The following values were not passed to `accelerate launch` and had defaults used instead:
	`--mixed_precision` was set to a value of `'no'`
	`--dynamo_backend` was set to a value of `'no'`
To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`.
Rank[3/8] 06/19/2025 14:10:14 INFO train.py:239 | if accelerator initialized:True
Rank[3/8] 06/19/2025 14:10:14 INFO train.py:240 | accelerator state: {'_cpu': False, 'backend': 'nccl', 'device': device(type='cuda', index=3), 'debug': False, 'distributed_type': <DistributedType.MULTI_GPU: 'MULTI_GPU'>, 'num_processes': 8, 'process_index': 3, 'local_process_index': 3, 'fork_launched': False, 'deepspeed_plugins': None, 'use_ipex': None, 'torch_tp_plugin': None, 'dynamo_plugin': TorchDynamoPlugin(backend=<DynamoBackend.NO: 'NO'>, mode='default', fullgraph=False, dynamic=False, options=None, disable=False), '_mixed_precision': 'no'}
Rank[0/8] 06/19/2025 14:10:14 INFO train.py:239 | if accelerator initialized:True
Rank[2/8] 06/19/2025 14:10:14 INFO train.py:239 | if accelerator initialized:True
Rank[0/8] 06/19/2025 14:10:14 INFO train.py:240 | accelerator state: {'_cpu': False, 'backend': 'nccl', 'device': device(type='cuda', index=0), 'debug': False, 'distributed_type': <DistributedType.MULTI_GPU: 'MULTI_GPU'>, 'num_processes': 8, 'process_index': 0, 'local_process_index': 0, 'fork_launched': False, 'deepspeed_plugins': None, 'use_ipex': None, 'torch_tp_plugin': None, 'dynamo_plugin': TorchDynamoPlugin(backend=<DynamoBackend.NO: 'NO'>, mode='default', fullgraph=False, dynamic=False, options=None, disable=False), '_mixed_precision': 'no'}
Rank[2/8] 06/19/2025 14:10:14 INFO train.py:240 | accelerator state: {'_cpu': False, 'backend': 'nccl', 'device': device(type='cuda', index=2), 'debug': False, 'distributed_type': <DistributedType.MULTI_GPU: 'MULTI_GPU'>, 'num_processes': 8, 'process_index': 2, 'local_process_index': 2, 'fork_launched': False, 'deepspeed_plugins': None, 'use_ipex': None, 'torch_tp_plugin': None, 'dynamo_plugin': TorchDynamoPlugin(backend=<DynamoBackend.NO: 'NO'>, mode='default', fullgraph=False, dynamic=False, options=None, disable=False), '_mixed_precision': 'no'}
Rank[6/8] 06/19/2025 14:10:14 INFO train.py:239 | if accelerator initialized:True
Rank[4/8] 06/19/2025 14:10:14 INFO train.py:239 | if accelerator initialized:True
Rank[5/8] 06/19/2025 14:10:14 INFO train.py:239 | if accelerator initialized:True
Rank[1/8] 06/19/2025 14:10:14 INFO train.py:239 | if accelerator initialized:True
Rank[7/8] 06/19/2025 14:10:14 INFO train.py:239 | if accelerator initialized:True
Rank[4/8] 06/19/2025 14:10:14 INFO train.py:240 | accelerator state: {'_cpu': False, 'backend': 'nccl', 'device': device(type='cuda', index=4), 'debug': False, 'distributed_type': <DistributedType.MULTI_GPU: 'MULTI_GPU'>, 'num_processes': 8, 'process_index': 4, 'local_process_index': 4, 'fork_launched': False, 'deepspeed_plugins': None, 'use_ipex': None, 'torch_tp_plugin': None, 'dynamo_plugin': TorchDynamoPlugin(backend=<DynamoBackend.NO: 'NO'>, mode='default', fullgraph=False, dynamic=False, options=None, disable=False), '_mixed_precision': 'no'}
Rank[6/8] 06/19/2025 14:10:14 INFO train.py:240 | accelerator state: {'_cpu': False, 'backend': 'nccl', 'device': device(type='cuda', index=6), 'debug': False, 'distributed_type': <DistributedType.MULTI_GPU: 'MULTI_GPU'>, 'num_processes': 8, 'process_index': 6, 'local_process_index': 6, 'fork_launched': False, 'deepspeed_plugins': None, 'use_ipex': None, 'torch_tp_plugin': None, 'dynamo_plugin': TorchDynamoPlugin(backend=<DynamoBackend.NO: 'NO'>, mode='default', fullgraph=False, dynamic=False, options=None, disable=False), '_mixed_precision': 'no'}
Rank[5/8] 06/19/2025 14:10:14 INFO train.py:240 | accelerator state: {'_cpu': False, 'backend': 'nccl', 'device': device(type='cuda', index=5), 'debug': False, 'distributed_type': <DistributedType.MULTI_GPU: 'MULTI_GPU'>, 'num_processes': 8, 'process_index': 5, 'local_process_index': 5, 'fork_launched': False, 'deepspeed_plugins': None, 'use_ipex': None, 'torch_tp_plugin': None, 'dynamo_plugin': TorchDynamoPlugin(backend=<DynamoBackend.NO: 'NO'>, mode='default', fullgraph=False, dynamic=False, options=None, disable=False), '_mixed_precision': 'no'}
Rank[1/8] 06/19/2025 14:10:14 INFO train.py:240 | accelerator state: {'_cpu': False, 'backend': 'nccl', 'device': device(type='cuda', index=1), 'debug': False, 'distributed_type': <DistributedType.MULTI_GPU: 'MULTI_GPU'>, 'num_processes': 8, 'process_index': 1, 'local_process_index': 1, 'fork_launched': False, 'deepspeed_plugins': None, 'use_ipex': None, 'torch_tp_plugin': None, 'dynamo_plugin': TorchDynamoPlugin(backend=<DynamoBackend.NO: 'NO'>, mode='default', fullgraph=False, dynamic=False, options=None, disable=False), '_mixed_precision': 'no'}
Rank[7/8] 06/19/2025 14:10:14 INFO train.py:240 | accelerator state: {'_cpu': False, 'backend': 'nccl', 'device': device(type='cuda', index=7), 'debug': False, 'distributed_type': <DistributedType.MULTI_GPU: 'MULTI_GPU'>, 'num_processes': 8, 'process_index': 7, 'local_process_index': 7, 'fork_launched': False, 'deepspeed_plugins': None, 'use_ipex': None, 'torch_tp_plugin': None, 'dynamo_plugin': TorchDynamoPlugin(backend=<DynamoBackend.NO: 'NO'>, mode='default', fullgraph=False, dynamic=False, options=None, disable=False), '_mixed_precision': 'no'}
Rank[0/8] 06/19/2025 14:10:14 INFO train.py:135 | 
{
    "mode": "det",
    "data_version": "v1",
    "trainval": false,
    "embed_dims": 256,
    "with_depth": true,
    "with_depth_loss": true,
    "min_depth": 0.25,
    "max_depth": 10,
    "num_depth": 64,
    "batch_size": 1,
    "val_batch_size": 1,
    "step_log_freq": 25,
    "num_workers": 8,
    "lr": 0.0002,
    "eval_only": false,
    "checkpoint": "./ckpt/groundingdino_swint_ogc_mmdet-822d7e9d-rename.pth",
    "bert_checkpoint": "./ckpt/bert-base-uncased",
    "anchor_file": "./anchor_files/embodiedscan_kmeans_det_cam_log_z-0.2-3.npy",
    "data_root": "./data/",
    "max_epoch": 240,
    "epoch_eval_freq": 10,
    "save_epoch_freq": 10
}
/usr/local/lib/python3.10/dist-packages/torch/utils/cpp_extension.py:1965: UserWarning: TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation. 
If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST'].
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/torch/utils/cpp_extension.py:1965: UserWarning: TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation. 
If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST'].
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/torch/utils/cpp_extension.py:1965: UserWarning: TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation. 
If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST'].
  warnings.warn(

Loading Train dataset:   0%|          | 0/3113 [00:00<?, ?it/s]
Loading Train dataset:   0%|          | 0/3113 [00:00<?, ?it/s]
Loading Train dataset:   0%|          | 0/3113 [00:00<?, ?it/s]
Loading Train dataset:   0%|          | 0/3113 [00:00<?, ?it/s]
Loading Train dataset:   0%|          | 0/3113 [00:00<?, ?it/s]
Loading Train dataset:   0%|          | 0/3113 [00:00<?, ?it/s]
Loading Train dataset:   0%|          | 0/3113 [00:00<?, ?it/s]
Loading Train dataset:   0%|          | 0/3113 [00:00<?, ?it/s]
Loading Train dataset: 100%|██████████| 3113/3113 [00:06<00:00, 449.19it/s]

Loading Train dataset: 100%|██████████| 3113/3113 [00:06<00:00, 445.99it/s]

Loading Train dataset: 100%|██████████| 3113/3113 [00:06<00:00, 445.18it/s]

Loading Train dataset: 100%|██████████| 3113/3113 [00:06<00:00, 445.86it/s]

Loading Train dataset: 100%|██████████| 3113/3113 [00:07<00:00, 443.05it/s]

Loading Train dataset: 100%|██████████| 3113/3113 [00:07<00:00, 439.70it/s]

Loading Train dataset: 100%|██████████| 3113/3113 [00:07<00:00, 439.04it/s]
Rank[5/8] 06/19/2025 14:12:05 INFO embodiedscan_det_grounding_dataset.py:227 | dataset length : 3113
Rank[5/8] 06/19/2025 14:12:05 INFO train.py:93 | load checkpoint: ./ckpt/groundingdino_swint_ogc_mmdet-822d7e9d-rename.pth
Rank[1/8] 06/19/2025 14:12:05 INFO embodiedscan_det_grounding_dataset.py:227 | dataset length : 3113
Rank[1/8] 06/19/2025 14:12:05 INFO train.py:93 | load checkpoint: ./ckpt/groundingdino_swint_ogc_mmdet-822d7e9d-rename.pth

Loading Train dataset: 100%|██████████| 3113/3113 [00:07<00:00, 437.17it/s]
Rank[0/8] 06/19/2025 14:12:05 INFO embodiedscan_det_grounding_dataset.py:227 | dataset length : 3113
Rank[0/8] 06/19/2025 14:12:05 INFO train.py:93 | load checkpoint: ./ckpt/groundingdino_swint_ogc_mmdet-822d7e9d-rename.pth
Rank[4/8] 06/19/2025 14:12:05 INFO embodiedscan_det_grounding_dataset.py:227 | dataset length : 3113
Rank[4/8] 06/19/2025 14:12:05 INFO train.py:93 | load checkpoint: ./ckpt/groundingdino_swint_ogc_mmdet-822d7e9d-rename.pth
Rank[7/8] 06/19/2025 14:12:05 INFO embodiedscan_det_grounding_dataset.py:227 | dataset length : 3113
Rank[7/8] 06/19/2025 14:12:05 INFO train.py:93 | load checkpoint: ./ckpt/groundingdino_swint_ogc_mmdet-822d7e9d-rename.pth
Rank[6/8] 06/19/2025 14:12:05 INFO embodiedscan_det_grounding_dataset.py:227 | dataset length : 3113
Rank[6/8] 06/19/2025 14:12:05 INFO train.py:93 | load checkpoint: ./ckpt/groundingdino_swint_ogc_mmdet-822d7e9d-rename.pth
Rank[3/8] 06/19/2025 14:12:05 INFO embodiedscan_det_grounding_dataset.py:227 | dataset length : 3113
Rank[3/8] 06/19/2025 14:12:05 INFO train.py:93 | load checkpoint: ./ckpt/groundingdino_swint_ogc_mmdet-822d7e9d-rename.pth
Rank[2/8] 06/19/2025 14:12:06 INFO embodiedscan_det_grounding_dataset.py:227 | dataset length : 3113
Rank[2/8] 06/19/2025 14:12:06 INFO train.py:93 | load checkpoint: ./ckpt/groundingdino_swint_ogc_mmdet-822d7e9d-rename.pth
/running_package/submit-sem/train.py:171: DeprecationWarning: Call to deprecated class SimpleTrainer. (This class is deprecated. Use `HookBasedTrainer` instead.) -- Deprecated since version 0.1.0.
  trainer = SimpleTrainer(
Rank[0/8] 06/19/2025 14:12:14 INFO train.py:107 | num of missing_keys: 430,num of unexpected_keys: 94
Rank[0/8] 06/19/2025 14:12:14 INFO train.py:111 | missing_keys:
 ['decoder.instance_bank.instance_feature', 'decoder.anchor_encoder.pos_fc.0.weight', 'decoder.anchor_encoder.pos_fc.0.bias', 'decoder.anchor_encoder.pos_fc.2.weight', 'decoder.anchor_encoder.pos_fc.2.bias', 'decoder.anchor_encoder.pos_fc.3.weight', 'decoder.anchor_encoder.pos_fc.3.bias', 'decoder.anchor_encoder.pos_fc.5.weight', 'decoder.anchor_encoder.pos_fc.5.bias', 'decoder.anchor_encoder.size_fc.0.weight', 'decoder.anchor_encoder.size_fc.0.bias', 'decoder.anchor_encoder.size_fc.2.weight', 'decoder.anchor_encoder.size_fc.2.bias', 'decoder.anchor_encoder.size_fc.3.weight', 'decoder.anchor_encoder.size_fc.3.bias', 'decoder.anchor_encoder.size_fc.5.weight', 'decoder.anchor_encoder.size_fc.5.bias', 'decoder.anchor_encoder.yaw_fc.0.weight', 'decoder.anchor_encoder.yaw_fc.0.bias', 'decoder.anchor_encoder.yaw_fc.2.weight', 'decoder.anchor_encoder.yaw_fc.2.bias', 'decoder.anchor_encoder.yaw_fc.3.weight', 'decoder.anchor_encoder.yaw_fc.3.bias', 'decoder.anchor_encoder.yaw_fc.5.weight', 'decoder.anchor_encoder.yaw_fc.5.bias', 'decoder.anchor_encoder.output_fc.0.weight', 'decoder.anchor_encoder.output_fc.0.bias', 'decoder.anchor_encoder.output_fc.2.weight', 'decoder.anchor_encoder.output_fc.2.bias', 'decoder.anchor_encoder.output_fc.3.weight', 'decoder.anchor_encoder.output_fc.3.bias', 'decoder.anchor_encoder.output_fc.5.weight', 'decoder.anchor_encoder.output_fc.5.bias', 'decoder.sampler.label_embedding.weight', 'decoder.layers.4.kps_generator.learnable_fc.weight', 'decoder.layers.4.kps_generator.learnable_fc.bias', 'decoder.layers.4.camera_encoder.0.weight', 'decoder.layers.4.camera_encoder.0.bias', 'decoder.layers.4.camera_encoder.2.weight', 'decoder.layers.4.camera_encoder.2.bias', 'decoder.layers.4.camera_encoder.3.weight', 'decoder.layers.4.camera_encoder.3.bias', 'decoder.layers.4.camera_encoder.5.weight', 'decoder.layers.4.camera_encoder.5.bias', 'decoder.layers.4.weights_fc.weight', 'decoder.layers.4.weights_fc.bias', 'decoder.layers.8.bias', 'decoder.layers.8.layers.0.weight', 'decoder.layers.8.layers.0.bias', 'decoder.layers.8.layers.2.weight', 'decoder.layers.8.layers.2.bias', 'decoder.layers.8.layers.4.weight', 'decoder.layers.8.layers.4.bias', 'decoder.layers.8.layers.5.weight', 'decoder.layers.8.layers.5.bias', 'decoder.layers.8.layers.7.weight', 'decoder.layers.8.layers.7.bias', 'decoder.layers.8.layers.9.weight', 'decoder.layers.8.layers.9.bias', 'decoder.layers.8.layers.10.weight', 'decoder.layers.8.layers.10.bias', 'decoder.layers.8.layers.11.weight', 'decoder.layers.13.kps_generator.learnable_fc.weight', 'decoder.layers.13.kps_generator.learnable_fc.bias', 'decoder.layers.13.camera_encoder.0.weight', 'decoder.layers.13.camera_encoder.0.bias', 'decoder.layers.13.camera_encoder.2.weight', 'decoder.layers.13.camera_encoder.2.bias', 'decoder.layers.13.camera_encoder.3.weight', 'decoder.layers.13.camera_encoder.3.bias', 'decoder.layers.13.camera_encoder.5.weight', 'decoder.layers.13.camera_encoder.5.bias', 'decoder.layers.13.weights_fc.weight', 'decoder.layers.13.weights_fc.bias', 'decoder.layers.17.bias', 'decoder.layers.17.layers.0.weight', 'decoder.layers.17.layers.0.bias', 'decoder.layers.17.layers.2.weight', 'decoder.layers.17.layers.2.bias', 'decoder.layers.17.layers.4.weight', 'decoder.layers.17.layers.4.bias', 'decoder.layers.17.layers.5.weight', 'decoder.layers.17.layers.5.bias', 'decoder.layers.17.layers.7.weight', 'decoder.layers.17.layers.7.bias', 'decoder.layers.17.layers.9.weight', 'decoder.layers.17.layers.9.bias', 'decoder.layers.17.layers.10.weight', 'decoder.layers.17.layers.10.bias', 'decoder.layers.17.layers.11.weight', 'decoder.layers.22.kps_generator.learnable_fc.weight', 'decoder.layers.22.kps_generator.learnable_fc.bias', 'decoder.layers.22.camera_encoder.0.weight', 'decoder.layers.22.camera_encoder.0.bias', 'decoder.layers.22.camera_encoder.2.weight', 'decoder.layers.22.camera_encoder.2.bias', 'decoder.layers.22.camera_encoder.3.weight', 'decoder.layers.22.camera_encoder.3.bias', 'decoder.layers.22.camera_encoder.5.weight', 'decoder.layers.22.camera_encoder.5.bias', 'decoder.layers.22.weights_fc.weight', 'decoder.layers.22.weights_fc.bias', 'decoder.layers.26.bias', 'decoder.layers.26.layers.0.weight', 'decoder.layers.26.layers.0.bias', 'decoder.layers.26.layers.2.weight', 'decoder.layers.26.layers.2.bias', 'decoder.layers.26.layers.4.weight', 'decoder.layers.26.layers.4.bias', 'decoder.layers.26.layers.5.weight', 'decoder.layers.26.layers.5.bias', 'decoder.layers.26.layers.7.weight', 'decoder.layers.26.layers.7.bias', 'decoder.layers.26.layers.9.weight', 'decoder.layers.26.layers.9.bias', 'decoder.layers.26.layers.10.weight', 'decoder.layers.26.layers.10.bias', 'decoder.layers.26.layers.11.weight', 'decoder.layers.31.kps_generator.learnable_fc.weight', 'decoder.layers.31.kps_generator.learnable_fc.bias', 'decoder.layers.31.camera_encoder.0.weight', 'decoder.layers.31.camera_encoder.0.bias', 'decoder.layers.31.camera_encoder.2.weight', 'decoder.layers.31.camera_encoder.2.bias', 'decoder.layers.31.camera_encoder.3.weight', 'decoder.layers.31.camera_encoder.3.bias', 'decoder.layers.31.camera_encoder.5.weight', 'decoder.layers.31.camera_encoder.5.bias', 'decoder.layers.31.weights_fc.weight', 'decoder.layers.31.weights_fc.bias', 'decoder.layers.35.bias', 'decoder.layers.35.layers.0.weight', 'decoder.layers.35.layers.0.bias', 'decoder.layers.35.layers.2.weight', 'decoder.layers.35.layers.2.bias', 'decoder.layers.35.layers.4.weight', 'decoder.layers.35.layers.4.bias', 'decoder.layers.35.layers.5.weight', 'decoder.layers.35.layers.5.bias', 'decoder.layers.35.layers.7.weight', 'decoder.layers.35.layers.7.bias', 'decoder.layers.35.layers.9.weight', 'decoder.layers.35.layers.9.bias', 'decoder.layers.35.layers.10.weight', 'decoder.layers.35.layers.10.bias', 'decoder.layers.35.layers.11.weight', 'decoder.layers.40.kps_generator.learnable_fc.weight', 'decoder.layers.40.kps_generator.learnable_fc.bias', 'decoder.layers.40.camera_encoder.0.weight', 'decoder.layers.40.camera_encoder.0.bias', 'decoder.layers.40.camera_encoder.2.weight', 'decoder.layers.40.camera_encoder.2.bias', 'decoder.layers.40.camera_encoder.3.weight', 'decoder.layers.40.camera_encoder.3.bias', 'decoder.layers.40.camera_encoder.5.weight', 'decoder.layers.40.camera_encoder.5.bias', 'decoder.layers.40.weights_fc.weight', 'decoder.layers.40.weights_fc.bias', 'decoder.layers.44.bias', 'decoder.layers.44.layers.0.weight', 'decoder.layers.44.layers.0.bias', 'decoder.layers.44.layers.2.weight', 'decoder.layers.44.layers.2.bias', 'decoder.layers.44.layers.4.weight', 'decoder.layers.44.layers.4.bias', 'decoder.layers.44.layers.5.weight', 'decoder.layers.44.layers.5.bias', 'decoder.layers.44.layers.7.weight', 'decoder.layers.44.layers.7.bias', 'decoder.layers.44.layers.9.weight', 'decoder.layers.44.layers.9.bias', 'decoder.layers.44.layers.10.weight', 'decoder.layers.44.layers.10.bias', 'decoder.layers.44.layers.11.weight', 'decoder.layers.49.kps_generator.learnable_fc.weight', 'decoder.layers.49.kps_generator.learnable_fc.bias', 'decoder.layers.49.camera_encoder.0.weight', 'decoder.layers.49.camera_encoder.0.bias', 'decoder.layers.49.camera_encoder.2.weight', 'decoder.layers.49.camera_encoder.2.bias', 'decoder.layers.49.camera_encoder.3.weight', 'decoder.layers.49.camera_encoder.3.bias', 'decoder.layers.49.camera_encoder.5.weight', 'decoder.layers.49.camera_encoder.5.bias', 'decoder.layers.49.weights_fc.weight', 'decoder.layers.49.weights_fc.bias', 'decoder.layers.53.bias', 'decoder.layers.53.layers.0.weight', 'decoder.layers.53.layers.0.bias', 'decoder.layers.53.layers.2.weight', 'decoder.layers.53.layers.2.bias', 'decoder.layers.53.layers.4.weight', 'decoder.layers.53.layers.4.bias', 'decoder.layers.53.layers.5.weight', 'decoder.layers.53.layers.5.bias', 'decoder.layers.53.layers.7.weight', 'decoder.layers.53.layers.7.bias', 'decoder.layers.53.layers.9.weight', 'decoder.layers.53.layers.9.bias', 'decoder.layers.53.layers.10.weight', 'decoder.layers.53.layers.10.bias', 'decoder.layers.53.layers.11.weight', 'neck.convs.0.0.weight', 'neck.convs.0.0.bias', 'neck.convs.0.1.weight', 'neck.convs.0.1.bias', 'neck.convs.1.0.weight', 'neck.convs.1.0.bias', 'neck.convs.1.1.weight', 'neck.convs.1.1.bias', 'neck.convs.2.0.weight', 'neck.convs.2.0.bias', 'neck.convs.2.1.weight', 'neck.convs.2.1.bias', 'neck.extra_convs.0.0.weight', 'neck.extra_convs.0.0.bias', 'neck.extra_convs.0.1.weight', 'neck.extra_convs.0.1.bias', 'spatial_enhancer.pts_prob_pre_fc.weight', 'spatial_enhancer.pts_prob_pre_fc.bias', 'spatial_enhancer.pts_prob_fc.layers.0.weight', 'spatial_enhancer.pts_prob_fc.layers.0.bias', 'spatial_enhancer.pts_prob_fc.layers.1.weight', 'spatial_enhancer.pts_prob_fc.layers.1.bias', 'spatial_enhancer.pts_fc.weight', 'spatial_enhancer.pts_fc.bias', 'spatial_enhancer.fusion_fc.0.layers.0.0.weight', 'spatial_enhancer.fusion_fc.0.layers.0.0.bias', 'spatial_enhancer.fusion_fc.0.layers.1.weight', 'spatial_enhancer.fusion_fc.0.layers.1.bias', 'spatial_enhancer.fusion_fc.1.weight', 'spatial_enhancer.fusion_fc.1.bias', 'spatial_enhancer.fusion_norm.weight', 'spatial_enhancer.fusion_norm.bias', 'backbone_3d.conv1.weight', 'backbone_3d.bn1.weight', 'backbone_3d.bn1.bias', 'backbone_3d.bn1.running_mean', 'backbone_3d.bn1.running_var', 'backbone_3d.layer1.0.conv1.weight', 'backbone_3d.layer1.0.bn1.weight', 'backbone_3d.layer1.0.bn1.bias', 'backbone_3d.layer1.0.bn1.running_mean', 'backbone_3d.layer1.0.bn1.running_var', 'backbone_3d.layer1.0.conv2.weight', 'backbone_3d.layer1.0.bn2.weight', 'backbone_3d.layer1.0.bn2.bias', 'backbone_3d.layer1.0.bn2.running_mean', 'backbone_3d.layer1.0.bn2.running_var', 'backbone_3d.layer1.1.conv1.weight', 'backbone_3d.layer1.1.bn1.weight', 'backbone_3d.layer1.1.bn1.bias', 'backbone_3d.layer1.1.bn1.running_mean', 'backbone_3d.layer1.1.bn1.running_var', 'backbone_3d.layer1.1.conv2.weight', 'backbone_3d.layer1.1.bn2.weight', 'backbone_3d.layer1.1.bn2.bias', 'backbone_3d.layer1.1.bn2.running_mean', 'backbone_3d.layer1.1.bn2.running_var', 'backbone_3d.layer1.2.conv1.weight', 'backbone_3d.layer1.2.bn1.weight', 'backbone_3d.layer1.2.bn1.bias', 'backbone_3d.layer1.2.bn1.running_mean', 'backbone_3d.layer1.2.bn1.running_var', 'backbone_3d.layer1.2.conv2.weight', 'backbone_3d.layer1.2.bn2.weight', 'backbone_3d.layer1.2.bn2.bias', 'backbone_3d.layer1.2.bn2.running_mean', 'backbone_3d.layer1.2.bn2.running_var', 'backbone_3d.layer2.0.conv1.weight', 'backbone_3d.layer2.0.bn1.weight', 'backbone_3d.layer2.0.bn1.bias', 'backbone_3d.layer2.0.bn1.running_mean', 'backbone_3d.layer2.0.bn1.running_var', 'backbone_3d.layer2.0.conv2.weight', 'backbone_3d.layer2.0.bn2.weight', 'backbone_3d.layer2.0.bn2.bias', 'backbone_3d.layer2.0.bn2.running_mean', 'backbone_3d.layer2.0.bn2.running_var', 'backbone_3d.layer2.0.downsample.0.weight', 'backbone_3d.layer2.0.downsample.1.weight', 'backbone_3d.layer2.0.downsample.1.bias', 'backbone_3d.layer2.0.downsample.1.running_mean', 'backbone_3d.layer2.0.downsample.1.running_var', 'backbone_3d.layer2.1.conv1.weight', 'backbone_3d.layer2.1.bn1.weight', 'backbone_3d.layer2.1.bn1.bias', 'backbone_3d.layer2.1.bn1.running_mean', 'backbone_3d.layer2.1.bn1.running_var', 'backbone_3d.layer2.1.conv2.weight', 'backbone_3d.layer2.1.bn2.weight', 'backbone_3d.layer2.1.bn2.bias', 'backbone_3d.layer2.1.bn2.running_mean', 'backbone_3d.layer2.1.bn2.running_var', 'backbone_3d.layer2.2.conv1.weight', 'backbone_3d.layer2.2.bn1.weight', 'backbone_3d.layer2.2.bn1.bias', 'backbone_3d.layer2.2.bn1.running_mean', 'backbone_3d.layer2.2.bn1.running_var', 'backbone_3d.layer2.2.conv2.weight', 'backbone_3d.layer2.2.bn2.weight', 'backbone_3d.layer2.2.bn2.bias', 'backbone_3d.layer2.2.bn2.running_mean', 'backbone_3d.layer2.2.bn2.running_var', 'backbone_3d.layer2.3.conv1.weight', 'backbone_3d.layer2.3.bn1.weight', 'backbone_3d.layer2.3.bn1.bias', 'backbone_3d.layer2.3.bn1.running_mean', 'backbone_3d.layer2.3.bn1.running_var', 'backbone_3d.layer2.3.conv2.weight', 'backbone_3d.layer2.3.bn2.weight', 'backbone_3d.layer2.3.bn2.bias', 'backbone_3d.layer2.3.bn2.running_mean', 'backbone_3d.layer2.3.bn2.running_var', 'backbone_3d.layer3.0.conv1.weight', 'backbone_3d.layer3.0.bn1.weight', 'backbone_3d.layer3.0.bn1.bias', 'backbone_3d.layer3.0.bn1.running_mean', 'backbone_3d.layer3.0.bn1.running_var', 'backbone_3d.layer3.0.conv2.weight', 'backbone_3d.layer3.0.bn2.weight', 'backbone_3d.layer3.0.bn2.bias', 'backbone_3d.layer3.0.bn2.running_mean', 'backbone_3d.layer3.0.bn2.running_var', 'backbone_3d.layer3.0.downsample.0.weight', 'backbone_3d.layer3.0.downsample.1.weight', 'backbone_3d.layer3.0.downsample.1.bias', 'backbone_3d.layer3.0.downsample.1.running_mean', 'backbone_3d.layer3.0.downsample.1.running_var', 'backbone_3d.layer3.1.conv1.weight', 'backbone_3d.layer3.1.bn1.weight', 'backbone_3d.layer3.1.bn1.bias', 'backbone_3d.layer3.1.bn1.running_mean', 'backbone_3d.layer3.1.bn1.running_var', 'backbone_3d.layer3.1.conv2.weight', 'backbone_3d.layer3.1.bn2.weight', 'backbone_3d.layer3.1.bn2.bias', 'backbone_3d.layer3.1.bn2.running_mean', 'backbone_3d.layer3.1.bn2.running_var', 'backbone_3d.layer3.2.conv1.weight', 'backbone_3d.layer3.2.bn1.weight', 'backbone_3d.layer3.2.bn1.bias', 'backbone_3d.layer3.2.bn1.running_mean', 'backbone_3d.layer3.2.bn1.running_var', 'backbone_3d.layer3.2.conv2.weight', 'backbone_3d.layer3.2.bn2.weight', 'backbone_3d.layer3.2.bn2.bias', 'backbone_3d.layer3.2.bn2.running_mean', 'backbone_3d.layer3.2.bn2.running_var', 'backbone_3d.layer3.3.conv1.weight', 'backbone_3d.layer3.3.bn1.weight', 'backbone_3d.layer3.3.bn1.bias', 'backbone_3d.layer3.3.bn1.running_mean', 'backbone_3d.layer3.3.bn1.running_var', 'backbone_3d.layer3.3.conv2.weight', 'backbone_3d.layer3.3.bn2.weight', 'backbone_3d.layer3.3.bn2.bias', 'backbone_3d.layer3.3.bn2.running_mean', 'backbone_3d.layer3.3.bn2.running_var', 'backbone_3d.layer3.4.conv1.weight', 'backbone_3d.layer3.4.bn1.weight', 'backbone_3d.layer3.4.bn1.bias', 'backbone_3d.layer3.4.bn1.running_mean', 'backbone_3d.layer3.4.bn1.running_var', 'backbone_3d.layer3.4.conv2.weight', 'backbone_3d.layer3.4.bn2.weight', 'backbone_3d.layer3.4.bn2.bias', 'backbone_3d.layer3.4.bn2.running_mean', 'backbone_3d.layer3.4.bn2.running_var', 'backbone_3d.layer3.5.conv1.weight', 'backbone_3d.layer3.5.bn1.weight', 'backbone_3d.layer3.5.bn1.bias', 'backbone_3d.layer3.5.bn1.running_mean', 'backbone_3d.layer3.5.bn1.running_var', 'backbone_3d.layer3.5.conv2.weight', 'backbone_3d.layer3.5.bn2.weight', 'backbone_3d.layer3.5.bn2.bias', 'backbone_3d.layer3.5.bn2.running_mean', 'backbone_3d.layer3.5.bn2.running_var', 'backbone_3d.layer4.0.conv1.weight', 'backbone_3d.layer4.0.bn1.weight', 'backbone_3d.layer4.0.bn1.bias', 'backbone_3d.layer4.0.bn1.running_mean', 'backbone_3d.layer4.0.bn1.running_var', 'backbone_3d.layer4.0.conv2.weight', 'backbone_3d.layer4.0.bn2.weight', 'backbone_3d.layer4.0.bn2.bias', 'backbone_3d.layer4.0.bn2.running_mean', 'backbone_3d.layer4.0.bn2.running_var', 'backbone_3d.layer4.0.downsample.0.weight', 'backbone_3d.layer4.0.downsample.1.weight', 'backbone_3d.layer4.0.downsample.1.bias', 'backbone_3d.layer4.0.downsample.1.running_mean', 'backbone_3d.layer4.0.downsample.1.running_var', 'backbone_3d.layer4.1.conv1.weight', 'backbone_3d.layer4.1.bn1.weight', 'backbone_3d.layer4.1.bn1.bias', 'backbone_3d.layer4.1.bn1.running_mean', 'backbone_3d.layer4.1.bn1.running_var', 'backbone_3d.layer4.1.conv2.weight', 'backbone_3d.layer4.1.bn2.weight', 'backbone_3d.layer4.1.bn2.bias', 'backbone_3d.layer4.1.bn2.running_mean', 'backbone_3d.layer4.1.bn2.running_var', 'backbone_3d.layer4.2.conv1.weight', 'backbone_3d.layer4.2.bn1.weight', 'backbone_3d.layer4.2.bn1.bias', 'backbone_3d.layer4.2.bn1.running_mean', 'backbone_3d.layer4.2.bn1.running_var', 'backbone_3d.layer4.2.conv2.weight', 'backbone_3d.layer4.2.bn2.weight', 'backbone_3d.layer4.2.bn2.bias', 'backbone_3d.layer4.2.bn2.running_mean', 'backbone_3d.layer4.2.bn2.running_var', 'neck_3d.convs.0.0.weight', 'neck_3d.convs.0.0.bias', 'neck_3d.convs.0.1.weight', 'neck_3d.convs.0.1.bias', 'neck_3d.convs.1.0.weight', 'neck_3d.convs.1.0.bias', 'neck_3d.convs.1.1.weight', 'neck_3d.convs.1.1.bias', 'neck_3d.convs.2.0.weight', 'neck_3d.convs.2.0.bias', 'neck_3d.convs.2.1.weight', 'neck_3d.convs.2.1.bias', 'neck_3d.extra_convs.0.0.weight', 'neck_3d.extra_convs.0.0.bias', 'neck_3d.extra_convs.0.1.weight', 'neck_3d.extra_convs.0.1.bias']
unexpected_keys:
 ['bbox_head.reg_branches.0.0.weight', 'bbox_head.reg_branches.0.0.bias', 'bbox_head.reg_branches.0.2.weight', 'bbox_head.reg_branches.0.2.bias', 'bbox_head.reg_branches.0.4.weight', 'bbox_head.reg_branches.0.4.bias', 'bbox_head.reg_branches.1.0.weight', 'bbox_head.reg_branches.1.0.bias', 'bbox_head.reg_branches.1.2.weight', 'bbox_head.reg_branches.1.2.bias', 'bbox_head.reg_branches.1.4.weight', 'bbox_head.reg_branches.1.4.bias', 'bbox_head.reg_branches.2.0.weight', 'bbox_head.reg_branches.2.0.bias', 'bbox_head.reg_branches.2.2.weight', 'bbox_head.reg_branches.2.2.bias', 'bbox_head.reg_branches.2.4.weight', 'bbox_head.reg_branches.2.4.bias', 'bbox_head.reg_branches.3.0.weight', 'bbox_head.reg_branches.3.0.bias', 'bbox_head.reg_branches.3.2.weight', 'bbox_head.reg_branches.3.2.bias', 'bbox_head.reg_branches.3.4.weight', 'bbox_head.reg_branches.3.4.bias', 'bbox_head.reg_branches.4.0.weight', 'bbox_head.reg_branches.4.0.bias', 'bbox_head.reg_branches.4.2.weight', 'bbox_head.reg_branches.4.2.bias', 'bbox_head.reg_branches.4.4.weight', 'bbox_head.reg_branches.4.4.bias', 'bbox_head.reg_branches.5.0.weight', 'bbox_head.reg_branches.5.0.bias', 'bbox_head.reg_branches.5.2.weight', 'bbox_head.reg_branches.5.2.bias', 'bbox_head.reg_branches.5.4.weight', 'bbox_head.reg_branches.5.4.bias', 'query_embedding.weight', 'memory_trans_fc.weight', 'memory_trans_fc.bias', 'memory_trans_norm.weight', 'memory_trans_norm.bias', 'bbox_head.reg_branches.6.0.weight', 'bbox_head.reg_branches.6.0.bias', 'bbox_head.reg_branches.6.2.weight', 'bbox_head.reg_branches.6.2.bias', 'bbox_head.reg_branches.6.4.weight', 'bbox_head.reg_branches.6.4.bias', 'decoder.ref_point_head.layers.0.weight', 'decoder.ref_point_head.layers.0.bias', 'decoder.ref_point_head.layers.1.weight', 'decoder.ref_point_head.layers.1.bias', 'decoder.layers.4.sampling_offsets.weight', 'decoder.layers.4.sampling_offsets.bias', 'decoder.layers.4.attention_weights.weight', 'decoder.layers.4.attention_weights.bias', 'decoder.layers.13.sampling_offsets.weight', 'decoder.layers.13.sampling_offsets.bias', 'decoder.layers.13.attention_weights.weight', 'decoder.layers.13.attention_weights.bias', 'decoder.layers.22.sampling_offsets.weight', 'decoder.layers.22.sampling_offsets.bias', 'decoder.layers.22.attention_weights.weight', 'decoder.layers.22.attention_weights.bias', 'decoder.layers.31.sampling_offsets.weight', 'decoder.layers.31.sampling_offsets.bias', 'decoder.layers.31.attention_weights.weight', 'decoder.layers.31.attention_weights.bias', 'decoder.layers.40.sampling_offsets.weight', 'decoder.layers.40.sampling_offsets.bias', 'decoder.layers.40.attention_weights.weight', 'decoder.layers.40.attention_weights.bias', 'decoder.layers.49.sampling_offsets.weight', 'decoder.layers.49.sampling_offsets.bias', 'decoder.layers.49.attention_weights.weight', 'decoder.layers.49.attention_weights.bias', 'neck.convs.0.conv.weight', 'neck.convs.0.conv.bias', 'neck.convs.0.gn.weight', 'neck.convs.0.gn.bias', 'neck.convs.1.conv.weight', 'neck.convs.1.conv.bias', 'neck.convs.1.gn.weight', 'neck.convs.1.gn.bias', 'neck.convs.2.conv.weight', 'neck.convs.2.conv.bias', 'neck.convs.2.gn.weight', 'neck.convs.2.gn.bias', 'neck.extra_convs.0.conv.weight', 'neck.extra_convs.0.conv.bias', 'neck.extra_convs.0.gn.weight', 'neck.extra_convs.0.gn.bias', 'text_encoder.language_backbone.body.model.pooler.dense.weight', 'text_encoder.language_backbone.body.model.pooler.dense.bias', 'text_encoder.language_backbone.body.model.embeddings.position_ids']
/running_package/submit-sem/train.py:171: DeprecationWarning: Call to deprecated class SimpleTrainer. (This class is deprecated. Use `HookBasedTrainer` instead.) -- Deprecated since version 0.1.0.
  trainer = SimpleTrainer(
/running_package/submit-sem/train.py:171: DeprecationWarning: Call to deprecated class SimpleTrainer. (This class is deprecated. Use `HookBasedTrainer` instead.) -- Deprecated since version 0.1.0.
  trainer = SimpleTrainer(
/running_package/submit-sem/train.py:171: DeprecationWarning: Call to deprecated class SimpleTrainer. (This class is deprecated. Use `HookBasedTrainer` instead.) -- Deprecated since version 0.1.0.
  trainer = SimpleTrainer(
/running_package/submit-sem/train.py:171: DeprecationWarning: Call to deprecated class SimpleTrainer. (This class is deprecated. Use `HookBasedTrainer` instead.) -- Deprecated since version 0.1.0.
  trainer = SimpleTrainer(
/running_package/submit-sem/train.py:171: DeprecationWarning: Call to deprecated class SimpleTrainer. (This class is deprecated. Use `HookBasedTrainer` instead.) -- Deprecated since version 0.1.0.
  trainer = SimpleTrainer(
/running_package/submit-sem/train.py:171: DeprecationWarning: Call to deprecated class SimpleTrainer. (This class is deprecated. Use `HookBasedTrainer` instead.) -- Deprecated since version 0.1.0.
  trainer = SimpleTrainer(
/running_package/submit-sem/train.py:171: DeprecationWarning: Call to deprecated class SimpleTrainer. (This class is deprecated. Use `HookBasedTrainer` instead.) -- Deprecated since version 0.1.0.
  trainer = SimpleTrainer(
Rank[0/8] 06/19/2025 14:12:15 INFO hook_based_trainer.py:323 | 
==================================================BEGIN TRAINING==================================================
Rank[0/8] 06/19/2025 14:12:15 INFO hook_based_trainer.py:327 | Start training loop from epoch 0 and step 0
/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/usr/local/lib/python3.10/dist-packages/torch/functional.py:513: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3609.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/usr/local/lib/python3.10/dist-packages/torch/functional.py:513: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3609.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/usr/local/lib/python3.10/dist-packages/torch/functional.py:513: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3609.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/functional.py:513: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3609.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/functional.py:513: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3609.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/functional.py:513: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3609.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/functional.py:513: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3609.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/functional.py:513: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3609.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:1399: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
  with device_autocast_ctx, torch.cpu.amp.autocast(**cpu_autocast_kwargs), recompute_context:  # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:1399: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
  with device_autocast_ctx, torch.cpu.amp.autocast(**cpu_autocast_kwargs), recompute_context:  # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:1399: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
  with device_autocast_ctx, torch.cpu.amp.autocast(**cpu_autocast_kwargs), recompute_context:  # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:1399: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
  with device_autocast_ctx, torch.cpu.amp.autocast(**cpu_autocast_kwargs), recompute_context:  # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:1399: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
  with device_autocast_ctx, torch.cpu.amp.autocast(**cpu_autocast_kwargs), recompute_context:  # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:1399: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
  with device_autocast_ctx, torch.cpu.amp.autocast(**cpu_autocast_kwargs), recompute_context:  # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:1399: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
  with device_autocast_ctx, torch.cpu.amp.autocast(**cpu_autocast_kwargs), recompute_context:  # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:1399: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
  with device_autocast_ctx, torch.cpu.amp.autocast(**cpu_autocast_kwargs), recompute_context:  # type: ignore[attr-defined]
Rank[0/8] 06/19/2025 14:14:04 INFO stats.py:314 | Epoch[0] Step[24] GlobalStep[24] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 4 days, 16:51:46.	Learning Rate: 1.01900e-05.	
Rank[0/8] 06/19/2025 14:14:04 INFO loss_tracker.py:84 | Epoch[0/239] Step[24] GlobalStep[24/NA]: det_loss_cls_0[1.3777] det_loss_box_0[1.4201] det_loss_cls_1[1.3624] det_loss_box_1[1.4182] det_loss_cls_2[1.3394] det_loss_box_2[1.4163] det_loss_cls_3[1.3629] det_loss_box_3[1.4149] det_loss_cls_4[1.3640] det_loss_box_4[1.4134] det_loss_cls_5[1.3639] det_loss_box_5[1.4119] det_loss_cls_dn_0[0.5647] det_loss_box_dn_0[0.5101] det_loss_cls_dn_1[0.5612] det_loss_box_dn_1[0.5092] det_loss_cls_dn_2[0.5503] det_loss_box_dn_2[0.5085] det_loss_cls_dn_3[0.5600] det_loss_box_dn_3[0.5079] det_loss_cls_dn_4[0.5594] det_loss_box_dn_4[0.5074] det_loss_cls_dn_5[0.5585] det_loss_box_dn_5[0.5070] loss_depth[0.0611] total_loss[23.1307]
Rank[0/8] 06/19/2025 14:14:33 INFO stats.py:314 | Epoch[0] Step[49] GlobalStep[49] Training Speed: 7.71 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 2 days, 23:30:35.	Learning Rate: 2.01800e-05.	
Rank[0/8] 06/19/2025 14:14:33 INFO loss_tracker.py:84 | Epoch[0/239] Step[49] GlobalStep[49/NA]: det_loss_cls_0[1.4484] det_loss_box_0[1.3480] det_loss_cls_1[1.3997] det_loss_box_1[1.3403] det_loss_cls_2[1.3918] det_loss_box_2[1.3341] det_loss_cls_3[1.4037] det_loss_box_3[1.3292] det_loss_cls_4[1.4146] det_loss_box_4[1.3250] det_loss_cls_5[1.4262] det_loss_box_5[1.3214] det_loss_cls_dn_0[0.5571] det_loss_box_dn_0[0.5172] det_loss_cls_dn_1[0.5417] det_loss_box_dn_1[0.5145] det_loss_cls_dn_2[0.5359] det_loss_box_dn_2[0.5137] det_loss_cls_dn_3[0.5409] det_loss_box_dn_3[0.5144] det_loss_cls_dn_4[0.5450] det_loss_box_dn_4[0.5163] det_loss_cls_dn_5[0.5496] det_loss_box_dn_5[0.5185] loss_depth[0.0581] total_loss[22.9052]
Rank[0/8] 06/19/2025 14:15:00 INFO stats.py:314 | Epoch[0] Step[74] GlobalStep[74] Training Speed: 7.90 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 2 days, 9:01:00.	Learning Rate: 3.01700e-05.	
Rank[0/8] 06/19/2025 14:15:00 INFO loss_tracker.py:84 | Epoch[0/239] Step[74] GlobalStep[74/NA]: det_loss_cls_0[1.0271] det_loss_box_0[1.1662] det_loss_cls_1[0.9627] det_loss_box_1[1.1508] det_loss_cls_2[0.9542] det_loss_box_2[1.1364] det_loss_cls_3[0.9446] det_loss_box_3[1.1267] det_loss_cls_4[0.9492] det_loss_box_4[1.1193] det_loss_cls_5[0.9659] det_loss_box_5[1.1082] det_loss_cls_dn_0[0.5561] det_loss_box_dn_0[0.6079] det_loss_cls_dn_1[0.5149] det_loss_box_dn_1[0.6077] det_loss_cls_dn_2[0.5090] det_loss_box_dn_2[0.6105] det_loss_cls_dn_3[0.5027] det_loss_box_dn_3[0.6139] det_loss_cls_dn_4[0.5067] det_loss_box_dn_4[0.6166] det_loss_cls_dn_5[0.5175] det_loss_box_dn_5[0.6197] loss_depth[0.0600] total_loss[19.4545]
Rank[0/8] 06/19/2025 14:15:27 INFO stats.py:314 | Epoch[0] Step[99] GlobalStep[99] Training Speed: 7.61 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 2 days, 1:51:27.	Learning Rate: 4.01600e-05.	
Rank[0/8] 06/19/2025 14:15:27 INFO loss_tracker.py:84 | Epoch[0/239] Step[99] GlobalStep[99/NA]: det_loss_cls_0[1.2280] det_loss_box_0[1.7002] det_loss_cls_1[1.1410] det_loss_box_1[1.6453] det_loss_cls_2[1.1497] det_loss_box_2[1.5942] det_loss_cls_3[1.1303] det_loss_box_3[1.5699] det_loss_cls_4[1.1370] det_loss_box_4[1.5439] det_loss_cls_5[1.1531] det_loss_box_5[1.5255] det_loss_cls_dn_0[0.4553] det_loss_box_dn_0[0.5748] det_loss_cls_dn_1[0.3842] det_loss_box_dn_1[0.5737] det_loss_cls_dn_2[0.3771] det_loss_box_dn_2[0.5756] det_loss_cls_dn_3[0.3469] det_loss_box_dn_3[0.5762] det_loss_cls_dn_4[0.3538] det_loss_box_dn_4[0.5765] det_loss_cls_dn_5[0.3664] det_loss_box_dn_5[0.5804] loss_depth[0.0600] total_loss[22.3191]
Rank[0/8] 06/19/2025 14:15:56 INFO stats.py:314 | Epoch[0] Step[124] GlobalStep[124] Training Speed: 6.93 samples/sec across all devices.	Average Step Time: 1.15 sec.	Estimated Remaining Time: 1 day, 21:52:53.	Learning Rate: 5.01500e-05.	
Rank[0/8] 06/19/2025 14:15:56 INFO loss_tracker.py:84 | Epoch[0/239] Step[124] GlobalStep[124/NA]: det_loss_cls_0[0.9855] det_loss_box_0[1.3326] det_loss_cls_1[0.9677] det_loss_box_1[1.2772] det_loss_cls_2[0.9817] det_loss_box_2[1.2334] det_loss_cls_3[0.9660] det_loss_box_3[1.2148] det_loss_cls_4[0.9849] det_loss_box_4[1.1817] det_loss_cls_5[0.9980] det_loss_box_5[1.1691] det_loss_cls_dn_0[0.3626] det_loss_box_dn_0[0.5418] det_loss_cls_dn_1[0.3170] det_loss_box_dn_1[0.5443] det_loss_cls_dn_2[0.3113] det_loss_box_dn_2[0.5505] det_loss_cls_dn_3[0.2883] det_loss_box_dn_3[0.5553] det_loss_cls_dn_4[0.2940] det_loss_box_dn_4[0.5610] det_loss_cls_dn_5[0.3051] det_loss_box_dn_5[0.5706] loss_depth[0.0602] total_loss[18.5546]
Rank[0/8] 06/19/2025 14:16:23 INFO stats.py:314 | Epoch[0] Step[149] GlobalStep[149] Training Speed: 7.55 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 1 day, 18:55:40.	Learning Rate: 6.01400e-05.	
Rank[0/8] 06/19/2025 14:16:23 INFO loss_tracker.py:84 | Epoch[0/239] Step[149] GlobalStep[149/NA]: det_loss_cls_0[0.9692] det_loss_box_0[1.3489] det_loss_cls_1[0.9581] det_loss_box_1[1.2967] det_loss_cls_2[0.9761] det_loss_box_2[1.2537] det_loss_cls_3[0.9719] det_loss_box_3[1.2367] det_loss_cls_4[0.9809] det_loss_box_4[1.2189] det_loss_cls_5[0.9948] det_loss_box_5[1.2144] det_loss_cls_dn_0[0.3088] det_loss_box_dn_0[0.5376] det_loss_cls_dn_1[0.2623] det_loss_box_dn_1[0.5349] det_loss_cls_dn_2[0.2567] det_loss_box_dn_2[0.5363] det_loss_cls_dn_3[0.2546] det_loss_box_dn_3[0.5387] det_loss_cls_dn_4[0.2547] det_loss_box_dn_4[0.5400] det_loss_cls_dn_5[0.2576] det_loss_box_dn_5[0.5448] loss_depth[0.0572] total_loss[18.3046]
Rank[0/8] 06/19/2025 14:16:50 INFO stats.py:314 | Epoch[0] Step[174] GlobalStep[174] Training Speed: 7.80 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 1 day, 16:44:17.	Learning Rate: 7.01300e-05.	
Rank[0/8] 06/19/2025 14:16:50 INFO loss_tracker.py:84 | Epoch[0/239] Step[174] GlobalStep[174/NA]: det_loss_cls_0[0.9846] det_loss_box_0[1.5359] det_loss_cls_1[0.9810] det_loss_box_1[1.4707] det_loss_cls_2[1.0007] det_loss_box_2[1.4214] det_loss_cls_3[1.0009] det_loss_box_3[1.3913] det_loss_cls_4[1.0103] det_loss_box_4[1.3610] det_loss_cls_5[1.0223] det_loss_box_5[1.3456] det_loss_cls_dn_0[0.2776] det_loss_box_dn_0[0.5507] det_loss_cls_dn_1[0.2556] det_loss_box_dn_1[0.5429] det_loss_cls_dn_2[0.2535] det_loss_box_dn_2[0.5430] det_loss_cls_dn_3[0.2513] det_loss_box_dn_3[0.5442] det_loss_cls_dn_4[0.2526] det_loss_box_dn_4[0.5444] det_loss_cls_dn_5[0.2541] det_loss_box_dn_5[0.5485] loss_depth[0.0593] total_loss[19.4035]
Rank[0/8] 06/19/2025 14:17:17 INFO stats.py:314 | Epoch[0] Step[199] GlobalStep[199] Training Speed: 7.79 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 1 day, 15:12:55.	Learning Rate: 8.01200e-05.	
Rank[0/8] 06/19/2025 14:17:17 INFO loss_tracker.py:84 | Epoch[0/239] Step[199] GlobalStep[199/NA]: det_loss_cls_0[0.9257] det_loss_box_0[1.2173] det_loss_cls_1[0.9212] det_loss_box_1[1.1443] det_loss_cls_2[0.9424] det_loss_box_2[1.1067] det_loss_cls_3[0.9427] det_loss_box_3[1.1007] det_loss_cls_4[0.9513] det_loss_box_4[1.0877] det_loss_cls_5[0.9563] det_loss_box_5[1.0830] det_loss_cls_dn_0[0.2477] det_loss_box_dn_0[0.4813] det_loss_cls_dn_1[0.2447] det_loss_box_dn_1[0.4654] det_loss_cls_dn_2[0.2421] det_loss_box_dn_2[0.4620] det_loss_cls_dn_3[0.2415] det_loss_box_dn_3[0.4625] det_loss_cls_dn_4[0.2429] det_loss_box_dn_4[0.4644] det_loss_cls_dn_5[0.2442] det_loss_box_dn_5[0.4709] loss_depth[0.0599] total_loss[16.7086]
Rank[0/8] 06/19/2025 14:17:45 INFO stats.py:314 | Epoch[0] Step[224] GlobalStep[224] Training Speed: 7.56 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 1 day, 14:03:47.	Learning Rate: 9.01100e-05.	
Rank[0/8] 06/19/2025 14:17:45 INFO loss_tracker.py:84 | Epoch[0/239] Step[224] GlobalStep[224/NA]: det_loss_cls_0[0.9472] det_loss_box_0[1.2415] det_loss_cls_1[0.9420] det_loss_box_1[1.1773] det_loss_cls_2[0.9550] det_loss_box_2[1.1457] det_loss_cls_3[0.9664] det_loss_box_3[1.1373] det_loss_cls_4[0.9776] det_loss_box_4[1.1250] det_loss_cls_5[0.9830] det_loss_box_5[1.1220] det_loss_cls_dn_0[0.2624] det_loss_box_dn_0[0.4891] det_loss_cls_dn_1[0.2598] det_loss_box_dn_1[0.4767] det_loss_cls_dn_2[0.2579] det_loss_box_dn_2[0.4749] det_loss_cls_dn_3[0.2556] det_loss_box_dn_3[0.4760] det_loss_cls_dn_4[0.2552] det_loss_box_dn_4[0.4775] det_loss_cls_dn_5[0.2589] det_loss_box_dn_5[0.4813] loss_depth[0.0604] total_loss[17.2057]
Rank[0/8] 06/19/2025 14:18:12 INFO stats.py:314 | Epoch[0] Step[249] GlobalStep[249] Training Speed: 7.44 samples/sec across all devices.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 1 day, 13:01:40.	Learning Rate: 1.00100e-04.	
Rank[0/8] 06/19/2025 14:18:12 INFO loss_tracker.py:84 | Epoch[0/239] Step[249] GlobalStep[249/NA]: det_loss_cls_0[1.0952] det_loss_box_0[1.4694] det_loss_cls_1[1.1047] det_loss_box_1[1.4084] det_loss_cls_2[1.1184] det_loss_box_2[1.3672] det_loss_cls_3[1.1176] det_loss_box_3[1.3538] det_loss_cls_4[1.1244] det_loss_box_4[1.3536] det_loss_cls_5[1.1281] det_loss_box_5[1.3571] det_loss_cls_dn_0[0.2546] det_loss_box_dn_0[0.4073] det_loss_cls_dn_1[0.2520] det_loss_box_dn_1[0.4016] det_loss_cls_dn_2[0.2476] det_loss_box_dn_2[0.4041] det_loss_cls_dn_3[0.2455] det_loss_box_dn_3[0.4058] det_loss_cls_dn_4[0.2462] det_loss_box_dn_4[0.4103] det_loss_cls_dn_5[0.2496] det_loss_box_dn_5[0.4160] loss_depth[0.0553] total_loss[18.9940]
Rank[0/8] 06/19/2025 14:18:38 INFO stats.py:314 | Epoch[0] Step[274] GlobalStep[274] Training Speed: 13.67 samples/sec across all devices.	Average Step Time: 0.59 sec.	Estimated Remaining Time: 1 day, 12:06:33.	Learning Rate: 1.10090e-04.	
Rank[0/8] 06/19/2025 14:18:38 INFO loss_tracker.py:84 | Epoch[0/239] Step[274] GlobalStep[274/NA]: det_loss_cls_0[0.8180] det_loss_box_0[1.0410] det_loss_cls_1[0.8243] det_loss_box_1[0.9871] det_loss_cls_2[0.8351] det_loss_box_2[0.9589] det_loss_cls_3[0.8384] det_loss_box_3[0.9561] det_loss_cls_4[0.8408] det_loss_box_4[0.9541] det_loss_cls_5[0.8407] det_loss_box_5[0.9554] det_loss_cls_dn_0[0.2438] det_loss_box_dn_0[0.4245] det_loss_cls_dn_1[0.2393] det_loss_box_dn_1[0.4167] det_loss_cls_dn_2[0.2384] det_loss_box_dn_2[0.4160] det_loss_cls_dn_3[0.2375] det_loss_box_dn_3[0.4193] det_loss_cls_dn_4[0.2350] det_loss_box_dn_4[0.4214] det_loss_cls_dn_5[0.2382] det_loss_box_dn_5[0.4242] loss_depth[0.0580] total_loss[14.8621]
Rank[0/8] 06/19/2025 14:19:06 INFO stats.py:314 | Epoch[0] Step[299] GlobalStep[299] Training Speed: 7.48 samples/sec across all devices.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 1 day, 11:32:37.	Learning Rate: 1.20080e-04.	
Rank[0/8] 06/19/2025 14:19:06 INFO loss_tracker.py:84 | Epoch[0/239] Step[299] GlobalStep[299/NA]: det_loss_cls_0[0.8449] det_loss_box_0[1.0935] det_loss_cls_1[0.8425] det_loss_box_1[1.0400] det_loss_cls_2[0.8468] det_loss_box_2[1.0214] det_loss_cls_3[0.8418] det_loss_box_3[1.0147] det_loss_cls_4[0.8491] det_loss_box_4[1.0096] det_loss_cls_5[0.8602] det_loss_box_5[1.0109] det_loss_cls_dn_0[0.2539] det_loss_box_dn_0[0.4522] det_loss_cls_dn_1[0.2496] det_loss_box_dn_1[0.4460] det_loss_cls_dn_2[0.2473] det_loss_box_dn_2[0.4476] det_loss_cls_dn_3[0.2433] det_loss_box_dn_3[0.4502] det_loss_cls_dn_4[0.2443] det_loss_box_dn_4[0.4542] det_loss_cls_dn_5[0.2465] det_loss_box_dn_5[0.4569] loss_depth[0.0597] total_loss[15.5270]
Rank[0/8] 06/19/2025 14:19:33 INFO stats.py:314 | Epoch[0] Step[324] GlobalStep[324] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 10:53:28.	Learning Rate: 1.30070e-04.	
Rank[0/8] 06/19/2025 14:19:33 INFO loss_tracker.py:84 | Epoch[0/239] Step[324] GlobalStep[324/NA]: det_loss_cls_0[0.8481] det_loss_box_0[1.2351] det_loss_cls_1[0.8429] det_loss_box_1[1.1791] det_loss_cls_2[0.8501] det_loss_box_2[1.1627] det_loss_cls_3[0.8432] det_loss_box_3[1.1574] det_loss_cls_4[0.8450] det_loss_box_4[1.1540] det_loss_cls_5[0.8530] det_loss_box_5[1.1541] det_loss_cls_dn_0[0.2626] det_loss_box_dn_0[0.4760] det_loss_cls_dn_1[0.2550] det_loss_box_dn_1[0.4702] det_loss_cls_dn_2[0.2519] det_loss_box_dn_2[0.4718] det_loss_cls_dn_3[0.2517] det_loss_box_dn_3[0.4754] det_loss_cls_dn_4[0.2530] det_loss_box_dn_4[0.4789] det_loss_cls_dn_5[0.2558] det_loss_box_dn_5[0.4837] loss_depth[0.0579] total_loss[16.5686]
Rank[0/8] 06/19/2025 14:19:59 INFO stats.py:314 | Epoch[0] Step[349] GlobalStep[349] Training Speed: 7.87 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1 day, 10:20:47.	Learning Rate: 1.40060e-04.	
Rank[0/8] 06/19/2025 14:19:59 INFO loss_tracker.py:84 | Epoch[0/239] Step[349] GlobalStep[349/NA]: det_loss_cls_0[0.8403] det_loss_box_0[0.9821] det_loss_cls_1[0.8183] det_loss_box_1[0.9462] det_loss_cls_2[0.8187] det_loss_box_2[0.9292] det_loss_cls_3[0.8092] det_loss_box_3[0.9302] det_loss_cls_4[0.8153] det_loss_box_4[0.9242] det_loss_cls_5[0.8203] det_loss_box_5[0.9234] det_loss_cls_dn_0[0.2449] det_loss_box_dn_0[0.4154] det_loss_cls_dn_1[0.2402] det_loss_box_dn_1[0.4089] det_loss_cls_dn_2[0.2338] det_loss_box_dn_2[0.4110] det_loss_cls_dn_3[0.2302] det_loss_box_dn_3[0.4151] det_loss_cls_dn_4[0.2307] det_loss_box_dn_4[0.4214] det_loss_cls_dn_5[0.2325] det_loss_box_dn_5[0.4279] loss_depth[0.0584] total_loss[14.5277]
Rank[0/8] 06/19/2025 14:20:27 INFO stats.py:314 | Epoch[0] Step[374] GlobalStep[374] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 9:59:39.	Learning Rate: 1.50050e-04.	
Rank[0/8] 06/19/2025 14:20:27 INFO loss_tracker.py:84 | Epoch[0/239] Step[374] GlobalStep[374/NA]: det_loss_cls_0[0.6135] det_loss_box_0[0.8421] det_loss_cls_1[0.5916] det_loss_box_1[0.8165] det_loss_cls_2[0.5902] det_loss_box_2[0.8029] det_loss_cls_3[0.5839] det_loss_box_3[0.7969] det_loss_cls_4[0.5884] det_loss_box_4[0.7923] det_loss_cls_5[0.5935] det_loss_box_5[0.7933] det_loss_cls_dn_0[0.2334] det_loss_box_dn_0[0.4939] det_loss_cls_dn_1[0.2237] det_loss_box_dn_1[0.4843] det_loss_cls_dn_2[0.2193] det_loss_box_dn_2[0.4841] det_loss_cls_dn_3[0.2147] det_loss_box_dn_3[0.4854] det_loss_cls_dn_4[0.2179] det_loss_box_dn_4[0.4886] det_loss_cls_dn_5[0.2241] det_loss_box_dn_5[0.4918] loss_depth[0.0591] total_loss[12.7253]
Rank[0/8] 06/19/2025 14:20:43 INFO stats.py:394 | Epoch[0] completed. Training Speed: 6.15 samples/sec across all devices.	Epoch Time: 507.68 sec.	Average Epoch Time: 507.68 sec.	Average Step Time: 1.30 sec.	Estimated Remaining Time: 1 day, 9:42:16.	Learning Rate: 1.56044e-04.	
Rank[0/8] 06/19/2025 14:20:59 INFO stats.py:314 | Epoch[1] Step[9] GlobalStep[399] Training Speed: 7.80 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 1 day, 9:54:31.	Learning Rate: 1.60040e-04.	
Rank[0/8] 06/19/2025 14:21:14 INFO loss_tracker.py:84 | Epoch[1/239] Step[24] GlobalStep[414/NA]: det_loss_cls_0[0.7141] det_loss_box_0[0.8092] det_loss_cls_1[0.7060] det_loss_box_1[0.7886] det_loss_cls_2[0.7025] det_loss_box_2[0.7829] det_loss_cls_3[0.6965] det_loss_box_3[0.7805] det_loss_cls_4[0.6917] det_loss_box_4[0.7760] det_loss_cls_5[0.6927] det_loss_box_5[0.7787] det_loss_cls_dn_0[0.2266] det_loss_box_dn_0[0.4087] det_loss_cls_dn_1[0.2183] det_loss_box_dn_1[0.4043] det_loss_cls_dn_2[0.2137] det_loss_box_dn_2[0.4071] det_loss_cls_dn_3[0.2116] det_loss_box_dn_3[0.4101] det_loss_cls_dn_4[0.2139] det_loss_box_dn_4[0.4155] det_loss_cls_dn_5[0.2197] det_loss_box_dn_5[0.4204] loss_depth[0.0582] total_loss[12.7475]
Rank[0/8] 06/19/2025 14:21:25 INFO stats.py:314 | Epoch[1] Step[34] GlobalStep[424] Training Speed: 7.95 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 9:29:44.	Learning Rate: 1.70030e-04.	
Rank[0/8] 06/19/2025 14:21:41 INFO loss_tracker.py:84 | Epoch[1/239] Step[49] GlobalStep[439/NA]: det_loss_cls_0[0.8465] det_loss_box_0[1.1159] det_loss_cls_1[0.8264] det_loss_box_1[1.1004] det_loss_cls_2[0.8284] det_loss_box_2[1.0907] det_loss_cls_3[0.8196] det_loss_box_3[1.0941] det_loss_cls_4[0.8163] det_loss_box_4[1.0919] det_loss_cls_5[0.8134] det_loss_box_5[1.0921] det_loss_cls_dn_0[0.2325] det_loss_box_dn_0[0.5394] det_loss_cls_dn_1[0.2256] det_loss_box_dn_1[0.5376] det_loss_cls_dn_2[0.2203] det_loss_box_dn_2[0.5412] det_loss_cls_dn_3[0.2191] det_loss_box_dn_3[0.5491] det_loss_cls_dn_4[0.2178] det_loss_box_dn_4[0.5559] det_loss_cls_dn_5[0.2238] det_loss_box_dn_5[0.5676] loss_depth[0.0554] total_loss[16.2212]
Rank[0/8] 06/19/2025 14:21:52 INFO stats.py:314 | Epoch[1] Step[59] GlobalStep[449] Training Speed: 7.68 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 9:12:05.	Learning Rate: 1.80020e-04.	
Rank[0/8] 06/19/2025 14:22:09 INFO loss_tracker.py:84 | Epoch[1/239] Step[74] GlobalStep[464/NA]: det_loss_cls_0[0.7360] det_loss_box_0[0.9194] det_loss_cls_1[0.7254] det_loss_box_1[0.8893] det_loss_cls_2[0.7341] det_loss_box_2[0.8778] det_loss_cls_3[0.7236] det_loss_box_3[0.8806] det_loss_cls_4[0.7284] det_loss_box_4[0.8762] det_loss_cls_5[0.7279] det_loss_box_5[0.8816] det_loss_cls_dn_0[0.2250] det_loss_box_dn_0[0.4576] det_loss_cls_dn_1[0.2168] det_loss_box_dn_1[0.4501] det_loss_cls_dn_2[0.2163] det_loss_box_dn_2[0.4549] det_loss_cls_dn_3[0.2192] det_loss_box_dn_3[0.4589] det_loss_cls_dn_4[0.2205] det_loss_box_dn_4[0.4655] det_loss_cls_dn_5[0.2222] det_loss_box_dn_5[0.4710] loss_depth[0.0551] total_loss[13.8334]
Rank[0/8] 06/19/2025 14:22:19 INFO stats.py:314 | Epoch[1] Step[84] GlobalStep[474] Training Speed: 7.87 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1 day, 8:53:28.	Learning Rate: 1.90010e-04.	
Rank[0/8] 06/19/2025 14:22:35 INFO loss_tracker.py:84 | Epoch[1/239] Step[99] GlobalStep[489/NA]: det_loss_cls_0[0.7063] det_loss_box_0[0.8738] det_loss_cls_1[0.6863] det_loss_box_1[0.8554] det_loss_cls_2[0.6862] det_loss_box_2[0.8439] det_loss_cls_3[0.6766] det_loss_box_3[0.8420] det_loss_cls_4[0.6817] det_loss_box_4[0.8357] det_loss_cls_5[0.6855] det_loss_box_5[0.8366] det_loss_cls_dn_0[0.2185] det_loss_box_dn_0[0.5345] det_loss_cls_dn_1[0.2154] det_loss_box_dn_1[0.5323] det_loss_cls_dn_2[0.2147] det_loss_box_dn_2[0.5340] det_loss_cls_dn_3[0.2141] det_loss_box_dn_3[0.5389] det_loss_cls_dn_4[0.2150] det_loss_box_dn_4[0.5456] det_loss_cls_dn_5[0.2179] det_loss_box_dn_5[0.5518] loss_depth[0.0576] total_loss[13.8003]
Rank[0/8] 06/19/2025 14:22:45 INFO stats.py:314 | Epoch[1] Step[109] GlobalStep[499] Training Speed: 8.13 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 8:36:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:23:01 INFO loss_tracker.py:84 | Epoch[1/239] Step[124] GlobalStep[514/NA]: det_loss_cls_0[0.8858] det_loss_box_0[1.1535] det_loss_cls_1[0.8579] det_loss_box_1[1.1280] det_loss_cls_2[0.8491] det_loss_box_2[1.1173] det_loss_cls_3[0.8534] det_loss_box_3[1.1139] det_loss_cls_4[0.8482] det_loss_box_4[1.1120] det_loss_cls_5[0.8549] det_loss_box_5[1.1142] det_loss_cls_dn_0[0.2077] det_loss_box_dn_0[0.3655] det_loss_cls_dn_1[0.2053] det_loss_box_dn_1[0.3616] det_loss_cls_dn_2[0.2034] det_loss_box_dn_2[0.3667] det_loss_cls_dn_3[0.2028] det_loss_box_dn_3[0.3738] det_loss_cls_dn_4[0.2032] det_loss_box_dn_4[0.3815] det_loss_cls_dn_5[0.2067] det_loss_box_dn_5[0.3910] loss_depth[0.0534] total_loss[15.4107]
Rank[0/8] 06/19/2025 14:23:12 INFO stats.py:314 | Epoch[1] Step[134] GlobalStep[524] Training Speed: 6.51 samples/sec across all devices.	Average Step Time: 1.23 sec.	Estimated Remaining Time: 1 day, 8:20:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:23:28 INFO loss_tracker.py:84 | Epoch[1/239] Step[149] GlobalStep[539/NA]: det_loss_cls_0[1.0345] det_loss_box_0[1.4696] det_loss_cls_1[1.0094] det_loss_box_1[1.4421] det_loss_cls_2[1.0005] det_loss_box_2[1.4316] det_loss_cls_3[0.9964] det_loss_box_3[1.4316] det_loss_cls_4[0.9967] det_loss_box_4[1.4239] det_loss_cls_5[0.9977] det_loss_box_5[1.4219] det_loss_cls_dn_0[0.2105] det_loss_box_dn_0[0.4055] det_loss_cls_dn_1[0.2067] det_loss_box_dn_1[0.4035] det_loss_cls_dn_2[0.2063] det_loss_box_dn_2[0.4083] det_loss_cls_dn_3[0.2068] det_loss_box_dn_3[0.4146] det_loss_cls_dn_4[0.2103] det_loss_box_dn_4[0.4195] det_loss_cls_dn_5[0.2114] det_loss_box_dn_5[0.4257] loss_depth[0.0548] total_loss[18.4398]
Rank[0/8] 06/19/2025 14:23:39 INFO stats.py:314 | Epoch[1] Step[159] GlobalStep[549] Training Speed: 7.82 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1 day, 8:08:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:23:55 INFO loss_tracker.py:84 | Epoch[1/239] Step[174] GlobalStep[564/NA]: det_loss_cls_0[0.7625] det_loss_box_0[0.9846] det_loss_cls_1[0.7488] det_loss_box_1[0.9613] det_loss_cls_2[0.7520] det_loss_box_2[0.9497] det_loss_cls_3[0.7452] det_loss_box_3[0.9417] det_loss_cls_4[0.7463] det_loss_box_4[0.9378] det_loss_cls_5[0.7505] det_loss_box_5[0.9383] det_loss_cls_dn_0[0.1822] det_loss_box_dn_0[0.3543] det_loss_cls_dn_1[0.1820] det_loss_box_dn_1[0.3506] det_loss_cls_dn_2[0.1812] det_loss_box_dn_2[0.3520] det_loss_cls_dn_3[0.1835] det_loss_box_dn_3[0.3546] det_loss_cls_dn_4[0.1838] det_loss_box_dn_4[0.3588] det_loss_cls_dn_5[0.1873] det_loss_box_dn_5[0.3629] loss_depth[0.0583] total_loss[13.5103]
Rank[0/8] 06/19/2025 14:24:05 INFO stats.py:314 | Epoch[1] Step[184] GlobalStep[574] Training Speed: 7.36 samples/sec across all devices.	Average Step Time: 1.09 sec.	Estimated Remaining Time: 1 day, 7:55:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:24:21 INFO loss_tracker.py:84 | Epoch[1/239] Step[199] GlobalStep[589/NA]: det_loss_cls_0[0.8633] det_loss_box_0[1.1108] det_loss_cls_1[0.8364] det_loss_box_1[1.1088] det_loss_cls_2[0.8339] det_loss_box_2[1.1020] det_loss_cls_3[0.8427] det_loss_box_3[1.1053] det_loss_cls_4[0.8490] det_loss_box_4[1.1022] det_loss_cls_5[0.8500] det_loss_box_5[1.1086] det_loss_cls_dn_0[0.1927] det_loss_box_dn_0[0.3822] det_loss_cls_dn_1[0.1895] det_loss_box_dn_1[0.3780] det_loss_cls_dn_2[0.1908] det_loss_box_dn_2[0.3790] det_loss_cls_dn_3[0.1922] det_loss_box_dn_3[0.3827] det_loss_cls_dn_4[0.1947] det_loss_box_dn_4[0.3853] det_loss_cls_dn_5[0.1964] det_loss_box_dn_5[0.3886] loss_depth[0.0565] total_loss[15.2216]
Rank[0/8] 06/19/2025 14:24:31 INFO stats.py:314 | Epoch[1] Step[209] GlobalStep[599] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 7:42:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:24:48 INFO loss_tracker.py:84 | Epoch[1/239] Step[224] GlobalStep[614/NA]: det_loss_cls_0[0.9362] det_loss_box_0[1.2407] det_loss_cls_1[0.9151] det_loss_box_1[1.2270] det_loss_cls_2[0.9112] det_loss_box_2[1.2077] det_loss_cls_3[0.9072] det_loss_box_3[1.2035] det_loss_cls_4[0.9013] det_loss_box_4[1.1987] det_loss_cls_5[0.9116] det_loss_box_5[1.1937] det_loss_cls_dn_0[0.1932] det_loss_box_dn_0[0.3691] det_loss_cls_dn_1[0.1944] det_loss_box_dn_1[0.3662] det_loss_cls_dn_2[0.1942] det_loss_box_dn_2[0.3687] det_loss_cls_dn_3[0.1943] det_loss_box_dn_3[0.3726] det_loss_cls_dn_4[0.1956] det_loss_box_dn_4[0.3763] det_loss_cls_dn_5[0.1969] det_loss_box_dn_5[0.3803] loss_depth[0.0554] total_loss[16.2111]
Rank[0/8] 06/19/2025 14:24:59 INFO stats.py:314 | Epoch[1] Step[234] GlobalStep[624] Training Speed: 7.74 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 1 day, 7:34:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:25:15 INFO loss_tracker.py:84 | Epoch[1/239] Step[249] GlobalStep[639/NA]: det_loss_cls_0[0.7996] det_loss_box_0[1.0976] det_loss_cls_1[0.7849] det_loss_box_1[1.0847] det_loss_cls_2[0.7814] det_loss_box_2[1.0881] det_loss_cls_3[0.7740] det_loss_box_3[1.0836] det_loss_cls_4[0.7730] det_loss_box_4[1.0814] det_loss_cls_5[0.7816] det_loss_box_5[1.0766] det_loss_cls_dn_0[0.2034] det_loss_box_dn_0[0.4965] det_loss_cls_dn_1[0.2030] det_loss_box_dn_1[0.4947] det_loss_cls_dn_2[0.2029] det_loss_box_dn_2[0.4952] det_loss_cls_dn_3[0.2052] det_loss_box_dn_3[0.4978] det_loss_cls_dn_4[0.2059] det_loss_box_dn_4[0.5004] det_loss_cls_dn_5[0.2080] det_loss_box_dn_5[0.5035] loss_depth[0.0545] total_loss[15.4774]
Rank[0/8] 06/19/2025 14:25:25 INFO stats.py:314 | Epoch[1] Step[259] GlobalStep[649] Training Speed: 7.63 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 7:23:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:25:41 INFO loss_tracker.py:84 | Epoch[1/239] Step[274] GlobalStep[664/NA]: det_loss_cls_0[0.9291] det_loss_box_0[1.5172] det_loss_cls_1[0.8965] det_loss_box_1[1.4754] det_loss_cls_2[0.8825] det_loss_box_2[1.4593] det_loss_cls_3[0.8842] det_loss_box_3[1.4432] det_loss_cls_4[0.8783] det_loss_box_4[1.4291] det_loss_cls_5[0.8820] det_loss_box_5[1.4250] det_loss_cls_dn_0[0.2050] det_loss_box_dn_0[0.4953] det_loss_cls_dn_1[0.2053] det_loss_box_dn_1[0.4914] det_loss_cls_dn_2[0.2069] det_loss_box_dn_2[0.4938] det_loss_cls_dn_3[0.2064] det_loss_box_dn_3[0.4962] det_loss_cls_dn_4[0.2068] det_loss_box_dn_4[0.4984] det_loss_cls_dn_5[0.2102] det_loss_box_dn_5[0.5011] loss_depth[0.0573] total_loss[18.3758]
Rank[0/8] 06/19/2025 14:25:51 INFO stats.py:314 | Epoch[1] Step[284] GlobalStep[674] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 7:12:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:26:07 INFO loss_tracker.py:84 | Epoch[1/239] Step[299] GlobalStep[689/NA]: det_loss_cls_0[0.6852] det_loss_box_0[0.8664] det_loss_cls_1[0.6634] det_loss_box_1[0.8398] det_loss_cls_2[0.6617] det_loss_box_2[0.8341] det_loss_cls_3[0.6612] det_loss_box_3[0.8262] det_loss_cls_4[0.6611] det_loss_box_4[0.8244] det_loss_cls_5[0.6686] det_loss_box_5[0.8233] det_loss_cls_dn_0[0.1891] det_loss_box_dn_0[0.4318] det_loss_cls_dn_1[0.1907] det_loss_box_dn_1[0.4259] det_loss_cls_dn_2[0.1906] det_loss_box_dn_2[0.4276] det_loss_cls_dn_3[0.1910] det_loss_box_dn_3[0.4276] det_loss_cls_dn_4[0.1927] det_loss_box_dn_4[0.4291] det_loss_cls_dn_5[0.1959] det_loss_box_dn_5[0.4309] loss_depth[0.0520] total_loss[12.7902]
Rank[0/8] 06/19/2025 14:26:18 INFO stats.py:314 | Epoch[1] Step[309] GlobalStep[699] Training Speed: 7.89 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 7:05:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:26:35 INFO loss_tracker.py:84 | Epoch[1/239] Step[324] GlobalStep[714/NA]: det_loss_cls_0[0.7178] det_loss_box_0[0.9751] det_loss_cls_1[0.6984] det_loss_box_1[0.9579] det_loss_cls_2[0.7015] det_loss_box_2[0.9545] det_loss_cls_3[0.6982] det_loss_box_3[0.9565] det_loss_cls_4[0.6923] det_loss_box_4[0.9504] det_loss_cls_5[0.6982] det_loss_box_5[0.9449] det_loss_cls_dn_0[0.1867] det_loss_box_dn_0[0.4203] det_loss_cls_dn_1[0.1872] det_loss_box_dn_1[0.4173] det_loss_cls_dn_2[0.1875] det_loss_box_dn_2[0.4188] det_loss_cls_dn_3[0.1888] det_loss_box_dn_3[0.4209] det_loss_cls_dn_4[0.1908] det_loss_box_dn_4[0.4227] det_loss_cls_dn_5[0.1937] det_loss_box_dn_5[0.4262] loss_depth[0.0529] total_loss[13.6596]
Rank[0/8] 06/19/2025 14:26:45 INFO stats.py:314 | Epoch[1] Step[334] GlobalStep[724] Training Speed: 7.47 samples/sec across all devices.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 1 day, 6:57:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:27:01 INFO loss_tracker.py:84 | Epoch[1/239] Step[349] GlobalStep[739/NA]: det_loss_cls_0[0.6861] det_loss_box_0[0.8662] det_loss_cls_1[0.6765] det_loss_box_1[0.8398] det_loss_cls_2[0.6719] det_loss_box_2[0.8285] det_loss_cls_3[0.6697] det_loss_box_3[0.8286] det_loss_cls_4[0.6698] det_loss_box_4[0.8267] det_loss_cls_5[0.6732] det_loss_box_5[0.8280] det_loss_cls_dn_0[0.1872] det_loss_box_dn_0[0.4139] det_loss_cls_dn_1[0.1871] det_loss_box_dn_1[0.4076] det_loss_cls_dn_2[0.1848] det_loss_box_dn_2[0.4088] det_loss_cls_dn_3[0.1860] det_loss_box_dn_3[0.4110] det_loss_cls_dn_4[0.1873] det_loss_box_dn_4[0.4137] det_loss_cls_dn_5[0.1911] det_loss_box_dn_5[0.4169] loss_depth[0.0533] total_loss[12.7137]
Rank[0/8] 06/19/2025 14:27:12 INFO stats.py:314 | Epoch[1] Step[359] GlobalStep[749] Training Speed: 7.66 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 6:49:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:27:27 INFO loss_tracker.py:84 | Epoch[1/239] Step[374] GlobalStep[764/NA]: det_loss_cls_0[0.6911] det_loss_box_0[1.0526] det_loss_cls_1[0.6761] det_loss_box_1[1.0249] det_loss_cls_2[0.6738] det_loss_box_2[1.0097] det_loss_cls_3[0.6751] det_loss_box_3[1.0016] det_loss_cls_4[0.6812] det_loss_box_4[0.9900] det_loss_cls_5[0.6847] det_loss_box_5[0.9900] det_loss_cls_dn_0[0.1994] det_loss_box_dn_0[0.4891] det_loss_cls_dn_1[0.1989] det_loss_box_dn_1[0.4858] det_loss_cls_dn_2[0.2009] det_loss_box_dn_2[0.4872] det_loss_cls_dn_3[0.2013] det_loss_box_dn_3[0.4892] det_loss_cls_dn_4[0.2033] det_loss_box_dn_4[0.4917] det_loss_cls_dn_5[0.2050] det_loss_box_dn_5[0.4940] loss_depth[0.0550] total_loss[14.3518]
Rank[0/8] 06/19/2025 14:27:37 INFO stats.py:314 | Epoch[1] Step[384] GlobalStep[774] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 6:41:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:27:42 INFO stats.py:394 | Epoch[1] completed. Training Speed: 7.44 samples/sec across all devices.	Epoch Time: 419.52 sec.	Average Epoch Time: 419.52 sec.	Average Step Time: 1.08 sec.	Estimated Remaining Time: 1 day, 6:38:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:28:09 INFO stats.py:314 | Epoch[2] Step[19] GlobalStep[799] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 6:43:30.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:28:14 INFO loss_tracker.py:84 | Epoch[2/239] Step[24] GlobalStep[804/NA]: det_loss_cls_0[0.7731] det_loss_box_0[1.0767] det_loss_cls_1[0.7491] det_loss_box_1[1.0356] det_loss_cls_2[0.7525] det_loss_box_2[1.0177] det_loss_cls_3[0.7615] det_loss_box_3[1.0000] det_loss_cls_4[0.7589] det_loss_box_4[0.9970] det_loss_cls_5[0.7593] det_loss_box_5[0.9923] det_loss_cls_dn_0[0.1793] det_loss_box_dn_0[0.4607] det_loss_cls_dn_1[0.1787] det_loss_box_dn_1[0.4528] det_loss_cls_dn_2[0.1795] det_loss_box_dn_2[0.4546] det_loss_cls_dn_3[0.1802] det_loss_box_dn_3[0.4569] det_loss_cls_dn_4[0.1812] det_loss_box_dn_4[0.4579] det_loss_cls_dn_5[0.1846] det_loss_box_dn_5[0.4612] loss_depth[0.0522] total_loss[14.5536]
Rank[0/8] 06/19/2025 14:28:35 INFO stats.py:314 | Epoch[2] Step[44] GlobalStep[824] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 6:36:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:28:40 INFO loss_tracker.py:84 | Epoch[2/239] Step[49] GlobalStep[829/NA]: det_loss_cls_0[0.6374] det_loss_box_0[0.8299] det_loss_cls_1[0.6265] det_loss_box_1[0.8050] det_loss_cls_2[0.6256] det_loss_box_2[0.7826] det_loss_cls_3[0.6269] det_loss_box_3[0.7707] det_loss_cls_4[0.6300] det_loss_box_4[0.7650] det_loss_cls_5[0.6280] det_loss_box_5[0.7597] det_loss_cls_dn_0[0.1692] det_loss_box_dn_0[0.3372] det_loss_cls_dn_1[0.1695] det_loss_box_dn_1[0.3325] det_loss_cls_dn_2[0.1696] det_loss_box_dn_2[0.3352] det_loss_cls_dn_3[0.1716] det_loss_box_dn_3[0.3379] det_loss_cls_dn_4[0.1720] det_loss_box_dn_4[0.3399] det_loss_cls_dn_5[0.1745] det_loss_box_dn_5[0.3430] loss_depth[0.0515] total_loss[11.5911]
Rank[0/8] 06/19/2025 14:29:01 INFO stats.py:314 | Epoch[2] Step[69] GlobalStep[849] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 6:29:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:29:06 INFO loss_tracker.py:84 | Epoch[2/239] Step[74] GlobalStep[854/NA]: det_loss_cls_0[0.9814] det_loss_box_0[1.4832] det_loss_cls_1[0.9541] det_loss_box_1[1.4271] det_loss_cls_2[0.9512] det_loss_box_2[1.3796] det_loss_cls_3[0.9521] det_loss_box_3[1.3637] det_loss_cls_4[0.9592] det_loss_box_4[1.3541] det_loss_cls_5[0.9691] det_loss_box_5[1.3526] det_loss_cls_dn_0[0.1827] det_loss_box_dn_0[0.4165] det_loss_cls_dn_1[0.1838] det_loss_box_dn_1[0.4090] det_loss_cls_dn_2[0.1838] det_loss_box_dn_2[0.4105] det_loss_cls_dn_3[0.1852] det_loss_box_dn_3[0.4113] det_loss_cls_dn_4[0.1856] det_loss_box_dn_4[0.4126] det_loss_cls_dn_5[0.1874] det_loss_box_dn_5[0.4142] loss_depth[0.0509] total_loss[17.7609]
Rank[0/8] 06/19/2025 14:29:29 INFO stats.py:314 | Epoch[2] Step[94] GlobalStep[874] Training Speed: 8.01 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 6:26:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:29:34 INFO loss_tracker.py:84 | Epoch[2/239] Step[99] GlobalStep[879/NA]: det_loss_cls_0[0.6578] det_loss_box_0[0.8638] det_loss_cls_1[0.6355] det_loss_box_1[0.8325] det_loss_cls_2[0.6330] det_loss_box_2[0.8111] det_loss_cls_3[0.6363] det_loss_box_3[0.7923] det_loss_cls_4[0.6348] det_loss_box_4[0.7870] det_loss_cls_5[0.6373] det_loss_box_5[0.7836] det_loss_cls_dn_0[0.1695] det_loss_box_dn_0[0.3699] det_loss_cls_dn_1[0.1697] det_loss_box_dn_1[0.3674] det_loss_cls_dn_2[0.1716] det_loss_box_dn_2[0.3687] det_loss_cls_dn_3[0.1715] det_loss_box_dn_3[0.3702] det_loss_cls_dn_4[0.1732] det_loss_box_dn_4[0.3721] det_loss_cls_dn_5[0.1772] det_loss_box_dn_5[0.3743] loss_depth[0.0513] total_loss[12.0117]
Rank[0/8] 06/19/2025 14:29:56 INFO stats.py:314 | Epoch[2] Step[119] GlobalStep[899] Training Speed: 7.50 samples/sec across all devices.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 1 day, 6:21:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:30:01 INFO loss_tracker.py:84 | Epoch[2/239] Step[124] GlobalStep[904/NA]: det_loss_cls_0[0.8963] det_loss_box_0[1.4022] det_loss_cls_1[0.8782] det_loss_box_1[1.3674] det_loss_cls_2[0.8765] det_loss_box_2[1.3274] det_loss_cls_3[0.8754] det_loss_box_3[1.3093] det_loss_cls_4[0.8856] det_loss_box_4[1.2983] det_loss_cls_5[0.8849] det_loss_box_5[1.2951] det_loss_cls_dn_0[0.1862] det_loss_box_dn_0[0.4125] det_loss_cls_dn_1[0.1859] det_loss_box_dn_1[0.4089] det_loss_cls_dn_2[0.1853] det_loss_box_dn_2[0.4112] det_loss_cls_dn_3[0.1872] det_loss_box_dn_3[0.4131] det_loss_cls_dn_4[0.1875] det_loss_box_dn_4[0.4151] det_loss_cls_dn_5[0.1915] det_loss_box_dn_5[0.4167] loss_depth[0.0501] total_loss[16.9478]
Rank[0/8] 06/19/2025 14:30:22 INFO stats.py:314 | Epoch[2] Step[144] GlobalStep[924] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 6:15:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:30:27 INFO loss_tracker.py:84 | Epoch[2/239] Step[149] GlobalStep[929/NA]: det_loss_cls_0[0.8418] det_loss_box_0[1.1722] det_loss_cls_1[0.8147] det_loss_box_1[1.1293] det_loss_cls_2[0.8134] det_loss_box_2[1.1294] det_loss_cls_3[0.8134] det_loss_box_3[1.1154] det_loss_cls_4[0.8158] det_loss_box_4[1.1094] det_loss_cls_5[0.8107] det_loss_box_5[1.1102] det_loss_cls_dn_0[0.1744] det_loss_box_dn_0[0.4019] det_loss_cls_dn_1[0.1754] det_loss_box_dn_1[0.3984] det_loss_cls_dn_2[0.1742] det_loss_box_dn_2[0.3999] det_loss_cls_dn_3[0.1754] det_loss_box_dn_3[0.4003] det_loss_cls_dn_4[0.1776] det_loss_box_dn_4[0.4025] det_loss_cls_dn_5[0.1803] det_loss_box_dn_5[0.4059] loss_depth[0.0508] total_loss[15.1929]
Rank[0/8] 06/19/2025 14:30:49 INFO stats.py:314 | Epoch[2] Step[169] GlobalStep[949] Training Speed: 7.88 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1 day, 6:11:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:30:55 INFO loss_tracker.py:84 | Epoch[2/239] Step[174] GlobalStep[954/NA]: det_loss_cls_0[0.7766] det_loss_box_0[1.0833] det_loss_cls_1[0.7637] det_loss_box_1[1.0182] det_loss_cls_2[0.7605] det_loss_box_2[0.9927] det_loss_cls_3[0.7616] det_loss_box_3[0.9774] det_loss_cls_4[0.7685] det_loss_box_4[0.9738] det_loss_cls_5[0.7715] det_loss_box_5[0.9731] det_loss_cls_dn_0[0.1783] det_loss_box_dn_0[0.4786] det_loss_cls_dn_1[0.1778] det_loss_box_dn_1[0.4681] det_loss_cls_dn_2[0.1785] det_loss_box_dn_2[0.4690] det_loss_cls_dn_3[0.1803] det_loss_box_dn_3[0.4633] det_loss_cls_dn_4[0.1795] det_loss_box_dn_4[0.4643] det_loss_cls_dn_5[0.1815] det_loss_box_dn_5[0.4651] loss_depth[0.0499] total_loss[14.5551]
Rank[0/8] 06/19/2025 14:31:16 INFO stats.py:314 | Epoch[2] Step[194] GlobalStep[974] Training Speed: 7.88 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1 day, 6:06:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:31:21 INFO loss_tracker.py:84 | Epoch[2/239] Step[199] GlobalStep[979/NA]: det_loss_cls_0[0.7884] det_loss_box_0[1.0683] det_loss_cls_1[0.7680] det_loss_box_1[1.0142] det_loss_cls_2[0.7745] det_loss_box_2[0.9807] det_loss_cls_3[0.7716] det_loss_box_3[0.9659] det_loss_cls_4[0.7686] det_loss_box_4[0.9586] det_loss_cls_5[0.7765] det_loss_box_5[0.9514] det_loss_cls_dn_0[0.1910] det_loss_box_dn_0[0.3857] det_loss_cls_dn_1[0.1860] det_loss_box_dn_1[0.3835] det_loss_cls_dn_2[0.1860] det_loss_box_dn_2[0.3860] det_loss_cls_dn_3[0.1875] det_loss_box_dn_3[0.3885] det_loss_cls_dn_4[0.1876] det_loss_box_dn_4[0.3903] det_loss_cls_dn_5[0.1914] det_loss_box_dn_5[0.3921] loss_depth[0.0476] total_loss[14.0901]
Rank[0/8] 06/19/2025 14:31:42 INFO stats.py:314 | Epoch[2] Step[219] GlobalStep[999] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 6:00:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:31:47 INFO loss_tracker.py:84 | Epoch[2/239] Step[224] GlobalStep[1004/NA]: det_loss_cls_0[0.6438] det_loss_box_0[0.8391] det_loss_cls_1[0.6292] det_loss_box_1[0.7894] det_loss_cls_2[0.6200] det_loss_box_2[0.7628] det_loss_cls_3[0.6178] det_loss_box_3[0.7510] det_loss_cls_4[0.6216] det_loss_box_4[0.7411] det_loss_cls_5[0.6287] det_loss_box_5[0.7381] det_loss_cls_dn_0[0.1832] det_loss_box_dn_0[0.4350] det_loss_cls_dn_1[0.1847] det_loss_box_dn_1[0.4300] det_loss_cls_dn_2[0.1842] det_loss_box_dn_2[0.4336] det_loss_cls_dn_3[0.1823] det_loss_box_dn_3[0.4356] det_loss_cls_dn_4[0.1850] det_loss_box_dn_4[0.4384] det_loss_cls_dn_5[0.1877] det_loss_box_dn_5[0.4403] loss_depth[0.0477] total_loss[12.1501]
Rank[0/8] 06/19/2025 14:32:08 INFO stats.py:314 | Epoch[2] Step[244] GlobalStep[1024] Training Speed: 8.63 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 5:56:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:32:14 INFO loss_tracker.py:84 | Epoch[2/239] Step[249] GlobalStep[1029/NA]: det_loss_cls_0[0.6656] det_loss_box_0[0.8918] det_loss_cls_1[0.6430] det_loss_box_1[0.8505] det_loss_cls_2[0.6499] det_loss_box_2[0.8233] det_loss_cls_3[0.6434] det_loss_box_3[0.8130] det_loss_cls_4[0.6518] det_loss_box_4[0.8034] det_loss_cls_5[0.6547] det_loss_box_5[0.7997] det_loss_cls_dn_0[0.1829] det_loss_box_dn_0[0.4938] det_loss_cls_dn_1[0.1809] det_loss_box_dn_1[0.4857] det_loss_cls_dn_2[0.1804] det_loss_box_dn_2[0.4869] det_loss_cls_dn_3[0.1813] det_loss_box_dn_3[0.4869] det_loss_cls_dn_4[0.1821] det_loss_box_dn_4[0.4885] det_loss_cls_dn_5[0.1845] det_loss_box_dn_5[0.4904] loss_depth[0.0464] total_loss[12.9607]
Rank[0/8] 06/19/2025 14:32:35 INFO stats.py:314 | Epoch[2] Step[269] GlobalStep[1049] Training Speed: 7.17 samples/sec across all devices.	Average Step Time: 1.12 sec.	Estimated Remaining Time: 1 day, 5:52:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:32:41 INFO loss_tracker.py:84 | Epoch[2/239] Step[274] GlobalStep[1054/NA]: det_loss_cls_0[0.7480] det_loss_box_0[0.9389] det_loss_cls_1[0.7369] det_loss_box_1[0.8888] det_loss_cls_2[0.7340] det_loss_box_2[0.8670] det_loss_cls_3[0.7295] det_loss_box_3[0.8623] det_loss_cls_4[0.7291] det_loss_box_4[0.8586] det_loss_cls_5[0.7308] det_loss_box_5[0.8566] det_loss_cls_dn_0[0.1608] det_loss_box_dn_0[0.3437] det_loss_cls_dn_1[0.1613] det_loss_box_dn_1[0.3473] det_loss_cls_dn_2[0.1614] det_loss_box_dn_2[0.3501] det_loss_cls_dn_3[0.1627] det_loss_box_dn_3[0.3538] det_loss_cls_dn_4[0.1636] det_loss_box_dn_4[0.3560] det_loss_cls_dn_5[0.1656] det_loss_box_dn_5[0.3581] loss_depth[0.0485] total_loss[12.8134]
Rank[0/8] 06/19/2025 14:33:01 INFO stats.py:314 | Epoch[2] Step[294] GlobalStep[1074] Training Speed: 7.77 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 1 day, 5:47:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:33:07 INFO loss_tracker.py:84 | Epoch[2/239] Step[299] GlobalStep[1079/NA]: det_loss_cls_0[0.7792] det_loss_box_0[1.1280] det_loss_cls_1[0.7579] det_loss_box_1[1.0665] det_loss_cls_2[0.7616] det_loss_box_2[1.0230] det_loss_cls_3[0.7564] det_loss_box_3[1.0071] det_loss_cls_4[0.7578] det_loss_box_4[0.9986] det_loss_cls_5[0.7578] det_loss_box_5[0.9989] det_loss_cls_dn_0[0.1653] det_loss_box_dn_0[0.3747] det_loss_cls_dn_1[0.1661] det_loss_box_dn_1[0.3736] det_loss_cls_dn_2[0.1658] det_loss_box_dn_2[0.3750] det_loss_cls_dn_3[0.1673] det_loss_box_dn_3[0.3766] det_loss_cls_dn_4[0.1671] det_loss_box_dn_4[0.3779] det_loss_cls_dn_5[0.1699] det_loss_box_dn_5[0.3797] loss_depth[0.0486] total_loss[14.1004]
Rank[0/8] 06/19/2025 14:33:28 INFO stats.py:314 | Epoch[2] Step[319] GlobalStep[1099] Training Speed: 8.00 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 5:44:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:33:33 INFO loss_tracker.py:84 | Epoch[2/239] Step[324] GlobalStep[1104/NA]: det_loss_cls_0[0.9118] det_loss_box_0[1.3942] det_loss_cls_1[0.8875] det_loss_box_1[1.3164] det_loss_cls_2[0.8825] det_loss_box_2[1.2791] det_loss_cls_3[0.8785] det_loss_box_3[1.2665] det_loss_cls_4[0.8836] det_loss_box_4[1.2561] det_loss_cls_5[0.8845] det_loss_box_5[1.2540] det_loss_cls_dn_0[0.1683] det_loss_box_dn_0[0.5014] det_loss_cls_dn_1[0.1675] det_loss_box_dn_1[0.4959] det_loss_cls_dn_2[0.1693] det_loss_box_dn_2[0.4951] det_loss_cls_dn_3[0.1696] det_loss_box_dn_3[0.4954] det_loss_cls_dn_4[0.1707] det_loss_box_dn_4[0.4947] det_loss_cls_dn_5[0.1730] det_loss_box_dn_5[0.4965] loss_depth[0.0507] total_loss[17.1426]
Rank[0/8] 06/19/2025 14:33:54 INFO stats.py:314 | Epoch[2] Step[344] GlobalStep[1124] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 5:40:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:34:00 INFO loss_tracker.py:84 | Epoch[2/239] Step[349] GlobalStep[1129/NA]: det_loss_cls_0[0.7480] det_loss_box_0[1.1408] det_loss_cls_1[0.7295] det_loss_box_1[1.0846] det_loss_cls_2[0.7386] det_loss_box_2[1.0435] det_loss_cls_3[0.7340] det_loss_box_3[1.0311] det_loss_cls_4[0.7408] det_loss_box_4[1.0190] det_loss_cls_5[0.7378] det_loss_box_5[1.0199] det_loss_cls_dn_0[0.1626] det_loss_box_dn_0[0.4768] det_loss_cls_dn_1[0.1624] det_loss_box_dn_1[0.4685] det_loss_cls_dn_2[0.1617] det_loss_box_dn_2[0.4683] det_loss_cls_dn_3[0.1636] det_loss_box_dn_3[0.4675] det_loss_cls_dn_4[0.1636] det_loss_box_dn_4[0.4686] det_loss_cls_dn_5[0.1662] det_loss_box_dn_5[0.4694] loss_depth[0.0499] total_loss[14.6167]
Rank[0/8] 06/19/2025 14:34:21 INFO stats.py:314 | Epoch[2] Step[369] GlobalStep[1149] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 5:36:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:34:26 INFO loss_tracker.py:84 | Epoch[2/239] Step[374] GlobalStep[1154/NA]: det_loss_cls_0[0.6419] det_loss_box_0[0.9782] det_loss_cls_1[0.6315] det_loss_box_1[0.9443] det_loss_cls_2[0.6304] det_loss_box_2[0.8961] det_loss_cls_3[0.6306] det_loss_box_3[0.8784] det_loss_cls_4[0.6375] det_loss_box_4[0.8666] det_loss_cls_5[0.6378] det_loss_box_5[0.8647] det_loss_cls_dn_0[0.1701] det_loss_box_dn_0[0.4976] det_loss_cls_dn_1[0.1684] det_loss_box_dn_1[0.4842] det_loss_cls_dn_2[0.1669] det_loss_box_dn_2[0.4828] det_loss_cls_dn_3[0.1681] det_loss_box_dn_3[0.4805] det_loss_cls_dn_4[0.1695] det_loss_box_dn_4[0.4804] det_loss_cls_dn_5[0.1728] det_loss_box_dn_5[0.4803] loss_depth[0.0489] total_loss[13.2085]
Rank[0/8] 06/19/2025 14:34:41 INFO stats.py:394 | Epoch[2] completed. Training Speed: 7.44 samples/sec across all devices.	Epoch Time: 419.14 sec.	Average Epoch Time: 419.14 sec.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 1 day, 5:32:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:34:52 INFO stats.py:314 | Epoch[3] Step[4] GlobalStep[1174] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 5:39:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:35:13 INFO loss_tracker.py:84 | Epoch[3/239] Step[24] GlobalStep[1194/NA]: det_loss_cls_0[0.7179] det_loss_box_0[1.0574] det_loss_cls_1[0.7007] det_loss_box_1[0.9826] det_loss_cls_2[0.7024] det_loss_box_2[0.9489] det_loss_cls_3[0.7001] det_loss_box_3[0.9405] det_loss_cls_4[0.7048] det_loss_box_4[0.9235] det_loss_cls_5[0.7097] det_loss_box_5[0.9183] det_loss_cls_dn_0[0.1718] det_loss_box_dn_0[0.4719] det_loss_cls_dn_1[0.1707] det_loss_box_dn_1[0.4593] det_loss_cls_dn_2[0.1704] det_loss_box_dn_2[0.4598] det_loss_cls_dn_3[0.1709] det_loss_box_dn_3[0.4567] det_loss_cls_dn_4[0.1717] det_loss_box_dn_4[0.4576] det_loss_cls_dn_5[0.1739] det_loss_box_dn_5[0.4586] loss_depth[0.0474] total_loss[13.8477]
Rank[0/8] 06/19/2025 14:35:19 INFO stats.py:314 | Epoch[3] Step[29] GlobalStep[1199] Training Speed: 6.62 samples/sec across all devices.	Average Step Time: 1.21 sec.	Estimated Remaining Time: 1 day, 5:35:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:35:41 INFO loss_tracker.py:84 | Epoch[3/239] Step[49] GlobalStep[1219/NA]: det_loss_cls_0[0.6241] det_loss_box_0[0.9272] det_loss_cls_1[0.6137] det_loss_box_1[0.8685] det_loss_cls_2[0.6110] det_loss_box_2[0.8481] det_loss_cls_3[0.6074] det_loss_box_3[0.8446] det_loss_cls_4[0.6163] det_loss_box_4[0.8318] det_loss_cls_5[0.6135] det_loss_box_5[0.8291] det_loss_cls_dn_0[0.1731] det_loss_box_dn_0[0.5001] det_loss_cls_dn_1[0.1712] det_loss_box_dn_1[0.4917] det_loss_cls_dn_2[0.1706] det_loss_box_dn_2[0.4938] det_loss_cls_dn_3[0.1719] det_loss_box_dn_3[0.4958] det_loss_cls_dn_4[0.1734] det_loss_box_dn_4[0.4982] det_loss_cls_dn_5[0.1761] det_loss_box_dn_5[0.5004] loss_depth[0.0484] total_loss[12.9000]
Rank[0/8] 06/19/2025 14:35:46 INFO stats.py:314 | Epoch[3] Step[54] GlobalStep[1224] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 5:32:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:36:06 INFO loss_tracker.py:84 | Epoch[3/239] Step[74] GlobalStep[1244/NA]: det_loss_cls_0[0.7328] det_loss_box_0[0.9608] det_loss_cls_1[0.7350] det_loss_box_1[0.8960] det_loss_cls_2[0.7264] det_loss_box_2[0.8640] det_loss_cls_3[0.7188] det_loss_box_3[0.8560] det_loss_cls_4[0.7316] det_loss_box_4[0.8542] det_loss_cls_5[0.7279] det_loss_box_5[0.8512] det_loss_cls_dn_0[0.1670] det_loss_box_dn_0[0.3797] det_loss_cls_dn_1[0.1652] det_loss_box_dn_1[0.3760] det_loss_cls_dn_2[0.1656] det_loss_box_dn_2[0.3747] det_loss_cls_dn_3[0.1644] det_loss_box_dn_3[0.3758] det_loss_cls_dn_4[0.1658] det_loss_box_dn_4[0.3776] det_loss_cls_dn_5[0.1686] det_loss_box_dn_5[0.3799] loss_depth[0.0465] total_loss[12.9616]
Rank[0/8] 06/19/2025 14:36:12 INFO stats.py:314 | Epoch[3] Step[79] GlobalStep[1249] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 5:29:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:36:33 INFO loss_tracker.py:84 | Epoch[3/239] Step[99] GlobalStep[1269/NA]: det_loss_cls_0[0.7559] det_loss_box_0[0.9875] det_loss_cls_1[0.7321] det_loss_box_1[0.9129] det_loss_cls_2[0.7275] det_loss_box_2[0.8588] det_loss_cls_3[0.7196] det_loss_box_3[0.8506] det_loss_cls_4[0.7253] det_loss_box_4[0.8357] det_loss_cls_5[0.7241] det_loss_box_5[0.8347] det_loss_cls_dn_0[0.1741] det_loss_box_dn_0[0.4069] det_loss_cls_dn_1[0.1727] det_loss_box_dn_1[0.4011] det_loss_cls_dn_2[0.1749] det_loss_box_dn_2[0.4036] det_loss_cls_dn_3[0.1771] det_loss_box_dn_3[0.4087] det_loss_cls_dn_4[0.1776] det_loss_box_dn_4[0.4111] det_loss_cls_dn_5[0.1804] det_loss_box_dn_5[0.4128] loss_depth[0.0482] total_loss[13.2138]
Rank[0/8] 06/19/2025 14:36:38 INFO stats.py:314 | Epoch[3] Step[104] GlobalStep[1274] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 5:25:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:37:00 INFO loss_tracker.py:84 | Epoch[3/239] Step[124] GlobalStep[1294/NA]: det_loss_cls_0[0.8364] det_loss_box_0[1.0077] det_loss_cls_1[0.8263] det_loss_box_1[0.9400] det_loss_cls_2[0.8183] det_loss_box_2[0.9190] det_loss_cls_3[0.8049] det_loss_box_3[0.9211] det_loss_cls_4[0.8085] det_loss_box_4[0.9157] det_loss_cls_5[0.8157] det_loss_box_5[0.9022] det_loss_cls_dn_0[0.1711] det_loss_box_dn_0[0.3628] det_loss_cls_dn_1[0.1671] det_loss_box_dn_1[0.3589] det_loss_cls_dn_2[0.1669] det_loss_box_dn_2[0.3595] det_loss_cls_dn_3[0.1676] det_loss_box_dn_3[0.3599] det_loss_cls_dn_4[0.1677] det_loss_box_dn_4[0.3608] det_loss_cls_dn_5[0.1685] det_loss_box_dn_5[0.3622] loss_depth[0.0450] total_loss[13.7338]
Rank[0/8] 06/19/2025 14:37:05 INFO stats.py:314 | Epoch[3] Step[129] GlobalStep[1299] Training Speed: 6.36 samples/sec across all devices.	Average Step Time: 1.26 sec.	Estimated Remaining Time: 1 day, 5:23:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:37:26 INFO loss_tracker.py:84 | Epoch[3/239] Step[149] GlobalStep[1319/NA]: det_loss_cls_0[0.6513] det_loss_box_0[0.8324] det_loss_cls_1[0.6495] det_loss_box_1[0.8013] det_loss_cls_2[0.6400] det_loss_box_2[0.7850] det_loss_cls_3[0.6513] det_loss_box_3[0.7568] det_loss_cls_4[0.6546] det_loss_box_4[0.7528] det_loss_cls_5[0.6585] det_loss_box_5[0.7500] det_loss_cls_dn_0[0.1629] det_loss_box_dn_0[0.5149] det_loss_cls_dn_1[0.1651] det_loss_box_dn_1[0.4949] det_loss_cls_dn_2[0.1661] det_loss_box_dn_2[0.4951] det_loss_cls_dn_3[0.1657] det_loss_box_dn_3[0.4918] det_loss_cls_dn_4[0.1671] det_loss_box_dn_4[0.4922] det_loss_cls_dn_5[0.1697] det_loss_box_dn_5[0.4940] loss_depth[0.0452] total_loss[12.6081]
Rank[0/8] 06/19/2025 14:37:32 INFO stats.py:314 | Epoch[3] Step[154] GlobalStep[1324] Training Speed: 6.22 samples/sec across all devices.	Average Step Time: 1.29 sec.	Estimated Remaining Time: 1 day, 5:20:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:37:53 INFO loss_tracker.py:84 | Epoch[3/239] Step[174] GlobalStep[1344/NA]: det_loss_cls_0[0.7120] det_loss_box_0[1.0060] det_loss_cls_1[0.7092] det_loss_box_1[0.9178] det_loss_cls_2[0.7108] det_loss_box_2[0.8696] det_loss_cls_3[0.7128] det_loss_box_3[0.8555] det_loss_cls_4[0.7042] det_loss_box_4[0.8526] det_loss_cls_5[0.7049] det_loss_box_5[0.8525] det_loss_cls_dn_0[0.1666] det_loss_box_dn_0[0.3802] det_loss_cls_dn_1[0.1664] det_loss_box_dn_1[0.3708] det_loss_cls_dn_2[0.1655] det_loss_box_dn_2[0.3715] det_loss_cls_dn_3[0.1667] det_loss_box_dn_3[0.3718] det_loss_cls_dn_4[0.1665] det_loss_box_dn_4[0.3726] det_loss_cls_dn_5[0.1687] det_loss_box_dn_5[0.3733] loss_depth[0.0468] total_loss[12.8953]
Rank[0/8] 06/19/2025 14:37:58 INFO stats.py:314 | Epoch[3] Step[179] GlobalStep[1349] Training Speed: 6.27 samples/sec across all devices.	Average Step Time: 1.28 sec.	Estimated Remaining Time: 1 day, 5:17:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:38:20 INFO loss_tracker.py:84 | Epoch[3/239] Step[199] GlobalStep[1369/NA]: det_loss_cls_0[0.8207] det_loss_box_0[1.1781] det_loss_cls_1[0.7955] det_loss_box_1[1.0977] det_loss_cls_2[0.7904] det_loss_box_2[1.0466] det_loss_cls_3[0.7800] det_loss_box_3[1.0377] det_loss_cls_4[0.7785] det_loss_box_4[1.0306] det_loss_cls_5[0.7826] det_loss_box_5[1.0298] det_loss_cls_dn_0[0.1787] det_loss_box_dn_0[0.4013] det_loss_cls_dn_1[0.1788] det_loss_box_dn_1[0.3948] det_loss_cls_dn_2[0.1783] det_loss_box_dn_2[0.3943] det_loss_cls_dn_3[0.1788] det_loss_box_dn_3[0.3950] det_loss_cls_dn_4[0.1787] det_loss_box_dn_4[0.3956] det_loss_cls_dn_5[0.1803] det_loss_box_dn_5[0.3966] loss_depth[0.0459] total_loss[14.6654]
Rank[0/8] 06/19/2025 14:38:25 INFO stats.py:314 | Epoch[3] Step[204] GlobalStep[1374] Training Speed: 7.87 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1 day, 5:14:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:38:46 INFO loss_tracker.py:84 | Epoch[3/239] Step[224] GlobalStep[1394/NA]: det_loss_cls_0[0.7105] det_loss_box_0[0.9856] det_loss_cls_1[0.6875] det_loss_box_1[0.9236] det_loss_cls_2[0.6933] det_loss_box_2[0.8963] det_loss_cls_3[0.6865] det_loss_box_3[0.8931] det_loss_cls_4[0.6860] det_loss_box_4[0.8855] det_loss_cls_5[0.6888] det_loss_box_5[0.8798] det_loss_cls_dn_0[0.1827] det_loss_box_dn_0[0.4347] det_loss_cls_dn_1[0.1832] det_loss_box_dn_1[0.4297] det_loss_cls_dn_2[0.1829] det_loss_box_dn_2[0.4307] det_loss_cls_dn_3[0.1824] det_loss_box_dn_3[0.4328] det_loss_cls_dn_4[0.1839] det_loss_box_dn_4[0.4348] det_loss_cls_dn_5[0.1863] det_loss_box_dn_5[0.4362] loss_depth[0.0486] total_loss[13.3654]
Rank[0/8] 06/19/2025 14:38:51 INFO stats.py:314 | Epoch[3] Step[229] GlobalStep[1399] Training Speed: 7.69 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 5:12:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:39:12 INFO loss_tracker.py:84 | Epoch[3/239] Step[249] GlobalStep[1419/NA]: det_loss_cls_0[0.7453] det_loss_box_0[1.1431] det_loss_cls_1[0.7271] det_loss_box_1[1.0545] det_loss_cls_2[0.7276] det_loss_box_2[1.0041] det_loss_cls_3[0.7270] det_loss_box_3[0.9870] det_loss_cls_4[0.7215] det_loss_box_4[0.9767] det_loss_cls_5[0.7243] det_loss_box_5[0.9729] det_loss_cls_dn_0[0.1672] det_loss_box_dn_0[0.3955] det_loss_cls_dn_1[0.1662] det_loss_box_dn_1[0.3887] det_loss_cls_dn_2[0.1656] det_loss_box_dn_2[0.3883] det_loss_cls_dn_3[0.1658] det_loss_box_dn_3[0.3901] det_loss_cls_dn_4[0.1661] det_loss_box_dn_4[0.3915] det_loss_cls_dn_5[0.1685] det_loss_box_dn_5[0.3924] loss_depth[0.0442] total_loss[13.9014]
Rank[0/8] 06/19/2025 14:39:17 INFO stats.py:314 | Epoch[3] Step[254] GlobalStep[1424] Training Speed: 7.97 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 5:08:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:39:38 INFO loss_tracker.py:84 | Epoch[3/239] Step[274] GlobalStep[1444/NA]: det_loss_cls_0[0.7877] det_loss_box_0[1.0623] det_loss_cls_1[0.7787] det_loss_box_1[0.9498] det_loss_cls_2[0.7760] det_loss_box_2[0.9244] det_loss_cls_3[0.7822] det_loss_box_3[0.9083] det_loss_cls_4[0.7801] det_loss_box_4[0.8988] det_loss_cls_5[0.7801] det_loss_box_5[0.8945] det_loss_cls_dn_0[0.1676] det_loss_box_dn_0[0.5338] det_loss_cls_dn_1[0.1673] det_loss_box_dn_1[0.5164] det_loss_cls_dn_2[0.1667] det_loss_box_dn_2[0.5187] det_loss_cls_dn_3[0.1685] det_loss_box_dn_3[0.5171] det_loss_cls_dn_4[0.1684] det_loss_box_dn_4[0.5203] det_loss_cls_dn_5[0.1701] det_loss_box_dn_5[0.5233] loss_depth[0.0411] total_loss[14.5020]
Rank[0/8] 06/19/2025 14:39:44 INFO stats.py:314 | Epoch[3] Step[279] GlobalStep[1449] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 5:06:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:40:05 INFO loss_tracker.py:84 | Epoch[3/239] Step[299] GlobalStep[1469/NA]: det_loss_cls_0[0.6855] det_loss_box_0[0.8679] det_loss_cls_1[0.6749] det_loss_box_1[0.8129] det_loss_cls_2[0.6831] det_loss_box_2[0.7796] det_loss_cls_3[0.6874] det_loss_box_3[0.7627] det_loss_cls_4[0.6858] det_loss_box_4[0.7543] det_loss_cls_5[0.6824] det_loss_box_5[0.7528] det_loss_cls_dn_0[0.1661] det_loss_box_dn_0[0.4086] det_loss_cls_dn_1[0.1663] det_loss_box_dn_1[0.3987] det_loss_cls_dn_2[0.1660] det_loss_box_dn_2[0.3995] det_loss_cls_dn_3[0.1669] det_loss_box_dn_3[0.3993] det_loss_cls_dn_4[0.1668] det_loss_box_dn_4[0.3997] det_loss_cls_dn_5[0.1677] det_loss_box_dn_5[0.4001] loss_depth[0.0441] total_loss[12.2792]
Rank[0/8] 06/19/2025 14:40:10 INFO stats.py:314 | Epoch[3] Step[304] GlobalStep[1474] Training Speed: 7.67 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 5:03:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:40:31 INFO loss_tracker.py:84 | Epoch[3/239] Step[324] GlobalStep[1494/NA]: det_loss_cls_0[0.7734] det_loss_box_0[1.0360] det_loss_cls_1[0.7604] det_loss_box_1[0.9672] det_loss_cls_2[0.7636] det_loss_box_2[0.9337] det_loss_cls_3[0.7601] det_loss_box_3[0.9254] det_loss_cls_4[0.7647] det_loss_box_4[0.9224] det_loss_cls_5[0.7713] det_loss_box_5[0.9160] det_loss_cls_dn_0[0.1697] det_loss_box_dn_0[0.4127] det_loss_cls_dn_1[0.1702] det_loss_box_dn_1[0.4040] det_loss_cls_dn_2[0.1701] det_loss_box_dn_2[0.4024] det_loss_cls_dn_3[0.1716] det_loss_box_dn_3[0.4024] det_loss_cls_dn_4[0.1724] det_loss_box_dn_4[0.4032] det_loss_cls_dn_5[0.1742] det_loss_box_dn_5[0.4034] loss_depth[0.0455] total_loss[13.7961]
Rank[0/8] 06/19/2025 14:40:36 INFO stats.py:314 | Epoch[3] Step[329] GlobalStep[1499] Training Speed: 8.00 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 5:00:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:40:58 INFO loss_tracker.py:84 | Epoch[3/239] Step[349] GlobalStep[1519/NA]: det_loss_cls_0[0.8530] det_loss_box_0[1.1278] det_loss_cls_1[0.8394] det_loss_box_1[1.0226] det_loss_cls_2[0.8458] det_loss_box_2[0.9735] det_loss_cls_3[0.8496] det_loss_box_3[0.9602] det_loss_cls_4[0.8382] det_loss_box_4[0.9574] det_loss_cls_5[0.8463] det_loss_box_5[0.9494] det_loss_cls_dn_0[0.1773] det_loss_box_dn_0[0.3606] det_loss_cls_dn_1[0.1753] det_loss_box_dn_1[0.3595] det_loss_cls_dn_2[0.1750] det_loss_box_dn_2[0.3606] det_loss_cls_dn_3[0.1752] det_loss_box_dn_3[0.3617] det_loss_cls_dn_4[0.1765] det_loss_box_dn_4[0.3624] det_loss_cls_dn_5[0.1791] det_loss_box_dn_5[0.3640] loss_depth[0.0424] total_loss[14.3327]
Rank[0/8] 06/19/2025 14:41:03 INFO stats.py:314 | Epoch[3] Step[354] GlobalStep[1524] Training Speed: 7.73 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 4:59:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:41:25 INFO loss_tracker.py:84 | Epoch[3/239] Step[374] GlobalStep[1544/NA]: det_loss_cls_0[0.7262] det_loss_box_0[0.9657] det_loss_cls_1[0.7259] det_loss_box_1[0.8721] det_loss_cls_2[0.7186] det_loss_box_2[0.8303] det_loss_cls_3[0.7152] det_loss_box_3[0.8218] det_loss_cls_4[0.7055] det_loss_box_4[0.8197] det_loss_cls_5[0.7076] det_loss_box_5[0.8117] det_loss_cls_dn_0[0.1581] det_loss_box_dn_0[0.4447] det_loss_cls_dn_1[0.1573] det_loss_box_dn_1[0.4196] det_loss_cls_dn_2[0.1573] det_loss_box_dn_2[0.4164] det_loss_cls_dn_3[0.1569] det_loss_box_dn_3[0.4118] det_loss_cls_dn_4[0.1570] det_loss_box_dn_4[0.4112] det_loss_cls_dn_5[0.1583] det_loss_box_dn_5[0.4130] loss_depth[0.0464] total_loss[12.9286]
Rank[0/8] 06/19/2025 14:41:30 INFO stats.py:314 | Epoch[3] Step[379] GlobalStep[1549] Training Speed: 8.02 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 4:57:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:41:40 INFO stats.py:394 | Epoch[3] completed. Training Speed: 7.45 samples/sec across all devices.	Epoch Time: 418.86 sec.	Average Epoch Time: 418.86 sec.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 1 day, 4:55:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:42:01 INFO stats.py:314 | Epoch[4] Step[14] GlobalStep[1574] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 4:59:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:42:12 INFO loss_tracker.py:84 | Epoch[4/239] Step[24] GlobalStep[1584/NA]: det_loss_cls_0[0.8099] det_loss_box_0[1.1163] det_loss_cls_1[0.8117] det_loss_box_1[1.0127] det_loss_cls_2[0.8056] det_loss_box_2[0.9718] det_loss_cls_3[0.7988] det_loss_box_3[0.9638] det_loss_cls_4[0.8063] det_loss_box_4[0.9445] det_loss_cls_5[0.8034] det_loss_box_5[0.9432] det_loss_cls_dn_0[0.1609] det_loss_box_dn_0[0.3830] det_loss_cls_dn_1[0.1597] det_loss_box_dn_1[0.3743] det_loss_cls_dn_2[0.1603] det_loss_box_dn_2[0.3740] det_loss_cls_dn_3[0.1612] det_loss_box_dn_3[0.3744] det_loss_cls_dn_4[0.1611] det_loss_box_dn_4[0.3754] det_loss_cls_dn_5[0.1628] det_loss_box_dn_5[0.3768] loss_depth[0.0443] total_loss[14.0560]
Rank[0/8] 06/19/2025 14:42:28 INFO stats.py:314 | Epoch[4] Step[39] GlobalStep[1599] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 4:57:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:42:39 INFO loss_tracker.py:84 | Epoch[4/239] Step[49] GlobalStep[1609/NA]: det_loss_cls_0[0.7336] det_loss_box_0[1.0998] det_loss_cls_1[0.7297] det_loss_box_1[1.0100] det_loss_cls_2[0.7233] det_loss_box_2[0.9504] det_loss_cls_3[0.7174] det_loss_box_3[0.9314] det_loss_cls_4[0.7183] det_loss_box_4[0.9196] det_loss_cls_5[0.7180] det_loss_box_5[0.9154] det_loss_cls_dn_0[0.1561] det_loss_box_dn_0[0.4694] det_loss_cls_dn_1[0.1556] det_loss_box_dn_1[0.4561] det_loss_cls_dn_2[0.1548] det_loss_box_dn_2[0.4557] det_loss_cls_dn_3[0.1541] det_loss_box_dn_3[0.4583] det_loss_cls_dn_4[0.1540] det_loss_box_dn_4[0.4605] det_loss_cls_dn_5[0.1559] det_loss_box_dn_5[0.4625] loss_depth[0.0459] total_loss[13.9057]
Rank[0/8] 06/19/2025 14:42:55 INFO stats.py:314 | Epoch[4] Step[64] GlobalStep[1624] Training Speed: 6.49 samples/sec across all devices.	Average Step Time: 1.23 sec.	Estimated Remaining Time: 1 day, 4:55:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:43:06 INFO loss_tracker.py:84 | Epoch[4/239] Step[74] GlobalStep[1634/NA]: det_loss_cls_0[0.6186] det_loss_box_0[0.8194] det_loss_cls_1[0.6075] det_loss_box_1[0.7455] det_loss_cls_2[0.6019] det_loss_box_2[0.7238] det_loss_cls_3[0.6070] det_loss_box_3[0.6974] det_loss_cls_4[0.6009] det_loss_box_4[0.6870] det_loss_cls_5[0.6076] det_loss_box_5[0.6841] det_loss_cls_dn_0[0.1566] det_loss_box_dn_0[0.3951] det_loss_cls_dn_1[0.1549] det_loss_box_dn_1[0.3826] det_loss_cls_dn_2[0.1542] det_loss_box_dn_2[0.3832] det_loss_cls_dn_3[0.1559] det_loss_box_dn_3[0.3836] det_loss_cls_dn_4[0.1565] det_loss_box_dn_4[0.3848] det_loss_cls_dn_5[0.1588] det_loss_box_dn_5[0.3867] loss_depth[0.0445] total_loss[11.2982]
Rank[0/8] 06/19/2025 14:43:21 INFO stats.py:314 | Epoch[4] Step[89] GlobalStep[1649] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 4:53:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:43:32 INFO loss_tracker.py:84 | Epoch[4/239] Step[99] GlobalStep[1659/NA]: det_loss_cls_0[0.7644] det_loss_box_0[1.0757] det_loss_cls_1[0.7527] det_loss_box_1[1.0030] det_loss_cls_2[0.7512] det_loss_box_2[0.9879] det_loss_cls_3[0.7516] det_loss_box_3[0.9714] det_loss_cls_4[0.7551] det_loss_box_4[0.9698] det_loss_cls_5[0.7560] det_loss_box_5[0.9622] det_loss_cls_dn_0[0.1676] det_loss_box_dn_0[0.3971] det_loss_cls_dn_1[0.1661] det_loss_box_dn_1[0.3901] det_loss_cls_dn_2[0.1656] det_loss_box_dn_2[0.3905] det_loss_cls_dn_3[0.1674] det_loss_box_dn_3[0.3915] det_loss_cls_dn_4[0.1670] det_loss_box_dn_4[0.3925] det_loss_cls_dn_5[0.1696] det_loss_box_dn_5[0.3934] loss_depth[0.0485] total_loss[13.9079]
Rank[0/8] 06/19/2025 14:43:47 INFO stats.py:314 | Epoch[4] Step[114] GlobalStep[1674] Training Speed: 6.22 samples/sec across all devices.	Average Step Time: 1.29 sec.	Estimated Remaining Time: 1 day, 4:50:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:43:58 INFO loss_tracker.py:84 | Epoch[4/239] Step[124] GlobalStep[1684/NA]: det_loss_cls_0[0.7908] det_loss_box_0[1.1708] det_loss_cls_1[0.7806] det_loss_box_1[1.0754] det_loss_cls_2[0.7813] det_loss_box_2[1.0294] det_loss_cls_3[0.7833] det_loss_box_3[1.0214] det_loss_cls_4[0.7823] det_loss_box_4[1.0180] det_loss_cls_5[0.7809] det_loss_box_5[1.0155] det_loss_cls_dn_0[0.1605] det_loss_box_dn_0[0.4401] det_loss_cls_dn_1[0.1615] det_loss_box_dn_1[0.4326] det_loss_cls_dn_2[0.1614] det_loss_box_dn_2[0.4324] det_loss_cls_dn_3[0.1616] det_loss_box_dn_3[0.4325] det_loss_cls_dn_4[0.1616] det_loss_box_dn_4[0.4334] det_loss_cls_dn_5[0.1632] det_loss_box_dn_5[0.4337] loss_depth[0.0488] total_loss[14.6532]
Rank[0/8] 06/19/2025 14:44:14 INFO stats.py:314 | Epoch[4] Step[139] GlobalStep[1699] Training Speed: 9.14 samples/sec across all devices.	Average Step Time: 0.87 sec.	Estimated Remaining Time: 1 day, 4:48:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:44:24 INFO loss_tracker.py:84 | Epoch[4/239] Step[149] GlobalStep[1709/NA]: det_loss_cls_0[0.7554] det_loss_box_0[1.1413] det_loss_cls_1[0.7600] det_loss_box_1[1.0141] det_loss_cls_2[0.7484] det_loss_box_2[0.9873] det_loss_cls_3[0.7437] det_loss_box_3[0.9818] det_loss_cls_4[0.7391] det_loss_box_4[0.9752] det_loss_cls_5[0.7409] det_loss_box_5[0.9737] det_loss_cls_dn_0[0.1619] det_loss_box_dn_0[0.4412] det_loss_cls_dn_1[0.1568] det_loss_box_dn_1[0.4288] det_loss_cls_dn_2[0.1573] det_loss_box_dn_2[0.4284] det_loss_cls_dn_3[0.1582] det_loss_box_dn_3[0.4285] det_loss_cls_dn_4[0.1581] det_loss_box_dn_4[0.4293] det_loss_cls_dn_5[0.1598] det_loss_box_dn_5[0.4306] loss_depth[0.0545] total_loss[14.1544]
Rank[0/8] 06/19/2025 14:44:40 INFO stats.py:314 | Epoch[4] Step[164] GlobalStep[1724] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 4:46:30.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:44:50 INFO loss_tracker.py:84 | Epoch[4/239] Step[174] GlobalStep[1734/NA]: det_loss_cls_0[0.7372] det_loss_box_0[1.0786] det_loss_cls_1[0.7261] det_loss_box_1[0.9717] det_loss_cls_2[0.7215] det_loss_box_2[0.9255] det_loss_cls_3[0.7289] det_loss_box_3[0.8995] det_loss_cls_4[0.7250] det_loss_box_4[0.8959] det_loss_cls_5[0.7237] det_loss_box_5[0.8948] det_loss_cls_dn_0[0.1659] det_loss_box_dn_0[0.4122] det_loss_cls_dn_1[0.1656] det_loss_box_dn_1[0.4018] det_loss_cls_dn_2[0.1664] det_loss_box_dn_2[0.4019] det_loss_cls_dn_3[0.1663] det_loss_box_dn_3[0.4029] det_loss_cls_dn_4[0.1674] det_loss_box_dn_4[0.4043] det_loss_cls_dn_5[0.1689] det_loss_box_dn_5[0.4055] loss_depth[0.0442] total_loss[13.5015]
Rank[0/8] 06/19/2025 14:45:06 INFO stats.py:314 | Epoch[4] Step[189] GlobalStep[1749] Training Speed: 7.69 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 4:44:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:45:17 INFO loss_tracker.py:84 | Epoch[4/239] Step[199] GlobalStep[1759/NA]: det_loss_cls_0[0.6329] det_loss_box_0[0.9121] det_loss_cls_1[0.6261] det_loss_box_1[0.8326] det_loss_cls_2[0.6290] det_loss_box_2[0.7924] det_loss_cls_3[0.6257] det_loss_box_3[0.7741] det_loss_cls_4[0.6286] det_loss_box_4[0.7712] det_loss_cls_5[0.6312] det_loss_box_5[0.7683] det_loss_cls_dn_0[0.1614] det_loss_box_dn_0[0.3885] det_loss_cls_dn_1[0.1593] det_loss_box_dn_1[0.3831] det_loss_cls_dn_2[0.1582] det_loss_box_dn_2[0.3847] det_loss_cls_dn_3[0.1590] det_loss_box_dn_3[0.3864] det_loss_cls_dn_4[0.1589] det_loss_box_dn_4[0.3876] det_loss_cls_dn_5[0.1613] det_loss_box_dn_5[0.3891] loss_depth[0.0434] total_loss[11.9452]
Rank[0/8] 06/19/2025 14:45:33 INFO stats.py:314 | Epoch[4] Step[214] GlobalStep[1774] Training Speed: 7.89 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 4:42:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:45:43 INFO loss_tracker.py:84 | Epoch[4/239] Step[224] GlobalStep[1784/NA]: det_loss_cls_0[0.6357] det_loss_box_0[0.8496] det_loss_cls_1[0.6245] det_loss_box_1[0.7888] det_loss_cls_2[0.6262] det_loss_box_2[0.7622] det_loss_cls_3[0.6367] det_loss_box_3[0.7409] det_loss_cls_4[0.6330] det_loss_box_4[0.7292] det_loss_cls_5[0.6413] det_loss_box_5[0.7198] det_loss_cls_dn_0[0.1529] det_loss_box_dn_0[0.4854] det_loss_cls_dn_1[0.1469] det_loss_box_dn_1[0.4607] det_loss_cls_dn_2[0.1483] det_loss_box_dn_2[0.4583] det_loss_cls_dn_3[0.1495] det_loss_box_dn_3[0.4577] det_loss_cls_dn_4[0.1503] det_loss_box_dn_4[0.4587] det_loss_cls_dn_5[0.1512] det_loss_box_dn_5[0.4588] loss_depth[0.0426] total_loss[12.1092]
Rank[0/8] 06/19/2025 14:45:59 INFO stats.py:314 | Epoch[4] Step[239] GlobalStep[1799] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 4:40:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:46:10 INFO loss_tracker.py:84 | Epoch[4/239] Step[249] GlobalStep[1809/NA]: det_loss_cls_0[0.7681] det_loss_box_0[1.0056] det_loss_cls_1[0.7544] det_loss_box_1[0.8779] det_loss_cls_2[0.7624] det_loss_box_2[0.8210] det_loss_cls_3[0.7531] det_loss_box_3[0.8128] det_loss_cls_4[0.7531] det_loss_box_4[0.7986] det_loss_cls_5[0.7529] det_loss_box_5[0.7958] det_loss_cls_dn_0[0.1569] det_loss_box_dn_0[0.3644] det_loss_cls_dn_1[0.1565] det_loss_box_dn_1[0.3563] det_loss_cls_dn_2[0.1561] det_loss_box_dn_2[0.3548] det_loss_cls_dn_3[0.1564] det_loss_box_dn_3[0.3547] det_loss_cls_dn_4[0.1559] det_loss_box_dn_4[0.3555] det_loss_cls_dn_5[0.1579] det_loss_box_dn_5[0.3560] loss_depth[0.0444] total_loss[12.7816]
Rank[0/8] 06/19/2025 14:46:25 INFO stats.py:314 | Epoch[4] Step[264] GlobalStep[1824] Training Speed: 7.62 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 4:38:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:46:37 INFO loss_tracker.py:84 | Epoch[4/239] Step[274] GlobalStep[1834/NA]: det_loss_cls_0[0.8260] det_loss_box_0[1.1715] det_loss_cls_1[0.8180] det_loss_box_1[1.0807] det_loss_cls_2[0.8123] det_loss_box_2[1.0400] det_loss_cls_3[0.8085] det_loss_box_3[1.0242] det_loss_cls_4[0.8026] det_loss_box_4[1.0147] det_loss_cls_5[0.8029] det_loss_box_5[1.0119] det_loss_cls_dn_0[0.1594] det_loss_box_dn_0[0.3621] det_loss_cls_dn_1[0.1578] det_loss_box_dn_1[0.3588] det_loss_cls_dn_2[0.1581] det_loss_box_dn_2[0.3616] det_loss_cls_dn_3[0.1591] det_loss_box_dn_3[0.3647] det_loss_cls_dn_4[0.1603] det_loss_box_dn_4[0.3665] det_loss_cls_dn_5[0.1612] det_loss_box_dn_5[0.3685] loss_depth[0.0470] total_loss[14.3984]
Rank[0/8] 06/19/2025 14:46:53 INFO stats.py:314 | Epoch[4] Step[289] GlobalStep[1849] Training Speed: 8.22 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 4:37:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:47:04 INFO loss_tracker.py:84 | Epoch[4/239] Step[299] GlobalStep[1859/NA]: det_loss_cls_0[0.6531] det_loss_box_0[0.9173] det_loss_cls_1[0.6405] det_loss_box_1[0.8248] det_loss_cls_2[0.6356] det_loss_box_2[0.7864] det_loss_cls_3[0.6359] det_loss_box_3[0.7636] det_loss_cls_4[0.6254] det_loss_box_4[0.7606] det_loss_cls_5[0.6290] det_loss_box_5[0.7607] det_loss_cls_dn_0[0.1506] det_loss_box_dn_0[0.3721] det_loss_cls_dn_1[0.1506] det_loss_box_dn_1[0.3641] det_loss_cls_dn_2[0.1498] det_loss_box_dn_2[0.3631] det_loss_cls_dn_3[0.1498] det_loss_box_dn_3[0.3632] det_loss_cls_dn_4[0.1511] det_loss_box_dn_4[0.3640] det_loss_cls_dn_5[0.1525] det_loss_box_dn_5[0.3650] loss_depth[0.0445] total_loss[11.7731]
Rank[0/8] 06/19/2025 14:47:20 INFO stats.py:314 | Epoch[4] Step[314] GlobalStep[1874] Training Speed: 7.68 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 4:35:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:47:30 INFO loss_tracker.py:84 | Epoch[4/239] Step[324] GlobalStep[1884/NA]: det_loss_cls_0[0.6689] det_loss_box_0[1.0358] det_loss_cls_1[0.6649] det_loss_box_1[0.9273] det_loss_cls_2[0.6519] det_loss_box_2[0.8694] det_loss_cls_3[0.6559] det_loss_box_3[0.8510] det_loss_cls_4[0.6511] det_loss_box_4[0.8481] det_loss_cls_5[0.6561] det_loss_box_5[0.8472] det_loss_cls_dn_0[0.1564] det_loss_box_dn_0[0.4547] det_loss_cls_dn_1[0.1556] det_loss_box_dn_1[0.4527] det_loss_cls_dn_2[0.1541] det_loss_box_dn_2[0.4528] det_loss_cls_dn_3[0.1548] det_loss_box_dn_3[0.4542] det_loss_cls_dn_4[0.1540] det_loss_box_dn_4[0.4542] det_loss_cls_dn_5[0.1560] det_loss_box_dn_5[0.4554] loss_depth[0.0488] total_loss[13.0313]
Rank[0/8] 06/19/2025 14:47:46 INFO stats.py:314 | Epoch[4] Step[339] GlobalStep[1899] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 4:33:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:47:56 INFO loss_tracker.py:84 | Epoch[4/239] Step[349] GlobalStep[1909/NA]: det_loss_cls_0[0.7227] det_loss_box_0[0.9683] det_loss_cls_1[0.7148] det_loss_box_1[0.8847] det_loss_cls_2[0.7151] det_loss_box_2[0.8279] det_loss_cls_3[0.7143] det_loss_box_3[0.8075] det_loss_cls_4[0.7139] det_loss_box_4[0.7994] det_loss_cls_5[0.7146] det_loss_box_5[0.7959] det_loss_cls_dn_0[0.1515] det_loss_box_dn_0[0.3597] det_loss_cls_dn_1[0.1513] det_loss_box_dn_1[0.3512] det_loss_cls_dn_2[0.1496] det_loss_box_dn_2[0.3492] det_loss_cls_dn_3[0.1497] det_loss_box_dn_3[0.3497] det_loss_cls_dn_4[0.1505] det_loss_box_dn_4[0.3511] det_loss_cls_dn_5[0.1513] det_loss_box_dn_5[0.3523] loss_depth[0.0446] total_loss[12.4410]
Rank[0/8] 06/19/2025 14:48:12 INFO stats.py:314 | Epoch[4] Step[364] GlobalStep[1924] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 4:32:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:48:23 INFO loss_tracker.py:84 | Epoch[4/239] Step[374] GlobalStep[1934/NA]: det_loss_cls_0[0.7256] det_loss_box_0[0.9586] det_loss_cls_1[0.7048] det_loss_box_1[0.8643] det_loss_cls_2[0.7066] det_loss_box_2[0.8149] det_loss_cls_3[0.6891] det_loss_box_3[0.8128] det_loss_cls_4[0.6912] det_loss_box_4[0.8101] det_loss_cls_5[0.6991] det_loss_box_5[0.8009] det_loss_cls_dn_0[0.1637] det_loss_box_dn_0[0.4036] det_loss_cls_dn_1[0.1630] det_loss_box_dn_1[0.3885] det_loss_cls_dn_2[0.1629] det_loss_box_dn_2[0.3856] det_loss_cls_dn_3[0.1627] det_loss_box_dn_3[0.3860] det_loss_cls_dn_4[0.1638] det_loss_box_dn_4[0.3862] det_loss_cls_dn_5[0.1658] det_loss_box_dn_5[0.3872] loss_depth[0.0467] total_loss[12.6436]
Rank[0/8] 06/19/2025 14:48:38 INFO stats.py:314 | Epoch[4] Step[389] GlobalStep[1949] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 4:30:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:48:38 INFO stats.py:394 | Epoch[4] completed. Training Speed: 7.46 samples/sec across all devices.	Epoch Time: 418.04 sec.	Average Epoch Time: 418.04 sec.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 1 day, 4:30:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:49:09 INFO stats.py:314 | Epoch[5] Step[24] GlobalStep[1974] Training Speed: 7.87 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1 day, 4:32:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:49:09 INFO loss_tracker.py:84 | Epoch[5/239] Step[24] GlobalStep[1974/NA]: det_loss_cls_0[0.7568] det_loss_box_0[1.0558] det_loss_cls_1[0.7434] det_loss_box_1[0.9530] det_loss_cls_2[0.7421] det_loss_box_2[0.9088] det_loss_cls_3[0.7346] det_loss_box_3[0.8965] det_loss_cls_4[0.7265] det_loss_box_4[0.8918] det_loss_cls_5[0.7266] det_loss_box_5[0.8873] det_loss_cls_dn_0[0.1579] det_loss_box_dn_0[0.4082] det_loss_cls_dn_1[0.1569] det_loss_box_dn_1[0.3967] det_loss_cls_dn_2[0.1559] det_loss_box_dn_2[0.3956] det_loss_cls_dn_3[0.1559] det_loss_box_dn_3[0.3961] det_loss_cls_dn_4[0.1564] det_loss_box_dn_4[0.3973] det_loss_cls_dn_5[0.1583] det_loss_box_dn_5[0.3983] loss_depth[0.0448] total_loss[13.4016]
Rank[0/8] 06/19/2025 14:49:35 INFO stats.py:314 | Epoch[5] Step[49] GlobalStep[1999] Training Speed: 8.08 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 4:30:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:49:36 INFO loss_tracker.py:84 | Epoch[5/239] Step[49] GlobalStep[1999/NA]: det_loss_cls_0[0.7318] det_loss_box_0[1.0587] det_loss_cls_1[0.7266] det_loss_box_1[0.9421] det_loss_cls_2[0.7112] det_loss_box_2[0.8981] det_loss_cls_3[0.7074] det_loss_box_3[0.8788] det_loss_cls_4[0.7177] det_loss_box_4[0.8693] det_loss_cls_5[0.7171] det_loss_box_5[0.8639] det_loss_cls_dn_0[0.1480] det_loss_box_dn_0[0.4159] det_loss_cls_dn_1[0.1482] det_loss_box_dn_1[0.4061] det_loss_cls_dn_2[0.1486] det_loss_box_dn_2[0.4048] det_loss_cls_dn_3[0.1480] det_loss_box_dn_3[0.4049] det_loss_cls_dn_4[0.1487] det_loss_box_dn_4[0.4056] det_loss_cls_dn_5[0.1498] det_loss_box_dn_5[0.4062] loss_depth[0.0428] total_loss[13.2002]
Rank[0/8] 06/19/2025 14:50:02 INFO stats.py:314 | Epoch[5] Step[74] GlobalStep[2024] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 4:28:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:50:02 INFO loss_tracker.py:84 | Epoch[5/239] Step[74] GlobalStep[2024/NA]: det_loss_cls_0[0.7225] det_loss_box_0[0.9348] det_loss_cls_1[0.7295] det_loss_box_1[0.8739] det_loss_cls_2[0.7267] det_loss_box_2[0.8436] det_loss_cls_3[0.7291] det_loss_box_3[0.8284] det_loss_cls_4[0.7248] det_loss_box_4[0.8247] det_loss_cls_5[0.7256] det_loss_box_5[0.8211] det_loss_cls_dn_0[0.1598] det_loss_box_dn_0[0.3878] det_loss_cls_dn_1[0.1591] det_loss_box_dn_1[0.3755] det_loss_cls_dn_2[0.1595] det_loss_box_dn_2[0.3734] det_loss_cls_dn_3[0.1600] det_loss_box_dn_3[0.3742] det_loss_cls_dn_4[0.1610] det_loss_box_dn_4[0.3752] det_loss_cls_dn_5[0.1622] det_loss_box_dn_5[0.3759] loss_depth[0.0394] total_loss[12.7477]
Rank[0/8] 06/19/2025 14:50:29 INFO stats.py:314 | Epoch[5] Step[99] GlobalStep[2049] Training Speed: 7.81 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1 day, 4:27:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:50:29 INFO loss_tracker.py:84 | Epoch[5/239] Step[99] GlobalStep[2049/NA]: det_loss_cls_0[0.5918] det_loss_box_0[0.8078] det_loss_cls_1[0.5733] det_loss_box_1[0.7232] det_loss_cls_2[0.5752] det_loss_box_2[0.6871] det_loss_cls_3[0.5690] det_loss_box_3[0.6761] det_loss_cls_4[0.5683] det_loss_box_4[0.6770] det_loss_cls_5[0.5710] det_loss_box_5[0.6756] det_loss_cls_dn_0[0.1625] det_loss_box_dn_0[0.4317] det_loss_cls_dn_1[0.1609] det_loss_box_dn_1[0.4220] det_loss_cls_dn_2[0.1617] det_loss_box_dn_2[0.4210] det_loss_cls_dn_3[0.1619] det_loss_box_dn_3[0.4216] det_loss_cls_dn_4[0.1619] det_loss_box_dn_4[0.4229] det_loss_cls_dn_5[0.1635] det_loss_box_dn_5[0.4243] loss_depth[0.0463] total_loss[11.2577]
Rank[0/8] 06/19/2025 14:50:55 INFO stats.py:314 | Epoch[5] Step[124] GlobalStep[2074] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 4:25:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:50:55 INFO loss_tracker.py:84 | Epoch[5/239] Step[124] GlobalStep[2074/NA]: det_loss_cls_0[0.7473] det_loss_box_0[0.9982] det_loss_cls_1[0.7298] det_loss_box_1[0.8648] det_loss_cls_2[0.7222] det_loss_box_2[0.8396] det_loss_cls_3[0.7190] det_loss_box_3[0.8209] det_loss_cls_4[0.7223] det_loss_box_4[0.8093] det_loss_cls_5[0.7160] det_loss_box_5[0.8058] det_loss_cls_dn_0[0.1495] det_loss_box_dn_0[0.3726] det_loss_cls_dn_1[0.1487] det_loss_box_dn_1[0.3522] det_loss_cls_dn_2[0.1498] det_loss_box_dn_2[0.3505] det_loss_cls_dn_3[0.1507] det_loss_box_dn_3[0.3503] det_loss_cls_dn_4[0.1500] det_loss_box_dn_4[0.3504] det_loss_cls_dn_5[0.1525] det_loss_box_dn_5[0.3510] loss_depth[0.0457] total_loss[12.5691]
Rank[0/8] 06/19/2025 14:51:22 INFO stats.py:314 | Epoch[5] Step[149] GlobalStep[2099] Training Speed: 7.79 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 1 day, 4:24:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:51:22 INFO loss_tracker.py:84 | Epoch[5/239] Step[149] GlobalStep[2099/NA]: det_loss_cls_0[0.8362] det_loss_box_0[1.2206] det_loss_cls_1[0.8310] det_loss_box_1[1.1003] det_loss_cls_2[0.8188] det_loss_box_2[1.0611] det_loss_cls_3[0.8118] det_loss_box_3[1.0431] det_loss_cls_4[0.8115] det_loss_box_4[1.0335] det_loss_cls_5[0.8068] det_loss_box_5[1.0346] det_loss_cls_dn_0[0.1603] det_loss_box_dn_0[0.4094] det_loss_cls_dn_1[0.1588] det_loss_box_dn_1[0.3969] det_loss_cls_dn_2[0.1596] det_loss_box_dn_2[0.3949] det_loss_cls_dn_3[0.1597] det_loss_box_dn_3[0.3949] det_loss_cls_dn_4[0.1606] det_loss_box_dn_4[0.3951] det_loss_cls_dn_5[0.1626] det_loss_box_dn_5[0.3953] loss_depth[0.0477] total_loss[14.8049]
Rank[0/8] 06/19/2025 14:51:48 INFO stats.py:314 | Epoch[5] Step[174] GlobalStep[2124] Training Speed: 8.56 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 4:22:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:51:48 INFO loss_tracker.py:84 | Epoch[5/239] Step[174] GlobalStep[2124/NA]: det_loss_cls_0[0.7081] det_loss_box_0[0.9534] det_loss_cls_1[0.6839] det_loss_box_1[0.8654] det_loss_cls_2[0.6807] det_loss_box_2[0.8223] det_loss_cls_3[0.6756] det_loss_box_3[0.8071] det_loss_cls_4[0.6832] det_loss_box_4[0.7887] det_loss_cls_5[0.6766] det_loss_box_5[0.7884] det_loss_cls_dn_0[0.1548] det_loss_box_dn_0[0.4370] det_loss_cls_dn_1[0.1525] det_loss_box_dn_1[0.4224] det_loss_cls_dn_2[0.1524] det_loss_box_dn_2[0.4184] det_loss_cls_dn_3[0.1519] det_loss_box_dn_3[0.4190] det_loss_cls_dn_4[0.1519] det_loss_box_dn_4[0.4198] det_loss_cls_dn_5[0.1533] det_loss_box_dn_5[0.4204] loss_depth[0.0452] total_loss[12.6325]
Rank[0/8] 06/19/2025 14:52:14 INFO stats.py:314 | Epoch[5] Step[199] GlobalStep[2149] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 4:20:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:52:14 INFO loss_tracker.py:84 | Epoch[5/239] Step[199] GlobalStep[2149/NA]: det_loss_cls_0[0.7219] det_loss_box_0[1.0594] det_loss_cls_1[0.6981] det_loss_box_1[0.9648] det_loss_cls_2[0.6919] det_loss_box_2[0.9206] det_loss_cls_3[0.6987] det_loss_box_3[0.8956] det_loss_cls_4[0.6991] det_loss_box_4[0.8897] det_loss_cls_5[0.6979] det_loss_box_5[0.8916] det_loss_cls_dn_0[0.1569] det_loss_box_dn_0[0.3996] det_loss_cls_dn_1[0.1566] det_loss_box_dn_1[0.3875] det_loss_cls_dn_2[0.1567] det_loss_box_dn_2[0.3853] det_loss_cls_dn_3[0.1572] det_loss_box_dn_3[0.3857] det_loss_cls_dn_4[0.1559] det_loss_box_dn_4[0.3855] det_loss_cls_dn_5[0.1564] det_loss_box_dn_5[0.3856] loss_depth[0.0466] total_loss[13.1447]
Rank[0/8] 06/19/2025 14:52:40 INFO stats.py:314 | Epoch[5] Step[224] GlobalStep[2174] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 4:19:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:52:40 INFO loss_tracker.py:84 | Epoch[5/239] Step[224] GlobalStep[2174/NA]: det_loss_cls_0[0.5527] det_loss_box_0[0.7807] det_loss_cls_1[0.5374] det_loss_box_1[0.7041] det_loss_cls_2[0.5400] det_loss_box_2[0.6466] det_loss_cls_3[0.5260] det_loss_box_3[0.6339] det_loss_cls_4[0.5272] det_loss_box_4[0.6233] det_loss_cls_5[0.5286] det_loss_box_5[0.6249] det_loss_cls_dn_0[0.1562] det_loss_box_dn_0[0.4963] det_loss_cls_dn_1[0.1535] det_loss_box_dn_1[0.4700] det_loss_cls_dn_2[0.1524] det_loss_box_dn_2[0.4642] det_loss_cls_dn_3[0.1513] det_loss_box_dn_3[0.4627] det_loss_cls_dn_4[0.1521] det_loss_box_dn_4[0.4639] det_loss_cls_dn_5[0.1537] det_loss_box_dn_5[0.4648] loss_depth[0.0472] total_loss[11.0135]
Rank[0/8] 06/19/2025 14:53:07 INFO stats.py:314 | Epoch[5] Step[249] GlobalStep[2199] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 4:18:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:53:07 INFO loss_tracker.py:84 | Epoch[5/239] Step[249] GlobalStep[2199/NA]: det_loss_cls_0[0.7197] det_loss_box_0[0.8973] det_loss_cls_1[0.7026] det_loss_box_1[0.8123] det_loss_cls_2[0.7086] det_loss_box_2[0.7653] det_loss_cls_3[0.7035] det_loss_box_3[0.7592] det_loss_cls_4[0.7072] det_loss_box_4[0.7466] det_loss_cls_5[0.7090] det_loss_box_5[0.7406] det_loss_cls_dn_0[0.1560] det_loss_box_dn_0[0.3297] det_loss_cls_dn_1[0.1541] det_loss_box_dn_1[0.3199] det_loss_cls_dn_2[0.1544] det_loss_box_dn_2[0.3200] det_loss_cls_dn_3[0.1558] det_loss_box_dn_3[0.3207] det_loss_cls_dn_4[0.1553] det_loss_box_dn_4[0.3212] det_loss_cls_dn_5[0.1568] det_loss_box_dn_5[0.3218] loss_depth[0.0415] total_loss[11.8792]
Rank[0/8] 06/19/2025 14:53:34 INFO stats.py:314 | Epoch[5] Step[274] GlobalStep[2224] Training Speed: 7.82 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1 day, 4:16:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:53:34 INFO loss_tracker.py:84 | Epoch[5/239] Step[274] GlobalStep[2224/NA]: det_loss_cls_0[0.6673] det_loss_box_0[0.7696] det_loss_cls_1[0.6537] det_loss_box_1[0.7015] det_loss_cls_2[0.6588] det_loss_box_2[0.6789] det_loss_cls_3[0.6501] det_loss_box_3[0.6704] det_loss_cls_4[0.6557] det_loss_box_4[0.6618] det_loss_cls_5[0.6516] det_loss_box_5[0.6608] det_loss_cls_dn_0[0.1538] det_loss_box_dn_0[0.4596] det_loss_cls_dn_1[0.1529] det_loss_box_dn_1[0.4401] det_loss_cls_dn_2[0.1528] det_loss_box_dn_2[0.4378] det_loss_cls_dn_3[0.1529] det_loss_box_dn_3[0.4370] det_loss_cls_dn_4[0.1536] det_loss_box_dn_4[0.4367] det_loss_cls_dn_5[0.1544] det_loss_box_dn_5[0.4380] loss_depth[0.0443] total_loss[11.6942]
Rank[0/8] 06/19/2025 14:54:00 INFO stats.py:314 | Epoch[5] Step[299] GlobalStep[2249] Training Speed: 8.00 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 4:14:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:54:00 INFO loss_tracker.py:84 | Epoch[5/239] Step[299] GlobalStep[2249/NA]: det_loss_cls_0[0.5457] det_loss_box_0[0.7419] det_loss_cls_1[0.5231] det_loss_box_1[0.6757] det_loss_cls_2[0.5293] det_loss_box_2[0.6483] det_loss_cls_3[0.5300] det_loss_box_3[0.6363] det_loss_cls_4[0.5231] det_loss_box_4[0.6378] det_loss_cls_5[0.5250] det_loss_box_5[0.6337] det_loss_cls_dn_0[0.1451] det_loss_box_dn_0[0.4890] det_loss_cls_dn_1[0.1436] det_loss_box_dn_1[0.4677] det_loss_cls_dn_2[0.1431] det_loss_box_dn_2[0.4639] det_loss_cls_dn_3[0.1435] det_loss_box_dn_3[0.4653] det_loss_cls_dn_4[0.1436] det_loss_box_dn_4[0.4664] det_loss_cls_dn_5[0.1451] det_loss_box_dn_5[0.4669] loss_depth[0.0442] total_loss[10.8772]
Rank[0/8] 06/19/2025 14:54:27 INFO stats.py:314 | Epoch[5] Step[324] GlobalStep[2274] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 4:13:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:54:27 INFO loss_tracker.py:84 | Epoch[5/239] Step[324] GlobalStep[2274/NA]: det_loss_cls_0[0.7470] det_loss_box_0[1.0765] det_loss_cls_1[0.7258] det_loss_box_1[0.9713] det_loss_cls_2[0.7319] det_loss_box_2[0.9219] det_loss_cls_3[0.7244] det_loss_box_3[0.9020] det_loss_cls_4[0.7170] det_loss_box_4[0.9012] det_loss_cls_5[0.7137] det_loss_box_5[0.8990] det_loss_cls_dn_0[0.1505] det_loss_box_dn_0[0.3942] det_loss_cls_dn_1[0.1464] det_loss_box_dn_1[0.3850] det_loss_cls_dn_2[0.1465] det_loss_box_dn_2[0.3825] det_loss_cls_dn_3[0.1458] det_loss_box_dn_3[0.3835] det_loss_cls_dn_4[0.1454] det_loss_box_dn_4[0.3843] det_loss_cls_dn_5[0.1471] det_loss_box_dn_5[0.3853] loss_depth[0.0466] total_loss[13.2751]
Rank[0/8] 06/19/2025 14:54:53 INFO stats.py:314 | Epoch[5] Step[349] GlobalStep[2299] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 4:12:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:54:53 INFO loss_tracker.py:84 | Epoch[5/239] Step[349] GlobalStep[2299/NA]: det_loss_cls_0[0.5712] det_loss_box_0[0.7018] det_loss_cls_1[0.5602] det_loss_box_1[0.6473] det_loss_cls_2[0.5552] det_loss_box_2[0.6304] det_loss_cls_3[0.5535] det_loss_box_3[0.6249] det_loss_cls_4[0.5591] det_loss_box_4[0.6176] det_loss_cls_5[0.5589] det_loss_box_5[0.6183] det_loss_cls_dn_0[0.1418] det_loss_box_dn_0[0.4269] det_loss_cls_dn_1[0.1409] det_loss_box_dn_1[0.4128] det_loss_cls_dn_2[0.1404] det_loss_box_dn_2[0.4076] det_loss_cls_dn_3[0.1401] det_loss_box_dn_3[0.4071] det_loss_cls_dn_4[0.1398] det_loss_box_dn_4[0.4078] det_loss_cls_dn_5[0.1420] det_loss_box_dn_5[0.4084] loss_depth[0.0423] total_loss[10.5564]
Rank[0/8] 06/19/2025 14:55:19 INFO stats.py:314 | Epoch[5] Step[374] GlobalStep[2324] Training Speed: 8.69 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 1 day, 4:10:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:55:19 INFO loss_tracker.py:84 | Epoch[5/239] Step[374] GlobalStep[2324/NA]: det_loss_cls_0[0.7205] det_loss_box_0[0.8688] det_loss_cls_1[0.7041] det_loss_box_1[0.7757] det_loss_cls_2[0.6929] det_loss_box_2[0.7458] det_loss_cls_3[0.6995] det_loss_box_3[0.7246] det_loss_cls_4[0.7033] det_loss_box_4[0.7126] det_loss_cls_5[0.7069] det_loss_box_5[0.7111] det_loss_cls_dn_0[0.1598] det_loss_box_dn_0[0.4515] det_loss_cls_dn_1[0.1580] det_loss_box_dn_1[0.4277] det_loss_cls_dn_2[0.1555] det_loss_box_dn_2[0.4236] det_loss_cls_dn_3[0.1556] det_loss_box_dn_3[0.4233] det_loss_cls_dn_4[0.1564] det_loss_box_dn_4[0.4233] det_loss_cls_dn_5[0.1576] det_loss_box_dn_5[0.4246] loss_depth[0.0418] total_loss[12.3243]
Rank[0/8] 06/19/2025 14:55:34 INFO stats.py:394 | Epoch[5] completed. Training Speed: 7.50 samples/sec across all devices.	Epoch Time: 416.09 sec.	Average Epoch Time: 416.09 sec.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 1 day, 4:09:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:55:49 INFO stats.py:314 | Epoch[6] Step[9] GlobalStep[2349] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 4:11:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:56:04 INFO loss_tracker.py:84 | Epoch[6/239] Step[24] GlobalStep[2364/NA]: det_loss_cls_0[0.7271] det_loss_box_0[0.9523] det_loss_cls_1[0.7136] det_loss_box_1[0.8659] det_loss_cls_2[0.7158] det_loss_box_2[0.8367] det_loss_cls_3[0.7035] det_loss_box_3[0.8289] det_loss_cls_4[0.7018] det_loss_box_4[0.8254] det_loss_cls_5[0.7032] det_loss_box_5[0.8256] det_loss_cls_dn_0[0.1609] det_loss_box_dn_0[0.3740] det_loss_cls_dn_1[0.1617] det_loss_box_dn_1[0.3621] det_loss_cls_dn_2[0.1637] det_loss_box_dn_2[0.3605] det_loss_cls_dn_3[0.1650] det_loss_box_dn_3[0.3603] det_loss_cls_dn_4[0.1653] det_loss_box_dn_4[0.3612] det_loss_cls_dn_5[0.1663] det_loss_box_dn_5[0.3623] loss_depth[0.0433] total_loss[12.6065]
Rank[0/8] 06/19/2025 14:56:14 INFO stats.py:314 | Epoch[6] Step[34] GlobalStep[2374] Training Speed: 7.80 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 1 day, 4:09:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:56:31 INFO loss_tracker.py:84 | Epoch[6/239] Step[49] GlobalStep[2389/NA]: det_loss_cls_0[0.5081] det_loss_box_0[0.6321] det_loss_cls_1[0.4947] det_loss_box_1[0.5798] det_loss_cls_2[0.4924] det_loss_box_2[0.5413] det_loss_cls_3[0.4924] det_loss_box_3[0.5358] det_loss_cls_4[0.4856] det_loss_box_4[0.5370] det_loss_cls_5[0.4891] det_loss_box_5[0.5345] det_loss_cls_dn_0[0.1370] det_loss_box_dn_0[0.3483] det_loss_cls_dn_1[0.1363] det_loss_box_dn_1[0.3355] det_loss_cls_dn_2[0.1365] det_loss_box_dn_2[0.3316] det_loss_cls_dn_3[0.1360] det_loss_box_dn_3[0.3318] det_loss_cls_dn_4[0.1354] det_loss_box_dn_4[0.3318] det_loss_cls_dn_5[0.1365] det_loss_box_dn_5[0.3319] loss_depth[0.0431] total_loss[9.1944]
Rank[0/8] 06/19/2025 14:56:41 INFO stats.py:314 | Epoch[6] Step[59] GlobalStep[2399] Training Speed: 8.02 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 4:08:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:56:57 INFO loss_tracker.py:84 | Epoch[6/239] Step[74] GlobalStep[2414/NA]: det_loss_cls_0[0.7104] det_loss_box_0[1.0047] det_loss_cls_1[0.6833] det_loss_box_1[0.9084] det_loss_cls_2[0.6924] det_loss_box_2[0.8520] det_loss_cls_3[0.6881] det_loss_box_3[0.8357] det_loss_cls_4[0.6835] det_loss_box_4[0.8329] det_loss_cls_5[0.6755] det_loss_box_5[0.8303] det_loss_cls_dn_0[0.1528] det_loss_box_dn_0[0.4347] det_loss_cls_dn_1[0.1513] det_loss_box_dn_1[0.4171] det_loss_cls_dn_2[0.1503] det_loss_box_dn_2[0.4120] det_loss_cls_dn_3[0.1507] det_loss_box_dn_3[0.4121] det_loss_cls_dn_4[0.1508] det_loss_box_dn_4[0.4123] det_loss_cls_dn_5[0.1523] det_loss_box_dn_5[0.4132] loss_depth[0.0471] total_loss[12.8540]
Rank[0/8] 06/19/2025 14:57:08 INFO stats.py:314 | Epoch[6] Step[84] GlobalStep[2424] Training Speed: 7.92 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 4:07:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:57:24 INFO loss_tracker.py:84 | Epoch[6/239] Step[99] GlobalStep[2439/NA]: det_loss_cls_0[0.5521] det_loss_box_0[0.8037] det_loss_cls_1[0.5445] det_loss_box_1[0.7317] det_loss_cls_2[0.5441] det_loss_box_2[0.6924] det_loss_cls_3[0.5347] det_loss_box_3[0.6860] det_loss_cls_4[0.5340] det_loss_box_4[0.6810] det_loss_cls_5[0.5359] det_loss_box_5[0.6757] det_loss_cls_dn_0[0.1424] det_loss_box_dn_0[0.7407] det_loss_cls_dn_1[0.1406] det_loss_box_dn_1[0.7193] det_loss_cls_dn_2[0.1407] det_loss_box_dn_2[0.7192] det_loss_cls_dn_3[0.1411] det_loss_box_dn_3[0.7195] det_loss_cls_dn_4[0.1419] det_loss_box_dn_4[0.7197] det_loss_cls_dn_5[0.1430] det_loss_box_dn_5[0.7202] loss_depth[0.0438] total_loss[12.7480]
Rank[0/8] 06/19/2025 14:57:34 INFO stats.py:314 | Epoch[6] Step[109] GlobalStep[2449] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 4:06:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:57:50 INFO loss_tracker.py:84 | Epoch[6/239] Step[124] GlobalStep[2464/NA]: det_loss_cls_0[0.5167] det_loss_box_0[0.6438] det_loss_cls_1[0.5036] det_loss_box_1[0.5838] det_loss_cls_2[0.4985] det_loss_box_2[0.5708] det_loss_cls_3[0.4960] det_loss_box_3[0.5554] det_loss_cls_4[0.5017] det_loss_box_4[0.5472] det_loss_cls_5[0.5017] det_loss_box_5[0.5418] det_loss_cls_dn_0[0.1353] det_loss_box_dn_0[0.3505] det_loss_cls_dn_1[0.1354] det_loss_box_dn_1[0.3389] det_loss_cls_dn_2[0.1341] det_loss_box_dn_2[0.3400] det_loss_cls_dn_3[0.1340] det_loss_box_dn_3[0.3415] det_loss_cls_dn_4[0.1351] det_loss_box_dn_4[0.3430] det_loss_cls_dn_5[0.1357] det_loss_box_dn_5[0.3441] loss_depth[0.0438] total_loss[9.3724]
Rank[0/8] 06/19/2025 14:58:01 INFO stats.py:314 | Epoch[6] Step[134] GlobalStep[2474] Training Speed: 7.45 samples/sec across all devices.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 1 day, 4:05:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:58:17 INFO loss_tracker.py:84 | Epoch[6/239] Step[149] GlobalStep[2489/NA]: det_loss_cls_0[0.5371] det_loss_box_0[0.6807] det_loss_cls_1[0.5261] det_loss_box_1[0.6025] det_loss_cls_2[0.5190] det_loss_box_2[0.5884] det_loss_cls_3[0.5168] det_loss_box_3[0.5779] det_loss_cls_4[0.5137] det_loss_box_4[0.5693] det_loss_cls_5[0.5129] det_loss_box_5[0.5712] det_loss_cls_dn_0[0.1505] det_loss_box_dn_0[0.4192] det_loss_cls_dn_1[0.1503] det_loss_box_dn_1[0.4095] det_loss_cls_dn_2[0.1491] det_loss_box_dn_2[0.4074] det_loss_cls_dn_3[0.1481] det_loss_box_dn_3[0.4095] det_loss_cls_dn_4[0.1481] det_loss_box_dn_4[0.4105] det_loss_cls_dn_5[0.1485] det_loss_box_dn_5[0.4119] loss_depth[0.0440] total_loss[10.1223]
Rank[0/8] 06/19/2025 14:58:27 INFO stats.py:314 | Epoch[6] Step[159] GlobalStep[2499] Training Speed: 7.82 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1 day, 4:03:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:58:42 INFO loss_tracker.py:84 | Epoch[6/239] Step[174] GlobalStep[2514/NA]: det_loss_cls_0[0.5594] det_loss_box_0[0.8078] det_loss_cls_1[0.5539] det_loss_box_1[0.7311] det_loss_cls_2[0.5546] det_loss_box_2[0.7059] det_loss_cls_3[0.5520] det_loss_box_3[0.6909] det_loss_cls_4[0.5546] det_loss_box_4[0.6875] det_loss_cls_5[0.5488] det_loss_box_5[0.6883] det_loss_cls_dn_0[0.1488] det_loss_box_dn_0[0.4259] det_loss_cls_dn_1[0.1466] det_loss_box_dn_1[0.4074] det_loss_cls_dn_2[0.1470] det_loss_box_dn_2[0.4056] det_loss_cls_dn_3[0.1466] det_loss_box_dn_3[0.4046] det_loss_cls_dn_4[0.1477] det_loss_box_dn_4[0.4050] det_loss_cls_dn_5[0.1485] det_loss_box_dn_5[0.4047] loss_depth[0.0450] total_loss[11.0183]
Rank[0/8] 06/19/2025 14:58:53 INFO stats.py:314 | Epoch[6] Step[184] GlobalStep[2524] Training Speed: 7.63 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 4:01:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:59:08 INFO loss_tracker.py:84 | Epoch[6/239] Step[199] GlobalStep[2539/NA]: det_loss_cls_0[0.6778] det_loss_box_0[0.9383] det_loss_cls_1[0.6643] det_loss_box_1[0.8583] det_loss_cls_2[0.6689] det_loss_box_2[0.8243] det_loss_cls_3[0.6686] det_loss_box_3[0.8116] det_loss_cls_4[0.6613] det_loss_box_4[0.8116] det_loss_cls_5[0.6610] det_loss_box_5[0.8111] det_loss_cls_dn_0[0.1502] det_loss_box_dn_0[0.4869] det_loss_cls_dn_1[0.1483] det_loss_box_dn_1[0.4651] det_loss_cls_dn_2[0.1481] det_loss_box_dn_2[0.4638] det_loss_cls_dn_3[0.1480] det_loss_box_dn_3[0.4646] det_loss_cls_dn_4[0.1477] det_loss_box_dn_4[0.4654] det_loss_cls_dn_5[0.1497] det_loss_box_dn_5[0.4659] loss_depth[0.0465] total_loss[12.8073]
Rank[0/8] 06/19/2025 14:59:19 INFO stats.py:314 | Epoch[6] Step[209] GlobalStep[2549] Training Speed: 6.52 samples/sec across all devices.	Average Step Time: 1.23 sec.	Estimated Remaining Time: 1 day, 4:00:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 14:59:35 INFO loss_tracker.py:84 | Epoch[6/239] Step[224] GlobalStep[2564/NA]: det_loss_cls_0[0.6939] det_loss_box_0[1.0058] det_loss_cls_1[0.6818] det_loss_box_1[0.9201] det_loss_cls_2[0.6824] det_loss_box_2[0.8755] det_loss_cls_3[0.6674] det_loss_box_3[0.8667] det_loss_cls_4[0.6668] det_loss_box_4[0.8489] det_loss_cls_5[0.6711] det_loss_box_5[0.8487] det_loss_cls_dn_0[0.1574] det_loss_box_dn_0[0.3778] det_loss_cls_dn_1[0.1552] det_loss_box_dn_1[0.3702] det_loss_cls_dn_2[0.1547] det_loss_box_dn_2[0.3679] det_loss_cls_dn_3[0.1553] det_loss_box_dn_3[0.3678] det_loss_cls_dn_4[0.1562] det_loss_box_dn_4[0.3688] det_loss_cls_dn_5[0.1576] det_loss_box_dn_5[0.3693] loss_depth[0.0435] total_loss[12.6309]
Rank[0/8] 06/19/2025 14:59:45 INFO stats.py:314 | Epoch[6] Step[234] GlobalStep[2574] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 3:59:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:00:01 INFO loss_tracker.py:84 | Epoch[6/239] Step[249] GlobalStep[2589/NA]: det_loss_cls_0[0.5481] det_loss_box_0[0.7607] det_loss_cls_1[0.5424] det_loss_box_1[0.6918] det_loss_cls_2[0.5442] det_loss_box_2[0.6739] det_loss_cls_3[0.5350] det_loss_box_3[0.6676] det_loss_cls_4[0.5311] det_loss_box_4[0.6564] det_loss_cls_5[0.5303] det_loss_box_5[0.6588] det_loss_cls_dn_0[0.1479] det_loss_box_dn_0[0.4699] det_loss_cls_dn_1[0.1463] det_loss_box_dn_1[0.4499] det_loss_cls_dn_2[0.1458] det_loss_box_dn_2[0.4468] det_loss_cls_dn_3[0.1456] det_loss_box_dn_3[0.4472] det_loss_cls_dn_4[0.1460] det_loss_box_dn_4[0.4483] det_loss_cls_dn_5[0.1477] det_loss_box_dn_5[0.4490] loss_depth[0.0404] total_loss[10.9712]
Rank[0/8] 06/19/2025 15:00:11 INFO stats.py:314 | Epoch[6] Step[259] GlobalStep[2599] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 3:57:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:00:27 INFO loss_tracker.py:84 | Epoch[6/239] Step[274] GlobalStep[2614/NA]: det_loss_cls_0[0.7149] det_loss_box_0[1.0689] det_loss_cls_1[0.7056] det_loss_box_1[0.9736] det_loss_cls_2[0.6998] det_loss_box_2[0.9443] det_loss_cls_3[0.6871] det_loss_box_3[0.9367] det_loss_cls_4[0.6848] det_loss_box_4[0.9337] det_loss_cls_5[0.6707] det_loss_box_5[0.9393] det_loss_cls_dn_0[0.1528] det_loss_box_dn_0[0.4612] det_loss_cls_dn_1[0.1522] det_loss_box_dn_1[0.4516] det_loss_cls_dn_2[0.1520] det_loss_box_dn_2[0.4498] det_loss_cls_dn_3[0.1519] det_loss_box_dn_3[0.4502] det_loss_cls_dn_4[0.1517] det_loss_box_dn_4[0.4513] det_loss_cls_dn_5[0.1533] det_loss_box_dn_5[0.4523] loss_depth[0.0469] total_loss[13.6363]
Rank[0/8] 06/19/2025 15:00:38 INFO stats.py:314 | Epoch[6] Step[284] GlobalStep[2624] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 3:56:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:00:54 INFO loss_tracker.py:84 | Epoch[6/239] Step[299] GlobalStep[2639/NA]: det_loss_cls_0[0.7708] det_loss_box_0[1.0648] det_loss_cls_1[0.7534] det_loss_box_1[0.9526] det_loss_cls_2[0.7450] det_loss_box_2[0.9060] det_loss_cls_3[0.7416] det_loss_box_3[0.8932] det_loss_cls_4[0.7423] det_loss_box_4[0.8771] det_loss_cls_5[0.7371] det_loss_box_5[0.8764] det_loss_cls_dn_0[0.1553] det_loss_box_dn_0[0.4173] det_loss_cls_dn_1[0.1505] det_loss_box_dn_1[0.4044] det_loss_cls_dn_2[0.1496] det_loss_box_dn_2[0.4009] det_loss_cls_dn_3[0.1493] det_loss_box_dn_3[0.4013] det_loss_cls_dn_4[0.1491] det_loss_box_dn_4[0.4016] det_loss_cls_dn_5[0.1499] det_loss_box_dn_5[0.4021] loss_depth[0.0414] total_loss[13.4332]
Rank[0/8] 06/19/2025 15:01:04 INFO stats.py:314 | Epoch[6] Step[309] GlobalStep[2649] Training Speed: 8.69 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 1 day, 3:55:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:01:20 INFO loss_tracker.py:84 | Epoch[6/239] Step[324] GlobalStep[2664/NA]: det_loss_cls_0[0.6925] det_loss_box_0[0.9143] det_loss_cls_1[0.6748] det_loss_box_1[0.8249] det_loss_cls_2[0.6783] det_loss_box_2[0.7764] det_loss_cls_3[0.6749] det_loss_box_3[0.7612] det_loss_cls_4[0.6780] det_loss_box_4[0.7491] det_loss_cls_5[0.6765] det_loss_box_5[0.7532] det_loss_cls_dn_0[0.1647] det_loss_box_dn_0[0.4030] det_loss_cls_dn_1[0.1609] det_loss_box_dn_1[0.3911] det_loss_cls_dn_2[0.1620] det_loss_box_dn_2[0.3899] det_loss_cls_dn_3[0.1611] det_loss_box_dn_3[0.3898] det_loss_cls_dn_4[0.1614] det_loss_box_dn_4[0.3906] det_loss_cls_dn_5[0.1618] det_loss_box_dn_5[0.3911] loss_depth[0.0420] total_loss[12.2234]
Rank[0/8] 06/19/2025 15:01:30 INFO stats.py:314 | Epoch[6] Step[334] GlobalStep[2674] Training Speed: 8.03 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 3:54:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:01:46 INFO loss_tracker.py:84 | Epoch[6/239] Step[349] GlobalStep[2689/NA]: det_loss_cls_0[0.7717] det_loss_box_0[1.0185] det_loss_cls_1[0.7633] det_loss_box_1[0.9038] det_loss_cls_2[0.7628] det_loss_box_2[0.8625] det_loss_cls_3[0.7541] det_loss_box_3[0.8549] det_loss_cls_4[0.7508] det_loss_box_4[0.8560] det_loss_cls_5[0.7469] det_loss_box_5[0.8501] det_loss_cls_dn_0[0.1468] det_loss_box_dn_0[0.3738] det_loss_cls_dn_1[0.1445] det_loss_box_dn_1[0.3605] det_loss_cls_dn_2[0.1448] det_loss_box_dn_2[0.3557] det_loss_cls_dn_3[0.1454] det_loss_box_dn_3[0.3558] det_loss_cls_dn_4[0.1461] det_loss_box_dn_4[0.3567] det_loss_cls_dn_5[0.1473] det_loss_box_dn_5[0.3567] loss_depth[0.0421] total_loss[12.9715]
Rank[0/8] 06/19/2025 15:01:56 INFO stats.py:314 | Epoch[6] Step[359] GlobalStep[2699] Training Speed: 7.68 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 3:52:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:02:12 INFO loss_tracker.py:84 | Epoch[6/239] Step[374] GlobalStep[2714/NA]: det_loss_cls_0[0.6949] det_loss_box_0[0.9770] det_loss_cls_1[0.6826] det_loss_box_1[0.8959] det_loss_cls_2[0.6765] det_loss_box_2[0.8612] det_loss_cls_3[0.6757] det_loss_box_3[0.8387] det_loss_cls_4[0.6787] det_loss_box_4[0.8300] det_loss_cls_5[0.6746] det_loss_box_5[0.8293] det_loss_cls_dn_0[0.1576] det_loss_box_dn_0[0.4825] det_loss_cls_dn_1[0.1568] det_loss_box_dn_1[0.4541] det_loss_cls_dn_2[0.1561] det_loss_box_dn_2[0.4493] det_loss_cls_dn_3[0.1567] det_loss_box_dn_3[0.4486] det_loss_cls_dn_4[0.1562] det_loss_box_dn_4[0.4490] det_loss_cls_dn_5[0.1579] det_loss_box_dn_5[0.4494] loss_depth[0.0440] total_loss[13.0333]
Rank[0/8] 06/19/2025 15:02:23 INFO stats.py:314 | Epoch[6] Step[384] GlobalStep[2724] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 3:51:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:02:28 INFO stats.py:394 | Epoch[6] completed. Training Speed: 7.55 samples/sec across all devices.	Epoch Time: 413.40 sec.	Average Epoch Time: 413.40 sec.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 1 day, 3:51:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:02:53 INFO stats.py:314 | Epoch[7] Step[19] GlobalStep[2749] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 3:52:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:02:59 INFO loss_tracker.py:84 | Epoch[7/239] Step[24] GlobalStep[2754/NA]: det_loss_cls_0[0.6852] det_loss_box_0[0.9344] det_loss_cls_1[0.6741] det_loss_box_1[0.8454] det_loss_cls_2[0.6812] det_loss_box_2[0.8021] det_loss_cls_3[0.6726] det_loss_box_3[0.7931] det_loss_cls_4[0.6725] det_loss_box_4[0.7799] det_loss_cls_5[0.6701] det_loss_box_5[0.7813] det_loss_cls_dn_0[0.1498] det_loss_box_dn_0[0.4215] det_loss_cls_dn_1[0.1486] det_loss_box_dn_1[0.3986] det_loss_cls_dn_2[0.1492] det_loss_box_dn_2[0.3972] det_loss_cls_dn_3[0.1503] det_loss_box_dn_3[0.3969] det_loss_cls_dn_4[0.1506] det_loss_box_dn_4[0.3979] det_loss_cls_dn_5[0.1517] det_loss_box_dn_5[0.3987] loss_depth[0.0441] total_loss[12.3472]
Rank[0/8] 06/19/2025 15:03:20 INFO stats.py:314 | Epoch[7] Step[44] GlobalStep[2774] Training Speed: 6.19 samples/sec across all devices.	Average Step Time: 1.29 sec.	Estimated Remaining Time: 1 day, 3:51:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:03:25 INFO loss_tracker.py:84 | Epoch[7/239] Step[49] GlobalStep[2779/NA]: det_loss_cls_0[0.8694] det_loss_box_0[1.0545] det_loss_cls_1[0.8469] det_loss_box_1[0.9589] det_loss_cls_2[0.8405] det_loss_box_2[0.9172] det_loss_cls_3[0.8389] det_loss_box_3[0.9017] det_loss_cls_4[0.8385] det_loss_box_4[0.8913] det_loss_cls_5[0.8300] det_loss_box_5[0.8916] det_loss_cls_dn_0[0.1502] det_loss_box_dn_0[0.3253] det_loss_cls_dn_1[0.1472] det_loss_box_dn_1[0.3130] det_loss_cls_dn_2[0.1466] det_loss_box_dn_2[0.3111] det_loss_cls_dn_3[0.1474] det_loss_box_dn_3[0.3116] det_loss_cls_dn_4[0.1472] det_loss_box_dn_4[0.3124] det_loss_cls_dn_5[0.1474] det_loss_box_dn_5[0.3133] loss_depth[0.0422] total_loss[13.4942]
Rank[0/8] 06/19/2025 15:03:46 INFO stats.py:314 | Epoch[7] Step[69] GlobalStep[2799] Training Speed: 8.65 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 3:50:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:03:52 INFO loss_tracker.py:84 | Epoch[7/239] Step[74] GlobalStep[2804/NA]: det_loss_cls_0[0.6434] det_loss_box_0[0.9409] det_loss_cls_1[0.6347] det_loss_box_1[0.8388] det_loss_cls_2[0.6319] det_loss_box_2[0.8065] det_loss_cls_3[0.6322] det_loss_box_3[0.7823] det_loss_cls_4[0.6271] det_loss_box_4[0.7719] det_loss_cls_5[0.6200] det_loss_box_5[0.7733] det_loss_cls_dn_0[0.1509] det_loss_box_dn_0[0.4323] det_loss_cls_dn_1[0.1486] det_loss_box_dn_1[0.4134] det_loss_cls_dn_2[0.1491] det_loss_box_dn_2[0.4101] det_loss_cls_dn_3[0.1484] det_loss_box_dn_3[0.4107] det_loss_cls_dn_4[0.1492] det_loss_box_dn_4[0.4110] det_loss_cls_dn_5[0.1493] det_loss_box_dn_5[0.4115] loss_depth[0.0451] total_loss[12.1327]
Rank[0/8] 06/19/2025 15:04:14 INFO stats.py:314 | Epoch[7] Step[94] GlobalStep[2824] Training Speed: 8.04 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 3:50:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:04:19 INFO loss_tracker.py:84 | Epoch[7/239] Step[99] GlobalStep[2829/NA]: det_loss_cls_0[0.7141] det_loss_box_0[0.9696] det_loss_cls_1[0.6965] det_loss_box_1[0.8757] det_loss_cls_2[0.6929] det_loss_box_2[0.8310] det_loss_cls_3[0.6919] det_loss_box_3[0.8209] det_loss_cls_4[0.6855] det_loss_box_4[0.8077] det_loss_cls_5[0.6875] det_loss_box_5[0.8020] det_loss_cls_dn_0[0.1502] det_loss_box_dn_0[0.4118] det_loss_cls_dn_1[0.1470] det_loss_box_dn_1[0.4000] det_loss_cls_dn_2[0.1468] det_loss_box_dn_2[0.3993] det_loss_cls_dn_3[0.1473] det_loss_box_dn_3[0.3998] det_loss_cls_dn_4[0.1478] det_loss_box_dn_4[0.4004] det_loss_cls_dn_5[0.1490] det_loss_box_dn_5[0.4009] loss_depth[0.0451] total_loss[12.6206]
Rank[0/8] 06/19/2025 15:04:39 INFO stats.py:314 | Epoch[7] Step[119] GlobalStep[2849] Training Speed: 7.49 samples/sec across all devices.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 1 day, 3:48:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:04:45 INFO loss_tracker.py:84 | Epoch[7/239] Step[124] GlobalStep[2854/NA]: det_loss_cls_0[0.5825] det_loss_box_0[0.7558] det_loss_cls_1[0.5773] det_loss_box_1[0.6764] det_loss_cls_2[0.5805] det_loss_box_2[0.6319] det_loss_cls_3[0.5725] det_loss_box_3[0.6295] det_loss_cls_4[0.5642] det_loss_box_4[0.6343] det_loss_cls_5[0.5663] det_loss_box_5[0.6286] det_loss_cls_dn_0[0.1408] det_loss_box_dn_0[0.3741] det_loss_cls_dn_1[0.1402] det_loss_box_dn_1[0.3620] det_loss_cls_dn_2[0.1396] det_loss_box_dn_2[0.3592] det_loss_cls_dn_3[0.1398] det_loss_box_dn_3[0.3599] det_loss_cls_dn_4[0.1402] det_loss_box_dn_4[0.3606] det_loss_cls_dn_5[0.1413] det_loss_box_dn_5[0.3609] loss_depth[0.0410] total_loss[10.4593]
Rank[0/8] 06/19/2025 15:05:07 INFO stats.py:314 | Epoch[7] Step[144] GlobalStep[2874] Training Speed: 7.93 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 3:48:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:05:12 INFO loss_tracker.py:84 | Epoch[7/239] Step[149] GlobalStep[2879/NA]: det_loss_cls_0[0.7398] det_loss_box_0[0.9287] det_loss_cls_1[0.7038] det_loss_box_1[0.8269] det_loss_cls_2[0.7056] det_loss_box_2[0.7827] det_loss_cls_3[0.7026] det_loss_box_3[0.7796] det_loss_cls_4[0.6990] det_loss_box_4[0.7707] det_loss_cls_5[0.6968] det_loss_box_5[0.7690] det_loss_cls_dn_0[0.1480] det_loss_box_dn_0[0.3719] det_loss_cls_dn_1[0.1473] det_loss_box_dn_1[0.3574] det_loss_cls_dn_2[0.1469] det_loss_box_dn_2[0.3536] det_loss_cls_dn_3[0.1464] det_loss_box_dn_3[0.3540] det_loss_cls_dn_4[0.1475] det_loss_box_dn_4[0.3541] det_loss_cls_dn_5[0.1483] det_loss_box_dn_5[0.3538] loss_depth[0.0416] total_loss[12.1760]
Rank[0/8] 06/19/2025 15:05:33 INFO stats.py:314 | Epoch[7] Step[169] GlobalStep[2899] Training Speed: 6.02 samples/sec across all devices.	Average Step Time: 1.33 sec.	Estimated Remaining Time: 1 day, 3:47:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:05:39 INFO loss_tracker.py:84 | Epoch[7/239] Step[174] GlobalStep[2904/NA]: det_loss_cls_0[0.8034] det_loss_box_0[1.1196] det_loss_cls_1[0.7844] det_loss_box_1[1.0030] det_loss_cls_2[0.7781] det_loss_box_2[0.9497] det_loss_cls_3[0.7673] det_loss_box_3[0.9362] det_loss_cls_4[0.7536] det_loss_box_4[0.9352] det_loss_cls_5[0.7566] det_loss_box_5[0.9310] det_loss_cls_dn_0[0.1501] det_loss_box_dn_0[0.3883] det_loss_cls_dn_1[0.1490] det_loss_box_dn_1[0.3745] det_loss_cls_dn_2[0.1489] det_loss_box_dn_2[0.3705] det_loss_cls_dn_3[0.1489] det_loss_box_dn_3[0.3709] det_loss_cls_dn_4[0.1494] det_loss_box_dn_4[0.3713] det_loss_cls_dn_5[0.1504] det_loss_box_dn_5[0.3716] loss_depth[0.0455] total_loss[13.7076]
Rank[0/8] 06/19/2025 15:05:59 INFO stats.py:314 | Epoch[7] Step[194] GlobalStep[2924] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 3:45:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:06:04 INFO loss_tracker.py:84 | Epoch[7/239] Step[199] GlobalStep[2929/NA]: det_loss_cls_0[0.6047] det_loss_box_0[0.8119] det_loss_cls_1[0.5874] det_loss_box_1[0.7364] det_loss_cls_2[0.5834] det_loss_box_2[0.6915] det_loss_cls_3[0.5787] det_loss_box_3[0.6769] det_loss_cls_4[0.5783] det_loss_box_4[0.6657] det_loss_cls_5[0.5732] det_loss_box_5[0.6642] det_loss_cls_dn_0[0.1375] det_loss_box_dn_0[0.4086] det_loss_cls_dn_1[0.1351] det_loss_box_dn_1[0.3922] det_loss_cls_dn_2[0.1360] det_loss_box_dn_2[0.3864] det_loss_cls_dn_3[0.1329] det_loss_box_dn_3[0.3862] det_loss_cls_dn_4[0.1324] det_loss_box_dn_4[0.3870] det_loss_cls_dn_5[0.1341] det_loss_box_dn_5[0.3875] loss_depth[0.0431] total_loss[10.9516]
Rank[0/8] 06/19/2025 15:06:26 INFO stats.py:314 | Epoch[7] Step[219] GlobalStep[2949] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 3:44:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:06:31 INFO loss_tracker.py:84 | Epoch[7/239] Step[224] GlobalStep[2954/NA]: det_loss_cls_0[0.6663] det_loss_box_0[0.8687] det_loss_cls_1[0.6542] det_loss_box_1[0.7754] det_loss_cls_2[0.6502] det_loss_box_2[0.7377] det_loss_cls_3[0.6409] det_loss_box_3[0.7277] det_loss_cls_4[0.6451] det_loss_box_4[0.7093] det_loss_cls_5[0.6295] det_loss_box_5[0.7082] det_loss_cls_dn_0[0.1474] det_loss_box_dn_0[0.3701] det_loss_cls_dn_1[0.1452] det_loss_box_dn_1[0.3530] det_loss_cls_dn_2[0.1448] det_loss_box_dn_2[0.3508] det_loss_cls_dn_3[0.1434] det_loss_box_dn_3[0.3513] det_loss_cls_dn_4[0.1436] det_loss_box_dn_4[0.3520] det_loss_cls_dn_5[0.1443] det_loss_box_dn_5[0.3524] loss_depth[0.0425] total_loss[11.4537]
Rank[0/8] 06/19/2025 15:06:52 INFO stats.py:314 | Epoch[7] Step[244] GlobalStep[2974] Training Speed: 8.13 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 3:43:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:06:57 INFO loss_tracker.py:84 | Epoch[7/239] Step[249] GlobalStep[2979/NA]: det_loss_cls_0[0.8159] det_loss_box_0[1.2122] det_loss_cls_1[0.7795] det_loss_box_1[1.0893] det_loss_cls_2[0.7838] det_loss_box_2[1.0456] det_loss_cls_3[0.7673] det_loss_box_3[1.0357] det_loss_cls_4[0.7652] det_loss_box_4[1.0291] det_loss_cls_5[0.7681] det_loss_box_5[1.0185] det_loss_cls_dn_0[0.1524] det_loss_box_dn_0[0.4579] det_loss_cls_dn_1[0.1494] det_loss_box_dn_1[0.4447] det_loss_cls_dn_2[0.1493] det_loss_box_dn_2[0.4410] det_loss_cls_dn_3[0.1500] det_loss_box_dn_3[0.4409] det_loss_cls_dn_4[0.1493] det_loss_box_dn_4[0.4416] det_loss_cls_dn_5[0.1517] det_loss_box_dn_5[0.4424] loss_depth[0.0460] total_loss[14.7268]
Rank[0/8] 06/19/2025 15:07:18 INFO stats.py:314 | Epoch[7] Step[269] GlobalStep[2999] Training Speed: 8.17 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 3:42:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:07:23 INFO loss_tracker.py:84 | Epoch[7/239] Step[274] GlobalStep[3004/NA]: det_loss_cls_0[0.6967] det_loss_box_0[0.8896] det_loss_cls_1[0.6700] det_loss_box_1[0.7934] det_loss_cls_2[0.6702] det_loss_box_2[0.7498] det_loss_cls_3[0.6650] det_loss_box_3[0.7436] det_loss_cls_4[0.6645] det_loss_box_4[0.7289] det_loss_cls_5[0.6591] det_loss_box_5[0.7318] det_loss_cls_dn_0[0.1435] det_loss_box_dn_0[0.3255] det_loss_cls_dn_1[0.1413] det_loss_box_dn_1[0.3170] det_loss_cls_dn_2[0.1413] det_loss_box_dn_2[0.3145] det_loss_cls_dn_3[0.1408] det_loss_box_dn_3[0.3142] det_loss_cls_dn_4[0.1404] det_loss_box_dn_4[0.3146] det_loss_cls_dn_5[0.1407] det_loss_box_dn_5[0.3153] loss_depth[0.0403] total_loss[11.4521]
Rank[0/8] 06/19/2025 15:07:44 INFO stats.py:314 | Epoch[7] Step[294] GlobalStep[3024] Training Speed: 8.13 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 3:41:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:07:50 INFO loss_tracker.py:84 | Epoch[7/239] Step[299] GlobalStep[3029/NA]: det_loss_cls_0[0.6294] det_loss_box_0[0.8314] det_loss_cls_1[0.6072] det_loss_box_1[0.7420] det_loss_cls_2[0.6020] det_loss_box_2[0.7031] det_loss_cls_3[0.6047] det_loss_box_3[0.6811] det_loss_cls_4[0.5957] det_loss_box_4[0.6749] det_loss_cls_5[0.5876] det_loss_box_5[0.6732] det_loss_cls_dn_0[0.1439] det_loss_box_dn_0[0.3954] det_loss_cls_dn_1[0.1422] det_loss_box_dn_1[0.3848] det_loss_cls_dn_2[0.1424] det_loss_box_dn_2[0.3801] det_loss_cls_dn_3[0.1428] det_loss_box_dn_3[0.3804] det_loss_cls_dn_4[0.1432] det_loss_box_dn_4[0.3813] det_loss_cls_dn_5[0.1452] det_loss_box_dn_5[0.3821] loss_depth[0.0438] total_loss[11.1400]
Rank[0/8] 06/19/2025 15:08:11 INFO stats.py:314 | Epoch[7] Step[319] GlobalStep[3049] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 3:40:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:08:16 INFO loss_tracker.py:84 | Epoch[7/239] Step[324] GlobalStep[3054/NA]: det_loss_cls_0[0.6553] det_loss_box_0[0.9850] det_loss_cls_1[0.6373] det_loss_box_1[0.8761] det_loss_cls_2[0.6375] det_loss_box_2[0.8383] det_loss_cls_3[0.6292] det_loss_box_3[0.8194] det_loss_cls_4[0.6254] det_loss_box_4[0.8097] det_loss_cls_5[0.6215] det_loss_box_5[0.8068] det_loss_cls_dn_0[0.1419] det_loss_box_dn_0[0.4020] det_loss_cls_dn_1[0.1409] det_loss_box_dn_1[0.3844] det_loss_cls_dn_2[0.1414] det_loss_box_dn_2[0.3810] det_loss_cls_dn_3[0.1407] det_loss_box_dn_3[0.3820] det_loss_cls_dn_4[0.1406] det_loss_box_dn_4[0.3824] det_loss_cls_dn_5[0.1420] det_loss_box_dn_5[0.3828] loss_depth[0.0425] total_loss[12.1458]
Rank[0/8] 06/19/2025 15:08:37 INFO stats.py:314 | Epoch[7] Step[344] GlobalStep[3074] Training Speed: 7.86 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1 day, 3:39:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:08:43 INFO loss_tracker.py:84 | Epoch[7/239] Step[349] GlobalStep[3079/NA]: det_loss_cls_0[0.7829] det_loss_box_0[1.0665] det_loss_cls_1[0.7617] det_loss_box_1[0.9319] det_loss_cls_2[0.7649] det_loss_box_2[0.8681] det_loss_cls_3[0.7567] det_loss_box_3[0.8567] det_loss_cls_4[0.7511] det_loss_box_4[0.8425] det_loss_cls_5[0.7484] det_loss_box_5[0.8385] det_loss_cls_dn_0[0.1340] det_loss_box_dn_0[0.4527] det_loss_cls_dn_1[0.1327] det_loss_box_dn_1[0.4192] det_loss_cls_dn_2[0.1333] det_loss_box_dn_2[0.4115] det_loss_cls_dn_3[0.1338] det_loss_box_dn_3[0.4108] det_loss_cls_dn_4[0.1339] det_loss_box_dn_4[0.4105] det_loss_cls_dn_5[0.1340] det_loss_box_dn_5[0.4105] loss_depth[0.0485] total_loss[13.3353]
Rank[0/8] 06/19/2025 15:09:03 INFO stats.py:314 | Epoch[7] Step[369] GlobalStep[3099] Training Speed: 7.98 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 3:38:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:09:09 INFO loss_tracker.py:84 | Epoch[7/239] Step[374] GlobalStep[3104/NA]: det_loss_cls_0[0.6497] det_loss_box_0[0.8516] det_loss_cls_1[0.6357] det_loss_box_1[0.7552] det_loss_cls_2[0.6253] det_loss_box_2[0.7209] det_loss_cls_3[0.6200] det_loss_box_3[0.7042] det_loss_cls_4[0.6125] det_loss_box_4[0.6947] det_loss_cls_5[0.6126] det_loss_box_5[0.6919] det_loss_cls_dn_0[0.1439] det_loss_box_dn_0[0.3831] det_loss_cls_dn_1[0.1425] det_loss_box_dn_1[0.3621] det_loss_cls_dn_2[0.1428] det_loss_box_dn_2[0.3565] det_loss_cls_dn_3[0.1420] det_loss_box_dn_3[0.3565] det_loss_cls_dn_4[0.1429] det_loss_box_dn_4[0.3574] det_loss_cls_dn_5[0.1438] det_loss_box_dn_5[0.3582] loss_depth[0.0436] total_loss[11.2496]
Rank[0/8] 06/19/2025 15:09:25 INFO stats.py:394 | Epoch[7] completed. Training Speed: 7.48 samples/sec across all devices.	Epoch Time: 417.04 sec.	Average Epoch Time: 417.04 sec.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 1 day, 3:37:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:09:34 INFO stats.py:314 | Epoch[8] Step[4] GlobalStep[3124] Training Speed: 8.01 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 3:39:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:09:56 INFO loss_tracker.py:84 | Epoch[8/239] Step[24] GlobalStep[3144/NA]: det_loss_cls_0[0.6577] det_loss_box_0[0.8531] det_loss_cls_1[0.6416] det_loss_box_1[0.7517] det_loss_cls_2[0.6356] det_loss_box_2[0.7216] det_loss_cls_3[0.6346] det_loss_box_3[0.7022] det_loss_cls_4[0.6380] det_loss_box_4[0.6975] det_loss_cls_5[0.6297] det_loss_box_5[0.6970] det_loss_cls_dn_0[0.1486] det_loss_box_dn_0[0.4227] det_loss_cls_dn_1[0.1470] det_loss_box_dn_1[0.4042] det_loss_cls_dn_2[0.1466] det_loss_box_dn_2[0.3994] det_loss_cls_dn_3[0.1458] det_loss_box_dn_3[0.3991] det_loss_cls_dn_4[0.1456] det_loss_box_dn_4[0.3995] det_loss_cls_dn_5[0.1464] det_loss_box_dn_5[0.4001] loss_depth[0.0461] total_loss[11.6113]
Rank[0/8] 06/19/2025 15:10:02 INFO stats.py:314 | Epoch[8] Step[29] GlobalStep[3149] Training Speed: 7.69 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 3:38:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:10:22 INFO loss_tracker.py:84 | Epoch[8/239] Step[49] GlobalStep[3169/NA]: det_loss_cls_0[0.6517] det_loss_box_0[0.8322] det_loss_cls_1[0.6404] det_loss_box_1[0.7539] det_loss_cls_2[0.6391] det_loss_box_2[0.7328] det_loss_cls_3[0.6328] det_loss_box_3[0.7220] det_loss_cls_4[0.6282] det_loss_box_4[0.7110] det_loss_cls_5[0.6236] det_loss_box_5[0.7105] det_loss_cls_dn_0[0.1586] det_loss_box_dn_0[0.4946] det_loss_cls_dn_1[0.1537] det_loss_box_dn_1[0.4682] det_loss_cls_dn_2[0.1554] det_loss_box_dn_2[0.4633] det_loss_cls_dn_3[0.1551] det_loss_box_dn_3[0.4633] det_loss_cls_dn_4[0.1542] det_loss_box_dn_4[0.4638] det_loss_cls_dn_5[0.1554] det_loss_box_dn_5[0.4636] loss_depth[0.0389] total_loss[12.0664]
Rank[0/8] 06/19/2025 15:10:27 INFO stats.py:314 | Epoch[8] Step[54] GlobalStep[3174] Training Speed: 7.64 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 3:37:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:10:49 INFO loss_tracker.py:84 | Epoch[8/239] Step[74] GlobalStep[3194/NA]: det_loss_cls_0[0.6098] det_loss_box_0[0.7900] det_loss_cls_1[0.6157] det_loss_box_1[0.6924] det_loss_cls_2[0.6091] det_loss_box_2[0.6659] det_loss_cls_3[0.6030] det_loss_box_3[0.6559] det_loss_cls_4[0.5960] det_loss_box_4[0.6486] det_loss_cls_5[0.5937] det_loss_box_5[0.6518] det_loss_cls_dn_0[0.1464] det_loss_box_dn_0[0.3505] det_loss_cls_dn_1[0.1448] det_loss_box_dn_1[0.3366] det_loss_cls_dn_2[0.1454] det_loss_box_dn_2[0.3313] det_loss_cls_dn_3[0.1436] det_loss_box_dn_3[0.3315] det_loss_cls_dn_4[0.1451] det_loss_box_dn_4[0.3319] det_loss_cls_dn_5[0.1449] det_loss_box_dn_5[0.3319] loss_depth[0.0390] total_loss[10.6547]
Rank[0/8] 06/19/2025 15:10:54 INFO stats.py:314 | Epoch[8] Step[79] GlobalStep[3199] Training Speed: 7.48 samples/sec across all devices.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 1 day, 3:36:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:11:15 INFO loss_tracker.py:84 | Epoch[8/239] Step[99] GlobalStep[3219/NA]: det_loss_cls_0[0.4855] det_loss_box_0[0.5769] det_loss_cls_1[0.4695] det_loss_box_1[0.5117] det_loss_cls_2[0.4614] det_loss_box_2[0.4825] det_loss_cls_3[0.4560] det_loss_box_3[0.4745] det_loss_cls_4[0.4494] det_loss_box_4[0.4672] det_loss_cls_5[0.4510] det_loss_box_5[0.4655] det_loss_cls_dn_0[0.1378] det_loss_box_dn_0[0.3606] det_loss_cls_dn_1[0.1356] det_loss_box_dn_1[0.3507] det_loss_cls_dn_2[0.1345] det_loss_box_dn_2[0.3453] det_loss_cls_dn_3[0.1342] det_loss_box_dn_3[0.3454] det_loss_cls_dn_4[0.1344] det_loss_box_dn_4[0.3459] det_loss_cls_dn_5[0.1351] det_loss_box_dn_5[0.3465] loss_depth[0.0401] total_loss[8.6972]
Rank[0/8] 06/19/2025 15:11:21 INFO stats.py:314 | Epoch[8] Step[104] GlobalStep[3224] Training Speed: 6.92 samples/sec across all devices.	Average Step Time: 1.16 sec.	Estimated Remaining Time: 1 day, 3:36:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:11:41 INFO loss_tracker.py:84 | Epoch[8/239] Step[124] GlobalStep[3244/NA]: det_loss_cls_0[0.6752] det_loss_box_0[0.9228] det_loss_cls_1[0.6456] det_loss_box_1[0.7980] det_loss_cls_2[0.6477] det_loss_box_2[0.7438] det_loss_cls_3[0.6434] det_loss_box_3[0.7213] det_loss_cls_4[0.6451] det_loss_box_4[0.7050] det_loss_cls_5[0.6431] det_loss_box_5[0.7026] det_loss_cls_dn_0[0.1372] det_loss_box_dn_0[0.3932] det_loss_cls_dn_1[0.1365] det_loss_box_dn_1[0.3747] det_loss_cls_dn_2[0.1365] det_loss_box_dn_2[0.3723] det_loss_cls_dn_3[0.1362] det_loss_box_dn_3[0.3720] det_loss_cls_dn_4[0.1362] det_loss_box_dn_4[0.3723] det_loss_cls_dn_5[0.1364] det_loss_box_dn_5[0.3726] loss_depth[0.0401] total_loss[11.6098]
Rank[0/8] 06/19/2025 15:11:47 INFO stats.py:314 | Epoch[8] Step[129] GlobalStep[3249] Training Speed: 7.97 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 3:34:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:12:08 INFO loss_tracker.py:84 | Epoch[8/239] Step[149] GlobalStep[3269/NA]: det_loss_cls_0[0.5942] det_loss_box_0[0.7231] det_loss_cls_1[0.5881] det_loss_box_1[0.6468] det_loss_cls_2[0.5893] det_loss_box_2[0.6143] det_loss_cls_3[0.5942] det_loss_box_3[0.5982] det_loss_cls_4[0.5889] det_loss_box_4[0.5859] det_loss_cls_5[0.5810] det_loss_box_5[0.5897] det_loss_cls_dn_0[0.1376] det_loss_box_dn_0[0.3845] det_loss_cls_dn_1[0.1366] det_loss_box_dn_1[0.3725] det_loss_cls_dn_2[0.1360] det_loss_box_dn_2[0.3672] det_loss_cls_dn_3[0.1352] det_loss_box_dn_3[0.3671] det_loss_cls_dn_4[0.1350] det_loss_box_dn_4[0.3681] det_loss_cls_dn_5[0.1376] det_loss_box_dn_5[0.3686] loss_depth[0.0396] total_loss[10.3793]
Rank[0/8] 06/19/2025 15:12:13 INFO stats.py:314 | Epoch[8] Step[154] GlobalStep[3274] Training Speed: 10.06 samples/sec across all devices.	Average Step Time: 0.79 sec.	Estimated Remaining Time: 1 day, 3:33:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:12:34 INFO loss_tracker.py:84 | Epoch[8/239] Step[174] GlobalStep[3294/NA]: det_loss_cls_0[0.5893] det_loss_box_0[0.7588] det_loss_cls_1[0.5775] det_loss_box_1[0.6655] det_loss_cls_2[0.5812] det_loss_box_2[0.6275] det_loss_cls_3[0.5776] det_loss_box_3[0.6190] det_loss_cls_4[0.5720] det_loss_box_4[0.6127] det_loss_cls_5[0.5761] det_loss_box_5[0.6052] det_loss_cls_dn_0[0.1390] det_loss_box_dn_0[0.3868] det_loss_cls_dn_1[0.1353] det_loss_box_dn_1[0.3693] det_loss_cls_dn_2[0.1346] det_loss_box_dn_2[0.3662] det_loss_cls_dn_3[0.1340] det_loss_box_dn_3[0.3659] det_loss_cls_dn_4[0.1340] det_loss_box_dn_4[0.3664] det_loss_cls_dn_5[0.1357] det_loss_box_dn_5[0.3668] loss_depth[0.0452] total_loss[10.4418]
Rank[0/8] 06/19/2025 15:12:40 INFO stats.py:314 | Epoch[8] Step[179] GlobalStep[3299] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 3:33:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:13:01 INFO loss_tracker.py:84 | Epoch[8/239] Step[199] GlobalStep[3319/NA]: det_loss_cls_0[0.5975] det_loss_box_0[0.9977] det_loss_cls_1[0.5953] det_loss_box_1[0.8902] det_loss_cls_2[0.5814] det_loss_box_2[0.8642] det_loss_cls_3[0.5862] det_loss_box_3[0.8359] det_loss_cls_4[0.5809] det_loss_box_4[0.8224] det_loss_cls_5[0.5827] det_loss_box_5[0.8177] det_loss_cls_dn_0[0.1476] det_loss_box_dn_0[0.4722] det_loss_cls_dn_1[0.1463] det_loss_box_dn_1[0.4576] det_loss_cls_dn_2[0.1477] det_loss_box_dn_2[0.4556] det_loss_cls_dn_3[0.1484] det_loss_box_dn_3[0.4578] det_loss_cls_dn_4[0.1494] det_loss_box_dn_4[0.4591] det_loss_cls_dn_5[0.1495] det_loss_box_dn_5[0.4601] loss_depth[0.0450] total_loss[12.4487]
Rank[0/8] 06/19/2025 15:13:06 INFO stats.py:314 | Epoch[8] Step[204] GlobalStep[3324] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 3:31:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:13:26 INFO loss_tracker.py:84 | Epoch[8/239] Step[224] GlobalStep[3344/NA]: det_loss_cls_0[0.6653] det_loss_box_0[0.9841] det_loss_cls_1[0.6452] det_loss_box_1[0.9053] det_loss_cls_2[0.6462] det_loss_box_2[0.8354] det_loss_cls_3[0.6468] det_loss_box_3[0.8267] det_loss_cls_4[0.6452] det_loss_box_4[0.8256] det_loss_cls_5[0.6338] det_loss_box_5[0.8297] det_loss_cls_dn_0[0.1508] det_loss_box_dn_0[0.3962] det_loss_cls_dn_1[0.1505] det_loss_box_dn_1[0.3788] det_loss_cls_dn_2[0.1509] det_loss_box_dn_2[0.3759] det_loss_cls_dn_3[0.1504] det_loss_box_dn_3[0.3769] det_loss_cls_dn_4[0.1497] det_loss_box_dn_4[0.3772] det_loss_cls_dn_5[0.1508] det_loss_box_dn_5[0.3775] loss_depth[0.0456] total_loss[12.3206]
Rank[0/8] 06/19/2025 15:13:31 INFO stats.py:314 | Epoch[8] Step[229] GlobalStep[3349] Training Speed: 7.45 samples/sec across all devices.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 1 day, 3:30:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:13:53 INFO loss_tracker.py:84 | Epoch[8/239] Step[249] GlobalStep[3369/NA]: det_loss_cls_0[0.8476] det_loss_box_0[1.0753] det_loss_cls_1[0.8354] det_loss_box_1[0.9618] det_loss_cls_2[0.8187] det_loss_box_2[0.9219] det_loss_cls_3[0.8025] det_loss_box_3[0.9081] det_loss_cls_4[0.7962] det_loss_box_4[0.8947] det_loss_cls_5[0.7977] det_loss_box_5[0.8857] det_loss_cls_dn_0[0.1527] det_loss_box_dn_0[0.3616] det_loss_cls_dn_1[0.1493] det_loss_box_dn_1[0.3483] det_loss_cls_dn_2[0.1497] det_loss_box_dn_2[0.3433] det_loss_cls_dn_3[0.1479] det_loss_box_dn_3[0.3435] det_loss_cls_dn_4[0.1478] det_loss_box_dn_4[0.3438] det_loss_cls_dn_5[0.1487] det_loss_box_dn_5[0.3440] loss_depth[0.0444] total_loss[13.5708]
Rank[0/8] 06/19/2025 15:13:58 INFO stats.py:314 | Epoch[8] Step[254] GlobalStep[3374] Training Speed: 7.89 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 3:29:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:14:19 INFO loss_tracker.py:84 | Epoch[8/239] Step[274] GlobalStep[3394/NA]: det_loss_cls_0[0.6680] det_loss_box_0[0.9651] det_loss_cls_1[0.6552] det_loss_box_1[0.8987] det_loss_cls_2[0.6561] det_loss_box_2[0.8590] det_loss_cls_3[0.6459] det_loss_box_3[0.8394] det_loss_cls_4[0.6427] det_loss_box_4[0.8375] det_loss_cls_5[0.6364] det_loss_box_5[0.8408] det_loss_cls_dn_0[0.1532] det_loss_box_dn_0[0.4524] det_loss_cls_dn_1[0.1519] det_loss_box_dn_1[0.4356] det_loss_cls_dn_2[0.1516] det_loss_box_dn_2[0.4310] det_loss_cls_dn_3[0.1516] det_loss_box_dn_3[0.4311] det_loss_cls_dn_4[0.1519] det_loss_box_dn_4[0.4314] det_loss_cls_dn_5[0.1524] det_loss_box_dn_5[0.4324] loss_depth[0.0427] total_loss[12.7141]
Rank[0/8] 06/19/2025 15:14:24 INFO stats.py:314 | Epoch[8] Step[279] GlobalStep[3399] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 3:29:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:14:45 INFO loss_tracker.py:84 | Epoch[8/239] Step[299] GlobalStep[3419/NA]: det_loss_cls_0[0.5984] det_loss_box_0[0.8384] det_loss_cls_1[0.5782] det_loss_box_1[0.7385] det_loss_cls_2[0.5822] det_loss_box_2[0.6989] det_loss_cls_3[0.5705] det_loss_box_3[0.6840] det_loss_cls_4[0.5720] det_loss_box_4[0.6798] det_loss_cls_5[0.5607] det_loss_box_5[0.6778] det_loss_cls_dn_0[0.1341] det_loss_box_dn_0[0.4126] det_loss_cls_dn_1[0.1320] det_loss_box_dn_1[0.3901] det_loss_cls_dn_2[0.1310] det_loss_box_dn_2[0.3861] det_loss_cls_dn_3[0.1298] det_loss_box_dn_3[0.3858] det_loss_cls_dn_4[0.1297] det_loss_box_dn_4[0.3869] det_loss_cls_dn_5[0.1294] det_loss_box_dn_5[0.3876] loss_depth[0.0433] total_loss[10.9580]
Rank[0/8] 06/19/2025 15:14:51 INFO stats.py:314 | Epoch[8] Step[304] GlobalStep[3424] Training Speed: 8.04 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 3:27:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:15:12 INFO loss_tracker.py:84 | Epoch[8/239] Step[324] GlobalStep[3444/NA]: det_loss_cls_0[0.6125] det_loss_box_0[0.8682] det_loss_cls_1[0.5970] det_loss_box_1[0.8235] det_loss_cls_2[0.5980] det_loss_box_2[0.7789] det_loss_cls_3[0.5891] det_loss_box_3[0.7686] det_loss_cls_4[0.5891] det_loss_box_4[0.7592] det_loss_cls_5[0.5889] det_loss_box_5[0.7576] det_loss_cls_dn_0[0.1408] det_loss_box_dn_0[0.5980] det_loss_cls_dn_1[0.1380] det_loss_box_dn_1[0.5711] det_loss_cls_dn_2[0.1388] det_loss_box_dn_2[0.5601] det_loss_cls_dn_3[0.1386] det_loss_box_dn_3[0.5640] det_loss_cls_dn_4[0.1379] det_loss_box_dn_4[0.5645] det_loss_cls_dn_5[0.1388] det_loss_box_dn_5[0.5659] loss_depth[0.0450] total_loss[12.6321]
Rank[0/8] 06/19/2025 15:15:17 INFO stats.py:314 | Epoch[8] Step[329] GlobalStep[3449] Training Speed: 7.64 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 3:27:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:15:39 INFO loss_tracker.py:84 | Epoch[8/239] Step[349] GlobalStep[3469/NA]: det_loss_cls_0[0.5706] det_loss_box_0[0.7534] det_loss_cls_1[0.5604] det_loss_box_1[0.6693] det_loss_cls_2[0.5599] det_loss_box_2[0.6375] det_loss_cls_3[0.5430] det_loss_box_3[0.6246] det_loss_cls_4[0.5484] det_loss_box_4[0.6137] det_loss_cls_5[0.5452] det_loss_box_5[0.6123] det_loss_cls_dn_0[0.1360] det_loss_box_dn_0[0.3860] det_loss_cls_dn_1[0.1335] det_loss_box_dn_1[0.3654] det_loss_cls_dn_2[0.1343] det_loss_box_dn_2[0.3606] det_loss_cls_dn_3[0.1336] det_loss_box_dn_3[0.3604] det_loss_cls_dn_4[0.1338] det_loss_box_dn_4[0.3607] det_loss_cls_dn_5[0.1351] det_loss_box_dn_5[0.3611] loss_depth[0.0442] total_loss[10.2831]
Rank[0/8] 06/19/2025 15:15:44 INFO stats.py:314 | Epoch[8] Step[354] GlobalStep[3474] Training Speed: 6.57 samples/sec across all devices.	Average Step Time: 1.22 sec.	Estimated Remaining Time: 1 day, 3:26:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:16:05 INFO loss_tracker.py:84 | Epoch[8/239] Step[374] GlobalStep[3494/NA]: det_loss_cls_0[0.6850] det_loss_box_0[0.8897] det_loss_cls_1[0.6679] det_loss_box_1[0.7675] det_loss_cls_2[0.6582] det_loss_box_2[0.7345] det_loss_cls_3[0.6518] det_loss_box_3[0.7250] det_loss_cls_4[0.6520] det_loss_box_4[0.7219] det_loss_cls_5[0.6459] det_loss_box_5[0.7156] det_loss_cls_dn_0[0.1473] det_loss_box_dn_0[0.3748] det_loss_cls_dn_1[0.1446] det_loss_box_dn_1[0.3596] det_loss_cls_dn_2[0.1453] det_loss_box_dn_2[0.3547] det_loss_cls_dn_3[0.1443] det_loss_box_dn_3[0.3548] det_loss_cls_dn_4[0.1448] det_loss_box_dn_4[0.3549] det_loss_cls_dn_5[0.1450] det_loss_box_dn_5[0.3552] loss_depth[0.0447] total_loss[11.5850]
Rank[0/8] 06/19/2025 15:16:10 INFO stats.py:314 | Epoch[8] Step[379] GlobalStep[3499] Training Speed: 7.82 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1 day, 3:25:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:16:20 INFO stats.py:394 | Epoch[8] completed. Training Speed: 7.52 samples/sec across all devices.	Epoch Time: 415.05 sec.	Average Epoch Time: 415.05 sec.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 1 day, 3:24:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:16:39 INFO stats.py:314 | Epoch[9] Step[14] GlobalStep[3524] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 3:25:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:16:50 INFO loss_tracker.py:84 | Epoch[9/239] Step[24] GlobalStep[3534/NA]: det_loss_cls_0[0.6691] det_loss_box_0[0.9546] det_loss_cls_1[0.6500] det_loss_box_1[0.8418] det_loss_cls_2[0.6450] det_loss_box_2[0.8030] det_loss_cls_3[0.6439] det_loss_box_3[0.7868] det_loss_cls_4[0.6459] det_loss_box_4[0.7745] det_loss_cls_5[0.6397] det_loss_box_5[0.7721] det_loss_cls_dn_0[0.1458] det_loss_box_dn_0[0.4034] det_loss_cls_dn_1[0.1439] det_loss_box_dn_1[0.3904] det_loss_cls_dn_2[0.1445] det_loss_box_dn_2[0.3820] det_loss_cls_dn_3[0.1448] det_loss_box_dn_3[0.3823] det_loss_cls_dn_4[0.1436] det_loss_box_dn_4[0.3830] det_loss_cls_dn_5[0.1437] det_loss_box_dn_5[0.3833] loss_depth[0.0446] total_loss[12.0615]
Rank[0/8] 06/19/2025 15:17:06 INFO stats.py:314 | Epoch[9] Step[39] GlobalStep[3549] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 3:24:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:17:16 INFO loss_tracker.py:84 | Epoch[9/239] Step[49] GlobalStep[3559/NA]: det_loss_cls_0[0.6127] det_loss_box_0[0.8243] det_loss_cls_1[0.5878] det_loss_box_1[0.7254] det_loss_cls_2[0.5906] det_loss_box_2[0.7046] det_loss_cls_3[0.5921] det_loss_box_3[0.6891] det_loss_cls_4[0.5915] det_loss_box_4[0.6891] det_loss_cls_5[0.5878] det_loss_box_5[0.6850] det_loss_cls_dn_0[0.1448] det_loss_box_dn_0[0.4255] det_loss_cls_dn_1[0.1428] det_loss_box_dn_1[0.4044] det_loss_cls_dn_2[0.1431] det_loss_box_dn_2[0.3988] det_loss_cls_dn_3[0.1426] det_loss_box_dn_3[0.3979] det_loss_cls_dn_4[0.1419] det_loss_box_dn_4[0.3983] det_loss_cls_dn_5[0.1431] det_loss_box_dn_5[0.3986] loss_depth[0.0426] total_loss[11.2044]
Rank[0/8] 06/19/2025 15:17:33 INFO stats.py:314 | Epoch[9] Step[64] GlobalStep[3574] Training Speed: 7.75 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 1 day, 3:24:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:17:43 INFO loss_tracker.py:84 | Epoch[9/239] Step[74] GlobalStep[3584/NA]: det_loss_cls_0[0.5028] det_loss_box_0[0.6908] det_loss_cls_1[0.4870] det_loss_box_1[0.5933] det_loss_cls_2[0.4861] det_loss_box_2[0.5691] det_loss_cls_3[0.4824] det_loss_box_3[0.5590] det_loss_cls_4[0.4853] det_loss_box_4[0.5440] det_loss_cls_5[0.4787] det_loss_box_5[0.5441] det_loss_cls_dn_0[0.1163] det_loss_box_dn_0[0.3767] det_loss_cls_dn_1[0.1135] det_loss_box_dn_1[0.3607] det_loss_cls_dn_2[0.1127] det_loss_box_dn_2[0.3548] det_loss_cls_dn_3[0.1120] det_loss_box_dn_3[0.3548] det_loss_cls_dn_4[0.1110] det_loss_box_dn_4[0.3550] det_loss_cls_dn_5[0.1110] det_loss_box_dn_5[0.3550] loss_depth[0.0427] total_loss[9.2988]
Rank[0/8] 06/19/2025 15:17:59 INFO stats.py:314 | Epoch[9] Step[89] GlobalStep[3599] Training Speed: 7.90 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 3:23:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:18:10 INFO loss_tracker.py:84 | Epoch[9/239] Step[99] GlobalStep[3609/NA]: det_loss_cls_0[0.5369] det_loss_box_0[0.7446] det_loss_cls_1[0.5234] det_loss_box_1[0.6699] det_loss_cls_2[0.5201] det_loss_box_2[0.6390] det_loss_cls_3[0.5249] det_loss_box_3[0.6238] det_loss_cls_4[0.5187] det_loss_box_4[0.6119] det_loss_cls_5[0.5173] det_loss_box_5[0.6102] det_loss_cls_dn_0[0.1424] det_loss_box_dn_0[0.4521] det_loss_cls_dn_1[0.1402] det_loss_box_dn_1[0.4326] det_loss_cls_dn_2[0.1385] det_loss_box_dn_2[0.4258] det_loss_cls_dn_3[0.1373] det_loss_box_dn_3[0.4260] det_loss_cls_dn_4[0.1390] det_loss_box_dn_4[0.4266] det_loss_cls_dn_5[0.1416] det_loss_box_dn_5[0.4274] loss_depth[0.0417] total_loss[10.5117]
Rank[0/8] 06/19/2025 15:18:25 INFO stats.py:314 | Epoch[9] Step[114] GlobalStep[3624] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 3:22:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:18:36 INFO loss_tracker.py:84 | Epoch[9/239] Step[124] GlobalStep[3634/NA]: det_loss_cls_0[0.6148] det_loss_box_0[0.7939] det_loss_cls_1[0.5956] det_loss_box_1[0.6985] det_loss_cls_2[0.5973] det_loss_box_2[0.6659] det_loss_cls_3[0.5951] det_loss_box_3[0.6600] det_loss_cls_4[0.5884] det_loss_box_4[0.6589] det_loss_cls_5[0.5754] det_loss_box_5[0.6656] det_loss_cls_dn_0[0.1349] det_loss_box_dn_0[0.3963] det_loss_cls_dn_1[0.1352] det_loss_box_dn_1[0.3764] det_loss_cls_dn_2[0.1351] det_loss_box_dn_2[0.3700] det_loss_cls_dn_3[0.1356] det_loss_box_dn_3[0.3708] det_loss_cls_dn_4[0.1342] det_loss_box_dn_4[0.3706] det_loss_cls_dn_5[0.1355] det_loss_box_dn_5[0.3704] loss_depth[0.0469] total_loss[10.8214]
Rank[0/8] 06/19/2025 15:18:52 INFO stats.py:314 | Epoch[9] Step[139] GlobalStep[3649] Training Speed: 7.85 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1 day, 3:21:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:19:02 INFO loss_tracker.py:84 | Epoch[9/239] Step[149] GlobalStep[3659/NA]: det_loss_cls_0[0.6465] det_loss_box_0[0.8875] det_loss_cls_1[0.6368] det_loss_box_1[0.8012] det_loss_cls_2[0.6309] det_loss_box_2[0.7618] det_loss_cls_3[0.6212] det_loss_box_3[0.7517] det_loss_cls_4[0.6209] det_loss_box_4[0.7475] det_loss_cls_5[0.6159] det_loss_box_5[0.7484] det_loss_cls_dn_0[0.1382] det_loss_box_dn_0[0.4224] det_loss_cls_dn_1[0.1359] det_loss_box_dn_1[0.4061] det_loss_cls_dn_2[0.1365] det_loss_box_dn_2[0.4002] det_loss_cls_dn_3[0.1368] det_loss_box_dn_3[0.4004] det_loss_cls_dn_4[0.1372] det_loss_box_dn_4[0.4009] det_loss_cls_dn_5[0.1382] det_loss_box_dn_5[0.4017] loss_depth[0.0433] total_loss[11.7685]
Rank[0/8] 06/19/2025 15:19:17 INFO stats.py:314 | Epoch[9] Step[164] GlobalStep[3674] Training Speed: 7.75 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 1 day, 3:20:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:19:28 INFO loss_tracker.py:84 | Epoch[9/239] Step[174] GlobalStep[3684/NA]: det_loss_cls_0[0.5954] det_loss_box_0[0.7268] det_loss_cls_1[0.5754] det_loss_box_1[0.6409] det_loss_cls_2[0.5719] det_loss_box_2[0.6186] det_loss_cls_3[0.5696] det_loss_box_3[0.6018] det_loss_cls_4[0.5660] det_loss_box_4[0.5865] det_loss_cls_5[0.5678] det_loss_box_5[0.5823] det_loss_cls_dn_0[0.1323] det_loss_box_dn_0[0.4175] det_loss_cls_dn_1[0.1306] det_loss_box_dn_1[0.3971] det_loss_cls_dn_2[0.1311] det_loss_box_dn_2[0.3864] det_loss_cls_dn_3[0.1308] det_loss_box_dn_3[0.3873] det_loss_cls_dn_4[0.1302] det_loss_box_dn_4[0.3877] det_loss_cls_dn_5[0.1303] det_loss_box_dn_5[0.3882] loss_depth[0.0414] total_loss[10.3940]
Rank[0/8] 06/19/2025 15:19:44 INFO stats.py:314 | Epoch[9] Step[189] GlobalStep[3699] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 3:19:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:19:54 INFO loss_tracker.py:84 | Epoch[9/239] Step[199] GlobalStep[3709/NA]: det_loss_cls_0[0.8118] det_loss_box_0[0.9731] det_loss_cls_1[0.7919] det_loss_box_1[0.8608] det_loss_cls_2[0.7727] det_loss_box_2[0.8300] det_loss_cls_3[0.7685] det_loss_box_3[0.8192] det_loss_cls_4[0.7679] det_loss_box_4[0.8134] det_loss_cls_5[0.7596] det_loss_box_5[0.8134] det_loss_cls_dn_0[0.1487] det_loss_box_dn_0[0.3453] det_loss_cls_dn_1[0.1464] det_loss_box_dn_1[0.3330] det_loss_cls_dn_2[0.1463] det_loss_box_dn_2[0.3283] det_loss_cls_dn_3[0.1452] det_loss_box_dn_3[0.3277] det_loss_cls_dn_4[0.1445] det_loss_box_dn_4[0.3282] det_loss_cls_dn_5[0.1449] det_loss_box_dn_5[0.3285] loss_depth[0.0394] total_loss[12.6888]
Rank[0/8] 06/19/2025 15:20:10 INFO stats.py:314 | Epoch[9] Step[214] GlobalStep[3724] Training Speed: 8.08 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 3:18:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:20:21 INFO loss_tracker.py:84 | Epoch[9/239] Step[224] GlobalStep[3734/NA]: det_loss_cls_0[0.5804] det_loss_box_0[0.8200] det_loss_cls_1[0.5630] det_loss_box_1[0.7379] det_loss_cls_2[0.5678] det_loss_box_2[0.6934] det_loss_cls_3[0.5559] det_loss_box_3[0.6681] det_loss_cls_4[0.5537] det_loss_box_4[0.6588] det_loss_cls_5[0.5393] det_loss_box_5[0.6670] det_loss_cls_dn_0[0.1412] det_loss_box_dn_0[0.4116] det_loss_cls_dn_1[0.1394] det_loss_box_dn_1[0.3990] det_loss_cls_dn_2[0.1395] det_loss_box_dn_2[0.3892] det_loss_cls_dn_3[0.1404] det_loss_box_dn_3[0.3890] det_loss_cls_dn_4[0.1406] det_loss_box_dn_4[0.3888] det_loss_cls_dn_5[0.1419] det_loss_box_dn_5[0.3889] loss_depth[0.0447] total_loss[10.8594]
Rank[0/8] 06/19/2025 15:20:36 INFO stats.py:314 | Epoch[9] Step[239] GlobalStep[3749] Training Speed: 7.61 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 3:17:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:20:46 INFO loss_tracker.py:84 | Epoch[9/239] Step[249] GlobalStep[3759/NA]: det_loss_cls_0[0.8058] det_loss_box_0[1.0490] det_loss_cls_1[0.7911] det_loss_box_1[0.9405] det_loss_cls_2[0.7905] det_loss_box_2[0.8952] det_loss_cls_3[0.7897] det_loss_box_3[0.8812] det_loss_cls_4[0.7849] det_loss_box_4[0.8692] det_loss_cls_5[0.7803] det_loss_box_5[0.8698] det_loss_cls_dn_0[0.1493] det_loss_box_dn_0[0.3628] det_loss_cls_dn_1[0.1470] det_loss_box_dn_1[0.3496] det_loss_cls_dn_2[0.1460] det_loss_box_dn_2[0.3461] det_loss_cls_dn_3[0.1455] det_loss_box_dn_3[0.3456] det_loss_cls_dn_4[0.1451] det_loss_box_dn_4[0.3455] det_loss_cls_dn_5[0.1463] det_loss_box_dn_5[0.3455] loss_depth[0.0407] total_loss[13.2624]
Rank[0/8] 06/19/2025 15:21:02 INFO stats.py:314 | Epoch[9] Step[264] GlobalStep[3774] Training Speed: 6.34 samples/sec across all devices.	Average Step Time: 1.26 sec.	Estimated Remaining Time: 1 day, 3:16:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:21:13 INFO loss_tracker.py:84 | Epoch[9/239] Step[274] GlobalStep[3784/NA]: det_loss_cls_0[0.5214] det_loss_box_0[0.6792] det_loss_cls_1[0.5082] det_loss_box_1[0.6096] det_loss_cls_2[0.4982] det_loss_box_2[0.5769] det_loss_cls_3[0.4989] det_loss_box_3[0.5590] det_loss_cls_4[0.4977] det_loss_box_4[0.5538] det_loss_cls_5[0.4947] det_loss_box_5[0.5541] det_loss_cls_dn_0[0.1275] det_loss_box_dn_0[0.3853] det_loss_cls_dn_1[0.1234] det_loss_box_dn_1[0.3618] det_loss_cls_dn_2[0.1214] det_loss_box_dn_2[0.3558] det_loss_cls_dn_3[0.1204] det_loss_box_dn_3[0.3560] det_loss_cls_dn_4[0.1217] det_loss_box_dn_4[0.3569] det_loss_cls_dn_5[0.1223] det_loss_box_dn_5[0.3573] loss_depth[0.0457] total_loss[9.5071]
Rank[0/8] 06/19/2025 15:21:29 INFO stats.py:314 | Epoch[9] Step[289] GlobalStep[3799] Training Speed: 6.98 samples/sec across all devices.	Average Step Time: 1.15 sec.	Estimated Remaining Time: 1 day, 3:15:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:21:39 INFO loss_tracker.py:84 | Epoch[9/239] Step[299] GlobalStep[3809/NA]: det_loss_cls_0[0.6527] det_loss_box_0[0.8629] det_loss_cls_1[0.6342] det_loss_box_1[0.7706] det_loss_cls_2[0.6416] det_loss_box_2[0.7309] det_loss_cls_3[0.6328] det_loss_box_3[0.7132] det_loss_cls_4[0.6293] det_loss_box_4[0.7005] det_loss_cls_5[0.6261] det_loss_box_5[0.6986] det_loss_cls_dn_0[0.1397] det_loss_box_dn_0[0.4940] det_loss_cls_dn_1[0.1359] det_loss_box_dn_1[0.4706] det_loss_cls_dn_2[0.1352] det_loss_box_dn_2[0.4614] det_loss_cls_dn_3[0.1356] det_loss_box_dn_3[0.4599] det_loss_cls_dn_4[0.1358] det_loss_box_dn_4[0.4599] det_loss_cls_dn_5[0.1352] det_loss_box_dn_5[0.4597] loss_depth[0.0409] total_loss[11.9574]
Rank[0/8] 06/19/2025 15:21:55 INFO stats.py:314 | Epoch[9] Step[314] GlobalStep[3824] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 3:15:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:22:05 INFO loss_tracker.py:84 | Epoch[9/239] Step[324] GlobalStep[3834/NA]: det_loss_cls_0[0.6300] det_loss_box_0[0.7914] det_loss_cls_1[0.6018] det_loss_box_1[0.7015] det_loss_cls_2[0.6117] det_loss_box_2[0.6553] det_loss_cls_3[0.6109] det_loss_box_3[0.6370] det_loss_cls_4[0.5947] det_loss_box_4[0.6228] det_loss_cls_5[0.5862] det_loss_box_5[0.6255] det_loss_cls_dn_0[0.1261] det_loss_box_dn_0[0.3602] det_loss_cls_dn_1[0.1243] det_loss_box_dn_1[0.3453] det_loss_cls_dn_2[0.1244] det_loss_box_dn_2[0.3406] det_loss_cls_dn_3[0.1234] det_loss_box_dn_3[0.3407] det_loss_cls_dn_4[0.1235] det_loss_box_dn_4[0.3408] det_loss_cls_dn_5[0.1239] det_loss_box_dn_5[0.3411] loss_depth[0.0445] total_loss[10.5277]
Rank[0/8] 06/19/2025 15:22:20 INFO stats.py:314 | Epoch[9] Step[339] GlobalStep[3849] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 3:13:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:22:31 INFO loss_tracker.py:84 | Epoch[9/239] Step[349] GlobalStep[3859/NA]: det_loss_cls_0[0.7108] det_loss_box_0[0.9552] det_loss_cls_1[0.7015] det_loss_box_1[0.8415] det_loss_cls_2[0.6938] det_loss_box_2[0.7903] det_loss_cls_3[0.6864] det_loss_box_3[0.7838] det_loss_cls_4[0.6798] det_loss_box_4[0.7790] det_loss_cls_5[0.6757] det_loss_box_5[0.7819] det_loss_cls_dn_0[0.1457] det_loss_box_dn_0[0.5323] det_loss_cls_dn_1[0.1426] det_loss_box_dn_1[0.5042] det_loss_cls_dn_2[0.1437] det_loss_box_dn_2[0.5004] det_loss_cls_dn_3[0.1422] det_loss_box_dn_3[0.5031] det_loss_cls_dn_4[0.1420] det_loss_box_dn_4[0.5058] det_loss_cls_dn_5[0.1429] det_loss_box_dn_5[0.5079] loss_depth[0.0414] total_loss[13.0337]
Rank[0/8] 06/19/2025 15:22:46 INFO stats.py:314 | Epoch[9] Step[364] GlobalStep[3874] Training Speed: 12.64 samples/sec across all devices.	Average Step Time: 0.63 sec.	Estimated Remaining Time: 1 day, 3:12:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:22:57 INFO loss_tracker.py:84 | Epoch[9/239] Step[374] GlobalStep[3884/NA]: det_loss_cls_0[0.5816] det_loss_box_0[0.7516] det_loss_cls_1[0.5687] det_loss_box_1[0.6641] det_loss_cls_2[0.5758] det_loss_box_2[0.6303] det_loss_cls_3[0.5686] det_loss_box_3[0.6187] det_loss_cls_4[0.5678] det_loss_box_4[0.6101] det_loss_cls_5[0.5635] det_loss_box_5[0.6064] det_loss_cls_dn_0[0.1312] det_loss_box_dn_0[0.3754] det_loss_cls_dn_1[0.1307] det_loss_box_dn_1[0.3636] det_loss_cls_dn_2[0.1303] det_loss_box_dn_2[0.3574] det_loss_cls_dn_3[0.1310] det_loss_box_dn_3[0.3568] det_loss_cls_dn_4[0.1295] det_loss_box_dn_4[0.3568] det_loss_cls_dn_5[0.1315] det_loss_box_dn_5[0.3566] loss_depth[0.0421] total_loss[10.3001]
Rank[0/8] 06/19/2025 15:23:13 INFO stats.py:314 | Epoch[9] Step[389] GlobalStep[3899] Training Speed: 7.74 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 1 day, 3:12:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:23:13 INFO trainer.py:268 | 
==================================================BEGIN EVAL==================================================

Loading Test dataset:   0%|          | 0/817 [00:00<?, ?it/s]
Loading Test dataset:   0%|          | 0/817 [00:00<?, ?it/s]
Loading Test dataset:   0%|          | 0/817 [00:00<?, ?it/s]
Loading Test dataset:   0%|          | 0/817 [00:00<?, ?it/s]
Loading Test dataset:   0%|          | 0/817 [00:00<?, ?it/s]
Loading Test dataset:   0%|          | 0/817 [00:00<?, ?it/s]
Loading Test dataset:   0%|          | 0/817 [00:00<?, ?it/s]
Loading Test dataset:   0%|          | 0/817 [00:00<?, ?it/s]
Loading Test dataset: 100%|██████████| 817/817 [00:01<00:00, 459.79it/s]

Loading Test dataset: 100%|██████████| 817/817 [00:01<00:00, 454.28it/s]

Loading Test dataset: 100%|██████████| 817/817 [00:01<00:00, 452.45it/s]
Rank[4/8] 06/19/2025 15:23:17 INFO embodiedscan_det_grounding_dataset.py:227 | dataset length : 817
Rank[0/8] 06/19/2025 15:23:17 INFO embodiedscan_det_grounding_dataset.py:227 | dataset length : 817

Loading Test dataset: 100%|██████████| 817/817 [00:01<00:00, 441.98it/s]
Rank[1/8] 06/19/2025 15:23:17 INFO embodiedscan_det_grounding_dataset.py:227 | dataset length : 817
Rank[2/8] 06/19/2025 15:23:17 INFO embodiedscan_det_grounding_dataset.py:227 | dataset length : 817

Loading Test dataset: 100%|██████████| 817/817 [00:01<00:00, 456.25it/s]

Loading Test dataset: 100%|██████████| 817/817 [00:01<00:00, 452.34it/s]

Loading Test dataset: 100%|██████████| 817/817 [00:01<00:00, 450.49it/s]

Loading Test dataset: 100%|██████████| 817/817 [00:01<00:00, 445.67it/s]
Rank[7/8] 06/19/2025 15:23:17 INFO embodiedscan_det_grounding_dataset.py:227 | dataset length : 817
Rank[3/8] 06/19/2025 15:23:17 INFO embodiedscan_det_grounding_dataset.py:227 | dataset length : 817
Rank[5/8] 06/19/2025 15:23:17 INFO embodiedscan_det_grounding_dataset.py:227 | dataset length : 817
Rank[6/8] 06/19/2025 15:23:17 INFO embodiedscan_det_grounding_dataset.py:227 | dataset length : 817
Rank[0/8] 06/19/2025 15:24:22 INFO trainer.py:275 | eval: 10
Rank[0/8] 06/19/2025 15:24:35 INFO trainer.py:275 | eval: 20
Rank[0/8] 06/19/2025 15:24:49 INFO trainer.py:275 | eval: 30
Rank[0/8] 06/19/2025 15:25:05 INFO trainer.py:275 | eval: 40
Rank[0/8] 06/19/2025 15:25:19 INFO trainer.py:275 | eval: 50
Rank[0/8] 06/19/2025 15:25:29 INFO trainer.py:275 | eval: 60
Rank[0/8] 06/19/2025 15:25:43 INFO trainer.py:275 | eval: 70
Rank[0/8] 06/19/2025 15:25:57 INFO trainer.py:275 | eval: 80
Rank[0/8] 06/19/2025 15:26:11 INFO trainer.py:275 | eval: 90
Rank[0/8] 06/19/2025 15:26:21 INFO trainer.py:275 | eval: 100
Rank[0/8] 06/19/2025 15:26:25 INFO metrics.py:428 | number of results: 824
Rank[0/8] 06/19/2025 15:31:48 INFO metrics.py:332 | eval results of overall:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| toilet              | 0.4451  | 0.8613  | 0.1559  | 0.5474  |
| toilet paper        | 0.0757  | 0.3543  | 0.0005  | 0.0315  |
| sink                | 0.2519  | 0.7160  | 0.0090  | 0.1440  |
| bathtub             | 0.3771  | 0.7794  | 0.0276  | 0.3382  |
| towel               | 0.0474  | 0.3839  | 0.0001  | 0.0194  |
| counter             | 0.0048  | 0.3119  | 0.0001  | 0.0550  |
| doorframe           | 0.0581  | 0.5762  | 0.0055  | 0.1349  |
| stopcock            | 0.0028  | 0.3103  | 0.0000  | 0.0000  |
| curtain             | 0.0458  | 0.5075  | 0.0052  | 0.1493  |
| bin                 | 0.1611  | 0.7486  | 0.0137  | 0.2626  |
| socket              | 0.0000  | 0.0265  | 0.0000  | 0.0000  |
| clothes             | 0.0204  | 0.3025  | 0.0003  | 0.0478  |
| vanity              | 0.1609  | 0.6000  | 0.0069  | 0.2444  |
| switch              | 0.0001  | 0.0132  | 0.0000  | 0.0000  |
| mirror              | 0.0060  | 0.0761  | 0.0000  | 0.0054  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dish           | 0.0015  | 0.3333  | 0.0000  | 0.0000  |
| door                | 0.0115  | 0.3301  | 0.0004  | 0.0465  |
| bag                 | 0.0431  | 0.5189  | 0.0003  | 0.0595  |
| ledge               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| box                 | 0.0228  | 0.3578  | 0.0006  | 0.0564  |
| picture             | 0.0004  | 0.0528  | 0.0000  | 0.0022  |
| faucet              | 0.0015  | 0.1277  | 0.0000  | 0.0000  |
| cabinet             | 0.0374  | 0.4571  | 0.0024  | 0.0900  |
| radiator            | 0.0322  | 0.3667  | 0.0000  | 0.0000  |
| cup                 | 0.0009  | 0.1525  | 0.0000  | 0.0085  |
| tissue box          | 0.0025  | 0.1515  | 0.0000  | 0.0000  |
| toothpaste          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dispenser           | 0.0447  | 0.5227  | 0.0056  | 0.0909  |
| shelf               | 0.0244  | 0.4245  | 0.0007  | 0.0845  |
| countertop          | 0.0023  | 0.1429  | 0.0000  | 0.0000  |
| basket              | 0.0439  | 0.4261  | 0.0023  | 0.0783  |
| blanket             | 0.0413  | 0.4900  | 0.0001  | 0.0500  |
| rail                | 0.0006  | 0.1562  | 0.0000  | 0.0000  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bottle              | 0.0047  | 0.2089  | 0.0000  | 0.0028  |
| shower              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| window              | 0.0167  | 0.2695  | 0.0001  | 0.0284  |
| plunger             | 0.3917  | 0.8000  | 0.0000  | 0.0000  |
| hair dryer          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue              | 0.0769  | 0.0769  | 0.0026  | 0.0769  |
| washbasin           | 0.0008  | 0.2000  | 0.0000  | 0.0000  |
| dresser             | 0.1388  | 0.5556  | 0.0334  | 0.3333  |
| bar                 | 0.0030  | 0.0303  | 0.0000  | 0.0000  |
| monitor             | 0.1408  | 0.6141  | 0.0015  | 0.0435  |
| chair               | 0.1968  | 0.8534  | 0.0639  | 0.5017  |
| table               | 0.0942  | 0.7727  | 0.0130  | 0.3777  |
| desk                | 0.0951  | 0.7753  | 0.0119  | 0.3933  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| keyboard            | 0.0023  | 0.0698  | 0.0000  | 0.0000  |
| mouse               | 0.0000  | 0.0270  | 0.0000  | 0.0000  |
| paper               | 0.0003  | 0.0710  | 0.0000  | 0.0000  |
| computer            | 0.0292  | 0.4098  | 0.0000  | 0.0164  |
| blackboard          | 0.0479  | 0.4146  | 0.0009  | 0.0732  |
| telephone           | 0.0068  | 0.1833  | 0.0000  | 0.0167  |
| tv                  | 0.0705  | 0.4752  | 0.0006  | 0.0396  |
| board               | 0.0075  | 0.1290  | 0.0000  | 0.0000  |
| bucket              | 0.0224  | 0.3333  | 0.0059  | 0.1538  |
| windowsill          | 0.0003  | 0.0435  | 0.0000  | 0.0000  |
| backpack            | 0.1524  | 0.6613  | 0.0157  | 0.2581  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| book                | 0.0087  | 0.3324  | 0.0001  | 0.0251  |
| pipe                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillow              | 0.1032  | 0.6709  | 0.0014  | 0.0879  |
| bed                 | 0.3165  | 0.8698  | 0.0634  | 0.5385  |
| stand               | 0.1044  | 0.7744  | 0.0303  | 0.3902  |
| case                | 0.0033  | 0.1525  | 0.0002  | 0.0452  |
| shoe                | 0.0082  | 0.2336  | 0.0000  | 0.0093  |
| cushion             | 0.0144  | 0.4510  | 0.0000  | 0.0196  |
| lamp                | 0.0732  | 0.3552  | 0.0010  | 0.0358  |
| couch               | 0.1256  | 0.7603  | 0.0159  | 0.3719  |
| light               | 0.0001  | 0.0491  | 0.0000  | 0.0064  |
| ottoman             | 0.0329  | 0.2778  | 0.0000  | 0.0000  |
| chandelier          | 0.0001  | 0.0476  | 0.0000  | 0.0000  |
| stool               | 0.0417  | 0.5210  | 0.0029  | 0.2017  |
| jacket              | 0.1503  | 0.5833  | 0.0000  | 0.0000  |
| dumbbell            | 0.0015  | 0.3333  | 0.0000  | 0.0000  |
| guitar              | 0.1054  | 0.2500  | 0.0833  | 0.0833  |
| refrigerator        | 0.1336  | 0.6800  | 0.0006  | 0.1000  |
| purse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| luggage             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coffee maker        | 0.0548  | 0.4483  | 0.0036  | 0.1724  |
| clock               | 0.0085  | 0.1277  | 0.0000  | 0.0000  |
| stall               | 0.0013  | 0.1176  | 0.0002  | 0.0588  |
| mop                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| carpet              | 0.0000  | 0.0119  | 0.0000  | 0.0000  |
| bench               | 0.0013  | 0.3396  | 0.0000  | 0.0377  |
| wardrobe            | 0.0100  | 0.2892  | 0.0001  | 0.0120  |
| toy                 | 0.0145  | 0.2424  | 0.0009  | 0.0303  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mat                 | 0.0000  | 0.0270  | 0.0000  | 0.0000  |
| soap dispenser      | 0.0088  | 0.2963  | 0.0000  | 0.0000  |
| grab bar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| paper cutter        | 0.0020  | 0.3333  | 0.0000  | 0.0000  |
| water cooler        | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| vent                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plant               | 0.0630  | 0.4965  | 0.0018  | 0.0878  |
| printer             | 0.1232  | 0.6122  | 0.0049  | 0.1633  |
| laptop              | 0.1166  | 0.3913  | 0.0054  | 0.0435  |
| person              | 0.0129  | 0.6667  | 0.0000  | 0.0000  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher   | 0.0264  | 0.6000  | 0.0020  | 0.2000  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball                | 0.0587  | 0.4286  | 0.0011  | 0.0952  |
| fan                 | 0.0017  | 0.1094  | 0.0003  | 0.0156  |
| container           | 0.0022  | 0.0339  | 0.0001  | 0.0169  |
| microwave           | 0.0968  | 0.5510  | 0.0032  | 0.0816  |
| copier              | 0.4174  | 0.9167  | 0.1553  | 0.4167  |
| sign                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toiletry            | 0.0027  | 0.1739  | 0.0000  | 0.0000  |
| dish rack           | 0.0070  | 0.4000  | 0.0000  | 0.0000  |
| dishwasher          | 0.0015  | 0.2000  | 0.0000  | 0.0000  |
| kettle              | 0.0129  | 0.4444  | 0.0042  | 0.1111  |
| machine             | 0.0103  | 0.2632  | 0.0075  | 0.0526  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stove               | 0.1666  | 0.6111  | 0.0305  | 0.2222  |
| plate               | 0.0029  | 0.1538  | 0.0000  | 0.0192  |
| oven                | 0.1300  | 0.4643  | 0.0512  | 0.1786  |
| teapot              | 0.0007  | 0.1111  | 0.0000  | 0.0000  |
| bowl                | 0.0023  | 0.2029  | 0.0000  | 0.0000  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.0036  | 0.2857  | 0.0000  | 0.0000  |
| toaster             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rack                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mailbox             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blinds              | 0.0004  | 0.1000  | 0.0000  | 0.0000  |
| vase                | 0.0141  | 0.3200  | 0.0008  | 0.0600  |
| hanger              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hamper              | 0.0378  | 0.5455  | 0.0010  | 0.0909  |
| vacuum cleaner      | 0.1429  | 0.1429  | 0.0000  | 0.0000  |
| holder              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| remote control      | 0.0123  | 0.0370  | 0.0000  | 0.0000  |
| stairs              | 0.0102  | 0.1528  | 0.0003  | 0.0278  |
| column              | 0.0003  | 0.2105  | 0.0000  | 0.0000  |
| pillar              | 0.0076  | 0.3488  | 0.0000  | 0.0000  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garbage             | 0.0402  | 0.5000  | 0.0029  | 0.2500  |
| file                | 0.0027  | 0.0588  | 0.0000  | 0.0000  |
| commode             | 0.0887  | 0.6346  | 0.0091  | 0.2308  |
| kitchenware         | 0.0247  | 0.2391  | 0.0000  | 0.0000  |
| food                | 0.0026  | 0.0909  | 0.0000  | 0.0000  |
| frame               | 0.0072  | 0.0408  | 0.0000  | 0.0000  |
| flowerpot           | 0.0049  | 0.3333  | 0.0002  | 0.0588  |
| decoration          | 0.0011  | 0.1071  | 0.0000  | 0.0000  |
| candle              | 0.0097  | 0.1111  | 0.0000  | 0.0000  |
| heater              | 0.0060  | 0.3103  | 0.0000  | 0.0000  |
| notebook            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| device              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| folder              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| humidifier          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| air conditioner     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| album               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sunglasses          | 0.0033  | 0.0769  | 0.0000  | 0.0000  |
| magazine            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| statue              | 0.0163  | 0.1053  | 0.0000  | 0.0000  |
| brush               | 0.0034  | 0.0714  | 0.0000  | 0.0000  |
| clothes dryer       | 0.0069  | 0.4000  | 0.0014  | 0.1333  |
| washing machine     | 0.2693  | 0.6071  | 0.0201  | 0.3214  |
| water heater        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scale               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| body loofah         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beanbag             | 0.7429  | 1.0000  | 0.0000  | 0.0000  |
| conducting wire     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pot                 | 0.0024  | 0.2963  | 0.0000  | 0.0000  |
| pan                 | 0.0098  | 0.1176  | 0.0000  | 0.0000  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pack                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit               | 0.0199  | 0.2143  | 0.0000  | 0.0000  |
| window frame        | 0.0042  | 0.1414  | 0.0000  | 0.0101  |
| jalousie            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crib                | 0.0338  | 0.6667  | 0.0238  | 0.3333  |
| drawer              | 0.0024  | 0.1111  | 0.0000  | 0.0000  |
| price tag           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| calendar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| roll                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| piano               | 0.0750  | 0.4000  | 0.0095  | 0.2000  |
| fireplace           | 0.0394  | 0.2143  | 0.0001  | 0.0357  |
| broom               | 0.0071  | 0.0714  | 0.0000  | 0.0000  |
| candlestick         | 0.0104  | 0.3333  | 0.0000  | 0.0000  |
| balcony             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet               | 0.0159  | 0.3333  | 0.0000  | 0.0000  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| roof                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| steps               | 0.0000  | 0.1000  | 0.0000  | 0.0000  |
| beam                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basin               | 0.0064  | 0.1765  | 0.0000  | 0.0000  |
| thermostat          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| doorway             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchen island      | 0.0148  | 0.4286  | 0.0035  | 0.2857  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool table          | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| screen              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.0043  | 0.1500  | 0.0000  | 0.0000  |
| ladder              | 0.0196  | 0.0588  | 0.0000  | 0.0000  |
| soap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| arch                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| footstool           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crate               | 0.2427  | 0.5714  | 0.0023  | 0.1429  |
| poster              | 0.0014  | 0.0303  | 0.0000  | 0.0000  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 0.0055  | 0.3333  | 0.0000  | 0.0000  |
| napkins             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| menu                | 0.0476  | 0.2857  | 0.0000  | 0.0000  |
| spoon               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| partition           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coil                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| handle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| treadmill           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garage door         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sponge              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| salt                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shampoo             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| barricade           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tablet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| letter              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wine                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| player              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sack                | 0.0714  | 0.1429  | 0.0000  | 0.0000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| map                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radio               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| overall             | 0.0362  | 0.2008  | 0.0071  | 0.0476  |
| head                | 0.0540  | 0.3474  | 0.0063  | 0.0868  |
| common              | 0.0300  | 0.1607  | 0.0036  | 0.0276  |
| tail                | 0.0205  | 0.0569  | 0.0130  | 0.0220  |
| small               | 0.0024  | 0.0464  | 0.0001  | 0.0022  |
| medium              | 0.0365  | 0.2239  | 0.0065  | 0.0486  |
| large               | 0.0324  | 0.2886  | 0.0056  | 0.0733  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/19/2025 15:31:48 INFO metrics.py:437 | number of scannet results: 250
Rank[0/8] 06/19/2025 15:33:41 INFO metrics.py:332 | eval results of scannet:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| toilet              | 0.5193  | 0.9107  | 0.2332  | 0.7321  |
| toilet paper        | 0.1117  | 0.4051  | 0.0010  | 0.0380  |
| sink                | 0.3809  | 0.8409  | 0.0208  | 0.2159  |
| bathtub             | 0.5115  | 0.8438  | 0.0493  | 0.3750  |
| towel               | 0.0658  | 0.4380  | 0.0001  | 0.0248  |
| counter             | 0.0070  | 0.4571  | 0.0000  | 0.0000  |
| doorframe           | 0.0315  | 0.5493  | 0.0014  | 0.1408  |
| stopcock            | 0.0032  | 0.3103  | 0.0000  | 0.0000  |
| curtain             | 0.0740  | 0.5487  | 0.0142  | 0.2301  |
| bin                 | 0.2034  | 0.7532  | 0.0150  | 0.2596  |
| socket              | 0.0000  | 0.0291  | 0.0000  | 0.0000  |
| clothes             | 0.0271  | 0.2810  | 0.0004  | 0.0458  |
| vanity              | 0.2399  | 0.8000  | 0.0104  | 0.3667  |
| switch              | 0.0002  | 0.0253  | 0.0000  | 0.0000  |
| mirror              | 0.0128  | 0.1084  | 0.0000  | 0.0120  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dish           | 0.0020  | 0.4167  | 0.0000  | 0.0000  |
| door                | 0.0361  | 0.4849  | 0.0039  | 0.1037  |
| bag                 | 0.0440  | 0.5616  | 0.0012  | 0.0959  |
| ledge               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| box                 | 0.0216  | 0.3135  | 0.0009  | 0.0635  |
| picture             | 0.0012  | 0.1007  | 0.0000  | 0.0037  |
| faucet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cabinet             | 0.0453  | 0.5079  | 0.0063  | 0.1270  |
| radiator            | 0.0553  | 0.5172  | 0.0000  | 0.0000  |
| cup                 | 0.0023  | 0.1884  | 0.0001  | 0.0145  |
| tissue box          | 0.0029  | 0.1818  | 0.0000  | 0.0000  |
| toothpaste          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dispenser           | 0.0613  | 0.5897  | 0.0073  | 0.1026  |
| shelf               | 0.0427  | 0.6397  | 0.0007  | 0.1029  |
| basket              | 0.0431  | 0.3462  | 0.0001  | 0.0385  |
| blanket             | 0.1208  | 0.4167  | 0.0000  | 0.0000  |
| rail                | 0.0006  | 0.1667  | 0.0000  | 0.0000  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bottle              | 0.0051  | 0.2316  | 0.0000  | 0.0053  |
| shower              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| window              | 0.0484  | 0.4691  | 0.0003  | 0.0741  |
| plunger             | 0.3924  | 0.8000  | 0.0000  | 0.0000  |
| hair dryer          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue              | 0.1111  | 0.1111  | 0.0139  | 0.1111  |
| dresser             | 0.1875  | 0.6757  | 0.0491  | 0.4865  |
| bar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| monitor             | 0.1600  | 0.6643  | 0.0024  | 0.0571  |
| chair               | 0.2019  | 0.8774  | 0.0833  | 0.5332  |
| table               | 0.1366  | 0.8154  | 0.0228  | 0.4654  |
| desk                | 0.1215  | 0.8987  | 0.0144  | 0.4430  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| keyboard            | 0.0039  | 0.0690  | 0.0000  | 0.0000  |
| mouse               | 0.0000  | 0.0476  | 0.0000  | 0.0000  |
| paper               | 0.0003  | 0.0566  | 0.0000  | 0.0000  |
| computer            | 0.0580  | 0.4359  | 0.0000  | 0.0000  |
| blackboard          | 0.0573  | 0.4928  | 0.0011  | 0.0870  |
| telephone           | 0.0062  | 0.1562  | 0.0000  | 0.0000  |
| tv                  | 0.1606  | 0.6522  | 0.0011  | 0.0435  |
| board               | 0.0112  | 0.1562  | 0.0000  | 0.0000  |
| bucket              | 0.0117  | 0.2857  | 0.0032  | 0.1429  |
| windowsill          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| backpack            | 0.2316  | 0.7857  | 0.0089  | 0.2143  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| book                | 0.0127  | 0.3667  | 0.0005  | 0.0310  |
| pipe                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillow              | 0.1186  | 0.7569  | 0.0041  | 0.1667  |
| bed                 | 0.4004  | 0.9649  | 0.1441  | 0.8596  |
| stand               | 0.2391  | 0.8491  | 0.0836  | 0.4717  |
| case                | 0.1566  | 0.6842  | 0.0034  | 0.2105  |
| shoe                | 0.0035  | 0.1750  | 0.0000  | 0.0000  |
| cushion             | 0.0119  | 1.0000  | 0.0000  | 0.0000  |
| lamp                | 0.1222  | 0.4762  | 0.0046  | 0.0595  |
| couch               | 0.1319  | 0.9125  | 0.0078  | 0.4250  |
| light               | 0.0007  | 0.0690  | 0.0000  | 0.0000  |
| ottoman             | 0.0047  | 0.4286  | 0.0000  | 0.0000  |
| stool               | 0.1574  | 0.4773  | 0.0141  | 0.2273  |
| jacket              | 0.1569  | 0.6087  | 0.0000  | 0.0000  |
| guitar              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| refrigerator        | 0.2588  | 0.8500  | 0.0007  | 0.0500  |
| purse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coffee maker        | 0.1209  | 0.6471  | 0.0091  | 0.2353  |
| clock               | 0.0262  | 0.2000  | 0.0000  | 0.0000  |
| stall               | 0.0035  | 0.1176  | 0.0004  | 0.0588  |
| carpet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bench               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wardrobe            | 0.0409  | 0.3750  | 0.0000  | 0.0000  |
| toy                 | 0.0097  | 0.1176  | 0.0097  | 0.1176  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mat                 | 0.0003  | 0.1000  | 0.0000  | 0.0000  |
| soap dispenser      | 0.0111  | 0.3333  | 0.0000  | 0.0000  |
| grab bar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| paper cutter        | 0.0025  | 0.4000  | 0.0000  | 0.0000  |
| water cooler        | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| plant               | 0.1092  | 0.5714  | 0.0021  | 0.1092  |
| printer             | 0.1701  | 0.7273  | 0.0049  | 0.1818  |
| laptop              | 0.1612  | 0.4000  | 0.0167  | 0.0667  |
| person              | 0.0159  | 0.6667  | 0.0000  | 0.0000  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher   | 0.0635  | 0.6667  | 0.0139  | 0.3333  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball                | 0.0164  | 0.3333  | 0.0000  | 0.0000  |
| fan                 | 0.0096  | 0.2500  | 0.0066  | 0.1250  |
| container           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| microwave           | 0.1331  | 0.6000  | 0.0044  | 0.1000  |
| copier              | 0.4174  | 0.9167  | 0.1553  | 0.4167  |
| sign                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dish rack           | 0.0119  | 0.5000  | 0.0000  | 0.0000  |
| dishwasher          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kettle              | 0.0386  | 0.7500  | 0.0000  | 0.0000  |
| machine             | 0.0110  | 0.2500  | 0.0089  | 0.0625  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stove               | 0.2711  | 1.0000  | 0.1381  | 0.6667  |
| plate               | 0.0014  | 0.2000  | 0.0000  | 0.0000  |
| oven                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| teapot              | 0.0066  | 0.2500  | 0.0000  | 0.0000  |
| bowl                | 0.0444  | 0.2222  | 0.0000  | 0.0000  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.0015  | 0.2000  | 0.0000  | 0.0000  |
| toaster             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rack                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mailbox             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hamper              | 0.0415  | 0.6000  | 0.0011  | 0.1000  |
| vacuum cleaner      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stairs              | 0.1053  | 0.4000  | 0.0080  | 0.2000  |
| pillar              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| decoration          | 0.0012  | 0.0833  | 0.0000  | 0.0000  |
| flowerpot           | 0.0091  | 0.5000  | 0.0000  | 0.0000  |
| pot                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crate               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| calendar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ladder              | 0.0667  | 0.2000  | 0.0000  | 0.0000  |
| speaker             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 0.0155  | 0.6667  | 0.0000  | 0.0000  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.4333  | 0.5000  | 0.1136  | 0.3333  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.0208  | 0.5000  | 0.0000  | 0.0000  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basin               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scale               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| broom               | 0.0417  | 0.0833  | 0.0000  | 0.0000  |
| fruit               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| scannet             | 0.0627  | 0.3031  | 0.0123  | 0.0802  |
| head                | 0.0840  | 0.4004  | 0.0134  | 0.1166  |
| common              | 0.0270  | 0.2009  | 0.0042  | 0.0330  |
| tail                | 0.0667  | 0.0988  | 0.0357  | 0.0357  |
| small               | 0.0029  | 0.0525  | 0.0000  | 0.0010  |
| medium              | 0.0715  | 0.3723  | 0.0149  | 0.0922  |
| large               | 0.1098  | 0.4898  | 0.0060  | 0.1710  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/19/2025 15:33:41 INFO metrics.py:437 | number of 3rscan results: 235
Rank[0/8] 06/19/2025 15:35:09 INFO metrics.py:332 | eval results of 3rscan:
+-------------------+---------+---------+---------+---------+
| classes           | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+-------------------+---------+---------+---------+---------+
| chair             | 0.2472  | 0.8656  | 0.0733  | 0.5451  |
| printer           | 0.0844  | 0.5556  | 0.0370  | 0.1111  |
| book              | 0.0080  | 0.3254  | 0.0000  | 0.0237  |
| table             | 0.0795  | 0.7928  | 0.0122  | 0.3739  |
| bin               | 0.1419  | 0.7927  | 0.0233  | 0.3293  |
| monitor           | 0.1168  | 0.4750  | 0.0000  | 0.0000  |
| desk              | 0.0923  | 0.7125  | 0.0143  | 0.3875  |
| computer          | 0.0326  | 0.3810  | 0.0001  | 0.0476  |
| box               | 0.0279  | 0.3834  | 0.0006  | 0.0545  |
| keyboard          | 0.0004  | 0.0833  | 0.0000  | 0.0000  |
| cup               | 0.0002  | 0.1026  | 0.0000  | 0.0000  |
| mouse             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bag               | 0.0514  | 0.5152  | 0.0001  | 0.0404  |
| paper             | 0.0007  | 0.1286  | 0.0000  | 0.0000  |
| stand             | 0.0653  | 0.7377  | 0.0204  | 0.2787  |
| door              | 0.0062  | 0.2647  | 0.0000  | 0.0147  |
| socket            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bottle            | 0.0057  | 0.1570  | 0.0000  | 0.0000  |
| window            | 0.0076  | 0.2603  | 0.0000  | 0.0205  |
| plant             | 0.0543  | 0.4307  | 0.0039  | 0.0792  |
| case              | 0.0006  | 0.1000  | 0.0000  | 0.0286  |
| tissue box        | 0.0175  | 0.3333  | 0.0000  | 0.0000  |
| garbage           | 0.0402  | 0.5000  | 0.0029  | 0.2500  |
| backpack          | 0.1184  | 0.5455  | 0.0293  | 0.2727  |
| plate             | 0.0046  | 0.1818  | 0.0000  | 0.0303  |
| shelf             | 0.0209  | 0.3746  | 0.0008  | 0.0707  |
| cabinet           | 0.0444  | 0.4957  | 0.0005  | 0.0769  |
| file              | 0.0033  | 0.0714  | 0.0000  | 0.0000  |
| paper cutter      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| couch             | 0.1820  | 0.6286  | 0.0237  | 0.3429  |
| stool             | 0.0495  | 0.6667  | 0.0033  | 0.2593  |
| blinds            | 0.0005  | 0.1282  | 0.0000  | 0.0000  |
| pen               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plug              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tv                | 0.0797  | 0.4857  | 0.0003  | 0.0571  |
| lamp              | 0.0471  | 0.2625  | 0.0001  | 0.0125  |
| pillow            | 0.1358  | 0.7227  | 0.0008  | 0.0703  |
| picture           | 0.0003  | 0.0407  | 0.0000  | 0.0000  |
| cushion           | 0.0581  | 0.4667  | 0.0001  | 0.0333  |
| bowl              | 0.0032  | 0.2593  | 0.0000  | 0.0000  |
| counter           | 0.0174  | 0.2683  | 0.0005  | 0.0976  |
| bed               | 0.3317  | 0.8545  | 0.0428  | 0.3818  |
| commode           | 0.0980  | 0.6346  | 0.0107  | 0.2308  |
| kitchenware       | 0.0259  | 0.2500  | 0.0000  | 0.0000  |
| light             | 0.0001  | 0.0227  | 0.0000  | 0.0000  |
| bench             | 0.0020  | 0.3929  | 0.0000  | 0.0357  |
| sink              | 0.2617  | 0.7679  | 0.0202  | 0.1071  |
| doorframe         | 0.0177  | 0.3600  | 0.0030  | 0.1000  |
| food              | 0.0031  | 0.1000  | 0.0000  | 0.0000  |
| blanket           | 0.0493  | 0.5385  | 0.0002  | 0.0615  |
| board             | 0.0171  | 0.1250  | 0.0000  | 0.0000  |
| wardrobe          | 0.0108  | 0.2500  | 0.0001  | 0.0156  |
| frame             | 0.0117  | 0.0667  | 0.0000  | 0.0000  |
| ottoman           | 0.1048  | 0.5455  | 0.0000  | 0.0000  |
| carpet            | 0.0001  | 0.0179  | 0.0000  | 0.0000  |
| clock             | 0.0004  | 0.0526  | 0.0000  | 0.0000  |
| curtain           | 0.0387  | 0.4524  | 0.0014  | 0.1111  |
| flowerpot         | 0.0043  | 0.2500  | 0.0004  | 0.0417  |
| mat               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vase              | 0.0400  | 0.2500  | 0.0100  | 0.0500  |
| decoration        | 0.0025  | 0.0938  | 0.0000  | 0.0000  |
| range hood        | 0.0024  | 0.2000  | 0.0000  | 0.0000  |
| candle            | 0.0157  | 0.1333  | 0.0000  | 0.0000  |
| heater            | 0.0060  | 0.2982  | 0.0000  | 0.0000  |
| laptop            | 0.0711  | 0.5000  | 0.0000  | 0.0000  |
| radiator          | 0.0094  | 0.1034  | 0.0000  | 0.0000  |
| fan               | 0.0455  | 0.5000  | 0.0000  | 0.0000  |
| bucket            | 0.0545  | 0.5333  | 0.0132  | 0.2000  |
| telephone         | 0.0131  | 0.2083  | 0.0001  | 0.0417  |
| switch            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| windowsill        | 0.0004  | 0.0513  | 0.0000  | 0.0000  |
| column            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blackboard        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| notebook          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| device            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| folder            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| humidifier        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mirror            | 0.0010  | 0.0741  | 0.0000  | 0.0000  |
| clothes           | 0.0236  | 0.3301  | 0.0006  | 0.0680  |
| air conditioner   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toilet paper      | 0.0299  | 0.1667  | 0.0000  | 0.0000  |
| album             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basket            | 0.0827  | 0.5625  | 0.0053  | 0.1250  |
| towel             | 0.0191  | 0.3038  | 0.0000  | 0.0000  |
| luggage           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| remote control    | 0.0167  | 0.0500  | 0.0000  | 0.0000  |
| shoe              | 0.0227  | 0.2619  | 0.0001  | 0.0238  |
| toy               | 0.0401  | 0.4615  | 0.0000  | 0.0000  |
| refrigerator      | 0.1035  | 0.6000  | 0.0030  | 0.2667  |
| sunglasses        | 0.0033  | 0.0769  | 0.0000  | 0.0000  |
| magazine          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toilet            | 0.4863  | 0.8235  | 0.1246  | 0.3529  |
| bathtub           | 0.5831  | 0.8889  | 0.0299  | 0.4444  |
| faucet            | 0.0040  | 0.3333  | 0.0000  | 0.0000  |
| statue            | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| brush             | 0.0083  | 0.1250  | 0.0000  | 0.0000  |
| clothes dryer     | 0.0020  | 0.3000  | 0.0000  | 0.0000  |
| shower            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine   | 0.2749  | 0.6429  | 0.0131  | 0.2143  |
| washbasin         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toiletry          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water heater      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rack              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tube              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scale             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| body loofah       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beanbag           | 0.7429  | 1.0000  | 0.0000  | 0.0000  |
| conducting wire   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wire              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pot               | 0.0068  | 0.3333  | 0.0000  | 0.0000  |
| microwave         | 0.1582  | 0.8235  | 0.0052  | 0.1176  |
| stove             | 0.2444  | 0.5882  | 0.0000  | 0.0000  |
| oven              | 0.2291  | 0.6842  | 0.0783  | 0.2632  |
| dishwasher        | 0.0163  | 0.6667  | 0.0000  | 0.0000  |
| kettle            | 0.0138  | 0.4167  | 0.0080  | 0.1667  |
| coffee maker      | 0.0014  | 0.1111  | 0.0013  | 0.1111  |
| pan               | 0.0139  | 0.1667  | 0.0000  | 0.0000  |
| jar               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dish rack         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pack              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| chandelier        | 0.0078  | 0.5000  | 0.0000  | 0.0000  |
| fruit             | 0.0667  | 0.4000  | 0.0000  | 0.0000  |
| pillar            | 0.0125  | 0.2353  | 0.0000  | 0.0000  |
| window frame      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jalousie          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crib              | 0.0338  | 0.6667  | 0.0238  | 0.3333  |
| drawer            | 0.0059  | 0.2222  | 0.0000  | 0.0000  |
| price tag         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| calendar          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dispenser         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| roll              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| piano             | 0.1111  | 0.3333  | 0.0196  | 0.3333  |
| fireplace         | 0.0083  | 0.2000  | 0.0000  | 0.0000  |
| stairs            | 0.0056  | 0.2143  | 0.0000  | 0.0000  |
| rail              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| broom             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candlestick       | 0.0312  | 1.0000  | 0.0000  | 0.0000  |
| dresser           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| balcony           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| container         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tray              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mop               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dish         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dispenser    | 0.0049  | 0.2000  | 0.0000  | 0.0000  |
| umbrella          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| napkins           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| teapot            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| menu              | 0.0476  | 0.2857  | 0.0000  | 0.0000  |
| poster            | 0.0015  | 0.0317  | 0.0000  | 0.0000  |
| hat               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| spoon             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pad               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basin             | 0.0073  | 0.1333  | 0.0000  | 0.0000  |
| hanger            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball              | 0.2029  | 0.4000  | 0.0000  | 0.0000  |
| pipe              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sign              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crate             | 0.3398  | 0.8000  | 0.0033  | 0.2000  |
| guitar            | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| speaker           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vacuum cleaner    | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| dumbbell          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bar               | 0.0333  | 0.3333  | 0.0000  | 0.0000  |
| partition         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coil              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ladder            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| furniture         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| salt              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shampoo           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| footstool         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toothbrush        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flush             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| player            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sack              | 0.0714  | 0.1429  | 0.0000  | 0.0000  |
| bicycle           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| letter            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| map               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radio             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| holder            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tablet            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glass             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| seasoning         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher | 0.2500  | 1.0000  | 0.0000  | 0.0000  |
| machine           | 0.3333  | 1.0000  | 0.0000  | 0.0000  |
| screen            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------           | ------- | ------- | ------- | ------- |
| 3rscan            | 0.0462  | 0.2273  | 0.0061  | 0.0463  |
| head              | 0.0571  | 0.3304  | 0.0059  | 0.0739  |
| common            | 0.0545  | 0.1769  | 0.0094  | 0.0281  |
| tail              | 0.0032  | 0.0558  | 0.0007  | 0.0095  |
| small             | 0.0040  | 0.0643  | 0.0002  | 0.0080  |
| medium            | 0.0491  | 0.2521  | 0.0063  | 0.0462  |
| large             | 0.0413  | 0.3869  | 0.0046  | 0.1410  |
+-------------------+---------+---------+---------+---------+
Rank[0/8] 06/19/2025 15:35:09 INFO metrics.py:437 | number of matterport3d results: 339
Rank[0/8] 06/19/2025 15:36:45 INFO metrics.py:332 | eval results of matterport3d:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| plant               | 0.0619  | 0.5357  | 0.0010  | 0.0804  |
| doorframe           | 0.0790  | 0.6148  | 0.0080  | 0.1372  |
| sink                | 0.1701  | 0.5758  | 0.0016  | 0.1010  |
| light               | 0.0001  | 0.0541  | 0.0000  | 0.0085  |
| countertop          | 0.0025  | 0.1429  | 0.0000  | 0.0000  |
| pillow              | 0.1018  | 0.5899  | 0.0013  | 0.0662  |
| lamp                | 0.1083  | 0.4066  | 0.0031  | 0.0549  |
| towel               | 0.0583  | 0.3818  | 0.0002  | 0.0273  |
| counter             | 0.0011  | 0.2121  | 0.0000  | 0.0606  |
| flowerpot           | 0.0065  | 0.4000  | 0.0002  | 0.0800  |
| washbasin           | 0.0013  | 0.2143  | 0.0000  | 0.0000  |
| decoration          | 0.0011  | 0.1190  | 0.0000  | 0.0000  |
| door                | 0.0063  | 0.2324  | 0.0000  | 0.0131  |
| vase                | 0.0146  | 0.3667  | 0.0002  | 0.0667  |
| faucet              | 0.0053  | 0.1389  | 0.0000  | 0.0000  |
| cushion             | 0.0013  | 0.2941  | 0.0000  | 0.0000  |
| bottle              | 0.0066  | 0.2500  | 0.0000  | 0.0000  |
| picture             | 0.0001  | 0.0290  | 0.0000  | 0.0024  |
| stand               | 0.1675  | 0.7400  | 0.0790  | 0.4400  |
| pot                 | 0.0024  | 0.3077  | 0.0000  | 0.0000  |
| box                 | 0.0126  | 0.3462  | 0.0010  | 0.0385  |
| toiletry            | 0.0047  | 0.1818  | 0.0000  | 0.0000  |
| heater              | 0.0135  | 1.0000  | 0.0000  | 0.0000  |
| case                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vent                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cabinet             | 0.0286  | 0.3644  | 0.0011  | 0.0636  |
| table               | 0.0893  | 0.6731  | 0.0072  | 0.2372  |
| shelf               | 0.0219  | 0.3139  | 0.0012  | 0.0949  |
| curtain             | 0.0345  | 0.5312  | 0.0007  | 0.1042  |
| basket              | 0.0005  | 0.1600  | 0.0000  | 0.0000  |
| roof                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| window              | 0.0124  | 0.1833  | 0.0000  | 0.0111  |
| mirror              | 0.0005  | 0.0405  | 0.0000  | 0.0000  |
| statue              | 0.0588  | 0.0588  | 0.0000  | 0.0000  |
| wardrobe            | 0.0044  | 0.4545  | 0.0000  | 0.0000  |
| drawer              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| switch              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| couch               | 0.1222  | 0.7283  | 0.0330  | 0.3478  |
| toilet              | 0.4487  | 0.8281  | 0.1327  | 0.4375  |
| bowl                | 0.0022  | 0.1515  | 0.0000  | 0.0000  |
| desk                | 0.0734  | 0.5263  | 0.0034  | 0.2105  |
| bathtub             | 0.2308  | 0.6667  | 0.0103  | 0.2593  |
| paper               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| chair               | 0.1601  | 0.7517  | 0.0352  | 0.3197  |
| tv                  | 0.0340  | 0.3721  | 0.0013  | 0.0233  |
| plate               | 0.0005  | 0.0714  | 0.0000  | 0.0000  |
| book                | 0.0108  | 0.2283  | 0.0000  | 0.0079  |
| bed                 | 0.2656  | 0.7895  | 0.0658  | 0.3684  |
| food                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bench               | 0.0041  | 0.3500  | 0.0000  | 0.0500  |
| blanket             | 0.0034  | 0.3636  | 0.0001  | 0.0909  |
| ottoman             | 0.0028  | 0.0556  | 0.0000  | 0.0000  |
| stool               | 0.0015  | 0.2381  | 0.0000  | 0.0000  |
| window frame        | 0.0055  | 0.1609  | 0.0000  | 0.0115  |
| carpet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fan                 | 0.0003  | 0.0741  | 0.0000  | 0.0000  |
| fruit               | 0.0019  | 0.1250  | 0.0000  | 0.0000  |
| mat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillar              | 0.0081  | 0.5238  | 0.0000  | 0.0000  |
| column              | 0.0004  | 0.2222  | 0.0000  | 0.0000  |
| chandelier          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| steps               | 0.0000  | 0.1000  | 0.0000  | 0.0000  |
| beam                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stairs              | 0.0046  | 0.1132  | 0.0000  | 0.0189  |
| bin                 | 0.1038  | 0.6341  | 0.0041  | 0.1463  |
| dispenser           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dispenser      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| windowsill          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shower              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.2734  | 0.6250  | 0.0277  | 0.5000  |
| vanity              | 0.0027  | 0.2000  | 0.0000  | 0.0000  |
| bidet               | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.0702  | 0.6667  | 0.0452  | 0.6667  |
| basin               | 0.0083  | 1.0000  | 0.0000  | 0.0000  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| thermostat          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dresser             | 0.1710  | 0.3333  | 0.0000  | 0.0000  |
| toilet paper        | 0.0343  | 0.3056  | 0.0000  | 0.0278  |
| rail                | 0.0010  | 0.1667  | 0.0000  | 0.0000  |
| headboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket              | 0.0017  | 0.1000  | 0.0017  | 0.1000  |
| tissue              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue box          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| air conditioner     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| doorway             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchen island      | 0.0148  | 0.4286  | 0.0035  | 0.2857  |
| cup                 | 0.0010  | 0.1000  | 0.0000  | 0.0000  |
| piano               | 0.0312  | 0.5000  | 0.0000  | 0.0000  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radiator            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| refrigerator        | 0.0749  | 0.5333  | 0.0000  | 0.0000  |
| scale               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toy                 | 0.0461  | 0.2222  | 0.0000  | 0.0000  |
| pool table          | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| telephone           | 0.0104  | 0.2500  | 0.0000  | 0.0000  |
| screen              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rack                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bag                 | 0.1569  | 0.3077  | 0.0000  | 0.0000  |
| socket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| microwave           | 0.0833  | 0.0833  | 0.0000  | 0.0000  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.0047  | 0.1667  | 0.0000  | 0.0000  |
| machine             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pipe                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| board               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water heater        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ladder              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| printer             | 0.0019  | 0.1429  | 0.0019  | 0.1429  |
| soap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| container           | 0.0370  | 0.0741  | 0.0019  | 0.0370  |
| frame               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dish           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fireplace           | 0.0475  | 0.2174  | 0.0002  | 0.0435  |
| arch                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes             | 0.0323  | 0.3103  | 0.0001  | 0.0172  |
| monitor             | 0.1250  | 0.2500  | 0.0000  | 0.0000  |
| ball                | 0.4435  | 0.7500  | 0.0232  | 0.5000  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hanger              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stove               | 0.0260  | 0.3000  | 0.0022  | 0.2000  |
| oven                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.0294  | 0.5000  | 0.0000  | 0.0000  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| teapot              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchenware         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| footstool           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| keyboard            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coffee maker        | 0.0238  | 0.3333  | 0.0000  | 0.0000  |
| laptop              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| guitar              | 0.0736  | 0.2857  | 0.0000  | 0.0000  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clock               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| handle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| backpack            | 0.1250  | 1.0000  | 0.0500  | 1.0000  |
| shoe                | 0.0324  | 0.2800  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garage door         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pan                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| computer            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kettle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| holder              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| partition           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell            | 0.0161  | 0.5000  | 0.0000  | 0.0000  |
| wine                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mouse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| matterport3d        | 0.0317  | 0.1809  | 0.0052  | 0.0461  |
| head                | 0.0424  | 0.2676  | 0.0054  | 0.0682  |
| common              | 0.0132  | 0.0958  | 0.0011  | 0.0195  |
| tail                | 0.0424  | 0.0631  | 0.0168  | 0.0310  |
| small               | 0.0015  | 0.0440  | 0.0000  | 0.0004  |
| medium              | 0.0302  | 0.1904  | 0.0031  | 0.0440  |
| large               | 0.0269  | 0.3026  | 0.0069  | 0.0735  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/19/2025 15:36:45 INFO metrics.py:437 | number of arkit results: 0
Rank[0/8] 06/19/2025 15:36:45 INFO stats.py:394 | Epoch[9] completed. Training Speed: 2.55 samples/sec across all devices.	Epoch Time: 1225.52 sec.	Average Epoch Time: 1225.52 sec.	Average Step Time: 3.14 sec.	Estimated Remaining Time: 1 day, 8:23:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:36:46 INFO accelerator.py:3111 | Saving current state to /job_data/checkpoints/checkpoint_0
Rank[1/8] 06/19/2025 15:36:46 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 9 to /job_data/checkpoints/checkpoint_0
Rank[6/8] 06/19/2025 15:36:46 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 9 to /job_data/checkpoints/checkpoint_0
Rank[4/8] 06/19/2025 15:36:46 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 9 to /job_data/checkpoints/checkpoint_0
Rank[2/8] 06/19/2025 15:36:46 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 9 to /job_data/checkpoints/checkpoint_0
Rank[7/8] 06/19/2025 15:36:46 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 9 to /job_data/checkpoints/checkpoint_0
Rank[3/8] 06/19/2025 15:36:46 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 9 to /job_data/checkpoints/checkpoint_0
Rank[5/8] 06/19/2025 15:36:46 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 9 to /job_data/checkpoints/checkpoint_0
Rank[0/8] 06/19/2025 15:36:48 INFO checkpointing.py:106 | Model weights saved in /job_data/checkpoints/checkpoint_0/model.safetensors
Rank[0/8] 06/19/2025 15:36:50 INFO checkpointing.py:113 | Optimizer state saved in /job_data/checkpoints/checkpoint_0/optimizer.bin
Rank[0/8] 06/19/2025 15:36:50 INFO checkpointing.py:120 | Scheduler state saved in /job_data/checkpoints/checkpoint_0/scheduler.bin
Rank[0/8] 06/19/2025 15:36:50 INFO checkpointing.py:137 | Sampler state for dataloader 0 saved in /job_data/checkpoints/checkpoint_0/sampler.bin
Rank[0/8] 06/19/2025 15:36:50 INFO checkpointing.py:137 | Sampler state for dataloader 1 saved in /job_data/checkpoints/checkpoint_0/sampler_1.bin
Rank[0/8] 06/19/2025 15:36:50 INFO checkpointing.py:164 | Random states saved in /job_data/checkpoints/checkpoint_0/random_states_0.pkl
Rank[0/8] 06/19/2025 15:36:50 INFO checkpointing.py:300 | Saving the state of TrainerProgressState to /job_data/checkpoints/checkpoint_0/custom_checkpoint_0.pkl
Rank[0/8] 06/19/2025 15:36:51 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 9 to /job_data/checkpoints/checkpoint_0
Rank[0/8] 06/19/2025 15:37:19 INFO stats.py:314 | Epoch[10] Step[24] GlobalStep[3924] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 8:23:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:37:19 INFO loss_tracker.py:84 | Epoch[10/239] Step[24] GlobalStep[3924/NA]: det_loss_cls_0[0.7253] det_loss_box_0[0.9385] det_loss_cls_1[0.7100] det_loss_box_1[0.8317] det_loss_cls_2[0.7131] det_loss_box_2[0.7806] det_loss_cls_3[0.7096] det_loss_box_3[0.7571] det_loss_cls_4[0.7044] det_loss_box_4[0.7434] det_loss_cls_5[0.6928] det_loss_box_5[0.7451] det_loss_cls_dn_0[0.1494] det_loss_box_dn_0[0.4199] det_loss_cls_dn_1[0.1431] det_loss_box_dn_1[0.4044] det_loss_cls_dn_2[0.1444] det_loss_box_dn_2[0.4008] det_loss_cls_dn_3[0.1444] det_loss_box_dn_3[0.4002] det_loss_cls_dn_4[0.1444] det_loss_box_dn_4[0.4005] det_loss_cls_dn_5[0.1457] det_loss_box_dn_5[0.4007] loss_depth[0.0427] total_loss[12.3922]
Rank[0/8] 06/19/2025 15:37:45 INFO stats.py:314 | Epoch[10] Step[49] GlobalStep[3949] Training Speed: 8.70 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 1 day, 8:20:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:37:45 INFO loss_tracker.py:84 | Epoch[10/239] Step[49] GlobalStep[3949/NA]: det_loss_cls_0[0.6254] det_loss_box_0[0.7867] det_loss_cls_1[0.6081] det_loss_box_1[0.7190] det_loss_cls_2[0.6033] det_loss_box_2[0.6752] det_loss_cls_3[0.5916] det_loss_box_3[0.6698] det_loss_cls_4[0.5938] det_loss_box_4[0.6648] det_loss_cls_5[0.5902] det_loss_box_5[0.6616] det_loss_cls_dn_0[0.1358] det_loss_box_dn_0[0.4007] det_loss_cls_dn_1[0.1326] det_loss_box_dn_1[0.3805] det_loss_cls_dn_2[0.1339] det_loss_box_dn_2[0.3767] det_loss_cls_dn_3[0.1326] det_loss_box_dn_3[0.3761] det_loss_cls_dn_4[0.1322] det_loss_box_dn_4[0.3766] det_loss_cls_dn_5[0.1323] det_loss_box_dn_5[0.3773] loss_depth[0.0399] total_loss[10.9167]
Rank[0/8] 06/19/2025 15:38:11 INFO stats.py:314 | Epoch[10] Step[74] GlobalStep[3974] Training Speed: 7.38 samples/sec across all devices.	Average Step Time: 1.08 sec.	Estimated Remaining Time: 1 day, 8:17:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:38:11 INFO loss_tracker.py:84 | Epoch[10/239] Step[74] GlobalStep[3974/NA]: det_loss_cls_0[0.5429] det_loss_box_0[0.7189] det_loss_cls_1[0.5331] det_loss_box_1[0.6203] det_loss_cls_2[0.5241] det_loss_box_2[0.5964] det_loss_cls_3[0.5222] det_loss_box_3[0.5788] det_loss_cls_4[0.5184] det_loss_box_4[0.5706] det_loss_cls_5[0.5162] det_loss_box_5[0.5679] det_loss_cls_dn_0[0.1418] det_loss_box_dn_0[0.4111] det_loss_cls_dn_1[0.1374] det_loss_box_dn_1[0.3843] det_loss_cls_dn_2[0.1376] det_loss_box_dn_2[0.3768] det_loss_cls_dn_3[0.1365] det_loss_box_dn_3[0.3775] det_loss_cls_dn_4[0.1370] det_loss_box_dn_4[0.3774] det_loss_cls_dn_5[0.1374] det_loss_box_dn_5[0.3769] loss_depth[0.0416] total_loss[9.9829]
Rank[0/8] 06/19/2025 15:38:37 INFO stats.py:314 | Epoch[10] Step[99] GlobalStep[3999] Training Speed: 7.45 samples/sec across all devices.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 1 day, 8:14:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:38:37 INFO loss_tracker.py:84 | Epoch[10/239] Step[99] GlobalStep[3999/NA]: det_loss_cls_0[0.5772] det_loss_box_0[0.8385] det_loss_cls_1[0.5529] det_loss_box_1[0.7466] det_loss_cls_2[0.5485] det_loss_box_2[0.7130] det_loss_cls_3[0.5449] det_loss_box_3[0.7064] det_loss_cls_4[0.5347] det_loss_box_4[0.6980] det_loss_cls_5[0.5350] det_loss_box_5[0.6906] det_loss_cls_dn_0[0.1437] det_loss_box_dn_0[0.4435] det_loss_cls_dn_1[0.1399] det_loss_box_dn_1[0.4288] det_loss_cls_dn_2[0.1400] det_loss_box_dn_2[0.4199] det_loss_cls_dn_3[0.1392] det_loss_box_dn_3[0.4209] det_loss_cls_dn_4[0.1384] det_loss_box_dn_4[0.4220] det_loss_cls_dn_5[0.1396] det_loss_box_dn_5[0.4221] loss_depth[0.0418] total_loss[11.1260]
Rank[0/8] 06/19/2025 15:39:03 INFO stats.py:314 | Epoch[10] Step[124] GlobalStep[4024] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 8:11:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:39:03 INFO loss_tracker.py:84 | Epoch[10/239] Step[124] GlobalStep[4024/NA]: det_loss_cls_0[0.5743] det_loss_box_0[0.7417] det_loss_cls_1[0.5621] det_loss_box_1[0.6342] det_loss_cls_2[0.5554] det_loss_box_2[0.5881] det_loss_cls_3[0.5465] det_loss_box_3[0.5712] det_loss_cls_4[0.5457] det_loss_box_4[0.5640] det_loss_cls_5[0.5475] det_loss_box_5[0.5611] det_loss_cls_dn_0[0.1395] det_loss_box_dn_0[0.3786] det_loss_cls_dn_1[0.1370] det_loss_box_dn_1[0.3551] det_loss_cls_dn_2[0.1366] det_loss_box_dn_2[0.3441] det_loss_cls_dn_3[0.1363] det_loss_box_dn_3[0.3432] det_loss_cls_dn_4[0.1359] det_loss_box_dn_4[0.3429] det_loss_cls_dn_5[0.1371] det_loss_box_dn_5[0.3426] loss_depth[0.0392] total_loss[9.9599]
Rank[0/8] 06/19/2025 15:39:30 INFO stats.py:314 | Epoch[10] Step[149] GlobalStep[4049] Training Speed: 6.48 samples/sec across all devices.	Average Step Time: 1.23 sec.	Estimated Remaining Time: 1 day, 8:09:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:39:30 INFO loss_tracker.py:84 | Epoch[10/239] Step[149] GlobalStep[4049/NA]: det_loss_cls_0[0.6006] det_loss_box_0[0.7100] det_loss_cls_1[0.5850] det_loss_box_1[0.6193] det_loss_cls_2[0.5873] det_loss_box_2[0.5750] det_loss_cls_3[0.5834] det_loss_box_3[0.5618] det_loss_cls_4[0.5735] det_loss_box_4[0.5583] det_loss_cls_5[0.5600] det_loss_box_5[0.5626] det_loss_cls_dn_0[0.1386] det_loss_box_dn_0[0.3722] det_loss_cls_dn_1[0.1380] det_loss_box_dn_1[0.3500] det_loss_cls_dn_2[0.1372] det_loss_box_dn_2[0.3399] det_loss_cls_dn_3[0.1357] det_loss_box_dn_3[0.3393] det_loss_cls_dn_4[0.1360] det_loss_box_dn_4[0.3398] det_loss_cls_dn_5[0.1370] det_loss_box_dn_5[0.3401] loss_depth[0.0413] total_loss[10.0218]
Rank[0/8] 06/19/2025 15:39:55 INFO stats.py:314 | Epoch[10] Step[174] GlobalStep[4074] Training Speed: 8.22 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 8:06:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:39:55 INFO loss_tracker.py:84 | Epoch[10/239] Step[174] GlobalStep[4074/NA]: det_loss_cls_0[0.6250] det_loss_box_0[0.9366] det_loss_cls_1[0.6089] det_loss_box_1[0.8297] det_loss_cls_2[0.6006] det_loss_box_2[0.7847] det_loss_cls_3[0.6036] det_loss_box_3[0.7683] det_loss_cls_4[0.6005] det_loss_box_4[0.7492] det_loss_cls_5[0.5984] det_loss_box_5[0.7473] det_loss_cls_dn_0[0.1506] det_loss_box_dn_0[0.4133] det_loss_cls_dn_1[0.1477] det_loss_box_dn_1[0.3968] det_loss_cls_dn_2[0.1479] det_loss_box_dn_2[0.3913] det_loss_cls_dn_3[0.1479] det_loss_box_dn_3[0.3913] det_loss_cls_dn_4[0.1475] det_loss_box_dn_4[0.3914] det_loss_cls_dn_5[0.1491] det_loss_box_dn_5[0.3916] loss_depth[0.0450] total_loss[11.7640]
Rank[0/8] 06/19/2025 15:40:22 INFO stats.py:314 | Epoch[10] Step[199] GlobalStep[4099] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 8:03:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:40:22 INFO loss_tracker.py:84 | Epoch[10/239] Step[199] GlobalStep[4099/NA]: det_loss_cls_0[0.7896] det_loss_box_0[1.1462] det_loss_cls_1[0.7733] det_loss_box_1[1.0212] det_loss_cls_2[0.7682] det_loss_box_2[0.9792] det_loss_cls_3[0.7598] det_loss_box_3[0.9653] det_loss_cls_4[0.7440] det_loss_box_4[0.9535] det_loss_cls_5[0.7427] det_loss_box_5[0.9511] det_loss_cls_dn_0[0.1412] det_loss_box_dn_0[0.3784] det_loss_cls_dn_1[0.1386] det_loss_box_dn_1[0.3633] det_loss_cls_dn_2[0.1386] det_loss_box_dn_2[0.3562] det_loss_cls_dn_3[0.1382] det_loss_box_dn_3[0.3562] det_loss_cls_dn_4[0.1380] det_loss_box_dn_4[0.3572] det_loss_cls_dn_5[0.1389] det_loss_box_dn_5[0.3577] loss_depth[0.0443] total_loss[13.6410]
Rank[0/8] 06/19/2025 15:40:48 INFO stats.py:314 | Epoch[10] Step[224] GlobalStep[4124] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 8:00:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:40:48 INFO loss_tracker.py:84 | Epoch[10/239] Step[224] GlobalStep[4124/NA]: det_loss_cls_0[0.7495] det_loss_box_0[0.9778] det_loss_cls_1[0.7205] det_loss_box_1[0.8443] det_loss_cls_2[0.7225] det_loss_box_2[0.7862] det_loss_cls_3[0.7121] det_loss_box_3[0.7690] det_loss_cls_4[0.7088] det_loss_box_4[0.7535] det_loss_cls_5[0.6968] det_loss_box_5[0.7560] det_loss_cls_dn_0[0.1461] det_loss_box_dn_0[0.3668] det_loss_cls_dn_1[0.1435] det_loss_box_dn_1[0.3481] det_loss_cls_dn_2[0.1440] det_loss_box_dn_2[0.3398] det_loss_cls_dn_3[0.1443] det_loss_box_dn_3[0.3391] det_loss_cls_dn_4[0.1441] det_loss_box_dn_4[0.3394] det_loss_cls_dn_5[0.1446] det_loss_box_dn_5[0.3396] loss_depth[0.0445] total_loss[12.1809]
Rank[0/8] 06/19/2025 15:41:14 INFO stats.py:314 | Epoch[10] Step[249] GlobalStep[4149] Training Speed: 6.00 samples/sec across all devices.	Average Step Time: 1.33 sec.	Estimated Remaining Time: 1 day, 7:58:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:41:14 INFO loss_tracker.py:84 | Epoch[10/239] Step[249] GlobalStep[4149/NA]: det_loss_cls_0[0.5985] det_loss_box_0[0.7675] det_loss_cls_1[0.5756] det_loss_box_1[0.6737] det_loss_cls_2[0.5753] det_loss_box_2[0.6452] det_loss_cls_3[0.5656] det_loss_box_3[0.6301] det_loss_cls_4[0.5534] det_loss_box_4[0.6240] det_loss_cls_5[0.5442] det_loss_box_5[0.6293] det_loss_cls_dn_0[0.1395] det_loss_box_dn_0[0.3997] det_loss_cls_dn_1[0.1376] det_loss_box_dn_1[0.3792] det_loss_cls_dn_2[0.1375] det_loss_box_dn_2[0.3685] det_loss_cls_dn_3[0.1379] det_loss_box_dn_3[0.3690] det_loss_cls_dn_4[0.1376] det_loss_box_dn_4[0.3690] det_loss_cls_dn_5[0.1376] det_loss_box_dn_5[0.3691] loss_depth[0.0414] total_loss[10.5061]
Rank[0/8] 06/19/2025 15:41:40 INFO stats.py:314 | Epoch[10] Step[274] GlobalStep[4174] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 7:55:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:41:40 INFO loss_tracker.py:84 | Epoch[10/239] Step[274] GlobalStep[4174/NA]: det_loss_cls_0[0.6821] det_loss_box_0[0.9169] det_loss_cls_1[0.6735] det_loss_box_1[0.7869] det_loss_cls_2[0.6703] det_loss_box_2[0.7453] det_loss_cls_3[0.6644] det_loss_box_3[0.7167] det_loss_cls_4[0.6493] det_loss_box_4[0.7106] det_loss_cls_5[0.6430] det_loss_box_5[0.7109] det_loss_cls_dn_0[0.1314] det_loss_box_dn_0[0.3468] det_loss_cls_dn_1[0.1298] det_loss_box_dn_1[0.3294] det_loss_cls_dn_2[0.1304] det_loss_box_dn_2[0.3223] det_loss_cls_dn_3[0.1305] det_loss_box_dn_3[0.3223] det_loss_cls_dn_4[0.1297] det_loss_box_dn_4[0.3225] det_loss_cls_dn_5[0.1299] det_loss_box_dn_5[0.3228] loss_depth[0.0426] total_loss[11.3604]
Rank[0/8] 06/19/2025 15:42:06 INFO stats.py:314 | Epoch[10] Step[299] GlobalStep[4199] Training Speed: 7.87 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1 day, 7:52:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:42:06 INFO loss_tracker.py:84 | Epoch[10/239] Step[299] GlobalStep[4199/NA]: det_loss_cls_0[0.5965] det_loss_box_0[0.7960] det_loss_cls_1[0.5788] det_loss_box_1[0.6906] det_loss_cls_2[0.5790] det_loss_box_2[0.6515] det_loss_cls_3[0.5658] det_loss_box_3[0.6390] det_loss_cls_4[0.5664] det_loss_box_4[0.6254] det_loss_cls_5[0.5569] det_loss_box_5[0.6332] det_loss_cls_dn_0[0.1358] det_loss_box_dn_0[0.4010] det_loss_cls_dn_1[0.1333] det_loss_box_dn_1[0.3862] det_loss_cls_dn_2[0.1344] det_loss_box_dn_2[0.3802] det_loss_cls_dn_3[0.1331] det_loss_box_dn_3[0.3805] det_loss_cls_dn_4[0.1329] det_loss_box_dn_4[0.3805] det_loss_cls_dn_5[0.1331] det_loss_box_dn_5[0.3802] loss_depth[0.0444] total_loss[10.6346]
Rank[0/8] 06/19/2025 15:42:32 INFO stats.py:314 | Epoch[10] Step[324] GlobalStep[4224] Training Speed: 13.57 samples/sec across all devices.	Average Step Time: 0.59 sec.	Estimated Remaining Time: 1 day, 7:49:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:42:32 INFO loss_tracker.py:84 | Epoch[10/239] Step[324] GlobalStep[4224/NA]: det_loss_cls_0[0.7066] det_loss_box_0[0.9185] det_loss_cls_1[0.6923] det_loss_box_1[0.8158] det_loss_cls_2[0.6905] det_loss_box_2[0.7656] det_loss_cls_3[0.6734] det_loss_box_3[0.7575] det_loss_cls_4[0.6843] det_loss_box_4[0.7231] det_loss_cls_5[0.6744] det_loss_box_5[0.7262] det_loss_cls_dn_0[0.1414] det_loss_box_dn_0[0.3791] det_loss_cls_dn_1[0.1382] det_loss_box_dn_1[0.3575] det_loss_cls_dn_2[0.1372] det_loss_box_dn_2[0.3505] det_loss_cls_dn_3[0.1364] det_loss_box_dn_3[0.3505] det_loss_cls_dn_4[0.1356] det_loss_box_dn_4[0.3503] det_loss_cls_dn_5[0.1363] det_loss_box_dn_5[0.3504] loss_depth[0.0429] total_loss[11.8347]
Rank[0/8] 06/19/2025 15:42:59 INFO stats.py:314 | Epoch[10] Step[349] GlobalStep[4249] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 7:47:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:42:59 INFO loss_tracker.py:84 | Epoch[10/239] Step[349] GlobalStep[4249/NA]: det_loss_cls_0[0.6331] det_loss_box_0[0.8445] det_loss_cls_1[0.6230] det_loss_box_1[0.7138] det_loss_cls_2[0.6165] det_loss_box_2[0.6841] det_loss_cls_3[0.6093] det_loss_box_3[0.6593] det_loss_cls_4[0.6005] det_loss_box_4[0.6465] det_loss_cls_5[0.5937] det_loss_box_5[0.6486] det_loss_cls_dn_0[0.1459] det_loss_box_dn_0[0.3686] det_loss_cls_dn_1[0.1424] det_loss_box_dn_1[0.3499] det_loss_cls_dn_2[0.1424] det_loss_box_dn_2[0.3408] det_loss_cls_dn_3[0.1427] det_loss_box_dn_3[0.3413] det_loss_cls_dn_4[0.1424] det_loss_box_dn_4[0.3415] det_loss_cls_dn_5[0.1436] det_loss_box_dn_5[0.3418] loss_depth[0.0449] total_loss[10.8609]
Rank[0/8] 06/19/2025 15:43:25 INFO stats.py:314 | Epoch[10] Step[374] GlobalStep[4274] Training Speed: 7.81 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1 day, 7:44:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:43:25 INFO loss_tracker.py:84 | Epoch[10/239] Step[374] GlobalStep[4274/NA]: det_loss_cls_0[0.7293] det_loss_box_0[0.9843] det_loss_cls_1[0.7010] det_loss_box_1[0.8514] det_loss_cls_2[0.7020] det_loss_box_2[0.7899] det_loss_cls_3[0.6911] det_loss_box_3[0.7771] det_loss_cls_4[0.6771] det_loss_box_4[0.7737] det_loss_cls_5[0.6768] det_loss_box_5[0.7689] det_loss_cls_dn_0[0.1461] det_loss_box_dn_0[0.4194] det_loss_cls_dn_1[0.1428] det_loss_box_dn_1[0.3955] det_loss_cls_dn_2[0.1421] det_loss_box_dn_2[0.3875] det_loss_cls_dn_3[0.1409] det_loss_box_dn_3[0.3877] det_loss_cls_dn_4[0.1419] det_loss_box_dn_4[0.3880] det_loss_cls_dn_5[0.1426] det_loss_box_dn_5[0.3874] loss_depth[0.0460] total_loss[12.3904]
Rank[0/8] 06/19/2025 15:43:40 INFO stats.py:394 | Epoch[10] completed. Training Speed: 7.62 samples/sec across all devices.	Epoch Time: 409.71 sec.	Average Epoch Time: 409.71 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 7:43:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:43:54 INFO stats.py:314 | Epoch[11] Step[9] GlobalStep[4299] Training Speed: 7.88 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1 day, 7:43:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:44:10 INFO loss_tracker.py:84 | Epoch[11/239] Step[24] GlobalStep[4314/NA]: det_loss_cls_0[0.5445] det_loss_box_0[0.7383] det_loss_cls_1[0.5192] det_loss_box_1[0.6509] det_loss_cls_2[0.5153] det_loss_box_2[0.6213] det_loss_cls_3[0.5084] det_loss_box_3[0.6061] det_loss_cls_4[0.5004] det_loss_box_4[0.5969] det_loss_cls_5[0.4922] det_loss_box_5[0.5969] det_loss_cls_dn_0[0.1361] det_loss_box_dn_0[0.4338] det_loss_cls_dn_1[0.1360] det_loss_box_dn_1[0.4128] det_loss_cls_dn_2[0.1352] det_loss_box_dn_2[0.3996] det_loss_cls_dn_3[0.1336] det_loss_box_dn_3[0.4003] det_loss_cls_dn_4[0.1344] det_loss_box_dn_4[0.4004] det_loss_cls_dn_5[0.1356] det_loss_box_dn_5[0.4008] loss_depth[0.0425] total_loss[10.1913]
Rank[0/8] 06/19/2025 15:44:20 INFO stats.py:314 | Epoch[11] Step[34] GlobalStep[4324] Training Speed: 8.56 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 7:40:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:44:36 INFO loss_tracker.py:84 | Epoch[11/239] Step[49] GlobalStep[4339/NA]: det_loss_cls_0[0.6469] det_loss_box_0[0.8489] det_loss_cls_1[0.6214] det_loss_box_1[0.7600] det_loss_cls_2[0.6174] det_loss_box_2[0.7172] det_loss_cls_3[0.6052] det_loss_box_3[0.7013] det_loss_cls_4[0.6028] det_loss_box_4[0.6836] det_loss_cls_5[0.6004] det_loss_box_5[0.6818] det_loss_cls_dn_0[0.1366] det_loss_box_dn_0[0.3866] det_loss_cls_dn_1[0.1312] det_loss_box_dn_1[0.3610] det_loss_cls_dn_2[0.1317] det_loss_box_dn_2[0.3519] det_loss_cls_dn_3[0.1303] det_loss_box_dn_3[0.3520] det_loss_cls_dn_4[0.1302] det_loss_box_dn_4[0.3522] det_loss_cls_dn_5[0.1312] det_loss_box_dn_5[0.3521] loss_depth[0.0416] total_loss[11.0757]
Rank[0/8] 06/19/2025 15:44:46 INFO stats.py:314 | Epoch[11] Step[59] GlobalStep[4349] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 7:38:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:45:02 INFO loss_tracker.py:84 | Epoch[11/239] Step[74] GlobalStep[4364/NA]: det_loss_cls_0[0.5392] det_loss_box_0[0.7678] det_loss_cls_1[0.5144] det_loss_box_1[0.6826] det_loss_cls_2[0.5170] det_loss_box_2[0.6459] det_loss_cls_3[0.5120] det_loss_box_3[0.6293] det_loss_cls_4[0.5076] det_loss_box_4[0.6227] det_loss_cls_5[0.5073] det_loss_box_5[0.6211] det_loss_cls_dn_0[0.1359] det_loss_box_dn_0[0.4882] det_loss_cls_dn_1[0.1328] det_loss_box_dn_1[0.4557] det_loss_cls_dn_2[0.1331] det_loss_box_dn_2[0.4457] det_loss_cls_dn_3[0.1323] det_loss_box_dn_3[0.4464] det_loss_cls_dn_4[0.1314] det_loss_box_dn_4[0.4470] det_loss_cls_dn_5[0.1318] det_loss_box_dn_5[0.4479] loss_depth[0.0441] total_loss[10.6392]
Rank[0/8] 06/19/2025 15:45:12 INFO stats.py:314 | Epoch[11] Step[84] GlobalStep[4374] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 7:35:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:45:28 INFO loss_tracker.py:84 | Epoch[11/239] Step[99] GlobalStep[4389/NA]: det_loss_cls_0[0.6294] det_loss_box_0[0.8377] det_loss_cls_1[0.6156] det_loss_box_1[0.7469] det_loss_cls_2[0.6233] det_loss_box_2[0.7101] det_loss_cls_3[0.6140] det_loss_box_3[0.6940] det_loss_cls_4[0.6024] det_loss_box_4[0.6881] det_loss_cls_5[0.6003] det_loss_box_5[0.6805] det_loss_cls_dn_0[0.1369] det_loss_box_dn_0[0.4461] det_loss_cls_dn_1[0.1319] det_loss_box_dn_1[0.4305] det_loss_cls_dn_2[0.1332] det_loss_box_dn_2[0.4229] det_loss_cls_dn_3[0.1335] det_loss_box_dn_3[0.4230] det_loss_cls_dn_4[0.1337] det_loss_box_dn_4[0.4232] det_loss_cls_dn_5[0.1351] det_loss_box_dn_5[0.4231] loss_depth[0.0424] total_loss[11.4579]
Rank[0/8] 06/19/2025 15:45:39 INFO stats.py:314 | Epoch[11] Step[109] GlobalStep[4399] Training Speed: 6.48 samples/sec across all devices.	Average Step Time: 1.24 sec.	Estimated Remaining Time: 1 day, 7:33:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:45:54 INFO loss_tracker.py:84 | Epoch[11/239] Step[124] GlobalStep[4414/NA]: det_loss_cls_0[0.7065] det_loss_box_0[0.8942] det_loss_cls_1[0.6823] det_loss_box_1[0.7937] det_loss_cls_2[0.6762] det_loss_box_2[0.7530] det_loss_cls_3[0.6804] det_loss_box_3[0.7307] det_loss_cls_4[0.6667] det_loss_box_4[0.7218] det_loss_cls_5[0.6561] det_loss_box_5[0.7231] det_loss_cls_dn_0[0.1315] det_loss_box_dn_0[0.3743] det_loss_cls_dn_1[0.1293] det_loss_box_dn_1[0.3439] det_loss_cls_dn_2[0.1281] det_loss_box_dn_2[0.3320] det_loss_cls_dn_3[0.1274] det_loss_box_dn_3[0.3309] det_loss_cls_dn_4[0.1256] det_loss_box_dn_4[0.3313] det_loss_cls_dn_5[0.1254] det_loss_box_dn_5[0.3314] loss_depth[0.0428] total_loss[11.5385]
Rank[0/8] 06/19/2025 15:46:05 INFO stats.py:314 | Epoch[11] Step[134] GlobalStep[4424] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 7:30:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:46:20 INFO loss_tracker.py:84 | Epoch[11/239] Step[149] GlobalStep[4439/NA]: det_loss_cls_0[0.4829] det_loss_box_0[0.5918] det_loss_cls_1[0.4635] det_loss_box_1[0.5103] det_loss_cls_2[0.4600] det_loss_box_2[0.4855] det_loss_cls_3[0.4585] det_loss_box_3[0.4733] det_loss_cls_4[0.4607] det_loss_box_4[0.4612] det_loss_cls_5[0.4571] det_loss_box_5[0.4625] det_loss_cls_dn_0[0.1339] det_loss_box_dn_0[0.3735] det_loss_cls_dn_1[0.1314] det_loss_box_dn_1[0.3478] det_loss_cls_dn_2[0.1321] det_loss_box_dn_2[0.3328] det_loss_cls_dn_3[0.1302] det_loss_box_dn_3[0.3330] det_loss_cls_dn_4[0.1290] det_loss_box_dn_4[0.3330] det_loss_cls_dn_5[0.1296] det_loss_box_dn_5[0.3331] loss_depth[0.0432] total_loss[8.6499]
Rank[0/8] 06/19/2025 15:46:31 INFO stats.py:314 | Epoch[11] Step[159] GlobalStep[4449] Training Speed: 8.65 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 7:28:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:46:47 INFO loss_tracker.py:84 | Epoch[11/239] Step[174] GlobalStep[4464/NA]: det_loss_cls_0[0.6847] det_loss_box_0[0.9558] det_loss_cls_1[0.6766] det_loss_box_1[0.8405] det_loss_cls_2[0.6742] det_loss_box_2[0.7904] det_loss_cls_3[0.6614] det_loss_box_3[0.7712] det_loss_cls_4[0.6458] det_loss_box_4[0.7624] det_loss_cls_5[0.6447] det_loss_box_5[0.7535] det_loss_cls_dn_0[0.1393] det_loss_box_dn_0[0.4187] det_loss_cls_dn_1[0.1348] det_loss_box_dn_1[0.3986] det_loss_cls_dn_2[0.1352] det_loss_box_dn_2[0.3898] det_loss_cls_dn_3[0.1334] det_loss_box_dn_3[0.3894] det_loss_cls_dn_4[0.1338] det_loss_box_dn_4[0.3895] det_loss_cls_dn_5[0.1347] det_loss_box_dn_5[0.3895] loss_depth[0.0467] total_loss[12.0947]
Rank[0/8] 06/19/2025 15:46:58 INFO stats.py:314 | Epoch[11] Step[184] GlobalStep[4474] Training Speed: 7.77 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 1 day, 7:26:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:47:13 INFO loss_tracker.py:84 | Epoch[11/239] Step[199] GlobalStep[4489/NA]: det_loss_cls_0[0.7196] det_loss_box_0[0.9880] det_loss_cls_1[0.6998] det_loss_box_1[0.8413] det_loss_cls_2[0.7063] det_loss_box_2[0.7782] det_loss_cls_3[0.6943] det_loss_box_3[0.7699] det_loss_cls_4[0.6933] det_loss_box_4[0.7502] det_loss_cls_5[0.6854] det_loss_box_5[0.7493] det_loss_cls_dn_0[0.1402] det_loss_box_dn_0[0.3625] det_loss_cls_dn_1[0.1377] det_loss_box_dn_1[0.3449] det_loss_cls_dn_2[0.1383] det_loss_box_dn_2[0.3325] det_loss_cls_dn_3[0.1378] det_loss_box_dn_3[0.3319] det_loss_cls_dn_4[0.1370] det_loss_box_dn_4[0.3321] det_loss_cls_dn_5[0.1376] det_loss_box_dn_5[0.3320] loss_depth[0.0430] total_loss[11.9831]
Rank[0/8] 06/19/2025 15:47:23 INFO stats.py:314 | Epoch[11] Step[209] GlobalStep[4499] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 7:23:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:47:38 INFO loss_tracker.py:84 | Epoch[11/239] Step[224] GlobalStep[4514/NA]: det_loss_cls_0[0.6360] det_loss_box_0[0.8961] det_loss_cls_1[0.6192] det_loss_box_1[0.7799] det_loss_cls_2[0.6252] det_loss_box_2[0.7395] det_loss_cls_3[0.6193] det_loss_box_3[0.7157] det_loss_cls_4[0.6088] det_loss_box_4[0.7033] det_loss_cls_5[0.6006] det_loss_box_5[0.7047] det_loss_cls_dn_0[0.1326] det_loss_box_dn_0[0.4017] det_loss_cls_dn_1[0.1298] det_loss_box_dn_1[0.3732] det_loss_cls_dn_2[0.1306] det_loss_box_dn_2[0.3623] det_loss_cls_dn_3[0.1308] det_loss_box_dn_3[0.3621] det_loss_cls_dn_4[0.1303] det_loss_box_dn_4[0.3622] det_loss_cls_dn_5[0.1301] det_loss_box_dn_5[0.3620] loss_depth[0.0458] total_loss[11.3019]
Rank[0/8] 06/19/2025 15:47:49 INFO stats.py:314 | Epoch[11] Step[234] GlobalStep[4524] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 7:21:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:48:05 INFO loss_tracker.py:84 | Epoch[11/239] Step[249] GlobalStep[4539/NA]: det_loss_cls_0[0.6673] det_loss_box_0[0.9026] det_loss_cls_1[0.6438] det_loss_box_1[0.7908] det_loss_cls_2[0.6498] det_loss_box_2[0.7289] det_loss_cls_3[0.6434] det_loss_box_3[0.7114] det_loss_cls_4[0.6255] det_loss_box_4[0.7027] det_loss_cls_5[0.6212] det_loss_box_5[0.6984] det_loss_cls_dn_0[0.1283] det_loss_box_dn_0[0.3994] det_loss_cls_dn_1[0.1259] det_loss_box_dn_1[0.3780] det_loss_cls_dn_2[0.1259] det_loss_box_dn_2[0.3613] det_loss_cls_dn_3[0.1247] det_loss_box_dn_3[0.3615] det_loss_cls_dn_4[0.1238] det_loss_box_dn_4[0.3617] det_loss_cls_dn_5[0.1237] det_loss_box_dn_5[0.3616] loss_depth[0.0425] total_loss[11.4040]
Rank[0/8] 06/19/2025 15:48:15 INFO stats.py:314 | Epoch[11] Step[259] GlobalStep[4549] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 7:18:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:48:32 INFO loss_tracker.py:84 | Epoch[11/239] Step[274] GlobalStep[4564/NA]: det_loss_cls_0[0.7308] det_loss_box_0[1.0393] det_loss_cls_1[0.7064] det_loss_box_1[0.9186] det_loss_cls_2[0.7133] det_loss_box_2[0.8714] det_loss_cls_3[0.7020] det_loss_box_3[0.8571] det_loss_cls_4[0.6958] det_loss_box_4[0.8569] det_loss_cls_5[0.6891] det_loss_box_5[0.8543] det_loss_cls_dn_0[0.1384] det_loss_box_dn_0[0.3700] det_loss_cls_dn_1[0.1366] det_loss_box_dn_1[0.3512] det_loss_cls_dn_2[0.1371] det_loss_box_dn_2[0.3377] det_loss_cls_dn_3[0.1359] det_loss_box_dn_3[0.3378] det_loss_cls_dn_4[0.1355] det_loss_box_dn_4[0.3377] det_loss_cls_dn_5[0.1356] det_loss_box_dn_5[0.3379] loss_depth[0.0455] total_loss[12.5719]
Rank[0/8] 06/19/2025 15:48:42 INFO stats.py:314 | Epoch[11] Step[284] GlobalStep[4574] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 7:16:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:48:57 INFO loss_tracker.py:84 | Epoch[11/239] Step[299] GlobalStep[4589/NA]: det_loss_cls_0[0.6095] det_loss_box_0[0.8872] det_loss_cls_1[0.5930] det_loss_box_1[0.7990] det_loss_cls_2[0.5908] det_loss_box_2[0.7642] det_loss_cls_3[0.5852] det_loss_box_3[0.7505] det_loss_cls_4[0.5873] det_loss_box_4[0.7295] det_loss_cls_5[0.5782] det_loss_box_5[0.7304] det_loss_cls_dn_0[0.1368] det_loss_box_dn_0[0.4663] det_loss_cls_dn_1[0.1330] det_loss_box_dn_1[0.4413] det_loss_cls_dn_2[0.1338] det_loss_box_dn_2[0.4347] det_loss_cls_dn_3[0.1333] det_loss_box_dn_3[0.4334] det_loss_cls_dn_4[0.1332] det_loss_box_dn_4[0.4332] det_loss_cls_dn_5[0.1328] det_loss_box_dn_5[0.4337] loss_depth[0.0417] total_loss[11.6920]
Rank[0/8] 06/19/2025 15:49:07 INFO stats.py:314 | Epoch[11] Step[309] GlobalStep[4599] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 7:14:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:49:23 INFO loss_tracker.py:84 | Epoch[11/239] Step[324] GlobalStep[4614/NA]: det_loss_cls_0[0.7887] det_loss_box_0[0.9414] det_loss_cls_1[0.7613] det_loss_box_1[0.8145] det_loss_cls_2[0.7579] det_loss_box_2[0.7753] det_loss_cls_3[0.7544] det_loss_box_3[0.7479] det_loss_cls_4[0.7504] det_loss_box_4[0.7338] det_loss_cls_5[0.7412] det_loss_box_5[0.7370] det_loss_cls_dn_0[0.1375] det_loss_box_dn_0[0.3707] det_loss_cls_dn_1[0.1349] det_loss_box_dn_1[0.3504] det_loss_cls_dn_2[0.1362] det_loss_box_dn_2[0.3401] det_loss_cls_dn_3[0.1346] det_loss_box_dn_3[0.3395] det_loss_cls_dn_4[0.1333] det_loss_box_dn_4[0.3399] det_loss_cls_dn_5[0.1330] det_loss_box_dn_5[0.3394] loss_depth[0.0408] total_loss[12.2343]
Rank[0/8] 06/19/2025 15:49:34 INFO stats.py:314 | Epoch[11] Step[334] GlobalStep[4624] Training Speed: 7.70 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 7:12:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:49:50 INFO loss_tracker.py:84 | Epoch[11/239] Step[349] GlobalStep[4639/NA]: det_loss_cls_0[0.6187] det_loss_box_0[0.7983] det_loss_cls_1[0.5832] det_loss_box_1[0.7225] det_loss_cls_2[0.5969] det_loss_box_2[0.6825] det_loss_cls_3[0.5843] det_loss_box_3[0.6742] det_loss_cls_4[0.5749] det_loss_box_4[0.6569] det_loss_cls_5[0.5695] det_loss_box_5[0.6570] det_loss_cls_dn_0[0.1279] det_loss_box_dn_0[0.3693] det_loss_cls_dn_1[0.1264] det_loss_box_dn_1[0.3525] det_loss_cls_dn_2[0.1261] det_loss_box_dn_2[0.3431] det_loss_cls_dn_3[0.1250] det_loss_box_dn_3[0.3436] det_loss_cls_dn_4[0.1259] det_loss_box_dn_4[0.3437] det_loss_cls_dn_5[0.1262] det_loss_box_dn_5[0.3438] loss_depth[0.0438] total_loss[10.6162]
Rank[0/8] 06/19/2025 15:50:00 INFO stats.py:314 | Epoch[11] Step[359] GlobalStep[4649] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 7:09:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:50:16 INFO loss_tracker.py:84 | Epoch[11/239] Step[374] GlobalStep[4664/NA]: det_loss_cls_0[0.5129] det_loss_box_0[0.6715] det_loss_cls_1[0.4935] det_loss_box_1[0.5878] det_loss_cls_2[0.4978] det_loss_box_2[0.5443] det_loss_cls_3[0.4845] det_loss_box_3[0.5391] det_loss_cls_4[0.4811] det_loss_box_4[0.5268] det_loss_cls_5[0.4763] det_loss_box_5[0.5213] det_loss_cls_dn_0[0.1292] det_loss_box_dn_0[0.4481] det_loss_cls_dn_1[0.1249] det_loss_box_dn_1[0.4256] det_loss_cls_dn_2[0.1252] det_loss_box_dn_2[0.4088] det_loss_cls_dn_3[0.1239] det_loss_box_dn_3[0.4078] det_loss_cls_dn_4[0.1233] det_loss_box_dn_4[0.4079] det_loss_cls_dn_5[0.1243] det_loss_box_dn_5[0.4071] loss_depth[0.0380] total_loss[9.6311]
Rank[0/8] 06/19/2025 15:50:26 INFO stats.py:314 | Epoch[11] Step[384] GlobalStep[4674] Training Speed: 7.79 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 1 day, 7:07:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:50:31 INFO stats.py:394 | Epoch[11] completed. Training Speed: 7.60 samples/sec across all devices.	Epoch Time: 410.43 sec.	Average Epoch Time: 410.43 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 7:06:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:50:56 INFO stats.py:314 | Epoch[12] Step[19] GlobalStep[4699] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 7:06:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:51:01 INFO loss_tracker.py:84 | Epoch[12/239] Step[24] GlobalStep[4704/NA]: det_loss_cls_0[0.6006] det_loss_box_0[0.8320] det_loss_cls_1[0.5788] det_loss_box_1[0.7131] det_loss_cls_2[0.5776] det_loss_box_2[0.6728] det_loss_cls_3[0.5785] det_loss_box_3[0.6598] det_loss_cls_4[0.5659] det_loss_box_4[0.6535] det_loss_cls_5[0.5579] det_loss_box_5[0.6550] det_loss_cls_dn_0[0.1376] det_loss_box_dn_0[0.4014] det_loss_cls_dn_1[0.1351] det_loss_box_dn_1[0.3832] det_loss_cls_dn_2[0.1348] det_loss_box_dn_2[0.3707] det_loss_cls_dn_3[0.1344] det_loss_box_dn_3[0.3717] det_loss_cls_dn_4[0.1336] det_loss_box_dn_4[0.3722] det_loss_cls_dn_5[0.1343] det_loss_box_dn_5[0.3726] loss_depth[0.0437] total_loss[10.7712]
Rank[0/8] 06/19/2025 15:51:22 INFO stats.py:314 | Epoch[12] Step[44] GlobalStep[4724] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 7:04:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:51:27 INFO loss_tracker.py:84 | Epoch[12/239] Step[49] GlobalStep[4729/NA]: det_loss_cls_0[0.6482] det_loss_box_0[0.8643] det_loss_cls_1[0.6309] det_loss_box_1[0.7624] det_loss_cls_2[0.6356] det_loss_box_2[0.7175] det_loss_cls_3[0.6316] det_loss_box_3[0.7056] det_loss_cls_4[0.6133] det_loss_box_4[0.6948] det_loss_cls_5[0.6112] det_loss_box_5[0.6901] det_loss_cls_dn_0[0.1336] det_loss_box_dn_0[0.3678] det_loss_cls_dn_1[0.1308] det_loss_box_dn_1[0.3461] det_loss_cls_dn_2[0.1301] det_loss_box_dn_2[0.3362] det_loss_cls_dn_3[0.1296] det_loss_box_dn_3[0.3362] det_loss_cls_dn_4[0.1293] det_loss_box_dn_4[0.3365] det_loss_cls_dn_5[0.1301] det_loss_box_dn_5[0.3366] loss_depth[0.0392] total_loss[11.0875]
Rank[0/8] 06/19/2025 15:51:48 INFO stats.py:314 | Epoch[12] Step[69] GlobalStep[4749] Training Speed: 8.06 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 7:02:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:51:53 INFO loss_tracker.py:84 | Epoch[12/239] Step[74] GlobalStep[4754/NA]: det_loss_cls_0[0.7037] det_loss_box_0[0.9365] det_loss_cls_1[0.6739] det_loss_box_1[0.8135] det_loss_cls_2[0.6766] det_loss_box_2[0.7605] det_loss_cls_3[0.6668] det_loss_box_3[0.7350] det_loss_cls_4[0.6665] det_loss_box_4[0.7185] det_loss_cls_5[0.6661] det_loss_box_5[0.7136] det_loss_cls_dn_0[0.1350] det_loss_box_dn_0[0.3665] det_loss_cls_dn_1[0.1337] det_loss_box_dn_1[0.3488] det_loss_cls_dn_2[0.1333] det_loss_box_dn_2[0.3382] det_loss_cls_dn_3[0.1338] det_loss_box_dn_3[0.3377] det_loss_cls_dn_4[0.1331] det_loss_box_dn_4[0.3381] det_loss_cls_dn_5[0.1348] det_loss_box_dn_5[0.3382] loss_depth[0.0412] total_loss[11.6437]
Rank[0/8] 06/19/2025 15:52:13 INFO stats.py:314 | Epoch[12] Step[94] GlobalStep[4774] Training Speed: 7.79 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 1 day, 6:59:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:52:19 INFO loss_tracker.py:84 | Epoch[12/239] Step[99] GlobalStep[4779/NA]: det_loss_cls_0[0.7277] det_loss_box_0[0.9700] det_loss_cls_1[0.7088] det_loss_box_1[0.8413] det_loss_cls_2[0.7034] det_loss_box_2[0.7925] det_loss_cls_3[0.6936] det_loss_box_3[0.7804] det_loss_cls_4[0.6843] det_loss_box_4[0.7595] det_loss_cls_5[0.6799] det_loss_box_5[0.7582] det_loss_cls_dn_0[0.1325] det_loss_box_dn_0[0.3708] det_loss_cls_dn_1[0.1289] det_loss_box_dn_1[0.3517] det_loss_cls_dn_2[0.1292] det_loss_box_dn_2[0.3383] det_loss_cls_dn_3[0.1292] det_loss_box_dn_3[0.3386] det_loss_cls_dn_4[0.1293] det_loss_box_dn_4[0.3389] det_loss_cls_dn_5[0.1300] det_loss_box_dn_5[0.3389] loss_depth[0.0365] total_loss[11.9924]
Rank[0/8] 06/19/2025 15:52:40 INFO stats.py:314 | Epoch[12] Step[119] GlobalStep[4799] Training Speed: 6.44 samples/sec across all devices.	Average Step Time: 1.24 sec.	Estimated Remaining Time: 1 day, 6:57:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:52:46 INFO loss_tracker.py:84 | Epoch[12/239] Step[124] GlobalStep[4804/NA]: det_loss_cls_0[0.6747] det_loss_box_0[0.8777] det_loss_cls_1[0.6715] det_loss_box_1[0.7634] det_loss_cls_2[0.6603] det_loss_box_2[0.7159] det_loss_cls_3[0.6552] det_loss_box_3[0.7057] det_loss_cls_4[0.6433] det_loss_box_4[0.7029] det_loss_cls_5[0.6327] det_loss_box_5[0.7015] det_loss_cls_dn_0[0.1378] det_loss_box_dn_0[0.4719] det_loss_cls_dn_1[0.1336] det_loss_box_dn_1[0.4439] det_loss_cls_dn_2[0.1339] det_loss_box_dn_2[0.4334] det_loss_cls_dn_3[0.1324] det_loss_box_dn_3[0.4330] det_loss_cls_dn_4[0.1327] det_loss_box_dn_4[0.4330] det_loss_cls_dn_5[0.1331] det_loss_box_dn_5[0.4330] loss_depth[0.0407] total_loss[11.8974]
Rank[0/8] 06/19/2025 15:53:07 INFO stats.py:314 | Epoch[12] Step[144] GlobalStep[4824] Training Speed: 7.90 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 6:55:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:53:12 INFO loss_tracker.py:84 | Epoch[12/239] Step[149] GlobalStep[4829/NA]: det_loss_cls_0[0.6403] det_loss_box_0[0.8637] det_loss_cls_1[0.6036] det_loss_box_1[0.7542] det_loss_cls_2[0.5974] det_loss_box_2[0.7175] det_loss_cls_3[0.6022] det_loss_box_3[0.6966] det_loss_cls_4[0.5811] det_loss_box_4[0.6882] det_loss_cls_5[0.5830] det_loss_box_5[0.6845] det_loss_cls_dn_0[0.1388] det_loss_box_dn_0[0.3559] det_loss_cls_dn_1[0.1355] det_loss_box_dn_1[0.3333] det_loss_cls_dn_2[0.1352] det_loss_box_dn_2[0.3210] det_loss_cls_dn_3[0.1351] det_loss_box_dn_3[0.3213] det_loss_cls_dn_4[0.1346] det_loss_box_dn_4[0.3213] det_loss_cls_dn_5[0.1345] det_loss_box_dn_5[0.3213] loss_depth[0.0395] total_loss[10.8396]
Rank[0/8] 06/19/2025 15:53:32 INFO stats.py:314 | Epoch[12] Step[169] GlobalStep[4849] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 6:53:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:53:37 INFO loss_tracker.py:84 | Epoch[12/239] Step[174] GlobalStep[4854/NA]: det_loss_cls_0[0.6262] det_loss_box_0[0.9619] det_loss_cls_1[0.6081] det_loss_box_1[0.8644] det_loss_cls_2[0.6042] det_loss_box_2[0.8140] det_loss_cls_3[0.6022] det_loss_box_3[0.7950] det_loss_cls_4[0.5921] det_loss_box_4[0.7788] det_loss_cls_5[0.5925] det_loss_box_5[0.7822] det_loss_cls_dn_0[0.1269] det_loss_box_dn_0[0.3966] det_loss_cls_dn_1[0.1239] det_loss_box_dn_1[0.3767] det_loss_cls_dn_2[0.1238] det_loss_box_dn_2[0.3645] det_loss_cls_dn_3[0.1223] det_loss_box_dn_3[0.3648] det_loss_cls_dn_4[0.1218] det_loss_box_dn_4[0.3648] det_loss_cls_dn_5[0.1213] det_loss_box_dn_5[0.3643] loss_depth[0.0416] total_loss[11.6349]
Rank[0/8] 06/19/2025 15:53:58 INFO stats.py:314 | Epoch[12] Step[194] GlobalStep[4874] Training Speed: 7.87 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1 day, 6:51:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:54:03 INFO loss_tracker.py:84 | Epoch[12/239] Step[199] GlobalStep[4879/NA]: det_loss_cls_0[0.7688] det_loss_box_0[1.0529] det_loss_cls_1[0.7434] det_loss_box_1[0.9334] det_loss_cls_2[0.7533] det_loss_box_2[0.8851] det_loss_cls_3[0.7509] det_loss_box_3[0.8675] det_loss_cls_4[0.7313] det_loss_box_4[0.8590] det_loss_cls_5[0.7265] det_loss_box_5[0.8604] det_loss_cls_dn_0[0.1387] det_loss_box_dn_0[0.4873] det_loss_cls_dn_1[0.1354] det_loss_box_dn_1[0.4595] det_loss_cls_dn_2[0.1354] det_loss_box_dn_2[0.4454] det_loss_cls_dn_3[0.1344] det_loss_box_dn_3[0.4426] det_loss_cls_dn_4[0.1333] det_loss_box_dn_4[0.4421] det_loss_cls_dn_5[0.1341] det_loss_box_dn_5[0.4414] loss_depth[0.0400] total_loss[13.5023]
Rank[0/8] 06/19/2025 15:54:25 INFO stats.py:314 | Epoch[12] Step[219] GlobalStep[4899] Training Speed: 7.76 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 1 day, 6:49:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:54:30 INFO loss_tracker.py:84 | Epoch[12/239] Step[224] GlobalStep[4904/NA]: det_loss_cls_0[0.6622] det_loss_box_0[0.8216] det_loss_cls_1[0.6424] det_loss_box_1[0.7302] det_loss_cls_2[0.6421] det_loss_box_2[0.6920] det_loss_cls_3[0.6371] det_loss_box_3[0.6870] det_loss_cls_4[0.6328] det_loss_box_4[0.6788] det_loss_cls_5[0.6225] det_loss_box_5[0.6792] det_loss_cls_dn_0[0.1439] det_loss_box_dn_0[0.3347] det_loss_cls_dn_1[0.1413] det_loss_box_dn_1[0.3147] det_loss_cls_dn_2[0.1403] det_loss_box_dn_2[0.2993] det_loss_cls_dn_3[0.1396] det_loss_box_dn_3[0.2989] det_loss_cls_dn_4[0.1392] det_loss_box_dn_4[0.2987] det_loss_cls_dn_5[0.1394] det_loss_box_dn_5[0.2986] loss_depth[0.0377] total_loss[10.8542]
Rank[0/8] 06/19/2025 15:54:51 INFO stats.py:314 | Epoch[12] Step[244] GlobalStep[4924] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 6:47:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:54:56 INFO loss_tracker.py:84 | Epoch[12/239] Step[249] GlobalStep[4929/NA]: det_loss_cls_0[0.6630] det_loss_box_0[0.8147] det_loss_cls_1[0.6199] det_loss_box_1[0.7422] det_loss_cls_2[0.6211] det_loss_box_2[0.7043] det_loss_cls_3[0.6060] det_loss_box_3[0.6971] det_loss_cls_4[0.5974] det_loss_box_4[0.6830] det_loss_cls_5[0.5885] det_loss_box_5[0.6798] det_loss_cls_dn_0[0.1365] det_loss_box_dn_0[0.3930] det_loss_cls_dn_1[0.1340] det_loss_box_dn_1[0.3710] det_loss_cls_dn_2[0.1332] det_loss_box_dn_2[0.3567] det_loss_cls_dn_3[0.1323] det_loss_box_dn_3[0.3557] det_loss_cls_dn_4[0.1320] det_loss_box_dn_4[0.3560] det_loss_cls_dn_5[0.1318] det_loss_box_dn_5[0.3568] loss_depth[0.0427] total_loss[11.0489]
Rank[0/8] 06/19/2025 15:55:16 INFO stats.py:314 | Epoch[12] Step[269] GlobalStep[4949] Training Speed: 8.06 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 6:45:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:55:21 INFO loss_tracker.py:84 | Epoch[12/239] Step[274] GlobalStep[4954/NA]: det_loss_cls_0[0.6902] det_loss_box_0[0.8945] det_loss_cls_1[0.6733] det_loss_box_1[0.7787] det_loss_cls_2[0.6725] det_loss_box_2[0.7278] det_loss_cls_3[0.6513] det_loss_box_3[0.7209] det_loss_cls_4[0.6476] det_loss_box_4[0.7088] det_loss_cls_5[0.6415] det_loss_box_5[0.7100] det_loss_cls_dn_0[0.1366] det_loss_box_dn_0[0.3342] det_loss_cls_dn_1[0.1345] det_loss_box_dn_1[0.3110] det_loss_cls_dn_2[0.1338] det_loss_box_dn_2[0.3007] det_loss_cls_dn_3[0.1323] det_loss_box_dn_3[0.3004] det_loss_cls_dn_4[0.1334] det_loss_box_dn_4[0.3006] det_loss_cls_dn_5[0.1328] det_loss_box_dn_5[0.3006] loss_depth[0.0438] total_loss[11.2117]
Rank[0/8] 06/19/2025 15:55:43 INFO stats.py:314 | Epoch[12] Step[294] GlobalStep[4974] Training Speed: 7.45 samples/sec across all devices.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 1 day, 6:43:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:55:48 INFO loss_tracker.py:84 | Epoch[12/239] Step[299] GlobalStep[4979/NA]: det_loss_cls_0[0.6210] det_loss_box_0[0.8509] det_loss_cls_1[0.6129] det_loss_box_1[0.7311] det_loss_cls_2[0.6166] det_loss_box_2[0.6795] det_loss_cls_3[0.6111] det_loss_box_3[0.6708] det_loss_cls_4[0.6052] det_loss_box_4[0.6585] det_loss_cls_5[0.5879] det_loss_box_5[0.6646] det_loss_cls_dn_0[0.1302] det_loss_box_dn_0[0.4034] det_loss_cls_dn_1[0.1276] det_loss_box_dn_1[0.3786] det_loss_cls_dn_2[0.1268] det_loss_box_dn_2[0.3618] det_loss_cls_dn_3[0.1254] det_loss_box_dn_3[0.3613] det_loss_cls_dn_4[0.1250] det_loss_box_dn_4[0.3613] det_loss_cls_dn_5[0.1251] det_loss_box_dn_5[0.3618] loss_depth[0.0442] total_loss[10.9428]
Rank[0/8] 06/19/2025 15:56:09 INFO stats.py:314 | Epoch[12] Step[319] GlobalStep[4999] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 6:41:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:56:14 INFO loss_tracker.py:84 | Epoch[12/239] Step[324] GlobalStep[5004/NA]: det_loss_cls_0[0.6441] det_loss_box_0[0.8287] det_loss_cls_1[0.6333] det_loss_box_1[0.7064] det_loss_cls_2[0.6395] det_loss_box_2[0.6571] det_loss_cls_3[0.6199] det_loss_box_3[0.6344] det_loss_cls_4[0.6103] det_loss_box_4[0.6183] det_loss_cls_5[0.5983] det_loss_box_5[0.6194] det_loss_cls_dn_0[0.1351] det_loss_box_dn_0[0.4560] det_loss_cls_dn_1[0.1308] det_loss_box_dn_1[0.4278] det_loss_cls_dn_2[0.1313] det_loss_box_dn_2[0.4121] det_loss_cls_dn_3[0.1311] det_loss_box_dn_3[0.4119] det_loss_cls_dn_4[0.1297] det_loss_box_dn_4[0.4120] det_loss_cls_dn_5[0.1298] det_loss_box_dn_5[0.4128] loss_depth[0.0419] total_loss[11.1720]
Rank[0/8] 06/19/2025 15:56:35 INFO stats.py:314 | Epoch[12] Step[344] GlobalStep[5024] Training Speed: 8.22 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 6:39:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:56:40 INFO loss_tracker.py:84 | Epoch[12/239] Step[349] GlobalStep[5029/NA]: det_loss_cls_0[0.5290] det_loss_box_0[0.7437] det_loss_cls_1[0.5009] det_loss_box_1[0.6593] det_loss_cls_2[0.5053] det_loss_box_2[0.6139] det_loss_cls_3[0.4979] det_loss_box_3[0.5939] det_loss_cls_4[0.4937] det_loss_box_4[0.5835] det_loss_cls_5[0.4829] det_loss_box_5[0.5829] det_loss_cls_dn_0[0.1301] det_loss_box_dn_0[0.4871] det_loss_cls_dn_1[0.1275] det_loss_box_dn_1[0.4566] det_loss_cls_dn_2[0.1283] det_loss_box_dn_2[0.4461] det_loss_cls_dn_3[0.1276] det_loss_box_dn_3[0.4436] det_loss_cls_dn_4[0.1278] det_loss_box_dn_4[0.4443] det_loss_cls_dn_5[0.1275] det_loss_box_dn_5[0.4442] loss_depth[0.0453] total_loss[10.3233]
Rank[0/8] 06/19/2025 15:57:02 INFO stats.py:314 | Epoch[12] Step[369] GlobalStep[5049] Training Speed: 6.27 samples/sec across all devices.	Average Step Time: 1.28 sec.	Estimated Remaining Time: 1 day, 6:37:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:57:07 INFO loss_tracker.py:84 | Epoch[12/239] Step[374] GlobalStep[5054/NA]: det_loss_cls_0[0.7146] det_loss_box_0[0.9285] det_loss_cls_1[0.6922] det_loss_box_1[0.7803] det_loss_cls_2[0.7007] det_loss_box_2[0.7191] det_loss_cls_3[0.6899] det_loss_box_3[0.7103] det_loss_cls_4[0.6749] det_loss_box_4[0.7049] det_loss_cls_5[0.6695] det_loss_box_5[0.7077] det_loss_cls_dn_0[0.1403] det_loss_box_dn_0[0.3255] det_loss_cls_dn_1[0.1343] det_loss_box_dn_1[0.3070] det_loss_cls_dn_2[0.1350] det_loss_box_dn_2[0.2919] det_loss_cls_dn_3[0.1346] det_loss_box_dn_3[0.2913] det_loss_cls_dn_4[0.1334] det_loss_box_dn_4[0.2914] det_loss_cls_dn_5[0.1335] det_loss_box_dn_5[0.2914] loss_depth[0.0428] total_loss[11.3450]
Rank[0/8] 06/19/2025 15:57:22 INFO stats.py:394 | Epoch[12] completed. Training Speed: 7.58 samples/sec across all devices.	Epoch Time: 411.37 sec.	Average Epoch Time: 411.37 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 6:35:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:57:30 INFO stats.py:314 | Epoch[13] Step[4] GlobalStep[5074] Training Speed: 8.73 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 1 day, 6:35:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:57:51 INFO loss_tracker.py:84 | Epoch[13/239] Step[24] GlobalStep[5094/NA]: det_loss_cls_0[0.5062] det_loss_box_0[0.7335] det_loss_cls_1[0.4894] det_loss_box_1[0.6490] det_loss_cls_2[0.4883] det_loss_box_2[0.6224] det_loss_cls_3[0.4843] det_loss_box_3[0.6126] det_loss_cls_4[0.4768] det_loss_box_4[0.5950] det_loss_cls_5[0.4691] det_loss_box_5[0.5941] det_loss_cls_dn_0[0.1296] det_loss_box_dn_0[0.4277] det_loss_cls_dn_1[0.1271] det_loss_box_dn_1[0.4029] det_loss_cls_dn_2[0.1269] det_loss_box_dn_2[0.3838] det_loss_cls_dn_3[0.1252] det_loss_box_dn_3[0.3837] det_loss_cls_dn_4[0.1244] det_loss_box_dn_4[0.3838] det_loss_cls_dn_5[0.1250] det_loss_box_dn_5[0.3837] loss_depth[0.0433] total_loss[9.8878]
Rank[0/8] 06/19/2025 15:57:56 INFO stats.py:314 | Epoch[13] Step[29] GlobalStep[5099] Training Speed: 8.72 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 1 day, 6:33:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:58:17 INFO loss_tracker.py:84 | Epoch[13/239] Step[49] GlobalStep[5119/NA]: det_loss_cls_0[0.6580] det_loss_box_0[0.8871] det_loss_cls_1[0.6287] det_loss_box_1[0.7890] det_loss_cls_2[0.6224] det_loss_box_2[0.7371] det_loss_cls_3[0.6175] det_loss_box_3[0.7327] det_loss_cls_4[0.6066] det_loss_box_4[0.7194] det_loss_cls_5[0.5964] det_loss_box_5[0.7181] det_loss_cls_dn_0[0.1400] det_loss_box_dn_0[0.3803] det_loss_cls_dn_1[0.1382] det_loss_box_dn_1[0.3584] det_loss_cls_dn_2[0.1381] det_loss_box_dn_2[0.3449] det_loss_cls_dn_3[0.1373] det_loss_box_dn_3[0.3445] det_loss_cls_dn_4[0.1378] det_loss_box_dn_4[0.3440] det_loss_cls_dn_5[0.1376] det_loss_box_dn_5[0.3435] loss_depth[0.0423] total_loss[11.2998]
Rank[0/8] 06/19/2025 15:58:23 INFO stats.py:314 | Epoch[13] Step[54] GlobalStep[5124] Training Speed: 6.01 samples/sec across all devices.	Average Step Time: 1.33 sec.	Estimated Remaining Time: 1 day, 6:32:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:58:43 INFO loss_tracker.py:84 | Epoch[13/239] Step[74] GlobalStep[5144/NA]: det_loss_cls_0[0.6058] det_loss_box_0[0.8324] det_loss_cls_1[0.5923] det_loss_box_1[0.7227] det_loss_cls_2[0.5907] det_loss_box_2[0.6601] det_loss_cls_3[0.5775] det_loss_box_3[0.6430] det_loss_cls_4[0.5722] det_loss_box_4[0.6349] det_loss_cls_5[0.5622] det_loss_box_5[0.6345] det_loss_cls_dn_0[0.1209] det_loss_box_dn_0[0.3745] det_loss_cls_dn_1[0.1188] det_loss_box_dn_1[0.3465] det_loss_cls_dn_2[0.1179] det_loss_box_dn_2[0.3282] det_loss_cls_dn_3[0.1159] det_loss_box_dn_3[0.3279] det_loss_cls_dn_4[0.1157] det_loss_box_dn_4[0.3284] det_loss_cls_dn_5[0.1149] det_loss_box_dn_5[0.3280] loss_depth[0.0415] total_loss[10.4072]
Rank[0/8] 06/19/2025 15:58:49 INFO stats.py:314 | Epoch[13] Step[79] GlobalStep[5149] Training Speed: 6.26 samples/sec across all devices.	Average Step Time: 1.28 sec.	Estimated Remaining Time: 1 day, 6:30:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:59:09 INFO loss_tracker.py:84 | Epoch[13/239] Step[99] GlobalStep[5169/NA]: det_loss_cls_0[0.5841] det_loss_box_0[0.8105] det_loss_cls_1[0.5702] det_loss_box_1[0.7117] det_loss_cls_2[0.5747] det_loss_box_2[0.6581] det_loss_cls_3[0.5754] det_loss_box_3[0.6438] det_loss_cls_4[0.5689] det_loss_box_4[0.6363] det_loss_cls_5[0.5531] det_loss_box_5[0.6399] det_loss_cls_dn_0[0.1315] det_loss_box_dn_0[0.3983] det_loss_cls_dn_1[0.1290] det_loss_box_dn_1[0.3702] det_loss_cls_dn_2[0.1286] det_loss_box_dn_2[0.3547] det_loss_cls_dn_3[0.1281] det_loss_box_dn_3[0.3556] det_loss_cls_dn_4[0.1286] det_loss_box_dn_4[0.3558] det_loss_cls_dn_5[0.1293] det_loss_box_dn_5[0.3558] loss_depth[0.0422] total_loss[10.5343]
Rank[0/8] 06/19/2025 15:59:14 INFO stats.py:314 | Epoch[13] Step[104] GlobalStep[5174] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 6:28:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 15:59:35 INFO loss_tracker.py:84 | Epoch[13/239] Step[124] GlobalStep[5194/NA]: det_loss_cls_0[0.5573] det_loss_box_0[0.7535] det_loss_cls_1[0.5387] det_loss_box_1[0.6354] det_loss_cls_2[0.5453] det_loss_box_2[0.5860] det_loss_cls_3[0.5275] det_loss_box_3[0.5677] det_loss_cls_4[0.5183] det_loss_box_4[0.5612] det_loss_cls_5[0.5124] det_loss_box_5[0.5618] det_loss_cls_dn_0[0.1305] det_loss_box_dn_0[0.4026] det_loss_cls_dn_1[0.1280] det_loss_box_dn_1[0.3705] det_loss_cls_dn_2[0.1281] det_loss_box_dn_2[0.3557] det_loss_cls_dn_3[0.1273] det_loss_box_dn_3[0.3549] det_loss_cls_dn_4[0.1267] det_loss_box_dn_4[0.3547] det_loss_cls_dn_5[0.1259] det_loss_box_dn_5[0.3550] loss_depth[0.0447] total_loss[9.8695]
Rank[0/8] 06/19/2025 15:59:40 INFO stats.py:314 | Epoch[13] Step[129] GlobalStep[5199] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 6:26:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:00:01 INFO loss_tracker.py:84 | Epoch[13/239] Step[149] GlobalStep[5219/NA]: det_loss_cls_0[0.6633] det_loss_box_0[1.0083] det_loss_cls_1[0.6614] det_loss_box_1[0.8828] det_loss_cls_2[0.6453] det_loss_box_2[0.8431] det_loss_cls_3[0.6257] det_loss_box_3[0.8254] det_loss_cls_4[0.6275] det_loss_box_4[0.7976] det_loss_cls_5[0.6206] det_loss_box_5[0.7967] det_loss_cls_dn_0[0.1285] det_loss_box_dn_0[0.5446] det_loss_cls_dn_1[0.1262] det_loss_box_dn_1[0.5134] det_loss_cls_dn_2[0.1260] det_loss_box_dn_2[0.5031] det_loss_cls_dn_3[0.1255] det_loss_box_dn_3[0.5007] det_loss_cls_dn_4[0.1243] det_loss_box_dn_4[0.5015] det_loss_cls_dn_5[0.1236] det_loss_box_dn_5[0.5023] loss_depth[0.0447] total_loss[12.8618]
Rank[0/8] 06/19/2025 16:00:06 INFO stats.py:314 | Epoch[13] Step[154] GlobalStep[5224] Training Speed: 7.90 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 6:24:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:00:27 INFO loss_tracker.py:84 | Epoch[13/239] Step[174] GlobalStep[5244/NA]: det_loss_cls_0[0.5739] det_loss_box_0[0.6992] det_loss_cls_1[0.5436] det_loss_box_1[0.5980] det_loss_cls_2[0.5424] det_loss_box_2[0.5541] det_loss_cls_3[0.5343] det_loss_box_3[0.5377] det_loss_cls_4[0.5177] det_loss_box_4[0.5245] det_loss_cls_5[0.5084] det_loss_box_5[0.5173] det_loss_cls_dn_0[0.1275] det_loss_box_dn_0[0.3919] det_loss_cls_dn_1[0.1255] det_loss_box_dn_1[0.3666] det_loss_cls_dn_2[0.1250] det_loss_box_dn_2[0.3473] det_loss_cls_dn_3[0.1244] det_loss_box_dn_3[0.3454] det_loss_cls_dn_4[0.1229] det_loss_box_dn_4[0.3449] det_loss_cls_dn_5[0.1224] det_loss_box_dn_5[0.3447] loss_depth[0.0406] total_loss[9.5799]
Rank[0/8] 06/19/2025 16:00:32 INFO stats.py:314 | Epoch[13] Step[179] GlobalStep[5249] Training Speed: 12.17 samples/sec across all devices.	Average Step Time: 0.66 sec.	Estimated Remaining Time: 1 day, 6:22:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:00:53 INFO loss_tracker.py:84 | Epoch[13/239] Step[199] GlobalStep[5269/NA]: det_loss_cls_0[0.6907] det_loss_box_0[0.9039] det_loss_cls_1[0.6639] det_loss_box_1[0.7782] det_loss_cls_2[0.6772] det_loss_box_2[0.7475] det_loss_cls_3[0.6631] det_loss_box_3[0.7243] det_loss_cls_4[0.6559] det_loss_box_4[0.6959] det_loss_cls_5[0.6481] det_loss_box_5[0.6975] det_loss_cls_dn_0[0.1305] det_loss_box_dn_0[0.4982] det_loss_cls_dn_1[0.1271] det_loss_box_dn_1[0.4723] det_loss_cls_dn_2[0.1278] det_loss_box_dn_2[0.4537] det_loss_cls_dn_3[0.1275] det_loss_box_dn_3[0.4537] det_loss_cls_dn_4[0.1273] det_loss_box_dn_4[0.4538] det_loss_cls_dn_5[0.1271] det_loss_box_dn_5[0.4541] loss_depth[0.0397] total_loss[12.1392]
Rank[0/8] 06/19/2025 16:00:58 INFO stats.py:314 | Epoch[13] Step[204] GlobalStep[5274] Training Speed: 8.08 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 6:20:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:01:20 INFO loss_tracker.py:84 | Epoch[13/239] Step[224] GlobalStep[5294/NA]: det_loss_cls_0[0.6340] det_loss_box_0[0.9301] det_loss_cls_1[0.6142] det_loss_box_1[0.7998] det_loss_cls_2[0.6253] det_loss_box_2[0.7467] det_loss_cls_3[0.6064] det_loss_box_3[0.7376] det_loss_cls_4[0.5951] det_loss_box_4[0.7230] det_loss_cls_5[0.5888] det_loss_box_5[0.7211] det_loss_cls_dn_0[0.1314] det_loss_box_dn_0[0.4273] det_loss_cls_dn_1[0.1285] det_loss_box_dn_1[0.3998] det_loss_cls_dn_2[0.1287] det_loss_box_dn_2[0.3820] det_loss_cls_dn_3[0.1273] det_loss_box_dn_3[0.3816] det_loss_cls_dn_4[0.1283] det_loss_box_dn_4[0.3811] det_loss_cls_dn_5[0.1281] det_loss_box_dn_5[0.3808] loss_depth[0.0449] total_loss[11.4921]
Rank[0/8] 06/19/2025 16:01:25 INFO stats.py:314 | Epoch[13] Step[229] GlobalStep[5299] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 6:18:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:01:46 INFO loss_tracker.py:84 | Epoch[13/239] Step[249] GlobalStep[5319/NA]: det_loss_cls_0[0.6499] det_loss_box_0[0.9421] det_loss_cls_1[0.6269] det_loss_box_1[0.8078] det_loss_cls_2[0.6304] det_loss_box_2[0.7600] det_loss_cls_3[0.6257] det_loss_box_3[0.7400] det_loss_cls_4[0.6073] det_loss_box_4[0.7387] det_loss_cls_5[0.6053] det_loss_box_5[0.7387] det_loss_cls_dn_0[0.1434] det_loss_box_dn_0[0.4256] det_loss_cls_dn_1[0.1392] det_loss_box_dn_1[0.4000] det_loss_cls_dn_2[0.1384] det_loss_box_dn_2[0.3837] det_loss_cls_dn_3[0.1376] det_loss_box_dn_3[0.3838] det_loss_cls_dn_4[0.1374] det_loss_box_dn_4[0.3843] det_loss_cls_dn_5[0.1380] det_loss_box_dn_5[0.3841] loss_depth[0.0449] total_loss[11.7132]
Rank[0/8] 06/19/2025 16:01:51 INFO stats.py:314 | Epoch[13] Step[254] GlobalStep[5324] Training Speed: 7.54 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 1 day, 6:16:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:02:12 INFO loss_tracker.py:84 | Epoch[13/239] Step[274] GlobalStep[5344/NA]: det_loss_cls_0[0.7533] det_loss_box_0[1.1011] det_loss_cls_1[0.7155] det_loss_box_1[0.9571] det_loss_cls_2[0.7261] det_loss_box_2[0.8906] det_loss_cls_3[0.7240] det_loss_box_3[0.8811] det_loss_cls_4[0.7122] det_loss_box_4[0.8710] det_loss_cls_5[0.7012] det_loss_box_5[0.8700] det_loss_cls_dn_0[0.1408] det_loss_box_dn_0[0.4152] det_loss_cls_dn_1[0.1386] det_loss_box_dn_1[0.3893] det_loss_cls_dn_2[0.1378] det_loss_box_dn_2[0.3790] det_loss_cls_dn_3[0.1374] det_loss_box_dn_3[0.3783] det_loss_cls_dn_4[0.1371] det_loss_box_dn_4[0.3783] det_loss_cls_dn_5[0.1373] det_loss_box_dn_5[0.3782] loss_depth[0.0454] total_loss[13.0961]
Rank[0/8] 06/19/2025 16:02:17 INFO stats.py:314 | Epoch[13] Step[279] GlobalStep[5349] Training Speed: 8.56 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 6:15:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:02:38 INFO loss_tracker.py:84 | Epoch[13/239] Step[299] GlobalStep[5369/NA]: det_loss_cls_0[0.6836] det_loss_box_0[0.8968] det_loss_cls_1[0.6533] det_loss_box_1[0.7854] det_loss_cls_2[0.6488] det_loss_box_2[0.7485] det_loss_cls_3[0.6442] det_loss_box_3[0.7332] det_loss_cls_4[0.6442] det_loss_box_4[0.7071] det_loss_cls_5[0.6322] det_loss_box_5[0.7084] det_loss_cls_dn_0[0.1309] det_loss_box_dn_0[0.3978] det_loss_cls_dn_1[0.1262] det_loss_box_dn_1[0.3707] det_loss_cls_dn_2[0.1269] det_loss_box_dn_2[0.3593] det_loss_cls_dn_3[0.1263] det_loss_box_dn_3[0.3588] det_loss_cls_dn_4[0.1273] det_loss_box_dn_4[0.3592] det_loss_cls_dn_5[0.1275] det_loss_box_dn_5[0.3593] loss_depth[0.0413] total_loss[11.4972]
Rank[0/8] 06/19/2025 16:02:43 INFO stats.py:314 | Epoch[13] Step[304] GlobalStep[5374] Training Speed: 7.85 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1 day, 6:13:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:03:04 INFO loss_tracker.py:84 | Epoch[13/239] Step[324] GlobalStep[5394/NA]: det_loss_cls_0[0.5046] det_loss_box_0[0.6362] det_loss_cls_1[0.4842] det_loss_box_1[0.5546] det_loss_cls_2[0.4865] det_loss_box_2[0.5190] det_loss_cls_3[0.4799] det_loss_box_3[0.5028] det_loss_cls_4[0.4743] det_loss_box_4[0.4901] det_loss_cls_5[0.4569] det_loss_box_5[0.4957] det_loss_cls_dn_0[0.1242] det_loss_box_dn_0[0.3792] det_loss_cls_dn_1[0.1210] det_loss_box_dn_1[0.3541] det_loss_cls_dn_2[0.1219] det_loss_box_dn_2[0.3370] det_loss_cls_dn_3[0.1212] det_loss_box_dn_3[0.3370] det_loss_cls_dn_4[0.1217] det_loss_box_dn_4[0.3371] det_loss_cls_dn_5[0.1207] det_loss_box_dn_5[0.3373] loss_depth[0.0428] total_loss[8.9400]
Rank[0/8] 06/19/2025 16:03:09 INFO stats.py:314 | Epoch[13] Step[329] GlobalStep[5399] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 6:11:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:03:31 INFO loss_tracker.py:84 | Epoch[13/239] Step[349] GlobalStep[5419/NA]: det_loss_cls_0[0.5534] det_loss_box_0[0.9401] det_loss_cls_1[0.5205] det_loss_box_1[0.8425] det_loss_cls_2[0.5304] det_loss_box_2[0.8080] det_loss_cls_3[0.5254] det_loss_box_3[0.7887] det_loss_cls_4[0.5019] det_loss_box_4[0.7590] det_loss_cls_5[0.4920] det_loss_box_5[0.7618] det_loss_cls_dn_0[0.1251] det_loss_box_dn_0[0.5815] det_loss_cls_dn_1[0.1227] det_loss_box_dn_1[0.5531] det_loss_cls_dn_2[0.1230] det_loss_box_dn_2[0.5360] det_loss_cls_dn_3[0.1216] det_loss_box_dn_3[0.5361] det_loss_cls_dn_4[0.1209] det_loss_box_dn_4[0.5361] det_loss_cls_dn_5[0.1210] det_loss_box_dn_5[0.5374] loss_depth[0.0420] total_loss[12.0802]
Rank[0/8] 06/19/2025 16:03:36 INFO stats.py:314 | Epoch[13] Step[354] GlobalStep[5424] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 6:09:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:03:57 INFO loss_tracker.py:84 | Epoch[13/239] Step[374] GlobalStep[5444/NA]: det_loss_cls_0[0.7872] det_loss_box_0[1.0342] det_loss_cls_1[0.7608] det_loss_box_1[0.8913] det_loss_cls_2[0.7644] det_loss_box_2[0.8301] det_loss_cls_3[0.7589] det_loss_box_3[0.8043] det_loss_cls_4[0.7390] det_loss_box_4[0.7931] det_loss_cls_5[0.7256] det_loss_box_5[0.7954] det_loss_cls_dn_0[0.1418] det_loss_box_dn_0[0.3409] det_loss_cls_dn_1[0.1400] det_loss_box_dn_1[0.3211] det_loss_cls_dn_2[0.1403] det_loss_box_dn_2[0.3107] det_loss_cls_dn_3[0.1393] det_loss_box_dn_3[0.3106] det_loss_cls_dn_4[0.1385] det_loss_box_dn_4[0.3110] det_loss_cls_dn_5[0.1386] det_loss_box_dn_5[0.3108] loss_depth[0.0404] total_loss[12.4682]
Rank[0/8] 06/19/2025 16:04:02 INFO stats.py:314 | Epoch[13] Step[379] GlobalStep[5449] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 6:07:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:04:12 INFO stats.py:394 | Epoch[13] completed. Training Speed: 7.61 samples/sec across all devices.	Epoch Time: 409.92 sec.	Average Epoch Time: 409.92 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 6:07:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:04:29 INFO stats.py:314 | Epoch[14] Step[14] GlobalStep[5474] Training Speed: 8.04 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 6:06:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:04:39 INFO loss_tracker.py:84 | Epoch[14/239] Step[24] GlobalStep[5484/NA]: det_loss_cls_0[0.5236] det_loss_box_0[0.6656] det_loss_cls_1[0.5210] det_loss_box_1[0.6014] det_loss_cls_2[0.5191] det_loss_box_2[0.5633] det_loss_cls_3[0.5051] det_loss_box_3[0.5488] det_loss_cls_4[0.4987] det_loss_box_4[0.5325] det_loss_cls_5[0.4884] det_loss_box_5[0.5352] det_loss_cls_dn_0[0.1321] det_loss_box_dn_0[0.4055] det_loss_cls_dn_1[0.1283] det_loss_box_dn_1[0.3744] det_loss_cls_dn_2[0.1282] det_loss_box_dn_2[0.3598] det_loss_cls_dn_3[0.1274] det_loss_box_dn_3[0.3591] det_loss_cls_dn_4[0.1273] det_loss_box_dn_4[0.3594] det_loss_cls_dn_5[0.1276] det_loss_box_dn_5[0.3593] loss_depth[0.0390] total_loss[9.5303]
Rank[0/8] 06/19/2025 16:04:56 INFO stats.py:314 | Epoch[14] Step[39] GlobalStep[5499] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 6:04:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:05:06 INFO loss_tracker.py:84 | Epoch[14/239] Step[49] GlobalStep[5509/NA]: det_loss_cls_0[0.5717] det_loss_box_0[0.7216] det_loss_cls_1[0.5594] det_loss_box_1[0.6191] det_loss_cls_2[0.5605] det_loss_box_2[0.5739] det_loss_cls_3[0.5500] det_loss_box_3[0.5526] det_loss_cls_4[0.5360] det_loss_box_4[0.5453] det_loss_cls_5[0.5283] det_loss_box_5[0.5489] det_loss_cls_dn_0[0.1367] det_loss_box_dn_0[0.4075] det_loss_cls_dn_1[0.1332] det_loss_box_dn_1[0.3838] det_loss_cls_dn_2[0.1321] det_loss_box_dn_2[0.3657] det_loss_cls_dn_3[0.1307] det_loss_box_dn_3[0.3661] det_loss_cls_dn_4[0.1292] det_loss_box_dn_4[0.3664] det_loss_cls_dn_5[0.1307] det_loss_box_dn_5[0.3662] loss_depth[0.0415] total_loss[9.9571]
Rank[0/8] 06/19/2025 16:05:22 INFO stats.py:314 | Epoch[14] Step[64] GlobalStep[5524] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 6:03:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:05:32 INFO loss_tracker.py:84 | Epoch[14/239] Step[74] GlobalStep[5534/NA]: det_loss_cls_0[0.7088] det_loss_box_0[0.9340] det_loss_cls_1[0.6737] det_loss_box_1[0.8297] det_loss_cls_2[0.6797] det_loss_box_2[0.7861] det_loss_cls_3[0.6728] det_loss_box_3[0.7600] det_loss_cls_4[0.6573] det_loss_box_4[0.7491] det_loss_cls_5[0.6476] det_loss_box_5[0.7477] det_loss_cls_dn_0[0.1370] det_loss_box_dn_0[0.3539] det_loss_cls_dn_1[0.1332] det_loss_box_dn_1[0.3337] det_loss_cls_dn_2[0.1330] det_loss_box_dn_2[0.3167] det_loss_cls_dn_3[0.1312] det_loss_box_dn_3[0.3170] det_loss_cls_dn_4[0.1321] det_loss_box_dn_4[0.3169] det_loss_cls_dn_5[0.1312] det_loss_box_dn_5[0.3172] loss_depth[0.0391] total_loss[11.6386]
Rank[0/8] 06/19/2025 16:05:47 INFO stats.py:314 | Epoch[14] Step[89] GlobalStep[5549] Training Speed: 7.62 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 6:01:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:05:58 INFO loss_tracker.py:84 | Epoch[14/239] Step[99] GlobalStep[5559/NA]: det_loss_cls_0[0.6988] det_loss_box_0[0.9773] det_loss_cls_1[0.6795] det_loss_box_1[0.8458] det_loss_cls_2[0.6771] det_loss_box_2[0.8032] det_loss_cls_3[0.6615] det_loss_box_3[0.7797] det_loss_cls_4[0.6547] det_loss_box_4[0.7626] det_loss_cls_5[0.6402] det_loss_box_5[0.7618] det_loss_cls_dn_0[0.1345] det_loss_box_dn_0[0.3834] det_loss_cls_dn_1[0.1299] det_loss_box_dn_1[0.3527] det_loss_cls_dn_2[0.1305] det_loss_box_dn_2[0.3367] det_loss_cls_dn_3[0.1286] det_loss_box_dn_3[0.3360] det_loss_cls_dn_4[0.1291] det_loss_box_dn_4[0.3355] det_loss_cls_dn_5[0.1286] det_loss_box_dn_5[0.3357] loss_depth[0.0416] total_loss[11.8449]
Rank[0/8] 06/19/2025 16:06:14 INFO stats.py:314 | Epoch[14] Step[114] GlobalStep[5574] Training Speed: 8.34 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 5:59:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:06:24 INFO loss_tracker.py:84 | Epoch[14/239] Step[124] GlobalStep[5584/NA]: det_loss_cls_0[0.6947] det_loss_box_0[0.8931] det_loss_cls_1[0.6582] det_loss_box_1[0.7784] det_loss_cls_2[0.6469] det_loss_box_2[0.7411] det_loss_cls_3[0.6433] det_loss_box_3[0.7188] det_loss_cls_4[0.6285] det_loss_box_4[0.7075] det_loss_cls_5[0.6129] det_loss_box_5[0.7134] det_loss_cls_dn_0[0.1346] det_loss_box_dn_0[0.4485] det_loss_cls_dn_1[0.1324] det_loss_box_dn_1[0.4242] det_loss_cls_dn_2[0.1310] det_loss_box_dn_2[0.4075] det_loss_cls_dn_3[0.1302] det_loss_box_dn_3[0.4067] det_loss_cls_dn_4[0.1291] det_loss_box_dn_4[0.4066] det_loss_cls_dn_5[0.1286] det_loss_box_dn_5[0.4066] loss_depth[0.0422] total_loss[11.7651]
Rank[0/8] 06/19/2025 16:06:40 INFO stats.py:314 | Epoch[14] Step[139] GlobalStep[5599] Training Speed: 8.77 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 1 day, 5:57:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:06:51 INFO loss_tracker.py:84 | Epoch[14/239] Step[149] GlobalStep[5609/NA]: det_loss_cls_0[0.6296] det_loss_box_0[0.8184] det_loss_cls_1[0.5905] det_loss_box_1[0.7245] det_loss_cls_2[0.5944] det_loss_box_2[0.6774] det_loss_cls_3[0.5852] det_loss_box_3[0.6547] det_loss_cls_4[0.5693] det_loss_box_4[0.6354] det_loss_cls_5[0.5605] det_loss_box_5[0.6401] det_loss_cls_dn_0[0.1243] det_loss_box_dn_0[0.3413] det_loss_cls_dn_1[0.1228] det_loss_box_dn_1[0.3166] det_loss_cls_dn_2[0.1210] det_loss_box_dn_2[0.3035] det_loss_cls_dn_3[0.1184] det_loss_box_dn_3[0.3033] det_loss_cls_dn_4[0.1179] det_loss_box_dn_4[0.3039] det_loss_cls_dn_5[0.1177] det_loss_box_dn_5[0.3039] loss_depth[0.0415] total_loss[10.3159]
Rank[0/8] 06/19/2025 16:07:06 INFO stats.py:314 | Epoch[14] Step[164] GlobalStep[5624] Training Speed: 12.74 samples/sec across all devices.	Average Step Time: 0.63 sec.	Estimated Remaining Time: 1 day, 5:56:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:07:17 INFO loss_tracker.py:84 | Epoch[14/239] Step[174] GlobalStep[5634/NA]: det_loss_cls_0[0.7425] det_loss_box_0[1.0497] det_loss_cls_1[0.7191] det_loss_box_1[0.9108] det_loss_cls_2[0.7200] det_loss_box_2[0.8706] det_loss_cls_3[0.7144] det_loss_box_3[0.8472] det_loss_cls_4[0.7108] det_loss_box_4[0.8229] det_loss_cls_5[0.6997] det_loss_box_5[0.8272] det_loss_cls_dn_0[0.1272] det_loss_box_dn_0[0.4383] det_loss_cls_dn_1[0.1257] det_loss_box_dn_1[0.4117] det_loss_cls_dn_2[0.1254] det_loss_box_dn_2[0.3955] det_loss_cls_dn_3[0.1241] det_loss_box_dn_3[0.3962] det_loss_cls_dn_4[0.1237] det_loss_box_dn_4[0.3954] det_loss_cls_dn_5[0.1238] det_loss_box_dn_5[0.3954] loss_depth[0.0419] total_loss[12.8593]
Rank[0/8] 06/19/2025 16:07:32 INFO stats.py:314 | Epoch[14] Step[189] GlobalStep[5649] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 5:54:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:07:42 INFO loss_tracker.py:84 | Epoch[14/239] Step[199] GlobalStep[5659/NA]: det_loss_cls_0[0.5999] det_loss_box_0[0.8626] det_loss_cls_1[0.5865] det_loss_box_1[0.7348] det_loss_cls_2[0.5876] det_loss_box_2[0.6820] det_loss_cls_3[0.5826] det_loss_box_3[0.6673] det_loss_cls_4[0.5714] det_loss_box_4[0.6610] det_loss_cls_5[0.5600] det_loss_box_5[0.6602] det_loss_cls_dn_0[0.1328] det_loss_box_dn_0[0.3765] det_loss_cls_dn_1[0.1310] det_loss_box_dn_1[0.3560] det_loss_cls_dn_2[0.1300] det_loss_box_dn_2[0.3436] det_loss_cls_dn_3[0.1289] det_loss_box_dn_3[0.3437] det_loss_cls_dn_4[0.1287] det_loss_box_dn_4[0.3437] det_loss_cls_dn_5[0.1285] det_loss_box_dn_5[0.3435] loss_depth[0.0432] total_loss[10.6859]
Rank[0/8] 06/19/2025 16:07:58 INFO stats.py:314 | Epoch[14] Step[214] GlobalStep[5674] Training Speed: 8.58 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 5:52:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:08:09 INFO loss_tracker.py:84 | Epoch[14/239] Step[224] GlobalStep[5684/NA]: det_loss_cls_0[0.7717] det_loss_box_0[1.1099] det_loss_cls_1[0.7572] det_loss_box_1[0.9758] det_loss_cls_2[0.7545] det_loss_box_2[0.9268] det_loss_cls_3[0.7491] det_loss_box_3[0.9094] det_loss_cls_4[0.7413] det_loss_box_4[0.8857] det_loss_cls_5[0.7313] det_loss_box_5[0.8869] det_loss_cls_dn_0[0.1438] det_loss_box_dn_0[0.4065] det_loss_cls_dn_1[0.1412] det_loss_box_dn_1[0.3868] det_loss_cls_dn_2[0.1408] det_loss_box_dn_2[0.3725] det_loss_cls_dn_3[0.1410] det_loss_box_dn_3[0.3719] det_loss_cls_dn_4[0.1404] det_loss_box_dn_4[0.3726] det_loss_cls_dn_5[0.1403] det_loss_box_dn_5[0.3724] loss_depth[0.0474] total_loss[13.3772]
Rank[0/8] 06/19/2025 16:08:24 INFO stats.py:314 | Epoch[14] Step[239] GlobalStep[5699] Training Speed: 7.93 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 5:51:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:08:34 INFO loss_tracker.py:84 | Epoch[14/239] Step[249] GlobalStep[5709/NA]: det_loss_cls_0[0.5483] det_loss_box_0[0.7148] det_loss_cls_1[0.5314] det_loss_box_1[0.6013] det_loss_cls_2[0.5222] det_loss_box_2[0.5641] det_loss_cls_3[0.5160] det_loss_box_3[0.5443] det_loss_cls_4[0.4906] det_loss_box_4[0.5490] det_loss_cls_5[0.4852] det_loss_box_5[0.5462] det_loss_cls_dn_0[0.1263] det_loss_box_dn_0[0.3560] det_loss_cls_dn_1[0.1225] det_loss_box_dn_1[0.3307] det_loss_cls_dn_2[0.1222] det_loss_box_dn_2[0.3151] det_loss_cls_dn_3[0.1216] det_loss_box_dn_3[0.3151] det_loss_cls_dn_4[0.1210] det_loss_box_dn_4[0.3153] det_loss_cls_dn_5[0.1209] det_loss_box_dn_5[0.3146] loss_depth[0.0422] total_loss[9.3371]
Rank[0/8] 06/19/2025 16:08:50 INFO stats.py:314 | Epoch[14] Step[264] GlobalStep[5724] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 5:49:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:09:00 INFO loss_tracker.py:84 | Epoch[14/239] Step[274] GlobalStep[5734/NA]: det_loss_cls_0[0.5488] det_loss_box_0[0.7670] det_loss_cls_1[0.5311] det_loss_box_1[0.6759] det_loss_cls_2[0.5380] det_loss_box_2[0.6210] det_loss_cls_3[0.5269] det_loss_box_3[0.6069] det_loss_cls_4[0.5078] det_loss_box_4[0.5993] det_loss_cls_5[0.5024] det_loss_box_5[0.5980] det_loss_cls_dn_0[0.1293] det_loss_box_dn_0[0.4081] det_loss_cls_dn_1[0.1267] det_loss_box_dn_1[0.3809] det_loss_cls_dn_2[0.1271] det_loss_box_dn_2[0.3654] det_loss_cls_dn_3[0.1262] det_loss_box_dn_3[0.3645] det_loss_cls_dn_4[0.1263] det_loss_box_dn_4[0.3649] det_loss_cls_dn_5[0.1258] det_loss_box_dn_5[0.3647] loss_depth[0.0453] total_loss[10.0782]
Rank[0/8] 06/19/2025 16:09:16 INFO stats.py:314 | Epoch[14] Step[289] GlobalStep[5749] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 5:47:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:09:27 INFO loss_tracker.py:84 | Epoch[14/239] Step[299] GlobalStep[5759/NA]: det_loss_cls_0[0.6955] det_loss_box_0[0.9251] det_loss_cls_1[0.6778] det_loss_box_1[0.7753] det_loss_cls_2[0.6735] det_loss_box_2[0.7245] det_loss_cls_3[0.6626] det_loss_box_3[0.7044] det_loss_cls_4[0.6396] det_loss_box_4[0.6975] det_loss_cls_5[0.6279] det_loss_box_5[0.7008] det_loss_cls_dn_0[0.1284] det_loss_box_dn_0[0.3936] det_loss_cls_dn_1[0.1253] det_loss_box_dn_1[0.3622] det_loss_cls_dn_2[0.1252] det_loss_box_dn_2[0.3406] det_loss_cls_dn_3[0.1238] det_loss_box_dn_3[0.3396] det_loss_cls_dn_4[0.1240] det_loss_box_dn_4[0.3389] det_loss_cls_dn_5[0.1234] det_loss_box_dn_5[0.3393] loss_depth[0.0425] total_loss[11.4113]
Rank[0/8] 06/19/2025 16:09:43 INFO stats.py:314 | Epoch[14] Step[314] GlobalStep[5774] Training Speed: 8.03 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 5:46:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:09:53 INFO loss_tracker.py:84 | Epoch[14/239] Step[324] GlobalStep[5784/NA]: det_loss_cls_0[0.4956] det_loss_box_0[0.6194] det_loss_cls_1[0.4794] det_loss_box_1[0.5397] det_loss_cls_2[0.4747] det_loss_box_2[0.5031] det_loss_cls_3[0.4645] det_loss_box_3[0.4981] det_loss_cls_4[0.4485] det_loss_box_4[0.4924] det_loss_cls_5[0.4416] det_loss_box_5[0.4943] det_loss_cls_dn_0[0.1194] det_loss_box_dn_0[0.3684] det_loss_cls_dn_1[0.1179] det_loss_box_dn_1[0.3434] det_loss_cls_dn_2[0.1184] det_loss_box_dn_2[0.3203] det_loss_cls_dn_3[0.1177] det_loss_box_dn_3[0.3194] det_loss_cls_dn_4[0.1177] det_loss_box_dn_4[0.3187] det_loss_cls_dn_5[0.1170] det_loss_box_dn_5[0.3187] loss_depth[0.0390] total_loss[8.6872]
Rank[0/8] 06/19/2025 16:10:09 INFO stats.py:314 | Epoch[14] Step[339] GlobalStep[5799] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 5:44:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:10:19 INFO loss_tracker.py:84 | Epoch[14/239] Step[349] GlobalStep[5809/NA]: det_loss_cls_0[0.6431] det_loss_box_0[0.8634] det_loss_cls_1[0.6134] det_loss_box_1[0.7534] det_loss_cls_2[0.6262] det_loss_box_2[0.7082] det_loss_cls_3[0.6105] det_loss_box_3[0.6931] det_loss_cls_4[0.6040] det_loss_box_4[0.6795] det_loss_cls_5[0.5890] det_loss_box_5[0.6833] det_loss_cls_dn_0[0.1394] det_loss_box_dn_0[0.4187] det_loss_cls_dn_1[0.1368] det_loss_box_dn_1[0.3954] det_loss_cls_dn_2[0.1372] det_loss_box_dn_2[0.3729] det_loss_cls_dn_3[0.1365] det_loss_box_dn_3[0.3729] det_loss_cls_dn_4[0.1349] det_loss_box_dn_4[0.3725] det_loss_cls_dn_5[0.1341] det_loss_box_dn_5[0.3722] loss_depth[0.0427] total_loss[11.2331]
Rank[0/8] 06/19/2025 16:10:35 INFO stats.py:314 | Epoch[14] Step[364] GlobalStep[5824] Training Speed: 7.45 samples/sec across all devices.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 1 day, 5:43:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:10:45 INFO loss_tracker.py:84 | Epoch[14/239] Step[374] GlobalStep[5834/NA]: det_loss_cls_0[0.6151] det_loss_box_0[0.7616] det_loss_cls_1[0.5924] det_loss_box_1[0.6476] det_loss_cls_2[0.5819] det_loss_box_2[0.6056] det_loss_cls_3[0.5635] det_loss_box_3[0.5946] det_loss_cls_4[0.5536] det_loss_box_4[0.5806] det_loss_cls_5[0.5442] det_loss_box_5[0.5824] det_loss_cls_dn_0[0.1351] det_loss_box_dn_0[0.3674] det_loss_cls_dn_1[0.1314] det_loss_box_dn_1[0.3358] det_loss_cls_dn_2[0.1320] det_loss_box_dn_2[0.3209] det_loss_cls_dn_3[0.1302] det_loss_box_dn_3[0.3199] det_loss_cls_dn_4[0.1299] det_loss_box_dn_4[0.3201] det_loss_cls_dn_5[0.1300] det_loss_box_dn_5[0.3200] loss_depth[0.0432] total_loss[10.0391]
Rank[0/8] 06/19/2025 16:11:01 INFO stats.py:314 | Epoch[14] Step[389] GlobalStep[5849] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 5:41:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:11:01 INFO stats.py:394 | Epoch[14] completed. Training Speed: 7.64 samples/sec across all devices.	Epoch Time: 408.48 sec.	Average Epoch Time: 408.48 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 5:41:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:11:32 INFO stats.py:314 | Epoch[15] Step[24] GlobalStep[5874] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 5:41:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:11:32 INFO loss_tracker.py:84 | Epoch[15/239] Step[24] GlobalStep[5874/NA]: det_loss_cls_0[0.6499] det_loss_box_0[0.8570] det_loss_cls_1[0.6252] det_loss_box_1[0.7402] det_loss_cls_2[0.6244] det_loss_box_2[0.6823] det_loss_cls_3[0.6170] det_loss_box_3[0.6657] det_loss_cls_4[0.6019] det_loss_box_4[0.6575] det_loss_cls_5[0.5884] det_loss_box_5[0.6558] det_loss_cls_dn_0[0.1292] det_loss_box_dn_0[0.3495] det_loss_cls_dn_1[0.1263] det_loss_box_dn_1[0.3273] det_loss_cls_dn_2[0.1268] det_loss_box_dn_2[0.3094] det_loss_cls_dn_3[0.1258] det_loss_box_dn_3[0.3090] det_loss_cls_dn_4[0.1262] det_loss_box_dn_4[0.3084] det_loss_cls_dn_5[0.1262] det_loss_box_dn_5[0.3080] loss_depth[0.0430] total_loss[10.6805]
Rank[0/8] 06/19/2025 16:11:58 INFO stats.py:314 | Epoch[15] Step[49] GlobalStep[5899] Training Speed: 6.95 samples/sec across all devices.	Average Step Time: 1.15 sec.	Estimated Remaining Time: 1 day, 5:39:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:11:58 INFO loss_tracker.py:84 | Epoch[15/239] Step[49] GlobalStep[5899/NA]: det_loss_cls_0[0.5596] det_loss_box_0[0.7079] det_loss_cls_1[0.5602] det_loss_box_1[0.6107] det_loss_cls_2[0.5582] det_loss_box_2[0.5804] det_loss_cls_3[0.5488] det_loss_box_3[0.5713] det_loss_cls_4[0.5368] det_loss_box_4[0.5619] det_loss_cls_5[0.5237] det_loss_box_5[0.5640] det_loss_cls_dn_0[0.1432] det_loss_box_dn_0[0.4063] det_loss_cls_dn_1[0.1393] det_loss_box_dn_1[0.3814] det_loss_cls_dn_2[0.1394] det_loss_box_dn_2[0.3630] det_loss_cls_dn_3[0.1383] det_loss_box_dn_3[0.3622] det_loss_cls_dn_4[0.1376] det_loss_box_dn_4[0.3622] det_loss_cls_dn_5[0.1376] det_loss_box_dn_5[0.3620] loss_depth[0.0388] total_loss[9.9948]
Rank[0/8] 06/19/2025 16:12:24 INFO stats.py:314 | Epoch[15] Step[74] GlobalStep[5924] Training Speed: 8.01 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 5:37:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:12:24 INFO loss_tracker.py:84 | Epoch[15/239] Step[74] GlobalStep[5924/NA]: det_loss_cls_0[0.6193] det_loss_box_0[0.8036] det_loss_cls_1[0.5945] det_loss_box_1[0.7147] det_loss_cls_2[0.5914] det_loss_box_2[0.6694] det_loss_cls_3[0.5909] det_loss_box_3[0.6540] det_loss_cls_4[0.5783] det_loss_box_4[0.6363] det_loss_cls_5[0.5631] det_loss_box_5[0.6431] det_loss_cls_dn_0[0.1299] det_loss_box_dn_0[0.3418] det_loss_cls_dn_1[0.1264] det_loss_box_dn_1[0.3176] det_loss_cls_dn_2[0.1254] det_loss_box_dn_2[0.2961] det_loss_cls_dn_3[0.1242] det_loss_box_dn_3[0.2957] det_loss_cls_dn_4[0.1247] det_loss_box_dn_4[0.2951] det_loss_cls_dn_5[0.1251] det_loss_box_dn_5[0.2946] loss_depth[0.0418] total_loss[10.2971]
Rank[0/8] 06/19/2025 16:12:50 INFO stats.py:314 | Epoch[15] Step[99] GlobalStep[5949] Training Speed: 8.72 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 1 day, 5:36:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:12:50 INFO loss_tracker.py:84 | Epoch[15/239] Step[99] GlobalStep[5949/NA]: det_loss_cls_0[0.6479] det_loss_box_0[0.9050] det_loss_cls_1[0.6200] det_loss_box_1[0.8070] det_loss_cls_2[0.6295] det_loss_box_2[0.7501] det_loss_cls_3[0.6062] det_loss_box_3[0.7226] det_loss_cls_4[0.6006] det_loss_box_4[0.7088] det_loss_cls_5[0.5901] det_loss_box_5[0.7167] det_loss_cls_dn_0[0.1343] det_loss_box_dn_0[0.3842] det_loss_cls_dn_1[0.1318] det_loss_box_dn_1[0.3535] det_loss_cls_dn_2[0.1319] det_loss_box_dn_2[0.3330] det_loss_cls_dn_3[0.1307] det_loss_box_dn_3[0.3323] det_loss_cls_dn_4[0.1303] det_loss_box_dn_4[0.3320] det_loss_cls_dn_5[0.1309] det_loss_box_dn_5[0.3317] loss_depth[0.0429] total_loss[11.2040]
Rank[0/8] 06/19/2025 16:13:17 INFO stats.py:314 | Epoch[15] Step[124] GlobalStep[5974] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 5:34:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:13:17 INFO loss_tracker.py:84 | Epoch[15/239] Step[124] GlobalStep[5974/NA]: det_loss_cls_0[0.4322] det_loss_box_0[0.6131] det_loss_cls_1[0.4170] det_loss_box_1[0.5194] det_loss_cls_2[0.4222] det_loss_box_2[0.4848] det_loss_cls_3[0.4214] det_loss_box_3[0.4842] det_loss_cls_4[0.4167] det_loss_box_4[0.4645] det_loss_cls_5[0.4005] det_loss_box_5[0.4715] det_loss_cls_dn_0[0.1119] det_loss_box_dn_0[0.4184] det_loss_cls_dn_1[0.1084] det_loss_box_dn_1[0.3850] det_loss_cls_dn_2[0.1077] det_loss_box_dn_2[0.3674] det_loss_cls_dn_3[0.1075] det_loss_box_dn_3[0.3662] det_loss_cls_dn_4[0.1074] det_loss_box_dn_4[0.3644] det_loss_cls_dn_5[0.1071] det_loss_box_dn_5[0.3642] loss_depth[0.0419] total_loss[8.5049]
Rank[0/8] 06/19/2025 16:13:43 INFO stats.py:314 | Epoch[15] Step[149] GlobalStep[5999] Training Speed: 8.17 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 5:33:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:13:43 INFO loss_tracker.py:84 | Epoch[15/239] Step[149] GlobalStep[5999/NA]: det_loss_cls_0[0.6549] det_loss_box_0[0.8304] det_loss_cls_1[0.6229] det_loss_box_1[0.7123] det_loss_cls_2[0.6271] det_loss_box_2[0.6771] det_loss_cls_3[0.6207] det_loss_box_3[0.6523] det_loss_cls_4[0.6008] det_loss_box_4[0.6440] det_loss_cls_5[0.5916] det_loss_box_5[0.6440] det_loss_cls_dn_0[0.1241] det_loss_box_dn_0[0.4112] det_loss_cls_dn_1[0.1198] det_loss_box_dn_1[0.3885] det_loss_cls_dn_2[0.1210] det_loss_box_dn_2[0.3717] det_loss_cls_dn_3[0.1186] det_loss_box_dn_3[0.3727] det_loss_cls_dn_4[0.1185] det_loss_box_dn_4[0.3730] det_loss_cls_dn_5[0.1199] det_loss_box_dn_5[0.3728] loss_depth[0.0447] total_loss[10.9345]
Rank[0/8] 06/19/2025 16:14:09 INFO stats.py:314 | Epoch[15] Step[174] GlobalStep[6024] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 5:31:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:14:09 INFO loss_tracker.py:84 | Epoch[15/239] Step[174] GlobalStep[6024/NA]: det_loss_cls_0[0.5889] det_loss_box_0[0.7906] det_loss_cls_1[0.5708] det_loss_box_1[0.6948] det_loss_cls_2[0.5733] det_loss_box_2[0.6548] det_loss_cls_3[0.5684] det_loss_box_3[0.6265] det_loss_cls_4[0.5505] det_loss_box_4[0.6166] det_loss_cls_5[0.5406] det_loss_box_5[0.6187] det_loss_cls_dn_0[0.1338] det_loss_box_dn_0[0.3684] det_loss_cls_dn_1[0.1322] det_loss_box_dn_1[0.3400] det_loss_cls_dn_2[0.1315] det_loss_box_dn_2[0.3249] det_loss_cls_dn_3[0.1306] det_loss_box_dn_3[0.3247] det_loss_cls_dn_4[0.1295] det_loss_box_dn_4[0.3255] det_loss_cls_dn_5[0.1288] det_loss_box_dn_5[0.3249] loss_depth[0.0406] total_loss[10.2301]
Rank[0/8] 06/19/2025 16:14:35 INFO stats.py:314 | Epoch[15] Step[199] GlobalStep[6049] Training Speed: 8.63 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 5:30:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:14:35 INFO loss_tracker.py:84 | Epoch[15/239] Step[199] GlobalStep[6049/NA]: det_loss_cls_0[0.6741] det_loss_box_0[0.8373] det_loss_cls_1[0.6601] det_loss_box_1[0.7161] det_loss_cls_2[0.6484] det_loss_box_2[0.6755] det_loss_cls_3[0.6471] det_loss_box_3[0.6468] det_loss_cls_4[0.6293] det_loss_box_4[0.6376] det_loss_cls_5[0.6234] det_loss_box_5[0.6348] det_loss_cls_dn_0[0.1326] det_loss_box_dn_0[0.3583] det_loss_cls_dn_1[0.1307] det_loss_box_dn_1[0.3313] det_loss_cls_dn_2[0.1295] det_loss_box_dn_2[0.3159] det_loss_cls_dn_3[0.1289] det_loss_box_dn_3[0.3153] det_loss_cls_dn_4[0.1278] det_loss_box_dn_4[0.3150] det_loss_cls_dn_5[0.1266] det_loss_box_dn_5[0.3150] loss_depth[0.0393] total_loss[10.7964]
Rank[0/8] 06/19/2025 16:15:01 INFO stats.py:314 | Epoch[15] Step[224] GlobalStep[6074] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 5:28:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:15:01 INFO loss_tracker.py:84 | Epoch[15/239] Step[224] GlobalStep[6074/NA]: det_loss_cls_0[0.6928] det_loss_box_0[0.9015] det_loss_cls_1[0.6830] det_loss_box_1[0.7818] det_loss_cls_2[0.6702] det_loss_box_2[0.7274] det_loss_cls_3[0.6659] det_loss_box_3[0.7004] det_loss_cls_4[0.6428] det_loss_box_4[0.7042] det_loss_cls_5[0.6245] det_loss_box_5[0.7056] det_loss_cls_dn_0[0.1264] det_loss_box_dn_0[0.3300] det_loss_cls_dn_1[0.1239] det_loss_box_dn_1[0.3053] det_loss_cls_dn_2[0.1247] det_loss_box_dn_2[0.2911] det_loss_cls_dn_3[0.1224] det_loss_box_dn_3[0.2901] det_loss_cls_dn_4[0.1215] det_loss_box_dn_4[0.2895] det_loss_cls_dn_5[0.1207] det_loss_box_dn_5[0.2895] loss_depth[0.0417] total_loss[11.0768]
Rank[0/8] 06/19/2025 16:15:27 INFO stats.py:314 | Epoch[15] Step[249] GlobalStep[6099] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 5:27:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:15:27 INFO loss_tracker.py:84 | Epoch[15/239] Step[249] GlobalStep[6099/NA]: det_loss_cls_0[0.6574] det_loss_box_0[0.8006] det_loss_cls_1[0.6326] det_loss_box_1[0.6895] det_loss_cls_2[0.6315] det_loss_box_2[0.6380] det_loss_cls_3[0.6292] det_loss_box_3[0.6154] det_loss_cls_4[0.6137] det_loss_box_4[0.6035] det_loss_cls_5[0.6081] det_loss_box_5[0.6058] det_loss_cls_dn_0[0.1283] det_loss_box_dn_0[0.3373] det_loss_cls_dn_1[0.1263] det_loss_box_dn_1[0.3128] det_loss_cls_dn_2[0.1262] det_loss_box_dn_2[0.2947] det_loss_cls_dn_3[0.1243] det_loss_box_dn_3[0.2929] det_loss_cls_dn_4[0.1242] det_loss_box_dn_4[0.2927] det_loss_cls_dn_5[0.1238] det_loss_box_dn_5[0.2927] loss_depth[0.0387] total_loss[10.3401]
Rank[0/8] 06/19/2025 16:15:53 INFO stats.py:314 | Epoch[15] Step[274] GlobalStep[6124] Training Speed: 7.63 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 5:25:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:15:53 INFO loss_tracker.py:84 | Epoch[15/239] Step[274] GlobalStep[6124/NA]: det_loss_cls_0[0.4818] det_loss_box_0[0.7279] det_loss_cls_1[0.4672] det_loss_box_1[0.6323] det_loss_cls_2[0.4607] det_loss_box_2[0.5930] det_loss_cls_3[0.4609] det_loss_box_3[0.5714] det_loss_cls_4[0.4503] det_loss_box_4[0.5697] det_loss_cls_5[0.4413] det_loss_box_5[0.5629] det_loss_cls_dn_0[0.1302] det_loss_box_dn_0[0.4351] det_loss_cls_dn_1[0.1286] det_loss_box_dn_1[0.4094] det_loss_cls_dn_2[0.1292] det_loss_box_dn_2[0.3872] det_loss_cls_dn_3[0.1274] det_loss_box_dn_3[0.3868] det_loss_cls_dn_4[0.1253] det_loss_box_dn_4[0.3876] det_loss_cls_dn_5[0.1251] det_loss_box_dn_5[0.3873] loss_depth[0.0431] total_loss[9.6217]
Rank[0/8] 06/19/2025 16:16:19 INFO stats.py:314 | Epoch[15] Step[299] GlobalStep[6149] Training Speed: 10.92 samples/sec across all devices.	Average Step Time: 0.73 sec.	Estimated Remaining Time: 1 day, 5:24:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:16:19 INFO loss_tracker.py:84 | Epoch[15/239] Step[299] GlobalStep[6149/NA]: det_loss_cls_0[0.5911] det_loss_box_0[0.7348] det_loss_cls_1[0.5626] det_loss_box_1[0.6324] det_loss_cls_2[0.5748] det_loss_box_2[0.5654] det_loss_cls_3[0.5660] det_loss_box_3[0.5472] det_loss_cls_4[0.5540] det_loss_box_4[0.5293] det_loss_cls_5[0.5403] det_loss_box_5[0.5329] det_loss_cls_dn_0[0.1313] det_loss_box_dn_0[0.3418] det_loss_cls_dn_1[0.1285] det_loss_box_dn_1[0.3196] det_loss_cls_dn_2[0.1292] det_loss_box_dn_2[0.3046] det_loss_cls_dn_3[0.1273] det_loss_box_dn_3[0.3047] det_loss_cls_dn_4[0.1270] det_loss_box_dn_4[0.3049] det_loss_cls_dn_5[0.1264] det_loss_box_dn_5[0.3048] loss_depth[0.0414] total_loss[9.6223]
Rank[0/8] 06/19/2025 16:16:45 INFO stats.py:314 | Epoch[15] Step[324] GlobalStep[6174] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 5:22:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:16:45 INFO loss_tracker.py:84 | Epoch[15/239] Step[324] GlobalStep[6174/NA]: det_loss_cls_0[0.6861] det_loss_box_0[1.0759] det_loss_cls_1[0.6533] det_loss_box_1[0.9308] det_loss_cls_2[0.6685] det_loss_box_2[0.8684] det_loss_cls_3[0.6504] det_loss_box_3[0.8484] det_loss_cls_4[0.6423] det_loss_box_4[0.8362] det_loss_cls_5[0.6257] det_loss_box_5[0.8348] det_loss_cls_dn_0[0.1232] det_loss_box_dn_0[0.4131] det_loss_cls_dn_1[0.1215] det_loss_box_dn_1[0.3858] det_loss_cls_dn_2[0.1206] det_loss_box_dn_2[0.3675] det_loss_cls_dn_3[0.1203] det_loss_box_dn_3[0.3668] det_loss_cls_dn_4[0.1196] det_loss_box_dn_4[0.3665] det_loss_cls_dn_5[0.1192] det_loss_box_dn_5[0.3665] loss_depth[0.0438] total_loss[12.3552]
Rank[0/8] 06/19/2025 16:17:11 INFO stats.py:314 | Epoch[15] Step[349] GlobalStep[6199] Training Speed: 7.69 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 5:21:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:17:11 INFO loss_tracker.py:84 | Epoch[15/239] Step[349] GlobalStep[6199/NA]: det_loss_cls_0[0.6225] det_loss_box_0[0.8320] det_loss_cls_1[0.6088] det_loss_box_1[0.7050] det_loss_cls_2[0.6054] det_loss_box_2[0.6636] det_loss_cls_3[0.5989] det_loss_box_3[0.6334] det_loss_cls_4[0.5812] det_loss_box_4[0.6168] det_loss_cls_5[0.5667] det_loss_box_5[0.6250] det_loss_cls_dn_0[0.1278] det_loss_box_dn_0[0.3252] det_loss_cls_dn_1[0.1244] det_loss_box_dn_1[0.2953] det_loss_cls_dn_2[0.1256] det_loss_box_dn_2[0.2789] det_loss_cls_dn_3[0.1246] det_loss_box_dn_3[0.2788] det_loss_cls_dn_4[0.1254] det_loss_box_dn_4[0.2790] det_loss_cls_dn_5[0.1245] det_loss_box_dn_5[0.2790] loss_depth[0.0459] total_loss[10.1937]
Rank[0/8] 06/19/2025 16:17:37 INFO stats.py:314 | Epoch[15] Step[374] GlobalStep[6224] Training Speed: 8.76 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 1 day, 5:19:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:17:37 INFO loss_tracker.py:84 | Epoch[15/239] Step[374] GlobalStep[6224/NA]: det_loss_cls_0[0.6777] det_loss_box_0[0.9148] det_loss_cls_1[0.6496] det_loss_box_1[0.7869] det_loss_cls_2[0.6422] det_loss_box_2[0.7354] det_loss_cls_3[0.6227] det_loss_box_3[0.7205] det_loss_cls_4[0.6074] det_loss_box_4[0.6943] det_loss_cls_5[0.6017] det_loss_box_5[0.6938] det_loss_cls_dn_0[0.1222] det_loss_box_dn_0[0.3498] det_loss_cls_dn_1[0.1193] det_loss_box_dn_1[0.3281] det_loss_cls_dn_2[0.1198] det_loss_box_dn_2[0.3105] det_loss_cls_dn_3[0.1177] det_loss_box_dn_3[0.3102] det_loss_cls_dn_4[0.1174] det_loss_box_dn_4[0.3104] det_loss_cls_dn_5[0.1168] det_loss_box_dn_5[0.3102] loss_depth[0.0395] total_loss[11.0190]
Rank[0/8] 06/19/2025 16:17:53 INFO stats.py:394 | Epoch[15] completed. Training Speed: 7.56 samples/sec across all devices.	Epoch Time: 412.47 sec.	Average Epoch Time: 412.47 sec.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 1 day, 5:18:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:18:11 INFO stats.py:314 | Epoch[16] Step[9] GlobalStep[6249] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 5:19:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:18:27 INFO loss_tracker.py:84 | Epoch[16/239] Step[24] GlobalStep[6264/NA]: det_loss_cls_0[0.4841] det_loss_box_0[0.6568] det_loss_cls_1[0.4684] det_loss_box_1[0.5720] det_loss_cls_2[0.4721] det_loss_box_2[0.5275] det_loss_cls_3[0.4670] det_loss_box_3[0.5135] det_loss_cls_4[0.4590] det_loss_box_4[0.4999] det_loss_cls_5[0.4460] det_loss_box_5[0.5005] det_loss_cls_dn_0[0.1245] det_loss_box_dn_0[0.4570] det_loss_cls_dn_1[0.1199] det_loss_box_dn_1[0.4307] det_loss_cls_dn_2[0.1202] det_loss_box_dn_2[0.4088] det_loss_cls_dn_3[0.1195] det_loss_box_dn_3[0.4088] det_loss_cls_dn_4[0.1186] det_loss_box_dn_4[0.4082] det_loss_cls_dn_5[0.1186] det_loss_box_dn_5[0.4078] loss_depth[0.0391] total_loss[9.3484]
Rank[0/8] 06/19/2025 16:18:38 INFO stats.py:314 | Epoch[16] Step[34] GlobalStep[6274] Training Speed: 7.90 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 5:18:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:18:53 INFO loss_tracker.py:84 | Epoch[16/239] Step[49] GlobalStep[6289/NA]: det_loss_cls_0[0.5858] det_loss_box_0[0.8172] det_loss_cls_1[0.5577] det_loss_box_1[0.7004] det_loss_cls_2[0.5647] det_loss_box_2[0.6532] det_loss_cls_3[0.5575] det_loss_box_3[0.6475] det_loss_cls_4[0.5469] det_loss_box_4[0.6286] det_loss_cls_5[0.5400] det_loss_box_5[0.6259] det_loss_cls_dn_0[0.1202] det_loss_box_dn_0[0.3904] det_loss_cls_dn_1[0.1175] det_loss_box_dn_1[0.3645] det_loss_cls_dn_2[0.1181] det_loss_box_dn_2[0.3409] det_loss_cls_dn_3[0.1174] det_loss_box_dn_3[0.3407] det_loss_cls_dn_4[0.1181] det_loss_box_dn_4[0.3411] det_loss_cls_dn_5[0.1187] det_loss_box_dn_5[0.3406] loss_depth[0.0428] total_loss[10.2967]
Rank[0/8] 06/19/2025 16:19:03 INFO stats.py:314 | Epoch[16] Step[59] GlobalStep[6299] Training Speed: 8.58 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 5:17:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:19:19 INFO loss_tracker.py:84 | Epoch[16/239] Step[74] GlobalStep[6314/NA]: det_loss_cls_0[0.5296] det_loss_box_0[0.6087] det_loss_cls_1[0.5082] det_loss_box_1[0.5327] det_loss_cls_2[0.5049] det_loss_box_2[0.4907] det_loss_cls_3[0.5109] det_loss_box_3[0.4689] det_loss_cls_4[0.4877] det_loss_box_4[0.4711] det_loss_cls_5[0.4885] det_loss_box_5[0.4672] det_loss_cls_dn_0[0.1200] det_loss_box_dn_0[0.3627] det_loss_cls_dn_1[0.1154] det_loss_box_dn_1[0.3390] det_loss_cls_dn_2[0.1150] det_loss_box_dn_2[0.3201] det_loss_cls_dn_3[0.1143] det_loss_box_dn_3[0.3207] det_loss_cls_dn_4[0.1129] det_loss_box_dn_4[0.3208] det_loss_cls_dn_5[0.1134] det_loss_box_dn_5[0.3195] loss_depth[0.0403] total_loss[8.7833]
Rank[0/8] 06/19/2025 16:19:29 INFO stats.py:314 | Epoch[16] Step[84] GlobalStep[6324] Training Speed: 8.12 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 5:15:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:19:45 INFO loss_tracker.py:84 | Epoch[16/239] Step[99] GlobalStep[6339/NA]: det_loss_cls_0[0.7669] det_loss_box_0[0.9110] det_loss_cls_1[0.7349] det_loss_box_1[0.7793] det_loss_cls_2[0.7363] det_loss_box_2[0.7230] det_loss_cls_3[0.7286] det_loss_box_3[0.6970] det_loss_cls_4[0.7118] det_loss_box_4[0.6780] det_loss_cls_5[0.7044] det_loss_box_5[0.6795] det_loss_cls_dn_0[0.1248] det_loss_box_dn_0[0.2933] det_loss_cls_dn_1[0.1253] det_loss_box_dn_1[0.2741] det_loss_cls_dn_2[0.1232] det_loss_box_dn_2[0.2603] det_loss_cls_dn_3[0.1216] det_loss_box_dn_3[0.2602] det_loss_cls_dn_4[0.1209] det_loss_box_dn_4[0.2596] det_loss_cls_dn_5[0.1208] det_loss_box_dn_5[0.2597] loss_depth[0.0413] total_loss[11.2358]
Rank[0/8] 06/19/2025 16:19:55 INFO stats.py:314 | Epoch[16] Step[109] GlobalStep[6349] Training Speed: 6.57 samples/sec across all devices.	Average Step Time: 1.22 sec.	Estimated Remaining Time: 1 day, 5:14:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:20:11 INFO loss_tracker.py:84 | Epoch[16/239] Step[124] GlobalStep[6364/NA]: det_loss_cls_0[0.6356] det_loss_box_0[0.8629] det_loss_cls_1[0.6102] det_loss_box_1[0.7644] det_loss_cls_2[0.6170] det_loss_box_2[0.7255] det_loss_cls_3[0.6038] det_loss_box_3[0.7046] det_loss_cls_4[0.5942] det_loss_box_4[0.6943] det_loss_cls_5[0.5788] det_loss_box_5[0.6970] det_loss_cls_dn_0[0.1244] det_loss_box_dn_0[0.4090] det_loss_cls_dn_1[0.1221] det_loss_box_dn_1[0.3867] det_loss_cls_dn_2[0.1220] det_loss_box_dn_2[0.3708] det_loss_cls_dn_3[0.1209] det_loss_box_dn_3[0.3688] det_loss_cls_dn_4[0.1199] det_loss_box_dn_4[0.3692] det_loss_cls_dn_5[0.1198] det_loss_box_dn_5[0.3693] loss_depth[0.0422] total_loss[11.1335]
Rank[0/8] 06/19/2025 16:20:22 INFO stats.py:314 | Epoch[16] Step[134] GlobalStep[6374] Training Speed: 7.70 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 5:12:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:20:37 INFO loss_tracker.py:84 | Epoch[16/239] Step[149] GlobalStep[6389/NA]: det_loss_cls_0[0.6047] det_loss_box_0[0.7488] det_loss_cls_1[0.5865] det_loss_box_1[0.6579] det_loss_cls_2[0.5906] det_loss_box_2[0.6073] det_loss_cls_3[0.5745] det_loss_box_3[0.5955] det_loss_cls_4[0.5546] det_loss_box_4[0.5945] det_loss_cls_5[0.5455] det_loss_box_5[0.5937] det_loss_cls_dn_0[0.1273] det_loss_box_dn_0[0.3308] det_loss_cls_dn_1[0.1245] det_loss_box_dn_1[0.3127] det_loss_cls_dn_2[0.1236] det_loss_box_dn_2[0.2943] det_loss_cls_dn_3[0.1222] det_loss_box_dn_3[0.2944] det_loss_cls_dn_4[0.1208] det_loss_box_dn_4[0.2943] det_loss_cls_dn_5[0.1200] det_loss_box_dn_5[0.2940] loss_depth[0.0396] total_loss[9.8525]
Rank[0/8] 06/19/2025 16:20:47 INFO stats.py:314 | Epoch[16] Step[159] GlobalStep[6399] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 5:11:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:21:03 INFO loss_tracker.py:84 | Epoch[16/239] Step[174] GlobalStep[6414/NA]: det_loss_cls_0[0.7850] det_loss_box_0[1.1237] det_loss_cls_1[0.7682] det_loss_box_1[0.9491] det_loss_cls_2[0.7652] det_loss_box_2[0.8765] det_loss_cls_3[0.7665] det_loss_box_3[0.8465] det_loss_cls_4[0.7594] det_loss_box_4[0.8249] det_loss_cls_5[0.7492] det_loss_box_5[0.8220] det_loss_cls_dn_0[0.1355] det_loss_box_dn_0[0.4420] det_loss_cls_dn_1[0.1325] det_loss_box_dn_1[0.4098] det_loss_cls_dn_2[0.1329] det_loss_box_dn_2[0.3965] det_loss_cls_dn_3[0.1318] det_loss_box_dn_3[0.3949] det_loss_cls_dn_4[0.1315] det_loss_box_dn_4[0.3951] det_loss_cls_dn_5[0.1321] det_loss_box_dn_5[0.3956] loss_depth[0.0497] total_loss[13.3160]
Rank[0/8] 06/19/2025 16:21:14 INFO stats.py:314 | Epoch[16] Step[184] GlobalStep[6424] Training Speed: 7.99 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 5:10:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:21:30 INFO loss_tracker.py:84 | Epoch[16/239] Step[199] GlobalStep[6439/NA]: det_loss_cls_0[0.6067] det_loss_box_0[0.8497] det_loss_cls_1[0.5888] det_loss_box_1[0.7147] det_loss_cls_2[0.5846] det_loss_box_2[0.6596] det_loss_cls_3[0.5799] det_loss_box_3[0.6366] det_loss_cls_4[0.5677] det_loss_box_4[0.6257] det_loss_cls_5[0.5517] det_loss_box_5[0.6300] det_loss_cls_dn_0[0.1338] det_loss_box_dn_0[0.5458] det_loss_cls_dn_1[0.1297] det_loss_box_dn_1[0.5084] det_loss_cls_dn_2[0.1301] det_loss_box_dn_2[0.4926] det_loss_cls_dn_3[0.1291] det_loss_box_dn_3[0.4911] det_loss_cls_dn_4[0.1285] det_loss_box_dn_4[0.4913] det_loss_cls_dn_5[0.1290] det_loss_box_dn_5[0.4913] loss_depth[0.0406] total_loss[11.4370]
Rank[0/8] 06/19/2025 16:21:40 INFO stats.py:314 | Epoch[16] Step[209] GlobalStep[6449] Training Speed: 7.72 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 5:08:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:21:56 INFO loss_tracker.py:84 | Epoch[16/239] Step[224] GlobalStep[6464/NA]: det_loss_cls_0[0.6702] det_loss_box_0[0.8995] det_loss_cls_1[0.6576] det_loss_box_1[0.7462] det_loss_cls_2[0.6492] det_loss_box_2[0.7021] det_loss_cls_3[0.6463] det_loss_box_3[0.6830] det_loss_cls_4[0.6204] det_loss_box_4[0.6822] det_loss_cls_5[0.6144] det_loss_box_5[0.6818] det_loss_cls_dn_0[0.1211] det_loss_box_dn_0[0.3175] det_loss_cls_dn_1[0.1182] det_loss_box_dn_1[0.2945] det_loss_cls_dn_2[0.1178] det_loss_box_dn_2[0.2759] det_loss_cls_dn_3[0.1171] det_loss_box_dn_3[0.2753] det_loss_cls_dn_4[0.1164] det_loss_box_dn_4[0.2751] det_loss_cls_dn_5[0.1159] det_loss_box_dn_5[0.2749] loss_depth[0.0412] total_loss[10.7141]
Rank[0/8] 06/19/2025 16:22:07 INFO stats.py:314 | Epoch[16] Step[234] GlobalStep[6474] Training Speed: 7.89 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 5:07:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:22:22 INFO loss_tracker.py:84 | Epoch[16/239] Step[249] GlobalStep[6489/NA]: det_loss_cls_0[0.5878] det_loss_box_0[0.7842] det_loss_cls_1[0.5549] det_loss_box_1[0.6664] det_loss_cls_2[0.5532] det_loss_box_2[0.6090] det_loss_cls_3[0.5418] det_loss_box_3[0.5926] det_loss_cls_4[0.5247] det_loss_box_4[0.5802] det_loss_cls_5[0.5171] det_loss_box_5[0.5802] det_loss_cls_dn_0[0.1306] det_loss_box_dn_0[0.4577] det_loss_cls_dn_1[0.1280] det_loss_box_dn_1[0.4269] det_loss_cls_dn_2[0.1278] det_loss_box_dn_2[0.4081] det_loss_cls_dn_3[0.1274] det_loss_box_dn_3[0.4075] det_loss_cls_dn_4[0.1268] det_loss_box_dn_4[0.4064] det_loss_cls_dn_5[0.1266] det_loss_box_dn_5[0.4058] loss_depth[0.0444] total_loss[10.4160]
Rank[0/8] 06/19/2025 16:22:33 INFO stats.py:314 | Epoch[16] Step[259] GlobalStep[6499] Training Speed: 7.93 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 5:05:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:22:49 INFO loss_tracker.py:84 | Epoch[16/239] Step[274] GlobalStep[6514/NA]: det_loss_cls_0[0.5523] det_loss_box_0[0.6869] det_loss_cls_1[0.5439] det_loss_box_1[0.5826] det_loss_cls_2[0.5417] det_loss_box_2[0.5396] det_loss_cls_3[0.5387] det_loss_box_3[0.5285] det_loss_cls_4[0.5260] det_loss_box_4[0.5208] det_loss_cls_5[0.5108] det_loss_box_5[0.5195] det_loss_cls_dn_0[0.1297] det_loss_box_dn_0[0.3611] det_loss_cls_dn_1[0.1251] det_loss_box_dn_1[0.3269] det_loss_cls_dn_2[0.1240] det_loss_box_dn_2[0.3080] det_loss_cls_dn_3[0.1233] det_loss_box_dn_3[0.3075] det_loss_cls_dn_4[0.1218] det_loss_box_dn_4[0.3053] det_loss_cls_dn_5[0.1227] det_loss_box_dn_5[0.3056] loss_depth[0.0437] total_loss[9.2962]
Rank[0/8] 06/19/2025 16:22:59 INFO stats.py:314 | Epoch[16] Step[284] GlobalStep[6524] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 5:04:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:23:15 INFO loss_tracker.py:84 | Epoch[16/239] Step[299] GlobalStep[6539/NA]: det_loss_cls_0[0.6534] det_loss_box_0[0.8074] det_loss_cls_1[0.6351] det_loss_box_1[0.6886] det_loss_cls_2[0.6390] det_loss_box_2[0.6405] det_loss_cls_3[0.6344] det_loss_box_3[0.6208] det_loss_cls_4[0.6247] det_loss_box_4[0.6092] det_loss_cls_5[0.6208] det_loss_box_5[0.6078] det_loss_cls_dn_0[0.1279] det_loss_box_dn_0[0.3569] det_loss_cls_dn_1[0.1253] det_loss_box_dn_1[0.3240] det_loss_cls_dn_2[0.1251] det_loss_box_dn_2[0.3083] det_loss_cls_dn_3[0.1234] det_loss_box_dn_3[0.3067] det_loss_cls_dn_4[0.1234] det_loss_box_dn_4[0.3057] det_loss_cls_dn_5[0.1228] det_loss_box_dn_5[0.3054] loss_depth[0.0424] total_loss[10.4789]
Rank[0/8] 06/19/2025 16:23:25 INFO stats.py:314 | Epoch[16] Step[309] GlobalStep[6549] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 5:03:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:23:40 INFO loss_tracker.py:84 | Epoch[16/239] Step[324] GlobalStep[6564/NA]: det_loss_cls_0[0.7365] det_loss_box_0[1.1117] det_loss_cls_1[0.7199] det_loss_box_1[0.9652] det_loss_cls_2[0.7261] det_loss_box_2[0.9068] det_loss_cls_3[0.7144] det_loss_box_3[0.8775] det_loss_cls_4[0.7049] det_loss_box_4[0.8438] det_loss_cls_5[0.6910] det_loss_box_5[0.8523] det_loss_cls_dn_0[0.1219] det_loss_box_dn_0[0.4058] det_loss_cls_dn_1[0.1202] det_loss_box_dn_1[0.3756] det_loss_cls_dn_2[0.1210] det_loss_box_dn_2[0.3566] det_loss_cls_dn_3[0.1192] det_loss_box_dn_3[0.3558] det_loss_cls_dn_4[0.1191] det_loss_box_dn_4[0.3560] det_loss_cls_dn_5[0.1185] det_loss_box_dn_5[0.3559] loss_depth[0.0423] total_loss[12.8181]
Rank[0/8] 06/19/2025 16:23:51 INFO stats.py:314 | Epoch[16] Step[334] GlobalStep[6574] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 5:01:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:24:07 INFO loss_tracker.py:84 | Epoch[16/239] Step[349] GlobalStep[6589/NA]: det_loss_cls_0[0.7425] det_loss_box_0[0.9656] det_loss_cls_1[0.7265] det_loss_box_1[0.8173] det_loss_cls_2[0.7372] det_loss_box_2[0.7578] det_loss_cls_3[0.7213] det_loss_box_3[0.7380] det_loss_cls_4[0.6982] det_loss_box_4[0.7290] det_loss_cls_5[0.6897] det_loss_box_5[0.7265] det_loss_cls_dn_0[0.1257] det_loss_box_dn_0[0.3906] det_loss_cls_dn_1[0.1244] det_loss_box_dn_1[0.3643] det_loss_cls_dn_2[0.1262] det_loss_box_dn_2[0.3442] det_loss_cls_dn_3[0.1251] det_loss_box_dn_3[0.3430] det_loss_cls_dn_4[0.1243] det_loss_box_dn_4[0.3427] det_loss_cls_dn_5[0.1241] det_loss_box_dn_5[0.3426] loss_depth[0.0412] total_loss[11.9681]
Rank[0/8] 06/19/2025 16:24:18 INFO stats.py:314 | Epoch[16] Step[359] GlobalStep[6599] Training Speed: 8.62 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 5:00:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:24:33 INFO loss_tracker.py:84 | Epoch[16/239] Step[374] GlobalStep[6614/NA]: det_loss_cls_0[0.6760] det_loss_box_0[0.9613] det_loss_cls_1[0.6565] det_loss_box_1[0.8031] det_loss_cls_2[0.6411] det_loss_box_2[0.7469] det_loss_cls_3[0.6351] det_loss_box_3[0.7213] det_loss_cls_4[0.6279] det_loss_box_4[0.7054] det_loss_cls_5[0.6144] det_loss_box_5[0.7057] det_loss_cls_dn_0[0.1288] det_loss_box_dn_0[0.4161] det_loss_cls_dn_1[0.1244] det_loss_box_dn_1[0.3892] det_loss_cls_dn_2[0.1248] det_loss_box_dn_2[0.3608] det_loss_cls_dn_3[0.1232] det_loss_box_dn_3[0.3602] det_loss_cls_dn_4[0.1225] det_loss_box_dn_4[0.3602] det_loss_cls_dn_5[0.1221] det_loss_box_dn_5[0.3603] loss_depth[0.0424] total_loss[11.5297]
Rank[0/8] 06/19/2025 16:24:43 INFO stats.py:314 | Epoch[16] Step[384] GlobalStep[6624] Training Speed: 7.92 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 4:59:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:24:49 INFO stats.py:394 | Epoch[16] completed. Training Speed: 7.51 samples/sec across all devices.	Epoch Time: 415.62 sec.	Average Epoch Time: 415.62 sec.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 1 day, 4:58:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:25:14 INFO stats.py:314 | Epoch[17] Step[19] GlobalStep[6649] Training Speed: 11.17 samples/sec across all devices.	Average Step Time: 0.72 sec.	Estimated Remaining Time: 1 day, 4:58:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:25:19 INFO loss_tracker.py:84 | Epoch[17/239] Step[24] GlobalStep[6654/NA]: det_loss_cls_0[0.6530] det_loss_box_0[0.8396] det_loss_cls_1[0.6363] det_loss_box_1[0.7219] det_loss_cls_2[0.6415] det_loss_box_2[0.6707] det_loss_cls_3[0.6289] det_loss_box_3[0.6476] det_loss_cls_4[0.6155] det_loss_box_4[0.6239] det_loss_cls_5[0.6046] det_loss_box_5[0.6250] det_loss_cls_dn_0[0.1321] det_loss_box_dn_0[0.3661] det_loss_cls_dn_1[0.1285] det_loss_box_dn_1[0.3385] det_loss_cls_dn_2[0.1283] det_loss_box_dn_2[0.3166] det_loss_cls_dn_3[0.1269] det_loss_box_dn_3[0.3162] det_loss_cls_dn_4[0.1267] det_loss_box_dn_4[0.3157] det_loss_cls_dn_5[0.1259] det_loss_box_dn_5[0.3158] loss_depth[0.0421] total_loss[10.6878]
Rank[0/8] 06/19/2025 16:25:40 INFO stats.py:314 | Epoch[17] Step[44] GlobalStep[6674] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 4:57:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:25:46 INFO loss_tracker.py:84 | Epoch[17/239] Step[49] GlobalStep[6679/NA]: det_loss_cls_0[0.6933] det_loss_box_0[0.9054] det_loss_cls_1[0.6640] det_loss_box_1[0.7742] det_loss_cls_2[0.6725] det_loss_box_2[0.7004] det_loss_cls_3[0.6523] det_loss_box_3[0.6791] det_loss_cls_4[0.6391] det_loss_box_4[0.6656] det_loss_cls_5[0.6316] det_loss_box_5[0.6669] det_loss_cls_dn_0[0.1230] det_loss_box_dn_0[0.3904] det_loss_cls_dn_1[0.1192] det_loss_box_dn_1[0.3621] det_loss_cls_dn_2[0.1188] det_loss_box_dn_2[0.3385] det_loss_cls_dn_3[0.1167] det_loss_box_dn_3[0.3376] det_loss_cls_dn_4[0.1164] det_loss_box_dn_4[0.3370] det_loss_cls_dn_5[0.1157] det_loss_box_dn_5[0.3369] loss_depth[0.0434] total_loss[11.2003]
Rank[0/8] 06/19/2025 16:26:07 INFO stats.py:314 | Epoch[17] Step[69] GlobalStep[6699] Training Speed: 8.06 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 4:56:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:26:12 INFO loss_tracker.py:84 | Epoch[17/239] Step[74] GlobalStep[6704/NA]: det_loss_cls_0[0.5356] det_loss_box_0[0.7008] det_loss_cls_1[0.5171] det_loss_box_1[0.6205] det_loss_cls_2[0.5333] det_loss_box_2[0.5724] det_loss_cls_3[0.5295] det_loss_box_3[0.5580] det_loss_cls_4[0.5009] det_loss_box_4[0.5559] det_loss_cls_5[0.4895] det_loss_box_5[0.5581] det_loss_cls_dn_0[0.1331] det_loss_box_dn_0[0.3822] det_loss_cls_dn_1[0.1288] det_loss_box_dn_1[0.3562] det_loss_cls_dn_2[0.1288] det_loss_box_dn_2[0.3409] det_loss_cls_dn_3[0.1287] det_loss_box_dn_3[0.3407] det_loss_cls_dn_4[0.1281] det_loss_box_dn_4[0.3404] det_loss_cls_dn_5[0.1275] det_loss_box_dn_5[0.3399] loss_depth[0.0410] total_loss[9.5877]
Rank[0/8] 06/19/2025 16:26:32 INFO stats.py:314 | Epoch[17] Step[94] GlobalStep[6724] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 4:54:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:26:37 INFO loss_tracker.py:84 | Epoch[17/239] Step[99] GlobalStep[6729/NA]: det_loss_cls_0[0.5928] det_loss_box_0[0.7096] det_loss_cls_1[0.5746] det_loss_box_1[0.6070] det_loss_cls_2[0.5820] det_loss_box_2[0.5595] det_loss_cls_3[0.5726] det_loss_box_3[0.5405] det_loss_cls_4[0.5570] det_loss_box_4[0.5281] det_loss_cls_5[0.5499] det_loss_box_5[0.5236] det_loss_cls_dn_0[0.1312] det_loss_box_dn_0[0.3830] det_loss_cls_dn_1[0.1290] det_loss_box_dn_1[0.3570] det_loss_cls_dn_2[0.1294] det_loss_box_dn_2[0.3313] det_loss_cls_dn_3[0.1279] det_loss_box_dn_3[0.3310] det_loss_cls_dn_4[0.1273] det_loss_box_dn_4[0.3297] det_loss_cls_dn_5[0.1269] det_loss_box_dn_5[0.3295] loss_depth[0.0410] total_loss[9.7714]
Rank[0/8] 06/19/2025 16:26:58 INFO stats.py:314 | Epoch[17] Step[119] GlobalStep[6749] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 4:53:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:27:03 INFO loss_tracker.py:84 | Epoch[17/239] Step[124] GlobalStep[6754/NA]: det_loss_cls_0[0.6580] det_loss_box_0[0.9288] det_loss_cls_1[0.6441] det_loss_box_1[0.8134] det_loss_cls_2[0.6465] det_loss_box_2[0.7706] det_loss_cls_3[0.6364] det_loss_box_3[0.7379] det_loss_cls_4[0.6224] det_loss_box_4[0.7283] det_loss_cls_5[0.6097] det_loss_box_5[0.7285] det_loss_cls_dn_0[0.1307] det_loss_box_dn_0[0.3637] det_loss_cls_dn_1[0.1271] det_loss_box_dn_1[0.3439] det_loss_cls_dn_2[0.1260] det_loss_box_dn_2[0.3253] det_loss_cls_dn_3[0.1259] det_loss_box_dn_3[0.3250] det_loss_cls_dn_4[0.1246] det_loss_box_dn_4[0.3245] det_loss_cls_dn_5[0.1238] det_loss_box_dn_5[0.3245] loss_depth[0.0397] total_loss[11.3293]
Rank[0/8] 06/19/2025 16:27:25 INFO stats.py:314 | Epoch[17] Step[144] GlobalStep[6774] Training Speed: 12.80 samples/sec across all devices.	Average Step Time: 0.62 sec.	Estimated Remaining Time: 1 day, 4:52:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:27:30 INFO loss_tracker.py:84 | Epoch[17/239] Step[149] GlobalStep[6779/NA]: det_loss_cls_0[0.5896] det_loss_box_0[0.8531] det_loss_cls_1[0.5720] det_loss_box_1[0.7171] det_loss_cls_2[0.5641] det_loss_box_2[0.6556] det_loss_cls_3[0.5514] det_loss_box_3[0.6407] det_loss_cls_4[0.5389] det_loss_box_4[0.6179] det_loss_cls_5[0.5236] det_loss_box_5[0.6237] det_loss_cls_dn_0[0.1144] det_loss_box_dn_0[0.3116] det_loss_cls_dn_1[0.1102] det_loss_box_dn_1[0.2815] det_loss_cls_dn_2[0.1094] det_loss_box_dn_2[0.2586] det_loss_cls_dn_3[0.1078] det_loss_box_dn_3[0.2585] det_loss_cls_dn_4[0.1070] det_loss_box_dn_4[0.2582] det_loss_cls_dn_5[0.1064] det_loss_box_dn_5[0.2581] loss_depth[0.0411] total_loss[9.7708]
Rank[0/8] 06/19/2025 16:27:51 INFO stats.py:314 | Epoch[17] Step[169] GlobalStep[6799] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 4:50:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:27:56 INFO loss_tracker.py:84 | Epoch[17/239] Step[174] GlobalStep[6804/NA]: det_loss_cls_0[0.7279] det_loss_box_0[0.9306] det_loss_cls_1[0.7035] det_loss_box_1[0.8012] det_loss_cls_2[0.7168] det_loss_box_2[0.7195] det_loss_cls_3[0.6978] det_loss_box_3[0.7009] det_loss_cls_4[0.6785] det_loss_box_4[0.6909] det_loss_cls_5[0.6682] det_loss_box_5[0.6900] det_loss_cls_dn_0[0.1275] det_loss_box_dn_0[0.3304] det_loss_cls_dn_1[0.1202] det_loss_box_dn_1[0.3120] det_loss_cls_dn_2[0.1202] det_loss_box_dn_2[0.2948] det_loss_cls_dn_3[0.1201] det_loss_box_dn_3[0.2940] det_loss_cls_dn_4[0.1193] det_loss_box_dn_4[0.2936] det_loss_cls_dn_5[0.1195] det_loss_box_dn_5[0.2937] loss_depth[0.0378] total_loss[11.3088]
Rank[0/8] 06/19/2025 16:28:17 INFO stats.py:314 | Epoch[17] Step[194] GlobalStep[6824] Training Speed: 8.15 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 4:49:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:28:22 INFO loss_tracker.py:84 | Epoch[17/239] Step[199] GlobalStep[6829/NA]: det_loss_cls_0[0.5068] det_loss_box_0[0.6708] det_loss_cls_1[0.4970] det_loss_box_1[0.5591] det_loss_cls_2[0.4904] det_loss_box_2[0.5132] det_loss_cls_3[0.4750] det_loss_box_3[0.4981] det_loss_cls_4[0.4615] det_loss_box_4[0.4978] det_loss_cls_5[0.4502] det_loss_box_5[0.4988] det_loss_cls_dn_0[0.1168] det_loss_box_dn_0[0.3582] det_loss_cls_dn_1[0.1148] det_loss_box_dn_1[0.3295] det_loss_cls_dn_2[0.1151] det_loss_box_dn_2[0.3111] det_loss_cls_dn_3[0.1144] det_loss_box_dn_3[0.3107] det_loss_cls_dn_4[0.1124] det_loss_box_dn_4[0.3110] det_loss_cls_dn_5[0.1120] det_loss_box_dn_5[0.3110] loss_depth[0.0432] total_loss[8.7787]
Rank[0/8] 06/19/2025 16:28:44 INFO stats.py:314 | Epoch[17] Step[219] GlobalStep[6849] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 4:48:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:28:49 INFO loss_tracker.py:84 | Epoch[17/239] Step[224] GlobalStep[6854/NA]: det_loss_cls_0[0.5637] det_loss_box_0[0.8103] det_loss_cls_1[0.5366] det_loss_box_1[0.6914] det_loss_cls_2[0.5412] det_loss_box_2[0.6286] det_loss_cls_3[0.5269] det_loss_box_3[0.6209] det_loss_cls_4[0.5127] det_loss_box_4[0.6141] det_loss_cls_5[0.5088] det_loss_box_5[0.6100] det_loss_cls_dn_0[0.1281] det_loss_box_dn_0[0.4187] det_loss_cls_dn_1[0.1246] det_loss_box_dn_1[0.3751] det_loss_cls_dn_2[0.1238] det_loss_box_dn_2[0.3540] det_loss_cls_dn_3[0.1229] det_loss_box_dn_3[0.3528] det_loss_cls_dn_4[0.1220] det_loss_box_dn_4[0.3531] det_loss_cls_dn_5[0.1213] det_loss_box_dn_5[0.3523] loss_depth[0.0418] total_loss[10.1560]
Rank[0/8] 06/19/2025 16:29:10 INFO stats.py:314 | Epoch[17] Step[244] GlobalStep[6874] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 4:47:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:29:15 INFO loss_tracker.py:84 | Epoch[17/239] Step[249] GlobalStep[6879/NA]: det_loss_cls_0[0.5815] det_loss_box_0[0.7213] det_loss_cls_1[0.5588] det_loss_box_1[0.6190] det_loss_cls_2[0.5615] det_loss_box_2[0.5637] det_loss_cls_3[0.5525] det_loss_box_3[0.5581] det_loss_cls_4[0.5305] det_loss_box_4[0.5523] det_loss_cls_5[0.5204] det_loss_box_5[0.5580] det_loss_cls_dn_0[0.1225] det_loss_box_dn_0[0.3921] det_loss_cls_dn_1[0.1189] det_loss_box_dn_1[0.3564] det_loss_cls_dn_2[0.1181] det_loss_box_dn_2[0.3356] det_loss_cls_dn_3[0.1169] det_loss_box_dn_3[0.3343] det_loss_cls_dn_4[0.1164] det_loss_box_dn_4[0.3336] det_loss_cls_dn_5[0.1154] det_loss_box_dn_5[0.3336] loss_depth[0.0424] total_loss[9.7138]
Rank[0/8] 06/19/2025 16:29:35 INFO stats.py:314 | Epoch[17] Step[269] GlobalStep[6899] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 4:45:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:29:40 INFO loss_tracker.py:84 | Epoch[17/239] Step[274] GlobalStep[6904/NA]: det_loss_cls_0[0.4679] det_loss_box_0[0.6398] det_loss_cls_1[0.4552] det_loss_box_1[0.5414] det_loss_cls_2[0.4514] det_loss_box_2[0.5004] det_loss_cls_3[0.4487] det_loss_box_3[0.4804] det_loss_cls_4[0.4358] det_loss_box_4[0.4754] det_loss_cls_5[0.4237] det_loss_box_5[0.4819] det_loss_cls_dn_0[0.1122] det_loss_box_dn_0[0.3760] det_loss_cls_dn_1[0.1099] det_loss_box_dn_1[0.3410] det_loss_cls_dn_2[0.1080] det_loss_box_dn_2[0.3119] det_loss_cls_dn_3[0.1069] det_loss_box_dn_3[0.3115] det_loss_cls_dn_4[0.1062] det_loss_box_dn_4[0.3088] det_loss_cls_dn_5[0.1051] det_loss_box_dn_5[0.3092] loss_depth[0.0406] total_loss[8.4492]
Rank[0/8] 06/19/2025 16:30:01 INFO stats.py:314 | Epoch[17] Step[294] GlobalStep[6924] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 4:44:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:30:07 INFO loss_tracker.py:84 | Epoch[17/239] Step[299] GlobalStep[6929/NA]: det_loss_cls_0[0.5339] det_loss_box_0[0.7710] det_loss_cls_1[0.5152] det_loss_box_1[0.6518] det_loss_cls_2[0.5174] det_loss_box_2[0.6004] det_loss_cls_3[0.5027] det_loss_box_3[0.5829] det_loss_cls_4[0.4922] det_loss_box_4[0.5679] det_loss_cls_5[0.4822] det_loss_box_5[0.5676] det_loss_cls_dn_0[0.1238] det_loss_box_dn_0[0.3616] det_loss_cls_dn_1[0.1209] det_loss_box_dn_1[0.3334] det_loss_cls_dn_2[0.1209] det_loss_box_dn_2[0.3103] det_loss_cls_dn_3[0.1203] det_loss_box_dn_3[0.3106] det_loss_cls_dn_4[0.1197] det_loss_box_dn_4[0.3105] det_loss_cls_dn_5[0.1188] det_loss_box_dn_5[0.3102] loss_depth[0.0368] total_loss[9.4831]
Rank[0/8] 06/19/2025 16:30:28 INFO stats.py:314 | Epoch[17] Step[319] GlobalStep[6949] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 4:43:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:30:33 INFO loss_tracker.py:84 | Epoch[17/239] Step[324] GlobalStep[6954/NA]: det_loss_cls_0[0.6271] det_loss_box_0[0.8189] det_loss_cls_1[0.6189] det_loss_box_1[0.7088] det_loss_cls_2[0.6258] det_loss_box_2[0.6697] det_loss_cls_3[0.6149] det_loss_box_3[0.6554] det_loss_cls_4[0.6000] det_loss_box_4[0.6403] det_loss_cls_5[0.5866] det_loss_box_5[0.6427] det_loss_cls_dn_0[0.1236] det_loss_box_dn_0[0.4318] det_loss_cls_dn_1[0.1193] det_loss_box_dn_1[0.4046] det_loss_cls_dn_2[0.1187] det_loss_box_dn_2[0.3876] det_loss_cls_dn_3[0.1179] det_loss_box_dn_3[0.3879] det_loss_cls_dn_4[0.1169] det_loss_box_dn_4[0.3878] det_loss_cls_dn_5[0.1175] det_loss_box_dn_5[0.3885] loss_depth[0.0381] total_loss[10.9493]
Rank[0/8] 06/19/2025 16:30:54 INFO stats.py:314 | Epoch[17] Step[344] GlobalStep[6974] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 4:41:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:30:59 INFO loss_tracker.py:84 | Epoch[17/239] Step[349] GlobalStep[6979/NA]: det_loss_cls_0[0.7040] det_loss_box_0[0.9187] det_loss_cls_1[0.6844] det_loss_box_1[0.7735] det_loss_cls_2[0.6717] det_loss_box_2[0.7243] det_loss_cls_3[0.6666] det_loss_box_3[0.6934] det_loss_cls_4[0.6443] det_loss_box_4[0.6830] det_loss_cls_5[0.6351] det_loss_box_5[0.6828] det_loss_cls_dn_0[0.1185] det_loss_box_dn_0[0.3447] det_loss_cls_dn_1[0.1151] det_loss_box_dn_1[0.3142] det_loss_cls_dn_2[0.1146] det_loss_box_dn_2[0.2943] det_loss_cls_dn_3[0.1138] det_loss_box_dn_3[0.2933] det_loss_cls_dn_4[0.1131] det_loss_box_dn_4[0.2932] det_loss_cls_dn_5[0.1134] det_loss_box_dn_5[0.2936] loss_depth[0.0424] total_loss[11.0463]
Rank[0/8] 06/19/2025 16:31:20 INFO stats.py:314 | Epoch[17] Step[369] GlobalStep[6999] Training Speed: 8.07 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 4:40:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:31:25 INFO loss_tracker.py:84 | Epoch[17/239] Step[374] GlobalStep[7004/NA]: det_loss_cls_0[0.4982] det_loss_box_0[0.6839] det_loss_cls_1[0.4786] det_loss_box_1[0.6032] det_loss_cls_2[0.4832] det_loss_box_2[0.5559] det_loss_cls_3[0.4695] det_loss_box_3[0.5525] det_loss_cls_4[0.4559] det_loss_box_4[0.5385] det_loss_cls_5[0.4417] det_loss_box_5[0.5377] det_loss_cls_dn_0[0.1294] det_loss_box_dn_0[0.4497] det_loss_cls_dn_1[0.1277] det_loss_box_dn_1[0.4193] det_loss_cls_dn_2[0.1292] det_loss_box_dn_2[0.3988] det_loss_cls_dn_3[0.1275] det_loss_box_dn_3[0.3980] det_loss_cls_dn_4[0.1258] det_loss_box_dn_4[0.3977] det_loss_cls_dn_5[0.1255] det_loss_box_dn_5[0.3973] loss_depth[0.0408] total_loss[9.5657]
Rank[0/8] 06/19/2025 16:31:41 INFO stats.py:394 | Epoch[17] completed. Training Speed: 7.57 samples/sec across all devices.	Epoch Time: 412.21 sec.	Average Epoch Time: 412.21 sec.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 1 day, 4:39:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:31:49 INFO stats.py:314 | Epoch[18] Step[4] GlobalStep[7024] Training Speed: 6.61 samples/sec across all devices.	Average Step Time: 1.21 sec.	Estimated Remaining Time: 1 day, 4:40:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:32:10 INFO loss_tracker.py:84 | Epoch[18/239] Step[24] GlobalStep[7044/NA]: det_loss_cls_0[0.5857] det_loss_box_0[0.8144] det_loss_cls_1[0.5619] det_loss_box_1[0.6926] det_loss_cls_2[0.5590] det_loss_box_2[0.6375] det_loss_cls_3[0.5447] det_loss_box_3[0.6181] det_loss_cls_4[0.5294] det_loss_box_4[0.5979] det_loss_cls_5[0.5116] det_loss_box_5[0.6002] det_loss_cls_dn_0[0.1231] det_loss_box_dn_0[0.4386] det_loss_cls_dn_1[0.1179] det_loss_box_dn_1[0.4016] det_loss_cls_dn_2[0.1190] det_loss_box_dn_2[0.3773] det_loss_cls_dn_3[0.1168] det_loss_box_dn_3[0.3769] det_loss_cls_dn_4[0.1156] det_loss_box_dn_4[0.3767] det_loss_cls_dn_5[0.1147] det_loss_box_dn_5[0.3754] loss_depth[0.0438] total_loss[10.3506]
Rank[0/8] 06/19/2025 16:32:15 INFO stats.py:314 | Epoch[18] Step[29] GlobalStep[7049] Training Speed: 8.68 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 1 day, 4:38:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:32:36 INFO loss_tracker.py:84 | Epoch[18/239] Step[49] GlobalStep[7069/NA]: det_loss_cls_0[0.6300] det_loss_box_0[0.8490] det_loss_cls_1[0.6211] det_loss_box_1[0.7143] det_loss_cls_2[0.6038] det_loss_box_2[0.6659] det_loss_cls_3[0.6020] det_loss_box_3[0.6488] det_loss_cls_4[0.5819] det_loss_box_4[0.6305] det_loss_cls_5[0.5663] det_loss_box_5[0.6315] det_loss_cls_dn_0[0.1161] det_loss_box_dn_0[0.3416] det_loss_cls_dn_1[0.1100] det_loss_box_dn_1[0.3266] det_loss_cls_dn_2[0.1092] det_loss_box_dn_2[0.3047] det_loss_cls_dn_3[0.1084] det_loss_box_dn_3[0.3045] det_loss_cls_dn_4[0.1072] det_loss_box_dn_4[0.3050] det_loss_cls_dn_5[0.1064] det_loss_box_dn_5[0.3047] loss_depth[0.0422] total_loss[10.3318]
Rank[0/8] 06/19/2025 16:32:41 INFO stats.py:314 | Epoch[18] Step[54] GlobalStep[7074] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 4:37:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:33:02 INFO loss_tracker.py:84 | Epoch[18/239] Step[74] GlobalStep[7094/NA]: det_loss_cls_0[0.6621] det_loss_box_0[0.9418] det_loss_cls_1[0.6400] det_loss_box_1[0.8161] det_loss_cls_2[0.6386] det_loss_box_2[0.7715] det_loss_cls_3[0.6355] det_loss_box_3[0.7474] det_loss_cls_4[0.6112] det_loss_box_4[0.7347] det_loss_cls_5[0.5993] det_loss_box_5[0.7350] det_loss_cls_dn_0[0.1206] det_loss_box_dn_0[0.4184] det_loss_cls_dn_1[0.1172] det_loss_box_dn_1[0.3766] det_loss_cls_dn_2[0.1162] det_loss_box_dn_2[0.3538] det_loss_cls_dn_3[0.1149] det_loss_box_dn_3[0.3520] det_loss_cls_dn_4[0.1134] det_loss_box_dn_4[0.3519] det_loss_cls_dn_5[0.1129] det_loss_box_dn_5[0.3517] loss_depth[0.0410] total_loss[11.4738]
Rank[0/8] 06/19/2025 16:33:08 INFO stats.py:314 | Epoch[18] Step[79] GlobalStep[7099] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 4:36:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:33:29 INFO loss_tracker.py:84 | Epoch[18/239] Step[99] GlobalStep[7119/NA]: det_loss_cls_0[0.5548] det_loss_box_0[0.7955] det_loss_cls_1[0.5335] det_loss_box_1[0.6778] det_loss_cls_2[0.5419] det_loss_box_2[0.6229] det_loss_cls_3[0.5299] det_loss_box_3[0.6087] det_loss_cls_4[0.5132] det_loss_box_4[0.6032] det_loss_cls_5[0.5023] det_loss_box_5[0.6021] det_loss_cls_dn_0[0.1242] det_loss_box_dn_0[0.3967] det_loss_cls_dn_1[0.1192] det_loss_box_dn_1[0.3685] det_loss_cls_dn_2[0.1193] det_loss_box_dn_2[0.3484] det_loss_cls_dn_3[0.1187] det_loss_box_dn_3[0.3480] det_loss_cls_dn_4[0.1181] det_loss_box_dn_4[0.3478] det_loss_cls_dn_5[0.1181] det_loss_box_dn_5[0.3475] loss_depth[0.0403] total_loss[10.0008]
Rank[0/8] 06/19/2025 16:33:34 INFO stats.py:314 | Epoch[18] Step[104] GlobalStep[7124] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 4:35:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:33:54 INFO loss_tracker.py:84 | Epoch[18/239] Step[124] GlobalStep[7144/NA]: det_loss_cls_0[0.5436] det_loss_box_0[0.7503] det_loss_cls_1[0.5319] det_loss_box_1[0.6551] det_loss_cls_2[0.5363] det_loss_box_2[0.5997] det_loss_cls_3[0.5309] det_loss_box_3[0.5891] det_loss_cls_4[0.5114] det_loss_box_4[0.5828] det_loss_cls_5[0.5031] det_loss_box_5[0.5821] det_loss_cls_dn_0[0.1271] det_loss_box_dn_0[0.4595] det_loss_cls_dn_1[0.1232] det_loss_box_dn_1[0.4284] det_loss_cls_dn_2[0.1225] det_loss_box_dn_2[0.4087] det_loss_cls_dn_3[0.1219] det_loss_box_dn_3[0.4078] det_loss_cls_dn_4[0.1208] det_loss_box_dn_4[0.4077] det_loss_cls_dn_5[0.1205] det_loss_box_dn_5[0.4070] loss_depth[0.0452] total_loss[10.2167]
Rank[0/8] 06/19/2025 16:33:59 INFO stats.py:314 | Epoch[18] Step[129] GlobalStep[7149] Training Speed: 7.95 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 4:33:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:34:20 INFO loss_tracker.py:84 | Epoch[18/239] Step[149] GlobalStep[7169/NA]: det_loss_cls_0[0.6128] det_loss_box_0[0.8926] det_loss_cls_1[0.5764] det_loss_box_1[0.7493] det_loss_cls_2[0.5849] det_loss_box_2[0.6986] det_loss_cls_3[0.5761] det_loss_box_3[0.6786] det_loss_cls_4[0.5613] det_loss_box_4[0.6538] det_loss_cls_5[0.5492] det_loss_box_5[0.6584] det_loss_cls_dn_0[0.1275] det_loss_box_dn_0[0.3954] det_loss_cls_dn_1[0.1258] det_loss_box_dn_1[0.3590] det_loss_cls_dn_2[0.1252] det_loss_box_dn_2[0.3362] det_loss_cls_dn_3[0.1237] det_loss_box_dn_3[0.3359] det_loss_cls_dn_4[0.1221] det_loss_box_dn_4[0.3358] det_loss_cls_dn_5[0.1213] det_loss_box_dn_5[0.3353] loss_depth[0.0437] total_loss[10.6789]
Rank[0/8] 06/19/2025 16:34:26 INFO stats.py:314 | Epoch[18] Step[154] GlobalStep[7174] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 4:32:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:34:48 INFO loss_tracker.py:84 | Epoch[18/239] Step[174] GlobalStep[7194/NA]: det_loss_cls_0[0.5115] det_loss_box_0[0.8132] det_loss_cls_1[0.4958] det_loss_box_1[0.6862] det_loss_cls_2[0.4925] det_loss_box_2[0.6393] det_loss_cls_3[0.4891] det_loss_box_3[0.6160] det_loss_cls_4[0.4694] det_loss_box_4[0.6077] det_loss_cls_5[0.4563] det_loss_box_5[0.6072] det_loss_cls_dn_0[0.1233] det_loss_box_dn_0[0.4198] det_loss_cls_dn_1[0.1210] det_loss_box_dn_1[0.3856] det_loss_cls_dn_2[0.1204] det_loss_box_dn_2[0.3627] det_loss_cls_dn_3[0.1190] det_loss_box_dn_3[0.3615] det_loss_cls_dn_4[0.1177] det_loss_box_dn_4[0.3613] det_loss_cls_dn_5[0.1166] det_loss_box_dn_5[0.3610] loss_depth[0.0442] total_loss[9.8982]
Rank[0/8] 06/19/2025 16:34:53 INFO stats.py:314 | Epoch[18] Step[179] GlobalStep[7199] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 4:31:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:35:13 INFO loss_tracker.py:84 | Epoch[18/239] Step[199] GlobalStep[7219/NA]: det_loss_cls_0[0.5700] det_loss_box_0[0.7882] det_loss_cls_1[0.5560] det_loss_box_1[0.6709] det_loss_cls_2[0.5620] det_loss_box_2[0.6124] det_loss_cls_3[0.5491] det_loss_box_3[0.5964] det_loss_cls_4[0.5273] det_loss_box_4[0.5895] det_loss_cls_5[0.5277] det_loss_box_5[0.5875] det_loss_cls_dn_0[0.1385] det_loss_box_dn_0[0.3783] det_loss_cls_dn_1[0.1337] det_loss_box_dn_1[0.3441] det_loss_cls_dn_2[0.1337] det_loss_box_dn_2[0.3253] det_loss_cls_dn_3[0.1322] det_loss_box_dn_3[0.3236] det_loss_cls_dn_4[0.1311] det_loss_box_dn_4[0.3233] det_loss_cls_dn_5[0.1307] det_loss_box_dn_5[0.3231] loss_depth[0.0424] total_loss[9.9971]
Rank[0/8] 06/19/2025 16:35:19 INFO stats.py:314 | Epoch[18] Step[204] GlobalStep[7224] Training Speed: 6.46 samples/sec across all devices.	Average Step Time: 1.24 sec.	Estimated Remaining Time: 1 day, 4:30:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:35:39 INFO loss_tracker.py:84 | Epoch[18/239] Step[224] GlobalStep[7244/NA]: det_loss_cls_0[0.5904] det_loss_box_0[0.7734] det_loss_cls_1[0.5678] det_loss_box_1[0.6548] det_loss_cls_2[0.5625] det_loss_box_2[0.6126] det_loss_cls_3[0.5634] det_loss_box_3[0.5805] det_loss_cls_4[0.5417] det_loss_box_4[0.5625] det_loss_cls_5[0.5363] det_loss_box_5[0.5576] det_loss_cls_dn_0[0.1269] det_loss_box_dn_0[0.3319] det_loss_cls_dn_1[0.1236] det_loss_box_dn_1[0.2958] det_loss_cls_dn_2[0.1227] det_loss_box_dn_2[0.2789] det_loss_cls_dn_3[0.1211] det_loss_box_dn_3[0.2781] det_loss_cls_dn_4[0.1204] det_loss_box_dn_4[0.2777] det_loss_cls_dn_5[0.1189] det_loss_box_dn_5[0.2774] loss_depth[0.0457] total_loss[9.6225]
Rank[0/8] 06/19/2025 16:35:44 INFO stats.py:314 | Epoch[18] Step[229] GlobalStep[7249] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 4:28:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:36:06 INFO loss_tracker.py:84 | Epoch[18/239] Step[249] GlobalStep[7269/NA]: det_loss_cls_0[0.6440] det_loss_box_0[0.9691] det_loss_cls_1[0.6309] det_loss_box_1[0.8437] det_loss_cls_2[0.6260] det_loss_box_2[0.7937] det_loss_cls_3[0.6134] det_loss_box_3[0.7782] det_loss_cls_4[0.5856] det_loss_box_4[0.7786] det_loss_cls_5[0.5800] det_loss_box_5[0.7736] det_loss_cls_dn_0[0.1301] det_loss_box_dn_0[0.3672] det_loss_cls_dn_1[0.1267] det_loss_box_dn_1[0.3432] det_loss_cls_dn_2[0.1274] det_loss_box_dn_2[0.3179] det_loss_cls_dn_3[0.1254] det_loss_box_dn_3[0.3182] det_loss_cls_dn_4[0.1252] det_loss_box_dn_4[0.3174] det_loss_cls_dn_5[0.1247] det_loss_box_dn_5[0.3175] loss_depth[0.0396] total_loss[11.3973]
Rank[0/8] 06/19/2025 16:36:11 INFO stats.py:314 | Epoch[18] Step[254] GlobalStep[7274] Training Speed: 5.91 samples/sec across all devices.	Average Step Time: 1.35 sec.	Estimated Remaining Time: 1 day, 4:27:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:36:32 INFO loss_tracker.py:84 | Epoch[18/239] Step[274] GlobalStep[7294/NA]: det_loss_cls_0[0.6648] det_loss_box_0[0.9594] det_loss_cls_1[0.6496] det_loss_box_1[0.8065] det_loss_cls_2[0.6512] det_loss_box_2[0.7627] det_loss_cls_3[0.6336] det_loss_box_3[0.7519] det_loss_cls_4[0.6137] det_loss_box_4[0.7483] det_loss_cls_5[0.6047] det_loss_box_5[0.7504] det_loss_cls_dn_0[0.1222] det_loss_box_dn_0[0.4504] det_loss_cls_dn_1[0.1189] det_loss_box_dn_1[0.4230] det_loss_cls_dn_2[0.1187] det_loss_box_dn_2[0.3965] det_loss_cls_dn_3[0.1167] det_loss_box_dn_3[0.3965] det_loss_cls_dn_4[0.1165] det_loss_box_dn_4[0.3969] det_loss_cls_dn_5[0.1156] det_loss_box_dn_5[0.3961] loss_depth[0.0441] total_loss[11.8091]
Rank[0/8] 06/19/2025 16:36:37 INFO stats.py:314 | Epoch[18] Step[279] GlobalStep[7299] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 4:26:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:36:58 INFO loss_tracker.py:84 | Epoch[18/239] Step[299] GlobalStep[7319/NA]: det_loss_cls_0[0.7071] det_loss_box_0[0.9292] det_loss_cls_1[0.6833] det_loss_box_1[0.7809] det_loss_cls_2[0.6962] det_loss_box_2[0.7254] det_loss_cls_3[0.6809] det_loss_box_3[0.7041] det_loss_cls_4[0.6539] det_loss_box_4[0.6978] det_loss_cls_5[0.6550] det_loss_box_5[0.6914] det_loss_cls_dn_0[0.1294] det_loss_box_dn_0[0.3036] det_loss_cls_dn_1[0.1270] det_loss_box_dn_1[0.2813] det_loss_cls_dn_2[0.1252] det_loss_box_dn_2[0.2627] det_loss_cls_dn_3[0.1244] det_loss_box_dn_3[0.2623] det_loss_cls_dn_4[0.1237] det_loss_box_dn_4[0.2620] det_loss_cls_dn_5[0.1232] det_loss_box_dn_5[0.2616] loss_depth[0.0400] total_loss[11.0315]
Rank[0/8] 06/19/2025 16:37:03 INFO stats.py:314 | Epoch[18] Step[304] GlobalStep[7324] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 4:25:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:37:24 INFO loss_tracker.py:84 | Epoch[18/239] Step[324] GlobalStep[7344/NA]: det_loss_cls_0[0.6570] det_loss_box_0[0.8846] det_loss_cls_1[0.6375] det_loss_box_1[0.7448] det_loss_cls_2[0.6381] det_loss_box_2[0.6679] det_loss_cls_3[0.6303] det_loss_box_3[0.6430] det_loss_cls_4[0.6071] det_loss_box_4[0.6294] det_loss_cls_5[0.5866] det_loss_box_5[0.6299] det_loss_cls_dn_0[0.1171] det_loss_box_dn_0[0.3282] det_loss_cls_dn_1[0.1136] det_loss_box_dn_1[0.3005] det_loss_cls_dn_2[0.1140] det_loss_box_dn_2[0.2780] det_loss_cls_dn_3[0.1136] det_loss_box_dn_3[0.2777] det_loss_cls_dn_4[0.1123] det_loss_box_dn_4[0.2770] det_loss_cls_dn_5[0.1117] det_loss_box_dn_5[0.2766] loss_depth[0.0400] total_loss[10.4164]
Rank[0/8] 06/19/2025 16:37:30 INFO stats.py:314 | Epoch[18] Step[329] GlobalStep[7349] Training Speed: 6.22 samples/sec across all devices.	Average Step Time: 1.29 sec.	Estimated Remaining Time: 1 day, 4:24:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:37:50 INFO loss_tracker.py:84 | Epoch[18/239] Step[349] GlobalStep[7369/NA]: det_loss_cls_0[0.5729] det_loss_box_0[0.6906] det_loss_cls_1[0.5506] det_loss_box_1[0.5980] det_loss_cls_2[0.5596] det_loss_box_2[0.5379] det_loss_cls_3[0.5519] det_loss_box_3[0.5150] det_loss_cls_4[0.5319] det_loss_box_4[0.5066] det_loss_cls_5[0.5129] det_loss_box_5[0.5141] det_loss_cls_dn_0[0.1220] det_loss_box_dn_0[0.3675] det_loss_cls_dn_1[0.1188] det_loss_box_dn_1[0.3385] det_loss_cls_dn_2[0.1193] det_loss_box_dn_2[0.3128] det_loss_cls_dn_3[0.1183] det_loss_box_dn_3[0.3124] det_loss_cls_dn_4[0.1179] det_loss_box_dn_4[0.3131] det_loss_cls_dn_5[0.1167] det_loss_box_dn_5[0.3137] loss_depth[0.0415] total_loss[9.3546]
Rank[0/8] 06/19/2025 16:37:55 INFO stats.py:314 | Epoch[18] Step[354] GlobalStep[7374] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 4:23:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:38:16 INFO loss_tracker.py:84 | Epoch[18/239] Step[374] GlobalStep[7394/NA]: det_loss_cls_0[0.5489] det_loss_box_0[0.7802] det_loss_cls_1[0.5384] det_loss_box_1[0.6646] det_loss_cls_2[0.5422] det_loss_box_2[0.6201] det_loss_cls_3[0.5359] det_loss_box_3[0.6023] det_loss_cls_4[0.5222] det_loss_box_4[0.5967] det_loss_cls_5[0.5059] det_loss_box_5[0.5976] det_loss_cls_dn_0[0.1246] det_loss_box_dn_0[0.3642] det_loss_cls_dn_1[0.1186] det_loss_box_dn_1[0.3420] det_loss_cls_dn_2[0.1181] det_loss_box_dn_2[0.3143] det_loss_cls_dn_3[0.1172] det_loss_box_dn_3[0.3142] det_loss_cls_dn_4[0.1170] det_loss_box_dn_4[0.3136] det_loss_cls_dn_5[0.1156] det_loss_box_dn_5[0.3125] loss_depth[0.0411] total_loss[9.7682]
Rank[0/8] 06/19/2025 16:38:21 INFO stats.py:314 | Epoch[18] Step[379] GlobalStep[7399] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 4:21:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:38:31 INFO stats.py:394 | Epoch[18] completed. Training Speed: 7.61 samples/sec across all devices.	Epoch Time: 410.17 sec.	Average Epoch Time: 410.17 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 4:21:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:38:48 INFO stats.py:314 | Epoch[19] Step[14] GlobalStep[7424] Training Speed: 6.27 samples/sec across all devices.	Average Step Time: 1.28 sec.	Estimated Remaining Time: 1 day, 4:20:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:38:59 INFO loss_tracker.py:84 | Epoch[19/239] Step[24] GlobalStep[7434/NA]: det_loss_cls_0[0.5122] det_loss_box_0[0.6753] det_loss_cls_1[0.4915] det_loss_box_1[0.5627] det_loss_cls_2[0.4904] det_loss_box_2[0.5244] det_loss_cls_3[0.4856] det_loss_box_3[0.5080] det_loss_cls_4[0.4644] det_loss_box_4[0.4984] det_loss_cls_5[0.4527] det_loss_box_5[0.4998] det_loss_cls_dn_0[0.1171] det_loss_box_dn_0[0.3624] det_loss_cls_dn_1[0.1122] det_loss_box_dn_1[0.3292] det_loss_cls_dn_2[0.1117] det_loss_box_dn_2[0.3071] det_loss_cls_dn_3[0.1109] det_loss_box_dn_3[0.3061] det_loss_cls_dn_4[0.1093] det_loss_box_dn_4[0.3056] det_loss_cls_dn_5[0.1089] det_loss_box_dn_5[0.3053] loss_depth[0.0414] total_loss[8.7923]
Rank[0/8] 06/19/2025 16:39:14 INFO stats.py:314 | Epoch[19] Step[39] GlobalStep[7449] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 4:19:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:39:25 INFO loss_tracker.py:84 | Epoch[19/239] Step[49] GlobalStep[7459/NA]: det_loss_cls_0[0.5158] det_loss_box_0[0.7065] det_loss_cls_1[0.5086] det_loss_box_1[0.5808] det_loss_cls_2[0.4946] det_loss_box_2[0.5514] det_loss_cls_3[0.4836] det_loss_box_3[0.5305] det_loss_cls_4[0.4665] det_loss_box_4[0.5199] det_loss_cls_5[0.4549] det_loss_box_5[0.5258] det_loss_cls_dn_0[0.1178] det_loss_box_dn_0[0.3785] det_loss_cls_dn_1[0.1136] det_loss_box_dn_1[0.3498] det_loss_cls_dn_2[0.1127] det_loss_box_dn_2[0.3330] det_loss_cls_dn_3[0.1114] det_loss_box_dn_3[0.3331] det_loss_cls_dn_4[0.1106] det_loss_box_dn_4[0.3340] det_loss_cls_dn_5[0.1101] det_loss_box_dn_5[0.3337] loss_depth[0.0398] total_loss[9.1170]
Rank[0/8] 06/19/2025 16:39:40 INFO stats.py:314 | Epoch[19] Step[64] GlobalStep[7474] Training Speed: 8.19 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 4:18:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:39:50 INFO loss_tracker.py:84 | Epoch[19/239] Step[74] GlobalStep[7484/NA]: det_loss_cls_0[0.6692] det_loss_box_0[0.9091] det_loss_cls_1[0.6500] det_loss_box_1[0.7899] det_loss_cls_2[0.6473] det_loss_box_2[0.7370] det_loss_cls_3[0.6358] det_loss_box_3[0.7170] det_loss_cls_4[0.6148] det_loss_box_4[0.6983] det_loss_cls_5[0.6083] det_loss_box_5[0.6965] det_loss_cls_dn_0[0.1273] det_loss_box_dn_0[0.3236] det_loss_cls_dn_1[0.1244] det_loss_box_dn_1[0.3033] det_loss_cls_dn_2[0.1232] det_loss_box_dn_2[0.2855] det_loss_cls_dn_3[0.1220] det_loss_box_dn_3[0.2852] det_loss_cls_dn_4[0.1209] det_loss_box_dn_4[0.2851] det_loss_cls_dn_5[0.1200] det_loss_box_dn_5[0.2843] loss_depth[0.0383] total_loss[10.9161]
Rank[0/8] 06/19/2025 16:40:06 INFO stats.py:314 | Epoch[19] Step[89] GlobalStep[7499] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 4:17:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:40:17 INFO loss_tracker.py:84 | Epoch[19/239] Step[99] GlobalStep[7509/NA]: det_loss_cls_0[0.6731] det_loss_box_0[0.9113] det_loss_cls_1[0.6518] det_loss_box_1[0.7897] det_loss_cls_2[0.6542] det_loss_box_2[0.7352] det_loss_cls_3[0.6503] det_loss_box_3[0.7126] det_loss_cls_4[0.6312] det_loss_box_4[0.7010] det_loss_cls_5[0.6163] det_loss_box_5[0.7088] det_loss_cls_dn_0[0.1325] det_loss_box_dn_0[0.4003] det_loss_cls_dn_1[0.1294] det_loss_box_dn_1[0.3717] det_loss_cls_dn_2[0.1293] det_loss_box_dn_2[0.3480] det_loss_cls_dn_3[0.1277] det_loss_box_dn_3[0.3485] det_loss_cls_dn_4[0.1269] det_loss_box_dn_4[0.3486] det_loss_cls_dn_5[0.1257] det_loss_box_dn_5[0.3479] loss_depth[0.0448] total_loss[11.4168]
Rank[0/8] 06/19/2025 16:40:33 INFO stats.py:314 | Epoch[19] Step[114] GlobalStep[7524] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 4:16:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:40:43 INFO loss_tracker.py:84 | Epoch[19/239] Step[124] GlobalStep[7534/NA]: det_loss_cls_0[0.5556] det_loss_box_0[0.8104] det_loss_cls_1[0.5535] det_loss_box_1[0.6923] det_loss_cls_2[0.5518] det_loss_box_2[0.6394] det_loss_cls_3[0.5376] det_loss_box_3[0.6194] det_loss_cls_4[0.5172] det_loss_box_4[0.6112] det_loss_cls_5[0.5026] det_loss_box_5[0.6139] det_loss_cls_dn_0[0.1256] det_loss_box_dn_0[0.3956] det_loss_cls_dn_1[0.1218] det_loss_box_dn_1[0.3591] det_loss_cls_dn_2[0.1211] det_loss_box_dn_2[0.3371] det_loss_cls_dn_3[0.1216] det_loss_box_dn_3[0.3365] det_loss_cls_dn_4[0.1204] det_loss_box_dn_4[0.3365] det_loss_cls_dn_5[0.1198] det_loss_box_dn_5[0.3360] loss_depth[0.0415] total_loss[10.0775]
Rank[0/8] 06/19/2025 16:40:58 INFO stats.py:314 | Epoch[19] Step[139] GlobalStep[7549] Training Speed: 7.42 samples/sec across all devices.	Average Step Time: 1.08 sec.	Estimated Remaining Time: 1 day, 4:15:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:41:09 INFO loss_tracker.py:84 | Epoch[19/239] Step[149] GlobalStep[7559/NA]: det_loss_cls_0[0.5355] det_loss_box_0[0.7764] det_loss_cls_1[0.5163] det_loss_box_1[0.6829] det_loss_cls_2[0.5179] det_loss_box_2[0.6360] det_loss_cls_3[0.5185] det_loss_box_3[0.6253] det_loss_cls_4[0.4943] det_loss_box_4[0.6195] det_loss_cls_5[0.4805] det_loss_box_5[0.6182] det_loss_cls_dn_0[0.1243] det_loss_box_dn_0[0.4225] det_loss_cls_dn_1[0.1215] det_loss_box_dn_1[0.3893] det_loss_cls_dn_2[0.1215] det_loss_box_dn_2[0.3650] det_loss_cls_dn_3[0.1208] det_loss_box_dn_3[0.3631] det_loss_cls_dn_4[0.1197] det_loss_box_dn_4[0.3632] det_loss_cls_dn_5[0.1204] det_loss_box_dn_5[0.3624] loss_depth[0.0430] total_loss[10.0578]
Rank[0/8] 06/19/2025 16:41:24 INFO stats.py:314 | Epoch[19] Step[164] GlobalStep[7574] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 4:13:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:41:35 INFO loss_tracker.py:84 | Epoch[19/239] Step[174] GlobalStep[7584/NA]: det_loss_cls_0[0.5832] det_loss_box_0[0.7768] det_loss_cls_1[0.5799] det_loss_box_1[0.6697] det_loss_cls_2[0.5716] det_loss_box_2[0.6268] det_loss_cls_3[0.5587] det_loss_box_3[0.6049] det_loss_cls_4[0.5391] det_loss_box_4[0.5937] det_loss_cls_5[0.5251] det_loss_box_5[0.6012] det_loss_cls_dn_0[0.1234] det_loss_box_dn_0[0.4087] det_loss_cls_dn_1[0.1198] det_loss_box_dn_1[0.3782] det_loss_cls_dn_2[0.1195] det_loss_box_dn_2[0.3631] det_loss_cls_dn_3[0.1186] det_loss_box_dn_3[0.3623] det_loss_cls_dn_4[0.1178] det_loss_box_dn_4[0.3619] det_loss_cls_dn_5[0.1166] det_loss_box_dn_5[0.3612] loss_depth[0.0447] total_loss[10.2266]
Rank[0/8] 06/19/2025 16:41:51 INFO stats.py:314 | Epoch[19] Step[189] GlobalStep[7599] Training Speed: 5.21 samples/sec across all devices.	Average Step Time: 1.54 sec.	Estimated Remaining Time: 1 day, 4:12:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:42:01 INFO loss_tracker.py:84 | Epoch[19/239] Step[199] GlobalStep[7609/NA]: det_loss_cls_0[0.7159] det_loss_box_0[0.9518] det_loss_cls_1[0.6935] det_loss_box_1[0.8156] det_loss_cls_2[0.6770] det_loss_box_2[0.7520] det_loss_cls_3[0.6660] det_loss_box_3[0.7365] det_loss_cls_4[0.6452] det_loss_box_4[0.7297] det_loss_cls_5[0.6363] det_loss_box_5[0.7280] det_loss_cls_dn_0[0.1266] det_loss_box_dn_0[0.3864] det_loss_cls_dn_1[0.1246] det_loss_box_dn_1[0.3581] det_loss_cls_dn_2[0.1249] det_loss_box_dn_2[0.3387] det_loss_cls_dn_3[0.1236] det_loss_box_dn_3[0.3384] det_loss_cls_dn_4[0.1224] det_loss_box_dn_4[0.3379] det_loss_cls_dn_5[0.1214] det_loss_box_dn_5[0.3375] loss_depth[0.0419] total_loss[11.6301]
Rank[0/8] 06/19/2025 16:42:17 INFO stats.py:314 | Epoch[19] Step[214] GlobalStep[7624] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 4:11:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:42:27 INFO loss_tracker.py:84 | Epoch[19/239] Step[224] GlobalStep[7634/NA]: det_loss_cls_0[0.6083] det_loss_box_0[0.8259] det_loss_cls_1[0.5981] det_loss_box_1[0.6989] det_loss_cls_2[0.6025] det_loss_box_2[0.6443] det_loss_cls_3[0.5961] det_loss_box_3[0.6226] det_loss_cls_4[0.5768] det_loss_box_4[0.6207] det_loss_cls_5[0.5693] det_loss_box_5[0.6163] det_loss_cls_dn_0[0.1299] det_loss_box_dn_0[0.4063] det_loss_cls_dn_1[0.1274] det_loss_box_dn_1[0.3731] det_loss_cls_dn_2[0.1263] det_loss_box_dn_2[0.3492] det_loss_cls_dn_3[0.1258] det_loss_box_dn_3[0.3477] det_loss_cls_dn_4[0.1261] det_loss_box_dn_4[0.3473] det_loss_cls_dn_5[0.1253] det_loss_box_dn_5[0.3472] loss_depth[0.0424] total_loss[10.5535]
Rank[0/8] 06/19/2025 16:42:42 INFO stats.py:314 | Epoch[19] Step[239] GlobalStep[7649] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 4:10:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:42:52 INFO loss_tracker.py:84 | Epoch[19/239] Step[249] GlobalStep[7659/NA]: det_loss_cls_0[0.6328] det_loss_box_0[0.8562] det_loss_cls_1[0.6009] det_loss_box_1[0.7229] det_loss_cls_2[0.5989] det_loss_box_2[0.6708] det_loss_cls_3[0.5915] det_loss_box_3[0.6546] det_loss_cls_4[0.5652] det_loss_box_4[0.6518] det_loss_cls_5[0.5525] det_loss_box_5[0.6513] det_loss_cls_dn_0[0.1179] det_loss_box_dn_0[0.3249] det_loss_cls_dn_1[0.1149] det_loss_box_dn_1[0.2983] det_loss_cls_dn_2[0.1146] det_loss_box_dn_2[0.2773] det_loss_cls_dn_3[0.1141] det_loss_box_dn_3[0.2758] det_loss_cls_dn_4[0.1135] det_loss_box_dn_4[0.2751] det_loss_cls_dn_5[0.1129] det_loss_box_dn_5[0.2752] loss_depth[0.0416] total_loss[10.2055]
Rank[0/8] 06/19/2025 16:43:08 INFO stats.py:314 | Epoch[19] Step[264] GlobalStep[7674] Training Speed: 8.12 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 4:09:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:43:19 INFO loss_tracker.py:84 | Epoch[19/239] Step[274] GlobalStep[7684/NA]: det_loss_cls_0[0.4755] det_loss_box_0[0.6054] det_loss_cls_1[0.4523] det_loss_box_1[0.5069] det_loss_cls_2[0.4497] det_loss_box_2[0.4547] det_loss_cls_3[0.4423] det_loss_box_3[0.4402] det_loss_cls_4[0.4178] det_loss_box_4[0.4404] det_loss_cls_5[0.4117] det_loss_box_5[0.4385] det_loss_cls_dn_0[0.1072] det_loss_box_dn_0[0.3561] det_loss_cls_dn_1[0.1033] det_loss_box_dn_1[0.3193] det_loss_cls_dn_2[0.1021] det_loss_box_dn_2[0.2946] det_loss_cls_dn_3[0.1010] det_loss_box_dn_3[0.2934] det_loss_cls_dn_4[0.1012] det_loss_box_dn_4[0.2926] det_loss_cls_dn_5[0.0995] det_loss_box_dn_5[0.2920] loss_depth[0.0365] total_loss[8.0343]
Rank[0/8] 06/19/2025 16:43:35 INFO stats.py:314 | Epoch[19] Step[289] GlobalStep[7699] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 4:08:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:43:45 INFO loss_tracker.py:84 | Epoch[19/239] Step[299] GlobalStep[7709/NA]: det_loss_cls_0[0.5551] det_loss_box_0[0.7724] det_loss_cls_1[0.5423] det_loss_box_1[0.6524] det_loss_cls_2[0.5496] det_loss_box_2[0.5937] det_loss_cls_3[0.5316] det_loss_box_3[0.5827] det_loss_cls_4[0.5029] det_loss_box_4[0.5746] det_loss_cls_5[0.4951] det_loss_box_5[0.5760] det_loss_cls_dn_0[0.1270] det_loss_box_dn_0[0.3802] det_loss_cls_dn_1[0.1248] det_loss_box_dn_1[0.3479] det_loss_cls_dn_2[0.1239] det_loss_box_dn_2[0.3261] det_loss_cls_dn_3[0.1236] det_loss_box_dn_3[0.3250] det_loss_cls_dn_4[0.1237] det_loss_box_dn_4[0.3253] det_loss_cls_dn_5[0.1231] det_loss_box_dn_5[0.3253] loss_depth[0.0426] total_loss[9.7471]
Rank[0/8] 06/19/2025 16:44:01 INFO stats.py:314 | Epoch[19] Step[314] GlobalStep[7724] Training Speed: 6.42 samples/sec across all devices.	Average Step Time: 1.25 sec.	Estimated Remaining Time: 1 day, 4:07:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:44:11 INFO loss_tracker.py:84 | Epoch[19/239] Step[324] GlobalStep[7734/NA]: det_loss_cls_0[0.5278] det_loss_box_0[0.6594] det_loss_cls_1[0.5083] det_loss_box_1[0.5627] det_loss_cls_2[0.4994] det_loss_box_2[0.5266] det_loss_cls_3[0.4906] det_loss_box_3[0.5174] det_loss_cls_4[0.4789] det_loss_box_4[0.5104] det_loss_cls_5[0.4714] det_loss_box_5[0.5101] det_loss_cls_dn_0[0.1128] det_loss_box_dn_0[0.3887] det_loss_cls_dn_1[0.1099] det_loss_box_dn_1[0.3606] det_loss_cls_dn_2[0.1095] det_loss_box_dn_2[0.3390] det_loss_cls_dn_3[0.1079] det_loss_box_dn_3[0.3381] det_loss_cls_dn_4[0.1077] det_loss_box_dn_4[0.3375] det_loss_cls_dn_5[0.1072] det_loss_box_dn_5[0.3373] loss_depth[0.0421] total_loss[9.0614]
Rank[0/8] 06/19/2025 16:44:26 INFO stats.py:314 | Epoch[19] Step[339] GlobalStep[7749] Training Speed: 11.88 samples/sec across all devices.	Average Step Time: 0.67 sec.	Estimated Remaining Time: 1 day, 4:05:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:44:36 INFO loss_tracker.py:84 | Epoch[19/239] Step[349] GlobalStep[7759/NA]: det_loss_cls_0[0.6236] det_loss_box_0[0.7734] det_loss_cls_1[0.6028] det_loss_box_1[0.6420] det_loss_cls_2[0.6071] det_loss_box_2[0.5887] det_loss_cls_3[0.5990] det_loss_box_3[0.5689] det_loss_cls_4[0.5722] det_loss_box_4[0.5539] det_loss_cls_5[0.5617] det_loss_box_5[0.5535] det_loss_cls_dn_0[0.1129] det_loss_box_dn_0[0.2928] det_loss_cls_dn_1[0.1114] det_loss_box_dn_1[0.2679] det_loss_cls_dn_2[0.1103] det_loss_box_dn_2[0.2507] det_loss_cls_dn_3[0.1105] det_loss_box_dn_3[0.2499] det_loss_cls_dn_4[0.1104] det_loss_box_dn_4[0.2498] det_loss_cls_dn_5[0.1106] det_loss_box_dn_5[0.2490] loss_depth[0.0372] total_loss[9.5102]
Rank[0/8] 06/19/2025 16:44:52 INFO stats.py:314 | Epoch[19] Step[364] GlobalStep[7774] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 4:04:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:45:03 INFO loss_tracker.py:84 | Epoch[19/239] Step[374] GlobalStep[7784/NA]: det_loss_cls_0[0.5018] det_loss_box_0[0.7445] det_loss_cls_1[0.4942] det_loss_box_1[0.6128] det_loss_cls_2[0.4871] det_loss_box_2[0.5893] det_loss_cls_3[0.4745] det_loss_box_3[0.5730] det_loss_cls_4[0.4588] det_loss_box_4[0.5512] det_loss_cls_5[0.4492] det_loss_box_5[0.5558] det_loss_cls_dn_0[0.1275] det_loss_box_dn_0[0.4270] det_loss_cls_dn_1[0.1238] det_loss_box_dn_1[0.4013] det_loss_cls_dn_2[0.1235] det_loss_box_dn_2[0.3803] det_loss_cls_dn_3[0.1225] det_loss_box_dn_3[0.3797] det_loss_cls_dn_4[0.1211] det_loss_box_dn_4[0.3808] det_loss_cls_dn_5[0.1200] det_loss_box_dn_5[0.3797] loss_depth[0.0443] total_loss[9.6238]
Rank[0/8] 06/19/2025 16:45:18 INFO stats.py:314 | Epoch[19] Step[389] GlobalStep[7799] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 4:03:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:45:18 INFO trainer.py:268 | 
==================================================BEGIN EVAL==================================================
Rank[0/8] 06/19/2025 16:46:23 INFO trainer.py:275 | eval: 10
Rank[0/8] 06/19/2025 16:46:30 INFO trainer.py:275 | eval: 20
Rank[0/8] 06/19/2025 16:46:46 INFO trainer.py:275 | eval: 30
Rank[0/8] 06/19/2025 16:47:02 INFO trainer.py:275 | eval: 40
Rank[0/8] 06/19/2025 16:47:12 INFO trainer.py:275 | eval: 50
Rank[0/8] 06/19/2025 16:47:24 INFO trainer.py:275 | eval: 60
Rank[0/8] 06/19/2025 16:47:33 INFO trainer.py:275 | eval: 70
Rank[0/8] 06/19/2025 16:47:43 INFO trainer.py:275 | eval: 80
Rank[0/8] 06/19/2025 16:47:52 INFO trainer.py:275 | eval: 90
Rank[0/8] 06/19/2025 16:48:00 INFO trainer.py:275 | eval: 100
Rank[0/8] 06/19/2025 16:48:05 INFO metrics.py:428 | number of results: 824
Rank[0/8] 06/19/2025 16:53:09 INFO metrics.py:332 | eval results of overall:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| toilet              | 0.5622  | 0.8832  | 0.2633  | 0.6131  |
| bathtub             | 0.3766  | 0.8529  | 0.0909  | 0.6029  |
| picture             | 0.0198  | 0.2349  | 0.0001  | 0.0205  |
| toilet paper        | 0.1232  | 0.3701  | 0.0018  | 0.0787  |
| vanity              | 0.2738  | 0.8000  | 0.1002  | 0.6889  |
| bin                 | 0.2913  | 0.7989  | 0.0604  | 0.3966  |
| doorframe           | 0.0840  | 0.6813  | 0.0109  | 0.2294  |
| towel               | 0.1009  | 0.5613  | 0.0016  | 0.0710  |
| stopcock            | 0.0214  | 0.3103  | 0.0000  | 0.0000  |
| door                | 0.0758  | 0.5269  | 0.0025  | 0.0856  |
| sink                | 0.3103  | 0.6996  | 0.0252  | 0.1728  |
| counter             | 0.0146  | 0.4037  | 0.0006  | 0.0826  |
| bar                 | 0.0004  | 0.0909  | 0.0000  | 0.0000  |
| rail                | 0.0011  | 0.2188  | 0.0000  | 0.0156  |
| bag                 | 0.1129  | 0.5135  | 0.0051  | 0.1243  |
| faucet              | 0.0092  | 0.2128  | 0.0000  | 0.0000  |
| curtain             | 0.1672  | 0.7194  | 0.0295  | 0.3313  |
| soap dish           | 0.0633  | 0.2667  | 0.0000  | 0.0000  |
| couch               | 0.2226  | 0.8430  | 0.1363  | 0.6529  |
| clothes             | 0.0408  | 0.4936  | 0.0020  | 0.1210  |
| mirror              | 0.0211  | 0.2935  | 0.0001  | 0.0380  |
| cabinet             | 0.0857  | 0.6690  | 0.0176  | 0.2825  |
| box                 | 0.0379  | 0.4849  | 0.0030  | 0.1206  |
| tissue box          | 0.0085  | 0.3333  | 0.0000  | 0.0303  |
| socket              | 0.0017  | 0.1239  | 0.0000  | 0.0000  |
| backpack            | 0.3739  | 0.7581  | 0.0326  | 0.2581  |
| bottle              | 0.0177  | 0.2451  | 0.0001  | 0.0223  |
| switch              | 0.0003  | 0.0658  | 0.0000  | 0.0000  |
| cup                 | 0.0179  | 0.2797  | 0.0001  | 0.0339  |
| paper               | 0.0020  | 0.1391  | 0.0000  | 0.0089  |
| body loofah         | 0.1146  | 1.0000  | 0.0000  | 0.0000  |
| basket              | 0.1027  | 0.6087  | 0.0284  | 0.2174  |
| hair dryer          | 0.0037  | 0.2857  | 0.0000  | 0.0000  |
| plunger             | 0.5333  | 0.8000  | 0.0000  | 0.0000  |
| shower              | 0.0361  | 0.1333  | 0.0000  | 0.0000  |
| ledge               | 0.0002  | 0.1667  | 0.0000  | 0.0000  |
| paper cutter        | 0.0609  | 0.6667  | 0.0104  | 0.1667  |
| soap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| grab bar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dispenser           | 0.1554  | 0.5455  | 0.0104  | 0.0909  |
| tissue              | 0.0288  | 0.2308  | 0.0007  | 0.1538  |
| jacket              | 0.2973  | 0.8750  | 0.0503  | 0.3750  |
| toothpaste          | 0.0020  | 0.2000  | 0.0000  | 0.0000  |
| frame               | 0.0038  | 0.2449  | 0.0000  | 0.0204  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blanket             | 0.0645  | 0.5900  | 0.0035  | 0.2300  |
| toiletry            | 0.0036  | 0.3478  | 0.0000  | 0.0000  |
| window              | 0.0595  | 0.5135  | 0.0018  | 0.1063  |
| bucket              | 0.1267  | 0.5897  | 0.0136  | 0.1282  |
| mat                 | 0.0001  | 0.0541  | 0.0000  | 0.0000  |
| scale               | 0.0909  | 0.0909  | 0.0000  | 0.0000  |
| chair               | 0.3069  | 0.8149  | 0.1580  | 0.5557  |
| keyboard            | 0.0020  | 0.0814  | 0.0000  | 0.0000  |
| monitor             | 0.2952  | 0.6793  | 0.0048  | 0.0870  |
| blackboard          | 0.1161  | 0.6829  | 0.0017  | 0.1098  |
| radiator            | 0.2506  | 0.6333  | 0.0078  | 0.1111  |
| desk                | 0.2090  | 0.8596  | 0.0921  | 0.6180  |
| telephone           | 0.0468  | 0.4333  | 0.0081  | 0.1333  |
| computer            | 0.0643  | 0.5246  | 0.0002  | 0.0328  |
| table               | 0.2094  | 0.8323  | 0.1032  | 0.5784  |
| mouse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stand               | 0.2127  | 0.8293  | 0.1007  | 0.5244  |
| book                | 0.0127  | 0.3547  | 0.0000  | 0.0168  |
| board               | 0.0042  | 0.2097  | 0.0001  | 0.0161  |
| remote control      | 0.0034  | 0.0370  | 0.0000  | 0.0000  |
| lamp                | 0.0960  | 0.4657  | 0.0042  | 0.0806  |
| headphones          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shelf               | 0.0854  | 0.5755  | 0.0120  | 0.2230  |
| pipe                | 0.0006  | 0.0357  | 0.0000  | 0.0000  |
| pillow              | 0.1754  | 0.6011  | 0.0028  | 0.0767  |
| dresser             | 0.2612  | 0.9259  | 0.1123  | 0.7593  |
| carpet              | 0.0001  | 0.0833  | 0.0000  | 0.0119  |
| bed                 | 0.4025  | 0.9053  | 0.1022  | 0.6450  |
| light               | 0.0066  | 0.1325  | 0.0001  | 0.0107  |
| shoe                | 0.0600  | 0.3364  | 0.0004  | 0.0467  |
| hamper              | 0.3434  | 0.6364  | 0.2063  | 0.5455  |
| stool               | 0.1048  | 0.5882  | 0.0364  | 0.3277  |
| windowsill          | 0.0049  | 0.2826  | 0.0000  | 0.0217  |
| wardrobe            | 0.0965  | 0.7711  | 0.0249  | 0.2530  |
| toy                 | 0.0533  | 0.3939  | 0.0001  | 0.0303  |
| clock               | 0.0025  | 0.1489  | 0.0000  | 0.0213  |
| decoration          | 0.0031  | 0.2214  | 0.0000  | 0.0214  |
| plug                | 0.0008  | 0.0526  | 0.0000  | 0.0000  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| case                | 0.0187  | 0.2938  | 0.0047  | 0.0847  |
| bench               | 0.0674  | 0.6415  | 0.0041  | 0.0943  |
| ottoman             | 0.0441  | 0.5000  | 0.0349  | 0.2778  |
| guitar              | 0.0978  | 0.4167  | 0.0000  | 0.0000  |
| tv                  | 0.2232  | 0.6535  | 0.0125  | 0.1584  |
| refrigerator        | 0.3321  | 0.7200  | 0.0484  | 0.3400  |
| luggage             | 0.0732  | 1.0000  | 0.0185  | 0.3333  |
| coffee maker        | 0.1051  | 0.5862  | 0.0386  | 0.1034  |
| purse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| laptop              | 0.0765  | 0.3913  | 0.0000  | 0.0000  |
| tray                | 0.0011  | 0.1429  | 0.0000  | 0.0000  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| broom               | 0.0192  | 0.2857  | 0.0051  | 0.0714  |
| mop                 | 0.6667  | 0.6667  | 0.0000  | 0.0000  |
| countertop          | 0.0021  | 0.2143  | 0.0000  | 0.0357  |
| holder              | 0.0101  | 0.1111  | 0.0000  | 0.0000  |
| brush               | 0.0014  | 0.1429  | 0.0000  | 0.0000  |
| container           | 0.0023  | 0.1864  | 0.0005  | 0.0678  |
| chandelier          | 0.0350  | 0.3333  | 0.0000  | 0.0000  |
| fan                 | 0.2396  | 0.7812  | 0.0018  | 0.1094  |
| ladder              | 0.1220  | 0.4706  | 0.1176  | 0.1176  |
| printer             | 0.3405  | 0.7347  | 0.0726  | 0.3673  |
| soap dispenser      | 0.0898  | 0.2963  | 0.0393  | 0.0741  |
| stall               | 0.0147  | 0.4118  | 0.0000  | 0.0000  |
| washbasin           | 0.0044  | 0.3333  | 0.0014  | 0.1333  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mailbox             | 0.0014  | 0.2000  | 0.0014  | 0.2000  |
| vent                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| roof                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| column              | 0.0134  | 0.2632  | 0.0005  | 0.1053  |
| pillar              | 0.1248  | 0.6047  | 0.0053  | 0.1395  |
| stairs              | 0.0047  | 0.2778  | 0.0003  | 0.0556  |
| cushion             | 0.0181  | 0.4510  | 0.0000  | 0.0000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher   | 0.1333  | 0.4000  | 0.0000  | 0.0000  |
| projector           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plant               | 0.1424  | 0.5797  | 0.0206  | 0.1617  |
| flowerpot           | 0.0237  | 0.5098  | 0.0032  | 0.1569  |
| hat                 | 0.0004  | 0.1111  | 0.0000  | 0.0000  |
| dumbbell            | 0.0556  | 0.3333  | 0.0556  | 0.3333  |
| person              | 0.0296  | 0.3333  | 0.0000  | 0.0000  |
| tube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball                | 0.2753  | 0.5714  | 0.0659  | 0.2857  |
| globe               | 0.0081  | 1.0000  | 0.0000  | 0.0000  |
| commode             | 0.1738  | 0.8077  | 0.0849  | 0.5577  |
| copier              | 0.4398  | 0.9167  | 0.1141  | 0.5833  |
| water cooler        | 0.1190  | 1.0000  | 0.0861  | 1.0000  |
| microwave           | 0.2644  | 0.7347  | 0.0256  | 0.2041  |
| sign                | 0.0028  | 0.0667  | 0.0007  | 0.0333  |
| hanger              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kettle              | 0.0648  | 0.3333  | 0.0033  | 0.0556  |
| teapot              | 0.0133  | 0.3333  | 0.0000  | 0.0000  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchenware         | 0.0993  | 0.4130  | 0.0001  | 0.0217  |
| bowl                | 0.0063  | 0.2319  | 0.0009  | 0.0580  |
| plate               | 0.0066  | 0.2308  | 0.0000  | 0.0192  |
| machine             | 0.1619  | 0.7895  | 0.0105  | 0.2632  |
| rack                | 0.0030  | 0.2308  | 0.0005  | 0.0962  |
| dishwasher          | 0.1117  | 0.3000  | 0.0016  | 0.1000  |
| dish rack           | 0.0746  | 0.6000  | 0.0000  | 0.0000  |
| toaster             | 0.0236  | 0.2000  | 0.0000  | 0.0000  |
| piano               | 0.3333  | 0.4000  | 0.2000  | 0.2000  |
| vase                | 0.0828  | 0.3600  | 0.0000  | 0.0000  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vacuum cleaner      | 0.0177  | 0.2857  | 0.0130  | 0.1429  |
| blinds              | 0.0075  | 0.2600  | 0.0008  | 0.0600  |
| poster              | 0.0009  | 0.0152  | 0.0000  | 0.0000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| calendar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.0677  | 0.7333  | 0.0053  | 0.2667  |
| garbage             | 0.0711  | 0.7500  | 0.0289  | 0.5000  |
| conducting wire     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| folder              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| partition           | 0.0025  | 0.3333  | 0.0000  | 0.0000  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candle              | 0.0002  | 0.0556  | 0.0000  | 0.0000  |
| furniture           | 0.0010  | 0.1667  | 0.0000  | 0.0000  |
| basin               | 0.0001  | 0.0588  | 0.0001  | 0.0588  |
| napkins             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stove               | 0.3240  | 0.7778  | 0.1600  | 0.4167  |
| price tag           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| menu                | 0.1553  | 0.4286  | 0.0000  | 0.0000  |
| magazine            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candlestick         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| heater              | 0.1736  | 0.6207  | 0.0004  | 0.0172  |
| humidifier          | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| device              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| notebook            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pot                 | 0.0275  | 0.4074  | 0.0003  | 0.0370  |
| air conditioner     | 0.0179  | 0.4000  | 0.0000  | 0.0000  |
| fruit               | 0.0437  | 0.5000  | 0.0000  | 0.0000  |
| sunglasses          | 0.0238  | 0.1538  | 0.0038  | 0.1538  |
| fireplace           | 0.1162  | 0.3571  | 0.0003  | 0.0714  |
| statue              | 0.0334  | 0.4211  | 0.0059  | 0.1579  |
| beanbag             | 0.7905  | 1.0000  | 0.7340  | 1.0000  |
| oven                | 0.0732  | 0.6071  | 0.0027  | 0.1786  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pan                 | 0.0018  | 0.0588  | 0.0000  | 0.0000  |
| pack                | 0.0010  | 0.1333  | 0.0000  | 0.0000  |
| food                | 0.0455  | 0.0909  | 0.0455  | 0.0909  |
| jalousie            | 0.0455  | 0.5000  | 0.0000  | 0.0000  |
| balcony             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crib                | 0.0772  | 1.0000  | 0.0513  | 0.6667  |
| crate               | 0.1409  | 0.5714  | 0.0042  | 0.1429  |
| drawer              | 0.0015  | 0.1667  | 0.0001  | 0.0556  |
| roll                | 0.0050  | 0.1000  | 0.0000  | 0.0000  |
| drum                | 0.0048  | 0.1250  | 0.0000  | 0.0000  |
| bicycle             | 0.1962  | 1.0000  | 0.0888  | 0.5000  |
| umbrella            | 0.0118  | 0.2000  | 0.0000  | 0.0000  |
| speaker             | 0.0455  | 0.0455  | 0.0000  | 0.0000  |
| bidet               | 0.3939  | 0.6667  | 0.0750  | 0.6667  |
| coil                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| screen              | 0.0314  | 0.4000  | 0.0200  | 0.2000  |
| footstool           | 0.2500  | 0.5000  | 0.0714  | 0.5000  |
| headboard           | 0.0057  | 0.3750  | 0.0000  | 0.0000  |
| steps               | 0.0008  | 0.2000  | 0.0001  | 0.1000  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| window frame        | 0.0121  | 0.2424  | 0.0010  | 0.0505  |
| doorway             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.4511  | 0.8214  | 0.1051  | 0.4286  |
| handle              | 0.0029  | 0.2500  | 0.0000  | 0.0000  |
| thermostat          | 0.0079  | 0.1667  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garage door         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchen island      | 0.1722  | 0.7143  | 0.1666  | 0.4286  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| album               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glove               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| helmets             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool table          | 0.0370  | 0.3333  | 0.0370  | 0.3333  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| arch                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.1573  | 0.8000  | 0.0023  | 0.1000  |
| treadmill           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beam                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tablet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.0192  | 0.3571  | 0.0000  | 0.0000  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bread               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| spoon               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sack                | 0.3333  | 0.5714  | 0.0000  | 0.0000  |
| flush               | 0.0111  | 0.5000  | 0.0000  | 0.0000  |
| shampoo             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| letter              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| salt                | 0.1333  | 1.0000  | 0.0000  | 0.0000  |
| cube                | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| wine                | 0.0268  | 0.1905  | 0.0000  | 0.0000  |
| glass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| barricade           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water heater        | 0.1667  | 0.5000  | 0.1667  | 0.5000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| map                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| overall             | 0.0776  | 0.3201  | 0.0224  | 0.1099  |
| head                | 0.1113  | 0.4757  | 0.0234  | 0.1621  |
| common              | 0.0635  | 0.2723  | 0.0220  | 0.0911  |
| tail                | 0.0543  | 0.1908  | 0.0217  | 0.0703  |
| small               | 0.0073  | 0.0908  | 0.0009  | 0.0066  |
| medium              | 0.0824  | 0.3625  | 0.0223  | 0.1174  |
| large               | 0.0528  | 0.4252  | 0.0171  | 0.1704  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/19/2025 16:53:09 INFO metrics.py:437 | number of scannet results: 250
Rank[0/8] 06/19/2025 16:54:47 INFO metrics.py:332 | eval results of scannet:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| toilet              | 0.5754  | 0.9643  | 0.3776  | 0.7143  |
| bathtub             | 0.5206  | 0.9375  | 0.1058  | 0.6250  |
| picture             | 0.0285  | 0.2948  | 0.0001  | 0.0261  |
| toilet paper        | 0.0970  | 0.3291  | 0.0009  | 0.0633  |
| vanity              | 0.3835  | 0.9000  | 0.1346  | 0.7667  |
| bin                 | 0.3169  | 0.8213  | 0.0673  | 0.4128  |
| doorframe           | 0.0767  | 0.6972  | 0.0083  | 0.2817  |
| towel               | 0.1259  | 0.6033  | 0.0062  | 0.0909  |
| stopcock            | 0.0216  | 0.3103  | 0.0000  | 0.0000  |
| door                | 0.1057  | 0.6756  | 0.0064  | 0.1438  |
| sink                | 0.4356  | 0.8068  | 0.0661  | 0.2273  |
| counter             | 0.0254  | 0.5714  | 0.0009  | 0.1143  |
| bar                 | 0.0003  | 0.0455  | 0.0000  | 0.0000  |
| rail                | 0.0044  | 0.6667  | 0.0000  | 0.0000  |
| bag                 | 0.0752  | 0.4795  | 0.0184  | 0.1781  |
| faucet              | 0.0054  | 0.2500  | 0.0000  | 0.0000  |
| curtain             | 0.2162  | 0.7257  | 0.0512  | 0.4248  |
| soap dish           | 0.0791  | 0.3333  | 0.0000  | 0.0000  |
| couch               | 0.3329  | 0.9875  | 0.2410  | 0.8875  |
| clothes             | 0.0414  | 0.4837  | 0.0032  | 0.1242  |
| mirror              | 0.0249  | 0.2651  | 0.0002  | 0.0241  |
| cabinet             | 0.1210  | 0.7341  | 0.0288  | 0.3373  |
| box                 | 0.0363  | 0.4206  | 0.0028  | 0.1151  |
| tissue box          | 0.0140  | 0.4091  | 0.0000  | 0.0455  |
| socket              | 0.0019  | 0.1262  | 0.0000  | 0.0000  |
| backpack            | 0.5113  | 0.8571  | 0.0450  | 0.3214  |
| bottle              | 0.0220  | 0.2421  | 0.0001  | 0.0211  |
| switch              | 0.0008  | 0.1013  | 0.0000  | 0.0000  |
| cup                 | 0.0230  | 0.3333  | 0.0003  | 0.0580  |
| paper               | 0.0023  | 0.1170  | 0.0000  | 0.0038  |
| basket              | 0.0791  | 0.5385  | 0.0057  | 0.1538  |
| hair dryer          | 0.0041  | 0.2857  | 0.0000  | 0.0000  |
| plunger             | 0.5333  | 0.8000  | 0.0000  | 0.0000  |
| shower              | 0.0771  | 0.2143  | 0.0000  | 0.0000  |
| ledge               | 0.0002  | 0.1667  | 0.0000  | 0.0000  |
| paper cutter        | 0.0234  | 0.6000  | 0.0167  | 0.2000  |
| grab bar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dispenser           | 0.2088  | 0.5897  | 0.0135  | 0.1026  |
| tissue              | 0.0802  | 0.2222  | 0.0005  | 0.1111  |
| jacket              | 0.3119  | 0.9130  | 0.0528  | 0.3913  |
| toothpaste          | 0.0020  | 0.2000  | 0.0000  | 0.0000  |
| frame               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blanket             | 0.0784  | 0.5000  | 0.0013  | 0.2083  |
| window              | 0.1085  | 0.7654  | 0.0020  | 0.1790  |
| bucket              | 0.0477  | 0.4286  | 0.0000  | 0.0000  |
| mat                 | 0.0006  | 0.1000  | 0.0000  | 0.0000  |
| scale               | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| chair               | 0.3214  | 0.8141  | 0.1588  | 0.5373  |
| keyboard            | 0.0021  | 0.0690  | 0.0000  | 0.0000  |
| monitor             | 0.3463  | 0.7000  | 0.0069  | 0.0929  |
| blackboard          | 0.1088  | 0.6957  | 0.0023  | 0.1304  |
| radiator            | 0.3498  | 0.7931  | 0.0100  | 0.1379  |
| desk                | 0.2950  | 0.9620  | 0.1399  | 0.7342  |
| telephone           | 0.0680  | 0.5625  | 0.0151  | 0.1875  |
| computer            | 0.1030  | 0.5385  | 0.0007  | 0.0513  |
| table               | 0.2685  | 0.8769  | 0.1647  | 0.6769  |
| mouse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stand               | 0.3739  | 0.8679  | 0.2170  | 0.6981  |
| book                | 0.0205  | 0.3738  | 0.0000  | 0.0190  |
| board               | 0.0054  | 0.1875  | 0.0000  | 0.0000  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| lamp                | 0.1335  | 0.5119  | 0.0174  | 0.1071  |
| headphones          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shelf               | 0.1730  | 0.7721  | 0.0399  | 0.4118  |
| pipe                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillow              | 0.2126  | 0.6458  | 0.0097  | 0.1875  |
| dresser             | 0.3221  | 0.9730  | 0.1294  | 0.8108  |
| carpet              | 0.0001  | 0.1000  | 0.0000  | 0.0000  |
| bed                 | 0.4132  | 1.0000  | 0.2075  | 0.9298  |
| light               | 0.0377  | 0.1724  | 0.0000  | 0.0000  |
| shoe                | 0.1488  | 0.5250  | 0.0008  | 0.0750  |
| hamper              | 0.3778  | 0.7000  | 0.2271  | 0.6000  |
| stool               | 0.1287  | 0.5455  | 0.0594  | 0.3182  |
| windowsill          | 0.0091  | 0.3333  | 0.0000  | 0.0000  |
| wardrobe            | 0.2307  | 1.0000  | 0.1458  | 0.2500  |
| toy                 | 0.0781  | 0.2353  | 0.0000  | 0.0000  |
| clock               | 0.0013  | 0.1600  | 0.0001  | 0.0400  |
| decoration          | 0.0038  | 0.1667  | 0.0000  | 0.0000  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| case                | 0.1735  | 0.7368  | 0.0389  | 0.4211  |
| bench               | 0.0174  | 0.8000  | 0.0000  | 0.0000  |
| ottoman             | 0.0048  | 0.4286  | 0.0016  | 0.1429  |
| guitar              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tv                  | 0.4418  | 0.7391  | 0.0208  | 0.1739  |
| refrigerator        | 0.6258  | 0.9500  | 0.0852  | 0.5000  |
| coffee maker        | 0.2390  | 0.7647  | 0.0709  | 0.1765  |
| purse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| laptop              | 0.1913  | 0.4000  | 0.0000  | 0.0000  |
| tray                | 0.0033  | 0.3333  | 0.0000  | 0.0000  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| broom               | 0.0616  | 0.3333  | 0.0208  | 0.0833  |
| holder              | 0.0286  | 0.2000  | 0.0000  | 0.0000  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| container           | 0.0012  | 0.1739  | 0.0004  | 0.0870  |
| fan                 | 0.0573  | 0.7500  | 0.0040  | 0.1250  |
| ladder              | 0.2103  | 0.4000  | 0.2000  | 0.2000  |
| printer             | 0.4250  | 0.8182  | 0.1030  | 0.4848  |
| soap dispenser      | 0.1178  | 0.3810  | 0.0508  | 0.0952  |
| stall               | 0.0148  | 0.4118  | 0.0000  | 0.0000  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mailbox             | 0.0014  | 0.2000  | 0.0014  | 0.2000  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillar              | 0.0658  | 0.6000  | 0.0061  | 0.2000  |
| stairs              | 0.1239  | 0.8000  | 0.0018  | 0.2000  |
| cushion             | 0.0399  | 0.7500  | 0.0000  | 0.0000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plant               | 0.1737  | 0.5966  | 0.0488  | 0.2017  |
| flowerpot           | 0.0017  | 0.5000  | 0.0000  | 0.0000  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| person              | 0.0298  | 0.3333  | 0.0000  | 0.0000  |
| tube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball                | 0.2355  | 0.5000  | 0.0233  | 0.1667  |
| copier              | 0.4398  | 0.9167  | 0.1149  | 0.5833  |
| water cooler        | 0.1190  | 1.0000  | 0.0865  | 1.0000  |
| microwave           | 0.2988  | 0.7500  | 0.0382  | 0.2000  |
| sign                | 0.0036  | 0.0714  | 0.0009  | 0.0357  |
| kettle              | 0.3333  | 0.5000  | 0.0000  | 0.0000  |
| teapot              | 0.0171  | 0.5000  | 0.0000  | 0.0000  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bowl                | 0.0158  | 0.2222  | 0.0151  | 0.2222  |
| plate               | 0.0155  | 0.4000  | 0.0000  | 0.0000  |
| machine             | 0.1640  | 0.7500  | 0.0084  | 0.1875  |
| rack                | 0.0043  | 0.1818  | 0.0025  | 0.0909  |
| dishwasher          | 0.3333  | 0.3333  | 0.0000  | 0.0000  |
| dish rack           | 0.0932  | 0.7500  | 0.0000  | 0.0000  |
| toaster             | 0.0833  | 0.2500  | 0.0000  | 0.0000  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vacuum cleaner      | 0.0869  | 0.6667  | 0.0667  | 0.3333  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| calendar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.5000  | 1.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.4714  | 0.6667  | 0.0500  | 0.3333  |
| pot                 | 0.0357  | 0.5000  | 0.0000  | 0.0000  |
| stove               | 0.8197  | 1.0000  | 0.6979  | 0.8889  |
| range hood          | 0.0344  | 0.6000  | 0.0000  | 0.0000  |
| oven                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crate               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 0.5455  | 1.0000  | 0.3333  | 0.3333  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basin               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| notebook            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| map                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.1111  | 1.0000  | 0.0278  | 0.5000  |
| -------             | ------- | ------- | ------- | ------- |
| scannet             | 0.1193  | 0.4111  | 0.0320  | 0.1462  |
| head                | 0.1488  | 0.5284  | 0.0416  | 0.1970  |
| common              | 0.0940  | 0.2989  | 0.0246  | 0.0867  |
| tail                | 0.0500  | 0.1740  | 0.0068  | 0.0802  |
| small               | 0.0110  | 0.0995  | 0.0012  | 0.0080  |
| medium              | 0.1356  | 0.5166  | 0.0316  | 0.1736  |
| large               | 0.1388  | 0.7281  | 0.0599  | 0.3513  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/19/2025 16:54:47 INFO metrics.py:437 | number of 3rscan results: 235
Rank[0/8] 06/19/2025 16:56:11 INFO metrics.py:332 | eval results of 3rscan:
+-------------------+---------+---------+---------+---------+
| classes           | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+-------------------+---------+---------+---------+---------+
| chair             | 0.3430  | 0.8445  | 0.1921  | 0.5893  |
| keyboard          | 0.0030  | 0.1250  | 0.0000  | 0.0000  |
| backpack          | 0.2667  | 0.6667  | 0.0345  | 0.2121  |
| monitor           | 0.2108  | 0.6250  | 0.0020  | 0.0750  |
| cup               | 0.0269  | 0.2051  | 0.0000  | 0.0000  |
| table             | 0.2135  | 0.8559  | 0.0930  | 0.5631  |
| computer          | 0.0423  | 0.5238  | 0.0000  | 0.0000  |
| desk              | 0.1804  | 0.8125  | 0.0654  | 0.5250  |
| bag               | 0.1499  | 0.5657  | 0.0012  | 0.0909  |
| bin               | 0.3227  | 0.8171  | 0.0600  | 0.4024  |
| mouse             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| commode           | 0.1751  | 0.8077  | 0.0856  | 0.5577  |
| book              | 0.0077  | 0.3254  | 0.0000  | 0.0178  |
| printer           | 0.3450  | 0.6667  | 0.1111  | 0.1111  |
| socket            | 0.0001  | 0.0625  | 0.0000  | 0.0000  |
| bottle            | 0.0072  | 0.1901  | 0.0002  | 0.0083  |
| paper cutter      | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| paper             | 0.0020  | 0.2286  | 0.0001  | 0.0286  |
| box               | 0.0523  | 0.5207  | 0.0049  | 0.1264  |
| garbage           | 0.0720  | 0.7500  | 0.0296  | 0.5000  |
| cabinet           | 0.0931  | 0.7051  | 0.0155  | 0.2778  |
| case              | 0.0050  | 0.2500  | 0.0022  | 0.0429  |
| shelf             | 0.0606  | 0.5300  | 0.0023  | 0.1449  |
| stand             | 0.1354  | 0.8525  | 0.0536  | 0.3770  |
| conducting wire   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| window            | 0.0493  | 0.5000  | 0.0025  | 0.0959  |
| plug              | 0.0111  | 0.1111  | 0.0000  | 0.0000  |
| tissue box        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wardrobe          | 0.0979  | 0.7812  | 0.0155  | 0.2500  |
| plant             | 0.1399  | 0.5446  | 0.0085  | 0.1139  |
| telephone         | 0.0383  | 0.2500  | 0.0060  | 0.0417  |
| curtain           | 0.1733  | 0.7222  | 0.0204  | 0.2222  |
| folder            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blinds            | 0.0098  | 0.3333  | 0.0011  | 0.0769  |
| pen               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stool             | 0.1619  | 0.7407  | 0.0533  | 0.4074  |
| partition         | 0.0038  | 0.4000  | 0.0000  | 0.0000  |
| remote control    | 0.0071  | 0.0500  | 0.0000  | 0.0000  |
| column            | 0.0020  | 0.5000  | 0.0000  | 0.0000  |
| blackboard        | 0.3126  | 0.6667  | 0.0000  | 0.0000  |
| picture           | 0.0140  | 0.1789  | 0.0001  | 0.0122  |
| pillar            | 0.1321  | 0.4706  | 0.0000  | 0.0000  |
| bench             | 0.1193  | 0.7857  | 0.0073  | 0.1429  |
| windowsill        | 0.0057  | 0.2821  | 0.0000  | 0.0256  |
| wire              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes           | 0.0516  | 0.5631  | 0.0014  | 0.1068  |
| couch             | 0.2357  | 0.7857  | 0.1177  | 0.5286  |
| plate             | 0.0048  | 0.2727  | 0.0000  | 0.0303  |
| tv                | 0.1652  | 0.6857  | 0.0080  | 0.1429  |
| pillow            | 0.2080  | 0.6328  | 0.0025  | 0.0547  |
| lamp              | 0.0472  | 0.3500  | 0.0009  | 0.0437  |
| frame             | 0.0063  | 0.2667  | 0.0000  | 0.0000  |
| door              | 0.0860  | 0.4853  | 0.0018  | 0.0662  |
| light             | 0.0034  | 0.1136  | 0.0000  | 0.0000  |
| doorframe         | 0.0303  | 0.5200  | 0.0047  | 0.1600  |
| switch            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchenware       | 0.1055  | 0.4318  | 0.0001  | 0.0227  |
| towel             | 0.0863  | 0.5443  | 0.0004  | 0.0506  |
| bed               | 0.4962  | 0.8909  | 0.0773  | 0.4909  |
| carpet            | 0.0002  | 0.1071  | 0.0000  | 0.0179  |
| bowl              | 0.0044  | 0.2222  | 0.0012  | 0.0741  |
| cushion           | 0.0378  | 0.5667  | 0.0000  | 0.0000  |
| teapot            | 0.0278  | 0.2500  | 0.0000  | 0.0000  |
| candle            | 0.0004  | 0.0667  | 0.0000  | 0.0000  |
| furniture         | 0.0027  | 0.3333  | 0.0000  | 0.0000  |
| basin             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| laptop            | 0.0069  | 0.3333  | 0.0000  | 0.0000  |
| mirror            | 0.0149  | 0.3333  | 0.0006  | 0.0741  |
| rack              | 0.0055  | 0.3333  | 0.0006  | 0.1333  |
| napkins           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blanket           | 0.0784  | 0.6615  | 0.0053  | 0.2615  |
| stove             | 0.1718  | 0.7647  | 0.0104  | 0.2941  |
| sink              | 0.4752  | 0.7857  | 0.0283  | 0.2143  |
| ottoman           | 0.1401  | 0.7273  | 0.1138  | 0.5455  |
| counter           | 0.0206  | 0.3415  | 0.0034  | 0.0732  |
| price tag         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vase              | 0.1265  | 0.4000  | 0.0000  | 0.0000  |
| flowerpot         | 0.0218  | 0.5000  | 0.0000  | 0.0000  |
| mat               | 0.0009  | 0.3333  | 0.0000  | 0.0000  |
| menu              | 0.1553  | 0.4286  | 0.0000  | 0.0000  |
| magazine          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| decoration        | 0.0097  | 0.1562  | 0.0001  | 0.0312  |
| poster            | 0.0011  | 0.0159  | 0.0000  | 0.0000  |
| mattress          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candlestick       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| refrigerator      | 0.2893  | 0.6667  | 0.0653  | 0.4000  |
| fan               | 0.3333  | 1.0000  | 0.0000  | 0.0000  |
| heater            | 0.1741  | 0.6140  | 0.0004  | 0.0175  |
| humidifier        | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| radiator          | 0.1273  | 0.3793  | 0.0092  | 0.0690  |
| device            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| notebook          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| board             | 0.0117  | 0.2917  | 0.0006  | 0.0417  |
| file              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pad               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shoe              | 0.0561  | 0.2857  | 0.0009  | 0.0476  |
| basket            | 0.1826  | 0.7188  | 0.0576  | 0.3125  |
| pot               | 0.1641  | 0.5833  | 0.0000  | 0.0000  |
| clock             | 0.0033  | 0.1053  | 0.0000  | 0.0000  |
| toy               | 0.0852  | 0.6923  | 0.0001  | 0.0769  |
| air conditioner   | 0.1061  | 0.5000  | 0.0000  | 0.0000  |
| dispenser         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toilet paper      | 0.0474  | 0.3333  | 0.0000  | 0.0000  |
| luggage           | 0.1500  | 1.0000  | 0.0303  | 0.3333  |
| dresser           | 0.0581  | 1.0000  | 0.0000  | 0.0000  |
| scale             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| microwave         | 0.4079  | 0.9412  | 0.0482  | 0.2941  |
| fruit             | 0.0601  | 0.6000  | 0.0000  | 0.0000  |
| guitar            | 0.6000  | 1.0000  | 0.0000  | 0.0000  |
| brush             | 0.0034  | 0.2500  | 0.0000  | 0.0000  |
| ladder            | 0.0091  | 1.0000  | 0.0000  | 0.0000  |
| ball              | 0.2228  | 0.6000  | 0.0286  | 0.2000  |
| sunglasses        | 0.0238  | 0.1538  | 0.0038  | 0.1538  |
| piano             | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| fireplace         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bathtub           | 0.4914  | 0.8889  | 0.2576  | 0.6667  |
| faucet            | 0.3333  | 0.3333  | 0.0000  | 0.0000  |
| statue            | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| body loofah       | 0.3929  | 1.0000  | 0.0000  | 0.0000  |
| toiletry          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer     | 0.0803  | 0.7000  | 0.0341  | 0.2000  |
| shower            | 0.0029  | 0.2500  | 0.0000  | 0.0000  |
| bucket            | 0.3079  | 0.8667  | 0.0350  | 0.2000  |
| toilet            | 0.6680  | 0.7647  | 0.0269  | 0.2353  |
| soap              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| container         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tube              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beanbag           | 0.7960  | 1.0000  | 0.7405  | 1.0000  |
| oven              | 0.1120  | 0.8421  | 0.0047  | 0.2632  |
| kettle            | 0.0597  | 0.3333  | 0.0069  | 0.0833  |
| jar               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pan               | 0.0029  | 0.0833  | 0.0000  | 0.0000  |
| tray              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pack              | 0.0012  | 0.1538  | 0.0000  | 0.0000  |
| coffee maker      | 0.0132  | 0.3333  | 0.0000  | 0.0000  |
| food              | 0.1000  | 0.1000  | 0.1000  | 0.1000  |
| jalousie          | 0.0455  | 0.5000  | 0.0000  | 0.0000  |
| balcony           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rail              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crib              | 0.0772  | 1.0000  | 0.0513  | 0.6667  |
| sign              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crate             | 0.1982  | 0.8000  | 0.0061  | 0.2000  |
| drawer            | 0.0013  | 0.1111  | 0.0013  | 0.1111  |
| stairs            | 0.0184  | 0.3571  | 0.0012  | 0.0714  |
| calendar          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| holder            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bar               | 0.0191  | 0.6667  | 0.0000  | 0.0000  |
| tissue            | 0.0076  | 0.3333  | 0.0076  | 0.3333  |
| soap dispenser    | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| roll              | 0.0050  | 0.1000  | 0.0000  | 0.0000  |
| bicycle           | 0.1333  | 1.0000  | 0.0333  | 0.5000  |
| umbrella          | 0.0769  | 1.0000  | 0.0000  | 0.0000  |
| speaker           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine           | 0.5000  | 1.0000  | 0.0000  | 0.0000  |
| mop               | 0.6667  | 0.6667  | 0.0000  | 0.0000  |
| broom             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet             | 1.0000  | 1.0000  | 0.1667  | 1.0000  |
| soap dish         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coil              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| chandelier        | 0.0556  | 0.5000  | 0.0000  | 0.0000  |
| washing machine   | 0.5090  | 0.8571  | 0.0689  | 0.4286  |
| range hood        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bread             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hat               | 0.0040  | 0.1429  | 0.0000  | 0.0000  |
| spoon             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hanger            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hook              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tablet            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| window frame      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washbasin         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sack              | 0.3333  | 0.5714  | 0.0000  | 0.0000  |
| toaster           | 0.0114  | 0.2500  | 0.0000  | 0.0000  |
| pipe              | 0.0109  | 0.2500  | 0.0000  | 0.0000  |
| dumbbell          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vacuum cleaner    | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| letter            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| salt              | 0.1333  | 1.0000  | 0.0000  | 0.0000  |
| shampoo           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flush             | 0.0147  | 0.5000  | 0.0000  | 0.0000  |
| cube              | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| footstool         | 1.0000  | 1.0000  | 0.2000  | 1.0000  |
| glove             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water heater      | 0.3333  | 1.0000  | 0.3333  | 1.0000  |
| album             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tool              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| map               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| dishwasher        | 0.0833  | 0.3333  | 0.0833  | 0.3333  |
| seasoning         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toothbrush        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| helmets           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beam              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| screen            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------           | ------- | ------- | ------- | ------- |
| 3rscan            | 0.1160  | 0.3748  | 0.0256  | 0.1083  |
| head              | 0.1202  | 0.4781  | 0.0199  | 0.1313  |
| common            | 0.1164  | 0.3049  | 0.0248  | 0.0817  |
| tail              | 0.1059  | 0.2561  | 0.0395  | 0.1000  |
| small             | 0.0122  | 0.1037  | 0.0015  | 0.0136  |
| medium            | 0.1269  | 0.4203  | 0.0260  | 0.1147  |
| large             | 0.0923  | 0.5760  | 0.0333  | 0.1864  |
+-------------------+---------+---------+---------+---------+
Rank[0/8] 06/19/2025 16:56:11 INFO metrics.py:437 | number of matterport3d results: 339
Rank[0/8] 06/19/2025 16:57:46 INFO metrics.py:332 | eval results of matterport3d:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| plant               | 0.1684  | 0.6250  | 0.0194  | 0.2054  |
| mirror              | 0.0319  | 0.3108  | 0.0001  | 0.0405  |
| sink                | 0.1170  | 0.5556  | 0.0062  | 0.1010  |
| doorframe           | 0.1345  | 0.6966  | 0.0223  | 0.2190  |
| faucet              | 0.0085  | 0.1944  | 0.0000  | 0.0000  |
| towel               | 0.1136  | 0.5273  | 0.0007  | 0.0636  |
| light               | 0.0101  | 0.1339  | 0.0004  | 0.0142  |
| door                | 0.0555  | 0.4256  | 0.0015  | 0.0470  |
| curtain             | 0.1446  | 0.7083  | 0.0319  | 0.3646  |
| picture             | 0.0261  | 0.2295  | 0.0007  | 0.0217  |
| flowerpot           | 0.0541  | 0.5200  | 0.0180  | 0.3200  |
| washbasin           | 0.0048  | 0.3571  | 0.0016  | 0.1429  |
| countertop          | 0.0021  | 0.2143  | 0.0000  | 0.0357  |
| cabinet             | 0.0549  | 0.5636  | 0.0125  | 0.2288  |
| box                 | 0.0101  | 0.4808  | 0.0004  | 0.0962  |
| toiletry            | 0.0056  | 0.3636  | 0.0000  | 0.0000  |
| lamp                | 0.2090  | 0.6264  | 0.0038  | 0.1209  |
| bottle              | 0.0599  | 0.3958  | 0.0002  | 0.0625  |
| wardrobe            | 0.1358  | 0.5455  | 0.0005  | 0.2727  |
| pot                 | 0.0020  | 0.2308  | 0.0015  | 0.0769  |
| shelf               | 0.0727  | 0.4745  | 0.0135  | 0.1971  |
| case                | 0.0001  | 0.1667  | 0.0000  | 0.0556  |
| drawer              | 0.0032  | 0.2500  | 0.0000  | 0.0000  |
| vase                | 0.0741  | 0.3333  | 0.0000  | 0.0000  |
| toilet              | 0.6409  | 0.8438  | 0.3731  | 0.6250  |
| bathtub             | 0.3119  | 0.7407  | 0.0951  | 0.5556  |
| clothes             | 0.0583  | 0.3966  | 0.0025  | 0.1379  |
| counter             | 0.0035  | 0.3030  | 0.0003  | 0.0606  |
| couch               | 0.1712  | 0.7609  | 0.0948  | 0.5435  |
| dresser             | 0.1745  | 0.8000  | 0.1540  | 0.7333  |
| switch              | 0.0003  | 0.1000  | 0.0000  | 0.0000  |
| decoration          | 0.0031  | 0.2619  | 0.0001  | 0.0238  |
| window              | 0.0511  | 0.4056  | 0.0029  | 0.0778  |
| table               | 0.1354  | 0.7244  | 0.0421  | 0.4359  |
| chair               | 0.2551  | 0.7653  | 0.1403  | 0.5578  |
| vent                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bed                 | 0.3645  | 0.8246  | 0.0688  | 0.5088  |
| carpet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bench               | 0.0419  | 0.4000  | 0.0038  | 0.0500  |
| stand               | 0.2512  | 0.7600  | 0.1250  | 0.5200  |
| soap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillow              | 0.1701  | 0.5552  | 0.0045  | 0.0442  |
| plate               | 0.0714  | 0.0714  | 0.0000  | 0.0000  |
| paper               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tv                  | 0.2414  | 0.5814  | 0.0319  | 0.1628  |
| book                | 0.0302  | 0.3307  | 0.0002  | 0.0079  |
| screen              | 0.0393  | 0.5000  | 0.0250  | 0.2500  |
| food                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bowl                | 0.0109  | 0.2424  | 0.0000  | 0.0000  |
| ottoman             | 0.0201  | 0.3889  | 0.0109  | 0.1667  |
| cushion             | 0.0007  | 0.1765  | 0.0000  | 0.0000  |
| fruit               | 0.0634  | 0.5000  | 0.0000  | 0.0000  |
| blanket             | 0.0185  | 0.3636  | 0.0013  | 0.0909  |
| fireplace           | 0.1455  | 0.4348  | 0.0004  | 0.0870  |
| stool               | 0.0022  | 0.2857  | 0.0015  | 0.1429  |
| teapot              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| desk                | 0.1387  | 0.6316  | 0.0608  | 0.5263  |
| footstool           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headboard           | 0.0057  | 0.3750  | 0.0000  | 0.0000  |
| frame               | 0.0031  | 0.2353  | 0.0001  | 0.0588  |
| statue              | 0.0200  | 0.4118  | 0.0138  | 0.1765  |
| candlestick         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cup                 | 0.0051  | 0.2000  | 0.0000  | 0.0000  |
| air conditioner     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basin               | 0.0045  | 1.0000  | 0.0023  | 1.0000  |
| clock               | 0.3333  | 0.3333  | 0.0000  | 0.0000  |
| roof                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| telephone           | 0.1297  | 0.5000  | 0.0023  | 0.2500  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| column              | 0.0176  | 0.2500  | 0.0006  | 0.1111  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillar              | 0.1706  | 0.7143  | 0.0294  | 0.2381  |
| steps               | 0.0009  | 0.2000  | 0.0001  | 0.1000  |
| mat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| window frame        | 0.0138  | 0.2759  | 0.0011  | 0.0575  |
| stairs              | 0.0021  | 0.2075  | 0.0002  | 0.0377  |
| doorway             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rail                | 0.0015  | 0.1852  | 0.0000  | 0.0185  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bin                 | 0.1607  | 0.6341  | 0.0745  | 0.2927  |
| dispenser           | 0.0250  | 0.5000  | 0.0000  | 0.0000  |
| toilet paper        | 0.2609  | 0.4722  | 0.0104  | 0.1389  |
| vanity              | 0.1602  | 0.6000  | 0.0721  | 0.5333  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.5135  | 0.8750  | 0.2911  | 0.5000  |
| handle              | 0.0029  | 0.2500  | 0.0000  | 0.0000  |
| windowsill          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue box          | 0.0078  | 0.2500  | 0.0000  | 0.0000  |
| tissue              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| heater              | 0.2500  | 1.0000  | 0.0000  | 0.0000  |
| shower              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| thermostat          | 0.0079  | 0.1667  | 0.0000  | 0.0000  |
| bucket              | 0.0243  | 0.4000  | 0.0051  | 0.2000  |
| soap dish           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| monitor             | 0.0817  | 0.5000  | 0.0000  | 0.0000  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine             | 0.3929  | 1.0000  | 0.1538  | 1.0000  |
| garage door         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pipe                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basket              | 0.0245  | 0.4000  | 0.0002  | 0.0400  |
| kitchen island      | 0.1722  | 0.7143  | 0.1666  | 0.4286  |
| piano               | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| radiator            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coffee maker        | 0.0128  | 0.3333  | 0.0000  | 0.0000  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scale               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fan                 | 0.3513  | 0.7778  | 0.0020  | 0.1111  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| magazine            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toy                 | 0.0764  | 0.3611  | 0.0003  | 0.0278  |
| bag                 | 0.1474  | 0.3077  | 0.0017  | 0.0769  |
| rack                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher   | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| pool table          | 0.0370  | 0.3333  | 0.0370  | 0.3333  |
| speaker             | 0.1250  | 0.1250  | 0.0000  | 0.0000  |
| projector           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| printer             | 0.3052  | 0.4286  | 0.1429  | 0.1429  |
| arch                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| keyboard            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.0319  | 0.6667  | 0.0056  | 0.6667  |
| excercise equipment | 0.1672  | 0.7778  | 0.0019  | 0.0556  |
| backpack            | 0.2500  | 1.0000  | 0.0000  | 0.0000  |
| board               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jacket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| microwave           | 0.2025  | 0.4167  | 0.0417  | 0.0833  |
| ladder              | 0.1132  | 0.3333  | 0.1111  | 0.1111  |
| bicycle             | 0.0067  | 1.0000  | 0.0067  | 1.0000  |
| container           | 0.0553  | 0.2593  | 0.0052  | 0.0741  |
| blackboard          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| holder              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| globe               | 0.0179  | 1.0000  | 0.0000  | 0.0000  |
| beam                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| partition           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball                | 0.6071  | 0.7500  | 0.4500  | 0.7500  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stove               | 0.1489  | 0.6000  | 0.0185  | 0.2000  |
| chandelier          | 0.0567  | 0.3158  | 0.0000  | 0.0000  |
| refrigerator        | 0.1449  | 0.4667  | 0.0333  | 0.0667  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| oven                | 0.0143  | 0.2000  | 0.0000  | 0.0000  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchenware         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| guitar              | 0.0171  | 0.4286  | 0.0000  | 0.0000  |
| shoe                | 0.0032  | 0.1200  | 0.0000  | 0.0000  |
| bidet               | 0.1000  | 0.5000  | 0.0500  | 0.5000  |
| soap dispenser      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hamper              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| socket              | 0.0278  | 0.2500  | 0.0000  | 0.0000  |
| hanger              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| laptop              | 0.1667  | 0.5000  | 0.0000  | 0.0000  |
| pan                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell            | 0.1000  | 0.5000  | 0.1000  | 0.5000  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| computer            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.0499  | 0.5000  | 0.0000  | 0.0000  |
| dishwasher          | 0.0278  | 0.2500  | 0.0000  | 0.0000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wine                | 0.0268  | 0.1905  | 0.0000  | 0.0000  |
| drum                | 0.0078  | 0.1250  | 0.0000  | 0.0000  |
| kettle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pack                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mouse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| matterport3d        | 0.0696  | 0.2820  | 0.0177  | 0.1043  |
| head                | 0.0961  | 0.3807  | 0.0226  | 0.1259  |
| common              | 0.0553  | 0.2138  | 0.0141  | 0.0957  |
| tail                | 0.0137  | 0.1136  | 0.0101  | 0.0505  |
| small               | 0.0394  | 0.1127  | 0.0001  | 0.0018  |
| medium              | 0.0644  | 0.3040  | 0.0189  | 0.1142  |
| large               | 0.0525  | 0.3681  | 0.0095  | 0.1546  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/19/2025 16:57:46 INFO metrics.py:437 | number of arkit results: 0
Rank[0/8] 06/19/2025 16:57:47 INFO stats.py:394 | Epoch[19] completed. Training Speed: 2.70 samples/sec across all devices.	Epoch Time: 1155.63 sec.	Average Epoch Time: 1155.63 sec.	Average Step Time: 2.96 sec.	Estimated Remaining Time: 1 day, 6:20:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:57:47 INFO accelerator.py:3111 | Saving current state to /job_data/checkpoints/checkpoint_1
Rank[2/8] 06/19/2025 16:57:48 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 19 to /job_data/checkpoints/checkpoint_1
Rank[1/8] 06/19/2025 16:57:48 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 19 to /job_data/checkpoints/checkpoint_1
Rank[5/8] 06/19/2025 16:57:48 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 19 to /job_data/checkpoints/checkpoint_1
Rank[4/8] 06/19/2025 16:57:48 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 19 to /job_data/checkpoints/checkpoint_1
Rank[3/8] 06/19/2025 16:57:48 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 19 to /job_data/checkpoints/checkpoint_1
Rank[6/8] 06/19/2025 16:57:48 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 19 to /job_data/checkpoints/checkpoint_1
Rank[7/8] 06/19/2025 16:57:48 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 19 to /job_data/checkpoints/checkpoint_1
Rank[0/8] 06/19/2025 16:57:49 INFO checkpointing.py:106 | Model weights saved in /job_data/checkpoints/checkpoint_1/model.safetensors
Rank[0/8] 06/19/2025 16:57:52 INFO checkpointing.py:113 | Optimizer state saved in /job_data/checkpoints/checkpoint_1/optimizer.bin
Rank[0/8] 06/19/2025 16:57:52 INFO checkpointing.py:120 | Scheduler state saved in /job_data/checkpoints/checkpoint_1/scheduler.bin
Rank[0/8] 06/19/2025 16:57:52 INFO checkpointing.py:137 | Sampler state for dataloader 0 saved in /job_data/checkpoints/checkpoint_1/sampler.bin
Rank[0/8] 06/19/2025 16:57:52 INFO checkpointing.py:137 | Sampler state for dataloader 1 saved in /job_data/checkpoints/checkpoint_1/sampler_1.bin
Rank[0/8] 06/19/2025 16:57:52 INFO checkpointing.py:164 | Random states saved in /job_data/checkpoints/checkpoint_1/random_states_0.pkl
Rank[0/8] 06/19/2025 16:57:52 INFO checkpointing.py:300 | Saving the state of TrainerProgressState to /job_data/checkpoints/checkpoint_1/custom_checkpoint_0.pkl
Rank[0/8] 06/19/2025 16:57:52 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 19 to /job_data/checkpoints/checkpoint_1
Rank[0/8] 06/19/2025 16:58:21 INFO stats.py:314 | Epoch[20] Step[24] GlobalStep[7824] Training Speed: 6.28 samples/sec across all devices.	Average Step Time: 1.27 sec.	Estimated Remaining Time: 1 day, 6:20:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:58:21 INFO loss_tracker.py:84 | Epoch[20/239] Step[24] GlobalStep[7824/NA]: det_loss_cls_0[0.7436] det_loss_box_0[1.0100] det_loss_cls_1[0.7198] det_loss_box_1[0.8665] det_loss_cls_2[0.7239] det_loss_box_2[0.8131] det_loss_cls_3[0.7146] det_loss_box_3[0.7780] det_loss_cls_4[0.6929] det_loss_box_4[0.7582] det_loss_cls_5[0.6835] det_loss_box_5[0.7556] det_loss_cls_dn_0[0.1289] det_loss_box_dn_0[0.3897] det_loss_cls_dn_1[0.1257] det_loss_box_dn_1[0.3620] det_loss_cls_dn_2[0.1250] det_loss_box_dn_2[0.3434] det_loss_cls_dn_3[0.1236] det_loss_box_dn_3[0.3429] det_loss_cls_dn_4[0.1233] det_loss_box_dn_4[0.3431] det_loss_cls_dn_5[0.1223] det_loss_box_dn_5[0.3427] loss_depth[0.0415] total_loss[12.1739]
Rank[0/8] 06/19/2025 16:58:46 INFO stats.py:314 | Epoch[20] Step[49] GlobalStep[7849] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 6:19:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:58:47 INFO loss_tracker.py:84 | Epoch[20/239] Step[49] GlobalStep[7849/NA]: det_loss_cls_0[0.6232] det_loss_box_0[0.8890] det_loss_cls_1[0.6034] det_loss_box_1[0.7623] det_loss_cls_2[0.6109] det_loss_box_2[0.7016] det_loss_cls_3[0.5911] det_loss_box_3[0.6748] det_loss_cls_4[0.5725] det_loss_box_4[0.6733] det_loss_cls_5[0.5625] det_loss_box_5[0.6782] det_loss_cls_dn_0[0.1239] det_loss_box_dn_0[0.4117] det_loss_cls_dn_1[0.1218] det_loss_box_dn_1[0.3760] det_loss_cls_dn_2[0.1230] det_loss_box_dn_2[0.3535] det_loss_cls_dn_3[0.1218] det_loss_box_dn_3[0.3519] det_loss_cls_dn_4[0.1206] det_loss_box_dn_4[0.3506] det_loss_cls_dn_5[0.1201] det_loss_box_dn_5[0.3503] loss_depth[0.0405] total_loss[10.9085]
Rank[0/8] 06/19/2025 16:59:13 INFO stats.py:314 | Epoch[20] Step[74] GlobalStep[7874] Training Speed: 6.15 samples/sec across all devices.	Average Step Time: 1.30 sec.	Estimated Remaining Time: 1 day, 6:17:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:59:13 INFO loss_tracker.py:84 | Epoch[20/239] Step[74] GlobalStep[7874/NA]: det_loss_cls_0[0.5817] det_loss_box_0[0.8556] det_loss_cls_1[0.5522] det_loss_box_1[0.7229] det_loss_cls_2[0.5554] det_loss_box_2[0.6841] det_loss_cls_3[0.5376] det_loss_box_3[0.6677] det_loss_cls_4[0.5208] det_loss_box_4[0.6553] det_loss_cls_5[0.5063] det_loss_box_5[0.6587] det_loss_cls_dn_0[0.1192] det_loss_box_dn_0[0.4159] det_loss_cls_dn_1[0.1166] det_loss_box_dn_1[0.3838] det_loss_cls_dn_2[0.1152] det_loss_box_dn_2[0.3554] det_loss_cls_dn_3[0.1136] det_loss_box_dn_3[0.3539] det_loss_cls_dn_4[0.1137] det_loss_box_dn_4[0.3527] det_loss_cls_dn_5[0.1129] det_loss_box_dn_5[0.3529] loss_depth[0.0387] total_loss[10.4427]
Rank[0/8] 06/19/2025 16:59:39 INFO stats.py:314 | Epoch[20] Step[99] GlobalStep[7899] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 6:16:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 16:59:39 INFO loss_tracker.py:84 | Epoch[20/239] Step[99] GlobalStep[7899/NA]: det_loss_cls_0[0.7016] det_loss_box_0[0.9932] det_loss_cls_1[0.6735] det_loss_box_1[0.8702] det_loss_cls_2[0.6765] det_loss_box_2[0.7963] det_loss_cls_3[0.6661] det_loss_box_3[0.7600] det_loss_cls_4[0.6417] det_loss_box_4[0.7527] det_loss_cls_5[0.6265] det_loss_box_5[0.7526] det_loss_cls_dn_0[0.1294] det_loss_box_dn_0[0.4262] det_loss_cls_dn_1[0.1252] det_loss_box_dn_1[0.3858] det_loss_cls_dn_2[0.1241] det_loss_box_dn_2[0.3643] det_loss_cls_dn_3[0.1228] det_loss_box_dn_3[0.3636] det_loss_cls_dn_4[0.1221] det_loss_box_dn_4[0.3623] det_loss_cls_dn_5[0.1212] det_loss_box_dn_5[0.3605] loss_depth[0.0418] total_loss[11.9601]
Rank[0/8] 06/19/2025 17:00:05 INFO stats.py:314 | Epoch[20] Step[124] GlobalStep[7924] Training Speed: 7.75 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 1 day, 6:14:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:00:05 INFO loss_tracker.py:84 | Epoch[20/239] Step[124] GlobalStep[7924/NA]: det_loss_cls_0[0.6345] det_loss_box_0[0.8401] det_loss_cls_1[0.6089] det_loss_box_1[0.7083] det_loss_cls_2[0.6027] det_loss_box_2[0.6474] det_loss_cls_3[0.5997] det_loss_box_3[0.6273] det_loss_cls_4[0.5740] det_loss_box_4[0.6151] det_loss_cls_5[0.5629] det_loss_box_5[0.6194] det_loss_cls_dn_0[0.1301] det_loss_box_dn_0[0.3450] det_loss_cls_dn_1[0.1269] det_loss_box_dn_1[0.3157] det_loss_cls_dn_2[0.1255] det_loss_box_dn_2[0.2957] det_loss_cls_dn_3[0.1236] det_loss_box_dn_3[0.2950] det_loss_cls_dn_4[0.1226] det_loss_box_dn_4[0.2950] det_loss_cls_dn_5[0.1222] det_loss_box_dn_5[0.2945] loss_depth[0.0394] total_loss[10.2715]
Rank[0/8] 06/19/2025 17:00:31 INFO stats.py:314 | Epoch[20] Step[149] GlobalStep[7949] Training Speed: 6.04 samples/sec across all devices.	Average Step Time: 1.32 sec.	Estimated Remaining Time: 1 day, 6:12:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:00:31 INFO loss_tracker.py:84 | Epoch[20/239] Step[149] GlobalStep[7949/NA]: det_loss_cls_0[0.6501] det_loss_box_0[0.8183] det_loss_cls_1[0.6264] det_loss_box_1[0.6894] det_loss_cls_2[0.6222] det_loss_box_2[0.6229] det_loss_cls_3[0.6169] det_loss_box_3[0.6037] det_loss_cls_4[0.5988] det_loss_box_4[0.5930] det_loss_cls_5[0.5879] det_loss_box_5[0.5930] det_loss_cls_dn_0[0.1190] det_loss_box_dn_0[0.3114] det_loss_cls_dn_1[0.1172] det_loss_box_dn_1[0.2894] det_loss_cls_dn_2[0.1181] det_loss_box_dn_2[0.2655] det_loss_cls_dn_3[0.1165] det_loss_box_dn_3[0.2652] det_loss_cls_dn_4[0.1166] det_loss_box_dn_4[0.2649] det_loss_cls_dn_5[0.1159] det_loss_box_dn_5[0.2655] loss_depth[0.0439] total_loss[10.0317]
Rank[0/8] 06/19/2025 17:00:57 INFO stats.py:314 | Epoch[20] Step[174] GlobalStep[7974] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 6:11:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:00:57 INFO loss_tracker.py:84 | Epoch[20/239] Step[174] GlobalStep[7974/NA]: det_loss_cls_0[0.5477] det_loss_box_0[0.7339] det_loss_cls_1[0.5252] det_loss_box_1[0.6192] det_loss_cls_2[0.5207] det_loss_box_2[0.5741] det_loss_cls_3[0.5154] det_loss_box_3[0.5514] det_loss_cls_4[0.4889] det_loss_box_4[0.5460] det_loss_cls_5[0.4730] det_loss_box_5[0.5485] det_loss_cls_dn_0[0.1209] det_loss_box_dn_0[0.3809] det_loss_cls_dn_1[0.1183] det_loss_box_dn_1[0.3516] det_loss_cls_dn_2[0.1171] det_loss_box_dn_2[0.3374] det_loss_cls_dn_3[0.1169] det_loss_box_dn_3[0.3375] det_loss_cls_dn_4[0.1164] det_loss_box_dn_4[0.3374] det_loss_cls_dn_5[0.1152] det_loss_box_dn_5[0.3370] loss_depth[0.0406] total_loss[9.4712]
Rank[0/8] 06/19/2025 17:01:23 INFO stats.py:314 | Epoch[20] Step[199] GlobalStep[7999] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 6:09:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:01:23 INFO loss_tracker.py:84 | Epoch[20/239] Step[199] GlobalStep[7999/NA]: det_loss_cls_0[0.7014] det_loss_box_0[0.9458] det_loss_cls_1[0.6846] det_loss_box_1[0.8157] det_loss_cls_2[0.6764] det_loss_box_2[0.7553] det_loss_cls_3[0.6646] det_loss_box_3[0.7314] det_loss_cls_4[0.6533] det_loss_box_4[0.7150] det_loss_cls_5[0.6377] det_loss_box_5[0.7149] det_loss_cls_dn_0[0.1361] det_loss_box_dn_0[0.4033] det_loss_cls_dn_1[0.1315] det_loss_box_dn_1[0.3758] det_loss_cls_dn_2[0.1310] det_loss_box_dn_2[0.3575] det_loss_cls_dn_3[0.1308] det_loss_box_dn_3[0.3574] det_loss_cls_dn_4[0.1306] det_loss_box_dn_4[0.3569] det_loss_cls_dn_5[0.1293] det_loss_box_dn_5[0.3565] loss_depth[0.0454] total_loss[11.7382]
Rank[0/8] 06/19/2025 17:01:49 INFO stats.py:314 | Epoch[20] Step[224] GlobalStep[8024] Training Speed: 8.08 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 6:08:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:01:49 INFO loss_tracker.py:84 | Epoch[20/239] Step[224] GlobalStep[8024/NA]: det_loss_cls_0[0.6623] det_loss_box_0[0.8650] det_loss_cls_1[0.6597] det_loss_box_1[0.7222] det_loss_cls_2[0.6613] det_loss_box_2[0.6652] det_loss_cls_3[0.6492] det_loss_box_3[0.6475] det_loss_cls_4[0.6160] det_loss_box_4[0.6449] det_loss_cls_5[0.6142] det_loss_box_5[0.6367] det_loss_cls_dn_0[0.1217] det_loss_box_dn_0[0.3265] det_loss_cls_dn_1[0.1203] det_loss_box_dn_1[0.2995] det_loss_cls_dn_2[0.1191] det_loss_box_dn_2[0.2775] det_loss_cls_dn_3[0.1174] det_loss_box_dn_3[0.2770] det_loss_cls_dn_4[0.1163] det_loss_box_dn_4[0.2758] det_loss_cls_dn_5[0.1156] det_loss_box_dn_5[0.2763] loss_depth[0.0378] total_loss[10.5254]
Rank[0/8] 06/19/2025 17:02:15 INFO stats.py:314 | Epoch[20] Step[249] GlobalStep[8049] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 6:06:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:02:15 INFO loss_tracker.py:84 | Epoch[20/239] Step[249] GlobalStep[8049/NA]: det_loss_cls_0[0.5826] det_loss_box_0[0.8340] det_loss_cls_1[0.5635] det_loss_box_1[0.6973] det_loss_cls_2[0.5605] det_loss_box_2[0.6548] det_loss_cls_3[0.5577] det_loss_box_3[0.6349] det_loss_cls_4[0.5373] det_loss_box_4[0.6276] det_loss_cls_5[0.5334] det_loss_box_5[0.6254] det_loss_cls_dn_0[0.1159] det_loss_box_dn_0[0.3410] det_loss_cls_dn_1[0.1139] det_loss_box_dn_1[0.3122] det_loss_cls_dn_2[0.1138] det_loss_box_dn_2[0.2934] det_loss_cls_dn_3[0.1128] det_loss_box_dn_3[0.2922] det_loss_cls_dn_4[0.1114] det_loss_box_dn_4[0.2918] det_loss_cls_dn_5[0.1112] det_loss_box_dn_5[0.2908] loss_depth[0.0434] total_loss[9.9527]
Rank[0/8] 06/19/2025 17:02:41 INFO stats.py:314 | Epoch[20] Step[274] GlobalStep[8074] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 6:05:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:02:41 INFO loss_tracker.py:84 | Epoch[20/239] Step[274] GlobalStep[8074/NA]: det_loss_cls_0[0.6989] det_loss_box_0[0.8997] det_loss_cls_1[0.6779] det_loss_box_1[0.7470] det_loss_cls_2[0.6737] det_loss_box_2[0.6933] det_loss_cls_3[0.6596] det_loss_box_3[0.6750] det_loss_cls_4[0.6377] det_loss_box_4[0.6538] det_loss_cls_5[0.6271] det_loss_box_5[0.6514] det_loss_cls_dn_0[0.1326] det_loss_box_dn_0[0.2981] det_loss_cls_dn_1[0.1290] det_loss_box_dn_1[0.2722] det_loss_cls_dn_2[0.1288] det_loss_box_dn_2[0.2528] det_loss_cls_dn_3[0.1271] det_loss_box_dn_3[0.2521] det_loss_cls_dn_4[0.1249] det_loss_box_dn_4[0.2517] det_loss_cls_dn_5[0.1246] det_loss_box_dn_5[0.2509] loss_depth[0.0417] total_loss[10.6814]
Rank[0/8] 06/19/2025 17:03:06 INFO stats.py:314 | Epoch[20] Step[299] GlobalStep[8099] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 6:03:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:03:06 INFO loss_tracker.py:84 | Epoch[20/239] Step[299] GlobalStep[8099/NA]: det_loss_cls_0[0.6248] det_loss_box_0[0.7809] det_loss_cls_1[0.6233] det_loss_box_1[0.6570] det_loss_cls_2[0.6058] det_loss_box_2[0.6010] det_loss_cls_3[0.6072] det_loss_box_3[0.5916] det_loss_cls_4[0.5714] det_loss_box_4[0.5672] det_loss_cls_5[0.5658] det_loss_box_5[0.5648] det_loss_cls_dn_0[0.1267] det_loss_box_dn_0[0.3294] det_loss_cls_dn_1[0.1230] det_loss_box_dn_1[0.2985] det_loss_cls_dn_2[0.1217] det_loss_box_dn_2[0.2820] det_loss_cls_dn_3[0.1209] det_loss_box_dn_3[0.2808] det_loss_cls_dn_4[0.1206] det_loss_box_dn_4[0.2800] det_loss_cls_dn_5[0.1199] det_loss_box_dn_5[0.2801] loss_depth[0.0394] total_loss[9.8837]
Rank[0/8] 06/19/2025 17:03:33 INFO stats.py:314 | Epoch[20] Step[324] GlobalStep[8124] Training Speed: 6.39 samples/sec across all devices.	Average Step Time: 1.25 sec.	Estimated Remaining Time: 1 day, 6:02:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:03:33 INFO loss_tracker.py:84 | Epoch[20/239] Step[324] GlobalStep[8124/NA]: det_loss_cls_0[0.4866] det_loss_box_0[0.6461] det_loss_cls_1[0.4780] det_loss_box_1[0.5325] det_loss_cls_2[0.4832] det_loss_box_2[0.4936] det_loss_cls_3[0.4704] det_loss_box_3[0.4758] det_loss_cls_4[0.4585] det_loss_box_4[0.4581] det_loss_cls_5[0.4396] det_loss_box_5[0.4662] det_loss_cls_dn_0[0.1160] det_loss_box_dn_0[0.3533] det_loss_cls_dn_1[0.1128] det_loss_box_dn_1[0.3167] det_loss_cls_dn_2[0.1114] det_loss_box_dn_2[0.2979] det_loss_cls_dn_3[0.1108] det_loss_box_dn_3[0.2970] det_loss_cls_dn_4[0.1100] det_loss_box_dn_4[0.2964] det_loss_cls_dn_5[0.1093] det_loss_box_dn_5[0.2964] loss_depth[0.0462] total_loss[8.4625]
Rank[0/8] 06/19/2025 17:03:58 INFO stats.py:314 | Epoch[20] Step[349] GlobalStep[8149] Training Speed: 7.92 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 6:00:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:03:58 INFO loss_tracker.py:84 | Epoch[20/239] Step[349] GlobalStep[8149/NA]: det_loss_cls_0[0.5226] det_loss_box_0[0.7772] det_loss_cls_1[0.5043] det_loss_box_1[0.6588] det_loss_cls_2[0.5133] det_loss_box_2[0.6006] det_loss_cls_3[0.5021] det_loss_box_3[0.5798] det_loss_cls_4[0.4849] det_loss_box_4[0.5716] det_loss_cls_5[0.4676] det_loss_box_5[0.5772] det_loss_cls_dn_0[0.1131] det_loss_box_dn_0[0.3734] det_loss_cls_dn_1[0.1094] det_loss_box_dn_1[0.3313] det_loss_cls_dn_2[0.1092] det_loss_box_dn_2[0.3125] det_loss_cls_dn_3[0.1091] det_loss_box_dn_3[0.3108] det_loss_cls_dn_4[0.1081] det_loss_box_dn_4[0.3103] det_loss_cls_dn_5[0.1067] det_loss_box_dn_5[0.3108] loss_depth[0.0431] total_loss[9.4077]
Rank[0/8] 06/19/2025 17:04:25 INFO stats.py:314 | Epoch[20] Step[374] GlobalStep[8174] Training Speed: 8.86 samples/sec across all devices.	Average Step Time: 0.90 sec.	Estimated Remaining Time: 1 day, 5:59:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:04:25 INFO loss_tracker.py:84 | Epoch[20/239] Step[374] GlobalStep[8174/NA]: det_loss_cls_0[0.4269] det_loss_box_0[0.6712] det_loss_cls_1[0.4203] det_loss_box_1[0.5794] det_loss_cls_2[0.4181] det_loss_box_2[0.5506] det_loss_cls_3[0.4065] det_loss_box_3[0.5440] det_loss_cls_4[0.3918] det_loss_box_4[0.5380] det_loss_cls_5[0.3787] det_loss_box_5[0.5365] det_loss_cls_dn_0[0.1186] det_loss_box_dn_0[0.5181] det_loss_cls_dn_1[0.1151] det_loss_box_dn_1[0.4757] det_loss_cls_dn_2[0.1152] det_loss_box_dn_2[0.4578] det_loss_cls_dn_3[0.1140] det_loss_box_dn_3[0.4588] det_loss_cls_dn_4[0.1132] det_loss_box_dn_4[0.4568] det_loss_cls_dn_5[0.1130] det_loss_box_dn_5[0.4553] loss_depth[0.0436] total_loss[9.4173]
Rank[0/8] 06/19/2025 17:04:40 INFO stats.py:394 | Epoch[20] completed. Training Speed: 7.65 samples/sec across all devices.	Epoch Time: 408.10 sec.	Average Epoch Time: 408.10 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 5:58:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:04:53 INFO stats.py:314 | Epoch[21] Step[9] GlobalStep[8199] Training Speed: 7.79 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 1 day, 5:57:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:05:09 INFO loss_tracker.py:84 | Epoch[21/239] Step[24] GlobalStep[8214/NA]: det_loss_cls_0[0.5970] det_loss_box_0[0.8203] det_loss_cls_1[0.5698] det_loss_box_1[0.7057] det_loss_cls_2[0.5782] det_loss_box_2[0.6481] det_loss_cls_3[0.5770] det_loss_box_3[0.6303] det_loss_cls_4[0.5487] det_loss_box_4[0.6218] det_loss_cls_5[0.5393] det_loss_box_5[0.6225] det_loss_cls_dn_0[0.1204] det_loss_box_dn_0[0.3375] det_loss_cls_dn_1[0.1174] det_loss_box_dn_1[0.3061] det_loss_cls_dn_2[0.1163] det_loss_box_dn_2[0.2813] det_loss_cls_dn_3[0.1147] det_loss_box_dn_3[0.2803] det_loss_cls_dn_4[0.1142] det_loss_box_dn_4[0.2790] det_loss_cls_dn_5[0.1143] det_loss_box_dn_5[0.2790] loss_depth[0.0416] total_loss[9.9610]
Rank[0/8] 06/19/2025 17:05:19 INFO stats.py:314 | Epoch[21] Step[34] GlobalStep[8224] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 5:56:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:05:35 INFO loss_tracker.py:84 | Epoch[21/239] Step[49] GlobalStep[8239/NA]: det_loss_cls_0[0.4891] det_loss_box_0[0.6253] det_loss_cls_1[0.4879] det_loss_box_1[0.5259] det_loss_cls_2[0.4734] det_loss_box_2[0.4850] det_loss_cls_3[0.4677] det_loss_box_3[0.4706] det_loss_cls_4[0.4451] det_loss_box_4[0.4608] det_loss_cls_5[0.4300] det_loss_box_5[0.4642] det_loss_cls_dn_0[0.1106] det_loss_box_dn_0[0.3359] det_loss_cls_dn_1[0.1077] det_loss_box_dn_1[0.3042] det_loss_cls_dn_2[0.1069] det_loss_box_dn_2[0.2858] det_loss_cls_dn_3[0.1055] det_loss_box_dn_3[0.2852] det_loss_cls_dn_4[0.1050] det_loss_box_dn_4[0.2844] det_loss_cls_dn_5[0.1047] det_loss_box_dn_5[0.2836] loss_depth[0.0396] total_loss[8.2842]
Rank[0/8] 06/19/2025 17:05:45 INFO stats.py:314 | Epoch[21] Step[59] GlobalStep[8249] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 5:54:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:06:01 INFO loss_tracker.py:84 | Epoch[21/239] Step[74] GlobalStep[8264/NA]: det_loss_cls_0[0.5528] det_loss_box_0[0.7124] det_loss_cls_1[0.5425] det_loss_box_1[0.6210] det_loss_cls_2[0.5432] det_loss_box_2[0.5717] det_loss_cls_3[0.5283] det_loss_box_3[0.5660] det_loss_cls_4[0.5147] det_loss_box_4[0.5610] det_loss_cls_5[0.4968] det_loss_box_5[0.5704] det_loss_cls_dn_0[0.1240] det_loss_box_dn_0[0.4242] det_loss_cls_dn_1[0.1219] det_loss_box_dn_1[0.3846] det_loss_cls_dn_2[0.1215] det_loss_box_dn_2[0.3572] det_loss_cls_dn_3[0.1203] det_loss_box_dn_3[0.3564] det_loss_cls_dn_4[0.1200] det_loss_box_dn_4[0.3544] det_loss_cls_dn_5[0.1191] det_loss_box_dn_5[0.3536] loss_depth[0.0431] total_loss[9.7809]
Rank[0/8] 06/19/2025 17:06:11 INFO stats.py:314 | Epoch[21] Step[84] GlobalStep[8274] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 5:53:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:06:27 INFO loss_tracker.py:84 | Epoch[21/239] Step[99] GlobalStep[8289/NA]: det_loss_cls_0[0.5451] det_loss_box_0[0.7600] det_loss_cls_1[0.5203] det_loss_box_1[0.6450] det_loss_cls_2[0.5225] det_loss_box_2[0.5971] det_loss_cls_3[0.5140] det_loss_box_3[0.5754] det_loss_cls_4[0.4907] det_loss_box_4[0.5681] det_loss_cls_5[0.4740] det_loss_box_5[0.5751] det_loss_cls_dn_0[0.1224] det_loss_box_dn_0[0.4243] det_loss_cls_dn_1[0.1193] det_loss_box_dn_1[0.3914] det_loss_cls_dn_2[0.1193] det_loss_box_dn_2[0.3764] det_loss_cls_dn_3[0.1189] det_loss_box_dn_3[0.3756] det_loss_cls_dn_4[0.1190] det_loss_box_dn_4[0.3763] det_loss_cls_dn_5[0.1177] det_loss_box_dn_5[0.3769] loss_depth[0.0411] total_loss[9.8659]
Rank[0/8] 06/19/2025 17:06:37 INFO stats.py:314 | Epoch[21] Step[109] GlobalStep[8299] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 5:51:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:06:52 INFO loss_tracker.py:84 | Epoch[21/239] Step[124] GlobalStep[8314/NA]: det_loss_cls_0[0.5220] det_loss_box_0[0.6773] det_loss_cls_1[0.4960] det_loss_box_1[0.5931] det_loss_cls_2[0.5053] det_loss_box_2[0.5327] det_loss_cls_3[0.4952] det_loss_box_3[0.5133] det_loss_cls_4[0.4737] det_loss_box_4[0.4922] det_loss_cls_5[0.4630] det_loss_box_5[0.4925] det_loss_cls_dn_0[0.1209] det_loss_box_dn_0[0.3797] det_loss_cls_dn_1[0.1167] det_loss_box_dn_1[0.3438] det_loss_cls_dn_2[0.1167] det_loss_box_dn_2[0.3237] det_loss_cls_dn_3[0.1159] det_loss_box_dn_3[0.3216] det_loss_cls_dn_4[0.1152] det_loss_box_dn_4[0.3209] det_loss_cls_dn_5[0.1148] det_loss_box_dn_5[0.3206] loss_depth[0.0424] total_loss[9.0092]
Rank[0/8] 06/19/2025 17:07:03 INFO stats.py:314 | Epoch[21] Step[134] GlobalStep[8324] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 5:50:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:07:19 INFO loss_tracker.py:84 | Epoch[21/239] Step[149] GlobalStep[8339/NA]: det_loss_cls_0[0.6024] det_loss_box_0[0.8196] det_loss_cls_1[0.5727] det_loss_box_1[0.6738] det_loss_cls_2[0.5772] det_loss_box_2[0.6033] det_loss_cls_3[0.5651] det_loss_box_3[0.5872] det_loss_cls_4[0.5357] det_loss_box_4[0.5872] det_loss_cls_5[0.5342] det_loss_box_5[0.5838] det_loss_cls_dn_0[0.1253] det_loss_box_dn_0[0.3358] det_loss_cls_dn_1[0.1227] det_loss_box_dn_1[0.3002] det_loss_cls_dn_2[0.1229] det_loss_box_dn_2[0.2870] det_loss_cls_dn_3[0.1225] det_loss_box_dn_3[0.2859] det_loss_cls_dn_4[0.1220] det_loss_box_dn_4[0.2850] det_loss_cls_dn_5[0.1225] det_loss_box_dn_5[0.2842] loss_depth[0.0434] total_loss[9.8014]
Rank[0/8] 06/19/2025 17:07:29 INFO stats.py:314 | Epoch[21] Step[159] GlobalStep[8349] Training Speed: 8.17 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 5:49:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:07:45 INFO loss_tracker.py:84 | Epoch[21/239] Step[174] GlobalStep[8364/NA]: det_loss_cls_0[0.6006] det_loss_box_0[0.8263] det_loss_cls_1[0.5793] det_loss_box_1[0.6924] det_loss_cls_2[0.5656] det_loss_box_2[0.6333] det_loss_cls_3[0.5662] det_loss_box_3[0.6160] det_loss_cls_4[0.5457] det_loss_box_4[0.6125] det_loss_cls_5[0.5256] det_loss_box_5[0.6194] det_loss_cls_dn_0[0.1157] det_loss_box_dn_0[0.4183] det_loss_cls_dn_1[0.1132] det_loss_box_dn_1[0.3783] det_loss_cls_dn_2[0.1121] det_loss_box_dn_2[0.3616] det_loss_cls_dn_3[0.1106] det_loss_box_dn_3[0.3610] det_loss_cls_dn_4[0.1116] det_loss_box_dn_4[0.3604] det_loss_cls_dn_5[0.1102] det_loss_box_dn_5[0.3601] loss_depth[0.0393] total_loss[10.3355]
Rank[0/8] 06/19/2025 17:07:55 INFO stats.py:314 | Epoch[21] Step[184] GlobalStep[8374] Training Speed: 6.24 samples/sec across all devices.	Average Step Time: 1.28 sec.	Estimated Remaining Time: 1 day, 5:47:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:08:11 INFO loss_tracker.py:84 | Epoch[21/239] Step[199] GlobalStep[8389/NA]: det_loss_cls_0[0.7154] det_loss_box_0[0.9558] det_loss_cls_1[0.6730] det_loss_box_1[0.7594] det_loss_cls_2[0.6729] det_loss_box_2[0.6894] det_loss_cls_3[0.6569] det_loss_box_3[0.6719] det_loss_cls_4[0.6333] det_loss_box_4[0.6567] det_loss_cls_5[0.6130] det_loss_box_5[0.6631] det_loss_cls_dn_0[0.1257] det_loss_box_dn_0[0.3238] det_loss_cls_dn_1[0.1195] det_loss_box_dn_1[0.2949] det_loss_cls_dn_2[0.1191] det_loss_box_dn_2[0.2777] det_loss_cls_dn_3[0.1177] det_loss_box_dn_3[0.2769] det_loss_cls_dn_4[0.1165] det_loss_box_dn_4[0.2762] det_loss_cls_dn_5[0.1160] det_loss_box_dn_5[0.2758] loss_depth[0.0438] total_loss[10.8446]
Rank[0/8] 06/19/2025 17:08:21 INFO stats.py:314 | Epoch[21] Step[209] GlobalStep[8399] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 5:46:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:08:37 INFO loss_tracker.py:84 | Epoch[21/239] Step[224] GlobalStep[8414/NA]: det_loss_cls_0[0.5583] det_loss_box_0[0.8724] det_loss_cls_1[0.5441] det_loss_box_1[0.7296] det_loss_cls_2[0.5426] det_loss_box_2[0.6902] det_loss_cls_3[0.5251] det_loss_box_3[0.6703] det_loss_cls_4[0.4945] det_loss_box_4[0.6682] det_loss_cls_5[0.4784] det_loss_box_5[0.6659] det_loss_cls_dn_0[0.1120] det_loss_box_dn_0[0.5158] det_loss_cls_dn_1[0.1054] det_loss_box_dn_1[0.4831] det_loss_cls_dn_2[0.1057] det_loss_box_dn_2[0.4639] det_loss_cls_dn_3[0.1048] det_loss_box_dn_3[0.4632] det_loss_cls_dn_4[0.1036] det_loss_box_dn_4[0.4629] det_loss_cls_dn_5[0.1032] det_loss_box_dn_5[0.4630] loss_depth[0.0441] total_loss[10.9702]
Rank[0/8] 06/19/2025 17:08:47 INFO stats.py:314 | Epoch[21] Step[234] GlobalStep[8424] Training Speed: 7.97 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 5:44:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:09:03 INFO loss_tracker.py:84 | Epoch[21/239] Step[249] GlobalStep[8439/NA]: det_loss_cls_0[0.5143] det_loss_box_0[0.6495] det_loss_cls_1[0.4981] det_loss_box_1[0.5224] det_loss_cls_2[0.4960] det_loss_box_2[0.4729] det_loss_cls_3[0.4887] det_loss_box_3[0.4470] det_loss_cls_4[0.4630] det_loss_box_4[0.4471] det_loss_cls_5[0.4505] det_loss_box_5[0.4484] det_loss_cls_dn_0[0.1231] det_loss_box_dn_0[0.3225] det_loss_cls_dn_1[0.1177] det_loss_box_dn_1[0.2966] det_loss_cls_dn_2[0.1169] det_loss_box_dn_2[0.2772] det_loss_cls_dn_3[0.1162] det_loss_box_dn_3[0.2761] det_loss_cls_dn_4[0.1156] det_loss_box_dn_4[0.2761] det_loss_cls_dn_5[0.1148] det_loss_box_dn_5[0.2757] loss_depth[0.0419] total_loss[8.3684]
Rank[0/8] 06/19/2025 17:09:14 INFO stats.py:314 | Epoch[21] Step[259] GlobalStep[8449] Training Speed: 7.97 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 5:43:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:09:29 INFO loss_tracker.py:84 | Epoch[21/239] Step[274] GlobalStep[8464/NA]: det_loss_cls_0[0.5875] det_loss_box_0[0.8312] det_loss_cls_1[0.5641] det_loss_box_1[0.6943] det_loss_cls_2[0.5716] det_loss_box_2[0.6273] det_loss_cls_3[0.5501] det_loss_box_3[0.5989] det_loss_cls_4[0.5328] det_loss_box_4[0.5738] det_loss_cls_5[0.5242] det_loss_box_5[0.5755] det_loss_cls_dn_0[0.1183] det_loss_box_dn_0[0.4166] det_loss_cls_dn_1[0.1152] det_loss_box_dn_1[0.3827] det_loss_cls_dn_2[0.1144] det_loss_box_dn_2[0.3647] det_loss_cls_dn_3[0.1132] det_loss_box_dn_3[0.3643] det_loss_cls_dn_4[0.1113] det_loss_box_dn_4[0.3630] det_loss_cls_dn_5[0.1112] det_loss_box_dn_5[0.3624] loss_depth[0.0407] total_loss[10.2094]
Rank[0/8] 06/19/2025 17:09:40 INFO stats.py:314 | Epoch[21] Step[284] GlobalStep[8474] Training Speed: 8.15 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 5:41:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:09:55 INFO loss_tracker.py:84 | Epoch[21/239] Step[299] GlobalStep[8489/NA]: det_loss_cls_0[0.6555] det_loss_box_0[0.9032] det_loss_cls_1[0.6295] det_loss_box_1[0.7603] det_loss_cls_2[0.6166] det_loss_box_2[0.6951] det_loss_cls_3[0.6043] det_loss_box_3[0.6791] det_loss_cls_4[0.5862] det_loss_box_4[0.6720] det_loss_cls_5[0.5753] det_loss_box_5[0.6738] det_loss_cls_dn_0[0.1299] det_loss_box_dn_0[0.3745] det_loss_cls_dn_1[0.1261] det_loss_box_dn_1[0.3386] det_loss_cls_dn_2[0.1254] det_loss_box_dn_2[0.3174] det_loss_cls_dn_3[0.1247] det_loss_box_dn_3[0.3159] det_loss_cls_dn_4[0.1235] det_loss_box_dn_4[0.3150] det_loss_cls_dn_5[0.1230] det_loss_box_dn_5[0.3147] loss_depth[0.0432] total_loss[10.8228]
Rank[0/8] 06/19/2025 17:10:06 INFO stats.py:314 | Epoch[21] Step[309] GlobalStep[8499] Training Speed: 8.05 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 5:40:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:10:21 INFO loss_tracker.py:84 | Epoch[21/239] Step[324] GlobalStep[8514/NA]: det_loss_cls_0[0.5302] det_loss_box_0[0.7593] det_loss_cls_1[0.5137] det_loss_box_1[0.6415] det_loss_cls_2[0.5144] det_loss_box_2[0.5986] det_loss_cls_3[0.5079] det_loss_box_3[0.5769] det_loss_cls_4[0.4827] det_loss_box_4[0.5690] det_loss_cls_5[0.4643] det_loss_box_5[0.5741] det_loss_cls_dn_0[0.1187] det_loss_box_dn_0[0.3986] det_loss_cls_dn_1[0.1169] det_loss_box_dn_1[0.3631] det_loss_cls_dn_2[0.1155] det_loss_box_dn_2[0.3422] det_loss_cls_dn_3[0.1146] det_loss_box_dn_3[0.3413] det_loss_cls_dn_4[0.1138] det_loss_box_dn_4[0.3422] det_loss_cls_dn_5[0.1134] det_loss_box_dn_5[0.3410] loss_depth[0.0459] total_loss[9.5999]
Rank[0/8] 06/19/2025 17:10:32 INFO stats.py:314 | Epoch[21] Step[334] GlobalStep[8524] Training Speed: 8.08 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 5:39:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:10:47 INFO loss_tracker.py:84 | Epoch[21/239] Step[349] GlobalStep[8539/NA]: det_loss_cls_0[0.6937] det_loss_box_0[0.9612] det_loss_cls_1[0.6726] det_loss_box_1[0.8040] det_loss_cls_2[0.6784] det_loss_box_2[0.7190] det_loss_cls_3[0.6533] det_loss_box_3[0.6826] det_loss_cls_4[0.6349] det_loss_box_4[0.6735] det_loss_cls_5[0.6374] det_loss_box_5[0.6694] det_loss_cls_dn_0[0.1218] det_loss_box_dn_0[0.4166] det_loss_cls_dn_1[0.1183] det_loss_box_dn_1[0.3708] det_loss_cls_dn_2[0.1180] det_loss_box_dn_2[0.3518] det_loss_cls_dn_3[0.1155] det_loss_box_dn_3[0.3506] det_loss_cls_dn_4[0.1146] det_loss_box_dn_4[0.3507] det_loss_cls_dn_5[0.1139] det_loss_box_dn_5[0.3503] loss_depth[0.0436] total_loss[11.4166]
Rank[0/8] 06/19/2025 17:10:58 INFO stats.py:314 | Epoch[21] Step[359] GlobalStep[8549] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 5:37:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:11:13 INFO loss_tracker.py:84 | Epoch[21/239] Step[374] GlobalStep[8564/NA]: det_loss_cls_0[0.4405] det_loss_box_0[0.5753] det_loss_cls_1[0.4302] det_loss_box_1[0.4764] det_loss_cls_2[0.4291] det_loss_box_2[0.4319] det_loss_cls_3[0.4216] det_loss_box_3[0.4114] det_loss_cls_4[0.3986] det_loss_box_4[0.4061] det_loss_cls_5[0.3823] det_loss_box_5[0.4072] det_loss_cls_dn_0[0.1135] det_loss_box_dn_0[0.3215] det_loss_cls_dn_1[0.1100] det_loss_box_dn_1[0.2941] det_loss_cls_dn_2[0.1090] det_loss_box_dn_2[0.2703] det_loss_cls_dn_3[0.1085] det_loss_box_dn_3[0.2692] det_loss_cls_dn_4[0.1079] det_loss_box_dn_4[0.2690] det_loss_cls_dn_5[0.1060] det_loss_box_dn_5[0.2687] loss_depth[0.0415] total_loss[7.5997]
Rank[0/8] 06/19/2025 17:11:24 INFO stats.py:314 | Epoch[21] Step[384] GlobalStep[8574] Training Speed: 8.59 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 5:36:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:11:29 INFO stats.py:394 | Epoch[21] completed. Training Speed: 7.63 samples/sec across all devices.	Epoch Time: 409.13 sec.	Average Epoch Time: 409.13 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 5:36:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:11:53 INFO stats.py:314 | Epoch[22] Step[19] GlobalStep[8599] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 5:35:30.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:11:59 INFO loss_tracker.py:84 | Epoch[22/239] Step[24] GlobalStep[8604/NA]: det_loss_cls_0[0.5094] det_loss_box_0[0.6742] det_loss_cls_1[0.4988] det_loss_box_1[0.5551] det_loss_cls_2[0.5023] det_loss_box_2[0.5155] det_loss_cls_3[0.4918] det_loss_box_3[0.4982] det_loss_cls_4[0.4665] det_loss_box_4[0.5024] det_loss_cls_5[0.4572] det_loss_box_5[0.5004] det_loss_cls_dn_0[0.1218] det_loss_box_dn_0[0.3457] det_loss_cls_dn_1[0.1181] det_loss_box_dn_1[0.3141] det_loss_cls_dn_2[0.1179] det_loss_box_dn_2[0.2949] det_loss_cls_dn_3[0.1167] det_loss_box_dn_3[0.2940] det_loss_cls_dn_4[0.1159] det_loss_box_dn_4[0.2932] det_loss_cls_dn_5[0.1155] det_loss_box_dn_5[0.2924] loss_depth[0.0416] total_loss[8.7538]
Rank[0/8] 06/19/2025 17:12:19 INFO stats.py:314 | Epoch[22] Step[44] GlobalStep[8624] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 5:34:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:12:24 INFO loss_tracker.py:84 | Epoch[22/239] Step[49] GlobalStep[8629/NA]: det_loss_cls_0[0.5355] det_loss_box_0[0.7392] det_loss_cls_1[0.5101] det_loss_box_1[0.6119] det_loss_cls_2[0.5067] det_loss_box_2[0.5579] det_loss_cls_3[0.4964] det_loss_box_3[0.5421] det_loss_cls_4[0.4780] det_loss_box_4[0.5380] det_loss_cls_5[0.4646] det_loss_box_5[0.5400] det_loss_cls_dn_0[0.1165] det_loss_box_dn_0[0.3466] det_loss_cls_dn_1[0.1110] det_loss_box_dn_1[0.3174] det_loss_cls_dn_2[0.1103] det_loss_box_dn_2[0.2858] det_loss_cls_dn_3[0.1090] det_loss_box_dn_3[0.2857] det_loss_cls_dn_4[0.1091] det_loss_box_dn_4[0.2844] det_loss_cls_dn_5[0.1078] det_loss_box_dn_5[0.2838] loss_depth[0.0396] total_loss[9.0274]
Rank[0/8] 06/19/2025 17:12:46 INFO stats.py:314 | Epoch[22] Step[69] GlobalStep[8649] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 5:32:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:12:51 INFO loss_tracker.py:84 | Epoch[22/239] Step[74] GlobalStep[8654/NA]: det_loss_cls_0[0.5473] det_loss_box_0[0.7764] det_loss_cls_1[0.5257] det_loss_box_1[0.6800] det_loss_cls_2[0.5309] det_loss_box_2[0.6192] det_loss_cls_3[0.5276] det_loss_box_3[0.6051] det_loss_cls_4[0.4915] det_loss_box_4[0.5848] det_loss_cls_5[0.4821] det_loss_box_5[0.5840] det_loss_cls_dn_0[0.1167] det_loss_box_dn_0[0.3747] det_loss_cls_dn_1[0.1133] det_loss_box_dn_1[0.3382] det_loss_cls_dn_2[0.1130] det_loss_box_dn_2[0.3198] det_loss_cls_dn_3[0.1122] det_loss_box_dn_3[0.3186] det_loss_cls_dn_4[0.1117] det_loss_box_dn_4[0.3183] det_loss_cls_dn_5[0.1108] det_loss_box_dn_5[0.3175] loss_depth[0.0434] total_loss[9.6630]
Rank[0/8] 06/19/2025 17:13:12 INFO stats.py:314 | Epoch[22] Step[94] GlobalStep[8674] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 5:31:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:13:17 INFO loss_tracker.py:84 | Epoch[22/239] Step[99] GlobalStep[8679/NA]: det_loss_cls_0[0.6556] det_loss_box_0[0.8910] det_loss_cls_1[0.6381] det_loss_box_1[0.7520] det_loss_cls_2[0.6383] det_loss_box_2[0.6858] det_loss_cls_3[0.6292] det_loss_box_3[0.6649] det_loss_cls_4[0.6051] det_loss_box_4[0.6450] det_loss_cls_5[0.5896] det_loss_box_5[0.6490] det_loss_cls_dn_0[0.1188] det_loss_box_dn_0[0.3354] det_loss_cls_dn_1[0.1159] det_loss_box_dn_1[0.3020] det_loss_cls_dn_2[0.1156] det_loss_box_dn_2[0.2804] det_loss_cls_dn_3[0.1145] det_loss_box_dn_3[0.2807] det_loss_cls_dn_4[0.1140] det_loss_box_dn_4[0.2804] det_loss_cls_dn_5[0.1135] det_loss_box_dn_5[0.2801] loss_depth[0.0419] total_loss[10.5368]
Rank[0/8] 06/19/2025 17:13:38 INFO stats.py:314 | Epoch[22] Step[119] GlobalStep[8699] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 5:30:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:13:43 INFO loss_tracker.py:84 | Epoch[22/239] Step[124] GlobalStep[8704/NA]: det_loss_cls_0[0.5418] det_loss_box_0[0.7933] det_loss_cls_1[0.5197] det_loss_box_1[0.6688] det_loss_cls_2[0.5164] det_loss_box_2[0.6265] det_loss_cls_3[0.5030] det_loss_box_3[0.6154] det_loss_cls_4[0.4822] det_loss_box_4[0.6092] det_loss_cls_5[0.4753] det_loss_box_5[0.6109] det_loss_cls_dn_0[0.1166] det_loss_box_dn_0[0.4376] det_loss_cls_dn_1[0.1144] det_loss_box_dn_1[0.4038] det_loss_cls_dn_2[0.1142] det_loss_box_dn_2[0.3808] det_loss_cls_dn_3[0.1132] det_loss_box_dn_3[0.3802] det_loss_cls_dn_4[0.1124] det_loss_box_dn_4[0.3798] det_loss_cls_dn_5[0.1116] det_loss_box_dn_5[0.3789] loss_depth[0.0431] total_loss[10.0493]
Rank[0/8] 06/19/2025 17:14:04 INFO stats.py:314 | Epoch[22] Step[144] GlobalStep[8724] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 5:28:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:14:09 INFO loss_tracker.py:84 | Epoch[22/239] Step[149] GlobalStep[8729/NA]: det_loss_cls_0[0.5081] det_loss_box_0[0.8402] det_loss_cls_1[0.4967] det_loss_box_1[0.7027] det_loss_cls_2[0.4966] det_loss_box_2[0.6475] det_loss_cls_3[0.4871] det_loss_box_3[0.6295] det_loss_cls_4[0.4630] det_loss_box_4[0.6163] det_loss_cls_5[0.4512] det_loss_box_5[0.6164] det_loss_cls_dn_0[0.1217] det_loss_box_dn_0[0.4312] det_loss_cls_dn_1[0.1188] det_loss_box_dn_1[0.3917] det_loss_cls_dn_2[0.1180] det_loss_box_dn_2[0.3705] det_loss_cls_dn_3[0.1175] det_loss_box_dn_3[0.3700] det_loss_cls_dn_4[0.1169] det_loss_box_dn_4[0.3697] det_loss_cls_dn_5[0.1163] det_loss_box_dn_5[0.3691] loss_depth[0.0422] total_loss[10.0091]
Rank[0/8] 06/19/2025 17:14:30 INFO stats.py:314 | Epoch[22] Step[169] GlobalStep[8749] Training Speed: 5.86 samples/sec across all devices.	Average Step Time: 1.37 sec.	Estimated Remaining Time: 1 day, 5:27:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:14:35 INFO loss_tracker.py:84 | Epoch[22/239] Step[174] GlobalStep[8754/NA]: det_loss_cls_0[0.6281] det_loss_box_0[0.8306] det_loss_cls_1[0.6078] det_loss_box_1[0.6974] det_loss_cls_2[0.6047] det_loss_box_2[0.6336] det_loss_cls_3[0.5918] det_loss_box_3[0.6123] det_loss_cls_4[0.5628] det_loss_box_4[0.6066] det_loss_cls_5[0.5537] det_loss_box_5[0.6040] det_loss_cls_dn_0[0.1289] det_loss_box_dn_0[0.4318] det_loss_cls_dn_1[0.1232] det_loss_box_dn_1[0.3912] det_loss_cls_dn_2[0.1231] det_loss_box_dn_2[0.3780] det_loss_cls_dn_3[0.1218] det_loss_box_dn_3[0.3753] det_loss_cls_dn_4[0.1206] det_loss_box_dn_4[0.3750] det_loss_cls_dn_5[0.1207] det_loss_box_dn_5[0.3737] loss_depth[0.0415] total_loss[10.6383]
Rank[0/8] 06/19/2025 17:14:55 INFO stats.py:314 | Epoch[22] Step[194] GlobalStep[8774] Training Speed: 7.66 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 5:25:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:15:01 INFO loss_tracker.py:84 | Epoch[22/239] Step[199] GlobalStep[8779/NA]: det_loss_cls_0[0.5957] det_loss_box_0[0.8056] det_loss_cls_1[0.5863] det_loss_box_1[0.6828] det_loss_cls_2[0.5750] det_loss_box_2[0.6438] det_loss_cls_3[0.5625] det_loss_box_3[0.6297] det_loss_cls_4[0.5394] det_loss_box_4[0.6154] det_loss_cls_5[0.5286] det_loss_box_5[0.6163] det_loss_cls_dn_0[0.1287] det_loss_box_dn_0[0.4014] det_loss_cls_dn_1[0.1220] det_loss_box_dn_1[0.3741] det_loss_cls_dn_2[0.1231] det_loss_box_dn_2[0.3547] det_loss_cls_dn_3[0.1223] det_loss_box_dn_3[0.3549] det_loss_cls_dn_4[0.1218] det_loss_box_dn_4[0.3539] det_loss_cls_dn_5[0.1210] det_loss_box_dn_5[0.3516] loss_depth[0.0404] total_loss[10.3510]
Rank[0/8] 06/19/2025 17:15:22 INFO stats.py:314 | Epoch[22] Step[219] GlobalStep[8799] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 5:24:30.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:15:27 INFO loss_tracker.py:84 | Epoch[22/239] Step[224] GlobalStep[8804/NA]: det_loss_cls_0[0.5145] det_loss_box_0[0.7204] det_loss_cls_1[0.4943] det_loss_box_1[0.6036] det_loss_cls_2[0.4951] det_loss_box_2[0.5596] det_loss_cls_3[0.4888] det_loss_box_3[0.5510] det_loss_cls_4[0.4637] det_loss_box_4[0.5395] det_loss_cls_5[0.4444] det_loss_box_5[0.5449] det_loss_cls_dn_0[0.1148] det_loss_box_dn_0[0.5326] det_loss_cls_dn_1[0.1129] det_loss_box_dn_1[0.4871] det_loss_cls_dn_2[0.1131] det_loss_box_dn_2[0.4690] det_loss_cls_dn_3[0.1123] det_loss_box_dn_3[0.4669] det_loss_cls_dn_4[0.1113] det_loss_box_dn_4[0.4678] det_loss_cls_dn_5[0.1110] det_loss_box_dn_5[0.4674] loss_depth[0.0457] total_loss[10.0317]
Rank[0/8] 06/19/2025 17:15:47 INFO stats.py:314 | Epoch[22] Step[244] GlobalStep[8824] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 5:23:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:15:53 INFO loss_tracker.py:84 | Epoch[22/239] Step[249] GlobalStep[8829/NA]: det_loss_cls_0[0.5602] det_loss_box_0[0.6938] det_loss_cls_1[0.5484] det_loss_box_1[0.5650] det_loss_cls_2[0.5412] det_loss_box_2[0.5259] det_loss_cls_3[0.5212] det_loss_box_3[0.5130] det_loss_cls_4[0.4905] det_loss_box_4[0.5140] det_loss_cls_5[0.4807] det_loss_box_5[0.5091] det_loss_cls_dn_0[0.1192] det_loss_box_dn_0[0.3233] det_loss_cls_dn_1[0.1163] det_loss_box_dn_1[0.2924] det_loss_cls_dn_2[0.1165] det_loss_box_dn_2[0.2752] det_loss_cls_dn_3[0.1156] det_loss_box_dn_3[0.2734] det_loss_cls_dn_4[0.1150] det_loss_box_dn_4[0.2720] det_loss_cls_dn_5[0.1150] det_loss_box_dn_5[0.2708] loss_depth[0.0387] total_loss[8.9063]
Rank[0/8] 06/19/2025 17:16:14 INFO stats.py:314 | Epoch[22] Step[269] GlobalStep[8849] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 5:21:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:16:19 INFO loss_tracker.py:84 | Epoch[22/239] Step[274] GlobalStep[8854/NA]: det_loss_cls_0[0.5722] det_loss_box_0[0.7663] det_loss_cls_1[0.5651] det_loss_box_1[0.6529] det_loss_cls_2[0.5625] det_loss_box_2[0.6086] det_loss_cls_3[0.5482] det_loss_box_3[0.5813] det_loss_cls_4[0.5222] det_loss_box_4[0.5729] det_loss_cls_5[0.5098] det_loss_box_5[0.5704] det_loss_cls_dn_0[0.1151] det_loss_box_dn_0[0.3292] det_loss_cls_dn_1[0.1114] det_loss_box_dn_1[0.2983] det_loss_cls_dn_2[0.1113] det_loss_box_dn_2[0.2834] det_loss_cls_dn_3[0.1101] det_loss_box_dn_3[0.2826] det_loss_cls_dn_4[0.1095] det_loss_box_dn_4[0.2829] det_loss_cls_dn_5[0.1089] det_loss_box_dn_5[0.2825] loss_depth[0.0405] total_loss[9.4982]
Rank[0/8] 06/19/2025 17:16:40 INFO stats.py:314 | Epoch[22] Step[294] GlobalStep[8874] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 5:20:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:16:45 INFO loss_tracker.py:84 | Epoch[22/239] Step[299] GlobalStep[8879/NA]: det_loss_cls_0[0.6211] det_loss_box_0[0.8931] det_loss_cls_1[0.6081] det_loss_box_1[0.7416] det_loss_cls_2[0.5971] det_loss_box_2[0.6771] det_loss_cls_3[0.5778] det_loss_box_3[0.6677] det_loss_cls_4[0.5598] det_loss_box_4[0.6580] det_loss_cls_5[0.5460] det_loss_box_5[0.6564] det_loss_cls_dn_0[0.1204] det_loss_box_dn_0[0.3507] det_loss_cls_dn_1[0.1177] det_loss_box_dn_1[0.3155] det_loss_cls_dn_2[0.1174] det_loss_box_dn_2[0.2959] det_loss_cls_dn_3[0.1159] det_loss_box_dn_3[0.2948] det_loss_cls_dn_4[0.1156] det_loss_box_dn_4[0.2944] det_loss_cls_dn_5[0.1147] det_loss_box_dn_5[0.2942] loss_depth[0.0386] total_loss[10.3896]
Rank[0/8] 06/19/2025 17:17:05 INFO stats.py:314 | Epoch[22] Step[319] GlobalStep[8899] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 5:19:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:17:11 INFO loss_tracker.py:84 | Epoch[22/239] Step[324] GlobalStep[8904/NA]: det_loss_cls_0[0.5544] det_loss_box_0[0.7194] det_loss_cls_1[0.5307] det_loss_box_1[0.5947] det_loss_cls_2[0.5252] det_loss_box_2[0.5456] det_loss_cls_3[0.5174] det_loss_box_3[0.5251] det_loss_cls_4[0.5036] det_loss_box_4[0.5155] det_loss_cls_5[0.4931] det_loss_box_5[0.5125] det_loss_cls_dn_0[0.1131] det_loss_box_dn_0[0.3631] det_loss_cls_dn_1[0.1099] det_loss_box_dn_1[0.3248] det_loss_cls_dn_2[0.1092] det_loss_box_dn_2[0.3058] det_loss_cls_dn_3[0.1089] det_loss_box_dn_3[0.3052] det_loss_cls_dn_4[0.1093] det_loss_box_dn_4[0.3042] det_loss_cls_dn_5[0.1088] det_loss_box_dn_5[0.3027] loss_depth[0.0408] total_loss[9.1431]
Rank[0/8] 06/19/2025 17:17:31 INFO stats.py:314 | Epoch[22] Step[344] GlobalStep[8924] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 5:17:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:17:36 INFO loss_tracker.py:84 | Epoch[22/239] Step[349] GlobalStep[8929/NA]: det_loss_cls_0[0.6125] det_loss_box_0[0.9024] det_loss_cls_1[0.5965] det_loss_box_1[0.7593] det_loss_cls_2[0.5917] det_loss_box_2[0.7116] det_loss_cls_3[0.5874] det_loss_box_3[0.6751] det_loss_cls_4[0.5678] det_loss_box_4[0.6593] det_loss_cls_5[0.5543] det_loss_box_5[0.6582] det_loss_cls_dn_0[0.1191] det_loss_box_dn_0[0.3859] det_loss_cls_dn_1[0.1151] det_loss_box_dn_1[0.3492] det_loss_cls_dn_2[0.1147] det_loss_box_dn_2[0.3317] det_loss_cls_dn_3[0.1146] det_loss_box_dn_3[0.3313] det_loss_cls_dn_4[0.1129] det_loss_box_dn_4[0.3309] det_loss_cls_dn_5[0.1123] det_loss_box_dn_5[0.3291] loss_depth[0.0445] total_loss[10.6673]
Rank[0/8] 06/19/2025 17:17:57 INFO stats.py:314 | Epoch[22] Step[369] GlobalStep[8949] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 5:16:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:18:02 INFO loss_tracker.py:84 | Epoch[22/239] Step[374] GlobalStep[8954/NA]: det_loss_cls_0[0.5505] det_loss_box_0[0.7376] det_loss_cls_1[0.5188] det_loss_box_1[0.6146] det_loss_cls_2[0.5125] det_loss_box_2[0.5574] det_loss_cls_3[0.5074] det_loss_box_3[0.5318] det_loss_cls_4[0.4831] det_loss_box_4[0.5291] det_loss_cls_5[0.4797] det_loss_box_5[0.5272] det_loss_cls_dn_0[0.1132] det_loss_box_dn_0[0.3460] det_loss_cls_dn_1[0.1095] det_loss_box_dn_1[0.3126] det_loss_cls_dn_2[0.1090] det_loss_box_dn_2[0.2911] det_loss_cls_dn_3[0.1078] det_loss_box_dn_3[0.2904] det_loss_cls_dn_4[0.1068] det_loss_box_dn_4[0.2890] det_loss_cls_dn_5[0.1058] det_loss_box_dn_5[0.2880] loss_depth[0.0408] total_loss[9.0597]
Rank[0/8] 06/19/2025 17:18:18 INFO stats.py:394 | Epoch[22] completed. Training Speed: 7.64 samples/sec across all devices.	Epoch Time: 408.52 sec.	Average Epoch Time: 408.52 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 5:15:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:18:27 INFO stats.py:314 | Epoch[23] Step[4] GlobalStep[8974] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 5:15:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:18:47 INFO loss_tracker.py:84 | Epoch[23/239] Step[24] GlobalStep[8994/NA]: det_loss_cls_0[0.7090] det_loss_box_0[1.0115] det_loss_cls_1[0.6973] det_loss_box_1[0.8598] det_loss_cls_2[0.7018] det_loss_box_2[0.7860] det_loss_cls_3[0.6821] det_loss_box_3[0.7614] det_loss_cls_4[0.6617] det_loss_box_4[0.7522] det_loss_cls_5[0.6490] det_loss_box_5[0.7493] det_loss_cls_dn_0[0.1202] det_loss_box_dn_0[0.3216] det_loss_cls_dn_1[0.1165] det_loss_box_dn_1[0.2940] det_loss_cls_dn_2[0.1160] det_loss_box_dn_2[0.2779] det_loss_cls_dn_3[0.1153] det_loss_box_dn_3[0.2764] det_loss_cls_dn_4[0.1141] det_loss_box_dn_4[0.2764] det_loss_cls_dn_5[0.1136] det_loss_box_dn_5[0.2752] loss_depth[0.0385] total_loss[11.4767]
Rank[0/8] 06/19/2025 17:18:53 INFO stats.py:314 | Epoch[23] Step[29] GlobalStep[8999] Training Speed: 7.88 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1 day, 5:14:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:19:13 INFO loss_tracker.py:84 | Epoch[23/239] Step[49] GlobalStep[9019/NA]: det_loss_cls_0[0.5423] det_loss_box_0[0.7383] det_loss_cls_1[0.5215] det_loss_box_1[0.6386] det_loss_cls_2[0.5173] det_loss_box_2[0.5885] det_loss_cls_3[0.5115] det_loss_box_3[0.5697] det_loss_cls_4[0.4899] det_loss_box_4[0.5666] det_loss_cls_5[0.4855] det_loss_box_5[0.5634] det_loss_cls_dn_0[0.1213] det_loss_box_dn_0[0.3974] det_loss_cls_dn_1[0.1189] det_loss_box_dn_1[0.3602] det_loss_cls_dn_2[0.1183] det_loss_box_dn_2[0.3415] det_loss_cls_dn_3[0.1171] det_loss_box_dn_3[0.3401] det_loss_cls_dn_4[0.1167] det_loss_box_dn_4[0.3395] det_loss_cls_dn_5[0.1162] det_loss_box_dn_5[0.3387] loss_depth[0.0395] total_loss[9.5986]
Rank[0/8] 06/19/2025 17:19:18 INFO stats.py:314 | Epoch[23] Step[54] GlobalStep[9024] Training Speed: 8.19 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 5:12:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:19:39 INFO loss_tracker.py:84 | Epoch[23/239] Step[74] GlobalStep[9044/NA]: det_loss_cls_0[0.5786] det_loss_box_0[0.8287] det_loss_cls_1[0.5728] det_loss_box_1[0.6802] det_loss_cls_2[0.5781] det_loss_box_2[0.6042] det_loss_cls_3[0.5627] det_loss_box_3[0.5850] det_loss_cls_4[0.5328] det_loss_box_4[0.5783] det_loss_cls_5[0.5211] det_loss_box_5[0.5812] det_loss_cls_dn_0[0.1263] det_loss_box_dn_0[0.3526] det_loss_cls_dn_1[0.1218] det_loss_box_dn_1[0.3103] det_loss_cls_dn_2[0.1223] det_loss_box_dn_2[0.2896] det_loss_cls_dn_3[0.1218] det_loss_box_dn_3[0.2883] det_loss_cls_dn_4[0.1202] det_loss_box_dn_4[0.2880] det_loss_cls_dn_5[0.1199] det_loss_box_dn_5[0.2872] loss_depth[0.0448] total_loss[9.7966]
Rank[0/8] 06/19/2025 17:19:45 INFO stats.py:314 | Epoch[23] Step[79] GlobalStep[9049] Training Speed: 8.04 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 5:11:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:20:06 INFO loss_tracker.py:84 | Epoch[23/239] Step[99] GlobalStep[9069/NA]: det_loss_cls_0[0.5124] det_loss_box_0[0.6668] det_loss_cls_1[0.4873] det_loss_box_1[0.5557] det_loss_cls_2[0.4816] det_loss_box_2[0.4941] det_loss_cls_3[0.4707] det_loss_box_3[0.4812] det_loss_cls_4[0.4485] det_loss_box_4[0.4667] det_loss_cls_5[0.4314] det_loss_box_5[0.4737] det_loss_cls_dn_0[0.1126] det_loss_box_dn_0[0.3573] det_loss_cls_dn_1[0.1116] det_loss_box_dn_1[0.3208] det_loss_cls_dn_2[0.1108] det_loss_box_dn_2[0.3035] det_loss_cls_dn_3[0.1103] det_loss_box_dn_3[0.3023] det_loss_cls_dn_4[0.1097] det_loss_box_dn_4[0.3016] det_loss_cls_dn_5[0.1086] det_loss_box_dn_5[0.3012] loss_depth[0.0418] total_loss[8.5622]
Rank[0/8] 06/19/2025 17:20:11 INFO stats.py:314 | Epoch[23] Step[104] GlobalStep[9074] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 5:10:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:20:32 INFO loss_tracker.py:84 | Epoch[23/239] Step[124] GlobalStep[9094/NA]: det_loss_cls_0[0.5294] det_loss_box_0[0.7531] det_loss_cls_1[0.5149] det_loss_box_1[0.6405] det_loss_cls_2[0.5081] det_loss_box_2[0.5891] det_loss_cls_3[0.4954] det_loss_box_3[0.5655] det_loss_cls_4[0.4600] det_loss_box_4[0.5674] det_loss_cls_5[0.4515] det_loss_box_5[0.5697] det_loss_cls_dn_0[0.1191] det_loss_box_dn_0[0.3965] det_loss_cls_dn_1[0.1152] det_loss_box_dn_1[0.3613] det_loss_cls_dn_2[0.1136] det_loss_box_dn_2[0.3433] det_loss_cls_dn_3[0.1132] det_loss_box_dn_3[0.3428] det_loss_cls_dn_4[0.1123] det_loss_box_dn_4[0.3427] det_loss_cls_dn_5[0.1118] det_loss_box_dn_5[0.3423] loss_depth[0.0427] total_loss[9.5013]
Rank[0/8] 06/19/2025 17:20:37 INFO stats.py:314 | Epoch[23] Step[129] GlobalStep[9099] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 5:09:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:20:57 INFO loss_tracker.py:84 | Epoch[23/239] Step[149] GlobalStep[9119/NA]: det_loss_cls_0[0.7421] det_loss_box_0[1.1038] det_loss_cls_1[0.7182] det_loss_box_1[0.9591] det_loss_cls_2[0.7105] det_loss_box_2[0.8782] det_loss_cls_3[0.7079] det_loss_box_3[0.8459] det_loss_cls_4[0.6818] det_loss_box_4[0.8320] det_loss_cls_5[0.6736] det_loss_box_5[0.8226] det_loss_cls_dn_0[0.1272] det_loss_box_dn_0[0.4030] det_loss_cls_dn_1[0.1226] det_loss_box_dn_1[0.3749] det_loss_cls_dn_2[0.1215] det_loss_box_dn_2[0.3616] det_loss_cls_dn_3[0.1206] det_loss_box_dn_3[0.3605] det_loss_cls_dn_4[0.1200] det_loss_box_dn_4[0.3602] det_loss_cls_dn_5[0.1196] det_loss_box_dn_5[0.3581] loss_depth[0.0453] total_loss[12.6707]
Rank[0/8] 06/19/2025 17:21:02 INFO stats.py:314 | Epoch[23] Step[154] GlobalStep[9124] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 5:07:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:21:24 INFO loss_tracker.py:84 | Epoch[23/239] Step[174] GlobalStep[9144/NA]: det_loss_cls_0[0.6990] det_loss_box_0[0.9897] det_loss_cls_1[0.6811] det_loss_box_1[0.8254] det_loss_cls_2[0.6734] det_loss_box_2[0.7644] det_loss_cls_3[0.6628] det_loss_box_3[0.7463] det_loss_cls_4[0.6296] det_loss_box_4[0.7417] det_loss_cls_5[0.6182] det_loss_box_5[0.7322] det_loss_cls_dn_0[0.1297] det_loss_box_dn_0[0.3710] det_loss_cls_dn_1[0.1271] det_loss_box_dn_1[0.3401] det_loss_cls_dn_2[0.1263] det_loss_box_dn_2[0.3204] det_loss_cls_dn_3[0.1249] det_loss_box_dn_3[0.3189] det_loss_cls_dn_4[0.1233] det_loss_box_dn_4[0.3180] det_loss_cls_dn_5[0.1236] det_loss_box_dn_5[0.3180] loss_depth[0.0439] total_loss[11.5491]
Rank[0/8] 06/19/2025 17:21:29 INFO stats.py:314 | Epoch[23] Step[179] GlobalStep[9149] Training Speed: 8.02 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 5:06:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:21:50 INFO loss_tracker.py:84 | Epoch[23/239] Step[199] GlobalStep[9169/NA]: det_loss_cls_0[0.5414] det_loss_box_0[0.7107] det_loss_cls_1[0.5367] det_loss_box_1[0.6038] det_loss_cls_2[0.5342] det_loss_box_2[0.5542] det_loss_cls_3[0.5272] det_loss_box_3[0.5298] det_loss_cls_4[0.4923] det_loss_box_4[0.5206] det_loss_cls_5[0.4775] det_loss_box_5[0.5218] det_loss_cls_dn_0[0.1240] det_loss_box_dn_0[0.4149] det_loss_cls_dn_1[0.1205] det_loss_box_dn_1[0.3785] det_loss_cls_dn_2[0.1198] det_loss_box_dn_2[0.3564] det_loss_cls_dn_3[0.1203] det_loss_box_dn_3[0.3545] det_loss_cls_dn_4[0.1188] det_loss_box_dn_4[0.3552] det_loss_cls_dn_5[0.1178] det_loss_box_dn_5[0.3548] loss_depth[0.0389] total_loss[9.5245]
Rank[0/8] 06/19/2025 17:21:55 INFO stats.py:314 | Epoch[23] Step[204] GlobalStep[9174] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 5:05:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:22:16 INFO loss_tracker.py:84 | Epoch[23/239] Step[224] GlobalStep[9194/NA]: det_loss_cls_0[0.4334] det_loss_box_0[0.5763] det_loss_cls_1[0.4182] det_loss_box_1[0.4835] det_loss_cls_2[0.4160] det_loss_box_2[0.4332] det_loss_cls_3[0.4111] det_loss_box_3[0.4228] det_loss_cls_4[0.3884] det_loss_box_4[0.4124] det_loss_cls_5[0.3719] det_loss_box_5[0.4197] det_loss_cls_dn_0[0.1121] det_loss_box_dn_0[0.4367] det_loss_cls_dn_1[0.1078] det_loss_box_dn_1[0.4002] det_loss_cls_dn_2[0.1059] det_loss_box_dn_2[0.3771] det_loss_cls_dn_3[0.1050] det_loss_box_dn_3[0.3759] det_loss_cls_dn_4[0.1030] det_loss_box_dn_4[0.3754] det_loss_cls_dn_5[0.1026] det_loss_box_dn_5[0.3761] loss_depth[0.0438] total_loss[8.2085]
Rank[0/8] 06/19/2025 17:22:21 INFO stats.py:314 | Epoch[23] Step[229] GlobalStep[9199] Training Speed: 6.25 samples/sec across all devices.	Average Step Time: 1.28 sec.	Estimated Remaining Time: 1 day, 5:03:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:22:42 INFO loss_tracker.py:84 | Epoch[23/239] Step[249] GlobalStep[9219/NA]: det_loss_cls_0[0.4866] det_loss_box_0[0.6891] det_loss_cls_1[0.4770] det_loss_box_1[0.5717] det_loss_cls_2[0.4795] det_loss_box_2[0.5198] det_loss_cls_3[0.4562] det_loss_box_3[0.5061] det_loss_cls_4[0.4328] det_loss_box_4[0.4955] det_loss_cls_5[0.4229] det_loss_box_5[0.4999] det_loss_cls_dn_0[0.1161] det_loss_box_dn_0[0.4041] det_loss_cls_dn_1[0.1144] det_loss_box_dn_1[0.3601] det_loss_cls_dn_2[0.1129] det_loss_box_dn_2[0.3372] det_loss_cls_dn_3[0.1107] det_loss_box_dn_3[0.3350] det_loss_cls_dn_4[0.1105] det_loss_box_dn_4[0.3345] det_loss_cls_dn_5[0.1099] det_loss_box_dn_5[0.3340] loss_depth[0.0405] total_loss[8.8572]
Rank[0/8] 06/19/2025 17:22:47 INFO stats.py:314 | Epoch[23] Step[254] GlobalStep[9224] Training Speed: 8.22 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 5:02:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:23:08 INFO loss_tracker.py:84 | Epoch[23/239] Step[274] GlobalStep[9244/NA]: det_loss_cls_0[0.5108] det_loss_box_0[0.6502] det_loss_cls_1[0.5108] det_loss_box_1[0.5511] det_loss_cls_2[0.5204] det_loss_box_2[0.5056] det_loss_cls_3[0.5044] det_loss_box_3[0.4832] det_loss_cls_4[0.4745] det_loss_box_4[0.4779] det_loss_cls_5[0.4617] det_loss_box_5[0.4765] det_loss_cls_dn_0[0.1144] det_loss_box_dn_0[0.3248] det_loss_cls_dn_1[0.1114] det_loss_box_dn_1[0.2915] det_loss_cls_dn_2[0.1114] det_loss_box_dn_2[0.2673] det_loss_cls_dn_3[0.1098] det_loss_box_dn_3[0.2657] det_loss_cls_dn_4[0.1087] det_loss_box_dn_4[0.2650] det_loss_cls_dn_5[0.1083] det_loss_box_dn_5[0.2651] loss_depth[0.0389] total_loss[8.5095]
Rank[0/8] 06/19/2025 17:23:14 INFO stats.py:314 | Epoch[23] Step[279] GlobalStep[9249] Training Speed: 8.59 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 5:01:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:23:34 INFO loss_tracker.py:84 | Epoch[23/239] Step[299] GlobalStep[9269/NA]: det_loss_cls_0[0.5745] det_loss_box_0[0.8080] det_loss_cls_1[0.5304] det_loss_box_1[0.6834] det_loss_cls_2[0.5361] det_loss_box_2[0.6194] det_loss_cls_3[0.5205] det_loss_box_3[0.5975] det_loss_cls_4[0.5027] det_loss_box_4[0.5878] det_loss_cls_5[0.4859] det_loss_box_5[0.5933] det_loss_cls_dn_0[0.1179] det_loss_box_dn_0[0.3670] det_loss_cls_dn_1[0.1141] det_loss_box_dn_1[0.3300] det_loss_cls_dn_2[0.1140] det_loss_box_dn_2[0.3120] det_loss_cls_dn_3[0.1138] det_loss_box_dn_3[0.3112] det_loss_cls_dn_4[0.1124] det_loss_box_dn_4[0.3102] det_loss_cls_dn_5[0.1125] det_loss_box_dn_5[0.3095] loss_depth[0.0403] total_loss[9.7045]
Rank[0/8] 06/19/2025 17:23:39 INFO stats.py:314 | Epoch[23] Step[304] GlobalStep[9274] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 5:00:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:24:00 INFO loss_tracker.py:84 | Epoch[23/239] Step[324] GlobalStep[9294/NA]: det_loss_cls_0[0.5274] det_loss_box_0[0.7881] det_loss_cls_1[0.5089] det_loss_box_1[0.6627] det_loss_cls_2[0.5099] det_loss_box_2[0.6052] det_loss_cls_3[0.4950] det_loss_box_3[0.5897] det_loss_cls_4[0.4656] det_loss_box_4[0.5830] det_loss_cls_5[0.4624] det_loss_box_5[0.5744] det_loss_cls_dn_0[0.1217] det_loss_box_dn_0[0.3979] det_loss_cls_dn_1[0.1189] det_loss_box_dn_1[0.3663] det_loss_cls_dn_2[0.1178] det_loss_box_dn_2[0.3464] det_loss_cls_dn_3[0.1176] det_loss_box_dn_3[0.3461] det_loss_cls_dn_4[0.1173] det_loss_box_dn_4[0.3457] det_loss_cls_dn_5[0.1169] det_loss_box_dn_5[0.3448] loss_depth[0.0414] total_loss[9.6710]
Rank[0/8] 06/19/2025 17:24:05 INFO stats.py:314 | Epoch[23] Step[329] GlobalStep[9299] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 4:58:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:24:25 INFO loss_tracker.py:84 | Epoch[23/239] Step[349] GlobalStep[9319/NA]: det_loss_cls_0[0.5754] det_loss_box_0[0.7039] det_loss_cls_1[0.5528] det_loss_box_1[0.5948] det_loss_cls_2[0.5479] det_loss_box_2[0.5419] det_loss_cls_3[0.5315] det_loss_box_3[0.5306] det_loss_cls_4[0.5149] det_loss_box_4[0.5169] det_loss_cls_5[0.5057] det_loss_box_5[0.5224] det_loss_cls_dn_0[0.1156] det_loss_box_dn_0[0.3477] det_loss_cls_dn_1[0.1118] det_loss_box_dn_1[0.3147] det_loss_cls_dn_2[0.1103] det_loss_box_dn_2[0.2985] det_loss_cls_dn_3[0.1100] det_loss_box_dn_3[0.2985] det_loss_cls_dn_4[0.1089] det_loss_box_dn_4[0.2973] det_loss_cls_dn_5[0.1084] det_loss_box_dn_5[0.2968] loss_depth[0.0405] total_loss[9.1976]
Rank[0/8] 06/19/2025 17:24:31 INFO stats.py:314 | Epoch[23] Step[354] GlobalStep[9324] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 4:57:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:24:52 INFO loss_tracker.py:84 | Epoch[23/239] Step[374] GlobalStep[9344/NA]: det_loss_cls_0[0.5598] det_loss_box_0[0.7521] det_loss_cls_1[0.5480] det_loss_box_1[0.6093] det_loss_cls_2[0.5383] det_loss_box_2[0.5446] det_loss_cls_3[0.5320] det_loss_box_3[0.5198] det_loss_cls_4[0.4990] det_loss_box_4[0.5116] det_loss_cls_5[0.4853] det_loss_box_5[0.5140] det_loss_cls_dn_0[0.1207] det_loss_box_dn_0[0.3244] det_loss_cls_dn_1[0.1177] det_loss_box_dn_1[0.2889] det_loss_cls_dn_2[0.1172] det_loss_box_dn_2[0.2657] det_loss_cls_dn_3[0.1163] det_loss_box_dn_3[0.2653] det_loss_cls_dn_4[0.1157] det_loss_box_dn_4[0.2643] det_loss_cls_dn_5[0.1153] det_loss_box_dn_5[0.2637] loss_depth[0.0413] total_loss[9.0303]
Rank[0/8] 06/19/2025 17:24:57 INFO stats.py:314 | Epoch[23] Step[379] GlobalStep[9349] Training Speed: 7.76 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 1 day, 4:56:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:25:07 INFO stats.py:394 | Epoch[23] completed. Training Speed: 7.63 samples/sec across all devices.	Epoch Time: 409.10 sec.	Average Epoch Time: 409.10 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 4:55:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:25:26 INFO stats.py:314 | Epoch[24] Step[14] GlobalStep[9374] Training Speed: 6.87 samples/sec across all devices.	Average Step Time: 1.17 sec.	Estimated Remaining Time: 1 day, 4:55:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:25:36 INFO loss_tracker.py:84 | Epoch[24/239] Step[24] GlobalStep[9384/NA]: det_loss_cls_0[0.6201] det_loss_box_0[0.8308] det_loss_cls_1[0.5822] det_loss_box_1[0.7039] det_loss_cls_2[0.5774] det_loss_box_2[0.6340] det_loss_cls_3[0.5609] det_loss_box_3[0.6158] det_loss_cls_4[0.5435] det_loss_box_4[0.6054] det_loss_cls_5[0.5225] det_loss_box_5[0.6128] det_loss_cls_dn_0[0.1217] det_loss_box_dn_0[0.3431] det_loss_cls_dn_1[0.1169] det_loss_box_dn_1[0.3104] det_loss_cls_dn_2[0.1165] det_loss_box_dn_2[0.2932] det_loss_cls_dn_3[0.1150] det_loss_box_dn_3[0.2908] det_loss_cls_dn_4[0.1145] det_loss_box_dn_4[0.2907] det_loss_cls_dn_5[0.1131] det_loss_box_dn_5[0.2894] loss_depth[0.0416] total_loss[9.9660]
Rank[0/8] 06/19/2025 17:25:51 INFO stats.py:314 | Epoch[24] Step[39] GlobalStep[9399] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 4:54:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:26:03 INFO loss_tracker.py:84 | Epoch[24/239] Step[49] GlobalStep[9409/NA]: det_loss_cls_0[0.6380] det_loss_box_0[0.8835] det_loss_cls_1[0.6096] det_loss_box_1[0.7618] det_loss_cls_2[0.6160] det_loss_box_2[0.6849] det_loss_cls_3[0.6105] det_loss_box_3[0.6560] det_loss_cls_4[0.5839] det_loss_box_4[0.6483] det_loss_cls_5[0.5657] det_loss_box_5[0.6482] det_loss_cls_dn_0[0.1110] det_loss_box_dn_0[0.3369] det_loss_cls_dn_1[0.1094] det_loss_box_dn_1[0.2989] det_loss_cls_dn_2[0.1088] det_loss_box_dn_2[0.2813] det_loss_cls_dn_3[0.1073] det_loss_box_dn_3[0.2792] det_loss_cls_dn_4[0.1066] det_loss_box_dn_4[0.2790] det_loss_cls_dn_5[0.1067] det_loss_box_dn_5[0.2784] loss_depth[0.0409] total_loss[10.3506]
Rank[0/8] 06/19/2025 17:26:18 INFO stats.py:314 | Epoch[24] Step[64] GlobalStep[9424] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 4:53:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:26:28 INFO loss_tracker.py:84 | Epoch[24/239] Step[74] GlobalStep[9434/NA]: det_loss_cls_0[0.7240] det_loss_box_0[1.0567] det_loss_cls_1[0.7042] det_loss_box_1[0.9066] det_loss_cls_2[0.7027] det_loss_box_2[0.8358] det_loss_cls_3[0.6929] det_loss_box_3[0.8049] det_loss_cls_4[0.6756] det_loss_box_4[0.7925] det_loss_cls_5[0.6660] det_loss_box_5[0.7932] det_loss_cls_dn_0[0.1234] det_loss_box_dn_0[0.3625] det_loss_cls_dn_1[0.1199] det_loss_box_dn_1[0.3301] det_loss_cls_dn_2[0.1181] det_loss_box_dn_2[0.3123] det_loss_cls_dn_3[0.1165] det_loss_box_dn_3[0.3114] det_loss_cls_dn_4[0.1150] det_loss_box_dn_4[0.3105] det_loss_cls_dn_5[0.1145] det_loss_box_dn_5[0.3094] loss_depth[0.0410] total_loss[12.0397]
Rank[0/8] 06/19/2025 17:26:44 INFO stats.py:314 | Epoch[24] Step[89] GlobalStep[9449] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 4:51:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:26:55 INFO loss_tracker.py:84 | Epoch[24/239] Step[99] GlobalStep[9459/NA]: det_loss_cls_0[0.7510] det_loss_box_0[1.5005] det_loss_cls_1[0.7431] det_loss_box_1[1.2932] det_loss_cls_2[0.7241] det_loss_box_2[1.1845] det_loss_cls_3[0.7082] det_loss_box_3[1.1412] det_loss_cls_4[0.6888] det_loss_box_4[1.1271] det_loss_cls_5[0.6724] det_loss_box_5[1.1252] det_loss_cls_dn_0[0.1200] det_loss_box_dn_0[0.3541] det_loss_cls_dn_1[0.1154] det_loss_box_dn_1[0.3240] det_loss_cls_dn_2[0.1149] det_loss_box_dn_2[0.3094] det_loss_cls_dn_3[0.1153] det_loss_box_dn_3[0.3088] det_loss_cls_dn_4[0.1143] det_loss_box_dn_4[0.3083] det_loss_cls_dn_5[0.1131] det_loss_box_dn_5[0.3075] loss_depth[0.0426] total_loss[14.3071]
Rank[0/8] 06/19/2025 17:27:11 INFO stats.py:314 | Epoch[24] Step[114] GlobalStep[9474] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 4:50:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:27:21 INFO loss_tracker.py:84 | Epoch[24/239] Step[124] GlobalStep[9484/NA]: det_loss_cls_0[0.5494] det_loss_box_0[0.7256] det_loss_cls_1[0.5182] det_loss_box_1[0.6059] det_loss_cls_2[0.5191] det_loss_box_2[0.5617] det_loss_cls_3[0.4998] det_loss_box_3[0.5415] det_loss_cls_4[0.4830] det_loss_box_4[0.5308] det_loss_cls_5[0.4745] det_loss_box_5[0.5286] det_loss_cls_dn_0[0.1146] det_loss_box_dn_0[0.3179] det_loss_cls_dn_1[0.1115] det_loss_box_dn_1[0.2852] det_loss_cls_dn_2[0.1104] det_loss_box_dn_2[0.2718] det_loss_cls_dn_3[0.1100] det_loss_box_dn_3[0.2703] det_loss_cls_dn_4[0.1090] det_loss_box_dn_4[0.2699] det_loss_cls_dn_5[0.1084] det_loss_box_dn_5[0.2690] loss_depth[0.0360] total_loss[8.9222]
Rank[0/8] 06/19/2025 17:27:37 INFO stats.py:314 | Epoch[24] Step[139] GlobalStep[9499] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 4:49:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:27:48 INFO loss_tracker.py:84 | Epoch[24/239] Step[149] GlobalStep[9509/NA]: det_loss_cls_0[0.6409] det_loss_box_0[0.8584] det_loss_cls_1[0.6229] det_loss_box_1[0.7245] det_loss_cls_2[0.6045] det_loss_box_2[0.6799] det_loss_cls_3[0.5947] det_loss_box_3[0.6584] det_loss_cls_4[0.5781] det_loss_box_4[0.6456] det_loss_cls_5[0.5596] det_loss_box_5[0.6527] det_loss_cls_dn_0[0.1122] det_loss_box_dn_0[0.3401] det_loss_cls_dn_1[0.1086] det_loss_box_dn_1[0.3074] det_loss_cls_dn_2[0.1088] det_loss_box_dn_2[0.2903] det_loss_cls_dn_3[0.1079] det_loss_box_dn_3[0.2898] det_loss_cls_dn_4[0.1074] det_loss_box_dn_4[0.2891] det_loss_cls_dn_5[0.1070] det_loss_box_dn_5[0.2877] loss_depth[0.0435] total_loss[10.3200]
Rank[0/8] 06/19/2025 17:28:03 INFO stats.py:314 | Epoch[24] Step[164] GlobalStep[9524] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 4:48:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:28:13 INFO loss_tracker.py:84 | Epoch[24/239] Step[174] GlobalStep[9534/NA]: det_loss_cls_0[0.5607] det_loss_box_0[0.7494] det_loss_cls_1[0.5431] det_loss_box_1[0.6445] det_loss_cls_2[0.5446] det_loss_box_2[0.5942] det_loss_cls_3[0.5365] det_loss_box_3[0.5756] det_loss_cls_4[0.5052] det_loss_box_4[0.5696] det_loss_cls_5[0.4915] det_loss_box_5[0.5672] det_loss_cls_dn_0[0.1224] det_loss_box_dn_0[0.3674] det_loss_cls_dn_1[0.1177] det_loss_box_dn_1[0.3259] det_loss_cls_dn_2[0.1170] det_loss_box_dn_2[0.3097] det_loss_cls_dn_3[0.1157] det_loss_box_dn_3[0.3081] det_loss_cls_dn_4[0.1147] det_loss_box_dn_4[0.3077] det_loss_cls_dn_5[0.1139] det_loss_box_dn_5[0.3082] loss_depth[0.0444] total_loss[9.5547]
Rank[0/8] 06/19/2025 17:28:29 INFO stats.py:314 | Epoch[24] Step[189] GlobalStep[9549] Training Speed: 6.44 samples/sec across all devices.	Average Step Time: 1.24 sec.	Estimated Remaining Time: 1 day, 4:47:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:28:39 INFO loss_tracker.py:84 | Epoch[24/239] Step[199] GlobalStep[9559/NA]: det_loss_cls_0[0.5086] det_loss_box_0[0.7218] det_loss_cls_1[0.4864] det_loss_box_1[0.6240] det_loss_cls_2[0.4857] det_loss_box_2[0.5776] det_loss_cls_3[0.4837] det_loss_box_3[0.5662] det_loss_cls_4[0.4579] det_loss_box_4[0.5493] det_loss_cls_5[0.4477] det_loss_box_5[0.5494] det_loss_cls_dn_0[0.1222] det_loss_box_dn_0[0.4278] det_loss_cls_dn_1[0.1167] det_loss_box_dn_1[0.4000] det_loss_cls_dn_2[0.1166] det_loss_box_dn_2[0.3721] det_loss_cls_dn_3[0.1156] det_loss_box_dn_3[0.3697] det_loss_cls_dn_4[0.1142] det_loss_box_dn_4[0.3666] det_loss_cls_dn_5[0.1126] det_loss_box_dn_5[0.3653] loss_depth[0.0420] total_loss[9.4996]
Rank[0/8] 06/19/2025 17:28:55 INFO stats.py:314 | Epoch[24] Step[214] GlobalStep[9574] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 4:45:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:29:05 INFO loss_tracker.py:84 | Epoch[24/239] Step[224] GlobalStep[9584/NA]: det_loss_cls_0[0.6012] det_loss_box_0[0.7564] det_loss_cls_1[0.5838] det_loss_box_1[0.6353] det_loss_cls_2[0.5768] det_loss_box_2[0.5878] det_loss_cls_3[0.5667] det_loss_box_3[0.5737] det_loss_cls_4[0.5417] det_loss_box_4[0.5645] det_loss_cls_5[0.5276] det_loss_box_5[0.5699] det_loss_cls_dn_0[0.1240] det_loss_box_dn_0[0.3990] det_loss_cls_dn_1[0.1201] det_loss_box_dn_1[0.3738] det_loss_cls_dn_2[0.1203] det_loss_box_dn_2[0.3594] det_loss_cls_dn_3[0.1199] det_loss_box_dn_3[0.3580] det_loss_cls_dn_4[0.1182] det_loss_box_dn_4[0.3568] det_loss_cls_dn_5[0.1176] det_loss_box_dn_5[0.3523] loss_depth[0.0392] total_loss[10.0437]
Rank[0/8] 06/19/2025 17:29:21 INFO stats.py:314 | Epoch[24] Step[239] GlobalStep[9599] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 4:44:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:29:31 INFO loss_tracker.py:84 | Epoch[24/239] Step[249] GlobalStep[9609/NA]: det_loss_cls_0[0.6496] det_loss_box_0[0.8944] det_loss_cls_1[0.6296] det_loss_box_1[0.7662] det_loss_cls_2[0.6157] det_loss_box_2[0.6989] det_loss_cls_3[0.6032] det_loss_box_3[0.6794] det_loss_cls_4[0.5719] det_loss_box_4[0.6793] det_loss_cls_5[0.5564] det_loss_box_5[0.6752] det_loss_cls_dn_0[0.1153] det_loss_box_dn_0[0.3759] det_loss_cls_dn_1[0.1127] det_loss_box_dn_1[0.3442] det_loss_cls_dn_2[0.1114] det_loss_box_dn_2[0.3238] det_loss_cls_dn_3[0.1098] det_loss_box_dn_3[0.3256] det_loss_cls_dn_4[0.1093] det_loss_box_dn_4[0.3246] det_loss_cls_dn_5[0.1083] det_loss_box_dn_5[0.3226] loss_depth[0.0396] total_loss[10.7428]
Rank[0/8] 06/19/2025 17:29:47 INFO stats.py:314 | Epoch[24] Step[264] GlobalStep[9624] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 4:43:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:29:57 INFO loss_tracker.py:84 | Epoch[24/239] Step[274] GlobalStep[9634/NA]: det_loss_cls_0[0.5989] det_loss_box_0[0.7277] det_loss_cls_1[0.5757] det_loss_box_1[0.6154] det_loss_cls_2[0.5770] det_loss_box_2[0.5684] det_loss_cls_3[0.5633] det_loss_box_3[0.5507] det_loss_cls_4[0.5414] det_loss_box_4[0.5418] det_loss_cls_5[0.5265] det_loss_box_5[0.5366] det_loss_cls_dn_0[0.1215] det_loss_box_dn_0[0.3438] det_loss_cls_dn_1[0.1200] det_loss_box_dn_1[0.3076] det_loss_cls_dn_2[0.1191] det_loss_box_dn_2[0.2914] det_loss_cls_dn_3[0.1177] det_loss_box_dn_3[0.2923] det_loss_cls_dn_4[0.1169] det_loss_box_dn_4[0.2929] det_loss_cls_dn_5[0.1163] det_loss_box_dn_5[0.2926] loss_depth[0.0408] total_loss[9.4962]
Rank[0/8] 06/19/2025 17:30:13 INFO stats.py:314 | Epoch[24] Step[289] GlobalStep[9649] Training Speed: 8.08 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 4:42:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:30:23 INFO loss_tracker.py:84 | Epoch[24/239] Step[299] GlobalStep[9659/NA]: det_loss_cls_0[0.5542] det_loss_box_0[0.7214] det_loss_cls_1[0.5369] det_loss_box_1[0.6100] det_loss_cls_2[0.5331] det_loss_box_2[0.5678] det_loss_cls_3[0.5263] det_loss_box_3[0.5435] det_loss_cls_4[0.5023] det_loss_box_4[0.5385] det_loss_cls_5[0.4876] det_loss_box_5[0.5422] det_loss_cls_dn_0[0.1270] det_loss_box_dn_0[0.4134] det_loss_cls_dn_1[0.1209] det_loss_box_dn_1[0.3673] det_loss_cls_dn_2[0.1206] det_loss_box_dn_2[0.3494] det_loss_cls_dn_3[0.1203] det_loss_box_dn_3[0.3471] det_loss_cls_dn_4[0.1191] det_loss_box_dn_4[0.3456] det_loss_cls_dn_5[0.1187] det_loss_box_dn_5[0.3451] loss_depth[0.0402] total_loss[9.5986]
Rank[0/8] 06/19/2025 17:30:39 INFO stats.py:314 | Epoch[24] Step[314] GlobalStep[9674] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 4:40:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:30:50 INFO loss_tracker.py:84 | Epoch[24/239] Step[324] GlobalStep[9684/NA]: det_loss_cls_0[0.6107] det_loss_box_0[0.9826] det_loss_cls_1[0.6121] det_loss_box_1[0.8435] det_loss_cls_2[0.6143] det_loss_box_2[0.7852] det_loss_cls_3[0.5912] det_loss_box_3[0.7674] det_loss_cls_4[0.5662] det_loss_box_4[0.7480] det_loss_cls_5[0.5587] det_loss_box_5[0.7411] det_loss_cls_dn_0[0.1241] det_loss_box_dn_0[0.4149] det_loss_cls_dn_1[0.1203] det_loss_box_dn_1[0.3812] det_loss_cls_dn_2[0.1196] det_loss_box_dn_2[0.3646] det_loss_cls_dn_3[0.1184] det_loss_box_dn_3[0.3631] det_loss_cls_dn_4[0.1175] det_loss_box_dn_4[0.3634] det_loss_cls_dn_5[0.1171] det_loss_box_dn_5[0.3618] loss_depth[0.0441] total_loss[11.4310]
Rank[0/8] 06/19/2025 17:31:05 INFO stats.py:314 | Epoch[24] Step[339] GlobalStep[9699] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 4:39:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:31:16 INFO loss_tracker.py:84 | Epoch[24/239] Step[349] GlobalStep[9709/NA]: det_loss_cls_0[0.4547] det_loss_box_0[0.6021] det_loss_cls_1[0.4358] det_loss_box_1[0.4972] det_loss_cls_2[0.4362] det_loss_box_2[0.4498] det_loss_cls_3[0.4191] det_loss_box_3[0.4362] det_loss_cls_4[0.3950] det_loss_box_4[0.4273] det_loss_cls_5[0.3790] det_loss_box_5[0.4271] det_loss_cls_dn_0[0.1061] det_loss_box_dn_0[0.3300] det_loss_cls_dn_1[0.1023] det_loss_box_dn_1[0.2882] det_loss_cls_dn_2[0.1016] det_loss_box_dn_2[0.2704] det_loss_cls_dn_3[0.1000] det_loss_box_dn_3[0.2688] det_loss_cls_dn_4[0.0993] det_loss_box_dn_4[0.2683] det_loss_cls_dn_5[0.0985] det_loss_box_dn_5[0.2682] loss_depth[0.0376] total_loss[7.6988]
Rank[0/8] 06/19/2025 17:31:31 INFO stats.py:314 | Epoch[24] Step[364] GlobalStep[9724] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 4:38:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:31:42 INFO loss_tracker.py:84 | Epoch[24/239] Step[374] GlobalStep[9734/NA]: det_loss_cls_0[0.5144] det_loss_box_0[0.6690] det_loss_cls_1[0.5089] det_loss_box_1[0.5564] det_loss_cls_2[0.5059] det_loss_box_2[0.4999] det_loss_cls_3[0.4928] det_loss_box_3[0.4780] det_loss_cls_4[0.4644] det_loss_box_4[0.4818] det_loss_cls_5[0.4465] det_loss_box_5[0.4827] det_loss_cls_dn_0[0.1051] det_loss_box_dn_0[0.3489] det_loss_cls_dn_1[0.0996] det_loss_box_dn_1[0.3087] det_loss_cls_dn_2[0.0997] det_loss_box_dn_2[0.2887] det_loss_cls_dn_3[0.0985] det_loss_box_dn_3[0.2857] det_loss_cls_dn_4[0.0981] det_loss_box_dn_4[0.2844] det_loss_cls_dn_5[0.0983] det_loss_box_dn_5[0.2833] loss_depth[0.0404] total_loss[8.5403]
Rank[0/8] 06/19/2025 17:31:57 INFO stats.py:314 | Epoch[24] Step[389] GlobalStep[9749] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 4:37:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:31:57 INFO stats.py:394 | Epoch[24] completed. Training Speed: 7.61 samples/sec across all devices.	Epoch Time: 410.07 sec.	Average Epoch Time: 410.07 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 4:37:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:32:27 INFO stats.py:314 | Epoch[25] Step[24] GlobalStep[9774] Training Speed: 7.60 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 4:36:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:32:27 INFO loss_tracker.py:84 | Epoch[25/239] Step[24] GlobalStep[9774/NA]: det_loss_cls_0[0.6279] det_loss_box_0[0.9468] det_loss_cls_1[0.6012] det_loss_box_1[0.8145] det_loss_cls_2[0.5936] det_loss_box_2[0.7508] det_loss_cls_3[0.5864] det_loss_box_3[0.7215] det_loss_cls_4[0.5592] det_loss_box_4[0.7134] det_loss_cls_5[0.5507] det_loss_box_5[0.7117] det_loss_cls_dn_0[0.1256] det_loss_box_dn_0[0.3835] det_loss_cls_dn_1[0.1221] det_loss_box_dn_1[0.3489] det_loss_cls_dn_2[0.1218] det_loss_box_dn_2[0.3319] det_loss_cls_dn_3[0.1217] det_loss_box_dn_3[0.3305] det_loss_cls_dn_4[0.1211] det_loss_box_dn_4[0.3299] det_loss_cls_dn_5[0.1214] det_loss_box_dn_5[0.3293] loss_depth[0.0423] total_loss[11.0077]
Rank[0/8] 06/19/2025 17:32:53 INFO stats.py:314 | Epoch[25] Step[49] GlobalStep[9799] Training Speed: 7.72 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 4:35:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:32:53 INFO loss_tracker.py:84 | Epoch[25/239] Step[49] GlobalStep[9799/NA]: det_loss_cls_0[0.5550] det_loss_box_0[0.7292] det_loss_cls_1[0.5327] det_loss_box_1[0.6061] det_loss_cls_2[0.5329] det_loss_box_2[0.5563] det_loss_cls_3[0.5190] det_loss_box_3[0.5382] det_loss_cls_4[0.5004] det_loss_box_4[0.5293] det_loss_cls_5[0.4827] det_loss_box_5[0.5309] det_loss_cls_dn_0[0.1274] det_loss_box_dn_0[0.3789] det_loss_cls_dn_1[0.1251] det_loss_box_dn_1[0.3511] det_loss_cls_dn_2[0.1248] det_loss_box_dn_2[0.3363] det_loss_cls_dn_3[0.1238] det_loss_box_dn_3[0.3345] det_loss_cls_dn_4[0.1232] det_loss_box_dn_4[0.3335] det_loss_cls_dn_5[0.1213] det_loss_box_dn_5[0.3322] loss_depth[0.0429] total_loss[9.4678]
Rank[0/8] 06/19/2025 17:33:19 INFO stats.py:314 | Epoch[25] Step[74] GlobalStep[9824] Training Speed: 8.34 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 4:34:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:33:19 INFO loss_tracker.py:84 | Epoch[25/239] Step[74] GlobalStep[9824/NA]: det_loss_cls_0[0.5952] det_loss_box_0[0.8825] det_loss_cls_1[0.5926] det_loss_box_1[0.7245] det_loss_cls_2[0.5792] det_loss_box_2[0.6631] det_loss_cls_3[0.5599] det_loss_box_3[0.6558] det_loss_cls_4[0.5394] det_loss_box_4[0.6392] det_loss_cls_5[0.5222] det_loss_box_5[0.6400] det_loss_cls_dn_0[0.1270] det_loss_box_dn_0[0.3966] det_loss_cls_dn_1[0.1184] det_loss_box_dn_1[0.3587] det_loss_cls_dn_2[0.1177] det_loss_box_dn_2[0.3428] det_loss_cls_dn_3[0.1161] det_loss_box_dn_3[0.3413] det_loss_cls_dn_4[0.1167] det_loss_box_dn_4[0.3403] det_loss_cls_dn_5[0.1160] det_loss_box_dn_5[0.3394] loss_depth[0.0399] total_loss[10.4646]
Rank[0/8] 06/19/2025 17:33:45 INFO stats.py:314 | Epoch[25] Step[99] GlobalStep[9849] Training Speed: 8.56 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 4:33:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:33:45 INFO loss_tracker.py:84 | Epoch[25/239] Step[99] GlobalStep[9849/NA]: det_loss_cls_0[0.5424] det_loss_box_0[0.7366] det_loss_cls_1[0.5254] det_loss_box_1[0.6077] det_loss_cls_2[0.5115] det_loss_box_2[0.5659] det_loss_cls_3[0.5052] det_loss_box_3[0.5457] det_loss_cls_4[0.4754] det_loss_box_4[0.5294] det_loss_cls_5[0.4507] det_loss_box_5[0.5352] det_loss_cls_dn_0[0.1041] det_loss_box_dn_0[0.3700] det_loss_cls_dn_1[0.1000] det_loss_box_dn_1[0.3344] det_loss_cls_dn_2[0.0997] det_loss_box_dn_2[0.3164] det_loss_cls_dn_3[0.0992] det_loss_box_dn_3[0.3154] det_loss_cls_dn_4[0.0977] det_loss_box_dn_4[0.3150] det_loss_cls_dn_5[0.0976] det_loss_box_dn_5[0.3145] loss_depth[0.0421] total_loss[9.1373]
Rank[0/8] 06/19/2025 17:34:11 INFO stats.py:314 | Epoch[25] Step[124] GlobalStep[9874] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 4:32:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:34:11 INFO loss_tracker.py:84 | Epoch[25/239] Step[124] GlobalStep[9874/NA]: det_loss_cls_0[0.5987] det_loss_box_0[0.8257] det_loss_cls_1[0.5740] det_loss_box_1[0.7092] det_loss_cls_2[0.5718] det_loss_box_2[0.6410] det_loss_cls_3[0.5619] det_loss_box_3[0.6275] det_loss_cls_4[0.5380] det_loss_box_4[0.6157] det_loss_cls_5[0.5358] det_loss_box_5[0.6145] det_loss_cls_dn_0[0.1159] det_loss_box_dn_0[0.3532] det_loss_cls_dn_1[0.1128] det_loss_box_dn_1[0.3220] det_loss_cls_dn_2[0.1126] det_loss_box_dn_2[0.3044] det_loss_cls_dn_3[0.1115] det_loss_box_dn_3[0.3027] det_loss_cls_dn_4[0.1107] det_loss_box_dn_4[0.3024] det_loss_cls_dn_5[0.1110] det_loss_box_dn_5[0.3020] loss_depth[0.0421] total_loss[10.0172]
Rank[0/8] 06/19/2025 17:34:38 INFO stats.py:314 | Epoch[25] Step[149] GlobalStep[9899] Training Speed: 8.17 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 4:31:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:34:38 INFO loss_tracker.py:84 | Epoch[25/239] Step[149] GlobalStep[9899/NA]: det_loss_cls_0[0.4952] det_loss_box_0[0.6681] det_loss_cls_1[0.4804] det_loss_box_1[0.5639] det_loss_cls_2[0.4764] det_loss_box_2[0.5210] det_loss_cls_3[0.4677] det_loss_box_3[0.5076] det_loss_cls_4[0.4453] det_loss_box_4[0.5014] det_loss_cls_5[0.4380] det_loss_box_5[0.4988] det_loss_cls_dn_0[0.1093] det_loss_box_dn_0[0.3451] det_loss_cls_dn_1[0.1078] det_loss_box_dn_1[0.3117] det_loss_cls_dn_2[0.1078] det_loss_box_dn_2[0.2920] det_loss_cls_dn_3[0.1078] det_loss_box_dn_3[0.2915] det_loss_cls_dn_4[0.1076] det_loss_box_dn_4[0.2904] det_loss_cls_dn_5[0.1073] det_loss_box_dn_5[0.2895] loss_depth[0.0438] total_loss[8.5755]
Rank[0/8] 06/19/2025 17:35:04 INFO stats.py:314 | Epoch[25] Step[174] GlobalStep[9924] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 4:29:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:35:04 INFO loss_tracker.py:84 | Epoch[25/239] Step[174] GlobalStep[9924/NA]: det_loss_cls_0[0.7010] det_loss_box_0[0.9152] det_loss_cls_1[0.6737] det_loss_box_1[0.7810] det_loss_cls_2[0.6689] det_loss_box_2[0.7161] det_loss_cls_3[0.6523] det_loss_box_3[0.6965] det_loss_cls_4[0.6219] det_loss_box_4[0.6828] det_loss_cls_5[0.6243] det_loss_box_5[0.6770] det_loss_cls_dn_0[0.1226] det_loss_box_dn_0[0.3289] det_loss_cls_dn_1[0.1179] det_loss_box_dn_1[0.3034] det_loss_cls_dn_2[0.1172] det_loss_box_dn_2[0.2908] det_loss_cls_dn_3[0.1179] det_loss_box_dn_3[0.2907] det_loss_cls_dn_4[0.1166] det_loss_box_dn_4[0.2897] det_loss_cls_dn_5[0.1155] det_loss_box_dn_5[0.2891] loss_depth[0.0415] total_loss[10.9526]
Rank[0/8] 06/19/2025 17:35:30 INFO stats.py:314 | Epoch[25] Step[199] GlobalStep[9949] Training Speed: 8.08 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 4:28:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:35:30 INFO loss_tracker.py:84 | Epoch[25/239] Step[199] GlobalStep[9949/NA]: det_loss_cls_0[0.5583] det_loss_box_0[0.7282] det_loss_cls_1[0.5518] det_loss_box_1[0.6153] det_loss_cls_2[0.5460] det_loss_box_2[0.5476] det_loss_cls_3[0.5171] det_loss_box_3[0.5298] det_loss_cls_4[0.4947] det_loss_box_4[0.5253] det_loss_cls_5[0.4828] det_loss_box_5[0.5275] det_loss_cls_dn_0[0.1120] det_loss_box_dn_0[0.3316] det_loss_cls_dn_1[0.1093] det_loss_box_dn_1[0.2972] det_loss_cls_dn_2[0.1086] det_loss_box_dn_2[0.2778] det_loss_cls_dn_3[0.1073] det_loss_box_dn_3[0.2760] det_loss_cls_dn_4[0.1054] det_loss_box_dn_4[0.2745] det_loss_cls_dn_5[0.1051] det_loss_box_dn_5[0.2716] loss_depth[0.0368] total_loss[9.0376]
Rank[0/8] 06/19/2025 17:35:56 INFO stats.py:314 | Epoch[25] Step[224] GlobalStep[9974] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 4:27:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:35:56 INFO loss_tracker.py:84 | Epoch[25/239] Step[224] GlobalStep[9974/NA]: det_loss_cls_0[0.5019] det_loss_box_0[0.6352] det_loss_cls_1[0.4858] det_loss_box_1[0.5271] det_loss_cls_2[0.4770] det_loss_box_2[0.4897] det_loss_cls_3[0.4649] det_loss_box_3[0.4724] det_loss_cls_4[0.4508] det_loss_box_4[0.4676] det_loss_cls_5[0.4398] det_loss_box_5[0.4609] det_loss_cls_dn_0[0.0994] det_loss_box_dn_0[0.2980] det_loss_cls_dn_1[0.0955] det_loss_box_dn_1[0.2649] det_loss_cls_dn_2[0.0944] det_loss_box_dn_2[0.2466] det_loss_cls_dn_3[0.0939] det_loss_box_dn_3[0.2458] det_loss_cls_dn_4[0.0921] det_loss_box_dn_4[0.2453] det_loss_cls_dn_5[0.0911] det_loss_box_dn_5[0.2452] loss_depth[0.0410] total_loss[8.0263]
Rank[0/8] 06/19/2025 17:36:22 INFO stats.py:314 | Epoch[25] Step[249] GlobalStep[9999] Training Speed: 6.49 samples/sec across all devices.	Average Step Time: 1.23 sec.	Estimated Remaining Time: 1 day, 4:26:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:36:22 INFO loss_tracker.py:84 | Epoch[25/239] Step[249] GlobalStep[9999/NA]: det_loss_cls_0[0.6200] det_loss_box_0[0.8533] det_loss_cls_1[0.6087] det_loss_box_1[0.6870] det_loss_cls_2[0.6118] det_loss_box_2[0.6241] det_loss_cls_3[0.5953] det_loss_box_3[0.5895] det_loss_cls_4[0.5602] det_loss_box_4[0.5864] det_loss_cls_5[0.5493] det_loss_box_5[0.5843] det_loss_cls_dn_0[0.1160] det_loss_box_dn_0[0.3361] det_loss_cls_dn_1[0.1138] det_loss_box_dn_1[0.3065] det_loss_cls_dn_2[0.1129] det_loss_box_dn_2[0.2881] det_loss_cls_dn_3[0.1116] det_loss_box_dn_3[0.2857] det_loss_cls_dn_4[0.1111] det_loss_box_dn_4[0.2846] det_loss_cls_dn_5[0.1106] det_loss_box_dn_5[0.2836] loss_depth[0.0396] total_loss[9.9702]
Rank[0/8] 06/19/2025 17:36:48 INFO stats.py:314 | Epoch[25] Step[274] GlobalStep[10024] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 4:25:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:36:48 INFO loss_tracker.py:84 | Epoch[25/239] Step[274] GlobalStep[10024/NA]: det_loss_cls_0[0.5866] det_loss_box_0[0.8291] det_loss_cls_1[0.5711] det_loss_box_1[0.7081] det_loss_cls_2[0.5725] det_loss_box_2[0.6449] det_loss_cls_3[0.5635] det_loss_box_3[0.6216] det_loss_cls_4[0.5349] det_loss_box_4[0.6144] det_loss_cls_5[0.5282] det_loss_box_5[0.6169] det_loss_cls_dn_0[0.1067] det_loss_box_dn_0[0.3621] det_loss_cls_dn_1[0.1036] det_loss_box_dn_1[0.3345] det_loss_cls_dn_2[0.1030] det_loss_box_dn_2[0.3161] det_loss_cls_dn_3[0.1026] det_loss_box_dn_3[0.3153] det_loss_cls_dn_4[0.1013] det_loss_box_dn_4[0.3151] det_loss_cls_dn_5[0.1019] det_loss_box_dn_5[0.3124] loss_depth[0.0393] total_loss[10.0056]
Rank[0/8] 06/19/2025 17:37:14 INFO stats.py:314 | Epoch[25] Step[299] GlobalStep[10049] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 4:24:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:37:14 INFO loss_tracker.py:84 | Epoch[25/239] Step[299] GlobalStep[10049/NA]: det_loss_cls_0[0.5525] det_loss_box_0[0.7320] det_loss_cls_1[0.5462] det_loss_box_1[0.5988] det_loss_cls_2[0.5458] det_loss_box_2[0.5444] det_loss_cls_3[0.5222] det_loss_box_3[0.5415] det_loss_cls_4[0.4985] det_loss_box_4[0.5289] det_loss_cls_5[0.4853] det_loss_box_5[0.5253] det_loss_cls_dn_0[0.1203] det_loss_box_dn_0[0.3814] det_loss_cls_dn_1[0.1176] det_loss_box_dn_1[0.3453] det_loss_cls_dn_2[0.1169] det_loss_box_dn_2[0.3296] det_loss_cls_dn_3[0.1161] det_loss_box_dn_3[0.3291] det_loss_cls_dn_4[0.1148] det_loss_box_dn_4[0.3289] det_loss_cls_dn_5[0.1140] det_loss_box_dn_5[0.3282] loss_depth[0.0405] total_loss[9.4042]
Rank[0/8] 06/19/2025 17:37:39 INFO stats.py:314 | Epoch[25] Step[324] GlobalStep[10074] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 4:22:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:37:40 INFO loss_tracker.py:84 | Epoch[25/239] Step[324] GlobalStep[10074/NA]: det_loss_cls_0[0.5575] det_loss_box_0[0.7538] det_loss_cls_1[0.5346] det_loss_box_1[0.6165] det_loss_cls_2[0.5363] det_loss_box_2[0.5565] det_loss_cls_3[0.5198] det_loss_box_3[0.5377] det_loss_cls_4[0.4981] det_loss_box_4[0.5298] det_loss_cls_5[0.4787] det_loss_box_5[0.5325] det_loss_cls_dn_0[0.1171] det_loss_box_dn_0[0.3684] det_loss_cls_dn_1[0.1155] det_loss_box_dn_1[0.3358] det_loss_cls_dn_2[0.1150] det_loss_box_dn_2[0.3198] det_loss_cls_dn_3[0.1150] det_loss_box_dn_3[0.3190] det_loss_cls_dn_4[0.1150] det_loss_box_dn_4[0.3183] det_loss_cls_dn_5[0.1140] det_loss_box_dn_5[0.3181] loss_depth[0.0441] total_loss[9.3671]
Rank[0/8] 06/19/2025 17:38:06 INFO stats.py:314 | Epoch[25] Step[349] GlobalStep[10099] Training Speed: 8.06 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 4:21:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:38:06 INFO loss_tracker.py:84 | Epoch[25/239] Step[349] GlobalStep[10099/NA]: det_loss_cls_0[0.6108] det_loss_box_0[0.9131] det_loss_cls_1[0.5914] det_loss_box_1[0.7832] det_loss_cls_2[0.5793] det_loss_box_2[0.7176] det_loss_cls_3[0.5727] det_loss_box_3[0.6970] det_loss_cls_4[0.5419] det_loss_box_4[0.6845] det_loss_cls_5[0.5279] det_loss_box_5[0.6863] det_loss_cls_dn_0[0.1138] det_loss_box_dn_0[0.3400] det_loss_cls_dn_1[0.1106] det_loss_box_dn_1[0.3086] det_loss_cls_dn_2[0.1094] det_loss_box_dn_2[0.2926] det_loss_cls_dn_3[0.1091] det_loss_box_dn_3[0.2915] det_loss_cls_dn_4[0.1081] det_loss_box_dn_4[0.2913] det_loss_cls_dn_5[0.1068] det_loss_box_dn_5[0.2910] loss_depth[0.0420] total_loss[10.4208]
Rank[0/8] 06/19/2025 17:38:32 INFO stats.py:314 | Epoch[25] Step[374] GlobalStep[10124] Training Speed: 8.72 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 1 day, 4:20:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:38:32 INFO loss_tracker.py:84 | Epoch[25/239] Step[374] GlobalStep[10124/NA]: det_loss_cls_0[0.6349] det_loss_box_0[0.8464] det_loss_cls_1[0.6089] det_loss_box_1[0.6908] det_loss_cls_2[0.6081] det_loss_box_2[0.6299] det_loss_cls_3[0.5897] det_loss_box_3[0.6090] det_loss_cls_4[0.5568] det_loss_box_4[0.6026] det_loss_cls_5[0.5393] det_loss_box_5[0.6032] det_loss_cls_dn_0[0.1159] det_loss_box_dn_0[0.2949] det_loss_cls_dn_1[0.1109] det_loss_box_dn_1[0.2656] det_loss_cls_dn_2[0.1106] det_loss_box_dn_2[0.2504] det_loss_cls_dn_3[0.1098] det_loss_box_dn_3[0.2495] det_loss_cls_dn_4[0.1083] det_loss_box_dn_4[0.2487] det_loss_cls_dn_5[0.1082] det_loss_box_dn_5[0.2473] loss_depth[0.0399] total_loss[9.7794]
Rank[0/8] 06/19/2025 17:38:48 INFO stats.py:394 | Epoch[25] completed. Training Speed: 7.60 samples/sec across all devices.	Epoch Time: 410.78 sec.	Average Epoch Time: 410.78 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 4:20:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:39:01 INFO stats.py:314 | Epoch[26] Step[9] GlobalStep[10149] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 4:19:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:39:16 INFO loss_tracker.py:84 | Epoch[26/239] Step[24] GlobalStep[10164/NA]: det_loss_cls_0[0.6266] det_loss_box_0[0.8240] det_loss_cls_1[0.5990] det_loss_box_1[0.6727] det_loss_cls_2[0.5936] det_loss_box_2[0.6065] det_loss_cls_3[0.5802] det_loss_box_3[0.5847] det_loss_cls_4[0.5547] det_loss_box_4[0.5858] det_loss_cls_5[0.5381] det_loss_box_5[0.5893] det_loss_cls_dn_0[0.1049] det_loss_box_dn_0[0.3164] det_loss_cls_dn_1[0.1019] det_loss_box_dn_1[0.2848] det_loss_cls_dn_2[0.1012] det_loss_box_dn_2[0.2682] det_loss_cls_dn_3[0.1004] det_loss_box_dn_3[0.2666] det_loss_cls_dn_4[0.0991] det_loss_box_dn_4[0.2663] det_loss_cls_dn_5[0.0987] det_loss_box_dn_5[0.2657] loss_depth[0.0396] total_loss[9.6690]
Rank[0/8] 06/19/2025 17:39:27 INFO stats.py:314 | Epoch[26] Step[34] GlobalStep[10174] Training Speed: 8.97 samples/sec across all devices.	Average Step Time: 0.89 sec.	Estimated Remaining Time: 1 day, 4:18:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:39:43 INFO loss_tracker.py:84 | Epoch[26/239] Step[49] GlobalStep[10189/NA]: det_loss_cls_0[0.5799] det_loss_box_0[0.8212] det_loss_cls_1[0.5597] det_loss_box_1[0.6899] det_loss_cls_2[0.5641] det_loss_box_2[0.6268] det_loss_cls_3[0.5475] det_loss_box_3[0.6107] det_loss_cls_4[0.5293] det_loss_box_4[0.6007] det_loss_cls_5[0.5194] det_loss_box_5[0.6027] det_loss_cls_dn_0[0.1107] det_loss_box_dn_0[0.3009] det_loss_cls_dn_1[0.1086] det_loss_box_dn_1[0.2736] det_loss_cls_dn_2[0.1076] det_loss_box_dn_2[0.2568] det_loss_cls_dn_3[0.1065] det_loss_box_dn_3[0.2554] det_loss_cls_dn_4[0.1056] det_loss_box_dn_4[0.2548] det_loss_cls_dn_5[0.1049] det_loss_box_dn_5[0.2537] loss_depth[0.0398] total_loss[9.5308]
Rank[0/8] 06/19/2025 17:39:53 INFO stats.py:314 | Epoch[26] Step[59] GlobalStep[10199] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 4:17:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:40:09 INFO loss_tracker.py:84 | Epoch[26/239] Step[74] GlobalStep[10214/NA]: det_loss_cls_0[0.6071] det_loss_box_0[0.8127] det_loss_cls_1[0.5698] det_loss_box_1[0.6805] det_loss_cls_2[0.5645] det_loss_box_2[0.6082] det_loss_cls_3[0.5524] det_loss_box_3[0.5844] det_loss_cls_4[0.5310] det_loss_box_4[0.5750] det_loss_cls_5[0.5197] det_loss_box_5[0.5717] det_loss_cls_dn_0[0.1198] det_loss_box_dn_0[0.3466] det_loss_cls_dn_1[0.1162] det_loss_box_dn_1[0.2994] det_loss_cls_dn_2[0.1159] det_loss_box_dn_2[0.2805] det_loss_cls_dn_3[0.1141] det_loss_box_dn_3[0.2792] det_loss_cls_dn_4[0.1129] det_loss_box_dn_4[0.2785] det_loss_cls_dn_5[0.1134] det_loss_box_dn_5[0.2776] loss_depth[0.0448] total_loss[9.6759]
Rank[0/8] 06/19/2025 17:40:19 INFO stats.py:314 | Epoch[26] Step[84] GlobalStep[10224] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 4:16:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:40:35 INFO loss_tracker.py:84 | Epoch[26/239] Step[99] GlobalStep[10239/NA]: det_loss_cls_0[0.6223] det_loss_box_0[0.7847] det_loss_cls_1[0.5973] det_loss_box_1[0.6350] det_loss_cls_2[0.5938] det_loss_box_2[0.5745] det_loss_cls_3[0.5759] det_loss_box_3[0.5566] det_loss_cls_4[0.5402] det_loss_box_4[0.5541] det_loss_cls_5[0.5244] det_loss_box_5[0.5571] det_loss_cls_dn_0[0.1057] det_loss_box_dn_0[0.2952] det_loss_cls_dn_1[0.1027] det_loss_box_dn_1[0.2626] det_loss_cls_dn_2[0.1014] det_loss_box_dn_2[0.2475] det_loss_cls_dn_3[0.1005] det_loss_box_dn_3[0.2460] det_loss_cls_dn_4[0.0988] det_loss_box_dn_4[0.2453] det_loss_cls_dn_5[0.0977] det_loss_box_dn_5[0.2448] loss_depth[0.0389] total_loss[9.3030]
Rank[0/8] 06/19/2025 17:40:45 INFO stats.py:314 | Epoch[26] Step[109] GlobalStep[10249] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 4:15:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:41:01 INFO loss_tracker.py:84 | Epoch[26/239] Step[124] GlobalStep[10264/NA]: det_loss_cls_0[0.7695] det_loss_box_0[1.0589] det_loss_cls_1[0.7415] det_loss_box_1[0.9004] det_loss_cls_2[0.7343] det_loss_box_2[0.8441] det_loss_cls_3[0.7296] det_loss_box_3[0.8177] det_loss_cls_4[0.6929] det_loss_box_4[0.7961] det_loss_cls_5[0.6901] det_loss_box_5[0.7870] det_loss_cls_dn_0[0.1266] det_loss_box_dn_0[0.3553] det_loss_cls_dn_1[0.1227] det_loss_box_dn_1[0.3206] det_loss_cls_dn_2[0.1226] det_loss_box_dn_2[0.3065] det_loss_cls_dn_3[0.1216] det_loss_box_dn_3[0.3047] det_loss_cls_dn_4[0.1210] det_loss_box_dn_4[0.3042] det_loss_cls_dn_5[0.1200] det_loss_box_dn_5[0.3030] loss_depth[0.0404] total_loss[12.2312]
Rank[0/8] 06/19/2025 17:41:11 INFO stats.py:314 | Epoch[26] Step[134] GlobalStep[10274] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 4:14:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:41:27 INFO loss_tracker.py:84 | Epoch[26/239] Step[149] GlobalStep[10289/NA]: det_loss_cls_0[0.5516] det_loss_box_0[0.8187] det_loss_cls_1[0.5345] det_loss_box_1[0.7086] det_loss_cls_2[0.5237] det_loss_box_2[0.6591] det_loss_cls_3[0.5134] det_loss_box_3[0.6304] det_loss_cls_4[0.4940] det_loss_box_4[0.6323] det_loss_cls_5[0.4803] det_loss_box_5[0.6356] det_loss_cls_dn_0[0.1059] det_loss_box_dn_0[0.3150] det_loss_cls_dn_1[0.1049] det_loss_box_dn_1[0.2863] det_loss_cls_dn_2[0.1046] det_loss_box_dn_2[0.2704] det_loss_cls_dn_3[0.1045] det_loss_box_dn_3[0.2691] det_loss_cls_dn_4[0.1028] det_loss_box_dn_4[0.2684] det_loss_cls_dn_5[0.1020] det_loss_box_dn_5[0.2678] loss_depth[0.0433] total_loss[9.5274]
Rank[0/8] 06/19/2025 17:41:37 INFO stats.py:314 | Epoch[26] Step[159] GlobalStep[10299] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 4:13:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:41:53 INFO loss_tracker.py:84 | Epoch[26/239] Step[174] GlobalStep[10314/NA]: det_loss_cls_0[0.6268] det_loss_box_0[0.9163] det_loss_cls_1[0.6025] det_loss_box_1[0.7489] det_loss_cls_2[0.5914] det_loss_box_2[0.6759] det_loss_cls_3[0.5762] det_loss_box_3[0.6518] det_loss_cls_4[0.5603] det_loss_box_4[0.6279] det_loss_cls_5[0.5381] det_loss_box_5[0.6307] det_loss_cls_dn_0[0.1145] det_loss_box_dn_0[0.3516] det_loss_cls_dn_1[0.1115] det_loss_box_dn_1[0.3134] det_loss_cls_dn_2[0.1105] det_loss_box_dn_2[0.2922] det_loss_cls_dn_3[0.1094] det_loss_box_dn_3[0.2918] det_loss_cls_dn_4[0.1080] det_loss_box_dn_4[0.2905] det_loss_cls_dn_5[0.1078] det_loss_box_dn_5[0.2904] loss_depth[0.0388] total_loss[10.2772]
Rank[0/8] 06/19/2025 17:42:03 INFO stats.py:314 | Epoch[26] Step[184] GlobalStep[10324] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 4:12:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:42:19 INFO loss_tracker.py:84 | Epoch[26/239] Step[199] GlobalStep[10339/NA]: det_loss_cls_0[0.4875] det_loss_box_0[0.7070] det_loss_cls_1[0.4730] det_loss_box_1[0.6091] det_loss_cls_2[0.4699] det_loss_box_2[0.5520] det_loss_cls_3[0.4531] det_loss_box_3[0.5357] det_loss_cls_4[0.4261] det_loss_box_4[0.5293] det_loss_cls_5[0.4126] det_loss_box_5[0.5348] det_loss_cls_dn_0[0.1117] det_loss_box_dn_0[0.3645] det_loss_cls_dn_1[0.1093] det_loss_box_dn_1[0.3238] det_loss_cls_dn_2[0.1080] det_loss_box_dn_2[0.3027] det_loss_cls_dn_3[0.1073] det_loss_box_dn_3[0.3008] det_loss_cls_dn_4[0.1072] det_loss_box_dn_4[0.3002] det_loss_cls_dn_5[0.1058] det_loss_box_dn_5[0.2984] loss_depth[0.0418] total_loss[8.7716]
Rank[0/8] 06/19/2025 17:42:29 INFO stats.py:314 | Epoch[26] Step[209] GlobalStep[10349] Training Speed: 8.22 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 4:11:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:42:45 INFO loss_tracker.py:84 | Epoch[26/239] Step[224] GlobalStep[10364/NA]: det_loss_cls_0[0.5567] det_loss_box_0[0.6995] det_loss_cls_1[0.5489] det_loss_box_1[0.5635] det_loss_cls_2[0.5479] det_loss_box_2[0.5107] det_loss_cls_3[0.5292] det_loss_box_3[0.4920] det_loss_cls_4[0.5006] det_loss_box_4[0.4884] det_loss_cls_5[0.4874] det_loss_box_5[0.4861] det_loss_cls_dn_0[0.1112] det_loss_box_dn_0[0.3381] det_loss_cls_dn_1[0.1091] det_loss_box_dn_1[0.2992] det_loss_cls_dn_2[0.1083] det_loss_box_dn_2[0.2829] det_loss_cls_dn_3[0.1088] det_loss_box_dn_3[0.2819] det_loss_cls_dn_4[0.1072] det_loss_box_dn_4[0.2816] det_loss_cls_dn_5[0.1060] det_loss_box_dn_5[0.2788] loss_depth[0.0401] total_loss[8.8642]
Rank[0/8] 06/19/2025 17:42:55 INFO stats.py:314 | Epoch[26] Step[234] GlobalStep[10374] Training Speed: 8.19 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 4:09:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:43:11 INFO loss_tracker.py:84 | Epoch[26/239] Step[249] GlobalStep[10389/NA]: det_loss_cls_0[0.5824] det_loss_box_0[0.8768] det_loss_cls_1[0.5501] det_loss_box_1[0.7224] det_loss_cls_2[0.5451] det_loss_box_2[0.6653] det_loss_cls_3[0.5417] det_loss_box_3[0.6434] det_loss_cls_4[0.5196] det_loss_box_4[0.6343] det_loss_cls_5[0.5135] det_loss_box_5[0.6306] det_loss_cls_dn_0[0.1163] det_loss_box_dn_0[0.4419] det_loss_cls_dn_1[0.1149] det_loss_box_dn_1[0.3941] det_loss_cls_dn_2[0.1148] det_loss_box_dn_2[0.3781] det_loss_cls_dn_3[0.1137] det_loss_box_dn_3[0.3749] det_loss_cls_dn_4[0.1130] det_loss_box_dn_4[0.3745] det_loss_cls_dn_5[0.1127] det_loss_box_dn_5[0.3740] loss_depth[0.0434] total_loss[10.4915]
Rank[0/8] 06/19/2025 17:43:21 INFO stats.py:314 | Epoch[26] Step[259] GlobalStep[10399] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 4:08:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:43:37 INFO loss_tracker.py:84 | Epoch[26/239] Step[274] GlobalStep[10414/NA]: det_loss_cls_0[0.5608] det_loss_box_0[0.7851] det_loss_cls_1[0.5376] det_loss_box_1[0.6458] det_loss_cls_2[0.5472] det_loss_box_2[0.5700] det_loss_cls_3[0.5246] det_loss_box_3[0.5528] det_loss_cls_4[0.4958] det_loss_box_4[0.5453] det_loss_cls_5[0.4853] det_loss_box_5[0.5488] det_loss_cls_dn_0[0.1227] det_loss_box_dn_0[0.3565] det_loss_cls_dn_1[0.1201] det_loss_box_dn_1[0.3117] det_loss_cls_dn_2[0.1198] det_loss_box_dn_2[0.2921] det_loss_cls_dn_3[0.1198] det_loss_box_dn_3[0.2890] det_loss_cls_dn_4[0.1184] det_loss_box_dn_4[0.2884] det_loss_cls_dn_5[0.1186] det_loss_box_dn_5[0.2866] loss_depth[0.0420] total_loss[9.3850]
Rank[0/8] 06/19/2025 17:43:47 INFO stats.py:314 | Epoch[26] Step[284] GlobalStep[10424] Training Speed: 8.64 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 4:07:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:44:03 INFO loss_tracker.py:84 | Epoch[26/239] Step[299] GlobalStep[10439/NA]: det_loss_cls_0[0.5731] det_loss_box_0[0.7863] det_loss_cls_1[0.5553] det_loss_box_1[0.6507] det_loss_cls_2[0.5568] det_loss_box_2[0.5888] det_loss_cls_3[0.5454] det_loss_box_3[0.5684] det_loss_cls_4[0.5170] det_loss_box_4[0.5662] det_loss_cls_5[0.5059] det_loss_box_5[0.5658] det_loss_cls_dn_0[0.1150] det_loss_box_dn_0[0.3388] det_loss_cls_dn_1[0.1114] det_loss_box_dn_1[0.3096] det_loss_cls_dn_2[0.1110] det_loss_box_dn_2[0.2966] det_loss_cls_dn_3[0.1104] det_loss_box_dn_3[0.2957] det_loss_cls_dn_4[0.1102] det_loss_box_dn_4[0.2954] det_loss_cls_dn_5[0.1085] det_loss_box_dn_5[0.2945] loss_depth[0.0400] total_loss[9.5169]
Rank[0/8] 06/19/2025 17:44:13 INFO stats.py:314 | Epoch[26] Step[309] GlobalStep[10449] Training Speed: 8.13 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 4:06:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:44:29 INFO loss_tracker.py:84 | Epoch[26/239] Step[324] GlobalStep[10464/NA]: det_loss_cls_0[0.5461] det_loss_box_0[0.7551] det_loss_cls_1[0.5345] det_loss_box_1[0.6389] det_loss_cls_2[0.5373] det_loss_box_2[0.5870] det_loss_cls_3[0.5185] det_loss_box_3[0.5660] det_loss_cls_4[0.4842] det_loss_box_4[0.5597] det_loss_cls_5[0.4730] det_loss_box_5[0.5589] det_loss_cls_dn_0[0.1034] det_loss_box_dn_0[0.4621] det_loss_cls_dn_1[0.0995] det_loss_box_dn_1[0.4218] det_loss_cls_dn_2[0.0985] det_loss_box_dn_2[0.4104] det_loss_cls_dn_3[0.0982] det_loss_box_dn_3[0.4095] det_loss_cls_dn_4[0.0974] det_loss_box_dn_4[0.4092] det_loss_cls_dn_5[0.0973] det_loss_box_dn_5[0.4085] loss_depth[0.0426] total_loss[9.9175]
Rank[0/8] 06/19/2025 17:44:39 INFO stats.py:314 | Epoch[26] Step[334] GlobalStep[10474] Training Speed: 8.22 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 4:05:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:44:55 INFO loss_tracker.py:84 | Epoch[26/239] Step[349] GlobalStep[10489/NA]: det_loss_cls_0[0.6908] det_loss_box_0[1.0233] det_loss_cls_1[0.6669] det_loss_box_1[0.8666] det_loss_cls_2[0.6501] det_loss_box_2[0.7861] det_loss_cls_3[0.6386] det_loss_box_3[0.7698] det_loss_cls_4[0.6125] det_loss_box_4[0.7517] det_loss_cls_5[0.6109] det_loss_box_5[0.7461] det_loss_cls_dn_0[0.1111] det_loss_box_dn_0[0.3399] det_loss_cls_dn_1[0.1088] det_loss_box_dn_1[0.3021] det_loss_cls_dn_2[0.1084] det_loss_box_dn_2[0.2851] det_loss_cls_dn_3[0.1077] det_loss_box_dn_3[0.2837] det_loss_cls_dn_4[0.1073] det_loss_box_dn_4[0.2828] det_loss_cls_dn_5[0.1071] det_loss_box_dn_5[0.2828] loss_depth[0.0420] total_loss[11.2822]
Rank[0/8] 06/19/2025 17:45:06 INFO stats.py:314 | Epoch[26] Step[359] GlobalStep[10499] Training Speed: 8.68 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 1 day, 4:04:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:45:21 INFO loss_tracker.py:84 | Epoch[26/239] Step[374] GlobalStep[10514/NA]: det_loss_cls_0[0.4723] det_loss_box_0[0.6388] det_loss_cls_1[0.4625] det_loss_box_1[0.5383] det_loss_cls_2[0.4517] det_loss_box_2[0.4966] det_loss_cls_3[0.4320] det_loss_box_3[0.4858] det_loss_cls_4[0.4201] det_loss_box_4[0.4706] det_loss_cls_5[0.4035] det_loss_box_5[0.4722] det_loss_cls_dn_0[0.1024] det_loss_box_dn_0[0.3027] det_loss_cls_dn_1[0.0979] det_loss_box_dn_1[0.2616] det_loss_cls_dn_2[0.0972] det_loss_box_dn_2[0.2440] det_loss_cls_dn_3[0.0955] det_loss_box_dn_3[0.2412] det_loss_cls_dn_4[0.0944] det_loss_box_dn_4[0.2415] det_loss_cls_dn_5[0.0941] det_loss_box_dn_5[0.2412] loss_depth[0.0406] total_loss[7.8989]
Rank[0/8] 06/19/2025 17:45:31 INFO stats.py:314 | Epoch[26] Step[384] GlobalStep[10524] Training Speed: 7.98 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 4:03:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:45:36 INFO stats.py:394 | Epoch[26] completed. Training Speed: 7.64 samples/sec across all devices.	Epoch Time: 408.61 sec.	Average Epoch Time: 408.61 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 4:03:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:46:01 INFO stats.py:314 | Epoch[27] Step[19] GlobalStep[10549] Training Speed: 7.83 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1 day, 4:02:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:46:06 INFO loss_tracker.py:84 | Epoch[27/239] Step[24] GlobalStep[10554/NA]: det_loss_cls_0[0.4608] det_loss_box_0[0.6443] det_loss_cls_1[0.4523] det_loss_box_1[0.5369] det_loss_cls_2[0.4448] det_loss_box_2[0.4868] det_loss_cls_3[0.4313] det_loss_box_3[0.4683] det_loss_cls_4[0.4105] det_loss_box_4[0.4666] det_loss_cls_5[0.3954] det_loss_box_5[0.4676] det_loss_cls_dn_0[0.1096] det_loss_box_dn_0[0.4148] det_loss_cls_dn_1[0.1057] det_loss_box_dn_1[0.3734] det_loss_cls_dn_2[0.1050] det_loss_box_dn_2[0.3568] det_loss_cls_dn_3[0.1042] det_loss_box_dn_3[0.3554] det_loss_cls_dn_4[0.1030] det_loss_box_dn_4[0.3545] det_loss_cls_dn_5[0.1019] det_loss_box_dn_5[0.3511] loss_depth[0.0431] total_loss[8.5442]
Rank[0/8] 06/19/2025 17:46:27 INFO stats.py:314 | Epoch[27] Step[44] GlobalStep[10574] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 4:01:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:46:33 INFO loss_tracker.py:84 | Epoch[27/239] Step[49] GlobalStep[10579/NA]: det_loss_cls_0[0.5830] det_loss_box_0[0.8267] det_loss_cls_1[0.5820] det_loss_box_1[0.7077] det_loss_cls_2[0.5783] det_loss_box_2[0.6511] det_loss_cls_3[0.5540] det_loss_box_3[0.6372] det_loss_cls_4[0.5318] det_loss_box_4[0.6303] det_loss_cls_5[0.5149] det_loss_box_5[0.6310] det_loss_cls_dn_0[0.1225] det_loss_box_dn_0[0.3570] det_loss_cls_dn_1[0.1193] det_loss_box_dn_1[0.3168] det_loss_cls_dn_2[0.1191] det_loss_box_dn_2[0.2984] det_loss_cls_dn_3[0.1178] det_loss_box_dn_3[0.2962] det_loss_cls_dn_4[0.1170] det_loss_box_dn_4[0.2953] det_loss_cls_dn_5[0.1162] det_loss_box_dn_5[0.2945] loss_depth[0.0424] total_loss[10.0405]
Rank[0/8] 06/19/2025 17:46:53 INFO stats.py:314 | Epoch[27] Step[69] GlobalStep[10599] Training Speed: 8.15 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 4:00:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:46:58 INFO loss_tracker.py:84 | Epoch[27/239] Step[74] GlobalStep[10604/NA]: det_loss_cls_0[0.6794] det_loss_box_0[1.0372] det_loss_cls_1[0.6669] det_loss_box_1[0.8860] det_loss_cls_2[0.6711] det_loss_box_2[0.8040] det_loss_cls_3[0.6623] det_loss_box_3[0.7744] det_loss_cls_4[0.6292] det_loss_box_4[0.7781] det_loss_cls_5[0.6168] det_loss_box_5[0.7814] det_loss_cls_dn_0[0.1108] det_loss_box_dn_0[0.3799] det_loss_cls_dn_1[0.1081] det_loss_box_dn_1[0.3474] det_loss_cls_dn_2[0.1078] det_loss_box_dn_2[0.3336] det_loss_cls_dn_3[0.1078] det_loss_box_dn_3[0.3320] det_loss_cls_dn_4[0.1068] det_loss_box_dn_4[0.3317] det_loss_cls_dn_5[0.1059] det_loss_box_dn_5[0.3298] loss_depth[0.0439] total_loss[11.7322]
Rank[0/8] 06/19/2025 17:47:19 INFO stats.py:314 | Epoch[27] Step[94] GlobalStep[10624] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 3:59:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:47:24 INFO loss_tracker.py:84 | Epoch[27/239] Step[99] GlobalStep[10629/NA]: det_loss_cls_0[0.5423] det_loss_box_0[0.7664] det_loss_cls_1[0.5220] det_loss_box_1[0.6573] det_loss_cls_2[0.5248] det_loss_box_2[0.6089] det_loss_cls_3[0.5113] det_loss_box_3[0.5936] det_loss_cls_4[0.4855] det_loss_box_4[0.5890] det_loss_cls_5[0.4697] det_loss_box_5[0.5908] det_loss_cls_dn_0[0.1139] det_loss_box_dn_0[0.3653] det_loss_cls_dn_1[0.1110] det_loss_box_dn_1[0.3269] det_loss_cls_dn_2[0.1101] det_loss_box_dn_2[0.3119] det_loss_cls_dn_3[0.1098] det_loss_box_dn_3[0.3098] det_loss_cls_dn_4[0.1081] det_loss_box_dn_4[0.3081] det_loss_cls_dn_5[0.1070] det_loss_box_dn_5[0.3076] loss_depth[0.0421] total_loss[9.4932]
Rank[0/8] 06/19/2025 17:47:45 INFO stats.py:314 | Epoch[27] Step[119] GlobalStep[10649] Training Speed: 6.35 samples/sec across all devices.	Average Step Time: 1.26 sec.	Estimated Remaining Time: 1 day, 3:58:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:47:50 INFO loss_tracker.py:84 | Epoch[27/239] Step[124] GlobalStep[10654/NA]: det_loss_cls_0[0.4573] det_loss_box_0[0.5701] det_loss_cls_1[0.4287] det_loss_box_1[0.4798] det_loss_cls_2[0.4262] det_loss_box_2[0.4409] det_loss_cls_3[0.4076] det_loss_box_3[0.4295] det_loss_cls_4[0.3934] det_loss_box_4[0.4225] det_loss_cls_5[0.3829] det_loss_box_5[0.4273] det_loss_cls_dn_0[0.1041] det_loss_box_dn_0[0.3518] det_loss_cls_dn_1[0.1018] det_loss_box_dn_1[0.3153] det_loss_cls_dn_2[0.1012] det_loss_box_dn_2[0.2986] det_loss_cls_dn_3[0.0997] det_loss_box_dn_3[0.2967] det_loss_cls_dn_4[0.0982] det_loss_box_dn_4[0.2961] det_loss_cls_dn_5[0.0973] det_loss_box_dn_5[0.2952] loss_depth[0.0368] total_loss[7.7591]
Rank[0/8] 06/19/2025 17:48:11 INFO stats.py:314 | Epoch[27] Step[144] GlobalStep[10674] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 3:57:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:48:16 INFO loss_tracker.py:84 | Epoch[27/239] Step[149] GlobalStep[10679/NA]: det_loss_cls_0[0.6539] det_loss_box_0[0.9359] det_loss_cls_1[0.6246] det_loss_box_1[0.7874] det_loss_cls_2[0.6261] det_loss_box_2[0.7265] det_loss_cls_3[0.6074] det_loss_box_3[0.6998] det_loss_cls_4[0.5706] det_loss_box_4[0.6957] det_loss_cls_5[0.5616] det_loss_box_5[0.6929] det_loss_cls_dn_0[0.1184] det_loss_box_dn_0[0.4359] det_loss_cls_dn_1[0.1145] det_loss_box_dn_1[0.3923] det_loss_cls_dn_2[0.1139] det_loss_box_dn_2[0.3680] det_loss_cls_dn_3[0.1122] det_loss_box_dn_3[0.3653] det_loss_cls_dn_4[0.1108] det_loss_box_dn_4[0.3588] det_loss_cls_dn_5[0.1100] det_loss_box_dn_5[0.3576] loss_depth[0.0390] total_loss[11.1792]
Rank[0/8] 06/19/2025 17:48:36 INFO stats.py:314 | Epoch[27] Step[169] GlobalStep[10699] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 3:56:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:48:41 INFO loss_tracker.py:84 | Epoch[27/239] Step[174] GlobalStep[10704/NA]: det_loss_cls_0[0.6400] det_loss_box_0[0.8571] det_loss_cls_1[0.6161] det_loss_box_1[0.7261] det_loss_cls_2[0.6023] det_loss_box_2[0.6732] det_loss_cls_3[0.5800] det_loss_box_3[0.6520] det_loss_cls_4[0.5487] det_loss_box_4[0.6418] det_loss_cls_5[0.5396] det_loss_box_5[0.6418] det_loss_cls_dn_0[0.1114] det_loss_box_dn_0[0.3285] det_loss_cls_dn_1[0.1079] det_loss_box_dn_1[0.2993] det_loss_cls_dn_2[0.1067] det_loss_box_dn_2[0.2823] det_loss_cls_dn_3[0.1052] det_loss_box_dn_3[0.2811] det_loss_cls_dn_4[0.1041] det_loss_box_dn_4[0.2821] det_loss_cls_dn_5[0.1026] det_loss_box_dn_5[0.2796] loss_depth[0.0399] total_loss[10.1494]
Rank[0/8] 06/19/2025 17:49:02 INFO stats.py:314 | Epoch[27] Step[194] GlobalStep[10724] Training Speed: 8.66 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 1 day, 3:55:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:49:07 INFO loss_tracker.py:84 | Epoch[27/239] Step[199] GlobalStep[10729/NA]: det_loss_cls_0[0.5560] det_loss_box_0[0.6943] det_loss_cls_1[0.5388] det_loss_box_1[0.5841] det_loss_cls_2[0.5270] det_loss_box_2[0.5242] det_loss_cls_3[0.5028] det_loss_box_3[0.5198] det_loss_cls_4[0.4827] det_loss_box_4[0.5049] det_loss_cls_5[0.4596] det_loss_box_5[0.5123] det_loss_cls_dn_0[0.1121] det_loss_box_dn_0[0.3554] det_loss_cls_dn_1[0.1106] det_loss_box_dn_1[0.3269] det_loss_cls_dn_2[0.1091] det_loss_box_dn_2[0.3102] det_loss_cls_dn_3[0.1094] det_loss_box_dn_3[0.3100] det_loss_cls_dn_4[0.1082] det_loss_box_dn_4[0.3087] det_loss_cls_dn_5[0.1078] det_loss_box_dn_5[0.3085] loss_depth[0.0416] total_loss[9.0248]
Rank[0/8] 06/19/2025 17:49:28 INFO stats.py:314 | Epoch[27] Step[219] GlobalStep[10749] Training Speed: 6.30 samples/sec across all devices.	Average Step Time: 1.27 sec.	Estimated Remaining Time: 1 day, 3:54:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:49:33 INFO loss_tracker.py:84 | Epoch[27/239] Step[224] GlobalStep[10754/NA]: det_loss_cls_0[0.6356] det_loss_box_0[0.8531] det_loss_cls_1[0.6198] det_loss_box_1[0.7171] det_loss_cls_2[0.6102] det_loss_box_2[0.6677] det_loss_cls_3[0.6004] det_loss_box_3[0.6456] det_loss_cls_4[0.5714] det_loss_box_4[0.6336] det_loss_cls_5[0.5582] det_loss_box_5[0.6335] det_loss_cls_dn_0[0.1211] det_loss_box_dn_0[0.3574] det_loss_cls_dn_1[0.1186] det_loss_box_dn_1[0.3188] det_loss_cls_dn_2[0.1177] det_loss_box_dn_2[0.3055] det_loss_cls_dn_3[0.1166] det_loss_box_dn_3[0.3039] det_loss_cls_dn_4[0.1156] det_loss_box_dn_4[0.3028] det_loss_cls_dn_5[0.1147] det_loss_box_dn_5[0.3026] loss_depth[0.0403] total_loss[10.3817]
Rank[0/8] 06/19/2025 17:49:54 INFO stats.py:314 | Epoch[27] Step[244] GlobalStep[10774] Training Speed: 8.34 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 3:53:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:50:00 INFO loss_tracker.py:84 | Epoch[27/239] Step[249] GlobalStep[10779/NA]: det_loss_cls_0[0.4340] det_loss_box_0[0.5656] det_loss_cls_1[0.4096] det_loss_box_1[0.4610] det_loss_cls_2[0.4032] det_loss_box_2[0.4209] det_loss_cls_3[0.3938] det_loss_box_3[0.4104] det_loss_cls_4[0.3774] det_loss_box_4[0.3996] det_loss_cls_5[0.3677] det_loss_box_5[0.4026] det_loss_cls_dn_0[0.1018] det_loss_box_dn_0[0.3682] det_loss_cls_dn_1[0.0996] det_loss_box_dn_1[0.3254] det_loss_cls_dn_2[0.0976] det_loss_box_dn_2[0.3095] det_loss_cls_dn_3[0.0970] det_loss_box_dn_3[0.3078] det_loss_cls_dn_4[0.0972] det_loss_box_dn_4[0.3072] det_loss_cls_dn_5[0.0962] det_loss_box_dn_5[0.3043] loss_depth[0.0413] total_loss[7.5989]
Rank[0/8] 06/19/2025 17:50:20 INFO stats.py:314 | Epoch[27] Step[269] GlobalStep[10799] Training Speed: 7.51 samples/sec across all devices.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 1 day, 3:51:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:50:25 INFO loss_tracker.py:84 | Epoch[27/239] Step[274] GlobalStep[10804/NA]: det_loss_cls_0[0.5832] det_loss_box_0[0.7875] det_loss_cls_1[0.5562] det_loss_box_1[0.6598] det_loss_cls_2[0.5452] det_loss_box_2[0.6013] det_loss_cls_3[0.5311] det_loss_box_3[0.5715] det_loss_cls_4[0.5067] det_loss_box_4[0.5586] det_loss_cls_5[0.4957] det_loss_box_5[0.5654] det_loss_cls_dn_0[0.1129] det_loss_box_dn_0[0.3665] det_loss_cls_dn_1[0.1102] det_loss_box_dn_1[0.3274] det_loss_cls_dn_2[0.1097] det_loss_box_dn_2[0.3117] det_loss_cls_dn_3[0.1089] det_loss_box_dn_3[0.3099] det_loss_cls_dn_4[0.1088] det_loss_box_dn_4[0.3100] det_loss_cls_dn_5[0.1081] det_loss_box_dn_5[0.3084] loss_depth[0.0413] total_loss[9.5957]
Rank[0/8] 06/19/2025 17:50:46 INFO stats.py:314 | Epoch[27] Step[294] GlobalStep[10824] Training Speed: 7.90 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 3:50:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:50:51 INFO loss_tracker.py:84 | Epoch[27/239] Step[299] GlobalStep[10829/NA]: det_loss_cls_0[0.5540] det_loss_box_0[0.7715] det_loss_cls_1[0.5254] det_loss_box_1[0.6520] det_loss_cls_2[0.5327] det_loss_box_2[0.6091] det_loss_cls_3[0.5241] det_loss_box_3[0.5875] det_loss_cls_4[0.5037] det_loss_box_4[0.5766] det_loss_cls_5[0.4903] det_loss_box_5[0.5840] det_loss_cls_dn_0[0.1052] det_loss_box_dn_0[0.3574] det_loss_cls_dn_1[0.1012] det_loss_box_dn_1[0.3223] det_loss_cls_dn_2[0.1018] det_loss_box_dn_2[0.3033] det_loss_cls_dn_3[0.1009] det_loss_box_dn_3[0.3018] det_loss_cls_dn_4[0.1020] det_loss_box_dn_4[0.3007] det_loss_cls_dn_5[0.1010] det_loss_box_dn_5[0.2991] loss_depth[0.0415] total_loss[9.4488]
Rank[0/8] 06/19/2025 17:51:12 INFO stats.py:314 | Epoch[27] Step[319] GlobalStep[10849] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 3:49:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:51:18 INFO loss_tracker.py:84 | Epoch[27/239] Step[324] GlobalStep[10854/NA]: det_loss_cls_0[0.6371] det_loss_box_0[0.8839] det_loss_cls_1[0.6166] det_loss_box_1[0.7255] det_loss_cls_2[0.6147] det_loss_box_2[0.6591] det_loss_cls_3[0.6026] det_loss_box_3[0.6403] det_loss_cls_4[0.5784] det_loss_box_4[0.6323] det_loss_cls_5[0.5528] det_loss_box_5[0.6402] det_loss_cls_dn_0[0.1208] det_loss_box_dn_0[0.3383] det_loss_cls_dn_1[0.1176] det_loss_box_dn_1[0.3006] det_loss_cls_dn_2[0.1166] det_loss_box_dn_2[0.2849] det_loss_cls_dn_3[0.1154] det_loss_box_dn_3[0.2828] det_loss_cls_dn_4[0.1144] det_loss_box_dn_4[0.2817] det_loss_cls_dn_5[0.1139] det_loss_box_dn_5[0.2813] loss_depth[0.0367] total_loss[10.2885]
Rank[0/8] 06/19/2025 17:51:38 INFO stats.py:314 | Epoch[27] Step[344] GlobalStep[10874] Training Speed: 8.66 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 1 day, 3:48:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:51:43 INFO loss_tracker.py:84 | Epoch[27/239] Step[349] GlobalStep[10879/NA]: det_loss_cls_0[0.5735] det_loss_box_0[0.7012] det_loss_cls_1[0.5549] det_loss_box_1[0.6053] det_loss_cls_2[0.5533] det_loss_box_2[0.5663] det_loss_cls_3[0.5423] det_loss_box_3[0.5456] det_loss_cls_4[0.5220] det_loss_box_4[0.5365] det_loss_cls_5[0.5196] det_loss_box_5[0.5326] det_loss_cls_dn_0[0.1174] det_loss_box_dn_0[0.3267] det_loss_cls_dn_1[0.1140] det_loss_box_dn_1[0.2932] det_loss_cls_dn_2[0.1129] det_loss_box_dn_2[0.2791] det_loss_cls_dn_3[0.1117] det_loss_box_dn_3[0.2783] det_loss_cls_dn_4[0.1102] det_loss_box_dn_4[0.2779] det_loss_cls_dn_5[0.1100] det_loss_box_dn_5[0.2777] loss_depth[0.0385] total_loss[9.2005]
Rank[0/8] 06/19/2025 17:52:04 INFO stats.py:314 | Epoch[27] Step[369] GlobalStep[10899] Training Speed: 6.73 samples/sec across all devices.	Average Step Time: 1.19 sec.	Estimated Remaining Time: 1 day, 3:47:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:52:09 INFO loss_tracker.py:84 | Epoch[27/239] Step[374] GlobalStep[10904/NA]: det_loss_cls_0[0.6514] det_loss_box_0[0.8477] det_loss_cls_1[0.6166] det_loss_box_1[0.7115] det_loss_cls_2[0.6219] det_loss_box_2[0.6445] det_loss_cls_3[0.6101] det_loss_box_3[0.6185] det_loss_cls_4[0.5875] det_loss_box_4[0.6048] det_loss_cls_5[0.5709] det_loss_box_5[0.6059] det_loss_cls_dn_0[0.1119] det_loss_box_dn_0[0.4140] det_loss_cls_dn_1[0.1077] det_loss_box_dn_1[0.3914] det_loss_cls_dn_2[0.1066] det_loss_box_dn_2[0.3823] det_loss_cls_dn_3[0.1061] det_loss_box_dn_3[0.3836] det_loss_cls_dn_4[0.1046] det_loss_box_dn_4[0.3829] det_loss_cls_dn_5[0.1036] det_loss_box_dn_5[0.3828] loss_depth[0.0427] total_loss[10.7113]
Rank[0/8] 06/19/2025 17:52:24 INFO stats.py:394 | Epoch[27] completed. Training Speed: 7.64 samples/sec across all devices.	Epoch Time: 408.25 sec.	Average Epoch Time: 408.25 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 3:46:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:52:33 INFO stats.py:314 | Epoch[28] Step[4] GlobalStep[10924] Training Speed: 7.74 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 1 day, 3:47:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:52:55 INFO loss_tracker.py:84 | Epoch[28/239] Step[24] GlobalStep[10944/NA]: det_loss_cls_0[0.5577] det_loss_box_0[0.8300] det_loss_cls_1[0.5374] det_loss_box_1[0.6855] det_loss_cls_2[0.5323] det_loss_box_2[0.6315] det_loss_cls_3[0.5310] det_loss_box_3[0.6092] det_loss_cls_4[0.5066] det_loss_box_4[0.6021] det_loss_cls_5[0.4942] det_loss_box_5[0.6036] det_loss_cls_dn_0[0.1093] det_loss_box_dn_0[0.3887] det_loss_cls_dn_1[0.1059] det_loss_box_dn_1[0.3551] det_loss_cls_dn_2[0.1058] det_loss_box_dn_2[0.3413] det_loss_cls_dn_3[0.1046] det_loss_box_dn_3[0.3399] det_loss_cls_dn_4[0.1040] det_loss_box_dn_4[0.3395] det_loss_cls_dn_5[0.1031] det_loss_box_dn_5[0.3381] loss_depth[0.0447] total_loss[9.9010]
Rank[0/8] 06/19/2025 17:53:00 INFO stats.py:314 | Epoch[28] Step[29] GlobalStep[10949] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 3:46:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:53:21 INFO loss_tracker.py:84 | Epoch[28/239] Step[49] GlobalStep[10969/NA]: det_loss_cls_0[0.4811] det_loss_box_0[0.7014] det_loss_cls_1[0.4738] det_loss_box_1[0.5780] det_loss_cls_2[0.4792] det_loss_box_2[0.5272] det_loss_cls_3[0.4636] det_loss_box_3[0.5158] det_loss_cls_4[0.4342] det_loss_box_4[0.5059] det_loss_cls_5[0.4206] det_loss_box_5[0.5107] det_loss_cls_dn_0[0.1103] det_loss_box_dn_0[0.4825] det_loss_cls_dn_1[0.1066] det_loss_box_dn_1[0.4340] det_loss_cls_dn_2[0.1054] det_loss_box_dn_2[0.4083] det_loss_cls_dn_3[0.1050] det_loss_box_dn_3[0.4073] det_loss_cls_dn_4[0.1040] det_loss_box_dn_4[0.4034] det_loss_cls_dn_5[0.1037] det_loss_box_dn_5[0.4015] loss_depth[0.0413] total_loss[9.3052]
Rank[0/8] 06/19/2025 17:53:26 INFO stats.py:314 | Epoch[28] Step[54] GlobalStep[10974] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 3:45:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:53:47 INFO loss_tracker.py:84 | Epoch[28/239] Step[74] GlobalStep[10994/NA]: det_loss_cls_0[0.6526] det_loss_box_0[0.9177] det_loss_cls_1[0.6404] det_loss_box_1[0.7565] det_loss_cls_2[0.6349] det_loss_box_2[0.7014] det_loss_cls_3[0.6190] det_loss_box_3[0.6687] det_loss_cls_4[0.5792] det_loss_box_4[0.6629] det_loss_cls_5[0.5603] det_loss_box_5[0.6660] det_loss_cls_dn_0[0.1212] det_loss_box_dn_0[0.4131] det_loss_cls_dn_1[0.1189] det_loss_box_dn_1[0.3821] det_loss_cls_dn_2[0.1182] det_loss_box_dn_2[0.3690] det_loss_cls_dn_3[0.1182] det_loss_box_dn_3[0.3661] det_loss_cls_dn_4[0.1184] det_loss_box_dn_4[0.3660] det_loss_cls_dn_5[0.1170] det_loss_box_dn_5[0.3641] loss_depth[0.0415] total_loss[11.0733]
Rank[0/8] 06/19/2025 17:53:52 INFO stats.py:314 | Epoch[28] Step[79] GlobalStep[10999] Training Speed: 8.04 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 3:44:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:54:13 INFO loss_tracker.py:84 | Epoch[28/239] Step[99] GlobalStep[11019/NA]: det_loss_cls_0[0.5109] det_loss_box_0[0.6838] det_loss_cls_1[0.5084] det_loss_box_1[0.5508] det_loss_cls_2[0.4960] det_loss_box_2[0.5033] det_loss_cls_3[0.4833] det_loss_box_3[0.4951] det_loss_cls_4[0.4501] det_loss_box_4[0.4826] det_loss_cls_5[0.4431] det_loss_box_5[0.4803] det_loss_cls_dn_0[0.1093] det_loss_box_dn_0[0.2998] det_loss_cls_dn_1[0.1058] det_loss_box_dn_1[0.2693] det_loss_cls_dn_2[0.1049] det_loss_box_dn_2[0.2522] det_loss_cls_dn_3[0.1032] det_loss_box_dn_3[0.2511] det_loss_cls_dn_4[0.1022] det_loss_box_dn_4[0.2508] det_loss_cls_dn_5[0.1013] det_loss_box_dn_5[0.2502] loss_depth[0.0402] total_loss[8.3279]
Rank[0/8] 06/19/2025 17:54:19 INFO stats.py:314 | Epoch[28] Step[104] GlobalStep[11024] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 3:43:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:54:39 INFO loss_tracker.py:84 | Epoch[28/239] Step[124] GlobalStep[11044/NA]: det_loss_cls_0[0.6245] det_loss_box_0[0.8993] det_loss_cls_1[0.6013] det_loss_box_1[0.7439] det_loss_cls_2[0.6042] det_loss_box_2[0.6806] det_loss_cls_3[0.5903] det_loss_box_3[0.6561] det_loss_cls_4[0.5581] det_loss_box_4[0.6485] det_loss_cls_5[0.5380] det_loss_box_5[0.6490] det_loss_cls_dn_0[0.1234] det_loss_box_dn_0[0.4078] det_loss_cls_dn_1[0.1193] det_loss_box_dn_1[0.3709] det_loss_cls_dn_2[0.1191] det_loss_box_dn_2[0.3536] det_loss_cls_dn_3[0.1178] det_loss_box_dn_3[0.3533] det_loss_cls_dn_4[0.1164] det_loss_box_dn_4[0.3528] det_loss_cls_dn_5[0.1159] det_loss_box_dn_5[0.3520] loss_depth[0.0395] total_loss[10.7354]
Rank[0/8] 06/19/2025 17:54:44 INFO stats.py:314 | Epoch[28] Step[129] GlobalStep[11049] Training Speed: 7.97 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 3:42:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:55:05 INFO loss_tracker.py:84 | Epoch[28/239] Step[149] GlobalStep[11069/NA]: det_loss_cls_0[0.4364] det_loss_box_0[0.5948] det_loss_cls_1[0.4098] det_loss_box_1[0.4929] det_loss_cls_2[0.4096] det_loss_box_2[0.4528] det_loss_cls_3[0.3993] det_loss_box_3[0.4433] det_loss_cls_4[0.3820] det_loss_box_4[0.4322] det_loss_cls_5[0.3757] det_loss_box_5[0.4292] det_loss_cls_dn_0[0.1105] det_loss_box_dn_0[0.3605] det_loss_cls_dn_1[0.1076] det_loss_box_dn_1[0.3155] det_loss_cls_dn_2[0.1071] det_loss_box_dn_2[0.2971] det_loss_cls_dn_3[0.1063] det_loss_box_dn_3[0.2949] det_loss_cls_dn_4[0.1058] det_loss_box_dn_4[0.2947] det_loss_cls_dn_5[0.1041] det_loss_box_dn_5[0.2940] loss_depth[0.0418] total_loss[7.7980]
Rank[0/8] 06/19/2025 17:55:10 INFO stats.py:314 | Epoch[28] Step[154] GlobalStep[11074] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 3:41:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:55:30 INFO loss_tracker.py:84 | Epoch[28/239] Step[174] GlobalStep[11094/NA]: det_loss_cls_0[0.5714] det_loss_box_0[0.7283] det_loss_cls_1[0.5638] det_loss_box_1[0.6056] det_loss_cls_2[0.5627] det_loss_box_2[0.5383] det_loss_cls_3[0.5325] det_loss_box_3[0.5316] det_loss_cls_4[0.4991] det_loss_box_4[0.5225] det_loss_cls_5[0.4928] det_loss_box_5[0.5206] det_loss_cls_dn_0[0.1253] det_loss_box_dn_0[0.3208] det_loss_cls_dn_1[0.1219] det_loss_box_dn_1[0.2798] det_loss_cls_dn_2[0.1217] det_loss_box_dn_2[0.2606] det_loss_cls_dn_3[0.1196] det_loss_box_dn_3[0.2598] det_loss_cls_dn_4[0.1186] det_loss_box_dn_4[0.2595] det_loss_cls_dn_5[0.1179] det_loss_box_dn_5[0.2582] loss_depth[0.0407] total_loss[9.0739]
Rank[0/8] 06/19/2025 17:55:36 INFO stats.py:314 | Epoch[28] Step[179] GlobalStep[11099] Training Speed: 8.19 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 3:40:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:55:57 INFO loss_tracker.py:84 | Epoch[28/239] Step[199] GlobalStep[11119/NA]: det_loss_cls_0[0.5949] det_loss_box_0[0.7080] det_loss_cls_1[0.5833] det_loss_box_1[0.5868] det_loss_cls_2[0.5893] det_loss_box_2[0.5410] det_loss_cls_3[0.5756] det_loss_box_3[0.5285] det_loss_cls_4[0.5481] det_loss_box_4[0.5222] det_loss_cls_5[0.5395] det_loss_box_5[0.5219] det_loss_cls_dn_0[0.1102] det_loss_box_dn_0[0.3903] det_loss_cls_dn_1[0.1078] det_loss_box_dn_1[0.3491] det_loss_cls_dn_2[0.1070] det_loss_box_dn_2[0.3333] det_loss_cls_dn_3[0.1055] det_loss_box_dn_3[0.3290] det_loss_cls_dn_4[0.1046] det_loss_box_dn_4[0.3284] det_loss_cls_dn_5[0.1035] det_loss_box_dn_5[0.3279] loss_depth[0.0426] total_loss[9.5783]
Rank[0/8] 06/19/2025 17:56:02 INFO stats.py:314 | Epoch[28] Step[204] GlobalStep[11124] Training Speed: 6.96 samples/sec across all devices.	Average Step Time: 1.15 sec.	Estimated Remaining Time: 1 day, 3:39:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:56:22 INFO loss_tracker.py:84 | Epoch[28/239] Step[224] GlobalStep[11144/NA]: det_loss_cls_0[0.5837] det_loss_box_0[0.8305] det_loss_cls_1[0.5625] det_loss_box_1[0.6867] det_loss_cls_2[0.5561] det_loss_box_2[0.6354] det_loss_cls_3[0.5451] det_loss_box_3[0.6047] det_loss_cls_4[0.5239] det_loss_box_4[0.5902] det_loss_cls_5[0.5112] det_loss_box_5[0.5908] det_loss_cls_dn_0[0.1238] det_loss_box_dn_0[0.3861] det_loss_cls_dn_1[0.1183] det_loss_box_dn_1[0.3463] det_loss_cls_dn_2[0.1161] det_loss_box_dn_2[0.3300] det_loss_cls_dn_3[0.1151] det_loss_box_dn_3[0.3280] det_loss_cls_dn_4[0.1132] det_loss_box_dn_4[0.3276] det_loss_cls_dn_5[0.1127] det_loss_box_dn_5[0.3270] loss_depth[0.0406] total_loss[10.0059]
Rank[0/8] 06/19/2025 17:56:27 INFO stats.py:314 | Epoch[28] Step[229] GlobalStep[11149] Training Speed: 7.93 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 3:37:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:56:48 INFO loss_tracker.py:84 | Epoch[28/239] Step[249] GlobalStep[11169/NA]: det_loss_cls_0[0.5763] det_loss_box_0[0.9596] det_loss_cls_1[0.5615] det_loss_box_1[0.8039] det_loss_cls_2[0.5542] det_loss_box_2[0.7235] det_loss_cls_3[0.5516] det_loss_box_3[0.6959] det_loss_cls_4[0.5309] det_loss_box_4[0.6727] det_loss_cls_5[0.5163] det_loss_box_5[0.6768] det_loss_cls_dn_0[0.1252] det_loss_box_dn_0[0.4300] det_loss_cls_dn_1[0.1226] det_loss_box_dn_1[0.3897] det_loss_cls_dn_2[0.1218] det_loss_box_dn_2[0.3723] det_loss_cls_dn_3[0.1209] det_loss_box_dn_3[0.3716] det_loss_cls_dn_4[0.1213] det_loss_box_dn_4[0.3705] det_loss_cls_dn_5[0.1203] det_loss_box_dn_5[0.3698] loss_depth[0.0432] total_loss[10.9024]
Rank[0/8] 06/19/2025 17:56:54 INFO stats.py:314 | Epoch[28] Step[254] GlobalStep[11174] Training Speed: 8.71 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 1 day, 3:36:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:57:15 INFO loss_tracker.py:84 | Epoch[28/239] Step[274] GlobalStep[11194/NA]: det_loss_cls_0[0.5571] det_loss_box_0[0.7590] det_loss_cls_1[0.5367] det_loss_box_1[0.6411] det_loss_cls_2[0.5296] det_loss_box_2[0.5787] det_loss_cls_3[0.5147] det_loss_box_3[0.5580] det_loss_cls_4[0.4871] det_loss_box_4[0.5578] det_loss_cls_5[0.4705] det_loss_box_5[0.5590] det_loss_cls_dn_0[0.1163] det_loss_box_dn_0[0.3486] det_loss_cls_dn_1[0.1126] det_loss_box_dn_1[0.3157] det_loss_cls_dn_2[0.1110] det_loss_box_dn_2[0.2962] det_loss_cls_dn_3[0.1101] det_loss_box_dn_3[0.2949] det_loss_cls_dn_4[0.1088] det_loss_box_dn_4[0.2944] det_loss_cls_dn_5[0.1089] det_loss_box_dn_5[0.2927] loss_depth[0.0408] total_loss[9.3005]
Rank[0/8] 06/19/2025 17:57:20 INFO stats.py:314 | Epoch[28] Step[279] GlobalStep[11199] Training Speed: 7.83 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1 day, 3:36:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:57:41 INFO loss_tracker.py:84 | Epoch[28/239] Step[299] GlobalStep[11219/NA]: det_loss_cls_0[0.6132] det_loss_box_0[0.8062] det_loss_cls_1[0.5994] det_loss_box_1[0.6668] det_loss_cls_2[0.5980] det_loss_box_2[0.6202] det_loss_cls_3[0.5738] det_loss_box_3[0.6026] det_loss_cls_4[0.5398] det_loss_box_4[0.5966] det_loss_cls_5[0.5191] det_loss_box_5[0.5932] det_loss_cls_dn_0[0.1122] det_loss_box_dn_0[0.3536] det_loss_cls_dn_1[0.1089] det_loss_box_dn_1[0.3113] det_loss_cls_dn_2[0.1087] det_loss_box_dn_2[0.2949] det_loss_cls_dn_3[0.1075] det_loss_box_dn_3[0.2940] det_loss_cls_dn_4[0.1070] det_loss_box_dn_4[0.2925] det_loss_cls_dn_5[0.1065] det_loss_box_dn_5[0.2917] loss_depth[0.0435] total_loss[9.8614]
Rank[0/8] 06/19/2025 17:57:46 INFO stats.py:314 | Epoch[28] Step[304] GlobalStep[11224] Training Speed: 8.02 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 3:34:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:58:07 INFO loss_tracker.py:84 | Epoch[28/239] Step[324] GlobalStep[11244/NA]: det_loss_cls_0[0.5252] det_loss_box_0[0.8062] det_loss_cls_1[0.4994] det_loss_box_1[0.6736] det_loss_cls_2[0.4932] det_loss_box_2[0.6058] det_loss_cls_3[0.4831] det_loss_box_3[0.5690] det_loss_cls_4[0.4572] det_loss_box_4[0.5657] det_loss_cls_5[0.4494] det_loss_box_5[0.5668] det_loss_cls_dn_0[0.1155] det_loss_box_dn_0[0.3816] det_loss_cls_dn_1[0.1129] det_loss_box_dn_1[0.3368] det_loss_cls_dn_2[0.1122] det_loss_box_dn_2[0.3221] det_loss_cls_dn_3[0.1117] det_loss_box_dn_3[0.3198] det_loss_cls_dn_4[0.1105] det_loss_box_dn_4[0.3192] det_loss_cls_dn_5[0.1101] det_loss_box_dn_5[0.3177] loss_depth[0.0419] total_loss[9.4067]
Rank[0/8] 06/19/2025 17:58:12 INFO stats.py:314 | Epoch[28] Step[329] GlobalStep[11249] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 3:34:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:58:33 INFO loss_tracker.py:84 | Epoch[28/239] Step[349] GlobalStep[11269/NA]: det_loss_cls_0[0.5077] det_loss_box_0[0.7139] det_loss_cls_1[0.4982] det_loss_box_1[0.5891] det_loss_cls_2[0.5028] det_loss_box_2[0.5363] det_loss_cls_3[0.4944] det_loss_box_3[0.5166] det_loss_cls_4[0.4706] det_loss_box_4[0.5145] det_loss_cls_5[0.4564] det_loss_box_5[0.5111] det_loss_cls_dn_0[0.1102] det_loss_box_dn_0[0.3916] det_loss_cls_dn_1[0.1061] det_loss_box_dn_1[0.3610] det_loss_cls_dn_2[0.1057] det_loss_box_dn_2[0.3467] det_loss_cls_dn_3[0.1045] det_loss_box_dn_3[0.3452] det_loss_cls_dn_4[0.1040] det_loss_box_dn_4[0.3450] det_loss_cls_dn_5[0.1032] det_loss_box_dn_5[0.3437] loss_depth[0.0451] total_loss[9.1238]
Rank[0/8] 06/19/2025 17:58:38 INFO stats.py:314 | Epoch[28] Step[354] GlobalStep[11274] Training Speed: 8.00 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 3:32:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:58:59 INFO loss_tracker.py:84 | Epoch[28/239] Step[374] GlobalStep[11294/NA]: det_loss_cls_0[0.6957] det_loss_box_0[0.8766] det_loss_cls_1[0.6760] det_loss_box_1[0.7443] det_loss_cls_2[0.6588] det_loss_box_2[0.6616] det_loss_cls_3[0.6483] det_loss_box_3[0.6276] det_loss_cls_4[0.6123] det_loss_box_4[0.6221] det_loss_cls_5[0.5969] det_loss_box_5[0.6203] det_loss_cls_dn_0[0.1300] det_loss_box_dn_0[0.3245] det_loss_cls_dn_1[0.1269] det_loss_box_dn_1[0.2888] det_loss_cls_dn_2[0.1269] det_loss_box_dn_2[0.2726] det_loss_cls_dn_3[0.1258] det_loss_box_dn_3[0.2713] det_loss_cls_dn_4[0.1240] det_loss_box_dn_4[0.2704] det_loss_cls_dn_5[0.1237] det_loss_box_dn_5[0.2690] loss_depth[0.0374] total_loss[10.5320]
Rank[0/8] 06/19/2025 17:59:04 INFO stats.py:314 | Epoch[28] Step[379] GlobalStep[11299] Training Speed: 9.18 samples/sec across all devices.	Average Step Time: 0.87 sec.	Estimated Remaining Time: 1 day, 3:31:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:59:14 INFO stats.py:394 | Epoch[28] completed. Training Speed: 7.62 samples/sec across all devices.	Epoch Time: 409.39 sec.	Average Epoch Time: 409.39 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 3:31:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:59:32 INFO stats.py:314 | Epoch[29] Step[14] GlobalStep[11324] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 3:31:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 17:59:44 INFO loss_tracker.py:84 | Epoch[29/239] Step[24] GlobalStep[11334/NA]: det_loss_cls_0[0.6722] det_loss_box_0[0.8616] det_loss_cls_1[0.6531] det_loss_box_1[0.7209] det_loss_cls_2[0.6457] det_loss_box_2[0.6627] det_loss_cls_3[0.6218] det_loss_box_3[0.6313] det_loss_cls_4[0.5888] det_loss_box_4[0.6246] det_loss_cls_5[0.5750] det_loss_box_5[0.6191] det_loss_cls_dn_0[0.1245] det_loss_box_dn_0[0.3393] det_loss_cls_dn_1[0.1207] det_loss_box_dn_1[0.3018] det_loss_cls_dn_2[0.1204] det_loss_box_dn_2[0.2857] det_loss_cls_dn_3[0.1196] det_loss_box_dn_3[0.2846] det_loss_cls_dn_4[0.1182] det_loss_box_dn_4[0.2839] det_loss_cls_dn_5[0.1174] det_loss_box_dn_5[0.2830] loss_depth[0.0401] total_loss[10.4160]
Rank[0/8] 06/19/2025 17:59:59 INFO stats.py:314 | Epoch[29] Step[39] GlobalStep[11349] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 3:30:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:00:10 INFO loss_tracker.py:84 | Epoch[29/239] Step[49] GlobalStep[11359/NA]: det_loss_cls_0[0.5791] det_loss_box_0[0.7962] det_loss_cls_1[0.5556] det_loss_box_1[0.6566] det_loss_cls_2[0.5441] det_loss_box_2[0.6106] det_loss_cls_3[0.5265] det_loss_box_3[0.6041] det_loss_cls_4[0.5095] det_loss_box_4[0.5931] det_loss_cls_5[0.4981] det_loss_box_5[0.5987] det_loss_cls_dn_0[0.1068] det_loss_box_dn_0[0.3462] det_loss_cls_dn_1[0.1040] det_loss_box_dn_1[0.3085] det_loss_cls_dn_2[0.1035] det_loss_box_dn_2[0.2882] det_loss_cls_dn_3[0.1031] det_loss_box_dn_3[0.2868] det_loss_cls_dn_4[0.1030] det_loss_box_dn_4[0.2856] det_loss_cls_dn_5[0.1018] det_loss_box_dn_5[0.2852] loss_depth[0.0433] total_loss[9.5384]
Rank[0/8] 06/19/2025 18:00:25 INFO stats.py:314 | Epoch[29] Step[64] GlobalStep[11374] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 3:29:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:00:35 INFO loss_tracker.py:84 | Epoch[29/239] Step[74] GlobalStep[11384/NA]: det_loss_cls_0[0.4742] det_loss_box_0[0.5942] det_loss_cls_1[0.4633] det_loss_box_1[0.4888] det_loss_cls_2[0.4596] det_loss_box_2[0.4469] det_loss_cls_3[0.4452] det_loss_box_3[0.4361] det_loss_cls_4[0.4165] det_loss_box_4[0.4377] det_loss_cls_5[0.4058] det_loss_box_5[0.4367] det_loss_cls_dn_0[0.1062] det_loss_box_dn_0[0.3332] det_loss_cls_dn_1[0.1005] det_loss_box_dn_1[0.3023] det_loss_cls_dn_2[0.1003] det_loss_box_dn_2[0.2836] det_loss_cls_dn_3[0.0996] det_loss_box_dn_3[0.2803] det_loss_cls_dn_4[0.0986] det_loss_box_dn_4[0.2805] det_loss_cls_dn_5[0.0984] det_loss_box_dn_5[0.2798] loss_depth[0.0397] total_loss[7.9081]
Rank[0/8] 06/19/2025 18:00:51 INFO stats.py:314 | Epoch[29] Step[89] GlobalStep[11399] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 3:28:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:01:01 INFO loss_tracker.py:84 | Epoch[29/239] Step[99] GlobalStep[11409/NA]: det_loss_cls_0[0.5168] det_loss_box_0[0.6716] det_loss_cls_1[0.4971] det_loss_box_1[0.5641] det_loss_cls_2[0.4879] det_loss_box_2[0.5161] det_loss_cls_3[0.4818] det_loss_box_3[0.4954] det_loss_cls_4[0.4467] det_loss_box_4[0.4876] det_loss_cls_5[0.4347] det_loss_box_5[0.4851] det_loss_cls_dn_0[0.1003] det_loss_box_dn_0[0.3536] det_loss_cls_dn_1[0.0977] det_loss_box_dn_1[0.3080] det_loss_cls_dn_2[0.0964] det_loss_box_dn_2[0.2895] det_loss_cls_dn_3[0.0947] det_loss_box_dn_3[0.2872] det_loss_cls_dn_4[0.0940] det_loss_box_dn_4[0.2867] det_loss_cls_dn_5[0.0933] det_loss_box_dn_5[0.2860] loss_depth[0.0415] total_loss[8.5137]
Rank[0/8] 06/19/2025 18:01:17 INFO stats.py:314 | Epoch[29] Step[114] GlobalStep[11424] Training Speed: 7.64 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 3:27:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:01:27 INFO loss_tracker.py:84 | Epoch[29/239] Step[124] GlobalStep[11434/NA]: det_loss_cls_0[0.5617] det_loss_box_0[0.6673] det_loss_cls_1[0.5272] det_loss_box_1[0.5506] det_loss_cls_2[0.5242] det_loss_box_2[0.4973] det_loss_cls_3[0.5190] det_loss_box_3[0.4728] det_loss_cls_4[0.4864] det_loss_box_4[0.4671] det_loss_cls_5[0.4694] det_loss_box_5[0.4652] det_loss_cls_dn_0[0.1038] det_loss_box_dn_0[0.2771] det_loss_cls_dn_1[0.1006] det_loss_box_dn_1[0.2408] det_loss_cls_dn_2[0.1005] det_loss_box_dn_2[0.2261] det_loss_cls_dn_3[0.0996] det_loss_box_dn_3[0.2251] det_loss_cls_dn_4[0.0987] det_loss_box_dn_4[0.2241] det_loss_cls_dn_5[0.0977] det_loss_box_dn_5[0.2232] loss_depth[0.0430] total_loss[8.2685]
Rank[0/8] 06/19/2025 18:01:43 INFO stats.py:314 | Epoch[29] Step[139] GlobalStep[11449] Training Speed: 9.35 samples/sec across all devices.	Average Step Time: 0.86 sec.	Estimated Remaining Time: 1 day, 3:26:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:01:54 INFO loss_tracker.py:84 | Epoch[29/239] Step[149] GlobalStep[11459/NA]: det_loss_cls_0[0.5124] det_loss_box_0[0.6908] det_loss_cls_1[0.4835] det_loss_box_1[0.5915] det_loss_cls_2[0.4835] det_loss_box_2[0.5295] det_loss_cls_3[0.4663] det_loss_box_3[0.5126] det_loss_cls_4[0.4391] det_loss_box_4[0.5037] det_loss_cls_5[0.4312] det_loss_box_5[0.4990] det_loss_cls_dn_0[0.1116] det_loss_box_dn_0[0.3673] det_loss_cls_dn_1[0.1067] det_loss_box_dn_1[0.3310] det_loss_cls_dn_2[0.1068] det_loss_box_dn_2[0.3160] det_loss_cls_dn_3[0.1060] det_loss_box_dn_3[0.3144] det_loss_cls_dn_4[0.1047] det_loss_box_dn_4[0.3130] det_loss_cls_dn_5[0.1040] det_loss_box_dn_5[0.3118] loss_depth[0.0399] total_loss[8.7762]
Rank[0/8] 06/19/2025 18:02:08 INFO stats.py:314 | Epoch[29] Step[164] GlobalStep[11474] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 3:25:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:02:19 INFO loss_tracker.py:84 | Epoch[29/239] Step[174] GlobalStep[11484/NA]: det_loss_cls_0[0.5586] det_loss_box_0[0.7813] det_loss_cls_1[0.5376] det_loss_box_1[0.6574] det_loss_cls_2[0.5379] det_loss_box_2[0.5944] det_loss_cls_3[0.5173] det_loss_box_3[0.5811] det_loss_cls_4[0.4912] det_loss_box_4[0.5755] det_loss_cls_5[0.4756] det_loss_box_5[0.5736] det_loss_cls_dn_0[0.1070] det_loss_box_dn_0[0.3810] det_loss_cls_dn_1[0.1042] det_loss_box_dn_1[0.3432] det_loss_cls_dn_2[0.1041] det_loss_box_dn_2[0.3275] det_loss_cls_dn_3[0.1024] det_loss_box_dn_3[0.3259] det_loss_cls_dn_4[0.1012] det_loss_box_dn_4[0.3257] det_loss_cls_dn_5[0.1011] det_loss_box_dn_5[0.3219] loss_depth[0.0389] total_loss[9.5654]
Rank[0/8] 06/19/2025 18:02:34 INFO stats.py:314 | Epoch[29] Step[189] GlobalStep[11499] Training Speed: 8.86 samples/sec across all devices.	Average Step Time: 0.90 sec.	Estimated Remaining Time: 1 day, 3:24:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:02:46 INFO loss_tracker.py:84 | Epoch[29/239] Step[199] GlobalStep[11509/NA]: det_loss_cls_0[0.5879] det_loss_box_0[0.9050] det_loss_cls_1[0.5653] det_loss_box_1[0.7732] det_loss_cls_2[0.5508] det_loss_box_2[0.7124] det_loss_cls_3[0.5391] det_loss_box_3[0.6812] det_loss_cls_4[0.5109] det_loss_box_4[0.6765] det_loss_cls_5[0.4924] det_loss_box_5[0.6814] det_loss_cls_dn_0[0.1108] det_loss_box_dn_0[0.3438] det_loss_cls_dn_1[0.1073] det_loss_box_dn_1[0.3076] det_loss_cls_dn_2[0.1074] det_loss_box_dn_2[0.2911] det_loss_cls_dn_3[0.1065] det_loss_box_dn_3[0.2890] det_loss_cls_dn_4[0.1054] det_loss_box_dn_4[0.2884] det_loss_cls_dn_5[0.1042] det_loss_box_dn_5[0.2870] loss_depth[0.0431] total_loss[10.1676]
Rank[0/8] 06/19/2025 18:03:02 INFO stats.py:314 | Epoch[29] Step[214] GlobalStep[11524] Training Speed: 7.58 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 1 day, 3:23:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:03:12 INFO loss_tracker.py:84 | Epoch[29/239] Step[224] GlobalStep[11534/NA]: det_loss_cls_0[0.4967] det_loss_box_0[0.6730] det_loss_cls_1[0.4817] det_loss_box_1[0.5841] det_loss_cls_2[0.4712] det_loss_box_2[0.5454] det_loss_cls_3[0.4635] det_loss_box_3[0.5318] det_loss_cls_4[0.4491] det_loss_box_4[0.5136] det_loss_cls_5[0.4340] det_loss_box_5[0.5137] det_loss_cls_dn_0[0.1110] det_loss_box_dn_0[0.3287] det_loss_cls_dn_1[0.1084] det_loss_box_dn_1[0.2848] det_loss_cls_dn_2[0.1081] det_loss_box_dn_2[0.2665] det_loss_cls_dn_3[0.1074] det_loss_box_dn_3[0.2636] det_loss_cls_dn_4[0.1071] det_loss_box_dn_4[0.2630] det_loss_cls_dn_5[0.1071] det_loss_box_dn_5[0.2622] loss_depth[0.0405] total_loss[8.5163]
Rank[0/8] 06/19/2025 18:03:27 INFO stats.py:314 | Epoch[29] Step[239] GlobalStep[11549] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 3:22:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:03:37 INFO loss_tracker.py:84 | Epoch[29/239] Step[249] GlobalStep[11559/NA]: det_loss_cls_0[0.5713] det_loss_box_0[0.7656] det_loss_cls_1[0.5520] det_loss_box_1[0.6157] det_loss_cls_2[0.5441] det_loss_box_2[0.5623] det_loss_cls_3[0.5205] det_loss_box_3[0.5495] det_loss_cls_4[0.4919] det_loss_box_4[0.5498] det_loss_cls_5[0.4789] det_loss_box_5[0.5492] det_loss_cls_dn_0[0.1072] det_loss_box_dn_0[0.3315] det_loss_cls_dn_1[0.1041] det_loss_box_dn_1[0.2901] det_loss_cls_dn_2[0.1031] det_loss_box_dn_2[0.2748] det_loss_cls_dn_3[0.1030] det_loss_box_dn_3[0.2732] det_loss_cls_dn_4[0.1018] det_loss_box_dn_4[0.2724] det_loss_cls_dn_5[0.1009] det_loss_box_dn_5[0.2718] loss_depth[0.0422] total_loss[9.1269]
Rank[0/8] 06/19/2025 18:03:52 INFO stats.py:314 | Epoch[29] Step[264] GlobalStep[11574] Training Speed: 8.34 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 3:21:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:04:03 INFO loss_tracker.py:84 | Epoch[29/239] Step[274] GlobalStep[11584/NA]: det_loss_cls_0[0.7824] det_loss_box_0[1.1517] det_loss_cls_1[0.7571] det_loss_box_1[0.9863] det_loss_cls_2[0.7601] det_loss_box_2[0.9047] det_loss_cls_3[0.7312] det_loss_box_3[0.8855] det_loss_cls_4[0.7044] det_loss_box_4[0.8669] det_loss_cls_5[0.6863] det_loss_box_5[0.8600] det_loss_cls_dn_0[0.1268] det_loss_box_dn_0[0.3388] det_loss_cls_dn_1[0.1244] det_loss_box_dn_1[0.3068] det_loss_cls_dn_2[0.1239] det_loss_box_dn_2[0.2951] det_loss_cls_dn_3[0.1234] det_loss_box_dn_3[0.2938] det_loss_cls_dn_4[0.1223] det_loss_box_dn_4[0.2937] det_loss_cls_dn_5[0.1207] det_loss_box_dn_5[0.2930] loss_depth[0.0449] total_loss[12.6842]
Rank[0/8] 06/19/2025 18:04:19 INFO stats.py:314 | Epoch[29] Step[289] GlobalStep[11599] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 3:20:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:04:29 INFO loss_tracker.py:84 | Epoch[29/239] Step[299] GlobalStep[11609/NA]: det_loss_cls_0[0.5756] det_loss_box_0[0.7406] det_loss_cls_1[0.5583] det_loss_box_1[0.5941] det_loss_cls_2[0.5545] det_loss_box_2[0.5399] det_loss_cls_3[0.5296] det_loss_box_3[0.5284] det_loss_cls_4[0.5007] det_loss_box_4[0.5202] det_loss_cls_5[0.4863] det_loss_box_5[0.5237] det_loss_cls_dn_0[0.1151] det_loss_box_dn_0[0.3954] det_loss_cls_dn_1[0.1116] det_loss_box_dn_1[0.3584] det_loss_cls_dn_2[0.1115] det_loss_box_dn_2[0.3426] det_loss_cls_dn_3[0.1105] det_loss_box_dn_3[0.3403] det_loss_cls_dn_4[0.1100] det_loss_box_dn_4[0.3408] det_loss_cls_dn_5[0.1096] det_loss_box_dn_5[0.3397] loss_depth[0.0398] total_loss[9.4775]
Rank[0/8] 06/19/2025 18:04:45 INFO stats.py:314 | Epoch[29] Step[314] GlobalStep[11624] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 3:19:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:04:55 INFO loss_tracker.py:84 | Epoch[29/239] Step[324] GlobalStep[11634/NA]: det_loss_cls_0[0.6272] det_loss_box_0[0.8490] det_loss_cls_1[0.5999] det_loss_box_1[0.7304] det_loss_cls_2[0.5963] det_loss_box_2[0.6696] det_loss_cls_3[0.5826] det_loss_box_3[0.6478] det_loss_cls_4[0.5460] det_loss_box_4[0.6431] det_loss_cls_5[0.5340] det_loss_box_5[0.6408] det_loss_cls_dn_0[0.1142] det_loss_box_dn_0[0.3453] det_loss_cls_dn_1[0.1123] det_loss_box_dn_1[0.3094] det_loss_cls_dn_2[0.1124] det_loss_box_dn_2[0.2949] det_loss_cls_dn_3[0.1121] det_loss_box_dn_3[0.2937] det_loss_cls_dn_4[0.1124] det_loss_box_dn_4[0.2931] det_loss_cls_dn_5[0.1126] det_loss_box_dn_5[0.2924] loss_depth[0.0383] total_loss[10.2098]
Rank[0/8] 06/19/2025 18:05:10 INFO stats.py:314 | Epoch[29] Step[339] GlobalStep[11649] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 3:18:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:05:21 INFO loss_tracker.py:84 | Epoch[29/239] Step[349] GlobalStep[11659/NA]: det_loss_cls_0[0.5103] det_loss_box_0[0.6415] det_loss_cls_1[0.4864] det_loss_box_1[0.5365] det_loss_cls_2[0.4894] det_loss_box_2[0.4933] det_loss_cls_3[0.4823] det_loss_box_3[0.4711] det_loss_cls_4[0.4637] det_loss_box_4[0.4594] det_loss_cls_5[0.4528] det_loss_box_5[0.4607] det_loss_cls_dn_0[0.1142] det_loss_box_dn_0[0.3207] det_loss_cls_dn_1[0.1119] det_loss_box_dn_1[0.2862] det_loss_cls_dn_2[0.1108] det_loss_box_dn_2[0.2691] det_loss_cls_dn_3[0.1095] det_loss_box_dn_3[0.2690] det_loss_cls_dn_4[0.1090] det_loss_box_dn_4[0.2680] det_loss_cls_dn_5[0.1086] det_loss_box_dn_5[0.2665] loss_depth[0.0406] total_loss[8.3317]
Rank[0/8] 06/19/2025 18:05:36 INFO stats.py:314 | Epoch[29] Step[364] GlobalStep[11674] Training Speed: 8.06 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 3:17:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:05:46 INFO loss_tracker.py:84 | Epoch[29/239] Step[374] GlobalStep[11684/NA]: det_loss_cls_0[0.5269] det_loss_box_0[0.7089] det_loss_cls_1[0.5275] det_loss_box_1[0.5942] det_loss_cls_2[0.5202] det_loss_box_2[0.5311] det_loss_cls_3[0.5023] det_loss_box_3[0.5186] det_loss_cls_4[0.4706] det_loss_box_4[0.5115] det_loss_cls_5[0.4584] det_loss_box_5[0.5121] det_loss_cls_dn_0[0.1182] det_loss_box_dn_0[0.3240] det_loss_cls_dn_1[0.1145] det_loss_box_dn_1[0.2829] det_loss_cls_dn_2[0.1136] det_loss_box_dn_2[0.2678] det_loss_cls_dn_3[0.1120] det_loss_box_dn_3[0.2655] det_loss_cls_dn_4[0.1104] det_loss_box_dn_4[0.2644] det_loss_cls_dn_5[0.1092] det_loss_box_dn_5[0.2635] loss_depth[0.0413] total_loss[8.7695]
Rank[0/8] 06/19/2025 18:06:02 INFO stats.py:314 | Epoch[29] Step[389] GlobalStep[11699] Training Speed: 9.25 samples/sec across all devices.	Average Step Time: 0.86 sec.	Estimated Remaining Time: 1 day, 3:16:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:06:02 INFO trainer.py:268 | 
==================================================BEGIN EVAL==================================================
Rank[0/8] 06/19/2025 18:07:07 INFO trainer.py:275 | eval: 10
Rank[0/8] 06/19/2025 18:07:19 INFO trainer.py:275 | eval: 20
Rank[0/8] 06/19/2025 18:07:28 INFO trainer.py:275 | eval: 30
Rank[0/8] 06/19/2025 18:07:41 INFO trainer.py:275 | eval: 40
Rank[0/8] 06/19/2025 18:07:48 INFO trainer.py:275 | eval: 50
Rank[0/8] 06/19/2025 18:07:57 INFO trainer.py:275 | eval: 60
Rank[0/8] 06/19/2025 18:08:08 INFO trainer.py:275 | eval: 70
Rank[0/8] 06/19/2025 18:08:22 INFO trainer.py:275 | eval: 80
Rank[0/8] 06/19/2025 18:08:29 INFO trainer.py:275 | eval: 90
Rank[0/8] 06/19/2025 18:08:40 INFO trainer.py:275 | eval: 100
Rank[0/8] 06/19/2025 18:08:45 INFO metrics.py:428 | number of results: 824
Rank[0/8] 06/19/2025 18:13:15 INFO metrics.py:332 | eval results of overall:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| picture             | 0.1118  | 0.3448  | 0.0022  | 0.0550  |
| toilet              | 0.7657  | 0.8759  | 0.3446  | 0.5766  |
| bin                 | 0.4435  | 0.8492  | 0.1029  | 0.4358  |
| curtain             | 0.2404  | 0.7731  | 0.0584  | 0.3642  |
| doorframe           | 0.1591  | 0.6725  | 0.0226  | 0.2942  |
| sink                | 0.4478  | 0.7490  | 0.0234  | 0.1770  |
| toilet paper        | 0.1664  | 0.4331  | 0.0027  | 0.0394  |
| vanity              | 0.3586  | 0.8667  | 0.2013  | 0.7333  |
| switch              | 0.0005  | 0.0921  | 0.0000  | 0.0000  |
| bathtub             | 0.5970  | 0.8676  | 0.2926  | 0.6765  |
| towel               | 0.2121  | 0.6097  | 0.0093  | 0.1097  |
| door                | 0.1640  | 0.5807  | 0.0106  | 0.1870  |
| bar                 | 0.0020  | 0.1818  | 0.0008  | 0.0303  |
| socket              | 0.0043  | 0.1593  | 0.0000  | 0.0000  |
| soap dish           | 0.4144  | 0.6667  | 0.0001  | 0.0667  |
| stopcock            | 0.0357  | 0.2759  | 0.0000  | 0.0000  |
| mirror              | 0.0572  | 0.4565  | 0.0004  | 0.0543  |
| rail                | 0.0063  | 0.2344  | 0.0001  | 0.0312  |
| hair dryer          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bag                 | 0.1517  | 0.6270  | 0.0122  | 0.1568  |
| cup                 | 0.0242  | 0.2288  | 0.0000  | 0.0000  |
| tissue box          | 0.0111  | 0.3939  | 0.0000  | 0.0000  |
| shelf               | 0.1484  | 0.6367  | 0.0336  | 0.2860  |
| cabinet             | 0.1367  | 0.6994  | 0.0429  | 0.3726  |
| tissue              | 0.0013  | 0.2308  | 0.0000  | 0.0000  |
| counter             | 0.0412  | 0.5046  | 0.0050  | 0.1376  |
| shower              | 0.0003  | 0.1333  | 0.0000  | 0.0000  |
| clothes             | 0.0821  | 0.4968  | 0.0099  | 0.1465  |
| bottle              | 0.0137  | 0.3008  | 0.0000  | 0.0167  |
| faucet              | 0.0031  | 0.1915  | 0.0000  | 0.0000  |
| ledge               | 0.0011  | 0.1667  | 0.0000  | 0.0000  |
| backpack            | 0.4232  | 0.7903  | 0.1501  | 0.4194  |
| toothpaste          | 0.0667  | 0.2000  | 0.0000  | 0.0000  |
| microwave           | 0.3770  | 0.6735  | 0.0974  | 0.4082  |
| paper               | 0.0080  | 0.2012  | 0.0001  | 0.0237  |
| paper cutter        | 0.2052  | 0.5000  | 0.0192  | 0.3333  |
| soap                | 0.0002  | 0.1667  | 0.0002  | 0.1667  |
| window              | 0.1235  | 0.5763  | 0.0122  | 0.1991  |
| box                 | 0.0921  | 0.4391  | 0.0034  | 0.0852  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| grab bar            | 0.0018  | 0.2500  | 0.0000  | 0.0000  |
| frame               | 0.0120  | 0.3469  | 0.0000  | 0.0204  |
| holder              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stall               | 0.0164  | 0.5882  | 0.0036  | 0.3529  |
| radiator            | 0.3924  | 0.6667  | 0.0623  | 0.2556  |
| plunger             | 0.8000  | 0.8000  | 0.0000  | 0.0000  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stool               | 0.1214  | 0.6723  | 0.0487  | 0.3697  |
| basket              | 0.1438  | 0.5217  | 0.0595  | 0.2348  |
| scale               | 0.0909  | 0.0909  | 0.0000  | 0.0000  |
| dispenser           | 0.3239  | 0.5227  | 0.0066  | 0.1364  |
| purse               | 0.0062  | 0.1667  | 0.0000  | 0.0000  |
| monitor             | 0.3512  | 0.6467  | 0.0115  | 0.1304  |
| computer            | 0.1320  | 0.5738  | 0.0004  | 0.0492  |
| blackboard          | 0.3328  | 0.7683  | 0.0267  | 0.1951  |
| chair               | 0.4351  | 0.7664  | 0.2735  | 0.5591  |
| keyboard            | 0.0006  | 0.0233  | 0.0000  | 0.0000  |
| telephone           | 0.0747  | 0.3000  | 0.0054  | 0.0667  |
| mouse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| desk                | 0.2564  | 0.8596  | 0.1100  | 0.6236  |
| book                | 0.0202  | 0.3017  | 0.0006  | 0.0279  |
| table               | 0.3083  | 0.8229  | 0.1577  | 0.6066  |
| board               | 0.0757  | 0.4677  | 0.0069  | 0.1129  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headphones          | 0.1000  | 0.2000  | 0.0000  | 0.0000  |
| plug                | 0.0002  | 0.0526  | 0.0000  | 0.0000  |
| jacket              | 0.5003  | 0.7917  | 0.0460  | 0.3333  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| file                | 0.0131  | 0.3529  | 0.0000  | 0.0000  |
| cart                | 0.0007  | 0.2500  | 0.0000  | 0.0000  |
| pipe                | 0.0002  | 0.0714  | 0.0000  | 0.0000  |
| pillow              | 0.2716  | 0.6067  | 0.0098  | 0.1088  |
| bed                 | 0.4526  | 0.9231  | 0.1763  | 0.7396  |
| stand               | 0.3915  | 0.8049  | 0.1812  | 0.5244  |
| toy                 | 0.0740  | 0.3485  | 0.0176  | 0.0455  |
| dresser             | 0.5044  | 0.8704  | 0.3243  | 0.7037  |
| lamp                | 0.2154  | 0.5224  | 0.0198  | 0.1164  |
| light               | 0.0102  | 0.1560  | 0.0000  | 0.0192  |
| couch               | 0.2521  | 0.8471  | 0.1763  | 0.6736  |
| blanket             | 0.0902  | 0.7100  | 0.0030  | 0.2400  |
| windowsill          | 0.0328  | 0.3913  | 0.0006  | 0.0870  |
| wardrobe            | 0.1649  | 0.7470  | 0.0410  | 0.4458  |
| shoe                | 0.0613  | 0.3551  | 0.0004  | 0.0467  |
| calendar            | 0.0005  | 0.1667  | 0.0000  | 0.0000  |
| hanger              | 0.0001  | 0.0833  | 0.0000  | 0.0000  |
| poster              | 0.0020  | 0.1212  | 0.0000  | 0.0000  |
| rack                | 0.0044  | 0.4231  | 0.0004  | 0.0769  |
| case                | 0.0356  | 0.3729  | 0.0046  | 0.0791  |
| container           | 0.0053  | 0.2034  | 0.0001  | 0.0169  |
| ottoman             | 0.0681  | 0.5556  | 0.0556  | 0.3611  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| luggage             | 0.1250  | 1.0000  | 0.0013  | 0.3333  |
| bowl                | 0.0177  | 0.2899  | 0.0016  | 0.0145  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| decoration          | 0.0265  | 0.2643  | 0.0002  | 0.0357  |
| bench               | 0.1510  | 0.7170  | 0.0365  | 0.2830  |
| clock               | 0.0318  | 0.3191  | 0.0000  | 0.0000  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| album               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cushion             | 0.0446  | 0.5098  | 0.0084  | 0.1373  |
| map                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| laptop              | 0.2761  | 0.6087  | 0.1018  | 0.2174  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| guitar              | 0.3630  | 0.4167  | 0.0052  | 0.0833  |
| tv                  | 0.3202  | 0.6733  | 0.0612  | 0.2673  |
| refrigerator        | 0.5077  | 0.8200  | 0.2490  | 0.5000  |
| coffee maker        | 0.2711  | 0.7586  | 0.0547  | 0.3448  |
| kettle              | 0.0290  | 0.4444  | 0.0007  | 0.0556  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dish rack           | 0.4026  | 0.6000  | 0.0000  | 0.0000  |
| printer             | 0.4882  | 0.7347  | 0.1936  | 0.4694  |
| tray                | 0.0029  | 0.2857  | 0.0000  | 0.0000  |
| notebook            | 0.0009  | 0.0769  | 0.0000  | 0.0000  |
| jar                 | 0.0049  | 0.1538  | 0.0003  | 0.0769  |
| brush               | 0.0029  | 0.3571  | 0.0000  | 0.0000  |
| broom               | 0.0440  | 0.4286  | 0.0000  | 0.0000  |
| mop                 | 0.6859  | 1.0000  | 0.0014  | 0.3333  |
| soap dispenser      | 0.2549  | 0.5926  | 0.0661  | 0.1852  |
| hamper              | 0.1656  | 0.5455  | 0.1205  | 0.4545  |
| bucket              | 0.1216  | 0.5385  | 0.0599  | 0.1795  |
| chandelier          | 0.0676  | 0.4762  | 0.0003  | 0.0476  |
| ladder              | 0.0732  | 0.4118  | 0.0620  | 0.1765  |
| sign                | 0.0230  | 0.2000  | 0.0000  | 0.0000  |
| mat                 | 0.0031  | 0.2162  | 0.0000  | 0.0000  |
| carpet              | 0.0173  | 0.3929  | 0.0002  | 0.0714  |
| mailbox             | 0.3381  | 0.8000  | 0.0302  | 0.4000  |
| washbasin           | 0.0119  | 0.4000  | 0.0073  | 0.1333  |
| arch                | 0.0014  | 0.0667  | 0.0000  | 0.0000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine             | 0.2413  | 0.8947  | 0.0898  | 0.3158  |
| vent                | 0.0002  | 0.0417  | 0.0000  | 0.0000  |
| column              | 0.0082  | 0.2632  | 0.0010  | 0.1053  |
| water cooler        | 0.2576  | 1.0000  | 0.2576  | 1.0000  |
| roof                | 0.0010  | 0.2083  | 0.0000  | 0.0000  |
| pillar              | 0.1883  | 0.6977  | 0.0314  | 0.2791  |
| window frame        | 0.0286  | 0.4242  | 0.0016  | 0.1313  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plant               | 0.2228  | 0.5751  | 0.0414  | 0.2125  |
| stairs              | 0.0163  | 0.3750  | 0.0005  | 0.0694  |
| projector           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fan                 | 0.2384  | 0.7656  | 0.0551  | 0.3438  |
| screen              | 0.0400  | 0.2000  | 0.0000  | 0.0000  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| can                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plate               | 0.0070  | 0.1538  | 0.0001  | 0.0385  |
| basin               | 0.0005  | 0.0588  | 0.0005  | 0.0588  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| copier              | 0.7265  | 0.9167  | 0.3260  | 0.7500  |
| folder              | 0.0017  | 0.2143  | 0.0000  | 0.0000  |
| drawer              | 0.0701  | 0.4444  | 0.0474  | 0.2778  |
| tube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball                | 0.2676  | 0.4286  | 0.0855  | 0.2381  |
| globe               | 0.0238  | 1.0000  | 0.0000  | 0.0000  |
| beanbag             | 0.7208  | 1.0000  | 0.4112  | 1.0000  |
| fire extinguisher   | 0.3080  | 0.6000  | 0.0222  | 0.2000  |
| cosmetics           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toiletry            | 0.0045  | 0.1739  | 0.0000  | 0.0000  |
| vacuum cleaner      | 0.0887  | 0.4286  | 0.0714  | 0.1429  |
| range hood          | 0.2322  | 0.5714  | 0.0005  | 0.0714  |
| teapot              | 0.0107  | 0.4444  | 0.0019  | 0.1111  |
| toaster             | 0.0984  | 0.5000  | 0.0000  | 0.0000  |
| dishwasher          | 0.1136  | 0.3000  | 0.0045  | 0.1000  |
| stove               | 0.2751  | 0.6389  | 0.1327  | 0.3333  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| umbrella            | 0.0006  | 0.2000  | 0.0000  | 0.0000  |
| blinds              | 0.0139  | 0.3200  | 0.0011  | 0.0800  |
| flowerpot           | 0.0381  | 0.5294  | 0.0005  | 0.0588  |
| price tag           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| player              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| piano               | 0.2800  | 0.4000  | 0.2800  | 0.4000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| countertop          | 0.0275  | 0.3214  | 0.0000  | 0.0357  |
| radio               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| conducting wire     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garbage             | 0.1017  | 0.6250  | 0.0614  | 0.3750  |
| sunglasses          | 0.0435  | 0.5385  | 0.0023  | 0.1538  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| heater              | 0.2541  | 0.6207  | 0.0264  | 0.1207  |
| salt                | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| partition           | 0.0160  | 0.3333  | 0.0000  | 0.0000  |
| device              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| commode             | 0.2725  | 0.8077  | 0.2166  | 0.5769  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candle              | 0.0093  | 0.0556  | 0.0000  | 0.0000  |
| kitchenware         | 0.0382  | 0.3478  | 0.0003  | 0.0217  |
| vase                | 0.0642  | 0.3200  | 0.0024  | 0.0800  |
| magazine            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| oven                | 0.1887  | 0.6071  | 0.0334  | 0.2857  |
| napkins             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jalousie            | 0.0018  | 0.5000  | 0.0000  | 0.0000  |
| humidifier          | 0.0556  | 0.5000  | 0.0000  | 0.0000  |
| roll                | 0.1162  | 0.3000  | 0.0000  | 0.0000  |
| speaker             | 0.0227  | 0.0455  | 0.0000  | 0.0000  |
| balcony             | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pot                 | 0.0258  | 0.4074  | 0.0009  | 0.0741  |
| remote control      | 0.0191  | 0.1111  | 0.0000  | 0.0000  |
| air conditioner     | 0.2364  | 0.4000  | 0.0000  | 0.0000  |
| sack                | 0.0672  | 0.7143  | 0.0000  | 0.0000  |
| statue              | 0.0489  | 0.3684  | 0.0268  | 0.1053  |
| clothes dryer       | 0.0642  | 0.8000  | 0.0280  | 0.4667  |
| shampoo             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water heater        | 0.5000  | 0.5000  | 0.2500  | 0.5000  |
| flush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.3963  | 0.6786  | 0.1591  | 0.5000  |
| body loofah         | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| pan                 | 0.0183  | 0.1765  | 0.0000  | 0.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pack                | 0.0644  | 0.2000  | 0.0000  | 0.0000  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| spoon               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crate               | 0.2711  | 0.7143  | 0.0110  | 0.1429  |
| food                | 0.0008  | 0.0909  | 0.0000  | 0.0000  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crib                | 0.1116  | 1.0000  | 0.0889  | 0.6667  |
| fireplace           | 0.1053  | 0.5000  | 0.0002  | 0.0714  |
| drum                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| doorway             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 0.1370  | 1.0000  | 0.0249  | 0.5000  |
| tablet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| menu                | 0.2052  | 0.4286  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coil                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| handle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| footstool           | 0.0500  | 0.5000  | 0.0500  | 0.5000  |
| fruit               | 0.0375  | 0.2143  | 0.0270  | 0.1429  |
| steps               | 0.0004  | 0.2000  | 0.0000  | 0.0000  |
| beam                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet               | 0.3846  | 0.6667  | 0.3423  | 0.6667  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| thermostat          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garage door         | 0.1667  | 0.1667  | 0.0000  | 0.0000  |
| kitchen island      | 0.0194  | 0.8571  | 0.0118  | 0.5714  |
| excercise equipment | 0.2279  | 0.8000  | 0.0235  | 0.2000  |
| wine                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool table          | 0.1111  | 0.3333  | 0.0833  | 0.3333  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| treadmill           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| person              | 0.2143  | 0.3333  | 0.0028  | 0.1667  |
| adhesive tape       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell            | 0.1667  | 0.3333  | 0.0000  | 0.0000  |
| candlestick         | 0.3333  | 0.3333  | 0.0000  | 0.0000  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glove               | 0.0222  | 0.3333  | 0.0000  | 0.0000  |
| letter              | 0.0017  | 0.2500  | 0.0000  | 0.0000  |
| sponge              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cube                | 0.0833  | 1.0000  | 0.0667  | 1.0000  |
| helmets             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cap                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bread               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| overall             | 0.1106  | 0.3433  | 0.0313  | 0.1319  |
| head                | 0.1638  | 0.5090  | 0.0489  | 0.2014  |
| common              | 0.0941  | 0.3133  | 0.0239  | 0.1117  |
| tail                | 0.0704  | 0.1936  | 0.0202  | 0.0778  |
| small               | 0.0178  | 0.1099  | 0.0016  | 0.0157  |
| medium              | 0.1165  | 0.3781  | 0.0334  | 0.1432  |
| large               | 0.0638  | 0.4379  | 0.0223  | 0.2048  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/19/2025 18:13:15 INFO metrics.py:437 | number of scannet results: 250
Rank[0/8] 06/19/2025 18:14:43 INFO metrics.py:332 | eval results of scannet:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| picture             | 0.1465  | 0.3955  | 0.0058  | 0.0896  |
| toilet              | 0.8602  | 0.9464  | 0.4872  | 0.6607  |
| bin                 | 0.4765  | 0.8553  | 0.1141  | 0.4511  |
| curtain             | 0.3149  | 0.7788  | 0.1001  | 0.4159  |
| doorframe           | 0.1452  | 0.6690  | 0.0141  | 0.2817  |
| sink                | 0.5969  | 0.8295  | 0.0204  | 0.1932  |
| toilet paper        | 0.1756  | 0.4304  | 0.0043  | 0.0506  |
| vanity              | 0.4856  | 0.9333  | 0.2667  | 0.7667  |
| switch              | 0.0008  | 0.1266  | 0.0000  | 0.0000  |
| bathtub             | 0.7747  | 0.9375  | 0.3550  | 0.6875  |
| towel               | 0.2574  | 0.7190  | 0.0113  | 0.1405  |
| door                | 0.2845  | 0.7258  | 0.0302  | 0.3378  |
| bar                 | 0.0017  | 0.1818  | 0.0012  | 0.0455  |
| socket              | 0.0049  | 0.1699  | 0.0000  | 0.0000  |
| soap dish           | 0.5187  | 0.8333  | 0.0002  | 0.0833  |
| stopcock            | 0.0358  | 0.2759  | 0.0000  | 0.0000  |
| mirror              | 0.0633  | 0.5301  | 0.0005  | 0.0723  |
| rail                | 0.0507  | 0.6667  | 0.0023  | 0.3333  |
| hair dryer          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bag                 | 0.1196  | 0.6301  | 0.0358  | 0.1507  |
| cup                 | 0.0393  | 0.2319  | 0.0000  | 0.0000  |
| tissue box          | 0.0190  | 0.5000  | 0.0000  | 0.0000  |
| shelf               | 0.2512  | 0.8235  | 0.0691  | 0.5074  |
| cabinet             | 0.2025  | 0.7421  | 0.0679  | 0.4365  |
| tissue              | 0.0051  | 0.3333  | 0.0000  | 0.0000  |
| counter             | 0.0788  | 0.7429  | 0.0031  | 0.2286  |
| shower              | 0.0004  | 0.1429  | 0.0000  | 0.0000  |
| clothes             | 0.1080  | 0.5033  | 0.0249  | 0.1699  |
| bottle              | 0.0226  | 0.3105  | 0.0001  | 0.0158  |
| faucet              | 0.0068  | 0.3750  | 0.0000  | 0.0000  |
| ledge               | 0.0011  | 0.1667  | 0.0000  | 0.0000  |
| backpack            | 0.5616  | 0.8929  | 0.2720  | 0.6071  |
| toothpaste          | 0.0667  | 0.2000  | 0.0000  | 0.0000  |
| microwave           | 0.5112  | 0.7000  | 0.2402  | 0.5000  |
| paper               | 0.0103  | 0.1887  | 0.0001  | 0.0226  |
| paper cutter        | 0.2013  | 0.4000  | 0.0005  | 0.2000  |
| window              | 0.2509  | 0.8210  | 0.0323  | 0.3889  |
| box                 | 0.0858  | 0.3968  | 0.0027  | 0.0714  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| grab bar            | 0.0018  | 0.2500  | 0.0000  | 0.0000  |
| frame               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| holder              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stall               | 0.0170  | 0.5882  | 0.0037  | 0.3529  |
| radiator            | 0.5113  | 0.7759  | 0.1025  | 0.3621  |
| plunger             | 0.8000  | 0.8000  | 0.0000  | 0.0000  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stool               | 0.1956  | 0.5909  | 0.0977  | 0.3864  |
| basket              | 0.0403  | 0.3077  | 0.0027  | 0.1154  |
| scale               | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| dispenser           | 0.3724  | 0.5641  | 0.0087  | 0.1538  |
| purse               | 0.0104  | 0.1667  | 0.0000  | 0.0000  |
| monitor             | 0.4125  | 0.6571  | 0.0234  | 0.1643  |
| computer            | 0.1886  | 0.5641  | 0.0008  | 0.0513  |
| blackboard          | 0.3696  | 0.8116  | 0.0353  | 0.2319  |
| chair               | 0.4498  | 0.7600  | 0.2741  | 0.5455  |
| keyboard            | 0.0012  | 0.0345  | 0.0000  | 0.0000  |
| telephone           | 0.0915  | 0.3438  | 0.0143  | 0.1250  |
| mouse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| desk                | 0.3286  | 0.9494  | 0.1549  | 0.7468  |
| book                | 0.0292  | 0.3286  | 0.0002  | 0.0333  |
| table               | 0.3683  | 0.8808  | 0.2127  | 0.7000  |
| board               | 0.1010  | 0.6875  | 0.0010  | 0.1250  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headphones          | 0.1000  | 0.2000  | 0.0000  | 0.0000  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jacket              | 0.5223  | 0.8261  | 0.0481  | 0.3478  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cart                | 0.0017  | 0.5000  | 0.0000  | 0.0000  |
| pipe                | 0.0002  | 0.0714  | 0.0000  | 0.0000  |
| pillow              | 0.3666  | 0.6042  | 0.0413  | 0.2431  |
| bed                 | 0.4555  | 1.0000  | 0.3476  | 0.9825  |
| stand               | 0.5981  | 0.8302  | 0.3196  | 0.6415  |
| toy                 | 0.0182  | 0.1765  | 0.0000  | 0.0000  |
| dresser             | 0.6165  | 0.8919  | 0.4183  | 0.7838  |
| lamp                | 0.3528  | 0.5952  | 0.0543  | 0.1667  |
| light               | 0.0226  | 0.2414  | 0.0000  | 0.0000  |
| couch               | 0.3815  | 1.0000  | 0.3134  | 0.8625  |
| blanket             | 0.0262  | 0.5000  | 0.0018  | 0.1667  |
| windowsill          | 0.0040  | 0.1667  | 0.0000  | 0.0000  |
| wardrobe            | 0.2907  | 0.8750  | 0.2154  | 0.5000  |
| shoe                | 0.0860  | 0.3750  | 0.0087  | 0.1000  |
| calendar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rack                | 0.0175  | 0.5455  | 0.0017  | 0.0909  |
| case                | 0.3268  | 0.7895  | 0.0555  | 0.3684  |
| container           | 0.0012  | 0.0870  | 0.0000  | 0.0000  |
| ottoman             | 0.0106  | 0.4286  | 0.0000  | 0.0000  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bowl                | 0.1127  | 0.3333  | 0.0000  | 0.0000  |
| decoration          | 0.0182  | 0.3333  | 0.0000  | 0.0000  |
| bench               | 0.6743  | 1.0000  | 0.0009  | 0.2000  |
| clock               | 0.0766  | 0.4400  | 0.0000  | 0.0000  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cushion             | 0.2000  | 0.5000  | 0.0000  | 0.0000  |
| map                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| laptop              | 0.3902  | 0.6000  | 0.0667  | 0.1333  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| guitar              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tv                  | 0.6193  | 0.7826  | 0.2788  | 0.4783  |
| refrigerator        | 0.7468  | 0.9500  | 0.3629  | 0.6500  |
| coffee maker        | 0.3987  | 0.8235  | 0.1387  | 0.5294  |
| kettle              | 0.1299  | 0.5000  | 0.0000  | 0.0000  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dish rack           | 0.5039  | 0.7500  | 0.0000  | 0.0000  |
| printer             | 0.5175  | 0.8182  | 0.2697  | 0.5758  |
| tray                | 0.0040  | 0.3333  | 0.0000  | 0.0000  |
| notebook            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| broom               | 0.0886  | 0.5000  | 0.0000  | 0.0000  |
| soap dispenser      | 0.3179  | 0.6667  | 0.0850  | 0.1905  |
| hamper              | 0.1854  | 0.6000  | 0.1358  | 0.5000  |
| bucket              | 0.0344  | 0.4286  | 0.0006  | 0.0714  |
| ladder              | 0.2160  | 0.4000  | 0.2000  | 0.2000  |
| sign                | 0.0270  | 0.2143  | 0.0000  | 0.0000  |
| mat                 | 0.0249  | 0.4000  | 0.0000  | 0.0000  |
| carpet              | 0.0100  | 0.3000  | 0.0000  | 0.0000  |
| mailbox             | 0.3381  | 0.8000  | 0.0302  | 0.4000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine             | 0.3044  | 0.9375  | 0.1069  | 0.3125  |
| water cooler        | 0.8333  | 1.0000  | 0.8333  | 1.0000  |
| pillar              | 0.0706  | 0.6000  | 0.0066  | 0.4000  |
| plant               | 0.2805  | 0.6134  | 0.0943  | 0.3025  |
| stairs              | 0.0637  | 0.8000  | 0.0091  | 0.2000  |
| fan                 | 0.3247  | 0.6250  | 0.2083  | 0.2500  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plate               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basin               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| copier              | 0.7265  | 0.9167  | 0.3266  | 0.7500  |
| drawer              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball                | 0.2962  | 0.4167  | 0.0667  | 0.1667  |
| fire extinguisher   | 0.0133  | 0.3333  | 0.0000  | 0.0000  |
| vacuum cleaner      | 0.4000  | 0.6667  | 0.3333  | 0.3333  |
| range hood          | 0.6136  | 0.8000  | 0.0000  | 0.0000  |
| teapot              | 0.0833  | 0.7500  | 0.0250  | 0.2500  |
| toaster             | 0.0147  | 0.5000  | 0.0000  | 0.0000  |
| dishwasher          | 0.1667  | 0.3333  | 0.0000  | 0.0000  |
| stove               | 0.7907  | 1.0000  | 0.4825  | 0.8889  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flowerpot           | 0.0370  | 1.0000  | 0.0000  | 0.0000  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pot                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| person              | 0.2143  | 0.3333  | 0.0028  | 0.1667  |
| oven                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crate               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 0.2466  | 1.0000  | 0.0370  | 0.3333  |
| speaker             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.2083  | 1.0000  | 0.2083  | 1.0000  |
| washing machine     | 0.5952  | 0.6667  | 0.5952  | 0.6667  |
| excercise equipment | 1.0000  | 1.0000  | 0.0455  | 0.5000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| scannet             | 0.1837  | 0.4305  | 0.0617  | 0.1794  |
| head                | 0.2267  | 0.5516  | 0.0830  | 0.2414  |
| common              | 0.1492  | 0.3364  | 0.0369  | 0.1278  |
| tail                | 0.1021  | 0.1929  | 0.0411  | 0.0667  |
| small               | 0.0243  | 0.1355  | 0.0023  | 0.0185  |
| medium              | 0.2114  | 0.5087  | 0.0724  | 0.2170  |
| large               | 0.1712  | 0.7196  | 0.0584  | 0.3927  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/19/2025 18:14:43 INFO metrics.py:437 | number of 3rscan results: 235
Rank[0/8] 06/19/2025 18:16:00 INFO metrics.py:332 | eval results of 3rscan:
+-------------------+---------+---------+---------+---------+
| classes           | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+-------------------+---------+---------+---------+---------+
| monitor           | 0.3395  | 0.6250  | 0.0005  | 0.0250  |
| chair             | 0.4913  | 0.8157  | 0.3303  | 0.6219  |
| backpack          | 0.3307  | 0.6970  | 0.0663  | 0.2424  |
| bag               | 0.1863  | 0.6465  | 0.0075  | 0.1616  |
| keyboard          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| computer          | 0.0784  | 0.6190  | 0.0004  | 0.0476  |
| desk              | 0.2190  | 0.8375  | 0.0917  | 0.5625  |
| table             | 0.3445  | 0.8423  | 0.1609  | 0.5811  |
| mouse             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blackboard        | 0.1696  | 0.5833  | 0.0000  | 0.0000  |
| paper             | 0.0050  | 0.2571  | 0.0000  | 0.0286  |
| cup               | 0.0137  | 0.2308  | 0.0000  | 0.0000  |
| conducting wire   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| box               | 0.1150  | 0.4510  | 0.0047  | 0.0784  |
| book              | 0.0116  | 0.2604  | 0.0001  | 0.0178  |
| window            | 0.0902  | 0.5205  | 0.0030  | 0.1301  |
| garbage           | 0.1017  | 0.6250  | 0.0614  | 0.3750  |
| case              | 0.0081  | 0.3286  | 0.0001  | 0.0429  |
| bottle            | 0.0068  | 0.2397  | 0.0000  | 0.0083  |
| tissue box        | 0.0159  | 0.3333  | 0.0000  | 0.0000  |
| bin               | 0.5065  | 0.8780  | 0.0874  | 0.4268  |
| blinds            | 0.0180  | 0.4103  | 0.0015  | 0.1026  |
| plug              | 0.0028  | 0.1111  | 0.0000  | 0.0000  |
| shelf             | 0.1242  | 0.5936  | 0.0265  | 0.1979  |
| file              | 0.0144  | 0.3929  | 0.0000  | 0.0000  |
| sunglasses        | 0.0443  | 0.5385  | 0.0024  | 0.1538  |
| cabinet           | 0.1662  | 0.7778  | 0.0550  | 0.3889  |
| stand             | 0.2862  | 0.8525  | 0.1298  | 0.4590  |
| pen               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wire              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| socket            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rack              | 0.0053  | 0.5000  | 0.0005  | 0.1000  |
| drawer            | 0.1467  | 0.7778  | 0.0976  | 0.5556  |
| curtain           | 0.1965  | 0.7937  | 0.0277  | 0.3571  |
| telephone         | 0.0991  | 0.2917  | 0.0000  | 0.0000  |
| map               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| printer           | 0.5556  | 0.6667  | 0.0159  | 0.1111  |
| board             | 0.1315  | 0.2500  | 0.0481  | 0.1250  |
| plate             | 0.0029  | 0.1212  | 0.0000  | 0.0303  |
| notebook          | 0.0011  | 0.0909  | 0.0000  | 0.0000  |
| wardrobe          | 0.1687  | 0.7812  | 0.0272  | 0.4531  |
| heater            | 0.2423  | 0.6140  | 0.0194  | 0.1053  |
| salt              | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| cushion           | 0.0819  | 0.6333  | 0.0165  | 0.2000  |
| folder            | 0.0019  | 0.2143  | 0.0000  | 0.0000  |
| paper cutter      | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| partition         | 0.0195  | 0.4000  | 0.0000  | 0.0000  |
| column            | 0.0060  | 0.5000  | 0.0060  | 0.5000  |
| tissue            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| umbrella          | 0.0043  | 1.0000  | 0.0000  | 0.0000  |
| device            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stool             | 0.1505  | 0.8333  | 0.0591  | 0.4259  |
| radiator          | 0.2254  | 0.5172  | 0.0021  | 0.0690  |
| calendar          | 0.0018  | 0.2000  | 0.0000  | 0.0000  |
| pillar            | 0.2606  | 0.5882  | 0.0057  | 0.1765  |
| clothes           | 0.0725  | 0.5534  | 0.0044  | 0.1359  |
| tv                | 0.2446  | 0.6000  | 0.0386  | 0.1143  |
| picture           | 0.0881  | 0.2967  | 0.0002  | 0.0203  |
| lamp              | 0.1305  | 0.4375  | 0.0031  | 0.0625  |
| frame             | 0.0224  | 0.4000  | 0.0000  | 0.0333  |
| commode           | 0.2789  | 0.8077  | 0.2222  | 0.5769  |
| bowl              | 0.0213  | 0.2963  | 0.0046  | 0.0370  |
| light             | 0.0317  | 0.1932  | 0.0000  | 0.0000  |
| couch             | 0.1827  | 0.7286  | 0.1045  | 0.5429  |
| door              | 0.1652  | 0.5809  | 0.0175  | 0.1103  |
| doorframe         | 0.0700  | 0.5800  | 0.0062  | 0.1800  |
| pillow            | 0.3038  | 0.6719  | 0.0079  | 0.0938  |
| basin             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| counter           | 0.0539  | 0.4146  | 0.0327  | 0.0976  |
| towel             | 0.1498  | 0.5570  | 0.0085  | 0.0886  |
| jar               | 0.0281  | 0.2500  | 0.0013  | 0.1250  |
| bar               | 0.1222  | 0.6667  | 0.0000  | 0.0000  |
| price tag         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bench             | 0.1662  | 0.8214  | 0.0895  | 0.3929  |
| switch            | 0.0005  | 0.0566  | 0.0000  | 0.0000  |
| dispenser         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| decoration        | 0.1155  | 0.4062  | 0.0017  | 0.0938  |
| sink              | 0.3953  | 0.7143  | 0.0347  | 0.1607  |
| hook              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candle            | 0.0111  | 0.0667  | 0.0000  | 0.0000  |
| blanket           | 0.1527  | 0.8615  | 0.0049  | 0.2923  |
| clock             | 0.0039  | 0.1053  | 0.0000  | 0.0000  |
| poster            | 0.0040  | 0.1270  | 0.0000  | 0.0000  |
| tray              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bed               | 0.5788  | 0.9455  | 0.1117  | 0.5455  |
| mirror            | 0.0799  | 0.3333  | 0.0000  | 0.0370  |
| carpet            | 0.0282  | 0.5357  | 0.0005  | 0.1071  |
| flowerpot         | 0.0428  | 0.3750  | 0.0000  | 0.0000  |
| kitchenware       | 0.0424  | 0.3636  | 0.0003  | 0.0227  |
| container         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plant             | 0.2118  | 0.5297  | 0.0311  | 0.1584  |
| vase              | 0.0952  | 0.3000  | 0.0003  | 0.0500  |
| player            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| magazine          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| oven              | 0.2963  | 0.7895  | 0.0607  | 0.4211  |
| ottoman           | 0.2151  | 0.9091  | 0.1990  | 0.7273  |
| album             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| teapot            | 0.0015  | 0.2500  | 0.0000  | 0.0000  |
| napkins           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| laptop            | 0.2607  | 0.5000  | 0.2222  | 0.3333  |
| fan               | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| jalousie          | 0.0018  | 0.5000  | 0.0000  | 0.0000  |
| windowsill        | 0.0426  | 0.4359  | 0.0008  | 0.1026  |
| humidifier        | 0.0556  | 0.5000  | 0.0000  | 0.0000  |
| roll              | 0.1167  | 0.3000  | 0.0000  | 0.0000  |
| speaker           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| balcony           | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| shoe              | 0.0821  | 0.4524  | 0.0000  | 0.0238  |
| chandelier        | 0.0417  | 1.0000  | 0.0104  | 0.5000  |
| pot               | 0.0531  | 0.5000  | 0.0002  | 0.0833  |
| basket            | 0.2513  | 0.6406  | 0.1242  | 0.3594  |
| kettle            | 0.0347  | 0.5000  | 0.0017  | 0.0833  |
| mat               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| furniture         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| remote control    | 0.0379  | 0.1500  | 0.0000  | 0.0000  |
| beanbag           | 0.7321  | 1.0000  | 0.4230  | 1.0000  |
| air conditioner   | 0.3056  | 0.5000  | 0.0000  | 0.0000  |
| refrigerator      | 0.5447  | 0.8000  | 0.3641  | 0.6667  |
| toilet paper      | 0.0024  | 0.1667  | 0.0000  | 0.0000  |
| luggage           | 0.2500  | 1.0000  | 0.0024  | 0.3333  |
| stairs            | 0.0586  | 0.5000  | 0.0028  | 0.1429  |
| microwave         | 0.5750  | 0.8824  | 0.0555  | 0.4118  |
| vacuum cleaner    | 0.0071  | 0.2500  | 0.0000  | 0.0000  |
| brush             | 0.0160  | 0.6250  | 0.0000  | 0.0000  |
| guitar            | 0.5000  | 0.5000  | 0.1667  | 0.5000  |
| holder            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rail              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hanger            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sack              | 0.0676  | 0.7143  | 0.0000  | 0.0000  |
| hat               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tube              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toy               | 0.2891  | 0.6923  | 0.1039  | 0.2308  |
| piano             | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| dresser           | 0.5667  | 1.0000  | 0.0000  | 0.0000  |
| bathtub           | 0.6806  | 1.0000  | 0.4654  | 0.8889  |
| faucet            | 0.0692  | 0.6667  | 0.0000  | 0.0000  |
| statue            | 0.1667  | 0.5000  | 0.0000  | 0.0000  |
| clothes dryer     | 0.0698  | 0.8000  | 0.0063  | 0.3000  |
| bucket            | 0.3060  | 0.8667  | 0.1683  | 0.3333  |
| shower            | 0.0040  | 0.2500  | 0.0000  | 0.0000  |
| toilet            | 0.6916  | 0.8235  | 0.0487  | 0.2941  |
| scale             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shampoo           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toiletry          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water heater      | 1.0000  | 1.0000  | 0.5000  | 1.0000  |
| flush             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine   | 0.4299  | 0.7143  | 0.1147  | 0.4286  |
| soap dish         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| body loofah       | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| pan               | 0.0269  | 0.2500  | 0.0000  | 0.0000  |
| can               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pack              | 0.0744  | 0.2308  | 0.0000  | 0.0000  |
| knife             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| spoon             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coffee maker      | 0.1932  | 0.7778  | 0.0062  | 0.1111  |
| range hood        | 0.0086  | 0.4000  | 0.0038  | 0.2000  |
| stove             | 0.0665  | 0.4706  | 0.0000  | 0.0000  |
| dishwasher        | 0.1111  | 0.3333  | 0.1111  | 0.3333  |
| tool              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster           | 0.3571  | 0.7500  | 0.0000  | 0.0000  |
| crate             | 0.3869  | 1.0000  | 0.0167  | 0.2000  |
| ladder            | 0.0076  | 0.3333  | 0.0076  | 0.3333  |
| food              | 0.0012  | 0.1000  | 0.0000  | 0.0000  |
| seasoning         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crib              | 0.1116  | 1.0000  | 0.0889  | 0.6667  |
| mattress          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sign              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fireplace         | 0.0063  | 0.2000  | 0.0000  | 0.0000  |
| broom             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mop               | 0.7255  | 1.0000  | 0.0037  | 0.3333  |
| bicycle           | 0.2222  | 1.0000  | 0.0556  | 0.5000  |
| screen            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| window frame      | 0.0115  | 0.1667  | 0.0083  | 0.0833  |
| tablet            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| menu              | 0.2052  | 0.4286  | 0.0000  | 0.0000  |
| shovel            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dispenser    | 0.1085  | 0.4000  | 0.0043  | 0.2000  |
| coil              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scissors          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pad               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washbasin         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball              | 0.2800  | 0.4000  | 0.0100  | 0.2000  |
| adhesive tape     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dish rack         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit             | 0.0667  | 0.2000  | 0.0667  | 0.2000  |
| pipe              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candlestick       | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| letter            | 0.0017  | 0.2500  | 0.0000  | 0.0000  |
| bidet             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| sponge            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cube              | 0.0833  | 1.0000  | 0.0667  | 1.0000  |
| footstool         | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| cosmetics         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine           | 0.1429  | 1.0000  | 0.0000  | 0.0000  |
| bread             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beam              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radio             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toothbrush        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glass             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glove             | 0.0256  | 0.3333  | 0.0000  | 0.0000  |
| fire extinguisher | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| -------           | ------- | ------- | ------- | ------- |
| 3rscan            | 0.1417  | 0.3934  | 0.0463  | 0.1443  |
| head              | 0.1652  | 0.5049  | 0.0422  | 0.1760  |
| common            | 0.1183  | 0.3447  | 0.0522  | 0.1307  |
| tail              | 0.1295  | 0.2481  | 0.0460  | 0.1028  |
| small             | 0.0191  | 0.1248  | 0.0011  | 0.0114  |
| medium            | 0.1562  | 0.4406  | 0.0513  | 0.1595  |
| large             | 0.1116  | 0.6062  | 0.0400  | 0.2968  |
+-------------------+---------+---------+---------+---------+
Rank[0/8] 06/19/2025 18:16:00 INFO metrics.py:437 | number of matterport3d results: 339
Rank[0/8] 06/19/2025 18:17:24 INFO metrics.py:332 | eval results of matterport3d:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| sink                | 0.3714  | 0.6970  | 0.0311  | 0.1717  |
| mirror              | 0.0601  | 0.4189  | 0.0008  | 0.0405  |
| towel               | 0.2483  | 0.5273  | 0.0127  | 0.0909  |
| picture             | 0.1144  | 0.3406  | 0.0053  | 0.0531  |
| light               | 0.0057  | 0.1396  | 0.0001  | 0.0256  |
| doorframe           | 0.2568  | 0.6860  | 0.0502  | 0.3140  |
| plant               | 0.2439  | 0.6161  | 0.0282  | 0.2143  |
| faucet              | 0.0004  | 0.1111  | 0.0000  | 0.0000  |
| case                | 0.0040  | 0.2778  | 0.0031  | 0.0556  |
| window              | 0.0967  | 0.4889  | 0.0121  | 0.1417  |
| door                | 0.0975  | 0.4674  | 0.0035  | 0.0966  |
| box                 | 0.0532  | 0.5385  | 0.0065  | 0.2115  |
| flowerpot           | 0.0563  | 0.6400  | 0.0029  | 0.1200  |
| clothes             | 0.0759  | 0.3793  | 0.0026  | 0.1034  |
| cabinet             | 0.0703  | 0.5763  | 0.0256  | 0.2881  |
| washbasin           | 0.0129  | 0.4286  | 0.0079  | 0.1429  |
| bottle              | 0.0355  | 0.4167  | 0.0001  | 0.0417  |
| pot                 | 0.0330  | 0.3846  | 0.0040  | 0.0769  |
| lamp                | 0.2558  | 0.6044  | 0.0376  | 0.1648  |
| drawer              | 0.0003  | 0.1250  | 0.0000  | 0.0000  |
| countertop          | 0.0275  | 0.3214  | 0.0000  | 0.0357  |
| shelf               | 0.1270  | 0.5401  | 0.0399  | 0.2482  |
| curtain             | 0.2310  | 0.7396  | 0.0564  | 0.3125  |
| roof                | 0.0010  | 0.2083  | 0.0000  | 0.0000  |
| bathtub             | 0.4125  | 0.7407  | 0.2174  | 0.5926  |
| counter             | 0.0018  | 0.3636  | 0.0001  | 0.0909  |
| window frame        | 0.0326  | 0.4598  | 0.0016  | 0.1379  |
| decoration          | 0.0077  | 0.1905  | 0.0001  | 0.0238  |
| toiletry            | 0.0055  | 0.1818  | 0.0000  | 0.0000  |
| dresser             | 0.2963  | 0.8000  | 0.1540  | 0.6000  |
| shower              | 0.0000  | 0.0833  | 0.0000  | 0.0000  |
| book                | 0.0357  | 0.2677  | 0.0079  | 0.0236  |
| toilet              | 0.7321  | 0.8281  | 0.4310  | 0.5781  |
| bag                 | 0.1405  | 0.4615  | 0.0105  | 0.1538  |
| vent                | 0.0003  | 0.0417  | 0.0000  | 0.0000  |
| wardrobe            | 0.2303  | 0.4545  | 0.0048  | 0.3636  |
| vanity              | 0.1467  | 0.7333  | 0.0915  | 0.6667  |
| stand               | 0.3429  | 0.7200  | 0.1516  | 0.4800  |
| vase                | 0.0541  | 0.3333  | 0.0093  | 0.1000  |
| switch              | 0.0001  | 0.0500  | 0.0000  | 0.0000  |
| table               | 0.1933  | 0.6987  | 0.0997  | 0.4872  |
| handle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blanket             | 0.0015  | 0.2727  | 0.0002  | 0.0909  |
| frame               | 0.0063  | 0.2941  | 0.0000  | 0.0000  |
| soap                | 0.0006  | 0.2000  | 0.0006  | 0.2000  |
| plate               | 0.0324  | 0.2857  | 0.0006  | 0.0714  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basin               | 0.0208  | 1.0000  | 0.0208  | 1.0000  |
| basket              | 0.0521  | 0.4400  | 0.0002  | 0.0400  |
| mat                 | 0.0011  | 0.1667  | 0.0000  | 0.0000  |
| bench               | 0.0929  | 0.5000  | 0.0047  | 0.1500  |
| tissue              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cup                 | 0.0022  | 0.2000  | 0.0000  | 0.0000  |
| rack                | 0.0001  | 0.0909  | 0.0000  | 0.0000  |
| couch               | 0.2523  | 0.8043  | 0.1439  | 0.6087  |
| soap dish           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| windowsill          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bin                 | 0.2195  | 0.7561  | 0.0929  | 0.3659  |
| bed                 | 0.4992  | 0.8246  | 0.1497  | 0.6842  |
| carpet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| paper               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tv                  | 0.3072  | 0.6744  | 0.0361  | 0.2791  |
| screen              | 0.0500  | 0.2500  | 0.0000  | 0.0000  |
| pillow              | 0.2295  | 0.5552  | 0.0021  | 0.0599  |
| bowl                | 0.0212  | 0.2727  | 0.0000  | 0.0000  |
| cushion             | 0.0023  | 0.2941  | 0.0007  | 0.0588  |
| desk                | 0.2110  | 0.5789  | 0.0618  | 0.3684  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| chair               | 0.3402  | 0.7007  | 0.1973  | 0.4932  |
| fireplace           | 0.1299  | 0.5652  | 0.0002  | 0.0870  |
| teapot              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stool               | 0.0119  | 0.4286  | 0.0007  | 0.1905  |
| statue              | 0.0803  | 0.3529  | 0.0594  | 0.1176  |
| ottoman             | 0.0390  | 0.3889  | 0.0201  | 0.2778  |
| air conditioner     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| board               | 0.0001  | 0.1667  | 0.0000  | 0.0000  |
| clock               | 0.0673  | 0.6667  | 0.0000  | 0.0000  |
| chandelier          | 0.0975  | 0.4211  | 0.0000  | 0.0000  |
| tray                | 0.0196  | 0.3333  | 0.0000  | 0.0000  |
| footstool           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| doorway             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toy                 | 0.0691  | 0.3056  | 0.0000  | 0.0000  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit               | 0.0340  | 0.2500  | 0.0066  | 0.1250  |
| pillar              | 0.1980  | 0.8095  | 0.1042  | 0.3333  |
| column              | 0.0118  | 0.2500  | 0.0012  | 0.0833  |
| steps               | 0.0004  | 0.2000  | 0.0000  | 0.0000  |
| beam                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| heater              | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| washing machine     | 0.3934  | 0.6250  | 0.1294  | 0.5000  |
| dispenser           | 0.0111  | 0.5000  | 0.0000  | 0.0000  |
| toilet paper        | 0.2455  | 0.5278  | 0.0025  | 0.0278  |
| fire extinguisher   | 1.0000  | 1.0000  | 0.2500  | 1.0000  |
| hamper              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radiator            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dispenser      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet               | 0.1250  | 0.5000  | 0.0093  | 0.5000  |
| fan                 | 0.3151  | 0.7963  | 0.0924  | 0.3704  |
| rail                | 0.0039  | 0.2037  | 0.0000  | 0.0000  |
| stairs              | 0.0070  | 0.3019  | 0.0001  | 0.0377  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| telephone           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| thermostat          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket              | 0.0108  | 0.2000  | 0.0059  | 0.1000  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garage door         | 0.1667  | 0.1667  | 0.0000  | 0.0000  |
| kitchen island      | 0.0195  | 0.8571  | 0.0118  | 0.5714  |
| piano               | 0.2500  | 0.5000  | 0.2500  | 0.5000  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stove               | 0.2288  | 0.6000  | 0.1693  | 0.4000  |
| kettle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| monitor             | 0.0576  | 0.5000  | 0.0000  | 0.0000  |
| dishwasher          | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| oven                | 0.0275  | 0.4000  | 0.0000  | 0.0000  |
| container           | 0.0643  | 0.3704  | 0.0009  | 0.0370  |
| printer             | 0.3929  | 0.4286  | 0.3929  | 0.4286  |
| refrigerator        | 0.2086  | 0.6667  | 0.0267  | 0.1333  |
| excercise equipment | 0.2329  | 0.7778  | 0.0248  | 0.1667  |
| scale               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pan                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| magazine            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue box          | 0.0003  | 0.1250  | 0.0000  | 0.0000  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| partition           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wine                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool table          | 0.1111  | 0.3333  | 0.0833  | 0.3333  |
| hanger              | 0.0005  | 0.1000  | 0.0000  | 0.0000  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| projector           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0625  | 0.1250  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| keyboard            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| computer            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| arch                | 0.0015  | 0.0667  | 0.0000  | 0.0000  |
| food                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.0869  | 0.6667  | 0.0869  | 0.6667  |
| microwave           | 0.0428  | 0.3333  | 0.0350  | 0.2500  |
| range hood          | 0.0590  | 0.5000  | 0.0000  | 0.0000  |
| machine             | 0.0026  | 0.5000  | 0.0026  | 0.5000  |
| socket              | 0.0250  | 0.2500  | 0.0000  | 0.0000  |
| ladder              | 0.0247  | 0.4444  | 0.0065  | 0.1111  |
| pipe                | 0.0004  | 0.1000  | 0.0000  | 0.0000  |
| bicycle             | 0.1250  | 1.0000  | 0.0588  | 1.0000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| backpack            | 0.0400  | 1.0000  | 0.0400  | 1.0000  |
| globe               | 0.0500  | 1.0000  | 0.0000  | 0.0000  |
| player              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coffee maker        | 0.1111  | 0.3333  | 0.0000  | 0.0000  |
| kitchenware         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| file                | 0.1250  | 0.2500  | 0.0000  | 0.0000  |
| laptop              | 0.2436  | 1.0000  | 0.1667  | 0.5000  |
| guitar              | 0.4643  | 0.5714  | 0.0000  | 0.0000  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shoe                | 0.0077  | 0.1600  | 0.0000  | 0.0000  |
| holder              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball                | 0.3750  | 0.5000  | 0.3333  | 0.5000  |
| pack                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blackboard          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jacket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drum                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water heater        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell            | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mouse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| matterport3d        | 0.0830  | 0.2872  | 0.0301  | 0.1267  |
| head                | 0.1223  | 0.4085  | 0.0482  | 0.1724  |
| common              | 0.0591  | 0.2058  | 0.0174  | 0.0988  |
| tail                | 0.0169  | 0.1020  | 0.0037  | 0.0502  |
| small               | 0.0205  | 0.1072  | 0.0041  | 0.0312  |
| medium              | 0.0802  | 0.3079  | 0.0297  | 0.1265  |
| large               | 0.0675  | 0.3661  | 0.0207  | 0.1823  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/19/2025 18:17:24 INFO metrics.py:437 | number of arkit results: 0
Rank[0/8] 06/19/2025 18:17:24 INFO stats.py:394 | Epoch[29] completed. Training Speed: 2.86 samples/sec across all devices.	Epoch Time: 1090.36 sec.	Average Epoch Time: 1090.36 sec.	Average Step Time: 2.80 sec.	Estimated Remaining Time: 1 day, 4:36:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:17:25 INFO accelerator.py:3111 | Saving current state to /job_data/checkpoints/checkpoint_2
Rank[3/8] 06/19/2025 18:17:25 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 29 to /job_data/checkpoints/checkpoint_2
Rank[2/8] 06/19/2025 18:17:25 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 29 to /job_data/checkpoints/checkpoint_2
Rank[1/8] 06/19/2025 18:17:25 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 29 to /job_data/checkpoints/checkpoint_2
Rank[7/8] 06/19/2025 18:17:25 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 29 to /job_data/checkpoints/checkpoint_2
Rank[5/8] 06/19/2025 18:17:25 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 29 to /job_data/checkpoints/checkpoint_2
Rank[4/8] 06/19/2025 18:17:25 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 29 to /job_data/checkpoints/checkpoint_2
Rank[6/8] 06/19/2025 18:17:26 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 29 to /job_data/checkpoints/checkpoint_2
Rank[0/8] 06/19/2025 18:17:27 INFO checkpointing.py:106 | Model weights saved in /job_data/checkpoints/checkpoint_2/model.safetensors
Rank[0/8] 06/19/2025 18:17:29 INFO checkpointing.py:113 | Optimizer state saved in /job_data/checkpoints/checkpoint_2/optimizer.bin
Rank[0/8] 06/19/2025 18:17:29 INFO checkpointing.py:120 | Scheduler state saved in /job_data/checkpoints/checkpoint_2/scheduler.bin
Rank[0/8] 06/19/2025 18:17:29 INFO checkpointing.py:137 | Sampler state for dataloader 0 saved in /job_data/checkpoints/checkpoint_2/sampler.bin
Rank[0/8] 06/19/2025 18:17:29 INFO checkpointing.py:137 | Sampler state for dataloader 1 saved in /job_data/checkpoints/checkpoint_2/sampler_1.bin
Rank[0/8] 06/19/2025 18:17:29 INFO checkpointing.py:164 | Random states saved in /job_data/checkpoints/checkpoint_2/random_states_0.pkl
Rank[0/8] 06/19/2025 18:17:29 INFO checkpointing.py:300 | Saving the state of TrainerProgressState to /job_data/checkpoints/checkpoint_2/custom_checkpoint_0.pkl
Rank[0/8] 06/19/2025 18:17:29 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 29 to /job_data/checkpoints/checkpoint_2
Rank[0/8] 06/19/2025 18:18:00 INFO stats.py:314 | Epoch[30] Step[24] GlobalStep[11724] Training Speed: 6.77 samples/sec across all devices.	Average Step Time: 1.18 sec.	Estimated Remaining Time: 1 day, 4:36:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:18:00 INFO loss_tracker.py:84 | Epoch[30/239] Step[24] GlobalStep[11724/NA]: det_loss_cls_0[0.5825] det_loss_box_0[0.7753] det_loss_cls_1[0.5778] det_loss_box_1[0.6294] det_loss_cls_2[0.5707] det_loss_box_2[0.5684] det_loss_cls_3[0.5486] det_loss_box_3[0.5541] det_loss_cls_4[0.5065] det_loss_box_4[0.5421] det_loss_cls_5[0.4945] det_loss_box_5[0.5390] det_loss_cls_dn_0[0.1006] det_loss_box_dn_0[0.3069] det_loss_cls_dn_1[0.0984] det_loss_box_dn_1[0.2652] det_loss_cls_dn_2[0.0971] det_loss_box_dn_2[0.2464] det_loss_cls_dn_3[0.0960] det_loss_box_dn_3[0.2450] det_loss_cls_dn_4[0.0955] det_loss_box_dn_4[0.2439] det_loss_cls_dn_5[0.0948] det_loss_box_dn_5[0.2432] loss_depth[0.0400] total_loss[9.0618]
Rank[0/8] 06/19/2025 18:18:27 INFO stats.py:314 | Epoch[30] Step[49] GlobalStep[11749] Training Speed: 4.94 samples/sec across all devices.	Average Step Time: 1.62 sec.	Estimated Remaining Time: 1 day, 4:34:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:18:27 INFO loss_tracker.py:84 | Epoch[30/239] Step[49] GlobalStep[11749/NA]: det_loss_cls_0[0.5996] det_loss_box_0[0.7969] det_loss_cls_1[0.5777] det_loss_box_1[0.6816] det_loss_cls_2[0.5762] det_loss_box_2[0.6147] det_loss_cls_3[0.5631] det_loss_box_3[0.5925] det_loss_cls_4[0.5399] det_loss_box_4[0.5854] det_loss_cls_5[0.5170] det_loss_box_5[0.5894] det_loss_cls_dn_0[0.1100] det_loss_box_dn_0[0.3221] det_loss_cls_dn_1[0.1060] det_loss_box_dn_1[0.2903] det_loss_cls_dn_2[0.1054] det_loss_box_dn_2[0.2759] det_loss_cls_dn_3[0.1043] det_loss_box_dn_3[0.2738] det_loss_cls_dn_4[0.1024] det_loss_box_dn_4[0.2730] det_loss_cls_dn_5[0.1021] det_loss_box_dn_5[0.2714] loss_depth[0.0428] total_loss[9.6136]
Rank[0/8] 06/19/2025 18:18:52 INFO stats.py:314 | Epoch[30] Step[74] GlobalStep[11774] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 4:33:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:18:52 INFO loss_tracker.py:84 | Epoch[30/239] Step[74] GlobalStep[11774/NA]: det_loss_cls_0[0.5407] det_loss_box_0[0.7165] det_loss_cls_1[0.5233] det_loss_box_1[0.6151] det_loss_cls_2[0.5210] det_loss_box_2[0.5599] det_loss_cls_3[0.5067] det_loss_box_3[0.5487] det_loss_cls_4[0.4913] det_loss_box_4[0.5334] det_loss_cls_5[0.4695] det_loss_box_5[0.5368] det_loss_cls_dn_0[0.1073] det_loss_box_dn_0[0.3476] det_loss_cls_dn_1[0.1038] det_loss_box_dn_1[0.3097] det_loss_cls_dn_2[0.1030] det_loss_box_dn_2[0.2940] det_loss_cls_dn_3[0.1017] det_loss_box_dn_3[0.2932] det_loss_cls_dn_4[0.1004] det_loss_box_dn_4[0.2931] det_loss_cls_dn_5[0.0996] det_loss_box_dn_5[0.2916] loss_depth[0.0402] total_loss[9.0483]
Rank[0/8] 06/19/2025 18:19:18 INFO stats.py:314 | Epoch[30] Step[99] GlobalStep[11799] Training Speed: 7.98 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 4:32:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:19:18 INFO loss_tracker.py:84 | Epoch[30/239] Step[99] GlobalStep[11799/NA]: det_loss_cls_0[0.5397] det_loss_box_0[0.7266] det_loss_cls_1[0.5243] det_loss_box_1[0.6109] det_loss_cls_2[0.5142] det_loss_box_2[0.5645] det_loss_cls_3[0.4966] det_loss_box_3[0.5475] det_loss_cls_4[0.4828] det_loss_box_4[0.5385] det_loss_cls_5[0.4648] det_loss_box_5[0.5365] det_loss_cls_dn_0[0.1130] det_loss_box_dn_0[0.3938] det_loss_cls_dn_1[0.1095] det_loss_box_dn_1[0.3559] det_loss_cls_dn_2[0.1095] det_loss_box_dn_2[0.3382] det_loss_cls_dn_3[0.1081] det_loss_box_dn_3[0.3362] det_loss_cls_dn_4[0.1068] det_loss_box_dn_4[0.3356] det_loss_cls_dn_5[0.1065] det_loss_box_dn_5[0.3349] loss_depth[0.0419] total_loss[9.3369]
Rank[0/8] 06/19/2025 18:19:43 INFO stats.py:314 | Epoch[30] Step[124] GlobalStep[11824] Training Speed: 7.81 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1 day, 4:31:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:19:43 INFO loss_tracker.py:84 | Epoch[30/239] Step[124] GlobalStep[11824/NA]: det_loss_cls_0[0.5183] det_loss_box_0[0.6926] det_loss_cls_1[0.5117] det_loss_box_1[0.5626] det_loss_cls_2[0.5043] det_loss_box_2[0.5098] det_loss_cls_3[0.4811] det_loss_box_3[0.5027] det_loss_cls_4[0.4533] det_loss_box_4[0.4964] det_loss_cls_5[0.4415] det_loss_box_5[0.4912] det_loss_cls_dn_0[0.1124] det_loss_box_dn_0[0.3395] det_loss_cls_dn_1[0.1081] det_loss_box_dn_1[0.3004] det_loss_cls_dn_2[0.1076] det_loss_box_dn_2[0.2838] det_loss_cls_dn_3[0.1077] det_loss_box_dn_3[0.2825] det_loss_cls_dn_4[0.1074] det_loss_box_dn_4[0.2819] det_loss_cls_dn_5[0.1067] det_loss_box_dn_5[0.2805] loss_depth[0.0408] total_loss[8.6247]
Rank[0/8] 06/19/2025 18:20:09 INFO stats.py:314 | Epoch[30] Step[149] GlobalStep[11849] Training Speed: 8.05 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 4:30:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:20:09 INFO loss_tracker.py:84 | Epoch[30/239] Step[149] GlobalStep[11849/NA]: det_loss_cls_0[0.6168] det_loss_box_0[0.9025] det_loss_cls_1[0.5783] det_loss_box_1[0.7481] det_loss_cls_2[0.5816] det_loss_box_2[0.6954] det_loss_cls_3[0.5663] det_loss_box_3[0.6528] det_loss_cls_4[0.5431] det_loss_box_4[0.6443] det_loss_cls_5[0.5334] det_loss_box_5[0.6356] det_loss_cls_dn_0[0.1207] det_loss_box_dn_0[0.3458] det_loss_cls_dn_1[0.1167] det_loss_box_dn_1[0.3079] det_loss_cls_dn_2[0.1159] det_loss_box_dn_2[0.2940] det_loss_cls_dn_3[0.1156] det_loss_box_dn_3[0.2928] det_loss_cls_dn_4[0.1139] det_loss_box_dn_4[0.2924] det_loss_cls_dn_5[0.1123] det_loss_box_dn_5[0.2909] loss_depth[0.0424] total_loss[10.2597]
Rank[0/8] 06/19/2025 18:20:35 INFO stats.py:314 | Epoch[30] Step[174] GlobalStep[11874] Training Speed: 8.08 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 4:29:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:20:35 INFO loss_tracker.py:84 | Epoch[30/239] Step[174] GlobalStep[11874/NA]: det_loss_cls_0[0.6417] det_loss_box_0[0.8925] det_loss_cls_1[0.6075] det_loss_box_1[0.7169] det_loss_cls_2[0.6053] det_loss_box_2[0.6549] det_loss_cls_3[0.5904] det_loss_box_3[0.6355] det_loss_cls_4[0.5659] det_loss_box_4[0.6205] det_loss_cls_5[0.5480] det_loss_box_5[0.6237] det_loss_cls_dn_0[0.1085] det_loss_box_dn_0[0.3819] det_loss_cls_dn_1[0.1051] det_loss_box_dn_1[0.3366] det_loss_cls_dn_2[0.1047] det_loss_box_dn_2[0.3168] det_loss_cls_dn_3[0.1039] det_loss_box_dn_3[0.3136] det_loss_cls_dn_4[0.1029] det_loss_box_dn_4[0.3126] det_loss_cls_dn_5[0.1020] det_loss_box_dn_5[0.3117] loss_depth[0.0392] total_loss[10.3425]
Rank[0/8] 06/19/2025 18:21:01 INFO stats.py:314 | Epoch[30] Step[199] GlobalStep[11899] Training Speed: 7.93 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 4:27:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:21:01 INFO loss_tracker.py:84 | Epoch[30/239] Step[199] GlobalStep[11899/NA]: det_loss_cls_0[0.5809] det_loss_box_0[0.7653] det_loss_cls_1[0.5587] det_loss_box_1[0.6317] det_loss_cls_2[0.5536] det_loss_box_2[0.5802] det_loss_cls_3[0.5439] det_loss_box_3[0.5626] det_loss_cls_4[0.5183] det_loss_box_4[0.5552] det_loss_cls_5[0.5018] det_loss_box_5[0.5550] det_loss_cls_dn_0[0.1124] det_loss_box_dn_0[0.3664] det_loss_cls_dn_1[0.1107] det_loss_box_dn_1[0.3211] det_loss_cls_dn_2[0.1093] det_loss_box_dn_2[0.3077] det_loss_cls_dn_3[0.1090] det_loss_box_dn_3[0.3067] det_loss_cls_dn_4[0.1076] det_loss_box_dn_4[0.3073] det_loss_cls_dn_5[0.1074] det_loss_box_dn_5[0.3055] loss_depth[0.0385] total_loss[9.5167]
Rank[0/8] 06/19/2025 18:21:28 INFO stats.py:314 | Epoch[30] Step[224] GlobalStep[11924] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 4:26:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:21:28 INFO loss_tracker.py:84 | Epoch[30/239] Step[224] GlobalStep[11924/NA]: det_loss_cls_0[0.6048] det_loss_box_0[0.8343] det_loss_cls_1[0.5790] det_loss_box_1[0.6819] det_loss_cls_2[0.5727] det_loss_box_2[0.6165] det_loss_cls_3[0.5565] det_loss_box_3[0.6121] det_loss_cls_4[0.5314] det_loss_box_4[0.6069] det_loss_cls_5[0.5229] det_loss_box_5[0.6055] det_loss_cls_dn_0[0.1098] det_loss_box_dn_0[0.3816] det_loss_cls_dn_1[0.1073] det_loss_box_dn_1[0.3333] det_loss_cls_dn_2[0.1060] det_loss_box_dn_2[0.3188] det_loss_cls_dn_3[0.1047] det_loss_box_dn_3[0.3151] det_loss_cls_dn_4[0.1041] det_loss_box_dn_4[0.3147] det_loss_cls_dn_5[0.1030] det_loss_box_dn_5[0.3134] loss_depth[0.0420] total_loss[9.9783]
Rank[0/8] 06/19/2025 18:21:53 INFO stats.py:314 | Epoch[30] Step[249] GlobalStep[11949] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 4:25:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:21:53 INFO loss_tracker.py:84 | Epoch[30/239] Step[249] GlobalStep[11949/NA]: det_loss_cls_0[0.6559] det_loss_box_0[0.9266] det_loss_cls_1[0.6422] det_loss_box_1[0.7590] det_loss_cls_2[0.6326] det_loss_box_2[0.6909] det_loss_cls_3[0.6171] det_loss_box_3[0.6582] det_loss_cls_4[0.5923] det_loss_box_4[0.6443] det_loss_cls_5[0.5801] det_loss_box_5[0.6473] det_loss_cls_dn_0[0.1182] det_loss_box_dn_0[0.3542] det_loss_cls_dn_1[0.1140] det_loss_box_dn_1[0.3166] det_loss_cls_dn_2[0.1132] det_loss_box_dn_2[0.3033] det_loss_cls_dn_3[0.1121] det_loss_box_dn_3[0.3019] det_loss_cls_dn_4[0.1120] det_loss_box_dn_4[0.3014] det_loss_cls_dn_5[0.1114] det_loss_box_dn_5[0.2993] loss_depth[0.0453] total_loss[10.6494]
Rank[0/8] 06/19/2025 18:22:19 INFO stats.py:314 | Epoch[30] Step[274] GlobalStep[11974] Training Speed: 8.00 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 4:24:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:22:19 INFO loss_tracker.py:84 | Epoch[30/239] Step[274] GlobalStep[11974/NA]: det_loss_cls_0[0.5955] det_loss_box_0[0.8100] det_loss_cls_1[0.5898] det_loss_box_1[0.6591] det_loss_cls_2[0.5888] det_loss_box_2[0.5926] det_loss_cls_3[0.5629] det_loss_box_3[0.5776] det_loss_cls_4[0.5495] det_loss_box_4[0.5571] det_loss_cls_5[0.5221] det_loss_box_5[0.5641] det_loss_cls_dn_0[0.1128] det_loss_box_dn_0[0.4283] det_loss_cls_dn_1[0.1086] det_loss_box_dn_1[0.3785] det_loss_cls_dn_2[0.1078] det_loss_box_dn_2[0.3624] det_loss_cls_dn_3[0.1061] det_loss_box_dn_3[0.3618] det_loss_cls_dn_4[0.1053] det_loss_box_dn_4[0.3605] det_loss_cls_dn_5[0.1043] det_loss_box_dn_5[0.3592] loss_depth[0.0414] total_loss[10.1060]
Rank[0/8] 06/19/2025 18:22:45 INFO stats.py:314 | Epoch[30] Step[299] GlobalStep[11999] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 4:23:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:22:45 INFO loss_tracker.py:84 | Epoch[30/239] Step[299] GlobalStep[11999/NA]: det_loss_cls_0[0.4612] det_loss_box_0[0.6093] det_loss_cls_1[0.4490] det_loss_box_1[0.4982] det_loss_cls_2[0.4542] det_loss_box_2[0.4577] det_loss_cls_3[0.4395] det_loss_box_3[0.4345] det_loss_cls_4[0.4153] det_loss_box_4[0.4317] det_loss_cls_5[0.4045] det_loss_box_5[0.4275] det_loss_cls_dn_0[0.1085] det_loss_box_dn_0[0.3662] det_loss_cls_dn_1[0.1052] det_loss_box_dn_1[0.3303] det_loss_cls_dn_2[0.1049] det_loss_box_dn_2[0.3099] det_loss_cls_dn_3[0.1049] det_loss_box_dn_3[0.3081] det_loss_cls_dn_4[0.1037] det_loss_box_dn_4[0.3071] det_loss_cls_dn_5[0.1028] det_loss_box_dn_5[0.3044] loss_depth[0.0420] total_loss[8.0804]
Rank[0/8] 06/19/2025 18:23:10 INFO stats.py:314 | Epoch[30] Step[324] GlobalStep[12024] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 4:22:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:23:11 INFO loss_tracker.py:84 | Epoch[30/239] Step[324] GlobalStep[12024/NA]: det_loss_cls_0[0.7380] det_loss_box_0[0.9823] det_loss_cls_1[0.7108] det_loss_box_1[0.8044] det_loss_cls_2[0.7086] det_loss_box_2[0.7299] det_loss_cls_3[0.6950] det_loss_box_3[0.7034] det_loss_cls_4[0.6615] det_loss_box_4[0.6956] det_loss_cls_5[0.6507] det_loss_box_5[0.6885] det_loss_cls_dn_0[0.1288] det_loss_box_dn_0[0.3585] det_loss_cls_dn_1[0.1255] det_loss_box_dn_1[0.3226] det_loss_cls_dn_2[0.1254] det_loss_box_dn_2[0.3059] det_loss_cls_dn_3[0.1236] det_loss_box_dn_3[0.3038] det_loss_cls_dn_4[0.1223] det_loss_box_dn_4[0.3023] det_loss_cls_dn_5[0.1221] det_loss_box_dn_5[0.3007] loss_depth[0.0417] total_loss[11.4520]
Rank[0/8] 06/19/2025 18:23:36 INFO stats.py:314 | Epoch[30] Step[349] GlobalStep[12049] Training Speed: 8.34 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 4:21:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:23:36 INFO loss_tracker.py:84 | Epoch[30/239] Step[349] GlobalStep[12049/NA]: det_loss_cls_0[0.6299] det_loss_box_0[0.9875] det_loss_cls_1[0.6026] det_loss_box_1[0.8594] det_loss_cls_2[0.6135] det_loss_box_2[0.7696] det_loss_cls_3[0.5864] det_loss_box_3[0.7544] det_loss_cls_4[0.5637] det_loss_box_4[0.7313] det_loss_cls_5[0.5582] det_loss_box_5[0.7224] det_loss_cls_dn_0[0.1201] det_loss_box_dn_0[0.4075] det_loss_cls_dn_1[0.1165] det_loss_box_dn_1[0.3618] det_loss_cls_dn_2[0.1169] det_loss_box_dn_2[0.3432] det_loss_cls_dn_3[0.1160] det_loss_box_dn_3[0.3417] det_loss_cls_dn_4[0.1150] det_loss_box_dn_4[0.3408] det_loss_cls_dn_5[0.1145] det_loss_box_dn_5[0.3388] loss_depth[0.0415] total_loss[11.2530]
Rank[0/8] 06/19/2025 18:24:02 INFO stats.py:314 | Epoch[30] Step[374] GlobalStep[12074] Training Speed: 6.34 samples/sec across all devices.	Average Step Time: 1.26 sec.	Estimated Remaining Time: 1 day, 4:19:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:24:02 INFO loss_tracker.py:84 | Epoch[30/239] Step[374] GlobalStep[12074/NA]: det_loss_cls_0[0.6077] det_loss_box_0[0.8419] det_loss_cls_1[0.5834] det_loss_box_1[0.6956] det_loss_cls_2[0.5798] det_loss_box_2[0.6382] det_loss_cls_3[0.5652] det_loss_box_3[0.6137] det_loss_cls_4[0.5353] det_loss_box_4[0.6046] det_loss_cls_5[0.5191] det_loss_box_5[0.6076] det_loss_cls_dn_0[0.1153] det_loss_box_dn_0[0.3452] det_loss_cls_dn_1[0.1112] det_loss_box_dn_1[0.3092] det_loss_cls_dn_2[0.1107] det_loss_box_dn_2[0.2923] det_loss_cls_dn_3[0.1094] det_loss_box_dn_3[0.2921] det_loss_cls_dn_4[0.1082] det_loss_box_dn_4[0.2921] det_loss_cls_dn_5[0.1072] det_loss_box_dn_5[0.2919] loss_depth[0.0444] total_loss[9.9212]
Rank[0/8] 06/19/2025 18:24:18 INFO stats.py:394 | Epoch[30] completed. Training Speed: 7.64 samples/sec across all devices.	Epoch Time: 408.58 sec.	Average Epoch Time: 408.58 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 4:19:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:24:32 INFO stats.py:314 | Epoch[31] Step[9] GlobalStep[12099] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 4:19:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:24:48 INFO loss_tracker.py:84 | Epoch[31/239] Step[24] GlobalStep[12114/NA]: det_loss_cls_0[0.5163] det_loss_box_0[0.7969] det_loss_cls_1[0.5071] det_loss_box_1[0.6648] det_loss_cls_2[0.4967] det_loss_box_2[0.6035] det_loss_cls_3[0.4804] det_loss_box_3[0.5832] det_loss_cls_4[0.4441] det_loss_box_4[0.5895] det_loss_cls_5[0.4377] det_loss_box_5[0.5831] det_loss_cls_dn_0[0.1159] det_loss_box_dn_0[0.4478] det_loss_cls_dn_1[0.1124] det_loss_box_dn_1[0.3985] det_loss_cls_dn_2[0.1123] det_loss_box_dn_2[0.3843] det_loss_cls_dn_3[0.1116] det_loss_box_dn_3[0.3815] det_loss_cls_dn_4[0.1110] det_loss_box_dn_4[0.3799] det_loss_cls_dn_5[0.1102] det_loss_box_dn_5[0.3779] loss_depth[0.0421] total_loss[9.7886]
Rank[0/8] 06/19/2025 18:24:58 INFO stats.py:314 | Epoch[31] Step[34] GlobalStep[12124] Training Speed: 6.36 samples/sec across all devices.	Average Step Time: 1.26 sec.	Estimated Remaining Time: 1 day, 4:18:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:25:14 INFO loss_tracker.py:84 | Epoch[31/239] Step[49] GlobalStep[12139/NA]: det_loss_cls_0[0.5321] det_loss_box_0[0.7371] det_loss_cls_1[0.5240] det_loss_box_1[0.6099] det_loss_cls_2[0.5099] det_loss_box_2[0.5605] det_loss_cls_3[0.5023] det_loss_box_3[0.5434] det_loss_cls_4[0.4734] det_loss_box_4[0.5482] det_loss_cls_5[0.4651] det_loss_box_5[0.5439] det_loss_cls_dn_0[0.1213] det_loss_box_dn_0[0.3695] det_loss_cls_dn_1[0.1184] det_loss_box_dn_1[0.3175] det_loss_cls_dn_2[0.1180] det_loss_box_dn_2[0.3026] det_loss_cls_dn_3[0.1174] det_loss_box_dn_3[0.3003] det_loss_cls_dn_4[0.1161] det_loss_box_dn_4[0.2994] det_loss_cls_dn_5[0.1156] det_loss_box_dn_5[0.2979] loss_depth[0.0434] total_loss[9.1871]
Rank[0/8] 06/19/2025 18:25:24 INFO stats.py:314 | Epoch[31] Step[59] GlobalStep[12149] Training Speed: 8.02 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 4:17:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:25:41 INFO loss_tracker.py:84 | Epoch[31/239] Step[74] GlobalStep[12164/NA]: det_loss_cls_0[0.5138] det_loss_box_0[0.7727] det_loss_cls_1[0.4849] det_loss_box_1[0.6537] det_loss_cls_2[0.4860] det_loss_box_2[0.6018] det_loss_cls_3[0.4792] det_loss_box_3[0.5778] det_loss_cls_4[0.4501] det_loss_box_4[0.5713] det_loss_cls_5[0.4361] det_loss_box_5[0.5703] det_loss_cls_dn_0[0.1130] det_loss_box_dn_0[0.5706] det_loss_cls_dn_1[0.1092] det_loss_box_dn_1[0.5215] det_loss_cls_dn_2[0.1085] det_loss_box_dn_2[0.4979] det_loss_cls_dn_3[0.1070] det_loss_box_dn_3[0.5074] det_loss_cls_dn_4[0.1061] det_loss_box_dn_4[0.5057] det_loss_cls_dn_5[0.1057] det_loss_box_dn_5[0.4955] loss_depth[0.0385] total_loss[10.3841]
Rank[0/8] 06/19/2025 18:25:51 INFO stats.py:314 | Epoch[31] Step[84] GlobalStep[12174] Training Speed: 7.90 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 4:16:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:26:06 INFO loss_tracker.py:84 | Epoch[31/239] Step[99] GlobalStep[12189/NA]: det_loss_cls_0[0.5247] det_loss_box_0[0.6979] det_loss_cls_1[0.5080] det_loss_box_1[0.5538] det_loss_cls_2[0.4988] det_loss_box_2[0.4890] det_loss_cls_3[0.4806] det_loss_box_3[0.4768] det_loss_cls_4[0.4349] det_loss_box_4[0.4813] det_loss_cls_5[0.4208] det_loss_box_5[0.4840] det_loss_cls_dn_0[0.1106] det_loss_box_dn_0[0.3232] det_loss_cls_dn_1[0.1074] det_loss_box_dn_1[0.2857] det_loss_cls_dn_2[0.1071] det_loss_box_dn_2[0.2695] det_loss_cls_dn_3[0.1045] det_loss_box_dn_3[0.2683] det_loss_cls_dn_4[0.1026] det_loss_box_dn_4[0.2680] det_loss_cls_dn_5[0.1018] det_loss_box_dn_5[0.2661] loss_depth[0.0367] total_loss[8.4021]
Rank[0/8] 06/19/2025 18:26:17 INFO stats.py:314 | Epoch[31] Step[109] GlobalStep[12199] Training Speed: 7.40 samples/sec across all devices.	Average Step Time: 1.08 sec.	Estimated Remaining Time: 1 day, 4:14:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:26:33 INFO loss_tracker.py:84 | Epoch[31/239] Step[124] GlobalStep[12214/NA]: det_loss_cls_0[0.6911] det_loss_box_0[0.9269] det_loss_cls_1[0.6691] det_loss_box_1[0.7684] det_loss_cls_2[0.6571] det_loss_box_2[0.7093] det_loss_cls_3[0.6491] det_loss_box_3[0.6806] det_loss_cls_4[0.6066] det_loss_box_4[0.6665] det_loss_cls_5[0.5876] det_loss_box_5[0.6639] det_loss_cls_dn_0[0.1135] det_loss_box_dn_0[0.3209] det_loss_cls_dn_1[0.1088] det_loss_box_dn_1[0.2893] det_loss_cls_dn_2[0.1072] det_loss_box_dn_2[0.2713] det_loss_cls_dn_3[0.1085] det_loss_box_dn_3[0.2705] det_loss_cls_dn_4[0.1077] det_loss_box_dn_4[0.2696] det_loss_cls_dn_5[0.1076] det_loss_box_dn_5[0.2683] loss_depth[0.0409] total_loss[10.6606]
Rank[0/8] 06/19/2025 18:26:43 INFO stats.py:314 | Epoch[31] Step[134] GlobalStep[12224] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 4:13:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:26:59 INFO loss_tracker.py:84 | Epoch[31/239] Step[149] GlobalStep[12239/NA]: det_loss_cls_0[0.5623] det_loss_box_0[0.7493] det_loss_cls_1[0.5508] det_loss_box_1[0.6173] det_loss_cls_2[0.5385] det_loss_box_2[0.5722] det_loss_cls_3[0.5098] det_loss_box_3[0.5680] det_loss_cls_4[0.4917] det_loss_box_4[0.5489] det_loss_cls_5[0.4672] det_loss_box_5[0.5551] det_loss_cls_dn_0[0.1046] det_loss_box_dn_0[0.2995] det_loss_cls_dn_1[0.1005] det_loss_box_dn_1[0.2628] det_loss_cls_dn_2[0.1004] det_loss_box_dn_2[0.2481] det_loss_cls_dn_3[0.0987] det_loss_box_dn_3[0.2468] det_loss_cls_dn_4[0.0976] det_loss_box_dn_4[0.2462] det_loss_cls_dn_5[0.0964] det_loss_box_dn_5[0.2455] loss_depth[0.0424] total_loss[8.9207]
Rank[0/8] 06/19/2025 18:27:10 INFO stats.py:314 | Epoch[31] Step[159] GlobalStep[12249] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 4:12:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:27:25 INFO loss_tracker.py:84 | Epoch[31/239] Step[174] GlobalStep[12264/NA]: det_loss_cls_0[0.5872] det_loss_box_0[0.7305] det_loss_cls_1[0.5661] det_loss_box_1[0.6081] det_loss_cls_2[0.5584] det_loss_box_2[0.5598] det_loss_cls_3[0.5434] det_loss_box_3[0.5382] det_loss_cls_4[0.5094] det_loss_box_4[0.5352] det_loss_cls_5[0.4945] det_loss_box_5[0.5353] det_loss_cls_dn_0[0.1212] det_loss_box_dn_0[0.2990] det_loss_cls_dn_1[0.1164] det_loss_box_dn_1[0.2689] det_loss_cls_dn_2[0.1161] det_loss_box_dn_2[0.2563] det_loss_cls_dn_3[0.1150] det_loss_box_dn_3[0.2548] det_loss_cls_dn_4[0.1138] det_loss_box_dn_4[0.2548] det_loss_cls_dn_5[0.1131] det_loss_box_dn_5[0.2538] loss_depth[0.0389] total_loss[9.0883]
Rank[0/8] 06/19/2025 18:27:35 INFO stats.py:314 | Epoch[31] Step[184] GlobalStep[12274] Training Speed: 7.79 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 1 day, 4:11:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:27:51 INFO loss_tracker.py:84 | Epoch[31/239] Step[199] GlobalStep[12289/NA]: det_loss_cls_0[0.5494] det_loss_box_0[0.7080] det_loss_cls_1[0.5298] det_loss_box_1[0.6012] det_loss_cls_2[0.5241] det_loss_box_2[0.5549] det_loss_cls_3[0.5050] det_loss_box_3[0.5466] det_loss_cls_4[0.4751] det_loss_box_4[0.5373] det_loss_cls_5[0.4560] det_loss_box_5[0.5381] det_loss_cls_dn_0[0.1213] det_loss_box_dn_0[0.3085] det_loss_cls_dn_1[0.1173] det_loss_box_dn_1[0.2746] det_loss_cls_dn_2[0.1166] det_loss_box_dn_2[0.2577] det_loss_cls_dn_3[0.1159] det_loss_box_dn_3[0.2568] det_loss_cls_dn_4[0.1147] det_loss_box_dn_4[0.2559] det_loss_cls_dn_5[0.1140] det_loss_box_dn_5[0.2546] loss_depth[0.0406] total_loss[8.8741]
Rank[0/8] 06/19/2025 18:28:02 INFO stats.py:314 | Epoch[31] Step[209] GlobalStep[12299] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 4:10:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:28:17 INFO loss_tracker.py:84 | Epoch[31/239] Step[224] GlobalStep[12314/NA]: det_loss_cls_0[0.5062] det_loss_box_0[0.7006] det_loss_cls_1[0.5012] det_loss_box_1[0.5863] det_loss_cls_2[0.4921] det_loss_box_2[0.5408] det_loss_cls_3[0.4775] det_loss_box_3[0.5199] det_loss_cls_4[0.4555] det_loss_box_4[0.5126] det_loss_cls_5[0.4411] det_loss_box_5[0.5149] det_loss_cls_dn_0[0.1092] det_loss_box_dn_0[0.4070] det_loss_cls_dn_1[0.1071] det_loss_box_dn_1[0.3627] det_loss_cls_dn_2[0.1067] det_loss_box_dn_2[0.3462] det_loss_cls_dn_3[0.1066] det_loss_box_dn_3[0.3445] det_loss_cls_dn_4[0.1052] det_loss_box_dn_4[0.3443] det_loss_cls_dn_5[0.1037] det_loss_box_dn_5[0.3433] loss_depth[0.0407] total_loss[9.0758]
Rank[0/8] 06/19/2025 18:28:28 INFO stats.py:314 | Epoch[31] Step[234] GlobalStep[12324] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 4:09:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:28:44 INFO loss_tracker.py:84 | Epoch[31/239] Step[249] GlobalStep[12339/NA]: det_loss_cls_0[0.5215] det_loss_box_0[0.7495] det_loss_cls_1[0.5010] det_loss_box_1[0.6082] det_loss_cls_2[0.4900] det_loss_box_2[0.5665] det_loss_cls_3[0.4794] det_loss_box_3[0.5449] det_loss_cls_4[0.4552] det_loss_box_4[0.5416] det_loss_cls_5[0.4349] det_loss_box_5[0.5448] det_loss_cls_dn_0[0.1051] det_loss_box_dn_0[0.3532] det_loss_cls_dn_1[0.1017] det_loss_box_dn_1[0.3154] det_loss_cls_dn_2[0.1010] det_loss_box_dn_2[0.2984] det_loss_cls_dn_3[0.1001] det_loss_box_dn_3[0.2974] det_loss_cls_dn_4[0.0984] det_loss_box_dn_4[0.2971] det_loss_cls_dn_5[0.0980] det_loss_box_dn_5[0.2980] loss_depth[0.0423] total_loss[8.9437]
Rank[0/8] 06/19/2025 18:28:54 INFO stats.py:314 | Epoch[31] Step[259] GlobalStep[12349] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 4:08:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:29:09 INFO loss_tracker.py:84 | Epoch[31/239] Step[274] GlobalStep[12364/NA]: det_loss_cls_0[0.7392] det_loss_box_0[1.1163] det_loss_cls_1[0.7088] det_loss_box_1[0.9442] det_loss_cls_2[0.6988] det_loss_box_2[0.8789] det_loss_cls_3[0.6840] det_loss_box_3[0.8536] det_loss_cls_4[0.6518] det_loss_box_4[0.8508] det_loss_cls_5[0.6465] det_loss_box_5[0.8413] det_loss_cls_dn_0[0.1164] det_loss_box_dn_0[0.3612] det_loss_cls_dn_1[0.1128] det_loss_box_dn_1[0.3301] det_loss_cls_dn_2[0.1129] det_loss_box_dn_2[0.3150] det_loss_cls_dn_3[0.1117] det_loss_box_dn_3[0.3128] det_loss_cls_dn_4[0.1107] det_loss_box_dn_4[0.3127] det_loss_cls_dn_5[0.1105] det_loss_box_dn_5[0.3107] loss_depth[0.0425] total_loss[12.2743]
Rank[0/8] 06/19/2025 18:29:20 INFO stats.py:314 | Epoch[31] Step[284] GlobalStep[12374] Training Speed: 8.56 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 4:07:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:29:36 INFO loss_tracker.py:84 | Epoch[31/239] Step[299] GlobalStep[12389/NA]: det_loss_cls_0[0.5054] det_loss_box_0[0.6900] det_loss_cls_1[0.4979] det_loss_box_1[0.5679] det_loss_cls_2[0.4875] det_loss_box_2[0.5161] det_loss_cls_3[0.4730] det_loss_box_3[0.4982] det_loss_cls_4[0.4485] det_loss_box_4[0.4977] det_loss_cls_5[0.4347] det_loss_box_5[0.4963] det_loss_cls_dn_0[0.1098] det_loss_box_dn_0[0.3242] det_loss_cls_dn_1[0.1060] det_loss_box_dn_1[0.2857] det_loss_cls_dn_2[0.1049] det_loss_box_dn_2[0.2720] det_loss_cls_dn_3[0.1035] det_loss_box_dn_3[0.2700] det_loss_cls_dn_4[0.1020] det_loss_box_dn_4[0.2698] det_loss_cls_dn_5[0.1016] det_loss_box_dn_5[0.2694] loss_depth[0.0398] total_loss[8.4717]
Rank[0/8] 06/19/2025 18:29:47 INFO stats.py:314 | Epoch[31] Step[309] GlobalStep[12399] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 4:06:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:30:02 INFO loss_tracker.py:84 | Epoch[31/239] Step[324] GlobalStep[12414/NA]: det_loss_cls_0[0.4785] det_loss_box_0[0.6149] det_loss_cls_1[0.4610] det_loss_box_1[0.5103] det_loss_cls_2[0.4428] det_loss_box_2[0.4562] det_loss_cls_3[0.4314] det_loss_box_3[0.4396] det_loss_cls_4[0.3930] det_loss_box_4[0.4334] det_loss_cls_5[0.3835] det_loss_box_5[0.4316] det_loss_cls_dn_0[0.1014] det_loss_box_dn_0[0.3207] det_loss_cls_dn_1[0.0981] det_loss_box_dn_1[0.2782] det_loss_cls_dn_2[0.0970] det_loss_box_dn_2[0.2574] det_loss_cls_dn_3[0.0958] det_loss_box_dn_3[0.2550] det_loss_cls_dn_4[0.0949] det_loss_box_dn_4[0.2539] det_loss_cls_dn_5[0.0948] det_loss_box_dn_5[0.2532] loss_depth[0.0388] total_loss[7.7153]
Rank[0/8] 06/19/2025 18:30:13 INFO stats.py:314 | Epoch[31] Step[334] GlobalStep[12424] Training Speed: 8.01 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 4:05:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:30:29 INFO loss_tracker.py:84 | Epoch[31/239] Step[349] GlobalStep[12439/NA]: det_loss_cls_0[0.5551] det_loss_box_0[0.7064] det_loss_cls_1[0.5410] det_loss_box_1[0.5917] det_loss_cls_2[0.5418] det_loss_box_2[0.5274] det_loss_cls_3[0.5253] det_loss_box_3[0.5156] det_loss_cls_4[0.4975] det_loss_box_4[0.5122] det_loss_cls_5[0.4874] det_loss_box_5[0.5115] det_loss_cls_dn_0[0.1144] det_loss_box_dn_0[0.2977] det_loss_cls_dn_1[0.1118] det_loss_box_dn_1[0.2600] det_loss_cls_dn_2[0.1116] det_loss_box_dn_2[0.2446] det_loss_cls_dn_3[0.1113] det_loss_box_dn_3[0.2428] det_loss_cls_dn_4[0.1106] det_loss_box_dn_4[0.2422] det_loss_cls_dn_5[0.1102] det_loss_box_dn_5[0.2415] loss_depth[0.0393] total_loss[8.7510]
Rank[0/8] 06/19/2025 18:30:39 INFO stats.py:314 | Epoch[31] Step[359] GlobalStep[12449] Training Speed: 8.05 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 4:04:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:30:55 INFO loss_tracker.py:84 | Epoch[31/239] Step[374] GlobalStep[12464/NA]: det_loss_cls_0[0.5377] det_loss_box_0[0.6803] det_loss_cls_1[0.5212] det_loss_box_1[0.5621] det_loss_cls_2[0.5097] det_loss_box_2[0.5180] det_loss_cls_3[0.4974] det_loss_box_3[0.5005] det_loss_cls_4[0.4705] det_loss_box_4[0.4944] det_loss_cls_5[0.4614] det_loss_box_5[0.4906] det_loss_cls_dn_0[0.1249] det_loss_box_dn_0[0.3866] det_loss_cls_dn_1[0.1208] det_loss_box_dn_1[0.3518] det_loss_cls_dn_2[0.1203] det_loss_box_dn_2[0.3396] det_loss_cls_dn_3[0.1200] det_loss_box_dn_3[0.3378] det_loss_cls_dn_4[0.1192] det_loss_box_dn_4[0.3370] det_loss_cls_dn_5[0.1187] det_loss_box_dn_5[0.3371] loss_depth[0.0411] total_loss[9.0989]
Rank[0/8] 06/19/2025 18:31:06 INFO stats.py:314 | Epoch[31] Step[384] GlobalStep[12474] Training Speed: 8.17 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 4:03:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:31:11 INFO stats.py:394 | Epoch[31] completed. Training Speed: 7.56 samples/sec across all devices.	Epoch Time: 412.67 sec.	Average Epoch Time: 412.67 sec.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 1 day, 4:03:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:31:36 INFO stats.py:314 | Epoch[32] Step[19] GlobalStep[12499] Training Speed: 7.90 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 4:02:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:31:42 INFO loss_tracker.py:84 | Epoch[32/239] Step[24] GlobalStep[12504/NA]: det_loss_cls_0[0.6166] det_loss_box_0[0.8054] det_loss_cls_1[0.5974] det_loss_box_1[0.6580] det_loss_cls_2[0.5820] det_loss_box_2[0.6120] det_loss_cls_3[0.5615] det_loss_box_3[0.5913] det_loss_cls_4[0.5259] det_loss_box_4[0.5771] det_loss_cls_5[0.5146] det_loss_box_5[0.5736] det_loss_cls_dn_0[0.1200] det_loss_box_dn_0[0.3228] det_loss_cls_dn_1[0.1157] det_loss_box_dn_1[0.2842] det_loss_cls_dn_2[0.1155] det_loss_box_dn_2[0.2703] det_loss_cls_dn_3[0.1150] det_loss_box_dn_3[0.2682] det_loss_cls_dn_4[0.1153] det_loss_box_dn_4[0.2673] det_loss_cls_dn_5[0.1141] det_loss_box_dn_5[0.2660] loss_depth[0.0431] total_loss[9.6327]
Rank[0/8] 06/19/2025 18:32:03 INFO stats.py:314 | Epoch[32] Step[44] GlobalStep[12524] Training Speed: 8.64 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 4:01:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:32:08 INFO loss_tracker.py:84 | Epoch[32/239] Step[49] GlobalStep[12529/NA]: det_loss_cls_0[0.5995] det_loss_box_0[0.8916] det_loss_cls_1[0.5849] det_loss_box_1[0.7320] det_loss_cls_2[0.5784] det_loss_box_2[0.6719] det_loss_cls_3[0.5598] det_loss_box_3[0.6580] det_loss_cls_4[0.5346] det_loss_box_4[0.6430] det_loss_cls_5[0.5209] det_loss_box_5[0.6375] det_loss_cls_dn_0[0.1157] det_loss_box_dn_0[0.3655] det_loss_cls_dn_1[0.1122] det_loss_box_dn_1[0.3314] det_loss_cls_dn_2[0.1108] det_loss_box_dn_2[0.3195] det_loss_cls_dn_3[0.1099] det_loss_box_dn_3[0.3176] det_loss_cls_dn_4[0.1091] det_loss_box_dn_4[0.3176] det_loss_cls_dn_5[0.1091] det_loss_box_dn_5[0.3161] loss_depth[0.0418] total_loss[10.2883]
Rank[0/8] 06/19/2025 18:32:29 INFO stats.py:314 | Epoch[32] Step[69] GlobalStep[12549] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 4:00:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:32:34 INFO loss_tracker.py:84 | Epoch[32/239] Step[74] GlobalStep[12554/NA]: det_loss_cls_0[0.6915] det_loss_box_0[1.0334] det_loss_cls_1[0.6871] det_loss_box_1[0.8558] det_loss_cls_2[0.6661] det_loss_box_2[0.7767] det_loss_cls_3[0.6504] det_loss_box_3[0.7538] det_loss_cls_4[0.6312] det_loss_box_4[0.7400] det_loss_cls_5[0.6126] det_loss_box_5[0.7483] det_loss_cls_dn_0[0.1239] det_loss_box_dn_0[0.3994] det_loss_cls_dn_1[0.1191] det_loss_box_dn_1[0.3641] det_loss_cls_dn_2[0.1191] det_loss_box_dn_2[0.3488] det_loss_cls_dn_3[0.1188] det_loss_box_dn_3[0.3472] det_loss_cls_dn_4[0.1175] det_loss_box_dn_4[0.3459] det_loss_cls_dn_5[0.1171] det_loss_box_dn_5[0.3431] loss_depth[0.0430] total_loss[11.7540]
Rank[0/8] 06/19/2025 18:32:54 INFO stats.py:314 | Epoch[32] Step[94] GlobalStep[12574] Training Speed: 7.92 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 3:59:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:33:00 INFO loss_tracker.py:84 | Epoch[32/239] Step[99] GlobalStep[12579/NA]: det_loss_cls_0[0.5313] det_loss_box_0[0.7182] det_loss_cls_1[0.5128] det_loss_box_1[0.5875] det_loss_cls_2[0.5058] det_loss_box_2[0.5414] det_loss_cls_3[0.4895] det_loss_box_3[0.5170] det_loss_cls_4[0.4566] det_loss_box_4[0.5075] det_loss_cls_5[0.4385] det_loss_box_5[0.5107] det_loss_cls_dn_0[0.1112] det_loss_box_dn_0[0.3310] det_loss_cls_dn_1[0.1065] det_loss_box_dn_1[0.2933] det_loss_cls_dn_2[0.1055] det_loss_box_dn_2[0.2752] det_loss_cls_dn_3[0.1040] det_loss_box_dn_3[0.2737] det_loss_cls_dn_4[0.1027] det_loss_box_dn_4[0.2711] det_loss_cls_dn_5[0.1012] det_loss_box_dn_5[0.2701] loss_depth[0.0384] total_loss[8.7006]
Rank[0/8] 06/19/2025 18:33:21 INFO stats.py:314 | Epoch[32] Step[119] GlobalStep[12599] Training Speed: 7.50 samples/sec across all devices.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 1 day, 3:58:30.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:33:26 INFO loss_tracker.py:84 | Epoch[32/239] Step[124] GlobalStep[12604/NA]: det_loss_cls_0[0.5440] det_loss_box_0[0.7212] det_loss_cls_1[0.5321] det_loss_box_1[0.6055] det_loss_cls_2[0.5209] det_loss_box_2[0.5370] det_loss_cls_3[0.5066] det_loss_box_3[0.5149] det_loss_cls_4[0.4823] det_loss_box_4[0.5017] det_loss_cls_5[0.4608] det_loss_box_5[0.5069] det_loss_cls_dn_0[0.1012] det_loss_box_dn_0[0.3554] det_loss_cls_dn_1[0.0976] det_loss_box_dn_1[0.3136] det_loss_cls_dn_2[0.0973] det_loss_box_dn_2[0.2974] det_loss_cls_dn_3[0.0960] det_loss_box_dn_3[0.2968] det_loss_cls_dn_4[0.0954] det_loss_box_dn_4[0.2956] det_loss_cls_dn_5[0.0949] det_loss_box_dn_5[0.2940] loss_depth[0.0427] total_loss[8.9120]
Rank[0/8] 06/19/2025 18:33:47 INFO stats.py:314 | Epoch[32] Step[144] GlobalStep[12624] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 3:57:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:33:52 INFO loss_tracker.py:84 | Epoch[32/239] Step[149] GlobalStep[12629/NA]: det_loss_cls_0[0.4930] det_loss_box_0[0.7083] det_loss_cls_1[0.4806] det_loss_box_1[0.6026] det_loss_cls_2[0.4710] det_loss_box_2[0.5406] det_loss_cls_3[0.4543] det_loss_box_3[0.5289] det_loss_cls_4[0.4384] det_loss_box_4[0.5171] det_loss_cls_5[0.4260] det_loss_box_5[0.5234] det_loss_cls_dn_0[0.1076] det_loss_box_dn_0[0.3895] det_loss_cls_dn_1[0.1025] det_loss_box_dn_1[0.3552] det_loss_cls_dn_2[0.1030] det_loss_box_dn_2[0.3422] det_loss_cls_dn_3[0.1005] det_loss_box_dn_3[0.3401] det_loss_cls_dn_4[0.0998] det_loss_box_dn_4[0.3397] det_loss_cls_dn_5[0.0987] det_loss_box_dn_5[0.3385] loss_depth[0.0399] total_loss[8.9414]
Rank[0/8] 06/19/2025 18:34:14 INFO stats.py:314 | Epoch[32] Step[169] GlobalStep[12649] Training Speed: 7.99 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 3:56:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:34:19 INFO loss_tracker.py:84 | Epoch[32/239] Step[174] GlobalStep[12654/NA]: det_loss_cls_0[0.5912] det_loss_box_0[0.7489] det_loss_cls_1[0.5877] det_loss_box_1[0.6457] det_loss_cls_2[0.5813] det_loss_box_2[0.5959] det_loss_cls_3[0.5687] det_loss_box_3[0.5762] det_loss_cls_4[0.5452] det_loss_box_4[0.5736] det_loss_cls_5[0.5336] det_loss_box_5[0.5733] det_loss_cls_dn_0[0.1169] det_loss_box_dn_0[0.3618] det_loss_cls_dn_1[0.1138] det_loss_box_dn_1[0.3281] det_loss_cls_dn_2[0.1130] det_loss_box_dn_2[0.3115] det_loss_cls_dn_3[0.1115] det_loss_box_dn_3[0.3117] det_loss_cls_dn_4[0.1105] det_loss_box_dn_4[0.3106] det_loss_cls_dn_5[0.1104] det_loss_box_dn_5[0.3082] loss_depth[0.0431] total_loss[9.7725]
Rank[0/8] 06/19/2025 18:34:41 INFO stats.py:314 | Epoch[32] Step[194] GlobalStep[12674] Training Speed: 8.66 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 1 day, 3:55:30.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:34:46 INFO loss_tracker.py:84 | Epoch[32/239] Step[199] GlobalStep[12679/NA]: det_loss_cls_0[0.5187] det_loss_box_0[0.7370] det_loss_cls_1[0.4858] det_loss_box_1[0.5974] det_loss_cls_2[0.4791] det_loss_box_2[0.5496] det_loss_cls_3[0.4608] det_loss_box_3[0.5282] det_loss_cls_4[0.4400] det_loss_box_4[0.5243] det_loss_cls_5[0.4314] det_loss_box_5[0.5232] det_loss_cls_dn_0[0.1125] det_loss_box_dn_0[0.4090] det_loss_cls_dn_1[0.1081] det_loss_box_dn_1[0.3622] det_loss_cls_dn_2[0.1071] det_loss_box_dn_2[0.3448] det_loss_cls_dn_3[0.1058] det_loss_box_dn_3[0.3413] det_loss_cls_dn_4[0.1047] det_loss_box_dn_4[0.3404] det_loss_cls_dn_5[0.1037] det_loss_box_dn_5[0.3341] loss_depth[0.0438] total_loss[9.0930]
Rank[0/8] 06/19/2025 18:35:06 INFO stats.py:314 | Epoch[32] Step[219] GlobalStep[12699] Training Speed: 7.95 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 3:54:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:35:11 INFO loss_tracker.py:84 | Epoch[32/239] Step[224] GlobalStep[12704/NA]: det_loss_cls_0[0.5303] det_loss_box_0[0.7150] det_loss_cls_1[0.5003] det_loss_box_1[0.5811] det_loss_cls_2[0.4972] det_loss_box_2[0.5150] det_loss_cls_3[0.4753] det_loss_box_3[0.4914] det_loss_cls_4[0.4453] det_loss_box_4[0.4787] det_loss_cls_5[0.4325] det_loss_box_5[0.4776] det_loss_cls_dn_0[0.1042] det_loss_box_dn_0[0.2886] det_loss_cls_dn_1[0.1000] det_loss_box_dn_1[0.2505] det_loss_cls_dn_2[0.0989] det_loss_box_dn_2[0.2369] det_loss_cls_dn_3[0.0982] det_loss_box_dn_3[0.2343] det_loss_cls_dn_4[0.0973] det_loss_box_dn_4[0.2329] det_loss_cls_dn_5[0.0956] det_loss_box_dn_5[0.2328] loss_depth[0.0413] total_loss[8.2510]
Rank[0/8] 06/19/2025 18:35:32 INFO stats.py:314 | Epoch[32] Step[244] GlobalStep[12724] Training Speed: 12.43 samples/sec across all devices.	Average Step Time: 0.64 sec.	Estimated Remaining Time: 1 day, 3:53:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:35:38 INFO loss_tracker.py:84 | Epoch[32/239] Step[249] GlobalStep[12729/NA]: det_loss_cls_0[0.4551] det_loss_box_0[0.6767] det_loss_cls_1[0.4342] det_loss_box_1[0.5679] det_loss_cls_2[0.4336] det_loss_box_2[0.5197] det_loss_cls_3[0.4229] det_loss_box_3[0.5166] det_loss_cls_4[0.4116] det_loss_box_4[0.5087] det_loss_cls_5[0.3948] det_loss_box_5[0.5128] det_loss_cls_dn_0[0.1111] det_loss_box_dn_0[0.5541] det_loss_cls_dn_1[0.1075] det_loss_box_dn_1[0.5271] det_loss_cls_dn_2[0.1068] det_loss_box_dn_2[0.5110] det_loss_cls_dn_3[0.1058] det_loss_box_dn_3[0.5100] det_loss_cls_dn_4[0.1047] det_loss_box_dn_4[0.5081] det_loss_cls_dn_5[0.1035] det_loss_box_dn_5[0.5062] loss_depth[0.0419] total_loss[9.6523]
Rank[0/8] 06/19/2025 18:35:59 INFO stats.py:314 | Epoch[32] Step[269] GlobalStep[12749] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 3:52:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:36:04 INFO loss_tracker.py:84 | Epoch[32/239] Step[274] GlobalStep[12754/NA]: det_loss_cls_0[0.5831] det_loss_box_0[0.7561] det_loss_cls_1[0.5663] det_loss_box_1[0.6213] det_loss_cls_2[0.5613] det_loss_box_2[0.5521] det_loss_cls_3[0.5520] det_loss_box_3[0.5362] det_loss_cls_4[0.5210] det_loss_box_4[0.5281] det_loss_cls_5[0.5072] det_loss_box_5[0.5316] det_loss_cls_dn_0[0.1230] det_loss_box_dn_0[0.3865] det_loss_cls_dn_1[0.1190] det_loss_box_dn_1[0.3475] det_loss_cls_dn_2[0.1184] det_loss_box_dn_2[0.3344] det_loss_cls_dn_3[0.1169] det_loss_box_dn_3[0.3333] det_loss_cls_dn_4[0.1160] det_loss_box_dn_4[0.3331] det_loss_cls_dn_5[0.1152] det_loss_box_dn_5[0.3315] loss_depth[0.0431] total_loss[9.6342]
Rank[0/8] 06/19/2025 18:36:25 INFO stats.py:314 | Epoch[32] Step[294] GlobalStep[12774] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 3:51:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:36:30 INFO loss_tracker.py:84 | Epoch[32/239] Step[299] GlobalStep[12779/NA]: det_loss_cls_0[0.6843] det_loss_box_0[0.9245] det_loss_cls_1[0.6658] det_loss_box_1[0.7734] det_loss_cls_2[0.6597] det_loss_box_2[0.7110] det_loss_cls_3[0.6449] det_loss_box_3[0.6936] det_loss_cls_4[0.6227] det_loss_box_4[0.6644] det_loss_cls_5[0.6102] det_loss_box_5[0.6600] det_loss_cls_dn_0[0.1292] det_loss_box_dn_0[0.3266] det_loss_cls_dn_1[0.1258] det_loss_box_dn_1[0.2875] det_loss_cls_dn_2[0.1250] det_loss_box_dn_2[0.2726] det_loss_cls_dn_3[0.1242] det_loss_box_dn_3[0.2710] det_loss_cls_dn_4[0.1238] det_loss_box_dn_4[0.2698] det_loss_cls_dn_5[0.1226] det_loss_box_dn_5[0.2692] loss_depth[0.0389] total_loss[10.8008]
Rank[0/8] 06/19/2025 18:36:51 INFO stats.py:314 | Epoch[32] Step[319] GlobalStep[12799] Training Speed: 8.58 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 3:50:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:36:56 INFO loss_tracker.py:84 | Epoch[32/239] Step[324] GlobalStep[12804/NA]: det_loss_cls_0[0.5286] det_loss_box_0[0.8034] det_loss_cls_1[0.5039] det_loss_box_1[0.6696] det_loss_cls_2[0.4989] det_loss_box_2[0.6019] det_loss_cls_3[0.4803] det_loss_box_3[0.5872] det_loss_cls_4[0.4568] det_loss_box_4[0.5690] det_loss_cls_5[0.4455] det_loss_box_5[0.5653] det_loss_cls_dn_0[0.1111] det_loss_box_dn_0[0.3480] det_loss_cls_dn_1[0.1072] det_loss_box_dn_1[0.3179] det_loss_cls_dn_2[0.1067] det_loss_box_dn_2[0.3055] det_loss_cls_dn_3[0.1055] det_loss_box_dn_3[0.3043] det_loss_cls_dn_4[0.1045] det_loss_box_dn_4[0.3048] det_loss_cls_dn_5[0.1032] det_loss_box_dn_5[0.3032] loss_depth[0.0424] total_loss[9.2748]
Rank[0/8] 06/19/2025 18:37:17 INFO stats.py:314 | Epoch[32] Step[344] GlobalStep[12824] Training Speed: 7.85 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1 day, 3:49:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:37:22 INFO loss_tracker.py:84 | Epoch[32/239] Step[349] GlobalStep[12829/NA]: det_loss_cls_0[0.5673] det_loss_box_0[0.7440] det_loss_cls_1[0.5320] det_loss_box_1[0.6285] det_loss_cls_2[0.5240] det_loss_box_2[0.5622] det_loss_cls_3[0.5201] det_loss_box_3[0.5325] det_loss_cls_4[0.4879] det_loss_box_4[0.5283] det_loss_cls_5[0.4718] det_loss_box_5[0.5305] det_loss_cls_dn_0[0.1142] det_loss_box_dn_0[0.3133] det_loss_cls_dn_1[0.1107] det_loss_box_dn_1[0.2775] det_loss_cls_dn_2[0.1102] det_loss_box_dn_2[0.2607] det_loss_cls_dn_3[0.1097] det_loss_box_dn_3[0.2599] det_loss_cls_dn_4[0.1085] det_loss_box_dn_4[0.2587] det_loss_cls_dn_5[0.1079] det_loss_box_dn_5[0.2572] loss_depth[0.0417] total_loss[8.9594]
Rank[0/8] 06/19/2025 18:37:43 INFO stats.py:314 | Epoch[32] Step[369] GlobalStep[12849] Training Speed: 8.01 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 3:48:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:37:48 INFO loss_tracker.py:84 | Epoch[32/239] Step[374] GlobalStep[12854/NA]: det_loss_cls_0[0.5319] det_loss_box_0[0.7242] det_loss_cls_1[0.5020] det_loss_box_1[0.6100] det_loss_cls_2[0.4954] det_loss_box_2[0.5530] det_loss_cls_3[0.4794] det_loss_box_3[0.5369] det_loss_cls_4[0.4630] det_loss_box_4[0.5254] det_loss_cls_5[0.4414] det_loss_box_5[0.5287] det_loss_cls_dn_0[0.1173] det_loss_box_dn_0[0.3202] det_loss_cls_dn_1[0.1118] det_loss_box_dn_1[0.2755] det_loss_cls_dn_2[0.1100] det_loss_box_dn_2[0.2587] det_loss_cls_dn_3[0.1088] det_loss_box_dn_3[0.2569] det_loss_cls_dn_4[0.1079] det_loss_box_dn_4[0.2563] det_loss_cls_dn_5[0.1075] det_loss_box_dn_5[0.2555] loss_depth[0.0414] total_loss[8.7191]
Rank[0/8] 06/19/2025 18:38:04 INFO stats.py:394 | Epoch[32] completed. Training Speed: 7.54 samples/sec across all devices.	Epoch Time: 413.62 sec.	Average Epoch Time: 413.62 sec.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 1 day, 3:47:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:38:14 INFO stats.py:314 | Epoch[33] Step[4] GlobalStep[12874] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 3:47:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:38:35 INFO loss_tracker.py:84 | Epoch[33/239] Step[24] GlobalStep[12894/NA]: det_loss_cls_0[0.5481] det_loss_box_0[0.7011] det_loss_cls_1[0.5352] det_loss_box_1[0.5837] det_loss_cls_2[0.5274] det_loss_box_2[0.5300] det_loss_cls_3[0.5112] det_loss_box_3[0.5089] det_loss_cls_4[0.4908] det_loss_box_4[0.4988] det_loss_cls_5[0.4758] det_loss_box_5[0.4969] det_loss_cls_dn_0[0.1085] det_loss_box_dn_0[0.3550] det_loss_cls_dn_1[0.1051] det_loss_box_dn_1[0.3108] det_loss_cls_dn_2[0.1043] det_loss_box_dn_2[0.2906] det_loss_cls_dn_3[0.1037] det_loss_box_dn_3[0.2885] det_loss_cls_dn_4[0.1024] det_loss_box_dn_4[0.2865] det_loss_cls_dn_5[0.1017] det_loss_box_dn_5[0.2855] loss_depth[0.0387] total_loss[8.8891]
Rank[0/8] 06/19/2025 18:38:40 INFO stats.py:314 | Epoch[33] Step[29] GlobalStep[12899] Training Speed: 8.13 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 3:46:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:39:00 INFO loss_tracker.py:84 | Epoch[33/239] Step[49] GlobalStep[12919/NA]: det_loss_cls_0[0.5217] det_loss_box_0[0.7155] det_loss_cls_1[0.5163] det_loss_box_1[0.5816] det_loss_cls_2[0.5061] det_loss_box_2[0.5129] det_loss_cls_3[0.4825] det_loss_box_3[0.5067] det_loss_cls_4[0.4383] det_loss_box_4[0.4982] det_loss_cls_5[0.4226] det_loss_box_5[0.5008] det_loss_cls_dn_0[0.1094] det_loss_box_dn_0[0.3422] det_loss_cls_dn_1[0.1060] det_loss_box_dn_1[0.2983] det_loss_cls_dn_2[0.1059] det_loss_box_dn_2[0.2841] det_loss_cls_dn_3[0.1045] det_loss_box_dn_3[0.2830] det_loss_cls_dn_4[0.1031] det_loss_box_dn_4[0.2825] det_loss_cls_dn_5[0.1019] det_loss_box_dn_5[0.2812] loss_depth[0.0377] total_loss[8.6432]
Rank[0/8] 06/19/2025 18:39:06 INFO stats.py:314 | Epoch[33] Step[54] GlobalStep[12924] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 3:45:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:39:26 INFO loss_tracker.py:84 | Epoch[33/239] Step[74] GlobalStep[12944/NA]: det_loss_cls_0[0.4463] det_loss_box_0[0.6551] det_loss_cls_1[0.4334] det_loss_box_1[0.5298] det_loss_cls_2[0.4248] det_loss_box_2[0.4832] det_loss_cls_3[0.4068] det_loss_box_3[0.4644] det_loss_cls_4[0.3915] det_loss_box_4[0.4536] det_loss_cls_5[0.3857] det_loss_box_5[0.4501] det_loss_cls_dn_0[0.1141] det_loss_box_dn_0[0.3858] det_loss_cls_dn_1[0.1108] det_loss_box_dn_1[0.3410] det_loss_cls_dn_2[0.1116] det_loss_box_dn_2[0.3232] det_loss_cls_dn_3[0.1101] det_loss_box_dn_3[0.3209] det_loss_cls_dn_4[0.1095] det_loss_box_dn_4[0.3200] det_loss_cls_dn_5[0.1094] det_loss_box_dn_5[0.3182] loss_depth[0.0430] total_loss[8.2422]
Rank[0/8] 06/19/2025 18:39:32 INFO stats.py:314 | Epoch[33] Step[79] GlobalStep[12949] Training Speed: 6.56 samples/sec across all devices.	Average Step Time: 1.22 sec.	Estimated Remaining Time: 1 day, 3:44:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:39:52 INFO loss_tracker.py:84 | Epoch[33/239] Step[99] GlobalStep[12969/NA]: det_loss_cls_0[0.4983] det_loss_box_0[0.6776] det_loss_cls_1[0.4783] det_loss_box_1[0.5502] det_loss_cls_2[0.4758] det_loss_box_2[0.5085] det_loss_cls_3[0.4631] det_loss_box_3[0.4968] det_loss_cls_4[0.4387] det_loss_box_4[0.4807] det_loss_cls_5[0.4217] det_loss_box_5[0.4827] det_loss_cls_dn_0[0.1114] det_loss_box_dn_0[0.3748] det_loss_cls_dn_1[0.1078] det_loss_box_dn_1[0.3312] det_loss_cls_dn_2[0.1078] det_loss_box_dn_2[0.3168] det_loss_cls_dn_3[0.1066] det_loss_box_dn_3[0.3144] det_loss_cls_dn_4[0.1053] det_loss_box_dn_4[0.3143] det_loss_cls_dn_5[0.1045] det_loss_box_dn_5[0.3125] loss_depth[0.0400] total_loss[8.6198]
Rank[0/8] 06/19/2025 18:39:58 INFO stats.py:314 | Epoch[33] Step[104] GlobalStep[12974] Training Speed: 6.01 samples/sec across all devices.	Average Step Time: 1.33 sec.	Estimated Remaining Time: 1 day, 3:43:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:40:19 INFO loss_tracker.py:84 | Epoch[33/239] Step[124] GlobalStep[12994/NA]: det_loss_cls_0[0.7201] det_loss_box_0[0.9793] det_loss_cls_1[0.7066] det_loss_box_1[0.8139] det_loss_cls_2[0.6993] det_loss_box_2[0.7489] det_loss_cls_3[0.6891] det_loss_box_3[0.7148] det_loss_cls_4[0.6566] det_loss_box_4[0.7091] det_loss_cls_5[0.6468] det_loss_box_5[0.7021] det_loss_cls_dn_0[0.1257] det_loss_box_dn_0[0.3319] det_loss_cls_dn_1[0.1225] det_loss_box_dn_1[0.2873] det_loss_cls_dn_2[0.1228] det_loss_box_dn_2[0.2715] det_loss_cls_dn_3[0.1218] det_loss_box_dn_3[0.2707] det_loss_cls_dn_4[0.1208] det_loss_box_dn_4[0.2706] det_loss_cls_dn_5[0.1197] det_loss_box_dn_5[0.2672] loss_depth[0.0385] total_loss[11.2575]
Rank[0/8] 06/19/2025 18:40:24 INFO stats.py:314 | Epoch[33] Step[129] GlobalStep[12999] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 3:42:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:40:44 INFO loss_tracker.py:84 | Epoch[33/239] Step[149] GlobalStep[13019/NA]: det_loss_cls_0[0.5359] det_loss_box_0[0.6791] det_loss_cls_1[0.5121] det_loss_box_1[0.5709] det_loss_cls_2[0.5104] det_loss_box_2[0.5282] det_loss_cls_3[0.4857] det_loss_box_3[0.5198] det_loss_cls_4[0.4582] det_loss_box_4[0.5116] det_loss_cls_5[0.4455] det_loss_box_5[0.5056] det_loss_cls_dn_0[0.1135] det_loss_box_dn_0[0.3149] det_loss_cls_dn_1[0.1108] det_loss_box_dn_1[0.2789] det_loss_cls_dn_2[0.1104] det_loss_box_dn_2[0.2670] det_loss_cls_dn_3[0.1101] det_loss_box_dn_3[0.2661] det_loss_cls_dn_4[0.1086] det_loss_box_dn_4[0.2663] det_loss_cls_dn_5[0.1078] det_loss_box_dn_5[0.2660] loss_depth[0.0416] total_loss[8.6250]
Rank[0/8] 06/19/2025 18:40:50 INFO stats.py:314 | Epoch[33] Step[154] GlobalStep[13024] Training Speed: 7.13 samples/sec across all devices.	Average Step Time: 1.12 sec.	Estimated Remaining Time: 1 day, 3:41:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:41:11 INFO loss_tracker.py:84 | Epoch[33/239] Step[174] GlobalStep[13044/NA]: det_loss_cls_0[0.5662] det_loss_box_0[0.7837] det_loss_cls_1[0.5504] det_loss_box_1[0.6259] det_loss_cls_2[0.5468] det_loss_box_2[0.5707] det_loss_cls_3[0.5360] det_loss_box_3[0.5511] det_loss_cls_4[0.5049] det_loss_box_4[0.5501] det_loss_cls_5[0.4882] det_loss_box_5[0.5532] det_loss_cls_dn_0[0.1057] det_loss_box_dn_0[0.3157] det_loss_cls_dn_1[0.1015] det_loss_box_dn_1[0.2761] det_loss_cls_dn_2[0.1012] det_loss_box_dn_2[0.2575] det_loss_cls_dn_3[0.1006] det_loss_box_dn_3[0.2551] det_loss_cls_dn_4[0.1001] det_loss_box_dn_4[0.2537] det_loss_cls_dn_5[0.0989] det_loss_box_dn_5[0.2534] loss_depth[0.0390] total_loss[9.0858]
Rank[0/8] 06/19/2025 18:41:16 INFO stats.py:314 | Epoch[33] Step[179] GlobalStep[13049] Training Speed: 7.89 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 3:40:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:41:37 INFO loss_tracker.py:84 | Epoch[33/239] Step[199] GlobalStep[13069/NA]: det_loss_cls_0[0.5469] det_loss_box_0[0.7072] det_loss_cls_1[0.5298] det_loss_box_1[0.5978] det_loss_cls_2[0.5339] det_loss_box_2[0.5361] det_loss_cls_3[0.5299] det_loss_box_3[0.5053] det_loss_cls_4[0.4950] det_loss_box_4[0.5066] det_loss_cls_5[0.4833] det_loss_box_5[0.5065] det_loss_cls_dn_0[0.1135] det_loss_box_dn_0[0.3135] det_loss_cls_dn_1[0.1097] det_loss_box_dn_1[0.2760] det_loss_cls_dn_2[0.1081] det_loss_box_dn_2[0.2596] det_loss_cls_dn_3[0.1078] det_loss_box_dn_3[0.2580] det_loss_cls_dn_4[0.1060] det_loss_box_dn_4[0.2573] det_loss_cls_dn_5[0.1043] det_loss_box_dn_5[0.2565] loss_depth[0.0365] total_loss[8.7852]
Rank[0/8] 06/19/2025 18:41:42 INFO stats.py:314 | Epoch[33] Step[204] GlobalStep[13074] Training Speed: 8.68 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 1 day, 3:39:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:42:03 INFO loss_tracker.py:84 | Epoch[33/239] Step[224] GlobalStep[13094/NA]: det_loss_cls_0[0.5243] det_loss_box_0[0.7807] det_loss_cls_1[0.5036] det_loss_box_1[0.6542] det_loss_cls_2[0.4994] det_loss_box_2[0.6040] det_loss_cls_3[0.4831] det_loss_box_3[0.5788] det_loss_cls_4[0.4632] det_loss_box_4[0.5705] det_loss_cls_5[0.4534] det_loss_box_5[0.5682] det_loss_cls_dn_0[0.1083] det_loss_box_dn_0[0.3590] det_loss_cls_dn_1[0.1061] det_loss_box_dn_1[0.3237] det_loss_cls_dn_2[0.1051] det_loss_box_dn_2[0.3132] det_loss_cls_dn_3[0.1039] det_loss_box_dn_3[0.3119] det_loss_cls_dn_4[0.1036] det_loss_box_dn_4[0.3122] det_loss_cls_dn_5[0.1028] det_loss_box_dn_5[0.3113] loss_depth[0.0428] total_loss[9.2874]
Rank[0/8] 06/19/2025 18:42:08 INFO stats.py:314 | Epoch[33] Step[229] GlobalStep[13099] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 3:38:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:42:29 INFO loss_tracker.py:84 | Epoch[33/239] Step[249] GlobalStep[13119/NA]: det_loss_cls_0[0.6008] det_loss_box_0[0.8415] det_loss_cls_1[0.5727] det_loss_box_1[0.6890] det_loss_cls_2[0.5678] det_loss_box_2[0.6327] det_loss_cls_3[0.5553] det_loss_box_3[0.5991] det_loss_cls_4[0.5294] det_loss_box_4[0.5862] det_loss_cls_5[0.5092] det_loss_box_5[0.5862] det_loss_cls_dn_0[0.1156] det_loss_box_dn_0[0.4061] det_loss_cls_dn_1[0.1094] det_loss_box_dn_1[0.3687] det_loss_cls_dn_2[0.1086] det_loss_box_dn_2[0.3510] det_loss_cls_dn_3[0.1078] det_loss_box_dn_3[0.3492] det_loss_cls_dn_4[0.1065] det_loss_box_dn_4[0.3490] det_loss_cls_dn_5[0.1062] det_loss_box_dn_5[0.3452] loss_depth[0.0364] total_loss[10.1297]
Rank[0/8] 06/19/2025 18:42:34 INFO stats.py:314 | Epoch[33] Step[254] GlobalStep[13124] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 3:37:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:42:55 INFO loss_tracker.py:84 | Epoch[33/239] Step[274] GlobalStep[13144/NA]: det_loss_cls_0[0.7036] det_loss_box_0[0.9048] det_loss_cls_1[0.6819] det_loss_box_1[0.7498] det_loss_cls_2[0.6594] det_loss_box_2[0.6878] det_loss_cls_3[0.6556] det_loss_box_3[0.6558] det_loss_cls_4[0.6169] det_loss_box_4[0.6464] det_loss_cls_5[0.5963] det_loss_box_5[0.6430] det_loss_cls_dn_0[0.1077] det_loss_box_dn_0[0.3040] det_loss_cls_dn_1[0.1045] det_loss_box_dn_1[0.2694] det_loss_cls_dn_2[0.1037] det_loss_box_dn_2[0.2551] det_loss_cls_dn_3[0.1030] det_loss_box_dn_3[0.2534] det_loss_cls_dn_4[0.1020] det_loss_box_dn_4[0.2531] det_loss_cls_dn_5[0.1007] det_loss_box_dn_5[0.2529] loss_depth[0.0409] total_loss[10.4518]
Rank[0/8] 06/19/2025 18:43:01 INFO stats.py:314 | Epoch[33] Step[279] GlobalStep[13149] Training Speed: 7.29 samples/sec across all devices.	Average Step Time: 1.10 sec.	Estimated Remaining Time: 1 day, 3:36:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:43:22 INFO loss_tracker.py:84 | Epoch[33/239] Step[299] GlobalStep[13169/NA]: det_loss_cls_0[0.5975] det_loss_box_0[0.7994] det_loss_cls_1[0.5689] det_loss_box_1[0.6734] det_loss_cls_2[0.5648] det_loss_box_2[0.6107] det_loss_cls_3[0.5476] det_loss_box_3[0.5931] det_loss_cls_4[0.5196] det_loss_box_4[0.5793] det_loss_cls_5[0.5062] det_loss_box_5[0.5752] det_loss_cls_dn_0[0.1076] det_loss_box_dn_0[0.3341] det_loss_cls_dn_1[0.1036] det_loss_box_dn_1[0.2925] det_loss_cls_dn_2[0.1039] det_loss_box_dn_2[0.2737] det_loss_cls_dn_3[0.1023] det_loss_box_dn_3[0.2717] det_loss_cls_dn_4[0.1012] det_loss_box_dn_4[0.2707] det_loss_cls_dn_5[0.0999] det_loss_box_dn_5[0.2683] loss_depth[0.0456] total_loss[9.5109]
Rank[0/8] 06/19/2025 18:43:27 INFO stats.py:314 | Epoch[33] Step[304] GlobalStep[13174] Training Speed: 7.99 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 3:35:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:43:47 INFO loss_tracker.py:84 | Epoch[33/239] Step[324] GlobalStep[13194/NA]: det_loss_cls_0[0.4036] det_loss_box_0[0.4803] det_loss_cls_1[0.3940] det_loss_box_1[0.3943] det_loss_cls_2[0.3813] det_loss_box_2[0.3593] det_loss_cls_3[0.3692] det_loss_box_3[0.3436] det_loss_cls_4[0.3448] det_loss_box_4[0.3444] det_loss_cls_5[0.3393] det_loss_box_5[0.3381] det_loss_cls_dn_0[0.0958] det_loss_box_dn_0[0.2879] det_loss_cls_dn_1[0.0926] det_loss_box_dn_1[0.2474] det_loss_cls_dn_2[0.0917] det_loss_box_dn_2[0.2329] det_loss_cls_dn_3[0.0910] det_loss_box_dn_3[0.2300] det_loss_cls_dn_4[0.0902] det_loss_box_dn_4[0.2283] det_loss_cls_dn_5[0.0889] det_loss_box_dn_5[0.2266] loss_depth[0.0385] total_loss[6.5340]
Rank[0/8] 06/19/2025 18:43:53 INFO stats.py:314 | Epoch[33] Step[329] GlobalStep[13199] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 3:34:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:44:14 INFO loss_tracker.py:84 | Epoch[33/239] Step[349] GlobalStep[13219/NA]: det_loss_cls_0[0.6273] det_loss_box_0[0.9208] det_loss_cls_1[0.6005] det_loss_box_1[0.7605] det_loss_cls_2[0.5959] det_loss_box_2[0.6804] det_loss_cls_3[0.5711] det_loss_box_3[0.6658] det_loss_cls_4[0.5350] det_loss_box_4[0.6501] det_loss_cls_5[0.5263] det_loss_box_5[0.6407] det_loss_cls_dn_0[0.1057] det_loss_box_dn_0[0.3317] det_loss_cls_dn_1[0.1026] det_loss_box_dn_1[0.2955] det_loss_cls_dn_2[0.1023] det_loss_box_dn_2[0.2838] det_loss_cls_dn_3[0.1019] det_loss_box_dn_3[0.2831] det_loss_cls_dn_4[0.1009] det_loss_box_dn_4[0.2831] det_loss_cls_dn_5[0.1002] det_loss_box_dn_5[0.2817] loss_depth[0.0408] total_loss[10.1877]
Rank[0/8] 06/19/2025 18:44:19 INFO stats.py:314 | Epoch[33] Step[354] GlobalStep[13224] Training Speed: 8.65 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 1 day, 3:33:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:44:40 INFO loss_tracker.py:84 | Epoch[33/239] Step[374] GlobalStep[13244/NA]: det_loss_cls_0[0.5691] det_loss_box_0[0.8023] det_loss_cls_1[0.5427] det_loss_box_1[0.6659] det_loss_cls_2[0.5400] det_loss_box_2[0.6109] det_loss_cls_3[0.5213] det_loss_box_3[0.5900] det_loss_cls_4[0.4957] det_loss_box_4[0.5860] det_loss_cls_5[0.4810] det_loss_box_5[0.5852] det_loss_cls_dn_0[0.1246] det_loss_box_dn_0[0.3652] det_loss_cls_dn_1[0.1219] det_loss_box_dn_1[0.3311] det_loss_cls_dn_2[0.1210] det_loss_box_dn_2[0.3177] det_loss_cls_dn_3[0.1204] det_loss_box_dn_3[0.3164] det_loss_cls_dn_4[0.1192] det_loss_box_dn_4[0.3160] det_loss_cls_dn_5[0.1191] det_loss_box_dn_5[0.3139] loss_depth[0.0444] total_loss[9.7211]
Rank[0/8] 06/19/2025 18:44:45 INFO stats.py:314 | Epoch[33] Step[379] GlobalStep[13249] Training Speed: 6.81 samples/sec across all devices.	Average Step Time: 1.18 sec.	Estimated Remaining Time: 1 day, 3:32:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:44:55 INFO stats.py:394 | Epoch[33] completed. Training Speed: 7.60 samples/sec across all devices.	Epoch Time: 410.60 sec.	Average Epoch Time: 410.60 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 3:32:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:45:13 INFO stats.py:314 | Epoch[34] Step[14] GlobalStep[13274] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 3:31:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:45:24 INFO loss_tracker.py:84 | Epoch[34/239] Step[24] GlobalStep[13284/NA]: det_loss_cls_0[0.5871] det_loss_box_0[0.8373] det_loss_cls_1[0.5624] det_loss_box_1[0.6893] det_loss_cls_2[0.5622] det_loss_box_2[0.6223] det_loss_cls_3[0.5484] det_loss_box_3[0.5978] det_loss_cls_4[0.5078] det_loss_box_4[0.5896] det_loss_cls_5[0.4964] det_loss_box_5[0.5895] det_loss_cls_dn_0[0.1110] det_loss_box_dn_0[0.3378] det_loss_cls_dn_1[0.1071] det_loss_box_dn_1[0.2973] det_loss_cls_dn_2[0.1056] det_loss_box_dn_2[0.2798] det_loss_cls_dn_3[0.1052] det_loss_box_dn_3[0.2783] det_loss_cls_dn_4[0.1039] det_loss_box_dn_4[0.2777] det_loss_cls_dn_5[0.1033] det_loss_box_dn_5[0.2771] loss_depth[0.0438] total_loss[9.6181]
Rank[0/8] 06/19/2025 18:45:40 INFO stats.py:314 | Epoch[34] Step[39] GlobalStep[13299] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 3:30:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:45:50 INFO loss_tracker.py:84 | Epoch[34/239] Step[49] GlobalStep[13309/NA]: det_loss_cls_0[0.6307] det_loss_box_0[0.7557] det_loss_cls_1[0.6128] det_loss_box_1[0.6435] det_loss_cls_2[0.6085] det_loss_box_2[0.5831] det_loss_cls_3[0.5905] det_loss_box_3[0.5571] det_loss_cls_4[0.5610] det_loss_box_4[0.5580] det_loss_cls_5[0.5397] det_loss_box_5[0.5616] det_loss_cls_dn_0[0.1161] det_loss_box_dn_0[0.2963] det_loss_cls_dn_1[0.1131] det_loss_box_dn_1[0.2559] det_loss_cls_dn_2[0.1117] det_loss_box_dn_2[0.2390] det_loss_cls_dn_3[0.1108] det_loss_box_dn_3[0.2363] det_loss_cls_dn_4[0.1100] det_loss_box_dn_4[0.2360] det_loss_cls_dn_5[0.1096] det_loss_box_dn_5[0.2345] loss_depth[0.0386] total_loss[9.4100]
Rank[0/8] 06/19/2025 18:46:05 INFO stats.py:314 | Epoch[34] Step[64] GlobalStep[13324] Training Speed: 7.99 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 3:29:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:46:16 INFO loss_tracker.py:84 | Epoch[34/239] Step[74] GlobalStep[13334/NA]: det_loss_cls_0[0.6206] det_loss_box_0[0.8583] det_loss_cls_1[0.6012] det_loss_box_1[0.7076] det_loss_cls_2[0.5838] det_loss_box_2[0.6572] det_loss_cls_3[0.5660] det_loss_box_3[0.6307] det_loss_cls_4[0.5339] det_loss_box_4[0.6277] det_loss_cls_5[0.5285] det_loss_box_5[0.6208] det_loss_cls_dn_0[0.1126] det_loss_box_dn_0[0.3232] det_loss_cls_dn_1[0.1081] det_loss_box_dn_1[0.2838] det_loss_cls_dn_2[0.1084] det_loss_box_dn_2[0.2681] det_loss_cls_dn_3[0.1073] det_loss_box_dn_3[0.2663] det_loss_cls_dn_4[0.1059] det_loss_box_dn_4[0.2654] det_loss_cls_dn_5[0.1054] det_loss_box_dn_5[0.2630] loss_depth[0.0408] total_loss[9.8945]
Rank[0/8] 06/19/2025 18:46:31 INFO stats.py:314 | Epoch[34] Step[89] GlobalStep[13349] Training Speed: 8.06 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 3:28:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:46:42 INFO loss_tracker.py:84 | Epoch[34/239] Step[99] GlobalStep[13359/NA]: det_loss_cls_0[0.6011] det_loss_box_0[0.7959] det_loss_cls_1[0.5903] det_loss_box_1[0.6761] det_loss_cls_2[0.5691] det_loss_box_2[0.6352] det_loss_cls_3[0.5493] det_loss_box_3[0.6176] det_loss_cls_4[0.5223] det_loss_box_4[0.6044] det_loss_cls_5[0.5101] det_loss_box_5[0.6057] det_loss_cls_dn_0[0.1090] det_loss_box_dn_0[0.3369] det_loss_cls_dn_1[0.1056] det_loss_box_dn_1[0.3048] det_loss_cls_dn_2[0.1040] det_loss_box_dn_2[0.2915] det_loss_cls_dn_3[0.1035] det_loss_box_dn_3[0.2905] det_loss_cls_dn_4[0.1018] det_loss_box_dn_4[0.2899] det_loss_cls_dn_5[0.1012] det_loss_box_dn_5[0.2871] loss_depth[0.0406] total_loss[9.7434]
Rank[0/8] 06/19/2025 18:46:57 INFO stats.py:314 | Epoch[34] Step[114] GlobalStep[13374] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 3:27:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:47:08 INFO loss_tracker.py:84 | Epoch[34/239] Step[124] GlobalStep[13384/NA]: det_loss_cls_0[0.5779] det_loss_box_0[0.8033] det_loss_cls_1[0.5575] det_loss_box_1[0.6600] det_loss_cls_2[0.5474] det_loss_box_2[0.5971] det_loss_cls_3[0.5442] det_loss_box_3[0.5653] det_loss_cls_4[0.5125] det_loss_box_4[0.5549] det_loss_cls_5[0.4964] det_loss_box_5[0.5579] det_loss_cls_dn_0[0.0969] det_loss_box_dn_0[0.3426] det_loss_cls_dn_1[0.0924] det_loss_box_dn_1[0.2904] det_loss_cls_dn_2[0.0924] det_loss_box_dn_2[0.2737] det_loss_cls_dn_3[0.0915] det_loss_box_dn_3[0.2681] det_loss_cls_dn_4[0.0908] det_loss_box_dn_4[0.2667] det_loss_cls_dn_5[0.0897] det_loss_box_dn_5[0.2653] loss_depth[0.0419] total_loss[9.2767]
Rank[0/8] 06/19/2025 18:47:24 INFO stats.py:314 | Epoch[34] Step[139] GlobalStep[13399] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 3:26:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:47:35 INFO loss_tracker.py:84 | Epoch[34/239] Step[149] GlobalStep[13409/NA]: det_loss_cls_0[0.4941] det_loss_box_0[0.6598] det_loss_cls_1[0.4814] det_loss_box_1[0.5553] det_loss_cls_2[0.4551] det_loss_box_2[0.5046] det_loss_cls_3[0.4432] det_loss_box_3[0.4806] det_loss_cls_4[0.4146] det_loss_box_4[0.4735] det_loss_cls_5[0.4030] det_loss_box_5[0.4771] det_loss_cls_dn_0[0.1092] det_loss_box_dn_0[0.3613] det_loss_cls_dn_1[0.1063] det_loss_box_dn_1[0.3177] det_loss_cls_dn_2[0.1050] det_loss_box_dn_2[0.3034] det_loss_cls_dn_3[0.1045] det_loss_box_dn_3[0.3016] det_loss_cls_dn_4[0.1033] det_loss_box_dn_4[0.3015] det_loss_cls_dn_5[0.1024] det_loss_box_dn_5[0.3008] loss_depth[0.0432] total_loss[8.4026]
Rank[0/8] 06/19/2025 18:47:51 INFO stats.py:314 | Epoch[34] Step[164] GlobalStep[13424] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 3:25:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:48:02 INFO loss_tracker.py:84 | Epoch[34/239] Step[174] GlobalStep[13434/NA]: det_loss_cls_0[0.6013] det_loss_box_0[0.8366] det_loss_cls_1[0.5797] det_loss_box_1[0.7363] det_loss_cls_2[0.5619] det_loss_box_2[0.6831] det_loss_cls_3[0.5527] det_loss_box_3[0.6413] det_loss_cls_4[0.5335] det_loss_box_4[0.6178] det_loss_cls_5[0.5180] det_loss_box_5[0.6168] det_loss_cls_dn_0[0.1055] det_loss_box_dn_0[0.3482] det_loss_cls_dn_1[0.1012] det_loss_box_dn_1[0.3096] det_loss_cls_dn_2[0.1002] det_loss_box_dn_2[0.2922] det_loss_cls_dn_3[0.0990] det_loss_box_dn_3[0.2903] det_loss_cls_dn_4[0.0981] det_loss_box_dn_4[0.2890] det_loss_cls_dn_5[0.0971] det_loss_box_dn_5[0.2880] loss_depth[0.0380] total_loss[9.9353]
Rank[0/8] 06/19/2025 18:48:17 INFO stats.py:314 | Epoch[34] Step[189] GlobalStep[13449] Training Speed: 8.22 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 3:24:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:48:28 INFO loss_tracker.py:84 | Epoch[34/239] Step[199] GlobalStep[13459/NA]: det_loss_cls_0[0.5657] det_loss_box_0[0.7095] det_loss_cls_1[0.5520] det_loss_box_1[0.5997] det_loss_cls_2[0.5499] det_loss_box_2[0.5372] det_loss_cls_3[0.5308] det_loss_box_3[0.5388] det_loss_cls_4[0.4926] det_loss_box_4[0.5307] det_loss_cls_5[0.4812] det_loss_box_5[0.5310] det_loss_cls_dn_0[0.1197] det_loss_box_dn_0[0.2893] det_loss_cls_dn_1[0.1164] det_loss_box_dn_1[0.2565] det_loss_cls_dn_2[0.1152] det_loss_box_dn_2[0.2407] det_loss_cls_dn_3[0.1151] det_loss_box_dn_3[0.2381] det_loss_cls_dn_4[0.1146] det_loss_box_dn_4[0.2369] det_loss_cls_dn_5[0.1131] det_loss_box_dn_5[0.2354] loss_depth[0.0367] total_loss[8.8467]
Rank[0/8] 06/19/2025 18:48:43 INFO stats.py:314 | Epoch[34] Step[214] GlobalStep[13474] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 3:23:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:48:54 INFO loss_tracker.py:84 | Epoch[34/239] Step[224] GlobalStep[13484/NA]: det_loss_cls_0[0.6560] det_loss_box_0[0.9535] det_loss_cls_1[0.6482] det_loss_box_1[0.8180] det_loss_cls_2[0.6466] det_loss_box_2[0.7581] det_loss_cls_3[0.6306] det_loss_box_3[0.7527] det_loss_cls_4[0.6075] det_loss_box_4[0.7283] det_loss_cls_5[0.6002] det_loss_box_5[0.7263] det_loss_cls_dn_0[0.1314] det_loss_box_dn_0[0.3535] det_loss_cls_dn_1[0.1276] det_loss_box_dn_1[0.3110] det_loss_cls_dn_2[0.1266] det_loss_box_dn_2[0.2948] det_loss_cls_dn_3[0.1257] det_loss_box_dn_3[0.2927] det_loss_cls_dn_4[0.1254] det_loss_box_dn_4[0.2913] det_loss_cls_dn_5[0.1245] det_loss_box_dn_5[0.2900] loss_depth[0.0390] total_loss[11.1593]
Rank[0/8] 06/19/2025 18:49:11 INFO stats.py:314 | Epoch[34] Step[239] GlobalStep[13499] Training Speed: 7.80 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 1 day, 3:23:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:49:21 INFO loss_tracker.py:84 | Epoch[34/239] Step[249] GlobalStep[13509/NA]: det_loss_cls_0[0.5199] det_loss_box_0[0.6703] det_loss_cls_1[0.4819] det_loss_box_1[0.5752] det_loss_cls_2[0.4716] det_loss_box_2[0.5256] det_loss_cls_3[0.4625] det_loss_box_3[0.5008] det_loss_cls_4[0.4360] det_loss_box_4[0.4945] det_loss_cls_5[0.4252] det_loss_box_5[0.4879] det_loss_cls_dn_0[0.1135] det_loss_box_dn_0[0.3972] det_loss_cls_dn_1[0.1096] det_loss_box_dn_1[0.3542] det_loss_cls_dn_2[0.1082] det_loss_box_dn_2[0.3357] det_loss_cls_dn_3[0.1072] det_loss_box_dn_3[0.3321] det_loss_cls_dn_4[0.1060] det_loss_box_dn_4[0.3318] det_loss_cls_dn_5[0.1050] det_loss_box_dn_5[0.3311] loss_depth[0.0424] total_loss[8.8255]
Rank[0/8] 06/19/2025 18:49:36 INFO stats.py:314 | Epoch[34] Step[264] GlobalStep[13524] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 3:22:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:49:47 INFO loss_tracker.py:84 | Epoch[34/239] Step[274] GlobalStep[13534/NA]: det_loss_cls_0[0.5032] det_loss_box_0[0.6685] det_loss_cls_1[0.5000] det_loss_box_1[0.5316] det_loss_cls_2[0.4851] det_loss_box_2[0.4714] det_loss_cls_3[0.4775] det_loss_box_3[0.4542] det_loss_cls_4[0.4544] det_loss_box_4[0.4478] det_loss_cls_5[0.4399] det_loss_box_5[0.4488] det_loss_cls_dn_0[0.1085] det_loss_box_dn_0[0.3072] det_loss_cls_dn_1[0.1054] det_loss_box_dn_1[0.2678] det_loss_cls_dn_2[0.1053] det_loss_box_dn_2[0.2511] det_loss_cls_dn_3[0.1051] det_loss_box_dn_3[0.2486] det_loss_cls_dn_4[0.1047] det_loss_box_dn_4[0.2475] det_loss_cls_dn_5[0.1036] det_loss_box_dn_5[0.2469] loss_depth[0.0431] total_loss[8.1271]
Rank[0/8] 06/19/2025 18:50:03 INFO stats.py:314 | Epoch[34] Step[289] GlobalStep[13549] Training Speed: 8.22 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 3:21:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:50:13 INFO loss_tracker.py:84 | Epoch[34/239] Step[299] GlobalStep[13559/NA]: det_loss_cls_0[0.6143] det_loss_box_0[0.9078] det_loss_cls_1[0.5898] det_loss_box_1[0.7400] det_loss_cls_2[0.5795] det_loss_box_2[0.6821] det_loss_cls_3[0.5646] det_loss_box_3[0.6570] det_loss_cls_4[0.5291] det_loss_box_4[0.6448] det_loss_cls_5[0.5192] det_loss_box_5[0.6403] det_loss_cls_dn_0[0.1220] det_loss_box_dn_0[0.4054] det_loss_cls_dn_1[0.1186] det_loss_box_dn_1[0.3655] det_loss_cls_dn_2[0.1182] det_loss_box_dn_2[0.3484] det_loss_cls_dn_3[0.1170] det_loss_box_dn_3[0.3459] det_loss_cls_dn_4[0.1162] det_loss_box_dn_4[0.3457] det_loss_cls_dn_5[0.1151] det_loss_box_dn_5[0.3440] loss_depth[0.0475] total_loss[10.5779]
Rank[0/8] 06/19/2025 18:50:30 INFO stats.py:314 | Epoch[34] Step[314] GlobalStep[13574] Training Speed: 7.65 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 3:20:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:50:40 INFO loss_tracker.py:84 | Epoch[34/239] Step[324] GlobalStep[13584/NA]: det_loss_cls_0[0.5178] det_loss_box_0[0.7803] det_loss_cls_1[0.5207] det_loss_box_1[0.6470] det_loss_cls_2[0.4995] det_loss_box_2[0.5807] det_loss_cls_3[0.4840] det_loss_box_3[0.5716] det_loss_cls_4[0.4496] det_loss_box_4[0.5563] det_loss_cls_5[0.4325] det_loss_box_5[0.5572] det_loss_cls_dn_0[0.1124] det_loss_box_dn_0[0.3825] det_loss_cls_dn_1[0.1061] det_loss_box_dn_1[0.3420] det_loss_cls_dn_2[0.1060] det_loss_box_dn_2[0.3232] det_loss_cls_dn_3[0.1048] det_loss_box_dn_3[0.3215] det_loss_cls_dn_4[0.1031] det_loss_box_dn_4[0.3207] det_loss_cls_dn_5[0.1017] det_loss_box_dn_5[0.3184] loss_depth[0.0403] total_loss[9.2798]
Rank[0/8] 06/19/2025 18:50:56 INFO stats.py:314 | Epoch[34] Step[339] GlobalStep[13599] Training Speed: 7.78 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 1 day, 3:19:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:51:06 INFO loss_tracker.py:84 | Epoch[34/239] Step[349] GlobalStep[13609/NA]: det_loss_cls_0[0.5462] det_loss_box_0[0.7197] det_loss_cls_1[0.5461] det_loss_box_1[0.5889] det_loss_cls_2[0.5280] det_loss_box_2[0.5482] det_loss_cls_3[0.5027] det_loss_box_3[0.5282] det_loss_cls_4[0.4860] det_loss_box_4[0.5207] det_loss_cls_5[0.4802] det_loss_box_5[0.5218] det_loss_cls_dn_0[0.1115] det_loss_box_dn_0[0.3423] det_loss_cls_dn_1[0.1089] det_loss_box_dn_1[0.3029] det_loss_cls_dn_2[0.1091] det_loss_box_dn_2[0.2887] det_loss_cls_dn_3[0.1078] det_loss_box_dn_3[0.2868] det_loss_cls_dn_4[0.1072] det_loss_box_dn_4[0.2856] det_loss_cls_dn_5[0.1076] det_loss_box_dn_5[0.2839] loss_depth[0.0425] total_loss[9.0014]
Rank[0/8] 06/19/2025 18:51:22 INFO stats.py:314 | Epoch[34] Step[364] GlobalStep[13624] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 3:18:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:51:32 INFO loss_tracker.py:84 | Epoch[34/239] Step[374] GlobalStep[13634/NA]: det_loss_cls_0[0.5099] det_loss_box_0[0.6690] det_loss_cls_1[0.4995] det_loss_box_1[0.5612] det_loss_cls_2[0.4846] det_loss_box_2[0.5171] det_loss_cls_3[0.4720] det_loss_box_3[0.5061] det_loss_cls_4[0.4485] det_loss_box_4[0.4938] det_loss_cls_5[0.4374] det_loss_box_5[0.4938] det_loss_cls_dn_0[0.1175] det_loss_box_dn_0[0.3094] det_loss_cls_dn_1[0.1128] det_loss_box_dn_1[0.2757] det_loss_cls_dn_2[0.1122] det_loss_box_dn_2[0.2634] det_loss_cls_dn_3[0.1117] det_loss_box_dn_3[0.2607] det_loss_cls_dn_4[0.1103] det_loss_box_dn_4[0.2606] det_loss_cls_dn_5[0.1089] det_loss_box_dn_5[0.2594] loss_depth[0.0380] total_loss[8.4334]
Rank[0/8] 06/19/2025 18:51:47 INFO stats.py:314 | Epoch[34] Step[389] GlobalStep[13649] Training Speed: 8.58 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 3:17:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:51:47 INFO stats.py:394 | Epoch[34] completed. Training Speed: 7.56 samples/sec across all devices.	Epoch Time: 412.56 sec.	Average Epoch Time: 412.56 sec.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 1 day, 3:17:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:52:17 INFO stats.py:314 | Epoch[35] Step[24] GlobalStep[13674] Training Speed: 7.52 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 1 day, 3:16:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:52:17 INFO loss_tracker.py:84 | Epoch[35/239] Step[24] GlobalStep[13674/NA]: det_loss_cls_0[0.6025] det_loss_box_0[0.7633] det_loss_cls_1[0.5876] det_loss_box_1[0.6267] det_loss_cls_2[0.5713] det_loss_box_2[0.5684] det_loss_cls_3[0.5507] det_loss_box_3[0.5488] det_loss_cls_4[0.5161] det_loss_box_4[0.5396] det_loss_cls_5[0.5004] det_loss_box_5[0.5455] det_loss_cls_dn_0[0.1239] det_loss_box_dn_0[0.3318] det_loss_cls_dn_1[0.1191] det_loss_box_dn_1[0.2940] det_loss_cls_dn_2[0.1177] det_loss_box_dn_2[0.2794] det_loss_cls_dn_3[0.1170] det_loss_box_dn_3[0.2773] det_loss_cls_dn_4[0.1161] det_loss_box_dn_4[0.2765] det_loss_cls_dn_5[0.1154] det_loss_box_dn_5[0.2741] loss_depth[0.0386] total_loss[9.4017]
Rank[0/8] 06/19/2025 18:52:44 INFO stats.py:314 | Epoch[35] Step[49] GlobalStep[13699] Training Speed: 8.67 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 1 day, 3:15:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:52:44 INFO loss_tracker.py:84 | Epoch[35/239] Step[49] GlobalStep[13699/NA]: det_loss_cls_0[0.5769] det_loss_box_0[0.7862] det_loss_cls_1[0.5505] det_loss_box_1[0.6442] det_loss_cls_2[0.5477] det_loss_box_2[0.5712] det_loss_cls_3[0.5287] det_loss_box_3[0.5568] det_loss_cls_4[0.4999] det_loss_box_4[0.5471] det_loss_cls_5[0.4876] det_loss_box_5[0.5463] det_loss_cls_dn_0[0.1148] det_loss_box_dn_0[0.3565] det_loss_cls_dn_1[0.1111] det_loss_box_dn_1[0.3148] det_loss_cls_dn_2[0.1104] det_loss_box_dn_2[0.2973] det_loss_cls_dn_3[0.1103] det_loss_box_dn_3[0.2948] det_loss_cls_dn_4[0.1091] det_loss_box_dn_4[0.2934] det_loss_cls_dn_5[0.1089] det_loss_box_dn_5[0.2919] loss_depth[0.0406] total_loss[9.3970]
Rank[0/8] 06/19/2025 18:53:10 INFO stats.py:314 | Epoch[35] Step[74] GlobalStep[13724] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 3:14:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:53:10 INFO loss_tracker.py:84 | Epoch[35/239] Step[74] GlobalStep[13724/NA]: det_loss_cls_0[0.6564] det_loss_box_0[0.9483] det_loss_cls_1[0.6216] det_loss_box_1[0.8122] det_loss_cls_2[0.6293] det_loss_box_2[0.7391] det_loss_cls_3[0.6056] det_loss_box_3[0.7318] det_loss_cls_4[0.5745] det_loss_box_4[0.7170] det_loss_cls_5[0.5606] det_loss_box_5[0.7178] det_loss_cls_dn_0[0.1076] det_loss_box_dn_0[0.3728] det_loss_cls_dn_1[0.1047] det_loss_box_dn_1[0.3330] det_loss_cls_dn_2[0.1046] det_loss_box_dn_2[0.3178] det_loss_cls_dn_3[0.1030] det_loss_box_dn_3[0.3173] det_loss_cls_dn_4[0.1015] det_loss_box_dn_4[0.3167] det_loss_cls_dn_5[0.1010] det_loss_box_dn_5[0.3149] loss_depth[0.0409] total_loss[10.9499]
Rank[0/8] 06/19/2025 18:53:37 INFO stats.py:314 | Epoch[35] Step[99] GlobalStep[13749] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 3:13:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:53:37 INFO loss_tracker.py:84 | Epoch[35/239] Step[99] GlobalStep[13749/NA]: det_loss_cls_0[0.4256] det_loss_box_0[0.5957] det_loss_cls_1[0.4063] det_loss_box_1[0.5168] det_loss_cls_2[0.4008] det_loss_box_2[0.4704] det_loss_cls_3[0.3982] det_loss_box_3[0.4624] det_loss_cls_4[0.3741] det_loss_box_4[0.4582] det_loss_cls_5[0.3595] det_loss_box_5[0.4567] det_loss_cls_dn_0[0.0955] det_loss_box_dn_0[0.4136] det_loss_cls_dn_1[0.0934] det_loss_box_dn_1[0.3709] det_loss_cls_dn_2[0.0925] det_loss_box_dn_2[0.3575] det_loss_cls_dn_3[0.0914] det_loss_box_dn_3[0.3568] det_loss_cls_dn_4[0.0895] det_loss_box_dn_4[0.3566] det_loss_cls_dn_5[0.0888] det_loss_box_dn_5[0.3559] loss_depth[0.0388] total_loss[8.1259]
Rank[0/8] 06/19/2025 18:54:02 INFO stats.py:314 | Epoch[35] Step[124] GlobalStep[13774] Training Speed: 7.44 samples/sec across all devices.	Average Step Time: 1.08 sec.	Estimated Remaining Time: 1 day, 3:12:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:54:02 INFO loss_tracker.py:84 | Epoch[35/239] Step[124] GlobalStep[13774/NA]: det_loss_cls_0[0.4515] det_loss_box_0[0.6166] det_loss_cls_1[0.4290] det_loss_box_1[0.4909] det_loss_cls_2[0.4259] det_loss_box_2[0.4382] det_loss_cls_3[0.4126] det_loss_box_3[0.4221] det_loss_cls_4[0.3956] det_loss_box_4[0.4166] det_loss_cls_5[0.3872] det_loss_box_5[0.4191] det_loss_cls_dn_0[0.0999] det_loss_box_dn_0[0.3963] det_loss_cls_dn_1[0.0980] det_loss_box_dn_1[0.3464] det_loss_cls_dn_2[0.0977] det_loss_box_dn_2[0.3287] det_loss_cls_dn_3[0.0968] det_loss_box_dn_3[0.3254] det_loss_cls_dn_4[0.0948] det_loss_box_dn_4[0.3249] det_loss_cls_dn_5[0.0940] det_loss_box_dn_5[0.3205] loss_depth[0.0405] total_loss[7.9694]
Rank[0/8] 06/19/2025 18:54:29 INFO stats.py:314 | Epoch[35] Step[149] GlobalStep[13799] Training Speed: 8.07 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 3:12:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:54:29 INFO loss_tracker.py:84 | Epoch[35/239] Step[149] GlobalStep[13799/NA]: det_loss_cls_0[0.5404] det_loss_box_0[0.6936] det_loss_cls_1[0.5206] det_loss_box_1[0.5603] det_loss_cls_2[0.5178] det_loss_box_2[0.5106] det_loss_cls_3[0.4942] det_loss_box_3[0.5062] det_loss_cls_4[0.4686] det_loss_box_4[0.5018] det_loss_cls_5[0.4517] det_loss_box_5[0.5045] det_loss_cls_dn_0[0.1184] det_loss_box_dn_0[0.3513] det_loss_cls_dn_1[0.1141] det_loss_box_dn_1[0.3061] det_loss_cls_dn_2[0.1137] det_loss_box_dn_2[0.2905] det_loss_cls_dn_3[0.1130] det_loss_box_dn_3[0.2872] det_loss_cls_dn_4[0.1123] det_loss_box_dn_4[0.2866] det_loss_cls_dn_5[0.1115] det_loss_box_dn_5[0.2847] loss_depth[0.0404] total_loss[8.8001]
Rank[0/8] 06/19/2025 18:54:56 INFO stats.py:314 | Epoch[35] Step[174] GlobalStep[13824] Training Speed: 8.03 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 3:11:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:54:56 INFO loss_tracker.py:84 | Epoch[35/239] Step[174] GlobalStep[13824/NA]: det_loss_cls_0[0.5968] det_loss_box_0[0.7383] det_loss_cls_1[0.5787] det_loss_box_1[0.6082] det_loss_cls_2[0.5751] det_loss_box_2[0.5614] det_loss_cls_3[0.5629] det_loss_box_3[0.5429] det_loss_cls_4[0.5491] det_loss_box_4[0.5269] det_loss_cls_5[0.5311] det_loss_box_5[0.5301] det_loss_cls_dn_0[0.1263] det_loss_box_dn_0[0.3120] det_loss_cls_dn_1[0.1226] det_loss_box_dn_1[0.2792] det_loss_cls_dn_2[0.1226] det_loss_box_dn_2[0.2633] det_loss_cls_dn_3[0.1219] det_loss_box_dn_3[0.2607] det_loss_cls_dn_4[0.1213] det_loss_box_dn_4[0.2600] det_loss_cls_dn_5[0.1207] det_loss_box_dn_5[0.2566] loss_depth[0.0422] total_loss[9.3108]
Rank[0/8] 06/19/2025 18:55:22 INFO stats.py:314 | Epoch[35] Step[199] GlobalStep[13849] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 3:10:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:55:22 INFO loss_tracker.py:84 | Epoch[35/239] Step[199] GlobalStep[13849/NA]: det_loss_cls_0[0.5679] det_loss_box_0[0.8118] det_loss_cls_1[0.5470] det_loss_box_1[0.6359] det_loss_cls_2[0.5344] det_loss_box_2[0.5746] det_loss_cls_3[0.5205] det_loss_box_3[0.5506] det_loss_cls_4[0.4986] det_loss_box_4[0.5434] det_loss_cls_5[0.4866] det_loss_box_5[0.5442] det_loss_cls_dn_0[0.1103] det_loss_box_dn_0[0.3552] det_loss_cls_dn_1[0.1068] det_loss_box_dn_1[0.3143] det_loss_cls_dn_2[0.1054] det_loss_box_dn_2[0.2953] det_loss_cls_dn_3[0.1051] det_loss_box_dn_3[0.2930] det_loss_cls_dn_4[0.1046] det_loss_box_dn_4[0.2919] det_loss_cls_dn_5[0.1036] det_loss_box_dn_5[0.2914] loss_depth[0.0412] total_loss[9.3336]
Rank[0/8] 06/19/2025 18:55:48 INFO stats.py:314 | Epoch[35] Step[224] GlobalStep[13874] Training Speed: 6.04 samples/sec across all devices.	Average Step Time: 1.32 sec.	Estimated Remaining Time: 1 day, 3:09:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:55:48 INFO loss_tracker.py:84 | Epoch[35/239] Step[224] GlobalStep[13874/NA]: det_loss_cls_0[0.6132] det_loss_box_0[0.8542] det_loss_cls_1[0.5904] det_loss_box_1[0.7195] det_loss_cls_2[0.5896] det_loss_box_2[0.6361] det_loss_cls_3[0.5692] det_loss_box_3[0.6205] det_loss_cls_4[0.5340] det_loss_box_4[0.6121] det_loss_cls_5[0.5174] det_loss_box_5[0.6075] det_loss_cls_dn_0[0.1171] det_loss_box_dn_0[0.3425] det_loss_cls_dn_1[0.1137] det_loss_box_dn_1[0.3021] det_loss_cls_dn_2[0.1129] det_loss_box_dn_2[0.2852] det_loss_cls_dn_3[0.1120] det_loss_box_dn_3[0.2841] det_loss_cls_dn_4[0.1110] det_loss_box_dn_4[0.2837] det_loss_cls_dn_5[0.1103] det_loss_box_dn_5[0.2820] loss_depth[0.0437] total_loss[9.9640]
Rank[0/8] 06/19/2025 18:56:15 INFO stats.py:314 | Epoch[35] Step[249] GlobalStep[13899] Training Speed: 8.00 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 3:08:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:56:15 INFO loss_tracker.py:84 | Epoch[35/239] Step[249] GlobalStep[13899/NA]: det_loss_cls_0[0.4755] det_loss_box_0[0.6613] det_loss_cls_1[0.4769] det_loss_box_1[0.5346] det_loss_cls_2[0.4709] det_loss_box_2[0.4869] det_loss_cls_3[0.4471] det_loss_box_3[0.4764] det_loss_cls_4[0.4293] det_loss_box_4[0.4701] det_loss_cls_5[0.4183] det_loss_box_5[0.4750] det_loss_cls_dn_0[0.1032] det_loss_box_dn_0[0.3811] det_loss_cls_dn_1[0.1004] det_loss_box_dn_1[0.3292] det_loss_cls_dn_2[0.1001] det_loss_box_dn_2[0.3130] det_loss_cls_dn_3[0.0991] det_loss_box_dn_3[0.3112] det_loss_cls_dn_4[0.0973] det_loss_box_dn_4[0.3108] det_loss_cls_dn_5[0.0971] det_loss_box_dn_5[0.3100] loss_depth[0.0436] total_loss[8.4184]
Rank[0/8] 06/19/2025 18:56:41 INFO stats.py:314 | Epoch[35] Step[274] GlobalStep[13924] Training Speed: 8.07 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 3:07:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:56:41 INFO loss_tracker.py:84 | Epoch[35/239] Step[274] GlobalStep[13924/NA]: det_loss_cls_0[0.5705] det_loss_box_0[0.7632] det_loss_cls_1[0.5574] det_loss_box_1[0.6390] det_loss_cls_2[0.5549] det_loss_box_2[0.5822] det_loss_cls_3[0.5224] det_loss_box_3[0.5652] det_loss_cls_4[0.4948] det_loss_box_4[0.5567] det_loss_cls_5[0.4819] det_loss_box_5[0.5575] det_loss_cls_dn_0[0.1050] det_loss_box_dn_0[0.3187] det_loss_cls_dn_1[0.1016] det_loss_box_dn_1[0.2808] det_loss_cls_dn_2[0.1011] det_loss_box_dn_2[0.2643] det_loss_cls_dn_3[0.1000] det_loss_box_dn_3[0.2624] det_loss_cls_dn_4[0.0986] det_loss_box_dn_4[0.2616] det_loss_cls_dn_5[0.0980] det_loss_box_dn_5[0.2611] loss_depth[0.0437] total_loss[9.1428]
Rank[0/8] 06/19/2025 18:57:07 INFO stats.py:314 | Epoch[35] Step[299] GlobalStep[13949] Training Speed: 7.75 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 1 day, 3:06:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:57:07 INFO loss_tracker.py:84 | Epoch[35/239] Step[299] GlobalStep[13949/NA]: det_loss_cls_0[0.6349] det_loss_box_0[0.8649] det_loss_cls_1[0.6074] det_loss_box_1[0.7215] det_loss_cls_2[0.6009] det_loss_box_2[0.6557] det_loss_cls_3[0.5812] det_loss_box_3[0.6410] det_loss_cls_4[0.5457] det_loss_box_4[0.6274] det_loss_cls_5[0.5350] det_loss_box_5[0.6226] det_loss_cls_dn_0[0.1084] det_loss_box_dn_0[0.3072] det_loss_cls_dn_1[0.1044] det_loss_box_dn_1[0.2653] det_loss_cls_dn_2[0.1032] det_loss_box_dn_2[0.2520] det_loss_cls_dn_3[0.1024] det_loss_box_dn_3[0.2506] det_loss_cls_dn_4[0.1012] det_loss_box_dn_4[0.2501] det_loss_cls_dn_5[0.1005] det_loss_box_dn_5[0.2488] loss_depth[0.0428] total_loss[9.8752]
Rank[0/8] 06/19/2025 18:57:33 INFO stats.py:314 | Epoch[35] Step[324] GlobalStep[13974] Training Speed: 7.30 samples/sec across all devices.	Average Step Time: 1.10 sec.	Estimated Remaining Time: 1 day, 3:05:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:57:33 INFO loss_tracker.py:84 | Epoch[35/239] Step[324] GlobalStep[13974/NA]: det_loss_cls_0[0.4422] det_loss_box_0[0.6032] det_loss_cls_1[0.4156] det_loss_box_1[0.4946] det_loss_cls_2[0.4108] det_loss_box_2[0.4575] det_loss_cls_3[0.3890] det_loss_box_3[0.4440] det_loss_cls_4[0.3657] det_loss_box_4[0.4345] det_loss_cls_5[0.3529] det_loss_box_5[0.4373] det_loss_cls_dn_0[0.1036] det_loss_box_dn_0[0.4611] det_loss_cls_dn_1[0.0982] det_loss_box_dn_1[0.4122] det_loss_cls_dn_2[0.0984] det_loss_box_dn_2[0.3914] det_loss_cls_dn_3[0.0967] det_loss_box_dn_3[0.3909] det_loss_cls_dn_4[0.0951] det_loss_box_dn_4[0.3853] det_loss_cls_dn_5[0.0937] det_loss_box_dn_5[0.3813] loss_depth[0.0395] total_loss[8.2945]
Rank[0/8] 06/19/2025 18:58:00 INFO stats.py:314 | Epoch[35] Step[349] GlobalStep[13999] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 3:04:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:58:00 INFO loss_tracker.py:84 | Epoch[35/239] Step[349] GlobalStep[13999/NA]: det_loss_cls_0[0.5712] det_loss_box_0[0.7869] det_loss_cls_1[0.5585] det_loss_box_1[0.6590] det_loss_cls_2[0.5416] det_loss_box_2[0.6114] det_loss_cls_3[0.5252] det_loss_box_3[0.5921] det_loss_cls_4[0.5008] det_loss_box_4[0.5792] det_loss_cls_5[0.4839] det_loss_box_5[0.5790] det_loss_cls_dn_0[0.1131] det_loss_box_dn_0[0.3377] det_loss_cls_dn_1[0.1092] det_loss_box_dn_1[0.3021] det_loss_cls_dn_2[0.1092] det_loss_box_dn_2[0.2915] det_loss_cls_dn_3[0.1085] det_loss_box_dn_3[0.2907] det_loss_cls_dn_4[0.1072] det_loss_box_dn_4[0.2906] det_loss_cls_dn_5[0.1068] det_loss_box_dn_5[0.2893] loss_depth[0.0410] total_loss[9.4855]
Rank[0/8] 06/19/2025 18:58:26 INFO stats.py:314 | Epoch[35] Step[374] GlobalStep[14024] Training Speed: 9.69 samples/sec across all devices.	Average Step Time: 0.83 sec.	Estimated Remaining Time: 1 day, 3:03:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:58:26 INFO loss_tracker.py:84 | Epoch[35/239] Step[374] GlobalStep[14024/NA]: det_loss_cls_0[0.6774] det_loss_box_0[0.9397] det_loss_cls_1[0.6351] det_loss_box_1[0.8040] det_loss_cls_2[0.6366] det_loss_box_2[0.7332] det_loss_cls_3[0.6209] det_loss_box_3[0.7066] det_loss_cls_4[0.5899] det_loss_box_4[0.6764] det_loss_cls_5[0.5763] det_loss_box_5[0.6782] det_loss_cls_dn_0[0.1056] det_loss_box_dn_0[0.3559] det_loss_cls_dn_1[0.1031] det_loss_box_dn_1[0.3165] det_loss_cls_dn_2[0.1023] det_loss_box_dn_2[0.3012] det_loss_cls_dn_3[0.1008] det_loss_box_dn_3[0.3010] det_loss_cls_dn_4[0.0997] det_loss_box_dn_4[0.2993] det_loss_cls_dn_5[0.0992] det_loss_box_dn_5[0.2992] loss_depth[0.0405] total_loss[10.7987]
Rank[0/8] 06/19/2025 18:58:42 INFO stats.py:394 | Epoch[35] completed. Training Speed: 7.53 samples/sec across all devices.	Epoch Time: 414.15 sec.	Average Epoch Time: 414.15 sec.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 1 day, 3:03:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:58:53 INFO stats.py:314 | Epoch[36] Step[9] GlobalStep[14049] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 3:02:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:59:09 INFO loss_tracker.py:84 | Epoch[36/239] Step[24] GlobalStep[14064/NA]: det_loss_cls_0[0.5275] det_loss_box_0[0.7112] det_loss_cls_1[0.5030] det_loss_box_1[0.5801] det_loss_cls_2[0.4952] det_loss_box_2[0.5302] det_loss_cls_3[0.4794] det_loss_box_3[0.5120] det_loss_cls_4[0.4511] det_loss_box_4[0.5042] det_loss_cls_5[0.4369] det_loss_box_5[0.5049] det_loss_cls_dn_0[0.1041] det_loss_box_dn_0[0.3287] det_loss_cls_dn_1[0.1000] det_loss_box_dn_1[0.2810] det_loss_cls_dn_2[0.0987] det_loss_box_dn_2[0.2632] det_loss_cls_dn_3[0.0977] det_loss_box_dn_3[0.2608] det_loss_cls_dn_4[0.0962] det_loss_box_dn_4[0.2596] det_loss_cls_dn_5[0.0951] det_loss_box_dn_5[0.2578] loss_depth[0.0393] total_loss[8.5180]
Rank[0/8] 06/19/2025 18:59:20 INFO stats.py:314 | Epoch[36] Step[34] GlobalStep[14074] Training Speed: 6.13 samples/sec across all devices.	Average Step Time: 1.31 sec.	Estimated Remaining Time: 1 day, 3:02:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 18:59:35 INFO loss_tracker.py:84 | Epoch[36/239] Step[49] GlobalStep[14089/NA]: det_loss_cls_0[0.5607] det_loss_box_0[0.7410] det_loss_cls_1[0.5384] det_loss_box_1[0.6231] det_loss_cls_2[0.5207] det_loss_box_2[0.5806] det_loss_cls_3[0.5130] det_loss_box_3[0.5572] det_loss_cls_4[0.4805] det_loss_box_4[0.5518] det_loss_cls_5[0.4638] det_loss_box_5[0.5555] det_loss_cls_dn_0[0.1091] det_loss_box_dn_0[0.3914] det_loss_cls_dn_1[0.1054] det_loss_box_dn_1[0.3427] det_loss_cls_dn_2[0.1055] det_loss_box_dn_2[0.3285] det_loss_cls_dn_3[0.1042] det_loss_box_dn_3[0.3255] det_loss_cls_dn_4[0.1034] det_loss_box_dn_4[0.3255] det_loss_cls_dn_5[0.1025] det_loss_box_dn_5[0.3246] loss_depth[0.0414] total_loss[9.3960]
Rank[0/8] 06/19/2025 18:59:45 INFO stats.py:314 | Epoch[36] Step[59] GlobalStep[14099] Training Speed: 8.05 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 3:01:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:00:01 INFO loss_tracker.py:84 | Epoch[36/239] Step[74] GlobalStep[14114/NA]: det_loss_cls_0[0.5391] det_loss_box_0[0.7239] det_loss_cls_1[0.5213] det_loss_box_1[0.6061] det_loss_cls_2[0.5193] det_loss_box_2[0.5460] det_loss_cls_3[0.5096] det_loss_box_3[0.5242] det_loss_cls_4[0.4725] det_loss_box_4[0.5302] det_loss_cls_5[0.4633] det_loss_box_5[0.5317] det_loss_cls_dn_0[0.1072] det_loss_box_dn_0[0.4016] det_loss_cls_dn_1[0.1030] det_loss_box_dn_1[0.3528] det_loss_cls_dn_2[0.1025] det_loss_box_dn_2[0.3379] det_loss_cls_dn_3[0.1016] det_loss_box_dn_3[0.3365] det_loss_cls_dn_4[0.1008] det_loss_box_dn_4[0.3356] det_loss_cls_dn_5[0.1004] det_loss_box_dn_5[0.3346] loss_depth[0.0385] total_loss[9.2400]
Rank[0/8] 06/19/2025 19:00:11 INFO stats.py:314 | Epoch[36] Step[84] GlobalStep[14124] Training Speed: 8.17 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 3:00:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:00:27 INFO loss_tracker.py:84 | Epoch[36/239] Step[99] GlobalStep[14139/NA]: det_loss_cls_0[0.4902] det_loss_box_0[0.7095] det_loss_cls_1[0.4795] det_loss_box_1[0.5665] det_loss_cls_2[0.4749] det_loss_box_2[0.5136] det_loss_cls_3[0.4654] det_loss_box_3[0.4940] det_loss_cls_4[0.4396] det_loss_box_4[0.4852] det_loss_cls_5[0.4254] det_loss_box_5[0.4889] det_loss_cls_dn_0[0.1076] det_loss_box_dn_0[0.3630] det_loss_cls_dn_1[0.1042] det_loss_box_dn_1[0.3173] det_loss_cls_dn_2[0.1041] det_loss_box_dn_2[0.2974] det_loss_cls_dn_3[0.1032] det_loss_box_dn_3[0.2959] det_loss_cls_dn_4[0.1026] det_loss_box_dn_4[0.2949] det_loss_cls_dn_5[0.1023] det_loss_box_dn_5[0.2939] loss_depth[0.0441] total_loss[8.5632]
Rank[0/8] 06/19/2025 19:00:37 INFO stats.py:314 | Epoch[36] Step[109] GlobalStep[14149] Training Speed: 8.64 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 2:59:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:00:53 INFO loss_tracker.py:84 | Epoch[36/239] Step[124] GlobalStep[14164/NA]: det_loss_cls_0[0.6203] det_loss_box_0[0.8666] det_loss_cls_1[0.5909] det_loss_box_1[0.7309] det_loss_cls_2[0.5786] det_loss_box_2[0.6778] det_loss_cls_3[0.5495] det_loss_box_3[0.6528] det_loss_cls_4[0.5166] det_loss_box_4[0.6451] det_loss_cls_5[0.5034] det_loss_box_5[0.6363] det_loss_cls_dn_0[0.1131] det_loss_box_dn_0[0.4319] det_loss_cls_dn_1[0.1085] det_loss_box_dn_1[0.3935] det_loss_cls_dn_2[0.1074] det_loss_box_dn_2[0.3796] det_loss_cls_dn_3[0.1064] det_loss_box_dn_3[0.3777] det_loss_cls_dn_4[0.1052] det_loss_box_dn_4[0.3776] det_loss_cls_dn_5[0.1043] det_loss_box_dn_5[0.3769] loss_depth[0.0399] total_loss[10.5908]
Rank[0/8] 06/19/2025 19:01:04 INFO stats.py:314 | Epoch[36] Step[134] GlobalStep[14174] Training Speed: 7.88 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1 day, 2:58:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:01:20 INFO loss_tracker.py:84 | Epoch[36/239] Step[149] GlobalStep[14189/NA]: det_loss_cls_0[0.5308] det_loss_box_0[0.6482] det_loss_cls_1[0.5179] det_loss_box_1[0.5182] det_loss_cls_2[0.5128] det_loss_box_2[0.4808] det_loss_cls_3[0.4936] det_loss_box_3[0.4686] det_loss_cls_4[0.4648] det_loss_box_4[0.4641] det_loss_cls_5[0.4543] det_loss_box_5[0.4659] det_loss_cls_dn_0[0.1094] det_loss_box_dn_0[0.3068] det_loss_cls_dn_1[0.1056] det_loss_box_dn_1[0.2664] det_loss_cls_dn_2[0.1055] det_loss_box_dn_2[0.2526] det_loss_cls_dn_3[0.1059] det_loss_box_dn_3[0.2503] det_loss_cls_dn_4[0.1054] det_loss_box_dn_4[0.2500] det_loss_cls_dn_5[0.1043] det_loss_box_dn_5[0.2486] loss_depth[0.0422] total_loss[8.2731]
Rank[0/8] 06/19/2025 19:01:30 INFO stats.py:314 | Epoch[36] Step[159] GlobalStep[14199] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 2:57:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:01:46 INFO loss_tracker.py:84 | Epoch[36/239] Step[174] GlobalStep[14214/NA]: det_loss_cls_0[0.5985] det_loss_box_0[0.8693] det_loss_cls_1[0.5809] det_loss_box_1[0.7391] det_loss_cls_2[0.5650] det_loss_box_2[0.6724] det_loss_cls_3[0.5529] det_loss_box_3[0.6433] det_loss_cls_4[0.5245] det_loss_box_4[0.6477] det_loss_cls_5[0.5126] det_loss_box_5[0.6460] det_loss_cls_dn_0[0.1227] det_loss_box_dn_0[0.3485] det_loss_cls_dn_1[0.1195] det_loss_box_dn_1[0.3126] det_loss_cls_dn_2[0.1189] det_loss_box_dn_2[0.2993] det_loss_cls_dn_3[0.1178] det_loss_box_dn_3[0.2980] det_loss_cls_dn_4[0.1172] det_loss_box_dn_4[0.2970] det_loss_cls_dn_5[0.1169] det_loss_box_dn_5[0.2952] loss_depth[0.0461] total_loss[10.1619]
Rank[0/8] 06/19/2025 19:01:56 INFO stats.py:314 | Epoch[36] Step[184] GlobalStep[14224] Training Speed: 7.90 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 2:56:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:02:11 INFO loss_tracker.py:84 | Epoch[36/239] Step[199] GlobalStep[14239/NA]: det_loss_cls_0[0.6233] det_loss_box_0[0.8008] det_loss_cls_1[0.6063] det_loss_box_1[0.6649] det_loss_cls_2[0.5838] det_loss_box_2[0.6167] det_loss_cls_3[0.5649] det_loss_box_3[0.5944] det_loss_cls_4[0.5428] det_loss_box_4[0.5759] det_loss_cls_5[0.5329] det_loss_box_5[0.5716] det_loss_cls_dn_0[0.1178] det_loss_box_dn_0[0.3104] det_loss_cls_dn_1[0.1140] det_loss_box_dn_1[0.2679] det_loss_cls_dn_2[0.1130] det_loss_box_dn_2[0.2548] det_loss_cls_dn_3[0.1121] det_loss_box_dn_3[0.2528] det_loss_cls_dn_4[0.1122] det_loss_box_dn_4[0.2531] det_loss_cls_dn_5[0.1109] det_loss_box_dn_5[0.2518] loss_depth[0.0417] total_loss[9.5910]
Rank[0/8] 06/19/2025 19:02:22 INFO stats.py:314 | Epoch[36] Step[209] GlobalStep[14249] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 2:55:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:02:38 INFO loss_tracker.py:84 | Epoch[36/239] Step[224] GlobalStep[14264/NA]: det_loss_cls_0[0.5436] det_loss_box_0[0.7354] det_loss_cls_1[0.5245] det_loss_box_1[0.6040] det_loss_cls_2[0.5148] det_loss_box_2[0.5617] det_loss_cls_3[0.4973] det_loss_box_3[0.5430] det_loss_cls_4[0.4752] det_loss_box_4[0.5343] det_loss_cls_5[0.4617] det_loss_box_5[0.5327] det_loss_cls_dn_0[0.1131] det_loss_box_dn_0[0.3355] det_loss_cls_dn_1[0.1110] det_loss_box_dn_1[0.2897] det_loss_cls_dn_2[0.1101] det_loss_box_dn_2[0.2745] det_loss_cls_dn_3[0.1086] det_loss_box_dn_3[0.2737] det_loss_cls_dn_4[0.1075] det_loss_box_dn_4[0.2740] det_loss_cls_dn_5[0.1069] det_loss_box_dn_5[0.2731] loss_depth[0.0449] total_loss[8.9507]
Rank[0/8] 06/19/2025 19:02:48 INFO stats.py:314 | Epoch[36] Step[234] GlobalStep[14274] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 2:54:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:03:05 INFO loss_tracker.py:84 | Epoch[36/239] Step[249] GlobalStep[14289/NA]: det_loss_cls_0[0.4774] det_loss_box_0[0.6654] det_loss_cls_1[0.4570] det_loss_box_1[0.5413] det_loss_cls_2[0.4452] det_loss_box_2[0.4897] det_loss_cls_3[0.4302] det_loss_box_3[0.4777] det_loss_cls_4[0.4048] det_loss_box_4[0.4809] det_loss_cls_5[0.3902] det_loss_box_5[0.4885] det_loss_cls_dn_0[0.1028] det_loss_box_dn_0[0.3177] det_loss_cls_dn_1[0.0980] det_loss_box_dn_1[0.2826] det_loss_cls_dn_2[0.0971] det_loss_box_dn_2[0.2685] det_loss_cls_dn_3[0.0962] det_loss_box_dn_3[0.2673] det_loss_cls_dn_4[0.0944] det_loss_box_dn_4[0.2673] det_loss_cls_dn_5[0.0945] det_loss_box_dn_5[0.2659] loss_depth[0.0406] total_loss[8.0414]
Rank[0/8] 06/19/2025 19:03:15 INFO stats.py:314 | Epoch[36] Step[259] GlobalStep[14299] Training Speed: 7.90 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 2:53:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:03:31 INFO loss_tracker.py:84 | Epoch[36/239] Step[274] GlobalStep[14314/NA]: det_loss_cls_0[0.5317] det_loss_box_0[0.6791] det_loss_cls_1[0.5160] det_loss_box_1[0.5549] det_loss_cls_2[0.5092] det_loss_box_2[0.5025] det_loss_cls_3[0.4857] det_loss_box_3[0.4782] det_loss_cls_4[0.4523] det_loss_box_4[0.4743] det_loss_cls_5[0.4443] det_loss_box_5[0.4702] det_loss_cls_dn_0[0.1063] det_loss_box_dn_0[0.3288] det_loss_cls_dn_1[0.1039] det_loss_box_dn_1[0.2848] det_loss_cls_dn_2[0.1029] det_loss_box_dn_2[0.2729] det_loss_cls_dn_3[0.1009] det_loss_box_dn_3[0.2701] det_loss_cls_dn_4[0.1008] det_loss_box_dn_4[0.2690] det_loss_cls_dn_5[0.0999] det_loss_box_dn_5[0.2668] loss_depth[0.0417] total_loss[8.4472]
Rank[0/8] 06/19/2025 19:03:41 INFO stats.py:314 | Epoch[36] Step[284] GlobalStep[14324] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 2:52:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:03:57 INFO loss_tracker.py:84 | Epoch[36/239] Step[299] GlobalStep[14339/NA]: det_loss_cls_0[0.4822] det_loss_box_0[0.6281] det_loss_cls_1[0.4719] det_loss_box_1[0.5177] det_loss_cls_2[0.4602] det_loss_box_2[0.4630] det_loss_cls_3[0.4388] det_loss_box_3[0.4497] det_loss_cls_4[0.4140] det_loss_box_4[0.4407] det_loss_cls_5[0.4028] det_loss_box_5[0.4411] det_loss_cls_dn_0[0.1114] det_loss_box_dn_0[0.3259] det_loss_cls_dn_1[0.1093] det_loss_box_dn_1[0.2865] det_loss_cls_dn_2[0.1082] det_loss_box_dn_2[0.2693] det_loss_cls_dn_3[0.1073] det_loss_box_dn_3[0.2677] det_loss_cls_dn_4[0.1069] det_loss_box_dn_4[0.2663] det_loss_cls_dn_5[0.1058] det_loss_box_dn_5[0.2644] loss_depth[0.0411] total_loss[7.9804]
Rank[0/8] 06/19/2025 19:04:08 INFO stats.py:314 | Epoch[36] Step[309] GlobalStep[14349] Training Speed: 8.83 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 1 day, 2:51:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:04:23 INFO loss_tracker.py:84 | Epoch[36/239] Step[324] GlobalStep[14364/NA]: det_loss_cls_0[0.6736] det_loss_box_0[0.9289] det_loss_cls_1[0.6428] det_loss_box_1[0.7650] det_loss_cls_2[0.6202] det_loss_box_2[0.6923] det_loss_cls_3[0.6120] det_loss_box_3[0.6622] det_loss_cls_4[0.5690] det_loss_box_4[0.6524] det_loss_cls_5[0.5549] det_loss_box_5[0.6463] det_loss_cls_dn_0[0.1099] det_loss_box_dn_0[0.2984] det_loss_cls_dn_1[0.1065] det_loss_box_dn_1[0.2686] det_loss_cls_dn_2[0.1061] det_loss_box_dn_2[0.2555] det_loss_cls_dn_3[0.1053] det_loss_box_dn_3[0.2543] det_loss_cls_dn_4[0.1043] det_loss_box_dn_4[0.2537] det_loss_cls_dn_5[0.1041] det_loss_box_dn_5[0.2527] loss_depth[0.0397] total_loss[10.2785]
Rank[0/8] 06/19/2025 19:04:34 INFO stats.py:314 | Epoch[36] Step[334] GlobalStep[14374] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 2:51:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:04:50 INFO loss_tracker.py:84 | Epoch[36/239] Step[349] GlobalStep[14389/NA]: det_loss_cls_0[0.5074] det_loss_box_0[0.6730] det_loss_cls_1[0.4940] det_loss_box_1[0.5413] det_loss_cls_2[0.4756] det_loss_box_2[0.5147] det_loss_cls_3[0.4545] det_loss_box_3[0.5019] det_loss_cls_4[0.4312] det_loss_box_4[0.4843] det_loss_cls_5[0.4227] det_loss_box_5[0.4830] det_loss_cls_dn_0[0.1140] det_loss_box_dn_0[0.3815] det_loss_cls_dn_1[0.1110] det_loss_box_dn_1[0.3378] det_loss_cls_dn_2[0.1103] det_loss_box_dn_2[0.3190] det_loss_cls_dn_3[0.1093] det_loss_box_dn_3[0.3177] det_loss_cls_dn_4[0.1081] det_loss_box_dn_4[0.3175] det_loss_cls_dn_5[0.1074] det_loss_box_dn_5[0.3155] loss_depth[0.0400] total_loss[8.6727]
Rank[0/8] 06/19/2025 19:05:00 INFO stats.py:314 | Epoch[36] Step[359] GlobalStep[14399] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 2:50:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:05:16 INFO loss_tracker.py:84 | Epoch[36/239] Step[374] GlobalStep[14414/NA]: det_loss_cls_0[0.5782] det_loss_box_0[0.8170] det_loss_cls_1[0.5515] det_loss_box_1[0.6810] det_loss_cls_2[0.5374] det_loss_box_2[0.6439] det_loss_cls_3[0.5239] det_loss_box_3[0.6177] det_loss_cls_4[0.5086] det_loss_box_4[0.5954] det_loss_cls_5[0.4880] det_loss_box_5[0.6005] det_loss_cls_dn_0[0.1121] det_loss_box_dn_0[0.3655] det_loss_cls_dn_1[0.1098] det_loss_box_dn_1[0.3212] det_loss_cls_dn_2[0.1096] det_loss_box_dn_2[0.3035] det_loss_cls_dn_3[0.1103] det_loss_box_dn_3[0.3012] det_loss_cls_dn_4[0.1085] det_loss_box_dn_4[0.3006] det_loss_cls_dn_5[0.1071] det_loss_box_dn_5[0.3002] loss_depth[0.0412] total_loss[9.7342]
Rank[0/8] 06/19/2025 19:05:26 INFO stats.py:314 | Epoch[36] Step[384] GlobalStep[14424] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 2:49:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:05:31 INFO stats.py:394 | Epoch[36] completed. Training Speed: 7.62 samples/sec across all devices.	Epoch Time: 409.56 sec.	Average Epoch Time: 409.56 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 2:49:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:05:56 INFO stats.py:314 | Epoch[37] Step[19] GlobalStep[14449] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 2:48:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:06:01 INFO loss_tracker.py:84 | Epoch[37/239] Step[24] GlobalStep[14454/NA]: det_loss_cls_0[0.5372] det_loss_box_0[0.7651] det_loss_cls_1[0.5189] det_loss_box_1[0.6381] det_loss_cls_2[0.5096] det_loss_box_2[0.5847] det_loss_cls_3[0.4967] det_loss_box_3[0.5633] det_loss_cls_4[0.4775] det_loss_box_4[0.5538] det_loss_cls_5[0.4614] det_loss_box_5[0.5560] det_loss_cls_dn_0[0.1085] det_loss_box_dn_0[0.3368] det_loss_cls_dn_1[0.1060] det_loss_box_dn_1[0.2972] det_loss_cls_dn_2[0.1048] det_loss_box_dn_2[0.2828] det_loss_cls_dn_3[0.1043] det_loss_box_dn_3[0.2801] det_loss_cls_dn_4[0.1037] det_loss_box_dn_4[0.2798] det_loss_cls_dn_5[0.1028] det_loss_box_dn_5[0.2771] loss_depth[0.0420] total_loss[9.0884]
Rank[0/8] 06/19/2025 19:06:22 INFO stats.py:314 | Epoch[37] Step[44] GlobalStep[14474] Training Speed: 7.07 samples/sec across all devices.	Average Step Time: 1.13 sec.	Estimated Remaining Time: 1 day, 2:47:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:06:27 INFO loss_tracker.py:84 | Epoch[37/239] Step[49] GlobalStep[14479/NA]: det_loss_cls_0[0.5748] det_loss_box_0[0.7515] det_loss_cls_1[0.5645] det_loss_box_1[0.6230] det_loss_cls_2[0.5434] det_loss_box_2[0.5690] det_loss_cls_3[0.5270] det_loss_box_3[0.5522] det_loss_cls_4[0.5047] det_loss_box_4[0.5411] det_loss_cls_5[0.4986] det_loss_box_5[0.5368] det_loss_cls_dn_0[0.1124] det_loss_box_dn_0[0.3220] det_loss_cls_dn_1[0.1103] det_loss_box_dn_1[0.2838] det_loss_cls_dn_2[0.1087] det_loss_box_dn_2[0.2703] det_loss_cls_dn_3[0.1079] det_loss_box_dn_3[0.2695] det_loss_cls_dn_4[0.1072] det_loss_box_dn_4[0.2689] det_loss_cls_dn_5[0.1068] det_loss_box_dn_5[0.2672] loss_depth[0.0385] total_loss[9.1602]
Rank[0/8] 06/19/2025 19:06:48 INFO stats.py:314 | Epoch[37] Step[69] GlobalStep[14499] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 2:46:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:06:54 INFO loss_tracker.py:84 | Epoch[37/239] Step[74] GlobalStep[14504/NA]: det_loss_cls_0[0.4998] det_loss_box_0[0.6920] det_loss_cls_1[0.4805] det_loss_box_1[0.5900] det_loss_cls_2[0.4756] det_loss_box_2[0.5452] det_loss_cls_3[0.4677] det_loss_box_3[0.5239] det_loss_cls_4[0.4514] det_loss_box_4[0.5135] det_loss_cls_5[0.4337] det_loss_box_5[0.5158] det_loss_cls_dn_0[0.1178] det_loss_box_dn_0[0.4052] det_loss_cls_dn_1[0.1147] det_loss_box_dn_1[0.3536] det_loss_cls_dn_2[0.1142] det_loss_box_dn_2[0.3357] det_loss_cls_dn_3[0.1136] det_loss_box_dn_3[0.3344] det_loss_cls_dn_4[0.1132] det_loss_box_dn_4[0.3341] det_loss_cls_dn_5[0.1125] det_loss_box_dn_5[0.3313] loss_depth[0.0397] total_loss[9.0092]
Rank[0/8] 06/19/2025 19:07:15 INFO stats.py:314 | Epoch[37] Step[94] GlobalStep[14524] Training Speed: 6.33 samples/sec across all devices.	Average Step Time: 1.26 sec.	Estimated Remaining Time: 1 day, 2:45:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:07:20 INFO loss_tracker.py:84 | Epoch[37/239] Step[99] GlobalStep[14529/NA]: det_loss_cls_0[0.6105] det_loss_box_0[0.8559] det_loss_cls_1[0.6033] det_loss_box_1[0.6993] det_loss_cls_2[0.5826] det_loss_box_2[0.6522] det_loss_cls_3[0.5636] det_loss_box_3[0.6318] det_loss_cls_4[0.5362] det_loss_box_4[0.6342] det_loss_cls_5[0.5252] det_loss_box_5[0.6283] det_loss_cls_dn_0[0.1011] det_loss_box_dn_0[0.3366] det_loss_cls_dn_1[0.0982] det_loss_box_dn_1[0.2894] det_loss_cls_dn_2[0.0983] det_loss_box_dn_2[0.2723] det_loss_cls_dn_3[0.0978] det_loss_box_dn_3[0.2709] det_loss_cls_dn_4[0.0968] det_loss_box_dn_4[0.2693] det_loss_cls_dn_5[0.0954] det_loss_box_dn_5[0.2694] loss_depth[0.0455] total_loss[9.8643]
Rank[0/8] 06/19/2025 19:07:40 INFO stats.py:314 | Epoch[37] Step[119] GlobalStep[14549] Training Speed: 8.15 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 2:45:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:07:46 INFO loss_tracker.py:84 | Epoch[37/239] Step[124] GlobalStep[14554/NA]: det_loss_cls_0[0.5039] det_loss_box_0[0.6874] det_loss_cls_1[0.4907] det_loss_box_1[0.5672] det_loss_cls_2[0.4705] det_loss_box_2[0.5145] det_loss_cls_3[0.4581] det_loss_box_3[0.4917] det_loss_cls_4[0.4293] det_loss_box_4[0.4878] det_loss_cls_5[0.4190] det_loss_box_5[0.4822] det_loss_cls_dn_0[0.1150] det_loss_box_dn_0[0.3379] det_loss_cls_dn_1[0.1093] det_loss_box_dn_1[0.2906] det_loss_cls_dn_2[0.1096] det_loss_box_dn_2[0.2722] det_loss_cls_dn_3[0.1086] det_loss_box_dn_3[0.2700] det_loss_cls_dn_4[0.1075] det_loss_box_dn_4[0.2682] det_loss_cls_dn_5[0.1070] det_loss_box_dn_5[0.2677] loss_depth[0.0428] total_loss[8.4087]
Rank[0/8] 06/19/2025 19:08:06 INFO stats.py:314 | Epoch[37] Step[144] GlobalStep[14574] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 2:44:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:08:12 INFO loss_tracker.py:84 | Epoch[37/239] Step[149] GlobalStep[14579/NA]: det_loss_cls_0[0.5757] det_loss_box_0[0.8214] det_loss_cls_1[0.5650] det_loss_box_1[0.6648] det_loss_cls_2[0.5504] det_loss_box_2[0.6112] det_loss_cls_3[0.5289] det_loss_box_3[0.5832] det_loss_cls_4[0.5028] det_loss_box_4[0.5646] det_loss_cls_5[0.4882] det_loss_box_5[0.5699] det_loss_cls_dn_0[0.1067] det_loss_box_dn_0[0.3697] det_loss_cls_dn_1[0.1030] det_loss_box_dn_1[0.3267] det_loss_cls_dn_2[0.1026] det_loss_box_dn_2[0.3083] det_loss_cls_dn_3[0.1021] det_loss_box_dn_3[0.3070] det_loss_cls_dn_4[0.1014] det_loss_box_dn_4[0.3063] det_loss_cls_dn_5[0.1009] det_loss_box_dn_5[0.3034] loss_depth[0.0441] total_loss[9.6082]
Rank[0/8] 06/19/2025 19:08:33 INFO stats.py:314 | Epoch[37] Step[169] GlobalStep[14599] Training Speed: 7.69 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 2:43:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:08:38 INFO loss_tracker.py:84 | Epoch[37/239] Step[174] GlobalStep[14604/NA]: det_loss_cls_0[0.6088] det_loss_box_0[0.8025] det_loss_cls_1[0.5810] det_loss_box_1[0.6525] det_loss_cls_2[0.5790] det_loss_box_2[0.5891] det_loss_cls_3[0.5522] det_loss_box_3[0.5709] det_loss_cls_4[0.5306] det_loss_box_4[0.5599] det_loss_cls_5[0.5183] det_loss_box_5[0.5587] det_loss_cls_dn_0[0.1142] det_loss_box_dn_0[0.3949] det_loss_cls_dn_1[0.1107] det_loss_box_dn_1[0.3502] det_loss_cls_dn_2[0.1095] det_loss_box_dn_2[0.3371] det_loss_cls_dn_3[0.1086] det_loss_box_dn_3[0.3365] det_loss_cls_dn_4[0.1068] det_loss_box_dn_4[0.3367] det_loss_cls_dn_5[0.1063] det_loss_box_dn_5[0.3335] loss_depth[0.0410] total_loss[9.8894]
Rank[0/8] 06/19/2025 19:08:59 INFO stats.py:314 | Epoch[37] Step[194] GlobalStep[14624] Training Speed: 6.39 samples/sec across all devices.	Average Step Time: 1.25 sec.	Estimated Remaining Time: 1 day, 2:42:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:09:04 INFO loss_tracker.py:84 | Epoch[37/239] Step[199] GlobalStep[14629/NA]: det_loss_cls_0[0.5800] det_loss_box_0[0.7186] det_loss_cls_1[0.5681] det_loss_box_1[0.5719] det_loss_cls_2[0.5637] det_loss_box_2[0.5213] det_loss_cls_3[0.5429] det_loss_box_3[0.5139] det_loss_cls_4[0.5166] det_loss_box_4[0.5089] det_loss_cls_5[0.5025] det_loss_box_5[0.5108] det_loss_cls_dn_0[0.1029] det_loss_box_dn_0[0.3189] det_loss_cls_dn_1[0.0987] det_loss_box_dn_1[0.2795] det_loss_cls_dn_2[0.0988] det_loss_box_dn_2[0.2643] det_loss_cls_dn_3[0.0983] det_loss_box_dn_3[0.2624] det_loss_cls_dn_4[0.0970] det_loss_box_dn_4[0.2613] det_loss_cls_dn_5[0.0974] det_loss_box_dn_5[0.2583] loss_depth[0.0394] total_loss[8.8966]
Rank[0/8] 06/19/2025 19:09:24 INFO stats.py:314 | Epoch[37] Step[219] GlobalStep[14649] Training Speed: 7.57 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 1 day, 2:41:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:09:30 INFO loss_tracker.py:84 | Epoch[37/239] Step[224] GlobalStep[14654/NA]: det_loss_cls_0[0.5386] det_loss_box_0[0.7061] det_loss_cls_1[0.5238] det_loss_box_1[0.5659] det_loss_cls_2[0.5145] det_loss_box_2[0.5126] det_loss_cls_3[0.4897] det_loss_box_3[0.5040] det_loss_cls_4[0.4507] det_loss_box_4[0.4899] det_loss_cls_5[0.4335] det_loss_box_5[0.4916] det_loss_cls_dn_0[0.1021] det_loss_box_dn_0[0.3256] det_loss_cls_dn_1[0.0990] det_loss_box_dn_1[0.2885] det_loss_cls_dn_2[0.0981] det_loss_box_dn_2[0.2754] det_loss_cls_dn_3[0.0981] det_loss_box_dn_3[0.2747] det_loss_cls_dn_4[0.0971] det_loss_box_dn_4[0.2740] det_loss_cls_dn_5[0.0961] det_loss_box_dn_5[0.2725] loss_depth[0.0412] total_loss[8.5635]
Rank[0/8] 06/19/2025 19:09:51 INFO stats.py:314 | Epoch[37] Step[244] GlobalStep[14674] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 2:40:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:09:57 INFO loss_tracker.py:84 | Epoch[37/239] Step[249] GlobalStep[14679/NA]: det_loss_cls_0[0.5499] det_loss_box_0[0.7869] det_loss_cls_1[0.5238] det_loss_box_1[0.6714] det_loss_cls_2[0.5273] det_loss_box_2[0.6048] det_loss_cls_3[0.5181] det_loss_box_3[0.5775] det_loss_cls_4[0.4878] det_loss_box_4[0.5677] det_loss_cls_5[0.4763] det_loss_box_5[0.5653] det_loss_cls_dn_0[0.1088] det_loss_box_dn_0[0.3534] det_loss_cls_dn_1[0.1057] det_loss_box_dn_1[0.3047] det_loss_cls_dn_2[0.1054] det_loss_box_dn_2[0.2876] det_loss_cls_dn_3[0.1040] det_loss_box_dn_3[0.2852] det_loss_cls_dn_4[0.1032] det_loss_box_dn_4[0.2845] det_loss_cls_dn_5[0.1023] det_loss_box_dn_5[0.2833] loss_depth[0.0392] total_loss[9.3240]
Rank[0/8] 06/19/2025 19:10:18 INFO stats.py:314 | Epoch[37] Step[269] GlobalStep[14699] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 2:39:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:10:23 INFO loss_tracker.py:84 | Epoch[37/239] Step[274] GlobalStep[14704/NA]: det_loss_cls_0[0.6132] det_loss_box_0[0.9161] det_loss_cls_1[0.5953] det_loss_box_1[0.7526] det_loss_cls_2[0.5836] det_loss_box_2[0.6832] det_loss_cls_3[0.5708] det_loss_box_3[0.6556] det_loss_cls_4[0.5503] det_loss_box_4[0.6449] det_loss_cls_5[0.5408] det_loss_box_5[0.6489] det_loss_cls_dn_0[0.1179] det_loss_box_dn_0[0.3650] det_loss_cls_dn_1[0.1148] det_loss_box_dn_1[0.3262] det_loss_cls_dn_2[0.1144] det_loss_box_dn_2[0.3119] det_loss_cls_dn_3[0.1134] det_loss_box_dn_3[0.3103] det_loss_cls_dn_4[0.1127] det_loss_box_dn_4[0.3091] det_loss_cls_dn_5[0.1127] det_loss_box_dn_5[0.3074] loss_depth[0.0417] total_loss[10.4126]
Rank[0/8] 06/19/2025 19:10:44 INFO stats.py:314 | Epoch[37] Step[294] GlobalStep[14724] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 2:38:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:10:49 INFO loss_tracker.py:84 | Epoch[37/239] Step[299] GlobalStep[14729/NA]: det_loss_cls_0[0.5205] det_loss_box_0[0.7890] det_loss_cls_1[0.5168] det_loss_box_1[0.6440] det_loss_cls_2[0.5018] det_loss_box_2[0.5553] det_loss_cls_3[0.4825] det_loss_box_3[0.5345] det_loss_cls_4[0.4537] det_loss_box_4[0.5302] det_loss_cls_5[0.4402] det_loss_box_5[0.5284] det_loss_cls_dn_0[0.1121] det_loss_box_dn_0[0.4140] det_loss_cls_dn_1[0.1096] det_loss_box_dn_1[0.3594] det_loss_cls_dn_2[0.1088] det_loss_box_dn_2[0.3392] det_loss_cls_dn_3[0.1081] det_loss_box_dn_3[0.3369] det_loss_cls_dn_4[0.1077] det_loss_box_dn_4[0.3352] det_loss_cls_dn_5[0.1062] det_loss_box_dn_5[0.3331] loss_depth[0.0427] total_loss[9.3099]
Rank[0/8] 06/19/2025 19:11:10 INFO stats.py:314 | Epoch[37] Step[319] GlobalStep[14749] Training Speed: 6.70 samples/sec across all devices.	Average Step Time: 1.19 sec.	Estimated Remaining Time: 1 day, 2:37:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:11:15 INFO loss_tracker.py:84 | Epoch[37/239] Step[324] GlobalStep[14754/NA]: det_loss_cls_0[0.6182] det_loss_box_0[0.8983] det_loss_cls_1[0.6020] det_loss_box_1[0.7536] det_loss_cls_2[0.5801] det_loss_box_2[0.6906] det_loss_cls_3[0.5756] det_loss_box_3[0.6567] det_loss_cls_4[0.5434] det_loss_box_4[0.6469] det_loss_cls_5[0.5275] det_loss_box_5[0.6413] det_loss_cls_dn_0[0.1124] det_loss_box_dn_0[0.3556] det_loss_cls_dn_1[0.1065] det_loss_box_dn_1[0.3149] det_loss_cls_dn_2[0.1062] det_loss_box_dn_2[0.3024] det_loss_cls_dn_3[0.1055] det_loss_box_dn_3[0.3000] det_loss_cls_dn_4[0.1048] det_loss_box_dn_4[0.2986] det_loss_cls_dn_5[0.1048] det_loss_box_dn_5[0.2966] loss_depth[0.0432] total_loss[10.2858]
Rank[0/8] 06/19/2025 19:11:37 INFO stats.py:314 | Epoch[37] Step[344] GlobalStep[14774] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 2:37:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:11:42 INFO loss_tracker.py:84 | Epoch[37/239] Step[349] GlobalStep[14779/NA]: det_loss_cls_0[0.6475] det_loss_box_0[0.9436] det_loss_cls_1[0.6151] det_loss_box_1[0.7893] det_loss_cls_2[0.5937] det_loss_box_2[0.7169] det_loss_cls_3[0.5922] det_loss_box_3[0.6842] det_loss_cls_4[0.5658] det_loss_box_4[0.6648] det_loss_cls_5[0.5516] det_loss_box_5[0.6673] det_loss_cls_dn_0[0.1091] det_loss_box_dn_0[0.3869] det_loss_cls_dn_1[0.1059] det_loss_box_dn_1[0.3368] det_loss_cls_dn_2[0.1049] det_loss_box_dn_2[0.3194] det_loss_cls_dn_3[0.1043] det_loss_box_dn_3[0.3160] det_loss_cls_dn_4[0.1027] det_loss_box_dn_4[0.3142] det_loss_cls_dn_5[0.1020] det_loss_box_dn_5[0.3117] loss_depth[0.0439] total_loss[10.6897]
Rank[0/8] 06/19/2025 19:12:02 INFO stats.py:314 | Epoch[37] Step[369] GlobalStep[14799] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 2:36:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:12:07 INFO loss_tracker.py:84 | Epoch[37/239] Step[374] GlobalStep[14804/NA]: det_loss_cls_0[0.6020] det_loss_box_0[0.7607] det_loss_cls_1[0.5654] det_loss_box_1[0.6205] det_loss_cls_2[0.5631] det_loss_box_2[0.5471] det_loss_cls_3[0.5511] det_loss_box_3[0.5279] det_loss_cls_4[0.5228] det_loss_box_4[0.5269] det_loss_cls_5[0.5035] det_loss_box_5[0.5290] det_loss_cls_dn_0[0.1081] det_loss_box_dn_0[0.3048] det_loss_cls_dn_1[0.1040] det_loss_box_dn_1[0.2708] det_loss_cls_dn_2[0.1038] det_loss_box_dn_2[0.2553] det_loss_cls_dn_3[0.1030] det_loss_box_dn_3[0.2530] det_loss_cls_dn_4[0.1028] det_loss_box_dn_4[0.2521] det_loss_cls_dn_5[0.1016] det_loss_box_dn_5[0.2488] loss_depth[0.0378] total_loss[9.0656]
Rank[0/8] 06/19/2025 19:12:22 INFO stats.py:394 | Epoch[37] completed. Training Speed: 7.59 samples/sec across all devices.	Epoch Time: 410.93 sec.	Average Epoch Time: 410.93 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 2:35:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:12:30 INFO stats.py:314 | Epoch[38] Step[4] GlobalStep[14824] Training Speed: 7.89 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 2:35:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:12:51 INFO loss_tracker.py:84 | Epoch[38/239] Step[24] GlobalStep[14844/NA]: det_loss_cls_0[0.5404] det_loss_box_0[0.6928] det_loss_cls_1[0.5327] det_loss_box_1[0.5528] det_loss_cls_2[0.5148] det_loss_box_2[0.5034] det_loss_cls_3[0.5036] det_loss_box_3[0.4787] det_loss_cls_4[0.4713] det_loss_box_4[0.4759] det_loss_cls_5[0.4531] det_loss_box_5[0.4801] det_loss_cls_dn_0[0.1102] det_loss_box_dn_0[0.3129] det_loss_cls_dn_1[0.1080] det_loss_box_dn_1[0.2696] det_loss_cls_dn_2[0.1076] det_loss_box_dn_2[0.2522] det_loss_cls_dn_3[0.1069] det_loss_box_dn_3[0.2506] det_loss_cls_dn_4[0.1061] det_loss_box_dn_4[0.2492] det_loss_cls_dn_5[0.1056] det_loss_box_dn_5[0.2484] loss_depth[0.0396] total_loss[8.4665]
Rank[0/8] 06/19/2025 19:12:57 INFO stats.py:314 | Epoch[38] Step[29] GlobalStep[14849] Training Speed: 7.30 samples/sec across all devices.	Average Step Time: 1.10 sec.	Estimated Remaining Time: 1 day, 2:34:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:13:18 INFO loss_tracker.py:84 | Epoch[38/239] Step[49] GlobalStep[14869/NA]: det_loss_cls_0[0.5854] det_loss_box_0[0.8032] det_loss_cls_1[0.5658] det_loss_box_1[0.6616] det_loss_cls_2[0.5588] det_loss_box_2[0.6049] det_loss_cls_3[0.5328] det_loss_box_3[0.5932] det_loss_cls_4[0.5041] det_loss_box_4[0.5869] det_loss_cls_5[0.4774] det_loss_box_5[0.5893] det_loss_cls_dn_0[0.1090] det_loss_box_dn_0[0.3225] det_loss_cls_dn_1[0.1041] det_loss_box_dn_1[0.2880] det_loss_cls_dn_2[0.1034] det_loss_box_dn_2[0.2722] det_loss_cls_dn_3[0.1031] det_loss_box_dn_3[0.2687] det_loss_cls_dn_4[0.1025] det_loss_box_dn_4[0.2673] det_loss_cls_dn_5[0.1006] det_loss_box_dn_5[0.2663] loss_depth[0.0391] total_loss[9.4102]
Rank[0/8] 06/19/2025 19:13:24 INFO stats.py:314 | Epoch[38] Step[54] GlobalStep[14874] Training Speed: 7.97 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 2:33:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:13:45 INFO loss_tracker.py:84 | Epoch[38/239] Step[74] GlobalStep[14894/NA]: det_loss_cls_0[0.6393] det_loss_box_0[0.8782] det_loss_cls_1[0.6291] det_loss_box_1[0.7480] det_loss_cls_2[0.6147] det_loss_box_2[0.6964] det_loss_cls_3[0.6017] det_loss_box_3[0.6690] det_loss_cls_4[0.5764] det_loss_box_4[0.6530] det_loss_cls_5[0.5546] det_loss_box_5[0.6552] det_loss_cls_dn_0[0.1213] det_loss_box_dn_0[0.3756] det_loss_cls_dn_1[0.1174] det_loss_box_dn_1[0.3371] det_loss_cls_dn_2[0.1171] det_loss_box_dn_2[0.3255] det_loss_cls_dn_3[0.1171] det_loss_box_dn_3[0.3227] det_loss_cls_dn_4[0.1171] det_loss_box_dn_4[0.3222] det_loss_cls_dn_5[0.1157] det_loss_box_dn_5[0.3220] loss_depth[0.0402] total_loss[10.6666]
Rank[0/8] 06/19/2025 19:13:50 INFO stats.py:314 | Epoch[38] Step[79] GlobalStep[14899] Training Speed: 7.55 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 1 day, 2:32:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:14:11 INFO loss_tracker.py:84 | Epoch[38/239] Step[99] GlobalStep[14919/NA]: det_loss_cls_0[0.5607] det_loss_box_0[0.8048] det_loss_cls_1[0.5417] det_loss_box_1[0.6598] det_loss_cls_2[0.5248] det_loss_box_2[0.5954] det_loss_cls_3[0.5113] det_loss_box_3[0.5671] det_loss_cls_4[0.4899] det_loss_box_4[0.5613] det_loss_cls_5[0.4751] det_loss_box_5[0.5659] det_loss_cls_dn_0[0.1210] det_loss_box_dn_0[0.3936] det_loss_cls_dn_1[0.1180] det_loss_box_dn_1[0.3445] det_loss_cls_dn_2[0.1170] det_loss_box_dn_2[0.3272] det_loss_cls_dn_3[0.1156] det_loss_box_dn_3[0.3243] det_loss_cls_dn_4[0.1138] det_loss_box_dn_4[0.3238] det_loss_cls_dn_5[0.1131] det_loss_box_dn_5[0.3219] loss_depth[0.0453] total_loss[9.6368]
Rank[0/8] 06/19/2025 19:14:16 INFO stats.py:314 | Epoch[38] Step[104] GlobalStep[14924] Training Speed: 7.63 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 2:32:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:14:38 INFO loss_tracker.py:84 | Epoch[38/239] Step[124] GlobalStep[14944/NA]: det_loss_cls_0[0.6509] det_loss_box_0[0.9456] det_loss_cls_1[0.6280] det_loss_box_1[0.8030] det_loss_cls_2[0.6261] det_loss_box_2[0.7450] det_loss_cls_3[0.5981] det_loss_box_3[0.7163] det_loss_cls_4[0.5755] det_loss_box_4[0.7051] det_loss_cls_5[0.5581] det_loss_box_5[0.7018] det_loss_cls_dn_0[0.1106] det_loss_box_dn_0[0.5216] det_loss_cls_dn_1[0.1079] det_loss_box_dn_1[0.4686] det_loss_cls_dn_2[0.1072] det_loss_box_dn_2[0.4595] det_loss_cls_dn_3[0.1064] det_loss_box_dn_3[0.4572] det_loss_cls_dn_4[0.1053] det_loss_box_dn_4[0.4556] det_loss_cls_dn_5[0.1044] det_loss_box_dn_5[0.4529] loss_depth[0.0395] total_loss[11.7502]
Rank[0/8] 06/19/2025 19:14:43 INFO stats.py:314 | Epoch[38] Step[129] GlobalStep[14949] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 2:31:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:15:04 INFO loss_tracker.py:84 | Epoch[38/239] Step[149] GlobalStep[14969/NA]: det_loss_cls_0[0.6125] det_loss_box_0[0.8375] det_loss_cls_1[0.5978] det_loss_box_1[0.6696] det_loss_cls_2[0.5909] det_loss_box_2[0.6129] det_loss_cls_3[0.5634] det_loss_box_3[0.6001] det_loss_cls_4[0.5351] det_loss_box_4[0.5875] det_loss_cls_5[0.5199] det_loss_box_5[0.5850] det_loss_cls_dn_0[0.1162] det_loss_box_dn_0[0.2998] det_loss_cls_dn_1[0.1119] det_loss_box_dn_1[0.2603] det_loss_cls_dn_2[0.1108] det_loss_box_dn_2[0.2450] det_loss_cls_dn_3[0.1104] det_loss_box_dn_3[0.2433] det_loss_cls_dn_4[0.1092] det_loss_box_dn_4[0.2431] det_loss_cls_dn_5[0.1083] det_loss_box_dn_5[0.2385] loss_depth[0.0371] total_loss[9.5462]
Rank[0/8] 06/19/2025 19:15:09 INFO stats.py:314 | Epoch[38] Step[154] GlobalStep[14974] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 2:30:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:15:30 INFO loss_tracker.py:84 | Epoch[38/239] Step[174] GlobalStep[14994/NA]: det_loss_cls_0[0.6371] det_loss_box_0[0.8654] det_loss_cls_1[0.6096] det_loss_box_1[0.7050] det_loss_cls_2[0.5928] det_loss_box_2[0.6393] det_loss_cls_3[0.5740] det_loss_box_3[0.6197] det_loss_cls_4[0.5505] det_loss_box_4[0.6035] det_loss_cls_5[0.5328] det_loss_box_5[0.6034] det_loss_cls_dn_0[0.1133] det_loss_box_dn_0[0.3141] det_loss_cls_dn_1[0.1095] det_loss_box_dn_1[0.2769] det_loss_cls_dn_2[0.1103] det_loss_box_dn_2[0.2618] det_loss_cls_dn_3[0.1105] det_loss_box_dn_3[0.2582] det_loss_cls_dn_4[0.1096] det_loss_box_dn_4[0.2569] det_loss_cls_dn_5[0.1090] det_loss_box_dn_5[0.2555] loss_depth[0.0415] total_loss[9.8604]
Rank[0/8] 06/19/2025 19:15:35 INFO stats.py:314 | Epoch[38] Step[179] GlobalStep[14999] Training Speed: 6.15 samples/sec across all devices.	Average Step Time: 1.30 sec.	Estimated Remaining Time: 1 day, 2:29:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:15:56 INFO loss_tracker.py:84 | Epoch[38/239] Step[199] GlobalStep[15019/NA]: det_loss_cls_0[0.5383] det_loss_box_0[0.7371] det_loss_cls_1[0.5307] det_loss_box_1[0.6159] det_loss_cls_2[0.5160] det_loss_box_2[0.5579] det_loss_cls_3[0.4932] det_loss_box_3[0.5351] det_loss_cls_4[0.4667] det_loss_box_4[0.5299] det_loss_cls_5[0.4658] det_loss_box_5[0.5200] det_loss_cls_dn_0[0.1151] det_loss_box_dn_0[0.3190] det_loss_cls_dn_1[0.1105] det_loss_box_dn_1[0.2788] det_loss_cls_dn_2[0.1099] det_loss_box_dn_2[0.2646] det_loss_cls_dn_3[0.1098] det_loss_box_dn_3[0.2615] det_loss_cls_dn_4[0.1088] det_loss_box_dn_4[0.2606] det_loss_cls_dn_5[0.1085] det_loss_box_dn_5[0.2589] loss_depth[0.0383] total_loss[8.8509]
Rank[0/8] 06/19/2025 19:16:01 INFO stats.py:314 | Epoch[38] Step[204] GlobalStep[15024] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 2:28:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:16:22 INFO loss_tracker.py:84 | Epoch[38/239] Step[224] GlobalStep[15044/NA]: det_loss_cls_0[0.5689] det_loss_box_0[0.7482] det_loss_cls_1[0.5556] det_loss_box_1[0.6206] det_loss_cls_2[0.5376] det_loss_box_2[0.5683] det_loss_cls_3[0.5135] det_loss_box_3[0.5606] det_loss_cls_4[0.4875] det_loss_box_4[0.5541] det_loss_cls_5[0.4714] det_loss_box_5[0.5535] det_loss_cls_dn_0[0.1129] det_loss_box_dn_0[0.3644] det_loss_cls_dn_1[0.1064] det_loss_box_dn_1[0.3273] det_loss_cls_dn_2[0.1056] det_loss_box_dn_2[0.3172] det_loss_cls_dn_3[0.1049] det_loss_box_dn_3[0.3163] det_loss_cls_dn_4[0.1042] det_loss_box_dn_4[0.3159] det_loss_cls_dn_5[0.1034] det_loss_box_dn_5[0.3155] loss_depth[0.0418] total_loss[9.3756]
Rank[0/8] 06/19/2025 19:16:27 INFO stats.py:314 | Epoch[38] Step[229] GlobalStep[15049] Training Speed: 7.93 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 2:27:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:16:48 INFO loss_tracker.py:84 | Epoch[38/239] Step[249] GlobalStep[15069/NA]: det_loss_cls_0[0.5469] det_loss_box_0[0.7387] det_loss_cls_1[0.5459] det_loss_box_1[0.5965] det_loss_cls_2[0.5273] det_loss_box_2[0.5294] det_loss_cls_3[0.5113] det_loss_box_3[0.5177] det_loss_cls_4[0.4723] det_loss_box_4[0.5153] det_loss_cls_5[0.4648] det_loss_box_5[0.5129] det_loss_cls_dn_0[0.1007] det_loss_box_dn_0[0.3382] det_loss_cls_dn_1[0.0969] det_loss_box_dn_1[0.3071] det_loss_cls_dn_2[0.0959] det_loss_box_dn_2[0.2916] det_loss_cls_dn_3[0.0951] det_loss_box_dn_3[0.2907] det_loss_cls_dn_4[0.0938] det_loss_box_dn_4[0.2896] det_loss_cls_dn_5[0.0933] det_loss_box_dn_5[0.2888] loss_depth[0.0402] total_loss[8.9010]
Rank[0/8] 06/19/2025 19:16:53 INFO stats.py:314 | Epoch[38] Step[254] GlobalStep[15074] Training Speed: 7.85 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1 day, 2:26:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:17:14 INFO loss_tracker.py:84 | Epoch[38/239] Step[274] GlobalStep[15094/NA]: det_loss_cls_0[0.5608] det_loss_box_0[0.7716] det_loss_cls_1[0.5401] det_loss_box_1[0.6290] det_loss_cls_2[0.5326] det_loss_box_2[0.5764] det_loss_cls_3[0.5059] det_loss_box_3[0.5643] det_loss_cls_4[0.4740] det_loss_box_4[0.5446] det_loss_cls_5[0.4578] det_loss_box_5[0.5437] det_loss_cls_dn_0[0.1101] det_loss_box_dn_0[0.3459] det_loss_cls_dn_1[0.1059] det_loss_box_dn_1[0.3007] det_loss_cls_dn_2[0.1046] det_loss_box_dn_2[0.2834] det_loss_cls_dn_3[0.1038] det_loss_box_dn_3[0.2810] det_loss_cls_dn_4[0.1029] det_loss_box_dn_4[0.2797] det_loss_cls_dn_5[0.1027] det_loss_box_dn_5[0.2737] loss_depth[0.0432] total_loss[9.1384]
Rank[0/8] 06/19/2025 19:17:20 INFO stats.py:314 | Epoch[38] Step[279] GlobalStep[15099] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 2:25:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:17:40 INFO loss_tracker.py:84 | Epoch[38/239] Step[299] GlobalStep[15119/NA]: det_loss_cls_0[0.5367] det_loss_box_0[0.7369] det_loss_cls_1[0.5365] det_loss_box_1[0.6138] det_loss_cls_2[0.5214] det_loss_box_2[0.5524] det_loss_cls_3[0.4939] det_loss_box_3[0.5482] det_loss_cls_4[0.4741] det_loss_box_4[0.5361] det_loss_cls_5[0.4557] det_loss_box_5[0.5376] det_loss_cls_dn_0[0.1041] det_loss_box_dn_0[0.3300] det_loss_cls_dn_1[0.1013] det_loss_box_dn_1[0.2887] det_loss_cls_dn_2[0.1001] det_loss_box_dn_2[0.2735] det_loss_cls_dn_3[0.0987] det_loss_box_dn_3[0.2722] det_loss_cls_dn_4[0.0980] det_loss_box_dn_4[0.2704] det_loss_cls_dn_5[0.0973] det_loss_box_dn_5[0.2691] loss_depth[0.0391] total_loss[8.8857]
Rank[0/8] 06/19/2025 19:17:45 INFO stats.py:314 | Epoch[38] Step[304] GlobalStep[15124] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 2:25:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:18:07 INFO loss_tracker.py:84 | Epoch[38/239] Step[324] GlobalStep[15144/NA]: det_loss_cls_0[0.6914] det_loss_box_0[0.9298] det_loss_cls_1[0.6703] det_loss_box_1[0.7909] det_loss_cls_2[0.6608] det_loss_box_2[0.7257] det_loss_cls_3[0.6505] det_loss_box_3[0.6889] det_loss_cls_4[0.6174] det_loss_box_4[0.6804] det_loss_cls_5[0.5940] det_loss_box_5[0.6836] det_loss_cls_dn_0[0.1195] det_loss_box_dn_0[0.3163] det_loss_cls_dn_1[0.1150] det_loss_box_dn_1[0.2803] det_loss_cls_dn_2[0.1142] det_loss_box_dn_2[0.2667] det_loss_cls_dn_3[0.1143] det_loss_box_dn_3[0.2647] det_loss_cls_dn_4[0.1133] det_loss_box_dn_4[0.2642] det_loss_cls_dn_5[0.1127] det_loss_box_dn_5[0.2625] loss_depth[0.0382] total_loss[10.7655]
Rank[0/8] 06/19/2025 19:18:12 INFO stats.py:314 | Epoch[38] Step[329] GlobalStep[15149] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 2:24:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:18:32 INFO loss_tracker.py:84 | Epoch[38/239] Step[349] GlobalStep[15169/NA]: det_loss_cls_0[0.6256] det_loss_box_0[0.8214] det_loss_cls_1[0.6139] det_loss_box_1[0.6871] det_loss_cls_2[0.6114] det_loss_box_2[0.6248] det_loss_cls_3[0.5918] det_loss_box_3[0.6009] det_loss_cls_4[0.5588] det_loss_box_4[0.5923] det_loss_cls_5[0.5358] det_loss_box_5[0.5941] det_loss_cls_dn_0[0.1051] det_loss_box_dn_0[0.2828] det_loss_cls_dn_1[0.1022] det_loss_box_dn_1[0.2508] det_loss_cls_dn_2[0.1021] det_loss_box_dn_2[0.2371] det_loss_cls_dn_3[0.1019] det_loss_box_dn_3[0.2358] det_loss_cls_dn_4[0.1007] det_loss_box_dn_4[0.2344] det_loss_cls_dn_5[0.1003] det_loss_box_dn_5[0.2330] loss_depth[0.0376] total_loss[9.5817]
Rank[0/8] 06/19/2025 19:18:37 INFO stats.py:314 | Epoch[38] Step[354] GlobalStep[15174] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 2:23:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:18:58 INFO loss_tracker.py:84 | Epoch[38/239] Step[374] GlobalStep[15194/NA]: det_loss_cls_0[0.5075] det_loss_box_0[0.7282] det_loss_cls_1[0.4975] det_loss_box_1[0.5764] det_loss_cls_2[0.4924] det_loss_box_2[0.5328] det_loss_cls_3[0.4721] det_loss_box_3[0.5194] det_loss_cls_4[0.4518] det_loss_box_4[0.5089] det_loss_cls_5[0.4483] det_loss_box_5[0.5094] det_loss_cls_dn_0[0.1180] det_loss_box_dn_0[0.4582] det_loss_cls_dn_1[0.1147] det_loss_box_dn_1[0.4270] det_loss_cls_dn_2[0.1135] det_loss_box_dn_2[0.4097] det_loss_cls_dn_3[0.1131] det_loss_box_dn_3[0.4112] det_loss_cls_dn_4[0.1122] det_loss_box_dn_4[0.4098] det_loss_cls_dn_5[0.1118] det_loss_box_dn_5[0.4088] loss_depth[0.0437] total_loss[9.4964]
Rank[0/8] 06/19/2025 19:19:03 INFO stats.py:314 | Epoch[38] Step[379] GlobalStep[15199] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 2:22:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:19:13 INFO stats.py:394 | Epoch[38] completed. Training Speed: 7.59 samples/sec across all devices.	Epoch Time: 411.03 sec.	Average Epoch Time: 411.03 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 2:22:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:19:32 INFO stats.py:314 | Epoch[39] Step[14] GlobalStep[15224] Training Speed: 7.57 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 1 day, 2:21:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:19:43 INFO loss_tracker.py:84 | Epoch[39/239] Step[24] GlobalStep[15234/NA]: det_loss_cls_0[0.5759] det_loss_box_0[0.8296] det_loss_cls_1[0.5454] det_loss_box_1[0.6923] det_loss_cls_2[0.5356] det_loss_box_2[0.6263] det_loss_cls_3[0.5211] det_loss_box_3[0.6044] det_loss_cls_4[0.4945] det_loss_box_4[0.5993] det_loss_cls_5[0.4820] det_loss_box_5[0.5974] det_loss_cls_dn_0[0.1167] det_loss_box_dn_0[0.3818] det_loss_cls_dn_1[0.1148] det_loss_box_dn_1[0.3409] det_loss_cls_dn_2[0.1142] det_loss_box_dn_2[0.3253] det_loss_cls_dn_3[0.1128] det_loss_box_dn_3[0.3226] det_loss_cls_dn_4[0.1116] det_loss_box_dn_4[0.3225] det_loss_cls_dn_5[0.1110] det_loss_box_dn_5[0.3209] loss_depth[0.0422] total_loss[9.8412]
Rank[0/8] 06/19/2025 19:19:58 INFO stats.py:314 | Epoch[39] Step[39] GlobalStep[15249] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 2:20:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:20:08 INFO loss_tracker.py:84 | Epoch[39/239] Step[49] GlobalStep[15259/NA]: det_loss_cls_0[0.6211] det_loss_box_0[0.8281] det_loss_cls_1[0.6046] det_loss_box_1[0.6680] det_loss_cls_2[0.5853] det_loss_box_2[0.6073] det_loss_cls_3[0.5627] det_loss_box_3[0.5870] det_loss_cls_4[0.5373] det_loss_box_4[0.5612] det_loss_cls_5[0.5243] det_loss_box_5[0.5631] det_loss_cls_dn_0[0.1106] det_loss_box_dn_0[0.3336] det_loss_cls_dn_1[0.1069] det_loss_box_dn_1[0.2871] det_loss_cls_dn_2[0.1070] det_loss_box_dn_2[0.2693] det_loss_cls_dn_3[0.1066] det_loss_box_dn_3[0.2663] det_loss_cls_dn_4[0.1051] det_loss_box_dn_4[0.2651] det_loss_cls_dn_5[0.1046] det_loss_box_dn_5[0.2631] loss_depth[0.0394] total_loss[9.6147]
Rank[0/8] 06/19/2025 19:20:24 INFO stats.py:314 | Epoch[39] Step[64] GlobalStep[15274] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 2:20:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:20:35 INFO loss_tracker.py:84 | Epoch[39/239] Step[74] GlobalStep[15284/NA]: det_loss_cls_0[0.5301] det_loss_box_0[0.7316] det_loss_cls_1[0.5092] det_loss_box_1[0.5979] det_loss_cls_2[0.5063] det_loss_box_2[0.5477] det_loss_cls_3[0.4917] det_loss_box_3[0.5311] det_loss_cls_4[0.4713] det_loss_box_4[0.5174] det_loss_cls_5[0.4576] det_loss_box_5[0.5207] det_loss_cls_dn_0[0.1160] det_loss_box_dn_0[0.3673] det_loss_cls_dn_1[0.1119] det_loss_box_dn_1[0.3228] det_loss_cls_dn_2[0.1114] det_loss_box_dn_2[0.3082] det_loss_cls_dn_3[0.1104] det_loss_box_dn_3[0.3064] det_loss_cls_dn_4[0.1102] det_loss_box_dn_4[0.3054] det_loss_cls_dn_5[0.1099] det_loss_box_dn_5[0.3045] loss_depth[0.0450] total_loss[9.0420]
Rank[0/8] 06/19/2025 19:20:50 INFO stats.py:314 | Epoch[39] Step[89] GlobalStep[15299] Training Speed: 7.79 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 1 day, 2:19:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:21:01 INFO loss_tracker.py:84 | Epoch[39/239] Step[99] GlobalStep[15309/NA]: det_loss_cls_0[0.5769] det_loss_box_0[0.7188] det_loss_cls_1[0.5363] det_loss_box_1[0.6019] det_loss_cls_2[0.5375] det_loss_box_2[0.5573] det_loss_cls_3[0.5205] det_loss_box_3[0.5367] det_loss_cls_4[0.4928] det_loss_box_4[0.5266] det_loss_cls_5[0.4811] det_loss_box_5[0.5285] det_loss_cls_dn_0[0.1011] det_loss_box_dn_0[0.2726] det_loss_cls_dn_1[0.0978] det_loss_box_dn_1[0.2390] det_loss_cls_dn_2[0.0984] det_loss_box_dn_2[0.2273] det_loss_cls_dn_3[0.0973] det_loss_box_dn_3[0.2252] det_loss_cls_dn_4[0.0957] det_loss_box_dn_4[0.2244] det_loss_cls_dn_5[0.0952] det_loss_box_dn_5[0.2238] loss_depth[0.0393] total_loss[8.6521]
Rank[0/8] 06/19/2025 19:21:17 INFO stats.py:314 | Epoch[39] Step[114] GlobalStep[15324] Training Speed: 6.18 samples/sec across all devices.	Average Step Time: 1.29 sec.	Estimated Remaining Time: 1 day, 2:18:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:21:27 INFO loss_tracker.py:84 | Epoch[39/239] Step[124] GlobalStep[15334/NA]: det_loss_cls_0[0.5787] det_loss_box_0[0.7454] det_loss_cls_1[0.5567] det_loss_box_1[0.6003] det_loss_cls_2[0.5532] det_loss_box_2[0.5596] det_loss_cls_3[0.5444] det_loss_box_3[0.5503] det_loss_cls_4[0.5206] det_loss_box_4[0.5379] det_loss_cls_5[0.5035] det_loss_box_5[0.5370] det_loss_cls_dn_0[0.1155] det_loss_box_dn_0[0.3912] det_loss_cls_dn_1[0.1111] det_loss_box_dn_1[0.3501] det_loss_cls_dn_2[0.1115] det_loss_box_dn_2[0.3387] det_loss_cls_dn_3[0.1103] det_loss_box_dn_3[0.3360] det_loss_cls_dn_4[0.1101] det_loss_box_dn_4[0.3346] det_loss_cls_dn_5[0.1087] det_loss_box_dn_5[0.3327] loss_depth[0.0419] total_loss[9.5798]
Rank[0/8] 06/19/2025 19:21:43 INFO stats.py:314 | Epoch[39] Step[139] GlobalStep[15349] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 2:17:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:21:53 INFO loss_tracker.py:84 | Epoch[39/239] Step[149] GlobalStep[15359/NA]: det_loss_cls_0[0.4464] det_loss_box_0[0.6304] det_loss_cls_1[0.4357] det_loss_box_1[0.5167] det_loss_cls_2[0.4273] det_loss_box_2[0.4656] det_loss_cls_3[0.4069] det_loss_box_3[0.4561] det_loss_cls_4[0.3767] det_loss_box_4[0.4479] det_loss_cls_5[0.3663] det_loss_box_5[0.4427] det_loss_cls_dn_0[0.1044] det_loss_box_dn_0[0.3608] det_loss_cls_dn_1[0.1013] det_loss_box_dn_1[0.3177] det_loss_cls_dn_2[0.1009] det_loss_box_dn_2[0.3005] det_loss_cls_dn_3[0.0998] det_loss_box_dn_3[0.2974] det_loss_cls_dn_4[0.0975] det_loss_box_dn_4[0.2966] det_loss_cls_dn_5[0.0967] det_loss_box_dn_5[0.2952] loss_depth[0.0388] total_loss[7.9264]
Rank[0/8] 06/19/2025 19:22:09 INFO stats.py:314 | Epoch[39] Step[164] GlobalStep[15374] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 2:16:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:22:19 INFO loss_tracker.py:84 | Epoch[39/239] Step[174] GlobalStep[15384/NA]: det_loss_cls_0[0.5390] det_loss_box_0[0.7167] det_loss_cls_1[0.5166] det_loss_box_1[0.5908] det_loss_cls_2[0.5030] det_loss_box_2[0.5391] det_loss_cls_3[0.4835] det_loss_box_3[0.5203] det_loss_cls_4[0.4477] det_loss_box_4[0.5142] det_loss_cls_5[0.4318] det_loss_box_5[0.5188] det_loss_cls_dn_0[0.1114] det_loss_box_dn_0[0.3303] det_loss_cls_dn_1[0.1078] det_loss_box_dn_1[0.2865] det_loss_cls_dn_2[0.1073] det_loss_box_dn_2[0.2676] det_loss_cls_dn_3[0.1059] det_loss_box_dn_3[0.2642] det_loss_cls_dn_4[0.1050] det_loss_box_dn_4[0.2627] det_loss_cls_dn_5[0.1041] det_loss_box_dn_5[0.2612] loss_depth[0.0384] total_loss[8.6737]
Rank[0/8] 06/19/2025 19:22:35 INFO stats.py:314 | Epoch[39] Step[189] GlobalStep[15399] Training Speed: 8.13 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 2:15:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:22:46 INFO loss_tracker.py:84 | Epoch[39/239] Step[199] GlobalStep[15409/NA]: det_loss_cls_0[0.5229] det_loss_box_0[0.6967] det_loss_cls_1[0.5164] det_loss_box_1[0.5741] det_loss_cls_2[0.5052] det_loss_box_2[0.5198] det_loss_cls_3[0.4859] det_loss_box_3[0.5091] det_loss_cls_4[0.4566] det_loss_box_4[0.5022] det_loss_cls_5[0.4373] det_loss_box_5[0.5064] det_loss_cls_dn_0[0.1012] det_loss_box_dn_0[0.2918] det_loss_cls_dn_1[0.0973] det_loss_box_dn_1[0.2608] det_loss_cls_dn_2[0.0970] det_loss_box_dn_2[0.2506] det_loss_cls_dn_3[0.0961] det_loss_box_dn_3[0.2502] det_loss_cls_dn_4[0.0941] det_loss_box_dn_4[0.2498] det_loss_cls_dn_5[0.0932] det_loss_box_dn_5[0.2488] loss_depth[0.0402] total_loss[8.4040]
Rank[0/8] 06/19/2025 19:23:02 INFO stats.py:314 | Epoch[39] Step[214] GlobalStep[15424] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 2:15:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:23:12 INFO loss_tracker.py:84 | Epoch[39/239] Step[224] GlobalStep[15434/NA]: det_loss_cls_0[0.5373] det_loss_box_0[0.7260] det_loss_cls_1[0.5190] det_loss_box_1[0.5864] det_loss_cls_2[0.5088] det_loss_box_2[0.5310] det_loss_cls_3[0.5054] det_loss_box_3[0.5069] det_loss_cls_4[0.4759] det_loss_box_4[0.4978] det_loss_cls_5[0.4555] det_loss_box_5[0.5004] det_loss_cls_dn_0[0.1042] det_loss_box_dn_0[0.2764] det_loss_cls_dn_1[0.1017] det_loss_box_dn_1[0.2391] det_loss_cls_dn_2[0.1013] det_loss_box_dn_2[0.2246] det_loss_cls_dn_3[0.1007] det_loss_box_dn_3[0.2215] det_loss_cls_dn_4[0.0991] det_loss_box_dn_4[0.2214] det_loss_cls_dn_5[0.0992] det_loss_box_dn_5[0.2202] loss_depth[0.0422] total_loss[8.4017]
Rank[0/8] 06/19/2025 19:23:28 INFO stats.py:314 | Epoch[39] Step[239] GlobalStep[15449] Training Speed: 8.88 samples/sec across all devices.	Average Step Time: 0.90 sec.	Estimated Remaining Time: 1 day, 2:14:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:23:38 INFO loss_tracker.py:84 | Epoch[39/239] Step[249] GlobalStep[15459/NA]: det_loss_cls_0[0.5383] det_loss_box_0[0.7080] det_loss_cls_1[0.5254] det_loss_box_1[0.5531] det_loss_cls_2[0.5130] det_loss_box_2[0.4908] det_loss_cls_3[0.4967] det_loss_box_3[0.4722] det_loss_cls_4[0.4510] det_loss_box_4[0.4694] det_loss_cls_5[0.4418] det_loss_box_5[0.4668] det_loss_cls_dn_0[0.1136] det_loss_box_dn_0[0.3108] det_loss_cls_dn_1[0.1100] det_loss_box_dn_1[0.2654] det_loss_cls_dn_2[0.1091] det_loss_box_dn_2[0.2462] det_loss_cls_dn_3[0.1085] det_loss_box_dn_3[0.2433] det_loss_cls_dn_4[0.1075] det_loss_box_dn_4[0.2428] det_loss_cls_dn_5[0.1068] det_loss_box_dn_5[0.2418] loss_depth[0.0398] total_loss[8.3718]
Rank[0/8] 06/19/2025 19:23:54 INFO stats.py:314 | Epoch[39] Step[264] GlobalStep[15474] Training Speed: 6.47 samples/sec across all devices.	Average Step Time: 1.24 sec.	Estimated Remaining Time: 1 day, 2:13:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:24:05 INFO loss_tracker.py:84 | Epoch[39/239] Step[274] GlobalStep[15484/NA]: det_loss_cls_0[0.5799] det_loss_box_0[0.8061] det_loss_cls_1[0.5712] det_loss_box_1[0.6731] det_loss_cls_2[0.5548] det_loss_box_2[0.6213] det_loss_cls_3[0.5348] det_loss_box_3[0.6025] det_loss_cls_4[0.5116] det_loss_box_4[0.5898] det_loss_cls_5[0.4943] det_loss_box_5[0.5934] det_loss_cls_dn_0[0.1019] det_loss_box_dn_0[0.3354] det_loss_cls_dn_1[0.0995] det_loss_box_dn_1[0.3017] det_loss_cls_dn_2[0.0979] det_loss_box_dn_2[0.2860] det_loss_cls_dn_3[0.0975] det_loss_box_dn_3[0.2836] det_loss_cls_dn_4[0.0959] det_loss_box_dn_4[0.2823] det_loss_cls_dn_5[0.0952] det_loss_box_dn_5[0.2814] loss_depth[0.0384] total_loss[9.5292]
Rank[0/8] 06/19/2025 19:24:21 INFO stats.py:314 | Epoch[39] Step[289] GlobalStep[15499] Training Speed: 7.84 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1 day, 2:12:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:24:32 INFO loss_tracker.py:84 | Epoch[39/239] Step[299] GlobalStep[15509/NA]: det_loss_cls_0[0.5062] det_loss_box_0[0.7389] det_loss_cls_1[0.4988] det_loss_box_1[0.6024] det_loss_cls_2[0.4761] det_loss_box_2[0.5546] det_loss_cls_3[0.4612] det_loss_box_3[0.5272] det_loss_cls_4[0.4334] det_loss_box_4[0.5238] det_loss_cls_5[0.4178] det_loss_box_5[0.5255] det_loss_cls_dn_0[0.1002] det_loss_box_dn_0[0.3429] det_loss_cls_dn_1[0.0978] det_loss_box_dn_1[0.2968] det_loss_cls_dn_2[0.0966] det_loss_box_dn_2[0.2815] det_loss_cls_dn_3[0.0948] det_loss_box_dn_3[0.2789] det_loss_cls_dn_4[0.0940] det_loss_box_dn_4[0.2784] det_loss_cls_dn_5[0.0927] det_loss_box_dn_5[0.2766] loss_depth[0.0422] total_loss[8.6392]
Rank[0/8] 06/19/2025 19:24:47 INFO stats.py:314 | Epoch[39] Step[314] GlobalStep[15524] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 2:11:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:24:58 INFO loss_tracker.py:84 | Epoch[39/239] Step[324] GlobalStep[15534/NA]: det_loss_cls_0[0.5868] det_loss_box_0[0.7358] det_loss_cls_1[0.5571] det_loss_box_1[0.5925] det_loss_cls_2[0.5462] det_loss_box_2[0.5443] det_loss_cls_3[0.5263] det_loss_box_3[0.5220] det_loss_cls_4[0.4961] det_loss_box_4[0.5274] det_loss_cls_5[0.4816] det_loss_box_5[0.5293] det_loss_cls_dn_0[0.1029] det_loss_box_dn_0[0.3286] det_loss_cls_dn_1[0.0980] det_loss_box_dn_1[0.2875] det_loss_cls_dn_2[0.0957] det_loss_box_dn_2[0.2713] det_loss_cls_dn_3[0.0963] det_loss_box_dn_3[0.2678] det_loss_cls_dn_4[0.0953] det_loss_box_dn_4[0.2665] det_loss_cls_dn_5[0.0940] det_loss_box_dn_5[0.2647] loss_depth[0.0407] total_loss[8.9547]
Rank[0/8] 06/19/2025 19:25:13 INFO stats.py:314 | Epoch[39] Step[339] GlobalStep[15549] Training Speed: 6.57 samples/sec across all devices.	Average Step Time: 1.22 sec.	Estimated Remaining Time: 1 day, 2:10:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:25:24 INFO loss_tracker.py:84 | Epoch[39/239] Step[349] GlobalStep[15559/NA]: det_loss_cls_0[0.5941] det_loss_box_0[0.7813] det_loss_cls_1[0.5654] det_loss_box_1[0.6460] det_loss_cls_2[0.5477] det_loss_box_2[0.5959] det_loss_cls_3[0.5351] det_loss_box_3[0.5738] det_loss_cls_4[0.5055] det_loss_box_4[0.5673] det_loss_cls_5[0.4867] det_loss_box_5[0.5755] det_loss_cls_dn_0[0.1084] det_loss_box_dn_0[0.3949] det_loss_cls_dn_1[0.1039] det_loss_box_dn_1[0.3537] det_loss_cls_dn_2[0.1028] det_loss_box_dn_2[0.3364] det_loss_cls_dn_3[0.1020] det_loss_box_dn_3[0.3347] det_loss_cls_dn_4[0.1009] det_loss_box_dn_4[0.3331] det_loss_cls_dn_5[0.0997] det_loss_box_dn_5[0.3298] loss_depth[0.0422] total_loss[9.7166]
Rank[0/8] 06/19/2025 19:25:40 INFO stats.py:314 | Epoch[39] Step[364] GlobalStep[15574] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 2:10:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:25:50 INFO loss_tracker.py:84 | Epoch[39/239] Step[374] GlobalStep[15584/NA]: det_loss_cls_0[0.5728] det_loss_box_0[0.7940] det_loss_cls_1[0.5502] det_loss_box_1[0.6353] det_loss_cls_2[0.5414] det_loss_box_2[0.5737] det_loss_cls_3[0.5360] det_loss_box_3[0.5478] det_loss_cls_4[0.4999] det_loss_box_4[0.5317] det_loss_cls_5[0.4818] det_loss_box_5[0.5338] det_loss_cls_dn_0[0.1104] det_loss_box_dn_0[0.2912] det_loss_cls_dn_1[0.1068] det_loss_box_dn_1[0.2534] det_loss_cls_dn_2[0.1056] det_loss_box_dn_2[0.2375] det_loss_cls_dn_3[0.1051] det_loss_box_dn_3[0.2357] det_loss_cls_dn_4[0.1038] det_loss_box_dn_4[0.2350] det_loss_cls_dn_5[0.1031] det_loss_box_dn_5[0.2336] loss_depth[0.0423] total_loss[8.9621]
Rank[0/8] 06/19/2025 19:26:05 INFO stats.py:314 | Epoch[39] Step[389] GlobalStep[15599] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 2:09:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:26:06 INFO trainer.py:268 | 
==================================================BEGIN EVAL==================================================
Rank[0/8] 06/19/2025 19:27:04 INFO trainer.py:275 | eval: 10
Rank[0/8] 06/19/2025 19:27:13 INFO trainer.py:275 | eval: 20
Rank[0/8] 06/19/2025 19:27:26 INFO trainer.py:275 | eval: 30
Rank[0/8] 06/19/2025 19:27:35 INFO trainer.py:275 | eval: 40
Rank[0/8] 06/19/2025 19:27:44 INFO trainer.py:275 | eval: 50
Rank[0/8] 06/19/2025 19:27:51 INFO trainer.py:275 | eval: 60
Rank[0/8] 06/19/2025 19:28:01 INFO trainer.py:275 | eval: 70
Rank[0/8] 06/19/2025 19:28:14 INFO trainer.py:275 | eval: 80
Rank[0/8] 06/19/2025 19:28:24 INFO trainer.py:275 | eval: 90
Rank[0/8] 06/19/2025 19:28:32 INFO trainer.py:275 | eval: 100
Rank[0/8] 06/19/2025 19:28:40 INFO metrics.py:428 | number of results: 824
Rank[0/8] 06/19/2025 19:32:47 INFO metrics.py:332 | eval results of overall:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| sink                | 0.6117  | 0.8313  | 0.1015  | 0.3374  |
| toilet              | 0.7142  | 0.8832  | 0.4128  | 0.6204  |
| bin                 | 0.5289  | 0.8212  | 0.2489  | 0.5363  |
| bathtub             | 0.6821  | 0.8824  | 0.5513  | 0.7206  |
| picture             | 0.1238  | 0.3524  | 0.0040  | 0.0603  |
| towel               | 0.2398  | 0.6194  | 0.0144  | 0.1677  |
| toilet paper        | 0.2769  | 0.5433  | 0.0150  | 0.1575  |
| curtain             | 0.3019  | 0.7821  | 0.0372  | 0.3164  |
| stopcock            | 0.0647  | 0.4828  | 0.0011  | 0.0345  |
| mirror              | 0.0795  | 0.5272  | 0.0050  | 0.0761  |
| bar                 | 0.0048  | 0.0909  | 0.0000  | 0.0303  |
| bag                 | 0.1818  | 0.6703  | 0.0248  | 0.2000  |
| door                | 0.1341  | 0.6125  | 0.0126  | 0.1968  |
| doorframe           | 0.1648  | 0.7426  | 0.0269  | 0.3503  |
| tissue box          | 0.0088  | 0.5152  | 0.0010  | 0.0606  |
| hair dryer          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vanity              | 0.3360  | 0.8000  | 0.2387  | 0.7111  |
| switch              | 0.0018  | 0.1316  | 0.0000  | 0.0000  |
| rail                | 0.0041  | 0.2031  | 0.0000  | 0.0000  |
| backpack            | 0.4048  | 0.7903  | 0.1760  | 0.4839  |
| clothes             | 0.0981  | 0.5414  | 0.0106  | 0.1847  |
| socket              | 0.0065  | 0.1460  | 0.0000  | 0.0000  |
| faucet              | 0.0149  | 0.1915  | 0.0000  | 0.0000  |
| ledge               | 0.0049  | 0.1667  | 0.0000  | 0.0000  |
| plunger             | 0.3321  | 1.0000  | 0.0400  | 0.6000  |
| light               | 0.0107  | 0.1774  | 0.0001  | 0.0171  |
| soap dish           | 0.3303  | 0.6667  | 0.0000  | 0.0000  |
| cabinet             | 0.1817  | 0.6884  | 0.0584  | 0.3850  |
| bottle              | 0.0253  | 0.3677  | 0.0002  | 0.0529  |
| box                 | 0.1219  | 0.5007  | 0.0187  | 0.1533  |
| shelf               | 0.1693  | 0.6601  | 0.0385  | 0.3147  |
| shower              | 0.0273  | 0.2667  | 0.0131  | 0.1000  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| paper               | 0.0079  | 0.2041  | 0.0002  | 0.0207  |
| counter             | 0.0395  | 0.5780  | 0.0043  | 0.1743  |
| blanket             | 0.0875  | 0.6400  | 0.0090  | 0.2000  |
| cup                 | 0.0255  | 0.3220  | 0.0002  | 0.0424  |
| tissue              | 0.0135  | 0.3846  | 0.0078  | 0.1538  |
| paper cutter        | 0.3978  | 0.8333  | 0.0128  | 0.1667  |
| mat                 | 0.0111  | 0.2973  | 0.0000  | 0.0000  |
| window              | 0.1278  | 0.5704  | 0.0149  | 0.1931  |
| hamper              | 0.1868  | 0.6364  | 0.1524  | 0.5455  |
| washbasin           | 0.0017  | 0.2000  | 0.0000  | 0.0667  |
| frame               | 0.0197  | 0.3469  | 0.0001  | 0.0204  |
| vent                | 0.0033  | 0.0833  | 0.0000  | 0.0000  |
| decoration          | 0.0138  | 0.3786  | 0.0004  | 0.0786  |
| stall               | 0.1605  | 0.7647  | 0.0014  | 0.1176  |
| basket              | 0.1656  | 0.6435  | 0.0694  | 0.2522  |
| toothpaste          | 0.0070  | 0.4000  | 0.0000  | 0.0000  |
| grab bar            | 0.0060  | 0.2500  | 0.0000  | 0.0000  |
| lamp                | 0.2429  | 0.5343  | 0.0349  | 0.1910  |
| hanger              | 0.0025  | 0.4167  | 0.0000  | 0.0000  |
| rack                | 0.0123  | 0.4423  | 0.0031  | 0.2500  |
| monitor             | 0.5072  | 0.7174  | 0.0524  | 0.1739  |
| chair               | 0.5437  | 0.8077  | 0.3418  | 0.5803  |
| keyboard            | 0.0201  | 0.1395  | 0.0000  | 0.0000  |
| computer            | 0.1931  | 0.6230  | 0.0009  | 0.1148  |
| mouse               | 0.0002  | 0.0270  | 0.0000  | 0.0000  |
| radiator            | 0.4122  | 0.6556  | 0.1616  | 0.2556  |
| desk                | 0.3341  | 0.8764  | 0.1907  | 0.6517  |
| table               | 0.3477  | 0.8511  | 0.2097  | 0.6426  |
| blackboard          | 0.2886  | 0.6707  | 0.0225  | 0.1220  |
| plug                | 0.0023  | 0.0526  | 0.0000  | 0.0000  |
| board               | 0.0946  | 0.5484  | 0.0147  | 0.2097  |
| telephone           | 0.1943  | 0.6000  | 0.0202  | 0.1000  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wardrobe            | 0.2336  | 0.7711  | 0.0754  | 0.4699  |
| can                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| file                | 0.0489  | 0.4412  | 0.0003  | 0.0294  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| windowsill          | 0.0191  | 0.4348  | 0.0010  | 0.0652  |
| jacket              | 0.6342  | 0.9583  | 0.0993  | 0.5000  |
| speaker             | 0.0064  | 0.4091  | 0.0000  | 0.0000  |
| book                | 0.0364  | 0.3478  | 0.0012  | 0.0391  |
| stand               | 0.4436  | 0.8415  | 0.2578  | 0.6098  |
| pipe                | 0.0016  | 0.1429  | 0.0000  | 0.0000  |
| bed                 | 0.4774  | 0.9112  | 0.2591  | 0.8225  |
| pillow              | 0.3003  | 0.5900  | 0.0238  | 0.1409  |
| shoe                | 0.0712  | 0.3271  | 0.0036  | 0.0374  |
| dresser             | 0.5128  | 0.9444  | 0.3690  | 0.8333  |
| couch               | 0.3419  | 0.8471  | 0.2182  | 0.6694  |
| stool               | 0.1557  | 0.7143  | 0.0972  | 0.4706  |
| tv                  | 0.4340  | 0.7228  | 0.0716  | 0.2277  |
| toy                 | 0.1280  | 0.4545  | 0.0273  | 0.1061  |
| ottoman             | 0.0674  | 0.7778  | 0.0443  | 0.5278  |
| scale               | 0.1242  | 0.3636  | 0.0000  | 0.0000  |
| cushion             | 0.0661  | 0.5686  | 0.0136  | 0.1373  |
| case                | 0.0468  | 0.3898  | 0.0311  | 0.1356  |
| container           | 0.0079  | 0.2712  | 0.0017  | 0.1525  |
| tube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clock               | 0.0627  | 0.2766  | 0.0107  | 0.0426  |
| carpet              | 0.0236  | 0.3571  | 0.0000  | 0.0238  |
| calendar            | 0.0009  | 0.1667  | 0.0000  | 0.0000  |
| bench               | 0.2018  | 0.6415  | 0.0908  | 0.3585  |
| vacuum cleaner      | 0.1259  | 0.4286  | 0.0516  | 0.4286  |
| treadmill           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stairs              | 0.0434  | 0.3472  | 0.0033  | 0.0833  |
| guitar              | 0.1897  | 0.5000  | 0.0196  | 0.1667  |
| luggage             | 0.0783  | 1.0000  | 0.0238  | 0.3333  |
| refrigerator        | 0.5196  | 0.8400  | 0.1913  | 0.4400  |
| coffee maker        | 0.4000  | 0.7586  | 0.1530  | 0.3793  |
| adhesive tape       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| remote control      | 0.0145  | 0.2222  | 0.0000  | 0.0000  |
| notebook            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| purse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| laptop              | 0.2879  | 0.6087  | 0.0172  | 0.1304  |
| tray                | 0.0014  | 0.1429  | 0.0000  | 0.0000  |
| printer             | 0.4868  | 0.7755  | 0.2247  | 0.4694  |
| broom               | 0.0161  | 0.2857  | 0.0000  | 0.0000  |
| mop                 | 0.6190  | 1.0000  | 0.0000  | 0.0000  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket              | 0.2673  | 0.6923  | 0.0927  | 0.2821  |
| soap dispenser      | 0.5281  | 0.8519  | 0.0716  | 0.1852  |
| dispenser           | 0.2596  | 0.7045  | 0.1071  | 0.3409  |
| mailbox             | 0.1009  | 1.0000  | 0.0242  | 0.4000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| copier              | 0.7842  | 0.9167  | 0.1299  | 0.5000  |
| beam                | 0.0001  | 0.0455  | 0.0000  | 0.0000  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sign                | 0.0341  | 0.3000  | 0.0021  | 0.0333  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| column              | 0.0110  | 0.3684  | 0.0015  | 0.1316  |
| pillar              | 0.2110  | 0.6744  | 0.0326  | 0.3256  |
| glass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine             | 0.3113  | 0.8947  | 0.1315  | 0.3684  |
| arch                | 0.0014  | 0.2333  | 0.0003  | 0.1000  |
| washing machine     | 0.4082  | 0.8571  | 0.1049  | 0.5000  |
| footstool           | 0.2500  | 0.5000  | 0.2500  | 0.5000  |
| projector           | 0.0556  | 0.5000  | 0.0000  | 0.0000  |
| window frame        | 0.0371  | 0.4242  | 0.0235  | 0.2121  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cube                | 0.2500  | 1.0000  | 0.2500  | 1.0000  |
| cart                | 0.0016  | 0.2500  | 0.0016  | 0.2500  |
| piano               | 0.4000  | 0.4000  | 0.2667  | 0.4000  |
| plant               | 0.2292  | 0.6490  | 0.0575  | 0.2887  |
| fireplace           | 0.0996  | 0.4286  | 0.0002  | 0.0714  |
| bicycle             | 0.0623  | 0.8333  | 0.0221  | 0.3333  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fan                 | 0.3198  | 0.7969  | 0.0640  | 0.3438  |
| person              | 0.1694  | 0.5000  | 0.0000  | 0.0000  |
| plate               | 0.0444  | 0.3077  | 0.0031  | 0.0577  |
| map                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headphones          | 0.0013  | 0.2000  | 0.0013  | 0.2000  |
| ball                | 0.4107  | 0.5238  | 0.1782  | 0.3333  |
| poster              | 0.0114  | 0.0758  | 0.0000  | 0.0000  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher   | 0.0725  | 0.6000  | 0.0000  | 0.0000  |
| microwave           | 0.4510  | 0.7347  | 0.0389  | 0.2653  |
| basin               | 0.0048  | 0.1176  | 0.0002  | 0.0588  |
| toiletry            | 0.0132  | 0.3478  | 0.0000  | 0.0000  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| holder              | 0.0241  | 0.2222  | 0.0000  | 0.0000  |
| tablet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water cooler        | 0.8333  | 1.0000  | 0.8333  | 1.0000  |
| dish rack           | 0.4111  | 0.6000  | 0.0037  | 0.2000  |
| toaster             | 0.2536  | 0.5000  | 0.0000  | 0.0000  |
| teapot              | 0.0035  | 0.3333  | 0.0000  | 0.0000  |
| kettle              | 0.0515  | 0.4444  | 0.0098  | 0.1111  |
| stove               | 0.4742  | 0.8056  | 0.1956  | 0.3333  |
| dishwasher          | 0.1548  | 0.3000  | 0.0333  | 0.1000  |
| bowl                | 0.0578  | 0.4058  | 0.0130  | 0.1594  |
| range hood          | 0.0587  | 0.5714  | 0.0000  | 0.0000  |
| oven                | 0.2599  | 0.7143  | 0.0359  | 0.2500  |
| flowerpot           | 0.0238  | 0.5294  | 0.0027  | 0.1176  |
| ladder              | 0.0938  | 0.4118  | 0.0635  | 0.2353  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| player              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sponge              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coat hanger         | 0.0100  | 1.0000  | 0.0100  | 1.0000  |
| dumbbell            | 0.0750  | 0.6667  | 0.0000  | 0.0000  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap                | 0.0041  | 0.3333  | 0.0009  | 0.1667  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| air conditioner     | 0.4000  | 0.4000  | 0.0000  | 0.0000  |
| blinds              | 0.0242  | 0.3000  | 0.0064  | 0.1200  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| folder              | 0.0063  | 0.2143  | 0.0012  | 0.0714  |
| thermostat          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garbage             | 0.2811  | 0.7500  | 0.0113  | 0.2500  |
| conducting wire     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| commode             | 0.3311  | 0.8077  | 0.2021  | 0.5769  |
| candle              | 0.0628  | 0.3889  | 0.0102  | 0.1111  |
| kitchenware         | 0.1129  | 0.5000  | 0.0003  | 0.0435  |
| napkins             | 0.0074  | 0.3333  | 0.0074  | 0.3333  |
| vase                | 0.0946  | 0.5000  | 0.0132  | 0.2600  |
| price tag           | 0.1429  | 0.1429  | 0.0000  | 0.0000  |
| album               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| magazine            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jar                 | 0.0008  | 0.0769  | 0.0000  | 0.0000  |
| heater              | 0.3628  | 0.6897  | 0.0402  | 0.1724  |
| humidifier          | 0.1667  | 0.5000  | 0.0000  | 0.0000  |
| roll                | 0.1226  | 0.4000  | 0.0000  | 0.0000  |
| umbrella            | 0.2000  | 0.2000  | 0.0000  | 0.0000  |
| jalousie            | 0.0909  | 1.0000  | 0.0030  | 0.5000  |
| drawer              | 0.1244  | 0.5000  | 0.0138  | 0.3889  |
| partition           | 0.0275  | 0.6667  | 0.0000  | 0.0000  |
| device              | 0.0025  | 0.0909  | 0.0000  | 0.0000  |
| pack                | 0.0360  | 0.6667  | 0.0027  | 0.0667  |
| chandelier          | 0.2193  | 0.4762  | 0.0068  | 0.0476  |
| clothes dryer       | 0.1585  | 0.7333  | 0.0467  | 0.4000  |
| brush               | 0.0695  | 0.2857  | 0.0000  | 0.0000  |
| sunglasses          | 0.0298  | 0.4615  | 0.0000  | 0.0000  |
| candlestick         | 0.3333  | 0.3333  | 0.0000  | 0.0000  |
| crib                | 0.1911  | 1.0000  | 0.0167  | 0.3333  |
| statue              | 0.1242  | 0.3158  | 0.0555  | 0.1053  |
| shampoo             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| body loofah         | 0.0167  | 0.5000  | 0.0031  | 0.5000  |
| beanbag             | 1.0000  | 1.0000  | 0.6944  | 0.8333  |
| food                | 0.0261  | 0.2727  | 0.0000  | 0.0000  |
| menu                | 0.1603  | 0.5714  | 0.0079  | 0.1429  |
| bread               | 0.0667  | 0.4000  | 0.0000  | 0.0000  |
| spoon               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pan                 | 0.0347  | 0.3529  | 0.0049  | 0.0588  |
| pot                 | 0.0750  | 0.4444  | 0.0036  | 0.1852  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| balcony             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| salt                | 0.8333  | 1.0000  | 0.0000  | 0.0000  |
| crate               | 0.4734  | 0.8571  | 0.0004  | 0.1429  |
| radio               | 0.0625  | 0.2500  | 0.0179  | 0.2500  |
| steps               | 0.0009  | 0.2000  | 0.0000  | 0.0000  |
| drum                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet               | 0.3889  | 0.6667  | 0.3889  | 0.6667  |
| countertop          | 0.0153  | 0.3571  | 0.0007  | 0.1071  |
| roof                | 0.0002  | 0.1250  | 0.0000  | 0.0417  |
| doorway             | 0.0001  | 0.1111  | 0.0000  | 0.0000  |
| screen              | 0.0699  | 0.4000  | 0.0667  | 0.2000  |
| wine                | 0.0079  | 0.0476  | 0.0000  | 0.0000  |
| fruit               | 0.0932  | 0.3571  | 0.0000  | 0.0000  |
| garage door         | 0.4051  | 0.6667  | 0.2292  | 0.5000  |
| handle              | 0.0125  | 0.2500  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headboard           | 0.0034  | 0.1250  | 0.0000  | 0.0000  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchen island      | 0.1079  | 0.8571  | 0.1036  | 0.8571  |
| excercise equipment | 0.2537  | 0.7000  | 0.0255  | 0.2500  |
| pool table          | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| helmets             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water heater        | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sack                | 0.5041  | 0.7143  | 0.0102  | 0.1429  |
| coil                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flush               | 0.0200  | 0.5000  | 0.0000  | 0.0000  |
| cosmetics           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| letter              | 0.0052  | 0.2500  | 0.0000  | 0.0000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| barricade           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| overall             | 0.1362  | 0.3963  | 0.0444  | 0.1590  |
| head                | 0.1954  | 0.5566  | 0.0662  | 0.2386  |
| common              | 0.1254  | 0.3682  | 0.0311  | 0.1177  |
| tail                | 0.0813  | 0.2468  | 0.0353  | 0.1171  |
| small               | 0.0280  | 0.1592  | 0.0012  | 0.0182  |
| medium              | 0.1421  | 0.4515  | 0.0476  | 0.1709  |
| large               | 0.0818  | 0.4837  | 0.0303  | 0.2230  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/19/2025 19:32:47 INFO metrics.py:437 | number of scannet results: 250
Rank[0/8] 06/19/2025 19:34:07 INFO metrics.py:332 | eval results of scannet:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| sink                | 0.7761  | 0.8750  | 0.1776  | 0.3977  |
| toilet              | 0.7837  | 0.9464  | 0.5228  | 0.7321  |
| bin                 | 0.5863  | 0.8298  | 0.2936  | 0.5660  |
| bathtub             | 0.8530  | 0.9688  | 0.7796  | 0.8750  |
| picture             | 0.1847  | 0.4776  | 0.0121  | 0.0970  |
| towel               | 0.2866  | 0.7025  | 0.0184  | 0.2149  |
| toilet paper        | 0.3745  | 0.5696  | 0.0253  | 0.1899  |
| curtain             | 0.3815  | 0.7788  | 0.0649  | 0.3894  |
| stopcock            | 0.0647  | 0.4828  | 0.0011  | 0.0345  |
| mirror              | 0.1006  | 0.5542  | 0.0131  | 0.0964  |
| bar                 | 0.0057  | 0.0455  | 0.0000  | 0.0000  |
| bag                 | 0.1812  | 0.7123  | 0.0605  | 0.2740  |
| door                | 0.2532  | 0.7625  | 0.0406  | 0.3512  |
| doorframe           | 0.1444  | 0.7676  | 0.0098  | 0.3028  |
| tissue box          | 0.0104  | 0.5455  | 0.0001  | 0.0455  |
| hair dryer          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vanity              | 0.4388  | 0.8667  | 0.3071  | 0.7667  |
| switch              | 0.0044  | 0.1899  | 0.0000  | 0.0000  |
| rail                | 0.0189  | 0.3333  | 0.0000  | 0.0000  |
| backpack            | 0.4884  | 0.8929  | 0.2683  | 0.6429  |
| clothes             | 0.0998  | 0.5229  | 0.0211  | 0.2157  |
| socket              | 0.0077  | 0.1602  | 0.0000  | 0.0000  |
| faucet              | 0.0034  | 0.2500  | 0.0000  | 0.0000  |
| ledge               | 0.0049  | 0.1667  | 0.0000  | 0.0000  |
| plunger             | 0.3347  | 1.0000  | 0.0403  | 0.6000  |
| light               | 0.0041  | 0.2414  | 0.0003  | 0.0345  |
| soap dish           | 0.4085  | 0.7500  | 0.0000  | 0.0000  |
| cabinet             | 0.2583  | 0.7619  | 0.0763  | 0.4127  |
| bottle              | 0.0299  | 0.3895  | 0.0005  | 0.0684  |
| box                 | 0.1249  | 0.4960  | 0.0194  | 0.1548  |
| shelf               | 0.2928  | 0.8676  | 0.1008  | 0.5074  |
| shower              | 0.0293  | 0.2857  | 0.0179  | 0.0714  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| paper               | 0.0065  | 0.1887  | 0.0001  | 0.0075  |
| counter             | 0.0754  | 0.8000  | 0.0040  | 0.2286  |
| blanket             | 0.1175  | 0.5833  | 0.0191  | 0.2500  |
| cup                 | 0.0398  | 0.3623  | 0.0006  | 0.0725  |
| tissue              | 0.0019  | 0.4444  | 0.0001  | 0.1111  |
| paper cutter        | 0.2184  | 0.8000  | 0.0167  | 0.2000  |
| mat                 | 0.0090  | 0.4000  | 0.0000  | 0.0000  |
| window              | 0.2857  | 0.7901  | 0.0492  | 0.3642  |
| hamper              | 0.2061  | 0.7000  | 0.1681  | 0.6000  |
| frame               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| decoration          | 0.0955  | 0.3750  | 0.0000  | 0.0000  |
| stall               | 0.1612  | 0.7647  | 0.0014  | 0.1176  |
| basket              | 0.0207  | 0.3846  | 0.0084  | 0.1538  |
| toothpaste          | 0.0078  | 0.4000  | 0.0000  | 0.0000  |
| grab bar            | 0.0060  | 0.2500  | 0.0000  | 0.0000  |
| lamp                | 0.3686  | 0.6190  | 0.0929  | 0.2619  |
| rack                | 0.0017  | 0.1818  | 0.0014  | 0.0909  |
| monitor             | 0.5411  | 0.7429  | 0.0680  | 0.2000  |
| chair               | 0.5848  | 0.8080  | 0.3617  | 0.5843  |
| keyboard            | 0.0382  | 0.1724  | 0.0000  | 0.0000  |
| computer            | 0.2043  | 0.6154  | 0.0015  | 0.1026  |
| mouse               | 0.0005  | 0.0476  | 0.0000  | 0.0000  |
| radiator            | 0.5306  | 0.7586  | 0.2432  | 0.3276  |
| desk                | 0.4864  | 0.9747  | 0.2862  | 0.7722  |
| table               | 0.4317  | 0.8962  | 0.2753  | 0.7538  |
| blackboard          | 0.2907  | 0.6667  | 0.0357  | 0.1449  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| board               | 0.1189  | 0.7188  | 0.0312  | 0.3125  |
| telephone           | 0.2553  | 0.6875  | 0.0433  | 0.1875  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wardrobe            | 0.3765  | 0.7500  | 0.2788  | 0.3750  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| windowsill          | 0.0104  | 0.5000  | 0.0000  | 0.0000  |
| jacket              | 0.6621  | 1.0000  | 0.1039  | 0.5217  |
| speaker             | 0.0131  | 1.0000  | 0.0000  | 0.0000  |
| book                | 0.0392  | 0.3619  | 0.0019  | 0.0381  |
| stand               | 0.6787  | 0.9245  | 0.4728  | 0.8113  |
| pipe                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bed                 | 0.6196  | 1.0000  | 0.4270  | 0.9825  |
| pillow              | 0.3790  | 0.6389  | 0.1047  | 0.2847  |
| shoe                | 0.0697  | 0.3000  | 0.0063  | 0.0250  |
| dresser             | 0.6224  | 0.9730  | 0.4517  | 0.8649  |
| couch               | 0.5509  | 1.0000  | 0.4033  | 0.8750  |
| stool               | 0.1946  | 0.7273  | 0.1337  | 0.4091  |
| tv                  | 0.6958  | 0.8261  | 0.0945  | 0.2609  |
| toy                 | 0.0876  | 0.2941  | 0.0017  | 0.0588  |
| ottoman             | 0.0205  | 0.8571  | 0.0114  | 0.4286  |
| scale               | 0.2753  | 0.7500  | 0.0000  | 0.0000  |
| cushion             | 0.5127  | 1.0000  | 0.0000  | 0.0000  |
| case                | 0.4167  | 0.8947  | 0.3613  | 0.6842  |
| container           | 0.0015  | 0.1739  | 0.0002  | 0.0435  |
| tube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clock               | 0.0841  | 0.3600  | 0.0000  | 0.0000  |
| carpet              | 0.0083  | 0.4000  | 0.0000  | 0.0000  |
| calendar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bench               | 0.5942  | 1.0000  | 0.5242  | 0.8000  |
| vacuum cleaner      | 0.6667  | 0.6667  | 0.1957  | 0.6667  |
| stairs              | 0.2561  | 0.8000  | 0.0021  | 0.2000  |
| guitar              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| refrigerator        | 0.7365  | 0.9500  | 0.3123  | 0.5000  |
| coffee maker        | 0.6838  | 0.8824  | 0.3198  | 0.5882  |
| adhesive tape       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| notebook            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| purse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| laptop              | 0.3397  | 0.5333  | 0.0048  | 0.0667  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| printer             | 0.5457  | 0.8485  | 0.3575  | 0.6061  |
| broom               | 0.0334  | 0.3333  | 0.0000  | 0.0000  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket              | 0.1438  | 0.5000  | 0.0809  | 0.2857  |
| soap dispenser      | 0.6298  | 0.8571  | 0.0923  | 0.2381  |
| dispenser           | 0.3966  | 0.7436  | 0.1648  | 0.3846  |
| mailbox             | 0.1009  | 1.0000  | 0.0242  | 0.4000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| copier              | 0.7842  | 0.9167  | 0.1299  | 0.5000  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sign                | 0.0358  | 0.2857  | 0.0022  | 0.0357  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillar              | 0.0703  | 0.6000  | 0.0009  | 0.2000  |
| machine             | 0.3321  | 1.0000  | 0.1563  | 0.4375  |
| washing machine     | 0.4175  | 0.6667  | 0.3367  | 0.6667  |
| cart                | 0.0034  | 0.5000  | 0.0034  | 0.5000  |
| plant               | 0.2947  | 0.6891  | 0.0766  | 0.2941  |
| bicycle             | 0.2973  | 1.0000  | 0.1319  | 0.6667  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fan                 | 0.4082  | 0.8750  | 0.2500  | 0.2500  |
| person              | 0.1698  | 0.5000  | 0.0000  | 0.0000  |
| plate               | 0.0145  | 0.4000  | 0.0000  | 0.0000  |
| map                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headphones          | 0.0023  | 0.2000  | 0.0023  | 0.2000  |
| ball                | 0.4028  | 0.4167  | 0.1111  | 0.1667  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher   | 0.0242  | 0.6667  | 0.0000  | 0.0000  |
| microwave           | 0.5009  | 0.8000  | 0.0575  | 0.3500  |
| basin               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| holder              | 0.0065  | 0.2000  | 0.0000  | 0.0000  |
| water cooler        | 0.8333  | 1.0000  | 0.8333  | 1.0000  |
| dish rack           | 0.5183  | 0.7500  | 0.0061  | 0.2500  |
| toaster             | 0.0833  | 0.2500  | 0.0000  | 0.0000  |
| teapot              | 0.0208  | 0.5000  | 0.0000  | 0.0000  |
| kettle              | 0.0908  | 0.5000  | 0.0000  | 0.0000  |
| stove               | 0.9041  | 1.0000  | 0.7906  | 0.8889  |
| dishwasher          | 0.1667  | 0.3333  | 0.0000  | 0.0000  |
| bowl                | 0.0397  | 0.2222  | 0.0370  | 0.1111  |
| range hood          | 0.1500  | 0.4000  | 0.0000  | 0.0000  |
| oven                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flowerpot           | 0.0022  | 0.5000  | 0.0000  | 0.0000  |
| ladder              | 0.2000  | 0.4000  | 0.1000  | 0.2000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coat hanger         | 0.0303  | 1.0000  | 0.0303  | 1.0000  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pot                 | 0.1000  | 0.5000  | 0.1000  | 0.5000  |
| drawer              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crate               | 0.0172  | 0.5000  | 0.0172  | 0.5000  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.0100  | 0.5000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.4000  | 1.0000  | 0.1000  | 0.5000  |
| -------             | ------- | ------- | ------- | ------- |
| scannet             | 0.1964  | 0.4765  | 0.0799  | 0.2156  |
| head                | 0.2609  | 0.5984  | 0.1177  | 0.2821  |
| common              | 0.1442  | 0.3705  | 0.0351  | 0.1393  |
| tail                | 0.0715  | 0.2617  | 0.0464  | 0.1500  |
| small               | 0.0280  | 0.1649  | 0.0026  | 0.0191  |
| medium              | 0.2254  | 0.5877  | 0.0955  | 0.2571  |
| large               | 0.1674  | 0.7486  | 0.0614  | 0.4179  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/19/2025 19:34:07 INFO metrics.py:437 | number of 3rscan results: 235
Rank[0/8] 06/19/2025 19:35:15 INFO metrics.py:332 | eval results of 3rscan:
+-------------------+---------+---------+---------+---------+
| classes           | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+-------------------+---------+---------+---------+---------+
| keyboard          | 0.0108  | 0.0833  | 0.0000  | 0.0000  |
| chair             | 0.5881  | 0.8388  | 0.3977  | 0.6257  |
| monitor           | 0.5027  | 0.6500  | 0.0261  | 0.1000  |
| computer          | 0.1882  | 0.6667  | 0.0011  | 0.1429  |
| mouse             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bag               | 0.2016  | 0.6869  | 0.0152  | 0.1717  |
| box               | 0.1445  | 0.4902  | 0.0244  | 0.1460  |
| paper             | 0.0210  | 0.2714  | 0.0010  | 0.0714  |
| stand             | 0.2996  | 0.8033  | 0.1472  | 0.5082  |
| garbage           | 0.2836  | 0.7500  | 0.0121  | 0.2500  |
| cup               | 0.0331  | 0.3077  | 0.0000  | 0.0000  |
| desk              | 0.2248  | 0.8125  | 0.1309  | 0.5500  |
| table             | 0.3509  | 0.8784  | 0.2042  | 0.6306  |
| socket            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue box        | 0.0012  | 0.3333  | 0.0000  | 0.0000  |
| cabinet           | 0.2367  | 0.7436  | 0.0795  | 0.4316  |
| bottle            | 0.0158  | 0.2562  | 0.0000  | 0.0000  |
| book              | 0.0343  | 0.3550  | 0.0007  | 0.0414  |
| backpack          | 0.4049  | 0.6970  | 0.1145  | 0.3333  |
| telephone         | 0.1376  | 0.5000  | 0.0000  | 0.0000  |
| window            | 0.1051  | 0.5685  | 0.0117  | 0.1575  |
| shelf             | 0.1422  | 0.6219  | 0.0197  | 0.2438  |
| plug              | 0.0159  | 0.1111  | 0.0000  | 0.0000  |
| conducting wire   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| curtain           | 0.2828  | 0.8175  | 0.0312  | 0.2857  |
| wardrobe          | 0.2701  | 0.7969  | 0.0646  | 0.5156  |
| case              | 0.0168  | 0.3429  | 0.0043  | 0.0714  |
| bin               | 0.5520  | 0.8171  | 0.2094  | 0.5122  |
| notebook          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plate             | 0.0483  | 0.3030  | 0.0063  | 0.0909  |
| clothes           | 0.1267  | 0.6602  | 0.0070  | 0.1650  |
| paper cutter      | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| pen               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pad               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| can               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillow            | 0.3175  | 0.5742  | 0.0089  | 0.1133  |
| blinds            | 0.0315  | 0.3846  | 0.0083  | 0.1538  |
| plant             | 0.2348  | 0.5891  | 0.0578  | 0.2525  |
| stool             | 0.2239  | 0.8333  | 0.1379  | 0.6111  |
| commode           | 0.3315  | 0.8077  | 0.2022  | 0.5769  |
| picture           | 0.1053  | 0.3171  | 0.0004  | 0.0285  |
| file              | 0.0554  | 0.5000  | 0.0005  | 0.0357  |
| door              | 0.1565  | 0.5956  | 0.0082  | 0.1250  |
| rack              | 0.0388  | 0.5333  | 0.0045  | 0.2667  |
| shoe              | 0.1082  | 0.3571  | 0.0064  | 0.0714  |
| tv                | 0.4475  | 0.8857  | 0.0832  | 0.1714  |
| lamp              | 0.1699  | 0.4437  | 0.0052  | 0.1000  |
| couch             | 0.2637  | 0.7286  | 0.1469  | 0.5143  |
| frame             | 0.0345  | 0.4667  | 0.0002  | 0.0333  |
| bed               | 0.5455  | 0.9091  | 0.2563  | 0.7273  |
| light             | 0.0249  | 0.2614  | 0.0000  | 0.0000  |
| bowl              | 0.1187  | 0.4815  | 0.0269  | 0.2593  |
| ottoman           | 0.1781  | 0.9091  | 0.1488  | 0.7273  |
| footstool         | 0.5000  | 1.0000  | 0.5000  | 1.0000  |
| cushion           | 0.1292  | 0.7000  | 0.0255  | 0.1667  |
| doorframe         | 0.0355  | 0.4400  | 0.0040  | 0.1600  |
| blanket           | 0.1169  | 0.6308  | 0.0094  | 0.2000  |
| switch            | 0.0004  | 0.0566  | 0.0000  | 0.0000  |
| candle            | 0.0727  | 0.4000  | 0.0124  | 0.1333  |
| towel             | 0.2667  | 0.6203  | 0.0260  | 0.1519  |
| decoration        | 0.0112  | 0.4062  | 0.0003  | 0.0938  |
| board             | 0.1282  | 0.4167  | 0.0070  | 0.1250  |
| bench             | 0.2163  | 0.6786  | 0.0832  | 0.3929  |
| furniture         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| carpet            | 0.0443  | 0.4643  | 0.0001  | 0.0357  |
| clock             | 0.0414  | 0.1053  | 0.0263  | 0.0526  |
| kitchenware       | 0.1197  | 0.5227  | 0.0003  | 0.0455  |
| napkins           | 0.0074  | 0.3333  | 0.0074  | 0.3333  |
| laptop            | 0.2578  | 0.8333  | 0.1688  | 0.3333  |
| counter           | 0.0640  | 0.5610  | 0.0159  | 0.2195  |
| tablet            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sink              | 0.5722  | 0.8036  | 0.0499  | 0.2321  |
| mirror            | 0.0654  | 0.5185  | 0.0000  | 0.0000  |
| folder            | 0.0063  | 0.2143  | 0.0012  | 0.0714  |
| vase              | 0.1566  | 0.4000  | 0.0258  | 0.1500  |
| teapot            | 0.0049  | 0.2500  | 0.0000  | 0.0000  |
| price tag         | 0.1429  | 0.1429  | 0.0000  | 0.0000  |
| container         | 0.0001  | 0.1111  | 0.0001  | 0.1111  |
| album             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| calendar          | 0.0018  | 0.2000  | 0.0000  | 0.0000  |
| printer           | 0.4938  | 0.7778  | 0.2185  | 0.3333  |
| poster            | 0.0146  | 0.0794  | 0.0000  | 0.0000  |
| refrigerator      | 0.5753  | 0.8000  | 0.2333  | 0.6000  |
| player            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| magazine          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mat               | 0.0115  | 0.3333  | 0.0000  | 0.0000  |
| flowerpot         | 0.0372  | 0.4583  | 0.0060  | 0.0417  |
| toy               | 0.3852  | 0.6923  | 0.1243  | 0.2308  |
| fireplace         | 0.0090  | 0.4000  | 0.0000  | 0.0000  |
| cube              | 0.2500  | 1.0000  | 0.2500  | 1.0000  |
| jar               | 0.0017  | 0.1250  | 0.0000  | 0.0000  |
| remote control    | 0.0337  | 0.3000  | 0.0000  | 0.0000  |
| adhesive tape     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rail              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blackboard        | 0.3970  | 0.7500  | 0.0000  | 0.0000  |
| radiator          | 0.2365  | 0.5172  | 0.0194  | 0.1379  |
| heater            | 0.3612  | 0.6842  | 0.0382  | 0.1579  |
| fan               | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| humidifier        | 0.1667  | 0.5000  | 0.0000  | 0.0000  |
| windowsill        | 0.0241  | 0.4359  | 0.0013  | 0.0769  |
| roll              | 0.1246  | 0.4000  | 0.0000  | 0.0000  |
| umbrella          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| guitar            | 0.6667  | 1.0000  | 0.1667  | 0.5000  |
| jalousie          | 0.0909  | 1.0000  | 0.0030  | 0.5000  |
| drawer            | 0.2612  | 0.7778  | 0.0427  | 0.6667  |
| partition         | 0.0330  | 0.8000  | 0.0000  | 0.0000  |
| coffee maker      | 0.1262  | 0.6667  | 0.0069  | 0.1111  |
| speaker           | 0.0005  | 0.0833  | 0.0000  | 0.0000  |
| device            | 0.0025  | 0.0909  | 0.0000  | 0.0000  |
| pack              | 0.0415  | 0.7692  | 0.0031  | 0.0769  |
| tube              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| luggage           | 0.1900  | 1.0000  | 0.0833  | 0.3333  |
| chandelier        | 0.0556  | 0.5000  | 0.0000  | 0.0000  |
| air conditioner   | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| scale             | 0.1111  | 0.3333  | 0.0000  | 0.0000  |
| toilet paper      | 0.0066  | 0.1667  | 0.0000  | 0.0000  |
| basket            | 0.2863  | 0.8125  | 0.1540  | 0.3594  |
| washing machine   | 0.4190  | 0.9286  | 0.0541  | 0.5000  |
| clothes dryer     | 0.1801  | 0.7000  | 0.0374  | 0.3000  |
| brush             | 0.1271  | 0.5000  | 0.0000  | 0.0000  |
| tissue            | 0.1111  | 0.3333  | 0.0667  | 0.3333  |
| dresser           | 0.5182  | 1.0000  | 0.5000  | 0.5000  |
| kettle            | 0.0737  | 0.5000  | 0.0148  | 0.1667  |
| microwave         | 0.6588  | 1.0000  | 0.0297  | 0.2353  |
| hanger            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sunglasses        | 0.0298  | 0.4615  | 0.0000  | 0.0000  |
| hat               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candlestick       | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| stairs            | 0.0898  | 0.5714  | 0.0268  | 0.1429  |
| crib              | 0.1911  | 1.0000  | 0.0167  | 0.3333  |
| statue            | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| bicycle           | 0.0042  | 0.5000  | 0.0000  | 0.0000  |
| bathtub           | 0.6809  | 1.0000  | 0.4826  | 0.7778  |
| faucet            | 0.1667  | 0.3333  | 0.0000  | 0.0000  |
| bucket            | 0.5693  | 1.0000  | 0.1856  | 0.3333  |
| toilet            | 0.7325  | 0.8824  | 0.2983  | 0.4706  |
| shampoo           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shower            | 0.1970  | 1.0000  | 0.0938  | 0.5000  |
| toiletry          | 0.0323  | 1.0000  | 0.0000  | 0.0000  |
| dispenser         | 0.0007  | 0.3333  | 0.0000  | 0.0000  |
| pipe              | 0.0179  | 0.2500  | 0.0000  | 0.0000  |
| body loofah       | 0.0455  | 0.5000  | 0.0076  | 0.5000  |
| basin             | 0.0004  | 0.0667  | 0.0004  | 0.0667  |
| soap              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beanbag           | 1.0000  | 1.0000  | 0.6944  | 0.8333  |
| ball              | 0.2958  | 0.6000  | 0.2044  | 0.4000  |
| food              | 0.0287  | 0.3000  | 0.0000  | 0.0000  |
| menu              | 0.1603  | 0.5714  | 0.0079  | 0.1429  |
| bread             | 0.0667  | 0.4000  | 0.0000  | 0.0000  |
| ladder            | 0.0338  | 1.0000  | 0.0220  | 0.6667  |
| spoon             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillar            | 0.2587  | 0.6471  | 0.0401  | 0.3529  |
| stove             | 0.4100  | 0.8824  | 0.0012  | 0.1176  |
| pan               | 0.0545  | 0.5000  | 0.0069  | 0.0833  |
| oven              | 0.4117  | 0.9474  | 0.0602  | 0.3158  |
| pot               | 0.2233  | 0.5833  | 0.0043  | 0.3333  |
| toaster           | 0.5909  | 1.0000  | 0.0000  | 0.0000  |
| range hood        | 0.0222  | 0.6000  | 0.0000  | 0.0000  |
| tray              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher        | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| knife             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| column            | 0.0160  | 1.0000  | 0.0000  | 0.0000  |
| balcony           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vacuum cleaner    | 0.0312  | 0.2500  | 0.0312  | 0.2500  |
| wire              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| seasoning         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| salt              | 0.8333  | 1.0000  | 0.0000  | 0.0000  |
| soap dispenser    | 0.2974  | 0.8000  | 0.0000  | 0.0000  |
| sign              | 0.0250  | 0.5000  | 0.0000  | 0.0000  |
| crate             | 0.6619  | 1.0000  | 0.0000  | 0.0000  |
| radio             | 1.0000  | 1.0000  | 0.1429  | 1.0000  |
| holder            | 0.5000  | 1.0000  | 0.0000  | 0.0000  |
| piano             | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| window frame      | 0.0256  | 0.1667  | 0.0000  | 0.0000  |
| bar               | 0.1083  | 0.6667  | 0.0009  | 0.3333  |
| bidet             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| broom             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mop               | 0.6190  | 1.0000  | 0.0000  | 0.0000  |
| soap dish         | 0.0556  | 0.5000  | 0.0000  | 0.0000  |
| tool              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mattress          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hook              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dish rack         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit             | 0.2546  | 0.6000  | 0.0000  | 0.0000  |
| sack              | 0.5041  | 0.7143  | 0.0102  | 0.1429  |
| water heater      | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| dumbbell          | 0.1667  | 1.0000  | 0.0000  | 0.0000  |
| helmets           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| treadmill         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| screen            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coil              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| letter            | 0.0052  | 0.2500  | 0.0000  | 0.0000  |
| map               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washbasin         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flush             | 0.0200  | 0.5000  | 0.0000  | 0.0000  |
| glass             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine           | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| sponge            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| scissors          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toothbrush        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------           | ------- | ------- | ------- | ------- |
| 3rscan            | 0.1932  | 0.4793  | 0.0526  | 0.1621  |
| head              | 0.2062  | 0.5690  | 0.0617  | 0.2133  |
| common            | 0.2007  | 0.4695  | 0.0394  | 0.1139  |
| tail              | 0.1578  | 0.3210  | 0.0536  | 0.1307  |
| small             | 0.0435  | 0.1971  | 0.0016  | 0.0212  |
| medium            | 0.2090  | 0.5308  | 0.0586  | 0.1777  |
| large             | 0.1040  | 0.5967  | 0.0489  | 0.3140  |
+-------------------+---------+---------+---------+---------+
Rank[0/8] 06/19/2025 19:35:15 INFO metrics.py:437 | number of matterport3d results: 339
Rank[0/8] 06/19/2025 19:36:35 INFO metrics.py:332 | eval results of matterport3d:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| mirror              | 0.1041  | 0.5000  | 0.0078  | 0.0811  |
| sink                | 0.5159  | 0.8081  | 0.1013  | 0.3434  |
| plant               | 0.2228  | 0.7143  | 0.0714  | 0.3482  |
| towel               | 0.2277  | 0.5273  | 0.0100  | 0.1273  |
| faucet              | 0.0063  | 0.1667  | 0.0000  | 0.0000  |
| doorframe           | 0.2450  | 0.7731  | 0.0591  | 0.3931  |
| door                | 0.0785  | 0.5013  | 0.0046  | 0.1018  |
| cabinet             | 0.0859  | 0.5551  | 0.0321  | 0.3093  |
| picture             | 0.1058  | 0.2923  | 0.0037  | 0.0556  |
| light               | 0.0116  | 0.1510  | 0.0002  | 0.0199  |
| countertop          | 0.0153  | 0.3571  | 0.0007  | 0.1071  |
| box                 | 0.0349  | 0.6154  | 0.0044  | 0.2115  |
| window              | 0.1041  | 0.4722  | 0.0091  | 0.1306  |
| shelf               | 0.1258  | 0.5328  | 0.0305  | 0.2701  |
| decoration          | 0.0136  | 0.3690  | 0.0009  | 0.0952  |
| case                | 0.0004  | 0.2222  | 0.0000  | 0.0556  |
| lamp                | 0.2746  | 0.6154  | 0.0762  | 0.2857  |
| counter             | 0.0053  | 0.3636  | 0.0001  | 0.0606  |
| dresser             | 0.3497  | 0.8667  | 0.2358  | 0.8000  |
| window frame        | 0.0434  | 0.4598  | 0.0293  | 0.2414  |
| switch              | 0.0002  | 0.1000  | 0.0000  | 0.0000  |
| shower              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flowerpot           | 0.0264  | 0.6000  | 0.0037  | 0.2000  |
| bathtub             | 0.4679  | 0.7407  | 0.2609  | 0.5185  |
| desk                | 0.2293  | 0.7368  | 0.1792  | 0.5789  |
| curtain             | 0.2746  | 0.7396  | 0.0267  | 0.2708  |
| dispenser           | 0.0065  | 0.5000  | 0.0000  | 0.0000  |
| washbasin           | 0.0021  | 0.2143  | 0.0000  | 0.0714  |
| drawer              | 0.0022  | 0.2500  | 0.0008  | 0.1250  |
| roof                | 0.0002  | 0.1250  | 0.0000  | 0.0417  |
| toilet              | 0.7169  | 0.8281  | 0.4191  | 0.5625  |
| thermostat          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| table               | 0.2478  | 0.7372  | 0.1543  | 0.4744  |
| windowsill          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toiletry            | 0.0132  | 0.3182  | 0.0000  | 0.0000  |
| blanket             | 0.0128  | 0.8182  | 0.0002  | 0.0909  |
| wardrobe            | 0.0446  | 0.6364  | 0.0038  | 0.2727  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bottle              | 0.0716  | 0.5625  | 0.0036  | 0.1250  |
| vase                | 0.1011  | 0.5667  | 0.0092  | 0.3333  |
| paper               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stand               | 0.4172  | 0.8000  | 0.2282  | 0.5200  |
| doorway             | 0.0001  | 0.1111  | 0.0000  | 0.0000  |
| chair               | 0.3591  | 0.7517  | 0.2002  | 0.4864  |
| bench               | 0.1180  | 0.5000  | 0.0671  | 0.2000  |
| vent                | 0.0040  | 0.0833  | 0.0000  | 0.0000  |
| footstool           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vanity              | 0.0974  | 0.6667  | 0.0871  | 0.6000  |
| mat                 | 0.0364  | 0.2500  | 0.0000  | 0.0000  |
| telephone           | 0.2548  | 0.5000  | 0.0000  | 0.0000  |
| rack                | 0.0091  | 0.4545  | 0.0085  | 0.3636  |
| board               | 0.0014  | 0.1667  | 0.0000  | 0.0000  |
| soap dispenser      | 0.0048  | 1.0000  | 0.0000  | 0.0000  |
| bin                 | 0.2162  | 0.7805  | 0.1336  | 0.4146  |
| pillow              | 0.2753  | 0.5804  | 0.0073  | 0.0978  |
| frame               | 0.0035  | 0.1765  | 0.0000  | 0.0000  |
| couch               | 0.2538  | 0.8043  | 0.1447  | 0.6087  |
| plate               | 0.0673  | 0.2857  | 0.0000  | 0.0000  |
| pot                 | 0.0218  | 0.3077  | 0.0000  | 0.0000  |
| tv                  | 0.2604  | 0.5349  | 0.1024  | 0.2558  |
| washing machine     | 0.3664  | 0.8750  | 0.0923  | 0.3750  |
| tissue box          | 0.0510  | 0.5000  | 0.0312  | 0.1250  |
| carpet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candle              | 0.0185  | 0.3333  | 0.0000  | 0.0000  |
| fan                 | 0.3883  | 0.7963  | 0.0576  | 0.3704  |
| screen              | 0.0880  | 0.5000  | 0.0833  | 0.2500  |
| cushion             | 0.0080  | 0.2353  | 0.0020  | 0.1176  |
| bowl                | 0.0699  | 0.3939  | 0.0024  | 0.0909  |
| ottoman             | 0.0789  | 0.6667  | 0.0337  | 0.4444  |
| teapot              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bed                 | 0.4042  | 0.8246  | 0.2149  | 0.7544  |
| book                | 0.0536  | 0.2913  | 0.0019  | 0.0394  |
| cup                 | 0.0001  | 0.1000  | 0.0000  | 0.0000  |
| ball                | 0.5469  | 0.7500  | 0.5144  | 0.7500  |
| projector           | 0.0833  | 0.5000  | 0.0000  | 0.0000  |
| stool               | 0.0070  | 0.3810  | 0.0027  | 0.2381  |
| statue              | 0.0795  | 0.2941  | 0.0633  | 0.1176  |
| food                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fireplace           | 0.1229  | 0.4348  | 0.0004  | 0.0870  |
| steps               | 0.0010  | 0.2000  | 0.0000  | 0.0000  |
| monitor             | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| stairs              | 0.0372  | 0.2453  | 0.0018  | 0.0566  |
| basin               | 0.3333  | 1.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0457  | 0.7500  | 0.0000  | 0.0000  |
| wine                | 0.0079  | 0.0476  | 0.0000  | 0.0000  |
| clock               | 0.4000  | 0.6667  | 0.0023  | 0.3333  |
| toy                 | 0.1060  | 0.4444  | 0.0208  | 0.0833  |
| tray                | 0.0370  | 0.3333  | 0.0000  | 0.0000  |
| fruit               | 0.0116  | 0.2500  | 0.0000  | 0.0000  |
| pillar              | 0.2412  | 0.7143  | 0.0446  | 0.3333  |
| column              | 0.0156  | 0.3333  | 0.0025  | 0.1389  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| chandelier          | 0.2390  | 0.4737  | 0.0075  | 0.0526  |
| socket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| heater              | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| hamper              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet               | 0.0714  | 0.5000  | 0.0714  | 0.5000  |
| bucket              | 0.0315  | 0.5000  | 0.0064  | 0.2000  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garage door         | 0.4051  | 0.6667  | 0.2292  | 0.5000  |
| pipe                | 0.0055  | 0.3000  | 0.0000  | 0.0000  |
| clothes             | 0.0959  | 0.3793  | 0.0057  | 0.1379  |
| handle              | 0.0147  | 0.2500  | 0.0000  | 0.0000  |
| soap dish           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radiator            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basket              | 0.0901  | 0.4800  | 0.0007  | 0.0800  |
| fire extinguisher   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toilet paper        | 0.1441  | 0.6111  | 0.0039  | 0.1389  |
| rail                | 0.0063  | 0.2037  | 0.0000  | 0.0000  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headboard           | 0.0034  | 0.1250  | 0.0000  | 0.0000  |
| laptop              | 0.0833  | 0.5000  | 0.0000  | 0.0000  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.1806  | 0.6667  | 0.1806  | 0.6667  |
| kitchen island      | 0.1079  | 0.8571  | 0.1036  | 0.8571  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| piano               | 0.5000  | 0.5000  | 0.1000  | 0.5000  |
| excercise equipment | 0.2805  | 0.7222  | 0.0288  | 0.2778  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stove               | 0.1913  | 0.5000  | 0.0048  | 0.2000  |
| printer             | 0.3929  | 0.4286  | 0.0000  | 0.0000  |
| coffee maker        | 0.0145  | 0.3333  | 0.0000  | 0.0000  |
| pan                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scale               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| magazine            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| keyboard            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool table          | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| guitar              | 0.2612  | 0.5714  | 0.0143  | 0.1429  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| computer            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| arch                | 0.0014  | 0.2333  | 0.0003  | 0.1000  |
| container           | 0.1515  | 0.4074  | 0.0342  | 0.2593  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bag                 | 0.1147  | 0.3077  | 0.0000  | 0.0000  |
| microwave           | 0.1682  | 0.2500  | 0.1000  | 0.1667  |
| backpack            | 0.1000  | 1.0000  | 0.1000  | 1.0000  |
| shoe                | 0.0756  | 0.3200  | 0.0000  | 0.0000  |
| refrigerator        | 0.2251  | 0.7333  | 0.0690  | 0.2000  |
| ladder              | 0.1146  | 0.2222  | 0.1111  | 0.1111  |
| bicycle             | 0.0556  | 1.0000  | 0.0000  | 0.0000  |
| hanger              | 0.0174  | 0.5000  | 0.0000  | 0.0000  |
| soap                | 0.0116  | 0.4000  | 0.0026  | 0.2000  |
| holder              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beam                | 0.0001  | 0.0476  | 0.0000  | 0.0000  |
| kettle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| air conditioner     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchenware         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.0222  | 0.7500  | 0.0000  | 0.0000  |
| oven                | 0.1364  | 0.4000  | 0.0182  | 0.2000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.0357  | 0.2500  | 0.0000  | 0.0000  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| file                | 0.0833  | 0.2500  | 0.0000  | 0.0000  |
| mouse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| partition           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jacket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candlestick         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drum                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pack                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water heater        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell            | 0.0714  | 0.5000  | 0.0000  | 0.0000  |
| blackboard          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| matterport3d        | 0.0922  | 0.3220  | 0.0351  | 0.1299  |
| head                | 0.1393  | 0.4444  | 0.0502  | 0.1931  |
| common              | 0.0512  | 0.2429  | 0.0178  | 0.0647  |
| tail                | 0.0385  | 0.1099  | 0.0284  | 0.0842  |
| small               | 0.0249  | 0.1587  | 0.0002  | 0.0264  |
| medium              | 0.0904  | 0.3504  | 0.0361  | 0.1357  |
| large               | 0.0849  | 0.4356  | 0.0274  | 0.1709  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/19/2025 19:36:35 INFO metrics.py:437 | number of arkit results: 0
Rank[0/8] 06/19/2025 19:36:35 INFO stats.py:394 | Epoch[39] completed. Training Speed: 2.99 samples/sec across all devices.	Epoch Time: 1042.04 sec.	Average Epoch Time: 1042.04 sec.	Average Step Time: 2.67 sec.	Estimated Remaining Time: 1 day, 3:01:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:36:35 WARNING accelerator.py:3099 | Deleting 1 checkpoints to make room for new checkpoint.
Rank[0/8] 06/19/2025 19:36:36 INFO accelerator.py:3111 | Saving current state to /job_data/checkpoints/checkpoint_3
Rank[2/8] 06/19/2025 19:36:36 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 39 to /job_data/checkpoints/checkpoint_3
Rank[4/8] 06/19/2025 19:36:36 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 39 to /job_data/checkpoints/checkpoint_3
Rank[6/8] 06/19/2025 19:36:36 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 39 to /job_data/checkpoints/checkpoint_3
Rank[5/8] 06/19/2025 19:36:36 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 39 to /job_data/checkpoints/checkpoint_3
Rank[3/8] 06/19/2025 19:36:36 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 39 to /job_data/checkpoints/checkpoint_3
Rank[1/8] 06/19/2025 19:36:36 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 39 to /job_data/checkpoints/checkpoint_3
Rank[7/8] 06/19/2025 19:36:36 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 39 to /job_data/checkpoints/checkpoint_3
Rank[0/8] 06/19/2025 19:36:37 INFO checkpointing.py:106 | Model weights saved in /job_data/checkpoints/checkpoint_3/model.safetensors
Rank[0/8] 06/19/2025 19:36:40 INFO checkpointing.py:113 | Optimizer state saved in /job_data/checkpoints/checkpoint_3/optimizer.bin
Rank[0/8] 06/19/2025 19:36:40 INFO checkpointing.py:120 | Scheduler state saved in /job_data/checkpoints/checkpoint_3/scheduler.bin
Rank[0/8] 06/19/2025 19:36:40 INFO checkpointing.py:137 | Sampler state for dataloader 0 saved in /job_data/checkpoints/checkpoint_3/sampler.bin
Rank[0/8] 06/19/2025 19:36:40 INFO checkpointing.py:137 | Sampler state for dataloader 1 saved in /job_data/checkpoints/checkpoint_3/sampler_1.bin
Rank[0/8] 06/19/2025 19:36:40 INFO checkpointing.py:164 | Random states saved in /job_data/checkpoints/checkpoint_3/random_states_0.pkl
Rank[0/8] 06/19/2025 19:36:40 INFO checkpointing.py:300 | Saving the state of TrainerProgressState to /job_data/checkpoints/checkpoint_3/custom_checkpoint_0.pkl
Rank[0/8] 06/19/2025 19:36:40 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 39 to /job_data/checkpoints/checkpoint_3
Rank[0/8] 06/19/2025 19:37:12 INFO stats.py:314 | Epoch[40] Step[24] GlobalStep[15624] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 3:01:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:37:12 INFO loss_tracker.py:84 | Epoch[40/239] Step[24] GlobalStep[15624/NA]: det_loss_cls_0[0.5334] det_loss_box_0[0.7762] det_loss_cls_1[0.5139] det_loss_box_1[0.6277] det_loss_cls_2[0.5033] det_loss_box_2[0.5688] det_loss_cls_3[0.4807] det_loss_box_3[0.5529] det_loss_cls_4[0.4562] det_loss_box_4[0.5400] det_loss_cls_5[0.4352] det_loss_box_5[0.5420] det_loss_cls_dn_0[0.1044] det_loss_box_dn_0[0.4485] det_loss_cls_dn_1[0.1014] det_loss_box_dn_1[0.3978] det_loss_cls_dn_2[0.1006] det_loss_box_dn_2[0.3816] det_loss_cls_dn_3[0.1006] det_loss_box_dn_3[0.3794] det_loss_cls_dn_4[0.0996] det_loss_box_dn_4[0.3781] det_loss_cls_dn_5[0.0987] det_loss_box_dn_5[0.3730] loss_depth[0.0413] total_loss[9.5352]
Rank[0/8] 06/19/2025 19:37:37 INFO stats.py:314 | Epoch[40] Step[49] GlobalStep[15649] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 3:00:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:37:37 INFO loss_tracker.py:84 | Epoch[40/239] Step[49] GlobalStep[15649/NA]: det_loss_cls_0[0.6416] det_loss_box_0[0.9834] det_loss_cls_1[0.6085] det_loss_box_1[0.8263] det_loss_cls_2[0.5992] det_loss_box_2[0.7640] det_loss_cls_3[0.5741] det_loss_box_3[0.7432] det_loss_cls_4[0.5532] det_loss_box_4[0.7232] det_loss_cls_5[0.5399] det_loss_box_5[0.7219] det_loss_cls_dn_0[0.1231] det_loss_box_dn_0[0.4707] det_loss_cls_dn_1[0.1194] det_loss_box_dn_1[0.4399] det_loss_cls_dn_2[0.1186] det_loss_box_dn_2[0.4289] det_loss_cls_dn_3[0.1172] det_loss_box_dn_3[0.4260] det_loss_cls_dn_4[0.1162] det_loss_box_dn_4[0.4255] det_loss_cls_dn_5[0.1152] det_loss_box_dn_5[0.4151] loss_depth[0.0422] total_loss[11.6365]
Rank[0/8] 06/19/2025 19:38:03 INFO stats.py:314 | Epoch[40] Step[74] GlobalStep[15674] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 2:59:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:38:03 INFO loss_tracker.py:84 | Epoch[40/239] Step[74] GlobalStep[15674/NA]: det_loss_cls_0[0.6626] det_loss_box_0[0.9505] det_loss_cls_1[0.6180] det_loss_box_1[0.7735] det_loss_cls_2[0.6127] det_loss_box_2[0.7086] det_loss_cls_3[0.5853] det_loss_box_3[0.6869] det_loss_cls_4[0.5540] det_loss_box_4[0.6784] det_loss_cls_5[0.5341] det_loss_box_5[0.6783] det_loss_cls_dn_0[0.1054] det_loss_box_dn_0[0.3124] det_loss_cls_dn_1[0.1027] det_loss_box_dn_1[0.2777] det_loss_cls_dn_2[0.1026] det_loss_box_dn_2[0.2623] det_loss_cls_dn_3[0.1018] det_loss_box_dn_3[0.2609] det_loss_cls_dn_4[0.1007] det_loss_box_dn_4[0.2605] det_loss_cls_dn_5[0.1002] det_loss_box_dn_5[0.2598] loss_depth[0.0413] total_loss[10.3312]
Rank[0/8] 06/19/2025 19:38:30 INFO stats.py:314 | Epoch[40] Step[99] GlobalStep[15699] Training Speed: 6.14 samples/sec across all devices.	Average Step Time: 1.30 sec.	Estimated Remaining Time: 1 day, 2:58:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:38:30 INFO loss_tracker.py:84 | Epoch[40/239] Step[99] GlobalStep[15699/NA]: det_loss_cls_0[0.5169] det_loss_box_0[0.6697] det_loss_cls_1[0.5070] det_loss_box_1[0.5375] det_loss_cls_2[0.4953] det_loss_box_2[0.4756] det_loss_cls_3[0.4722] det_loss_box_3[0.4645] det_loss_cls_4[0.4416] det_loss_box_4[0.4612] det_loss_cls_5[0.4324] det_loss_box_5[0.4652] det_loss_cls_dn_0[0.1016] det_loss_box_dn_0[0.3269] det_loss_cls_dn_1[0.0978] det_loss_box_dn_1[0.2835] det_loss_cls_dn_2[0.0980] det_loss_box_dn_2[0.2664] det_loss_cls_dn_3[0.0966] det_loss_box_dn_3[0.2644] det_loss_cls_dn_4[0.0953] det_loss_box_dn_4[0.2629] det_loss_cls_dn_5[0.0951] det_loss_box_dn_5[0.2586] loss_depth[0.0393] total_loss[8.2253]
Rank[0/8] 06/19/2025 19:38:55 INFO stats.py:314 | Epoch[40] Step[124] GlobalStep[15724] Training Speed: 7.77 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 1 day, 2:57:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:38:55 INFO loss_tracker.py:84 | Epoch[40/239] Step[124] GlobalStep[15724/NA]: det_loss_cls_0[0.5667] det_loss_box_0[0.7856] det_loss_cls_1[0.5448] det_loss_box_1[0.6742] det_loss_cls_2[0.5420] det_loss_box_2[0.6201] det_loss_cls_3[0.5297] det_loss_box_3[0.5992] det_loss_cls_4[0.5052] det_loss_box_4[0.5766] det_loss_cls_5[0.4863] det_loss_box_5[0.5825] det_loss_cls_dn_0[0.1017] det_loss_box_dn_0[0.3880] det_loss_cls_dn_1[0.1000] det_loss_box_dn_1[0.3402] det_loss_cls_dn_2[0.0985] det_loss_box_dn_2[0.3229] det_loss_cls_dn_3[0.0973] det_loss_box_dn_3[0.3205] det_loss_cls_dn_4[0.0959] det_loss_box_dn_4[0.3195] det_loss_cls_dn_5[0.0958] det_loss_box_dn_5[0.3184] loss_depth[0.0408] total_loss[9.6525]
Rank[0/8] 06/19/2025 19:39:21 INFO stats.py:314 | Epoch[40] Step[149] GlobalStep[15749] Training Speed: 8.58 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 2:56:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:39:21 INFO loss_tracker.py:84 | Epoch[40/239] Step[149] GlobalStep[15749/NA]: det_loss_cls_0[0.4385] det_loss_box_0[0.6092] det_loss_cls_1[0.4144] det_loss_box_1[0.4882] det_loss_cls_2[0.4141] det_loss_box_2[0.4330] det_loss_cls_3[0.3952] det_loss_box_3[0.4117] det_loss_cls_4[0.3682] det_loss_box_4[0.4061] det_loss_cls_5[0.3541] det_loss_box_5[0.4100] det_loss_cls_dn_0[0.1048] det_loss_box_dn_0[0.3675] det_loss_cls_dn_1[0.1020] det_loss_box_dn_1[0.3116] det_loss_cls_dn_2[0.1017] det_loss_box_dn_2[0.2955] det_loss_cls_dn_3[0.0997] det_loss_box_dn_3[0.2929] det_loss_cls_dn_4[0.0990] det_loss_box_dn_4[0.2934] det_loss_cls_dn_5[0.0981] det_loss_box_dn_5[0.2913] loss_depth[0.0443] total_loss[7.6445]
Rank[0/8] 06/19/2025 19:39:47 INFO stats.py:314 | Epoch[40] Step[174] GlobalStep[15774] Training Speed: 8.01 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 2:55:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:39:47 INFO loss_tracker.py:84 | Epoch[40/239] Step[174] GlobalStep[15774/NA]: det_loss_cls_0[0.5820] det_loss_box_0[0.7338] det_loss_cls_1[0.5598] det_loss_box_1[0.6070] det_loss_cls_2[0.5560] det_loss_box_2[0.5383] det_loss_cls_3[0.5152] det_loss_box_3[0.5295] det_loss_cls_4[0.4786] det_loss_box_4[0.5223] det_loss_cls_5[0.4620] det_loss_box_5[0.5292] det_loss_cls_dn_0[0.1080] det_loss_box_dn_0[0.3656] det_loss_cls_dn_1[0.1047] det_loss_box_dn_1[0.3123] det_loss_cls_dn_2[0.1045] det_loss_box_dn_2[0.2956] det_loss_cls_dn_3[0.1027] det_loss_box_dn_3[0.2926] det_loss_cls_dn_4[0.1016] det_loss_box_dn_4[0.2904] det_loss_cls_dn_5[0.1010] det_loss_box_dn_5[0.2880] loss_depth[0.0427] total_loss[9.1238]
Rank[0/8] 06/19/2025 19:40:13 INFO stats.py:314 | Epoch[40] Step[199] GlobalStep[15799] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 2:54:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:40:13 INFO loss_tracker.py:84 | Epoch[40/239] Step[199] GlobalStep[15799/NA]: det_loss_cls_0[0.6306] det_loss_box_0[0.8579] det_loss_cls_1[0.6269] det_loss_box_1[0.7031] det_loss_cls_2[0.6079] det_loss_box_2[0.6416] det_loss_cls_3[0.5826] det_loss_box_3[0.6199] det_loss_cls_4[0.5451] det_loss_box_4[0.6035] det_loss_cls_5[0.5252] det_loss_box_5[0.5992] det_loss_cls_dn_0[0.1130] det_loss_box_dn_0[0.3522] det_loss_cls_dn_1[0.1098] det_loss_box_dn_1[0.3101] det_loss_cls_dn_2[0.1093] det_loss_box_dn_2[0.2947] det_loss_cls_dn_3[0.1081] det_loss_box_dn_3[0.2921] det_loss_cls_dn_4[0.1070] det_loss_box_dn_4[0.2915] det_loss_cls_dn_5[0.1062] det_loss_box_dn_5[0.2902] loss_depth[0.0403] total_loss[10.0678]
Rank[0/8] 06/19/2025 19:40:39 INFO stats.py:314 | Epoch[40] Step[224] GlobalStep[15824] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 2:53:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:40:39 INFO loss_tracker.py:84 | Epoch[40/239] Step[224] GlobalStep[15824/NA]: det_loss_cls_0[0.5186] det_loss_box_0[0.6783] det_loss_cls_1[0.5067] det_loss_box_1[0.5396] det_loss_cls_2[0.4918] det_loss_box_2[0.4967] det_loss_cls_3[0.4739] det_loss_box_3[0.4747] det_loss_cls_4[0.4508] det_loss_box_4[0.4618] det_loss_cls_5[0.4359] det_loss_box_5[0.4626] det_loss_cls_dn_0[0.1134] det_loss_box_dn_0[0.3246] det_loss_cls_dn_1[0.1111] det_loss_box_dn_1[0.2833] det_loss_cls_dn_2[0.1102] det_loss_box_dn_2[0.2598] det_loss_cls_dn_3[0.1097] det_loss_box_dn_3[0.2579] det_loss_cls_dn_4[0.1087] det_loss_box_dn_4[0.2528] det_loss_cls_dn_5[0.1077] det_loss_box_dn_5[0.2501] loss_depth[0.0435] total_loss[8.3242]
Rank[0/8] 06/19/2025 19:41:05 INFO stats.py:314 | Epoch[40] Step[249] GlobalStep[15849] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 2:53:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:41:05 INFO loss_tracker.py:84 | Epoch[40/239] Step[249] GlobalStep[15849/NA]: det_loss_cls_0[0.5132] det_loss_box_0[0.6424] det_loss_cls_1[0.4907] det_loss_box_1[0.5188] det_loss_cls_2[0.4836] det_loss_box_2[0.4567] det_loss_cls_3[0.4539] det_loss_box_3[0.4486] det_loss_cls_4[0.4203] det_loss_box_4[0.4429] det_loss_cls_5[0.4120] det_loss_box_5[0.4375] det_loss_cls_dn_0[0.0992] det_loss_box_dn_0[0.2985] det_loss_cls_dn_1[0.0954] det_loss_box_dn_1[0.2608] det_loss_cls_dn_2[0.0949] det_loss_box_dn_2[0.2454] det_loss_cls_dn_3[0.0943] det_loss_box_dn_3[0.2429] det_loss_cls_dn_4[0.0929] det_loss_box_dn_4[0.2431] det_loss_cls_dn_5[0.0920] det_loss_box_dn_5[0.2414] loss_depth[0.0417] total_loss[7.8631]
Rank[0/8] 06/19/2025 19:41:31 INFO stats.py:314 | Epoch[40] Step[274] GlobalStep[15874] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 2:52:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:41:31 INFO loss_tracker.py:84 | Epoch[40/239] Step[274] GlobalStep[15874/NA]: det_loss_cls_0[0.6476] det_loss_box_0[0.8076] det_loss_cls_1[0.6317] det_loss_box_1[0.6621] det_loss_cls_2[0.6107] det_loss_box_2[0.6075] det_loss_cls_3[0.6039] det_loss_box_3[0.5859] det_loss_cls_4[0.5694] det_loss_box_4[0.5825] det_loss_cls_5[0.5555] det_loss_box_5[0.5850] det_loss_cls_dn_0[0.1104] det_loss_box_dn_0[0.2841] det_loss_cls_dn_1[0.1078] det_loss_box_dn_1[0.2489] det_loss_cls_dn_2[0.1072] det_loss_box_dn_2[0.2359] det_loss_cls_dn_3[0.1064] det_loss_box_dn_3[0.2341] det_loss_cls_dn_4[0.1048] det_loss_box_dn_4[0.2338] det_loss_cls_dn_5[0.1037] det_loss_box_dn_5[0.2318] loss_depth[0.0445] total_loss[9.6028]
Rank[0/8] 06/19/2025 19:41:56 INFO stats.py:314 | Epoch[40] Step[299] GlobalStep[15899] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 2:51:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:41:57 INFO loss_tracker.py:84 | Epoch[40/239] Step[299] GlobalStep[15899/NA]: det_loss_cls_0[0.5075] det_loss_box_0[0.7115] det_loss_cls_1[0.4892] det_loss_box_1[0.5824] det_loss_cls_2[0.4795] det_loss_box_2[0.5398] det_loss_cls_3[0.4652] det_loss_box_3[0.5196] det_loss_cls_4[0.4498] det_loss_box_4[0.5085] det_loss_cls_5[0.4461] det_loss_box_5[0.5037] det_loss_cls_dn_0[0.1039] det_loss_box_dn_0[0.3562] det_loss_cls_dn_1[0.1007] det_loss_box_dn_1[0.3124] det_loss_cls_dn_2[0.1006] det_loss_box_dn_2[0.2909] det_loss_cls_dn_3[0.1009] det_loss_box_dn_3[0.2880] det_loss_cls_dn_4[0.1003] det_loss_box_dn_4[0.2864] det_loss_cls_dn_5[0.0996] det_loss_box_dn_5[0.2848] loss_depth[0.0404] total_loss[8.6679]
Rank[0/8] 06/19/2025 19:42:23 INFO stats.py:314 | Epoch[40] Step[324] GlobalStep[15924] Training Speed: 6.53 samples/sec across all devices.	Average Step Time: 1.22 sec.	Estimated Remaining Time: 1 day, 2:50:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:42:23 INFO loss_tracker.py:84 | Epoch[40/239] Step[324] GlobalStep[15924/NA]: det_loss_cls_0[0.6094] det_loss_box_0[0.7237] det_loss_cls_1[0.5887] det_loss_box_1[0.6043] det_loss_cls_2[0.5810] det_loss_box_2[0.5516] det_loss_cls_3[0.5610] det_loss_box_3[0.5298] det_loss_cls_4[0.5332] det_loss_box_4[0.5255] det_loss_cls_5[0.5269] det_loss_box_5[0.5236] det_loss_cls_dn_0[0.1204] det_loss_box_dn_0[0.2819] det_loss_cls_dn_1[0.1174] det_loss_box_dn_1[0.2475] det_loss_cls_dn_2[0.1166] det_loss_box_dn_2[0.2365] det_loss_cls_dn_3[0.1156] det_loss_box_dn_3[0.2343] det_loss_cls_dn_4[0.1151] det_loss_box_dn_4[0.2336] det_loss_cls_dn_5[0.1140] det_loss_box_dn_5[0.2313] loss_depth[0.0411] total_loss[9.0637]
Rank[0/8] 06/19/2025 19:42:48 INFO stats.py:314 | Epoch[40] Step[349] GlobalStep[15949] Training Speed: 8.15 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 2:49:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:42:48 INFO loss_tracker.py:84 | Epoch[40/239] Step[349] GlobalStep[15949/NA]: det_loss_cls_0[0.6120] det_loss_box_0[0.7372] det_loss_cls_1[0.5953] det_loss_box_1[0.6058] det_loss_cls_2[0.5869] det_loss_box_2[0.5601] det_loss_cls_3[0.5696] det_loss_box_3[0.5369] det_loss_cls_4[0.5434] det_loss_box_4[0.5241] det_loss_cls_5[0.5335] det_loss_box_5[0.5243] det_loss_cls_dn_0[0.1177] det_loss_box_dn_0[0.3077] det_loss_cls_dn_1[0.1132] det_loss_box_dn_1[0.2706] det_loss_cls_dn_2[0.1134] det_loss_box_dn_2[0.2523] det_loss_cls_dn_3[0.1125] det_loss_box_dn_3[0.2500] det_loss_cls_dn_4[0.1114] det_loss_box_dn_4[0.2483] det_loss_cls_dn_5[0.1111] det_loss_box_dn_5[0.2468] loss_depth[0.0405] total_loss[9.2249]
Rank[0/8] 06/19/2025 19:43:14 INFO stats.py:314 | Epoch[40] Step[374] GlobalStep[15974] Training Speed: 8.04 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 2:48:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:43:14 INFO loss_tracker.py:84 | Epoch[40/239] Step[374] GlobalStep[15974/NA]: det_loss_cls_0[0.5662] det_loss_box_0[0.7925] det_loss_cls_1[0.5472] det_loss_box_1[0.6267] det_loss_cls_2[0.5292] det_loss_box_2[0.5707] det_loss_cls_3[0.5039] det_loss_box_3[0.5559] det_loss_cls_4[0.4733] det_loss_box_4[0.5560] det_loss_cls_5[0.4695] det_loss_box_5[0.5483] det_loss_cls_dn_0[0.1078] det_loss_box_dn_0[0.3063] det_loss_cls_dn_1[0.1038] det_loss_box_dn_1[0.2705] det_loss_cls_dn_2[0.1024] det_loss_box_dn_2[0.2560] det_loss_cls_dn_3[0.1021] det_loss_box_dn_3[0.2522] det_loss_cls_dn_4[0.1012] det_loss_box_dn_4[0.2516] det_loss_cls_dn_5[0.1008] det_loss_box_dn_5[0.2505] loss_depth[0.0433] total_loss[8.9881]
Rank[0/8] 06/19/2025 19:43:29 INFO stats.py:394 | Epoch[40] completed. Training Speed: 7.63 samples/sec across all devices.	Epoch Time: 408.74 sec.	Average Epoch Time: 408.74 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 2:47:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:43:42 INFO stats.py:314 | Epoch[41] Step[9] GlobalStep[15999] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 2:47:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:43:58 INFO loss_tracker.py:84 | Epoch[41/239] Step[24] GlobalStep[16014/NA]: det_loss_cls_0[0.4871] det_loss_box_0[0.5898] det_loss_cls_1[0.4693] det_loss_box_1[0.4838] det_loss_cls_2[0.4537] det_loss_box_2[0.4468] det_loss_cls_3[0.4403] det_loss_box_3[0.4389] det_loss_cls_4[0.4095] det_loss_box_4[0.4354] det_loss_cls_5[0.3984] det_loss_box_5[0.4338] det_loss_cls_dn_0[0.1123] det_loss_box_dn_0[0.3270] det_loss_cls_dn_1[0.1087] det_loss_box_dn_1[0.2938] det_loss_cls_dn_2[0.1078] det_loss_box_dn_2[0.2789] det_loss_cls_dn_3[0.1075] det_loss_box_dn_3[0.2766] det_loss_cls_dn_4[0.1054] det_loss_box_dn_4[0.2762] det_loss_cls_dn_5[0.1047] det_loss_box_dn_5[0.2738] loss_depth[0.0386] total_loss[7.8984]
Rank[0/8] 06/19/2025 19:44:08 INFO stats.py:314 | Epoch[41] Step[34] GlobalStep[16024] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 2:46:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:44:24 INFO loss_tracker.py:84 | Epoch[41/239] Step[49] GlobalStep[16039/NA]: det_loss_cls_0[0.5344] det_loss_box_0[0.6642] det_loss_cls_1[0.5220] det_loss_box_1[0.5355] det_loss_cls_2[0.5104] det_loss_box_2[0.4929] det_loss_cls_3[0.4860] det_loss_box_3[0.4743] det_loss_cls_4[0.4612] det_loss_box_4[0.4698] det_loss_cls_5[0.4423] det_loss_box_5[0.4700] det_loss_cls_dn_0[0.1083] det_loss_box_dn_0[0.3032] det_loss_cls_dn_1[0.1046] det_loss_box_dn_1[0.2652] det_loss_cls_dn_2[0.1036] det_loss_box_dn_2[0.2471] det_loss_cls_dn_3[0.1030] det_loss_box_dn_3[0.2455] det_loss_cls_dn_4[0.1019] det_loss_box_dn_4[0.2435] det_loss_cls_dn_5[0.1017] det_loss_box_dn_5[0.2406] loss_depth[0.0390] total_loss[8.2702]
Rank[0/8] 06/19/2025 19:44:34 INFO stats.py:314 | Epoch[41] Step[59] GlobalStep[16049] Training Speed: 8.17 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 2:45:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:44:49 INFO loss_tracker.py:84 | Epoch[41/239] Step[74] GlobalStep[16064/NA]: det_loss_cls_0[0.6028] det_loss_box_0[0.7542] det_loss_cls_1[0.5831] det_loss_box_1[0.6031] det_loss_cls_2[0.5663] det_loss_box_2[0.5597] det_loss_cls_3[0.5399] det_loss_box_3[0.5373] det_loss_cls_4[0.5141] det_loss_box_4[0.5271] det_loss_cls_5[0.4988] det_loss_box_5[0.5252] det_loss_cls_dn_0[0.1076] det_loss_box_dn_0[0.2860] det_loss_cls_dn_1[0.1034] det_loss_box_dn_1[0.2495] det_loss_cls_dn_2[0.1037] det_loss_box_dn_2[0.2344] det_loss_cls_dn_3[0.1022] det_loss_box_dn_3[0.2333] det_loss_cls_dn_4[0.1007] det_loss_box_dn_4[0.2332] det_loss_cls_dn_5[0.1001] det_loss_box_dn_5[0.2324] loss_depth[0.0406] total_loss[8.9389]
Rank[0/8] 06/19/2025 19:45:00 INFO stats.py:314 | Epoch[41] Step[84] GlobalStep[16074] Training Speed: 6.10 samples/sec across all devices.	Average Step Time: 1.31 sec.	Estimated Remaining Time: 1 day, 2:44:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:45:16 INFO loss_tracker.py:84 | Epoch[41/239] Step[99] GlobalStep[16089/NA]: det_loss_cls_0[0.6753] det_loss_box_0[0.9062] det_loss_cls_1[0.6594] det_loss_box_1[0.7336] det_loss_cls_2[0.6472] det_loss_box_2[0.6760] det_loss_cls_3[0.6237] det_loss_box_3[0.6635] det_loss_cls_4[0.5913] det_loss_box_4[0.6505] det_loss_cls_5[0.5747] det_loss_box_5[0.6454] det_loss_cls_dn_0[0.1111] det_loss_box_dn_0[0.3058] det_loss_cls_dn_1[0.1090] det_loss_box_dn_1[0.2710] det_loss_cls_dn_2[0.1084] det_loss_box_dn_2[0.2594] det_loss_cls_dn_3[0.1083] det_loss_box_dn_3[0.2578] det_loss_cls_dn_4[0.1075] det_loss_box_dn_4[0.2563] det_loss_cls_dn_5[0.1075] det_loss_box_dn_5[0.2538] loss_depth[0.0401] total_loss[10.3429]
Rank[0/8] 06/19/2025 19:45:26 INFO stats.py:314 | Epoch[41] Step[109] GlobalStep[16099] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 2:43:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:45:41 INFO loss_tracker.py:84 | Epoch[41/239] Step[124] GlobalStep[16114/NA]: det_loss_cls_0[0.5969] det_loss_box_0[0.7929] det_loss_cls_1[0.5821] det_loss_box_1[0.6337] det_loss_cls_2[0.5721] det_loss_box_2[0.5705] det_loss_cls_3[0.5481] det_loss_box_3[0.5482] det_loss_cls_4[0.5227] det_loss_box_4[0.5353] det_loss_cls_5[0.5099] det_loss_box_5[0.5338] det_loss_cls_dn_0[0.1116] det_loss_box_dn_0[0.3111] det_loss_cls_dn_1[0.1080] det_loss_box_dn_1[0.2725] det_loss_cls_dn_2[0.1078] det_loss_box_dn_2[0.2548] det_loss_cls_dn_3[0.1055] det_loss_box_dn_3[0.2533] det_loss_cls_dn_4[0.1044] det_loss_box_dn_4[0.2530] det_loss_cls_dn_5[0.1041] det_loss_box_dn_5[0.2521] loss_depth[0.0415] total_loss[9.2260]
Rank[0/8] 06/19/2025 19:45:52 INFO stats.py:314 | Epoch[41] Step[134] GlobalStep[16124] Training Speed: 7.96 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 2:42:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:46:08 INFO loss_tracker.py:84 | Epoch[41/239] Step[149] GlobalStep[16139/NA]: det_loss_cls_0[0.6098] det_loss_box_0[0.7780] det_loss_cls_1[0.5870] det_loss_box_1[0.6167] det_loss_cls_2[0.5798] det_loss_box_2[0.5582] det_loss_cls_3[0.5571] det_loss_box_3[0.5406] det_loss_cls_4[0.5286] det_loss_box_4[0.5313] det_loss_cls_5[0.5223] det_loss_box_5[0.5252] det_loss_cls_dn_0[0.1112] det_loss_box_dn_0[0.3076] det_loss_cls_dn_1[0.1080] det_loss_box_dn_1[0.2666] det_loss_cls_dn_2[0.1074] det_loss_box_dn_2[0.2485] det_loss_cls_dn_3[0.1061] det_loss_box_dn_3[0.2462] det_loss_cls_dn_4[0.1052] det_loss_box_dn_4[0.2462] det_loss_cls_dn_5[0.1047] det_loss_box_dn_5[0.2448] loss_depth[0.0422] total_loss[9.1790]
Rank[0/8] 06/19/2025 19:46:18 INFO stats.py:314 | Epoch[41] Step[159] GlobalStep[16149] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 2:41:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:46:34 INFO loss_tracker.py:84 | Epoch[41/239] Step[174] GlobalStep[16164/NA]: det_loss_cls_0[0.6235] det_loss_box_0[0.8882] det_loss_cls_1[0.5888] det_loss_box_1[0.7272] det_loss_cls_2[0.5790] det_loss_box_2[0.6638] det_loss_cls_3[0.5497] det_loss_box_3[0.6496] det_loss_cls_4[0.5151] det_loss_box_4[0.6356] det_loss_cls_5[0.4977] det_loss_box_5[0.6294] det_loss_cls_dn_0[0.1058] det_loss_box_dn_0[0.3427] det_loss_cls_dn_1[0.1032] det_loss_box_dn_1[0.2887] det_loss_cls_dn_2[0.1021] det_loss_box_dn_2[0.2728] det_loss_cls_dn_3[0.1016] det_loss_box_dn_3[0.2675] det_loss_cls_dn_4[0.1008] det_loss_box_dn_4[0.2657] det_loss_cls_dn_5[0.0999] det_loss_box_dn_5[0.2641] loss_depth[0.0415] total_loss[9.9039]
Rank[0/8] 06/19/2025 19:46:44 INFO stats.py:314 | Epoch[41] Step[184] GlobalStep[16174] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 2:41:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:47:00 INFO loss_tracker.py:84 | Epoch[41/239] Step[199] GlobalStep[16189/NA]: det_loss_cls_0[0.5555] det_loss_box_0[0.7609] det_loss_cls_1[0.5356] det_loss_box_1[0.5927] det_loss_cls_2[0.5135] det_loss_box_2[0.5399] det_loss_cls_3[0.4926] det_loss_box_3[0.5180] det_loss_cls_4[0.4574] det_loss_box_4[0.5045] det_loss_cls_5[0.4356] det_loss_box_5[0.5079] det_loss_cls_dn_0[0.1092] det_loss_box_dn_0[0.3038] det_loss_cls_dn_1[0.1053] det_loss_box_dn_1[0.2583] det_loss_cls_dn_2[0.1041] det_loss_box_dn_2[0.2414] det_loss_cls_dn_3[0.1039] det_loss_box_dn_3[0.2393] det_loss_cls_dn_4[0.1021] det_loss_box_dn_4[0.2372] det_loss_cls_dn_5[0.1010] det_loss_box_dn_5[0.2362] loss_depth[0.0424] total_loss[8.5982]
Rank[0/8] 06/19/2025 19:47:10 INFO stats.py:314 | Epoch[41] Step[209] GlobalStep[16199] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 2:40:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:47:25 INFO loss_tracker.py:84 | Epoch[41/239] Step[224] GlobalStep[16214/NA]: det_loss_cls_0[0.5671] det_loss_box_0[0.8096] det_loss_cls_1[0.5453] det_loss_box_1[0.6582] det_loss_cls_2[0.5372] det_loss_box_2[0.5790] det_loss_cls_3[0.5191] det_loss_box_3[0.5641] det_loss_cls_4[0.4669] det_loss_box_4[0.5620] det_loss_cls_5[0.4587] det_loss_box_5[0.5614] det_loss_cls_dn_0[0.1053] det_loss_box_dn_0[0.3597] det_loss_cls_dn_1[0.1016] det_loss_box_dn_1[0.3165] det_loss_cls_dn_2[0.1006] det_loss_box_dn_2[0.3008] det_loss_cls_dn_3[0.1000] det_loss_box_dn_3[0.2982] det_loss_cls_dn_4[0.0990] det_loss_box_dn_4[0.2957] det_loss_cls_dn_5[0.0979] det_loss_box_dn_5[0.2950] loss_depth[0.0416] total_loss[9.3405]
Rank[0/8] 06/19/2025 19:47:35 INFO stats.py:314 | Epoch[41] Step[234] GlobalStep[16224] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 2:39:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:47:51 INFO loss_tracker.py:84 | Epoch[41/239] Step[249] GlobalStep[16239/NA]: det_loss_cls_0[0.4886] det_loss_box_0[0.6841] det_loss_cls_1[0.4723] det_loss_box_1[0.5688] det_loss_cls_2[0.4736] det_loss_box_2[0.5032] det_loss_cls_3[0.4594] det_loss_box_3[0.4949] det_loss_cls_4[0.4312] det_loss_box_4[0.4898] det_loss_cls_5[0.4191] det_loss_box_5[0.4973] det_loss_cls_dn_0[0.1151] det_loss_box_dn_0[0.3612] det_loss_cls_dn_1[0.1132] det_loss_box_dn_1[0.3200] det_loss_cls_dn_2[0.1125] det_loss_box_dn_2[0.3066] det_loss_cls_dn_3[0.1134] det_loss_box_dn_3[0.3055] det_loss_cls_dn_4[0.1121] det_loss_box_dn_4[0.3047] det_loss_cls_dn_5[0.1113] det_loss_box_dn_5[0.3032] loss_depth[0.0411] total_loss[8.6020]
Rank[0/8] 06/19/2025 19:48:02 INFO stats.py:314 | Epoch[41] Step[259] GlobalStep[16249] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 2:38:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:48:17 INFO loss_tracker.py:84 | Epoch[41/239] Step[274] GlobalStep[16264/NA]: det_loss_cls_0[0.5119] det_loss_box_0[0.6820] det_loss_cls_1[0.4965] det_loss_box_1[0.5321] det_loss_cls_2[0.4728] det_loss_box_2[0.4926] det_loss_cls_3[0.4567] det_loss_box_3[0.4757] det_loss_cls_4[0.4356] det_loss_box_4[0.4598] det_loss_cls_5[0.4200] det_loss_box_5[0.4576] det_loss_cls_dn_0[0.0995] det_loss_box_dn_0[0.3708] det_loss_cls_dn_1[0.0958] det_loss_box_dn_1[0.3178] det_loss_cls_dn_2[0.0941] det_loss_box_dn_2[0.3000] det_loss_cls_dn_3[0.0937] det_loss_box_dn_3[0.2981] det_loss_cls_dn_4[0.0932] det_loss_box_dn_4[0.2977] det_loss_cls_dn_5[0.0925] det_loss_box_dn_5[0.2949] loss_depth[0.0440] total_loss[8.3855]
Rank[0/8] 06/19/2025 19:48:28 INFO stats.py:314 | Epoch[41] Step[284] GlobalStep[16274] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 2:37:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:48:43 INFO loss_tracker.py:84 | Epoch[41/239] Step[299] GlobalStep[16289/NA]: det_loss_cls_0[0.4834] det_loss_box_0[0.6685] det_loss_cls_1[0.4676] det_loss_box_1[0.5345] det_loss_cls_2[0.4563] det_loss_box_2[0.4804] det_loss_cls_3[0.4360] det_loss_box_3[0.4636] det_loss_cls_4[0.4121] det_loss_box_4[0.4540] det_loss_cls_5[0.3965] det_loss_box_5[0.4548] det_loss_cls_dn_0[0.1086] det_loss_box_dn_0[0.3536] det_loss_cls_dn_1[0.1038] det_loss_box_dn_1[0.3218] det_loss_cls_dn_2[0.1029] det_loss_box_dn_2[0.3084] det_loss_cls_dn_3[0.1028] det_loss_box_dn_3[0.3064] det_loss_cls_dn_4[0.1026] det_loss_box_dn_4[0.3052] det_loss_cls_dn_5[0.1017] det_loss_box_dn_5[0.3024] loss_depth[0.0442] total_loss[8.2721]
Rank[0/8] 06/19/2025 19:48:54 INFO stats.py:314 | Epoch[41] Step[309] GlobalStep[16299] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 2:36:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:49:09 INFO loss_tracker.py:84 | Epoch[41/239] Step[324] GlobalStep[16314/NA]: det_loss_cls_0[0.4987] det_loss_box_0[0.7303] det_loss_cls_1[0.4838] det_loss_box_1[0.5751] det_loss_cls_2[0.4703] det_loss_box_2[0.5208] det_loss_cls_3[0.4592] det_loss_box_3[0.5011] det_loss_cls_4[0.4318] det_loss_box_4[0.4903] det_loss_cls_5[0.4271] det_loss_box_5[0.4883] det_loss_cls_dn_0[0.1037] det_loss_box_dn_0[0.3058] det_loss_cls_dn_1[0.0983] det_loss_box_dn_1[0.2620] det_loss_cls_dn_2[0.0983] det_loss_box_dn_2[0.2454] det_loss_cls_dn_3[0.0972] det_loss_box_dn_3[0.2435] det_loss_cls_dn_4[0.0967] det_loss_box_dn_4[0.2420] det_loss_cls_dn_5[0.0960] det_loss_box_dn_5[0.2415] loss_depth[0.0401] total_loss[8.2473]
Rank[0/8] 06/19/2025 19:49:20 INFO stats.py:314 | Epoch[41] Step[334] GlobalStep[16324] Training Speed: 7.99 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 2:35:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:49:35 INFO loss_tracker.py:84 | Epoch[41/239] Step[349] GlobalStep[16339/NA]: det_loss_cls_0[0.5601] det_loss_box_0[0.7050] det_loss_cls_1[0.5429] det_loss_box_1[0.5763] det_loss_cls_2[0.5268] det_loss_box_2[0.5079] det_loss_cls_3[0.4981] det_loss_box_3[0.4973] det_loss_cls_4[0.4632] det_loss_box_4[0.4884] det_loss_cls_5[0.4546] det_loss_box_5[0.4833] det_loss_cls_dn_0[0.1068] det_loss_box_dn_0[0.2786] det_loss_cls_dn_1[0.1019] det_loss_box_dn_1[0.2371] det_loss_cls_dn_2[0.1017] det_loss_box_dn_2[0.2212] det_loss_cls_dn_3[0.1007] det_loss_box_dn_3[0.2193] det_loss_cls_dn_4[0.0998] det_loss_box_dn_4[0.2186] det_loss_cls_dn_5[0.0993] det_loss_box_dn_5[0.2174] loss_depth[0.0393] total_loss[8.3455]
Rank[0/8] 06/19/2025 19:49:45 INFO stats.py:314 | Epoch[41] Step[359] GlobalStep[16349] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 2:34:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:50:00 INFO loss_tracker.py:84 | Epoch[41/239] Step[374] GlobalStep[16364/NA]: det_loss_cls_0[0.6596] det_loss_box_0[0.8521] det_loss_cls_1[0.6309] det_loss_box_1[0.6899] det_loss_cls_2[0.6195] det_loss_box_2[0.6254] det_loss_cls_3[0.5988] det_loss_box_3[0.6148] det_loss_cls_4[0.5679] det_loss_box_4[0.6061] det_loss_cls_5[0.5512] det_loss_box_5[0.6062] det_loss_cls_dn_0[0.1172] det_loss_box_dn_0[0.2911] det_loss_cls_dn_1[0.1116] det_loss_box_dn_1[0.2579] det_loss_cls_dn_2[0.1116] det_loss_box_dn_2[0.2468] det_loss_cls_dn_3[0.1114] det_loss_box_dn_3[0.2449] det_loss_cls_dn_4[0.1112] det_loss_box_dn_4[0.2437] det_loss_cls_dn_5[0.1095] det_loss_box_dn_5[0.2413] loss_depth[0.0403] total_loss[9.8608]
Rank[0/8] 06/19/2025 19:50:11 INFO stats.py:314 | Epoch[41] Step[384] GlobalStep[16374] Training Speed: 7.95 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 2:33:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:50:16 INFO stats.py:394 | Epoch[41] completed. Training Speed: 7.67 samples/sec across all devices.	Epoch Time: 406.70 sec.	Average Epoch Time: 406.70 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 2:33:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:50:39 INFO stats.py:314 | Epoch[42] Step[19] GlobalStep[16399] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 2:32:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:50:45 INFO loss_tracker.py:84 | Epoch[42/239] Step[24] GlobalStep[16404/NA]: det_loss_cls_0[0.6352] det_loss_box_0[0.8779] det_loss_cls_1[0.6176] det_loss_box_1[0.7108] det_loss_cls_2[0.6018] det_loss_box_2[0.6410] det_loss_cls_3[0.5826] det_loss_box_3[0.6104] det_loss_cls_4[0.5511] det_loss_box_4[0.6028] det_loss_cls_5[0.5385] det_loss_box_5[0.6022] det_loss_cls_dn_0[0.1156] det_loss_box_dn_0[0.3438] det_loss_cls_dn_1[0.1122] det_loss_box_dn_1[0.2976] det_loss_cls_dn_2[0.1117] det_loss_box_dn_2[0.2764] det_loss_cls_dn_3[0.1099] det_loss_box_dn_3[0.2744] det_loss_cls_dn_4[0.1086] det_loss_box_dn_4[0.2719] det_loss_cls_dn_5[0.1079] det_loss_box_dn_5[0.2702] loss_depth[0.0409] total_loss[10.0131]
Rank[0/8] 06/19/2025 19:51:06 INFO stats.py:314 | Epoch[42] Step[44] GlobalStep[16424] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 2:32:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:51:11 INFO loss_tracker.py:84 | Epoch[42/239] Step[49] GlobalStep[16429/NA]: det_loss_cls_0[0.5545] det_loss_box_0[0.8008] det_loss_cls_1[0.5301] det_loss_box_1[0.6465] det_loss_cls_2[0.5351] det_loss_box_2[0.5726] det_loss_cls_3[0.5073] det_loss_box_3[0.5557] det_loss_cls_4[0.4867] det_loss_box_4[0.5340] det_loss_cls_5[0.4682] det_loss_box_5[0.5391] det_loss_cls_dn_0[0.1079] det_loss_box_dn_0[0.4138] det_loss_cls_dn_1[0.1039] det_loss_box_dn_1[0.3692] det_loss_cls_dn_2[0.1030] det_loss_box_dn_2[0.3550] det_loss_cls_dn_3[0.1021] det_loss_box_dn_3[0.3530] det_loss_cls_dn_4[0.1007] det_loss_box_dn_4[0.3514] det_loss_cls_dn_5[0.0988] det_loss_box_dn_5[0.3495] loss_depth[0.0400] total_loss[9.5788]
Rank[0/8] 06/19/2025 19:51:32 INFO stats.py:314 | Epoch[42] Step[69] GlobalStep[16449] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 2:31:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:51:37 INFO loss_tracker.py:84 | Epoch[42/239] Step[74] GlobalStep[16454/NA]: det_loss_cls_0[0.4267] det_loss_box_0[0.5232] det_loss_cls_1[0.4141] det_loss_box_1[0.4185] det_loss_cls_2[0.4042] det_loss_box_2[0.3752] det_loss_cls_3[0.3749] det_loss_box_3[0.3760] det_loss_cls_4[0.3469] det_loss_box_4[0.3704] det_loss_cls_5[0.3260] det_loss_box_5[0.3684] det_loss_cls_dn_0[0.0973] det_loss_box_dn_0[0.3289] det_loss_cls_dn_1[0.0920] det_loss_box_dn_1[0.2837] det_loss_cls_dn_2[0.0918] det_loss_box_dn_2[0.2666] det_loss_cls_dn_3[0.0910] det_loss_box_dn_3[0.2638] det_loss_cls_dn_4[0.0900] det_loss_box_dn_4[0.2603] det_loss_cls_dn_5[0.0892] det_loss_box_dn_5[0.2587] loss_depth[0.0420] total_loss[6.9799]
Rank[0/8] 06/19/2025 19:51:57 INFO stats.py:314 | Epoch[42] Step[94] GlobalStep[16474] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 2:30:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:52:03 INFO loss_tracker.py:84 | Epoch[42/239] Step[99] GlobalStep[16479/NA]: det_loss_cls_0[0.4932] det_loss_box_0[0.6347] det_loss_cls_1[0.4721] det_loss_box_1[0.5132] det_loss_cls_2[0.4639] det_loss_box_2[0.4531] det_loss_cls_3[0.4437] det_loss_box_3[0.4430] det_loss_cls_4[0.4174] det_loss_box_4[0.4413] det_loss_cls_5[0.4013] det_loss_box_5[0.4376] det_loss_cls_dn_0[0.0991] det_loss_box_dn_0[0.3213] det_loss_cls_dn_1[0.0967] det_loss_box_dn_1[0.2772] det_loss_cls_dn_2[0.0949] det_loss_box_dn_2[0.2620] det_loss_cls_dn_3[0.0942] det_loss_box_dn_3[0.2592] det_loss_cls_dn_4[0.0940] det_loss_box_dn_4[0.2585] det_loss_cls_dn_5[0.0928] det_loss_box_dn_5[0.2573] loss_depth[0.0396] total_loss[7.8613]
Rank[0/8] 06/19/2025 19:52:24 INFO stats.py:314 | Epoch[42] Step[119] GlobalStep[16499] Training Speed: 8.73 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 1 day, 2:29:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:52:29 INFO loss_tracker.py:84 | Epoch[42/239] Step[124] GlobalStep[16504/NA]: det_loss_cls_0[0.6255] det_loss_box_0[0.8660] det_loss_cls_1[0.6104] det_loss_box_1[0.6908] det_loss_cls_2[0.5943] det_loss_box_2[0.6186] det_loss_cls_3[0.5640] det_loss_box_3[0.5946] det_loss_cls_4[0.5206] det_loss_box_4[0.5859] det_loss_cls_5[0.5023] det_loss_box_5[0.5902] det_loss_cls_dn_0[0.1036] det_loss_box_dn_0[0.3056] det_loss_cls_dn_1[0.1008] det_loss_box_dn_1[0.2645] det_loss_cls_dn_2[0.0997] det_loss_box_dn_2[0.2493] det_loss_cls_dn_3[0.0985] det_loss_box_dn_3[0.2469] det_loss_cls_dn_4[0.0980] det_loss_box_dn_4[0.2457] det_loss_cls_dn_5[0.0974] det_loss_box_dn_5[0.2445] loss_depth[0.0390] total_loss[9.5567]
Rank[0/8] 06/19/2025 19:52:49 INFO stats.py:314 | Epoch[42] Step[144] GlobalStep[16524] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 2:28:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:52:54 INFO loss_tracker.py:84 | Epoch[42/239] Step[149] GlobalStep[16529/NA]: det_loss_cls_0[0.5595] det_loss_box_0[0.7804] det_loss_cls_1[0.5390] det_loss_box_1[0.6354] det_loss_cls_2[0.5272] det_loss_box_2[0.5806] det_loss_cls_3[0.5162] det_loss_box_3[0.5623] det_loss_cls_4[0.4914] det_loss_box_4[0.5589] det_loss_cls_5[0.4822] det_loss_box_5[0.5618] det_loss_cls_dn_0[0.1115] det_loss_box_dn_0[0.3531] det_loss_cls_dn_1[0.1088] det_loss_box_dn_1[0.3097] det_loss_cls_dn_2[0.1085] det_loss_box_dn_2[0.2958] det_loss_cls_dn_3[0.1086] det_loss_box_dn_3[0.2944] det_loss_cls_dn_4[0.1068] det_loss_box_dn_4[0.2931] det_loss_cls_dn_5[0.1062] det_loss_box_dn_5[0.2919] loss_depth[0.0408] total_loss[9.3240]
Rank[0/8] 06/19/2025 19:53:15 INFO stats.py:314 | Epoch[42] Step[169] GlobalStep[16549] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 2:27:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:53:20 INFO loss_tracker.py:84 | Epoch[42/239] Step[174] GlobalStep[16554/NA]: det_loss_cls_0[0.6618] det_loss_box_0[0.8979] det_loss_cls_1[0.6434] det_loss_box_1[0.7491] det_loss_cls_2[0.6407] det_loss_box_2[0.6893] det_loss_cls_3[0.6179] det_loss_box_3[0.6605] det_loss_cls_4[0.5908] det_loss_box_4[0.6461] det_loss_cls_5[0.5878] det_loss_box_5[0.6350] det_loss_cls_dn_0[0.1034] det_loss_box_dn_0[0.2843] det_loss_cls_dn_1[0.0999] det_loss_box_dn_1[0.2551] det_loss_cls_dn_2[0.0993] det_loss_box_dn_2[0.2418] det_loss_cls_dn_3[0.0989] det_loss_box_dn_3[0.2400] det_loss_cls_dn_4[0.0976] det_loss_box_dn_4[0.2395] det_loss_cls_dn_5[0.0970] det_loss_box_dn_5[0.2386] loss_depth[0.0368] total_loss[10.1527]
Rank[0/8] 06/19/2025 19:53:41 INFO stats.py:314 | Epoch[42] Step[194] GlobalStep[16574] Training Speed: 8.70 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 1 day, 2:26:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:53:46 INFO loss_tracker.py:84 | Epoch[42/239] Step[199] GlobalStep[16579/NA]: det_loss_cls_0[0.5625] det_loss_box_0[0.7459] det_loss_cls_1[0.5397] det_loss_box_1[0.6088] det_loss_cls_2[0.5265] det_loss_box_2[0.5583] det_loss_cls_3[0.5162] det_loss_box_3[0.5442] det_loss_cls_4[0.4896] det_loss_box_4[0.5426] det_loss_cls_5[0.4782] det_loss_box_5[0.5439] det_loss_cls_dn_0[0.1108] det_loss_box_dn_0[0.3552] det_loss_cls_dn_1[0.1069] det_loss_box_dn_1[0.3156] det_loss_cls_dn_2[0.1070] det_loss_box_dn_2[0.3049] det_loss_cls_dn_3[0.1058] det_loss_box_dn_3[0.3013] det_loss_cls_dn_4[0.1047] det_loss_box_dn_4[0.3016] det_loss_cls_dn_5[0.1039] det_loss_box_dn_5[0.2995] loss_depth[0.0387] total_loss[9.2123]
Rank[0/8] 06/19/2025 19:54:06 INFO stats.py:314 | Epoch[42] Step[219] GlobalStep[16599] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 2:25:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:54:12 INFO loss_tracker.py:84 | Epoch[42/239] Step[224] GlobalStep[16604/NA]: det_loss_cls_0[0.4933] det_loss_box_0[0.6395] det_loss_cls_1[0.4781] det_loss_box_1[0.5309] det_loss_cls_2[0.4659] det_loss_box_2[0.4839] det_loss_cls_3[0.4416] det_loss_box_3[0.4799] det_loss_cls_4[0.4103] det_loss_box_4[0.4761] det_loss_cls_5[0.3983] det_loss_box_5[0.4778] det_loss_cls_dn_0[0.1058] det_loss_box_dn_0[0.2776] det_loss_cls_dn_1[0.1006] det_loss_box_dn_1[0.2478] det_loss_cls_dn_2[0.0996] det_loss_box_dn_2[0.2315] det_loss_cls_dn_3[0.0987] det_loss_box_dn_3[0.2282] det_loss_cls_dn_4[0.0978] det_loss_box_dn_4[0.2287] det_loss_cls_dn_5[0.0971] det_loss_box_dn_5[0.2272] loss_depth[0.0397] total_loss[7.8560]
Rank[0/8] 06/19/2025 19:54:32 INFO stats.py:314 | Epoch[42] Step[244] GlobalStep[16624] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 2:24:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:54:37 INFO loss_tracker.py:84 | Epoch[42/239] Step[249] GlobalStep[16629/NA]: det_loss_cls_0[0.5771] det_loss_box_0[0.7080] det_loss_cls_1[0.5634] det_loss_box_1[0.5757] det_loss_cls_2[0.5515] det_loss_box_2[0.5370] det_loss_cls_3[0.5315] det_loss_box_3[0.5346] det_loss_cls_4[0.5147] det_loss_box_4[0.5163] det_loss_cls_5[0.5046] det_loss_box_5[0.5138] det_loss_cls_dn_0[0.1087] det_loss_box_dn_0[0.3950] det_loss_cls_dn_1[0.1057] det_loss_box_dn_1[0.3453] det_loss_cls_dn_2[0.1056] det_loss_box_dn_2[0.3260] det_loss_cls_dn_3[0.1043] det_loss_box_dn_3[0.3241] det_loss_cls_dn_4[0.1037] det_loss_box_dn_4[0.3201] det_loss_cls_dn_5[0.1035] det_loss_box_dn_5[0.3198] loss_depth[0.0406] total_loss[9.3306]
Rank[0/8] 06/19/2025 19:54:58 INFO stats.py:314 | Epoch[42] Step[269] GlobalStep[16649] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 2:23:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:55:03 INFO loss_tracker.py:84 | Epoch[42/239] Step[274] GlobalStep[16654/NA]: det_loss_cls_0[0.5355] det_loss_box_0[0.7247] det_loss_cls_1[0.5246] det_loss_box_1[0.5683] det_loss_cls_2[0.5049] det_loss_box_2[0.5207] det_loss_cls_3[0.4809] det_loss_box_3[0.5131] det_loss_cls_4[0.4587] det_loss_box_4[0.5037] det_loss_cls_5[0.4437] det_loss_box_5[0.5046] det_loss_cls_dn_0[0.1114] det_loss_box_dn_0[0.3104] det_loss_cls_dn_1[0.1073] det_loss_box_dn_1[0.2753] det_loss_cls_dn_2[0.1075] det_loss_box_dn_2[0.2617] det_loss_cls_dn_3[0.1065] det_loss_box_dn_3[0.2602] det_loss_cls_dn_4[0.1056] det_loss_box_dn_4[0.2587] det_loss_cls_dn_5[0.1045] det_loss_box_dn_5[0.2565] loss_depth[0.0418] total_loss[8.5908]
Rank[0/8] 06/19/2025 19:55:24 INFO stats.py:314 | Epoch[42] Step[294] GlobalStep[16674] Training Speed: 7.87 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1 day, 2:23:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:55:29 INFO loss_tracker.py:84 | Epoch[42/239] Step[299] GlobalStep[16679/NA]: det_loss_cls_0[0.5349] det_loss_box_0[0.8003] det_loss_cls_1[0.5231] det_loss_box_1[0.6543] det_loss_cls_2[0.5181] det_loss_box_2[0.5870] det_loss_cls_3[0.4971] det_loss_box_3[0.5598] det_loss_cls_4[0.4694] det_loss_box_4[0.5547] det_loss_cls_5[0.4549] det_loss_box_5[0.5579] det_loss_cls_dn_0[0.1116] det_loss_box_dn_0[0.3906] det_loss_cls_dn_1[0.1086] det_loss_box_dn_1[0.3499] det_loss_cls_dn_2[0.1081] det_loss_box_dn_2[0.3367] det_loss_cls_dn_3[0.1079] det_loss_box_dn_3[0.3349] det_loss_cls_dn_4[0.1057] det_loss_box_dn_4[0.3338] det_loss_cls_dn_5[0.1044] det_loss_box_dn_5[0.3318] loss_depth[0.0445] total_loss[9.4800]
Rank[0/8] 06/19/2025 19:55:50 INFO stats.py:314 | Epoch[42] Step[319] GlobalStep[16699] Training Speed: 6.14 samples/sec across all devices.	Average Step Time: 1.30 sec.	Estimated Remaining Time: 1 day, 2:22:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:55:55 INFO loss_tracker.py:84 | Epoch[42/239] Step[324] GlobalStep[16704/NA]: det_loss_cls_0[0.5766] det_loss_box_0[0.7496] det_loss_cls_1[0.5795] det_loss_box_1[0.5938] det_loss_cls_2[0.5626] det_loss_box_2[0.5432] det_loss_cls_3[0.5433] det_loss_box_3[0.5297] det_loss_cls_4[0.5138] det_loss_box_4[0.5100] det_loss_cls_5[0.4984] det_loss_box_5[0.5116] det_loss_cls_dn_0[0.1130] det_loss_box_dn_0[0.3193] det_loss_cls_dn_1[0.1098] det_loss_box_dn_1[0.2800] det_loss_cls_dn_2[0.1089] det_loss_box_dn_2[0.2652] det_loss_cls_dn_3[0.1074] det_loss_box_dn_3[0.2632] det_loss_cls_dn_4[0.1085] det_loss_box_dn_4[0.2620] det_loss_cls_dn_5[0.1070] det_loss_box_dn_5[0.2605] loss_depth[0.0411] total_loss[9.0578]
Rank[0/8] 06/19/2025 19:56:16 INFO stats.py:314 | Epoch[42] Step[344] GlobalStep[16724] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 2:21:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:56:21 INFO loss_tracker.py:84 | Epoch[42/239] Step[349] GlobalStep[16729/NA]: det_loss_cls_0[0.6053] det_loss_box_0[0.7222] det_loss_cls_1[0.5906] det_loss_box_1[0.5946] det_loss_cls_2[0.5602] det_loss_box_2[0.5526] det_loss_cls_3[0.5450] det_loss_box_3[0.5331] det_loss_cls_4[0.5041] det_loss_box_4[0.5198] det_loss_cls_5[0.4849] det_loss_box_5[0.5216] det_loss_cls_dn_0[0.1027] det_loss_box_dn_0[0.3300] det_loss_cls_dn_1[0.0981] det_loss_box_dn_1[0.2979] det_loss_cls_dn_2[0.0977] det_loss_box_dn_2[0.2894] det_loss_cls_dn_3[0.0974] det_loss_box_dn_3[0.2869] det_loss_cls_dn_4[0.0961] det_loss_box_dn_4[0.2861] det_loss_cls_dn_5[0.0956] det_loss_box_dn_5[0.2857] loss_depth[0.0406] total_loss[9.1380]
Rank[0/8] 06/19/2025 19:56:41 INFO stats.py:314 | Epoch[42] Step[369] GlobalStep[16749] Training Speed: 8.65 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 1 day, 2:20:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:56:46 INFO loss_tracker.py:84 | Epoch[42/239] Step[374] GlobalStep[16754/NA]: det_loss_cls_0[0.6158] det_loss_box_0[0.8008] det_loss_cls_1[0.5984] det_loss_box_1[0.6570] det_loss_cls_2[0.5863] det_loss_box_2[0.6106] det_loss_cls_3[0.5684] det_loss_box_3[0.5845] det_loss_cls_4[0.5502] det_loss_box_4[0.5752] det_loss_cls_5[0.5257] det_loss_box_5[0.5809] det_loss_cls_dn_0[0.1183] det_loss_box_dn_0[0.3494] det_loss_cls_dn_1[0.1153] det_loss_box_dn_1[0.3099] det_loss_cls_dn_2[0.1155] det_loss_box_dn_2[0.2995] det_loss_cls_dn_3[0.1149] det_loss_box_dn_3[0.2968] det_loss_cls_dn_4[0.1140] det_loss_box_dn_4[0.2952] det_loss_cls_dn_5[0.1134] det_loss_box_dn_5[0.2940] loss_depth[0.0413] total_loss[9.8313]
Rank[0/8] 06/19/2025 19:57:02 INFO stats.py:394 | Epoch[42] completed. Training Speed: 7.69 samples/sec across all devices.	Epoch Time: 405.89 sec.	Average Epoch Time: 405.89 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 2:19:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:57:09 INFO stats.py:314 | Epoch[43] Step[4] GlobalStep[16774] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 2:19:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:57:30 INFO loss_tracker.py:84 | Epoch[43/239] Step[24] GlobalStep[16794/NA]: det_loss_cls_0[0.5440] det_loss_box_0[0.7120] det_loss_cls_1[0.5334] det_loss_box_1[0.5851] det_loss_cls_2[0.5130] det_loss_box_2[0.5355] det_loss_cls_3[0.4916] det_loss_box_3[0.5232] det_loss_cls_4[0.4671] det_loss_box_4[0.5102] det_loss_cls_5[0.4558] det_loss_box_5[0.5113] det_loss_cls_dn_0[0.1087] det_loss_box_dn_0[0.3264] det_loss_cls_dn_1[0.1055] det_loss_box_dn_1[0.2819] det_loss_cls_dn_2[0.1044] det_loss_box_dn_2[0.2645] det_loss_cls_dn_3[0.1035] det_loss_box_dn_3[0.2619] det_loss_cls_dn_4[0.1027] det_loss_box_dn_4[0.2601] det_loss_cls_dn_5[0.1022] det_loss_box_dn_5[0.2579] loss_depth[0.0400] total_loss[8.7019]
Rank[0/8] 06/19/2025 19:57:35 INFO stats.py:314 | Epoch[43] Step[29] GlobalStep[16799] Training Speed: 8.58 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 2:18:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:57:56 INFO loss_tracker.py:84 | Epoch[43/239] Step[49] GlobalStep[16819/NA]: det_loss_cls_0[0.5119] det_loss_box_0[0.7355] det_loss_cls_1[0.5013] det_loss_box_1[0.5788] det_loss_cls_2[0.4799] det_loss_box_2[0.5217] det_loss_cls_3[0.4639] det_loss_box_3[0.5030] det_loss_cls_4[0.4378] det_loss_box_4[0.4881] det_loss_cls_5[0.4268] det_loss_box_5[0.4891] det_loss_cls_dn_0[0.1104] det_loss_box_dn_0[0.3226] det_loss_cls_dn_1[0.1073] det_loss_box_dn_1[0.2790] det_loss_cls_dn_2[0.1068] det_loss_box_dn_2[0.2614] det_loss_cls_dn_3[0.1055] det_loss_box_dn_3[0.2588] det_loss_cls_dn_4[0.1038] det_loss_box_dn_4[0.2564] det_loss_cls_dn_5[0.1029] det_loss_box_dn_5[0.2552] loss_depth[0.0435] total_loss[8.4513]
Rank[0/8] 06/19/2025 19:58:01 INFO stats.py:314 | Epoch[43] Step[54] GlobalStep[16824] Training Speed: 8.00 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 2:17:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:58:21 INFO loss_tracker.py:84 | Epoch[43/239] Step[74] GlobalStep[16844/NA]: det_loss_cls_0[0.5311] det_loss_box_0[0.6635] det_loss_cls_1[0.5051] det_loss_box_1[0.5187] det_loss_cls_2[0.4854] det_loss_box_2[0.4754] det_loss_cls_3[0.4655] det_loss_box_3[0.4675] det_loss_cls_4[0.4296] det_loss_box_4[0.4624] det_loss_cls_5[0.4177] det_loss_box_5[0.4594] det_loss_cls_dn_0[0.1051] det_loss_box_dn_0[0.3079] det_loss_cls_dn_1[0.1009] det_loss_box_dn_1[0.2663] det_loss_cls_dn_2[0.0993] det_loss_box_dn_2[0.2503] det_loss_cls_dn_3[0.0987] det_loss_box_dn_3[0.2490] det_loss_cls_dn_4[0.0977] det_loss_box_dn_4[0.2472] det_loss_cls_dn_5[0.0968] det_loss_box_dn_5[0.2465] loss_depth[0.0400] total_loss[8.0868]
Rank[0/8] 06/19/2025 19:58:27 INFO stats.py:314 | Epoch[43] Step[79] GlobalStep[16849] Training Speed: 8.08 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 2:16:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:58:47 INFO loss_tracker.py:84 | Epoch[43/239] Step[99] GlobalStep[16869/NA]: det_loss_cls_0[0.5500] det_loss_box_0[0.7114] det_loss_cls_1[0.5307] det_loss_box_1[0.5745] det_loss_cls_2[0.5233] det_loss_box_2[0.5236] det_loss_cls_3[0.4997] det_loss_box_3[0.5132] det_loss_cls_4[0.4729] det_loss_box_4[0.5064] det_loss_cls_5[0.4587] det_loss_box_5[0.5074] det_loss_cls_dn_0[0.1142] det_loss_box_dn_0[0.3122] det_loss_cls_dn_1[0.1098] det_loss_box_dn_1[0.2766] det_loss_cls_dn_2[0.1090] det_loss_box_dn_2[0.2635] det_loss_cls_dn_3[0.1083] det_loss_box_dn_3[0.2610] det_loss_cls_dn_4[0.1072] det_loss_box_dn_4[0.2601] det_loss_cls_dn_5[0.1064] det_loss_box_dn_5[0.2581] loss_depth[0.0413] total_loss[8.6994]
Rank[0/8] 06/19/2025 19:58:52 INFO stats.py:314 | Epoch[43] Step[104] GlobalStep[16874] Training Speed: 6.46 samples/sec across all devices.	Average Step Time: 1.24 sec.	Estimated Remaining Time: 1 day, 2:15:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:59:13 INFO loss_tracker.py:84 | Epoch[43/239] Step[124] GlobalStep[16894/NA]: det_loss_cls_0[0.4942] det_loss_box_0[0.6729] det_loss_cls_1[0.4763] det_loss_box_1[0.5121] det_loss_cls_2[0.4665] det_loss_box_2[0.4727] det_loss_cls_3[0.4509] det_loss_box_3[0.4411] det_loss_cls_4[0.4170] det_loss_box_4[0.4418] det_loss_cls_5[0.4068] det_loss_box_5[0.4359] det_loss_cls_dn_0[0.1067] det_loss_box_dn_0[0.4163] det_loss_cls_dn_1[0.1034] det_loss_box_dn_1[0.3545] det_loss_cls_dn_2[0.1023] det_loss_box_dn_2[0.3364] det_loss_cls_dn_3[0.1018] det_loss_box_dn_3[0.3311] det_loss_cls_dn_4[0.1007] det_loss_box_dn_4[0.3294] det_loss_cls_dn_5[0.1000] det_loss_box_dn_5[0.3258] loss_depth[0.0433] total_loss[8.4402]
Rank[0/8] 06/19/2025 19:59:18 INFO stats.py:314 | Epoch[43] Step[129] GlobalStep[16899] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 2:15:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 19:59:38 INFO loss_tracker.py:84 | Epoch[43/239] Step[149] GlobalStep[16919/NA]: det_loss_cls_0[0.6691] det_loss_box_0[0.9145] det_loss_cls_1[0.6454] det_loss_box_1[0.7336] det_loss_cls_2[0.6252] det_loss_box_2[0.6693] det_loss_cls_3[0.6100] det_loss_box_3[0.6316] det_loss_cls_4[0.5726] det_loss_box_4[0.6238] det_loss_cls_5[0.5590] det_loss_box_5[0.6214] det_loss_cls_dn_0[0.1100] det_loss_box_dn_0[0.3103] det_loss_cls_dn_1[0.1067] det_loss_box_dn_1[0.2682] det_loss_cls_dn_2[0.1062] det_loss_box_dn_2[0.2521] det_loss_cls_dn_3[0.1054] det_loss_box_dn_3[0.2485] det_loss_cls_dn_4[0.1048] det_loss_box_dn_4[0.2472] det_loss_cls_dn_5[0.1041] det_loss_box_dn_5[0.2454] loss_depth[0.0411] total_loss[10.1256]
Rank[0/8] 06/19/2025 19:59:44 INFO stats.py:314 | Epoch[43] Step[154] GlobalStep[16924] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 2:14:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:00:04 INFO loss_tracker.py:84 | Epoch[43/239] Step[174] GlobalStep[16944/NA]: det_loss_cls_0[0.5091] det_loss_box_0[0.6875] det_loss_cls_1[0.4958] det_loss_box_1[0.5491] det_loss_cls_2[0.4828] det_loss_box_2[0.5132] det_loss_cls_3[0.4633] det_loss_box_3[0.4958] det_loss_cls_4[0.4396] det_loss_box_4[0.4887] det_loss_cls_5[0.4246] det_loss_box_5[0.4886] det_loss_cls_dn_0[0.1072] det_loss_box_dn_0[0.3326] det_loss_cls_dn_1[0.1025] det_loss_box_dn_1[0.2892] det_loss_cls_dn_2[0.1023] det_loss_box_dn_2[0.2742] det_loss_cls_dn_3[0.1018] det_loss_box_dn_3[0.2697] det_loss_cls_dn_4[0.1009] det_loss_box_dn_4[0.2701] det_loss_cls_dn_5[0.1000] det_loss_box_dn_5[0.2678] loss_depth[0.0407] total_loss[8.3969]
Rank[0/8] 06/19/2025 20:00:09 INFO stats.py:314 | Epoch[43] Step[179] GlobalStep[16949] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 2:13:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:00:30 INFO loss_tracker.py:84 | Epoch[43/239] Step[199] GlobalStep[16969/NA]: det_loss_cls_0[0.6649] det_loss_box_0[0.8992] det_loss_cls_1[0.6336] det_loss_box_1[0.7536] det_loss_cls_2[0.6203] det_loss_box_2[0.6884] det_loss_cls_3[0.6060] det_loss_box_3[0.6702] det_loss_cls_4[0.5721] det_loss_box_4[0.6523] det_loss_cls_5[0.5560] det_loss_box_5[0.6509] det_loss_cls_dn_0[0.1122] det_loss_box_dn_0[0.3232] det_loss_cls_dn_1[0.1083] det_loss_box_dn_1[0.2832] det_loss_cls_dn_2[0.1082] det_loss_box_dn_2[0.2662] det_loss_cls_dn_3[0.1079] det_loss_box_dn_3[0.2635] det_loss_cls_dn_4[0.1068] det_loss_box_dn_4[0.2619] det_loss_cls_dn_5[0.1057] det_loss_box_dn_5[0.2594] loss_depth[0.0430] total_loss[10.3169]
Rank[0/8] 06/19/2025 20:00:36 INFO stats.py:314 | Epoch[43] Step[204] GlobalStep[16974] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 2:12:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:00:56 INFO loss_tracker.py:84 | Epoch[43/239] Step[224] GlobalStep[16994/NA]: det_loss_cls_0[0.4783] det_loss_box_0[0.6205] det_loss_cls_1[0.4648] det_loss_box_1[0.4993] det_loss_cls_2[0.4533] det_loss_box_2[0.4559] det_loss_cls_3[0.4311] det_loss_box_3[0.4421] det_loss_cls_4[0.4059] det_loss_box_4[0.4402] det_loss_cls_5[0.4014] det_loss_box_5[0.4386] det_loss_cls_dn_0[0.1017] det_loss_box_dn_0[0.3367] det_loss_cls_dn_1[0.0992] det_loss_box_dn_1[0.2885] det_loss_cls_dn_2[0.1001] det_loss_box_dn_2[0.2687] det_loss_cls_dn_3[0.0989] det_loss_box_dn_3[0.2654] det_loss_cls_dn_4[0.0983] det_loss_box_dn_4[0.2641] det_loss_cls_dn_5[0.0975] det_loss_box_dn_5[0.2632] loss_depth[0.0391] total_loss[7.8528]
Rank[0/8] 06/19/2025 20:01:01 INFO stats.py:314 | Epoch[43] Step[229] GlobalStep[16999] Training Speed: 8.17 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 2:11:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:01:22 INFO loss_tracker.py:84 | Epoch[43/239] Step[249] GlobalStep[17019/NA]: det_loss_cls_0[0.5029] det_loss_box_0[0.6877] det_loss_cls_1[0.4899] det_loss_box_1[0.5498] det_loss_cls_2[0.4772] det_loss_box_2[0.5150] det_loss_cls_3[0.4541] det_loss_box_3[0.5013] det_loss_cls_4[0.4256] det_loss_box_4[0.4974] det_loss_cls_5[0.4163] det_loss_box_5[0.4963] det_loss_cls_dn_0[0.1046] det_loss_box_dn_0[0.3564] det_loss_cls_dn_1[0.1003] det_loss_box_dn_1[0.3099] det_loss_cls_dn_2[0.1004] det_loss_box_dn_2[0.2924] det_loss_cls_dn_3[0.1000] det_loss_box_dn_3[0.2902] det_loss_cls_dn_4[0.0989] det_loss_box_dn_4[0.2883] det_loss_cls_dn_5[0.0981] det_loss_box_dn_5[0.2865] loss_depth[0.0416] total_loss[8.4811]
Rank[0/8] 06/19/2025 20:01:27 INFO stats.py:314 | Epoch[43] Step[254] GlobalStep[17024] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 2:10:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:01:47 INFO loss_tracker.py:84 | Epoch[43/239] Step[274] GlobalStep[17044/NA]: det_loss_cls_0[0.5357] det_loss_box_0[0.7439] det_loss_cls_1[0.5155] det_loss_box_1[0.6242] det_loss_cls_2[0.5114] det_loss_box_2[0.5836] det_loss_cls_3[0.4883] det_loss_box_3[0.5734] det_loss_cls_4[0.4650] det_loss_box_4[0.5653] det_loss_cls_5[0.4518] det_loss_box_5[0.5605] det_loss_cls_dn_0[0.1081] det_loss_box_dn_0[0.3239] det_loss_cls_dn_1[0.1039] det_loss_box_dn_1[0.2939] det_loss_cls_dn_2[0.1043] det_loss_box_dn_2[0.2787] det_loss_cls_dn_3[0.1032] det_loss_box_dn_3[0.2776] det_loss_cls_dn_4[0.1027] det_loss_box_dn_4[0.2759] det_loss_cls_dn_5[0.1015] det_loss_box_dn_5[0.2730] loss_depth[0.0410] total_loss[9.0062]
Rank[0/8] 06/19/2025 20:01:53 INFO stats.py:314 | Epoch[43] Step[279] GlobalStep[17049] Training Speed: 7.92 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 2:09:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:02:14 INFO loss_tracker.py:84 | Epoch[43/239] Step[299] GlobalStep[17069/NA]: det_loss_cls_0[0.5891] det_loss_box_0[0.8106] det_loss_cls_1[0.5560] det_loss_box_1[0.6720] det_loss_cls_2[0.5518] det_loss_box_2[0.6080] det_loss_cls_3[0.5394] det_loss_box_3[0.5802] det_loss_cls_4[0.5104] det_loss_box_4[0.5693] det_loss_cls_5[0.4955] det_loss_box_5[0.5705] det_loss_cls_dn_0[0.1118] det_loss_box_dn_0[0.3410] det_loss_cls_dn_1[0.1070] det_loss_box_dn_1[0.3046] det_loss_cls_dn_2[0.1078] det_loss_box_dn_2[0.2903] det_loss_cls_dn_3[0.1056] det_loss_box_dn_3[0.2877] det_loss_cls_dn_4[0.1037] det_loss_box_dn_4[0.2866] det_loss_cls_dn_5[0.1023] det_loss_box_dn_5[0.2831] loss_depth[0.0390] total_loss[9.5232]
Rank[0/8] 06/19/2025 20:02:19 INFO stats.py:314 | Epoch[43] Step[304] GlobalStep[17074] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 2:08:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:02:39 INFO loss_tracker.py:84 | Epoch[43/239] Step[324] GlobalStep[17094/NA]: det_loss_cls_0[0.5643] det_loss_box_0[0.7679] det_loss_cls_1[0.5447] det_loss_box_1[0.6545] det_loss_cls_2[0.5305] det_loss_box_2[0.6078] det_loss_cls_3[0.5084] det_loss_box_3[0.5997] det_loss_cls_4[0.4862] det_loss_box_4[0.5935] det_loss_cls_5[0.4741] det_loss_box_5[0.5858] det_loss_cls_dn_0[0.1204] det_loss_box_dn_0[0.4727] det_loss_cls_dn_1[0.1177] det_loss_box_dn_1[0.4323] det_loss_cls_dn_2[0.1179] det_loss_box_dn_2[0.4157] det_loss_cls_dn_3[0.1167] det_loss_box_dn_3[0.4159] det_loss_cls_dn_4[0.1156] det_loss_box_dn_4[0.4152] det_loss_cls_dn_5[0.1156] det_loss_box_dn_5[0.4120] loss_depth[0.0387] total_loss[10.2239]
Rank[0/8] 06/19/2025 20:02:44 INFO stats.py:314 | Epoch[43] Step[329] GlobalStep[17099] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 2:07:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:03:05 INFO loss_tracker.py:84 | Epoch[43/239] Step[349] GlobalStep[17119/NA]: det_loss_cls_0[0.5357] det_loss_box_0[0.7467] det_loss_cls_1[0.5137] det_loss_box_1[0.6320] det_loss_cls_2[0.5085] det_loss_box_2[0.5646] det_loss_cls_3[0.4854] det_loss_box_3[0.5506] det_loss_cls_4[0.4683] det_loss_box_4[0.5287] det_loss_cls_5[0.4569] det_loss_box_5[0.5236] det_loss_cls_dn_0[0.1034] det_loss_box_dn_0[0.3351] det_loss_cls_dn_1[0.0999] det_loss_box_dn_1[0.2947] det_loss_cls_dn_2[0.0996] det_loss_box_dn_2[0.2750] det_loss_cls_dn_3[0.0981] det_loss_box_dn_3[0.2722] det_loss_cls_dn_4[0.0974] det_loss_box_dn_4[0.2733] det_loss_cls_dn_5[0.0964] det_loss_box_dn_5[0.2707] loss_depth[0.0404] total_loss[8.8709]
Rank[0/8] 06/19/2025 20:03:10 INFO stats.py:314 | Epoch[43] Step[354] GlobalStep[17124] Training Speed: 8.08 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 2:07:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:03:30 INFO loss_tracker.py:84 | Epoch[43/239] Step[374] GlobalStep[17144/NA]: det_loss_cls_0[0.5579] det_loss_box_0[0.8052] det_loss_cls_1[0.5370] det_loss_box_1[0.6724] det_loss_cls_2[0.5326] det_loss_box_2[0.5853] det_loss_cls_3[0.5088] det_loss_box_3[0.5781] det_loss_cls_4[0.4801] det_loss_box_4[0.5728] det_loss_cls_5[0.4702] det_loss_box_5[0.5692] det_loss_cls_dn_0[0.1094] det_loss_box_dn_0[0.3692] det_loss_cls_dn_1[0.1073] det_loss_box_dn_1[0.3252] det_loss_cls_dn_2[0.1067] det_loss_box_dn_2[0.3050] det_loss_cls_dn_3[0.1060] det_loss_box_dn_3[0.3031] det_loss_cls_dn_4[0.1050] det_loss_box_dn_4[0.3011] det_loss_cls_dn_5[0.1045] det_loss_box_dn_5[0.2992] loss_depth[0.0416] total_loss[9.4529]
Rank[0/8] 06/19/2025 20:03:35 INFO stats.py:314 | Epoch[43] Step[379] GlobalStep[17149] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 2:06:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:03:46 INFO stats.py:394 | Epoch[43] completed. Training Speed: 7.72 samples/sec across all devices.	Epoch Time: 404.23 sec.	Average Epoch Time: 404.23 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 2:05:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:04:05 INFO stats.py:314 | Epoch[44] Step[14] GlobalStep[17174] Training Speed: 8.02 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 2:05:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:04:15 INFO loss_tracker.py:84 | Epoch[44/239] Step[24] GlobalStep[17184/NA]: det_loss_cls_0[0.6539] det_loss_box_0[0.8670] det_loss_cls_1[0.6340] det_loss_box_1[0.7159] det_loss_cls_2[0.6188] det_loss_box_2[0.6503] det_loss_cls_3[0.5959] det_loss_box_3[0.6335] det_loss_cls_4[0.5617] det_loss_box_4[0.6145] det_loss_cls_5[0.5519] det_loss_box_5[0.6059] det_loss_cls_dn_0[0.1143] det_loss_box_dn_0[0.3701] det_loss_cls_dn_1[0.1107] det_loss_box_dn_1[0.3214] det_loss_cls_dn_2[0.1100] det_loss_box_dn_2[0.3071] det_loss_cls_dn_3[0.1093] det_loss_box_dn_3[0.3066] det_loss_cls_dn_4[0.1077] det_loss_box_dn_4[0.3067] det_loss_cls_dn_5[0.1067] det_loss_box_dn_5[0.3057] loss_depth[0.0394] total_loss[10.3190]
Rank[0/8] 06/19/2025 20:04:31 INFO stats.py:314 | Epoch[44] Step[39] GlobalStep[17199] Training Speed: 6.36 samples/sec across all devices.	Average Step Time: 1.26 sec.	Estimated Remaining Time: 1 day, 2:04:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:04:41 INFO loss_tracker.py:84 | Epoch[44/239] Step[49] GlobalStep[17209/NA]: det_loss_cls_0[0.5083] det_loss_box_0[0.6188] det_loss_cls_1[0.4980] det_loss_box_1[0.5045] det_loss_cls_2[0.4847] det_loss_box_2[0.4589] det_loss_cls_3[0.4672] det_loss_box_3[0.4534] det_loss_cls_4[0.4411] det_loss_box_4[0.4397] det_loss_cls_5[0.4315] det_loss_box_5[0.4377] det_loss_cls_dn_0[0.1106] det_loss_box_dn_0[0.2926] det_loss_cls_dn_1[0.1073] det_loss_box_dn_1[0.2526] det_loss_cls_dn_2[0.1073] det_loss_box_dn_2[0.2376] det_loss_cls_dn_3[0.1059] det_loss_box_dn_3[0.2349] det_loss_cls_dn_4[0.1061] det_loss_box_dn_4[0.2335] det_loss_cls_dn_5[0.1051] det_loss_box_dn_5[0.2325] loss_depth[0.0384] total_loss[7.9078]
Rank[0/8] 06/19/2025 20:04:56 INFO stats.py:314 | Epoch[44] Step[64] GlobalStep[17224] Training Speed: 6.97 samples/sec across all devices.	Average Step Time: 1.15 sec.	Estimated Remaining Time: 1 day, 2:03:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:05:06 INFO loss_tracker.py:84 | Epoch[44/239] Step[74] GlobalStep[17234/NA]: det_loss_cls_0[0.6190] det_loss_box_0[0.8284] det_loss_cls_1[0.6004] det_loss_box_1[0.6928] det_loss_cls_2[0.5883] det_loss_box_2[0.6397] det_loss_cls_3[0.5673] det_loss_box_3[0.6222] det_loss_cls_4[0.5390] det_loss_box_4[0.6129] det_loss_cls_5[0.5267] det_loss_box_5[0.6139] det_loss_cls_dn_0[0.1101] det_loss_box_dn_0[0.3467] det_loss_cls_dn_1[0.1065] det_loss_box_dn_1[0.3060] det_loss_cls_dn_2[0.1067] det_loss_box_dn_2[0.2898] det_loss_cls_dn_3[0.1058] det_loss_box_dn_3[0.2878] det_loss_cls_dn_4[0.1052] det_loss_box_dn_4[0.2875] det_loss_cls_dn_5[0.1047] det_loss_box_dn_5[0.2855] loss_depth[0.0416] total_loss[9.9341]
Rank[0/8] 06/19/2025 20:05:23 INFO stats.py:314 | Epoch[44] Step[89] GlobalStep[17249] Training Speed: 6.52 samples/sec across all devices.	Average Step Time: 1.23 sec.	Estimated Remaining Time: 1 day, 2:02:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:05:33 INFO loss_tracker.py:84 | Epoch[44/239] Step[99] GlobalStep[17259/NA]: det_loss_cls_0[0.6448] det_loss_box_0[0.7842] det_loss_cls_1[0.6276] det_loss_box_1[0.6299] det_loss_cls_2[0.6047] det_loss_box_2[0.5770] det_loss_cls_3[0.5916] det_loss_box_3[0.5580] det_loss_cls_4[0.5577] det_loss_box_4[0.5520] det_loss_cls_5[0.5381] det_loss_box_5[0.5531] det_loss_cls_dn_0[0.1082] det_loss_box_dn_0[0.2820] det_loss_cls_dn_1[0.1024] det_loss_box_dn_1[0.2501] det_loss_cls_dn_2[0.1014] det_loss_box_dn_2[0.2363] det_loss_cls_dn_3[0.1007] det_loss_box_dn_3[0.2342] det_loss_cls_dn_4[0.1003] det_loss_box_dn_4[0.2337] det_loss_cls_dn_5[0.0997] det_loss_box_dn_5[0.2318] loss_depth[0.0402] total_loss[9.3396]
Rank[0/8] 06/19/2025 20:05:48 INFO stats.py:314 | Epoch[44] Step[114] GlobalStep[17274] Training Speed: 8.78 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 1 day, 2:02:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:05:59 INFO loss_tracker.py:84 | Epoch[44/239] Step[124] GlobalStep[17284/NA]: det_loss_cls_0[0.5212] det_loss_box_0[0.7542] det_loss_cls_1[0.5070] det_loss_box_1[0.6061] det_loss_cls_2[0.4984] det_loss_box_2[0.5594] det_loss_cls_3[0.4780] det_loss_box_3[0.5370] det_loss_cls_4[0.4499] det_loss_box_4[0.5369] det_loss_cls_5[0.4375] det_loss_box_5[0.5377] det_loss_cls_dn_0[0.0999] det_loss_box_dn_0[0.3171] det_loss_cls_dn_1[0.0954] det_loss_box_dn_1[0.2749] det_loss_cls_dn_2[0.0952] det_loss_box_dn_2[0.2567] det_loss_cls_dn_3[0.0933] det_loss_box_dn_3[0.2549] det_loss_cls_dn_4[0.0929] det_loss_box_dn_4[0.2548] det_loss_cls_dn_5[0.0917] det_loss_box_dn_5[0.2521] loss_depth[0.0415] total_loss[8.6438]
Rank[0/8] 06/19/2025 20:06:14 INFO stats.py:314 | Epoch[44] Step[139] GlobalStep[17299] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 2:01:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:06:24 INFO loss_tracker.py:84 | Epoch[44/239] Step[149] GlobalStep[17309/NA]: det_loss_cls_0[0.6007] det_loss_box_0[0.7960] det_loss_cls_1[0.5944] det_loss_box_1[0.6685] det_loss_cls_2[0.5818] det_loss_box_2[0.6139] det_loss_cls_3[0.5638] det_loss_box_3[0.5896] det_loss_cls_4[0.5353] det_loss_box_4[0.5816] det_loss_cls_5[0.5129] det_loss_box_5[0.5862] det_loss_cls_dn_0[0.1080] det_loss_box_dn_0[0.3211] det_loss_cls_dn_1[0.1050] det_loss_box_dn_1[0.2853] det_loss_cls_dn_2[0.1042] det_loss_box_dn_2[0.2712] det_loss_cls_dn_3[0.1036] det_loss_box_dn_3[0.2704] det_loss_cls_dn_4[0.1022] det_loss_box_dn_4[0.2686] det_loss_cls_dn_5[0.1016] det_loss_box_dn_5[0.2670] loss_depth[0.0397] total_loss[9.5727]
Rank[0/8] 06/19/2025 20:06:40 INFO stats.py:314 | Epoch[44] Step[164] GlobalStep[17324] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 2:00:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:06:50 INFO loss_tracker.py:84 | Epoch[44/239] Step[174] GlobalStep[17334/NA]: det_loss_cls_0[0.6987] det_loss_box_0[0.9449] det_loss_cls_1[0.6854] det_loss_box_1[0.7675] det_loss_cls_2[0.6548] det_loss_box_2[0.7038] det_loss_cls_3[0.6494] det_loss_box_3[0.6801] det_loss_cls_4[0.6212] det_loss_box_4[0.6623] det_loss_cls_5[0.6066] det_loss_box_5[0.6623] det_loss_cls_dn_0[0.1164] det_loss_box_dn_0[0.3056] det_loss_cls_dn_1[0.1127] det_loss_box_dn_1[0.2793] det_loss_cls_dn_2[0.1125] det_loss_box_dn_2[0.2654] det_loss_cls_dn_3[0.1113] det_loss_box_dn_3[0.2642] det_loss_cls_dn_4[0.1104] det_loss_box_dn_4[0.2632] det_loss_cls_dn_5[0.1095] det_loss_box_dn_5[0.2615] loss_depth[0.0422] total_loss[10.6910]
Rank[0/8] 06/19/2025 20:07:06 INFO stats.py:314 | Epoch[44] Step[189] GlobalStep[17349] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 1:59:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:07:16 INFO loss_tracker.py:84 | Epoch[44/239] Step[199] GlobalStep[17359/NA]: det_loss_cls_0[0.6071] det_loss_box_0[0.7963] det_loss_cls_1[0.5986] det_loss_box_1[0.6430] det_loss_cls_2[0.5865] det_loss_box_2[0.5961] det_loss_cls_3[0.5627] det_loss_box_3[0.5785] det_loss_cls_4[0.5176] det_loss_box_4[0.5826] det_loss_cls_5[0.5044] det_loss_box_5[0.5849] det_loss_cls_dn_0[0.1014] det_loss_box_dn_0[0.3251] det_loss_cls_dn_1[0.0970] det_loss_box_dn_1[0.2876] det_loss_cls_dn_2[0.0966] det_loss_box_dn_2[0.2691] det_loss_cls_dn_3[0.0961] det_loss_box_dn_3[0.2666] det_loss_cls_dn_4[0.0947] det_loss_box_dn_4[0.2650] det_loss_cls_dn_5[0.0941] det_loss_box_dn_5[0.2634] loss_depth[0.0394] total_loss[9.4544]
Rank[0/8] 06/19/2025 20:07:31 INFO stats.py:314 | Epoch[44] Step[214] GlobalStep[17374] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 1:58:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:07:41 INFO loss_tracker.py:84 | Epoch[44/239] Step[224] GlobalStep[17384/NA]: det_loss_cls_0[0.4543] det_loss_box_0[0.5541] det_loss_cls_1[0.4378] det_loss_box_1[0.4337] det_loss_cls_2[0.4184] det_loss_box_2[0.3890] det_loss_cls_3[0.3882] det_loss_box_3[0.3823] det_loss_cls_4[0.3592] det_loss_box_4[0.3736] det_loss_cls_5[0.3465] det_loss_box_5[0.3745] det_loss_cls_dn_0[0.0962] det_loss_box_dn_0[0.2875] det_loss_cls_dn_1[0.0919] det_loss_box_dn_1[0.2431] det_loss_cls_dn_2[0.0906] det_loss_box_dn_2[0.2264] det_loss_cls_dn_3[0.0894] det_loss_box_dn_3[0.2241] det_loss_cls_dn_4[0.0881] det_loss_box_dn_4[0.2229] det_loss_cls_dn_5[0.0869] det_loss_box_dn_5[0.2215] loss_depth[0.0402] total_loss[6.9204]
Rank[0/8] 06/19/2025 20:07:57 INFO stats.py:314 | Epoch[44] Step[239] GlobalStep[17399] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 1:57:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:08:07 INFO loss_tracker.py:84 | Epoch[44/239] Step[249] GlobalStep[17409/NA]: det_loss_cls_0[0.5123] det_loss_box_0[0.7076] det_loss_cls_1[0.4914] det_loss_box_1[0.5540] det_loss_cls_2[0.4822] det_loss_box_2[0.5035] det_loss_cls_3[0.4563] det_loss_box_3[0.4987] det_loss_cls_4[0.4328] det_loss_box_4[0.4779] det_loss_cls_5[0.4148] det_loss_box_5[0.4836] det_loss_cls_dn_0[0.1073] det_loss_box_dn_0[0.3332] det_loss_cls_dn_1[0.1035] det_loss_box_dn_1[0.2899] det_loss_cls_dn_2[0.1021] det_loss_box_dn_2[0.2744] det_loss_cls_dn_3[0.1016] det_loss_box_dn_3[0.2725] det_loss_cls_dn_4[0.1010] det_loss_box_dn_4[0.2708] det_loss_cls_dn_5[0.1003] det_loss_box_dn_5[0.2695] loss_depth[0.0409] total_loss[8.3823]
Rank[0/8] 06/19/2025 20:08:24 INFO stats.py:314 | Epoch[44] Step[264] GlobalStep[17424] Training Speed: 7.68 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 1:56:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:08:34 INFO loss_tracker.py:84 | Epoch[44/239] Step[274] GlobalStep[17434/NA]: det_loss_cls_0[0.5607] det_loss_box_0[0.7739] det_loss_cls_1[0.5570] det_loss_box_1[0.6311] det_loss_cls_2[0.5354] det_loss_box_2[0.5741] det_loss_cls_3[0.5220] det_loss_box_3[0.5459] det_loss_cls_4[0.4917] det_loss_box_4[0.5386] det_loss_cls_5[0.4800] det_loss_box_5[0.5431] det_loss_cls_dn_0[0.1106] det_loss_box_dn_0[0.3484] det_loss_cls_dn_1[0.1072] det_loss_box_dn_1[0.3008] det_loss_cls_dn_2[0.1074] det_loss_box_dn_2[0.2844] det_loss_cls_dn_3[0.1059] det_loss_box_dn_3[0.2822] det_loss_cls_dn_4[0.1047] det_loss_box_dn_4[0.2805] det_loss_cls_dn_5[0.1038] det_loss_box_dn_5[0.2789] loss_depth[0.0396] total_loss[9.2080]
Rank[0/8] 06/19/2025 20:08:49 INFO stats.py:314 | Epoch[44] Step[289] GlobalStep[17449] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 1:56:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:08:59 INFO loss_tracker.py:84 | Epoch[44/239] Step[299] GlobalStep[17459/NA]: det_loss_cls_0[0.5372] det_loss_box_0[0.6802] det_loss_cls_1[0.5106] det_loss_box_1[0.5378] det_loss_cls_2[0.4948] det_loss_box_2[0.4868] det_loss_cls_3[0.4676] det_loss_box_3[0.4763] det_loss_cls_4[0.4379] det_loss_box_4[0.4680] det_loss_cls_5[0.4276] det_loss_box_5[0.4678] det_loss_cls_dn_0[0.1034] det_loss_box_dn_0[0.3040] det_loss_cls_dn_1[0.0997] det_loss_box_dn_1[0.2683] det_loss_cls_dn_2[0.0995] det_loss_box_dn_2[0.2537] det_loss_cls_dn_3[0.0976] det_loss_box_dn_3[0.2515] det_loss_cls_dn_4[0.0969] det_loss_box_dn_4[0.2503] det_loss_cls_dn_5[0.0963] det_loss_box_dn_5[0.2483] loss_depth[0.0388] total_loss[8.2011]
Rank[0/8] 06/19/2025 20:09:14 INFO stats.py:314 | Epoch[44] Step[314] GlobalStep[17474] Training Speed: 7.73 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 1:55:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:09:25 INFO loss_tracker.py:84 | Epoch[44/239] Step[324] GlobalStep[17484/NA]: det_loss_cls_0[0.4630] det_loss_box_0[0.5330] det_loss_cls_1[0.4560] det_loss_box_1[0.4298] det_loss_cls_2[0.4379] det_loss_box_2[0.4036] det_loss_cls_3[0.4117] det_loss_box_3[0.3829] det_loss_cls_4[0.3764] det_loss_box_4[0.3832] det_loss_cls_5[0.3536] det_loss_box_5[0.3871] det_loss_cls_dn_0[0.0928] det_loss_box_dn_0[0.3004] det_loss_cls_dn_1[0.0886] det_loss_box_dn_1[0.2558] det_loss_cls_dn_2[0.0879] det_loss_box_dn_2[0.2390] det_loss_cls_dn_3[0.0873] det_loss_box_dn_3[0.2359] det_loss_cls_dn_4[0.0870] det_loss_box_dn_4[0.2351] det_loss_cls_dn_5[0.0863] det_loss_box_dn_5[0.2339] loss_depth[0.0380] total_loss[7.0863]
Rank[0/8] 06/19/2025 20:09:41 INFO stats.py:314 | Epoch[44] Step[339] GlobalStep[17499] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 1:54:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:09:52 INFO loss_tracker.py:84 | Epoch[44/239] Step[349] GlobalStep[17509/NA]: det_loss_cls_0[0.5452] det_loss_box_0[0.7210] det_loss_cls_1[0.5186] det_loss_box_1[0.5809] det_loss_cls_2[0.5092] det_loss_box_2[0.5085] det_loss_cls_3[0.4932] det_loss_box_3[0.4932] det_loss_cls_4[0.4549] det_loss_box_4[0.4858] det_loss_cls_5[0.4329] det_loss_box_5[0.4880] det_loss_cls_dn_0[0.1105] det_loss_box_dn_0[0.2842] det_loss_cls_dn_1[0.1087] det_loss_box_dn_1[0.2445] det_loss_cls_dn_2[0.1078] det_loss_box_dn_2[0.2273] det_loss_cls_dn_3[0.1070] det_loss_box_dn_3[0.2256] det_loss_cls_dn_4[0.1053] det_loss_box_dn_4[0.2241] det_loss_cls_dn_5[0.1045] det_loss_box_dn_5[0.2223] loss_depth[0.0410] total_loss[8.3442]
Rank[0/8] 06/19/2025 20:10:07 INFO stats.py:314 | Epoch[44] Step[364] GlobalStep[17524] Training Speed: 8.19 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 1:53:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:10:17 INFO loss_tracker.py:84 | Epoch[44/239] Step[374] GlobalStep[17534/NA]: det_loss_cls_0[0.5285] det_loss_box_0[0.7409] det_loss_cls_1[0.5072] det_loss_box_1[0.6034] det_loss_cls_2[0.4879] det_loss_box_2[0.5485] det_loss_cls_3[0.4635] det_loss_box_3[0.5280] det_loss_cls_4[0.4231] det_loss_box_4[0.5182] det_loss_cls_5[0.4078] det_loss_box_5[0.5194] det_loss_cls_dn_0[0.1069] det_loss_box_dn_0[0.3909] det_loss_cls_dn_1[0.1036] det_loss_box_dn_1[0.3444] det_loss_cls_dn_2[0.1036] det_loss_box_dn_2[0.3308] det_loss_cls_dn_3[0.1021] det_loss_box_dn_3[0.3284] det_loss_cls_dn_4[0.1015] det_loss_box_dn_4[0.3270] det_loss_cls_dn_5[0.1010] det_loss_box_dn_5[0.3257] loss_depth[0.0390] total_loss[8.9812]
Rank[0/8] 06/19/2025 20:10:32 INFO stats.py:314 | Epoch[44] Step[389] GlobalStep[17549] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 1:52:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:10:32 INFO stats.py:394 | Epoch[44] completed. Training Speed: 7.68 samples/sec across all devices.	Epoch Time: 406.26 sec.	Average Epoch Time: 406.26 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 1:52:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:11:00 INFO stats.py:314 | Epoch[45] Step[24] GlobalStep[17574] Training Speed: 8.85 samples/sec across all devices.	Average Step Time: 0.90 sec.	Estimated Remaining Time: 1 day, 1:51:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:11:00 INFO loss_tracker.py:84 | Epoch[45/239] Step[24] GlobalStep[17574/NA]: det_loss_cls_0[0.5678] det_loss_box_0[0.8067] det_loss_cls_1[0.5478] det_loss_box_1[0.6768] det_loss_cls_2[0.5364] det_loss_box_2[0.5962] det_loss_cls_3[0.5090] det_loss_box_3[0.5878] det_loss_cls_4[0.4883] det_loss_box_4[0.5714] det_loss_cls_5[0.4679] det_loss_box_5[0.5754] det_loss_cls_dn_0[0.1051] det_loss_box_dn_0[0.3838] det_loss_cls_dn_1[0.1012] det_loss_box_dn_1[0.3360] det_loss_cls_dn_2[0.1011] det_loss_box_dn_2[0.3177] det_loss_cls_dn_3[0.0998] det_loss_box_dn_3[0.3142] det_loss_cls_dn_4[0.0995] det_loss_box_dn_4[0.3117] det_loss_cls_dn_5[0.0983] det_loss_box_dn_5[0.3095] loss_depth[0.0392] total_loss[9.5485]
Rank[0/8] 06/19/2025 20:11:26 INFO stats.py:314 | Epoch[45] Step[49] GlobalStep[17599] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 1:51:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:11:26 INFO loss_tracker.py:84 | Epoch[45/239] Step[49] GlobalStep[17599/NA]: det_loss_cls_0[0.5669] det_loss_box_0[0.7573] det_loss_cls_1[0.5398] det_loss_box_1[0.6253] det_loss_cls_2[0.5303] det_loss_box_2[0.5727] det_loss_cls_3[0.5039] det_loss_box_3[0.5572] det_loss_cls_4[0.4782] det_loss_box_4[0.5332] det_loss_cls_5[0.4629] det_loss_box_5[0.5382] det_loss_cls_dn_0[0.1023] det_loss_box_dn_0[0.3185] det_loss_cls_dn_1[0.0977] det_loss_box_dn_1[0.2873] det_loss_cls_dn_2[0.0982] det_loss_box_dn_2[0.2747] det_loss_cls_dn_3[0.0962] det_loss_box_dn_3[0.2749] det_loss_cls_dn_4[0.0959] det_loss_box_dn_4[0.2734] det_loss_cls_dn_5[0.0955] det_loss_box_dn_5[0.2706] loss_depth[0.0417] total_loss[8.9930]
Rank[0/8] 06/19/2025 20:11:51 INFO stats.py:314 | Epoch[45] Step[74] GlobalStep[17624] Training Speed: 8.62 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 1:50:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:11:51 INFO loss_tracker.py:84 | Epoch[45/239] Step[74] GlobalStep[17624/NA]: det_loss_cls_0[0.5581] det_loss_box_0[0.6367] det_loss_cls_1[0.5437] det_loss_box_1[0.5252] det_loss_cls_2[0.5375] det_loss_box_2[0.4742] det_loss_cls_3[0.5106] det_loss_box_3[0.4671] det_loss_cls_4[0.4827] det_loss_box_4[0.4582] det_loss_cls_5[0.4705] det_loss_box_5[0.4608] det_loss_cls_dn_0[0.1124] det_loss_box_dn_0[0.2880] det_loss_cls_dn_1[0.1093] det_loss_box_dn_1[0.2474] det_loss_cls_dn_2[0.1092] det_loss_box_dn_2[0.2355] det_loss_cls_dn_3[0.1086] det_loss_box_dn_3[0.2341] det_loss_cls_dn_4[0.1076] det_loss_box_dn_4[0.2346] det_loss_cls_dn_5[0.1072] det_loss_box_dn_5[0.2336] loss_depth[0.0400] total_loss[8.2926]
Rank[0/8] 06/19/2025 20:12:18 INFO stats.py:314 | Epoch[45] Step[99] GlobalStep[17649] Training Speed: 7.12 samples/sec across all devices.	Average Step Time: 1.12 sec.	Estimated Remaining Time: 1 day, 1:49:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:12:18 INFO loss_tracker.py:84 | Epoch[45/239] Step[99] GlobalStep[17649/NA]: det_loss_cls_0[0.5290] det_loss_box_0[0.7527] det_loss_cls_1[0.5268] det_loss_box_1[0.6037] det_loss_cls_2[0.5067] det_loss_box_2[0.5434] det_loss_cls_3[0.4824] det_loss_box_3[0.5309] det_loss_cls_4[0.4436] det_loss_box_4[0.5387] det_loss_cls_5[0.4336] det_loss_box_5[0.5365] det_loss_cls_dn_0[0.1118] det_loss_box_dn_0[0.3182] det_loss_cls_dn_1[0.1088] det_loss_box_dn_1[0.2776] det_loss_cls_dn_2[0.1080] det_loss_box_dn_2[0.2602] det_loss_cls_dn_3[0.1072] det_loss_box_dn_3[0.2561] det_loss_cls_dn_4[0.1055] det_loss_box_dn_4[0.2551] det_loss_cls_dn_5[0.1050] det_loss_box_dn_5[0.2530] loss_depth[0.0435] total_loss[8.7377]
Rank[0/8] 06/19/2025 20:12:44 INFO stats.py:314 | Epoch[45] Step[124] GlobalStep[17674] Training Speed: 7.98 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 1:48:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:12:44 INFO loss_tracker.py:84 | Epoch[45/239] Step[124] GlobalStep[17674/NA]: det_loss_cls_0[0.4320] det_loss_box_0[0.5864] det_loss_cls_1[0.4149] det_loss_box_1[0.4681] det_loss_cls_2[0.4023] det_loss_box_2[0.4213] det_loss_cls_3[0.3951] det_loss_box_3[0.4093] det_loss_cls_4[0.3614] det_loss_box_4[0.4011] det_loss_cls_5[0.3535] det_loss_box_5[0.3991] det_loss_cls_dn_0[0.1069] det_loss_box_dn_0[0.3542] det_loss_cls_dn_1[0.1021] det_loss_box_dn_1[0.2985] det_loss_cls_dn_2[0.1009] det_loss_box_dn_2[0.2849] det_loss_cls_dn_3[0.1003] det_loss_box_dn_3[0.2826] det_loss_cls_dn_4[0.0991] det_loss_box_dn_4[0.2818] det_loss_cls_dn_5[0.0983] det_loss_box_dn_5[0.2790] loss_depth[0.0407] total_loss[7.4741]
Rank[0/8] 06/19/2025 20:13:09 INFO stats.py:314 | Epoch[45] Step[149] GlobalStep[17699] Training Speed: 8.22 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 1:47:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:13:09 INFO loss_tracker.py:84 | Epoch[45/239] Step[149] GlobalStep[17699/NA]: det_loss_cls_0[0.5535] det_loss_box_0[0.7066] det_loss_cls_1[0.5290] det_loss_box_1[0.6049] det_loss_cls_2[0.5152] det_loss_box_2[0.5462] det_loss_cls_3[0.4977] det_loss_box_3[0.5356] det_loss_cls_4[0.4727] det_loss_box_4[0.5185] det_loss_cls_5[0.4569] det_loss_box_5[0.5213] det_loss_cls_dn_0[0.1073] det_loss_box_dn_0[0.3190] det_loss_cls_dn_1[0.1037] det_loss_box_dn_1[0.2806] det_loss_cls_dn_2[0.1035] det_loss_box_dn_2[0.2683] det_loss_cls_dn_3[0.1037] det_loss_box_dn_3[0.2656] det_loss_cls_dn_4[0.1025] det_loss_box_dn_4[0.2652] det_loss_cls_dn_5[0.1014] det_loss_box_dn_5[0.2636] loss_depth[0.0369] total_loss[8.7792]
Rank[0/8] 06/19/2025 20:13:35 INFO stats.py:314 | Epoch[45] Step[174] GlobalStep[17724] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 1:46:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:13:35 INFO loss_tracker.py:84 | Epoch[45/239] Step[174] GlobalStep[17724/NA]: det_loss_cls_0[0.4668] det_loss_box_0[0.5607] det_loss_cls_1[0.4421] det_loss_box_1[0.4681] det_loss_cls_2[0.4355] det_loss_box_2[0.4220] det_loss_cls_3[0.4252] det_loss_box_3[0.4029] det_loss_cls_4[0.3972] det_loss_box_4[0.3996] det_loss_cls_5[0.3878] det_loss_box_5[0.3975] det_loss_cls_dn_0[0.1039] det_loss_box_dn_0[0.3176] det_loss_cls_dn_1[0.0993] det_loss_box_dn_1[0.2756] det_loss_cls_dn_2[0.0991] det_loss_box_dn_2[0.2604] det_loss_cls_dn_3[0.0980] det_loss_box_dn_3[0.2585] det_loss_cls_dn_4[0.0964] det_loss_box_dn_4[0.2576] det_loss_cls_dn_5[0.0959] det_loss_box_dn_5[0.2563] loss_depth[0.0380] total_loss[7.4619]
Rank[0/8] 06/19/2025 20:14:01 INFO stats.py:314 | Epoch[45] Step[199] GlobalStep[17749] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 1:45:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:14:01 INFO loss_tracker.py:84 | Epoch[45/239] Step[199] GlobalStep[17749/NA]: det_loss_cls_0[0.5559] det_loss_box_0[0.7583] det_loss_cls_1[0.5442] det_loss_box_1[0.5947] det_loss_cls_2[0.5330] det_loss_box_2[0.5280] det_loss_cls_3[0.5046] det_loss_box_3[0.5070] det_loss_cls_4[0.4785] det_loss_box_4[0.4974] det_loss_cls_5[0.4704] det_loss_box_5[0.4908] det_loss_cls_dn_0[0.1000] det_loss_box_dn_0[0.2753] det_loss_cls_dn_1[0.0967] det_loss_box_dn_1[0.2427] det_loss_cls_dn_2[0.0962] det_loss_box_dn_2[0.2277] det_loss_cls_dn_3[0.0951] det_loss_box_dn_3[0.2265] det_loss_cls_dn_4[0.0938] det_loss_box_dn_4[0.2247] det_loss_cls_dn_5[0.0932] det_loss_box_dn_5[0.2239] loss_depth[0.0430] total_loss[8.5016]
Rank[0/8] 06/19/2025 20:14:27 INFO stats.py:314 | Epoch[45] Step[224] GlobalStep[17774] Training Speed: 8.00 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 1:45:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:14:27 INFO loss_tracker.py:84 | Epoch[45/239] Step[224] GlobalStep[17774/NA]: det_loss_cls_0[0.4922] det_loss_box_0[0.6270] det_loss_cls_1[0.4743] det_loss_box_1[0.5005] det_loss_cls_2[0.4624] det_loss_box_2[0.4623] det_loss_cls_3[0.4414] det_loss_box_3[0.4492] det_loss_cls_4[0.4154] det_loss_box_4[0.4468] det_loss_cls_5[0.3993] det_loss_box_5[0.4507] det_loss_cls_dn_0[0.1045] det_loss_box_dn_0[0.3217] det_loss_cls_dn_1[0.0999] det_loss_box_dn_1[0.2833] det_loss_cls_dn_2[0.0992] det_loss_box_dn_2[0.2681] det_loss_cls_dn_3[0.0985] det_loss_box_dn_3[0.2665] det_loss_cls_dn_4[0.0978] det_loss_box_dn_4[0.2647] det_loss_cls_dn_5[0.0972] det_loss_box_dn_5[0.2641] loss_depth[0.0428] total_loss[7.9297]
Rank[0/8] 06/19/2025 20:14:53 INFO stats.py:314 | Epoch[45] Step[249] GlobalStep[17799] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 1:44:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:14:53 INFO loss_tracker.py:84 | Epoch[45/239] Step[249] GlobalStep[17799/NA]: det_loss_cls_0[0.5938] det_loss_box_0[0.9002] det_loss_cls_1[0.5688] det_loss_box_1[0.7667] det_loss_cls_2[0.5622] det_loss_box_2[0.6772] det_loss_cls_3[0.5491] det_loss_box_3[0.6486] det_loss_cls_4[0.5154] det_loss_box_4[0.6442] det_loss_cls_5[0.5083] det_loss_box_5[0.6374] det_loss_cls_dn_0[0.1067] det_loss_box_dn_0[0.3779] det_loss_cls_dn_1[0.1042] det_loss_box_dn_1[0.3397] det_loss_cls_dn_2[0.1039] det_loss_box_dn_2[0.3319] det_loss_cls_dn_3[0.1031] det_loss_box_dn_3[0.3300] det_loss_cls_dn_4[0.1027] det_loss_box_dn_4[0.3289] det_loss_cls_dn_5[0.1014] det_loss_box_dn_5[0.3259] loss_depth[0.0411] total_loss[10.2691]
Rank[0/8] 06/19/2025 20:15:19 INFO stats.py:314 | Epoch[45] Step[274] GlobalStep[17824] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 1:43:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:15:19 INFO loss_tracker.py:84 | Epoch[45/239] Step[274] GlobalStep[17824/NA]: det_loss_cls_0[0.4693] det_loss_box_0[0.6693] det_loss_cls_1[0.4544] det_loss_box_1[0.5449] det_loss_cls_2[0.4439] det_loss_box_2[0.5114] det_loss_cls_3[0.4318] det_loss_box_3[0.4853] det_loss_cls_4[0.3927] det_loss_box_4[0.4807] det_loss_cls_5[0.3820] det_loss_box_5[0.4824] det_loss_cls_dn_0[0.1002] det_loss_box_dn_0[0.3718] det_loss_cls_dn_1[0.0965] det_loss_box_dn_1[0.3269] det_loss_cls_dn_2[0.0948] det_loss_box_dn_2[0.3119] det_loss_cls_dn_3[0.0934] det_loss_box_dn_3[0.3095] det_loss_cls_dn_4[0.0922] det_loss_box_dn_4[0.3090] det_loss_cls_dn_5[0.0910] det_loss_box_dn_5[0.3049] loss_depth[0.0433] total_loss[8.2936]
Rank[0/8] 06/19/2025 20:15:45 INFO stats.py:314 | Epoch[45] Step[299] GlobalStep[17849] Training Speed: 8.19 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 1:42:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:15:45 INFO loss_tracker.py:84 | Epoch[45/239] Step[299] GlobalStep[17849/NA]: det_loss_cls_0[0.4981] det_loss_box_0[0.6484] det_loss_cls_1[0.4953] det_loss_box_1[0.5264] det_loss_cls_2[0.4704] det_loss_box_2[0.4821] det_loss_cls_3[0.4437] det_loss_box_3[0.4734] det_loss_cls_4[0.4097] det_loss_box_4[0.4679] det_loss_cls_5[0.3976] det_loss_box_5[0.4724] det_loss_cls_dn_0[0.1080] det_loss_box_dn_0[0.3189] det_loss_cls_dn_1[0.1026] det_loss_box_dn_1[0.2779] det_loss_cls_dn_2[0.1022] det_loss_box_dn_2[0.2635] det_loss_cls_dn_3[0.1014] det_loss_box_dn_3[0.2619] det_loss_cls_dn_4[0.1006] det_loss_box_dn_4[0.2608] det_loss_cls_dn_5[0.0996] det_loss_box_dn_5[0.2596] loss_depth[0.0392] total_loss[8.0816]
Rank[0/8] 06/19/2025 20:16:11 INFO stats.py:314 | Epoch[45] Step[324] GlobalStep[17874] Training Speed: 7.71 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 1:41:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:16:11 INFO loss_tracker.py:84 | Epoch[45/239] Step[324] GlobalStep[17874/NA]: det_loss_cls_0[0.5347] det_loss_box_0[0.6489] det_loss_cls_1[0.5119] det_loss_box_1[0.5284] det_loss_cls_2[0.5042] det_loss_box_2[0.4604] det_loss_cls_3[0.4825] det_loss_box_3[0.4508] det_loss_cls_4[0.4515] det_loss_box_4[0.4455] det_loss_cls_5[0.4295] det_loss_box_5[0.4478] det_loss_cls_dn_0[0.1108] det_loss_box_dn_0[0.2857] det_loss_cls_dn_1[0.1069] det_loss_box_dn_1[0.2414] det_loss_cls_dn_2[0.1069] det_loss_box_dn_2[0.2261] det_loss_cls_dn_3[0.1055] det_loss_box_dn_3[0.2227] det_loss_cls_dn_4[0.1049] det_loss_box_dn_4[0.2217] det_loss_cls_dn_5[0.1040] det_loss_box_dn_5[0.2202] loss_depth[0.0395] total_loss[7.9924]
Rank[0/8] 06/19/2025 20:16:36 INFO stats.py:314 | Epoch[45] Step[349] GlobalStep[17899] Training Speed: 8.62 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 1:40:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:16:36 INFO loss_tracker.py:84 | Epoch[45/239] Step[349] GlobalStep[17899/NA]: det_loss_cls_0[0.6124] det_loss_box_0[0.9114] det_loss_cls_1[0.5844] det_loss_box_1[0.7706] det_loss_cls_2[0.5588] det_loss_box_2[0.7021] det_loss_cls_3[0.5447] det_loss_box_3[0.6673] det_loss_cls_4[0.5253] det_loss_box_4[0.6396] det_loss_cls_5[0.5151] det_loss_box_5[0.6358] det_loss_cls_dn_0[0.1062] det_loss_box_dn_0[0.3384] det_loss_cls_dn_1[0.1032] det_loss_box_dn_1[0.2990] det_loss_cls_dn_2[0.1018] det_loss_box_dn_2[0.2862] det_loss_cls_dn_3[0.1004] det_loss_box_dn_3[0.2838] det_loss_cls_dn_4[0.0998] det_loss_box_dn_4[0.2824] det_loss_cls_dn_5[0.0992] det_loss_box_dn_5[0.2824] loss_depth[0.0365] total_loss[10.0867]
Rank[0/8] 06/19/2025 20:17:03 INFO stats.py:314 | Epoch[45] Step[374] GlobalStep[17924] Training Speed: 7.85 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1 day, 1:40:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:17:03 INFO loss_tracker.py:84 | Epoch[45/239] Step[374] GlobalStep[17924/NA]: det_loss_cls_0[0.4870] det_loss_box_0[0.6160] det_loss_cls_1[0.4780] det_loss_box_1[0.4848] det_loss_cls_2[0.4545] det_loss_box_2[0.4430] det_loss_cls_3[0.4453] det_loss_box_3[0.4251] det_loss_cls_4[0.4077] det_loss_box_4[0.4261] det_loss_cls_5[0.3944] det_loss_box_5[0.4239] det_loss_cls_dn_0[0.1090] det_loss_box_dn_0[0.3187] det_loss_cls_dn_1[0.1050] det_loss_box_dn_1[0.2789] det_loss_cls_dn_2[0.1051] det_loss_box_dn_2[0.2662] det_loss_cls_dn_3[0.1045] det_loss_box_dn_3[0.2638] det_loss_cls_dn_4[0.1031] det_loss_box_dn_4[0.2638] det_loss_cls_dn_5[0.1028] det_loss_box_dn_5[0.2634] loss_depth[0.0389] total_loss[7.8090]
Rank[0/8] 06/19/2025 20:17:19 INFO stats.py:394 | Epoch[45] completed. Training Speed: 7.67 samples/sec across all devices.	Epoch Time: 406.66 sec.	Average Epoch Time: 406.66 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 1:39:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:17:31 INFO stats.py:314 | Epoch[46] Step[9] GlobalStep[17949] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 1:39:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:17:47 INFO loss_tracker.py:84 | Epoch[46/239] Step[24] GlobalStep[17964/NA]: det_loss_cls_0[0.5564] det_loss_box_0[0.7178] det_loss_cls_1[0.5348] det_loss_box_1[0.5897] det_loss_cls_2[0.5261] det_loss_box_2[0.5284] det_loss_cls_3[0.4959] det_loss_box_3[0.5160] det_loss_cls_4[0.4684] det_loss_box_4[0.5020] det_loss_cls_5[0.4503] det_loss_box_5[0.5035] det_loss_cls_dn_0[0.1078] det_loss_box_dn_0[0.3042] det_loss_cls_dn_1[0.1034] det_loss_box_dn_1[0.2627] det_loss_cls_dn_2[0.1037] det_loss_box_dn_2[0.2473] det_loss_cls_dn_3[0.1029] det_loss_box_dn_3[0.2447] det_loss_cls_dn_4[0.1017] det_loss_box_dn_4[0.2434] det_loss_cls_dn_5[0.1009] det_loss_box_dn_5[0.2418] loss_depth[0.0406] total_loss[8.5944]
Rank[0/8] 06/19/2025 20:17:57 INFO stats.py:314 | Epoch[46] Step[34] GlobalStep[17974] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 1:38:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:18:12 INFO loss_tracker.py:84 | Epoch[46/239] Step[49] GlobalStep[17989/NA]: det_loss_cls_0[0.5444] det_loss_box_0[0.7329] det_loss_cls_1[0.5250] det_loss_box_1[0.5976] det_loss_cls_2[0.5093] det_loss_box_2[0.5363] det_loss_cls_3[0.4946] det_loss_box_3[0.5146] det_loss_cls_4[0.4770] det_loss_box_4[0.4974] det_loss_cls_5[0.4660] det_loss_box_5[0.4941] det_loss_cls_dn_0[0.1073] det_loss_box_dn_0[0.3716] det_loss_cls_dn_1[0.1013] det_loss_box_dn_1[0.3302] det_loss_cls_dn_2[0.1012] det_loss_box_dn_2[0.3125] det_loss_cls_dn_3[0.1004] det_loss_box_dn_3[0.3097] det_loss_cls_dn_4[0.0994] det_loss_box_dn_4[0.3088] det_loss_cls_dn_5[0.0979] det_loss_box_dn_5[0.3038] loss_depth[0.0387] total_loss[8.9718]
Rank[0/8] 06/19/2025 20:18:23 INFO stats.py:314 | Epoch[46] Step[59] GlobalStep[17999] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 1:37:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:18:39 INFO loss_tracker.py:84 | Epoch[46/239] Step[74] GlobalStep[18014/NA]: det_loss_cls_0[0.5146] det_loss_box_0[0.6945] det_loss_cls_1[0.4898] det_loss_box_1[0.5743] det_loss_cls_2[0.4695] det_loss_box_2[0.5239] det_loss_cls_3[0.4507] det_loss_box_3[0.5056] det_loss_cls_4[0.4306] det_loss_box_4[0.4974] det_loss_cls_5[0.4148] det_loss_box_5[0.4991] det_loss_cls_dn_0[0.1017] det_loss_box_dn_0[0.3074] det_loss_cls_dn_1[0.0988] det_loss_box_dn_1[0.2755] det_loss_cls_dn_2[0.0986] det_loss_box_dn_2[0.2581] det_loss_cls_dn_3[0.0971] det_loss_box_dn_3[0.2577] det_loss_cls_dn_4[0.0964] det_loss_box_dn_4[0.2577] det_loss_cls_dn_5[0.0960] det_loss_box_dn_5[0.2560] loss_depth[0.0393] total_loss[8.3050]
Rank[0/8] 06/19/2025 20:18:49 INFO stats.py:314 | Epoch[46] Step[84] GlobalStep[18024] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 1:36:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:19:04 INFO loss_tracker.py:84 | Epoch[46/239] Step[99] GlobalStep[18039/NA]: det_loss_cls_0[0.5492] det_loss_box_0[0.8160] det_loss_cls_1[0.5229] det_loss_box_1[0.6659] det_loss_cls_2[0.5221] det_loss_box_2[0.6066] det_loss_cls_3[0.4999] det_loss_box_3[0.5921] det_loss_cls_4[0.4673] det_loss_box_4[0.5882] det_loss_cls_5[0.4532] det_loss_box_5[0.5901] det_loss_cls_dn_0[0.0958] det_loss_box_dn_0[0.4221] det_loss_cls_dn_1[0.0915] det_loss_box_dn_1[0.3789] det_loss_cls_dn_2[0.0912] det_loss_box_dn_2[0.3665] det_loss_cls_dn_3[0.0902] det_loss_box_dn_3[0.3673] det_loss_cls_dn_4[0.0887] det_loss_box_dn_4[0.3667] det_loss_cls_dn_5[0.0877] det_loss_box_dn_5[0.3631] loss_depth[0.0428] total_loss[9.7259]
Rank[0/8] 06/19/2025 20:19:14 INFO stats.py:314 | Epoch[46] Step[109] GlobalStep[18049] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 1:36:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:19:30 INFO loss_tracker.py:84 | Epoch[46/239] Step[124] GlobalStep[18064/NA]: det_loss_cls_0[0.5799] det_loss_box_0[0.7459] det_loss_cls_1[0.5554] det_loss_box_1[0.5833] det_loss_cls_2[0.5299] det_loss_box_2[0.5221] det_loss_cls_3[0.5019] det_loss_box_3[0.5078] det_loss_cls_4[0.4715] det_loss_box_4[0.5031] det_loss_cls_5[0.4606] det_loss_box_5[0.5001] det_loss_cls_dn_0[0.1103] det_loss_box_dn_0[0.2905] det_loss_cls_dn_1[0.1073] det_loss_box_dn_1[0.2548] det_loss_cls_dn_2[0.1075] det_loss_box_dn_2[0.2421] det_loss_cls_dn_3[0.1069] det_loss_box_dn_3[0.2407] det_loss_cls_dn_4[0.1063] det_loss_box_dn_4[0.2401] det_loss_cls_dn_5[0.1052] det_loss_box_dn_5[0.2364] loss_depth[0.0400] total_loss[8.6498]
Rank[0/8] 06/19/2025 20:19:40 INFO stats.py:314 | Epoch[46] Step[134] GlobalStep[18074] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 1:35:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:19:56 INFO loss_tracker.py:84 | Epoch[46/239] Step[149] GlobalStep[18089/NA]: det_loss_cls_0[0.6254] det_loss_box_0[0.8367] det_loss_cls_1[0.6118] det_loss_box_1[0.6953] det_loss_cls_2[0.5947] det_loss_box_2[0.6429] det_loss_cls_3[0.5737] det_loss_box_3[0.6120] det_loss_cls_4[0.5382] det_loss_box_4[0.5960] det_loss_cls_5[0.5219] det_loss_box_5[0.5943] det_loss_cls_dn_0[0.1114] det_loss_box_dn_0[0.3140] det_loss_cls_dn_1[0.1077] det_loss_box_dn_1[0.2732] det_loss_cls_dn_2[0.1071] det_loss_box_dn_2[0.2573] det_loss_cls_dn_3[0.1064] det_loss_box_dn_3[0.2561] det_loss_cls_dn_4[0.1059] det_loss_box_dn_4[0.2552] det_loss_cls_dn_5[0.1048] det_loss_box_dn_5[0.2552] loss_depth[0.0391] total_loss[9.7366]
Rank[0/8] 06/19/2025 20:20:06 INFO stats.py:314 | Epoch[46] Step[159] GlobalStep[18099] Training Speed: 8.68 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 1 day, 1:34:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:20:21 INFO loss_tracker.py:84 | Epoch[46/239] Step[174] GlobalStep[18114/NA]: det_loss_cls_0[0.6796] det_loss_box_0[0.9339] det_loss_cls_1[0.6585] det_loss_box_1[0.7517] det_loss_cls_2[0.6421] det_loss_box_2[0.6792] det_loss_cls_3[0.6176] det_loss_box_3[0.6711] det_loss_cls_4[0.5971] det_loss_box_4[0.6591] det_loss_cls_5[0.5869] det_loss_box_5[0.6585] det_loss_cls_dn_0[0.1343] det_loss_box_dn_0[0.3876] det_loss_cls_dn_1[0.1271] det_loss_box_dn_1[0.3460] det_loss_cls_dn_2[0.1264] det_loss_box_dn_2[0.3300] det_loss_cls_dn_3[0.1259] det_loss_box_dn_3[0.3277] det_loss_cls_dn_4[0.1242] det_loss_box_dn_4[0.3257] det_loss_cls_dn_5[0.1236] det_loss_box_dn_5[0.3228] loss_depth[0.0396] total_loss[10.9763]
Rank[0/8] 06/19/2025 20:20:31 INFO stats.py:314 | Epoch[46] Step[184] GlobalStep[18124] Training Speed: 8.66 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 1 day, 1:33:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:20:47 INFO loss_tracker.py:84 | Epoch[46/239] Step[199] GlobalStep[18139/NA]: det_loss_cls_0[0.5810] det_loss_box_0[0.8294] det_loss_cls_1[0.5576] det_loss_box_1[0.6767] det_loss_cls_2[0.5513] det_loss_box_2[0.6155] det_loss_cls_3[0.5254] det_loss_box_3[0.6049] det_loss_cls_4[0.5053] det_loss_box_4[0.5993] det_loss_cls_5[0.4891] det_loss_box_5[0.5995] det_loss_cls_dn_0[0.1028] det_loss_box_dn_0[0.3835] det_loss_cls_dn_1[0.0986] det_loss_box_dn_1[0.3286] det_loss_cls_dn_2[0.0992] det_loss_box_dn_2[0.3119] det_loss_cls_dn_3[0.0970] det_loss_box_dn_3[0.3092] det_loss_cls_dn_4[0.0972] det_loss_box_dn_4[0.3083] det_loss_cls_dn_5[0.0968] det_loss_box_dn_5[0.3087] loss_depth[0.0425] total_loss[9.7193]
Rank[0/8] 06/19/2025 20:20:57 INFO stats.py:314 | Epoch[46] Step[209] GlobalStep[18149] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 1:32:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:21:13 INFO loss_tracker.py:84 | Epoch[46/239] Step[224] GlobalStep[18164/NA]: det_loss_cls_0[0.6652] det_loss_box_0[0.8723] det_loss_cls_1[0.6416] det_loss_box_1[0.7003] det_loss_cls_2[0.6223] det_loss_box_2[0.6375] det_loss_cls_3[0.6012] det_loss_box_3[0.6140] det_loss_cls_4[0.5646] det_loss_box_4[0.6262] det_loss_cls_5[0.5601] det_loss_box_5[0.6224] det_loss_cls_dn_0[0.1068] det_loss_box_dn_0[0.3224] det_loss_cls_dn_1[0.1021] det_loss_box_dn_1[0.2774] det_loss_cls_dn_2[0.1018] det_loss_box_dn_2[0.2650] det_loss_cls_dn_3[0.1009] det_loss_box_dn_3[0.2621] det_loss_cls_dn_4[0.0995] det_loss_box_dn_4[0.2608] det_loss_cls_dn_5[0.0998] det_loss_box_dn_5[0.2593] loss_depth[0.0407] total_loss[10.0264]
Rank[0/8] 06/19/2025 20:21:23 INFO stats.py:314 | Epoch[46] Step[234] GlobalStep[18174] Training Speed: 7.73 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 1:31:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:21:39 INFO loss_tracker.py:84 | Epoch[46/239] Step[249] GlobalStep[18189/NA]: det_loss_cls_0[0.4887] det_loss_box_0[0.5988] det_loss_cls_1[0.4638] det_loss_box_1[0.4967] det_loss_cls_2[0.4587] det_loss_box_2[0.4345] det_loss_cls_3[0.4411] det_loss_box_3[0.4305] det_loss_cls_4[0.4120] det_loss_box_4[0.4305] det_loss_cls_5[0.4004] det_loss_box_5[0.4332] det_loss_cls_dn_0[0.1036] det_loss_box_dn_0[0.3162] det_loss_cls_dn_1[0.1010] det_loss_box_dn_1[0.2765] det_loss_cls_dn_2[0.0994] det_loss_box_dn_2[0.2620] det_loss_cls_dn_3[0.0986] det_loss_box_dn_3[0.2604] det_loss_cls_dn_4[0.0978] det_loss_box_dn_4[0.2589] det_loss_cls_dn_5[0.0969] det_loss_box_dn_5[0.2575] loss_depth[0.0407] total_loss[7.7582]
Rank[0/8] 06/19/2025 20:21:49 INFO stats.py:314 | Epoch[46] Step[259] GlobalStep[18199] Training Speed: 8.01 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 1:31:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:22:05 INFO loss_tracker.py:84 | Epoch[46/239] Step[274] GlobalStep[18214/NA]: det_loss_cls_0[0.4997] det_loss_box_0[0.7176] det_loss_cls_1[0.4905] det_loss_box_1[0.5836] det_loss_cls_2[0.4850] det_loss_box_2[0.5226] det_loss_cls_3[0.4607] det_loss_box_3[0.5019] det_loss_cls_4[0.4306] det_loss_box_4[0.4942] det_loss_cls_5[0.4097] det_loss_box_5[0.4978] det_loss_cls_dn_0[0.0933] det_loss_box_dn_0[0.3429] det_loss_cls_dn_1[0.0878] det_loss_box_dn_1[0.3068] det_loss_cls_dn_2[0.0874] det_loss_box_dn_2[0.2870] det_loss_cls_dn_3[0.0872] det_loss_box_dn_3[0.2847] det_loss_cls_dn_4[0.0861] det_loss_box_dn_4[0.2817] det_loss_cls_dn_5[0.0854] det_loss_box_dn_5[0.2792] loss_depth[0.0404] total_loss[8.4438]
Rank[0/8] 06/19/2025 20:22:15 INFO stats.py:314 | Epoch[46] Step[284] GlobalStep[18224] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 1:30:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:22:30 INFO loss_tracker.py:84 | Epoch[46/239] Step[299] GlobalStep[18239/NA]: det_loss_cls_0[0.5478] det_loss_box_0[0.7208] det_loss_cls_1[0.5196] det_loss_box_1[0.5898] det_loss_cls_2[0.5011] det_loss_box_2[0.5311] det_loss_cls_3[0.4861] det_loss_box_3[0.5094] det_loss_cls_4[0.4567] det_loss_box_4[0.5008] det_loss_cls_5[0.4386] det_loss_box_5[0.5037] det_loss_cls_dn_0[0.1075] det_loss_box_dn_0[0.3270] det_loss_cls_dn_1[0.1037] det_loss_box_dn_1[0.2788] det_loss_cls_dn_2[0.1044] det_loss_box_dn_2[0.2644] det_loss_cls_dn_3[0.1033] det_loss_box_dn_3[0.2618] det_loss_cls_dn_4[0.1025] det_loss_box_dn_4[0.2602] det_loss_cls_dn_5[0.1022] det_loss_box_dn_5[0.2585] loss_depth[0.0390] total_loss[8.6187]
Rank[0/8] 06/19/2025 20:22:41 INFO stats.py:314 | Epoch[46] Step[309] GlobalStep[18249] Training Speed: 8.34 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 1:29:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:22:57 INFO loss_tracker.py:84 | Epoch[46/239] Step[324] GlobalStep[18264/NA]: det_loss_cls_0[0.4846] det_loss_box_0[0.6578] det_loss_cls_1[0.4773] det_loss_box_1[0.5227] det_loss_cls_2[0.4613] det_loss_box_2[0.4740] det_loss_cls_3[0.4440] det_loss_box_3[0.4586] det_loss_cls_4[0.4244] det_loss_box_4[0.4472] det_loss_cls_5[0.4084] det_loss_box_5[0.4527] det_loss_cls_dn_0[0.1069] det_loss_box_dn_0[0.3679] det_loss_cls_dn_1[0.1034] det_loss_box_dn_1[0.3170] det_loss_cls_dn_2[0.1034] det_loss_box_dn_2[0.2983] det_loss_cls_dn_3[0.1029] det_loss_box_dn_3[0.2965] det_loss_cls_dn_4[0.1019] det_loss_box_dn_4[0.2939] det_loss_cls_dn_5[0.1002] det_loss_box_dn_5[0.2910] loss_depth[0.0392] total_loss[8.2355]
Rank[0/8] 06/19/2025 20:23:07 INFO stats.py:314 | Epoch[46] Step[334] GlobalStep[18274] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 1:28:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:23:22 INFO loss_tracker.py:84 | Epoch[46/239] Step[349] GlobalStep[18289/NA]: det_loss_cls_0[0.4780] det_loss_box_0[0.6199] det_loss_cls_1[0.4705] det_loss_box_1[0.5137] det_loss_cls_2[0.4475] det_loss_box_2[0.4763] det_loss_cls_3[0.4282] det_loss_box_3[0.4652] det_loss_cls_4[0.4000] det_loss_box_4[0.4616] det_loss_cls_5[0.3854] det_loss_box_5[0.4636] det_loss_cls_dn_0[0.1086] det_loss_box_dn_0[0.3247] det_loss_cls_dn_1[0.1052] det_loss_box_dn_1[0.2861] det_loss_cls_dn_2[0.1042] det_loss_box_dn_2[0.2675] det_loss_cls_dn_3[0.1040] det_loss_box_dn_3[0.2640] det_loss_cls_dn_4[0.1028] det_loss_box_dn_4[0.2625] det_loss_cls_dn_5[0.1019] det_loss_box_dn_5[0.2616] loss_depth[0.0374] total_loss[7.9404]
Rank[0/8] 06/19/2025 20:23:32 INFO stats.py:314 | Epoch[46] Step[359] GlobalStep[18299] Training Speed: 7.80 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 1 day, 1:27:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:23:47 INFO loss_tracker.py:84 | Epoch[46/239] Step[374] GlobalStep[18314/NA]: det_loss_cls_0[0.4855] det_loss_box_0[0.6473] det_loss_cls_1[0.4696] det_loss_box_1[0.5269] det_loss_cls_2[0.4575] det_loss_box_2[0.4727] det_loss_cls_3[0.4386] det_loss_box_3[0.4584] det_loss_cls_4[0.4065] det_loss_box_4[0.4547] det_loss_cls_5[0.3945] det_loss_box_5[0.4519] det_loss_cls_dn_0[0.1060] det_loss_box_dn_0[0.3022] det_loss_cls_dn_1[0.1020] det_loss_box_dn_1[0.2629] det_loss_cls_dn_2[0.1014] det_loss_box_dn_2[0.2520] det_loss_cls_dn_3[0.1004] det_loss_box_dn_3[0.2501] det_loss_cls_dn_4[0.0989] det_loss_box_dn_4[0.2496] det_loss_cls_dn_5[0.0984] det_loss_box_dn_5[0.2477] loss_depth[0.0428] total_loss[7.8785]
Rank[0/8] 06/19/2025 20:23:58 INFO stats.py:314 | Epoch[46] Step[384] GlobalStep[18324] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 1:26:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:24:03 INFO stats.py:394 | Epoch[46] completed. Training Speed: 7.73 samples/sec across all devices.	Epoch Time: 403.83 sec.	Average Epoch Time: 403.83 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 1:26:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:24:26 INFO stats.py:314 | Epoch[47] Step[19] GlobalStep[18349] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 1:26:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:24:31 INFO loss_tracker.py:84 | Epoch[47/239] Step[24] GlobalStep[18354/NA]: det_loss_cls_0[0.6048] det_loss_box_0[0.8290] det_loss_cls_1[0.5868] det_loss_box_1[0.6745] det_loss_cls_2[0.5672] det_loss_box_2[0.6123] det_loss_cls_3[0.5403] det_loss_box_3[0.6038] det_loss_cls_4[0.5147] det_loss_box_4[0.5923] det_loss_cls_5[0.5020] det_loss_box_5[0.5875] det_loss_cls_dn_0[0.1133] det_loss_box_dn_0[0.3276] det_loss_cls_dn_1[0.1083] det_loss_box_dn_1[0.2838] det_loss_cls_dn_2[0.1080] det_loss_box_dn_2[0.2684] det_loss_cls_dn_3[0.1077] det_loss_box_dn_3[0.2663] det_loss_cls_dn_4[0.1064] det_loss_box_dn_4[0.2656] det_loss_cls_dn_5[0.1050] det_loss_box_dn_5[0.2635] loss_depth[0.0412] total_loss[9.5805]
Rank[0/8] 06/19/2025 20:24:51 INFO stats.py:314 | Epoch[47] Step[44] GlobalStep[18374] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 1:25:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:24:56 INFO loss_tracker.py:84 | Epoch[47/239] Step[49] GlobalStep[18379/NA]: det_loss_cls_0[0.5156] det_loss_box_0[0.7317] det_loss_cls_1[0.4994] det_loss_box_1[0.5818] det_loss_cls_2[0.4883] det_loss_box_2[0.5222] det_loss_cls_3[0.4675] det_loss_box_3[0.5065] det_loss_cls_4[0.4490] det_loss_box_4[0.4981] det_loss_cls_5[0.4328] det_loss_box_5[0.4966] det_loss_cls_dn_0[0.1030] det_loss_box_dn_0[0.3504] det_loss_cls_dn_1[0.1010] det_loss_box_dn_1[0.3077] det_loss_cls_dn_2[0.1001] det_loss_box_dn_2[0.2887] det_loss_cls_dn_3[0.0987] det_loss_box_dn_3[0.2874] det_loss_cls_dn_4[0.0975] det_loss_box_dn_4[0.2860] det_loss_cls_dn_5[0.0964] det_loss_box_dn_5[0.2843] loss_depth[0.0421] total_loss[8.6329]
Rank[0/8] 06/19/2025 20:25:17 INFO stats.py:314 | Epoch[47] Step[69] GlobalStep[18399] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 1:24:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:25:22 INFO loss_tracker.py:84 | Epoch[47/239] Step[74] GlobalStep[18404/NA]: det_loss_cls_0[0.6099] det_loss_box_0[0.7586] det_loss_cls_1[0.5792] det_loss_box_1[0.6247] det_loss_cls_2[0.5713] det_loss_box_2[0.5747] det_loss_cls_3[0.5561] det_loss_box_3[0.5538] det_loss_cls_4[0.5293] det_loss_box_4[0.5500] det_loss_cls_5[0.5211] det_loss_box_5[0.5510] det_loss_cls_dn_0[0.1131] det_loss_box_dn_0[0.2848] det_loss_cls_dn_1[0.1100] det_loss_box_dn_1[0.2449] det_loss_cls_dn_2[0.1099] det_loss_box_dn_2[0.2286] det_loss_cls_dn_3[0.1087] det_loss_box_dn_3[0.2262] det_loss_cls_dn_4[0.1081] det_loss_box_dn_4[0.2252] det_loss_cls_dn_5[0.1076] det_loss_box_dn_5[0.2240] loss_depth[0.0440] total_loss[9.1147]
Rank[0/8] 06/19/2025 20:25:43 INFO stats.py:314 | Epoch[47] Step[94] GlobalStep[18424] Training Speed: 7.82 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1 day, 1:23:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:25:49 INFO loss_tracker.py:84 | Epoch[47/239] Step[99] GlobalStep[18429/NA]: det_loss_cls_0[0.5376] det_loss_box_0[0.7306] det_loss_cls_1[0.5319] det_loss_box_1[0.5796] det_loss_cls_2[0.5181] det_loss_box_2[0.5146] det_loss_cls_3[0.4863] det_loss_box_3[0.4903] det_loss_cls_4[0.4558] det_loss_box_4[0.4780] det_loss_cls_5[0.4416] det_loss_box_5[0.4734] det_loss_cls_dn_0[0.0967] det_loss_box_dn_0[0.2665] det_loss_cls_dn_1[0.0942] det_loss_box_dn_1[0.2288] det_loss_cls_dn_2[0.0934] det_loss_box_dn_2[0.2154] det_loss_cls_dn_3[0.0918] det_loss_box_dn_3[0.2132] det_loss_cls_dn_4[0.0909] det_loss_box_dn_4[0.2118] det_loss_cls_dn_5[0.0902] det_loss_box_dn_5[0.2104] loss_depth[0.0432] total_loss[8.1842]
Rank[0/8] 06/19/2025 20:26:09 INFO stats.py:314 | Epoch[47] Step[119] GlobalStep[18449] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 1:22:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:26:14 INFO loss_tracker.py:84 | Epoch[47/239] Step[124] GlobalStep[18454/NA]: det_loss_cls_0[0.4492] det_loss_box_0[0.5903] det_loss_cls_1[0.4409] det_loss_box_1[0.4813] det_loss_cls_2[0.4200] det_loss_box_2[0.4299] det_loss_cls_3[0.3945] det_loss_box_3[0.4206] det_loss_cls_4[0.3644] det_loss_box_4[0.4225] det_loss_cls_5[0.3485] det_loss_box_5[0.4227] det_loss_cls_dn_0[0.1065] det_loss_box_dn_0[0.4048] det_loss_cls_dn_1[0.1027] det_loss_box_dn_1[0.3624] det_loss_cls_dn_2[0.1022] det_loss_box_dn_2[0.3460] det_loss_cls_dn_3[0.1020] det_loss_box_dn_3[0.3448] det_loss_cls_dn_4[0.1018] det_loss_box_dn_4[0.3431] det_loss_cls_dn_5[0.1012] det_loss_box_dn_5[0.3260] loss_depth[0.0393] total_loss[7.9678]
Rank[0/8] 06/19/2025 20:26:34 INFO stats.py:314 | Epoch[47] Step[144] GlobalStep[18474] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 1:22:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:26:39 INFO loss_tracker.py:84 | Epoch[47/239] Step[149] GlobalStep[18479/NA]: det_loss_cls_0[0.4593] det_loss_box_0[0.6806] det_loss_cls_1[0.4408] det_loss_box_1[0.5726] det_loss_cls_2[0.4384] det_loss_box_2[0.5182] det_loss_cls_3[0.4194] det_loss_box_3[0.4978] det_loss_cls_4[0.3902] det_loss_box_4[0.4900] det_loss_cls_5[0.3792] det_loss_box_5[0.4894] det_loss_cls_dn_0[0.0983] det_loss_box_dn_0[0.3947] det_loss_cls_dn_1[0.0949] det_loss_box_dn_1[0.3532] det_loss_cls_dn_2[0.0945] det_loss_box_dn_2[0.3390] det_loss_cls_dn_3[0.0936] det_loss_box_dn_3[0.3376] det_loss_cls_dn_4[0.0923] det_loss_box_dn_4[0.3374] det_loss_cls_dn_5[0.0913] det_loss_box_dn_5[0.3350] loss_depth[0.0424] total_loss[8.4804]
Rank[0/8] 06/19/2025 20:27:00 INFO stats.py:314 | Epoch[47] Step[169] GlobalStep[18499] Training Speed: 8.69 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 1 day, 1:21:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:27:06 INFO loss_tracker.py:84 | Epoch[47/239] Step[174] GlobalStep[18504/NA]: det_loss_cls_0[0.6091] det_loss_box_0[0.7889] det_loss_cls_1[0.5760] det_loss_box_1[0.6414] det_loss_cls_2[0.5807] det_loss_box_2[0.5713] det_loss_cls_3[0.5408] det_loss_box_3[0.5671] det_loss_cls_4[0.5154] det_loss_box_4[0.5389] det_loss_cls_5[0.4984] det_loss_box_5[0.5421] det_loss_cls_dn_0[0.1061] det_loss_box_dn_0[0.3313] det_loss_cls_dn_1[0.1024] det_loss_box_dn_1[0.2848] det_loss_cls_dn_2[0.1018] det_loss_box_dn_2[0.2677] det_loss_cls_dn_3[0.1013] det_loss_box_dn_3[0.2654] det_loss_cls_dn_4[0.0997] det_loss_box_dn_4[0.2636] det_loss_cls_dn_5[0.0987] det_loss_box_dn_5[0.2630] loss_depth[0.0407] total_loss[9.2968]
Rank[0/8] 06/19/2025 20:27:26 INFO stats.py:314 | Epoch[47] Step[194] GlobalStep[18524] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 1:20:30.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:27:31 INFO loss_tracker.py:84 | Epoch[47/239] Step[199] GlobalStep[18529/NA]: det_loss_cls_0[0.6109] det_loss_box_0[0.7799] det_loss_cls_1[0.6002] det_loss_box_1[0.6339] det_loss_cls_2[0.5830] det_loss_box_2[0.5686] det_loss_cls_3[0.5641] det_loss_box_3[0.5396] det_loss_cls_4[0.5273] det_loss_box_4[0.5423] det_loss_cls_5[0.5039] det_loss_box_5[0.5442] det_loss_cls_dn_0[0.1060] det_loss_box_dn_0[0.2725] det_loss_cls_dn_1[0.1031] det_loss_box_dn_1[0.2392] det_loss_cls_dn_2[0.1027] det_loss_box_dn_2[0.2240] det_loss_cls_dn_3[0.1024] det_loss_box_dn_3[0.2223] det_loss_cls_dn_4[0.1015] det_loss_box_dn_4[0.2218] det_loss_cls_dn_5[0.1004] det_loss_box_dn_5[0.2214] loss_depth[0.0397] total_loss[9.0549]
Rank[0/8] 06/19/2025 20:27:51 INFO stats.py:314 | Epoch[47] Step[219] GlobalStep[18549] Training Speed: 7.91 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 1:19:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:27:57 INFO loss_tracker.py:84 | Epoch[47/239] Step[224] GlobalStep[18554/NA]: det_loss_cls_0[0.6402] det_loss_box_0[1.0268] det_loss_cls_1[0.6193] det_loss_box_1[0.8325] det_loss_cls_2[0.6071] det_loss_box_2[0.7635] det_loss_cls_3[0.5855] det_loss_box_3[0.7390] det_loss_cls_4[0.5580] det_loss_box_4[0.7195] det_loss_cls_5[0.5413] det_loss_box_5[0.7155] det_loss_cls_dn_0[0.1095] det_loss_box_dn_0[0.3548] det_loss_cls_dn_1[0.1054] det_loss_box_dn_1[0.3140] det_loss_cls_dn_2[0.1045] det_loss_box_dn_2[0.2982] det_loss_cls_dn_3[0.1037] det_loss_box_dn_3[0.2958] det_loss_cls_dn_4[0.1033] det_loss_box_dn_4[0.2948] det_loss_cls_dn_5[0.1021] det_loss_box_dn_5[0.2936] loss_depth[0.0428] total_loss[10.8706]
Rank[0/8] 06/19/2025 20:28:18 INFO stats.py:314 | Epoch[47] Step[244] GlobalStep[18574] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 1:18:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:28:23 INFO loss_tracker.py:84 | Epoch[47/239] Step[249] GlobalStep[18579/NA]: det_loss_cls_0[0.5579] det_loss_box_0[0.7383] det_loss_cls_1[0.5526] det_loss_box_1[0.5976] det_loss_cls_2[0.5304] det_loss_box_2[0.5434] det_loss_cls_3[0.5092] det_loss_box_3[0.5222] det_loss_cls_4[0.4766] det_loss_box_4[0.5112] det_loss_cls_5[0.4572] det_loss_box_5[0.5094] det_loss_cls_dn_0[0.1103] det_loss_box_dn_0[0.3182] det_loss_cls_dn_1[0.1067] det_loss_box_dn_1[0.2736] det_loss_cls_dn_2[0.1060] det_loss_box_dn_2[0.2589] det_loss_cls_dn_3[0.1057] det_loss_box_dn_3[0.2559] det_loss_cls_dn_4[0.1036] det_loss_box_dn_4[0.2544] det_loss_cls_dn_5[0.1032] det_loss_box_dn_5[0.2535] loss_depth[0.0384] total_loss[8.7944]
Rank[0/8] 06/19/2025 20:28:44 INFO stats.py:314 | Epoch[47] Step[269] GlobalStep[18599] Training Speed: 7.69 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 1:18:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:28:49 INFO loss_tracker.py:84 | Epoch[47/239] Step[274] GlobalStep[18604/NA]: det_loss_cls_0[0.5483] det_loss_box_0[0.7444] det_loss_cls_1[0.5211] det_loss_box_1[0.5987] det_loss_cls_2[0.5194] det_loss_box_2[0.5409] det_loss_cls_3[0.4860] det_loss_box_3[0.5173] det_loss_cls_4[0.4634] det_loss_box_4[0.5039] det_loss_cls_5[0.4430] det_loss_box_5[0.5030] det_loss_cls_dn_0[0.0960] det_loss_box_dn_0[0.3450] det_loss_cls_dn_1[0.0915] det_loss_box_dn_1[0.3005] det_loss_cls_dn_2[0.0904] det_loss_box_dn_2[0.2823] det_loss_cls_dn_3[0.0890] det_loss_box_dn_3[0.2795] det_loss_cls_dn_4[0.0879] det_loss_box_dn_4[0.2776] det_loss_cls_dn_5[0.0871] det_loss_box_dn_5[0.2755] loss_depth[0.0411] total_loss[8.7326]
Rank[0/8] 06/19/2025 20:29:09 INFO stats.py:314 | Epoch[47] Step[294] GlobalStep[18624] Training Speed: 8.34 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 1:17:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:29:14 INFO loss_tracker.py:84 | Epoch[47/239] Step[299] GlobalStep[18629/NA]: det_loss_cls_0[0.3874] det_loss_box_0[0.4775] det_loss_cls_1[0.3812] det_loss_box_1[0.3781] det_loss_cls_2[0.3602] det_loss_box_2[0.3491] det_loss_cls_3[0.3438] det_loss_box_3[0.3368] det_loss_cls_4[0.3176] det_loss_box_4[0.3276] det_loss_cls_5[0.3077] det_loss_box_5[0.3239] det_loss_cls_dn_0[0.0900] det_loss_box_dn_0[0.2990] det_loss_cls_dn_1[0.0864] det_loss_box_dn_1[0.2546] det_loss_cls_dn_2[0.0861] det_loss_box_dn_2[0.2374] det_loss_cls_dn_3[0.0859] det_loss_box_dn_3[0.2345] det_loss_cls_dn_4[0.0856] det_loss_box_dn_4[0.2315] det_loss_cls_dn_5[0.0847] det_loss_box_dn_5[0.2304] loss_depth[0.0410] total_loss[6.3379]
Rank[0/8] 06/19/2025 20:29:34 INFO stats.py:314 | Epoch[47] Step[319] GlobalStep[18649] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 1:16:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:29:40 INFO loss_tracker.py:84 | Epoch[47/239] Step[324] GlobalStep[18654/NA]: det_loss_cls_0[0.5762] det_loss_box_0[0.8430] det_loss_cls_1[0.5575] det_loss_box_1[0.7050] det_loss_cls_2[0.5376] det_loss_box_2[0.6446] det_loss_cls_3[0.5218] det_loss_box_3[0.6328] det_loss_cls_4[0.4896] det_loss_box_4[0.6243] det_loss_cls_5[0.4762] det_loss_box_5[0.6237] det_loss_cls_dn_0[0.1172] det_loss_box_dn_0[0.4017] det_loss_cls_dn_1[0.1139] det_loss_box_dn_1[0.3554] det_loss_cls_dn_2[0.1136] det_loss_box_dn_2[0.3420] det_loss_cls_dn_3[0.1125] det_loss_box_dn_3[0.3386] det_loss_cls_dn_4[0.1118] det_loss_box_dn_4[0.3377] det_loss_cls_dn_5[0.1106] det_loss_box_dn_5[0.3334] loss_depth[0.0424] total_loss[10.0631]
Rank[0/8] 06/19/2025 20:30:01 INFO stats.py:314 | Epoch[47] Step[344] GlobalStep[18674] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 1:15:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:30:06 INFO loss_tracker.py:84 | Epoch[47/239] Step[349] GlobalStep[18679/NA]: det_loss_cls_0[0.4866] det_loss_box_0[0.6858] det_loss_cls_1[0.4778] det_loss_box_1[0.5667] det_loss_cls_2[0.4678] det_loss_box_2[0.5204] det_loss_cls_3[0.4553] det_loss_box_3[0.4996] det_loss_cls_4[0.4314] det_loss_box_4[0.4934] det_loss_cls_5[0.4223] det_loss_box_5[0.4888] det_loss_cls_dn_0[0.1079] det_loss_box_dn_0[0.3890] det_loss_cls_dn_1[0.1042] det_loss_box_dn_1[0.3446] det_loss_cls_dn_2[0.1030] det_loss_box_dn_2[0.3297] det_loss_cls_dn_3[0.1026] det_loss_box_dn_3[0.3286] det_loss_cls_dn_4[0.1013] det_loss_box_dn_4[0.3271] det_loss_cls_dn_5[0.1004] det_loss_box_dn_5[0.3246] loss_depth[0.0400] total_loss[8.6988]
Rank[0/8] 06/19/2025 20:30:27 INFO stats.py:314 | Epoch[47] Step[369] GlobalStep[18699] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 1:14:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:30:32 INFO loss_tracker.py:84 | Epoch[47/239] Step[374] GlobalStep[18704/NA]: det_loss_cls_0[0.6116] det_loss_box_0[0.9422] det_loss_cls_1[0.5868] det_loss_box_1[0.7687] det_loss_cls_2[0.5773] det_loss_box_2[0.6983] det_loss_cls_3[0.5407] det_loss_box_3[0.6823] det_loss_cls_4[0.5128] det_loss_box_4[0.6619] det_loss_cls_5[0.4993] det_loss_box_5[0.6585] det_loss_cls_dn_0[0.1005] det_loss_box_dn_0[0.3603] det_loss_cls_dn_1[0.0963] det_loss_box_dn_1[0.3156] det_loss_cls_dn_2[0.0965] det_loss_box_dn_2[0.2944] det_loss_cls_dn_3[0.0961] det_loss_box_dn_3[0.2928] det_loss_cls_dn_4[0.0952] det_loss_box_dn_4[0.2912] det_loss_cls_dn_5[0.0943] det_loss_box_dn_5[0.2900] loss_depth[0.0427] total_loss[10.2066]
Rank[0/8] 06/19/2025 20:30:47 INFO stats.py:394 | Epoch[47] completed. Training Speed: 7.72 samples/sec across all devices.	Epoch Time: 403.98 sec.	Average Epoch Time: 403.98 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 1:14:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:30:56 INFO stats.py:314 | Epoch[48] Step[4] GlobalStep[18724] Training Speed: 7.95 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 1:14:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:31:17 INFO loss_tracker.py:84 | Epoch[48/239] Step[24] GlobalStep[18744/NA]: det_loss_cls_0[0.5159] det_loss_box_0[0.7271] det_loss_cls_1[0.5024] det_loss_box_1[0.5880] det_loss_cls_2[0.4933] det_loss_box_2[0.5375] det_loss_cls_3[0.4768] det_loss_box_3[0.5147] det_loss_cls_4[0.4419] det_loss_box_4[0.5116] det_loss_cls_5[0.4306] det_loss_box_5[0.5090] det_loss_cls_dn_0[0.1074] det_loss_box_dn_0[0.3278] det_loss_cls_dn_1[0.1037] det_loss_box_dn_1[0.2805] det_loss_cls_dn_2[0.1038] det_loss_box_dn_2[0.2574] det_loss_cls_dn_3[0.1030] det_loss_box_dn_3[0.2547] det_loss_cls_dn_4[0.1027] det_loss_box_dn_4[0.2535] det_loss_cls_dn_5[0.1018] det_loss_box_dn_5[0.2522] loss_depth[0.0421] total_loss[8.5395]
Rank[0/8] 06/19/2025 20:31:22 INFO stats.py:314 | Epoch[48] Step[29] GlobalStep[18749] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 1:13:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:31:43 INFO loss_tracker.py:84 | Epoch[48/239] Step[49] GlobalStep[18769/NA]: det_loss_cls_0[0.5595] det_loss_box_0[0.7682] det_loss_cls_1[0.5269] det_loss_box_1[0.6357] det_loss_cls_2[0.5192] det_loss_box_2[0.5769] det_loss_cls_3[0.4954] det_loss_box_3[0.5642] det_loss_cls_4[0.4759] det_loss_box_4[0.5572] det_loss_cls_5[0.4658] det_loss_box_5[0.5555] det_loss_cls_dn_0[0.1071] det_loss_box_dn_0[0.3208] det_loss_cls_dn_1[0.1033] det_loss_box_dn_1[0.2801] det_loss_cls_dn_2[0.1026] det_loss_box_dn_2[0.2638] det_loss_cls_dn_3[0.1018] det_loss_box_dn_3[0.2620] det_loss_cls_dn_4[0.1011] det_loss_box_dn_4[0.2605] det_loss_cls_dn_5[0.0996] det_loss_box_dn_5[0.2593] loss_depth[0.0356] total_loss[8.9980]
Rank[0/8] 06/19/2025 20:31:48 INFO stats.py:314 | Epoch[48] Step[54] GlobalStep[18774] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 1:12:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:32:08 INFO loss_tracker.py:84 | Epoch[48/239] Step[74] GlobalStep[18794/NA]: det_loss_cls_0[0.6979] det_loss_box_0[0.9936] det_loss_cls_1[0.6655] det_loss_box_1[0.8244] det_loss_cls_2[0.6624] det_loss_box_2[0.7381] det_loss_cls_3[0.6386] det_loss_box_3[0.7198] det_loss_cls_4[0.6123] det_loss_box_4[0.7002] det_loss_cls_5[0.5977] det_loss_box_5[0.6991] det_loss_cls_dn_0[0.1064] det_loss_box_dn_0[0.3383] det_loss_cls_dn_1[0.1028] det_loss_box_dn_1[0.2935] det_loss_cls_dn_2[0.1018] det_loss_box_dn_2[0.2772] det_loss_cls_dn_3[0.1019] det_loss_box_dn_3[0.2734] det_loss_cls_dn_4[0.0996] det_loss_box_dn_4[0.2717] det_loss_cls_dn_5[0.0989] det_loss_box_dn_5[0.2706] loss_depth[0.0413] total_loss[10.9268]
Rank[0/8] 06/19/2025 20:32:13 INFO stats.py:314 | Epoch[48] Step[79] GlobalStep[18799] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 1:11:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:32:34 INFO loss_tracker.py:84 | Epoch[48/239] Step[99] GlobalStep[18819/NA]: det_loss_cls_0[0.5154] det_loss_box_0[0.6870] det_loss_cls_1[0.4969] det_loss_box_1[0.5636] det_loss_cls_2[0.4892] det_loss_box_2[0.5052] det_loss_cls_3[0.4614] det_loss_box_3[0.4927] det_loss_cls_4[0.4321] det_loss_box_4[0.4813] det_loss_cls_5[0.4254] det_loss_box_5[0.4740] det_loss_cls_dn_0[0.1094] det_loss_box_dn_0[0.3819] det_loss_cls_dn_1[0.1054] det_loss_box_dn_1[0.3308] det_loss_cls_dn_2[0.1049] det_loss_box_dn_2[0.3217] det_loss_cls_dn_3[0.1051] det_loss_box_dn_3[0.3185] det_loss_cls_dn_4[0.1041] det_loss_box_dn_4[0.3178] det_loss_cls_dn_5[0.1029] det_loss_box_dn_5[0.3146] loss_depth[0.0413] total_loss[8.6828]
Rank[0/8] 06/19/2025 20:32:39 INFO stats.py:314 | Epoch[48] Step[104] GlobalStep[18824] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 1:11:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:33:00 INFO loss_tracker.py:84 | Epoch[48/239] Step[124] GlobalStep[18844/NA]: det_loss_cls_0[0.5900] det_loss_box_0[0.8626] det_loss_cls_1[0.5724] det_loss_box_1[0.7344] det_loss_cls_2[0.5615] det_loss_box_2[0.6645] det_loss_cls_3[0.5423] det_loss_box_3[0.6335] det_loss_cls_4[0.5217] det_loss_box_4[0.6158] det_loss_cls_5[0.4969] det_loss_box_5[0.6144] det_loss_cls_dn_0[0.1116] det_loss_box_dn_0[0.3453] det_loss_cls_dn_1[0.1089] det_loss_box_dn_1[0.3093] det_loss_cls_dn_2[0.1086] det_loss_box_dn_2[0.2905] det_loss_cls_dn_3[0.1068] det_loss_box_dn_3[0.2887] det_loss_cls_dn_4[0.1071] det_loss_box_dn_4[0.2881] det_loss_cls_dn_5[0.1059] det_loss_box_dn_5[0.2861] loss_depth[0.0430] total_loss[9.9099]
Rank[0/8] 06/19/2025 20:33:05 INFO stats.py:314 | Epoch[48] Step[129] GlobalStep[18849] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 1:10:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:33:26 INFO loss_tracker.py:84 | Epoch[48/239] Step[149] GlobalStep[18869/NA]: det_loss_cls_0[0.5244] det_loss_box_0[0.7992] det_loss_cls_1[0.4971] det_loss_box_1[0.6710] det_loss_cls_2[0.4806] det_loss_box_2[0.6147] det_loss_cls_3[0.4684] det_loss_box_3[0.5927] det_loss_cls_4[0.4421] det_loss_box_4[0.5755] det_loss_cls_5[0.4271] det_loss_box_5[0.5788] det_loss_cls_dn_0[0.1033] det_loss_box_dn_0[0.3049] det_loss_cls_dn_1[0.0990] det_loss_box_dn_1[0.2685] det_loss_cls_dn_2[0.0977] det_loss_box_dn_2[0.2527] det_loss_cls_dn_3[0.0972] det_loss_box_dn_3[0.2506] det_loss_cls_dn_4[0.0957] det_loss_box_dn_4[0.2505] det_loss_cls_dn_5[0.0951] det_loss_box_dn_5[0.2492] loss_depth[0.0427] total_loss[8.8789]
Rank[0/8] 06/19/2025 20:33:31 INFO stats.py:314 | Epoch[48] Step[154] GlobalStep[18874] Training Speed: 8.58 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 1:09:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:33:51 INFO loss_tracker.py:84 | Epoch[48/239] Step[174] GlobalStep[18894/NA]: det_loss_cls_0[0.4206] det_loss_box_0[0.5664] det_loss_cls_1[0.4179] det_loss_box_1[0.4559] det_loss_cls_2[0.4107] det_loss_box_2[0.4144] det_loss_cls_3[0.3807] det_loss_box_3[0.4053] det_loss_cls_4[0.3631] det_loss_box_4[0.3876] det_loss_cls_5[0.3543] det_loss_box_5[0.3846] det_loss_cls_dn_0[0.1077] det_loss_box_dn_0[0.3812] det_loss_cls_dn_1[0.1035] det_loss_box_dn_1[0.3359] det_loss_cls_dn_2[0.1039] det_loss_box_dn_2[0.3230] det_loss_cls_dn_3[0.1037] det_loss_box_dn_3[0.3191] det_loss_cls_dn_4[0.1025] det_loss_box_dn_4[0.3178] det_loss_cls_dn_5[0.1018] det_loss_box_dn_5[0.3163] loss_depth[0.0384] total_loss[7.6164]
Rank[0/8] 06/19/2025 20:33:56 INFO stats.py:314 | Epoch[48] Step[179] GlobalStep[18899] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 1:08:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:34:17 INFO loss_tracker.py:84 | Epoch[48/239] Step[199] GlobalStep[18919/NA]: det_loss_cls_0[0.5086] det_loss_box_0[0.7041] det_loss_cls_1[0.5092] det_loss_box_1[0.5684] det_loss_cls_2[0.4967] det_loss_box_2[0.5110] det_loss_cls_3[0.4829] det_loss_box_3[0.4905] det_loss_cls_4[0.4495] det_loss_box_4[0.4863] det_loss_cls_5[0.4324] det_loss_box_5[0.4833] det_loss_cls_dn_0[0.1153] det_loss_box_dn_0[0.3278] det_loss_cls_dn_1[0.1127] det_loss_box_dn_1[0.2866] det_loss_cls_dn_2[0.1125] det_loss_box_dn_2[0.2737] det_loss_cls_dn_3[0.1119] det_loss_box_dn_3[0.2715] det_loss_cls_dn_4[0.1112] det_loss_box_dn_4[0.2715] det_loss_cls_dn_5[0.1103] det_loss_box_dn_5[0.2705] loss_depth[0.0403] total_loss[8.5388]
Rank[0/8] 06/19/2025 20:34:23 INFO stats.py:314 | Epoch[48] Step[204] GlobalStep[18924] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 1:07:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:34:43 INFO loss_tracker.py:84 | Epoch[48/239] Step[224] GlobalStep[18944/NA]: det_loss_cls_0[0.4981] det_loss_box_0[0.7561] det_loss_cls_1[0.4900] det_loss_box_1[0.6136] det_loss_cls_2[0.4796] det_loss_box_2[0.5550] det_loss_cls_3[0.4530] det_loss_box_3[0.5244] det_loss_cls_4[0.4254] det_loss_box_4[0.5227] det_loss_cls_5[0.4110] det_loss_box_5[0.5163] det_loss_cls_dn_0[0.1008] det_loss_box_dn_0[0.3211] det_loss_cls_dn_1[0.0966] det_loss_box_dn_1[0.2724] det_loss_cls_dn_2[0.0961] det_loss_box_dn_2[0.2536] det_loss_cls_dn_3[0.0950] det_loss_box_dn_3[0.2507] det_loss_cls_dn_4[0.0939] det_loss_box_dn_4[0.2494] det_loss_cls_dn_5[0.0935] det_loss_box_dn_5[0.2482] loss_depth[0.0394] total_loss[8.4557]
Rank[0/8] 06/19/2025 20:34:48 INFO stats.py:314 | Epoch[48] Step[229] GlobalStep[18949] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 1:06:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:35:08 INFO loss_tracker.py:84 | Epoch[48/239] Step[249] GlobalStep[18969/NA]: det_loss_cls_0[0.5679] det_loss_box_0[0.8333] det_loss_cls_1[0.5696] det_loss_box_1[0.6666] det_loss_cls_2[0.5533] det_loss_box_2[0.5920] det_loss_cls_3[0.5355] det_loss_box_3[0.5722] det_loss_cls_4[0.5089] det_loss_box_4[0.5639] det_loss_cls_5[0.4936] det_loss_box_5[0.5603] det_loss_cls_dn_0[0.1052] det_loss_box_dn_0[0.3242] det_loss_cls_dn_1[0.1015] det_loss_box_dn_1[0.2777] det_loss_cls_dn_2[0.1011] det_loss_box_dn_2[0.2599] det_loss_cls_dn_3[0.1006] det_loss_box_dn_3[0.2566] det_loss_cls_dn_4[0.0994] det_loss_box_dn_4[0.2559] det_loss_cls_dn_5[0.0987] det_loss_box_dn_5[0.2541] loss_depth[0.0419] total_loss[9.2939]
Rank[0/8] 06/19/2025 20:35:13 INFO stats.py:314 | Epoch[48] Step[254] GlobalStep[18974] Training Speed: 8.62 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 1:06:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:35:34 INFO loss_tracker.py:84 | Epoch[48/239] Step[274] GlobalStep[18994/NA]: det_loss_cls_0[0.4699] det_loss_box_0[0.6464] det_loss_cls_1[0.4648] det_loss_box_1[0.5343] det_loss_cls_2[0.4467] det_loss_box_2[0.4961] det_loss_cls_3[0.4377] det_loss_box_3[0.4865] det_loss_cls_4[0.4069] det_loss_box_4[0.4816] det_loss_cls_5[0.3877] det_loss_box_5[0.4776] det_loss_cls_dn_0[0.0947] det_loss_box_dn_0[0.3064] det_loss_cls_dn_1[0.0913] det_loss_box_dn_1[0.2634] det_loss_cls_dn_2[0.0907] det_loss_box_dn_2[0.2477] det_loss_cls_dn_3[0.0903] det_loss_box_dn_3[0.2445] det_loss_cls_dn_4[0.0900] det_loss_box_dn_4[0.2435] det_loss_cls_dn_5[0.0896] det_loss_box_dn_5[0.2416] loss_depth[0.0372] total_loss[7.8670]
Rank[0/8] 06/19/2025 20:35:40 INFO stats.py:314 | Epoch[48] Step[279] GlobalStep[18999] Training Speed: 6.21 samples/sec across all devices.	Average Step Time: 1.29 sec.	Estimated Remaining Time: 1 day, 1:05:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:36:01 INFO loss_tracker.py:84 | Epoch[48/239] Step[299] GlobalStep[19019/NA]: det_loss_cls_0[0.6268] det_loss_box_0[0.7635] det_loss_cls_1[0.5964] det_loss_box_1[0.6202] det_loss_cls_2[0.5856] det_loss_box_2[0.5667] det_loss_cls_3[0.5680] det_loss_box_3[0.5433] det_loss_cls_4[0.5346] det_loss_box_4[0.5385] det_loss_cls_5[0.5105] det_loss_box_5[0.5462] det_loss_cls_dn_0[0.1055] det_loss_box_dn_0[0.3163] det_loss_cls_dn_1[0.1013] det_loss_box_dn_1[0.2774] det_loss_cls_dn_2[0.1008] det_loss_box_dn_2[0.2611] det_loss_cls_dn_3[0.1001] det_loss_box_dn_3[0.2571] det_loss_cls_dn_4[0.0993] det_loss_box_dn_4[0.2550] det_loss_cls_dn_5[0.0980] det_loss_box_dn_5[0.2537] loss_depth[0.0399] total_loss[9.2659]
Rank[0/8] 06/19/2025 20:36:06 INFO stats.py:314 | Epoch[48] Step[304] GlobalStep[19024] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 1:04:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:36:26 INFO loss_tracker.py:84 | Epoch[48/239] Step[324] GlobalStep[19044/NA]: det_loss_cls_0[0.6086] det_loss_box_0[0.8070] det_loss_cls_1[0.6108] det_loss_box_1[0.6697] det_loss_cls_2[0.5899] det_loss_box_2[0.5989] det_loss_cls_3[0.5714] det_loss_box_3[0.5902] det_loss_cls_4[0.5393] det_loss_box_4[0.5836] det_loss_cls_5[0.5337] det_loss_box_5[0.5780] det_loss_cls_dn_0[0.1111] det_loss_box_dn_0[0.2792] det_loss_cls_dn_1[0.1078] det_loss_box_dn_1[0.2374] det_loss_cls_dn_2[0.1082] det_loss_box_dn_2[0.2206] det_loss_cls_dn_3[0.1080] det_loss_box_dn_3[0.2179] det_loss_cls_dn_4[0.1073] det_loss_box_dn_4[0.2169] det_loss_cls_dn_5[0.1068] det_loss_box_dn_5[0.2157] loss_depth[0.0391] total_loss[9.3571]
Rank[0/8] 06/19/2025 20:36:31 INFO stats.py:314 | Epoch[48] Step[329] GlobalStep[19049] Training Speed: 8.56 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 1:03:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:36:51 INFO loss_tracker.py:84 | Epoch[48/239] Step[349] GlobalStep[19069/NA]: det_loss_cls_0[0.5445] det_loss_box_0[0.7952] det_loss_cls_1[0.5353] det_loss_box_1[0.6502] det_loss_cls_2[0.5200] det_loss_box_2[0.6118] det_loss_cls_3[0.5043] det_loss_box_3[0.5959] det_loss_cls_4[0.4753] det_loss_box_4[0.5681] det_loss_cls_5[0.4663] det_loss_box_5[0.5685] det_loss_cls_dn_0[0.1074] det_loss_box_dn_0[0.3555] det_loss_cls_dn_1[0.1023] det_loss_box_dn_1[0.3095] det_loss_cls_dn_2[0.1013] det_loss_box_dn_2[0.2904] det_loss_cls_dn_3[0.1005] det_loss_box_dn_3[0.2863] det_loss_cls_dn_4[0.0993] det_loss_box_dn_4[0.2856] det_loss_cls_dn_5[0.0983] det_loss_box_dn_5[0.2829] loss_depth[0.0424] total_loss[9.2973]
Rank[0/8] 06/19/2025 20:36:56 INFO stats.py:314 | Epoch[48] Step[354] GlobalStep[19074] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 1:02:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:37:18 INFO loss_tracker.py:84 | Epoch[48/239] Step[374] GlobalStep[19094/NA]: det_loss_cls_0[0.4249] det_loss_box_0[0.5687] det_loss_cls_1[0.4074] det_loss_box_1[0.4634] det_loss_cls_2[0.3900] det_loss_box_2[0.4160] det_loss_cls_3[0.3701] det_loss_box_3[0.4048] det_loss_cls_4[0.3483] det_loss_box_4[0.3963] det_loss_cls_5[0.3410] det_loss_box_5[0.4018] det_loss_cls_dn_0[0.1043] det_loss_box_dn_0[0.3043] det_loss_cls_dn_1[0.0998] det_loss_box_dn_1[0.2632] det_loss_cls_dn_2[0.0990] det_loss_box_dn_2[0.2489] det_loss_cls_dn_3[0.0976] det_loss_box_dn_3[0.2465] det_loss_cls_dn_4[0.0959] det_loss_box_dn_4[0.2455] det_loss_cls_dn_5[0.0953] det_loss_box_dn_5[0.2439] loss_depth[0.0376] total_loss[7.1143]
Rank[0/8] 06/19/2025 20:37:23 INFO stats.py:314 | Epoch[48] Step[379] GlobalStep[19099] Training Speed: 8.64 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 1:02:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:37:33 INFO stats.py:394 | Epoch[48] completed. Training Speed: 7.68 samples/sec across all devices.	Epoch Time: 406.31 sec.	Average Epoch Time: 406.31 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 1:01:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:37:50 INFO stats.py:314 | Epoch[49] Step[14] GlobalStep[19124] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 1:01:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:38:00 INFO loss_tracker.py:84 | Epoch[49/239] Step[24] GlobalStep[19134/NA]: det_loss_cls_0[0.5496] det_loss_box_0[0.6982] det_loss_cls_1[0.5302] det_loss_box_1[0.5620] det_loss_cls_2[0.5147] det_loss_box_2[0.5061] det_loss_cls_3[0.4872] det_loss_box_3[0.4946] det_loss_cls_4[0.4563] det_loss_box_4[0.4858] det_loss_cls_5[0.4402] det_loss_box_5[0.4912] det_loss_cls_dn_0[0.1073] det_loss_box_dn_0[0.3584] det_loss_cls_dn_1[0.1035] det_loss_box_dn_1[0.3215] det_loss_cls_dn_2[0.1027] det_loss_box_dn_2[0.3032] det_loss_cls_dn_3[0.1020] det_loss_box_dn_3[0.3012] det_loss_cls_dn_4[0.1013] det_loss_box_dn_4[0.2994] det_loss_cls_dn_5[0.1000] det_loss_box_dn_5[0.2953] loss_depth[0.0424] total_loss[8.7545]
Rank[0/8] 06/19/2025 20:38:16 INFO stats.py:314 | Epoch[49] Step[39] GlobalStep[19149] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 1:00:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:38:26 INFO loss_tracker.py:84 | Epoch[49/239] Step[49] GlobalStep[19159/NA]: det_loss_cls_0[0.4760] det_loss_box_0[0.6247] det_loss_cls_1[0.4688] det_loss_box_1[0.4844] det_loss_cls_2[0.4302] det_loss_box_2[0.4498] det_loss_cls_3[0.4038] det_loss_box_3[0.4418] det_loss_cls_4[0.3760] det_loss_box_4[0.4378] det_loss_cls_5[0.3616] det_loss_box_5[0.4338] det_loss_cls_dn_0[0.0978] det_loss_box_dn_0[0.3396] det_loss_cls_dn_1[0.0934] det_loss_box_dn_1[0.2912] det_loss_cls_dn_2[0.0932] det_loss_box_dn_2[0.2739] det_loss_cls_dn_3[0.0932] det_loss_box_dn_3[0.2701] det_loss_cls_dn_4[0.0925] det_loss_box_dn_4[0.2678] det_loss_cls_dn_5[0.0915] det_loss_box_dn_5[0.2652] loss_depth[0.0410] total_loss[7.6991]
Rank[0/8] 06/19/2025 20:38:42 INFO stats.py:314 | Epoch[49] Step[64] GlobalStep[19174] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 0:59:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:38:53 INFO loss_tracker.py:84 | Epoch[49/239] Step[74] GlobalStep[19184/NA]: det_loss_cls_0[0.5190] det_loss_box_0[0.7065] det_loss_cls_1[0.4996] det_loss_box_1[0.5738] det_loss_cls_2[0.4895] det_loss_box_2[0.5127] det_loss_cls_3[0.4653] det_loss_box_3[0.5004] det_loss_cls_4[0.4440] det_loss_box_4[0.4893] det_loss_cls_5[0.4354] det_loss_box_5[0.4849] det_loss_cls_dn_0[0.1047] det_loss_box_dn_0[0.3269] det_loss_cls_dn_1[0.1032] det_loss_box_dn_1[0.2774] det_loss_cls_dn_2[0.1029] det_loss_box_dn_2[0.2604] det_loss_cls_dn_3[0.1027] det_loss_box_dn_3[0.2595] det_loss_cls_dn_4[0.1015] det_loss_box_dn_4[0.2572] det_loss_cls_dn_5[0.1010] det_loss_box_dn_5[0.2524] loss_depth[0.0436] total_loss[8.4135]
Rank[0/8] 06/19/2025 20:39:08 INFO stats.py:314 | Epoch[49] Step[89] GlobalStep[19199] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 0:59:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:39:18 INFO loss_tracker.py:84 | Epoch[49/239] Step[99] GlobalStep[19209/NA]: det_loss_cls_0[0.6425] det_loss_box_0[0.9010] det_loss_cls_1[0.6420] det_loss_box_1[0.7378] det_loss_cls_2[0.6188] det_loss_box_2[0.6671] det_loss_cls_3[0.5986] det_loss_box_3[0.6447] det_loss_cls_4[0.5697] det_loss_box_4[0.6410] det_loss_cls_5[0.5584] det_loss_box_5[0.6344] det_loss_cls_dn_0[0.1046] det_loss_box_dn_0[0.3667] det_loss_cls_dn_1[0.0991] det_loss_box_dn_1[0.3303] det_loss_cls_dn_2[0.0988] det_loss_box_dn_2[0.3158] det_loss_cls_dn_3[0.0975] det_loss_box_dn_3[0.3132] det_loss_cls_dn_4[0.0969] det_loss_box_dn_4[0.3107] det_loss_cls_dn_5[0.0956] det_loss_box_dn_5[0.3058] loss_depth[0.0407] total_loss[10.4316]
Rank[0/8] 06/19/2025 20:39:33 INFO stats.py:314 | Epoch[49] Step[114] GlobalStep[19224] Training Speed: 7.89 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 0:58:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:39:43 INFO loss_tracker.py:84 | Epoch[49/239] Step[124] GlobalStep[19234/NA]: det_loss_cls_0[0.5249] det_loss_box_0[0.6232] det_loss_cls_1[0.5135] det_loss_box_1[0.5038] det_loss_cls_2[0.4929] det_loss_box_2[0.4583] det_loss_cls_3[0.4783] det_loss_box_3[0.4360] det_loss_cls_4[0.4466] det_loss_box_4[0.4388] det_loss_cls_5[0.4361] det_loss_box_5[0.4389] det_loss_cls_dn_0[0.1118] det_loss_box_dn_0[0.3051] det_loss_cls_dn_1[0.1083] det_loss_box_dn_1[0.2631] det_loss_cls_dn_2[0.1081] det_loss_box_dn_2[0.2461] det_loss_cls_dn_3[0.1064] det_loss_box_dn_3[0.2445] det_loss_cls_dn_4[0.1052] det_loss_box_dn_4[0.2430] det_loss_cls_dn_5[0.1046] det_loss_box_dn_5[0.2427] loss_depth[0.0419] total_loss[8.0221]
Rank[0/8] 06/19/2025 20:39:59 INFO stats.py:314 | Epoch[49] Step[139] GlobalStep[19249] Training Speed: 6.79 samples/sec across all devices.	Average Step Time: 1.18 sec.	Estimated Remaining Time: 1 day, 0:57:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:40:10 INFO loss_tracker.py:84 | Epoch[49/239] Step[149] GlobalStep[19259/NA]: det_loss_cls_0[0.6513] det_loss_box_0[0.9900] det_loss_cls_1[0.6402] det_loss_box_1[0.8372] det_loss_cls_2[0.6320] det_loss_box_2[0.7542] det_loss_cls_3[0.6133] det_loss_box_3[0.7233] det_loss_cls_4[0.5786] det_loss_box_4[0.7095] det_loss_cls_5[0.5598] det_loss_box_5[0.7074] det_loss_cls_dn_0[0.1149] det_loss_box_dn_0[0.4231] det_loss_cls_dn_1[0.1118] det_loss_box_dn_1[0.3813] det_loss_cls_dn_2[0.1103] det_loss_box_dn_2[0.3702] det_loss_cls_dn_3[0.1089] det_loss_box_dn_3[0.3696] det_loss_cls_dn_4[0.1074] det_loss_box_dn_4[0.3683] det_loss_cls_dn_5[0.1065] det_loss_box_dn_5[0.3663] loss_depth[0.0408] total_loss[11.3763]
Rank[0/8] 06/19/2025 20:40:25 INFO stats.py:314 | Epoch[49] Step[164] GlobalStep[19274] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 0:56:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:40:35 INFO loss_tracker.py:84 | Epoch[49/239] Step[174] GlobalStep[19284/NA]: det_loss_cls_0[0.5164] det_loss_box_0[0.6593] det_loss_cls_1[0.4830] det_loss_box_1[0.5421] det_loss_cls_2[0.4825] det_loss_box_2[0.4753] det_loss_cls_3[0.4488] det_loss_box_3[0.4608] det_loss_cls_4[0.4097] det_loss_box_4[0.4608] det_loss_cls_5[0.3992] det_loss_box_5[0.4581] det_loss_cls_dn_0[0.0980] det_loss_box_dn_0[0.2663] det_loss_cls_dn_1[0.0941] det_loss_box_dn_1[0.2262] det_loss_cls_dn_2[0.0932] det_loss_box_dn_2[0.2115] det_loss_cls_dn_3[0.0920] det_loss_box_dn_3[0.2091] det_loss_cls_dn_4[0.0907] det_loss_box_dn_4[0.2079] det_loss_cls_dn_5[0.0894] det_loss_box_dn_5[0.2065] loss_depth[0.0416] total_loss[7.7224]
Rank[0/8] 06/19/2025 20:40:50 INFO stats.py:314 | Epoch[49] Step[189] GlobalStep[19299] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 0:55:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:41:00 INFO loss_tracker.py:84 | Epoch[49/239] Step[199] GlobalStep[19309/NA]: det_loss_cls_0[0.5650] det_loss_box_0[0.7785] det_loss_cls_1[0.5441] det_loss_box_1[0.6245] det_loss_cls_2[0.5352] det_loss_box_2[0.5414] det_loss_cls_3[0.5085] det_loss_box_3[0.5320] det_loss_cls_4[0.4799] det_loss_box_4[0.5235] det_loss_cls_5[0.4637] det_loss_box_5[0.5268] det_loss_cls_dn_0[0.1194] det_loss_box_dn_0[0.3247] det_loss_cls_dn_1[0.1141] det_loss_box_dn_1[0.2854] det_loss_cls_dn_2[0.1145] det_loss_box_dn_2[0.2715] det_loss_cls_dn_3[0.1131] det_loss_box_dn_3[0.2699] det_loss_cls_dn_4[0.1126] det_loss_box_dn_4[0.2689] det_loss_cls_dn_5[0.1124] det_loss_box_dn_5[0.2673] loss_depth[0.0405] total_loss[9.0376]
Rank[0/8] 06/19/2025 20:41:15 INFO stats.py:314 | Epoch[49] Step[214] GlobalStep[19324] Training Speed: 6.09 samples/sec across all devices.	Average Step Time: 1.31 sec.	Estimated Remaining Time: 1 day, 0:55:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:41:27 INFO loss_tracker.py:84 | Epoch[49/239] Step[224] GlobalStep[19334/NA]: det_loss_cls_0[0.4986] det_loss_box_0[0.6554] det_loss_cls_1[0.4828] det_loss_box_1[0.5207] det_loss_cls_2[0.4673] det_loss_box_2[0.4692] det_loss_cls_3[0.4448] det_loss_box_3[0.4651] det_loss_cls_4[0.4120] det_loss_box_4[0.4613] det_loss_cls_5[0.3994] det_loss_box_5[0.4617] det_loss_cls_dn_0[0.0985] det_loss_box_dn_0[0.3251] det_loss_cls_dn_1[0.0941] det_loss_box_dn_1[0.2812] det_loss_cls_dn_2[0.0941] det_loss_box_dn_2[0.2649] det_loss_cls_dn_3[0.0930] det_loss_box_dn_3[0.2627] det_loss_cls_dn_4[0.0930] det_loss_box_dn_4[0.2604] det_loss_cls_dn_5[0.0925] det_loss_box_dn_5[0.2601] loss_depth[0.0409] total_loss[7.9987]
Rank[0/8] 06/19/2025 20:41:43 INFO stats.py:314 | Epoch[49] Step[239] GlobalStep[19349] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 0:54:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:41:53 INFO loss_tracker.py:84 | Epoch[49/239] Step[249] GlobalStep[19359/NA]: det_loss_cls_0[0.6809] det_loss_box_0[0.9311] det_loss_cls_1[0.6552] det_loss_box_1[0.7638] det_loss_cls_2[0.6483] det_loss_box_2[0.6770] det_loss_cls_3[0.6215] det_loss_box_3[0.6633] det_loss_cls_4[0.5956] det_loss_box_4[0.6530] det_loss_cls_5[0.5813] det_loss_box_5[0.6520] det_loss_cls_dn_0[0.1198] det_loss_box_dn_0[0.3358] det_loss_cls_dn_1[0.1177] det_loss_box_dn_1[0.2968] det_loss_cls_dn_2[0.1174] det_loss_box_dn_2[0.2827] det_loss_cls_dn_3[0.1162] det_loss_box_dn_3[0.2814] det_loss_cls_dn_4[0.1140] det_loss_box_dn_4[0.2806] det_loss_cls_dn_5[0.1139] det_loss_box_dn_5[0.2771] loss_depth[0.0419] total_loss[10.6181]
Rank[0/8] 06/19/2025 20:42:08 INFO stats.py:314 | Epoch[49] Step[264] GlobalStep[19374] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 0:53:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:42:18 INFO loss_tracker.py:84 | Epoch[49/239] Step[274] GlobalStep[19384/NA]: det_loss_cls_0[0.5432] det_loss_box_0[0.7298] det_loss_cls_1[0.5369] det_loss_box_1[0.5849] det_loss_cls_2[0.5265] det_loss_box_2[0.5119] det_loss_cls_3[0.4966] det_loss_box_3[0.4926] det_loss_cls_4[0.4661] det_loss_box_4[0.4878] det_loss_cls_5[0.4579] det_loss_box_5[0.4894] det_loss_cls_dn_0[0.1064] det_loss_box_dn_0[0.2809] det_loss_cls_dn_1[0.1019] det_loss_box_dn_1[0.2423] det_loss_cls_dn_2[0.1016] det_loss_box_dn_2[0.2308] det_loss_cls_dn_3[0.1009] det_loss_box_dn_3[0.2287] det_loss_cls_dn_4[0.0995] det_loss_box_dn_4[0.2277] det_loss_cls_dn_5[0.0987] det_loss_box_dn_5[0.2261] loss_depth[0.0422] total_loss[8.4116]
Rank[0/8] 06/19/2025 20:42:33 INFO stats.py:314 | Epoch[49] Step[289] GlobalStep[19399] Training Speed: 6.38 samples/sec across all devices.	Average Step Time: 1.25 sec.	Estimated Remaining Time: 1 day, 0:52:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:42:43 INFO loss_tracker.py:84 | Epoch[49/239] Step[299] GlobalStep[19409/NA]: det_loss_cls_0[0.5136] det_loss_box_0[0.6953] det_loss_cls_1[0.5187] det_loss_box_1[0.5581] det_loss_cls_2[0.5025] det_loss_box_2[0.5127] det_loss_cls_3[0.4761] det_loss_box_3[0.5050] det_loss_cls_4[0.4567] det_loss_box_4[0.4870] det_loss_cls_5[0.4312] det_loss_box_5[0.4961] det_loss_cls_dn_0[0.1031] det_loss_box_dn_0[0.3003] det_loss_cls_dn_1[0.0991] det_loss_box_dn_1[0.2581] det_loss_cls_dn_2[0.0980] det_loss_box_dn_2[0.2423] det_loss_cls_dn_3[0.0975] det_loss_box_dn_3[0.2407] det_loss_cls_dn_4[0.0961] det_loss_box_dn_4[0.2391] det_loss_cls_dn_5[0.0953] det_loss_box_dn_5[0.2379] loss_depth[0.0395] total_loss[8.3000]
Rank[0/8] 06/19/2025 20:43:00 INFO stats.py:314 | Epoch[49] Step[314] GlobalStep[19424] Training Speed: 7.16 samples/sec across all devices.	Average Step Time: 1.12 sec.	Estimated Remaining Time: 1 day, 0:52:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:43:09 INFO loss_tracker.py:84 | Epoch[49/239] Step[324] GlobalStep[19434/NA]: det_loss_cls_0[0.4905] det_loss_box_0[0.6696] det_loss_cls_1[0.4812] det_loss_box_1[0.5641] det_loss_cls_2[0.4608] det_loss_box_2[0.5128] det_loss_cls_3[0.4391] det_loss_box_3[0.5034] det_loss_cls_4[0.4132] det_loss_box_4[0.4897] det_loss_cls_5[0.4034] det_loss_box_5[0.4858] det_loss_cls_dn_0[0.0977] det_loss_box_dn_0[0.3407] det_loss_cls_dn_1[0.0934] det_loss_box_dn_1[0.2928] det_loss_cls_dn_2[0.0922] det_loss_box_dn_2[0.2771] det_loss_cls_dn_3[0.0914] det_loss_box_dn_3[0.2743] det_loss_cls_dn_4[0.0899] det_loss_box_dn_4[0.2719] det_loss_cls_dn_5[0.0892] det_loss_box_dn_5[0.2688] loss_depth[0.0380] total_loss[8.2310]
Rank[0/8] 06/19/2025 20:43:25 INFO stats.py:314 | Epoch[49] Step[339] GlobalStep[19449] Training Speed: 8.71 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 1 day, 0:51:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:43:35 INFO loss_tracker.py:84 | Epoch[49/239] Step[349] GlobalStep[19459/NA]: det_loss_cls_0[0.5099] det_loss_box_0[0.7842] det_loss_cls_1[0.5242] det_loss_box_1[0.6734] det_loss_cls_2[0.5017] det_loss_box_2[0.6244] det_loss_cls_3[0.4815] det_loss_box_3[0.6102] det_loss_cls_4[0.4566] det_loss_box_4[0.5984] det_loss_cls_5[0.4406] det_loss_box_5[0.6045] det_loss_cls_dn_0[0.1104] det_loss_box_dn_0[0.3651] det_loss_cls_dn_1[0.1057] det_loss_box_dn_1[0.3275] det_loss_cls_dn_2[0.1047] det_loss_box_dn_2[0.3141] det_loss_cls_dn_3[0.1043] det_loss_box_dn_3[0.3112] det_loss_cls_dn_4[0.1035] det_loss_box_dn_4[0.3100] det_loss_cls_dn_5[0.1022] det_loss_box_dn_5[0.3066] loss_depth[0.0394] total_loss[9.4144]
Rank[0/8] 06/19/2025 20:43:50 INFO stats.py:314 | Epoch[49] Step[364] GlobalStep[19474] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 0:50:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:44:00 INFO loss_tracker.py:84 | Epoch[49/239] Step[374] GlobalStep[19484/NA]: det_loss_cls_0[0.6470] det_loss_box_0[0.8803] det_loss_cls_1[0.6342] det_loss_box_1[0.7004] det_loss_cls_2[0.6195] det_loss_box_2[0.6399] det_loss_cls_3[0.5886] det_loss_box_3[0.6208] det_loss_cls_4[0.5500] det_loss_box_4[0.6212] det_loss_cls_5[0.5342] det_loss_box_5[0.6201] det_loss_cls_dn_0[0.1108] det_loss_box_dn_0[0.2894] det_loss_cls_dn_1[0.1077] det_loss_box_dn_1[0.2553] det_loss_cls_dn_2[0.1072] det_loss_box_dn_2[0.2426] det_loss_cls_dn_3[0.1063] det_loss_box_dn_3[0.2408] det_loss_cls_dn_4[0.1058] det_loss_box_dn_4[0.2394] det_loss_cls_dn_5[0.1050] det_loss_box_dn_5[0.2381] loss_depth[0.0404] total_loss[9.8450]
Rank[0/8] 06/19/2025 20:44:16 INFO stats.py:314 | Epoch[49] Step[389] GlobalStep[19499] Training Speed: 7.10 samples/sec across all devices.	Average Step Time: 1.13 sec.	Estimated Remaining Time: 1 day, 0:49:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:44:17 INFO trainer.py:268 | 
==================================================BEGIN EVAL==================================================
Rank[0/8] 06/19/2025 20:45:21 INFO trainer.py:275 | eval: 10
Rank[0/8] 06/19/2025 20:45:29 INFO trainer.py:275 | eval: 20
Rank[0/8] 06/19/2025 20:45:42 INFO trainer.py:275 | eval: 30
Rank[0/8] 06/19/2025 20:45:51 INFO trainer.py:275 | eval: 40
Rank[0/8] 06/19/2025 20:46:00 INFO trainer.py:275 | eval: 50
Rank[0/8] 06/19/2025 20:46:07 INFO trainer.py:275 | eval: 60
Rank[0/8] 06/19/2025 20:46:18 INFO trainer.py:275 | eval: 70
Rank[0/8] 06/19/2025 20:46:25 INFO trainer.py:275 | eval: 80
Rank[0/8] 06/19/2025 20:46:35 INFO trainer.py:275 | eval: 90
Rank[0/8] 06/19/2025 20:46:45 INFO trainer.py:275 | eval: 100
Rank[0/8] 06/19/2025 20:46:50 INFO metrics.py:428 | number of results: 824
Rank[0/8] 06/19/2025 20:50:33 INFO metrics.py:332 | eval results of overall:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| picture             | 0.1172  | 0.3114  | 0.0021  | 0.0388  |
| bin                 | 0.5443  | 0.8017  | 0.1870  | 0.4693  |
| sink                | 0.5660  | 0.7695  | 0.0836  | 0.2428  |
| bathtub             | 0.7166  | 0.8971  | 0.5516  | 0.7206  |
| toilet              | 0.8292  | 0.8540  | 0.4669  | 0.6350  |
| curtain             | 0.3699  | 0.7731  | 0.0836  | 0.3284  |
| towel               | 0.2577  | 0.6097  | 0.0319  | 0.1742  |
| door                | 0.1703  | 0.5526  | 0.0060  | 0.1308  |
| toilet paper        | 0.2604  | 0.5591  | 0.0137  | 0.2047  |
| stopcock            | 0.1416  | 0.2759  | 0.0000  | 0.0000  |
| vanity              | 0.4257  | 0.8889  | 0.2057  | 0.5778  |
| rail                | 0.0154  | 0.4062  | 0.0001  | 0.0312  |
| doorframe           | 0.1911  | 0.6883  | 0.0355  | 0.3047  |
| bar                 | 0.0437  | 0.1212  | 0.0000  | 0.0303  |
| bag                 | 0.2180  | 0.7027  | 0.0560  | 0.2432  |
| bottle              | 0.0307  | 0.3538  | 0.0004  | 0.0418  |
| faucet              | 0.0221  | 0.1064  | 0.0000  | 0.0000  |
| mirror              | 0.0893  | 0.5870  | 0.0015  | 0.0978  |
| tissue box          | 0.0214  | 0.3939  | 0.0000  | 0.0000  |
| ledge               | 0.0033  | 0.5000  | 0.0000  | 0.0000  |
| socket              | 0.0045  | 0.1814  | 0.0000  | 0.0000  |
| hair dryer          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| counter             | 0.0645  | 0.6697  | 0.0031  | 0.2844  |
| cup                 | 0.0450  | 0.3898  | 0.0006  | 0.0339  |
| shelf               | 0.2269  | 0.6763  | 0.0455  | 0.2824  |
| plunger             | 0.7600  | 0.8000  | 0.1800  | 0.4000  |
| switch              | 0.0023  | 0.0658  | 0.0000  | 0.0000  |
| paper               | 0.0118  | 0.2367  | 0.0002  | 0.0385  |
| shower              | 0.0632  | 0.2000  | 0.0001  | 0.0333  |
| clothes             | 0.1019  | 0.5000  | 0.0070  | 0.1369  |
| backpack            | 0.4488  | 0.7581  | 0.1240  | 0.3871  |
| bidet               | 0.3478  | 0.6667  | 0.3478  | 0.6667  |
| cabinet             | 0.2438  | 0.7064  | 0.0910  | 0.3643  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dish           | 0.4131  | 0.6000  | 0.0010  | 0.0667  |
| paper cutter        | 0.7188  | 0.8333  | 0.3333  | 0.3333  |
| box                 | 0.1351  | 0.4849  | 0.0065  | 0.1022  |
| frame               | 0.0248  | 0.3673  | 0.0000  | 0.0000  |
| washbasin           | 0.0058  | 0.4667  | 0.0032  | 0.2000  |
| light               | 0.0103  | 0.1774  | 0.0002  | 0.0171  |
| tissue              | 0.0166  | 0.3077  | 0.0000  | 0.0000  |
| soap                | 0.0035  | 0.1667  | 0.0000  | 0.0000  |
| stand               | 0.4327  | 0.8476  | 0.2205  | 0.5427  |
| grab bar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hanger              | 0.0011  | 0.2500  | 0.0000  | 0.0000  |
| plug                | 0.0055  | 0.1053  | 0.0000  | 0.0000  |
| vent                | 0.0043  | 0.1667  | 0.0000  | 0.0000  |
| mat                 | 0.0029  | 0.1892  | 0.0000  | 0.0000  |
| stall               | 0.0847  | 0.9412  | 0.0029  | 0.2941  |
| dispenser           | 0.2897  | 0.5682  | 0.1370  | 0.2955  |
| purse               | 0.0052  | 0.1667  | 0.0000  | 0.0000  |
| microwave           | 0.5739  | 0.7959  | 0.1974  | 0.3878  |
| basket              | 0.1913  | 0.6609  | 0.0386  | 0.2174  |
| basin               | 0.0026  | 0.2941  | 0.0000  | 0.0588  |
| bucket              | 0.2111  | 0.5385  | 0.0826  | 0.2821  |
| monitor             | 0.4583  | 0.6630  | 0.0361  | 0.1630  |
| keyboard            | 0.0169  | 0.1395  | 0.0000  | 0.0000  |
| chair               | 0.5185  | 0.7341  | 0.3042  | 0.4961  |
| radiator            | 0.4960  | 0.6889  | 0.1492  | 0.2556  |
| computer            | 0.2117  | 0.5574  | 0.0036  | 0.0984  |
| mouse               | 0.0097  | 0.0811  | 0.0000  | 0.0000  |
| blackboard          | 0.2323  | 0.5488  | 0.0457  | 0.2073  |
| window              | 0.1156  | 0.5030  | 0.0041  | 0.1168  |
| table               | 0.3937  | 0.8307  | 0.2174  | 0.5815  |
| desk                | 0.3542  | 0.8876  | 0.2023  | 0.6124  |
| telephone           | 0.2206  | 0.5000  | 0.0107  | 0.2000  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| windowsill          | 0.0508  | 0.6304  | 0.0008  | 0.0870  |
| blinds              | 0.0435  | 0.4400  | 0.0009  | 0.1200  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| board               | 0.1134  | 0.5000  | 0.0459  | 0.1774  |
| book                | 0.0661  | 0.4050  | 0.0030  | 0.0405  |
| jacket              | 0.5193  | 0.9167  | 0.0515  | 0.2917  |
| tablet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stool               | 0.2278  | 0.6891  | 0.1107  | 0.4370  |
| mailbox             | 0.0281  | 0.6000  | 0.0008  | 0.2000  |
| couch               | 0.3711  | 0.8719  | 0.2138  | 0.6777  |
| sunglasses          | 0.0065  | 0.3077  | 0.0000  | 0.0000  |
| can                 | 0.0083  | 0.0833  | 0.0000  | 0.0000  |
| pipe                | 0.0050  | 0.2857  | 0.0000  | 0.0000  |
| bed                 | 0.5293  | 0.9290  | 0.3074  | 0.7870  |
| dresser             | 0.5442  | 0.9259  | 0.4582  | 0.8148  |
| wardrobe            | 0.2513  | 0.7711  | 0.0338  | 0.3373  |
| pillow              | 0.2933  | 0.6360  | 0.0181  | 0.1492  |
| blanket             | 0.0778  | 0.8100  | 0.0076  | 0.2900  |
| toy                 | 0.0709  | 0.4697  | 0.0043  | 0.0909  |
| lamp                | 0.2817  | 0.5463  | 0.0194  | 0.1194  |
| ottoman             | 0.1068  | 0.7500  | 0.0576  | 0.4444  |
| shoe                | 0.1044  | 0.4112  | 0.0038  | 0.0374  |
| carpet              | 0.0318  | 0.3690  | 0.0016  | 0.0833  |
| tv                  | 0.3481  | 0.6634  | 0.0395  | 0.2277  |
| calendar            | 0.0003  | 0.1667  | 0.0000  | 0.0000  |
| case                | 0.0524  | 0.3955  | 0.0215  | 0.0904  |
| bowl                | 0.0880  | 0.4638  | 0.0064  | 0.1449  |
| decoration          | 0.0208  | 0.3571  | 0.0007  | 0.0643  |
| heater              | 0.3316  | 0.6897  | 0.0658  | 0.2414  |
| cushion             | 0.1225  | 0.4706  | 0.0077  | 0.0980  |
| printer             | 0.5171  | 0.7959  | 0.2073  | 0.6327  |
| container           | 0.0074  | 0.2712  | 0.0012  | 0.1356  |
| poster              | 0.0100  | 0.1364  | 0.0000  | 0.0152  |
| fan                 | 0.3179  | 0.7812  | 0.1222  | 0.4219  |
| plate               | 0.0328  | 0.3654  | 0.0000  | 0.0192  |
| laptop              | 0.3840  | 0.6522  | 0.0979  | 0.1739  |
| statue              | 0.1154  | 0.5789  | 0.0310  | 0.1579  |
| clock               | 0.0912  | 0.3617  | 0.0000  | 0.0213  |
| remote control      | 0.0433  | 0.1852  | 0.0232  | 0.1111  |
| guitar              | 0.2057  | 0.5000  | 0.0857  | 0.1667  |
| luggage             | 0.3773  | 1.0000  | 0.3679  | 1.0000  |
| refrigerator        | 0.6098  | 0.8000  | 0.1062  | 0.3200  |
| coffee maker        | 0.4103  | 0.7241  | 0.0985  | 0.2069  |
| notebook            | 0.0001  | 0.0769  | 0.0000  | 0.0000  |
| tray                | 0.0026  | 0.4286  | 0.0009  | 0.1429  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kettle              | 0.1089  | 0.6111  | 0.0333  | 0.2778  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bench               | 0.1891  | 0.6604  | 0.1193  | 0.3962  |
| holder              | 0.1371  | 0.4444  | 0.0000  | 0.0000  |
| tube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hamper              | 0.2928  | 0.8182  | 0.1801  | 0.5455  |
| ladder              | 0.1499  | 0.5882  | 0.0588  | 0.0588  |
| stairs              | 0.0534  | 0.4028  | 0.0054  | 0.0972  |
| soap dispenser      | 0.5708  | 0.7037  | 0.1019  | 0.1111  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillar              | 0.2796  | 0.6512  | 0.0127  | 0.2326  |
| column              | 0.0071  | 0.2632  | 0.0017  | 0.1053  |
| sign                | 0.0267  | 0.3667  | 0.0000  | 0.0000  |
| fire extinguisher   | 0.6074  | 0.8000  | 0.0000  | 0.0000  |
| arch                | 0.0045  | 0.1667  | 0.0000  | 0.0333  |
| steps               | 0.0034  | 0.2000  | 0.0001  | 0.1000  |
| garage door         | 0.1307  | 0.6667  | 0.0830  | 0.6667  |
| window frame        | 0.0304  | 0.4141  | 0.0026  | 0.1313  |
| beam                | 0.0005  | 0.1364  | 0.0000  | 0.0000  |
| roof                | 0.0013  | 0.3750  | 0.0000  | 0.0000  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| projector           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cart                | 0.0172  | 0.5000  | 0.0159  | 0.5000  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| screen              | 0.2000  | 0.2000  | 0.0000  | 0.0000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| grass               | 0.0034  | 1.0000  | 0.0000  | 0.0000  |
| rack                | 0.0457  | 0.4231  | 0.0091  | 0.2115  |
| plant               | 0.2731  | 0.6143  | 0.0442  | 0.1917  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| folder              | 0.0303  | 0.1429  | 0.0000  | 0.0000  |
| furniture           | 0.0017  | 0.1667  | 0.0017  | 0.1667  |
| radio               | 0.0100  | 0.2500  | 0.0100  | 0.2500  |
| hat                 | 0.0219  | 0.3333  | 0.0000  | 0.0000  |
| person              | 0.0083  | 0.1667  | 0.0000  | 0.0000  |
| file                | 0.0647  | 0.3235  | 0.0016  | 0.0882  |
| ball                | 0.3081  | 0.4762  | 0.0680  | 0.2381  |
| commode             | 0.3580  | 0.8077  | 0.2298  | 0.5962  |
| globe               | 0.0278  | 1.0000  | 0.0000  | 0.0000  |
| copier              | 0.6140  | 0.9167  | 0.4060  | 0.5833  |
| water cooler        | 0.7500  | 1.0000  | 0.5000  | 0.5000  |
| machine             | 0.4952  | 0.9474  | 0.1699  | 0.7895  |
| toothpaste          | 0.0024  | 0.2000  | 0.0000  | 0.0000  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headboard           | 0.0013  | 0.1250  | 0.0000  | 0.0000  |
| thermostat          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| teapot              | 0.0063  | 0.3333  | 0.0000  | 0.0000  |
| pitcher             | 0.0238  | 0.3333  | 0.0238  | 0.3333  |
| stove               | 0.4560  | 0.8333  | 0.2086  | 0.3611  |
| dish rack           | 0.0049  | 0.4000  | 0.0000  | 0.0000  |
| toaster             | 0.2423  | 0.5000  | 0.0000  | 0.0000  |
| oven                | 0.2006  | 0.7143  | 0.0239  | 0.1786  |
| dishwasher          | 0.1833  | 0.3000  | 0.0333  | 0.1000  |
| kitchenware         | 0.1790  | 0.5870  | 0.0410  | 0.1522  |
| pot                 | 0.0545  | 0.4815  | 0.0030  | 0.1111  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| spoon               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scale               | 0.1818  | 0.1818  | 0.0455  | 0.0909  |
| pack                | 0.0207  | 0.5333  | 0.0002  | 0.0667  |
| pan                 | 0.0400  | 0.2941  | 0.0035  | 0.0588  |
| fruit               | 0.1515  | 0.3571  | 0.0000  | 0.0000  |
| flowerpot           | 0.0307  | 0.5882  | 0.0034  | 0.1373  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fireplace           | 0.0543  | 0.4286  | 0.0018  | 0.0357  |
| piano               | 0.2903  | 0.6000  | 0.2000  | 0.2000  |
| player              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| magazine            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jar                 | 0.0066  | 0.5385  | 0.0001  | 0.0769  |
| map                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vacuum cleaner      | 0.1055  | 0.4286  | 0.0027  | 0.1429  |
| cosmetics           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toiletry            | 0.0226  | 0.2609  | 0.0000  | 0.0000  |
| headphones          | 0.1067  | 0.4000  | 0.0200  | 0.2000  |
| device              | 0.0909  | 0.0909  | 0.0000  | 0.0000  |
| conducting wire     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garbage             | 0.0832  | 0.6250  | 0.0262  | 0.3750  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drawer              | 0.0458  | 0.5000  | 0.0024  | 0.3333  |
| candle              | 0.0025  | 0.0556  | 0.0000  | 0.0000  |
| price tag           | 0.1541  | 0.4286  | 0.0000  | 0.0000  |
| vase                | 0.1035  | 0.4200  | 0.0058  | 0.1200  |
| chandelier          | 0.0697  | 0.4762  | 0.0043  | 0.0952  |
| jalousie            | 0.0698  | 1.0000  | 0.0000  | 0.0000  |
| humidifier          | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| umbrella            | 0.2000  | 0.2000  | 0.0000  | 0.0000  |
| speaker             | 0.0004  | 0.0909  | 0.0001  | 0.0455  |
| roll                | 0.1467  | 0.4000  | 0.0008  | 0.1000  |
| air conditioner     | 0.5200  | 0.6000  | 0.0000  | 0.0000  |
| flush               | 0.0263  | 0.5000  | 0.0000  | 0.0000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| brush               | 0.0239  | 0.3571  | 0.0000  | 0.0000  |
| balcony             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cap                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candlestick         | 0.3333  | 0.3333  | 0.0000  | 0.0000  |
| footstool           | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.4184  | 0.6667  | 0.2120  | 0.4667  |
| shampoo             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| body loofah         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beanbag             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| letter              | 0.1146  | 0.5000  | 0.0000  | 0.0000  |
| album               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.2244  | 0.6429  | 0.0014  | 0.0714  |
| bread               | 0.0351  | 0.4000  | 0.0000  | 0.0000  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| food                | 0.0455  | 0.0909  | 0.0000  | 0.0000  |
| partition           | 0.1035  | 0.6667  | 0.0000  | 0.0000  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crib                | 0.2111  | 1.0000  | 0.0167  | 0.3333  |
| helmets             | 0.0126  | 0.6667  | 0.0000  | 0.0000  |
| crate               | 0.1633  | 0.5714  | 0.0476  | 0.1429  |
| broom               | 0.0204  | 0.2857  | 0.0008  | 0.0714  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| napkins             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mop                 | 0.8333  | 1.0000  | 0.3333  | 0.3333  |
| sack                | 0.4923  | 0.7143  | 0.0143  | 0.1429  |
| countertop          | 0.0240  | 0.5714  | 0.0004  | 0.0714  |
| doorway             | 0.0006  | 0.2222  | 0.0000  | 0.1111  |
| glass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wine                | 0.0526  | 0.0952  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| handle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchen island      | 0.3328  | 0.8571  | 0.3031  | 0.5714  |
| excercise equipment | 0.2843  | 0.7000  | 0.0331  | 0.3500  |
| pool table          | 0.0833  | 0.3333  | 0.0833  | 0.3333  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.4236  | 0.7857  | 0.1688  | 0.4286  |
| sponge              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 0.3304  | 0.6667  | 0.1991  | 0.5000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drum                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coil                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water heater        | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| salt                | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| menu                | 0.4286  | 0.4286  | 0.0000  | 0.0000  |
| cube                | 0.1111  | 1.0000  | 0.1111  | 1.0000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glove               | 0.3939  | 0.6667  | 0.0044  | 0.3333  |
| treadmill           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| overall             | 0.1516  | 0.3990  | 0.0488  | 0.1475  |
| head                | 0.2127  | 0.5539  | 0.0699  | 0.2274  |
| common              | 0.1378  | 0.3788  | 0.0400  | 0.1182  |
| tail                | 0.0996  | 0.2501  | 0.0354  | 0.0925  |
| small               | 0.0438  | 0.1756  | 0.0032  | 0.0267  |
| medium              | 0.1584  | 0.4433  | 0.0525  | 0.1619  |
| large               | 0.0859  | 0.5187  | 0.0306  | 0.2250  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/19/2025 20:50:33 INFO metrics.py:437 | number of scannet results: 250
Rank[0/8] 06/19/2025 20:51:43 INFO metrics.py:332 | eval results of scannet:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| picture             | 0.1910  | 0.3843  | 0.0037  | 0.0373  |
| bin                 | 0.5996  | 0.8085  | 0.2142  | 0.4979  |
| sink                | 0.7461  | 0.8523  | 0.1280  | 0.3182  |
| bathtub             | 0.8735  | 0.9688  | 0.7615  | 0.8438  |
| toilet              | 0.9134  | 0.9286  | 0.5872  | 0.7321  |
| curtain             | 0.4715  | 0.7522  | 0.0987  | 0.3451  |
| towel               | 0.3100  | 0.6777  | 0.0484  | 0.1901  |
| door                | 0.2259  | 0.6421  | 0.0098  | 0.1739  |
| toilet paper        | 0.3103  | 0.6076  | 0.0222  | 0.2785  |
| stopcock            | 0.1416  | 0.2759  | 0.0000  | 0.0000  |
| vanity              | 0.5583  | 0.9333  | 0.2717  | 0.5667  |
| rail                | 0.0430  | 0.6667  | 0.0000  | 0.0000  |
| doorframe           | 0.2028  | 0.6761  | 0.0307  | 0.2465  |
| bar                 | 0.0524  | 0.0909  | 0.0000  | 0.0000  |
| bag                 | 0.2080  | 0.7397  | 0.1285  | 0.3151  |
| bottle              | 0.0353  | 0.3526  | 0.0007  | 0.0368  |
| faucet              | 0.0023  | 0.1250  | 0.0000  | 0.0000  |
| mirror              | 0.1211  | 0.6867  | 0.0011  | 0.0843  |
| tissue box          | 0.0082  | 0.3182  | 0.0000  | 0.0000  |
| ledge               | 0.0033  | 0.5000  | 0.0000  | 0.0000  |
| socket              | 0.0053  | 0.1990  | 0.0000  | 0.0000  |
| hair dryer          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| counter             | 0.1084  | 0.8571  | 0.0108  | 0.4571  |
| cup                 | 0.1514  | 0.4348  | 0.0022  | 0.0580  |
| shelf               | 0.4322  | 0.8235  | 0.1233  | 0.4559  |
| plunger             | 0.7600  | 0.8000  | 0.1800  | 0.4000  |
| switch              | 0.0062  | 0.1139  | 0.0000  | 0.0000  |
| paper               | 0.0154  | 0.2264  | 0.0004  | 0.0302  |
| shower              | 0.0920  | 0.2143  | 0.0000  | 0.0000  |
| clothes             | 0.1146  | 0.5163  | 0.0064  | 0.1438  |
| backpack            | 0.6240  | 0.8571  | 0.2027  | 0.5000  |
| cabinet             | 0.3402  | 0.7500  | 0.1192  | 0.3730  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dish           | 0.5181  | 0.7500  | 0.0014  | 0.0833  |
| paper cutter        | 0.6615  | 0.8000  | 0.2000  | 0.2000  |
| box                 | 0.1540  | 0.4444  | 0.0067  | 0.0833  |
| frame               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| light               | 0.0700  | 0.2069  | 0.0000  | 0.0345  |
| tissue              | 0.0296  | 0.3333  | 0.0000  | 0.0000  |
| stand               | 0.6816  | 0.8679  | 0.4088  | 0.6415  |
| grab bar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mat                 | 0.0047  | 0.2000  | 0.0000  | 0.0000  |
| stall               | 0.0870  | 0.9412  | 0.0031  | 0.2941  |
| dispenser           | 0.3716  | 0.5897  | 0.1727  | 0.3333  |
| purse               | 0.0093  | 0.1667  | 0.0000  | 0.0000  |
| microwave           | 0.5784  | 0.8000  | 0.1922  | 0.4500  |
| basket              | 0.0476  | 0.5769  | 0.0097  | 0.1923  |
| basin               | 0.0037  | 1.0000  | 0.0000  | 0.0000  |
| bucket              | 0.1763  | 0.6429  | 0.1018  | 0.3571  |
| monitor             | 0.5379  | 0.6786  | 0.0556  | 0.2000  |
| keyboard            | 0.0416  | 0.1897  | 0.0000  | 0.0000  |
| chair               | 0.5615  | 0.7518  | 0.3280  | 0.5087  |
| radiator            | 0.6335  | 0.7586  | 0.2184  | 0.3448  |
| computer            | 0.2488  | 0.5385  | 0.0081  | 0.1026  |
| mouse               | 0.0498  | 0.1429  | 0.0000  | 0.0000  |
| blackboard          | 0.2402  | 0.5217  | 0.0476  | 0.2029  |
| window              | 0.3078  | 0.7469  | 0.0082  | 0.1914  |
| table               | 0.5356  | 0.8731  | 0.3029  | 0.6923  |
| desk                | 0.4799  | 0.9620  | 0.3051  | 0.6962  |
| telephone           | 0.3070  | 0.5312  | 0.0162  | 0.2500  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| windowsill          | 0.0091  | 0.8333  | 0.0000  | 0.0000  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| board               | 0.1030  | 0.6875  | 0.0195  | 0.2500  |
| book                | 0.0777  | 0.4548  | 0.0030  | 0.0452  |
| jacket              | 0.5424  | 0.9565  | 0.0539  | 0.3043  |
| stool               | 0.2598  | 0.6818  | 0.1076  | 0.3864  |
| mailbox             | 0.0281  | 0.6000  | 0.0008  | 0.2000  |
| couch               | 0.5515  | 0.9625  | 0.3901  | 0.8125  |
| pipe                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bed                 | 0.6308  | 1.0000  | 0.5515  | 0.9649  |
| dresser             | 0.6327  | 0.9730  | 0.5637  | 0.8919  |
| wardrobe            | 0.3456  | 1.0000  | 0.0471  | 0.3750  |
| pillow              | 0.4090  | 0.7083  | 0.0955  | 0.3056  |
| blanket             | 0.0352  | 0.6250  | 0.0032  | 0.1667  |
| toy                 | 0.0550  | 0.2353  | 0.0000  | 0.0000  |
| lamp                | 0.4719  | 0.6429  | 0.0473  | 0.1310  |
| ottoman             | 0.0566  | 1.0000  | 0.0057  | 0.4286  |
| shoe                | 0.1192  | 0.4000  | 0.0000  | 0.0250  |
| carpet              | 0.0008  | 0.3000  | 0.0000  | 0.0000  |
| tv                  | 0.4431  | 0.6522  | 0.1382  | 0.3478  |
| calendar            | 0.0038  | 1.0000  | 0.0000  | 0.0000  |
| case                | 0.4331  | 0.9474  | 0.2190  | 0.4737  |
| bowl                | 0.0156  | 0.2222  | 0.0156  | 0.2222  |
| decoration          | 0.0246  | 0.2083  | 0.0000  | 0.0000  |
| cushion             | 0.1548  | 0.5000  | 0.0000  | 0.0000  |
| printer             | 0.5346  | 0.8788  | 0.2334  | 0.7576  |
| container           | 0.0069  | 0.2174  | 0.0002  | 0.0870  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fan                 | 0.2478  | 0.6250  | 0.1042  | 0.2500  |
| plate               | 0.0170  | 0.4000  | 0.0000  | 0.0000  |
| laptop              | 0.4834  | 0.6000  | 0.1111  | 0.1333  |
| clock               | 0.1543  | 0.4400  | 0.0000  | 0.0000  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| guitar              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| refrigerator        | 0.8868  | 0.9000  | 0.2516  | 0.5000  |
| coffee maker        | 0.5566  | 0.7647  | 0.2059  | 0.3529  |
| notebook            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tray                | 0.0079  | 0.6667  | 0.0035  | 0.3333  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kettle              | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| adhesive tape       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bench               | 0.7914  | 1.0000  | 0.6071  | 0.8000  |
| holder              | 0.0725  | 0.4000  | 0.0000  | 0.0000  |
| tube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hamper              | 0.3304  | 0.9000  | 0.2038  | 0.6000  |
| ladder              | 0.4000  | 0.6000  | 0.2000  | 0.2000  |
| stairs              | 0.2375  | 0.8000  | 0.0181  | 0.4000  |
| soap dispenser      | 0.6376  | 0.7143  | 0.1310  | 0.1429  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillar              | 0.0185  | 0.4000  | 0.0000  | 0.0000  |
| sign                | 0.0239  | 0.3214  | 0.0000  | 0.0000  |
| fire extinguisher   | 0.3446  | 0.6667  | 0.0000  | 0.0000  |
| cart                | 0.0344  | 1.0000  | 0.0318  | 1.0000  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rack                | 0.0312  | 0.1818  | 0.0066  | 0.1818  |
| plant               | 0.3168  | 0.5882  | 0.0471  | 0.1345  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| person              | 0.0083  | 0.1667  | 0.0000  | 0.0000  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball                | 0.2500  | 0.3333  | 0.0694  | 0.1667  |
| copier              | 0.6140  | 0.9167  | 0.4060  | 0.5833  |
| water cooler        | 0.7500  | 1.0000  | 0.5000  | 0.5000  |
| machine             | 0.5857  | 1.0000  | 0.2108  | 0.8125  |
| toothpaste          | 0.0024  | 0.2000  | 0.0000  | 0.0000  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| teapot              | 0.0086  | 0.2500  | 0.0000  | 0.0000  |
| pitcher             | 0.0278  | 0.3333  | 0.0278  | 0.3333  |
| stove               | 1.0000  | 1.0000  | 0.7610  | 0.8889  |
| dish rack           | 0.0147  | 0.5000  | 0.0000  | 0.0000  |
| toaster             | 0.0500  | 0.2500  | 0.0000  | 0.0000  |
| oven                | 0.0417  | 0.2500  | 0.0000  | 0.0000  |
| dishwasher          | 0.1667  | 0.3333  | 0.0000  | 0.0000  |
| pot                 | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scale               | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| fruit               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flowerpot           | 0.0114  | 1.0000  | 0.0039  | 0.5000  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| map                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vacuum cleaner      | 0.0667  | 0.3333  | 0.0667  | 0.3333  |
| headphones          | 0.1571  | 0.4000  | 0.0286  | 0.2000  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| broom               | 0.0385  | 0.3333  | 0.0015  | 0.0833  |
| drawer              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.4861  | 0.6667  | 0.4861  | 0.6667  |
| crate               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0167  | 0.5000  | 0.0167  | 0.5000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.3000  | 0.4000  | 0.0000  | 0.0000  |
| bicycle             | 0.5556  | 0.6667  | 0.0667  | 0.3333  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer       | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| -------             | ------- | ------- | ------- | ------- |
| scannet             | 0.2113  | 0.4557  | 0.0822  | 0.1966  |
| head                | 0.2795  | 0.5885  | 0.1133  | 0.2682  |
| common              | 0.1662  | 0.3809  | 0.0566  | 0.1430  |
| tail                | 0.0720  | 0.1529  | 0.0309  | 0.0659  |
| small               | 0.0375  | 0.1947  | 0.0042  | 0.0347  |
| medium              | 0.2498  | 0.5532  | 0.0986  | 0.2377  |
| large               | 0.1925  | 0.7085  | 0.0566  | 0.3341  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/19/2025 20:51:43 INFO metrics.py:437 | number of 3rscan results: 235
Rank[0/8] 06/19/2025 20:52:44 INFO metrics.py:332 | eval results of 3rscan:
+-------------------+---------+---------+---------+---------+
| classes           | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+-------------------+---------+---------+---------+---------+
| keyboard          | 0.0006  | 0.0417  | 0.0000  | 0.0000  |
| monitor           | 0.3014  | 0.6250  | 0.0037  | 0.0500  |
| bin               | 0.5610  | 0.8293  | 0.1912  | 0.4512  |
| bag               | 0.2694  | 0.7071  | 0.0404  | 0.2222  |
| chair             | 0.5523  | 0.7370  | 0.3379  | 0.5221  |
| mouse             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| desk              | 0.3043  | 0.8500  | 0.1487  | 0.5625  |
| cup               | 0.0029  | 0.3333  | 0.0000  | 0.0000  |
| box               | 0.1409  | 0.5054  | 0.0078  | 0.1046  |
| computer          | 0.2032  | 0.6190  | 0.0010  | 0.0952  |
| book              | 0.0655  | 0.3550  | 0.0004  | 0.0237  |
| paper             | 0.0094  | 0.2857  | 0.0003  | 0.0714  |
| socket            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bottle            | 0.0281  | 0.3223  | 0.0003  | 0.0331  |
| plug              | 0.1111  | 0.2222  | 0.0000  | 0.0000  |
| pad               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blinds            | 0.0570  | 0.5128  | 0.0013  | 0.1538  |
| cabinet           | 0.2974  | 0.7863  | 0.1281  | 0.4658  |
| pen               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| backpack          | 0.3197  | 0.6970  | 0.0730  | 0.3030  |
| case              | 0.0247  | 0.3429  | 0.0037  | 0.0357  |
| stand             | 0.2983  | 0.8689  | 0.1318  | 0.4590  |
| conducting wire   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shelf             | 0.1704  | 0.6643  | 0.0195  | 0.2261  |
| tissue box        | 0.0108  | 0.3333  | 0.0000  | 0.0000  |
| device            | 0.0909  | 0.0909  | 0.0000  | 0.0000  |
| table             | 0.3907  | 0.8514  | 0.2079  | 0.5450  |
| plate             | 0.0244  | 0.3636  | 0.0001  | 0.0303  |
| window            | 0.1113  | 0.5479  | 0.0074  | 0.1096  |
| plant             | 0.2730  | 0.5990  | 0.0297  | 0.1485  |
| wardrobe          | 0.2934  | 0.7969  | 0.0345  | 0.3438  |
| notebook          | 0.0002  | 0.0909  | 0.0000  | 0.0000  |
| garbage           | 0.1138  | 0.6250  | 0.0313  | 0.3750  |
| paper cutter      | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| folder            | 0.0500  | 0.1429  | 0.0000  | 0.0000  |
| curtain           | 0.3138  | 0.8175  | 0.1031  | 0.3333  |
| board             | 0.1810  | 0.3333  | 0.0899  | 0.1250  |
| kettle            | 0.1299  | 0.8333  | 0.0518  | 0.4167  |
| spoon             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| picture           | 0.0929  | 0.3252  | 0.0024  | 0.0447  |
| poster            | 0.0143  | 0.1429  | 0.0000  | 0.0159  |
| clothes           | 0.1226  | 0.5534  | 0.0147  | 0.1650  |
| column            | 0.0113  | 1.0000  | 0.0070  | 0.5000  |
| scissors          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| printer           | 0.6607  | 0.7778  | 0.2696  | 0.4444  |
| telephone         | 0.1546  | 0.3750  | 0.0084  | 0.1667  |
| stool             | 0.3044  | 0.8333  | 0.1592  | 0.5741  |
| rack              | 0.0849  | 0.6000  | 0.0191  | 0.3000  |
| cushion           | 0.2027  | 0.6333  | 0.0139  | 0.1333  |
| wire              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| lamp              | 0.1793  | 0.4500  | 0.0055  | 0.0625  |
| pillar            | 0.4027  | 0.6471  | 0.0239  | 0.2941  |
| radiator          | 0.2350  | 0.6207  | 0.0242  | 0.1034  |
| clock             | 0.0402  | 0.2105  | 0.0000  | 0.0000  |
| counter           | 0.0724  | 0.6098  | 0.0023  | 0.2683  |
| drawer            | 0.1197  | 0.7778  | 0.0058  | 0.5556  |
| tv                | 0.3416  | 0.7429  | 0.0208  | 0.1143  |
| commode           | 0.3695  | 0.8077  | 0.2363  | 0.5962  |
| pillow            | 0.3325  | 0.6484  | 0.0062  | 0.1133  |
| couch             | 0.2539  | 0.8429  | 0.1036  | 0.6143  |
| bowl              | 0.1788  | 0.5926  | 0.0082  | 0.1852  |
| frame             | 0.0496  | 0.5333  | 0.0000  | 0.0000  |
| light             | 0.0135  | 0.3068  | 0.0000  | 0.0114  |
| doorframe         | 0.0938  | 0.6200  | 0.0107  | 0.2200  |
| blanket           | 0.1301  | 0.9231  | 0.0136  | 0.3538  |
| towel             | 0.1619  | 0.5949  | 0.0178  | 0.1139  |
| switch            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| door              | 0.1752  | 0.5221  | 0.0072  | 0.1103  |
| kitchenware       | 0.1882  | 0.6136  | 0.0429  | 0.1591  |
| laptop            | 0.2767  | 0.6667  | 0.0689  | 0.3333  |
| sink              | 0.4802  | 0.6964  | 0.0898  | 0.2143  |
| candle            | 0.0042  | 0.0667  | 0.0000  | 0.0000  |
| bed               | 0.6231  | 0.9636  | 0.2521  | 0.6909  |
| mirror            | 0.0987  | 0.4444  | 0.0001  | 0.0741  |
| toilet            | 0.7605  | 0.7647  | 0.1723  | 0.3529  |
| basin             | 0.0047  | 0.2000  | 0.0000  | 0.0000  |
| ottoman           | 0.2243  | 0.8182  | 0.1866  | 0.6364  |
| teapot            | 0.0114  | 0.5000  | 0.0000  | 0.0000  |
| decoration        | 0.0308  | 0.3438  | 0.0000  | 0.0000  |
| bench             | 0.1575  | 0.7500  | 0.0921  | 0.4286  |
| carpet            | 0.0549  | 0.5000  | 0.0029  | 0.1250  |
| price tag         | 0.1541  | 0.4286  | 0.0000  | 0.0000  |
| dispenser         | 0.0071  | 0.3333  | 0.0000  | 0.0000  |
| bar               | 0.3684  | 0.6667  | 0.0017  | 0.3333  |
| sign              | 0.1156  | 1.0000  | 0.0000  | 0.0000  |
| remote control    | 0.1188  | 0.2500  | 0.0755  | 0.1500  |
| flowerpot         | 0.0351  | 0.5833  | 0.0035  | 0.0417  |
| mat               | 0.0139  | 0.3333  | 0.0000  | 0.0000  |
| vase              | 0.1660  | 0.4000  | 0.0028  | 0.0500  |
| container         | 0.0006  | 0.1111  | 0.0006  | 0.1111  |
| windowsill        | 0.0651  | 0.6154  | 0.0012  | 0.1026  |
| calendar          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| magazine          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toy               | 0.1160  | 0.6154  | 0.0308  | 0.1538  |
| chandelier        | 0.0501  | 1.0000  | 0.0263  | 0.5000  |
| refrigerator      | 0.5007  | 0.8000  | 0.1087  | 0.2000  |
| blackboard        | 0.3182  | 0.7500  | 0.0539  | 0.2500  |
| heater            | 0.3290  | 0.6842  | 0.0671  | 0.2456  |
| fan               | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| jalousie          | 0.0698  | 1.0000  | 0.0000  | 0.0000  |
| humidifier        | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| file              | 0.0370  | 0.3214  | 0.0017  | 0.0714  |
| umbrella          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| pack              | 0.0239  | 0.6154  | 0.0002  | 0.0769  |
| coffee maker      | 0.2542  | 0.7778  | 0.0000  | 0.0000  |
| tray              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sunglasses        | 0.0069  | 0.3077  | 0.0000  | 0.0000  |
| roll              | 0.1575  | 0.4000  | 0.0009  | 0.1000  |
| shoe              | 0.1549  | 0.5000  | 0.0108  | 0.0714  |
| basket            | 0.2978  | 0.7656  | 0.0786  | 0.2500  |
| holder            | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| air conditioner   | 0.6500  | 0.7500  | 0.0000  | 0.0000  |
| toilet paper      | 0.1925  | 0.4167  | 0.0017  | 0.0833  |
| hanger            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue            | 0.0667  | 0.3333  | 0.0000  | 0.0000  |
| faucet            | 0.3356  | 0.6667  | 0.0000  | 0.0000  |
| flush             | 0.0263  | 0.5000  | 0.0000  | 0.0000  |
| hook              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bathtub           | 0.7069  | 1.0000  | 0.5616  | 0.7778  |
| dumbbell          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| brush             | 0.0454  | 0.6250  | 0.0000  | 0.0000  |
| scale             | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| microwave         | 0.8989  | 1.0000  | 0.3926  | 0.5294  |
| dresser           | 0.8333  | 1.0000  | 0.0000  | 0.0000  |
| fruit             | 0.2633  | 0.6000  | 0.0000  | 0.0000  |
| balcony           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vacuum cleaner    | 0.1705  | 0.5000  | 0.0000  | 0.0000  |
| luggage           | 0.4879  | 1.0000  | 0.4287  | 1.0000  |
| jar               | 0.0108  | 0.6250  | 0.0000  | 0.0000  |
| hat               | 0.0141  | 0.2857  | 0.0000  | 0.0000  |
| candlestick       | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| statue            | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| footstool         | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| shower            | 0.3036  | 0.7500  | 0.0012  | 0.2500  |
| bucket            | 0.5186  | 0.6667  | 0.1504  | 0.2667  |
| toiletry          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beam              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer     | 0.4524  | 0.7000  | 0.1458  | 0.4000  |
| shampoo           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| tube              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| body loofah       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beanbag           | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| letter            | 0.1146  | 0.5000  | 0.0000  | 0.0000  |
| album             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tablet            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| can               | 0.0139  | 0.1250  | 0.0000  | 0.0000  |
| stove             | 0.2441  | 0.8235  | 0.0363  | 0.2353  |
| range hood        | 0.4478  | 1.0000  | 0.0061  | 0.2000  |
| pot               | 0.0563  | 0.6667  | 0.0118  | 0.2500  |
| pan               | 0.0704  | 0.3333  | 0.0069  | 0.0833  |
| oven              | 0.3135  | 0.8947  | 0.0413  | 0.2632  |
| bread             | 0.0363  | 0.4000  | 0.0000  | 0.0000  |
| knife             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ladder            | 0.0977  | 1.0000  | 0.0000  | 0.0000  |
| dish rack         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| food              | 0.0500  | 0.1000  | 0.0000  | 0.0000  |
| shovel            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stairs            | 0.2107  | 0.5714  | 0.0732  | 0.1429  |
| toaster           | 0.5801  | 1.0000  | 0.0000  | 0.0000  |
| partition         | 0.1247  | 0.8000  | 0.0000  | 0.0000  |
| screen            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rail              | 0.0013  | 0.2500  | 0.0000  | 0.0000  |
| radio             | 0.0500  | 1.0000  | 0.0500  | 1.0000  |
| crib              | 0.2111  | 1.0000  | 0.0167  | 0.3333  |
| helmets           | 0.0201  | 0.6667  | 0.0000  | 0.0000  |
| crate             | 0.2339  | 0.8000  | 0.0667  | 0.2000  |
| pipe              | 0.0048  | 0.7500  | 0.0000  | 0.0000  |
| soap dispenser    | 0.4867  | 0.8000  | 0.0000  | 0.0000  |
| soap dish         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| broom             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| piano             | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| fireplace         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| furniture         | 0.0256  | 0.3333  | 0.0256  | 0.3333  |
| ball              | 0.4500  | 0.6000  | 0.2333  | 0.4000  |
| napkins           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washbasin         | 0.0286  | 1.0000  | 0.0000  | 0.0000  |
| mop               | 0.8333  | 1.0000  | 0.3333  | 0.3333  |
| sack              | 0.4942  | 0.7143  | 0.0143  | 0.1429  |
| bicycle           | 0.5213  | 1.0000  | 0.5213  | 1.0000  |
| washing machine   | 0.5281  | 0.8571  | 0.1051  | 0.2857  |
| dishwasher        | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| tool              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| window frame      | 0.0054  | 0.1667  | 0.0000  | 0.0000  |
| mattress          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| seasoning         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| guitar            | 0.5714  | 1.0000  | 0.5000  | 0.5000  |
| glass             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coil              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water heater      | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| sponge            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| salt              | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| menu              | 0.4286  | 0.4286  | 0.0000  | 0.0000  |
| cube              | 0.1111  | 1.0000  | 0.1111  | 1.0000  |
| player            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glove             | 0.3939  | 0.6667  | 0.0044  | 0.3333  |
| map               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cosmetics         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toothbrush        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| -------           | ------- | ------- | ------- | ------- |
| 3rscan            | 0.2096  | 0.4862  | 0.0629  | 0.1634  |
| head              | 0.2219  | 0.5793  | 0.0579  | 0.2041  |
| common            | 0.2203  | 0.4687  | 0.0709  | 0.1359  |
| tail              | 0.1718  | 0.3355  | 0.0613  | 0.1250  |
| small             | 0.0685  | 0.2098  | 0.0100  | 0.0304  |
| medium            | 0.2182  | 0.5440  | 0.0658  | 0.1770  |
| large             | 0.1619  | 0.6606  | 0.0550  | 0.2693  |
+-------------------+---------+---------+---------+---------+
Rank[0/8] 06/19/2025 20:52:44 INFO metrics.py:437 | number of matterport3d results: 339
Rank[0/8] 06/19/2025 20:53:59 INFO metrics.py:332 | eval results of matterport3d:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| sink                | 0.4746  | 0.7374  | 0.0552  | 0.1919  |
| plant               | 0.2741  | 0.6696  | 0.0989  | 0.3304  |
| mirror              | 0.0755  | 0.5270  | 0.0069  | 0.1216  |
| doorframe           | 0.2461  | 0.7018  | 0.0561  | 0.3377  |
| towel               | 0.3088  | 0.5455  | 0.0450  | 0.2000  |
| countertop          | 0.0240  | 0.5714  | 0.0004  | 0.0714  |
| decoration          | 0.0201  | 0.4048  | 0.0017  | 0.1071  |
| bathtub             | 0.5193  | 0.7778  | 0.2778  | 0.5556  |
| light               | 0.0148  | 0.1425  | 0.0031  | 0.0171  |
| washbasin           | 0.0067  | 0.4286  | 0.0040  | 0.2143  |
| picture             | 0.0924  | 0.2560  | 0.0016  | 0.0362  |
| counter             | 0.0527  | 0.5455  | 0.0003  | 0.1212  |
| bottle              | 0.0437  | 0.4375  | 0.0007  | 0.0833  |
| shelf               | 0.1305  | 0.5547  | 0.0400  | 0.2263  |
| door                | 0.1369  | 0.4935  | 0.0039  | 0.1044  |
| cabinet             | 0.1154  | 0.5805  | 0.0400  | 0.2542  |
| lamp                | 0.3029  | 0.6264  | 0.0495  | 0.2088  |
| drawer              | 0.0027  | 0.2500  | 0.0007  | 0.1250  |
| case                | 0.0005  | 0.2222  | 0.0001  | 0.1111  |
| window frame        | 0.0353  | 0.4483  | 0.0031  | 0.1494  |
| dresser             | 0.4015  | 0.8000  | 0.3431  | 0.7333  |
| toiletry            | 0.0263  | 0.2727  | 0.0000  | 0.0000  |
| box                 | 0.0548  | 0.5000  | 0.0100  | 0.1731  |
| roof                | 0.0013  | 0.3750  | 0.0000  | 0.0000  |
| clothes             | 0.0924  | 0.3621  | 0.0019  | 0.0690  |
| vent                | 0.0066  | 0.1667  | 0.0000  | 0.0000  |
| window              | 0.0576  | 0.3750  | 0.0031  | 0.0861  |
| curtain             | 0.3441  | 0.7396  | 0.0547  | 0.3021  |
| toilet              | 0.7620  | 0.8125  | 0.4897  | 0.6250  |
| vase                | 0.0799  | 0.4333  | 0.0131  | 0.1667  |
| wardrobe            | 0.2484  | 0.4545  | 0.1335  | 0.2727  |
| soap                | 0.0333  | 0.2000  | 0.0000  | 0.0000  |
| faucet              | 0.0005  | 0.0556  | 0.0000  | 0.0000  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vanity              | 0.1658  | 0.8000  | 0.0724  | 0.6000  |
| table               | 0.2241  | 0.7308  | 0.1277  | 0.4487  |
| doorway             | 0.0006  | 0.2222  | 0.0000  | 0.1111  |
| flowerpot           | 0.0419  | 0.5600  | 0.0092  | 0.2000  |
| shower              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toilet paper        | 0.2097  | 0.5000  | 0.0109  | 0.0833  |
| switch              | 0.0002  | 0.0500  | 0.0000  | 0.0000  |
| blanket             | 0.0143  | 0.5455  | 0.0006  | 0.1818  |
| tissue              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| windowsill          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cup                 | 0.0050  | 0.3000  | 0.0000  | 0.0000  |
| pillow              | 0.2247  | 0.5931  | 0.0061  | 0.1073  |
| bench               | 0.1456  | 0.4500  | 0.0758  | 0.2500  |
| dispenser           | 0.0056  | 0.5000  | 0.0000  | 0.0000  |
| chair               | 0.3635  | 0.6701  | 0.2023  | 0.4082  |
| statue              | 0.1120  | 0.5882  | 0.0828  | 0.1765  |
| paper               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stand               | 0.4566  | 0.8000  | 0.2178  | 0.5400  |
| blinds              | 0.0046  | 0.2000  | 0.0000  | 0.0000  |
| mat                 | 0.0024  | 0.1667  | 0.0000  | 0.0000  |
| stool               | 0.0354  | 0.3333  | 0.0287  | 0.1905  |
| pot                 | 0.0539  | 0.3077  | 0.0000  | 0.0000  |
| bin                 | 0.2813  | 0.7073  | 0.0769  | 0.3415  |
| bidet               | 0.0714  | 0.5000  | 0.0714  | 0.5000  |
| book                | 0.0891  | 0.3071  | 0.0107  | 0.0472  |
| tissue box          | 0.2173  | 0.6250  | 0.0000  | 0.0000  |
| column              | 0.0083  | 0.2222  | 0.0018  | 0.0833  |
| soap dish           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basket              | 0.1301  | 0.4800  | 0.0078  | 0.1600  |
| hanger              | 0.0108  | 0.3000  | 0.0000  | 0.0000  |
| candle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basin               | 0.0014  | 1.0000  | 0.0014  | 1.0000  |
| pillar              | 0.2851  | 0.7143  | 0.0133  | 0.2381  |
| soap dispenser      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| couch               | 0.3834  | 0.8152  | 0.2102  | 0.6087  |
| holder              | 0.0010  | 0.3333  | 0.0000  | 0.0000  |
| plate               | 0.1283  | 0.3571  | 0.0000  | 0.0000  |
| tv                  | 0.3947  | 0.6047  | 0.0504  | 0.2558  |
| cushion             | 0.0033  | 0.1765  | 0.0001  | 0.0588  |
| bowl                | 0.0881  | 0.4242  | 0.0320  | 0.0909  |
| teapot              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ottoman             | 0.2008  | 0.6111  | 0.1168  | 0.3333  |
| bed                 | 0.4747  | 0.8246  | 0.2284  | 0.7018  |
| screen              | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| fireplace           | 0.0846  | 0.5217  | 0.0031  | 0.0435  |
| clock               | 0.2500  | 0.6667  | 0.0007  | 0.3333  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| carpet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stairs              | 0.0175  | 0.3208  | 0.0002  | 0.0566  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| food                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| desk                | 0.1679  | 0.7368  | 0.0813  | 0.4737  |
| footstool           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| telephone           | 0.1354  | 1.0000  | 0.0000  | 0.0000  |
| frame               | 0.0007  | 0.1176  | 0.0000  | 0.0000  |
| steps               | 0.0034  | 0.2000  | 0.0001  | 0.1000  |
| thermostat          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| board               | 0.0054  | 0.1667  | 0.0000  | 0.0000  |
| toy                 | 0.0936  | 0.5278  | 0.0023  | 0.1111  |
| wine                | 0.0526  | 0.0952  | 0.0000  | 0.0000  |
| fruit               | 0.1458  | 0.2500  | 0.0000  | 0.0000  |
| grass               | 0.0038  | 1.0000  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garage door         | 0.1307  | 0.6667  | 0.0830  | 0.6667  |
| handle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beam                | 0.0006  | 0.1429  | 0.0000  | 0.0000  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fan                 | 0.3649  | 0.8148  | 0.1718  | 0.4630  |
| monitor             | 0.2083  | 0.5000  | 0.0000  | 0.0000  |
| fire extinguisher   | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| partition           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| socket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rail                | 0.0270  | 0.3889  | 0.0004  | 0.0370  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headboard           | 0.0013  | 0.1250  | 0.0000  | 0.0000  |
| hat                 | 0.2500  | 1.0000  | 0.0000  | 0.0000  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tray                | 0.0085  | 0.3333  | 0.0000  | 0.0000  |
| kettle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket              | 0.0011  | 0.2000  | 0.0009  | 0.2000  |
| machine             | 0.4000  | 1.0000  | 0.1455  | 1.0000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchen island      | 0.3348  | 0.8571  | 0.3036  | 0.5714  |
| piano               | 0.1852  | 1.0000  | 0.0000  | 0.0000  |
| heater              | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| printer             | 0.4286  | 0.4286  | 0.2381  | 0.2857  |
| jar                 | 0.0052  | 0.6667  | 0.0029  | 0.3333  |
| rack                | 0.0006  | 0.1818  | 0.0000  | 0.0000  |
| excercise equipment | 0.3391  | 0.7778  | 0.0445  | 0.3889  |
| blackboard          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radiator            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stove               | 0.2494  | 0.7000  | 0.0333  | 0.1000  |
| bar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| refrigerator        | 0.2909  | 0.6667  | 0.0035  | 0.2000  |
| computer            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| container           | 0.0557  | 0.3704  | 0.0179  | 0.1852  |
| scale               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| globe               | 0.1000  | 1.0000  | 0.0000  | 0.0000  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| keyboard            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool table          | 0.0833  | 0.3333  | 0.0833  | 0.3333  |
| guitar              | 0.1555  | 0.5714  | 0.0026  | 0.1429  |
| speaker             | 0.0006  | 0.1250  | 0.0000  | 0.0000  |
| bag                 | 0.1123  | 0.4615  | 0.0000  | 0.0000  |
| pipe                | 0.1094  | 0.5000  | 0.0000  | 0.0000  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| arch                | 0.0045  | 0.1667  | 0.0000  | 0.0333  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| projector           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.2823  | 0.7500  | 0.0857  | 0.5000  |
| clothes dryer       | 0.1667  | 0.3333  | 0.1667  | 0.3333  |
| shoe                | 0.0537  | 0.2800  | 0.0000  | 0.0000  |
| microwave           | 0.1526  | 0.5000  | 0.0833  | 0.0833  |
| backpack            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pan                 | 0.0143  | 0.2000  | 0.0000  | 0.0000  |
| chandelier          | 0.1727  | 0.4211  | 0.0029  | 0.0526  |
| ladder              | 0.1156  | 0.4444  | 0.0000  | 0.0000  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| air conditioner     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| oven                | 0.0169  | 0.4000  | 0.0000  | 0.0000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.0833  | 0.2500  | 0.0000  | 0.0000  |
| kitchenware         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| laptop              | 0.8333  | 1.0000  | 0.0000  | 0.0000  |
| mouse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball                | 0.5150  | 0.7500  | 0.1250  | 0.2500  |
| file                | 0.2593  | 0.5000  | 0.0114  | 0.2500  |
| coffee maker        | 0.0476  | 0.3333  | 0.0000  | 0.0000  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hamper              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.1406  | 0.5000  | 0.0000  | 0.0000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jacket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drum                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pack                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| can                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radio               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cap                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| player              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| matterport3d        | 0.1067  | 0.3313  | 0.0275  | 0.1148  |
| head                | 0.1583  | 0.4332  | 0.0436  | 0.1567  |
| common              | 0.0724  | 0.2701  | 0.0098  | 0.0769  |
| tail                | 0.0259  | 0.1556  | 0.0193  | 0.0740  |
| small               | 0.0329  | 0.1440  | 0.0017  | 0.0218  |
| medium              | 0.1052  | 0.3522  | 0.0266  | 0.1174  |
| large               | 0.0701  | 0.4662  | 0.0264  | 0.2273  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/19/2025 20:53:59 INFO metrics.py:437 | number of arkit results: 0
Rank[0/8] 06/19/2025 20:54:00 INFO stats.py:394 | Epoch[49] completed. Training Speed: 3.16 samples/sec across all devices.	Epoch Time: 986.97 sec.	Average Epoch Time: 986.97 sec.	Average Step Time: 2.53 sec.	Estimated Remaining Time: 1 day, 1:26:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:54:00 WARNING accelerator.py:3099 | Deleting 1 checkpoints to make room for new checkpoint.
Rank[0/8] 06/19/2025 20:54:01 INFO accelerator.py:3111 | Saving current state to /job_data/checkpoints/checkpoint_4
Rank[5/8] 06/19/2025 20:54:02 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 49 to /job_data/checkpoints/checkpoint_4
Rank[7/8] 06/19/2025 20:54:02 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 49 to /job_data/checkpoints/checkpoint_4
Rank[4/8] 06/19/2025 20:54:02 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 49 to /job_data/checkpoints/checkpoint_4
Rank[2/8] 06/19/2025 20:54:02 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 49 to /job_data/checkpoints/checkpoint_4
Rank[1/8] 06/19/2025 20:54:02 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 49 to /job_data/checkpoints/checkpoint_4
Rank[3/8] 06/19/2025 20:54:02 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 49 to /job_data/checkpoints/checkpoint_4
Rank[6/8] 06/19/2025 20:54:02 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 49 to /job_data/checkpoints/checkpoint_4
Rank[0/8] 06/19/2025 20:54:03 INFO checkpointing.py:106 | Model weights saved in /job_data/checkpoints/checkpoint_4/model.safetensors
Rank[0/8] 06/19/2025 20:54:06 INFO checkpointing.py:113 | Optimizer state saved in /job_data/checkpoints/checkpoint_4/optimizer.bin
Rank[0/8] 06/19/2025 20:54:06 INFO checkpointing.py:120 | Scheduler state saved in /job_data/checkpoints/checkpoint_4/scheduler.bin
Rank[0/8] 06/19/2025 20:54:06 INFO checkpointing.py:137 | Sampler state for dataloader 0 saved in /job_data/checkpoints/checkpoint_4/sampler.bin
Rank[0/8] 06/19/2025 20:54:06 INFO checkpointing.py:137 | Sampler state for dataloader 1 saved in /job_data/checkpoints/checkpoint_4/sampler_1.bin
Rank[0/8] 06/19/2025 20:54:06 INFO checkpointing.py:164 | Random states saved in /job_data/checkpoints/checkpoint_4/random_states_0.pkl
Rank[0/8] 06/19/2025 20:54:06 INFO checkpointing.py:300 | Saving the state of TrainerProgressState to /job_data/checkpoints/checkpoint_4/custom_checkpoint_0.pkl
Rank[0/8] 06/19/2025 20:54:06 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 49 to /job_data/checkpoints/checkpoint_4
Rank[0/8] 06/19/2025 20:54:35 INFO stats.py:314 | Epoch[50] Step[24] GlobalStep[19524] Training Speed: 7.99 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 1:26:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:54:35 INFO loss_tracker.py:84 | Epoch[50/239] Step[24] GlobalStep[19524/NA]: det_loss_cls_0[0.5237] det_loss_box_0[0.7499] det_loss_cls_1[0.5074] det_loss_box_1[0.6179] det_loss_cls_2[0.4816] det_loss_box_2[0.5554] det_loss_cls_3[0.4632] det_loss_box_3[0.5314] det_loss_cls_4[0.4368] det_loss_box_4[0.5172] det_loss_cls_5[0.4284] det_loss_box_5[0.5102] det_loss_cls_dn_0[0.1127] det_loss_box_dn_0[0.3830] det_loss_cls_dn_1[0.1091] det_loss_box_dn_1[0.3458] det_loss_cls_dn_2[0.1090] det_loss_box_dn_2[0.3301] det_loss_cls_dn_3[0.1083] det_loss_box_dn_3[0.3287] det_loss_cls_dn_4[0.1072] det_loss_box_dn_4[0.3268] det_loss_cls_dn_5[0.1065] det_loss_box_dn_5[0.3219] loss_depth[0.0393] total_loss[9.0512]
Rank[0/8] 06/19/2025 20:55:01 INFO stats.py:314 | Epoch[50] Step[49] GlobalStep[19549] Training Speed: 6.44 samples/sec across all devices.	Average Step Time: 1.24 sec.	Estimated Remaining Time: 1 day, 1:25:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:55:01 INFO loss_tracker.py:84 | Epoch[50/239] Step[49] GlobalStep[19549/NA]: det_loss_cls_0[0.5701] det_loss_box_0[0.7767] det_loss_cls_1[0.5615] det_loss_box_1[0.6198] det_loss_cls_2[0.5593] det_loss_box_2[0.5603] det_loss_cls_3[0.5406] det_loss_box_3[0.5412] det_loss_cls_4[0.5084] det_loss_box_4[0.5367] det_loss_cls_5[0.4905] det_loss_box_5[0.5393] det_loss_cls_dn_0[0.1124] det_loss_box_dn_0[0.3530] det_loss_cls_dn_1[0.1105] det_loss_box_dn_1[0.3113] det_loss_cls_dn_2[0.1101] det_loss_box_dn_2[0.2966] det_loss_cls_dn_3[0.1096] det_loss_box_dn_3[0.2929] det_loss_cls_dn_4[0.1086] det_loss_box_dn_4[0.2921] det_loss_cls_dn_5[0.1084] det_loss_box_dn_5[0.2885] loss_depth[0.0428] total_loss[9.3409]
Rank[0/8] 06/19/2025 20:55:27 INFO stats.py:314 | Epoch[50] Step[74] GlobalStep[19574] Training Speed: 7.68 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 1:24:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:55:27 INFO loss_tracker.py:84 | Epoch[50/239] Step[74] GlobalStep[19574/NA]: det_loss_cls_0[0.5593] det_loss_box_0[0.7383] det_loss_cls_1[0.5397] det_loss_box_1[0.5992] det_loss_cls_2[0.5194] det_loss_box_2[0.5381] det_loss_cls_3[0.5015] det_loss_box_3[0.5145] det_loss_cls_4[0.4747] det_loss_box_4[0.4958] det_loss_cls_5[0.4594] det_loss_box_5[0.4973] det_loss_cls_dn_0[0.1081] det_loss_box_dn_0[0.3352] det_loss_cls_dn_1[0.1053] det_loss_box_dn_1[0.2867] det_loss_cls_dn_2[0.1046] det_loss_box_dn_2[0.2640] det_loss_cls_dn_3[0.1029] det_loss_box_dn_3[0.2605] det_loss_cls_dn_4[0.1022] det_loss_box_dn_4[0.2565] det_loss_cls_dn_5[0.1014] det_loss_box_dn_5[0.2548] loss_depth[0.0365] total_loss[8.7558]
Rank[0/8] 06/19/2025 20:55:52 INFO stats.py:314 | Epoch[50] Step[99] GlobalStep[19599] Training Speed: 7.77 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 1 day, 1:23:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:55:52 INFO loss_tracker.py:84 | Epoch[50/239] Step[99] GlobalStep[19599/NA]: det_loss_cls_0[0.4084] det_loss_box_0[0.6304] det_loss_cls_1[0.4040] det_loss_box_1[0.5453] det_loss_cls_2[0.3809] det_loss_box_2[0.5106] det_loss_cls_3[0.3626] det_loss_box_3[0.4951] det_loss_cls_4[0.3447] det_loss_box_4[0.4887] det_loss_cls_5[0.3334] det_loss_box_5[0.4889] det_loss_cls_dn_0[0.0965] det_loss_box_dn_0[0.4874] det_loss_cls_dn_1[0.0923] det_loss_box_dn_1[0.4334] det_loss_cls_dn_2[0.0912] det_loss_box_dn_2[0.4162] det_loss_cls_dn_3[0.0899] det_loss_box_dn_3[0.4144] det_loss_cls_dn_4[0.0888] det_loss_box_dn_4[0.4150] det_loss_cls_dn_5[0.0884] det_loss_box_dn_5[0.4139] loss_depth[0.0412] total_loss[8.5618]
Rank[0/8] 06/19/2025 20:56:18 INFO stats.py:314 | Epoch[50] Step[124] GlobalStep[19624] Training Speed: 6.36 samples/sec across all devices.	Average Step Time: 1.26 sec.	Estimated Remaining Time: 1 day, 1:23:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:56:18 INFO loss_tracker.py:84 | Epoch[50/239] Step[124] GlobalStep[19624/NA]: det_loss_cls_0[0.5151] det_loss_box_0[0.7146] det_loss_cls_1[0.5069] det_loss_box_1[0.6096] det_loss_cls_2[0.4891] det_loss_box_2[0.5629] det_loss_cls_3[0.4690] det_loss_box_3[0.5442] det_loss_cls_4[0.4324] det_loss_box_4[0.5358] det_loss_cls_5[0.4173] det_loss_box_5[0.5342] det_loss_cls_dn_0[0.1100] det_loss_box_dn_0[0.3454] det_loss_cls_dn_1[0.1071] det_loss_box_dn_1[0.3053] det_loss_cls_dn_2[0.1066] det_loss_box_dn_2[0.2907] det_loss_cls_dn_3[0.1060] det_loss_box_dn_3[0.2865] det_loss_cls_dn_4[0.1052] det_loss_box_dn_4[0.2860] det_loss_cls_dn_5[0.1042] det_loss_box_dn_5[0.2847] loss_depth[0.0400] total_loss[8.8088]
Rank[0/8] 06/19/2025 20:56:44 INFO stats.py:314 | Epoch[50] Step[149] GlobalStep[19649] Training Speed: 14.26 samples/sec across all devices.	Average Step Time: 0.56 sec.	Estimated Remaining Time: 1 day, 1:22:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:56:44 INFO loss_tracker.py:84 | Epoch[50/239] Step[149] GlobalStep[19649/NA]: det_loss_cls_0[0.4840] det_loss_box_0[0.6686] det_loss_cls_1[0.4794] det_loss_box_1[0.5331] det_loss_cls_2[0.4488] det_loss_box_2[0.4612] det_loss_cls_3[0.4322] det_loss_box_3[0.4347] det_loss_cls_4[0.3998] det_loss_box_4[0.4186] det_loss_cls_5[0.3844] det_loss_box_5[0.4205] det_loss_cls_dn_0[0.1024] det_loss_box_dn_0[0.2973] det_loss_cls_dn_1[0.0996] det_loss_box_dn_1[0.2475] det_loss_cls_dn_2[0.0986] det_loss_box_dn_2[0.2289] det_loss_cls_dn_3[0.0978] det_loss_box_dn_3[0.2270] det_loss_cls_dn_4[0.0959] det_loss_box_dn_4[0.2257] det_loss_cls_dn_5[0.0950] det_loss_box_dn_5[0.2248] loss_depth[0.0408] total_loss[7.6465]
Rank[0/8] 06/19/2025 20:57:09 INFO stats.py:314 | Epoch[50] Step[174] GlobalStep[19674] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 1:21:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:57:09 INFO loss_tracker.py:84 | Epoch[50/239] Step[174] GlobalStep[19674/NA]: det_loss_cls_0[0.6053] det_loss_box_0[0.8367] det_loss_cls_1[0.5968] det_loss_box_1[0.6765] det_loss_cls_2[0.5788] det_loss_box_2[0.6033] det_loss_cls_3[0.5351] det_loss_box_3[0.5981] det_loss_cls_4[0.5043] det_loss_box_4[0.5889] det_loss_cls_5[0.4838] det_loss_box_5[0.5903] det_loss_cls_dn_0[0.1057] det_loss_box_dn_0[0.3131] det_loss_cls_dn_1[0.0990] det_loss_box_dn_1[0.2755] det_loss_cls_dn_2[0.0991] det_loss_box_dn_2[0.2618] det_loss_cls_dn_3[0.0984] det_loss_box_dn_3[0.2603] det_loss_cls_dn_4[0.0974] det_loss_box_dn_4[0.2587] det_loss_cls_dn_5[0.0963] det_loss_box_dn_5[0.2575] loss_depth[0.0425] total_loss[9.4633]
Rank[0/8] 06/19/2025 20:57:35 INFO stats.py:314 | Epoch[50] Step[199] GlobalStep[19699] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 1:20:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:57:35 INFO loss_tracker.py:84 | Epoch[50/239] Step[199] GlobalStep[19699/NA]: det_loss_cls_0[0.5658] det_loss_box_0[0.7437] det_loss_cls_1[0.5476] det_loss_box_1[0.5994] det_loss_cls_2[0.5197] det_loss_box_2[0.5426] det_loss_cls_3[0.4865] det_loss_box_3[0.5272] det_loss_cls_4[0.4619] det_loss_box_4[0.5124] det_loss_cls_5[0.4420] det_loss_box_5[0.5133] det_loss_cls_dn_0[0.1086] det_loss_box_dn_0[0.3171] det_loss_cls_dn_1[0.1052] det_loss_box_dn_1[0.2821] det_loss_cls_dn_2[0.1041] det_loss_box_dn_2[0.2683] det_loss_cls_dn_3[0.1021] det_loss_box_dn_3[0.2654] det_loss_cls_dn_4[0.1006] det_loss_box_dn_4[0.2641] det_loss_cls_dn_5[0.0995] det_loss_box_dn_5[0.2633] loss_depth[0.0436] total_loss[8.7860]
Rank[0/8] 06/19/2025 20:58:02 INFO stats.py:314 | Epoch[50] Step[224] GlobalStep[19724] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 1:19:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:58:02 INFO loss_tracker.py:84 | Epoch[50/239] Step[224] GlobalStep[19724/NA]: det_loss_cls_0[0.5878] det_loss_box_0[0.7516] det_loss_cls_1[0.5678] det_loss_box_1[0.6128] det_loss_cls_2[0.5523] det_loss_box_2[0.5608] det_loss_cls_3[0.5361] det_loss_box_3[0.5433] det_loss_cls_4[0.5028] det_loss_box_4[0.5409] det_loss_cls_5[0.4959] det_loss_box_5[0.5383] det_loss_cls_dn_0[0.1105] det_loss_box_dn_0[0.3280] det_loss_cls_dn_1[0.1081] det_loss_box_dn_1[0.2936] det_loss_cls_dn_2[0.1084] det_loss_box_dn_2[0.2819] det_loss_cls_dn_3[0.1072] det_loss_box_dn_3[0.2787] det_loss_cls_dn_4[0.1065] det_loss_box_dn_4[0.2780] det_loss_cls_dn_5[0.1057] det_loss_box_dn_5[0.2770] loss_depth[0.0415] total_loss[9.2153]
Rank[0/8] 06/19/2025 20:58:27 INFO stats.py:314 | Epoch[50] Step[249] GlobalStep[19749] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 1:18:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:58:27 INFO loss_tracker.py:84 | Epoch[50/239] Step[249] GlobalStep[19749/NA]: det_loss_cls_0[0.6984] det_loss_box_0[0.9667] det_loss_cls_1[0.6584] det_loss_box_1[0.8017] det_loss_cls_2[0.6549] det_loss_box_2[0.7225] det_loss_cls_3[0.6452] det_loss_box_3[0.6982] det_loss_cls_4[0.6182] det_loss_box_4[0.6807] det_loss_cls_5[0.5883] det_loss_box_5[0.6891] det_loss_cls_dn_0[0.1080] det_loss_box_dn_0[0.3255] det_loss_cls_dn_1[0.1049] det_loss_box_dn_1[0.2846] det_loss_cls_dn_2[0.1042] det_loss_box_dn_2[0.2690] det_loss_cls_dn_3[0.1031] det_loss_box_dn_3[0.2661] det_loss_cls_dn_4[0.1024] det_loss_box_dn_4[0.2648] det_loss_cls_dn_5[0.1013] det_loss_box_dn_5[0.2641] loss_depth[0.0436] total_loss[10.7641]
Rank[0/8] 06/19/2025 20:58:53 INFO stats.py:314 | Epoch[50] Step[274] GlobalStep[19774] Training Speed: 8.17 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 1:18:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:58:53 INFO loss_tracker.py:84 | Epoch[50/239] Step[274] GlobalStep[19774/NA]: det_loss_cls_0[0.6997] det_loss_box_0[0.9644] det_loss_cls_1[0.6798] det_loss_box_1[0.7775] det_loss_cls_2[0.6622] det_loss_box_2[0.7145] det_loss_cls_3[0.6374] det_loss_box_3[0.6924] det_loss_cls_4[0.6050] det_loss_box_4[0.6737] det_loss_cls_5[0.5778] det_loss_box_5[0.6740] det_loss_cls_dn_0[0.1039] det_loss_box_dn_0[0.2894] det_loss_cls_dn_1[0.0998] det_loss_box_dn_1[0.2532] det_loss_cls_dn_2[0.0994] det_loss_box_dn_2[0.2395] det_loss_cls_dn_3[0.0984] det_loss_box_dn_3[0.2378] det_loss_cls_dn_4[0.0976] det_loss_box_dn_4[0.2359] det_loss_cls_dn_5[0.0967] det_loss_box_dn_5[0.2338] loss_depth[0.0401] total_loss[10.4840]
Rank[0/8] 06/19/2025 20:59:19 INFO stats.py:314 | Epoch[50] Step[299] GlobalStep[19799] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 1:17:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:59:19 INFO loss_tracker.py:84 | Epoch[50/239] Step[299] GlobalStep[19799/NA]: det_loss_cls_0[0.5425] det_loss_box_0[0.7850] det_loss_cls_1[0.5279] det_loss_box_1[0.6255] det_loss_cls_2[0.5121] det_loss_box_2[0.5622] det_loss_cls_3[0.4855] det_loss_box_3[0.5389] det_loss_cls_4[0.4544] det_loss_box_4[0.5424] det_loss_cls_5[0.4383] det_loss_box_5[0.5402] det_loss_cls_dn_0[0.1085] det_loss_box_dn_0[0.3459] det_loss_cls_dn_1[0.1042] det_loss_box_dn_1[0.3044] det_loss_cls_dn_2[0.1043] det_loss_box_dn_2[0.2902] det_loss_cls_dn_3[0.1027] det_loss_box_dn_3[0.2875] det_loss_cls_dn_4[0.1018] det_loss_box_dn_4[0.2868] det_loss_cls_dn_5[0.1013] det_loss_box_dn_5[0.2844] loss_depth[0.0392] total_loss[9.0160]
Rank[0/8] 06/19/2025 20:59:45 INFO stats.py:314 | Epoch[50] Step[324] GlobalStep[19824] Training Speed: 7.88 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1 day, 1:16:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 20:59:45 INFO loss_tracker.py:84 | Epoch[50/239] Step[324] GlobalStep[19824/NA]: det_loss_cls_0[0.5049] det_loss_box_0[0.6763] det_loss_cls_1[0.5045] det_loss_box_1[0.5416] det_loss_cls_2[0.4821] det_loss_box_2[0.4927] det_loss_cls_3[0.4543] det_loss_box_3[0.4767] det_loss_cls_4[0.4246] det_loss_box_4[0.4708] det_loss_cls_5[0.4127] det_loss_box_5[0.4687] det_loss_cls_dn_0[0.0971] det_loss_box_dn_0[0.2923] det_loss_cls_dn_1[0.0944] det_loss_box_dn_1[0.2495] det_loss_cls_dn_2[0.0941] det_loss_box_dn_2[0.2333] det_loss_cls_dn_3[0.0931] det_loss_box_dn_3[0.2321] det_loss_cls_dn_4[0.0918] det_loss_box_dn_4[0.2313] det_loss_cls_dn_5[0.0904] det_loss_box_dn_5[0.2301] loss_depth[0.0400] total_loss[7.9793]
Rank[0/8] 06/19/2025 21:00:10 INFO stats.py:314 | Epoch[50] Step[349] GlobalStep[19849] Training Speed: 8.19 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 1:15:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:00:10 INFO loss_tracker.py:84 | Epoch[50/239] Step[349] GlobalStep[19849/NA]: det_loss_cls_0[0.4985] det_loss_box_0[0.6328] det_loss_cls_1[0.4814] det_loss_box_1[0.5138] det_loss_cls_2[0.4680] det_loss_box_2[0.4705] det_loss_cls_3[0.4500] det_loss_box_3[0.4598] det_loss_cls_4[0.4256] det_loss_box_4[0.4588] det_loss_cls_5[0.4206] det_loss_box_5[0.4542] det_loss_cls_dn_0[0.1067] det_loss_box_dn_0[0.3269] det_loss_cls_dn_1[0.1026] det_loss_box_dn_1[0.2891] det_loss_cls_dn_2[0.1019] det_loss_box_dn_2[0.2732] det_loss_cls_dn_3[0.1008] det_loss_box_dn_3[0.2700] det_loss_cls_dn_4[0.0996] det_loss_box_dn_4[0.2683] det_loss_cls_dn_5[0.0991] det_loss_box_dn_5[0.2664] loss_depth[0.0379] total_loss[8.0765]
Rank[0/8] 06/19/2025 21:00:36 INFO stats.py:314 | Epoch[50] Step[374] GlobalStep[19874] Training Speed: 8.19 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 1:14:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:00:36 INFO loss_tracker.py:84 | Epoch[50/239] Step[374] GlobalStep[19874/NA]: det_loss_cls_0[0.6073] det_loss_box_0[0.8371] det_loss_cls_1[0.6051] det_loss_box_1[0.6715] det_loss_cls_2[0.5801] det_loss_box_2[0.5913] det_loss_cls_3[0.5519] det_loss_box_3[0.5653] det_loss_cls_4[0.5229] det_loss_box_4[0.5596] det_loss_cls_5[0.5039] det_loss_box_5[0.5595] det_loss_cls_dn_0[0.1198] det_loss_box_dn_0[0.3799] det_loss_cls_dn_1[0.1158] det_loss_box_dn_1[0.3336] det_loss_cls_dn_2[0.1153] det_loss_box_dn_2[0.3116] det_loss_cls_dn_3[0.1144] det_loss_box_dn_3[0.3090] det_loss_cls_dn_4[0.1136] det_loss_box_dn_4[0.3071] det_loss_cls_dn_5[0.1130] det_loss_box_dn_5[0.3071] loss_depth[0.0413] total_loss[9.8370]
Rank[0/8] 06/19/2025 21:00:52 INFO stats.py:394 | Epoch[50] completed. Training Speed: 7.68 samples/sec across all devices.	Epoch Time: 406.12 sec.	Average Epoch Time: 406.12 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 1:14:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:01:10 INFO stats.py:314 | Epoch[51] Step[9] GlobalStep[19899] Training Speed: 8.15 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 1:14:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:01:25 INFO loss_tracker.py:84 | Epoch[51/239] Step[24] GlobalStep[19914/NA]: det_loss_cls_0[0.5103] det_loss_box_0[0.6892] det_loss_cls_1[0.4924] det_loss_box_1[0.5545] det_loss_cls_2[0.4864] det_loss_box_2[0.4950] det_loss_cls_3[0.4596] det_loss_box_3[0.4865] det_loss_cls_4[0.4212] det_loss_box_4[0.4821] det_loss_cls_5[0.4067] det_loss_box_5[0.4835] det_loss_cls_dn_0[0.1004] det_loss_box_dn_0[0.3190] det_loss_cls_dn_1[0.0979] det_loss_box_dn_1[0.2796] det_loss_cls_dn_2[0.0972] det_loss_box_dn_2[0.2646] det_loss_cls_dn_3[0.0968] det_loss_box_dn_3[0.2626] det_loss_cls_dn_4[0.0962] det_loss_box_dn_4[0.2604] det_loss_cls_dn_5[0.0959] det_loss_box_dn_5[0.2563] loss_depth[0.0397] total_loss[8.2340]
Rank[0/8] 06/19/2025 21:01:36 INFO stats.py:314 | Epoch[51] Step[34] GlobalStep[19924] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 1:13:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:01:51 INFO loss_tracker.py:84 | Epoch[51/239] Step[49] GlobalStep[19939/NA]: det_loss_cls_0[0.5231] det_loss_box_0[0.7388] det_loss_cls_1[0.4882] det_loss_box_1[0.6134] det_loss_cls_2[0.4810] det_loss_box_2[0.5551] det_loss_cls_3[0.4632] det_loss_box_3[0.5398] det_loss_cls_4[0.4387] det_loss_box_4[0.5326] det_loss_cls_5[0.4207] det_loss_box_5[0.5355] det_loss_cls_dn_0[0.1134] det_loss_box_dn_0[0.3514] det_loss_cls_dn_1[0.1097] det_loss_box_dn_1[0.3107] det_loss_cls_dn_2[0.1095] det_loss_box_dn_2[0.2951] det_loss_cls_dn_3[0.1097] det_loss_box_dn_3[0.2923] det_loss_cls_dn_4[0.1083] det_loss_box_dn_4[0.2917] det_loss_cls_dn_5[0.1074] det_loss_box_dn_5[0.2900] loss_depth[0.0414] total_loss[8.8605]
Rank[0/8] 06/19/2025 21:02:01 INFO stats.py:314 | Epoch[51] Step[59] GlobalStep[19949] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 1:12:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:02:17 INFO loss_tracker.py:84 | Epoch[51/239] Step[74] GlobalStep[19964/NA]: det_loss_cls_0[0.4410] det_loss_box_0[0.6079] det_loss_cls_1[0.4243] det_loss_box_1[0.5244] det_loss_cls_2[0.4208] det_loss_box_2[0.4660] det_loss_cls_3[0.4077] det_loss_box_3[0.4433] det_loss_cls_4[0.3726] det_loss_box_4[0.4395] det_loss_cls_5[0.3613] det_loss_box_5[0.4416] det_loss_cls_dn_0[0.0977] det_loss_box_dn_0[0.4033] det_loss_cls_dn_1[0.0930] det_loss_box_dn_1[0.3657] det_loss_cls_dn_2[0.0922] det_loss_box_dn_2[0.3441] det_loss_cls_dn_3[0.0918] det_loss_box_dn_3[0.3414] det_loss_cls_dn_4[0.0904] det_loss_box_dn_4[0.3391] det_loss_cls_dn_5[0.0895] det_loss_box_dn_5[0.3390] loss_depth[0.0388] total_loss[8.0765]
Rank[0/8] 06/19/2025 21:02:28 INFO stats.py:314 | Epoch[51] Step[84] GlobalStep[19974] Training Speed: 7.95 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 1:11:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:02:43 INFO loss_tracker.py:84 | Epoch[51/239] Step[99] GlobalStep[19989/NA]: det_loss_cls_0[0.4462] det_loss_box_0[0.6130] det_loss_cls_1[0.4464] det_loss_box_1[0.5055] det_loss_cls_2[0.4311] det_loss_box_2[0.4661] det_loss_cls_3[0.4081] det_loss_box_3[0.4584] det_loss_cls_4[0.3863] det_loss_box_4[0.4526] det_loss_cls_5[0.3742] det_loss_box_5[0.4538] det_loss_cls_dn_0[0.1018] det_loss_box_dn_0[0.3289] det_loss_cls_dn_1[0.0980] det_loss_box_dn_1[0.2859] det_loss_cls_dn_2[0.0978] det_loss_box_dn_2[0.2718] det_loss_cls_dn_3[0.0963] det_loss_box_dn_3[0.2687] det_loss_cls_dn_4[0.0957] det_loss_box_dn_4[0.2679] det_loss_cls_dn_5[0.0946] det_loss_box_dn_5[0.2651] loss_depth[0.0386] total_loss[7.7526]
Rank[0/8] 06/19/2025 21:02:53 INFO stats.py:314 | Epoch[51] Step[109] GlobalStep[19999] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 1:11:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:03:08 INFO loss_tracker.py:84 | Epoch[51/239] Step[124] GlobalStep[20014/NA]: det_loss_cls_0[0.6417] det_loss_box_0[0.8795] det_loss_cls_1[0.6294] det_loss_box_1[0.7144] det_loss_cls_2[0.6151] det_loss_box_2[0.6630] det_loss_cls_3[0.5965] det_loss_box_3[0.6454] det_loss_cls_4[0.5627] det_loss_box_4[0.6397] det_loss_cls_5[0.5501] det_loss_box_5[0.6380] det_loss_cls_dn_0[0.1089] det_loss_box_dn_0[0.3119] det_loss_cls_dn_1[0.1075] det_loss_box_dn_1[0.2718] det_loss_cls_dn_2[0.1065] det_loss_box_dn_2[0.2584] det_loss_cls_dn_3[0.1052] det_loss_box_dn_3[0.2541] det_loss_cls_dn_4[0.1041] det_loss_box_dn_4[0.2531] det_loss_cls_dn_5[0.1035] det_loss_box_dn_5[0.2504] loss_depth[0.0432] total_loss[10.0539]
Rank[0/8] 06/19/2025 21:03:18 INFO stats.py:314 | Epoch[51] Step[134] GlobalStep[20024] Training Speed: 8.67 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 1 day, 1:10:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:03:35 INFO loss_tracker.py:84 | Epoch[51/239] Step[149] GlobalStep[20039/NA]: det_loss_cls_0[0.5287] det_loss_box_0[0.7008] det_loss_cls_1[0.5131] det_loss_box_1[0.5656] det_loss_cls_2[0.5025] det_loss_box_2[0.5005] det_loss_cls_3[0.4765] det_loss_box_3[0.4890] det_loss_cls_4[0.4486] det_loss_box_4[0.4861] det_loss_cls_5[0.4336] det_loss_box_5[0.4853] det_loss_cls_dn_0[0.1041] det_loss_box_dn_0[0.3374] det_loss_cls_dn_1[0.1018] det_loss_box_dn_1[0.2937] det_loss_cls_dn_2[0.1019] det_loss_box_dn_2[0.2778] det_loss_cls_dn_3[0.1013] det_loss_box_dn_3[0.2747] det_loss_cls_dn_4[0.0999] det_loss_box_dn_4[0.2739] det_loss_cls_dn_5[0.0995] det_loss_box_dn_5[0.2730] loss_depth[0.0408] total_loss[8.5102]
Rank[0/8] 06/19/2025 21:03:45 INFO stats.py:314 | Epoch[51] Step[159] GlobalStep[20049] Training Speed: 7.14 samples/sec across all devices.	Average Step Time: 1.12 sec.	Estimated Remaining Time: 1 day, 1:09:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:04:01 INFO loss_tracker.py:84 | Epoch[51/239] Step[174] GlobalStep[20064/NA]: det_loss_cls_0[0.5595] det_loss_box_0[0.7769] det_loss_cls_1[0.5372] det_loss_box_1[0.6504] det_loss_cls_2[0.5223] det_loss_box_2[0.5999] det_loss_cls_3[0.5018] det_loss_box_3[0.5952] det_loss_cls_4[0.4850] det_loss_box_4[0.5735] det_loss_cls_5[0.4580] det_loss_box_5[0.5773] det_loss_cls_dn_0[0.1111] det_loss_box_dn_0[0.3453] det_loss_cls_dn_1[0.1062] det_loss_box_dn_1[0.3088] det_loss_cls_dn_2[0.1063] det_loss_box_dn_2[0.2932] det_loss_cls_dn_3[0.1063] det_loss_box_dn_3[0.2913] det_loss_cls_dn_4[0.1051] det_loss_box_dn_4[0.2903] det_loss_cls_dn_5[0.1043] det_loss_box_dn_5[0.2887] loss_depth[0.0374] total_loss[9.3313]
Rank[0/8] 06/19/2025 21:04:11 INFO stats.py:314 | Epoch[51] Step[184] GlobalStep[20074] Training Speed: 8.03 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 1:08:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:04:26 INFO loss_tracker.py:84 | Epoch[51/239] Step[199] GlobalStep[20089/NA]: det_loss_cls_0[0.6278] det_loss_box_0[0.9068] det_loss_cls_1[0.6260] det_loss_box_1[0.7177] det_loss_cls_2[0.6021] det_loss_box_2[0.6654] det_loss_cls_3[0.5799] det_loss_box_3[0.6471] det_loss_cls_4[0.5452] det_loss_box_4[0.6373] det_loss_cls_5[0.5328] det_loss_box_5[0.6291] det_loss_cls_dn_0[0.1035] det_loss_box_dn_0[0.3098] det_loss_cls_dn_1[0.1010] det_loss_box_dn_1[0.2653] det_loss_cls_dn_2[0.1002] det_loss_box_dn_2[0.2540] det_loss_cls_dn_3[0.0995] det_loss_box_dn_3[0.2509] det_loss_cls_dn_4[0.0987] det_loss_box_dn_4[0.2492] det_loss_cls_dn_5[0.0978] det_loss_box_dn_5[0.2480] loss_depth[0.0407] total_loss[9.9358]
Rank[0/8] 06/19/2025 21:04:36 INFO stats.py:314 | Epoch[51] Step[209] GlobalStep[20099] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 1:07:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:04:52 INFO loss_tracker.py:84 | Epoch[51/239] Step[224] GlobalStep[20114/NA]: det_loss_cls_0[0.5608] det_loss_box_0[0.8146] det_loss_cls_1[0.5380] det_loss_box_1[0.6606] det_loss_cls_2[0.5180] det_loss_box_2[0.5950] det_loss_cls_3[0.4928] det_loss_box_3[0.5723] det_loss_cls_4[0.4502] det_loss_box_4[0.5670] det_loss_cls_5[0.4420] det_loss_box_5[0.5595] det_loss_cls_dn_0[0.1067] det_loss_box_dn_0[0.3701] det_loss_cls_dn_1[0.1036] det_loss_box_dn_1[0.3221] det_loss_cls_dn_2[0.1031] det_loss_box_dn_2[0.3003] det_loss_cls_dn_3[0.1032] det_loss_box_dn_3[0.2985] det_loss_cls_dn_4[0.1014] det_loss_box_dn_4[0.2969] det_loss_cls_dn_5[0.1008] det_loss_box_dn_5[0.2950] loss_depth[0.0440] total_loss[9.3164]
Rank[0/8] 06/19/2025 21:05:03 INFO stats.py:314 | Epoch[51] Step[234] GlobalStep[20124] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 1:07:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:05:18 INFO loss_tracker.py:84 | Epoch[51/239] Step[249] GlobalStep[20139/NA]: det_loss_cls_0[0.6323] det_loss_box_0[0.8431] det_loss_cls_1[0.6297] det_loss_box_1[0.7055] det_loss_cls_2[0.6148] det_loss_box_2[0.6460] det_loss_cls_3[0.5909] det_loss_box_3[0.6349] det_loss_cls_4[0.5586] det_loss_box_4[0.6201] det_loss_cls_5[0.5433] det_loss_box_5[0.6215] det_loss_cls_dn_0[0.1113] det_loss_box_dn_0[0.3407] det_loss_cls_dn_1[0.1082] det_loss_box_dn_1[0.2993] det_loss_cls_dn_2[0.1075] det_loss_box_dn_2[0.2840] det_loss_cls_dn_3[0.1067] det_loss_box_dn_3[0.2804] det_loss_cls_dn_4[0.1051] det_loss_box_dn_4[0.2800] det_loss_cls_dn_5[0.1045] det_loss_box_dn_5[0.2791] loss_depth[0.0353] total_loss[10.0829]
Rank[0/8] 06/19/2025 21:05:29 INFO stats.py:314 | Epoch[51] Step[259] GlobalStep[20149] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 1:06:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:05:44 INFO loss_tracker.py:84 | Epoch[51/239] Step[274] GlobalStep[20164/NA]: det_loss_cls_0[0.5007] det_loss_box_0[0.7556] det_loss_cls_1[0.4912] det_loss_box_1[0.6252] det_loss_cls_2[0.4787] det_loss_box_2[0.5705] det_loss_cls_3[0.4534] det_loss_box_3[0.5476] det_loss_cls_4[0.4274] det_loss_box_4[0.5381] det_loss_cls_5[0.4109] det_loss_box_5[0.5384] det_loss_cls_dn_0[0.1044] det_loss_box_dn_0[0.4397] det_loss_cls_dn_1[0.1002] det_loss_box_dn_1[0.3910] det_loss_cls_dn_2[0.0992] det_loss_box_dn_2[0.3737] det_loss_cls_dn_3[0.0980] det_loss_box_dn_3[0.3714] det_loss_cls_dn_4[0.0963] det_loss_box_dn_4[0.3696] det_loss_cls_dn_5[0.0950] det_loss_box_dn_5[0.3664] loss_depth[0.0399] total_loss[9.2825]
Rank[0/8] 06/19/2025 21:05:54 INFO stats.py:314 | Epoch[51] Step[284] GlobalStep[20174] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 1:05:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:06:10 INFO loss_tracker.py:84 | Epoch[51/239] Step[299] GlobalStep[20189/NA]: det_loss_cls_0[0.5424] det_loss_box_0[0.6300] det_loss_cls_1[0.5371] det_loss_box_1[0.5003] det_loss_cls_2[0.5069] det_loss_box_2[0.4688] det_loss_cls_3[0.4912] det_loss_box_3[0.4459] det_loss_cls_4[0.4504] det_loss_box_4[0.4536] det_loss_cls_5[0.4311] det_loss_box_5[0.4549] det_loss_cls_dn_0[0.1020] det_loss_box_dn_0[0.2664] det_loss_cls_dn_1[0.0979] det_loss_box_dn_1[0.2243] det_loss_cls_dn_2[0.0971] det_loss_box_dn_2[0.2112] det_loss_cls_dn_3[0.0964] det_loss_box_dn_3[0.2086] det_loss_cls_dn_4[0.0945] det_loss_box_dn_4[0.2080] det_loss_cls_dn_5[0.0934] det_loss_box_dn_5[0.2070] loss_depth[0.0384] total_loss[7.8579]
Rank[0/8] 06/19/2025 21:06:20 INFO stats.py:314 | Epoch[51] Step[309] GlobalStep[20199] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 1:04:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:06:36 INFO loss_tracker.py:84 | Epoch[51/239] Step[324] GlobalStep[20214/NA]: det_loss_cls_0[0.6136] det_loss_box_0[0.8610] det_loss_cls_1[0.6116] det_loss_box_1[0.6797] det_loss_cls_2[0.5870] det_loss_box_2[0.6200] det_loss_cls_3[0.5648] det_loss_box_3[0.5928] det_loss_cls_4[0.5286] det_loss_box_4[0.5714] det_loss_cls_5[0.5165] det_loss_box_5[0.5643] det_loss_cls_dn_0[0.1036] det_loss_box_dn_0[0.2733] det_loss_cls_dn_1[0.1001] det_loss_box_dn_1[0.2353] det_loss_cls_dn_2[0.0991] det_loss_box_dn_2[0.2239] det_loss_cls_dn_3[0.0986] det_loss_box_dn_3[0.2206] det_loss_cls_dn_4[0.0978] det_loss_box_dn_4[0.2196] det_loss_cls_dn_5[0.0965] det_loss_box_dn_5[0.2182] loss_depth[0.0368] total_loss[9.3348]
Rank[0/8] 06/19/2025 21:06:46 INFO stats.py:314 | Epoch[51] Step[334] GlobalStep[20224] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 1:03:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:07:02 INFO loss_tracker.py:84 | Epoch[51/239] Step[349] GlobalStep[20239/NA]: det_loss_cls_0[0.4388] det_loss_box_0[0.6417] det_loss_cls_1[0.4131] det_loss_box_1[0.5256] det_loss_cls_2[0.4016] det_loss_box_2[0.4567] det_loss_cls_3[0.3781] det_loss_box_3[0.4375] det_loss_cls_4[0.3532] det_loss_box_4[0.4270] det_loss_cls_5[0.3437] det_loss_box_5[0.4233] det_loss_cls_dn_0[0.0935] det_loss_box_dn_0[0.3330] det_loss_cls_dn_1[0.0884] det_loss_box_dn_1[0.2840] det_loss_cls_dn_2[0.0886] det_loss_box_dn_2[0.2639] det_loss_cls_dn_3[0.0877] det_loss_box_dn_3[0.2611] det_loss_cls_dn_4[0.0867] det_loss_box_dn_4[0.2598] det_loss_cls_dn_5[0.0858] det_loss_box_dn_5[0.2580] loss_depth[0.0451] total_loss[7.4760]
Rank[0/8] 06/19/2025 21:07:12 INFO stats.py:314 | Epoch[51] Step[359] GlobalStep[20249] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 1:03:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:07:27 INFO loss_tracker.py:84 | Epoch[51/239] Step[374] GlobalStep[20264/NA]: det_loss_cls_0[0.4486] det_loss_box_0[0.6292] det_loss_cls_1[0.4457] det_loss_box_1[0.4928] det_loss_cls_2[0.4307] det_loss_box_2[0.4365] det_loss_cls_3[0.4070] det_loss_box_3[0.4207] det_loss_cls_4[0.3732] det_loss_box_4[0.4102] det_loss_cls_5[0.3551] det_loss_box_5[0.4151] det_loss_cls_dn_0[0.1065] det_loss_box_dn_0[0.3316] det_loss_cls_dn_1[0.1013] det_loss_box_dn_1[0.2775] det_loss_cls_dn_2[0.1014] det_loss_box_dn_2[0.2619] det_loss_cls_dn_3[0.1001] det_loss_box_dn_3[0.2581] det_loss_cls_dn_4[0.0997] det_loss_box_dn_4[0.2561] det_loss_cls_dn_5[0.0985] det_loss_box_dn_5[0.2532] loss_depth[0.0420] total_loss[7.5531]
Rank[0/8] 06/19/2025 21:07:38 INFO stats.py:314 | Epoch[51] Step[384] GlobalStep[20274] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 1:02:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:07:43 INFO stats.py:394 | Epoch[51] completed. Training Speed: 7.60 samples/sec across all devices.	Epoch Time: 410.56 sec.	Average Epoch Time: 410.56 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 1:02:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:08:09 INFO stats.py:314 | Epoch[52] Step[19] GlobalStep[20299] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 1:01:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:08:14 INFO loss_tracker.py:84 | Epoch[52/239] Step[24] GlobalStep[20304/NA]: det_loss_cls_0[0.5885] det_loss_box_0[0.8053] det_loss_cls_1[0.5737] det_loss_box_1[0.6631] det_loss_cls_2[0.5534] det_loss_box_2[0.5986] det_loss_cls_3[0.5415] det_loss_box_3[0.5599] det_loss_cls_4[0.5098] det_loss_box_4[0.5510] det_loss_cls_5[0.4851] det_loss_box_5[0.5579] det_loss_cls_dn_0[0.1078] det_loss_box_dn_0[0.3312] det_loss_cls_dn_1[0.1042] det_loss_box_dn_1[0.2893] det_loss_cls_dn_2[0.1036] det_loss_box_dn_2[0.2773] det_loss_cls_dn_3[0.1026] det_loss_box_dn_3[0.2743] det_loss_cls_dn_4[0.1011] det_loss_box_dn_4[0.2732] det_loss_cls_dn_5[0.1002] det_loss_box_dn_5[0.2700] loss_depth[0.0430] total_loss[9.3657]
Rank[0/8] 06/19/2025 21:08:35 INFO stats.py:314 | Epoch[52] Step[44] GlobalStep[20324] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 1:00:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:08:40 INFO loss_tracker.py:84 | Epoch[52/239] Step[49] GlobalStep[20329/NA]: det_loss_cls_0[0.4868] det_loss_box_0[0.6124] det_loss_cls_1[0.4701] det_loss_box_1[0.5164] det_loss_cls_2[0.4643] det_loss_box_2[0.4717] det_loss_cls_3[0.4340] det_loss_box_3[0.4596] det_loss_cls_4[0.3970] det_loss_box_4[0.4483] det_loss_cls_5[0.3880] det_loss_box_5[0.4469] det_loss_cls_dn_0[0.0954] det_loss_box_dn_0[0.3221] det_loss_cls_dn_1[0.0915] det_loss_box_dn_1[0.2865] det_loss_cls_dn_2[0.0905] det_loss_box_dn_2[0.2713] det_loss_cls_dn_3[0.0895] det_loss_box_dn_3[0.2679] det_loss_cls_dn_4[0.0886] det_loss_box_dn_4[0.2663] det_loss_cls_dn_5[0.0879] det_loss_box_dn_5[0.2643] loss_depth[0.0378] total_loss[7.8549]
Rank[0/8] 06/19/2025 21:09:00 INFO stats.py:314 | Epoch[52] Step[69] GlobalStep[20349] Training Speed: 6.13 samples/sec across all devices.	Average Step Time: 1.30 sec.	Estimated Remaining Time: 1 day, 1:00:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:09:05 INFO loss_tracker.py:84 | Epoch[52/239] Step[74] GlobalStep[20354/NA]: det_loss_cls_0[0.5543] det_loss_box_0[0.7988] det_loss_cls_1[0.5258] det_loss_box_1[0.6633] det_loss_cls_2[0.5046] det_loss_box_2[0.6073] det_loss_cls_3[0.4858] det_loss_box_3[0.5819] det_loss_cls_4[0.4649] det_loss_box_4[0.5671] det_loss_cls_5[0.4503] det_loss_box_5[0.5625] det_loss_cls_dn_0[0.1047] det_loss_box_dn_0[0.3466] det_loss_cls_dn_1[0.1017] det_loss_box_dn_1[0.3010] det_loss_cls_dn_2[0.1023] det_loss_box_dn_2[0.2851] det_loss_cls_dn_3[0.1010] det_loss_box_dn_3[0.2812] det_loss_cls_dn_4[0.1001] det_loss_box_dn_4[0.2801] det_loss_cls_dn_5[0.0995] det_loss_box_dn_5[0.2765] loss_depth[0.0435] total_loss[9.1900]
Rank[0/8] 06/19/2025 21:09:26 INFO stats.py:314 | Epoch[52] Step[94] GlobalStep[20374] Training Speed: 8.67 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 1 day, 0:59:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:09:32 INFO loss_tracker.py:84 | Epoch[52/239] Step[99] GlobalStep[20379/NA]: det_loss_cls_0[0.5408] det_loss_box_0[0.9002] det_loss_cls_1[0.5274] det_loss_box_1[0.7518] det_loss_cls_2[0.5119] det_loss_box_2[0.6848] det_loss_cls_3[0.4995] det_loss_box_3[0.6728] det_loss_cls_4[0.4784] det_loss_box_4[0.6617] det_loss_cls_5[0.4702] det_loss_box_5[0.6614] det_loss_cls_dn_0[0.1076] det_loss_box_dn_0[0.4080] det_loss_cls_dn_1[0.1052] det_loss_box_dn_1[0.3541] det_loss_cls_dn_2[0.1050] det_loss_box_dn_2[0.3391] det_loss_cls_dn_3[0.1036] det_loss_box_dn_3[0.3352] det_loss_cls_dn_4[0.1026] det_loss_box_dn_4[0.3346] det_loss_cls_dn_5[0.1017] det_loss_box_dn_5[0.3297] loss_depth[0.0432] total_loss[10.1306]
Rank[0/8] 06/19/2025 21:09:52 INFO stats.py:314 | Epoch[52] Step[119] GlobalStep[20399] Training Speed: 7.60 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 0:58:30.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:09:57 INFO loss_tracker.py:84 | Epoch[52/239] Step[124] GlobalStep[20404/NA]: det_loss_cls_0[0.4855] det_loss_box_0[0.6678] det_loss_cls_1[0.4804] det_loss_box_1[0.5189] det_loss_cls_2[0.4538] det_loss_box_2[0.4717] det_loss_cls_3[0.4260] det_loss_box_3[0.4603] det_loss_cls_4[0.3985] det_loss_box_4[0.4541] det_loss_cls_5[0.3882] det_loss_box_5[0.4483] det_loss_cls_dn_0[0.1096] det_loss_box_dn_0[0.3269] det_loss_cls_dn_1[0.1048] det_loss_box_dn_1[0.2853] det_loss_cls_dn_2[0.1032] det_loss_box_dn_2[0.2685] det_loss_cls_dn_3[0.1026] det_loss_box_dn_3[0.2652] det_loss_cls_dn_4[0.1007] det_loss_box_dn_4[0.2635] det_loss_cls_dn_5[0.1005] det_loss_box_dn_5[0.2618] loss_depth[0.0463] total_loss[7.9923]
Rank[0/8] 06/19/2025 21:10:18 INFO stats.py:314 | Epoch[52] Step[144] GlobalStep[20424] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 0:57:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:10:23 INFO loss_tracker.py:84 | Epoch[52/239] Step[149] GlobalStep[20429/NA]: det_loss_cls_0[0.5229] det_loss_box_0[0.7521] det_loss_cls_1[0.5084] det_loss_box_1[0.6205] det_loss_cls_2[0.4896] det_loss_box_2[0.5682] det_loss_cls_3[0.4721] det_loss_box_3[0.5525] det_loss_cls_4[0.4444] det_loss_box_4[0.5475] det_loss_cls_5[0.4347] det_loss_box_5[0.5470] det_loss_cls_dn_0[0.1041] det_loss_box_dn_0[0.3334] det_loss_cls_dn_1[0.0993] det_loss_box_dn_1[0.2880] det_loss_cls_dn_2[0.0992] det_loss_box_dn_2[0.2712] det_loss_cls_dn_3[0.0983] det_loss_box_dn_3[0.2693] det_loss_cls_dn_4[0.0971] det_loss_box_dn_4[0.2678] det_loss_cls_dn_5[0.0958] det_loss_box_dn_5[0.2652] loss_depth[0.0408] total_loss[8.7893]
Rank[0/8] 06/19/2025 21:10:44 INFO stats.py:314 | Epoch[52] Step[169] GlobalStep[20449] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 0:56:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:10:49 INFO loss_tracker.py:84 | Epoch[52/239] Step[174] GlobalStep[20454/NA]: det_loss_cls_0[0.5403] det_loss_box_0[0.6474] det_loss_cls_1[0.5366] det_loss_box_1[0.5147] det_loss_cls_2[0.5030] det_loss_box_2[0.4608] det_loss_cls_3[0.4818] det_loss_box_3[0.4488] det_loss_cls_4[0.4506] det_loss_box_4[0.4371] det_loss_cls_5[0.4376] det_loss_box_5[0.4398] det_loss_cls_dn_0[0.1157] det_loss_box_dn_0[0.3394] det_loss_cls_dn_1[0.1125] det_loss_box_dn_1[0.2895] det_loss_cls_dn_2[0.1122] det_loss_box_dn_2[0.2739] det_loss_cls_dn_3[0.1110] det_loss_box_dn_3[0.2723] det_loss_cls_dn_4[0.1096] det_loss_box_dn_4[0.2708] det_loss_cls_dn_5[0.1089] det_loss_box_dn_5[0.2687] loss_depth[0.0404] total_loss[8.3235]
Rank[0/8] 06/19/2025 21:11:10 INFO stats.py:314 | Epoch[52] Step[194] GlobalStep[20474] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 0:56:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:11:15 INFO loss_tracker.py:84 | Epoch[52/239] Step[199] GlobalStep[20479/NA]: det_loss_cls_0[0.5870] det_loss_box_0[0.7914] det_loss_cls_1[0.5635] det_loss_box_1[0.6125] det_loss_cls_2[0.5484] det_loss_box_2[0.5503] det_loss_cls_3[0.5255] det_loss_box_3[0.5286] det_loss_cls_4[0.4972] det_loss_box_4[0.5244] det_loss_cls_5[0.4839] det_loss_box_5[0.5218] det_loss_cls_dn_0[0.1036] det_loss_box_dn_0[0.3293] det_loss_cls_dn_1[0.0999] det_loss_box_dn_1[0.2790] det_loss_cls_dn_2[0.0996] det_loss_box_dn_2[0.2613] det_loss_cls_dn_3[0.0985] det_loss_box_dn_3[0.2586] det_loss_cls_dn_4[0.0976] det_loss_box_dn_4[0.2573] det_loss_cls_dn_5[0.0970] det_loss_box_dn_5[0.2580] loss_depth[0.0405] total_loss[9.0143]
Rank[0/8] 06/19/2025 21:11:36 INFO stats.py:314 | Epoch[52] Step[219] GlobalStep[20499] Training Speed: 7.91 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 0:55:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:11:41 INFO loss_tracker.py:84 | Epoch[52/239] Step[224] GlobalStep[20504/NA]: det_loss_cls_0[0.4348] det_loss_box_0[0.5656] det_loss_cls_1[0.4227] det_loss_box_1[0.4543] det_loss_cls_2[0.4077] det_loss_box_2[0.3950] det_loss_cls_3[0.3786] det_loss_box_3[0.3844] det_loss_cls_4[0.3486] det_loss_box_4[0.3780] det_loss_cls_5[0.3327] det_loss_box_5[0.3830] det_loss_cls_dn_0[0.0921] det_loss_box_dn_0[0.3275] det_loss_cls_dn_1[0.0882] det_loss_box_dn_1[0.2696] det_loss_cls_dn_2[0.0871] det_loss_box_dn_2[0.2511] det_loss_cls_dn_3[0.0853] det_loss_box_dn_3[0.2470] det_loss_cls_dn_4[0.0849] det_loss_box_dn_4[0.2455] det_loss_cls_dn_5[0.0844] det_loss_box_dn_5[0.2440] loss_depth[0.0382] total_loss[7.0303]
Rank[0/8] 06/19/2025 21:12:01 INFO stats.py:314 | Epoch[52] Step[244] GlobalStep[20524] Training Speed: 7.72 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 0:54:30.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:12:07 INFO loss_tracker.py:84 | Epoch[52/239] Step[249] GlobalStep[20529/NA]: det_loss_cls_0[0.5411] det_loss_box_0[0.7778] det_loss_cls_1[0.5279] det_loss_box_1[0.6240] det_loss_cls_2[0.5025] det_loss_box_2[0.5634] det_loss_cls_3[0.4918] det_loss_box_3[0.5292] det_loss_cls_4[0.4608] det_loss_box_4[0.5254] det_loss_cls_5[0.4495] det_loss_box_5[0.5251] det_loss_cls_dn_0[0.1024] det_loss_box_dn_0[0.3401] det_loss_cls_dn_1[0.0989] det_loss_box_dn_1[0.2943] det_loss_cls_dn_2[0.0980] det_loss_box_dn_2[0.2762] det_loss_cls_dn_3[0.0968] det_loss_box_dn_3[0.2710] det_loss_cls_dn_4[0.0954] det_loss_box_dn_4[0.2692] det_loss_cls_dn_5[0.0950] det_loss_box_dn_5[0.2682] loss_depth[0.0375] total_loss[8.8615]
Rank[0/8] 06/19/2025 21:12:27 INFO stats.py:314 | Epoch[52] Step[269] GlobalStep[20549] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 0:53:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:12:33 INFO loss_tracker.py:84 | Epoch[52/239] Step[274] GlobalStep[20554/NA]: det_loss_cls_0[0.6636] det_loss_box_0[0.8617] det_loss_cls_1[0.6422] det_loss_box_1[0.6996] det_loss_cls_2[0.6258] det_loss_box_2[0.6257] det_loss_cls_3[0.5843] det_loss_box_3[0.6014] det_loss_cls_4[0.5501] det_loss_box_4[0.5861] det_loss_cls_5[0.5298] det_loss_box_5[0.5892] det_loss_cls_dn_0[0.1139] det_loss_box_dn_0[0.2942] det_loss_cls_dn_1[0.1097] det_loss_box_dn_1[0.2558] det_loss_cls_dn_2[0.1096] det_loss_box_dn_2[0.2417] det_loss_cls_dn_3[0.1088] det_loss_box_dn_3[0.2394] det_loss_cls_dn_4[0.1075] det_loss_box_dn_4[0.2385] det_loss_cls_dn_5[0.1073] det_loss_box_dn_5[0.2356] loss_depth[0.0403] total_loss[9.7619]
Rank[0/8] 06/19/2025 21:12:53 INFO stats.py:314 | Epoch[52] Step[294] GlobalStep[20574] Training Speed: 7.09 samples/sec across all devices.	Average Step Time: 1.13 sec.	Estimated Remaining Time: 1 day, 0:52:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:12:59 INFO loss_tracker.py:84 | Epoch[52/239] Step[299] GlobalStep[20579/NA]: det_loss_cls_0[0.5652] det_loss_box_0[0.8464] det_loss_cls_1[0.5504] det_loss_box_1[0.6926] det_loss_cls_2[0.5411] det_loss_box_2[0.6442] det_loss_cls_3[0.5141] det_loss_box_3[0.6228] det_loss_cls_4[0.4909] det_loss_box_4[0.6088] det_loss_cls_5[0.4808] det_loss_box_5[0.6074] det_loss_cls_dn_0[0.1117] det_loss_box_dn_0[0.4025] det_loss_cls_dn_1[0.1084] det_loss_box_dn_1[0.3613] det_loss_cls_dn_2[0.1087] det_loss_box_dn_2[0.3476] det_loss_cls_dn_3[0.1075] det_loss_box_dn_3[0.3455] det_loss_cls_dn_4[0.1069] det_loss_box_dn_4[0.3447] det_loss_cls_dn_5[0.1061] det_loss_box_dn_5[0.3426] loss_depth[0.0411] total_loss[9.9992]
Rank[0/8] 06/19/2025 21:13:19 INFO stats.py:314 | Epoch[52] Step[319] GlobalStep[20599] Training Speed: 8.56 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 0:52:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:13:24 INFO loss_tracker.py:84 | Epoch[52/239] Step[324] GlobalStep[20604/NA]: det_loss_cls_0[0.5859] det_loss_box_0[0.7549] det_loss_cls_1[0.5808] det_loss_box_1[0.5906] det_loss_cls_2[0.5566] det_loss_box_2[0.5330] det_loss_cls_3[0.5371] det_loss_box_3[0.5084] det_loss_cls_4[0.5054] det_loss_box_4[0.5030] det_loss_cls_5[0.4854] det_loss_box_5[0.5058] det_loss_cls_dn_0[0.1129] det_loss_box_dn_0[0.2983] det_loss_cls_dn_1[0.1092] det_loss_box_dn_1[0.2557] det_loss_cls_dn_2[0.1095] det_loss_box_dn_2[0.2387] det_loss_cls_dn_3[0.1082] det_loss_box_dn_3[0.2357] det_loss_cls_dn_4[0.1066] det_loss_box_dn_4[0.2353] det_loss_cls_dn_5[0.1055] det_loss_box_dn_5[0.2340] loss_depth[0.0414] total_loss[8.8379]
Rank[0/8] 06/19/2025 21:13:45 INFO stats.py:314 | Epoch[52] Step[344] GlobalStep[20624] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 0:51:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:13:50 INFO loss_tracker.py:84 | Epoch[52/239] Step[349] GlobalStep[20629/NA]: det_loss_cls_0[0.4241] det_loss_box_0[0.6241] det_loss_cls_1[0.4040] det_loss_box_1[0.5128] det_loss_cls_2[0.3960] det_loss_box_2[0.4601] det_loss_cls_3[0.3717] det_loss_box_3[0.4469] det_loss_cls_4[0.3512] det_loss_box_4[0.4327] det_loss_cls_5[0.3375] det_loss_box_5[0.4353] det_loss_cls_dn_0[0.1060] det_loss_box_dn_0[0.3997] det_loss_cls_dn_1[0.1026] det_loss_box_dn_1[0.3553] det_loss_cls_dn_2[0.1016] det_loss_box_dn_2[0.3405] det_loss_cls_dn_3[0.1002] det_loss_box_dn_3[0.3362] det_loss_cls_dn_4[0.1000] det_loss_box_dn_4[0.3346] det_loss_cls_dn_5[0.0988] det_loss_box_dn_5[0.3320] loss_depth[0.0412] total_loss[7.9451]
Rank[0/8] 06/19/2025 21:14:10 INFO stats.py:314 | Epoch[52] Step[369] GlobalStep[20649] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 0:50:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:14:15 INFO loss_tracker.py:84 | Epoch[52/239] Step[374] GlobalStep[20654/NA]: det_loss_cls_0[0.4678] det_loss_box_0[0.5874] det_loss_cls_1[0.4525] det_loss_box_1[0.4483] det_loss_cls_2[0.4367] det_loss_box_2[0.4043] det_loss_cls_3[0.4155] det_loss_box_3[0.3920] det_loss_cls_4[0.3924] det_loss_box_4[0.3797] det_loss_cls_5[0.3787] det_loss_box_5[0.3793] det_loss_cls_dn_0[0.1128] det_loss_box_dn_0[0.3708] det_loss_cls_dn_1[0.1099] det_loss_box_dn_1[0.3315] det_loss_cls_dn_2[0.1101] det_loss_box_dn_2[0.3136] det_loss_cls_dn_3[0.1092] det_loss_box_dn_3[0.3111] det_loss_cls_dn_4[0.1091] det_loss_box_dn_4[0.3089] det_loss_cls_dn_5[0.1085] det_loss_box_dn_5[0.3071] loss_depth[0.0394] total_loss[7.7766]
Rank[0/8] 06/19/2025 21:14:30 INFO stats.py:394 | Epoch[52] completed. Training Speed: 7.65 samples/sec across all devices.	Epoch Time: 407.86 sec.	Average Epoch Time: 407.86 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1 day, 0:49:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:14:39 INFO stats.py:314 | Epoch[53] Step[4] GlobalStep[20674] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 0:49:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:14:59 INFO loss_tracker.py:84 | Epoch[53/239] Step[24] GlobalStep[20694/NA]: det_loss_cls_0[0.5470] det_loss_box_0[0.7362] det_loss_cls_1[0.5345] det_loss_box_1[0.5926] det_loss_cls_2[0.5169] det_loss_box_2[0.5499] det_loss_cls_3[0.4877] det_loss_box_3[0.5325] det_loss_cls_4[0.4577] det_loss_box_4[0.5227] det_loss_cls_5[0.4447] det_loss_box_5[0.5192] det_loss_cls_dn_0[0.1094] det_loss_box_dn_0[0.3229] det_loss_cls_dn_1[0.1047] det_loss_box_dn_1[0.2833] det_loss_cls_dn_2[0.1049] det_loss_box_dn_2[0.2671] det_loss_cls_dn_3[0.1032] det_loss_box_dn_3[0.2644] det_loss_cls_dn_4[0.1019] det_loss_box_dn_4[0.2633] det_loss_cls_dn_5[0.1009] det_loss_box_dn_5[0.2612] loss_depth[0.0399] total_loss[8.7687]
Rank[0/8] 06/19/2025 21:15:05 INFO stats.py:314 | Epoch[53] Step[29] GlobalStep[20699] Training Speed: 8.58 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 0:49:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:15:26 INFO loss_tracker.py:84 | Epoch[53/239] Step[49] GlobalStep[20719/NA]: det_loss_cls_0[0.5309] det_loss_box_0[0.7177] det_loss_cls_1[0.5143] det_loss_box_1[0.5706] det_loss_cls_2[0.4897] det_loss_box_2[0.5230] det_loss_cls_3[0.4602] det_loss_box_3[0.5133] det_loss_cls_4[0.4333] det_loss_box_4[0.5088] det_loss_cls_5[0.4198] det_loss_box_5[0.5108] det_loss_cls_dn_0[0.0996] det_loss_box_dn_0[0.3083] det_loss_cls_dn_1[0.0959] det_loss_box_dn_1[0.2724] det_loss_cls_dn_2[0.0957] det_loss_box_dn_2[0.2576] det_loss_cls_dn_3[0.0942] det_loss_box_dn_3[0.2547] det_loss_cls_dn_4[0.0928] det_loss_box_dn_4[0.2544] det_loss_cls_dn_5[0.0916] det_loss_box_dn_5[0.2527] loss_depth[0.0382] total_loss[8.4004]
Rank[0/8] 06/19/2025 21:15:31 INFO stats.py:314 | Epoch[53] Step[54] GlobalStep[20724] Training Speed: 8.05 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 0:48:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:15:52 INFO loss_tracker.py:84 | Epoch[53/239] Step[74] GlobalStep[20744/NA]: det_loss_cls_0[0.5005] det_loss_box_0[0.6521] det_loss_cls_1[0.4882] det_loss_box_1[0.5037] det_loss_cls_2[0.4587] det_loss_box_2[0.4679] det_loss_cls_3[0.4397] det_loss_box_3[0.4549] det_loss_cls_4[0.4224] det_loss_box_4[0.4474] det_loss_cls_5[0.3981] det_loss_box_5[0.4560] det_loss_cls_dn_0[0.0942] det_loss_box_dn_0[0.3222] det_loss_cls_dn_1[0.0884] det_loss_box_dn_1[0.2877] det_loss_cls_dn_2[0.0877] det_loss_box_dn_2[0.2753] det_loss_cls_dn_3[0.0861] det_loss_box_dn_3[0.2734] det_loss_cls_dn_4[0.0854] det_loss_box_dn_4[0.2731] det_loss_cls_dn_5[0.0841] det_loss_box_dn_5[0.2624] loss_depth[0.0426] total_loss[7.9522]
Rank[0/8] 06/19/2025 21:15:57 INFO stats.py:314 | Epoch[53] Step[79] GlobalStep[20749] Training Speed: 8.15 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 0:47:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:16:17 INFO loss_tracker.py:84 | Epoch[53/239] Step[99] GlobalStep[20769/NA]: det_loss_cls_0[0.5904] det_loss_box_0[0.8203] det_loss_cls_1[0.5686] det_loss_box_1[0.6830] det_loss_cls_2[0.5493] det_loss_box_2[0.6205] det_loss_cls_3[0.5247] det_loss_box_3[0.5933] det_loss_cls_4[0.4885] det_loss_box_4[0.5849] det_loss_cls_5[0.4754] det_loss_box_5[0.5747] det_loss_cls_dn_0[0.1024] det_loss_box_dn_0[0.3300] det_loss_cls_dn_1[0.0983] det_loss_box_dn_1[0.2912] det_loss_cls_dn_2[0.0982] det_loss_box_dn_2[0.2771] det_loss_cls_dn_3[0.0972] det_loss_box_dn_3[0.2742] det_loss_cls_dn_4[0.0962] det_loss_box_dn_4[0.2729] det_loss_cls_dn_5[0.0955] det_loss_box_dn_5[0.2707] loss_depth[0.0411] total_loss[9.4183]
Rank[0/8] 06/19/2025 21:16:22 INFO stats.py:314 | Epoch[53] Step[104] GlobalStep[20774] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 0:46:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:16:42 INFO loss_tracker.py:84 | Epoch[53/239] Step[124] GlobalStep[20794/NA]: det_loss_cls_0[0.5405] det_loss_box_0[0.7276] det_loss_cls_1[0.5427] det_loss_box_1[0.5800] det_loss_cls_2[0.5150] det_loss_box_2[0.5252] det_loss_cls_3[0.4932] det_loss_box_3[0.5083] det_loss_cls_4[0.4594] det_loss_box_4[0.4911] det_loss_cls_5[0.4519] det_loss_box_5[0.4851] det_loss_cls_dn_0[0.1071] det_loss_box_dn_0[0.3035] det_loss_cls_dn_1[0.1031] det_loss_box_dn_1[0.2620] det_loss_cls_dn_2[0.1028] det_loss_box_dn_2[0.2483] det_loss_cls_dn_3[0.1014] det_loss_box_dn_3[0.2444] det_loss_cls_dn_4[0.1001] det_loss_box_dn_4[0.2425] det_loss_cls_dn_5[0.0986] det_loss_box_dn_5[0.2415] loss_depth[0.0408] total_loss[8.5161]
Rank[0/8] 06/19/2025 21:16:47 INFO stats.py:314 | Epoch[53] Step[129] GlobalStep[20799] Training Speed: 7.34 samples/sec across all devices.	Average Step Time: 1.09 sec.	Estimated Remaining Time: 1 day, 0:45:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:17:08 INFO loss_tracker.py:84 | Epoch[53/239] Step[149] GlobalStep[20819/NA]: det_loss_cls_0[0.5583] det_loss_box_0[0.7600] det_loss_cls_1[0.5327] det_loss_box_1[0.6318] det_loss_cls_2[0.5184] det_loss_box_2[0.5710] det_loss_cls_3[0.4889] det_loss_box_3[0.5535] det_loss_cls_4[0.4677] det_loss_box_4[0.5417] det_loss_cls_5[0.4641] det_loss_box_5[0.5368] det_loss_cls_dn_0[0.1051] det_loss_box_dn_0[0.3083] det_loss_cls_dn_1[0.1021] det_loss_box_dn_1[0.2707] det_loss_cls_dn_2[0.1012] det_loss_box_dn_2[0.2510] det_loss_cls_dn_3[0.1000] det_loss_box_dn_3[0.2500] det_loss_cls_dn_4[0.0998] det_loss_box_dn_4[0.2467] det_loss_cls_dn_5[0.0987] det_loss_box_dn_5[0.2441] loss_depth[0.0381] total_loss[8.8408]
Rank[0/8] 06/19/2025 21:17:13 INFO stats.py:314 | Epoch[53] Step[154] GlobalStep[20824] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 0:45:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:17:34 INFO loss_tracker.py:84 | Epoch[53/239] Step[174] GlobalStep[20844/NA]: det_loss_cls_0[0.5513] det_loss_box_0[0.7822] det_loss_cls_1[0.5444] det_loss_box_1[0.6311] det_loss_cls_2[0.5250] det_loss_box_2[0.5940] det_loss_cls_3[0.4966] det_loss_box_3[0.5612] det_loss_cls_4[0.4584] det_loss_box_4[0.5417] det_loss_cls_5[0.4456] det_loss_box_5[0.5408] det_loss_cls_dn_0[0.0992] det_loss_box_dn_0[0.3353] det_loss_cls_dn_1[0.0956] det_loss_box_dn_1[0.2952] det_loss_cls_dn_2[0.0940] det_loss_box_dn_2[0.2809] det_loss_cls_dn_3[0.0937] det_loss_box_dn_3[0.2784] det_loss_cls_dn_4[0.0918] det_loss_box_dn_4[0.2775] det_loss_cls_dn_5[0.0917] det_loss_box_dn_5[0.2747] loss_depth[0.0404] total_loss[9.0207]
Rank[0/8] 06/19/2025 21:17:39 INFO stats.py:314 | Epoch[53] Step[179] GlobalStep[20849] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 0:44:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:18:00 INFO loss_tracker.py:84 | Epoch[53/239] Step[199] GlobalStep[20869/NA]: det_loss_cls_0[0.6150] det_loss_box_0[0.8544] det_loss_cls_1[0.5882] det_loss_box_1[0.6935] det_loss_cls_2[0.5720] det_loss_box_2[0.6163] det_loss_cls_3[0.5518] det_loss_box_3[0.5897] det_loss_cls_4[0.5268] det_loss_box_4[0.5781] det_loss_cls_5[0.5126] det_loss_box_5[0.5753] det_loss_cls_dn_0[0.1094] det_loss_box_dn_0[0.2984] det_loss_cls_dn_1[0.1043] det_loss_box_dn_1[0.2540] det_loss_cls_dn_2[0.1032] det_loss_box_dn_2[0.2397] det_loss_cls_dn_3[0.1022] det_loss_box_dn_3[0.2362] det_loss_cls_dn_4[0.1007] det_loss_box_dn_4[0.2362] det_loss_cls_dn_5[0.0996] det_loss_box_dn_5[0.2349] loss_depth[0.0393] total_loss[9.4317]
Rank[0/8] 06/19/2025 21:18:05 INFO stats.py:314 | Epoch[53] Step[204] GlobalStep[20874] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 0:43:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:18:26 INFO loss_tracker.py:84 | Epoch[53/239] Step[224] GlobalStep[20894/NA]: det_loss_cls_0[0.5375] det_loss_box_0[0.7068] det_loss_cls_1[0.5289] det_loss_box_1[0.5803] det_loss_cls_2[0.5174] det_loss_box_2[0.5455] det_loss_cls_3[0.5007] det_loss_box_3[0.5255] det_loss_cls_4[0.4670] det_loss_box_4[0.5123] det_loss_cls_5[0.4526] det_loss_box_5[0.5076] det_loss_cls_dn_0[0.1103] det_loss_box_dn_0[0.3060] det_loss_cls_dn_1[0.1074] det_loss_box_dn_1[0.2585] det_loss_cls_dn_2[0.1069] det_loss_box_dn_2[0.2409] det_loss_cls_dn_3[0.1057] det_loss_box_dn_3[0.2386] det_loss_cls_dn_4[0.1053] det_loss_box_dn_4[0.2374] det_loss_cls_dn_5[0.1041] det_loss_box_dn_5[0.2366] loss_depth[0.0404] total_loss[8.5802]
Rank[0/8] 06/19/2025 21:18:31 INFO stats.py:314 | Epoch[53] Step[229] GlobalStep[20899] Training Speed: 7.88 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1 day, 0:42:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:18:52 INFO loss_tracker.py:84 | Epoch[53/239] Step[249] GlobalStep[20919/NA]: det_loss_cls_0[0.7160] det_loss_box_0[1.0362] det_loss_cls_1[0.6913] det_loss_box_1[0.8338] det_loss_cls_2[0.6739] det_loss_box_2[0.7736] det_loss_cls_3[0.6455] det_loss_box_3[0.7353] det_loss_cls_4[0.6146] det_loss_box_4[0.7284] det_loss_cls_5[0.6020] det_loss_box_5[0.7264] det_loss_cls_dn_0[0.1143] det_loss_box_dn_0[0.3560] det_loss_cls_dn_1[0.1110] det_loss_box_dn_1[0.3161] det_loss_cls_dn_2[0.1105] det_loss_box_dn_2[0.2995] det_loss_cls_dn_3[0.1105] det_loss_box_dn_3[0.2970] det_loss_cls_dn_4[0.1092] det_loss_box_dn_4[0.2960] det_loss_cls_dn_5[0.1075] det_loss_box_dn_5[0.2942] loss_depth[0.0436] total_loss[11.3426]
Rank[0/8] 06/19/2025 21:18:57 INFO stats.py:314 | Epoch[53] Step[254] GlobalStep[20924] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 0:42:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:19:18 INFO loss_tracker.py:84 | Epoch[53/239] Step[274] GlobalStep[20944/NA]: det_loss_cls_0[0.4409] det_loss_box_0[0.6027] det_loss_cls_1[0.4259] det_loss_box_1[0.4918] det_loss_cls_2[0.4180] det_loss_box_2[0.4466] det_loss_cls_3[0.3958] det_loss_box_3[0.4274] det_loss_cls_4[0.3693] det_loss_box_4[0.4194] det_loss_cls_5[0.3545] det_loss_box_5[0.4181] det_loss_cls_dn_0[0.0964] det_loss_box_dn_0[0.3165] det_loss_cls_dn_1[0.0936] det_loss_box_dn_1[0.2740] det_loss_cls_dn_2[0.0936] det_loss_box_dn_2[0.2560] det_loss_cls_dn_3[0.0922] det_loss_box_dn_3[0.2525] det_loss_cls_dn_4[0.0911] det_loss_box_dn_4[0.2509] det_loss_cls_dn_5[0.0911] det_loss_box_dn_5[0.2495] loss_depth[0.0407] total_loss[7.4084]
Rank[0/8] 06/19/2025 21:19:23 INFO stats.py:314 | Epoch[53] Step[279] GlobalStep[20949] Training Speed: 8.17 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 0:41:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:19:44 INFO loss_tracker.py:84 | Epoch[53/239] Step[299] GlobalStep[20969/NA]: det_loss_cls_0[0.5144] det_loss_box_0[0.5946] det_loss_cls_1[0.5139] det_loss_box_1[0.4899] det_loss_cls_2[0.4908] det_loss_box_2[0.4412] det_loss_cls_3[0.4634] det_loss_box_3[0.4311] det_loss_cls_4[0.4398] det_loss_box_4[0.4213] det_loss_cls_5[0.4254] det_loss_box_5[0.4221] det_loss_cls_dn_0[0.0995] det_loss_box_dn_0[0.3255] det_loss_cls_dn_1[0.0967] det_loss_box_dn_1[0.2816] det_loss_cls_dn_2[0.0958] det_loss_box_dn_2[0.2632] det_loss_cls_dn_3[0.0947] det_loss_box_dn_3[0.2611] det_loss_cls_dn_4[0.0941] det_loss_box_dn_4[0.2605] det_loss_cls_dn_5[0.0934] det_loss_box_dn_5[0.2536] loss_depth[0.0385] total_loss[7.9061]
Rank[0/8] 06/19/2025 21:19:49 INFO stats.py:314 | Epoch[53] Step[304] GlobalStep[20974] Training Speed: 8.19 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 0:40:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:20:09 INFO loss_tracker.py:84 | Epoch[53/239] Step[324] GlobalStep[20994/NA]: det_loss_cls_0[0.6089] det_loss_box_0[0.7970] det_loss_cls_1[0.6016] det_loss_box_1[0.6297] det_loss_cls_2[0.5791] det_loss_box_2[0.5775] det_loss_cls_3[0.5533] det_loss_box_3[0.5479] det_loss_cls_4[0.5183] det_loss_box_4[0.5420] det_loss_cls_5[0.5111] det_loss_box_5[0.5366] det_loss_cls_dn_0[0.1087] det_loss_box_dn_0[0.2745] det_loss_cls_dn_1[0.1062] det_loss_box_dn_1[0.2373] det_loss_cls_dn_2[0.1051] det_loss_box_dn_2[0.2220] det_loss_cls_dn_3[0.1046] det_loss_box_dn_3[0.2210] det_loss_cls_dn_4[0.1038] det_loss_box_dn_4[0.2210] det_loss_cls_dn_5[0.1021] det_loss_box_dn_5[0.2188] loss_depth[0.0402] total_loss[9.0682]
Rank[0/8] 06/19/2025 21:20:15 INFO stats.py:314 | Epoch[53] Step[329] GlobalStep[20999] Training Speed: 6.65 samples/sec across all devices.	Average Step Time: 1.20 sec.	Estimated Remaining Time: 1 day, 0:39:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:20:35 INFO loss_tracker.py:84 | Epoch[53/239] Step[349] GlobalStep[21019/NA]: det_loss_cls_0[0.4403] det_loss_box_0[0.5132] det_loss_cls_1[0.4443] det_loss_box_1[0.4129] det_loss_cls_2[0.4135] det_loss_box_2[0.3767] det_loss_cls_3[0.3926] det_loss_box_3[0.3647] det_loss_cls_4[0.3622] det_loss_box_4[0.3633] det_loss_cls_5[0.3522] det_loss_box_5[0.3628] det_loss_cls_dn_0[0.1004] det_loss_box_dn_0[0.3088] det_loss_cls_dn_1[0.0975] det_loss_box_dn_1[0.2601] det_loss_cls_dn_2[0.0964] det_loss_box_dn_2[0.2426] det_loss_cls_dn_3[0.0949] det_loss_box_dn_3[0.2380] det_loss_cls_dn_4[0.0932] det_loss_box_dn_4[0.2349] det_loss_cls_dn_5[0.0919] det_loss_box_dn_5[0.2341] loss_depth[0.0409] total_loss[6.9326]
Rank[0/8] 06/19/2025 21:20:40 INFO stats.py:314 | Epoch[53] Step[354] GlobalStep[21024] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 0:38:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:21:01 INFO loss_tracker.py:84 | Epoch[53/239] Step[374] GlobalStep[21044/NA]: det_loss_cls_0[0.4613] det_loss_box_0[0.6719] det_loss_cls_1[0.4591] det_loss_box_1[0.5414] det_loss_cls_2[0.4330] det_loss_box_2[0.4889] det_loss_cls_3[0.4113] det_loss_box_3[0.4708] det_loss_cls_4[0.3921] det_loss_box_4[0.4595] det_loss_cls_5[0.3827] det_loss_box_5[0.4603] det_loss_cls_dn_0[0.1059] det_loss_box_dn_0[0.3338] det_loss_cls_dn_1[0.1017] det_loss_box_dn_1[0.2932] det_loss_cls_dn_2[0.1016] det_loss_box_dn_2[0.2754] det_loss_cls_dn_3[0.1002] det_loss_box_dn_3[0.2723] det_loss_cls_dn_4[0.0999] det_loss_box_dn_4[0.2703] det_loss_cls_dn_5[0.0988] det_loss_box_dn_5[0.2685] loss_depth[0.0387] total_loss[7.9925]
Rank[0/8] 06/19/2025 21:21:06 INFO stats.py:314 | Epoch[53] Step[379] GlobalStep[21049] Training Speed: 7.90 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 0:38:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:21:16 INFO stats.py:394 | Epoch[53] completed. Training Speed: 7.69 samples/sec across all devices.	Epoch Time: 405.67 sec.	Average Epoch Time: 405.67 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 0:37:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:21:35 INFO stats.py:314 | Epoch[54] Step[14] GlobalStep[21074] Training Speed: 8.67 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 1 day, 0:37:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:21:46 INFO loss_tracker.py:84 | Epoch[54/239] Step[24] GlobalStep[21084/NA]: det_loss_cls_0[0.4543] det_loss_box_0[0.6168] det_loss_cls_1[0.4383] det_loss_box_1[0.4964] det_loss_cls_2[0.4177] det_loss_box_2[0.4450] det_loss_cls_3[0.4009] det_loss_box_3[0.4339] det_loss_cls_4[0.3707] det_loss_box_4[0.4299] det_loss_cls_5[0.3606] det_loss_box_5[0.4281] det_loss_cls_dn_0[0.0983] det_loss_box_dn_0[0.3538] det_loss_cls_dn_1[0.0945] det_loss_box_dn_1[0.3119] det_loss_cls_dn_2[0.0944] det_loss_box_dn_2[0.2907] det_loss_cls_dn_3[0.0932] det_loss_box_dn_3[0.2884] det_loss_cls_dn_4[0.0922] det_loss_box_dn_4[0.2867] det_loss_cls_dn_5[0.0916] det_loss_box_dn_5[0.2832] loss_depth[0.0412] total_loss[7.7126]
Rank[0/8] 06/19/2025 21:22:01 INFO stats.py:314 | Epoch[54] Step[39] GlobalStep[21099] Training Speed: 8.19 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 0:36:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:22:11 INFO loss_tracker.py:84 | Epoch[54/239] Step[49] GlobalStep[21109/NA]: det_loss_cls_0[0.5647] det_loss_box_0[0.7547] det_loss_cls_1[0.5614] det_loss_box_1[0.5889] det_loss_cls_2[0.5356] det_loss_box_2[0.5355] det_loss_cls_3[0.4858] det_loss_box_3[0.5201] det_loss_cls_4[0.4557] det_loss_box_4[0.5064] det_loss_cls_5[0.4382] det_loss_box_5[0.5080] det_loss_cls_dn_0[0.1032] det_loss_box_dn_0[0.3049] det_loss_cls_dn_1[0.0996] det_loss_box_dn_1[0.2595] det_loss_cls_dn_2[0.0993] det_loss_box_dn_2[0.2413] det_loss_cls_dn_3[0.0982] det_loss_box_dn_3[0.2385] det_loss_cls_dn_4[0.0975] det_loss_box_dn_4[0.2372] det_loss_cls_dn_5[0.0958] det_loss_box_dn_5[0.2353] loss_depth[0.0390] total_loss[8.6042]
Rank[0/8] 06/19/2025 21:22:27 INFO stats.py:314 | Epoch[54] Step[64] GlobalStep[21124] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 0:35:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:22:37 INFO loss_tracker.py:84 | Epoch[54/239] Step[74] GlobalStep[21134/NA]: det_loss_cls_0[0.5057] det_loss_box_0[0.7755] det_loss_cls_1[0.4967] det_loss_box_1[0.6233] det_loss_cls_2[0.4731] det_loss_box_2[0.5716] det_loss_cls_3[0.4502] det_loss_box_3[0.5560] det_loss_cls_4[0.4363] det_loss_box_4[0.5330] det_loss_cls_5[0.4234] det_loss_box_5[0.5360] det_loss_cls_dn_0[0.1056] det_loss_box_dn_0[0.4341] det_loss_cls_dn_1[0.1024] det_loss_box_dn_1[0.3784] det_loss_cls_dn_2[0.1023] det_loss_box_dn_2[0.3620] det_loss_cls_dn_3[0.1011] det_loss_box_dn_3[0.3574] det_loss_cls_dn_4[0.1000] det_loss_box_dn_4[0.3557] det_loss_cls_dn_5[0.0995] det_loss_box_dn_5[0.3528] loss_depth[0.0394] total_loss[9.2714]
Rank[0/8] 06/19/2025 21:22:53 INFO stats.py:314 | Epoch[54] Step[89] GlobalStep[21149] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 0:35:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:23:03 INFO loss_tracker.py:84 | Epoch[54/239] Step[99] GlobalStep[21159/NA]: det_loss_cls_0[0.4974] det_loss_box_0[0.7621] det_loss_cls_1[0.4794] det_loss_box_1[0.6499] det_loss_cls_2[0.4644] det_loss_box_2[0.6011] det_loss_cls_3[0.4423] det_loss_box_3[0.5911] det_loss_cls_4[0.4163] det_loss_box_4[0.5764] det_loss_cls_5[0.4027] det_loss_box_5[0.5771] det_loss_cls_dn_0[0.1109] det_loss_box_dn_0[0.5466] det_loss_cls_dn_1[0.1073] det_loss_box_dn_1[0.4776] det_loss_cls_dn_2[0.1071] det_loss_box_dn_2[0.4546] det_loss_cls_dn_3[0.1055] det_loss_box_dn_3[0.4519] det_loss_cls_dn_4[0.1048] det_loss_box_dn_4[0.4509] det_loss_cls_dn_5[0.1040] det_loss_box_dn_5[0.4415] loss_depth[0.0427] total_loss[9.9657]
Rank[0/8] 06/19/2025 21:23:19 INFO stats.py:314 | Epoch[54] Step[114] GlobalStep[21174] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 0:34:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:23:29 INFO loss_tracker.py:84 | Epoch[54/239] Step[124] GlobalStep[21184/NA]: det_loss_cls_0[0.6356] det_loss_box_0[0.8158] det_loss_cls_1[0.6157] det_loss_box_1[0.6617] det_loss_cls_2[0.5934] det_loss_box_2[0.6099] det_loss_cls_3[0.5674] det_loss_box_3[0.5913] det_loss_cls_4[0.5301] det_loss_box_4[0.5874] det_loss_cls_5[0.5233] det_loss_box_5[0.5782] det_loss_cls_dn_0[0.1092] det_loss_box_dn_0[0.3151] det_loss_cls_dn_1[0.1058] det_loss_box_dn_1[0.2797] det_loss_cls_dn_2[0.1047] det_loss_box_dn_2[0.2651] det_loss_cls_dn_3[0.1035] det_loss_box_dn_3[0.2630] det_loss_cls_dn_4[0.1029] det_loss_box_dn_4[0.2628] det_loss_cls_dn_5[0.1020] det_loss_box_dn_5[0.2607] loss_depth[0.0394] total_loss[9.6238]
Rank[0/8] 06/19/2025 21:23:44 INFO stats.py:314 | Epoch[54] Step[139] GlobalStep[21199] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 0:33:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:23:54 INFO loss_tracker.py:84 | Epoch[54/239] Step[149] GlobalStep[21209/NA]: det_loss_cls_0[0.6430] det_loss_box_0[0.8933] det_loss_cls_1[0.6224] det_loss_box_1[0.7603] det_loss_cls_2[0.6043] det_loss_box_2[0.6816] det_loss_cls_3[0.5778] det_loss_box_3[0.6708] det_loss_cls_4[0.5403] det_loss_box_4[0.6655] det_loss_cls_5[0.5314] det_loss_box_5[0.6721] det_loss_cls_dn_0[0.1120] det_loss_box_dn_0[0.3214] det_loss_cls_dn_1[0.1079] det_loss_box_dn_1[0.2902] det_loss_cls_dn_2[0.1078] det_loss_box_dn_2[0.2763] det_loss_cls_dn_3[0.1061] det_loss_box_dn_3[0.2744] det_loss_cls_dn_4[0.1050] det_loss_box_dn_4[0.2740] det_loss_cls_dn_5[0.1041] det_loss_box_dn_5[0.2719] loss_depth[0.0377] total_loss[10.2518]
Rank[0/8] 06/19/2025 21:24:11 INFO stats.py:314 | Epoch[54] Step[164] GlobalStep[21224] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 0:32:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:24:21 INFO loss_tracker.py:84 | Epoch[54/239] Step[174] GlobalStep[21234/NA]: det_loss_cls_0[0.6370] det_loss_box_0[0.7868] det_loss_cls_1[0.6062] det_loss_box_1[0.6307] det_loss_cls_2[0.5861] det_loss_box_2[0.5714] det_loss_cls_3[0.5555] det_loss_box_3[0.5539] det_loss_cls_4[0.5303] det_loss_box_4[0.5382] det_loss_cls_5[0.5128] det_loss_box_5[0.5435] det_loss_cls_dn_0[0.1100] det_loss_box_dn_0[0.2995] det_loss_cls_dn_1[0.1068] det_loss_box_dn_1[0.2606] det_loss_cls_dn_2[0.1072] det_loss_box_dn_2[0.2446] det_loss_cls_dn_3[0.1064] det_loss_box_dn_3[0.2430] det_loss_cls_dn_4[0.1051] det_loss_box_dn_4[0.2428] det_loss_cls_dn_5[0.1040] det_loss_box_dn_5[0.2413] loss_depth[0.0404] total_loss[9.2642]
Rank[0/8] 06/19/2025 21:24:36 INFO stats.py:314 | Epoch[54] Step[189] GlobalStep[21249] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 0:32:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:24:46 INFO loss_tracker.py:84 | Epoch[54/239] Step[199] GlobalStep[21259/NA]: det_loss_cls_0[0.4998] det_loss_box_0[0.6614] det_loss_cls_1[0.4967] det_loss_box_1[0.5528] det_loss_cls_2[0.4748] det_loss_box_2[0.5196] det_loss_cls_3[0.4584] det_loss_box_3[0.4983] det_loss_cls_4[0.4285] det_loss_box_4[0.4918] det_loss_cls_5[0.4226] det_loss_box_5[0.4821] det_loss_cls_dn_0[0.1062] det_loss_box_dn_0[0.3470] det_loss_cls_dn_1[0.1018] det_loss_box_dn_1[0.3000] det_loss_cls_dn_2[0.1011] det_loss_box_dn_2[0.2832] det_loss_cls_dn_3[0.1012] det_loss_box_dn_3[0.2799] det_loss_cls_dn_4[0.0997] det_loss_box_dn_4[0.2784] det_loss_cls_dn_5[0.0987] det_loss_box_dn_5[0.2776] loss_depth[0.0391] total_loss[8.4007]
Rank[0/8] 06/19/2025 21:25:01 INFO stats.py:314 | Epoch[54] Step[214] GlobalStep[21274] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 0:31:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:25:12 INFO loss_tracker.py:84 | Epoch[54/239] Step[224] GlobalStep[21284/NA]: det_loss_cls_0[0.6377] det_loss_box_0[0.8298] det_loss_cls_1[0.6238] det_loss_box_1[0.6559] det_loss_cls_2[0.6012] det_loss_box_2[0.5951] det_loss_cls_3[0.5825] det_loss_box_3[0.5588] det_loss_cls_4[0.5482] det_loss_box_4[0.5604] det_loss_cls_5[0.5337] det_loss_box_5[0.5603] det_loss_cls_dn_0[0.1122] det_loss_box_dn_0[0.2812] det_loss_cls_dn_1[0.1080] det_loss_box_dn_1[0.2407] det_loss_cls_dn_2[0.1072] det_loss_box_dn_2[0.2267] det_loss_cls_dn_3[0.1067] det_loss_box_dn_3[0.2235] det_loss_cls_dn_4[0.1051] det_loss_box_dn_4[0.2222] det_loss_cls_dn_5[0.1040] det_loss_box_dn_5[0.2213] loss_depth[0.0413] total_loss[9.3874]
Rank[0/8] 06/19/2025 21:25:27 INFO stats.py:314 | Epoch[54] Step[239] GlobalStep[21299] Training Speed: 7.31 samples/sec across all devices.	Average Step Time: 1.09 sec.	Estimated Remaining Time: 1 day, 0:30:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:25:38 INFO loss_tracker.py:84 | Epoch[54/239] Step[249] GlobalStep[21309/NA]: det_loss_cls_0[0.5647] det_loss_box_0[0.7133] det_loss_cls_1[0.5527] det_loss_box_1[0.5763] det_loss_cls_2[0.5289] det_loss_box_2[0.5196] det_loss_cls_3[0.5123] det_loss_box_3[0.5114] det_loss_cls_4[0.4848] det_loss_box_4[0.5023] det_loss_cls_5[0.4724] det_loss_box_5[0.4984] det_loss_cls_dn_0[0.1112] det_loss_box_dn_0[0.3264] det_loss_cls_dn_1[0.1080] det_loss_box_dn_1[0.2920] det_loss_cls_dn_2[0.1077] det_loss_box_dn_2[0.2792] det_loss_cls_dn_3[0.1062] det_loss_box_dn_3[0.2778] det_loss_cls_dn_4[0.1060] det_loss_box_dn_4[0.2771] det_loss_cls_dn_5[0.1048] det_loss_box_dn_5[0.2750] loss_depth[0.0380] total_loss[8.8465]
Rank[0/8] 06/19/2025 21:25:54 INFO stats.py:314 | Epoch[54] Step[264] GlobalStep[21324] Training Speed: 7.72 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 0:29:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:26:04 INFO loss_tracker.py:84 | Epoch[54/239] Step[274] GlobalStep[21334/NA]: det_loss_cls_0[0.5650] det_loss_box_0[0.6898] det_loss_cls_1[0.5457] det_loss_box_1[0.5629] det_loss_cls_2[0.5317] det_loss_box_2[0.5139] det_loss_cls_3[0.5149] det_loss_box_3[0.4938] det_loss_cls_4[0.4860] det_loss_box_4[0.4934] det_loss_cls_5[0.4768] det_loss_box_5[0.4937] det_loss_cls_dn_0[0.1037] det_loss_box_dn_0[0.3531] det_loss_cls_dn_1[0.1011] det_loss_box_dn_1[0.3038] det_loss_cls_dn_2[0.0999] det_loss_box_dn_2[0.2870] det_loss_cls_dn_3[0.0995] det_loss_box_dn_3[0.2825] det_loss_cls_dn_4[0.0999] det_loss_box_dn_4[0.2809] det_loss_cls_dn_5[0.0996] det_loss_box_dn_5[0.2785] loss_depth[0.0383] total_loss[8.7954]
Rank[0/8] 06/19/2025 21:26:19 INFO stats.py:314 | Epoch[54] Step[289] GlobalStep[21349] Training Speed: 7.86 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1 day, 0:28:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:26:30 INFO loss_tracker.py:84 | Epoch[54/239] Step[299] GlobalStep[21359/NA]: det_loss_cls_0[0.5521] det_loss_box_0[0.7396] det_loss_cls_1[0.5378] det_loss_box_1[0.5736] det_loss_cls_2[0.5146] det_loss_box_2[0.5244] det_loss_cls_3[0.4949] det_loss_box_3[0.4969] det_loss_cls_4[0.4549] det_loss_box_4[0.4788] det_loss_cls_5[0.4387] det_loss_box_5[0.4800] det_loss_cls_dn_0[0.0999] det_loss_box_dn_0[0.2836] det_loss_cls_dn_1[0.0961] det_loss_box_dn_1[0.2445] det_loss_cls_dn_2[0.0957] det_loss_box_dn_2[0.2306] det_loss_cls_dn_3[0.0942] det_loss_box_dn_3[0.2275] det_loss_cls_dn_4[0.0928] det_loss_box_dn_4[0.2265] det_loss_cls_dn_5[0.0916] det_loss_box_dn_5[0.2247] loss_depth[0.0406] total_loss[8.3346]
Rank[0/8] 06/19/2025 21:26:45 INFO stats.py:314 | Epoch[54] Step[314] GlobalStep[21374] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 0:28:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:26:55 INFO loss_tracker.py:84 | Epoch[54/239] Step[324] GlobalStep[21384/NA]: det_loss_cls_0[0.5395] det_loss_box_0[0.7377] det_loss_cls_1[0.5342] det_loss_box_1[0.6047] det_loss_cls_2[0.5107] det_loss_box_2[0.5579] det_loss_cls_3[0.4839] det_loss_box_3[0.5357] det_loss_cls_4[0.4473] det_loss_box_4[0.5314] det_loss_cls_5[0.4374] det_loss_box_5[0.5269] det_loss_cls_dn_0[0.1025] det_loss_box_dn_0[0.3254] det_loss_cls_dn_1[0.1000] det_loss_box_dn_1[0.2831] det_loss_cls_dn_2[0.0989] det_loss_box_dn_2[0.2677] det_loss_cls_dn_3[0.0979] det_loss_box_dn_3[0.2632] det_loss_cls_dn_4[0.0970] det_loss_box_dn_4[0.2628] det_loss_cls_dn_5[0.0964] det_loss_box_dn_5[0.2610] loss_depth[0.0391] total_loss[8.7422]
Rank[0/8] 06/19/2025 21:27:10 INFO stats.py:314 | Epoch[54] Step[339] GlobalStep[21399] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 0:27:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:27:21 INFO loss_tracker.py:84 | Epoch[54/239] Step[349] GlobalStep[21409/NA]: det_loss_cls_0[0.6063] det_loss_box_0[0.8999] det_loss_cls_1[0.5807] det_loss_box_1[0.7289] det_loss_cls_2[0.5561] det_loss_box_2[0.6527] det_loss_cls_3[0.5346] det_loss_box_3[0.6277] det_loss_cls_4[0.5088] det_loss_box_4[0.6168] det_loss_cls_5[0.4998] det_loss_box_5[0.6204] det_loss_cls_dn_0[0.1144] det_loss_box_dn_0[0.3619] det_loss_cls_dn_1[0.1110] det_loss_box_dn_1[0.3237] det_loss_cls_dn_2[0.1103] det_loss_box_dn_2[0.3077] det_loss_cls_dn_3[0.1093] det_loss_box_dn_3[0.3052] det_loss_cls_dn_4[0.1088] det_loss_box_dn_4[0.3037] det_loss_cls_dn_5[0.1082] det_loss_box_dn_5[0.3027] loss_depth[0.0437] total_loss[10.0432]
Rank[0/8] 06/19/2025 21:27:37 INFO stats.py:314 | Epoch[54] Step[364] GlobalStep[21424] Training Speed: 8.56 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 0:26:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:27:47 INFO loss_tracker.py:84 | Epoch[54/239] Step[374] GlobalStep[21434/NA]: det_loss_cls_0[0.4850] det_loss_box_0[0.6436] det_loss_cls_1[0.4812] det_loss_box_1[0.5220] det_loss_cls_2[0.4515] det_loss_box_2[0.4713] det_loss_cls_3[0.4195] det_loss_box_3[0.4627] det_loss_cls_4[0.3947] det_loss_box_4[0.4532] det_loss_cls_5[0.3805] det_loss_box_5[0.4528] det_loss_cls_dn_0[0.0968] det_loss_box_dn_0[0.3336] det_loss_cls_dn_1[0.0926] det_loss_box_dn_1[0.2875] det_loss_cls_dn_2[0.0918] det_loss_box_dn_2[0.2704] det_loss_cls_dn_3[0.0901] det_loss_box_dn_3[0.2656] det_loss_cls_dn_4[0.0891] det_loss_box_dn_4[0.2645] det_loss_cls_dn_5[0.0881] det_loss_box_dn_5[0.2613] loss_depth[0.0428] total_loss[7.8921]
Rank[0/8] 06/19/2025 21:28:02 INFO stats.py:314 | Epoch[54] Step[389] GlobalStep[21449] Training Speed: 8.68 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 1 day, 0:25:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:28:02 INFO stats.py:394 | Epoch[54] completed. Training Speed: 7.68 samples/sec across all devices.	Epoch Time: 406.28 sec.	Average Epoch Time: 406.28 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 0:25:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:28:30 INFO stats.py:314 | Epoch[55] Step[24] GlobalStep[21474] Training Speed: 8.64 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 0:25:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:28:30 INFO loss_tracker.py:84 | Epoch[55/239] Step[24] GlobalStep[21474/NA]: det_loss_cls_0[0.5264] det_loss_box_0[0.6543] det_loss_cls_1[0.5183] det_loss_box_1[0.5314] det_loss_cls_2[0.4948] det_loss_box_2[0.4822] det_loss_cls_3[0.4704] det_loss_box_3[0.4602] det_loss_cls_4[0.4381] det_loss_box_4[0.4534] det_loss_cls_5[0.4243] det_loss_box_5[0.4559] det_loss_cls_dn_0[0.1047] det_loss_box_dn_0[0.2870] det_loss_cls_dn_1[0.1020] det_loss_box_dn_1[0.2481] det_loss_cls_dn_2[0.1020] det_loss_box_dn_2[0.2342] det_loss_cls_dn_3[0.1009] det_loss_box_dn_3[0.2312] det_loss_cls_dn_4[0.0999] det_loss_box_dn_4[0.2305] det_loss_cls_dn_5[0.0997] det_loss_box_dn_5[0.2298] loss_depth[0.0395] total_loss[8.0194]
Rank[0/8] 06/19/2025 21:28:56 INFO stats.py:314 | Epoch[55] Step[49] GlobalStep[21499] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 0:24:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:28:56 INFO loss_tracker.py:84 | Epoch[55/239] Step[49] GlobalStep[21499/NA]: det_loss_cls_0[0.6183] det_loss_box_0[0.7714] det_loss_cls_1[0.5907] det_loss_box_1[0.6364] det_loss_cls_2[0.5694] det_loss_box_2[0.5649] det_loss_cls_3[0.5425] det_loss_box_3[0.5501] det_loss_cls_4[0.5126] det_loss_box_4[0.5434] det_loss_cls_5[0.5004] det_loss_box_5[0.5407] det_loss_cls_dn_0[0.1143] det_loss_box_dn_0[0.2890] det_loss_cls_dn_1[0.1109] det_loss_box_dn_1[0.2550] det_loss_cls_dn_2[0.1104] det_loss_box_dn_2[0.2405] det_loss_cls_dn_3[0.1098] det_loss_box_dn_3[0.2388] det_loss_cls_dn_4[0.1089] det_loss_box_dn_4[0.2374] det_loss_cls_dn_5[0.1083] det_loss_box_dn_5[0.2359] loss_depth[0.0411] total_loss[9.1414]
Rank[0/8] 06/19/2025 21:29:21 INFO stats.py:314 | Epoch[55] Step[74] GlobalStep[21524] Training Speed: 7.38 samples/sec across all devices.	Average Step Time: 1.08 sec.	Estimated Remaining Time: 1 day, 0:23:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:29:21 INFO loss_tracker.py:84 | Epoch[55/239] Step[74] GlobalStep[21524/NA]: det_loss_cls_0[0.6243] det_loss_box_0[0.8536] det_loss_cls_1[0.6039] det_loss_box_1[0.6885] det_loss_cls_2[0.5766] det_loss_box_2[0.6398] det_loss_cls_3[0.5511] det_loss_box_3[0.6153] det_loss_cls_4[0.5263] det_loss_box_4[0.5975] det_loss_cls_5[0.5117] det_loss_box_5[0.5982] det_loss_cls_dn_0[0.1048] det_loss_box_dn_0[0.3161] det_loss_cls_dn_1[0.1008] det_loss_box_dn_1[0.2749] det_loss_cls_dn_2[0.0995] det_loss_box_dn_2[0.2585] det_loss_cls_dn_3[0.0992] det_loss_box_dn_3[0.2552] det_loss_cls_dn_4[0.0984] det_loss_box_dn_4[0.2544] det_loss_cls_dn_5[0.0976] det_loss_box_dn_5[0.2542] loss_depth[0.0428] total_loss[9.6433]
Rank[0/8] 06/19/2025 21:29:47 INFO stats.py:314 | Epoch[55] Step[99] GlobalStep[21549] Training Speed: 8.75 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 1 day, 0:22:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:29:48 INFO loss_tracker.py:84 | Epoch[55/239] Step[99] GlobalStep[21549/NA]: det_loss_cls_0[0.5689] det_loss_box_0[0.7546] det_loss_cls_1[0.5495] det_loss_box_1[0.6300] det_loss_cls_2[0.5343] det_loss_box_2[0.5873] det_loss_cls_3[0.5117] det_loss_box_3[0.5653] det_loss_cls_4[0.4853] det_loss_box_4[0.5462] det_loss_cls_5[0.4761] det_loss_box_5[0.5434] det_loss_cls_dn_0[0.1130] det_loss_box_dn_0[0.3159] det_loss_cls_dn_1[0.1088] det_loss_box_dn_1[0.2816] det_loss_cls_dn_2[0.1085] det_loss_box_dn_2[0.2674] det_loss_cls_dn_3[0.1076] det_loss_box_dn_3[0.2631] det_loss_cls_dn_4[0.1069] det_loss_box_dn_4[0.2626] det_loss_cls_dn_5[0.1062] det_loss_box_dn_5[0.2600] loss_depth[0.0389] total_loss[9.0932]
Rank[0/8] 06/19/2025 21:30:13 INFO stats.py:314 | Epoch[55] Step[124] GlobalStep[21574] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 0:22:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:30:13 INFO loss_tracker.py:84 | Epoch[55/239] Step[124] GlobalStep[21574/NA]: det_loss_cls_0[0.4688] det_loss_box_0[0.5925] det_loss_cls_1[0.4373] det_loss_box_1[0.4684] det_loss_cls_2[0.4174] det_loss_box_2[0.4200] det_loss_cls_3[0.3891] det_loss_box_3[0.4065] det_loss_cls_4[0.3546] det_loss_box_4[0.4007] det_loss_cls_5[0.3413] det_loss_box_5[0.3992] det_loss_cls_dn_0[0.0959] det_loss_box_dn_0[0.3067] det_loss_cls_dn_1[0.0911] det_loss_box_dn_1[0.2638] det_loss_cls_dn_2[0.0904] det_loss_box_dn_2[0.2462] det_loss_cls_dn_3[0.0888] det_loss_box_dn_3[0.2428] det_loss_cls_dn_4[0.0879] det_loss_box_dn_4[0.2408] det_loss_cls_dn_5[0.0873] det_loss_box_dn_5[0.2387] loss_depth[0.0393] total_loss[7.2158]
Rank[0/8] 06/19/2025 21:30:39 INFO stats.py:314 | Epoch[55] Step[149] GlobalStep[21599] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 0:21:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:30:39 INFO loss_tracker.py:84 | Epoch[55/239] Step[149] GlobalStep[21599/NA]: det_loss_cls_0[0.6242] det_loss_box_0[0.8388] det_loss_cls_1[0.6073] det_loss_box_1[0.6804] det_loss_cls_2[0.5961] det_loss_box_2[0.6352] det_loss_cls_3[0.5635] det_loss_box_3[0.6271] det_loss_cls_4[0.5326] det_loss_box_4[0.6125] det_loss_cls_5[0.5150] det_loss_box_5[0.6169] det_loss_cls_dn_0[0.1070] det_loss_box_dn_0[0.3569] det_loss_cls_dn_1[0.1044] det_loss_box_dn_1[0.3203] det_loss_cls_dn_2[0.1050] det_loss_box_dn_2[0.3094] det_loss_cls_dn_3[0.1046] det_loss_box_dn_3[0.3071] det_loss_cls_dn_4[0.1034] det_loss_box_dn_4[0.3069] det_loss_cls_dn_5[0.1028] det_loss_box_dn_5[0.3062] loss_depth[0.0356] total_loss[10.0188]
Rank[0/8] 06/19/2025 21:31:04 INFO stats.py:314 | Epoch[55] Step[174] GlobalStep[21624] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 0:20:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:31:05 INFO loss_tracker.py:84 | Epoch[55/239] Step[174] GlobalStep[21624/NA]: det_loss_cls_0[0.5956] det_loss_box_0[0.8053] det_loss_cls_1[0.5990] det_loss_box_1[0.6475] det_loss_cls_2[0.5776] det_loss_box_2[0.5838] det_loss_cls_3[0.5419] det_loss_box_3[0.5644] det_loss_cls_4[0.5071] det_loss_box_4[0.5431] det_loss_cls_5[0.4990] det_loss_box_5[0.5430] det_loss_cls_dn_0[0.1015] det_loss_box_dn_0[0.3503] det_loss_cls_dn_1[0.0980] det_loss_box_dn_1[0.3142] det_loss_cls_dn_2[0.0979] det_loss_box_dn_2[0.3011] det_loss_cls_dn_3[0.0972] det_loss_box_dn_3[0.2970] det_loss_cls_dn_4[0.0964] det_loss_box_dn_4[0.2959] det_loss_cls_dn_5[0.0948] det_loss_box_dn_5[0.2929] loss_depth[0.0415] total_loss[9.4861]
Rank[0/8] 06/19/2025 21:31:31 INFO stats.py:314 | Epoch[55] Step[199] GlobalStep[21649] Training Speed: 8.06 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 0:19:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:31:31 INFO loss_tracker.py:84 | Epoch[55/239] Step[199] GlobalStep[21649/NA]: det_loss_cls_0[0.4070] det_loss_box_0[0.6301] det_loss_cls_1[0.3880] det_loss_box_1[0.5119] det_loss_cls_2[0.3764] det_loss_box_2[0.4763] det_loss_cls_3[0.3643] det_loss_box_3[0.4727] det_loss_cls_4[0.3453] det_loss_box_4[0.4610] det_loss_cls_5[0.3359] det_loss_box_5[0.4603] det_loss_cls_dn_0[0.1080] det_loss_box_dn_0[0.5114] det_loss_cls_dn_1[0.1036] det_loss_box_dn_1[0.4751] det_loss_cls_dn_2[0.1025] det_loss_box_dn_2[0.4540] det_loss_cls_dn_3[0.1015] det_loss_box_dn_3[0.4541] det_loss_cls_dn_4[0.1005] det_loss_box_dn_4[0.4529] det_loss_cls_dn_5[0.0994] det_loss_box_dn_5[0.4458] loss_depth[0.0410] total_loss[8.6790]
Rank[0/8] 06/19/2025 21:31:57 INFO stats.py:314 | Epoch[55] Step[224] GlobalStep[21674] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 0:19:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:31:57 INFO loss_tracker.py:84 | Epoch[55/239] Step[224] GlobalStep[21674/NA]: det_loss_cls_0[0.5064] det_loss_box_0[0.6872] det_loss_cls_1[0.4811] det_loss_box_1[0.5606] det_loss_cls_2[0.4656] det_loss_box_2[0.5156] det_loss_cls_3[0.4427] det_loss_box_3[0.4988] det_loss_cls_4[0.4187] det_loss_box_4[0.4894] det_loss_cls_5[0.4043] det_loss_box_5[0.4941] det_loss_cls_dn_0[0.1052] det_loss_box_dn_0[0.3250] det_loss_cls_dn_1[0.1024] det_loss_box_dn_1[0.2835] det_loss_cls_dn_2[0.1009] det_loss_box_dn_2[0.2658] det_loss_cls_dn_3[0.0999] det_loss_box_dn_3[0.2629] det_loss_cls_dn_4[0.0983] det_loss_box_dn_4[0.2617] det_loss_cls_dn_5[0.0975] det_loss_box_dn_5[0.2605] loss_depth[0.0370] total_loss[8.2651]
Rank[0/8] 06/19/2025 21:32:22 INFO stats.py:314 | Epoch[55] Step[249] GlobalStep[21699] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 0:18:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:32:22 INFO loss_tracker.py:84 | Epoch[55/239] Step[249] GlobalStep[21699/NA]: det_loss_cls_0[0.6198] det_loss_box_0[0.8497] det_loss_cls_1[0.6018] det_loss_box_1[0.7007] det_loss_cls_2[0.5858] det_loss_box_2[0.6369] det_loss_cls_3[0.5740] det_loss_box_3[0.6151] det_loss_cls_4[0.5297] det_loss_box_4[0.6195] det_loss_cls_5[0.5151] det_loss_box_5[0.6187] det_loss_cls_dn_0[0.1059] det_loss_box_dn_0[0.3368] det_loss_cls_dn_1[0.1036] det_loss_box_dn_1[0.3034] det_loss_cls_dn_2[0.1030] det_loss_box_dn_2[0.2881] det_loss_cls_dn_3[0.1030] det_loss_box_dn_3[0.2858] det_loss_cls_dn_4[0.1022] det_loss_box_dn_4[0.2853] det_loss_cls_dn_5[0.1009] det_loss_box_dn_5[0.2818] loss_depth[0.0385] total_loss[9.9052]
Rank[0/8] 06/19/2025 21:32:48 INFO stats.py:314 | Epoch[55] Step[274] GlobalStep[21724] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 0:17:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:32:48 INFO loss_tracker.py:84 | Epoch[55/239] Step[274] GlobalStep[21724/NA]: det_loss_cls_0[0.5622] det_loss_box_0[0.7654] det_loss_cls_1[0.5440] det_loss_box_1[0.6420] det_loss_cls_2[0.5211] det_loss_box_2[0.5885] det_loss_cls_3[0.4964] det_loss_box_3[0.5679] det_loss_cls_4[0.4672] det_loss_box_4[0.5532] det_loss_cls_5[0.4582] det_loss_box_5[0.5526] det_loss_cls_dn_0[0.1106] det_loss_box_dn_0[0.3150] det_loss_cls_dn_1[0.1054] det_loss_box_dn_1[0.2741] det_loss_cls_dn_2[0.1046] det_loss_box_dn_2[0.2606] det_loss_cls_dn_3[0.1045] det_loss_box_dn_3[0.2582] det_loss_cls_dn_4[0.1028] det_loss_box_dn_4[0.2574] det_loss_cls_dn_5[0.1017] det_loss_box_dn_5[0.2542] loss_depth[0.0430] total_loss[9.0107]
Rank[0/8] 06/19/2025 21:33:14 INFO stats.py:314 | Epoch[55] Step[299] GlobalStep[21749] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 0:16:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:33:14 INFO loss_tracker.py:84 | Epoch[55/239] Step[299] GlobalStep[21749/NA]: det_loss_cls_0[0.5692] det_loss_box_0[0.7696] det_loss_cls_1[0.5512] det_loss_box_1[0.6342] det_loss_cls_2[0.5424] det_loss_box_2[0.5756] det_loss_cls_3[0.5056] det_loss_box_3[0.5606] det_loss_cls_4[0.4673] det_loss_box_4[0.5501] det_loss_cls_5[0.4412] det_loss_box_5[0.5550] det_loss_cls_dn_0[0.1037] det_loss_box_dn_0[0.3166] det_loss_cls_dn_1[0.1003] det_loss_box_dn_1[0.2764] det_loss_cls_dn_2[0.0986] det_loss_box_dn_2[0.2569] det_loss_cls_dn_3[0.0985] det_loss_box_dn_3[0.2537] det_loss_cls_dn_4[0.0963] det_loss_box_dn_4[0.2525] det_loss_cls_dn_5[0.0950] det_loss_box_dn_5[0.2504] loss_depth[0.0394] total_loss[8.9602]
Rank[0/8] 06/19/2025 21:33:39 INFO stats.py:314 | Epoch[55] Step[324] GlobalStep[21774] Training Speed: 8.34 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 0:15:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:33:39 INFO loss_tracker.py:84 | Epoch[55/239] Step[324] GlobalStep[21774/NA]: det_loss_cls_0[0.6007] det_loss_box_0[0.8361] det_loss_cls_1[0.5809] det_loss_box_1[0.6741] det_loss_cls_2[0.5753] det_loss_box_2[0.6135] det_loss_cls_3[0.5543] det_loss_box_3[0.5957] det_loss_cls_4[0.5135] det_loss_box_4[0.5883] det_loss_cls_5[0.5021] det_loss_box_5[0.5851] det_loss_cls_dn_0[0.1011] det_loss_box_dn_0[0.3715] det_loss_cls_dn_1[0.0979] det_loss_box_dn_1[0.3241] det_loss_cls_dn_2[0.0971] det_loss_box_dn_2[0.3042] det_loss_cls_dn_3[0.0961] det_loss_box_dn_3[0.3009] det_loss_cls_dn_4[0.0943] det_loss_box_dn_4[0.2999] det_loss_cls_dn_5[0.0939] det_loss_box_dn_5[0.2991] loss_depth[0.0433] total_loss[9.7430]
Rank[0/8] 06/19/2025 21:34:05 INFO stats.py:314 | Epoch[55] Step[349] GlobalStep[21799] Training Speed: 6.85 samples/sec across all devices.	Average Step Time: 1.17 sec.	Estimated Remaining Time: 1 day, 0:15:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:34:05 INFO loss_tracker.py:84 | Epoch[55/239] Step[349] GlobalStep[21799/NA]: det_loss_cls_0[0.5149] det_loss_box_0[0.6395] det_loss_cls_1[0.4897] det_loss_box_1[0.5281] det_loss_cls_2[0.4764] det_loss_box_2[0.4704] det_loss_cls_3[0.4575] det_loss_box_3[0.4613] det_loss_cls_4[0.4354] det_loss_box_4[0.4430] det_loss_cls_5[0.4205] det_loss_box_5[0.4427] det_loss_cls_dn_0[0.1032] det_loss_box_dn_0[0.3207] det_loss_cls_dn_1[0.0991] det_loss_box_dn_1[0.2728] det_loss_cls_dn_2[0.0990] det_loss_box_dn_2[0.2543] det_loss_cls_dn_3[0.0987] det_loss_box_dn_3[0.2510] det_loss_cls_dn_4[0.0986] det_loss_box_dn_4[0.2497] det_loss_cls_dn_5[0.0976] det_loss_box_dn_5[0.2478] loss_depth[0.0399] total_loss[8.0118]
Rank[0/8] 06/19/2025 21:34:31 INFO stats.py:314 | Epoch[55] Step[374] GlobalStep[21824] Training Speed: 8.19 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 0:14:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:34:31 INFO loss_tracker.py:84 | Epoch[55/239] Step[374] GlobalStep[21824/NA]: det_loss_cls_0[0.6301] det_loss_box_0[0.8034] det_loss_cls_1[0.6128] det_loss_box_1[0.6459] det_loss_cls_2[0.5877] det_loss_box_2[0.6021] det_loss_cls_3[0.5660] det_loss_box_3[0.5818] det_loss_cls_4[0.5390] det_loss_box_4[0.5602] det_loss_cls_5[0.5222] det_loss_box_5[0.5597] det_loss_cls_dn_0[0.0990] det_loss_box_dn_0[0.3484] det_loss_cls_dn_1[0.0954] det_loss_box_dn_1[0.3127] det_loss_cls_dn_2[0.0951] det_loss_box_dn_2[0.2952] det_loss_cls_dn_3[0.0943] det_loss_box_dn_3[0.2944] det_loss_cls_dn_4[0.0932] det_loss_box_dn_4[0.2937] det_loss_cls_dn_5[0.0928] det_loss_box_dn_5[0.2928] loss_depth[0.0384] total_loss[9.6560]
Rank[0/8] 06/19/2025 21:34:46 INFO stats.py:394 | Epoch[55] completed. Training Speed: 7.73 samples/sec across all devices.	Epoch Time: 403.70 sec.	Average Epoch Time: 403.70 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 0:13:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:34:58 INFO stats.py:314 | Epoch[56] Step[9] GlobalStep[21849] Training Speed: 14.59 samples/sec across all devices.	Average Step Time: 0.55 sec.	Estimated Remaining Time: 1 day, 0:13:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:35:13 INFO loss_tracker.py:84 | Epoch[56/239] Step[24] GlobalStep[21864/NA]: det_loss_cls_0[0.5950] det_loss_box_0[0.8275] det_loss_cls_1[0.5841] det_loss_box_1[0.6992] det_loss_cls_2[0.5599] det_loss_box_2[0.6456] det_loss_cls_3[0.5344] det_loss_box_3[0.6128] det_loss_cls_4[0.5085] det_loss_box_4[0.6050] det_loss_cls_5[0.4922] det_loss_box_5[0.6053] det_loss_cls_dn_0[0.1054] det_loss_box_dn_0[0.3518] det_loss_cls_dn_1[0.1024] det_loss_box_dn_1[0.3149] det_loss_cls_dn_2[0.1024] det_loss_box_dn_2[0.2991] det_loss_cls_dn_3[0.1006] det_loss_box_dn_3[0.2972] det_loss_cls_dn_4[0.0996] det_loss_box_dn_4[0.2964] det_loss_cls_dn_5[0.0989] det_loss_box_dn_5[0.2953] loss_depth[0.0384] total_loss[9.7717]
Rank[0/8] 06/19/2025 21:35:23 INFO stats.py:314 | Epoch[56] Step[34] GlobalStep[21874] Training Speed: 7.39 samples/sec across all devices.	Average Step Time: 1.08 sec.	Estimated Remaining Time: 1 day, 0:12:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:35:39 INFO loss_tracker.py:84 | Epoch[56/239] Step[49] GlobalStep[21889/NA]: det_loss_cls_0[0.4674] det_loss_box_0[0.6331] det_loss_cls_1[0.4504] det_loss_box_1[0.5292] det_loss_cls_2[0.4151] det_loss_box_2[0.4946] det_loss_cls_3[0.3980] det_loss_box_3[0.4734] det_loss_cls_4[0.3755] det_loss_box_4[0.4670] det_loss_cls_5[0.3694] det_loss_box_5[0.4560] det_loss_cls_dn_0[0.0989] det_loss_box_dn_0[0.4684] det_loss_cls_dn_1[0.0943] det_loss_box_dn_1[0.4231] det_loss_cls_dn_2[0.0938] det_loss_box_dn_2[0.4137] det_loss_cls_dn_3[0.0932] det_loss_box_dn_3[0.4108] det_loss_cls_dn_4[0.0917] det_loss_box_dn_4[0.4101] det_loss_cls_dn_5[0.0911] det_loss_box_dn_5[0.4085] loss_depth[0.0385] total_loss[8.6652]
Rank[0/8] 06/19/2025 21:35:49 INFO stats.py:314 | Epoch[56] Step[59] GlobalStep[21899] Training Speed: 7.89 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 0:12:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:36:05 INFO loss_tracker.py:84 | Epoch[56/239] Step[74] GlobalStep[21914/NA]: det_loss_cls_0[0.6352] det_loss_box_0[0.7854] det_loss_cls_1[0.6138] det_loss_box_1[0.6110] det_loss_cls_2[0.5926] det_loss_box_2[0.5554] det_loss_cls_3[0.5655] det_loss_box_3[0.5356] det_loss_cls_4[0.5245] det_loss_box_4[0.5305] det_loss_cls_5[0.5039] det_loss_box_5[0.5298] det_loss_cls_dn_0[0.1012] det_loss_box_dn_0[0.2788] det_loss_cls_dn_1[0.0973] det_loss_box_dn_1[0.2386] det_loss_cls_dn_2[0.0970] det_loss_box_dn_2[0.2230] det_loss_cls_dn_3[0.0962] det_loss_box_dn_3[0.2197] det_loss_cls_dn_4[0.0963] det_loss_box_dn_4[0.2191] det_loss_cls_dn_5[0.0949] det_loss_box_dn_5[0.2179] loss_depth[0.0409] total_loss[9.0044]
Rank[0/8] 06/19/2025 21:36:15 INFO stats.py:314 | Epoch[56] Step[84] GlobalStep[21924] Training Speed: 7.99 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1 day, 0:11:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:36:31 INFO loss_tracker.py:84 | Epoch[56/239] Step[99] GlobalStep[21939/NA]: det_loss_cls_0[0.5990] det_loss_box_0[0.7362] det_loss_cls_1[0.5849] det_loss_box_1[0.6026] det_loss_cls_2[0.5749] det_loss_box_2[0.5487] det_loss_cls_3[0.5600] det_loss_box_3[0.5256] det_loss_cls_4[0.5308] det_loss_box_4[0.5154] det_loss_cls_5[0.5196] det_loss_box_5[0.5153] det_loss_cls_dn_0[0.1235] det_loss_box_dn_0[0.3025] det_loss_cls_dn_1[0.1211] det_loss_box_dn_1[0.2666] det_loss_cls_dn_2[0.1208] det_loss_box_dn_2[0.2526] det_loss_cls_dn_3[0.1197] det_loss_box_dn_3[0.2505] det_loss_cls_dn_4[0.1184] det_loss_box_dn_4[0.2499] det_loss_cls_dn_5[0.1189] det_loss_box_dn_5[0.2481] loss_depth[0.0385] total_loss[9.1439]
Rank[0/8] 06/19/2025 21:36:41 INFO stats.py:314 | Epoch[56] Step[109] GlobalStep[21949] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1 day, 0:10:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:36:56 INFO loss_tracker.py:84 | Epoch[56/239] Step[124] GlobalStep[21964/NA]: det_loss_cls_0[0.4365] det_loss_box_0[0.5931] det_loss_cls_1[0.4181] det_loss_box_1[0.4688] det_loss_cls_2[0.4028] det_loss_box_2[0.4275] det_loss_cls_3[0.3821] det_loss_box_3[0.4103] det_loss_cls_4[0.3591] det_loss_box_4[0.4102] det_loss_cls_5[0.3476] det_loss_box_5[0.4123] det_loss_cls_dn_0[0.0875] det_loss_box_dn_0[0.3403] det_loss_cls_dn_1[0.0843] det_loss_box_dn_1[0.2827] det_loss_cls_dn_2[0.0839] det_loss_box_dn_2[0.2603] det_loss_cls_dn_3[0.0829] det_loss_box_dn_3[0.2576] det_loss_cls_dn_4[0.0816] det_loss_box_dn_4[0.2558] det_loss_cls_dn_5[0.0804] det_loss_box_dn_5[0.2544] loss_depth[0.0405] total_loss[7.2606]
Rank[0/8] 06/19/2025 21:37:07 INFO stats.py:314 | Epoch[56] Step[134] GlobalStep[21974] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1 day, 0:09:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:37:23 INFO loss_tracker.py:84 | Epoch[56/239] Step[149] GlobalStep[21989/NA]: det_loss_cls_0[0.5767] det_loss_box_0[0.7187] det_loss_cls_1[0.5554] det_loss_box_1[0.5831] det_loss_cls_2[0.5332] det_loss_box_2[0.5171] det_loss_cls_3[0.5046] det_loss_box_3[0.4984] det_loss_cls_4[0.4716] det_loss_box_4[0.4910] det_loss_cls_5[0.4676] det_loss_box_5[0.4857] det_loss_cls_dn_0[0.1017] det_loss_box_dn_0[0.2631] det_loss_cls_dn_1[0.0980] det_loss_box_dn_1[0.2254] det_loss_cls_dn_2[0.0973] det_loss_box_dn_2[0.2114] det_loss_cls_dn_3[0.0960] det_loss_box_dn_3[0.2103] det_loss_cls_dn_4[0.0962] det_loss_box_dn_4[0.2089] det_loss_cls_dn_5[0.0950] det_loss_box_dn_5[0.2084] loss_depth[0.0404] total_loss[8.3553]
Rank[0/8] 06/19/2025 21:37:33 INFO stats.py:314 | Epoch[56] Step[159] GlobalStep[21999] Training Speed: 7.56 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 1 day, 0:09:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:37:48 INFO loss_tracker.py:84 | Epoch[56/239] Step[174] GlobalStep[22014/NA]: det_loss_cls_0[0.5993] det_loss_box_0[0.7970] det_loss_cls_1[0.5960] det_loss_box_1[0.6462] det_loss_cls_2[0.5700] det_loss_box_2[0.5814] det_loss_cls_3[0.5414] det_loss_box_3[0.5582] det_loss_cls_4[0.5190] det_loss_box_4[0.5486] det_loss_cls_5[0.5007] det_loss_box_5[0.5494] det_loss_cls_dn_0[0.0984] det_loss_box_dn_0[0.3103] det_loss_cls_dn_1[0.0953] det_loss_box_dn_1[0.2699] det_loss_cls_dn_2[0.0942] det_loss_box_dn_2[0.2544] det_loss_cls_dn_3[0.0928] det_loss_box_dn_3[0.2516] det_loss_cls_dn_4[0.0918] det_loss_box_dn_4[0.2513] det_loss_cls_dn_5[0.0911] det_loss_box_dn_5[0.2504] loss_depth[0.0388] total_loss[9.1975]
Rank[0/8] 06/19/2025 21:37:59 INFO stats.py:314 | Epoch[56] Step[184] GlobalStep[22024] Training Speed: 8.62 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1 day, 0:08:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:38:14 INFO loss_tracker.py:84 | Epoch[56/239] Step[199] GlobalStep[22039/NA]: det_loss_cls_0[0.7493] det_loss_box_0[1.0422] det_loss_cls_1[0.7345] det_loss_box_1[0.8172] det_loss_cls_2[0.7275] det_loss_box_2[0.7458] det_loss_cls_3[0.6966] det_loss_box_3[0.7169] det_loss_cls_4[0.6571] det_loss_box_4[0.7021] det_loss_cls_5[0.6449] det_loss_box_5[0.6957] det_loss_cls_dn_0[0.1092] det_loss_box_dn_0[0.3133] det_loss_cls_dn_1[0.1055] det_loss_box_dn_1[0.2714] det_loss_cls_dn_2[0.1048] det_loss_box_dn_2[0.2530] det_loss_cls_dn_3[0.1047] det_loss_box_dn_3[0.2501] det_loss_cls_dn_4[0.1033] det_loss_box_dn_4[0.2478] det_loss_cls_dn_5[0.1027] det_loss_box_dn_5[0.2463] loss_depth[0.0422] total_loss[11.1841]
Rank[0/8] 06/19/2025 21:38:24 INFO stats.py:314 | Epoch[56] Step[209] GlobalStep[22049] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1 day, 0:07:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:38:41 INFO loss_tracker.py:84 | Epoch[56/239] Step[224] GlobalStep[22064/NA]: det_loss_cls_0[0.5283] det_loss_box_0[0.7522] det_loss_cls_1[0.5002] det_loss_box_1[0.6017] det_loss_cls_2[0.4873] det_loss_box_2[0.5475] det_loss_cls_3[0.4705] det_loss_box_3[0.5286] det_loss_cls_4[0.4331] det_loss_box_4[0.5225] det_loss_cls_5[0.4206] det_loss_box_5[0.5161] det_loss_cls_dn_0[0.1024] det_loss_box_dn_0[0.3871] det_loss_cls_dn_1[0.0994] det_loss_box_dn_1[0.3395] det_loss_cls_dn_2[0.0985] det_loss_box_dn_2[0.3119] det_loss_cls_dn_3[0.0972] det_loss_box_dn_3[0.3082] det_loss_cls_dn_4[0.0968] det_loss_box_dn_4[0.3094] det_loss_cls_dn_5[0.0959] det_loss_box_dn_5[0.3102] loss_depth[0.0403] total_loss[8.9055]
Rank[0/8] 06/19/2025 21:38:51 INFO stats.py:314 | Epoch[56] Step[234] GlobalStep[22074] Training Speed: 7.28 samples/sec across all devices.	Average Step Time: 1.10 sec.	Estimated Remaining Time: 1 day, 0:07:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:39:06 INFO loss_tracker.py:84 | Epoch[56/239] Step[249] GlobalStep[22089/NA]: det_loss_cls_0[0.5256] det_loss_box_0[0.6606] det_loss_cls_1[0.5060] det_loss_box_1[0.5249] det_loss_cls_2[0.4902] det_loss_box_2[0.4680] det_loss_cls_3[0.4614] det_loss_box_3[0.4541] det_loss_cls_4[0.4241] det_loss_box_4[0.4551] det_loss_cls_5[0.4142] det_loss_box_5[0.4514] det_loss_cls_dn_0[0.0974] det_loss_box_dn_0[0.2859] det_loss_cls_dn_1[0.0935] det_loss_box_dn_1[0.2482] det_loss_cls_dn_2[0.0929] det_loss_box_dn_2[0.2323] det_loss_cls_dn_3[0.0921] det_loss_box_dn_3[0.2290] det_loss_cls_dn_4[0.0911] det_loss_box_dn_4[0.2278] det_loss_cls_dn_5[0.0903] det_loss_box_dn_5[0.2268] loss_depth[0.0408] total_loss[7.8835]
Rank[0/8] 06/19/2025 21:39:16 INFO stats.py:314 | Epoch[56] Step[259] GlobalStep[22099] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 0:06:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:39:32 INFO loss_tracker.py:84 | Epoch[56/239] Step[274] GlobalStep[22114/NA]: det_loss_cls_0[0.6295] det_loss_box_0[0.8620] det_loss_cls_1[0.6196] det_loss_box_1[0.6878] det_loss_cls_2[0.5904] det_loss_box_2[0.6158] det_loss_cls_3[0.5672] det_loss_box_3[0.5912] det_loss_cls_4[0.5398] det_loss_box_4[0.5724] det_loss_cls_5[0.5223] det_loss_box_5[0.5713] det_loss_cls_dn_0[0.1018] det_loss_box_dn_0[0.2722] det_loss_cls_dn_1[0.0983] det_loss_box_dn_1[0.2338] det_loss_cls_dn_2[0.0979] det_loss_box_dn_2[0.2217] det_loss_cls_dn_3[0.0961] det_loss_box_dn_3[0.2193] det_loss_cls_dn_4[0.0949] det_loss_box_dn_4[0.2186] det_loss_cls_dn_5[0.0941] det_loss_box_dn_5[0.2176] loss_depth[0.0389] total_loss[9.3744]
Rank[0/8] 06/19/2025 21:39:42 INFO stats.py:314 | Epoch[56] Step[284] GlobalStep[22124] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 0:05:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:39:58 INFO loss_tracker.py:84 | Epoch[56/239] Step[299] GlobalStep[22139/NA]: det_loss_cls_0[0.5811] det_loss_box_0[0.7819] det_loss_cls_1[0.5636] det_loss_box_1[0.6296] det_loss_cls_2[0.5393] det_loss_box_2[0.5714] det_loss_cls_3[0.5215] det_loss_box_3[0.5514] det_loss_cls_4[0.4954] det_loss_box_4[0.5355] det_loss_cls_5[0.4722] det_loss_box_5[0.5385] det_loss_cls_dn_0[0.0970] det_loss_box_dn_0[0.3309] det_loss_cls_dn_1[0.0942] det_loss_box_dn_1[0.3061] det_loss_cls_dn_2[0.0939] det_loss_box_dn_2[0.2909] det_loss_cls_dn_3[0.0935] det_loss_box_dn_3[0.2902] det_loss_cls_dn_4[0.0927] det_loss_box_dn_4[0.2896] det_loss_cls_dn_5[0.0909] det_loss_box_dn_5[0.2883] loss_depth[0.0411] total_loss[9.1805]
Rank[0/8] 06/19/2025 21:40:08 INFO stats.py:314 | Epoch[56] Step[309] GlobalStep[22149] Training Speed: 7.79 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 1 day, 0:04:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:40:24 INFO loss_tracker.py:84 | Epoch[56/239] Step[324] GlobalStep[22164/NA]: det_loss_cls_0[0.4839] det_loss_box_0[0.7669] det_loss_cls_1[0.4598] det_loss_box_1[0.6466] det_loss_cls_2[0.4484] det_loss_box_2[0.5831] det_loss_cls_3[0.4259] det_loss_box_3[0.5629] det_loss_cls_4[0.4080] det_loss_box_4[0.5477] det_loss_cls_5[0.3983] det_loss_box_5[0.5445] det_loss_cls_dn_0[0.0939] det_loss_box_dn_0[0.3702] det_loss_cls_dn_1[0.0901] det_loss_box_dn_1[0.3188] det_loss_cls_dn_2[0.0896] det_loss_box_dn_2[0.3026] det_loss_cls_dn_3[0.0888] det_loss_box_dn_3[0.3006] det_loss_cls_dn_4[0.0877] det_loss_box_dn_4[0.2982] det_loss_cls_dn_5[0.0873] det_loss_box_dn_5[0.2954] loss_depth[0.0424] total_loss[8.7415]
Rank[0/8] 06/19/2025 21:40:34 INFO stats.py:314 | Epoch[56] Step[334] GlobalStep[22174] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 0:04:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:40:49 INFO loss_tracker.py:84 | Epoch[56/239] Step[349] GlobalStep[22189/NA]: det_loss_cls_0[0.4496] det_loss_box_0[0.6812] det_loss_cls_1[0.4262] det_loss_box_1[0.5211] det_loss_cls_2[0.4126] det_loss_box_2[0.4642] det_loss_cls_3[0.3898] det_loss_box_3[0.4575] det_loss_cls_4[0.3651] det_loss_box_4[0.4526] det_loss_cls_5[0.3547] det_loss_box_5[0.4489] det_loss_cls_dn_0[0.1012] det_loss_box_dn_0[0.3433] det_loss_cls_dn_1[0.0984] det_loss_box_dn_1[0.2899] det_loss_cls_dn_2[0.0978] det_loss_box_dn_2[0.2708] det_loss_cls_dn_3[0.0967] det_loss_box_dn_3[0.2696] det_loss_cls_dn_4[0.0960] det_loss_box_dn_4[0.2687] det_loss_cls_dn_5[0.0953] det_loss_box_dn_5[0.2655] loss_depth[0.0457] total_loss[7.7625]
Rank[0/8] 06/19/2025 21:40:59 INFO stats.py:314 | Epoch[56] Step[359] GlobalStep[22199] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1 day, 0:03:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:41:14 INFO loss_tracker.py:84 | Epoch[56/239] Step[374] GlobalStep[22214/NA]: det_loss_cls_0[0.5054] det_loss_box_0[0.6857] det_loss_cls_1[0.4865] det_loss_box_1[0.5653] det_loss_cls_2[0.4621] det_loss_box_2[0.5074] det_loss_cls_3[0.4503] det_loss_box_3[0.4868] det_loss_cls_4[0.4265] det_loss_box_4[0.4838] det_loss_cls_5[0.4181] det_loss_box_5[0.4830] det_loss_cls_dn_0[0.0980] det_loss_box_dn_0[0.3346] det_loss_cls_dn_1[0.0951] det_loss_box_dn_1[0.2847] det_loss_cls_dn_2[0.0948] det_loss_box_dn_2[0.2662] det_loss_cls_dn_3[0.0935] det_loss_box_dn_3[0.2617] det_loss_cls_dn_4[0.0934] det_loss_box_dn_4[0.2605] det_loss_cls_dn_5[0.0919] det_loss_box_dn_5[0.2575] loss_depth[0.0399] total_loss[8.2329]
Rank[0/8] 06/19/2025 21:41:25 INFO stats.py:314 | Epoch[56] Step[384] GlobalStep[22224] Training Speed: 8.19 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1 day, 0:02:30.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:41:30 INFO stats.py:394 | Epoch[56] completed. Training Speed: 7.72 samples/sec across all devices.	Epoch Time: 404.23 sec.	Average Epoch Time: 404.23 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1 day, 0:02:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:41:57 INFO stats.py:314 | Epoch[57] Step[19] GlobalStep[22249] Training Speed: 8.69 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 1 day, 0:02:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:42:02 INFO loss_tracker.py:84 | Epoch[57/239] Step[24] GlobalStep[22254/NA]: det_loss_cls_0[0.5495] det_loss_box_0[0.6803] det_loss_cls_1[0.5373] det_loss_box_1[0.5357] det_loss_cls_2[0.5171] det_loss_box_2[0.4900] det_loss_cls_3[0.4963] det_loss_box_3[0.4703] det_loss_cls_4[0.4614] det_loss_box_4[0.4571] det_loss_cls_5[0.4453] det_loss_box_5[0.4556] det_loss_cls_dn_0[0.1059] det_loss_box_dn_0[0.2748] det_loss_cls_dn_1[0.1022] det_loss_box_dn_1[0.2309] det_loss_cls_dn_2[0.1015] det_loss_box_dn_2[0.2158] det_loss_cls_dn_3[0.1008] det_loss_box_dn_3[0.2131] det_loss_cls_dn_4[0.1003] det_loss_box_dn_4[0.2120] det_loss_cls_dn_5[0.0994] det_loss_box_dn_5[0.2104] loss_depth[0.0377] total_loss[8.1006]
Rank[0/8] 06/19/2025 21:42:22 INFO stats.py:314 | Epoch[57] Step[44] GlobalStep[22274] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1 day, 0:01:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:42:27 INFO loss_tracker.py:84 | Epoch[57/239] Step[49] GlobalStep[22279/NA]: det_loss_cls_0[0.4548] det_loss_box_0[0.5373] det_loss_cls_1[0.4587] det_loss_box_1[0.4232] det_loss_cls_2[0.4265] det_loss_box_2[0.3837] det_loss_cls_3[0.4045] det_loss_box_3[0.3720] det_loss_cls_4[0.3826] det_loss_box_4[0.3551] det_loss_cls_5[0.3675] det_loss_box_5[0.3556] det_loss_cls_dn_0[0.1009] det_loss_box_dn_0[0.2665] det_loss_cls_dn_1[0.0973] det_loss_box_dn_1[0.2271] det_loss_cls_dn_2[0.0966] det_loss_box_dn_2[0.2106] det_loss_cls_dn_3[0.0956] det_loss_box_dn_3[0.2076] det_loss_cls_dn_4[0.0947] det_loss_box_dn_4[0.2065] det_loss_cls_dn_5[0.0940] det_loss_box_dn_5[0.2050] loss_depth[0.0389] total_loss[6.8628]
Rank[0/8] 06/19/2025 21:42:48 INFO stats.py:314 | Epoch[57] Step[69] GlobalStep[22299] Training Speed: 6.35 samples/sec across all devices.	Average Step Time: 1.26 sec.	Estimated Remaining Time: 1 day, 0:00:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:42:54 INFO loss_tracker.py:84 | Epoch[57/239] Step[74] GlobalStep[22304/NA]: det_loss_cls_0[0.5180] det_loss_box_0[0.6830] det_loss_cls_1[0.5031] det_loss_box_1[0.5851] det_loss_cls_2[0.4869] det_loss_box_2[0.5322] det_loss_cls_3[0.4581] det_loss_box_3[0.5159] det_loss_cls_4[0.4366] det_loss_box_4[0.5077] det_loss_cls_5[0.4243] det_loss_box_5[0.5059] det_loss_cls_dn_0[0.1052] det_loss_box_dn_0[0.3225] det_loss_cls_dn_1[0.1021] det_loss_box_dn_1[0.2801] det_loss_cls_dn_2[0.1019] det_loss_box_dn_2[0.2681] det_loss_cls_dn_3[0.1004] det_loss_box_dn_3[0.2653] det_loss_cls_dn_4[0.0995] det_loss_box_dn_4[0.2637] det_loss_cls_dn_5[0.0981] det_loss_box_dn_5[0.2625] loss_depth[0.0404] total_loss[8.4667]
Rank[0/8] 06/19/2025 21:43:15 INFO stats.py:314 | Epoch[57] Step[94] GlobalStep[22324] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 23:59:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:43:20 INFO loss_tracker.py:84 | Epoch[57/239] Step[99] GlobalStep[22329/NA]: det_loss_cls_0[0.4840] det_loss_box_0[0.6651] det_loss_cls_1[0.4680] det_loss_box_1[0.5466] det_loss_cls_2[0.4595] det_loss_box_2[0.4822] det_loss_cls_3[0.4386] det_loss_box_3[0.4678] det_loss_cls_4[0.4185] det_loss_box_4[0.4580] det_loss_cls_5[0.4046] det_loss_box_5[0.4609] det_loss_cls_dn_0[0.0938] det_loss_box_dn_0[0.3094] det_loss_cls_dn_1[0.0903] det_loss_box_dn_1[0.2666] det_loss_cls_dn_2[0.0891] det_loss_box_dn_2[0.2529] det_loss_cls_dn_3[0.0890] det_loss_box_dn_3[0.2488] det_loss_cls_dn_4[0.0880] det_loss_box_dn_4[0.2478] det_loss_cls_dn_5[0.0872] det_loss_box_dn_5[0.2466] loss_depth[0.0393] total_loss[7.9027]
Rank[0/8] 06/19/2025 21:43:40 INFO stats.py:314 | Epoch[57] Step[119] GlobalStep[22349] Training Speed: 8.56 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 23:59:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:43:45 INFO loss_tracker.py:84 | Epoch[57/239] Step[124] GlobalStep[22354/NA]: det_loss_cls_0[0.4817] det_loss_box_0[0.6427] det_loss_cls_1[0.4630] det_loss_box_1[0.5060] det_loss_cls_2[0.4486] det_loss_box_2[0.4458] det_loss_cls_3[0.4226] det_loss_box_3[0.4376] det_loss_cls_4[0.3970] det_loss_box_4[0.4293] det_loss_cls_5[0.3840] det_loss_box_5[0.4291] det_loss_cls_dn_0[0.1029] det_loss_box_dn_0[0.3669] det_loss_cls_dn_1[0.0988] det_loss_box_dn_1[0.3222] det_loss_cls_dn_2[0.0985] det_loss_box_dn_2[0.3036] det_loss_cls_dn_3[0.0972] det_loss_box_dn_3[0.3003] det_loss_cls_dn_4[0.0961] det_loss_box_dn_4[0.2982] det_loss_cls_dn_5[0.0955] det_loss_box_dn_5[0.2902] loss_depth[0.0400] total_loss[7.9979]
Rank[0/8] 06/19/2025 21:44:05 INFO stats.py:314 | Epoch[57] Step[144] GlobalStep[22374] Training Speed: 8.59 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 23:58:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:44:10 INFO loss_tracker.py:84 | Epoch[57/239] Step[149] GlobalStep[22379/NA]: det_loss_cls_0[0.5537] det_loss_box_0[0.7126] det_loss_cls_1[0.5394] det_loss_box_1[0.5681] det_loss_cls_2[0.5095] det_loss_box_2[0.5078] det_loss_cls_3[0.4958] det_loss_box_3[0.4880] det_loss_cls_4[0.4545] det_loss_box_4[0.4877] det_loss_cls_5[0.4409] det_loss_box_5[0.4867] det_loss_cls_dn_0[0.1030] det_loss_box_dn_0[0.3557] det_loss_cls_dn_1[0.0980] det_loss_box_dn_1[0.3072] det_loss_cls_dn_2[0.0981] det_loss_box_dn_2[0.2885] det_loss_cls_dn_3[0.0976] det_loss_box_dn_3[0.2842] det_loss_cls_dn_4[0.0959] det_loss_box_dn_4[0.2824] det_loss_cls_dn_5[0.0949] det_loss_box_dn_5[0.2795] loss_depth[0.0395] total_loss[8.6692]
Rank[0/8] 06/19/2025 21:44:31 INFO stats.py:314 | Epoch[57] Step[169] GlobalStep[22399] Training Speed: 8.05 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 23:57:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:44:37 INFO loss_tracker.py:84 | Epoch[57/239] Step[174] GlobalStep[22404/NA]: det_loss_cls_0[0.5134] det_loss_box_0[0.7060] det_loss_cls_1[0.4916] det_loss_box_1[0.5639] det_loss_cls_2[0.4715] det_loss_box_2[0.4986] det_loss_cls_3[0.4460] det_loss_box_3[0.4808] det_loss_cls_4[0.4253] det_loss_box_4[0.4780] det_loss_cls_5[0.4142] det_loss_box_5[0.4765] det_loss_cls_dn_0[0.0979] det_loss_box_dn_0[0.3481] det_loss_cls_dn_1[0.0944] det_loss_box_dn_1[0.3056] det_loss_cls_dn_2[0.0938] det_loss_box_dn_2[0.2898] det_loss_cls_dn_3[0.0927] det_loss_box_dn_3[0.2857] det_loss_cls_dn_4[0.0917] det_loss_box_dn_4[0.2842] det_loss_cls_dn_5[0.0908] det_loss_box_dn_5[0.2811] loss_depth[0.0373] total_loss[8.3588]
Rank[0/8] 06/19/2025 21:44:57 INFO stats.py:314 | Epoch[57] Step[194] GlobalStep[22424] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 23:56:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:45:02 INFO loss_tracker.py:84 | Epoch[57/239] Step[199] GlobalStep[22429/NA]: det_loss_cls_0[0.5395] det_loss_box_0[0.7399] det_loss_cls_1[0.5233] det_loss_box_1[0.5531] det_loss_cls_2[0.4875] det_loss_box_2[0.5169] det_loss_cls_3[0.4575] det_loss_box_3[0.5045] det_loss_cls_4[0.4412] det_loss_box_4[0.4796] det_loss_cls_5[0.4322] det_loss_box_5[0.4760] det_loss_cls_dn_0[0.1125] det_loss_box_dn_0[0.3104] det_loss_cls_dn_1[0.1089] det_loss_box_dn_1[0.2729] det_loss_cls_dn_2[0.1080] det_loss_box_dn_2[0.2593] det_loss_cls_dn_3[0.1068] det_loss_box_dn_3[0.2576] det_loss_cls_dn_4[0.1057] det_loss_box_dn_4[0.2568] det_loss_cls_dn_5[0.1050] det_loss_box_dn_5[0.2543] loss_depth[0.0438] total_loss[8.4532]
Rank[0/8] 06/19/2025 21:45:23 INFO stats.py:314 | Epoch[57] Step[219] GlobalStep[22449] Training Speed: 7.98 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 23:56:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:45:28 INFO loss_tracker.py:84 | Epoch[57/239] Step[224] GlobalStep[22454/NA]: det_loss_cls_0[0.5342] det_loss_box_0[0.6718] det_loss_cls_1[0.5231] det_loss_box_1[0.5298] det_loss_cls_2[0.4994] det_loss_box_2[0.4987] det_loss_cls_3[0.4758] det_loss_box_3[0.4817] det_loss_cls_4[0.4495] det_loss_box_4[0.4748] det_loss_cls_5[0.4356] det_loss_box_5[0.4756] det_loss_cls_dn_0[0.1022] det_loss_box_dn_0[0.3282] det_loss_cls_dn_1[0.0982] det_loss_box_dn_1[0.2759] det_loss_cls_dn_2[0.0968] det_loss_box_dn_2[0.2601] det_loss_cls_dn_3[0.0959] det_loss_box_dn_3[0.2555] det_loss_cls_dn_4[0.0961] det_loss_box_dn_4[0.2543] det_loss_cls_dn_5[0.0952] det_loss_box_dn_5[0.2519] loss_depth[0.0406] total_loss[8.3008]
Rank[0/8] 06/19/2025 21:45:49 INFO stats.py:314 | Epoch[57] Step[244] GlobalStep[22474] Training Speed: 7.83 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 23:55:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:45:54 INFO loss_tracker.py:84 | Epoch[57/239] Step[249] GlobalStep[22479/NA]: det_loss_cls_0[0.5265] det_loss_box_0[0.6861] det_loss_cls_1[0.5021] det_loss_box_1[0.5457] det_loss_cls_2[0.4843] det_loss_box_2[0.5063] det_loss_cls_3[0.4630] det_loss_box_3[0.4945] det_loss_cls_4[0.4360] det_loss_box_4[0.4822] det_loss_cls_5[0.4222] det_loss_box_5[0.4786] det_loss_cls_dn_0[0.1026] det_loss_box_dn_0[0.3732] det_loss_cls_dn_1[0.0995] det_loss_box_dn_1[0.3328] det_loss_cls_dn_2[0.0987] det_loss_box_dn_2[0.3152] det_loss_cls_dn_3[0.0978] det_loss_box_dn_3[0.3133] det_loss_cls_dn_4[0.0960] det_loss_box_dn_4[0.3122] det_loss_cls_dn_5[0.0955] det_loss_box_dn_5[0.3108] loss_depth[0.0396] total_loss[8.6150]
Rank[0/8] 06/19/2025 21:46:14 INFO stats.py:314 | Epoch[57] Step[269] GlobalStep[22499] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 23:54:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:46:19 INFO loss_tracker.py:84 | Epoch[57/239] Step[274] GlobalStep[22504/NA]: det_loss_cls_0[0.5594] det_loss_box_0[0.7372] det_loss_cls_1[0.5593] det_loss_box_1[0.6065] det_loss_cls_2[0.5493] det_loss_box_2[0.5620] det_loss_cls_3[0.5213] det_loss_box_3[0.5492] det_loss_cls_4[0.5011] det_loss_box_4[0.5389] det_loss_cls_5[0.4842] det_loss_box_5[0.5364] det_loss_cls_dn_0[0.1193] det_loss_box_dn_0[0.3448] det_loss_cls_dn_1[0.1163] det_loss_box_dn_1[0.3038] det_loss_cls_dn_2[0.1164] det_loss_box_dn_2[0.2871] det_loss_cls_dn_3[0.1155] det_loss_box_dn_3[0.2849] det_loss_cls_dn_4[0.1139] det_loss_box_dn_4[0.2844] det_loss_cls_dn_5[0.1128] det_loss_box_dn_5[0.2831] loss_depth[0.0388] total_loss[9.2262]
Rank[0/8] 06/19/2025 21:46:40 INFO stats.py:314 | Epoch[57] Step[294] GlobalStep[22524] Training Speed: 8.07 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 23:53:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:46:45 INFO loss_tracker.py:84 | Epoch[57/239] Step[299] GlobalStep[22529/NA]: det_loss_cls_0[0.4273] det_loss_box_0[0.5982] det_loss_cls_1[0.4194] det_loss_box_1[0.4940] det_loss_cls_2[0.4044] det_loss_box_2[0.4766] det_loss_cls_3[0.3934] det_loss_box_3[0.4515] det_loss_cls_4[0.3711] det_loss_box_4[0.4472] det_loss_cls_5[0.3591] det_loss_box_5[0.4463] det_loss_cls_dn_0[0.0962] det_loss_box_dn_0[0.3819] det_loss_cls_dn_1[0.0909] det_loss_box_dn_1[0.3335] det_loss_cls_dn_2[0.0915] det_loss_box_dn_2[0.3219] det_loss_cls_dn_3[0.0908] det_loss_box_dn_3[0.3170] det_loss_cls_dn_4[0.0892] det_loss_box_dn_4[0.3147] det_loss_cls_dn_5[0.0889] det_loss_box_dn_5[0.3125] loss_depth[0.0390] total_loss[7.8567]
Rank[0/8] 06/19/2025 21:47:06 INFO stats.py:314 | Epoch[57] Step[319] GlobalStep[22549] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 23:53:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:47:11 INFO loss_tracker.py:84 | Epoch[57/239] Step[324] GlobalStep[22554/NA]: det_loss_cls_0[0.4763] det_loss_box_0[0.6221] det_loss_cls_1[0.4635] det_loss_box_1[0.4900] det_loss_cls_2[0.4419] det_loss_box_2[0.4409] det_loss_cls_3[0.4070] det_loss_box_3[0.4288] det_loss_cls_4[0.3763] det_loss_box_4[0.4186] det_loss_cls_5[0.3595] det_loss_box_5[0.4203] det_loss_cls_dn_0[0.1044] det_loss_box_dn_0[0.3538] det_loss_cls_dn_1[0.0998] det_loss_box_dn_1[0.3115] det_loss_cls_dn_2[0.0995] det_loss_box_dn_2[0.2963] det_loss_cls_dn_3[0.0978] det_loss_box_dn_3[0.2930] det_loss_cls_dn_4[0.0969] det_loss_box_dn_4[0.2930] det_loss_cls_dn_5[0.0954] det_loss_box_dn_5[0.2902] loss_depth[0.0367] total_loss[7.8135]
Rank[0/8] 06/19/2025 21:47:32 INFO stats.py:314 | Epoch[57] Step[344] GlobalStep[22574] Training Speed: 8.17 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 23:52:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:47:37 INFO loss_tracker.py:84 | Epoch[57/239] Step[349] GlobalStep[22579/NA]: det_loss_cls_0[0.5585] det_loss_box_0[0.7467] det_loss_cls_1[0.5199] det_loss_box_1[0.5986] det_loss_cls_2[0.5147] det_loss_box_2[0.5452] det_loss_cls_3[0.4947] det_loss_box_3[0.5229] det_loss_cls_4[0.4698] det_loss_box_4[0.5103] det_loss_cls_5[0.4615] det_loss_box_5[0.5056] det_loss_cls_dn_0[0.1089] det_loss_box_dn_0[0.3312] det_loss_cls_dn_1[0.1056] det_loss_box_dn_1[0.2925] det_loss_cls_dn_2[0.1046] det_loss_box_dn_2[0.2809] det_loss_cls_dn_3[0.1039] det_loss_box_dn_3[0.2780] det_loss_cls_dn_4[0.1027] det_loss_box_dn_4[0.2765] det_loss_cls_dn_5[0.1025] det_loss_box_dn_5[0.2748] loss_depth[0.0440] total_loss[8.8541]
Rank[0/8] 06/19/2025 21:47:58 INFO stats.py:314 | Epoch[57] Step[369] GlobalStep[22599] Training Speed: 8.63 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 23:51:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:48:03 INFO loss_tracker.py:84 | Epoch[57/239] Step[374] GlobalStep[22604/NA]: det_loss_cls_0[0.5738] det_loss_box_0[0.7064] det_loss_cls_1[0.5587] det_loss_box_1[0.5369] det_loss_cls_2[0.5274] det_loss_box_2[0.4846] det_loss_cls_3[0.4993] det_loss_box_3[0.4773] det_loss_cls_4[0.4707] det_loss_box_4[0.4748] det_loss_cls_5[0.4570] det_loss_box_5[0.4725] det_loss_cls_dn_0[0.1142] det_loss_box_dn_0[0.3191] det_loss_cls_dn_1[0.1120] det_loss_box_dn_1[0.2684] det_loss_cls_dn_2[0.1122] det_loss_box_dn_2[0.2479] det_loss_cls_dn_3[0.1108] det_loss_box_dn_3[0.2455] det_loss_cls_dn_4[0.1095] det_loss_box_dn_4[0.2438] det_loss_cls_dn_5[0.1085] det_loss_box_dn_5[0.2415] loss_depth[0.0389] total_loss[8.5117]
Rank[0/8] 06/19/2025 21:48:18 INFO stats.py:394 | Epoch[57] completed. Training Speed: 7.65 samples/sec across all devices.	Epoch Time: 407.93 sec.	Average Epoch Time: 407.93 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 23:51:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:48:25 INFO stats.py:314 | Epoch[58] Step[4] GlobalStep[22624] Training Speed: 6.40 samples/sec across all devices.	Average Step Time: 1.25 sec.	Estimated Remaining Time: 23:51:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:48:46 INFO loss_tracker.py:84 | Epoch[58/239] Step[24] GlobalStep[22644/NA]: det_loss_cls_0[0.5739] det_loss_box_0[0.7546] det_loss_cls_1[0.5534] det_loss_box_1[0.6113] det_loss_cls_2[0.5361] det_loss_box_2[0.5534] det_loss_cls_3[0.5137] det_loss_box_3[0.5382] det_loss_cls_4[0.4849] det_loss_box_4[0.5212] det_loss_cls_5[0.4693] det_loss_box_5[0.5231] det_loss_cls_dn_0[0.1061] det_loss_box_dn_0[0.3272] det_loss_cls_dn_1[0.1033] det_loss_box_dn_1[0.2845] det_loss_cls_dn_2[0.1027] det_loss_box_dn_2[0.2697] det_loss_cls_dn_3[0.1016] det_loss_box_dn_3[0.2668] det_loss_cls_dn_4[0.1009] det_loss_box_dn_4[0.2661] det_loss_cls_dn_5[0.1003] det_loss_box_dn_5[0.2631] loss_depth[0.0402] total_loss[8.9657]
Rank[0/8] 06/19/2025 21:48:51 INFO stats.py:314 | Epoch[58] Step[29] GlobalStep[22649] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 23:50:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:49:12 INFO loss_tracker.py:84 | Epoch[58/239] Step[49] GlobalStep[22669/NA]: det_loss_cls_0[0.5303] det_loss_box_0[0.6540] det_loss_cls_1[0.5130] det_loss_box_1[0.5233] det_loss_cls_2[0.4892] det_loss_box_2[0.4789] det_loss_cls_3[0.4691] det_loss_box_3[0.4608] det_loss_cls_4[0.4397] det_loss_box_4[0.4568] det_loss_cls_5[0.4292] det_loss_box_5[0.4520] det_loss_cls_dn_0[0.1175] det_loss_box_dn_0[0.3215] det_loss_cls_dn_1[0.1138] det_loss_box_dn_1[0.2749] det_loss_cls_dn_2[0.1128] det_loss_box_dn_2[0.2600] det_loss_cls_dn_3[0.1124] det_loss_box_dn_3[0.2577] det_loss_cls_dn_4[0.1110] det_loss_box_dn_4[0.2573] det_loss_cls_dn_5[0.1107] det_loss_box_dn_5[0.2567] loss_depth[0.0402] total_loss[8.2428]
Rank[0/8] 06/19/2025 21:49:17 INFO stats.py:314 | Epoch[58] Step[54] GlobalStep[22674] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 23:49:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:49:37 INFO loss_tracker.py:84 | Epoch[58/239] Step[74] GlobalStep[22694/NA]: det_loss_cls_0[0.6904] det_loss_box_0[0.9697] det_loss_cls_1[0.6782] det_loss_box_1[0.7980] det_loss_cls_2[0.6627] det_loss_box_2[0.7453] det_loss_cls_3[0.6383] det_loss_box_3[0.7233] det_loss_cls_4[0.6128] det_loss_box_4[0.7161] det_loss_cls_5[0.6014] det_loss_box_5[0.7144] det_loss_cls_dn_0[0.1049] det_loss_box_dn_0[0.3189] det_loss_cls_dn_1[0.1011] det_loss_box_dn_1[0.2758] det_loss_cls_dn_2[0.1002] det_loss_box_dn_2[0.2616] det_loss_cls_dn_3[0.0992] det_loss_box_dn_3[0.2590] det_loss_cls_dn_4[0.0984] det_loss_box_dn_4[0.2587] det_loss_cls_dn_5[0.0977] det_loss_box_dn_5[0.2576] loss_depth[0.0409] total_loss[10.8247]
Rank[0/8] 06/19/2025 21:49:43 INFO stats.py:314 | Epoch[58] Step[79] GlobalStep[22699] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 23:48:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:50:03 INFO loss_tracker.py:84 | Epoch[58/239] Step[99] GlobalStep[22719/NA]: det_loss_cls_0[0.5582] det_loss_box_0[0.7409] det_loss_cls_1[0.5355] det_loss_box_1[0.6007] det_loss_cls_2[0.5081] det_loss_box_2[0.5437] det_loss_cls_3[0.4897] det_loss_box_3[0.5146] det_loss_cls_4[0.4627] det_loss_box_4[0.5032] det_loss_cls_5[0.4418] det_loss_box_5[0.5038] det_loss_cls_dn_0[0.1144] det_loss_box_dn_0[0.3349] det_loss_cls_dn_1[0.1110] det_loss_box_dn_1[0.2904] det_loss_cls_dn_2[0.1098] det_loss_box_dn_2[0.2721] det_loss_cls_dn_3[0.1096] det_loss_box_dn_3[0.2705] det_loss_cls_dn_4[0.1076] det_loss_box_dn_4[0.2693] det_loss_cls_dn_5[0.1069] det_loss_box_dn_5[0.2665] loss_depth[0.0424] total_loss[8.8084]
Rank[0/8] 06/19/2025 21:50:09 INFO stats.py:314 | Epoch[58] Step[104] GlobalStep[22724] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 23:48:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:50:29 INFO loss_tracker.py:84 | Epoch[58/239] Step[124] GlobalStep[22744/NA]: det_loss_cls_0[0.5314] det_loss_box_0[0.7524] det_loss_cls_1[0.5181] det_loss_box_1[0.6156] det_loss_cls_2[0.4929] det_loss_box_2[0.5636] det_loss_cls_3[0.4688] det_loss_box_3[0.5484] det_loss_cls_4[0.4415] det_loss_box_4[0.5306] det_loss_cls_5[0.4268] det_loss_box_5[0.5375] det_loss_cls_dn_0[0.1102] det_loss_box_dn_0[0.3418] det_loss_cls_dn_1[0.1069] det_loss_box_dn_1[0.3006] det_loss_cls_dn_2[0.1066] det_loss_box_dn_2[0.2840] det_loss_cls_dn_3[0.1055] det_loss_box_dn_3[0.2807] det_loss_cls_dn_4[0.1037] det_loss_box_dn_4[0.2798] det_loss_cls_dn_5[0.1036] det_loss_box_dn_5[0.2788] loss_depth[0.0421] total_loss[8.8721]
Rank[0/8] 06/19/2025 21:50:35 INFO stats.py:314 | Epoch[58] Step[129] GlobalStep[22749] Training Speed: 7.26 samples/sec across all devices.	Average Step Time: 1.10 sec.	Estimated Remaining Time: 23:47:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:50:55 INFO loss_tracker.py:84 | Epoch[58/239] Step[149] GlobalStep[22769/NA]: det_loss_cls_0[0.4913] det_loss_box_0[0.6476] det_loss_cls_1[0.4850] det_loss_box_1[0.5306] det_loss_cls_2[0.4590] det_loss_box_2[0.4975] det_loss_cls_3[0.4404] det_loss_box_3[0.4793] det_loss_cls_4[0.4173] det_loss_box_4[0.4691] det_loss_cls_5[0.4071] det_loss_box_5[0.4705] det_loss_cls_dn_0[0.0997] det_loss_box_dn_0[0.2848] det_loss_cls_dn_1[0.0964] det_loss_box_dn_1[0.2403] det_loss_cls_dn_2[0.0957] det_loss_box_dn_2[0.2243] det_loss_cls_dn_3[0.0946] det_loss_box_dn_3[0.2215] det_loss_cls_dn_4[0.0937] det_loss_box_dn_4[0.2201] det_loss_cls_dn_5[0.0932] det_loss_box_dn_5[0.2190] loss_depth[0.0389] total_loss[7.8169]
Rank[0/8] 06/19/2025 21:51:00 INFO stats.py:314 | Epoch[58] Step[154] GlobalStep[22774] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 23:46:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:51:21 INFO loss_tracker.py:84 | Epoch[58/239] Step[174] GlobalStep[22794/NA]: det_loss_cls_0[0.6214] det_loss_box_0[0.8388] det_loss_cls_1[0.5917] det_loss_box_1[0.6472] det_loss_cls_2[0.5753] det_loss_box_2[0.5862] det_loss_cls_3[0.5498] det_loss_box_3[0.5647] det_loss_cls_4[0.5241] det_loss_box_4[0.5507] det_loss_cls_5[0.5109] det_loss_box_5[0.5502] det_loss_cls_dn_0[0.1167] det_loss_box_dn_0[0.3405] det_loss_cls_dn_1[0.1135] det_loss_box_dn_1[0.2906] det_loss_cls_dn_2[0.1130] det_loss_box_dn_2[0.2721] det_loss_cls_dn_3[0.1117] det_loss_box_dn_3[0.2682] det_loss_cls_dn_4[0.1111] det_loss_box_dn_4[0.2685] det_loss_cls_dn_5[0.1099] det_loss_box_dn_5[0.2671] loss_depth[0.0446] total_loss[9.5384]
Rank[0/8] 06/19/2025 21:51:26 INFO stats.py:314 | Epoch[58] Step[179] GlobalStep[22799] Training Speed: 6.32 samples/sec across all devices.	Average Step Time: 1.27 sec.	Estimated Remaining Time: 23:45:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:51:47 INFO loss_tracker.py:84 | Epoch[58/239] Step[199] GlobalStep[22819/NA]: det_loss_cls_0[0.5897] det_loss_box_0[0.7342] det_loss_cls_1[0.5752] det_loss_box_1[0.5926] det_loss_cls_2[0.5454] det_loss_box_2[0.5448] det_loss_cls_3[0.5273] det_loss_box_3[0.5230] det_loss_cls_4[0.4956] det_loss_box_4[0.5165] det_loss_cls_5[0.4816] det_loss_box_5[0.5160] det_loss_cls_dn_0[0.1060] det_loss_box_dn_0[0.3000] det_loss_cls_dn_1[0.1029] det_loss_box_dn_1[0.2616] det_loss_cls_dn_2[0.1032] det_loss_box_dn_2[0.2451] det_loss_cls_dn_3[0.1034] det_loss_box_dn_3[0.2413] det_loss_cls_dn_4[0.1032] det_loss_box_dn_4[0.2399] det_loss_cls_dn_5[0.1036] det_loss_box_dn_5[0.2383] loss_depth[0.0377] total_loss[8.8282]
Rank[0/8] 06/19/2025 21:51:52 INFO stats.py:314 | Epoch[58] Step[204] GlobalStep[22824] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 23:45:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:52:13 INFO loss_tracker.py:84 | Epoch[58/239] Step[224] GlobalStep[22844/NA]: det_loss_cls_0[0.6311] det_loss_box_0[0.8040] det_loss_cls_1[0.6190] det_loss_box_1[0.6394] det_loss_cls_2[0.6047] det_loss_box_2[0.5713] det_loss_cls_3[0.5760] det_loss_box_3[0.5551] det_loss_cls_4[0.5490] det_loss_box_4[0.5436] det_loss_cls_5[0.5345] det_loss_box_5[0.5473] det_loss_cls_dn_0[0.0998] det_loss_box_dn_0[0.2821] det_loss_cls_dn_1[0.0963] det_loss_box_dn_1[0.2391] det_loss_cls_dn_2[0.0958] det_loss_box_dn_2[0.2233] det_loss_cls_dn_3[0.0951] det_loss_box_dn_3[0.2208] det_loss_cls_dn_4[0.0941] det_loss_box_dn_4[0.2198] det_loss_cls_dn_5[0.0935] det_loss_box_dn_5[0.2192] loss_depth[0.0415] total_loss[9.1954]
Rank[0/8] 06/19/2025 21:52:18 INFO stats.py:314 | Epoch[58] Step[229] GlobalStep[22849] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 23:44:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:52:38 INFO loss_tracker.py:84 | Epoch[58/239] Step[249] GlobalStep[22869/NA]: det_loss_cls_0[0.5020] det_loss_box_0[0.6726] det_loss_cls_1[0.4928] det_loss_box_1[0.5425] det_loss_cls_2[0.4814] det_loss_box_2[0.4872] det_loss_cls_3[0.4502] det_loss_box_3[0.4729] det_loss_cls_4[0.4242] det_loss_box_4[0.4632] det_loss_cls_5[0.4118] det_loss_box_5[0.4625] det_loss_cls_dn_0[0.1003] det_loss_box_dn_0[0.3158] det_loss_cls_dn_1[0.0975] det_loss_box_dn_1[0.2718] det_loss_cls_dn_2[0.0969] det_loss_box_dn_2[0.2565] det_loss_cls_dn_3[0.0965] det_loss_box_dn_3[0.2539] det_loss_cls_dn_4[0.0954] det_loss_box_dn_4[0.2528] det_loss_cls_dn_5[0.0948] det_loss_box_dn_5[0.2517] loss_depth[0.0370] total_loss[8.0841]
Rank[0/8] 06/19/2025 21:52:43 INFO stats.py:314 | Epoch[58] Step[254] GlobalStep[22874] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 23:43:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:53:04 INFO loss_tracker.py:84 | Epoch[58/239] Step[274] GlobalStep[22894/NA]: det_loss_cls_0[0.4816] det_loss_box_0[0.6680] det_loss_cls_1[0.4574] det_loss_box_1[0.5219] det_loss_cls_2[0.4370] det_loss_box_2[0.4754] det_loss_cls_3[0.4194] det_loss_box_3[0.4487] det_loss_cls_4[0.3842] det_loss_box_4[0.4446] det_loss_cls_5[0.3702] det_loss_box_5[0.4448] det_loss_cls_dn_0[0.0881] det_loss_box_dn_0[0.3651] det_loss_cls_dn_1[0.0845] det_loss_box_dn_1[0.3145] det_loss_cls_dn_2[0.0847] det_loss_box_dn_2[0.3005] det_loss_cls_dn_3[0.0832] det_loss_box_dn_3[0.2975] det_loss_cls_dn_4[0.0819] det_loss_box_dn_4[0.2962] det_loss_cls_dn_5[0.0817] det_loss_box_dn_5[0.2936] loss_depth[0.0376] total_loss[7.9623]
Rank[0/8] 06/19/2025 21:53:09 INFO stats.py:314 | Epoch[58] Step[279] GlobalStep[22899] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 23:42:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:53:30 INFO loss_tracker.py:84 | Epoch[58/239] Step[299] GlobalStep[22919/NA]: det_loss_cls_0[0.4729] det_loss_box_0[0.6756] det_loss_cls_1[0.4666] det_loss_box_1[0.5387] det_loss_cls_2[0.4509] det_loss_box_2[0.4920] det_loss_cls_3[0.4258] det_loss_box_3[0.4775] det_loss_cls_4[0.4004] det_loss_box_4[0.4737] det_loss_cls_5[0.3833] det_loss_box_5[0.4760] det_loss_cls_dn_0[0.1087] det_loss_box_dn_0[0.3593] det_loss_cls_dn_1[0.1054] det_loss_box_dn_1[0.3072] det_loss_cls_dn_2[0.1043] det_loss_box_dn_2[0.2880] det_loss_cls_dn_3[0.1032] det_loss_box_dn_3[0.2839] det_loss_cls_dn_4[0.1024] det_loss_box_dn_4[0.2819] det_loss_cls_dn_5[0.1022] det_loss_box_dn_5[0.2799] loss_depth[0.0409] total_loss[8.2007]
Rank[0/8] 06/19/2025 21:53:35 INFO stats.py:314 | Epoch[58] Step[304] GlobalStep[22924] Training Speed: 8.04 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 23:42:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:53:56 INFO loss_tracker.py:84 | Epoch[58/239] Step[324] GlobalStep[22944/NA]: det_loss_cls_0[0.5227] det_loss_box_0[0.6689] det_loss_cls_1[0.5156] det_loss_box_1[0.5323] det_loss_cls_2[0.4925] det_loss_box_2[0.4850] det_loss_cls_3[0.4689] det_loss_box_3[0.4638] det_loss_cls_4[0.4470] det_loss_box_4[0.4519] det_loss_cls_5[0.4331] det_loss_box_5[0.4517] det_loss_cls_dn_0[0.1063] det_loss_box_dn_0[0.3154] det_loss_cls_dn_1[0.1040] det_loss_box_dn_1[0.2642] det_loss_cls_dn_2[0.1029] det_loss_box_dn_2[0.2475] det_loss_cls_dn_3[0.1021] det_loss_box_dn_3[0.2432] det_loss_cls_dn_4[0.1013] det_loss_box_dn_4[0.2429] det_loss_cls_dn_5[0.1003] det_loss_box_dn_5[0.2416] loss_depth[0.0405] total_loss[8.1459]
Rank[0/8] 06/19/2025 21:54:01 INFO stats.py:314 | Epoch[58] Step[329] GlobalStep[22949] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 23:41:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:54:21 INFO loss_tracker.py:84 | Epoch[58/239] Step[349] GlobalStep[22969/NA]: det_loss_cls_0[0.4310] det_loss_box_0[0.6055] det_loss_cls_1[0.4279] det_loss_box_1[0.4540] det_loss_cls_2[0.4075] det_loss_box_2[0.4209] det_loss_cls_3[0.3842] det_loss_box_3[0.4067] det_loss_cls_4[0.3549] det_loss_box_4[0.4034] det_loss_cls_5[0.3456] det_loss_box_5[0.3972] det_loss_cls_dn_0[0.0990] det_loss_box_dn_0[0.3193] det_loss_cls_dn_1[0.0956] det_loss_box_dn_1[0.2804] det_loss_cls_dn_2[0.0944] det_loss_box_dn_2[0.2655] det_loss_cls_dn_3[0.0942] det_loss_box_dn_3[0.2625] det_loss_cls_dn_4[0.0932] det_loss_box_dn_4[0.2617] det_loss_cls_dn_5[0.0928] det_loss_box_dn_5[0.2589] loss_depth[0.0391] total_loss[7.2952]
Rank[0/8] 06/19/2025 21:54:27 INFO stats.py:314 | Epoch[58] Step[354] GlobalStep[22974] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 23:40:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:54:48 INFO loss_tracker.py:84 | Epoch[58/239] Step[374] GlobalStep[22994/NA]: det_loss_cls_0[0.5919] det_loss_box_0[0.7801] det_loss_cls_1[0.5847] det_loss_box_1[0.6231] det_loss_cls_2[0.5644] det_loss_box_2[0.5618] det_loss_cls_3[0.5454] det_loss_box_3[0.5351] det_loss_cls_4[0.5187] det_loss_box_4[0.5168] det_loss_cls_5[0.5046] det_loss_box_5[0.5249] det_loss_cls_dn_0[0.1048] det_loss_box_dn_0[0.2977] det_loss_cls_dn_1[0.1022] det_loss_box_dn_1[0.2563] det_loss_cls_dn_2[0.1011] det_loss_box_dn_2[0.2406] det_loss_cls_dn_3[0.1009] det_loss_box_dn_3[0.2374] det_loss_cls_dn_4[0.0997] det_loss_box_dn_4[0.2358] det_loss_cls_dn_5[0.0991] det_loss_box_dn_5[0.2349] loss_depth[0.0375] total_loss[8.9995]
Rank[0/8] 06/19/2025 21:54:53 INFO stats.py:314 | Epoch[58] Step[379] GlobalStep[22999] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 23:40:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:55:03 INFO stats.py:394 | Epoch[58] completed. Training Speed: 7.71 samples/sec across all devices.	Epoch Time: 404.50 sec.	Average Epoch Time: 404.50 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 23:39:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:55:19 INFO stats.py:314 | Epoch[59] Step[14] GlobalStep[23024] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 23:39:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:55:30 INFO loss_tracker.py:84 | Epoch[59/239] Step[24] GlobalStep[23034/NA]: det_loss_cls_0[0.4637] det_loss_box_0[0.5956] det_loss_cls_1[0.4561] det_loss_box_1[0.4651] det_loss_cls_2[0.4377] det_loss_box_2[0.4200] det_loss_cls_3[0.4187] det_loss_box_3[0.4070] det_loss_cls_4[0.3875] det_loss_box_4[0.4032] det_loss_cls_5[0.3728] det_loss_box_5[0.4034] det_loss_cls_dn_0[0.1041] det_loss_box_dn_0[0.2686] det_loss_cls_dn_1[0.0997] det_loss_box_dn_1[0.2278] det_loss_cls_dn_2[0.0985] det_loss_box_dn_2[0.2161] det_loss_cls_dn_3[0.0977] det_loss_box_dn_3[0.2140] det_loss_cls_dn_4[0.0965] det_loss_box_dn_4[0.2136] det_loss_cls_dn_5[0.0958] det_loss_box_dn_5[0.2120] loss_depth[0.0408] total_loss[7.2159]
Rank[0/8] 06/19/2025 21:55:46 INFO stats.py:314 | Epoch[59] Step[39] GlobalStep[23049] Training Speed: 7.96 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 23:38:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:55:56 INFO loss_tracker.py:84 | Epoch[59/239] Step[49] GlobalStep[23059/NA]: det_loss_cls_0[0.5305] det_loss_box_0[0.6747] det_loss_cls_1[0.5169] det_loss_box_1[0.5471] det_loss_cls_2[0.4914] det_loss_box_2[0.4957] det_loss_cls_3[0.4656] det_loss_box_3[0.4793] det_loss_cls_4[0.4292] det_loss_box_4[0.4780] det_loss_cls_5[0.4165] det_loss_box_5[0.4797] det_loss_cls_dn_0[0.1073] det_loss_box_dn_0[0.3394] det_loss_cls_dn_1[0.1040] det_loss_box_dn_1[0.2920] det_loss_cls_dn_2[0.1029] det_loss_box_dn_2[0.2727] det_loss_cls_dn_3[0.1022] det_loss_box_dn_3[0.2699] det_loss_cls_dn_4[0.1015] det_loss_box_dn_4[0.2686] det_loss_cls_dn_5[0.1011] det_loss_box_dn_5[0.2664] loss_depth[0.0392] total_loss[8.3719]
Rank[0/8] 06/19/2025 21:56:12 INFO stats.py:314 | Epoch[59] Step[64] GlobalStep[23074] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 23:37:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:56:22 INFO loss_tracker.py:84 | Epoch[59/239] Step[74] GlobalStep[23084/NA]: det_loss_cls_0[0.4957] det_loss_box_0[0.6417] det_loss_cls_1[0.4734] det_loss_box_1[0.5085] det_loss_cls_2[0.4533] det_loss_box_2[0.4514] det_loss_cls_3[0.4317] det_loss_box_3[0.4359] det_loss_cls_4[0.4097] det_loss_box_4[0.4328] det_loss_cls_5[0.4042] det_loss_box_5[0.4292] det_loss_cls_dn_0[0.1072] det_loss_box_dn_0[0.3323] det_loss_cls_dn_1[0.1036] det_loss_box_dn_1[0.2945] det_loss_cls_dn_2[0.1027] det_loss_box_dn_2[0.2766] det_loss_cls_dn_3[0.1015] det_loss_box_dn_3[0.2728] det_loss_cls_dn_4[0.1004] det_loss_box_dn_4[0.2705] det_loss_cls_dn_5[0.0996] det_loss_box_dn_5[0.2686] loss_depth[0.0363] total_loss[7.9342]
Rank[0/8] 06/19/2025 21:56:37 INFO stats.py:314 | Epoch[59] Step[89] GlobalStep[23099] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 23:37:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:56:48 INFO loss_tracker.py:84 | Epoch[59/239] Step[99] GlobalStep[23109/NA]: det_loss_cls_0[0.5656] det_loss_box_0[0.7066] det_loss_cls_1[0.5343] det_loss_box_1[0.5608] det_loss_cls_2[0.5103] det_loss_box_2[0.5107] det_loss_cls_3[0.4807] det_loss_box_3[0.5004] det_loss_cls_4[0.4492] det_loss_box_4[0.4937] det_loss_cls_5[0.4346] det_loss_box_5[0.4866] det_loss_cls_dn_0[0.0992] det_loss_box_dn_0[0.3215] det_loss_cls_dn_1[0.0960] det_loss_box_dn_1[0.2708] det_loss_cls_dn_2[0.0954] det_loss_box_dn_2[0.2548] det_loss_cls_dn_3[0.0943] det_loss_box_dn_3[0.2512] det_loss_cls_dn_4[0.0930] det_loss_box_dn_4[0.2496] det_loss_cls_dn_5[0.0925] det_loss_box_dn_5[0.2488] loss_depth[0.0412] total_loss[8.4417]
Rank[0/8] 06/19/2025 21:57:03 INFO stats.py:314 | Epoch[59] Step[114] GlobalStep[23124] Training Speed: 8.76 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 23:36:30.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:57:13 INFO loss_tracker.py:84 | Epoch[59/239] Step[124] GlobalStep[23134/NA]: det_loss_cls_0[0.7378] det_loss_box_0[1.0854] det_loss_cls_1[0.7160] det_loss_box_1[0.8744] det_loss_cls_2[0.7005] det_loss_box_2[0.7876] det_loss_cls_3[0.6736] det_loss_box_3[0.7778] det_loss_cls_4[0.6456] det_loss_box_4[0.7604] det_loss_cls_5[0.6313] det_loss_box_5[0.7549] det_loss_cls_dn_0[0.1162] det_loss_box_dn_0[0.3709] det_loss_cls_dn_1[0.1134] det_loss_box_dn_1[0.3369] det_loss_cls_dn_2[0.1134] det_loss_box_dn_2[0.3256] det_loss_cls_dn_3[0.1123] det_loss_box_dn_3[0.3220] det_loss_cls_dn_4[0.1123] det_loss_box_dn_4[0.3225] det_loss_cls_dn_5[0.1114] det_loss_box_dn_5[0.3208] loss_depth[0.0455] total_loss[11.8687]
Rank[0/8] 06/19/2025 21:57:29 INFO stats.py:314 | Epoch[59] Step[139] GlobalStep[23149] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 23:35:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:57:39 INFO loss_tracker.py:84 | Epoch[59/239] Step[149] GlobalStep[23159/NA]: det_loss_cls_0[0.5704] det_loss_box_0[0.7651] det_loss_cls_1[0.5542] det_loss_box_1[0.6058] det_loss_cls_2[0.5299] det_loss_box_2[0.5586] det_loss_cls_3[0.5102] det_loss_box_3[0.5356] det_loss_cls_4[0.4799] det_loss_box_4[0.5269] det_loss_cls_5[0.4633] det_loss_box_5[0.5296] det_loss_cls_dn_0[0.1043] det_loss_box_dn_0[0.2944] det_loss_cls_dn_1[0.1013] det_loss_box_dn_1[0.2532] det_loss_cls_dn_2[0.1003] det_loss_box_dn_2[0.2393] det_loss_cls_dn_3[0.0994] det_loss_box_dn_3[0.2371] det_loss_cls_dn_4[0.0990] det_loss_box_dn_4[0.2368] det_loss_cls_dn_5[0.0979] det_loss_box_dn_5[0.2356] loss_depth[0.0438] total_loss[8.7719]
Rank[0/8] 06/19/2025 21:57:55 INFO stats.py:314 | Epoch[59] Step[164] GlobalStep[23174] Training Speed: 8.04 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 23:35:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:58:05 INFO loss_tracker.py:84 | Epoch[59/239] Step[174] GlobalStep[23184/NA]: det_loss_cls_0[0.5528] det_loss_box_0[0.7622] det_loss_cls_1[0.5349] det_loss_box_1[0.6095] det_loss_cls_2[0.5148] det_loss_box_2[0.5568] det_loss_cls_3[0.4914] det_loss_box_3[0.5310] det_loss_cls_4[0.4582] det_loss_box_4[0.5135] det_loss_cls_5[0.4366] det_loss_box_5[0.5196] det_loss_cls_dn_0[0.1019] det_loss_box_dn_0[0.2978] det_loss_cls_dn_1[0.0969] det_loss_box_dn_1[0.2600] det_loss_cls_dn_2[0.0962] det_loss_box_dn_2[0.2450] det_loss_cls_dn_3[0.0957] det_loss_box_dn_3[0.2420] det_loss_cls_dn_4[0.0943] det_loss_box_dn_4[0.2415] det_loss_cls_dn_5[0.0935] det_loss_box_dn_5[0.2401] loss_depth[0.0405] total_loss[8.6268]
Rank[0/8] 06/19/2025 21:58:20 INFO stats.py:314 | Epoch[59] Step[189] GlobalStep[23199] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 23:34:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:58:30 INFO loss_tracker.py:84 | Epoch[59/239] Step[199] GlobalStep[23209/NA]: det_loss_cls_0[0.6247] det_loss_box_0[0.8534] det_loss_cls_1[0.5926] det_loss_box_1[0.6878] det_loss_cls_2[0.5708] det_loss_box_2[0.6194] det_loss_cls_3[0.5485] det_loss_box_3[0.5870] det_loss_cls_4[0.5234] det_loss_box_4[0.5705] det_loss_cls_5[0.5095] det_loss_box_5[0.5692] det_loss_cls_dn_0[0.1116] det_loss_box_dn_0[0.3196] det_loss_cls_dn_1[0.1090] det_loss_box_dn_1[0.2829] det_loss_cls_dn_2[0.1076] det_loss_box_dn_2[0.2667] det_loss_cls_dn_3[0.1065] det_loss_box_dn_3[0.2649] det_loss_cls_dn_4[0.1060] det_loss_box_dn_4[0.2653] det_loss_cls_dn_5[0.1050] det_loss_box_dn_5[0.2635] loss_depth[0.0452] total_loss[9.6105]
Rank[0/8] 06/19/2025 21:58:46 INFO stats.py:314 | Epoch[59] Step[214] GlobalStep[23224] Training Speed: 7.83 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 23:33:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:58:56 INFO loss_tracker.py:84 | Epoch[59/239] Step[224] GlobalStep[23234/NA]: det_loss_cls_0[0.5113] det_loss_box_0[0.7046] det_loss_cls_1[0.4867] det_loss_box_1[0.5635] det_loss_cls_2[0.4651] det_loss_box_2[0.5348] det_loss_cls_3[0.4465] det_loss_box_3[0.5143] det_loss_cls_4[0.4151] det_loss_box_4[0.5100] det_loss_cls_5[0.4033] det_loss_box_5[0.5049] det_loss_cls_dn_0[0.0960] det_loss_box_dn_0[0.3644] det_loss_cls_dn_1[0.0932] det_loss_box_dn_1[0.3102] det_loss_cls_dn_2[0.0929] det_loss_box_dn_2[0.2886] det_loss_cls_dn_3[0.0920] det_loss_box_dn_3[0.2854] det_loss_cls_dn_4[0.0910] det_loss_box_dn_4[0.2842] det_loss_cls_dn_5[0.0899] det_loss_box_dn_5[0.2827] loss_depth[0.0413] total_loss[8.4717]
Rank[0/8] 06/19/2025 21:59:12 INFO stats.py:314 | Epoch[59] Step[239] GlobalStep[23249] Training Speed: 7.79 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 23:32:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:59:23 INFO loss_tracker.py:84 | Epoch[59/239] Step[249] GlobalStep[23259/NA]: det_loss_cls_0[0.6118] det_loss_box_0[0.7026] det_loss_cls_1[0.5953] det_loss_box_1[0.5738] det_loss_cls_2[0.5616] det_loss_box_2[0.5246] det_loss_cls_3[0.5473] det_loss_box_3[0.5062] det_loss_cls_4[0.5115] det_loss_box_4[0.4920] det_loss_cls_5[0.5055] det_loss_box_5[0.4841] det_loss_cls_dn_0[0.1107] det_loss_box_dn_0[0.2836] det_loss_cls_dn_1[0.1080] det_loss_box_dn_1[0.2476] det_loss_cls_dn_2[0.1080] det_loss_box_dn_2[0.2347] det_loss_cls_dn_3[0.1070] det_loss_box_dn_3[0.2335] det_loss_cls_dn_4[0.1060] det_loss_box_dn_4[0.2330] det_loss_cls_dn_5[0.1051] det_loss_box_dn_5[0.2322] loss_depth[0.0387] total_loss[8.7643]
Rank[0/8] 06/19/2025 21:59:38 INFO stats.py:314 | Epoch[59] Step[264] GlobalStep[23274] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 23:32:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 21:59:49 INFO loss_tracker.py:84 | Epoch[59/239] Step[274] GlobalStep[23284/NA]: det_loss_cls_0[0.5356] det_loss_box_0[0.6954] det_loss_cls_1[0.5199] det_loss_box_1[0.5622] det_loss_cls_2[0.5018] det_loss_box_2[0.5102] det_loss_cls_3[0.4846] det_loss_box_3[0.4862] det_loss_cls_4[0.4523] det_loss_box_4[0.4835] det_loss_cls_5[0.4430] det_loss_box_5[0.4797] det_loss_cls_dn_0[0.1041] det_loss_box_dn_0[0.3229] det_loss_cls_dn_1[0.1003] det_loss_box_dn_1[0.2775] det_loss_cls_dn_2[0.1001] det_loss_box_dn_2[0.2566] det_loss_cls_dn_3[0.0990] det_loss_box_dn_3[0.2525] det_loss_cls_dn_4[0.0981] det_loss_box_dn_4[0.2508] det_loss_cls_dn_5[0.0976] det_loss_box_dn_5[0.2493] loss_depth[0.0429] total_loss[8.4060]
Rank[0/8] 06/19/2025 22:00:04 INFO stats.py:314 | Epoch[59] Step[289] GlobalStep[23299] Training Speed: 7.90 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 23:31:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:00:15 INFO loss_tracker.py:84 | Epoch[59/239] Step[299] GlobalStep[23309/NA]: det_loss_cls_0[0.4086] det_loss_box_0[0.5257] det_loss_cls_1[0.4023] det_loss_box_1[0.4206] det_loss_cls_2[0.3758] det_loss_box_2[0.3817] det_loss_cls_3[0.3497] det_loss_box_3[0.3743] det_loss_cls_4[0.3228] det_loss_box_4[0.3727] det_loss_cls_5[0.3007] det_loss_box_5[0.3787] det_loss_cls_dn_0[0.0919] det_loss_box_dn_0[0.3208] det_loss_cls_dn_1[0.0889] det_loss_box_dn_1[0.2723] det_loss_cls_dn_2[0.0870] det_loss_box_dn_2[0.2568] det_loss_cls_dn_3[0.0854] det_loss_box_dn_3[0.2533] det_loss_cls_dn_4[0.0842] det_loss_box_dn_4[0.2523] det_loss_cls_dn_5[0.0836] det_loss_box_dn_5[0.2519] loss_depth[0.0393] total_loss[6.7813]
Rank[0/8] 06/19/2025 22:00:31 INFO stats.py:314 | Epoch[59] Step[314] GlobalStep[23324] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 23:30:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:00:41 INFO loss_tracker.py:84 | Epoch[59/239] Step[324] GlobalStep[23334/NA]: det_loss_cls_0[0.4830] det_loss_box_0[0.6537] det_loss_cls_1[0.4708] det_loss_box_1[0.5345] det_loss_cls_2[0.4483] det_loss_box_2[0.4774] det_loss_cls_3[0.4262] det_loss_box_3[0.4606] det_loss_cls_4[0.4024] det_loss_box_4[0.4510] det_loss_cls_5[0.3918] det_loss_box_5[0.4466] det_loss_cls_dn_0[0.0956] det_loss_box_dn_0[0.3574] det_loss_cls_dn_1[0.0911] det_loss_box_dn_1[0.3091] det_loss_cls_dn_2[0.0906] det_loss_box_dn_2[0.2895] det_loss_cls_dn_3[0.0901] det_loss_box_dn_3[0.2862] det_loss_cls_dn_4[0.0879] det_loss_box_dn_4[0.2855] det_loss_cls_dn_5[0.0874] det_loss_box_dn_5[0.2835] loss_depth[0.0383] total_loss[8.0384]
Rank[0/8] 06/19/2025 22:00:57 INFO stats.py:314 | Epoch[59] Step[339] GlobalStep[23349] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 23:30:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:01:07 INFO loss_tracker.py:84 | Epoch[59/239] Step[349] GlobalStep[23359/NA]: det_loss_cls_0[0.5468] det_loss_box_0[0.7483] det_loss_cls_1[0.5490] det_loss_box_1[0.5939] det_loss_cls_2[0.5239] det_loss_box_2[0.5450] det_loss_cls_3[0.4989] det_loss_box_3[0.5240] det_loss_cls_4[0.4811] det_loss_box_4[0.5134] det_loss_cls_5[0.4590] det_loss_box_5[0.5210] det_loss_cls_dn_0[0.1114] det_loss_box_dn_0[0.3502] det_loss_cls_dn_1[0.1072] det_loss_box_dn_1[0.3049] det_loss_cls_dn_2[0.1072] det_loss_box_dn_2[0.2911] det_loss_cls_dn_3[0.1064] det_loss_box_dn_3[0.2888] det_loss_cls_dn_4[0.1054] det_loss_box_dn_4[0.2873] det_loss_cls_dn_5[0.1043] det_loss_box_dn_5[0.2849] loss_depth[0.0417] total_loss[8.9950]
Rank[0/8] 06/19/2025 22:01:22 INFO stats.py:314 | Epoch[59] Step[364] GlobalStep[23374] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 23:29:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:01:33 INFO loss_tracker.py:84 | Epoch[59/239] Step[374] GlobalStep[23384/NA]: det_loss_cls_0[0.5916] det_loss_box_0[0.7947] det_loss_cls_1[0.5498] det_loss_box_1[0.6370] det_loss_cls_2[0.5257] det_loss_box_2[0.5826] det_loss_cls_3[0.4964] det_loss_box_3[0.5569] det_loss_cls_4[0.4775] det_loss_box_4[0.5394] det_loss_cls_5[0.4505] det_loss_box_5[0.5450] det_loss_cls_dn_0[0.1039] det_loss_box_dn_0[0.3498] det_loss_cls_dn_1[0.0990] det_loss_box_dn_1[0.3082] det_loss_cls_dn_2[0.0982] det_loss_box_dn_2[0.2950] det_loss_cls_dn_3[0.0974] det_loss_box_dn_3[0.2922] det_loss_cls_dn_4[0.0956] det_loss_box_dn_4[0.2911] det_loss_cls_dn_5[0.0944] det_loss_box_dn_5[0.2894] loss_depth[0.0423] total_loss[9.2032]
Rank[0/8] 06/19/2025 22:01:48 INFO stats.py:314 | Epoch[59] Step[389] GlobalStep[23399] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 23:28:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:01:48 INFO trainer.py:268 | 
==================================================BEGIN EVAL==================================================
Rank[0/8] 06/19/2025 22:02:50 INFO trainer.py:275 | eval: 10
Rank[0/8] 06/19/2025 22:03:00 INFO trainer.py:275 | eval: 20
Rank[0/8] 06/19/2025 22:03:11 INFO trainer.py:275 | eval: 30
Rank[0/8] 06/19/2025 22:03:29 INFO trainer.py:275 | eval: 40
Rank[0/8] 06/19/2025 22:03:43 INFO trainer.py:275 | eval: 50
Rank[0/8] 06/19/2025 22:03:52 INFO trainer.py:275 | eval: 60
Rank[0/8] 06/19/2025 22:04:04 INFO trainer.py:275 | eval: 70
Rank[0/8] 06/19/2025 22:04:17 INFO trainer.py:275 | eval: 80
Rank[0/8] 06/19/2025 22:04:30 INFO trainer.py:275 | eval: 90
Rank[0/8] 06/19/2025 22:04:37 INFO trainer.py:275 | eval: 100
Rank[0/8] 06/19/2025 22:04:41 INFO metrics.py:428 | number of results: 824
Rank[0/8] 06/19/2025 22:08:41 INFO metrics.py:332 | eval results of overall:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| bin                 | 0.5445  | 0.7961  | 0.2324  | 0.4581  |
| toilet              | 0.8353  | 0.8686  | 0.5932  | 0.7226  |
| sink                | 0.6159  | 0.7942  | 0.0951  | 0.2840  |
| bathtub             | 0.7773  | 0.8676  | 0.5080  | 0.6176  |
| picture             | 0.2131  | 0.4569  | 0.0109  | 0.0884  |
| curtain             | 0.4141  | 0.8209  | 0.0795  | 0.3821  |
| doorframe           | 0.2875  | 0.7461  | 0.0604  | 0.3730  |
| door                | 0.2055  | 0.6614  | 0.0193  | 0.2237  |
| stopcock            | 0.1599  | 0.4483  | 0.0000  | 0.0000  |
| toilet paper        | 0.3190  | 0.5512  | 0.0347  | 0.1654  |
| towel               | 0.3035  | 0.6065  | 0.0278  | 0.1548  |
| vanity              | 0.4036  | 0.9111  | 0.3043  | 0.7556  |
| bar                 | 0.0073  | 0.0909  | 0.0000  | 0.0303  |
| tissue box          | 0.0509  | 0.4545  | 0.0011  | 0.1515  |
| rail                | 0.0120  | 0.3438  | 0.0000  | 0.0469  |
| bottle              | 0.0386  | 0.3955  | 0.0006  | 0.0669  |
| switch              | 0.0017  | 0.1250  | 0.0000  | 0.0000  |
| bag                 | 0.2252  | 0.6432  | 0.0260  | 0.1892  |
| counter             | 0.0947  | 0.6789  | 0.0088  | 0.2294  |
| mirror              | 0.1527  | 0.6522  | 0.0073  | 0.1576  |
| hair dryer          | 0.0476  | 0.1429  | 0.0000  | 0.0000  |
| box                 | 0.1348  | 0.5033  | 0.0194  | 0.1402  |
| shelf               | 0.1994  | 0.6727  | 0.0761  | 0.3921  |
| shower              | 0.0184  | 0.1333  | 0.0043  | 0.0667  |
| socket              | 0.0098  | 0.2124  | 0.0001  | 0.0177  |
| paper               | 0.0130  | 0.2337  | 0.0004  | 0.0296  |
| cup                 | 0.0642  | 0.2966  | 0.0018  | 0.0508  |
| tissue              | 0.0027  | 0.2308  | 0.0012  | 0.1538  |
| backpack            | 0.5319  | 0.7903  | 0.2878  | 0.4516  |
| cabinet             | 0.2315  | 0.7091  | 0.1015  | 0.4515  |
| bed                 | 0.7373  | 0.9290  | 0.5331  | 0.8698  |
| faucet              | 0.0153  | 0.2553  | 0.0000  | 0.0000  |
| light               | 0.0147  | 0.1774  | 0.0001  | 0.0235  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ledge               | 0.0034  | 0.5000  | 0.0002  | 0.1667  |
| microwave           | 0.5108  | 0.7143  | 0.1297  | 0.4082  |
| purse               | 0.0008  | 0.1667  | 0.0000  | 0.0000  |
| decoration          | 0.0231  | 0.3857  | 0.0001  | 0.0357  |
| stand               | 0.5298  | 0.8415  | 0.3316  | 0.6341  |
| toothpaste          | 0.0007  | 0.2000  | 0.0000  | 0.0000  |
| plant               | 0.2561  | 0.6236  | 0.0428  | 0.2794  |
| clothes             | 0.1257  | 0.5478  | 0.0063  | 0.1306  |
| book                | 0.0465  | 0.4022  | 0.0020  | 0.0642  |
| pillow              | 0.3475  | 0.6346  | 0.0345  | 0.1548  |
| lamp                | 0.3224  | 0.5791  | 0.0509  | 0.2060  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blanket             | 0.1487  | 0.7000  | 0.0067  | 0.2500  |
| bench               | 0.2326  | 0.7358  | 0.1144  | 0.4528  |
| rack                | 0.0476  | 0.5577  | 0.0128  | 0.2500  |
| mat                 | 0.0201  | 0.3243  | 0.0004  | 0.0270  |
| soap dish           | 0.5017  | 0.6667  | 0.0241  | 0.1333  |
| soap                | 0.0009  | 0.3333  | 0.0000  | 0.0000  |
| stool               | 0.2058  | 0.6807  | 0.1160  | 0.4286  |
| hamper              | 0.4221  | 0.5455  | 0.2273  | 0.2727  |
| frame               | 0.0596  | 0.5102  | 0.0007  | 0.0408  |
| table               | 0.4255  | 0.8433  | 0.2783  | 0.6223  |
| scale               | 0.2500  | 0.2727  | 0.0303  | 0.0909  |
| basket              | 0.2260  | 0.5478  | 0.0674  | 0.2174  |
| case                | 0.0719  | 0.3785  | 0.0337  | 0.1243  |
| window              | 0.2112  | 0.6407  | 0.0378  | 0.2695  |
| column              | 0.0073  | 0.3158  | 0.0007  | 0.0789  |
| refrigerator        | 0.5362  | 0.7800  | 0.2719  | 0.5600  |
| jacket              | 0.5668  | 0.8750  | 0.1636  | 0.3333  |
| cosmetics           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bowl                | 0.0851  | 0.3623  | 0.0094  | 0.1159  |
| stall               | 0.0406  | 0.9412  | 0.0090  | 0.4118  |
| paper cutter        | 0.4638  | 0.8333  | 0.0000  | 0.0000  |
| couch               | 0.5039  | 0.8223  | 0.3369  | 0.6736  |
| wrap                | 0.0323  | 1.0000  | 0.0000  | 0.0000  |
| grab bar            | 0.0143  | 0.5000  | 0.0000  | 0.0000  |
| container           | 0.0082  | 0.3390  | 0.0024  | 0.1525  |
| chair               | 0.5518  | 0.7620  | 0.3554  | 0.5596  |
| radiator            | 0.5923  | 0.7667  | 0.0561  | 0.1667  |
| monitor             | 0.5048  | 0.6902  | 0.0325  | 0.1739  |
| mouse               | 0.0003  | 0.0541  | 0.0000  | 0.0000  |
| keyboard            | 0.0110  | 0.0930  | 0.0000  | 0.0000  |
| blackboard          | 0.4766  | 0.8415  | 0.0591  | 0.2561  |
| computer            | 0.2548  | 0.5738  | 0.0023  | 0.0984  |
| telephone           | 0.2477  | 0.5000  | 0.0350  | 0.1333  |
| plug                | 0.0013  | 0.0526  | 0.0000  | 0.0000  |
| wardrobe            | 0.2249  | 0.7831  | 0.0578  | 0.4819  |
| windowsill          | 0.0247  | 0.5652  | 0.0036  | 0.0435  |
| desk                | 0.3446  | 0.8989  | 0.1893  | 0.6742  |
| blinds              | 0.1081  | 0.4200  | 0.0099  | 0.0800  |
| mailbox             | 0.0261  | 1.0000  | 0.0153  | 0.6000  |
| board               | 0.1102  | 0.6290  | 0.0317  | 0.2258  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| can                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| remote control      | 0.0375  | 0.2222  | 0.0077  | 0.0741  |
| tube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| projector           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| furniture           | 0.0008  | 0.5000  | 0.0004  | 0.3333  |
| laptop              | 0.3411  | 0.6087  | 0.0245  | 0.0870  |
| headphones          | 0.0917  | 0.4000  | 0.0000  | 0.0000  |
| dresser             | 0.6300  | 0.9259  | 0.5366  | 0.8704  |
| pipe                | 0.0182  | 0.1429  | 0.0000  | 0.0000  |
| ottoman             | 0.1379  | 0.8056  | 0.0916  | 0.5556  |
| shoe                | 0.1436  | 0.3551  | 0.0023  | 0.0561  |
| toy                 | 0.1508  | 0.4394  | 0.0043  | 0.0606  |
| tv                  | 0.5391  | 0.7921  | 0.1161  | 0.3465  |
| clock               | 0.1996  | 0.5106  | 0.0006  | 0.0213  |
| cushion             | 0.1261  | 0.5294  | 0.0131  | 0.1176  |
| calendar            | 0.0017  | 0.3333  | 0.0007  | 0.1667  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| heater              | 0.4466  | 0.8103  | 0.0824  | 0.2931  |
| printer             | 0.5207  | 0.8367  | 0.1912  | 0.5306  |
| carpet              | 0.0488  | 0.4286  | 0.0032  | 0.1071  |
| statue              | 0.1424  | 0.3158  | 0.0566  | 0.1579  |
| poster              | 0.0618  | 0.1667  | 0.0000  | 0.0000  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hat                 | 0.0868  | 0.4444  | 0.0000  | 0.0000  |
| notebook            | 0.0005  | 0.0769  | 0.0000  | 0.0000  |
| cart                | 0.0031  | 0.5000  | 0.0031  | 0.5000  |
| partition           | 0.0103  | 0.6667  | 0.0000  | 0.0000  |
| guitar              | 0.2708  | 0.5000  | 0.0104  | 0.0833  |
| luggage             | 0.0652  | 1.0000  | 0.0072  | 0.3333  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water cooler        | 0.6429  | 1.0000  | 0.5141  | 1.0000  |
| tray                | 0.0018  | 0.2857  | 0.0000  | 0.0000  |
| coffee maker        | 0.2813  | 0.6897  | 0.0652  | 0.3103  |
| machine             | 0.3895  | 1.0000  | 0.0627  | 0.5789  |
| plunger             | 0.8000  | 0.8000  | 0.0533  | 0.4000  |
| commode             | 0.4715  | 0.8269  | 0.3810  | 0.6538  |
| roof                | 0.0017  | 0.2500  | 0.0000  | 0.0417  |
| vent                | 0.0216  | 0.1250  | 0.0000  | 0.0000  |
| soap dispenser      | 0.4607  | 0.7407  | 0.1606  | 0.2963  |
| dish rack           | 0.1641  | 0.6000  | 0.0000  | 0.0000  |
| bucket              | 0.2434  | 0.6154  | 0.0488  | 0.2564  |
| brush               | 0.0902  | 0.2857  | 0.0000  | 0.0000  |
| roll                | 0.0525  | 0.4000  | 0.0000  | 0.0000  |
| hanger              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dispenser           | 0.4185  | 0.7500  | 0.0692  | 0.3409  |
| alarm               | 0.0005  | 0.1250  | 0.0000  | 0.0000  |
| pillar              | 0.3329  | 0.7209  | 0.1217  | 0.3721  |
| broom               | 0.0149  | 0.2857  | 0.0000  | 0.0000  |
| mop                 | 0.2619  | 0.6667  | 0.0000  | 0.0000  |
| fire extinguisher   | 0.2261  | 0.6000  | 0.0674  | 0.4000  |
| glass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beam                | 0.0002  | 0.0455  | 0.0000  | 0.0000  |
| sign                | 0.0633  | 0.3333  | 0.0056  | 0.0333  |
| person              | 0.1081  | 0.6667  | 0.0446  | 0.3333  |
| copier              | 0.9167  | 0.9167  | 0.3409  | 0.5000  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ladder              | 0.0482  | 0.3529  | 0.0338  | 0.1765  |
| fireplace           | 0.1173  | 0.3571  | 0.0032  | 0.0714  |
| holder              | 0.0021  | 0.2222  | 0.0000  | 0.0000  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stairs              | 0.0764  | 0.3750  | 0.0044  | 0.1667  |
| arch                | 0.0045  | 0.2333  | 0.0014  | 0.1000  |
| drum                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crate               | 0.5032  | 0.7143  | 0.0476  | 0.1429  |
| doorway             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| window frame        | 0.0534  | 0.5455  | 0.0047  | 0.2020  |
| cube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drawer              | 0.0496  | 0.5556  | 0.0026  | 0.3889  |
| screen              | 0.0600  | 0.4000  | 0.0000  | 0.0000  |
| bicycle             | 0.3847  | 0.6667  | 0.0667  | 0.3333  |
| dishwasher          | 0.2750  | 0.4000  | 0.0090  | 0.2000  |
| tablet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| folder              | 0.0070  | 0.4286  | 0.0001  | 0.0714  |
| file                | 0.1392  | 0.4706  | 0.0061  | 0.0588  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flowerpot           | 0.0328  | 0.6275  | 0.0029  | 0.1176  |
| air conditioner     | 0.2733  | 0.6000  | 0.1000  | 0.2000  |
| fan                 | 0.3656  | 0.7656  | 0.0793  | 0.4375  |
| sunglasses          | 0.0502  | 0.5385  | 0.0007  | 0.0769  |
| ball                | 0.3983  | 0.5238  | 0.3104  | 0.4286  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plate               | 0.0517  | 0.3654  | 0.0035  | 0.0769  |
| flush               | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| headboard           | 0.0089  | 0.2500  | 0.0000  | 0.0000  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pitcher             | 0.0133  | 0.3333  | 0.0040  | 0.3333  |
| teapot              | 0.0021  | 0.2222  | 0.0000  | 0.0000  |
| kettle              | 0.0785  | 0.6111  | 0.0105  | 0.1667  |
| oven                | 0.2597  | 0.7143  | 0.0413  | 0.3214  |
| toaster             | 0.2250  | 0.3000  | 0.0000  | 0.0000  |
| pack                | 0.0488  | 0.7333  | 0.0136  | 0.2000  |
| stove               | 0.5150  | 0.8611  | 0.2773  | 0.4444  |
| kitchenware         | 0.1214  | 0.3913  | 0.0027  | 0.0652  |
| beanbag             | 0.9762  | 1.0000  | 0.9762  | 1.0000  |
| vase                | 0.1234  | 0.5400  | 0.0110  | 0.1600  |
| price tag           | 0.1500  | 0.4286  | 0.0000  | 0.0000  |
| pot                 | 0.0405  | 0.4074  | 0.0093  | 0.1111  |
| footstool           | 0.0238  | 0.5000  | 0.0238  | 0.5000  |
| player              | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| jar                 | 0.0011  | 0.0769  | 0.0000  | 0.0000  |
| vacuum cleaner      | 0.1429  | 0.4286  | 0.0159  | 0.1429  |
| toiletry            | 0.0402  | 0.3913  | 0.0152  | 0.0870  |
| radio               | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| umbrella            | 0.2000  | 0.2000  | 0.0000  | 0.0000  |
| speaker             | 0.0049  | 0.1364  | 0.0000  | 0.0000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garbage             | 0.1261  | 0.5000  | 0.0335  | 0.3750  |
| candle              | 0.0871  | 0.3333  | 0.0008  | 0.1111  |
| napkins             | 0.1667  | 0.3333  | 0.0000  | 0.0000  |
| crib                | 0.2963  | 1.0000  | 0.0370  | 0.3333  |
| magazine            | 0.0016  | 0.2000  | 0.0000  | 0.0000  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| humidifier          | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| conducting wire     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| device              | 0.0105  | 0.1818  | 0.0076  | 0.0909  |
| clothes dryer       | 0.2077  | 0.7333  | 0.1040  | 0.5333  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jalousie            | 0.0078  | 0.5000  | 0.0000  | 0.0000  |
| chandelier          | 0.2555  | 0.3810  | 0.0503  | 0.1905  |
| adhesive tape       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| album               | 0.0130  | 1.0000  | 0.0000  | 0.0000  |
| candlestick         | 0.3333  | 0.3333  | 0.0047  | 0.3333  |
| shampoo             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basin               | 0.0098  | 0.1765  | 0.0001  | 0.0588  |
| body loofah         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.4761  | 0.8214  | 0.2481  | 0.5714  |
| pan                 | 0.0759  | 0.2941  | 0.0042  | 0.1176  |
| range hood          | 0.2781  | 0.7143  | 0.0032  | 0.1429  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bread               | 0.0808  | 0.4000  | 0.0000  | 0.0000  |
| fruit               | 0.1934  | 0.3571  | 0.0194  | 0.1429  |
| spoon               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| food                | 0.0909  | 0.0909  | 0.0909  | 0.0909  |
| balcony             | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet               | 0.3548  | 0.6667  | 0.3548  | 0.6667  |
| piano               | 0.4000  | 0.4000  | 0.2069  | 0.4000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sack                | 0.6523  | 0.8571  | 0.2234  | 0.4286  |
| coil                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| countertop          | 0.0145  | 0.4643  | 0.0005  | 0.0714  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washbasin           | 0.0057  | 0.2667  | 0.0040  | 0.2000  |
| steps               | 0.0007  | 0.3000  | 0.0003  | 0.1000  |
| wine                | 0.0286  | 0.1429  | 0.0000  | 0.0000  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| handle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| banister            | 0.0286  | 0.2000  | 0.0000  | 0.0000  |
| thermostat          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchen island      | 0.0647  | 0.7143  | 0.0495  | 0.4286  |
| seasoning           | 0.0208  | 0.1250  | 0.0000  | 0.0000  |
| excercise equipment | 0.1725  | 0.7000  | 0.0100  | 0.2000  |
| treadmill           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool table          | 0.1667  | 0.3333  | 0.1667  | 0.3333  |
| garage door         | 0.1111  | 0.3333  | 0.0278  | 0.1667  |
| map                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell            | 0.0029  | 0.3333  | 0.0000  | 0.0000  |
| helmets             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool                | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| barricade           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| letter              | 0.0125  | 0.2500  | 0.0000  | 0.0000  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glove               | 0.1944  | 0.6667  | 0.0000  | 0.0000  |
| sponge              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| salt                | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| water heater        | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cap                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| menu                | 0.2381  | 0.2857  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| overall             | 0.1604  | 0.4058  | 0.0511  | 0.1600  |
| head                | 0.2457  | 0.5789  | 0.0865  | 0.2552  |
| common              | 0.1401  | 0.3895  | 0.0431  | 0.1496  |
| tail                | 0.0897  | 0.2342  | 0.0212  | 0.0673  |
| small               | 0.0403  | 0.1995  | 0.0058  | 0.0381  |
| medium              | 0.1685  | 0.4439  | 0.0531  | 0.1754  |
| large               | 0.0944  | 0.5221  | 0.0335  | 0.2467  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/19/2025 22:08:41 INFO metrics.py:437 | number of scannet results: 250
Rank[0/8] 06/19/2025 22:09:52 INFO metrics.py:332 | eval results of scannet:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| bin                 | 0.5846  | 0.7915  | 0.2812  | 0.5021  |
| toilet              | 0.9191  | 0.9286  | 0.7031  | 0.8036  |
| sink                | 0.7587  | 0.8295  | 0.1753  | 0.3636  |
| bathtub             | 0.9142  | 0.9688  | 0.7057  | 0.7500  |
| picture             | 0.3106  | 0.5597  | 0.0227  | 0.1306  |
| curtain             | 0.5273  | 0.8407  | 0.1326  | 0.4513  |
| doorframe           | 0.3323  | 0.7324  | 0.0298  | 0.2817  |
| door                | 0.3688  | 0.8127  | 0.0585  | 0.3378  |
| stopcock            | 0.1599  | 0.4483  | 0.0000  | 0.0000  |
| toilet paper        | 0.3753  | 0.5696  | 0.0402  | 0.1646  |
| towel               | 0.3541  | 0.6446  | 0.0079  | 0.1405  |
| vanity              | 0.5318  | 0.9667  | 0.3999  | 0.7667  |
| bar                 | 0.0065  | 0.0455  | 0.0000  | 0.0000  |
| tissue box          | 0.0495  | 0.4091  | 0.0004  | 0.1364  |
| rail                | 0.0677  | 0.8333  | 0.0004  | 0.1667  |
| bottle              | 0.0412  | 0.4000  | 0.0009  | 0.0737  |
| switch              | 0.0046  | 0.2025  | 0.0000  | 0.0000  |
| bag                 | 0.2075  | 0.6301  | 0.0486  | 0.2466  |
| counter             | 0.1732  | 0.8000  | 0.0235  | 0.3714  |
| mirror              | 0.1780  | 0.6988  | 0.0030  | 0.1325  |
| hair dryer          | 0.0476  | 0.1429  | 0.0000  | 0.0000  |
| box                 | 0.1473  | 0.4683  | 0.0233  | 0.1310  |
| shelf               | 0.3413  | 0.8603  | 0.1673  | 0.6618  |
| shower              | 0.0055  | 0.0714  | 0.0038  | 0.0714  |
| socket              | 0.0115  | 0.2282  | 0.0001  | 0.0194  |
| paper               | 0.0135  | 0.2302  | 0.0001  | 0.0189  |
| cup                 | 0.1106  | 0.3768  | 0.0016  | 0.0580  |
| tissue              | 0.0041  | 0.2222  | 0.0005  | 0.1111  |
| backpack            | 0.6570  | 0.8929  | 0.4594  | 0.5714  |
| cabinet             | 0.3059  | 0.7500  | 0.1338  | 0.4683  |
| bed                 | 0.8636  | 1.0000  | 0.7486  | 0.9825  |
| faucet              | 0.0005  | 0.1250  | 0.0000  | 0.0000  |
| light               | 0.0478  | 0.3448  | 0.0008  | 0.0690  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ledge               | 0.0034  | 0.5000  | 0.0002  | 0.1667  |
| microwave           | 0.5541  | 0.7500  | 0.1276  | 0.4000  |
| purse               | 0.0013  | 0.1667  | 0.0000  | 0.0000  |
| decoration          | 0.0893  | 0.4583  | 0.0000  | 0.0000  |
| stand               | 0.7660  | 0.8868  | 0.5175  | 0.7358  |
| toothpaste          | 0.0008  | 0.2000  | 0.0000  | 0.0000  |
| plant               | 0.2553  | 0.5630  | 0.0544  | 0.2773  |
| clothes             | 0.1183  | 0.5033  | 0.0122  | 0.1699  |
| book                | 0.0579  | 0.4214  | 0.0043  | 0.0833  |
| pillow              | 0.4346  | 0.6528  | 0.1172  | 0.2847  |
| lamp                | 0.4477  | 0.6905  | 0.0921  | 0.2500  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blanket             | 0.0415  | 0.5417  | 0.0029  | 0.1667  |
| bench               | 0.9029  | 1.0000  | 0.1193  | 0.8000  |
| rack                | 0.0068  | 0.4545  | 0.0000  | 0.0000  |
| mat                 | 0.0188  | 0.3000  | 0.0000  | 0.0000  |
| soap dish           | 0.6346  | 0.8333  | 0.0301  | 0.1667  |
| stool               | 0.2170  | 0.7045  | 0.0983  | 0.4091  |
| hamper              | 0.4643  | 0.6000  | 0.2500  | 0.3000  |
| frame               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| table               | 0.5026  | 0.8923  | 0.3573  | 0.6769  |
| scale               | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| basket              | 0.1086  | 0.3077  | 0.0337  | 0.1154  |
| case                | 0.4157  | 0.8421  | 0.3434  | 0.6842  |
| window              | 0.4100  | 0.8519  | 0.1069  | 0.5123  |
| refrigerator        | 0.7934  | 1.0000  | 0.4253  | 0.7500  |
| jacket              | 0.5916  | 0.9130  | 0.1707  | 0.3478  |
| bowl                | 0.1148  | 0.2222  | 0.1111  | 0.1111  |
| stall               | 0.0443  | 0.9412  | 0.0099  | 0.4118  |
| paper cutter        | 0.4366  | 0.8000  | 0.0000  | 0.0000  |
| couch               | 0.7853  | 0.9625  | 0.6319  | 0.8250  |
| wrap                | 0.0323  | 1.0000  | 0.0000  | 0.0000  |
| grab bar            | 0.0144  | 0.5000  | 0.0000  | 0.0000  |
| container           | 0.0049  | 0.1739  | 0.0017  | 0.0870  |
| chair               | 0.5891  | 0.7508  | 0.3547  | 0.5352  |
| radiator            | 0.7085  | 0.8276  | 0.0826  | 0.2241  |
| monitor             | 0.5619  | 0.7214  | 0.0509  | 0.2000  |
| mouse               | 0.0009  | 0.0952  | 0.0000  | 0.0000  |
| keyboard            | 0.0188  | 0.1207  | 0.0000  | 0.0000  |
| blackboard          | 0.4599  | 0.8406  | 0.0632  | 0.2899  |
| computer            | 0.2913  | 0.5385  | 0.0025  | 0.1026  |
| telephone           | 0.2228  | 0.5000  | 0.0186  | 0.1562  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wardrobe            | 0.2368  | 0.8750  | 0.1723  | 0.3750  |
| windowsill          | 0.0016  | 0.3333  | 0.0000  | 0.0000  |
| desk                | 0.4591  | 0.9747  | 0.2706  | 0.7215  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mailbox             | 0.0318  | 1.0000  | 0.0171  | 0.6000  |
| board               | 0.0988  | 0.7188  | 0.0162  | 0.3438  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| laptop              | 0.3886  | 0.5333  | 0.0333  | 0.0667  |
| headphones          | 0.1111  | 0.4000  | 0.0000  | 0.0000  |
| dresser             | 0.7472  | 1.0000  | 0.6934  | 0.9730  |
| pipe                | 0.0001  | 0.0714  | 0.0000  | 0.0000  |
| ottoman             | 0.0419  | 0.8571  | 0.0102  | 0.2857  |
| shoe                | 0.1655  | 0.4500  | 0.0052  | 0.0750  |
| toy                 | 0.0889  | 0.2941  | 0.0118  | 0.0588  |
| tv                  | 0.6835  | 0.8261  | 0.1755  | 0.3478  |
| clock               | 0.2834  | 0.6400  | 0.0000  | 0.0000  |
| cushion             | 0.1980  | 0.7500  | 0.0000  | 0.0000  |
| calendar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| printer             | 0.5601  | 0.9091  | 0.3183  | 0.6667  |
| carpet              | 0.0065  | 0.4000  | 0.0000  | 0.0000  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| notebook            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cart                | 0.0077  | 1.0000  | 0.0077  | 1.0000  |
| guitar              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water cooler        | 0.6429  | 1.0000  | 0.5169  | 1.0000  |
| tray                | 0.0015  | 0.3333  | 0.0000  | 0.0000  |
| coffee maker        | 0.4592  | 0.8235  | 0.2228  | 0.5294  |
| machine             | 0.4293  | 1.0000  | 0.1361  | 0.6875  |
| plunger             | 0.8000  | 0.8000  | 0.0533  | 0.4000  |
| soap dispenser      | 0.5650  | 0.8095  | 0.2083  | 0.3810  |
| dish rack           | 0.3414  | 0.7500  | 0.0000  | 0.0000  |
| bucket              | 0.1313  | 0.5000  | 0.1040  | 0.2857  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dispenser           | 0.5516  | 0.7949  | 0.0968  | 0.3590  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillar              | 0.3140  | 0.8000  | 0.0296  | 0.6000  |
| broom               | 0.0261  | 0.3333  | 0.0000  | 0.0000  |
| fire extinguisher   | 0.0278  | 0.3333  | 0.0278  | 0.3333  |
| sign                | 0.0638  | 0.3214  | 0.0060  | 0.0357  |
| person              | 0.1086  | 0.6667  | 0.0446  | 0.3333  |
| copier              | 0.9167  | 0.9167  | 0.3409  | 0.5000  |
| ladder              | 0.0625  | 0.4000  | 0.0063  | 0.2000  |
| holder              | 0.0034  | 0.2000  | 0.0000  | 0.0000  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stairs              | 0.2932  | 0.8000  | 0.0134  | 0.6000  |
| crate               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drawer              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 0.3909  | 1.0000  | 0.1905  | 0.6667  |
| dishwasher          | 0.3333  | 0.3333  | 0.0000  | 0.0000  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flowerpot           | 0.0012  | 0.5000  | 0.0000  | 0.0000  |
| fan                 | 0.5321  | 0.7500  | 0.1250  | 0.1250  |
| ball                | 0.4028  | 0.4167  | 0.3056  | 0.3333  |
| plate               | 0.0215  | 0.6000  | 0.0000  | 0.0000  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pitcher             | 0.0167  | 0.3333  | 0.0056  | 0.3333  |
| teapot              | 0.0147  | 0.2500  | 0.0000  | 0.0000  |
| kettle              | 0.0361  | 0.5000  | 0.0000  | 0.0000  |
| oven                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stove               | 0.9355  | 1.0000  | 0.9253  | 1.0000  |
| pot                 | 0.0091  | 0.5000  | 0.0000  | 0.0000  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vacuum cleaner      | 0.6667  | 0.6667  | 0.1667  | 0.3333  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basin               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.4444  | 0.6667  | 0.4444  | 0.6667  |
| adhesive tape       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.4000  | 1.0000  | 0.4000  | 1.0000  |
| map                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.5657  | 0.8000  | 0.0000  | 0.0000  |
| barricade           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.0132  | 0.5000  | 0.0000  | 0.0000  |
| fruit               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| scannet             | 0.2277  | 0.4784  | 0.0904  | 0.2215  |
| head                | 0.3067  | 0.6077  | 0.1303  | 0.2917  |
| common              | 0.1625  | 0.3629  | 0.0522  | 0.1536  |
| tail                | 0.0782  | 0.2583  | 0.0296  | 0.1167  |
| small               | 0.0384  | 0.1718  | 0.0072  | 0.0442  |
| medium              | 0.2582  | 0.5700  | 0.0995  | 0.2483  |
| large               | 0.2001  | 0.8158  | 0.0906  | 0.5079  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/19/2025 22:09:52 INFO metrics.py:437 | number of 3rscan results: 235
Rank[0/8] 06/19/2025 22:10:59 INFO metrics.py:332 | eval results of 3rscan:
+-------------------+---------+---------+---------+---------+
| classes           | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+-------------------+---------+---------+---------+---------+
| keyboard          | 0.0069  | 0.0417  | 0.0000  | 0.0000  |
| chair             | 0.6004  | 0.7869  | 0.4336  | 0.6161  |
| monitor           | 0.4660  | 0.6000  | 0.0114  | 0.1000  |
| bin               | 0.5522  | 0.8171  | 0.1865  | 0.4390  |
| desk              | 0.2838  | 0.8750  | 0.1333  | 0.6375  |
| bag               | 0.2672  | 0.6869  | 0.0267  | 0.1717  |
| cup               | 0.0396  | 0.1795  | 0.0051  | 0.0256  |
| box               | 0.1571  | 0.5054  | 0.0231  | 0.1373  |
| tissue box        | 0.0313  | 0.6667  | 0.0278  | 0.3333  |
| mouse             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| paper             | 0.0205  | 0.2571  | 0.0041  | 0.0714  |
| pen               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stand             | 0.3988  | 0.8689  | 0.2310  | 0.5902  |
| radiator          | 0.4320  | 0.7241  | 0.0079  | 0.0690  |
| case              | 0.0413  | 0.3429  | 0.0002  | 0.0571  |
| book              | 0.0291  | 0.3728  | 0.0005  | 0.0355  |
| table             | 0.4421  | 0.8694  | 0.2576  | 0.6126  |
| bottle            | 0.0219  | 0.3223  | 0.0000  | 0.0248  |
| computer          | 0.2382  | 0.6190  | 0.0067  | 0.0952  |
| socket            | 0.0001  | 0.0625  | 0.0000  | 0.0000  |
| cabinet           | 0.2870  | 0.7778  | 0.1288  | 0.5214  |
| telephone         | 0.3177  | 0.5417  | 0.0657  | 0.1250  |
| clock             | 0.1172  | 0.3684  | 0.0000  | 0.0000  |
| plug              | 0.0556  | 0.1111  | 0.0000  | 0.0000  |
| curtain           | 0.4018  | 0.8651  | 0.0778  | 0.4206  |
| window            | 0.1588  | 0.6849  | 0.0294  | 0.2192  |
| wardrobe          | 0.2730  | 0.8125  | 0.0647  | 0.5156  |
| blinds            | 0.1414  | 0.5128  | 0.0128  | 0.1026  |
| shelf             | 0.1797  | 0.6360  | 0.0480  | 0.2862  |
| garbage           | 0.1306  | 0.5000  | 0.0364  | 0.3750  |
| container         | 0.0025  | 0.2222  | 0.0006  | 0.1111  |
| commode           | 0.4762  | 0.8269  | 0.3835  | 0.6538  |
| paper cutter      | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| printer           | 0.6866  | 0.7778  | 0.0826  | 0.3333  |
| pillow            | 0.3889  | 0.6602  | 0.0269  | 0.1172  |
| stool             | 0.2962  | 0.7963  | 0.1831  | 0.5185  |
| plant             | 0.2882  | 0.6139  | 0.0471  | 0.2030  |
| folder            | 0.0071  | 0.4286  | 0.0001  | 0.0714  |
| picture           | 0.1871  | 0.4390  | 0.0082  | 0.0691  |
| rack              | 0.1226  | 0.7000  | 0.0415  | 0.4333  |
| clothes           | 0.1913  | 0.6990  | 0.0027  | 0.0874  |
| notebook          | 0.0007  | 0.0909  | 0.0000  | 0.0000  |
| file              | 0.1665  | 0.5357  | 0.0075  | 0.0714  |
| plate             | 0.0461  | 0.3333  | 0.0055  | 0.0909  |
| furniture         | 0.0046  | 1.0000  | 0.0023  | 0.6667  |
| backpack          | 0.4487  | 0.6970  | 0.1599  | 0.3333  |
| drawer            | 0.1363  | 0.8889  | 0.0104  | 0.6667  |
| column            | 0.0188  | 1.0000  | 0.0005  | 0.5000  |
| calendar          | 0.0032  | 0.4000  | 0.0013  | 0.2000  |
| basket            | 0.3323  | 0.6719  | 0.1163  | 0.3281  |
| board             | 0.1524  | 0.5833  | 0.0722  | 0.1250  |
| machine           | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| cushion           | 0.2234  | 0.6667  | 0.0237  | 0.1667  |
| bar               | 0.2500  | 0.6667  | 0.0008  | 0.3333  |
| tablet            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tv                | 0.6165  | 0.8857  | 0.1255  | 0.3429  |
| lamp              | 0.2568  | 0.4813  | 0.0151  | 0.1000  |
| couch             | 0.3707  | 0.7143  | 0.1696  | 0.5714  |
| frame             | 0.1165  | 0.6667  | 0.0014  | 0.0667  |
| bed               | 0.8331  | 0.9455  | 0.5476  | 0.8545  |
| bowl              | 0.1647  | 0.4074  | 0.0114  | 0.1481  |
| ottoman           | 0.2735  | 0.9091  | 0.2198  | 0.8182  |
| light             | 0.0368  | 0.2500  | 0.0000  | 0.0000  |
| bench             | 0.1935  | 0.8214  | 0.1580  | 0.5357  |
| flowerpot         | 0.0466  | 0.6250  | 0.0038  | 0.0417  |
| candle            | 0.1041  | 0.3333  | 0.0009  | 0.0667  |
| blanket           | 0.2385  | 0.8154  | 0.0115  | 0.3077  |
| vase              | 0.2106  | 0.5500  | 0.0076  | 0.1500  |
| footstool         | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| decoration        | 0.0597  | 0.4062  | 0.0001  | 0.0312  |
| door              | 0.2515  | 0.6985  | 0.0257  | 0.2426  |
| switch            | 0.0002  | 0.0189  | 0.0000  | 0.0000  |
| teapot            | 0.0013  | 0.2500  | 0.0000  | 0.0000  |
| napkins           | 0.1667  | 0.3333  | 0.0000  | 0.0000  |
| towel             | 0.2112  | 0.5949  | 0.0246  | 0.1266  |
| doorframe         | 0.1529  | 0.6000  | 0.0188  | 0.2400  |
| jar               | 0.0028  | 0.1250  | 0.0000  | 0.0000  |
| price tag         | 0.1504  | 0.4286  | 0.0000  | 0.0000  |
| laptop            | 0.3304  | 0.8333  | 0.0111  | 0.1667  |
| mirror            | 0.1416  | 0.5926  | 0.0076  | 0.1111  |
| counter           | 0.1234  | 0.6829  | 0.0069  | 0.1707  |
| ladder            | 0.3444  | 0.6667  | 0.3333  | 0.3333  |
| carpet            | 0.0906  | 0.5536  | 0.0062  | 0.1607  |
| player            | 0.3333  | 0.3333  | 0.0000  | 0.0000  |
| crib              | 0.2963  | 1.0000  | 0.0370  | 0.3333  |
| magazine          | 0.0023  | 0.2500  | 0.0000  | 0.0000  |
| poster            | 0.0666  | 0.1746  | 0.0000  | 0.0000  |
| cube              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fan               | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| refrigerator      | 0.5896  | 0.8000  | 0.3133  | 0.6000  |
| toy               | 0.3640  | 0.6154  | 0.0198  | 0.1538  |
| mat               | 0.0256  | 0.3333  | 0.0256  | 0.3333  |
| pad               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| humidifier        | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| blackboard        | 0.6851  | 0.9167  | 0.0833  | 0.0833  |
| heater            | 0.4494  | 0.8070  | 0.0815  | 0.2807  |
| windowsill        | 0.0334  | 0.6154  | 0.0043  | 0.0513  |
| roll              | 0.0625  | 0.4000  | 0.0000  | 0.0000  |
| umbrella          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| coffee maker      | 0.2351  | 0.5556  | 0.0000  | 0.0000  |
| conducting wire   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| device            | 0.0105  | 0.1818  | 0.0076  | 0.0909  |
| clothes dryer     | 0.1734  | 0.7000  | 0.0334  | 0.4000  |
| tool              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| remote control    | 0.0868  | 0.3000  | 0.0204  | 0.1000  |
| partition         | 0.0153  | 0.8000  | 0.0000  | 0.0000  |
| jalousie          | 0.0078  | 0.5000  | 0.0000  | 0.0000  |
| pack              | 0.0564  | 0.8462  | 0.0157  | 0.2308  |
| bucket            | 0.5600  | 0.9333  | 0.0732  | 0.3333  |
| chandelier        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kettle            | 0.1074  | 0.6667  | 0.0169  | 0.2500  |
| sink              | 0.5714  | 0.7500  | 0.0529  | 0.1964  |
| tray              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pot               | 0.2590  | 0.4167  | 0.0845  | 0.2500  |
| air conditioner   | 0.3470  | 0.7500  | 0.1250  | 0.2500  |
| toilet paper      | 0.1488  | 0.4167  | 0.0000  | 0.0000  |
| scale             | 0.5556  | 0.6667  | 0.1667  | 0.3333  |
| holder            | 0.0100  | 1.0000  | 0.0000  | 0.0000  |
| brush             | 0.1714  | 0.5000  | 0.0000  | 0.0000  |
| adhesive tape     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| luggage           | 0.1071  | 1.0000  | 0.0119  | 0.3333  |
| shoe              | 0.2052  | 0.3333  | 0.0022  | 0.0476  |
| tissue            | 0.0833  | 0.3333  | 0.0667  | 0.3333  |
| album             | 0.0278  | 1.0000  | 0.0000  | 0.0000  |
| dresser           | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| hanger            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bathtub           | 0.8021  | 0.8889  | 0.0617  | 0.2222  |
| microwave         | 0.7357  | 0.9412  | 0.2735  | 0.5294  |
| rail              | 0.0066  | 0.2500  | 0.0000  | 0.0000  |
| sunglasses        | 0.0503  | 0.5385  | 0.0007  | 0.0769  |
| hat               | 0.1533  | 0.4286  | 0.0000  | 0.0000  |
| cosmetics         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candlestick       | 1.0000  | 1.0000  | 0.0145  | 1.0000  |
| guitar            | 0.8333  | 1.0000  | 0.0000  | 0.0000  |
| statue            | 1.0000  | 1.0000  | 0.5000  | 0.5000  |
| shampoo           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| faucet            | 0.1232  | 0.6667  | 0.0000  | 0.0000  |
| shower            | 0.2812  | 0.7500  | 0.0278  | 0.2500  |
| basin             | 0.0173  | 0.1333  | 0.0003  | 0.0667  |
| toilet            | 0.7521  | 0.7647  | 0.4379  | 0.4706  |
| toiletry          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pipe              | 0.1250  | 0.2500  | 0.0000  | 0.0000  |
| mattress          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| body loofah       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine   | 0.5714  | 0.8571  | 0.2229  | 0.5000  |
| pillar            | 0.3689  | 0.6471  | 0.3283  | 0.4706  |
| beam              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dispenser         | 0.0101  | 0.3333  | 0.0000  | 0.0000  |
| tube              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beanbag           | 0.9762  | 1.0000  | 0.9762  | 1.0000  |
| ball              | 0.3240  | 0.6000  | 0.2069  | 0.4000  |
| can               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stairs            | 0.1904  | 0.5714  | 0.0484  | 0.3571  |
| stove             | 0.3622  | 0.8824  | 0.0493  | 0.2353  |
| pan               | 0.1097  | 0.4167  | 0.0064  | 0.1667  |
| kitchenware       | 0.1296  | 0.4091  | 0.0030  | 0.0682  |
| oven              | 0.4066  | 0.9474  | 0.0652  | 0.4211  |
| range hood        | 0.1300  | 0.8000  | 0.0174  | 0.4000  |
| shovel            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster           | 0.5625  | 0.7500  | 0.0000  | 0.0000  |
| bread             | 0.0808  | 0.4000  | 0.0000  | 0.0000  |
| dish rack         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit             | 0.4333  | 0.6000  | 0.0000  | 0.0000  |
| dishwasher        | 0.6667  | 0.6667  | 0.0476  | 0.3333  |
| spoon             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| knife             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| food              | 0.1000  | 0.1000  | 0.1000  | 0.1000  |
| balcony           | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| crate             | 0.8256  | 1.0000  | 0.0667  | 0.2000  |
| window frame      | 0.0980  | 0.3333  | 0.0019  | 0.0833  |
| wire              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radio             | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| sign              | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| fireplace         | 0.0154  | 0.2000  | 0.0000  | 0.0000  |
| broom             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| mop               | 0.5556  | 0.6667  | 0.0000  | 0.0000  |
| piano             | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| screen            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle           | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| sack              | 0.6537  | 0.8571  | 0.2262  | 0.4286  |
| flush             | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| soap dish         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dispenser    | 0.2333  | 0.6000  | 0.0000  | 0.0000  |
| coil              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hook              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell          | 0.1111  | 1.0000  | 0.0000  | 0.0000  |
| speaker           | 0.0044  | 0.0833  | 0.0000  | 0.0000  |
| helmets           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| treadmill         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glass             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| letter            | 0.0125  | 0.2500  | 0.0000  | 0.0000  |
| scissors          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glove             | 0.1944  | 0.6667  | 0.0000  | 0.0000  |
| sponge            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| salt              | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| washbasin         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toothbrush        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vacuum cleaner    | 0.0500  | 0.2500  | 0.0000  | 0.0000  |
| water heater      | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| map               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| seasoning         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| menu              | 0.2381  | 0.2857  | 0.0000  | 0.0000  |
| fire extinguisher | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| -------           | ------- | ------- | ------- | ------- |
| 3rscan            | 0.2392  | 0.4918  | 0.0632  | 0.1700  |
| head              | 0.2711  | 0.6063  | 0.0725  | 0.2342  |
| common            | 0.2482  | 0.4757  | 0.0661  | 0.1516  |
| tail              | 0.1686  | 0.3030  | 0.0423  | 0.0767  |
| small             | 0.0582  | 0.2388  | 0.0105  | 0.0319  |
| medium            | 0.2658  | 0.5458  | 0.0658  | 0.1845  |
| large             | 0.1758  | 0.6418  | 0.0634  | 0.3198  |
+-------------------+---------+---------+---------+---------+
Rank[0/8] 06/19/2025 22:10:59 INFO metrics.py:437 | number of matterport3d results: 339
Rank[0/8] 06/19/2025 22:12:20 INFO metrics.py:332 | eval results of matterport3d:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| sink                | 0.5209  | 0.7879  | 0.0801  | 0.2626  |
| picture             | 0.1740  | 0.4010  | 0.0078  | 0.0725  |
| plant               | 0.2292  | 0.7054  | 0.0485  | 0.4196  |
| faucet              | 0.0149  | 0.2500  | 0.0000  | 0.0000  |
| door                | 0.1126  | 0.5300  | 0.0058  | 0.1279  |
| towel               | 0.3508  | 0.5727  | 0.0695  | 0.1909  |
| doorframe           | 0.3147  | 0.7704  | 0.1041  | 0.4248  |
| mirror              | 0.1551  | 0.6216  | 0.0259  | 0.2027  |
| countertop          | 0.0145  | 0.4643  | 0.0005  | 0.0714  |
| bathtub             | 0.6029  | 0.7407  | 0.4446  | 0.5926  |
| shelf               | 0.1158  | 0.5620  | 0.0640  | 0.3431  |
| case                | 0.0003  | 0.1667  | 0.0000  | 0.0556  |
| bottle              | 0.1221  | 0.5625  | 0.0045  | 0.1458  |
| cabinet             | 0.1139  | 0.5975  | 0.0525  | 0.3644  |
| lamp                | 0.3351  | 0.6484  | 0.1144  | 0.3516  |
| drawer              | 0.0007  | 0.2500  | 0.0001  | 0.1250  |
| flowerpot           | 0.0369  | 0.6400  | 0.0047  | 0.2000  |
| dresser             | 0.4205  | 0.8000  | 0.2943  | 0.7333  |
| vent                | 0.0226  | 0.1250  | 0.0000  | 0.0000  |
| switch              | 0.0003  | 0.1000  | 0.0000  | 0.0000  |
| counter             | 0.0270  | 0.5455  | 0.0021  | 0.1515  |
| toilet              | 0.7725  | 0.8438  | 0.5542  | 0.7188  |
| light               | 0.0150  | 0.1453  | 0.0004  | 0.0256  |
| vanity              | 0.1552  | 0.8000  | 0.1390  | 0.7333  |
| decoration          | 0.0108  | 0.3571  | 0.0003  | 0.0476  |
| desk                | 0.2190  | 0.6842  | 0.1504  | 0.6316  |
| window              | 0.1543  | 0.5278  | 0.0164  | 0.1806  |
| toiletry            | 0.0489  | 0.4091  | 0.0161  | 0.0909  |
| wardrobe            | 0.2123  | 0.5455  | 0.0939  | 0.3636  |
| box                 | 0.0444  | 0.6538  | 0.0102  | 0.2115  |
| bed                 | 0.6137  | 0.8421  | 0.3464  | 0.7719  |
| table               | 0.2999  | 0.7244  | 0.2014  | 0.5449  |
| book                | 0.0749  | 0.3780  | 0.0048  | 0.0394  |
| curtain             | 0.3187  | 0.7396  | 0.0295  | 0.2500  |
| container           | 0.0422  | 0.5185  | 0.0127  | 0.2222  |
| vase                | 0.0754  | 0.5333  | 0.0215  | 0.1667  |
| stand               | 0.4506  | 0.7600  | 0.2953  | 0.5800  |
| pot                 | 0.0340  | 0.3846  | 0.0000  | 0.0000  |
| chair               | 0.4220  | 0.7551  | 0.2656  | 0.5408  |
| window frame        | 0.0566  | 0.5747  | 0.0055  | 0.2184  |
| roof                | 0.0017  | 0.2500  | 0.0000  | 0.0417  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| partition           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| alarm               | 0.0007  | 0.1667  | 0.0000  | 0.0000  |
| blanket             | 0.0086  | 0.3636  | 0.0015  | 0.0909  |
| shower              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bin                 | 0.3956  | 0.7805  | 0.1228  | 0.2439  |
| soap                | 0.0018  | 0.4000  | 0.0000  | 0.0000  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| frame               | 0.0038  | 0.2941  | 0.0000  | 0.0000  |
| pillow              | 0.2795  | 0.6057  | 0.0060  | 0.1262  |
| bench               | 0.1301  | 0.5500  | 0.0692  | 0.2500  |
| toilet paper        | 0.2630  | 0.5556  | 0.0518  | 0.2222  |
| plate               | 0.1824  | 0.3571  | 0.0008  | 0.0714  |
| tv                  | 0.4226  | 0.6977  | 0.0807  | 0.3488  |
| clothes             | 0.0825  | 0.3966  | 0.0133  | 0.1034  |
| washbasin           | 0.0061  | 0.2857  | 0.0043  | 0.2143  |
| tissue              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kettle              | 0.0075  | 0.5000  | 0.0000  | 0.0000  |
| rack                | 0.0005  | 0.2727  | 0.0000  | 0.0000  |
| column              | 0.0104  | 0.2778  | 0.0013  | 0.0556  |
| stool               | 0.0288  | 0.3333  | 0.0255  | 0.2381  |
| cup                 | 0.0019  | 0.2000  | 0.0002  | 0.1000  |
| bowl                | 0.0338  | 0.3636  | 0.0084  | 0.0909  |
| statue              | 0.0312  | 0.2353  | 0.0027  | 0.1176  |
| steps               | 0.0007  | 0.3000  | 0.0003  | 0.1000  |
| mat                 | 0.0357  | 0.3333  | 0.0000  | 0.0000  |
| basin               | 0.0179  | 1.0000  | 0.0000  | 0.0000  |
| paper               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue box          | 0.0707  | 0.5000  | 0.0031  | 0.1250  |
| windowsill          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillar              | 0.3360  | 0.7619  | 0.0610  | 0.2381  |
| basket              | 0.1430  | 0.4800  | 0.0004  | 0.0400  |
| doorway             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| couch               | 0.3801  | 0.7826  | 0.2239  | 0.6196  |
| screen              | 0.0774  | 0.5000  | 0.0000  | 0.0000  |
| teapot              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| footstool           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ottoman             | 0.1722  | 0.7222  | 0.0878  | 0.5000  |
| cushion             | 0.0048  | 0.2353  | 0.0015  | 0.0588  |
| stairs              | 0.0319  | 0.2830  | 0.0015  | 0.0755  |
| food                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wine                | 0.0286  | 0.1429  | 0.0000  | 0.0000  |
| clock               | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| keyboard            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candle              | 0.0020  | 0.3333  | 0.0020  | 0.3333  |
| carpet              | 0.0000  | 0.0556  | 0.0000  | 0.0000  |
| board               | 0.0926  | 0.3333  | 0.0000  | 0.0000  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| telephone           | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| tray                | 0.0056  | 0.3333  | 0.0000  | 0.0000  |
| monitor             | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| bag                 | 0.1428  | 0.3846  | 0.0000  | 0.0000  |
| fireplace           | 0.1604  | 0.3913  | 0.0042  | 0.0870  |
| toy                 | 0.1518  | 0.4444  | 0.0012  | 0.0278  |
| ball                | 0.5625  | 0.7500  | 0.5625  | 0.7500  |
| radiator            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blinds              | 0.0011  | 0.1000  | 0.0000  | 0.0000  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit               | 0.0729  | 0.2500  | 0.0729  | 0.2500  |
| beam                | 0.0003  | 0.0476  | 0.0000  | 0.0000  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| handle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fan                 | 0.3803  | 0.7593  | 0.1188  | 0.5000  |
| socket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dispenser           | 0.0035  | 0.5000  | 0.0035  | 0.5000  |
| washing machine     | 0.3472  | 0.8750  | 0.1842  | 0.6250  |
| soap dispenser      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dish           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket              | 0.0079  | 0.3000  | 0.0008  | 0.1000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rail                | 0.0110  | 0.2963  | 0.0000  | 0.0370  |
| headboard           | 0.0089  | 0.2500  | 0.0000  | 0.0000  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| laptop              | 0.1250  | 0.5000  | 0.0000  | 0.0000  |
| banister            | 0.0286  | 0.2000  | 0.0000  | 0.0000  |
| thermostat          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pipe                | 0.0034  | 0.2000  | 0.0000  | 0.0000  |
| fire extinguisher   | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| kitchen island      | 0.0651  | 0.7143  | 0.0500  | 0.4286  |
| heater              | 0.5000  | 1.0000  | 0.5000  | 1.0000  |
| piano               | 0.5000  | 0.5000  | 0.0106  | 0.5000  |
| seasoning           | 0.0714  | 0.1429  | 0.0000  | 0.0000  |
| printer             | 0.4306  | 0.5714  | 0.0476  | 0.1429  |
| excercise equipment | 0.2273  | 0.7222  | 0.0130  | 0.2222  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| refrigerator        | 0.1757  | 0.4667  | 0.0814  | 0.2667  |
| scale               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hat                 | 0.2000  | 1.0000  | 0.0000  | 0.0000  |
| pan                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| holder              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool table          | 0.1667  | 0.3333  | 0.1667  | 0.3333  |
| mouse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| guitar              | 0.2262  | 0.5714  | 0.0238  | 0.1429  |
| computer            | 0.0065  | 1.0000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0156  | 0.2500  | 0.0000  | 0.0000  |
| projector           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.2778  | 0.6667  | 0.2778  | 0.6667  |
| backpack            | 0.0120  | 1.0000  | 0.0120  | 1.0000  |
| machine             | 0.2000  | 1.0000  | 0.0000  | 0.0000  |
| kitchenware         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garage door         | 0.1111  | 0.3333  | 0.0278  | 0.1667  |
| ladder              | 0.0212  | 0.2222  | 0.0101  | 0.1111  |
| arch                | 0.0045  | 0.2333  | 0.0014  | 0.1000  |
| bidet               | 0.0294  | 0.5000  | 0.0294  | 0.5000  |
| air conditioner     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candlestick         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hanger              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| chandelier          | 0.3086  | 0.4211  | 0.0689  | 0.2105  |
| microwave           | 0.1856  | 0.3333  | 0.0160  | 0.2500  |
| stove               | 0.3452  | 0.7000  | 0.1176  | 0.3000  |
| oven                | 0.0436  | 0.4000  | 0.0333  | 0.2000  |
| shoe                | 0.0250  | 0.2400  | 0.0009  | 0.0400  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| file                | 0.1250  | 0.2500  | 0.0000  | 0.0000  |
| dishwasher          | 0.0250  | 0.2500  | 0.0250  | 0.2500  |
| coffee maker        | 0.0093  | 0.3333  | 0.0000  | 0.0000  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drum                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hamper              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool                | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.1429  | 0.5000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blackboard          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water heater        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pack                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jacket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radio               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| player              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| matterport3d        | 0.1070  | 0.3270  | 0.0435  | 0.1439  |
| head                | 0.1602  | 0.4613  | 0.0619  | 0.2121  |
| common              | 0.0661  | 0.2421  | 0.0329  | 0.0916  |
| tail                | 0.0360  | 0.1009  | 0.0101  | 0.0529  |
| small               | 0.0445  | 0.1922  | 0.0262  | 0.0644  |
| medium              | 0.0969  | 0.3384  | 0.0379  | 0.1483  |
| large               | 0.0934  | 0.4429  | 0.0284  | 0.2012  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/19/2025 22:12:20 INFO metrics.py:437 | number of arkit results: 0
Rank[0/8] 06/19/2025 22:12:20 INFO stats.py:394 | Epoch[59] completed. Training Speed: 3.01 samples/sec across all devices.	Epoch Time: 1037.49 sec.	Average Epoch Time: 1037.49 sec.	Average Step Time: 2.66 sec.	Estimated Remaining Time: 1 day, 0:00:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:12:21 WARNING accelerator.py:3099 | Deleting 1 checkpoints to make room for new checkpoint.
Rank[0/8] 06/19/2025 22:12:22 INFO accelerator.py:3111 | Saving current state to /job_data/checkpoints/checkpoint_5
Rank[7/8] 06/19/2025 22:12:22 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 59 to /job_data/checkpoints/checkpoint_5
Rank[4/8] 06/19/2025 22:12:22 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 59 to /job_data/checkpoints/checkpoint_5
Rank[1/8] 06/19/2025 22:12:22 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 59 to /job_data/checkpoints/checkpoint_5
Rank[3/8] 06/19/2025 22:12:22 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 59 to /job_data/checkpoints/checkpoint_5
Rank[5/8] 06/19/2025 22:12:22 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 59 to /job_data/checkpoints/checkpoint_5
Rank[2/8] 06/19/2025 22:12:22 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 59 to /job_data/checkpoints/checkpoint_5
Rank[6/8] 06/19/2025 22:12:22 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 59 to /job_data/checkpoints/checkpoint_5
Rank[0/8] 06/19/2025 22:12:24 INFO checkpointing.py:106 | Model weights saved in /job_data/checkpoints/checkpoint_5/model.safetensors
Rank[0/8] 06/19/2025 22:12:26 INFO checkpointing.py:113 | Optimizer state saved in /job_data/checkpoints/checkpoint_5/optimizer.bin
Rank[0/8] 06/19/2025 22:12:27 INFO checkpointing.py:120 | Scheduler state saved in /job_data/checkpoints/checkpoint_5/scheduler.bin
Rank[0/8] 06/19/2025 22:12:27 INFO checkpointing.py:137 | Sampler state for dataloader 0 saved in /job_data/checkpoints/checkpoint_5/sampler.bin
Rank[0/8] 06/19/2025 22:12:27 INFO checkpointing.py:137 | Sampler state for dataloader 1 saved in /job_data/checkpoints/checkpoint_5/sampler_1.bin
Rank[0/8] 06/19/2025 22:12:27 INFO checkpointing.py:164 | Random states saved in /job_data/checkpoints/checkpoint_5/random_states_0.pkl
Rank[0/8] 06/19/2025 22:12:27 INFO checkpointing.py:300 | Saving the state of TrainerProgressState to /job_data/checkpoints/checkpoint_5/custom_checkpoint_0.pkl
Rank[0/8] 06/19/2025 22:12:27 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 59 to /job_data/checkpoints/checkpoint_5
Rank[0/8] 06/19/2025 22:12:56 INFO stats.py:314 | Epoch[60] Step[24] GlobalStep[23424] Training Speed: 7.89 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1 day, 0:00:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:12:56 INFO loss_tracker.py:84 | Epoch[60/239] Step[24] GlobalStep[23424/NA]: det_loss_cls_0[0.4549] det_loss_box_0[0.5489] det_loss_cls_1[0.4577] det_loss_box_1[0.4396] det_loss_cls_2[0.4321] det_loss_box_2[0.3977] det_loss_cls_3[0.4114] det_loss_box_3[0.3852] det_loss_cls_4[0.3848] det_loss_box_4[0.3839] det_loss_cls_5[0.3694] det_loss_box_5[0.3863] det_loss_cls_dn_0[0.1031] det_loss_box_dn_0[0.2939] det_loss_cls_dn_1[0.0997] det_loss_box_dn_1[0.2501] det_loss_cls_dn_2[0.0995] det_loss_box_dn_2[0.2333] det_loss_cls_dn_3[0.0983] det_loss_box_dn_3[0.2300] det_loss_cls_dn_4[0.0973] det_loss_box_dn_4[0.2287] det_loss_cls_dn_5[0.0965] det_loss_box_dn_5[0.2253] loss_depth[0.0416] total_loss[7.1493]
Rank[0/8] 06/19/2025 22:13:22 INFO stats.py:314 | Epoch[60] Step[49] GlobalStep[23449] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 23:59:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:13:23 INFO loss_tracker.py:84 | Epoch[60/239] Step[49] GlobalStep[23449/NA]: det_loss_cls_0[0.5914] det_loss_box_0[0.7809] det_loss_cls_1[0.5729] det_loss_box_1[0.6289] det_loss_cls_2[0.5478] det_loss_box_2[0.5646] det_loss_cls_3[0.5157] det_loss_box_3[0.5501] det_loss_cls_4[0.4953] det_loss_box_4[0.5397] det_loss_cls_5[0.4762] det_loss_box_5[0.5407] det_loss_cls_dn_0[0.1047] det_loss_box_dn_0[0.3617] det_loss_cls_dn_1[0.1016] det_loss_box_dn_1[0.3149] det_loss_cls_dn_2[0.1000] det_loss_box_dn_2[0.2972] det_loss_cls_dn_3[0.0990] det_loss_box_dn_3[0.2935] det_loss_cls_dn_4[0.0976] det_loss_box_dn_4[0.2922] det_loss_cls_dn_5[0.0964] det_loss_box_dn_5[0.2926] loss_depth[0.0403] total_loss[9.2956]
Rank[0/8] 06/19/2025 22:13:48 INFO stats.py:314 | Epoch[60] Step[74] GlobalStep[23474] Training Speed: 7.96 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 23:58:30.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:13:48 INFO loss_tracker.py:84 | Epoch[60/239] Step[74] GlobalStep[23474/NA]: det_loss_cls_0[0.5085] det_loss_box_0[0.6707] det_loss_cls_1[0.4990] det_loss_box_1[0.5513] det_loss_cls_2[0.4834] det_loss_box_2[0.5072] det_loss_cls_3[0.4593] det_loss_box_3[0.4892] det_loss_cls_4[0.4316] det_loss_box_4[0.4833] det_loss_cls_5[0.4230] det_loss_box_5[0.4764] det_loss_cls_dn_0[0.1048] det_loss_box_dn_0[0.3811] det_loss_cls_dn_1[0.1013] det_loss_box_dn_1[0.3318] det_loss_cls_dn_2[0.1008] det_loss_box_dn_2[0.3152] det_loss_cls_dn_3[0.1014] det_loss_box_dn_3[0.3105] det_loss_cls_dn_4[0.1009] det_loss_box_dn_4[0.3090] det_loss_cls_dn_5[0.1009] det_loss_box_dn_5[0.3061] loss_depth[0.0359] total_loss[8.5827]
Rank[0/8] 06/19/2025 22:14:14 INFO stats.py:314 | Epoch[60] Step[99] GlobalStep[23499] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 23:57:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:14:14 INFO loss_tracker.py:84 | Epoch[60/239] Step[99] GlobalStep[23499/NA]: det_loss_cls_0[0.5517] det_loss_box_0[0.6934] det_loss_cls_1[0.5276] det_loss_box_1[0.5758] det_loss_cls_2[0.5184] det_loss_box_2[0.5199] det_loss_cls_3[0.4931] det_loss_box_3[0.4994] det_loss_cls_4[0.4776] det_loss_box_4[0.4872] det_loss_cls_5[0.4612] det_loss_box_5[0.4944] det_loss_cls_dn_0[0.1047] det_loss_box_dn_0[0.2859] det_loss_cls_dn_1[0.1021] det_loss_box_dn_1[0.2488] det_loss_cls_dn_2[0.1012] det_loss_box_dn_2[0.2356] det_loss_cls_dn_3[0.1012] det_loss_box_dn_3[0.2346] det_loss_cls_dn_4[0.1001] det_loss_box_dn_4[0.2325] det_loss_cls_dn_5[0.0996] det_loss_box_dn_5[0.2321] loss_depth[0.0404] total_loss[8.4187]
Rank[0/8] 06/19/2025 22:14:39 INFO stats.py:314 | Epoch[60] Step[124] GlobalStep[23524] Training Speed: 7.98 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 23:56:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:14:39 INFO loss_tracker.py:84 | Epoch[60/239] Step[124] GlobalStep[23524/NA]: det_loss_cls_0[0.5821] det_loss_box_0[0.7609] det_loss_cls_1[0.5671] det_loss_box_1[0.6338] det_loss_cls_2[0.5549] det_loss_box_2[0.5759] det_loss_cls_3[0.5331] det_loss_box_3[0.5517] det_loss_cls_4[0.5041] det_loss_box_4[0.5414] det_loss_cls_5[0.4944] det_loss_box_5[0.5402] det_loss_cls_dn_0[0.1127] det_loss_box_dn_0[0.2986] det_loss_cls_dn_1[0.1087] det_loss_box_dn_1[0.2579] det_loss_cls_dn_2[0.1082] det_loss_box_dn_2[0.2438] det_loss_cls_dn_3[0.1081] det_loss_box_dn_3[0.2409] det_loss_cls_dn_4[0.1072] det_loss_box_dn_4[0.2397] det_loss_cls_dn_5[0.1063] det_loss_box_dn_5[0.2377] loss_depth[0.0377] total_loss[9.0473]
Rank[0/8] 06/19/2025 22:15:05 INFO stats.py:314 | Epoch[60] Step[149] GlobalStep[23549] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 23:56:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:15:05 INFO loss_tracker.py:84 | Epoch[60/239] Step[149] GlobalStep[23549/NA]: det_loss_cls_0[0.5004] det_loss_box_0[0.5916] det_loss_cls_1[0.4793] det_loss_box_1[0.4781] det_loss_cls_2[0.4528] det_loss_box_2[0.4391] det_loss_cls_3[0.4234] det_loss_box_3[0.4218] det_loss_cls_4[0.4050] det_loss_box_4[0.4092] det_loss_cls_5[0.3885] det_loss_box_5[0.4141] det_loss_cls_dn_0[0.1040] det_loss_box_dn_0[0.2771] det_loss_cls_dn_1[0.0999] det_loss_box_dn_1[0.2385] det_loss_cls_dn_2[0.0998] det_loss_box_dn_2[0.2264] det_loss_cls_dn_3[0.0993] det_loss_box_dn_3[0.2235] det_loss_cls_dn_4[0.0978] det_loss_box_dn_4[0.2222] det_loss_cls_dn_5[0.0968] det_loss_box_dn_5[0.2195] loss_depth[0.0387] total_loss[7.4466]
Rank[0/8] 06/19/2025 22:15:30 INFO stats.py:314 | Epoch[60] Step[174] GlobalStep[23574] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 23:55:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:15:30 INFO loss_tracker.py:84 | Epoch[60/239] Step[174] GlobalStep[23574/NA]: det_loss_cls_0[0.5184] det_loss_box_0[0.6910] det_loss_cls_1[0.5080] det_loss_box_1[0.5575] det_loss_cls_2[0.4864] det_loss_box_2[0.5033] det_loss_cls_3[0.4674] det_loss_box_3[0.4812] det_loss_cls_4[0.4370] det_loss_box_4[0.4614] det_loss_cls_5[0.4151] det_loss_box_5[0.4631] det_loss_cls_dn_0[0.0907] det_loss_box_dn_0[0.3148] det_loss_cls_dn_1[0.0884] det_loss_box_dn_1[0.2722] det_loss_cls_dn_2[0.0876] det_loss_box_dn_2[0.2557] det_loss_cls_dn_3[0.0879] det_loss_box_dn_3[0.2531] det_loss_cls_dn_4[0.0881] det_loss_box_dn_4[0.2518] det_loss_cls_dn_5[0.0873] det_loss_box_dn_5[0.2500] loss_depth[0.0410] total_loss[8.1583]
Rank[0/8] 06/19/2025 22:15:56 INFO stats.py:314 | Epoch[60] Step[199] GlobalStep[23599] Training Speed: 6.31 samples/sec across all devices.	Average Step Time: 1.27 sec.	Estimated Remaining Time: 23:54:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:15:56 INFO loss_tracker.py:84 | Epoch[60/239] Step[199] GlobalStep[23599/NA]: det_loss_cls_0[0.5809] det_loss_box_0[0.9417] det_loss_cls_1[0.5714] det_loss_box_1[0.7921] det_loss_cls_2[0.5598] det_loss_box_2[0.7278] det_loss_cls_3[0.5380] det_loss_box_3[0.6937] det_loss_cls_4[0.5028] det_loss_box_4[0.6817] det_loss_cls_5[0.4945] det_loss_box_5[0.6749] det_loss_cls_dn_0[0.1102] det_loss_box_dn_0[0.3488] det_loss_cls_dn_1[0.1065] det_loss_box_dn_1[0.3096] det_loss_cls_dn_2[0.1058] det_loss_box_dn_2[0.2947] det_loss_cls_dn_3[0.1050] det_loss_box_dn_3[0.2917] det_loss_cls_dn_4[0.1047] det_loss_box_dn_4[0.2899] det_loss_cls_dn_5[0.1040] det_loss_box_dn_5[0.2889] loss_depth[0.0417] total_loss[10.2609]
Rank[0/8] 06/19/2025 22:16:22 INFO stats.py:314 | Epoch[60] Step[224] GlobalStep[23624] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 23:53:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:16:22 INFO loss_tracker.py:84 | Epoch[60/239] Step[224] GlobalStep[23624/NA]: det_loss_cls_0[0.5344] det_loss_box_0[0.7128] det_loss_cls_1[0.5259] det_loss_box_1[0.5781] det_loss_cls_2[0.5016] det_loss_box_2[0.5109] det_loss_cls_3[0.4773] det_loss_box_3[0.4958] det_loss_cls_4[0.4502] det_loss_box_4[0.4865] det_loss_cls_5[0.4296] det_loss_box_5[0.4939] det_loss_cls_dn_0[0.1078] det_loss_box_dn_0[0.3301] det_loss_cls_dn_1[0.1049] det_loss_box_dn_1[0.2797] det_loss_cls_dn_2[0.1040] det_loss_box_dn_2[0.2614] det_loss_cls_dn_3[0.1036] det_loss_box_dn_3[0.2584] det_loss_cls_dn_4[0.1016] det_loss_box_dn_4[0.2573] det_loss_cls_dn_5[0.1017] det_loss_box_dn_5[0.2552] loss_depth[0.0397] total_loss[8.5023]
Rank[0/8] 06/19/2025 22:16:48 INFO stats.py:314 | Epoch[60] Step[249] GlobalStep[23649] Training Speed: 6.19 samples/sec across all devices.	Average Step Time: 1.29 sec.	Estimated Remaining Time: 23:53:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:16:48 INFO loss_tracker.py:84 | Epoch[60/239] Step[249] GlobalStep[23649/NA]: det_loss_cls_0[0.5521] det_loss_box_0[0.7702] det_loss_cls_1[0.5269] det_loss_box_1[0.6088] det_loss_cls_2[0.5091] det_loss_box_2[0.5553] det_loss_cls_3[0.4854] det_loss_box_3[0.5314] det_loss_cls_4[0.4539] det_loss_box_4[0.5234] det_loss_cls_5[0.4369] det_loss_box_5[0.5262] det_loss_cls_dn_0[0.1087] det_loss_box_dn_0[0.3144] det_loss_cls_dn_1[0.1046] det_loss_box_dn_1[0.2689] det_loss_cls_dn_2[0.1032] det_loss_box_dn_2[0.2523] det_loss_cls_dn_3[0.1026] det_loss_box_dn_3[0.2510] det_loss_cls_dn_4[0.1009] det_loss_box_dn_4[0.2495] det_loss_cls_dn_5[0.1004] det_loss_box_dn_5[0.2461] loss_depth[0.0447] total_loss[8.7268]
Rank[0/8] 06/19/2025 22:17:13 INFO stats.py:314 | Epoch[60] Step[274] GlobalStep[23674] Training Speed: 7.57 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 23:52:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:17:13 INFO loss_tracker.py:84 | Epoch[60/239] Step[274] GlobalStep[23674/NA]: det_loss_cls_0[0.6191] det_loss_box_0[0.8227] det_loss_cls_1[0.6093] det_loss_box_1[0.6529] det_loss_cls_2[0.5882] det_loss_box_2[0.5899] det_loss_cls_3[0.5631] det_loss_box_3[0.5672] det_loss_cls_4[0.5330] det_loss_box_4[0.5586] det_loss_cls_5[0.5178] det_loss_box_5[0.5582] det_loss_cls_dn_0[0.1071] det_loss_box_dn_0[0.2696] det_loss_cls_dn_1[0.1039] det_loss_box_dn_1[0.2302] det_loss_cls_dn_2[0.1037] det_loss_box_dn_2[0.2168] det_loss_cls_dn_3[0.1027] det_loss_box_dn_3[0.2146] det_loss_cls_dn_4[0.1017] det_loss_box_dn_4[0.2147] det_loss_cls_dn_5[0.1009] det_loss_box_dn_5[0.2139] loss_depth[0.0370] total_loss[9.1969]
Rank[0/8] 06/19/2025 22:17:39 INFO stats.py:314 | Epoch[60] Step[299] GlobalStep[23699] Training Speed: 7.96 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 23:51:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:17:40 INFO loss_tracker.py:84 | Epoch[60/239] Step[299] GlobalStep[23699/NA]: det_loss_cls_0[0.5923] det_loss_box_0[0.7535] det_loss_cls_1[0.5567] det_loss_box_1[0.5876] det_loss_cls_2[0.5349] det_loss_box_2[0.5305] det_loss_cls_3[0.5149] det_loss_box_3[0.5001] det_loss_cls_4[0.4714] det_loss_box_4[0.4973] det_loss_cls_5[0.4574] det_loss_box_5[0.4966] det_loss_cls_dn_0[0.1022] det_loss_box_dn_0[0.2575] det_loss_cls_dn_1[0.0982] det_loss_box_dn_1[0.2158] det_loss_cls_dn_2[0.0976] det_loss_box_dn_2[0.2014] det_loss_cls_dn_3[0.0967] det_loss_box_dn_3[0.1986] det_loss_cls_dn_4[0.0950] det_loss_box_dn_4[0.1980] det_loss_cls_dn_5[0.0941] det_loss_box_dn_5[0.1973] loss_depth[0.0381] total_loss[8.3836]
Rank[0/8] 06/19/2025 22:18:06 INFO stats.py:314 | Epoch[60] Step[324] GlobalStep[23724] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 23:50:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:18:06 INFO loss_tracker.py:84 | Epoch[60/239] Step[324] GlobalStep[23724/NA]: det_loss_cls_0[0.7010] det_loss_box_0[0.9882] det_loss_cls_1[0.6735] det_loss_box_1[0.7790] det_loss_cls_2[0.6467] det_loss_box_2[0.7173] det_loss_cls_3[0.6144] det_loss_box_3[0.6925] det_loss_cls_4[0.5888] det_loss_box_4[0.6840] det_loss_cls_5[0.5619] det_loss_box_5[0.6919] det_loss_cls_dn_0[0.1091] det_loss_box_dn_0[0.2964] det_loss_cls_dn_1[0.1048] det_loss_box_dn_1[0.2576] det_loss_cls_dn_2[0.1045] det_loss_box_dn_2[0.2388] det_loss_cls_dn_3[0.1031] det_loss_box_dn_3[0.2371] det_loss_cls_dn_4[0.1022] det_loss_box_dn_4[0.2374] det_loss_cls_dn_5[0.1017] det_loss_box_dn_5[0.2363] loss_depth[0.0426] total_loss[10.5108]
Rank[0/8] 06/19/2025 22:18:31 INFO stats.py:314 | Epoch[60] Step[349] GlobalStep[23749] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 23:50:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:18:31 INFO loss_tracker.py:84 | Epoch[60/239] Step[349] GlobalStep[23749/NA]: det_loss_cls_0[0.5894] det_loss_box_0[0.8811] det_loss_cls_1[0.5618] det_loss_box_1[0.7310] det_loss_cls_2[0.5478] det_loss_box_2[0.6732] det_loss_cls_3[0.5284] det_loss_box_3[0.6480] det_loss_cls_4[0.5026] det_loss_box_4[0.6434] det_loss_cls_5[0.4866] det_loss_box_5[0.6418] det_loss_cls_dn_0[0.1082] det_loss_box_dn_0[0.3903] det_loss_cls_dn_1[0.1048] det_loss_box_dn_1[0.3466] det_loss_cls_dn_2[0.1057] det_loss_box_dn_2[0.3271] det_loss_cls_dn_3[0.1034] det_loss_box_dn_3[0.3252] det_loss_cls_dn_4[0.1022] det_loss_box_dn_4[0.3242] det_loss_cls_dn_5[0.1011] det_loss_box_dn_5[0.3235] loss_depth[0.0392] total_loss[10.1366]
Rank[0/8] 06/19/2025 22:18:57 INFO stats.py:314 | Epoch[60] Step[374] GlobalStep[23774] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 23:49:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:18:57 INFO loss_tracker.py:84 | Epoch[60/239] Step[374] GlobalStep[23774/NA]: det_loss_cls_0[0.6162] det_loss_box_0[0.8134] det_loss_cls_1[0.5947] det_loss_box_1[0.6597] det_loss_cls_2[0.5765] det_loss_box_2[0.5920] det_loss_cls_3[0.5505] det_loss_box_3[0.5549] det_loss_cls_4[0.5165] det_loss_box_4[0.5420] det_loss_cls_5[0.5042] det_loss_box_5[0.5390] det_loss_cls_dn_0[0.1070] det_loss_box_dn_0[0.2796] det_loss_cls_dn_1[0.1039] det_loss_box_dn_1[0.2420] det_loss_cls_dn_2[0.1040] det_loss_box_dn_2[0.2280] det_loss_cls_dn_3[0.1032] det_loss_box_dn_3[0.2249] det_loss_cls_dn_4[0.1018] det_loss_box_dn_4[0.2240] det_loss_cls_dn_5[0.1011] det_loss_box_dn_5[0.2225] loss_depth[0.0394] total_loss[9.1409]
Rank[0/8] 06/19/2025 22:19:13 INFO stats.py:394 | Epoch[60] completed. Training Speed: 7.69 samples/sec across all devices.	Epoch Time: 405.84 sec.	Average Epoch Time: 405.84 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 23:48:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:19:26 INFO stats.py:314 | Epoch[61] Step[9] GlobalStep[23799] Training Speed: 7.32 samples/sec across all devices.	Average Step Time: 1.09 sec.	Estimated Remaining Time: 23:48:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:19:41 INFO loss_tracker.py:84 | Epoch[61/239] Step[24] GlobalStep[23814/NA]: det_loss_cls_0[0.6385] det_loss_box_0[0.8907] det_loss_cls_1[0.6219] det_loss_box_1[0.7144] det_loss_cls_2[0.6057] det_loss_box_2[0.6350] det_loss_cls_3[0.5840] det_loss_box_3[0.6105] det_loss_cls_4[0.5546] det_loss_box_4[0.5943] det_loss_cls_5[0.5391] det_loss_box_5[0.5991] det_loss_cls_dn_0[0.1092] det_loss_box_dn_0[0.3273] det_loss_cls_dn_1[0.1058] det_loss_box_dn_1[0.2856] det_loss_cls_dn_2[0.1061] det_loss_box_dn_2[0.2751] det_loss_cls_dn_3[0.1052] det_loss_box_dn_3[0.2708] det_loss_cls_dn_4[0.1046] det_loss_box_dn_4[0.2704] det_loss_cls_dn_5[0.1036] det_loss_box_dn_5[0.2680] loss_depth[0.0440] total_loss[9.9635]
Rank[0/8] 06/19/2025 22:19:52 INFO stats.py:314 | Epoch[61] Step[34] GlobalStep[23824] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 23:48:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:20:07 INFO loss_tracker.py:84 | Epoch[61/239] Step[49] GlobalStep[23839/NA]: det_loss_cls_0[0.5557] det_loss_box_0[0.7414] det_loss_cls_1[0.5338] det_loss_box_1[0.6031] det_loss_cls_2[0.5107] det_loss_box_2[0.5476] det_loss_cls_3[0.4936] det_loss_box_3[0.5179] det_loss_cls_4[0.4653] det_loss_box_4[0.5120] det_loss_cls_5[0.4477] det_loss_box_5[0.5135] det_loss_cls_dn_0[0.0955] det_loss_box_dn_0[0.3154] det_loss_cls_dn_1[0.0910] det_loss_box_dn_1[0.2736] det_loss_cls_dn_2[0.0907] det_loss_box_dn_2[0.2548] det_loss_cls_dn_3[0.0901] det_loss_box_dn_3[0.2521] det_loss_cls_dn_4[0.0891] det_loss_box_dn_4[0.2512] det_loss_cls_dn_5[0.0881] det_loss_box_dn_5[0.2487] loss_depth[0.0390] total_loss[8.6215]
Rank[0/8] 06/19/2025 22:20:17 INFO stats.py:314 | Epoch[61] Step[59] GlobalStep[23849] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 23:47:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:20:33 INFO loss_tracker.py:84 | Epoch[61/239] Step[74] GlobalStep[23864/NA]: det_loss_cls_0[0.5101] det_loss_box_0[0.6661] det_loss_cls_1[0.4760] det_loss_box_1[0.5408] det_loss_cls_2[0.4597] det_loss_box_2[0.4869] det_loss_cls_3[0.4338] det_loss_box_3[0.4665] det_loss_cls_4[0.4027] det_loss_box_4[0.4533] det_loss_cls_5[0.3919] det_loss_box_5[0.4507] det_loss_cls_dn_0[0.1006] det_loss_box_dn_0[0.3197] det_loss_cls_dn_1[0.0965] det_loss_box_dn_1[0.2761] det_loss_cls_dn_2[0.0954] det_loss_box_dn_2[0.2604] det_loss_cls_dn_3[0.0951] det_loss_box_dn_3[0.2589] det_loss_cls_dn_4[0.0940] det_loss_box_dn_4[0.2582] det_loss_cls_dn_5[0.0923] det_loss_box_dn_5[0.2567] loss_depth[0.0390] total_loss[7.9816]
Rank[0/8] 06/19/2025 22:20:43 INFO stats.py:314 | Epoch[61] Step[84] GlobalStep[23874] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 23:46:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:20:59 INFO loss_tracker.py:84 | Epoch[61/239] Step[99] GlobalStep[23889/NA]: det_loss_cls_0[0.5160] det_loss_box_0[0.6776] det_loss_cls_1[0.5077] det_loss_box_1[0.5362] det_loss_cls_2[0.4843] det_loss_box_2[0.4751] det_loss_cls_3[0.4571] det_loss_box_3[0.4482] det_loss_cls_4[0.4216] det_loss_box_4[0.4359] det_loss_cls_5[0.4051] det_loss_box_5[0.4325] det_loss_cls_dn_0[0.1019] det_loss_box_dn_0[0.2933] det_loss_cls_dn_1[0.0987] det_loss_box_dn_1[0.2539] det_loss_cls_dn_2[0.0986] det_loss_box_dn_2[0.2399] det_loss_cls_dn_3[0.0978] det_loss_box_dn_3[0.2356] det_loss_cls_dn_4[0.0972] det_loss_box_dn_4[0.2346] det_loss_cls_dn_5[0.0962] det_loss_box_dn_5[0.2319] loss_depth[0.0413] total_loss[7.9183]
Rank[0/8] 06/19/2025 22:21:09 INFO stats.py:314 | Epoch[61] Step[109] GlobalStep[23899] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 23:45:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:21:24 INFO loss_tracker.py:84 | Epoch[61/239] Step[124] GlobalStep[23914/NA]: det_loss_cls_0[0.4845] det_loss_box_0[0.6520] det_loss_cls_1[0.4577] det_loss_box_1[0.5115] det_loss_cls_2[0.4379] det_loss_box_2[0.4617] det_loss_cls_3[0.4130] det_loss_box_3[0.4458] det_loss_cls_4[0.3885] det_loss_box_4[0.4417] det_loss_cls_5[0.3731] det_loss_box_5[0.4453] det_loss_cls_dn_0[0.0980] det_loss_box_dn_0[0.3104] det_loss_cls_dn_1[0.0927] det_loss_box_dn_1[0.2724] det_loss_cls_dn_2[0.0921] det_loss_box_dn_2[0.2564] det_loss_cls_dn_3[0.0916] det_loss_box_dn_3[0.2513] det_loss_cls_dn_4[0.0906] det_loss_box_dn_4[0.2497] det_loss_cls_dn_5[0.0896] det_loss_box_dn_5[0.2494] loss_depth[0.0396] total_loss[7.6963]
Rank[0/8] 06/19/2025 22:21:34 INFO stats.py:314 | Epoch[61] Step[134] GlobalStep[23924] Training Speed: 8.56 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 23:45:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:21:50 INFO loss_tracker.py:84 | Epoch[61/239] Step[149] GlobalStep[23939/NA]: det_loss_cls_0[0.6224] det_loss_box_0[0.8176] det_loss_cls_1[0.5921] det_loss_box_1[0.6576] det_loss_cls_2[0.5742] det_loss_box_2[0.6016] det_loss_cls_3[0.5308] det_loss_box_3[0.5845] det_loss_cls_4[0.5088] det_loss_box_4[0.5702] det_loss_cls_5[0.4867] det_loss_box_5[0.5707] det_loss_cls_dn_0[0.1019] det_loss_box_dn_0[0.3697] det_loss_cls_dn_1[0.0971] det_loss_box_dn_1[0.3224] det_loss_cls_dn_2[0.0967] det_loss_box_dn_2[0.3054] det_loss_cls_dn_3[0.0954] det_loss_box_dn_3[0.3007] det_loss_cls_dn_4[0.0944] det_loss_box_dn_4[0.2995] det_loss_cls_dn_5[0.0933] det_loss_box_dn_5[0.2979] loss_depth[0.0376] total_loss[9.6293]
Rank[0/8] 06/19/2025 22:22:00 INFO stats.py:314 | Epoch[61] Step[159] GlobalStep[23949] Training Speed: 8.19 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 23:44:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:22:16 INFO loss_tracker.py:84 | Epoch[61/239] Step[174] GlobalStep[23964/NA]: det_loss_cls_0[0.6278] det_loss_box_0[0.7845] det_loss_cls_1[0.6071] det_loss_box_1[0.6437] det_loss_cls_2[0.5836] det_loss_box_2[0.5912] det_loss_cls_3[0.5553] det_loss_box_3[0.5755] det_loss_cls_4[0.5270] det_loss_box_4[0.5575] det_loss_cls_5[0.5105] det_loss_box_5[0.5628] det_loss_cls_dn_0[0.1090] det_loss_box_dn_0[0.2599] det_loss_cls_dn_1[0.1051] det_loss_box_dn_1[0.2183] det_loss_cls_dn_2[0.1058] det_loss_box_dn_2[0.2045] det_loss_cls_dn_3[0.1077] det_loss_box_dn_3[0.2012] det_loss_cls_dn_4[0.1072] det_loss_box_dn_4[0.2003] det_loss_cls_dn_5[0.1059] det_loss_box_dn_5[0.1989] loss_depth[0.0412] total_loss[9.0917]
Rank[0/8] 06/19/2025 22:22:26 INFO stats.py:314 | Epoch[61] Step[184] GlobalStep[23974] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 23:43:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:22:42 INFO loss_tracker.py:84 | Epoch[61/239] Step[199] GlobalStep[23989/NA]: det_loss_cls_0[0.5248] det_loss_box_0[0.6181] det_loss_cls_1[0.5043] det_loss_box_1[0.4988] det_loss_cls_2[0.4913] det_loss_box_2[0.4405] det_loss_cls_3[0.4571] det_loss_box_3[0.4350] det_loss_cls_4[0.4308] det_loss_box_4[0.4310] det_loss_cls_5[0.4167] det_loss_box_5[0.4316] det_loss_cls_dn_0[0.0999] det_loss_box_dn_0[0.2890] det_loss_cls_dn_1[0.0953] det_loss_box_dn_1[0.2400] det_loss_cls_dn_2[0.0938] det_loss_box_dn_2[0.2183] det_loss_cls_dn_3[0.0931] det_loss_box_dn_3[0.2155] det_loss_cls_dn_4[0.0908] det_loss_box_dn_4[0.2137] det_loss_cls_dn_5[0.0901] det_loss_box_dn_5[0.2132] loss_depth[0.0408] total_loss[7.6735]
Rank[0/8] 06/19/2025 22:22:52 INFO stats.py:314 | Epoch[61] Step[209] GlobalStep[23999] Training Speed: 8.02 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 23:42:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:23:07 INFO loss_tracker.py:84 | Epoch[61/239] Step[224] GlobalStep[24014/NA]: det_loss_cls_0[0.4723] det_loss_box_0[0.6104] det_loss_cls_1[0.4421] det_loss_box_1[0.5042] det_loss_cls_2[0.4404] det_loss_box_2[0.4505] det_loss_cls_3[0.4191] det_loss_box_3[0.4346] det_loss_cls_4[0.3922] det_loss_box_4[0.4221] det_loss_cls_5[0.3790] det_loss_box_5[0.4233] det_loss_cls_dn_0[0.1103] det_loss_box_dn_0[0.3635] det_loss_cls_dn_1[0.1065] det_loss_box_dn_1[0.3098] det_loss_cls_dn_2[0.1061] det_loss_box_dn_2[0.2939] det_loss_cls_dn_3[0.1054] det_loss_box_dn_3[0.2900] det_loss_cls_dn_4[0.1044] det_loss_box_dn_4[0.2885] det_loss_cls_dn_5[0.1032] det_loss_box_dn_5[0.2854] loss_depth[0.0416] total_loss[7.8989]
Rank[0/8] 06/19/2025 22:23:17 INFO stats.py:314 | Epoch[61] Step[234] GlobalStep[24024] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 23:42:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:23:33 INFO loss_tracker.py:84 | Epoch[61/239] Step[249] GlobalStep[24039/NA]: det_loss_cls_0[0.6034] det_loss_box_0[0.8355] det_loss_cls_1[0.5783] det_loss_box_1[0.6756] det_loss_cls_2[0.5493] det_loss_box_2[0.6131] det_loss_cls_3[0.5352] det_loss_box_3[0.5800] det_loss_cls_4[0.5021] det_loss_box_4[0.5729] det_loss_cls_5[0.4891] det_loss_box_5[0.5657] det_loss_cls_dn_0[0.1095] det_loss_box_dn_0[0.2883] det_loss_cls_dn_1[0.1061] det_loss_box_dn_1[0.2463] det_loss_cls_dn_2[0.1052] det_loss_box_dn_2[0.2333] det_loss_cls_dn_3[0.1041] det_loss_box_dn_3[0.2307] det_loss_cls_dn_4[0.1030] det_loss_box_dn_4[0.2300] det_loss_cls_dn_5[0.1020] det_loss_box_dn_5[0.2292] loss_depth[0.0384] total_loss[9.2261]
Rank[0/8] 06/19/2025 22:23:44 INFO stats.py:314 | Epoch[61] Step[259] GlobalStep[24049] Training Speed: 6.13 samples/sec across all devices.	Average Step Time: 1.31 sec.	Estimated Remaining Time: 23:41:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:23:59 INFO loss_tracker.py:84 | Epoch[61/239] Step[274] GlobalStep[24064/NA]: det_loss_cls_0[0.5590] det_loss_box_0[0.6985] det_loss_cls_1[0.5412] det_loss_box_1[0.5474] det_loss_cls_2[0.5173] det_loss_box_2[0.4964] det_loss_cls_3[0.4874] det_loss_box_3[0.4806] det_loss_cls_4[0.4532] det_loss_box_4[0.4800] det_loss_cls_5[0.4487] det_loss_box_5[0.4685] det_loss_cls_dn_0[0.1111] det_loss_box_dn_0[0.3031] det_loss_cls_dn_1[0.1077] det_loss_box_dn_1[0.2543] det_loss_cls_dn_2[0.1071] det_loss_box_dn_2[0.2364] det_loss_cls_dn_3[0.1065] det_loss_box_dn_3[0.2322] det_loss_cls_dn_4[0.1051] det_loss_box_dn_4[0.2306] det_loss_cls_dn_5[0.1042] det_loss_box_dn_5[0.2287] loss_depth[0.0408] total_loss[8.3462]
Rank[0/8] 06/19/2025 22:24:10 INFO stats.py:314 | Epoch[61] Step[284] GlobalStep[24074] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 23:40:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:24:25 INFO loss_tracker.py:84 | Epoch[61/239] Step[299] GlobalStep[24089/NA]: det_loss_cls_0[0.4766] det_loss_box_0[0.6052] det_loss_cls_1[0.4751] det_loss_box_1[0.4991] det_loss_cls_2[0.4512] det_loss_box_2[0.4413] det_loss_cls_3[0.4314] det_loss_box_3[0.4221] det_loss_cls_4[0.3988] det_loss_box_4[0.4138] det_loss_cls_5[0.3904] det_loss_box_5[0.4085] det_loss_cls_dn_0[0.0951] det_loss_box_dn_0[0.3188] det_loss_cls_dn_1[0.0914] det_loss_box_dn_1[0.2764] det_loss_cls_dn_2[0.0908] det_loss_box_dn_2[0.2639] det_loss_cls_dn_3[0.0901] det_loss_box_dn_3[0.2618] det_loss_cls_dn_4[0.0889] det_loss_box_dn_4[0.2613] det_loss_cls_dn_5[0.0877] det_loss_box_dn_5[0.2576] loss_depth[0.0407] total_loss[7.6381]
Rank[0/8] 06/19/2025 22:24:35 INFO stats.py:314 | Epoch[61] Step[309] GlobalStep[24099] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 23:39:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:24:51 INFO loss_tracker.py:84 | Epoch[61/239] Step[324] GlobalStep[24114/NA]: det_loss_cls_0[0.4534] det_loss_box_0[0.5575] det_loss_cls_1[0.4486] det_loss_box_1[0.4426] det_loss_cls_2[0.4296] det_loss_box_2[0.3890] det_loss_cls_3[0.3997] det_loss_box_3[0.3744] det_loss_cls_4[0.3743] det_loss_box_4[0.3660] det_loss_cls_5[0.3677] det_loss_box_5[0.3591] det_loss_cls_dn_0[0.0989] det_loss_box_dn_0[0.2917] det_loss_cls_dn_1[0.0954] det_loss_box_dn_1[0.2475] det_loss_cls_dn_2[0.0957] det_loss_box_dn_2[0.2274] det_loss_cls_dn_3[0.0937] det_loss_box_dn_3[0.2230] det_loss_cls_dn_4[0.0929] det_loss_box_dn_4[0.2211] det_loss_cls_dn_5[0.0919] det_loss_box_dn_5[0.2194] loss_depth[0.0383] total_loss[6.9986]
Rank[0/8] 06/19/2025 22:25:01 INFO stats.py:314 | Epoch[61] Step[334] GlobalStep[24124] Training Speed: 11.85 samples/sec across all devices.	Average Step Time: 0.68 sec.	Estimated Remaining Time: 23:39:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:25:17 INFO loss_tracker.py:84 | Epoch[61/239] Step[349] GlobalStep[24139/NA]: det_loss_cls_0[0.6366] det_loss_box_0[0.9378] det_loss_cls_1[0.6276] det_loss_box_1[0.7719] det_loss_cls_2[0.6225] det_loss_box_2[0.7138] det_loss_cls_3[0.5930] det_loss_box_3[0.6935] det_loss_cls_4[0.5689] det_loss_box_4[0.6862] det_loss_cls_5[0.5598] det_loss_box_5[0.6800] det_loss_cls_dn_0[0.1141] det_loss_box_dn_0[0.3233] det_loss_cls_dn_1[0.1100] det_loss_box_dn_1[0.2910] det_loss_cls_dn_2[0.1100] det_loss_box_dn_2[0.2751] det_loss_cls_dn_3[0.1085] det_loss_box_dn_3[0.2723] det_loss_cls_dn_4[0.1082] det_loss_box_dn_4[0.2714] det_loss_cls_dn_5[0.1076] det_loss_box_dn_5[0.2693] loss_depth[0.0377] total_loss[10.4901]
Rank[0/8] 06/19/2025 22:25:27 INFO stats.py:314 | Epoch[61] Step[359] GlobalStep[24149] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 23:38:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:25:42 INFO loss_tracker.py:84 | Epoch[61/239] Step[374] GlobalStep[24164/NA]: det_loss_cls_0[0.5688] det_loss_box_0[0.6940] det_loss_cls_1[0.5387] det_loss_box_1[0.5757] det_loss_cls_2[0.5227] det_loss_box_2[0.5202] det_loss_cls_3[0.5015] det_loss_box_3[0.4973] det_loss_cls_4[0.4669] det_loss_box_4[0.4854] det_loss_cls_5[0.4565] det_loss_box_5[0.4786] det_loss_cls_dn_0[0.1050] det_loss_box_dn_0[0.3866] det_loss_cls_dn_1[0.1010] det_loss_box_dn_1[0.3382] det_loss_cls_dn_2[0.1005] det_loss_box_dn_2[0.3155] det_loss_cls_dn_3[0.0991] det_loss_box_dn_3[0.3105] det_loss_cls_dn_4[0.0975] det_loss_box_dn_4[0.3087] det_loss_cls_dn_5[0.0964] det_loss_box_dn_5[0.3067] loss_depth[0.0393] total_loss[8.9111]
Rank[0/8] 06/19/2025 22:25:53 INFO stats.py:314 | Epoch[61] Step[384] GlobalStep[24174] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 23:37:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:25:58 INFO stats.py:394 | Epoch[61] completed. Training Speed: 7.70 samples/sec across all devices.	Epoch Time: 404.97 sec.	Average Epoch Time: 404.97 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 23:37:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:26:21 INFO stats.py:314 | Epoch[62] Step[19] GlobalStep[24199] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 23:36:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:26:27 INFO loss_tracker.py:84 | Epoch[62/239] Step[24] GlobalStep[24204/NA]: det_loss_cls_0[0.6020] det_loss_box_0[0.7410] det_loss_cls_1[0.5790] det_loss_box_1[0.6000] det_loss_cls_2[0.5515] det_loss_box_2[0.5464] det_loss_cls_3[0.5296] det_loss_box_3[0.5212] det_loss_cls_4[0.4935] det_loss_box_4[0.5145] det_loss_cls_5[0.4801] det_loss_box_5[0.5104] det_loss_cls_dn_0[0.1125] det_loss_box_dn_0[0.2945] det_loss_cls_dn_1[0.1093] det_loss_box_dn_1[0.2552] det_loss_cls_dn_2[0.1087] det_loss_box_dn_2[0.2423] det_loss_cls_dn_3[0.1073] det_loss_box_dn_3[0.2398] det_loss_cls_dn_4[0.1057] det_loss_box_dn_4[0.2385] det_loss_cls_dn_5[0.1048] det_loss_box_dn_5[0.2378] loss_depth[0.0395] total_loss[8.8650]
Rank[0/8] 06/19/2025 22:26:47 INFO stats.py:314 | Epoch[62] Step[44] GlobalStep[24224] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 23:36:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:26:52 INFO loss_tracker.py:84 | Epoch[62/239] Step[49] GlobalStep[24229/NA]: det_loss_cls_0[0.5784] det_loss_box_0[0.6909] det_loss_cls_1[0.5530] det_loss_box_1[0.5411] det_loss_cls_2[0.5320] det_loss_box_2[0.5002] det_loss_cls_3[0.4997] det_loss_box_3[0.4773] det_loss_cls_4[0.4665] det_loss_box_4[0.4755] det_loss_cls_5[0.4612] det_loss_box_5[0.4700] det_loss_cls_dn_0[0.1067] det_loss_box_dn_0[0.3143] det_loss_cls_dn_1[0.1035] det_loss_box_dn_1[0.2558] det_loss_cls_dn_2[0.1024] det_loss_box_dn_2[0.2324] det_loss_cls_dn_3[0.1013] det_loss_box_dn_3[0.2289] det_loss_cls_dn_4[0.1004] det_loss_box_dn_4[0.2264] det_loss_cls_dn_5[0.0995] det_loss_box_dn_5[0.2250] loss_depth[0.0401] total_loss[8.3827]
Rank[0/8] 06/19/2025 22:27:13 INFO stats.py:314 | Epoch[62] Step[69] GlobalStep[24249] Training Speed: 8.59 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 23:35:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:27:18 INFO loss_tracker.py:84 | Epoch[62/239] Step[74] GlobalStep[24254/NA]: det_loss_cls_0[0.5964] det_loss_box_0[0.8298] det_loss_cls_1[0.5714] det_loss_box_1[0.7077] det_loss_cls_2[0.5577] det_loss_box_2[0.6479] det_loss_cls_3[0.5266] det_loss_box_3[0.6230] det_loss_cls_4[0.5021] det_loss_box_4[0.6181] det_loss_cls_5[0.4914] det_loss_box_5[0.6172] det_loss_cls_dn_0[0.1071] det_loss_box_dn_0[0.5465] det_loss_cls_dn_1[0.1027] det_loss_box_dn_1[0.5032] det_loss_cls_dn_2[0.1017] det_loss_box_dn_2[0.4860] det_loss_cls_dn_3[0.1014] det_loss_box_dn_3[0.4820] det_loss_cls_dn_4[0.0999] det_loss_box_dn_4[0.4819] det_loss_cls_dn_5[0.0990] det_loss_box_dn_5[0.4779] loss_depth[0.0415] total_loss[10.9201]
Rank[0/8] 06/19/2025 22:27:39 INFO stats.py:314 | Epoch[62] Step[94] GlobalStep[24274] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 23:34:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:27:44 INFO loss_tracker.py:84 | Epoch[62/239] Step[99] GlobalStep[24279/NA]: det_loss_cls_0[0.4185] det_loss_box_0[0.5445] det_loss_cls_1[0.3925] det_loss_box_1[0.4494] det_loss_cls_2[0.3763] det_loss_box_2[0.4119] det_loss_cls_3[0.3571] det_loss_box_3[0.3920] det_loss_cls_4[0.3392] det_loss_box_4[0.3856] det_loss_cls_5[0.3322] det_loss_box_5[0.3865] det_loss_cls_dn_0[0.1002] det_loss_box_dn_0[0.3072] det_loss_cls_dn_1[0.0958] det_loss_box_dn_1[0.2638] det_loss_cls_dn_2[0.0957] det_loss_box_dn_2[0.2476] det_loss_cls_dn_3[0.0940] det_loss_box_dn_3[0.2446] det_loss_cls_dn_4[0.0941] det_loss_box_dn_4[0.2430] det_loss_cls_dn_5[0.0931] det_loss_box_dn_5[0.2419] loss_depth[0.0413] total_loss[6.9481]
Rank[0/8] 06/19/2025 22:28:05 INFO stats.py:314 | Epoch[62] Step[119] GlobalStep[24299] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 23:34:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:28:10 INFO loss_tracker.py:84 | Epoch[62/239] Step[124] GlobalStep[24304/NA]: det_loss_cls_0[0.5991] det_loss_box_0[0.9041] det_loss_cls_1[0.5781] det_loss_box_1[0.7461] det_loss_cls_2[0.5489] det_loss_box_2[0.6966] det_loss_cls_3[0.5241] det_loss_box_3[0.6667] det_loss_cls_4[0.5037] det_loss_box_4[0.6460] det_loss_cls_5[0.4921] det_loss_box_5[0.6469] det_loss_cls_dn_0[0.1117] det_loss_box_dn_0[0.3270] det_loss_cls_dn_1[0.1070] det_loss_box_dn_1[0.2868] det_loss_cls_dn_2[0.1066] det_loss_box_dn_2[0.2693] det_loss_cls_dn_3[0.1052] det_loss_box_dn_3[0.2667] det_loss_cls_dn_4[0.1044] det_loss_box_dn_4[0.2664] det_loss_cls_dn_5[0.1035] det_loss_box_dn_5[0.2647] loss_depth[0.0395] total_loss[9.9112]
Rank[0/8] 06/19/2025 22:28:30 INFO stats.py:314 | Epoch[62] Step[144] GlobalStep[24324] Training Speed: 8.13 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 23:33:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:28:35 INFO loss_tracker.py:84 | Epoch[62/239] Step[149] GlobalStep[24329/NA]: det_loss_cls_0[0.5258] det_loss_box_0[0.6833] det_loss_cls_1[0.5065] det_loss_box_1[0.5573] det_loss_cls_2[0.4884] det_loss_box_2[0.4986] det_loss_cls_3[0.4579] det_loss_box_3[0.4813] det_loss_cls_4[0.4402] det_loss_box_4[0.4558] det_loss_cls_5[0.4224] det_loss_box_5[0.4615] det_loss_cls_dn_0[0.0999] det_loss_box_dn_0[0.3026] det_loss_cls_dn_1[0.0958] det_loss_box_dn_1[0.2635] det_loss_cls_dn_2[0.0949] det_loss_box_dn_2[0.2501] det_loss_cls_dn_3[0.0937] det_loss_box_dn_3[0.2489] det_loss_cls_dn_4[0.0925] det_loss_box_dn_4[0.2475] det_loss_cls_dn_5[0.0917] det_loss_box_dn_5[0.2459] loss_depth[0.0415] total_loss[8.1475]
Rank[0/8] 06/19/2025 22:28:56 INFO stats.py:314 | Epoch[62] Step[169] GlobalStep[24349] Training Speed: 6.72 samples/sec across all devices.	Average Step Time: 1.19 sec.	Estimated Remaining Time: 23:32:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:29:01 INFO loss_tracker.py:84 | Epoch[62/239] Step[174] GlobalStep[24354/NA]: det_loss_cls_0[0.5238] det_loss_box_0[0.6627] det_loss_cls_1[0.5096] det_loss_box_1[0.5414] det_loss_cls_2[0.4888] det_loss_box_2[0.4912] det_loss_cls_3[0.4681] det_loss_box_3[0.4856] det_loss_cls_4[0.4404] det_loss_box_4[0.4756] det_loss_cls_5[0.4361] det_loss_box_5[0.4669] det_loss_cls_dn_0[0.1059] det_loss_box_dn_0[0.3511] det_loss_cls_dn_1[0.1022] det_loss_box_dn_1[0.2979] det_loss_cls_dn_2[0.1018] det_loss_box_dn_2[0.2777] det_loss_cls_dn_3[0.1003] det_loss_box_dn_3[0.2742] det_loss_cls_dn_4[0.0995] det_loss_box_dn_4[0.2727] det_loss_cls_dn_5[0.0990] det_loss_box_dn_5[0.2711] loss_depth[0.0401] total_loss[8.3839]
Rank[0/8] 06/19/2025 22:29:22 INFO stats.py:314 | Epoch[62] Step[194] GlobalStep[24374] Training Speed: 6.53 samples/sec across all devices.	Average Step Time: 1.23 sec.	Estimated Remaining Time: 23:31:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:29:27 INFO loss_tracker.py:84 | Epoch[62/239] Step[199] GlobalStep[24379/NA]: det_loss_cls_0[0.4730] det_loss_box_0[0.5955] det_loss_cls_1[0.4736] det_loss_box_1[0.4601] det_loss_cls_2[0.4520] det_loss_box_2[0.4191] det_loss_cls_3[0.4202] det_loss_box_3[0.4072] det_loss_cls_4[0.4038] det_loss_box_4[0.3947] det_loss_cls_5[0.3973] det_loss_box_5[0.3897] det_loss_cls_dn_0[0.0966] det_loss_box_dn_0[0.2874] det_loss_cls_dn_1[0.0933] det_loss_box_dn_1[0.2493] det_loss_cls_dn_2[0.0928] det_loss_box_dn_2[0.2324] det_loss_cls_dn_3[0.0921] det_loss_box_dn_3[0.2294] det_loss_cls_dn_4[0.0910] det_loss_box_dn_4[0.2269] det_loss_cls_dn_5[0.0898] det_loss_box_dn_5[0.2254] loss_depth[0.0407] total_loss[7.3332]
Rank[0/8] 06/19/2025 22:29:47 INFO stats.py:314 | Epoch[62] Step[219] GlobalStep[24399] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 23:31:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:29:52 INFO loss_tracker.py:84 | Epoch[62/239] Step[224] GlobalStep[24404/NA]: det_loss_cls_0[0.5821] det_loss_box_0[0.8570] det_loss_cls_1[0.5537] det_loss_box_1[0.6872] det_loss_cls_2[0.5244] det_loss_box_2[0.6117] det_loss_cls_3[0.4912] det_loss_box_3[0.5801] det_loss_cls_4[0.4679] det_loss_box_4[0.5700] det_loss_cls_5[0.4529] det_loss_box_5[0.5693] det_loss_cls_dn_0[0.1001] det_loss_box_dn_0[0.3321] det_loss_cls_dn_1[0.0967] det_loss_box_dn_1[0.2818] det_loss_cls_dn_2[0.0964] det_loss_box_dn_2[0.2653] det_loss_cls_dn_3[0.0954] det_loss_box_dn_3[0.2620] det_loss_cls_dn_4[0.0939] det_loss_box_dn_4[0.2617] det_loss_cls_dn_5[0.0927] det_loss_box_dn_5[0.2597] loss_depth[0.0427] total_loss[9.2279]
Rank[0/8] 06/19/2025 22:30:13 INFO stats.py:314 | Epoch[62] Step[244] GlobalStep[24424] Training Speed: 7.73 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 23:30:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:30:18 INFO loss_tracker.py:84 | Epoch[62/239] Step[249] GlobalStep[24429/NA]: det_loss_cls_0[0.4526] det_loss_box_0[0.6422] det_loss_cls_1[0.4365] det_loss_box_1[0.5223] det_loss_cls_2[0.4212] det_loss_box_2[0.4566] det_loss_cls_3[0.3990] det_loss_box_3[0.4480] det_loss_cls_4[0.3784] det_loss_box_4[0.4324] det_loss_cls_5[0.3704] det_loss_box_5[0.4330] det_loss_cls_dn_0[0.1000] det_loss_box_dn_0[0.3612] det_loss_cls_dn_1[0.0962] det_loss_box_dn_1[0.3120] det_loss_cls_dn_2[0.0970] det_loss_box_dn_2[0.2962] det_loss_cls_dn_3[0.0960] det_loss_box_dn_3[0.2921] det_loss_cls_dn_4[0.0953] det_loss_box_dn_4[0.2921] det_loss_cls_dn_5[0.0950] det_loss_box_dn_5[0.2900] loss_depth[0.0418] total_loss[7.8574]
Rank[0/8] 06/19/2025 22:30:39 INFO stats.py:314 | Epoch[62] Step[269] GlobalStep[24449] Training Speed: 7.99 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 23:29:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:30:44 INFO loss_tracker.py:84 | Epoch[62/239] Step[274] GlobalStep[24454/NA]: det_loss_cls_0[0.4224] det_loss_box_0[0.5543] det_loss_cls_1[0.4157] det_loss_box_1[0.4384] det_loss_cls_2[0.4054] det_loss_box_2[0.3939] det_loss_cls_3[0.3741] det_loss_box_3[0.3847] det_loss_cls_4[0.3514] det_loss_box_4[0.3762] det_loss_cls_5[0.3409] det_loss_box_5[0.3757] det_loss_cls_dn_0[0.1024] det_loss_box_dn_0[0.3234] det_loss_cls_dn_1[0.0967] det_loss_box_dn_1[0.2783] det_loss_cls_dn_2[0.0966] det_loss_box_dn_2[0.2647] det_loss_cls_dn_3[0.0959] det_loss_box_dn_3[0.2630] det_loss_cls_dn_4[0.0943] det_loss_box_dn_4[0.2611] det_loss_cls_dn_5[0.0937] det_loss_box_dn_5[0.2586] loss_depth[0.0431] total_loss[7.1049]
Rank[0/8] 06/19/2025 22:31:05 INFO stats.py:314 | Epoch[62] Step[294] GlobalStep[24474] Training Speed: 8.62 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 23:28:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:31:10 INFO loss_tracker.py:84 | Epoch[62/239] Step[299] GlobalStep[24479/NA]: det_loss_cls_0[0.5123] det_loss_box_0[0.6667] det_loss_cls_1[0.4854] det_loss_box_1[0.5448] det_loss_cls_2[0.4670] det_loss_box_2[0.4973] det_loss_cls_3[0.4504] det_loss_box_3[0.4793] det_loss_cls_4[0.4185] det_loss_box_4[0.4755] det_loss_cls_5[0.4101] det_loss_box_5[0.4737] det_loss_cls_dn_0[0.1069] det_loss_box_dn_0[0.2816] det_loss_cls_dn_1[0.1035] det_loss_box_dn_1[0.2325] det_loss_cls_dn_2[0.1028] det_loss_box_dn_2[0.2170] det_loss_cls_dn_3[0.1020] det_loss_box_dn_3[0.2139] det_loss_cls_dn_4[0.1010] det_loss_box_dn_4[0.2131] det_loss_cls_dn_5[0.1005] det_loss_box_dn_5[0.2123] loss_depth[0.0446] total_loss[7.9128]
Rank[0/8] 06/19/2025 22:31:31 INFO stats.py:314 | Epoch[62] Step[319] GlobalStep[24499] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 23:28:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:31:36 INFO loss_tracker.py:84 | Epoch[62/239] Step[324] GlobalStep[24504/NA]: det_loss_cls_0[0.5947] det_loss_box_0[0.7961] det_loss_cls_1[0.5744] det_loss_box_1[0.6256] det_loss_cls_2[0.5532] det_loss_box_2[0.5764] det_loss_cls_3[0.5297] det_loss_box_3[0.5461] det_loss_cls_4[0.4966] det_loss_box_4[0.5500] det_loss_cls_5[0.4825] det_loss_box_5[0.5513] det_loss_cls_dn_0[0.0919] det_loss_box_dn_0[0.4105] det_loss_cls_dn_1[0.0864] det_loss_box_dn_1[0.3554] det_loss_cls_dn_2[0.0866] det_loss_box_dn_2[0.3395] det_loss_cls_dn_3[0.0860] det_loss_box_dn_3[0.3351] det_loss_cls_dn_4[0.0858] det_loss_box_dn_4[0.3344] det_loss_cls_dn_5[0.0848] det_loss_box_dn_5[0.3319] loss_depth[0.0389] total_loss[9.5439]
Rank[0/8] 06/19/2025 22:31:57 INFO stats.py:314 | Epoch[62] Step[344] GlobalStep[24524] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 23:27:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:32:02 INFO loss_tracker.py:84 | Epoch[62/239] Step[349] GlobalStep[24529/NA]: det_loss_cls_0[0.5523] det_loss_box_0[0.7662] det_loss_cls_1[0.5353] det_loss_box_1[0.6102] det_loss_cls_2[0.5142] det_loss_box_2[0.5550] det_loss_cls_3[0.4825] det_loss_box_3[0.5386] det_loss_cls_4[0.4494] det_loss_box_4[0.5299] det_loss_cls_5[0.4407] det_loss_box_5[0.5219] det_loss_cls_dn_0[0.1017] det_loss_box_dn_0[0.3274] det_loss_cls_dn_1[0.0959] det_loss_box_dn_1[0.2878] det_loss_cls_dn_2[0.0947] det_loss_box_dn_2[0.2725] det_loss_cls_dn_3[0.0940] det_loss_box_dn_3[0.2685] det_loss_cls_dn_4[0.0932] det_loss_box_dn_4[0.2675] det_loss_cls_dn_5[0.0923] det_loss_box_dn_5[0.2653] loss_depth[0.0393] total_loss[8.7963]
Rank[0/8] 06/19/2025 22:32:23 INFO stats.py:314 | Epoch[62] Step[369] GlobalStep[24549] Training Speed: 6.49 samples/sec across all devices.	Average Step Time: 1.23 sec.	Estimated Remaining Time: 23:26:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:32:28 INFO loss_tracker.py:84 | Epoch[62/239] Step[374] GlobalStep[24554/NA]: det_loss_cls_0[0.5726] det_loss_box_0[0.8175] det_loss_cls_1[0.5688] det_loss_box_1[0.6240] det_loss_cls_2[0.5526] det_loss_box_2[0.5581] det_loss_cls_3[0.5218] det_loss_box_3[0.5280] det_loss_cls_4[0.4984] det_loss_box_4[0.5172] det_loss_cls_5[0.4801] det_loss_box_5[0.5204] det_loss_cls_dn_0[0.1060] det_loss_box_dn_0[0.3531] det_loss_cls_dn_1[0.1022] det_loss_box_dn_1[0.3062] det_loss_cls_dn_2[0.1014] det_loss_box_dn_2[0.2928] det_loss_cls_dn_3[0.0996] det_loss_box_dn_3[0.2895] det_loss_cls_dn_4[0.0990] det_loss_box_dn_4[0.2889] det_loss_cls_dn_5[0.0983] det_loss_box_dn_5[0.2824] loss_depth[0.0426] total_loss[9.2214]
Rank[0/8] 06/19/2025 22:32:43 INFO stats.py:394 | Epoch[62] completed. Training Speed: 7.69 samples/sec across all devices.	Epoch Time: 405.55 sec.	Average Epoch Time: 405.55 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 23:26:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:32:53 INFO stats.py:314 | Epoch[63] Step[4] GlobalStep[24574] Training Speed: 8.03 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 23:26:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:33:13 INFO loss_tracker.py:84 | Epoch[63/239] Step[24] GlobalStep[24594/NA]: det_loss_cls_0[0.6007] det_loss_box_0[0.7761] det_loss_cls_1[0.5942] det_loss_box_1[0.6245] det_loss_cls_2[0.5715] det_loss_box_2[0.5576] det_loss_cls_3[0.5460] det_loss_box_3[0.5400] det_loss_cls_4[0.5017] det_loss_box_4[0.5351] det_loss_cls_5[0.4799] det_loss_box_5[0.5414] det_loss_cls_dn_0[0.1023] det_loss_box_dn_0[0.2929] det_loss_cls_dn_1[0.0977] det_loss_box_dn_1[0.2562] det_loss_cls_dn_2[0.0974] det_loss_box_dn_2[0.2375] det_loss_cls_dn_3[0.0963] det_loss_box_dn_3[0.2348] det_loss_cls_dn_4[0.0957] det_loss_box_dn_4[0.2341] det_loss_cls_dn_5[0.0951] det_loss_box_dn_5[0.2325] loss_depth[0.0401] total_loss[8.9815]
Rank[0/8] 06/19/2025 22:33:18 INFO stats.py:314 | Epoch[63] Step[29] GlobalStep[24599] Training Speed: 8.05 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 23:25:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:33:40 INFO loss_tracker.py:84 | Epoch[63/239] Step[49] GlobalStep[24619/NA]: det_loss_cls_0[0.5776] det_loss_box_0[0.7484] det_loss_cls_1[0.5723] det_loss_box_1[0.5930] det_loss_cls_2[0.5471] det_loss_box_2[0.5430] det_loss_cls_3[0.5237] det_loss_box_3[0.5246] det_loss_cls_4[0.4882] det_loss_box_4[0.5131] det_loss_cls_5[0.4737] det_loss_box_5[0.5100] det_loss_cls_dn_0[0.1012] det_loss_box_dn_0[0.2979] det_loss_cls_dn_1[0.0972] det_loss_box_dn_1[0.2651] det_loss_cls_dn_2[0.0964] det_loss_box_dn_2[0.2547] det_loss_cls_dn_3[0.0952] det_loss_box_dn_3[0.2528] det_loss_cls_dn_4[0.0944] det_loss_box_dn_4[0.2509] det_loss_cls_dn_5[0.0931] det_loss_box_dn_5[0.2498] loss_depth[0.0390] total_loss[8.8022]
Rank[0/8] 06/19/2025 22:33:44 INFO stats.py:314 | Epoch[63] Step[54] GlobalStep[24624] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 23:24:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:34:05 INFO loss_tracker.py:84 | Epoch[63/239] Step[74] GlobalStep[24644/NA]: det_loss_cls_0[0.5713] det_loss_box_0[0.8470] det_loss_cls_1[0.5527] det_loss_box_1[0.6736] det_loss_cls_2[0.5364] det_loss_box_2[0.6141] det_loss_cls_3[0.5065] det_loss_box_3[0.5992] det_loss_cls_4[0.4887] det_loss_box_4[0.5818] det_loss_cls_5[0.4718] det_loss_box_5[0.5799] det_loss_cls_dn_0[0.1035] det_loss_box_dn_0[0.3444] det_loss_cls_dn_1[0.0981] det_loss_box_dn_1[0.3120] det_loss_cls_dn_2[0.0978] det_loss_box_dn_2[0.2966] det_loss_cls_dn_3[0.0968] det_loss_box_dn_3[0.2939] det_loss_cls_dn_4[0.0962] det_loss_box_dn_4[0.2929] det_loss_cls_dn_5[0.0957] det_loss_box_dn_5[0.2905] loss_depth[0.0422] total_loss[9.4837]
Rank[0/8] 06/19/2025 22:34:11 INFO stats.py:314 | Epoch[63] Step[79] GlobalStep[24649] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 23:23:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:34:31 INFO loss_tracker.py:84 | Epoch[63/239] Step[99] GlobalStep[24669/NA]: det_loss_cls_0[0.4811] det_loss_box_0[0.6912] det_loss_cls_1[0.4853] det_loss_box_1[0.5379] det_loss_cls_2[0.4565] det_loss_box_2[0.4925] det_loss_cls_3[0.4312] det_loss_box_3[0.4743] det_loss_cls_4[0.3965] det_loss_box_4[0.4660] det_loss_cls_5[0.3856] det_loss_box_5[0.4607] det_loss_cls_dn_0[0.1038] det_loss_box_dn_0[0.2890] det_loss_cls_dn_1[0.0997] det_loss_box_dn_1[0.2394] det_loss_cls_dn_2[0.0993] det_loss_box_dn_2[0.2231] det_loss_cls_dn_3[0.0977] det_loss_box_dn_3[0.2193] det_loss_cls_dn_4[0.0964] det_loss_box_dn_4[0.2178] det_loss_cls_dn_5[0.0953] det_loss_box_dn_5[0.2161] loss_depth[0.0393] total_loss[7.7949]
Rank[0/8] 06/19/2025 22:34:36 INFO stats.py:314 | Epoch[63] Step[104] GlobalStep[24674] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 23:23:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:34:57 INFO loss_tracker.py:84 | Epoch[63/239] Step[124] GlobalStep[24694/NA]: det_loss_cls_0[0.6390] det_loss_box_0[0.8103] det_loss_cls_1[0.6187] det_loss_box_1[0.6447] det_loss_cls_2[0.5968] det_loss_box_2[0.5736] det_loss_cls_3[0.5658] det_loss_box_3[0.5446] det_loss_cls_4[0.5312] det_loss_box_4[0.5384] det_loss_cls_5[0.5246] det_loss_box_5[0.5327] det_loss_cls_dn_0[0.1158] det_loss_box_dn_0[0.3141] det_loss_cls_dn_1[0.1112] det_loss_box_dn_1[0.2741] det_loss_cls_dn_2[0.1111] det_loss_box_dn_2[0.2588] det_loss_cls_dn_3[0.1106] det_loss_box_dn_3[0.2568] det_loss_cls_dn_4[0.1095] det_loss_box_dn_4[0.2565] det_loss_cls_dn_5[0.1087] det_loss_box_dn_5[0.2557] loss_depth[0.0416] total_loss[9.4449]
Rank[0/8] 06/19/2025 22:35:02 INFO stats.py:314 | Epoch[63] Step[129] GlobalStep[24699] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 23:22:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:35:22 INFO loss_tracker.py:84 | Epoch[63/239] Step[149] GlobalStep[24719/NA]: det_loss_cls_0[0.7046] det_loss_box_0[0.9030] det_loss_cls_1[0.6725] det_loss_box_1[0.7209] det_loss_cls_2[0.6437] det_loss_box_2[0.6508] det_loss_cls_3[0.6156] det_loss_box_3[0.6353] det_loss_cls_4[0.5719] det_loss_box_4[0.6313] det_loss_cls_5[0.5619] det_loss_box_5[0.6230] det_loss_cls_dn_0[0.1082] det_loss_box_dn_0[0.2889] det_loss_cls_dn_1[0.1056] det_loss_box_dn_1[0.2580] det_loss_cls_dn_2[0.1040] det_loss_box_dn_2[0.2460] det_loss_cls_dn_3[0.1035] det_loss_box_dn_3[0.2435] det_loss_cls_dn_4[0.1026] det_loss_box_dn_4[0.2428] det_loss_cls_dn_5[0.1019] det_loss_box_dn_5[0.2404] loss_depth[0.0391] total_loss[10.1190]
Rank[0/8] 06/19/2025 22:35:27 INFO stats.py:314 | Epoch[63] Step[154] GlobalStep[24724] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 23:21:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:35:48 INFO loss_tracker.py:84 | Epoch[63/239] Step[174] GlobalStep[24744/NA]: det_loss_cls_0[0.6458] det_loss_box_0[0.8923] det_loss_cls_1[0.6512] det_loss_box_1[0.7399] det_loss_cls_2[0.6326] det_loss_box_2[0.6556] det_loss_cls_3[0.6067] det_loss_box_3[0.6215] det_loss_cls_4[0.5792] det_loss_box_4[0.6004] det_loss_cls_5[0.5597] det_loss_box_5[0.6024] det_loss_cls_dn_0[0.1020] det_loss_box_dn_0[0.2793] det_loss_cls_dn_1[0.0971] det_loss_box_dn_1[0.2397] det_loss_cls_dn_2[0.0982] det_loss_box_dn_2[0.2270] det_loss_cls_dn_3[0.0977] det_loss_box_dn_3[0.2244] det_loss_cls_dn_4[0.0961] det_loss_box_dn_4[0.2228] det_loss_cls_dn_5[0.0949] det_loss_box_dn_5[0.2215] loss_depth[0.0413] total_loss[9.8294]
Rank[0/8] 06/19/2025 22:35:54 INFO stats.py:314 | Epoch[63] Step[179] GlobalStep[24749] Training Speed: 7.97 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 23:21:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:36:15 INFO loss_tracker.py:84 | Epoch[63/239] Step[199] GlobalStep[24769/NA]: det_loss_cls_0[0.5020] det_loss_box_0[0.6603] det_loss_cls_1[0.4929] det_loss_box_1[0.5262] det_loss_cls_2[0.4666] det_loss_box_2[0.4826] det_loss_cls_3[0.4452] det_loss_box_3[0.4670] det_loss_cls_4[0.4214] det_loss_box_4[0.4577] det_loss_cls_5[0.4061] det_loss_box_5[0.4567] det_loss_cls_dn_0[0.1090] det_loss_box_dn_0[0.3130] det_loss_cls_dn_1[0.1058] det_loss_box_dn_1[0.2709] det_loss_cls_dn_2[0.1051] det_loss_box_dn_2[0.2566] det_loss_cls_dn_3[0.1049] det_loss_box_dn_3[0.2541] det_loss_cls_dn_4[0.1038] det_loss_box_dn_4[0.2529] det_loss_cls_dn_5[0.1031] det_loss_box_dn_5[0.2523] loss_depth[0.0387] total_loss[8.0551]
Rank[0/8] 06/19/2025 22:36:20 INFO stats.py:314 | Epoch[63] Step[204] GlobalStep[24774] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 23:20:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:36:41 INFO loss_tracker.py:84 | Epoch[63/239] Step[224] GlobalStep[24794/NA]: det_loss_cls_0[0.4976] det_loss_box_0[0.6468] det_loss_cls_1[0.4889] det_loss_box_1[0.5246] det_loss_cls_2[0.4801] det_loss_box_2[0.4750] det_loss_cls_3[0.4606] det_loss_box_3[0.4576] det_loss_cls_4[0.4347] det_loss_box_4[0.4512] det_loss_cls_5[0.4185] det_loss_box_5[0.4536] det_loss_cls_dn_0[0.0995] det_loss_box_dn_0[0.3504] det_loss_cls_dn_1[0.0964] det_loss_box_dn_1[0.2987] det_loss_cls_dn_2[0.0963] det_loss_box_dn_2[0.2825] det_loss_cls_dn_3[0.0955] det_loss_box_dn_3[0.2798] det_loss_cls_dn_4[0.0947] det_loss_box_dn_4[0.2798] det_loss_cls_dn_5[0.0936] det_loss_box_dn_5[0.2783] loss_depth[0.0408] total_loss[8.1756]
Rank[0/8] 06/19/2025 22:36:46 INFO stats.py:314 | Epoch[63] Step[229] GlobalStep[24799] Training Speed: 8.02 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 23:19:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:37:06 INFO loss_tracker.py:84 | Epoch[63/239] Step[249] GlobalStep[24819/NA]: det_loss_cls_0[0.5031] det_loss_box_0[0.6576] det_loss_cls_1[0.4990] det_loss_box_1[0.5281] det_loss_cls_2[0.4831] det_loss_box_2[0.4727] det_loss_cls_3[0.4461] det_loss_box_3[0.4669] det_loss_cls_4[0.4228] det_loss_box_4[0.4531] det_loss_cls_5[0.4109] det_loss_box_5[0.4536] det_loss_cls_dn_0[0.1043] det_loss_box_dn_0[0.2866] det_loss_cls_dn_1[0.1011] det_loss_box_dn_1[0.2484] det_loss_cls_dn_2[0.1009] det_loss_box_dn_2[0.2345] det_loss_cls_dn_3[0.1002] det_loss_box_dn_3[0.2316] det_loss_cls_dn_4[0.0995] det_loss_box_dn_4[0.2308] det_loss_cls_dn_5[0.0983] det_loss_box_dn_5[0.2291] loss_depth[0.0374] total_loss[7.8997]
Rank[0/8] 06/19/2025 22:37:12 INFO stats.py:314 | Epoch[63] Step[254] GlobalStep[24824] Training Speed: 6.01 samples/sec across all devices.	Average Step Time: 1.33 sec.	Estimated Remaining Time: 23:18:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:37:32 INFO loss_tracker.py:84 | Epoch[63/239] Step[274] GlobalStep[24844/NA]: det_loss_cls_0[0.6178] det_loss_box_0[0.8969] det_loss_cls_1[0.6144] det_loss_box_1[0.7075] det_loss_cls_2[0.5925] det_loss_box_2[0.6469] det_loss_cls_3[0.5598] det_loss_box_3[0.6326] det_loss_cls_4[0.5303] det_loss_box_4[0.6340] det_loss_cls_5[0.5064] det_loss_box_5[0.6337] det_loss_cls_dn_0[0.0924] det_loss_box_dn_0[0.3588] det_loss_cls_dn_1[0.0890] det_loss_box_dn_1[0.3144] det_loss_cls_dn_2[0.0876] det_loss_box_dn_2[0.3010] det_loss_cls_dn_3[0.0851] det_loss_box_dn_3[0.3008] det_loss_cls_dn_4[0.0842] det_loss_box_dn_4[0.2989] det_loss_cls_dn_5[0.0833] det_loss_box_dn_5[0.2961] loss_depth[0.0410] total_loss[10.0056]
Rank[0/8] 06/19/2025 22:37:38 INFO stats.py:314 | Epoch[63] Step[279] GlobalStep[24849] Training Speed: 6.39 samples/sec across all devices.	Average Step Time: 1.25 sec.	Estimated Remaining Time: 23:18:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:37:59 INFO loss_tracker.py:84 | Epoch[63/239] Step[299] GlobalStep[24869/NA]: det_loss_cls_0[0.5198] det_loss_box_0[0.7241] det_loss_cls_1[0.5036] det_loss_box_1[0.5706] det_loss_cls_2[0.4942] det_loss_box_2[0.5172] det_loss_cls_3[0.4644] det_loss_box_3[0.4996] det_loss_cls_4[0.4417] det_loss_box_4[0.4920] det_loss_cls_5[0.4282] det_loss_box_5[0.4897] det_loss_cls_dn_0[0.1001] det_loss_box_dn_0[0.3374] det_loss_cls_dn_1[0.0967] det_loss_box_dn_1[0.2919] det_loss_cls_dn_2[0.0953] det_loss_box_dn_2[0.2752] det_loss_cls_dn_3[0.0941] det_loss_box_dn_3[0.2728] det_loss_cls_dn_4[0.0940] det_loss_box_dn_4[0.2734] det_loss_cls_dn_5[0.0925] det_loss_box_dn_5[0.2709] loss_depth[0.0389] total_loss[8.4785]
Rank[0/8] 06/19/2025 22:38:04 INFO stats.py:314 | Epoch[63] Step[304] GlobalStep[24874] Training Speed: 7.30 samples/sec across all devices.	Average Step Time: 1.10 sec.	Estimated Remaining Time: 23:17:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:38:24 INFO loss_tracker.py:84 | Epoch[63/239] Step[324] GlobalStep[24894/NA]: det_loss_cls_0[0.4167] det_loss_box_0[0.5696] det_loss_cls_1[0.3986] det_loss_box_1[0.4363] det_loss_cls_2[0.3911] det_loss_box_2[0.3926] det_loss_cls_3[0.3662] det_loss_box_3[0.3833] det_loss_cls_4[0.3453] det_loss_box_4[0.3820] det_loss_cls_5[0.3371] det_loss_box_5[0.3802] det_loss_cls_dn_0[0.0964] det_loss_box_dn_0[0.3644] det_loss_cls_dn_1[0.0929] det_loss_box_dn_1[0.3113] det_loss_cls_dn_2[0.0928] det_loss_box_dn_2[0.2941] det_loss_cls_dn_3[0.0918] det_loss_box_dn_3[0.2898] det_loss_cls_dn_4[0.0906] det_loss_box_dn_4[0.2881] det_loss_cls_dn_5[0.0901] det_loss_box_dn_5[0.2859] loss_depth[0.0368] total_loss[7.2242]
Rank[0/8] 06/19/2025 22:38:29 INFO stats.py:314 | Epoch[63] Step[329] GlobalStep[24899] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 23:16:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:38:50 INFO loss_tracker.py:84 | Epoch[63/239] Step[349] GlobalStep[24919/NA]: det_loss_cls_0[0.4917] det_loss_box_0[0.6179] det_loss_cls_1[0.4921] det_loss_box_1[0.4912] det_loss_cls_2[0.4639] det_loss_box_2[0.4697] det_loss_cls_3[0.4280] det_loss_box_3[0.4606] det_loss_cls_4[0.3933] det_loss_box_4[0.4353] det_loss_cls_5[0.3763] det_loss_box_5[0.4358] det_loss_cls_dn_0[0.0980] det_loss_box_dn_0[0.3863] det_loss_cls_dn_1[0.0959] det_loss_box_dn_1[0.3414] det_loss_cls_dn_2[0.0939] det_loss_box_dn_2[0.3273] det_loss_cls_dn_3[0.0931] det_loss_box_dn_3[0.3240] det_loss_cls_dn_4[0.0919] det_loss_box_dn_4[0.3224] det_loss_cls_dn_5[0.0907] det_loss_box_dn_5[0.3186] loss_depth[0.0402] total_loss[8.1795]
Rank[0/8] 06/19/2025 22:38:55 INFO stats.py:314 | Epoch[63] Step[354] GlobalStep[24924] Training Speed: 6.45 samples/sec across all devices.	Average Step Time: 1.24 sec.	Estimated Remaining Time: 23:16:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:39:16 INFO loss_tracker.py:84 | Epoch[63/239] Step[374] GlobalStep[24944/NA]: det_loss_cls_0[0.5552] det_loss_box_0[0.6951] det_loss_cls_1[0.5369] det_loss_box_1[0.5438] det_loss_cls_2[0.5019] det_loss_box_2[0.4853] det_loss_cls_3[0.4776] det_loss_box_3[0.4690] det_loss_cls_4[0.4486] det_loss_box_4[0.4550] det_loss_cls_5[0.4321] det_loss_box_5[0.4576] det_loss_cls_dn_0[0.1009] det_loss_box_dn_0[0.3629] det_loss_cls_dn_1[0.0972] det_loss_box_dn_1[0.3045] det_loss_cls_dn_2[0.0965] det_loss_box_dn_2[0.2819] det_loss_cls_dn_3[0.0951] det_loss_box_dn_3[0.2769] det_loss_cls_dn_4[0.0943] det_loss_box_dn_4[0.2739] det_loss_cls_dn_5[0.0945] det_loss_box_dn_5[0.2732] loss_depth[0.0393] total_loss[8.4490]
Rank[0/8] 06/19/2025 22:39:21 INFO stats.py:314 | Epoch[63] Step[379] GlobalStep[24949] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 23:15:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:39:31 INFO stats.py:394 | Epoch[63] completed. Training Speed: 7.65 samples/sec across all devices.	Epoch Time: 407.83 sec.	Average Epoch Time: 407.83 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 23:14:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:39:50 INFO stats.py:314 | Epoch[64] Step[14] GlobalStep[24974] Training Speed: 8.02 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 23:14:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:40:01 INFO loss_tracker.py:84 | Epoch[64/239] Step[24] GlobalStep[24984/NA]: det_loss_cls_0[0.5500] det_loss_box_0[0.7110] det_loss_cls_1[0.5285] det_loss_box_1[0.5739] det_loss_cls_2[0.5099] det_loss_box_2[0.5203] det_loss_cls_3[0.4749] det_loss_box_3[0.4997] det_loss_cls_4[0.4432] det_loss_box_4[0.4893] det_loss_cls_5[0.4246] det_loss_box_5[0.4937] det_loss_cls_dn_0[0.1070] det_loss_box_dn_0[0.3073] det_loss_cls_dn_1[0.1020] det_loss_box_dn_1[0.2583] det_loss_cls_dn_2[0.1008] det_loss_box_dn_2[0.2428] det_loss_cls_dn_3[0.1001] det_loss_box_dn_3[0.2400] det_loss_cls_dn_4[0.0990] det_loss_box_dn_4[0.2383] det_loss_cls_dn_5[0.0987] det_loss_box_dn_5[0.2364] loss_depth[0.0391] total_loss[8.3886]
Rank[0/8] 06/19/2025 22:40:16 INFO stats.py:314 | Epoch[64] Step[39] GlobalStep[24999] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 23:14:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:40:26 INFO loss_tracker.py:84 | Epoch[64/239] Step[49] GlobalStep[25009/NA]: det_loss_cls_0[0.4098] det_loss_box_0[0.6480] det_loss_cls_1[0.4112] det_loss_box_1[0.5288] det_loss_cls_2[0.3950] det_loss_box_2[0.4729] det_loss_cls_3[0.3775] det_loss_box_3[0.4590] det_loss_cls_4[0.3480] det_loss_box_4[0.4536] det_loss_cls_5[0.3429] det_loss_box_5[0.4569] det_loss_cls_dn_0[0.1044] det_loss_box_dn_0[0.4575] det_loss_cls_dn_1[0.1034] det_loss_box_dn_1[0.3785] det_loss_cls_dn_2[0.1019] det_loss_box_dn_2[0.3510] det_loss_cls_dn_3[0.1007] det_loss_box_dn_3[0.3442] det_loss_cls_dn_4[0.1002] det_loss_box_dn_4[0.3396] det_loss_cls_dn_5[0.0993] det_loss_box_dn_5[0.3374] loss_depth[0.0430] total_loss[8.1646]
Rank[0/8] 06/19/2025 22:40:42 INFO stats.py:314 | Epoch[64] Step[64] GlobalStep[25024] Training Speed: 8.70 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 23:13:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:40:52 INFO loss_tracker.py:84 | Epoch[64/239] Step[74] GlobalStep[25034/NA]: det_loss_cls_0[0.5539] det_loss_box_0[0.7081] det_loss_cls_1[0.5415] det_loss_box_1[0.5557] det_loss_cls_2[0.5057] det_loss_box_2[0.5018] det_loss_cls_3[0.4688] det_loss_box_3[0.4832] det_loss_cls_4[0.4400] det_loss_box_4[0.4683] det_loss_cls_5[0.4281] det_loss_box_5[0.4694] det_loss_cls_dn_0[0.0870] det_loss_box_dn_0[0.2696] det_loss_cls_dn_1[0.0833] det_loss_box_dn_1[0.2326] det_loss_cls_dn_2[0.0821] det_loss_box_dn_2[0.2162] det_loss_cls_dn_3[0.0812] det_loss_box_dn_3[0.2139] det_loss_cls_dn_4[0.0805] det_loss_box_dn_4[0.2133] det_loss_cls_dn_5[0.0799] det_loss_box_dn_5[0.2116] loss_depth[0.0391] total_loss[8.0145]
Rank[0/8] 06/19/2025 22:41:08 INFO stats.py:314 | Epoch[64] Step[89] GlobalStep[25049] Training Speed: 7.78 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 23:12:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:41:18 INFO loss_tracker.py:84 | Epoch[64/239] Step[99] GlobalStep[25059/NA]: det_loss_cls_0[0.4860] det_loss_box_0[0.6003] det_loss_cls_1[0.4798] det_loss_box_1[0.4642] det_loss_cls_2[0.4456] det_loss_box_2[0.4146] det_loss_cls_3[0.4217] det_loss_box_3[0.3989] det_loss_cls_4[0.3922] det_loss_box_4[0.3907] det_loss_cls_5[0.3770] det_loss_box_5[0.3901] det_loss_cls_dn_0[0.0936] det_loss_box_dn_0[0.3203] det_loss_cls_dn_1[0.0901] det_loss_box_dn_1[0.2724] det_loss_cls_dn_2[0.0885] det_loss_box_dn_2[0.2589] det_loss_cls_dn_3[0.0869] det_loss_box_dn_3[0.2561] det_loss_cls_dn_4[0.0868] det_loss_box_dn_4[0.2559] det_loss_cls_dn_5[0.0856] det_loss_box_dn_5[0.2523] loss_depth[0.0418] total_loss[7.4498]
Rank[0/8] 06/19/2025 22:41:33 INFO stats.py:314 | Epoch[64] Step[114] GlobalStep[25074] Training Speed: 8.58 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 23:11:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:41:44 INFO loss_tracker.py:84 | Epoch[64/239] Step[124] GlobalStep[25084/NA]: det_loss_cls_0[0.5286] det_loss_box_0[0.7312] det_loss_cls_1[0.5234] det_loss_box_1[0.5970] det_loss_cls_2[0.4969] det_loss_box_2[0.5370] det_loss_cls_3[0.4737] det_loss_box_3[0.5218] det_loss_cls_4[0.4465] det_loss_box_4[0.5067] det_loss_cls_5[0.4318] det_loss_box_5[0.5140] det_loss_cls_dn_0[0.1074] det_loss_box_dn_0[0.3534] det_loss_cls_dn_1[0.1043] det_loss_box_dn_1[0.3128] det_loss_cls_dn_2[0.1034] det_loss_box_dn_2[0.2936] det_loss_cls_dn_3[0.1023] det_loss_box_dn_3[0.2903] det_loss_cls_dn_4[0.1017] det_loss_box_dn_4[0.2895] det_loss_cls_dn_5[0.1011] det_loss_box_dn_5[0.2877] loss_depth[0.0451] total_loss[8.8011]
Rank[0/8] 06/19/2025 22:41:59 INFO stats.py:314 | Epoch[64] Step[139] GlobalStep[25099] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 23:11:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:42:09 INFO loss_tracker.py:84 | Epoch[64/239] Step[149] GlobalStep[25109/NA]: det_loss_cls_0[0.5572] det_loss_box_0[0.7404] det_loss_cls_1[0.5300] det_loss_box_1[0.5964] det_loss_cls_2[0.5107] det_loss_box_2[0.5317] det_loss_cls_3[0.4869] det_loss_box_3[0.5119] det_loss_cls_4[0.4633] det_loss_box_4[0.5094] det_loss_cls_5[0.4456] det_loss_box_5[0.5094] det_loss_cls_dn_0[0.1041] det_loss_box_dn_0[0.3112] det_loss_cls_dn_1[0.1002] det_loss_box_dn_1[0.2684] det_loss_cls_dn_2[0.0998] det_loss_box_dn_2[0.2537] det_loss_cls_dn_3[0.0999] det_loss_box_dn_3[0.2513] det_loss_cls_dn_4[0.0981] det_loss_box_dn_4[0.2504] det_loss_cls_dn_5[0.0970] det_loss_box_dn_5[0.2496] loss_depth[0.0381] total_loss[8.6148]
Rank[0/8] 06/19/2025 22:42:25 INFO stats.py:314 | Epoch[64] Step[164] GlobalStep[25124] Training Speed: 7.65 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 23:10:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:42:35 INFO loss_tracker.py:84 | Epoch[64/239] Step[174] GlobalStep[25134/NA]: det_loss_cls_0[0.5960] det_loss_box_0[0.8597] det_loss_cls_1[0.5903] det_loss_box_1[0.7166] det_loss_cls_2[0.5658] det_loss_box_2[0.6550] det_loss_cls_3[0.5324] det_loss_box_3[0.6347] det_loss_cls_4[0.5000] det_loss_box_4[0.6292] det_loss_cls_5[0.4757] det_loss_box_5[0.6374] det_loss_cls_dn_0[0.1002] det_loss_box_dn_0[0.4336] det_loss_cls_dn_1[0.0960] det_loss_box_dn_1[0.3788] det_loss_cls_dn_2[0.0962] det_loss_box_dn_2[0.3638] det_loss_cls_dn_3[0.0945] det_loss_box_dn_3[0.3625] det_loss_cls_dn_4[0.0932] det_loss_box_dn_4[0.3622] det_loss_cls_dn_5[0.0923] det_loss_box_dn_5[0.3607] loss_depth[0.0422] total_loss[10.2692]
Rank[0/8] 06/19/2025 22:42:51 INFO stats.py:314 | Epoch[64] Step[189] GlobalStep[25149] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 23:09:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:43:01 INFO loss_tracker.py:84 | Epoch[64/239] Step[199] GlobalStep[25159/NA]: det_loss_cls_0[0.4818] det_loss_box_0[0.7179] det_loss_cls_1[0.4702] det_loss_box_1[0.5816] det_loss_cls_2[0.4532] det_loss_box_2[0.5315] det_loss_cls_3[0.4320] det_loss_box_3[0.5071] det_loss_cls_4[0.4062] det_loss_box_4[0.4896] det_loss_cls_5[0.3896] det_loss_box_5[0.4900] det_loss_cls_dn_0[0.1058] det_loss_box_dn_0[0.3755] det_loss_cls_dn_1[0.1031] det_loss_box_dn_1[0.3307] det_loss_cls_dn_2[0.1019] det_loss_box_dn_2[0.3155] det_loss_cls_dn_3[0.1009] det_loss_box_dn_3[0.3104] det_loss_cls_dn_4[0.1006] det_loss_box_dn_4[0.3101] det_loss_cls_dn_5[0.1000] det_loss_box_dn_5[0.3082] loss_depth[0.0430] total_loss[8.5564]
Rank[0/8] 06/19/2025 22:43:17 INFO stats.py:314 | Epoch[64] Step[214] GlobalStep[25174] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 23:08:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:43:27 INFO loss_tracker.py:84 | Epoch[64/239] Step[224] GlobalStep[25184/NA]: det_loss_cls_0[0.6204] det_loss_box_0[0.9212] det_loss_cls_1[0.6052] det_loss_box_1[0.7135] det_loss_cls_2[0.5815] det_loss_box_2[0.6317] det_loss_cls_3[0.5553] det_loss_box_3[0.6032] det_loss_cls_4[0.5111] det_loss_box_4[0.5946] det_loss_cls_5[0.4914] det_loss_box_5[0.5949] det_loss_cls_dn_0[0.0945] det_loss_box_dn_0[0.2813] det_loss_cls_dn_1[0.0908] det_loss_box_dn_1[0.2402] det_loss_cls_dn_2[0.0902] det_loss_box_dn_2[0.2254] det_loss_cls_dn_3[0.0887] det_loss_box_dn_3[0.2215] det_loss_cls_dn_4[0.0871] det_loss_box_dn_4[0.2206] det_loss_cls_dn_5[0.0864] det_loss_box_dn_5[0.2180] loss_depth[0.0401] total_loss[9.4088]
Rank[0/8] 06/19/2025 22:43:43 INFO stats.py:314 | Epoch[64] Step[239] GlobalStep[25199] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 23:08:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:43:53 INFO loss_tracker.py:84 | Epoch[64/239] Step[249] GlobalStep[25209/NA]: det_loss_cls_0[0.4823] det_loss_box_0[0.6778] det_loss_cls_1[0.4704] det_loss_box_1[0.5328] det_loss_cls_2[0.4588] det_loss_box_2[0.4914] det_loss_cls_3[0.4405] det_loss_box_3[0.4744] det_loss_cls_4[0.4095] det_loss_box_4[0.4662] det_loss_cls_5[0.3990] det_loss_box_5[0.4652] det_loss_cls_dn_0[0.1016] det_loss_box_dn_0[0.3585] det_loss_cls_dn_1[0.0990] det_loss_box_dn_1[0.3063] det_loss_cls_dn_2[0.0991] det_loss_box_dn_2[0.2810] det_loss_cls_dn_3[0.0977] det_loss_box_dn_3[0.2758] det_loss_cls_dn_4[0.0968] det_loss_box_dn_4[0.2726] det_loss_cls_dn_5[0.0965] det_loss_box_dn_5[0.2718] loss_depth[0.0416] total_loss[8.1666]
Rank[0/8] 06/19/2025 22:44:09 INFO stats.py:314 | Epoch[64] Step[264] GlobalStep[25224] Training Speed: 7.96 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 23:07:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:44:20 INFO loss_tracker.py:84 | Epoch[64/239] Step[274] GlobalStep[25234/NA]: det_loss_cls_0[0.4752] det_loss_box_0[0.6085] det_loss_cls_1[0.4452] det_loss_box_1[0.4873] det_loss_cls_2[0.4288] det_loss_box_2[0.4481] det_loss_cls_3[0.4050] det_loss_box_3[0.4317] det_loss_cls_4[0.3859] det_loss_box_4[0.4244] det_loss_cls_5[0.3764] det_loss_box_5[0.4264] det_loss_cls_dn_0[0.1026] det_loss_box_dn_0[0.3153] det_loss_cls_dn_1[0.0983] det_loss_box_dn_1[0.2706] det_loss_cls_dn_2[0.0976] det_loss_box_dn_2[0.2550] det_loss_cls_dn_3[0.0966] det_loss_box_dn_3[0.2517] det_loss_cls_dn_4[0.0959] det_loss_box_dn_4[0.2507] det_loss_cls_dn_5[0.0950] det_loss_box_dn_5[0.2486] loss_depth[0.0401] total_loss[7.5607]
Rank[0/8] 06/19/2025 22:44:35 INFO stats.py:314 | Epoch[64] Step[289] GlobalStep[25249] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 23:06:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:44:45 INFO loss_tracker.py:84 | Epoch[64/239] Step[299] GlobalStep[25259/NA]: det_loss_cls_0[0.4344] det_loss_box_0[0.6680] det_loss_cls_1[0.4226] det_loss_box_1[0.5370] det_loss_cls_2[0.4067] det_loss_box_2[0.4798] det_loss_cls_3[0.3873] det_loss_box_3[0.4549] det_loss_cls_4[0.3739] det_loss_box_4[0.4561] det_loss_cls_5[0.3606] det_loss_box_5[0.4617] det_loss_cls_dn_0[0.1001] det_loss_box_dn_0[0.3802] det_loss_cls_dn_1[0.0972] det_loss_box_dn_1[0.3277] det_loss_cls_dn_2[0.0969] det_loss_box_dn_2[0.3171] det_loss_cls_dn_3[0.0958] det_loss_box_dn_3[0.3112] det_loss_cls_dn_4[0.0958] det_loss_box_dn_4[0.3082] det_loss_cls_dn_5[0.0945] det_loss_box_dn_5[0.3066] loss_depth[0.0401] total_loss[8.0145]
Rank[0/8] 06/19/2025 22:45:01 INFO stats.py:314 | Epoch[64] Step[314] GlobalStep[25274] Training Speed: 8.71 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 23:06:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:45:11 INFO loss_tracker.py:84 | Epoch[64/239] Step[324] GlobalStep[25284/NA]: det_loss_cls_0[0.5730] det_loss_box_0[0.8387] det_loss_cls_1[0.5631] det_loss_box_1[0.6940] det_loss_cls_2[0.5513] det_loss_box_2[0.6167] det_loss_cls_3[0.5195] det_loss_box_3[0.5865] det_loss_cls_4[0.4933] det_loss_box_4[0.5742] det_loss_cls_5[0.4817] det_loss_box_5[0.5792] det_loss_cls_dn_0[0.1054] det_loss_box_dn_0[0.3206] det_loss_cls_dn_1[0.1008] det_loss_box_dn_1[0.2862] det_loss_cls_dn_2[0.1008] det_loss_box_dn_2[0.2705] det_loss_cls_dn_3[0.0991] det_loss_box_dn_3[0.2689] det_loss_cls_dn_4[0.0989] det_loss_box_dn_4[0.2687] det_loss_cls_dn_5[0.0978] det_loss_box_dn_5[0.2676] loss_depth[0.0393] total_loss[9.3958]
Rank[0/8] 06/19/2025 22:45:26 INFO stats.py:314 | Epoch[64] Step[339] GlobalStep[25299] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 23:05:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:45:37 INFO loss_tracker.py:84 | Epoch[64/239] Step[349] GlobalStep[25309/NA]: det_loss_cls_0[0.4376] det_loss_box_0[0.5947] det_loss_cls_1[0.4283] det_loss_box_1[0.4721] det_loss_cls_2[0.4050] det_loss_box_2[0.4297] det_loss_cls_3[0.3744] det_loss_box_3[0.4227] det_loss_cls_4[0.3671] det_loss_box_4[0.4054] det_loss_cls_5[0.3533] det_loss_box_5[0.4065] det_loss_cls_dn_0[0.1026] det_loss_box_dn_0[0.3011] det_loss_cls_dn_1[0.0984] det_loss_box_dn_1[0.2614] det_loss_cls_dn_2[0.0979] det_loss_box_dn_2[0.2501] det_loss_cls_dn_3[0.0975] det_loss_box_dn_3[0.2470] det_loss_cls_dn_4[0.0969] det_loss_box_dn_4[0.2461] det_loss_cls_dn_5[0.0952] det_loss_box_dn_5[0.2443] loss_depth[0.0407] total_loss[7.2757]
Rank[0/8] 06/19/2025 22:45:52 INFO stats.py:314 | Epoch[64] Step[364] GlobalStep[25324] Training Speed: 8.59 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 23:04:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:46:03 INFO loss_tracker.py:84 | Epoch[64/239] Step[374] GlobalStep[25334/NA]: det_loss_cls_0[0.4627] det_loss_box_0[0.6242] det_loss_cls_1[0.4409] det_loss_box_1[0.4935] det_loss_cls_2[0.4188] det_loss_box_2[0.4378] det_loss_cls_3[0.3935] det_loss_box_3[0.4327] det_loss_cls_4[0.3742] det_loss_box_4[0.4221] det_loss_cls_5[0.3633] det_loss_box_5[0.4243] det_loss_cls_dn_0[0.1086] det_loss_box_dn_0[0.3279] det_loss_cls_dn_1[0.1051] det_loss_box_dn_1[0.2814] det_loss_cls_dn_2[0.1044] det_loss_box_dn_2[0.2671] det_loss_cls_dn_3[0.1044] det_loss_box_dn_3[0.2626] det_loss_cls_dn_4[0.1032] det_loss_box_dn_4[0.2610] det_loss_cls_dn_5[0.1029] det_loss_box_dn_5[0.2594] loss_depth[0.0406] total_loss[7.6168]
Rank[0/8] 06/19/2025 22:46:17 INFO stats.py:314 | Epoch[64] Step[389] GlobalStep[25349] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 23:03:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:46:17 INFO stats.py:394 | Epoch[64] completed. Training Speed: 7.68 samples/sec across all devices.	Epoch Time: 406.38 sec.	Average Epoch Time: 406.38 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 23:03:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:46:49 INFO stats.py:314 | Epoch[65] Step[24] GlobalStep[25374] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 23:03:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:46:49 INFO loss_tracker.py:84 | Epoch[65/239] Step[24] GlobalStep[25374/NA]: det_loss_cls_0[0.5111] det_loss_box_0[0.6526] det_loss_cls_1[0.4960] det_loss_box_1[0.5249] det_loss_cls_2[0.4725] det_loss_box_2[0.4828] det_loss_cls_3[0.4503] det_loss_box_3[0.4679] det_loss_cls_4[0.4281] det_loss_box_4[0.4623] det_loss_cls_5[0.4149] det_loss_box_5[0.4620] det_loss_cls_dn_0[0.1045] det_loss_box_dn_0[0.3330] det_loss_cls_dn_1[0.1012] det_loss_box_dn_1[0.2942] det_loss_cls_dn_2[0.1004] det_loss_box_dn_2[0.2802] det_loss_cls_dn_3[0.0997] det_loss_box_dn_3[0.2774] det_loss_cls_dn_4[0.0981] det_loss_box_dn_4[0.2762] det_loss_cls_dn_5[0.0975] det_loss_box_dn_5[0.2751] loss_depth[0.0414] total_loss[8.2044]
Rank[0/8] 06/19/2025 22:47:14 INFO stats.py:314 | Epoch[65] Step[49] GlobalStep[25399] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 23:02:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:47:14 INFO loss_tracker.py:84 | Epoch[65/239] Step[49] GlobalStep[25399/NA]: det_loss_cls_0[0.6252] det_loss_box_0[0.8099] det_loss_cls_1[0.6182] det_loss_box_1[0.6485] det_loss_cls_2[0.5936] det_loss_box_2[0.5871] det_loss_cls_3[0.5767] det_loss_box_3[0.5732] det_loss_cls_4[0.5419] det_loss_box_4[0.5610] det_loss_cls_5[0.5268] det_loss_box_5[0.5602] det_loss_cls_dn_0[0.1075] det_loss_box_dn_0[0.2979] det_loss_cls_dn_1[0.1034] det_loss_box_dn_1[0.2613] det_loss_cls_dn_2[0.1035] det_loss_box_dn_2[0.2480] det_loss_cls_dn_3[0.1024] det_loss_box_dn_3[0.2450] det_loss_cls_dn_4[0.1013] det_loss_box_dn_4[0.2444] det_loss_cls_dn_5[0.1002] det_loss_box_dn_5[0.2406] loss_depth[0.0399] total_loss[9.4181]
Rank[0/8] 06/19/2025 22:47:40 INFO stats.py:314 | Epoch[65] Step[74] GlobalStep[25424] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 23:02:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:47:40 INFO loss_tracker.py:84 | Epoch[65/239] Step[74] GlobalStep[25424/NA]: det_loss_cls_0[0.6781] det_loss_box_0[0.8589] det_loss_cls_1[0.6596] det_loss_box_1[0.6558] det_loss_cls_2[0.6219] det_loss_box_2[0.5725] det_loss_cls_3[0.5833] det_loss_box_3[0.5509] det_loss_cls_4[0.5393] det_loss_box_4[0.5414] det_loss_cls_5[0.5156] det_loss_box_5[0.5426] det_loss_cls_dn_0[0.1028] det_loss_box_dn_0[0.2642] det_loss_cls_dn_1[0.0991] det_loss_box_dn_1[0.2257] det_loss_cls_dn_2[0.0989] det_loss_box_dn_2[0.2125] det_loss_cls_dn_3[0.0981] det_loss_box_dn_3[0.2095] det_loss_cls_dn_4[0.0971] det_loss_box_dn_4[0.2085] det_loss_cls_dn_5[0.0963] det_loss_box_dn_5[0.2080] loss_depth[0.0413] total_loss[9.2820]
Rank[0/8] 06/19/2025 22:48:06 INFO stats.py:314 | Epoch[65] Step[99] GlobalStep[25449] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 23:01:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:48:06 INFO loss_tracker.py:84 | Epoch[65/239] Step[99] GlobalStep[25449/NA]: det_loss_cls_0[0.5116] det_loss_box_0[0.6421] det_loss_cls_1[0.5118] det_loss_box_1[0.4825] det_loss_cls_2[0.4828] det_loss_box_2[0.4455] det_loss_cls_3[0.4527] det_loss_box_3[0.4198] det_loss_cls_4[0.4387] det_loss_box_4[0.4061] det_loss_cls_5[0.4224] det_loss_box_5[0.4066] det_loss_cls_dn_0[0.1063] det_loss_box_dn_0[0.2565] det_loss_cls_dn_1[0.1022] det_loss_box_dn_1[0.2187] det_loss_cls_dn_2[0.1024] det_loss_box_dn_2[0.2071] det_loss_cls_dn_3[0.1018] det_loss_box_dn_3[0.2037] det_loss_cls_dn_4[0.1012] det_loss_box_dn_4[0.2031] det_loss_cls_dn_5[0.1007] det_loss_box_dn_5[0.2012] loss_depth[0.0397] total_loss[7.5671]
Rank[0/8] 06/19/2025 22:48:32 INFO stats.py:314 | Epoch[65] Step[124] GlobalStep[25474] Training Speed: 7.66 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 23:00:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:48:32 INFO loss_tracker.py:84 | Epoch[65/239] Step[124] GlobalStep[25474/NA]: det_loss_cls_0[0.5290] det_loss_box_0[0.7050] det_loss_cls_1[0.5298] det_loss_box_1[0.5683] det_loss_cls_2[0.5040] det_loss_box_2[0.5171] det_loss_cls_3[0.4762] det_loss_box_3[0.4981] det_loss_cls_4[0.4454] det_loss_box_4[0.4900] det_loss_cls_5[0.4330] det_loss_box_5[0.4888] det_loss_cls_dn_0[0.1083] det_loss_box_dn_0[0.3059] det_loss_cls_dn_1[0.1038] det_loss_box_dn_1[0.2714] det_loss_cls_dn_2[0.1038] det_loss_box_dn_2[0.2595] det_loss_cls_dn_3[0.1019] det_loss_box_dn_3[0.2576] det_loss_cls_dn_4[0.1015] det_loss_box_dn_4[0.2578] det_loss_cls_dn_5[0.1001] det_loss_box_dn_5[0.2558] loss_depth[0.0386] total_loss[8.4506]
Rank[0/8] 06/19/2025 22:48:58 INFO stats.py:314 | Epoch[65] Step[149] GlobalStep[25499] Training Speed: 8.58 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 22:59:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:48:58 INFO loss_tracker.py:84 | Epoch[65/239] Step[149] GlobalStep[25499/NA]: det_loss_cls_0[0.6439] det_loss_box_0[0.8957] det_loss_cls_1[0.6323] det_loss_box_1[0.7183] det_loss_cls_2[0.6118] det_loss_box_2[0.6456] det_loss_cls_3[0.5764] det_loss_box_3[0.6270] det_loss_cls_4[0.5457] det_loss_box_4[0.6052] det_loss_cls_5[0.5290] det_loss_box_5[0.6097] det_loss_cls_dn_0[0.1054] det_loss_box_dn_0[0.2945] det_loss_cls_dn_1[0.1019] det_loss_box_dn_1[0.2566] det_loss_cls_dn_2[0.1010] det_loss_box_dn_2[0.2423] det_loss_cls_dn_3[0.1006] det_loss_box_dn_3[0.2389] det_loss_cls_dn_4[0.0989] det_loss_box_dn_4[0.2374] det_loss_cls_dn_5[0.0978] det_loss_box_dn_5[0.2369] loss_depth[0.0420] total_loss[9.7949]
Rank[0/8] 06/19/2025 22:49:23 INFO stats.py:314 | Epoch[65] Step[174] GlobalStep[25524] Training Speed: 6.05 samples/sec across all devices.	Average Step Time: 1.32 sec.	Estimated Remaining Time: 22:59:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:49:23 INFO loss_tracker.py:84 | Epoch[65/239] Step[174] GlobalStep[25524/NA]: det_loss_cls_0[0.5284] det_loss_box_0[0.7285] det_loss_cls_1[0.4988] det_loss_box_1[0.5817] det_loss_cls_2[0.4775] det_loss_box_2[0.5203] det_loss_cls_3[0.4551] det_loss_box_3[0.4984] det_loss_cls_4[0.4241] det_loss_box_4[0.4863] det_loss_cls_5[0.4099] det_loss_box_5[0.4878] det_loss_cls_dn_0[0.1056] det_loss_box_dn_0[0.3362] det_loss_cls_dn_1[0.1015] det_loss_box_dn_1[0.2919] det_loss_cls_dn_2[0.1002] det_loss_box_dn_2[0.2728] det_loss_cls_dn_3[0.0991] det_loss_box_dn_3[0.2683] det_loss_cls_dn_4[0.0972] det_loss_box_dn_4[0.2674] det_loss_cls_dn_5[0.0964] det_loss_box_dn_5[0.2678] loss_depth[0.0458] total_loss[8.4468]
Rank[0/8] 06/19/2025 22:49:50 INFO stats.py:314 | Epoch[65] Step[199] GlobalStep[25549] Training Speed: 7.60 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 22:58:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:49:50 INFO loss_tracker.py:84 | Epoch[65/239] Step[199] GlobalStep[25549/NA]: det_loss_cls_0[0.5457] det_loss_box_0[0.6464] det_loss_cls_1[0.5445] det_loss_box_1[0.5161] det_loss_cls_2[0.5245] det_loss_box_2[0.4710] det_loss_cls_3[0.4975] det_loss_box_3[0.4510] det_loss_cls_4[0.4440] det_loss_box_4[0.4499] det_loss_cls_5[0.4316] det_loss_box_5[0.4500] det_loss_cls_dn_0[0.1023] det_loss_box_dn_0[0.3064] det_loss_cls_dn_1[0.0984] det_loss_box_dn_1[0.2601] det_loss_cls_dn_2[0.0980] det_loss_box_dn_2[0.2448] det_loss_cls_dn_3[0.0971] det_loss_box_dn_3[0.2413] det_loss_cls_dn_4[0.0960] det_loss_box_dn_4[0.2402] det_loss_cls_dn_5[0.0948] det_loss_box_dn_5[0.2382] loss_depth[0.0384] total_loss[8.1283]
Rank[0/8] 06/19/2025 22:50:15 INFO stats.py:314 | Epoch[65] Step[224] GlobalStep[25574] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 22:57:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:50:15 INFO loss_tracker.py:84 | Epoch[65/239] Step[224] GlobalStep[25574/NA]: det_loss_cls_0[0.4490] det_loss_box_0[0.5759] det_loss_cls_1[0.4338] det_loss_box_1[0.4570] det_loss_cls_2[0.4074] det_loss_box_2[0.4088] det_loss_cls_3[0.3992] det_loss_box_3[0.3869] det_loss_cls_4[0.3695] det_loss_box_4[0.3826] det_loss_cls_5[0.3508] det_loss_box_5[0.3858] det_loss_cls_dn_0[0.0953] det_loss_box_dn_0[0.3203] det_loss_cls_dn_1[0.0906] det_loss_box_dn_1[0.2757] det_loss_cls_dn_2[0.0905] det_loss_box_dn_2[0.2562] det_loss_cls_dn_3[0.0884] det_loss_box_dn_3[0.2534] det_loss_cls_dn_4[0.0870] det_loss_box_dn_4[0.2521] det_loss_cls_dn_5[0.0860] det_loss_box_dn_5[0.2513] loss_depth[0.0386] total_loss[7.1922]
Rank[0/8] 06/19/2025 22:50:41 INFO stats.py:314 | Epoch[65] Step[249] GlobalStep[25599] Training Speed: 7.89 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 22:57:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:50:41 INFO loss_tracker.py:84 | Epoch[65/239] Step[249] GlobalStep[25599/NA]: det_loss_cls_0[0.5499] det_loss_box_0[0.7648] det_loss_cls_1[0.5193] det_loss_box_1[0.6428] det_loss_cls_2[0.4932] det_loss_box_2[0.5802] det_loss_cls_3[0.4774] det_loss_box_3[0.5559] det_loss_cls_4[0.4596] det_loss_box_4[0.5395] det_loss_cls_5[0.4528] det_loss_box_5[0.5390] det_loss_cls_dn_0[0.1010] det_loss_box_dn_0[0.3448] det_loss_cls_dn_1[0.0972] det_loss_box_dn_1[0.2942] det_loss_cls_dn_2[0.0977] det_loss_box_dn_2[0.2774] det_loss_cls_dn_3[0.0968] det_loss_box_dn_3[0.2719] det_loss_cls_dn_4[0.0958] det_loss_box_dn_4[0.2710] det_loss_cls_dn_5[0.0952] det_loss_box_dn_5[0.2692] loss_depth[0.0431] total_loss[8.9296]
Rank[0/8] 06/19/2025 22:51:07 INFO stats.py:314 | Epoch[65] Step[274] GlobalStep[25624] Training Speed: 8.12 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 22:56:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:51:07 INFO loss_tracker.py:84 | Epoch[65/239] Step[274] GlobalStep[25624/NA]: det_loss_cls_0[0.5265] det_loss_box_0[0.7398] det_loss_cls_1[0.5093] det_loss_box_1[0.5789] det_loss_cls_2[0.4836] det_loss_box_2[0.5235] det_loss_cls_3[0.4587] det_loss_box_3[0.5061] det_loss_cls_4[0.4350] det_loss_box_4[0.5005] det_loss_cls_5[0.4273] det_loss_box_5[0.4952] det_loss_cls_dn_0[0.1011] det_loss_box_dn_0[0.2960] det_loss_cls_dn_1[0.0977] det_loss_box_dn_1[0.2522] det_loss_cls_dn_2[0.0968] det_loss_box_dn_2[0.2364] det_loss_cls_dn_3[0.0959] det_loss_box_dn_3[0.2334] det_loss_cls_dn_4[0.0945] det_loss_box_dn_4[0.2319] det_loss_cls_dn_5[0.0939] det_loss_box_dn_5[0.2301] loss_depth[0.0440] total_loss[8.2884]
Rank[0/8] 06/19/2025 22:51:33 INFO stats.py:314 | Epoch[65] Step[299] GlobalStep[25649] Training Speed: 8.15 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 22:55:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:51:33 INFO loss_tracker.py:84 | Epoch[65/239] Step[299] GlobalStep[25649/NA]: det_loss_cls_0[0.4914] det_loss_box_0[0.6535] det_loss_cls_1[0.4782] det_loss_box_1[0.5106] det_loss_cls_2[0.4605] det_loss_box_2[0.4533] det_loss_cls_3[0.4284] det_loss_box_3[0.4388] det_loss_cls_4[0.3966] det_loss_box_4[0.4212] det_loss_cls_5[0.3895] det_loss_box_5[0.4213] det_loss_cls_dn_0[0.1014] det_loss_box_dn_0[0.3327] det_loss_cls_dn_1[0.0979] det_loss_box_dn_1[0.2752] det_loss_cls_dn_2[0.0974] det_loss_box_dn_2[0.2571] det_loss_cls_dn_3[0.0969] det_loss_box_dn_3[0.2531] det_loss_cls_dn_4[0.0958] det_loss_box_dn_4[0.2514] det_loss_cls_dn_5[0.0951] det_loss_box_dn_5[0.2497] loss_depth[0.0396] total_loss[7.7865]
Rank[0/8] 06/19/2025 22:51:59 INFO stats.py:314 | Epoch[65] Step[324] GlobalStep[25674] Training Speed: 7.84 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 22:54:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:51:59 INFO loss_tracker.py:84 | Epoch[65/239] Step[324] GlobalStep[25674/NA]: det_loss_cls_0[0.5106] det_loss_box_0[0.6992] det_loss_cls_1[0.4933] det_loss_box_1[0.5384] det_loss_cls_2[0.4667] det_loss_box_2[0.5016] det_loss_cls_3[0.4546] det_loss_box_3[0.4751] det_loss_cls_4[0.4275] det_loss_box_4[0.4666] det_loss_cls_5[0.4163] det_loss_box_5[0.4686] det_loss_cls_dn_0[0.1078] det_loss_box_dn_0[0.3118] det_loss_cls_dn_1[0.1049] det_loss_box_dn_1[0.2718] det_loss_cls_dn_2[0.1042] det_loss_box_dn_2[0.2567] det_loss_cls_dn_3[0.1029] det_loss_box_dn_3[0.2548] det_loss_cls_dn_4[0.1025] det_loss_box_dn_4[0.2538] det_loss_cls_dn_5[0.1015] det_loss_box_dn_5[0.2528] loss_depth[0.0400] total_loss[8.1840]
Rank[0/8] 06/19/2025 22:52:25 INFO stats.py:314 | Epoch[65] Step[349] GlobalStep[25699] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 22:54:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:52:25 INFO loss_tracker.py:84 | Epoch[65/239] Step[349] GlobalStep[25699/NA]: det_loss_cls_0[0.6814] det_loss_box_0[0.8322] det_loss_cls_1[0.6789] det_loss_box_1[0.6694] det_loss_cls_2[0.6491] det_loss_box_2[0.6051] det_loss_cls_3[0.6167] det_loss_box_3[0.5884] det_loss_cls_4[0.5840] det_loss_box_4[0.5738] det_loss_cls_5[0.5686] det_loss_box_5[0.5743] det_loss_cls_dn_0[0.1063] det_loss_box_dn_0[0.3088] det_loss_cls_dn_1[0.1018] det_loss_box_dn_1[0.2625] det_loss_cls_dn_2[0.1009] det_loss_box_dn_2[0.2453] det_loss_cls_dn_3[0.1007] det_loss_box_dn_3[0.2427] det_loss_cls_dn_4[0.0997] det_loss_box_dn_4[0.2433] det_loss_cls_dn_5[0.0986] det_loss_box_dn_5[0.2424] loss_depth[0.0377] total_loss[9.8125]
Rank[0/8] 06/19/2025 22:52:52 INFO stats.py:314 | Epoch[65] Step[374] GlobalStep[25724] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 22:53:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:52:52 INFO loss_tracker.py:84 | Epoch[65/239] Step[374] GlobalStep[25724/NA]: det_loss_cls_0[0.4383] det_loss_box_0[0.5899] det_loss_cls_1[0.4310] det_loss_box_1[0.4650] det_loss_cls_2[0.4189] det_loss_box_2[0.4058] det_loss_cls_3[0.4029] det_loss_box_3[0.3917] det_loss_cls_4[0.3699] det_loss_box_4[0.3986] det_loss_cls_5[0.3653] det_loss_box_5[0.3935] det_loss_cls_dn_0[0.0955] det_loss_box_dn_0[0.3374] det_loss_cls_dn_1[0.0911] det_loss_box_dn_1[0.2897] det_loss_cls_dn_2[0.0905] det_loss_box_dn_2[0.2688] det_loss_cls_dn_3[0.0899] det_loss_box_dn_3[0.2651] det_loss_cls_dn_4[0.0889] det_loss_box_dn_4[0.2638] det_loss_cls_dn_5[0.0878] det_loss_box_dn_5[0.2616] loss_depth[0.0374] total_loss[7.3383]
Rank[0/8] 06/19/2025 22:53:07 INFO stats.py:394 | Epoch[65] completed. Training Speed: 7.62 samples/sec across all devices.	Epoch Time: 409.26 sec.	Average Epoch Time: 409.26 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 22:53:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:53:20 INFO stats.py:314 | Epoch[66] Step[9] GlobalStep[25749] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 22:53:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:53:35 INFO loss_tracker.py:84 | Epoch[66/239] Step[24] GlobalStep[25764/NA]: det_loss_cls_0[0.5340] det_loss_box_0[0.7129] det_loss_cls_1[0.5196] det_loss_box_1[0.5605] det_loss_cls_2[0.4963] det_loss_box_2[0.4962] det_loss_cls_3[0.4682] det_loss_box_3[0.4857] det_loss_cls_4[0.4335] det_loss_box_4[0.4773] det_loss_cls_5[0.4251] det_loss_box_5[0.4753] det_loss_cls_dn_0[0.0959] det_loss_box_dn_0[0.2942] det_loss_cls_dn_1[0.0926] det_loss_box_dn_1[0.2571] det_loss_cls_dn_2[0.0913] det_loss_box_dn_2[0.2410] det_loss_cls_dn_3[0.0903] det_loss_box_dn_3[0.2377] det_loss_cls_dn_4[0.0893] det_loss_box_dn_4[0.2355] det_loss_cls_dn_5[0.0885] det_loss_box_dn_5[0.2347] loss_depth[0.0426] total_loss[8.1755]
Rank[0/8] 06/19/2025 22:53:45 INFO stats.py:314 | Epoch[66] Step[34] GlobalStep[25774] Training Speed: 9.14 samples/sec across all devices.	Average Step Time: 0.88 sec.	Estimated Remaining Time: 22:52:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:54:02 INFO loss_tracker.py:84 | Epoch[66/239] Step[49] GlobalStep[25789/NA]: det_loss_cls_0[0.4451] det_loss_box_0[0.6250] det_loss_cls_1[0.4373] det_loss_box_1[0.5052] det_loss_cls_2[0.4083] det_loss_box_2[0.4628] det_loss_cls_3[0.4004] det_loss_box_3[0.4376] det_loss_cls_4[0.3804] det_loss_box_4[0.4187] det_loss_cls_5[0.3605] det_loss_box_5[0.4242] det_loss_cls_dn_0[0.1105] det_loss_box_dn_0[0.3312] det_loss_cls_dn_1[0.1061] det_loss_box_dn_1[0.2866] det_loss_cls_dn_2[0.1060] det_loss_box_dn_2[0.2684] det_loss_cls_dn_3[0.1046] det_loss_box_dn_3[0.2648] det_loss_cls_dn_4[0.1039] det_loss_box_dn_4[0.2632] det_loss_cls_dn_5[0.1029] det_loss_box_dn_5[0.2621] loss_depth[0.0372] total_loss[7.6529]
Rank[0/8] 06/19/2025 22:54:12 INFO stats.py:314 | Epoch[66] Step[59] GlobalStep[25799] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 22:51:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:54:27 INFO loss_tracker.py:84 | Epoch[66/239] Step[74] GlobalStep[25814/NA]: det_loss_cls_0[0.4091] det_loss_box_0[0.5346] det_loss_cls_1[0.3849] det_loss_box_1[0.4305] det_loss_cls_2[0.3731] det_loss_box_2[0.3788] det_loss_cls_3[0.3468] det_loss_box_3[0.3671] det_loss_cls_4[0.3324] det_loss_box_4[0.3610] det_loss_cls_5[0.3128] det_loss_box_5[0.3675] det_loss_cls_dn_0[0.1016] det_loss_box_dn_0[0.3497] det_loss_cls_dn_1[0.0967] det_loss_box_dn_1[0.2993] det_loss_cls_dn_2[0.0959] det_loss_box_dn_2[0.2809] det_loss_cls_dn_3[0.0947] det_loss_box_dn_3[0.2776] det_loss_cls_dn_4[0.0942] det_loss_box_dn_4[0.2762] det_loss_cls_dn_5[0.0930] det_loss_box_dn_5[0.2734] loss_depth[0.0421] total_loss[6.9741]
Rank[0/8] 06/19/2025 22:54:38 INFO stats.py:314 | Epoch[66] Step[84] GlobalStep[25824] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 22:50:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:54:53 INFO loss_tracker.py:84 | Epoch[66/239] Step[99] GlobalStep[25839/NA]: det_loss_cls_0[0.5189] det_loss_box_0[0.6547] det_loss_cls_1[0.5124] det_loss_box_1[0.5117] det_loss_cls_2[0.4706] det_loss_box_2[0.4731] det_loss_cls_3[0.4455] det_loss_box_3[0.4536] det_loss_cls_4[0.4185] det_loss_box_4[0.4332] det_loss_cls_5[0.3971] det_loss_box_5[0.4405] det_loss_cls_dn_0[0.0989] det_loss_box_dn_0[0.2821] det_loss_cls_dn_1[0.0952] det_loss_box_dn_1[0.2459] det_loss_cls_dn_2[0.0944] det_loss_box_dn_2[0.2309] det_loss_cls_dn_3[0.0920] det_loss_box_dn_3[0.2282] det_loss_cls_dn_4[0.0915] det_loss_box_dn_4[0.2277] det_loss_cls_dn_5[0.0906] det_loss_box_dn_5[0.2262] loss_depth[0.0388] total_loss[7.7720]
Rank[0/8] 06/19/2025 22:55:03 INFO stats.py:314 | Epoch[66] Step[109] GlobalStep[25849] Training Speed: 7.91 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 22:50:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:55:19 INFO loss_tracker.py:84 | Epoch[66/239] Step[124] GlobalStep[25864/NA]: det_loss_cls_0[0.5249] det_loss_box_0[0.7208] det_loss_cls_1[0.4970] det_loss_box_1[0.5800] det_loss_cls_2[0.4700] det_loss_box_2[0.5103] det_loss_cls_3[0.4480] det_loss_box_3[0.4869] det_loss_cls_4[0.4114] det_loss_box_4[0.4845] det_loss_cls_5[0.3992] det_loss_box_5[0.4782] det_loss_cls_dn_0[0.1012] det_loss_box_dn_0[0.3310] det_loss_cls_dn_1[0.0978] det_loss_box_dn_1[0.2917] det_loss_cls_dn_2[0.0970] det_loss_box_dn_2[0.2773] det_loss_cls_dn_3[0.0958] det_loss_box_dn_3[0.2743] det_loss_cls_dn_4[0.0950] det_loss_box_dn_4[0.2726] det_loss_cls_dn_5[0.0934] det_loss_box_dn_5[0.2705] loss_depth[0.0417] total_loss[8.3506]
Rank[0/8] 06/19/2025 22:55:30 INFO stats.py:314 | Epoch[66] Step[134] GlobalStep[25874] Training Speed: 7.86 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 22:49:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:55:45 INFO loss_tracker.py:84 | Epoch[66/239] Step[149] GlobalStep[25889/NA]: det_loss_cls_0[0.5454] det_loss_box_0[0.6828] det_loss_cls_1[0.5121] det_loss_box_1[0.5521] det_loss_cls_2[0.4936] det_loss_box_2[0.4878] det_loss_cls_3[0.4608] det_loss_box_3[0.4821] det_loss_cls_4[0.4459] det_loss_box_4[0.4595] det_loss_cls_5[0.4301] det_loss_box_5[0.4605] det_loss_cls_dn_0[0.0983] det_loss_box_dn_0[0.3153] det_loss_cls_dn_1[0.0951] det_loss_box_dn_1[0.2726] det_loss_cls_dn_2[0.0943] det_loss_box_dn_2[0.2561] det_loss_cls_dn_3[0.0936] det_loss_box_dn_3[0.2533] det_loss_cls_dn_4[0.0928] det_loss_box_dn_4[0.2517] det_loss_cls_dn_5[0.0920] det_loss_box_dn_5[0.2425] loss_depth[0.0403] total_loss[8.2106]
Rank[0/8] 06/19/2025 22:55:55 INFO stats.py:314 | Epoch[66] Step[159] GlobalStep[25899] Training Speed: 7.95 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 22:48:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:56:11 INFO loss_tracker.py:84 | Epoch[66/239] Step[174] GlobalStep[25914/NA]: det_loss_cls_0[0.4394] det_loss_box_0[0.6253] det_loss_cls_1[0.4196] det_loss_box_1[0.5147] det_loss_cls_2[0.4054] det_loss_box_2[0.4559] det_loss_cls_3[0.3837] det_loss_box_3[0.4502] det_loss_cls_4[0.3584] det_loss_box_4[0.4457] det_loss_cls_5[0.3490] det_loss_box_5[0.4398] det_loss_cls_dn_0[0.1053] det_loss_box_dn_0[0.4230] det_loss_cls_dn_1[0.1022] det_loss_box_dn_1[0.3609] det_loss_cls_dn_2[0.1006] det_loss_box_dn_2[0.3429] det_loss_cls_dn_3[0.0995] det_loss_box_dn_3[0.3392] det_loss_cls_dn_4[0.0992] det_loss_box_dn_4[0.3389] det_loss_cls_dn_5[0.0978] det_loss_box_dn_5[0.3365] loss_depth[0.0425] total_loss[8.0756]
Rank[0/8] 06/19/2025 22:56:21 INFO stats.py:314 | Epoch[66] Step[184] GlobalStep[25924] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 22:48:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:56:36 INFO loss_tracker.py:84 | Epoch[66/239] Step[199] GlobalStep[25939/NA]: det_loss_cls_0[0.4793] det_loss_box_0[0.7010] det_loss_cls_1[0.4616] det_loss_box_1[0.5596] det_loss_cls_2[0.4340] det_loss_box_2[0.5021] det_loss_cls_3[0.4157] det_loss_box_3[0.4935] det_loss_cls_4[0.3925] det_loss_box_4[0.4814] det_loss_cls_5[0.3796] det_loss_box_5[0.4782] det_loss_cls_dn_0[0.0980] det_loss_box_dn_0[0.3344] det_loss_cls_dn_1[0.0946] det_loss_box_dn_1[0.2907] det_loss_cls_dn_2[0.0942] det_loss_box_dn_2[0.2736] det_loss_cls_dn_3[0.0933] det_loss_box_dn_3[0.2704] det_loss_cls_dn_4[0.0923] det_loss_box_dn_4[0.2709] det_loss_cls_dn_5[0.0908] det_loss_box_dn_5[0.2697] loss_depth[0.0450] total_loss[8.0964]
Rank[0/8] 06/19/2025 22:56:47 INFO stats.py:314 | Epoch[66] Step[209] GlobalStep[25949] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 22:47:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:57:02 INFO loss_tracker.py:84 | Epoch[66/239] Step[224] GlobalStep[25964/NA]: det_loss_cls_0[0.5981] det_loss_box_0[0.7733] det_loss_cls_1[0.5850] det_loss_box_1[0.6237] det_loss_cls_2[0.5707] det_loss_box_2[0.5503] det_loss_cls_3[0.5360] det_loss_box_3[0.5318] det_loss_cls_4[0.4910] det_loss_box_4[0.5359] det_loss_cls_5[0.4789] det_loss_box_5[0.5389] det_loss_cls_dn_0[0.1097] det_loss_box_dn_0[0.3114] det_loss_cls_dn_1[0.1039] det_loss_box_dn_1[0.2735] det_loss_cls_dn_2[0.1036] det_loss_box_dn_2[0.2589] det_loss_cls_dn_3[0.1029] det_loss_box_dn_3[0.2563] det_loss_cls_dn_4[0.1015] det_loss_box_dn_4[0.2557] det_loss_cls_dn_5[0.1005] det_loss_box_dn_5[0.2536] loss_depth[0.0387] total_loss[9.0835]
Rank[0/8] 06/19/2025 22:57:12 INFO stats.py:314 | Epoch[66] Step[234] GlobalStep[25974] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 22:46:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:57:27 INFO loss_tracker.py:84 | Epoch[66/239] Step[249] GlobalStep[25989/NA]: det_loss_cls_0[0.4459] det_loss_box_0[0.6427] det_loss_cls_1[0.4290] det_loss_box_1[0.5160] det_loss_cls_2[0.4003] det_loss_box_2[0.4624] det_loss_cls_3[0.3842] det_loss_box_3[0.4405] det_loss_cls_4[0.3644] det_loss_box_4[0.4334] det_loss_cls_5[0.3562] det_loss_box_5[0.4297] det_loss_cls_dn_0[0.1027] det_loss_box_dn_0[0.4055] det_loss_cls_dn_1[0.0997] det_loss_box_dn_1[0.3454] det_loss_cls_dn_2[0.0985] det_loss_box_dn_2[0.3260] det_loss_cls_dn_3[0.0975] det_loss_box_dn_3[0.3230] det_loss_cls_dn_4[0.0963] det_loss_box_dn_4[0.3216] det_loss_cls_dn_5[0.0954] det_loss_box_dn_5[0.3191] loss_depth[0.0409] total_loss[7.9764]
Rank[0/8] 06/19/2025 22:57:37 INFO stats.py:314 | Epoch[66] Step[259] GlobalStep[25999] Training Speed: 7.97 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 22:45:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:57:53 INFO loss_tracker.py:84 | Epoch[66/239] Step[274] GlobalStep[26014/NA]: det_loss_cls_0[0.6015] det_loss_box_0[0.8939] det_loss_cls_1[0.5817] det_loss_box_1[0.7195] det_loss_cls_2[0.5730] det_loss_box_2[0.6328] det_loss_cls_3[0.5425] det_loss_box_3[0.6104] det_loss_cls_4[0.5041] det_loss_box_4[0.6032] det_loss_cls_5[0.4956] det_loss_box_5[0.6031] det_loss_cls_dn_0[0.1022] det_loss_box_dn_0[0.3663] det_loss_cls_dn_1[0.0987] det_loss_box_dn_1[0.3219] det_loss_cls_dn_2[0.0989] det_loss_box_dn_2[0.3024] det_loss_cls_dn_3[0.0977] det_loss_box_dn_3[0.2983] det_loss_cls_dn_4[0.0967] det_loss_box_dn_4[0.2971] det_loss_cls_dn_5[0.0961] det_loss_box_dn_5[0.2965] loss_depth[0.0405] total_loss[9.8747]
Rank[0/8] 06/19/2025 22:58:03 INFO stats.py:314 | Epoch[66] Step[284] GlobalStep[26024] Training Speed: 6.41 samples/sec across all devices.	Average Step Time: 1.25 sec.	Estimated Remaining Time: 22:45:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:58:19 INFO loss_tracker.py:84 | Epoch[66/239] Step[299] GlobalStep[26039/NA]: det_loss_cls_0[0.6068] det_loss_box_0[0.8038] det_loss_cls_1[0.5779] det_loss_box_1[0.6329] det_loss_cls_2[0.5588] det_loss_box_2[0.5724] det_loss_cls_3[0.5323] det_loss_box_3[0.5555] det_loss_cls_4[0.4909] det_loss_box_4[0.5602] det_loss_cls_5[0.4790] det_loss_box_5[0.5599] det_loss_cls_dn_0[0.1093] det_loss_box_dn_0[0.2861] det_loss_cls_dn_1[0.1048] det_loss_box_dn_1[0.2488] det_loss_cls_dn_2[0.1047] det_loss_box_dn_2[0.2341] det_loss_cls_dn_3[0.1049] det_loss_box_dn_3[0.2316] det_loss_cls_dn_4[0.1033] det_loss_box_dn_4[0.2305] det_loss_cls_dn_5[0.1030] det_loss_box_dn_5[0.2301] loss_depth[0.0370] total_loss[9.0585]
Rank[0/8] 06/19/2025 22:58:29 INFO stats.py:314 | Epoch[66] Step[309] GlobalStep[26049] Training Speed: 7.93 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 22:44:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:58:45 INFO loss_tracker.py:84 | Epoch[66/239] Step[324] GlobalStep[26064/NA]: det_loss_cls_0[0.4347] det_loss_box_0[0.5896] det_loss_cls_1[0.4183] det_loss_box_1[0.4443] det_loss_cls_2[0.3964] det_loss_box_2[0.4048] det_loss_cls_3[0.3797] det_loss_box_3[0.3759] det_loss_cls_4[0.3486] det_loss_box_4[0.3695] det_loss_cls_5[0.3440] det_loss_box_5[0.3672] det_loss_cls_dn_0[0.0925] det_loss_box_dn_0[0.3030] det_loss_cls_dn_1[0.0901] det_loss_box_dn_1[0.2521] det_loss_cls_dn_2[0.0892] det_loss_box_dn_2[0.2342] det_loss_cls_dn_3[0.0880] det_loss_box_dn_3[0.2303] det_loss_cls_dn_4[0.0871] det_loss_box_dn_4[0.2276] det_loss_cls_dn_5[0.0856] det_loss_box_dn_5[0.2263] loss_depth[0.0404] total_loss[6.9193]
Rank[0/8] 06/19/2025 22:58:55 INFO stats.py:314 | Epoch[66] Step[334] GlobalStep[26074] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 22:43:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:59:11 INFO loss_tracker.py:84 | Epoch[66/239] Step[349] GlobalStep[26089/NA]: det_loss_cls_0[0.5833] det_loss_box_0[0.7882] det_loss_cls_1[0.5698] det_loss_box_1[0.6317] det_loss_cls_2[0.5507] det_loss_box_2[0.5644] det_loss_cls_3[0.5229] det_loss_box_3[0.5391] det_loss_cls_4[0.4871] det_loss_box_4[0.5352] det_loss_cls_5[0.4715] det_loss_box_5[0.5368] det_loss_cls_dn_0[0.1002] det_loss_box_dn_0[0.2674] det_loss_cls_dn_1[0.0955] det_loss_box_dn_1[0.2288] det_loss_cls_dn_2[0.0940] det_loss_box_dn_2[0.2122] det_loss_cls_dn_3[0.0930] det_loss_box_dn_3[0.2096] det_loss_cls_dn_4[0.0925] det_loss_box_dn_4[0.2080] det_loss_cls_dn_5[0.0921] det_loss_box_dn_5[0.2063] loss_depth[0.0397] total_loss[8.7199]
Rank[0/8] 06/19/2025 22:59:21 INFO stats.py:314 | Epoch[66] Step[359] GlobalStep[26099] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 22:43:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:59:36 INFO loss_tracker.py:84 | Epoch[66/239] Step[374] GlobalStep[26114/NA]: det_loss_cls_0[0.3822] det_loss_box_0[0.5143] det_loss_cls_1[0.3773] det_loss_box_1[0.4249] det_loss_cls_2[0.3652] det_loss_box_2[0.3873] det_loss_cls_3[0.3391] det_loss_box_3[0.3764] det_loss_cls_4[0.3221] det_loss_box_4[0.3742] det_loss_cls_5[0.3119] det_loss_box_5[0.3741] det_loss_cls_dn_0[0.0948] det_loss_box_dn_0[0.3640] det_loss_cls_dn_1[0.0907] det_loss_box_dn_1[0.3178] det_loss_cls_dn_2[0.0893] det_loss_box_dn_2[0.2995] det_loss_cls_dn_3[0.0883] det_loss_box_dn_3[0.2973] det_loss_cls_dn_4[0.0880] det_loss_box_dn_4[0.2979] det_loss_cls_dn_5[0.0870] det_loss_box_dn_5[0.2985] loss_depth[0.0376] total_loss[7.0000]
Rank[0/8] 06/19/2025 22:59:46 INFO stats.py:314 | Epoch[66] Step[384] GlobalStep[26124] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 22:42:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 22:59:51 INFO stats.py:394 | Epoch[66] completed. Training Speed: 7.71 samples/sec across all devices.	Epoch Time: 404.53 sec.	Average Epoch Time: 404.53 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 22:42:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:00:15 INFO stats.py:314 | Epoch[67] Step[19] GlobalStep[26149] Training Speed: 6.43 samples/sec across all devices.	Average Step Time: 1.24 sec.	Estimated Remaining Time: 22:41:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:00:20 INFO loss_tracker.py:84 | Epoch[67/239] Step[24] GlobalStep[26154/NA]: det_loss_cls_0[0.5397] det_loss_box_0[0.6769] det_loss_cls_1[0.5308] det_loss_box_1[0.5395] det_loss_cls_2[0.5047] det_loss_box_2[0.4813] det_loss_cls_3[0.4809] det_loss_box_3[0.4673] det_loss_cls_4[0.4503] det_loss_box_4[0.4624] det_loss_cls_5[0.4345] det_loss_box_5[0.4690] det_loss_cls_dn_0[0.1063] det_loss_box_dn_0[0.2799] det_loss_cls_dn_1[0.1023] det_loss_box_dn_1[0.2420] det_loss_cls_dn_2[0.1019] det_loss_box_dn_2[0.2285] det_loss_cls_dn_3[0.1006] det_loss_box_dn_3[0.2260] det_loss_cls_dn_4[0.0994] det_loss_box_dn_4[0.2247] det_loss_cls_dn_5[0.0983] det_loss_box_dn_5[0.2238] loss_depth[0.0383] total_loss[8.1090]
Rank[0/8] 06/19/2025 23:00:40 INFO stats.py:314 | Epoch[67] Step[44] GlobalStep[26174] Training Speed: 7.92 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 22:41:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:00:46 INFO loss_tracker.py:84 | Epoch[67/239] Step[49] GlobalStep[26179/NA]: det_loss_cls_0[0.5886] det_loss_box_0[0.8169] det_loss_cls_1[0.5689] det_loss_box_1[0.6492] det_loss_cls_2[0.5322] det_loss_box_2[0.5885] det_loss_cls_3[0.5106] det_loss_box_3[0.5637] det_loss_cls_4[0.4670] det_loss_box_4[0.5519] det_loss_cls_5[0.4573] det_loss_box_5[0.5476] det_loss_cls_dn_0[0.1014] det_loss_box_dn_0[0.3325] det_loss_cls_dn_1[0.0977] det_loss_box_dn_1[0.2874] det_loss_cls_dn_2[0.0971] det_loss_box_dn_2[0.2719] det_loss_cls_dn_3[0.0958] det_loss_box_dn_3[0.2672] det_loss_cls_dn_4[0.0946] det_loss_box_dn_4[0.2650] det_loss_cls_dn_5[0.0938] det_loss_box_dn_5[0.2632] loss_depth[0.0430] total_loss[9.1530]
Rank[0/8] 06/19/2025 23:01:06 INFO stats.py:314 | Epoch[67] Step[69] GlobalStep[26199] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 22:40:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:01:12 INFO loss_tracker.py:84 | Epoch[67/239] Step[74] GlobalStep[26204/NA]: det_loss_cls_0[0.4626] det_loss_box_0[0.5692] det_loss_cls_1[0.4564] det_loss_box_1[0.4552] det_loss_cls_2[0.4406] det_loss_box_2[0.4321] det_loss_cls_3[0.4240] det_loss_box_3[0.4167] det_loss_cls_4[0.3950] det_loss_box_4[0.4070] det_loss_cls_5[0.3840] det_loss_box_5[0.4056] det_loss_cls_dn_0[0.1027] det_loss_box_dn_0[0.3166] det_loss_cls_dn_1[0.1001] det_loss_box_dn_1[0.2686] det_loss_cls_dn_2[0.0994] det_loss_box_dn_2[0.2535] det_loss_cls_dn_3[0.0986] det_loss_box_dn_3[0.2498] det_loss_cls_dn_4[0.0975] det_loss_box_dn_4[0.2478] det_loss_cls_dn_5[0.0973] det_loss_box_dn_5[0.2474] loss_depth[0.0374] total_loss[7.4651]
Rank[0/8] 06/19/2025 23:01:33 INFO stats.py:314 | Epoch[67] Step[94] GlobalStep[26224] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 22:39:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:01:38 INFO loss_tracker.py:84 | Epoch[67/239] Step[99] GlobalStep[26229/NA]: det_loss_cls_0[0.5426] det_loss_box_0[0.7341] det_loss_cls_1[0.5261] det_loss_box_1[0.5572] det_loss_cls_2[0.4952] det_loss_box_2[0.5101] det_loss_cls_3[0.4632] det_loss_box_3[0.4932] det_loss_cls_4[0.4381] det_loss_box_4[0.4879] det_loss_cls_5[0.4183] det_loss_box_5[0.4942] det_loss_cls_dn_0[0.1009] det_loss_box_dn_0[0.2808] det_loss_cls_dn_1[0.0980] det_loss_box_dn_1[0.2438] det_loss_cls_dn_2[0.0967] det_loss_box_dn_2[0.2298] det_loss_cls_dn_3[0.0956] det_loss_box_dn_3[0.2274] det_loss_cls_dn_4[0.0946] det_loss_box_dn_4[0.2274] det_loss_cls_dn_5[0.0943] det_loss_box_dn_5[0.2260] loss_depth[0.0387] total_loss[8.2142]
Rank[0/8] 06/19/2025 23:01:58 INFO stats.py:314 | Epoch[67] Step[119] GlobalStep[26249] Training Speed: 7.97 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 22:39:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:02:03 INFO loss_tracker.py:84 | Epoch[67/239] Step[124] GlobalStep[26254/NA]: det_loss_cls_0[0.4468] det_loss_box_0[0.5652] det_loss_cls_1[0.4441] det_loss_box_1[0.4414] det_loss_cls_2[0.4220] det_loss_box_2[0.4073] det_loss_cls_3[0.4045] det_loss_box_3[0.3960] det_loss_cls_4[0.3773] det_loss_box_4[0.3894] det_loss_cls_5[0.3589] det_loss_box_5[0.3934] det_loss_cls_dn_0[0.0969] det_loss_box_dn_0[0.2913] det_loss_cls_dn_1[0.0946] det_loss_box_dn_1[0.2530] det_loss_cls_dn_2[0.0943] det_loss_box_dn_2[0.2408] det_loss_cls_dn_3[0.0918] det_loss_box_dn_3[0.2380] det_loss_cls_dn_4[0.0905] det_loss_box_dn_4[0.2361] det_loss_cls_dn_5[0.0894] det_loss_box_dn_5[0.2347] loss_depth[0.0360] total_loss[7.1336]
Rank[0/8] 06/19/2025 23:02:23 INFO stats.py:314 | Epoch[67] Step[144] GlobalStep[26274] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 22:38:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:02:29 INFO loss_tracker.py:84 | Epoch[67/239] Step[149] GlobalStep[26279/NA]: det_loss_cls_0[0.4560] det_loss_box_0[0.5653] det_loss_cls_1[0.4392] det_loss_box_1[0.4559] det_loss_cls_2[0.4272] det_loss_box_2[0.4082] det_loss_cls_3[0.4130] det_loss_box_3[0.3942] det_loss_cls_4[0.3927] det_loss_box_4[0.3871] det_loss_cls_5[0.3788] det_loss_box_5[0.3947] det_loss_cls_dn_0[0.0911] det_loss_box_dn_0[0.3469] det_loss_cls_dn_1[0.0882] det_loss_box_dn_1[0.3109] det_loss_cls_dn_2[0.0871] det_loss_box_dn_2[0.2958] det_loss_cls_dn_3[0.0859] det_loss_box_dn_3[0.2935] det_loss_cls_dn_4[0.0851] det_loss_box_dn_4[0.2915] det_loss_cls_dn_5[0.0838] det_loss_box_dn_5[0.2888] loss_depth[0.0435] total_loss[7.5043]
Rank[0/8] 06/19/2025 23:02:50 INFO stats.py:314 | Epoch[67] Step[169] GlobalStep[26299] Training Speed: 6.98 samples/sec across all devices.	Average Step Time: 1.15 sec.	Estimated Remaining Time: 22:37:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:02:55 INFO loss_tracker.py:84 | Epoch[67/239] Step[174] GlobalStep[26304/NA]: det_loss_cls_0[0.6691] det_loss_box_0[0.7913] det_loss_cls_1[0.6332] det_loss_box_1[0.6272] det_loss_cls_2[0.6041] det_loss_box_2[0.5718] det_loss_cls_3[0.5740] det_loss_box_3[0.5565] det_loss_cls_4[0.5532] det_loss_box_4[0.5347] det_loss_cls_5[0.5349] det_loss_box_5[0.5407] det_loss_cls_dn_0[0.1031] det_loss_box_dn_0[0.3791] det_loss_cls_dn_1[0.0995] det_loss_box_dn_1[0.3187] det_loss_cls_dn_2[0.0990] det_loss_box_dn_2[0.3023] det_loss_cls_dn_3[0.0975] det_loss_box_dn_3[0.2982] det_loss_cls_dn_4[0.0974] det_loss_box_dn_4[0.2963] det_loss_cls_dn_5[0.0962] det_loss_box_dn_5[0.2913] loss_depth[0.0385] total_loss[9.7077]
Rank[0/8] 06/19/2025 23:03:15 INFO stats.py:314 | Epoch[67] Step[194] GlobalStep[26324] Training Speed: 7.79 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 22:37:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:03:20 INFO loss_tracker.py:84 | Epoch[67/239] Step[199] GlobalStep[26329/NA]: det_loss_cls_0[0.5016] det_loss_box_0[0.6536] det_loss_cls_1[0.4659] det_loss_box_1[0.5255] det_loss_cls_2[0.4497] det_loss_box_2[0.4817] det_loss_cls_3[0.4058] det_loss_box_3[0.4642] det_loss_cls_4[0.3919] det_loss_box_4[0.4438] det_loss_cls_5[0.3766] det_loss_box_5[0.4463] det_loss_cls_dn_0[0.0918] det_loss_box_dn_0[0.3142] det_loss_cls_dn_1[0.0873] det_loss_box_dn_1[0.2684] det_loss_cls_dn_2[0.0864] det_loss_box_dn_2[0.2490] det_loss_cls_dn_3[0.0854] det_loss_box_dn_3[0.2461] det_loss_cls_dn_4[0.0851] det_loss_box_dn_4[0.2444] det_loss_cls_dn_5[0.0839] det_loss_box_dn_5[0.2423] loss_depth[0.0386] total_loss[7.7295]
Rank[0/8] 06/19/2025 23:03:41 INFO stats.py:314 | Epoch[67] Step[219] GlobalStep[26349] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 22:36:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:03:46 INFO loss_tracker.py:84 | Epoch[67/239] Step[224] GlobalStep[26354/NA]: det_loss_cls_0[0.5012] det_loss_box_0[0.6609] det_loss_cls_1[0.4880] det_loss_box_1[0.5132] det_loss_cls_2[0.4569] det_loss_box_2[0.4634] det_loss_cls_3[0.4437] det_loss_box_3[0.4396] det_loss_cls_4[0.4047] det_loss_box_4[0.4313] det_loss_cls_5[0.3956] det_loss_box_5[0.4317] det_loss_cls_dn_0[0.0954] det_loss_box_dn_0[0.2807] det_loss_cls_dn_1[0.0926] det_loss_box_dn_1[0.2423] det_loss_cls_dn_2[0.0913] det_loss_box_dn_2[0.2276] det_loss_cls_dn_3[0.0908] det_loss_box_dn_3[0.2248] det_loss_cls_dn_4[0.0901] det_loss_box_dn_4[0.2235] det_loss_cls_dn_5[0.0892] det_loss_box_dn_5[0.2219] loss_depth[0.0392] total_loss[7.6396]
Rank[0/8] 06/19/2025 23:04:07 INFO stats.py:314 | Epoch[67] Step[244] GlobalStep[26374] Training Speed: 8.63 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 22:35:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:04:12 INFO loss_tracker.py:84 | Epoch[67/239] Step[249] GlobalStep[26379/NA]: det_loss_cls_0[0.5432] det_loss_box_0[0.7277] det_loss_cls_1[0.5239] det_loss_box_1[0.5909] det_loss_cls_2[0.5076] det_loss_box_2[0.5402] det_loss_cls_3[0.4802] det_loss_box_3[0.5170] det_loss_cls_4[0.4486] det_loss_box_4[0.5031] det_loss_cls_5[0.4362] det_loss_box_5[0.5062] det_loss_cls_dn_0[0.0938] det_loss_box_dn_0[0.3920] det_loss_cls_dn_1[0.0898] det_loss_box_dn_1[0.3423] det_loss_cls_dn_2[0.0890] det_loss_box_dn_2[0.3230] det_loss_cls_dn_3[0.0878] det_loss_box_dn_3[0.3178] det_loss_cls_dn_4[0.0873] det_loss_box_dn_4[0.3162] det_loss_cls_dn_5[0.0865] det_loss_box_dn_5[0.3099] loss_depth[0.0399] total_loss[8.8999]
Rank[0/8] 06/19/2025 23:04:33 INFO stats.py:314 | Epoch[67] Step[269] GlobalStep[26399] Training Speed: 7.71 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 22:34:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:04:38 INFO loss_tracker.py:84 | Epoch[67/239] Step[274] GlobalStep[26404/NA]: det_loss_cls_0[0.6009] det_loss_box_0[0.7689] det_loss_cls_1[0.5944] det_loss_box_1[0.6232] det_loss_cls_2[0.5715] det_loss_box_2[0.5706] det_loss_cls_3[0.5475] det_loss_box_3[0.5567] det_loss_cls_4[0.5226] det_loss_box_4[0.5535] det_loss_cls_5[0.5038] det_loss_box_5[0.5508] det_loss_cls_dn_0[0.1067] det_loss_box_dn_0[0.2776] det_loss_cls_dn_1[0.1021] det_loss_box_dn_1[0.2413] det_loss_cls_dn_2[0.1023] det_loss_box_dn_2[0.2250] det_loss_cls_dn_3[0.1011] det_loss_box_dn_3[0.2218] det_loss_cls_dn_4[0.0996] det_loss_box_dn_4[0.2213] det_loss_cls_dn_5[0.0986] det_loss_box_dn_5[0.2198] loss_depth[0.0400] total_loss[9.0214]
Rank[0/8] 06/19/2025 23:04:58 INFO stats.py:314 | Epoch[67] Step[294] GlobalStep[26424] Training Speed: 8.00 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 22:34:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:05:03 INFO loss_tracker.py:84 | Epoch[67/239] Step[299] GlobalStep[26429/NA]: det_loss_cls_0[0.5368] det_loss_box_0[0.7530] det_loss_cls_1[0.5244] det_loss_box_1[0.6163] det_loss_cls_2[0.4953] det_loss_box_2[0.5828] det_loss_cls_3[0.4677] det_loss_box_3[0.5596] det_loss_cls_4[0.4356] det_loss_box_4[0.5533] det_loss_cls_5[0.4275] det_loss_box_5[0.5520] det_loss_cls_dn_0[0.1023] det_loss_box_dn_0[0.3076] det_loss_cls_dn_1[0.0971] det_loss_box_dn_1[0.2703] det_loss_cls_dn_2[0.0958] det_loss_box_dn_2[0.2516] det_loss_cls_dn_3[0.0951] det_loss_box_dn_3[0.2476] det_loss_cls_dn_4[0.0942] det_loss_box_dn_4[0.2472] det_loss_cls_dn_5[0.0932] det_loss_box_dn_5[0.2461] loss_depth[0.0363] total_loss[8.6887]
Rank[0/8] 06/19/2025 23:05:25 INFO stats.py:314 | Epoch[67] Step[319] GlobalStep[26449] Training Speed: 7.83 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 22:33:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:05:30 INFO loss_tracker.py:84 | Epoch[67/239] Step[324] GlobalStep[26454/NA]: det_loss_cls_0[0.5013] det_loss_box_0[0.6278] det_loss_cls_1[0.4945] det_loss_box_1[0.4986] det_loss_cls_2[0.4520] det_loss_box_2[0.4490] det_loss_cls_3[0.4318] det_loss_box_3[0.4339] det_loss_cls_4[0.4015] det_loss_box_4[0.4200] det_loss_cls_5[0.3898] det_loss_box_5[0.4149] det_loss_cls_dn_0[0.1017] det_loss_box_dn_0[0.3035] det_loss_cls_dn_1[0.0969] det_loss_box_dn_1[0.2582] det_loss_cls_dn_2[0.0955] det_loss_box_dn_2[0.2420] det_loss_cls_dn_3[0.0946] det_loss_box_dn_3[0.2374] det_loss_cls_dn_4[0.0936] det_loss_box_dn_4[0.2368] det_loss_cls_dn_5[0.0931] det_loss_box_dn_5[0.2332] loss_depth[0.0390] total_loss[7.6405]
Rank[0/8] 06/19/2025 23:05:50 INFO stats.py:314 | Epoch[67] Step[344] GlobalStep[26474] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 22:32:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:05:56 INFO loss_tracker.py:84 | Epoch[67/239] Step[349] GlobalStep[26479/NA]: det_loss_cls_0[0.4176] det_loss_box_0[0.5879] det_loss_cls_1[0.4161] det_loss_box_1[0.4628] det_loss_cls_2[0.3824] det_loss_box_2[0.4265] det_loss_cls_3[0.3674] det_loss_box_3[0.4091] det_loss_cls_4[0.3517] det_loss_box_4[0.4038] det_loss_cls_5[0.3365] det_loss_box_5[0.4069] det_loss_cls_dn_0[0.1063] det_loss_box_dn_0[0.3095] det_loss_cls_dn_1[0.1020] det_loss_box_dn_1[0.2684] det_loss_cls_dn_2[0.1020] det_loss_box_dn_2[0.2524] det_loss_cls_dn_3[0.1010] det_loss_box_dn_3[0.2500] det_loss_cls_dn_4[0.1005] det_loss_box_dn_4[0.2482] det_loss_cls_dn_5[0.0994] det_loss_box_dn_5[0.2464] loss_depth[0.0391] total_loss[7.1940]
Rank[0/8] 06/19/2025 23:06:16 INFO stats.py:314 | Epoch[67] Step[369] GlobalStep[26499] Training Speed: 7.73 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 22:32:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:06:21 INFO loss_tracker.py:84 | Epoch[67/239] Step[374] GlobalStep[26504/NA]: det_loss_cls_0[0.4893] det_loss_box_0[0.7219] det_loss_cls_1[0.4682] det_loss_box_1[0.5682] det_loss_cls_2[0.4426] det_loss_box_2[0.5104] det_loss_cls_3[0.4135] det_loss_box_3[0.4881] det_loss_cls_4[0.3864] det_loss_box_4[0.4809] det_loss_cls_5[0.3708] det_loss_box_5[0.4843] det_loss_cls_dn_0[0.1032] det_loss_box_dn_0[0.4112] det_loss_cls_dn_1[0.0985] det_loss_box_dn_1[0.3603] det_loss_cls_dn_2[0.0977] det_loss_box_dn_2[0.3467] det_loss_cls_dn_3[0.0960] det_loss_box_dn_3[0.3435] det_loss_cls_dn_4[0.0955] det_loss_box_dn_4[0.3424] det_loss_cls_dn_5[0.0944] det_loss_box_dn_5[0.3357] loss_depth[0.0432] total_loss[8.5930]
Rank[0/8] 06/19/2025 23:06:36 INFO stats.py:394 | Epoch[67] completed. Training Speed: 7.71 samples/sec across all devices.	Epoch Time: 404.49 sec.	Average Epoch Time: 404.49 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 22:31:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:06:44 INFO stats.py:314 | Epoch[68] Step[4] GlobalStep[26524] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 22:31:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:07:05 INFO loss_tracker.py:84 | Epoch[68/239] Step[24] GlobalStep[26544/NA]: det_loss_cls_0[0.5838] det_loss_box_0[0.7930] det_loss_cls_1[0.5706] det_loss_box_1[0.6269] det_loss_cls_2[0.5434] det_loss_box_2[0.5641] det_loss_cls_3[0.5185] det_loss_box_3[0.5414] det_loss_cls_4[0.4928] det_loss_box_4[0.5321] det_loss_cls_5[0.4839] det_loss_box_5[0.5309] det_loss_cls_dn_0[0.1131] det_loss_box_dn_0[0.3150] det_loss_cls_dn_1[0.1091] det_loss_box_dn_1[0.2697] det_loss_cls_dn_2[0.1088] det_loss_box_dn_2[0.2500] det_loss_cls_dn_3[0.1077] det_loss_box_dn_3[0.2460] det_loss_cls_dn_4[0.1067] det_loss_box_dn_4[0.2448] det_loss_cls_dn_5[0.1058] det_loss_box_dn_5[0.2437] loss_depth[0.0420] total_loss[9.0440]
Rank[0/8] 06/19/2025 23:07:10 INFO stats.py:314 | Epoch[68] Step[29] GlobalStep[26549] Training Speed: 7.98 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 22:30:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:07:30 INFO loss_tracker.py:84 | Epoch[68/239] Step[49] GlobalStep[26569/NA]: det_loss_cls_0[0.5981] det_loss_box_0[0.8032] det_loss_cls_1[0.5934] det_loss_box_1[0.6414] det_loss_cls_2[0.5606] det_loss_box_2[0.5782] det_loss_cls_3[0.5378] det_loss_box_3[0.5493] det_loss_cls_4[0.5118] det_loss_box_4[0.5345] det_loss_cls_5[0.4927] det_loss_box_5[0.5382] det_loss_cls_dn_0[0.1062] det_loss_box_dn_0[0.2764] det_loss_cls_dn_1[0.1028] det_loss_box_dn_1[0.2348] det_loss_cls_dn_2[0.1018] det_loss_box_dn_2[0.2195] det_loss_cls_dn_3[0.1014] det_loss_box_dn_3[0.2158] det_loss_cls_dn_4[0.1003] det_loss_box_dn_4[0.2141] det_loss_cls_dn_5[0.0996] det_loss_box_dn_5[0.2121] loss_depth[0.0407] total_loss[8.9646]
Rank[0/8] 06/19/2025 23:07:35 INFO stats.py:314 | Epoch[68] Step[54] GlobalStep[26574] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 22:30:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:07:55 INFO loss_tracker.py:84 | Epoch[68/239] Step[74] GlobalStep[26594/NA]: det_loss_cls_0[0.5361] det_loss_box_0[0.6649] det_loss_cls_1[0.5397] det_loss_box_1[0.5274] det_loss_cls_2[0.5179] det_loss_box_2[0.4790] det_loss_cls_3[0.4835] det_loss_box_3[0.4750] det_loss_cls_4[0.4670] det_loss_box_4[0.4555] det_loss_cls_5[0.4563] det_loss_box_5[0.4528] det_loss_cls_dn_0[0.1097] det_loss_box_dn_0[0.2942] det_loss_cls_dn_1[0.1065] det_loss_box_dn_1[0.2511] det_loss_cls_dn_2[0.1052] det_loss_box_dn_2[0.2367] det_loss_cls_dn_3[0.1042] det_loss_box_dn_3[0.2349] det_loss_cls_dn_4[0.1031] det_loss_box_dn_4[0.2341] det_loss_cls_dn_5[0.1022] det_loss_box_dn_5[0.2329] loss_depth[0.0412] total_loss[8.2109]
Rank[0/8] 06/19/2025 23:08:00 INFO stats.py:314 | Epoch[68] Step[79] GlobalStep[26599] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 22:29:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:08:22 INFO loss_tracker.py:84 | Epoch[68/239] Step[99] GlobalStep[26619/NA]: det_loss_cls_0[0.6228] det_loss_box_0[0.8496] det_loss_cls_1[0.6148] det_loss_box_1[0.7043] det_loss_cls_2[0.5907] det_loss_box_2[0.6458] det_loss_cls_3[0.5660] det_loss_box_3[0.6181] det_loss_cls_4[0.5294] det_loss_box_4[0.6045] det_loss_cls_5[0.5087] det_loss_box_5[0.6065] det_loss_cls_dn_0[0.1050] det_loss_box_dn_0[0.3301] det_loss_cls_dn_1[0.1016] det_loss_box_dn_1[0.2872] det_loss_cls_dn_2[0.1009] det_loss_box_dn_2[0.2679] det_loss_cls_dn_3[0.1004] det_loss_box_dn_3[0.2655] det_loss_cls_dn_4[0.0993] det_loss_box_dn_4[0.2653] det_loss_cls_dn_5[0.0984] det_loss_box_dn_5[0.2635] loss_depth[0.0383] total_loss[9.7845]
Rank[0/8] 06/19/2025 23:08:27 INFO stats.py:314 | Epoch[68] Step[104] GlobalStep[26624] Training Speed: 7.82 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 22:28:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:08:48 INFO loss_tracker.py:84 | Epoch[68/239] Step[124] GlobalStep[26644/NA]: det_loss_cls_0[0.5239] det_loss_box_0[0.7186] det_loss_cls_1[0.5030] det_loss_box_1[0.5522] det_loss_cls_2[0.4828] det_loss_box_2[0.5059] det_loss_cls_3[0.4576] det_loss_box_3[0.4916] det_loss_cls_4[0.4228] det_loss_box_4[0.4761] det_loss_cls_5[0.4184] det_loss_box_5[0.4683] det_loss_cls_dn_0[0.1027] det_loss_box_dn_0[0.3216] det_loss_cls_dn_1[0.1005] det_loss_box_dn_1[0.2786] det_loss_cls_dn_2[0.0998] det_loss_box_dn_2[0.2617] det_loss_cls_dn_3[0.0989] det_loss_box_dn_3[0.2593] det_loss_cls_dn_4[0.0983] det_loss_box_dn_4[0.2577] det_loss_cls_dn_5[0.0976] det_loss_box_dn_5[0.2551] loss_depth[0.0377] total_loss[8.2903]
Rank[0/8] 06/19/2025 23:08:53 INFO stats.py:314 | Epoch[68] Step[129] GlobalStep[26649] Training Speed: 7.57 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 22:28:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:09:13 INFO loss_tracker.py:84 | Epoch[68/239] Step[149] GlobalStep[26669/NA]: det_loss_cls_0[0.5783] det_loss_box_0[0.7948] det_loss_cls_1[0.5698] det_loss_box_1[0.6504] det_loss_cls_2[0.5429] det_loss_box_2[0.5974] det_loss_cls_3[0.5151] det_loss_box_3[0.5806] det_loss_cls_4[0.4844] det_loss_box_4[0.5647] det_loss_cls_5[0.4679] det_loss_box_5[0.5647] det_loss_cls_dn_0[0.0979] det_loss_box_dn_0[0.3609] det_loss_cls_dn_1[0.0939] det_loss_box_dn_1[0.3211] det_loss_cls_dn_2[0.0933] det_loss_box_dn_2[0.3065] det_loss_cls_dn_3[0.0923] det_loss_box_dn_3[0.3028] det_loss_cls_dn_4[0.0917] det_loss_box_dn_4[0.3015] det_loss_cls_dn_5[0.0909] det_loss_box_dn_5[0.2996] loss_depth[0.0401] total_loss[9.4038]
Rank[0/8] 06/19/2025 23:09:19 INFO stats.py:314 | Epoch[68] Step[154] GlobalStep[26674] Training Speed: 8.62 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 22:27:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:09:39 INFO loss_tracker.py:84 | Epoch[68/239] Step[174] GlobalStep[26694/NA]: det_loss_cls_0[0.4371] det_loss_box_0[0.5516] det_loss_cls_1[0.4227] det_loss_box_1[0.4392] det_loss_cls_2[0.3973] det_loss_box_2[0.3929] det_loss_cls_3[0.3719] det_loss_box_3[0.3755] det_loss_cls_4[0.3513] det_loss_box_4[0.3617] det_loss_cls_5[0.3374] det_loss_box_5[0.3653] det_loss_cls_dn_0[0.1040] det_loss_box_dn_0[0.3410] det_loss_cls_dn_1[0.1007] det_loss_box_dn_1[0.2984] det_loss_cls_dn_2[0.0995] det_loss_box_dn_2[0.2800] det_loss_cls_dn_3[0.0978] det_loss_box_dn_3[0.2775] det_loss_cls_dn_4[0.0962] det_loss_box_dn_4[0.2759] det_loss_cls_dn_5[0.0954] det_loss_box_dn_5[0.2658] loss_depth[0.0390] total_loss[7.1751]
Rank[0/8] 06/19/2025 23:09:45 INFO stats.py:314 | Epoch[68] Step[179] GlobalStep[26699] Training Speed: 6.37 samples/sec across all devices.	Average Step Time: 1.26 sec.	Estimated Remaining Time: 22:26:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:10:05 INFO loss_tracker.py:84 | Epoch[68/239] Step[199] GlobalStep[26719/NA]: det_loss_cls_0[0.5362] det_loss_box_0[0.7510] det_loss_cls_1[0.5150] det_loss_box_1[0.6082] det_loss_cls_2[0.4939] det_loss_box_2[0.5550] det_loss_cls_3[0.4616] det_loss_box_3[0.5348] det_loss_cls_4[0.4326] det_loss_box_4[0.5158] det_loss_cls_5[0.4213] det_loss_box_5[0.5140] det_loss_cls_dn_0[0.0930] det_loss_box_dn_0[0.3277] det_loss_cls_dn_1[0.0877] det_loss_box_dn_1[0.2815] det_loss_cls_dn_2[0.0868] det_loss_box_dn_2[0.2661] det_loss_cls_dn_3[0.0853] det_loss_box_dn_3[0.2631] det_loss_cls_dn_4[0.0844] det_loss_box_dn_4[0.2610] det_loss_cls_dn_5[0.0844] det_loss_box_dn_5[0.2557] loss_depth[0.0404] total_loss[8.5566]
Rank[0/8] 06/19/2025 23:10:10 INFO stats.py:314 | Epoch[68] Step[204] GlobalStep[26724] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 22:26:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:10:31 INFO loss_tracker.py:84 | Epoch[68/239] Step[224] GlobalStep[26744/NA]: det_loss_cls_0[0.4152] det_loss_box_0[0.5852] det_loss_cls_1[0.4024] det_loss_box_1[0.4599] det_loss_cls_2[0.3823] det_loss_box_2[0.4098] det_loss_cls_3[0.3640] det_loss_box_3[0.3893] det_loss_cls_4[0.3418] det_loss_box_4[0.3818] det_loss_cls_5[0.3330] det_loss_box_5[0.3816] det_loss_cls_dn_0[0.0981] det_loss_box_dn_0[0.3520] det_loss_cls_dn_1[0.0949] det_loss_box_dn_1[0.3107] det_loss_cls_dn_2[0.0946] det_loss_box_dn_2[0.2973] det_loss_cls_dn_3[0.0933] det_loss_box_dn_3[0.2938] det_loss_cls_dn_4[0.0924] det_loss_box_dn_4[0.2930] det_loss_cls_dn_5[0.0916] det_loss_box_dn_5[0.2909] loss_depth[0.0389] total_loss[7.2879]
Rank[0/8] 06/19/2025 23:10:36 INFO stats.py:314 | Epoch[68] Step[229] GlobalStep[26749] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 22:25:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:10:56 INFO loss_tracker.py:84 | Epoch[68/239] Step[249] GlobalStep[26769/NA]: det_loss_cls_0[0.5013] det_loss_box_0[0.7953] det_loss_cls_1[0.5037] det_loss_box_1[0.6759] det_loss_cls_2[0.4911] det_loss_box_2[0.6075] det_loss_cls_3[0.4825] det_loss_box_3[0.5967] det_loss_cls_4[0.4442] det_loss_box_4[0.5629] det_loss_cls_5[0.4329] det_loss_box_5[0.5609] det_loss_cls_dn_0[0.1088] det_loss_box_dn_0[0.4351] det_loss_cls_dn_1[0.1048] det_loss_box_dn_1[0.3962] det_loss_cls_dn_2[0.1036] det_loss_box_dn_2[0.3791] det_loss_cls_dn_3[0.1029] det_loss_box_dn_3[0.3743] det_loss_cls_dn_4[0.1025] det_loss_box_dn_4[0.3731] det_loss_cls_dn_5[0.1020] det_loss_box_dn_5[0.3677] loss_depth[0.0365] total_loss[9.6415]
Rank[0/8] 06/19/2025 23:11:01 INFO stats.py:314 | Epoch[68] Step[254] GlobalStep[26774] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 22:24:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:11:23 INFO loss_tracker.py:84 | Epoch[68/239] Step[274] GlobalStep[26794/NA]: det_loss_cls_0[0.4064] det_loss_box_0[0.5509] det_loss_cls_1[0.3956] det_loss_box_1[0.4333] det_loss_cls_2[0.3674] det_loss_box_2[0.4066] det_loss_cls_3[0.3491] det_loss_box_3[0.3892] det_loss_cls_4[0.3255] det_loss_box_4[0.3758] det_loss_cls_5[0.3153] det_loss_box_5[0.3763] det_loss_cls_dn_0[0.1013] det_loss_box_dn_0[0.3950] det_loss_cls_dn_1[0.0973] det_loss_box_dn_1[0.3408] det_loss_cls_dn_2[0.0970] det_loss_box_dn_2[0.3248] det_loss_cls_dn_3[0.0970] det_loss_box_dn_3[0.3199] det_loss_cls_dn_4[0.0952] det_loss_box_dn_4[0.3176] det_loss_cls_dn_5[0.0948] det_loss_box_dn_5[0.3149] loss_depth[0.0424] total_loss[7.3295]
Rank[0/8] 06/19/2025 23:11:28 INFO stats.py:314 | Epoch[68] Step[279] GlobalStep[26799] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 22:24:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:11:48 INFO loss_tracker.py:84 | Epoch[68/239] Step[299] GlobalStep[26819/NA]: det_loss_cls_0[0.5507] det_loss_box_0[0.6985] det_loss_cls_1[0.5262] det_loss_box_1[0.5789] det_loss_cls_2[0.5231] det_loss_box_2[0.5242] det_loss_cls_3[0.4889] det_loss_box_3[0.5065] det_loss_cls_4[0.4579] det_loss_box_4[0.4958] det_loss_cls_5[0.4453] det_loss_box_5[0.4929] det_loss_cls_dn_0[0.1206] det_loss_box_dn_0[0.2911] det_loss_cls_dn_1[0.1194] det_loss_box_dn_1[0.2543] det_loss_cls_dn_2[0.1188] det_loss_box_dn_2[0.2406] det_loss_cls_dn_3[0.1209] det_loss_box_dn_3[0.2377] det_loss_cls_dn_4[0.1215] det_loss_box_dn_4[0.2361] det_loss_cls_dn_5[0.1200] det_loss_box_dn_5[0.2346] loss_depth[0.0399] total_loss[8.5443]
Rank[0/8] 06/19/2025 23:11:54 INFO stats.py:314 | Epoch[68] Step[304] GlobalStep[26824] Training Speed: 7.52 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 22:23:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:12:14 INFO loss_tracker.py:84 | Epoch[68/239] Step[324] GlobalStep[26844/NA]: det_loss_cls_0[0.6538] det_loss_box_0[0.7989] det_loss_cls_1[0.6216] det_loss_box_1[0.6324] det_loss_cls_2[0.6107] det_loss_box_2[0.5663] det_loss_cls_3[0.5797] det_loss_box_3[0.5576] det_loss_cls_4[0.5402] det_loss_box_4[0.5486] det_loss_cls_5[0.5371] det_loss_box_5[0.5412] det_loss_cls_dn_0[0.1028] det_loss_box_dn_0[0.2927] det_loss_cls_dn_1[0.0976] det_loss_box_dn_1[0.2489] det_loss_cls_dn_2[0.0962] det_loss_box_dn_2[0.2348] det_loss_cls_dn_3[0.0956] det_loss_box_dn_3[0.2314] det_loss_cls_dn_4[0.0941] det_loss_box_dn_4[0.2311] det_loss_cls_dn_5[0.0939] det_loss_box_dn_5[0.2302] loss_depth[0.0400] total_loss[9.2772]
Rank[0/8] 06/19/2025 23:12:19 INFO stats.py:314 | Epoch[68] Step[329] GlobalStep[26849] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 22:22:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:12:41 INFO loss_tracker.py:84 | Epoch[68/239] Step[349] GlobalStep[26869/NA]: det_loss_cls_0[0.6121] det_loss_box_0[0.7702] det_loss_cls_1[0.5917] det_loss_box_1[0.6192] det_loss_cls_2[0.5649] det_loss_box_2[0.5463] det_loss_cls_3[0.5350] det_loss_box_3[0.5283] det_loss_cls_4[0.5194] det_loss_box_4[0.5166] det_loss_cls_5[0.5114] det_loss_box_5[0.5185] det_loss_cls_dn_0[0.1081] det_loss_box_dn_0[0.3359] det_loss_cls_dn_1[0.1049] det_loss_box_dn_1[0.3012] det_loss_cls_dn_2[0.1049] det_loss_box_dn_2[0.2905] det_loss_cls_dn_3[0.1040] det_loss_box_dn_3[0.2884] det_loss_cls_dn_4[0.1028] det_loss_box_dn_4[0.2881] det_loss_cls_dn_5[0.1026] det_loss_box_dn_5[0.2868] loss_depth[0.0390] total_loss[9.2908]
Rank[0/8] 06/19/2025 23:12:46 INFO stats.py:314 | Epoch[68] Step[354] GlobalStep[26874] Training Speed: 5.84 samples/sec across all devices.	Average Step Time: 1.37 sec.	Estimated Remaining Time: 22:21:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:13:07 INFO loss_tracker.py:84 | Epoch[68/239] Step[374] GlobalStep[26894/NA]: det_loss_cls_0[0.4961] det_loss_box_0[0.6219] det_loss_cls_1[0.4823] det_loss_box_1[0.4851] det_loss_cls_2[0.4488] det_loss_box_2[0.4401] det_loss_cls_3[0.4197] det_loss_box_3[0.4255] det_loss_cls_4[0.3998] det_loss_box_4[0.4167] det_loss_cls_5[0.3845] det_loss_box_5[0.4205] det_loss_cls_dn_0[0.1072] det_loss_box_dn_0[0.2975] det_loss_cls_dn_1[0.1041] det_loss_box_dn_1[0.2511] det_loss_cls_dn_2[0.1031] det_loss_box_dn_2[0.2319] det_loss_cls_dn_3[0.1024] det_loss_box_dn_3[0.2284] det_loss_cls_dn_4[0.1013] det_loss_box_dn_4[0.2252] det_loss_cls_dn_5[0.1005] det_loss_box_dn_5[0.2234] loss_depth[0.0415] total_loss[7.5588]
Rank[0/8] 06/19/2025 23:13:11 INFO stats.py:314 | Epoch[68] Step[379] GlobalStep[26899] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 22:21:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:13:22 INFO stats.py:394 | Epoch[68] completed. Training Speed: 7.68 samples/sec across all devices.	Epoch Time: 406.04 sec.	Average Epoch Time: 406.04 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 22:21:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:13:39 INFO stats.py:314 | Epoch[69] Step[14] GlobalStep[26924] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 22:20:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:13:49 INFO loss_tracker.py:84 | Epoch[69/239] Step[24] GlobalStep[26934/NA]: det_loss_cls_0[0.6196] det_loss_box_0[0.8392] det_loss_cls_1[0.6064] det_loss_box_1[0.6706] det_loss_cls_2[0.5790] det_loss_box_2[0.6073] det_loss_cls_3[0.5556] det_loss_box_3[0.5940] det_loss_cls_4[0.5281] det_loss_box_4[0.5882] det_loss_cls_5[0.5145] det_loss_box_5[0.5863] det_loss_cls_dn_0[0.1091] det_loss_box_dn_0[0.3389] det_loss_cls_dn_1[0.1056] det_loss_box_dn_1[0.2888] det_loss_cls_dn_2[0.1048] det_loss_box_dn_2[0.2676] det_loss_cls_dn_3[0.1031] det_loss_box_dn_3[0.2642] det_loss_cls_dn_4[0.1021] det_loss_box_dn_4[0.2620] det_loss_cls_dn_5[0.1015] det_loss_box_dn_5[0.2605] loss_depth[0.0390] total_loss[9.6361]
Rank[0/8] 06/19/2025 23:14:05 INFO stats.py:314 | Epoch[69] Step[39] GlobalStep[26949] Training Speed: 7.96 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 22:19:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:14:15 INFO loss_tracker.py:84 | Epoch[69/239] Step[49] GlobalStep[26959/NA]: det_loss_cls_0[0.5043] det_loss_box_0[0.7364] det_loss_cls_1[0.4783] det_loss_box_1[0.6020] det_loss_cls_2[0.4660] det_loss_box_2[0.5328] det_loss_cls_3[0.4381] det_loss_box_3[0.5151] det_loss_cls_4[0.4152] det_loss_box_4[0.4969] det_loss_cls_5[0.4080] det_loss_box_5[0.4965] det_loss_cls_dn_0[0.1120] det_loss_box_dn_0[0.3389] det_loss_cls_dn_1[0.1071] det_loss_box_dn_1[0.2954] det_loss_cls_dn_2[0.1074] det_loss_box_dn_2[0.2822] det_loss_cls_dn_3[0.1064] det_loss_box_dn_3[0.2789] det_loss_cls_dn_4[0.1061] det_loss_box_dn_4[0.2771] det_loss_cls_dn_5[0.1043] det_loss_box_dn_5[0.2759] loss_depth[0.0387] total_loss[8.5199]
Rank[0/8] 06/19/2025 23:14:31 INFO stats.py:314 | Epoch[69] Step[64] GlobalStep[26974] Training Speed: 7.48 samples/sec across all devices.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 22:19:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:14:41 INFO loss_tracker.py:84 | Epoch[69/239] Step[74] GlobalStep[26984/NA]: det_loss_cls_0[0.5962] det_loss_box_0[0.8303] det_loss_cls_1[0.5854] det_loss_box_1[0.6685] det_loss_cls_2[0.5692] det_loss_box_2[0.6136] det_loss_cls_3[0.5415] det_loss_box_3[0.5973] det_loss_cls_4[0.5122] det_loss_box_4[0.5798] det_loss_cls_5[0.5042] det_loss_box_5[0.5811] det_loss_cls_dn_0[0.1106] det_loss_box_dn_0[0.3044] det_loss_cls_dn_1[0.1071] det_loss_box_dn_1[0.2557] det_loss_cls_dn_2[0.1064] det_loss_box_dn_2[0.2395] det_loss_cls_dn_3[0.1048] det_loss_box_dn_3[0.2369] det_loss_cls_dn_4[0.1030] det_loss_box_dn_4[0.2353] det_loss_cls_dn_5[0.1026] det_loss_box_dn_5[0.2344] loss_depth[0.0363] total_loss[9.3562]
Rank[0/8] 06/19/2025 23:14:56 INFO stats.py:314 | Epoch[69] Step[89] GlobalStep[26999] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 22:18:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:15:07 INFO loss_tracker.py:84 | Epoch[69/239] Step[99] GlobalStep[27009/NA]: det_loss_cls_0[0.5672] det_loss_box_0[0.7866] det_loss_cls_1[0.5479] det_loss_box_1[0.6213] det_loss_cls_2[0.5113] det_loss_box_2[0.5553] det_loss_cls_3[0.4923] det_loss_box_3[0.5237] det_loss_cls_4[0.4586] det_loss_box_4[0.5214] det_loss_cls_5[0.4391] det_loss_box_5[0.5281] det_loss_cls_dn_0[0.0965] det_loss_box_dn_0[0.3527] det_loss_cls_dn_1[0.0920] det_loss_box_dn_1[0.3012] det_loss_cls_dn_2[0.0915] det_loss_box_dn_2[0.2791] det_loss_cls_dn_3[0.0902] det_loss_box_dn_3[0.2740] det_loss_cls_dn_4[0.0894] det_loss_box_dn_4[0.2725] det_loss_cls_dn_5[0.0881] det_loss_box_dn_5[0.2718] loss_depth[0.0419] total_loss[8.8939]
Rank[0/8] 06/19/2025 23:15:22 INFO stats.py:314 | Epoch[69] Step[114] GlobalStep[27024] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 22:17:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:15:33 INFO loss_tracker.py:84 | Epoch[69/239] Step[124] GlobalStep[27034/NA]: det_loss_cls_0[0.7215] det_loss_box_0[0.9903] det_loss_cls_1[0.7051] det_loss_box_1[0.7952] det_loss_cls_2[0.6798] det_loss_box_2[0.7041] det_loss_cls_3[0.6514] det_loss_box_3[0.6774] det_loss_cls_4[0.6182] det_loss_box_4[0.6624] det_loss_cls_5[0.6073] det_loss_box_5[0.6540] det_loss_cls_dn_0[0.1010] det_loss_box_dn_0[0.3101] det_loss_cls_dn_1[0.0968] det_loss_box_dn_1[0.2734] det_loss_cls_dn_2[0.0965] det_loss_box_dn_2[0.2563] det_loss_cls_dn_3[0.0957] det_loss_box_dn_3[0.2538] det_loss_cls_dn_4[0.0937] det_loss_box_dn_4[0.2525] det_loss_cls_dn_5[0.0929] det_loss_box_dn_5[0.2517] loss_depth[0.0405] total_loss[10.6817]
Rank[0/8] 06/19/2025 23:15:49 INFO stats.py:314 | Epoch[69] Step[139] GlobalStep[27049] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 22:17:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:15:59 INFO loss_tracker.py:84 | Epoch[69/239] Step[149] GlobalStep[27059/NA]: det_loss_cls_0[0.4364] det_loss_box_0[0.5999] det_loss_cls_1[0.4237] det_loss_box_1[0.4647] det_loss_cls_2[0.4085] det_loss_box_2[0.4210] det_loss_cls_3[0.3825] det_loss_box_3[0.4133] det_loss_cls_4[0.3588] det_loss_box_4[0.4006] det_loss_cls_5[0.3491] det_loss_box_5[0.3981] det_loss_cls_dn_0[0.0936] det_loss_box_dn_0[0.3205] det_loss_cls_dn_1[0.0902] det_loss_box_dn_1[0.2699] det_loss_cls_dn_2[0.0899] det_loss_box_dn_2[0.2536] det_loss_cls_dn_3[0.0885] det_loss_box_dn_3[0.2472] det_loss_cls_dn_4[0.0874] det_loss_box_dn_4[0.2468] det_loss_cls_dn_5[0.0866] det_loss_box_dn_5[0.2462] loss_depth[0.0406] total_loss[7.2175]
Rank[0/8] 06/19/2025 23:16:15 INFO stats.py:314 | Epoch[69] Step[164] GlobalStep[27074] Training Speed: 8.03 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 22:16:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:16:25 INFO loss_tracker.py:84 | Epoch[69/239] Step[174] GlobalStep[27084/NA]: det_loss_cls_0[0.4234] det_loss_box_0[0.6167] det_loss_cls_1[0.4124] det_loss_box_1[0.4731] det_loss_cls_2[0.3996] det_loss_box_2[0.4210] det_loss_cls_3[0.3750] det_loss_box_3[0.4096] det_loss_cls_4[0.3593] det_loss_box_4[0.3979] det_loss_cls_5[0.3499] det_loss_box_5[0.3997] det_loss_cls_dn_0[0.0931] det_loss_box_dn_0[0.2975] det_loss_cls_dn_1[0.0890] det_loss_box_dn_1[0.2547] det_loss_cls_dn_2[0.0886] det_loss_box_dn_2[0.2422] det_loss_cls_dn_3[0.0879] det_loss_box_dn_3[0.2386] det_loss_cls_dn_4[0.0870] det_loss_box_dn_4[0.2376] det_loss_cls_dn_5[0.0856] det_loss_box_dn_5[0.2358] loss_depth[0.0390] total_loss[7.1144]
Rank[0/8] 06/19/2025 23:16:41 INFO stats.py:314 | Epoch[69] Step[189] GlobalStep[27099] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 22:15:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:16:51 INFO loss_tracker.py:84 | Epoch[69/239] Step[199] GlobalStep[27109/NA]: det_loss_cls_0[0.5091] det_loss_box_0[0.6344] det_loss_cls_1[0.5006] det_loss_box_1[0.4916] det_loss_cls_2[0.4662] det_loss_box_2[0.4511] det_loss_cls_3[0.4374] det_loss_box_3[0.4312] det_loss_cls_4[0.4089] det_loss_box_4[0.4228] det_loss_cls_5[0.3997] det_loss_box_5[0.4202] det_loss_cls_dn_0[0.0885] det_loss_box_dn_0[0.2563] det_loss_cls_dn_1[0.0855] det_loss_box_dn_1[0.2091] det_loss_cls_dn_2[0.0841] det_loss_box_dn_2[0.1917] det_loss_cls_dn_3[0.0838] det_loss_box_dn_3[0.1880] det_loss_cls_dn_4[0.0831] det_loss_box_dn_4[0.1866] det_loss_cls_dn_5[0.0822] det_loss_box_dn_5[0.1854] loss_depth[0.0383] total_loss[7.3359]
Rank[0/8] 06/19/2025 23:17:07 INFO stats.py:314 | Epoch[69] Step[214] GlobalStep[27124] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 22:15:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:17:17 INFO loss_tracker.py:84 | Epoch[69/239] Step[224] GlobalStep[27134/NA]: det_loss_cls_0[0.4769] det_loss_box_0[0.6331] det_loss_cls_1[0.4656] det_loss_box_1[0.5171] det_loss_cls_2[0.4430] det_loss_box_2[0.4625] det_loss_cls_3[0.4179] det_loss_box_3[0.4482] det_loss_cls_4[0.3965] det_loss_box_4[0.4451] det_loss_cls_5[0.3831] det_loss_box_5[0.4454] det_loss_cls_dn_0[0.1113] det_loss_box_dn_0[0.3192] det_loss_cls_dn_1[0.1066] det_loss_box_dn_1[0.2777] det_loss_cls_dn_2[0.1064] det_loss_box_dn_2[0.2630] det_loss_cls_dn_3[0.1059] det_loss_box_dn_3[0.2622] det_loss_cls_dn_4[0.1049] det_loss_box_dn_4[0.2610] det_loss_cls_dn_5[0.1044] det_loss_box_dn_5[0.2588] loss_depth[0.0392] total_loss[7.8550]
Rank[0/8] 06/19/2025 23:17:32 INFO stats.py:314 | Epoch[69] Step[239] GlobalStep[27149] Training Speed: 8.70 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 22:14:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:17:42 INFO loss_tracker.py:84 | Epoch[69/239] Step[249] GlobalStep[27159/NA]: det_loss_cls_0[0.5125] det_loss_box_0[0.6612] det_loss_cls_1[0.5006] det_loss_box_1[0.5253] det_loss_cls_2[0.4798] det_loss_box_2[0.4785] det_loss_cls_3[0.4741] det_loss_box_3[0.4570] det_loss_cls_4[0.4351] det_loss_box_4[0.4465] det_loss_cls_5[0.4231] det_loss_box_5[0.4461] det_loss_cls_dn_0[0.0950] det_loss_box_dn_0[0.4011] det_loss_cls_dn_1[0.0921] det_loss_box_dn_1[0.3456] det_loss_cls_dn_2[0.0910] det_loss_box_dn_2[0.3208] det_loss_cls_dn_3[0.0904] det_loss_box_dn_3[0.3168] det_loss_cls_dn_4[0.0891] det_loss_box_dn_4[0.3147] det_loss_cls_dn_5[0.0879] det_loss_box_dn_5[0.3126] loss_depth[0.0385] total_loss[8.4354]
Rank[0/8] 06/19/2025 23:17:58 INFO stats.py:314 | Epoch[69] Step[264] GlobalStep[27174] Training Speed: 8.81 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 22:13:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:18:08 INFO loss_tracker.py:84 | Epoch[69/239] Step[274] GlobalStep[27184/NA]: det_loss_cls_0[0.5114] det_loss_box_0[0.6947] det_loss_cls_1[0.5066] det_loss_box_1[0.5620] det_loss_cls_2[0.4915] det_loss_box_2[0.5084] det_loss_cls_3[0.4643] det_loss_box_3[0.4970] det_loss_cls_4[0.4344] det_loss_box_4[0.4882] det_loss_cls_5[0.4276] det_loss_box_5[0.4874] det_loss_cls_dn_0[0.1074] det_loss_box_dn_0[0.3135] det_loss_cls_dn_1[0.1033] det_loss_box_dn_1[0.2719] det_loss_cls_dn_2[0.1027] det_loss_box_dn_2[0.2585] det_loss_cls_dn_3[0.1019] det_loss_box_dn_3[0.2549] det_loss_cls_dn_4[0.1010] det_loss_box_dn_4[0.2530] det_loss_cls_dn_5[0.1005] det_loss_box_dn_5[0.2515] loss_depth[0.0403] total_loss[8.3339]
Rank[0/8] 06/19/2025 23:18:24 INFO stats.py:314 | Epoch[69] Step[289] GlobalStep[27199] Training Speed: 6.31 samples/sec across all devices.	Average Step Time: 1.27 sec.	Estimated Remaining Time: 22:13:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:18:34 INFO loss_tracker.py:84 | Epoch[69/239] Step[299] GlobalStep[27209/NA]: det_loss_cls_0[0.4881] det_loss_box_0[0.6512] det_loss_cls_1[0.4600] det_loss_box_1[0.5253] det_loss_cls_2[0.4336] det_loss_box_2[0.4734] det_loss_cls_3[0.4230] det_loss_box_3[0.4628] det_loss_cls_4[0.3928] det_loss_box_4[0.4559] det_loss_cls_5[0.3785] det_loss_box_5[0.4546] det_loss_cls_dn_0[0.0967] det_loss_box_dn_0[0.3186] det_loss_cls_dn_1[0.0923] det_loss_box_dn_1[0.2799] det_loss_cls_dn_2[0.0930] det_loss_box_dn_2[0.2621] det_loss_cls_dn_3[0.0913] det_loss_box_dn_3[0.2581] det_loss_cls_dn_4[0.0904] det_loss_box_dn_4[0.2562] det_loss_cls_dn_5[0.0891] det_loss_box_dn_5[0.2543] loss_depth[0.0430] total_loss[7.8241]
Rank[0/8] 06/19/2025 23:18:50 INFO stats.py:314 | Epoch[69] Step[314] GlobalStep[27224] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 22:12:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:19:00 INFO loss_tracker.py:84 | Epoch[69/239] Step[324] GlobalStep[27234/NA]: det_loss_cls_0[0.5611] det_loss_box_0[0.7423] det_loss_cls_1[0.5434] det_loss_box_1[0.5960] det_loss_cls_2[0.5175] det_loss_box_2[0.5357] det_loss_cls_3[0.4889] det_loss_box_3[0.5088] det_loss_cls_4[0.4672] det_loss_box_4[0.4994] det_loss_cls_5[0.4497] det_loss_box_5[0.5007] det_loss_cls_dn_0[0.1038] det_loss_box_dn_0[0.3137] det_loss_cls_dn_1[0.1014] det_loss_box_dn_1[0.2700] det_loss_cls_dn_2[0.1007] det_loss_box_dn_2[0.2528] det_loss_cls_dn_3[0.0996] det_loss_box_dn_3[0.2493] det_loss_cls_dn_4[0.0986] det_loss_box_dn_4[0.2478] det_loss_cls_dn_5[0.0982] det_loss_box_dn_5[0.2454] loss_depth[0.0425] total_loss[8.6341]
Rank[0/8] 06/19/2025 23:19:15 INFO stats.py:314 | Epoch[69] Step[339] GlobalStep[27249] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 22:11:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:19:26 INFO loss_tracker.py:84 | Epoch[69/239] Step[349] GlobalStep[27259/NA]: det_loss_cls_0[0.4679] det_loss_box_0[0.6887] det_loss_cls_1[0.4566] det_loss_box_1[0.5345] det_loss_cls_2[0.4227] det_loss_box_2[0.4616] det_loss_cls_3[0.4186] det_loss_box_3[0.4317] det_loss_cls_4[0.3921] det_loss_box_4[0.4268] det_loss_cls_5[0.3770] det_loss_box_5[0.4295] det_loss_cls_dn_0[0.1016] det_loss_box_dn_0[0.3443] det_loss_cls_dn_1[0.0963] det_loss_box_dn_1[0.2806] det_loss_cls_dn_2[0.0956] det_loss_box_dn_2[0.2624] det_loss_cls_dn_3[0.0939] det_loss_box_dn_3[0.2587] det_loss_cls_dn_4[0.0929] det_loss_box_dn_4[0.2573] det_loss_cls_dn_5[0.0923] det_loss_box_dn_5[0.2564] loss_depth[0.0414] total_loss[7.7812]
Rank[0/8] 06/19/2025 23:19:41 INFO stats.py:314 | Epoch[69] Step[364] GlobalStep[27274] Training Speed: 8.64 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 22:11:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:19:52 INFO loss_tracker.py:84 | Epoch[69/239] Step[374] GlobalStep[27284/NA]: det_loss_cls_0[0.4232] det_loss_box_0[0.5365] det_loss_cls_1[0.4161] det_loss_box_1[0.4208] det_loss_cls_2[0.3906] det_loss_box_2[0.3845] det_loss_cls_3[0.3719] det_loss_box_3[0.3590] det_loss_cls_4[0.3436] det_loss_box_4[0.3436] det_loss_cls_5[0.3324] det_loss_box_5[0.3434] det_loss_cls_dn_0[0.0942] det_loss_box_dn_0[0.2986] det_loss_cls_dn_1[0.0896] det_loss_box_dn_1[0.2587] det_loss_cls_dn_2[0.0890] det_loss_box_dn_2[0.2419] det_loss_cls_dn_3[0.0874] det_loss_box_dn_3[0.2369] det_loss_cls_dn_4[0.0870] det_loss_box_dn_4[0.2353] det_loss_cls_dn_5[0.0859] det_loss_box_dn_5[0.2342] loss_depth[0.0356] total_loss[6.7400]
Rank[0/8] 06/19/2025 23:20:07 INFO stats.py:314 | Epoch[69] Step[389] GlobalStep[27299] Training Speed: 7.55 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 22:10:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:20:08 INFO trainer.py:268 | 
==================================================BEGIN EVAL==================================================
Rank[0/8] 06/19/2025 23:21:07 INFO trainer.py:275 | eval: 10
Rank[0/8] 06/19/2025 23:21:15 INFO trainer.py:275 | eval: 20
Rank[0/8] 06/19/2025 23:21:23 INFO trainer.py:275 | eval: 30
Rank[0/8] 06/19/2025 23:21:34 INFO trainer.py:275 | eval: 40
Rank[0/8] 06/19/2025 23:21:45 INFO trainer.py:275 | eval: 50
Rank[0/8] 06/19/2025 23:21:52 INFO trainer.py:275 | eval: 60
Rank[0/8] 06/19/2025 23:22:03 INFO trainer.py:275 | eval: 70
Rank[0/8] 06/19/2025 23:22:14 INFO trainer.py:275 | eval: 80
Rank[0/8] 06/19/2025 23:22:22 INFO trainer.py:275 | eval: 90
Rank[0/8] 06/19/2025 23:22:32 INFO trainer.py:275 | eval: 100
Rank[0/8] 06/19/2025 23:22:40 INFO metrics.py:428 | number of results: 824
Rank[0/8] 06/19/2025 23:27:21 INFO metrics.py:332 | eval results of overall:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| bathtub             | 0.7755  | 0.8382  | 0.5692  | 0.6471  |
| bin                 | 0.5644  | 0.7709  | 0.1477  | 0.3575  |
| picture             | 0.1397  | 0.3513  | 0.0088  | 0.0700  |
| toilet paper        | 0.2559  | 0.4252  | 0.0074  | 0.0709  |
| toilet              | 0.8350  | 0.8832  | 0.4946  | 0.6350  |
| sink                | 0.5921  | 0.7984  | 0.1171  | 0.3745  |
| towel               | 0.2799  | 0.5806  | 0.0385  | 0.1452  |
| vanity              | 0.4167  | 0.9333  | 0.2584  | 0.7111  |
| doorframe           | 0.2241  | 0.7058  | 0.0521  | 0.3503  |
| stopcock            | 0.0980  | 0.4828  | 0.0007  | 0.0345  |
| rail                | 0.0066  | 0.2969  | 0.0010  | 0.0312  |
| backpack            | 0.5201  | 0.8226  | 0.1790  | 0.3710  |
| bottle              | 0.0395  | 0.3203  | 0.0009  | 0.0557  |
| curtain             | 0.4058  | 0.8358  | 0.0805  | 0.4716  |
| door                | 0.2113  | 0.6858  | 0.0255  | 0.2616  |
| bar                 | 0.0142  | 0.1515  | 0.0000  | 0.0303  |
| hair dryer          | 0.0156  | 0.2857  | 0.0000  | 0.0000  |
| switch              | 0.0007  | 0.0461  | 0.0000  | 0.0066  |
| mirror              | 0.0609  | 0.5272  | 0.0026  | 0.1304  |
| tissue box          | 0.0722  | 0.5152  | 0.0138  | 0.1515  |
| microwave           | 0.5550  | 0.8367  | 0.1168  | 0.3469  |
| bag                 | 0.2281  | 0.6324  | 0.0152  | 0.1676  |
| shower              | 0.0342  | 0.2333  | 0.0013  | 0.0667  |
| light               | 0.0204  | 0.1987  | 0.0002  | 0.0321  |
| counter             | 0.0957  | 0.6422  | 0.0139  | 0.2385  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| grab bar            | 0.0242  | 0.2500  | 0.0000  | 0.0000  |
| cup                 | 0.0862  | 0.3898  | 0.0063  | 0.0508  |
| paper               | 0.0154  | 0.2515  | 0.0006  | 0.0355  |
| tissue              | 0.0189  | 0.3077  | 0.0005  | 0.0769  |
| cabinet             | 0.2300  | 0.7202  | 0.1083  | 0.4529  |
| paper cutter        | 0.5333  | 0.8333  | 0.0067  | 0.1667  |
| ledge               | 0.0170  | 0.6667  | 0.0152  | 0.1667  |
| toothpaste          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basket              | 0.2908  | 0.5913  | 0.0801  | 0.2087  |
| blanket             | 0.1246  | 0.7500  | 0.0285  | 0.3800  |
| faucet              | 0.0213  | 0.2766  | 0.0005  | 0.0213  |
| shelf               | 0.1742  | 0.6691  | 0.0565  | 0.3705  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kettle              | 0.1097  | 0.6667  | 0.0387  | 0.2222  |
| soap                | 0.0046  | 0.5000  | 0.0000  | 0.0000  |
| box                 | 0.1431  | 0.4993  | 0.0215  | 0.1455  |
| plunger             | 0.6071  | 0.8000  | 0.0053  | 0.4000  |
| soap dish           | 0.5347  | 0.6000  | 0.1568  | 0.3333  |
| clothes             | 0.1316  | 0.5828  | 0.0092  | 0.1656  |
| mat                 | 0.0102  | 0.1892  | 0.0000  | 0.0000  |
| washbasin           | 0.0358  | 0.2667  | 0.0333  | 0.0667  |
| frame               | 0.0089  | 0.2653  | 0.0000  | 0.0204  |
| bed                 | 0.6413  | 0.9112  | 0.4002  | 0.8047  |
| socket              | 0.0061  | 0.1593  | 0.0000  | 0.0000  |
| holder              | 0.1165  | 0.3333  | 0.0000  | 0.0000  |
| purse               | 0.0007  | 0.1667  | 0.0000  | 0.0000  |
| wrap                | 0.0070  | 1.0000  | 0.0000  | 0.0000  |
| cosmetics           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vent                | 0.0430  | 0.0833  | 0.0000  | 0.0000  |
| decoration          | 0.0276  | 0.3571  | 0.0045  | 0.0929  |
| laptop              | 0.3189  | 0.6087  | 0.0486  | 0.3043  |
| hat                 | 0.0044  | 0.2222  | 0.0000  | 0.0000  |
| plant               | 0.2649  | 0.5727  | 0.0398  | 0.2079  |
| candle              | 0.0303  | 0.2222  | 0.0000  | 0.0000  |
| couch               | 0.3960  | 0.8884  | 0.2591  | 0.7397  |
| refrigerator        | 0.5841  | 0.8200  | 0.2089  | 0.4600  |
| dish rack           | 0.3034  | 0.8000  | 0.0000  | 0.0000  |
| monitor             | 0.4659  | 0.6467  | 0.0390  | 0.1576  |
| stool               | 0.1726  | 0.6471  | 0.0571  | 0.3109  |
| dresser             | 0.5626  | 0.9259  | 0.3689  | 0.7778  |
| case                | 0.0773  | 0.4294  | 0.0245  | 0.1186  |
| toaster             | 0.3072  | 0.6000  | 0.0000  | 0.0000  |
| hamper              | 0.3119  | 0.5455  | 0.3019  | 0.3636  |
| chair               | 0.5709  | 0.8122  | 0.3550  | 0.5864  |
| keyboard            | 0.0106  | 0.0814  | 0.0000  | 0.0000  |
| mouse               | 0.0022  | 0.0541  | 0.0000  | 0.0000  |
| radiator            | 0.4887  | 0.6333  | 0.0970  | 0.2222  |
| blackboard          | 0.4339  | 0.7439  | 0.1452  | 0.3780  |
| table               | 0.4086  | 0.8386  | 0.2590  | 0.6332  |
| window              | 0.1603  | 0.5928  | 0.0243  | 0.2126  |
| desk                | 0.3993  | 0.8764  | 0.1783  | 0.6573  |
| telephone           | 0.3018  | 0.5500  | 0.0322  | 0.1667  |
| plug                | 0.0023  | 0.1053  | 0.0000  | 0.0000  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| book                | 0.0575  | 0.3785  | 0.0035  | 0.0810  |
| computer            | 0.1246  | 0.5246  | 0.0058  | 0.0820  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blinds              | 0.1028  | 0.4600  | 0.0030  | 0.1800  |
| windowsill          | 0.0262  | 0.5435  | 0.0032  | 0.1522  |
| board               | 0.0900  | 0.6129  | 0.0119  | 0.1613  |
| can                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| notebook            | 0.0001  | 0.0769  | 0.0000  | 0.0000  |
| tablet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pipe                | 0.0371  | 0.1429  | 0.0000  | 0.0000  |
| pillow              | 0.3209  | 0.6541  | 0.0266  | 0.1729  |
| stand               | 0.5305  | 0.8232  | 0.3695  | 0.6402  |
| lamp                | 0.2687  | 0.5493  | 0.0481  | 0.1910  |
| tv                  | 0.4952  | 0.7525  | 0.0708  | 0.2871  |
| wardrobe            | 0.2851  | 0.7349  | 0.0822  | 0.4699  |
| shoe                | 0.0803  | 0.3178  | 0.0054  | 0.0467  |
| printer             | 0.5359  | 0.8776  | 0.2043  | 0.4694  |
| toy                 | 0.0907  | 0.4242  | 0.0123  | 0.1061  |
| bench               | 0.2580  | 0.6981  | 0.1576  | 0.4717  |
| ottoman             | 0.0584  | 0.6667  | 0.0253  | 0.4167  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clock               | 0.2023  | 0.4468  | 0.0007  | 0.0426  |
| calendar            | 0.0006  | 0.1667  | 0.0000  | 0.0000  |
| furniture           | 0.0018  | 0.5000  | 0.0003  | 0.1667  |
| stairs              | 0.0456  | 0.3611  | 0.0030  | 0.1389  |
| cushion             | 0.0946  | 0.5294  | 0.0064  | 0.1373  |
| carpet              | 0.0465  | 0.3571  | 0.0000  | 0.0119  |
| poster              | 0.0042  | 0.1364  | 0.0000  | 0.0000  |
| fan                 | 0.4344  | 0.7812  | 0.0464  | 0.2344  |
| rack                | 0.0245  | 0.5385  | 0.0097  | 0.2885  |
| container           | 0.0254  | 0.3390  | 0.0042  | 0.1186  |
| guitar              | 0.0628  | 0.3333  | 0.0208  | 0.1667  |
| luggage             | 0.1491  | 1.0000  | 0.0833  | 0.3333  |
| jacket              | 0.5193  | 0.7500  | 0.0870  | 0.3333  |
| magazine            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| person              | 0.0797  | 0.6667  | 0.0006  | 0.1667  |
| coffee maker        | 0.4480  | 0.6207  | 0.1057  | 0.2414  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sign                | 0.0666  | 0.2667  | 0.0021  | 0.0667  |
| speaker             | 0.0077  | 0.2727  | 0.0000  | 0.0000  |
| jar                 | 0.0109  | 0.3077  | 0.0000  | 0.0000  |
| plate               | 0.0226  | 0.3462  | 0.0000  | 0.0192  |
| broom               | 0.1246  | 0.4286  | 0.0029  | 0.0714  |
| stall               | 0.0159  | 0.8824  | 0.0049  | 0.2353  |
| hanger              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| brush               | 0.0557  | 0.2857  | 0.0000  | 0.0000  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ladder              | 0.0654  | 0.4706  | 0.0044  | 0.1765  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket              | 0.2388  | 0.5897  | 0.0439  | 0.1538  |
| scale               | 0.2291  | 0.3636  | 0.0000  | 0.0000  |
| dispenser           | 0.5535  | 0.7045  | 0.0844  | 0.2045  |
| soap dispenser      | 0.3333  | 0.5556  | 0.0025  | 0.0370  |
| mailbox             | 0.0108  | 0.6000  | 0.0041  | 0.4000  |
| machine             | 0.5434  | 1.0000  | 0.1956  | 0.7895  |
| glass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher   | 0.0553  | 0.4000  | 0.0000  | 0.0000  |
| pillar              | 0.2488  | 0.6512  | 0.0585  | 0.2558  |
| beam                | 0.0004  | 0.1364  | 0.0000  | 0.0455  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| projector           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| footstool           | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| window frame        | 0.0230  | 0.4444  | 0.0031  | 0.1818  |
| fireplace           | 0.1212  | 0.4643  | 0.0006  | 0.0714  |
| roof                | 0.0015  | 0.2917  | 0.0001  | 0.0417  |
| column              | 0.0127  | 0.3421  | 0.0015  | 0.1316  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| statue              | 0.0764  | 0.3684  | 0.0105  | 0.1579  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| screen              | 0.0286  | 0.2000  | 0.0025  | 0.2000  |
| bowl                | 0.0672  | 0.4058  | 0.0058  | 0.1014  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| folder              | 0.0334  | 0.3571  | 0.0000  | 0.0000  |
| device              | 0.0471  | 0.1818  | 0.0000  | 0.0000  |
| tube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| file                | 0.0916  | 0.4706  | 0.0156  | 0.1765  |
| headphones          | 0.2838  | 0.6000  | 0.0000  | 0.0000  |
| ball                | 0.4164  | 0.5238  | 0.3016  | 0.3333  |
| map                 | 0.0714  | 0.5000  | 0.0000  | 0.0000  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| thermostat          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water cooler        | 0.5080  | 1.0000  | 0.5000  | 0.5000  |
| toiletry            | 0.0752  | 0.3043  | 0.0019  | 0.0435  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| partition           | 0.0463  | 0.6667  | 0.0009  | 0.1667  |
| doorway             | 0.0004  | 0.1111  | 0.0000  | 0.0000  |
| remote control      | 0.0332  | 0.1852  | 0.0023  | 0.0370  |
| teapot              | 0.0094  | 0.3333  | 0.0074  | 0.1111  |
| oven                | 0.3249  | 0.7143  | 0.0400  | 0.1786  |
| dishwasher          | 0.1237  | 0.4000  | 0.0250  | 0.1000  |
| stove               | 0.3580  | 0.7500  | 0.1302  | 0.2778  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drawer              | 0.0647  | 0.4444  | 0.0440  | 0.4444  |
| vase                | 0.1459  | 0.5200  | 0.0110  | 0.1800  |
| pot                 | 0.0721  | 0.4444  | 0.0014  | 0.0370  |
| flowerpot           | 0.0423  | 0.5490  | 0.0013  | 0.1176  |
| range hood          | 0.1604  | 0.5000  | 0.0002  | 0.0714  |
| dumbbell            | 0.0067  | 0.3333  | 0.0000  | 0.0000  |
| vacuum cleaner      | 0.1786  | 0.4286  | 0.1429  | 0.2857  |
| piano               | 0.2138  | 0.4000  | 0.2073  | 0.4000  |
| bicycle             | 0.5335  | 0.8333  | 0.1039  | 0.6667  |
| air conditioner     | 0.3333  | 0.4000  | 0.0000  | 0.0000  |
| copier              | 0.6873  | 0.9167  | 0.2073  | 0.5000  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garbage             | 0.3945  | 0.7500  | 0.1029  | 0.3750  |
| commode             | 0.3958  | 0.8462  | 0.3169  | 0.6731  |
| price tag           | 0.0738  | 0.2857  | 0.0000  | 0.0000  |
| kitchenware         | 0.1430  | 0.4348  | 0.0196  | 0.1087  |
| napkins             | 0.3430  | 0.6667  | 0.0000  | 0.0000  |
| player              | 0.1250  | 0.2500  | 0.1250  | 0.2500  |
| candlestick         | 0.3524  | 1.0000  | 0.0000  | 0.0000  |
| humidifier          | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| heater              | 0.3873  | 0.6724  | 0.0853  | 0.1897  |
| roll                | 0.1172  | 0.4000  | 0.0000  | 0.0000  |
| umbrella            | 0.0154  | 0.2000  | 0.0000  | 0.0000  |
| conducting wire     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| chandelier          | 0.1020  | 0.3333  | 0.0000  | 0.0000  |
| album               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit               | 0.1369  | 0.2143  | 0.0714  | 0.0714  |
| adhesive tape       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sunglasses          | 0.0894  | 0.3846  | 0.0037  | 0.0769  |
| clothes dryer       | 0.1685  | 0.6667  | 0.0784  | 0.4667  |
| shampoo             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basin               | 0.0150  | 0.1176  | 0.0001  | 0.0588  |
| washing machine     | 0.4667  | 0.7857  | 0.2105  | 0.4286  |
| body loofah         | 0.0556  | 0.5000  | 0.0000  | 0.0000  |
| beanbag             | 1.0000  | 1.0000  | 0.5000  | 0.5000  |
| pack                | 0.0407  | 0.6667  | 0.0015  | 0.0667  |
| pan                 | 0.0222  | 0.3529  | 0.0032  | 0.1176  |
| food                | 0.0917  | 0.1818  | 0.0040  | 0.0909  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jalousie            | 0.2500  | 1.0000  | 0.2500  | 1.0000  |
| balcony             | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radio               | 0.2500  | 0.2500  | 0.0039  | 0.2500  |
| crib                | 0.4474  | 1.0000  | 0.4474  | 1.0000  |
| drum                | 0.0312  | 0.1250  | 0.0000  | 0.0000  |
| arch                | 0.0009  | 0.0333  | 0.0009  | 0.0333  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bread               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| barricade           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cube                | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mop                 | 0.6842  | 1.0000  | 0.0000  | 0.0000  |
| flush               | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| countertop          | 0.0184  | 0.4286  | 0.0002  | 0.0714  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| steps               | 0.0010  | 0.2000  | 0.0000  | 0.1000  |
| wine                | 0.0176  | 0.1429  | 0.0000  | 0.0000  |
| garage door         | 0.1333  | 0.3333  | 0.0104  | 0.3333  |
| handle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headboard           | 0.0104  | 0.1250  | 0.0000  | 0.0000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchen island      | 0.1308  | 0.5714  | 0.1169  | 0.4286  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crate               | 0.6370  | 0.8571  | 0.0639  | 0.4286  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| menu                | 0.4759  | 0.7143  | 0.0062  | 0.1429  |
| spoon               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coil                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| grass               | 0.0042  | 1.0000  | 0.0000  | 0.0000  |
| letter              | 0.0100  | 0.2500  | 0.0000  | 0.0000  |
| sack                | 0.5332  | 0.8571  | 0.0000  | 0.0000  |
| glove               | 0.1833  | 0.6667  | 0.0000  | 0.0000  |
| salt                | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| bidet               | 0.4000  | 0.6667  | 0.3333  | 0.3333  |
| pool table          | 0.1111  | 0.3333  | 0.1111  | 0.3333  |
| treadmill           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.3734  | 0.5500  | 0.0388  | 0.2000  |
| water heater        | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| overall             | 0.1661  | 0.4077  | 0.0516  | 0.1532  |
| head                | 0.2317  | 0.5592  | 0.0757  | 0.2407  |
| common              | 0.1419  | 0.3749  | 0.0400  | 0.1241  |
| tail                | 0.1198  | 0.2739  | 0.0380  | 0.0879  |
| small               | 0.0462  | 0.1821  | 0.0048  | 0.0161  |
| medium              | 0.1764  | 0.4440  | 0.0570  | 0.1646  |
| large               | 0.0974  | 0.5251  | 0.0368  | 0.2824  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/19/2025 23:27:21 INFO metrics.py:437 | number of scannet results: 250
Rank[0/8] 06/19/2025 23:28:51 INFO metrics.py:332 | eval results of scannet:
+-------------------+---------+---------+---------+---------+
| classes           | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+-------------------+---------+---------+---------+---------+
| bathtub           | 0.8782  | 0.9062  | 0.7558  | 0.7812  |
| bin               | 0.6250  | 0.7830  | 0.1856  | 0.4000  |
| picture           | 0.2157  | 0.4291  | 0.0232  | 0.1119  |
| toilet paper      | 0.2981  | 0.4051  | 0.0084  | 0.0380  |
| toilet            | 0.9388  | 0.9643  | 0.5948  | 0.7321  |
| sink              | 0.7464  | 0.8523  | 0.2206  | 0.5341  |
| towel             | 0.3535  | 0.6116  | 0.0494  | 0.1818  |
| vanity            | 0.5704  | 1.0000  | 0.3454  | 0.7333  |
| doorframe         | 0.2674  | 0.7606  | 0.0531  | 0.3732  |
| stopcock          | 0.0980  | 0.4828  | 0.0007  | 0.0345  |
| rail              | 0.0337  | 0.5000  | 0.0111  | 0.1667  |
| backpack          | 0.6116  | 0.9286  | 0.2714  | 0.5357  |
| bottle            | 0.0440  | 0.3053  | 0.0015  | 0.0579  |
| curtain           | 0.5176  | 0.8407  | 0.0872  | 0.5044  |
| door              | 0.3146  | 0.8161  | 0.0500  | 0.3779  |
| bar               | 0.0059  | 0.1364  | 0.0000  | 0.0000  |
| hair dryer        | 0.0203  | 0.2857  | 0.0000  | 0.0000  |
| switch            | 0.0019  | 0.0633  | 0.0000  | 0.0000  |
| mirror            | 0.0584  | 0.6024  | 0.0052  | 0.1446  |
| tissue box        | 0.1184  | 0.5000  | 0.0214  | 0.1818  |
| microwave         | 0.6366  | 0.8500  | 0.1518  | 0.4000  |
| bag               | 0.2065  | 0.6575  | 0.0639  | 0.2329  |
| shower            | 0.0595  | 0.1429  | 0.0025  | 0.0714  |
| light             | 0.0918  | 0.3448  | 0.0000  | 0.0345  |
| counter           | 0.2103  | 0.8571  | 0.0485  | 0.4286  |
| rod               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| grab bar          | 0.0242  | 0.2500  | 0.0000  | 0.0000  |
| cup               | 0.1562  | 0.4348  | 0.0114  | 0.0725  |
| paper             | 0.0184  | 0.2491  | 0.0007  | 0.0264  |
| tissue            | 0.0610  | 0.3333  | 0.0022  | 0.1111  |
| cabinet           | 0.3131  | 0.7857  | 0.1480  | 0.5119  |
| paper cutter      | 0.3905  | 0.8000  | 0.0095  | 0.2000  |
| ledge             | 0.0171  | 0.6667  | 0.0152  | 0.1667  |
| toothpaste        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basket            | 0.1227  | 0.4231  | 0.0225  | 0.1154  |
| blanket           | 0.0409  | 0.5000  | 0.0044  | 0.3750  |
| faucet            | 0.0357  | 0.2500  | 0.0089  | 0.1250  |
| shelf             | 0.3537  | 0.8529  | 0.1524  | 0.6176  |
| toothbrush        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kettle            | 0.1013  | 1.0000  | 0.0417  | 0.2500  |
| box               | 0.1330  | 0.4405  | 0.0216  | 0.1389  |
| plunger           | 0.6071  | 0.8000  | 0.0053  | 0.4000  |
| soap dish         | 0.6703  | 0.7500  | 0.2002  | 0.4167  |
| clothes           | 0.1570  | 0.5817  | 0.0115  | 0.1830  |
| mat               | 0.0449  | 0.4000  | 0.0000  | 0.0000  |
| frame             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bed               | 0.8245  | 1.0000  | 0.7309  | 1.0000  |
| socket            | 0.0070  | 0.1748  | 0.0000  | 0.0000  |
| holder            | 0.2050  | 0.4000  | 0.0000  | 0.0000  |
| purse             | 0.0008  | 0.1667  | 0.0000  | 0.0000  |
| wrap              | 0.0070  | 1.0000  | 0.0000  | 0.0000  |
| decoration        | 0.0330  | 0.3333  | 0.0097  | 0.1250  |
| laptop            | 0.3829  | 0.4667  | 0.1194  | 0.2667  |
| hat               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plant             | 0.2825  | 0.4706  | 0.0445  | 0.1597  |
| couch             | 0.5876  | 0.9875  | 0.4411  | 0.9000  |
| refrigerator      | 0.7724  | 0.9000  | 0.2329  | 0.4500  |
| dish rack         | 0.3793  | 1.0000  | 0.0000  | 0.0000  |
| monitor           | 0.4972  | 0.6357  | 0.0515  | 0.1786  |
| stool             | 0.2543  | 0.7273  | 0.0593  | 0.2955  |
| dresser           | 0.6425  | 0.9459  | 0.4688  | 0.8108  |
| case              | 0.4914  | 0.9474  | 0.2403  | 0.5789  |
| toaster           | 0.0729  | 0.5000  | 0.0000  | 0.0000  |
| hamper            | 0.3455  | 0.6000  | 0.3321  | 0.4000  |
| chair             | 0.6030  | 0.7926  | 0.3660  | 0.5669  |
| keyboard          | 0.0384  | 0.0862  | 0.0000  | 0.0000  |
| mouse             | 0.0056  | 0.0952  | 0.0000  | 0.0000  |
| radiator          | 0.5930  | 0.7241  | 0.1480  | 0.3276  |
| blackboard        | 0.4397  | 0.7246  | 0.1619  | 0.3913  |
| table             | 0.4801  | 0.9038  | 0.3185  | 0.7154  |
| window            | 0.4118  | 0.8395  | 0.1001  | 0.4198  |
| desk              | 0.5417  | 0.9620  | 0.2636  | 0.7848  |
| telephone         | 0.3695  | 0.5938  | 0.0468  | 0.1875  |
| plug              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pen               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| book              | 0.0662  | 0.3667  | 0.0024  | 0.0833  |
| computer          | 0.1694  | 0.5128  | 0.0051  | 0.0256  |
| pad               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blinds            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| windowsill        | 0.0129  | 0.8333  | 0.0003  | 0.1667  |
| board             | 0.0424  | 0.6562  | 0.0081  | 0.2188  |
| notebook          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pipe              | 0.0034  | 0.0714  | 0.0000  | 0.0000  |
| pillow            | 0.4292  | 0.6806  | 0.0982  | 0.3056  |
| stand             | 0.7857  | 0.9245  | 0.6827  | 0.8302  |
| lamp              | 0.3178  | 0.6429  | 0.1362  | 0.2738  |
| tv                | 0.7784  | 0.9130  | 0.1065  | 0.3478  |
| wardrobe          | 0.4844  | 0.8750  | 0.2750  | 0.5000  |
| shoe              | 0.1187  | 0.3750  | 0.0138  | 0.0500  |
| printer           | 0.6069  | 0.9697  | 0.2996  | 0.5758  |
| toy               | 0.0592  | 0.2353  | 0.0000  | 0.0000  |
| bench             | 0.6823  | 1.0000  | 0.3375  | 0.8000  |
| ottoman           | 0.0197  | 0.5714  | 0.0025  | 0.2857  |
| clock             | 0.3629  | 0.6000  | 0.0002  | 0.0400  |
| calendar          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stairs            | 0.2195  | 0.8000  | 0.0039  | 0.2000  |
| cushion           | 0.4722  | 1.0000  | 0.0022  | 0.2500  |
| carpet            | 0.0108  | 0.3000  | 0.0000  | 0.0000  |
| poster            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fan               | 0.6815  | 0.8750  | 0.2083  | 0.2500  |
| rack              | 0.0120  | 0.3636  | 0.0104  | 0.1818  |
| container         | 0.0042  | 0.2609  | 0.0001  | 0.0435  |
| guitar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jacket            | 0.5419  | 0.7826  | 0.0908  | 0.3478  |
| person            | 0.0797  | 0.6667  | 0.0007  | 0.1667  |
| coffee maker      | 0.6554  | 0.7059  | 0.1559  | 0.2941  |
| tray              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sign              | 0.0701  | 0.2500  | 0.0023  | 0.0714  |
| speaker           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jar               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plate             | 0.0889  | 0.4000  | 0.0000  | 0.0000  |
| broom             | 0.2137  | 0.5000  | 0.0044  | 0.0833  |
| stall             | 0.0249  | 0.8824  | 0.0071  | 0.2353  |
| brush             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| alarm             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ladder            | 0.1174  | 0.6000  | 0.0045  | 0.2000  |
| scissors          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket            | 0.1139  | 0.5714  | 0.0300  | 0.1429  |
| scale             | 0.2609  | 0.5000  | 0.0000  | 0.0000  |
| dispenser         | 0.6308  | 0.7436  | 0.0924  | 0.2051  |
| soap dispenser    | 0.4439  | 0.6667  | 0.0034  | 0.0476  |
| mailbox           | 0.0108  | 0.6000  | 0.0041  | 0.4000  |
| machine           | 0.5620  | 1.0000  | 0.2608  | 0.8125  |
| fire extinguisher | 0.0054  | 0.3333  | 0.0000  | 0.0000  |
| pillar            | 0.2000  | 0.2000  | 0.0000  | 0.0000  |
| car               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cart              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| eraser            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bowl              | 0.0926  | 0.2222  | 0.0370  | 0.1111  |
| dvd               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tube              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| file              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headphones        | 0.3040  | 0.6000  | 0.0000  | 0.0000  |
| ball              | 0.3955  | 0.5000  | 0.2500  | 0.2500  |
| map               | 0.1429  | 1.0000  | 0.0000  | 0.0000  |
| water cooler      | 0.5114  | 1.0000  | 0.5000  | 0.5000  |
| coat hanger       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| remote control    | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| teapot            | 0.0090  | 0.5000  | 0.0000  | 0.0000  |
| oven              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher        | 0.1667  | 0.3333  | 0.0000  | 0.0000  |
| stove             | 0.7324  | 1.0000  | 0.5168  | 0.7778  |
| pitcher           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drawer            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pot               | 0.0053  | 0.5000  | 0.0000  | 0.0000  |
| flowerpot         | 0.0059  | 0.5000  | 0.0000  | 0.0000  |
| range hood        | 0.3000  | 0.4000  | 0.0000  | 0.0000  |
| vacuum cleaner    | 0.6667  | 0.6667  | 0.6667  | 0.6667  |
| bicycle           | 0.7500  | 1.0000  | 0.2170  | 1.0000  |
| copier            | 0.6873  | 0.9167  | 0.2073  | 0.5000  |
| structure         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stapler           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wire              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basin             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mattress          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crate             | 0.0114  | 0.5000  | 0.0114  | 0.5000  |
| clothes dryer     | 0.4500  | 1.0000  | 0.1000  | 0.5000  |
| fruit             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine   | 0.4444  | 0.6667  | 0.4444  | 0.6667  |
| -------           | ------- | ------- | ------- | ------- |
| scannet           | 0.2294  | 0.4783  | 0.0852  | 0.2090  |
| head              | 0.3037  | 0.5942  | 0.1203  | 0.2851  |
| common            | 0.1735  | 0.3796  | 0.0542  | 0.1464  |
| tail              | 0.0757  | 0.2690  | 0.0245  | 0.0659  |
| small             | 0.0395  | 0.1661  | 0.0043  | 0.0203  |
| medium            | 0.2652  | 0.5735  | 0.0937  | 0.2386  |
| large             | 0.2265  | 0.8213  | 0.0936  | 0.5122  |
+-------------------+---------+---------+---------+---------+
Rank[0/8] 06/19/2025 23:28:51 INFO metrics.py:437 | number of 3rscan results: 235
Rank[0/8] 06/19/2025 23:30:11 INFO metrics.py:332 | eval results of 3rscan:
+-------------------+---------+---------+---------+---------+
| classes           | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+-------------------+---------+---------+---------+---------+
| monitor           | 0.4815  | 0.7250  | 0.0220  | 0.1000  |
| keyboard          | 0.0049  | 0.0833  | 0.0000  | 0.0000  |
| chair             | 0.6390  | 0.8752  | 0.4207  | 0.6526  |
| mouse             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| box               | 0.1792  | 0.5185  | 0.0256  | 0.1394  |
| paper             | 0.0222  | 0.2714  | 0.0019  | 0.0714  |
| desk              | 0.3148  | 0.8250  | 0.1308  | 0.5625  |
| cup               | 0.0158  | 0.3590  | 0.0012  | 0.0256  |
| case              | 0.0381  | 0.3857  | 0.0009  | 0.0643  |
| table             | 0.4404  | 0.8559  | 0.2583  | 0.6126  |
| bag               | 0.2677  | 0.6465  | 0.0051  | 0.1313  |
| cabinet           | 0.2827  | 0.8248  | 0.1408  | 0.5256  |
| tissue box        | 0.0470  | 0.6667  | 0.0303  | 0.3333  |
| bin               | 0.5713  | 0.7805  | 0.1191  | 0.3049  |
| pen               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bottle            | 0.0338  | 0.3223  | 0.0003  | 0.0331  |
| book              | 0.0517  | 0.4083  | 0.0036  | 0.0592  |
| window            | 0.1470  | 0.6233  | 0.0124  | 0.2055  |
| plug              | 0.0802  | 0.2222  | 0.0000  | 0.0000  |
| curtain           | 0.3662  | 0.8889  | 0.0766  | 0.5000  |
| computer          | 0.1754  | 0.5714  | 0.0145  | 0.1905  |
| garbage           | 0.4202  | 0.7500  | 0.1092  | 0.3750  |
| stand             | 0.3472  | 0.7869  | 0.1763  | 0.5574  |
| notebook          | 0.0002  | 0.0909  | 0.0000  | 0.0000  |
| paper cutter      | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| pad               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plate             | 0.0232  | 0.3333  | 0.0000  | 0.0303  |
| device            | 0.0471  | 0.1818  | 0.0000  | 0.0000  |
| rack              | 0.0745  | 0.6667  | 0.0461  | 0.3667  |
| picture           | 0.0855  | 0.3130  | 0.0053  | 0.0528  |
| shelf             | 0.1282  | 0.6572  | 0.0303  | 0.2968  |
| stool             | 0.1993  | 0.6852  | 0.0870  | 0.3704  |
| telephone         | 0.2319  | 0.5000  | 0.0217  | 0.1667  |
| blinds            | 0.1372  | 0.5641  | 0.0042  | 0.2308  |
| folder            | 0.0411  | 0.3571  | 0.0000  | 0.0000  |
| printer           | 0.6256  | 0.7778  | 0.1111  | 0.2222  |
| toy               | 0.3189  | 0.7692  | 0.1310  | 0.3077  |
| file              | 0.1337  | 0.5000  | 0.0210  | 0.1786  |
| container         | 0.0002  | 0.1111  | 0.0000  | 0.0000  |
| socket            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket            | 0.6021  | 0.8667  | 0.0780  | 0.2000  |
| wardrobe          | 0.3110  | 0.7500  | 0.0715  | 0.4844  |
| hat               | 0.0026  | 0.1429  | 0.0000  | 0.0000  |
| tv                | 0.5270  | 0.8571  | 0.1015  | 0.2571  |
| commode           | 0.3963  | 0.8462  | 0.3172  | 0.6731  |
| lamp              | 0.2345  | 0.4625  | 0.0032  | 0.0750  |
| pillow            | 0.3770  | 0.7305  | 0.0138  | 0.1406  |
| door              | 0.2210  | 0.6618  | 0.0332  | 0.2500  |
| couch             | 0.3196  | 0.8571  | 0.1932  | 0.6857  |
| light             | 0.0359  | 0.3068  | 0.0017  | 0.0682  |
| cushion           | 0.1731  | 0.6667  | 0.0148  | 0.1667  |
| ottoman           | 0.1538  | 0.9091  | 0.0779  | 0.7273  |
| candle            | 0.0372  | 0.2667  | 0.0000  | 0.0000  |
| price tag         | 0.0738  | 0.2857  | 0.0000  | 0.0000  |
| frame             | 0.0146  | 0.3333  | 0.0001  | 0.0333  |
| kitchenware       | 0.1515  | 0.4545  | 0.0206  | 0.1136  |
| switch            | 0.0000  | 0.0189  | 0.0000  | 0.0000  |
| teapot            | 0.0357  | 0.2500  | 0.0357  | 0.2500  |
| bowl              | 0.1057  | 0.5556  | 0.0078  | 0.1111  |
| flowerpot         | 0.0438  | 0.4583  | 0.0002  | 0.0417  |
| plant             | 0.2734  | 0.5693  | 0.0357  | 0.1832  |
| vase              | 0.2645  | 0.4500  | 0.0168  | 0.1000  |
| blanket           | 0.2293  | 0.8923  | 0.0699  | 0.4308  |
| decoration        | 0.0772  | 0.3438  | 0.0175  | 0.0938  |
| doorframe         | 0.1258  | 0.6400  | 0.0106  | 0.1800  |
| bench             | 0.3348  | 0.8214  | 0.1972  | 0.5357  |
| napkins           | 0.3430  | 0.6667  | 0.0000  | 0.0000  |
| laptop            | 0.1884  | 0.8333  | 0.0097  | 0.3333  |
| poster            | 0.0093  | 0.1429  | 0.0000  | 0.0000  |
| dispenser         | 0.0063  | 0.3333  | 0.0000  | 0.0000  |
| clock             | 0.0518  | 0.2105  | 0.0000  | 0.0000  |
| footstool         | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| bar               | 0.4286  | 0.6667  | 0.0004  | 0.3333  |
| bed               | 0.6729  | 0.9091  | 0.2836  | 0.6727  |
| player            | 0.1667  | 0.3333  | 0.1667  | 0.3333  |
| carpet            | 0.0725  | 0.4821  | 0.0000  | 0.0179  |
| towel             | 0.1847  | 0.5949  | 0.0408  | 0.1266  |
| magazine          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes           | 0.1442  | 0.6408  | 0.0147  | 0.1748  |
| mat               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mirror            | 0.1041  | 0.4074  | 0.0080  | 0.0741  |
| candlestick       | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| blackboard        | 0.5579  | 0.9167  | 0.0697  | 0.3333  |
| backpack          | 0.4826  | 0.7576  | 0.0995  | 0.2424  |
| humidifier        | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| fan               | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| heater            | 0.3840  | 0.6667  | 0.0825  | 0.1754  |
| roll              | 0.1281  | 0.4000  | 0.0000  | 0.0000  |
| windowsill        | 0.0315  | 0.5128  | 0.0039  | 0.1538  |
| radiator          | 0.3386  | 0.5172  | 0.0172  | 0.0345  |
| umbrella          | 0.0769  | 1.0000  | 0.0000  | 0.0000  |
| conducting wire   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| partition         | 0.0567  | 0.8000  | 0.0012  | 0.2000  |
| shoe              | 0.0951  | 0.3571  | 0.0003  | 0.0476  |
| board             | 0.1827  | 0.5833  | 0.0213  | 0.1250  |
| pot               | 0.2019  | 0.5000  | 0.0076  | 0.0833  |
| kettle            | 0.1457  | 0.6667  | 0.0467  | 0.2500  |
| chandelier        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| album             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| remote control    | 0.0476  | 0.2500  | 0.0033  | 0.0500  |
| toilet paper      | 0.0239  | 0.2500  | 0.0000  | 0.0000  |
| luggage           | 0.2720  | 1.0000  | 0.1667  | 0.3333  |
| guitar            | 0.2019  | 1.0000  | 0.1250  | 0.5000  |
| microwave         | 0.7884  | 1.0000  | 0.1507  | 0.4118  |
| hanger            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basket            | 0.4275  | 0.6719  | 0.1366  | 0.2656  |
| dresser           | 0.8333  | 1.0000  | 0.0114  | 0.5000  |
| fruit             | 0.3879  | 0.6000  | 0.2000  | 0.2000  |
| counter           | 0.0913  | 0.6341  | 0.0033  | 0.1951  |
| rail              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bathtub           | 0.8889  | 0.8889  | 0.5139  | 0.5556  |
| holder            | 0.0435  | 1.0000  | 0.0000  | 0.0000  |
| sink              | 0.5939  | 0.8214  | 0.0542  | 0.1786  |
| adhesive tape     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jar               | 0.0206  | 0.5000  | 0.0000  | 0.0000  |
| scale             | 0.5556  | 0.6667  | 0.0000  | 0.0000  |
| sunglasses        | 0.0894  | 0.3846  | 0.0037  | 0.0769  |
| furniture         | 0.0370  | 1.0000  | 0.0062  | 0.3333  |
| statue            | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| faucet            | 0.0556  | 0.3333  | 0.0000  | 0.0000  |
| clothes dryer     | 0.2418  | 0.6000  | 0.1100  | 0.4000  |
| shower            | 0.0519  | 0.7500  | 0.0056  | 0.2500  |
| toilet            | 0.7369  | 0.7647  | 0.3209  | 0.4706  |
| toiletry          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| brush             | 0.1302  | 0.5000  | 0.0000  | 0.0000  |
| shampoo           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue            | 0.0167  | 0.3333  | 0.0000  | 0.0000  |
| soap dish         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basin             | 0.0167  | 0.0667  | 0.0000  | 0.0000  |
| washing machine   | 0.5237  | 0.7857  | 0.1344  | 0.2857  |
| body loofah       | 0.0833  | 0.5000  | 0.0000  | 0.0000  |
| tube              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beanbag           | 1.0000  | 1.0000  | 0.5000  | 0.5000  |
| speaker           | 0.0022  | 0.1667  | 0.0000  | 0.0000  |
| can               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stairs            | 0.1429  | 0.5714  | 0.0051  | 0.3571  |
| pillar            | 0.2865  | 0.6471  | 0.0957  | 0.2941  |
| pack              | 0.0474  | 0.7692  | 0.0017  | 0.0769  |
| column            | 0.0538  | 1.0000  | 0.0294  | 0.5000  |
| sign              | 0.1667  | 0.5000  | 0.0000  | 0.0000  |
| stove             | 0.2795  | 0.7647  | 0.0392  | 0.1176  |
| oven              | 0.5078  | 0.9474  | 0.0658  | 0.2632  |
| pan               | 0.0387  | 0.3333  | 0.0062  | 0.1667  |
| refrigerator      | 0.5879  | 0.8667  | 0.3154  | 0.5333  |
| range hood        | 0.0783  | 0.6000  | 0.0011  | 0.2000  |
| coffee maker      | 0.1007  | 0.5556  | 0.0437  | 0.2222  |
| food              | 0.1014  | 0.2000  | 0.0043  | 0.1000  |
| knife             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jalousie          | 0.2500  | 1.0000  | 0.2500  | 1.0000  |
| balcony           | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| screen            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wire              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drawer            | 0.3204  | 0.6667  | 0.2648  | 0.6667  |
| radio             | 1.0000  | 1.0000  | 0.0159  | 1.0000  |
| crib              | 0.4474  | 1.0000  | 0.4474  | 1.0000  |
| calendar          | 0.0016  | 0.2000  | 0.0000  | 0.0000  |
| fireplace         | 0.0750  | 0.4000  | 0.0000  | 0.0000  |
| ball              | 0.3750  | 0.6000  | 0.2500  | 0.4000  |
| piano             | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| bread             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tablet            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine           | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| bicycle           | 0.5357  | 1.0000  | 0.1250  | 0.5000  |
| mattress          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cube              | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| ladder            | 0.1667  | 1.0000  | 0.0357  | 0.6667  |
| pipe              | 0.2535  | 0.7500  | 0.0000  | 0.0000  |
| broom             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mop               | 0.7167  | 1.0000  | 0.0000  | 0.0000  |
| flush             | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| toothbrush        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| menu              | 0.4759  | 0.7143  | 0.0062  | 0.1429  |
| spoon             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| air conditioner   | 0.4167  | 0.5000  | 0.0000  | 0.0000  |
| hook              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| window frame      | 0.0059  | 0.1667  | 0.0013  | 0.0833  |
| toaster           | 0.6991  | 1.0000  | 0.0000  | 0.0000  |
| dumbbell          | 0.0588  | 1.0000  | 0.0000  | 0.0000  |
| vacuum cleaner    | 0.0250  | 0.2500  | 0.0000  | 0.0000  |
| crate             | 0.9250  | 1.0000  | 0.0952  | 0.4000  |
| coil              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tray              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| letter            | 0.0100  | 0.2500  | 0.0000  | 0.0000  |
| sack              | 0.5332  | 0.8571  | 0.0000  | 0.0000  |
| glove             | 0.1833  | 0.6667  | 0.0000  | 0.0000  |
| soap dispenser    | 0.0154  | 0.2000  | 0.0000  | 0.0000  |
| salt              | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| glass             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water heater      | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| bidet             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| dish rack         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tool              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scissors          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cosmetics         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| seasoning         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| treadmill         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher        | 0.5000  | 0.6667  | 0.3333  | 0.3333  |
| washbasin         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| -------           | ------- | ------- | ------- | ------- |
| 3rscan            | 0.2389  | 0.5032  | 0.0617  | 0.1743  |
| head              | 0.2427  | 0.5793  | 0.0627  | 0.2256  |
| common            | 0.2363  | 0.4939  | 0.0572  | 0.1342  |
| tail              | 0.2353  | 0.3674  | 0.0664  | 0.1306  |
| small             | 0.0605  | 0.2253  | 0.0087  | 0.0171  |
| medium            | 0.2548  | 0.5540  | 0.0707  | 0.2007  |
| large             | 0.1672  | 0.6654  | 0.0683  | 0.3799  |
+-------------------+---------+---------+---------+---------+
Rank[0/8] 06/19/2025 23:30:11 INFO metrics.py:437 | number of matterport3d results: 339
Rank[0/8] 06/19/2025 23:31:38 INFO metrics.py:332 | eval results of matterport3d:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| doorframe           | 0.2779  | 0.6939  | 0.0844  | 0.3641  |
| mirror              | 0.0610  | 0.4865  | 0.0014  | 0.1351  |
| door                | 0.1408  | 0.5927  | 0.0103  | 0.1749  |
| plant               | 0.2634  | 0.6875  | 0.0708  | 0.3036  |
| picture             | 0.1347  | 0.3237  | 0.0019  | 0.0531  |
| light               | 0.0143  | 0.1595  | 0.0001  | 0.0228  |
| towel               | 0.2976  | 0.5364  | 0.0359  | 0.1182  |
| sink                | 0.4880  | 0.7374  | 0.1261  | 0.3434  |
| dresser             | 0.4264  | 0.8667  | 0.2537  | 0.7333  |
| countertop          | 0.0184  | 0.4286  | 0.0002  | 0.0714  |
| toilet              | 0.7822  | 0.8438  | 0.5065  | 0.5938  |
| vent                | 0.0440  | 0.0833  | 0.0000  | 0.0000  |
| bathtub             | 0.6088  | 0.7407  | 0.3451  | 0.5185  |
| faucet              | 0.0296  | 0.2778  | 0.0000  | 0.0000  |
| shower              | 0.0002  | 0.1667  | 0.0000  | 0.0000  |
| cabinet             | 0.0999  | 0.5466  | 0.0466  | 0.3178  |
| case                | 0.0006  | 0.2222  | 0.0000  | 0.0556  |
| drawer              | 0.0050  | 0.2500  | 0.0048  | 0.2500  |
| decoration          | 0.0144  | 0.3690  | 0.0012  | 0.0833  |
| vase                | 0.1089  | 0.5667  | 0.0212  | 0.2333  |
| roof                | 0.0015  | 0.2917  | 0.0001  | 0.0417  |
| counter             | 0.0085  | 0.4242  | 0.0008  | 0.0909  |
| window              | 0.0766  | 0.4694  | 0.0078  | 0.1222  |
| lamp                | 0.3372  | 0.6154  | 0.0855  | 0.3187  |
| vanity              | 0.0911  | 0.8000  | 0.0815  | 0.6667  |
| chair               | 0.3759  | 0.7653  | 0.2216  | 0.5340  |
| flowerpot           | 0.0514  | 0.6400  | 0.0043  | 0.2000  |
| curtain             | 0.3808  | 0.7604  | 0.1043  | 0.3958  |
| washbasin           | 0.0385  | 0.2857  | 0.0357  | 0.0714  |
| bottle              | 0.0704  | 0.3750  | 0.0038  | 0.1042  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fan                 | 0.4800  | 0.7778  | 0.0288  | 0.2407  |
| soap                | 0.0202  | 0.6000  | 0.0000  | 0.0000  |
| blanket             | 0.0310  | 0.4545  | 0.0003  | 0.0909  |
| window frame        | 0.0269  | 0.4828  | 0.0037  | 0.1954  |
| toiletry            | 0.0641  | 0.2727  | 0.0023  | 0.0455  |
| switch              | 0.0001  | 0.0500  | 0.0001  | 0.0500  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pot                 | 0.0283  | 0.3846  | 0.0000  | 0.0000  |
| box                 | 0.0327  | 0.6154  | 0.0100  | 0.2308  |
| shelf               | 0.1022  | 0.5109  | 0.0334  | 0.2774  |
| bench               | 0.0916  | 0.4500  | 0.0868  | 0.3000  |
| thermostat          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| container           | 0.2814  | 0.4815  | 0.0510  | 0.2222  |
| stand               | 0.4918  | 0.7600  | 0.2440  | 0.5400  |
| table               | 0.2603  | 0.7051  | 0.1851  | 0.5256  |
| blinds              | 0.0005  | 0.1000  | 0.0000  | 0.0000  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dispenser           | 0.0417  | 0.5000  | 0.0417  | 0.5000  |
| book                | 0.0747  | 0.3780  | 0.0205  | 0.1024  |
| plate               | 0.0150  | 0.3571  | 0.0000  | 0.0000  |
| pillow              | 0.2378  | 0.5804  | 0.0154  | 0.1388  |
| statue              | 0.0212  | 0.3529  | 0.0159  | 0.1765  |
| windowsill          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| paper               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| couch               | 0.3217  | 0.8261  | 0.1661  | 0.6413  |
| cushion             | 0.0026  | 0.1765  | 0.0002  | 0.0588  |
| tv                  | 0.3224  | 0.5814  | 0.0548  | 0.2791  |
| screen              | 0.0357  | 0.2500  | 0.0034  | 0.2500  |
| ottoman             | 0.0868  | 0.5556  | 0.0387  | 0.2778  |
| stairs              | 0.0336  | 0.2642  | 0.0051  | 0.0755  |
| teapot              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| footstool           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| carpet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fireplace           | 0.2034  | 0.4783  | 0.0012  | 0.0870  |
| stool               | 0.0144  | 0.3810  | 0.0113  | 0.1905  |
| speaker             | 0.0895  | 0.5000  | 0.0000  | 0.0000  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bowl                | 0.0674  | 0.3333  | 0.0047  | 0.0909  |
| food                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bed                 | 0.5511  | 0.8246  | 0.3490  | 0.7368  |
| steps               | 0.0010  | 0.2000  | 0.0000  | 0.1000  |
| candlestick         | 0.1536  | 1.0000  | 0.0000  | 0.0000  |
| clock               | 0.3373  | 0.6667  | 0.3333  | 0.3333  |
| toy                 | 0.0919  | 0.3889  | 0.0060  | 0.0833  |
| frame               | 0.0038  | 0.1765  | 0.0000  | 0.0000  |
| column              | 0.0121  | 0.3056  | 0.0007  | 0.1111  |
| wine                | 0.0176  | 0.1429  | 0.0000  | 0.0000  |
| wardrobe            | 0.1918  | 0.5455  | 0.0984  | 0.3636  |
| pillar              | 0.3620  | 0.7619  | 0.0885  | 0.2857  |
| laptop              | 0.8333  | 1.0000  | 0.0094  | 0.5000  |
| desk                | 0.2270  | 0.7368  | 0.0928  | 0.5263  |
| fruit               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garage door         | 0.1333  | 0.3333  | 0.0104  | 0.3333  |
| mat                 | 0.0026  | 0.1250  | 0.0000  | 0.0000  |
| handle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bin                 | 0.2245  | 0.6829  | 0.0607  | 0.2195  |
| toilet paper        | 0.3429  | 0.5278  | 0.0324  | 0.1667  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rail                | 0.0071  | 0.2963  | 0.0000  | 0.0185  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headboard           | 0.0104  | 0.1250  | 0.0000  | 0.0000  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| monitor             | 0.0357  | 0.2500  | 0.0000  | 0.0000  |
| telephone           | 0.4167  | 0.5000  | 0.0000  | 0.0000  |
| soap dispenser      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.4938  | 0.8750  | 0.3138  | 0.5000  |
| doorway             | 0.0004  | 0.1111  | 0.0000  | 0.0000  |
| clothes dryer       | 0.1481  | 0.6667  | 0.1481  | 0.6667  |
| hanger              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| partition           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rack                | 0.0022  | 0.3636  | 0.0002  | 0.1818  |
| tissue              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket              | 0.0057  | 0.2000  | 0.0031  | 0.1000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchen island      | 0.1308  | 0.5714  | 0.1169  | 0.4286  |
| heater              | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| cup                 | 0.0080  | 0.2000  | 0.0000  | 0.0000  |
| board               | 0.0404  | 0.5000  | 0.0000  | 0.0000  |
| piano               | 0.0278  | 0.5000  | 0.0135  | 0.5000  |
| printer             | 0.4350  | 0.5714  | 0.2381  | 0.2857  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radiator            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball                | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| blackboard          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| keyboard            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kettle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue box          | 0.0624  | 0.5000  | 0.0000  | 0.0000  |
| refrigerator        | 0.3026  | 0.6667  | 0.0897  | 0.4000  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hat                 | 0.1429  | 1.0000  | 0.0000  | 0.0000  |
| scale               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basket              | 0.1375  | 0.5600  | 0.0076  | 0.1600  |
| magazine            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basin               | 0.0043  | 1.0000  | 0.0043  | 1.0000  |
| computer            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bag                 | 0.1088  | 0.3846  | 0.0026  | 0.0769  |
| guitar              | 0.0833  | 0.2857  | 0.0179  | 0.1429  |
| bar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pipe                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| projector           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| microwave           | 0.1527  | 0.5833  | 0.0851  | 0.1667  |
| clothes             | 0.1174  | 0.4828  | 0.0060  | 0.1034  |
| fire extinguisher   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ladder              | 0.0494  | 0.2222  | 0.0000  | 0.0000  |
| pan                 | 0.0426  | 0.4000  | 0.0000  | 0.0000  |
| machine             | 0.2000  | 1.0000  | 0.2000  | 1.0000  |
| soap dish           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beam                | 0.0004  | 0.1429  | 0.0000  | 0.0476  |
| arch                | 0.0009  | 0.0333  | 0.0009  | 0.0333  |
| holder              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| air conditioner     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| chandelier          | 0.1200  | 0.3684  | 0.0000  | 0.0000  |
| oven                | 0.0336  | 0.4000  | 0.0000  | 0.0000  |
| stove               | 0.1919  | 0.5000  | 0.0009  | 0.1000  |
| range hood          | 0.1000  | 0.5000  | 0.0000  | 0.0000  |
| kitchenware         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.0074  | 0.2500  | 0.0000  | 0.0000  |
| file                | 0.1583  | 0.5000  | 0.0250  | 0.2500  |
| toaster             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hamper              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| backpack            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shoe                | 0.0338  | 0.1600  | 0.0057  | 0.0400  |
| socket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jacket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| grass               | 0.0042  | 1.0000  | 0.0000  | 0.0000  |
| bidet               | 0.0833  | 0.5000  | 0.0000  | 0.0000  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool table          | 0.1111  | 0.3333  | 0.1111  | 0.3333  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mouse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.4149  | 0.6111  | 0.0431  | 0.2222  |
| player              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drum                | 0.0625  | 0.1250  | 0.0000  | 0.0000  |
| coffee maker        | 0.0667  | 0.3333  | 0.0000  | 0.0000  |
| pack                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| matterport3d        | 0.1009  | 0.3130  | 0.0392  | 0.1355  |
| head                | 0.1666  | 0.4372  | 0.0647  | 0.1944  |
| common              | 0.0412  | 0.2073  | 0.0164  | 0.0914  |
| tail                | 0.0272  | 0.1545  | 0.0095  | 0.0438  |
| small               | 0.0297  | 0.1206  | 0.0142  | 0.0331  |
| medium              | 0.1000  | 0.3404  | 0.0376  | 0.1313  |
| large               | 0.0760  | 0.4673  | 0.0263  | 0.2164  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/19/2025 23:31:38 INFO metrics.py:437 | number of arkit results: 0
Rank[0/8] 06/19/2025 23:31:38 INFO stats.py:394 | Epoch[69] completed. Training Speed: 2.85 samples/sec across all devices.	Epoch Time: 1096.43 sec.	Average Epoch Time: 1096.43 sec.	Average Step Time: 2.81 sec.	Estimated Remaining Time: 22:38:30.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:31:39 WARNING accelerator.py:3099 | Deleting 1 checkpoints to make room for new checkpoint.
Rank[0/8] 06/19/2025 23:31:39 INFO accelerator.py:3111 | Saving current state to /job_data/checkpoints/checkpoint_6
Rank[7/8] 06/19/2025 23:31:40 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 69 to /job_data/checkpoints/checkpoint_6
Rank[3/8] 06/19/2025 23:31:40 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 69 to /job_data/checkpoints/checkpoint_6
Rank[6/8] 06/19/2025 23:31:40 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 69 to /job_data/checkpoints/checkpoint_6
Rank[5/8] 06/19/2025 23:31:40 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 69 to /job_data/checkpoints/checkpoint_6
Rank[2/8] 06/19/2025 23:31:40 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 69 to /job_data/checkpoints/checkpoint_6
Rank[4/8] 06/19/2025 23:31:40 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 69 to /job_data/checkpoints/checkpoint_6
Rank[1/8] 06/19/2025 23:31:40 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 69 to /job_data/checkpoints/checkpoint_6
Rank[0/8] 06/19/2025 23:31:41 INFO checkpointing.py:106 | Model weights saved in /job_data/checkpoints/checkpoint_6/model.safetensors
Rank[0/8] 06/19/2025 23:31:44 INFO checkpointing.py:113 | Optimizer state saved in /job_data/checkpoints/checkpoint_6/optimizer.bin
Rank[0/8] 06/19/2025 23:31:44 INFO checkpointing.py:120 | Scheduler state saved in /job_data/checkpoints/checkpoint_6/scheduler.bin
Rank[0/8] 06/19/2025 23:31:44 INFO checkpointing.py:137 | Sampler state for dataloader 0 saved in /job_data/checkpoints/checkpoint_6/sampler.bin
Rank[0/8] 06/19/2025 23:31:44 INFO checkpointing.py:137 | Sampler state for dataloader 1 saved in /job_data/checkpoints/checkpoint_6/sampler_1.bin
Rank[0/8] 06/19/2025 23:31:44 INFO checkpointing.py:164 | Random states saved in /job_data/checkpoints/checkpoint_6/random_states_0.pkl
Rank[0/8] 06/19/2025 23:31:44 INFO checkpointing.py:300 | Saving the state of TrainerProgressState to /job_data/checkpoints/checkpoint_6/custom_checkpoint_0.pkl
Rank[0/8] 06/19/2025 23:31:44 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 69 to /job_data/checkpoints/checkpoint_6
Rank[0/8] 06/19/2025 23:32:13 INFO stats.py:314 | Epoch[70] Step[24] GlobalStep[27324] Training Speed: 6.71 samples/sec across all devices.	Average Step Time: 1.19 sec.	Estimated Remaining Time: 22:38:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:32:13 INFO loss_tracker.py:84 | Epoch[70/239] Step[24] GlobalStep[27324/NA]: det_loss_cls_0[0.5669] det_loss_box_0[0.7346] det_loss_cls_1[0.5434] det_loss_box_1[0.5808] det_loss_cls_2[0.5287] det_loss_box_2[0.5153] det_loss_cls_3[0.5020] det_loss_box_3[0.4981] det_loss_cls_4[0.4691] det_loss_box_4[0.4866] det_loss_cls_5[0.4553] det_loss_box_5[0.4868] det_loss_cls_dn_0[0.1026] det_loss_box_dn_0[0.2794] det_loss_cls_dn_1[0.0997] det_loss_box_dn_1[0.2396] det_loss_cls_dn_2[0.1001] det_loss_box_dn_2[0.2262] det_loss_cls_dn_3[0.0985] det_loss_box_dn_3[0.2235] det_loss_cls_dn_4[0.0976] det_loss_box_dn_4[0.2224] det_loss_cls_dn_5[0.0977] det_loss_box_dn_5[0.2217] loss_depth[0.0385] total_loss[8.4153]
Rank[0/8] 06/19/2025 23:32:39 INFO stats.py:314 | Epoch[70] Step[49] GlobalStep[27349] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 22:37:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:32:39 INFO loss_tracker.py:84 | Epoch[70/239] Step[49] GlobalStep[27349/NA]: det_loss_cls_0[0.5097] det_loss_box_0[0.7708] det_loss_cls_1[0.4963] det_loss_box_1[0.6175] det_loss_cls_2[0.4761] det_loss_box_2[0.5603] det_loss_cls_3[0.4545] det_loss_box_3[0.5284] det_loss_cls_4[0.4288] det_loss_box_4[0.5225] det_loss_cls_5[0.4216] det_loss_box_5[0.5196] det_loss_cls_dn_0[0.1027] det_loss_box_dn_0[0.3417] det_loss_cls_dn_1[0.0987] det_loss_box_dn_1[0.2944] det_loss_cls_dn_2[0.0980] det_loss_box_dn_2[0.2812] det_loss_cls_dn_3[0.0979] det_loss_box_dn_3[0.2779] det_loss_cls_dn_4[0.0968] det_loss_box_dn_4[0.2775] det_loss_cls_dn_5[0.0966] det_loss_box_dn_5[0.2760] loss_depth[0.0408] total_loss[8.6862]
Rank[0/8] 06/19/2025 23:33:05 INFO stats.py:314 | Epoch[70] Step[74] GlobalStep[27374] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 22:36:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:33:05 INFO loss_tracker.py:84 | Epoch[70/239] Step[74] GlobalStep[27374/NA]: det_loss_cls_0[0.5574] det_loss_box_0[0.7179] det_loss_cls_1[0.5311] det_loss_box_1[0.5700] det_loss_cls_2[0.4989] det_loss_box_2[0.5088] det_loss_cls_3[0.4621] det_loss_box_3[0.4896] det_loss_cls_4[0.4276] det_loss_box_4[0.4867] det_loss_cls_5[0.4158] det_loss_box_5[0.4827] det_loss_cls_dn_0[0.1024] det_loss_box_dn_0[0.3225] det_loss_cls_dn_1[0.0983] det_loss_box_dn_1[0.2770] det_loss_cls_dn_2[0.0981] det_loss_box_dn_2[0.2600] det_loss_cls_dn_3[0.0969] det_loss_box_dn_3[0.2555] det_loss_cls_dn_4[0.0959] det_loss_box_dn_4[0.2540] det_loss_cls_dn_5[0.0950] det_loss_box_dn_5[0.2504] loss_depth[0.0449] total_loss[8.3993]
Rank[0/8] 06/19/2025 23:33:30 INFO stats.py:314 | Epoch[70] Step[99] GlobalStep[27399] Training Speed: 7.78 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 22:36:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:33:30 INFO loss_tracker.py:84 | Epoch[70/239] Step[99] GlobalStep[27399/NA]: det_loss_cls_0[0.5261] det_loss_box_0[0.6621] det_loss_cls_1[0.5012] det_loss_box_1[0.5234] det_loss_cls_2[0.4843] det_loss_box_2[0.4632] det_loss_cls_3[0.4653] det_loss_box_3[0.4456] det_loss_cls_4[0.4321] det_loss_box_4[0.4410] det_loss_cls_5[0.4086] det_loss_box_5[0.4501] det_loss_cls_dn_0[0.0935] det_loss_box_dn_0[0.3068] det_loss_cls_dn_1[0.0898] det_loss_box_dn_1[0.2533] det_loss_cls_dn_2[0.0898] det_loss_box_dn_2[0.2355] det_loss_cls_dn_3[0.0884] det_loss_box_dn_3[0.2316] det_loss_cls_dn_4[0.0876] det_loss_box_dn_4[0.2289] det_loss_cls_dn_5[0.0872] det_loss_box_dn_5[0.2277] loss_depth[0.0400] total_loss[7.8631]
Rank[0/8] 06/19/2025 23:33:56 INFO stats.py:314 | Epoch[70] Step[124] GlobalStep[27424] Training Speed: 8.75 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 22:35:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:33:56 INFO loss_tracker.py:84 | Epoch[70/239] Step[124] GlobalStep[27424/NA]: det_loss_cls_0[0.6554] det_loss_box_0[0.9905] det_loss_cls_1[0.6239] det_loss_box_1[0.7858] det_loss_cls_2[0.5996] det_loss_box_2[0.7078] det_loss_cls_3[0.5679] det_loss_box_3[0.6635] det_loss_cls_4[0.5290] det_loss_box_4[0.6518] det_loss_cls_5[0.5077] det_loss_box_5[0.6611] det_loss_cls_dn_0[0.1014] det_loss_box_dn_0[0.3702] det_loss_cls_dn_1[0.0980] det_loss_box_dn_1[0.3184] det_loss_cls_dn_2[0.0977] det_loss_box_dn_2[0.3042] det_loss_cls_dn_3[0.0965] det_loss_box_dn_3[0.2990] det_loss_cls_dn_4[0.0959] det_loss_box_dn_4[0.2980] det_loss_cls_dn_5[0.0956] det_loss_box_dn_5[0.2953] loss_depth[0.0406] total_loss[10.4549]
Rank[0/8] 06/19/2025 23:34:22 INFO stats.py:314 | Epoch[70] Step[149] GlobalStep[27449] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 22:34:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:34:22 INFO loss_tracker.py:84 | Epoch[70/239] Step[149] GlobalStep[27449/NA]: det_loss_cls_0[0.5741] det_loss_box_0[0.8317] det_loss_cls_1[0.5522] det_loss_box_1[0.6550] det_loss_cls_2[0.5367] det_loss_box_2[0.5995] det_loss_cls_3[0.5044] det_loss_box_3[0.5820] det_loss_cls_4[0.4689] det_loss_box_4[0.5660] det_loss_cls_5[0.4599] det_loss_box_5[0.5635] det_loss_cls_dn_0[0.1017] det_loss_box_dn_0[0.3314] det_loss_cls_dn_1[0.0988] det_loss_box_dn_1[0.2828] det_loss_cls_dn_2[0.0979] det_loss_box_dn_2[0.2648] det_loss_cls_dn_3[0.0966] det_loss_box_dn_3[0.2621] det_loss_cls_dn_4[0.0959] det_loss_box_dn_4[0.2614] det_loss_cls_dn_5[0.0956] det_loss_box_dn_5[0.2601] loss_depth[0.0397] total_loss[9.1824]
Rank[0/8] 06/19/2025 23:34:48 INFO stats.py:314 | Epoch[70] Step[174] GlobalStep[27474] Training Speed: 8.95 samples/sec across all devices.	Average Step Time: 0.89 sec.	Estimated Remaining Time: 22:33:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:34:48 INFO loss_tracker.py:84 | Epoch[70/239] Step[174] GlobalStep[27474/NA]: det_loss_cls_0[0.4292] det_loss_box_0[0.5599] det_loss_cls_1[0.4051] det_loss_box_1[0.4567] det_loss_cls_2[0.3938] det_loss_box_2[0.4158] det_loss_cls_3[0.3807] det_loss_box_3[0.4012] det_loss_cls_4[0.3513] det_loss_box_4[0.3975] det_loss_cls_5[0.3412] det_loss_box_5[0.3980] det_loss_cls_dn_0[0.0982] det_loss_box_dn_0[0.3388] det_loss_cls_dn_1[0.0939] det_loss_box_dn_1[0.2998] det_loss_cls_dn_2[0.0938] det_loss_box_dn_2[0.2811] det_loss_cls_dn_3[0.0924] det_loss_box_dn_3[0.2792] det_loss_cls_dn_4[0.0912] det_loss_box_dn_4[0.2793] det_loss_cls_dn_5[0.0903] det_loss_box_dn_5[0.2769] loss_depth[0.0370] total_loss[7.2822]
Rank[0/8] 06/19/2025 23:35:14 INFO stats.py:314 | Epoch[70] Step[199] GlobalStep[27499] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 22:33:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:35:14 INFO loss_tracker.py:84 | Epoch[70/239] Step[199] GlobalStep[27499/NA]: det_loss_cls_0[0.6137] det_loss_box_0[0.7540] det_loss_cls_1[0.5920] det_loss_box_1[0.6124] det_loss_cls_2[0.5591] det_loss_box_2[0.5644] det_loss_cls_3[0.5305] det_loss_box_3[0.5471] det_loss_cls_4[0.5019] det_loss_box_4[0.5400] det_loss_cls_5[0.4946] det_loss_box_5[0.5375] det_loss_cls_dn_0[0.0997] det_loss_box_dn_0[0.2964] det_loss_cls_dn_1[0.0970] det_loss_box_dn_1[0.2634] det_loss_cls_dn_2[0.0961] det_loss_box_dn_2[0.2522] det_loss_cls_dn_3[0.0949] det_loss_box_dn_3[0.2489] det_loss_cls_dn_4[0.0941] det_loss_box_dn_4[0.2476] det_loss_cls_dn_5[0.0933] det_loss_box_dn_5[0.2434] loss_depth[0.0428] total_loss[9.0170]
Rank[0/8] 06/19/2025 23:35:40 INFO stats.py:314 | Epoch[70] Step[224] GlobalStep[27524] Training Speed: 8.06 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 22:32:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:35:40 INFO loss_tracker.py:84 | Epoch[70/239] Step[224] GlobalStep[27524/NA]: det_loss_cls_0[0.5783] det_loss_box_0[0.7859] det_loss_cls_1[0.5595] det_loss_box_1[0.6397] det_loss_cls_2[0.5310] det_loss_box_2[0.5914] det_loss_cls_3[0.5109] det_loss_box_3[0.5688] det_loss_cls_4[0.4864] det_loss_box_4[0.5619] det_loss_cls_5[0.4770] det_loss_box_5[0.5576] det_loss_cls_dn_0[0.1022] det_loss_box_dn_0[0.2946] det_loss_cls_dn_1[0.0996] det_loss_box_dn_1[0.2533] det_loss_cls_dn_2[0.0980] det_loss_box_dn_2[0.2401] det_loss_cls_dn_3[0.0980] det_loss_box_dn_3[0.2374] det_loss_cls_dn_4[0.0966] det_loss_box_dn_4[0.2360] det_loss_cls_dn_5[0.0956] det_loss_box_dn_5[0.2357] loss_depth[0.0408] total_loss[8.9765]
Rank[0/8] 06/19/2025 23:36:06 INFO stats.py:314 | Epoch[70] Step[249] GlobalStep[27549] Training Speed: 7.59 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 22:31:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:36:06 INFO loss_tracker.py:84 | Epoch[70/239] Step[249] GlobalStep[27549/NA]: det_loss_cls_0[0.4395] det_loss_box_0[0.7352] det_loss_cls_1[0.4176] det_loss_box_1[0.6007] det_loss_cls_2[0.3915] det_loss_box_2[0.5447] det_loss_cls_3[0.3702] det_loss_box_3[0.5349] det_loss_cls_4[0.3500] det_loss_box_4[0.5195] det_loss_cls_5[0.3391] det_loss_box_5[0.5221] det_loss_cls_dn_0[0.0918] det_loss_box_dn_0[0.3461] det_loss_cls_dn_1[0.0886] det_loss_box_dn_1[0.3017] det_loss_cls_dn_2[0.0878] det_loss_box_dn_2[0.2814] det_loss_cls_dn_3[0.0865] det_loss_box_dn_3[0.2796] det_loss_cls_dn_4[0.0854] det_loss_box_dn_4[0.2786] det_loss_cls_dn_5[0.0844] det_loss_box_dn_5[0.2770] loss_depth[0.0447] total_loss[8.0986]
Rank[0/8] 06/19/2025 23:36:32 INFO stats.py:314 | Epoch[70] Step[274] GlobalStep[27574] Training Speed: 6.61 samples/sec across all devices.	Average Step Time: 1.21 sec.	Estimated Remaining Time: 22:31:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:36:32 INFO loss_tracker.py:84 | Epoch[70/239] Step[274] GlobalStep[27574/NA]: det_loss_cls_0[0.4864] det_loss_box_0[0.5979] det_loss_cls_1[0.4651] det_loss_box_1[0.4849] det_loss_cls_2[0.4439] det_loss_box_2[0.4363] det_loss_cls_3[0.4208] det_loss_box_3[0.4322] det_loss_cls_4[0.3949] det_loss_box_4[0.4267] det_loss_cls_5[0.3906] det_loss_box_5[0.4217] det_loss_cls_dn_0[0.0999] det_loss_box_dn_0[0.3001] det_loss_cls_dn_1[0.0959] det_loss_box_dn_1[0.2553] det_loss_cls_dn_2[0.0957] det_loss_box_dn_2[0.2393] det_loss_cls_dn_3[0.0946] det_loss_box_dn_3[0.2351] det_loss_cls_dn_4[0.0934] det_loss_box_dn_4[0.2362] det_loss_cls_dn_5[0.0927] det_loss_box_dn_5[0.2354] loss_depth[0.0409] total_loss[7.5160]
Rank[0/8] 06/19/2025 23:36:58 INFO stats.py:314 | Epoch[70] Step[299] GlobalStep[27599] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 22:30:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:36:58 INFO loss_tracker.py:84 | Epoch[70/239] Step[299] GlobalStep[27599/NA]: det_loss_cls_0[0.5745] det_loss_box_0[0.8205] det_loss_cls_1[0.5498] det_loss_box_1[0.6280] det_loss_cls_2[0.5261] det_loss_box_2[0.5529] det_loss_cls_3[0.4993] det_loss_box_3[0.5426] det_loss_cls_4[0.4625] det_loss_box_4[0.5414] det_loss_cls_5[0.4517] det_loss_box_5[0.5394] det_loss_cls_dn_0[0.1072] det_loss_box_dn_0[0.3093] det_loss_cls_dn_1[0.1022] det_loss_box_dn_1[0.2655] det_loss_cls_dn_2[0.1018] det_loss_box_dn_2[0.2464] det_loss_cls_dn_3[0.1012] det_loss_box_dn_3[0.2435] det_loss_cls_dn_4[0.1000] det_loss_box_dn_4[0.2424] det_loss_cls_dn_5[0.0994] det_loss_box_dn_5[0.2411] loss_depth[0.0422] total_loss[8.8910]
Rank[0/8] 06/19/2025 23:37:24 INFO stats.py:314 | Epoch[70] Step[324] GlobalStep[27624] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 22:29:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:37:24 INFO loss_tracker.py:84 | Epoch[70/239] Step[324] GlobalStep[27624/NA]: det_loss_cls_0[0.5481] det_loss_box_0[0.7384] det_loss_cls_1[0.5361] det_loss_box_1[0.5758] det_loss_cls_2[0.5064] det_loss_box_2[0.5174] det_loss_cls_3[0.4718] det_loss_box_3[0.5067] det_loss_cls_4[0.4354] det_loss_box_4[0.4934] det_loss_cls_5[0.4220] det_loss_box_5[0.4889] det_loss_cls_dn_0[0.1010] det_loss_box_dn_0[0.3214] det_loss_cls_dn_1[0.0985] det_loss_box_dn_1[0.2780] det_loss_cls_dn_2[0.0976] det_loss_box_dn_2[0.2578] det_loss_cls_dn_3[0.0968] det_loss_box_dn_3[0.2557] det_loss_cls_dn_4[0.0952] det_loss_box_dn_4[0.2549] det_loss_cls_dn_5[0.0941] det_loss_box_dn_5[0.2531] loss_depth[0.0390] total_loss[8.4834]
Rank[0/8] 06/19/2025 23:37:49 INFO stats.py:314 | Epoch[70] Step[349] GlobalStep[27649] Training Speed: 7.74 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 22:28:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:37:49 INFO loss_tracker.py:84 | Epoch[70/239] Step[349] GlobalStep[27649/NA]: det_loss_cls_0[0.4476] det_loss_box_0[0.6843] det_loss_cls_1[0.4336] det_loss_box_1[0.5399] det_loss_cls_2[0.4137] det_loss_box_2[0.4806] det_loss_cls_3[0.3865] det_loss_box_3[0.4666] det_loss_cls_4[0.3573] det_loss_box_4[0.4614] det_loss_cls_5[0.3460] det_loss_box_5[0.4601] det_loss_cls_dn_0[0.0955] det_loss_box_dn_0[0.3350] det_loss_cls_dn_1[0.0920] det_loss_box_dn_1[0.2929] det_loss_cls_dn_2[0.0917] det_loss_box_dn_2[0.2787] det_loss_cls_dn_3[0.0901] det_loss_box_dn_3[0.2763] det_loss_cls_dn_4[0.0885] det_loss_box_dn_4[0.2745] det_loss_cls_dn_5[0.0878] det_loss_box_dn_5[0.2720] loss_depth[0.0437] total_loss[7.7963]
Rank[0/8] 06/19/2025 23:38:15 INFO stats.py:314 | Epoch[70] Step[374] GlobalStep[27674] Training Speed: 7.56 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 22:28:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:38:15 INFO loss_tracker.py:84 | Epoch[70/239] Step[374] GlobalStep[27674/NA]: det_loss_cls_0[0.4463] det_loss_box_0[0.6114] det_loss_cls_1[0.4315] det_loss_box_1[0.4852] det_loss_cls_2[0.4006] det_loss_box_2[0.4410] det_loss_cls_3[0.3815] det_loss_box_3[0.4288] det_loss_cls_4[0.3604] det_loss_box_4[0.4153] det_loss_cls_5[0.3461] det_loss_box_5[0.4208] det_loss_cls_dn_0[0.0987] det_loss_box_dn_0[0.3281] det_loss_cls_dn_1[0.0942] det_loss_box_dn_1[0.2848] det_loss_cls_dn_2[0.0938] det_loss_box_dn_2[0.2696] det_loss_cls_dn_3[0.0923] det_loss_box_dn_3[0.2666] det_loss_cls_dn_4[0.0908] det_loss_box_dn_4[0.2651] det_loss_cls_dn_5[0.0899] det_loss_box_dn_5[0.2601] loss_depth[0.0401] total_loss[7.4429]
Rank[0/8] 06/19/2025 23:38:31 INFO stats.py:394 | Epoch[70] completed. Training Speed: 7.67 samples/sec across all devices.	Epoch Time: 406.92 sec.	Average Epoch Time: 406.92 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 22:27:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:38:52 INFO stats.py:314 | Epoch[71] Step[9] GlobalStep[27699] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 22:27:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:39:07 INFO loss_tracker.py:84 | Epoch[71/239] Step[24] GlobalStep[27714/NA]: det_loss_cls_0[0.5558] det_loss_box_0[0.8372] det_loss_cls_1[0.5386] det_loss_box_1[0.6565] det_loss_cls_2[0.5232] det_loss_box_2[0.5954] det_loss_cls_3[0.4900] det_loss_box_3[0.5803] det_loss_cls_4[0.4632] det_loss_box_4[0.5694] det_loss_cls_5[0.4548] det_loss_box_5[0.5675] det_loss_cls_dn_0[0.1029] det_loss_box_dn_0[0.3724] det_loss_cls_dn_1[0.0996] det_loss_box_dn_1[0.3194] det_loss_cls_dn_2[0.0987] det_loss_box_dn_2[0.3008] det_loss_cls_dn_3[0.0975] det_loss_box_dn_3[0.2982] det_loss_cls_dn_4[0.0960] det_loss_box_dn_4[0.2956] det_loss_cls_dn_5[0.0954] det_loss_box_dn_5[0.2937] loss_depth[0.0407] total_loss[9.3428]
Rank[0/8] 06/19/2025 23:39:17 INFO stats.py:314 | Epoch[71] Step[34] GlobalStep[27724] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 22:27:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:39:33 INFO loss_tracker.py:84 | Epoch[71/239] Step[49] GlobalStep[27739/NA]: det_loss_cls_0[0.4606] det_loss_box_0[0.6231] det_loss_cls_1[0.4480] det_loss_box_1[0.5064] det_loss_cls_2[0.4156] det_loss_box_2[0.4598] det_loss_cls_3[0.3974] det_loss_box_3[0.4411] det_loss_cls_4[0.3728] det_loss_box_4[0.4296] det_loss_cls_5[0.3626] det_loss_box_5[0.4285] det_loss_cls_dn_0[0.0940] det_loss_box_dn_0[0.2951] det_loss_cls_dn_1[0.0908] det_loss_box_dn_1[0.2545] det_loss_cls_dn_2[0.0899] det_loss_box_dn_2[0.2400] det_loss_cls_dn_3[0.0897] det_loss_box_dn_3[0.2377] det_loss_cls_dn_4[0.0881] det_loss_box_dn_4[0.2364] det_loss_cls_dn_5[0.0874] det_loss_box_dn_5[0.2341] loss_depth[0.0402] total_loss[7.4235]
Rank[0/8] 06/19/2025 23:39:43 INFO stats.py:314 | Epoch[71] Step[59] GlobalStep[27749] Training Speed: 6.32 samples/sec across all devices.	Average Step Time: 1.27 sec.	Estimated Remaining Time: 22:26:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:39:59 INFO loss_tracker.py:84 | Epoch[71/239] Step[74] GlobalStep[27764/NA]: det_loss_cls_0[0.5759] det_loss_box_0[0.7314] det_loss_cls_1[0.5435] det_loss_box_1[0.5758] det_loss_cls_2[0.5209] det_loss_box_2[0.5149] det_loss_cls_3[0.4965] det_loss_box_3[0.4965] det_loss_cls_4[0.4710] det_loss_box_4[0.4946] det_loss_cls_5[0.4536] det_loss_box_5[0.4958] det_loss_cls_dn_0[0.0949] det_loss_box_dn_0[0.2810] det_loss_cls_dn_1[0.0909] det_loss_box_dn_1[0.2439] det_loss_cls_dn_2[0.0909] det_loss_box_dn_2[0.2327] det_loss_cls_dn_3[0.0900] det_loss_box_dn_3[0.2301] det_loss_cls_dn_4[0.0891] det_loss_box_dn_4[0.2291] det_loss_cls_dn_5[0.0882] det_loss_box_dn_5[0.2254] loss_depth[0.0380] total_loss[8.3948]
Rank[0/8] 06/19/2025 23:40:09 INFO stats.py:314 | Epoch[71] Step[84] GlobalStep[27774] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 22:25:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:40:24 INFO loss_tracker.py:84 | Epoch[71/239] Step[99] GlobalStep[27789/NA]: det_loss_cls_0[0.4961] det_loss_box_0[0.6930] det_loss_cls_1[0.4942] det_loss_box_1[0.5273] det_loss_cls_2[0.4679] det_loss_box_2[0.4874] det_loss_cls_3[0.4527] det_loss_box_3[0.4661] det_loss_cls_4[0.4213] det_loss_box_4[0.4681] det_loss_cls_5[0.4063] det_loss_box_5[0.4695] det_loss_cls_dn_0[0.0990] det_loss_box_dn_0[0.3299] det_loss_cls_dn_1[0.0954] det_loss_box_dn_1[0.2787] det_loss_cls_dn_2[0.0945] det_loss_box_dn_2[0.2631] det_loss_cls_dn_3[0.0934] det_loss_box_dn_3[0.2608] det_loss_cls_dn_4[0.0927] det_loss_box_dn_4[0.2617] det_loss_cls_dn_5[0.0919] det_loss_box_dn_5[0.2603] loss_depth[0.0396] total_loss[8.1109]
Rank[0/8] 06/19/2025 23:40:35 INFO stats.py:314 | Epoch[71] Step[109] GlobalStep[27799] Training Speed: 8.05 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 22:25:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:40:50 INFO loss_tracker.py:84 | Epoch[71/239] Step[124] GlobalStep[27814/NA]: det_loss_cls_0[0.5397] det_loss_box_0[0.7478] det_loss_cls_1[0.5265] det_loss_box_1[0.5708] det_loss_cls_2[0.5122] det_loss_box_2[0.5055] det_loss_cls_3[0.4846] det_loss_box_3[0.4876] det_loss_cls_4[0.4604] det_loss_box_4[0.4773] det_loss_cls_5[0.4469] det_loss_box_5[0.4773] det_loss_cls_dn_0[0.1046] det_loss_box_dn_0[0.3034] det_loss_cls_dn_1[0.1015] det_loss_box_dn_1[0.2630] det_loss_cls_dn_2[0.1008] det_loss_box_dn_2[0.2494] det_loss_cls_dn_3[0.0998] det_loss_box_dn_3[0.2457] det_loss_cls_dn_4[0.0994] det_loss_box_dn_4[0.2438] det_loss_cls_dn_5[0.0987] det_loss_box_dn_5[0.2411] loss_depth[0.0436] total_loss[8.4314]
Rank[0/8] 06/19/2025 23:41:01 INFO stats.py:314 | Epoch[71] Step[134] GlobalStep[27824] Training Speed: 7.69 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 22:24:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:41:17 INFO loss_tracker.py:84 | Epoch[71/239] Step[149] GlobalStep[27839/NA]: det_loss_cls_0[0.7034] det_loss_box_0[0.9105] det_loss_cls_1[0.6612] det_loss_box_1[0.6944] det_loss_cls_2[0.6561] det_loss_box_2[0.6293] det_loss_cls_3[0.6276] det_loss_box_3[0.6083] det_loss_cls_4[0.6029] det_loss_box_4[0.6012] det_loss_cls_5[0.5826] det_loss_box_5[0.6054] det_loss_cls_dn_0[0.1045] det_loss_box_dn_0[0.2872] det_loss_cls_dn_1[0.1015] det_loss_box_dn_1[0.2446] det_loss_cls_dn_2[0.1004] det_loss_box_dn_2[0.2291] det_loss_cls_dn_3[0.1004] det_loss_box_dn_3[0.2260] det_loss_cls_dn_4[0.0997] det_loss_box_dn_4[0.2249] det_loss_cls_dn_5[0.0991] det_loss_box_dn_5[0.2238] loss_depth[0.0431] total_loss[9.9672]
Rank[0/8] 06/19/2025 23:41:27 INFO stats.py:314 | Epoch[71] Step[159] GlobalStep[27849] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 22:23:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:41:42 INFO loss_tracker.py:84 | Epoch[71/239] Step[174] GlobalStep[27864/NA]: det_loss_cls_0[0.6038] det_loss_box_0[0.8105] det_loss_cls_1[0.5707] det_loss_box_1[0.6515] det_loss_cls_2[0.5517] det_loss_box_2[0.5930] det_loss_cls_3[0.5452] det_loss_box_3[0.5672] det_loss_cls_4[0.5148] det_loss_box_4[0.5541] det_loss_cls_5[0.5018] det_loss_box_5[0.5558] det_loss_cls_dn_0[0.1049] det_loss_box_dn_0[0.3532] det_loss_cls_dn_1[0.0997] det_loss_box_dn_1[0.3095] det_loss_cls_dn_2[0.0993] det_loss_box_dn_2[0.2939] det_loss_cls_dn_3[0.0975] det_loss_box_dn_3[0.2913] det_loss_cls_dn_4[0.0973] det_loss_box_dn_4[0.2917] det_loss_cls_dn_5[0.0958] det_loss_box_dn_5[0.2875] loss_depth[0.0413] total_loss[9.4829]
Rank[0/8] 06/19/2025 23:41:53 INFO stats.py:314 | Epoch[71] Step[184] GlobalStep[27874] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 22:23:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:42:08 INFO loss_tracker.py:84 | Epoch[71/239] Step[199] GlobalStep[27889/NA]: det_loss_cls_0[0.5132] det_loss_box_0[0.7510] det_loss_cls_1[0.4951] det_loss_box_1[0.6107] det_loss_cls_2[0.4851] det_loss_box_2[0.5599] det_loss_cls_3[0.4619] det_loss_box_3[0.5339] det_loss_cls_4[0.4309] det_loss_box_4[0.5277] det_loss_cls_5[0.4142] det_loss_box_5[0.5271] det_loss_cls_dn_0[0.1066] det_loss_box_dn_0[0.3261] det_loss_cls_dn_1[0.1033] det_loss_box_dn_1[0.2811] det_loss_cls_dn_2[0.1020] det_loss_box_dn_2[0.2662] det_loss_cls_dn_3[0.1014] det_loss_box_dn_3[0.2629] det_loss_cls_dn_4[0.1001] det_loss_box_dn_4[0.2615] det_loss_cls_dn_5[0.0994] det_loss_box_dn_5[0.2597] loss_depth[0.0383] total_loss[8.6193]
Rank[0/8] 06/19/2025 23:42:18 INFO stats.py:314 | Epoch[71] Step[209] GlobalStep[27899] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 22:22:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:42:34 INFO loss_tracker.py:84 | Epoch[71/239] Step[224] GlobalStep[27914/NA]: det_loss_cls_0[0.5527] det_loss_box_0[0.6991] det_loss_cls_1[0.5411] det_loss_box_1[0.5766] det_loss_cls_2[0.5293] det_loss_box_2[0.5041] det_loss_cls_3[0.5038] det_loss_box_3[0.4913] det_loss_cls_4[0.4698] det_loss_box_4[0.4886] det_loss_cls_5[0.4609] det_loss_box_5[0.4860] det_loss_cls_dn_0[0.0995] det_loss_box_dn_0[0.3135] det_loss_cls_dn_1[0.0955] det_loss_box_dn_1[0.2707] det_loss_cls_dn_2[0.0951] det_loss_box_dn_2[0.2533] det_loss_cls_dn_3[0.0934] det_loss_box_dn_3[0.2522] det_loss_cls_dn_4[0.0924] det_loss_box_dn_4[0.2511] det_loss_cls_dn_5[0.0917] det_loss_box_dn_5[0.2499] loss_depth[0.0388] total_loss[8.5007]
Rank[0/8] 06/19/2025 23:42:44 INFO stats.py:314 | Epoch[71] Step[234] GlobalStep[27924] Training Speed: 7.68 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 22:21:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:43:00 INFO loss_tracker.py:84 | Epoch[71/239] Step[249] GlobalStep[27939/NA]: det_loss_cls_0[0.5545] det_loss_box_0[0.8095] det_loss_cls_1[0.5363] det_loss_box_1[0.6396] det_loss_cls_2[0.5171] det_loss_box_2[0.5620] det_loss_cls_3[0.4911] det_loss_box_3[0.5270] det_loss_cls_4[0.4603] det_loss_box_4[0.5184] det_loss_cls_5[0.4502] det_loss_box_5[0.5189] det_loss_cls_dn_0[0.1030] det_loss_box_dn_0[0.4042] det_loss_cls_dn_1[0.0990] det_loss_box_dn_1[0.3568] det_loss_cls_dn_2[0.0987] det_loss_box_dn_2[0.3447] det_loss_cls_dn_3[0.0972] det_loss_box_dn_3[0.3388] det_loss_cls_dn_4[0.0965] det_loss_box_dn_4[0.3384] det_loss_cls_dn_5[0.0956] det_loss_box_dn_5[0.3363] loss_depth[0.0411] total_loss[9.3353]
Rank[0/8] 06/19/2025 23:43:10 INFO stats.py:314 | Epoch[71] Step[259] GlobalStep[27949] Training Speed: 6.69 samples/sec across all devices.	Average Step Time: 1.20 sec.	Estimated Remaining Time: 22:20:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:43:25 INFO loss_tracker.py:84 | Epoch[71/239] Step[274] GlobalStep[27964/NA]: det_loss_cls_0[0.5181] det_loss_box_0[0.6277] det_loss_cls_1[0.5275] det_loss_box_1[0.4834] det_loss_cls_2[0.5078] det_loss_box_2[0.4357] det_loss_cls_3[0.4848] det_loss_box_3[0.4230] det_loss_cls_4[0.4595] det_loss_box_4[0.4157] det_loss_cls_5[0.4508] det_loss_box_5[0.4119] det_loss_cls_dn_0[0.1137] det_loss_box_dn_0[0.3010] det_loss_cls_dn_1[0.1102] det_loss_box_dn_1[0.2563] det_loss_cls_dn_2[0.1096] det_loss_box_dn_2[0.2427] det_loss_cls_dn_3[0.1082] det_loss_box_dn_3[0.2393] det_loss_cls_dn_4[0.1072] det_loss_box_dn_4[0.2383] det_loss_cls_dn_5[0.1065] det_loss_box_dn_5[0.2361] loss_depth[0.0393] total_loss[7.9544]
Rank[0/8] 06/19/2025 23:43:36 INFO stats.py:314 | Epoch[71] Step[284] GlobalStep[27974] Training Speed: 8.13 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 22:20:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:43:51 INFO loss_tracker.py:84 | Epoch[71/239] Step[299] GlobalStep[27989/NA]: det_loss_cls_0[0.6281] det_loss_box_0[0.9288] det_loss_cls_1[0.6229] det_loss_box_1[0.7103] det_loss_cls_2[0.6048] det_loss_box_2[0.6447] det_loss_cls_3[0.5728] det_loss_box_3[0.6194] det_loss_cls_4[0.5381] det_loss_box_4[0.6056] det_loss_cls_5[0.5217] det_loss_box_5[0.6060] det_loss_cls_dn_0[0.1030] det_loss_box_dn_0[0.3371] det_loss_cls_dn_1[0.0998] det_loss_box_dn_1[0.2946] det_loss_cls_dn_2[0.0996] det_loss_box_dn_2[0.2781] det_loss_cls_dn_3[0.0984] det_loss_box_dn_3[0.2781] det_loss_cls_dn_4[0.0974] det_loss_box_dn_4[0.2772] det_loss_cls_dn_5[0.0965] det_loss_box_dn_5[0.2761] loss_depth[0.0400] total_loss[9.9791]
Rank[0/8] 06/19/2025 23:44:01 INFO stats.py:314 | Epoch[71] Step[309] GlobalStep[27999] Training Speed: 8.07 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 22:19:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:44:17 INFO loss_tracker.py:84 | Epoch[71/239] Step[324] GlobalStep[28014/NA]: det_loss_cls_0[0.4630] det_loss_box_0[0.6233] det_loss_cls_1[0.4489] det_loss_box_1[0.4770] det_loss_cls_2[0.4260] det_loss_box_2[0.4255] det_loss_cls_3[0.4036] det_loss_box_3[0.4107] det_loss_cls_4[0.3764] det_loss_box_4[0.4051] det_loss_cls_5[0.3603] det_loss_box_5[0.4061] det_loss_cls_dn_0[0.0988] det_loss_box_dn_0[0.3042] det_loss_cls_dn_1[0.0936] det_loss_box_dn_1[0.2574] det_loss_cls_dn_2[0.0927] det_loss_box_dn_2[0.2458] det_loss_cls_dn_3[0.0916] det_loss_box_dn_3[0.2423] det_loss_cls_dn_4[0.0903] det_loss_box_dn_4[0.2417] det_loss_cls_dn_5[0.0899] det_loss_box_dn_5[0.2413] loss_depth[0.0376] total_loss[7.3532]
Rank[0/8] 06/19/2025 23:44:27 INFO stats.py:314 | Epoch[71] Step[334] GlobalStep[28024] Training Speed: 8.75 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 22:18:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:44:43 INFO loss_tracker.py:84 | Epoch[71/239] Step[349] GlobalStep[28039/NA]: det_loss_cls_0[0.5535] det_loss_box_0[0.8256] det_loss_cls_1[0.5275] det_loss_box_1[0.6693] det_loss_cls_2[0.5100] det_loss_box_2[0.5954] det_loss_cls_3[0.4871] det_loss_box_3[0.5721] det_loss_cls_4[0.4647] det_loss_box_4[0.5600] det_loss_cls_5[0.4505] det_loss_box_5[0.5592] det_loss_cls_dn_0[0.1123] det_loss_box_dn_0[0.3566] det_loss_cls_dn_1[0.1095] det_loss_box_dn_1[0.3039] det_loss_cls_dn_2[0.1093] det_loss_box_dn_2[0.2885] det_loss_cls_dn_3[0.1095] det_loss_box_dn_3[0.2833] det_loss_cls_dn_4[0.1078] det_loss_box_dn_4[0.2819] det_loss_cls_dn_5[0.1067] det_loss_box_dn_5[0.2806] loss_depth[0.0391] total_loss[9.2639]
Rank[0/8] 06/19/2025 23:44:53 INFO stats.py:314 | Epoch[71] Step[359] GlobalStep[28049] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 22:18:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:45:08 INFO loss_tracker.py:84 | Epoch[71/239] Step[374] GlobalStep[28064/NA]: det_loss_cls_0[0.4271] det_loss_box_0[0.5683] det_loss_cls_1[0.4213] det_loss_box_1[0.4446] det_loss_cls_2[0.4007] det_loss_box_2[0.3986] det_loss_cls_3[0.3889] det_loss_box_3[0.3941] det_loss_cls_4[0.3690] det_loss_box_4[0.3882] det_loss_cls_5[0.3481] det_loss_box_5[0.3926] det_loss_cls_dn_0[0.0992] det_loss_box_dn_0[0.3519] det_loss_cls_dn_1[0.0961] det_loss_box_dn_1[0.3080] det_loss_cls_dn_2[0.0952] det_loss_box_dn_2[0.2945] det_loss_cls_dn_3[0.0937] det_loss_box_dn_3[0.2909] det_loss_cls_dn_4[0.0928] det_loss_box_dn_4[0.2895] det_loss_cls_dn_5[0.0920] det_loss_box_dn_5[0.2860] loss_depth[0.0363] total_loss[7.3677]
Rank[0/8] 06/19/2025 23:45:18 INFO stats.py:314 | Epoch[71] Step[384] GlobalStep[28074] Training Speed: 8.77 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 22:17:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:45:23 INFO stats.py:394 | Epoch[71] completed. Training Speed: 7.57 samples/sec across all devices.	Epoch Time: 412.19 sec.	Average Epoch Time: 412.19 sec.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 22:17:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:45:47 INFO stats.py:314 | Epoch[72] Step[19] GlobalStep[28099] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 22:16:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:45:53 INFO loss_tracker.py:84 | Epoch[72/239] Step[24] GlobalStep[28104/NA]: det_loss_cls_0[0.4471] det_loss_box_0[0.5735] det_loss_cls_1[0.4321] det_loss_box_1[0.4628] det_loss_cls_2[0.4128] det_loss_box_2[0.4148] det_loss_cls_3[0.3972] det_loss_box_3[0.4000] det_loss_cls_4[0.3692] det_loss_box_4[0.3994] det_loss_cls_5[0.3615] det_loss_box_5[0.3962] det_loss_cls_dn_0[0.0946] det_loss_box_dn_0[0.3059] det_loss_cls_dn_1[0.0915] det_loss_box_dn_1[0.2617] det_loss_cls_dn_2[0.0909] det_loss_box_dn_2[0.2448] det_loss_cls_dn_3[0.0898] det_loss_box_dn_3[0.2417] det_loss_cls_dn_4[0.0889] det_loss_box_dn_4[0.2408] det_loss_cls_dn_5[0.0883] det_loss_box_dn_5[0.2381] loss_depth[0.0393] total_loss[7.1831]
Rank[0/8] 06/19/2025 23:46:14 INFO stats.py:314 | Epoch[72] Step[44] GlobalStep[28124] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 22:16:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:46:19 INFO loss_tracker.py:84 | Epoch[72/239] Step[49] GlobalStep[28129/NA]: det_loss_cls_0[0.4366] det_loss_box_0[0.5381] det_loss_cls_1[0.4295] det_loss_box_1[0.4276] det_loss_cls_2[0.4045] det_loss_box_2[0.3838] det_loss_cls_3[0.3661] det_loss_box_3[0.3818] det_loss_cls_4[0.3464] det_loss_box_4[0.3720] det_loss_cls_5[0.3344] det_loss_box_5[0.3764] det_loss_cls_dn_0[0.0905] det_loss_box_dn_0[0.2424] det_loss_cls_dn_1[0.0856] det_loss_box_dn_1[0.2045] det_loss_cls_dn_2[0.0842] det_loss_box_dn_2[0.1904] det_loss_cls_dn_3[0.0832] det_loss_box_dn_3[0.1879] det_loss_cls_dn_4[0.0815] det_loss_box_dn_4[0.1870] det_loss_cls_dn_5[0.0813] det_loss_box_dn_5[0.1855] loss_depth[0.0365] total_loss[6.5377]
Rank[0/8] 06/19/2025 23:46:39 INFO stats.py:314 | Epoch[72] Step[69] GlobalStep[28149] Training Speed: 7.96 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 22:15:30.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:46:44 INFO loss_tracker.py:84 | Epoch[72/239] Step[74] GlobalStep[28154/NA]: det_loss_cls_0[0.5131] det_loss_box_0[0.6639] det_loss_cls_1[0.4914] det_loss_box_1[0.5264] det_loss_cls_2[0.4731] det_loss_box_2[0.4694] det_loss_cls_3[0.4575] det_loss_box_3[0.4429] det_loss_cls_4[0.4287] det_loss_box_4[0.4372] det_loss_cls_5[0.4189] det_loss_box_5[0.4385] det_loss_cls_dn_0[0.0961] det_loss_box_dn_0[0.2683] det_loss_cls_dn_1[0.0938] det_loss_box_dn_1[0.2274] det_loss_cls_dn_2[0.0926] det_loss_box_dn_2[0.2142] det_loss_cls_dn_3[0.0923] det_loss_box_dn_3[0.2118] det_loss_cls_dn_4[0.0908] det_loss_box_dn_4[0.2112] det_loss_cls_dn_5[0.0903] det_loss_box_dn_5[0.2099] loss_depth[0.0414] total_loss[7.7011]
Rank[0/8] 06/19/2025 23:47:04 INFO stats.py:314 | Epoch[72] Step[94] GlobalStep[28174] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 22:14:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:47:09 INFO loss_tracker.py:84 | Epoch[72/239] Step[99] GlobalStep[28179/NA]: det_loss_cls_0[0.5263] det_loss_box_0[0.7023] det_loss_cls_1[0.5055] det_loss_box_1[0.5666] det_loss_cls_2[0.4765] det_loss_box_2[0.5015] det_loss_cls_3[0.4617] det_loss_box_3[0.4784] det_loss_cls_4[0.4355] det_loss_box_4[0.4706] det_loss_cls_5[0.4276] det_loss_box_5[0.4651] det_loss_cls_dn_0[0.1011] det_loss_box_dn_0[0.3266] det_loss_cls_dn_1[0.0984] det_loss_box_dn_1[0.2826] det_loss_cls_dn_2[0.0977] det_loss_box_dn_2[0.2669] det_loss_cls_dn_3[0.0966] det_loss_box_dn_3[0.2645] det_loss_cls_dn_4[0.0953] det_loss_box_dn_4[0.2626] det_loss_cls_dn_5[0.0944] det_loss_box_dn_5[0.2620] loss_depth[0.0368] total_loss[8.3030]
Rank[0/8] 06/19/2025 23:47:31 INFO stats.py:314 | Epoch[72] Step[119] GlobalStep[28199] Training Speed: 7.59 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 22:14:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:47:36 INFO loss_tracker.py:84 | Epoch[72/239] Step[124] GlobalStep[28204/NA]: det_loss_cls_0[0.6269] det_loss_box_0[0.8854] det_loss_cls_1[0.6110] det_loss_box_1[0.7390] det_loss_cls_2[0.5981] det_loss_box_2[0.6562] det_loss_cls_3[0.5700] det_loss_box_3[0.6308] det_loss_cls_4[0.5463] det_loss_box_4[0.6101] det_loss_cls_5[0.5308] det_loss_box_5[0.6144] det_loss_cls_dn_0[0.1089] det_loss_box_dn_0[0.3145] det_loss_cls_dn_1[0.1061] det_loss_box_dn_1[0.2748] det_loss_cls_dn_2[0.1050] det_loss_box_dn_2[0.2574] det_loss_cls_dn_3[0.1052] det_loss_box_dn_3[0.2529] det_loss_cls_dn_4[0.1045] det_loss_box_dn_4[0.2513] det_loss_cls_dn_5[0.1036] det_loss_box_dn_5[0.2483] loss_depth[0.0398] total_loss[9.8914]
Rank[0/8] 06/19/2025 23:47:57 INFO stats.py:314 | Epoch[72] Step[144] GlobalStep[28224] Training Speed: 7.55 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 22:13:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:48:02 INFO loss_tracker.py:84 | Epoch[72/239] Step[149] GlobalStep[28229/NA]: det_loss_cls_0[0.6003] det_loss_box_0[0.7770] det_loss_cls_1[0.5926] det_loss_box_1[0.6114] det_loss_cls_2[0.5532] det_loss_box_2[0.5526] det_loss_cls_3[0.5280] det_loss_box_3[0.5297] det_loss_cls_4[0.4909] det_loss_box_4[0.5215] det_loss_cls_5[0.4760] det_loss_box_5[0.5166] det_loss_cls_dn_0[0.0959] det_loss_box_dn_0[0.2882] det_loss_cls_dn_1[0.0917] det_loss_box_dn_1[0.2561] det_loss_cls_dn_2[0.0915] det_loss_box_dn_2[0.2418] det_loss_cls_dn_3[0.0906] det_loss_box_dn_3[0.2398] det_loss_cls_dn_4[0.0889] det_loss_box_dn_4[0.2395] det_loss_cls_dn_5[0.0884] det_loss_box_dn_5[0.2365] loss_depth[0.0412] total_loss[8.8399]
Rank[0/8] 06/19/2025 23:48:23 INFO stats.py:314 | Epoch[72] Step[169] GlobalStep[28249] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 22:12:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:48:28 INFO loss_tracker.py:84 | Epoch[72/239] Step[174] GlobalStep[28254/NA]: det_loss_cls_0[0.6562] det_loss_box_0[0.8339] det_loss_cls_1[0.6393] det_loss_box_1[0.6529] det_loss_cls_2[0.6006] det_loss_box_2[0.5837] det_loss_cls_3[0.5760] det_loss_box_3[0.5659] det_loss_cls_4[0.5507] det_loss_box_4[0.5568] det_loss_cls_5[0.5306] det_loss_box_5[0.5585] det_loss_cls_dn_0[0.1024] det_loss_box_dn_0[0.2704] det_loss_cls_dn_1[0.0982] det_loss_box_dn_1[0.2342] det_loss_cls_dn_2[0.0975] det_loss_box_dn_2[0.2221] det_loss_cls_dn_3[0.0966] det_loss_box_dn_3[0.2202] det_loss_cls_dn_4[0.0951] det_loss_box_dn_4[0.2187] det_loss_cls_dn_5[0.0942] det_loss_box_dn_5[0.2174] loss_depth[0.0366] total_loss[9.3088]
Rank[0/8] 06/19/2025 23:48:49 INFO stats.py:314 | Epoch[72] Step[194] GlobalStep[28274] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 22:12:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:48:54 INFO loss_tracker.py:84 | Epoch[72/239] Step[199] GlobalStep[28279/NA]: det_loss_cls_0[0.5925] det_loss_box_0[0.8751] det_loss_cls_1[0.5637] det_loss_box_1[0.7016] det_loss_cls_2[0.5431] det_loss_box_2[0.6392] det_loss_cls_3[0.5266] det_loss_box_3[0.6134] det_loss_cls_4[0.4996] det_loss_box_4[0.6139] det_loss_cls_5[0.4837] det_loss_box_5[0.6166] det_loss_cls_dn_0[0.1061] det_loss_box_dn_0[0.3337] det_loss_cls_dn_1[0.1031] det_loss_box_dn_1[0.2865] det_loss_cls_dn_2[0.1030] det_loss_box_dn_2[0.2769] det_loss_cls_dn_3[0.1019] det_loss_box_dn_3[0.2724] det_loss_cls_dn_4[0.1012] det_loss_box_dn_4[0.2709] det_loss_cls_dn_5[0.1003] det_loss_box_dn_5[0.2734] loss_depth[0.0388] total_loss[9.6373]
Rank[0/8] 06/19/2025 23:49:15 INFO stats.py:314 | Epoch[72] Step[219] GlobalStep[28299] Training Speed: 8.56 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 22:11:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:49:20 INFO loss_tracker.py:84 | Epoch[72/239] Step[224] GlobalStep[28304/NA]: det_loss_cls_0[0.5843] det_loss_box_0[0.8430] det_loss_cls_1[0.5669] det_loss_box_1[0.7235] det_loss_cls_2[0.5414] det_loss_box_2[0.6648] det_loss_cls_3[0.5126] det_loss_box_3[0.6483] det_loss_cls_4[0.4938] det_loss_box_4[0.6329] det_loss_cls_5[0.4798] det_loss_box_5[0.6342] det_loss_cls_dn_0[0.1047] det_loss_box_dn_0[0.3142] det_loss_cls_dn_1[0.1017] det_loss_box_dn_1[0.2755] det_loss_cls_dn_2[0.1007] det_loss_box_dn_2[0.2586] det_loss_cls_dn_3[0.0994] det_loss_box_dn_3[0.2551] det_loss_cls_dn_4[0.0991] det_loss_box_dn_4[0.2541] det_loss_cls_dn_5[0.0981] det_loss_box_dn_5[0.2522] loss_depth[0.0393] total_loss[9.5780]
Rank[0/8] 06/19/2025 23:49:40 INFO stats.py:314 | Epoch[72] Step[244] GlobalStep[28324] Training Speed: 8.08 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 22:10:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:49:46 INFO loss_tracker.py:84 | Epoch[72/239] Step[249] GlobalStep[28329/NA]: det_loss_cls_0[0.5564] det_loss_box_0[0.6774] det_loss_cls_1[0.5486] det_loss_box_1[0.5354] det_loss_cls_2[0.5119] det_loss_box_2[0.4882] det_loss_cls_3[0.4811] det_loss_box_3[0.4741] det_loss_cls_4[0.4560] det_loss_box_4[0.4599] det_loss_cls_5[0.4416] det_loss_box_5[0.4586] det_loss_cls_dn_0[0.1000] det_loss_box_dn_0[0.2944] det_loss_cls_dn_1[0.0967] det_loss_box_dn_1[0.2553] det_loss_cls_dn_2[0.0949] det_loss_box_dn_2[0.2337] det_loss_cls_dn_3[0.0947] det_loss_box_dn_3[0.2307] det_loss_cls_dn_4[0.0939] det_loss_box_dn_4[0.2297] det_loss_cls_dn_5[0.0930] det_loss_box_dn_5[0.2291] loss_depth[0.0400] total_loss[8.1755]
Rank[0/8] 06/19/2025 23:50:07 INFO stats.py:314 | Epoch[72] Step[269] GlobalStep[28349] Training Speed: 7.52 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 22:09:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:50:12 INFO loss_tracker.py:84 | Epoch[72/239] Step[274] GlobalStep[28354/NA]: det_loss_cls_0[0.4993] det_loss_box_0[0.6914] det_loss_cls_1[0.4909] det_loss_box_1[0.5673] det_loss_cls_2[0.4707] det_loss_box_2[0.5126] det_loss_cls_3[0.4441] det_loss_box_3[0.5013] det_loss_cls_4[0.4128] det_loss_box_4[0.4907] det_loss_cls_5[0.4034] det_loss_box_5[0.4870] det_loss_cls_dn_0[0.1063] det_loss_box_dn_0[0.3424] det_loss_cls_dn_1[0.1026] det_loss_box_dn_1[0.3040] det_loss_cls_dn_2[0.1022] det_loss_box_dn_2[0.2898] det_loss_cls_dn_3[0.1011] det_loss_box_dn_3[0.2884] det_loss_cls_dn_4[0.0999] det_loss_box_dn_4[0.2875] det_loss_cls_dn_5[0.0990] det_loss_box_dn_5[0.2857] loss_depth[0.0456] total_loss[8.4261]
Rank[0/8] 06/19/2025 23:50:33 INFO stats.py:314 | Epoch[72] Step[294] GlobalStep[28374] Training Speed: 8.05 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 22:09:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:50:38 INFO loss_tracker.py:84 | Epoch[72/239] Step[299] GlobalStep[28379/NA]: det_loss_cls_0[0.5060] det_loss_box_0[0.7107] det_loss_cls_1[0.4939] det_loss_box_1[0.5537] det_loss_cls_2[0.4706] det_loss_box_2[0.5189] det_loss_cls_3[0.4303] det_loss_box_3[0.5146] det_loss_cls_4[0.4139] det_loss_box_4[0.5101] det_loss_cls_5[0.3973] det_loss_box_5[0.5129] det_loss_cls_dn_0[0.1098] det_loss_box_dn_0[0.3180] det_loss_cls_dn_1[0.1060] det_loss_box_dn_1[0.2726] det_loss_cls_dn_2[0.1050] det_loss_box_dn_2[0.2581] det_loss_cls_dn_3[0.1037] det_loss_box_dn_3[0.2554] det_loss_cls_dn_4[0.1028] det_loss_box_dn_4[0.2546] det_loss_cls_dn_5[0.1012] det_loss_box_dn_5[0.2530] loss_depth[0.0400] total_loss[8.3131]
Rank[0/8] 06/19/2025 23:50:59 INFO stats.py:314 | Epoch[72] Step[319] GlobalStep[28399] Training Speed: 8.13 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 22:08:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:51:04 INFO loss_tracker.py:84 | Epoch[72/239] Step[324] GlobalStep[28404/NA]: det_loss_cls_0[0.5956] det_loss_box_0[0.8096] det_loss_cls_1[0.5829] det_loss_box_1[0.6513] det_loss_cls_2[0.5671] det_loss_box_2[0.5979] det_loss_cls_3[0.5395] det_loss_box_3[0.5828] det_loss_cls_4[0.5183] det_loss_box_4[0.5760] det_loss_cls_5[0.5062] det_loss_box_5[0.5784] det_loss_cls_dn_0[0.1135] det_loss_box_dn_0[0.3768] det_loss_cls_dn_1[0.1101] det_loss_box_dn_1[0.3219] det_loss_cls_dn_2[0.1103] det_loss_box_dn_2[0.3051] det_loss_cls_dn_3[0.1094] det_loss_box_dn_3[0.3025] det_loss_cls_dn_4[0.1076] det_loss_box_dn_4[0.3011] det_loss_cls_dn_5[0.1068] det_loss_box_dn_5[0.2987] loss_depth[0.0458] total_loss[9.7151]
Rank[0/8] 06/19/2025 23:51:25 INFO stats.py:314 | Epoch[72] Step[344] GlobalStep[28424] Training Speed: 6.46 samples/sec across all devices.	Average Step Time: 1.24 sec.	Estimated Remaining Time: 22:07:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:51:30 INFO loss_tracker.py:84 | Epoch[72/239] Step[349] GlobalStep[28429/NA]: det_loss_cls_0[0.5444] det_loss_box_0[0.6935] det_loss_cls_1[0.5287] det_loss_box_1[0.5629] det_loss_cls_2[0.5085] det_loss_box_2[0.4883] det_loss_cls_3[0.4804] det_loss_box_3[0.4698] det_loss_cls_4[0.4406] det_loss_box_4[0.4717] det_loss_cls_5[0.4284] det_loss_box_5[0.4703] det_loss_cls_dn_0[0.1013] det_loss_box_dn_0[0.2851] det_loss_cls_dn_1[0.0979] det_loss_box_dn_1[0.2415] det_loss_cls_dn_2[0.0972] det_loss_box_dn_2[0.2282] det_loss_cls_dn_3[0.0966] det_loss_box_dn_3[0.2251] det_loss_cls_dn_4[0.0945] det_loss_box_dn_4[0.2239] det_loss_cls_dn_5[0.0941] det_loss_box_dn_5[0.2233] loss_depth[0.0409] total_loss[8.1370]
Rank[0/8] 06/19/2025 23:51:51 INFO stats.py:314 | Epoch[72] Step[369] GlobalStep[28449] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 22:07:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:51:56 INFO loss_tracker.py:84 | Epoch[72/239] Step[374] GlobalStep[28454/NA]: det_loss_cls_0[0.5447] det_loss_box_0[0.7167] det_loss_cls_1[0.5305] det_loss_box_1[0.5684] det_loss_cls_2[0.5036] det_loss_box_2[0.5124] det_loss_cls_3[0.4739] det_loss_box_3[0.4928] det_loss_cls_4[0.4552] det_loss_box_4[0.4821] det_loss_cls_5[0.4382] det_loss_box_5[0.4853] det_loss_cls_dn_0[0.1014] det_loss_box_dn_0[0.2968] det_loss_cls_dn_1[0.0973] det_loss_box_dn_1[0.2552] det_loss_cls_dn_2[0.0963] det_loss_box_dn_2[0.2410] det_loss_cls_dn_3[0.0958] det_loss_box_dn_3[0.2383] det_loss_cls_dn_4[0.0949] det_loss_box_dn_4[0.2372] det_loss_cls_dn_5[0.0939] det_loss_box_dn_5[0.2358] loss_depth[0.0402] total_loss[8.3279]
Rank[0/8] 06/19/2025 23:52:11 INFO stats.py:394 | Epoch[72] completed. Training Speed: 7.65 samples/sec across all devices.	Epoch Time: 407.94 sec.	Average Epoch Time: 407.94 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 22:06:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:52:18 INFO stats.py:314 | Epoch[73] Step[4] GlobalStep[28474] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 22:06:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:52:39 INFO loss_tracker.py:84 | Epoch[73/239] Step[24] GlobalStep[28494/NA]: det_loss_cls_0[0.5105] det_loss_box_0[0.6110] det_loss_cls_1[0.4839] det_loss_box_1[0.4946] det_loss_cls_2[0.4640] det_loss_box_2[0.4507] det_loss_cls_3[0.4431] det_loss_box_3[0.4338] det_loss_cls_4[0.4194] det_loss_box_4[0.4240] det_loss_cls_5[0.4079] det_loss_box_5[0.4251] det_loss_cls_dn_0[0.0995] det_loss_box_dn_0[0.2998] det_loss_cls_dn_1[0.0948] det_loss_box_dn_1[0.2559] det_loss_cls_dn_2[0.0936] det_loss_box_dn_2[0.2388] det_loss_cls_dn_3[0.0931] det_loss_box_dn_3[0.2348] det_loss_cls_dn_4[0.0923] det_loss_box_dn_4[0.2333] det_loss_cls_dn_5[0.0919] det_loss_box_dn_5[0.2325] loss_depth[0.0398] total_loss[7.6683]
Rank[0/8] 06/19/2025 23:52:44 INFO stats.py:314 | Epoch[73] Step[29] GlobalStep[28499] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 22:05:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:53:04 INFO loss_tracker.py:84 | Epoch[73/239] Step[49] GlobalStep[28519/NA]: det_loss_cls_0[0.6663] det_loss_box_0[0.9063] det_loss_cls_1[0.6322] det_loss_box_1[0.7393] det_loss_cls_2[0.6123] det_loss_box_2[0.6619] det_loss_cls_3[0.5875] det_loss_box_3[0.6292] det_loss_cls_4[0.5531] det_loss_box_4[0.6208] det_loss_cls_5[0.5428] det_loss_box_5[0.6138] det_loss_cls_dn_0[0.1136] det_loss_box_dn_0[0.3239] det_loss_cls_dn_1[0.1097] det_loss_box_dn_1[0.2773] det_loss_cls_dn_2[0.1092] det_loss_box_dn_2[0.2602] det_loss_cls_dn_3[0.1078] det_loss_box_dn_3[0.2577] det_loss_cls_dn_4[0.1063] det_loss_box_dn_4[0.2552] det_loss_cls_dn_5[0.1050] det_loss_box_dn_5[0.2532] loss_depth[0.0434] total_loss[10.0880]
Rank[0/8] 06/19/2025 23:53:10 INFO stats.py:314 | Epoch[73] Step[54] GlobalStep[28524] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 22:05:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:53:31 INFO loss_tracker.py:84 | Epoch[73/239] Step[74] GlobalStep[28544/NA]: det_loss_cls_0[0.5244] det_loss_box_0[0.6011] det_loss_cls_1[0.5220] det_loss_box_1[0.4758] det_loss_cls_2[0.5002] det_loss_box_2[0.4363] det_loss_cls_3[0.4716] det_loss_box_3[0.4333] det_loss_cls_4[0.4395] det_loss_box_4[0.4210] det_loss_cls_5[0.4286] det_loss_box_5[0.4223] det_loss_cls_dn_0[0.1077] det_loss_box_dn_0[0.2738] det_loss_cls_dn_1[0.1043] det_loss_box_dn_1[0.2358] det_loss_cls_dn_2[0.1030] det_loss_box_dn_2[0.2230] det_loss_cls_dn_3[0.1017] det_loss_box_dn_3[0.2205] det_loss_cls_dn_4[0.1010] det_loss_box_dn_4[0.2193] det_loss_cls_dn_5[0.1000] det_loss_box_dn_5[0.2185] loss_depth[0.0373] total_loss[7.7220]
Rank[0/8] 06/19/2025 23:53:36 INFO stats.py:314 | Epoch[73] Step[79] GlobalStep[28549] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 22:04:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:53:56 INFO loss_tracker.py:84 | Epoch[73/239] Step[99] GlobalStep[28569/NA]: det_loss_cls_0[0.4683] det_loss_box_0[0.6337] det_loss_cls_1[0.4562] det_loss_box_1[0.5236] det_loss_cls_2[0.4392] det_loss_box_2[0.4817] det_loss_cls_3[0.4192] det_loss_box_3[0.4531] det_loss_cls_4[0.3953] det_loss_box_4[0.4456] det_loss_cls_5[0.3847] det_loss_box_5[0.4438] det_loss_cls_dn_0[0.0940] det_loss_box_dn_0[0.3195] det_loss_cls_dn_1[0.0903] det_loss_box_dn_1[0.2739] det_loss_cls_dn_2[0.0893] det_loss_box_dn_2[0.2556] det_loss_cls_dn_3[0.0882] det_loss_box_dn_3[0.2530] det_loss_cls_dn_4[0.0873] det_loss_box_dn_4[0.2518] det_loss_cls_dn_5[0.0864] det_loss_box_dn_5[0.2522] loss_depth[0.0361] total_loss[7.7221]
Rank[0/8] 06/19/2025 23:54:02 INFO stats.py:314 | Epoch[73] Step[104] GlobalStep[28574] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 22:03:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:54:22 INFO loss_tracker.py:84 | Epoch[73/239] Step[124] GlobalStep[28594/NA]: det_loss_cls_0[0.6508] det_loss_box_0[0.8234] det_loss_cls_1[0.6308] det_loss_box_1[0.6573] det_loss_cls_2[0.6118] det_loss_box_2[0.5907] det_loss_cls_3[0.5910] det_loss_box_3[0.5748] det_loss_cls_4[0.5569] det_loss_box_4[0.5594] det_loss_cls_5[0.5450] det_loss_box_5[0.5539] det_loss_cls_dn_0[0.1178] det_loss_box_dn_0[0.3376] det_loss_cls_dn_1[0.1138] det_loss_box_dn_1[0.2978] det_loss_cls_dn_2[0.1135] det_loss_box_dn_2[0.2851] det_loss_cls_dn_3[0.1128] det_loss_box_dn_3[0.2813] det_loss_cls_dn_4[0.1120] det_loss_box_dn_4[0.2811] det_loss_cls_dn_5[0.1109] det_loss_box_dn_5[0.2793] loss_depth[0.0393] total_loss[9.8280]
Rank[0/8] 06/19/2025 23:54:28 INFO stats.py:314 | Epoch[73] Step[129] GlobalStep[28599] Training Speed: 8.01 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 22:03:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:54:49 INFO loss_tracker.py:84 | Epoch[73/239] Step[149] GlobalStep[28619/NA]: det_loss_cls_0[0.4984] det_loss_box_0[0.5864] det_loss_cls_1[0.4856] det_loss_box_1[0.4646] det_loss_cls_2[0.4479] det_loss_box_2[0.4113] det_loss_cls_3[0.4226] det_loss_box_3[0.3995] det_loss_cls_4[0.3907] det_loss_box_4[0.3926] det_loss_cls_5[0.3771] det_loss_box_5[0.3934] det_loss_cls_dn_0[0.0990] det_loss_box_dn_0[0.3215] det_loss_cls_dn_1[0.0960] det_loss_box_dn_1[0.2711] det_loss_cls_dn_2[0.0946] det_loss_box_dn_2[0.2515] det_loss_cls_dn_3[0.0941] det_loss_box_dn_3[0.2459] det_loss_cls_dn_4[0.0933] det_loss_box_dn_4[0.2438] det_loss_cls_dn_5[0.0927] det_loss_box_dn_5[0.2419] loss_depth[0.0384] total_loss[7.4540]
Rank[0/8] 06/19/2025 23:54:54 INFO stats.py:314 | Epoch[73] Step[154] GlobalStep[28624] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 22:02:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:55:14 INFO loss_tracker.py:84 | Epoch[73/239] Step[174] GlobalStep[28644/NA]: det_loss_cls_0[0.5603] det_loss_box_0[0.8288] det_loss_cls_1[0.5604] det_loss_box_1[0.6631] det_loss_cls_2[0.5452] det_loss_box_2[0.5954] det_loss_cls_3[0.5118] det_loss_box_3[0.5975] det_loss_cls_4[0.4967] det_loss_box_4[0.5770] det_loss_cls_5[0.4831] det_loss_box_5[0.5714] det_loss_cls_dn_0[0.1086] det_loss_box_dn_0[0.3453] det_loss_cls_dn_1[0.1069] det_loss_box_dn_1[0.3013] det_loss_cls_dn_2[0.1064] det_loss_box_dn_2[0.2842] det_loss_cls_dn_3[0.1056] det_loss_box_dn_3[0.2807] det_loss_cls_dn_4[0.1051] det_loss_box_dn_4[0.2807] det_loss_cls_dn_5[0.1041] det_loss_box_dn_5[0.2792] loss_depth[0.0411] total_loss[9.4398]
Rank[0/8] 06/19/2025 23:55:19 INFO stats.py:314 | Epoch[73] Step[179] GlobalStep[28649] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 22:01:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:55:40 INFO loss_tracker.py:84 | Epoch[73/239] Step[199] GlobalStep[28669/NA]: det_loss_cls_0[0.6444] det_loss_box_0[0.8131] det_loss_cls_1[0.6246] det_loss_box_1[0.6440] det_loss_cls_2[0.5897] det_loss_box_2[0.5913] det_loss_cls_3[0.5506] det_loss_box_3[0.5708] det_loss_cls_4[0.5107] det_loss_box_4[0.5573] det_loss_cls_5[0.4962] det_loss_box_5[0.5570] det_loss_cls_dn_0[0.1089] det_loss_box_dn_0[0.2866] det_loss_cls_dn_1[0.1059] det_loss_box_dn_1[0.2499] det_loss_cls_dn_2[0.1053] det_loss_box_dn_2[0.2350] det_loss_cls_dn_3[0.1049] det_loss_box_dn_3[0.2329] det_loss_cls_dn_4[0.1037] det_loss_box_dn_4[0.2308] det_loss_cls_dn_5[0.1027] det_loss_box_dn_5[0.2299] loss_depth[0.0424] total_loss[9.2887]
Rank[0/8] 06/19/2025 23:55:45 INFO stats.py:314 | Epoch[73] Step[204] GlobalStep[28674] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 22:01:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:56:06 INFO loss_tracker.py:84 | Epoch[73/239] Step[224] GlobalStep[28694/NA]: det_loss_cls_0[0.6239] det_loss_box_0[0.8458] det_loss_cls_1[0.6077] det_loss_box_1[0.6881] det_loss_cls_2[0.5856] det_loss_box_2[0.6186] det_loss_cls_3[0.5593] det_loss_box_3[0.5967] det_loss_cls_4[0.5218] det_loss_box_4[0.5833] det_loss_cls_5[0.5035] det_loss_box_5[0.5856] det_loss_cls_dn_0[0.1037] det_loss_box_dn_0[0.3333] det_loss_cls_dn_1[0.1006] det_loss_box_dn_1[0.2925] det_loss_cls_dn_2[0.0996] det_loss_box_dn_2[0.2746] det_loss_cls_dn_3[0.0982] det_loss_box_dn_3[0.2724] det_loss_cls_dn_4[0.0972] det_loss_box_dn_4[0.2712] det_loss_cls_dn_5[0.0962] det_loss_box_dn_5[0.2700] loss_depth[0.0392] total_loss[9.6687]
Rank[0/8] 06/19/2025 23:56:11 INFO stats.py:314 | Epoch[73] Step[229] GlobalStep[28699] Training Speed: 6.58 samples/sec across all devices.	Average Step Time: 1.22 sec.	Estimated Remaining Time: 22:00:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:56:31 INFO loss_tracker.py:84 | Epoch[73/239] Step[249] GlobalStep[28719/NA]: det_loss_cls_0[0.5861] det_loss_box_0[0.7566] det_loss_cls_1[0.5665] det_loss_box_1[0.5876] det_loss_cls_2[0.5477] det_loss_box_2[0.5221] det_loss_cls_3[0.5223] det_loss_box_3[0.5079] det_loss_cls_4[0.4960] det_loss_box_4[0.5078] det_loss_cls_5[0.4807] det_loss_box_5[0.5061] det_loss_cls_dn_0[0.1036] det_loss_box_dn_0[0.3000] det_loss_cls_dn_1[0.1006] det_loss_box_dn_1[0.2557] det_loss_cls_dn_2[0.0996] det_loss_box_dn_2[0.2418] det_loss_cls_dn_3[0.0982] det_loss_box_dn_3[0.2373] det_loss_cls_dn_4[0.0976] det_loss_box_dn_4[0.2370] det_loss_cls_dn_5[0.0967] det_loss_box_dn_5[0.2350] loss_depth[0.0429] total_loss[8.7333]
Rank[0/8] 06/19/2025 23:56:37 INFO stats.py:314 | Epoch[73] Step[254] GlobalStep[28724] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 21:59:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:56:57 INFO loss_tracker.py:84 | Epoch[73/239] Step[274] GlobalStep[28744/NA]: det_loss_cls_0[0.5066] det_loss_box_0[0.6326] det_loss_cls_1[0.4870] det_loss_box_1[0.5165] det_loss_cls_2[0.4728] det_loss_box_2[0.4690] det_loss_cls_3[0.4477] det_loss_box_3[0.4586] det_loss_cls_4[0.4262] det_loss_box_4[0.4476] det_loss_cls_5[0.4124] det_loss_box_5[0.4498] det_loss_cls_dn_0[0.0998] det_loss_box_dn_0[0.2952] det_loss_cls_dn_1[0.0961] det_loss_box_dn_1[0.2578] det_loss_cls_dn_2[0.0956] det_loss_box_dn_2[0.2417] det_loss_cls_dn_3[0.0944] det_loss_box_dn_3[0.2404] det_loss_cls_dn_4[0.0943] det_loss_box_dn_4[0.2398] det_loss_cls_dn_5[0.0925] det_loss_box_dn_5[0.2384] loss_depth[0.0388] total_loss[7.8515]
Rank[0/8] 06/19/2025 23:57:03 INFO stats.py:314 | Epoch[73] Step[279] GlobalStep[28749] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 21:59:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:57:23 INFO loss_tracker.py:84 | Epoch[73/239] Step[299] GlobalStep[28769/NA]: det_loss_cls_0[0.4841] det_loss_box_0[0.6175] det_loss_cls_1[0.4728] det_loss_box_1[0.4694] det_loss_cls_2[0.4399] det_loss_box_2[0.4388] det_loss_cls_3[0.4110] det_loss_box_3[0.4327] det_loss_cls_4[0.3833] det_loss_box_4[0.4147] det_loss_cls_5[0.3762] det_loss_box_5[0.4159] det_loss_cls_dn_0[0.0973] det_loss_box_dn_0[0.4060] det_loss_cls_dn_1[0.0933] det_loss_box_dn_1[0.3504] det_loss_cls_dn_2[0.0917] det_loss_box_dn_2[0.3342] det_loss_cls_dn_3[0.0907] det_loss_box_dn_3[0.3306] det_loss_cls_dn_4[0.0900] det_loss_box_dn_4[0.3282] det_loss_cls_dn_5[0.0894] det_loss_box_dn_5[0.3248] loss_depth[0.0392] total_loss[8.0221]
Rank[0/8] 06/19/2025 23:57:29 INFO stats.py:314 | Epoch[73] Step[304] GlobalStep[28774] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 21:58:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:57:49 INFO loss_tracker.py:84 | Epoch[73/239] Step[324] GlobalStep[28794/NA]: det_loss_cls_0[0.5593] det_loss_box_0[0.7589] det_loss_cls_1[0.5494] det_loss_box_1[0.5959] det_loss_cls_2[0.5268] det_loss_box_2[0.5240] det_loss_cls_3[0.5027] det_loss_box_3[0.5042] det_loss_cls_4[0.4799] det_loss_box_4[0.4844] det_loss_cls_5[0.4656] det_loss_box_5[0.4839] det_loss_cls_dn_0[0.1102] det_loss_box_dn_0[0.3126] det_loss_cls_dn_1[0.1072] det_loss_box_dn_1[0.2633] det_loss_cls_dn_2[0.1076] det_loss_box_dn_2[0.2493] det_loss_cls_dn_3[0.1064] det_loss_box_dn_3[0.2475] det_loss_cls_dn_4[0.1057] det_loss_box_dn_4[0.2464] det_loss_cls_dn_5[0.1054] det_loss_box_dn_5[0.2442] loss_depth[0.0447] total_loss[8.6855]
Rank[0/8] 06/19/2025 23:57:54 INFO stats.py:314 | Epoch[73] Step[329] GlobalStep[28799] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 21:57:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:58:15 INFO loss_tracker.py:84 | Epoch[73/239] Step[349] GlobalStep[28819/NA]: det_loss_cls_0[0.6240] det_loss_box_0[0.8720] det_loss_cls_1[0.5997] det_loss_box_1[0.6610] det_loss_cls_2[0.5772] det_loss_box_2[0.6169] det_loss_cls_3[0.5525] det_loss_box_3[0.5890] det_loss_cls_4[0.5289] det_loss_box_4[0.5725] det_loss_cls_5[0.5082] det_loss_box_5[0.5733] det_loss_cls_dn_0[0.0965] det_loss_box_dn_0[0.3754] det_loss_cls_dn_1[0.0924] det_loss_box_dn_1[0.3146] det_loss_cls_dn_2[0.0912] det_loss_box_dn_2[0.2983] det_loss_cls_dn_3[0.0903] det_loss_box_dn_3[0.2934] det_loss_cls_dn_4[0.0894] det_loss_box_dn_4[0.2908] det_loss_cls_dn_5[0.0882] det_loss_box_dn_5[0.2892] loss_depth[0.0416] total_loss[9.7266]
Rank[0/8] 06/19/2025 23:58:20 INFO stats.py:314 | Epoch[73] Step[354] GlobalStep[28824] Training Speed: 6.08 samples/sec across all devices.	Average Step Time: 1.32 sec.	Estimated Remaining Time: 21:57:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:58:41 INFO loss_tracker.py:84 | Epoch[73/239] Step[374] GlobalStep[28844/NA]: det_loss_cls_0[0.4757] det_loss_box_0[0.6699] det_loss_cls_1[0.4672] det_loss_box_1[0.5551] det_loss_cls_2[0.4352] det_loss_box_2[0.5055] det_loss_cls_3[0.4108] det_loss_box_3[0.4901] det_loss_cls_4[0.3827] det_loss_box_4[0.4813] det_loss_cls_5[0.3660] det_loss_box_5[0.4827] det_loss_cls_dn_0[0.0973] det_loss_box_dn_0[0.3184] det_loss_cls_dn_1[0.0930] det_loss_box_dn_1[0.2769] det_loss_cls_dn_2[0.0916] det_loss_box_dn_2[0.2586] det_loss_cls_dn_3[0.0903] det_loss_box_dn_3[0.2562] det_loss_cls_dn_4[0.0897] det_loss_box_dn_4[0.2533] det_loss_cls_dn_5[0.0890] det_loss_box_dn_5[0.2523] loss_depth[0.0400] total_loss[7.9290]
Rank[0/8] 06/19/2025 23:58:46 INFO stats.py:314 | Epoch[73] Step[379] GlobalStep[28849] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 21:56:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:58:56 INFO stats.py:394 | Epoch[73] completed. Training Speed: 7.71 samples/sec across all devices.	Epoch Time: 404.83 sec.	Average Epoch Time: 404.83 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 21:56:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:59:15 INFO stats.py:314 | Epoch[74] Step[14] GlobalStep[28874] Training Speed: 8.07 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 21:55:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:59:25 INFO loss_tracker.py:84 | Epoch[74/239] Step[24] GlobalStep[28884/NA]: det_loss_cls_0[0.4507] det_loss_box_0[0.6287] det_loss_cls_1[0.4488] det_loss_box_1[0.4918] det_loss_cls_2[0.4170] det_loss_box_2[0.4594] det_loss_cls_3[0.3962] det_loss_box_3[0.4390] det_loss_cls_4[0.3681] det_loss_box_4[0.4348] det_loss_cls_5[0.3561] det_loss_box_5[0.4339] det_loss_cls_dn_0[0.1030] det_loss_box_dn_0[0.3339] det_loss_cls_dn_1[0.1001] det_loss_box_dn_1[0.2907] det_loss_cls_dn_2[0.0987] det_loss_box_dn_2[0.2742] det_loss_cls_dn_3[0.0983] det_loss_box_dn_3[0.2706] det_loss_cls_dn_4[0.0968] det_loss_box_dn_4[0.2697] det_loss_cls_dn_5[0.0958] det_loss_box_dn_5[0.2673] loss_depth[0.0391] total_loss[7.6625]
Rank[0/8] 06/19/2025 23:59:41 INFO stats.py:314 | Epoch[74] Step[39] GlobalStep[28899] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 21:55:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/19/2025 23:59:51 INFO loss_tracker.py:84 | Epoch[74/239] Step[49] GlobalStep[28909/NA]: det_loss_cls_0[0.5285] det_loss_box_0[0.6654] det_loss_cls_1[0.5171] det_loss_box_1[0.5215] det_loss_cls_2[0.4952] det_loss_box_2[0.4819] det_loss_cls_3[0.4641] det_loss_box_3[0.4632] det_loss_cls_4[0.4306] det_loss_box_4[0.4591] det_loss_cls_5[0.4147] det_loss_box_5[0.4556] det_loss_cls_dn_0[0.1013] det_loss_box_dn_0[0.3149] det_loss_cls_dn_1[0.0981] det_loss_box_dn_1[0.2710] det_loss_cls_dn_2[0.0976] det_loss_box_dn_2[0.2569] det_loss_cls_dn_3[0.0967] det_loss_box_dn_3[0.2541] det_loss_cls_dn_4[0.0958] det_loss_box_dn_4[0.2523] det_loss_cls_dn_5[0.0952] det_loss_box_dn_5[0.2501] loss_depth[0.0386] total_loss[8.1194]
Rank[0/8] 06/20/2025 00:00:07 INFO stats.py:314 | Epoch[74] Step[64] GlobalStep[28924] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 21:54:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:00:17 INFO loss_tracker.py:84 | Epoch[74/239] Step[74] GlobalStep[28934/NA]: det_loss_cls_0[0.4331] det_loss_box_0[0.6446] det_loss_cls_1[0.4251] det_loss_box_1[0.5442] det_loss_cls_2[0.4133] det_loss_box_2[0.4926] det_loss_cls_3[0.3875] det_loss_box_3[0.4726] det_loss_cls_4[0.3687] det_loss_box_4[0.4609] det_loss_cls_5[0.3516] det_loss_box_5[0.4614] det_loss_cls_dn_0[0.0982] det_loss_box_dn_0[0.3721] det_loss_cls_dn_1[0.0958] det_loss_box_dn_1[0.3238] det_loss_cls_dn_2[0.0955] det_loss_box_dn_2[0.3094] det_loss_cls_dn_3[0.0931] det_loss_box_dn_3[0.3072] det_loss_cls_dn_4[0.0927] det_loss_box_dn_4[0.3066] det_loss_cls_dn_5[0.0922] det_loss_box_dn_5[0.3038] loss_depth[0.0377] total_loss[7.9836]
Rank[0/8] 06/20/2025 00:00:33 INFO stats.py:314 | Epoch[74] Step[89] GlobalStep[28949] Training Speed: 8.65 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 21:53:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:00:44 INFO loss_tracker.py:84 | Epoch[74/239] Step[99] GlobalStep[28959/NA]: det_loss_cls_0[0.6448] det_loss_box_0[0.8090] det_loss_cls_1[0.6378] det_loss_box_1[0.6340] det_loss_cls_2[0.6082] det_loss_box_2[0.5640] det_loss_cls_3[0.5845] det_loss_box_3[0.5426] det_loss_cls_4[0.5477] det_loss_box_4[0.5290] det_loss_cls_5[0.5354] det_loss_box_5[0.5284] det_loss_cls_dn_0[0.1055] det_loss_box_dn_0[0.2859] det_loss_cls_dn_1[0.1032] det_loss_box_dn_1[0.2454] det_loss_cls_dn_2[0.1024] det_loss_box_dn_2[0.2290] det_loss_cls_dn_3[0.1012] det_loss_box_dn_3[0.2257] det_loss_cls_dn_4[0.1002] det_loss_box_dn_4[0.2242] det_loss_cls_dn_5[0.0998] det_loss_box_dn_5[0.2228] loss_depth[0.0409] total_loss[9.2517]
Rank[0/8] 06/20/2025 00:00:59 INFO stats.py:314 | Epoch[74] Step[114] GlobalStep[28974] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 21:53:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:01:09 INFO loss_tracker.py:84 | Epoch[74/239] Step[124] GlobalStep[28984/NA]: det_loss_cls_0[0.5385] det_loss_box_0[0.7587] det_loss_cls_1[0.5200] det_loss_box_1[0.6037] det_loss_cls_2[0.4882] det_loss_box_2[0.5409] det_loss_cls_3[0.4588] det_loss_box_3[0.5243] det_loss_cls_4[0.4339] det_loss_box_4[0.5127] det_loss_cls_5[0.4217] det_loss_box_5[0.5098] det_loss_cls_dn_0[0.0923] det_loss_box_dn_0[0.3306] det_loss_cls_dn_1[0.0895] det_loss_box_dn_1[0.2930] det_loss_cls_dn_2[0.0885] det_loss_box_dn_2[0.2784] det_loss_cls_dn_3[0.0877] det_loss_box_dn_3[0.2755] det_loss_cls_dn_4[0.0864] det_loss_box_dn_4[0.2750] det_loss_cls_dn_5[0.0849] det_loss_box_dn_5[0.2736] loss_depth[0.0395] total_loss[8.6060]
Rank[0/8] 06/20/2025 00:01:25 INFO stats.py:314 | Epoch[74] Step[139] GlobalStep[28999] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 21:52:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:01:35 INFO loss_tracker.py:84 | Epoch[74/239] Step[149] GlobalStep[29009/NA]: det_loss_cls_0[0.6159] det_loss_box_0[0.8454] det_loss_cls_1[0.6074] det_loss_box_1[0.6408] det_loss_cls_2[0.5668] det_loss_box_2[0.5694] det_loss_cls_3[0.5340] det_loss_box_3[0.5490] det_loss_cls_4[0.5062] det_loss_box_4[0.5534] det_loss_cls_5[0.4995] det_loss_box_5[0.5514] det_loss_cls_dn_0[0.1166] det_loss_box_dn_0[0.3285] det_loss_cls_dn_1[0.1141] det_loss_box_dn_1[0.2849] det_loss_cls_dn_2[0.1141] det_loss_box_dn_2[0.2669] det_loss_cls_dn_3[0.1132] det_loss_box_dn_3[0.2641] det_loss_cls_dn_4[0.1120] det_loss_box_dn_4[0.2616] det_loss_cls_dn_5[0.1113] det_loss_box_dn_5[0.2605] loss_depth[0.0413] total_loss[9.4283]
Rank[0/8] 06/20/2025 00:01:51 INFO stats.py:314 | Epoch[74] Step[164] GlobalStep[29024] Training Speed: 6.34 samples/sec across all devices.	Average Step Time: 1.26 sec.	Estimated Remaining Time: 21:51:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:02:01 INFO loss_tracker.py:84 | Epoch[74/239] Step[174] GlobalStep[29034/NA]: det_loss_cls_0[0.5876] det_loss_box_0[0.8199] det_loss_cls_1[0.5531] det_loss_box_1[0.6471] det_loss_cls_2[0.5351] det_loss_box_2[0.6006] det_loss_cls_3[0.5156] det_loss_box_3[0.5705] det_loss_cls_4[0.4906] det_loss_box_4[0.5661] det_loss_cls_5[0.4782] det_loss_box_5[0.5664] det_loss_cls_dn_0[0.0959] det_loss_box_dn_0[0.3029] det_loss_cls_dn_1[0.0911] det_loss_box_dn_1[0.2625] det_loss_cls_dn_2[0.0902] det_loss_box_dn_2[0.2472] det_loss_cls_dn_3[0.0893] det_loss_box_dn_3[0.2458] det_loss_cls_dn_4[0.0884] det_loss_box_dn_4[0.2437] det_loss_cls_dn_5[0.0879] det_loss_box_dn_5[0.2392] loss_depth[0.0408] total_loss[9.0556]
Rank[0/8] 06/20/2025 00:02:17 INFO stats.py:314 | Epoch[74] Step[189] GlobalStep[29049] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 21:51:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:02:27 INFO loss_tracker.py:84 | Epoch[74/239] Step[199] GlobalStep[29059/NA]: det_loss_cls_0[0.5241] det_loss_box_0[0.7962] det_loss_cls_1[0.5012] det_loss_box_1[0.6402] det_loss_cls_2[0.4843] det_loss_box_2[0.5742] det_loss_cls_3[0.4588] det_loss_box_3[0.5576] det_loss_cls_4[0.4321] det_loss_box_4[0.5441] det_loss_cls_5[0.4151] det_loss_box_5[0.5389] det_loss_cls_dn_0[0.0974] det_loss_box_dn_0[0.3195] det_loss_cls_dn_1[0.0944] det_loss_box_dn_1[0.2747] det_loss_cls_dn_2[0.0944] det_loss_box_dn_2[0.2578] det_loss_cls_dn_3[0.0937] det_loss_box_dn_3[0.2554] det_loss_cls_dn_4[0.0933] det_loss_box_dn_4[0.2538] det_loss_cls_dn_5[0.0927] det_loss_box_dn_5[0.2522] loss_depth[0.0434] total_loss[8.6893]
Rank[0/8] 06/20/2025 00:02:43 INFO stats.py:314 | Epoch[74] Step[214] GlobalStep[29074] Training Speed: 8.56 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 21:50:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:02:53 INFO loss_tracker.py:84 | Epoch[74/239] Step[224] GlobalStep[29084/NA]: det_loss_cls_0[0.4525] det_loss_box_0[0.5984] det_loss_cls_1[0.4387] det_loss_box_1[0.4716] det_loss_cls_2[0.4017] det_loss_box_2[0.4183] det_loss_cls_3[0.3796] det_loss_box_3[0.4003] det_loss_cls_4[0.3529] det_loss_box_4[0.3911] det_loss_cls_5[0.3375] det_loss_box_5[0.3927] det_loss_cls_dn_0[0.1008] det_loss_box_dn_0[0.3291] det_loss_cls_dn_1[0.0955] det_loss_box_dn_1[0.2814] det_loss_cls_dn_2[0.0949] det_loss_box_dn_2[0.2627] det_loss_cls_dn_3[0.0934] det_loss_box_dn_3[0.2601] det_loss_cls_dn_4[0.0929] det_loss_box_dn_4[0.2575] det_loss_cls_dn_5[0.0914] det_loss_box_dn_5[0.2535] loss_depth[0.0414] total_loss[7.2897]
Rank[0/8] 06/20/2025 00:03:08 INFO stats.py:314 | Epoch[74] Step[239] GlobalStep[29099] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 21:49:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:03:18 INFO loss_tracker.py:84 | Epoch[74/239] Step[249] GlobalStep[29109/NA]: det_loss_cls_0[0.6272] det_loss_box_0[0.8975] det_loss_cls_1[0.6046] det_loss_box_1[0.7165] det_loss_cls_2[0.5691] det_loss_box_2[0.6631] det_loss_cls_3[0.5482] det_loss_box_3[0.6303] det_loss_cls_4[0.5287] det_loss_box_4[0.6222] det_loss_cls_5[0.5088] det_loss_box_5[0.6229] det_loss_cls_dn_0[0.1041] det_loss_box_dn_0[0.3023] det_loss_cls_dn_1[0.1008] det_loss_box_dn_1[0.2620] det_loss_cls_dn_2[0.0999] det_loss_box_dn_2[0.2504] det_loss_cls_dn_3[0.0989] det_loss_box_dn_3[0.2483] det_loss_cls_dn_4[0.0981] det_loss_box_dn_4[0.2475] det_loss_cls_dn_5[0.0971] det_loss_box_dn_5[0.2452] loss_depth[0.0411] total_loss[9.7345]
Rank[0/8] 06/20/2025 00:03:34 INFO stats.py:314 | Epoch[74] Step[264] GlobalStep[29124] Training Speed: 6.08 samples/sec across all devices.	Average Step Time: 1.32 sec.	Estimated Remaining Time: 21:49:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:03:45 INFO loss_tracker.py:84 | Epoch[74/239] Step[274] GlobalStep[29134/NA]: det_loss_cls_0[0.5931] det_loss_box_0[0.7674] det_loss_cls_1[0.5866] det_loss_box_1[0.6128] det_loss_cls_2[0.5512] det_loss_box_2[0.5379] det_loss_cls_3[0.5227] det_loss_box_3[0.5160] det_loss_cls_4[0.4859] det_loss_box_4[0.5124] det_loss_cls_5[0.4707] det_loss_box_5[0.5129] det_loss_cls_dn_0[0.0950] det_loss_box_dn_0[0.2794] det_loss_cls_dn_1[0.0916] det_loss_box_dn_1[0.2332] det_loss_cls_dn_2[0.0911] det_loss_box_dn_2[0.2132] det_loss_cls_dn_3[0.0898] det_loss_box_dn_3[0.2110] det_loss_cls_dn_4[0.0882] det_loss_box_dn_4[0.2087] det_loss_cls_dn_5[0.0877] det_loss_box_dn_5[0.2073] loss_depth[0.0394] total_loss[8.6053]
Rank[0/8] 06/20/2025 00:04:00 INFO stats.py:314 | Epoch[74] Step[289] GlobalStep[29149] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 21:48:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:04:10 INFO loss_tracker.py:84 | Epoch[74/239] Step[299] GlobalStep[29159/NA]: det_loss_cls_0[0.4475] det_loss_box_0[0.6413] det_loss_cls_1[0.4360] det_loss_box_1[0.4959] det_loss_cls_2[0.4197] det_loss_box_2[0.4343] det_loss_cls_3[0.3938] det_loss_box_3[0.4172] det_loss_cls_4[0.3764] det_loss_box_4[0.4172] det_loss_cls_5[0.3631] det_loss_box_5[0.4232] det_loss_cls_dn_0[0.1081] det_loss_box_dn_0[0.3334] det_loss_cls_dn_1[0.1028] det_loss_box_dn_1[0.2841] det_loss_cls_dn_2[0.1014] det_loss_box_dn_2[0.2672] det_loss_cls_dn_3[0.1011] det_loss_box_dn_3[0.2630] det_loss_cls_dn_4[0.1002] det_loss_box_dn_4[0.2616] det_loss_cls_dn_5[0.0998] det_loss_box_dn_5[0.2596] loss_depth[0.0434] total_loss[7.5913]
Rank[0/8] 06/20/2025 00:04:25 INFO stats.py:314 | Epoch[74] Step[314] GlobalStep[29174] Training Speed: 8.63 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 21:47:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:04:36 INFO loss_tracker.py:84 | Epoch[74/239] Step[324] GlobalStep[29184/NA]: det_loss_cls_0[0.4685] det_loss_box_0[0.6274] det_loss_cls_1[0.4669] det_loss_box_1[0.4959] det_loss_cls_2[0.4386] det_loss_box_2[0.4564] det_loss_cls_3[0.4109] det_loss_box_3[0.4492] det_loss_cls_4[0.3803] det_loss_box_4[0.4413] det_loss_cls_5[0.3661] det_loss_box_5[0.4386] det_loss_cls_dn_0[0.0929] det_loss_box_dn_0[0.3332] det_loss_cls_dn_1[0.0890] det_loss_box_dn_1[0.2798] det_loss_cls_dn_2[0.0888] det_loss_box_dn_2[0.2633] det_loss_cls_dn_3[0.0883] det_loss_box_dn_3[0.2590] det_loss_cls_dn_4[0.0876] det_loss_box_dn_4[0.2575] det_loss_cls_dn_5[0.0866] det_loss_box_dn_5[0.2555] loss_depth[0.0391] total_loss[7.6607]
Rank[0/8] 06/20/2025 00:04:51 INFO stats.py:314 | Epoch[74] Step[339] GlobalStep[29199] Training Speed: 6.78 samples/sec across all devices.	Average Step Time: 1.18 sec.	Estimated Remaining Time: 21:46:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:05:02 INFO loss_tracker.py:84 | Epoch[74/239] Step[349] GlobalStep[29209/NA]: det_loss_cls_0[0.4117] det_loss_box_0[0.5517] det_loss_cls_1[0.3972] det_loss_box_1[0.4481] det_loss_cls_2[0.3678] det_loss_box_2[0.4103] det_loss_cls_3[0.3578] det_loss_box_3[0.3893] det_loss_cls_4[0.3302] det_loss_box_4[0.3874] det_loss_cls_5[0.3173] det_loss_box_5[0.3860] det_loss_cls_dn_0[0.1027] det_loss_box_dn_0[0.4164] det_loss_cls_dn_1[0.0981] det_loss_box_dn_1[0.3699] det_loss_cls_dn_2[0.0976] det_loss_box_dn_2[0.3531] det_loss_cls_dn_3[0.0962] det_loss_box_dn_3[0.3520] det_loss_cls_dn_4[0.0951] det_loss_box_dn_4[0.3493] det_loss_cls_dn_5[0.0930] det_loss_box_dn_5[0.3434] loss_depth[0.0394] total_loss[7.5610]
Rank[0/8] 06/20/2025 00:05:18 INFO stats.py:314 | Epoch[74] Step[364] GlobalStep[29224] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 21:46:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:05:28 INFO loss_tracker.py:84 | Epoch[74/239] Step[374] GlobalStep[29234/NA]: det_loss_cls_0[0.5020] det_loss_box_0[0.6382] det_loss_cls_1[0.4970] det_loss_box_1[0.4998] det_loss_cls_2[0.4618] det_loss_box_2[0.4492] det_loss_cls_3[0.4400] det_loss_box_3[0.4247] det_loss_cls_4[0.4155] det_loss_box_4[0.4203] det_loss_cls_5[0.4056] det_loss_box_5[0.4245] det_loss_cls_dn_0[0.0943] det_loss_box_dn_0[0.3106] det_loss_cls_dn_1[0.0911] det_loss_box_dn_1[0.2671] det_loss_cls_dn_2[0.0902] det_loss_box_dn_2[0.2507] det_loss_cls_dn_3[0.0897] det_loss_box_dn_3[0.2468] det_loss_cls_dn_4[0.0882] det_loss_box_dn_4[0.2455] det_loss_cls_dn_5[0.0874] det_loss_box_dn_5[0.2447] loss_depth[0.0369] total_loss[7.7213]
Rank[0/8] 06/20/2025 00:05:43 INFO stats.py:314 | Epoch[74] Step[389] GlobalStep[29249] Training Speed: 8.67 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 21:45:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:05:43 INFO stats.py:394 | Epoch[74] completed. Training Speed: 7.66 samples/sec across all devices.	Epoch Time: 407.53 sec.	Average Epoch Time: 407.53 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 21:45:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:06:12 INFO stats.py:314 | Epoch[75] Step[24] GlobalStep[29274] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 21:45:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:06:12 INFO loss_tracker.py:84 | Epoch[75/239] Step[24] GlobalStep[29274/NA]: det_loss_cls_0[0.5249] det_loss_box_0[0.6676] det_loss_cls_1[0.5209] det_loss_box_1[0.5312] det_loss_cls_2[0.4848] det_loss_box_2[0.4690] det_loss_cls_3[0.4703] det_loss_box_3[0.4474] det_loss_cls_4[0.4292] det_loss_box_4[0.4470] det_loss_cls_5[0.4173] det_loss_box_5[0.4466] det_loss_cls_dn_0[0.1060] det_loss_box_dn_0[0.2915] det_loss_cls_dn_1[0.1016] det_loss_box_dn_1[0.2492] det_loss_cls_dn_2[0.1010] det_loss_box_dn_2[0.2349] det_loss_cls_dn_3[0.1003] det_loss_box_dn_3[0.2330] det_loss_cls_dn_4[0.0995] det_loss_box_dn_4[0.2320] det_loss_cls_dn_5[0.0988] det_loss_box_dn_5[0.2314] loss_depth[0.0388] total_loss[7.9741]
Rank[0/8] 06/20/2025 00:06:39 INFO stats.py:314 | Epoch[75] Step[49] GlobalStep[29299] Training Speed: 8.07 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 21:44:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:06:39 INFO loss_tracker.py:84 | Epoch[75/239] Step[49] GlobalStep[29299/NA]: det_loss_cls_0[0.5374] det_loss_box_0[0.7183] det_loss_cls_1[0.5395] det_loss_box_1[0.5717] det_loss_cls_2[0.5110] det_loss_box_2[0.5075] det_loss_cls_3[0.4768] det_loss_box_3[0.4945] det_loss_cls_4[0.4479] det_loss_box_4[0.4826] det_loss_cls_5[0.4411] det_loss_box_5[0.4803] det_loss_cls_dn_0[0.0989] det_loss_box_dn_0[0.3134] det_loss_cls_dn_1[0.0952] det_loss_box_dn_1[0.2726] det_loss_cls_dn_2[0.0946] det_loss_box_dn_2[0.2594] det_loss_cls_dn_3[0.0940] det_loss_box_dn_3[0.2570] det_loss_cls_dn_4[0.0923] det_loss_box_dn_4[0.2561] det_loss_cls_dn_5[0.0914] det_loss_box_dn_5[0.2542] loss_depth[0.0391] total_loss[8.4268]
Rank[0/8] 06/20/2025 00:07:04 INFO stats.py:314 | Epoch[75] Step[74] GlobalStep[29324] Training Speed: 8.65 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 21:43:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:07:04 INFO loss_tracker.py:84 | Epoch[75/239] Step[74] GlobalStep[29324/NA]: det_loss_cls_0[0.6399] det_loss_box_0[0.9697] det_loss_cls_1[0.6207] det_loss_box_1[0.7720] det_loss_cls_2[0.5982] det_loss_box_2[0.7020] det_loss_cls_3[0.5742] det_loss_box_3[0.6854] det_loss_cls_4[0.5360] det_loss_box_4[0.6712] det_loss_cls_5[0.5213] det_loss_box_5[0.6688] det_loss_cls_dn_0[0.1090] det_loss_box_dn_0[0.3485] det_loss_cls_dn_1[0.1050] det_loss_box_dn_1[0.3023] det_loss_cls_dn_2[0.1047] det_loss_box_dn_2[0.2861] det_loss_cls_dn_3[0.1040] det_loss_box_dn_3[0.2833] det_loss_cls_dn_4[0.1030] det_loss_box_dn_4[0.2820] det_loss_cls_dn_5[0.1018] det_loss_box_dn_5[0.2802] loss_depth[0.0408] total_loss[10.4102]
Rank[0/8] 06/20/2025 00:07:30 INFO stats.py:314 | Epoch[75] Step[99] GlobalStep[29349] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 21:43:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:07:30 INFO loss_tracker.py:84 | Epoch[75/239] Step[99] GlobalStep[29349/NA]: det_loss_cls_0[0.4790] det_loss_box_0[0.6701] det_loss_cls_1[0.4693] det_loss_box_1[0.5276] det_loss_cls_2[0.4425] det_loss_box_2[0.4753] det_loss_cls_3[0.4181] det_loss_box_3[0.4599] det_loss_cls_4[0.3880] det_loss_box_4[0.4492] det_loss_cls_5[0.3750] det_loss_box_5[0.4514] det_loss_cls_dn_0[0.0931] det_loss_box_dn_0[0.3377] det_loss_cls_dn_1[0.0890] det_loss_box_dn_1[0.2835] det_loss_cls_dn_2[0.0877] det_loss_box_dn_2[0.2676] det_loss_cls_dn_3[0.0871] det_loss_box_dn_3[0.2643] det_loss_cls_dn_4[0.0853] det_loss_box_dn_4[0.2627] det_loss_cls_dn_5[0.0839] det_loss_box_dn_5[0.2571] loss_depth[0.0375] total_loss[7.8419]
Rank[0/8] 06/20/2025 00:07:56 INFO stats.py:314 | Epoch[75] Step[124] GlobalStep[29374] Training Speed: 8.66 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 21:42:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:07:56 INFO loss_tracker.py:84 | Epoch[75/239] Step[124] GlobalStep[29374/NA]: det_loss_cls_0[0.4852] det_loss_box_0[0.6283] det_loss_cls_1[0.4605] det_loss_box_1[0.5203] det_loss_cls_2[0.4537] det_loss_box_2[0.4680] det_loss_cls_3[0.4246] det_loss_box_3[0.4510] det_loss_cls_4[0.3926] det_loss_box_4[0.4383] det_loss_cls_5[0.3816] det_loss_box_5[0.4364] det_loss_cls_dn_0[0.1057] det_loss_box_dn_0[0.3684] det_loss_cls_dn_1[0.1030] det_loss_box_dn_1[0.3228] det_loss_cls_dn_2[0.1026] det_loss_box_dn_2[0.3041] det_loss_cls_dn_3[0.1011] det_loss_box_dn_3[0.2997] det_loss_cls_dn_4[0.0998] det_loss_box_dn_4[0.2996] det_loss_cls_dn_5[0.0985] det_loss_box_dn_5[0.2979] loss_depth[0.0409] total_loss[8.0846]
Rank[0/8] 06/20/2025 00:08:21 INFO stats.py:314 | Epoch[75] Step[149] GlobalStep[29399] Training Speed: 11.63 samples/sec across all devices.	Average Step Time: 0.69 sec.	Estimated Remaining Time: 21:41:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:08:22 INFO loss_tracker.py:84 | Epoch[75/239] Step[149] GlobalStep[29399/NA]: det_loss_cls_0[0.4631] det_loss_box_0[0.6162] det_loss_cls_1[0.4531] det_loss_box_1[0.4837] det_loss_cls_2[0.4261] det_loss_box_2[0.4340] det_loss_cls_3[0.4047] det_loss_box_3[0.4146] det_loss_cls_4[0.3767] det_loss_box_4[0.4114] det_loss_cls_5[0.3663] det_loss_box_5[0.4081] det_loss_cls_dn_0[0.1050] det_loss_box_dn_0[0.3071] det_loss_cls_dn_1[0.1022] det_loss_box_dn_1[0.2620] det_loss_cls_dn_2[0.1018] det_loss_box_dn_2[0.2466] det_loss_cls_dn_3[0.1007] det_loss_box_dn_3[0.2440] det_loss_cls_dn_4[0.0998] det_loss_box_dn_4[0.2410] det_loss_cls_dn_5[0.0988] det_loss_box_dn_5[0.2401] loss_depth[0.0385] total_loss[7.4456]
Rank[0/8] 06/20/2025 00:08:48 INFO stats.py:314 | Epoch[75] Step[174] GlobalStep[29424] Training Speed: 8.13 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 21:41:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:08:48 INFO loss_tracker.py:84 | Epoch[75/239] Step[174] GlobalStep[29424/NA]: det_loss_cls_0[0.4653] det_loss_box_0[0.6055] det_loss_cls_1[0.4485] det_loss_box_1[0.4814] det_loss_cls_2[0.4337] det_loss_box_2[0.4305] det_loss_cls_3[0.4063] det_loss_box_3[0.4099] det_loss_cls_4[0.3773] det_loss_box_4[0.4065] det_loss_cls_5[0.3666] det_loss_box_5[0.4038] det_loss_cls_dn_0[0.1024] det_loss_box_dn_0[0.3374] det_loss_cls_dn_1[0.1003] det_loss_box_dn_1[0.2886] det_loss_cls_dn_2[0.1002] det_loss_box_dn_2[0.2741] det_loss_cls_dn_3[0.0982] det_loss_box_dn_3[0.2692] det_loss_cls_dn_4[0.0967] det_loss_box_dn_4[0.2681] det_loss_cls_dn_5[0.0960] det_loss_box_dn_5[0.2661] loss_depth[0.0398] total_loss[7.5721]
Rank[0/8] 06/20/2025 00:09:14 INFO stats.py:314 | Epoch[75] Step[199] GlobalStep[29449] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 21:40:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:09:14 INFO loss_tracker.py:84 | Epoch[75/239] Step[199] GlobalStep[29449/NA]: det_loss_cls_0[0.4888] det_loss_box_0[0.5681] det_loss_cls_1[0.4807] det_loss_box_1[0.4421] det_loss_cls_2[0.4563] det_loss_box_2[0.4039] det_loss_cls_3[0.4221] det_loss_box_3[0.3859] det_loss_cls_4[0.3963] det_loss_box_4[0.3877] det_loss_cls_5[0.3839] det_loss_box_5[0.3816] det_loss_cls_dn_0[0.0947] det_loss_box_dn_0[0.3469] det_loss_cls_dn_1[0.0905] det_loss_box_dn_1[0.3048] det_loss_cls_dn_2[0.0893] det_loss_box_dn_2[0.2946] det_loss_cls_dn_3[0.0890] det_loss_box_dn_3[0.2908] det_loss_cls_dn_4[0.0883] det_loss_box_dn_4[0.2875] det_loss_cls_dn_5[0.0871] det_loss_box_dn_5[0.2835] loss_depth[0.0369] total_loss[7.5813]
Rank[0/8] 06/20/2025 00:09:40 INFO stats.py:314 | Epoch[75] Step[224] GlobalStep[29474] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 21:39:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:09:40 INFO loss_tracker.py:84 | Epoch[75/239] Step[224] GlobalStep[29474/NA]: det_loss_cls_0[0.4389] det_loss_box_0[0.5782] det_loss_cls_1[0.4176] det_loss_box_1[0.4436] det_loss_cls_2[0.4029] det_loss_box_2[0.3971] det_loss_cls_3[0.3680] det_loss_box_3[0.4003] det_loss_cls_4[0.3478] det_loss_box_4[0.3860] det_loss_cls_5[0.3338] det_loss_box_5[0.3874] det_loss_cls_dn_0[0.0973] det_loss_box_dn_0[0.3404] det_loss_cls_dn_1[0.0930] det_loss_box_dn_1[0.2933] det_loss_cls_dn_2[0.0931] det_loss_box_dn_2[0.2794] det_loss_cls_dn_3[0.0910] det_loss_box_dn_3[0.2761] det_loss_cls_dn_4[0.0905] det_loss_box_dn_4[0.2762] det_loss_cls_dn_5[0.0898] det_loss_box_dn_5[0.2751] loss_depth[0.0417] total_loss[7.2386]
Rank[0/8] 06/20/2025 00:10:06 INFO stats.py:314 | Epoch[75] Step[249] GlobalStep[29499] Training Speed: 7.89 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 21:39:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:10:06 INFO loss_tracker.py:84 | Epoch[75/239] Step[249] GlobalStep[29499/NA]: det_loss_cls_0[0.4742] det_loss_box_0[0.6854] det_loss_cls_1[0.4643] det_loss_box_1[0.5619] det_loss_cls_2[0.4401] det_loss_box_2[0.5258] det_loss_cls_3[0.4161] det_loss_box_3[0.5131] det_loss_cls_4[0.3917] det_loss_box_4[0.4897] det_loss_cls_5[0.3804] det_loss_box_5[0.4787] det_loss_cls_dn_0[0.0960] det_loss_box_dn_0[0.3002] det_loss_cls_dn_1[0.0914] det_loss_box_dn_1[0.2526] det_loss_cls_dn_2[0.0908] det_loss_box_dn_2[0.2376] det_loss_cls_dn_3[0.0898] det_loss_box_dn_3[0.2343] det_loss_cls_dn_4[0.0897] det_loss_box_dn_4[0.2325] det_loss_cls_dn_5[0.0885] det_loss_box_dn_5[0.2302] loss_depth[0.0398] total_loss[7.8949]
Rank[0/8] 06/20/2025 00:10:32 INFO stats.py:314 | Epoch[75] Step[274] GlobalStep[29524] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 21:38:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:10:32 INFO loss_tracker.py:84 | Epoch[75/239] Step[274] GlobalStep[29524/NA]: det_loss_cls_0[0.4984] det_loss_box_0[0.6161] det_loss_cls_1[0.4820] det_loss_box_1[0.4944] det_loss_cls_2[0.4605] det_loss_box_2[0.4443] det_loss_cls_3[0.4401] det_loss_box_3[0.4252] det_loss_cls_4[0.4120] det_loss_box_4[0.4118] det_loss_cls_5[0.4007] det_loss_box_5[0.4133] det_loss_cls_dn_0[0.1037] det_loss_box_dn_0[0.2663] det_loss_cls_dn_1[0.0987] det_loss_box_dn_1[0.2304] det_loss_cls_dn_2[0.0975] det_loss_box_dn_2[0.2198] det_loss_cls_dn_3[0.0961] det_loss_box_dn_3[0.2173] det_loss_cls_dn_4[0.0953] det_loss_box_dn_4[0.2148] det_loss_cls_dn_5[0.0940] det_loss_box_dn_5[0.2133] loss_depth[0.0395] total_loss[7.4857]
Rank[0/8] 06/20/2025 00:10:58 INFO stats.py:314 | Epoch[75] Step[299] GlobalStep[29549] Training Speed: 8.02 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 21:37:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:10:58 INFO loss_tracker.py:84 | Epoch[75/239] Step[299] GlobalStep[29549/NA]: det_loss_cls_0[0.4641] det_loss_box_0[0.5953] det_loss_cls_1[0.4660] det_loss_box_1[0.4581] det_loss_cls_2[0.4444] det_loss_box_2[0.4176] det_loss_cls_3[0.4092] det_loss_box_3[0.4133] det_loss_cls_4[0.3791] det_loss_box_4[0.4026] det_loss_cls_5[0.3630] det_loss_box_5[0.4036] det_loss_cls_dn_0[0.0943] det_loss_box_dn_0[0.3348] det_loss_cls_dn_1[0.0900] det_loss_box_dn_1[0.2933] det_loss_cls_dn_2[0.0885] det_loss_box_dn_2[0.2764] det_loss_cls_dn_3[0.0872] det_loss_box_dn_3[0.2708] det_loss_cls_dn_4[0.0862] det_loss_box_dn_4[0.2689] det_loss_cls_dn_5[0.0850] det_loss_box_dn_5[0.2652] loss_depth[0.0367] total_loss[7.4936]
Rank[0/8] 06/20/2025 00:11:24 INFO stats.py:314 | Epoch[75] Step[324] GlobalStep[29574] Training Speed: 8.05 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 21:37:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:11:24 INFO loss_tracker.py:84 | Epoch[75/239] Step[324] GlobalStep[29574/NA]: det_loss_cls_0[0.4512] det_loss_box_0[0.6082] det_loss_cls_1[0.4466] det_loss_box_1[0.4685] det_loss_cls_2[0.4107] det_loss_box_2[0.4270] det_loss_cls_3[0.3845] det_loss_box_3[0.4140] det_loss_cls_4[0.3522] det_loss_box_4[0.4127] det_loss_cls_5[0.3413] det_loss_box_5[0.4171] det_loss_cls_dn_0[0.0956] det_loss_box_dn_0[0.3230] det_loss_cls_dn_1[0.0899] det_loss_box_dn_1[0.2844] det_loss_cls_dn_2[0.0904] det_loss_box_dn_2[0.2702] det_loss_cls_dn_3[0.0894] det_loss_box_dn_3[0.2667] det_loss_cls_dn_4[0.0881] det_loss_box_dn_4[0.2663] det_loss_cls_dn_5[0.0862] det_loss_box_dn_5[0.2620] loss_depth[0.0384] total_loss[7.3846]
Rank[0/8] 06/20/2025 00:11:49 INFO stats.py:314 | Epoch[75] Step[349] GlobalStep[29599] Training Speed: 7.65 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 21:36:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:11:49 INFO loss_tracker.py:84 | Epoch[75/239] Step[349] GlobalStep[29599/NA]: det_loss_cls_0[0.5658] det_loss_box_0[0.7093] det_loss_cls_1[0.5617] det_loss_box_1[0.5506] det_loss_cls_2[0.5349] det_loss_box_2[0.4923] det_loss_cls_3[0.4951] det_loss_box_3[0.4807] det_loss_cls_4[0.4652] det_loss_box_4[0.4741] det_loss_cls_5[0.4396] det_loss_box_5[0.4765] det_loss_cls_dn_0[0.0921] det_loss_box_dn_0[0.2598] det_loss_cls_dn_1[0.0883] det_loss_box_dn_1[0.2158] det_loss_cls_dn_2[0.0873] det_loss_box_dn_2[0.2007] det_loss_cls_dn_3[0.0859] det_loss_box_dn_3[0.1976] det_loss_cls_dn_4[0.0850] det_loss_box_dn_4[0.1959] det_loss_cls_dn_5[0.0845] det_loss_box_dn_5[0.1954] loss_depth[0.0385] total_loss[8.0727]
Rank[0/8] 06/20/2025 00:12:16 INFO stats.py:314 | Epoch[75] Step[374] GlobalStep[29624] Training Speed: 7.25 samples/sec across all devices.	Average Step Time: 1.10 sec.	Estimated Remaining Time: 21:35:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:12:16 INFO loss_tracker.py:84 | Epoch[75/239] Step[374] GlobalStep[29624/NA]: det_loss_cls_0[0.6017] det_loss_box_0[0.7815] det_loss_cls_1[0.5756] det_loss_box_1[0.6273] det_loss_cls_2[0.5447] det_loss_box_2[0.5632] det_loss_cls_3[0.5099] det_loss_box_3[0.5540] det_loss_cls_4[0.4888] det_loss_box_4[0.5305] det_loss_cls_5[0.4790] det_loss_box_5[0.5273] det_loss_cls_dn_0[0.1030] det_loss_box_dn_0[0.3250] det_loss_cls_dn_1[0.0998] det_loss_box_dn_1[0.2864] det_loss_cls_dn_2[0.0998] det_loss_box_dn_2[0.2728] det_loss_cls_dn_3[0.0985] det_loss_box_dn_3[0.2705] det_loss_cls_dn_4[0.0977] det_loss_box_dn_4[0.2693] det_loss_cls_dn_5[0.0967] det_loss_box_dn_5[0.2667] loss_depth[0.0414] total_loss[9.1110]
Rank[0/8] 06/20/2025 00:12:31 INFO stats.py:394 | Epoch[75] completed. Training Speed: 7.66 samples/sec across all devices.	Epoch Time: 407.32 sec.	Average Epoch Time: 407.32 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 21:35:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:12:44 INFO stats.py:314 | Epoch[76] Step[9] GlobalStep[29649] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 21:35:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:13:00 INFO loss_tracker.py:84 | Epoch[76/239] Step[24] GlobalStep[29664/NA]: det_loss_cls_0[0.5037] det_loss_box_0[0.7382] det_loss_cls_1[0.4976] det_loss_box_1[0.5971] det_loss_cls_2[0.4779] det_loss_box_2[0.5304] det_loss_cls_3[0.4453] det_loss_box_3[0.5189] det_loss_cls_4[0.4170] det_loss_box_4[0.5123] det_loss_cls_5[0.4024] det_loss_box_5[0.5145] det_loss_cls_dn_0[0.1014] det_loss_box_dn_0[0.3186] det_loss_cls_dn_1[0.0972] det_loss_box_dn_1[0.2762] det_loss_cls_dn_2[0.0970] det_loss_box_dn_2[0.2612] det_loss_cls_dn_3[0.0964] det_loss_box_dn_3[0.2585] det_loss_cls_dn_4[0.0951] det_loss_box_dn_4[0.2560] det_loss_cls_dn_5[0.0942] det_loss_box_dn_5[0.2501] loss_depth[0.0408] total_loss[8.3980]
Rank[0/8] 06/20/2025 00:13:10 INFO stats.py:314 | Epoch[76] Step[34] GlobalStep[29674] Training Speed: 8.78 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 21:34:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:13:25 INFO loss_tracker.py:84 | Epoch[76/239] Step[49] GlobalStep[29689/NA]: det_loss_cls_0[0.4717] det_loss_box_0[0.6024] det_loss_cls_1[0.4589] det_loss_box_1[0.4640] det_loss_cls_2[0.4401] det_loss_box_2[0.4101] det_loss_cls_3[0.3971] det_loss_box_3[0.4024] det_loss_cls_4[0.3643] det_loss_box_4[0.4013] det_loss_cls_5[0.3524] det_loss_box_5[0.4034] det_loss_cls_dn_0[0.0946] det_loss_box_dn_0[0.2785] det_loss_cls_dn_1[0.0896] det_loss_box_dn_1[0.2397] det_loss_cls_dn_2[0.0886] det_loss_box_dn_2[0.2254] det_loss_cls_dn_3[0.0870] det_loss_box_dn_3[0.2226] det_loss_cls_dn_4[0.0857] det_loss_box_dn_4[0.2217] det_loss_cls_dn_5[0.0853] det_loss_box_dn_5[0.2203] loss_depth[0.0368] total_loss[7.1438]
Rank[0/8] 06/20/2025 00:13:36 INFO stats.py:314 | Epoch[76] Step[59] GlobalStep[29699] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 21:33:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:13:52 INFO loss_tracker.py:84 | Epoch[76/239] Step[74] GlobalStep[29714/NA]: det_loss_cls_0[0.4404] det_loss_box_0[0.5867] det_loss_cls_1[0.4207] det_loss_box_1[0.4599] det_loss_cls_2[0.4041] det_loss_box_2[0.3995] det_loss_cls_3[0.3792] det_loss_box_3[0.3802] det_loss_cls_4[0.3467] det_loss_box_4[0.3729] det_loss_cls_5[0.3337] det_loss_box_5[0.3714] det_loss_cls_dn_0[0.0990] det_loss_box_dn_0[0.3044] det_loss_cls_dn_1[0.0960] det_loss_box_dn_1[0.2626] det_loss_cls_dn_2[0.0951] det_loss_box_dn_2[0.2468] det_loss_cls_dn_3[0.0947] det_loss_box_dn_3[0.2435] det_loss_cls_dn_4[0.0941] det_loss_box_dn_4[0.2428] det_loss_cls_dn_5[0.0933] det_loss_box_dn_5[0.2410] loss_depth[0.0411] total_loss[7.0496]
Rank[0/8] 06/20/2025 00:14:02 INFO stats.py:314 | Epoch[76] Step[84] GlobalStep[29724] Training Speed: 8.71 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 21:33:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:14:18 INFO loss_tracker.py:84 | Epoch[76/239] Step[99] GlobalStep[29739/NA]: det_loss_cls_0[0.4988] det_loss_box_0[0.6938] det_loss_cls_1[0.4873] det_loss_box_1[0.5516] det_loss_cls_2[0.4592] det_loss_box_2[0.5035] det_loss_cls_3[0.4389] det_loss_box_3[0.4894] det_loss_cls_4[0.4172] det_loss_box_4[0.4758] det_loss_cls_5[0.4049] det_loss_box_5[0.4745] det_loss_cls_dn_0[0.0912] det_loss_box_dn_0[0.3204] det_loss_cls_dn_1[0.0882] det_loss_box_dn_1[0.2814] det_loss_cls_dn_2[0.0882] det_loss_box_dn_2[0.2625] det_loss_cls_dn_3[0.0874] det_loss_box_dn_3[0.2596] det_loss_cls_dn_4[0.0865] det_loss_box_dn_4[0.2592] det_loss_cls_dn_5[0.0854] det_loss_box_dn_5[0.2557] loss_depth[0.0373] total_loss[8.0979]
Rank[0/8] 06/20/2025 00:14:28 INFO stats.py:314 | Epoch[76] Step[109] GlobalStep[29749] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 21:32:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:14:43 INFO loss_tracker.py:84 | Epoch[76/239] Step[124] GlobalStep[29764/NA]: det_loss_cls_0[0.5398] det_loss_box_0[0.7010] det_loss_cls_1[0.5286] det_loss_box_1[0.5519] det_loss_cls_2[0.4934] det_loss_box_2[0.4937] det_loss_cls_3[0.4784] det_loss_box_3[0.4734] det_loss_cls_4[0.4340] det_loss_box_4[0.4646] det_loss_cls_5[0.4207] det_loss_box_5[0.4637] det_loss_cls_dn_0[0.0994] det_loss_box_dn_0[0.3343] det_loss_cls_dn_1[0.0957] det_loss_box_dn_1[0.2852] det_loss_cls_dn_2[0.0957] det_loss_box_dn_2[0.2725] det_loss_cls_dn_3[0.0951] det_loss_box_dn_3[0.2700] det_loss_cls_dn_4[0.0937] det_loss_box_dn_4[0.2691] det_loss_cls_dn_5[0.0924] det_loss_box_dn_5[0.2660] loss_depth[0.0409] total_loss[8.3533]
Rank[0/8] 06/20/2025 00:14:53 INFO stats.py:314 | Epoch[76] Step[134] GlobalStep[29774] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 21:31:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:15:09 INFO loss_tracker.py:84 | Epoch[76/239] Step[149] GlobalStep[29789/NA]: det_loss_cls_0[0.5693] det_loss_box_0[0.7750] det_loss_cls_1[0.5485] det_loss_box_1[0.6148] det_loss_cls_2[0.5245] det_loss_box_2[0.5653] det_loss_cls_3[0.4920] det_loss_box_3[0.5531] det_loss_cls_4[0.4657] det_loss_box_4[0.5434] det_loss_cls_5[0.4605] det_loss_box_5[0.5383] det_loss_cls_dn_0[0.1117] det_loss_box_dn_0[0.3067] det_loss_cls_dn_1[0.1085] det_loss_box_dn_1[0.2677] det_loss_cls_dn_2[0.1080] det_loss_box_dn_2[0.2535] det_loss_cls_dn_3[0.1064] det_loss_box_dn_3[0.2509] det_loss_cls_dn_4[0.1057] det_loss_box_dn_4[0.2519] det_loss_cls_dn_5[0.1048] det_loss_box_dn_5[0.2493] loss_depth[0.0449] total_loss[8.9204]
Rank[0/8] 06/20/2025 00:15:20 INFO stats.py:314 | Epoch[76] Step[159] GlobalStep[29799] Training Speed: 7.69 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 21:31:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:15:35 INFO loss_tracker.py:84 | Epoch[76/239] Step[174] GlobalStep[29814/NA]: det_loss_cls_0[0.6129] det_loss_box_0[0.8207] det_loss_cls_1[0.6130] det_loss_box_1[0.6643] det_loss_cls_2[0.5882] det_loss_box_2[0.5816] det_loss_cls_3[0.5526] det_loss_box_3[0.5562] det_loss_cls_4[0.5181] det_loss_box_4[0.5442] det_loss_cls_5[0.5016] det_loss_box_5[0.5491] det_loss_cls_dn_0[0.0990] det_loss_box_dn_0[0.3566] det_loss_cls_dn_1[0.0960] det_loss_box_dn_1[0.3089] det_loss_cls_dn_2[0.0953] det_loss_box_dn_2[0.2927] det_loss_cls_dn_3[0.0938] det_loss_box_dn_3[0.2888] det_loss_cls_dn_4[0.0927] det_loss_box_dn_4[0.2885] det_loss_cls_dn_5[0.0920] det_loss_box_dn_5[0.2870] loss_depth[0.0397] total_loss[9.5335]
Rank[0/8] 06/20/2025 00:15:46 INFO stats.py:314 | Epoch[76] Step[184] GlobalStep[29824] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 21:30:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:16:01 INFO loss_tracker.py:84 | Epoch[76/239] Step[199] GlobalStep[29839/NA]: det_loss_cls_0[0.5369] det_loss_box_0[0.7199] det_loss_cls_1[0.5243] det_loss_box_1[0.5921] det_loss_cls_2[0.5019] det_loss_box_2[0.5443] det_loss_cls_3[0.4867] det_loss_box_3[0.5278] det_loss_cls_4[0.4559] det_loss_box_4[0.5214] det_loss_cls_5[0.4518] det_loss_box_5[0.5164] det_loss_cls_dn_0[0.1125] det_loss_box_dn_0[0.3294] det_loss_cls_dn_1[0.1085] det_loss_box_dn_1[0.2864] det_loss_cls_dn_2[0.1085] det_loss_box_dn_2[0.2721] det_loss_cls_dn_3[0.1071] det_loss_box_dn_3[0.2694] det_loss_cls_dn_4[0.1068] det_loss_box_dn_4[0.2668] det_loss_cls_dn_5[0.1060] det_loss_box_dn_5[0.2630] loss_depth[0.0389] total_loss[8.7550]
Rank[0/8] 06/20/2025 00:16:11 INFO stats.py:314 | Epoch[76] Step[209] GlobalStep[29849] Training Speed: 8.72 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 21:29:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:16:27 INFO loss_tracker.py:84 | Epoch[76/239] Step[224] GlobalStep[29864/NA]: det_loss_cls_0[0.5836] det_loss_box_0[0.7800] det_loss_cls_1[0.5767] det_loss_box_1[0.6336] det_loss_cls_2[0.5450] det_loss_box_2[0.5677] det_loss_cls_3[0.5150] det_loss_box_3[0.5474] det_loss_cls_4[0.4792] det_loss_box_4[0.5453] det_loss_cls_5[0.4735] det_loss_box_5[0.5352] det_loss_cls_dn_0[0.1003] det_loss_box_dn_0[0.2783] det_loss_cls_dn_1[0.0967] det_loss_box_dn_1[0.2415] det_loss_cls_dn_2[0.0959] det_loss_box_dn_2[0.2249] det_loss_cls_dn_3[0.0947] det_loss_box_dn_3[0.2217] det_loss_cls_dn_4[0.0936] det_loss_box_dn_4[0.2197] det_loss_cls_dn_5[0.0926] det_loss_box_dn_5[0.2186] loss_depth[0.0382] total_loss[8.7988]
Rank[0/8] 06/20/2025 00:16:37 INFO stats.py:314 | Epoch[76] Step[234] GlobalStep[29874] Training Speed: 7.73 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 21:29:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:16:53 INFO loss_tracker.py:84 | Epoch[76/239] Step[249] GlobalStep[29889/NA]: det_loss_cls_0[0.5190] det_loss_box_0[0.6945] det_loss_cls_1[0.4943] det_loss_box_1[0.5663] det_loss_cls_2[0.4741] det_loss_box_2[0.4945] det_loss_cls_3[0.4488] det_loss_box_3[0.4849] det_loss_cls_4[0.4181] det_loss_box_4[0.4662] det_loss_cls_5[0.4054] det_loss_box_5[0.4645] det_loss_cls_dn_0[0.1079] det_loss_box_dn_0[0.3307] det_loss_cls_dn_1[0.1036] det_loss_box_dn_1[0.2816] det_loss_cls_dn_2[0.1028] det_loss_box_dn_2[0.2632] det_loss_cls_dn_3[0.1021] det_loss_box_dn_3[0.2592] det_loss_cls_dn_4[0.1009] det_loss_box_dn_4[0.2579] det_loss_cls_dn_5[0.1003] det_loss_box_dn_5[0.2561] loss_depth[0.0405] total_loss[8.2376]
Rank[0/8] 06/20/2025 00:17:03 INFO stats.py:314 | Epoch[76] Step[259] GlobalStep[29899] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 21:28:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:17:18 INFO loss_tracker.py:84 | Epoch[76/239] Step[274] GlobalStep[29914/NA]: det_loss_cls_0[0.5558] det_loss_box_0[0.7963] det_loss_cls_1[0.5415] det_loss_box_1[0.6547] det_loss_cls_2[0.5063] det_loss_box_2[0.6063] det_loss_cls_3[0.4823] det_loss_box_3[0.5820] det_loss_cls_4[0.4587] det_loss_box_4[0.5598] det_loss_cls_5[0.4493] det_loss_box_5[0.5575] det_loss_cls_dn_0[0.0987] det_loss_box_dn_0[0.2781] det_loss_cls_dn_1[0.0949] det_loss_box_dn_1[0.2399] det_loss_cls_dn_2[0.0946] det_loss_box_dn_2[0.2252] det_loss_cls_dn_3[0.0930] det_loss_box_dn_3[0.2223] det_loss_cls_dn_4[0.0924] det_loss_box_dn_4[0.2205] det_loss_cls_dn_5[0.0914] det_loss_box_dn_5[0.2188] loss_depth[0.0373] total_loss[8.7574]
Rank[0/8] 06/20/2025 00:17:28 INFO stats.py:314 | Epoch[76] Step[284] GlobalStep[29924] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 21:27:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:17:44 INFO loss_tracker.py:84 | Epoch[76/239] Step[299] GlobalStep[29939/NA]: det_loss_cls_0[0.6290] det_loss_box_0[0.8489] det_loss_cls_1[0.6199] det_loss_box_1[0.6847] det_loss_cls_2[0.5958] det_loss_box_2[0.6301] det_loss_cls_3[0.5759] det_loss_box_3[0.6029] det_loss_cls_4[0.5441] det_loss_box_4[0.5967] det_loss_cls_5[0.5320] det_loss_box_5[0.5901] det_loss_cls_dn_0[0.1083] det_loss_box_dn_0[0.3101] det_loss_cls_dn_1[0.1047] det_loss_box_dn_1[0.2698] det_loss_cls_dn_2[0.1046] det_loss_box_dn_2[0.2542] det_loss_cls_dn_3[0.1045] det_loss_box_dn_3[0.2515] det_loss_cls_dn_4[0.1038] det_loss_box_dn_4[0.2504] det_loss_cls_dn_5[0.1027] det_loss_box_dn_5[0.2489] loss_depth[0.0401] total_loss[9.7037]
Rank[0/8] 06/20/2025 00:17:54 INFO stats.py:314 | Epoch[76] Step[309] GlobalStep[29949] Training Speed: 6.19 samples/sec across all devices.	Average Step Time: 1.29 sec.	Estimated Remaining Time: 21:27:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:18:10 INFO loss_tracker.py:84 | Epoch[76/239] Step[324] GlobalStep[29964/NA]: det_loss_cls_0[0.4358] det_loss_box_0[0.5484] det_loss_cls_1[0.4395] det_loss_box_1[0.4304] det_loss_cls_2[0.4090] det_loss_box_2[0.3823] det_loss_cls_3[0.3869] det_loss_box_3[0.3661] det_loss_cls_4[0.3534] det_loss_box_4[0.3610] det_loss_cls_5[0.3418] det_loss_box_5[0.3626] det_loss_cls_dn_0[0.0939] det_loss_box_dn_0[0.3108] det_loss_cls_dn_1[0.0889] det_loss_box_dn_1[0.2726] det_loss_cls_dn_2[0.0878] det_loss_box_dn_2[0.2477] det_loss_cls_dn_3[0.0863] det_loss_box_dn_3[0.2448] det_loss_cls_dn_4[0.0859] det_loss_box_dn_4[0.2418] det_loss_cls_dn_5[0.0851] det_loss_box_dn_5[0.2412] loss_depth[0.0411] total_loss[6.9451]
Rank[0/8] 06/20/2025 00:18:20 INFO stats.py:314 | Epoch[76] Step[334] GlobalStep[29974] Training Speed: 8.17 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 21:26:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:18:35 INFO loss_tracker.py:84 | Epoch[76/239] Step[349] GlobalStep[29989/NA]: det_loss_cls_0[0.5460] det_loss_box_0[0.6824] det_loss_cls_1[0.5173] det_loss_box_1[0.5344] det_loss_cls_2[0.4901] det_loss_box_2[0.4903] det_loss_cls_3[0.4693] det_loss_box_3[0.4701] det_loss_cls_4[0.4314] det_loss_box_4[0.4680] det_loss_cls_5[0.4227] det_loss_box_5[0.4683] det_loss_cls_dn_0[0.1034] det_loss_box_dn_0[0.3026] det_loss_cls_dn_1[0.1010] det_loss_box_dn_1[0.2590] det_loss_cls_dn_2[0.0998] det_loss_box_dn_2[0.2414] det_loss_cls_dn_3[0.0985] det_loss_box_dn_3[0.2386] det_loss_cls_dn_4[0.0975] det_loss_box_dn_4[0.2370] det_loss_cls_dn_5[0.0962] det_loss_box_dn_5[0.2353] loss_depth[0.0401] total_loss[8.1407]
Rank[0/8] 06/20/2025 00:18:46 INFO stats.py:314 | Epoch[76] Step[359] GlobalStep[29999] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 21:25:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:19:01 INFO loss_tracker.py:84 | Epoch[76/239] Step[374] GlobalStep[30014/NA]: det_loss_cls_0[0.5034] det_loss_box_0[0.6537] det_loss_cls_1[0.4911] det_loss_box_1[0.5262] det_loss_cls_2[0.4621] det_loss_box_2[0.4786] det_loss_cls_3[0.4273] det_loss_box_3[0.4647] det_loss_cls_4[0.4097] det_loss_box_4[0.4585] det_loss_cls_5[0.3971] det_loss_box_5[0.4623] det_loss_cls_dn_0[0.0997] det_loss_box_dn_0[0.3629] det_loss_cls_dn_1[0.0964] det_loss_box_dn_1[0.3130] det_loss_cls_dn_2[0.0959] det_loss_box_dn_2[0.2960] det_loss_cls_dn_3[0.0951] det_loss_box_dn_3[0.2921] det_loss_cls_dn_4[0.0948] det_loss_box_dn_4[0.2910] det_loss_cls_dn_5[0.0920] det_loss_box_dn_5[0.2869] loss_depth[0.0376] total_loss[8.1880]
Rank[0/8] 06/20/2025 00:19:11 INFO stats.py:314 | Epoch[76] Step[384] GlobalStep[30024] Training Speed: 6.98 samples/sec across all devices.	Average Step Time: 1.15 sec.	Estimated Remaining Time: 21:25:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:19:17 INFO stats.py:394 | Epoch[76] completed. Training Speed: 7.69 samples/sec across all devices.	Epoch Time: 405.72 sec.	Average Epoch Time: 405.72 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 21:25:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:19:39 INFO stats.py:314 | Epoch[77] Step[19] GlobalStep[30049] Training Speed: 7.99 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 21:24:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:19:44 INFO loss_tracker.py:84 | Epoch[77/239] Step[24] GlobalStep[30054/NA]: det_loss_cls_0[0.4652] det_loss_box_0[0.6396] det_loss_cls_1[0.4569] det_loss_box_1[0.5265] det_loss_cls_2[0.4348] det_loss_box_2[0.4880] det_loss_cls_3[0.4098] det_loss_box_3[0.4682] det_loss_cls_4[0.3775] det_loss_box_4[0.4623] det_loss_cls_5[0.3645] det_loss_box_5[0.4623] det_loss_cls_dn_0[0.0994] det_loss_box_dn_0[0.3159] det_loss_cls_dn_1[0.0957] det_loss_box_dn_1[0.2756] det_loss_cls_dn_2[0.0948] det_loss_box_dn_2[0.2616] det_loss_cls_dn_3[0.0934] det_loss_box_dn_3[0.2590] det_loss_cls_dn_4[0.0923] det_loss_box_dn_4[0.2583] det_loss_cls_dn_5[0.0912] det_loss_box_dn_5[0.2559] loss_depth[0.0362] total_loss[7.7850]
Rank[0/8] 06/20/2025 00:20:05 INFO stats.py:314 | Epoch[77] Step[44] GlobalStep[30074] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 21:23:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:20:10 INFO loss_tracker.py:84 | Epoch[77/239] Step[49] GlobalStep[30079/NA]: det_loss_cls_0[0.3795] det_loss_box_0[0.5535] det_loss_cls_1[0.3688] det_loss_box_1[0.4333] det_loss_cls_2[0.3427] det_loss_box_2[0.3893] det_loss_cls_3[0.3173] det_loss_box_3[0.3748] det_loss_cls_4[0.3057] det_loss_box_4[0.3627] det_loss_cls_5[0.2972] det_loss_box_5[0.3611] det_loss_cls_dn_0[0.0999] det_loss_box_dn_0[0.3343] det_loss_cls_dn_1[0.0972] det_loss_box_dn_1[0.2869] det_loss_cls_dn_2[0.0966] det_loss_box_dn_2[0.2716] det_loss_cls_dn_3[0.0954] det_loss_box_dn_3[0.2681] det_loss_cls_dn_4[0.0949] det_loss_box_dn_4[0.2671] det_loss_cls_dn_5[0.0943] det_loss_box_dn_5[0.2639] loss_depth[0.0385] total_loss[6.7947]
Rank[0/8] 06/20/2025 00:20:31 INFO stats.py:314 | Epoch[77] Step[69] GlobalStep[30099] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 21:23:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:20:36 INFO loss_tracker.py:84 | Epoch[77/239] Step[74] GlobalStep[30104/NA]: det_loss_cls_0[0.6954] det_loss_box_0[0.8817] det_loss_cls_1[0.6634] det_loss_box_1[0.6789] det_loss_cls_2[0.6217] det_loss_box_2[0.5958] det_loss_cls_3[0.5968] det_loss_box_3[0.5718] det_loss_cls_4[0.5707] det_loss_box_4[0.5595] det_loss_cls_5[0.5489] det_loss_box_5[0.5619] det_loss_cls_dn_0[0.1158] det_loss_box_dn_0[0.3086] det_loss_cls_dn_1[0.1127] det_loss_box_dn_1[0.2588] det_loss_cls_dn_2[0.1116] det_loss_box_dn_2[0.2426] det_loss_cls_dn_3[0.1103] det_loss_box_dn_3[0.2390] det_loss_cls_dn_4[0.1093] det_loss_box_dn_4[0.2366] det_loss_cls_dn_5[0.1084] det_loss_box_dn_5[0.2362] loss_depth[0.0419] total_loss[9.7785]
Rank[0/8] 06/20/2025 00:20:57 INFO stats.py:314 | Epoch[77] Step[94] GlobalStep[30124] Training Speed: 7.92 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 21:22:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:21:02 INFO loss_tracker.py:84 | Epoch[77/239] Step[99] GlobalStep[30129/NA]: det_loss_cls_0[0.4575] det_loss_box_0[0.6362] det_loss_cls_1[0.4451] det_loss_box_1[0.4983] det_loss_cls_2[0.4201] det_loss_box_2[0.4359] det_loss_cls_3[0.3927] det_loss_box_3[0.4229] det_loss_cls_4[0.3681] det_loss_box_4[0.4184] det_loss_cls_5[0.3574] det_loss_box_5[0.4151] det_loss_cls_dn_0[0.0957] det_loss_box_dn_0[0.2901] det_loss_cls_dn_1[0.0922] det_loss_box_dn_1[0.2467] det_loss_cls_dn_2[0.0913] det_loss_box_dn_2[0.2315] det_loss_cls_dn_3[0.0899] det_loss_box_dn_3[0.2287] det_loss_cls_dn_4[0.0889] det_loss_box_dn_4[0.2263] det_loss_cls_dn_5[0.0878] det_loss_box_dn_5[0.2243] loss_depth[0.0419] total_loss[7.3029]
Rank[0/8] 06/20/2025 00:21:22 INFO stats.py:314 | Epoch[77] Step[119] GlobalStep[30149] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 21:21:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:21:27 INFO loss_tracker.py:84 | Epoch[77/239] Step[124] GlobalStep[30154/NA]: det_loss_cls_0[0.4312] det_loss_box_0[0.5399] det_loss_cls_1[0.4063] det_loss_box_1[0.4341] det_loss_cls_2[0.3837] det_loss_box_2[0.3900] det_loss_cls_3[0.3622] det_loss_box_3[0.3745] det_loss_cls_4[0.3329] det_loss_box_4[0.3677] det_loss_cls_5[0.3158] det_loss_box_5[0.3701] det_loss_cls_dn_0[0.0925] det_loss_box_dn_0[0.3002] det_loss_cls_dn_1[0.0879] det_loss_box_dn_1[0.2556] det_loss_cls_dn_2[0.0869] det_loss_box_dn_2[0.2373] det_loss_cls_dn_3[0.0856] det_loss_box_dn_3[0.2348] det_loss_cls_dn_4[0.0843] det_loss_box_dn_4[0.2320] det_loss_cls_dn_5[0.0832] det_loss_box_dn_5[0.2304] loss_depth[0.0354] total_loss[6.7546]
Rank[0/8] 06/20/2025 00:21:48 INFO stats.py:314 | Epoch[77] Step[144] GlobalStep[30174] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 21:21:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:21:53 INFO loss_tracker.py:84 | Epoch[77/239] Step[149] GlobalStep[30179/NA]: det_loss_cls_0[0.6158] det_loss_box_0[0.8212] det_loss_cls_1[0.6062] det_loss_box_1[0.6510] det_loss_cls_2[0.5842] det_loss_box_2[0.5729] det_loss_cls_3[0.5450] det_loss_box_3[0.5621] det_loss_cls_4[0.5208] det_loss_box_4[0.5474] det_loss_cls_5[0.4943] det_loss_box_5[0.5489] det_loss_cls_dn_0[0.0976] det_loss_box_dn_0[0.2905] det_loss_cls_dn_1[0.0937] det_loss_box_dn_1[0.2556] det_loss_cls_dn_2[0.0926] det_loss_box_dn_2[0.2412] det_loss_cls_dn_3[0.0917] det_loss_box_dn_3[0.2385] det_loss_cls_dn_4[0.0910] det_loss_box_dn_4[0.2375] det_loss_cls_dn_5[0.0899] det_loss_box_dn_5[0.2327] loss_depth[0.0421] total_loss[9.1644]
Rank[0/8] 06/20/2025 00:22:14 INFO stats.py:314 | Epoch[77] Step[169] GlobalStep[30199] Training Speed: 7.90 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 21:20:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:22:19 INFO loss_tracker.py:84 | Epoch[77/239] Step[174] GlobalStep[30204/NA]: det_loss_cls_0[0.4469] det_loss_box_0[0.5750] det_loss_cls_1[0.4485] det_loss_box_1[0.4578] det_loss_cls_2[0.4278] det_loss_box_2[0.4212] det_loss_cls_3[0.3921] det_loss_box_3[0.4191] det_loss_cls_4[0.3727] det_loss_box_4[0.4074] det_loss_cls_5[0.3583] det_loss_box_5[0.4107] det_loss_cls_dn_0[0.0902] det_loss_box_dn_0[0.3516] det_loss_cls_dn_1[0.0867] det_loss_box_dn_1[0.2991] det_loss_cls_dn_2[0.0851] det_loss_box_dn_2[0.2771] det_loss_cls_dn_3[0.0843] det_loss_box_dn_3[0.2725] det_loss_cls_dn_4[0.0834] det_loss_box_dn_4[0.2703] det_loss_cls_dn_5[0.0826] det_loss_box_dn_5[0.2670] loss_depth[0.0403] total_loss[7.4278]
Rank[0/8] 06/20/2025 00:22:40 INFO stats.py:314 | Epoch[77] Step[194] GlobalStep[30224] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 21:19:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:22:45 INFO loss_tracker.py:84 | Epoch[77/239] Step[199] GlobalStep[30229/NA]: det_loss_cls_0[0.5540] det_loss_box_0[0.7670] det_loss_cls_1[0.5319] det_loss_box_1[0.6337] det_loss_cls_2[0.5068] det_loss_box_2[0.5621] det_loss_cls_3[0.4847] det_loss_box_3[0.5376] det_loss_cls_4[0.4537] det_loss_box_4[0.5317] det_loss_cls_5[0.4396] det_loss_box_5[0.5316] det_loss_cls_dn_0[0.1041] det_loss_box_dn_0[0.3928] det_loss_cls_dn_1[0.0994] det_loss_box_dn_1[0.3400] det_loss_cls_dn_2[0.0982] det_loss_box_dn_2[0.3188] det_loss_cls_dn_3[0.0971] det_loss_box_dn_3[0.3145] det_loss_cls_dn_4[0.0959] det_loss_box_dn_4[0.3124] det_loss_cls_dn_5[0.0951] det_loss_box_dn_5[0.3083] loss_depth[0.0449] total_loss[9.1559]
Rank[0/8] 06/20/2025 00:23:06 INFO stats.py:314 | Epoch[77] Step[219] GlobalStep[30249] Training Speed: 7.62 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 21:19:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:23:11 INFO loss_tracker.py:84 | Epoch[77/239] Step[224] GlobalStep[30254/NA]: det_loss_cls_0[0.3911] det_loss_box_0[0.4823] det_loss_cls_1[0.3778] det_loss_box_1[0.3958] det_loss_cls_2[0.3430] det_loss_box_2[0.3716] det_loss_cls_3[0.3274] det_loss_box_3[0.3528] det_loss_cls_4[0.3076] det_loss_box_4[0.3317] det_loss_cls_5[0.2920] det_loss_box_5[0.3358] det_loss_cls_dn_0[0.1000] det_loss_box_dn_0[0.3276] det_loss_cls_dn_1[0.0969] det_loss_box_dn_1[0.2728] det_loss_cls_dn_2[0.0967] det_loss_box_dn_2[0.2553] det_loss_cls_dn_3[0.0959] det_loss_box_dn_3[0.2523] det_loss_cls_dn_4[0.0962] det_loss_box_dn_4[0.2509] det_loss_cls_dn_5[0.0956] det_loss_box_dn_5[0.2490] loss_depth[0.0432] total_loss[6.5410]
Rank[0/8] 06/20/2025 00:23:31 INFO stats.py:314 | Epoch[77] Step[244] GlobalStep[30274] Training Speed: 7.76 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 21:18:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:23:37 INFO loss_tracker.py:84 | Epoch[77/239] Step[249] GlobalStep[30279/NA]: det_loss_cls_0[0.5075] det_loss_box_0[0.6319] det_loss_cls_1[0.4862] det_loss_box_1[0.5059] det_loss_cls_2[0.4558] det_loss_box_2[0.4605] det_loss_cls_3[0.4333] det_loss_box_3[0.4474] det_loss_cls_4[0.4098] det_loss_box_4[0.4328] det_loss_cls_5[0.3921] det_loss_box_5[0.4356] det_loss_cls_dn_0[0.0996] det_loss_box_dn_0[0.2885] det_loss_cls_dn_1[0.0972] det_loss_box_dn_1[0.2468] det_loss_cls_dn_2[0.0964] det_loss_box_dn_2[0.2293] det_loss_cls_dn_3[0.0951] det_loss_box_dn_3[0.2262] det_loss_cls_dn_4[0.0943] det_loss_box_dn_4[0.2247] det_loss_cls_dn_5[0.0937] det_loss_box_dn_5[0.2241] loss_depth[0.0397] total_loss[7.6542]
Rank[0/8] 06/20/2025 00:23:58 INFO stats.py:314 | Epoch[77] Step[269] GlobalStep[30299] Training Speed: 8.34 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 21:17:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:24:03 INFO loss_tracker.py:84 | Epoch[77/239] Step[274] GlobalStep[30304/NA]: det_loss_cls_0[0.4661] det_loss_box_0[0.7033] det_loss_cls_1[0.4547] det_loss_box_1[0.5626] det_loss_cls_2[0.4216] det_loss_box_2[0.5167] det_loss_cls_3[0.4132] det_loss_box_3[0.4884] det_loss_cls_4[0.3828] det_loss_box_4[0.4822] det_loss_cls_5[0.3743] det_loss_box_5[0.4850] det_loss_cls_dn_0[0.1064] det_loss_box_dn_0[0.3339] det_loss_cls_dn_1[0.1025] det_loss_box_dn_1[0.2806] det_loss_cls_dn_2[0.1023] det_loss_box_dn_2[0.2634] det_loss_cls_dn_3[0.1016] det_loss_box_dn_3[0.2587] det_loss_cls_dn_4[0.1001] det_loss_box_dn_4[0.2574] det_loss_cls_dn_5[0.0988] det_loss_box_dn_5[0.2558] loss_depth[0.0426] total_loss[8.0551]
Rank[0/8] 06/20/2025 00:24:23 INFO stats.py:314 | Epoch[77] Step[294] GlobalStep[30324] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 21:17:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:24:28 INFO loss_tracker.py:84 | Epoch[77/239] Step[299] GlobalStep[30329/NA]: det_loss_cls_0[0.4369] det_loss_box_0[0.6096] det_loss_cls_1[0.4129] det_loss_box_1[0.4881] det_loss_cls_2[0.3888] det_loss_box_2[0.4283] det_loss_cls_3[0.3709] det_loss_box_3[0.4040] det_loss_cls_4[0.3391] det_loss_box_4[0.3990] det_loss_cls_5[0.3221] det_loss_box_5[0.3963] det_loss_cls_dn_0[0.0885] det_loss_box_dn_0[0.2855] det_loss_cls_dn_1[0.0853] det_loss_box_dn_1[0.2405] det_loss_cls_dn_2[0.0856] det_loss_box_dn_2[0.2227] det_loss_cls_dn_3[0.0851] det_loss_box_dn_3[0.2181] det_loss_cls_dn_4[0.0838] det_loss_box_dn_4[0.2178] det_loss_cls_dn_5[0.0822] det_loss_box_dn_5[0.2170] loss_depth[0.0359] total_loss[6.9439]
Rank[0/8] 06/20/2025 00:24:49 INFO stats.py:314 | Epoch[77] Step[319] GlobalStep[30349] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 21:16:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:24:54 INFO loss_tracker.py:84 | Epoch[77/239] Step[324] GlobalStep[30354/NA]: det_loss_cls_0[0.5205] det_loss_box_0[0.6342] det_loss_cls_1[0.5103] det_loss_box_1[0.4878] det_loss_cls_2[0.4782] det_loss_box_2[0.4381] det_loss_cls_3[0.4407] det_loss_box_3[0.4216] det_loss_cls_4[0.4165] det_loss_box_4[0.4116] det_loss_cls_5[0.3975] det_loss_box_5[0.4136] det_loss_cls_dn_0[0.1038] det_loss_box_dn_0[0.2913] det_loss_cls_dn_1[0.1005] det_loss_box_dn_1[0.2416] det_loss_cls_dn_2[0.0999] det_loss_box_dn_2[0.2227] det_loss_cls_dn_3[0.0990] det_loss_box_dn_3[0.2195] det_loss_cls_dn_4[0.0984] det_loss_box_dn_4[0.2185] det_loss_cls_dn_5[0.0967] det_loss_box_dn_5[0.2174] loss_depth[0.0414] total_loss[7.6213]
Rank[0/8] 06/20/2025 00:25:15 INFO stats.py:314 | Epoch[77] Step[344] GlobalStep[30374] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 21:15:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:25:20 INFO loss_tracker.py:84 | Epoch[77/239] Step[349] GlobalStep[30379/NA]: det_loss_cls_0[0.4803] det_loss_box_0[0.6824] det_loss_cls_1[0.4757] det_loss_box_1[0.5345] det_loss_cls_2[0.4479] det_loss_box_2[0.4824] det_loss_cls_3[0.4179] det_loss_box_3[0.4693] det_loss_cls_4[0.3836] det_loss_box_4[0.4689] det_loss_cls_5[0.3801] det_loss_box_5[0.4608] det_loss_cls_dn_0[0.0961] det_loss_box_dn_0[0.3071] det_loss_cls_dn_1[0.0934] det_loss_box_dn_1[0.2620] det_loss_cls_dn_2[0.0926] det_loss_box_dn_2[0.2454] det_loss_cls_dn_3[0.0914] det_loss_box_dn_3[0.2403] det_loss_cls_dn_4[0.0910] det_loss_box_dn_4[0.2395] det_loss_cls_dn_5[0.0900] det_loss_box_dn_5[0.2376] loss_depth[0.0374] total_loss[7.8076]
Rank[0/8] 06/20/2025 00:25:40 INFO stats.py:314 | Epoch[77] Step[369] GlobalStep[30399] Training Speed: 8.03 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 21:15:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:25:46 INFO loss_tracker.py:84 | Epoch[77/239] Step[374] GlobalStep[30404/NA]: det_loss_cls_0[0.4416] det_loss_box_0[0.6199] det_loss_cls_1[0.4421] det_loss_box_1[0.4715] det_loss_cls_2[0.4178] det_loss_box_2[0.4333] det_loss_cls_3[0.3829] det_loss_box_3[0.4161] det_loss_cls_4[0.3641] det_loss_box_4[0.4049] det_loss_cls_5[0.3532] det_loss_box_5[0.4068] det_loss_cls_dn_0[0.0972] det_loss_box_dn_0[0.3586] det_loss_cls_dn_1[0.0932] det_loss_box_dn_1[0.3114] det_loss_cls_dn_2[0.0921] det_loss_box_dn_2[0.2889] det_loss_cls_dn_3[0.0913] det_loss_box_dn_3[0.2852] det_loss_cls_dn_4[0.0903] det_loss_box_dn_4[0.2829] det_loss_cls_dn_5[0.0893] det_loss_box_dn_5[0.2800] loss_depth[0.0409] total_loss[7.5556]
Rank[0/8] 06/20/2025 00:26:01 INFO stats.py:394 | Epoch[77] completed. Training Speed: 7.72 samples/sec across all devices.	Epoch Time: 404.33 sec.	Average Epoch Time: 404.33 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 21:14:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:26:08 INFO stats.py:314 | Epoch[78] Step[4] GlobalStep[30424] Training Speed: 7.77 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 21:14:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:26:29 INFO loss_tracker.py:84 | Epoch[78/239] Step[24] GlobalStep[30444/NA]: det_loss_cls_0[0.5528] det_loss_box_0[0.6727] det_loss_cls_1[0.5417] det_loss_box_1[0.5355] det_loss_cls_2[0.5142] det_loss_box_2[0.4781] det_loss_cls_3[0.4796] det_loss_box_3[0.4713] det_loss_cls_4[0.4596] det_loss_box_4[0.4568] det_loss_cls_5[0.4446] det_loss_box_5[0.4576] det_loss_cls_dn_0[0.1047] det_loss_box_dn_0[0.2740] det_loss_cls_dn_1[0.1007] det_loss_box_dn_1[0.2367] det_loss_cls_dn_2[0.1001] det_loss_box_dn_2[0.2209] det_loss_cls_dn_3[0.0989] det_loss_box_dn_3[0.2183] det_loss_cls_dn_4[0.0976] det_loss_box_dn_4[0.2170] det_loss_cls_dn_5[0.0970] det_loss_box_dn_5[0.2152] loss_depth[0.0399] total_loss[8.0857]
Rank[0/8] 06/20/2025 00:26:34 INFO stats.py:314 | Epoch[78] Step[29] GlobalStep[30449] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 21:14:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:26:55 INFO loss_tracker.py:84 | Epoch[78/239] Step[49] GlobalStep[30469/NA]: det_loss_cls_0[0.5734] det_loss_box_0[0.7455] det_loss_cls_1[0.5676] det_loss_box_1[0.5876] det_loss_cls_2[0.5357] det_loss_box_2[0.5404] det_loss_cls_3[0.5119] det_loss_box_3[0.5089] det_loss_cls_4[0.4906] det_loss_box_4[0.5006] det_loss_cls_5[0.4705] det_loss_box_5[0.5050] det_loss_cls_dn_0[0.0946] det_loss_box_dn_0[0.2655] det_loss_cls_dn_1[0.0924] det_loss_box_dn_1[0.2225] det_loss_cls_dn_2[0.0917] det_loss_box_dn_2[0.2065] det_loss_cls_dn_3[0.0907] det_loss_box_dn_3[0.2028] det_loss_cls_dn_4[0.0908] det_loss_box_dn_4[0.2007] det_loss_cls_dn_5[0.0896] det_loss_box_dn_5[0.2000] loss_depth[0.0389] total_loss[8.4241]
Rank[0/8] 06/20/2025 00:27:01 INFO stats.py:314 | Epoch[78] Step[54] GlobalStep[30474] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 21:13:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:27:21 INFO loss_tracker.py:84 | Epoch[78/239] Step[74] GlobalStep[30494/NA]: det_loss_cls_0[0.4955] det_loss_box_0[0.7760] det_loss_cls_1[0.4904] det_loss_box_1[0.6415] det_loss_cls_2[0.4658] det_loss_box_2[0.5833] det_loss_cls_3[0.4405] det_loss_box_3[0.5617] det_loss_cls_4[0.4122] det_loss_box_4[0.5489] det_loss_cls_5[0.4011] det_loss_box_5[0.5435] det_loss_cls_dn_0[0.1099] det_loss_box_dn_0[0.4646] det_loss_cls_dn_1[0.1054] det_loss_box_dn_1[0.4101] det_loss_cls_dn_2[0.1048] det_loss_box_dn_2[0.3913] det_loss_cls_dn_3[0.1033] det_loss_box_dn_3[0.3884] det_loss_cls_dn_4[0.1020] det_loss_box_dn_4[0.3865] det_loss_cls_dn_5[0.1011] det_loss_box_dn_5[0.3831] loss_depth[0.0390] total_loss[9.4501]
Rank[0/8] 06/20/2025 00:27:26 INFO stats.py:314 | Epoch[78] Step[79] GlobalStep[30499] Training Speed: 8.03 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 21:12:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:27:47 INFO loss_tracker.py:84 | Epoch[78/239] Step[99] GlobalStep[30519/NA]: det_loss_cls_0[0.5561] det_loss_box_0[0.7036] det_loss_cls_1[0.5427] det_loss_box_1[0.5627] det_loss_cls_2[0.5139] det_loss_box_2[0.5169] det_loss_cls_3[0.4905] det_loss_box_3[0.5005] det_loss_cls_4[0.4620] det_loss_box_4[0.4965] det_loss_cls_5[0.4519] det_loss_box_5[0.4919] det_loss_cls_dn_0[0.1019] det_loss_box_dn_0[0.2546] det_loss_cls_dn_1[0.0987] det_loss_box_dn_1[0.2181] det_loss_cls_dn_2[0.0985] det_loss_box_dn_2[0.2056] det_loss_cls_dn_3[0.0979] det_loss_box_dn_3[0.2024] det_loss_cls_dn_4[0.0965] det_loss_box_dn_4[0.2010] det_loss_cls_dn_5[0.0961] det_loss_box_dn_5[0.1999] loss_depth[0.0383] total_loss[8.1986]
Rank[0/8] 06/20/2025 00:27:52 INFO stats.py:314 | Epoch[78] Step[104] GlobalStep[30524] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 21:12:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:28:13 INFO loss_tracker.py:84 | Epoch[78/239] Step[124] GlobalStep[30544/NA]: det_loss_cls_0[0.5148] det_loss_box_0[0.6692] det_loss_cls_1[0.4965] det_loss_box_1[0.5231] det_loss_cls_2[0.4662] det_loss_box_2[0.4655] det_loss_cls_3[0.4388] det_loss_box_3[0.4436] det_loss_cls_4[0.4145] det_loss_box_4[0.4336] det_loss_cls_5[0.3973] det_loss_box_5[0.4395] det_loss_cls_dn_0[0.0949] det_loss_box_dn_0[0.2842] det_loss_cls_dn_1[0.0917] det_loss_box_dn_1[0.2439] det_loss_cls_dn_2[0.0916] det_loss_box_dn_2[0.2293] det_loss_cls_dn_3[0.0902] det_loss_box_dn_3[0.2267] det_loss_cls_dn_4[0.0894] det_loss_box_dn_4[0.2262] det_loss_cls_dn_5[0.0882] det_loss_box_dn_5[0.2246] loss_depth[0.0400] total_loss[7.7235]
Rank[0/8] 06/20/2025 00:28:18 INFO stats.py:314 | Epoch[78] Step[129] GlobalStep[30549] Training Speed: 8.84 samples/sec across all devices.	Average Step Time: 0.90 sec.	Estimated Remaining Time: 21:11:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:28:39 INFO loss_tracker.py:84 | Epoch[78/239] Step[149] GlobalStep[30569/NA]: det_loss_cls_0[0.5177] det_loss_box_0[0.6727] det_loss_cls_1[0.5160] det_loss_box_1[0.5356] det_loss_cls_2[0.4940] det_loss_box_2[0.4817] det_loss_cls_3[0.4637] det_loss_box_3[0.4740] det_loss_cls_4[0.4313] det_loss_box_4[0.4705] det_loss_cls_5[0.4188] det_loss_box_5[0.4731] det_loss_cls_dn_0[0.1004] det_loss_box_dn_0[0.3120] det_loss_cls_dn_1[0.0968] det_loss_box_dn_1[0.2648] det_loss_cls_dn_2[0.0961] det_loss_box_dn_2[0.2433] det_loss_cls_dn_3[0.0949] det_loss_box_dn_3[0.2399] det_loss_cls_dn_4[0.0940] det_loss_box_dn_4[0.2392] det_loss_cls_dn_5[0.0933] det_loss_box_dn_5[0.2382] loss_depth[0.0412] total_loss[8.1030]
Rank[0/8] 06/20/2025 00:28:44 INFO stats.py:314 | Epoch[78] Step[154] GlobalStep[30574] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 21:10:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:29:04 INFO loss_tracker.py:84 | Epoch[78/239] Step[174] GlobalStep[30594/NA]: det_loss_cls_0[0.5298] det_loss_box_0[0.6518] det_loss_cls_1[0.5179] det_loss_box_1[0.4875] det_loss_cls_2[0.4780] det_loss_box_2[0.4381] det_loss_cls_3[0.4470] det_loss_box_3[0.4202] det_loss_cls_4[0.4101] det_loss_box_4[0.4147] det_loss_cls_5[0.3965] det_loss_box_5[0.4142] det_loss_cls_dn_0[0.0934] det_loss_box_dn_0[0.2690] det_loss_cls_dn_1[0.0898] det_loss_box_dn_1[0.2235] det_loss_cls_dn_2[0.0896] det_loss_box_dn_2[0.2076] det_loss_cls_dn_3[0.0879] det_loss_box_dn_3[0.2039] det_loss_cls_dn_4[0.0873] det_loss_box_dn_4[0.2020] det_loss_cls_dn_5[0.0862] det_loss_box_dn_5[0.2015] loss_depth[0.0409] total_loss[7.4884]
Rank[0/8] 06/20/2025 00:29:09 INFO stats.py:314 | Epoch[78] Step[179] GlobalStep[30599] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 21:10:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:29:30 INFO loss_tracker.py:84 | Epoch[78/239] Step[199] GlobalStep[30619/NA]: det_loss_cls_0[0.5483] det_loss_box_0[0.7642] det_loss_cls_1[0.5367] det_loss_box_1[0.6341] det_loss_cls_2[0.5062] det_loss_box_2[0.5780] det_loss_cls_3[0.4815] det_loss_box_3[0.5606] det_loss_cls_4[0.4570] det_loss_box_4[0.5471] det_loss_cls_5[0.4430] det_loss_box_5[0.5512] det_loss_cls_dn_0[0.0910] det_loss_box_dn_0[0.3310] det_loss_cls_dn_1[0.0875] det_loss_box_dn_1[0.2907] det_loss_cls_dn_2[0.0869] det_loss_box_dn_2[0.2778] det_loss_cls_dn_3[0.0857] det_loss_box_dn_3[0.2742] det_loss_cls_dn_4[0.0852] det_loss_box_dn_4[0.2741] det_loss_cls_dn_5[0.0842] det_loss_box_dn_5[0.2727] loss_depth[0.0387] total_loss[8.8874]
Rank[0/8] 06/20/2025 00:29:35 INFO stats.py:314 | Epoch[78] Step[204] GlobalStep[30624] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 21:09:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:29:56 INFO loss_tracker.py:84 | Epoch[78/239] Step[224] GlobalStep[30644/NA]: det_loss_cls_0[0.4852] det_loss_box_0[0.6440] det_loss_cls_1[0.4872] det_loss_box_1[0.5128] det_loss_cls_2[0.4682] det_loss_box_2[0.4607] det_loss_cls_3[0.4386] det_loss_box_3[0.4438] det_loss_cls_4[0.4086] det_loss_box_4[0.4359] det_loss_cls_5[0.3977] det_loss_box_5[0.4373] det_loss_cls_dn_0[0.1009] det_loss_box_dn_0[0.3689] det_loss_cls_dn_1[0.0972] det_loss_box_dn_1[0.3184] det_loss_cls_dn_2[0.0963] det_loss_box_dn_2[0.2987] det_loss_cls_dn_3[0.0956] det_loss_box_dn_3[0.2953] det_loss_cls_dn_4[0.0942] det_loss_box_dn_4[0.2908] det_loss_cls_dn_5[0.0933] det_loss_box_dn_5[0.2828] loss_depth[0.0392] total_loss[8.0916]
Rank[0/8] 06/20/2025 00:30:02 INFO stats.py:314 | Epoch[78] Step[229] GlobalStep[30649] Training Speed: 7.02 samples/sec across all devices.	Average Step Time: 1.14 sec.	Estimated Remaining Time: 21:08:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:30:23 INFO loss_tracker.py:84 | Epoch[78/239] Step[249] GlobalStep[30669/NA]: det_loss_cls_0[0.5298] det_loss_box_0[0.7386] det_loss_cls_1[0.5140] det_loss_box_1[0.5934] det_loss_cls_2[0.4932] det_loss_box_2[0.5415] det_loss_cls_3[0.4692] det_loss_box_3[0.5294] det_loss_cls_4[0.4435] det_loss_box_4[0.5240] det_loss_cls_5[0.4302] det_loss_box_5[0.5209] det_loss_cls_dn_0[0.1142] det_loss_box_dn_0[0.3870] det_loss_cls_dn_1[0.1104] det_loss_box_dn_1[0.3511] det_loss_cls_dn_2[0.1106] det_loss_box_dn_2[0.3349] det_loss_cls_dn_3[0.1099] det_loss_box_dn_3[0.3328] det_loss_cls_dn_4[0.1084] det_loss_box_dn_4[0.3328] det_loss_cls_dn_5[0.1076] det_loss_box_dn_5[0.3314] loss_depth[0.0398] total_loss[9.0986]
Rank[0/8] 06/20/2025 00:30:28 INFO stats.py:314 | Epoch[78] Step[254] GlobalStep[30674] Training Speed: 7.33 samples/sec across all devices.	Average Step Time: 1.09 sec.	Estimated Remaining Time: 21:08:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:30:49 INFO loss_tracker.py:84 | Epoch[78/239] Step[274] GlobalStep[30694/NA]: det_loss_cls_0[0.5738] det_loss_box_0[0.7627] det_loss_cls_1[0.5405] det_loss_box_1[0.6250] det_loss_cls_2[0.5295] det_loss_box_2[0.5640] det_loss_cls_3[0.5091] det_loss_box_3[0.5411] det_loss_cls_4[0.4705] det_loss_box_4[0.5440] det_loss_cls_5[0.4508] det_loss_box_5[0.5423] det_loss_cls_dn_0[0.0956] det_loss_box_dn_0[0.3107] det_loss_cls_dn_1[0.0909] det_loss_box_dn_1[0.2722] det_loss_cls_dn_2[0.0905] det_loss_box_dn_2[0.2573] det_loss_cls_dn_3[0.0903] det_loss_box_dn_3[0.2534] det_loss_cls_dn_4[0.0883] det_loss_box_dn_4[0.2527] det_loss_cls_dn_5[0.0870] det_loss_box_dn_5[0.2517] loss_depth[0.0392] total_loss[8.8330]
Rank[0/8] 06/20/2025 00:30:54 INFO stats.py:314 | Epoch[78] Step[279] GlobalStep[30699] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 21:07:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:31:14 INFO loss_tracker.py:84 | Epoch[78/239] Step[299] GlobalStep[30719/NA]: det_loss_cls_0[0.5296] det_loss_box_0[0.6824] det_loss_cls_1[0.5276] det_loss_box_1[0.5217] det_loss_cls_2[0.4935] det_loss_box_2[0.4648] det_loss_cls_3[0.4648] det_loss_box_3[0.4450] det_loss_cls_4[0.4237] det_loss_box_4[0.4395] det_loss_cls_5[0.4094] det_loss_box_5[0.4400] det_loss_cls_dn_0[0.0847] det_loss_box_dn_0[0.2588] det_loss_cls_dn_1[0.0818] det_loss_box_dn_1[0.2182] det_loss_cls_dn_2[0.0809] det_loss_box_dn_2[0.2003] det_loss_cls_dn_3[0.0799] det_loss_box_dn_3[0.1956] det_loss_cls_dn_4[0.0790] det_loss_box_dn_4[0.1940] det_loss_cls_dn_5[0.0792] det_loss_box_dn_5[0.1917] loss_depth[0.0378] total_loss[7.6240]
Rank[0/8] 06/20/2025 00:31:20 INFO stats.py:314 | Epoch[78] Step[304] GlobalStep[30724] Training Speed: 8.58 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 21:06:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:31:40 INFO loss_tracker.py:84 | Epoch[78/239] Step[324] GlobalStep[30744/NA]: det_loss_cls_0[0.5848] det_loss_box_0[0.7435] det_loss_cls_1[0.5874] det_loss_box_1[0.5673] det_loss_cls_2[0.5524] det_loss_box_2[0.5325] det_loss_cls_3[0.5303] det_loss_box_3[0.5115] det_loss_cls_4[0.5059] det_loss_box_4[0.4922] det_loss_cls_5[0.4862] det_loss_box_5[0.4965] det_loss_cls_dn_0[0.1072] det_loss_box_dn_0[0.3143] det_loss_cls_dn_1[0.1040] det_loss_box_dn_1[0.2665] det_loss_cls_dn_2[0.1036] det_loss_box_dn_2[0.2505] det_loss_cls_dn_3[0.1022] det_loss_box_dn_3[0.2470] det_loss_cls_dn_4[0.1012] det_loss_box_dn_4[0.2456] det_loss_cls_dn_5[0.0998] det_loss_box_dn_5[0.2443] loss_depth[0.0373] total_loss[8.8141]
Rank[0/8] 06/20/2025 00:31:45 INFO stats.py:314 | Epoch[78] Step[329] GlobalStep[30749] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 21:06:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:32:06 INFO loss_tracker.py:84 | Epoch[78/239] Step[349] GlobalStep[30769/NA]: det_loss_cls_0[0.4771] det_loss_box_0[0.6851] det_loss_cls_1[0.4612] det_loss_box_1[0.5374] det_loss_cls_2[0.4378] det_loss_box_2[0.4772] det_loss_cls_3[0.4062] det_loss_box_3[0.4649] det_loss_cls_4[0.3784] det_loss_box_4[0.4536] det_loss_cls_5[0.3641] det_loss_box_5[0.4556] det_loss_cls_dn_0[0.0931] det_loss_box_dn_0[0.3262] det_loss_cls_dn_1[0.0905] det_loss_box_dn_1[0.2770] det_loss_cls_dn_2[0.0900] det_loss_box_dn_2[0.2587] det_loss_cls_dn_3[0.0882] det_loss_box_dn_3[0.2557] det_loss_cls_dn_4[0.0871] det_loss_box_dn_4[0.2543] det_loss_cls_dn_5[0.0860] det_loss_box_dn_5[0.2525] loss_depth[0.0384] total_loss[7.7961]
Rank[0/8] 06/20/2025 00:32:11 INFO stats.py:314 | Epoch[78] Step[354] GlobalStep[30774] Training Speed: 7.89 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 21:05:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:32:32 INFO loss_tracker.py:84 | Epoch[78/239] Step[374] GlobalStep[30794/NA]: det_loss_cls_0[0.5172] det_loss_box_0[0.6895] det_loss_cls_1[0.4920] det_loss_box_1[0.5562] det_loss_cls_2[0.4743] det_loss_box_2[0.5134] det_loss_cls_3[0.4588] det_loss_box_3[0.4944] det_loss_cls_4[0.4341] det_loss_box_4[0.4865] det_loss_cls_5[0.4138] det_loss_box_5[0.4862] det_loss_cls_dn_0[0.1034] det_loss_box_dn_0[0.2846] det_loss_cls_dn_1[0.0993] det_loss_box_dn_1[0.2485] det_loss_cls_dn_2[0.0991] det_loss_box_dn_2[0.2288] det_loss_cls_dn_3[0.0972] det_loss_box_dn_3[0.2257] det_loss_cls_dn_4[0.0962] det_loss_box_dn_4[0.2244] det_loss_cls_dn_5[0.0953] det_loss_box_dn_5[0.2221] loss_depth[0.0374] total_loss[8.0785]
Rank[0/8] 06/20/2025 00:32:37 INFO stats.py:314 | Epoch[78] Step[379] GlobalStep[30799] Training Speed: 7.26 samples/sec across all devices.	Average Step Time: 1.10 sec.	Estimated Remaining Time: 21:04:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:32:47 INFO stats.py:394 | Epoch[78] completed. Training Speed: 7.68 samples/sec across all devices.	Epoch Time: 406.41 sec.	Average Epoch Time: 406.41 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 21:04:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:33:04 INFO stats.py:314 | Epoch[79] Step[14] GlobalStep[30824] Training Speed: 7.78 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 21:04:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:33:14 INFO loss_tracker.py:84 | Epoch[79/239] Step[24] GlobalStep[30834/NA]: det_loss_cls_0[0.5326] det_loss_box_0[0.6704] det_loss_cls_1[0.5068] det_loss_box_1[0.5225] det_loss_cls_2[0.4869] det_loss_box_2[0.4695] det_loss_cls_3[0.4551] det_loss_box_3[0.4534] det_loss_cls_4[0.4346] det_loss_box_4[0.4325] det_loss_cls_5[0.4217] det_loss_box_5[0.4335] det_loss_cls_dn_0[0.0908] det_loss_box_dn_0[0.2985] det_loss_cls_dn_1[0.0868] det_loss_box_dn_1[0.2585] det_loss_cls_dn_2[0.0866] det_loss_box_dn_2[0.2428] det_loss_cls_dn_3[0.0859] det_loss_box_dn_3[0.2401] det_loss_cls_dn_4[0.0845] det_loss_box_dn_4[0.2379] det_loss_cls_dn_5[0.0835] det_loss_box_dn_5[0.2311] loss_depth[0.0410] total_loss[7.8875]
Rank[0/8] 06/20/2025 00:33:29 INFO stats.py:314 | Epoch[79] Step[39] GlobalStep[30849] Training Speed: 8.59 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 21:03:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:33:39 INFO loss_tracker.py:84 | Epoch[79/239] Step[49] GlobalStep[30859/NA]: det_loss_cls_0[0.6338] det_loss_box_0[0.8551] det_loss_cls_1[0.6143] det_loss_box_1[0.6812] det_loss_cls_2[0.5878] det_loss_box_2[0.6099] det_loss_cls_3[0.5568] det_loss_box_3[0.5858] det_loss_cls_4[0.5255] det_loss_box_4[0.5827] det_loss_cls_5[0.5113] det_loss_box_5[0.5842] det_loss_cls_dn_0[0.1061] det_loss_box_dn_0[0.2843] det_loss_cls_dn_1[0.1024] det_loss_box_dn_1[0.2473] det_loss_cls_dn_2[0.1022] det_loss_box_dn_2[0.2328] det_loss_cls_dn_3[0.1006] det_loss_box_dn_3[0.2293] det_loss_cls_dn_4[0.0993] det_loss_box_dn_4[0.2284] det_loss_cls_dn_5[0.0984] det_loss_box_dn_5[0.2273] loss_depth[0.0385] total_loss[9.4252]
Rank[0/8] 06/20/2025 00:33:55 INFO stats.py:314 | Epoch[79] Step[64] GlobalStep[30874] Training Speed: 8.64 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 21:02:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:34:06 INFO loss_tracker.py:84 | Epoch[79/239] Step[74] GlobalStep[30884/NA]: det_loss_cls_0[0.5609] det_loss_box_0[0.7506] det_loss_cls_1[0.5434] det_loss_box_1[0.6222] det_loss_cls_2[0.5234] det_loss_box_2[0.5439] det_loss_cls_3[0.4939] det_loss_box_3[0.5228] det_loss_cls_4[0.4723] det_loss_box_4[0.5066] det_loss_cls_5[0.4595] det_loss_box_5[0.5046] det_loss_cls_dn_0[0.1011] det_loss_box_dn_0[0.3295] det_loss_cls_dn_1[0.0977] det_loss_box_dn_1[0.2808] det_loss_cls_dn_2[0.0965] det_loss_box_dn_2[0.2619] det_loss_cls_dn_3[0.0958] det_loss_box_dn_3[0.2561] det_loss_cls_dn_4[0.0951] det_loss_box_dn_4[0.2560] det_loss_cls_dn_5[0.0948] det_loss_box_dn_5[0.2531] loss_depth[0.0377] total_loss[8.7603]
Rank[0/8] 06/20/2025 00:34:22 INFO stats.py:314 | Epoch[79] Step[89] GlobalStep[30899] Training Speed: 7.97 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 21:02:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:34:32 INFO loss_tracker.py:84 | Epoch[79/239] Step[99] GlobalStep[30909/NA]: det_loss_cls_0[0.5063] det_loss_box_0[0.7212] det_loss_cls_1[0.4913] det_loss_box_1[0.5784] det_loss_cls_2[0.4703] det_loss_box_2[0.5143] det_loss_cls_3[0.4385] det_loss_box_3[0.4993] det_loss_cls_4[0.4098] det_loss_box_4[0.5027] det_loss_cls_5[0.3942] det_loss_box_5[0.5016] det_loss_cls_dn_0[0.1006] det_loss_box_dn_0[0.3141] det_loss_cls_dn_1[0.0961] det_loss_box_dn_1[0.2718] det_loss_cls_dn_2[0.0956] det_loss_box_dn_2[0.2542] det_loss_cls_dn_3[0.0943] det_loss_box_dn_3[0.2506] det_loss_cls_dn_4[0.0934] det_loss_box_dn_4[0.2503] det_loss_cls_dn_5[0.0925] det_loss_box_dn_5[0.2481] loss_depth[0.0370] total_loss[8.2265]
Rank[0/8] 06/20/2025 00:34:47 INFO stats.py:314 | Epoch[79] Step[114] GlobalStep[30924] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 21:01:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:34:57 INFO loss_tracker.py:84 | Epoch[79/239] Step[124] GlobalStep[30934/NA]: det_loss_cls_0[0.4913] det_loss_box_0[0.7014] det_loss_cls_1[0.4857] det_loss_box_1[0.5664] det_loss_cls_2[0.4438] det_loss_box_2[0.5312] det_loss_cls_3[0.4281] det_loss_box_3[0.5068] det_loss_cls_4[0.4086] det_loss_box_4[0.4933] det_loss_cls_5[0.3903] det_loss_box_5[0.4955] det_loss_cls_dn_0[0.0979] det_loss_box_dn_0[0.4014] det_loss_cls_dn_1[0.0933] det_loss_box_dn_1[0.3584] det_loss_cls_dn_2[0.0930] det_loss_box_dn_2[0.3465] det_loss_cls_dn_3[0.0920] det_loss_box_dn_3[0.3444] det_loss_cls_dn_4[0.0908] det_loss_box_dn_4[0.3439] det_loss_cls_dn_5[0.0903] det_loss_box_dn_5[0.3428] loss_depth[0.0419] total_loss[8.6789]
Rank[0/8] 06/20/2025 00:35:13 INFO stats.py:314 | Epoch[79] Step[139] GlobalStep[30949] Training Speed: 6.90 samples/sec across all devices.	Average Step Time: 1.16 sec.	Estimated Remaining Time: 21:01:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:35:23 INFO loss_tracker.py:84 | Epoch[79/239] Step[149] GlobalStep[30959/NA]: det_loss_cls_0[0.5262] det_loss_box_0[0.6555] det_loss_cls_1[0.5134] det_loss_box_1[0.5216] det_loss_cls_2[0.4888] det_loss_box_2[0.4739] det_loss_cls_3[0.4580] det_loss_box_3[0.4585] det_loss_cls_4[0.4332] det_loss_box_4[0.4482] det_loss_cls_5[0.4239] det_loss_box_5[0.4416] det_loss_cls_dn_0[0.1126] det_loss_box_dn_0[0.2912] det_loss_cls_dn_1[0.1099] det_loss_box_dn_1[0.2491] det_loss_cls_dn_2[0.1091] det_loss_box_dn_2[0.2333] det_loss_cls_dn_3[0.1080] det_loss_box_dn_3[0.2309] det_loss_cls_dn_4[0.1068] det_loss_box_dn_4[0.2302] det_loss_cls_dn_5[0.1060] det_loss_box_dn_5[0.2281] loss_depth[0.0382] total_loss[7.9960]
Rank[0/8] 06/20/2025 00:35:39 INFO stats.py:314 | Epoch[79] Step[164] GlobalStep[30974] Training Speed: 5.80 samples/sec across all devices.	Average Step Time: 1.38 sec.	Estimated Remaining Time: 21:00:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:35:50 INFO loss_tracker.py:84 | Epoch[79/239] Step[174] GlobalStep[30984/NA]: det_loss_cls_0[0.6069] det_loss_box_0[0.7654] det_loss_cls_1[0.5902] det_loss_box_1[0.5732] det_loss_cls_2[0.5531] det_loss_box_2[0.5129] det_loss_cls_3[0.5157] det_loss_box_3[0.5021] det_loss_cls_4[0.4988] det_loss_box_4[0.4838] det_loss_cls_5[0.4901] det_loss_box_5[0.4844] det_loss_cls_dn_0[0.1046] det_loss_box_dn_0[0.2430] det_loss_cls_dn_1[0.1009] det_loss_box_dn_1[0.2047] det_loss_cls_dn_2[0.1006] det_loss_box_dn_2[0.1893] det_loss_cls_dn_3[0.0995] det_loss_box_dn_3[0.1871] det_loss_cls_dn_4[0.0983] det_loss_box_dn_4[0.1859] det_loss_cls_dn_5[0.0971] det_loss_box_dn_5[0.1846] loss_depth[0.0383] total_loss[8.4104]
Rank[0/8] 06/20/2025 00:36:05 INFO stats.py:314 | Epoch[79] Step[189] GlobalStep[30999] Training Speed: 8.06 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 20:59:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:36:15 INFO loss_tracker.py:84 | Epoch[79/239] Step[199] GlobalStep[31009/NA]: det_loss_cls_0[0.5767] det_loss_box_0[0.6894] det_loss_cls_1[0.5446] det_loss_box_1[0.5293] det_loss_cls_2[0.5094] det_loss_box_2[0.4660] det_loss_cls_3[0.4708] det_loss_box_3[0.4575] det_loss_cls_4[0.4410] det_loss_box_4[0.4511] det_loss_cls_5[0.4262] det_loss_box_5[0.4476] det_loss_cls_dn_0[0.1016] det_loss_box_dn_0[0.3127] det_loss_cls_dn_1[0.0977] det_loss_box_dn_1[0.2675] det_loss_cls_dn_2[0.0973] det_loss_box_dn_2[0.2478] det_loss_cls_dn_3[0.0965] det_loss_box_dn_3[0.2462] det_loss_cls_dn_4[0.0957] det_loss_box_dn_4[0.2441] det_loss_cls_dn_5[0.0949] det_loss_box_dn_5[0.2414] loss_depth[0.0394] total_loss[8.1926]
Rank[0/8] 06/20/2025 00:36:31 INFO stats.py:314 | Epoch[79] Step[214] GlobalStep[31024] Training Speed: 7.56 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 20:59:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:36:41 INFO loss_tracker.py:84 | Epoch[79/239] Step[224] GlobalStep[31034/NA]: det_loss_cls_0[0.4520] det_loss_box_0[0.5342] det_loss_cls_1[0.4543] det_loss_box_1[0.4001] det_loss_cls_2[0.4236] det_loss_box_2[0.3673] det_loss_cls_3[0.3995] det_loss_box_3[0.3499] det_loss_cls_4[0.3716] det_loss_box_4[0.3431] det_loss_cls_5[0.3521] det_loss_box_5[0.3467] det_loss_cls_dn_0[0.0908] det_loss_box_dn_0[0.2512] det_loss_cls_dn_1[0.0881] det_loss_box_dn_1[0.2079] det_loss_cls_dn_2[0.0867] det_loss_box_dn_2[0.1939] det_loss_cls_dn_3[0.0854] det_loss_box_dn_3[0.1910] det_loss_cls_dn_4[0.0847] det_loss_box_dn_4[0.1898] det_loss_cls_dn_5[0.0837] det_loss_box_dn_5[0.1891] loss_depth[0.0383] total_loss[6.5753]
Rank[0/8] 06/20/2025 00:36:57 INFO stats.py:314 | Epoch[79] Step[239] GlobalStep[31049] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 20:58:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:37:08 INFO loss_tracker.py:84 | Epoch[79/239] Step[249] GlobalStep[31059/NA]: det_loss_cls_0[0.5858] det_loss_box_0[1.0172] det_loss_cls_1[0.5793] det_loss_box_1[0.8439] det_loss_cls_2[0.5391] det_loss_box_2[0.7672] det_loss_cls_3[0.5228] det_loss_box_3[0.7314] det_loss_cls_4[0.4995] det_loss_box_4[0.7176] det_loss_cls_5[0.4889] det_loss_box_5[0.7108] det_loss_cls_dn_0[0.1053] det_loss_box_dn_0[0.3395] det_loss_cls_dn_1[0.1023] det_loss_box_dn_1[0.2850] det_loss_cls_dn_2[0.1024] det_loss_box_dn_2[0.2705] det_loss_cls_dn_3[0.1015] det_loss_box_dn_3[0.2745] det_loss_cls_dn_4[0.1004] det_loss_box_dn_4[0.2714] det_loss_cls_dn_5[0.0990] det_loss_box_dn_5[0.2659] loss_depth[0.0397] total_loss[10.3610]
Rank[0/8] 06/20/2025 00:37:23 INFO stats.py:314 | Epoch[79] Step[264] GlobalStep[31074] Training Speed: 8.77 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 20:57:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:37:34 INFO loss_tracker.py:84 | Epoch[79/239] Step[274] GlobalStep[31084/NA]: det_loss_cls_0[0.6008] det_loss_box_0[0.7985] det_loss_cls_1[0.5767] det_loss_box_1[0.6264] det_loss_cls_2[0.5471] det_loss_box_2[0.5659] det_loss_cls_3[0.5274] det_loss_box_3[0.5383] det_loss_cls_4[0.4961] det_loss_box_4[0.5261] det_loss_cls_5[0.4829] det_loss_box_5[0.5261] det_loss_cls_dn_0[0.1026] det_loss_box_dn_0[0.2980] det_loss_cls_dn_1[0.0994] det_loss_box_dn_1[0.2579] det_loss_cls_dn_2[0.0990] det_loss_box_dn_2[0.2414] det_loss_cls_dn_3[0.0989] det_loss_box_dn_3[0.2371] det_loss_cls_dn_4[0.0977] det_loss_box_dn_4[0.2369] det_loss_cls_dn_5[0.0964] det_loss_box_dn_5[0.2354] loss_depth[0.0417] total_loss[8.9548]
Rank[0/8] 06/20/2025 00:37:49 INFO stats.py:314 | Epoch[79] Step[289] GlobalStep[31099] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 20:57:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:37:59 INFO loss_tracker.py:84 | Epoch[79/239] Step[299] GlobalStep[31109/NA]: det_loss_cls_0[0.4077] det_loss_box_0[0.5728] det_loss_cls_1[0.3877] det_loss_box_1[0.4464] det_loss_cls_2[0.3689] det_loss_box_2[0.4041] det_loss_cls_3[0.3467] det_loss_box_3[0.3918] det_loss_cls_4[0.3299] det_loss_box_4[0.3751] det_loss_cls_5[0.3153] det_loss_box_5[0.3803] det_loss_cls_dn_0[0.0900] det_loss_box_dn_0[0.3556] det_loss_cls_dn_1[0.0854] det_loss_box_dn_1[0.3103] det_loss_cls_dn_2[0.0856] det_loss_box_dn_2[0.2953] det_loss_cls_dn_3[0.0845] det_loss_box_dn_3[0.2917] det_loss_cls_dn_4[0.0836] det_loss_box_dn_4[0.2901] det_loss_cls_dn_5[0.0826] det_loss_box_dn_5[0.2889] loss_depth[0.0381] total_loss[7.1086]
Rank[0/8] 06/20/2025 00:38:15 INFO stats.py:314 | Epoch[79] Step[314] GlobalStep[31124] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 20:56:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:38:26 INFO loss_tracker.py:84 | Epoch[79/239] Step[324] GlobalStep[31134/NA]: det_loss_cls_0[0.4254] det_loss_box_0[0.5409] det_loss_cls_1[0.4010] det_loss_box_1[0.4254] det_loss_cls_2[0.3633] det_loss_box_2[0.3913] det_loss_cls_3[0.3514] det_loss_box_3[0.3662] det_loss_cls_4[0.3295] det_loss_box_4[0.3639] det_loss_cls_5[0.3170] det_loss_box_5[0.3615] det_loss_cls_dn_0[0.0888] det_loss_box_dn_0[0.3522] det_loss_cls_dn_1[0.0854] det_loss_box_dn_1[0.3025] det_loss_cls_dn_2[0.0840] det_loss_box_dn_2[0.2866] det_loss_cls_dn_3[0.0844] det_loss_box_dn_3[0.2830] det_loss_cls_dn_4[0.0833] det_loss_box_dn_4[0.2802] det_loss_cls_dn_5[0.0832] det_loss_box_dn_5[0.2780] loss_depth[0.0423] total_loss[6.9705]
Rank[0/8] 06/20/2025 00:38:41 INFO stats.py:314 | Epoch[79] Step[339] GlobalStep[31149] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 20:55:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:38:51 INFO loss_tracker.py:84 | Epoch[79/239] Step[349] GlobalStep[31159/NA]: det_loss_cls_0[0.4832] det_loss_box_0[0.6332] det_loss_cls_1[0.4614] det_loss_box_1[0.5055] det_loss_cls_2[0.4426] det_loss_box_2[0.4519] det_loss_cls_3[0.4130] det_loss_box_3[0.4277] det_loss_cls_4[0.3908] det_loss_box_4[0.4206] det_loss_cls_5[0.3791] det_loss_box_5[0.4229] det_loss_cls_dn_0[0.1047] det_loss_box_dn_0[0.3080] det_loss_cls_dn_1[0.1013] det_loss_box_dn_1[0.2667] det_loss_cls_dn_2[0.1010] det_loss_box_dn_2[0.2481] det_loss_cls_dn_3[0.0989] det_loss_box_dn_3[0.2433] det_loss_cls_dn_4[0.0985] det_loss_box_dn_4[0.2412] det_loss_cls_dn_5[0.0974] det_loss_box_dn_5[0.2383] loss_depth[0.0402] total_loss[7.6196]
Rank[0/8] 06/20/2025 00:39:06 INFO stats.py:314 | Epoch[79] Step[364] GlobalStep[31174] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 20:55:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:39:16 INFO loss_tracker.py:84 | Epoch[79/239] Step[374] GlobalStep[31184/NA]: det_loss_cls_0[0.3872] det_loss_box_0[0.5221] det_loss_cls_1[0.3706] det_loss_box_1[0.3949] det_loss_cls_2[0.3619] det_loss_box_2[0.3553] det_loss_cls_3[0.3301] det_loss_box_3[0.3519] det_loss_cls_4[0.3111] det_loss_box_4[0.3426] det_loss_cls_5[0.2966] det_loss_box_5[0.3445] det_loss_cls_dn_0[0.0921] det_loss_box_dn_0[0.3260] det_loss_cls_dn_1[0.0873] det_loss_box_dn_1[0.2697] det_loss_cls_dn_2[0.0870] det_loss_box_dn_2[0.2496] det_loss_cls_dn_3[0.0856] det_loss_box_dn_3[0.2460] det_loss_cls_dn_4[0.0844] det_loss_box_dn_4[0.2447] det_loss_cls_dn_5[0.0833] det_loss_box_dn_5[0.2412] loss_depth[0.0408] total_loss[6.5064]
Rank[0/8] 06/20/2025 00:39:32 INFO stats.py:314 | Epoch[79] Step[389] GlobalStep[31199] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 20:54:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:39:32 INFO trainer.py:268 | 
==================================================BEGIN EVAL==================================================
Rank[0/8] 06/20/2025 00:40:31 INFO trainer.py:275 | eval: 10
Rank[0/8] 06/20/2025 00:40:39 INFO trainer.py:275 | eval: 20
Rank[0/8] 06/20/2025 00:40:47 INFO trainer.py:275 | eval: 30
Rank[0/8] 06/20/2025 00:40:58 INFO trainer.py:275 | eval: 40
Rank[0/8] 06/20/2025 00:41:09 INFO trainer.py:275 | eval: 50
Rank[0/8] 06/20/2025 00:41:16 INFO trainer.py:275 | eval: 60
Rank[0/8] 06/20/2025 00:41:28 INFO trainer.py:275 | eval: 70
Rank[0/8] 06/20/2025 00:41:35 INFO trainer.py:275 | eval: 80
Rank[0/8] 06/20/2025 00:41:44 INFO trainer.py:275 | eval: 90
Rank[0/8] 06/20/2025 00:41:51 INFO trainer.py:275 | eval: 100
Rank[0/8] 06/20/2025 00:41:56 INFO metrics.py:428 | number of results: 824
Rank[0/8] 06/20/2025 00:46:23 INFO metrics.py:332 | eval results of overall:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| bin                 | 0.6222  | 0.7961  | 0.3019  | 0.5056  |
| sink                | 0.6400  | 0.8066  | 0.1689  | 0.3663  |
| bathtub             | 0.8238  | 0.8971  | 0.7012  | 0.7941  |
| picture             | 0.1520  | 0.3502  | 0.0049  | 0.0560  |
| toilet paper        | 0.2807  | 0.4882  | 0.0231  | 0.1732  |
| toilet              | 0.8254  | 0.8905  | 0.5571  | 0.7299  |
| vanity              | 0.4608  | 0.8667  | 0.3333  | 0.7778  |
| towel               | 0.3131  | 0.5645  | 0.0639  | 0.2258  |
| doorframe           | 0.2480  | 0.7478  | 0.0446  | 0.3818  |
| hair dryer          | 0.0600  | 0.2857  | 0.0062  | 0.1429  |
| curtain             | 0.4424  | 0.8179  | 0.0814  | 0.3493  |
| door                | 0.2274  | 0.6797  | 0.0204  | 0.2531  |
| bar                 | 0.0042  | 0.0909  | 0.0033  | 0.0606  |
| rail                | 0.0143  | 0.3594  | 0.0000  | 0.0312  |
| mirror              | 0.1947  | 0.6250  | 0.0068  | 0.0978  |
| stopcock            | 0.0653  | 0.4828  | 0.0021  | 0.0690  |
| switch              | 0.0023  | 0.1053  | 0.0000  | 0.0000  |
| tissue box          | 0.0781  | 0.4242  | 0.0284  | 0.1818  |
| bottle              | 0.0369  | 0.2702  | 0.0003  | 0.0390  |
| faucet              | 0.0261  | 0.4681  | 0.0000  | 0.0213  |
| bag                 | 0.2505  | 0.6432  | 0.0606  | 0.2324  |
| socket              | 0.0097  | 0.1681  | 0.0000  | 0.0044  |
| light               | 0.0267  | 0.2500  | 0.0001  | 0.0299  |
| purse               | 0.0015  | 0.3333  | 0.0000  | 0.0000  |
| rod                 | 0.0083  | 0.0250  | 0.0000  | 0.0000  |
| shower              | 0.1097  | 0.3000  | 0.0431  | 0.1333  |
| paper               | 0.0128  | 0.2041  | 0.0007  | 0.0207  |
| cup                 | 0.0685  | 0.2288  | 0.0009  | 0.0254  |
| basket              | 0.2016  | 0.5391  | 0.0896  | 0.2957  |
| paper cutter        | 0.6477  | 1.0000  | 0.0000  | 0.0000  |
| box                 | 0.1297  | 0.3958  | 0.0295  | 0.1363  |
| ledge               | 0.0128  | 0.3333  | 0.0080  | 0.3333  |
| grab bar            | 0.0173  | 0.3750  | 0.0000  | 0.0000  |
| hat                 | 0.1255  | 0.4444  | 0.0000  | 0.0000  |
| cabinet             | 0.2444  | 0.7382  | 0.1350  | 0.4945  |
| backpack            | 0.5496  | 0.7903  | 0.3320  | 0.5000  |
| vent                | 0.0156  | 0.0833  | 0.0000  | 0.0000  |
| tissue              | 0.0021  | 0.1538  | 0.0015  | 0.0769  |
| mat                 | 0.0180  | 0.2703  | 0.0002  | 0.0270  |
| shelf               | 0.2337  | 0.6871  | 0.0881  | 0.4119  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bed                 | 0.6764  | 0.9408  | 0.5072  | 0.8639  |
| lamp                | 0.2705  | 0.5642  | 0.0659  | 0.2478  |
| toothpaste          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blanket             | 0.2017  | 0.8000  | 0.0308  | 0.3700  |
| counter             | 0.1252  | 0.6789  | 0.0607  | 0.3119  |
| plunger             | 0.8185  | 1.0000  | 0.0511  | 0.4000  |
| frame               | 0.0722  | 0.4694  | 0.0074  | 0.1020  |
| desk                | 0.4338  | 0.9270  | 0.2569  | 0.7303  |
| microwave           | 0.5555  | 0.7347  | 0.3307  | 0.4694  |
| soap                | 0.0039  | 0.3333  | 0.0000  | 0.0000  |
| cosmetics           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| carpet              | 0.0635  | 0.4405  | 0.0013  | 0.1190  |
| couch               | 0.4511  | 0.8512  | 0.3332  | 0.7231  |
| pillow              | 0.2898  | 0.6541  | 0.0370  | 0.1980  |
| chair               | 0.6138  | 0.8038  | 0.4368  | 0.6254  |
| keyboard            | 0.0404  | 0.1512  | 0.0000  | 0.0000  |
| monitor             | 0.5253  | 0.6957  | 0.0818  | 0.2391  |
| radiator            | 0.6034  | 0.7444  | 0.1922  | 0.3444  |
| computer            | 0.3247  | 0.6393  | 0.0236  | 0.1967  |
| table               | 0.4676  | 0.8480  | 0.3333  | 0.7006  |
| mouse               | 0.0008  | 0.0541  | 0.0000  | 0.0000  |
| window              | 0.2055  | 0.6647  | 0.0348  | 0.2934  |
| blinds              | 0.1127  | 0.4400  | 0.0074  | 0.1200  |
| blackboard          | 0.4234  | 0.7195  | 0.0556  | 0.1463  |
| plug                | 0.0543  | 0.1053  | 0.0000  | 0.0000  |
| telephone           | 0.3301  | 0.5833  | 0.0124  | 0.0833  |
| jacket              | 0.6026  | 0.8750  | 0.2425  | 0.4167  |
| book                | 0.0467  | 0.3841  | 0.0031  | 0.0838  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stand               | 0.4757  | 0.8598  | 0.3405  | 0.6524  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stool               | 0.2238  | 0.7143  | 0.1089  | 0.4454  |
| pipe                | 0.0013  | 0.1786  | 0.0000  | 0.0000  |
| windowsill          | 0.0457  | 0.5217  | 0.0002  | 0.0217  |
| clothes             | 0.1396  | 0.5605  | 0.0170  | 0.1752  |
| dresser             | 0.6098  | 0.8704  | 0.4967  | 0.7778  |
| wardrobe            | 0.2579  | 0.7108  | 0.1144  | 0.5783  |
| clothes dryer       | 0.2230  | 0.7333  | 0.1399  | 0.6000  |
| rack                | 0.0514  | 0.5577  | 0.0117  | 0.3269  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tv                  | 0.4543  | 0.7327  | 0.1184  | 0.3465  |
| case                | 0.0929  | 0.3051  | 0.0352  | 0.1299  |
| toy                 | 0.1068  | 0.3333  | 0.0313  | 0.1212  |
| heater              | 0.5384  | 0.8103  | 0.0405  | 0.2069  |
| laptop              | 0.3584  | 0.6522  | 0.0607  | 0.1739  |
| shoe                | 0.1206  | 0.3458  | 0.0046  | 0.0654  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| calendar            | 0.0012  | 0.3333  | 0.0000  | 0.0000  |
| decoration          | 0.0455  | 0.3571  | 0.0015  | 0.0714  |
| ottoman             | 0.1043  | 0.6389  | 0.0780  | 0.4722  |
| container           | 0.0034  | 0.2034  | 0.0010  | 0.1017  |
| cushion             | 0.1909  | 0.6275  | 0.0393  | 0.1765  |
| bench               | 0.2595  | 0.7547  | 0.1558  | 0.4528  |
| poster              | 0.0068  | 0.0606  | 0.0000  | 0.0000  |
| notebook            | 0.0003  | 0.0769  | 0.0000  | 0.0000  |
| scale               | 0.1136  | 0.1818  | 0.0000  | 0.0000  |
| clock               | 0.1612  | 0.3830  | 0.0223  | 0.0638  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| remote control      | 0.0173  | 0.1111  | 0.0019  | 0.0370  |
| guitar              | 0.2120  | 0.5000  | 0.0417  | 0.0833  |
| refrigerator        | 0.5423  | 0.8200  | 0.3071  | 0.6200  |
| coffee maker        | 0.4065  | 0.7241  | 0.1252  | 0.2759  |
| luggage             | 0.4278  | 1.0000  | 0.4278  | 1.0000  |
| sunglasses          | 0.0928  | 0.6154  | 0.0008  | 0.1538  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| magazine            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| broom               | 0.0278  | 0.2857  | 0.0005  | 0.0714  |
| soap dish           | 0.4563  | 0.6000  | 0.0000  | 0.0000  |
| brush               | 0.1074  | 0.3571  | 0.0000  | 0.0000  |
| roll                | 0.1212  | 0.4000  | 0.0000  | 0.0000  |
| tube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| chandelier          | 0.0629  | 0.3333  | 0.0048  | 0.0476  |
| sign                | 0.0867  | 0.2333  | 0.0000  | 0.0000  |
| soap dispenser      | 0.4528  | 0.7778  | 0.0312  | 0.2593  |
| dispenser           | 0.4860  | 0.7273  | 0.2022  | 0.3864  |
| alarm               | 0.0001  | 0.1250  | 0.0000  | 0.0000  |
| stall               | 0.1271  | 0.9412  | 0.1001  | 0.4118  |
| mailbox             | 0.1497  | 1.0000  | 0.0013  | 0.2000  |
| roof                | 0.0007  | 0.2083  | 0.0001  | 0.0833  |
| board               | 0.1263  | 0.5806  | 0.0352  | 0.1452  |
| window frame        | 0.0310  | 0.5455  | 0.0053  | 0.2222  |
| stairs              | 0.0509  | 0.3750  | 0.0197  | 0.1250  |
| cube                | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| fireplace           | 0.0738  | 0.4643  | 0.0090  | 0.1786  |
| pillar              | 0.3416  | 0.6977  | 0.0944  | 0.3023  |
| projector           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fan                 | 0.4665  | 0.7969  | 0.1728  | 0.5000  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crate               | 0.2773  | 0.7143  | 0.0486  | 0.2857  |
| plant               | 0.2553  | 0.5658  | 0.0369  | 0.2102  |
| file                | 0.0229  | 0.2647  | 0.0024  | 0.1176  |
| printer             | 0.5590  | 0.7551  | 0.3352  | 0.5918  |
| machine             | 0.5715  | 1.0000  | 0.0564  | 0.5789  |
| person              | 0.0152  | 0.1667  | 0.0000  | 0.0000  |
| dumbbell            | 0.5000  | 0.6667  | 0.0370  | 0.3333  |
| map                 | 0.0312  | 0.5000  | 0.0312  | 0.5000  |
| headphones          | 0.0455  | 0.4000  | 0.0000  | 0.0000  |
| ball                | 0.4236  | 0.5238  | 0.1915  | 0.4762  |
| hamper              | 0.4939  | 0.5455  | 0.4939  | 0.5455  |
| bicycle             | 0.5336  | 1.0000  | 0.0683  | 0.6667  |
| fire extinguisher   | 0.2000  | 0.2000  | 0.0000  | 0.0000  |
| water cooler        | 0.6250  | 1.0000  | 0.6250  | 1.0000  |
| speaker             | 0.0101  | 0.2273  | 0.0001  | 0.0455  |
| stove               | 0.4808  | 0.8333  | 0.2642  | 0.4444  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| holder              | 0.0010  | 0.1111  | 0.0000  | 0.0000  |
| hanger              | 0.0027  | 0.2500  | 0.0000  | 0.0000  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| doorway             | 0.0004  | 0.1111  | 0.0000  | 0.0000  |
| piano               | 0.2000  | 0.2000  | 0.2000  | 0.2000  |
| dish rack           | 0.4570  | 0.8000  | 0.0018  | 0.2000  |
| kitchenware         | 0.2024  | 0.5217  | 0.0303  | 0.0870  |
| teapot              | 0.0297  | 0.2222  | 0.0000  | 0.0000  |
| kettle              | 0.1025  | 0.5556  | 0.0242  | 0.2222  |
| toaster             | 0.4355  | 0.8000  | 0.0030  | 0.1000  |
| dishwasher          | 0.1061  | 0.3000  | 0.0250  | 0.1000  |
| plate               | 0.0346  | 0.3077  | 0.0007  | 0.0577  |
| oven                | 0.3154  | 0.7500  | 0.1249  | 0.3929  |
| washing machine     | 0.5253  | 0.8214  | 0.2711  | 0.5357  |
| range hood          | 0.3490  | 0.7857  | 0.0000  | 0.0000  |
| bowl                | 0.0698  | 0.4058  | 0.0055  | 0.0870  |
| bucket              | 0.1800  | 0.5641  | 0.0487  | 0.2564  |
| column              | 0.0144  | 0.2632  | 0.0069  | 0.1579  |
| flowerpot           | 0.0365  | 0.4510  | 0.0053  | 0.1569  |
| vase                | 0.1509  | 0.4400  | 0.0523  | 0.1200  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ladder              | 0.0813  | 0.5882  | 0.0462  | 0.2941  |
| candle              | 0.0675  | 0.2222  | 0.0055  | 0.1111  |
| air conditioner     | 0.5200  | 0.6000  | 0.0010  | 0.2000  |
| album               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jar                 | 0.0298  | 0.2308  | 0.0000  | 0.0000  |
| vacuum cleaner      | 0.0168  | 0.2857  | 0.0000  | 0.0000  |
| humidifier          | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| copier              | 0.9167  | 0.9167  | 0.3393  | 0.5000  |
| folder              | 0.0040  | 0.2143  | 0.0000  | 0.0000  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| thermostat          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garbage             | 0.2673  | 0.7500  | 0.0134  | 0.3750  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| conducting wire     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| device              | 0.0629  | 0.2727  | 0.0000  | 0.0000  |
| beanbag             | 1.0000  | 1.0000  | 0.7778  | 0.8333  |
| commode             | 0.3791  | 0.8269  | 0.2994  | 0.6731  |
| price tag           | 0.1760  | 0.4286  | 0.0000  | 0.0000  |
| napkins             | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| player              | 0.1250  | 0.2500  | 0.0000  | 0.0000  |
| candlestick         | 0.3846  | 0.6667  | 0.0000  | 0.0000  |
| jalousie            | 0.1032  | 1.0000  | 0.0238  | 0.5000  |
| drawer              | 0.0227  | 0.5556  | 0.0180  | 0.4444  |
| balcony             | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| umbrella            | 0.2000  | 0.2000  | 0.2000  | 0.2000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shampoo             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| statue              | 0.1120  | 0.3684  | 0.0161  | 0.1053  |
| basin               | 0.0002  | 0.0588  | 0.0002  | 0.0588  |
| toiletry            | 0.0069  | 0.3043  | 0.0004  | 0.0435  |
| body loofah         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| letter              | 0.1213  | 0.7500  | 0.0000  | 0.0000  |
| can                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pack                | 0.0652  | 0.4667  | 0.0422  | 0.1333  |
| food                | 0.0909  | 0.0909  | 0.0000  | 0.0000  |
| partition           | 0.1365  | 0.6667  | 0.0079  | 0.1667  |
| glass               | 0.0417  | 1.0000  | 0.0000  | 0.0000  |
| spoon               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pan                 | 0.0315  | 0.2941  | 0.0007  | 0.0588  |
| pot                 | 0.0839  | 0.4815  | 0.0008  | 0.1852  |
| steps               | 0.0015  | 0.3000  | 0.0000  | 0.0000  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radio               | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| crib                | 0.2059  | 0.6667  | 0.2059  | 0.6667  |
| drum                | 0.0274  | 0.2500  | 0.0000  | 0.0000  |
| screen              | 0.0889  | 0.4000  | 0.0500  | 0.2000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tablet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| barricade           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mop                 | 0.8667  | 1.0000  | 0.0000  | 0.0000  |
| flush               | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| sack                | 0.3751  | 1.0000  | 0.1444  | 0.2857  |
| coil                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| countertop          | 0.0157  | 0.3929  | 0.0003  | 0.1071  |
| washbasin           | 0.0152  | 0.3333  | 0.0109  | 0.1333  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| footstool           | 0.1250  | 0.5000  | 0.1250  | 0.5000  |
| fruit               | 0.1439  | 0.2857  | 0.0420  | 0.2143  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| handle              | 0.0004  | 0.2500  | 0.0000  | 0.0000  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headboard           | 0.0331  | 0.5000  | 0.0000  | 0.0000  |
| garage door         | 0.2819  | 0.5000  | 0.0631  | 0.5000  |
| kitchen island      | 0.0455  | 0.4286  | 0.0381  | 0.2857  |
| wine                | 0.0238  | 0.0476  | 0.0000  | 0.0000  |
| seasoning           | 0.0163  | 0.2500  | 0.0000  | 0.0000  |
| pool table          | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sponge              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beam                | 0.0236  | 0.1364  | 0.0153  | 0.0909  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| arch                | 0.0007  | 0.1667  | 0.0005  | 0.0667  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| helmets             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bread               | 0.0667  | 0.2000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water heater        | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| salt                | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| menu                | 0.4558  | 0.5714  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet               | 0.3548  | 0.6667  | 0.3548  | 0.6667  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| treadmill           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.3509  | 0.6500  | 0.0471  | 0.2000  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glove               | 0.3333  | 0.6667  | 0.0000  | 0.0000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| overall             | 0.1827  | 0.4084  | 0.0692  | 0.1778  |
| head                | 0.2543  | 0.5681  | 0.1052  | 0.2750  |
| common              | 0.1495  | 0.3781  | 0.0454  | 0.1429  |
| tail                | 0.1407  | 0.2639  | 0.0563  | 0.1090  |
| small               | 0.0548  | 0.1542  | 0.0020  | 0.0204  |
| medium              | 0.1906  | 0.4649  | 0.0767  | 0.1961  |
| large               | 0.1059  | 0.5281  | 0.0453  | 0.2877  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 00:46:23 INFO metrics.py:437 | number of scannet results: 250
Rank[0/8] 06/20/2025 00:47:46 INFO metrics.py:332 | eval results of scannet:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| bin                 | 0.6494  | 0.7915  | 0.3356  | 0.5319  |
| sink                | 0.7644  | 0.8636  | 0.2471  | 0.4432  |
| bathtub             | 0.9280  | 0.9688  | 0.8167  | 0.8750  |
| picture             | 0.1996  | 0.3769  | 0.0084  | 0.0597  |
| toilet paper        | 0.3598  | 0.5190  | 0.0281  | 0.1772  |
| toilet              | 0.8892  | 0.9643  | 0.5644  | 0.7143  |
| vanity              | 0.6454  | 1.0000  | 0.4598  | 0.8667  |
| towel               | 0.3815  | 0.5868  | 0.0856  | 0.2645  |
| doorframe           | 0.3147  | 0.7606  | 0.0330  | 0.2887  |
| hair dryer          | 0.0606  | 0.2857  | 0.0065  | 0.1429  |
| curtain             | 0.5572  | 0.8584  | 0.1514  | 0.4159  |
| door                | 0.3620  | 0.8294  | 0.0538  | 0.4181  |
| bar                 | 0.0045  | 0.0455  | 0.0045  | 0.0455  |
| rail                | 0.0804  | 0.6667  | 0.0000  | 0.0000  |
| mirror              | 0.1883  | 0.6024  | 0.0051  | 0.0723  |
| stopcock            | 0.0653  | 0.4828  | 0.0021  | 0.0690  |
| switch              | 0.0040  | 0.1519  | 0.0000  | 0.0000  |
| tissue box          | 0.1027  | 0.4545  | 0.0479  | 0.2273  |
| bottle              | 0.0418  | 0.2684  | 0.0006  | 0.0368  |
| faucet              | 0.0152  | 0.8750  | 0.0005  | 0.1250  |
| bag                 | 0.2135  | 0.6164  | 0.1272  | 0.2740  |
| socket              | 0.0112  | 0.1845  | 0.0000  | 0.0049  |
| light               | 0.0370  | 0.3448  | 0.0000  | 0.0000  |
| purse               | 0.0023  | 0.3333  | 0.0000  | 0.0000  |
| rod                 | 0.0238  | 0.0714  | 0.0000  | 0.0000  |
| shower              | 0.1549  | 0.3571  | 0.0719  | 0.1429  |
| paper               | 0.0092  | 0.1887  | 0.0004  | 0.0189  |
| cup                 | 0.1267  | 0.2609  | 0.0029  | 0.0435  |
| basket              | 0.1104  | 0.2308  | 0.0414  | 0.1538  |
| paper cutter        | 0.6625  | 1.0000  | 0.0000  | 0.0000  |
| box                 | 0.1306  | 0.3492  | 0.0324  | 0.1270  |
| ledge               | 0.0128  | 0.3333  | 0.0080  | 0.3333  |
| grab bar            | 0.0173  | 0.3750  | 0.0000  | 0.0000  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cabinet             | 0.3339  | 0.7540  | 0.1799  | 0.5317  |
| backpack            | 0.6235  | 0.8571  | 0.4782  | 0.6071  |
| tissue              | 0.0028  | 0.1111  | 0.0000  | 0.0000  |
| mat                 | 0.0257  | 0.4000  | 0.0000  | 0.0000  |
| shelf               | 0.4323  | 0.8824  | 0.2225  | 0.6765  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bed                 | 0.8062  | 1.0000  | 0.7047  | 0.9825  |
| lamp                | 0.4092  | 0.6786  | 0.1583  | 0.3452  |
| toothpaste          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blanket             | 0.1294  | 0.5417  | 0.0305  | 0.3750  |
| counter             | 0.2908  | 0.9429  | 0.2255  | 0.6000  |
| plunger             | 0.8185  | 1.0000  | 0.0511  | 0.4000  |
| frame               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| desk                | 0.5554  | 0.9620  | 0.3160  | 0.7848  |
| microwave           | 0.6021  | 0.7500  | 0.2821  | 0.4500  |
| carpet              | 0.0112  | 0.3000  | 0.0000  | 0.0000  |
| couch               | 0.6696  | 0.9625  | 0.5979  | 0.8875  |
| pillow              | 0.3998  | 0.6875  | 0.1421  | 0.3681  |
| chair               | 0.6570  | 0.8192  | 0.4561  | 0.6333  |
| keyboard            | 0.0585  | 0.1552  | 0.0000  | 0.0000  |
| monitor             | 0.5502  | 0.6857  | 0.1270  | 0.2929  |
| radiator            | 0.7401  | 0.8103  | 0.2596  | 0.4138  |
| computer            | 0.3710  | 0.6410  | 0.0077  | 0.1795  |
| table               | 0.5736  | 0.9154  | 0.4331  | 0.8192  |
| mouse               | 0.0026  | 0.0952  | 0.0000  | 0.0000  |
| window              | 0.4809  | 0.8519  | 0.1055  | 0.5432  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blackboard          | 0.4929  | 0.7536  | 0.0655  | 0.1594  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| telephone           | 0.4281  | 0.6562  | 0.0302  | 0.1250  |
| jacket              | 0.6389  | 0.9130  | 0.2539  | 0.4348  |
| book                | 0.0661  | 0.4214  | 0.0072  | 0.1000  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stand               | 0.7680  | 0.9057  | 0.6565  | 0.8113  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stool               | 0.2637  | 0.7500  | 0.1395  | 0.4318  |
| pipe                | 0.0001  | 0.0714  | 0.0000  | 0.0000  |
| windowsill          | 0.0137  | 0.5000  | 0.0000  | 0.0000  |
| clothes             | 0.1433  | 0.5621  | 0.0310  | 0.1961  |
| dresser             | 0.7073  | 0.9189  | 0.6377  | 0.8649  |
| wardrobe            | 0.4115  | 0.7500  | 0.2969  | 0.3750  |
| clothes dryer       | 0.3333  | 1.0000  | 0.3333  | 1.0000  |
| rack                | 0.0080  | 0.4545  | 0.0019  | 0.2727  |
| tv                  | 0.5806  | 0.7826  | 0.2018  | 0.3478  |
| case                | 0.6278  | 0.8947  | 0.3245  | 0.6842  |
| toy                 | 0.0816  | 0.2353  | 0.0784  | 0.1176  |
| laptop              | 0.3766  | 0.5333  | 0.0974  | 0.2000  |
| shoe                | 0.1890  | 0.4750  | 0.0068  | 0.1000  |
| calendar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| decoration          | 0.0942  | 0.3750  | 0.0112  | 0.1250  |
| ottoman             | 0.0464  | 0.7143  | 0.0150  | 0.2857  |
| container           | 0.0050  | 0.2174  | 0.0018  | 0.1304  |
| cushion             | 0.2794  | 1.0000  | 0.0000  | 0.0000  |
| bench               | 0.6769  | 1.0000  | 0.3001  | 0.8000  |
| poster              | 0.2500  | 1.0000  | 0.0000  | 0.0000  |
| notebook            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scale               | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| clock               | 0.1913  | 0.4400  | 0.0009  | 0.0400  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| guitar              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| refrigerator        | 0.7530  | 0.9500  | 0.4189  | 0.7000  |
| coffee maker        | 0.5469  | 0.7059  | 0.1967  | 0.3529  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| broom               | 0.0727  | 0.3333  | 0.0020  | 0.0833  |
| soap dish           | 0.5881  | 0.7500  | 0.0000  | 0.0000  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sign                | 0.0822  | 0.2143  | 0.0000  | 0.0000  |
| soap dispenser      | 0.4799  | 0.8095  | 0.0398  | 0.2857  |
| dispenser           | 0.6073  | 0.7692  | 0.2431  | 0.4103  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stall               | 0.1271  | 0.9412  | 0.1001  | 0.4118  |
| mailbox             | 0.1511  | 1.0000  | 0.0013  | 0.2000  |
| board               | 0.0402  | 0.7188  | 0.0051  | 0.1875  |
| stairs              | 0.0364  | 0.6000  | 0.0014  | 0.2000  |
| pillar              | 0.2049  | 0.8000  | 0.0077  | 0.4000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fan                 | 0.6592  | 0.8750  | 0.1667  | 0.3750  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crate               | 0.0027  | 0.5000  | 0.0027  | 0.5000  |
| plant               | 0.2641  | 0.4874  | 0.0331  | 0.1933  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| printer             | 0.6267  | 0.8182  | 0.4469  | 0.7273  |
| machine             | 0.5942  | 1.0000  | 0.0800  | 0.5625  |
| person              | 0.0152  | 0.1667  | 0.0000  | 0.0000  |
| map                 | 0.0625  | 1.0000  | 0.0625  | 1.0000  |
| headphones          | 0.0722  | 0.4000  | 0.0000  | 0.0000  |
| ball                | 0.4167  | 0.4167  | 0.1960  | 0.3333  |
| hamper              | 0.5433  | 0.6000  | 0.5433  | 0.6000  |
| bicycle             | 0.5990  | 1.0000  | 0.1528  | 0.6667  |
| fire extinguisher   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water cooler        | 0.6250  | 1.0000  | 0.6250  | 1.0000  |
| speaker             | 0.0286  | 1.0000  | 0.0071  | 0.5000  |
| stove               | 0.8759  | 1.0000  | 0.7822  | 1.0000  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| holder              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dish rack           | 0.5762  | 1.0000  | 0.0034  | 0.2500  |
| teapot              | 0.0119  | 0.2500  | 0.0000  | 0.0000  |
| kettle              | 0.2955  | 0.5000  | 0.0000  | 0.0000  |
| toaster             | 0.2389  | 1.0000  | 0.0833  | 0.2500  |
| dishwasher          | 0.1111  | 0.3333  | 0.0000  | 0.0000  |
| plate               | 0.0143  | 0.2000  | 0.0143  | 0.2000  |
| oven                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.6523  | 0.8333  | 0.6333  | 0.6667  |
| range hood          | 0.4533  | 0.6000  | 0.0000  | 0.0000  |
| bowl                | 0.1231  | 0.3333  | 0.0009  | 0.1111  |
| bucket              | 0.1248  | 0.5714  | 0.0317  | 0.1429  |
| flowerpot           | 0.0044  | 0.5000  | 0.0000  | 0.0000  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ladder              | 0.0896  | 0.6000  | 0.0500  | 0.2000  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vacuum cleaner      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| copier              | 0.9167  | 0.9167  | 0.3393  | 0.5000  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pot                 | 0.0833  | 0.5000  | 0.0000  | 0.0000  |
| basin               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drawer              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| scannet             | 0.2383  | 0.4743  | 0.1036  | 0.2321  |
| head                | 0.3200  | 0.6079  | 0.1556  | 0.3141  |
| common              | 0.1728  | 0.3564  | 0.0505  | 0.1465  |
| tail                | 0.0903  | 0.2595  | 0.0375  | 0.1333  |
| small               | 0.0402  | 0.1584  | 0.0022  | 0.0275  |
| medium              | 0.2810  | 0.5677  | 0.1221  | 0.2849  |
| large               | 0.2261  | 0.7526  | 0.1013  | 0.4671  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 00:47:46 INFO metrics.py:437 | number of 3rscan results: 235
Rank[0/8] 06/20/2025 00:48:59 INFO metrics.py:332 | eval results of 3rscan:
+-------------------+---------+---------+---------+---------+
| classes           | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+-------------------+---------+---------+---------+---------+
| keyboard          | 0.0382  | 0.1667  | 0.0000  | 0.0000  |
| chair             | 0.6698  | 0.8311  | 0.5185  | 0.6756  |
| monitor           | 0.6001  | 0.7500  | 0.0482  | 0.0750  |
| desk              | 0.3676  | 0.9250  | 0.2306  | 0.6750  |
| bag               | 0.3013  | 0.6970  | 0.0446  | 0.2323  |
| bin               | 0.7176  | 0.8902  | 0.3003  | 0.5244  |
| mouse             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cup               | 0.0152  | 0.2051  | 0.0000  | 0.0000  |
| paper             | 0.0417  | 0.2714  | 0.0048  | 0.0286  |
| box               | 0.1477  | 0.4074  | 0.0326  | 0.1351  |
| case              | 0.0315  | 0.2571  | 0.0051  | 0.0714  |
| garbage           | 0.2722  | 0.7500  | 0.0149  | 0.3750  |
| socket            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| paper cutter      | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| stand             | 0.3919  | 0.8689  | 0.2337  | 0.6066  |
| plug              | 0.1270  | 0.2222  | 0.0000  | 0.0000  |
| table             | 0.4897  | 0.8649  | 0.3196  | 0.6712  |
| computer          | 0.3092  | 0.6667  | 0.0547  | 0.2381  |
| pen               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue box        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| window            | 0.1483  | 0.7055  | 0.0182  | 0.2055  |
| bottle            | 0.0254  | 0.2066  | 0.0001  | 0.0248  |
| notebook          | 0.0004  | 0.0909  | 0.0000  | 0.0000  |
| file              | 0.0262  | 0.2857  | 0.0032  | 0.1429  |
| backpack          | 0.5120  | 0.7273  | 0.2205  | 0.4242  |
| shelf             | 0.1901  | 0.6608  | 0.0558  | 0.3286  |
| book              | 0.0411  | 0.3787  | 0.0013  | 0.0710  |
| blinds            | 0.1676  | 0.5641  | 0.0129  | 0.1538  |
| telephone         | 0.2565  | 0.5000  | 0.0002  | 0.0417  |
| curtain           | 0.4524  | 0.8651  | 0.0503  | 0.3730  |
| cushion           | 0.2895  | 0.7667  | 0.0657  | 0.2333  |
| cabinet           | 0.2992  | 0.8248  | 0.1671  | 0.5641  |
| plate             | 0.0413  | 0.3333  | 0.0010  | 0.0303  |
| pad               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes           | 0.1853  | 0.6505  | 0.0148  | 0.1748  |
| wardrobe          | 0.2751  | 0.7344  | 0.1031  | 0.6406  |
| candle            | 0.0824  | 0.2000  | 0.0080  | 0.1333  |
| radiator          | 0.3704  | 0.6552  | 0.0847  | 0.2414  |
| picture           | 0.1195  | 0.3577  | 0.0041  | 0.0528  |
| plant             | 0.2736  | 0.5545  | 0.0380  | 0.1485  |
| conducting wire   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| carpet            | 0.1038  | 0.6071  | 0.0023  | 0.1786  |
| device            | 0.0629  | 0.2727  | 0.0000  | 0.0000  |
| pillow            | 0.3761  | 0.7344  | 0.0312  | 0.1836  |
| beanbag           | 1.0000  | 1.0000  | 0.7778  | 0.8333  |
| clock             | 0.1437  | 0.3158  | 0.0526  | 0.0526  |
| bucket            | 0.4544  | 0.8000  | 0.0993  | 0.4000  |
| light             | 0.0789  | 0.3864  | 0.0000  | 0.0227  |
| frame             | 0.1244  | 0.6667  | 0.0122  | 0.1333  |
| tv                | 0.5021  | 0.7714  | 0.1356  | 0.3429  |
| lamp              | 0.2146  | 0.4750  | 0.0287  | 0.1187  |
| stool             | 0.3044  | 0.8519  | 0.1432  | 0.5370  |
| commode           | 0.3826  | 0.8269  | 0.3013  | 0.6731  |
| couch             | 0.3955  | 0.8000  | 0.2203  | 0.6286  |
| door              | 0.1791  | 0.7132  | 0.0096  | 0.2279  |
| price tag         | 0.1760  | 0.4286  | 0.0000  | 0.0000  |
| bed               | 0.7581  | 0.9818  | 0.5511  | 0.8364  |
| bowl              | 0.1155  | 0.4815  | 0.0125  | 0.0741  |
| switch            | 0.0010  | 0.0566  | 0.0000  | 0.0000  |
| bench             | 0.3624  | 0.8929  | 0.2508  | 0.5357  |
| bar               | 0.0889  | 0.6667  | 0.0128  | 0.3333  |
| doorframe         | 0.1615  | 0.6600  | 0.0295  | 0.2200  |
| blanket           | 0.2730  | 0.9077  | 0.0380  | 0.4000  |
| napkins           | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| decoration        | 0.1307  | 0.4375  | 0.0047  | 0.0938  |
| teapot            | 0.0833  | 0.2500  | 0.0000  | 0.0000  |
| jar               | 0.0634  | 0.3750  | 0.0000  | 0.0000  |
| ottoman           | 0.3608  | 0.9091  | 0.2958  | 0.7273  |
| kettle            | 0.1258  | 0.6667  | 0.0381  | 0.3333  |
| remote control    | 0.0300  | 0.1500  | 0.0033  | 0.0500  |
| player            | 0.1667  | 0.3333  | 0.0000  | 0.0000  |
| flowerpot         | 0.0235  | 0.3333  | 0.0009  | 0.0417  |
| towel             | 0.1886  | 0.5316  | 0.0363  | 0.1772  |
| container         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vase              | 0.2645  | 0.4500  | 0.1000  | 0.1000  |
| mat               | 0.0333  | 0.3333  | 0.0000  | 0.0000  |
| album             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mirror            | 0.2528  | 0.5926  | 0.0379  | 0.1111  |
| magazine          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candlestick       | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| roll              | 0.2042  | 0.4000  | 0.0000  | 0.0000  |
| folder            | 0.0044  | 0.2143  | 0.0000  | 0.0000  |
| poster            | 0.0051  | 0.0476  | 0.0000  | 0.0000  |
| refrigerator      | 0.6231  | 0.8000  | 0.5651  | 0.7333  |
| printer           | 0.5334  | 0.7778  | 0.0762  | 0.3333  |
| toy               | 0.3431  | 0.5385  | 0.0513  | 0.1538  |
| calendar          | 0.0030  | 0.4000  | 0.0000  | 0.0000  |
| blackboard        | 0.2150  | 0.5833  | 0.0167  | 0.0833  |
| humidifier        | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| fan               | 0.5000  | 1.0000  | 0.1667  | 0.5000  |
| windowsill        | 0.0559  | 0.5385  | 0.0002  | 0.0256  |
| heater            | 0.5340  | 0.8070  | 0.0374  | 0.1930  |
| jalousie          | 0.1032  | 1.0000  | 0.0238  | 0.5000  |
| laptop            | 0.2731  | 0.8333  | 0.0278  | 0.1667  |
| coffee maker      | 0.1695  | 0.8889  | 0.0342  | 0.2222  |
| window frame      | 0.0273  | 0.3333  | 0.0024  | 0.0833  |
| drawer            | 0.0930  | 0.8889  | 0.0756  | 0.6667  |
| balcony           | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| umbrella          | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| speaker           | 0.0335  | 0.1667  | 0.0000  | 0.0000  |
| shoe              | 0.1330  | 0.3333  | 0.0084  | 0.0714  |
| chandelier        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rack              | 0.1279  | 0.7000  | 0.0252  | 0.4000  |
| basket            | 0.3527  | 0.7031  | 0.1708  | 0.4219  |
| sink              | 0.7106  | 0.8571  | 0.1649  | 0.2857  |
| air conditioner   | 0.6500  | 0.7500  | 0.0016  | 0.2500  |
| clothes dryer     | 0.2321  | 0.7000  | 0.1135  | 0.5000  |
| hanger            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scale             | 0.0833  | 0.3333  | 0.0000  | 0.0000  |
| hook              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| brush             | 0.1894  | 0.6250  | 0.0000  | 0.0000  |
| bathtub           | 0.9722  | 1.0000  | 0.6852  | 0.7778  |
| counter           | 0.0960  | 0.6341  | 0.0074  | 0.1951  |
| microwave         | 0.8173  | 0.9412  | 0.6473  | 0.7647  |
| luggage           | 0.4611  | 1.0000  | 0.4611  | 1.0000  |
| rail              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| guitar            | 0.6250  | 1.0000  | 0.5000  | 0.5000  |
| board             | 0.2965  | 0.4583  | 0.0869  | 0.1250  |
| sunglasses        | 0.0929  | 0.6154  | 0.0008  | 0.1538  |
| hat               | 0.1477  | 0.4286  | 0.0000  | 0.0000  |
| sign              | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| furniture         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| faucet            | 0.1905  | 0.6667  | 0.0000  | 0.0000  |
| shampoo           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| statue            | 1.0000  | 1.0000  | 0.2500  | 0.5000  |
| toilet            | 0.8559  | 0.8824  | 0.3509  | 0.5882  |
| shower            | 0.4260  | 1.0000  | 0.2503  | 0.5000  |
| toilet paper      | 0.0531  | 0.2500  | 0.0000  | 0.0000  |
| dispenser         | 0.0016  | 0.3333  | 0.0000  | 0.0000  |
| basin             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue            | 0.0333  | 0.3333  | 0.0333  | 0.3333  |
| soap              | 0.0476  | 1.0000  | 0.0000  | 0.0000  |
| washing machine   | 0.4858  | 0.7857  | 0.1193  | 0.3571  |
| toiletry          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dish         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchenware       | 0.2139  | 0.5455  | 0.0317  | 0.0909  |
| tube              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| body loofah       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| letter            | 0.1213  | 0.7500  | 0.0000  | 0.0000  |
| can               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillar            | 0.4305  | 0.6471  | 0.1658  | 0.3529  |
| column            | 0.0346  | 1.0000  | 0.0346  | 1.0000  |
| pack              | 0.0755  | 0.5385  | 0.0487  | 0.1538  |
| food              | 0.1000  | 0.1000  | 0.0000  | 0.0000  |
| partition         | 0.1659  | 0.8000  | 0.0095  | 0.2000  |
| glass             | 0.0526  | 1.0000  | 0.0000  | 0.0000  |
| stairs            | 0.2012  | 0.5714  | 0.1220  | 0.3571  |
| spoon             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pan               | 0.0541  | 0.3333  | 0.0013  | 0.0833  |
| stove             | 0.3801  | 0.8824  | 0.0415  | 0.2353  |
| oven              | 0.4425  | 1.0000  | 0.1649  | 0.5263  |
| range hood        | 0.5111  | 1.0000  | 0.0000  | 0.0000  |
| pot               | 0.2672  | 0.5833  | 0.0068  | 0.3333  |
| ladder            | 0.5370  | 1.0000  | 0.1481  | 1.0000  |
| toaster           | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| knife             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radio             | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| crib              | 0.2059  | 0.6667  | 0.2059  | 0.6667  |
| fireplace         | 0.0468  | 0.4000  | 0.0100  | 0.2000  |
| broom             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dispenser    | 0.5067  | 0.8000  | 0.0143  | 0.2000  |
| holder            | 0.0154  | 1.0000  | 0.0000  | 0.0000  |
| tray              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| piano             | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| dresser           | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| screen            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine           | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| tablet            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle           | 0.8333  | 1.0000  | 0.0455  | 0.5000  |
| ball              | 0.3128  | 0.6000  | 0.0411  | 0.6000  |
| mattress          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crate             | 0.4905  | 0.8000  | 0.0667  | 0.2000  |
| pipe              | 0.0088  | 0.5000  | 0.0000  | 0.0000  |
| mop               | 0.8667  | 1.0000  | 0.0000  | 0.0000  |
| flush             | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| sack              | 0.3757  | 1.0000  | 0.1445  | 0.2857  |
| coil              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tool              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bread             | 0.1000  | 0.2000  | 0.0000  | 0.0000  |
| shovel            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cosmetics         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| helmets           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water heater      | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| salt              | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| seasoning         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit             | 0.4095  | 0.8000  | 0.1205  | 0.6000  |
| vacuum cleaner    | 0.0345  | 0.5000  | 0.0000  | 0.0000  |
| menu              | 0.4558  | 0.5714  | 0.0000  | 0.0000  |
| wire              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washbasin         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| cube              | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| treadmill         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| footstool         | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| toothbrush        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dish rack         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beam              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scissors          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| map               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glove             | 0.3333  | 0.6667  | 0.0000  | 0.0000  |
| dishwasher        | 0.4444  | 0.6667  | 0.3333  | 0.3333  |
| fire extinguisher | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| -------           | ------- | ------- | ------- | ------- |
| 3rscan            | 0.2684  | 0.5129  | 0.0920  | 0.2065  |
| head              | 0.2793  | 0.5993  | 0.0988  | 0.2626  |
| common            | 0.2654  | 0.5026  | 0.0808  | 0.1863  |
| tail              | 0.2522  | 0.3651  | 0.0945  | 0.1292  |
| small             | 0.0747  | 0.2021  | 0.0033  | 0.0284  |
| medium            | 0.2862  | 0.5723  | 0.1019  | 0.2251  |
| large             | 0.1887  | 0.7398  | 0.0932  | 0.4605  |
+-------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 00:48:59 INFO metrics.py:437 | number of matterport3d results: 339
Rank[0/8] 06/20/2025 00:50:29 INFO metrics.py:332 | eval results of matterport3d:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| bathtub             | 0.6577  | 0.7778  | 0.5607  | 0.7037  |
| door                | 0.1691  | 0.5509  | 0.0098  | 0.1332  |
| doorframe           | 0.2816  | 0.7546  | 0.0805  | 0.4380  |
| light               | 0.0159  | 0.2080  | 0.0004  | 0.0342  |
| mirror              | 0.2323  | 0.6622  | 0.0022  | 0.1216  |
| sink                | 0.4971  | 0.7273  | 0.1017  | 0.3434  |
| picture             | 0.1559  | 0.3285  | 0.0040  | 0.0556  |
| faucet              | 0.0380  | 0.3611  | 0.0000  | 0.0000  |
| cabinet             | 0.1182  | 0.6356  | 0.0703  | 0.3856  |
| countertop          | 0.0157  | 0.3929  | 0.0003  | 0.1071  |
| plant               | 0.2332  | 0.6696  | 0.0863  | 0.3393  |
| towel               | 0.3695  | 0.5636  | 0.0822  | 0.2182  |
| vent                | 0.0198  | 0.0833  | 0.0000  | 0.0000  |
| washbasin           | 0.0166  | 0.3571  | 0.0117  | 0.1429  |
| shelf               | 0.1287  | 0.5474  | 0.0276  | 0.3212  |
| drawer              | 0.0020  | 0.2500  | 0.0019  | 0.2500  |
| toilet              | 0.7771  | 0.8281  | 0.6702  | 0.7812  |
| decoration          | 0.0105  | 0.3214  | 0.0002  | 0.0476  |
| roof                | 0.0007  | 0.2083  | 0.0001  | 0.0833  |
| dresser             | 0.3380  | 0.7333  | 0.2835  | 0.6667  |
| counter             | 0.0089  | 0.4545  | 0.0006  | 0.1515  |
| bed                 | 0.5513  | 0.8421  | 0.2768  | 0.7719  |
| window              | 0.1336  | 0.5639  | 0.0186  | 0.2167  |
| wardrobe            | 0.1252  | 0.5455  | 0.0188  | 0.3636  |
| lamp                | 0.2610  | 0.6154  | 0.0829  | 0.3846  |
| shower              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| desk                | 0.2885  | 0.7895  | 0.2330  | 0.7368  |
| flowerpot           | 0.0757  | 0.5600  | 0.0248  | 0.2800  |
| window frame        | 0.0372  | 0.5747  | 0.0072  | 0.2414  |
| vanity              | 0.1328  | 0.6000  | 0.1328  | 0.6000  |
| stand               | 0.2783  | 0.8000  | 0.1814  | 0.5400  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toiletry            | 0.0159  | 0.3182  | 0.0006  | 0.0455  |
| case                | 0.0005  | 0.0556  | 0.0000  | 0.0000  |
| table               | 0.2788  | 0.7115  | 0.2232  | 0.5449  |
| frame               | 0.0027  | 0.1765  | 0.0014  | 0.0588  |
| chair               | 0.4032  | 0.7041  | 0.2375  | 0.5102  |
| book                | 0.0524  | 0.2677  | 0.0033  | 0.0472  |
| bench               | 0.1479  | 0.5000  | 0.0568  | 0.2500  |
| bottle              | 0.0690  | 0.4375  | 0.0014  | 0.0833  |
| pillow              | 0.1611  | 0.5741  | 0.0062  | 0.1325  |
| vase                | 0.1140  | 0.4333  | 0.0412  | 0.1333  |
| blanket             | 0.0630  | 0.7273  | 0.0069  | 0.1818  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dispenser           | 0.0238  | 0.5000  | 0.0238  | 0.5000  |
| soap                | 0.0022  | 0.2000  | 0.0000  | 0.0000  |
| doorway             | 0.0004  | 0.1111  | 0.0000  | 0.0000  |
| switch              | 0.0014  | 0.0500  | 0.0000  | 0.0000  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| box                 | 0.0512  | 0.5192  | 0.0139  | 0.1923  |
| paper               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| curtain             | 0.3261  | 0.7083  | 0.0487  | 0.2396  |
| bowl                | 0.0588  | 0.3636  | 0.0023  | 0.0909  |
| couch               | 0.3269  | 0.7935  | 0.1959  | 0.6522  |
| plate               | 0.0409  | 0.2857  | 0.0002  | 0.0714  |
| tv                  | 0.4496  | 0.6744  | 0.1013  | 0.3488  |
| stairs              | 0.0253  | 0.3019  | 0.0019  | 0.0566  |
| screen              | 0.1127  | 0.5000  | 0.0625  | 0.2500  |
| steps               | 0.0015  | 0.3000  | 0.0000  | 0.0000  |
| stool               | 0.0510  | 0.2857  | 0.0034  | 0.2381  |
| ottoman             | 0.0645  | 0.4444  | 0.0483  | 0.3889  |
| cushion             | 0.0232  | 0.2941  | 0.0028  | 0.1176  |
| food                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| statue              | 0.0425  | 0.2941  | 0.0039  | 0.0588  |
| rail                | 0.0105  | 0.3519  | 0.0000  | 0.0370  |
| fireplace           | 0.1613  | 0.4783  | 0.0238  | 0.1739  |
| fan                 | 0.5346  | 0.7778  | 0.2493  | 0.5185  |
| carpet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candle              | 0.0196  | 0.3333  | 0.0000  | 0.0000  |
| alarm               | 0.0001  | 0.1667  | 0.0000  | 0.0000  |
| footstool           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillar              | 0.3487  | 0.7143  | 0.0795  | 0.2381  |
| mat                 | 0.0516  | 0.2083  | 0.0010  | 0.0417  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| teapot              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clock               | 0.1111  | 0.3333  | 0.1111  | 0.3333  |
| toy                 | 0.0936  | 0.3056  | 0.0081  | 0.1111  |
| pot                 | 0.0292  | 0.3846  | 0.0002  | 0.0769  |
| column              | 0.0220  | 0.2222  | 0.0133  | 0.1111  |
| cup                 | 0.0008  | 0.1000  | 0.0000  | 0.0000  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| monitor             | 0.0853  | 0.5000  | 0.0000  | 0.0000  |
| kettle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bin                 | 0.2369  | 0.6341  | 0.1081  | 0.3171  |
| socket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| handle              | 0.0004  | 0.2500  | 0.0000  | 0.0000  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| windowsill          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dispenser      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toilet paper        | 0.1916  | 0.5000  | 0.0535  | 0.2222  |
| headboard           | 0.0331  | 0.5000  | 0.0000  | 0.0000  |
| basket              | 0.0764  | 0.4400  | 0.0007  | 0.1200  |
| washing machine     | 0.5994  | 0.8750  | 0.4421  | 0.7500  |
| bucket              | 0.0091  | 0.2000  | 0.0091  | 0.2000  |
| garage door         | 0.2819  | 0.5000  | 0.0631  | 0.5000  |
| telephone           | 0.2652  | 0.5000  | 0.0000  | 0.0000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hanger              | 0.0128  | 0.3000  | 0.0000  | 0.0000  |
| holder              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchen island      | 0.0455  | 0.4286  | 0.0381  | 0.2857  |
| heater              | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| piano               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wine                | 0.0238  | 0.0476  | 0.0000  | 0.0000  |
| radiator            | 0.0303  | 0.3333  | 0.0000  | 0.0000  |
| printer             | 0.4286  | 0.4286  | 0.2857  | 0.2857  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0024  | 0.1250  | 0.0000  | 0.0000  |
| computer            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| board               | 0.0657  | 0.3333  | 0.0000  | 0.0000  |
| seasoning           | 0.0442  | 0.2857  | 0.0000  | 0.0000  |
| scale               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hat                 | 0.1000  | 1.0000  | 0.0000  | 0.0000  |
| tissue box          | 0.1796  | 0.5000  | 0.0179  | 0.1250  |
| magazine            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| container           | 0.0182  | 0.2593  | 0.0035  | 0.1111  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| partition           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchenware         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| file                | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| rack                | 0.0004  | 0.2727  | 0.0002  | 0.1818  |
| laptop              | 0.7500  | 1.0000  | 0.0000  | 0.0000  |
| pool table          | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| thermostat          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shoe                | 0.0217  | 0.1600  | 0.0000  | 0.0000  |
| pipe                | 0.0020  | 0.2000  | 0.0000  | 0.0000  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beam                | 0.0247  | 0.1429  | 0.0161  | 0.0952  |
| projector           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.2222  | 0.6667  | 0.2222  | 0.6667  |
| bag                 | 0.1007  | 0.3846  | 0.0000  | 0.0000  |
| clothes             | 0.1455  | 0.3966  | 0.0063  | 0.1207  |
| air conditioner     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pan                 | 0.0091  | 0.2000  | 0.0000  | 0.0000  |
| oven                | 0.2400  | 0.4000  | 0.2000  | 0.2000  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| arch                | 0.0007  | 0.1667  | 0.0005  | 0.0667  |
| chandelier          | 0.0857  | 0.3684  | 0.0066  | 0.0526  |
| fire extinguisher   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ladder              | 0.0679  | 0.4444  | 0.0556  | 0.1111  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dish           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.2623  | 0.7500  | 0.0000  | 0.0000  |
| refrigerator        | 0.2013  | 0.6667  | 0.0163  | 0.4000  |
| microwave           | 0.1316  | 0.4167  | 0.0833  | 0.0833  |
| stove               | 0.2229  | 0.6000  | 0.1500  | 0.3000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine             | 0.0903  | 1.0000  | 0.0377  | 1.0000  |
| keyboard            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mouse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| guitar              | 0.2305  | 0.5714  | 0.0000  | 0.0000  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 0.0625  | 1.0000  | 0.0127  | 1.0000  |
| ball                | 0.5170  | 0.7500  | 0.5170  | 0.7500  |
| basin               | 0.0097  | 1.0000  | 0.0097  | 1.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hamper              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| backpack            | 0.0588  | 1.0000  | 0.0000  | 0.0000  |
| jacket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet               | 0.0200  | 0.5000  | 0.0200  | 0.5000  |
| toaster             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drum                | 0.0339  | 0.2500  | 0.0000  | 0.0000  |
| coffee maker        | 0.1111  | 0.3333  | 0.0000  | 0.0000  |
| dishwasher          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.3920  | 0.7222  | 0.0523  | 0.2222  |
| radio               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell            | 0.5000  | 0.5000  | 0.2500  | 0.5000  |
| candlestick         | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| pack                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| matterport3d        | 0.1069  | 0.3210  | 0.0473  | 0.1536  |
| head                | 0.1652  | 0.4434  | 0.0737  | 0.2136  |
| common              | 0.0550  | 0.2360  | 0.0235  | 0.1069  |
| tail                | 0.0420  | 0.1166  | 0.0189  | 0.0675  |
| small               | 0.0164  | 0.0766  | 0.0015  | 0.0170  |
| medium              | 0.1106  | 0.3619  | 0.0487  | 0.1599  |
| large               | 0.0816  | 0.4381  | 0.0408  | 0.2486  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 00:50:29 INFO metrics.py:437 | number of arkit results: 0
Rank[0/8] 06/20/2025 00:50:30 INFO stats.py:394 | Epoch[79] completed. Training Speed: 2.94 samples/sec across all devices.	Epoch Time: 1062.64 sec.	Average Epoch Time: 1062.64 sec.	Average Step Time: 2.72 sec.	Estimated Remaining Time: 21:16:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:50:31 WARNING accelerator.py:3099 | Deleting 1 checkpoints to make room for new checkpoint.
Rank[0/8] 06/20/2025 00:50:31 INFO accelerator.py:3111 | Saving current state to /job_data/checkpoints/checkpoint_7
Rank[5/8] 06/20/2025 00:50:32 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 79 to /job_data/checkpoints/checkpoint_7
Rank[7/8] 06/20/2025 00:50:32 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 79 to /job_data/checkpoints/checkpoint_7
Rank[6/8] 06/20/2025 00:50:32 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 79 to /job_data/checkpoints/checkpoint_7
Rank[3/8] 06/20/2025 00:50:32 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 79 to /job_data/checkpoints/checkpoint_7
Rank[1/8] 06/20/2025 00:50:32 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 79 to /job_data/checkpoints/checkpoint_7
Rank[4/8] 06/20/2025 00:50:32 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 79 to /job_data/checkpoints/checkpoint_7
Rank[2/8] 06/20/2025 00:50:32 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 79 to /job_data/checkpoints/checkpoint_7
Rank[0/8] 06/20/2025 00:50:33 INFO checkpointing.py:106 | Model weights saved in /job_data/checkpoints/checkpoint_7/model.safetensors
Rank[0/8] 06/20/2025 00:50:36 INFO checkpointing.py:113 | Optimizer state saved in /job_data/checkpoints/checkpoint_7/optimizer.bin
Rank[0/8] 06/20/2025 00:50:36 INFO checkpointing.py:120 | Scheduler state saved in /job_data/checkpoints/checkpoint_7/scheduler.bin
Rank[0/8] 06/20/2025 00:50:36 INFO checkpointing.py:137 | Sampler state for dataloader 0 saved in /job_data/checkpoints/checkpoint_7/sampler.bin
Rank[0/8] 06/20/2025 00:50:36 INFO checkpointing.py:137 | Sampler state for dataloader 1 saved in /job_data/checkpoints/checkpoint_7/sampler_1.bin
Rank[0/8] 06/20/2025 00:50:36 INFO checkpointing.py:164 | Random states saved in /job_data/checkpoints/checkpoint_7/random_states_0.pkl
Rank[0/8] 06/20/2025 00:50:36 INFO checkpointing.py:300 | Saving the state of TrainerProgressState to /job_data/checkpoints/checkpoint_7/custom_checkpoint_0.pkl
Rank[0/8] 06/20/2025 00:50:36 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 79 to /job_data/checkpoints/checkpoint_7
Rank[0/8] 06/20/2025 00:51:05 INFO stats.py:314 | Epoch[80] Step[24] GlobalStep[31224] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 21:16:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:51:05 INFO loss_tracker.py:84 | Epoch[80/239] Step[24] GlobalStep[31224/NA]: det_loss_cls_0[0.4614] det_loss_box_0[0.5859] det_loss_cls_1[0.4470] det_loss_box_1[0.4592] det_loss_cls_2[0.4227] det_loss_box_2[0.4225] det_loss_cls_3[0.3985] det_loss_box_3[0.4086] det_loss_cls_4[0.3707] det_loss_box_4[0.4047] det_loss_cls_5[0.3614] det_loss_box_5[0.4037] det_loss_cls_dn_0[0.0952] det_loss_box_dn_0[0.3068] det_loss_cls_dn_1[0.0907] det_loss_box_dn_1[0.2571] det_loss_cls_dn_2[0.0902] det_loss_box_dn_2[0.2369] det_loss_cls_dn_3[0.0888] det_loss_box_dn_3[0.2343] det_loss_cls_dn_4[0.0879] det_loss_box_dn_4[0.2329] det_loss_cls_dn_5[0.0869] det_loss_box_dn_5[0.2300] loss_depth[0.0413] total_loss[7.2250]
Rank[0/8] 06/20/2025 00:51:31 INFO stats.py:314 | Epoch[80] Step[49] GlobalStep[31249] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 21:15:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:51:31 INFO loss_tracker.py:84 | Epoch[80/239] Step[49] GlobalStep[31249/NA]: det_loss_cls_0[0.5248] det_loss_box_0[0.7984] det_loss_cls_1[0.5072] det_loss_box_1[0.6236] det_loss_cls_2[0.4760] det_loss_box_2[0.5569] det_loss_cls_3[0.4531] det_loss_box_3[0.5275] det_loss_cls_4[0.4315] det_loss_box_4[0.5206] det_loss_cls_5[0.4192] det_loss_box_5[0.5238] det_loss_cls_dn_0[0.0961] det_loss_box_dn_0[0.3574] det_loss_cls_dn_1[0.0925] det_loss_box_dn_1[0.3010] det_loss_cls_dn_2[0.0916] det_loss_box_dn_2[0.2815] det_loss_cls_dn_3[0.0905] det_loss_box_dn_3[0.2782] det_loss_cls_dn_4[0.0892] det_loss_box_dn_4[0.2773] det_loss_cls_dn_5[0.0881] det_loss_box_dn_5[0.2741] loss_depth[0.0419] total_loss[8.7221]
Rank[0/8] 06/20/2025 00:51:58 INFO stats.py:314 | Epoch[80] Step[74] GlobalStep[31274] Training Speed: 5.78 samples/sec across all devices.	Average Step Time: 1.38 sec.	Estimated Remaining Time: 21:14:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:51:58 INFO loss_tracker.py:84 | Epoch[80/239] Step[74] GlobalStep[31274/NA]: det_loss_cls_0[0.5693] det_loss_box_0[0.7324] det_loss_cls_1[0.5384] det_loss_box_1[0.5839] det_loss_cls_2[0.5058] det_loss_box_2[0.5215] det_loss_cls_3[0.4853] det_loss_box_3[0.4989] det_loss_cls_4[0.4612] det_loss_box_4[0.4940] det_loss_cls_5[0.4457] det_loss_box_5[0.4926] det_loss_cls_dn_0[0.0970] det_loss_box_dn_0[0.3328] det_loss_cls_dn_1[0.0933] det_loss_box_dn_1[0.2746] det_loss_cls_dn_2[0.0928] det_loss_box_dn_2[0.2540] det_loss_cls_dn_3[0.0920] det_loss_box_dn_3[0.2497] det_loss_cls_dn_4[0.0907] det_loss_box_dn_4[0.2474] det_loss_cls_dn_5[0.0898] det_loss_box_dn_5[0.2460] loss_depth[0.0455] total_loss[8.5347]
Rank[0/8] 06/20/2025 00:52:23 INFO stats.py:314 | Epoch[80] Step[99] GlobalStep[31299] Training Speed: 8.81 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 21:14:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:52:23 INFO loss_tracker.py:84 | Epoch[80/239] Step[99] GlobalStep[31299/NA]: det_loss_cls_0[0.4840] det_loss_box_0[0.6356] det_loss_cls_1[0.4815] det_loss_box_1[0.4743] det_loss_cls_2[0.4453] det_loss_box_2[0.4383] det_loss_cls_3[0.4136] det_loss_box_3[0.4201] det_loss_cls_4[0.3918] det_loss_box_4[0.4079] det_loss_cls_5[0.3789] det_loss_box_5[0.4091] det_loss_cls_dn_0[0.0971] det_loss_box_dn_0[0.3075] det_loss_cls_dn_1[0.0939] det_loss_box_dn_1[0.2603] det_loss_cls_dn_2[0.0934] det_loss_box_dn_2[0.2453] det_loss_cls_dn_3[0.0918] det_loss_box_dn_3[0.2436] det_loss_cls_dn_4[0.0906] det_loss_box_dn_4[0.2434] det_loss_cls_dn_5[0.0897] det_loss_box_dn_5[0.2433] loss_depth[0.0386] total_loss[7.5189]
Rank[0/8] 06/20/2025 00:52:49 INFO stats.py:314 | Epoch[80] Step[124] GlobalStep[31324] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 21:13:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:52:49 INFO loss_tracker.py:84 | Epoch[80/239] Step[124] GlobalStep[31324/NA]: det_loss_cls_0[0.4840] det_loss_box_0[0.6203] det_loss_cls_1[0.4854] det_loss_box_1[0.4881] det_loss_cls_2[0.4535] det_loss_box_2[0.4416] det_loss_cls_3[0.4274] det_loss_box_3[0.4283] det_loss_cls_4[0.3983] det_loss_box_4[0.4237] det_loss_cls_5[0.3801] det_loss_box_5[0.4300] det_loss_cls_dn_0[0.1029] det_loss_box_dn_0[0.2942] det_loss_cls_dn_1[0.0986] det_loss_box_dn_1[0.2496] det_loss_cls_dn_2[0.0982] det_loss_box_dn_2[0.2334] det_loss_cls_dn_3[0.0973] det_loss_box_dn_3[0.2316] det_loss_cls_dn_4[0.0959] det_loss_box_dn_4[0.2303] det_loss_cls_dn_5[0.0945] det_loss_box_dn_5[0.2289] loss_depth[0.0354] total_loss[7.5516]
Rank[0/8] 06/20/2025 00:53:15 INFO stats.py:314 | Epoch[80] Step[149] GlobalStep[31349] Training Speed: 8.13 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 21:12:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:53:15 INFO loss_tracker.py:84 | Epoch[80/239] Step[149] GlobalStep[31349/NA]: det_loss_cls_0[0.5108] det_loss_box_0[0.6448] det_loss_cls_1[0.4946] det_loss_box_1[0.4935] det_loss_cls_2[0.4616] det_loss_box_2[0.4419] det_loss_cls_3[0.4277] det_loss_box_3[0.4295] det_loss_cls_4[0.3938] det_loss_box_4[0.4244] det_loss_cls_5[0.3888] det_loss_box_5[0.4206] det_loss_cls_dn_0[0.0936] det_loss_box_dn_0[0.2706] det_loss_cls_dn_1[0.0906] det_loss_box_dn_1[0.2246] det_loss_cls_dn_2[0.0900] det_loss_box_dn_2[0.2061] det_loss_cls_dn_3[0.0901] det_loss_box_dn_3[0.2031] det_loss_cls_dn_4[0.0883] det_loss_box_dn_4[0.2025] det_loss_cls_dn_5[0.0873] det_loss_box_dn_5[0.2014] loss_depth[0.0396] total_loss[7.4198]
Rank[0/8] 06/20/2025 00:53:41 INFO stats.py:314 | Epoch[80] Step[174] GlobalStep[31374] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 21:12:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:53:41 INFO loss_tracker.py:84 | Epoch[80/239] Step[174] GlobalStep[31374/NA]: det_loss_cls_0[0.4154] det_loss_box_0[0.5954] det_loss_cls_1[0.3991] det_loss_box_1[0.4627] det_loss_cls_2[0.3744] det_loss_box_2[0.4112] det_loss_cls_3[0.3546] det_loss_box_3[0.3956] det_loss_cls_4[0.3320] det_loss_box_4[0.3911] det_loss_cls_5[0.3183] det_loss_box_5[0.3911] det_loss_cls_dn_0[0.0986] det_loss_box_dn_0[0.4670] det_loss_cls_dn_1[0.0960] det_loss_box_dn_1[0.4005] det_loss_cls_dn_2[0.0957] det_loss_box_dn_2[0.3844] det_loss_cls_dn_3[0.0941] det_loss_box_dn_3[0.3880] det_loss_cls_dn_4[0.0931] det_loss_box_dn_4[0.3923] det_loss_cls_dn_5[0.0915] det_loss_box_dn_5[0.3901] loss_depth[0.0417] total_loss[7.8739]
Rank[0/8] 06/20/2025 00:54:07 INFO stats.py:314 | Epoch[80] Step[199] GlobalStep[31399] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 21:11:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:54:07 INFO loss_tracker.py:84 | Epoch[80/239] Step[199] GlobalStep[31399/NA]: det_loss_cls_0[0.5140] det_loss_box_0[0.7309] det_loss_cls_1[0.4764] det_loss_box_1[0.6022] det_loss_cls_2[0.4627] det_loss_box_2[0.5262] det_loss_cls_3[0.4367] det_loss_box_3[0.5147] det_loss_cls_4[0.4162] det_loss_box_4[0.5040] det_loss_cls_5[0.4021] det_loss_box_5[0.5045] det_loss_cls_dn_0[0.0940] det_loss_box_dn_0[0.3238] det_loss_cls_dn_1[0.0912] det_loss_box_dn_1[0.2877] det_loss_cls_dn_2[0.0907] det_loss_box_dn_2[0.2723] det_loss_cls_dn_3[0.0896] det_loss_box_dn_3[0.2700] det_loss_cls_dn_4[0.0887] det_loss_box_dn_4[0.2689] det_loss_cls_dn_5[0.0877] det_loss_box_dn_5[0.2685] loss_depth[0.0394] total_loss[8.3633]
Rank[0/8] 06/20/2025 00:54:32 INFO stats.py:314 | Epoch[80] Step[224] GlobalStep[31424] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 21:10:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:54:32 INFO loss_tracker.py:84 | Epoch[80/239] Step[224] GlobalStep[31424/NA]: det_loss_cls_0[0.4677] det_loss_box_0[0.6486] det_loss_cls_1[0.4543] det_loss_box_1[0.5044] det_loss_cls_2[0.4229] det_loss_box_2[0.4507] det_loss_cls_3[0.4050] det_loss_box_3[0.4415] det_loss_cls_4[0.3762] det_loss_box_4[0.4345] det_loss_cls_5[0.3662] det_loss_box_5[0.4324] det_loss_cls_dn_0[0.1007] det_loss_box_dn_0[0.3445] det_loss_cls_dn_1[0.0966] det_loss_box_dn_1[0.2942] det_loss_cls_dn_2[0.0962] det_loss_box_dn_2[0.2751] det_loss_cls_dn_3[0.0956] det_loss_box_dn_3[0.2721] det_loss_cls_dn_4[0.0939] det_loss_box_dn_4[0.2720] det_loss_cls_dn_5[0.0930] det_loss_box_dn_5[0.2688] loss_depth[0.0400] total_loss[7.7472]
Rank[0/8] 06/20/2025 00:54:59 INFO stats.py:314 | Epoch[80] Step[249] GlobalStep[31449] Training Speed: 7.59 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 21:10:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:54:59 INFO loss_tracker.py:84 | Epoch[80/239] Step[249] GlobalStep[31449/NA]: det_loss_cls_0[0.4854] det_loss_box_0[0.6004] det_loss_cls_1[0.4775] det_loss_box_1[0.4567] det_loss_cls_2[0.4439] det_loss_box_2[0.4102] det_loss_cls_3[0.4128] det_loss_box_3[0.3949] det_loss_cls_4[0.3883] det_loss_box_4[0.3827] det_loss_cls_5[0.3765] det_loss_box_5[0.3850] det_loss_cls_dn_0[0.0971] det_loss_box_dn_0[0.2768] det_loss_cls_dn_1[0.0936] det_loss_box_dn_1[0.2271] det_loss_cls_dn_2[0.0931] det_loss_box_dn_2[0.2095] det_loss_cls_dn_3[0.0917] det_loss_box_dn_3[0.2059] det_loss_cls_dn_4[0.0907] det_loss_box_dn_4[0.2036] det_loss_cls_dn_5[0.0897] det_loss_box_dn_5[0.2018] loss_depth[0.0366] total_loss[7.1316]
Rank[0/8] 06/20/2025 00:55:24 INFO stats.py:314 | Epoch[80] Step[274] GlobalStep[31474] Training Speed: 8.67 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 21:09:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:55:24 INFO loss_tracker.py:84 | Epoch[80/239] Step[274] GlobalStep[31474/NA]: det_loss_cls_0[0.5715] det_loss_box_0[0.7454] det_loss_cls_1[0.5527] det_loss_box_1[0.6070] det_loss_cls_2[0.5251] det_loss_box_2[0.5414] det_loss_cls_3[0.4988] det_loss_box_3[0.5135] det_loss_cls_4[0.4719] det_loss_box_4[0.5039] det_loss_cls_5[0.4560] det_loss_box_5[0.5073] det_loss_cls_dn_0[0.1075] det_loss_box_dn_0[0.3373] det_loss_cls_dn_1[0.1043] det_loss_box_dn_1[0.2928] det_loss_cls_dn_2[0.1047] det_loss_box_dn_2[0.2800] det_loss_cls_dn_3[0.1031] det_loss_box_dn_3[0.2755] det_loss_cls_dn_4[0.1022] det_loss_box_dn_4[0.2740] det_loss_cls_dn_5[0.1018] det_loss_box_dn_5[0.2716] loss_depth[0.0390] total_loss[8.8882]
Rank[0/8] 06/20/2025 00:55:49 INFO stats.py:314 | Epoch[80] Step[299] GlobalStep[31499] Training Speed: 8.34 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 21:08:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:55:49 INFO loss_tracker.py:84 | Epoch[80/239] Step[299] GlobalStep[31499/NA]: det_loss_cls_0[0.5580] det_loss_box_0[0.7576] det_loss_cls_1[0.5489] det_loss_box_1[0.6183] det_loss_cls_2[0.5250] det_loss_box_2[0.5595] det_loss_cls_3[0.5001] det_loss_box_3[0.5360] det_loss_cls_4[0.4735] det_loss_box_4[0.5245] det_loss_cls_5[0.4587] det_loss_box_5[0.5293] det_loss_cls_dn_0[0.1029] det_loss_box_dn_0[0.3442] det_loss_cls_dn_1[0.0997] det_loss_box_dn_1[0.3027] det_loss_cls_dn_2[0.0994] det_loss_box_dn_2[0.2896] det_loss_cls_dn_3[0.0984] det_loss_box_dn_3[0.2857] det_loss_cls_dn_4[0.0971] det_loss_box_dn_4[0.2848] det_loss_cls_dn_5[0.0965] det_loss_box_dn_5[0.2788] loss_depth[0.0403] total_loss[9.0095]
Rank[0/8] 06/20/2025 00:56:16 INFO stats.py:314 | Epoch[80] Step[324] GlobalStep[31524] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 21:08:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:56:16 INFO loss_tracker.py:84 | Epoch[80/239] Step[324] GlobalStep[31524/NA]: det_loss_cls_0[0.5285] det_loss_box_0[0.7280] det_loss_cls_1[0.5080] det_loss_box_1[0.5682] det_loss_cls_2[0.4870] det_loss_box_2[0.5141] det_loss_cls_3[0.4610] det_loss_box_3[0.4979] det_loss_cls_4[0.4442] det_loss_box_4[0.4797] det_loss_cls_5[0.4339] det_loss_box_5[0.4807] det_loss_cls_dn_0[0.0972] det_loss_box_dn_0[0.3025] det_loss_cls_dn_1[0.0943] det_loss_box_dn_1[0.2685] det_loss_cls_dn_2[0.0947] det_loss_box_dn_2[0.2536] det_loss_cls_dn_3[0.0924] det_loss_box_dn_3[0.2508] det_loss_cls_dn_4[0.0920] det_loss_box_dn_4[0.2497] det_loss_cls_dn_5[0.0908] det_loss_box_dn_5[0.2476] loss_depth[0.0394] total_loss[8.3045]
Rank[0/8] 06/20/2025 00:56:42 INFO stats.py:314 | Epoch[80] Step[349] GlobalStep[31549] Training Speed: 8.22 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 21:07:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:56:42 INFO loss_tracker.py:84 | Epoch[80/239] Step[349] GlobalStep[31549/NA]: det_loss_cls_0[0.4962] det_loss_box_0[0.6261] det_loss_cls_1[0.4761] det_loss_box_1[0.4806] det_loss_cls_2[0.4563] det_loss_box_2[0.4350] det_loss_cls_3[0.4261] det_loss_box_3[0.4135] det_loss_cls_4[0.4051] det_loss_box_4[0.3987] det_loss_cls_5[0.3868] det_loss_box_5[0.4025] det_loss_cls_dn_0[0.0895] det_loss_box_dn_0[0.2755] det_loss_cls_dn_1[0.0858] det_loss_box_dn_1[0.2313] det_loss_cls_dn_2[0.0849] det_loss_box_dn_2[0.2138] det_loss_cls_dn_3[0.0847] det_loss_box_dn_3[0.2094] det_loss_cls_dn_4[0.0826] det_loss_box_dn_4[0.2086] det_loss_cls_dn_5[0.0816] det_loss_box_dn_5[0.2078] loss_depth[0.0347] total_loss[7.2932]
Rank[0/8] 06/20/2025 00:57:07 INFO stats.py:314 | Epoch[80] Step[374] GlobalStep[31574] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 21:06:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:57:07 INFO loss_tracker.py:84 | Epoch[80/239] Step[374] GlobalStep[31574/NA]: det_loss_cls_0[0.5718] det_loss_box_0[0.7265] det_loss_cls_1[0.5515] det_loss_box_1[0.6013] det_loss_cls_2[0.5202] det_loss_box_2[0.5493] det_loss_cls_3[0.5023] det_loss_box_3[0.5340] det_loss_cls_4[0.4661] det_loss_box_4[0.5290] det_loss_cls_5[0.4512] det_loss_box_5[0.5254] det_loss_cls_dn_0[0.1116] det_loss_box_dn_0[0.3258] det_loss_cls_dn_1[0.1086] det_loss_box_dn_1[0.2778] det_loss_cls_dn_2[0.1080] det_loss_box_dn_2[0.2621] det_loss_cls_dn_3[0.1070] det_loss_box_dn_3[0.2580] det_loss_cls_dn_4[0.1060] det_loss_box_dn_4[0.2587] det_loss_cls_dn_5[0.1049] det_loss_box_dn_5[0.2563] loss_depth[0.0377] total_loss[8.8513]
Rank[0/8] 06/20/2025 00:57:22 INFO stats.py:394 | Epoch[80] completed. Training Speed: 7.68 samples/sec across all devices.	Epoch Time: 406.03 sec.	Average Epoch Time: 406.03 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 21:06:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:57:36 INFO stats.py:314 | Epoch[81] Step[9] GlobalStep[31599] Training Speed: 8.13 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 21:06:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:57:51 INFO loss_tracker.py:84 | Epoch[81/239] Step[24] GlobalStep[31614/NA]: det_loss_cls_0[0.4414] det_loss_box_0[0.5460] det_loss_cls_1[0.4268] det_loss_box_1[0.4309] det_loss_cls_2[0.4026] det_loss_box_2[0.3937] det_loss_cls_3[0.3770] det_loss_box_3[0.3838] det_loss_cls_4[0.3510] det_loss_box_4[0.3773] det_loss_cls_5[0.3366] det_loss_box_5[0.3794] det_loss_cls_dn_0[0.0870] det_loss_box_dn_0[0.2907] det_loss_cls_dn_1[0.0830] det_loss_box_dn_1[0.2548] det_loss_cls_dn_2[0.0828] det_loss_box_dn_2[0.2385] det_loss_cls_dn_3[0.0817] det_loss_box_dn_3[0.2361] det_loss_cls_dn_4[0.0807] det_loss_box_dn_4[0.2348] det_loss_cls_dn_5[0.0801] det_loss_box_dn_5[0.2334] loss_depth[0.0376] total_loss[6.8676]
Rank[0/8] 06/20/2025 00:58:02 INFO stats.py:314 | Epoch[81] Step[34] GlobalStep[31624] Training Speed: 8.73 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 21:05:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:58:17 INFO loss_tracker.py:84 | Epoch[81/239] Step[49] GlobalStep[31639/NA]: det_loss_cls_0[0.4848] det_loss_box_0[0.6727] det_loss_cls_1[0.4667] det_loss_box_1[0.5248] det_loss_cls_2[0.4332] det_loss_box_2[0.4604] det_loss_cls_3[0.4108] det_loss_box_3[0.4382] det_loss_cls_4[0.3897] det_loss_box_4[0.4337] det_loss_cls_5[0.3749] det_loss_box_5[0.4305] det_loss_cls_dn_0[0.0971] det_loss_box_dn_0[0.3048] det_loss_cls_dn_1[0.0947] det_loss_box_dn_1[0.2565] det_loss_cls_dn_2[0.0939] det_loss_box_dn_2[0.2408] det_loss_cls_dn_3[0.0917] det_loss_box_dn_3[0.2374] det_loss_cls_dn_4[0.0907] det_loss_box_dn_4[0.2355] det_loss_cls_dn_5[0.0893] det_loss_box_dn_5[0.2339] loss_depth[0.0384] total_loss[7.6250]
Rank[0/8] 06/20/2025 00:58:27 INFO stats.py:314 | Epoch[81] Step[59] GlobalStep[31649] Training Speed: 7.81 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 21:04:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:58:42 INFO loss_tracker.py:84 | Epoch[81/239] Step[74] GlobalStep[31664/NA]: det_loss_cls_0[0.5391] det_loss_box_0[0.7123] det_loss_cls_1[0.5298] det_loss_box_1[0.5706] det_loss_cls_2[0.4964] det_loss_box_2[0.5315] det_loss_cls_3[0.4803] det_loss_box_3[0.5126] det_loss_cls_4[0.4587] det_loss_box_4[0.4891] det_loss_cls_5[0.4489] det_loss_box_5[0.4896] det_loss_cls_dn_0[0.1070] det_loss_box_dn_0[0.3159] det_loss_cls_dn_1[0.1032] det_loss_box_dn_1[0.2758] det_loss_cls_dn_2[0.1022] det_loss_box_dn_2[0.2601] det_loss_cls_dn_3[0.1001] det_loss_box_dn_3[0.2569] det_loss_cls_dn_4[0.0985] det_loss_box_dn_4[0.2564] det_loss_cls_dn_5[0.0980] det_loss_box_dn_5[0.2545] loss_depth[0.0402] total_loss[8.5278]
Rank[0/8] 06/20/2025 00:58:53 INFO stats.py:314 | Epoch[81] Step[84] GlobalStep[31674] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 21:04:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:59:08 INFO loss_tracker.py:84 | Epoch[81/239] Step[99] GlobalStep[31689/NA]: det_loss_cls_0[0.4715] det_loss_box_0[0.5958] det_loss_cls_1[0.4600] det_loss_box_1[0.4907] det_loss_cls_2[0.4309] det_loss_box_2[0.4431] det_loss_cls_3[0.3990] det_loss_box_3[0.4324] det_loss_cls_4[0.3791] det_loss_box_4[0.4254] det_loss_cls_5[0.3672] det_loss_box_5[0.4221] det_loss_cls_dn_0[0.0938] det_loss_box_dn_0[0.3291] det_loss_cls_dn_1[0.0898] det_loss_box_dn_1[0.2827] det_loss_cls_dn_2[0.0899] det_loss_box_dn_2[0.2615] det_loss_cls_dn_3[0.0885] det_loss_box_dn_3[0.2584] det_loss_cls_dn_4[0.0881] det_loss_box_dn_4[0.2545] det_loss_cls_dn_5[0.0868] det_loss_box_dn_5[0.2529] loss_depth[0.0366] total_loss[7.5297]
Rank[0/8] 06/20/2025 00:59:19 INFO stats.py:314 | Epoch[81] Step[109] GlobalStep[31699] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 21:03:30.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:59:34 INFO loss_tracker.py:84 | Epoch[81/239] Step[124] GlobalStep[31714/NA]: det_loss_cls_0[0.4761] det_loss_box_0[0.6743] det_loss_cls_1[0.4605] det_loss_box_1[0.5348] det_loss_cls_2[0.4298] det_loss_box_2[0.4914] det_loss_cls_3[0.4022] det_loss_box_3[0.4693] det_loss_cls_4[0.3766] det_loss_box_4[0.4610] det_loss_cls_5[0.3613] det_loss_box_5[0.4579] det_loss_cls_dn_0[0.0967] det_loss_box_dn_0[0.3313] det_loss_cls_dn_1[0.0938] det_loss_box_dn_1[0.2782] det_loss_cls_dn_2[0.0938] det_loss_box_dn_2[0.2574] det_loss_cls_dn_3[0.0919] det_loss_box_dn_3[0.2530] det_loss_cls_dn_4[0.0911] det_loss_box_dn_4[0.2516] det_loss_cls_dn_5[0.0899] det_loss_box_dn_5[0.2494] loss_depth[0.0393] total_loss[7.8127]
Rank[0/8] 06/20/2025 00:59:44 INFO stats.py:314 | Epoch[81] Step[134] GlobalStep[31724] Training Speed: 7.85 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 21:02:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 00:59:59 INFO loss_tracker.py:84 | Epoch[81/239] Step[149] GlobalStep[31739/NA]: det_loss_cls_0[0.5822] det_loss_box_0[0.7468] det_loss_cls_1[0.5837] det_loss_box_1[0.5805] det_loss_cls_2[0.5418] det_loss_box_2[0.5251] det_loss_cls_3[0.5057] det_loss_box_3[0.5045] det_loss_cls_4[0.4721] det_loss_box_4[0.4930] det_loss_cls_5[0.4476] det_loss_box_5[0.4954] det_loss_cls_dn_0[0.0975] det_loss_box_dn_0[0.2542] det_loss_cls_dn_1[0.0928] det_loss_box_dn_1[0.2146] det_loss_cls_dn_2[0.0916] det_loss_box_dn_2[0.2016] det_loss_cls_dn_3[0.0906] det_loss_box_dn_3[0.1983] det_loss_cls_dn_4[0.0893] det_loss_box_dn_4[0.1967] det_loss_cls_dn_5[0.0877] det_loss_box_dn_5[0.1956] loss_depth[0.0384] total_loss[8.3270]
Rank[0/8] 06/20/2025 01:00:10 INFO stats.py:314 | Epoch[81] Step[159] GlobalStep[31749] Training Speed: 8.87 samples/sec across all devices.	Average Step Time: 0.90 sec.	Estimated Remaining Time: 21:02:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:00:26 INFO loss_tracker.py:84 | Epoch[81/239] Step[174] GlobalStep[31764/NA]: det_loss_cls_0[0.5599] det_loss_box_0[0.7413] det_loss_cls_1[0.5543] det_loss_box_1[0.5729] det_loss_cls_2[0.5211] det_loss_box_2[0.5089] det_loss_cls_3[0.4899] det_loss_box_3[0.4885] det_loss_cls_4[0.4635] det_loss_box_4[0.4819] det_loss_cls_5[0.4464] det_loss_box_5[0.4853] det_loss_cls_dn_0[0.1066] det_loss_box_dn_0[0.2974] det_loss_cls_dn_1[0.1032] det_loss_box_dn_1[0.2458] det_loss_cls_dn_2[0.1020] det_loss_box_dn_2[0.2306] det_loss_cls_dn_3[0.1019] det_loss_box_dn_3[0.2267] det_loss_cls_dn_4[0.1008] det_loss_box_dn_4[0.2265] det_loss_cls_dn_5[0.1003] det_loss_box_dn_5[0.2242] loss_depth[0.0401] total_loss[8.4201]
Rank[0/8] 06/20/2025 01:00:36 INFO stats.py:314 | Epoch[81] Step[184] GlobalStep[31774] Training Speed: 7.69 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 21:01:30.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:00:52 INFO loss_tracker.py:84 | Epoch[81/239] Step[199] GlobalStep[31789/NA]: det_loss_cls_0[0.5380] det_loss_box_0[0.7654] det_loss_cls_1[0.5072] det_loss_box_1[0.6098] det_loss_cls_2[0.4913] det_loss_box_2[0.5392] det_loss_cls_3[0.4565] det_loss_box_3[0.5139] det_loss_cls_4[0.4379] det_loss_box_4[0.4955] det_loss_cls_5[0.4291] det_loss_box_5[0.4943] det_loss_cls_dn_0[0.0873] det_loss_box_dn_0[0.2802] det_loss_cls_dn_1[0.0834] det_loss_box_dn_1[0.2454] det_loss_cls_dn_2[0.0826] det_loss_box_dn_2[0.2296] det_loss_cls_dn_3[0.0813] det_loss_box_dn_3[0.2272] det_loss_cls_dn_4[0.0805] det_loss_box_dn_4[0.2261] det_loss_cls_dn_5[0.0798] det_loss_box_dn_5[0.2234] loss_depth[0.0408] total_loss[8.2458]
Rank[0/8] 06/20/2025 01:01:02 INFO stats.py:314 | Epoch[81] Step[209] GlobalStep[31799] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 21:00:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:01:17 INFO loss_tracker.py:84 | Epoch[81/239] Step[224] GlobalStep[31814/NA]: det_loss_cls_0[0.7162] det_loss_box_0[0.8430] det_loss_cls_1[0.6760] det_loss_box_1[0.6630] det_loss_cls_2[0.6371] det_loss_box_2[0.5909] det_loss_cls_3[0.6145] det_loss_box_3[0.5511] det_loss_cls_4[0.5797] det_loss_box_4[0.5372] det_loss_cls_5[0.5662] det_loss_box_5[0.5341] det_loss_cls_dn_0[0.1240] det_loss_box_dn_0[0.2821] det_loss_cls_dn_1[0.1209] det_loss_box_dn_1[0.2437] det_loss_cls_dn_2[0.1209] det_loss_box_dn_2[0.2278] det_loss_cls_dn_3[0.1200] det_loss_box_dn_3[0.2239] det_loss_cls_dn_4[0.1194] det_loss_box_dn_4[0.2223] det_loss_cls_dn_5[0.1182] det_loss_box_dn_5[0.2218] loss_depth[0.0388] total_loss[9.6928]
Rank[0/8] 06/20/2025 01:01:27 INFO stats.py:314 | Epoch[81] Step[234] GlobalStep[31824] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 21:00:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:01:43 INFO loss_tracker.py:84 | Epoch[81/239] Step[249] GlobalStep[31839/NA]: det_loss_cls_0[0.5946] det_loss_box_0[0.8187] det_loss_cls_1[0.5637] det_loss_box_1[0.6235] det_loss_cls_2[0.5401] det_loss_box_2[0.5543] det_loss_cls_3[0.5075] det_loss_box_3[0.5244] det_loss_cls_4[0.4765] det_loss_box_4[0.5203] det_loss_cls_5[0.4595] det_loss_box_5[0.5227] det_loss_cls_dn_0[0.1037] det_loss_box_dn_0[0.2816] det_loss_cls_dn_1[0.0991] det_loss_box_dn_1[0.2402] det_loss_cls_dn_2[0.0993] det_loss_box_dn_2[0.2220] det_loss_cls_dn_3[0.0980] det_loss_box_dn_3[0.2175] det_loss_cls_dn_4[0.0970] det_loss_box_dn_4[0.2165] det_loss_cls_dn_5[0.0966] det_loss_box_dn_5[0.2151] loss_depth[0.0419] total_loss[8.7343]
Rank[0/8] 06/20/2025 01:01:53 INFO stats.py:314 | Epoch[81] Step[259] GlobalStep[31849] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 20:59:30.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:02:09 INFO loss_tracker.py:84 | Epoch[81/239] Step[274] GlobalStep[31864/NA]: det_loss_cls_0[0.5592] det_loss_box_0[0.8092] det_loss_cls_1[0.5453] det_loss_box_1[0.6372] det_loss_cls_2[0.5104] det_loss_box_2[0.5721] det_loss_cls_3[0.4857] det_loss_box_3[0.5459] det_loss_cls_4[0.4557] det_loss_box_4[0.5450] det_loss_cls_5[0.4444] det_loss_box_5[0.5440] det_loss_cls_dn_0[0.1007] det_loss_box_dn_0[0.3585] det_loss_cls_dn_1[0.0979] det_loss_box_dn_1[0.3166] det_loss_cls_dn_2[0.0971] det_loss_box_dn_2[0.2968] det_loss_cls_dn_3[0.0970] det_loss_box_dn_3[0.2932] det_loss_cls_dn_4[0.0957] det_loss_box_dn_4[0.2918] det_loss_cls_dn_5[0.0946] det_loss_box_dn_5[0.2886] loss_depth[0.0426] total_loss[9.1252]
Rank[0/8] 06/20/2025 01:02:20 INFO stats.py:314 | Epoch[81] Step[284] GlobalStep[31874] Training Speed: 7.71 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 20:58:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:02:35 INFO loss_tracker.py:84 | Epoch[81/239] Step[299] GlobalStep[31889/NA]: det_loss_cls_0[0.6044] det_loss_box_0[0.8067] det_loss_cls_1[0.5841] det_loss_box_1[0.6491] det_loss_cls_2[0.5531] det_loss_box_2[0.5881] det_loss_cls_3[0.5264] det_loss_box_3[0.5636] det_loss_cls_4[0.4963] det_loss_box_4[0.5562] det_loss_cls_5[0.4769] det_loss_box_5[0.5590] det_loss_cls_dn_0[0.0873] det_loss_box_dn_0[0.3256] det_loss_cls_dn_1[0.0838] det_loss_box_dn_1[0.2747] det_loss_cls_dn_2[0.0829] det_loss_box_dn_2[0.2519] det_loss_cls_dn_3[0.0822] det_loss_box_dn_3[0.2481] det_loss_cls_dn_4[0.0813] det_loss_box_dn_4[0.2464] det_loss_cls_dn_5[0.0804] det_loss_box_dn_5[0.2370] loss_depth[0.0397] total_loss[9.0855]
Rank[0/8] 06/20/2025 01:02:45 INFO stats.py:314 | Epoch[81] Step[309] GlobalStep[31899] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 20:58:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:03:01 INFO loss_tracker.py:84 | Epoch[81/239] Step[324] GlobalStep[31914/NA]: det_loss_cls_0[0.5489] det_loss_box_0[0.7546] det_loss_cls_1[0.5478] det_loss_box_1[0.5922] det_loss_cls_2[0.5211] det_loss_box_2[0.5353] det_loss_cls_3[0.4944] det_loss_box_3[0.5159] det_loss_cls_4[0.4702] det_loss_box_4[0.5141] det_loss_cls_5[0.4597] det_loss_box_5[0.5142] det_loss_cls_dn_0[0.1036] det_loss_box_dn_0[0.2963] det_loss_cls_dn_1[0.1021] det_loss_box_dn_1[0.2562] det_loss_cls_dn_2[0.1017] det_loss_box_dn_2[0.2391] det_loss_cls_dn_3[0.1005] det_loss_box_dn_3[0.2357] det_loss_cls_dn_4[0.0993] det_loss_box_dn_4[0.2342] det_loss_cls_dn_5[0.0988] det_loss_box_dn_5[0.2325] loss_depth[0.0412] total_loss[8.6096]
Rank[0/8] 06/20/2025 01:03:11 INFO stats.py:314 | Epoch[81] Step[334] GlobalStep[31924] Training Speed: 8.00 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 20:57:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:03:27 INFO loss_tracker.py:84 | Epoch[81/239] Step[349] GlobalStep[31939/NA]: det_loss_cls_0[0.4944] det_loss_box_0[0.6238] det_loss_cls_1[0.4756] det_loss_box_1[0.4941] det_loss_cls_2[0.4373] det_loss_box_2[0.4434] det_loss_cls_3[0.4092] det_loss_box_3[0.4275] det_loss_cls_4[0.3800] det_loss_box_4[0.4179] det_loss_cls_5[0.3638] det_loss_box_5[0.4212] det_loss_cls_dn_0[0.1044] det_loss_box_dn_0[0.3220] det_loss_cls_dn_1[0.1009] det_loss_box_dn_1[0.2729] det_loss_cls_dn_2[0.1008] det_loss_box_dn_2[0.2544] det_loss_cls_dn_3[0.0994] det_loss_box_dn_3[0.2518] det_loss_cls_dn_4[0.0986] det_loss_box_dn_4[0.2511] det_loss_cls_dn_5[0.0978] det_loss_box_dn_5[0.2494] loss_depth[0.0405] total_loss[7.6319]
Rank[0/8] 06/20/2025 01:03:37 INFO stats.py:314 | Epoch[81] Step[359] GlobalStep[31949] Training Speed: 7.86 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 20:56:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:03:53 INFO loss_tracker.py:84 | Epoch[81/239] Step[374] GlobalStep[31964/NA]: det_loss_cls_0[0.5505] det_loss_box_0[0.7933] det_loss_cls_1[0.5468] det_loss_box_1[0.6376] det_loss_cls_2[0.5139] det_loss_box_2[0.5731] det_loss_cls_3[0.4969] det_loss_box_3[0.5495] det_loss_cls_4[0.4652] det_loss_box_4[0.5341] det_loss_cls_5[0.4530] det_loss_box_5[0.5377] det_loss_cls_dn_0[0.1058] det_loss_box_dn_0[0.3795] det_loss_cls_dn_1[0.1014] det_loss_box_dn_1[0.3197] det_loss_cls_dn_2[0.1016] det_loss_box_dn_2[0.3019] det_loss_cls_dn_3[0.1010] det_loss_box_dn_3[0.2960] det_loss_cls_dn_4[0.0995] det_loss_box_dn_4[0.2933] det_loss_cls_dn_5[0.0988] det_loss_box_dn_5[0.2916] loss_depth[0.0388] total_loss[9.1805]
Rank[0/8] 06/20/2025 01:04:02 INFO stats.py:314 | Epoch[81] Step[384] GlobalStep[31974] Training Speed: 8.81 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 20:56:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:04:07 INFO stats.py:394 | Epoch[81] completed. Training Speed: 7.70 samples/sec across all devices.	Epoch Time: 405.30 sec.	Average Epoch Time: 405.30 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 20:56:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:04:30 INFO stats.py:314 | Epoch[82] Step[19] GlobalStep[31999] Training Speed: 8.02 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 20:55:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:04:35 INFO loss_tracker.py:84 | Epoch[82/239] Step[24] GlobalStep[32004/NA]: det_loss_cls_0[0.5775] det_loss_box_0[0.7501] det_loss_cls_1[0.5554] det_loss_box_1[0.6026] det_loss_cls_2[0.5222] det_loss_box_2[0.5409] det_loss_cls_3[0.4976] det_loss_box_3[0.5164] det_loss_cls_4[0.4651] det_loss_box_4[0.5099] det_loss_cls_5[0.4497] det_loss_box_5[0.5118] det_loss_cls_dn_0[0.1027] det_loss_box_dn_0[0.3230] det_loss_cls_dn_1[0.0990] det_loss_box_dn_1[0.2746] det_loss_cls_dn_2[0.0978] det_loss_box_dn_2[0.2557] det_loss_cls_dn_3[0.0969] det_loss_box_dn_3[0.2525] det_loss_cls_dn_4[0.0961] det_loss_box_dn_4[0.2500] det_loss_cls_dn_5[0.0954] det_loss_box_dn_5[0.2481] loss_depth[0.0390] total_loss[8.7298]
Rank[0/8] 06/20/2025 01:04:55 INFO stats.py:314 | Epoch[82] Step[44] GlobalStep[32024] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 20:54:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:05:00 INFO loss_tracker.py:84 | Epoch[82/239] Step[49] GlobalStep[32029/NA]: det_loss_cls_0[0.4769] det_loss_box_0[0.6196] det_loss_cls_1[0.4692] det_loss_box_1[0.4937] det_loss_cls_2[0.4409] det_loss_box_2[0.4521] det_loss_cls_3[0.4197] det_loss_box_3[0.4368] det_loss_cls_4[0.3963] det_loss_box_4[0.4272] det_loss_cls_5[0.3843] det_loss_box_5[0.4260] det_loss_cls_dn_0[0.0993] det_loss_box_dn_0[0.3214] det_loss_cls_dn_1[0.0961] det_loss_box_dn_1[0.2786] det_loss_cls_dn_2[0.0956] det_loss_box_dn_2[0.2648] det_loss_cls_dn_3[0.0952] det_loss_box_dn_3[0.2626] det_loss_cls_dn_4[0.0935] det_loss_box_dn_4[0.2618] det_loss_cls_dn_5[0.0927] det_loss_box_dn_5[0.2533] loss_depth[0.0371] total_loss[7.6948]
Rank[0/8] 06/20/2025 01:05:21 INFO stats.py:314 | Epoch[82] Step[69] GlobalStep[32049] Training Speed: 8.64 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 20:54:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:05:26 INFO loss_tracker.py:84 | Epoch[82/239] Step[74] GlobalStep[32054/NA]: det_loss_cls_0[0.5748] det_loss_box_0[0.7052] det_loss_cls_1[0.5531] det_loss_box_1[0.5538] det_loss_cls_2[0.5216] det_loss_box_2[0.5078] det_loss_cls_3[0.5004] det_loss_box_3[0.4900] det_loss_cls_4[0.4647] det_loss_box_4[0.4835] det_loss_cls_5[0.4516] det_loss_box_5[0.4806] det_loss_cls_dn_0[0.0976] det_loss_box_dn_0[0.2705] det_loss_cls_dn_1[0.0936] det_loss_box_dn_1[0.2274] det_loss_cls_dn_2[0.0925] det_loss_box_dn_2[0.2117] det_loss_cls_dn_3[0.0920] det_loss_box_dn_3[0.2099] det_loss_cls_dn_4[0.0912] det_loss_box_dn_4[0.2092] det_loss_cls_dn_5[0.0903] det_loss_box_dn_5[0.2069] loss_depth[0.0406] total_loss[8.2206]
Rank[0/8] 06/20/2025 01:05:47 INFO stats.py:314 | Epoch[82] Step[94] GlobalStep[32074] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 20:53:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:05:52 INFO loss_tracker.py:84 | Epoch[82/239] Step[99] GlobalStep[32079/NA]: det_loss_cls_0[0.5410] det_loss_box_0[0.6823] det_loss_cls_1[0.5303] det_loss_box_1[0.5323] det_loss_cls_2[0.4896] det_loss_box_2[0.4837] det_loss_cls_3[0.4587] det_loss_box_3[0.4595] det_loss_cls_4[0.4343] det_loss_box_4[0.4456] det_loss_cls_5[0.4118] det_loss_box_5[0.4503] det_loss_cls_dn_0[0.0993] det_loss_box_dn_0[0.2579] det_loss_cls_dn_1[0.0960] det_loss_box_dn_1[0.2190] det_loss_cls_dn_2[0.0947] det_loss_box_dn_2[0.2038] det_loss_cls_dn_3[0.0939] det_loss_box_dn_3[0.2002] det_loss_cls_dn_4[0.0932] det_loss_box_dn_4[0.1986] det_loss_cls_dn_5[0.0919] det_loss_box_dn_5[0.1969] loss_depth[0.0375] total_loss[7.8023]
Rank[0/8] 06/20/2025 01:06:13 INFO stats.py:314 | Epoch[82] Step[119] GlobalStep[32099] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 20:52:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:06:18 INFO loss_tracker.py:84 | Epoch[82/239] Step[124] GlobalStep[32104/NA]: det_loss_cls_0[0.4224] det_loss_box_0[0.5288] det_loss_cls_1[0.4029] det_loss_box_1[0.4185] det_loss_cls_2[0.3776] det_loss_box_2[0.3809] det_loss_cls_3[0.3446] det_loss_box_3[0.3764] det_loss_cls_4[0.3282] det_loss_box_4[0.3664] det_loss_cls_5[0.3178] det_loss_box_5[0.3654] det_loss_cls_dn_0[0.0933] det_loss_box_dn_0[0.2744] det_loss_cls_dn_1[0.0891] det_loss_box_dn_1[0.2345] det_loss_cls_dn_2[0.0885] det_loss_box_dn_2[0.2224] det_loss_cls_dn_3[0.0878] det_loss_box_dn_3[0.2202] det_loss_cls_dn_4[0.0868] det_loss_box_dn_4[0.2189] det_loss_cls_dn_5[0.0865] det_loss_box_dn_5[0.2190] loss_depth[0.0385] total_loss[6.5898]
Rank[0/8] 06/20/2025 01:06:39 INFO stats.py:314 | Epoch[82] Step[144] GlobalStep[32124] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 20:52:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:06:44 INFO loss_tracker.py:84 | Epoch[82/239] Step[149] GlobalStep[32129/NA]: det_loss_cls_0[0.4536] det_loss_box_0[0.5955] det_loss_cls_1[0.4426] det_loss_box_1[0.4625] det_loss_cls_2[0.4134] det_loss_box_2[0.4224] det_loss_cls_3[0.3800] det_loss_box_3[0.4062] det_loss_cls_4[0.3669] det_loss_box_4[0.3941] det_loss_cls_5[0.3508] det_loss_box_5[0.3907] det_loss_cls_dn_0[0.0929] det_loss_box_dn_0[0.2754] det_loss_cls_dn_1[0.0891] det_loss_box_dn_1[0.2390] det_loss_cls_dn_2[0.0886] det_loss_box_dn_2[0.2252] det_loss_cls_dn_3[0.0866] det_loss_box_dn_3[0.2211] det_loss_cls_dn_4[0.0855] det_loss_box_dn_4[0.2213] det_loss_cls_dn_5[0.0845] det_loss_box_dn_5[0.2201] loss_depth[0.0371] total_loss[7.0451]
Rank[0/8] 06/20/2025 01:07:04 INFO stats.py:314 | Epoch[82] Step[169] GlobalStep[32149] Training Speed: 8.03 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 20:51:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:07:09 INFO loss_tracker.py:84 | Epoch[82/239] Step[174] GlobalStep[32154/NA]: det_loss_cls_0[0.4611] det_loss_box_0[0.5826] det_loss_cls_1[0.4500] det_loss_box_1[0.4303] det_loss_cls_2[0.4174] det_loss_box_2[0.3900] det_loss_cls_3[0.3834] det_loss_box_3[0.3808] det_loss_cls_4[0.3582] det_loss_box_4[0.3693] det_loss_cls_5[0.3402] det_loss_box_5[0.3703] det_loss_cls_dn_0[0.0905] det_loss_box_dn_0[0.2629] det_loss_cls_dn_1[0.0874] det_loss_box_dn_1[0.2183] det_loss_cls_dn_2[0.0870] det_loss_box_dn_2[0.2034] det_loss_cls_dn_3[0.0863] det_loss_box_dn_3[0.2011] det_loss_cls_dn_4[0.0846] det_loss_box_dn_4[0.1994] det_loss_cls_dn_5[0.0836] det_loss_box_dn_5[0.1986] loss_depth[0.0399] total_loss[6.7766]
Rank[0/8] 06/20/2025 01:07:31 INFO stats.py:314 | Epoch[82] Step[194] GlobalStep[32174] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 20:50:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:07:36 INFO loss_tracker.py:84 | Epoch[82/239] Step[199] GlobalStep[32179/NA]: det_loss_cls_0[0.4542] det_loss_box_0[0.6151] det_loss_cls_1[0.4405] det_loss_box_1[0.4737] det_loss_cls_2[0.4082] det_loss_box_2[0.4231] det_loss_cls_3[0.3820] det_loss_box_3[0.4122] det_loss_cls_4[0.3569] det_loss_box_4[0.4032] det_loss_cls_5[0.3435] det_loss_box_5[0.4019] det_loss_cls_dn_0[0.0912] det_loss_box_dn_0[0.3175] det_loss_cls_dn_1[0.0867] det_loss_box_dn_1[0.2597] det_loss_cls_dn_2[0.0857] det_loss_box_dn_2[0.2442] det_loss_cls_dn_3[0.0841] det_loss_box_dn_3[0.2381] det_loss_cls_dn_4[0.0831] det_loss_box_dn_4[0.2367] det_loss_cls_dn_5[0.0820] det_loss_box_dn_5[0.2330] loss_depth[0.0423] total_loss[7.1988]
Rank[0/8] 06/20/2025 01:07:57 INFO stats.py:314 | Epoch[82] Step[219] GlobalStep[32199] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 20:50:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:08:02 INFO loss_tracker.py:84 | Epoch[82/239] Step[224] GlobalStep[32204/NA]: det_loss_cls_0[0.5582] det_loss_box_0[0.7732] det_loss_cls_1[0.5359] det_loss_box_1[0.6307] det_loss_cls_2[0.5146] det_loss_box_2[0.5688] det_loss_cls_3[0.4913] det_loss_box_3[0.5371] det_loss_cls_4[0.4687] det_loss_box_4[0.5293] det_loss_cls_5[0.4567] det_loss_box_5[0.5288] det_loss_cls_dn_0[0.1018] det_loss_box_dn_0[0.3220] det_loss_cls_dn_1[0.0972] det_loss_box_dn_1[0.2850] det_loss_cls_dn_2[0.0969] det_loss_box_dn_2[0.2729] det_loss_cls_dn_3[0.0951] det_loss_box_dn_3[0.2697] det_loss_cls_dn_4[0.0938] det_loss_box_dn_4[0.2686] det_loss_cls_dn_5[0.0928] det_loss_box_dn_5[0.2668] loss_depth[0.0403] total_loss[8.8963]
Rank[0/8] 06/20/2025 01:08:22 INFO stats.py:314 | Epoch[82] Step[244] GlobalStep[32224] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 20:49:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:08:27 INFO loss_tracker.py:84 | Epoch[82/239] Step[249] GlobalStep[32229/NA]: det_loss_cls_0[0.5629] det_loss_box_0[0.7262] det_loss_cls_1[0.5454] det_loss_box_1[0.5643] det_loss_cls_2[0.5216] det_loss_box_2[0.5161] det_loss_cls_3[0.4923] det_loss_box_3[0.4984] det_loss_cls_4[0.4559] det_loss_box_4[0.4957] det_loss_cls_5[0.4453] det_loss_box_5[0.4911] det_loss_cls_dn_0[0.0910] det_loss_box_dn_0[0.2718] det_loss_cls_dn_1[0.0870] det_loss_box_dn_1[0.2251] det_loss_cls_dn_2[0.0869] det_loss_box_dn_2[0.2105] det_loss_cls_dn_3[0.0859] det_loss_box_dn_3[0.2073] det_loss_cls_dn_4[0.0849] det_loss_box_dn_4[0.2063] det_loss_cls_dn_5[0.0835] det_loss_box_dn_5[0.2046] loss_depth[0.0397] total_loss[8.1997]
Rank[0/8] 06/20/2025 01:08:48 INFO stats.py:314 | Epoch[82] Step[269] GlobalStep[32249] Training Speed: 8.56 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 20:48:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:08:53 INFO loss_tracker.py:84 | Epoch[82/239] Step[274] GlobalStep[32254/NA]: det_loss_cls_0[0.6092] det_loss_box_0[0.8942] det_loss_cls_1[0.6039] det_loss_box_1[0.6754] det_loss_cls_2[0.5769] det_loss_box_2[0.6146] det_loss_cls_3[0.5523] det_loss_box_3[0.6015] det_loss_cls_4[0.5280] det_loss_box_4[0.5834] det_loss_cls_5[0.5151] det_loss_box_5[0.5833] det_loss_cls_dn_0[0.1023] det_loss_box_dn_0[0.3261] det_loss_cls_dn_1[0.0999] det_loss_box_dn_1[0.2841] det_loss_cls_dn_2[0.0999] det_loss_box_dn_2[0.2688] det_loss_cls_dn_3[0.0980] det_loss_box_dn_3[0.2659] det_loss_cls_dn_4[0.0961] det_loss_box_dn_4[0.2640] det_loss_cls_dn_5[0.0950] det_loss_box_dn_5[0.2624] loss_depth[0.0425] total_loss[9.6429]
Rank[0/8] 06/20/2025 01:09:13 INFO stats.py:314 | Epoch[82] Step[294] GlobalStep[32274] Training Speed: 7.62 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 20:48:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:09:19 INFO loss_tracker.py:84 | Epoch[82/239] Step[299] GlobalStep[32279/NA]: det_loss_cls_0[0.4485] det_loss_box_0[0.7330] det_loss_cls_1[0.4294] det_loss_box_1[0.5952] det_loss_cls_2[0.4056] det_loss_box_2[0.5596] det_loss_cls_3[0.3919] det_loss_box_3[0.5323] det_loss_cls_4[0.3689] det_loss_box_4[0.5127] det_loss_cls_5[0.3666] det_loss_box_5[0.5068] det_loss_cls_dn_0[0.0976] det_loss_box_dn_0[0.5144] det_loss_cls_dn_1[0.0946] det_loss_box_dn_1[0.4520] det_loss_cls_dn_2[0.0936] det_loss_box_dn_2[0.4307] det_loss_cls_dn_3[0.0924] det_loss_box_dn_3[0.4266] det_loss_cls_dn_4[0.0911] det_loss_box_dn_4[0.4245] det_loss_cls_dn_5[0.0903] det_loss_box_dn_5[0.4219] loss_depth[0.0376] total_loss[9.1178]
Rank[0/8] 06/20/2025 01:09:39 INFO stats.py:314 | Epoch[82] Step[319] GlobalStep[32299] Training Speed: 8.12 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 20:47:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:09:44 INFO loss_tracker.py:84 | Epoch[82/239] Step[324] GlobalStep[32304/NA]: det_loss_cls_0[0.4845] det_loss_box_0[0.6071] det_loss_cls_1[0.4815] det_loss_box_1[0.4923] det_loss_cls_2[0.4516] det_loss_box_2[0.4575] det_loss_cls_3[0.4205] det_loss_box_3[0.4520] det_loss_cls_4[0.3926] det_loss_box_4[0.4505] det_loss_cls_5[0.3792] det_loss_box_5[0.4484] det_loss_cls_dn_0[0.0954] det_loss_box_dn_0[0.2910] det_loss_cls_dn_1[0.0919] det_loss_box_dn_1[0.2467] det_loss_cls_dn_2[0.0919] det_loss_box_dn_2[0.2330] det_loss_cls_dn_3[0.0909] det_loss_box_dn_3[0.2288] det_loss_cls_dn_4[0.0896] det_loss_box_dn_4[0.2289] det_loss_cls_dn_5[0.0888] det_loss_box_dn_5[0.2286] loss_depth[0.0379] total_loss[7.5611]
Rank[0/8] 06/20/2025 01:10:05 INFO stats.py:314 | Epoch[82] Step[344] GlobalStep[32324] Training Speed: 7.87 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 20:46:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:10:10 INFO loss_tracker.py:84 | Epoch[82/239] Step[349] GlobalStep[32329/NA]: det_loss_cls_0[0.4680] det_loss_box_0[0.5731] det_loss_cls_1[0.4537] det_loss_box_1[0.4537] det_loss_cls_2[0.4189] det_loss_box_2[0.4254] det_loss_cls_3[0.4025] det_loss_box_3[0.4028] det_loss_cls_4[0.3756] det_loss_box_4[0.3898] det_loss_cls_5[0.3638] det_loss_box_5[0.3895] det_loss_cls_dn_0[0.0963] det_loss_box_dn_0[0.2929] det_loss_cls_dn_1[0.0928] det_loss_box_dn_1[0.2530] det_loss_cls_dn_2[0.0926] det_loss_box_dn_2[0.2389] det_loss_cls_dn_3[0.0915] det_loss_box_dn_3[0.2362] det_loss_cls_dn_4[0.0899] det_loss_box_dn_4[0.2332] det_loss_cls_dn_5[0.0890] det_loss_box_dn_5[0.2321] loss_depth[0.0382] total_loss[7.1935]
Rank[0/8] 06/20/2025 01:10:31 INFO stats.py:314 | Epoch[82] Step[369] GlobalStep[32349] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 20:46:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:10:36 INFO loss_tracker.py:84 | Epoch[82/239] Step[374] GlobalStep[32354/NA]: det_loss_cls_0[0.4806] det_loss_box_0[0.6855] det_loss_cls_1[0.4641] det_loss_box_1[0.5789] det_loss_cls_2[0.4272] det_loss_box_2[0.5288] det_loss_cls_3[0.4076] det_loss_box_3[0.5022] det_loss_cls_4[0.3837] det_loss_box_4[0.4768] det_loss_cls_5[0.3708] det_loss_box_5[0.4798] det_loss_cls_dn_0[0.0917] det_loss_box_dn_0[0.3497] det_loss_cls_dn_1[0.0878] det_loss_box_dn_1[0.3101] det_loss_cls_dn_2[0.0876] det_loss_box_dn_2[0.2972] det_loss_cls_dn_3[0.0865] det_loss_box_dn_3[0.2935] det_loss_cls_dn_4[0.0856] det_loss_box_dn_4[0.2924] det_loss_cls_dn_5[0.0846] det_loss_box_dn_5[0.2905] loss_depth[0.0408] total_loss[8.1839]
Rank[0/8] 06/20/2025 01:10:51 INFO stats.py:394 | Epoch[82] completed. Training Speed: 7.73 samples/sec across all devices.	Epoch Time: 403.41 sec.	Average Epoch Time: 403.41 sec.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 20:45:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:10:57 INFO stats.py:314 | Epoch[83] Step[4] GlobalStep[32374] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 20:45:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:11:18 INFO loss_tracker.py:84 | Epoch[83/239] Step[24] GlobalStep[32394/NA]: det_loss_cls_0[0.4543] det_loss_box_0[0.5853] det_loss_cls_1[0.4344] det_loss_box_1[0.4437] det_loss_cls_2[0.4020] det_loss_box_2[0.4089] det_loss_cls_3[0.3807] det_loss_box_3[0.3956] det_loss_cls_4[0.3538] det_loss_box_4[0.3878] det_loss_cls_5[0.3429] det_loss_box_5[0.3892] det_loss_cls_dn_0[0.0954] det_loss_box_dn_0[0.2992] det_loss_cls_dn_1[0.0929] det_loss_box_dn_1[0.2478] det_loss_cls_dn_2[0.0919] det_loss_box_dn_2[0.2297] det_loss_cls_dn_3[0.0906] det_loss_box_dn_3[0.2266] det_loss_cls_dn_4[0.0899] det_loss_box_dn_4[0.2251] det_loss_cls_dn_5[0.0888] det_loss_box_dn_5[0.2236] loss_depth[0.0398] total_loss[7.0198]
Rank[0/8] 06/20/2025 01:11:22 INFO stats.py:314 | Epoch[83] Step[29] GlobalStep[32399] Training Speed: 11.96 samples/sec across all devices.	Average Step Time: 0.67 sec.	Estimated Remaining Time: 20:45:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:11:44 INFO loss_tracker.py:84 | Epoch[83/239] Step[49] GlobalStep[32419/NA]: det_loss_cls_0[0.5245] det_loss_box_0[0.6740] det_loss_cls_1[0.4998] det_loss_box_1[0.4863] det_loss_cls_2[0.4693] det_loss_box_2[0.4389] det_loss_cls_3[0.4455] det_loss_box_3[0.4262] det_loss_cls_4[0.4231] det_loss_box_4[0.4107] det_loss_cls_5[0.4096] det_loss_box_5[0.4152] det_loss_cls_dn_0[0.0993] det_loss_box_dn_0[0.2864] det_loss_cls_dn_1[0.0957] det_loss_box_dn_1[0.2446] det_loss_cls_dn_2[0.0946] det_loss_box_dn_2[0.2276] det_loss_cls_dn_3[0.0934] det_loss_box_dn_3[0.2240] det_loss_cls_dn_4[0.0928] det_loss_box_dn_4[0.2227] det_loss_cls_dn_5[0.0915] det_loss_box_dn_5[0.2210] loss_depth[0.0386] total_loss[7.6555]
Rank[0/8] 06/20/2025 01:11:49 INFO stats.py:314 | Epoch[83] Step[54] GlobalStep[32424] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 20:44:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:12:09 INFO loss_tracker.py:84 | Epoch[83/239] Step[74] GlobalStep[32444/NA]: det_loss_cls_0[0.4670] det_loss_box_0[0.6476] det_loss_cls_1[0.4501] det_loss_box_1[0.5335] det_loss_cls_2[0.4179] det_loss_box_2[0.4979] det_loss_cls_3[0.4002] det_loss_box_3[0.4755] det_loss_cls_4[0.3652] det_loss_box_4[0.4747] det_loss_cls_5[0.3533] det_loss_box_5[0.4727] det_loss_cls_dn_0[0.0921] det_loss_box_dn_0[0.4243] det_loss_cls_dn_1[0.0889] det_loss_box_dn_1[0.3796] det_loss_cls_dn_2[0.0886] det_loss_box_dn_2[0.3649] det_loss_cls_dn_3[0.0875] det_loss_box_dn_3[0.3597] det_loss_cls_dn_4[0.0864] det_loss_box_dn_4[0.3520] det_loss_cls_dn_5[0.0860] det_loss_box_dn_5[0.3484] loss_depth[0.0394] total_loss[8.3535]
Rank[0/8] 06/20/2025 01:12:15 INFO stats.py:314 | Epoch[83] Step[79] GlobalStep[32449] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 20:43:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:12:36 INFO loss_tracker.py:84 | Epoch[83/239] Step[99] GlobalStep[32469/NA]: det_loss_cls_0[0.6734] det_loss_box_0[0.8339] det_loss_cls_1[0.6520] det_loss_box_1[0.6456] det_loss_cls_2[0.6111] det_loss_box_2[0.5780] det_loss_cls_3[0.5758] det_loss_box_3[0.5593] det_loss_cls_4[0.5404] det_loss_box_4[0.5422] det_loss_cls_5[0.5266] det_loss_box_5[0.5407] det_loss_cls_dn_0[0.0997] det_loss_box_dn_0[0.2655] det_loss_cls_dn_1[0.0959] det_loss_box_dn_1[0.2245] det_loss_cls_dn_2[0.0946] det_loss_box_dn_2[0.2108] det_loss_cls_dn_3[0.0940] det_loss_box_dn_3[0.2092] det_loss_cls_dn_4[0.0931] det_loss_box_dn_4[0.2073] det_loss_cls_dn_5[0.0918] det_loss_box_dn_5[0.2067] loss_depth[0.0385] total_loss[9.2107]
Rank[0/8] 06/20/2025 01:12:41 INFO stats.py:314 | Epoch[83] Step[104] GlobalStep[32474] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 20:43:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:13:01 INFO loss_tracker.py:84 | Epoch[83/239] Step[124] GlobalStep[32494/NA]: det_loss_cls_0[0.5242] det_loss_box_0[0.5761] det_loss_cls_1[0.5056] det_loss_box_1[0.4446] det_loss_cls_2[0.4662] det_loss_box_2[0.4048] det_loss_cls_3[0.4271] det_loss_box_3[0.3928] det_loss_cls_4[0.3977] det_loss_box_4[0.3812] det_loss_cls_5[0.3755] det_loss_box_5[0.3861] det_loss_cls_dn_0[0.0956] det_loss_box_dn_0[0.2884] det_loss_cls_dn_1[0.0915] det_loss_box_dn_1[0.2465] det_loss_cls_dn_2[0.0920] det_loss_box_dn_2[0.2283] det_loss_cls_dn_3[0.0906] det_loss_box_dn_3[0.2252] det_loss_cls_dn_4[0.0896] det_loss_box_dn_4[0.2224] det_loss_cls_dn_5[0.0882] det_loss_box_dn_5[0.2197] loss_depth[0.0378] total_loss[7.2977]
Rank[0/8] 06/20/2025 01:13:06 INFO stats.py:314 | Epoch[83] Step[129] GlobalStep[32499] Training Speed: 7.72 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 20:42:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:13:27 INFO loss_tracker.py:84 | Epoch[83/239] Step[149] GlobalStep[32519/NA]: det_loss_cls_0[0.4625] det_loss_box_0[0.5781] det_loss_cls_1[0.4527] det_loss_box_1[0.4619] det_loss_cls_2[0.4359] det_loss_box_2[0.4308] det_loss_cls_3[0.4137] det_loss_box_3[0.4311] det_loss_cls_4[0.3825] det_loss_box_4[0.4076] det_loss_cls_5[0.3717] det_loss_box_5[0.4091] det_loss_cls_dn_0[0.1043] det_loss_box_dn_0[0.3282] det_loss_cls_dn_1[0.1010] det_loss_box_dn_1[0.2804] det_loss_cls_dn_2[0.1006] det_loss_box_dn_2[0.2620] det_loss_cls_dn_3[0.0991] det_loss_box_dn_3[0.2591] det_loss_cls_dn_4[0.0978] det_loss_box_dn_4[0.2574] det_loss_cls_dn_5[0.0970] det_loss_box_dn_5[0.2577] loss_depth[0.0401] total_loss[7.5225]
Rank[0/8] 06/20/2025 01:13:32 INFO stats.py:314 | Epoch[83] Step[154] GlobalStep[32524] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 20:41:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:13:52 INFO loss_tracker.py:84 | Epoch[83/239] Step[174] GlobalStep[32544/NA]: det_loss_cls_0[0.4571] det_loss_box_0[0.6359] det_loss_cls_1[0.4458] det_loss_box_1[0.4909] det_loss_cls_2[0.4306] det_loss_box_2[0.4462] det_loss_cls_3[0.3961] det_loss_box_3[0.4361] det_loss_cls_4[0.3673] det_loss_box_4[0.4232] det_loss_cls_5[0.3541] det_loss_box_5[0.4280] det_loss_cls_dn_0[0.0980] det_loss_box_dn_0[0.3276] det_loss_cls_dn_1[0.0929] det_loss_box_dn_1[0.2854] det_loss_cls_dn_2[0.0918] det_loss_box_dn_2[0.2687] det_loss_cls_dn_3[0.0917] det_loss_box_dn_3[0.2646] det_loss_cls_dn_4[0.0905] det_loss_box_dn_4[0.2631] det_loss_cls_dn_5[0.0905] det_loss_box_dn_5[0.2618] loss_depth[0.0432] total_loss[7.5813]
Rank[0/8] 06/20/2025 01:13:58 INFO stats.py:314 | Epoch[83] Step[179] GlobalStep[32549] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 20:41:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:14:18 INFO loss_tracker.py:84 | Epoch[83/239] Step[199] GlobalStep[32569/NA]: det_loss_cls_0[0.6074] det_loss_box_0[0.8083] det_loss_cls_1[0.5848] det_loss_box_1[0.6633] det_loss_cls_2[0.5721] det_loss_box_2[0.5996] det_loss_cls_3[0.5369] det_loss_box_3[0.5969] det_loss_cls_4[0.5166] det_loss_box_4[0.5851] det_loss_cls_5[0.5006] det_loss_box_5[0.5895] det_loss_cls_dn_0[0.0991] det_loss_box_dn_0[0.3153] det_loss_cls_dn_1[0.0955] det_loss_box_dn_1[0.2745] det_loss_cls_dn_2[0.0952] det_loss_box_dn_2[0.2602] det_loss_cls_dn_3[0.0948] det_loss_box_dn_3[0.2580] det_loss_cls_dn_4[0.0934] det_loss_box_dn_4[0.2561] det_loss_cls_dn_5[0.0927] det_loss_box_dn_5[0.2513] loss_depth[0.0407] total_loss[9.3877]
Rank[0/8] 06/20/2025 01:14:23 INFO stats.py:314 | Epoch[83] Step[204] GlobalStep[32574] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 20:40:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:14:44 INFO loss_tracker.py:84 | Epoch[83/239] Step[224] GlobalStep[32594/NA]: det_loss_cls_0[0.5311] det_loss_box_0[0.6695] det_loss_cls_1[0.5278] det_loss_box_1[0.5248] det_loss_cls_2[0.5089] det_loss_box_2[0.4782] det_loss_cls_3[0.4774] det_loss_box_3[0.4703] det_loss_cls_4[0.4451] det_loss_box_4[0.4654] det_loss_cls_5[0.4382] det_loss_box_5[0.4602] det_loss_cls_dn_0[0.0949] det_loss_box_dn_0[0.3288] det_loss_cls_dn_1[0.0912] det_loss_box_dn_1[0.2718] det_loss_cls_dn_2[0.0900] det_loss_box_dn_2[0.2541] det_loss_cls_dn_3[0.0892] det_loss_box_dn_3[0.2491] det_loss_cls_dn_4[0.0882] det_loss_box_dn_4[0.2496] det_loss_cls_dn_5[0.0867] det_loss_box_dn_5[0.2451] loss_depth[0.0433] total_loss[8.1785]
Rank[0/8] 06/20/2025 01:14:49 INFO stats.py:314 | Epoch[83] Step[229] GlobalStep[32599] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 20:39:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:15:10 INFO loss_tracker.py:84 | Epoch[83/239] Step[249] GlobalStep[32619/NA]: det_loss_cls_0[0.5107] det_loss_box_0[0.6624] det_loss_cls_1[0.4962] det_loss_box_1[0.5165] det_loss_cls_2[0.4713] det_loss_box_2[0.4649] det_loss_cls_3[0.4510] det_loss_box_3[0.4510] det_loss_cls_4[0.4206] det_loss_box_4[0.4457] det_loss_cls_5[0.4107] det_loss_box_5[0.4492] det_loss_cls_dn_0[0.1027] det_loss_box_dn_0[0.3186] det_loss_cls_dn_1[0.0987] det_loss_box_dn_1[0.2719] det_loss_cls_dn_2[0.0982] det_loss_box_dn_2[0.2551] det_loss_cls_dn_3[0.0973] det_loss_box_dn_3[0.2531] det_loss_cls_dn_4[0.0968] det_loss_box_dn_4[0.2516] det_loss_cls_dn_5[0.0963] det_loss_box_dn_5[0.2488] loss_depth[0.0403] total_loss[7.9795]
Rank[0/8] 06/20/2025 01:15:15 INFO stats.py:314 | Epoch[83] Step[254] GlobalStep[32624] Training Speed: 8.69 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 20:39:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:15:36 INFO loss_tracker.py:84 | Epoch[83/239] Step[274] GlobalStep[32644/NA]: det_loss_cls_0[0.4197] det_loss_box_0[0.6495] det_loss_cls_1[0.4136] det_loss_box_1[0.5352] det_loss_cls_2[0.3931] det_loss_box_2[0.4841] det_loss_cls_3[0.3750] det_loss_box_3[0.4668] det_loss_cls_4[0.3533] det_loss_box_4[0.4660] det_loss_cls_5[0.3467] det_loss_box_5[0.4665] det_loss_cls_dn_0[0.1065] det_loss_box_dn_0[0.3323] det_loss_cls_dn_1[0.1027] det_loss_box_dn_1[0.2892] det_loss_cls_dn_2[0.1018] det_loss_box_dn_2[0.2715] det_loss_cls_dn_3[0.1000] det_loss_box_dn_3[0.2674] det_loss_cls_dn_4[0.0995] det_loss_box_dn_4[0.2651] det_loss_cls_dn_5[0.0978] det_loss_box_dn_5[0.2649] loss_depth[0.0391] total_loss[7.7073]
Rank[0/8] 06/20/2025 01:15:41 INFO stats.py:314 | Epoch[83] Step[279] GlobalStep[32649] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 20:38:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:16:02 INFO loss_tracker.py:84 | Epoch[83/239] Step[299] GlobalStep[32669/NA]: det_loss_cls_0[0.4818] det_loss_box_0[0.6359] det_loss_cls_1[0.4730] det_loss_box_1[0.4909] det_loss_cls_2[0.4435] det_loss_box_2[0.4500] det_loss_cls_3[0.4174] det_loss_box_3[0.4443] det_loss_cls_4[0.3890] det_loss_box_4[0.4383] det_loss_cls_5[0.3771] det_loss_box_5[0.4338] det_loss_cls_dn_0[0.0993] det_loss_box_dn_0[0.2766] det_loss_cls_dn_1[0.0947] det_loss_box_dn_1[0.2388] det_loss_cls_dn_2[0.0939] det_loss_box_dn_2[0.2207] det_loss_cls_dn_3[0.0936] det_loss_box_dn_3[0.2178] det_loss_cls_dn_4[0.0914] det_loss_box_dn_4[0.2162] det_loss_cls_dn_5[0.0908] det_loss_box_dn_5[0.2143] loss_depth[0.0423] total_loss[7.4656]
Rank[0/8] 06/20/2025 01:16:07 INFO stats.py:314 | Epoch[83] Step[304] GlobalStep[32674] Training Speed: 6.52 samples/sec across all devices.	Average Step Time: 1.23 sec.	Estimated Remaining Time: 20:37:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:16:27 INFO loss_tracker.py:84 | Epoch[83/239] Step[324] GlobalStep[32694/NA]: det_loss_cls_0[0.4630] det_loss_box_0[0.6148] det_loss_cls_1[0.4624] det_loss_box_1[0.4692] det_loss_cls_2[0.4391] det_loss_box_2[0.4316] det_loss_cls_3[0.4043] det_loss_box_3[0.4187] det_loss_cls_4[0.3791] det_loss_box_4[0.4116] det_loss_cls_5[0.3649] det_loss_box_5[0.4088] det_loss_cls_dn_0[0.0990] det_loss_box_dn_0[0.3282] det_loss_cls_dn_1[0.0963] det_loss_box_dn_1[0.2801] det_loss_cls_dn_2[0.0967] det_loss_box_dn_2[0.2641] det_loss_cls_dn_3[0.0945] det_loss_box_dn_3[0.2612] det_loss_cls_dn_4[0.0942] det_loss_box_dn_4[0.2604] det_loss_cls_dn_5[0.0933] det_loss_box_dn_5[0.2588] loss_depth[0.0407] total_loss[7.5351]
Rank[0/8] 06/20/2025 01:16:32 INFO stats.py:314 | Epoch[83] Step[329] GlobalStep[32699] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 20:37:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:16:53 INFO loss_tracker.py:84 | Epoch[83/239] Step[349] GlobalStep[32719/NA]: det_loss_cls_0[0.5053] det_loss_box_0[0.7724] det_loss_cls_1[0.4860] det_loss_box_1[0.5833] det_loss_cls_2[0.4606] det_loss_box_2[0.5319] det_loss_cls_3[0.4345] det_loss_box_3[0.5022] det_loss_cls_4[0.3997] det_loss_box_4[0.4844] det_loss_cls_5[0.3926] det_loss_box_5[0.4819] det_loss_cls_dn_0[0.1000] det_loss_box_dn_0[0.3223] det_loss_cls_dn_1[0.0966] det_loss_box_dn_1[0.2804] det_loss_cls_dn_2[0.0957] det_loss_box_dn_2[0.2631] det_loss_cls_dn_3[0.0949] det_loss_box_dn_3[0.2606] det_loss_cls_dn_4[0.0932] det_loss_box_dn_4[0.2594] det_loss_cls_dn_5[0.0918] det_loss_box_dn_5[0.2555] loss_depth[0.0404] total_loss[8.2886]
Rank[0/8] 06/20/2025 01:16:58 INFO stats.py:314 | Epoch[83] Step[354] GlobalStep[32724] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 20:36:30.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:17:19 INFO loss_tracker.py:84 | Epoch[83/239] Step[374] GlobalStep[32744/NA]: det_loss_cls_0[0.4951] det_loss_box_0[0.6797] det_loss_cls_1[0.4910] det_loss_box_1[0.5267] det_loss_cls_2[0.4556] det_loss_box_2[0.4919] det_loss_cls_3[0.4206] det_loss_box_3[0.4720] det_loss_cls_4[0.3972] det_loss_box_4[0.4534] det_loss_cls_5[0.3757] det_loss_box_5[0.4618] det_loss_cls_dn_0[0.0976] det_loss_box_dn_0[0.3111] det_loss_cls_dn_1[0.0929] det_loss_box_dn_1[0.2648] det_loss_cls_dn_2[0.0924] det_loss_box_dn_2[0.2475] det_loss_cls_dn_3[0.0914] det_loss_box_dn_3[0.2436] det_loss_cls_dn_4[0.0905] det_loss_box_dn_4[0.2416] det_loss_cls_dn_5[0.0897] det_loss_box_dn_5[0.2379] loss_depth[0.0390] total_loss[7.8603]
Rank[0/8] 06/20/2025 01:17:24 INFO stats.py:314 | Epoch[83] Step[379] GlobalStep[32749] Training Speed: 8.15 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 20:35:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:17:34 INFO stats.py:394 | Epoch[83] completed. Training Speed: 7.74 samples/sec across all devices.	Epoch Time: 403.34 sec.	Average Epoch Time: 403.34 sec.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 20:35:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:17:51 INFO stats.py:314 | Epoch[84] Step[14] GlobalStep[32774] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 20:35:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:18:01 INFO loss_tracker.py:84 | Epoch[84/239] Step[24] GlobalStep[32784/NA]: det_loss_cls_0[0.5724] det_loss_box_0[0.7303] det_loss_cls_1[0.5637] det_loss_box_1[0.5698] det_loss_cls_2[0.5313] det_loss_box_2[0.5183] det_loss_cls_3[0.5035] det_loss_box_3[0.4932] det_loss_cls_4[0.4748] det_loss_box_4[0.4836] det_loss_cls_5[0.4534] det_loss_box_5[0.4869] det_loss_cls_dn_0[0.1044] det_loss_box_dn_0[0.3017] det_loss_cls_dn_1[0.1010] det_loss_box_dn_1[0.2637] det_loss_cls_dn_2[0.1008] det_loss_box_dn_2[0.2491] det_loss_cls_dn_3[0.1004] det_loss_box_dn_3[0.2461] det_loss_cls_dn_4[0.0998] det_loss_box_dn_4[0.2447] det_loss_cls_dn_5[0.0990] det_loss_box_dn_5[0.2431] loss_depth[0.0391] total_loss[8.5741]
Rank[0/8] 06/20/2025 01:18:17 INFO stats.py:314 | Epoch[84] Step[39] GlobalStep[32799] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 20:34:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:18:27 INFO loss_tracker.py:84 | Epoch[84/239] Step[49] GlobalStep[32809/NA]: det_loss_cls_0[0.4882] det_loss_box_0[0.6289] det_loss_cls_1[0.4604] det_loss_box_1[0.4898] det_loss_cls_2[0.4399] det_loss_box_2[0.4432] det_loss_cls_3[0.4143] det_loss_box_3[0.4299] det_loss_cls_4[0.3957] det_loss_box_4[0.4162] det_loss_cls_5[0.3791] det_loss_box_5[0.4221] det_loss_cls_dn_0[0.0990] det_loss_box_dn_0[0.2878] det_loss_cls_dn_1[0.0956] det_loss_box_dn_1[0.2469] det_loss_cls_dn_2[0.0950] det_loss_box_dn_2[0.2308] det_loss_cls_dn_3[0.0939] det_loss_box_dn_3[0.2268] det_loss_cls_dn_4[0.0929] det_loss_box_dn_4[0.2255] det_loss_cls_dn_5[0.0923] det_loss_box_dn_5[0.2243] loss_depth[0.0397] total_loss[7.4583]
Rank[0/8] 06/20/2025 01:18:43 INFO stats.py:314 | Epoch[84] Step[64] GlobalStep[32824] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 20:33:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:18:53 INFO loss_tracker.py:84 | Epoch[84/239] Step[74] GlobalStep[32834/NA]: det_loss_cls_0[0.4116] det_loss_box_0[0.4922] det_loss_cls_1[0.4079] det_loss_box_1[0.3749] det_loss_cls_2[0.3680] det_loss_box_2[0.3464] det_loss_cls_3[0.3336] det_loss_box_3[0.3351] det_loss_cls_4[0.3083] det_loss_box_4[0.3263] det_loss_cls_5[0.2981] det_loss_box_5[0.3259] det_loss_cls_dn_0[0.0927] det_loss_box_dn_0[0.3067] det_loss_cls_dn_1[0.0889] det_loss_box_dn_1[0.2653] det_loss_cls_dn_2[0.0887] det_loss_box_dn_2[0.2470] det_loss_cls_dn_3[0.0872] det_loss_box_dn_3[0.2409] det_loss_cls_dn_4[0.0861] det_loss_box_dn_4[0.2400] det_loss_cls_dn_5[0.0860] det_loss_box_dn_5[0.2381] loss_depth[0.0371] total_loss[6.4333]
Rank[0/8] 06/20/2025 01:19:08 INFO stats.py:314 | Epoch[84] Step[89] GlobalStep[32849] Training Speed: 7.76 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 20:33:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:19:19 INFO loss_tracker.py:84 | Epoch[84/239] Step[99] GlobalStep[32859/NA]: det_loss_cls_0[0.4485] det_loss_box_0[0.5622] det_loss_cls_1[0.4156] det_loss_box_1[0.4607] det_loss_cls_2[0.3898] det_loss_box_2[0.4148] det_loss_cls_3[0.3691] det_loss_box_3[0.3970] det_loss_cls_4[0.3294] det_loss_box_4[0.3908] det_loss_cls_5[0.3218] det_loss_box_5[0.3903] det_loss_cls_dn_0[0.1002] det_loss_box_dn_0[0.3358] det_loss_cls_dn_1[0.0958] det_loss_box_dn_1[0.2874] det_loss_cls_dn_2[0.0951] det_loss_box_dn_2[0.2727] det_loss_cls_dn_3[0.0942] det_loss_box_dn_3[0.2694] det_loss_cls_dn_4[0.0931] det_loss_box_dn_4[0.2668] det_loss_cls_dn_5[0.0924] det_loss_box_dn_5[0.2644] loss_depth[0.0389] total_loss[7.1963]
Rank[0/8] 06/20/2025 01:19:34 INFO stats.py:314 | Epoch[84] Step[114] GlobalStep[32874] Training Speed: 8.05 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 20:32:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:19:45 INFO loss_tracker.py:84 | Epoch[84/239] Step[124] GlobalStep[32884/NA]: det_loss_cls_0[0.5449] det_loss_box_0[0.7126] det_loss_cls_1[0.5161] det_loss_box_1[0.5735] det_loss_cls_2[0.4974] det_loss_box_2[0.5158] det_loss_cls_3[0.4752] det_loss_box_3[0.4967] det_loss_cls_4[0.4565] det_loss_box_4[0.4852] det_loss_cls_5[0.4377] det_loss_box_5[0.4911] det_loss_cls_dn_0[0.0961] det_loss_box_dn_0[0.3708] det_loss_cls_dn_1[0.0919] det_loss_box_dn_1[0.3215] det_loss_cls_dn_2[0.0918] det_loss_box_dn_2[0.3079] det_loss_cls_dn_3[0.0906] det_loss_box_dn_3[0.3021] det_loss_cls_dn_4[0.0897] det_loss_box_dn_4[0.2997] det_loss_cls_dn_5[0.0891] det_loss_box_dn_5[0.2972] loss_depth[0.0366] total_loss[8.6878]
Rank[0/8] 06/20/2025 01:20:00 INFO stats.py:314 | Epoch[84] Step[139] GlobalStep[32899] Training Speed: 8.04 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 20:31:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:20:11 INFO loss_tracker.py:84 | Epoch[84/239] Step[149] GlobalStep[32909/NA]: det_loss_cls_0[0.4689] det_loss_box_0[0.6242] det_loss_cls_1[0.4459] det_loss_box_1[0.5054] det_loss_cls_2[0.4105] det_loss_box_2[0.4734] det_loss_cls_3[0.3823] det_loss_box_3[0.4566] det_loss_cls_4[0.3555] det_loss_box_4[0.4564] det_loss_cls_5[0.3482] det_loss_box_5[0.4485] det_loss_cls_dn_0[0.0984] det_loss_box_dn_0[0.3464] det_loss_cls_dn_1[0.0950] det_loss_box_dn_1[0.2876] det_loss_cls_dn_2[0.0942] det_loss_box_dn_2[0.2659] det_loss_cls_dn_3[0.0931] det_loss_box_dn_3[0.2624] det_loss_cls_dn_4[0.0924] det_loss_box_dn_4[0.2595] det_loss_cls_dn_5[0.0911] det_loss_box_dn_5[0.2586] loss_depth[0.0382] total_loss[7.6586]
Rank[0/8] 06/20/2025 01:20:26 INFO stats.py:314 | Epoch[84] Step[164] GlobalStep[32924] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 20:31:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:20:36 INFO loss_tracker.py:84 | Epoch[84/239] Step[174] GlobalStep[32934/NA]: det_loss_cls_0[0.6128] det_loss_box_0[0.7637] det_loss_cls_1[0.5815] det_loss_box_1[0.6233] det_loss_cls_2[0.5369] det_loss_box_2[0.5627] det_loss_cls_3[0.5100] det_loss_box_3[0.5452] det_loss_cls_4[0.4799] det_loss_box_4[0.5394] det_loss_cls_5[0.4594] det_loss_box_5[0.5351] det_loss_cls_dn_0[0.1001] det_loss_box_dn_0[0.2674] det_loss_cls_dn_1[0.0971] det_loss_box_dn_1[0.2328] det_loss_cls_dn_2[0.0964] det_loss_box_dn_2[0.2174] det_loss_cls_dn_3[0.0955] det_loss_box_dn_3[0.2159] det_loss_cls_dn_4[0.0950] det_loss_box_dn_4[0.2143] det_loss_cls_dn_5[0.0939] det_loss_box_dn_5[0.2139] loss_depth[0.0366] total_loss[8.7261]
Rank[0/8] 06/20/2025 01:20:51 INFO stats.py:314 | Epoch[84] Step[189] GlobalStep[32949] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 20:30:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:21:01 INFO loss_tracker.py:84 | Epoch[84/239] Step[199] GlobalStep[32959/NA]: det_loss_cls_0[0.5505] det_loss_box_0[0.7075] det_loss_cls_1[0.5300] det_loss_box_1[0.5446] det_loss_cls_2[0.5025] det_loss_box_2[0.4850] det_loss_cls_3[0.4750] det_loss_box_3[0.4750] det_loss_cls_4[0.4405] det_loss_box_4[0.4738] det_loss_cls_5[0.4237] det_loss_box_5[0.4776] det_loss_cls_dn_0[0.1010] det_loss_box_dn_0[0.2791] det_loss_cls_dn_1[0.0977] det_loss_box_dn_1[0.2464] det_loss_cls_dn_2[0.0967] det_loss_box_dn_2[0.2326] det_loss_cls_dn_3[0.0954] det_loss_box_dn_3[0.2306] det_loss_cls_dn_4[0.0946] det_loss_box_dn_4[0.2303] det_loss_cls_dn_5[0.0936] det_loss_box_dn_5[0.2283] loss_depth[0.0397] total_loss[8.1515]
Rank[0/8] 06/20/2025 01:21:17 INFO stats.py:314 | Epoch[84] Step[214] GlobalStep[32974] Training Speed: 8.69 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 20:30:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:21:27 INFO loss_tracker.py:84 | Epoch[84/239] Step[224] GlobalStep[32984/NA]: det_loss_cls_0[0.5367] det_loss_box_0[0.7930] det_loss_cls_1[0.5331] det_loss_box_1[0.6140] det_loss_cls_2[0.5053] det_loss_box_2[0.5381] det_loss_cls_3[0.4805] det_loss_box_3[0.5257] det_loss_cls_4[0.4515] det_loss_box_4[0.5238] det_loss_cls_5[0.4448] det_loss_box_5[0.5167] det_loss_cls_dn_0[0.0996] det_loss_box_dn_0[0.3301] det_loss_cls_dn_1[0.0951] det_loss_box_dn_1[0.2834] det_loss_cls_dn_2[0.0946] det_loss_box_dn_2[0.2665] det_loss_cls_dn_3[0.0936] det_loss_box_dn_3[0.2610] det_loss_cls_dn_4[0.0927] det_loss_box_dn_4[0.2600] det_loss_cls_dn_5[0.0919] det_loss_box_dn_5[0.2583] loss_depth[0.0422] total_loss[8.7324]
Rank[0/8] 06/20/2025 01:21:43 INFO stats.py:314 | Epoch[84] Step[239] GlobalStep[32999] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 20:29:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:21:54 INFO loss_tracker.py:84 | Epoch[84/239] Step[249] GlobalStep[33009/NA]: det_loss_cls_0[0.5469] det_loss_box_0[0.7348] det_loss_cls_1[0.5112] det_loss_box_1[0.5718] det_loss_cls_2[0.4992] det_loss_box_2[0.5172] det_loss_cls_3[0.4759] det_loss_box_3[0.5002] det_loss_cls_4[0.4528] det_loss_box_4[0.4970] det_loss_cls_5[0.4405] det_loss_box_5[0.4940] det_loss_cls_dn_0[0.1101] det_loss_box_dn_0[0.3833] det_loss_cls_dn_1[0.1061] det_loss_box_dn_1[0.3360] det_loss_cls_dn_2[0.1066] det_loss_box_dn_2[0.3200] det_loss_cls_dn_3[0.1061] det_loss_box_dn_3[0.3167] det_loss_cls_dn_4[0.1056] det_loss_box_dn_4[0.3157] det_loss_cls_dn_5[0.1050] det_loss_box_dn_5[0.3123] loss_depth[0.0441] total_loss[8.9092]
Rank[0/8] 06/20/2025 01:22:09 INFO stats.py:314 | Epoch[84] Step[264] GlobalStep[33024] Training Speed: 8.66 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 20:28:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:22:20 INFO loss_tracker.py:84 | Epoch[84/239] Step[274] GlobalStep[33034/NA]: det_loss_cls_0[0.6003] det_loss_box_0[0.8105] det_loss_cls_1[0.5785] det_loss_box_1[0.6617] det_loss_cls_2[0.5572] det_loss_box_2[0.6094] det_loss_cls_3[0.5313] det_loss_box_3[0.5904] det_loss_cls_4[0.5119] det_loss_box_4[0.5739] det_loss_cls_5[0.5020] det_loss_box_5[0.5708] det_loss_cls_dn_0[0.1133] det_loss_box_dn_0[0.3293] det_loss_cls_dn_1[0.1089] det_loss_box_dn_1[0.2912] det_loss_cls_dn_2[0.1093] det_loss_box_dn_2[0.2755] det_loss_cls_dn_3[0.1079] det_loss_box_dn_3[0.2720] det_loss_cls_dn_4[0.1071] det_loss_box_dn_4[0.2719] det_loss_cls_dn_5[0.1057] det_loss_box_dn_5[0.2695] loss_depth[0.0406] total_loss[9.5001]
Rank[0/8] 06/20/2025 01:22:35 INFO stats.py:314 | Epoch[84] Step[289] GlobalStep[33049] Training Speed: 8.34 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 20:28:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:22:46 INFO loss_tracker.py:84 | Epoch[84/239] Step[299] GlobalStep[33059/NA]: det_loss_cls_0[0.4186] det_loss_box_0[0.5847] det_loss_cls_1[0.3990] det_loss_box_1[0.4625] det_loss_cls_2[0.3739] det_loss_box_2[0.4120] det_loss_cls_3[0.3479] det_loss_box_3[0.3963] det_loss_cls_4[0.3266] det_loss_box_4[0.3922] det_loss_cls_5[0.3186] det_loss_box_5[0.3924] det_loss_cls_dn_0[0.0927] det_loss_box_dn_0[0.3358] det_loss_cls_dn_1[0.0893] det_loss_box_dn_1[0.2884] det_loss_cls_dn_2[0.0894] det_loss_box_dn_2[0.2687] det_loss_cls_dn_3[0.0877] det_loss_box_dn_3[0.2654] det_loss_cls_dn_4[0.0869] det_loss_box_dn_4[0.2636] det_loss_cls_dn_5[0.0862] det_loss_box_dn_5[0.2626] loss_depth[0.0377] total_loss[7.0789]
Rank[0/8] 06/20/2025 01:23:01 INFO stats.py:314 | Epoch[84] Step[314] GlobalStep[33074] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 20:27:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:23:11 INFO loss_tracker.py:84 | Epoch[84/239] Step[324] GlobalStep[33084/NA]: det_loss_cls_0[0.5837] det_loss_box_0[0.7083] det_loss_cls_1[0.5686] det_loss_box_1[0.5463] det_loss_cls_2[0.5232] det_loss_box_2[0.4906] det_loss_cls_3[0.4898] det_loss_box_3[0.4713] det_loss_cls_4[0.4645] det_loss_box_4[0.4568] det_loss_cls_5[0.4449] det_loss_box_5[0.4583] det_loss_cls_dn_0[0.1030] det_loss_box_dn_0[0.2841] det_loss_cls_dn_1[0.0994] det_loss_box_dn_1[0.2369] det_loss_cls_dn_2[0.0989] det_loss_box_dn_2[0.2198] det_loss_cls_dn_3[0.0975] det_loss_box_dn_3[0.2166] det_loss_cls_dn_4[0.0963] det_loss_box_dn_4[0.2154] det_loss_cls_dn_5[0.0951] det_loss_box_dn_5[0.2130] loss_depth[0.0393] total_loss[8.2216]
Rank[0/8] 06/20/2025 01:23:27 INFO stats.py:314 | Epoch[84] Step[339] GlobalStep[33099] Training Speed: 8.59 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 20:26:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:23:38 INFO loss_tracker.py:84 | Epoch[84/239] Step[349] GlobalStep[33109/NA]: det_loss_cls_0[0.5631] det_loss_box_0[0.7095] det_loss_cls_1[0.5492] det_loss_box_1[0.5620] det_loss_cls_2[0.4963] det_loss_box_2[0.5070] det_loss_cls_3[0.4804] det_loss_box_3[0.4896] det_loss_cls_4[0.4575] det_loss_box_4[0.4836] det_loss_cls_5[0.4467] det_loss_box_5[0.4812] det_loss_cls_dn_0[0.0982] det_loss_box_dn_0[0.2798] det_loss_cls_dn_1[0.0937] det_loss_box_dn_1[0.2358] det_loss_cls_dn_2[0.0927] det_loss_box_dn_2[0.2178] det_loss_cls_dn_3[0.0924] det_loss_box_dn_3[0.2142] det_loss_cls_dn_4[0.0911] det_loss_box_dn_4[0.2142] det_loss_cls_dn_5[0.0896] det_loss_box_dn_5[0.2123] loss_depth[0.0361] total_loss[8.1937]
Rank[0/8] 06/20/2025 01:23:53 INFO stats.py:314 | Epoch[84] Step[364] GlobalStep[33124] Training Speed: 8.70 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 20:26:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:24:04 INFO loss_tracker.py:84 | Epoch[84/239] Step[374] GlobalStep[33134/NA]: det_loss_cls_0[0.5617] det_loss_box_0[0.7686] det_loss_cls_1[0.5423] det_loss_box_1[0.5958] det_loss_cls_2[0.5090] det_loss_box_2[0.5441] det_loss_cls_3[0.4873] det_loss_box_3[0.5165] det_loss_cls_4[0.4668] det_loss_box_4[0.5075] det_loss_cls_5[0.4457] det_loss_box_5[0.5077] det_loss_cls_dn_0[0.1077] det_loss_box_dn_0[0.3273] det_loss_cls_dn_1[0.1038] det_loss_box_dn_1[0.2854] det_loss_cls_dn_2[0.1032] det_loss_box_dn_2[0.2674] det_loss_cls_dn_3[0.1030] det_loss_box_dn_3[0.2652] det_loss_cls_dn_4[0.1035] det_loss_box_dn_4[0.2633] det_loss_cls_dn_5[0.1027] det_loss_box_dn_5[0.2613] loss_depth[0.0387] total_loss[8.7856]
Rank[0/8] 06/20/2025 01:24:19 INFO stats.py:314 | Epoch[84] Step[389] GlobalStep[33149] Training Speed: 6.44 samples/sec across all devices.	Average Step Time: 1.24 sec.	Estimated Remaining Time: 20:25:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:24:19 INFO stats.py:394 | Epoch[84] completed. Training Speed: 7.70 samples/sec across all devices.	Epoch Time: 404.95 sec.	Average Epoch Time: 404.95 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 20:25:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:24:46 INFO stats.py:314 | Epoch[85] Step[24] GlobalStep[33174] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 20:24:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:24:46 INFO loss_tracker.py:84 | Epoch[85/239] Step[24] GlobalStep[33174/NA]: det_loss_cls_0[0.4482] det_loss_box_0[0.5654] det_loss_cls_1[0.4284] det_loss_box_1[0.4429] det_loss_cls_2[0.4022] det_loss_box_2[0.3931] det_loss_cls_3[0.3832] det_loss_box_3[0.3712] det_loss_cls_4[0.3609] det_loss_box_4[0.3628] det_loss_cls_5[0.3480] det_loss_box_5[0.3662] det_loss_cls_dn_0[0.0941] det_loss_box_dn_0[0.2891] det_loss_cls_dn_1[0.0901] det_loss_box_dn_1[0.2451] det_loss_cls_dn_2[0.0895] det_loss_box_dn_2[0.2273] det_loss_cls_dn_3[0.0889] det_loss_box_dn_3[0.2243] det_loss_cls_dn_4[0.0881] det_loss_box_dn_4[0.2232] det_loss_cls_dn_5[0.0869] det_loss_box_dn_5[0.2209] loss_depth[0.0382] total_loss[6.8780]
Rank[0/8] 06/20/2025 01:25:12 INFO stats.py:314 | Epoch[85] Step[49] GlobalStep[33199] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 20:24:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:25:12 INFO loss_tracker.py:84 | Epoch[85/239] Step[49] GlobalStep[33199/NA]: det_loss_cls_0[0.4041] det_loss_box_0[0.4763] det_loss_cls_1[0.3962] det_loss_box_1[0.3512] det_loss_cls_2[0.3687] det_loss_box_2[0.3102] det_loss_cls_3[0.3350] det_loss_box_3[0.3028] det_loss_cls_4[0.3124] det_loss_box_4[0.2987] det_loss_cls_5[0.2985] det_loss_box_5[0.3023] det_loss_cls_dn_0[0.0907] det_loss_box_dn_0[0.2685] det_loss_cls_dn_1[0.0882] det_loss_box_dn_1[0.2190] det_loss_cls_dn_2[0.0872] det_loss_box_dn_2[0.2057] det_loss_cls_dn_3[0.0866] det_loss_box_dn_3[0.2012] det_loss_cls_dn_4[0.0864] det_loss_box_dn_4[0.1987] det_loss_cls_dn_5[0.0873] det_loss_box_dn_5[0.1970] loss_depth[0.0405] total_loss[6.0133]
Rank[0/8] 06/20/2025 01:25:38 INFO stats.py:314 | Epoch[85] Step[74] GlobalStep[33224] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 20:23:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:25:38 INFO loss_tracker.py:84 | Epoch[85/239] Step[74] GlobalStep[33224/NA]: det_loss_cls_0[0.6114] det_loss_box_0[0.8552] det_loss_cls_1[0.6077] det_loss_box_1[0.7036] det_loss_cls_2[0.5804] det_loss_box_2[0.6384] det_loss_cls_3[0.5518] det_loss_box_3[0.6206] det_loss_cls_4[0.5102] det_loss_box_4[0.6080] det_loss_cls_5[0.4965] det_loss_box_5[0.6099] det_loss_cls_dn_0[0.1029] det_loss_box_dn_0[0.2895] det_loss_cls_dn_1[0.0992] det_loss_box_dn_1[0.2513] det_loss_cls_dn_2[0.0985] det_loss_box_dn_2[0.2363] det_loss_cls_dn_3[0.0976] det_loss_box_dn_3[0.2327] det_loss_cls_dn_4[0.0972] det_loss_box_dn_4[0.2314] det_loss_cls_dn_5[0.0967] det_loss_box_dn_5[0.2311] loss_depth[0.0370] total_loss[9.4948]
Rank[0/8] 06/20/2025 01:26:04 INFO stats.py:314 | Epoch[85] Step[99] GlobalStep[33249] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 20:22:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:26:04 INFO loss_tracker.py:84 | Epoch[85/239] Step[99] GlobalStep[33249/NA]: det_loss_cls_0[0.5932] det_loss_box_0[0.8651] det_loss_cls_1[0.5691] det_loss_box_1[0.7313] det_loss_cls_2[0.5469] det_loss_box_2[0.6643] det_loss_cls_3[0.5254] det_loss_box_3[0.6399] det_loss_cls_4[0.4996] det_loss_box_4[0.6359] det_loss_cls_5[0.4931] det_loss_box_5[0.6252] det_loss_cls_dn_0[0.0970] det_loss_box_dn_0[0.3476] det_loss_cls_dn_1[0.0914] det_loss_box_dn_1[0.3082] det_loss_cls_dn_2[0.0907] det_loss_box_dn_2[0.2954] det_loss_cls_dn_3[0.0897] det_loss_box_dn_3[0.2911] det_loss_cls_dn_4[0.0893] det_loss_box_dn_4[0.2917] det_loss_cls_dn_5[0.0887] det_loss_box_dn_5[0.2884] loss_depth[0.0384] total_loss[9.7966]
Rank[0/8] 06/20/2025 01:26:30 INFO stats.py:314 | Epoch[85] Step[124] GlobalStep[33274] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 20:22:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:26:30 INFO loss_tracker.py:84 | Epoch[85/239] Step[124] GlobalStep[33274/NA]: det_loss_cls_0[0.6142] det_loss_box_0[0.8335] det_loss_cls_1[0.5974] det_loss_box_1[0.6472] det_loss_cls_2[0.5746] det_loss_box_2[0.5802] det_loss_cls_3[0.5453] det_loss_box_3[0.5589] det_loss_cls_4[0.5098] det_loss_box_4[0.5499] det_loss_cls_5[0.4949] det_loss_box_5[0.5491] det_loss_cls_dn_0[0.1062] det_loss_box_dn_0[0.2890] det_loss_cls_dn_1[0.1022] det_loss_box_dn_1[0.2482] det_loss_cls_dn_2[0.1016] det_loss_box_dn_2[0.2336] det_loss_cls_dn_3[0.1007] det_loss_box_dn_3[0.2303] det_loss_cls_dn_4[0.1000] det_loss_box_dn_4[0.2294] det_loss_cls_dn_5[0.0995] det_loss_box_dn_5[0.2282] loss_depth[0.0393] total_loss[9.1631]
Rank[0/8] 06/20/2025 01:26:55 INFO stats.py:314 | Epoch[85] Step[149] GlobalStep[33299] Training Speed: 7.77 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 20:21:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:26:55 INFO loss_tracker.py:84 | Epoch[85/239] Step[149] GlobalStep[33299/NA]: det_loss_cls_0[0.5977] det_loss_box_0[0.7492] det_loss_cls_1[0.5838] det_loss_box_1[0.6067] det_loss_cls_2[0.5439] det_loss_box_2[0.5332] det_loss_cls_3[0.5105] det_loss_box_3[0.5151] det_loss_cls_4[0.4713] det_loss_box_4[0.5185] det_loss_cls_5[0.4605] det_loss_box_5[0.5172] det_loss_cls_dn_0[0.0932] det_loss_box_dn_0[0.2753] det_loss_cls_dn_1[0.0901] det_loss_box_dn_1[0.2384] det_loss_cls_dn_2[0.0892] det_loss_box_dn_2[0.2247] det_loss_cls_dn_3[0.0880] det_loss_box_dn_3[0.2234] det_loss_cls_dn_4[0.0874] det_loss_box_dn_4[0.2221] det_loss_cls_dn_5[0.0870] det_loss_box_dn_5[0.2200] loss_depth[0.0368] total_loss[8.5834]
Rank[0/8] 06/20/2025 01:27:21 INFO stats.py:314 | Epoch[85] Step[174] GlobalStep[33324] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 20:21:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:27:21 INFO loss_tracker.py:84 | Epoch[85/239] Step[174] GlobalStep[33324/NA]: det_loss_cls_0[0.4100] det_loss_box_0[0.5207] det_loss_cls_1[0.3917] det_loss_box_1[0.4072] det_loss_cls_2[0.3724] det_loss_box_2[0.3622] det_loss_cls_3[0.3513] det_loss_box_3[0.3540] det_loss_cls_4[0.3346] det_loss_box_4[0.3495] det_loss_cls_5[0.3258] det_loss_box_5[0.3430] det_loss_cls_dn_0[0.1096] det_loss_box_dn_0[0.3429] det_loss_cls_dn_1[0.1061] det_loss_box_dn_1[0.2844] det_loss_cls_dn_2[0.1044] det_loss_box_dn_2[0.2594] det_loss_cls_dn_3[0.1036] det_loss_box_dn_3[0.2559] det_loss_cls_dn_4[0.1027] det_loss_box_dn_4[0.2546] det_loss_cls_dn_5[0.1021] det_loss_box_dn_5[0.2522] loss_depth[0.0412] total_loss[6.8415]
Rank[0/8] 06/20/2025 01:27:47 INFO stats.py:314 | Epoch[85] Step[199] GlobalStep[33349] Training Speed: 6.94 samples/sec across all devices.	Average Step Time: 1.15 sec.	Estimated Remaining Time: 20:20:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:27:47 INFO loss_tracker.py:84 | Epoch[85/239] Step[199] GlobalStep[33349/NA]: det_loss_cls_0[0.4957] det_loss_box_0[0.6588] det_loss_cls_1[0.4678] det_loss_box_1[0.5102] det_loss_cls_2[0.4396] det_loss_box_2[0.4495] det_loss_cls_3[0.4102] det_loss_box_3[0.4255] det_loss_cls_4[0.3821] det_loss_box_4[0.4130] det_loss_cls_5[0.3700] det_loss_box_5[0.4176] det_loss_cls_dn_0[0.0912] det_loss_box_dn_0[0.3108] det_loss_cls_dn_1[0.0870] det_loss_box_dn_1[0.2711] det_loss_cls_dn_2[0.0862] det_loss_box_dn_2[0.2513] det_loss_cls_dn_3[0.0856] det_loss_box_dn_3[0.2473] det_loss_cls_dn_4[0.0843] det_loss_box_dn_4[0.2464] det_loss_cls_dn_5[0.0834] det_loss_box_dn_5[0.2439] loss_depth[0.0410] total_loss[7.5697]
Rank[0/8] 06/20/2025 01:28:13 INFO stats.py:314 | Epoch[85] Step[224] GlobalStep[33374] Training Speed: 7.75 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 20:19:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:28:13 INFO loss_tracker.py:84 | Epoch[85/239] Step[224] GlobalStep[33374/NA]: det_loss_cls_0[0.4337] det_loss_box_0[0.4982] det_loss_cls_1[0.4226] det_loss_box_1[0.3800] det_loss_cls_2[0.3860] det_loss_box_2[0.3419] det_loss_cls_3[0.3627] det_loss_box_3[0.3378] det_loss_cls_4[0.3390] det_loss_box_4[0.3365] det_loss_cls_5[0.3210] det_loss_box_5[0.3357] det_loss_cls_dn_0[0.0933] det_loss_box_dn_0[0.2464] det_loss_cls_dn_1[0.0907] det_loss_box_dn_1[0.2020] det_loss_cls_dn_2[0.0895] det_loss_box_dn_2[0.1839] det_loss_cls_dn_3[0.0881] det_loss_box_dn_3[0.1810] det_loss_cls_dn_4[0.0878] det_loss_box_dn_4[0.1799] det_loss_cls_dn_5[0.0864] det_loss_box_dn_5[0.1789] loss_depth[0.0411] total_loss[6.2439]
Rank[0/8] 06/20/2025 01:28:39 INFO stats.py:314 | Epoch[85] Step[249] GlobalStep[33399] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 20:19:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:28:39 INFO loss_tracker.py:84 | Epoch[85/239] Step[249] GlobalStep[33399/NA]: det_loss_cls_0[0.4463] det_loss_box_0[0.5558] det_loss_cls_1[0.4393] det_loss_box_1[0.4166] det_loss_cls_2[0.3999] det_loss_box_2[0.3820] det_loss_cls_3[0.3607] det_loss_box_3[0.3748] det_loss_cls_4[0.3273] det_loss_box_4[0.3668] det_loss_cls_5[0.3114] det_loss_box_5[0.3746] det_loss_cls_dn_0[0.0897] det_loss_box_dn_0[0.3141] det_loss_cls_dn_1[0.0868] det_loss_box_dn_1[0.2628] det_loss_cls_dn_2[0.0863] det_loss_box_dn_2[0.2391] det_loss_cls_dn_3[0.0851] det_loss_box_dn_3[0.2354] det_loss_cls_dn_4[0.0846] det_loss_box_dn_4[0.2329] det_loss_cls_dn_5[0.0836] det_loss_box_dn_5[0.2326] loss_depth[0.0396] total_loss[6.8282]
Rank[0/8] 06/20/2025 01:29:05 INFO stats.py:314 | Epoch[85] Step[274] GlobalStep[33424] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 20:18:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:29:05 INFO loss_tracker.py:84 | Epoch[85/239] Step[274] GlobalStep[33424/NA]: det_loss_cls_0[0.5640] det_loss_box_0[0.7015] det_loss_cls_1[0.5594] det_loss_box_1[0.5708] det_loss_cls_2[0.5266] det_loss_box_2[0.5382] det_loss_cls_3[0.4979] det_loss_box_3[0.5142] det_loss_cls_4[0.4694] det_loss_box_4[0.5165] det_loss_cls_5[0.4609] det_loss_box_5[0.5098] det_loss_cls_dn_0[0.1149] det_loss_box_dn_0[0.2951] det_loss_cls_dn_1[0.1121] det_loss_box_dn_1[0.2507] det_loss_cls_dn_2[0.1122] det_loss_box_dn_2[0.2336] det_loss_cls_dn_3[0.1111] det_loss_box_dn_3[0.2298] det_loss_cls_dn_4[0.1109] det_loss_box_dn_4[0.2284] det_loss_cls_dn_5[0.1103] det_loss_box_dn_5[0.2277] loss_depth[0.0360] total_loss[8.6019]
Rank[0/8] 06/20/2025 01:29:31 INFO stats.py:314 | Epoch[85] Step[299] GlobalStep[33449] Training Speed: 8.69 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 20:17:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:29:31 INFO loss_tracker.py:84 | Epoch[85/239] Step[299] GlobalStep[33449/NA]: det_loss_cls_0[0.4036] det_loss_box_0[0.5268] det_loss_cls_1[0.3856] det_loss_box_1[0.4066] det_loss_cls_2[0.3590] det_loss_box_2[0.3746] det_loss_cls_3[0.3441] det_loss_box_3[0.3608] det_loss_cls_4[0.3011] det_loss_box_4[0.3682] det_loss_cls_5[0.2979] det_loss_box_5[0.3647] det_loss_cls_dn_0[0.0973] det_loss_box_dn_0[0.3301] det_loss_cls_dn_1[0.0934] det_loss_box_dn_1[0.2929] det_loss_cls_dn_2[0.0927] det_loss_box_dn_2[0.2791] det_loss_cls_dn_3[0.0913] det_loss_box_dn_3[0.2762] det_loss_cls_dn_4[0.0901] det_loss_box_dn_4[0.2747] det_loss_cls_dn_5[0.0897] det_loss_box_dn_5[0.2741] loss_depth[0.0371] total_loss[6.8119]
Rank[0/8] 06/20/2025 01:29:57 INFO stats.py:314 | Epoch[85] Step[324] GlobalStep[33474] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 20:17:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:29:57 INFO loss_tracker.py:84 | Epoch[85/239] Step[324] GlobalStep[33474/NA]: det_loss_cls_0[0.5118] det_loss_box_0[0.6359] det_loss_cls_1[0.5018] det_loss_box_1[0.5038] det_loss_cls_2[0.4653] det_loss_box_2[0.4595] det_loss_cls_3[0.4445] det_loss_box_3[0.4412] det_loss_cls_4[0.4091] det_loss_box_4[0.4309] det_loss_cls_5[0.3970] det_loss_box_5[0.4275] det_loss_cls_dn_0[0.0992] det_loss_box_dn_0[0.2801] det_loss_cls_dn_1[0.0952] det_loss_box_dn_1[0.2365] det_loss_cls_dn_2[0.0946] det_loss_box_dn_2[0.2208] det_loss_cls_dn_3[0.0940] det_loss_box_dn_3[0.2180] det_loss_cls_dn_4[0.0929] det_loss_box_dn_4[0.2158] det_loss_cls_dn_5[0.0921] det_loss_box_dn_5[0.2153] loss_depth[0.0389] total_loss[7.6221]
Rank[0/8] 06/20/2025 01:30:23 INFO stats.py:314 | Epoch[85] Step[349] GlobalStep[33499] Training Speed: 8.08 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 20:16:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:30:23 INFO loss_tracker.py:84 | Epoch[85/239] Step[349] GlobalStep[33499/NA]: det_loss_cls_0[0.5764] det_loss_box_0[0.8727] det_loss_cls_1[0.5709] det_loss_box_1[0.7073] det_loss_cls_2[0.5360] det_loss_box_2[0.6406] det_loss_cls_3[0.5012] det_loss_box_3[0.6121] det_loss_cls_4[0.4666] det_loss_box_4[0.5966] det_loss_cls_5[0.4548] det_loss_box_5[0.5908] det_loss_cls_dn_0[0.0991] det_loss_box_dn_0[0.4313] det_loss_cls_dn_1[0.0973] det_loss_box_dn_1[0.3753] det_loss_cls_dn_2[0.0985] det_loss_box_dn_2[0.3608] det_loss_cls_dn_3[0.0955] det_loss_box_dn_3[0.3575] det_loss_cls_dn_4[0.0960] det_loss_box_dn_4[0.3567] det_loss_cls_dn_5[0.0956] det_loss_box_dn_5[0.3548] loss_depth[0.0379] total_loss[9.9822]
Rank[0/8] 06/20/2025 01:30:49 INFO stats.py:314 | Epoch[85] Step[374] GlobalStep[33524] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 20:15:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:30:49 INFO loss_tracker.py:84 | Epoch[85/239] Step[374] GlobalStep[33524/NA]: det_loss_cls_0[0.5277] det_loss_box_0[0.6872] det_loss_cls_1[0.5271] det_loss_box_1[0.5271] det_loss_cls_2[0.4931] det_loss_box_2[0.4764] det_loss_cls_3[0.4653] det_loss_box_3[0.4545] det_loss_cls_4[0.4338] det_loss_box_4[0.4501] det_loss_cls_5[0.4215] det_loss_box_5[0.4498] det_loss_cls_dn_0[0.0950] det_loss_box_dn_0[0.3114] det_loss_cls_dn_1[0.0921] det_loss_box_dn_1[0.2703] det_loss_cls_dn_2[0.0916] det_loss_box_dn_2[0.2580] det_loss_cls_dn_3[0.0906] det_loss_box_dn_3[0.2554] det_loss_cls_dn_4[0.0898] det_loss_box_dn_4[0.2549] det_loss_cls_dn_5[0.0895] det_loss_box_dn_5[0.2513] loss_depth[0.0411] total_loss[8.1047]
Rank[0/8] 06/20/2025 01:31:04 INFO stats.py:394 | Epoch[85] completed. Training Speed: 7.71 samples/sec across all devices.	Epoch Time: 404.77 sec.	Average Epoch Time: 404.77 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 20:15:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:31:17 INFO stats.py:314 | Epoch[86] Step[9] GlobalStep[33549] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 20:15:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:31:32 INFO loss_tracker.py:84 | Epoch[86/239] Step[24] GlobalStep[33564/NA]: det_loss_cls_0[0.5423] det_loss_box_0[0.7107] det_loss_cls_1[0.5307] det_loss_box_1[0.5489] det_loss_cls_2[0.5095] det_loss_box_2[0.4884] det_loss_cls_3[0.4872] det_loss_box_3[0.4694] det_loss_cls_4[0.4610] det_loss_box_4[0.4666] det_loss_cls_5[0.4531] det_loss_box_5[0.4684] det_loss_cls_dn_0[0.0985] det_loss_box_dn_0[0.3322] det_loss_cls_dn_1[0.0952] det_loss_box_dn_1[0.2714] det_loss_cls_dn_2[0.0941] det_loss_box_dn_2[0.2485] det_loss_cls_dn_3[0.0933] det_loss_box_dn_3[0.2452] det_loss_cls_dn_4[0.0921] det_loss_box_dn_4[0.2441] det_loss_cls_dn_5[0.0915] det_loss_box_dn_5[0.2422] loss_depth[0.0393] total_loss[8.3234]
Rank[0/8] 06/20/2025 01:31:42 INFO stats.py:314 | Epoch[86] Step[34] GlobalStep[33574] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 20:14:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:31:58 INFO loss_tracker.py:84 | Epoch[86/239] Step[49] GlobalStep[33589/NA]: det_loss_cls_0[0.5061] det_loss_box_0[0.6782] det_loss_cls_1[0.5097] det_loss_box_1[0.5254] det_loss_cls_2[0.4717] det_loss_box_2[0.4826] det_loss_cls_3[0.4471] det_loss_box_3[0.4686] det_loss_cls_4[0.4160] det_loss_box_4[0.4584] det_loss_cls_5[0.3978] det_loss_box_5[0.4579] det_loss_cls_dn_0[0.0999] det_loss_box_dn_0[0.3342] det_loss_cls_dn_1[0.0959] det_loss_box_dn_1[0.2860] det_loss_cls_dn_2[0.0959] det_loss_box_dn_2[0.2600] det_loss_cls_dn_3[0.0943] det_loss_box_dn_3[0.2544] det_loss_cls_dn_4[0.0940] det_loss_box_dn_4[0.2520] det_loss_cls_dn_5[0.0924] det_loss_box_dn_5[0.2496] loss_depth[0.0375] total_loss[8.0659]
Rank[0/8] 06/20/2025 01:32:09 INFO stats.py:314 | Epoch[86] Step[59] GlobalStep[33599] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 20:14:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:32:24 INFO loss_tracker.py:84 | Epoch[86/239] Step[74] GlobalStep[33614/NA]: det_loss_cls_0[0.6419] det_loss_box_0[0.8997] det_loss_cls_1[0.6185] det_loss_box_1[0.7204] det_loss_cls_2[0.5970] det_loss_box_2[0.6658] det_loss_cls_3[0.5723] det_loss_box_3[0.6490] det_loss_cls_4[0.5509] det_loss_box_4[0.6310] det_loss_cls_5[0.5377] det_loss_box_5[0.6294] det_loss_cls_dn_0[0.1121] det_loss_box_dn_0[0.3104] det_loss_cls_dn_1[0.1089] det_loss_box_dn_1[0.2652] det_loss_cls_dn_2[0.1079] det_loss_box_dn_2[0.2476] det_loss_cls_dn_3[0.1069] det_loss_box_dn_3[0.2442] det_loss_cls_dn_4[0.1068] det_loss_box_dn_4[0.2432] det_loss_cls_dn_5[0.1054] det_loss_box_dn_5[0.2420] loss_depth[0.0414] total_loss[9.9556]
Rank[0/8] 06/20/2025 01:32:34 INFO stats.py:314 | Epoch[86] Step[84] GlobalStep[33624] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 20:13:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:32:50 INFO loss_tracker.py:84 | Epoch[86/239] Step[99] GlobalStep[33639/NA]: det_loss_cls_0[0.4085] det_loss_box_0[0.5329] det_loss_cls_1[0.3947] det_loss_box_1[0.4147] det_loss_cls_2[0.3770] det_loss_box_2[0.3850] det_loss_cls_3[0.3579] det_loss_box_3[0.3615] det_loss_cls_4[0.3266] det_loss_box_4[0.3528] det_loss_cls_5[0.3195] det_loss_box_5[0.3519] det_loss_cls_dn_0[0.0912] det_loss_box_dn_0[0.3179] det_loss_cls_dn_1[0.0886] det_loss_box_dn_1[0.2520] det_loss_cls_dn_2[0.0880] det_loss_box_dn_2[0.2280] det_loss_cls_dn_3[0.0864] det_loss_box_dn_3[0.2257] det_loss_cls_dn_4[0.0857] det_loss_box_dn_4[0.2193] det_loss_cls_dn_5[0.0849] det_loss_box_dn_5[0.2173] loss_depth[0.0395] total_loss[6.6077]
Rank[0/8] 06/20/2025 01:33:00 INFO stats.py:314 | Epoch[86] Step[109] GlobalStep[33649] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 20:12:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:33:16 INFO loss_tracker.py:84 | Epoch[86/239] Step[124] GlobalStep[33664/NA]: det_loss_cls_0[0.4365] det_loss_box_0[0.5935] det_loss_cls_1[0.4220] det_loss_box_1[0.4442] det_loss_cls_2[0.3808] det_loss_box_2[0.4081] det_loss_cls_3[0.3638] det_loss_box_3[0.3867] det_loss_cls_4[0.3347] det_loss_box_4[0.3887] det_loss_cls_5[0.3195] det_loss_box_5[0.3874] det_loss_cls_dn_0[0.0987] det_loss_box_dn_0[0.3421] det_loss_cls_dn_1[0.0944] det_loss_box_dn_1[0.2896] det_loss_cls_dn_2[0.0935] det_loss_box_dn_2[0.2692] det_loss_cls_dn_3[0.0916] det_loss_box_dn_3[0.2652] det_loss_cls_dn_4[0.0906] det_loss_box_dn_4[0.2645] det_loss_cls_dn_5[0.0893] det_loss_box_dn_5[0.2625] loss_depth[0.0423] total_loss[7.1596]
Rank[0/8] 06/20/2025 01:33:26 INFO stats.py:314 | Epoch[86] Step[134] GlobalStep[33674] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 20:12:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:33:42 INFO loss_tracker.py:84 | Epoch[86/239] Step[149] GlobalStep[33689/NA]: det_loss_cls_0[0.5045] det_loss_box_0[0.6937] det_loss_cls_1[0.4717] det_loss_box_1[0.5440] det_loss_cls_2[0.4475] det_loss_box_2[0.4876] det_loss_cls_3[0.4263] det_loss_box_3[0.4610] det_loss_cls_4[0.4013] det_loss_box_4[0.4453] det_loss_cls_5[0.3939] det_loss_box_5[0.4474] det_loss_cls_dn_0[0.1120] det_loss_box_dn_0[0.3250] det_loss_cls_dn_1[0.1070] det_loss_box_dn_1[0.2790] det_loss_cls_dn_2[0.1057] det_loss_box_dn_2[0.2627] det_loss_cls_dn_3[0.1047] det_loss_box_dn_3[0.2601] det_loss_cls_dn_4[0.1041] det_loss_box_dn_4[0.2587] det_loss_cls_dn_5[0.1030] det_loss_box_dn_5[0.2566] loss_depth[0.0420] total_loss[8.0447]
Rank[0/8] 06/20/2025 01:33:52 INFO stats.py:314 | Epoch[86] Step[159] GlobalStep[33699] Training Speed: 7.90 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 20:11:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:34:08 INFO loss_tracker.py:84 | Epoch[86/239] Step[174] GlobalStep[33714/NA]: det_loss_cls_0[0.4931] det_loss_box_0[0.6716] det_loss_cls_1[0.4814] det_loss_box_1[0.5237] det_loss_cls_2[0.4434] det_loss_box_2[0.4746] det_loss_cls_3[0.4155] det_loss_box_3[0.4628] det_loss_cls_4[0.3914] det_loss_box_4[0.4468] det_loss_cls_5[0.3743] det_loss_box_5[0.4487] det_loss_cls_dn_0[0.0956] det_loss_box_dn_0[0.3135] det_loss_cls_dn_1[0.0905] det_loss_box_dn_1[0.2724] det_loss_cls_dn_2[0.0901] det_loss_box_dn_2[0.2590] det_loss_cls_dn_3[0.0889] det_loss_box_dn_3[0.2557] det_loss_cls_dn_4[0.0879] det_loss_box_dn_4[0.2547] det_loss_cls_dn_5[0.0867] det_loss_box_dn_5[0.2532] loss_depth[0.0404] total_loss[7.8159]
Rank[0/8] 06/20/2025 01:34:18 INFO stats.py:314 | Epoch[86] Step[184] GlobalStep[33724] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 20:10:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:34:33 INFO loss_tracker.py:84 | Epoch[86/239] Step[199] GlobalStep[33739/NA]: det_loss_cls_0[0.5756] det_loss_box_0[0.7209] det_loss_cls_1[0.5697] det_loss_box_1[0.6004] det_loss_cls_2[0.5383] det_loss_box_2[0.5533] det_loss_cls_3[0.5134] det_loss_box_3[0.5381] det_loss_cls_4[0.4916] det_loss_box_4[0.5312] det_loss_cls_5[0.4758] det_loss_box_5[0.5322] det_loss_cls_dn_0[0.1016] det_loss_box_dn_0[0.3117] det_loss_cls_dn_1[0.0972] det_loss_box_dn_1[0.2627] det_loss_cls_dn_2[0.0973] det_loss_box_dn_2[0.2458] det_loss_cls_dn_3[0.0963] det_loss_box_dn_3[0.2431] det_loss_cls_dn_4[0.0953] det_loss_box_dn_4[0.2422] det_loss_cls_dn_5[0.0946] det_loss_box_dn_5[0.2399] loss_depth[0.0384] total_loss[8.8069]
Rank[0/8] 06/20/2025 01:34:44 INFO stats.py:314 | Epoch[86] Step[209] GlobalStep[33749] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 20:10:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:35:00 INFO loss_tracker.py:84 | Epoch[86/239] Step[224] GlobalStep[33764/NA]: det_loss_cls_0[0.5216] det_loss_box_0[0.6066] det_loss_cls_1[0.5106] det_loss_box_1[0.4722] det_loss_cls_2[0.4759] det_loss_box_2[0.4365] det_loss_cls_3[0.4644] det_loss_box_3[0.4199] det_loss_cls_4[0.4265] det_loss_box_4[0.4230] det_loss_cls_5[0.4152] det_loss_box_5[0.4223] det_loss_cls_dn_0[0.1032] det_loss_box_dn_0[0.2944] det_loss_cls_dn_1[0.0992] det_loss_box_dn_1[0.2462] det_loss_cls_dn_2[0.0983] det_loss_box_dn_2[0.2358] det_loss_cls_dn_3[0.0970] det_loss_box_dn_3[0.2306] det_loss_cls_dn_4[0.0960] det_loss_box_dn_4[0.2286] det_loss_cls_dn_5[0.0950] det_loss_box_dn_5[0.2270] loss_depth[0.0395] total_loss[7.6856]
Rank[0/8] 06/20/2025 01:35:10 INFO stats.py:314 | Epoch[86] Step[234] GlobalStep[33774] Training Speed: 8.05 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 20:09:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:35:26 INFO loss_tracker.py:84 | Epoch[86/239] Step[249] GlobalStep[33789/NA]: det_loss_cls_0[0.5414] det_loss_box_0[0.7830] det_loss_cls_1[0.5395] det_loss_box_1[0.5889] det_loss_cls_2[0.4978] det_loss_box_2[0.5298] det_loss_cls_3[0.4768] det_loss_box_3[0.5081] det_loss_cls_4[0.4468] det_loss_box_4[0.5010] det_loss_cls_5[0.4280] det_loss_box_5[0.5036] det_loss_cls_dn_0[0.1035] det_loss_box_dn_0[0.3101] det_loss_cls_dn_1[0.1004] det_loss_box_dn_1[0.2644] det_loss_cls_dn_2[0.1002] det_loss_box_dn_2[0.2497] det_loss_cls_dn_3[0.0989] det_loss_box_dn_3[0.2462] det_loss_cls_dn_4[0.0978] det_loss_box_dn_4[0.2456] det_loss_cls_dn_5[0.0974] det_loss_box_dn_5[0.2434] loss_depth[0.0405] total_loss[8.5427]
Rank[0/8] 06/20/2025 01:35:36 INFO stats.py:314 | Epoch[86] Step[259] GlobalStep[33799] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 20:09:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:35:52 INFO loss_tracker.py:84 | Epoch[86/239] Step[274] GlobalStep[33814/NA]: det_loss_cls_0[0.4837] det_loss_box_0[0.6704] det_loss_cls_1[0.4611] det_loss_box_1[0.5477] det_loss_cls_2[0.4239] det_loss_box_2[0.5122] det_loss_cls_3[0.4123] det_loss_box_3[0.4960] det_loss_cls_4[0.3900] det_loss_box_4[0.4853] det_loss_cls_5[0.3681] det_loss_box_5[0.4855] det_loss_cls_dn_0[0.0928] det_loss_box_dn_0[0.3054] det_loss_cls_dn_1[0.0879] det_loss_box_dn_1[0.2597] det_loss_cls_dn_2[0.0879] det_loss_box_dn_2[0.2432] det_loss_cls_dn_3[0.0872] det_loss_box_dn_3[0.2385] det_loss_cls_dn_4[0.0864] det_loss_box_dn_4[0.2381] det_loss_cls_dn_5[0.0860] det_loss_box_dn_5[0.2362] loss_depth[0.0368] total_loss[7.8224]
Rank[0/8] 06/20/2025 01:36:02 INFO stats.py:314 | Epoch[86] Step[284] GlobalStep[33824] Training Speed: 7.62 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 20:08:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:36:18 INFO loss_tracker.py:84 | Epoch[86/239] Step[299] GlobalStep[33839/NA]: det_loss_cls_0[0.5977] det_loss_box_0[0.7623] det_loss_cls_1[0.5820] det_loss_box_1[0.5986] det_loss_cls_2[0.5482] det_loss_box_2[0.5373] det_loss_cls_3[0.5104] det_loss_box_3[0.5196] det_loss_cls_4[0.4886] det_loss_box_4[0.5016] det_loss_cls_5[0.4716] det_loss_box_5[0.5050] det_loss_cls_dn_0[0.1096] det_loss_box_dn_0[0.2831] det_loss_cls_dn_1[0.1060] det_loss_box_dn_1[0.2473] det_loss_cls_dn_2[0.1051] det_loss_box_dn_2[0.2287] det_loss_cls_dn_3[0.1043] det_loss_box_dn_3[0.2266] det_loss_cls_dn_4[0.1034] det_loss_box_dn_4[0.2251] det_loss_cls_dn_5[0.1023] det_loss_box_dn_5[0.2235] loss_depth[0.0380] total_loss[8.7257]
Rank[0/8] 06/20/2025 01:36:28 INFO stats.py:314 | Epoch[86] Step[309] GlobalStep[33849] Training Speed: 8.06 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 20:07:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:36:43 INFO loss_tracker.py:84 | Epoch[86/239] Step[324] GlobalStep[33864/NA]: det_loss_cls_0[0.6215] det_loss_box_0[0.9571] det_loss_cls_1[0.5994] det_loss_box_1[0.7582] det_loss_cls_2[0.5773] det_loss_box_2[0.6615] det_loss_cls_3[0.5448] det_loss_box_3[0.6259] det_loss_cls_4[0.5230] det_loss_box_4[0.6164] det_loss_cls_5[0.5041] det_loss_box_5[0.6175] det_loss_cls_dn_0[0.0941] det_loss_box_dn_0[0.4178] det_loss_cls_dn_1[0.0915] det_loss_box_dn_1[0.3693] det_loss_cls_dn_2[0.0917] det_loss_box_dn_2[0.3506] det_loss_cls_dn_3[0.0906] det_loss_box_dn_3[0.3477] det_loss_cls_dn_4[0.0904] det_loss_box_dn_4[0.3443] det_loss_cls_dn_5[0.0891] det_loss_box_dn_5[0.3452] loss_depth[0.0395] total_loss[10.3682]
Rank[0/8] 06/20/2025 01:36:54 INFO stats.py:314 | Epoch[86] Step[334] GlobalStep[33874] Training Speed: 7.98 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 20:07:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:37:09 INFO loss_tracker.py:84 | Epoch[86/239] Step[349] GlobalStep[33889/NA]: det_loss_cls_0[0.5823] det_loss_box_0[0.7931] det_loss_cls_1[0.5685] det_loss_box_1[0.6280] det_loss_cls_2[0.5358] det_loss_box_2[0.5558] det_loss_cls_3[0.5025] det_loss_box_3[0.5282] det_loss_cls_4[0.4726] det_loss_box_4[0.5204] det_loss_cls_5[0.4541] det_loss_box_5[0.5282] det_loss_cls_dn_0[0.1036] det_loss_box_dn_0[0.3201] det_loss_cls_dn_1[0.0996] det_loss_box_dn_1[0.2705] det_loss_cls_dn_2[0.0995] det_loss_box_dn_2[0.2538] det_loss_cls_dn_3[0.0989] det_loss_box_dn_3[0.2494] det_loss_cls_dn_4[0.0980] det_loss_box_dn_4[0.2483] det_loss_cls_dn_5[0.0970] det_loss_box_dn_5[0.2477] loss_depth[0.0395] total_loss[8.8955]
Rank[0/8] 06/20/2025 01:37:19 INFO stats.py:314 | Epoch[86] Step[359] GlobalStep[33899] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 20:06:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:37:35 INFO loss_tracker.py:84 | Epoch[86/239] Step[374] GlobalStep[33914/NA]: det_loss_cls_0[0.5677] det_loss_box_0[0.7059] det_loss_cls_1[0.5490] det_loss_box_1[0.5446] det_loss_cls_2[0.5259] det_loss_box_2[0.4806] det_loss_cls_3[0.4976] det_loss_box_3[0.4596] det_loss_cls_4[0.4686] det_loss_box_4[0.4509] det_loss_cls_5[0.4603] det_loss_box_5[0.4471] det_loss_cls_dn_0[0.1069] det_loss_box_dn_0[0.2893] det_loss_cls_dn_1[0.1036] det_loss_box_dn_1[0.2512] det_loss_cls_dn_2[0.1033] det_loss_box_dn_2[0.2357] det_loss_cls_dn_3[0.1012] det_loss_box_dn_3[0.2323] det_loss_cls_dn_4[0.1004] det_loss_box_dn_4[0.2303] det_loss_cls_dn_5[0.0992] det_loss_box_dn_5[0.2261] loss_depth[0.0372] total_loss[8.2745]
Rank[0/8] 06/20/2025 01:37:45 INFO stats.py:314 | Epoch[86] Step[384] GlobalStep[33924] Training Speed: 7.92 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 20:05:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:37:50 INFO stats.py:394 | Epoch[86] completed. Training Speed: 7.68 samples/sec across all devices.	Epoch Time: 406.44 sec.	Average Epoch Time: 406.44 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 20:05:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:38:13 INFO stats.py:314 | Epoch[87] Step[19] GlobalStep[33949] Training Speed: 8.66 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 20:05:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:38:19 INFO loss_tracker.py:84 | Epoch[87/239] Step[24] GlobalStep[33954/NA]: det_loss_cls_0[0.5159] det_loss_box_0[0.6752] det_loss_cls_1[0.5155] det_loss_box_1[0.5266] det_loss_cls_2[0.4779] det_loss_box_2[0.4756] det_loss_cls_3[0.4523] det_loss_box_3[0.4645] det_loss_cls_4[0.4253] det_loss_box_4[0.4512] det_loss_cls_5[0.4125] det_loss_box_5[0.4506] det_loss_cls_dn_0[0.0986] det_loss_box_dn_0[0.3165] det_loss_cls_dn_1[0.0942] det_loss_box_dn_1[0.2707] det_loss_cls_dn_2[0.0944] det_loss_box_dn_2[0.2532] det_loss_cls_dn_3[0.0931] det_loss_box_dn_3[0.2491] det_loss_cls_dn_4[0.0919] det_loss_box_dn_4[0.2474] det_loss_cls_dn_5[0.0916] det_loss_box_dn_5[0.2447] loss_depth[0.0386] total_loss[8.0272]
Rank[0/8] 06/20/2025 01:38:39 INFO stats.py:314 | Epoch[87] Step[44] GlobalStep[33974] Training Speed: 8.05 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 20:04:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:38:44 INFO loss_tracker.py:84 | Epoch[87/239] Step[49] GlobalStep[33979/NA]: det_loss_cls_0[0.5419] det_loss_box_0[0.7550] det_loss_cls_1[0.5188] det_loss_box_1[0.5893] det_loss_cls_2[0.4883] det_loss_box_2[0.5279] det_loss_cls_3[0.4592] det_loss_box_3[0.5084] det_loss_cls_4[0.4267] det_loss_box_4[0.5041] det_loss_cls_5[0.4108] det_loss_box_5[0.5002] det_loss_cls_dn_0[0.1091] det_loss_box_dn_0[0.3407] det_loss_cls_dn_1[0.1056] det_loss_box_dn_1[0.2920] det_loss_cls_dn_2[0.1054] det_loss_box_dn_2[0.2718] det_loss_cls_dn_3[0.1040] det_loss_box_dn_3[0.2675] det_loss_cls_dn_4[0.1036] det_loss_box_dn_4[0.2654] det_loss_cls_dn_5[0.1027] det_loss_box_dn_5[0.2632] loss_depth[0.0398] total_loss[8.6013]
Rank[0/8] 06/20/2025 01:39:05 INFO stats.py:314 | Epoch[87] Step[69] GlobalStep[33999] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 20:03:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:39:11 INFO loss_tracker.py:84 | Epoch[87/239] Step[74] GlobalStep[34004/NA]: det_loss_cls_0[0.4754] det_loss_box_0[0.6423] det_loss_cls_1[0.4674] det_loss_box_1[0.4866] det_loss_cls_2[0.4407] det_loss_box_2[0.4336] det_loss_cls_3[0.4070] det_loss_box_3[0.4199] det_loss_cls_4[0.3877] det_loss_box_4[0.4139] det_loss_cls_5[0.3749] det_loss_box_5[0.4142] det_loss_cls_dn_0[0.1086] det_loss_box_dn_0[0.2666] det_loss_cls_dn_1[0.1067] det_loss_box_dn_1[0.2286] det_loss_cls_dn_2[0.1054] det_loss_box_dn_2[0.2142] det_loss_cls_dn_3[0.1051] det_loss_box_dn_3[0.2115] det_loss_cls_dn_4[0.1036] det_loss_box_dn_4[0.2105] det_loss_cls_dn_5[0.1027] det_loss_box_dn_5[0.2092] loss_depth[0.0370] total_loss[7.3732]
Rank[0/8] 06/20/2025 01:39:31 INFO stats.py:314 | Epoch[87] Step[94] GlobalStep[34024] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 20:03:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:39:36 INFO loss_tracker.py:84 | Epoch[87/239] Step[99] GlobalStep[34029/NA]: det_loss_cls_0[0.4311] det_loss_box_0[0.5561] det_loss_cls_1[0.4111] det_loss_box_1[0.4554] det_loss_cls_2[0.3968] det_loss_box_2[0.4004] det_loss_cls_3[0.3647] det_loss_box_3[0.3875] det_loss_cls_4[0.3348] det_loss_box_4[0.3806] det_loss_cls_5[0.3195] det_loss_box_5[0.3835] det_loss_cls_dn_0[0.0861] det_loss_box_dn_0[0.3064] det_loss_cls_dn_1[0.0826] det_loss_box_dn_1[0.2538] det_loss_cls_dn_2[0.0823] det_loss_box_dn_2[0.2387] det_loss_cls_dn_3[0.0809] det_loss_box_dn_3[0.2339] det_loss_cls_dn_4[0.0790] det_loss_box_dn_4[0.2318] det_loss_cls_dn_5[0.0783] det_loss_box_dn_5[0.2311] loss_depth[0.0344] total_loss[6.8408]
Rank[0/8] 06/20/2025 01:39:57 INFO stats.py:314 | Epoch[87] Step[119] GlobalStep[34049] Training Speed: 7.78 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 20:02:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:40:02 INFO loss_tracker.py:84 | Epoch[87/239] Step[124] GlobalStep[34054/NA]: det_loss_cls_0[0.4470] det_loss_box_0[0.5617] det_loss_cls_1[0.4440] det_loss_box_1[0.4454] det_loss_cls_2[0.4040] det_loss_box_2[0.4101] det_loss_cls_3[0.3732] det_loss_box_3[0.3958] det_loss_cls_4[0.3538] det_loss_box_4[0.3872] det_loss_cls_5[0.3410] det_loss_box_5[0.3870] det_loss_cls_dn_0[0.1030] det_loss_box_dn_0[0.3075] det_loss_cls_dn_1[0.0990] det_loss_box_dn_1[0.2602] det_loss_cls_dn_2[0.0979] det_loss_box_dn_2[0.2441] det_loss_cls_dn_3[0.0971] det_loss_box_dn_3[0.2410] det_loss_cls_dn_4[0.0966] det_loss_box_dn_4[0.2388] det_loss_cls_dn_5[0.0957] det_loss_box_dn_5[0.2382] loss_depth[0.0352] total_loss[7.1045]
Rank[0/8] 06/20/2025 01:40:23 INFO stats.py:314 | Epoch[87] Step[144] GlobalStep[34074] Training Speed: 8.59 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 20:02:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:40:28 INFO loss_tracker.py:84 | Epoch[87/239] Step[149] GlobalStep[34079/NA]: det_loss_cls_0[0.5726] det_loss_box_0[0.7407] det_loss_cls_1[0.5391] det_loss_box_1[0.5664] det_loss_cls_2[0.4972] det_loss_box_2[0.5182] det_loss_cls_3[0.4758] det_loss_box_3[0.5033] det_loss_cls_4[0.4517] det_loss_box_4[0.4849] det_loss_cls_5[0.4357] det_loss_box_5[0.4844] det_loss_cls_dn_0[0.0971] det_loss_box_dn_0[0.2694] det_loss_cls_dn_1[0.0939] det_loss_box_dn_1[0.2337] det_loss_cls_dn_2[0.0929] det_loss_box_dn_2[0.2172] det_loss_cls_dn_3[0.0911] det_loss_box_dn_3[0.2131] det_loss_cls_dn_4[0.0899] det_loss_box_dn_4[0.2114] det_loss_cls_dn_5[0.0888] det_loss_box_dn_5[0.2086] loss_depth[0.0389] total_loss[8.2162]
Rank[0/8] 06/20/2025 01:40:49 INFO stats.py:314 | Epoch[87] Step[169] GlobalStep[34099] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 20:01:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:40:54 INFO loss_tracker.py:84 | Epoch[87/239] Step[174] GlobalStep[34104/NA]: det_loss_cls_0[0.5383] det_loss_box_0[0.7342] det_loss_cls_1[0.5228] det_loss_box_1[0.5798] det_loss_cls_2[0.5036] det_loss_box_2[0.5121] det_loss_cls_3[0.4727] det_loss_box_3[0.5083] det_loss_cls_4[0.4400] det_loss_box_4[0.4942] det_loss_cls_5[0.4218] det_loss_box_5[0.4919] det_loss_cls_dn_0[0.0986] det_loss_box_dn_0[0.2698] det_loss_cls_dn_1[0.0947] det_loss_box_dn_1[0.2295] det_loss_cls_dn_2[0.0942] det_loss_box_dn_2[0.2155] det_loss_cls_dn_3[0.0934] det_loss_box_dn_3[0.2123] det_loss_cls_dn_4[0.0929] det_loss_box_dn_4[0.2105] det_loss_cls_dn_5[0.0923] det_loss_box_dn_5[0.2093] loss_depth[0.0414] total_loss[8.1742]
Rank[0/8] 06/20/2025 01:41:15 INFO stats.py:314 | Epoch[87] Step[194] GlobalStep[34124] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 20:00:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:41:20 INFO loss_tracker.py:84 | Epoch[87/239] Step[199] GlobalStep[34129/NA]: det_loss_cls_0[0.6170] det_loss_box_0[0.9391] det_loss_cls_1[0.6286] det_loss_box_1[0.7386] det_loss_cls_2[0.5858] det_loss_box_2[0.6595] det_loss_cls_3[0.5576] det_loss_box_3[0.6322] det_loss_cls_4[0.5330] det_loss_box_4[0.6146] det_loss_cls_5[0.5137] det_loss_box_5[0.6184] det_loss_cls_dn_0[0.1042] det_loss_box_dn_0[0.3184] det_loss_cls_dn_1[0.1013] det_loss_box_dn_1[0.2755] det_loss_cls_dn_2[0.1009] det_loss_box_dn_2[0.2595] det_loss_cls_dn_3[0.0997] det_loss_box_dn_3[0.2563] det_loss_cls_dn_4[0.0992] det_loss_box_dn_4[0.2552] det_loss_cls_dn_5[0.0984] det_loss_box_dn_5[0.2538] loss_depth[0.0405] total_loss[9.9010]
Rank[0/8] 06/20/2025 01:41:40 INFO stats.py:314 | Epoch[87] Step[219] GlobalStep[34149] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 20:00:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:41:45 INFO loss_tracker.py:84 | Epoch[87/239] Step[224] GlobalStep[34154/NA]: det_loss_cls_0[0.4663] det_loss_box_0[0.5766] det_loss_cls_1[0.4409] det_loss_box_1[0.4421] det_loss_cls_2[0.3890] det_loss_box_2[0.3961] det_loss_cls_3[0.3650] det_loss_box_3[0.3718] det_loss_cls_4[0.3363] det_loss_box_4[0.3607] det_loss_cls_5[0.3093] det_loss_box_5[0.3701] det_loss_cls_dn_0[0.0950] det_loss_box_dn_0[0.2864] det_loss_cls_dn_1[0.0912] det_loss_box_dn_1[0.2443] det_loss_cls_dn_2[0.0894] det_loss_box_dn_2[0.2278] det_loss_cls_dn_3[0.0882] det_loss_box_dn_3[0.2246] det_loss_cls_dn_4[0.0873] det_loss_box_dn_4[0.2233] det_loss_cls_dn_5[0.0859] det_loss_box_dn_5[0.2186] loss_depth[0.0400] total_loss[6.8261]
Rank[0/8] 06/20/2025 01:42:07 INFO stats.py:314 | Epoch[87] Step[244] GlobalStep[34174] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 19:59:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:42:12 INFO loss_tracker.py:84 | Epoch[87/239] Step[249] GlobalStep[34179/NA]: det_loss_cls_0[0.4284] det_loss_box_0[0.5678] det_loss_cls_1[0.4137] det_loss_box_1[0.4563] det_loss_cls_2[0.3848] det_loss_box_2[0.4252] det_loss_cls_3[0.3694] det_loss_box_3[0.4061] det_loss_cls_4[0.3424] det_loss_box_4[0.3932] det_loss_cls_5[0.3327] det_loss_box_5[0.3913] det_loss_cls_dn_0[0.0959] det_loss_box_dn_0[0.3095] det_loss_cls_dn_1[0.0922] det_loss_box_dn_1[0.2643] det_loss_cls_dn_2[0.0912] det_loss_box_dn_2[0.2461] det_loss_cls_dn_3[0.0903] det_loss_box_dn_3[0.2415] det_loss_cls_dn_4[0.0893] det_loss_box_dn_4[0.2406] det_loss_cls_dn_5[0.0893] det_loss_box_dn_5[0.2394] loss_depth[0.0383] total_loss[7.0393]
Rank[0/8] 06/20/2025 01:42:32 INFO stats.py:314 | Epoch[87] Step[269] GlobalStep[34199] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 19:58:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:42:37 INFO loss_tracker.py:84 | Epoch[87/239] Step[274] GlobalStep[34204/NA]: det_loss_cls_0[0.3892] det_loss_box_0[0.5243] det_loss_cls_1[0.3777] det_loss_box_1[0.4158] det_loss_cls_2[0.3490] det_loss_box_2[0.3804] det_loss_cls_3[0.3316] det_loss_box_3[0.3645] det_loss_cls_4[0.3112] det_loss_box_4[0.3550] det_loss_cls_5[0.2944] det_loss_box_5[0.3567] det_loss_cls_dn_0[0.0869] det_loss_box_dn_0[0.3129] det_loss_cls_dn_1[0.0834] det_loss_box_dn_1[0.2708] det_loss_cls_dn_2[0.0829] det_loss_box_dn_2[0.2547] det_loss_cls_dn_3[0.0818] det_loss_box_dn_3[0.2542] det_loss_cls_dn_4[0.0806] det_loss_box_dn_4[0.2523] det_loss_cls_dn_5[0.0800] det_loss_box_dn_5[0.2504] loss_depth[0.0391] total_loss[6.5800]
Rank[0/8] 06/20/2025 01:42:58 INFO stats.py:314 | Epoch[87] Step[294] GlobalStep[34224] Training Speed: 7.97 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 19:58:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:43:03 INFO loss_tracker.py:84 | Epoch[87/239] Step[299] GlobalStep[34229/NA]: det_loss_cls_0[0.5256] det_loss_box_0[0.6669] det_loss_cls_1[0.5180] det_loss_box_1[0.5219] det_loss_cls_2[0.4852] det_loss_box_2[0.4714] det_loss_cls_3[0.4510] det_loss_box_3[0.4481] det_loss_cls_4[0.4175] det_loss_box_4[0.4343] det_loss_cls_5[0.4009] det_loss_box_5[0.4366] det_loss_cls_dn_0[0.0953] det_loss_box_dn_0[0.2736] det_loss_cls_dn_1[0.0910] det_loss_box_dn_1[0.2269] det_loss_cls_dn_2[0.0903] det_loss_box_dn_2[0.2098] det_loss_cls_dn_3[0.0891] det_loss_box_dn_3[0.2074] det_loss_cls_dn_4[0.0878] det_loss_box_dn_4[0.2049] det_loss_cls_dn_5[0.0868] det_loss_box_dn_5[0.2038] loss_depth[0.0366] total_loss[7.6807]
Rank[0/8] 06/20/2025 01:43:24 INFO stats.py:314 | Epoch[87] Step[319] GlobalStep[34249] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 19:57:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:43:29 INFO loss_tracker.py:84 | Epoch[87/239] Step[324] GlobalStep[34254/NA]: det_loss_cls_0[0.6390] det_loss_box_0[0.8727] det_loss_cls_1[0.6264] det_loss_box_1[0.7134] det_loss_cls_2[0.5902] det_loss_box_2[0.6332] det_loss_cls_3[0.5693] det_loss_box_3[0.6106] det_loss_cls_4[0.5478] det_loss_box_4[0.5964] det_loss_cls_5[0.5259] det_loss_box_5[0.6020] det_loss_cls_dn_0[0.0987] det_loss_box_dn_0[0.3371] det_loss_cls_dn_1[0.0935] det_loss_box_dn_1[0.2847] det_loss_cls_dn_2[0.0928] det_loss_box_dn_2[0.2690] det_loss_cls_dn_3[0.0914] det_loss_box_dn_3[0.2655] det_loss_cls_dn_4[0.0915] det_loss_box_dn_4[0.2639] det_loss_cls_dn_5[0.0911] det_loss_box_dn_5[0.2631] loss_depth[0.0390] total_loss[9.8083]
Rank[0/8] 06/20/2025 01:43:50 INFO stats.py:314 | Epoch[87] Step[344] GlobalStep[34274] Training Speed: 7.91 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 19:57:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:43:55 INFO loss_tracker.py:84 | Epoch[87/239] Step[349] GlobalStep[34279/NA]: det_loss_cls_0[0.5740] det_loss_box_0[0.7074] det_loss_cls_1[0.5500] det_loss_box_1[0.5558] det_loss_cls_2[0.5136] det_loss_box_2[0.5005] det_loss_cls_3[0.4822] det_loss_box_3[0.4851] det_loss_cls_4[0.4513] det_loss_box_4[0.4782] det_loss_cls_5[0.4299] det_loss_box_5[0.4782] det_loss_cls_dn_0[0.1018] det_loss_box_dn_0[0.2855] det_loss_cls_dn_1[0.0983] det_loss_box_dn_1[0.2390] det_loss_cls_dn_2[0.0977] det_loss_box_dn_2[0.2229] det_loss_cls_dn_3[0.0962] det_loss_box_dn_3[0.2197] det_loss_cls_dn_4[0.0955] det_loss_box_dn_4[0.2174] det_loss_cls_dn_5[0.0945] det_loss_box_dn_5[0.2152] loss_depth[0.0391] total_loss[8.2292]
Rank[0/8] 06/20/2025 01:44:15 INFO stats.py:314 | Epoch[87] Step[369] GlobalStep[34299] Training Speed: 7.80 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 19:56:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:44:20 INFO loss_tracker.py:84 | Epoch[87/239] Step[374] GlobalStep[34304/NA]: det_loss_cls_0[0.5626] det_loss_box_0[0.8795] det_loss_cls_1[0.5378] det_loss_box_1[0.6659] det_loss_cls_2[0.5099] det_loss_box_2[0.6169] det_loss_cls_3[0.4784] det_loss_box_3[0.5919] det_loss_cls_4[0.4518] det_loss_box_4[0.5712] det_loss_cls_5[0.4400] det_loss_box_5[0.5714] det_loss_cls_dn_0[0.0968] det_loss_box_dn_0[0.3020] det_loss_cls_dn_1[0.0932] det_loss_box_dn_1[0.2600] det_loss_cls_dn_2[0.0919] det_loss_box_dn_2[0.2459] det_loss_cls_dn_3[0.0911] det_loss_box_dn_3[0.2437] det_loss_cls_dn_4[0.0906] det_loss_box_dn_4[0.2423] det_loss_cls_dn_5[0.0897] det_loss_box_dn_5[0.2389] loss_depth[0.0414] total_loss[9.0049]
Rank[0/8] 06/20/2025 01:44:36 INFO stats.py:394 | Epoch[87] completed. Training Speed: 7.68 samples/sec across all devices.	Epoch Time: 406.01 sec.	Average Epoch Time: 406.01 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 19:55:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:44:44 INFO stats.py:314 | Epoch[88] Step[4] GlobalStep[34324] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 19:55:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:45:05 INFO loss_tracker.py:84 | Epoch[88/239] Step[24] GlobalStep[34344/NA]: det_loss_cls_0[0.5073] det_loss_box_0[0.6500] det_loss_cls_1[0.4883] det_loss_box_1[0.4847] det_loss_cls_2[0.4461] det_loss_box_2[0.4462] det_loss_cls_3[0.4119] det_loss_box_3[0.4273] det_loss_cls_4[0.3874] det_loss_box_4[0.4161] det_loss_cls_5[0.3699] det_loss_box_5[0.4160] det_loss_cls_dn_0[0.0867] det_loss_box_dn_0[0.2809] det_loss_cls_dn_1[0.0831] det_loss_box_dn_1[0.2370] det_loss_cls_dn_2[0.0824] det_loss_box_dn_2[0.2184] det_loss_cls_dn_3[0.0807] det_loss_box_dn_3[0.2143] det_loss_cls_dn_4[0.0800] det_loss_box_dn_4[0.2124] det_loss_cls_dn_5[0.0793] det_loss_box_dn_5[0.2102] loss_depth[0.0384] total_loss[7.3553]
Rank[0/8] 06/20/2025 01:45:10 INFO stats.py:314 | Epoch[88] Step[29] GlobalStep[34349] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 19:55:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:45:31 INFO loss_tracker.py:84 | Epoch[88/239] Step[49] GlobalStep[34369/NA]: det_loss_cls_0[0.5606] det_loss_box_0[0.7940] det_loss_cls_1[0.5455] det_loss_box_1[0.6324] det_loss_cls_2[0.5098] det_loss_box_2[0.5655] det_loss_cls_3[0.4766] det_loss_box_3[0.5489] det_loss_cls_4[0.4606] det_loss_box_4[0.5371] det_loss_cls_5[0.4450] det_loss_box_5[0.5277] det_loss_cls_dn_0[0.1016] det_loss_box_dn_0[0.2948] det_loss_cls_dn_1[0.0983] det_loss_box_dn_1[0.2483] det_loss_cls_dn_2[0.0980] det_loss_box_dn_2[0.2336] det_loss_cls_dn_3[0.0975] det_loss_box_dn_3[0.2307] det_loss_cls_dn_4[0.0967] det_loss_box_dn_4[0.2285] det_loss_cls_dn_5[0.0962] det_loss_box_dn_5[0.2279] loss_depth[0.0382] total_loss[8.6940]
Rank[0/8] 06/20/2025 01:45:36 INFO stats.py:314 | Epoch[88] Step[54] GlobalStep[34374] Training Speed: 7.80 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 19:54:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:45:57 INFO loss_tracker.py:84 | Epoch[88/239] Step[74] GlobalStep[34394/NA]: det_loss_cls_0[0.5578] det_loss_box_0[0.7807] det_loss_cls_1[0.5486] det_loss_box_1[0.6322] det_loss_cls_2[0.5172] det_loss_box_2[0.5611] det_loss_cls_3[0.4923] det_loss_box_3[0.5455] det_loss_cls_4[0.4736] det_loss_box_4[0.5309] det_loss_cls_5[0.4665] det_loss_box_5[0.5261] det_loss_cls_dn_0[0.1088] det_loss_box_dn_0[0.3255] det_loss_cls_dn_1[0.1044] det_loss_box_dn_1[0.2885] det_loss_cls_dn_2[0.1031] det_loss_box_dn_2[0.2711] det_loss_cls_dn_3[0.1023] det_loss_box_dn_3[0.2691] det_loss_cls_dn_4[0.1017] det_loss_box_dn_4[0.2675] det_loss_cls_dn_5[0.1005] det_loss_box_dn_5[0.2652] loss_depth[0.0380] total_loss[8.9782]
Rank[0/8] 06/20/2025 01:46:02 INFO stats.py:314 | Epoch[88] Step[79] GlobalStep[34399] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 19:53:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:46:22 INFO loss_tracker.py:84 | Epoch[88/239] Step[99] GlobalStep[34419/NA]: det_loss_cls_0[0.5066] det_loss_box_0[0.6618] det_loss_cls_1[0.4964] det_loss_box_1[0.5030] det_loss_cls_2[0.4633] det_loss_box_2[0.4555] det_loss_cls_3[0.4369] det_loss_box_3[0.4402] det_loss_cls_4[0.4002] det_loss_box_4[0.4383] det_loss_cls_5[0.3935] det_loss_box_5[0.4305] det_loss_cls_dn_0[0.0944] det_loss_box_dn_0[0.2888] det_loss_cls_dn_1[0.0919] det_loss_box_dn_1[0.2468] det_loss_cls_dn_2[0.0911] det_loss_box_dn_2[0.2288] det_loss_cls_dn_3[0.0896] det_loss_box_dn_3[0.2257] det_loss_cls_dn_4[0.0890] det_loss_box_dn_4[0.2250] det_loss_cls_dn_5[0.0880] det_loss_box_dn_5[0.2231] loss_depth[0.0358] total_loss[7.6441]
Rank[0/8] 06/20/2025 01:46:28 INFO stats.py:314 | Epoch[88] Step[104] GlobalStep[34424] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 19:53:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:46:49 INFO loss_tracker.py:84 | Epoch[88/239] Step[124] GlobalStep[34444/NA]: det_loss_cls_0[0.4213] det_loss_box_0[0.6780] det_loss_cls_1[0.4151] det_loss_box_1[0.5359] det_loss_cls_2[0.3888] det_loss_box_2[0.4914] det_loss_cls_3[0.3700] det_loss_box_3[0.4664] det_loss_cls_4[0.3458] det_loss_box_4[0.4619] det_loss_cls_5[0.3344] det_loss_box_5[0.4578] det_loss_cls_dn_0[0.0975] det_loss_box_dn_0[0.3757] det_loss_cls_dn_1[0.0946] det_loss_box_dn_1[0.3269] det_loss_cls_dn_2[0.0935] det_loss_box_dn_2[0.3059] det_loss_cls_dn_3[0.0924] det_loss_box_dn_3[0.3018] det_loss_cls_dn_4[0.0912] det_loss_box_dn_4[0.2997] det_loss_cls_dn_5[0.0908] det_loss_box_dn_5[0.2979] loss_depth[0.0402] total_loss[7.8746]
Rank[0/8] 06/20/2025 01:46:54 INFO stats.py:314 | Epoch[88] Step[129] GlobalStep[34449] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 19:52:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:47:14 INFO loss_tracker.py:84 | Epoch[88/239] Step[149] GlobalStep[34469/NA]: det_loss_cls_0[0.4740] det_loss_box_0[0.6139] det_loss_cls_1[0.4597] det_loss_box_1[0.4856] det_loss_cls_2[0.4358] det_loss_box_2[0.4383] det_loss_cls_3[0.4113] det_loss_box_3[0.4175] det_loss_cls_4[0.3845] det_loss_box_4[0.4056] det_loss_cls_5[0.3678] det_loss_box_5[0.4056] det_loss_cls_dn_0[0.0918] det_loss_box_dn_0[0.3456] det_loss_cls_dn_1[0.0872] det_loss_box_dn_1[0.2989] det_loss_cls_dn_2[0.0859] det_loss_box_dn_2[0.2797] det_loss_cls_dn_3[0.0860] det_loss_box_dn_3[0.2748] det_loss_cls_dn_4[0.0844] det_loss_box_dn_4[0.2741] det_loss_cls_dn_5[0.0828] det_loss_box_dn_5[0.2724] loss_depth[0.0356] total_loss[7.5989]
Rank[0/8] 06/20/2025 01:47:20 INFO stats.py:314 | Epoch[88] Step[154] GlobalStep[34474] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 19:52:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:47:40 INFO loss_tracker.py:84 | Epoch[88/239] Step[174] GlobalStep[34494/NA]: det_loss_cls_0[0.5422] det_loss_box_0[0.7016] det_loss_cls_1[0.5212] det_loss_box_1[0.5412] det_loss_cls_2[0.4908] det_loss_box_2[0.4904] det_loss_cls_3[0.4679] det_loss_box_3[0.4808] det_loss_cls_4[0.4346] det_loss_box_4[0.4743] det_loss_cls_5[0.4190] det_loss_box_5[0.4757] det_loss_cls_dn_0[0.1039] det_loss_box_dn_0[0.2973] det_loss_cls_dn_1[0.1000] det_loss_box_dn_1[0.2536] det_loss_cls_dn_2[0.0989] det_loss_box_dn_2[0.2401] det_loss_cls_dn_3[0.0986] det_loss_box_dn_3[0.2371] det_loss_cls_dn_4[0.0977] det_loss_box_dn_4[0.2359] det_loss_cls_dn_5[0.0968] det_loss_box_dn_5[0.2343] loss_depth[0.0380] total_loss[8.1719]
Rank[0/8] 06/20/2025 01:47:45 INFO stats.py:314 | Epoch[88] Step[179] GlobalStep[34499] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 19:51:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:48:06 INFO loss_tracker.py:84 | Epoch[88/239] Step[199] GlobalStep[34519/NA]: det_loss_cls_0[0.4503] det_loss_box_0[0.6130] det_loss_cls_1[0.4388] det_loss_box_1[0.4873] det_loss_cls_2[0.4065] det_loss_box_2[0.4505] det_loss_cls_3[0.3797] det_loss_box_3[0.4388] det_loss_cls_4[0.3597] det_loss_box_4[0.4189] det_loss_cls_5[0.3458] det_loss_box_5[0.4121] det_loss_cls_dn_0[0.0971] det_loss_box_dn_0[0.3087] det_loss_cls_dn_1[0.0934] det_loss_box_dn_1[0.2627] det_loss_cls_dn_2[0.0931] det_loss_box_dn_2[0.2477] det_loss_cls_dn_3[0.0927] det_loss_box_dn_3[0.2448] det_loss_cls_dn_4[0.0919] det_loss_box_dn_4[0.2433] det_loss_cls_dn_5[0.0904] det_loss_box_dn_5[0.2408] loss_depth[0.0379] total_loss[7.3459]
Rank[0/8] 06/20/2025 01:48:11 INFO stats.py:314 | Epoch[88] Step[204] GlobalStep[34524] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 19:50:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:48:32 INFO loss_tracker.py:84 | Epoch[88/239] Step[224] GlobalStep[34544/NA]: det_loss_cls_0[0.5457] det_loss_box_0[0.7307] det_loss_cls_1[0.5312] det_loss_box_1[0.5783] det_loss_cls_2[0.4983] det_loss_box_2[0.5221] det_loss_cls_3[0.4599] det_loss_box_3[0.5078] det_loss_cls_4[0.4267] det_loss_box_4[0.4984] det_loss_cls_5[0.4065] det_loss_box_5[0.5014] det_loss_cls_dn_0[0.1026] det_loss_box_dn_0[0.3241] det_loss_cls_dn_1[0.0991] det_loss_box_dn_1[0.2772] det_loss_cls_dn_2[0.0981] det_loss_box_dn_2[0.2647] det_loss_cls_dn_3[0.0975] det_loss_box_dn_3[0.2619] det_loss_cls_dn_4[0.0961] det_loss_box_dn_4[0.2607] det_loss_cls_dn_5[0.0950] det_loss_box_dn_5[0.2597] loss_depth[0.0393] total_loss[8.4831]
Rank[0/8] 06/20/2025 01:48:37 INFO stats.py:314 | Epoch[88] Step[229] GlobalStep[34549] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 19:50:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:48:58 INFO loss_tracker.py:84 | Epoch[88/239] Step[249] GlobalStep[34569/NA]: det_loss_cls_0[0.6071] det_loss_box_0[0.8463] det_loss_cls_1[0.6079] det_loss_box_1[0.6591] det_loss_cls_2[0.5792] det_loss_box_2[0.5989] det_loss_cls_3[0.5483] det_loss_box_3[0.5885] det_loss_cls_4[0.5306] det_loss_box_4[0.5745] det_loss_cls_5[0.5168] det_loss_box_5[0.5805] det_loss_cls_dn_0[0.1025] det_loss_box_dn_0[0.3339] det_loss_cls_dn_1[0.1007] det_loss_box_dn_1[0.2839] det_loss_cls_dn_2[0.0997] det_loss_box_dn_2[0.2653] det_loss_cls_dn_3[0.0990] det_loss_box_dn_3[0.2625] det_loss_cls_dn_4[0.0983] det_loss_box_dn_4[0.2586] det_loss_cls_dn_5[0.0973] det_loss_box_dn_5[0.2566] loss_depth[0.0375] total_loss[9.5334]
Rank[0/8] 06/20/2025 01:49:03 INFO stats.py:314 | Epoch[88] Step[254] GlobalStep[34574] Training Speed: 8.13 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 19:49:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:49:24 INFO loss_tracker.py:84 | Epoch[88/239] Step[274] GlobalStep[34594/NA]: det_loss_cls_0[0.4755] det_loss_box_0[0.6373] det_loss_cls_1[0.4601] det_loss_box_1[0.5142] det_loss_cls_2[0.4308] det_loss_box_2[0.4821] det_loss_cls_3[0.4116] det_loss_box_3[0.4615] det_loss_cls_4[0.3856] det_loss_box_4[0.4452] det_loss_cls_5[0.3687] det_loss_box_5[0.4469] det_loss_cls_dn_0[0.0980] det_loss_box_dn_0[0.3463] det_loss_cls_dn_1[0.0942] det_loss_box_dn_1[0.3065] det_loss_cls_dn_2[0.0934] det_loss_box_dn_2[0.2908] det_loss_cls_dn_3[0.0922] det_loss_box_dn_3[0.2880] det_loss_cls_dn_4[0.0903] det_loss_box_dn_4[0.2875] det_loss_cls_dn_5[0.0893] det_loss_box_dn_5[0.2868] loss_depth[0.0369] total_loss[7.9197]
Rank[0/8] 06/20/2025 01:49:29 INFO stats.py:314 | Epoch[88] Step[279] GlobalStep[34599] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 19:48:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:49:50 INFO loss_tracker.py:84 | Epoch[88/239] Step[299] GlobalStep[34619/NA]: det_loss_cls_0[0.5086] det_loss_box_0[0.6750] det_loss_cls_1[0.4971] det_loss_box_1[0.5220] det_loss_cls_2[0.4678] det_loss_box_2[0.4692] det_loss_cls_3[0.4308] det_loss_box_3[0.4525] det_loss_cls_4[0.4041] det_loss_box_4[0.4444] det_loss_cls_5[0.3877] det_loss_box_5[0.4448] det_loss_cls_dn_0[0.0994] det_loss_box_dn_0[0.2722] det_loss_cls_dn_1[0.0970] det_loss_box_dn_1[0.2293] det_loss_cls_dn_2[0.0962] det_loss_box_dn_2[0.2131] det_loss_cls_dn_3[0.0953] det_loss_box_dn_3[0.2095] det_loss_cls_dn_4[0.0942] det_loss_box_dn_4[0.2084] det_loss_cls_dn_5[0.0926] det_loss_box_dn_5[0.2066] loss_depth[0.0387] total_loss[7.6565]
Rank[0/8] 06/20/2025 01:49:55 INFO stats.py:314 | Epoch[88] Step[304] GlobalStep[34624] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 19:48:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:50:15 INFO loss_tracker.py:84 | Epoch[88/239] Step[324] GlobalStep[34644/NA]: det_loss_cls_0[0.4589] det_loss_box_0[0.6021] det_loss_cls_1[0.4707] det_loss_box_1[0.4582] det_loss_cls_2[0.4287] det_loss_box_2[0.4157] det_loss_cls_3[0.3987] det_loss_box_3[0.4107] det_loss_cls_4[0.3701] det_loss_box_4[0.4025] det_loss_cls_5[0.3544] det_loss_box_5[0.4049] det_loss_cls_dn_0[0.0944] det_loss_box_dn_0[0.3057] det_loss_cls_dn_1[0.0906] det_loss_box_dn_1[0.2595] det_loss_cls_dn_2[0.0898] det_loss_box_dn_2[0.2480] det_loss_cls_dn_3[0.0883] det_loss_box_dn_3[0.2446] det_loss_cls_dn_4[0.0876] det_loss_box_dn_4[0.2438] det_loss_cls_dn_5[0.0865] det_loss_box_dn_5[0.2428] loss_depth[0.0380] total_loss[7.2954]
Rank[0/8] 06/20/2025 01:50:20 INFO stats.py:314 | Epoch[88] Step[329] GlobalStep[34649] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 19:47:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:50:41 INFO loss_tracker.py:84 | Epoch[88/239] Step[349] GlobalStep[34669/NA]: det_loss_cls_0[0.4434] det_loss_box_0[0.6246] det_loss_cls_1[0.4299] det_loss_box_1[0.4996] det_loss_cls_2[0.4054] det_loss_box_2[0.4502] det_loss_cls_3[0.3881] det_loss_box_3[0.4268] det_loss_cls_4[0.3606] det_loss_box_4[0.4208] det_loss_cls_5[0.3464] det_loss_box_5[0.4213] det_loss_cls_dn_0[0.0907] det_loss_box_dn_0[0.3488] det_loss_cls_dn_1[0.0849] det_loss_box_dn_1[0.3076] det_loss_cls_dn_2[0.0839] det_loss_box_dn_2[0.2847] det_loss_cls_dn_3[0.0823] det_loss_box_dn_3[0.2818] det_loss_cls_dn_4[0.0818] det_loss_box_dn_4[0.2802] det_loss_cls_dn_5[0.0810] det_loss_box_dn_5[0.2777] loss_depth[0.0369] total_loss[7.5395]
Rank[0/8] 06/20/2025 01:50:46 INFO stats.py:314 | Epoch[88] Step[354] GlobalStep[34674] Training Speed: 5.78 samples/sec across all devices.	Average Step Time: 1.38 sec.	Estimated Remaining Time: 19:47:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:51:07 INFO loss_tracker.py:84 | Epoch[88/239] Step[374] GlobalStep[34694/NA]: det_loss_cls_0[0.4737] det_loss_box_0[0.6705] det_loss_cls_1[0.4644] det_loss_box_1[0.5303] det_loss_cls_2[0.4255] det_loss_box_2[0.4700] det_loss_cls_3[0.4015] det_loss_box_3[0.4563] det_loss_cls_4[0.3815] det_loss_box_4[0.4471] det_loss_cls_5[0.3535] det_loss_box_5[0.4543] det_loss_cls_dn_0[0.0999] det_loss_box_dn_0[0.3549] det_loss_cls_dn_1[0.0971] det_loss_box_dn_1[0.2984] det_loss_cls_dn_2[0.0967] det_loss_box_dn_2[0.2836] det_loss_cls_dn_3[0.0950] det_loss_box_dn_3[0.2799] det_loss_cls_dn_4[0.0949] det_loss_box_dn_4[0.2776] det_loss_cls_dn_5[0.0941] det_loss_box_dn_5[0.2745] loss_depth[0.0407] total_loss[7.9159]
Rank[0/8] 06/20/2025 01:51:12 INFO stats.py:314 | Epoch[88] Step[379] GlobalStep[34699] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 19:46:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:51:22 INFO stats.py:394 | Epoch[88] completed. Training Speed: 7.68 samples/sec across all devices.	Epoch Time: 406.08 sec.	Average Epoch Time: 406.08 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 19:46:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:51:40 INFO stats.py:314 | Epoch[89] Step[14] GlobalStep[34724] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 19:45:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:51:50 INFO loss_tracker.py:84 | Epoch[89/239] Step[24] GlobalStep[34734/NA]: det_loss_cls_0[0.5541] det_loss_box_0[0.7634] det_loss_cls_1[0.5343] det_loss_box_1[0.5956] det_loss_cls_2[0.5080] det_loss_box_2[0.5424] det_loss_cls_3[0.4770] det_loss_box_3[0.5200] det_loss_cls_4[0.4469] det_loss_box_4[0.5106] det_loss_cls_5[0.4367] det_loss_box_5[0.5087] det_loss_cls_dn_0[0.1021] det_loss_box_dn_0[0.3169] det_loss_cls_dn_1[0.0986] det_loss_box_dn_1[0.2688] det_loss_cls_dn_2[0.0979] det_loss_box_dn_2[0.2536] det_loss_cls_dn_3[0.0963] det_loss_box_dn_3[0.2497] det_loss_cls_dn_4[0.0956] det_loss_box_dn_4[0.2480] det_loss_cls_dn_5[0.0950] det_loss_box_dn_5[0.2453] loss_depth[0.0397] total_loss[8.6052]
Rank[0/8] 06/20/2025 01:52:06 INFO stats.py:314 | Epoch[89] Step[39] GlobalStep[34749] Training Speed: 8.15 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 19:45:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:52:16 INFO loss_tracker.py:84 | Epoch[89/239] Step[49] GlobalStep[34759/NA]: det_loss_cls_0[0.5275] det_loss_box_0[0.6530] det_loss_cls_1[0.5176] det_loss_box_1[0.5272] det_loss_cls_2[0.4893] det_loss_box_2[0.4789] det_loss_cls_3[0.4688] det_loss_box_3[0.4580] det_loss_cls_4[0.4539] det_loss_box_4[0.4428] det_loss_cls_5[0.4373] det_loss_box_5[0.4473] det_loss_cls_dn_0[0.0996] det_loss_box_dn_0[0.2755] det_loss_cls_dn_1[0.0961] det_loss_box_dn_1[0.2411] det_loss_cls_dn_2[0.0969] det_loss_box_dn_2[0.2297] det_loss_cls_dn_3[0.0954] det_loss_box_dn_3[0.2265] det_loss_cls_dn_4[0.0954] det_loss_box_dn_4[0.2262] det_loss_cls_dn_5[0.0945] det_loss_box_dn_5[0.2240] loss_depth[0.0366] total_loss[7.9390]
Rank[0/8] 06/20/2025 01:52:32 INFO stats.py:314 | Epoch[89] Step[64] GlobalStep[34774] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 19:44:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:52:42 INFO loss_tracker.py:84 | Epoch[89/239] Step[74] GlobalStep[34784/NA]: det_loss_cls_0[0.5656] det_loss_box_0[0.6999] det_loss_cls_1[0.5448] det_loss_box_1[0.5424] det_loss_cls_2[0.5112] det_loss_box_2[0.4991] det_loss_cls_3[0.4724] det_loss_box_3[0.4860] det_loss_cls_4[0.4431] det_loss_box_4[0.4688] det_loss_cls_5[0.4298] det_loss_box_5[0.4729] det_loss_cls_dn_0[0.0953] det_loss_box_dn_0[0.2463] det_loss_cls_dn_1[0.0923] det_loss_box_dn_1[0.2028] det_loss_cls_dn_2[0.0915] det_loss_box_dn_2[0.1880] det_loss_cls_dn_3[0.0900] det_loss_box_dn_3[0.1856] det_loss_cls_dn_4[0.0889] det_loss_box_dn_4[0.1841] det_loss_cls_dn_5[0.0881] det_loss_box_dn_5[0.1832] loss_depth[0.0379] total_loss[7.9099]
Rank[0/8] 06/20/2025 01:52:58 INFO stats.py:314 | Epoch[89] Step[89] GlobalStep[34799] Training Speed: 7.66 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 19:43:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:53:08 INFO loss_tracker.py:84 | Epoch[89/239] Step[99] GlobalStep[34809/NA]: det_loss_cls_0[0.4644] det_loss_box_0[0.6044] det_loss_cls_1[0.4524] det_loss_box_1[0.4764] det_loss_cls_2[0.4193] det_loss_box_2[0.4430] det_loss_cls_3[0.4032] det_loss_box_3[0.4205] det_loss_cls_4[0.3843] det_loss_box_4[0.4098] det_loss_cls_5[0.3755] det_loss_box_5[0.4131] det_loss_cls_dn_0[0.0949] det_loss_box_dn_0[0.3129] det_loss_cls_dn_1[0.0906] det_loss_box_dn_1[0.2616] det_loss_cls_dn_2[0.0899] det_loss_box_dn_2[0.2472] det_loss_cls_dn_3[0.0890] det_loss_box_dn_3[0.2441] det_loss_cls_dn_4[0.0876] det_loss_box_dn_4[0.2440] det_loss_cls_dn_5[0.0869] det_loss_box_dn_5[0.2403] loss_depth[0.0390] total_loss[7.3942]
Rank[0/8] 06/20/2025 01:53:24 INFO stats.py:314 | Epoch[89] Step[114] GlobalStep[34824] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 19:43:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:53:34 INFO loss_tracker.py:84 | Epoch[89/239] Step[124] GlobalStep[34834/NA]: det_loss_cls_0[0.4901] det_loss_box_0[0.6933] det_loss_cls_1[0.4673] det_loss_box_1[0.5371] det_loss_cls_2[0.4372] det_loss_box_2[0.4883] det_loss_cls_3[0.4039] det_loss_box_3[0.4776] det_loss_cls_4[0.3810] det_loss_box_4[0.4743] det_loss_cls_5[0.3717] det_loss_box_5[0.4771] det_loss_cls_dn_0[0.0984] det_loss_box_dn_0[0.3211] det_loss_cls_dn_1[0.0953] det_loss_box_dn_1[0.2782] det_loss_cls_dn_2[0.0954] det_loss_box_dn_2[0.2607] det_loss_cls_dn_3[0.0943] det_loss_box_dn_3[0.2568] det_loss_cls_dn_4[0.0925] det_loss_box_dn_4[0.2561] det_loss_cls_dn_5[0.0917] det_loss_box_dn_5[0.2530] loss_depth[0.0384] total_loss[7.9308]
Rank[0/8] 06/20/2025 01:53:49 INFO stats.py:314 | Epoch[89] Step[139] GlobalStep[34849] Training Speed: 8.03 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 19:42:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:54:00 INFO loss_tracker.py:84 | Epoch[89/239] Step[149] GlobalStep[34859/NA]: det_loss_cls_0[0.4809] det_loss_box_0[0.6235] det_loss_cls_1[0.4803] det_loss_box_1[0.5047] det_loss_cls_2[0.4414] det_loss_box_2[0.4573] det_loss_cls_3[0.4168] det_loss_box_3[0.4398] det_loss_cls_4[0.3922] det_loss_box_4[0.4342] det_loss_cls_5[0.3820] det_loss_box_5[0.4344] det_loss_cls_dn_0[0.1006] det_loss_box_dn_0[0.3541] det_loss_cls_dn_1[0.0960] det_loss_box_dn_1[0.3108] det_loss_cls_dn_2[0.0952] det_loss_box_dn_2[0.2925] det_loss_cls_dn_3[0.0943] det_loss_box_dn_3[0.2890] det_loss_cls_dn_4[0.0931] det_loss_box_dn_4[0.2874] det_loss_cls_dn_5[0.0925] det_loss_box_dn_5[0.2831] loss_depth[0.0381] total_loss[7.9142]
Rank[0/8] 06/20/2025 01:54:16 INFO stats.py:314 | Epoch[89] Step[164] GlobalStep[34874] Training Speed: 8.69 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 19:42:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:54:26 INFO loss_tracker.py:84 | Epoch[89/239] Step[174] GlobalStep[34884/NA]: det_loss_cls_0[0.5575] det_loss_box_0[0.7367] det_loss_cls_1[0.5488] det_loss_box_1[0.5699] det_loss_cls_2[0.5200] det_loss_box_2[0.4962] det_loss_cls_3[0.4872] det_loss_box_3[0.4800] det_loss_cls_4[0.4516] det_loss_box_4[0.4675] det_loss_cls_5[0.4443] det_loss_box_5[0.4618] det_loss_cls_dn_0[0.1063] det_loss_box_dn_0[0.3278] det_loss_cls_dn_1[0.1031] det_loss_box_dn_1[0.2826] det_loss_cls_dn_2[0.1020] det_loss_box_dn_2[0.2634] det_loss_cls_dn_3[0.1013] det_loss_box_dn_3[0.2589] det_loss_cls_dn_4[0.0997] det_loss_box_dn_4[0.2565] det_loss_cls_dn_5[0.0993] det_loss_box_dn_5[0.2557] loss_depth[0.0399] total_loss[8.5182]
Rank[0/8] 06/20/2025 01:54:42 INFO stats.py:314 | Epoch[89] Step[189] GlobalStep[34899] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 19:41:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:54:52 INFO loss_tracker.py:84 | Epoch[89/239] Step[199] GlobalStep[34909/NA]: det_loss_cls_0[0.4163] det_loss_box_0[0.5406] det_loss_cls_1[0.4250] det_loss_box_1[0.4331] det_loss_cls_2[0.3893] det_loss_box_2[0.3846] det_loss_cls_3[0.3571] det_loss_box_3[0.3775] det_loss_cls_4[0.3361] det_loss_box_4[0.3689] det_loss_cls_5[0.3238] det_loss_box_5[0.3679] det_loss_cls_dn_0[0.0908] det_loss_box_dn_0[0.2826] det_loss_cls_dn_1[0.0866] det_loss_box_dn_1[0.2439] det_loss_cls_dn_2[0.0857] det_loss_box_dn_2[0.2310] det_loss_cls_dn_3[0.0845] det_loss_box_dn_3[0.2274] det_loss_cls_dn_4[0.0832] det_loss_box_dn_4[0.2267] det_loss_cls_dn_5[0.0824] det_loss_box_dn_5[0.2252] loss_depth[0.0385] total_loss[6.7089]
Rank[0/8] 06/20/2025 01:55:07 INFO stats.py:314 | Epoch[89] Step[214] GlobalStep[34924] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 19:40:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:55:18 INFO loss_tracker.py:84 | Epoch[89/239] Step[224] GlobalStep[34934/NA]: det_loss_cls_0[0.4777] det_loss_box_0[0.6282] det_loss_cls_1[0.4563] det_loss_box_1[0.4979] det_loss_cls_2[0.4277] det_loss_box_2[0.4383] det_loss_cls_3[0.3953] det_loss_box_3[0.4231] det_loss_cls_4[0.3700] det_loss_box_4[0.4180] det_loss_cls_5[0.3526] det_loss_box_5[0.4222] det_loss_cls_dn_0[0.0868] det_loss_box_dn_0[0.2905] det_loss_cls_dn_1[0.0839] det_loss_box_dn_1[0.2436] det_loss_cls_dn_2[0.0827] det_loss_box_dn_2[0.2250] det_loss_cls_dn_3[0.0819] det_loss_box_dn_3[0.2218] det_loss_cls_dn_4[0.0807] det_loss_box_dn_4[0.2205] det_loss_cls_dn_5[0.0794] det_loss_box_dn_5[0.2194] loss_depth[0.0404] total_loss[7.2640]
Rank[0/8] 06/20/2025 01:55:34 INFO stats.py:314 | Epoch[89] Step[239] GlobalStep[34949] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 19:40:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:55:44 INFO loss_tracker.py:84 | Epoch[89/239] Step[249] GlobalStep[34959/NA]: det_loss_cls_0[0.4638] det_loss_box_0[0.6228] det_loss_cls_1[0.4693] det_loss_box_1[0.4978] det_loss_cls_2[0.4341] det_loss_box_2[0.4530] det_loss_cls_3[0.4080] det_loss_box_3[0.4441] det_loss_cls_4[0.3895] det_loss_box_4[0.4293] det_loss_cls_5[0.3794] det_loss_box_5[0.4257] det_loss_cls_dn_0[0.0895] det_loss_box_dn_0[0.2967] det_loss_cls_dn_1[0.0863] det_loss_box_dn_1[0.2584] det_loss_cls_dn_2[0.0864] det_loss_box_dn_2[0.2455] det_loss_cls_dn_3[0.0852] det_loss_box_dn_3[0.2427] det_loss_cls_dn_4[0.0847] det_loss_box_dn_4[0.2422] det_loss_cls_dn_5[0.0838] det_loss_box_dn_5[0.2345] loss_depth[0.0365] total_loss[7.4889]
Rank[0/8] 06/20/2025 01:56:00 INFO stats.py:314 | Epoch[89] Step[264] GlobalStep[34974] Training Speed: 8.22 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 19:39:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:56:10 INFO loss_tracker.py:84 | Epoch[89/239] Step[274] GlobalStep[34984/NA]: det_loss_cls_0[0.5400] det_loss_box_0[0.7140] det_loss_cls_1[0.5177] det_loss_box_1[0.5614] det_loss_cls_2[0.4798] det_loss_box_2[0.5086] det_loss_cls_3[0.4565] det_loss_box_3[0.4854] det_loss_cls_4[0.4184] det_loss_box_4[0.4788] det_loss_cls_5[0.3993] det_loss_box_5[0.4812] det_loss_cls_dn_0[0.0997] det_loss_box_dn_0[0.3179] det_loss_cls_dn_1[0.0955] det_loss_box_dn_1[0.2709] det_loss_cls_dn_2[0.0952] det_loss_box_dn_2[0.2535] det_loss_cls_dn_3[0.0942] det_loss_box_dn_3[0.2513] det_loss_cls_dn_4[0.0935] det_loss_box_dn_4[0.2489] det_loss_cls_dn_5[0.0932] det_loss_box_dn_5[0.2413] loss_depth[0.0407] total_loss[8.2367]
Rank[0/8] 06/20/2025 01:56:25 INFO stats.py:314 | Epoch[89] Step[289] GlobalStep[34999] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 19:38:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:56:36 INFO loss_tracker.py:84 | Epoch[89/239] Step[299] GlobalStep[35009/NA]: det_loss_cls_0[0.5002] det_loss_box_0[0.6768] det_loss_cls_1[0.4859] det_loss_box_1[0.5301] det_loss_cls_2[0.4550] det_loss_box_2[0.4852] det_loss_cls_3[0.4368] det_loss_box_3[0.4687] det_loss_cls_4[0.4019] det_loss_box_4[0.4664] det_loss_cls_5[0.3912] det_loss_box_5[0.4689] det_loss_cls_dn_0[0.0942] det_loss_box_dn_0[0.2961] det_loss_cls_dn_1[0.0899] det_loss_box_dn_1[0.2516] det_loss_cls_dn_2[0.0883] det_loss_box_dn_2[0.2349] det_loss_cls_dn_3[0.0873] det_loss_box_dn_3[0.2319] det_loss_cls_dn_4[0.0863] det_loss_box_dn_4[0.2311] det_loss_cls_dn_5[0.0853] det_loss_box_dn_5[0.2306] loss_depth[0.0415] total_loss[7.8161]
Rank[0/8] 06/20/2025 01:56:51 INFO stats.py:314 | Epoch[89] Step[314] GlobalStep[35024] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 19:38:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:57:01 INFO loss_tracker.py:84 | Epoch[89/239] Step[324] GlobalStep[35034/NA]: det_loss_cls_0[0.5062] det_loss_box_0[0.6513] det_loss_cls_1[0.4900] det_loss_box_1[0.4869] det_loss_cls_2[0.4430] det_loss_box_2[0.4332] det_loss_cls_3[0.4201] det_loss_box_3[0.4187] det_loss_cls_4[0.3962] det_loss_box_4[0.4050] det_loss_cls_5[0.3807] det_loss_box_5[0.4011] det_loss_cls_dn_0[0.0958] det_loss_box_dn_0[0.2876] det_loss_cls_dn_1[0.0923] det_loss_box_dn_1[0.2463] det_loss_cls_dn_2[0.0920] det_loss_box_dn_2[0.2310] det_loss_cls_dn_3[0.0914] det_loss_box_dn_3[0.2264] det_loss_cls_dn_4[0.0910] det_loss_box_dn_4[0.2260] det_loss_cls_dn_5[0.0905] det_loss_box_dn_5[0.2239] loss_depth[0.0390] total_loss[7.4656]
Rank[0/8] 06/20/2025 01:57:17 INFO stats.py:314 | Epoch[89] Step[339] GlobalStep[35049] Training Speed: 7.53 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 19:37:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:57:27 INFO loss_tracker.py:84 | Epoch[89/239] Step[349] GlobalStep[35059/NA]: det_loss_cls_0[0.4548] det_loss_box_0[0.6555] det_loss_cls_1[0.4392] det_loss_box_1[0.5063] det_loss_cls_2[0.4066] det_loss_box_2[0.4660] det_loss_cls_3[0.3846] det_loss_box_3[0.4544] det_loss_cls_4[0.3671] det_loss_box_4[0.4386] det_loss_cls_5[0.3444] det_loss_box_5[0.4454] det_loss_cls_dn_0[0.0951] det_loss_box_dn_0[0.3287] det_loss_cls_dn_1[0.0909] det_loss_box_dn_1[0.2750] det_loss_cls_dn_2[0.0901] det_loss_box_dn_2[0.2539] det_loss_cls_dn_3[0.0893] det_loss_box_dn_3[0.2496] det_loss_cls_dn_4[0.0889] det_loss_box_dn_4[0.2496] det_loss_cls_dn_5[0.0878] det_loss_box_dn_5[0.2474] loss_depth[0.0393] total_loss[7.5485]
Rank[0/8] 06/20/2025 01:57:43 INFO stats.py:314 | Epoch[89] Step[364] GlobalStep[35074] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 19:37:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:57:53 INFO loss_tracker.py:84 | Epoch[89/239] Step[374] GlobalStep[35084/NA]: det_loss_cls_0[0.5889] det_loss_box_0[0.8820] det_loss_cls_1[0.5732] det_loss_box_1[0.7060] det_loss_cls_2[0.5461] det_loss_box_2[0.6308] det_loss_cls_3[0.5166] det_loss_box_3[0.6058] det_loss_cls_4[0.4898] det_loss_box_4[0.5908] det_loss_cls_5[0.4701] det_loss_box_5[0.5950] det_loss_cls_dn_0[0.1056] det_loss_box_dn_0[0.3083] det_loss_cls_dn_1[0.1025] det_loss_box_dn_1[0.2643] det_loss_cls_dn_2[0.1018] det_loss_box_dn_2[0.2513] det_loss_cls_dn_3[0.1009] det_loss_box_dn_3[0.2477] det_loss_cls_dn_4[0.0997] det_loss_box_dn_4[0.2463] det_loss_cls_dn_5[0.0987] det_loss_box_dn_5[0.2454] loss_depth[0.0403] total_loss[9.4078]
Rank[0/8] 06/20/2025 01:58:08 INFO stats.py:314 | Epoch[89] Step[389] GlobalStep[35099] Training Speed: 8.77 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 19:36:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 01:58:08 INFO trainer.py:268 | 
==================================================BEGIN EVAL==================================================
Rank[0/8] 06/20/2025 01:59:11 INFO trainer.py:275 | eval: 10
Rank[0/8] 06/20/2025 01:59:19 INFO trainer.py:275 | eval: 20
Rank[0/8] 06/20/2025 01:59:28 INFO trainer.py:275 | eval: 30
Rank[0/8] 06/20/2025 01:59:41 INFO trainer.py:275 | eval: 40
Rank[0/8] 06/20/2025 01:59:48 INFO trainer.py:275 | eval: 50
Rank[0/8] 06/20/2025 01:59:56 INFO trainer.py:275 | eval: 60
Rank[0/8] 06/20/2025 02:00:06 INFO trainer.py:275 | eval: 70
Rank[0/8] 06/20/2025 02:00:17 INFO trainer.py:275 | eval: 80
Rank[0/8] 06/20/2025 02:00:26 INFO trainer.py:275 | eval: 90
Rank[0/8] 06/20/2025 02:00:35 INFO trainer.py:275 | eval: 100
Rank[0/8] 06/20/2025 02:00:40 INFO metrics.py:428 | number of results: 824
Rank[0/8] 06/20/2025 02:04:39 INFO metrics.py:332 | eval results of overall:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| bathtub             | 0.7812  | 0.8676  | 0.4488  | 0.6471  |
| toilet              | 0.8215  | 0.8832  | 0.6102  | 0.7299  |
| curtain             | 0.4728  | 0.8119  | 0.1021  | 0.4388  |
| bin                 | 0.6128  | 0.8101  | 0.2860  | 0.5028  |
| picture             | 0.1436  | 0.3470  | 0.0061  | 0.0603  |
| sink                | 0.6419  | 0.8066  | 0.1816  | 0.3992  |
| toilet paper        | 0.3754  | 0.5906  | 0.0652  | 0.2283  |
| door                | 0.2725  | 0.6932  | 0.0296  | 0.2604  |
| rail                | 0.0061  | 0.2812  | 0.0000  | 0.0469  |
| vanity              | 0.4232  | 0.8889  | 0.3350  | 0.7778  |
| towel               | 0.3395  | 0.6097  | 0.0712  | 0.2677  |
| doorframe           | 0.2671  | 0.7443  | 0.0595  | 0.3818  |
| bar                 | 0.0024  | 0.1515  | 0.0003  | 0.0606  |
| mirror              | 0.1427  | 0.5489  | 0.0116  | 0.1304  |
| hair dryer          | 0.0317  | 0.2857  | 0.0000  | 0.0000  |
| stopcock            | 0.1148  | 0.5862  | 0.0082  | 0.1379  |
| bottle              | 0.0560  | 0.4568  | 0.0020  | 0.0864  |
| switch              | 0.0011  | 0.0855  | 0.0000  | 0.0000  |
| tissue box          | 0.0879  | 0.4848  | 0.0112  | 0.1515  |
| bag                 | 0.2524  | 0.7081  | 0.0490  | 0.2865  |
| cup                 | 0.0821  | 0.3559  | 0.0046  | 0.0678  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| counter             | 0.1905  | 0.7339  | 0.0338  | 0.3119  |
| ledge               | 0.0049  | 0.5000  | 0.0009  | 0.1667  |
| socket              | 0.0042  | 0.1681  | 0.0000  | 0.0133  |
| clothes             | 0.1298  | 0.5860  | 0.0246  | 0.2357  |
| box                 | 0.1353  | 0.5164  | 0.0204  | 0.1651  |
| light               | 0.0316  | 0.2607  | 0.0007  | 0.0342  |
| backpack            | 0.5468  | 0.8065  | 0.3230  | 0.5000  |
| bed                 | 0.7856  | 0.9172  | 0.5465  | 0.8402  |
| shower              | 0.1001  | 0.2333  | 0.0043  | 0.0667  |
| soap dish           | 0.5341  | 0.6000  | 0.0000  | 0.0000  |
| purse               | 0.0008  | 0.1667  | 0.0000  | 0.0000  |
| paper               | 0.0301  | 0.2870  | 0.0008  | 0.0503  |
| cabinet             | 0.2532  | 0.7493  | 0.1053  | 0.4751  |
| mat                 | 0.0110  | 0.3514  | 0.0005  | 0.0811  |
| blanket             | 0.2001  | 0.7600  | 0.0440  | 0.4600  |
| faucet              | 0.0176  | 0.2553  | 0.0001  | 0.0426  |
| tissue              | 0.0014  | 0.1538  | 0.0001  | 0.0769  |
| shelf               | 0.2541  | 0.6960  | 0.1114  | 0.4245  |
| toothpaste          | 0.0035  | 0.4000  | 0.0009  | 0.2000  |
| basket              | 0.2234  | 0.5652  | 0.1124  | 0.2870  |
| wrap                | 0.0025  | 1.0000  | 0.0000  | 0.0000  |
| soap                | 0.0015  | 0.3333  | 0.0006  | 0.1667  |
| scale               | 0.1854  | 0.3636  | 0.0455  | 0.0909  |
| plant               | 0.2792  | 0.6328  | 0.0508  | 0.2679  |
| lamp                | 0.2667  | 0.5642  | 0.0564  | 0.2149  |
| vent                | 0.0518  | 0.2083  | 0.0000  | 0.0000  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mouse               | 0.0032  | 0.1351  | 0.0000  | 0.0000  |
| grab bar            | 0.0042  | 0.1250  | 0.0000  | 0.0000  |
| microwave           | 0.6396  | 0.7959  | 0.1752  | 0.4490  |
| plunger             | 0.7600  | 0.8000  | 0.6000  | 0.6000  |
| dispenser           | 0.4310  | 0.6591  | 0.1916  | 0.3636  |
| dish rack           | 0.1158  | 0.6000  | 0.0000  | 0.0000  |
| couch               | 0.4478  | 0.8388  | 0.3202  | 0.6942  |
| chair               | 0.5717  | 0.7408  | 0.4129  | 0.5691  |
| monitor             | 0.4213  | 0.5543  | 0.0282  | 0.1413  |
| computer            | 0.3045  | 0.6230  | 0.0028  | 0.1311  |
| radiator            | 0.6445  | 0.7667  | 0.2211  | 0.2889  |
| keyboard            | 0.0155  | 0.1512  | 0.0000  | 0.0000  |
| table               | 0.4701  | 0.8433  | 0.3163  | 0.6959  |
| desk                | 0.4911  | 0.8876  | 0.2681  | 0.6854  |
| blackboard          | 0.5263  | 0.7927  | 0.1006  | 0.3049  |
| window              | 0.1961  | 0.5734  | 0.0253  | 0.2171  |
| telephone           | 0.3002  | 0.6000  | 0.0344  | 0.1333  |
| plug                | 0.0040  | 0.1579  | 0.0000  | 0.0000  |
| wardrobe            | 0.3277  | 0.7952  | 0.1471  | 0.5181  |
| stand               | 0.5318  | 0.8841  | 0.3683  | 0.6463  |
| book                | 0.0388  | 0.3408  | 0.0019  | 0.0642  |
| windowsill          | 0.0272  | 0.6087  | 0.0013  | 0.1957  |
| shoe                | 0.1576  | 0.5047  | 0.0120  | 0.0654  |
| laptop              | 0.4549  | 0.7391  | 0.0716  | 0.2609  |
| notebook            | 0.0001  | 0.0769  | 0.0000  | 0.0000  |
| blinds              | 0.1085  | 0.4000  | 0.0053  | 0.1600  |
| board               | 0.1633  | 0.6452  | 0.0502  | 0.1452  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jacket              | 0.5858  | 0.7917  | 0.2213  | 0.3750  |
| screen              | 0.0500  | 0.2000  | 0.0000  | 0.0000  |
| pipe                | 0.0022  | 0.1071  | 0.0000  | 0.0000  |
| pillow              | 0.3429  | 0.6318  | 0.0508  | 0.2106  |
| dresser             | 0.6427  | 0.8889  | 0.5482  | 0.7778  |
| toy                 | 0.1338  | 0.4091  | 0.0499  | 0.1212  |
| case                | 0.0953  | 0.4746  | 0.0392  | 0.1356  |
| cushion             | 0.1690  | 0.4902  | 0.0222  | 0.1765  |
| adhesive tape       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| carpet              | 0.0605  | 0.4881  | 0.0027  | 0.1548  |
| clock               | 0.2283  | 0.5319  | 0.0061  | 0.0638  |
| decoration          | 0.0441  | 0.4500  | 0.0044  | 0.0786  |
| tv                  | 0.5072  | 0.7426  | 0.0942  | 0.3465  |
| frame               | 0.0347  | 0.4490  | 0.0002  | 0.0204  |
| container           | 0.0090  | 0.3559  | 0.0028  | 0.1356  |
| statue              | 0.1043  | 0.5263  | 0.0192  | 0.2632  |
| stool               | 0.2471  | 0.7059  | 0.1203  | 0.4622  |
| poster              | 0.0025  | 0.0909  | 0.0000  | 0.0000  |
| guitar              | 0.1649  | 0.5000  | 0.0000  | 0.0000  |
| luggage             | 0.3556  | 1.0000  | 0.3450  | 0.6667  |
| refrigerator        | 0.6093  | 0.8400  | 0.2744  | 0.4800  |
| ottoman             | 0.1058  | 0.6944  | 0.0915  | 0.5278  |
| remote control      | 0.0861  | 0.2222  | 0.0000  | 0.0000  |
| coffee maker        | 0.2715  | 0.6552  | 0.0134  | 0.2414  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0292  | 0.3636  | 0.0000  | 0.0000  |
| bench               | 0.3289  | 0.6981  | 0.2576  | 0.5094  |
| plate               | 0.0265  | 0.3077  | 0.0007  | 0.0192  |
| fan                 | 0.5736  | 0.8125  | 0.2639  | 0.5781  |
| broom               | 0.0124  | 0.4286  | 0.0000  | 0.0000  |
| paper cutter        | 0.8402  | 1.0000  | 0.0000  | 0.0000  |
| brush               | 0.1039  | 0.4286  | 0.0000  | 0.0000  |
| rack                | 0.0614  | 0.5577  | 0.0244  | 0.3654  |
| bowl                | 0.1060  | 0.4638  | 0.0172  | 0.1014  |
| ladder              | 0.0795  | 0.5882  | 0.0458  | 0.2941  |
| flowerpot           | 0.0558  | 0.5882  | 0.0028  | 0.1765  |
| hat                 | 0.0568  | 0.2222  | 0.0000  | 0.0000  |
| bucket              | 0.2543  | 0.6923  | 0.0691  | 0.2308  |
| hamper              | 0.3188  | 0.5455  | 0.2235  | 0.3636  |
| stairs              | 0.0160  | 0.3611  | 0.0021  | 0.1111  |
| roll                | 0.0684  | 0.4000  | 0.0000  | 0.0000  |
| tube                | 0.0006  | 0.0769  | 0.0000  | 0.0000  |
| soap dispenser      | 0.4456  | 0.5926  | 0.0896  | 0.2222  |
| stall               | 0.0518  | 0.8824  | 0.0167  | 0.4118  |
| hanger              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| alarm               | 0.0007  | 0.1250  | 0.0000  | 0.0000  |
| pillar              | 0.3189  | 0.7674  | 0.0125  | 0.3721  |
| holder              | 0.0556  | 0.1111  | 0.0000  | 0.0000  |
| calendar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mailbox             | 0.2308  | 0.4000  | 0.2000  | 0.2000  |
| partition           | 0.0188  | 0.6667  | 0.0000  | 0.0000  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| window frame        | 0.0379  | 0.3838  | 0.0042  | 0.1818  |
| roof                | 0.0005  | 0.3750  | 0.0000  | 0.0417  |
| cube                | 0.5000  | 1.0000  | 0.5000  | 1.0000  |
| tablet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| projector           | 0.2500  | 0.5000  | 0.0238  | 0.5000  |
| furniture           | 0.0008  | 0.1667  | 0.0000  | 0.0000  |
| cart                | 0.0023  | 0.2500  | 0.0000  | 0.0000  |
| column              | 0.0118  | 0.3158  | 0.0006  | 0.1053  |
| printer             | 0.5689  | 0.7959  | 0.3546  | 0.6122  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vacuum cleaner      | 0.0891  | 0.5714  | 0.0816  | 0.2857  |
| file                | 0.1079  | 0.3824  | 0.0495  | 0.2059  |
| folder              | 0.0992  | 0.5714  | 0.0179  | 0.0714  |
| person              | 0.2222  | 0.3333  | 0.0027  | 0.1667  |
| ball                | 0.4237  | 0.6190  | 0.1609  | 0.3810  |
| bicycle             | 0.5621  | 0.8333  | 0.1019  | 0.3333  |
| fruit               | 0.1748  | 0.4286  | 0.0918  | 0.1429  |
| fire extinguisher   | 0.6129  | 0.8000  | 0.2000  | 0.2000  |
| copier              | 0.8958  | 0.9167  | 0.4620  | 0.6667  |
| teapot              | 0.1111  | 0.1111  | 0.0000  | 0.0000  |
| basin               | 0.0099  | 0.1765  | 0.0006  | 0.0588  |
| piano               | 0.2000  | 0.2000  | 0.2000  | 0.2000  |
| machine             | 0.4479  | 0.8947  | 0.2156  | 0.4737  |
| sign                | 0.0231  | 0.2333  | 0.0004  | 0.0333  |
| water cooler        | 0.7500  | 1.0000  | 0.7500  | 1.0000  |
| kettle              | 0.1096  | 0.5000  | 0.0855  | 0.2778  |
| range hood          | 0.2923  | 0.7143  | 0.0216  | 0.1429  |
| stove               | 0.4730  | 0.8333  | 0.2769  | 0.4722  |
| toaster             | 0.2455  | 0.5000  | 0.0000  | 0.0000  |
| crate               | 0.3873  | 0.5714  | 0.2476  | 0.4286  |
| heater              | 0.5034  | 0.7759  | 0.0663  | 0.2414  |
| oven                | 0.4589  | 0.7857  | 0.1790  | 0.4643  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jar                 | 0.0158  | 0.4615  | 0.0009  | 0.0769  |
| vase                | 0.1103  | 0.5400  | 0.0513  | 0.2800  |
| pot                 | 0.0951  | 0.4815  | 0.0081  | 0.1481  |
| dumbbell            | 0.4444  | 0.6667  | 0.1111  | 0.3333  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toiletry            | 0.0341  | 0.5217  | 0.0024  | 0.0435  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| umbrella            | 0.2000  | 0.2000  | 0.0000  | 0.0000  |
| air conditioner     | 0.5500  | 0.6000  | 0.2000  | 0.2000  |
| garbage             | 0.4893  | 0.6250  | 0.0190  | 0.2500  |
| headphones          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| countertop          | 0.0454  | 0.4286  | 0.0004  | 0.0714  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| conducting wire     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| device              | 0.0932  | 0.1818  | 0.0000  | 0.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| commode             | 0.4048  | 0.8269  | 0.2869  | 0.7115  |
| price tag           | 0.0459  | 0.2857  | 0.0000  | 0.0000  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| napkins             | 0.1667  | 0.3333  | 0.1667  | 0.3333  |
| player              | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| magazine            | 0.0024  | 0.2000  | 0.0000  | 0.0000  |
| fireplace           | 0.1005  | 0.5000  | 0.0129  | 0.1429  |
| candle              | 0.0589  | 0.1667  | 0.0000  | 0.0000  |
| humidifier          | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| drawer              | 0.1571  | 0.5556  | 0.0883  | 0.3889  |
| jalousie            | 0.2500  | 0.5000  | 0.2500  | 0.5000  |
| album               | 0.0278  | 1.0000  | 0.0000  | 0.0000  |
| sunglasses          | 0.0353  | 0.4615  | 0.0011  | 0.0769  |
| food                | 0.1547  | 0.2727  | 0.0000  | 0.0000  |
| seasoning           | 0.0015  | 0.1250  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shampoo             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.4232  | 0.8214  | 0.2050  | 0.5714  |
| clothes dryer       | 0.3591  | 0.8667  | 0.1824  | 0.6000  |
| flush               | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| beanbag             | 1.0000  | 1.0000  | 0.8214  | 1.0000  |
| cap                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| can                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchenware         | 0.1595  | 0.4783  | 0.0031  | 0.0435  |
| pan                 | 0.0866  | 0.4118  | 0.0081  | 0.1176  |
| pack                | 0.0392  | 0.6667  | 0.0001  | 0.0667  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| balcony             | 0.1250  | 0.5000  | 0.1250  | 0.5000  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radio               | 0.2500  | 0.2500  | 0.1250  | 0.2500  |
| crib                | 0.0667  | 0.3333  | 0.0667  | 0.3333  |
| mop                 | 0.6667  | 0.6667  | 0.0098  | 0.3333  |
| chandelier          | 0.0423  | 0.1905  | 0.0026  | 0.0476  |
| steps               | 0.0000  | 0.1000  | 0.0000  | 0.0000  |
| candlestick         | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coil                | 0.0023  | 0.1667  | 0.0000  | 0.0000  |
| washbasin           | 0.0062  | 0.2667  | 0.0018  | 0.1333  |
| footstool           | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| doorway             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| handle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| thermostat          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchen island      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool table          | 0.1111  | 0.3333  | 0.1111  | 0.3333  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beam                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| arch                | 0.0005  | 0.1000  | 0.0000  | 0.0333  |
| cosmetics           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.2267  | 0.3000  | 0.1667  | 0.2000  |
| helmets             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| map                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| spoon               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sack                | 0.4393  | 1.0000  | 0.1571  | 0.2857  |
| bread               | 0.1000  | 0.2000  | 0.0000  | 0.0000  |
| barricade           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| salt                | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| drum                | 0.0521  | 0.2500  | 0.0000  | 0.0000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| menu                | 0.1429  | 0.1429  | 0.0000  | 0.0000  |
| sponge              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glove               | 0.0857  | 0.6667  | 0.0000  | 0.0000  |
| excercise equipment | 0.4179  | 0.6500  | 0.0294  | 0.2500  |
| treadmill           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet               | 0.4667  | 0.6667  | 0.4667  | 0.6667  |
| garage door         | 0.3333  | 0.3333  | 0.2222  | 0.3333  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| letter              | 0.0625  | 0.2500  | 0.0000  | 0.0000  |
| wine                | 0.0325  | 0.0952  | 0.0000  | 0.0000  |
| water heater        | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| overall             | 0.1847  | 0.4051  | 0.0745  | 0.1788  |
| head                | 0.2659  | 0.5881  | 0.1038  | 0.2823  |
| common              | 0.1653  | 0.3798  | 0.0585  | 0.1402  |
| tail                | 0.1159  | 0.2286  | 0.0600  | 0.1073  |
| small               | 0.0514  | 0.1975  | 0.0045  | 0.0236  |
| medium              | 0.1970  | 0.4494  | 0.0835  | 0.2066  |
| large               | 0.0996  | 0.4716  | 0.0414  | 0.2388  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 02:04:39 INFO metrics.py:437 | number of scannet results: 250
Rank[0/8] 06/20/2025 02:05:52 INFO metrics.py:332 | eval results of scannet:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| bathtub             | 0.8916  | 0.9375  | 0.4813  | 0.6250  |
| toilet              | 0.9072  | 0.9286  | 0.7155  | 0.8036  |
| curtain             | 0.5438  | 0.8142  | 0.1494  | 0.4248  |
| bin                 | 0.6445  | 0.8043  | 0.3394  | 0.5617  |
| picture             | 0.1685  | 0.3396  | 0.0111  | 0.0634  |
| sink                | 0.7858  | 0.8750  | 0.3079  | 0.4886  |
| toilet paper        | 0.4334  | 0.5949  | 0.0903  | 0.2405  |
| door                | 0.3655  | 0.7860  | 0.0844  | 0.4181  |
| rail                | 0.0510  | 0.5000  | 0.0008  | 0.3333  |
| vanity              | 0.5536  | 1.0000  | 0.4298  | 0.8667  |
| towel               | 0.3854  | 0.6446  | 0.1090  | 0.2645  |
| doorframe           | 0.2597  | 0.7113  | 0.0517  | 0.3521  |
| bar                 | 0.0003  | 0.1364  | 0.0000  | 0.0455  |
| mirror              | 0.2397  | 0.6265  | 0.0277  | 0.1446  |
| hair dryer          | 0.0336  | 0.2857  | 0.0000  | 0.0000  |
| stopcock            | 0.1148  | 0.5862  | 0.0082  | 0.1379  |
| bottle              | 0.0788  | 0.4842  | 0.0040  | 0.1053  |
| switch              | 0.0020  | 0.1013  | 0.0000  | 0.0000  |
| tissue box          | 0.1528  | 0.5000  | 0.0208  | 0.1364  |
| bag                 | 0.2349  | 0.6986  | 0.1062  | 0.3699  |
| cup                 | 0.1623  | 0.4348  | 0.0144  | 0.1014  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| counter             | 0.4082  | 0.9429  | 0.0970  | 0.5429  |
| ledge               | 0.0049  | 0.5000  | 0.0009  | 0.1667  |
| socket              | 0.0048  | 0.1845  | 0.0000  | 0.0146  |
| clothes             | 0.1377  | 0.6078  | 0.0557  | 0.3007  |
| box                 | 0.1174  | 0.4722  | 0.0183  | 0.1349  |
| light               | 0.2496  | 0.5172  | 0.0355  | 0.1034  |
| backpack            | 0.6122  | 0.8571  | 0.4372  | 0.6071  |
| bed                 | 0.9515  | 1.0000  | 0.8542  | 0.9649  |
| shower              | 0.1787  | 0.2857  | 0.0002  | 0.0714  |
| soap dish           | 0.6683  | 0.7500  | 0.0000  | 0.0000  |
| purse               | 0.0012  | 0.1667  | 0.0000  | 0.0000  |
| paper               | 0.0224  | 0.2717  | 0.0009  | 0.0528  |
| cabinet             | 0.3351  | 0.7698  | 0.1393  | 0.4921  |
| mat                 | 0.0064  | 0.2000  | 0.0003  | 0.1000  |
| blanket             | 0.0674  | 0.5833  | 0.0258  | 0.4167  |
| faucet              | 0.0009  | 0.1250  | 0.0000  | 0.0000  |
| tissue              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shelf               | 0.4263  | 0.8824  | 0.2695  | 0.6691  |
| toothpaste          | 0.0035  | 0.4000  | 0.0009  | 0.2000  |
| basket              | 0.0665  | 0.3077  | 0.0444  | 0.2308  |
| wrap                | 0.0025  | 1.0000  | 0.0000  | 0.0000  |
| scale               | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| plant               | 0.3266  | 0.6303  | 0.0464  | 0.2437  |
| lamp                | 0.3968  | 0.6667  | 0.1463  | 0.2857  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mouse               | 0.0088  | 0.2381  | 0.0000  | 0.0000  |
| grab bar            | 0.0042  | 0.1250  | 0.0000  | 0.0000  |
| microwave           | 0.6758  | 0.8000  | 0.1940  | 0.5000  |
| plunger             | 0.7600  | 0.8000  | 0.6000  | 0.6000  |
| dispenser           | 0.5173  | 0.6667  | 0.2333  | 0.3846  |
| dish rack           | 0.0278  | 0.5000  | 0.0000  | 0.0000  |
| couch               | 0.6470  | 0.9625  | 0.4988  | 0.8125  |
| chair               | 0.5929  | 0.7426  | 0.4222  | 0.5577  |
| monitor             | 0.4474  | 0.5429  | 0.0446  | 0.1643  |
| computer            | 0.3252  | 0.5897  | 0.0022  | 0.1282  |
| radiator            | 0.7201  | 0.8103  | 0.3163  | 0.3793  |
| keyboard            | 0.0247  | 0.1552  | 0.0000  | 0.0000  |
| table               | 0.5706  | 0.8923  | 0.3842  | 0.7692  |
| desk                | 0.6313  | 0.9620  | 0.3542  | 0.7089  |
| blackboard          | 0.5567  | 0.8116  | 0.1289  | 0.3478  |
| window              | 0.4243  | 0.7963  | 0.0672  | 0.3457  |
| telephone           | 0.4042  | 0.6562  | 0.1052  | 0.2188  |
| plug                | 0.0001  | 0.1111  | 0.0000  | 0.0000  |
| wardrobe            | 0.4437  | 0.8750  | 0.3438  | 0.3750  |
| stand               | 0.7962  | 0.9057  | 0.6448  | 0.7925  |
| book                | 0.0414  | 0.3429  | 0.0023  | 0.0714  |
| windowsill          | 0.0039  | 0.5000  | 0.0000  | 0.0000  |
| shoe                | 0.1567  | 0.5750  | 0.0285  | 0.0750  |
| laptop              | 0.4911  | 0.6000  | 0.1652  | 0.3333  |
| notebook            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| board               | 0.1188  | 0.7188  | 0.0035  | 0.1875  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jacket              | 0.6114  | 0.8261  | 0.2309  | 0.3913  |
| pipe                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillow              | 0.4514  | 0.6736  | 0.1737  | 0.4097  |
| dresser             | 0.7745  | 0.8919  | 0.7217  | 0.8108  |
| toy                 | 0.1260  | 0.2941  | 0.1176  | 0.1176  |
| case                | 0.5271  | 1.0000  | 0.3870  | 0.7368  |
| cushion             | 0.0137  | 0.5000  | 0.0000  | 0.0000  |
| adhesive tape       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| carpet              | 0.0144  | 0.4000  | 0.0000  | 0.0000  |
| clock               | 0.2507  | 0.6000  | 0.0133  | 0.1200  |
| decoration          | 0.0576  | 0.4167  | 0.0006  | 0.0417  |
| tv                  | 0.6044  | 0.7826  | 0.1315  | 0.3913  |
| frame               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| container           | 0.0027  | 0.2174  | 0.0003  | 0.0435  |
| stool               | 0.2881  | 0.7273  | 0.1547  | 0.4773  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| guitar              | 0.0041  | 0.3333  | 0.0000  | 0.0000  |
| refrigerator        | 0.8677  | 0.9500  | 0.3726  | 0.6000  |
| ottoman             | 0.0398  | 0.7143  | 0.0131  | 0.4286  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coffee maker        | 0.5010  | 0.7647  | 0.1037  | 0.4118  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0127  | 1.0000  | 0.0000  | 0.0000  |
| bench               | 0.5167  | 1.0000  | 0.4550  | 1.0000  |
| plate               | 0.0205  | 0.4000  | 0.0000  | 0.0000  |
| fan                 | 0.5646  | 0.8750  | 0.0850  | 0.3750  |
| broom               | 0.0227  | 0.5000  | 0.0000  | 0.0000  |
| paper cutter        | 0.8092  | 1.0000  | 0.0000  | 0.0000  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rack                | 0.0113  | 0.3636  | 0.0109  | 0.2727  |
| bowl                | 0.1405  | 0.4444  | 0.0101  | 0.2222  |
| ladder              | 0.1238  | 0.4000  | 0.0667  | 0.2000  |
| flowerpot           | 0.0364  | 1.0000  | 0.0152  | 0.5000  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket              | 0.1252  | 0.7143  | 0.0257  | 0.1429  |
| hamper              | 0.3507  | 0.6000  | 0.2459  | 0.4000  |
| stairs              | 0.0257  | 0.8000  | 0.0011  | 0.2000  |
| tube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dispenser      | 0.5343  | 0.6667  | 0.1148  | 0.2381  |
| stall               | 0.0524  | 0.8824  | 0.0169  | 0.4118  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillar              | 0.2358  | 0.8000  | 0.0107  | 0.6000  |
| holder              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| calendar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mailbox             | 0.2308  | 0.4000  | 0.2000  | 0.2000  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cart                | 0.0047  | 0.5000  | 0.0000  | 0.0000  |
| printer             | 0.6239  | 0.9091  | 0.4822  | 0.7273  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vacuum cleaner      | 0.5679  | 1.0000  | 0.5556  | 0.6667  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| person              | 0.2222  | 0.3333  | 0.0027  | 0.1667  |
| ball                | 0.4175  | 0.5000  | 0.0833  | 0.1667  |
| bicycle             | 0.6911  | 1.0000  | 0.0476  | 0.3333  |
| fruit               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher   | 0.3535  | 0.6667  | 0.0000  | 0.0000  |
| copier              | 0.8958  | 0.9167  | 0.4620  | 0.6667  |
| teapot              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basin               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine             | 0.4686  | 0.9375  | 0.2833  | 0.5000  |
| sign                | 0.0163  | 0.2143  | 0.0005  | 0.0357  |
| water cooler        | 0.7500  | 1.0000  | 0.7500  | 1.0000  |
| kettle              | 0.2500  | 0.5000  | 0.0833  | 0.2500  |
| range hood          | 0.6596  | 1.0000  | 0.0667  | 0.2000  |
| stove               | 0.8417  | 1.0000  | 0.8417  | 1.0000  |
| toaster             | 0.0100  | 0.2500  | 0.0000  | 0.0000  |
| crate               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| oven                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pot                 | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headphones          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.2611  | 0.6667  | 0.2611  | 0.6667  |
| dishwasher          | 0.1111  | 0.3333  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| map                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.7500  | 1.0000  | 0.7500  | 1.0000  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drawer              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| scannet             | 0.2421  | 0.4750  | 0.1126  | 0.2310  |
| head                | 0.3201  | 0.6175  | 0.1537  | 0.3298  |
| common              | 0.1797  | 0.3511  | 0.0634  | 0.1296  |
| tail                | 0.0892  | 0.2237  | 0.0775  | 0.1000  |
| small               | 0.0427  | 0.1960  | 0.0056  | 0.0274  |
| medium              | 0.2802  | 0.5570  | 0.1297  | 0.2855  |
| large               | 0.2371  | 0.7456  | 0.1084  | 0.4287  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 02:05:52 INFO metrics.py:437 | number of 3rscan results: 235
Rank[0/8] 06/20/2025 02:06:59 INFO metrics.py:332 | eval results of 3rscan:
+-------------------+---------+---------+---------+---------+
| classes           | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+-------------------+---------+---------+---------+---------+
| chair             | 0.6476  | 0.7774  | 0.4821  | 0.6219  |
| monitor           | 0.4262  | 0.6000  | 0.0109  | 0.0750  |
| keyboard          | 0.0075  | 0.1667  | 0.0000  | 0.0000  |
| desk              | 0.4380  | 0.8625  | 0.2222  | 0.6750  |
| case              | 0.0594  | 0.4214  | 0.0049  | 0.0643  |
| mouse             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bag               | 0.3064  | 0.7374  | 0.0368  | 0.2626  |
| bin               | 0.6720  | 0.8659  | 0.2506  | 0.4512  |
| paper             | 0.0926  | 0.3571  | 0.0029  | 0.0429  |
| computer          | 0.3117  | 0.7143  | 0.0073  | 0.1429  |
| stand             | 0.4109  | 0.9180  | 0.2139  | 0.5082  |
| table             | 0.4884  | 0.8694  | 0.3388  | 0.6982  |
| cup               | 0.0205  | 0.2821  | 0.0000  | 0.0000  |
| backpack          | 0.5245  | 0.7879  | 0.2476  | 0.4242  |
| garbage           | 0.4893  | 0.6250  | 0.0192  | 0.2500  |
| bottle            | 0.0294  | 0.3554  | 0.0000  | 0.0083  |
| box               | 0.1763  | 0.5185  | 0.0264  | 0.1656  |
| plug              | 0.1429  | 0.2222  | 0.0000  | 0.0000  |
| pen               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| telephone         | 0.2256  | 0.5000  | 0.0004  | 0.0417  |
| book              | 0.0317  | 0.3669  | 0.0008  | 0.0533  |
| window            | 0.2125  | 0.6027  | 0.0267  | 0.2055  |
| shelf             | 0.2140  | 0.6643  | 0.0629  | 0.3463  |
| curtain           | 0.4955  | 0.8571  | 0.0724  | 0.4603  |
| paper cutter      | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| wardrobe          | 0.3736  | 0.8281  | 0.1450  | 0.5625  |
| cabinet           | 0.2889  | 0.8205  | 0.1378  | 0.5641  |
| conducting wire   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| socket            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plant             | 0.2847  | 0.5941  | 0.0595  | 0.2178  |
| plate             | 0.0149  | 0.2727  | 0.0012  | 0.0303  |
| tissue box        | 0.0035  | 0.3333  | 0.0035  | 0.3333  |
| notebook          | 0.0002  | 0.0909  | 0.0000  | 0.0000  |
| pillow            | 0.4276  | 0.6719  | 0.0380  | 0.1836  |
| file              | 0.1153  | 0.4286  | 0.0573  | 0.2143  |
| blinds            | 0.1499  | 0.5128  | 0.0075  | 0.2051  |
| cushion           | 0.2684  | 0.6000  | 0.0360  | 0.2000  |
| hat               | 0.1492  | 0.2857  | 0.0000  | 0.0000  |
| device            | 0.0932  | 0.1818  | 0.0000  | 0.0000  |
| couch             | 0.3942  | 0.7714  | 0.2743  | 0.6429  |
| shoe              | 0.2646  | 0.5714  | 0.0041  | 0.0952  |
| container         | 0.0446  | 0.6667  | 0.0374  | 0.2222  |
| stool             | 0.3290  | 0.8519  | 0.1690  | 0.5556  |
| printer           | 0.6508  | 0.6667  | 0.1589  | 0.4444  |
| clothes           | 0.1704  | 0.6602  | 0.0082  | 0.1748  |
| folder            | 0.0992  | 0.5714  | 0.0179  | 0.0714  |
| rack              | 0.1600  | 0.7000  | 0.0572  | 0.4667  |
| tv                | 0.6338  | 0.8857  | 0.1570  | 0.3429  |
| commode           | 0.4052  | 0.8269  | 0.2872  | 0.7115  |
| lamp              | 0.2083  | 0.4688  | 0.0124  | 0.1187  |
| frame             | 0.0714  | 0.6333  | 0.0004  | 0.0333  |
| picture           | 0.1345  | 0.3455  | 0.0031  | 0.0488  |
| blanket           | 0.3385  | 0.8769  | 0.0711  | 0.5077  |
| bench             | 0.4425  | 0.8214  | 0.3721  | 0.6786  |
| bed               | 0.7923  | 0.9455  | 0.4597  | 0.7818  |
| ottoman           | 0.3691  | 1.0000  | 0.3516  | 0.8182  |
| door              | 0.3041  | 0.7279  | 0.0329  | 0.2206  |
| light             | 0.0406  | 0.4091  | 0.0009  | 0.0227  |
| bowl              | 0.1545  | 0.5556  | 0.0653  | 0.1111  |
| decoration        | 0.1399  | 0.5000  | 0.0201  | 0.0938  |
| doorframe         | 0.1385  | 0.6800  | 0.0331  | 0.2800  |
| carpet            | 0.1027  | 0.6429  | 0.0054  | 0.2321  |
| flowerpot         | 0.0715  | 0.5833  | 0.0013  | 0.0833  |
| switch            | 0.0012  | 0.0566  | 0.0000  | 0.0000  |
| poster            | 0.0106  | 0.0952  | 0.0000  | 0.0000  |
| counter           | 0.1367  | 0.8293  | 0.0106  | 0.2439  |
| mat               | 0.0185  | 0.3333  | 0.0039  | 0.3333  |
| clock             | 0.2448  | 0.4211  | 0.0000  | 0.0000  |
| jar               | 0.0312  | 0.6250  | 0.0019  | 0.1250  |
| bar               | 0.4667  | 0.6667  | 0.0139  | 0.3333  |
| price tag         | 0.0459  | 0.2857  | 0.0000  | 0.0000  |
| mattress          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dispenser         | 0.0692  | 0.6667  | 0.0013  | 0.3333  |
| board             | 0.2315  | 0.6250  | 0.1250  | 0.1250  |
| vase              | 0.1874  | 0.5500  | 0.1029  | 0.2500  |
| kettle            | 0.1548  | 0.5833  | 0.1248  | 0.3333  |
| napkins           | 0.1667  | 0.3333  | 0.1667  | 0.3333  |
| stairs            | 0.1000  | 0.7143  | 0.0074  | 0.2857  |
| sink              | 0.6456  | 0.7857  | 0.1179  | 0.2679  |
| laptop            | 0.4387  | 1.0000  | 0.0333  | 0.1667  |
| towel             | 0.2299  | 0.5949  | 0.0353  | 0.2152  |
| oven              | 0.6618  | 1.0000  | 0.2637  | 0.6316  |
| player            | 0.3333  | 0.3333  | 0.0000  | 0.0000  |
| magazine          | 0.0030  | 0.2500  | 0.0000  | 0.0000  |
| teapot            | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| fireplace         | 0.0226  | 0.4000  | 0.0000  | 0.0000  |
| mirror            | 0.1267  | 0.5185  | 0.0077  | 0.1481  |
| calendar          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candle            | 0.0697  | 0.1333  | 0.0000  | 0.0000  |
| toy               | 0.2710  | 0.6154  | 0.0733  | 0.2308  |
| basket            | 0.3952  | 0.7188  | 0.2128  | 0.3906  |
| humidifier        | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| blackboard        | 0.4754  | 0.7500  | 0.0030  | 0.0833  |
| windowsill        | 0.0492  | 0.6410  | 0.0024  | 0.2308  |
| heater            | 0.5081  | 0.7719  | 0.0677  | 0.2456  |
| fan               | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| roll              | 0.0807  | 0.4000  | 0.0000  | 0.0000  |
| partition         | 0.0232  | 0.8000  | 0.0000  | 0.0000  |
| radiator          | 0.5460  | 0.7241  | 0.0387  | 0.1379  |
| umbrella          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| coffee maker      | 0.1635  | 0.5556  | 0.0000  | 0.0000  |
| drawer            | 0.3340  | 1.0000  | 0.1861  | 0.7778  |
| jalousie          | 0.2500  | 0.5000  | 0.2500  | 0.5000  |
| speaker           | 0.0701  | 0.2500  | 0.0000  | 0.0000  |
| tablet            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball              | 0.5286  | 0.8000  | 0.5000  | 0.6000  |
| album             | 0.0667  | 1.0000  | 0.0000  | 0.0000  |
| remote control    | 0.1218  | 0.3000  | 0.0000  | 0.0000  |
| hanger            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toilet paper      | 0.1081  | 0.4167  | 0.0278  | 0.0833  |
| scale             | 0.3519  | 0.6667  | 0.3333  | 0.3333  |
| soap dispenser    | 0.2058  | 0.4000  | 0.0029  | 0.2000  |
| rail              | 0.0007  | 0.2500  | 0.0000  | 0.0000  |
| luggage           | 0.3946  | 1.0000  | 0.3636  | 0.6667  |
| fruit             | 0.4500  | 0.6000  | 0.2000  | 0.2000  |
| toilet            | 0.8107  | 0.8824  | 0.3275  | 0.4706  |
| air conditioner   | 0.6875  | 0.7500  | 0.2500  | 0.2500  |
| sunglasses        | 0.0354  | 0.4615  | 0.0011  | 0.0769  |
| food              | 0.1714  | 0.3000  | 0.0000  | 0.0000  |
| pad               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| seasoning         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dresser           | 0.5073  | 1.0000  | 0.0000  | 0.0000  |
| furniture         | 0.0175  | 0.3333  | 0.0000  | 0.0000  |
| bathtub           | 0.8642  | 0.8889  | 0.5661  | 0.6667  |
| statue            | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| faucet            | 0.1273  | 1.0000  | 0.0000  | 0.0000  |
| shower            | 0.2512  | 0.7500  | 0.2500  | 0.2500  |
| shampoo           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toiletry          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine   | 0.6348  | 0.8571  | 0.2219  | 0.4286  |
| brush             | 0.2177  | 0.6250  | 0.0000  | 0.0000  |
| clothes dryer     | 0.3865  | 0.9000  | 0.0804  | 0.5000  |
| bucket            | 0.6702  | 0.9333  | 0.1951  | 0.4000  |
| tube              | 0.0014  | 0.1429  | 0.0000  | 0.0000  |
| tissue            | 0.0293  | 0.6667  | 0.0014  | 0.3333  |
| flush             | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| soap              | 0.1429  | 1.0000  | 0.0000  | 0.0000  |
| pipe              | 0.0174  | 0.5000  | 0.0000  | 0.0000  |
| beanbag           | 1.0000  | 1.0000  | 0.8306  | 1.0000  |
| can               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillar            | 0.4408  | 0.7647  | 0.0270  | 0.3529  |
| column            | 0.0465  | 1.0000  | 0.0208  | 0.5000  |
| microwave         | 0.9372  | 1.0000  | 0.2677  | 0.5882  |
| stove             | 0.3139  | 0.8235  | 0.0381  | 0.2941  |
| kitchenware       | 0.1674  | 0.5000  | 0.0032  | 0.0455  |
| refrigerator      | 0.6806  | 0.8667  | 0.4374  | 0.6000  |
| pan               | 0.1585  | 0.5000  | 0.0153  | 0.1667  |
| range hood        | 0.1833  | 0.6000  | 0.0000  | 0.0000  |
| pot               | 0.3646  | 0.5833  | 0.0955  | 0.2500  |
| dish rack         | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| pack              | 0.0468  | 0.7692  | 0.0001  | 0.0769  |
| basin             | 0.0121  | 0.1333  | 0.0000  | 0.0000  |
| knife             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| balcony           | 0.1250  | 0.5000  | 0.1250  | 0.5000  |
| crate             | 0.6833  | 0.8000  | 0.4000  | 0.6000  |
| wire              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radio             | 1.0000  | 1.0000  | 0.5000  | 1.0000  |
| sign              | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| crib              | 0.0667  | 0.3333  | 0.0667  | 0.3333  |
| holder            | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| broom             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mop               | 0.6667  | 0.6667  | 0.0370  | 0.3333  |
| chandelier        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| piano             | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| tray              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candlestick       | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| machine           | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| bicycle           | 1.0000  | 1.0000  | 0.2500  | 0.5000  |
| screen            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| guitar            | 0.7500  | 1.0000  | 0.0000  | 0.0000  |
| window frame      | 0.1929  | 0.3333  | 0.0000  | 0.0000  |
| soap dish         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cube              | 0.5000  | 1.0000  | 0.5000  | 1.0000  |
| adhesive tape     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vacuum cleaner    | 0.0040  | 0.2500  | 0.0000  | 0.0000  |
| ladder            | 0.1156  | 1.0000  | 0.0952  | 1.0000  |
| coil              | 0.0023  | 0.1667  | 0.0000  | 0.0000  |
| spoon             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tool              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hook              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell          | 0.5000  | 1.0000  | 0.0000  | 0.0000  |
| sack              | 0.4468  | 1.0000  | 0.1579  | 0.2857  |
| helmets           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bread             | 0.1000  | 0.2000  | 0.0000  | 0.0000  |
| glass             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| salt              | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| menu              | 0.1429  | 0.1429  | 0.0000  | 0.0000  |
| sponge            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glove             | 0.0857  | 0.6667  | 0.0000  | 0.0000  |
| map               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| letter            | 0.0625  | 0.2500  | 0.0000  | 0.0000  |
| shovel            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher        | 0.6667  | 0.6667  | 0.6667  | 0.6667  |
| beam              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washbasin         | 0.1111  | 1.0000  | 0.1111  | 1.0000  |
| treadmill         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| footstool         | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| bidet             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| water heater      | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| fire extinguisher | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| toaster           | 0.6080  | 1.0000  | 0.0000  | 0.0000  |
| cosmetics         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scissors          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------           | ------- | ------- | ------- | ------- |
| 3rscan            | 0.2792  | 0.5350  | 0.0998  | 0.2186  |
| head              | 0.2931  | 0.6402  | 0.0927  | 0.2650  |
| common            | 0.3116  | 0.5361  | 0.1048  | 0.1948  |
| tail              | 0.2072  | 0.3321  | 0.1066  | 0.1631  |
| small             | 0.0783  | 0.2492  | 0.0130  | 0.0279  |
| medium            | 0.2961  | 0.6040  | 0.1085  | 0.2577  |
| large             | 0.2057  | 0.6772  | 0.0875  | 0.3889  |
+-------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 02:06:59 INFO metrics.py:437 | number of matterport3d results: 339
Rank[0/8] 06/20/2025 02:08:20 INFO metrics.py:332 | eval results of matterport3d:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| mirror              | 0.0988  | 0.4730  | 0.0012  | 0.1081  |
| sink                | 0.5109  | 0.7576  | 0.1242  | 0.3939  |
| door                | 0.2112  | 0.6084  | 0.0071  | 0.1514  |
| doorframe           | 0.3308  | 0.7652  | 0.0814  | 0.4063  |
| light               | 0.0188  | 0.2023  | 0.0002  | 0.0313  |
| bathtub             | 0.5962  | 0.7778  | 0.4249  | 0.6667  |
| towel               | 0.3874  | 0.5818  | 0.0788  | 0.3091  |
| faucet              | 0.0177  | 0.2222  | 0.0003  | 0.0556  |
| shelf               | 0.1787  | 0.5766  | 0.0553  | 0.3431  |
| dresser             | 0.2983  | 0.8667  | 0.1931  | 0.8000  |
| bottle              | 0.1035  | 0.6042  | 0.0125  | 0.2083  |
| plant               | 0.2665  | 0.7054  | 0.0691  | 0.3839  |
| clothes             | 0.1025  | 0.3966  | 0.0133  | 0.1724  |
| drawer              | 0.0003  | 0.1250  | 0.0000  | 0.0000  |
| countertop          | 0.0463  | 0.4286  | 0.0004  | 0.0714  |
| case                | 0.0015  | 0.3333  | 0.0000  | 0.0556  |
| picture             | 0.1444  | 0.3527  | 0.0095  | 0.0652  |
| cabinet             | 0.1422  | 0.6568  | 0.0514  | 0.3686  |
| blanket             | 0.0657  | 0.4545  | 0.0221  | 0.2727  |
| toilet              | 0.7322  | 0.8438  | 0.6066  | 0.7344  |
| lamp                | 0.2743  | 0.6374  | 0.1099  | 0.3187  |
| roof                | 0.0005  | 0.3750  | 0.0000  | 0.0417  |
| decoration          | 0.0174  | 0.4405  | 0.0011  | 0.0833  |
| window              | 0.1017  | 0.4611  | 0.0104  | 0.1639  |
| counter             | 0.0387  | 0.3939  | 0.0012  | 0.1515  |
| toiletry            | 0.0413  | 0.5455  | 0.0027  | 0.0455  |
| washbasin           | 0.0059  | 0.2143  | 0.0014  | 0.0714  |
| vent                | 0.0558  | 0.2083  | 0.0000  | 0.0000  |
| table               | 0.2828  | 0.7244  | 0.1987  | 0.5705  |
| switch              | 0.0002  | 0.1000  | 0.0000  | 0.0000  |
| stand               | 0.4909  | 0.8200  | 0.4042  | 0.6600  |
| vase                | 0.0626  | 0.5333  | 0.0163  | 0.3000  |
| shower              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| curtain             | 0.4050  | 0.7500  | 0.0942  | 0.4271  |
| bed                 | 0.6185  | 0.8070  | 0.2998  | 0.7719  |
| soap                | 0.0016  | 0.2000  | 0.0016  | 0.2000  |
| vanity              | 0.2606  | 0.6667  | 0.2567  | 0.6000  |
| wardrobe            | 0.1037  | 0.5455  | 0.0649  | 0.3636  |
| flowerpot           | 0.0685  | 0.5600  | 0.0069  | 0.2400  |
| statue              | 0.0855  | 0.5294  | 0.0671  | 0.2941  |
| mat                 | 0.0238  | 0.4167  | 0.0009  | 0.0417  |
| pillow              | 0.2142  | 0.5804  | 0.0187  | 0.1420  |
| pot                 | 0.0198  | 0.3846  | 0.0002  | 0.0769  |
| basket              | 0.0589  | 0.4400  | 0.0029  | 0.0800  |
| box                 | 0.0483  | 0.7115  | 0.0162  | 0.3077  |
| frame               | 0.0010  | 0.1765  | 0.0000  | 0.0000  |
| paper               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| alarm               | 0.0009  | 0.1667  | 0.0000  | 0.0000  |
| couch               | 0.3498  | 0.7826  | 0.2253  | 0.6304  |
| bowl                | 0.0823  | 0.3939  | 0.0022  | 0.0606  |
| plate               | 0.2215  | 0.3571  | 0.0000  | 0.0000  |
| ottoman             | 0.0403  | 0.5000  | 0.0311  | 0.3889  |
| tv                  | 0.4315  | 0.6047  | 0.1110  | 0.3256  |
| chair               | 0.4142  | 0.6701  | 0.2688  | 0.5136  |
| book                | 0.0640  | 0.2992  | 0.0046  | 0.0551  |
| stairs              | 0.0102  | 0.2264  | 0.0033  | 0.0566  |
| stool               | 0.0221  | 0.2857  | 0.0031  | 0.1905  |
| screen              | 0.0625  | 0.2500  | 0.0000  | 0.0000  |
| bench               | 0.1452  | 0.4500  | 0.0929  | 0.1500  |
| cushion             | 0.0387  | 0.2941  | 0.0045  | 0.1765  |
| desk                | 0.2123  | 0.6842  | 0.1596  | 0.6316  |
| food                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| footstool           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| telephone           | 0.2895  | 0.7500  | 0.0000  | 0.0000  |
| steps               | 0.0000  | 0.1000  | 0.0000  | 0.0000  |
| clock               | 0.3431  | 0.6667  | 0.0000  | 0.0000  |
| carpet              | 0.0003  | 0.0556  | 0.0000  | 0.0000  |
| fireplace           | 0.1605  | 0.5217  | 0.0190  | 0.1739  |
| doorway             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toy                 | 0.1548  | 0.3889  | 0.0167  | 0.0833  |
| cup                 | 0.0045  | 0.1000  | 0.0045  | 0.1000  |
| bin                 | 0.2563  | 0.7317  | 0.0355  | 0.2683  |
| bar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit               | 0.1232  | 0.3750  | 0.0625  | 0.1250  |
| column              | 0.0141  | 0.2778  | 0.0003  | 0.0833  |
| pillar              | 0.2870  | 0.7619  | 0.0077  | 0.3333  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| window frame        | 0.0310  | 0.3908  | 0.0050  | 0.2069  |
| handle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| windowsill          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rail                | 0.0033  | 0.2593  | 0.0000  | 0.0185  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dispenser           | 0.0048  | 0.5000  | 0.0000  | 0.0000  |
| socket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.3269  | 0.8750  | 0.2704  | 0.7500  |
| toilet paper        | 0.2912  | 0.6389  | 0.0431  | 0.2500  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dispenser      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fan                 | 0.6369  | 0.8148  | 0.3604  | 0.6296  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| thermostat          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| heater              | 0.2000  | 1.0000  | 0.0000  | 0.0000  |
| bucket              | 0.0138  | 0.3000  | 0.0100  | 0.1000  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hanger              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue box          | 0.0536  | 0.5000  | 0.0004  | 0.1250  |
| tissue              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchen island      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| monitor             | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| teapot              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| seasoning           | 0.0053  | 0.1429  | 0.0000  | 0.0000  |
| board               | 0.1875  | 0.3333  | 0.0000  | 0.0000  |
| jar                 | 0.0016  | 0.3333  | 0.0000  | 0.0000  |
| rack                | 0.0048  | 0.3636  | 0.0016  | 0.1818  |
| pack                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| printer             | 0.4286  | 0.4286  | 0.2381  | 0.2857  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| keyboard            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stove               | 0.3685  | 0.7000  | 0.1400  | 0.3000  |
| container           | 0.0740  | 0.3704  | 0.0083  | 0.1852  |
| radiator            | 0.1667  | 0.3333  | 0.0000  | 0.0000  |
| scale               | 0.0016  | 0.2500  | 0.0000  | 0.0000  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pan                 | 0.0333  | 0.2000  | 0.0000  | 0.0000  |
| basin               | 0.0500  | 1.0000  | 0.0500  | 1.0000  |
| computer            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bag                 | 0.0485  | 0.5385  | 0.0000  | 0.0000  |
| pool table          | 0.1111  | 0.3333  | 0.1111  | 0.3333  |
| partition           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| guitar              | 0.1429  | 0.4286  | 0.0000  | 0.0000  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0508  | 0.3750  | 0.0000  | 0.0000  |
| projector           | 0.5000  | 0.5000  | 0.1000  | 0.5000  |
| mouse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.1905  | 0.6667  | 0.1905  | 0.6667  |
| microwave           | 0.1529  | 0.5000  | 0.0841  | 0.1667  |
| shoe                | 0.0249  | 0.2800  | 0.0000  | 0.0000  |
| fire extinguisher   | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| ladder              | 0.1407  | 0.5556  | 0.1111  | 0.1111  |
| pipe                | 0.0002  | 0.1000  | 0.0000  | 0.0000  |
| dumbbell            | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| brush               | 0.0015  | 0.2500  | 0.0000  | 0.0000  |
| soap dish           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beam                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candle              | 0.0083  | 0.3333  | 0.0000  | 0.0000  |
| kettle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| arch                | 0.0005  | 0.1000  | 0.0000  | 0.0333  |
| ball                | 0.4467  | 0.7500  | 0.2300  | 0.7500  |
| air conditioner     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| chandelier          | 0.0526  | 0.2105  | 0.0033  | 0.0526  |
| refrigerator        | 0.1790  | 0.6667  | 0.0681  | 0.2000  |
| range hood          | 0.0711  | 0.5000  | 0.0417  | 0.2500  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| laptop              | 0.8333  | 1.0000  | 0.0000  | 0.0000  |
| oven                | 0.2758  | 0.6000  | 0.0667  | 0.2000  |
| kitchenware         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| file                | 0.1250  | 0.2500  | 0.0500  | 0.2500  |
| machine             | 0.0063  | 0.5000  | 0.0063  | 0.5000  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| piano               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.4645  | 0.7222  | 0.0327  | 0.2778  |
| backpack            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet               | 0.1667  | 0.5000  | 0.1667  | 0.5000  |
| garage door         | 0.3333  | 0.3333  | 0.2222  | 0.3333  |
| bicycle             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| holder              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wine                | 0.0325  | 0.0952  | 0.0000  | 0.0000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hamper              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drum                | 0.0530  | 0.2500  | 0.0000  | 0.0000  |
| coffee maker        | 0.1667  | 0.3333  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radio               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| player              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| matterport3d        | 0.1138  | 0.3170  | 0.0416  | 0.1420  |
| head                | 0.1670  | 0.4572  | 0.0591  | 0.2008  |
| common              | 0.0678  | 0.2171  | 0.0249  | 0.0914  |
| tail                | 0.0487  | 0.0858  | 0.0250  | 0.0694  |
| small               | 0.0458  | 0.1562  | 0.0011  | 0.0184  |
| medium              | 0.1099  | 0.3527  | 0.0419  | 0.1520  |
| large               | 0.0753  | 0.3728  | 0.0335  | 0.1930  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 02:08:20 INFO metrics.py:437 | number of arkit results: 0
Rank[0/8] 06/20/2025 02:08:21 INFO stats.py:394 | Epoch[89] completed. Training Speed: 3.06 samples/sec across all devices.	Epoch Time: 1018.09 sec.	Average Epoch Time: 1018.09 sec.	Average Step Time: 2.61 sec.	Estimated Remaining Time: 19:53:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:08:21 WARNING accelerator.py:3099 | Deleting 1 checkpoints to make room for new checkpoint.
Rank[0/8] 06/20/2025 02:08:22 INFO accelerator.py:3111 | Saving current state to /job_data/checkpoints/checkpoint_8
Rank[3/8] 06/20/2025 02:08:22 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 89 to /job_data/checkpoints/checkpoint_8
Rank[6/8] 06/20/2025 02:08:22 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 89 to /job_data/checkpoints/checkpoint_8
Rank[1/8] 06/20/2025 02:08:22 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 89 to /job_data/checkpoints/checkpoint_8
Rank[7/8] 06/20/2025 02:08:22 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 89 to /job_data/checkpoints/checkpoint_8
Rank[5/8] 06/20/2025 02:08:22 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 89 to /job_data/checkpoints/checkpoint_8
Rank[2/8] 06/20/2025 02:08:22 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 89 to /job_data/checkpoints/checkpoint_8
Rank[4/8] 06/20/2025 02:08:22 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 89 to /job_data/checkpoints/checkpoint_8
Rank[0/8] 06/20/2025 02:08:23 INFO checkpointing.py:106 | Model weights saved in /job_data/checkpoints/checkpoint_8/model.safetensors
Rank[0/8] 06/20/2025 02:08:26 INFO checkpointing.py:113 | Optimizer state saved in /job_data/checkpoints/checkpoint_8/optimizer.bin
Rank[0/8] 06/20/2025 02:08:26 INFO checkpointing.py:120 | Scheduler state saved in /job_data/checkpoints/checkpoint_8/scheduler.bin
Rank[0/8] 06/20/2025 02:08:26 INFO checkpointing.py:137 | Sampler state for dataloader 0 saved in /job_data/checkpoints/checkpoint_8/sampler.bin
Rank[0/8] 06/20/2025 02:08:26 INFO checkpointing.py:137 | Sampler state for dataloader 1 saved in /job_data/checkpoints/checkpoint_8/sampler_1.bin
Rank[0/8] 06/20/2025 02:08:26 INFO checkpointing.py:164 | Random states saved in /job_data/checkpoints/checkpoint_8/random_states_0.pkl
Rank[0/8] 06/20/2025 02:08:26 INFO checkpointing.py:300 | Saving the state of TrainerProgressState to /job_data/checkpoints/checkpoint_8/custom_checkpoint_0.pkl
Rank[0/8] 06/20/2025 02:08:26 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 89 to /job_data/checkpoints/checkpoint_8
Rank[0/8] 06/20/2025 02:08:54 INFO stats.py:314 | Epoch[90] Step[24] GlobalStep[35124] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 19:53:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:08:54 INFO loss_tracker.py:84 | Epoch[90/239] Step[24] GlobalStep[35124/NA]: det_loss_cls_0[0.4990] det_loss_box_0[0.6644] det_loss_cls_1[0.4871] det_loss_box_1[0.5211] det_loss_cls_2[0.4537] det_loss_box_2[0.4662] det_loss_cls_3[0.4298] det_loss_box_3[0.4521] det_loss_cls_4[0.4109] det_loss_box_4[0.4455] det_loss_cls_5[0.3963] det_loss_box_5[0.4454] det_loss_cls_dn_0[0.1030] det_loss_box_dn_0[0.2943] det_loss_cls_dn_1[0.0986] det_loss_box_dn_1[0.2544] det_loss_cls_dn_2[0.0983] det_loss_box_dn_2[0.2393] det_loss_cls_dn_3[0.0971] det_loss_box_dn_3[0.2367] det_loss_cls_dn_4[0.0959] det_loss_box_dn_4[0.2365] det_loss_cls_dn_5[0.0948] det_loss_box_dn_5[0.2346] loss_depth[0.0383] total_loss[7.7935]
Rank[0/8] 06/20/2025 02:09:20 INFO stats.py:314 | Epoch[90] Step[49] GlobalStep[35149] Training Speed: 7.62 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 19:52:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:09:20 INFO loss_tracker.py:84 | Epoch[90/239] Step[49] GlobalStep[35149/NA]: det_loss_cls_0[0.4537] det_loss_box_0[0.7160] det_loss_cls_1[0.4502] det_loss_box_1[0.5704] det_loss_cls_2[0.4223] det_loss_box_2[0.4988] det_loss_cls_3[0.3958] det_loss_box_3[0.4755] det_loss_cls_4[0.3757] det_loss_box_4[0.4607] det_loss_cls_5[0.3581] det_loss_box_5[0.4593] det_loss_cls_dn_0[0.0968] det_loss_box_dn_0[0.3699] det_loss_cls_dn_1[0.0937] det_loss_box_dn_1[0.3237] det_loss_cls_dn_2[0.0930] det_loss_box_dn_2[0.3059] det_loss_cls_dn_3[0.0916] det_loss_box_dn_3[0.3012] det_loss_cls_dn_4[0.0908] det_loss_box_dn_4[0.2999] det_loss_cls_dn_5[0.0900] det_loss_box_dn_5[0.2928] loss_depth[0.0391] total_loss[8.1250]
Rank[0/8] 06/20/2025 02:09:46 INFO stats.py:314 | Epoch[90] Step[74] GlobalStep[35174] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 19:51:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:09:46 INFO loss_tracker.py:84 | Epoch[90/239] Step[74] GlobalStep[35174/NA]: det_loss_cls_0[0.4535] det_loss_box_0[0.5906] det_loss_cls_1[0.4484] det_loss_box_1[0.4624] det_loss_cls_2[0.4228] det_loss_box_2[0.4116] det_loss_cls_3[0.3979] det_loss_box_3[0.3987] det_loss_cls_4[0.3666] det_loss_box_4[0.3963] det_loss_cls_5[0.3579] det_loss_box_5[0.3914] det_loss_cls_dn_0[0.0924] det_loss_box_dn_0[0.2679] det_loss_cls_dn_1[0.0885] det_loss_box_dn_1[0.2253] det_loss_cls_dn_2[0.0883] det_loss_box_dn_2[0.2102] det_loss_cls_dn_3[0.0871] det_loss_box_dn_3[0.2080] det_loss_cls_dn_4[0.0860] det_loss_box_dn_4[0.2069] det_loss_cls_dn_5[0.0847] det_loss_box_dn_5[0.2064] loss_depth[0.0368] total_loss[6.9866]
Rank[0/8] 06/20/2025 02:10:11 INFO stats.py:314 | Epoch[90] Step[99] GlobalStep[35199] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 19:51:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:10:11 INFO loss_tracker.py:84 | Epoch[90/239] Step[99] GlobalStep[35199/NA]: det_loss_cls_0[0.7250] det_loss_box_0[0.9977] det_loss_cls_1[0.6932] det_loss_box_1[0.7765] det_loss_cls_2[0.6663] det_loss_box_2[0.6840] det_loss_cls_3[0.6392] det_loss_box_3[0.6574] det_loss_cls_4[0.6079] det_loss_box_4[0.6430] det_loss_cls_5[0.5933] det_loss_box_5[0.6448] det_loss_cls_dn_0[0.1223] det_loss_box_dn_0[0.3307] det_loss_cls_dn_1[0.1183] det_loss_box_dn_1[0.2849] det_loss_cls_dn_2[0.1175] det_loss_box_dn_2[0.2696] det_loss_cls_dn_3[0.1160] det_loss_box_dn_3[0.2665] det_loss_cls_dn_4[0.1154] det_loss_box_dn_4[0.2640] det_loss_cls_dn_5[0.1151] det_loss_box_dn_5[0.2625] loss_depth[0.0412] total_loss[10.7522]
Rank[0/8] 06/20/2025 02:10:37 INFO stats.py:314 | Epoch[90] Step[124] GlobalStep[35224] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 19:50:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:10:37 INFO loss_tracker.py:84 | Epoch[90/239] Step[124] GlobalStep[35224/NA]: det_loss_cls_0[0.5411] det_loss_box_0[0.7281] det_loss_cls_1[0.5179] det_loss_box_1[0.5663] det_loss_cls_2[0.4830] det_loss_box_2[0.5092] det_loss_cls_3[0.4534] det_loss_box_3[0.4931] det_loss_cls_4[0.4179] det_loss_box_4[0.4889] det_loss_cls_5[0.4078] det_loss_box_5[0.4856] det_loss_cls_dn_0[0.1075] det_loss_box_dn_0[0.3314] det_loss_cls_dn_1[0.1042] det_loss_box_dn_1[0.2814] det_loss_cls_dn_2[0.1037] det_loss_box_dn_2[0.2603] det_loss_cls_dn_3[0.1022] det_loss_box_dn_3[0.2568] det_loss_cls_dn_4[0.1014] det_loss_box_dn_4[0.2545] det_loss_cls_dn_5[0.1001] det_loss_box_dn_5[0.2527] loss_depth[0.0392] total_loss[8.3877]
Rank[0/8] 06/20/2025 02:11:03 INFO stats.py:314 | Epoch[90] Step[149] GlobalStep[35249] Training Speed: 7.53 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 19:49:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:11:03 INFO loss_tracker.py:84 | Epoch[90/239] Step[149] GlobalStep[35249/NA]: det_loss_cls_0[0.4692] det_loss_box_0[0.6595] det_loss_cls_1[0.4563] det_loss_box_1[0.5346] det_loss_cls_2[0.4368] det_loss_box_2[0.4835] det_loss_cls_3[0.4049] det_loss_box_3[0.4786] det_loss_cls_4[0.3842] det_loss_box_4[0.4707] det_loss_cls_5[0.3746] det_loss_box_5[0.4695] det_loss_cls_dn_0[0.0946] det_loss_box_dn_0[0.3202] det_loss_cls_dn_1[0.0906] det_loss_box_dn_1[0.2825] det_loss_cls_dn_2[0.0911] det_loss_box_dn_2[0.2679] det_loss_cls_dn_3[0.0900] det_loss_box_dn_3[0.2633] det_loss_cls_dn_4[0.0897] det_loss_box_dn_4[0.2609] det_loss_cls_dn_5[0.0887] det_loss_box_dn_5[0.2592] loss_depth[0.0395] total_loss[7.8605]
Rank[0/8] 06/20/2025 02:11:28 INFO stats.py:314 | Epoch[90] Step[174] GlobalStep[35274] Training Speed: 7.89 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 19:49:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:11:28 INFO loss_tracker.py:84 | Epoch[90/239] Step[174] GlobalStep[35274/NA]: det_loss_cls_0[0.6134] det_loss_box_0[0.7498] det_loss_cls_1[0.5998] det_loss_box_1[0.5687] det_loss_cls_2[0.5682] det_loss_box_2[0.5082] det_loss_cls_3[0.5235] det_loss_box_3[0.4909] det_loss_cls_4[0.5022] det_loss_box_4[0.4800] det_loss_cls_5[0.4840] det_loss_box_5[0.4830] det_loss_cls_dn_0[0.1037] det_loss_box_dn_0[0.2791] det_loss_cls_dn_1[0.0999] det_loss_box_dn_1[0.2380] det_loss_cls_dn_2[0.0992] det_loss_box_dn_2[0.2225] det_loss_cls_dn_3[0.0982] det_loss_box_dn_3[0.2197] det_loss_cls_dn_4[0.0968] det_loss_box_dn_4[0.2178] det_loss_cls_dn_5[0.0960] det_loss_box_dn_5[0.2173] loss_depth[0.0378] total_loss[8.5978]
Rank[0/8] 06/20/2025 02:11:54 INFO stats.py:314 | Epoch[90] Step[199] GlobalStep[35299] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 19:48:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:11:54 INFO loss_tracker.py:84 | Epoch[90/239] Step[199] GlobalStep[35299/NA]: det_loss_cls_0[0.5692] det_loss_box_0[0.7914] det_loss_cls_1[0.5490] det_loss_box_1[0.6375] det_loss_cls_2[0.5307] det_loss_box_2[0.5686] det_loss_cls_3[0.4956] det_loss_box_3[0.5553] det_loss_cls_4[0.4717] det_loss_box_4[0.5418] det_loss_cls_5[0.4597] det_loss_box_5[0.5388] det_loss_cls_dn_0[0.1051] det_loss_box_dn_0[0.3488] det_loss_cls_dn_1[0.1005] det_loss_box_dn_1[0.3074] det_loss_cls_dn_2[0.0998] det_loss_box_dn_2[0.2934] det_loss_cls_dn_3[0.0987] det_loss_box_dn_3[0.2904] det_loss_cls_dn_4[0.0984] det_loss_box_dn_4[0.2895] det_loss_cls_dn_5[0.0971] det_loss_box_dn_5[0.2839] loss_depth[0.0404] total_loss[9.1630]
Rank[0/8] 06/20/2025 02:12:20 INFO stats.py:314 | Epoch[90] Step[224] GlobalStep[35324] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 19:47:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:12:20 INFO loss_tracker.py:84 | Epoch[90/239] Step[224] GlobalStep[35324/NA]: det_loss_cls_0[0.4682] det_loss_box_0[0.6319] det_loss_cls_1[0.4461] det_loss_box_1[0.4888] det_loss_cls_2[0.4176] det_loss_box_2[0.4382] det_loss_cls_3[0.4007] det_loss_box_3[0.4157] det_loss_cls_4[0.3647] det_loss_box_4[0.4160] det_loss_cls_5[0.3492] det_loss_box_5[0.4210] det_loss_cls_dn_0[0.0944] det_loss_box_dn_0[0.2810] det_loss_cls_dn_1[0.0909] det_loss_box_dn_1[0.2347] det_loss_cls_dn_2[0.0898] det_loss_box_dn_2[0.2190] det_loss_cls_dn_3[0.0887] det_loss_box_dn_3[0.2158] det_loss_cls_dn_4[0.0875] det_loss_box_dn_4[0.2149] det_loss_cls_dn_5[0.0868] det_loss_box_dn_5[0.2138] loss_depth[0.0376] total_loss[7.2128]
Rank[0/8] 06/20/2025 02:12:46 INFO stats.py:314 | Epoch[90] Step[249] GlobalStep[35349] Training Speed: 7.99 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 19:47:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:12:46 INFO loss_tracker.py:84 | Epoch[90/239] Step[249] GlobalStep[35349/NA]: det_loss_cls_0[0.4085] det_loss_box_0[0.5466] det_loss_cls_1[0.3971] det_loss_box_1[0.4263] det_loss_cls_2[0.3709] det_loss_box_2[0.3912] det_loss_cls_3[0.3508] det_loss_box_3[0.3711] det_loss_cls_4[0.3381] det_loss_box_4[0.3605] det_loss_cls_5[0.3195] det_loss_box_5[0.3681] det_loss_cls_dn_0[0.0973] det_loss_box_dn_0[0.3556] det_loss_cls_dn_1[0.0948] det_loss_box_dn_1[0.3010] det_loss_cls_dn_2[0.0946] det_loss_box_dn_2[0.2807] det_loss_cls_dn_3[0.0938] det_loss_box_dn_3[0.2768] det_loss_cls_dn_4[0.0938] det_loss_box_dn_4[0.2750] det_loss_cls_dn_5[0.0928] det_loss_box_dn_5[0.2731] loss_depth[0.0400] total_loss[7.0183]
Rank[0/8] 06/20/2025 02:13:11 INFO stats.py:314 | Epoch[90] Step[274] GlobalStep[35374] Training Speed: 6.88 samples/sec across all devices.	Average Step Time: 1.16 sec.	Estimated Remaining Time: 19:46:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:13:11 INFO loss_tracker.py:84 | Epoch[90/239] Step[274] GlobalStep[35374/NA]: det_loss_cls_0[0.5178] det_loss_box_0[0.7269] det_loss_cls_1[0.5088] det_loss_box_1[0.5563] det_loss_cls_2[0.4802] det_loss_box_2[0.5053] det_loss_cls_3[0.4582] det_loss_box_3[0.4882] det_loss_cls_4[0.4348] det_loss_box_4[0.4743] det_loss_cls_5[0.4197] det_loss_box_5[0.4767] det_loss_cls_dn_0[0.1013] det_loss_box_dn_0[0.2802] det_loss_cls_dn_1[0.0977] det_loss_box_dn_1[0.2446] det_loss_cls_dn_2[0.0969] det_loss_box_dn_2[0.2290] det_loss_cls_dn_3[0.0959] det_loss_box_dn_3[0.2254] det_loss_cls_dn_4[0.0945] det_loss_box_dn_4[0.2243] det_loss_cls_dn_5[0.0936] det_loss_box_dn_5[0.2235] loss_depth[0.0371] total_loss[8.0911]
Rank[0/8] 06/20/2025 02:13:37 INFO stats.py:314 | Epoch[90] Step[299] GlobalStep[35399] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 19:45:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:13:37 INFO loss_tracker.py:84 | Epoch[90/239] Step[299] GlobalStep[35399/NA]: det_loss_cls_0[0.5158] det_loss_box_0[0.6851] det_loss_cls_1[0.5030] det_loss_box_1[0.5446] det_loss_cls_2[0.4792] det_loss_box_2[0.4917] det_loss_cls_3[0.4481] det_loss_box_3[0.4766] det_loss_cls_4[0.4231] det_loss_box_4[0.4599] det_loss_cls_5[0.4018] det_loss_box_5[0.4689] det_loss_cls_dn_0[0.1030] det_loss_box_dn_0[0.3390] det_loss_cls_dn_1[0.0997] det_loss_box_dn_1[0.2927] det_loss_cls_dn_2[0.0990] det_loss_box_dn_2[0.2787] det_loss_cls_dn_3[0.0982] det_loss_box_dn_3[0.2741] det_loss_cls_dn_4[0.0966] det_loss_box_dn_4[0.2726] det_loss_cls_dn_5[0.0961] det_loss_box_dn_5[0.2717] loss_depth[0.0397] total_loss[8.2588]
Rank[0/8] 06/20/2025 02:14:03 INFO stats.py:314 | Epoch[90] Step[324] GlobalStep[35424] Training Speed: 6.38 samples/sec across all devices.	Average Step Time: 1.25 sec.	Estimated Remaining Time: 19:45:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:14:03 INFO loss_tracker.py:84 | Epoch[90/239] Step[324] GlobalStep[35424/NA]: det_loss_cls_0[0.5251] det_loss_box_0[0.6985] det_loss_cls_1[0.5039] det_loss_box_1[0.5496] det_loss_cls_2[0.4644] det_loss_box_2[0.5062] det_loss_cls_3[0.4418] det_loss_box_3[0.4914] det_loss_cls_4[0.4082] det_loss_box_4[0.4862] det_loss_cls_5[0.3920] det_loss_box_5[0.4894] det_loss_cls_dn_0[0.0935] det_loss_box_dn_0[0.3507] det_loss_cls_dn_1[0.0902] det_loss_box_dn_1[0.3053] det_loss_cls_dn_2[0.0899] det_loss_box_dn_2[0.2880] det_loss_cls_dn_3[0.0891] det_loss_box_dn_3[0.2844] det_loss_cls_dn_4[0.0881] det_loss_box_dn_4[0.2824] det_loss_cls_dn_5[0.0878] det_loss_box_dn_5[0.2794] loss_depth[0.0413] total_loss[8.3267]
Rank[0/8] 06/20/2025 02:14:28 INFO stats.py:314 | Epoch[90] Step[349] GlobalStep[35449] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 19:44:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:14:28 INFO loss_tracker.py:84 | Epoch[90/239] Step[349] GlobalStep[35449/NA]: det_loss_cls_0[0.4799] det_loss_box_0[0.5991] det_loss_cls_1[0.4619] det_loss_box_1[0.4637] det_loss_cls_2[0.4292] det_loss_box_2[0.4121] det_loss_cls_3[0.3897] det_loss_box_3[0.3979] det_loss_cls_4[0.3624] det_loss_box_4[0.3943] det_loss_cls_5[0.3423] det_loss_box_5[0.3975] det_loss_cls_dn_0[0.0926] det_loss_box_dn_0[0.2743] det_loss_cls_dn_1[0.0871] det_loss_box_dn_1[0.2335] det_loss_cls_dn_2[0.0862] det_loss_box_dn_2[0.2207] det_loss_cls_dn_3[0.0847] det_loss_box_dn_3[0.2185] det_loss_cls_dn_4[0.0835] det_loss_box_dn_4[0.2172] det_loss_cls_dn_5[0.0825] det_loss_box_dn_5[0.2145] loss_depth[0.0351] total_loss[7.0603]
Rank[0/8] 06/20/2025 02:14:54 INFO stats.py:314 | Epoch[90] Step[374] GlobalStep[35474] Training Speed: 8.56 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 19:44:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:14:54 INFO loss_tracker.py:84 | Epoch[90/239] Step[374] GlobalStep[35474/NA]: det_loss_cls_0[0.5133] det_loss_box_0[0.6855] det_loss_cls_1[0.5041] det_loss_box_1[0.5250] det_loss_cls_2[0.4691] det_loss_box_2[0.4758] det_loss_cls_3[0.4349] det_loss_box_3[0.4559] det_loss_cls_4[0.4149] det_loss_box_4[0.4465] det_loss_cls_5[0.4035] det_loss_box_5[0.4432] det_loss_cls_dn_0[0.1023] det_loss_box_dn_0[0.2858] det_loss_cls_dn_1[0.0986] det_loss_box_dn_1[0.2395] det_loss_cls_dn_2[0.0982] det_loss_box_dn_2[0.2231] det_loss_cls_dn_3[0.0972] det_loss_box_dn_3[0.2205] det_loss_cls_dn_4[0.0965] det_loss_box_dn_4[0.2189] det_loss_cls_dn_5[0.0955] det_loss_box_dn_5[0.2159] loss_depth[0.0419] total_loss[7.8057]
Rank[0/8] 06/20/2025 02:15:09 INFO stats.py:394 | Epoch[90] completed. Training Speed: 7.74 samples/sec across all devices.	Epoch Time: 403.27 sec.	Average Epoch Time: 403.27 sec.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 19:43:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:15:22 INFO stats.py:314 | Epoch[91] Step[9] GlobalStep[35499] Training Speed: 8.05 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 19:43:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:15:38 INFO loss_tracker.py:84 | Epoch[91/239] Step[24] GlobalStep[35514/NA]: det_loss_cls_0[0.3585] det_loss_box_0[0.4641] det_loss_cls_1[0.3509] det_loss_box_1[0.3711] det_loss_cls_2[0.3226] det_loss_box_2[0.3467] det_loss_cls_3[0.3072] det_loss_box_3[0.3338] det_loss_cls_4[0.2937] det_loss_box_4[0.3243] det_loss_cls_5[0.2861] det_loss_box_5[0.3239] det_loss_cls_dn_0[0.0964] det_loss_box_dn_0[0.3395] det_loss_cls_dn_1[0.0919] det_loss_box_dn_1[0.2884] det_loss_cls_dn_2[0.0910] det_loss_box_dn_2[0.2744] det_loss_cls_dn_3[0.0898] det_loss_box_dn_3[0.2699] det_loss_cls_dn_4[0.0890] det_loss_box_dn_4[0.2676] det_loss_cls_dn_5[0.0884] det_loss_box_dn_5[0.2656] loss_depth[0.0393] total_loss[6.3739]
Rank[0/8] 06/20/2025 02:15:48 INFO stats.py:314 | Epoch[91] Step[34] GlobalStep[35524] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 19:42:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:16:03 INFO loss_tracker.py:84 | Epoch[91/239] Step[49] GlobalStep[35539/NA]: det_loss_cls_0[0.5359] det_loss_box_0[0.6992] det_loss_cls_1[0.5104] det_loss_box_1[0.5481] det_loss_cls_2[0.4808] det_loss_box_2[0.5096] det_loss_cls_3[0.4619] det_loss_box_3[0.4972] det_loss_cls_4[0.4431] det_loss_box_4[0.4834] det_loss_cls_5[0.4227] det_loss_box_5[0.4909] det_loss_cls_dn_0[0.0946] det_loss_box_dn_0[0.2542] det_loss_cls_dn_1[0.0904] det_loss_box_dn_1[0.2163] det_loss_cls_dn_2[0.0897] det_loss_box_dn_2[0.2048] det_loss_cls_dn_3[0.0890] det_loss_box_dn_3[0.2020] det_loss_cls_dn_4[0.0883] det_loss_box_dn_4[0.2009] det_loss_cls_dn_5[0.0876] det_loss_box_dn_5[0.1999] loss_depth[0.0374] total_loss[7.9386]
Rank[0/8] 06/20/2025 02:16:14 INFO stats.py:314 | Epoch[91] Step[59] GlobalStep[35549] Training Speed: 7.95 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 19:42:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:16:30 INFO loss_tracker.py:84 | Epoch[91/239] Step[74] GlobalStep[35564/NA]: det_loss_cls_0[0.6244] det_loss_box_0[0.8531] det_loss_cls_1[0.6281] det_loss_box_1[0.6558] det_loss_cls_2[0.5832] det_loss_box_2[0.5811] det_loss_cls_3[0.5439] det_loss_box_3[0.5596] det_loss_cls_4[0.5085] det_loss_box_4[0.5376] det_loss_cls_5[0.4974] det_loss_box_5[0.5395] det_loss_cls_dn_0[0.1040] det_loss_box_dn_0[0.2751] det_loss_cls_dn_1[0.1003] det_loss_box_dn_1[0.2302] det_loss_cls_dn_2[0.1004] det_loss_box_dn_2[0.2191] det_loss_cls_dn_3[0.0984] det_loss_box_dn_3[0.2155] det_loss_cls_dn_4[0.0979] det_loss_box_dn_4[0.2136] det_loss_cls_dn_5[0.0967] det_loss_box_dn_5[0.2122] loss_depth[0.0394] total_loss[9.1148]
Rank[0/8] 06/20/2025 02:16:41 INFO stats.py:314 | Epoch[91] Step[84] GlobalStep[35574] Training Speed: 6.58 samples/sec across all devices.	Average Step Time: 1.22 sec.	Estimated Remaining Time: 19:41:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:16:56 INFO loss_tracker.py:84 | Epoch[91/239] Step[99] GlobalStep[35589/NA]: det_loss_cls_0[0.4698] det_loss_box_0[0.6385] det_loss_cls_1[0.4604] det_loss_box_1[0.5097] det_loss_cls_2[0.4250] det_loss_box_2[0.4701] det_loss_cls_3[0.4012] det_loss_box_3[0.4585] det_loss_cls_4[0.3698] det_loss_box_4[0.4577] det_loss_cls_5[0.3685] det_loss_box_5[0.4517] det_loss_cls_dn_0[0.1066] det_loss_box_dn_0[0.3531] det_loss_cls_dn_1[0.1024] det_loss_box_dn_1[0.3106] det_loss_cls_dn_2[0.1033] det_loss_box_dn_2[0.2955] det_loss_cls_dn_3[0.1021] det_loss_box_dn_3[0.2924] det_loss_cls_dn_4[0.1012] det_loss_box_dn_4[0.2913] det_loss_cls_dn_5[0.1000] det_loss_box_dn_5[0.2887] loss_depth[0.0401] total_loss[7.9681]
Rank[0/8] 06/20/2025 02:17:06 INFO stats.py:314 | Epoch[91] Step[109] GlobalStep[35599] Training Speed: 7.82 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 19:40:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:17:21 INFO loss_tracker.py:84 | Epoch[91/239] Step[124] GlobalStep[35614/NA]: det_loss_cls_0[0.4471] det_loss_box_0[0.5308] det_loss_cls_1[0.4315] det_loss_box_1[0.4182] det_loss_cls_2[0.3982] det_loss_box_2[0.3706] det_loss_cls_3[0.3667] det_loss_box_3[0.3580] det_loss_cls_4[0.3408] det_loss_box_4[0.3478] det_loss_cls_5[0.3236] det_loss_box_5[0.3546] det_loss_cls_dn_0[0.0972] det_loss_box_dn_0[0.2607] det_loss_cls_dn_1[0.0925] det_loss_box_dn_1[0.2129] det_loss_cls_dn_2[0.0917] det_loss_box_dn_2[0.1957] det_loss_cls_dn_3[0.0907] det_loss_box_dn_3[0.1927] det_loss_cls_dn_4[0.0896] det_loss_box_dn_4[0.1913] det_loss_cls_dn_5[0.0893] det_loss_box_dn_5[0.1905] loss_depth[0.0358] total_loss[6.5186]
Rank[0/8] 06/20/2025 02:17:31 INFO stats.py:314 | Epoch[91] Step[134] GlobalStep[35624] Training Speed: 8.83 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 19:40:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:17:46 INFO loss_tracker.py:84 | Epoch[91/239] Step[149] GlobalStep[35639/NA]: det_loss_cls_0[0.4883] det_loss_box_0[0.6864] det_loss_cls_1[0.4838] det_loss_box_1[0.5434] det_loss_cls_2[0.4502] det_loss_box_2[0.4839] det_loss_cls_3[0.4272] det_loss_box_3[0.4750] det_loss_cls_4[0.4095] det_loss_box_4[0.4717] det_loss_cls_5[0.3976] det_loss_box_5[0.4663] det_loss_cls_dn_0[0.0910] det_loss_box_dn_0[0.3080] det_loss_cls_dn_1[0.0882] det_loss_box_dn_1[0.2712] det_loss_cls_dn_2[0.0873] det_loss_box_dn_2[0.2570] det_loss_cls_dn_3[0.0864] det_loss_box_dn_3[0.2535] det_loss_cls_dn_4[0.0857] det_loss_box_dn_4[0.2519] det_loss_cls_dn_5[0.0845] det_loss_box_dn_5[0.2495] loss_depth[0.0369] total_loss[7.9347]
Rank[0/8] 06/20/2025 02:17:57 INFO stats.py:314 | Epoch[91] Step[159] GlobalStep[35649] Training Speed: 7.76 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 19:39:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:18:13 INFO loss_tracker.py:84 | Epoch[91/239] Step[174] GlobalStep[35664/NA]: det_loss_cls_0[0.4681] det_loss_box_0[0.6125] det_loss_cls_1[0.4494] det_loss_box_1[0.4637] det_loss_cls_2[0.4164] det_loss_box_2[0.4266] det_loss_cls_3[0.4009] det_loss_box_3[0.4021] det_loss_cls_4[0.3678] det_loss_box_4[0.4048] det_loss_cls_5[0.3539] det_loss_box_5[0.4051] det_loss_cls_dn_0[0.0864] det_loss_box_dn_0[0.3051] det_loss_cls_dn_1[0.0831] det_loss_box_dn_1[0.2594] det_loss_cls_dn_2[0.0825] det_loss_box_dn_2[0.2422] det_loss_cls_dn_3[0.0809] det_loss_box_dn_3[0.2364] det_loss_cls_dn_4[0.0800] det_loss_box_dn_4[0.2358] det_loss_cls_dn_5[0.0788] det_loss_box_dn_5[0.2354] loss_depth[0.0363] total_loss[7.2138]
Rank[0/8] 06/20/2025 02:18:23 INFO stats.py:314 | Epoch[91] Step[184] GlobalStep[35674] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 19:39:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:18:39 INFO loss_tracker.py:84 | Epoch[91/239] Step[199] GlobalStep[35689/NA]: det_loss_cls_0[0.5969] det_loss_box_0[0.7695] det_loss_cls_1[0.5869] det_loss_box_1[0.5864] det_loss_cls_2[0.5519] det_loss_box_2[0.5288] det_loss_cls_3[0.5172] det_loss_box_3[0.5044] det_loss_cls_4[0.4936] det_loss_box_4[0.4957] det_loss_cls_5[0.4818] det_loss_box_5[0.4942] det_loss_cls_dn_0[0.1140] det_loss_box_dn_0[0.2871] det_loss_cls_dn_1[0.1100] det_loss_box_dn_1[0.2457] det_loss_cls_dn_2[0.1084] det_loss_box_dn_2[0.2304] det_loss_cls_dn_3[0.1070] det_loss_box_dn_3[0.2287] det_loss_cls_dn_4[0.1058] det_loss_box_dn_4[0.2278] det_loss_cls_dn_5[0.1051] det_loss_box_dn_5[0.2234] loss_depth[0.0426] total_loss[8.7432]
Rank[0/8] 06/20/2025 02:18:49 INFO stats.py:314 | Epoch[91] Step[209] GlobalStep[35699] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 19:38:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:19:05 INFO loss_tracker.py:84 | Epoch[91/239] Step[224] GlobalStep[35714/NA]: det_loss_cls_0[0.7171] det_loss_box_0[1.0230] det_loss_cls_1[0.6933] det_loss_box_1[0.8217] det_loss_cls_2[0.6562] det_loss_box_2[0.7404] det_loss_cls_3[0.6119] det_loss_box_3[0.7284] det_loss_cls_4[0.5863] det_loss_box_4[0.7057] det_loss_cls_5[0.5740] det_loss_box_5[0.7028] det_loss_cls_dn_0[0.1007] det_loss_box_dn_0[0.3135] det_loss_cls_dn_1[0.0974] det_loss_box_dn_1[0.2708] det_loss_cls_dn_2[0.0976] det_loss_box_dn_2[0.2579] det_loss_cls_dn_3[0.0963] det_loss_box_dn_3[0.2553] det_loss_cls_dn_4[0.0965] det_loss_box_dn_4[0.2544] det_loss_cls_dn_5[0.0963] det_loss_box_dn_5[0.2528] loss_depth[0.0413] total_loss[10.7917]
Rank[0/8] 06/20/2025 02:19:15 INFO stats.py:314 | Epoch[91] Step[234] GlobalStep[35724] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 19:37:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:19:31 INFO loss_tracker.py:84 | Epoch[91/239] Step[249] GlobalStep[35739/NA]: det_loss_cls_0[0.5699] det_loss_box_0[0.7027] det_loss_cls_1[0.5531] det_loss_box_1[0.5478] det_loss_cls_2[0.5050] det_loss_box_2[0.4948] det_loss_cls_3[0.4739] det_loss_box_3[0.4807] det_loss_cls_4[0.4545] det_loss_box_4[0.4631] det_loss_cls_5[0.4355] det_loss_box_5[0.4688] det_loss_cls_dn_0[0.1021] det_loss_box_dn_0[0.2735] det_loss_cls_dn_1[0.0989] det_loss_box_dn_1[0.2242] det_loss_cls_dn_2[0.0983] det_loss_box_dn_2[0.2058] det_loss_cls_dn_3[0.0979] det_loss_box_dn_3[0.2026] det_loss_cls_dn_4[0.0967] det_loss_box_dn_4[0.2008] det_loss_cls_dn_5[0.0955] det_loss_box_dn_5[0.1989] loss_depth[0.0396] total_loss[8.0845]
Rank[0/8] 06/20/2025 02:19:41 INFO stats.py:314 | Epoch[91] Step[259] GlobalStep[35749] Training Speed: 14.20 samples/sec across all devices.	Average Step Time: 0.56 sec.	Estimated Remaining Time: 19:37:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:19:57 INFO loss_tracker.py:84 | Epoch[91/239] Step[274] GlobalStep[35764/NA]: det_loss_cls_0[0.4793] det_loss_box_0[0.6920] det_loss_cls_1[0.4696] det_loss_box_1[0.5336] det_loss_cls_2[0.4371] det_loss_box_2[0.5007] det_loss_cls_3[0.4121] det_loss_box_3[0.4726] det_loss_cls_4[0.3965] det_loss_box_4[0.4540] det_loss_cls_5[0.3940] det_loss_box_5[0.4540] det_loss_cls_dn_0[0.0962] det_loss_box_dn_0[0.4737] det_loss_cls_dn_1[0.0913] det_loss_box_dn_1[0.4187] det_loss_cls_dn_2[0.0902] det_loss_box_dn_2[0.3990] det_loss_cls_dn_3[0.0894] det_loss_box_dn_3[0.3894] det_loss_cls_dn_4[0.0881] det_loss_box_dn_4[0.3879] det_loss_cls_dn_5[0.0873] det_loss_box_dn_5[0.3812] loss_depth[0.0371] total_loss[8.7254]
Rank[0/8] 06/20/2025 02:20:07 INFO stats.py:314 | Epoch[91] Step[284] GlobalStep[35774] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 19:36:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:20:22 INFO loss_tracker.py:84 | Epoch[91/239] Step[299] GlobalStep[35789/NA]: det_loss_cls_0[0.5490] det_loss_box_0[0.7014] det_loss_cls_1[0.5221] det_loss_box_1[0.5448] det_loss_cls_2[0.4989] det_loss_box_2[0.4881] det_loss_cls_3[0.4633] det_loss_box_3[0.4730] det_loss_cls_4[0.4360] det_loss_box_4[0.4672] det_loss_cls_5[0.4262] det_loss_box_5[0.4694] det_loss_cls_dn_0[0.1063] det_loss_box_dn_0[0.3358] det_loss_cls_dn_1[0.1025] det_loss_box_dn_1[0.2829] det_loss_cls_dn_2[0.1019] det_loss_box_dn_2[0.2673] det_loss_cls_dn_3[0.1011] det_loss_box_dn_3[0.2632] det_loss_cls_dn_4[0.1005] det_loss_box_dn_4[0.2606] det_loss_cls_dn_5[0.0992] det_loss_box_dn_5[0.2564] loss_depth[0.0401] total_loss[8.3573]
Rank[0/8] 06/20/2025 02:20:33 INFO stats.py:314 | Epoch[91] Step[309] GlobalStep[35799] Training Speed: 7.91 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 19:35:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:20:49 INFO loss_tracker.py:84 | Epoch[91/239] Step[324] GlobalStep[35814/NA]: det_loss_cls_0[0.5042] det_loss_box_0[0.6899] det_loss_cls_1[0.4855] det_loss_box_1[0.5365] det_loss_cls_2[0.4452] det_loss_box_2[0.5016] det_loss_cls_3[0.4194] det_loss_box_3[0.4854] det_loss_cls_4[0.3874] det_loss_box_4[0.4847] det_loss_cls_5[0.3767] det_loss_box_5[0.4892] det_loss_cls_dn_0[0.0933] det_loss_box_dn_0[0.2891] det_loss_cls_dn_1[0.0895] det_loss_box_dn_1[0.2480] det_loss_cls_dn_2[0.0886] det_loss_box_dn_2[0.2346] det_loss_cls_dn_3[0.0880] det_loss_box_dn_3[0.2322] det_loss_cls_dn_4[0.0870] det_loss_box_dn_4[0.2300] det_loss_cls_dn_5[0.0859] det_loss_box_dn_5[0.2288] loss_depth[0.0354] total_loss[7.8360]
Rank[0/8] 06/20/2025 02:20:59 INFO stats.py:314 | Epoch[91] Step[334] GlobalStep[35824] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 19:35:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:21:15 INFO loss_tracker.py:84 | Epoch[91/239] Step[349] GlobalStep[35839/NA]: det_loss_cls_0[0.4979] det_loss_box_0[0.6618] det_loss_cls_1[0.4935] det_loss_box_1[0.5103] det_loss_cls_2[0.4617] det_loss_box_2[0.4789] det_loss_cls_3[0.4379] det_loss_box_3[0.4617] det_loss_cls_4[0.4053] det_loss_box_4[0.4459] det_loss_cls_5[0.3961] det_loss_box_5[0.4390] det_loss_cls_dn_0[0.0990] det_loss_box_dn_0[0.3320] det_loss_cls_dn_1[0.0957] det_loss_box_dn_1[0.2797] det_loss_cls_dn_2[0.0955] det_loss_box_dn_2[0.2647] det_loss_cls_dn_3[0.0940] det_loss_box_dn_3[0.2605] det_loss_cls_dn_4[0.0935] det_loss_box_dn_4[0.2577] det_loss_cls_dn_5[0.0918] det_loss_box_dn_5[0.2560] loss_depth[0.0422] total_loss[7.9524]
Rank[0/8] 06/20/2025 02:21:25 INFO stats.py:314 | Epoch[91] Step[359] GlobalStep[35849] Training Speed: 8.59 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 19:34:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:21:40 INFO loss_tracker.py:84 | Epoch[91/239] Step[374] GlobalStep[35864/NA]: det_loss_cls_0[0.5597] det_loss_box_0[0.8199] det_loss_cls_1[0.5358] det_loss_box_1[0.6931] det_loss_cls_2[0.4969] det_loss_box_2[0.6473] det_loss_cls_3[0.4643] det_loss_box_3[0.6216] det_loss_cls_4[0.4459] det_loss_box_4[0.5998] det_loss_cls_5[0.4261] det_loss_box_5[0.6025] det_loss_cls_dn_0[0.1055] det_loss_box_dn_0[0.3457] det_loss_cls_dn_1[0.1022] det_loss_box_dn_1[0.2947] det_loss_cls_dn_2[0.1020] det_loss_box_dn_2[0.2791] det_loss_cls_dn_3[0.1014] det_loss_box_dn_3[0.2773] det_loss_cls_dn_4[0.0999] det_loss_box_dn_4[0.2771] det_loss_cls_dn_5[0.0988] det_loss_box_dn_5[0.2764] loss_depth[0.0413] total_loss[9.3143]
Rank[0/8] 06/20/2025 02:21:51 INFO stats.py:314 | Epoch[91] Step[384] GlobalStep[35874] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 19:33:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:21:56 INFO stats.py:394 | Epoch[91] completed. Training Speed: 7.68 samples/sec across all devices.	Epoch Time: 406.08 sec.	Average Epoch Time: 406.08 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 19:33:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:22:19 INFO stats.py:314 | Epoch[92] Step[19] GlobalStep[35899] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 19:33:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:22:25 INFO loss_tracker.py:84 | Epoch[92/239] Step[24] GlobalStep[35904/NA]: det_loss_cls_0[0.5533] det_loss_box_0[0.7338] det_loss_cls_1[0.5467] det_loss_box_1[0.5783] det_loss_cls_2[0.5092] det_loss_box_2[0.5240] det_loss_cls_3[0.4822] det_loss_box_3[0.5077] det_loss_cls_4[0.4574] det_loss_box_4[0.4936] det_loss_cls_5[0.4429] det_loss_box_5[0.4927] det_loss_cls_dn_0[0.1139] det_loss_box_dn_0[0.3090] det_loss_cls_dn_1[0.1103] det_loss_box_dn_1[0.2723] det_loss_cls_dn_2[0.1096] det_loss_box_dn_2[0.2571] det_loss_cls_dn_3[0.1082] det_loss_box_dn_3[0.2535] det_loss_cls_dn_4[0.1074] det_loss_box_dn_4[0.2516] det_loss_cls_dn_5[0.1064] det_loss_box_dn_5[0.2495] loss_depth[0.0392] total_loss[8.6099]
Rank[0/8] 06/20/2025 02:22:46 INFO stats.py:314 | Epoch[92] Step[44] GlobalStep[35924] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 19:32:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:22:51 INFO loss_tracker.py:84 | Epoch[92/239] Step[49] GlobalStep[35929/NA]: det_loss_cls_0[0.6855] det_loss_box_0[0.9026] det_loss_cls_1[0.6610] det_loss_box_1[0.7344] det_loss_cls_2[0.6258] det_loss_box_2[0.6820] det_loss_cls_3[0.6056] det_loss_box_3[0.6444] det_loss_cls_4[0.5763] det_loss_box_4[0.6333] det_loss_cls_5[0.5572] det_loss_box_5[0.6318] det_loss_cls_dn_0[0.1164] det_loss_box_dn_0[0.3205] det_loss_cls_dn_1[0.1127] det_loss_box_dn_1[0.2793] det_loss_cls_dn_2[0.1127] det_loss_box_dn_2[0.2626] det_loss_cls_dn_3[0.1121] det_loss_box_dn_3[0.2587] det_loss_cls_dn_4[0.1119] det_loss_box_dn_4[0.2581] det_loss_cls_dn_5[0.1102] det_loss_box_dn_5[0.2558] loss_depth[0.0395] total_loss[10.2904]
Rank[0/8] 06/20/2025 02:23:11 INFO stats.py:314 | Epoch[92] Step[69] GlobalStep[35949] Training Speed: 8.04 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 19:32:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:23:16 INFO loss_tracker.py:84 | Epoch[92/239] Step[74] GlobalStep[35954/NA]: det_loss_cls_0[0.4782] det_loss_box_0[0.6243] det_loss_cls_1[0.4688] det_loss_box_1[0.4975] det_loss_cls_2[0.4430] det_loss_box_2[0.4468] det_loss_cls_3[0.4188] det_loss_box_3[0.4310] det_loss_cls_4[0.3882] det_loss_box_4[0.4294] det_loss_cls_5[0.3756] det_loss_box_5[0.4318] det_loss_cls_dn_0[0.0953] det_loss_box_dn_0[0.3161] det_loss_cls_dn_1[0.0912] det_loss_box_dn_1[0.2704] det_loss_cls_dn_2[0.0907] det_loss_box_dn_2[0.2526] det_loss_cls_dn_3[0.0900] det_loss_box_dn_3[0.2489] det_loss_cls_dn_4[0.0892] det_loss_box_dn_4[0.2479] det_loss_cls_dn_5[0.0882] det_loss_box_dn_5[0.2463] loss_depth[0.0348] total_loss[7.5952]
Rank[0/8] 06/20/2025 02:23:37 INFO stats.py:314 | Epoch[92] Step[94] GlobalStep[35974] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 19:31:30.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:23:42 INFO loss_tracker.py:84 | Epoch[92/239] Step[99] GlobalStep[35979/NA]: det_loss_cls_0[0.5596] det_loss_box_0[0.7000] det_loss_cls_1[0.5527] det_loss_box_1[0.5351] det_loss_cls_2[0.5193] det_loss_box_2[0.4832] det_loss_cls_3[0.4813] det_loss_box_3[0.4649] det_loss_cls_4[0.4483] det_loss_box_4[0.4572] det_loss_cls_5[0.4249] det_loss_box_5[0.4612] det_loss_cls_dn_0[0.0993] det_loss_box_dn_0[0.2498] det_loss_cls_dn_1[0.0963] det_loss_box_dn_1[0.2106] det_loss_cls_dn_2[0.0954] det_loss_box_dn_2[0.1947] det_loss_cls_dn_3[0.0949] det_loss_box_dn_3[0.1930] det_loss_cls_dn_4[0.0944] det_loss_box_dn_4[0.1917] det_loss_cls_dn_5[0.0935] det_loss_box_dn_5[0.1890] loss_depth[0.0350] total_loss[7.9252]
Rank[0/8] 06/20/2025 02:24:03 INFO stats.py:314 | Epoch[92] Step[119] GlobalStep[35999] Training Speed: 8.75 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 19:30:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:24:08 INFO loss_tracker.py:84 | Epoch[92/239] Step[124] GlobalStep[36004/NA]: det_loss_cls_0[0.5822] det_loss_box_0[0.7576] det_loss_cls_1[0.5766] det_loss_box_1[0.5622] det_loss_cls_2[0.5359] det_loss_box_2[0.5146] det_loss_cls_3[0.5010] det_loss_box_3[0.4956] det_loss_cls_4[0.4635] det_loss_box_4[0.4963] det_loss_cls_5[0.4528] det_loss_box_5[0.4853] det_loss_cls_dn_0[0.1063] det_loss_box_dn_0[0.2637] det_loss_cls_dn_1[0.1028] det_loss_box_dn_1[0.2220] det_loss_cls_dn_2[0.1017] det_loss_box_dn_2[0.2054] det_loss_cls_dn_3[0.1010] det_loss_box_dn_3[0.2015] det_loss_cls_dn_4[0.1002] det_loss_box_dn_4[0.2001] det_loss_cls_dn_5[0.0999] det_loss_box_dn_5[0.1993] loss_depth[0.0404] total_loss[8.3678]
Rank[0/8] 06/20/2025 02:24:29 INFO stats.py:314 | Epoch[92] Step[144] GlobalStep[36024] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 19:30:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:24:34 INFO loss_tracker.py:84 | Epoch[92/239] Step[149] GlobalStep[36029/NA]: det_loss_cls_0[0.6318] det_loss_box_0[0.7702] det_loss_cls_1[0.6152] det_loss_box_1[0.6142] det_loss_cls_2[0.5699] det_loss_box_2[0.5583] det_loss_cls_3[0.5415] det_loss_box_3[0.5290] det_loss_cls_4[0.5017] det_loss_box_4[0.5308] det_loss_cls_5[0.4774] det_loss_box_5[0.5367] det_loss_cls_dn_0[0.1020] det_loss_box_dn_0[0.2884] det_loss_cls_dn_1[0.0982] det_loss_box_dn_1[0.2427] det_loss_cls_dn_2[0.0973] det_loss_box_dn_2[0.2257] det_loss_cls_dn_3[0.0964] det_loss_box_dn_3[0.2230] det_loss_cls_dn_4[0.0956] det_loss_box_dn_4[0.2215] det_loss_cls_dn_5[0.0942] det_loss_box_dn_5[0.2209] loss_depth[0.0373] total_loss[8.9201]
Rank[0/8] 06/20/2025 02:24:54 INFO stats.py:314 | Epoch[92] Step[169] GlobalStep[36049] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 19:29:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:24:59 INFO loss_tracker.py:84 | Epoch[92/239] Step[174] GlobalStep[36054/NA]: det_loss_cls_0[0.5802] det_loss_box_0[0.8337] det_loss_cls_1[0.5536] det_loss_box_1[0.6535] det_loss_cls_2[0.5272] det_loss_box_2[0.5948] det_loss_cls_3[0.4985] det_loss_box_3[0.5715] det_loss_cls_4[0.4728] det_loss_box_4[0.5568] det_loss_cls_5[0.4567] det_loss_box_5[0.5586] det_loss_cls_dn_0[0.0974] det_loss_box_dn_0[0.2980] det_loss_cls_dn_1[0.0939] det_loss_box_dn_1[0.2567] det_loss_cls_dn_2[0.0931] det_loss_box_dn_2[0.2410] det_loss_cls_dn_3[0.0924] det_loss_box_dn_3[0.2372] det_loss_cls_dn_4[0.0920] det_loss_box_dn_4[0.2362] det_loss_cls_dn_5[0.0914] det_loss_box_dn_5[0.2360] loss_depth[0.0405] total_loss[8.9639]
Rank[0/8] 06/20/2025 02:25:19 INFO stats.py:314 | Epoch[92] Step[194] GlobalStep[36074] Training Speed: 8.69 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 19:28:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:25:25 INFO loss_tracker.py:84 | Epoch[92/239] Step[199] GlobalStep[36079/NA]: det_loss_cls_0[0.6251] det_loss_box_0[0.9056] det_loss_cls_1[0.5955] det_loss_box_1[0.7214] det_loss_cls_2[0.5617] det_loss_box_2[0.6428] det_loss_cls_3[0.5366] det_loss_box_3[0.6130] det_loss_cls_4[0.5081] det_loss_box_4[0.5908] det_loss_cls_5[0.4979] det_loss_box_5[0.5833] det_loss_cls_dn_0[0.1069] det_loss_box_dn_0[0.3007] det_loss_cls_dn_1[0.1025] det_loss_box_dn_1[0.2571] det_loss_cls_dn_2[0.1023] det_loss_box_dn_2[0.2407] det_loss_cls_dn_3[0.1008] det_loss_box_dn_3[0.2388] det_loss_cls_dn_4[0.0996] det_loss_box_dn_4[0.2373] det_loss_cls_dn_5[0.0985] det_loss_box_dn_5[0.2363] loss_depth[0.0383] total_loss[9.5416]
Rank[0/8] 06/20/2025 02:25:45 INFO stats.py:314 | Epoch[92] Step[219] GlobalStep[36099] Training Speed: 7.91 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 19:28:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:25:50 INFO loss_tracker.py:84 | Epoch[92/239] Step[224] GlobalStep[36104/NA]: det_loss_cls_0[0.5368] det_loss_box_0[0.7366] det_loss_cls_1[0.5188] det_loss_box_1[0.5828] det_loss_cls_2[0.4875] det_loss_box_2[0.5157] det_loss_cls_3[0.4615] det_loss_box_3[0.4926] det_loss_cls_4[0.4350] det_loss_box_4[0.4879] det_loss_cls_5[0.4231] det_loss_box_5[0.4878] det_loss_cls_dn_0[0.0996] det_loss_box_dn_0[0.3299] det_loss_cls_dn_1[0.0949] det_loss_box_dn_1[0.2819] det_loss_cls_dn_2[0.0941] det_loss_box_dn_2[0.2619] det_loss_cls_dn_3[0.0930] det_loss_box_dn_3[0.2588] det_loss_cls_dn_4[0.0918] det_loss_box_dn_4[0.2576] det_loss_cls_dn_5[0.0911] det_loss_box_dn_5[0.2546] loss_depth[0.0392] total_loss[8.4145]
Rank[0/8] 06/20/2025 02:26:11 INFO stats.py:314 | Epoch[92] Step[244] GlobalStep[36124] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 19:27:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:26:16 INFO loss_tracker.py:84 | Epoch[92/239] Step[249] GlobalStep[36129/NA]: det_loss_cls_0[0.5187] det_loss_box_0[0.7588] det_loss_cls_1[0.5184] det_loss_box_1[0.6222] det_loss_cls_2[0.5016] det_loss_box_2[0.5702] det_loss_cls_3[0.4719] det_loss_box_3[0.5432] det_loss_cls_4[0.4524] det_loss_box_4[0.5391] det_loss_cls_5[0.4351] det_loss_box_5[0.5452] det_loss_cls_dn_0[0.0953] det_loss_box_dn_0[0.3419] det_loss_cls_dn_1[0.0927] det_loss_box_dn_1[0.2989] det_loss_cls_dn_2[0.0929] det_loss_box_dn_2[0.2867] det_loss_cls_dn_3[0.0921] det_loss_box_dn_3[0.2834] det_loss_cls_dn_4[0.0907] det_loss_box_dn_4[0.2802] det_loss_cls_dn_5[0.0900] det_loss_box_dn_5[0.2783] loss_depth[0.0352] total_loss[8.8351]
Rank[0/8] 06/20/2025 02:26:37 INFO stats.py:314 | Epoch[92] Step[269] GlobalStep[36149] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 19:27:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:26:42 INFO loss_tracker.py:84 | Epoch[92/239] Step[274] GlobalStep[36154/NA]: det_loss_cls_0[0.5638] det_loss_box_0[0.7068] det_loss_cls_1[0.5415] det_loss_box_1[0.5666] det_loss_cls_2[0.5049] det_loss_box_2[0.5126] det_loss_cls_3[0.4833] det_loss_box_3[0.4844] det_loss_cls_4[0.4459] det_loss_box_4[0.4705] det_loss_cls_5[0.4274] det_loss_box_5[0.4704] det_loss_cls_dn_0[0.0949] det_loss_box_dn_0[0.2807] det_loss_cls_dn_1[0.0909] det_loss_box_dn_1[0.2429] det_loss_cls_dn_2[0.0896] det_loss_box_dn_2[0.2284] det_loss_cls_dn_3[0.0888] det_loss_box_dn_3[0.2252] det_loss_cls_dn_4[0.0868] det_loss_box_dn_4[0.2241] det_loss_cls_dn_5[0.0860] det_loss_box_dn_5[0.2235] loss_depth[0.0381] total_loss[8.1781]
Rank[0/8] 06/20/2025 02:27:04 INFO stats.py:314 | Epoch[92] Step[294] GlobalStep[36174] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 19:26:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:27:09 INFO loss_tracker.py:84 | Epoch[92/239] Step[299] GlobalStep[36179/NA]: det_loss_cls_0[0.6895] det_loss_box_0[0.9427] det_loss_cls_1[0.6728] det_loss_box_1[0.7447] det_loss_cls_2[0.6292] det_loss_box_2[0.6742] det_loss_cls_3[0.5907] det_loss_box_3[0.6465] det_loss_cls_4[0.5678] det_loss_box_4[0.6298] det_loss_cls_5[0.5520] det_loss_box_5[0.6283] det_loss_cls_dn_0[0.1158] det_loss_box_dn_0[0.2929] det_loss_cls_dn_1[0.1127] det_loss_box_dn_1[0.2521] det_loss_cls_dn_2[0.1122] det_loss_box_dn_2[0.2357] det_loss_cls_dn_3[0.1110] det_loss_box_dn_3[0.2332] det_loss_cls_dn_4[0.1100] det_loss_box_dn_4[0.2309] det_loss_cls_dn_5[0.1090] det_loss_box_dn_5[0.2291] loss_depth[0.0420] total_loss[10.1548]
Rank[0/8] 06/20/2025 02:27:29 INFO stats.py:314 | Epoch[92] Step[319] GlobalStep[36199] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 19:25:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:27:35 INFO loss_tracker.py:84 | Epoch[92/239] Step[324] GlobalStep[36204/NA]: det_loss_cls_0[0.5333] det_loss_box_0[0.7555] det_loss_cls_1[0.5133] det_loss_box_1[0.5810] det_loss_cls_2[0.4827] det_loss_box_2[0.5208] det_loss_cls_3[0.4568] det_loss_box_3[0.4970] det_loss_cls_4[0.4293] det_loss_box_4[0.4901] det_loss_cls_5[0.4155] det_loss_box_5[0.4863] det_loss_cls_dn_0[0.0929] det_loss_box_dn_0[0.3004] det_loss_cls_dn_1[0.0882] det_loss_box_dn_1[0.2487] det_loss_cls_dn_2[0.0873] det_loss_box_dn_2[0.2308] det_loss_cls_dn_3[0.0860] det_loss_box_dn_3[0.2272] det_loss_cls_dn_4[0.0846] det_loss_box_dn_4[0.2251] det_loss_cls_dn_5[0.0834] det_loss_box_dn_5[0.2248] loss_depth[0.0389] total_loss[8.1798]
Rank[0/8] 06/20/2025 02:27:55 INFO stats.py:314 | Epoch[92] Step[344] GlobalStep[36224] Training Speed: 7.99 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 19:25:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:28:00 INFO loss_tracker.py:84 | Epoch[92/239] Step[349] GlobalStep[36229/NA]: det_loss_cls_0[0.5409] det_loss_box_0[0.7348] det_loss_cls_1[0.5232] det_loss_box_1[0.5770] det_loss_cls_2[0.4927] det_loss_box_2[0.5325] det_loss_cls_3[0.4752] det_loss_box_3[0.5065] det_loss_cls_4[0.4486] det_loss_box_4[0.4980] det_loss_cls_5[0.4381] det_loss_box_5[0.4940] det_loss_cls_dn_0[0.1050] det_loss_box_dn_0[0.3029] det_loss_cls_dn_1[0.1022] det_loss_box_dn_1[0.2591] det_loss_cls_dn_2[0.1016] det_loss_box_dn_2[0.2410] det_loss_cls_dn_3[0.1006] det_loss_box_dn_3[0.2363] det_loss_cls_dn_4[0.0996] det_loss_box_dn_4[0.2347] det_loss_cls_dn_5[0.0986] det_loss_box_dn_5[0.2336] loss_depth[0.0368] total_loss[8.4136]
Rank[0/8] 06/20/2025 02:28:21 INFO stats.py:314 | Epoch[92] Step[369] GlobalStep[36249] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 19:24:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:28:26 INFO loss_tracker.py:84 | Epoch[92/239] Step[374] GlobalStep[36254/NA]: det_loss_cls_0[0.4789] det_loss_box_0[0.5886] det_loss_cls_1[0.4692] det_loss_box_1[0.4574] det_loss_cls_2[0.4321] det_loss_box_2[0.4143] det_loss_cls_3[0.3967] det_loss_box_3[0.4080] det_loss_cls_4[0.3808] det_loss_box_4[0.3937] det_loss_cls_5[0.3673] det_loss_box_5[0.3961] det_loss_cls_dn_0[0.1140] det_loss_box_dn_0[0.3269] det_loss_cls_dn_1[0.1115] det_loss_box_dn_1[0.2796] det_loss_cls_dn_2[0.1111] det_loss_box_dn_2[0.2629] det_loss_cls_dn_3[0.1099] det_loss_box_dn_3[0.2598] det_loss_cls_dn_4[0.1096] det_loss_box_dn_4[0.2580] det_loss_cls_dn_5[0.1088] det_loss_box_dn_5[0.2523] loss_depth[0.0362] total_loss[7.5239]
Rank[0/8] 06/20/2025 02:28:41 INFO stats.py:394 | Epoch[92] completed. Training Speed: 7.70 samples/sec across all devices.	Epoch Time: 405.34 sec.	Average Epoch Time: 405.34 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 19:24:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:28:49 INFO stats.py:314 | Epoch[93] Step[4] GlobalStep[36274] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 19:23:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:29:10 INFO loss_tracker.py:84 | Epoch[93/239] Step[24] GlobalStep[36294/NA]: det_loss_cls_0[0.5483] det_loss_box_0[0.7188] det_loss_cls_1[0.5279] det_loss_box_1[0.5574] det_loss_cls_2[0.4924] det_loss_box_2[0.5027] det_loss_cls_3[0.4622] det_loss_box_3[0.4926] det_loss_cls_4[0.4369] det_loss_box_4[0.4768] det_loss_cls_5[0.4268] det_loss_box_5[0.4757] det_loss_cls_dn_0[0.1024] det_loss_box_dn_0[0.2736] det_loss_cls_dn_1[0.0982] det_loss_box_dn_1[0.2335] det_loss_cls_dn_2[0.0976] det_loss_box_dn_2[0.2180] det_loss_cls_dn_3[0.0968] det_loss_box_dn_3[0.2158] det_loss_cls_dn_4[0.0960] det_loss_box_dn_4[0.2143] det_loss_cls_dn_5[0.0950] det_loss_box_dn_5[0.2123] loss_depth[0.0400] total_loss[8.1121]
Rank[0/8] 06/20/2025 02:29:15 INFO stats.py:314 | Epoch[93] Step[29] GlobalStep[36299] Training Speed: 7.70 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 19:23:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:29:35 INFO loss_tracker.py:84 | Epoch[93/239] Step[49] GlobalStep[36319/NA]: det_loss_cls_0[0.4279] det_loss_box_0[0.5413] det_loss_cls_1[0.4143] det_loss_box_1[0.4215] det_loss_cls_2[0.3768] det_loss_box_2[0.3738] det_loss_cls_3[0.3574] det_loss_box_3[0.3556] det_loss_cls_4[0.3322] det_loss_box_4[0.3469] det_loss_cls_5[0.3223] det_loss_box_5[0.3482] det_loss_cls_dn_0[0.0885] det_loss_box_dn_0[0.2899] det_loss_cls_dn_1[0.0850] det_loss_box_dn_1[0.2425] det_loss_cls_dn_2[0.0838] det_loss_box_dn_2[0.2280] det_loss_cls_dn_3[0.0834] det_loss_box_dn_3[0.2250] det_loss_cls_dn_4[0.0822] det_loss_box_dn_4[0.2235] det_loss_cls_dn_5[0.0820] det_loss_box_dn_5[0.2218] loss_depth[0.0395] total_loss[6.5933]
Rank[0/8] 06/20/2025 02:29:40 INFO stats.py:314 | Epoch[93] Step[54] GlobalStep[36324] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 19:22:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:30:01 INFO loss_tracker.py:84 | Epoch[93/239] Step[74] GlobalStep[36344/NA]: det_loss_cls_0[0.4702] det_loss_box_0[0.6207] det_loss_cls_1[0.4549] det_loss_box_1[0.4666] det_loss_cls_2[0.4288] det_loss_box_2[0.4279] det_loss_cls_3[0.4070] det_loss_box_3[0.4174] det_loss_cls_4[0.3791] det_loss_box_4[0.4047] det_loss_cls_5[0.3614] det_loss_box_5[0.4033] det_loss_cls_dn_0[0.1024] det_loss_box_dn_0[0.2992] det_loss_cls_dn_1[0.0994] det_loss_box_dn_1[0.2447] det_loss_cls_dn_2[0.0982] det_loss_box_dn_2[0.2252] det_loss_cls_dn_3[0.0980] det_loss_box_dn_3[0.2221] det_loss_cls_dn_4[0.0969] det_loss_box_dn_4[0.2199] det_loss_cls_dn_5[0.0961] det_loss_box_dn_5[0.2183] loss_depth[0.0394] total_loss[7.3019]
Rank[0/8] 06/20/2025 02:30:06 INFO stats.py:314 | Epoch[93] Step[79] GlobalStep[36349] Training Speed: 7.78 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 19:22:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:30:28 INFO loss_tracker.py:84 | Epoch[93/239] Step[99] GlobalStep[36369/NA]: det_loss_cls_0[0.4310] det_loss_box_0[0.6239] det_loss_cls_1[0.4330] det_loss_box_1[0.4965] det_loss_cls_2[0.3912] det_loss_box_2[0.4569] det_loss_cls_3[0.3670] det_loss_box_3[0.4364] det_loss_cls_4[0.3516] det_loss_box_4[0.4270] det_loss_cls_5[0.3429] det_loss_box_5[0.4242] det_loss_cls_dn_0[0.0962] det_loss_box_dn_0[0.3634] det_loss_cls_dn_1[0.0931] det_loss_box_dn_1[0.3128] det_loss_cls_dn_2[0.0924] det_loss_box_dn_2[0.2910] det_loss_cls_dn_3[0.0918] det_loss_box_dn_3[0.2873] det_loss_cls_dn_4[0.0914] det_loss_box_dn_4[0.2844] det_loss_cls_dn_5[0.0908] det_loss_box_dn_5[0.2792] loss_depth[0.0379] total_loss[7.5933]
Rank[0/8] 06/20/2025 02:30:33 INFO stats.py:314 | Epoch[93] Step[104] GlobalStep[36374] Training Speed: 6.62 samples/sec across all devices.	Average Step Time: 1.21 sec.	Estimated Remaining Time: 19:21:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:30:53 INFO loss_tracker.py:84 | Epoch[93/239] Step[124] GlobalStep[36394/NA]: det_loss_cls_0[0.5327] det_loss_box_0[0.7648] det_loss_cls_1[0.5249] det_loss_box_1[0.6089] det_loss_cls_2[0.4911] det_loss_box_2[0.5624] det_loss_cls_3[0.4771] det_loss_box_3[0.5388] det_loss_cls_4[0.4440] det_loss_box_4[0.5318] det_loss_cls_5[0.4339] det_loss_box_5[0.5267] det_loss_cls_dn_0[0.0949] det_loss_box_dn_0[0.2989] det_loss_cls_dn_1[0.0909] det_loss_box_dn_1[0.2619] det_loss_cls_dn_2[0.0904] det_loss_box_dn_2[0.2524] det_loss_cls_dn_3[0.0894] det_loss_box_dn_3[0.2494] det_loss_cls_dn_4[0.0884] det_loss_box_dn_4[0.2488] det_loss_cls_dn_5[0.0876] det_loss_box_dn_5[0.2454] loss_depth[0.0374] total_loss[8.5726]
Rank[0/8] 06/20/2025 02:30:59 INFO stats.py:314 | Epoch[93] Step[129] GlobalStep[36399] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 19:20:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:31:20 INFO loss_tracker.py:84 | Epoch[93/239] Step[149] GlobalStep[36419/NA]: det_loss_cls_0[0.5147] det_loss_box_0[0.7008] det_loss_cls_1[0.4985] det_loss_box_1[0.5385] det_loss_cls_2[0.4673] det_loss_box_2[0.4805] det_loss_cls_3[0.4334] det_loss_box_3[0.4650] det_loss_cls_4[0.4104] det_loss_box_4[0.4546] det_loss_cls_5[0.3918] det_loss_box_5[0.4593] det_loss_cls_dn_0[0.0997] det_loss_box_dn_0[0.2767] det_loss_cls_dn_1[0.0953] det_loss_box_dn_1[0.2340] det_loss_cls_dn_2[0.0946] det_loss_box_dn_2[0.2224] det_loss_cls_dn_3[0.0932] det_loss_box_dn_3[0.2187] det_loss_cls_dn_4[0.0925] det_loss_box_dn_4[0.2177] det_loss_cls_dn_5[0.0918] det_loss_box_dn_5[0.2146] loss_depth[0.0407] total_loss[7.8066]
Rank[0/8] 06/20/2025 02:31:25 INFO stats.py:314 | Epoch[93] Step[154] GlobalStep[36424] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 19:20:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:31:45 INFO loss_tracker.py:84 | Epoch[93/239] Step[174] GlobalStep[36444/NA]: det_loss_cls_0[0.4224] det_loss_box_0[0.5215] det_loss_cls_1[0.4073] det_loss_box_1[0.4240] det_loss_cls_2[0.3699] det_loss_box_2[0.3875] det_loss_cls_3[0.3482] det_loss_box_3[0.3733] det_loss_cls_4[0.3315] det_loss_box_4[0.3697] det_loss_cls_5[0.3247] det_loss_box_5[0.3680] det_loss_cls_dn_0[0.0996] det_loss_box_dn_0[0.3136] det_loss_cls_dn_1[0.0939] det_loss_box_dn_1[0.2765] det_loss_cls_dn_2[0.0930] det_loss_box_dn_2[0.2639] det_loss_cls_dn_3[0.0929] det_loss_box_dn_3[0.2589] det_loss_cls_dn_4[0.0925] det_loss_box_dn_4[0.2576] det_loss_cls_dn_5[0.0916] det_loss_box_dn_5[0.2501] loss_depth[0.0403] total_loss[6.8723]
Rank[0/8] 06/20/2025 02:31:51 INFO stats.py:314 | Epoch[93] Step[179] GlobalStep[36449] Training Speed: 6.54 samples/sec across all devices.	Average Step Time: 1.22 sec.	Estimated Remaining Time: 19:19:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:32:11 INFO loss_tracker.py:84 | Epoch[93/239] Step[199] GlobalStep[36469/NA]: det_loss_cls_0[0.4718] det_loss_box_0[0.5877] det_loss_cls_1[0.4651] det_loss_box_1[0.4553] det_loss_cls_2[0.4345] det_loss_box_2[0.4106] det_loss_cls_3[0.4139] det_loss_box_3[0.3915] det_loss_cls_4[0.3869] det_loss_box_4[0.3802] det_loss_cls_5[0.3649] det_loss_box_5[0.3886] det_loss_cls_dn_0[0.0985] det_loss_box_dn_0[0.2585] det_loss_cls_dn_1[0.0949] det_loss_box_dn_1[0.2233] det_loss_cls_dn_2[0.0942] det_loss_box_dn_2[0.2106] det_loss_cls_dn_3[0.0930] det_loss_box_dn_3[0.2087] det_loss_cls_dn_4[0.0917] det_loss_box_dn_4[0.2080] det_loss_cls_dn_5[0.0915] det_loss_box_dn_5[0.2074] loss_depth[0.0378] total_loss[7.0691]
Rank[0/8] 06/20/2025 02:32:16 INFO stats.py:314 | Epoch[93] Step[204] GlobalStep[36474] Training Speed: 7.71 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 19:18:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:32:37 INFO loss_tracker.py:84 | Epoch[93/239] Step[224] GlobalStep[36494/NA]: det_loss_cls_0[0.4857] det_loss_box_0[0.5690] det_loss_cls_1[0.4799] det_loss_box_1[0.4249] det_loss_cls_2[0.4414] det_loss_box_2[0.3924] det_loss_cls_3[0.4184] det_loss_box_3[0.3721] det_loss_cls_4[0.3829] det_loss_box_4[0.3735] det_loss_cls_5[0.3772] det_loss_box_5[0.3674] det_loss_cls_dn_0[0.0873] det_loss_box_dn_0[0.2868] det_loss_cls_dn_1[0.0837] det_loss_box_dn_1[0.2387] det_loss_cls_dn_2[0.0830] det_loss_box_dn_2[0.2239] det_loss_cls_dn_3[0.0811] det_loss_box_dn_3[0.2197] det_loss_cls_dn_4[0.0804] det_loss_box_dn_4[0.2189] det_loss_cls_dn_5[0.0798] det_loss_box_dn_5[0.2183] loss_depth[0.0386] total_loss[7.0250]
Rank[0/8] 06/20/2025 02:32:42 INFO stats.py:314 | Epoch[93] Step[229] GlobalStep[36499] Training Speed: 8.80 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 19:18:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:33:02 INFO loss_tracker.py:84 | Epoch[93/239] Step[249] GlobalStep[36519/NA]: det_loss_cls_0[0.5597] det_loss_box_0[0.7188] det_loss_cls_1[0.5261] det_loss_box_1[0.5495] det_loss_cls_2[0.4777] det_loss_box_2[0.4926] det_loss_cls_3[0.4488] det_loss_box_3[0.4708] det_loss_cls_4[0.4129] det_loss_box_4[0.4676] det_loss_cls_5[0.4009] det_loss_box_5[0.4675] det_loss_cls_dn_0[0.0916] det_loss_box_dn_0[0.2717] det_loss_cls_dn_1[0.0871] det_loss_box_dn_1[0.2297] det_loss_cls_dn_2[0.0862] det_loss_box_dn_2[0.2140] det_loss_cls_dn_3[0.0849] det_loss_box_dn_3[0.2114] det_loss_cls_dn_4[0.0831] det_loss_box_dn_4[0.2095] det_loss_cls_dn_5[0.0819] det_loss_box_dn_5[0.2080] loss_depth[0.0392] total_loss[7.8912]
Rank[0/8] 06/20/2025 02:33:08 INFO stats.py:314 | Epoch[93] Step[254] GlobalStep[36524] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 19:17:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:33:29 INFO loss_tracker.py:84 | Epoch[93/239] Step[274] GlobalStep[36544/NA]: det_loss_cls_0[0.4966] det_loss_box_0[0.6286] det_loss_cls_1[0.4776] det_loss_box_1[0.4897] det_loss_cls_2[0.4475] det_loss_box_2[0.4396] det_loss_cls_3[0.4240] det_loss_box_3[0.4294] det_loss_cls_4[0.3986] det_loss_box_4[0.4174] det_loss_cls_5[0.3909] det_loss_box_5[0.4133] det_loss_cls_dn_0[0.0903] det_loss_box_dn_0[0.2879] det_loss_cls_dn_1[0.0866] det_loss_box_dn_1[0.2365] det_loss_cls_dn_2[0.0864] det_loss_box_dn_2[0.2223] det_loss_cls_dn_3[0.0851] det_loss_box_dn_3[0.2189] det_loss_cls_dn_4[0.0840] det_loss_box_dn_4[0.2166] det_loss_cls_dn_5[0.0834] det_loss_box_dn_5[0.2151] loss_depth[0.0404] total_loss[7.4067]
Rank[0/8] 06/20/2025 02:33:34 INFO stats.py:314 | Epoch[93] Step[279] GlobalStep[36549] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 19:17:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:33:54 INFO loss_tracker.py:84 | Epoch[93/239] Step[299] GlobalStep[36569/NA]: det_loss_cls_0[0.5325] det_loss_box_0[0.6644] det_loss_cls_1[0.5119] det_loss_box_1[0.4998] det_loss_cls_2[0.4708] det_loss_box_2[0.4628] det_loss_cls_3[0.4464] det_loss_box_3[0.4510] det_loss_cls_4[0.4106] det_loss_box_4[0.4449] det_loss_cls_5[0.3997] det_loss_box_5[0.4439] det_loss_cls_dn_0[0.1044] det_loss_box_dn_0[0.3051] det_loss_cls_dn_1[0.1010] det_loss_box_dn_1[0.2653] det_loss_cls_dn_2[0.1010] det_loss_box_dn_2[0.2536] det_loss_cls_dn_3[0.0994] det_loss_box_dn_3[0.2510] det_loss_cls_dn_4[0.0982] det_loss_box_dn_4[0.2501] det_loss_cls_dn_5[0.0977] det_loss_box_dn_5[0.2491] loss_depth[0.0404] total_loss[7.9551]
Rank[0/8] 06/20/2025 02:33:59 INFO stats.py:314 | Epoch[93] Step[304] GlobalStep[36574] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 19:16:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:34:20 INFO loss_tracker.py:84 | Epoch[93/239] Step[324] GlobalStep[36594/NA]: det_loss_cls_0[0.5330] det_loss_box_0[0.6658] det_loss_cls_1[0.5335] det_loss_box_1[0.5089] det_loss_cls_2[0.5000] det_loss_box_2[0.4642] det_loss_cls_3[0.4713] det_loss_box_3[0.4496] det_loss_cls_4[0.4448] det_loss_box_4[0.4445] det_loss_cls_5[0.4283] det_loss_box_5[0.4507] det_loss_cls_dn_0[0.1022] det_loss_box_dn_0[0.3738] det_loss_cls_dn_1[0.0982] det_loss_box_dn_1[0.3174] det_loss_cls_dn_2[0.0982] det_loss_box_dn_2[0.3003] det_loss_cls_dn_3[0.0974] det_loss_box_dn_3[0.2938] det_loss_cls_dn_4[0.0971] det_loss_box_dn_4[0.2909] det_loss_cls_dn_5[0.0968] det_loss_box_dn_5[0.2829] loss_depth[0.0413] total_loss[8.3849]
Rank[0/8] 06/20/2025 02:34:26 INFO stats.py:314 | Epoch[93] Step[329] GlobalStep[36599] Training Speed: 5.99 samples/sec across all devices.	Average Step Time: 1.34 sec.	Estimated Remaining Time: 19:15:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:34:46 INFO loss_tracker.py:84 | Epoch[93/239] Step[349] GlobalStep[36619/NA]: det_loss_cls_0[0.5501] det_loss_box_0[0.7013] det_loss_cls_1[0.5397] det_loss_box_1[0.5144] det_loss_cls_2[0.4964] det_loss_box_2[0.4635] det_loss_cls_3[0.4622] det_loss_box_3[0.4483] det_loss_cls_4[0.4399] det_loss_box_4[0.4332] det_loss_cls_5[0.4277] det_loss_box_5[0.4337] det_loss_cls_dn_0[0.1066] det_loss_box_dn_0[0.2664] det_loss_cls_dn_1[0.1027] det_loss_box_dn_1[0.2242] det_loss_cls_dn_2[0.1017] det_loss_box_dn_2[0.2068] det_loss_cls_dn_3[0.1001] det_loss_box_dn_3[0.2037] det_loss_cls_dn_4[0.0989] det_loss_box_dn_4[0.2023] det_loss_cls_dn_5[0.0980] det_loss_box_dn_5[0.2006] loss_depth[0.0370] total_loss[7.8596]
Rank[0/8] 06/20/2025 02:34:51 INFO stats.py:314 | Epoch[93] Step[354] GlobalStep[36624] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 19:15:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:35:12 INFO loss_tracker.py:84 | Epoch[93/239] Step[374] GlobalStep[36644/NA]: det_loss_cls_0[0.4117] det_loss_box_0[0.5363] det_loss_cls_1[0.3961] det_loss_box_1[0.4060] det_loss_cls_2[0.3769] det_loss_box_2[0.3552] det_loss_cls_3[0.3495] det_loss_box_3[0.3441] det_loss_cls_4[0.3185] det_loss_box_4[0.3403] det_loss_cls_5[0.3057] det_loss_box_5[0.3401] det_loss_cls_dn_0[0.0884] det_loss_box_dn_0[0.3295] det_loss_cls_dn_1[0.0851] det_loss_box_dn_1[0.2778] det_loss_cls_dn_2[0.0846] det_loss_box_dn_2[0.2517] det_loss_cls_dn_3[0.0834] det_loss_box_dn_3[0.2492] det_loss_cls_dn_4[0.0827] det_loss_box_dn_4[0.2469] det_loss_cls_dn_5[0.0815] det_loss_box_dn_5[0.2461] loss_depth[0.0395] total_loss[6.6269]
Rank[0/8] 06/20/2025 02:35:17 INFO stats.py:314 | Epoch[93] Step[379] GlobalStep[36649] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 19:14:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:35:27 INFO stats.py:394 | Epoch[93] completed. Training Speed: 7.68 samples/sec across all devices.	Epoch Time: 406.47 sec.	Average Epoch Time: 406.47 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 19:14:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:35:46 INFO stats.py:314 | Epoch[94] Step[14] GlobalStep[36674] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 19:14:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:35:57 INFO loss_tracker.py:84 | Epoch[94/239] Step[24] GlobalStep[36684/NA]: det_loss_cls_0[0.5195] det_loss_box_0[0.6858] det_loss_cls_1[0.5174] det_loss_box_1[0.5379] det_loss_cls_2[0.4815] det_loss_box_2[0.4937] det_loss_cls_3[0.4582] det_loss_box_3[0.4830] det_loss_cls_4[0.4276] det_loss_box_4[0.4775] det_loss_cls_5[0.4139] det_loss_box_5[0.4784] det_loss_cls_dn_0[0.0924] det_loss_box_dn_0[0.2672] det_loss_cls_dn_1[0.0888] det_loss_box_dn_1[0.2306] det_loss_cls_dn_2[0.0881] det_loss_box_dn_2[0.2170] det_loss_cls_dn_3[0.0873] det_loss_box_dn_3[0.2152] det_loss_cls_dn_4[0.0860] det_loss_box_dn_4[0.2141] det_loss_cls_dn_5[0.0850] det_loss_box_dn_5[0.2129] loss_depth[0.0355] total_loss[7.8945]
Rank[0/8] 06/20/2025 02:36:13 INFO stats.py:314 | Epoch[94] Step[39] GlobalStep[36699] Training Speed: 7.90 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 19:13:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:36:23 INFO loss_tracker.py:84 | Epoch[94/239] Step[49] GlobalStep[36709/NA]: det_loss_cls_0[0.4601] det_loss_box_0[0.5828] det_loss_cls_1[0.4414] det_loss_box_1[0.4500] det_loss_cls_2[0.3991] det_loss_box_2[0.3947] det_loss_cls_3[0.3830] det_loss_box_3[0.3833] det_loss_cls_4[0.3603] det_loss_box_4[0.3736] det_loss_cls_5[0.3485] det_loss_box_5[0.3769] det_loss_cls_dn_0[0.1032] det_loss_box_dn_0[0.3399] det_loss_cls_dn_1[0.1002] det_loss_box_dn_1[0.2817] det_loss_cls_dn_2[0.0993] det_loss_box_dn_2[0.2603] det_loss_cls_dn_3[0.0981] det_loss_box_dn_3[0.2558] det_loss_cls_dn_4[0.0970] det_loss_box_dn_4[0.2551] det_loss_cls_dn_5[0.0962] det_loss_box_dn_5[0.2535] loss_depth[0.0402] total_loss[7.2341]
Rank[0/8] 06/20/2025 02:36:38 INFO stats.py:314 | Epoch[94] Step[64] GlobalStep[36724] Training Speed: 7.79 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 19:12:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:36:49 INFO loss_tracker.py:84 | Epoch[94/239] Step[74] GlobalStep[36734/NA]: det_loss_cls_0[0.5115] det_loss_box_0[0.6436] det_loss_cls_1[0.4992] det_loss_box_1[0.5001] det_loss_cls_2[0.4682] det_loss_box_2[0.4520] det_loss_cls_3[0.4464] det_loss_box_3[0.4411] det_loss_cls_4[0.4161] det_loss_box_4[0.4317] det_loss_cls_5[0.3962] det_loss_box_5[0.4358] det_loss_cls_dn_0[0.1028] det_loss_box_dn_0[0.3631] det_loss_cls_dn_1[0.0991] det_loss_box_dn_1[0.3023] det_loss_cls_dn_2[0.0986] det_loss_box_dn_2[0.2853] det_loss_cls_dn_3[0.0976] det_loss_box_dn_3[0.2790] det_loss_cls_dn_4[0.0957] det_loss_box_dn_4[0.2774] det_loss_cls_dn_5[0.0947] det_loss_box_dn_5[0.2740] loss_depth[0.0364] total_loss[8.0479]
Rank[0/8] 06/20/2025 02:37:04 INFO stats.py:314 | Epoch[94] Step[89] GlobalStep[36749] Training Speed: 7.49 samples/sec across all devices.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 19:12:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:37:15 INFO loss_tracker.py:84 | Epoch[94/239] Step[99] GlobalStep[36759/NA]: det_loss_cls_0[0.4457] det_loss_box_0[0.5608] det_loss_cls_1[0.4321] det_loss_box_1[0.4322] det_loss_cls_2[0.3913] det_loss_box_2[0.4003] det_loss_cls_3[0.3640] det_loss_box_3[0.3854] det_loss_cls_4[0.3370] det_loss_box_4[0.3747] det_loss_cls_5[0.3275] det_loss_box_5[0.3757] det_loss_cls_dn_0[0.1010] det_loss_box_dn_0[0.2836] det_loss_cls_dn_1[0.0975] det_loss_box_dn_1[0.2402] det_loss_cls_dn_2[0.0973] det_loss_box_dn_2[0.2223] det_loss_cls_dn_3[0.0962] det_loss_box_dn_3[0.2192] det_loss_cls_dn_4[0.0954] det_loss_box_dn_4[0.2180] det_loss_cls_dn_5[0.0943] det_loss_box_dn_5[0.2156] loss_depth[0.0376] total_loss[6.8446]
Rank[0/8] 06/20/2025 02:37:31 INFO stats.py:314 | Epoch[94] Step[114] GlobalStep[36774] Training Speed: 7.97 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 19:11:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:37:40 INFO loss_tracker.py:84 | Epoch[94/239] Step[124] GlobalStep[36784/NA]: det_loss_cls_0[0.5281] det_loss_box_0[0.6086] det_loss_cls_1[0.5060] det_loss_box_1[0.4644] det_loss_cls_2[0.4697] det_loss_box_2[0.4214] det_loss_cls_3[0.4431] det_loss_box_3[0.4119] det_loss_cls_4[0.4224] det_loss_box_4[0.4010] det_loss_cls_5[0.4037] det_loss_box_5[0.4046] det_loss_cls_dn_0[0.0986] det_loss_box_dn_0[0.2670] det_loss_cls_dn_1[0.0951] det_loss_box_dn_1[0.2181] det_loss_cls_dn_2[0.0949] det_loss_box_dn_2[0.1956] det_loss_cls_dn_3[0.0937] det_loss_box_dn_3[0.1924] det_loss_cls_dn_4[0.0925] det_loss_box_dn_4[0.1913] det_loss_cls_dn_5[0.0917] det_loss_box_dn_5[0.1901] loss_depth[0.0394] total_loss[7.3450]
Rank[0/8] 06/20/2025 02:37:56 INFO stats.py:314 | Epoch[94] Step[139] GlobalStep[36799] Training Speed: 8.65 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 19:10:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:38:06 INFO loss_tracker.py:84 | Epoch[94/239] Step[149] GlobalStep[36809/NA]: det_loss_cls_0[0.5772] det_loss_box_0[0.7449] det_loss_cls_1[0.5663] det_loss_box_1[0.5615] det_loss_cls_2[0.5267] det_loss_box_2[0.5057] det_loss_cls_3[0.4911] det_loss_box_3[0.4846] det_loss_cls_4[0.4533] det_loss_box_4[0.4768] det_loss_cls_5[0.4407] det_loss_box_5[0.4792] det_loss_cls_dn_0[0.0988] det_loss_box_dn_0[0.2692] det_loss_cls_dn_1[0.0947] det_loss_box_dn_1[0.2202] det_loss_cls_dn_2[0.0954] det_loss_box_dn_2[0.2026] det_loss_cls_dn_3[0.0945] det_loss_box_dn_3[0.1986] det_loss_cls_dn_4[0.0931] det_loss_box_dn_4[0.1966] det_loss_cls_dn_5[0.0921] det_loss_box_dn_5[0.1951] loss_depth[0.0381] total_loss[8.1971]
Rank[0/8] 06/20/2025 02:38:22 INFO stats.py:314 | Epoch[94] Step[164] GlobalStep[36824] Training Speed: 6.41 samples/sec across all devices.	Average Step Time: 1.25 sec.	Estimated Remaining Time: 19:10:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:38:32 INFO loss_tracker.py:84 | Epoch[94/239] Step[174] GlobalStep[36834/NA]: det_loss_cls_0[0.4092] det_loss_box_0[0.5371] det_loss_cls_1[0.3862] det_loss_box_1[0.4069] det_loss_cls_2[0.3587] det_loss_box_2[0.3645] det_loss_cls_3[0.3380] det_loss_box_3[0.3559] det_loss_cls_4[0.3158] det_loss_box_4[0.3456] det_loss_cls_5[0.3039] det_loss_box_5[0.3480] det_loss_cls_dn_0[0.0895] det_loss_box_dn_0[0.2873] det_loss_cls_dn_1[0.0862] det_loss_box_dn_1[0.2402] det_loss_cls_dn_2[0.0855] det_loss_box_dn_2[0.2246] det_loss_cls_dn_3[0.0847] det_loss_box_dn_3[0.2227] det_loss_cls_dn_4[0.0837] det_loss_box_dn_4[0.2195] det_loss_cls_dn_5[0.0839] det_loss_box_dn_5[0.2169] loss_depth[0.0398] total_loss[6.4344]
Rank[0/8] 06/20/2025 02:38:48 INFO stats.py:314 | Epoch[94] Step[189] GlobalStep[36849] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 19:09:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:38:59 INFO loss_tracker.py:84 | Epoch[94/239] Step[199] GlobalStep[36859/NA]: det_loss_cls_0[0.5366] det_loss_box_0[0.7208] det_loss_cls_1[0.5324] det_loss_box_1[0.5651] det_loss_cls_2[0.4876] det_loss_box_2[0.5130] det_loss_cls_3[0.4592] det_loss_box_3[0.4994] det_loss_cls_4[0.4301] det_loss_box_4[0.4922] det_loss_cls_5[0.4189] det_loss_box_5[0.4835] det_loss_cls_dn_0[0.0948] det_loss_box_dn_0[0.3322] det_loss_cls_dn_1[0.0923] det_loss_box_dn_1[0.2903] det_loss_cls_dn_2[0.0914] det_loss_box_dn_2[0.2778] det_loss_cls_dn_3[0.0900] det_loss_box_dn_3[0.2745] det_loss_cls_dn_4[0.0885] det_loss_box_dn_4[0.2727] det_loss_cls_dn_5[0.0881] det_loss_box_dn_5[0.2679] loss_depth[0.0387] total_loss[8.4378]
Rank[0/8] 06/20/2025 02:39:14 INFO stats.py:314 | Epoch[94] Step[214] GlobalStep[36874] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 19:09:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:39:24 INFO loss_tracker.py:84 | Epoch[94/239] Step[224] GlobalStep[36884/NA]: det_loss_cls_0[0.5125] det_loss_box_0[0.6560] det_loss_cls_1[0.4990] det_loss_box_1[0.5059] det_loss_cls_2[0.4759] det_loss_box_2[0.4681] det_loss_cls_3[0.4481] det_loss_box_3[0.4418] det_loss_cls_4[0.4164] det_loss_box_4[0.4298] det_loss_cls_5[0.4045] det_loss_box_5[0.4271] det_loss_cls_dn_0[0.1036] det_loss_box_dn_0[0.3148] det_loss_cls_dn_1[0.1009] det_loss_box_dn_1[0.2718] det_loss_cls_dn_2[0.1004] det_loss_box_dn_2[0.2515] det_loss_cls_dn_3[0.1006] det_loss_box_dn_3[0.2488] det_loss_cls_dn_4[0.1000] det_loss_box_dn_4[0.2469] det_loss_cls_dn_5[0.0991] det_loss_box_dn_5[0.2442] loss_depth[0.0391] total_loss[7.9071]
Rank[0/8] 06/20/2025 02:39:39 INFO stats.py:314 | Epoch[94] Step[239] GlobalStep[36899] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 19:08:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:39:49 INFO loss_tracker.py:84 | Epoch[94/239] Step[249] GlobalStep[36909/NA]: det_loss_cls_0[0.6596] det_loss_box_0[0.8726] det_loss_cls_1[0.6445] det_loss_box_1[0.7012] det_loss_cls_2[0.6021] det_loss_box_2[0.6284] det_loss_cls_3[0.5551] det_loss_box_3[0.6140] det_loss_cls_4[0.5224] det_loss_box_4[0.5987] det_loss_cls_5[0.5068] det_loss_box_5[0.5984] det_loss_cls_dn_0[0.0988] det_loss_box_dn_0[0.2985] det_loss_cls_dn_1[0.0955] det_loss_box_dn_1[0.2583] det_loss_cls_dn_2[0.0943] det_loss_box_dn_2[0.2424] det_loss_cls_dn_3[0.0926] det_loss_box_dn_3[0.2395] det_loss_cls_dn_4[0.0914] det_loss_box_dn_4[0.2380] det_loss_cls_dn_5[0.0904] det_loss_box_dn_5[0.2385] loss_depth[0.0378] total_loss[9.6199]
Rank[0/8] 06/20/2025 02:40:06 INFO stats.py:314 | Epoch[94] Step[264] GlobalStep[36924] Training Speed: 6.15 samples/sec across all devices.	Average Step Time: 1.30 sec.	Estimated Remaining Time: 19:07:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:40:16 INFO loss_tracker.py:84 | Epoch[94/239] Step[274] GlobalStep[36934/NA]: det_loss_cls_0[0.6813] det_loss_box_0[0.9992] det_loss_cls_1[0.6599] det_loss_box_1[0.7875] det_loss_cls_2[0.6101] det_loss_box_2[0.7270] det_loss_cls_3[0.5759] det_loss_box_3[0.7067] det_loss_cls_4[0.5435] det_loss_box_4[0.6792] det_loss_cls_5[0.5297] det_loss_box_5[0.6793] det_loss_cls_dn_0[0.0930] det_loss_box_dn_0[0.2965] det_loss_cls_dn_1[0.0888] det_loss_box_dn_1[0.2526] det_loss_cls_dn_2[0.0877] det_loss_box_dn_2[0.2371] det_loss_cls_dn_3[0.0873] det_loss_box_dn_3[0.2337] det_loss_cls_dn_4[0.0857] det_loss_box_dn_4[0.2319] det_loss_cls_dn_5[0.0846] det_loss_box_dn_5[0.2295] loss_depth[0.0418] total_loss[10.2296]
Rank[0/8] 06/20/2025 02:40:32 INFO stats.py:314 | Epoch[94] Step[289] GlobalStep[36949] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 19:07:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:40:42 INFO loss_tracker.py:84 | Epoch[94/239] Step[299] GlobalStep[36959/NA]: det_loss_cls_0[0.4606] det_loss_box_0[0.6299] det_loss_cls_1[0.4449] det_loss_box_1[0.4967] det_loss_cls_2[0.4098] det_loss_box_2[0.4440] det_loss_cls_3[0.3885] det_loss_box_3[0.4279] det_loss_cls_4[0.3608] det_loss_box_4[0.4221] det_loss_cls_5[0.3454] det_loss_box_5[0.4222] det_loss_cls_dn_0[0.0967] det_loss_box_dn_0[0.3423] det_loss_cls_dn_1[0.0914] det_loss_box_dn_1[0.2946] det_loss_cls_dn_2[0.0904] det_loss_box_dn_2[0.2790] det_loss_cls_dn_3[0.0894] det_loss_box_dn_3[0.2787] det_loss_cls_dn_4[0.0886] det_loss_box_dn_4[0.2774] det_loss_cls_dn_5[0.0879] det_loss_box_dn_5[0.2736] loss_depth[0.0393] total_loss[7.5821]
Rank[0/8] 06/20/2025 02:40:58 INFO stats.py:314 | Epoch[94] Step[314] GlobalStep[36974] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 19:06:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:41:08 INFO loss_tracker.py:84 | Epoch[94/239] Step[324] GlobalStep[36984/NA]: det_loss_cls_0[0.4381] det_loss_box_0[0.5539] det_loss_cls_1[0.4275] det_loss_box_1[0.4257] det_loss_cls_2[0.3983] det_loss_box_2[0.3887] det_loss_cls_3[0.3727] det_loss_box_3[0.3733] det_loss_cls_4[0.3453] det_loss_box_4[0.3658] det_loss_cls_5[0.3358] det_loss_box_5[0.3667] det_loss_cls_dn_0[0.1083] det_loss_box_dn_0[0.3269] det_loss_cls_dn_1[0.1036] det_loss_box_dn_1[0.2793] det_loss_cls_dn_2[0.1028] det_loss_box_dn_2[0.2615] det_loss_cls_dn_3[0.1016] det_loss_box_dn_3[0.2583] det_loss_cls_dn_4[0.1005] det_loss_box_dn_4[0.2567] det_loss_cls_dn_5[0.0996] det_loss_box_dn_5[0.2538] loss_depth[0.0368] total_loss[7.0816]
Rank[0/8] 06/20/2025 02:41:24 INFO stats.py:314 | Epoch[94] Step[339] GlobalStep[36999] Training Speed: 6.14 samples/sec across all devices.	Average Step Time: 1.30 sec.	Estimated Remaining Time: 19:05:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:41:34 INFO loss_tracker.py:84 | Epoch[94/239] Step[349] GlobalStep[37009/NA]: det_loss_cls_0[0.4645] det_loss_box_0[0.6021] det_loss_cls_1[0.4405] det_loss_box_1[0.4771] det_loss_cls_2[0.4163] det_loss_box_2[0.4163] det_loss_cls_3[0.3871] det_loss_box_3[0.4062] det_loss_cls_4[0.3632] det_loss_box_4[0.3985] det_loss_cls_5[0.3483] det_loss_box_5[0.3988] det_loss_cls_dn_0[0.0944] det_loss_box_dn_0[0.2724] det_loss_cls_dn_1[0.0904] det_loss_box_dn_1[0.2271] det_loss_cls_dn_2[0.0893] det_loss_box_dn_2[0.2119] det_loss_cls_dn_3[0.0880] det_loss_box_dn_3[0.2087] det_loss_cls_dn_4[0.0871] det_loss_box_dn_4[0.2069] det_loss_cls_dn_5[0.0861] det_loss_box_dn_5[0.2051] loss_depth[0.0404] total_loss[7.0266]
Rank[0/8] 06/20/2025 02:41:50 INFO stats.py:314 | Epoch[94] Step[364] GlobalStep[37024] Training Speed: 6.94 samples/sec across all devices.	Average Step Time: 1.15 sec.	Estimated Remaining Time: 19:05:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:42:01 INFO loss_tracker.py:84 | Epoch[94/239] Step[374] GlobalStep[37034/NA]: det_loss_cls_0[0.5188] det_loss_box_0[0.7366] det_loss_cls_1[0.5037] det_loss_box_1[0.5909] det_loss_cls_2[0.4557] det_loss_box_2[0.5283] det_loss_cls_3[0.4234] det_loss_box_3[0.5228] det_loss_cls_4[0.4016] det_loss_box_4[0.5065] det_loss_cls_5[0.3851] det_loss_box_5[0.5074] det_loss_cls_dn_0[0.0918] det_loss_box_dn_0[0.2870] det_loss_cls_dn_1[0.0878] det_loss_box_dn_1[0.2447] det_loss_cls_dn_2[0.0873] det_loss_box_dn_2[0.2306] det_loss_cls_dn_3[0.0861] det_loss_box_dn_3[0.2283] det_loss_cls_dn_4[0.0854] det_loss_box_dn_4[0.2274] det_loss_cls_dn_5[0.0850] det_loss_box_dn_5[0.2263] loss_depth[0.0390] total_loss[8.0874]
Rank[0/8] 06/20/2025 02:42:16 INFO stats.py:314 | Epoch[94] Step[389] GlobalStep[37049] Training Speed: 7.17 samples/sec across all devices.	Average Step Time: 1.12 sec.	Estimated Remaining Time: 19:04:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:42:16 INFO stats.py:394 | Epoch[94] completed. Training Speed: 7.64 samples/sec across all devices.	Epoch Time: 408.48 sec.	Average Epoch Time: 408.48 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 19:04:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:42:43 INFO stats.py:314 | Epoch[95] Step[24] GlobalStep[37074] Training Speed: 7.52 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 19:04:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:42:43 INFO loss_tracker.py:84 | Epoch[95/239] Step[24] GlobalStep[37074/NA]: det_loss_cls_0[0.5164] det_loss_box_0[0.7001] det_loss_cls_1[0.4939] det_loss_box_1[0.5744] det_loss_cls_2[0.4677] det_loss_box_2[0.5177] det_loss_cls_3[0.4508] det_loss_box_3[0.4858] det_loss_cls_4[0.4298] det_loss_box_4[0.4771] det_loss_cls_5[0.4154] det_loss_box_5[0.4793] det_loss_cls_dn_0[0.1052] det_loss_box_dn_0[0.3171] det_loss_cls_dn_1[0.1019] det_loss_box_dn_1[0.2715] det_loss_cls_dn_2[0.1013] det_loss_box_dn_2[0.2548] det_loss_cls_dn_3[0.0996] det_loss_box_dn_3[0.2513] det_loss_cls_dn_4[0.0984] det_loss_box_dn_4[0.2503] det_loss_cls_dn_5[0.0975] det_loss_box_dn_5[0.2494] loss_depth[0.0373] total_loss[8.2441]
Rank[0/8] 06/20/2025 02:43:09 INFO stats.py:314 | Epoch[95] Step[49] GlobalStep[37099] Training Speed: 8.01 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 19:03:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:43:09 INFO loss_tracker.py:84 | Epoch[95/239] Step[49] GlobalStep[37099/NA]: det_loss_cls_0[0.5102] det_loss_box_0[0.8343] det_loss_cls_1[0.4893] det_loss_box_1[0.6590] det_loss_cls_2[0.4661] det_loss_box_2[0.5847] det_loss_cls_3[0.4385] det_loss_box_3[0.5672] det_loss_cls_4[0.4139] det_loss_box_4[0.5655] det_loss_cls_5[0.4045] det_loss_box_5[0.5683] det_loss_cls_dn_0[0.0912] det_loss_box_dn_0[0.3620] det_loss_cls_dn_1[0.0879] det_loss_box_dn_1[0.3087] det_loss_cls_dn_2[0.0877] det_loss_box_dn_2[0.2919] det_loss_cls_dn_3[0.0861] det_loss_box_dn_3[0.2874] det_loss_cls_dn_4[0.0846] det_loss_box_dn_4[0.2855] det_loss_cls_dn_5[0.0840] det_loss_box_dn_5[0.2844] loss_depth[0.0417] total_loss[8.8846]
Rank[0/8] 06/20/2025 02:43:35 INFO stats.py:314 | Epoch[95] Step[74] GlobalStep[37124] Training Speed: 7.87 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 19:02:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:43:35 INFO loss_tracker.py:84 | Epoch[95/239] Step[74] GlobalStep[37124/NA]: det_loss_cls_0[0.5861] det_loss_box_0[0.7869] det_loss_cls_1[0.5653] det_loss_box_1[0.6298] det_loss_cls_2[0.5296] det_loss_box_2[0.5637] det_loss_cls_3[0.5007] det_loss_box_3[0.5385] det_loss_cls_4[0.4715] det_loss_box_4[0.5204] det_loss_cls_5[0.4540] det_loss_box_5[0.5198] det_loss_cls_dn_0[0.1128] det_loss_box_dn_0[0.3206] det_loss_cls_dn_1[0.1082] det_loss_box_dn_1[0.2741] det_loss_cls_dn_2[0.1076] det_loss_box_dn_2[0.2553] det_loss_cls_dn_3[0.1066] det_loss_box_dn_3[0.2519] det_loss_cls_dn_4[0.1051] det_loss_box_dn_4[0.2495] det_loss_cls_dn_5[0.1043] det_loss_box_dn_5[0.2486] loss_depth[0.0414] total_loss[8.9525]
Rank[0/8] 06/20/2025 02:44:00 INFO stats.py:314 | Epoch[95] Step[99] GlobalStep[37149] Training Speed: 13.50 samples/sec across all devices.	Average Step Time: 0.59 sec.	Estimated Remaining Time: 19:02:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:44:01 INFO loss_tracker.py:84 | Epoch[95/239] Step[99] GlobalStep[37149/NA]: det_loss_cls_0[0.5424] det_loss_box_0[0.7280] det_loss_cls_1[0.5340] det_loss_box_1[0.5614] det_loss_cls_2[0.5014] det_loss_box_2[0.5129] det_loss_cls_3[0.4685] det_loss_box_3[0.4956] det_loss_cls_4[0.4473] det_loss_box_4[0.4812] det_loss_cls_5[0.4337] det_loss_box_5[0.4862] det_loss_cls_dn_0[0.0956] det_loss_box_dn_0[0.3019] det_loss_cls_dn_1[0.0917] det_loss_box_dn_1[0.2633] det_loss_cls_dn_2[0.0915] det_loss_box_dn_2[0.2523] det_loss_cls_dn_3[0.0907] det_loss_box_dn_3[0.2502] det_loss_cls_dn_4[0.0895] det_loss_box_dn_4[0.2498] det_loss_cls_dn_5[0.0883] det_loss_box_dn_5[0.2465] loss_depth[0.0404] total_loss[8.3445]
Rank[0/8] 06/20/2025 02:44:26 INFO stats.py:314 | Epoch[95] Step[124] GlobalStep[37174] Training Speed: 8.13 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 19:01:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:44:26 INFO loss_tracker.py:84 | Epoch[95/239] Step[124] GlobalStep[37174/NA]: det_loss_cls_0[0.5696] det_loss_box_0[0.8417] det_loss_cls_1[0.5572] det_loss_box_1[0.6670] det_loss_cls_2[0.5177] det_loss_box_2[0.6181] det_loss_cls_3[0.4948] det_loss_box_3[0.5901] det_loss_cls_4[0.4698] det_loss_box_4[0.5674] det_loss_cls_5[0.4568] det_loss_box_5[0.5666] det_loss_cls_dn_0[0.1063] det_loss_box_dn_0[0.4437] det_loss_cls_dn_1[0.1024] det_loss_box_dn_1[0.3888] det_loss_cls_dn_2[0.1019] det_loss_box_dn_2[0.3656] det_loss_cls_dn_3[0.1015] det_loss_box_dn_3[0.3624] det_loss_cls_dn_4[0.1013] det_loss_box_dn_4[0.3606] det_loss_cls_dn_5[0.1001] det_loss_box_dn_5[0.3603] loss_depth[0.0368] total_loss[9.8485]
Rank[0/8] 06/20/2025 02:44:52 INFO stats.py:314 | Epoch[95] Step[149] GlobalStep[37199] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 19:01:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:44:52 INFO loss_tracker.py:84 | Epoch[95/239] Step[149] GlobalStep[37199/NA]: det_loss_cls_0[0.4542] det_loss_box_0[0.5857] det_loss_cls_1[0.4449] det_loss_box_1[0.4367] det_loss_cls_2[0.4056] det_loss_box_2[0.4072] det_loss_cls_3[0.3848] det_loss_box_3[0.3877] det_loss_cls_4[0.3614] det_loss_box_4[0.3784] det_loss_cls_5[0.3493] det_loss_box_5[0.3807] det_loss_cls_dn_0[0.0937] det_loss_box_dn_0[0.3009] det_loss_cls_dn_1[0.0896] det_loss_box_dn_1[0.2509] det_loss_cls_dn_2[0.0886] det_loss_box_dn_2[0.2370] det_loss_cls_dn_3[0.0874] det_loss_box_dn_3[0.2320] det_loss_cls_dn_4[0.0864] det_loss_box_dn_4[0.2304] det_loss_cls_dn_5[0.0852] det_loss_box_dn_5[0.2283] loss_depth[0.0378] total_loss[7.0248]
Rank[0/8] 06/20/2025 02:45:18 INFO stats.py:314 | Epoch[95] Step[174] GlobalStep[37224] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 19:00:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:45:18 INFO loss_tracker.py:84 | Epoch[95/239] Step[174] GlobalStep[37224/NA]: det_loss_cls_0[0.4688] det_loss_box_0[0.6099] det_loss_cls_1[0.4597] det_loss_box_1[0.4805] det_loss_cls_2[0.4199] det_loss_box_2[0.4413] det_loss_cls_3[0.3900] det_loss_box_3[0.4316] det_loss_cls_4[0.3675] det_loss_box_4[0.4235] det_loss_cls_5[0.3499] det_loss_box_5[0.4263] det_loss_cls_dn_0[0.0992] det_loss_box_dn_0[0.3308] det_loss_cls_dn_1[0.0961] det_loss_box_dn_1[0.2906] det_loss_cls_dn_2[0.0953] det_loss_box_dn_2[0.2765] det_loss_cls_dn_3[0.0945] det_loss_box_dn_3[0.2743] det_loss_cls_dn_4[0.0939] det_loss_box_dn_4[0.2735] det_loss_cls_dn_5[0.0936] det_loss_box_dn_5[0.2709] loss_depth[0.0370] total_loss[7.5950]
Rank[0/8] 06/20/2025 02:45:44 INFO stats.py:314 | Epoch[95] Step[199] GlobalStep[37249] Training Speed: 6.29 samples/sec across all devices.	Average Step Time: 1.27 sec.	Estimated Remaining Time: 18:59:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:45:44 INFO loss_tracker.py:84 | Epoch[95/239] Step[199] GlobalStep[37249/NA]: det_loss_cls_0[0.4602] det_loss_box_0[0.6392] det_loss_cls_1[0.4575] det_loss_box_1[0.5172] det_loss_cls_2[0.4281] det_loss_box_2[0.4802] det_loss_cls_3[0.3972] det_loss_box_3[0.4659] det_loss_cls_4[0.3752] det_loss_box_4[0.4597] det_loss_cls_5[0.3649] det_loss_box_5[0.4590] det_loss_cls_dn_0[0.0955] det_loss_box_dn_0[0.4739] det_loss_cls_dn_1[0.0930] det_loss_box_dn_1[0.3892] det_loss_cls_dn_2[0.0923] det_loss_box_dn_2[0.3705] det_loss_cls_dn_3[0.0921] det_loss_box_dn_3[0.3610] det_loss_cls_dn_4[0.0911] det_loss_box_dn_4[0.3610] det_loss_cls_dn_5[0.0901] det_loss_box_dn_5[0.3575] loss_depth[0.0402] total_loss[8.4120]
Rank[0/8] 06/20/2025 02:46:11 INFO stats.py:314 | Epoch[95] Step[224] GlobalStep[37274] Training Speed: 6.92 samples/sec across all devices.	Average Step Time: 1.16 sec.	Estimated Remaining Time: 18:59:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:46:11 INFO loss_tracker.py:84 | Epoch[95/239] Step[224] GlobalStep[37274/NA]: det_loss_cls_0[0.5420] det_loss_box_0[0.7323] det_loss_cls_1[0.5270] det_loss_box_1[0.5679] det_loss_cls_2[0.4872] det_loss_box_2[0.5200] det_loss_cls_3[0.4620] det_loss_box_3[0.5040] det_loss_cls_4[0.4255] det_loss_box_4[0.4978] det_loss_cls_5[0.4131] det_loss_box_5[0.4907] det_loss_cls_dn_0[0.1057] det_loss_box_dn_0[0.3579] det_loss_cls_dn_1[0.1020] det_loss_box_dn_1[0.3042] det_loss_cls_dn_2[0.1015] det_loss_box_dn_2[0.2837] det_loss_cls_dn_3[0.1009] det_loss_box_dn_3[0.2837] det_loss_cls_dn_4[0.1006] det_loss_box_dn_4[0.2826] det_loss_cls_dn_5[0.0991] det_loss_box_dn_5[0.2805] loss_depth[0.0382] total_loss[8.6099]
Rank[0/8] 06/20/2025 02:46:36 INFO stats.py:314 | Epoch[95] Step[249] GlobalStep[37299] Training Speed: 7.89 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 18:58:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:46:36 INFO loss_tracker.py:84 | Epoch[95/239] Step[249] GlobalStep[37299/NA]: det_loss_cls_0[0.4975] det_loss_box_0[0.7332] det_loss_cls_1[0.4807] det_loss_box_1[0.5754] det_loss_cls_2[0.4391] det_loss_box_2[0.5106] det_loss_cls_3[0.4072] det_loss_box_3[0.4932] det_loss_cls_4[0.3785] det_loss_box_4[0.4828] det_loss_cls_5[0.3625] det_loss_box_5[0.4832] det_loss_cls_dn_0[0.0937] det_loss_box_dn_0[0.3744] det_loss_cls_dn_1[0.0904] det_loss_box_dn_1[0.3239] det_loss_cls_dn_2[0.0895] det_loss_box_dn_2[0.3052] det_loss_cls_dn_3[0.0886] det_loss_box_dn_3[0.3007] det_loss_cls_dn_4[0.0883] det_loss_box_dn_4[0.2989] det_loss_cls_dn_5[0.0868] det_loss_box_dn_5[0.2979] loss_depth[0.0416] total_loss[8.3239]
Rank[0/8] 06/20/2025 02:47:01 INFO stats.py:314 | Epoch[95] Step[274] GlobalStep[37324] Training Speed: 8.02 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 18:57:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:47:02 INFO loss_tracker.py:84 | Epoch[95/239] Step[274] GlobalStep[37324/NA]: det_loss_cls_0[0.5667] det_loss_box_0[0.7520] det_loss_cls_1[0.5641] det_loss_box_1[0.5727] det_loss_cls_2[0.5141] det_loss_box_2[0.5208] det_loss_cls_3[0.4748] det_loss_box_3[0.5128] det_loss_cls_4[0.4469] det_loss_box_4[0.5066] det_loss_cls_5[0.4253] det_loss_box_5[0.5099] det_loss_cls_dn_0[0.1046] det_loss_box_dn_0[0.3322] det_loss_cls_dn_1[0.1011] det_loss_box_dn_1[0.2885] det_loss_cls_dn_2[0.1006] det_loss_box_dn_2[0.2743] det_loss_cls_dn_3[0.0985] det_loss_box_dn_3[0.2709] det_loss_cls_dn_4[0.0975] det_loss_box_dn_4[0.2699] det_loss_cls_dn_5[0.0966] det_loss_box_dn_5[0.2671] loss_depth[0.0400] total_loss[8.7084]
Rank[0/8] 06/20/2025 02:47:27 INFO stats.py:314 | Epoch[95] Step[299] GlobalStep[37349] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 18:57:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:47:27 INFO loss_tracker.py:84 | Epoch[95/239] Step[299] GlobalStep[37349/NA]: det_loss_cls_0[0.4750] det_loss_box_0[0.6232] det_loss_cls_1[0.4553] det_loss_box_1[0.4633] det_loss_cls_2[0.4233] det_loss_box_2[0.4126] det_loss_cls_3[0.3916] det_loss_box_3[0.4009] det_loss_cls_4[0.3586] det_loss_box_4[0.3961] det_loss_cls_5[0.3521] det_loss_box_5[0.3912] det_loss_cls_dn_0[0.1000] det_loss_box_dn_0[0.3427] det_loss_cls_dn_1[0.0975] det_loss_box_dn_1[0.2973] det_loss_cls_dn_2[0.0968] det_loss_box_dn_2[0.2794] det_loss_cls_dn_3[0.0958] det_loss_box_dn_3[0.2754] det_loss_cls_dn_4[0.0946] det_loss_box_dn_4[0.2740] det_loss_cls_dn_5[0.0938] det_loss_box_dn_5[0.2704] loss_depth[0.0406] total_loss[7.5013]
Rank[0/8] 06/20/2025 02:47:53 INFO stats.py:314 | Epoch[95] Step[324] GlobalStep[37374] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 18:56:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:47:53 INFO loss_tracker.py:84 | Epoch[95/239] Step[324] GlobalStep[37374/NA]: det_loss_cls_0[0.4907] det_loss_box_0[0.6913] det_loss_cls_1[0.4801] det_loss_box_1[0.5453] det_loss_cls_2[0.4491] det_loss_box_2[0.4960] det_loss_cls_3[0.4173] det_loss_box_3[0.4775] det_loss_cls_4[0.3976] det_loss_box_4[0.4598] det_loss_cls_5[0.3819] det_loss_box_5[0.4629] det_loss_cls_dn_0[0.0913] det_loss_box_dn_0[0.2803] det_loss_cls_dn_1[0.0865] det_loss_box_dn_1[0.2394] det_loss_cls_dn_2[0.0852] det_loss_box_dn_2[0.2253] det_loss_cls_dn_3[0.0836] det_loss_box_dn_3[0.2218] det_loss_cls_dn_4[0.0826] det_loss_box_dn_4[0.2203] det_loss_cls_dn_5[0.0814] det_loss_box_dn_5[0.2145] loss_depth[0.0385] total_loss[7.7002]
Rank[0/8] 06/20/2025 02:48:19 INFO stats.py:314 | Epoch[95] Step[349] GlobalStep[37399] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 18:56:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:48:19 INFO loss_tracker.py:84 | Epoch[95/239] Step[349] GlobalStep[37399/NA]: det_loss_cls_0[0.5185] det_loss_box_0[0.7199] det_loss_cls_1[0.5139] det_loss_box_1[0.5477] det_loss_cls_2[0.4775] det_loss_box_2[0.4952] det_loss_cls_3[0.4594] det_loss_box_3[0.4759] det_loss_cls_4[0.4344] det_loss_box_4[0.4684] det_loss_cls_5[0.4185] det_loss_box_5[0.4678] det_loss_cls_dn_0[0.1079] det_loss_box_dn_0[0.2735] det_loss_cls_dn_1[0.1028] det_loss_box_dn_1[0.2311] det_loss_cls_dn_2[0.1022] det_loss_box_dn_2[0.2170] det_loss_cls_dn_3[0.1011] det_loss_box_dn_3[0.2130] det_loss_cls_dn_4[0.0994] det_loss_box_dn_4[0.2117] det_loss_cls_dn_5[0.0986] det_loss_box_dn_5[0.2101] loss_depth[0.0370] total_loss[8.0026]
Rank[0/8] 06/20/2025 02:48:45 INFO stats.py:314 | Epoch[95] Step[374] GlobalStep[37424] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 18:55:30.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:48:45 INFO loss_tracker.py:84 | Epoch[95/239] Step[374] GlobalStep[37424/NA]: det_loss_cls_0[0.4415] det_loss_box_0[0.5839] det_loss_cls_1[0.4217] det_loss_box_1[0.4521] det_loss_cls_2[0.3844] det_loss_box_2[0.4143] det_loss_cls_3[0.3685] det_loss_box_3[0.4014] det_loss_cls_4[0.3346] det_loss_box_4[0.4007] det_loss_cls_5[0.3255] det_loss_box_5[0.3990] det_loss_cls_dn_0[0.0858] det_loss_box_dn_0[0.2998] det_loss_cls_dn_1[0.0825] det_loss_box_dn_1[0.2545] det_loss_cls_dn_2[0.0818] det_loss_box_dn_2[0.2391] det_loss_cls_dn_3[0.0806] det_loss_box_dn_3[0.2361] det_loss_cls_dn_4[0.0793] det_loss_box_dn_4[0.2352] det_loss_cls_dn_5[0.0785] det_loss_box_dn_5[0.2344] loss_depth[0.0398] total_loss[6.9549]
Rank[0/8] 06/20/2025 02:49:00 INFO stats.py:394 | Epoch[95] completed. Training Speed: 7.72 samples/sec across all devices.	Epoch Time: 404.29 sec.	Average Epoch Time: 404.29 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 18:55:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:49:13 INFO stats.py:314 | Epoch[96] Step[9] GlobalStep[37449] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 18:54:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:49:29 INFO loss_tracker.py:84 | Epoch[96/239] Step[24] GlobalStep[37464/NA]: det_loss_cls_0[0.5763] det_loss_box_0[0.7006] det_loss_cls_1[0.5591] det_loss_box_1[0.5614] det_loss_cls_2[0.5277] det_loss_box_2[0.5101] det_loss_cls_3[0.4931] det_loss_box_3[0.4926] det_loss_cls_4[0.4627] det_loss_box_4[0.4822] det_loss_cls_5[0.4488] det_loss_box_5[0.4806] det_loss_cls_dn_0[0.1053] det_loss_box_dn_0[0.3137] det_loss_cls_dn_1[0.1016] det_loss_box_dn_1[0.2667] det_loss_cls_dn_2[0.1008] det_loss_box_dn_2[0.2499] det_loss_cls_dn_3[0.0990] det_loss_box_dn_3[0.2456] det_loss_cls_dn_4[0.0979] det_loss_box_dn_4[0.2446] det_loss_cls_dn_5[0.0966] det_loss_box_dn_5[0.2412] loss_depth[0.0377] total_loss[8.4958]
Rank[0/8] 06/20/2025 02:49:39 INFO stats.py:314 | Epoch[96] Step[34] GlobalStep[37474] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 18:54:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:49:55 INFO loss_tracker.py:84 | Epoch[96/239] Step[49] GlobalStep[37489/NA]: det_loss_cls_0[0.4134] det_loss_box_0[0.5516] det_loss_cls_1[0.4009] det_loss_box_1[0.4272] det_loss_cls_2[0.3665] det_loss_box_2[0.3871] det_loss_cls_3[0.3413] det_loss_box_3[0.3774] det_loss_cls_4[0.3195] det_loss_box_4[0.3663] det_loss_cls_5[0.3087] det_loss_box_5[0.3617] det_loss_cls_dn_0[0.0843] det_loss_box_dn_0[0.3100] det_loss_cls_dn_1[0.0812] det_loss_box_dn_1[0.2565] det_loss_cls_dn_2[0.0792] det_loss_box_dn_2[0.2373] det_loss_cls_dn_3[0.0777] det_loss_box_dn_3[0.2340] det_loss_cls_dn_4[0.0764] det_loss_box_dn_4[0.2327] det_loss_cls_dn_5[0.0750] det_loss_box_dn_5[0.2313] loss_depth[0.0358] total_loss[6.6331]
Rank[0/8] 06/20/2025 02:50:05 INFO stats.py:314 | Epoch[96] Step[59] GlobalStep[37499] Training Speed: 8.17 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 18:53:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:50:20 INFO loss_tracker.py:84 | Epoch[96/239] Step[74] GlobalStep[37514/NA]: det_loss_cls_0[0.5732] det_loss_box_0[0.7147] det_loss_cls_1[0.5784] det_loss_box_1[0.5458] det_loss_cls_2[0.5302] det_loss_box_2[0.5134] det_loss_cls_3[0.4857] det_loss_box_3[0.5151] det_loss_cls_4[0.4454] det_loss_box_4[0.5145] det_loss_cls_5[0.4374] det_loss_box_5[0.5088] det_loss_cls_dn_0[0.0982] det_loss_box_dn_0[0.2810] det_loss_cls_dn_1[0.0944] det_loss_box_dn_1[0.2404] det_loss_cls_dn_2[0.0936] det_loss_box_dn_2[0.2244] det_loss_cls_dn_3[0.0921] det_loss_box_dn_3[0.2215] det_loss_cls_dn_4[0.0916] det_loss_box_dn_4[0.2193] det_loss_cls_dn_5[0.0905] det_loss_box_dn_5[0.2182] loss_depth[0.0384] total_loss[8.3665]
Rank[0/8] 06/20/2025 02:50:31 INFO stats.py:314 | Epoch[96] Step[84] GlobalStep[37524] Training Speed: 8.63 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 18:53:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:50:47 INFO loss_tracker.py:84 | Epoch[96/239] Step[99] GlobalStep[37539/NA]: det_loss_cls_0[0.4609] det_loss_box_0[0.6756] det_loss_cls_1[0.4495] det_loss_box_1[0.5592] det_loss_cls_2[0.4178] det_loss_box_2[0.5252] det_loss_cls_3[0.3992] det_loss_box_3[0.5063] det_loss_cls_4[0.3819] det_loss_box_4[0.4889] det_loss_cls_5[0.3668] det_loss_box_5[0.4928] det_loss_cls_dn_0[0.0949] det_loss_box_dn_0[0.3565] det_loss_cls_dn_1[0.0909] det_loss_box_dn_1[0.3138] det_loss_cls_dn_2[0.0900] det_loss_box_dn_2[0.3002] det_loss_cls_dn_3[0.0887] det_loss_box_dn_3[0.2975] det_loss_cls_dn_4[0.0883] det_loss_box_dn_4[0.2950] det_loss_cls_dn_5[0.0869] det_loss_box_dn_5[0.2914] loss_depth[0.0378] total_loss[8.1560]
Rank[0/8] 06/20/2025 02:50:57 INFO stats.py:314 | Epoch[96] Step[109] GlobalStep[37549] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 18:52:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:51:12 INFO loss_tracker.py:84 | Epoch[96/239] Step[124] GlobalStep[37564/NA]: det_loss_cls_0[0.5777] det_loss_box_0[0.7957] det_loss_cls_1[0.5468] det_loss_box_1[0.6175] det_loss_cls_2[0.5111] det_loss_box_2[0.5659] det_loss_cls_3[0.4732] det_loss_box_3[0.5449] det_loss_cls_4[0.4443] det_loss_box_4[0.5311] det_loss_cls_5[0.4295] det_loss_box_5[0.5292] det_loss_cls_dn_0[0.1069] det_loss_box_dn_0[0.3068] det_loss_cls_dn_1[0.1030] det_loss_box_dn_1[0.2658] det_loss_cls_dn_2[0.1021] det_loss_box_dn_2[0.2483] det_loss_cls_dn_3[0.1013] det_loss_box_dn_3[0.2454] det_loss_cls_dn_4[0.1001] det_loss_box_dn_4[0.2441] det_loss_cls_dn_5[0.0996] det_loss_box_dn_5[0.2431] loss_depth[0.0394] total_loss[8.7729]
Rank[0/8] 06/20/2025 02:51:22 INFO stats.py:314 | Epoch[96] Step[134] GlobalStep[37574] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 18:51:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:51:38 INFO loss_tracker.py:84 | Epoch[96/239] Step[149] GlobalStep[37589/NA]: det_loss_cls_0[0.4590] det_loss_box_0[0.6001] det_loss_cls_1[0.4421] det_loss_box_1[0.4534] det_loss_cls_2[0.4126] det_loss_box_2[0.4059] det_loss_cls_3[0.3984] det_loss_box_3[0.3805] det_loss_cls_4[0.3644] det_loss_box_4[0.3798] det_loss_cls_5[0.3552] det_loss_box_5[0.3782] det_loss_cls_dn_0[0.0940] det_loss_box_dn_0[0.3033] det_loss_cls_dn_1[0.0905] det_loss_box_dn_1[0.2566] det_loss_cls_dn_2[0.0904] det_loss_box_dn_2[0.2352] det_loss_cls_dn_3[0.0892] det_loss_box_dn_3[0.2310] det_loss_cls_dn_4[0.0879] det_loss_box_dn_4[0.2286] det_loss_cls_dn_5[0.0868] det_loss_box_dn_5[0.2274] loss_depth[0.0379] total_loss[7.0883]
Rank[0/8] 06/20/2025 02:51:48 INFO stats.py:314 | Epoch[96] Step[159] GlobalStep[37599] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 18:51:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:52:04 INFO loss_tracker.py:84 | Epoch[96/239] Step[174] GlobalStep[37614/NA]: det_loss_cls_0[0.4316] det_loss_box_0[0.6169] det_loss_cls_1[0.4250] det_loss_box_1[0.4777] det_loss_cls_2[0.3846] det_loss_box_2[0.4553] det_loss_cls_3[0.3608] det_loss_box_3[0.4359] det_loss_cls_4[0.3382] det_loss_box_4[0.4248] det_loss_cls_5[0.3291] det_loss_box_5[0.4251] det_loss_cls_dn_0[0.0997] det_loss_box_dn_0[0.3706] det_loss_cls_dn_1[0.0954] det_loss_box_dn_1[0.3141] det_loss_cls_dn_2[0.0953] det_loss_box_dn_2[0.2934] det_loss_cls_dn_3[0.0938] det_loss_box_dn_3[0.2897] det_loss_cls_dn_4[0.0924] det_loss_box_dn_4[0.2844] det_loss_cls_dn_5[0.0915] det_loss_box_dn_5[0.2829] loss_depth[0.0377] total_loss[7.5459]
Rank[0/8] 06/20/2025 02:52:15 INFO stats.py:314 | Epoch[96] Step[184] GlobalStep[37624] Training Speed: 7.88 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 18:50:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:52:30 INFO loss_tracker.py:84 | Epoch[96/239] Step[199] GlobalStep[37639/NA]: det_loss_cls_0[0.5899] det_loss_box_0[0.7774] det_loss_cls_1[0.5838] det_loss_box_1[0.6008] det_loss_cls_2[0.5449] det_loss_box_2[0.5484] det_loss_cls_3[0.5193] det_loss_box_3[0.5287] det_loss_cls_4[0.4861] det_loss_box_4[0.5156] det_loss_cls_5[0.4748] det_loss_box_5[0.5145] det_loss_cls_dn_0[0.1100] det_loss_box_dn_0[0.3404] det_loss_cls_dn_1[0.1072] det_loss_box_dn_1[0.2940] det_loss_cls_dn_2[0.1063] det_loss_box_dn_2[0.2770] det_loss_cls_dn_3[0.1056] det_loss_box_dn_3[0.2731] det_loss_cls_dn_4[0.1050] det_loss_box_dn_4[0.2718] det_loss_cls_dn_5[0.1041] det_loss_box_dn_5[0.2701] loss_depth[0.0380] total_loss[9.0868]
Rank[0/8] 06/20/2025 02:52:40 INFO stats.py:314 | Epoch[96] Step[209] GlobalStep[37649] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 18:50:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:52:56 INFO loss_tracker.py:84 | Epoch[96/239] Step[224] GlobalStep[37664/NA]: det_loss_cls_0[0.4790] det_loss_box_0[0.6290] det_loss_cls_1[0.4676] det_loss_box_1[0.5040] det_loss_cls_2[0.4380] det_loss_box_2[0.4578] det_loss_cls_3[0.4073] det_loss_box_3[0.4503] det_loss_cls_4[0.3857] det_loss_box_4[0.4423] det_loss_cls_5[0.3820] det_loss_box_5[0.4383] det_loss_cls_dn_0[0.0917] det_loss_box_dn_0[0.2948] det_loss_cls_dn_1[0.0872] det_loss_box_dn_1[0.2527] det_loss_cls_dn_2[0.0867] det_loss_box_dn_2[0.2383] det_loss_cls_dn_3[0.0855] det_loss_box_dn_3[0.2347] det_loss_cls_dn_4[0.0848] det_loss_box_dn_4[0.2340] det_loss_cls_dn_5[0.0842] det_loss_box_dn_5[0.2323] loss_depth[0.0390] total_loss[7.5270]
Rank[0/8] 06/20/2025 02:53:06 INFO stats.py:314 | Epoch[96] Step[234] GlobalStep[37674] Training Speed: 6.55 samples/sec across all devices.	Average Step Time: 1.22 sec.	Estimated Remaining Time: 18:49:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:53:22 INFO loss_tracker.py:84 | Epoch[96/239] Step[249] GlobalStep[37689/NA]: det_loss_cls_0[0.5211] det_loss_box_0[0.7026] det_loss_cls_1[0.5156] det_loss_box_1[0.5483] det_loss_cls_2[0.4812] det_loss_box_2[0.5014] det_loss_cls_3[0.4561] det_loss_box_3[0.4782] det_loss_cls_4[0.4324] det_loss_box_4[0.4639] det_loss_cls_5[0.4138] det_loss_box_5[0.4654] det_loss_cls_dn_0[0.1029] det_loss_box_dn_0[0.2941] det_loss_cls_dn_1[0.0991] det_loss_box_dn_1[0.2472] det_loss_cls_dn_2[0.0982] det_loss_box_dn_2[0.2302] det_loss_cls_dn_3[0.0973] det_loss_box_dn_3[0.2281] det_loss_cls_dn_4[0.0962] det_loss_box_dn_4[0.2272] det_loss_cls_dn_5[0.0954] det_loss_box_dn_5[0.2260] loss_depth[0.0386] total_loss[8.0606]
Rank[0/8] 06/20/2025 02:53:32 INFO stats.py:314 | Epoch[96] Step[259] GlobalStep[37699] Training Speed: 8.13 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 18:48:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:53:48 INFO loss_tracker.py:84 | Epoch[96/239] Step[274] GlobalStep[37714/NA]: det_loss_cls_0[0.5645] det_loss_box_0[0.7727] det_loss_cls_1[0.5477] det_loss_box_1[0.6137] det_loss_cls_2[0.5145] det_loss_box_2[0.5713] det_loss_cls_3[0.4998] det_loss_box_3[0.5530] det_loss_cls_4[0.4744] det_loss_box_4[0.5299] det_loss_cls_5[0.4622] det_loss_box_5[0.5301] det_loss_cls_dn_0[0.1053] det_loss_box_dn_0[0.3127] det_loss_cls_dn_1[0.1005] det_loss_box_dn_1[0.2700] det_loss_cls_dn_2[0.0999] det_loss_box_dn_2[0.2532] det_loss_cls_dn_3[0.0988] det_loss_box_dn_3[0.2496] det_loss_cls_dn_4[0.0983] det_loss_box_dn_4[0.2477] det_loss_cls_dn_5[0.0975] det_loss_box_dn_5[0.2482] loss_depth[0.0432] total_loss[8.8587]
Rank[0/8] 06/20/2025 02:53:58 INFO stats.py:314 | Epoch[96] Step[284] GlobalStep[37724] Training Speed: 8.17 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 18:48:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:54:13 INFO loss_tracker.py:84 | Epoch[96/239] Step[299] GlobalStep[37739/NA]: det_loss_cls_0[0.5599] det_loss_box_0[0.7979] det_loss_cls_1[0.5456] det_loss_box_1[0.6210] det_loss_cls_2[0.5064] det_loss_box_2[0.5603] det_loss_cls_3[0.4737] det_loss_box_3[0.5422] det_loss_cls_4[0.4506] det_loss_box_4[0.5216] det_loss_cls_5[0.4329] det_loss_box_5[0.5247] det_loss_cls_dn_0[0.0996] det_loss_box_dn_0[0.3140] det_loss_cls_dn_1[0.0957] det_loss_box_dn_1[0.2662] det_loss_cls_dn_2[0.0952] det_loss_box_dn_2[0.2461] det_loss_cls_dn_3[0.0934] det_loss_box_dn_3[0.2417] det_loss_cls_dn_4[0.0928] det_loss_box_dn_4[0.2419] det_loss_cls_dn_5[0.0918] det_loss_box_dn_5[0.2403] loss_depth[0.0401] total_loss[8.6956]
Rank[0/8] 06/20/2025 02:54:24 INFO stats.py:314 | Epoch[96] Step[309] GlobalStep[37749] Training Speed: 6.90 samples/sec across all devices.	Average Step Time: 1.16 sec.	Estimated Remaining Time: 18:47:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:54:39 INFO loss_tracker.py:84 | Epoch[96/239] Step[324] GlobalStep[37764/NA]: det_loss_cls_0[0.5081] det_loss_box_0[0.8278] det_loss_cls_1[0.4945] det_loss_box_1[0.6829] det_loss_cls_2[0.4750] det_loss_box_2[0.6122] det_loss_cls_3[0.4473] det_loss_box_3[0.5973] det_loss_cls_4[0.4323] det_loss_box_4[0.5677] det_loss_cls_5[0.4184] det_loss_box_5[0.5736] det_loss_cls_dn_0[0.0945] det_loss_box_dn_0[0.5299] det_loss_cls_dn_1[0.0909] det_loss_box_dn_1[0.4691] det_loss_cls_dn_2[0.0899] det_loss_box_dn_2[0.4453] det_loss_cls_dn_3[0.0891] det_loss_box_dn_3[0.4345] det_loss_cls_dn_4[0.0877] det_loss_box_dn_4[0.4300] det_loss_cls_dn_5[0.0865] det_loss_box_dn_5[0.4278] loss_depth[0.0412] total_loss[9.9536]
Rank[0/8] 06/20/2025 02:54:50 INFO stats.py:314 | Epoch[96] Step[334] GlobalStep[37774] Training Speed: 7.57 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 18:46:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:55:05 INFO loss_tracker.py:84 | Epoch[96/239] Step[349] GlobalStep[37789/NA]: det_loss_cls_0[0.4706] det_loss_box_0[0.6036] det_loss_cls_1[0.4599] det_loss_box_1[0.4543] det_loss_cls_2[0.4293] det_loss_box_2[0.4158] det_loss_cls_3[0.3967] det_loss_box_3[0.4119] det_loss_cls_4[0.3781] det_loss_box_4[0.3994] det_loss_cls_5[0.3594] det_loss_box_5[0.4005] det_loss_cls_dn_0[0.0996] det_loss_box_dn_0[0.2971] det_loss_cls_dn_1[0.0955] det_loss_box_dn_1[0.2480] det_loss_cls_dn_2[0.0957] det_loss_box_dn_2[0.2304] det_loss_cls_dn_3[0.0945] det_loss_box_dn_3[0.2265] det_loss_cls_dn_4[0.0938] det_loss_box_dn_4[0.2222] det_loss_cls_dn_5[0.0931] det_loss_box_dn_5[0.2209] loss_depth[0.0402] total_loss[7.2370]
Rank[0/8] 06/20/2025 02:55:16 INFO stats.py:314 | Epoch[96] Step[359] GlobalStep[37799] Training Speed: 7.49 samples/sec across all devices.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 18:46:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:55:31 INFO loss_tracker.py:84 | Epoch[96/239] Step[374] GlobalStep[37814/NA]: det_loss_cls_0[0.5009] det_loss_box_0[0.6690] det_loss_cls_1[0.4953] det_loss_box_1[0.5277] det_loss_cls_2[0.4564] det_loss_box_2[0.4701] det_loss_cls_3[0.4235] det_loss_box_3[0.4583] det_loss_cls_4[0.4029] det_loss_box_4[0.4461] det_loss_cls_5[0.3917] det_loss_box_5[0.4455] det_loss_cls_dn_0[0.0971] det_loss_box_dn_0[0.3072] det_loss_cls_dn_1[0.0935] det_loss_box_dn_1[0.2585] det_loss_cls_dn_2[0.0931] det_loss_box_dn_2[0.2450] det_loss_cls_dn_3[0.0914] det_loss_box_dn_3[0.2404] det_loss_cls_dn_4[0.0912] det_loss_box_dn_4[0.2392] det_loss_cls_dn_5[0.0897] det_loss_box_dn_5[0.2322] loss_depth[0.0397] total_loss[7.8054]
Rank[0/8] 06/20/2025 02:55:41 INFO stats.py:314 | Epoch[96] Step[384] GlobalStep[37824] Training Speed: 8.84 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 18:45:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:55:46 INFO stats.py:394 | Epoch[96] completed. Training Speed: 7.68 samples/sec across all devices.	Epoch Time: 406.08 sec.	Average Epoch Time: 406.08 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 18:45:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:56:10 INFO stats.py:314 | Epoch[97] Step[19] GlobalStep[37849] Training Speed: 8.01 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 18:45:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:56:15 INFO loss_tracker.py:84 | Epoch[97/239] Step[24] GlobalStep[37854/NA]: det_loss_cls_0[0.5028] det_loss_box_0[0.7432] det_loss_cls_1[0.5008] det_loss_box_1[0.5852] det_loss_cls_2[0.4741] det_loss_box_2[0.5275] det_loss_cls_3[0.4496] det_loss_box_3[0.5043] det_loss_cls_4[0.4221] det_loss_box_4[0.4917] det_loss_cls_5[0.4134] det_loss_box_5[0.4885] det_loss_cls_dn_0[0.1052] det_loss_box_dn_0[0.2990] det_loss_cls_dn_1[0.1013] det_loss_box_dn_1[0.2570] det_loss_cls_dn_2[0.1010] det_loss_box_dn_2[0.2434] det_loss_cls_dn_3[0.1009] det_loss_box_dn_3[0.2390] det_loss_cls_dn_4[0.0996] det_loss_box_dn_4[0.2373] det_loss_cls_dn_5[0.0993] det_loss_box_dn_5[0.2348] loss_depth[0.0423] total_loss[8.2631]
Rank[0/8] 06/20/2025 02:56:37 INFO stats.py:314 | Epoch[97] Step[44] GlobalStep[37874] Training Speed: 7.25 samples/sec across all devices.	Average Step Time: 1.10 sec.	Estimated Remaining Time: 18:44:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:56:42 INFO loss_tracker.py:84 | Epoch[97/239] Step[49] GlobalStep[37879/NA]: det_loss_cls_0[0.5260] det_loss_box_0[0.7580] det_loss_cls_1[0.5173] det_loss_box_1[0.6102] det_loss_cls_2[0.4834] det_loss_box_2[0.5606] det_loss_cls_3[0.4611] det_loss_box_3[0.5498] det_loss_cls_4[0.4356] det_loss_box_4[0.5343] det_loss_cls_5[0.4292] det_loss_box_5[0.5317] det_loss_cls_dn_0[0.0939] det_loss_box_dn_0[0.4123] det_loss_cls_dn_1[0.0902] det_loss_box_dn_1[0.3690] det_loss_cls_dn_2[0.0898] det_loss_box_dn_2[0.3508] det_loss_cls_dn_3[0.0881] det_loss_box_dn_3[0.3459] det_loss_cls_dn_4[0.0876] det_loss_box_dn_4[0.3444] det_loss_cls_dn_5[0.0862] det_loss_box_dn_5[0.3405] loss_depth[0.0373] total_loss[9.1332]
Rank[0/8] 06/20/2025 02:57:02 INFO stats.py:314 | Epoch[97] Step[69] GlobalStep[37899] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 18:43:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:57:07 INFO loss_tracker.py:84 | Epoch[97/239] Step[74] GlobalStep[37904/NA]: det_loss_cls_0[0.4533] det_loss_box_0[0.5826] det_loss_cls_1[0.4398] det_loss_box_1[0.4459] det_loss_cls_2[0.4014] det_loss_box_2[0.3847] det_loss_cls_3[0.3745] det_loss_box_3[0.3683] det_loss_cls_4[0.3440] det_loss_box_4[0.3592] det_loss_cls_5[0.3291] det_loss_box_5[0.3584] det_loss_cls_dn_0[0.0797] det_loss_box_dn_0[0.2729] det_loss_cls_dn_1[0.0753] det_loss_box_dn_1[0.2226] det_loss_cls_dn_2[0.0746] det_loss_box_dn_2[0.2041] det_loss_cls_dn_3[0.0738] det_loss_box_dn_3[0.1998] det_loss_cls_dn_4[0.0726] det_loss_box_dn_4[0.1969] det_loss_cls_dn_5[0.0720] det_loss_box_dn_5[0.1954] loss_depth[0.0382] total_loss[6.6189]
Rank[0/8] 06/20/2025 02:57:28 INFO stats.py:314 | Epoch[97] Step[94] GlobalStep[37924] Training Speed: 14.18 samples/sec across all devices.	Average Step Time: 0.56 sec.	Estimated Remaining Time: 18:43:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:57:34 INFO loss_tracker.py:84 | Epoch[97/239] Step[99] GlobalStep[37929/NA]: det_loss_cls_0[0.4901] det_loss_box_0[0.6950] det_loss_cls_1[0.4715] det_loss_box_1[0.5523] det_loss_cls_2[0.4300] det_loss_box_2[0.5224] det_loss_cls_3[0.4114] det_loss_box_3[0.5042] det_loss_cls_4[0.3897] det_loss_box_4[0.4824] det_loss_cls_5[0.3731] det_loss_box_5[0.4815] det_loss_cls_dn_0[0.0869] det_loss_box_dn_0[0.3200] det_loss_cls_dn_1[0.0826] det_loss_box_dn_1[0.2751] det_loss_cls_dn_2[0.0820] det_loss_box_dn_2[0.2601] det_loss_cls_dn_3[0.0811] det_loss_box_dn_3[0.2572] det_loss_cls_dn_4[0.0805] det_loss_box_dn_4[0.2542] det_loss_cls_dn_5[0.0788] det_loss_box_dn_5[0.2513] loss_depth[0.0378] total_loss[7.9512]
Rank[0/8] 06/20/2025 02:57:55 INFO stats.py:314 | Epoch[97] Step[119] GlobalStep[37949] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 18:42:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:58:00 INFO loss_tracker.py:84 | Epoch[97/239] Step[124] GlobalStep[37954/NA]: det_loss_cls_0[0.4626] det_loss_box_0[0.5928] det_loss_cls_1[0.4533] det_loss_box_1[0.4531] det_loss_cls_2[0.4246] det_loss_box_2[0.4042] det_loss_cls_3[0.3926] det_loss_box_3[0.3974] det_loss_cls_4[0.3647] det_loss_box_4[0.3929] det_loss_cls_5[0.3573] det_loss_box_5[0.3938] det_loss_cls_dn_0[0.0867] det_loss_box_dn_0[0.3039] det_loss_cls_dn_1[0.0831] det_loss_box_dn_1[0.2630] det_loss_cls_dn_2[0.0821] det_loss_box_dn_2[0.2516] det_loss_cls_dn_3[0.0810] det_loss_box_dn_3[0.2469] det_loss_cls_dn_4[0.0801] det_loss_box_dn_4[0.2441] det_loss_cls_dn_5[0.0793] det_loss_box_dn_5[0.2372] loss_depth[0.0378] total_loss[7.1664]
Rank[0/8] 06/20/2025 02:58:20 INFO stats.py:314 | Epoch[97] Step[144] GlobalStep[37974] Training Speed: 8.12 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 18:42:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:58:26 INFO loss_tracker.py:84 | Epoch[97/239] Step[149] GlobalStep[37979/NA]: det_loss_cls_0[0.5177] det_loss_box_0[0.6153] det_loss_cls_1[0.5035] det_loss_box_1[0.4890] det_loss_cls_2[0.4749] det_loss_box_2[0.4301] det_loss_cls_3[0.4347] det_loss_box_3[0.4297] det_loss_cls_4[0.4014] det_loss_box_4[0.4172] det_loss_cls_5[0.3826] det_loss_box_5[0.4252] det_loss_cls_dn_0[0.0917] det_loss_box_dn_0[0.2914] det_loss_cls_dn_1[0.0893] det_loss_box_dn_1[0.2384] det_loss_cls_dn_2[0.0883] det_loss_box_dn_2[0.2184] det_loss_cls_dn_3[0.0877] det_loss_box_dn_3[0.2160] det_loss_cls_dn_4[0.0868] det_loss_box_dn_4[0.2139] det_loss_cls_dn_5[0.0863] det_loss_box_dn_5[0.2117] loss_depth[0.0393] total_loss[7.4807]
Rank[0/8] 06/20/2025 02:58:46 INFO stats.py:314 | Epoch[97] Step[169] GlobalStep[37999] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 18:41:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:58:51 INFO loss_tracker.py:84 | Epoch[97/239] Step[174] GlobalStep[38004/NA]: det_loss_cls_0[0.5358] det_loss_box_0[0.6400] det_loss_cls_1[0.5085] det_loss_box_1[0.5226] det_loss_cls_2[0.4797] det_loss_box_2[0.4797] det_loss_cls_3[0.4538] det_loss_box_3[0.4577] det_loss_cls_4[0.4236] det_loss_box_4[0.4497] det_loss_cls_5[0.4032] det_loss_box_5[0.4529] det_loss_cls_dn_0[0.0977] det_loss_box_dn_0[0.2668] det_loss_cls_dn_1[0.0943] det_loss_box_dn_1[0.2274] det_loss_cls_dn_2[0.0934] det_loss_box_dn_2[0.2156] det_loss_cls_dn_3[0.0925] det_loss_box_dn_3[0.2123] det_loss_cls_dn_4[0.0918] det_loss_box_dn_4[0.2114] det_loss_cls_dn_5[0.0904] det_loss_box_dn_5[0.2104] loss_depth[0.0389] total_loss[7.7500]
Rank[0/8] 06/20/2025 02:59:12 INFO stats.py:314 | Epoch[97] Step[194] GlobalStep[38024] Training Speed: 7.99 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 18:40:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:59:17 INFO loss_tracker.py:84 | Epoch[97/239] Step[199] GlobalStep[38029/NA]: det_loss_cls_0[0.4975] det_loss_box_0[0.6948] det_loss_cls_1[0.4987] det_loss_box_1[0.5445] det_loss_cls_2[0.4663] det_loss_box_2[0.4941] det_loss_cls_3[0.4391] det_loss_box_3[0.4840] det_loss_cls_4[0.4175] det_loss_box_4[0.4687] det_loss_cls_5[0.4043] det_loss_box_5[0.4729] det_loss_cls_dn_0[0.0974] det_loss_box_dn_0[0.3204] det_loss_cls_dn_1[0.0941] det_loss_box_dn_1[0.2796] det_loss_cls_dn_2[0.0937] det_loss_box_dn_2[0.2671] det_loss_cls_dn_3[0.0928] det_loss_box_dn_3[0.2651] det_loss_cls_dn_4[0.0912] det_loss_box_dn_4[0.2636] det_loss_cls_dn_5[0.0903] det_loss_box_dn_5[0.2608] loss_depth[0.0377] total_loss[8.1363]
Rank[0/8] 06/20/2025 02:59:38 INFO stats.py:314 | Epoch[97] Step[219] GlobalStep[38049] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 18:40:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 02:59:43 INFO loss_tracker.py:84 | Epoch[97/239] Step[224] GlobalStep[38054/NA]: det_loss_cls_0[0.5792] det_loss_box_0[0.9250] det_loss_cls_1[0.5670] det_loss_box_1[0.7565] det_loss_cls_2[0.5412] det_loss_box_2[0.6802] det_loss_cls_3[0.5080] det_loss_box_3[0.6654] det_loss_cls_4[0.4833] det_loss_box_4[0.6507] det_loss_cls_5[0.4713] det_loss_box_5[0.6414] det_loss_cls_dn_0[0.1067] det_loss_box_dn_0[0.3428] det_loss_cls_dn_1[0.1034] det_loss_box_dn_1[0.3035] det_loss_cls_dn_2[0.1032] det_loss_box_dn_2[0.2893] det_loss_cls_dn_3[0.1025] det_loss_box_dn_3[0.2869] det_loss_cls_dn_4[0.1019] det_loss_box_dn_4[0.2865] det_loss_cls_dn_5[0.1009] det_loss_box_dn_5[0.2851] loss_depth[0.0420] total_loss[9.9240]
Rank[0/8] 06/20/2025 03:00:04 INFO stats.py:314 | Epoch[97] Step[244] GlobalStep[38074] Training Speed: 7.95 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 18:39:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:00:09 INFO loss_tracker.py:84 | Epoch[97/239] Step[249] GlobalStep[38079/NA]: det_loss_cls_0[0.4322] det_loss_box_0[0.5785] det_loss_cls_1[0.4253] det_loss_box_1[0.4609] det_loss_cls_2[0.3969] det_loss_box_2[0.4095] det_loss_cls_3[0.3644] det_loss_box_3[0.4040] det_loss_cls_4[0.3456] det_loss_box_4[0.3949] det_loss_cls_5[0.3370] det_loss_box_5[0.3898] det_loss_cls_dn_0[0.1006] det_loss_box_dn_0[0.3062] det_loss_cls_dn_1[0.0967] det_loss_box_dn_1[0.2601] det_loss_cls_dn_2[0.0966] det_loss_box_dn_2[0.2406] det_loss_cls_dn_3[0.0959] det_loss_box_dn_3[0.2386] det_loss_cls_dn_4[0.0950] det_loss_box_dn_4[0.2372] det_loss_cls_dn_5[0.0942] det_loss_box_dn_5[0.2347] loss_depth[0.0379] total_loss[7.0731]
Rank[0/8] 06/20/2025 03:00:29 INFO stats.py:314 | Epoch[97] Step[269] GlobalStep[38099] Training Speed: 8.22 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 18:39:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:00:34 INFO loss_tracker.py:84 | Epoch[97/239] Step[274] GlobalStep[38104/NA]: det_loss_cls_0[0.4902] det_loss_box_0[0.6541] det_loss_cls_1[0.4759] det_loss_box_1[0.5178] det_loss_cls_2[0.4386] det_loss_box_2[0.4645] det_loss_cls_3[0.4022] det_loss_box_3[0.4481] det_loss_cls_4[0.3772] det_loss_box_4[0.4370] det_loss_cls_5[0.3731] det_loss_box_5[0.4338] det_loss_cls_dn_0[0.1012] det_loss_box_dn_0[0.2940] det_loss_cls_dn_1[0.0961] det_loss_box_dn_1[0.2425] det_loss_cls_dn_2[0.0952] det_loss_box_dn_2[0.2243] det_loss_cls_dn_3[0.0940] det_loss_box_dn_3[0.2213] det_loss_cls_dn_4[0.0934] det_loss_box_dn_4[0.2198] det_loss_cls_dn_5[0.0921] det_loss_box_dn_5[0.2188] loss_depth[0.0389] total_loss[7.5442]
Rank[0/8] 06/20/2025 03:00:55 INFO stats.py:314 | Epoch[97] Step[294] GlobalStep[38124] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 18:38:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:01:00 INFO loss_tracker.py:84 | Epoch[97/239] Step[299] GlobalStep[38129/NA]: det_loss_cls_0[0.4907] det_loss_box_0[0.6436] det_loss_cls_1[0.4679] det_loss_box_1[0.4992] det_loss_cls_2[0.4254] det_loss_box_2[0.4411] det_loss_cls_3[0.3934] det_loss_box_3[0.4255] det_loss_cls_4[0.3673] det_loss_box_4[0.4112] det_loss_cls_5[0.3553] det_loss_box_5[0.4089] det_loss_cls_dn_0[0.0967] det_loss_box_dn_0[0.3335] det_loss_cls_dn_1[0.0933] det_loss_box_dn_1[0.2895] det_loss_cls_dn_2[0.0925] det_loss_box_dn_2[0.2733] det_loss_cls_dn_3[0.0915] det_loss_box_dn_3[0.2700] det_loss_cls_dn_4[0.0908] det_loss_box_dn_4[0.2690] det_loss_cls_dn_5[0.0901] det_loss_box_dn_5[0.2653] loss_depth[0.0414] total_loss[7.6265]
Rank[0/8] 06/20/2025 03:01:21 INFO stats.py:314 | Epoch[97] Step[319] GlobalStep[38149] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 18:37:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:01:26 INFO loss_tracker.py:84 | Epoch[97/239] Step[324] GlobalStep[38154/NA]: det_loss_cls_0[0.4191] det_loss_box_0[0.6107] det_loss_cls_1[0.4091] det_loss_box_1[0.4882] det_loss_cls_2[0.3718] det_loss_box_2[0.4481] det_loss_cls_3[0.3523] det_loss_box_3[0.4297] det_loss_cls_4[0.3258] det_loss_box_4[0.4247] det_loss_cls_5[0.3146] det_loss_box_5[0.4223] det_loss_cls_dn_0[0.0998] det_loss_box_dn_0[0.3493] det_loss_cls_dn_1[0.0951] det_loss_box_dn_1[0.2990] det_loss_cls_dn_2[0.0944] det_loss_box_dn_2[0.2830] det_loss_cls_dn_3[0.0938] det_loss_box_dn_3[0.2782] det_loss_cls_dn_4[0.0930] det_loss_box_dn_4[0.2754] det_loss_cls_dn_5[0.0919] det_loss_box_dn_5[0.2741] loss_depth[0.0413] total_loss[7.3846]
Rank[0/8] 06/20/2025 03:01:46 INFO stats.py:314 | Epoch[97] Step[344] GlobalStep[38174] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 18:37:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:01:52 INFO loss_tracker.py:84 | Epoch[97/239] Step[349] GlobalStep[38179/NA]: det_loss_cls_0[0.5637] det_loss_box_0[0.7055] det_loss_cls_1[0.5366] det_loss_box_1[0.5242] det_loss_cls_2[0.4983] det_loss_box_2[0.4903] det_loss_cls_3[0.4725] det_loss_box_3[0.4764] det_loss_cls_4[0.4394] det_loss_box_4[0.4623] det_loss_cls_5[0.4265] det_loss_box_5[0.4581] det_loss_cls_dn_0[0.1089] det_loss_box_dn_0[0.2768] det_loss_cls_dn_1[0.1070] det_loss_box_dn_1[0.2390] det_loss_cls_dn_2[0.1050] det_loss_box_dn_2[0.2262] det_loss_cls_dn_3[0.1039] det_loss_box_dn_3[0.2238] det_loss_cls_dn_4[0.1023] det_loss_box_dn_4[0.2223] det_loss_cls_dn_5[0.1012] det_loss_box_dn_5[0.2206] loss_depth[0.0398] total_loss[8.1307]
Rank[0/8] 06/20/2025 03:02:12 INFO stats.py:314 | Epoch[97] Step[369] GlobalStep[38199] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 18:36:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:02:18 INFO loss_tracker.py:84 | Epoch[97/239] Step[374] GlobalStep[38204/NA]: det_loss_cls_0[0.4168] det_loss_box_0[0.5765] det_loss_cls_1[0.4045] det_loss_box_1[0.4516] det_loss_cls_2[0.3785] det_loss_box_2[0.4074] det_loss_cls_3[0.3606] det_loss_box_3[0.4028] det_loss_cls_4[0.3358] det_loss_box_4[0.3962] det_loss_cls_5[0.3265] det_loss_box_5[0.3964] det_loss_cls_dn_0[0.0956] det_loss_box_dn_0[0.2791] det_loss_cls_dn_1[0.0914] det_loss_box_dn_1[0.2402] det_loss_cls_dn_2[0.0910] det_loss_box_dn_2[0.2288] det_loss_cls_dn_3[0.0907] det_loss_box_dn_3[0.2267] det_loss_cls_dn_4[0.0889] det_loss_box_dn_4[0.2256] det_loss_cls_dn_5[0.0879] det_loss_box_dn_5[0.2255] loss_depth[0.0380] total_loss[6.8631]
Rank[0/8] 06/20/2025 03:02:33 INFO stats.py:394 | Epoch[97] completed. Training Speed: 7.67 samples/sec across all devices.	Epoch Time: 406.76 sec.	Average Epoch Time: 406.76 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 18:36:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:02:40 INFO stats.py:314 | Epoch[98] Step[4] GlobalStep[38224] Training Speed: 7.78 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 18:36:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:03:00 INFO loss_tracker.py:84 | Epoch[98/239] Step[24] GlobalStep[38244/NA]: det_loss_cls_0[0.4721] det_loss_box_0[0.6459] det_loss_cls_1[0.4715] det_loss_box_1[0.5183] det_loss_cls_2[0.4501] det_loss_box_2[0.4663] det_loss_cls_3[0.4137] det_loss_box_3[0.4534] det_loss_cls_4[0.3833] det_loss_box_4[0.4487] det_loss_cls_5[0.3718] det_loss_box_5[0.4462] det_loss_cls_dn_0[0.0938] det_loss_box_dn_0[0.3113] det_loss_cls_dn_1[0.0900] det_loss_box_dn_1[0.2712] det_loss_cls_dn_2[0.0892] det_loss_box_dn_2[0.2550] det_loss_cls_dn_3[0.0882] det_loss_box_dn_3[0.2510] det_loss_cls_dn_4[0.0870] det_loss_box_dn_4[0.2499] det_loss_cls_dn_5[0.0863] det_loss_box_dn_5[0.2468] loss_depth[0.0364] total_loss[7.6977]
Rank[0/8] 06/20/2025 03:03:06 INFO stats.py:314 | Epoch[98] Step[29] GlobalStep[38249] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 18:35:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:03:26 INFO loss_tracker.py:84 | Epoch[98/239] Step[49] GlobalStep[38269/NA]: det_loss_cls_0[0.4587] det_loss_box_0[0.6090] det_loss_cls_1[0.4512] det_loss_box_1[0.4667] det_loss_cls_2[0.4155] det_loss_box_2[0.4261] det_loss_cls_3[0.4048] det_loss_box_3[0.4090] det_loss_cls_4[0.3772] det_loss_box_4[0.4102] det_loss_cls_5[0.3572] det_loss_box_5[0.4076] det_loss_cls_dn_0[0.1009] det_loss_box_dn_0[0.2983] det_loss_cls_dn_1[0.0981] det_loss_box_dn_1[0.2470] det_loss_cls_dn_2[0.0972] det_loss_box_dn_2[0.2321] det_loss_cls_dn_3[0.0963] det_loss_box_dn_3[0.2281] det_loss_cls_dn_4[0.0946] det_loss_box_dn_4[0.2271] det_loss_cls_dn_5[0.0941] det_loss_box_dn_5[0.2234] loss_depth[0.0422] total_loss[7.2724]
Rank[0/8] 06/20/2025 03:03:31 INFO stats.py:314 | Epoch[98] Step[54] GlobalStep[38274] Training Speed: 6.21 samples/sec across all devices.	Average Step Time: 1.29 sec.	Estimated Remaining Time: 18:34:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:03:53 INFO loss_tracker.py:84 | Epoch[98/239] Step[74] GlobalStep[38294/NA]: det_loss_cls_0[0.5748] det_loss_box_0[0.8674] det_loss_cls_1[0.5528] det_loss_box_1[0.6692] det_loss_cls_2[0.5251] det_loss_box_2[0.6007] det_loss_cls_3[0.5097] det_loss_box_3[0.5716] det_loss_cls_4[0.4802] det_loss_box_4[0.5617] det_loss_cls_5[0.4623] det_loss_box_5[0.5544] det_loss_cls_dn_0[0.1056] det_loss_box_dn_0[0.3381] det_loss_cls_dn_1[0.1021] det_loss_box_dn_1[0.2925] det_loss_cls_dn_2[0.1022] det_loss_box_dn_2[0.2795] det_loss_cls_dn_3[0.1019] det_loss_box_dn_3[0.2753] det_loss_cls_dn_4[0.1004] det_loss_box_dn_4[0.2739] det_loss_cls_dn_5[0.1000] det_loss_box_dn_5[0.2706] loss_depth[0.0406] total_loss[9.3123]
Rank[0/8] 06/20/2025 03:03:58 INFO stats.py:314 | Epoch[98] Step[79] GlobalStep[38299] Training Speed: 8.69 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 18:34:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:04:18 INFO loss_tracker.py:84 | Epoch[98/239] Step[99] GlobalStep[38319/NA]: det_loss_cls_0[0.5329] det_loss_box_0[0.7669] det_loss_cls_1[0.5156] det_loss_box_1[0.6168] det_loss_cls_2[0.4921] det_loss_box_2[0.5622] det_loss_cls_3[0.4647] det_loss_box_3[0.5381] det_loss_cls_4[0.4363] det_loss_box_4[0.5261] det_loss_cls_5[0.4266] det_loss_box_5[0.5221] det_loss_cls_dn_0[0.0949] det_loss_box_dn_0[0.3218] det_loss_cls_dn_1[0.0918] det_loss_box_dn_1[0.2822] det_loss_cls_dn_2[0.0917] det_loss_box_dn_2[0.2639] det_loss_cls_dn_3[0.0903] det_loss_box_dn_3[0.2613] det_loss_cls_dn_4[0.0894] det_loss_box_dn_4[0.2604] det_loss_cls_dn_5[0.0884] det_loss_box_dn_5[0.2596] loss_depth[0.0414] total_loss[8.6377]
Rank[0/8] 06/20/2025 03:04:23 INFO stats.py:314 | Epoch[98] Step[104] GlobalStep[38324] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 18:33:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:04:43 INFO loss_tracker.py:84 | Epoch[98/239] Step[124] GlobalStep[38344/NA]: det_loss_cls_0[0.4886] det_loss_box_0[0.6346] det_loss_cls_1[0.4747] det_loss_box_1[0.4906] det_loss_cls_2[0.4493] det_loss_box_2[0.4349] det_loss_cls_3[0.4101] det_loss_box_3[0.4212] det_loss_cls_4[0.3739] det_loss_box_4[0.4160] det_loss_cls_5[0.3601] det_loss_box_5[0.4155] det_loss_cls_dn_0[0.0893] det_loss_box_dn_0[0.2888] det_loss_cls_dn_1[0.0855] det_loss_box_dn_1[0.2505] det_loss_cls_dn_2[0.0846] det_loss_box_dn_2[0.2371] det_loss_cls_dn_3[0.0838] det_loss_box_dn_3[0.2341] det_loss_cls_dn_4[0.0828] det_loss_box_dn_4[0.2329] det_loss_cls_dn_5[0.0813] det_loss_box_dn_5[0.2314] loss_depth[0.0387] total_loss[7.3904]
Rank[0/8] 06/20/2025 03:04:49 INFO stats.py:314 | Epoch[98] Step[129] GlobalStep[38349] Training Speed: 8.08 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 18:33:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:05:10 INFO loss_tracker.py:84 | Epoch[98/239] Step[149] GlobalStep[38369/NA]: det_loss_cls_0[0.4356] det_loss_box_0[0.5535] det_loss_cls_1[0.4193] det_loss_box_1[0.4359] det_loss_cls_2[0.3945] det_loss_box_2[0.3990] det_loss_cls_3[0.3868] det_loss_box_3[0.3821] det_loss_cls_4[0.3608] det_loss_box_4[0.3767] det_loss_cls_5[0.3570] det_loss_box_5[0.3765] det_loss_cls_dn_0[0.0990] det_loss_box_dn_0[0.2842] det_loss_cls_dn_1[0.0949] det_loss_box_dn_1[0.2363] det_loss_cls_dn_2[0.0948] det_loss_box_dn_2[0.2181] det_loss_cls_dn_3[0.0939] det_loss_box_dn_3[0.2138] det_loss_cls_dn_4[0.0933] det_loss_box_dn_4[0.2118] det_loss_cls_dn_5[0.0919] det_loss_box_dn_5[0.2105] loss_depth[0.0392] total_loss[6.8595]
Rank[0/8] 06/20/2025 03:05:15 INFO stats.py:314 | Epoch[98] Step[154] GlobalStep[38374] Training Speed: 8.07 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 18:32:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:05:36 INFO loss_tracker.py:84 | Epoch[98/239] Step[174] GlobalStep[38394/NA]: det_loss_cls_0[0.4695] det_loss_box_0[0.5667] det_loss_cls_1[0.4505] det_loss_box_1[0.4468] det_loss_cls_2[0.4239] det_loss_box_2[0.3955] det_loss_cls_3[0.4066] det_loss_box_3[0.3820] det_loss_cls_4[0.3895] det_loss_box_4[0.3650] det_loss_cls_5[0.3715] det_loss_box_5[0.3696] det_loss_cls_dn_0[0.0952] det_loss_box_dn_0[0.2860] det_loss_cls_dn_1[0.0906] det_loss_box_dn_1[0.2409] det_loss_cls_dn_2[0.0898] det_loss_box_dn_2[0.2254] det_loss_cls_dn_3[0.0890] det_loss_box_dn_3[0.2221] det_loss_cls_dn_4[0.0883] det_loss_box_dn_4[0.2208] det_loss_cls_dn_5[0.0873] det_loss_box_dn_5[0.2190] loss_depth[0.0373] total_loss[7.0288]
Rank[0/8] 06/20/2025 03:05:41 INFO stats.py:314 | Epoch[98] Step[179] GlobalStep[38399] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 18:31:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:06:01 INFO loss_tracker.py:84 | Epoch[98/239] Step[199] GlobalStep[38419/NA]: det_loss_cls_0[0.4726] det_loss_box_0[0.6052] det_loss_cls_1[0.4632] det_loss_box_1[0.4800] det_loss_cls_2[0.4304] det_loss_box_2[0.4518] det_loss_cls_3[0.4060] det_loss_box_3[0.4354] det_loss_cls_4[0.3797] det_loss_box_4[0.4343] det_loss_cls_5[0.3691] det_loss_box_5[0.4299] det_loss_cls_dn_0[0.0974] det_loss_box_dn_0[0.3011] det_loss_cls_dn_1[0.0928] det_loss_box_dn_1[0.2556] det_loss_cls_dn_2[0.0918] det_loss_box_dn_2[0.2337] det_loss_cls_dn_3[0.0904] det_loss_box_dn_3[0.2295] det_loss_cls_dn_4[0.0892] det_loss_box_dn_4[0.2256] det_loss_cls_dn_5[0.0884] det_loss_box_dn_5[0.2247] loss_depth[0.0351] total_loss[7.4128]
Rank[0/8] 06/20/2025 03:06:06 INFO stats.py:314 | Epoch[98] Step[204] GlobalStep[38424] Training Speed: 7.62 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 18:31:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:06:27 INFO loss_tracker.py:84 | Epoch[98/239] Step[224] GlobalStep[38444/NA]: det_loss_cls_0[0.6194] det_loss_box_0[0.8784] det_loss_cls_1[0.6075] det_loss_box_1[0.6795] det_loss_cls_2[0.5606] det_loss_box_2[0.6177] det_loss_cls_3[0.5319] det_loss_box_3[0.5827] det_loss_cls_4[0.4990] det_loss_box_4[0.5690] det_loss_cls_5[0.4794] det_loss_box_5[0.5645] det_loss_cls_dn_0[0.0936] det_loss_box_dn_0[0.2936] det_loss_cls_dn_1[0.0885] det_loss_box_dn_1[0.2431] det_loss_cls_dn_2[0.0875] det_loss_box_dn_2[0.2257] det_loss_cls_dn_3[0.0864] det_loss_box_dn_3[0.2203] det_loss_cls_dn_4[0.0856] det_loss_box_dn_4[0.2184] det_loss_cls_dn_5[0.0842] det_loss_box_dn_5[0.2171] loss_depth[0.0398] total_loss[9.1734]
Rank[0/8] 06/20/2025 03:06:32 INFO stats.py:314 | Epoch[98] Step[229] GlobalStep[38449] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 18:30:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:06:54 INFO loss_tracker.py:84 | Epoch[98/239] Step[249] GlobalStep[38469/NA]: det_loss_cls_0[0.3727] det_loss_box_0[0.4400] det_loss_cls_1[0.3607] det_loss_box_1[0.3596] det_loss_cls_2[0.3283] det_loss_box_2[0.3277] det_loss_cls_3[0.3173] det_loss_box_3[0.3089] det_loss_cls_4[0.2926] det_loss_box_4[0.3122] det_loss_cls_5[0.2817] det_loss_box_5[0.3103] det_loss_cls_dn_0[0.0895] det_loss_box_dn_0[0.2779] det_loss_cls_dn_1[0.0863] det_loss_box_dn_1[0.2307] det_loss_cls_dn_2[0.0863] det_loss_box_dn_2[0.2141] det_loss_cls_dn_3[0.0855] det_loss_box_dn_3[0.2115] det_loss_cls_dn_4[0.0843] det_loss_box_dn_4[0.2097] det_loss_cls_dn_5[0.0830] det_loss_box_dn_5[0.2080] loss_depth[0.0396] total_loss[5.9183]
Rank[0/8] 06/20/2025 03:06:59 INFO stats.py:314 | Epoch[98] Step[254] GlobalStep[38474] Training Speed: 7.67 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 18:29:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:07:19 INFO loss_tracker.py:84 | Epoch[98/239] Step[274] GlobalStep[38494/NA]: det_loss_cls_0[0.4474] det_loss_box_0[0.5875] det_loss_cls_1[0.4155] det_loss_box_1[0.4351] det_loss_cls_2[0.3903] det_loss_box_2[0.3935] det_loss_cls_3[0.3616] det_loss_box_3[0.3813] det_loss_cls_4[0.3404] det_loss_box_4[0.3727] det_loss_cls_5[0.3272] det_loss_box_5[0.3750] det_loss_cls_dn_0[0.0994] det_loss_box_dn_0[0.3071] det_loss_cls_dn_1[0.0965] det_loss_box_dn_1[0.2671] det_loss_cls_dn_2[0.0957] det_loss_box_dn_2[0.2501] det_loss_cls_dn_3[0.0948] det_loss_box_dn_3[0.2478] det_loss_cls_dn_4[0.0937] det_loss_box_dn_4[0.2481] det_loss_cls_dn_5[0.0930] det_loss_box_dn_5[0.2472] loss_depth[0.0371] total_loss[7.0050]
Rank[0/8] 06/20/2025 03:07:24 INFO stats.py:314 | Epoch[98] Step[279] GlobalStep[38499] Training Speed: 13.05 samples/sec across all devices.	Average Step Time: 0.61 sec.	Estimated Remaining Time: 18:29:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:07:45 INFO loss_tracker.py:84 | Epoch[98/239] Step[299] GlobalStep[38519/NA]: det_loss_cls_0[0.4683] det_loss_box_0[0.6268] det_loss_cls_1[0.4539] det_loss_box_1[0.5118] det_loss_cls_2[0.4280] det_loss_box_2[0.4580] det_loss_cls_3[0.3933] det_loss_box_3[0.4552] det_loss_cls_4[0.3669] det_loss_box_4[0.4423] det_loss_cls_5[0.3502] det_loss_box_5[0.4434] det_loss_cls_dn_0[0.0856] det_loss_box_dn_0[0.3577] det_loss_cls_dn_1[0.0815] det_loss_box_dn_1[0.3007] det_loss_cls_dn_2[0.0803] det_loss_box_dn_2[0.2846] det_loss_cls_dn_3[0.0788] det_loss_box_dn_3[0.2846] det_loss_cls_dn_4[0.0783] det_loss_box_dn_4[0.2807] det_loss_cls_dn_5[0.0773] det_loss_box_dn_5[0.2762] loss_depth[0.0379] total_loss[7.7023]
Rank[0/8] 06/20/2025 03:07:51 INFO stats.py:314 | Epoch[98] Step[304] GlobalStep[38524] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 18:28:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:08:11 INFO loss_tracker.py:84 | Epoch[98/239] Step[324] GlobalStep[38544/NA]: det_loss_cls_0[0.5183] det_loss_box_0[0.6662] det_loss_cls_1[0.5133] det_loss_box_1[0.5138] det_loss_cls_2[0.4685] det_loss_box_2[0.4608] det_loss_cls_3[0.4439] det_loss_box_3[0.4433] det_loss_cls_4[0.4158] det_loss_box_4[0.4358] det_loss_cls_5[0.4052] det_loss_box_5[0.4350] det_loss_cls_dn_0[0.0894] det_loss_box_dn_0[0.2619] det_loss_cls_dn_1[0.0857] det_loss_box_dn_1[0.2232] det_loss_cls_dn_2[0.0850] det_loss_box_dn_2[0.2081] det_loss_cls_dn_3[0.0842] det_loss_box_dn_3[0.2042] det_loss_cls_dn_4[0.0827] det_loss_box_dn_4[0.2027] det_loss_cls_dn_5[0.0819] det_loss_box_dn_5[0.2000] loss_depth[0.0363] total_loss[7.5650]
Rank[0/8] 06/20/2025 03:08:16 INFO stats.py:314 | Epoch[98] Step[329] GlobalStep[38549] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 18:28:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:08:37 INFO loss_tracker.py:84 | Epoch[98/239] Step[349] GlobalStep[38569/NA]: det_loss_cls_0[0.5014] det_loss_box_0[0.6440] det_loss_cls_1[0.4894] det_loss_box_1[0.4702] det_loss_cls_2[0.4490] det_loss_box_2[0.4164] det_loss_cls_3[0.4289] det_loss_box_3[0.3942] det_loss_cls_4[0.3927] det_loss_box_4[0.3914] det_loss_cls_5[0.3810] det_loss_box_5[0.3855] det_loss_cls_dn_0[0.1006] det_loss_box_dn_0[0.2628] det_loss_cls_dn_1[0.0980] det_loss_box_dn_1[0.2209] det_loss_cls_dn_2[0.0971] det_loss_box_dn_2[0.2045] det_loss_cls_dn_3[0.0960] det_loss_box_dn_3[0.1999] det_loss_cls_dn_4[0.0958] det_loss_box_dn_4[0.1988] det_loss_cls_dn_5[0.0946] det_loss_box_dn_5[0.1968] loss_depth[0.0391] total_loss[7.2491]
Rank[0/8] 06/20/2025 03:08:42 INFO stats.py:314 | Epoch[98] Step[354] GlobalStep[38574] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 18:27:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:09:02 INFO loss_tracker.py:84 | Epoch[98/239] Step[374] GlobalStep[38594/NA]: det_loss_cls_0[0.4805] det_loss_box_0[0.7263] det_loss_cls_1[0.4561] det_loss_box_1[0.5870] det_loss_cls_2[0.4228] det_loss_box_2[0.5253] det_loss_cls_3[0.4006] det_loss_box_3[0.5077] det_loss_cls_4[0.3803] det_loss_box_4[0.4957] det_loss_cls_5[0.3669] det_loss_box_5[0.4955] det_loss_cls_dn_0[0.0967] det_loss_box_dn_0[0.3372] det_loss_cls_dn_1[0.0927] det_loss_box_dn_1[0.2916] det_loss_cls_dn_2[0.0921] det_loss_box_dn_2[0.2777] det_loss_cls_dn_3[0.0917] det_loss_box_dn_3[0.2751] det_loss_cls_dn_4[0.0910] det_loss_box_dn_4[0.2744] det_loss_cls_dn_5[0.0899] det_loss_box_dn_5[0.2688] loss_depth[0.0398] total_loss[8.1634]
Rank[0/8] 06/20/2025 03:09:07 INFO stats.py:314 | Epoch[98] Step[379] GlobalStep[38599] Training Speed: 8.91 samples/sec across all devices.	Average Step Time: 0.90 sec.	Estimated Remaining Time: 18:26:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:09:17 INFO stats.py:394 | Epoch[98] completed. Training Speed: 7.71 samples/sec across all devices.	Epoch Time: 404.48 sec.	Average Epoch Time: 404.48 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 18:26:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:09:35 INFO stats.py:314 | Epoch[99] Step[14] GlobalStep[38624] Training Speed: 7.84 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 18:26:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:09:46 INFO loss_tracker.py:84 | Epoch[99/239] Step[24] GlobalStep[38634/NA]: det_loss_cls_0[0.4928] det_loss_box_0[0.6397] det_loss_cls_1[0.4998] det_loss_box_1[0.4917] det_loss_cls_2[0.4702] det_loss_box_2[0.4466] det_loss_cls_3[0.4311] det_loss_box_3[0.4355] det_loss_cls_4[0.3980] det_loss_box_4[0.4298] det_loss_cls_5[0.3876] det_loss_box_5[0.4287] det_loss_cls_dn_0[0.0997] det_loss_box_dn_0[0.2823] det_loss_cls_dn_1[0.0958] det_loss_box_dn_1[0.2407] det_loss_cls_dn_2[0.0954] det_loss_box_dn_2[0.2282] det_loss_cls_dn_3[0.0940] det_loss_box_dn_3[0.2249] det_loss_cls_dn_4[0.0932] det_loss_box_dn_4[0.2233] det_loss_cls_dn_5[0.0924] det_loss_box_dn_5[0.2220] loss_depth[0.0379] total_loss[7.5813]
Rank[0/8] 06/20/2025 03:10:01 INFO stats.py:314 | Epoch[99] Step[39] GlobalStep[38649] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 18:25:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:10:11 INFO loss_tracker.py:84 | Epoch[99/239] Step[49] GlobalStep[38659/NA]: det_loss_cls_0[0.4180] det_loss_box_0[0.5635] det_loss_cls_1[0.4132] det_loss_box_1[0.4455] det_loss_cls_2[0.3827] det_loss_box_2[0.4032] det_loss_cls_3[0.3494] det_loss_box_3[0.3934] det_loss_cls_4[0.3200] det_loss_box_4[0.3847] det_loss_cls_5[0.3030] det_loss_box_5[0.3863] det_loss_cls_dn_0[0.0880] det_loss_box_dn_0[0.2668] det_loss_cls_dn_1[0.0842] det_loss_box_dn_1[0.2220] det_loss_cls_dn_2[0.0834] det_loss_box_dn_2[0.2039] det_loss_cls_dn_3[0.0823] det_loss_box_dn_3[0.2010] det_loss_cls_dn_4[0.0814] det_loss_box_dn_4[0.1992] det_loss_cls_dn_5[0.0810] det_loss_box_dn_5[0.1960] loss_depth[0.0375] total_loss[6.5895]
Rank[0/8] 06/20/2025 03:10:26 INFO stats.py:314 | Epoch[99] Step[64] GlobalStep[38674] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 18:25:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:10:36 INFO loss_tracker.py:84 | Epoch[99/239] Step[74] GlobalStep[38684/NA]: det_loss_cls_0[0.4950] det_loss_box_0[0.6441] det_loss_cls_1[0.4777] det_loss_box_1[0.4870] det_loss_cls_2[0.4351] det_loss_box_2[0.4360] det_loss_cls_3[0.4131] det_loss_box_3[0.4238] det_loss_cls_4[0.3759] det_loss_box_4[0.4187] det_loss_cls_5[0.3570] det_loss_box_5[0.4213] det_loss_cls_dn_0[0.0950] det_loss_box_dn_0[0.2828] det_loss_cls_dn_1[0.0923] det_loss_box_dn_1[0.2410] det_loss_cls_dn_2[0.0911] det_loss_box_dn_2[0.2224] det_loss_cls_dn_3[0.0909] det_loss_box_dn_3[0.2188] det_loss_cls_dn_4[0.0902] det_loss_box_dn_4[0.2173] det_loss_cls_dn_5[0.0898] det_loss_box_dn_5[0.2161] loss_depth[0.0383] total_loss[7.3706]
Rank[0/8] 06/20/2025 03:10:52 INFO stats.py:314 | Epoch[99] Step[89] GlobalStep[38699] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 18:24:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:11:03 INFO loss_tracker.py:84 | Epoch[99/239] Step[99] GlobalStep[38709/NA]: det_loss_cls_0[0.3601] det_loss_box_0[0.4385] det_loss_cls_1[0.3571] det_loss_box_1[0.3492] det_loss_cls_2[0.3168] det_loss_box_2[0.3220] det_loss_cls_3[0.3019] det_loss_box_3[0.3168] det_loss_cls_4[0.2857] det_loss_box_4[0.3124] det_loss_cls_5[0.2701] det_loss_box_5[0.3149] det_loss_cls_dn_0[0.0931] det_loss_box_dn_0[0.3021] det_loss_cls_dn_1[0.0896] det_loss_box_dn_1[0.2559] det_loss_cls_dn_2[0.0895] det_loss_box_dn_2[0.2389] det_loss_cls_dn_3[0.0884] det_loss_box_dn_3[0.2350] det_loss_cls_dn_4[0.0877] det_loss_box_dn_4[0.2325] det_loss_cls_dn_5[0.0867] det_loss_box_dn_5[0.2305] loss_depth[0.0368] total_loss[6.0122]
Rank[0/8] 06/20/2025 03:11:18 INFO stats.py:314 | Epoch[99] Step[114] GlobalStep[38724] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 18:23:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:11:28 INFO loss_tracker.py:84 | Epoch[99/239] Step[124] GlobalStep[38734/NA]: det_loss_cls_0[0.6021] det_loss_box_0[0.8890] det_loss_cls_1[0.5807] det_loss_box_1[0.7166] det_loss_cls_2[0.5490] det_loss_box_2[0.6528] det_loss_cls_3[0.5233] det_loss_box_3[0.6254] det_loss_cls_4[0.5022] det_loss_box_4[0.6050] det_loss_cls_5[0.4886] det_loss_box_5[0.6054] det_loss_cls_dn_0[0.1018] det_loss_box_dn_0[0.3233] det_loss_cls_dn_1[0.0985] det_loss_box_dn_1[0.2766] det_loss_cls_dn_2[0.0975] det_loss_box_dn_2[0.2597] det_loss_cls_dn_3[0.0963] det_loss_box_dn_3[0.2569] det_loss_cls_dn_4[0.0949] det_loss_box_dn_4[0.2560] det_loss_cls_dn_5[0.0939] det_loss_box_dn_5[0.2540] loss_depth[0.0413] total_loss[9.5910]
Rank[0/8] 06/20/2025 03:11:44 INFO stats.py:314 | Epoch[99] Step[139] GlobalStep[38749] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 18:23:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:11:55 INFO loss_tracker.py:84 | Epoch[99/239] Step[149] GlobalStep[38759/NA]: det_loss_cls_0[0.5500] det_loss_box_0[0.6864] det_loss_cls_1[0.5278] det_loss_box_1[0.5214] det_loss_cls_2[0.4905] det_loss_box_2[0.4670] det_loss_cls_3[0.4625] det_loss_box_3[0.4463] det_loss_cls_4[0.4398] det_loss_box_4[0.4329] det_loss_cls_5[0.4225] det_loss_box_5[0.4302] det_loss_cls_dn_0[0.1047] det_loss_box_dn_0[0.2706] det_loss_cls_dn_1[0.1016] det_loss_box_dn_1[0.2311] det_loss_cls_dn_2[0.1009] det_loss_box_dn_2[0.2182] det_loss_cls_dn_3[0.1003] det_loss_box_dn_3[0.2150] det_loss_cls_dn_4[0.0991] det_loss_box_dn_4[0.2142] det_loss_cls_dn_5[0.0985] det_loss_box_dn_5[0.2127] loss_depth[0.0387] total_loss[7.8830]
Rank[0/8] 06/20/2025 03:12:10 INFO stats.py:314 | Epoch[99] Step[164] GlobalStep[38774] Training Speed: 7.66 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 18:22:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:12:21 INFO loss_tracker.py:84 | Epoch[99/239] Step[174] GlobalStep[38784/NA]: det_loss_cls_0[0.5722] det_loss_box_0[0.7274] det_loss_cls_1[0.5598] det_loss_box_1[0.5550] det_loss_cls_2[0.5273] det_loss_box_2[0.4950] det_loss_cls_3[0.4906] det_loss_box_3[0.4879] det_loss_cls_4[0.4563] det_loss_box_4[0.4802] det_loss_cls_5[0.4388] det_loss_box_5[0.4837] det_loss_cls_dn_0[0.0991] det_loss_box_dn_0[0.3062] det_loss_cls_dn_1[0.0952] det_loss_box_dn_1[0.2615] det_loss_cls_dn_2[0.0947] det_loss_box_dn_2[0.2451] det_loss_cls_dn_3[0.0931] det_loss_box_dn_3[0.2423] det_loss_cls_dn_4[0.0921] det_loss_box_dn_4[0.2414] det_loss_cls_dn_5[0.0911] det_loss_box_dn_5[0.2400] loss_depth[0.0424] total_loss[8.4182]
Rank[0/8] 06/20/2025 03:12:36 INFO stats.py:314 | Epoch[99] Step[189] GlobalStep[38799] Training Speed: 11.22 samples/sec across all devices.	Average Step Time: 0.71 sec.	Estimated Remaining Time: 18:22:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:12:47 INFO loss_tracker.py:84 | Epoch[99/239] Step[199] GlobalStep[38809/NA]: det_loss_cls_0[0.4839] det_loss_box_0[0.6224] det_loss_cls_1[0.4713] det_loss_box_1[0.4994] det_loss_cls_2[0.4372] det_loss_box_2[0.4399] det_loss_cls_3[0.4160] det_loss_box_3[0.4255] det_loss_cls_4[0.3948] det_loss_box_4[0.4117] det_loss_cls_5[0.3859] det_loss_box_5[0.4081] det_loss_cls_dn_0[0.1102] det_loss_box_dn_0[0.3260] det_loss_cls_dn_1[0.1056] det_loss_box_dn_1[0.2775] det_loss_cls_dn_2[0.1055] det_loss_box_dn_2[0.2596] det_loss_cls_dn_3[0.1042] det_loss_box_dn_3[0.2574] det_loss_cls_dn_4[0.1038] det_loss_box_dn_4[0.2561] det_loss_cls_dn_5[0.1024] det_loss_box_dn_5[0.2544] loss_depth[0.0357] total_loss[7.6946]
Rank[0/8] 06/20/2025 03:13:02 INFO stats.py:314 | Epoch[99] Step[214] GlobalStep[38824] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 18:21:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:13:12 INFO loss_tracker.py:84 | Epoch[99/239] Step[224] GlobalStep[38834/NA]: det_loss_cls_0[0.5144] det_loss_box_0[0.6440] det_loss_cls_1[0.4939] det_loss_box_1[0.5244] det_loss_cls_2[0.4524] det_loss_box_2[0.4825] det_loss_cls_3[0.4086] det_loss_box_3[0.4676] det_loss_cls_4[0.3880] det_loss_box_4[0.4571] det_loss_cls_5[0.3758] det_loss_box_5[0.4535] det_loss_cls_dn_0[0.0973] det_loss_box_dn_0[0.3330] det_loss_cls_dn_1[0.0947] det_loss_box_dn_1[0.2902] det_loss_cls_dn_2[0.0932] det_loss_box_dn_2[0.2714] det_loss_cls_dn_3[0.0930] det_loss_box_dn_3[0.2660] det_loss_cls_dn_4[0.0915] det_loss_box_dn_4[0.2654] det_loss_cls_dn_5[0.0906] det_loss_box_dn_5[0.2634] loss_depth[0.0365] total_loss[7.9483]
Rank[0/8] 06/20/2025 03:13:27 INFO stats.py:314 | Epoch[99] Step[239] GlobalStep[38849] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 18:20:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:13:38 INFO loss_tracker.py:84 | Epoch[99/239] Step[249] GlobalStep[38859/NA]: det_loss_cls_0[0.5237] det_loss_box_0[0.7629] det_loss_cls_1[0.5152] det_loss_box_1[0.6167] det_loss_cls_2[0.4793] det_loss_box_2[0.5705] det_loss_cls_3[0.4532] det_loss_box_3[0.5587] det_loss_cls_4[0.4290] det_loss_box_4[0.5445] det_loss_cls_5[0.4094] det_loss_box_5[0.5494] det_loss_cls_dn_0[0.1031] det_loss_box_dn_0[0.5030] det_loss_cls_dn_1[0.0995] det_loss_box_dn_1[0.4502] det_loss_cls_dn_2[0.0990] det_loss_box_dn_2[0.4244] det_loss_cls_dn_3[0.0983] det_loss_box_dn_3[0.4192] det_loss_cls_dn_4[0.0969] det_loss_box_dn_4[0.4194] det_loss_cls_dn_5[0.0961] det_loss_box_dn_5[0.4154] loss_depth[0.0432] total_loss[9.6801]
Rank[0/8] 06/20/2025 03:13:54 INFO stats.py:314 | Epoch[99] Step[264] GlobalStep[38874] Training Speed: 6.25 samples/sec across all devices.	Average Step Time: 1.28 sec.	Estimated Remaining Time: 18:20:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:14:04 INFO loss_tracker.py:84 | Epoch[99/239] Step[274] GlobalStep[38884/NA]: det_loss_cls_0[0.5142] det_loss_box_0[0.6963] det_loss_cls_1[0.5022] det_loss_box_1[0.5440] det_loss_cls_2[0.4680] det_loss_box_2[0.5128] det_loss_cls_3[0.4454] det_loss_box_3[0.4933] det_loss_cls_4[0.4150] det_loss_box_4[0.4794] det_loss_cls_5[0.4064] det_loss_box_5[0.4731] det_loss_cls_dn_0[0.0927] det_loss_box_dn_0[0.3290] det_loss_cls_dn_1[0.0895] det_loss_box_dn_1[0.2898] det_loss_cls_dn_2[0.0880] det_loss_box_dn_2[0.2780] det_loss_cls_dn_3[0.0868] det_loss_box_dn_3[0.2751] det_loss_cls_dn_4[0.0854] det_loss_box_dn_4[0.2726] det_loss_cls_dn_5[0.0839] det_loss_box_dn_5[0.2713] loss_depth[0.0354] total_loss[8.2276]
Rank[0/8] 06/20/2025 03:14:19 INFO stats.py:314 | Epoch[99] Step[289] GlobalStep[38899] Training Speed: 8.71 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 18:19:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:14:30 INFO loss_tracker.py:84 | Epoch[99/239] Step[299] GlobalStep[38909/NA]: det_loss_cls_0[0.5388] det_loss_box_0[0.7649] det_loss_cls_1[0.5078] det_loss_box_1[0.6049] det_loss_cls_2[0.4798] det_loss_box_2[0.5414] det_loss_cls_3[0.4437] det_loss_box_3[0.5148] det_loss_cls_4[0.4197] det_loss_box_4[0.5069] det_loss_cls_5[0.4115] det_loss_box_5[0.5040] det_loss_cls_dn_0[0.1025] det_loss_box_dn_0[0.3263] det_loss_cls_dn_1[0.0988] det_loss_box_dn_1[0.2796] det_loss_cls_dn_2[0.0982] det_loss_box_dn_2[0.2581] det_loss_cls_dn_3[0.0970] det_loss_box_dn_3[0.2536] det_loss_cls_dn_4[0.0963] det_loss_box_dn_4[0.2518] det_loss_cls_dn_5[0.0955] det_loss_box_dn_5[0.2464] loss_depth[0.0399] total_loss[8.4823]
Rank[0/8] 06/20/2025 03:14:45 INFO stats.py:314 | Epoch[99] Step[314] GlobalStep[38924] Training Speed: 8.56 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 18:19:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:14:55 INFO loss_tracker.py:84 | Epoch[99/239] Step[324] GlobalStep[38934/NA]: det_loss_cls_0[0.4554] det_loss_box_0[0.6054] det_loss_cls_1[0.4482] det_loss_box_1[0.4706] det_loss_cls_2[0.4133] det_loss_box_2[0.4260] det_loss_cls_3[0.3834] det_loss_box_3[0.4023] det_loss_cls_4[0.3527] det_loss_box_4[0.3995] det_loss_cls_5[0.3421] det_loss_box_5[0.3941] det_loss_cls_dn_0[0.0943] det_loss_box_dn_0[0.2783] det_loss_cls_dn_1[0.0906] det_loss_box_dn_1[0.2344] det_loss_cls_dn_2[0.0899] det_loss_box_dn_2[0.2157] det_loss_cls_dn_3[0.0891] det_loss_box_dn_3[0.2129] det_loss_cls_dn_4[0.0887] det_loss_box_dn_4[0.2112] det_loss_cls_dn_5[0.0875] det_loss_box_dn_5[0.2095] loss_depth[0.0356] total_loss[7.0305]
Rank[0/8] 06/20/2025 03:15:10 INFO stats.py:314 | Epoch[99] Step[339] GlobalStep[38949] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 18:18:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:15:21 INFO loss_tracker.py:84 | Epoch[99/239] Step[349] GlobalStep[38959/NA]: det_loss_cls_0[0.5352] det_loss_box_0[0.7005] det_loss_cls_1[0.5267] det_loss_box_1[0.5416] det_loss_cls_2[0.4759] det_loss_box_2[0.4960] det_loss_cls_3[0.4639] det_loss_box_3[0.4694] det_loss_cls_4[0.4298] det_loss_box_4[0.4647] det_loss_cls_5[0.4225] det_loss_box_5[0.4619] det_loss_cls_dn_0[0.0926] det_loss_box_dn_0[0.3177] det_loss_cls_dn_1[0.0882] det_loss_box_dn_1[0.2735] det_loss_cls_dn_2[0.0881] det_loss_box_dn_2[0.2552] det_loss_cls_dn_3[0.0873] det_loss_box_dn_3[0.2522] det_loss_cls_dn_4[0.0862] det_loss_box_dn_4[0.2511] det_loss_cls_dn_5[0.0856] det_loss_box_dn_5[0.2431] loss_depth[0.0382] total_loss[8.1471]
Rank[0/8] 06/20/2025 03:15:37 INFO stats.py:314 | Epoch[99] Step[364] GlobalStep[38974] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 18:17:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:15:47 INFO loss_tracker.py:84 | Epoch[99/239] Step[374] GlobalStep[38984/NA]: det_loss_cls_0[0.4531] det_loss_box_0[0.5656] det_loss_cls_1[0.4432] det_loss_box_1[0.4328] det_loss_cls_2[0.4021] det_loss_box_2[0.4022] det_loss_cls_3[0.3806] det_loss_box_3[0.3937] det_loss_cls_4[0.3602] det_loss_box_4[0.3747] det_loss_cls_5[0.3441] det_loss_box_5[0.3796] det_loss_cls_dn_0[0.0974] det_loss_box_dn_0[0.3088] det_loss_cls_dn_1[0.0929] det_loss_box_dn_1[0.2646] det_loss_cls_dn_2[0.0921] det_loss_box_dn_2[0.2491] det_loss_cls_dn_3[0.0905] det_loss_box_dn_3[0.2464] det_loss_cls_dn_4[0.0901] det_loss_box_dn_4[0.2452] det_loss_cls_dn_5[0.0885] det_loss_box_dn_5[0.2441] loss_depth[0.0393] total_loss[7.0810]
Rank[0/8] 06/20/2025 03:16:02 INFO stats.py:314 | Epoch[99] Step[389] GlobalStep[38999] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 18:17:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:16:02 INFO trainer.py:268 | 
==================================================BEGIN EVAL==================================================
Rank[0/8] 06/20/2025 03:16:58 INFO trainer.py:275 | eval: 10
Rank[0/8] 06/20/2025 03:17:07 INFO trainer.py:275 | eval: 20
Rank[0/8] 06/20/2025 03:17:19 INFO trainer.py:275 | eval: 30
Rank[0/8] 06/20/2025 03:17:26 INFO trainer.py:275 | eval: 40
Rank[0/8] 06/20/2025 03:17:35 INFO trainer.py:275 | eval: 50
Rank[0/8] 06/20/2025 03:17:41 INFO trainer.py:275 | eval: 60
Rank[0/8] 06/20/2025 03:17:52 INFO trainer.py:275 | eval: 70
Rank[0/8] 06/20/2025 03:17:59 INFO trainer.py:275 | eval: 80
Rank[0/8] 06/20/2025 03:18:10 INFO trainer.py:275 | eval: 90
Rank[0/8] 06/20/2025 03:18:18 INFO trainer.py:275 | eval: 100
Rank[0/8] 06/20/2025 03:18:26 INFO metrics.py:428 | number of results: 824
Rank[0/8] 06/20/2025 03:22:47 INFO metrics.py:332 | eval results of overall:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| bin                 | 0.5801  | 0.8101  | 0.2114  | 0.4749  |
| picture             | 0.1278  | 0.3739  | 0.0024  | 0.0603  |
| toilet              | 0.8403  | 0.8832  | 0.6149  | 0.7445  |
| bathtub             | 0.8153  | 0.8676  | 0.6244  | 0.6912  |
| toilet paper        | 0.3277  | 0.5039  | 0.0187  | 0.1024  |
| vanity              | 0.5429  | 0.9333  | 0.4173  | 0.8000  |
| sink                | 0.6459  | 0.8189  | 0.1718  | 0.3868  |
| towel               | 0.3059  | 0.5935  | 0.0414  | 0.1806  |
| door                | 0.2660  | 0.6785  | 0.0274  | 0.2482  |
| doorframe           | 0.2452  | 0.7198  | 0.0519  | 0.3503  |
| rail                | 0.0128  | 0.3594  | 0.0001  | 0.0469  |
| bar                 | 0.0053  | 0.0909  | 0.0004  | 0.0303  |
| socket              | 0.0051  | 0.1947  | 0.0000  | 0.0044  |
| stopcock            | 0.0371  | 0.4138  | 0.0004  | 0.0345  |
| curtain             | 0.4373  | 0.8149  | 0.1364  | 0.4448  |
| switch              | 0.0012  | 0.0921  | 0.0000  | 0.0066  |
| mirror              | 0.2007  | 0.6902  | 0.0101  | 0.2011  |
| bag                 | 0.2650  | 0.7027  | 0.0553  | 0.2486  |
| bottle              | 0.0377  | 0.2981  | 0.0017  | 0.0696  |
| backpack            | 0.5302  | 0.8065  | 0.2673  | 0.5000  |
| grab bar            | 0.0049  | 0.3750  | 0.0000  | 0.0000  |
| cup                 | 0.0857  | 0.2712  | 0.0170  | 0.0508  |
| shower              | 0.1054  | 0.3000  | 0.0134  | 0.1000  |
| box                 | 0.1226  | 0.4469  | 0.0231  | 0.1691  |
| ledge               | 0.0225  | 0.5000  | 0.0000  | 0.0000  |
| tissue box          | 0.0683  | 0.4848  | 0.0345  | 0.1818  |
| paper               | 0.0199  | 0.2751  | 0.0000  | 0.0237  |
| rod                 | 0.0000  | 0.0250  | 0.0000  | 0.0000  |
| hair dryer          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cabinet             | 0.2648  | 0.7410  | 0.1407  | 0.5083  |
| clothes             | 0.1303  | 0.6083  | 0.0140  | 0.1975  |
| basket              | 0.2506  | 0.5304  | 0.1134  | 0.2696  |
| tissue              | 0.0081  | 0.2308  | 0.0066  | 0.1538  |
| vent                | 0.0197  | 0.1250  | 0.0000  | 0.0000  |
| lamp                | 0.2738  | 0.5910  | 0.0718  | 0.2448  |
| light               | 0.0263  | 0.2094  | 0.0003  | 0.0278  |
| microwave           | 0.4844  | 0.6735  | 0.1840  | 0.4082  |
| counter             | 0.0856  | 0.7798  | 0.0252  | 0.2661  |
| shelf               | 0.2855  | 0.6996  | 0.1178  | 0.4263  |
| plunger             | 0.4226  | 1.0000  | 0.4062  | 0.8000  |
| mouse               | 0.0296  | 0.0811  | 0.0000  | 0.0000  |
| cosmetics           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| decoration          | 0.0292  | 0.3357  | 0.0033  | 0.0643  |
| carpet              | 0.0431  | 0.4286  | 0.0024  | 0.1190  |
| purse               | 0.0032  | 0.5000  | 0.0000  | 0.0000  |
| holder              | 0.2222  | 0.2222  | 0.0000  | 0.0000  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stand               | 0.4785  | 0.8415  | 0.3460  | 0.6768  |
| window              | 0.1933  | 0.6078  | 0.0202  | 0.2620  |
| telephone           | 0.2858  | 0.4833  | 0.0292  | 0.1500  |
| bed                 | 0.7638  | 0.9290  | 0.5390  | 0.7811  |
| stool               | 0.2571  | 0.7311  | 0.1346  | 0.4454  |
| plant               | 0.2527  | 0.5935  | 0.0478  | 0.2540  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clock               | 0.2126  | 0.4894  | 0.0532  | 0.1064  |
| plug                | 0.0010  | 0.1053  | 0.0000  | 0.0000  |
| toothpaste          | 0.0099  | 0.6000  | 0.0000  | 0.0000  |
| mat                 | 0.0085  | 0.2162  | 0.0000  | 0.0000  |
| frame               | 0.0416  | 0.3673  | 0.0024  | 0.1020  |
| chair               | 0.6108  | 0.8027  | 0.4487  | 0.6522  |
| computer            | 0.2278  | 0.7049  | 0.0114  | 0.1639  |
| radiator            | 0.6114  | 0.8000  | 0.2266  | 0.3889  |
| monitor             | 0.5975  | 0.7337  | 0.0985  | 0.2826  |
| keyboard            | 0.0297  | 0.1512  | 0.0000  | 0.0000  |
| table               | 0.4654  | 0.8386  | 0.3297  | 0.6818  |
| blackboard          | 0.5367  | 0.8171  | 0.1222  | 0.4024  |
| desk                | 0.4822  | 0.9157  | 0.3109  | 0.7022  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| book                | 0.0566  | 0.3673  | 0.0018  | 0.0684  |
| board               | 0.1140  | 0.5323  | 0.0499  | 0.1774  |
| notebook            | 0.0004  | 0.1538  | 0.0000  | 0.0000  |
| windowsill          | 0.0252  | 0.3913  | 0.0003  | 0.0435  |
| tube                | 0.0002  | 0.0769  | 0.0000  | 0.0000  |
| fan                 | 0.5401  | 0.7812  | 0.2937  | 0.5156  |
| blinds              | 0.0979  | 0.3600  | 0.0073  | 0.1600  |
| laptop              | 0.4424  | 0.7391  | 0.0167  | 0.2174  |
| conducting wire     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jacket              | 0.5942  | 0.7917  | 0.0200  | 0.2083  |
| pipe                | 0.0365  | 0.1786  | 0.0000  | 0.0000  |
| pillow              | 0.3344  | 0.6960  | 0.0536  | 0.2190  |
| ottoman             | 0.1360  | 0.8056  | 0.0857  | 0.5556  |
| blanket             | 0.1473  | 0.7700  | 0.0342  | 0.4300  |
| toy                 | 0.1196  | 0.3939  | 0.0305  | 0.0909  |
| dresser             | 0.5770  | 0.9074  | 0.4882  | 0.8333  |
| couch               | 0.4813  | 0.8306  | 0.3468  | 0.6942  |
| case                | 0.0929  | 0.3616  | 0.0520  | 0.1299  |
| wardrobe            | 0.2781  | 0.7831  | 0.1207  | 0.5663  |
| dumbbell            | 0.3364  | 0.6667  | 0.0000  | 0.0000  |
| shoe                | 0.1807  | 0.4019  | 0.0233  | 0.0654  |
| washing machine     | 0.4996  | 0.9643  | 0.3068  | 0.7857  |
| heater              | 0.5404  | 0.7931  | 0.1284  | 0.3793  |
| rack                | 0.0612  | 0.5000  | 0.0367  | 0.3269  |
| tv                  | 0.4391  | 0.7525  | 0.1191  | 0.3465  |
| paper cutter        | 0.5703  | 0.8333  | 0.0333  | 0.1667  |
| remote control      | 0.0119  | 0.0741  | 0.0000  | 0.0000  |
| scale               | 0.1818  | 0.1818  | 0.0455  | 0.0909  |
| cushion             | 0.1602  | 0.5882  | 0.0272  | 0.1373  |
| bench               | 0.2793  | 0.7358  | 0.2357  | 0.5283  |
| statue              | 0.0800  | 0.3158  | 0.0000  | 0.0000  |
| furniture           | 0.0026  | 0.1667  | 0.0000  | 0.0000  |
| luggage             | 0.4789  | 1.0000  | 0.4444  | 0.6667  |
| tray                | 0.0008  | 0.1429  | 0.0008  | 0.1429  |
| calendar            | 0.0008  | 0.1667  | 0.0000  | 0.0000  |
| stairs              | 0.0680  | 0.3611  | 0.0044  | 0.1250  |
| plate               | 0.0456  | 0.4038  | 0.0011  | 0.0769  |
| commode             | 0.4636  | 0.7885  | 0.3613  | 0.6538  |
| guitar              | 0.2322  | 0.5000  | 0.0041  | 0.1667  |
| refrigerator        | 0.5405  | 0.8600  | 0.3614  | 0.6400  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jar                 | 0.0058  | 0.2308  | 0.0000  | 0.0000  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bowl                | 0.0723  | 0.4058  | 0.0153  | 0.0580  |
| coffee maker        | 0.4021  | 0.6552  | 0.1119  | 0.3103  |
| brush               | 0.1261  | 0.2857  | 0.0000  | 0.0000  |
| soap dish           | 0.0800  | 0.4000  | 0.0083  | 0.0667  |
| stall               | 0.0615  | 1.0000  | 0.0082  | 0.5882  |
| roll                | 0.0823  | 0.4000  | 0.0000  | 0.0000  |
| container           | 0.0030  | 0.2034  | 0.0002  | 0.0508  |
| bucket              | 0.1816  | 0.4872  | 0.0500  | 0.2308  |
| dispenser           | 0.5079  | 0.7955  | 0.1756  | 0.3182  |
| soap dispenser      | 0.4710  | 0.7407  | 0.0866  | 0.2593  |
| pillar              | 0.3343  | 0.6977  | 0.0918  | 0.4419  |
| sign                | 0.0449  | 0.4000  | 0.0004  | 0.0333  |
| column              | 0.0059  | 0.3684  | 0.0005  | 0.1316  |
| machine             | 0.4686  | 0.9474  | 0.3183  | 0.7895  |
| hanger              | 0.0001  | 0.0833  | 0.0000  | 0.0000  |
| partition           | 0.0894  | 0.6667  | 0.0000  | 0.0000  |
| faucet              | 0.0149  | 0.1489  | 0.0000  | 0.0000  |
| window frame        | 0.0266  | 0.4242  | 0.0021  | 0.1616  |
| fireplace           | 0.1574  | 0.5000  | 0.0039  | 0.1429  |
| cube                | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| tablet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stove               | 0.5436  | 0.8333  | 0.3773  | 0.6111  |
| fire extinguisher   | 0.2154  | 0.4000  | 0.0000  | 0.0000  |
| printer             | 0.6089  | 0.8367  | 0.3148  | 0.5510  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| person              | 0.1821  | 0.6667  | 0.0128  | 0.1667  |
| drawer              | 0.1728  | 0.5556  | 0.0605  | 0.4444  |
| screen              | 0.0222  | 0.2000  | 0.0022  | 0.2000  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vacuum cleaner      | 0.3121  | 0.5714  | 0.0952  | 0.2857  |
| oven                | 0.3754  | 0.7500  | 0.1468  | 0.4286  |
| doorway             | 0.0002  | 0.1111  | 0.0000  | 0.0000  |
| ladder              | 0.0912  | 0.3529  | 0.0811  | 0.1765  |
| map                 | 0.0128  | 0.5000  | 0.0000  | 0.0000  |
| hat                 | 0.0370  | 0.1111  | 0.0000  | 0.0000  |
| humidifier          | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| ball                | 0.3977  | 0.4762  | 0.3271  | 0.3810  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| poster              | 0.0139  | 0.1364  | 0.0000  | 0.0000  |
| basin               | 0.0001  | 0.0588  | 0.0001  | 0.0588  |
| hamper              | 0.4621  | 0.5455  | 0.4212  | 0.5455  |
| bicycle             | 0.6431  | 1.0000  | 0.3329  | 1.0000  |
| speaker             | 0.0124  | 0.3182  | 0.0022  | 0.0455  |
| soap                | 0.0053  | 0.5000  | 0.0006  | 0.1667  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| piano               | 0.2024  | 0.4000  | 0.0012  | 0.2000  |
| water cooler        | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| kettle              | 0.0681  | 0.4444  | 0.0045  | 0.1111  |
| toaster             | 0.4365  | 0.6000  | 0.0053  | 0.1000  |
| teapot              | 0.0234  | 0.2222  | 0.0000  | 0.0000  |
| dish rack           | 0.2379  | 1.0000  | 0.0100  | 0.2000  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchenware         | 0.2051  | 0.4130  | 0.0038  | 0.0652  |
| range hood          | 0.3623  | 0.7857  | 0.0000  | 0.0000  |
| mailbox             | 0.0122  | 0.6000  | 0.0000  | 0.0000  |
| flowerpot           | 0.0570  | 0.3529  | 0.0216  | 0.1176  |
| vase                | 0.1398  | 0.4200  | 0.0582  | 0.2000  |
| file                | 0.1378  | 0.3824  | 0.0150  | 0.1176  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pot                 | 0.0977  | 0.4444  | 0.0381  | 0.1852  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toiletry            | 0.0141  | 0.3043  | 0.0000  | 0.0000  |
| coil                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headphones          | 0.3455  | 0.8000  | 0.0000  | 0.0000  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| copier              | 0.9167  | 0.9167  | 0.3909  | 0.5000  |
| countertop          | 0.0263  | 0.6071  | 0.0002  | 0.0714  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crate               | 0.3770  | 0.5714  | 0.1429  | 0.1429  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garbage             | 0.2958  | 0.6250  | 0.2311  | 0.5000  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| folder              | 0.0272  | 0.4286  | 0.0079  | 0.0714  |
| candle              | 0.0595  | 0.3333  | 0.0000  | 0.0000  |
| candlestick         | 0.3359  | 0.6667  | 0.0000  | 0.0000  |
| napkins             | 0.5556  | 0.6667  | 0.0000  | 0.0000  |
| price tag           | 0.1122  | 0.2857  | 0.0204  | 0.1429  |
| pack                | 0.0612  | 0.6667  | 0.0010  | 0.1333  |
| magazine            | 0.0008  | 0.2000  | 0.0000  | 0.0000  |
| player              | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| album               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| device              | 0.0256  | 0.2727  | 0.0057  | 0.0909  |
| umbrella            | 0.0500  | 0.2000  | 0.0012  | 0.2000  |
| jalousie            | 0.6429  | 1.0000  | 0.5000  | 0.5000  |
| menu                | 0.4206  | 0.5714  | 0.0000  | 0.0000  |
| chandelier          | 0.2437  | 0.4286  | 0.0171  | 0.1429  |
| air conditioner     | 0.5500  | 0.6000  | 0.5500  | 0.6000  |
| beanbag             | 0.8433  | 1.0000  | 0.7524  | 0.8333  |
| clothes dryer       | 0.3134  | 0.7333  | 0.2155  | 0.5333  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit               | 0.1764  | 0.4286  | 0.0210  | 0.2143  |
| balcony             | 0.0625  | 0.5000  | 0.0086  | 0.5000  |
| sunglasses          | 0.1080  | 0.5385  | 0.0015  | 0.0769  |
| footstool           | 0.2500  | 0.5000  | 0.2500  | 0.5000  |
| shampoo             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glove               | 0.5712  | 1.0000  | 0.1111  | 0.3333  |
| water heater        | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| body loofah         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| can                 | 0.0104  | 0.0833  | 0.0000  | 0.0000  |
| salt                | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| spoon               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| food                | 0.1212  | 0.1818  | 0.0000  | 0.0000  |
| bread               | 0.2400  | 0.4000  | 0.0000  | 0.0000  |
| pan                 | 0.0368  | 0.3529  | 0.0147  | 0.0588  |
| sack                | 0.5381  | 1.0000  | 0.4951  | 0.7143  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radio               | 0.2500  | 0.2500  | 0.0034  | 0.2500  |
| crib                | 0.1880  | 1.0000  | 0.1603  | 1.0000  |
| broom               | 0.0062  | 0.2143  | 0.0000  | 0.0000  |
| drum                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| helmets             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flush               | 0.7500  | 1.0000  | 0.0000  | 0.0000  |
| mop                 | 0.4264  | 1.0000  | 0.0000  | 0.0000  |
| roof                | 0.0009  | 0.3333  | 0.0000  | 0.0833  |
| glass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wine                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| steps               | 0.0001  | 0.2000  | 0.0000  | 0.0000  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washbasin           | 0.0018  | 0.2667  | 0.0013  | 0.1333  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchen island      | 0.2143  | 0.2857  | 0.2143  | 0.2857  |
| excercise equipment | 0.3792  | 0.7000  | 0.0180  | 0.2500  |
| pool table          | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sponge              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| projector           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| treadmill           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garage door         | 0.3333  | 0.3333  | 0.0327  | 0.3333  |
| handle              | 0.0014  | 0.2500  | 0.0000  | 0.0000  |
| beam                | 0.0000  | 0.0455  | 0.0000  | 0.0000  |
| arch                | 0.0045  | 0.1333  | 0.0002  | 0.0667  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape       | 0.0143  | 0.2000  | 0.0000  | 0.0000  |
| dishwasher          | 0.2081  | 0.3000  | 0.0682  | 0.2000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cap                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| letter              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| thermostat          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet               | 0.4000  | 0.6667  | 0.4000  | 0.6667  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| overall             | 0.1870  | 0.4086  | 0.0775  | 0.1830  |
| head                | 0.2615  | 0.5718  | 0.1117  | 0.2862  |
| common              | 0.1583  | 0.3914  | 0.0619  | 0.1563  |
| tail                | 0.1372  | 0.2472  | 0.0574  | 0.0988  |
| small               | 0.0534  | 0.1781  | 0.0043  | 0.0253  |
| medium              | 0.2013  | 0.4608  | 0.0889  | 0.2048  |
| large               | 0.1158  | 0.5398  | 0.0394  | 0.2676  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 03:22:47 INFO metrics.py:437 | number of scannet results: 250
Rank[0/8] 06/20/2025 03:24:09 INFO metrics.py:332 | eval results of scannet:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| bin                 | 0.6117  | 0.7957  | 0.2424  | 0.4894  |
| picture             | 0.1911  | 0.4366  | 0.0024  | 0.0485  |
| toilet              | 0.9358  | 0.9643  | 0.7557  | 0.8750  |
| bathtub             | 0.9150  | 0.9375  | 0.8571  | 0.8750  |
| toilet paper        | 0.4047  | 0.5190  | 0.0260  | 0.1139  |
| vanity              | 0.7336  | 1.0000  | 0.5550  | 0.8667  |
| sink                | 0.8246  | 0.8864  | 0.2829  | 0.5000  |
| towel               | 0.4093  | 0.6942  | 0.0468  | 0.1983  |
| door                | 0.4271  | 0.8161  | 0.0886  | 0.4181  |
| doorframe           | 0.3310  | 0.7324  | 0.0681  | 0.2958  |
| rail                | 0.0475  | 0.5000  | 0.0000  | 0.0000  |
| bar                 | 0.0025  | 0.0455  | 0.0000  | 0.0000  |
| socket              | 0.0073  | 0.2136  | 0.0000  | 0.0049  |
| stopcock            | 0.0374  | 0.4138  | 0.0004  | 0.0345  |
| curtain             | 0.5813  | 0.8407  | 0.2294  | 0.5929  |
| switch              | 0.0043  | 0.1646  | 0.0000  | 0.0127  |
| mirror              | 0.2497  | 0.7590  | 0.0095  | 0.1807  |
| bag                 | 0.2321  | 0.7260  | 0.0987  | 0.2877  |
| bottle              | 0.0570  | 0.3158  | 0.0040  | 0.0895  |
| backpack            | 0.6982  | 0.8929  | 0.4410  | 0.6429  |
| grab bar            | 0.0049  | 0.3750  | 0.0000  | 0.0000  |
| cup                 | 0.1357  | 0.3333  | 0.0394  | 0.0725  |
| shower              | 0.1621  | 0.2857  | 0.0144  | 0.1429  |
| box                 | 0.1219  | 0.4444  | 0.0409  | 0.1587  |
| ledge               | 0.0225  | 0.5000  | 0.0000  | 0.0000  |
| tissue box          | 0.0781  | 0.5000  | 0.0564  | 0.1818  |
| paper               | 0.0220  | 0.2717  | 0.0000  | 0.0189  |
| rod                 | 0.0001  | 0.0714  | 0.0000  | 0.0000  |
| hair dryer          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cabinet             | 0.3413  | 0.7738  | 0.1793  | 0.5119  |
| clothes             | 0.1406  | 0.5882  | 0.0195  | 0.1895  |
| basket              | 0.0808  | 0.3077  | 0.0609  | 0.1923  |
| tissue              | 0.0004  | 0.1111  | 0.0004  | 0.1111  |
| lamp                | 0.4511  | 0.7738  | 0.1916  | 0.4048  |
| light               | 0.0679  | 0.4138  | 0.0009  | 0.0345  |
| microwave           | 0.3946  | 0.6500  | 0.1162  | 0.4000  |
| counter             | 0.1462  | 0.9143  | 0.0559  | 0.5143  |
| shelf               | 0.5119  | 0.8529  | 0.3030  | 0.6544  |
| plunger             | 0.4226  | 1.0000  | 0.4062  | 0.8000  |
| mouse               | 0.0544  | 0.1429  | 0.0000  | 0.0000  |
| decoration          | 0.0386  | 0.2917  | 0.0117  | 0.0833  |
| carpet              | 0.0050  | 0.3000  | 0.0000  | 0.0000  |
| purse               | 0.0081  | 0.5000  | 0.0000  | 0.0000  |
| holder              | 0.2000  | 0.2000  | 0.0000  | 0.0000  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stand               | 0.6901  | 0.8868  | 0.5517  | 0.7547  |
| window              | 0.4244  | 0.8210  | 0.0656  | 0.4630  |
| telephone           | 0.4027  | 0.6250  | 0.0422  | 0.2188  |
| bed                 | 0.9484  | 1.0000  | 0.7967  | 0.9649  |
| stool               | 0.3367  | 0.8182  | 0.1569  | 0.4318  |
| plant               | 0.2730  | 0.5882  | 0.0519  | 0.2437  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clock               | 0.2921  | 0.5600  | 0.0417  | 0.1200  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toothpaste          | 0.0119  | 0.6000  | 0.0000  | 0.0000  |
| mat                 | 0.0237  | 0.3000  | 0.0000  | 0.0000  |
| frame               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| chair               | 0.6367  | 0.8008  | 0.4673  | 0.6496  |
| computer            | 0.2127  | 0.7179  | 0.0189  | 0.1795  |
| radiator            | 0.7326  | 0.8966  | 0.3292  | 0.5172  |
| monitor             | 0.6556  | 0.7500  | 0.1380  | 0.3143  |
| keyboard            | 0.0653  | 0.1897  | 0.0000  | 0.0000  |
| table               | 0.5800  | 0.9154  | 0.4008  | 0.7846  |
| blackboard          | 0.5767  | 0.8406  | 0.1412  | 0.4348  |
| desk                | 0.6025  | 0.9747  | 0.4017  | 0.7722  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| book                | 0.0641  | 0.3690  | 0.0023  | 0.0810  |
| board               | 0.0540  | 0.6250  | 0.0035  | 0.2500  |
| notebook            | 0.0056  | 0.5000  | 0.0000  | 0.0000  |
| windowsill          | 0.0224  | 0.3333  | 0.0008  | 0.1667  |
| tube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fan                 | 0.5038  | 0.7500  | 0.3292  | 0.5000  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| laptop              | 0.4424  | 0.6667  | 0.0002  | 0.0667  |
| jacket              | 0.6206  | 0.8261  | 0.0209  | 0.2174  |
| pipe                | 0.0004  | 0.0714  | 0.0000  | 0.0000  |
| pillow              | 0.4510  | 0.7083  | 0.1709  | 0.4167  |
| ottoman             | 0.0945  | 1.0000  | 0.0561  | 0.7143  |
| blanket             | 0.0722  | 0.5000  | 0.0053  | 0.2500  |
| toy                 | 0.0877  | 0.2941  | 0.0784  | 0.1176  |
| dresser             | 0.7164  | 0.9459  | 0.6469  | 0.9189  |
| couch               | 0.7433  | 0.9625  | 0.5995  | 0.9000  |
| case                | 0.6084  | 1.0000  | 0.4652  | 0.7895  |
| wardrobe            | 0.3733  | 0.8750  | 0.2917  | 0.3750  |
| shoe                | 0.2527  | 0.5000  | 0.0481  | 0.1000  |
| washing machine     | 0.4563  | 1.0000  | 0.4563  | 1.0000  |
| rack                | 0.0032  | 0.2727  | 0.0009  | 0.1818  |
| tv                  | 0.4802  | 0.6957  | 0.2652  | 0.4348  |
| paper cutter        | 0.4547  | 0.8000  | 0.0500  | 0.2000  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scale               | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| cushion             | 0.0086  | 0.5000  | 0.0000  | 0.0000  |
| bench               | 0.7333  | 1.0000  | 0.7333  | 1.0000  |
| tray                | 0.0022  | 0.3333  | 0.0022  | 0.3333  |
| calendar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stairs              | 0.4188  | 0.6000  | 0.0007  | 0.2000  |
| plate               | 0.0285  | 0.4000  | 0.0000  | 0.0000  |
| guitar              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| refrigerator        | 0.7040  | 0.9500  | 0.5378  | 0.8500  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bowl                | 0.0665  | 0.3333  | 0.0029  | 0.1111  |
| coffee maker        | 0.5929  | 0.7059  | 0.2267  | 0.4706  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dish           | 0.1214  | 0.4167  | 0.0104  | 0.0833  |
| stall               | 0.0616  | 1.0000  | 0.0082  | 0.5882  |
| container           | 0.0012  | 0.0870  | 0.0000  | 0.0000  |
| bucket              | 0.0239  | 0.2857  | 0.0057  | 0.2143  |
| dispenser           | 0.5887  | 0.8205  | 0.2039  | 0.3590  |
| soap dispenser      | 0.5318  | 0.8095  | 0.1106  | 0.2857  |
| pillar              | 0.0275  | 0.4000  | 0.0250  | 0.2000  |
| sign                | 0.0449  | 0.3929  | 0.0005  | 0.0357  |
| machine             | 0.5105  | 1.0000  | 0.4477  | 0.8750  |
| faucet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stove               | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| fire extinguisher   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| printer             | 0.6787  | 0.9091  | 0.5419  | 0.7273  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| person              | 0.1821  | 0.6667  | 0.0128  | 0.1667  |
| drawer              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vacuum cleaner      | 0.6816  | 1.0000  | 0.6667  | 0.6667  |
| oven                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ladder              | 0.0388  | 0.4000  | 0.0000  | 0.0000  |
| map                 | 0.0256  | 1.0000  | 0.0000  | 0.0000  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball                | 0.3477  | 0.4167  | 0.2500  | 0.2500  |
| poster              | 0.2000  | 1.0000  | 0.0000  | 0.0000  |
| basin               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hamper              | 0.5083  | 0.6000  | 0.4649  | 0.6000  |
| bicycle             | 0.7667  | 1.0000  | 0.1571  | 1.0000  |
| speaker             | 0.0030  | 0.5000  | 0.0000  | 0.0000  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water cooler        | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| kettle              | 0.1458  | 0.5000  | 0.0000  | 0.0000  |
| toaster             | 0.0576  | 0.5000  | 0.0312  | 0.2500  |
| teapot              | 0.0833  | 0.2500  | 0.0000  | 0.0000  |
| dish rack           | 0.2500  | 1.0000  | 0.0250  | 0.2500  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.7709  | 1.0000  | 0.0000  | 0.0000  |
| mailbox             | 0.0131  | 0.6000  | 0.0000  | 0.0000  |
| flowerpot           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pot                 | 0.0263  | 0.5000  | 0.0000  | 0.0000  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headphones          | 0.4183  | 0.8000  | 0.0000  | 0.0000  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| copier              | 0.9167  | 0.9167  | 0.3909  | 0.5000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crate               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.0278  | 0.3333  | 0.0000  | 0.0000  |
| broom               | 0.0977  | 0.2500  | 0.0000  | 0.0000  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.3333  | 1.0000  | 0.3333  | 1.0000  |
| fruit               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.0217  | 0.5000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| scannet             | 0.2311  | 0.4675  | 0.1158  | 0.2320  |
| head                | 0.3198  | 0.5953  | 0.1705  | 0.3328  |
| common              | 0.1767  | 0.3898  | 0.0667  | 0.1524  |
| tail                | 0.0472  | 0.2010  | 0.0391  | 0.0646  |
| small               | 0.0368  | 0.1532  | 0.0040  | 0.0155  |
| medium              | 0.2778  | 0.5700  | 0.1401  | 0.2845  |
| large               | 0.2610  | 0.7912  | 0.1079  | 0.4913  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 03:24:09 INFO metrics.py:437 | number of 3rscan results: 235
Rank[0/8] 06/20/2025 03:25:19 INFO metrics.py:332 | eval results of 3rscan:
+-------------------+---------+---------+---------+---------+
| classes           | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+-------------------+---------+---------+---------+---------+
| chair             | 0.6972  | 0.8580  | 0.5285  | 0.7159  |
| monitor           | 0.5667  | 0.7000  | 0.0592  | 0.2000  |
| keyboard          | 0.0092  | 0.0833  | 0.0000  | 0.0000  |
| backpack          | 0.4143  | 0.7576  | 0.1416  | 0.3939  |
| bin               | 0.6495  | 0.8780  | 0.2241  | 0.5244  |
| case              | 0.0443  | 0.3000  | 0.0100  | 0.0500  |
| cup               | 0.0465  | 0.2308  | 0.0000  | 0.0256  |
| paper             | 0.0322  | 0.3000  | 0.0002  | 0.0429  |
| bag               | 0.3208  | 0.7172  | 0.0477  | 0.2525  |
| desk              | 0.4133  | 0.9125  | 0.2604  | 0.6625  |
| mouse             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stand             | 0.4332  | 0.8361  | 0.2645  | 0.6230  |
| computer          | 0.2897  | 0.7143  | 0.0062  | 0.1429  |
| plug              | 0.0068  | 0.2222  | 0.0000  | 0.0000  |
| cabinet           | 0.3414  | 0.8248  | 0.1829  | 0.6197  |
| book              | 0.0571  | 0.3905  | 0.0029  | 0.0414  |
| table             | 0.4948  | 0.8604  | 0.3511  | 0.6577  |
| pen               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plate             | 0.0483  | 0.4242  | 0.0019  | 0.0909  |
| box               | 0.1355  | 0.4336  | 0.0147  | 0.1569  |
| garbage           | 0.2982  | 0.6250  | 0.2325  | 0.5000  |
| bottle            | 0.0246  | 0.2397  | 0.0003  | 0.0248  |
| socket            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| conducting wire   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shelf             | 0.2383  | 0.6961  | 0.0584  | 0.3498  |
| file              | 0.1582  | 0.4286  | 0.0147  | 0.1071  |
| wardrobe          | 0.3027  | 0.8281  | 0.1175  | 0.6406  |
| notebook          | 0.0002  | 0.0909  | 0.0000  | 0.0000  |
| telephone         | 0.1771  | 0.2917  | 0.0444  | 0.0833  |
| window            | 0.1623  | 0.6233  | 0.0056  | 0.1712  |
| pillow            | 0.4079  | 0.7305  | 0.0373  | 0.1797  |
| plant             | 0.2856  | 0.5594  | 0.0522  | 0.1931  |
| curtain           | 0.4437  | 0.8492  | 0.1222  | 0.3968  |
| pad               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| paper cutter      | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| cushion           | 0.2654  | 0.7667  | 0.0461  | 0.2000  |
| hat               | 0.1429  | 0.1429  | 0.0000  | 0.0000  |
| clock             | 0.1574  | 0.4211  | 0.0677  | 0.1053  |
| blinds            | 0.1257  | 0.4615  | 0.0095  | 0.2051  |
| door              | 0.2662  | 0.6397  | 0.0241  | 0.2206  |
| picture           | 0.0682  | 0.3171  | 0.0006  | 0.0488  |
| switch            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes           | 0.1458  | 0.6796  | 0.0105  | 0.2039  |
| toy               | 0.3264  | 0.5385  | 0.0258  | 0.1538  |
| bucket            | 0.7043  | 0.9333  | 0.1776  | 0.4000  |
| laptop            | 0.4444  | 0.8333  | 0.2040  | 0.5000  |
| folder            | 0.0273  | 0.4286  | 0.0079  | 0.0714  |
| wire              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| printer           | 0.6134  | 0.8889  | 0.0131  | 0.2222  |
| drawer            | 0.4679  | 0.8889  | 0.1795  | 0.7778  |
| frame             | 0.0694  | 0.5333  | 0.0039  | 0.1333  |
| tv                | 0.5045  | 0.8857  | 0.0755  | 0.2571  |
| lamp              | 0.2280  | 0.4813  | 0.0357  | 0.1250  |
| commode           | 0.4640  | 0.7885  | 0.3616  | 0.6538  |
| stool             | 0.3299  | 0.8333  | 0.1837  | 0.5556  |
| couch             | 0.3805  | 0.7143  | 0.2537  | 0.5714  |
| light             | 0.0476  | 0.3295  | 0.0000  | 0.0114  |
| blanket           | 0.2238  | 0.8615  | 0.0763  | 0.5231  |
| candle            | 0.0705  | 0.3333  | 0.0000  | 0.0000  |
| bowl              | 0.1235  | 0.4815  | 0.0389  | 0.1111  |
| ottoman           | 0.3664  | 0.9091  | 0.2185  | 0.6364  |
| bed               | 0.7894  | 0.9636  | 0.5852  | 0.8364  |
| teapot            | 0.0208  | 0.2500  | 0.0000  | 0.0000  |
| doorframe         | 0.1298  | 0.6400  | 0.0082  | 0.2000  |
| vase              | 0.2542  | 0.4000  | 0.1375  | 0.1500  |
| candlestick       | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| bench             | 0.3307  | 0.8571  | 0.2773  | 0.6429  |
| decoration        | 0.1080  | 0.3750  | 0.0104  | 0.0312  |
| napkins           | 0.5556  | 0.6667  | 0.0000  | 0.0000  |
| kettle            | 0.0826  | 0.5000  | 0.0073  | 0.1667  |
| poster            | 0.0147  | 0.1270  | 0.0000  | 0.0000  |
| container         | 0.0055  | 0.4444  | 0.0003  | 0.1111  |
| remote control    | 0.0295  | 0.1000  | 0.0000  | 0.0000  |
| price tag         | 0.1122  | 0.2857  | 0.0204  | 0.1429  |
| towel             | 0.1987  | 0.5570  | 0.0357  | 0.1772  |
| pack              | 0.0707  | 0.7692  | 0.0012  | 0.1538  |
| jar               | 0.0098  | 0.2500  | 0.0000  | 0.0000  |
| toilet paper      | 0.0867  | 0.5000  | 0.0042  | 0.0833  |
| counter           | 0.1057  | 0.8293  | 0.0398  | 0.1951  |
| carpet            | 0.0707  | 0.5893  | 0.0040  | 0.1786  |
| magazine          | 0.0010  | 0.2500  | 0.0000  | 0.0000  |
| roll              | 0.0883  | 0.4000  | 0.0000  | 0.0000  |
| basket            | 0.4215  | 0.7031  | 0.2026  | 0.3594  |
| flowerpot         | 0.0879  | 0.2917  | 0.0417  | 0.0417  |
| player            | 0.3333  | 0.3333  | 0.0000  | 0.0000  |
| calendar          | 0.0014  | 0.2000  | 0.0000  | 0.0000  |
| oven              | 0.5712  | 1.0000  | 0.2324  | 0.6316  |
| statue            | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| album             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cube              | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| blackboard        | 0.4225  | 0.7500  | 0.0500  | 0.2500  |
| humidifier        | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| heater            | 0.5459  | 0.7895  | 0.1288  | 0.3684  |
| windowsill        | 0.0306  | 0.4103  | 0.0004  | 0.0256  |
| fan               | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| radiator          | 0.4273  | 0.6897  | 0.0303  | 0.1724  |
| device            | 0.0256  | 0.2727  | 0.0057  | 0.0909  |
| umbrella          | 0.2500  | 1.0000  | 0.0060  | 1.0000  |
| partition         | 0.1073  | 0.8000  | 0.0000  | 0.0000  |
| jalousie          | 0.6429  | 1.0000  | 0.5000  | 0.5000  |
| speaker           | 0.0121  | 0.1667  | 0.0093  | 0.0833  |
| coffee maker      | 0.2161  | 0.7778  | 0.0048  | 0.1111  |
| menu              | 0.4206  | 0.5714  | 0.0000  | 0.0000  |
| ball              | 0.5200  | 0.6000  | 0.4500  | 0.6000  |
| chandelier        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shoe              | 0.2321  | 0.4762  | 0.0202  | 0.0714  |
| mirror            | 0.1103  | 0.5556  | 0.0210  | 0.1852  |
| furniture         | 0.0057  | 0.3333  | 0.0000  | 0.0000  |
| rack              | 0.1726  | 0.7000  | 0.1021  | 0.4667  |
| pot               | 0.3061  | 0.5833  | 0.0904  | 0.4167  |
| sink              | 0.6159  | 0.8393  | 0.0805  | 0.2143  |
| air conditioner   | 0.6875  | 0.7500  | 0.6875  | 0.7500  |
| beanbag           | 0.9028  | 1.0000  | 0.7778  | 0.8333  |
| hanger            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scale             | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| clothes dryer     | 0.3788  | 0.7000  | 0.1976  | 0.4000  |
| shower            | 0.3917  | 0.7500  | 0.2500  | 0.2500  |
| brush             | 0.2300  | 0.5000  | 0.0000  | 0.0000  |
| soap dish         | 0.0057  | 0.5000  | 0.0000  | 0.0000  |
| hook              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| luggage           | 0.5111  | 1.0000  | 0.4444  | 0.6667  |
| fruit             | 0.5569  | 0.8000  | 0.0363  | 0.4000  |
| balcony           | 0.0625  | 0.5000  | 0.0086  | 0.5000  |
| board             | 0.1847  | 0.4583  | 0.1250  | 0.1250  |
| microwave         | 0.9144  | 1.0000  | 0.4420  | 0.5882  |
| sunglasses        | 0.1080  | 0.5385  | 0.0015  | 0.0769  |
| footstool         | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| bathtub           | 0.9324  | 1.0000  | 0.3843  | 0.5556  |
| shampoo           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toilet            | 0.8217  | 0.8824  | 0.3354  | 0.4118  |
| faucet            | 0.1769  | 0.6667  | 0.0000  | 0.0000  |
| toiletry          | 0.0042  | 1.0000  | 0.0000  | 0.0000  |
| washing machine   | 0.6025  | 1.0000  | 0.2871  | 0.7857  |
| tube              | 0.0005  | 0.1429  | 0.0000  | 0.0000  |
| holder            | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| bar               | 0.4000  | 0.6667  | 0.0303  | 0.3333  |
| cosmetics         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pipe              | 0.2553  | 0.7500  | 0.0000  | 0.0000  |
| glove             | 0.5712  | 1.0000  | 0.1111  | 0.3333  |
| dispenser         | 0.0099  | 0.6667  | 0.0000  | 0.0000  |
| crate             | 0.5771  | 0.8000  | 0.2000  | 0.2000  |
| water heater      | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| soap              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basin             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| body loofah       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| can               | 0.0156  | 0.1250  | 0.0000  | 0.0000  |
| pillar            | 0.5235  | 0.7059  | 0.1374  | 0.4118  |
| column            | 0.0380  | 1.0000  | 0.0172  | 0.5000  |
| salt              | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| stairs            | 0.2306  | 0.5714  | 0.0130  | 0.3571  |
| coil              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| spoon             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| food              | 0.1333  | 0.2000  | 0.0000  | 0.0000  |
| stove             | 0.3668  | 0.8824  | 0.1015  | 0.5294  |
| refrigerator      | 0.6584  | 0.8000  | 0.4269  | 0.6667  |
| kitchenware       | 0.2145  | 0.4318  | 0.0039  | 0.0682  |
| bread             | 0.2400  | 0.4000  | 0.0000  | 0.0000  |
| pan               | 0.0549  | 0.5000  | 0.0208  | 0.0833  |
| range hood        | 0.2383  | 0.8000  | 0.0000  | 0.0000  |
| sack              | 0.5392  | 1.0000  | 0.4951  | 0.7143  |
| toaster           | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| tissue            | 0.0829  | 0.6667  | 0.0667  | 0.3333  |
| soap dispenser    | 0.2967  | 0.6000  | 0.0100  | 0.2000  |
| shovel            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| knife             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tray              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sign              | 0.1667  | 0.5000  | 0.0000  | 0.0000  |
| mattress          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radio             | 1.0000  | 1.0000  | 0.0135  | 1.0000  |
| rail              | 0.0833  | 0.2500  | 0.0000  | 0.0000  |
| crib              | 0.1880  | 1.0000  | 0.1603  | 1.0000  |
| fireplace         | 0.0182  | 0.2000  | 0.0000  | 0.0000  |
| tablet            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue box        | 0.0167  | 0.3333  | 0.0167  | 0.3333  |
| broom             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| piano             | 0.3333  | 0.3333  | 0.0000  | 0.0000  |
| vacuum cleaner    | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| helmets           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dresser           | 0.6000  | 1.0000  | 0.0000  | 0.0000  |
| screen            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| window frame      | 0.0728  | 0.3333  | 0.0000  | 0.0000  |
| bicycle           | 0.8333  | 1.0000  | 0.8333  | 1.0000  |
| ladder            | 0.3535  | 0.6667  | 0.3535  | 0.6667  |
| mat               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flush             | 0.7500  | 1.0000  | 0.0000  | 0.0000  |
| mop               | 0.4269  | 1.0000  | 0.0000  | 0.0000  |
| tool              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine           | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| beam              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell          | 0.0435  | 1.0000  | 0.0000  | 0.0000  |
| guitar            | 0.6667  | 1.0000  | 0.0417  | 0.5000  |
| glass             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scissors          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| letter            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape     | 0.0179  | 0.2500  | 0.0000  | 0.0000  |
| seasoning         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sponge            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washbasin         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| dish rack         | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| toothbrush        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| treadmill         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher        | 0.6667  | 0.6667  | 0.2619  | 0.6667  |
| fire extinguisher | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| map               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------           | ------- | ------- | ------- | ------- |
| 3rscan            | 0.2773  | 0.5194  | 0.0977  | 0.2159  |
| head              | 0.2857  | 0.6068  | 0.1030  | 0.2704  |
| common            | 0.2826  | 0.5136  | 0.0984  | 0.1923  |
| tail              | 0.2547  | 0.3664  | 0.0870  | 0.1476  |
| small             | 0.0834  | 0.2209  | 0.0114  | 0.0355  |
| medium            | 0.2959  | 0.5845  | 0.1143  | 0.2574  |
| large             | 0.2118  | 0.7169  | 0.0636  | 0.3913  |
+-------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 03:25:19 INFO metrics.py:437 | number of matterport3d results: 339
Rank[0/8] 06/20/2025 03:26:49 INFO metrics.py:332 | eval results of matterport3d:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| sink                | 0.5166  | 0.7475  | 0.1956  | 0.3838  |
| door                | 0.1824  | 0.5849  | 0.0056  | 0.1253  |
| bathtub             | 0.6500  | 0.7407  | 0.3941  | 0.5185  |
| doorframe           | 0.2572  | 0.7256  | 0.0692  | 0.3905  |
| mirror              | 0.2063  | 0.6622  | 0.0109  | 0.2297  |
| towel               | 0.3127  | 0.5091  | 0.0522  | 0.1636  |
| light               | 0.0218  | 0.1624  | 0.0008  | 0.0313  |
| cabinet             | 0.1299  | 0.6229  | 0.0716  | 0.3941  |
| picture             | 0.1555  | 0.3671  | 0.0078  | 0.0749  |
| countertop          | 0.0263  | 0.6071  | 0.0002  | 0.0714  |
| plant               | 0.1985  | 0.6607  | 0.0621  | 0.3750  |
| toiletry            | 0.0161  | 0.2727  | 0.0000  | 0.0000  |
| case                | 0.0006  | 0.1667  | 0.0000  | 0.0556  |
| window              | 0.1301  | 0.5056  | 0.0203  | 0.2083  |
| switch              | 0.0001  | 0.0500  | 0.0000  | 0.0000  |
| soap                | 0.0106  | 0.6000  | 0.0013  | 0.2000  |
| shelf               | 0.1600  | 0.5547  | 0.0745  | 0.3577  |
| toilet              | 0.7641  | 0.8125  | 0.6339  | 0.7188  |
| drawer              | 0.0014  | 0.2500  | 0.0010  | 0.1250  |
| clothes             | 0.1537  | 0.5345  | 0.0496  | 0.2069  |
| vent                | 0.0575  | 0.1250  | 0.0000  | 0.0000  |
| decoration          | 0.0071  | 0.3333  | 0.0004  | 0.0714  |
| roof                | 0.0009  | 0.3333  | 0.0000  | 0.0833  |
| vanity              | 0.1503  | 0.8000  | 0.1460  | 0.6667  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dresser             | 0.2547  | 0.8000  | 0.1910  | 0.7333  |
| flowerpot           | 0.0447  | 0.4400  | 0.0070  | 0.2000  |
| table               | 0.2690  | 0.6795  | 0.2202  | 0.5449  |
| pot                 | 0.0400  | 0.3077  | 0.0000  | 0.0000  |
| faucet              | 0.0071  | 0.1389  | 0.0000  | 0.0000  |
| lamp                | 0.2122  | 0.6154  | 0.0563  | 0.3077  |
| vase                | 0.1075  | 0.4333  | 0.0066  | 0.2333  |
| stand               | 0.3534  | 0.8000  | 0.3108  | 0.6600  |
| frame               | 0.0003  | 0.1176  | 0.0001  | 0.0588  |
| box                 | 0.0733  | 0.5769  | 0.0356  | 0.3269  |
| counter             | 0.0264  | 0.5758  | 0.0003  | 0.0909  |
| shower              | 0.0536  | 0.1667  | 0.0000  | 0.0000  |
| blanket             | 0.1123  | 0.8182  | 0.0009  | 0.2727  |
| wardrobe            | 0.1358  | 0.4545  | 0.1155  | 0.2727  |
| curtain             | 0.2761  | 0.7396  | 0.0557  | 0.3333  |
| window frame        | 0.0248  | 0.4368  | 0.0029  | 0.1839  |
| pillow              | 0.2135  | 0.6625  | 0.0178  | 0.1609  |
| socket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| desk                | 0.2905  | 0.6842  | 0.1483  | 0.5789  |
| paper               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bench               | 0.1370  | 0.5000  | 0.0972  | 0.2500  |
| bottle              | 0.0537  | 0.3750  | 0.0023  | 0.1042  |
| basket              | 0.0840  | 0.3200  | 0.0016  | 0.1200  |
| bin                 | 0.2877  | 0.7561  | 0.0467  | 0.2927  |
| soap dish           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| chair               | 0.4000  | 0.7109  | 0.2662  | 0.5476  |
| tv                  | 0.4408  | 0.6744  | 0.1511  | 0.3721  |
| ottoman             | 0.1037  | 0.6667  | 0.0736  | 0.4444  |
| screen              | 0.0500  | 0.2500  | 0.0037  | 0.2500  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| couch               | 0.3122  | 0.8043  | 0.1891  | 0.6087  |
| plate               | 0.1248  | 0.3571  | 0.0003  | 0.0714  |
| footstool           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bed                 | 0.5579  | 0.8246  | 0.2131  | 0.5439  |
| bowl                | 0.0657  | 0.3636  | 0.0000  | 0.0000  |
| book                | 0.0748  | 0.3307  | 0.0059  | 0.0630  |
| cushion             | 0.0091  | 0.2941  | 0.0003  | 0.0588  |
| statue              | 0.0305  | 0.2941  | 0.0000  | 0.0000  |
| telephone           | 0.2609  | 0.5000  | 0.0000  | 0.0000  |
| food                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stool               | 0.0138  | 0.2857  | 0.0122  | 0.1905  |
| fireplace           | 0.2838  | 0.5652  | 0.0075  | 0.1739  |
| clock               | 0.0010  | 0.3333  | 0.0000  | 0.0000  |
| bag                 | 0.1092  | 0.4615  | 0.0000  | 0.0000  |
| teapot              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wine                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toy                 | 0.1139  | 0.3889  | 0.0079  | 0.0556  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| steps               | 0.0001  | 0.2000  | 0.0000  | 0.0000  |
| backpack            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stairs              | 0.0202  | 0.2830  | 0.0037  | 0.0566  |
| fruit               | 0.0495  | 0.2500  | 0.0417  | 0.1250  |
| carpet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillar              | 0.2581  | 0.7619  | 0.1116  | 0.5238  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| column              | 0.0048  | 0.3333  | 0.0002  | 0.1111  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mat                 | 0.0107  | 0.2083  | 0.0000  | 0.0000  |
| fan                 | 0.5685  | 0.7963  | 0.2835  | 0.5185  |
| rail                | 0.0144  | 0.3519  | 0.0004  | 0.0556  |
| doorway             | 0.0002  | 0.1111  | 0.0000  | 0.0000  |
| washing machine     | 0.4988  | 0.8750  | 0.3757  | 0.6250  |
| washbasin           | 0.0021  | 0.2857  | 0.0015  | 0.1429  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| heater              | 0.2500  | 1.0000  | 0.2500  | 1.0000  |
| pipe                | 0.0003  | 0.1000  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toilet paper        | 0.2030  | 0.4722  | 0.0142  | 0.0833  |
| windowsill          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket              | 0.0003  | 0.1000  | 0.0000  | 0.0000  |
| candle              | 0.0370  | 0.3333  | 0.0000  | 0.0000  |
| cup                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basin               | 0.0045  | 1.0000  | 0.0045  | 1.0000  |
| headboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shoe                | 0.0031  | 0.1200  | 0.0000  | 0.0000  |
| monitor             | 0.3333  | 0.5000  | 0.0000  | 0.0000  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dispenser           | 0.0333  | 0.5000  | 0.0000  | 0.0000  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchen island      | 0.2143  | 0.2857  | 0.2143  | 0.2857  |
| printer             | 0.4286  | 0.4286  | 0.0714  | 0.1429  |
| board               | 0.1677  | 0.3333  | 0.0000  | 0.0000  |
| jar                 | 0.0051  | 0.3333  | 0.0000  | 0.0000  |
| tissue box          | 0.0917  | 0.5000  | 0.0004  | 0.1250  |
| container           | 0.0405  | 0.2222  | 0.0044  | 0.0741  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| piano               | 0.0172  | 0.5000  | 0.0172  | 0.5000  |
| refrigerator        | 0.2807  | 0.8000  | 0.1135  | 0.3333  |
| stove               | 0.2434  | 0.6000  | 0.2138  | 0.4000  |
| computer            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.4440  | 0.7222  | 0.0234  | 0.2778  |
| radiator            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rack                | 0.0004  | 0.1818  | 0.0002  | 0.0909  |
| scale               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball                | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| laptop              | 0.7500  | 1.0000  | 0.0179  | 0.5000  |
| kettle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| keyboard            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radio               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| guitar              | 0.2821  | 0.5714  | 0.0026  | 0.1429  |
| pool table          | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| tissue              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0873  | 0.5000  | 0.0000  | 0.0000  |
| bar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.3939  | 0.6667  | 0.3939  | 0.6667  |
| machine             | 0.0714  | 0.5000  | 0.0714  | 0.5000  |
| dumbbell            | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| fire extinguisher   | 0.5000  | 1.0000  | 0.0000  | 0.0000  |
| ladder              | 0.1181  | 0.2222  | 0.1111  | 0.1111  |
| garage door         | 0.3333  | 0.3333  | 0.0327  | 0.3333  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| handle              | 0.0014  | 0.2500  | 0.0000  | 0.0000  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beam                | 0.0000  | 0.0476  | 0.0000  | 0.0000  |
| candlestick         | 0.1000  | 0.5000  | 0.0000  | 0.0000  |
| player              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| arch                | 0.0045  | 0.1333  | 0.0002  | 0.0667  |
| chandelier          | 0.3489  | 0.4737  | 0.0263  | 0.1579  |
| air conditioner     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| holder              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| microwave           | 0.1290  | 0.2500  | 0.0859  | 0.1667  |
| oven                | 0.2048  | 0.4000  | 0.0000  | 0.0000  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchenware         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pack                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| magazine            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| file                | 0.2500  | 0.2500  | 0.2500  | 0.2500  |
| bicycle             | 0.0417  | 1.0000  | 0.0081  | 1.0000  |
| partition           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| thermostat          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blackboard          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mouse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dispenser      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hanger              | 0.0006  | 0.1000  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water heater        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet               | 0.1000  | 0.5000  | 0.1000  | 0.5000  |
| range hood          | 0.0539  | 0.5000  | 0.0000  | 0.0000  |
| pan                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hamper              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drum                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cap                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jacket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| can                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coffee maker        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| matterport3d        | 0.1035  | 0.2939  | 0.0411  | 0.1397  |
| head                | 0.1553  | 0.4242  | 0.0610  | 0.1999  |
| common              | 0.0659  | 0.2253  | 0.0234  | 0.1016  |
| tail                | 0.0360  | 0.0651  | 0.0227  | 0.0484  |
| small               | 0.0222  | 0.1175  | 0.0007  | 0.0132  |
| medium              | 0.1048  | 0.3205  | 0.0428  | 0.1452  |
| large               | 0.0830  | 0.4361  | 0.0338  | 0.2115  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 03:26:49 INFO metrics.py:437 | number of arkit results: 0
Rank[0/8] 06/20/2025 03:26:49 INFO stats.py:394 | Epoch[99] completed. Training Speed: 2.97 samples/sec across all devices.	Epoch Time: 1051.79 sec.	Average Epoch Time: 1051.79 sec.	Average Step Time: 2.70 sec.	Estimated Remaining Time: 18:32:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:26:49 WARNING accelerator.py:3099 | Deleting 1 checkpoints to make room for new checkpoint.
Rank[0/8] 06/20/2025 03:26:50 INFO accelerator.py:3111 | Saving current state to /job_data/checkpoints/checkpoint_9
Rank[5/8] 06/20/2025 03:26:50 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 99 to /job_data/checkpoints/checkpoint_9
Rank[3/8] 06/20/2025 03:26:50 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 99 to /job_data/checkpoints/checkpoint_9
Rank[2/8] 06/20/2025 03:26:50 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 99 to /job_data/checkpoints/checkpoint_9
Rank[7/8] 06/20/2025 03:26:50 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 99 to /job_data/checkpoints/checkpoint_9
Rank[1/8] 06/20/2025 03:26:50 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 99 to /job_data/checkpoints/checkpoint_9
Rank[4/8] 06/20/2025 03:26:50 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 99 to /job_data/checkpoints/checkpoint_9
Rank[6/8] 06/20/2025 03:26:50 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 99 to /job_data/checkpoints/checkpoint_9
Rank[0/8] 06/20/2025 03:26:52 INFO checkpointing.py:106 | Model weights saved in /job_data/checkpoints/checkpoint_9/model.safetensors
Rank[0/8] 06/20/2025 03:26:55 INFO checkpointing.py:113 | Optimizer state saved in /job_data/checkpoints/checkpoint_9/optimizer.bin
Rank[0/8] 06/20/2025 03:26:55 INFO checkpointing.py:120 | Scheduler state saved in /job_data/checkpoints/checkpoint_9/scheduler.bin
Rank[0/8] 06/20/2025 03:26:55 INFO checkpointing.py:137 | Sampler state for dataloader 0 saved in /job_data/checkpoints/checkpoint_9/sampler.bin
Rank[0/8] 06/20/2025 03:26:55 INFO checkpointing.py:137 | Sampler state for dataloader 1 saved in /job_data/checkpoints/checkpoint_9/sampler_1.bin
Rank[0/8] 06/20/2025 03:26:55 INFO checkpointing.py:164 | Random states saved in /job_data/checkpoints/checkpoint_9/random_states_0.pkl
Rank[0/8] 06/20/2025 03:26:55 INFO checkpointing.py:300 | Saving the state of TrainerProgressState to /job_data/checkpoints/checkpoint_9/custom_checkpoint_0.pkl
Rank[0/8] 06/20/2025 03:26:55 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 99 to /job_data/checkpoints/checkpoint_9
Rank[0/8] 06/20/2025 03:27:25 INFO stats.py:314 | Epoch[100] Step[24] GlobalStep[39024] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 18:32:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:27:25 INFO loss_tracker.py:84 | Epoch[100/239] Step[24] GlobalStep[39024/NA]: det_loss_cls_0[0.5086] det_loss_box_0[0.5915] det_loss_cls_1[0.4936] det_loss_box_1[0.4594] det_loss_cls_2[0.4569] det_loss_box_2[0.4148] det_loss_cls_3[0.4266] det_loss_box_3[0.4047] det_loss_cls_4[0.3983] det_loss_box_4[0.3946] det_loss_cls_5[0.3831] det_loss_box_5[0.3979] det_loss_cls_dn_0[0.1016] det_loss_box_dn_0[0.2710] det_loss_cls_dn_1[0.0979] det_loss_box_dn_1[0.2252] det_loss_cls_dn_2[0.0966] det_loss_box_dn_2[0.2127] det_loss_cls_dn_3[0.0964] det_loss_box_dn_3[0.2093] det_loss_cls_dn_4[0.0952] det_loss_box_dn_4[0.2080] det_loss_cls_dn_5[0.0945] det_loss_box_dn_5[0.2069] loss_depth[0.0400] total_loss[7.2849]
Rank[0/8] 06/20/2025 03:27:51 INFO stats.py:314 | Epoch[100] Step[49] GlobalStep[39049] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 18:31:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:27:51 INFO loss_tracker.py:84 | Epoch[100/239] Step[49] GlobalStep[39049/NA]: det_loss_cls_0[0.5608] det_loss_box_0[0.7476] det_loss_cls_1[0.5376] det_loss_box_1[0.6009] det_loss_cls_2[0.5122] det_loss_box_2[0.5287] det_loss_cls_3[0.4860] det_loss_box_3[0.5086] det_loss_cls_4[0.4559] det_loss_box_4[0.4960] det_loss_cls_5[0.4352] det_loss_box_5[0.5014] det_loss_cls_dn_0[0.1093] det_loss_box_dn_0[0.3369] det_loss_cls_dn_1[0.1052] det_loss_box_dn_1[0.2939] det_loss_cls_dn_2[0.1045] det_loss_box_dn_2[0.2760] det_loss_cls_dn_3[0.1036] det_loss_box_dn_3[0.2723] det_loss_cls_dn_4[0.1033] det_loss_box_dn_4[0.2700] det_loss_cls_dn_5[0.1027] det_loss_box_dn_5[0.2687] loss_depth[0.0396] total_loss[8.7567]
Rank[0/8] 06/20/2025 03:28:16 INFO stats.py:314 | Epoch[100] Step[74] GlobalStep[39074] Training Speed: 7.89 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 18:30:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:28:16 INFO loss_tracker.py:84 | Epoch[100/239] Step[74] GlobalStep[39074/NA]: det_loss_cls_0[0.4177] det_loss_box_0[0.5787] det_loss_cls_1[0.4177] det_loss_box_1[0.4824] det_loss_cls_2[0.3916] det_loss_box_2[0.4448] det_loss_cls_3[0.3717] det_loss_box_3[0.4222] det_loss_cls_4[0.3484] det_loss_box_4[0.4214] det_loss_cls_5[0.3389] det_loss_box_5[0.4182] det_loss_cls_dn_0[0.0916] det_loss_box_dn_0[0.4075] det_loss_cls_dn_1[0.0881] det_loss_box_dn_1[0.3281] det_loss_cls_dn_2[0.0875] det_loss_box_dn_2[0.3053] det_loss_cls_dn_3[0.0863] det_loss_box_dn_3[0.3020] det_loss_cls_dn_4[0.0862] det_loss_box_dn_4[0.2981] det_loss_cls_dn_5[0.0858] det_loss_box_dn_5[0.2940] loss_depth[0.0392] total_loss[7.5535]
Rank[0/8] 06/20/2025 03:28:43 INFO stats.py:314 | Epoch[100] Step[99] GlobalStep[39099] Training Speed: 8.07 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 18:30:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:28:43 INFO loss_tracker.py:84 | Epoch[100/239] Step[99] GlobalStep[39099/NA]: det_loss_cls_0[0.4433] det_loss_box_0[0.5773] det_loss_cls_1[0.4287] det_loss_box_1[0.4518] det_loss_cls_2[0.3963] det_loss_box_2[0.4042] det_loss_cls_3[0.3635] det_loss_box_3[0.3922] det_loss_cls_4[0.3396] det_loss_box_4[0.3802] det_loss_cls_5[0.3245] det_loss_box_5[0.3819] det_loss_cls_dn_0[0.0874] det_loss_box_dn_0[0.2635] det_loss_cls_dn_1[0.0835] det_loss_box_dn_1[0.2185] det_loss_cls_dn_2[0.0825] det_loss_box_dn_2[0.2024] det_loss_cls_dn_3[0.0812] det_loss_box_dn_3[0.1992] det_loss_cls_dn_4[0.0801] det_loss_box_dn_4[0.1977] det_loss_cls_dn_5[0.0791] det_loss_box_dn_5[0.1967] loss_depth[0.0402] total_loss[6.6955]
Rank[0/8] 06/20/2025 03:29:08 INFO stats.py:314 | Epoch[100] Step[124] GlobalStep[39124] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 18:29:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:29:08 INFO loss_tracker.py:84 | Epoch[100/239] Step[124] GlobalStep[39124/NA]: det_loss_cls_0[0.5238] det_loss_box_0[0.6952] det_loss_cls_1[0.5022] det_loss_box_1[0.5615] det_loss_cls_2[0.4767] det_loss_box_2[0.5065] det_loss_cls_3[0.4529] det_loss_box_3[0.4935] det_loss_cls_4[0.4322] det_loss_box_4[0.4781] det_loss_cls_5[0.4202] det_loss_box_5[0.4772] det_loss_cls_dn_0[0.1117] det_loss_box_dn_0[0.3679] det_loss_cls_dn_1[0.1089] det_loss_box_dn_1[0.3159] det_loss_cls_dn_2[0.1091] det_loss_box_dn_2[0.2934] det_loss_cls_dn_3[0.1081] det_loss_box_dn_3[0.2888] det_loss_cls_dn_4[0.1078] det_loss_box_dn_4[0.2875] det_loss_cls_dn_5[0.1067] det_loss_box_dn_5[0.2870] loss_depth[0.0393] total_loss[8.5520]
Rank[0/8] 06/20/2025 03:29:34 INFO stats.py:314 | Epoch[100] Step[149] GlobalStep[39149] Training Speed: 6.25 samples/sec across all devices.	Average Step Time: 1.28 sec.	Estimated Remaining Time: 18:28:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:29:34 INFO loss_tracker.py:84 | Epoch[100/239] Step[149] GlobalStep[39149/NA]: det_loss_cls_0[0.4957] det_loss_box_0[0.6504] det_loss_cls_1[0.4723] det_loss_box_1[0.5145] det_loss_cls_2[0.4352] det_loss_box_2[0.4610] det_loss_cls_3[0.4062] det_loss_box_3[0.4465] det_loss_cls_4[0.3737] det_loss_box_4[0.4431] det_loss_cls_5[0.3583] det_loss_box_5[0.4429] det_loss_cls_dn_0[0.0874] det_loss_box_dn_0[0.2904] det_loss_cls_dn_1[0.0834] det_loss_box_dn_1[0.2458] det_loss_cls_dn_2[0.0824] det_loss_box_dn_2[0.2273] det_loss_cls_dn_3[0.0815] det_loss_box_dn_3[0.2244] det_loss_cls_dn_4[0.0807] det_loss_box_dn_4[0.2237] det_loss_cls_dn_5[0.0791] det_loss_box_dn_5[0.2226] loss_depth[0.0411] total_loss[7.4693]
Rank[0/8] 06/20/2025 03:29:59 INFO stats.py:314 | Epoch[100] Step[174] GlobalStep[39174] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 18:28:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:29:59 INFO loss_tracker.py:84 | Epoch[100/239] Step[174] GlobalStep[39174/NA]: det_loss_cls_0[0.6029] det_loss_box_0[0.8587] det_loss_cls_1[0.5902] det_loss_box_1[0.6837] det_loss_cls_2[0.5683] det_loss_box_2[0.6149] det_loss_cls_3[0.5511] det_loss_box_3[0.5967] det_loss_cls_4[0.5167] det_loss_box_4[0.5893] det_loss_cls_5[0.5045] det_loss_box_5[0.5910] det_loss_cls_dn_0[0.1083] det_loss_box_dn_0[0.2982] det_loss_cls_dn_1[0.1048] det_loss_box_dn_1[0.2544] det_loss_cls_dn_2[0.1038] det_loss_box_dn_2[0.2388] det_loss_cls_dn_3[0.1032] det_loss_box_dn_3[0.2351] det_loss_cls_dn_4[0.1026] det_loss_box_dn_4[0.2336] det_loss_cls_dn_5[0.1017] det_loss_box_dn_5[0.2326] loss_depth[0.0374] total_loss[9.4224]
Rank[0/8] 06/20/2025 03:30:26 INFO stats.py:314 | Epoch[100] Step[199] GlobalStep[39199] Training Speed: 8.02 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 18:27:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:30:26 INFO loss_tracker.py:84 | Epoch[100/239] Step[199] GlobalStep[39199/NA]: det_loss_cls_0[0.5638] det_loss_box_0[0.7946] det_loss_cls_1[0.5382] det_loss_box_1[0.6177] det_loss_cls_2[0.5012] det_loss_box_2[0.5733] det_loss_cls_3[0.4764] det_loss_box_3[0.5563] det_loss_cls_4[0.4607] det_loss_box_4[0.5409] det_loss_cls_5[0.4477] det_loss_box_5[0.5374] det_loss_cls_dn_0[0.1023] det_loss_box_dn_0[0.2962] det_loss_cls_dn_1[0.0982] det_loss_box_dn_1[0.2456] det_loss_cls_dn_2[0.0975] det_loss_box_dn_2[0.2264] det_loss_cls_dn_3[0.0964] det_loss_box_dn_3[0.2222] det_loss_cls_dn_4[0.0957] det_loss_box_dn_4[0.2206] det_loss_cls_dn_5[0.0953] det_loss_box_dn_5[0.2206] loss_depth[0.0390] total_loss[8.6642]
Rank[0/8] 06/20/2025 03:30:51 INFO stats.py:314 | Epoch[100] Step[224] GlobalStep[39224] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 18:27:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:30:51 INFO loss_tracker.py:84 | Epoch[100/239] Step[224] GlobalStep[39224/NA]: det_loss_cls_0[0.5520] det_loss_box_0[0.7894] det_loss_cls_1[0.5374] det_loss_box_1[0.6453] det_loss_cls_2[0.4997] det_loss_box_2[0.5887] det_loss_cls_3[0.4696] det_loss_box_3[0.5629] det_loss_cls_4[0.4476] det_loss_box_4[0.5570] det_loss_cls_5[0.4331] det_loss_box_5[0.5601] det_loss_cls_dn_0[0.1021] det_loss_box_dn_0[0.3272] det_loss_cls_dn_1[0.0975] det_loss_box_dn_1[0.2868] det_loss_cls_dn_2[0.0970] det_loss_box_dn_2[0.2724] det_loss_cls_dn_3[0.0962] det_loss_box_dn_3[0.2698] det_loss_cls_dn_4[0.0950] det_loss_box_dn_4[0.2696] det_loss_cls_dn_5[0.0954] det_loss_box_dn_5[0.2681] loss_depth[0.0416] total_loss[8.9616]
Rank[0/8] 06/20/2025 03:31:16 INFO stats.py:314 | Epoch[100] Step[249] GlobalStep[39249] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 18:26:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:31:16 INFO loss_tracker.py:84 | Epoch[100/239] Step[249] GlobalStep[39249/NA]: det_loss_cls_0[0.4745] det_loss_box_0[0.6630] det_loss_cls_1[0.4652] det_loss_box_1[0.4900] det_loss_cls_2[0.4314] det_loss_box_2[0.4451] det_loss_cls_3[0.3995] det_loss_box_3[0.4275] det_loss_cls_4[0.3747] det_loss_box_4[0.4219] det_loss_cls_5[0.3552] det_loss_box_5[0.4228] det_loss_cls_dn_0[0.0917] det_loss_box_dn_0[0.2720] det_loss_cls_dn_1[0.0868] det_loss_box_dn_1[0.2274] det_loss_cls_dn_2[0.0866] det_loss_box_dn_2[0.2144] det_loss_cls_dn_3[0.0853] det_loss_box_dn_3[0.2095] det_loss_cls_dn_4[0.0845] det_loss_box_dn_4[0.2078] det_loss_cls_dn_5[0.0838] det_loss_box_dn_5[0.2067] loss_depth[0.0405] total_loss[7.2678]
Rank[0/8] 06/20/2025 03:31:43 INFO stats.py:314 | Epoch[100] Step[274] GlobalStep[39274] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 18:25:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:31:43 INFO loss_tracker.py:84 | Epoch[100/239] Step[274] GlobalStep[39274/NA]: det_loss_cls_0[0.5207] det_loss_box_0[0.6402] det_loss_cls_1[0.5092] det_loss_box_1[0.4849] det_loss_cls_2[0.4786] det_loss_box_2[0.4375] det_loss_cls_3[0.4384] det_loss_box_3[0.4246] det_loss_cls_4[0.4102] det_loss_box_4[0.4218] det_loss_cls_5[0.3924] det_loss_box_5[0.4234] det_loss_cls_dn_0[0.0919] det_loss_box_dn_0[0.2334] det_loss_cls_dn_1[0.0891] det_loss_box_dn_1[0.1934] det_loss_cls_dn_2[0.0879] det_loss_box_dn_2[0.1783] det_loss_cls_dn_3[0.0873] det_loss_box_dn_3[0.1755] det_loss_cls_dn_4[0.0869] det_loss_box_dn_4[0.1740] det_loss_cls_dn_5[0.0861] det_loss_box_dn_5[0.1733] loss_depth[0.0366] total_loss[7.2758]
Rank[0/8] 06/20/2025 03:32:08 INFO stats.py:314 | Epoch[100] Step[299] GlobalStep[39299] Training Speed: 8.58 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 18:25:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:32:08 INFO loss_tracker.py:84 | Epoch[100/239] Step[299] GlobalStep[39299/NA]: det_loss_cls_0[0.4295] det_loss_box_0[0.5703] det_loss_cls_1[0.4228] det_loss_box_1[0.4508] det_loss_cls_2[0.3942] det_loss_box_2[0.4088] det_loss_cls_3[0.3684] det_loss_box_3[0.3998] det_loss_cls_4[0.3497] det_loss_box_4[0.3916] det_loss_cls_5[0.3413] det_loss_box_5[0.3892] det_loss_cls_dn_0[0.0887] det_loss_box_dn_0[0.3001] det_loss_cls_dn_1[0.0852] det_loss_box_dn_1[0.2532] det_loss_cls_dn_2[0.0844] det_loss_box_dn_2[0.2375] det_loss_cls_dn_3[0.0831] det_loss_box_dn_3[0.2332] det_loss_cls_dn_4[0.0825] det_loss_box_dn_4[0.2313] det_loss_cls_dn_5[0.0816] det_loss_box_dn_5[0.2309] loss_depth[0.0368] total_loss[6.9449]
Rank[0/8] 06/20/2025 03:32:34 INFO stats.py:314 | Epoch[100] Step[324] GlobalStep[39324] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 18:24:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:32:35 INFO loss_tracker.py:84 | Epoch[100/239] Step[324] GlobalStep[39324/NA]: det_loss_cls_0[0.5646] det_loss_box_0[0.8187] det_loss_cls_1[0.5454] det_loss_box_1[0.6456] det_loss_cls_2[0.5062] det_loss_box_2[0.5819] det_loss_cls_3[0.4739] det_loss_box_3[0.5635] det_loss_cls_4[0.4496] det_loss_box_4[0.5348] det_loss_cls_5[0.4339] det_loss_box_5[0.5376] det_loss_cls_dn_0[0.0983] det_loss_box_dn_0[0.3449] det_loss_cls_dn_1[0.0937] det_loss_box_dn_1[0.3010] det_loss_cls_dn_2[0.0926] det_loss_box_dn_2[0.2854] det_loss_cls_dn_3[0.0912] det_loss_box_dn_3[0.2833] det_loss_cls_dn_4[0.0910] det_loss_box_dn_4[0.2820] det_loss_cls_dn_5[0.0909] det_loss_box_dn_5[0.2780] loss_depth[0.0414] total_loss[9.0293]
Rank[0/8] 06/20/2025 03:33:01 INFO stats.py:314 | Epoch[100] Step[349] GlobalStep[39349] Training Speed: 7.84 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 18:23:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:33:01 INFO loss_tracker.py:84 | Epoch[100/239] Step[349] GlobalStep[39349/NA]: det_loss_cls_0[0.5467] det_loss_box_0[0.7108] det_loss_cls_1[0.5347] det_loss_box_1[0.5526] det_loss_cls_2[0.4999] det_loss_box_2[0.5187] det_loss_cls_3[0.4772] det_loss_box_3[0.5047] det_loss_cls_4[0.4489] det_loss_box_4[0.4966] det_loss_cls_5[0.4334] det_loss_box_5[0.4967] det_loss_cls_dn_0[0.1058] det_loss_box_dn_0[0.2977] det_loss_cls_dn_1[0.1022] det_loss_box_dn_1[0.2581] det_loss_cls_dn_2[0.1018] det_loss_box_dn_2[0.2428] det_loss_cls_dn_3[0.1003] det_loss_box_dn_3[0.2389] det_loss_cls_dn_4[0.0990] det_loss_box_dn_4[0.2380] det_loss_cls_dn_5[0.0983] det_loss_box_dn_5[0.2368] loss_depth[0.0411] total_loss[8.3817]
Rank[0/8] 06/20/2025 03:33:27 INFO stats.py:314 | Epoch[100] Step[374] GlobalStep[39374] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 18:23:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:33:27 INFO loss_tracker.py:84 | Epoch[100/239] Step[374] GlobalStep[39374/NA]: det_loss_cls_0[0.5083] det_loss_box_0[0.6280] det_loss_cls_1[0.4973] det_loss_box_1[0.4837] det_loss_cls_2[0.4530] det_loss_box_2[0.4432] det_loss_cls_3[0.4124] det_loss_box_3[0.4408] det_loss_cls_4[0.3874] det_loss_box_4[0.4321] det_loss_cls_5[0.3667] det_loss_box_5[0.4361] det_loss_cls_dn_0[0.0897] det_loss_box_dn_0[0.2529] det_loss_cls_dn_1[0.0869] det_loss_box_dn_1[0.2101] det_loss_cls_dn_2[0.0861] det_loss_box_dn_2[0.1940] det_loss_cls_dn_3[0.0846] det_loss_box_dn_3[0.1905] det_loss_cls_dn_4[0.0836] det_loss_box_dn_4[0.1890] det_loss_cls_dn_5[0.0827] det_loss_box_dn_5[0.1879] loss_depth[0.0385] total_loss[7.2654]
Rank[0/8] 06/20/2025 03:33:42 INFO stats.py:394 | Epoch[100] completed. Training Speed: 7.67 samples/sec across all devices.	Epoch Time: 407.00 sec.	Average Epoch Time: 407.00 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 18:22:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:33:55 INFO stats.py:314 | Epoch[101] Step[9] GlobalStep[39399] Training Speed: 8.15 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 18:22:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:34:11 INFO loss_tracker.py:84 | Epoch[101/239] Step[24] GlobalStep[39414/NA]: det_loss_cls_0[0.4458] det_loss_box_0[0.5605] det_loss_cls_1[0.4409] det_loss_box_1[0.4424] det_loss_cls_2[0.4097] det_loss_box_2[0.4023] det_loss_cls_3[0.3776] det_loss_box_3[0.3980] det_loss_cls_4[0.3514] det_loss_box_4[0.3917] det_loss_cls_5[0.3406] det_loss_box_5[0.3943] det_loss_cls_dn_0[0.1013] det_loss_box_dn_0[0.3251] det_loss_cls_dn_1[0.0967] det_loss_box_dn_1[0.2774] det_loss_cls_dn_2[0.0966] det_loss_box_dn_2[0.2599] det_loss_cls_dn_3[0.0948] det_loss_box_dn_3[0.2574] det_loss_cls_dn_4[0.0938] det_loss_box_dn_4[0.2535] det_loss_cls_dn_5[0.0931] det_loss_box_dn_5[0.2511] loss_depth[0.0370] total_loss[7.1930]
Rank[0/8] 06/20/2025 03:34:21 INFO stats.py:314 | Epoch[101] Step[34] GlobalStep[39424] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 18:22:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:34:36 INFO loss_tracker.py:84 | Epoch[101/239] Step[49] GlobalStep[39439/NA]: det_loss_cls_0[0.5769] det_loss_box_0[0.7082] det_loss_cls_1[0.5658] det_loss_box_1[0.5480] det_loss_cls_2[0.5353] det_loss_box_2[0.5185] det_loss_cls_3[0.5100] det_loss_box_3[0.5074] det_loss_cls_4[0.4760] det_loss_box_4[0.5046] det_loss_cls_5[0.4562] det_loss_box_5[0.5102] det_loss_cls_dn_0[0.1027] det_loss_box_dn_0[0.2857] det_loss_cls_dn_1[0.0991] det_loss_box_dn_1[0.2507] det_loss_cls_dn_2[0.0982] det_loss_box_dn_2[0.2384] det_loss_cls_dn_3[0.0966] det_loss_box_dn_3[0.2363] det_loss_cls_dn_4[0.0963] det_loss_box_dn_4[0.2355] det_loss_cls_dn_5[0.0957] det_loss_box_dn_5[0.2334] loss_depth[0.0400] total_loss[8.5257]
Rank[0/8] 06/20/2025 03:34:47 INFO stats.py:314 | Epoch[101] Step[59] GlobalStep[39449] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 18:21:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:35:02 INFO loss_tracker.py:84 | Epoch[101/239] Step[74] GlobalStep[39464/NA]: det_loss_cls_0[0.5650] det_loss_box_0[0.7688] det_loss_cls_1[0.5550] det_loss_box_1[0.5879] det_loss_cls_2[0.5063] det_loss_box_2[0.5369] det_loss_cls_3[0.4859] det_loss_box_3[0.5210] det_loss_cls_4[0.4496] det_loss_box_4[0.4981] det_loss_cls_5[0.4300] det_loss_box_5[0.4993] det_loss_cls_dn_0[0.0995] det_loss_box_dn_0[0.2813] det_loss_cls_dn_1[0.0958] det_loss_box_dn_1[0.2369] det_loss_cls_dn_2[0.0949] det_loss_box_dn_2[0.2204] det_loss_cls_dn_3[0.0944] det_loss_box_dn_3[0.2168] det_loss_cls_dn_4[0.0936] det_loss_box_dn_4[0.2149] det_loss_cls_dn_5[0.0931] det_loss_box_dn_5[0.2128] loss_depth[0.0393] total_loss[8.3972]
Rank[0/8] 06/20/2025 03:35:12 INFO stats.py:314 | Epoch[101] Step[84] GlobalStep[39474] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 18:20:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:35:28 INFO loss_tracker.py:84 | Epoch[101/239] Step[99] GlobalStep[39489/NA]: det_loss_cls_0[0.4506] det_loss_box_0[0.5834] det_loss_cls_1[0.4413] det_loss_box_1[0.4438] det_loss_cls_2[0.4098] det_loss_box_2[0.4085] det_loss_cls_3[0.3763] det_loss_box_3[0.3955] det_loss_cls_4[0.3488] det_loss_box_4[0.3946] det_loss_cls_5[0.3282] det_loss_box_5[0.3985] det_loss_cls_dn_0[0.0949] det_loss_box_dn_0[0.3331] det_loss_cls_dn_1[0.0916] det_loss_box_dn_1[0.2894] det_loss_cls_dn_2[0.0908] det_loss_box_dn_2[0.2722] det_loss_cls_dn_3[0.0906] det_loss_box_dn_3[0.2679] det_loss_cls_dn_4[0.0902] det_loss_box_dn_4[0.2667] det_loss_cls_dn_5[0.0883] det_loss_box_dn_5[0.2642] loss_depth[0.0376] total_loss[7.2569]
Rank[0/8] 06/20/2025 03:35:38 INFO stats.py:314 | Epoch[101] Step[109] GlobalStep[39499] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 18:20:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:35:54 INFO loss_tracker.py:84 | Epoch[101/239] Step[124] GlobalStep[39514/NA]: det_loss_cls_0[0.5968] det_loss_box_0[0.8461] det_loss_cls_1[0.6042] det_loss_box_1[0.7066] det_loss_cls_2[0.5704] det_loss_box_2[0.6470] det_loss_cls_3[0.5468] det_loss_box_3[0.6313] det_loss_cls_4[0.5179] det_loss_box_4[0.6258] det_loss_cls_5[0.5032] det_loss_box_5[0.6257] det_loss_cls_dn_0[0.1002] det_loss_box_dn_0[0.3089] det_loss_cls_dn_1[0.0967] det_loss_box_dn_1[0.2612] det_loss_cls_dn_2[0.0956] det_loss_box_dn_2[0.2419] det_loss_cls_dn_3[0.0950] det_loss_box_dn_3[0.2378] det_loss_cls_dn_4[0.0935] det_loss_box_dn_4[0.2344] det_loss_cls_dn_5[0.0929] det_loss_box_dn_5[0.2317] loss_depth[0.0385] total_loss[9.5501]
Rank[0/8] 06/20/2025 03:36:05 INFO stats.py:314 | Epoch[101] Step[134] GlobalStep[39524] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 18:19:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:36:20 INFO loss_tracker.py:84 | Epoch[101/239] Step[149] GlobalStep[39539/NA]: det_loss_cls_0[0.5933] det_loss_box_0[0.7235] det_loss_cls_1[0.5812] det_loss_box_1[0.5737] det_loss_cls_2[0.5421] det_loss_box_2[0.5341] det_loss_cls_3[0.5231] det_loss_box_3[0.5083] det_loss_cls_4[0.4904] det_loss_box_4[0.5096] det_loss_cls_5[0.4833] det_loss_box_5[0.5064] det_loss_cls_dn_0[0.1045] det_loss_box_dn_0[0.2699] det_loss_cls_dn_1[0.1011] det_loss_box_dn_1[0.2311] det_loss_cls_dn_2[0.1017] det_loss_box_dn_2[0.2177] det_loss_cls_dn_3[0.1004] det_loss_box_dn_3[0.2149] det_loss_cls_dn_4[0.1001] det_loss_box_dn_4[0.2128] det_loss_cls_dn_5[0.0991] det_loss_box_dn_5[0.2118] loss_depth[0.0360] total_loss[8.5701]
Rank[0/8] 06/20/2025 03:36:30 INFO stats.py:314 | Epoch[101] Step[159] GlobalStep[39549] Training Speed: 8.63 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 18:19:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:36:46 INFO loss_tracker.py:84 | Epoch[101/239] Step[174] GlobalStep[39564/NA]: det_loss_cls_0[0.5702] det_loss_box_0[0.6980] det_loss_cls_1[0.5603] det_loss_box_1[0.5192] det_loss_cls_2[0.5102] det_loss_box_2[0.4795] det_loss_cls_3[0.4839] det_loss_box_3[0.4560] det_loss_cls_4[0.4559] det_loss_box_4[0.4391] det_loss_cls_5[0.4433] det_loss_box_5[0.4396] det_loss_cls_dn_0[0.1072] det_loss_box_dn_0[0.2692] det_loss_cls_dn_1[0.1027] det_loss_box_dn_1[0.2246] det_loss_cls_dn_2[0.1010] det_loss_box_dn_2[0.2071] det_loss_cls_dn_3[0.0998] det_loss_box_dn_3[0.2045] det_loss_cls_dn_4[0.0992] det_loss_box_dn_4[0.2033] det_loss_cls_dn_5[0.0985] det_loss_box_dn_5[0.2020] loss_depth[0.0402] total_loss[8.0146]
Rank[0/8] 06/20/2025 03:36:57 INFO stats.py:314 | Epoch[101] Step[184] GlobalStep[39574] Training Speed: 6.55 samples/sec across all devices.	Average Step Time: 1.22 sec.	Estimated Remaining Time: 18:18:30.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:37:12 INFO loss_tracker.py:84 | Epoch[101/239] Step[199] GlobalStep[39589/NA]: det_loss_cls_0[0.5331] det_loss_box_0[0.7358] det_loss_cls_1[0.5078] det_loss_box_1[0.5773] det_loss_cls_2[0.4841] det_loss_box_2[0.5276] det_loss_cls_3[0.4432] det_loss_box_3[0.5151] det_loss_cls_4[0.4165] det_loss_box_4[0.4958] det_loss_cls_5[0.4025] det_loss_box_5[0.4968] det_loss_cls_dn_0[0.1010] det_loss_box_dn_0[0.3237] det_loss_cls_dn_1[0.0960] det_loss_box_dn_1[0.2902] det_loss_cls_dn_2[0.0952] det_loss_box_dn_2[0.2771] det_loss_cls_dn_3[0.0942] det_loss_box_dn_3[0.2730] det_loss_cls_dn_4[0.0930] det_loss_box_dn_4[0.2711] det_loss_cls_dn_5[0.0925] det_loss_box_dn_5[0.2709] loss_depth[0.0378] total_loss[8.4509]
Rank[0/8] 06/20/2025 03:37:22 INFO stats.py:314 | Epoch[101] Step[209] GlobalStep[39599] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 18:17:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:37:37 INFO loss_tracker.py:84 | Epoch[101/239] Step[224] GlobalStep[39614/NA]: det_loss_cls_0[0.5775] det_loss_box_0[0.7922] det_loss_cls_1[0.5853] det_loss_box_1[0.5908] det_loss_cls_2[0.5285] det_loss_box_2[0.5430] det_loss_cls_3[0.5000] det_loss_box_3[0.5215] det_loss_cls_4[0.4627] det_loss_box_4[0.5191] det_loss_cls_5[0.4507] det_loss_box_5[0.5175] det_loss_cls_dn_0[0.0941] det_loss_box_dn_0[0.2964] det_loss_cls_dn_1[0.0915] det_loss_box_dn_1[0.2597] det_loss_cls_dn_2[0.0905] det_loss_box_dn_2[0.2450] det_loss_cls_dn_3[0.0894] det_loss_box_dn_3[0.2429] det_loss_cls_dn_4[0.0886] det_loss_box_dn_4[0.2412] det_loss_cls_dn_5[0.0874] det_loss_box_dn_5[0.2404] loss_depth[0.0356] total_loss[8.6917]
Rank[0/8] 06/20/2025 03:37:48 INFO stats.py:314 | Epoch[101] Step[234] GlobalStep[39624] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 18:17:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:38:03 INFO loss_tracker.py:84 | Epoch[101/239] Step[249] GlobalStep[39639/NA]: det_loss_cls_0[0.6564] det_loss_box_0[0.9071] det_loss_cls_1[0.6207] det_loss_box_1[0.7323] det_loss_cls_2[0.5903] det_loss_box_2[0.6659] det_loss_cls_3[0.5546] det_loss_box_3[0.6465] det_loss_cls_4[0.5233] det_loss_box_4[0.6191] det_loss_cls_5[0.5032] det_loss_box_5[0.6183] det_loss_cls_dn_0[0.0950] det_loss_box_dn_0[0.2963] det_loss_cls_dn_1[0.0916] det_loss_box_dn_1[0.2534] det_loss_cls_dn_2[0.0910] det_loss_box_dn_2[0.2394] det_loss_cls_dn_3[0.0897] det_loss_box_dn_3[0.2358] det_loss_cls_dn_4[0.0882] det_loss_box_dn_4[0.2333] det_loss_cls_dn_5[0.0873] det_loss_box_dn_5[0.2306] loss_depth[0.0377] total_loss[9.7071]
Rank[0/8] 06/20/2025 03:38:14 INFO stats.py:314 | Epoch[101] Step[259] GlobalStep[39649] Training Speed: 6.70 samples/sec across all devices.	Average Step Time: 1.19 sec.	Estimated Remaining Time: 18:16:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:38:29 INFO loss_tracker.py:84 | Epoch[101/239] Step[274] GlobalStep[39664/NA]: det_loss_cls_0[0.5547] det_loss_box_0[0.7448] det_loss_cls_1[0.5547] det_loss_box_1[0.5783] det_loss_cls_2[0.4998] det_loss_box_2[0.5328] det_loss_cls_3[0.4840] det_loss_box_3[0.4992] det_loss_cls_4[0.4466] det_loss_box_4[0.5004] det_loss_cls_5[0.4321] det_loss_box_5[0.5025] det_loss_cls_dn_0[0.1012] det_loss_box_dn_0[0.2849] det_loss_cls_dn_1[0.0970] det_loss_box_dn_1[0.2434] det_loss_cls_dn_2[0.0965] det_loss_box_dn_2[0.2292] det_loss_cls_dn_3[0.0954] det_loss_box_dn_3[0.2249] det_loss_cls_dn_4[0.0942] det_loss_box_dn_4[0.2235] det_loss_cls_dn_5[0.0938] det_loss_box_dn_5[0.2206] loss_depth[0.0377] total_loss[8.3722]
Rank[0/8] 06/20/2025 03:38:40 INFO stats.py:314 | Epoch[101] Step[284] GlobalStep[39674] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 18:16:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:38:55 INFO loss_tracker.py:84 | Epoch[101/239] Step[299] GlobalStep[39689/NA]: det_loss_cls_0[0.3967] det_loss_box_0[0.5168] det_loss_cls_1[0.3977] det_loss_box_1[0.4208] det_loss_cls_2[0.3611] det_loss_box_2[0.3941] det_loss_cls_3[0.3460] det_loss_box_3[0.3710] det_loss_cls_4[0.3265] det_loss_box_4[0.3616] det_loss_cls_5[0.3153] det_loss_box_5[0.3560] det_loss_cls_dn_0[0.0933] det_loss_box_dn_0[0.3712] det_loss_cls_dn_1[0.0907] det_loss_box_dn_1[0.3216] det_loss_cls_dn_2[0.0896] det_loss_box_dn_2[0.3056] det_loss_cls_dn_3[0.0890] det_loss_box_dn_3[0.2999] det_loss_cls_dn_4[0.0884] det_loss_box_dn_4[0.2961] det_loss_cls_dn_5[0.0875] det_loss_box_dn_5[0.2943] loss_depth[0.0394] total_loss[7.0300]
Rank[0/8] 06/20/2025 03:39:06 INFO stats.py:314 | Epoch[101] Step[309] GlobalStep[39699] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 18:15:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:39:21 INFO loss_tracker.py:84 | Epoch[101/239] Step[324] GlobalStep[39714/NA]: det_loss_cls_0[0.5826] det_loss_box_0[0.8241] det_loss_cls_1[0.5688] det_loss_box_1[0.6333] det_loss_cls_2[0.5234] det_loss_box_2[0.5870] det_loss_cls_3[0.5060] det_loss_box_3[0.5604] det_loss_cls_4[0.4766] det_loss_box_4[0.5503] det_loss_cls_5[0.4604] det_loss_box_5[0.5485] det_loss_cls_dn_0[0.0940] det_loss_box_dn_0[0.2955] det_loss_cls_dn_1[0.0910] det_loss_box_dn_1[0.2526] det_loss_cls_dn_2[0.0905] det_loss_box_dn_2[0.2379] det_loss_cls_dn_3[0.0891] det_loss_box_dn_3[0.2339] det_loss_cls_dn_4[0.0884] det_loss_box_dn_4[0.2324] det_loss_cls_dn_5[0.0873] det_loss_box_dn_5[0.2314] loss_depth[0.0401] total_loss[8.8853]
Rank[0/8] 06/20/2025 03:39:32 INFO stats.py:314 | Epoch[101] Step[334] GlobalStep[39724] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 18:14:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:39:47 INFO loss_tracker.py:84 | Epoch[101/239] Step[349] GlobalStep[39739/NA]: det_loss_cls_0[0.5683] det_loss_box_0[0.7018] det_loss_cls_1[0.5369] det_loss_box_1[0.5259] det_loss_cls_2[0.4876] det_loss_box_2[0.4689] det_loss_cls_3[0.4550] det_loss_box_3[0.4495] det_loss_cls_4[0.4255] det_loss_box_4[0.4419] det_loss_cls_5[0.4023] det_loss_box_5[0.4434] det_loss_cls_dn_0[0.1017] det_loss_box_dn_0[0.2719] det_loss_cls_dn_1[0.0988] det_loss_box_dn_1[0.2282] det_loss_cls_dn_2[0.0989] det_loss_box_dn_2[0.2122] det_loss_cls_dn_3[0.0980] det_loss_box_dn_3[0.2088] det_loss_cls_dn_4[0.0971] det_loss_box_dn_4[0.2079] det_loss_cls_dn_5[0.0964] det_loss_box_dn_5[0.2068] loss_depth[0.0391] total_loss[7.8730]
Rank[0/8] 06/20/2025 03:39:58 INFO stats.py:314 | Epoch[101] Step[359] GlobalStep[39749] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 18:14:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:40:13 INFO loss_tracker.py:84 | Epoch[101/239] Step[374] GlobalStep[39764/NA]: det_loss_cls_0[0.4885] det_loss_box_0[0.6275] det_loss_cls_1[0.4875] det_loss_box_1[0.4964] det_loss_cls_2[0.4528] det_loss_box_2[0.4633] det_loss_cls_3[0.4303] det_loss_box_3[0.4589] det_loss_cls_4[0.4106] det_loss_box_4[0.4501] det_loss_cls_5[0.3932] det_loss_box_5[0.4531] det_loss_cls_dn_0[0.1058] det_loss_box_dn_0[0.2811] det_loss_cls_dn_1[0.1012] det_loss_box_dn_1[0.2441] det_loss_cls_dn_2[0.0999] det_loss_box_dn_2[0.2288] det_loss_cls_dn_3[0.0995] det_loss_box_dn_3[0.2262] det_loss_cls_dn_4[0.0995] det_loss_box_dn_4[0.2251] det_loss_cls_dn_5[0.0985] det_loss_box_dn_5[0.2244] loss_depth[0.0399] total_loss[7.6861]
Rank[0/8] 06/20/2025 03:40:23 INFO stats.py:314 | Epoch[101] Step[384] GlobalStep[39774] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 18:13:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:40:29 INFO stats.py:394 | Epoch[101] completed. Training Speed: 7.66 samples/sec across all devices.	Epoch Time: 407.47 sec.	Average Epoch Time: 407.47 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 18:13:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:40:51 INFO stats.py:314 | Epoch[102] Step[19] GlobalStep[39799] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 18:13:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:40:56 INFO loss_tracker.py:84 | Epoch[102/239] Step[24] GlobalStep[39804/NA]: det_loss_cls_0[0.4320] det_loss_box_0[0.5818] det_loss_cls_1[0.4161] det_loss_box_1[0.4532] det_loss_cls_2[0.3863] det_loss_box_2[0.4140] det_loss_cls_3[0.3681] det_loss_box_3[0.4031] det_loss_cls_4[0.3506] det_loss_box_4[0.3957] det_loss_cls_5[0.3364] det_loss_box_5[0.3939] det_loss_cls_dn_0[0.0938] det_loss_box_dn_0[0.3126] det_loss_cls_dn_1[0.0898] det_loss_box_dn_1[0.2609] det_loss_cls_dn_2[0.0894] det_loss_box_dn_2[0.2442] det_loss_cls_dn_3[0.0889] det_loss_box_dn_3[0.2405] det_loss_cls_dn_4[0.0881] det_loss_box_dn_4[0.2395] det_loss_cls_dn_5[0.0865] det_loss_box_dn_5[0.2366] loss_depth[0.0372] total_loss[7.0392]
Rank[0/8] 06/20/2025 03:41:17 INFO stats.py:314 | Epoch[102] Step[44] GlobalStep[39824] Training Speed: 8.77 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 18:12:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:41:22 INFO loss_tracker.py:84 | Epoch[102/239] Step[49] GlobalStep[39829/NA]: det_loss_cls_0[0.4848] det_loss_box_0[0.5969] det_loss_cls_1[0.4766] det_loss_box_1[0.4708] det_loss_cls_2[0.4343] det_loss_box_2[0.4342] det_loss_cls_3[0.4132] det_loss_box_3[0.4220] det_loss_cls_4[0.3894] det_loss_box_4[0.4198] det_loss_cls_5[0.3768] det_loss_box_5[0.4229] det_loss_cls_dn_0[0.1022] det_loss_box_dn_0[0.3220] det_loss_cls_dn_1[0.0972] det_loss_box_dn_1[0.2774] det_loss_cls_dn_2[0.0971] det_loss_box_dn_2[0.2587] det_loss_cls_dn_3[0.0958] det_loss_box_dn_3[0.2546] det_loss_cls_dn_4[0.0956] det_loss_box_dn_4[0.2522] det_loss_cls_dn_5[0.0946] det_loss_box_dn_5[0.2505] loss_depth[0.0354] total_loss[7.5753]
Rank[0/8] 06/20/2025 03:41:43 INFO stats.py:314 | Epoch[102] Step[69] GlobalStep[39849] Training Speed: 8.83 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 18:11:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:41:48 INFO loss_tracker.py:84 | Epoch[102/239] Step[74] GlobalStep[39854/NA]: det_loss_cls_0[0.5063] det_loss_box_0[0.7020] det_loss_cls_1[0.5027] det_loss_box_1[0.5612] det_loss_cls_2[0.4763] det_loss_box_2[0.5057] det_loss_cls_3[0.4451] det_loss_box_3[0.4886] det_loss_cls_4[0.4089] det_loss_box_4[0.4800] det_loss_cls_5[0.3984] det_loss_box_5[0.4756] det_loss_cls_dn_0[0.0964] det_loss_box_dn_0[0.2752] det_loss_cls_dn_1[0.0923] det_loss_box_dn_1[0.2291] det_loss_cls_dn_2[0.0920] det_loss_box_dn_2[0.2115] det_loss_cls_dn_3[0.0912] det_loss_box_dn_3[0.2083] det_loss_cls_dn_4[0.0909] det_loss_box_dn_4[0.2059] det_loss_cls_dn_5[0.0896] det_loss_box_dn_5[0.2050] loss_depth[0.0368] total_loss[7.8750]
Rank[0/8] 06/20/2025 03:42:08 INFO stats.py:314 | Epoch[102] Step[94] GlobalStep[39874] Training Speed: 13.81 samples/sec across all devices.	Average Step Time: 0.58 sec.	Estimated Remaining Time: 18:11:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:42:14 INFO loss_tracker.py:84 | Epoch[102/239] Step[99] GlobalStep[39879/NA]: det_loss_cls_0[0.4467] det_loss_box_0[0.6225] det_loss_cls_1[0.4320] det_loss_box_1[0.4936] det_loss_cls_2[0.4077] det_loss_box_2[0.4526] det_loss_cls_3[0.3875] det_loss_box_3[0.4364] det_loss_cls_4[0.3650] det_loss_box_4[0.4277] det_loss_cls_5[0.3572] det_loss_box_5[0.4291] det_loss_cls_dn_0[0.0947] det_loss_box_dn_0[0.2827] det_loss_cls_dn_1[0.0918] det_loss_box_dn_1[0.2404] det_loss_cls_dn_2[0.0909] det_loss_box_dn_2[0.2288] det_loss_cls_dn_3[0.0890] det_loss_box_dn_3[0.2260] det_loss_cls_dn_4[0.0884] det_loss_box_dn_4[0.2247] det_loss_cls_dn_5[0.0869] det_loss_box_dn_5[0.2242] loss_depth[0.0376] total_loss[7.2641]
Rank[0/8] 06/20/2025 03:42:34 INFO stats.py:314 | Epoch[102] Step[119] GlobalStep[39899] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 18:10:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:42:40 INFO loss_tracker.py:84 | Epoch[102/239] Step[124] GlobalStep[39904/NA]: det_loss_cls_0[0.6440] det_loss_box_0[0.8535] det_loss_cls_1[0.6342] det_loss_box_1[0.6874] det_loss_cls_2[0.5929] det_loss_box_2[0.6158] det_loss_cls_3[0.5553] det_loss_box_3[0.5975] det_loss_cls_4[0.5137] det_loss_box_4[0.5863] det_loss_cls_5[0.4995] det_loss_box_5[0.5862] det_loss_cls_dn_0[0.0954] det_loss_box_dn_0[0.2501] det_loss_cls_dn_1[0.0916] det_loss_box_dn_1[0.2061] det_loss_cls_dn_2[0.0905] det_loss_box_dn_2[0.1930] det_loss_cls_dn_3[0.0894] det_loss_box_dn_3[0.1893] det_loss_cls_dn_4[0.0887] det_loss_box_dn_4[0.1886] det_loss_cls_dn_5[0.0880] det_loss_box_dn_5[0.1883] loss_depth[0.0361] total_loss[9.1614]
Rank[0/8] 06/20/2025 03:43:01 INFO stats.py:314 | Epoch[102] Step[144] GlobalStep[39924] Training Speed: 7.96 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 18:09:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:43:06 INFO loss_tracker.py:84 | Epoch[102/239] Step[149] GlobalStep[39929/NA]: det_loss_cls_0[0.5037] det_loss_box_0[0.6979] det_loss_cls_1[0.4811] det_loss_box_1[0.5611] det_loss_cls_2[0.4589] det_loss_box_2[0.4997] det_loss_cls_3[0.4387] det_loss_box_3[0.4786] det_loss_cls_4[0.4134] det_loss_box_4[0.4769] det_loss_cls_5[0.4023] det_loss_box_5[0.4731] det_loss_cls_dn_0[0.0952] det_loss_box_dn_0[0.3088] det_loss_cls_dn_1[0.0904] det_loss_box_dn_1[0.2699] det_loss_cls_dn_2[0.0901] det_loss_box_dn_2[0.2523] det_loss_cls_dn_3[0.0891] det_loss_box_dn_3[0.2478] det_loss_cls_dn_4[0.0876] det_loss_box_dn_4[0.2452] det_loss_cls_dn_5[0.0870] det_loss_box_dn_5[0.2427] loss_depth[0.0374] total_loss[8.0289]
Rank[0/8] 06/20/2025 03:43:27 INFO stats.py:314 | Epoch[102] Step[169] GlobalStep[39949] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 18:09:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:43:32 INFO loss_tracker.py:84 | Epoch[102/239] Step[174] GlobalStep[39954/NA]: det_loss_cls_0[0.5520] det_loss_box_0[0.7267] det_loss_cls_1[0.5408] det_loss_box_1[0.5745] det_loss_cls_2[0.4967] det_loss_box_2[0.5352] det_loss_cls_3[0.4619] det_loss_box_3[0.5061] det_loss_cls_4[0.4243] det_loss_box_4[0.4893] det_loss_cls_5[0.4104] det_loss_box_5[0.4874] det_loss_cls_dn_0[0.0989] det_loss_box_dn_0[0.3002] det_loss_cls_dn_1[0.0956] det_loss_box_dn_1[0.2599] det_loss_cls_dn_2[0.0958] det_loss_box_dn_2[0.2433] det_loss_cls_dn_3[0.0948] det_loss_box_dn_3[0.2415] det_loss_cls_dn_4[0.0941] det_loss_box_dn_4[0.2402] det_loss_cls_dn_5[0.0936] det_loss_box_dn_5[0.2397] loss_depth[0.0370] total_loss[8.3399]
Rank[0/8] 06/20/2025 03:43:52 INFO stats.py:314 | Epoch[102] Step[194] GlobalStep[39974] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 18:08:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:43:57 INFO loss_tracker.py:84 | Epoch[102/239] Step[199] GlobalStep[39979/NA]: det_loss_cls_0[0.6936] det_loss_box_0[0.8979] det_loss_cls_1[0.6784] det_loss_box_1[0.6968] det_loss_cls_2[0.6307] det_loss_box_2[0.6153] det_loss_cls_3[0.5932] det_loss_box_3[0.5937] det_loss_cls_4[0.5530] det_loss_box_4[0.5910] det_loss_cls_5[0.5495] det_loss_box_5[0.5924] det_loss_cls_dn_0[0.1054] det_loss_box_dn_0[0.2928] det_loss_cls_dn_1[0.1035] det_loss_box_dn_1[0.2484] det_loss_cls_dn_2[0.1020] det_loss_box_dn_2[0.2325] det_loss_cls_dn_3[0.1012] det_loss_box_dn_3[0.2293] det_loss_cls_dn_4[0.1002] det_loss_box_dn_4[0.2286] det_loss_cls_dn_5[0.0991] det_loss_box_dn_5[0.2270] loss_depth[0.0385] total_loss[9.7940]
Rank[0/8] 06/20/2025 03:44:18 INFO stats.py:314 | Epoch[102] Step[219] GlobalStep[39999] Training Speed: 6.79 samples/sec across all devices.	Average Step Time: 1.18 sec.	Estimated Remaining Time: 18:08:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:44:23 INFO loss_tracker.py:84 | Epoch[102/239] Step[224] GlobalStep[40004/NA]: det_loss_cls_0[0.5339] det_loss_box_0[0.6756] det_loss_cls_1[0.5177] det_loss_box_1[0.5072] det_loss_cls_2[0.4743] det_loss_box_2[0.4774] det_loss_cls_3[0.4398] det_loss_box_3[0.4621] det_loss_cls_4[0.4169] det_loss_box_4[0.4603] det_loss_cls_5[0.4044] det_loss_box_5[0.4611] det_loss_cls_dn_0[0.0973] det_loss_box_dn_0[0.3145] det_loss_cls_dn_1[0.0934] det_loss_box_dn_1[0.2626] det_loss_cls_dn_2[0.0923] det_loss_box_dn_2[0.2461] det_loss_cls_dn_3[0.0912] det_loss_box_dn_3[0.2421] det_loss_cls_dn_4[0.0901] det_loss_box_dn_4[0.2406] det_loss_cls_dn_5[0.0889] det_loss_box_dn_5[0.2383] loss_depth[0.0400] total_loss[7.9681]
Rank[0/8] 06/20/2025 03:44:44 INFO stats.py:314 | Epoch[102] Step[244] GlobalStep[40024] Training Speed: 6.78 samples/sec across all devices.	Average Step Time: 1.18 sec.	Estimated Remaining Time: 18:07:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:44:49 INFO loss_tracker.py:84 | Epoch[102/239] Step[249] GlobalStep[40029/NA]: det_loss_cls_0[0.4995] det_loss_box_0[0.7573] det_loss_cls_1[0.4738] det_loss_box_1[0.6137] det_loss_cls_2[0.4408] det_loss_box_2[0.5458] det_loss_cls_3[0.4206] det_loss_box_3[0.5344] det_loss_cls_4[0.3981] det_loss_box_4[0.5143] det_loss_cls_5[0.3760] det_loss_box_5[0.5116] det_loss_cls_dn_0[0.0985] det_loss_box_dn_0[0.3974] det_loss_cls_dn_1[0.0940] det_loss_box_dn_1[0.3465] det_loss_cls_dn_2[0.0934] det_loss_box_dn_2[0.3291] det_loss_cls_dn_3[0.0923] det_loss_box_dn_3[0.3224] det_loss_cls_dn_4[0.0906] det_loss_box_dn_4[0.3209] det_loss_cls_dn_5[0.0892] det_loss_box_dn_5[0.3188] loss_depth[0.0418] total_loss[8.7207]
Rank[0/8] 06/20/2025 03:45:10 INFO stats.py:314 | Epoch[102] Step[269] GlobalStep[40049] Training Speed: 8.12 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 18:06:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:45:15 INFO loss_tracker.py:84 | Epoch[102/239] Step[274] GlobalStep[40054/NA]: det_loss_cls_0[0.4672] det_loss_box_0[0.6321] det_loss_cls_1[0.4639] det_loss_box_1[0.4967] det_loss_cls_2[0.4187] det_loss_box_2[0.4541] det_loss_cls_3[0.3888] det_loss_box_3[0.4441] det_loss_cls_4[0.3674] det_loss_box_4[0.4451] det_loss_cls_5[0.3576] det_loss_box_5[0.4387] det_loss_cls_dn_0[0.1004] det_loss_box_dn_0[0.3333] det_loss_cls_dn_1[0.0970] det_loss_box_dn_1[0.2851] det_loss_cls_dn_2[0.0956] det_loss_box_dn_2[0.2679] det_loss_cls_dn_3[0.0945] det_loss_box_dn_3[0.2636] det_loss_cls_dn_4[0.0936] det_loss_box_dn_4[0.2624] det_loss_cls_dn_5[0.0932] det_loss_box_dn_5[0.2549] loss_depth[0.0393] total_loss[7.6553]
Rank[0/8] 06/20/2025 03:45:36 INFO stats.py:314 | Epoch[102] Step[294] GlobalStep[40074] Training Speed: 6.19 samples/sec across all devices.	Average Step Time: 1.29 sec.	Estimated Remaining Time: 18:06:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:45:41 INFO loss_tracker.py:84 | Epoch[102/239] Step[299] GlobalStep[40079/NA]: det_loss_cls_0[0.6379] det_loss_box_0[0.8175] det_loss_cls_1[0.6244] det_loss_box_1[0.6370] det_loss_cls_2[0.5679] det_loss_box_2[0.5882] det_loss_cls_3[0.5417] det_loss_box_3[0.5683] det_loss_cls_4[0.5108] det_loss_box_4[0.5520] det_loss_cls_5[0.5032] det_loss_box_5[0.5544] det_loss_cls_dn_0[0.0994] det_loss_box_dn_0[0.2821] det_loss_cls_dn_1[0.0963] det_loss_box_dn_1[0.2403] det_loss_cls_dn_2[0.0958] det_loss_box_dn_2[0.2238] det_loss_cls_dn_3[0.0950] det_loss_box_dn_3[0.2204] det_loss_cls_dn_4[0.0942] det_loss_box_dn_4[0.2194] det_loss_cls_dn_5[0.0935] det_loss_box_dn_5[0.2185] loss_depth[0.0362] total_loss[9.1182]
Rank[0/8] 06/20/2025 03:46:02 INFO stats.py:314 | Epoch[102] Step[319] GlobalStep[40099] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 18:05:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:46:07 INFO loss_tracker.py:84 | Epoch[102/239] Step[324] GlobalStep[40104/NA]: det_loss_cls_0[0.5427] det_loss_box_0[0.6556] det_loss_cls_1[0.5165] det_loss_box_1[0.4843] det_loss_cls_2[0.4657] det_loss_box_2[0.4287] det_loss_cls_3[0.4384] det_loss_box_3[0.4112] det_loss_cls_4[0.4082] det_loss_box_4[0.4090] det_loss_cls_5[0.3911] det_loss_box_5[0.4128] det_loss_cls_dn_0[0.1043] det_loss_box_dn_0[0.2765] det_loss_cls_dn_1[0.1008] det_loss_box_dn_1[0.2282] det_loss_cls_dn_2[0.1010] det_loss_box_dn_2[0.2074] det_loss_cls_dn_3[0.0998] det_loss_box_dn_3[0.2038] det_loss_cls_dn_4[0.1000] det_loss_box_dn_4[0.2015] det_loss_cls_dn_5[0.0993] det_loss_box_dn_5[0.2009] loss_depth[0.0371] total_loss[7.5249]
Rank[0/8] 06/20/2025 03:46:28 INFO stats.py:314 | Epoch[102] Step[344] GlobalStep[40124] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 18:05:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:46:33 INFO loss_tracker.py:84 | Epoch[102/239] Step[349] GlobalStep[40129/NA]: det_loss_cls_0[0.4677] det_loss_box_0[0.7007] det_loss_cls_1[0.4488] det_loss_box_1[0.5466] det_loss_cls_2[0.4000] det_loss_box_2[0.4863] det_loss_cls_3[0.3722] det_loss_box_3[0.4674] det_loss_cls_4[0.3598] det_loss_box_4[0.4645] det_loss_cls_5[0.3489] det_loss_box_5[0.4659] det_loss_cls_dn_0[0.0954] det_loss_box_dn_0[0.3331] det_loss_cls_dn_1[0.0915] det_loss_box_dn_1[0.2804] det_loss_cls_dn_2[0.0909] det_loss_box_dn_2[0.2613] det_loss_cls_dn_3[0.0898] det_loss_box_dn_3[0.2573] det_loss_cls_dn_4[0.0885] det_loss_box_dn_4[0.2558] det_loss_cls_dn_5[0.0874] det_loss_box_dn_5[0.2539] loss_depth[0.0391] total_loss[7.7532]
Rank[0/8] 06/20/2025 03:46:53 INFO stats.py:314 | Epoch[102] Step[369] GlobalStep[40149] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 18:04:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:46:58 INFO loss_tracker.py:84 | Epoch[102/239] Step[374] GlobalStep[40154/NA]: det_loss_cls_0[0.5176] det_loss_box_0[0.7073] det_loss_cls_1[0.4955] det_loss_box_1[0.5506] det_loss_cls_2[0.4527] det_loss_box_2[0.5155] det_loss_cls_3[0.4292] det_loss_box_3[0.4962] det_loss_cls_4[0.4024] det_loss_box_4[0.4743] det_loss_cls_5[0.3827] det_loss_box_5[0.4821] det_loss_cls_dn_0[0.0943] det_loss_box_dn_0[0.3210] det_loss_cls_dn_1[0.0917] det_loss_box_dn_1[0.2708] det_loss_cls_dn_2[0.0917] det_loss_box_dn_2[0.2482] det_loss_cls_dn_3[0.0905] det_loss_box_dn_3[0.2452] det_loss_cls_dn_4[0.0895] det_loss_box_dn_4[0.2424] det_loss_cls_dn_5[0.0885] det_loss_box_dn_5[0.2408] loss_depth[0.0351] total_loss[8.0560]
Rank[0/8] 06/20/2025 03:47:14 INFO stats.py:394 | Epoch[102] completed. Training Speed: 7.71 samples/sec across all devices.	Epoch Time: 404.88 sec.	Average Epoch Time: 404.88 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 18:04:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:47:21 INFO stats.py:314 | Epoch[103] Step[4] GlobalStep[40174] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 18:03:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:47:41 INFO loss_tracker.py:84 | Epoch[103/239] Step[24] GlobalStep[40194/NA]: det_loss_cls_0[0.4954] det_loss_box_0[0.5949] det_loss_cls_1[0.4888] det_loss_box_1[0.4508] det_loss_cls_2[0.4530] det_loss_box_2[0.4090] det_loss_cls_3[0.4180] det_loss_box_3[0.3981] det_loss_cls_4[0.3903] det_loss_box_4[0.3922] det_loss_cls_5[0.3758] det_loss_box_5[0.3974] det_loss_cls_dn_0[0.1007] det_loss_box_dn_0[0.3023] det_loss_cls_dn_1[0.0975] det_loss_box_dn_1[0.2601] det_loss_cls_dn_2[0.0977] det_loss_box_dn_2[0.2412] det_loss_cls_dn_3[0.0970] det_loss_box_dn_3[0.2381] det_loss_cls_dn_4[0.0955] det_loss_box_dn_4[0.2365] det_loss_cls_dn_5[0.0953] det_loss_box_dn_5[0.2355] loss_depth[0.0368] total_loss[7.3981]
Rank[0/8] 06/20/2025 03:47:46 INFO stats.py:314 | Epoch[103] Step[29] GlobalStep[40199] Training Speed: 7.89 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 18:03:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:48:07 INFO loss_tracker.py:84 | Epoch[103/239] Step[49] GlobalStep[40219/NA]: det_loss_cls_0[0.4557] det_loss_box_0[0.5963] det_loss_cls_1[0.4388] det_loss_box_1[0.4773] det_loss_cls_2[0.4166] det_loss_box_2[0.4203] det_loss_cls_3[0.3864] det_loss_box_3[0.4148] det_loss_cls_4[0.3639] det_loss_box_4[0.4186] det_loss_cls_5[0.3563] det_loss_box_5[0.4161] det_loss_cls_dn_0[0.1105] det_loss_box_dn_0[0.3244] det_loss_cls_dn_1[0.1075] det_loss_box_dn_1[0.2737] det_loss_cls_dn_2[0.1077] det_loss_box_dn_2[0.2564] det_loss_cls_dn_3[0.1073] det_loss_box_dn_3[0.2535] det_loss_cls_dn_4[0.1076] det_loss_box_dn_4[0.2519] det_loss_cls_dn_5[0.1064] det_loss_box_dn_5[0.2497] loss_depth[0.0376] total_loss[7.4553]
Rank[0/8] 06/20/2025 03:48:12 INFO stats.py:314 | Epoch[103] Step[54] GlobalStep[40224] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 18:02:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:48:33 INFO loss_tracker.py:84 | Epoch[103/239] Step[74] GlobalStep[40244/NA]: det_loss_cls_0[0.3687] det_loss_box_0[0.4771] det_loss_cls_1[0.3617] det_loss_box_1[0.3564] det_loss_cls_2[0.3388] det_loss_box_2[0.3185] det_loss_cls_3[0.3102] det_loss_box_3[0.3096] det_loss_cls_4[0.2944] det_loss_box_4[0.2944] det_loss_cls_5[0.2822] det_loss_box_5[0.2942] det_loss_cls_dn_0[0.0831] det_loss_box_dn_0[0.2781] det_loss_cls_dn_1[0.0799] det_loss_box_dn_1[0.2242] det_loss_cls_dn_2[0.0790] det_loss_box_dn_2[0.2087] det_loss_cls_dn_3[0.0778] det_loss_box_dn_3[0.2035] det_loss_cls_dn_4[0.0770] det_loss_box_dn_4[0.2002] det_loss_cls_dn_5[0.0758] det_loss_box_dn_5[0.1976] loss_depth[0.0373] total_loss[5.8284]
Rank[0/8] 06/20/2025 03:48:38 INFO stats.py:314 | Epoch[103] Step[79] GlobalStep[40249] Training Speed: 7.96 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 18:02:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:48:59 INFO loss_tracker.py:84 | Epoch[103/239] Step[99] GlobalStep[40269/NA]: det_loss_cls_0[0.4318] det_loss_box_0[0.6251] det_loss_cls_1[0.4329] det_loss_box_1[0.4529] det_loss_cls_2[0.3969] det_loss_box_2[0.4019] det_loss_cls_3[0.3709] det_loss_box_3[0.3899] det_loss_cls_4[0.3423] det_loss_box_4[0.3819] det_loss_cls_5[0.3248] det_loss_box_5[0.3825] det_loss_cls_dn_0[0.0944] det_loss_box_dn_0[0.3201] det_loss_cls_dn_1[0.0907] det_loss_box_dn_1[0.2675] det_loss_cls_dn_2[0.0891] det_loss_box_dn_2[0.2519] det_loss_cls_dn_3[0.0881] det_loss_box_dn_3[0.2491] det_loss_cls_dn_4[0.0867] det_loss_box_dn_4[0.2470] det_loss_cls_dn_5[0.0852] det_loss_box_dn_5[0.2430] loss_depth[0.0406] total_loss[7.0875]
Rank[0/8] 06/20/2025 03:49:04 INFO stats.py:314 | Epoch[103] Step[104] GlobalStep[40274] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 18:01:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:49:25 INFO loss_tracker.py:84 | Epoch[103/239] Step[124] GlobalStep[40294/NA]: det_loss_cls_0[0.4603] det_loss_box_0[0.6388] det_loss_cls_1[0.4518] det_loss_box_1[0.4791] det_loss_cls_2[0.4120] det_loss_box_2[0.4423] det_loss_cls_3[0.3952] det_loss_box_3[0.4315] det_loss_cls_4[0.3627] det_loss_box_4[0.4237] det_loss_cls_5[0.3506] det_loss_box_5[0.4196] det_loss_cls_dn_0[0.0942] det_loss_box_dn_0[0.3120] det_loss_cls_dn_1[0.0907] det_loss_box_dn_1[0.2651] det_loss_cls_dn_2[0.0898] det_loss_box_dn_2[0.2470] det_loss_cls_dn_3[0.0888] det_loss_box_dn_3[0.2430] det_loss_cls_dn_4[0.0877] det_loss_box_dn_4[0.2399] det_loss_cls_dn_5[0.0872] det_loss_box_dn_5[0.2386] loss_depth[0.0394] total_loss[7.3909]
Rank[0/8] 06/20/2025 03:49:30 INFO stats.py:314 | Epoch[103] Step[129] GlobalStep[40299] Training Speed: 6.60 samples/sec across all devices.	Average Step Time: 1.21 sec.	Estimated Remaining Time: 18:00:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:49:51 INFO loss_tracker.py:84 | Epoch[103/239] Step[149] GlobalStep[40319/NA]: det_loss_cls_0[0.5171] det_loss_box_0[0.7644] det_loss_cls_1[0.4975] det_loss_box_1[0.6082] det_loss_cls_2[0.4673] det_loss_box_2[0.5452] det_loss_cls_3[0.4478] det_loss_box_3[0.5258] det_loss_cls_4[0.4083] det_loss_box_4[0.5252] det_loss_cls_5[0.3946] det_loss_box_5[0.5198] det_loss_cls_dn_0[0.1088] det_loss_box_dn_0[0.3721] det_loss_cls_dn_1[0.1058] det_loss_box_dn_1[0.3270] det_loss_cls_dn_2[0.1046] det_loss_box_dn_2[0.3078] det_loss_cls_dn_3[0.1036] det_loss_box_dn_3[0.3018] det_loss_cls_dn_4[0.1024] det_loss_box_dn_4[0.3013] det_loss_cls_dn_5[0.1016] det_loss_box_dn_5[0.3000] loss_depth[0.0422] total_loss[8.8003]
Rank[0/8] 06/20/2025 03:49:56 INFO stats.py:314 | Epoch[103] Step[154] GlobalStep[40324] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 18:00:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:50:17 INFO loss_tracker.py:84 | Epoch[103/239] Step[174] GlobalStep[40344/NA]: det_loss_cls_0[0.5077] det_loss_box_0[0.6624] det_loss_cls_1[0.4935] det_loss_box_1[0.5263] det_loss_cls_2[0.4734] det_loss_box_2[0.4733] det_loss_cls_3[0.4332] det_loss_box_3[0.4580] det_loss_cls_4[0.4016] det_loss_box_4[0.4483] det_loss_cls_5[0.3830] det_loss_box_5[0.4523] det_loss_cls_dn_0[0.0867] det_loss_box_dn_0[0.2876] det_loss_cls_dn_1[0.0835] det_loss_box_dn_1[0.2461] det_loss_cls_dn_2[0.0824] det_loss_box_dn_2[0.2264] det_loss_cls_dn_3[0.0813] det_loss_box_dn_3[0.2229] det_loss_cls_dn_4[0.0799] det_loss_box_dn_4[0.2213] det_loss_cls_dn_5[0.0787] det_loss_box_dn_5[0.2196] loss_depth[0.0352] total_loss[7.6643]
Rank[0/8] 06/20/2025 03:50:22 INFO stats.py:314 | Epoch[103] Step[179] GlobalStep[40349] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 17:59:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:50:43 INFO loss_tracker.py:84 | Epoch[103/239] Step[199] GlobalStep[40369/NA]: det_loss_cls_0[0.4512] det_loss_box_0[0.6158] det_loss_cls_1[0.4381] det_loss_box_1[0.4885] det_loss_cls_2[0.4155] det_loss_box_2[0.4282] det_loss_cls_3[0.3839] det_loss_box_3[0.4200] det_loss_cls_4[0.3587] det_loss_box_4[0.4177] det_loss_cls_5[0.3448] det_loss_box_5[0.4175] det_loss_cls_dn_0[0.0959] det_loss_box_dn_0[0.3133] det_loss_cls_dn_1[0.0923] det_loss_box_dn_1[0.2746] det_loss_cls_dn_2[0.0919] det_loss_box_dn_2[0.2589] det_loss_cls_dn_3[0.0906] det_loss_box_dn_3[0.2580] det_loss_cls_dn_4[0.0895] det_loss_box_dn_4[0.2579] det_loss_cls_dn_5[0.0888] det_loss_box_dn_5[0.2552] loss_depth[0.0411] total_loss[7.3879]
Rank[0/8] 06/20/2025 03:50:48 INFO stats.py:314 | Epoch[103] Step[204] GlobalStep[40374] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 17:59:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:51:08 INFO loss_tracker.py:84 | Epoch[103/239] Step[224] GlobalStep[40394/NA]: det_loss_cls_0[0.4431] det_loss_box_0[0.6223] det_loss_cls_1[0.4229] det_loss_box_1[0.4865] det_loss_cls_2[0.4020] det_loss_box_2[0.4314] det_loss_cls_3[0.3688] det_loss_box_3[0.4192] det_loss_cls_4[0.3409] det_loss_box_4[0.4070] det_loss_cls_5[0.3327] det_loss_box_5[0.4054] det_loss_cls_dn_0[0.0881] det_loss_box_dn_0[0.3428] det_loss_cls_dn_1[0.0851] det_loss_box_dn_1[0.2957] det_loss_cls_dn_2[0.0850] det_loss_box_dn_2[0.2765] det_loss_cls_dn_3[0.0838] det_loss_box_dn_3[0.2738] det_loss_cls_dn_4[0.0827] det_loss_box_dn_4[0.2694] det_loss_cls_dn_5[0.0816] det_loss_box_dn_5[0.2679] loss_depth[0.0393] total_loss[7.3537]
Rank[0/8] 06/20/2025 03:51:14 INFO stats.py:314 | Epoch[103] Step[229] GlobalStep[40399] Training Speed: 4.92 samples/sec across all devices.	Average Step Time: 1.63 sec.	Estimated Remaining Time: 17:58:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:51:34 INFO loss_tracker.py:84 | Epoch[103/239] Step[249] GlobalStep[40419/NA]: det_loss_cls_0[0.5404] det_loss_box_0[0.7490] det_loss_cls_1[0.5267] det_loss_box_1[0.6201] det_loss_cls_2[0.4953] det_loss_box_2[0.5553] det_loss_cls_3[0.4663] det_loss_box_3[0.5253] det_loss_cls_4[0.4396] det_loss_box_4[0.5092] det_loss_cls_5[0.4254] det_loss_box_5[0.5062] det_loss_cls_dn_0[0.0937] det_loss_box_dn_0[0.3396] det_loss_cls_dn_1[0.0918] det_loss_box_dn_1[0.2928] det_loss_cls_dn_2[0.0915] det_loss_box_dn_2[0.2684] det_loss_cls_dn_3[0.0910] det_loss_box_dn_3[0.2633] det_loss_cls_dn_4[0.0896] det_loss_box_dn_4[0.2592] det_loss_cls_dn_5[0.0884] det_loss_box_dn_5[0.2578] loss_depth[0.0382] total_loss[8.6240]
Rank[0/8] 06/20/2025 03:51:40 INFO stats.py:314 | Epoch[103] Step[254] GlobalStep[40424] Training Speed: 5.96 samples/sec across all devices.	Average Step Time: 1.34 sec.	Estimated Remaining Time: 17:57:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:52:01 INFO loss_tracker.py:84 | Epoch[103/239] Step[274] GlobalStep[40444/NA]: det_loss_cls_0[0.4458] det_loss_box_0[0.6162] det_loss_cls_1[0.4441] det_loss_box_1[0.4808] det_loss_cls_2[0.4112] det_loss_box_2[0.4356] det_loss_cls_3[0.3730] det_loss_box_3[0.4237] det_loss_cls_4[0.3440] det_loss_box_4[0.4175] det_loss_cls_5[0.3336] det_loss_box_5[0.4148] det_loss_cls_dn_0[0.0879] det_loss_box_dn_0[0.2894] det_loss_cls_dn_1[0.0845] det_loss_box_dn_1[0.2385] det_loss_cls_dn_2[0.0834] det_loss_box_dn_2[0.2198] det_loss_cls_dn_3[0.0822] det_loss_box_dn_3[0.2148] det_loss_cls_dn_4[0.0816] det_loss_box_dn_4[0.2141] det_loss_cls_dn_5[0.0809] det_loss_box_dn_5[0.2112] loss_depth[0.0326] total_loss[7.0612]
Rank[0/8] 06/20/2025 03:52:06 INFO stats.py:314 | Epoch[103] Step[279] GlobalStep[40449] Training Speed: 7.60 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 17:57:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:52:27 INFO loss_tracker.py:84 | Epoch[103/239] Step[299] GlobalStep[40469/NA]: det_loss_cls_0[0.4977] det_loss_box_0[0.6527] det_loss_cls_1[0.4859] det_loss_box_1[0.5271] det_loss_cls_2[0.4503] det_loss_box_2[0.4693] det_loss_cls_3[0.4319] det_loss_box_3[0.4675] det_loss_cls_4[0.3961] det_loss_box_4[0.4544] det_loss_cls_5[0.3806] det_loss_box_5[0.4557] det_loss_cls_dn_0[0.1014] det_loss_box_dn_0[0.3745] det_loss_cls_dn_1[0.0979] det_loss_box_dn_1[0.3253] det_loss_cls_dn_2[0.0966] det_loss_box_dn_2[0.3075] det_loss_cls_dn_3[0.0958] det_loss_box_dn_3[0.2997] det_loss_cls_dn_4[0.0950] det_loss_box_dn_4[0.2966] det_loss_cls_dn_5[0.0938] det_loss_box_dn_5[0.2933] loss_depth[0.0374] total_loss[8.1842]
Rank[0/8] 06/20/2025 03:52:32 INFO stats.py:314 | Epoch[103] Step[304] GlobalStep[40474] Training Speed: 8.17 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 17:56:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:52:52 INFO loss_tracker.py:84 | Epoch[103/239] Step[324] GlobalStep[40494/NA]: det_loss_cls_0[0.5931] det_loss_box_0[0.7705] det_loss_cls_1[0.5821] det_loss_box_1[0.5933] det_loss_cls_2[0.5513] det_loss_box_2[0.5304] det_loss_cls_3[0.5323] det_loss_box_3[0.5142] det_loss_cls_4[0.4955] det_loss_box_4[0.5033] det_loss_cls_5[0.4721] det_loss_box_5[0.5094] det_loss_cls_dn_0[0.1016] det_loss_box_dn_0[0.3176] det_loss_cls_dn_1[0.0990] det_loss_box_dn_1[0.2747] det_loss_cls_dn_2[0.0983] det_loss_box_dn_2[0.2595] det_loss_cls_dn_3[0.0970] det_loss_box_dn_3[0.2567] det_loss_cls_dn_4[0.0960] det_loss_box_dn_4[0.2537] det_loss_cls_dn_5[0.0952] det_loss_box_dn_5[0.2471] loss_depth[0.0367] total_loss[8.8807]
Rank[0/8] 06/20/2025 03:52:57 INFO stats.py:314 | Epoch[103] Step[329] GlobalStep[40499] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 17:56:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:53:18 INFO loss_tracker.py:84 | Epoch[103/239] Step[349] GlobalStep[40519/NA]: det_loss_cls_0[0.4360] det_loss_box_0[0.5263] det_loss_cls_1[0.4299] det_loss_box_1[0.4076] det_loss_cls_2[0.4024] det_loss_box_2[0.3660] det_loss_cls_3[0.3734] det_loss_box_3[0.3530] det_loss_cls_4[0.3535] det_loss_box_4[0.3423] det_loss_cls_5[0.3323] det_loss_box_5[0.3478] det_loss_cls_dn_0[0.1060] det_loss_box_dn_0[0.3022] det_loss_cls_dn_1[0.1022] det_loss_box_dn_1[0.2542] det_loss_cls_dn_2[0.1013] det_loss_box_dn_2[0.2338] det_loss_cls_dn_3[0.1005] det_loss_box_dn_3[0.2302] det_loss_cls_dn_4[0.0989] det_loss_box_dn_4[0.2275] det_loss_cls_dn_5[0.0983] det_loss_box_dn_5[0.2255] loss_depth[0.0400] total_loss[6.7913]
Rank[0/8] 06/20/2025 03:53:23 INFO stats.py:314 | Epoch[103] Step[354] GlobalStep[40524] Training Speed: 6.65 samples/sec across all devices.	Average Step Time: 1.20 sec.	Estimated Remaining Time: 17:55:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:53:44 INFO loss_tracker.py:84 | Epoch[103/239] Step[374] GlobalStep[40544/NA]: det_loss_cls_0[0.5250] det_loss_box_0[0.6882] det_loss_cls_1[0.5383] det_loss_box_1[0.5293] det_loss_cls_2[0.5011] det_loss_box_2[0.4849] det_loss_cls_3[0.4699] det_loss_box_3[0.4636] det_loss_cls_4[0.4323] det_loss_box_4[0.4616] det_loss_cls_5[0.4242] det_loss_box_5[0.4546] det_loss_cls_dn_0[0.0980] det_loss_box_dn_0[0.2862] det_loss_cls_dn_1[0.0951] det_loss_box_dn_1[0.2444] det_loss_cls_dn_2[0.0946] det_loss_box_dn_2[0.2323] det_loss_cls_dn_3[0.0942] det_loss_box_dn_3[0.2274] det_loss_cls_dn_4[0.0925] det_loss_box_dn_4[0.2250] det_loss_cls_dn_5[0.0916] det_loss_box_dn_5[0.2219] loss_depth[0.0395] total_loss[8.0158]
Rank[0/8] 06/20/2025 03:53:49 INFO stats.py:314 | Epoch[103] Step[379] GlobalStep[40549] Training Speed: 7.67 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 17:54:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:53:59 INFO stats.py:394 | Epoch[103] completed. Training Speed: 7.71 samples/sec across all devices.	Epoch Time: 404.82 sec.	Average Epoch Time: 404.82 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 17:54:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:54:17 INFO stats.py:314 | Epoch[104] Step[14] GlobalStep[40574] Training Speed: 8.66 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 17:54:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:54:27 INFO loss_tracker.py:84 | Epoch[104/239] Step[24] GlobalStep[40584/NA]: det_loss_cls_0[0.4960] det_loss_box_0[0.6573] det_loss_cls_1[0.4840] det_loss_box_1[0.5105] det_loss_cls_2[0.4474] det_loss_box_2[0.4576] det_loss_cls_3[0.4205] det_loss_box_3[0.4350] det_loss_cls_4[0.3889] det_loss_box_4[0.4280] det_loss_cls_5[0.3730] det_loss_box_5[0.4289] det_loss_cls_dn_0[0.0925] det_loss_box_dn_0[0.3025] det_loss_cls_dn_1[0.0894] det_loss_box_dn_1[0.2592] det_loss_cls_dn_2[0.0891] det_loss_box_dn_2[0.2412] det_loss_cls_dn_3[0.0877] det_loss_box_dn_3[0.2389] det_loss_cls_dn_4[0.0866] det_loss_box_dn_4[0.2375] det_loss_cls_dn_5[0.0857] det_loss_box_dn_5[0.2363] loss_depth[0.0386] total_loss[7.6122]
Rank[0/8] 06/20/2025 03:54:43 INFO stats.py:314 | Epoch[104] Step[39] GlobalStep[40599] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 17:53:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:54:54 INFO loss_tracker.py:84 | Epoch[104/239] Step[49] GlobalStep[40609/NA]: det_loss_cls_0[0.5958] det_loss_box_0[0.7386] det_loss_cls_1[0.5773] det_loss_box_1[0.5717] det_loss_cls_2[0.5369] det_loss_box_2[0.5270] det_loss_cls_3[0.5079] det_loss_box_3[0.5065] det_loss_cls_4[0.4786] det_loss_box_4[0.4990] det_loss_cls_5[0.4544] det_loss_box_5[0.5050] det_loss_cls_dn_0[0.1007] det_loss_box_dn_0[0.2854] det_loss_cls_dn_1[0.0979] det_loss_box_dn_1[0.2327] det_loss_cls_dn_2[0.0975] det_loss_box_dn_2[0.2175] det_loss_cls_dn_3[0.0970] det_loss_box_dn_3[0.2136] det_loss_cls_dn_4[0.0959] det_loss_box_dn_4[0.2130] det_loss_cls_dn_5[0.0953] det_loss_box_dn_5[0.2122] loss_depth[0.0366] total_loss[8.4940]
Rank[0/8] 06/20/2025 03:55:09 INFO stats.py:314 | Epoch[104] Step[64] GlobalStep[40624] Training Speed: 8.72 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 17:53:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:55:20 INFO loss_tracker.py:84 | Epoch[104/239] Step[74] GlobalStep[40634/NA]: det_loss_cls_0[0.4916] det_loss_box_0[0.6201] det_loss_cls_1[0.4862] det_loss_box_1[0.4809] det_loss_cls_2[0.4477] det_loss_box_2[0.4357] det_loss_cls_3[0.4210] det_loss_box_3[0.4273] det_loss_cls_4[0.3911] det_loss_box_4[0.4306] det_loss_cls_5[0.3785] det_loss_box_5[0.4279] det_loss_cls_dn_0[0.0933] det_loss_box_dn_0[0.2852] det_loss_cls_dn_1[0.0903] det_loss_box_dn_1[0.2463] det_loss_cls_dn_2[0.0892] det_loss_box_dn_2[0.2318] det_loss_cls_dn_3[0.0882] det_loss_box_dn_3[0.2303] det_loss_cls_dn_4[0.0879] det_loss_box_dn_4[0.2286] det_loss_cls_dn_5[0.0872] det_loss_box_dn_5[0.2256] loss_depth[0.0392] total_loss[7.4613]
Rank[0/8] 06/20/2025 03:55:36 INFO stats.py:314 | Epoch[104] Step[89] GlobalStep[40649] Training Speed: 6.25 samples/sec across all devices.	Average Step Time: 1.28 sec.	Estimated Remaining Time: 17:52:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:55:46 INFO loss_tracker.py:84 | Epoch[104/239] Step[99] GlobalStep[40659/NA]: det_loss_cls_0[0.5643] det_loss_box_0[0.8108] det_loss_cls_1[0.5440] det_loss_box_1[0.6243] det_loss_cls_2[0.5087] det_loss_box_2[0.5580] det_loss_cls_3[0.4796] det_loss_box_3[0.5483] det_loss_cls_4[0.4579] det_loss_box_4[0.5245] det_loss_cls_5[0.4429] det_loss_box_5[0.5267] det_loss_cls_dn_0[0.1023] det_loss_box_dn_0[0.3870] det_loss_cls_dn_1[0.0990] det_loss_box_dn_1[0.3294] det_loss_cls_dn_2[0.0987] det_loss_box_dn_2[0.3114] det_loss_cls_dn_3[0.0978] det_loss_box_dn_3[0.3066] det_loss_cls_dn_4[0.0964] det_loss_box_dn_4[0.3019] det_loss_cls_dn_5[0.0954] det_loss_box_dn_5[0.2996] loss_depth[0.0419] total_loss[9.1574]
Rank[0/8] 06/20/2025 03:56:01 INFO stats.py:314 | Epoch[104] Step[114] GlobalStep[40674] Training Speed: 8.22 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 17:51:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:56:12 INFO loss_tracker.py:84 | Epoch[104/239] Step[124] GlobalStep[40684/NA]: det_loss_cls_0[0.5268] det_loss_box_0[0.6175] det_loss_cls_1[0.5142] det_loss_box_1[0.4677] det_loss_cls_2[0.4694] det_loss_box_2[0.4257] det_loss_cls_3[0.4351] det_loss_box_3[0.4124] det_loss_cls_4[0.4112] det_loss_box_4[0.4029] det_loss_cls_5[0.3918] det_loss_box_5[0.4094] det_loss_cls_dn_0[0.0930] det_loss_box_dn_0[0.2823] det_loss_cls_dn_1[0.0907] det_loss_box_dn_1[0.2429] det_loss_cls_dn_2[0.0905] det_loss_box_dn_2[0.2277] det_loss_cls_dn_3[0.0895] det_loss_box_dn_3[0.2251] det_loss_cls_dn_4[0.0885] det_loss_box_dn_4[0.2237] det_loss_cls_dn_5[0.0875] det_loss_box_dn_5[0.2229] loss_depth[0.0390] total_loss[7.4877]
Rank[0/8] 06/20/2025 03:56:28 INFO stats.py:314 | Epoch[104] Step[139] GlobalStep[40699] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 17:51:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:56:38 INFO loss_tracker.py:84 | Epoch[104/239] Step[149] GlobalStep[40709/NA]: det_loss_cls_0[0.3393] det_loss_box_0[0.4538] det_loss_cls_1[0.3430] det_loss_box_1[0.3563] det_loss_cls_2[0.3176] det_loss_box_2[0.3197] det_loss_cls_3[0.2923] det_loss_box_3[0.3093] det_loss_cls_4[0.2677] det_loss_box_4[0.3028] det_loss_cls_5[0.2617] det_loss_box_5[0.2995] det_loss_cls_dn_0[0.0967] det_loss_box_dn_0[0.3476] det_loss_cls_dn_1[0.0937] det_loss_box_dn_1[0.2978] det_loss_cls_dn_2[0.0928] det_loss_box_dn_2[0.2762] det_loss_cls_dn_3[0.0918] det_loss_box_dn_3[0.2716] det_loss_cls_dn_4[0.0914] det_loss_box_dn_4[0.2699] det_loss_cls_dn_5[0.0907] det_loss_box_dn_5[0.2665] loss_depth[0.0381] total_loss[6.1877]
Rank[0/8] 06/20/2025 03:56:53 INFO stats.py:314 | Epoch[104] Step[164] GlobalStep[40724] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 17:50:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:57:04 INFO loss_tracker.py:84 | Epoch[104/239] Step[174] GlobalStep[40734/NA]: det_loss_cls_0[0.5677] det_loss_box_0[0.7140] det_loss_cls_1[0.5525] det_loss_box_1[0.5570] det_loss_cls_2[0.5140] det_loss_box_2[0.5169] det_loss_cls_3[0.4831] det_loss_box_3[0.4998] det_loss_cls_4[0.4608] det_loss_box_4[0.4835] det_loss_cls_5[0.4373] det_loss_box_5[0.4892] det_loss_cls_dn_0[0.0986] det_loss_box_dn_0[0.2884] det_loss_cls_dn_1[0.0948] det_loss_box_dn_1[0.2519] det_loss_cls_dn_2[0.0945] det_loss_box_dn_2[0.2367] det_loss_cls_dn_3[0.0935] det_loss_box_dn_3[0.2353] det_loss_cls_dn_4[0.0924] det_loss_box_dn_4[0.2346] det_loss_cls_dn_5[0.0912] det_loss_box_dn_5[0.2332] loss_depth[0.0366] total_loss[8.3575]
Rank[0/8] 06/20/2025 03:57:19 INFO stats.py:314 | Epoch[104] Step[189] GlobalStep[40749] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 17:50:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:57:29 INFO loss_tracker.py:84 | Epoch[104/239] Step[199] GlobalStep[40759/NA]: det_loss_cls_0[0.4258] det_loss_box_0[0.5680] det_loss_cls_1[0.4240] det_loss_box_1[0.4335] det_loss_cls_2[0.3767] det_loss_box_2[0.3904] det_loss_cls_3[0.3587] det_loss_box_3[0.3763] det_loss_cls_4[0.3381] det_loss_box_4[0.3703] det_loss_cls_5[0.3230] det_loss_box_5[0.3737] det_loss_cls_dn_0[0.0927] det_loss_box_dn_0[0.2991] det_loss_cls_dn_1[0.0896] det_loss_box_dn_1[0.2580] det_loss_cls_dn_2[0.0891] det_loss_box_dn_2[0.2429] det_loss_cls_dn_3[0.0882] det_loss_box_dn_3[0.2405] det_loss_cls_dn_4[0.0878] det_loss_box_dn_4[0.2393] det_loss_cls_dn_5[0.0873] det_loss_box_dn_5[0.2375] loss_depth[0.0379] total_loss[6.8482]
Rank[0/8] 06/20/2025 03:57:45 INFO stats.py:314 | Epoch[104] Step[214] GlobalStep[40774] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 17:49:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:57:55 INFO loss_tracker.py:84 | Epoch[104/239] Step[224] GlobalStep[40784/NA]: det_loss_cls_0[0.5697] det_loss_box_0[0.6834] det_loss_cls_1[0.5563] det_loss_box_1[0.5354] det_loss_cls_2[0.5146] det_loss_box_2[0.4979] det_loss_cls_3[0.4984] det_loss_box_3[0.4728] det_loss_cls_4[0.4731] det_loss_box_4[0.4674] det_loss_cls_5[0.4537] det_loss_box_5[0.4693] det_loss_cls_dn_0[0.1074] det_loss_box_dn_0[0.2707] det_loss_cls_dn_1[0.1046] det_loss_box_dn_1[0.2295] det_loss_cls_dn_2[0.1044] det_loss_box_dn_2[0.2130] det_loss_cls_dn_3[0.1036] det_loss_box_dn_3[0.2089] det_loss_cls_dn_4[0.1023] det_loss_box_dn_4[0.2076] det_loss_cls_dn_5[0.1018] det_loss_box_dn_5[0.2050] loss_depth[0.0363] total_loss[8.1868]
Rank[0/8] 06/20/2025 03:58:11 INFO stats.py:314 | Epoch[104] Step[239] GlobalStep[40799] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 17:48:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:58:21 INFO loss_tracker.py:84 | Epoch[104/239] Step[249] GlobalStep[40809/NA]: det_loss_cls_0[0.4471] det_loss_box_0[0.5574] det_loss_cls_1[0.4366] det_loss_box_1[0.4184] det_loss_cls_2[0.3945] det_loss_box_2[0.3902] det_loss_cls_3[0.3622] det_loss_box_3[0.3776] det_loss_cls_4[0.3347] det_loss_box_4[0.3779] det_loss_cls_5[0.3279] det_loss_box_5[0.3762] det_loss_cls_dn_0[0.0938] det_loss_box_dn_0[0.3138] det_loss_cls_dn_1[0.0902] det_loss_box_dn_1[0.2601] det_loss_cls_dn_2[0.0904] det_loss_box_dn_2[0.2452] det_loss_cls_dn_3[0.0896] det_loss_box_dn_3[0.2400] det_loss_cls_dn_4[0.0878] det_loss_box_dn_4[0.2387] det_loss_cls_dn_5[0.0864] det_loss_box_dn_5[0.2316] loss_depth[0.0396] total_loss[6.9080]
Rank[0/8] 06/20/2025 03:58:37 INFO stats.py:314 | Epoch[104] Step[264] GlobalStep[40824] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 17:48:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:58:47 INFO loss_tracker.py:84 | Epoch[104/239] Step[274] GlobalStep[40834/NA]: det_loss_cls_0[0.5362] det_loss_box_0[0.6413] det_loss_cls_1[0.5161] det_loss_box_1[0.5056] det_loss_cls_2[0.4835] det_loss_box_2[0.4590] det_loss_cls_3[0.4497] det_loss_box_3[0.4453] det_loss_cls_4[0.4207] det_loss_box_4[0.4372] det_loss_cls_5[0.4016] det_loss_box_5[0.4410] det_loss_cls_dn_0[0.0930] det_loss_box_dn_0[0.2456] det_loss_cls_dn_1[0.0889] det_loss_box_dn_1[0.2103] det_loss_cls_dn_2[0.0882] det_loss_box_dn_2[0.1957] det_loss_cls_dn_3[0.0874] det_loss_box_dn_3[0.1932] det_loss_cls_dn_4[0.0864] det_loss_box_dn_4[0.1922] det_loss_cls_dn_5[0.0855] det_loss_box_dn_5[0.1906] loss_depth[0.0353] total_loss[7.5293]
Rank[0/8] 06/20/2025 03:59:03 INFO stats.py:314 | Epoch[104] Step[289] GlobalStep[40849] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 17:47:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:59:13 INFO loss_tracker.py:84 | Epoch[104/239] Step[299] GlobalStep[40859/NA]: det_loss_cls_0[0.4695] det_loss_box_0[0.5574] det_loss_cls_1[0.4506] det_loss_box_1[0.4236] det_loss_cls_2[0.4139] det_loss_box_2[0.3765] det_loss_cls_3[0.3882] det_loss_box_3[0.3751] det_loss_cls_4[0.3582] det_loss_box_4[0.3596] det_loss_cls_5[0.3446] det_loss_box_5[0.3560] det_loss_cls_dn_0[0.0952] det_loss_box_dn_0[0.2443] det_loss_cls_dn_1[0.0914] det_loss_box_dn_1[0.2018] det_loss_cls_dn_2[0.0907] det_loss_box_dn_2[0.1878] det_loss_cls_dn_3[0.0900] det_loss_box_dn_3[0.1848] det_loss_cls_dn_4[0.0885] det_loss_box_dn_4[0.1844] det_loss_cls_dn_5[0.0879] det_loss_box_dn_5[0.1824] loss_depth[0.0416] total_loss[6.6439]
Rank[0/8] 06/20/2025 03:59:29 INFO stats.py:314 | Epoch[104] Step[314] GlobalStep[40874] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 17:47:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 03:59:39 INFO loss_tracker.py:84 | Epoch[104/239] Step[324] GlobalStep[40884/NA]: det_loss_cls_0[0.5475] det_loss_box_0[0.7167] det_loss_cls_1[0.5299] det_loss_box_1[0.5696] det_loss_cls_2[0.4925] det_loss_box_2[0.5229] det_loss_cls_3[0.4688] det_loss_box_3[0.5056] det_loss_cls_4[0.4434] det_loss_box_4[0.4949] det_loss_cls_5[0.4312] det_loss_box_5[0.4898] det_loss_cls_dn_0[0.1032] det_loss_box_dn_0[0.2918] det_loss_cls_dn_1[0.0988] det_loss_box_dn_1[0.2538] det_loss_cls_dn_2[0.0990] det_loss_box_dn_2[0.2389] det_loss_cls_dn_3[0.0974] det_loss_box_dn_3[0.2359] det_loss_cls_dn_4[0.0964] det_loss_box_dn_4[0.2347] det_loss_cls_dn_5[0.0952] det_loss_box_dn_5[0.2322] loss_depth[0.0407] total_loss[8.3310]
Rank[0/8] 06/20/2025 03:59:54 INFO stats.py:314 | Epoch[104] Step[339] GlobalStep[40899] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 17:46:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:00:04 INFO loss_tracker.py:84 | Epoch[104/239] Step[349] GlobalStep[40909/NA]: det_loss_cls_0[0.5073] det_loss_box_0[0.6633] det_loss_cls_1[0.5087] det_loss_box_1[0.5037] det_loss_cls_2[0.4715] det_loss_box_2[0.4567] det_loss_cls_3[0.4392] det_loss_box_3[0.4423] det_loss_cls_4[0.4292] det_loss_box_4[0.4254] det_loss_cls_5[0.4097] det_loss_box_5[0.4318] det_loss_cls_dn_0[0.0890] det_loss_box_dn_0[0.2618] det_loss_cls_dn_1[0.0854] det_loss_box_dn_1[0.2216] det_loss_cls_dn_2[0.0840] det_loss_box_dn_2[0.2073] det_loss_cls_dn_3[0.0831] det_loss_box_dn_3[0.2043] det_loss_cls_dn_4[0.0819] det_loss_box_dn_4[0.2020] det_loss_cls_dn_5[0.0807] det_loss_box_dn_5[0.1991] loss_depth[0.0376] total_loss[7.5268]
Rank[0/8] 06/20/2025 04:00:20 INFO stats.py:314 | Epoch[104] Step[364] GlobalStep[40924] Training Speed: 7.92 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 17:45:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:00:30 INFO loss_tracker.py:84 | Epoch[104/239] Step[374] GlobalStep[40934/NA]: det_loss_cls_0[0.6250] det_loss_box_0[0.8065] det_loss_cls_1[0.6083] det_loss_box_1[0.6503] det_loss_cls_2[0.5576] det_loss_box_2[0.6018] det_loss_cls_3[0.5167] det_loss_box_3[0.5887] det_loss_cls_4[0.4871] det_loss_box_4[0.5783] det_loss_cls_5[0.4806] det_loss_box_5[0.5737] det_loss_cls_dn_0[0.1054] det_loss_box_dn_0[0.3049] det_loss_cls_dn_1[0.1016] det_loss_box_dn_1[0.2639] det_loss_cls_dn_2[0.1009] det_loss_box_dn_2[0.2458] det_loss_cls_dn_3[0.1001] det_loss_box_dn_3[0.2439] det_loss_cls_dn_4[0.0996] det_loss_box_dn_4[0.2416] det_loss_cls_dn_5[0.0987] det_loss_box_dn_5[0.2407] loss_depth[0.0389] total_loss[9.2607]
Rank[0/8] 06/20/2025 04:00:46 INFO stats.py:314 | Epoch[104] Step[389] GlobalStep[40949] Training Speed: 8.66 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 17:45:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:00:46 INFO stats.py:394 | Epoch[104] completed. Training Speed: 7.67 samples/sec across all devices.	Epoch Time: 406.57 sec.	Average Epoch Time: 406.57 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 17:45:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:01:13 INFO stats.py:314 | Epoch[105] Step[24] GlobalStep[40974] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 17:44:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:01:13 INFO loss_tracker.py:84 | Epoch[105/239] Step[24] GlobalStep[40974/NA]: det_loss_cls_0[0.4080] det_loss_box_0[0.5386] det_loss_cls_1[0.4041] det_loss_box_1[0.4233] det_loss_cls_2[0.3695] det_loss_box_2[0.3831] det_loss_cls_3[0.3391] det_loss_box_3[0.3757] det_loss_cls_4[0.3160] det_loss_box_4[0.3694] det_loss_cls_5[0.3087] det_loss_box_5[0.3723] det_loss_cls_dn_0[0.0913] det_loss_box_dn_0[0.3529] det_loss_cls_dn_1[0.0891] det_loss_box_dn_1[0.2921] det_loss_cls_dn_2[0.0884] det_loss_box_dn_2[0.2684] det_loss_cls_dn_3[0.0873] det_loss_box_dn_3[0.2629] det_loss_cls_dn_4[0.0868] det_loss_box_dn_4[0.2601] det_loss_cls_dn_5[0.0857] det_loss_box_dn_5[0.2577] loss_depth[0.0359] total_loss[6.8664]
Rank[0/8] 06/20/2025 04:01:39 INFO stats.py:314 | Epoch[105] Step[49] GlobalStep[40999] Training Speed: 8.76 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 17:44:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:01:39 INFO loss_tracker.py:84 | Epoch[105/239] Step[49] GlobalStep[40999/NA]: det_loss_cls_0[0.4303] det_loss_box_0[0.5572] det_loss_cls_1[0.4168] det_loss_box_1[0.4399] det_loss_cls_2[0.3802] det_loss_box_2[0.4037] det_loss_cls_3[0.3563] det_loss_box_3[0.3993] det_loss_cls_4[0.3395] det_loss_box_4[0.3780] det_loss_cls_5[0.3298] det_loss_box_5[0.3792] det_loss_cls_dn_0[0.0926] det_loss_box_dn_0[0.3307] det_loss_cls_dn_1[0.0887] det_loss_box_dn_1[0.2720] det_loss_cls_dn_2[0.0879] det_loss_box_dn_2[0.2513] det_loss_cls_dn_3[0.0859] det_loss_box_dn_3[0.2471] det_loss_cls_dn_4[0.0851] det_loss_box_dn_4[0.2455] det_loss_cls_dn_5[0.0841] det_loss_box_dn_5[0.2399] loss_depth[0.0366] total_loss[6.9576]
Rank[0/8] 06/20/2025 04:02:05 INFO stats.py:314 | Epoch[105] Step[74] GlobalStep[41024] Training Speed: 8.06 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 17:43:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:02:05 INFO loss_tracker.py:84 | Epoch[105/239] Step[74] GlobalStep[41024/NA]: det_loss_cls_0[0.6256] det_loss_box_0[0.8022] det_loss_cls_1[0.6027] det_loss_box_1[0.6385] det_loss_cls_2[0.5765] det_loss_box_2[0.5752] det_loss_cls_3[0.5475] det_loss_box_3[0.5579] det_loss_cls_4[0.5263] det_loss_box_4[0.5410] det_loss_cls_5[0.5137] det_loss_box_5[0.5441] det_loss_cls_dn_0[0.1053] det_loss_box_dn_0[0.2942] det_loss_cls_dn_1[0.1021] det_loss_box_dn_1[0.2542] det_loss_cls_dn_2[0.1019] det_loss_box_dn_2[0.2394] det_loss_cls_dn_3[0.1009] det_loss_box_dn_3[0.2381] det_loss_cls_dn_4[0.1001] det_loss_box_dn_4[0.2389] det_loss_cls_dn_5[0.0991] det_loss_box_dn_5[0.2376] loss_depth[0.0397] total_loss[9.2028]
Rank[0/8] 06/20/2025 04:02:31 INFO stats.py:314 | Epoch[105] Step[99] GlobalStep[41049] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 17:42:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:02:31 INFO loss_tracker.py:84 | Epoch[105/239] Step[99] GlobalStep[41049/NA]: det_loss_cls_0[0.5121] det_loss_box_0[0.6742] det_loss_cls_1[0.5058] det_loss_box_1[0.5191] det_loss_cls_2[0.4599] det_loss_box_2[0.4741] det_loss_cls_3[0.4396] det_loss_box_3[0.4467] det_loss_cls_4[0.4011] det_loss_box_4[0.4506] det_loss_cls_5[0.3833] det_loss_box_5[0.4498] det_loss_cls_dn_0[0.0979] det_loss_box_dn_0[0.2911] det_loss_cls_dn_1[0.0927] det_loss_box_dn_1[0.2438] det_loss_cls_dn_2[0.0919] det_loss_box_dn_2[0.2300] det_loss_cls_dn_3[0.0913] det_loss_box_dn_3[0.2275] det_loss_cls_dn_4[0.0907] det_loss_box_dn_4[0.2266] det_loss_cls_dn_5[0.0896] det_loss_box_dn_5[0.2239] loss_depth[0.0395] total_loss[7.7529]
Rank[0/8] 06/20/2025 04:02:57 INFO stats.py:314 | Epoch[105] Step[124] GlobalStep[41074] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 17:42:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:02:57 INFO loss_tracker.py:84 | Epoch[105/239] Step[124] GlobalStep[41074/NA]: det_loss_cls_0[0.5593] det_loss_box_0[0.7278] det_loss_cls_1[0.5379] det_loss_box_1[0.5535] det_loss_cls_2[0.5023] det_loss_box_2[0.4793] det_loss_cls_3[0.4661] det_loss_box_3[0.4681] det_loss_cls_4[0.4485] det_loss_box_4[0.4570] det_loss_cls_5[0.4310] det_loss_box_5[0.4589] det_loss_cls_dn_0[0.0972] det_loss_box_dn_0[0.3842] det_loss_cls_dn_1[0.0936] det_loss_box_dn_1[0.3310] det_loss_cls_dn_2[0.0927] det_loss_box_dn_2[0.3201] det_loss_cls_dn_3[0.0917] det_loss_box_dn_3[0.3120] det_loss_cls_dn_4[0.0903] det_loss_box_dn_4[0.3097] det_loss_cls_dn_5[0.0892] det_loss_box_dn_5[0.3079] loss_depth[0.0398] total_loss[8.6487]
Rank[0/8] 06/20/2025 04:03:23 INFO stats.py:314 | Epoch[105] Step[149] GlobalStep[41099] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 17:41:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:03:23 INFO loss_tracker.py:84 | Epoch[105/239] Step[149] GlobalStep[41099/NA]: det_loss_cls_0[0.4923] det_loss_box_0[0.6657] det_loss_cls_1[0.4716] det_loss_box_1[0.5259] det_loss_cls_2[0.4436] det_loss_box_2[0.4704] det_loss_cls_3[0.4133] det_loss_box_3[0.4548] det_loss_cls_4[0.3880] det_loss_box_4[0.4446] det_loss_cls_5[0.3764] det_loss_box_5[0.4390] det_loss_cls_dn_0[0.1030] det_loss_box_dn_0[0.3317] det_loss_cls_dn_1[0.0989] det_loss_box_dn_1[0.2915] det_loss_cls_dn_2[0.0976] det_loss_box_dn_2[0.2747] det_loss_cls_dn_3[0.0966] det_loss_box_dn_3[0.2708] det_loss_cls_dn_4[0.0955] det_loss_box_dn_4[0.2692] det_loss_cls_dn_5[0.0943] det_loss_box_dn_5[0.2664] loss_depth[0.0374] total_loss[7.9130]
Rank[0/8] 06/20/2025 04:03:49 INFO stats.py:314 | Epoch[105] Step[174] GlobalStep[41124] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 17:41:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:03:49 INFO loss_tracker.py:84 | Epoch[105/239] Step[174] GlobalStep[41124/NA]: det_loss_cls_0[0.3918] det_loss_box_0[0.5219] det_loss_cls_1[0.3966] det_loss_box_1[0.3949] det_loss_cls_2[0.3526] det_loss_box_2[0.3679] det_loss_cls_3[0.3298] det_loss_box_3[0.3565] det_loss_cls_4[0.3167] det_loss_box_4[0.3444] det_loss_cls_5[0.3080] det_loss_box_5[0.3441] det_loss_cls_dn_0[0.0859] det_loss_box_dn_0[0.2848] det_loss_cls_dn_1[0.0830] det_loss_box_dn_1[0.2327] det_loss_cls_dn_2[0.0828] det_loss_box_dn_2[0.2167] det_loss_cls_dn_3[0.0812] det_loss_box_dn_3[0.2089] det_loss_cls_dn_4[0.0805] det_loss_box_dn_4[0.2049] det_loss_cls_dn_5[0.0796] det_loss_box_dn_5[0.2032] loss_depth[0.0390] total_loss[6.3084]
Rank[0/8] 06/20/2025 04:04:14 INFO stats.py:314 | Epoch[105] Step[199] GlobalStep[41149] Training Speed: 8.02 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 17:40:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:04:14 INFO loss_tracker.py:84 | Epoch[105/239] Step[199] GlobalStep[41149/NA]: det_loss_cls_0[0.4815] det_loss_box_0[0.5583] det_loss_cls_1[0.4731] det_loss_box_1[0.4360] det_loss_cls_2[0.4315] det_loss_box_2[0.3979] det_loss_cls_3[0.4115] det_loss_box_3[0.3866] det_loss_cls_4[0.3844] det_loss_box_4[0.3844] det_loss_cls_5[0.3710] det_loss_box_5[0.3851] det_loss_cls_dn_0[0.1073] det_loss_box_dn_0[0.2923] det_loss_cls_dn_1[0.1044] det_loss_box_dn_1[0.2509] det_loss_cls_dn_2[0.1040] det_loss_box_dn_2[0.2352] det_loss_cls_dn_3[0.1028] det_loss_box_dn_3[0.2333] det_loss_cls_dn_4[0.1018] det_loss_box_dn_4[0.2321] det_loss_cls_dn_5[0.1000] det_loss_box_dn_5[0.2305] loss_depth[0.0388] total_loss[7.2345]
Rank[0/8] 06/20/2025 04:04:40 INFO stats.py:314 | Epoch[105] Step[224] GlobalStep[41174] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 17:39:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:04:40 INFO loss_tracker.py:84 | Epoch[105/239] Step[224] GlobalStep[41174/NA]: det_loss_cls_0[0.5300] det_loss_box_0[0.6942] det_loss_cls_1[0.5105] det_loss_box_1[0.5458] det_loss_cls_2[0.4675] det_loss_box_2[0.4996] det_loss_cls_3[0.4466] det_loss_box_3[0.4733] det_loss_cls_4[0.4222] det_loss_box_4[0.4591] det_loss_cls_5[0.4064] det_loss_box_5[0.4668] det_loss_cls_dn_0[0.0867] det_loss_box_dn_0[0.3195] det_loss_cls_dn_1[0.0833] det_loss_box_dn_1[0.2678] det_loss_cls_dn_2[0.0830] det_loss_box_dn_2[0.2480] det_loss_cls_dn_3[0.0824] det_loss_box_dn_3[0.2449] det_loss_cls_dn_4[0.0812] det_loss_box_dn_4[0.2431] det_loss_cls_dn_5[0.0805] det_loss_box_dn_5[0.2411] loss_depth[0.0384] total_loss[8.0218]
Rank[0/8] 06/20/2025 04:05:06 INFO stats.py:314 | Epoch[105] Step[249] GlobalStep[41199] Training Speed: 8.68 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 17:39:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:05:06 INFO loss_tracker.py:84 | Epoch[105/239] Step[249] GlobalStep[41199/NA]: det_loss_cls_0[0.4346] det_loss_box_0[0.5815] det_loss_cls_1[0.4327] det_loss_box_1[0.4371] det_loss_cls_2[0.3951] det_loss_box_2[0.3950] det_loss_cls_3[0.3792] det_loss_box_3[0.3787] det_loss_cls_4[0.3642] det_loss_box_4[0.3633] det_loss_cls_5[0.3543] det_loss_box_5[0.3674] det_loss_cls_dn_0[0.1003] det_loss_box_dn_0[0.3092] det_loss_cls_dn_1[0.0968] det_loss_box_dn_1[0.2575] det_loss_cls_dn_2[0.0959] det_loss_box_dn_2[0.2392] det_loss_cls_dn_3[0.0952] det_loss_box_dn_3[0.2357] det_loss_cls_dn_4[0.0946] det_loss_box_dn_4[0.2346] det_loss_cls_dn_5[0.0937] det_loss_box_dn_5[0.2314] loss_depth[0.0401] total_loss[7.0073]
Rank[0/8] 06/20/2025 04:05:32 INFO stats.py:314 | Epoch[105] Step[274] GlobalStep[41224] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 17:38:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:05:32 INFO loss_tracker.py:84 | Epoch[105/239] Step[274] GlobalStep[41224/NA]: det_loss_cls_0[0.5753] det_loss_box_0[0.7392] det_loss_cls_1[0.5470] det_loss_box_1[0.5560] det_loss_cls_2[0.4933] det_loss_box_2[0.5019] det_loss_cls_3[0.4738] det_loss_box_3[0.4868] det_loss_cls_4[0.4357] det_loss_box_4[0.4811] det_loss_cls_5[0.4292] det_loss_box_5[0.4746] det_loss_cls_dn_0[0.0931] det_loss_box_dn_0[0.2499] det_loss_cls_dn_1[0.0900] det_loss_box_dn_1[0.2052] det_loss_cls_dn_2[0.0891] det_loss_box_dn_2[0.1884] det_loss_cls_dn_3[0.0888] det_loss_box_dn_3[0.1856] det_loss_cls_dn_4[0.0879] det_loss_box_dn_4[0.1840] det_loss_cls_dn_5[0.0873] det_loss_box_dn_5[0.1827] loss_depth[0.0373] total_loss[7.9630]
Rank[0/8] 06/20/2025 04:05:58 INFO stats.py:314 | Epoch[105] Step[299] GlobalStep[41249] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 17:38:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:05:58 INFO loss_tracker.py:84 | Epoch[105/239] Step[299] GlobalStep[41249/NA]: det_loss_cls_0[0.9011] det_loss_box_0[1.1454] det_loss_cls_1[0.8589] det_loss_box_1[0.8805] det_loss_cls_2[0.8076] det_loss_box_2[0.8113] det_loss_cls_3[0.7674] det_loss_box_3[0.7804] det_loss_cls_4[0.7362] det_loss_box_4[0.7602] det_loss_cls_5[0.7147] det_loss_box_5[0.7602] det_loss_cls_dn_0[0.1078] det_loss_box_dn_0[0.2658] det_loss_cls_dn_1[0.1048] det_loss_box_dn_1[0.2265] det_loss_cls_dn_2[0.1048] det_loss_box_dn_2[0.2145] det_loss_cls_dn_3[0.1042] det_loss_box_dn_3[0.2121] det_loss_cls_dn_4[0.1026] det_loss_box_dn_4[0.2112] det_loss_cls_dn_5[0.1027] det_loss_box_dn_5[0.2088] loss_depth[0.0425] total_loss[11.9324]
Rank[0/8] 06/20/2025 04:06:24 INFO stats.py:314 | Epoch[105] Step[324] GlobalStep[41274] Training Speed: 6.63 samples/sec across all devices.	Average Step Time: 1.21 sec.	Estimated Remaining Time: 17:37:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:06:24 INFO loss_tracker.py:84 | Epoch[105/239] Step[324] GlobalStep[41274/NA]: det_loss_cls_0[0.5405] det_loss_box_0[0.6385] det_loss_cls_1[0.5233] det_loss_box_1[0.4875] det_loss_cls_2[0.4765] det_loss_box_2[0.4533] det_loss_cls_3[0.4488] det_loss_box_3[0.4366] det_loss_cls_4[0.4175] det_loss_box_4[0.4316] det_loss_cls_5[0.4076] det_loss_box_5[0.4299] det_loss_cls_dn_0[0.1152] det_loss_box_dn_0[0.2934] det_loss_cls_dn_1[0.1122] det_loss_box_dn_1[0.2494] det_loss_cls_dn_2[0.1119] det_loss_box_dn_2[0.2320] det_loss_cls_dn_3[0.1106] det_loss_box_dn_3[0.2292] det_loss_cls_dn_4[0.1100] det_loss_box_dn_4[0.2273] det_loss_cls_dn_5[0.1091] det_loss_box_dn_5[0.2264] loss_depth[0.0402] total_loss[7.8588]
Rank[0/8] 06/20/2025 04:06:50 INFO stats.py:314 | Epoch[105] Step[349] GlobalStep[41299] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 17:36:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:06:50 INFO loss_tracker.py:84 | Epoch[105/239] Step[349] GlobalStep[41299/NA]: det_loss_cls_0[0.4885] det_loss_box_0[0.6256] det_loss_cls_1[0.4740] det_loss_box_1[0.5076] det_loss_cls_2[0.4350] det_loss_box_2[0.4670] det_loss_cls_3[0.3994] det_loss_box_3[0.4515] det_loss_cls_4[0.3778] det_loss_box_4[0.4532] det_loss_cls_5[0.3644] det_loss_box_5[0.4480] det_loss_cls_dn_0[0.0893] det_loss_box_dn_0[0.2718] det_loss_cls_dn_1[0.0863] det_loss_box_dn_1[0.2312] det_loss_cls_dn_2[0.0853] det_loss_box_dn_2[0.2149] det_loss_cls_dn_3[0.0839] det_loss_box_dn_3[0.2120] det_loss_cls_dn_4[0.0829] det_loss_box_dn_4[0.2108] det_loss_cls_dn_5[0.0823] det_loss_box_dn_5[0.2094] loss_depth[0.0348] total_loss[7.3870]
Rank[0/8] 06/20/2025 04:07:16 INFO stats.py:314 | Epoch[105] Step[374] GlobalStep[41324] Training Speed: 8.03 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 17:36:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:07:16 INFO loss_tracker.py:84 | Epoch[105/239] Step[374] GlobalStep[41324/NA]: det_loss_cls_0[0.4520] det_loss_box_0[0.5831] det_loss_cls_1[0.4498] det_loss_box_1[0.4577] det_loss_cls_2[0.4188] det_loss_box_2[0.4088] det_loss_cls_3[0.3899] det_loss_box_3[0.4024] det_loss_cls_4[0.3623] det_loss_box_4[0.3856] det_loss_cls_5[0.3587] det_loss_box_5[0.3840] det_loss_cls_dn_0[0.0961] det_loss_box_dn_0[0.2975] det_loss_cls_dn_1[0.0907] det_loss_box_dn_1[0.2537] det_loss_cls_dn_2[0.0901] det_loss_box_dn_2[0.2375] det_loss_cls_dn_3[0.0896] det_loss_box_dn_3[0.2342] det_loss_cls_dn_4[0.0879] det_loss_box_dn_4[0.2331] det_loss_cls_dn_5[0.0870] det_loss_box_dn_5[0.2305] loss_depth[0.0389] total_loss[7.1200]
Rank[0/8] 06/20/2025 04:07:31 INFO stats.py:394 | Epoch[105] completed. Training Speed: 7.69 samples/sec across all devices.	Epoch Time: 405.83 sec.	Average Epoch Time: 405.83 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 17:35:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:07:44 INFO stats.py:314 | Epoch[106] Step[9] GlobalStep[41349] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 17:35:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:08:00 INFO loss_tracker.py:84 | Epoch[106/239] Step[24] GlobalStep[41364/NA]: det_loss_cls_0[0.6678] det_loss_box_0[0.8179] det_loss_cls_1[0.6301] det_loss_box_1[0.6362] det_loss_cls_2[0.5923] det_loss_box_2[0.5764] det_loss_cls_3[0.5629] det_loss_box_3[0.5559] det_loss_cls_4[0.5416] det_loss_box_4[0.5380] det_loss_cls_5[0.5164] det_loss_box_5[0.5418] det_loss_cls_dn_0[0.0963] det_loss_box_dn_0[0.2896] det_loss_cls_dn_1[0.0923] det_loss_box_dn_1[0.2510] det_loss_cls_dn_2[0.0911] det_loss_box_dn_2[0.2359] det_loss_cls_dn_3[0.0905] det_loss_box_dn_3[0.2327] det_loss_cls_dn_4[0.0894] det_loss_box_dn_4[0.2309] det_loss_cls_dn_5[0.0887] det_loss_box_dn_5[0.2296] loss_depth[0.0396] total_loss[9.2348]
Rank[0/8] 06/20/2025 04:08:11 INFO stats.py:314 | Epoch[106] Step[34] GlobalStep[41374] Training Speed: 7.55 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 17:35:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:08:27 INFO loss_tracker.py:84 | Epoch[106/239] Step[49] GlobalStep[41389/NA]: det_loss_cls_0[0.4150] det_loss_box_0[0.5399] det_loss_cls_1[0.4016] det_loss_box_1[0.4065] det_loss_cls_2[0.3659] det_loss_box_2[0.3723] det_loss_cls_3[0.3427] det_loss_box_3[0.3598] det_loss_cls_4[0.3161] det_loss_box_4[0.3525] det_loss_cls_5[0.3028] det_loss_box_5[0.3496] det_loss_cls_dn_0[0.0926] det_loss_box_dn_0[0.3310] det_loss_cls_dn_1[0.0890] det_loss_box_dn_1[0.2734] det_loss_cls_dn_2[0.0877] det_loss_box_dn_2[0.2543] det_loss_cls_dn_3[0.0869] det_loss_box_dn_3[0.2492] det_loss_cls_dn_4[0.0858] det_loss_box_dn_4[0.2476] det_loss_cls_dn_5[0.0843] det_loss_box_dn_5[0.2437] loss_depth[0.0393] total_loss[6.6895]
Rank[0/8] 06/20/2025 04:08:37 INFO stats.py:314 | Epoch[106] Step[59] GlobalStep[41399] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 17:34:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:08:52 INFO loss_tracker.py:84 | Epoch[106/239] Step[74] GlobalStep[41414/NA]: det_loss_cls_0[0.4465] det_loss_box_0[0.5559] det_loss_cls_1[0.4168] det_loss_box_1[0.4382] det_loss_cls_2[0.3845] det_loss_box_2[0.3927] det_loss_cls_3[0.3611] det_loss_box_3[0.3735] det_loss_cls_4[0.3379] det_loss_box_4[0.3742] det_loss_cls_5[0.3188] det_loss_box_5[0.3753] det_loss_cls_dn_0[0.0900] det_loss_box_dn_0[0.2895] det_loss_cls_dn_1[0.0868] det_loss_box_dn_1[0.2479] det_loss_cls_dn_2[0.0855] det_loss_box_dn_2[0.2282] det_loss_cls_dn_3[0.0847] det_loss_box_dn_3[0.2240] det_loss_cls_dn_4[0.0841] det_loss_box_dn_4[0.2223] det_loss_cls_dn_5[0.0829] det_loss_box_dn_5[0.2210] loss_depth[0.0336] total_loss[6.7558]
Rank[0/8] 06/20/2025 04:09:02 INFO stats.py:314 | Epoch[106] Step[84] GlobalStep[41424] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 17:33:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:09:18 INFO loss_tracker.py:84 | Epoch[106/239] Step[99] GlobalStep[41439/NA]: det_loss_cls_0[0.4748] det_loss_box_0[0.6702] det_loss_cls_1[0.4703] det_loss_box_1[0.5169] det_loss_cls_2[0.4397] det_loss_box_2[0.4661] det_loss_cls_3[0.4105] det_loss_box_3[0.4381] det_loss_cls_4[0.3955] det_loss_box_4[0.4173] det_loss_cls_5[0.3767] det_loss_box_5[0.4279] det_loss_cls_dn_0[0.0965] det_loss_box_dn_0[0.3285] det_loss_cls_dn_1[0.0925] det_loss_box_dn_1[0.2851] det_loss_cls_dn_2[0.0921] det_loss_box_dn_2[0.2693] det_loss_cls_dn_3[0.0904] det_loss_box_dn_3[0.2661] det_loss_cls_dn_4[0.0902] det_loss_box_dn_4[0.2645] det_loss_cls_dn_5[0.0894] det_loss_box_dn_5[0.2612] loss_depth[0.0403] total_loss[7.7702]
Rank[0/8] 06/20/2025 04:09:29 INFO stats.py:314 | Epoch[106] Step[109] GlobalStep[41449] Training Speed: 7.83 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 17:33:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:09:44 INFO loss_tracker.py:84 | Epoch[106/239] Step[124] GlobalStep[41464/NA]: det_loss_cls_0[0.5442] det_loss_box_0[0.6518] det_loss_cls_1[0.5158] det_loss_box_1[0.5199] det_loss_cls_2[0.4830] det_loss_box_2[0.4791] det_loss_cls_3[0.4484] det_loss_box_3[0.4650] det_loss_cls_4[0.4224] det_loss_box_4[0.4519] det_loss_cls_5[0.4066] det_loss_box_5[0.4589] det_loss_cls_dn_0[0.0958] det_loss_box_dn_0[0.2882] det_loss_cls_dn_1[0.0918] det_loss_box_dn_1[0.2456] det_loss_cls_dn_2[0.0902] det_loss_box_dn_2[0.2335] det_loss_cls_dn_3[0.0896] det_loss_box_dn_3[0.2322] det_loss_cls_dn_4[0.0890] det_loss_box_dn_4[0.2323] det_loss_cls_dn_5[0.0885] det_loss_box_dn_5[0.2311] loss_depth[0.0366] total_loss[7.8915]
Rank[0/8] 06/20/2025 04:09:54 INFO stats.py:314 | Epoch[106] Step[134] GlobalStep[41474] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 17:32:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:10:10 INFO loss_tracker.py:84 | Epoch[106/239] Step[149] GlobalStep[41489/NA]: det_loss_cls_0[0.4502] det_loss_box_0[0.6241] det_loss_cls_1[0.4401] det_loss_box_1[0.4939] det_loss_cls_2[0.4222] det_loss_box_2[0.4394] det_loss_cls_3[0.3975] det_loss_box_3[0.4196] det_loss_cls_4[0.3770] det_loss_box_4[0.4105] det_loss_cls_5[0.3612] det_loss_box_5[0.4113] det_loss_cls_dn_0[0.1016] det_loss_box_dn_0[0.3652] det_loss_cls_dn_1[0.0966] det_loss_box_dn_1[0.3196] det_loss_cls_dn_2[0.0954] det_loss_box_dn_2[0.3007] det_loss_cls_dn_3[0.0938] det_loss_box_dn_3[0.2981] det_loss_cls_dn_4[0.0925] det_loss_box_dn_4[0.2972] det_loss_cls_dn_5[0.0916] det_loss_box_dn_5[0.2942] loss_depth[0.0391] total_loss[7.7325]
Rank[0/8] 06/20/2025 04:10:20 INFO stats.py:314 | Epoch[106] Step[159] GlobalStep[41499] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 17:32:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:10:36 INFO loss_tracker.py:84 | Epoch[106/239] Step[174] GlobalStep[41514/NA]: det_loss_cls_0[0.4612] det_loss_box_0[0.6060] det_loss_cls_1[0.4453] det_loss_box_1[0.4571] det_loss_cls_2[0.4095] det_loss_box_2[0.4086] det_loss_cls_3[0.3806] det_loss_box_3[0.4032] det_loss_cls_4[0.3592] det_loss_box_4[0.4008] det_loss_cls_5[0.3509] det_loss_box_5[0.3979] det_loss_cls_dn_0[0.1022] det_loss_box_dn_0[0.3170] det_loss_cls_dn_1[0.0985] det_loss_box_dn_1[0.2748] det_loss_cls_dn_2[0.0978] det_loss_box_dn_2[0.2569] det_loss_cls_dn_3[0.0972] det_loss_box_dn_3[0.2545] det_loss_cls_dn_4[0.0959] det_loss_box_dn_4[0.2525] det_loss_cls_dn_5[0.0953] det_loss_box_dn_5[0.2500] loss_depth[0.0375] total_loss[7.3104]
Rank[0/8] 06/20/2025 04:10:46 INFO stats.py:314 | Epoch[106] Step[184] GlobalStep[41524] Training Speed: 7.59 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 17:31:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:11:02 INFO loss_tracker.py:84 | Epoch[106/239] Step[199] GlobalStep[41539/NA]: det_loss_cls_0[0.4092] det_loss_box_0[0.5235] det_loss_cls_1[0.4095] det_loss_box_1[0.4107] det_loss_cls_2[0.3869] det_loss_box_2[0.3807] det_loss_cls_3[0.3566] det_loss_box_3[0.3790] det_loss_cls_4[0.3349] det_loss_box_4[0.3689] det_loss_cls_5[0.3279] det_loss_box_5[0.3662] det_loss_cls_dn_0[0.1001] det_loss_box_dn_0[0.3141] det_loss_cls_dn_1[0.0961] det_loss_box_dn_1[0.2628] det_loss_cls_dn_2[0.0959] det_loss_box_dn_2[0.2456] det_loss_cls_dn_3[0.0943] det_loss_box_dn_3[0.2412] det_loss_cls_dn_4[0.0927] det_loss_box_dn_4[0.2388] det_loss_cls_dn_5[0.0915] det_loss_box_dn_5[0.2365] loss_depth[0.0370] total_loss[6.8005]
Rank[0/8] 06/20/2025 04:11:12 INFO stats.py:314 | Epoch[106] Step[209] GlobalStep[41549] Training Speed: 6.13 samples/sec across all devices.	Average Step Time: 1.30 sec.	Estimated Remaining Time: 17:30:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:11:27 INFO loss_tracker.py:84 | Epoch[106/239] Step[224] GlobalStep[41564/NA]: det_loss_cls_0[0.4467] det_loss_box_0[0.5967] det_loss_cls_1[0.4293] det_loss_box_1[0.4510] det_loss_cls_2[0.3851] det_loss_box_2[0.4069] det_loss_cls_3[0.3498] det_loss_box_3[0.4011] det_loss_cls_4[0.3310] det_loss_box_4[0.3962] det_loss_cls_5[0.3196] det_loss_box_5[0.3914] det_loss_cls_dn_0[0.0946] det_loss_box_dn_0[0.2880] det_loss_cls_dn_1[0.0924] det_loss_box_dn_1[0.2410] det_loss_cls_dn_2[0.0917] det_loss_box_dn_2[0.2215] det_loss_cls_dn_3[0.0908] det_loss_box_dn_3[0.2182] det_loss_cls_dn_4[0.0896] det_loss_box_dn_4[0.2169] det_loss_cls_dn_5[0.0889] det_loss_box_dn_5[0.2169] loss_depth[0.0377] total_loss[6.8929]
Rank[0/8] 06/20/2025 04:11:37 INFO stats.py:314 | Epoch[106] Step[234] GlobalStep[41574] Training Speed: 7.78 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 17:30:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:11:53 INFO loss_tracker.py:84 | Epoch[106/239] Step[249] GlobalStep[41589/NA]: det_loss_cls_0[0.4455] det_loss_box_0[0.5981] det_loss_cls_1[0.4378] det_loss_box_1[0.4689] det_loss_cls_2[0.4097] det_loss_box_2[0.4225] det_loss_cls_3[0.3724] det_loss_box_3[0.4213] det_loss_cls_4[0.3471] det_loss_box_4[0.4110] det_loss_cls_5[0.3353] det_loss_box_5[0.4105] det_loss_cls_dn_0[0.0940] det_loss_box_dn_0[0.3357] det_loss_cls_dn_1[0.0894] det_loss_box_dn_1[0.2900] det_loss_cls_dn_2[0.0894] det_loss_box_dn_2[0.2687] det_loss_cls_dn_3[0.0893] det_loss_box_dn_3[0.2655] det_loss_cls_dn_4[0.0879] det_loss_box_dn_4[0.2641] det_loss_cls_dn_5[0.0873] det_loss_box_dn_5[0.2633] loss_depth[0.0401] total_loss[7.3445]
Rank[0/8] 06/20/2025 04:12:03 INFO stats.py:314 | Epoch[106] Step[259] GlobalStep[41599] Training Speed: 6.49 samples/sec across all devices.	Average Step Time: 1.23 sec.	Estimated Remaining Time: 17:29:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:12:19 INFO loss_tracker.py:84 | Epoch[106/239] Step[274] GlobalStep[41614/NA]: det_loss_cls_0[0.4698] det_loss_box_0[0.6917] det_loss_cls_1[0.4532] det_loss_box_1[0.5444] det_loss_cls_2[0.4201] det_loss_box_2[0.4952] det_loss_cls_3[0.4015] det_loss_box_3[0.4815] det_loss_cls_4[0.3758] det_loss_box_4[0.4709] det_loss_cls_5[0.3640] det_loss_box_5[0.4687] det_loss_cls_dn_0[0.1025] det_loss_box_dn_0[0.3854] det_loss_cls_dn_1[0.0976] det_loss_box_dn_1[0.3409] det_loss_cls_dn_2[0.0974] det_loss_box_dn_2[0.3266] det_loss_cls_dn_3[0.0973] det_loss_box_dn_3[0.3236] det_loss_cls_dn_4[0.0958] det_loss_box_dn_4[0.3224] det_loss_cls_dn_5[0.0950] det_loss_box_dn_5[0.3169] loss_depth[0.0395] total_loss[8.2775]
Rank[0/8] 06/20/2025 04:12:29 INFO stats.py:314 | Epoch[106] Step[284] GlobalStep[41624] Training Speed: 8.22 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 17:29:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:12:45 INFO loss_tracker.py:84 | Epoch[106/239] Step[299] GlobalStep[41639/NA]: det_loss_cls_0[0.4474] det_loss_box_0[0.5400] det_loss_cls_1[0.4506] det_loss_box_1[0.4243] det_loss_cls_2[0.4069] det_loss_box_2[0.3880] det_loss_cls_3[0.3817] det_loss_box_3[0.3794] det_loss_cls_4[0.3572] det_loss_box_4[0.3739] det_loss_cls_5[0.3426] det_loss_box_5[0.3761] det_loss_cls_dn_0[0.0948] det_loss_box_dn_0[0.2494] det_loss_cls_dn_1[0.0911] det_loss_box_dn_1[0.2091] det_loss_cls_dn_2[0.0900] det_loss_box_dn_2[0.1948] det_loss_cls_dn_3[0.0890] det_loss_box_dn_3[0.1916] det_loss_cls_dn_4[0.0880] det_loss_box_dn_4[0.1906] det_loss_cls_dn_5[0.0869] det_loss_box_dn_5[0.1894] loss_depth[0.0374] total_loss[6.6702]
Rank[0/8] 06/20/2025 04:12:55 INFO stats.py:314 | Epoch[106] Step[309] GlobalStep[41649] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 17:28:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:13:10 INFO loss_tracker.py:84 | Epoch[106/239] Step[324] GlobalStep[41664/NA]: det_loss_cls_0[0.6448] det_loss_box_0[0.8151] det_loss_cls_1[0.6286] det_loss_box_1[0.6000] det_loss_cls_2[0.5769] det_loss_box_2[0.5377] det_loss_cls_3[0.5389] det_loss_box_3[0.5107] det_loss_cls_4[0.4940] det_loss_box_4[0.5102] det_loss_cls_5[0.4731] det_loss_box_5[0.5111] det_loss_cls_dn_0[0.0994] det_loss_box_dn_0[0.2575] det_loss_cls_dn_1[0.0961] det_loss_box_dn_1[0.2159] det_loss_cls_dn_2[0.0949] det_loss_box_dn_2[0.2004] det_loss_cls_dn_3[0.0944] det_loss_box_dn_3[0.1983] det_loss_cls_dn_4[0.0936] det_loss_box_dn_4[0.1968] det_loss_cls_dn_5[0.0926] det_loss_box_dn_5[0.1958] loss_depth[0.0413] total_loss[8.7181]
Rank[0/8] 06/20/2025 04:13:21 INFO stats.py:314 | Epoch[106] Step[334] GlobalStep[41674] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 17:27:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:13:36 INFO loss_tracker.py:84 | Epoch[106/239] Step[349] GlobalStep[41689/NA]: det_loss_cls_0[0.4814] det_loss_box_0[0.6742] det_loss_cls_1[0.4791] det_loss_box_1[0.5284] det_loss_cls_2[0.4461] det_loss_box_2[0.4726] det_loss_cls_3[0.4186] det_loss_box_3[0.4480] det_loss_cls_4[0.3915] det_loss_box_4[0.4464] det_loss_cls_5[0.3755] det_loss_box_5[0.4488] det_loss_cls_dn_0[0.0912] det_loss_box_dn_0[0.3187] det_loss_cls_dn_1[0.0880] det_loss_box_dn_1[0.2730] det_loss_cls_dn_2[0.0870] det_loss_box_dn_2[0.2562] det_loss_cls_dn_3[0.0865] det_loss_box_dn_3[0.2531] det_loss_cls_dn_4[0.0856] det_loss_box_dn_4[0.2521] det_loss_cls_dn_5[0.0847] det_loss_box_dn_5[0.2459] loss_depth[0.0374] total_loss[7.7700]
Rank[0/8] 06/20/2025 04:13:47 INFO stats.py:314 | Epoch[106] Step[359] GlobalStep[41699] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 17:27:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:14:03 INFO loss_tracker.py:84 | Epoch[106/239] Step[374] GlobalStep[41714/NA]: det_loss_cls_0[0.6375] det_loss_box_0[0.8022] det_loss_cls_1[0.6279] det_loss_box_1[0.6338] det_loss_cls_2[0.5828] det_loss_box_2[0.5686] det_loss_cls_3[0.5618] det_loss_box_3[0.5472] det_loss_cls_4[0.5358] det_loss_box_4[0.5365] det_loss_cls_5[0.5177] det_loss_box_5[0.5365] det_loss_cls_dn_0[0.0954] det_loss_box_dn_0[0.2782] det_loss_cls_dn_1[0.0928] det_loss_box_dn_1[0.2373] det_loss_cls_dn_2[0.0910] det_loss_box_dn_2[0.2216] det_loss_cls_dn_3[0.0899] det_loss_box_dn_3[0.2186] det_loss_cls_dn_4[0.0889] det_loss_box_dn_4[0.2167] det_loss_cls_dn_5[0.0874] det_loss_box_dn_5[0.2151] loss_depth[0.0372] total_loss[9.0584]
Rank[0/8] 06/20/2025 04:14:13 INFO stats.py:314 | Epoch[106] Step[384] GlobalStep[41724] Training Speed: 8.66 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 17:26:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:14:18 INFO stats.py:394 | Epoch[106] completed. Training Speed: 7.68 samples/sec across all devices.	Epoch Time: 406.19 sec.	Average Epoch Time: 406.19 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 17:26:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:14:40 INFO stats.py:314 | Epoch[107] Step[19] GlobalStep[41749] Training Speed: 8.68 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 17:26:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:14:45 INFO loss_tracker.py:84 | Epoch[107/239] Step[24] GlobalStep[41754/NA]: det_loss_cls_0[0.5252] det_loss_box_0[0.7101] det_loss_cls_1[0.5064] det_loss_box_1[0.5387] det_loss_cls_2[0.4730] det_loss_box_2[0.4861] det_loss_cls_3[0.4342] det_loss_box_3[0.4749] det_loss_cls_4[0.4077] det_loss_box_4[0.4643] det_loss_cls_5[0.3912] det_loss_box_5[0.4666] det_loss_cls_dn_0[0.1012] det_loss_box_dn_0[0.3045] det_loss_cls_dn_1[0.0973] det_loss_box_dn_1[0.2598] det_loss_cls_dn_2[0.0964] det_loss_box_dn_2[0.2393] det_loss_cls_dn_3[0.0956] det_loss_box_dn_3[0.2364] det_loss_cls_dn_4[0.0952] det_loss_box_dn_4[0.2343] det_loss_cls_dn_5[0.0943] det_loss_box_dn_5[0.2316] loss_depth[0.0394] total_loss[8.0039]
Rank[0/8] 06/20/2025 04:15:06 INFO stats.py:314 | Epoch[107] Step[44] GlobalStep[41774] Training Speed: 8.56 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 17:25:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:15:12 INFO loss_tracker.py:84 | Epoch[107/239] Step[49] GlobalStep[41779/NA]: det_loss_cls_0[0.5410] det_loss_box_0[0.7037] det_loss_cls_1[0.5347] det_loss_box_1[0.5266] det_loss_cls_2[0.4853] det_loss_box_2[0.4876] det_loss_cls_3[0.4611] det_loss_box_3[0.4604] det_loss_cls_4[0.4217] det_loss_box_4[0.4603] det_loss_cls_5[0.4106] det_loss_box_5[0.4593] det_loss_cls_dn_0[0.0980] det_loss_box_dn_0[0.3077] det_loss_cls_dn_1[0.0945] det_loss_box_dn_1[0.2620] det_loss_cls_dn_2[0.0935] det_loss_box_dn_2[0.2457] det_loss_cls_dn_3[0.0928] det_loss_box_dn_3[0.2425] det_loss_cls_dn_4[0.0917] det_loss_box_dn_4[0.2394] det_loss_cls_dn_5[0.0906] det_loss_box_dn_5[0.2368] loss_depth[0.0372] total_loss[8.0848]
Rank[0/8] 06/20/2025 04:15:33 INFO stats.py:314 | Epoch[107] Step[69] GlobalStep[41799] Training Speed: 7.70 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 17:25:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:15:38 INFO loss_tracker.py:84 | Epoch[107/239] Step[74] GlobalStep[41804/NA]: det_loss_cls_0[0.5316] det_loss_box_0[0.7290] det_loss_cls_1[0.5153] det_loss_box_1[0.5690] det_loss_cls_2[0.4762] det_loss_box_2[0.5151] det_loss_cls_3[0.4615] det_loss_box_3[0.4944] det_loss_cls_4[0.4294] det_loss_box_4[0.4925] det_loss_cls_5[0.4221] det_loss_box_5[0.4892] det_loss_cls_dn_0[0.1098] det_loss_box_dn_0[0.3228] det_loss_cls_dn_1[0.1069] det_loss_box_dn_1[0.2749] det_loss_cls_dn_2[0.1071] det_loss_box_dn_2[0.2576] det_loss_cls_dn_3[0.1058] det_loss_box_dn_3[0.2549] det_loss_cls_dn_4[0.1045] det_loss_box_dn_4[0.2537] det_loss_cls_dn_5[0.1037] det_loss_box_dn_5[0.2494] loss_depth[0.0423] total_loss[8.4186]
Rank[0/8] 06/20/2025 04:15:58 INFO stats.py:314 | Epoch[107] Step[94] GlobalStep[41824] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 17:24:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:16:03 INFO loss_tracker.py:84 | Epoch[107/239] Step[99] GlobalStep[41829/NA]: det_loss_cls_0[0.4076] det_loss_box_0[0.5494] det_loss_cls_1[0.4088] det_loss_box_1[0.4017] det_loss_cls_2[0.3653] det_loss_box_2[0.3701] det_loss_cls_3[0.3292] det_loss_box_3[0.3599] det_loss_cls_4[0.3057] det_loss_box_4[0.3492] det_loss_cls_5[0.3041] det_loss_box_5[0.3431] det_loss_cls_dn_0[0.0886] det_loss_box_dn_0[0.2960] det_loss_cls_dn_1[0.0839] det_loss_box_dn_1[0.2443] det_loss_cls_dn_2[0.0837] det_loss_box_dn_2[0.2260] det_loss_cls_dn_3[0.0828] det_loss_box_dn_3[0.2228] det_loss_cls_dn_4[0.0825] det_loss_box_dn_4[0.2214] det_loss_cls_dn_5[0.0815] det_loss_box_dn_5[0.2176] loss_depth[0.0368] total_loss[6.4621]
Rank[0/8] 06/20/2025 04:16:24 INFO stats.py:314 | Epoch[107] Step[119] GlobalStep[41849] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 17:23:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:16:29 INFO loss_tracker.py:84 | Epoch[107/239] Step[124] GlobalStep[41854/NA]: det_loss_cls_0[0.4145] det_loss_box_0[0.5528] det_loss_cls_1[0.4054] det_loss_box_1[0.4296] det_loss_cls_2[0.3688] det_loss_box_2[0.3883] det_loss_cls_3[0.3468] det_loss_box_3[0.3795] det_loss_cls_4[0.3303] det_loss_box_4[0.3657] det_loss_cls_5[0.3237] det_loss_box_5[0.3632] det_loss_cls_dn_0[0.0995] det_loss_box_dn_0[0.3161] det_loss_cls_dn_1[0.0957] det_loss_box_dn_1[0.2716] det_loss_cls_dn_2[0.0946] det_loss_box_dn_2[0.2519] det_loss_cls_dn_3[0.0934] det_loss_box_dn_3[0.2495] det_loss_cls_dn_4[0.0916] det_loss_box_dn_4[0.2473] det_loss_cls_dn_5[0.0905] det_loss_box_dn_5[0.2450] loss_depth[0.0383] total_loss[6.8535]
Rank[0/8] 06/20/2025 04:16:50 INFO stats.py:314 | Epoch[107] Step[144] GlobalStep[41874] Training Speed: 8.12 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 17:23:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:16:55 INFO loss_tracker.py:84 | Epoch[107/239] Step[149] GlobalStep[41879/NA]: det_loss_cls_0[0.4427] det_loss_box_0[0.5931] det_loss_cls_1[0.4334] det_loss_box_1[0.4752] det_loss_cls_2[0.4055] det_loss_box_2[0.4205] det_loss_cls_3[0.3722] det_loss_box_3[0.4161] det_loss_cls_4[0.3492] det_loss_box_4[0.3956] det_loss_cls_5[0.3349] det_loss_box_5[0.3962] det_loss_cls_dn_0[0.1010] det_loss_box_dn_0[0.3545] det_loss_cls_dn_1[0.0978] det_loss_box_dn_1[0.3085] det_loss_cls_dn_2[0.0972] det_loss_box_dn_2[0.2947] det_loss_cls_dn_3[0.0967] det_loss_box_dn_3[0.2925] det_loss_cls_dn_4[0.0960] det_loss_box_dn_4[0.2907] det_loss_cls_dn_5[0.0955] det_loss_box_dn_5[0.2881] loss_depth[0.0386] total_loss[7.4866]
Rank[0/8] 06/20/2025 04:17:16 INFO stats.py:314 | Epoch[107] Step[169] GlobalStep[41899] Training Speed: 7.78 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 17:22:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:17:21 INFO loss_tracker.py:84 | Epoch[107/239] Step[174] GlobalStep[41904/NA]: det_loss_cls_0[0.4524] det_loss_box_0[0.5742] det_loss_cls_1[0.4586] det_loss_box_1[0.4374] det_loss_cls_2[0.4126] det_loss_box_2[0.4117] det_loss_cls_3[0.3940] det_loss_box_3[0.3963] det_loss_cls_4[0.3633] det_loss_box_4[0.3861] det_loss_cls_5[0.3485] det_loss_box_5[0.3842] det_loss_cls_dn_0[0.1016] det_loss_box_dn_0[0.2954] det_loss_cls_dn_1[0.0972] det_loss_box_dn_1[0.2472] det_loss_cls_dn_2[0.0966] det_loss_box_dn_2[0.2276] det_loss_cls_dn_3[0.0951] det_loss_box_dn_3[0.2247] det_loss_cls_dn_4[0.0939] det_loss_box_dn_4[0.2225] det_loss_cls_dn_5[0.0932] det_loss_box_dn_5[0.2219] loss_depth[0.0373] total_loss[7.0737]
Rank[0/8] 06/20/2025 04:17:41 INFO stats.py:314 | Epoch[107] Step[194] GlobalStep[41924] Training Speed: 8.56 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 17:22:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:17:46 INFO loss_tracker.py:84 | Epoch[107/239] Step[199] GlobalStep[41929/NA]: det_loss_cls_0[0.5450] det_loss_box_0[0.7269] det_loss_cls_1[0.5319] det_loss_box_1[0.5760] det_loss_cls_2[0.4796] det_loss_box_2[0.5388] det_loss_cls_3[0.4514] det_loss_box_3[0.5153] det_loss_cls_4[0.4400] det_loss_box_4[0.4869] det_loss_cls_5[0.4271] det_loss_box_5[0.4860] det_loss_cls_dn_0[0.0927] det_loss_box_dn_0[0.3105] det_loss_cls_dn_1[0.0881] det_loss_box_dn_1[0.2696] det_loss_cls_dn_2[0.0876] det_loss_box_dn_2[0.2551] det_loss_cls_dn_3[0.0870] det_loss_box_dn_3[0.2525] det_loss_cls_dn_4[0.0858] det_loss_box_dn_4[0.2506] det_loss_cls_dn_5[0.0846] det_loss_box_dn_5[0.2489] loss_depth[0.0378] total_loss[8.3559]
Rank[0/8] 06/20/2025 04:18:07 INFO stats.py:314 | Epoch[107] Step[219] GlobalStep[41949] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 17:21:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:18:13 INFO loss_tracker.py:84 | Epoch[107/239] Step[224] GlobalStep[41954/NA]: det_loss_cls_0[0.4552] det_loss_box_0[0.5445] det_loss_cls_1[0.4487] det_loss_box_1[0.4315] det_loss_cls_2[0.4065] det_loss_box_2[0.4212] det_loss_cls_3[0.3803] det_loss_box_3[0.4089] det_loss_cls_4[0.3544] det_loss_box_4[0.4018] det_loss_cls_5[0.3404] det_loss_box_5[0.4024] det_loss_cls_dn_0[0.1028] det_loss_box_dn_0[0.2921] det_loss_cls_dn_1[0.0995] det_loss_box_dn_1[0.2500] det_loss_cls_dn_2[0.0992] det_loss_box_dn_2[0.2366] det_loss_cls_dn_3[0.0975] det_loss_box_dn_3[0.2342] det_loss_cls_dn_4[0.0960] det_loss_box_dn_4[0.2331] det_loss_cls_dn_5[0.0954] det_loss_box_dn_5[0.2320] loss_depth[0.0354] total_loss[7.0997]
Rank[0/8] 06/20/2025 04:18:34 INFO stats.py:314 | Epoch[107] Step[244] GlobalStep[41974] Training Speed: 11.13 samples/sec across all devices.	Average Step Time: 0.72 sec.	Estimated Remaining Time: 17:20:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:18:39 INFO loss_tracker.py:84 | Epoch[107/239] Step[249] GlobalStep[41979/NA]: det_loss_cls_0[0.5695] det_loss_box_0[0.7032] det_loss_cls_1[0.5514] det_loss_box_1[0.5388] det_loss_cls_2[0.5087] det_loss_box_2[0.5026] det_loss_cls_3[0.4751] det_loss_box_3[0.4889] det_loss_cls_4[0.4274] det_loss_box_4[0.4902] det_loss_cls_5[0.4183] det_loss_box_5[0.4926] det_loss_cls_dn_0[0.1012] det_loss_box_dn_0[0.2901] det_loss_cls_dn_1[0.0975] det_loss_box_dn_1[0.2504] det_loss_cls_dn_2[0.0970] det_loss_box_dn_2[0.2369] det_loss_cls_dn_3[0.0960] det_loss_box_dn_3[0.2314] det_loss_cls_dn_4[0.0948] det_loss_box_dn_4[0.2305] det_loss_cls_dn_5[0.0936] det_loss_box_dn_5[0.2282] loss_depth[0.0403] total_loss[8.2547]
Rank[0/8] 06/20/2025 04:18:59 INFO stats.py:314 | Epoch[107] Step[269] GlobalStep[41999] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 17:20:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:19:04 INFO loss_tracker.py:84 | Epoch[107/239] Step[274] GlobalStep[42004/NA]: det_loss_cls_0[0.6875] det_loss_box_0[0.8547] det_loss_cls_1[0.6674] det_loss_box_1[0.6533] det_loss_cls_2[0.6336] det_loss_box_2[0.5890] det_loss_cls_3[0.5887] det_loss_box_3[0.5699] det_loss_cls_4[0.5561] det_loss_box_4[0.5655] det_loss_cls_5[0.5447] det_loss_box_5[0.5682] det_loss_cls_dn_0[0.1131] det_loss_box_dn_0[0.2814] det_loss_cls_dn_1[0.1104] det_loss_box_dn_1[0.2364] det_loss_cls_dn_2[0.1100] det_loss_box_dn_2[0.2186] det_loss_cls_dn_3[0.1088] det_loss_box_dn_3[0.2164] det_loss_cls_dn_4[0.1076] det_loss_box_dn_4[0.2149] det_loss_cls_dn_5[0.1070] det_loss_box_dn_5[0.2139] loss_depth[0.0407] total_loss[9.5578]
Rank[0/8] 06/20/2025 04:19:25 INFO stats.py:314 | Epoch[107] Step[294] GlobalStep[42024] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 17:19:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:19:30 INFO loss_tracker.py:84 | Epoch[107/239] Step[299] GlobalStep[42029/NA]: det_loss_cls_0[0.5332] det_loss_box_0[0.6589] det_loss_cls_1[0.5412] det_loss_box_1[0.5012] det_loss_cls_2[0.4982] det_loss_box_2[0.4697] det_loss_cls_3[0.4684] det_loss_box_3[0.4518] det_loss_cls_4[0.4320] det_loss_box_4[0.4484] det_loss_cls_5[0.4185] det_loss_box_5[0.4487] det_loss_cls_dn_0[0.1006] det_loss_box_dn_0[0.3072] det_loss_cls_dn_1[0.0969] det_loss_box_dn_1[0.2592] det_loss_cls_dn_2[0.0963] det_loss_box_dn_2[0.2396] det_loss_cls_dn_3[0.0954] det_loss_box_dn_3[0.2344] det_loss_cls_dn_4[0.0945] det_loss_box_dn_4[0.2319] det_loss_cls_dn_5[0.0945] det_loss_box_dn_5[0.2296] loss_depth[0.0371] total_loss[7.9873]
Rank[0/8] 06/20/2025 04:19:51 INFO stats.py:314 | Epoch[107] Step[319] GlobalStep[42049] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 17:19:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:19:57 INFO loss_tracker.py:84 | Epoch[107/239] Step[324] GlobalStep[42054/NA]: det_loss_cls_0[0.5641] det_loss_box_0[0.7344] det_loss_cls_1[0.5593] det_loss_box_1[0.5735] det_loss_cls_2[0.5147] det_loss_box_2[0.5211] det_loss_cls_3[0.4797] det_loss_box_3[0.5031] det_loss_cls_4[0.4556] det_loss_box_4[0.4901] det_loss_cls_5[0.4394] det_loss_box_5[0.4877] det_loss_cls_dn_0[0.1024] det_loss_box_dn_0[0.2817] det_loss_cls_dn_1[0.0971] det_loss_box_dn_1[0.2429] det_loss_cls_dn_2[0.0966] det_loss_box_dn_2[0.2293] det_loss_cls_dn_3[0.0957] det_loss_box_dn_3[0.2262] det_loss_cls_dn_4[0.0947] det_loss_box_dn_4[0.2249] det_loss_cls_dn_5[0.0943] det_loss_box_dn_5[0.2241] loss_depth[0.0406] total_loss[8.3731]
Rank[0/8] 06/20/2025 04:20:17 INFO stats.py:314 | Epoch[107] Step[344] GlobalStep[42074] Training Speed: 8.22 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 17:18:30.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:20:22 INFO loss_tracker.py:84 | Epoch[107/239] Step[349] GlobalStep[42079/NA]: det_loss_cls_0[0.5018] det_loss_box_0[0.6838] det_loss_cls_1[0.4832] det_loss_box_1[0.5296] det_loss_cls_2[0.4494] det_loss_box_2[0.4671] det_loss_cls_3[0.4272] det_loss_box_3[0.4366] det_loss_cls_4[0.4043] det_loss_box_4[0.4287] det_loss_cls_5[0.3874] det_loss_box_5[0.4286] det_loss_cls_dn_0[0.0995] det_loss_box_dn_0[0.3530] det_loss_cls_dn_1[0.0973] det_loss_box_dn_1[0.3071] det_loss_cls_dn_2[0.0963] det_loss_box_dn_2[0.2867] det_loss_cls_dn_3[0.0956] det_loss_box_dn_3[0.2829] det_loss_cls_dn_4[0.0948] det_loss_box_dn_4[0.2796] det_loss_cls_dn_5[0.0933] det_loss_box_dn_5[0.2776] loss_depth[0.0383] total_loss[8.0295]
Rank[0/8] 06/20/2025 04:20:43 INFO stats.py:314 | Epoch[107] Step[369] GlobalStep[42099] Training Speed: 8.34 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 17:17:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:20:48 INFO loss_tracker.py:84 | Epoch[107/239] Step[374] GlobalStep[42104/NA]: det_loss_cls_0[0.4642] det_loss_box_0[0.6368] det_loss_cls_1[0.4494] det_loss_box_1[0.5060] det_loss_cls_2[0.4278] det_loss_box_2[0.4397] det_loss_cls_3[0.4052] det_loss_box_3[0.4288] det_loss_cls_4[0.3730] det_loss_box_4[0.4249] det_loss_cls_5[0.3631] det_loss_box_5[0.4247] det_loss_cls_dn_0[0.0941] det_loss_box_dn_0[0.2772] det_loss_cls_dn_1[0.0905] det_loss_box_dn_1[0.2386] det_loss_cls_dn_2[0.0899] det_loss_box_dn_2[0.2233] det_loss_cls_dn_3[0.0892] det_loss_box_dn_3[0.2206] det_loss_cls_dn_4[0.0880] det_loss_box_dn_4[0.2187] det_loss_cls_dn_5[0.0873] det_loss_box_dn_5[0.2167] loss_depth[0.0398] total_loss[7.3173]
Rank[0/8] 06/20/2025 04:21:04 INFO stats.py:394 | Epoch[107] completed. Training Speed: 7.68 samples/sec across all devices.	Epoch Time: 406.04 sec.	Average Epoch Time: 406.04 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 17:17:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:21:12 INFO stats.py:314 | Epoch[108] Step[4] GlobalStep[42124] Training Speed: 7.15 samples/sec across all devices.	Average Step Time: 1.12 sec.	Estimated Remaining Time: 17:17:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:21:33 INFO loss_tracker.py:84 | Epoch[108/239] Step[24] GlobalStep[42144/NA]: det_loss_cls_0[0.5329] det_loss_box_0[0.6993] det_loss_cls_1[0.5182] det_loss_box_1[0.5400] det_loss_cls_2[0.4813] det_loss_box_2[0.5053] det_loss_cls_3[0.4546] det_loss_box_3[0.4903] det_loss_cls_4[0.4308] det_loss_box_4[0.4711] det_loss_cls_5[0.4211] det_loss_box_5[0.4689] det_loss_cls_dn_0[0.1060] det_loss_box_dn_0[0.4037] det_loss_cls_dn_1[0.1018] det_loss_box_dn_1[0.3499] det_loss_cls_dn_2[0.1012] det_loss_box_dn_2[0.3310] det_loss_cls_dn_3[0.1010] det_loss_box_dn_3[0.3274] det_loss_cls_dn_4[0.1001] det_loss_box_dn_4[0.3265] det_loss_cls_dn_5[0.0991] det_loss_box_dn_5[0.3235] loss_depth[0.0411] total_loss[8.7263]
Rank[0/8] 06/20/2025 04:21:38 INFO stats.py:314 | Epoch[108] Step[29] GlobalStep[42149] Training Speed: 8.06 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 17:16:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:21:58 INFO loss_tracker.py:84 | Epoch[108/239] Step[49] GlobalStep[42169/NA]: det_loss_cls_0[0.5148] det_loss_box_0[0.6710] det_loss_cls_1[0.5049] det_loss_box_1[0.5211] det_loss_cls_2[0.4598] det_loss_box_2[0.4702] det_loss_cls_3[0.4261] det_loss_box_3[0.4513] det_loss_cls_4[0.4007] det_loss_box_4[0.4341] det_loss_cls_5[0.3837] det_loss_box_5[0.4361] det_loss_cls_dn_0[0.0912] det_loss_box_dn_0[0.2901] det_loss_cls_dn_1[0.0875] det_loss_box_dn_1[0.2440] det_loss_cls_dn_2[0.0865] det_loss_box_dn_2[0.2274] det_loss_cls_dn_3[0.0851] det_loss_box_dn_3[0.2235] det_loss_cls_dn_4[0.0844] det_loss_box_dn_4[0.2212] det_loss_cls_dn_5[0.0831] det_loss_box_dn_5[0.2168] loss_depth[0.0375] total_loss[7.6518]
Rank[0/8] 06/20/2025 04:22:03 INFO stats.py:314 | Epoch[108] Step[54] GlobalStep[42174] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 17:16:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:22:24 INFO loss_tracker.py:84 | Epoch[108/239] Step[74] GlobalStep[42194/NA]: det_loss_cls_0[0.4538] det_loss_box_0[0.6176] det_loss_cls_1[0.4371] det_loss_box_1[0.4732] det_loss_cls_2[0.4028] det_loss_box_2[0.4383] det_loss_cls_3[0.3733] det_loss_box_3[0.4306] det_loss_cls_4[0.3506] det_loss_box_4[0.4200] det_loss_cls_5[0.3331] det_loss_box_5[0.4233] det_loss_cls_dn_0[0.0938] det_loss_box_dn_0[0.4891] det_loss_cls_dn_1[0.0900] det_loss_box_dn_1[0.4118] det_loss_cls_dn_2[0.0900] det_loss_box_dn_2[0.3878] det_loss_cls_dn_3[0.0888] det_loss_box_dn_3[0.3849] det_loss_cls_dn_4[0.0877] det_loss_box_dn_4[0.3800] det_loss_cls_dn_5[0.0873] det_loss_box_dn_5[0.3740] loss_depth[0.0395] total_loss[8.1585]
Rank[0/8] 06/20/2025 04:22:29 INFO stats.py:314 | Epoch[108] Step[79] GlobalStep[42199] Training Speed: 7.84 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 17:15:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:22:51 INFO loss_tracker.py:84 | Epoch[108/239] Step[99] GlobalStep[42219/NA]: det_loss_cls_0[0.5508] det_loss_box_0[0.6977] det_loss_cls_1[0.5418] det_loss_box_1[0.5337] det_loss_cls_2[0.5017] det_loss_box_2[0.4805] det_loss_cls_3[0.4672] det_loss_box_3[0.4713] det_loss_cls_4[0.4449] det_loss_box_4[0.4563] det_loss_cls_5[0.4198] det_loss_box_5[0.4555] det_loss_cls_dn_0[0.0979] det_loss_box_dn_0[0.2758] det_loss_cls_dn_1[0.0939] det_loss_box_dn_1[0.2370] det_loss_cls_dn_2[0.0927] det_loss_box_dn_2[0.2203] det_loss_cls_dn_3[0.0916] det_loss_box_dn_3[0.2177] det_loss_cls_dn_4[0.0905] det_loss_box_dn_4[0.2161] det_loss_cls_dn_5[0.0893] det_loss_box_dn_5[0.2118] loss_depth[0.0390] total_loss[7.9950]
Rank[0/8] 06/20/2025 04:22:56 INFO stats.py:314 | Epoch[108] Step[104] GlobalStep[42224] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 17:15:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:23:16 INFO loss_tracker.py:84 | Epoch[108/239] Step[124] GlobalStep[42244/NA]: det_loss_cls_0[0.5719] det_loss_box_0[0.7708] det_loss_cls_1[0.5592] det_loss_box_1[0.6427] det_loss_cls_2[0.5156] det_loss_box_2[0.5827] det_loss_cls_3[0.4893] det_loss_box_3[0.5571] det_loss_cls_4[0.4690] det_loss_box_4[0.5369] det_loss_cls_5[0.4591] det_loss_box_5[0.5328] det_loss_cls_dn_0[0.1008] det_loss_box_dn_0[0.3286] det_loss_cls_dn_1[0.0976] det_loss_box_dn_1[0.2808] det_loss_cls_dn_2[0.0976] det_loss_box_dn_2[0.2668] det_loss_cls_dn_3[0.0969] det_loss_box_dn_3[0.2644] det_loss_cls_dn_4[0.0964] det_loss_box_dn_4[0.2631] det_loss_cls_dn_5[0.0956] det_loss_box_dn_5[0.2625] loss_depth[0.0408] total_loss[8.9791]
Rank[0/8] 06/20/2025 04:23:21 INFO stats.py:314 | Epoch[108] Step[129] GlobalStep[42249] Training Speed: 7.74 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 17:14:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:23:42 INFO loss_tracker.py:84 | Epoch[108/239] Step[149] GlobalStep[42269/NA]: det_loss_cls_0[0.5940] det_loss_box_0[0.7619] det_loss_cls_1[0.5894] det_loss_box_1[0.5957] det_loss_cls_2[0.5496] det_loss_box_2[0.5442] det_loss_cls_3[0.5193] det_loss_box_3[0.5189] det_loss_cls_4[0.4917] det_loss_box_4[0.5052] det_loss_cls_5[0.4793] det_loss_box_5[0.5081] det_loss_cls_dn_0[0.1013] det_loss_box_dn_0[0.2873] det_loss_cls_dn_1[0.0972] det_loss_box_dn_1[0.2424] det_loss_cls_dn_2[0.0964] det_loss_box_dn_2[0.2231] det_loss_cls_dn_3[0.0952] det_loss_box_dn_3[0.2214] det_loss_cls_dn_4[0.0945] det_loss_box_dn_4[0.2198] det_loss_cls_dn_5[0.0939] det_loss_box_dn_5[0.2178] loss_depth[0.0364] total_loss[8.6839]
Rank[0/8] 06/20/2025 04:23:47 INFO stats.py:314 | Epoch[108] Step[154] GlobalStep[42274] Training Speed: 8.34 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 17:13:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:24:08 INFO loss_tracker.py:84 | Epoch[108/239] Step[174] GlobalStep[42294/NA]: det_loss_cls_0[0.4150] det_loss_box_0[0.5186] det_loss_cls_1[0.4079] det_loss_box_1[0.4113] det_loss_cls_2[0.3814] det_loss_box_2[0.3705] det_loss_cls_3[0.3431] det_loss_box_3[0.3622] det_loss_cls_4[0.3226] det_loss_box_4[0.3552] det_loss_cls_5[0.3105] det_loss_box_5[0.3553] det_loss_cls_dn_0[0.0935] det_loss_box_dn_0[0.3313] det_loss_cls_dn_1[0.0900] det_loss_box_dn_1[0.2761] det_loss_cls_dn_2[0.0896] det_loss_box_dn_2[0.2553] det_loss_cls_dn_3[0.0881] det_loss_box_dn_3[0.2506] det_loss_cls_dn_4[0.0875] det_loss_box_dn_4[0.2482] det_loss_cls_dn_5[0.0868] det_loss_box_dn_5[0.2450] loss_depth[0.0359] total_loss[6.7315]
Rank[0/8] 06/20/2025 04:24:14 INFO stats.py:314 | Epoch[108] Step[179] GlobalStep[42299] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 17:13:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:24:34 INFO loss_tracker.py:84 | Epoch[108/239] Step[199] GlobalStep[42319/NA]: det_loss_cls_0[0.4729] det_loss_box_0[0.6586] det_loss_cls_1[0.4602] det_loss_box_1[0.5101] det_loss_cls_2[0.4468] det_loss_box_2[0.4455] det_loss_cls_3[0.4130] det_loss_box_3[0.4243] det_loss_cls_4[0.3939] det_loss_box_4[0.4108] det_loss_cls_5[0.3720] det_loss_box_5[0.4203] det_loss_cls_dn_0[0.1045] det_loss_box_dn_0[0.2980] det_loss_cls_dn_1[0.1006] det_loss_box_dn_1[0.2569] det_loss_cls_dn_2[0.0996] det_loss_box_dn_2[0.2415] det_loss_cls_dn_3[0.0985] det_loss_box_dn_3[0.2375] det_loss_cls_dn_4[0.0981] det_loss_box_dn_4[0.2357] det_loss_cls_dn_5[0.0967] det_loss_box_dn_5[0.2334] loss_depth[0.0408] total_loss[7.5701]
Rank[0/8] 06/20/2025 04:24:39 INFO stats.py:314 | Epoch[108] Step[204] GlobalStep[42324] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 17:12:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:24:59 INFO loss_tracker.py:84 | Epoch[108/239] Step[224] GlobalStep[42344/NA]: det_loss_cls_0[0.6039] det_loss_box_0[0.7640] det_loss_cls_1[0.5970] det_loss_box_1[0.5732] det_loss_cls_2[0.5469] det_loss_box_2[0.5107] det_loss_cls_3[0.5147] det_loss_box_3[0.4933] det_loss_cls_4[0.4645] det_loss_box_4[0.4924] det_loss_cls_5[0.4482] det_loss_box_5[0.4924] det_loss_cls_dn_0[0.0999] det_loss_box_dn_0[0.2715] det_loss_cls_dn_1[0.0957] det_loss_box_dn_1[0.2336] det_loss_cls_dn_2[0.0948] det_loss_box_dn_2[0.2181] det_loss_cls_dn_3[0.0950] det_loss_box_dn_3[0.2134] det_loss_cls_dn_4[0.0937] det_loss_box_dn_4[0.2121] det_loss_cls_dn_5[0.0928] det_loss_box_dn_5[0.2114] loss_depth[0.0388] total_loss[8.4720]
Rank[0/8] 06/20/2025 04:25:05 INFO stats.py:314 | Epoch[108] Step[229] GlobalStep[42349] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 17:12:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:25:25 INFO loss_tracker.py:84 | Epoch[108/239] Step[249] GlobalStep[42369/NA]: det_loss_cls_0[0.4614] det_loss_box_0[0.6333] det_loss_cls_1[0.4563] det_loss_box_1[0.4814] det_loss_cls_2[0.4084] det_loss_box_2[0.4311] det_loss_cls_3[0.3802] det_loss_box_3[0.4169] det_loss_cls_4[0.3475] det_loss_box_4[0.4119] det_loss_cls_5[0.3392] det_loss_box_5[0.4104] det_loss_cls_dn_0[0.0942] det_loss_box_dn_0[0.3213] det_loss_cls_dn_1[0.0900] det_loss_box_dn_1[0.2632] det_loss_cls_dn_2[0.0894] det_loss_box_dn_2[0.2484] det_loss_cls_dn_3[0.0884] det_loss_box_dn_3[0.2431] det_loss_cls_dn_4[0.0871] det_loss_box_dn_4[0.2445] det_loss_cls_dn_5[0.0859] det_loss_box_dn_5[0.2433] loss_depth[0.0399] total_loss[7.3166]
Rank[0/8] 06/20/2025 04:25:30 INFO stats.py:314 | Epoch[108] Step[254] GlobalStep[42374] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 17:11:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:25:51 INFO loss_tracker.py:84 | Epoch[108/239] Step[274] GlobalStep[42394/NA]: det_loss_cls_0[0.5802] det_loss_box_0[0.7120] det_loss_cls_1[0.5592] det_loss_box_1[0.5450] det_loss_cls_2[0.5176] det_loss_box_2[0.5032] det_loss_cls_3[0.4893] det_loss_box_3[0.4765] det_loss_cls_4[0.4603] det_loss_box_4[0.4716] det_loss_cls_5[0.4418] det_loss_box_5[0.4741] det_loss_cls_dn_0[0.1037] det_loss_box_dn_0[0.3065] det_loss_cls_dn_1[0.1009] det_loss_box_dn_1[0.2598] det_loss_cls_dn_2[0.1007] det_loss_box_dn_2[0.2453] det_loss_cls_dn_3[0.0998] det_loss_box_dn_3[0.2420] det_loss_cls_dn_4[0.0990] det_loss_box_dn_4[0.2395] det_loss_cls_dn_5[0.0977] det_loss_box_dn_5[0.2383] loss_depth[0.0396] total_loss[8.4034]
Rank[0/8] 06/20/2025 04:25:56 INFO stats.py:314 | Epoch[108] Step[279] GlobalStep[42399] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 17:10:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:26:16 INFO loss_tracker.py:84 | Epoch[108/239] Step[299] GlobalStep[42419/NA]: det_loss_cls_0[0.4144] det_loss_box_0[0.5406] det_loss_cls_1[0.3940] det_loss_box_1[0.4084] det_loss_cls_2[0.3707] det_loss_box_2[0.3565] det_loss_cls_3[0.3401] det_loss_box_3[0.3496] det_loss_cls_4[0.3183] det_loss_box_4[0.3318] det_loss_cls_5[0.3012] det_loss_box_5[0.3358] det_loss_cls_dn_0[0.0957] det_loss_box_dn_0[0.3070] det_loss_cls_dn_1[0.0924] det_loss_box_dn_1[0.2542] det_loss_cls_dn_2[0.0912] det_loss_box_dn_2[0.2372] det_loss_cls_dn_3[0.0901] det_loss_box_dn_3[0.2338] det_loss_cls_dn_4[0.0894] det_loss_box_dn_4[0.2322] det_loss_cls_dn_5[0.0882] det_loss_box_dn_5[0.2309] loss_depth[0.0391] total_loss[6.5426]
Rank[0/8] 06/20/2025 04:26:22 INFO stats.py:314 | Epoch[108] Step[304] GlobalStep[42424] Training Speed: 7.84 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 17:10:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:26:43 INFO loss_tracker.py:84 | Epoch[108/239] Step[324] GlobalStep[42444/NA]: det_loss_cls_0[0.4835] det_loss_box_0[0.5908] det_loss_cls_1[0.4678] det_loss_box_1[0.4453] det_loss_cls_2[0.4251] det_loss_box_2[0.4106] det_loss_cls_3[0.3993] det_loss_box_3[0.3968] det_loss_cls_4[0.3618] det_loss_box_4[0.3943] det_loss_cls_5[0.3472] det_loss_box_5[0.3954] det_loss_cls_dn_0[0.0966] det_loss_box_dn_0[0.2614] det_loss_cls_dn_1[0.0937] det_loss_box_dn_1[0.2176] det_loss_cls_dn_2[0.0929] det_loss_box_dn_2[0.2027] det_loss_cls_dn_3[0.0918] det_loss_box_dn_3[0.1997] det_loss_cls_dn_4[0.0910] det_loss_box_dn_4[0.1986] det_loss_cls_dn_5[0.0903] det_loss_box_dn_5[0.1915] loss_depth[0.0380] total_loss[6.9836]
Rank[0/8] 06/20/2025 04:26:48 INFO stats.py:314 | Epoch[108] Step[329] GlobalStep[42449] Training Speed: 8.34 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 17:09:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:27:08 INFO loss_tracker.py:84 | Epoch[108/239] Step[349] GlobalStep[42469/NA]: det_loss_cls_0[0.7076] det_loss_box_0[0.9690] det_loss_cls_1[0.6863] det_loss_box_1[0.7655] det_loss_cls_2[0.6431] det_loss_box_2[0.7097] det_loss_cls_3[0.6186] det_loss_box_3[0.6759] det_loss_cls_4[0.5847] det_loss_box_4[0.6723] det_loss_cls_5[0.5803] det_loss_box_5[0.6760] det_loss_cls_dn_0[0.1053] det_loss_box_dn_0[0.2876] det_loss_cls_dn_1[0.1026] det_loss_box_dn_1[0.2479] det_loss_cls_dn_2[0.1022] det_loss_box_dn_2[0.2289] det_loss_cls_dn_3[0.1019] det_loss_box_dn_3[0.2246] det_loss_cls_dn_4[0.1011] det_loss_box_dn_4[0.2248] det_loss_cls_dn_5[0.1007] det_loss_box_dn_5[0.2235] loss_depth[0.0442] total_loss[10.3843]
Rank[0/8] 06/20/2025 04:27:14 INFO stats.py:314 | Epoch[108] Step[354] GlobalStep[42474] Training Speed: 13.27 samples/sec across all devices.	Average Step Time: 0.60 sec.	Estimated Remaining Time: 17:09:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:27:34 INFO loss_tracker.py:84 | Epoch[108/239] Step[374] GlobalStep[42494/NA]: det_loss_cls_0[0.4628] det_loss_box_0[0.5593] det_loss_cls_1[0.4418] det_loss_box_1[0.4335] det_loss_cls_2[0.4033] det_loss_box_2[0.3906] det_loss_cls_3[0.3736] det_loss_box_3[0.3868] det_loss_cls_4[0.3513] det_loss_box_4[0.3785] det_loss_cls_5[0.3311] det_loss_box_5[0.3840] det_loss_cls_dn_0[0.1016] det_loss_box_dn_0[0.3037] det_loss_cls_dn_1[0.0982] det_loss_box_dn_1[0.2647] det_loss_cls_dn_2[0.0978] det_loss_box_dn_2[0.2415] det_loss_cls_dn_3[0.0961] det_loss_box_dn_3[0.2373] det_loss_cls_dn_4[0.0958] det_loss_box_dn_4[0.2345] det_loss_cls_dn_5[0.0945] det_loss_box_dn_5[0.2335] loss_depth[0.0404] total_loss[7.0363]
Rank[0/8] 06/20/2025 04:27:39 INFO stats.py:314 | Epoch[108] Step[379] GlobalStep[42499] Training Speed: 8.68 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 17:08:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:27:49 INFO stats.py:394 | Epoch[108] completed. Training Speed: 7.69 samples/sec across all devices.	Epoch Time: 405.83 sec.	Average Epoch Time: 405.83 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 17:08:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:28:07 INFO stats.py:314 | Epoch[109] Step[14] GlobalStep[42524] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 17:07:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:28:18 INFO loss_tracker.py:84 | Epoch[109/239] Step[24] GlobalStep[42534/NA]: det_loss_cls_0[0.5457] det_loss_box_0[0.7763] det_loss_cls_1[0.5296] det_loss_box_1[0.6307] det_loss_cls_2[0.4910] det_loss_box_2[0.5882] det_loss_cls_3[0.4558] det_loss_box_3[0.5867] det_loss_cls_4[0.4415] det_loss_box_4[0.5663] det_loss_cls_5[0.4320] det_loss_box_5[0.5640] det_loss_cls_dn_0[0.0983] det_loss_box_dn_0[0.3411] det_loss_cls_dn_1[0.0953] det_loss_box_dn_1[0.2940] det_loss_cls_dn_2[0.0949] det_loss_box_dn_2[0.2778] det_loss_cls_dn_3[0.0945] det_loss_box_dn_3[0.2744] det_loss_cls_dn_4[0.0938] det_loss_box_dn_4[0.2717] det_loss_cls_dn_5[0.0933] det_loss_box_dn_5[0.2696] loss_depth[0.0383] total_loss[8.9448]
Rank[0/8] 06/20/2025 04:28:34 INFO stats.py:314 | Epoch[109] Step[39] GlobalStep[42549] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 17:07:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:28:45 INFO loss_tracker.py:84 | Epoch[109/239] Step[49] GlobalStep[42559/NA]: det_loss_cls_0[0.5548] det_loss_box_0[0.6862] det_loss_cls_1[0.5318] det_loss_box_1[0.5029] det_loss_cls_2[0.4911] det_loss_box_2[0.4584] det_loss_cls_3[0.4662] det_loss_box_3[0.4394] det_loss_cls_4[0.4281] det_loss_box_4[0.4392] det_loss_cls_5[0.4104] det_loss_box_5[0.4437] det_loss_cls_dn_0[0.0986] det_loss_box_dn_0[0.2440] det_loss_cls_dn_1[0.0958] det_loss_box_dn_1[0.1999] det_loss_cls_dn_2[0.0945] det_loss_box_dn_2[0.1839] det_loss_cls_dn_3[0.0942] det_loss_box_dn_3[0.1816] det_loss_cls_dn_4[0.0927] det_loss_box_dn_4[0.1805] det_loss_cls_dn_5[0.0919] det_loss_box_dn_5[0.1796] loss_depth[0.0392] total_loss[7.6286]
Rank[0/8] 06/20/2025 04:29:00 INFO stats.py:314 | Epoch[109] Step[64] GlobalStep[42574] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 17:06:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:29:10 INFO loss_tracker.py:84 | Epoch[109/239] Step[74] GlobalStep[42584/NA]: det_loss_cls_0[0.4218] det_loss_box_0[0.5144] det_loss_cls_1[0.4195] det_loss_box_1[0.4186] det_loss_cls_2[0.3834] det_loss_box_2[0.3816] det_loss_cls_3[0.3581] det_loss_box_3[0.3712] det_loss_cls_4[0.3361] det_loss_box_4[0.3622] det_loss_cls_5[0.3193] det_loss_box_5[0.3635] det_loss_cls_dn_0[0.0961] det_loss_box_dn_0[0.3079] det_loss_cls_dn_1[0.0937] det_loss_box_dn_1[0.2713] det_loss_cls_dn_2[0.0923] det_loss_box_dn_2[0.2563] det_loss_cls_dn_3[0.0911] det_loss_box_dn_3[0.2523] det_loss_cls_dn_4[0.0906] det_loss_box_dn_4[0.2517] det_loss_cls_dn_5[0.0896] det_loss_box_dn_5[0.2510] loss_depth[0.0365] total_loss[6.8299]
Rank[0/8] 06/20/2025 04:29:25 INFO stats.py:314 | Epoch[109] Step[89] GlobalStep[42599] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 17:06:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:29:35 INFO loss_tracker.py:84 | Epoch[109/239] Step[99] GlobalStep[42609/NA]: det_loss_cls_0[0.6704] det_loss_box_0[0.8301] det_loss_cls_1[0.6580] det_loss_box_1[0.6361] det_loss_cls_2[0.6037] det_loss_box_2[0.5765] det_loss_cls_3[0.5694] det_loss_box_3[0.5611] det_loss_cls_4[0.5405] det_loss_box_4[0.5409] det_loss_cls_5[0.5212] det_loss_box_5[0.5416] det_loss_cls_dn_0[0.1040] det_loss_box_dn_0[0.2664] det_loss_cls_dn_1[0.1013] det_loss_box_dn_1[0.2281] det_loss_cls_dn_2[0.1005] det_loss_box_dn_2[0.2118] det_loss_cls_dn_3[0.0986] det_loss_box_dn_3[0.2089] det_loss_cls_dn_4[0.0974] det_loss_box_dn_4[0.2069] det_loss_cls_dn_5[0.0964] det_loss_box_dn_5[0.2044] loss_depth[0.0382] total_loss[9.2122]
Rank[0/8] 06/20/2025 04:29:51 INFO stats.py:314 | Epoch[109] Step[114] GlobalStep[42624] Training Speed: 8.03 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 17:05:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:30:02 INFO loss_tracker.py:84 | Epoch[109/239] Step[124] GlobalStep[42634/NA]: det_loss_cls_0[0.4704] det_loss_box_0[0.6433] det_loss_cls_1[0.4570] det_loss_box_1[0.4821] det_loss_cls_2[0.4198] det_loss_box_2[0.4341] det_loss_cls_3[0.3884] det_loss_box_3[0.4127] det_loss_cls_4[0.3690] det_loss_box_4[0.3974] det_loss_cls_5[0.3502] det_loss_box_5[0.3989] det_loss_cls_dn_0[0.0913] det_loss_box_dn_0[0.2773] det_loss_cls_dn_1[0.0876] det_loss_box_dn_1[0.2275] det_loss_cls_dn_2[0.0865] det_loss_box_dn_2[0.2085] det_loss_cls_dn_3[0.0848] det_loss_box_dn_3[0.2040] det_loss_cls_dn_4[0.0837] det_loss_box_dn_4[0.2024] det_loss_cls_dn_5[0.0827] det_loss_box_dn_5[0.2014] loss_depth[0.0388] total_loss[7.0997]
Rank[0/8] 06/20/2025 04:30:17 INFO stats.py:314 | Epoch[109] Step[139] GlobalStep[42649] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 17:05:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:30:27 INFO loss_tracker.py:84 | Epoch[109/239] Step[149] GlobalStep[42659/NA]: det_loss_cls_0[0.4825] det_loss_box_0[0.6321] det_loss_cls_1[0.4680] det_loss_box_1[0.4641] det_loss_cls_2[0.4220] det_loss_box_2[0.4095] det_loss_cls_3[0.4031] det_loss_box_3[0.3964] det_loss_cls_4[0.3797] det_loss_box_4[0.3931] det_loss_cls_5[0.3674] det_loss_box_5[0.3876] det_loss_cls_dn_0[0.0988] det_loss_box_dn_0[0.3403] det_loss_cls_dn_1[0.0952] det_loss_box_dn_1[0.2912] det_loss_cls_dn_2[0.0944] det_loss_box_dn_2[0.2704] det_loss_cls_dn_3[0.0935] det_loss_box_dn_3[0.2642] det_loss_cls_dn_4[0.0928] det_loss_box_dn_4[0.2620] det_loss_cls_dn_5[0.0917] det_loss_box_dn_5[0.2597] loss_depth[0.0401] total_loss[7.4997]
Rank[0/8] 06/20/2025 04:30:42 INFO stats.py:314 | Epoch[109] Step[164] GlobalStep[42674] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 17:04:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:30:53 INFO loss_tracker.py:84 | Epoch[109/239] Step[174] GlobalStep[42684/NA]: det_loss_cls_0[0.4903] det_loss_box_0[0.6217] det_loss_cls_1[0.4755] det_loss_box_1[0.4821] det_loss_cls_2[0.4474] det_loss_box_2[0.4610] det_loss_cls_3[0.4154] det_loss_box_3[0.4413] det_loss_cls_4[0.3884] det_loss_box_4[0.4301] det_loss_cls_5[0.3795] det_loss_box_5[0.4302] det_loss_cls_dn_0[0.1071] det_loss_box_dn_0[0.3413] det_loss_cls_dn_1[0.1052] det_loss_box_dn_1[0.2886] det_loss_cls_dn_2[0.1045] det_loss_box_dn_2[0.2705] det_loss_cls_dn_3[0.1034] det_loss_box_dn_3[0.2669] det_loss_cls_dn_4[0.1029] det_loss_box_dn_4[0.2639] det_loss_cls_dn_5[0.1023] det_loss_box_dn_5[0.2620] loss_depth[0.0399] total_loss[7.8214]
Rank[0/8] 06/20/2025 04:31:09 INFO stats.py:314 | Epoch[109] Step[189] GlobalStep[42699] Training Speed: 8.04 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 17:03:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:31:19 INFO loss_tracker.py:84 | Epoch[109/239] Step[199] GlobalStep[42709/NA]: det_loss_cls_0[0.5193] det_loss_box_0[0.7537] det_loss_cls_1[0.5009] det_loss_box_1[0.5964] det_loss_cls_2[0.4473] det_loss_box_2[0.5504] det_loss_cls_3[0.4181] det_loss_box_3[0.5376] det_loss_cls_4[0.3954] det_loss_box_4[0.5218] det_loss_cls_5[0.3835] det_loss_box_5[0.5172] det_loss_cls_dn_0[0.0931] det_loss_box_dn_0[0.3508] det_loss_cls_dn_1[0.0884] det_loss_box_dn_1[0.2992] det_loss_cls_dn_2[0.0882] det_loss_box_dn_2[0.2806] det_loss_cls_dn_3[0.0867] det_loss_box_dn_3[0.2753] det_loss_cls_dn_4[0.0860] det_loss_box_dn_4[0.2737] det_loss_cls_dn_5[0.0850] det_loss_box_dn_5[0.2704] loss_depth[0.0380] total_loss[8.4570]
Rank[0/8] 06/20/2025 04:31:35 INFO stats.py:314 | Epoch[109] Step[214] GlobalStep[42724] Training Speed: 7.46 samples/sec across all devices.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 17:03:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:31:45 INFO loss_tracker.py:84 | Epoch[109/239] Step[224] GlobalStep[42734/NA]: det_loss_cls_0[0.5561] det_loss_box_0[0.8042] det_loss_cls_1[0.5333] det_loss_box_1[0.6278] det_loss_cls_2[0.4910] det_loss_box_2[0.5652] det_loss_cls_3[0.4630] det_loss_box_3[0.5403] det_loss_cls_4[0.4318] det_loss_box_4[0.5296] det_loss_cls_5[0.4204] det_loss_box_5[0.5228] det_loss_cls_dn_0[0.0984] det_loss_box_dn_0[0.3110] det_loss_cls_dn_1[0.0944] det_loss_box_dn_1[0.2705] det_loss_cls_dn_2[0.0929] det_loss_box_dn_2[0.2521] det_loss_cls_dn_3[0.0915] det_loss_box_dn_3[0.2488] det_loss_cls_dn_4[0.0909] det_loss_box_dn_4[0.2477] det_loss_cls_dn_5[0.0905] det_loss_box_dn_5[0.2469] loss_depth[0.0370] total_loss[8.6581]
Rank[0/8] 06/20/2025 04:32:01 INFO stats.py:314 | Epoch[109] Step[239] GlobalStep[42749] Training Speed: 8.34 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 17:02:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:32:11 INFO loss_tracker.py:84 | Epoch[109/239] Step[249] GlobalStep[42759/NA]: det_loss_cls_0[0.4418] det_loss_box_0[0.5658] det_loss_cls_1[0.4366] det_loss_box_1[0.4243] det_loss_cls_2[0.3988] det_loss_box_2[0.3803] det_loss_cls_3[0.3690] det_loss_box_3[0.3726] det_loss_cls_4[0.3456] det_loss_box_4[0.3694] det_loss_cls_5[0.3344] det_loss_box_5[0.3748] det_loss_cls_dn_0[0.0900] det_loss_box_dn_0[0.2662] det_loss_cls_dn_1[0.0860] det_loss_box_dn_1[0.2239] det_loss_cls_dn_2[0.0856] det_loss_box_dn_2[0.2065] det_loss_cls_dn_3[0.0840] det_loss_box_dn_3[0.2038] det_loss_cls_dn_4[0.0831] det_loss_box_dn_4[0.2026] det_loss_cls_dn_5[0.0825] det_loss_box_dn_5[0.2009] loss_depth[0.0381] total_loss[6.6663]
Rank[0/8] 06/20/2025 04:32:26 INFO stats.py:314 | Epoch[109] Step[264] GlobalStep[42774] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 17:02:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:32:37 INFO loss_tracker.py:84 | Epoch[109/239] Step[274] GlobalStep[42784/NA]: det_loss_cls_0[0.4600] det_loss_box_0[0.5769] det_loss_cls_1[0.4557] det_loss_box_1[0.4228] det_loss_cls_2[0.4111] det_loss_box_2[0.3861] det_loss_cls_3[0.3823] det_loss_box_3[0.3748] det_loss_cls_4[0.3520] det_loss_box_4[0.3617] det_loss_cls_5[0.3420] det_loss_box_5[0.3643] det_loss_cls_dn_0[0.0989] det_loss_box_dn_0[0.2788] det_loss_cls_dn_1[0.0954] det_loss_box_dn_1[0.2329] det_loss_cls_dn_2[0.0943] det_loss_box_dn_2[0.2170] det_loss_cls_dn_3[0.0937] det_loss_box_dn_3[0.2129] det_loss_cls_dn_4[0.0920] det_loss_box_dn_4[0.2116] det_loss_cls_dn_5[0.0916] det_loss_box_dn_5[0.2110] loss_depth[0.0391] total_loss[6.8589]
Rank[0/8] 06/20/2025 04:32:52 INFO stats.py:314 | Epoch[109] Step[289] GlobalStep[42799] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 17:01:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:33:03 INFO loss_tracker.py:84 | Epoch[109/239] Step[299] GlobalStep[42809/NA]: det_loss_cls_0[0.5144] det_loss_box_0[0.6389] det_loss_cls_1[0.5043] det_loss_box_1[0.4904] det_loss_cls_2[0.4563] det_loss_box_2[0.4494] det_loss_cls_3[0.4319] det_loss_box_3[0.4336] det_loss_cls_4[0.4005] det_loss_box_4[0.4299] det_loss_cls_5[0.3887] det_loss_box_5[0.4255] det_loss_cls_dn_0[0.0883] det_loss_box_dn_0[0.2958] det_loss_cls_dn_1[0.0856] det_loss_box_dn_1[0.2455] det_loss_cls_dn_2[0.0844] det_loss_box_dn_2[0.2329] det_loss_cls_dn_3[0.0831] det_loss_box_dn_3[0.2291] det_loss_cls_dn_4[0.0821] det_loss_box_dn_4[0.2276] det_loss_cls_dn_5[0.0809] det_loss_box_dn_5[0.2267] loss_depth[0.0400] total_loss[7.5657]
Rank[0/8] 06/20/2025 04:33:19 INFO stats.py:314 | Epoch[109] Step[314] GlobalStep[42824] Training Speed: 8.05 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 17:00:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:33:29 INFO loss_tracker.py:84 | Epoch[109/239] Step[324] GlobalStep[42834/NA]: det_loss_cls_0[0.5052] det_loss_box_0[0.6260] det_loss_cls_1[0.4897] det_loss_box_1[0.4951] det_loss_cls_2[0.4441] det_loss_box_2[0.4366] det_loss_cls_3[0.4035] det_loss_box_3[0.4216] det_loss_cls_4[0.3813] det_loss_box_4[0.4131] det_loss_cls_5[0.3595] det_loss_box_5[0.4148] det_loss_cls_dn_0[0.0965] det_loss_box_dn_0[0.2830] det_loss_cls_dn_1[0.0928] det_loss_box_dn_1[0.2374] det_loss_cls_dn_2[0.0929] det_loss_box_dn_2[0.2188] det_loss_cls_dn_3[0.0921] det_loss_box_dn_3[0.2160] det_loss_cls_dn_4[0.0909] det_loss_box_dn_4[0.2142] det_loss_cls_dn_5[0.0905] det_loss_box_dn_5[0.2131] loss_depth[0.0362] total_loss[7.3649]
Rank[0/8] 06/20/2025 04:33:45 INFO stats.py:314 | Epoch[109] Step[339] GlobalStep[42849] Training Speed: 7.26 samples/sec across all devices.	Average Step Time: 1.10 sec.	Estimated Remaining Time: 17:00:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:33:55 INFO loss_tracker.py:84 | Epoch[109/239] Step[349] GlobalStep[42859/NA]: det_loss_cls_0[0.4809] det_loss_box_0[0.7086] det_loss_cls_1[0.4758] det_loss_box_1[0.5420] det_loss_cls_2[0.4411] det_loss_box_2[0.5005] det_loss_cls_3[0.4264] det_loss_box_3[0.4898] det_loss_cls_4[0.3972] det_loss_box_4[0.4802] det_loss_cls_5[0.3868] det_loss_box_5[0.4835] det_loss_cls_dn_0[0.1003] det_loss_box_dn_0[0.2912] det_loss_cls_dn_1[0.0979] det_loss_box_dn_1[0.2429] det_loss_cls_dn_2[0.0968] det_loss_box_dn_2[0.2254] det_loss_cls_dn_3[0.0954] det_loss_box_dn_3[0.2209] det_loss_cls_dn_4[0.0946] det_loss_box_dn_4[0.2191] det_loss_cls_dn_5[0.0936] det_loss_box_dn_5[0.2178] loss_depth[0.0383] total_loss[7.8472]
Rank[0/8] 06/20/2025 04:34:11 INFO stats.py:314 | Epoch[109] Step[364] GlobalStep[42874] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 16:59:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:34:21 INFO loss_tracker.py:84 | Epoch[109/239] Step[374] GlobalStep[42884/NA]: det_loss_cls_0[0.5495] det_loss_box_0[0.7175] det_loss_cls_1[0.5203] det_loss_box_1[0.5613] det_loss_cls_2[0.4908] det_loss_box_2[0.5070] det_loss_cls_3[0.4490] det_loss_box_3[0.4919] det_loss_cls_4[0.4304] det_loss_box_4[0.4862] det_loss_cls_5[0.4095] det_loss_box_5[0.4960] det_loss_cls_dn_0[0.0989] det_loss_box_dn_0[0.3317] det_loss_cls_dn_1[0.0945] det_loss_box_dn_1[0.2872] det_loss_cls_dn_2[0.0940] det_loss_box_dn_2[0.2654] det_loss_cls_dn_3[0.0933] det_loss_box_dn_3[0.2614] det_loss_cls_dn_4[0.0923] det_loss_box_dn_4[0.2599] det_loss_cls_dn_5[0.0911] det_loss_box_dn_5[0.2573] loss_depth[0.0375] total_loss[8.3737]
Rank[0/8] 06/20/2025 04:34:36 INFO stats.py:314 | Epoch[109] Step[389] GlobalStep[42899] Training Speed: 7.83 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 16:59:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:34:37 INFO trainer.py:268 | 
==================================================BEGIN EVAL==================================================
Rank[0/8] 06/20/2025 04:35:36 INFO trainer.py:275 | eval: 10
Rank[0/8] 06/20/2025 04:35:48 INFO trainer.py:275 | eval: 20
Rank[0/8] 06/20/2025 04:35:59 INFO trainer.py:275 | eval: 30
Rank[0/8] 06/20/2025 04:36:13 INFO trainer.py:275 | eval: 40
Rank[0/8] 06/20/2025 04:36:21 INFO trainer.py:275 | eval: 50
Rank[0/8] 06/20/2025 04:36:33 INFO trainer.py:275 | eval: 60
Rank[0/8] 06/20/2025 04:36:40 INFO trainer.py:275 | eval: 70
Rank[0/8] 06/20/2025 04:36:51 INFO trainer.py:275 | eval: 80
Rank[0/8] 06/20/2025 04:36:59 INFO trainer.py:275 | eval: 90
Rank[0/8] 06/20/2025 04:37:08 INFO trainer.py:275 | eval: 100
Rank[0/8] 06/20/2025 04:37:14 INFO metrics.py:428 | number of results: 824
Rank[0/8] 06/20/2025 04:41:33 INFO metrics.py:332 | eval results of overall:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| bin                 | 0.6344  | 0.8212  | 0.3558  | 0.5698  |
| toilet              | 0.8419  | 0.8978  | 0.6290  | 0.7591  |
| bathtub             | 0.7976  | 0.8824  | 0.5703  | 0.7206  |
| picture             | 0.1714  | 0.4170  | 0.0072  | 0.0830  |
| sink                | 0.6492  | 0.8272  | 0.1846  | 0.4115  |
| toilet paper        | 0.3422  | 0.5512  | 0.0748  | 0.2677  |
| curtain             | 0.5391  | 0.8179  | 0.0884  | 0.4448  |
| towel               | 0.3184  | 0.6355  | 0.0538  | 0.2226  |
| doorframe           | 0.3113  | 0.7741  | 0.0693  | 0.4081  |
| door                | 0.2854  | 0.6687  | 0.0226  | 0.2286  |
| vanity              | 0.4772  | 0.8667  | 0.4499  | 0.8000  |
| rail                | 0.0132  | 0.3125  | 0.0000  | 0.0156  |
| bar                 | 0.0141  | 0.1515  | 0.0008  | 0.0303  |
| stopcock            | 0.1670  | 0.4828  | 0.0003  | 0.0345  |
| cup                 | 0.0839  | 0.4068  | 0.0018  | 0.0339  |
| switch              | 0.0026  | 0.0789  | 0.0000  | 0.0000  |
| mirror              | 0.1296  | 0.6467  | 0.0058  | 0.1685  |
| bottle              | 0.0352  | 0.3287  | 0.0008  | 0.0474  |
| tissue              | 0.0011  | 0.3077  | 0.0003  | 0.0769  |
| hair dryer          | 0.0063  | 0.2857  | 0.0000  | 0.0000  |
| shelf               | 0.2801  | 0.7140  | 0.1176  | 0.4299  |
| paper               | 0.0205  | 0.2692  | 0.0005  | 0.0533  |
| shower              | 0.0573  | 0.3000  | 0.0070  | 0.0667  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| grab bar            | 0.2721  | 0.6250  | 0.0000  | 0.0000  |
| bag                 | 0.2561  | 0.6595  | 0.0368  | 0.2541  |
| light               | 0.0286  | 0.2521  | 0.0001  | 0.0278  |
| socket              | 0.0084  | 0.1991  | 0.0000  | 0.0044  |
| ledge               | 0.0626  | 0.8333  | 0.0000  | 0.0000  |
| tissue box          | 0.1080  | 0.6364  | 0.0119  | 0.2121  |
| counter             | 0.1327  | 0.7523  | 0.0110  | 0.3394  |
| box                 | 0.1731  | 0.4875  | 0.0285  | 0.1638  |
| vent                | 0.0113  | 0.1250  | 0.0000  | 0.0000  |
| toothpaste          | 0.0026  | 0.4000  | 0.0000  | 0.0000  |
| hanger              | 0.0028  | 0.3333  | 0.0000  | 0.0000  |
| lamp                | 0.3297  | 0.6328  | 0.0419  | 0.2328  |
| faucet              | 0.0228  | 0.2553  | 0.0024  | 0.0213  |
| backpack            | 0.5330  | 0.8387  | 0.2536  | 0.4677  |
| cosmetics           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dish           | 0.5340  | 0.6000  | 0.0463  | 0.2000  |
| bed                 | 0.7780  | 0.9290  | 0.6201  | 0.8757  |
| plunger             | 0.8000  | 0.8000  | 0.6000  | 0.6000  |
| frame               | 0.0653  | 0.5102  | 0.0009  | 0.1020  |
| blanket             | 0.1749  | 0.7600  | 0.0451  | 0.4400  |
| basket              | 0.2780  | 0.5565  | 0.1064  | 0.2870  |
| beam                | 0.0004  | 0.1364  | 0.0000  | 0.0000  |
| scale               | 0.2500  | 0.2727  | 0.0001  | 0.0909  |
| pipe                | 0.0006  | 0.1429  | 0.0000  | 0.0000  |
| cabinet             | 0.2479  | 0.7438  | 0.1087  | 0.4917  |
| clothes             | 0.1698  | 0.5637  | 0.0211  | 0.2006  |
| mat                 | 0.0114  | 0.3784  | 0.0004  | 0.0811  |
| door knob           | 0.0238  | 0.5000  | 0.0000  | 0.0000  |
| paper cutter        | 0.8056  | 0.8333  | 0.0000  | 0.0000  |
| candle              | 0.0670  | 0.2222  | 0.0020  | 0.1111  |
| toy                 | 0.1334  | 0.3485  | 0.0243  | 0.0455  |
| container           | 0.0077  | 0.2542  | 0.0037  | 0.1186  |
| stool               | 0.3072  | 0.7227  | 0.2148  | 0.5546  |
| radiator            | 0.6235  | 0.7333  | 0.2214  | 0.3333  |
| chair               | 0.5920  | 0.7915  | 0.4162  | 0.6104  |
| computer            | 0.3401  | 0.6557  | 0.0097  | 0.1148  |
| keyboard            | 0.0318  | 0.1628  | 0.0000  | 0.0000  |
| mouse               | 0.0043  | 0.0811  | 0.0000  | 0.0000  |
| blackboard          | 0.5401  | 0.7561  | 0.1066  | 0.2805  |
| monitor             | 0.5379  | 0.7554  | 0.1249  | 0.3913  |
| desk                | 0.4959  | 0.9157  | 0.2842  | 0.7809  |
| window              | 0.2109  | 0.6018  | 0.0238  | 0.2260  |
| windowsill          | 0.0912  | 0.6522  | 0.0231  | 0.1522  |
| table               | 0.5106  | 0.8668  | 0.3406  | 0.7022  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stand               | 0.5562  | 0.8841  | 0.3922  | 0.6646  |
| telephone           | 0.3414  | 0.6000  | 0.0358  | 0.1667  |
| blinds              | 0.1171  | 0.4200  | 0.0121  | 0.2000  |
| book                | 0.0623  | 0.3687  | 0.0076  | 0.0684  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| couch               | 0.4704  | 0.8595  | 0.3861  | 0.7603  |
| jacket              | 0.7156  | 0.7917  | 0.2459  | 0.5417  |
| laptop              | 0.4253  | 0.6087  | 0.1484  | 0.3043  |
| headphones          | 0.2379  | 0.6000  | 0.0167  | 0.2000  |
| soap dispenser      | 0.3453  | 0.4444  | 0.0241  | 0.1111  |
| bench               | 0.3282  | 0.7358  | 0.1745  | 0.5283  |
| wardrobe            | 0.4443  | 0.8193  | 0.2215  | 0.5663  |
| pillow              | 0.3061  | 0.6876  | 0.0345  | 0.2050  |
| window frame        | 0.0363  | 0.4343  | 0.0132  | 0.1616  |
| ottoman             | 0.1060  | 0.6389  | 0.0747  | 0.4444  |
| dresser             | 0.5875  | 0.9259  | 0.4372  | 0.7963  |
| remote control      | 0.0312  | 0.2222  | 0.0037  | 0.0370  |
| tv                  | 0.5650  | 0.7723  | 0.0857  | 0.3168  |
| rack                | 0.0670  | 0.5000  | 0.0331  | 0.2692  |
| shoe                | 0.1769  | 0.4019  | 0.0051  | 0.0654  |
| carpet              | 0.0535  | 0.4524  | 0.0020  | 0.1310  |
| cart                | 0.0288  | 0.5000  | 0.0005  | 0.2500  |
| case                | 0.0935  | 0.3390  | 0.0491  | 0.1356  |
| clock               | 0.1469  | 0.5745  | 0.0001  | 0.0638  |
| cushion             | 0.1655  | 0.4902  | 0.0215  | 0.1373  |
| heater              | 0.4863  | 0.7241  | 0.1721  | 0.3448  |
| poster              | 0.0490  | 0.1970  | 0.0015  | 0.0152  |
| sponge              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stairs              | 0.0506  | 0.4722  | 0.0132  | 0.1667  |
| piano               | 0.2148  | 0.4000  | 0.2148  | 0.4000  |
| calendar            | 0.0009  | 0.1667  | 0.0000  | 0.0000  |
| guitar              | 0.1299  | 0.4167  | 0.0019  | 0.0833  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bowl                | 0.0976  | 0.3623  | 0.0229  | 0.1014  |
| board               | 0.2229  | 0.6129  | 0.0517  | 0.1774  |
| printer             | 0.5854  | 0.7959  | 0.4115  | 0.6327  |
| purse               | 0.0141  | 0.3333  | 0.0000  | 0.0000  |
| luggage             | 0.3205  | 1.0000  | 0.2333  | 0.6667  |
| refrigerator        | 0.6259  | 0.7800  | 0.3992  | 0.5600  |
| coffee maker        | 0.3997  | 0.6552  | 0.0444  | 0.1724  |
| person              | 0.3013  | 0.6667  | 0.0854  | 0.3333  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plate               | 0.0261  | 0.3077  | 0.0036  | 0.0962  |
| map                 | 0.0417  | 0.5000  | 0.0417  | 0.5000  |
| can                 | 0.0167  | 0.0833  | 0.0167  | 0.0833  |
| microwave           | 0.5808  | 0.7755  | 0.2500  | 0.4286  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| brush               | 0.1644  | 0.3571  | 0.0158  | 0.1429  |
| letter              | 0.4261  | 1.0000  | 0.0000  | 0.0000  |
| tube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| roll                | 0.1228  | 0.3000  | 0.0000  | 0.0000  |
| fireplace           | 0.1388  | 0.4643  | 0.0103  | 0.2143  |
| ball                | 0.4326  | 0.5714  | 0.2461  | 0.3810  |
| fan                 | 0.6135  | 0.7969  | 0.2505  | 0.4844  |
| basin               | 0.0148  | 0.1176  | 0.0000  | 0.0588  |
| washing machine     | 0.3898  | 0.8929  | 0.2088  | 0.7143  |
| notebook            | 0.0005  | 0.1538  | 0.0000  | 0.0000  |
| dispenser           | 0.5812  | 0.8182  | 0.2143  | 0.3864  |
| stall               | 0.1817  | 0.7059  | 0.0543  | 0.4706  |
| mailbox             | 0.2843  | 0.6000  | 0.0021  | 0.2000  |
| water cooler        | 0.6250  | 1.0000  | 0.6250  | 1.0000  |
| pillar              | 0.3026  | 0.7209  | 0.0720  | 0.4651  |
| sign                | 0.0075  | 0.3333  | 0.0000  | 0.0000  |
| column              | 0.0377  | 0.3421  | 0.0115  | 0.1842  |
| doorway             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| arch                | 0.0005  | 0.0667  | 0.0001  | 0.0333  |
| cube                | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| projector           | 0.1667  | 0.5000  | 0.0000  | 0.0000  |
| crib                | 0.1111  | 0.3333  | 0.0000  | 0.0000  |
| roof                | 0.0033  | 0.4167  | 0.0006  | 0.0833  |
| stove               | 0.5640  | 0.8889  | 0.2913  | 0.5556  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher   | 0.2971  | 0.6000  | 0.0000  | 0.0000  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| screen              | 0.3407  | 0.6000  | 0.0105  | 0.2000  |
| tablet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plant               | 0.2878  | 0.5727  | 0.0606  | 0.2540  |
| tray                | 0.0005  | 0.1429  | 0.0000  | 0.0000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0040  | 0.1818  | 0.0002  | 0.0455  |
| copier              | 0.9097  | 0.9167  | 0.6508  | 0.8333  |
| file                | 0.0972  | 0.5294  | 0.0157  | 0.2059  |
| hat                 | 0.0288  | 0.2222  | 0.0000  | 0.0000  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pen                 | 0.0002  | 0.0667  | 0.0000  | 0.0000  |
| hamper              | 0.3485  | 0.4545  | 0.3485  | 0.4545  |
| decoration          | 0.0392  | 0.3000  | 0.0016  | 0.0643  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket              | 0.2867  | 0.5641  | 0.0713  | 0.3333  |
| machine             | 0.4182  | 1.0000  | 0.2096  | 0.6842  |
| magazine            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| holder              | 0.0111  | 0.2222  | 0.0046  | 0.1111  |
| soap                | 0.0046  | 0.3333  | 0.0000  | 0.0000  |
| headboard           | 0.0006  | 0.1250  | 0.0000  | 0.0000  |
| dish rack           | 0.3690  | 1.0000  | 0.0667  | 0.2000  |
| dishwasher          | 0.0748  | 0.4000  | 0.0048  | 0.1000  |
| kettle              | 0.0867  | 0.5000  | 0.0398  | 0.3333  |
| teapot              | 0.0565  | 0.3333  | 0.0000  | 0.0000  |
| oven                | 0.4200  | 0.8214  | 0.0749  | 0.3571  |
| toaster             | 0.3800  | 0.4000  | 0.0333  | 0.1000  |
| flowerpot           | 0.0463  | 0.4510  | 0.0038  | 0.0784  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ladder              | 0.1080  | 0.5294  | 0.0042  | 0.0588  |
| vase                | 0.1428  | 0.3800  | 0.0267  | 0.1800  |
| chandelier          | 0.0518  | 0.2381  | 0.0348  | 0.1429  |
| vacuum cleaner      | 0.5637  | 0.8571  | 0.0755  | 0.4286  |
| jar                 | 0.0130  | 0.3077  | 0.0028  | 0.1538  |
| dumbbell            | 0.1059  | 0.6667  | 0.0000  | 0.0000  |
| air conditioner     | 0.6000  | 0.6000  | 0.2679  | 0.6000  |
| wire                | 0.0020  | 0.5000  | 0.0000  | 0.0000  |
| crate               | 0.4071  | 0.5714  | 0.1429  | 0.1429  |
| garbage             | 0.4494  | 0.6250  | 0.1759  | 0.5000  |
| spoon               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| conducting wire     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| commode             | 0.4570  | 0.7885  | 0.3592  | 0.6346  |
| price tag           | 0.0085  | 0.2857  | 0.0000  | 0.0000  |
| candlestick         | 0.0417  | 0.3333  | 0.0026  | 0.3333  |
| album               | 0.0244  | 1.0000  | 0.0000  | 0.0000  |
| player              | 0.2500  | 0.2500  | 0.2500  | 0.2500  |
| range hood          | 0.3493  | 0.7143  | 0.0179  | 0.0714  |
| humidifier          | 0.5500  | 1.0000  | 0.0000  | 0.0000  |
| jalousie            | 0.8333  | 1.0000  | 0.5000  | 0.5000  |
| umbrella            | 0.0154  | 0.2000  | 0.0087  | 0.2000  |
| folder              | 0.0174  | 0.3571  | 0.0000  | 0.0000  |
| adhesive tape       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| device              | 0.0092  | 0.3636  | 0.0000  | 0.0000  |
| drawer              | 0.1583  | 0.6111  | 0.0566  | 0.4444  |
| clothes dryer       | 0.2766  | 0.8667  | 0.1333  | 0.6000  |
| balcony             | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| fruit               | 0.2335  | 0.3571  | 0.0000  | 0.0000  |
| sunglasses          | 0.1196  | 0.6154  | 0.0017  | 0.1538  |
| napkins             | 0.3333  | 0.3333  | 0.0000  | 0.0000  |
| footstool           | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| statue              | 0.0844  | 0.3684  | 0.0126  | 0.1579  |
| toiletry            | 0.0638  | 0.2609  | 0.0000  | 0.0000  |
| shampoo             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washbasin           | 0.0005  | 0.4000  | 0.0001  | 0.2000  |
| flush               | 0.2935  | 1.0000  | 0.0000  | 0.0000  |
| body loofah         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beanbag             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| pack                | 0.0383  | 0.6667  | 0.0020  | 0.2000  |
| pot                 | 0.0773  | 0.3704  | 0.0054  | 0.2593  |
| pan                 | 0.0550  | 0.2941  | 0.0172  | 0.1176  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchenware         | 0.1530  | 0.5000  | 0.0070  | 0.0870  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sack                | 0.5458  | 0.8571  | 0.0159  | 0.1429  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| food                | 0.0385  | 0.2727  | 0.0053  | 0.0909  |
| handle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radio               | 0.2500  | 0.2500  | 0.2500  | 0.2500  |
| broom               | 0.0092  | 0.2143  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| steps               | 0.0129  | 0.2000  | 0.0000  | 0.1000  |
| drum                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mop                 | 1.0000  | 1.0000  | 0.0048  | 0.3333  |
| helmets             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coil                | 0.0049  | 0.1667  | 0.0000  | 0.0000  |
| countertop          | 0.0238  | 0.4643  | 0.0014  | 0.0714  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| banister            | 0.2000  | 0.2000  | 0.0000  | 0.0000  |
| thermostat          | 0.0022  | 0.1667  | 0.0000  | 0.0000  |
| garage door         | 0.2808  | 0.5000  | 0.2778  | 0.3333  |
| kitchen island      | 0.1639  | 0.5714  | 0.1639  | 0.5714  |
| treadmill           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool table          | 0.1667  | 0.3333  | 0.0061  | 0.3333  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.3017  | 0.6500  | 0.0235  | 0.3000  |
| wine                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 0.6550  | 1.0000  | 0.4483  | 0.8333  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| partition           | 0.0812  | 0.8333  | 0.0000  | 0.0000  |
| bidet               | 0.5556  | 0.6667  | 0.1111  | 0.3333  |
| water heater        | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| menu                | 0.6905  | 0.7143  | 0.2857  | 0.2857  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| barricade           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cap                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glove               | 0.6923  | 1.0000  | 0.0000  | 0.0000  |
| bread               | 0.0667  | 0.2000  | 0.0000  | 0.0000  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| salt                | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| overall             | 0.2032  | 0.4304  | 0.0798  | 0.1909  |
| head                | 0.2777  | 0.5862  | 0.1146  | 0.2962  |
| common              | 0.1738  | 0.4059  | 0.0628  | 0.1674  |
| tail                | 0.1541  | 0.2855  | 0.0605  | 0.1009  |
| small               | 0.0572  | 0.2177  | 0.0047  | 0.0293  |
| medium              | 0.2091  | 0.4655  | 0.0899  | 0.2212  |
| large               | 0.1305  | 0.5117  | 0.0510  | 0.3010  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 04:41:33 INFO metrics.py:437 | number of scannet results: 250
Rank[0/8] 06/20/2025 04:42:51 INFO metrics.py:332 | eval results of scannet:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| bin                 | 0.6640  | 0.8298  | 0.4118  | 0.6043  |
| toilet              | 0.9234  | 0.9643  | 0.6855  | 0.8571  |
| bathtub             | 0.9033  | 0.9375  | 0.6650  | 0.7188  |
| picture             | 0.2530  | 0.4627  | 0.0115  | 0.1007  |
| sink                | 0.8160  | 0.9205  | 0.2867  | 0.5114  |
| toilet paper        | 0.4085  | 0.5443  | 0.0989  | 0.2785  |
| curtain             | 0.6529  | 0.8584  | 0.0949  | 0.5044  |
| towel               | 0.4081  | 0.6942  | 0.0781  | 0.2479  |
| doorframe           | 0.3249  | 0.7465  | 0.0719  | 0.3380  |
| door                | 0.3709  | 0.7759  | 0.0503  | 0.3244  |
| vanity              | 0.6563  | 0.9667  | 0.6375  | 0.8667  |
| rail                | 0.0176  | 0.3333  | 0.0000  | 0.0000  |
| bar                 | 0.0070  | 0.1364  | 0.0000  | 0.0000  |
| stopcock            | 0.1671  | 0.4828  | 0.0003  | 0.0345  |
| cup                 | 0.1315  | 0.4348  | 0.0052  | 0.0435  |
| switch              | 0.0050  | 0.1139  | 0.0000  | 0.0000  |
| mirror              | 0.1542  | 0.7108  | 0.0022  | 0.1446  |
| bottle              | 0.0404  | 0.3158  | 0.0017  | 0.0526  |
| tissue              | 0.0008  | 0.2222  | 0.0000  | 0.0000  |
| hair dryer          | 0.0066  | 0.2857  | 0.0000  | 0.0000  |
| shelf               | 0.4881  | 0.8897  | 0.2840  | 0.6618  |
| paper               | 0.0204  | 0.2679  | 0.0006  | 0.0528  |
| shower              | 0.0503  | 0.3571  | 0.0079  | 0.0714  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| grab bar            | 0.2721  | 0.6250  | 0.0000  | 0.0000  |
| bag                 | 0.2276  | 0.6301  | 0.0808  | 0.2877  |
| light               | 0.0963  | 0.3793  | 0.0000  | 0.0345  |
| socket              | 0.0096  | 0.2136  | 0.0000  | 0.0049  |
| ledge               | 0.0626  | 0.8333  | 0.0000  | 0.0000  |
| tissue box          | 0.1555  | 0.6818  | 0.0050  | 0.1818  |
| counter             | 0.3638  | 0.9143  | 0.0790  | 0.5429  |
| box                 | 0.1482  | 0.3929  | 0.0389  | 0.1270  |
| toothpaste          | 0.0029  | 0.4000  | 0.0000  | 0.0000  |
| lamp                | 0.4077  | 0.7500  | 0.0911  | 0.3214  |
| faucet              | 0.0012  | 0.1250  | 0.0000  | 0.0000  |
| backpack            | 0.5834  | 0.8929  | 0.4212  | 0.6071  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dish           | 0.6683  | 0.7500  | 0.0591  | 0.2500  |
| bed                 | 0.9794  | 1.0000  | 0.9596  | 1.0000  |
| plunger             | 0.8000  | 0.8000  | 0.6000  | 0.6000  |
| frame               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blanket             | 0.0587  | 0.5417  | 0.0123  | 0.2917  |
| basket              | 0.0404  | 0.3077  | 0.0141  | 0.1923  |
| scale               | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| pipe                | 0.0003  | 0.0714  | 0.0000  | 0.0000  |
| cabinet             | 0.3425  | 0.7897  | 0.1478  | 0.5278  |
| clothes             | 0.1764  | 0.6013  | 0.0319  | 0.2484  |
| mat                 | 0.0547  | 0.5000  | 0.0036  | 0.2000  |
| paper cutter        | 0.7600  | 0.8000  | 0.0000  | 0.0000  |
| toy                 | 0.1098  | 0.1765  | 0.0196  | 0.0588  |
| container           | 0.0042  | 0.1739  | 0.0020  | 0.0870  |
| stool               | 0.4205  | 0.8182  | 0.3393  | 0.6591  |
| radiator            | 0.7500  | 0.7931  | 0.3213  | 0.4310  |
| chair               | 0.6259  | 0.7916  | 0.4107  | 0.5832  |
| computer            | 0.3481  | 0.6154  | 0.0005  | 0.0769  |
| keyboard            | 0.0585  | 0.1724  | 0.0000  | 0.0000  |
| mouse               | 0.0118  | 0.1429  | 0.0000  | 0.0000  |
| blackboard          | 0.5403  | 0.7536  | 0.1073  | 0.2609  |
| monitor             | 0.5960  | 0.8000  | 0.1810  | 0.4643  |
| desk                | 0.5857  | 0.9747  | 0.3471  | 0.8608  |
| window              | 0.4459  | 0.8210  | 0.0741  | 0.3765  |
| windowsill          | 0.0576  | 0.6667  | 0.0002  | 0.1667  |
| table               | 0.6148  | 0.9154  | 0.4323  | 0.8077  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stand               | 0.7382  | 0.9434  | 0.5923  | 0.7925  |
| telephone           | 0.4360  | 0.6875  | 0.0418  | 0.1875  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| book                | 0.0557  | 0.3667  | 0.0100  | 0.0833  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| couch               | 0.7615  | 1.0000  | 0.6722  | 0.9125  |
| jacket              | 0.7481  | 0.8261  | 0.2580  | 0.5652  |
| laptop              | 0.4423  | 0.4667  | 0.1173  | 0.2667  |
| headphones          | 0.2510  | 0.6000  | 0.0222  | 0.2000  |
| soap dispenser      | 0.4414  | 0.5238  | 0.0317  | 0.1429  |
| bench               | 0.7556  | 1.0000  | 0.7556  | 1.0000  |
| wardrobe            | 0.6366  | 1.0000  | 0.3267  | 0.5000  |
| pillow              | 0.4283  | 0.6597  | 0.1986  | 0.4097  |
| ottoman             | 0.0700  | 1.0000  | 0.0057  | 0.2857  |
| dresser             | 0.7198  | 0.9730  | 0.5961  | 0.8919  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tv                  | 0.8883  | 0.9130  | 0.1387  | 0.3478  |
| rack                | 0.0013  | 0.0909  | 0.0000  | 0.0000  |
| shoe                | 0.1630  | 0.4500  | 0.0139  | 0.0750  |
| carpet              | 0.0012  | 0.4000  | 0.0000  | 0.0000  |
| cart                | 0.0017  | 0.5000  | 0.0017  | 0.5000  |
| case                | 0.5608  | 0.8947  | 0.4180  | 0.6316  |
| clock               | 0.2330  | 0.6400  | 0.0001  | 0.0800  |
| cushion             | 0.0064  | 0.2500  | 0.0000  | 0.0000  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stairs              | 0.0979  | 1.0000  | 0.0192  | 0.4000  |
| calendar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| guitar              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bowl                | 0.2143  | 0.3333  | 0.0000  | 0.0000  |
| board               | 0.2575  | 0.7188  | 0.0045  | 0.1875  |
| printer             | 0.6646  | 0.8788  | 0.5961  | 0.8182  |
| purse               | 0.0174  | 0.3333  | 0.0000  | 0.0000  |
| refrigerator        | 0.8443  | 0.9500  | 0.5536  | 0.7000  |
| coffee maker        | 0.6054  | 0.8235  | 0.0957  | 0.2941  |
| person              | 0.3013  | 0.6667  | 0.0861  | 0.3333  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plate               | 0.0159  | 0.4000  | 0.0000  | 0.0000  |
| map                 | 0.0833  | 1.0000  | 0.0833  | 1.0000  |
| microwave           | 0.6133  | 0.8000  | 0.2014  | 0.4000  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball                | 0.4219  | 0.5000  | 0.1875  | 0.2500  |
| fan                 | 0.6460  | 0.8750  | 0.4000  | 0.5000  |
| basin               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.3194  | 0.8333  | 0.3194  | 0.8333  |
| notebook            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dispenser           | 0.6938  | 0.8462  | 0.2495  | 0.4103  |
| stall               | 0.1818  | 0.7059  | 0.0543  | 0.4706  |
| mailbox             | 0.2843  | 0.6000  | 0.0021  | 0.2000  |
| water cooler        | 0.6250  | 1.0000  | 0.6250  | 1.0000  |
| pillar              | 0.2000  | 0.2000  | 0.0056  | 0.2000  |
| sign                | 0.0082  | 0.3571  | 0.0000  | 0.0000  |
| stove               | 0.9667  | 1.0000  | 0.9667  | 1.0000  |
| fire extinguisher   | 0.0123  | 0.3333  | 0.0000  | 0.0000  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plant               | 0.2379  | 0.5210  | 0.0546  | 0.2269  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| copier              | 0.9097  | 0.9167  | 0.6508  | 0.8333  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pen                 | 0.0040  | 0.1111  | 0.0000  | 0.0000  |
| hamper              | 0.3833  | 0.5000  | 0.3833  | 0.5000  |
| decoration          | 0.0767  | 0.2917  | 0.0086  | 0.1250  |
| bucket              | 0.2480  | 0.5714  | 0.0718  | 0.2857  |
| machine             | 0.5289  | 1.0000  | 0.2248  | 0.6875  |
| holder              | 0.0286  | 0.2000  | 0.0286  | 0.2000  |
| dish rack           | 0.3611  | 1.0000  | 0.0833  | 0.2500  |
| dishwasher          | 0.0046  | 0.3333  | 0.0000  | 0.0000  |
| kettle              | 0.0393  | 0.5000  | 0.0167  | 0.2500  |
| teapot              | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| oven                | 0.0132  | 0.2500  | 0.0000  | 0.0000  |
| flowerpot           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ladder              | 0.1692  | 0.6000  | 0.0000  | 0.0000  |
| vacuum cleaner      | 0.8333  | 1.0000  | 0.6667  | 0.6667  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crate               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 0.7193  | 1.0000  | 0.3623  | 0.6667  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| broom               | 0.0318  | 0.1667  | 0.0000  | 0.0000  |
| range hood          | 0.5833  | 0.8000  | 0.0500  | 0.2000  |
| pot                 | 0.0833  | 0.5000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.4000  | 1.0000  | 0.3095  | 1.0000  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drawer              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| scannet             | 0.2573  | 0.4719  | 0.1226  | 0.2449  |
| head                | 0.3396  | 0.6065  | 0.1692  | 0.3269  |
| common              | 0.1963  | 0.3714  | 0.0766  | 0.1666  |
| tail                | 0.0954  | 0.2087  | 0.0596  | 0.1273  |
| small               | 0.0464  | 0.1776  | 0.0035  | 0.0279  |
| medium              | 0.2958  | 0.5634  | 0.1439  | 0.2903  |
| large               | 0.2695  | 0.8121  | 0.1248  | 0.5471  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 04:42:51 INFO metrics.py:437 | number of 3rscan results: 235
Rank[0/8] 06/20/2025 04:44:03 INFO metrics.py:332 | eval results of 3rscan:
+-------------------+---------+---------+---------+---------+
| classes           | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+-------------------+---------+---------+---------+---------+
| chair             | 0.6729  | 0.8253  | 0.5226  | 0.6756  |
| monitor           | 0.5273  | 0.6250  | 0.0510  | 0.1500  |
| keyboard          | 0.0202  | 0.1667  | 0.0000  | 0.0000  |
| desk              | 0.4748  | 0.9000  | 0.2597  | 0.7375  |
| bag               | 0.3076  | 0.7071  | 0.0294  | 0.2626  |
| mouse             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| backpack          | 0.5243  | 0.8182  | 0.1341  | 0.3636  |
| cup               | 0.0431  | 0.3590  | 0.0001  | 0.0256  |
| stand             | 0.4707  | 0.9016  | 0.2678  | 0.5738  |
| computer          | 0.3985  | 0.7619  | 0.1075  | 0.1905  |
| case              | 0.0450  | 0.3000  | 0.0093  | 0.0857  |
| paper             | 0.0393  | 0.2857  | 0.0007  | 0.0571  |
| cabinet           | 0.2959  | 0.7906  | 0.1490  | 0.5684  |
| box               | 0.2068  | 0.5229  | 0.0265  | 0.1699  |
| bin               | 0.6988  | 0.8293  | 0.3281  | 0.5244  |
| table             | 0.5556  | 0.8784  | 0.3465  | 0.6667  |
| pen               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plate             | 0.0283  | 0.3030  | 0.0059  | 0.1212  |
| file              | 0.1161  | 0.6071  | 0.0192  | 0.2500  |
| garbage           | 0.4521  | 0.6250  | 0.1828  | 0.5000  |
| plug              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bottle            | 0.0245  | 0.2975  | 0.0002  | 0.0248  |
| socket            | 0.0004  | 0.0625  | 0.0000  | 0.0000  |
| book              | 0.0889  | 0.4083  | 0.0078  | 0.0533  |
| paper cutter      | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| window            | 0.2134  | 0.6438  | 0.0229  | 0.2055  |
| shelf             | 0.2328  | 0.7067  | 0.0611  | 0.3746  |
| blinds            | 0.1502  | 0.5385  | 0.0156  | 0.2564  |
| wardrobe          | 0.4944  | 0.8438  | 0.2392  | 0.6250  |
| picture           | 0.1268  | 0.4024  | 0.0098  | 0.0772  |
| telephone         | 0.2751  | 0.4583  | 0.0405  | 0.1667  |
| tissue box        | 0.0888  | 0.6667  | 0.0833  | 0.3333  |
| cushion           | 0.2866  | 0.7667  | 0.0373  | 0.2333  |
| curtain           | 0.5402  | 0.8333  | 0.0813  | 0.3730  |
| candle            | 0.0800  | 0.2000  | 0.0022  | 0.0667  |
| hat               | 0.0744  | 0.2857  | 0.0000  | 0.0000  |
| couch             | 0.3621  | 0.8143  | 0.2536  | 0.6857  |
| door              | 0.3163  | 0.6912  | 0.0280  | 0.2059  |
| spoon             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pad               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| conducting wire   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bench             | 0.3964  | 0.8571  | 0.2585  | 0.6071  |
| notebook          | 0.0006  | 0.1818  | 0.0000  | 0.0000  |
| stool             | 0.3569  | 0.7963  | 0.2340  | 0.5926  |
| pillow            | 0.3798  | 0.7578  | 0.0187  | 0.1719  |
| switch            | 0.0001  | 0.0377  | 0.0000  | 0.0000  |
| clock             | 0.0820  | 0.4737  | 0.0000  | 0.0000  |
| laptop            | 0.4444  | 0.8333  | 0.2037  | 0.3333  |
| bowl              | 0.1438  | 0.4074  | 0.0561  | 0.1111  |
| tv                | 0.5402  | 0.8571  | 0.0810  | 0.3429  |
| lamp              | 0.2806  | 0.5375  | 0.0211  | 0.1125  |
| frame             | 0.1117  | 0.7333  | 0.0016  | 0.1333  |
| commode           | 0.4580  | 0.7885  | 0.3598  | 0.6346  |
| bed               | 0.7696  | 0.9455  | 0.6259  | 0.8727  |
| light             | 0.0377  | 0.3750  | 0.0001  | 0.0455  |
| blanket           | 0.2783  | 0.9077  | 0.0804  | 0.5385  |
| carpet            | 0.0851  | 0.6071  | 0.0033  | 0.1964  |
| towel             | 0.2221  | 0.6456  | 0.0531  | 0.2152  |
| counter           | 0.1199  | 0.7805  | 0.0026  | 0.3171  |
| rack              | 0.1656  | 0.7000  | 0.0741  | 0.4000  |
| clothes           | 0.2238  | 0.6117  | 0.0174  | 0.1748  |
| doorframe         | 0.1906  | 0.7000  | 0.0346  | 0.3000  |
| decoration        | 0.1104  | 0.3438  | 0.0048  | 0.0625  |
| container         | 0.0666  | 0.4444  | 0.0564  | 0.2222  |
| teapot            | 0.0308  | 0.5000  | 0.0000  | 0.0000  |
| ottoman           | 0.4004  | 1.0000  | 0.3831  | 0.8182  |
| hook              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vase              | 0.3002  | 0.4000  | 0.0568  | 0.2000  |
| toilet            | 0.8094  | 0.8824  | 0.4938  | 0.5882  |
| price tag         | 0.0085  | 0.2857  | 0.0000  | 0.0000  |
| magazine          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flowerpot         | 0.0683  | 0.4583  | 0.0069  | 0.0417  |
| candlestick       | 0.1250  | 1.0000  | 0.0079  | 1.0000  |
| album             | 0.0385  | 1.0000  | 0.0000  | 0.0000  |
| plant             | 0.3277  | 0.5446  | 0.0503  | 0.1980  |
| jar               | 0.0290  | 0.5000  | 0.0060  | 0.2500  |
| printer           | 0.5961  | 0.7778  | 0.1333  | 0.2222  |
| oven              | 0.6227  | 1.0000  | 0.1198  | 0.4737  |
| player            | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| fan               | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| basket            | 0.4642  | 0.7031  | 0.2109  | 0.3750  |
| sink              | 0.6294  | 0.8393  | 0.1174  | 0.2500  |
| air conditioner   | 0.7500  | 0.7500  | 0.3355  | 0.7500  |
| range hood        | 0.3383  | 1.0000  | 0.0000  | 0.0000  |
| blackboard        | 0.6417  | 0.8333  | 0.1663  | 0.4167  |
| heater            | 0.4885  | 0.7193  | 0.1719  | 0.3333  |
| humidifier        | 0.5500  | 1.0000  | 0.0000  | 0.0000  |
| windowsill        | 0.1163  | 0.6667  | 0.0279  | 0.1538  |
| board             | 0.2699  | 0.5417  | 0.1291  | 0.2083  |
| roll              | 0.1253  | 0.3000  | 0.0000  | 0.0000  |
| jalousie          | 0.8333  | 1.0000  | 0.5000  | 0.5000  |
| umbrella          | 0.0769  | 1.0000  | 0.0435  | 1.0000  |
| folder            | 0.0174  | 0.3571  | 0.0000  | 0.0000  |
| wire              | 0.0040  | 1.0000  | 0.0000  | 0.0000  |
| adhesive tape     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| remote control    | 0.0474  | 0.3000  | 0.0056  | 0.0500  |
| device            | 0.0092  | 0.3636  | 0.0000  | 0.0000  |
| coffee maker      | 0.1840  | 0.5556  | 0.0000  | 0.0000  |
| drawer            | 0.3684  | 0.8889  | 0.1708  | 0.6667  |
| speaker           | 0.0104  | 0.0833  | 0.0000  | 0.0000  |
| mirror            | 0.0918  | 0.5926  | 0.0005  | 0.1111  |
| shoe              | 0.2918  | 0.5476  | 0.0071  | 0.0952  |
| clothes dryer     | 0.4048  | 0.9000  | 0.0874  | 0.5000  |
| stairs            | 0.2025  | 0.7143  | 0.0377  | 0.3571  |
| radiator          | 0.3855  | 0.6552  | 0.0184  | 0.1724  |
| hanger            | 0.0013  | 0.5000  | 0.0000  | 0.0000  |
| scale             | 0.3333  | 0.3333  | 0.0010  | 0.3333  |
| balcony           | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| rail              | 0.0013  | 0.2500  | 0.0000  | 0.0000  |
| microwave         | 0.8876  | 1.0000  | 0.4849  | 0.6471  |
| fruit             | 0.6538  | 1.0000  | 0.0000  | 0.0000  |
| shower            | 0.3503  | 1.0000  | 0.0357  | 0.2500  |
| guitar            | 0.8333  | 1.0000  | 0.0000  | 0.0000  |
| toy               | 0.4188  | 0.5385  | 0.1026  | 0.1538  |
| basin             | 0.0167  | 0.0667  | 0.0000  | 0.0000  |
| toilet paper      | 0.0933  | 0.4167  | 0.0000  | 0.0000  |
| ball              | 0.3933  | 0.6000  | 0.2400  | 0.4000  |
| piano             | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| sunglasses        | 0.1196  | 0.6154  | 0.0017  | 0.1538  |
| cosmetics         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| napkins           | 0.3333  | 0.3333  | 0.0000  | 0.0000  |
| bar               | 0.2667  | 0.6667  | 0.0667  | 0.3333  |
| footstool         | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| cube              | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| bathtub           | 0.9390  | 1.0000  | 0.4222  | 0.7778  |
| statue            | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| washing machine   | 0.5204  | 0.9286  | 0.1993  | 0.7143  |
| soap dish         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| faucet            | 0.1212  | 0.6667  | 0.0476  | 0.3333  |
| brush             | 0.3610  | 0.6250  | 0.0339  | 0.2500  |
| toiletry          | 0.2000  | 1.0000  | 0.0000  | 0.0000  |
| bucket            | 0.6065  | 0.8000  | 0.1846  | 0.4667  |
| tissue            | 0.0155  | 0.6667  | 0.0088  | 0.3333  |
| shampoo           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washbasin         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dispenser         | 0.0417  | 0.6667  | 0.0000  | 0.0000  |
| flush             | 0.2935  | 1.0000  | 0.0000  | 0.0000  |
| beam              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tube              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beanbag           | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| letter            | 0.4262  | 1.0000  | 0.0000  | 0.0000  |
| can               | 0.0250  | 0.1250  | 0.0250  | 0.1250  |
| pillar            | 0.4257  | 0.8235  | 0.1876  | 0.4706  |
| column            | 0.0516  | 1.0000  | 0.0455  | 0.5000  |
| pack              | 0.0442  | 0.7692  | 0.0023  | 0.2308  |
| stove             | 0.4403  | 0.9412  | 0.0431  | 0.4118  |
| pot               | 0.2582  | 0.5833  | 0.0188  | 0.4167  |
| pan               | 0.0782  | 0.4167  | 0.0243  | 0.1667  |
| refrigerator      | 0.6537  | 0.7333  | 0.5834  | 0.7333  |
| mat               | 0.0175  | 0.3333  | 0.0000  | 0.0000  |
| kettle            | 0.1288  | 0.5833  | 0.0598  | 0.4167  |
| glass             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchenware       | 0.1602  | 0.5227  | 0.0073  | 0.0909  |
| knife             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sack              | 0.5458  | 0.8571  | 0.0159  | 0.1429  |
| tool              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| food              | 0.0425  | 0.3000  | 0.0059  | 0.1000  |
| soap dispenser    | 0.0045  | 0.2000  | 0.0000  | 0.0000  |
| calendar          | 0.0013  | 0.2000  | 0.0000  | 0.0000  |
| holder            | 0.0333  | 1.0000  | 0.0000  | 0.0000  |
| radio             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| sign              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| poster            | 0.0755  | 0.2063  | 0.0032  | 0.0159  |
| crib              | 0.1111  | 0.3333  | 0.0000  | 0.0000  |
| luggage           | 0.6444  | 1.0000  | 0.4444  | 0.6667  |
| tray              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| broom             | 0.0036  | 0.5000  | 0.0000  | 0.0000  |
| chandelier        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fireplace         | 0.0500  | 0.2000  | 0.0125  | 0.2000  |
| dresser           | 0.5526  | 1.0000  | 0.0000  | 0.0000  |
| tablet            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| screen            | 0.0769  | 1.0000  | 0.0000  | 0.0000  |
| mop               | 1.0000  | 1.0000  | 0.0056  | 0.3333  |
| soap              | 0.0556  | 1.0000  | 0.0000  | 0.0000  |
| helmets           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| furniture         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coil              | 0.0049  | 0.1667  | 0.0000  | 0.0000  |
| ladder            | 0.1356  | 1.0000  | 0.0667  | 0.3333  |
| window frame      | 0.0249  | 0.3333  | 0.0000  | 0.0000  |
| pipe              | 0.0038  | 0.5000  | 0.0000  | 0.0000  |
| mattress          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet             | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| water heater      | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| crate             | 0.6227  | 0.8000  | 0.2000  | 0.2000  |
| treadmill         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine           | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| dumbbell          | 0.1250  | 1.0000  | 0.0000  | 0.0000  |
| vacuum cleaner    | 0.6875  | 0.7500  | 0.0033  | 0.2500  |
| bicycle           | 0.8333  | 1.0000  | 0.8333  | 1.0000  |
| partition         | 0.1001  | 1.0000  | 0.0000  | 0.0000  |
| seasoning         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| menu              | 0.6905  | 0.7143  | 0.2857  | 0.2857  |
| map               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glove             | 0.6923  | 1.0000  | 0.0000  | 0.0000  |
| toothbrush        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bread             | 0.0667  | 0.2000  | 0.0000  | 0.0000  |
| shovel            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sponge            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| salt              | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| toaster           | 0.9500  | 1.0000  | 0.0833  | 0.2500  |
| scissors          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dish rack         | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| dishwasher        | 0.3000  | 0.6667  | 0.0833  | 0.3333  |
| fire extinguisher | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| -------           | ------- | ------- | ------- | ------- |
| 3rscan            | 0.2793  | 0.5584  | 0.1021  | 0.2241  |
| head              | 0.3019  | 0.6385  | 0.1132  | 0.2914  |
| common            | 0.2660  | 0.5468  | 0.0904  | 0.1967  |
| tail              | 0.2551  | 0.4241  | 0.0976  | 0.1353  |
| small             | 0.0690  | 0.2732  | 0.0068  | 0.0441  |
| medium            | 0.3034  | 0.6066  | 0.1180  | 0.2531  |
| large             | 0.2175  | 0.7450  | 0.0870  | 0.4078  |
+-------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 04:44:03 INFO metrics.py:437 | number of matterport3d results: 339
Rank[0/8] 06/20/2025 04:45:30 INFO metrics.py:332 | eval results of matterport3d:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| mirror              | 0.1479  | 0.5946  | 0.0267  | 0.2162  |
| light               | 0.0285  | 0.2108  | 0.0004  | 0.0228  |
| doorframe           | 0.3556  | 0.7942  | 0.0909  | 0.4485  |
| door                | 0.2302  | 0.5770  | 0.0100  | 0.1619  |
| plant               | 0.3031  | 0.6786  | 0.1147  | 0.3839  |
| sink                | 0.4927  | 0.7374  | 0.1432  | 0.4141  |
| window              | 0.1272  | 0.4861  | 0.0112  | 0.1667  |
| towel               | 0.3371  | 0.5636  | 0.0449  | 0.2000  |
| countertop          | 0.0238  | 0.4643  | 0.0014  | 0.0714  |
| picture             | 0.1492  | 0.3961  | 0.0058  | 0.0749  |
| bottle              | 0.0767  | 0.4583  | 0.0014  | 0.0833  |
| drawer              | 0.0031  | 0.3750  | 0.0007  | 0.2500  |
| cabinet             | 0.1210  | 0.6483  | 0.0467  | 0.3771  |
| shelf               | 0.1555  | 0.5547  | 0.0619  | 0.3139  |
| roof                | 0.0033  | 0.4167  | 0.0006  | 0.0833  |
| bathtub             | 0.5993  | 0.7778  | 0.5144  | 0.7037  |
| counter             | 0.0090  | 0.5455  | 0.0006  | 0.1515  |
| flowerpot           | 0.0533  | 0.4800  | 0.0042  | 0.1200  |
| faucet              | 0.0297  | 0.2500  | 0.0000  | 0.0000  |
| lamp                | 0.3657  | 0.6923  | 0.0579  | 0.3626  |
| window frame        | 0.0407  | 0.4483  | 0.0155  | 0.1839  |
| toilet              | 0.7743  | 0.8438  | 0.6437  | 0.7188  |
| fan                 | 0.6284  | 0.7963  | 0.2369  | 0.4815  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| table               | 0.2757  | 0.7692  | 0.2158  | 0.5769  |
| vent                | 0.0252  | 0.1250  | 0.0000  | 0.0000  |
| dresser             | 0.3287  | 0.8000  | 0.2130  | 0.6667  |
| blanket             | 0.0548  | 0.3636  | 0.0007  | 0.1818  |
| windowsill          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| case                | 0.0000  | 0.0556  | 0.0000  | 0.0000  |
| vase                | 0.0623  | 0.3667  | 0.0225  | 0.1667  |
| book                | 0.0732  | 0.3228  | 0.0086  | 0.0394  |
| switch              | 0.0002  | 0.0500  | 0.0000  | 0.0000  |
| frame               | 0.0002  | 0.1765  | 0.0000  | 0.0588  |
| pillow              | 0.1841  | 0.6435  | 0.0048  | 0.1388  |
| tv                  | 0.4077  | 0.6279  | 0.1150  | 0.2791  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| paper               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| box                 | 0.0587  | 0.6346  | 0.0156  | 0.2885  |
| desk                | 0.3315  | 0.7368  | 0.2137  | 0.6316  |
| decoration          | 0.0088  | 0.2857  | 0.0001  | 0.0476  |
| tissue              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plate               | 0.0762  | 0.2857  | 0.0005  | 0.0714  |
| curtain             | 0.4072  | 0.7500  | 0.1252  | 0.4688  |
| screen              | 0.4167  | 0.5000  | 0.0250  | 0.2500  |
| chair               | 0.4441  | 0.7313  | 0.2785  | 0.5850  |
| couch               | 0.2995  | 0.7717  | 0.2386  | 0.6848  |
| bowl                | 0.1110  | 0.3333  | 0.0060  | 0.1212  |
| stand               | 0.5544  | 0.8000  | 0.4595  | 0.6400  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bed                 | 0.6013  | 0.8421  | 0.2612  | 0.7544  |
| stool               | 0.0138  | 0.3333  | 0.0042  | 0.2381  |
| bench               | 0.1849  | 0.5000  | 0.0255  | 0.3000  |
| clock               | 0.3509  | 0.6667  | 0.0020  | 0.3333  |
| stairs              | 0.0342  | 0.3585  | 0.0091  | 0.0943  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ottoman             | 0.0787  | 0.2778  | 0.0648  | 0.2778  |
| wardrobe            | 0.0332  | 0.5455  | 0.0278  | 0.2727  |
| statue              | 0.0713  | 0.3529  | 0.0619  | 0.1765  |
| doorway             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| door knob           | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| cushion             | 0.0005  | 0.0588  | 0.0000  | 0.0000  |
| teapot              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| steps               | 0.0129  | 0.2000  | 0.0000  | 0.1000  |
| cup                 | 0.0023  | 0.4000  | 0.0000  | 0.0000  |
| carpet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillar              | 0.2750  | 0.7619  | 0.0224  | 0.5238  |
| column              | 0.0602  | 0.3056  | 0.0141  | 0.1667  |
| mat                 | 0.0051  | 0.3333  | 0.0005  | 0.0417  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bin                 | 0.2962  | 0.7561  | 0.1363  | 0.4634  |
| washing machine     | 0.2895  | 0.8750  | 0.1776  | 0.6250  |
| shower              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washbasin           | 0.0005  | 0.4286  | 0.0001  | 0.2143  |
| handle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vanity              | 0.2196  | 0.6667  | 0.1173  | 0.6667  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toilet paper        | 0.3365  | 0.6111  | 0.0668  | 0.3333  |
| rail                | 0.0365  | 0.3148  | 0.0001  | 0.0185  |
| banister            | 0.2000  | 0.2000  | 0.0000  | 0.0000  |
| headboard           | 0.0006  | 0.1250  | 0.0000  | 0.0000  |
| telephone           | 0.1337  | 0.7500  | 0.0000  | 0.0000  |
| footstool           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pot                 | 0.0022  | 0.1538  | 0.0022  | 0.1538  |
| pipe                | 0.0006  | 0.1000  | 0.0000  | 0.0000  |
| thermostat          | 0.0022  | 0.1667  | 0.0000  | 0.0000  |
| ball                | 0.5089  | 0.7500  | 0.5089  | 0.7500  |
| dispenser           | 0.0026  | 0.5000  | 0.0026  | 0.5000  |
| soap dispenser      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket              | 0.0028  | 0.2000  | 0.0028  | 0.2000  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| holder              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garage door         | 0.2809  | 0.5000  | 0.2778  | 0.3333  |
| socket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchen island      | 0.1641  | 0.5714  | 0.1641  | 0.5714  |
| heater              | 0.5000  | 1.0000  | 0.5000  | 1.0000  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| piano               | 0.1000  | 0.5000  | 0.1000  | 0.5000  |
| rack                | 0.0014  | 0.3636  | 0.0004  | 0.1818  |
| stove               | 0.3608  | 0.7000  | 0.1160  | 0.4000  |
| fireplace           | 0.2784  | 0.5217  | 0.0167  | 0.2174  |
| pack                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue box          | 0.0547  | 0.5000  | 0.0375  | 0.2500  |
| basin               | 0.0011  | 1.0000  | 0.0011  | 1.0000  |
| candle              | 0.1667  | 0.3333  | 0.1667  | 0.3333  |
| radiator            | 0.1111  | 0.3333  | 0.0000  | 0.0000  |
| tray                | 0.0085  | 0.3333  | 0.0000  | 0.0000  |
| board               | 0.1863  | 0.3333  | 0.0000  | 0.0000  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cart                | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| printer             | 0.4286  | 0.4286  | 0.1905  | 0.2857  |
| computer            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| air conditioner     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| refrigerator        | 0.2860  | 0.6000  | 0.0693  | 0.2000  |
| container           | 0.0357  | 0.2593  | 0.0043  | 0.1111  |
| kettle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basket              | 0.1094  | 0.4400  | 0.0035  | 0.1600  |
| scale               | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| pan                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| laptop              | 0.6667  | 1.0000  | 0.2500  | 0.5000  |
| toy                 | 0.0663  | 0.3611  | 0.0000  | 0.0000  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool table          | 0.1667  | 0.3333  | 0.0063  | 0.3333  |
| monitor             | 0.2083  | 0.5000  | 0.1250  | 0.2500  |
| guitar              | 0.1068  | 0.4286  | 0.0065  | 0.1429  |
| hanger              | 0.0252  | 0.3000  | 0.0000  | 0.0000  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bag                 | 0.1197  | 0.4615  | 0.0000  | 0.0000  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| projector           | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| speaker             | 0.0030  | 0.3750  | 0.0014  | 0.1250  |
| food                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.3750  | 0.6667  | 0.3750  | 0.6667  |
| excercise equipment | 0.3364  | 0.7222  | 0.0267  | 0.3333  |
| microwave           | 0.1333  | 0.4167  | 0.0906  | 0.1667  |
| clothes             | 0.1152  | 0.3793  | 0.0231  | 0.1207  |
| fire extinguisher   | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| ladder              | 0.1274  | 0.3333  | 0.0000  | 0.0000  |
| beam                | 0.0004  | 0.1429  | 0.0000  | 0.0000  |
| toiletry            | 0.0641  | 0.2273  | 0.0000  | 0.0000  |
| soap                | 0.0007  | 0.2000  | 0.0000  | 0.0000  |
| machine             | 0.0339  | 1.0000  | 0.0085  | 0.5000  |
| soap dish           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| arch                | 0.0005  | 0.0667  | 0.0001  | 0.0333  |
| chandelier          | 0.1028  | 0.2632  | 0.0707  | 0.1579  |
| file                | 0.1250  | 0.2500  | 0.0000  | 0.0000  |
| oven                | 0.3600  | 0.6000  | 0.0065  | 0.2000  |
| keyboard            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wine                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 0.0769  | 1.0000  | 0.0769  | 1.0000  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shoe                | 0.0414  | 0.0800  | 0.0000  | 0.0000  |
| dishwasher          | 0.0192  | 0.2500  | 0.0000  | 0.0000  |
| kitchenware         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mouse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blackboard          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| backpack            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet               | 0.2500  | 0.5000  | 0.2500  | 0.5000  |
| magazine            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hamper              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.0156  | 0.2500  | 0.0000  | 0.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drum                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coffee maker        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell            | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| partition           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jacket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| matterport3d        | 0.1239  | 0.3216  | 0.0464  | 0.1630  |
| head                | 0.1671  | 0.4353  | 0.0694  | 0.2265  |
| common              | 0.0826  | 0.2429  | 0.0231  | 0.1143  |
| tail                | 0.0826  | 0.1309  | 0.0279  | 0.0695  |
| small               | 0.0495  | 0.1513  | 0.0021  | 0.0359  |
| medium              | 0.1190  | 0.3449  | 0.0486  | 0.1684  |
| large               | 0.0975  | 0.4130  | 0.0412  | 0.2760  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 04:45:30 INFO metrics.py:437 | number of arkit results: 0
Rank[0/8] 06/20/2025 04:45:31 INFO stats.py:394 | Epoch[109] completed. Training Speed: 2.94 samples/sec across all devices.	Epoch Time: 1061.29 sec.	Average Epoch Time: 1061.29 sec.	Average Step Time: 2.72 sec.	Estimated Remaining Time: 17:12:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:45:31 WARNING accelerator.py:3099 | Deleting 1 checkpoints to make room for new checkpoint.
Rank[0/8] 06/20/2025 04:45:32 INFO accelerator.py:3111 | Saving current state to /job_data/checkpoints/checkpoint_10
Rank[7/8] 06/20/2025 04:45:32 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 109 to /job_data/checkpoints/checkpoint_10
Rank[2/8] 06/20/2025 04:45:32 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 109 to /job_data/checkpoints/checkpoint_10
Rank[6/8] 06/20/2025 04:45:32 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 109 to /job_data/checkpoints/checkpoint_10
Rank[3/8] 06/20/2025 04:45:32 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 109 to /job_data/checkpoints/checkpoint_10
Rank[5/8] 06/20/2025 04:45:33 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 109 to /job_data/checkpoints/checkpoint_10
Rank[4/8] 06/20/2025 04:45:33 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 109 to /job_data/checkpoints/checkpoint_10
Rank[1/8] 06/20/2025 04:45:33 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 109 to /job_data/checkpoints/checkpoint_10
Rank[0/8] 06/20/2025 04:45:34 INFO checkpointing.py:106 | Model weights saved in /job_data/checkpoints/checkpoint_10/model.safetensors
Rank[0/8] 06/20/2025 04:45:37 INFO checkpointing.py:113 | Optimizer state saved in /job_data/checkpoints/checkpoint_10/optimizer.bin
Rank[0/8] 06/20/2025 04:45:37 INFO checkpointing.py:120 | Scheduler state saved in /job_data/checkpoints/checkpoint_10/scheduler.bin
Rank[0/8] 06/20/2025 04:45:37 INFO checkpointing.py:137 | Sampler state for dataloader 0 saved in /job_data/checkpoints/checkpoint_10/sampler.bin
Rank[0/8] 06/20/2025 04:45:37 INFO checkpointing.py:137 | Sampler state for dataloader 1 saved in /job_data/checkpoints/checkpoint_10/sampler_1.bin
Rank[0/8] 06/20/2025 04:45:37 INFO checkpointing.py:164 | Random states saved in /job_data/checkpoints/checkpoint_10/random_states_0.pkl
Rank[0/8] 06/20/2025 04:45:37 INFO checkpointing.py:300 | Saving the state of TrainerProgressState to /job_data/checkpoints/checkpoint_10/custom_checkpoint_0.pkl
Rank[0/8] 06/20/2025 04:45:37 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 109 to /job_data/checkpoints/checkpoint_10
Rank[0/8] 06/20/2025 04:46:03 INFO stats.py:314 | Epoch[110] Step[24] GlobalStep[42924] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 17:11:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:46:03 INFO loss_tracker.py:84 | Epoch[110/239] Step[24] GlobalStep[42924/NA]: det_loss_cls_0[0.4648] det_loss_box_0[0.6365] det_loss_cls_1[0.4537] det_loss_box_1[0.4856] det_loss_cls_2[0.4193] det_loss_box_2[0.4379] det_loss_cls_3[0.3813] det_loss_box_3[0.4232] det_loss_cls_4[0.3635] det_loss_box_4[0.4130] det_loss_cls_5[0.3482] det_loss_box_5[0.4145] det_loss_cls_dn_0[0.0980] det_loss_box_dn_0[0.2973] det_loss_cls_dn_1[0.0935] det_loss_box_dn_1[0.2469] det_loss_cls_dn_2[0.0932] det_loss_box_dn_2[0.2289] det_loss_cls_dn_3[0.0920] det_loss_box_dn_3[0.2253] det_loss_cls_dn_4[0.0915] det_loss_box_dn_4[0.2235] det_loss_cls_dn_5[0.0906] det_loss_box_dn_5[0.2218] loss_depth[0.0386] total_loss[7.2825]
Rank[0/8] 06/20/2025 04:46:30 INFO stats.py:314 | Epoch[110] Step[49] GlobalStep[42949] Training Speed: 6.77 samples/sec across all devices.	Average Step Time: 1.18 sec.	Estimated Remaining Time: 17:10:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:46:30 INFO loss_tracker.py:84 | Epoch[110/239] Step[49] GlobalStep[42949/NA]: det_loss_cls_0[0.5652] det_loss_box_0[0.7481] det_loss_cls_1[0.5406] det_loss_box_1[0.5724] det_loss_cls_2[0.5017] det_loss_box_2[0.5178] det_loss_cls_3[0.4607] det_loss_box_3[0.5063] det_loss_cls_4[0.4395] det_loss_box_4[0.4897] det_loss_cls_5[0.4294] det_loss_box_5[0.4894] det_loss_cls_dn_0[0.0909] det_loss_box_dn_0[0.2840] det_loss_cls_dn_1[0.0869] det_loss_box_dn_1[0.2389] det_loss_cls_dn_2[0.0861] det_loss_box_dn_2[0.2237] det_loss_cls_dn_3[0.0851] det_loss_box_dn_3[0.2192] det_loss_cls_dn_4[0.0843] det_loss_box_dn_4[0.2163] det_loss_cls_dn_5[0.0833] det_loss_box_dn_5[0.2156] loss_depth[0.0380] total_loss[8.2131]
Rank[0/8] 06/20/2025 04:46:55 INFO stats.py:314 | Epoch[110] Step[74] GlobalStep[42974] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 17:10:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:46:55 INFO loss_tracker.py:84 | Epoch[110/239] Step[74] GlobalStep[42974/NA]: det_loss_cls_0[0.4482] det_loss_box_0[0.6651] det_loss_cls_1[0.4289] det_loss_box_1[0.5129] det_loss_cls_2[0.3947] det_loss_box_2[0.4497] det_loss_cls_3[0.3655] det_loss_box_3[0.4331] det_loss_cls_4[0.3400] det_loss_box_4[0.4239] det_loss_cls_5[0.3313] det_loss_box_5[0.4164] det_loss_cls_dn_0[0.0893] det_loss_box_dn_0[0.3415] det_loss_cls_dn_1[0.0868] det_loss_box_dn_1[0.2789] det_loss_cls_dn_2[0.0871] det_loss_box_dn_2[0.2571] det_loss_cls_dn_3[0.0858] det_loss_box_dn_3[0.2544] det_loss_cls_dn_4[0.0850] det_loss_box_dn_4[0.2532] det_loss_cls_dn_5[0.0839] det_loss_box_dn_5[0.2505] loss_depth[0.0385] total_loss[7.4016]
Rank[0/8] 06/20/2025 04:47:21 INFO stats.py:314 | Epoch[110] Step[99] GlobalStep[42999] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 17:09:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:47:21 INFO loss_tracker.py:84 | Epoch[110/239] Step[99] GlobalStep[42999/NA]: det_loss_cls_0[0.5035] det_loss_box_0[0.6222] det_loss_cls_1[0.4777] det_loss_box_1[0.4694] det_loss_cls_2[0.4443] det_loss_box_2[0.4207] det_loss_cls_3[0.3998] det_loss_box_3[0.4152] det_loss_cls_4[0.3708] det_loss_box_4[0.4152] det_loss_cls_5[0.3574] det_loss_box_5[0.4119] det_loss_cls_dn_0[0.0863] det_loss_box_dn_0[0.3008] det_loss_cls_dn_1[0.0825] det_loss_box_dn_1[0.2441] det_loss_cls_dn_2[0.0816] det_loss_box_dn_2[0.2226] det_loss_cls_dn_3[0.0806] det_loss_box_dn_3[0.2183] det_loss_cls_dn_4[0.0802] det_loss_box_dn_4[0.2148] det_loss_cls_dn_5[0.0797] det_loss_box_dn_5[0.2143] loss_depth[0.0375] total_loss[7.2514]
Rank[0/8] 06/20/2025 04:47:47 INFO stats.py:314 | Epoch[110] Step[124] GlobalStep[43024] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 17:09:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:47:47 INFO loss_tracker.py:84 | Epoch[110/239] Step[124] GlobalStep[43024/NA]: det_loss_cls_0[0.4750] det_loss_box_0[0.6494] det_loss_cls_1[0.4478] det_loss_box_1[0.4820] det_loss_cls_2[0.4247] det_loss_box_2[0.4223] det_loss_cls_3[0.4004] det_loss_box_3[0.4069] det_loss_cls_4[0.3711] det_loss_box_4[0.4086] det_loss_cls_5[0.3579] det_loss_box_5[0.4107] det_loss_cls_dn_0[0.0916] det_loss_box_dn_0[0.3023] det_loss_cls_dn_1[0.0874] det_loss_box_dn_1[0.2549] det_loss_cls_dn_2[0.0875] det_loss_box_dn_2[0.2400] det_loss_cls_dn_3[0.0860] det_loss_box_dn_3[0.2370] det_loss_cls_dn_4[0.0852] det_loss_box_dn_4[0.2352] det_loss_cls_dn_5[0.0843] det_loss_box_dn_5[0.2244] loss_depth[0.0384] total_loss[7.3111]
Rank[0/8] 06/20/2025 04:48:13 INFO stats.py:314 | Epoch[110] Step[149] GlobalStep[43049] Training Speed: 7.76 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 17:08:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:48:13 INFO loss_tracker.py:84 | Epoch[110/239] Step[149] GlobalStep[43049/NA]: det_loss_cls_0[0.4616] det_loss_box_0[0.5649] det_loss_cls_1[0.4653] det_loss_box_1[0.4308] det_loss_cls_2[0.4264] det_loss_box_2[0.3949] det_loss_cls_3[0.3936] det_loss_box_3[0.3811] det_loss_cls_4[0.3739] det_loss_box_4[0.3726] det_loss_cls_5[0.3653] det_loss_box_5[0.3679] det_loss_cls_dn_0[0.0970] det_loss_box_dn_0[0.2634] det_loss_cls_dn_1[0.0934] det_loss_box_dn_1[0.2213] det_loss_cls_dn_2[0.0932] det_loss_box_dn_2[0.2076] det_loss_cls_dn_3[0.0921] det_loss_box_dn_3[0.2041] det_loss_cls_dn_4[0.0912] det_loss_box_dn_4[0.2017] det_loss_cls_dn_5[0.0906] det_loss_box_dn_5[0.1991] loss_depth[0.0382] total_loss[6.8913]
Rank[0/8] 06/20/2025 04:48:39 INFO stats.py:314 | Epoch[110] Step[174] GlobalStep[43074] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 17:07:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:48:39 INFO loss_tracker.py:84 | Epoch[110/239] Step[174] GlobalStep[43074/NA]: det_loss_cls_0[0.5351] det_loss_box_0[0.7359] det_loss_cls_1[0.5234] det_loss_box_1[0.5814] det_loss_cls_2[0.4820] det_loss_box_2[0.5414] det_loss_cls_3[0.4555] det_loss_box_3[0.5134] det_loss_cls_4[0.4280] det_loss_box_4[0.5059] det_loss_cls_5[0.4170] det_loss_box_5[0.5088] det_loss_cls_dn_0[0.1021] det_loss_box_dn_0[0.3149] det_loss_cls_dn_1[0.0989] det_loss_box_dn_1[0.2679] det_loss_cls_dn_2[0.0975] det_loss_box_dn_2[0.2527] det_loss_cls_dn_3[0.0963] det_loss_box_dn_3[0.2480] det_loss_cls_dn_4[0.0955] det_loss_box_dn_4[0.2456] det_loss_cls_dn_5[0.0945] det_loss_box_dn_5[0.2436] loss_depth[0.0392] total_loss[8.4246]
Rank[0/8] 06/20/2025 04:49:05 INFO stats.py:314 | Epoch[110] Step[199] GlobalStep[43099] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 17:07:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:49:05 INFO loss_tracker.py:84 | Epoch[110/239] Step[199] GlobalStep[43099/NA]: det_loss_cls_0[0.4077] det_loss_box_0[0.4933] det_loss_cls_1[0.3985] det_loss_box_1[0.3867] det_loss_cls_2[0.3654] det_loss_box_2[0.3451] det_loss_cls_3[0.3396] det_loss_box_3[0.3341] det_loss_cls_4[0.3196] det_loss_box_4[0.3326] det_loss_cls_5[0.3062] det_loss_box_5[0.3349] det_loss_cls_dn_0[0.0948] det_loss_box_dn_0[0.3047] det_loss_cls_dn_1[0.0904] det_loss_box_dn_1[0.2546] det_loss_cls_dn_2[0.0897] det_loss_box_dn_2[0.2353] det_loss_cls_dn_3[0.0889] det_loss_box_dn_3[0.2304] det_loss_cls_dn_4[0.0880] det_loss_box_dn_4[0.2283] det_loss_cls_dn_5[0.0870] det_loss_box_dn_5[0.2270] loss_depth[0.0381] total_loss[6.4210]
Rank[0/8] 06/20/2025 04:49:31 INFO stats.py:314 | Epoch[110] Step[224] GlobalStep[43124] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 17:06:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:49:31 INFO loss_tracker.py:84 | Epoch[110/239] Step[224] GlobalStep[43124/NA]: det_loss_cls_0[0.4051] det_loss_box_0[0.5076] det_loss_cls_1[0.3880] det_loss_box_1[0.3911] det_loss_cls_2[0.3523] det_loss_box_2[0.3635] det_loss_cls_3[0.3216] det_loss_box_3[0.3513] det_loss_cls_4[0.3085] det_loss_box_4[0.3383] det_loss_cls_5[0.2973] det_loss_box_5[0.3376] det_loss_cls_dn_0[0.0774] det_loss_box_dn_0[0.2812] det_loss_cls_dn_1[0.0740] det_loss_box_dn_1[0.2344] det_loss_cls_dn_2[0.0732] det_loss_box_dn_2[0.2234] det_loss_cls_dn_3[0.0721] det_loss_box_dn_3[0.2196] det_loss_cls_dn_4[0.0718] det_loss_box_dn_4[0.2172] det_loss_cls_dn_5[0.0713] det_loss_box_dn_5[0.2153] loss_depth[0.0391] total_loss[6.2323]
Rank[0/8] 06/20/2025 04:49:57 INFO stats.py:314 | Epoch[110] Step[249] GlobalStep[43149] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 17:06:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:49:57 INFO loss_tracker.py:84 | Epoch[110/239] Step[249] GlobalStep[43149/NA]: det_loss_cls_0[0.5784] det_loss_box_0[0.7685] det_loss_cls_1[0.5623] det_loss_box_1[0.5820] det_loss_cls_2[0.5205] det_loss_box_2[0.5234] det_loss_cls_3[0.4904] det_loss_box_3[0.5058] det_loss_cls_4[0.4628] det_loss_box_4[0.4903] det_loss_cls_5[0.4454] det_loss_box_5[0.4968] det_loss_cls_dn_0[0.0937] det_loss_box_dn_0[0.2641] det_loss_cls_dn_1[0.0906] det_loss_box_dn_1[0.2246] det_loss_cls_dn_2[0.0904] det_loss_box_dn_2[0.2099] det_loss_cls_dn_3[0.0892] det_loss_box_dn_3[0.2068] det_loss_cls_dn_4[0.0890] det_loss_box_dn_4[0.2060] det_loss_cls_dn_5[0.0877] det_loss_box_dn_5[0.2044] loss_depth[0.0360] total_loss[8.3190]
Rank[0/8] 06/20/2025 04:50:23 INFO stats.py:314 | Epoch[110] Step[274] GlobalStep[43174] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 17:05:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:50:23 INFO loss_tracker.py:84 | Epoch[110/239] Step[274] GlobalStep[43174/NA]: det_loss_cls_0[0.7041] det_loss_box_0[0.9078] det_loss_cls_1[0.6977] det_loss_box_1[0.7032] det_loss_cls_2[0.6436] det_loss_box_2[0.6325] det_loss_cls_3[0.6033] det_loss_box_3[0.6072] det_loss_cls_4[0.5682] det_loss_box_4[0.5936] det_loss_cls_5[0.5514] det_loss_box_5[0.6009] det_loss_cls_dn_0[0.1016] det_loss_box_dn_0[0.2599] det_loss_cls_dn_1[0.0981] det_loss_box_dn_1[0.2214] det_loss_cls_dn_2[0.0976] det_loss_box_dn_2[0.2073] det_loss_cls_dn_3[0.0963] det_loss_box_dn_3[0.2034] det_loss_cls_dn_4[0.0955] det_loss_box_dn_4[0.2024] det_loss_cls_dn_5[0.0945] det_loss_box_dn_5[0.2005] loss_depth[0.0349] total_loss[9.7270]
Rank[0/8] 06/20/2025 04:50:48 INFO stats.py:314 | Epoch[110] Step[299] GlobalStep[43199] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 17:04:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:50:48 INFO loss_tracker.py:84 | Epoch[110/239] Step[299] GlobalStep[43199/NA]: det_loss_cls_0[0.5418] det_loss_box_0[0.7027] det_loss_cls_1[0.5372] det_loss_box_1[0.5382] det_loss_cls_2[0.5099] det_loss_box_2[0.4737] det_loss_cls_3[0.4726] det_loss_box_3[0.4603] det_loss_cls_4[0.4521] det_loss_box_4[0.4495] det_loss_cls_5[0.4374] det_loss_box_5[0.4544] det_loss_cls_dn_0[0.1043] det_loss_box_dn_0[0.3079] det_loss_cls_dn_1[0.1007] det_loss_box_dn_1[0.2643] det_loss_cls_dn_2[0.1008] det_loss_box_dn_2[0.2498] det_loss_cls_dn_3[0.1000] det_loss_box_dn_3[0.2464] det_loss_cls_dn_4[0.0991] det_loss_box_dn_4[0.2453] det_loss_cls_dn_5[0.0987] det_loss_box_dn_5[0.2417] loss_depth[0.0408] total_loss[8.2295]
Rank[0/8] 06/20/2025 04:51:14 INFO stats.py:314 | Epoch[110] Step[324] GlobalStep[43224] Training Speed: 6.23 samples/sec across all devices.	Average Step Time: 1.28 sec.	Estimated Remaining Time: 17:04:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:51:14 INFO loss_tracker.py:84 | Epoch[110/239] Step[324] GlobalStep[43224/NA]: det_loss_cls_0[0.5205] det_loss_box_0[0.6772] det_loss_cls_1[0.5094] det_loss_box_1[0.5215] det_loss_cls_2[0.4632] det_loss_box_2[0.4666] det_loss_cls_3[0.4190] det_loss_box_3[0.4579] det_loss_cls_4[0.3959] det_loss_box_4[0.4411] det_loss_cls_5[0.3784] det_loss_box_5[0.4447] det_loss_cls_dn_0[0.0942] det_loss_box_dn_0[0.3250] det_loss_cls_dn_1[0.0914] det_loss_box_dn_1[0.2743] det_loss_cls_dn_2[0.0913] det_loss_box_dn_2[0.2570] det_loss_cls_dn_3[0.0905] det_loss_box_dn_3[0.2540] det_loss_cls_dn_4[0.0895] det_loss_box_dn_4[0.2520] det_loss_cls_dn_5[0.0881] det_loss_box_dn_5[0.2502] loss_depth[0.0390] total_loss[7.8915]
Rank[0/8] 06/20/2025 04:51:41 INFO stats.py:314 | Epoch[110] Step[349] GlobalStep[43249] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 17:03:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:51:41 INFO loss_tracker.py:84 | Epoch[110/239] Step[349] GlobalStep[43249/NA]: det_loss_cls_0[0.5888] det_loss_box_0[0.6905] det_loss_cls_1[0.5772] det_loss_box_1[0.5457] det_loss_cls_2[0.5412] det_loss_box_2[0.4846] det_loss_cls_3[0.5197] det_loss_box_3[0.4637] det_loss_cls_4[0.4832] det_loss_box_4[0.4596] det_loss_cls_5[0.4708] det_loss_box_5[0.4611] det_loss_cls_dn_0[0.1028] det_loss_box_dn_0[0.3208] det_loss_cls_dn_1[0.0989] det_loss_box_dn_1[0.2734] det_loss_cls_dn_2[0.0973] det_loss_box_dn_2[0.2596] det_loss_cls_dn_3[0.0973] det_loss_box_dn_3[0.2559] det_loss_cls_dn_4[0.0958] det_loss_box_dn_4[0.2555] det_loss_cls_dn_5[0.0956] det_loss_box_dn_5[0.2532] loss_depth[0.0375] total_loss[8.5295]
Rank[0/8] 06/20/2025 04:52:07 INFO stats.py:314 | Epoch[110] Step[374] GlobalStep[43274] Training Speed: 6.52 samples/sec across all devices.	Average Step Time: 1.23 sec.	Estimated Remaining Time: 17:03:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:52:07 INFO loss_tracker.py:84 | Epoch[110/239] Step[374] GlobalStep[43274/NA]: det_loss_cls_0[0.5301] det_loss_box_0[0.7227] det_loss_cls_1[0.5207] det_loss_box_1[0.5706] det_loss_cls_2[0.4875] det_loss_box_2[0.5129] det_loss_cls_3[0.4553] det_loss_box_3[0.4886] det_loss_cls_4[0.4296] det_loss_box_4[0.4780] det_loss_cls_5[0.4147] det_loss_box_5[0.4779] det_loss_cls_dn_0[0.0980] det_loss_box_dn_0[0.3502] det_loss_cls_dn_1[0.0954] det_loss_box_dn_1[0.2989] det_loss_cls_dn_2[0.0951] det_loss_box_dn_2[0.2778] det_loss_cls_dn_3[0.0941] det_loss_box_dn_3[0.2752] det_loss_cls_dn_4[0.0932] det_loss_box_dn_4[0.2725] det_loss_cls_dn_5[0.0929] det_loss_box_dn_5[0.2712] loss_depth[0.0408] total_loss[8.4439]
Rank[0/8] 06/20/2025 04:52:22 INFO stats.py:394 | Epoch[110] completed. Training Speed: 7.70 samples/sec across all devices.	Epoch Time: 404.97 sec.	Average Epoch Time: 404.97 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 17:02:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:52:34 INFO stats.py:314 | Epoch[111] Step[9] GlobalStep[43299] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 17:02:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:52:50 INFO loss_tracker.py:84 | Epoch[111/239] Step[24] GlobalStep[43314/NA]: det_loss_cls_0[0.4591] det_loss_box_0[0.5852] det_loss_cls_1[0.4538] det_loss_box_1[0.4468] det_loss_cls_2[0.4139] det_loss_box_2[0.4055] det_loss_cls_3[0.3753] det_loss_box_3[0.3990] det_loss_cls_4[0.3536] det_loss_box_4[0.3897] det_loss_cls_5[0.3401] det_loss_box_5[0.3881] det_loss_cls_dn_0[0.0947] det_loss_box_dn_0[0.2615] det_loss_cls_dn_1[0.0916] det_loss_box_dn_1[0.2189] det_loss_cls_dn_2[0.0904] det_loss_box_dn_2[0.2018] det_loss_cls_dn_3[0.0894] det_loss_box_dn_3[0.1986] det_loss_cls_dn_4[0.0887] det_loss_box_dn_4[0.1979] det_loss_cls_dn_5[0.0878] det_loss_box_dn_5[0.1965] loss_depth[0.0362] total_loss[6.8641]
Rank[0/8] 06/20/2025 04:53:00 INFO stats.py:314 | Epoch[111] Step[34] GlobalStep[43324] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 17:02:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:53:15 INFO loss_tracker.py:84 | Epoch[111/239] Step[49] GlobalStep[43339/NA]: det_loss_cls_0[0.4377] det_loss_box_0[0.5910] det_loss_cls_1[0.4251] det_loss_box_1[0.4649] det_loss_cls_2[0.3980] det_loss_box_2[0.4310] det_loss_cls_3[0.3704] det_loss_box_3[0.4234] det_loss_cls_4[0.3444] det_loss_box_4[0.4185] det_loss_cls_5[0.3348] det_loss_box_5[0.4215] det_loss_cls_dn_0[0.0964] det_loss_box_dn_0[0.3575] det_loss_cls_dn_1[0.0929] det_loss_box_dn_1[0.3053] det_loss_cls_dn_2[0.0922] det_loss_box_dn_2[0.2862] det_loss_cls_dn_3[0.0910] det_loss_box_dn_3[0.2844] det_loss_cls_dn_4[0.0899] det_loss_box_dn_4[0.2820] det_loss_cls_dn_5[0.0889] det_loss_box_dn_5[0.2780] loss_depth[0.0388] total_loss[7.4440]
Rank[0/8] 06/20/2025 04:53:26 INFO stats.py:314 | Epoch[111] Step[59] GlobalStep[43349] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 17:01:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:53:42 INFO loss_tracker.py:84 | Epoch[111/239] Step[74] GlobalStep[43364/NA]: det_loss_cls_0[0.4802] det_loss_box_0[0.7407] det_loss_cls_1[0.4666] det_loss_box_1[0.5834] det_loss_cls_2[0.4293] det_loss_box_2[0.5407] det_loss_cls_3[0.3950] det_loss_box_3[0.5445] det_loss_cls_4[0.3779] det_loss_box_4[0.5354] det_loss_cls_5[0.3616] det_loss_box_5[0.5370] det_loss_cls_dn_0[0.1039] det_loss_box_dn_0[0.2913] det_loss_cls_dn_1[0.0994] det_loss_box_dn_1[0.2482] det_loss_cls_dn_2[0.0985] det_loss_box_dn_2[0.2358] det_loss_cls_dn_3[0.0978] det_loss_box_dn_3[0.2339] det_loss_cls_dn_4[0.0968] det_loss_box_dn_4[0.2328] det_loss_cls_dn_5[0.0964] det_loss_box_dn_5[0.2314] loss_depth[0.0426] total_loss[8.1013]
Rank[0/8] 06/20/2025 04:53:52 INFO stats.py:314 | Epoch[111] Step[84] GlobalStep[43374] Training Speed: 7.93 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 17:00:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:54:08 INFO loss_tracker.py:84 | Epoch[111/239] Step[99] GlobalStep[43389/NA]: det_loss_cls_0[0.4757] det_loss_box_0[0.6372] det_loss_cls_1[0.4521] det_loss_box_1[0.4746] det_loss_cls_2[0.4197] det_loss_box_2[0.4254] det_loss_cls_3[0.3939] det_loss_box_3[0.4101] det_loss_cls_4[0.3655] det_loss_box_4[0.3998] det_loss_cls_5[0.3467] det_loss_box_5[0.3999] det_loss_cls_dn_0[0.1000] det_loss_box_dn_0[0.2670] det_loss_cls_dn_1[0.0962] det_loss_box_dn_1[0.2190] det_loss_cls_dn_2[0.0942] det_loss_box_dn_2[0.2002] det_loss_cls_dn_3[0.0935] det_loss_box_dn_3[0.1945] det_loss_cls_dn_4[0.0926] det_loss_box_dn_4[0.1920] det_loss_cls_dn_5[0.0920] det_loss_box_dn_5[0.1897] loss_depth[0.0409] total_loss[7.0724]
Rank[0/8] 06/20/2025 04:54:18 INFO stats.py:314 | Epoch[111] Step[109] GlobalStep[43399] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 17:00:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:54:34 INFO loss_tracker.py:84 | Epoch[111/239] Step[124] GlobalStep[43414/NA]: det_loss_cls_0[0.5661] det_loss_box_0[0.8012] det_loss_cls_1[0.5635] det_loss_box_1[0.6235] det_loss_cls_2[0.5153] det_loss_box_2[0.5844] det_loss_cls_3[0.4846] det_loss_box_3[0.5569] det_loss_cls_4[0.4556] det_loss_box_4[0.5488] det_loss_cls_5[0.4405] det_loss_box_5[0.5453] det_loss_cls_dn_0[0.1064] det_loss_box_dn_0[0.3107] det_loss_cls_dn_1[0.1027] det_loss_box_dn_1[0.2688] det_loss_cls_dn_2[0.1021] det_loss_box_dn_2[0.2506] det_loss_cls_dn_3[0.1008] det_loss_box_dn_3[0.2466] det_loss_cls_dn_4[0.1000] det_loss_box_dn_4[0.2444] det_loss_cls_dn_5[0.0993] det_loss_box_dn_5[0.2435] loss_depth[0.0409] total_loss[8.9023]
Rank[0/8] 06/20/2025 04:54:44 INFO stats.py:314 | Epoch[111] Step[134] GlobalStep[43424] Training Speed: 7.73 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 16:59:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:54:59 INFO loss_tracker.py:84 | Epoch[111/239] Step[149] GlobalStep[43439/NA]: det_loss_cls_0[0.4350] det_loss_box_0[0.5571] det_loss_cls_1[0.4312] det_loss_box_1[0.4291] det_loss_cls_2[0.3930] det_loss_box_2[0.4025] det_loss_cls_3[0.3701] det_loss_box_3[0.3942] det_loss_cls_4[0.3487] det_loss_box_4[0.3891] det_loss_cls_5[0.3375] det_loss_box_5[0.3915] det_loss_cls_dn_0[0.0835] det_loss_box_dn_0[0.2868] det_loss_cls_dn_1[0.0797] det_loss_box_dn_1[0.2410] det_loss_cls_dn_2[0.0789] det_loss_box_dn_2[0.2252] det_loss_cls_dn_3[0.0781] det_loss_box_dn_3[0.2208] det_loss_cls_dn_4[0.0776] det_loss_box_dn_4[0.2200] det_loss_cls_dn_5[0.0765] det_loss_box_dn_5[0.2179] loss_depth[0.0361] total_loss[6.8011]
Rank[0/8] 06/20/2025 04:55:09 INFO stats.py:314 | Epoch[111] Step[159] GlobalStep[43449] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 16:59:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:55:25 INFO loss_tracker.py:84 | Epoch[111/239] Step[174] GlobalStep[43464/NA]: det_loss_cls_0[0.5920] det_loss_box_0[0.8198] det_loss_cls_1[0.5819] det_loss_box_1[0.6408] det_loss_cls_2[0.5505] det_loss_box_2[0.5745] det_loss_cls_3[0.5301] det_loss_box_3[0.5595] det_loss_cls_4[0.4941] det_loss_box_4[0.5555] det_loss_cls_5[0.4850] det_loss_box_5[0.5577] det_loss_cls_dn_0[0.1053] det_loss_box_dn_0[0.2977] det_loss_cls_dn_1[0.1021] det_loss_box_dn_1[0.2600] det_loss_cls_dn_2[0.1007] det_loss_box_dn_2[0.2442] det_loss_cls_dn_3[0.1000] det_loss_box_dn_3[0.2409] det_loss_cls_dn_4[0.0991] det_loss_box_dn_4[0.2409] det_loss_cls_dn_5[0.0983] det_loss_box_dn_5[0.2390] loss_depth[0.0367] total_loss[9.1062]
Rank[0/8] 06/20/2025 04:55:35 INFO stats.py:314 | Epoch[111] Step[184] GlobalStep[43474] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 16:58:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:55:51 INFO loss_tracker.py:84 | Epoch[111/239] Step[199] GlobalStep[43489/NA]: det_loss_cls_0[0.5111] det_loss_box_0[0.6511] det_loss_cls_1[0.4931] det_loss_box_1[0.5184] det_loss_cls_2[0.4518] det_loss_box_2[0.4691] det_loss_cls_3[0.4437] det_loss_box_3[0.4439] det_loss_cls_4[0.4153] det_loss_box_4[0.4576] det_loss_cls_5[0.3962] det_loss_box_5[0.4562] det_loss_cls_dn_0[0.1058] det_loss_box_dn_0[0.3078] det_loss_cls_dn_1[0.1027] det_loss_box_dn_1[0.2609] det_loss_cls_dn_2[0.1017] det_loss_box_dn_2[0.2501] det_loss_cls_dn_3[0.1017] det_loss_box_dn_3[0.2468] det_loss_cls_dn_4[0.1006] det_loss_box_dn_4[0.2461] det_loss_cls_dn_5[0.0999] det_loss_box_dn_5[0.2448] loss_depth[0.0392] total_loss[7.9155]
Rank[0/8] 06/20/2025 04:56:01 INFO stats.py:314 | Epoch[111] Step[209] GlobalStep[43499] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 16:57:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:56:16 INFO loss_tracker.py:84 | Epoch[111/239] Step[224] GlobalStep[43514/NA]: det_loss_cls_0[0.5366] det_loss_box_0[0.7395] det_loss_cls_1[0.5309] det_loss_box_1[0.5802] det_loss_cls_2[0.4893] det_loss_box_2[0.5299] det_loss_cls_3[0.4489] det_loss_box_3[0.5123] det_loss_cls_4[0.4262] det_loss_box_4[0.5040] det_loss_cls_5[0.4116] det_loss_box_5[0.5021] det_loss_cls_dn_0[0.0994] det_loss_box_dn_0[0.3175] det_loss_cls_dn_1[0.0953] det_loss_box_dn_1[0.2807] det_loss_cls_dn_2[0.0944] det_loss_box_dn_2[0.2655] det_loss_cls_dn_3[0.0932] det_loss_box_dn_3[0.2629] det_loss_cls_dn_4[0.0919] det_loss_box_dn_4[0.2621] det_loss_cls_dn_5[0.0907] det_loss_box_dn_5[0.2597] loss_depth[0.0379] total_loss[8.4626]
Rank[0/8] 06/20/2025 04:56:27 INFO stats.py:314 | Epoch[111] Step[234] GlobalStep[43524] Training Speed: 6.75 samples/sec across all devices.	Average Step Time: 1.19 sec.	Estimated Remaining Time: 16:57:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:56:42 INFO loss_tracker.py:84 | Epoch[111/239] Step[249] GlobalStep[43539/NA]: det_loss_cls_0[0.6013] det_loss_box_0[0.7726] det_loss_cls_1[0.5958] det_loss_box_1[0.5733] det_loss_cls_2[0.5447] det_loss_box_2[0.5217] det_loss_cls_3[0.5167] det_loss_box_3[0.5101] det_loss_cls_4[0.4900] det_loss_box_4[0.4955] det_loss_cls_5[0.4742] det_loss_box_5[0.4936] det_loss_cls_dn_0[0.1001] det_loss_box_dn_0[0.2710] det_loss_cls_dn_1[0.0960] det_loss_box_dn_1[0.2245] det_loss_cls_dn_2[0.0955] det_loss_box_dn_2[0.2094] det_loss_cls_dn_3[0.0949] det_loss_box_dn_3[0.2066] det_loss_cls_dn_4[0.0940] det_loss_box_dn_4[0.2046] det_loss_cls_dn_5[0.0930] det_loss_box_dn_5[0.2028] loss_depth[0.0402] total_loss[8.5220]
Rank[0/8] 06/20/2025 04:56:52 INFO stats.py:314 | Epoch[111] Step[259] GlobalStep[43549] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 16:56:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:57:08 INFO loss_tracker.py:84 | Epoch[111/239] Step[274] GlobalStep[43564/NA]: det_loss_cls_0[0.5120] det_loss_box_0[0.6762] det_loss_cls_1[0.4884] det_loss_box_1[0.5263] det_loss_cls_2[0.4558] det_loss_box_2[0.4551] det_loss_cls_3[0.4225] det_loss_box_3[0.4581] det_loss_cls_4[0.3940] det_loss_box_4[0.4383] det_loss_cls_5[0.3762] det_loss_box_5[0.4406] det_loss_cls_dn_0[0.1034] det_loss_box_dn_0[0.3587] det_loss_cls_dn_1[0.0991] det_loss_box_dn_1[0.3120] det_loss_cls_dn_2[0.0987] det_loss_box_dn_2[0.2931] det_loss_cls_dn_3[0.0981] det_loss_box_dn_3[0.2913] det_loss_cls_dn_4[0.0977] det_loss_box_dn_4[0.2858] det_loss_cls_dn_5[0.0975] det_loss_box_dn_5[0.2830] loss_depth[0.0394] total_loss[8.1013]
Rank[0/8] 06/20/2025 04:57:18 INFO stats.py:314 | Epoch[111] Step[284] GlobalStep[43574] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 16:56:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:57:33 INFO loss_tracker.py:84 | Epoch[111/239] Step[299] GlobalStep[43589/NA]: det_loss_cls_0[0.4848] det_loss_box_0[0.5847] det_loss_cls_1[0.4807] det_loss_box_1[0.4690] det_loss_cls_2[0.4387] det_loss_box_2[0.4272] det_loss_cls_3[0.4095] det_loss_box_3[0.4140] det_loss_cls_4[0.3773] det_loss_box_4[0.4151] det_loss_cls_5[0.3660] det_loss_box_5[0.4133] det_loss_cls_dn_0[0.1002] det_loss_box_dn_0[0.3078] det_loss_cls_dn_1[0.0954] det_loss_box_dn_1[0.2681] det_loss_cls_dn_2[0.0948] det_loss_box_dn_2[0.2557] det_loss_cls_dn_3[0.0939] det_loss_box_dn_3[0.2530] det_loss_cls_dn_4[0.0932] det_loss_box_dn_4[0.2524] det_loss_cls_dn_5[0.0925] det_loss_box_dn_5[0.2515] loss_depth[0.0385] total_loss[7.4775]
Rank[0/8] 06/20/2025 04:57:44 INFO stats.py:314 | Epoch[111] Step[309] GlobalStep[43599] Training Speed: 7.91 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 16:55:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:57:59 INFO loss_tracker.py:84 | Epoch[111/239] Step[324] GlobalStep[43614/NA]: det_loss_cls_0[0.6454] det_loss_box_0[0.7986] det_loss_cls_1[0.6234] det_loss_box_1[0.6482] det_loss_cls_2[0.5941] det_loss_box_2[0.5942] det_loss_cls_3[0.5651] det_loss_box_3[0.5873] det_loss_cls_4[0.5367] det_loss_box_4[0.5771] det_loss_cls_5[0.5264] det_loss_box_5[0.5747] det_loss_cls_dn_0[0.1007] det_loss_box_dn_0[0.3224] det_loss_cls_dn_1[0.0979] det_loss_box_dn_1[0.2824] det_loss_cls_dn_2[0.0973] det_loss_box_dn_2[0.2693] det_loss_cls_dn_3[0.0957] det_loss_box_dn_3[0.2651] det_loss_cls_dn_4[0.0949] det_loss_box_dn_4[0.2650] det_loss_cls_dn_5[0.0936] det_loss_box_dn_5[0.2609] loss_depth[0.0396] total_loss[9.5560]
Rank[0/8] 06/20/2025 04:58:09 INFO stats.py:314 | Epoch[111] Step[334] GlobalStep[43624] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 16:54:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:58:25 INFO loss_tracker.py:84 | Epoch[111/239] Step[349] GlobalStep[43639/NA]: det_loss_cls_0[0.4837] det_loss_box_0[0.6399] det_loss_cls_1[0.4648] det_loss_box_1[0.4996] det_loss_cls_2[0.4198] det_loss_box_2[0.4695] det_loss_cls_3[0.3853] det_loss_box_3[0.4624] det_loss_cls_4[0.3659] det_loss_box_4[0.4523] det_loss_cls_5[0.3515] det_loss_box_5[0.4503] det_loss_cls_dn_0[0.1024] det_loss_box_dn_0[0.3267] det_loss_cls_dn_1[0.0990] det_loss_box_dn_1[0.2792] det_loss_cls_dn_2[0.0982] det_loss_box_dn_2[0.2639] det_loss_cls_dn_3[0.0975] det_loss_box_dn_3[0.2623] det_loss_cls_dn_4[0.0970] det_loss_box_dn_4[0.2603] det_loss_cls_dn_5[0.0962] det_loss_box_dn_5[0.2587] loss_depth[0.0386] total_loss[7.7248]
Rank[0/8] 06/20/2025 04:58:36 INFO stats.py:314 | Epoch[111] Step[359] GlobalStep[43649] Training Speed: 6.59 samples/sec across all devices.	Average Step Time: 1.21 sec.	Estimated Remaining Time: 16:54:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:58:51 INFO loss_tracker.py:84 | Epoch[111/239] Step[374] GlobalStep[43664/NA]: det_loss_cls_0[0.4178] det_loss_box_0[0.5378] det_loss_cls_1[0.4168] det_loss_box_1[0.3935] det_loss_cls_2[0.3691] det_loss_box_2[0.3568] det_loss_cls_3[0.3381] det_loss_box_3[0.3435] det_loss_cls_4[0.3115] det_loss_box_4[0.3382] det_loss_cls_5[0.3004] det_loss_box_5[0.3353] det_loss_cls_dn_0[0.0831] det_loss_box_dn_0[0.2624] det_loss_cls_dn_1[0.0793] det_loss_box_dn_1[0.2192] det_loss_cls_dn_2[0.0793] det_loss_box_dn_2[0.1997] det_loss_cls_dn_3[0.0775] det_loss_box_dn_3[0.1968] det_loss_cls_dn_4[0.0767] det_loss_box_dn_4[0.1950] det_loss_cls_dn_5[0.0757] det_loss_box_dn_5[0.1950] loss_depth[0.0380] total_loss[6.2363]
Rank[0/8] 06/20/2025 04:59:01 INFO stats.py:314 | Epoch[111] Step[384] GlobalStep[43674] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 16:53:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:59:06 INFO stats.py:394 | Epoch[111] completed. Training Speed: 7.72 samples/sec across all devices.	Epoch Time: 404.17 sec.	Average Epoch Time: 404.17 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 16:53:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:59:30 INFO stats.py:314 | Epoch[112] Step[19] GlobalStep[43699] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 16:53:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 04:59:35 INFO loss_tracker.py:84 | Epoch[112/239] Step[24] GlobalStep[43704/NA]: det_loss_cls_0[0.4022] det_loss_box_0[0.5506] det_loss_cls_1[0.3932] det_loss_box_1[0.4009] det_loss_cls_2[0.3562] det_loss_box_2[0.3626] det_loss_cls_3[0.3318] det_loss_box_3[0.3398] det_loss_cls_4[0.3028] det_loss_box_4[0.3296] det_loss_cls_5[0.2892] det_loss_box_5[0.3296] det_loss_cls_dn_0[0.0897] det_loss_box_dn_0[0.2821] det_loss_cls_dn_1[0.0842] det_loss_box_dn_1[0.2397] det_loss_cls_dn_2[0.0839] det_loss_box_dn_2[0.2224] det_loss_cls_dn_3[0.0825] det_loss_box_dn_3[0.2176] det_loss_cls_dn_4[0.0820] det_loss_box_dn_4[0.2163] det_loss_cls_dn_5[0.0806] det_loss_box_dn_5[0.2121] loss_depth[0.0369] total_loss[6.3186]
Rank[0/8] 06/20/2025 04:59:55 INFO stats.py:314 | Epoch[112] Step[44] GlobalStep[43724] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 16:52:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:00:01 INFO loss_tracker.py:84 | Epoch[112/239] Step[49] GlobalStep[43729/NA]: det_loss_cls_0[0.5427] det_loss_box_0[0.6964] det_loss_cls_1[0.5319] det_loss_box_1[0.5393] det_loss_cls_2[0.4944] det_loss_box_2[0.4954] det_loss_cls_3[0.4654] det_loss_box_3[0.4853] det_loss_cls_4[0.4408] det_loss_box_4[0.4757] det_loss_cls_5[0.4245] det_loss_box_5[0.4719] det_loss_cls_dn_0[0.1079] det_loss_box_dn_0[0.2839] det_loss_cls_dn_1[0.1049] det_loss_box_dn_1[0.2383] det_loss_cls_dn_2[0.1037] det_loss_box_dn_2[0.2205] det_loss_cls_dn_3[0.1023] det_loss_box_dn_3[0.2169] det_loss_cls_dn_4[0.1014] det_loss_box_dn_4[0.2162] det_loss_cls_dn_5[0.1008] det_loss_box_dn_5[0.2150] loss_depth[0.0362] total_loss[8.1118]
Rank[0/8] 06/20/2025 05:00:21 INFO stats.py:314 | Epoch[112] Step[69] GlobalStep[43749] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 16:51:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:00:26 INFO loss_tracker.py:84 | Epoch[112/239] Step[74] GlobalStep[43754/NA]: det_loss_cls_0[0.5286] det_loss_box_0[0.6412] det_loss_cls_1[0.5175] det_loss_box_1[0.4861] det_loss_cls_2[0.4845] det_loss_box_2[0.4344] det_loss_cls_3[0.4674] det_loss_box_3[0.4191] det_loss_cls_4[0.4319] det_loss_box_4[0.4098] det_loss_cls_5[0.4162] det_loss_box_5[0.4125] det_loss_cls_dn_0[0.1002] det_loss_box_dn_0[0.2784] det_loss_cls_dn_1[0.0969] det_loss_box_dn_1[0.2375] det_loss_cls_dn_2[0.0965] det_loss_box_dn_2[0.2264] det_loss_cls_dn_3[0.0953] det_loss_box_dn_3[0.2234] det_loss_cls_dn_4[0.0946] det_loss_box_dn_4[0.2218] det_loss_cls_dn_5[0.0935] det_loss_box_dn_5[0.2194] loss_depth[0.0404] total_loss[7.6735]
Rank[0/8] 06/20/2025 05:00:47 INFO stats.py:314 | Epoch[112] Step[94] GlobalStep[43774] Training Speed: 6.42 samples/sec across all devices.	Average Step Time: 1.25 sec.	Estimated Remaining Time: 16:51:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:00:53 INFO loss_tracker.py:84 | Epoch[112/239] Step[99] GlobalStep[43779/NA]: det_loss_cls_0[0.4524] det_loss_box_0[0.5471] det_loss_cls_1[0.4518] det_loss_box_1[0.4310] det_loss_cls_2[0.4240] det_loss_box_2[0.3981] det_loss_cls_3[0.3999] det_loss_box_3[0.3832] det_loss_cls_4[0.3752] det_loss_box_4[0.3775] det_loss_cls_5[0.3645] det_loss_box_5[0.3853] det_loss_cls_dn_0[0.0909] det_loss_box_dn_0[0.2800] det_loss_cls_dn_1[0.0876] det_loss_box_dn_1[0.2411] det_loss_cls_dn_2[0.0869] det_loss_box_dn_2[0.2238] det_loss_cls_dn_3[0.0859] det_loss_box_dn_3[0.2204] det_loss_cls_dn_4[0.0848] det_loss_box_dn_4[0.2199] det_loss_cls_dn_5[0.0835] det_loss_box_dn_5[0.2177] loss_depth[0.0343] total_loss[6.9468]
Rank[0/8] 06/20/2025 05:01:13 INFO stats.py:314 | Epoch[112] Step[119] GlobalStep[43799] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 16:50:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:01:18 INFO loss_tracker.py:84 | Epoch[112/239] Step[124] GlobalStep[43804/NA]: det_loss_cls_0[0.4334] det_loss_box_0[0.5592] det_loss_cls_1[0.4397] det_loss_box_1[0.4530] det_loss_cls_2[0.4079] det_loss_box_2[0.4125] det_loss_cls_3[0.3744] det_loss_box_3[0.3976] det_loss_cls_4[0.3660] det_loss_box_4[0.3855] det_loss_cls_5[0.3456] det_loss_box_5[0.3931] det_loss_cls_dn_0[0.0873] det_loss_box_dn_0[0.2916] det_loss_cls_dn_1[0.0833] det_loss_box_dn_1[0.2414] det_loss_cls_dn_2[0.0828] det_loss_box_dn_2[0.2230] det_loss_cls_dn_3[0.0818] det_loss_box_dn_3[0.2218] det_loss_cls_dn_4[0.0811] det_loss_box_dn_4[0.2180] det_loss_cls_dn_5[0.0800] det_loss_box_dn_5[0.2163] loss_depth[0.0384] total_loss[6.9146]
Rank[0/8] 06/20/2025 05:01:39 INFO stats.py:314 | Epoch[112] Step[144] GlobalStep[43824] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 16:50:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:01:44 INFO loss_tracker.py:84 | Epoch[112/239] Step[149] GlobalStep[43829/NA]: det_loss_cls_0[0.4735] det_loss_box_0[0.6047] det_loss_cls_1[0.4588] det_loss_box_1[0.4624] det_loss_cls_2[0.4211] det_loss_box_2[0.4147] det_loss_cls_3[0.3898] det_loss_box_3[0.3994] det_loss_cls_4[0.3641] det_loss_box_4[0.3943] det_loss_cls_5[0.3483] det_loss_box_5[0.4001] det_loss_cls_dn_0[0.0920] det_loss_box_dn_0[0.2703] det_loss_cls_dn_1[0.0896] det_loss_box_dn_1[0.2244] det_loss_cls_dn_2[0.0885] det_loss_box_dn_2[0.2078] det_loss_cls_dn_3[0.0877] det_loss_box_dn_3[0.2031] det_loss_cls_dn_4[0.0881] det_loss_box_dn_4[0.2019] det_loss_cls_dn_5[0.0865] det_loss_box_dn_5[0.1997] loss_depth[0.0357] total_loss[7.0066]
Rank[0/8] 06/20/2025 05:02:05 INFO stats.py:314 | Epoch[112] Step[169] GlobalStep[43849] Training Speed: 7.98 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 16:49:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:02:10 INFO loss_tracker.py:84 | Epoch[112/239] Step[174] GlobalStep[43854/NA]: det_loss_cls_0[0.5488] det_loss_box_0[0.7609] det_loss_cls_1[0.5245] det_loss_box_1[0.5909] det_loss_cls_2[0.4923] det_loss_box_2[0.5230] det_loss_cls_3[0.4636] det_loss_box_3[0.5001] det_loss_cls_4[0.4399] det_loss_box_4[0.4888] det_loss_cls_5[0.4253] det_loss_box_5[0.4920] det_loss_cls_dn_0[0.1082] det_loss_box_dn_0[0.3503] det_loss_cls_dn_1[0.1034] det_loss_box_dn_1[0.3008] det_loss_cls_dn_2[0.1028] det_loss_box_dn_2[0.2819] det_loss_cls_dn_3[0.1022] det_loss_box_dn_3[0.2775] det_loss_cls_dn_4[0.1014] det_loss_box_dn_4[0.2753] det_loss_cls_dn_5[0.1006] det_loss_box_dn_5[0.2736] loss_depth[0.0418] total_loss[8.6698]
Rank[0/8] 06/20/2025 05:02:31 INFO stats.py:314 | Epoch[112] Step[194] GlobalStep[43874] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 16:48:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:02:36 INFO loss_tracker.py:84 | Epoch[112/239] Step[199] GlobalStep[43879/NA]: det_loss_cls_0[0.4993] det_loss_box_0[0.6279] det_loss_cls_1[0.4799] det_loss_box_1[0.4874] det_loss_cls_2[0.4459] det_loss_box_2[0.4464] det_loss_cls_3[0.4238] det_loss_box_3[0.4304] det_loss_cls_4[0.3953] det_loss_box_4[0.4214] det_loss_cls_5[0.3889] det_loss_box_5[0.4150] det_loss_cls_dn_0[0.0959] det_loss_box_dn_0[0.2809] det_loss_cls_dn_1[0.0926] det_loss_box_dn_1[0.2415] det_loss_cls_dn_2[0.0925] det_loss_box_dn_2[0.2291] det_loss_cls_dn_3[0.0922] det_loss_box_dn_3[0.2241] det_loss_cls_dn_4[0.0902] det_loss_box_dn_4[0.2227] det_loss_cls_dn_5[0.0900] det_loss_box_dn_5[0.2221] loss_depth[0.0369] total_loss[7.4722]
Rank[0/8] 06/20/2025 05:02:57 INFO stats.py:314 | Epoch[112] Step[219] GlobalStep[43899] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 16:48:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:03:02 INFO loss_tracker.py:84 | Epoch[112/239] Step[224] GlobalStep[43904/NA]: det_loss_cls_0[0.4782] det_loss_box_0[0.5486] det_loss_cls_1[0.4694] det_loss_box_1[0.4053] det_loss_cls_2[0.4366] det_loss_box_2[0.3865] det_loss_cls_3[0.4038] det_loss_box_3[0.3782] det_loss_cls_4[0.3767] det_loss_box_4[0.3714] det_loss_cls_5[0.3665] det_loss_box_5[0.3690] det_loss_cls_dn_0[0.1083] det_loss_box_dn_0[0.3096] det_loss_cls_dn_1[0.1039] det_loss_box_dn_1[0.2604] det_loss_cls_dn_2[0.1037] det_loss_box_dn_2[0.2449] det_loss_cls_dn_3[0.1034] det_loss_box_dn_3[0.2397] det_loss_cls_dn_4[0.1028] det_loss_box_dn_4[0.2387] det_loss_cls_dn_5[0.1026] det_loss_box_dn_5[0.2363] loss_depth[0.0410] total_loss[7.1854]
Rank[0/8] 06/20/2025 05:03:23 INFO stats.py:314 | Epoch[112] Step[244] GlobalStep[43924] Training Speed: 8.22 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 16:47:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:03:28 INFO loss_tracker.py:84 | Epoch[112/239] Step[249] GlobalStep[43929/NA]: det_loss_cls_0[0.4986] det_loss_box_0[0.6316] det_loss_cls_1[0.4780] det_loss_box_1[0.4780] det_loss_cls_2[0.4522] det_loss_box_2[0.4312] det_loss_cls_3[0.4219] det_loss_box_3[0.4188] det_loss_cls_4[0.3957] det_loss_box_4[0.4106] det_loss_cls_5[0.3779] det_loss_box_5[0.4136] det_loss_cls_dn_0[0.1024] det_loss_box_dn_0[0.2661] det_loss_cls_dn_1[0.0989] det_loss_box_dn_1[0.2255] det_loss_cls_dn_2[0.0982] det_loss_box_dn_2[0.2089] det_loss_cls_dn_3[0.0976] det_loss_box_dn_3[0.2053] det_loss_cls_dn_4[0.0961] det_loss_box_dn_4[0.2042] det_loss_cls_dn_5[0.0949] det_loss_box_dn_5[0.2016] loss_depth[0.0363] total_loss[7.3439]
Rank[0/8] 06/20/2025 05:03:49 INFO stats.py:314 | Epoch[112] Step[269] GlobalStep[43949] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 16:47:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:03:54 INFO loss_tracker.py:84 | Epoch[112/239] Step[274] GlobalStep[43954/NA]: det_loss_cls_0[0.5061] det_loss_box_0[0.6359] det_loss_cls_1[0.5011] det_loss_box_1[0.5260] det_loss_cls_2[0.4722] det_loss_box_2[0.4794] det_loss_cls_3[0.4395] det_loss_box_3[0.4605] det_loss_cls_4[0.4260] det_loss_box_4[0.4417] det_loss_cls_5[0.4074] det_loss_box_5[0.4471] det_loss_cls_dn_0[0.1058] det_loss_box_dn_0[0.3256] det_loss_cls_dn_1[0.1030] det_loss_box_dn_1[0.2689] det_loss_cls_dn_2[0.1021] det_loss_box_dn_2[0.2513] det_loss_cls_dn_3[0.1017] det_loss_box_dn_3[0.2464] det_loss_cls_dn_4[0.1012] det_loss_box_dn_4[0.2452] det_loss_cls_dn_5[0.1001] det_loss_box_dn_5[0.2428] loss_depth[0.0394] total_loss[7.9763]
Rank[0/8] 06/20/2025 05:04:16 INFO stats.py:314 | Epoch[112] Step[294] GlobalStep[43974] Training Speed: 6.06 samples/sec across all devices.	Average Step Time: 1.32 sec.	Estimated Remaining Time: 16:46:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:04:20 INFO loss_tracker.py:84 | Epoch[112/239] Step[299] GlobalStep[43979/NA]: det_loss_cls_0[0.5595] det_loss_box_0[0.7330] det_loss_cls_1[0.5419] det_loss_box_1[0.5884] det_loss_cls_2[0.5103] det_loss_box_2[0.5330] det_loss_cls_3[0.4813] det_loss_box_3[0.5204] det_loss_cls_4[0.4580] det_loss_box_4[0.5059] det_loss_cls_5[0.4455] det_loss_box_5[0.5063] det_loss_cls_dn_0[0.0999] det_loss_box_dn_0[0.2822] det_loss_cls_dn_1[0.0966] det_loss_box_dn_1[0.2458] det_loss_cls_dn_2[0.0959] det_loss_box_dn_2[0.2326] det_loss_cls_dn_3[0.0947] det_loss_box_dn_3[0.2291] det_loss_cls_dn_4[0.0937] det_loss_box_dn_4[0.2283] det_loss_cls_dn_5[0.0929] det_loss_box_dn_5[0.2253] loss_depth[0.0342] total_loss[8.4347]
Rank[0/8] 06/20/2025 05:04:41 INFO stats.py:314 | Epoch[112] Step[319] GlobalStep[43999] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 16:46:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:04:46 INFO loss_tracker.py:84 | Epoch[112/239] Step[324] GlobalStep[44004/NA]: det_loss_cls_0[0.4940] det_loss_box_0[0.6811] det_loss_cls_1[0.4778] det_loss_box_1[0.5672] det_loss_cls_2[0.4371] det_loss_box_2[0.5179] det_loss_cls_3[0.4168] det_loss_box_3[0.4949] det_loss_cls_4[0.3932] det_loss_box_4[0.4949] det_loss_cls_5[0.3775] det_loss_box_5[0.4985] det_loss_cls_dn_0[0.0926] det_loss_box_dn_0[0.3898] det_loss_cls_dn_1[0.0892] det_loss_box_dn_1[0.3263] det_loss_cls_dn_2[0.0881] det_loss_box_dn_2[0.3060] det_loss_cls_dn_3[0.0864] det_loss_box_dn_3[0.3022] det_loss_cls_dn_4[0.0857] det_loss_box_dn_4[0.2999] det_loss_cls_dn_5[0.0849] det_loss_box_dn_5[0.2907] loss_depth[0.0377] total_loss[8.3303]
Rank[0/8] 06/20/2025 05:05:07 INFO stats.py:314 | Epoch[112] Step[344] GlobalStep[44024] Training Speed: 8.07 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 16:45:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:05:12 INFO loss_tracker.py:84 | Epoch[112/239] Step[349] GlobalStep[44029/NA]: det_loss_cls_0[0.4912] det_loss_box_0[0.6581] det_loss_cls_1[0.4806] det_loss_box_1[0.4944] det_loss_cls_2[0.4436] det_loss_box_2[0.4534] det_loss_cls_3[0.4149] det_loss_box_3[0.4410] det_loss_cls_4[0.3844] det_loss_box_4[0.4399] det_loss_cls_5[0.3767] det_loss_box_5[0.4414] det_loss_cls_dn_0[0.0979] det_loss_box_dn_0[0.2562] det_loss_cls_dn_1[0.0940] det_loss_box_dn_1[0.2128] det_loss_cls_dn_2[0.0937] det_loss_box_dn_2[0.1974] det_loss_cls_dn_3[0.0926] det_loss_box_dn_3[0.1936] det_loss_cls_dn_4[0.0919] det_loss_box_dn_4[0.1929] det_loss_cls_dn_5[0.0911] det_loss_box_dn_5[0.1902] loss_depth[0.0399] total_loss[7.3639]
Rank[0/8] 06/20/2025 05:05:33 INFO stats.py:314 | Epoch[112] Step[369] GlobalStep[44049] Training Speed: 11.04 samples/sec across all devices.	Average Step Time: 0.72 sec.	Estimated Remaining Time: 16:44:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:05:38 INFO loss_tracker.py:84 | Epoch[112/239] Step[374] GlobalStep[44054/NA]: det_loss_cls_0[0.3683] det_loss_box_0[0.4606] det_loss_cls_1[0.3522] det_loss_box_1[0.3593] det_loss_cls_2[0.3114] det_loss_box_2[0.3243] det_loss_cls_3[0.2837] det_loss_box_3[0.3138] det_loss_cls_4[0.2661] det_loss_box_4[0.3034] det_loss_cls_5[0.2529] det_loss_box_5[0.3045] det_loss_cls_dn_0[0.0887] det_loss_box_dn_0[0.2966] det_loss_cls_dn_1[0.0840] det_loss_box_dn_1[0.2503] det_loss_cls_dn_2[0.0829] det_loss_box_dn_2[0.2330] det_loss_cls_dn_3[0.0813] det_loss_box_dn_3[0.2286] det_loss_cls_dn_4[0.0812] det_loss_box_dn_4[0.2268] det_loss_cls_dn_5[0.0796] det_loss_box_dn_5[0.2241] loss_depth[0.0369] total_loss[5.8946]
Rank[0/8] 06/20/2025 05:05:53 INFO stats.py:394 | Epoch[112] completed. Training Speed: 7.67 samples/sec across all devices.	Epoch Time: 407.02 sec.	Average Epoch Time: 407.02 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 16:44:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:06:00 INFO stats.py:314 | Epoch[113] Step[4] GlobalStep[44074] Training Speed: 8.15 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 16:44:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:06:21 INFO loss_tracker.py:84 | Epoch[113/239] Step[24] GlobalStep[44094/NA]: det_loss_cls_0[0.5568] det_loss_box_0[0.7344] det_loss_cls_1[0.5503] det_loss_box_1[0.5823] det_loss_cls_2[0.5089] det_loss_box_2[0.5450] det_loss_cls_3[0.4856] det_loss_box_3[0.5261] det_loss_cls_4[0.4571] det_loss_box_4[0.5295] det_loss_cls_5[0.4439] det_loss_box_5[0.5275] det_loss_cls_dn_0[0.1019] det_loss_box_dn_0[0.3242] det_loss_cls_dn_1[0.0987] det_loss_box_dn_1[0.2802] det_loss_cls_dn_2[0.0986] det_loss_box_dn_2[0.2644] det_loss_cls_dn_3[0.0972] det_loss_box_dn_3[0.2618] det_loss_cls_dn_4[0.0965] det_loss_box_dn_4[0.2620] det_loss_cls_dn_5[0.0957] det_loss_box_dn_5[0.2598] loss_depth[0.0374] total_loss[8.7257]
Rank[0/8] 06/20/2025 05:06:26 INFO stats.py:314 | Epoch[113] Step[29] GlobalStep[44099] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 16:43:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:06:47 INFO loss_tracker.py:84 | Epoch[113/239] Step[49] GlobalStep[44119/NA]: det_loss_cls_0[0.4941] det_loss_box_0[0.6588] det_loss_cls_1[0.4771] det_loss_box_1[0.5121] det_loss_cls_2[0.4460] det_loss_box_2[0.4614] det_loss_cls_3[0.4043] det_loss_box_3[0.4536] det_loss_cls_4[0.3832] det_loss_box_4[0.4423] det_loss_cls_5[0.3737] det_loss_box_5[0.4437] det_loss_cls_dn_0[0.0840] det_loss_box_dn_0[0.2954] det_loss_cls_dn_1[0.0807] det_loss_box_dn_1[0.2430] det_loss_cls_dn_2[0.0794] det_loss_box_dn_2[0.2226] det_loss_cls_dn_3[0.0779] det_loss_box_dn_3[0.2182] det_loss_cls_dn_4[0.0769] det_loss_box_dn_4[0.2167] det_loss_cls_dn_5[0.0757] det_loss_box_dn_5[0.2154] loss_depth[0.0362] total_loss[7.4724]
Rank[0/8] 06/20/2025 05:06:52 INFO stats.py:314 | Epoch[113] Step[54] GlobalStep[44124] Training Speed: 8.66 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 16:43:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:07:13 INFO loss_tracker.py:84 | Epoch[113/239] Step[74] GlobalStep[44144/NA]: det_loss_cls_0[0.5059] det_loss_box_0[0.6360] det_loss_cls_1[0.4794] det_loss_box_1[0.5059] det_loss_cls_2[0.4319] det_loss_box_2[0.4488] det_loss_cls_3[0.4081] det_loss_box_3[0.4320] det_loss_cls_4[0.3839] det_loss_box_4[0.4243] det_loss_cls_5[0.3691] det_loss_box_5[0.4262] det_loss_cls_dn_0[0.0963] det_loss_box_dn_0[0.3078] det_loss_cls_dn_1[0.0919] det_loss_box_dn_1[0.2599] det_loss_cls_dn_2[0.0912] det_loss_box_dn_2[0.2412] det_loss_cls_dn_3[0.0904] det_loss_box_dn_3[0.2372] det_loss_cls_dn_4[0.0899] det_loss_box_dn_4[0.2351] det_loss_cls_dn_5[0.0892] det_loss_box_dn_5[0.2332] loss_depth[0.0376] total_loss[7.5525]
Rank[0/8] 06/20/2025 05:07:18 INFO stats.py:314 | Epoch[113] Step[79] GlobalStep[44149] Training Speed: 9.80 samples/sec across all devices.	Average Step Time: 0.82 sec.	Estimated Remaining Time: 16:42:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:07:39 INFO loss_tracker.py:84 | Epoch[113/239] Step[99] GlobalStep[44169/NA]: det_loss_cls_0[0.4255] det_loss_box_0[0.5728] det_loss_cls_1[0.3995] det_loss_box_1[0.4498] det_loss_cls_2[0.3649] det_loss_box_2[0.3951] det_loss_cls_3[0.3468] det_loss_box_3[0.3783] det_loss_cls_4[0.3110] det_loss_box_4[0.3757] det_loss_cls_5[0.2991] det_loss_box_5[0.3713] det_loss_cls_dn_0[0.0914] det_loss_box_dn_0[0.3043] det_loss_cls_dn_1[0.0877] det_loss_box_dn_1[0.2530] det_loss_cls_dn_2[0.0879] det_loss_box_dn_2[0.2346] det_loss_cls_dn_3[0.0857] det_loss_box_dn_3[0.2308] det_loss_cls_dn_4[0.0848] det_loss_box_dn_4[0.2290] det_loss_cls_dn_5[0.0836] det_loss_box_dn_5[0.2275] loss_depth[0.0380] total_loss[6.7281]
Rank[0/8] 06/20/2025 05:07:44 INFO stats.py:314 | Epoch[113] Step[104] GlobalStep[44174] Training Speed: 8.13 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 16:41:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:08:05 INFO loss_tracker.py:84 | Epoch[113/239] Step[124] GlobalStep[44194/NA]: det_loss_cls_0[0.5355] det_loss_box_0[0.6817] det_loss_cls_1[0.5101] det_loss_box_1[0.5104] det_loss_cls_2[0.4761] det_loss_box_2[0.4547] det_loss_cls_3[0.4527] det_loss_box_3[0.4377] det_loss_cls_4[0.4262] det_loss_box_4[0.4365] det_loss_cls_5[0.4098] det_loss_box_5[0.4394] det_loss_cls_dn_0[0.1046] det_loss_box_dn_0[0.2842] det_loss_cls_dn_1[0.1008] det_loss_box_dn_1[0.2356] det_loss_cls_dn_2[0.0994] det_loss_box_dn_2[0.2186] det_loss_cls_dn_3[0.0988] det_loss_box_dn_3[0.2168] det_loss_cls_dn_4[0.0978] det_loss_box_dn_4[0.2149] det_loss_cls_dn_5[0.0968] det_loss_box_dn_5[0.2144] loss_depth[0.0365] total_loss[7.7901]
Rank[0/8] 06/20/2025 05:08:09 INFO stats.py:314 | Epoch[113] Step[129] GlobalStep[44199] Training Speed: 8.68 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 16:41:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:08:30 INFO loss_tracker.py:84 | Epoch[113/239] Step[149] GlobalStep[44219/NA]: det_loss_cls_0[0.5602] det_loss_box_0[0.7181] det_loss_cls_1[0.5480] det_loss_box_1[0.5607] det_loss_cls_2[0.5186] det_loss_box_2[0.5203] det_loss_cls_3[0.4927] det_loss_box_3[0.4962] det_loss_cls_4[0.4626] det_loss_box_4[0.4848] det_loss_cls_5[0.4458] det_loss_box_5[0.4857] det_loss_cls_dn_0[0.0988] det_loss_box_dn_0[0.2936] det_loss_cls_dn_1[0.0953] det_loss_box_dn_1[0.2517] det_loss_cls_dn_2[0.0945] det_loss_box_dn_2[0.2390] det_loss_cls_dn_3[0.0932] det_loss_box_dn_3[0.2365] det_loss_cls_dn_4[0.0916] det_loss_box_dn_4[0.2347] det_loss_cls_dn_5[0.0912] det_loss_box_dn_5[0.2334] loss_depth[0.0362] total_loss[8.3833]
Rank[0/8] 06/20/2025 05:08:36 INFO stats.py:314 | Epoch[113] Step[154] GlobalStep[44224] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 16:40:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:08:56 INFO loss_tracker.py:84 | Epoch[113/239] Step[174] GlobalStep[44244/NA]: det_loss_cls_0[0.5775] det_loss_box_0[0.7605] det_loss_cls_1[0.5615] det_loss_box_1[0.6397] det_loss_cls_2[0.5007] det_loss_box_2[0.5843] det_loss_cls_3[0.4713] det_loss_box_3[0.5744] det_loss_cls_4[0.4299] det_loss_box_4[0.5549] det_loss_cls_5[0.4155] det_loss_box_5[0.5608] det_loss_cls_dn_0[0.0879] det_loss_box_dn_0[0.2809] det_loss_cls_dn_1[0.0832] det_loss_box_dn_1[0.2400] det_loss_cls_dn_2[0.0821] det_loss_box_dn_2[0.2258] det_loss_cls_dn_3[0.0816] det_loss_box_dn_3[0.2230] det_loss_cls_dn_4[0.0806] det_loss_box_dn_4[0.2217] det_loss_cls_dn_5[0.0795] det_loss_box_dn_5[0.2203] loss_depth[0.0367] total_loss[8.5746]
Rank[0/8] 06/20/2025 05:09:01 INFO stats.py:314 | Epoch[113] Step[179] GlobalStep[44249] Training Speed: 7.81 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 16:40:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:09:22 INFO loss_tracker.py:84 | Epoch[113/239] Step[199] GlobalStep[44269/NA]: det_loss_cls_0[0.5188] det_loss_box_0[0.7086] det_loss_cls_1[0.5191] det_loss_box_1[0.5296] det_loss_cls_2[0.4780] det_loss_box_2[0.4739] det_loss_cls_3[0.4536] det_loss_box_3[0.4574] det_loss_cls_4[0.4268] det_loss_box_4[0.4556] det_loss_cls_5[0.4180] det_loss_box_5[0.4525] det_loss_cls_dn_0[0.1044] det_loss_box_dn_0[0.3073] det_loss_cls_dn_1[0.1020] det_loss_box_dn_1[0.2678] det_loss_cls_dn_2[0.1016] det_loss_box_dn_2[0.2517] det_loss_cls_dn_3[0.1003] det_loss_box_dn_3[0.2492] det_loss_cls_dn_4[0.1002] det_loss_box_dn_4[0.2474] det_loss_cls_dn_5[0.0995] det_loss_box_dn_5[0.2471] loss_depth[0.0375] total_loss[8.1080]
Rank[0/8] 06/20/2025 05:09:27 INFO stats.py:314 | Epoch[113] Step[204] GlobalStep[44274] Training Speed: 8.59 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 16:39:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:09:48 INFO loss_tracker.py:84 | Epoch[113/239] Step[224] GlobalStep[44294/NA]: det_loss_cls_0[0.5607] det_loss_box_0[0.7495] det_loss_cls_1[0.5566] det_loss_box_1[0.5944] det_loss_cls_2[0.5090] det_loss_box_2[0.5428] det_loss_cls_3[0.4808] det_loss_box_3[0.5272] det_loss_cls_4[0.4626] det_loss_box_4[0.5088] det_loss_cls_5[0.4502] det_loss_box_5[0.5091] det_loss_cls_dn_0[0.1031] det_loss_box_dn_0[0.3243] det_loss_cls_dn_1[0.0987] det_loss_box_dn_1[0.2793] det_loss_cls_dn_2[0.0981] det_loss_box_dn_2[0.2640] det_loss_cls_dn_3[0.0973] det_loss_box_dn_3[0.2597] det_loss_cls_dn_4[0.0969] det_loss_box_dn_4[0.2578] det_loss_cls_dn_5[0.0959] det_loss_box_dn_5[0.2549] loss_depth[0.0363] total_loss[8.7181]
Rank[0/8] 06/20/2025 05:09:53 INFO stats.py:314 | Epoch[113] Step[229] GlobalStep[44299] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 16:38:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:10:14 INFO loss_tracker.py:84 | Epoch[113/239] Step[249] GlobalStep[44319/NA]: det_loss_cls_0[0.5406] det_loss_box_0[0.7178] det_loss_cls_1[0.5209] det_loss_box_1[0.5468] det_loss_cls_2[0.4845] det_loss_box_2[0.4970] det_loss_cls_3[0.4518] det_loss_box_3[0.4865] det_loss_cls_4[0.4324] det_loss_box_4[0.4676] det_loss_cls_5[0.4126] det_loss_box_5[0.4740] det_loss_cls_dn_0[0.0906] det_loss_box_dn_0[0.3702] det_loss_cls_dn_1[0.0863] det_loss_box_dn_1[0.3173] det_loss_cls_dn_2[0.0865] det_loss_box_dn_2[0.2922] det_loss_cls_dn_3[0.0855] det_loss_box_dn_3[0.2889] det_loss_cls_dn_4[0.0848] det_loss_box_dn_4[0.2864] det_loss_cls_dn_5[0.0836] det_loss_box_dn_5[0.2813] loss_depth[0.0383] total_loss[8.4245]
Rank[0/8] 06/20/2025 05:10:19 INFO stats.py:314 | Epoch[113] Step[254] GlobalStep[44324] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 16:38:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:10:40 INFO loss_tracker.py:84 | Epoch[113/239] Step[274] GlobalStep[44344/NA]: det_loss_cls_0[0.4421] det_loss_box_0[0.5430] det_loss_cls_1[0.4137] det_loss_box_1[0.4164] det_loss_cls_2[0.3792] det_loss_box_2[0.3643] det_loss_cls_3[0.3495] det_loss_box_3[0.3495] det_loss_cls_4[0.3232] det_loss_box_4[0.3419] det_loss_cls_5[0.3123] det_loss_box_5[0.3427] det_loss_cls_dn_0[0.1004] det_loss_box_dn_0[0.3037] det_loss_cls_dn_1[0.0966] det_loss_box_dn_1[0.2510] det_loss_cls_dn_2[0.0957] det_loss_box_dn_2[0.2323] det_loss_cls_dn_3[0.0944] det_loss_box_dn_3[0.2277] det_loss_cls_dn_4[0.0944] det_loss_box_dn_4[0.2236] det_loss_cls_dn_5[0.0926] det_loss_box_dn_5[0.2229] loss_depth[0.0396] total_loss[6.6526]
Rank[0/8] 06/20/2025 05:10:45 INFO stats.py:314 | Epoch[113] Step[279] GlobalStep[44349] Training Speed: 8.15 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 16:37:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:11:06 INFO loss_tracker.py:84 | Epoch[113/239] Step[299] GlobalStep[44369/NA]: det_loss_cls_0[0.5958] det_loss_box_0[0.8112] det_loss_cls_1[0.5759] det_loss_box_1[0.6578] det_loss_cls_2[0.5324] det_loss_box_2[0.6110] det_loss_cls_3[0.5005] det_loss_box_3[0.5976] det_loss_cls_4[0.4811] det_loss_box_4[0.5698] det_loss_cls_5[0.4592] det_loss_box_5[0.5788] det_loss_cls_dn_0[0.1095] det_loss_box_dn_0[0.2974] det_loss_cls_dn_1[0.1058] det_loss_box_dn_1[0.2527] det_loss_cls_dn_2[0.1051] det_loss_box_dn_2[0.2375] det_loss_cls_dn_3[0.1044] det_loss_box_dn_3[0.2343] det_loss_cls_dn_4[0.1034] det_loss_box_dn_4[0.2326] det_loss_cls_dn_5[0.1033] det_loss_box_dn_5[0.2299] loss_depth[0.0384] total_loss[9.1255]
Rank[0/8] 06/20/2025 05:11:11 INFO stats.py:314 | Epoch[113] Step[304] GlobalStep[44374] Training Speed: 7.93 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 16:37:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:11:32 INFO loss_tracker.py:84 | Epoch[113/239] Step[324] GlobalStep[44394/NA]: det_loss_cls_0[0.6724] det_loss_box_0[0.8587] det_loss_cls_1[0.6609] det_loss_box_1[0.6849] det_loss_cls_2[0.6113] det_loss_box_2[0.6179] det_loss_cls_3[0.5806] det_loss_box_3[0.5898] det_loss_cls_4[0.5436] det_loss_box_4[0.5867] det_loss_cls_5[0.5269] det_loss_box_5[0.5893] det_loss_cls_dn_0[0.0995] det_loss_box_dn_0[0.3128] det_loss_cls_dn_1[0.0971] det_loss_box_dn_1[0.2681] det_loss_cls_dn_2[0.0963] det_loss_box_dn_2[0.2514] det_loss_cls_dn_3[0.0958] det_loss_box_dn_3[0.2476] det_loss_cls_dn_4[0.0948] det_loss_box_dn_4[0.2471] det_loss_cls_dn_5[0.0938] det_loss_box_dn_5[0.2440] loss_depth[0.0385] total_loss[9.7098]
Rank[0/8] 06/20/2025 05:11:38 INFO stats.py:314 | Epoch[113] Step[329] GlobalStep[44399] Training Speed: 8.00 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 16:36:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:11:58 INFO loss_tracker.py:84 | Epoch[113/239] Step[349] GlobalStep[44419/NA]: det_loss_cls_0[0.5501] det_loss_box_0[0.7349] det_loss_cls_1[0.5363] det_loss_box_1[0.5574] det_loss_cls_2[0.4967] det_loss_box_2[0.4916] det_loss_cls_3[0.4632] det_loss_box_3[0.4791] det_loss_cls_4[0.4300] det_loss_box_4[0.4732] det_loss_cls_5[0.4114] det_loss_box_5[0.4735] det_loss_cls_dn_0[0.0914] det_loss_box_dn_0[0.2467] det_loss_cls_dn_1[0.0880] det_loss_box_dn_1[0.2064] det_loss_cls_dn_2[0.0868] det_loss_box_dn_2[0.1889] det_loss_cls_dn_3[0.0859] det_loss_box_dn_3[0.1855] det_loss_cls_dn_4[0.0852] det_loss_box_dn_4[0.1842] det_loss_cls_dn_5[0.0843] det_loss_box_dn_5[0.1828] loss_depth[0.0394] total_loss[7.8529]
Rank[0/8] 06/20/2025 05:12:03 INFO stats.py:314 | Epoch[113] Step[354] GlobalStep[44424] Training Speed: 7.71 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 16:36:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:12:24 INFO loss_tracker.py:84 | Epoch[113/239] Step[374] GlobalStep[44444/NA]: det_loss_cls_0[0.5221] det_loss_box_0[0.6785] det_loss_cls_1[0.5231] det_loss_box_1[0.5316] det_loss_cls_2[0.4864] det_loss_box_2[0.4861] det_loss_cls_3[0.4609] det_loss_box_3[0.4736] det_loss_cls_4[0.4342] det_loss_box_4[0.4713] det_loss_cls_5[0.4247] det_loss_box_5[0.4696] det_loss_cls_dn_0[0.1018] det_loss_box_dn_0[0.3293] det_loss_cls_dn_1[0.0989] det_loss_box_dn_1[0.2751] det_loss_cls_dn_2[0.0988] det_loss_box_dn_2[0.2602] det_loss_cls_dn_3[0.0972] det_loss_box_dn_3[0.2547] det_loss_cls_dn_4[0.0967] det_loss_box_dn_4[0.2517] det_loss_cls_dn_5[0.0963] det_loss_box_dn_5[0.2481] loss_depth[0.0402] total_loss[8.2111]
Rank[0/8] 06/20/2025 05:12:29 INFO stats.py:314 | Epoch[113] Step[379] GlobalStep[44449] Training Speed: 8.65 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 16:35:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:12:39 INFO stats.py:394 | Epoch[113] completed. Training Speed: 7.69 samples/sec across all devices.	Epoch Time: 405.79 sec.	Average Epoch Time: 405.79 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 16:35:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:12:56 INFO stats.py:314 | Epoch[114] Step[14] GlobalStep[44474] Training Speed: 8.12 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 16:34:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:13:06 INFO loss_tracker.py:84 | Epoch[114/239] Step[24] GlobalStep[44484/NA]: det_loss_cls_0[0.4968] det_loss_box_0[0.6745] det_loss_cls_1[0.4823] det_loss_box_1[0.5307] det_loss_cls_2[0.4406] det_loss_box_2[0.4871] det_loss_cls_3[0.4179] det_loss_box_3[0.4641] det_loss_cls_4[0.3912] det_loss_box_4[0.4489] det_loss_cls_5[0.3796] det_loss_box_5[0.4474] det_loss_cls_dn_0[0.1019] det_loss_box_dn_0[0.2881] det_loss_cls_dn_1[0.0993] det_loss_box_dn_1[0.2474] det_loss_cls_dn_2[0.0988] det_loss_box_dn_2[0.2331] det_loss_cls_dn_3[0.0974] det_loss_box_dn_3[0.2309] det_loss_cls_dn_4[0.0964] det_loss_box_dn_4[0.2300] det_loss_cls_dn_5[0.0952] det_loss_box_dn_5[0.2294] loss_depth[0.0381] total_loss[7.7470]
Rank[0/8] 06/20/2025 05:13:22 INFO stats.py:314 | Epoch[114] Step[39] GlobalStep[44499] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 16:34:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:13:32 INFO loss_tracker.py:84 | Epoch[114/239] Step[49] GlobalStep[44509/NA]: det_loss_cls_0[0.4918] det_loss_box_0[0.6172] det_loss_cls_1[0.4778] det_loss_box_1[0.4709] det_loss_cls_2[0.4403] det_loss_box_2[0.4325] det_loss_cls_3[0.4024] det_loss_box_3[0.4184] det_loss_cls_4[0.3763] det_loss_box_4[0.4093] det_loss_cls_5[0.3641] det_loss_box_5[0.4064] det_loss_cls_dn_0[0.1000] det_loss_box_dn_0[0.3146] det_loss_cls_dn_1[0.0961] det_loss_box_dn_1[0.2753] det_loss_cls_dn_2[0.0956] det_loss_box_dn_2[0.2593] det_loss_cls_dn_3[0.0933] det_loss_box_dn_3[0.2576] det_loss_cls_dn_4[0.0924] det_loss_box_dn_4[0.2560] det_loss_cls_dn_5[0.0909] det_loss_box_dn_5[0.2517] loss_depth[0.0388] total_loss[7.5289]
Rank[0/8] 06/20/2025 05:13:48 INFO stats.py:314 | Epoch[114] Step[64] GlobalStep[44524] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 16:33:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:13:58 INFO loss_tracker.py:84 | Epoch[114/239] Step[74] GlobalStep[44534/NA]: det_loss_cls_0[0.3304] det_loss_box_0[0.4416] det_loss_cls_1[0.3194] det_loss_box_1[0.3495] det_loss_cls_2[0.2971] det_loss_box_2[0.3109] det_loss_cls_3[0.2784] det_loss_box_3[0.2969] det_loss_cls_4[0.2540] det_loss_box_4[0.3003] det_loss_cls_5[0.2525] det_loss_box_5[0.2925] det_loss_cls_dn_0[0.0986] det_loss_box_dn_0[0.3240] det_loss_cls_dn_1[0.0948] det_loss_box_dn_1[0.2738] det_loss_cls_dn_2[0.0946] det_loss_box_dn_2[0.2544] det_loss_cls_dn_3[0.0932] det_loss_box_dn_3[0.2515] det_loss_cls_dn_4[0.0924] det_loss_box_dn_4[0.2494] det_loss_cls_dn_5[0.0914] det_loss_box_dn_5[0.2480] loss_depth[0.0417] total_loss[5.9312]
Rank[0/8] 06/20/2025 05:14:13 INFO stats.py:314 | Epoch[114] Step[89] GlobalStep[44549] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 16:33:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:14:24 INFO loss_tracker.py:84 | Epoch[114/239] Step[99] GlobalStep[44559/NA]: det_loss_cls_0[0.5214] det_loss_box_0[0.6862] det_loss_cls_1[0.4962] det_loss_box_1[0.5447] det_loss_cls_2[0.4742] det_loss_box_2[0.4981] det_loss_cls_3[0.4419] det_loss_box_3[0.4838] det_loss_cls_4[0.4220] det_loss_box_4[0.4747] det_loss_cls_5[0.4062] det_loss_box_5[0.4790] det_loss_cls_dn_0[0.1044] det_loss_box_dn_0[0.3037] det_loss_cls_dn_1[0.1009] det_loss_box_dn_1[0.2601] det_loss_cls_dn_2[0.0999] det_loss_box_dn_2[0.2449] det_loss_cls_dn_3[0.0978] det_loss_box_dn_3[0.2382] det_loss_cls_dn_4[0.0970] det_loss_box_dn_4[0.2372] det_loss_cls_dn_5[0.0959] det_loss_box_dn_5[0.2350] loss_depth[0.0350] total_loss[8.0782]
Rank[0/8] 06/20/2025 05:14:39 INFO stats.py:314 | Epoch[114] Step[114] GlobalStep[44574] Training Speed: 8.05 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 16:32:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:14:49 INFO loss_tracker.py:84 | Epoch[114/239] Step[124] GlobalStep[44584/NA]: det_loss_cls_0[0.5709] det_loss_box_0[0.7310] det_loss_cls_1[0.5407] det_loss_box_1[0.5467] det_loss_cls_2[0.4995] det_loss_box_2[0.4985] det_loss_cls_3[0.4720] det_loss_box_3[0.4806] det_loss_cls_4[0.4487] det_loss_box_4[0.4704] det_loss_cls_5[0.4221] det_loss_box_5[0.4781] det_loss_cls_dn_0[0.1049] det_loss_box_dn_0[0.2871] det_loss_cls_dn_1[0.1014] det_loss_box_dn_1[0.2452] det_loss_cls_dn_2[0.1003] det_loss_box_dn_2[0.2292] det_loss_cls_dn_3[0.0998] det_loss_box_dn_3[0.2260] det_loss_cls_dn_4[0.0995] det_loss_box_dn_4[0.2245] det_loss_cls_dn_5[0.0980] det_loss_box_dn_5[0.2227] loss_depth[0.0412] total_loss[8.2390]
Rank[0/8] 06/20/2025 05:15:05 INFO stats.py:314 | Epoch[114] Step[139] GlobalStep[44599] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 16:31:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:15:15 INFO loss_tracker.py:84 | Epoch[114/239] Step[149] GlobalStep[44609/NA]: det_loss_cls_0[0.5953] det_loss_box_0[0.7685] det_loss_cls_1[0.5813] det_loss_box_1[0.5959] det_loss_cls_2[0.5328] det_loss_box_2[0.5592] det_loss_cls_3[0.4968] det_loss_box_3[0.5391] det_loss_cls_4[0.4683] det_loss_box_4[0.5394] det_loss_cls_5[0.4610] det_loss_box_5[0.5360] det_loss_cls_dn_0[0.1053] det_loss_box_dn_0[0.2819] det_loss_cls_dn_1[0.1014] det_loss_box_dn_1[0.2376] det_loss_cls_dn_2[0.0994] det_loss_box_dn_2[0.2218] det_loss_cls_dn_3[0.0991] det_loss_box_dn_3[0.2174] det_loss_cls_dn_4[0.0976] det_loss_box_dn_4[0.2172] det_loss_cls_dn_5[0.0964] det_loss_box_dn_5[0.2156] loss_depth[0.0405] total_loss[8.7049]
Rank[0/8] 06/20/2025 05:15:31 INFO stats.py:314 | Epoch[114] Step[164] GlobalStep[44624] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 16:31:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:15:41 INFO loss_tracker.py:84 | Epoch[114/239] Step[174] GlobalStep[44634/NA]: det_loss_cls_0[0.3601] det_loss_box_0[0.4929] det_loss_cls_1[0.3541] det_loss_box_1[0.3854] det_loss_cls_2[0.3197] det_loss_box_2[0.3600] det_loss_cls_3[0.3040] det_loss_box_3[0.3437] det_loss_cls_4[0.2878] det_loss_box_4[0.3289] det_loss_cls_5[0.2753] det_loss_box_5[0.3306] det_loss_cls_dn_0[0.0874] det_loss_box_dn_0[0.3424] det_loss_cls_dn_1[0.0833] det_loss_box_dn_1[0.2904] det_loss_cls_dn_2[0.0829] det_loss_box_dn_2[0.2697] det_loss_cls_dn_3[0.0815] det_loss_box_dn_3[0.2664] det_loss_cls_dn_4[0.0813] det_loss_box_dn_4[0.2658] det_loss_cls_dn_5[0.0805] det_loss_box_dn_5[0.2638] loss_depth[0.0410] total_loss[6.3787]
Rank[0/8] 06/20/2025 05:15:57 INFO stats.py:314 | Epoch[114] Step[189] GlobalStep[44649] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 16:30:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:16:07 INFO loss_tracker.py:84 | Epoch[114/239] Step[199] GlobalStep[44659/NA]: det_loss_cls_0[0.6047] det_loss_box_0[0.8267] det_loss_cls_1[0.5788] det_loss_box_1[0.6643] det_loss_cls_2[0.5357] det_loss_box_2[0.6128] det_loss_cls_3[0.5119] det_loss_box_3[0.6017] det_loss_cls_4[0.4859] det_loss_box_4[0.5943] det_loss_cls_5[0.4724] det_loss_box_5[0.5780] det_loss_cls_dn_0[0.0994] det_loss_box_dn_0[0.3227] det_loss_cls_dn_1[0.0956] det_loss_box_dn_1[0.2694] det_loss_cls_dn_2[0.0950] det_loss_box_dn_2[0.2546] det_loss_cls_dn_3[0.0935] det_loss_box_dn_3[0.2517] det_loss_cls_dn_4[0.0922] det_loss_box_dn_4[0.2503] det_loss_cls_dn_5[0.0912] det_loss_box_dn_5[0.2470] loss_depth[0.0394] total_loss[9.2690]
Rank[0/8] 06/20/2025 05:16:23 INFO stats.py:314 | Epoch[114] Step[214] GlobalStep[44674] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 16:30:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:16:33 INFO loss_tracker.py:84 | Epoch[114/239] Step[224] GlobalStep[44684/NA]: det_loss_cls_0[0.4048] det_loss_box_0[0.5295] det_loss_cls_1[0.3861] det_loss_box_1[0.4142] det_loss_cls_2[0.3457] det_loss_box_2[0.3722] det_loss_cls_3[0.3236] det_loss_box_3[0.3601] det_loss_cls_4[0.3020] det_loss_box_4[0.3431] det_loss_cls_5[0.2923] det_loss_box_5[0.3402] det_loss_cls_dn_0[0.0879] det_loss_box_dn_0[0.3371] det_loss_cls_dn_1[0.0854] det_loss_box_dn_1[0.2773] det_loss_cls_dn_2[0.0848] det_loss_box_dn_2[0.2525] det_loss_cls_dn_3[0.0829] det_loss_box_dn_3[0.2481] det_loss_cls_dn_4[0.0828] det_loss_box_dn_4[0.2457] det_loss_cls_dn_5[0.0811] det_loss_box_dn_5[0.2383] loss_depth[0.0398] total_loss[6.5574]
Rank[0/8] 06/20/2025 05:16:49 INFO stats.py:314 | Epoch[114] Step[239] GlobalStep[44699] Training Speed: 6.96 samples/sec across all devices.	Average Step Time: 1.15 sec.	Estimated Remaining Time: 16:29:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:16:59 INFO loss_tracker.py:84 | Epoch[114/239] Step[249] GlobalStep[44709/NA]: det_loss_cls_0[0.4060] det_loss_box_0[0.4935] det_loss_cls_1[0.4026] det_loss_box_1[0.3640] det_loss_cls_2[0.3509] det_loss_box_2[0.3456] det_loss_cls_3[0.3253] det_loss_box_3[0.3344] det_loss_cls_4[0.3046] det_loss_box_4[0.3234] det_loss_cls_5[0.2918] det_loss_box_5[0.3236] det_loss_cls_dn_0[0.0909] det_loss_box_dn_0[0.2726] det_loss_cls_dn_1[0.0873] det_loss_box_dn_1[0.2317] det_loss_cls_dn_2[0.0869] det_loss_box_dn_2[0.2173] det_loss_cls_dn_3[0.0860] det_loss_box_dn_3[0.2147] det_loss_cls_dn_4[0.0858] det_loss_box_dn_4[0.2129] det_loss_cls_dn_5[0.0844] det_loss_box_dn_5[0.2119] loss_depth[0.0374] total_loss[6.1855]
Rank[0/8] 06/20/2025 05:17:15 INFO stats.py:314 | Epoch[114] Step[264] GlobalStep[44724] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 16:28:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:17:25 INFO loss_tracker.py:84 | Epoch[114/239] Step[274] GlobalStep[44734/NA]: det_loss_cls_0[0.4086] det_loss_box_0[0.5492] det_loss_cls_1[0.4074] det_loss_box_1[0.4168] det_loss_cls_2[0.3634] det_loss_box_2[0.3843] det_loss_cls_3[0.3403] det_loss_box_3[0.3732] det_loss_cls_4[0.3146] det_loss_box_4[0.3675] det_loss_cls_5[0.3083] det_loss_box_5[0.3636] det_loss_cls_dn_0[0.0968] det_loss_box_dn_0[0.3116] det_loss_cls_dn_1[0.0926] det_loss_box_dn_1[0.2656] det_loss_cls_dn_2[0.0914] det_loss_box_dn_2[0.2494] det_loss_cls_dn_3[0.0902] det_loss_box_dn_3[0.2467] det_loss_cls_dn_4[0.0904] det_loss_box_dn_4[0.2451] det_loss_cls_dn_5[0.0893] det_loss_box_dn_5[0.2440] loss_depth[0.0382] total_loss[6.7483]
Rank[0/8] 06/20/2025 05:17:41 INFO stats.py:314 | Epoch[114] Step[289] GlobalStep[44749] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 16:28:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:17:51 INFO loss_tracker.py:84 | Epoch[114/239] Step[299] GlobalStep[44759/NA]: det_loss_cls_0[0.5436] det_loss_box_0[0.7298] det_loss_cls_1[0.5440] det_loss_box_1[0.5663] det_loss_cls_2[0.5062] det_loss_box_2[0.5122] det_loss_cls_3[0.4696] det_loss_box_3[0.4940] det_loss_cls_4[0.4435] det_loss_box_4[0.4855] det_loss_cls_5[0.4242] det_loss_box_5[0.4851] det_loss_cls_dn_0[0.0955] det_loss_box_dn_0[0.3031] det_loss_cls_dn_1[0.0927] det_loss_box_dn_1[0.2587] det_loss_cls_dn_2[0.0926] det_loss_box_dn_2[0.2429] det_loss_cls_dn_3[0.0918] det_loss_box_dn_3[0.2403] det_loss_cls_dn_4[0.0918] det_loss_box_dn_4[0.2390] det_loss_cls_dn_5[0.0907] det_loss_box_dn_5[0.2391] loss_depth[0.0396] total_loss[8.3219]
Rank[0/8] 06/20/2025 05:18:07 INFO stats.py:314 | Epoch[114] Step[314] GlobalStep[44774] Training Speed: 7.72 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 16:27:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:18:17 INFO loss_tracker.py:84 | Epoch[114/239] Step[324] GlobalStep[44784/NA]: det_loss_cls_0[0.5102] det_loss_box_0[0.6640] det_loss_cls_1[0.5013] det_loss_box_1[0.5367] det_loss_cls_2[0.4710] det_loss_box_2[0.4884] det_loss_cls_3[0.4383] det_loss_box_3[0.4762] det_loss_cls_4[0.4072] det_loss_box_4[0.4655] det_loss_cls_5[0.3883] det_loss_box_5[0.4620] det_loss_cls_dn_0[0.0922] det_loss_box_dn_0[0.2793] det_loss_cls_dn_1[0.0891] det_loss_box_dn_1[0.2446] det_loss_cls_dn_2[0.0878] det_loss_box_dn_2[0.2326] det_loss_cls_dn_3[0.0872] det_loss_box_dn_3[0.2312] det_loss_cls_dn_4[0.0863] det_loss_box_dn_4[0.2292] det_loss_cls_dn_5[0.0857] det_loss_box_dn_5[0.2232] loss_depth[0.0362] total_loss[7.8137]
Rank[0/8] 06/20/2025 05:18:33 INFO stats.py:314 | Epoch[114] Step[339] GlobalStep[44799] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 16:27:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:18:43 INFO loss_tracker.py:84 | Epoch[114/239] Step[349] GlobalStep[44809/NA]: det_loss_cls_0[0.5742] det_loss_box_0[0.7000] det_loss_cls_1[0.5622] det_loss_box_1[0.5299] det_loss_cls_2[0.5058] det_loss_box_2[0.4814] det_loss_cls_3[0.4751] det_loss_box_3[0.4588] det_loss_cls_4[0.4349] det_loss_box_4[0.4539] det_loss_cls_5[0.4201] det_loss_box_5[0.4576] det_loss_cls_dn_0[0.0922] det_loss_box_dn_0[0.2671] det_loss_cls_dn_1[0.0879] det_loss_box_dn_1[0.2229] det_loss_cls_dn_2[0.0879] det_loss_box_dn_2[0.2064] det_loss_cls_dn_3[0.0868] det_loss_box_dn_3[0.2032] det_loss_cls_dn_4[0.0862] det_loss_box_dn_4[0.2021] det_loss_cls_dn_5[0.0855] det_loss_box_dn_5[0.2002] loss_depth[0.0372] total_loss[7.9196]
Rank[0/8] 06/20/2025 05:18:58 INFO stats.py:314 | Epoch[114] Step[364] GlobalStep[44824] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 16:26:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:19:08 INFO loss_tracker.py:84 | Epoch[114/239] Step[374] GlobalStep[44834/NA]: det_loss_cls_0[0.4804] det_loss_box_0[0.7203] det_loss_cls_1[0.4750] det_loss_box_1[0.5553] det_loss_cls_2[0.4296] det_loss_box_2[0.5098] det_loss_cls_3[0.4019] det_loss_box_3[0.4987] det_loss_cls_4[0.3736] det_loss_box_4[0.4876] det_loss_cls_5[0.3668] det_loss_box_5[0.4798] det_loss_cls_dn_0[0.1022] det_loss_box_dn_0[0.3226] det_loss_cls_dn_1[0.0999] det_loss_box_dn_1[0.2765] det_loss_cls_dn_2[0.0992] det_loss_box_dn_2[0.2614] det_loss_cls_dn_3[0.0983] det_loss_box_dn_3[0.2568] det_loss_cls_dn_4[0.0965] det_loss_box_dn_4[0.2547] det_loss_cls_dn_5[0.0957] det_loss_box_dn_5[0.2526] loss_depth[0.0389] total_loss[8.0343]
Rank[0/8] 06/20/2025 05:19:24 INFO stats.py:314 | Epoch[114] Step[389] GlobalStep[44849] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 16:26:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:19:24 INFO stats.py:394 | Epoch[114] completed. Training Speed: 7.70 samples/sec across all devices.	Epoch Time: 404.98 sec.	Average Epoch Time: 404.98 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 16:26:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:19:51 INFO stats.py:314 | Epoch[115] Step[24] GlobalStep[44874] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 16:25:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:19:51 INFO loss_tracker.py:84 | Epoch[115/239] Step[24] GlobalStep[44874/NA]: det_loss_cls_0[0.6280] det_loss_box_0[0.9150] det_loss_cls_1[0.6064] det_loss_box_1[0.7199] det_loss_cls_2[0.5682] det_loss_box_2[0.6575] det_loss_cls_3[0.5425] det_loss_box_3[0.6324] det_loss_cls_4[0.5124] det_loss_box_4[0.6217] det_loss_cls_5[0.4996] det_loss_box_5[0.6163] det_loss_cls_dn_0[0.0999] det_loss_box_dn_0[0.2903] det_loss_cls_dn_1[0.0970] det_loss_box_dn_1[0.2483] det_loss_cls_dn_2[0.0965] det_loss_box_dn_2[0.2328] det_loss_cls_dn_3[0.0957] det_loss_box_dn_3[0.2305] det_loss_cls_dn_4[0.0948] det_loss_box_dn_4[0.2288] det_loss_cls_dn_5[0.0932] det_loss_box_dn_5[0.2274] loss_depth[0.0412] total_loss[9.5964]
Rank[0/8] 06/20/2025 05:20:17 INFO stats.py:314 | Epoch[115] Step[49] GlobalStep[44899] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 16:24:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:20:17 INFO loss_tracker.py:84 | Epoch[115/239] Step[49] GlobalStep[44899/NA]: det_loss_cls_0[0.7549] det_loss_box_0[1.1021] det_loss_cls_1[0.7557] det_loss_box_1[0.8578] det_loss_cls_2[0.7041] det_loss_box_2[0.7689] det_loss_cls_3[0.6739] det_loss_box_3[0.7452] det_loss_cls_4[0.6376] det_loss_box_4[0.7417] det_loss_cls_5[0.6207] det_loss_box_5[0.7367] det_loss_cls_dn_0[0.1034] det_loss_box_dn_0[0.2756] det_loss_cls_dn_1[0.1006] det_loss_box_dn_1[0.2417] det_loss_cls_dn_2[0.1002] det_loss_box_dn_2[0.2265] det_loss_cls_dn_3[0.1000] det_loss_box_dn_3[0.2243] det_loss_cls_dn_4[0.0993] det_loss_box_dn_4[0.2237] det_loss_cls_dn_5[0.0987] det_loss_box_dn_5[0.2231] loss_depth[0.0401] total_loss[11.1565]
Rank[0/8] 06/20/2025 05:20:42 INFO stats.py:314 | Epoch[115] Step[74] GlobalStep[44924] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 16:24:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:20:42 INFO loss_tracker.py:84 | Epoch[115/239] Step[74] GlobalStep[44924/NA]: det_loss_cls_0[0.4875] det_loss_box_0[0.6163] det_loss_cls_1[0.4734] det_loss_box_1[0.4696] det_loss_cls_2[0.4325] det_loss_box_2[0.4283] det_loss_cls_3[0.3966] det_loss_box_3[0.4171] det_loss_cls_4[0.3781] det_loss_box_4[0.4035] det_loss_cls_5[0.3663] det_loss_box_5[0.4038] det_loss_cls_dn_0[0.0977] det_loss_box_dn_0[0.2518] det_loss_cls_dn_1[0.0929] det_loss_box_dn_1[0.2092] det_loss_cls_dn_2[0.0926] det_loss_box_dn_2[0.1936] det_loss_cls_dn_3[0.0919] det_loss_box_dn_3[0.1912] det_loss_cls_dn_4[0.0914] det_loss_box_dn_4[0.1907] det_loss_cls_dn_5[0.0913] det_loss_box_dn_5[0.1898] loss_depth[0.0391] total_loss[7.0963]
Rank[0/8] 06/20/2025 05:21:09 INFO stats.py:314 | Epoch[115] Step[99] GlobalStep[44949] Training Speed: 6.43 samples/sec across all devices.	Average Step Time: 1.24 sec.	Estimated Remaining Time: 16:23:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:21:09 INFO loss_tracker.py:84 | Epoch[115/239] Step[99] GlobalStep[44949/NA]: det_loss_cls_0[0.6067] det_loss_box_0[0.7697] det_loss_cls_1[0.5747] det_loss_box_1[0.5868] det_loss_cls_2[0.5305] det_loss_box_2[0.5348] det_loss_cls_3[0.4821] det_loss_box_3[0.5231] det_loss_cls_4[0.4541] det_loss_box_4[0.5063] det_loss_cls_5[0.4310] det_loss_box_5[0.5066] det_loss_cls_dn_0[0.0985] det_loss_box_dn_0[0.2715] det_loss_cls_dn_1[0.0940] det_loss_box_dn_1[0.2356] det_loss_cls_dn_2[0.0936] det_loss_box_dn_2[0.2224] det_loss_cls_dn_3[0.0923] det_loss_box_dn_3[0.2191] det_loss_cls_dn_4[0.0912] det_loss_box_dn_4[0.2181] det_loss_cls_dn_5[0.0905] det_loss_box_dn_5[0.2163] loss_depth[0.0385] total_loss[8.4878]
Rank[0/8] 06/20/2025 05:21:34 INFO stats.py:314 | Epoch[115] Step[124] GlobalStep[44974] Training Speed: 8.56 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 16:23:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:21:34 INFO loss_tracker.py:84 | Epoch[115/239] Step[124] GlobalStep[44974/NA]: det_loss_cls_0[0.4575] det_loss_box_0[0.6225] det_loss_cls_1[0.4391] det_loss_box_1[0.4880] det_loss_cls_2[0.4024] det_loss_box_2[0.4447] det_loss_cls_3[0.3781] det_loss_box_3[0.4315] det_loss_cls_4[0.3505] det_loss_box_4[0.4178] det_loss_cls_5[0.3375] det_loss_box_5[0.4218] det_loss_cls_dn_0[0.1015] det_loss_box_dn_0[0.3445] det_loss_cls_dn_1[0.0985] det_loss_box_dn_1[0.2956] det_loss_cls_dn_2[0.0977] det_loss_box_dn_2[0.2789] det_loss_cls_dn_3[0.0969] det_loss_box_dn_3[0.2740] det_loss_cls_dn_4[0.0964] det_loss_box_dn_4[0.2716] det_loss_cls_dn_5[0.0957] det_loss_box_dn_5[0.2655] loss_depth[0.0398] total_loss[7.5480]
Rank[0/8] 06/20/2025 05:22:00 INFO stats.py:314 | Epoch[115] Step[149] GlobalStep[44999] Training Speed: 8.07 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 16:22:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:22:00 INFO loss_tracker.py:84 | Epoch[115/239] Step[149] GlobalStep[44999/NA]: det_loss_cls_0[0.4769] det_loss_box_0[0.6131] det_loss_cls_1[0.4530] det_loss_box_1[0.4484] det_loss_cls_2[0.4010] det_loss_box_2[0.4149] det_loss_cls_3[0.3724] det_loss_box_3[0.3943] det_loss_cls_4[0.3519] det_loss_box_4[0.3864] det_loss_cls_5[0.3425] det_loss_box_5[0.3884] det_loss_cls_dn_0[0.0914] det_loss_box_dn_0[0.2584] det_loss_cls_dn_1[0.0873] det_loss_box_dn_1[0.2123] det_loss_cls_dn_2[0.0864] det_loss_box_dn_2[0.1951] det_loss_cls_dn_3[0.0855] det_loss_box_dn_3[0.1903] det_loss_cls_dn_4[0.0853] det_loss_box_dn_4[0.1895] det_loss_cls_dn_5[0.0846] det_loss_box_dn_5[0.1871] loss_depth[0.0364] total_loss[6.8330]
Rank[0/8] 06/20/2025 05:22:26 INFO stats.py:314 | Epoch[115] Step[174] GlobalStep[45024] Training Speed: 7.79 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 16:21:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:22:26 INFO loss_tracker.py:84 | Epoch[115/239] Step[174] GlobalStep[45024/NA]: det_loss_cls_0[0.5179] det_loss_box_0[0.6159] det_loss_cls_1[0.5140] det_loss_box_1[0.4623] det_loss_cls_2[0.4843] det_loss_box_2[0.4123] det_loss_cls_3[0.4456] det_loss_box_3[0.3982] det_loss_cls_4[0.4165] det_loss_box_4[0.3943] det_loss_cls_5[0.4076] det_loss_box_5[0.3875] det_loss_cls_dn_0[0.1045] det_loss_box_dn_0[0.2838] det_loss_cls_dn_1[0.1013] det_loss_box_dn_1[0.2399] det_loss_cls_dn_2[0.0996] det_loss_box_dn_2[0.2231] det_loss_cls_dn_3[0.0995] det_loss_box_dn_3[0.2191] det_loss_cls_dn_4[0.0986] det_loss_box_dn_4[0.2181] det_loss_cls_dn_5[0.0980] det_loss_box_dn_5[0.2141] loss_depth[0.0380] total_loss[7.4941]
Rank[0/8] 06/20/2025 05:22:52 INFO stats.py:314 | Epoch[115] Step[199] GlobalStep[45049] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 16:21:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:22:52 INFO loss_tracker.py:84 | Epoch[115/239] Step[199] GlobalStep[45049/NA]: det_loss_cls_0[0.4389] det_loss_box_0[0.5413] det_loss_cls_1[0.4288] det_loss_box_1[0.4090] det_loss_cls_2[0.3967] det_loss_box_2[0.3671] det_loss_cls_3[0.3615] det_loss_box_3[0.3554] det_loss_cls_4[0.3311] det_loss_box_4[0.3541] det_loss_cls_5[0.3218] det_loss_box_5[0.3503] det_loss_cls_dn_0[0.0870] det_loss_box_dn_0[0.2707] det_loss_cls_dn_1[0.0839] det_loss_box_dn_1[0.2220] det_loss_cls_dn_2[0.0839] det_loss_box_dn_2[0.2037] det_loss_cls_dn_3[0.0819] det_loss_box_dn_3[0.2003] det_loss_cls_dn_4[0.0816] det_loss_box_dn_4[0.2002] det_loss_cls_dn_5[0.0813] det_loss_box_dn_5[0.1985] loss_depth[0.0403] total_loss[6.4912]
Rank[0/8] 06/20/2025 05:23:18 INFO stats.py:314 | Epoch[115] Step[224] GlobalStep[45074] Training Speed: 7.88 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 16:20:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:23:18 INFO loss_tracker.py:84 | Epoch[115/239] Step[224] GlobalStep[45074/NA]: det_loss_cls_0[0.4428] det_loss_box_0[0.6250] det_loss_cls_1[0.4222] det_loss_box_1[0.4988] det_loss_cls_2[0.4036] det_loss_box_2[0.4374] det_loss_cls_3[0.3750] det_loss_box_3[0.4238] det_loss_cls_4[0.3604] det_loss_box_4[0.4038] det_loss_cls_5[0.3447] det_loss_box_5[0.4082] det_loss_cls_dn_0[0.0896] det_loss_box_dn_0[0.3171] det_loss_cls_dn_1[0.0863] det_loss_box_dn_1[0.2624] det_loss_cls_dn_2[0.0858] det_loss_box_dn_2[0.2437] det_loss_cls_dn_3[0.0849] det_loss_box_dn_3[0.2380] det_loss_cls_dn_4[0.0843] det_loss_box_dn_4[0.2351] det_loss_cls_dn_5[0.0836] det_loss_box_dn_5[0.2332] loss_depth[0.0413] total_loss[7.2309]
Rank[0/8] 06/20/2025 05:23:44 INFO stats.py:314 | Epoch[115] Step[249] GlobalStep[45099] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 16:20:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:23:44 INFO loss_tracker.py:84 | Epoch[115/239] Step[249] GlobalStep[45099/NA]: det_loss_cls_0[0.3845] det_loss_box_0[0.5372] det_loss_cls_1[0.3750] det_loss_box_1[0.4209] det_loss_cls_2[0.3522] det_loss_box_2[0.3863] det_loss_cls_3[0.3333] det_loss_box_3[0.3743] det_loss_cls_4[0.3137] det_loss_box_4[0.3685] det_loss_cls_5[0.2972] det_loss_box_5[0.3736] det_loss_cls_dn_0[0.0942] det_loss_box_dn_0[0.3819] det_loss_cls_dn_1[0.0905] det_loss_box_dn_1[0.3198] det_loss_cls_dn_2[0.0912] det_loss_box_dn_2[0.2957] det_loss_cls_dn_3[0.0892] det_loss_box_dn_3[0.2920] det_loss_cls_dn_4[0.0885] det_loss_box_dn_4[0.2915] det_loss_cls_dn_5[0.0873] det_loss_box_dn_5[0.2843] loss_depth[0.0401] total_loss[6.9627]
Rank[0/8] 06/20/2025 05:24:10 INFO stats.py:314 | Epoch[115] Step[274] GlobalStep[45124] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 16:19:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:24:10 INFO loss_tracker.py:84 | Epoch[115/239] Step[274] GlobalStep[45124/NA]: det_loss_cls_0[0.5620] det_loss_box_0[0.6983] det_loss_cls_1[0.5572] det_loss_box_1[0.5445] det_loss_cls_2[0.4980] det_loss_box_2[0.4996] det_loss_cls_3[0.4782] det_loss_box_3[0.4813] det_loss_cls_4[0.4479] det_loss_box_4[0.4704] det_loss_cls_5[0.4277] det_loss_box_5[0.4730] det_loss_cls_dn_0[0.0945] det_loss_box_dn_0[0.2792] det_loss_cls_dn_1[0.0900] det_loss_box_dn_1[0.2360] det_loss_cls_dn_2[0.0885] det_loss_box_dn_2[0.2202] det_loss_cls_dn_3[0.0880] det_loss_box_dn_3[0.2177] det_loss_cls_dn_4[0.0867] det_loss_box_dn_4[0.2162] det_loss_cls_dn_5[0.0854] det_loss_box_dn_5[0.2153] loss_depth[0.0375] total_loss[8.0931]
Rank[0/8] 06/20/2025 05:24:36 INFO stats.py:314 | Epoch[115] Step[299] GlobalStep[45149] Training Speed: 7.99 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 16:19:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:24:36 INFO loss_tracker.py:84 | Epoch[115/239] Step[299] GlobalStep[45149/NA]: det_loss_cls_0[0.4804] det_loss_box_0[0.6193] det_loss_cls_1[0.4688] det_loss_box_1[0.4570] det_loss_cls_2[0.4230] det_loss_box_2[0.4061] det_loss_cls_3[0.3885] det_loss_box_3[0.3853] det_loss_cls_4[0.3532] det_loss_box_4[0.3866] det_loss_cls_5[0.3456] det_loss_box_5[0.3836] det_loss_cls_dn_0[0.1032] det_loss_box_dn_0[0.3036] det_loss_cls_dn_1[0.1011] det_loss_box_dn_1[0.2535] det_loss_cls_dn_2[0.1003] det_loss_box_dn_2[0.2350] det_loss_cls_dn_3[0.0985] det_loss_box_dn_3[0.2290] det_loss_cls_dn_4[0.0981] det_loss_box_dn_4[0.2266] det_loss_cls_dn_5[0.0973] det_loss_box_dn_5[0.2257] loss_depth[0.0366] total_loss[7.2059]
Rank[0/8] 06/20/2025 05:25:01 INFO stats.py:314 | Epoch[115] Step[324] GlobalStep[45174] Training Speed: 6.57 samples/sec across all devices.	Average Step Time: 1.22 sec.	Estimated Remaining Time: 16:18:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:25:01 INFO loss_tracker.py:84 | Epoch[115/239] Step[324] GlobalStep[45174/NA]: det_loss_cls_0[0.5142] det_loss_box_0[0.6649] det_loss_cls_1[0.4937] det_loss_box_1[0.5187] det_loss_cls_2[0.4577] det_loss_box_2[0.4677] det_loss_cls_3[0.4356] det_loss_box_3[0.4535] det_loss_cls_4[0.4035] det_loss_box_4[0.4506] det_loss_cls_5[0.3976] det_loss_box_5[0.4448] det_loss_cls_dn_0[0.1077] det_loss_box_dn_0[0.2814] det_loss_cls_dn_1[0.1045] det_loss_box_dn_1[0.2390] det_loss_cls_dn_2[0.1042] det_loss_box_dn_2[0.2226] det_loss_cls_dn_3[0.1035] det_loss_box_dn_3[0.2203] det_loss_cls_dn_4[0.1036] det_loss_box_dn_4[0.2175] det_loss_cls_dn_5[0.1025] det_loss_box_dn_5[0.2168] loss_depth[0.0363] total_loss[7.7623]
Rank[0/8] 06/20/2025 05:25:28 INFO stats.py:314 | Epoch[115] Step[349] GlobalStep[45199] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 16:17:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:25:28 INFO loss_tracker.py:84 | Epoch[115/239] Step[349] GlobalStep[45199/NA]: det_loss_cls_0[0.5756] det_loss_box_0[0.8044] det_loss_cls_1[0.5606] det_loss_box_1[0.6176] det_loss_cls_2[0.5133] det_loss_box_2[0.5495] det_loss_cls_3[0.4992] det_loss_box_3[0.5257] det_loss_cls_4[0.4589] det_loss_box_4[0.5210] det_loss_cls_5[0.4446] det_loss_box_5[0.5183] det_loss_cls_dn_0[0.1044] det_loss_box_dn_0[0.2832] det_loss_cls_dn_1[0.1013] det_loss_box_dn_1[0.2368] det_loss_cls_dn_2[0.1003] det_loss_box_dn_2[0.2211] det_loss_cls_dn_3[0.0999] det_loss_box_dn_3[0.2184] det_loss_cls_dn_4[0.0989] det_loss_box_dn_4[0.2192] det_loss_cls_dn_5[0.0982] det_loss_box_dn_5[0.2172] loss_depth[0.0390] total_loss[8.6265]
Rank[0/8] 06/20/2025 05:25:54 INFO stats.py:314 | Epoch[115] Step[374] GlobalStep[45224] Training Speed: 8.67 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 16:17:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:25:54 INFO loss_tracker.py:84 | Epoch[115/239] Step[374] GlobalStep[45224/NA]: det_loss_cls_0[0.5735] det_loss_box_0[0.7082] det_loss_cls_1[0.5595] det_loss_box_1[0.5385] det_loss_cls_2[0.5179] det_loss_box_2[0.4770] det_loss_cls_3[0.4863] det_loss_box_3[0.4599] det_loss_cls_4[0.4555] det_loss_box_4[0.4567] det_loss_cls_5[0.4466] det_loss_box_5[0.4551] det_loss_cls_dn_0[0.1012] det_loss_box_dn_0[0.2937] det_loss_cls_dn_1[0.0981] det_loss_box_dn_1[0.2468] det_loss_cls_dn_2[0.0969] det_loss_box_dn_2[0.2299] det_loss_cls_dn_3[0.0960] det_loss_box_dn_3[0.2271] det_loss_cls_dn_4[0.0952] det_loss_box_dn_4[0.2245] det_loss_cls_dn_5[0.0945] det_loss_box_dn_5[0.2197] loss_depth[0.0415] total_loss[8.1998]
Rank[0/8] 06/20/2025 05:26:09 INFO stats.py:394 | Epoch[115] completed. Training Speed: 7.70 samples/sec across all devices.	Epoch Time: 404.95 sec.	Average Epoch Time: 404.95 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 16:16:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:26:22 INFO stats.py:314 | Epoch[116] Step[9] GlobalStep[45249] Training Speed: 8.04 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 16:16:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:26:38 INFO loss_tracker.py:84 | Epoch[116/239] Step[24] GlobalStep[45264/NA]: det_loss_cls_0[0.5602] det_loss_box_0[0.7224] det_loss_cls_1[0.5453] det_loss_box_1[0.5624] det_loss_cls_2[0.4992] det_loss_box_2[0.5163] det_loss_cls_3[0.4805] det_loss_box_3[0.4917] det_loss_cls_4[0.4462] det_loss_box_4[0.4844] det_loss_cls_5[0.4353] det_loss_box_5[0.4784] det_loss_cls_dn_0[0.0930] det_loss_box_dn_0[0.2930] det_loss_cls_dn_1[0.0895] det_loss_box_dn_1[0.2466] det_loss_cls_dn_2[0.0889] det_loss_box_dn_2[0.2274] det_loss_cls_dn_3[0.0881] det_loss_box_dn_3[0.2242] det_loss_cls_dn_4[0.0865] det_loss_box_dn_4[0.2210] det_loss_cls_dn_5[0.0853] det_loss_box_dn_5[0.2200] loss_depth[0.0362] total_loss[8.2219]
Rank[0/8] 06/20/2025 05:26:48 INFO stats.py:314 | Epoch[116] Step[34] GlobalStep[45274] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 16:16:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:27:04 INFO loss_tracker.py:84 | Epoch[116/239] Step[49] GlobalStep[45289/NA]: det_loss_cls_0[0.4745] det_loss_box_0[0.6666] det_loss_cls_1[0.4807] det_loss_box_1[0.5142] det_loss_cls_2[0.4416] det_loss_box_2[0.4585] det_loss_cls_3[0.4164] det_loss_box_3[0.4584] det_loss_cls_4[0.3866] det_loss_box_4[0.4500] det_loss_cls_5[0.3709] det_loss_box_5[0.4545] det_loss_cls_dn_0[0.1011] det_loss_box_dn_0[0.3067] det_loss_cls_dn_1[0.0975] det_loss_box_dn_1[0.2596] det_loss_cls_dn_2[0.0966] det_loss_box_dn_2[0.2418] det_loss_cls_dn_3[0.0953] det_loss_box_dn_3[0.2376] det_loss_cls_dn_4[0.0941] det_loss_box_dn_4[0.2352] det_loss_cls_dn_5[0.0929] det_loss_box_dn_5[0.2329] loss_depth[0.0367] total_loss[7.7009]
Rank[0/8] 06/20/2025 05:27:14 INFO stats.py:314 | Epoch[116] Step[59] GlobalStep[45299] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 16:15:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:27:29 INFO loss_tracker.py:84 | Epoch[116/239] Step[74] GlobalStep[45314/NA]: det_loss_cls_0[0.4940] det_loss_box_0[0.7323] det_loss_cls_1[0.4783] det_loss_box_1[0.5710] det_loss_cls_2[0.4512] det_loss_box_2[0.5159] det_loss_cls_3[0.4179] det_loss_box_3[0.5199] det_loss_cls_4[0.3978] det_loss_box_4[0.5069] det_loss_cls_5[0.3896] det_loss_box_5[0.5107] det_loss_cls_dn_0[0.0937] det_loss_box_dn_0[0.3249] det_loss_cls_dn_1[0.0904] det_loss_box_dn_1[0.2797] det_loss_cls_dn_2[0.0898] det_loss_box_dn_2[0.2653] det_loss_cls_dn_3[0.0891] det_loss_box_dn_3[0.2619] det_loss_cls_dn_4[0.0886] det_loss_box_dn_4[0.2604] det_loss_cls_dn_5[0.0877] det_loss_box_dn_5[0.2578] loss_depth[0.0385] total_loss[8.2133]
Rank[0/8] 06/20/2025 05:27:39 INFO stats.py:314 | Epoch[116] Step[84] GlobalStep[45324] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 16:14:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:27:55 INFO loss_tracker.py:84 | Epoch[116/239] Step[99] GlobalStep[45339/NA]: det_loss_cls_0[0.5553] det_loss_box_0[0.6578] det_loss_cls_1[0.5411] det_loss_box_1[0.5018] det_loss_cls_2[0.5038] det_loss_box_2[0.4483] det_loss_cls_3[0.4787] det_loss_box_3[0.4336] det_loss_cls_4[0.4476] det_loss_box_4[0.4289] det_loss_cls_5[0.4356] det_loss_box_5[0.4331] det_loss_cls_dn_0[0.0981] det_loss_box_dn_0[0.2727] det_loss_cls_dn_1[0.0946] det_loss_box_dn_1[0.2312] det_loss_cls_dn_2[0.0937] det_loss_box_dn_2[0.2154] det_loss_cls_dn_3[0.0927] det_loss_box_dn_3[0.2119] det_loss_cls_dn_4[0.0920] det_loss_box_dn_4[0.2112] det_loss_cls_dn_5[0.0914] det_loss_box_dn_5[0.2102] loss_depth[0.0378] total_loss[7.8186]
Rank[0/8] 06/20/2025 05:28:05 INFO stats.py:314 | Epoch[116] Step[109] GlobalStep[45349] Training Speed: 7.27 samples/sec across all devices.	Average Step Time: 1.10 sec.	Estimated Remaining Time: 16:14:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:28:21 INFO loss_tracker.py:84 | Epoch[116/239] Step[124] GlobalStep[45364/NA]: det_loss_cls_0[0.5533] det_loss_box_0[0.6963] det_loss_cls_1[0.5412] det_loss_box_1[0.5391] det_loss_cls_2[0.4964] det_loss_box_2[0.4836] det_loss_cls_3[0.4597] det_loss_box_3[0.4707] det_loss_cls_4[0.4264] det_loss_box_4[0.4621] det_loss_cls_5[0.4032] det_loss_box_5[0.4684] det_loss_cls_dn_0[0.0958] det_loss_box_dn_0[0.2617] det_loss_cls_dn_1[0.0925] det_loss_box_dn_1[0.2191] det_loss_cls_dn_2[0.0923] det_loss_box_dn_2[0.2019] det_loss_cls_dn_3[0.0907] det_loss_box_dn_3[0.1987] det_loss_cls_dn_4[0.0903] det_loss_box_dn_4[0.1975] det_loss_cls_dn_5[0.0889] det_loss_box_dn_5[0.1960] loss_depth[0.0410] total_loss[7.8669]
Rank[0/8] 06/20/2025 05:28:31 INFO stats.py:314 | Epoch[116] Step[134] GlobalStep[45374] Training Speed: 8.62 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 16:13:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:28:46 INFO loss_tracker.py:84 | Epoch[116/239] Step[149] GlobalStep[45389/NA]: det_loss_cls_0[0.5874] det_loss_box_0[0.6740] det_loss_cls_1[0.5824] det_loss_box_1[0.5228] det_loss_cls_2[0.5554] det_loss_box_2[0.4716] det_loss_cls_3[0.5138] det_loss_box_3[0.4665] det_loss_cls_4[0.4789] det_loss_box_4[0.4581] det_loss_cls_5[0.4619] det_loss_box_5[0.4600] det_loss_cls_dn_0[0.1090] det_loss_box_dn_0[0.2695] det_loss_cls_dn_1[0.1058] det_loss_box_dn_1[0.2299] det_loss_cls_dn_2[0.1048] det_loss_box_dn_2[0.2127] det_loss_cls_dn_3[0.1045] det_loss_box_dn_3[0.2093] det_loss_cls_dn_4[0.1039] det_loss_box_dn_4[0.2080] det_loss_cls_dn_5[0.1030] det_loss_box_dn_5[0.2047] loss_depth[0.0372] total_loss[8.2350]
Rank[0/8] 06/20/2025 05:28:57 INFO stats.py:314 | Epoch[116] Step[159] GlobalStep[45399] Training Speed: 8.56 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 16:13:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:29:12 INFO loss_tracker.py:84 | Epoch[116/239] Step[174] GlobalStep[45414/NA]: det_loss_cls_0[0.6047] det_loss_box_0[0.8408] det_loss_cls_1[0.5952] det_loss_box_1[0.6745] det_loss_cls_2[0.5491] det_loss_box_2[0.6145] det_loss_cls_3[0.5212] det_loss_box_3[0.5899] det_loss_cls_4[0.4929] det_loss_box_4[0.5725] det_loss_cls_5[0.4837] det_loss_box_5[0.5653] det_loss_cls_dn_0[0.1089] det_loss_box_dn_0[0.3173] det_loss_cls_dn_1[0.1059] det_loss_box_dn_1[0.2703] det_loss_cls_dn_2[0.1055] det_loss_box_dn_2[0.2545] det_loss_cls_dn_3[0.1052] det_loss_box_dn_3[0.2506] det_loss_cls_dn_4[0.1041] det_loss_box_dn_4[0.2491] det_loss_cls_dn_5[0.1036] det_loss_box_dn_5[0.2470] loss_depth[0.0405] total_loss[9.3669]
Rank[0/8] 06/20/2025 05:29:23 INFO stats.py:314 | Epoch[116] Step[184] GlobalStep[45424] Training Speed: 6.98 samples/sec across all devices.	Average Step Time: 1.15 sec.	Estimated Remaining Time: 16:12:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:29:38 INFO loss_tracker.py:84 | Epoch[116/239] Step[199] GlobalStep[45439/NA]: det_loss_cls_0[0.5751] det_loss_box_0[0.7144] det_loss_cls_1[0.5516] det_loss_box_1[0.5805] det_loss_cls_2[0.5172] det_loss_box_2[0.5369] det_loss_cls_3[0.4979] det_loss_box_3[0.5124] det_loss_cls_4[0.4588] det_loss_box_4[0.5010] det_loss_cls_5[0.4492] det_loss_box_5[0.4986] det_loss_cls_dn_0[0.1029] det_loss_box_dn_0[0.2904] det_loss_cls_dn_1[0.1003] det_loss_box_dn_1[0.2440] det_loss_cls_dn_2[0.0997] det_loss_box_dn_2[0.2280] det_loss_cls_dn_3[0.0986] det_loss_box_dn_3[0.2244] det_loss_cls_dn_4[0.0982] det_loss_box_dn_4[0.2215] det_loss_cls_dn_5[0.0973] det_loss_box_dn_5[0.2193] loss_depth[0.0392] total_loss[8.4574]
Rank[0/8] 06/20/2025 05:29:49 INFO stats.py:314 | Epoch[116] Step[209] GlobalStep[45449] Training Speed: 6.50 samples/sec across all devices.	Average Step Time: 1.23 sec.	Estimated Remaining Time: 16:12:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:30:04 INFO loss_tracker.py:84 | Epoch[116/239] Step[224] GlobalStep[45464/NA]: det_loss_cls_0[0.5804] det_loss_box_0[0.7527] det_loss_cls_1[0.5727] det_loss_box_1[0.6009] det_loss_cls_2[0.5303] det_loss_box_2[0.5498] det_loss_cls_3[0.5058] det_loss_box_3[0.5281] det_loss_cls_4[0.4736] det_loss_box_4[0.5158] det_loss_cls_5[0.4628] det_loss_box_5[0.5164] det_loss_cls_dn_0[0.1066] det_loss_box_dn_0[0.2968] det_loss_cls_dn_1[0.1024] det_loss_box_dn_1[0.2592] det_loss_cls_dn_2[0.1015] det_loss_box_dn_2[0.2445] det_loss_cls_dn_3[0.1005] det_loss_box_dn_3[0.2429] det_loss_cls_dn_4[0.0993] det_loss_box_dn_4[0.2409] det_loss_cls_dn_5[0.0983] det_loss_box_dn_5[0.2407] loss_depth[0.0368] total_loss[8.7596]
Rank[0/8] 06/20/2025 05:30:14 INFO stats.py:314 | Epoch[116] Step[234] GlobalStep[45474] Training Speed: 7.70 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 16:11:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:30:29 INFO loss_tracker.py:84 | Epoch[116/239] Step[249] GlobalStep[45489/NA]: det_loss_cls_0[0.5797] det_loss_box_0[0.7333] det_loss_cls_1[0.5695] det_loss_box_1[0.5934] det_loss_cls_2[0.5229] det_loss_box_2[0.5317] det_loss_cls_3[0.4856] det_loss_box_3[0.5094] det_loss_cls_4[0.4487] det_loss_box_4[0.5092] det_loss_cls_5[0.4306] det_loss_box_5[0.5074] det_loss_cls_dn_0[0.1019] det_loss_box_dn_0[0.3048] det_loss_cls_dn_1[0.0971] det_loss_box_dn_1[0.2607] det_loss_cls_dn_2[0.0970] det_loss_box_dn_2[0.2457] det_loss_cls_dn_3[0.0960] det_loss_box_dn_3[0.2427] det_loss_cls_dn_4[0.0948] det_loss_box_dn_4[0.2409] det_loss_cls_dn_5[0.0941] det_loss_box_dn_5[0.2390] loss_depth[0.0359] total_loss[8.5718]
Rank[0/8] 06/20/2025 05:30:40 INFO stats.py:314 | Epoch[116] Step[259] GlobalStep[45499] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 16:10:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:30:56 INFO loss_tracker.py:84 | Epoch[116/239] Step[274] GlobalStep[45514/NA]: det_loss_cls_0[0.3808] det_loss_box_0[0.5135] det_loss_cls_1[0.3702] det_loss_box_1[0.3982] det_loss_cls_2[0.3289] det_loss_box_2[0.3629] det_loss_cls_3[0.3045] det_loss_box_3[0.3589] det_loss_cls_4[0.2828] det_loss_box_4[0.3522] det_loss_cls_5[0.2670] det_loss_box_5[0.3588] det_loss_cls_dn_0[0.0889] det_loss_box_dn_0[0.2861] det_loss_cls_dn_1[0.0850] det_loss_box_dn_1[0.2443] det_loss_cls_dn_2[0.0836] det_loss_box_dn_2[0.2282] det_loss_cls_dn_3[0.0833] det_loss_box_dn_3[0.2257] det_loss_cls_dn_4[0.0822] det_loss_box_dn_4[0.2240] det_loss_cls_dn_5[0.0814] det_loss_box_dn_5[0.2214] loss_depth[0.0386] total_loss[6.2516]
Rank[0/8] 06/20/2025 05:31:06 INFO stats.py:314 | Epoch[116] Step[284] GlobalStep[45524] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 16:10:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:31:21 INFO loss_tracker.py:84 | Epoch[116/239] Step[299] GlobalStep[45539/NA]: det_loss_cls_0[0.4517] det_loss_box_0[0.5508] det_loss_cls_1[0.4408] det_loss_box_1[0.4097] det_loss_cls_2[0.3957] det_loss_box_2[0.3709] det_loss_cls_3[0.3634] det_loss_box_3[0.3684] det_loss_cls_4[0.3412] det_loss_box_4[0.3548] det_loss_cls_5[0.3252] det_loss_box_5[0.3512] det_loss_cls_dn_0[0.0913] det_loss_box_dn_0[0.2620] det_loss_cls_dn_1[0.0873] det_loss_box_dn_1[0.2184] det_loss_cls_dn_2[0.0862] det_loss_box_dn_2[0.2072] det_loss_cls_dn_3[0.0853] det_loss_box_dn_3[0.2050] det_loss_cls_dn_4[0.0839] det_loss_box_dn_4[0.2019] det_loss_cls_dn_5[0.0826] det_loss_box_dn_5[0.1997] loss_depth[0.0362] total_loss[6.5709]
Rank[0/8] 06/20/2025 05:31:31 INFO stats.py:314 | Epoch[116] Step[309] GlobalStep[45549] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 16:09:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:31:47 INFO loss_tracker.py:84 | Epoch[116/239] Step[324] GlobalStep[45564/NA]: det_loss_cls_0[0.5116] det_loss_box_0[0.7312] det_loss_cls_1[0.4868] det_loss_box_1[0.5784] det_loss_cls_2[0.4498] det_loss_box_2[0.5219] det_loss_cls_3[0.4168] det_loss_box_3[0.5081] det_loss_cls_4[0.4004] det_loss_box_4[0.4914] det_loss_cls_5[0.3852] det_loss_box_5[0.4931] det_loss_cls_dn_0[0.0902] det_loss_box_dn_0[0.3332] det_loss_cls_dn_1[0.0864] det_loss_box_dn_1[0.2874] det_loss_cls_dn_2[0.0863] det_loss_box_dn_2[0.2652] det_loss_cls_dn_3[0.0853] det_loss_box_dn_3[0.2602] det_loss_cls_dn_4[0.0841] det_loss_box_dn_4[0.2593] det_loss_cls_dn_5[0.0828] det_loss_box_dn_5[0.2557] loss_depth[0.0413] total_loss[8.1922]
Rank[0/8] 06/20/2025 05:31:57 INFO stats.py:314 | Epoch[116] Step[334] GlobalStep[45574] Training Speed: 8.08 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 16:09:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:32:13 INFO loss_tracker.py:84 | Epoch[116/239] Step[349] GlobalStep[45589/NA]: det_loss_cls_0[0.4261] det_loss_box_0[0.5879] det_loss_cls_1[0.4172] det_loss_box_1[0.4497] det_loss_cls_2[0.3730] det_loss_box_2[0.4073] det_loss_cls_3[0.3462] det_loss_box_3[0.3928] det_loss_cls_4[0.3269] det_loss_box_4[0.3837] det_loss_cls_5[0.3197] det_loss_box_5[0.3825] det_loss_cls_dn_0[0.0928] det_loss_box_dn_0[0.2664] det_loss_cls_dn_1[0.0888] det_loss_box_dn_1[0.2235] det_loss_cls_dn_2[0.0878] det_loss_box_dn_2[0.2052] det_loss_cls_dn_3[0.0863] det_loss_box_dn_3[0.2022] det_loss_cls_dn_4[0.0856] det_loss_box_dn_4[0.2007] det_loss_cls_dn_5[0.0844] det_loss_box_dn_5[0.1999] loss_depth[0.0393] total_loss[6.6759]
Rank[0/8] 06/20/2025 05:32:23 INFO stats.py:314 | Epoch[116] Step[359] GlobalStep[45599] Training Speed: 7.87 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 16:08:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:32:38 INFO loss_tracker.py:84 | Epoch[116/239] Step[374] GlobalStep[45614/NA]: det_loss_cls_0[0.4367] det_loss_box_0[0.5966] det_loss_cls_1[0.4192] det_loss_box_1[0.4585] det_loss_cls_2[0.3802] det_loss_box_2[0.4202] det_loss_cls_3[0.3496] det_loss_box_3[0.4097] det_loss_cls_4[0.3293] det_loss_box_4[0.3980] det_loss_cls_5[0.3146] det_loss_box_5[0.4015] det_loss_cls_dn_0[0.0852] det_loss_box_dn_0[0.2874] det_loss_cls_dn_1[0.0808] det_loss_box_dn_1[0.2415] det_loss_cls_dn_2[0.0794] det_loss_box_dn_2[0.2259] det_loss_cls_dn_3[0.0773] det_loss_box_dn_3[0.2204] det_loss_cls_dn_4[0.0773] det_loss_box_dn_4[0.2193] det_loss_cls_dn_5[0.0759] det_loss_box_dn_5[0.2172] loss_depth[0.0345] total_loss[6.8362]
Rank[0/8] 06/20/2025 05:32:48 INFO stats.py:314 | Epoch[116] Step[384] GlobalStep[45624] Training Speed: 7.86 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 16:07:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:32:53 INFO stats.py:394 | Epoch[116] completed. Training Speed: 7.72 samples/sec across all devices.	Epoch Time: 404.35 sec.	Average Epoch Time: 404.35 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 16:07:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:33:16 INFO stats.py:314 | Epoch[117] Step[19] GlobalStep[45649] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 16:07:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:33:21 INFO loss_tracker.py:84 | Epoch[117/239] Step[24] GlobalStep[45654/NA]: det_loss_cls_0[0.5229] det_loss_box_0[0.6909] det_loss_cls_1[0.5155] det_loss_box_1[0.5379] det_loss_cls_2[0.4702] det_loss_box_2[0.4830] det_loss_cls_3[0.4328] det_loss_box_3[0.4667] det_loss_cls_4[0.4093] det_loss_box_4[0.4453] det_loss_cls_5[0.3951] det_loss_box_5[0.4458] det_loss_cls_dn_0[0.1046] det_loss_box_dn_0[0.2798] det_loss_cls_dn_1[0.1005] det_loss_box_dn_1[0.2390] det_loss_cls_dn_2[0.0996] det_loss_box_dn_2[0.2215] det_loss_cls_dn_3[0.0990] det_loss_box_dn_3[0.2177] det_loss_cls_dn_4[0.0984] det_loss_box_dn_4[0.2158] det_loss_cls_dn_5[0.0969] det_loss_box_dn_5[0.2135] loss_depth[0.0353] total_loss[7.8369]
Rank[0/8] 06/20/2025 05:33:42 INFO stats.py:314 | Epoch[117] Step[44] GlobalStep[45674] Training Speed: 7.89 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 16:06:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:33:47 INFO loss_tracker.py:84 | Epoch[117/239] Step[49] GlobalStep[45679/NA]: det_loss_cls_0[0.3912] det_loss_box_0[0.4747] det_loss_cls_1[0.3866] det_loss_box_1[0.3849] det_loss_cls_2[0.3511] det_loss_box_2[0.3537] det_loss_cls_3[0.3135] det_loss_box_3[0.3513] det_loss_cls_4[0.2908] det_loss_box_4[0.3355] det_loss_cls_5[0.2798] det_loss_box_5[0.3407] det_loss_cls_dn_0[0.0961] det_loss_box_dn_0[0.2605] det_loss_cls_dn_1[0.0928] det_loss_box_dn_1[0.2215] det_loss_cls_dn_2[0.0915] det_loss_box_dn_2[0.2058] det_loss_cls_dn_3[0.0899] det_loss_box_dn_3[0.2027] det_loss_cls_dn_4[0.0893] det_loss_box_dn_4[0.2000] det_loss_cls_dn_5[0.0888] det_loss_box_dn_5[0.1982] loss_depth[0.0343] total_loss[6.1255]
Rank[0/8] 06/20/2025 05:34:07 INFO stats.py:314 | Epoch[117] Step[69] GlobalStep[45699] Training Speed: 8.02 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 16:06:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:34:13 INFO loss_tracker.py:84 | Epoch[117/239] Step[74] GlobalStep[45704/NA]: det_loss_cls_0[0.5129] det_loss_box_0[0.6702] det_loss_cls_1[0.4960] det_loss_box_1[0.5113] det_loss_cls_2[0.4548] det_loss_box_2[0.4592] det_loss_cls_3[0.4272] det_loss_box_3[0.4469] det_loss_cls_4[0.3875] det_loss_box_4[0.4428] det_loss_cls_5[0.3758] det_loss_box_5[0.4378] det_loss_cls_dn_0[0.0870] det_loss_box_dn_0[0.2767] det_loss_cls_dn_1[0.0824] det_loss_box_dn_1[0.2346] det_loss_cls_dn_2[0.0817] det_loss_box_dn_2[0.2180] det_loss_cls_dn_3[0.0804] det_loss_box_dn_3[0.2143] det_loss_cls_dn_4[0.0790] det_loss_box_dn_4[0.2128] det_loss_cls_dn_5[0.0782] det_loss_box_dn_5[0.2107] loss_depth[0.0400] total_loss[7.5179]
Rank[0/8] 06/20/2025 05:34:33 INFO stats.py:314 | Epoch[117] Step[94] GlobalStep[45724] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 16:05:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:34:38 INFO loss_tracker.py:84 | Epoch[117/239] Step[99] GlobalStep[45729/NA]: det_loss_cls_0[0.4201] det_loss_box_0[0.5927] det_loss_cls_1[0.4086] det_loss_box_1[0.4548] det_loss_cls_2[0.3701] det_loss_box_2[0.4217] det_loss_cls_3[0.3534] det_loss_box_3[0.4020] det_loss_cls_4[0.3231] det_loss_box_4[0.4028] det_loss_cls_5[0.3059] det_loss_box_5[0.4023] det_loss_cls_dn_0[0.0826] det_loss_box_dn_0[0.2705] det_loss_cls_dn_1[0.0786] det_loss_box_dn_1[0.2277] det_loss_cls_dn_2[0.0783] det_loss_box_dn_2[0.2132] det_loss_cls_dn_3[0.0770] det_loss_box_dn_3[0.2116] det_loss_cls_dn_4[0.0764] det_loss_box_dn_4[0.2112] det_loss_cls_dn_5[0.0785] det_loss_box_dn_5[0.2095] loss_depth[0.0374] total_loss[6.7102]
Rank[0/8] 06/20/2025 05:34:59 INFO stats.py:314 | Epoch[117] Step[119] GlobalStep[45749] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 16:05:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:35:05 INFO loss_tracker.py:84 | Epoch[117/239] Step[124] GlobalStep[45754/NA]: det_loss_cls_0[0.5056] det_loss_box_0[0.6547] det_loss_cls_1[0.4795] det_loss_box_1[0.5219] det_loss_cls_2[0.4503] det_loss_box_2[0.4672] det_loss_cls_3[0.4203] det_loss_box_3[0.4488] det_loss_cls_4[0.4019] det_loss_box_4[0.4380] det_loss_cls_5[0.3843] det_loss_box_5[0.4371] det_loss_cls_dn_0[0.0896] det_loss_box_dn_0[0.3848] det_loss_cls_dn_1[0.0866] det_loss_box_dn_1[0.3344] det_loss_cls_dn_2[0.0858] det_loss_box_dn_2[0.3005] det_loss_cls_dn_3[0.0854] det_loss_box_dn_3[0.2953] det_loss_cls_dn_4[0.0841] det_loss_box_dn_4[0.2919] det_loss_cls_dn_5[0.0825] det_loss_box_dn_5[0.2903] loss_depth[0.0393] total_loss[8.0598]
Rank[0/8] 06/20/2025 05:35:25 INFO stats.py:314 | Epoch[117] Step[144] GlobalStep[45774] Training Speed: 8.58 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 16:04:30.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:35:30 INFO loss_tracker.py:84 | Epoch[117/239] Step[149] GlobalStep[45779/NA]: det_loss_cls_0[0.4877] det_loss_box_0[0.6543] det_loss_cls_1[0.4692] det_loss_box_1[0.4958] det_loss_cls_2[0.4253] det_loss_box_2[0.4553] det_loss_cls_3[0.4067] det_loss_box_3[0.4320] det_loss_cls_4[0.3739] det_loss_box_4[0.4272] det_loss_cls_5[0.3644] det_loss_box_5[0.4230] det_loss_cls_dn_0[0.0953] det_loss_box_dn_0[0.3384] det_loss_cls_dn_1[0.0911] det_loss_box_dn_1[0.2945] det_loss_cls_dn_2[0.0900] det_loss_box_dn_2[0.2769] det_loss_cls_dn_3[0.0890] det_loss_box_dn_3[0.2732] det_loss_cls_dn_4[0.0877] det_loss_box_dn_4[0.2716] det_loss_cls_dn_5[0.0867] det_loss_box_dn_5[0.2636] loss_depth[0.0369] total_loss[7.7095]
Rank[0/8] 06/20/2025 05:35:51 INFO stats.py:314 | Epoch[117] Step[169] GlobalStep[45799] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 16:03:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:35:56 INFO loss_tracker.py:84 | Epoch[117/239] Step[174] GlobalStep[45804/NA]: det_loss_cls_0[0.4800] det_loss_box_0[0.6022] det_loss_cls_1[0.4777] det_loss_box_1[0.4607] det_loss_cls_2[0.4354] det_loss_box_2[0.4141] det_loss_cls_3[0.4039] det_loss_box_3[0.3983] det_loss_cls_4[0.3805] det_loss_box_4[0.3925] det_loss_cls_5[0.3612] det_loss_box_5[0.3942] det_loss_cls_dn_0[0.0912] det_loss_box_dn_0[0.2495] det_loss_cls_dn_1[0.0874] det_loss_box_dn_1[0.2055] det_loss_cls_dn_2[0.0865] det_loss_box_dn_2[0.1899] det_loss_cls_dn_3[0.0853] det_loss_box_dn_3[0.1876] det_loss_cls_dn_4[0.0844] det_loss_box_dn_4[0.1851] det_loss_cls_dn_5[0.0834] det_loss_box_dn_5[0.1843] loss_depth[0.0398] total_loss[6.9606]
Rank[0/8] 06/20/2025 05:36:17 INFO stats.py:314 | Epoch[117] Step[194] GlobalStep[45824] Training Speed: 8.72 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 16:03:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:36:22 INFO loss_tracker.py:84 | Epoch[117/239] Step[199] GlobalStep[45829/NA]: det_loss_cls_0[0.5097] det_loss_box_0[0.7246] det_loss_cls_1[0.5097] det_loss_box_1[0.5337] det_loss_cls_2[0.4580] det_loss_box_2[0.4954] det_loss_cls_3[0.4077] det_loss_box_3[0.4886] det_loss_cls_4[0.3954] det_loss_box_4[0.4744] det_loss_cls_5[0.3753] det_loss_box_5[0.4840] det_loss_cls_dn_0[0.0942] det_loss_box_dn_0[0.3225] det_loss_cls_dn_1[0.0910] det_loss_box_dn_1[0.2770] det_loss_cls_dn_2[0.0902] det_loss_box_dn_2[0.2619] det_loss_cls_dn_3[0.0890] det_loss_box_dn_3[0.2591] det_loss_cls_dn_4[0.0883] det_loss_box_dn_4[0.2567] det_loss_cls_dn_5[0.0873] det_loss_box_dn_5[0.2555] loss_depth[0.0390] total_loss[8.0683]
Rank[0/8] 06/20/2025 05:36:43 INFO stats.py:314 | Epoch[117] Step[219] GlobalStep[45849] Training Speed: 8.17 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 16:02:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:36:48 INFO loss_tracker.py:84 | Epoch[117/239] Step[224] GlobalStep[45854/NA]: det_loss_cls_0[0.5672] det_loss_box_0[0.7154] det_loss_cls_1[0.5518] det_loss_box_1[0.5515] det_loss_cls_2[0.5029] det_loss_box_2[0.5041] det_loss_cls_3[0.4719] det_loss_box_3[0.4952] det_loss_cls_4[0.4452] det_loss_box_4[0.4795] det_loss_cls_5[0.4332] det_loss_box_5[0.4746] det_loss_cls_dn_0[0.1086] det_loss_box_dn_0[0.3329] det_loss_cls_dn_1[0.1042] det_loss_box_dn_1[0.2784] det_loss_cls_dn_2[0.1034] det_loss_box_dn_2[0.2638] det_loss_cls_dn_3[0.1033] det_loss_box_dn_3[0.2588] det_loss_cls_dn_4[0.1011] det_loss_box_dn_4[0.2582] det_loss_cls_dn_5[0.0998] det_loss_box_dn_5[0.2574] loss_depth[0.0384] total_loss[8.5007]
Rank[0/8] 06/20/2025 05:37:09 INFO stats.py:314 | Epoch[117] Step[244] GlobalStep[45874] Training Speed: 6.74 samples/sec across all devices.	Average Step Time: 1.19 sec.	Estimated Remaining Time: 16:02:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:37:14 INFO loss_tracker.py:84 | Epoch[117/239] Step[249] GlobalStep[45879/NA]: det_loss_cls_0[0.5707] det_loss_box_0[0.7116] det_loss_cls_1[0.5621] det_loss_box_1[0.5475] det_loss_cls_2[0.5154] det_loss_box_2[0.4970] det_loss_cls_3[0.4981] det_loss_box_3[0.4682] det_loss_cls_4[0.4637] det_loss_box_4[0.4656] det_loss_cls_5[0.4534] det_loss_box_5[0.4620] det_loss_cls_dn_0[0.1026] det_loss_box_dn_0[0.2962] det_loss_cls_dn_1[0.0995] det_loss_box_dn_1[0.2486] det_loss_cls_dn_2[0.0980] det_loss_box_dn_2[0.2316] det_loss_cls_dn_3[0.0973] det_loss_box_dn_3[0.2272] det_loss_cls_dn_4[0.0964] det_loss_box_dn_4[0.2257] det_loss_cls_dn_5[0.0956] det_loss_box_dn_5[0.2239] loss_depth[0.0397] total_loss[8.2978]
Rank[0/8] 06/20/2025 05:37:35 INFO stats.py:314 | Epoch[117] Step[269] GlobalStep[45899] Training Speed: 8.69 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 16:01:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:37:40 INFO loss_tracker.py:84 | Epoch[117/239] Step[274] GlobalStep[45904/NA]: det_loss_cls_0[0.4630] det_loss_box_0[0.6676] det_loss_cls_1[0.4537] det_loss_box_1[0.5194] det_loss_cls_2[0.4241] det_loss_box_2[0.4762] det_loss_cls_3[0.4013] det_loss_box_3[0.4631] det_loss_cls_4[0.3743] det_loss_box_4[0.4547] det_loss_cls_5[0.3655] det_loss_box_5[0.4602] det_loss_cls_dn_0[0.0955] det_loss_box_dn_0[0.3201] det_loss_cls_dn_1[0.0924] det_loss_box_dn_1[0.2734] det_loss_cls_dn_2[0.0921] det_loss_box_dn_2[0.2545] det_loss_cls_dn_3[0.0915] det_loss_box_dn_3[0.2502] det_loss_cls_dn_4[0.0904] det_loss_box_dn_4[0.2476] det_loss_cls_dn_5[0.0892] det_loss_box_dn_5[0.2423] loss_depth[0.0377] total_loss[7.7001]
Rank[0/8] 06/20/2025 05:38:00 INFO stats.py:314 | Epoch[117] Step[294] GlobalStep[45924] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 16:01:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:38:05 INFO loss_tracker.py:84 | Epoch[117/239] Step[299] GlobalStep[45929/NA]: det_loss_cls_0[0.3746] det_loss_box_0[0.5829] det_loss_cls_1[0.3701] det_loss_box_1[0.4589] det_loss_cls_2[0.3381] det_loss_box_2[0.4095] det_loss_cls_3[0.3159] det_loss_box_3[0.3954] det_loss_cls_4[0.2951] det_loss_box_4[0.3928] det_loss_cls_5[0.2836] det_loss_box_5[0.3940] det_loss_cls_dn_0[0.0931] det_loss_box_dn_0[0.5383] det_loss_cls_dn_1[0.0895] det_loss_box_dn_1[0.4740] det_loss_cls_dn_2[0.0886] det_loss_box_dn_2[0.4570] det_loss_cls_dn_3[0.0874] det_loss_box_dn_3[0.4555] det_loss_cls_dn_4[0.0867] det_loss_box_dn_4[0.4534] det_loss_cls_dn_5[0.0864] det_loss_box_dn_5[0.4477] loss_depth[0.0395] total_loss[8.0079]
Rank[0/8] 06/20/2025 05:38:26 INFO stats.py:314 | Epoch[117] Step[319] GlobalStep[45949] Training Speed: 7.93 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 16:00:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:38:32 INFO loss_tracker.py:84 | Epoch[117/239] Step[324] GlobalStep[45954/NA]: det_loss_cls_0[0.4597] det_loss_box_0[0.5859] det_loss_cls_1[0.4444] det_loss_box_1[0.4449] det_loss_cls_2[0.4174] det_loss_box_2[0.3983] det_loss_cls_3[0.3869] det_loss_box_3[0.3852] det_loss_cls_4[0.3588] det_loss_box_4[0.3782] det_loss_cls_5[0.3471] det_loss_box_5[0.3812] det_loss_cls_dn_0[0.0943] det_loss_box_dn_0[0.3432] det_loss_cls_dn_1[0.0903] det_loss_box_dn_1[0.2859] det_loss_cls_dn_2[0.0901] det_loss_box_dn_2[0.2722] det_loss_cls_dn_3[0.0885] det_loss_box_dn_3[0.2680] det_loss_cls_dn_4[0.0874] det_loss_box_dn_4[0.2660] det_loss_cls_dn_5[0.0866] det_loss_box_dn_5[0.2601] loss_depth[0.0398] total_loss[7.2606]
Rank[0/8] 06/20/2025 05:38:52 INFO stats.py:314 | Epoch[117] Step[344] GlobalStep[45974] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 15:59:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:38:57 INFO loss_tracker.py:84 | Epoch[117/239] Step[349] GlobalStep[45979/NA]: det_loss_cls_0[0.5102] det_loss_box_0[0.7670] det_loss_cls_1[0.4910] det_loss_box_1[0.6001] det_loss_cls_2[0.4634] det_loss_box_2[0.5323] det_loss_cls_3[0.4371] det_loss_box_3[0.5101] det_loss_cls_4[0.4107] det_loss_box_4[0.5117] det_loss_cls_5[0.3955] det_loss_box_5[0.5121] det_loss_cls_dn_0[0.0981] det_loss_box_dn_0[0.5274] det_loss_cls_dn_1[0.0953] det_loss_box_dn_1[0.4718] det_loss_cls_dn_2[0.0954] det_loss_box_dn_2[0.4500] det_loss_cls_dn_3[0.0951] det_loss_box_dn_3[0.4426] det_loss_cls_dn_4[0.0937] det_loss_box_dn_4[0.4444] det_loss_cls_dn_5[0.0926] det_loss_box_dn_5[0.4410] loss_depth[0.0411] total_loss[9.5296]
Rank[0/8] 06/20/2025 05:39:18 INFO stats.py:314 | Epoch[117] Step[369] GlobalStep[45999] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 15:59:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:39:23 INFO loss_tracker.py:84 | Epoch[117/239] Step[374] GlobalStep[46004/NA]: det_loss_cls_0[0.4110] det_loss_box_0[0.5930] det_loss_cls_1[0.3886] det_loss_box_1[0.4260] det_loss_cls_2[0.3449] det_loss_box_2[0.3922] det_loss_cls_3[0.3199] det_loss_box_3[0.3820] det_loss_cls_4[0.2886] det_loss_box_4[0.3771] det_loss_cls_5[0.2812] det_loss_box_5[0.3796] det_loss_cls_dn_0[0.0899] det_loss_box_dn_0[0.3247] det_loss_cls_dn_1[0.0863] det_loss_box_dn_1[0.2718] det_loss_cls_dn_2[0.0847] det_loss_box_dn_2[0.2559] det_loss_cls_dn_3[0.0835] det_loss_box_dn_3[0.2500] det_loss_cls_dn_4[0.0829] det_loss_box_dn_4[0.2450] det_loss_cls_dn_5[0.0816] det_loss_box_dn_5[0.2441] loss_depth[0.0412] total_loss[6.7256]
Rank[0/8] 06/20/2025 05:39:39 INFO stats.py:394 | Epoch[117] completed. Training Speed: 7.70 samples/sec across all devices.	Epoch Time: 405.35 sec.	Average Epoch Time: 405.35 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 15:58:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:39:45 INFO stats.py:314 | Epoch[118] Step[4] GlobalStep[46024] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 15:58:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:40:07 INFO loss_tracker.py:84 | Epoch[118/239] Step[24] GlobalStep[46044/NA]: det_loss_cls_0[0.5186] det_loss_box_0[0.7002] det_loss_cls_1[0.4956] det_loss_box_1[0.5332] det_loss_cls_2[0.4581] det_loss_box_2[0.4855] det_loss_cls_3[0.4263] det_loss_box_3[0.4664] det_loss_cls_4[0.3990] det_loss_box_4[0.4575] det_loss_cls_5[0.3841] det_loss_box_5[0.4598] det_loss_cls_dn_0[0.1107] det_loss_box_dn_0[0.3233] det_loss_cls_dn_1[0.1075] det_loss_box_dn_1[0.2784] det_loss_cls_dn_2[0.1080] det_loss_box_dn_2[0.2613] det_loss_cls_dn_3[0.1068] det_loss_box_dn_3[0.2595] det_loss_cls_dn_4[0.1055] det_loss_box_dn_4[0.2573] det_loss_cls_dn_5[0.1042] det_loss_box_dn_5[0.2546] loss_depth[0.0379] total_loss[8.0995]
Rank[0/8] 06/20/2025 05:40:12 INFO stats.py:314 | Epoch[118] Step[29] GlobalStep[46049] Training Speed: 8.72 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 15:58:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:40:33 INFO loss_tracker.py:84 | Epoch[118/239] Step[49] GlobalStep[46069/NA]: det_loss_cls_0[0.6006] det_loss_box_0[0.7849] det_loss_cls_1[0.5751] det_loss_box_1[0.6218] det_loss_cls_2[0.5439] det_loss_box_2[0.5612] det_loss_cls_3[0.5081] det_loss_box_3[0.5332] det_loss_cls_4[0.4829] det_loss_box_4[0.5171] det_loss_cls_5[0.4710] det_loss_box_5[0.5118] det_loss_cls_dn_0[0.1048] det_loss_box_dn_0[0.2991] det_loss_cls_dn_1[0.1015] det_loss_box_dn_1[0.2534] det_loss_cls_dn_2[0.1008] det_loss_box_dn_2[0.2359] det_loss_cls_dn_3[0.1000] det_loss_box_dn_3[0.2329] det_loss_cls_dn_4[0.0991] det_loss_box_dn_4[0.2306] det_loss_cls_dn_5[0.0984] det_loss_box_dn_5[0.2275] loss_depth[0.0369] total_loss[8.8323]
Rank[0/8] 06/20/2025 05:40:39 INFO stats.py:314 | Epoch[118] Step[54] GlobalStep[46074] Training Speed: 8.15 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 15:57:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:40:59 INFO loss_tracker.py:84 | Epoch[118/239] Step[74] GlobalStep[46094/NA]: det_loss_cls_0[0.5167] det_loss_box_0[0.6195] det_loss_cls_1[0.5070] det_loss_box_1[0.4760] det_loss_cls_2[0.4656] det_loss_box_2[0.4326] det_loss_cls_3[0.4394] det_loss_box_3[0.4215] det_loss_cls_4[0.4150] det_loss_box_4[0.4131] det_loss_cls_5[0.3980] det_loss_box_5[0.4147] det_loss_cls_dn_0[0.0953] det_loss_box_dn_0[0.2527] det_loss_cls_dn_1[0.0913] det_loss_box_dn_1[0.2134] det_loss_cls_dn_2[0.0908] det_loss_box_dn_2[0.1967] det_loss_cls_dn_3[0.0897] det_loss_box_dn_3[0.1937] det_loss_cls_dn_4[0.0886] det_loss_box_dn_4[0.1917] det_loss_cls_dn_5[0.0884] det_loss_box_dn_5[0.1903] loss_depth[0.0357] total_loss[7.3372]
Rank[0/8] 06/20/2025 05:41:04 INFO stats.py:314 | Epoch[118] Step[79] GlobalStep[46099] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 15:57:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:41:25 INFO loss_tracker.py:84 | Epoch[118/239] Step[99] GlobalStep[46119/NA]: det_loss_cls_0[0.6116] det_loss_box_0[0.7668] det_loss_cls_1[0.5964] det_loss_box_1[0.5942] det_loss_cls_2[0.5465] det_loss_box_2[0.5390] det_loss_cls_3[0.4975] det_loss_box_3[0.5240] det_loss_cls_4[0.4661] det_loss_box_4[0.5141] det_loss_cls_5[0.4465] det_loss_box_5[0.5162] det_loss_cls_dn_0[0.0995] det_loss_box_dn_0[0.2872] det_loss_cls_dn_1[0.0948] det_loss_box_dn_1[0.2468] det_loss_cls_dn_2[0.0945] det_loss_box_dn_2[0.2331] det_loss_cls_dn_3[0.0932] det_loss_box_dn_3[0.2303] det_loss_cls_dn_4[0.0925] det_loss_box_dn_4[0.2295] det_loss_cls_dn_5[0.0917] det_loss_box_dn_5[0.2292] loss_depth[0.0403] total_loss[8.6815]
Rank[0/8] 06/20/2025 05:41:30 INFO stats.py:314 | Epoch[118] Step[104] GlobalStep[46124] Training Speed: 7.76 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 15:56:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:41:51 INFO loss_tracker.py:84 | Epoch[118/239] Step[124] GlobalStep[46144/NA]: det_loss_cls_0[0.5686] det_loss_box_0[0.7529] det_loss_cls_1[0.5483] det_loss_box_1[0.5706] det_loss_cls_2[0.4959] det_loss_box_2[0.5114] det_loss_cls_3[0.4579] det_loss_box_3[0.4932] det_loss_cls_4[0.4268] det_loss_box_4[0.4856] det_loss_cls_5[0.4070] det_loss_box_5[0.4870] det_loss_cls_dn_0[0.0989] det_loss_box_dn_0[0.2891] det_loss_cls_dn_1[0.0946] det_loss_box_dn_1[0.2470] det_loss_cls_dn_2[0.0946] det_loss_box_dn_2[0.2274] det_loss_cls_dn_3[0.0930] det_loss_box_dn_3[0.2236] det_loss_cls_dn_4[0.0923] det_loss_box_dn_4[0.2223] det_loss_cls_dn_5[0.0913] det_loss_box_dn_5[0.2209] loss_depth[0.0408] total_loss[8.2412]
Rank[0/8] 06/20/2025 05:41:56 INFO stats.py:314 | Epoch[118] Step[129] GlobalStep[46149] Training Speed: 7.69 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 15:55:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:42:17 INFO loss_tracker.py:84 | Epoch[118/239] Step[149] GlobalStep[46169/NA]: det_loss_cls_0[0.5350] det_loss_box_0[0.8204] det_loss_cls_1[0.5232] det_loss_box_1[0.6465] det_loss_cls_2[0.4814] det_loss_box_2[0.5982] det_loss_cls_3[0.4594] det_loss_box_3[0.5804] det_loss_cls_4[0.4295] det_loss_box_4[0.5644] det_loss_cls_5[0.4178] det_loss_box_5[0.5624] det_loss_cls_dn_0[0.1039] det_loss_box_dn_0[0.3039] det_loss_cls_dn_1[0.1012] det_loss_box_dn_1[0.2508] det_loss_cls_dn_2[0.1012] det_loss_box_dn_2[0.2373] det_loss_cls_dn_3[0.0998] det_loss_box_dn_3[0.2330] det_loss_cls_dn_4[0.0991] det_loss_box_dn_4[0.2316] det_loss_cls_dn_5[0.0979] det_loss_box_dn_5[0.2304] loss_depth[0.0405] total_loss[8.7493]
Rank[0/8] 06/20/2025 05:42:22 INFO stats.py:314 | Epoch[118] Step[154] GlobalStep[46174] Training Speed: 8.91 samples/sec across all devices.	Average Step Time: 0.90 sec.	Estimated Remaining Time: 15:55:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:42:43 INFO loss_tracker.py:84 | Epoch[118/239] Step[174] GlobalStep[46194/NA]: det_loss_cls_0[0.4799] det_loss_box_0[0.6287] det_loss_cls_1[0.4801] det_loss_box_1[0.4879] det_loss_cls_2[0.4369] det_loss_box_2[0.4328] det_loss_cls_3[0.4011] det_loss_box_3[0.4175] det_loss_cls_4[0.3727] det_loss_box_4[0.4086] det_loss_cls_5[0.3577] det_loss_box_5[0.4097] det_loss_cls_dn_0[0.1014] det_loss_box_dn_0[0.2601] det_loss_cls_dn_1[0.0976] det_loss_box_dn_1[0.2155] det_loss_cls_dn_2[0.0972] det_loss_box_dn_2[0.2021] det_loss_cls_dn_3[0.0963] det_loss_box_dn_3[0.1986] det_loss_cls_dn_4[0.0955] det_loss_box_dn_4[0.1975] det_loss_cls_dn_5[0.0948] det_loss_box_dn_5[0.1964] loss_depth[0.0401] total_loss[7.2065]
Rank[0/8] 06/20/2025 05:42:48 INFO stats.py:314 | Epoch[118] Step[179] GlobalStep[46199] Training Speed: 7.33 samples/sec across all devices.	Average Step Time: 1.09 sec.	Estimated Remaining Time: 15:54:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:43:09 INFO loss_tracker.py:84 | Epoch[118/239] Step[199] GlobalStep[46219/NA]: det_loss_cls_0[0.5394] det_loss_box_0[0.6660] det_loss_cls_1[0.5238] det_loss_box_1[0.5145] det_loss_cls_2[0.4853] det_loss_box_2[0.4667] det_loss_cls_3[0.4564] det_loss_box_3[0.4517] det_loss_cls_4[0.4266] det_loss_box_4[0.4481] det_loss_cls_5[0.4148] det_loss_box_5[0.4544] det_loss_cls_dn_0[0.0984] det_loss_box_dn_0[0.2989] det_loss_cls_dn_1[0.0953] det_loss_box_dn_1[0.2542] det_loss_cls_dn_2[0.0948] det_loss_box_dn_2[0.2389] det_loss_cls_dn_3[0.0933] det_loss_box_dn_3[0.2351] det_loss_cls_dn_4[0.0922] det_loss_box_dn_4[0.2347] det_loss_cls_dn_5[0.0915] det_loss_box_dn_5[0.2333] loss_depth[0.0364] total_loss[7.9448]
Rank[0/8] 06/20/2025 05:43:14 INFO stats.py:314 | Epoch[118] Step[204] GlobalStep[46224] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 15:54:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:43:34 INFO loss_tracker.py:84 | Epoch[118/239] Step[224] GlobalStep[46244/NA]: det_loss_cls_0[0.5780] det_loss_box_0[0.7132] det_loss_cls_1[0.5702] det_loss_box_1[0.5531] det_loss_cls_2[0.5329] det_loss_box_2[0.5137] det_loss_cls_3[0.4989] det_loss_box_3[0.4986] det_loss_cls_4[0.4742] det_loss_box_4[0.4879] det_loss_cls_5[0.4601] det_loss_box_5[0.4899] det_loss_cls_dn_0[0.0938] det_loss_box_dn_0[0.2873] det_loss_cls_dn_1[0.0893] det_loss_box_dn_1[0.2581] det_loss_cls_dn_2[0.0879] det_loss_box_dn_2[0.2463] det_loss_cls_dn_3[0.0864] det_loss_box_dn_3[0.2421] det_loss_cls_dn_4[0.0854] det_loss_box_dn_4[0.2410] det_loss_cls_dn_5[0.0847] det_loss_box_dn_5[0.2336] loss_depth[0.0377] total_loss[8.4443]
Rank[0/8] 06/20/2025 05:43:39 INFO stats.py:314 | Epoch[118] Step[229] GlobalStep[46249] Training Speed: 7.84 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 15:53:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:44:01 INFO loss_tracker.py:84 | Epoch[118/239] Step[249] GlobalStep[46269/NA]: det_loss_cls_0[0.5586] det_loss_box_0[0.6792] det_loss_cls_1[0.5442] det_loss_box_1[0.5151] det_loss_cls_2[0.4988] det_loss_box_2[0.4759] det_loss_cls_3[0.4616] det_loss_box_3[0.4623] det_loss_cls_4[0.4262] det_loss_box_4[0.4633] det_loss_cls_5[0.4153] det_loss_box_5[0.4630] det_loss_cls_dn_0[0.0949] det_loss_box_dn_0[0.2540] det_loss_cls_dn_1[0.0894] det_loss_box_dn_1[0.2183] det_loss_cls_dn_2[0.0884] det_loss_box_dn_2[0.2040] det_loss_cls_dn_3[0.0867] det_loss_box_dn_3[0.2009] det_loss_cls_dn_4[0.0856] det_loss_box_dn_4[0.2005] det_loss_cls_dn_5[0.0848] det_loss_box_dn_5[0.1991] loss_depth[0.0391] total_loss[7.8090]
Rank[0/8] 06/20/2025 05:44:06 INFO stats.py:314 | Epoch[118] Step[254] GlobalStep[46274] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 15:52:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:44:27 INFO loss_tracker.py:84 | Epoch[118/239] Step[274] GlobalStep[46294/NA]: det_loss_cls_0[0.5122] det_loss_box_0[0.6803] det_loss_cls_1[0.5099] det_loss_box_1[0.5255] det_loss_cls_2[0.4696] det_loss_box_2[0.4917] det_loss_cls_3[0.4372] det_loss_box_3[0.4818] det_loss_cls_4[0.4152] det_loss_box_4[0.4726] det_loss_cls_5[0.4065] det_loss_box_5[0.4749] det_loss_cls_dn_0[0.0947] det_loss_box_dn_0[0.2681] det_loss_cls_dn_1[0.0912] det_loss_box_dn_1[0.2273] det_loss_cls_dn_2[0.0910] det_loss_box_dn_2[0.2104] det_loss_cls_dn_3[0.0901] det_loss_box_dn_3[0.2072] det_loss_cls_dn_4[0.0894] det_loss_box_dn_4[0.2056] det_loss_cls_dn_5[0.0889] det_loss_box_dn_5[0.2051] loss_depth[0.0351] total_loss[7.7816]
Rank[0/8] 06/20/2025 05:44:32 INFO stats.py:314 | Epoch[118] Step[279] GlobalStep[46299] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 15:52:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:44:53 INFO loss_tracker.py:84 | Epoch[118/239] Step[299] GlobalStep[46319/NA]: det_loss_cls_0[0.5063] det_loss_box_0[0.6499] det_loss_cls_1[0.4956] det_loss_box_1[0.5203] det_loss_cls_2[0.4570] det_loss_box_2[0.4737] det_loss_cls_3[0.4278] det_loss_box_3[0.4563] det_loss_cls_4[0.4062] det_loss_box_4[0.4425] det_loss_cls_5[0.3900] det_loss_box_5[0.4522] det_loss_cls_dn_0[0.0983] det_loss_box_dn_0[0.2746] det_loss_cls_dn_1[0.0942] det_loss_box_dn_1[0.2347] det_loss_cls_dn_2[0.0934] det_loss_box_dn_2[0.2206] det_loss_cls_dn_3[0.0925] det_loss_box_dn_3[0.2168] det_loss_cls_dn_4[0.0916] det_loss_box_dn_4[0.2161] det_loss_cls_dn_5[0.0907] det_loss_box_dn_5[0.2146] loss_depth[0.0399] total_loss[7.6558]
Rank[0/8] 06/20/2025 05:44:58 INFO stats.py:314 | Epoch[118] Step[304] GlobalStep[46324] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 15:51:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:45:19 INFO loss_tracker.py:84 | Epoch[118/239] Step[324] GlobalStep[46344/NA]: det_loss_cls_0[0.4891] det_loss_box_0[0.7508] det_loss_cls_1[0.4723] det_loss_box_1[0.5881] det_loss_cls_2[0.4448] det_loss_box_2[0.5246] det_loss_cls_3[0.4225] det_loss_box_3[0.5076] det_loss_cls_4[0.4000] det_loss_box_4[0.4929] det_loss_cls_5[0.3906] det_loss_box_5[0.4912] det_loss_cls_dn_0[0.0980] det_loss_box_dn_0[0.2780] det_loss_cls_dn_1[0.0939] det_loss_box_dn_1[0.2337] det_loss_cls_dn_2[0.0929] det_loss_box_dn_2[0.2185] det_loss_cls_dn_3[0.0912] det_loss_box_dn_3[0.2160] det_loss_cls_dn_4[0.0907] det_loss_box_dn_4[0.2146] det_loss_cls_dn_5[0.0898] det_loss_box_dn_5[0.2127] loss_depth[0.0410] total_loss[7.9455]
Rank[0/8] 06/20/2025 05:45:24 INFO stats.py:314 | Epoch[118] Step[329] GlobalStep[46349] Training Speed: 7.41 samples/sec across all devices.	Average Step Time: 1.08 sec.	Estimated Remaining Time: 15:51:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:45:45 INFO loss_tracker.py:84 | Epoch[118/239] Step[349] GlobalStep[46369/NA]: det_loss_cls_0[0.5141] det_loss_box_0[0.6563] det_loss_cls_1[0.5017] det_loss_box_1[0.5095] det_loss_cls_2[0.4674] det_loss_box_2[0.4577] det_loss_cls_3[0.4331] det_loss_box_3[0.4495] det_loss_cls_4[0.4096] det_loss_box_4[0.4289] det_loss_cls_5[0.3902] det_loss_box_5[0.4377] det_loss_cls_dn_0[0.0972] det_loss_box_dn_0[0.3057] det_loss_cls_dn_1[0.0929] det_loss_box_dn_1[0.2560] det_loss_cls_dn_2[0.0917] det_loss_box_dn_2[0.2387] det_loss_cls_dn_3[0.0908] det_loss_box_dn_3[0.2334] det_loss_cls_dn_4[0.0900] det_loss_box_dn_4[0.2308] det_loss_cls_dn_5[0.0893] det_loss_box_dn_5[0.2296] loss_depth[0.0368] total_loss[7.7388]
Rank[0/8] 06/20/2025 05:45:50 INFO stats.py:314 | Epoch[118] Step[354] GlobalStep[46374] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 15:50:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:46:10 INFO loss_tracker.py:84 | Epoch[118/239] Step[374] GlobalStep[46394/NA]: det_loss_cls_0[0.4984] det_loss_box_0[0.6430] det_loss_cls_1[0.4743] det_loss_box_1[0.4893] det_loss_cls_2[0.4410] det_loss_box_2[0.4243] det_loss_cls_3[0.4069] det_loss_box_3[0.4178] det_loss_cls_4[0.3788] det_loss_box_4[0.3937] det_loss_cls_5[0.3658] det_loss_box_5[0.3944] det_loss_cls_dn_0[0.0923] det_loss_box_dn_0[0.2876] det_loss_cls_dn_1[0.0901] det_loss_box_dn_1[0.2381] det_loss_cls_dn_2[0.0894] det_loss_box_dn_2[0.2176] det_loss_cls_dn_3[0.0887] det_loss_box_dn_3[0.2145] det_loss_cls_dn_4[0.0883] det_loss_box_dn_4[0.2114] det_loss_cls_dn_5[0.0874] det_loss_box_dn_5[0.2101] loss_depth[0.0359] total_loss[7.2792]
Rank[0/8] 06/20/2025 05:46:15 INFO stats.py:314 | Epoch[118] Step[379] GlobalStep[46399] Training Speed: 8.85 samples/sec across all devices.	Average Step Time: 0.90 sec.	Estimated Remaining Time: 15:50:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:46:25 INFO stats.py:394 | Epoch[118] completed. Training Speed: 7.67 samples/sec across all devices.	Epoch Time: 406.77 sec.	Average Epoch Time: 406.77 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 15:49:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:46:43 INFO stats.py:314 | Epoch[119] Step[14] GlobalStep[46424] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 15:49:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:46:53 INFO loss_tracker.py:84 | Epoch[119/239] Step[24] GlobalStep[46434/NA]: det_loss_cls_0[0.4700] det_loss_box_0[0.6242] det_loss_cls_1[0.4495] det_loss_box_1[0.5084] det_loss_cls_2[0.4133] det_loss_box_2[0.4647] det_loss_cls_3[0.3902] det_loss_box_3[0.4498] det_loss_cls_4[0.3718] det_loss_box_4[0.4352] det_loss_cls_5[0.3504] det_loss_box_5[0.4388] det_loss_cls_dn_0[0.0997] det_loss_box_dn_0[0.3017] det_loss_cls_dn_1[0.0954] det_loss_box_dn_1[0.2513] det_loss_cls_dn_2[0.0946] det_loss_box_dn_2[0.2324] det_loss_cls_dn_3[0.0937] det_loss_box_dn_3[0.2286] det_loss_cls_dn_4[0.0926] det_loss_box_dn_4[0.2260] det_loss_cls_dn_5[0.0919] det_loss_box_dn_5[0.2262] loss_depth[0.0385] total_loss[7.4389]
Rank[0/8] 06/20/2025 05:47:09 INFO stats.py:314 | Epoch[119] Step[39] GlobalStep[46449] Training Speed: 7.97 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 15:48:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:47:19 INFO loss_tracker.py:84 | Epoch[119/239] Step[49] GlobalStep[46459/NA]: det_loss_cls_0[0.4380] det_loss_box_0[0.5593] det_loss_cls_1[0.4502] det_loss_box_1[0.4247] det_loss_cls_2[0.4029] det_loss_box_2[0.3807] det_loss_cls_3[0.3702] det_loss_box_3[0.3709] det_loss_cls_4[0.3411] det_loss_box_4[0.3689] det_loss_cls_5[0.3242] det_loss_box_5[0.3746] det_loss_cls_dn_0[0.0855] det_loss_box_dn_0[0.2588] det_loss_cls_dn_1[0.0814] det_loss_box_dn_1[0.2156] det_loss_cls_dn_2[0.0806] det_loss_box_dn_2[0.1988] det_loss_cls_dn_3[0.0787] det_loss_box_dn_3[0.1971] det_loss_cls_dn_4[0.0777] det_loss_box_dn_4[0.1951] det_loss_cls_dn_5[0.0773] det_loss_box_dn_5[0.1927] loss_depth[0.0366] total_loss[6.5815]
Rank[0/8] 06/20/2025 05:47:34 INFO stats.py:314 | Epoch[119] Step[64] GlobalStep[46474] Training Speed: 8.19 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 15:48:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:47:45 INFO loss_tracker.py:84 | Epoch[119/239] Step[74] GlobalStep[46484/NA]: det_loss_cls_0[0.5454] det_loss_box_0[0.7004] det_loss_cls_1[0.5287] det_loss_box_1[0.5522] det_loss_cls_2[0.4867] det_loss_box_2[0.5098] det_loss_cls_3[0.4519] det_loss_box_3[0.4974] det_loss_cls_4[0.4259] det_loss_box_4[0.4888] det_loss_cls_5[0.4062] det_loss_box_5[0.4958] det_loss_cls_dn_0[0.0927] det_loss_box_dn_0[0.2861] det_loss_cls_dn_1[0.0886] det_loss_box_dn_1[0.2421] det_loss_cls_dn_2[0.0875] det_loss_box_dn_2[0.2243] det_loss_cls_dn_3[0.0866] det_loss_box_dn_3[0.2223] det_loss_cls_dn_4[0.0853] det_loss_box_dn_4[0.2208] det_loss_cls_dn_5[0.0849] det_loss_box_dn_5[0.2180] loss_depth[0.0406] total_loss[8.0690]
Rank[0/8] 06/20/2025 05:48:00 INFO stats.py:314 | Epoch[119] Step[89] GlobalStep[46499] Training Speed: 8.17 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 15:47:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:48:11 INFO loss_tracker.py:84 | Epoch[119/239] Step[99] GlobalStep[46509/NA]: det_loss_cls_0[0.4572] det_loss_box_0[0.5992] det_loss_cls_1[0.4516] det_loss_box_1[0.4449] det_loss_cls_2[0.4169] det_loss_box_2[0.3941] det_loss_cls_3[0.3823] det_loss_box_3[0.3870] det_loss_cls_4[0.3561] det_loss_box_4[0.3851] det_loss_cls_5[0.3448] det_loss_box_5[0.3852] det_loss_cls_dn_0[0.0995] det_loss_box_dn_0[0.2789] det_loss_cls_dn_1[0.0960] det_loss_box_dn_1[0.2330] det_loss_cls_dn_2[0.0955] det_loss_box_dn_2[0.2200] det_loss_cls_dn_3[0.0940] det_loss_box_dn_3[0.2161] det_loss_cls_dn_4[0.0931] det_loss_box_dn_4[0.2140] det_loss_cls_dn_5[0.0922] det_loss_box_dn_5[0.2122] loss_depth[0.0378] total_loss[6.9865]
Rank[0/8] 06/20/2025 05:48:26 INFO stats.py:314 | Epoch[119] Step[114] GlobalStep[46524] Training Speed: 8.67 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 15:47:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:48:37 INFO loss_tracker.py:84 | Epoch[119/239] Step[124] GlobalStep[46534/NA]: det_loss_cls_0[0.5195] det_loss_box_0[0.6682] det_loss_cls_1[0.5025] det_loss_box_1[0.5455] det_loss_cls_2[0.4698] det_loss_box_2[0.4958] det_loss_cls_3[0.4326] det_loss_box_3[0.4806] det_loss_cls_4[0.4034] det_loss_box_4[0.4648] det_loss_cls_5[0.3818] det_loss_box_5[0.4667] det_loss_cls_dn_0[0.1004] det_loss_box_dn_0[0.3071] det_loss_cls_dn_1[0.0945] det_loss_box_dn_1[0.2631] det_loss_cls_dn_2[0.0935] det_loss_box_dn_2[0.2475] det_loss_cls_dn_3[0.0926] det_loss_box_dn_3[0.2450] det_loss_cls_dn_4[0.0914] det_loss_box_dn_4[0.2436] det_loss_cls_dn_5[0.0903] det_loss_box_dn_5[0.2414] loss_depth[0.0372] total_loss[7.9785]
Rank[0/8] 06/20/2025 05:48:52 INFO stats.py:314 | Epoch[119] Step[139] GlobalStep[46549] Training Speed: 8.62 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 15:46:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:49:02 INFO loss_tracker.py:84 | Epoch[119/239] Step[149] GlobalStep[46559/NA]: det_loss_cls_0[0.3966] det_loss_box_0[0.5389] det_loss_cls_1[0.3885] det_loss_box_1[0.4058] det_loss_cls_2[0.3487] det_loss_box_2[0.3672] det_loss_cls_3[0.3260] det_loss_box_3[0.3548] det_loss_cls_4[0.3030] det_loss_box_4[0.3429] det_loss_cls_5[0.2944] det_loss_box_5[0.3444] det_loss_cls_dn_0[0.0836] det_loss_box_dn_0[0.3056] det_loss_cls_dn_1[0.0787] det_loss_box_dn_1[0.2560] det_loss_cls_dn_2[0.0776] det_loss_box_dn_2[0.2346] det_loss_cls_dn_3[0.0766] det_loss_box_dn_3[0.2314] det_loss_cls_dn_4[0.0751] det_loss_box_dn_4[0.2299] det_loss_cls_dn_5[0.0740] det_loss_box_dn_5[0.2293] loss_depth[0.0367] total_loss[6.4002]
Rank[0/8] 06/20/2025 05:49:17 INFO stats.py:314 | Epoch[119] Step[164] GlobalStep[46574] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 15:46:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:49:27 INFO loss_tracker.py:84 | Epoch[119/239] Step[174] GlobalStep[46584/NA]: det_loss_cls_0[0.5519] det_loss_box_0[0.7470] det_loss_cls_1[0.5454] det_loss_box_1[0.5798] det_loss_cls_2[0.5107] det_loss_box_2[0.5348] det_loss_cls_3[0.4863] det_loss_box_3[0.5096] det_loss_cls_4[0.4642] det_loss_box_4[0.4876] det_loss_cls_5[0.4499] det_loss_box_5[0.4869] det_loss_cls_dn_0[0.1073] det_loss_box_dn_0[0.2896] det_loss_cls_dn_1[0.1046] det_loss_box_dn_1[0.2465] det_loss_cls_dn_2[0.1035] det_loss_box_dn_2[0.2306] det_loss_cls_dn_3[0.1024] det_loss_box_dn_3[0.2252] det_loss_cls_dn_4[0.1013] det_loss_box_dn_4[0.2227] det_loss_cls_dn_5[0.1002] det_loss_box_dn_5[0.2210] loss_depth[0.0354] total_loss[8.4444]
Rank[0/8] 06/20/2025 05:49:44 INFO stats.py:314 | Epoch[119] Step[189] GlobalStep[46599] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 15:45:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:49:54 INFO loss_tracker.py:84 | Epoch[119/239] Step[199] GlobalStep[46609/NA]: det_loss_cls_0[0.4875] det_loss_box_0[0.5803] det_loss_cls_1[0.4754] det_loss_box_1[0.4639] det_loss_cls_2[0.4404] det_loss_box_2[0.4294] det_loss_cls_3[0.4146] det_loss_box_3[0.4222] det_loss_cls_4[0.3879] det_loss_box_4[0.4174] det_loss_cls_5[0.3751] det_loss_box_5[0.4182] det_loss_cls_dn_0[0.1051] det_loss_box_dn_0[0.2768] det_loss_cls_dn_1[0.1017] det_loss_box_dn_1[0.2320] det_loss_cls_dn_2[0.1000] det_loss_box_dn_2[0.2172] det_loss_cls_dn_3[0.0988] det_loss_box_dn_3[0.2150] det_loss_cls_dn_4[0.0979] det_loss_box_dn_4[0.2134] det_loss_cls_dn_5[0.0967] det_loss_box_dn_5[0.2116] loss_depth[0.0369] total_loss[7.3151]
Rank[0/8] 06/20/2025 05:50:09 INFO stats.py:314 | Epoch[119] Step[214] GlobalStep[46624] Training Speed: 8.58 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 15:44:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:50:19 INFO loss_tracker.py:84 | Epoch[119/239] Step[224] GlobalStep[46634/NA]: det_loss_cls_0[0.5919] det_loss_box_0[0.8040] det_loss_cls_1[0.5638] det_loss_box_1[0.6370] det_loss_cls_2[0.5263] det_loss_box_2[0.5818] det_loss_cls_3[0.4971] det_loss_box_3[0.5728] det_loss_cls_4[0.4756] det_loss_box_4[0.5686] det_loss_cls_5[0.4660] det_loss_box_5[0.5610] det_loss_cls_dn_0[0.0942] det_loss_box_dn_0[0.3178] det_loss_cls_dn_1[0.0907] det_loss_box_dn_1[0.2767] det_loss_cls_dn_2[0.0897] det_loss_box_dn_2[0.2600] det_loss_cls_dn_3[0.0896] det_loss_box_dn_3[0.2556] det_loss_cls_dn_4[0.0885] det_loss_box_dn_4[0.2543] det_loss_cls_dn_5[0.0874] det_loss_box_dn_5[0.2513] loss_depth[0.0397] total_loss[9.0415]
Rank[0/8] 06/20/2025 05:50:34 INFO stats.py:314 | Epoch[119] Step[239] GlobalStep[46649] Training Speed: 8.73 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 15:44:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:50:45 INFO loss_tracker.py:84 | Epoch[119/239] Step[249] GlobalStep[46659/NA]: det_loss_cls_0[0.6132] det_loss_box_0[0.7758] det_loss_cls_1[0.6117] det_loss_box_1[0.5863] det_loss_cls_2[0.5541] det_loss_box_2[0.5493] det_loss_cls_3[0.5139] det_loss_box_3[0.5216] det_loss_cls_4[0.4815] det_loss_box_4[0.5151] det_loss_cls_5[0.4742] det_loss_box_5[0.5121] det_loss_cls_dn_0[0.0961] det_loss_box_dn_0[0.2552] det_loss_cls_dn_1[0.0931] det_loss_box_dn_1[0.2148] det_loss_cls_dn_2[0.0930] det_loss_box_dn_2[0.2000] det_loss_cls_dn_3[0.0916] det_loss_box_dn_3[0.1968] det_loss_cls_dn_4[0.0905] det_loss_box_dn_4[0.1961] det_loss_cls_dn_5[0.0897] det_loss_box_dn_5[0.1952] loss_depth[0.0437] total_loss[8.5645]
Rank[0/8] 06/20/2025 05:51:00 INFO stats.py:314 | Epoch[119] Step[264] GlobalStep[46674] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 15:43:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:51:11 INFO loss_tracker.py:84 | Epoch[119/239] Step[274] GlobalStep[46684/NA]: det_loss_cls_0[0.5007] det_loss_box_0[0.6854] det_loss_cls_1[0.4890] det_loss_box_1[0.5348] det_loss_cls_2[0.4499] det_loss_box_2[0.4822] det_loss_cls_3[0.4309] det_loss_box_3[0.4686] det_loss_cls_4[0.4093] det_loss_box_4[0.4656] det_loss_cls_5[0.4005] det_loss_box_5[0.4599] det_loss_cls_dn_0[0.0935] det_loss_box_dn_0[0.3658] det_loss_cls_dn_1[0.0906] det_loss_box_dn_1[0.3145] det_loss_cls_dn_2[0.0901] det_loss_box_dn_2[0.2987] det_loss_cls_dn_3[0.0898] det_loss_box_dn_3[0.2945] det_loss_cls_dn_4[0.0896] det_loss_box_dn_4[0.2919] det_loss_cls_dn_5[0.0885] det_loss_box_dn_5[0.2845] loss_depth[0.0383] total_loss[8.2069]
Rank[0/8] 06/20/2025 05:51:26 INFO stats.py:314 | Epoch[119] Step[289] GlobalStep[46699] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 15:43:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:51:36 INFO loss_tracker.py:84 | Epoch[119/239] Step[299] GlobalStep[46709/NA]: det_loss_cls_0[0.4912] det_loss_box_0[0.6531] det_loss_cls_1[0.4684] det_loss_box_1[0.5102] det_loss_cls_2[0.4241] det_loss_box_2[0.4573] det_loss_cls_3[0.3995] det_loss_box_3[0.4345] det_loss_cls_4[0.3693] det_loss_box_4[0.4226] det_loss_cls_5[0.3567] det_loss_box_5[0.4202] det_loss_cls_dn_0[0.0955] det_loss_box_dn_0[0.2689] det_loss_cls_dn_1[0.0929] det_loss_box_dn_1[0.2254] det_loss_cls_dn_2[0.0919] det_loss_box_dn_2[0.2071] det_loss_cls_dn_3[0.0912] det_loss_box_dn_3[0.2018] det_loss_cls_dn_4[0.0900] det_loss_box_dn_4[0.2004] det_loss_cls_dn_5[0.0891] det_loss_box_dn_5[0.1994] loss_depth[0.0368] total_loss[7.2973]
Rank[0/8] 06/20/2025 05:51:52 INFO stats.py:314 | Epoch[119] Step[314] GlobalStep[46724] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 15:42:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:52:02 INFO loss_tracker.py:84 | Epoch[119/239] Step[324] GlobalStep[46734/NA]: det_loss_cls_0[0.5328] det_loss_box_0[0.6754] det_loss_cls_1[0.5254] det_loss_box_1[0.5281] det_loss_cls_2[0.4780] det_loss_box_2[0.4733] det_loss_cls_3[0.4533] det_loss_box_3[0.4546] det_loss_cls_4[0.4250] det_loss_box_4[0.4446] det_loss_cls_5[0.3988] det_loss_box_5[0.4497] det_loss_cls_dn_0[0.0990] det_loss_box_dn_0[0.3372] det_loss_cls_dn_1[0.0948] det_loss_box_dn_1[0.2850] det_loss_cls_dn_2[0.0946] det_loss_box_dn_2[0.2683] det_loss_cls_dn_3[0.0933] det_loss_box_dn_3[0.2632] det_loss_cls_dn_4[0.0925] det_loss_box_dn_4[0.2618] det_loss_cls_dn_5[0.0914] det_loss_box_dn_5[0.2571] loss_depth[0.0417] total_loss[8.1188]
Rank[0/8] 06/20/2025 05:52:18 INFO stats.py:314 | Epoch[119] Step[339] GlobalStep[46749] Training Speed: 6.53 samples/sec across all devices.	Average Step Time: 1.23 sec.	Estimated Remaining Time: 15:42:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:52:28 INFO loss_tracker.py:84 | Epoch[119/239] Step[349] GlobalStep[46759/NA]: det_loss_cls_0[0.4629] det_loss_box_0[0.5444] det_loss_cls_1[0.4510] det_loss_box_1[0.4382] det_loss_cls_2[0.4175] det_loss_box_2[0.3961] det_loss_cls_3[0.3950] det_loss_box_3[0.3807] det_loss_cls_4[0.3691] det_loss_box_4[0.3735] det_loss_cls_5[0.3539] det_loss_box_5[0.3747] det_loss_cls_dn_0[0.0977] det_loss_box_dn_0[0.3027] det_loss_cls_dn_1[0.0934] det_loss_box_dn_1[0.2583] det_loss_cls_dn_2[0.0925] det_loss_box_dn_2[0.2427] det_loss_cls_dn_3[0.0916] det_loss_box_dn_3[0.2389] det_loss_cls_dn_4[0.0911] det_loss_box_dn_4[0.2384] det_loss_cls_dn_5[0.0898] det_loss_box_dn_5[0.2379] loss_depth[0.0385] total_loss[7.0703]
Rank[0/8] 06/20/2025 05:52:44 INFO stats.py:314 | Epoch[119] Step[364] GlobalStep[46774] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 15:41:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:52:54 INFO loss_tracker.py:84 | Epoch[119/239] Step[374] GlobalStep[46784/NA]: det_loss_cls_0[0.5466] det_loss_box_0[0.7574] det_loss_cls_1[0.5205] det_loss_box_1[0.5760] det_loss_cls_2[0.4794] det_loss_box_2[0.5103] det_loss_cls_3[0.4479] det_loss_box_3[0.4801] det_loss_cls_4[0.4210] det_loss_box_4[0.4726] det_loss_cls_5[0.3981] det_loss_box_5[0.4760] det_loss_cls_dn_0[0.0972] det_loss_box_dn_0[0.3163] det_loss_cls_dn_1[0.0935] det_loss_box_dn_1[0.2727] det_loss_cls_dn_2[0.0926] det_loss_box_dn_2[0.2531] det_loss_cls_dn_3[0.0908] det_loss_box_dn_3[0.2478] det_loss_cls_dn_4[0.0899] det_loss_box_dn_4[0.2456] det_loss_cls_dn_5[0.0878] det_loss_box_dn_5[0.2444] loss_depth[0.0378] total_loss[8.2553]
Rank[0/8] 06/20/2025 05:53:10 INFO stats.py:314 | Epoch[119] Step[389] GlobalStep[46799] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 15:40:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 05:53:10 INFO trainer.py:268 | 
==================================================BEGIN EVAL==================================================
Rank[0/8] 06/20/2025 05:54:09 INFO trainer.py:275 | eval: 10
Rank[0/8] 06/20/2025 05:54:17 INFO trainer.py:275 | eval: 20
Rank[0/8] 06/20/2025 05:54:29 INFO trainer.py:275 | eval: 30
Rank[0/8] 06/20/2025 05:54:42 INFO trainer.py:275 | eval: 40
Rank[0/8] 06/20/2025 05:54:49 INFO trainer.py:275 | eval: 50
Rank[0/8] 06/20/2025 05:54:57 INFO trainer.py:275 | eval: 60
Rank[0/8] 06/20/2025 05:55:04 INFO trainer.py:275 | eval: 70
Rank[0/8] 06/20/2025 05:55:13 INFO trainer.py:275 | eval: 80
Rank[0/8] 06/20/2025 05:55:21 INFO trainer.py:275 | eval: 90
Rank[0/8] 06/20/2025 05:55:28 INFO trainer.py:275 | eval: 100
Rank[0/8] 06/20/2025 05:55:33 INFO metrics.py:428 | number of results: 824
Rank[0/8] 06/20/2025 05:59:32 INFO metrics.py:332 | eval results of overall:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| sink                | 0.6484  | 0.8148  | 0.1369  | 0.3004  |
| toilet              | 0.8507  | 0.8832  | 0.6127  | 0.7007  |
| picture             | 0.2256  | 0.4526  | 0.0190  | 0.1121  |
| bathtub             | 0.8311  | 0.8676  | 0.6325  | 0.7059  |
| curtain             | 0.4826  | 0.8179  | 0.0816  | 0.3970  |
| bin                 | 0.6209  | 0.8296  | 0.3784  | 0.5754  |
| stopcock            | 0.1241  | 0.5172  | 0.0007  | 0.0345  |
| vanity              | 0.5168  | 0.8667  | 0.4913  | 0.7556  |
| toilet paper        | 0.2889  | 0.5669  | 0.0551  | 0.2441  |
| doorframe           | 0.2490  | 0.7110  | 0.0471  | 0.3643  |
| bag                 | 0.2341  | 0.6649  | 0.0451  | 0.2108  |
| switch              | 0.0039  | 0.1316  | 0.0000  | 0.0000  |
| bar                 | 0.0253  | 0.1515  | 0.0013  | 0.0303  |
| rail                | 0.0155  | 0.3438  | 0.0000  | 0.0156  |
| towel               | 0.3442  | 0.6516  | 0.0820  | 0.2548  |
| door                | 0.3062  | 0.6760  | 0.0420  | 0.2824  |
| bottle              | 0.0538  | 0.3928  | 0.0033  | 0.0919  |
| mirror              | 0.1327  | 0.6467  | 0.0060  | 0.1739  |
| socket              | 0.0063  | 0.2434  | 0.0000  | 0.0044  |
| shower              | 0.0690  | 0.2667  | 0.0017  | 0.0667  |
| paper               | 0.0288  | 0.2899  | 0.0006  | 0.0680  |
| cup                 | 0.0878  | 0.3814  | 0.0047  | 0.0678  |
| box                 | 0.1649  | 0.5033  | 0.0254  | 0.1782  |
| rod                 | 0.0250  | 0.0250  | 0.0000  | 0.0000  |
| hair dryer          | 0.0023  | 0.2857  | 0.0000  | 0.0000  |
| ledge               | 0.0061  | 0.3333  | 0.0000  | 0.0000  |
| tissue box          | 0.0713  | 0.4242  | 0.0542  | 0.2424  |
| faucet              | 0.0365  | 0.2340  | 0.0000  | 0.0000  |
| grab bar            | 0.0117  | 0.3750  | 0.0000  | 0.0000  |
| shelf               | 0.2859  | 0.7068  | 0.0971  | 0.4011  |
| tissue              | 0.0057  | 0.3077  | 0.0005  | 0.1538  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basket              | 0.2491  | 0.5043  | 0.1159  | 0.2783  |
| light               | 0.0251  | 0.2158  | 0.0005  | 0.0299  |
| mat                 | 0.0168  | 0.2432  | 0.0001  | 0.0270  |
| lamp                | 0.2606  | 0.5851  | 0.0382  | 0.1582  |
| hanger              | 0.0015  | 0.2500  | 0.0000  | 0.0000  |
| vent                | 0.0232  | 0.2083  | 0.0000  | 0.0000  |
| cabinet             | 0.2790  | 0.7701  | 0.1419  | 0.5014  |
| bowl                | 0.0895  | 0.4203  | 0.0096  | 0.0725  |
| toothpaste          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dish           | 0.4911  | 0.6000  | 0.1200  | 0.2000  |
| glass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| frame               | 0.0549  | 0.4898  | 0.0073  | 0.0816  |
| stand               | 0.5568  | 0.8780  | 0.4445  | 0.7195  |
| plug                | 0.0095  | 0.1053  | 0.0000  | 0.0000  |
| blanket             | 0.2806  | 0.7900  | 0.0415  | 0.3500  |
| decoration          | 0.0389  | 0.3929  | 0.0041  | 0.0929  |
| scale               | 0.3333  | 0.3636  | 0.0909  | 0.0909  |
| door knob           | 0.0714  | 0.5000  | 0.0000  | 0.0000  |
| counter             | 0.1083  | 0.7064  | 0.0190  | 0.2936  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candle              | 0.0650  | 0.3333  | 0.0012  | 0.0556  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| microwave           | 0.6214  | 0.7551  | 0.2702  | 0.4490  |
| plant               | 0.3159  | 0.5797  | 0.0721  | 0.2517  |
| clock               | 0.3654  | 0.6170  | 0.0407  | 0.1277  |
| cosmetics           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| backpack            | 0.5376  | 0.8387  | 0.2672  | 0.5000  |
| holder              | 0.1200  | 0.2222  | 0.0000  | 0.0000  |
| stool               | 0.2924  | 0.8151  | 0.1899  | 0.5294  |
| mouse               | 0.0088  | 0.1351  | 0.0000  | 0.0000  |
| purse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap                | 0.1675  | 0.3333  | 0.0000  | 0.0000  |
| bucket              | 0.2886  | 0.6923  | 0.1134  | 0.3333  |
| radiator            | 0.6089  | 0.7556  | 0.2470  | 0.3667  |
| keyboard            | 0.0446  | 0.1395  | 0.0000  | 0.0000  |
| chair               | 0.6424  | 0.8372  | 0.4611  | 0.6399  |
| monitor             | 0.4453  | 0.6087  | 0.0375  | 0.1522  |
| blackboard          | 0.4464  | 0.7073  | 0.0607  | 0.1463  |
| computer            | 0.2705  | 0.6557  | 0.0140  | 0.0984  |
| window              | 0.1790  | 0.5689  | 0.0168  | 0.1766  |
| desk                | 0.5421  | 0.8876  | 0.3214  | 0.6461  |
| table               | 0.4936  | 0.8495  | 0.3295  | 0.6708  |
| wardrobe            | 0.3756  | 0.7590  | 0.2165  | 0.5783  |
| book                | 0.0533  | 0.3408  | 0.0053  | 0.0754  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| telephone           | 0.4146  | 0.6167  | 0.0306  | 0.2167  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blinds              | 0.0678  | 0.4800  | 0.0037  | 0.2200  |
| clothes             | 0.1660  | 0.5892  | 0.0222  | 0.1688  |
| tablet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| laptop              | 0.3064  | 0.6087  | 0.0854  | 0.2174  |
| couch               | 0.5992  | 0.8719  | 0.3885  | 0.7231  |
| balcony             | 0.0018  | 0.5000  | 0.0000  | 0.0000  |
| pipe                | 0.0399  | 0.2857  | 0.0000  | 0.0000  |
| coil                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jacket              | 0.6078  | 0.7083  | 0.2455  | 0.4167  |
| board               | 0.2760  | 0.6290  | 0.0872  | 0.2419  |
| alarm               | 0.0026  | 0.1250  | 0.0000  | 0.0000  |
| windowsill          | 0.0331  | 0.4565  | 0.0000  | 0.0217  |
| spoon               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| refrigerator        | 0.6900  | 0.8200  | 0.4422  | 0.5400  |
| bed                 | 0.8423  | 0.9231  | 0.6127  | 0.8402  |
| pillow              | 0.3472  | 0.5997  | 0.0446  | 0.1897  |
| dresser             | 0.6682  | 0.9630  | 0.5913  | 0.8704  |
| air conditioner     | 0.1346  | 0.6000  | 0.0667  | 0.2000  |
| toy                 | 0.1539  | 0.4545  | 0.0191  | 0.1061  |
| calendar            | 0.0421  | 0.6667  | 0.0000  | 0.0000  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shoe                | 0.1783  | 0.3738  | 0.0252  | 0.0935  |
| tv                  | 0.4809  | 0.7426  | 0.0492  | 0.2673  |
| remote control      | 0.0229  | 0.2593  | 0.0010  | 0.0741  |
| poster              | 0.0086  | 0.1061  | 0.0002  | 0.0152  |
| map                 | 0.0238  | 0.5000  | 0.0238  | 0.5000  |
| rack                | 0.0867  | 0.5385  | 0.0437  | 0.3077  |
| sign                | 0.0499  | 0.4667  | 0.0005  | 0.0667  |
| fan                 | 0.6095  | 0.7656  | 0.0509  | 0.1562  |
| printer             | 0.4220  | 0.7551  | 0.1482  | 0.4898  |
| case                | 0.1065  | 0.4237  | 0.0711  | 0.1525  |
| carpet              | 0.0481  | 0.4762  | 0.0055  | 0.1190  |
| heater              | 0.5425  | 0.7241  | 0.1277  | 0.2931  |
| jar                 | 0.1037  | 0.4615  | 0.0772  | 0.1538  |
| container           | 0.0070  | 0.2712  | 0.0002  | 0.0678  |
| notebook            | 0.0053  | 0.1538  | 0.0000  | 0.0000  |
| guitar              | 0.2537  | 0.5000  | 0.0060  | 0.0833  |
| luggage             | 0.6181  | 1.0000  | 0.6181  | 1.0000  |
| ottoman             | 0.2096  | 0.7778  | 0.1809  | 0.5833  |
| bench               | 0.2617  | 0.7358  | 0.1324  | 0.5094  |
| sunglasses          | 0.0037  | 0.2308  | 0.0000  | 0.0000  |
| furniture           | 0.0054  | 0.3333  | 0.0054  | 0.3333  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine             | 0.4900  | 0.9474  | 0.3895  | 0.6842  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| person              | 0.0351  | 0.3333  | 0.0333  | 0.1667  |
| coffee maker        | 0.3826  | 0.5517  | 0.1241  | 0.2759  |
| plunger             | 0.8000  | 0.8000  | 0.8000  | 0.8000  |
| stall               | 0.1336  | 0.8824  | 0.0355  | 0.4118  |
| broom               | 0.0237  | 0.2857  | 0.0000  | 0.0000  |
| brush               | 0.1035  | 0.2857  | 0.0001  | 0.0714  |
| paper cutter        | 0.7857  | 0.8333  | 0.0417  | 0.1667  |
| handle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| letter              | 0.0252  | 0.5000  | 0.0000  | 0.0000  |
| tube                | 0.0010  | 0.0769  | 0.0000  | 0.0000  |
| piano               | 0.4000  | 0.4000  | 0.4000  | 0.4000  |
| roll                | 0.0623  | 0.5000  | 0.0002  | 0.1000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ladder              | 0.1968  | 0.4706  | 0.0449  | 0.2941  |
| partition           | 0.5833  | 0.6667  | 0.0079  | 0.1667  |
| crate               | 0.3259  | 0.7143  | 0.0744  | 0.2857  |
| doorway             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dispenser           | 0.5090  | 0.7273  | 0.2608  | 0.3636  |
| soap dispenser      | 0.4932  | 0.6667  | 0.1708  | 0.2963  |
| mailbox             | 0.1374  | 0.6000  | 0.0440  | 0.4000  |
| pillar              | 0.3311  | 0.7674  | 0.2392  | 0.4651  |
| column              | 0.0115  | 0.3158  | 0.0052  | 0.1316  |
| copier              | 0.8584  | 0.9167  | 0.4189  | 0.6667  |
| stairs              | 0.0580  | 0.4444  | 0.0040  | 0.1389  |
| fire extinguisher   | 0.4095  | 0.8000  | 0.0667  | 0.2000  |
| tray                | 0.0006  | 0.1429  | 0.0000  | 0.0000  |
| cube                | 0.5000  | 1.0000  | 0.5000  | 1.0000  |
| cushion             | 0.1972  | 0.3922  | 0.0394  | 0.1765  |
| window frame        | 0.0334  | 0.3737  | 0.0052  | 0.1414  |
| stove               | 0.4840  | 0.7500  | 0.2665  | 0.4722  |
| screen              | 0.0333  | 0.2000  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beam                | 0.0006  | 0.0909  | 0.0002  | 0.0909  |
| projector           | 0.0200  | 0.5000  | 0.0000  | 0.0000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plate               | 0.0141  | 0.2885  | 0.0027  | 0.0962  |
| sponge              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball                | 0.4390  | 0.5714  | 0.2356  | 0.3810  |
| folder              | 0.0172  | 0.4286  | 0.0021  | 0.0714  |
| humidifier          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| vacuum cleaner      | 0.6473  | 0.8571  | 0.0643  | 0.2857  |
| hat                 | 0.0319  | 0.2222  | 0.0000  | 0.0000  |
| statue              | 0.1151  | 0.3158  | 0.0033  | 0.1579  |
| headphones          | 0.2080  | 0.4000  | 0.0040  | 0.2000  |
| hamper              | 0.3988  | 0.5455  | 0.3778  | 0.4545  |
| commode             | 0.5117  | 0.8654  | 0.4037  | 0.6731  |
| bicycle             | 0.6340  | 1.0000  | 0.0481  | 0.5000  |
| basin               | 0.0297  | 0.1176  | 0.0001  | 0.0588  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water cooler        | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fireplace           | 0.1447  | 0.4643  | 0.0019  | 0.0714  |
| drawer              | 0.2226  | 0.6667  | 0.0247  | 0.3333  |
| roof                | 0.0015  | 0.2917  | 0.0000  | 0.0833  |
| headboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| arch                | 0.0001  | 0.0667  | 0.0000  | 0.0333  |
| oven                | 0.4645  | 0.8214  | 0.1307  | 0.3571  |
| dish rack           | 0.1223  | 1.0000  | 0.0400  | 0.2000  |
| dishwasher          | 0.1665  | 0.5000  | 0.0500  | 0.1000  |
| kettle              | 0.1013  | 0.5556  | 0.0652  | 0.4444  |
| teapot              | 0.0343  | 0.2222  | 0.0000  | 0.0000  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster             | 0.4063  | 0.5000  | 0.1000  | 0.1000  |
| pan                 | 0.1190  | 0.4706  | 0.0000  | 0.0000  |
| fruit               | 0.1625  | 0.4286  | 0.0003  | 0.0714  |
| kitchenware         | 0.2384  | 0.4348  | 0.0070  | 0.0652  |
| flowerpot           | 0.0660  | 0.5490  | 0.0314  | 0.2157  |
| vase                | 0.1175  | 0.4200  | 0.0705  | 0.2000  |
| price tag           | 0.2170  | 0.5714  | 0.0000  | 0.0000  |
| washing machine     | 0.5575  | 0.8571  | 0.4102  | 0.6786  |
| file                | 0.0740  | 0.4706  | 0.0031  | 0.1471  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| magazine            | 0.0080  | 0.2000  | 0.0000  | 0.0000  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toiletry            | 0.1163  | 0.3478  | 0.0000  | 0.0000  |
| speaker             | 0.0069  | 0.2273  | 0.0000  | 0.0000  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radio               | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| garbage             | 0.6000  | 0.7500  | 0.3958  | 0.6250  |
| device              | 0.0117  | 0.1818  | 0.0029  | 0.0909  |
| player              | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| crib                | 0.3131  | 1.0000  | 0.3131  | 1.0000  |
| napkins             | 0.0591  | 0.6667  | 0.0000  | 0.0000  |
| jalousie            | 0.8333  | 1.0000  | 0.0000  | 0.0000  |
| umbrella            | 0.0077  | 0.2000  | 0.0000  | 0.0000  |
| chandelier          | 0.1555  | 0.4286  | 0.0105  | 0.0952  |
| pot                 | 0.1849  | 0.3704  | 0.0378  | 0.1111  |
| album               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| conducting wire     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pack                | 0.0377  | 0.7333  | 0.0022  | 0.2000  |
| food                | 0.1223  | 0.2727  | 0.0000  | 0.0000  |
| clothes dryer       | 0.2737  | 0.7333  | 0.1988  | 0.6667  |
| shampoo             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mop                 | 0.7333  | 1.0000  | 0.0030  | 0.3333  |
| body loofah         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beanbag             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| sack                | 0.3417  | 0.7143  | 0.0079  | 0.1429  |
| adhesive tape       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| can                 | 0.0074  | 0.1667  | 0.0000  | 0.0000  |
| bread               | 0.0500  | 0.2000  | 0.0000  | 0.0000  |
| range hood          | 0.2595  | 0.5000  | 0.0253  | 0.2143  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drum                | 0.0250  | 0.2500  | 0.0000  | 0.0000  |
| steps               | 0.0019  | 0.4000  | 0.0000  | 0.0000  |
| candlestick         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| footstool           | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flush               | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| countertop          | 0.0065  | 0.3929  | 0.0015  | 0.0714  |
| washbasin           | 0.0050  | 0.1333  | 0.0050  | 0.1333  |
| wine                | 0.0093  | 0.1429  | 0.0000  | 0.0000  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| thermostat          | 0.0129  | 0.3333  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garage door         | 0.2778  | 0.3333  | 0.2778  | 0.3333  |
| treadmill           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet               | 0.6667  | 0.6667  | 0.6667  | 0.6667  |
| kitchen island      | 0.1192  | 0.8571  | 0.0867  | 0.8571  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool table          | 0.1667  | 0.3333  | 0.1667  | 0.3333  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.4621  | 0.6500  | 0.0113  | 0.1500  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell            | 0.2667  | 0.6667  | 0.0000  | 0.0000  |
| helmets             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| menu                | 0.5368  | 0.7143  | 0.0000  | 0.0000  |
| cap                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| barricade           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| salt                | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| water heater        | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| glove               | 0.9167  | 1.0000  | 0.0000  | 0.0000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| overall             | 0.2028  | 0.4197  | 0.0792  | 0.1801  |
| head                | 0.2820  | 0.5828  | 0.1195  | 0.2800  |
| common              | 0.1741  | 0.4039  | 0.0658  | 0.1554  |
| tail                | 0.1485  | 0.2586  | 0.0501  | 0.0986  |
| small               | 0.0672  | 0.2068  | 0.0082  | 0.0377  |
| medium              | 0.2010  | 0.4696  | 0.0834  | 0.2001  |
| large               | 0.1425  | 0.5096  | 0.0556  | 0.2634  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 05:59:32 INFO metrics.py:437 | number of scannet results: 250
Rank[0/8] 06/20/2025 06:00:43 INFO metrics.py:332 | eval results of scannet:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| sink                | 0.8118  | 0.8750  | 0.2033  | 0.3409  |
| toilet              | 0.9406  | 0.9643  | 0.7277  | 0.7857  |
| picture             | 0.3212  | 0.5410  | 0.0374  | 0.1493  |
| bathtub             | 0.9230  | 0.9375  | 0.7902  | 0.8125  |
| curtain             | 0.5617  | 0.8407  | 0.1058  | 0.3894  |
| bin                 | 0.6455  | 0.8170  | 0.4149  | 0.5830  |
| stopcock            | 0.1245  | 0.5172  | 0.0007  | 0.0345  |
| vanity              | 0.6727  | 0.9333  | 0.6443  | 0.8333  |
| toilet paper        | 0.3599  | 0.5823  | 0.0733  | 0.2658  |
| doorframe           | 0.3127  | 0.7746  | 0.0347  | 0.2887  |
| bag                 | 0.2620  | 0.6575  | 0.1195  | 0.2740  |
| switch              | 0.0082  | 0.2025  | 0.0000  | 0.0000  |
| bar                 | 0.0146  | 0.1364  | 0.0000  | 0.0000  |
| rail                | 0.0848  | 0.6667  | 0.0000  | 0.0000  |
| towel               | 0.4985  | 0.7273  | 0.1288  | 0.2810  |
| door                | 0.4343  | 0.7793  | 0.1064  | 0.4247  |
| bottle              | 0.0634  | 0.4158  | 0.0031  | 0.0842  |
| mirror              | 0.1884  | 0.7108  | 0.0082  | 0.1807  |
| socket              | 0.0079  | 0.2621  | 0.0000  | 0.0049  |
| shower              | 0.0954  | 0.2143  | 0.0009  | 0.0714  |
| paper               | 0.0253  | 0.2868  | 0.0007  | 0.0604  |
| cup                 | 0.1788  | 0.4493  | 0.0220  | 0.0580  |
| box                 | 0.1405  | 0.4286  | 0.0152  | 0.1627  |
| rod                 | 0.0714  | 0.0714  | 0.0000  | 0.0000  |
| hair dryer          | 0.0033  | 0.2857  | 0.0000  | 0.0000  |
| ledge               | 0.0061  | 0.3333  | 0.0000  | 0.0000  |
| tissue box          | 0.1182  | 0.4091  | 0.0989  | 0.1818  |
| faucet              | 0.0078  | 0.2500  | 0.0000  | 0.0000  |
| grab bar            | 0.0123  | 0.3750  | 0.0000  | 0.0000  |
| shelf               | 0.5384  | 0.8676  | 0.2583  | 0.5956  |
| tissue              | 0.0159  | 0.2222  | 0.0010  | 0.1111  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basket              | 0.0792  | 0.2308  | 0.0761  | 0.1538  |
| light               | 0.0999  | 0.4828  | 0.0115  | 0.0690  |
| mat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| lamp                | 0.3599  | 0.6310  | 0.0636  | 0.2143  |
| cabinet             | 0.3982  | 0.7897  | 0.1848  | 0.4960  |
| bowl                | 0.2822  | 0.4444  | 0.1111  | 0.1111  |
| toothpaste          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dish           | 0.6232  | 0.7500  | 0.1500  | 0.2500  |
| frame               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stand               | 0.8395  | 0.9434  | 0.6879  | 0.8113  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blanket             | 0.1330  | 0.5000  | 0.0083  | 0.2500  |
| decoration          | 0.0704  | 0.3333  | 0.0000  | 0.0000  |
| scale               | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| counter             | 0.2098  | 0.8571  | 0.0546  | 0.4286  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| microwave           | 0.6664  | 0.8000  | 0.3485  | 0.5500  |
| plant               | 0.3222  | 0.5042  | 0.0939  | 0.2521  |
| clock               | 0.4840  | 0.7600  | 0.0951  | 0.1600  |
| backpack            | 0.6568  | 0.9286  | 0.3652  | 0.5357  |
| holder              | 0.0333  | 0.2000  | 0.0000  | 0.0000  |
| stool               | 0.2795  | 0.9091  | 0.1293  | 0.4545  |
| mouse               | 0.0236  | 0.2381  | 0.0000  | 0.0000  |
| purse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket              | 0.2043  | 0.6429  | 0.0891  | 0.2143  |
| radiator            | 0.7227  | 0.8103  | 0.3133  | 0.4138  |
| keyboard            | 0.0575  | 0.1207  | 0.0000  | 0.0000  |
| chair               | 0.6885  | 0.8437  | 0.4698  | 0.6231  |
| monitor             | 0.4953  | 0.6000  | 0.0567  | 0.1786  |
| blackboard          | 0.5065  | 0.7246  | 0.0815  | 0.1739  |
| computer            | 0.2563  | 0.6667  | 0.0126  | 0.0769  |
| window              | 0.3717  | 0.7284  | 0.0384  | 0.2346  |
| desk                | 0.6811  | 0.9620  | 0.4028  | 0.7215  |
| table               | 0.5744  | 0.8962  | 0.4309  | 0.7577  |
| wardrobe            | 0.6088  | 1.0000  | 0.2574  | 0.6250  |
| book                | 0.0566  | 0.3167  | 0.0059  | 0.0738  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| telephone           | 0.5741  | 0.7500  | 0.0484  | 0.2500  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes             | 0.1738  | 0.5752  | 0.0313  | 0.1765  |
| laptop              | 0.3741  | 0.4667  | 0.0667  | 0.0667  |
| couch               | 0.8448  | 0.9875  | 0.6672  | 0.9125  |
| pipe                | 0.0014  | 0.2143  | 0.0000  | 0.0000  |
| jacket              | 0.6360  | 0.7391  | 0.2571  | 0.4348  |
| board               | 0.3222  | 0.6875  | 0.0681  | 0.3438  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| windowsill          | 0.0148  | 0.3333  | 0.0000  | 0.0000  |
| refrigerator        | 0.9017  | 0.9500  | 0.5967  | 0.6500  |
| bed                 | 0.9809  | 1.0000  | 0.8531  | 0.9474  |
| pillow              | 0.4351  | 0.6458  | 0.1623  | 0.3472  |
| dresser             | 0.7921  | 0.9730  | 0.7525  | 0.9189  |
| toy                 | 0.1031  | 0.1765  | 0.0000  | 0.0000  |
| calendar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shoe                | 0.2519  | 0.4000  | 0.0565  | 0.1250  |
| tv                  | 0.6652  | 0.8261  | 0.0846  | 0.2174  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| map                 | 0.0476  | 1.0000  | 0.0476  | 1.0000  |
| rack                | 0.0104  | 0.1818  | 0.0002  | 0.0909  |
| sign                | 0.0592  | 0.4643  | 0.0004  | 0.0357  |
| fan                 | 0.6053  | 0.8750  | 0.1250  | 0.1250  |
| printer             | 0.5010  | 0.8485  | 0.2481  | 0.6061  |
| case                | 0.6892  | 0.9474  | 0.6047  | 0.7368  |
| carpet              | 0.0143  | 0.5000  | 0.0000  | 0.0000  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| container           | 0.0060  | 0.1304  | 0.0000  | 0.0000  |
| notebook            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| guitar              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ottoman             | 0.0576  | 0.8571  | 0.0391  | 0.4286  |
| bench               | 0.2796  | 1.0000  | 0.1671  | 0.8000  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine             | 0.5585  | 1.0000  | 0.4150  | 0.7500  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| person              | 0.0357  | 0.3333  | 0.0333  | 0.1667  |
| coffee maker        | 0.5324  | 0.5882  | 0.2774  | 0.4118  |
| plunger             | 0.8000  | 0.8000  | 0.8000  | 0.8000  |
| stall               | 0.1348  | 0.8824  | 0.0358  | 0.4118  |
| broom               | 0.0545  | 0.3333  | 0.0000  | 0.0000  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| paper cutter        | 0.7333  | 0.8000  | 0.0667  | 0.2000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ladder              | 0.3550  | 0.6000  | 0.1000  | 0.4000  |
| crate               | 0.0104  | 0.5000  | 0.0104  | 0.5000  |
| dispenser           | 0.6187  | 0.7692  | 0.2902  | 0.3590  |
| soap dispenser      | 0.5209  | 0.6667  | 0.2168  | 0.3333  |
| mailbox             | 0.1374  | 0.6000  | 0.0440  | 0.4000  |
| pillar              | 0.2132  | 0.8000  | 0.0000  | 0.0000  |
| copier              | 0.8584  | 0.9167  | 0.4189  | 0.6667  |
| stairs              | 0.1800  | 0.8000  | 0.0506  | 0.8000  |
| fire extinguisher   | 0.1933  | 0.6667  | 0.0000  | 0.0000  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cushion             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stove               | 0.9381  | 1.0000  | 0.8509  | 1.0000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plate               | 0.2000  | 0.4000  | 0.0667  | 0.2000  |
| ball                | 0.4196  | 0.5000  | 0.2500  | 0.2500  |
| vacuum cleaner      | 0.6833  | 1.0000  | 0.6667  | 0.6667  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headphones          | 0.2148  | 0.4000  | 0.0074  | 0.2000  |
| hamper              | 0.4386  | 0.6000  | 0.4156  | 0.5000  |
| bicycle             | 0.6961  | 1.0000  | 0.0025  | 0.3333  |
| basin               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water cooler        | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| drawer              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| oven                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dish rack           | 0.1895  | 1.0000  | 0.0625  | 0.2500  |
| dishwasher          | 0.1111  | 0.3333  | 0.0000  | 0.0000  |
| kettle              | 0.2250  | 0.5000  | 0.0917  | 0.5000  |
| teapot              | 0.0833  | 0.2500  | 0.0000  | 0.0000  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster             | 0.1250  | 0.2500  | 0.0000  | 0.0000  |
| fruit               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flowerpot           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.5186  | 1.0000  | 0.4883  | 0.8333  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pot                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.6400  | 0.8000  | 0.0707  | 0.4000  |
| clothes dryer       | 0.2222  | 1.0000  | 0.2222  | 1.0000  |
| excercise equipment | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| scannet             | 0.2529  | 0.4538  | 0.1183  | 0.2305  |
| head                | 0.3443  | 0.5977  | 0.1649  | 0.3037  |
| common              | 0.1885  | 0.3537  | 0.0803  | 0.1753  |
| tail                | 0.0729  | 0.1678  | 0.0388  | 0.0957  |
| small               | 0.0514  | 0.1470  | 0.0119  | 0.0372  |
| medium              | 0.2943  | 0.5620  | 0.1340  | 0.2684  |
| large               | 0.2841  | 0.7574  | 0.1314  | 0.4663  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 06:00:43 INFO metrics.py:437 | number of 3rscan results: 235
Rank[0/8] 06/20/2025 06:01:48 INFO metrics.py:332 | eval results of 3rscan:
+-------------------+---------+---------+---------+---------+
| classes           | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+-------------------+---------+---------+---------+---------+
| monitor           | 0.4507  | 0.6750  | 0.0186  | 0.0750  |
| keyboard          | 0.0545  | 0.2083  | 0.0000  | 0.0000  |
| chair             | 0.6981  | 0.8695  | 0.5500  | 0.7102  |
| desk              | 0.4637  | 0.8500  | 0.3063  | 0.6250  |
| cup               | 0.0417  | 0.2821  | 0.0002  | 0.0769  |
| bag               | 0.2540  | 0.6970  | 0.0189  | 0.1919  |
| paper             | 0.0538  | 0.3143  | 0.0021  | 0.1000  |
| mouse             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| table             | 0.5503  | 0.8649  | 0.3337  | 0.6396  |
| computer          | 0.3709  | 0.6667  | 0.0200  | 0.1429  |
| stand             | 0.4388  | 0.9016  | 0.3468  | 0.6721  |
| case              | 0.0458  | 0.3857  | 0.0152  | 0.0786  |
| bin               | 0.7226  | 0.9146  | 0.3794  | 0.6220  |
| bottle            | 0.0387  | 0.3058  | 0.0017  | 0.0579  |
| garbage           | 0.6000  | 0.7500  | 0.3973  | 0.6250  |
| pen               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| window            | 0.1492  | 0.6233  | 0.0096  | 0.1781  |
| plug              | 0.0741  | 0.2222  | 0.0000  | 0.0000  |
| cabinet           | 0.3232  | 0.8504  | 0.1874  | 0.6026  |
| wardrobe          | 0.4185  | 0.7812  | 0.2807  | 0.6094  |
| book              | 0.0682  | 0.3964  | 0.0084  | 0.0888  |
| box               | 0.2010  | 0.5338  | 0.0382  | 0.1743  |
| socket            | 0.0003  | 0.0625  | 0.0000  | 0.0000  |
| plate             | 0.0107  | 0.3030  | 0.0033  | 0.0909  |
| backpack          | 0.4514  | 0.7879  | 0.2060  | 0.4848  |
| file              | 0.0899  | 0.5714  | 0.0038  | 0.1786  |
| blinds            | 0.1096  | 0.5641  | 0.0058  | 0.2564  |
| curtain           | 0.5307  | 0.8571  | 0.1031  | 0.4365  |
| shelf             | 0.2341  | 0.7244  | 0.0444  | 0.3498  |
| frame             | 0.1141  | 0.7000  | 0.0146  | 0.1000  |
| cushion           | 0.3411  | 0.6000  | 0.0666  | 0.2333  |
| picture           | 0.2016  | 0.4106  | 0.0169  | 0.1057  |
| telephone         | 0.2660  | 0.4583  | 0.0195  | 0.2083  |
| couch             | 0.4507  | 0.8000  | 0.2540  | 0.6286  |
| clothes           | 0.2184  | 0.6893  | 0.0255  | 0.2039  |
| rack              | 0.1577  | 0.7000  | 0.0790  | 0.4333  |
| pillow            | 0.4370  | 0.6289  | 0.0267  | 0.1289  |
| plant             | 0.3249  | 0.5495  | 0.0659  | 0.1980  |
| calendar          | 0.0578  | 0.8000  | 0.0000  | 0.0000  |
| bed               | 0.8598  | 0.9818  | 0.5649  | 0.8182  |
| clock             | 0.2933  | 0.4211  | 0.0000  | 0.0000  |
| lamp              | 0.2034  | 0.5375  | 0.0152  | 0.0750  |
| notebook          | 0.0071  | 0.1818  | 0.0000  | 0.0000  |
| carpet            | 0.0748  | 0.6071  | 0.0091  | 0.1786  |
| stool             | 0.4656  | 0.9074  | 0.3271  | 0.6852  |
| light             | 0.0697  | 0.3523  | 0.0009  | 0.0114  |
| bench             | 0.4233  | 0.8571  | 0.2614  | 0.6429  |
| stairs            | 0.1241  | 0.7143  | 0.0329  | 0.2857  |
| switch            | 0.0004  | 0.0189  | 0.0000  | 0.0000  |
| column            | 0.0162  | 1.0000  | 0.0081  | 0.5000  |
| ladder            | 0.3205  | 1.0000  | 0.2215  | 1.0000  |
| bucket            | 0.7775  | 0.9333  | 0.2695  | 0.5333  |
| container         | 0.0058  | 0.5556  | 0.0002  | 0.1111  |
| counter           | 0.1137  | 0.7073  | 0.0153  | 0.2683  |
| toy               | 0.4243  | 0.6923  | 0.0962  | 0.1538  |
| tv                | 0.5867  | 0.8286  | 0.0245  | 0.2000  |
| shoe              | 0.2032  | 0.5000  | 0.0132  | 0.1190  |
| hat               | 0.0714  | 0.1429  | 0.0000  | 0.0000  |
| balcony           | 0.0023  | 0.5000  | 0.0000  | 0.0000  |
| laptop            | 0.2125  | 0.8333  | 0.2098  | 0.6667  |
| basket            | 0.4290  | 0.6875  | 0.2006  | 0.3906  |
| device            | 0.0117  | 0.1818  | 0.0029  | 0.0909  |
| bicycle           | 1.0000  | 1.0000  | 0.1667  | 0.5000  |
| commode           | 0.5132  | 0.8654  | 0.4043  | 0.6731  |
| bowl              | 0.1288  | 0.4444  | 0.0160  | 0.1111  |
| teapot            | 0.0833  | 0.2500  | 0.0000  | 0.0000  |
| poster            | 0.0209  | 0.1111  | 0.0005  | 0.0159  |
| candle            | 0.0805  | 0.3333  | 0.0018  | 0.0667  |
| blanket           | 0.4025  | 0.9077  | 0.0694  | 0.4000  |
| decoration        | 0.0979  | 0.4688  | 0.0208  | 0.1250  |
| folder            | 0.0172  | 0.4286  | 0.0021  | 0.0714  |
| ottoman           | 0.6034  | 1.0000  | 0.5677  | 1.0000  |
| refrigerator      | 0.6647  | 0.8000  | 0.5466  | 0.6667  |
| remote control    | 0.0421  | 0.3500  | 0.0016  | 0.1000  |
| towel             | 0.1897  | 0.6203  | 0.0706  | 0.2658  |
| toilet            | 0.7921  | 0.8235  | 0.3366  | 0.4118  |
| vase              | 0.2489  | 0.4000  | 0.1500  | 0.1500  |
| door              | 0.2949  | 0.6838  | 0.0521  | 0.2868  |
| drawer            | 0.5037  | 1.0000  | 0.0699  | 0.6667  |
| flowerpot         | 0.0802  | 0.4583  | 0.0564  | 0.1667  |
| board             | 0.2575  | 0.6250  | 0.1276  | 0.1667  |
| pan               | 0.1683  | 0.5833  | 0.0000  | 0.0000  |
| bar               | 0.6667  | 0.6667  | 0.3333  | 0.3333  |
| player            | 0.3333  | 0.3333  | 0.0000  | 0.0000  |
| sink              | 0.6148  | 0.8393  | 0.1327  | 0.2857  |
| magazine          | 0.0100  | 0.2500  | 0.0000  | 0.0000  |
| crib              | 0.3131  | 1.0000  | 0.3131  | 1.0000  |
| printer           | 0.5250  | 0.6667  | 0.0833  | 0.2222  |
| fireplace         | 0.0500  | 0.2000  | 0.0000  | 0.0000  |
| oven              | 0.6621  | 1.0000  | 0.2009  | 0.5263  |
| cube              | 0.5000  | 1.0000  | 0.5000  | 1.0000  |
| jar               | 0.1749  | 0.6250  | 0.1257  | 0.2500  |
| roll              | 0.0804  | 0.5000  | 0.0002  | 0.1000  |
| pad               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| price tag         | 0.2171  | 0.5714  | 0.0000  | 0.0000  |
| napkins           | 0.0591  | 0.6667  | 0.0000  | 0.0000  |
| heater            | 0.5514  | 0.7193  | 0.1290  | 0.2807  |
| blackboard        | 0.2868  | 0.6667  | 0.0000  | 0.0000  |
| humidifier        | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| windowsill        | 0.0415  | 0.4872  | 0.0000  | 0.0256  |
| doorframe         | 0.1548  | 0.6800  | 0.0174  | 0.1800  |
| fan               | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| jalousie          | 0.8333  | 1.0000  | 0.0000  | 0.0000  |
| ball              | 0.3879  | 0.6000  | 0.0800  | 0.4000  |
| air conditioner   | 0.5000  | 0.7500  | 0.2500  | 0.2500  |
| tablet            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radiator          | 0.3654  | 0.6552  | 0.1414  | 0.3103  |
| umbrella          | 0.0455  | 1.0000  | 0.0000  | 0.0000  |
| coffee maker      | 0.2858  | 0.6667  | 0.0012  | 0.1111  |
| tube              | 0.0039  | 0.1429  | 0.0000  | 0.0000  |
| speaker           | 0.0068  | 0.1667  | 0.0000  | 0.0000  |
| pillar            | 0.5902  | 0.8235  | 0.4706  | 0.4706  |
| mirror            | 0.1285  | 0.5185  | 0.0076  | 0.1481  |
| chandelier        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine           | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| pot               | 0.5333  | 0.5833  | 0.1528  | 0.2500  |
| mattress          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| album             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scale             | 0.6667  | 0.6667  | 0.3333  | 0.3333  |
| tool              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| window frame      | 0.0411  | 0.2500  | 0.0006  | 0.0833  |
| conducting wire   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hanger            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| brush             | 0.1958  | 0.5000  | 0.0004  | 0.1250  |
| hook              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toilet paper      | 0.1498  | 0.4167  | 0.0000  | 0.0000  |
| holder            | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| guitar            | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| kettle            | 0.1224  | 0.6667  | 0.0883  | 0.5000  |
| mat               | 0.1667  | 0.3333  | 0.0069  | 0.3333  |
| fruit             | 0.4375  | 0.6000  | 0.0000  | 0.0000  |
| pack              | 0.0435  | 0.8462  | 0.0025  | 0.2308  |
| microwave         | 0.8423  | 0.8824  | 0.3532  | 0.4706  |
| rail              | 0.0021  | 0.2500  | 0.0000  | 0.0000  |
| crate             | 0.5442  | 0.8000  | 0.1000  | 0.2000  |
| luggage           | 0.6465  | 1.0000  | 0.6465  | 1.0000  |
| vacuum cleaner    | 0.7500  | 0.7500  | 0.0000  | 0.0000  |
| piano             | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| sunglasses        | 0.0042  | 0.2308  | 0.0000  | 0.0000  |
| furniture         | 0.0206  | 0.6667  | 0.0206  | 0.6667  |
| dresser           | 0.7500  | 1.0000  | 0.1667  | 0.5000  |
| food              | 0.1348  | 0.3000  | 0.0000  | 0.0000  |
| bathtub           | 0.9798  | 1.0000  | 0.6818  | 0.7778  |
| clothes dryer     | 0.4044  | 0.7000  | 0.2487  | 0.6000  |
| statue            | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| faucet            | 0.4702  | 1.0000  | 0.0000  | 0.0000  |
| shampoo           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shower            | 0.1030  | 0.7500  | 0.0312  | 0.2500  |
| toiletry          | 0.1111  | 1.0000  | 0.0000  | 0.0000  |
| soap              | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| coil              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pipe              | 0.2724  | 0.7500  | 0.0000  | 0.0000  |
| tissue            | 0.0219  | 0.6667  | 0.0018  | 0.3333  |
| mop               | 0.7576  | 1.0000  | 0.0048  | 0.3333  |
| tray              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| body loofah       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beanbag           | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| sack              | 0.3420  | 0.7143  | 0.0079  | 0.1429  |
| letter            | 0.0252  | 0.5000  | 0.0000  | 0.0000  |
| adhesive tape     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basin             | 0.0333  | 0.0667  | 0.0000  | 0.0000  |
| can               | 0.0111  | 0.2500  | 0.0000  | 0.0000  |
| washing machine   | 0.6407  | 0.7857  | 0.4840  | 0.6429  |
| partition         | 0.7000  | 0.8000  | 0.0095  | 0.2000  |
| stove             | 0.4098  | 0.7647  | 0.0630  | 0.2941  |
| bread             | 0.0500  | 0.2000  | 0.0000  | 0.0000  |
| range hood        | 0.0571  | 0.6000  | 0.0029  | 0.2000  |
| kitchenware       | 0.2504  | 0.4545  | 0.0073  | 0.0682  |
| knife             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| spoon             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher        | 0.7556  | 1.0000  | 0.3333  | 0.3333  |
| radio             | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| sign              | 0.0066  | 0.5000  | 0.0066  | 0.5000  |
| broom             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dispenser         | 0.0133  | 0.3333  | 0.0133  | 0.3333  |
| map               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candlestick       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| footstool         | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| screen            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flush             | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| soap dish         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toothbrush        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue box        | 0.1111  | 0.3333  | 0.1111  | 0.3333  |
| shovel            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wire              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell          | 0.3333  | 1.0000  | 0.0000  | 0.0000  |
| helmets           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glass             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| menu              | 0.5368  | 0.7143  | 0.0000  | 0.0000  |
| treadmill         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| sponge            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dispenser    | 0.7600  | 0.8000  | 0.0400  | 0.2000  |
| salt              | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| water heater      | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| beam              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glove             | 0.9167  | 1.0000  | 0.0000  | 0.0000  |
| toaster           | 0.9500  | 1.0000  | 0.2500  | 0.2500  |
| dish rack         | 0.0625  | 1.0000  | 0.0000  | 0.0000  |
| cosmetics         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scissors          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| paper cutter      | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| seasoning         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washbasin         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| -------           | ------- | ------- | ------- | ------- |
| 3rscan            | 0.3000  | 0.5441  | 0.1077  | 0.2224  |
| head              | 0.3085  | 0.6248  | 0.1219  | 0.2881  |
| common            | 0.3238  | 0.5735  | 0.1117  | 0.2090  |
| tail              | 0.2525  | 0.3565  | 0.0761  | 0.1199  |
| small             | 0.1074  | 0.2894  | 0.0108  | 0.0508  |
| medium            | 0.3077  | 0.5987  | 0.1160  | 0.2494  |
| large             | 0.2423  | 0.6816  | 0.0981  | 0.3888  |
+-------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 06:01:48 INFO metrics.py:437 | number of matterport3d results: 339
Rank[0/8] 06/20/2025 06:03:12 INFO metrics.py:332 | eval results of matterport3d:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| mirror              | 0.1072  | 0.6216  | 0.0061  | 0.1757  |
| picture             | 0.1807  | 0.4203  | 0.0122  | 0.0918  |
| towel               | 0.3510  | 0.5909  | 0.0598  | 0.2182  |
| light               | 0.0123  | 0.1595  | 0.0004  | 0.0313  |
| bathtub             | 0.6341  | 0.7407  | 0.3911  | 0.5556  |
| doorframe           | 0.2887  | 0.6913  | 0.0828  | 0.4169  |
| sink                | 0.4864  | 0.7475  | 0.0787  | 0.2727  |
| cabinet             | 0.1299  | 0.6695  | 0.0716  | 0.4068  |
| plant               | 0.2962  | 0.7143  | 0.0834  | 0.3482  |
| door                | 0.2300  | 0.5927  | 0.0107  | 0.1697  |
| switch              | 0.0030  | 0.1500  | 0.0000  | 0.0000  |
| drawer              | 0.0024  | 0.3750  | 0.0000  | 0.0000  |
| counter             | 0.0330  | 0.5455  | 0.0012  | 0.1818  |
| paper               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| lamp                | 0.3151  | 0.6264  | 0.0911  | 0.2527  |
| table               | 0.3033  | 0.7500  | 0.2100  | 0.5705  |
| desk                | 0.3593  | 0.7368  | 0.1503  | 0.4211  |
| shower              | 0.1389  | 0.1667  | 0.0000  | 0.0000  |
| flowerpot           | 0.0693  | 0.6800  | 0.0138  | 0.2800  |
| handle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dresser             | 0.3648  | 0.9333  | 0.2984  | 0.8000  |
| toilet              | 0.7968  | 0.8281  | 0.6584  | 0.7031  |
| window              | 0.1116  | 0.4750  | 0.0181  | 0.1500  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| countertop          | 0.0065  | 0.3929  | 0.0015  | 0.0714  |
| decoration          | 0.0102  | 0.3810  | 0.0010  | 0.1071  |
| doorway             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| faucet              | 0.0120  | 0.1667  | 0.0000  | 0.0000  |
| shelf               | 0.1487  | 0.5109  | 0.0848  | 0.3139  |
| frame               | 0.0003  | 0.1765  | 0.0000  | 0.0588  |
| bottle              | 0.0962  | 0.5208  | 0.0184  | 0.2083  |
| vanity              | 0.2574  | 0.7333  | 0.2463  | 0.6000  |
| vent                | 0.0243  | 0.2083  | 0.0000  | 0.0000  |
| blanket             | 0.0257  | 0.7273  | 0.0086  | 0.2727  |
| socket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toiletry            | 0.1205  | 0.3182  | 0.0000  | 0.0000  |
| roof                | 0.0015  | 0.2917  | 0.0000  | 0.0833  |
| case                | 0.0002  | 0.1667  | 0.0001  | 0.1111  |
| vase                | 0.0391  | 0.4333  | 0.0256  | 0.2333  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blinds              | 0.0087  | 0.2000  | 0.0001  | 0.1000  |
| washbasin           | 0.0055  | 0.1429  | 0.0055  | 0.1429  |
| book                | 0.0677  | 0.3465  | 0.0065  | 0.0630  |
| statue              | 0.0068  | 0.2353  | 0.0052  | 0.1765  |
| stand               | 0.4133  | 0.7800  | 0.3475  | 0.6800  |
| soap                | 0.0007  | 0.2000  | 0.0000  | 0.0000  |
| bench               | 0.1231  | 0.5000  | 0.0082  | 0.2500  |
| fan                 | 0.6685  | 0.7593  | 0.0531  | 0.1667  |
| plate               | 0.0258  | 0.2143  | 0.0004  | 0.0714  |
| washing machine     | 0.4842  | 0.8750  | 0.1690  | 0.6250  |
| computer            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basket              | 0.0594  | 0.3200  | 0.0030  | 0.1200  |
| bar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| window frame        | 0.0344  | 0.3908  | 0.0065  | 0.1494  |
| cushion             | 0.0020  | 0.1176  | 0.0020  | 0.1176  |
| windowsill          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mat                 | 0.0333  | 0.3333  | 0.0000  | 0.0000  |
| pot                 | 0.0327  | 0.2308  | 0.0000  | 0.0000  |
| tv                  | 0.2939  | 0.6279  | 0.0977  | 0.3488  |
| pillow              | 0.2327  | 0.5552  | 0.0128  | 0.1672  |
| bed                 | 0.6866  | 0.7895  | 0.4241  | 0.7544  |
| clothes             | 0.1384  | 0.4483  | 0.0106  | 0.0862  |
| box                 | 0.0402  | 0.5962  | 0.0151  | 0.2885  |
| tissue              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cup                 | 0.0047  | 0.3000  | 0.0003  | 0.1000  |
| telephone           | 0.2917  | 0.5000  | 0.0000  | 0.0000  |
| chair               | 0.4617  | 0.7585  | 0.2932  | 0.5714  |
| couch               | 0.5039  | 0.8261  | 0.2341  | 0.6304  |
| bowl                | 0.0418  | 0.3939  | 0.0001  | 0.0303  |
| curtain             | 0.3863  | 0.7396  | 0.0621  | 0.3542  |
| screen              | 0.0833  | 0.2500  | 0.0000  | 0.0000  |
| stairs              | 0.0429  | 0.3396  | 0.0004  | 0.0377  |
| teapot              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ottoman             | 0.0861  | 0.6111  | 0.0429  | 0.3889  |
| stool               | 0.0247  | 0.3810  | 0.0243  | 0.2857  |
| alarm               | 0.0045  | 0.1667  | 0.0000  | 0.0000  |
| footstool           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toy                 | 0.1301  | 0.5000  | 0.0038  | 0.1389  |
| air conditioner     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fireplace           | 0.1988  | 0.5217  | 0.0029  | 0.0870  |
| candle              | 0.0028  | 0.3333  | 0.0000  | 0.0000  |
| clock               | 0.3377  | 0.6667  | 0.0050  | 0.6667  |
| wine                | 0.0093  | 0.1429  | 0.0000  | 0.0000  |
| piano               | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| wardrobe            | 0.1180  | 0.4545  | 0.0143  | 0.3636  |
| backpack            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| steps               | 0.0019  | 0.4000  | 0.0000  | 0.0000  |
| bicycle             | 0.0345  | 1.0000  | 0.0345  | 1.0000  |
| kettle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stove               | 0.1807  | 0.5000  | 0.1536  | 0.3000  |
| rack                | 0.0023  | 0.4545  | 0.0002  | 0.1818  |
| column              | 0.0152  | 0.2778  | 0.0077  | 0.1111  |
| carpet              | 0.0000  | 0.0556  | 0.0000  | 0.0000  |
| monitor             | 0.0500  | 0.2500  | 0.0000  | 0.0000  |
| refrigerator        | 0.3722  | 0.6667  | 0.1106  | 0.2667  |
| door knob           | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit               | 0.0729  | 0.3750  | 0.0139  | 0.1250  |
| pillar              | 0.1661  | 0.7143  | 0.0900  | 0.5714  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rail                | 0.0139  | 0.3148  | 0.0000  | 0.0185  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bin                 | 0.3422  | 0.7317  | 0.1909  | 0.4390  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shoe                | 0.0433  | 0.1200  | 0.0000  | 0.0000  |
| thermostat          | 0.0129  | 0.3333  | 0.0000  | 0.0000  |
| dispenser           | 0.0119  | 0.5000  | 0.0119  | 0.5000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dispenser      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| holder              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garage door         | 0.2778  | 0.3333  | 0.2778  | 0.3333  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| partition           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toilet paper        | 0.1863  | 0.5833  | 0.0533  | 0.2778  |
| bag                 | 0.0748  | 0.4615  | 0.0000  | 0.0000  |
| headboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket              | 0.0065  | 0.4000  | 0.0045  | 0.2000  |
| machine             | 0.0100  | 0.5000  | 0.0000  | 0.0000  |
| pipe                | 0.0077  | 0.2000  | 0.0000  | 0.0000  |
| jar                 | 0.0030  | 0.3333  | 0.0000  | 0.0000  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dish           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.1481  | 0.6667  | 0.1481  | 0.6667  |
| bidet               | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| kitchen island      | 0.1192  | 0.8571  | 0.0867  | 0.8571  |
| radiator            | 0.4444  | 0.6667  | 0.0000  | 0.0000  |
| heater              | 0.0556  | 1.0000  | 0.0556  | 1.0000  |
| board               | 0.0639  | 0.3333  | 0.0000  | 0.0000  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| keyboard            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball                | 0.5312  | 0.7500  | 0.5312  | 0.7500  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scale               | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| basin               | 0.0045  | 1.0000  | 0.0045  | 1.0000  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pan                 | 0.0044  | 0.2000  | 0.0000  | 0.0000  |
| pool table          | 0.1667  | 0.3333  | 0.1667  | 0.3333  |
| tissue box          | 0.0561  | 0.5000  | 0.0275  | 0.3750  |
| guitar              | 0.3012  | 0.5714  | 0.0179  | 0.1429  |
| hanger              | 0.0123  | 0.3000  | 0.0000  | 0.0000  |
| speaker             | 0.0157  | 0.3750  | 0.0000  | 0.0000  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| projector           | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| food                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.5138  | 0.7222  | 0.0127  | 0.1667  |
| microwave           | 0.2382  | 0.5000  | 0.1180  | 0.2500  |
| beam                | 0.0007  | 0.0952  | 0.0002  | 0.0952  |
| chandelier          | 0.1756  | 0.4737  | 0.0118  | 0.1053  |
| fire extinguisher   | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| ladder              | 0.1170  | 0.2222  | 0.0000  | 0.0000  |
| printer             | 0.3929  | 0.4286  | 0.2857  | 0.2857  |
| hat                 | 0.0476  | 1.0000  | 0.0000  | 0.0000  |
| player              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| arch                | 0.0001  | 0.0667  | 0.0000  | 0.0333  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radio               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tray                | 0.0128  | 0.3333  | 0.0000  | 0.0000  |
| container           | 0.0674  | 0.2963  | 0.0084  | 0.1111  |
| range hood          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| oven                | 0.1614  | 0.8000  | 0.0000  | 0.0000  |
| kitchenware         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drum                | 0.0333  | 0.2500  | 0.0000  | 0.0000  |
| laptop              | 0.2778  | 1.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.0312  | 0.2500  | 0.0000  | 0.0000  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blackboard          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| magazine            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water heater        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pack                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jacket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coffee maker        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell            | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| hamper              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mouse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| matterport3d        | 0.1164  | 0.3317  | 0.0429  | 0.1498  |
| head                | 0.1616  | 0.4521  | 0.0599  | 0.2224  |
| common              | 0.0715  | 0.2492  | 0.0223  | 0.0833  |
| tail                | 0.0811  | 0.1387  | 0.0397  | 0.0778  |
| small               | 0.0540  | 0.1713  | 0.0029  | 0.0610  |
| medium              | 0.1039  | 0.3544  | 0.0402  | 0.1517  |
| large               | 0.1078  | 0.4220  | 0.0482  | 0.2253  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 06:03:12 INFO metrics.py:437 | number of arkit results: 0
Rank[0/8] 06/20/2025 06:03:13 INFO stats.py:394 | Epoch[119] completed. Training Speed: 3.10 samples/sec across all devices.	Epoch Time: 1007.09 sec.	Average Epoch Time: 1007.09 sec.	Average Step Time: 2.58 sec.	Estimated Remaining Time: 15:50:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:03:13 WARNING accelerator.py:3099 | Deleting 1 checkpoints to make room for new checkpoint.
Rank[0/8] 06/20/2025 06:03:14 INFO accelerator.py:3111 | Saving current state to /job_data/checkpoints/checkpoint_11
Rank[5/8] 06/20/2025 06:03:14 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 119 to /job_data/checkpoints/checkpoint_11
Rank[3/8] 06/20/2025 06:03:14 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 119 to /job_data/checkpoints/checkpoint_11
Rank[7/8] 06/20/2025 06:03:14 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 119 to /job_data/checkpoints/checkpoint_11
Rank[2/8] 06/20/2025 06:03:14 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 119 to /job_data/checkpoints/checkpoint_11
Rank[6/8] 06/20/2025 06:03:14 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 119 to /job_data/checkpoints/checkpoint_11
Rank[4/8] 06/20/2025 06:03:14 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 119 to /job_data/checkpoints/checkpoint_11
Rank[1/8] 06/20/2025 06:03:15 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 119 to /job_data/checkpoints/checkpoint_11
Rank[0/8] 06/20/2025 06:03:16 INFO checkpointing.py:106 | Model weights saved in /job_data/checkpoints/checkpoint_11/model.safetensors
Rank[0/8] 06/20/2025 06:03:19 INFO checkpointing.py:113 | Optimizer state saved in /job_data/checkpoints/checkpoint_11/optimizer.bin
Rank[0/8] 06/20/2025 06:03:19 INFO checkpointing.py:120 | Scheduler state saved in /job_data/checkpoints/checkpoint_11/scheduler.bin
Rank[0/8] 06/20/2025 06:03:19 INFO checkpointing.py:137 | Sampler state for dataloader 0 saved in /job_data/checkpoints/checkpoint_11/sampler.bin
Rank[0/8] 06/20/2025 06:03:19 INFO checkpointing.py:137 | Sampler state for dataloader 1 saved in /job_data/checkpoints/checkpoint_11/sampler_1.bin
Rank[0/8] 06/20/2025 06:03:19 INFO checkpointing.py:164 | Random states saved in /job_data/checkpoints/checkpoint_11/random_states_0.pkl
Rank[0/8] 06/20/2025 06:03:19 INFO checkpointing.py:300 | Saving the state of TrainerProgressState to /job_data/checkpoints/checkpoint_11/custom_checkpoint_0.pkl
Rank[0/8] 06/20/2025 06:03:19 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 119 to /job_data/checkpoints/checkpoint_11
Rank[0/8] 06/20/2025 06:03:48 INFO stats.py:314 | Epoch[120] Step[24] GlobalStep[46824] Training Speed: 8.70 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 15:50:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:03:48 INFO loss_tracker.py:84 | Epoch[120/239] Step[24] GlobalStep[46824/NA]: det_loss_cls_0[0.6459] det_loss_box_0[0.8233] det_loss_cls_1[0.6357] det_loss_box_1[0.6261] det_loss_cls_2[0.5894] det_loss_box_2[0.5735] det_loss_cls_3[0.5547] det_loss_box_3[0.5560] det_loss_cls_4[0.5089] det_loss_box_4[0.5563] det_loss_cls_5[0.5001] det_loss_box_5[0.5541] det_loss_cls_dn_0[0.1057] det_loss_box_dn_0[0.2967] det_loss_cls_dn_1[0.1025] det_loss_box_dn_1[0.2489] det_loss_cls_dn_2[0.1018] det_loss_box_dn_2[0.2334] det_loss_cls_dn_3[0.1001] det_loss_box_dn_3[0.2303] det_loss_cls_dn_4[0.0992] det_loss_box_dn_4[0.2273] det_loss_cls_dn_5[0.0983] det_loss_box_dn_5[0.2260] loss_depth[0.0391] total_loss[9.2332]
Rank[0/8] 06/20/2025 06:04:14 INFO stats.py:314 | Epoch[120] Step[49] GlobalStep[46849] Training Speed: 7.99 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 15:49:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:04:14 INFO loss_tracker.py:84 | Epoch[120/239] Step[49] GlobalStep[46849/NA]: det_loss_cls_0[0.5764] det_loss_box_0[0.8147] det_loss_cls_1[0.5595] det_loss_box_1[0.6438] det_loss_cls_2[0.5046] det_loss_box_2[0.6014] det_loss_cls_3[0.4704] det_loss_box_3[0.5939] det_loss_cls_4[0.4362] det_loss_box_4[0.5636] det_loss_cls_5[0.4234] det_loss_box_5[0.5585] det_loss_cls_dn_0[0.0939] det_loss_box_dn_0[0.3526] det_loss_cls_dn_1[0.0903] det_loss_box_dn_1[0.3021] det_loss_cls_dn_2[0.0899] det_loss_box_dn_2[0.2846] det_loss_cls_dn_3[0.0881] det_loss_box_dn_3[0.2797] det_loss_cls_dn_4[0.0870] det_loss_box_dn_4[0.2780] det_loss_cls_dn_5[0.0861] det_loss_box_dn_5[0.2770] loss_depth[0.0379] total_loss[9.0936]
Rank[0/8] 06/20/2025 06:04:39 INFO stats.py:314 | Epoch[120] Step[74] GlobalStep[46874] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 15:49:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:04:39 INFO loss_tracker.py:84 | Epoch[120/239] Step[74] GlobalStep[46874/NA]: det_loss_cls_0[0.6229] det_loss_box_0[0.8146] det_loss_cls_1[0.5962] det_loss_box_1[0.6267] det_loss_cls_2[0.5462] det_loss_box_2[0.5603] det_loss_cls_3[0.5042] det_loss_box_3[0.5309] det_loss_cls_4[0.4902] det_loss_box_4[0.5097] det_loss_cls_5[0.4719] det_loss_box_5[0.5133] det_loss_cls_dn_0[0.1023] det_loss_box_dn_0[0.2888] det_loss_cls_dn_1[0.0988] det_loss_box_dn_1[0.2452] det_loss_cls_dn_2[0.0984] det_loss_box_dn_2[0.2250] det_loss_cls_dn_3[0.0974] det_loss_box_dn_3[0.2233] det_loss_cls_dn_4[0.0968] det_loss_box_dn_4[0.2217] det_loss_cls_dn_5[0.0960] det_loss_box_dn_5[0.2206] loss_depth[0.0385] total_loss[8.8398]
Rank[0/8] 06/20/2025 06:05:06 INFO stats.py:314 | Epoch[120] Step[99] GlobalStep[46899] Training Speed: 7.55 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 15:48:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:05:06 INFO loss_tracker.py:84 | Epoch[120/239] Step[99] GlobalStep[46899/NA]: det_loss_cls_0[0.6993] det_loss_box_0[0.9520] det_loss_cls_1[0.6757] det_loss_box_1[0.7456] det_loss_cls_2[0.6475] det_loss_box_2[0.6795] det_loss_cls_3[0.6136] det_loss_box_3[0.6551] det_loss_cls_4[0.5873] det_loss_box_4[0.6386] det_loss_cls_5[0.5796] det_loss_box_5[0.6314] det_loss_cls_dn_0[0.1068] det_loss_box_dn_0[0.3275] det_loss_cls_dn_1[0.1032] det_loss_box_dn_1[0.2794] det_loss_cls_dn_2[0.1020] det_loss_box_dn_2[0.2604] det_loss_cls_dn_3[0.1009] det_loss_box_dn_3[0.2557] det_loss_cls_dn_4[0.1003] det_loss_box_dn_4[0.2527] det_loss_cls_dn_5[0.0995] det_loss_box_dn_5[0.2510] loss_depth[0.0375] total_loss[10.3823]
Rank[0/8] 06/20/2025 06:05:31 INFO stats.py:314 | Epoch[120] Step[124] GlobalStep[46924] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 15:48:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:05:31 INFO loss_tracker.py:84 | Epoch[120/239] Step[124] GlobalStep[46924/NA]: det_loss_cls_0[0.4927] det_loss_box_0[0.6441] det_loss_cls_1[0.4757] det_loss_box_1[0.4883] det_loss_cls_2[0.4278] det_loss_box_2[0.4451] det_loss_cls_3[0.4026] det_loss_box_3[0.4221] det_loss_cls_4[0.3723] det_loss_box_4[0.4147] det_loss_cls_5[0.3630] det_loss_box_5[0.4109] det_loss_cls_dn_0[0.1056] det_loss_box_dn_0[0.3214] det_loss_cls_dn_1[0.1019] det_loss_box_dn_1[0.2637] det_loss_cls_dn_2[0.1005] det_loss_box_dn_2[0.2463] det_loss_cls_dn_3[0.0994] det_loss_box_dn_3[0.2402] det_loss_cls_dn_4[0.0982] det_loss_box_dn_4[0.2391] det_loss_cls_dn_5[0.0974] det_loss_box_dn_5[0.2371] loss_depth[0.0405] total_loss[7.5505]
Rank[0/8] 06/20/2025 06:05:56 INFO stats.py:314 | Epoch[120] Step[149] GlobalStep[46949] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 15:47:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:05:56 INFO loss_tracker.py:84 | Epoch[120/239] Step[149] GlobalStep[46949/NA]: det_loss_cls_0[0.4787] det_loss_box_0[0.6034] det_loss_cls_1[0.4666] det_loss_box_1[0.4695] det_loss_cls_2[0.4286] det_loss_box_2[0.4182] det_loss_cls_3[0.3946] det_loss_box_3[0.4019] det_loss_cls_4[0.3638] det_loss_box_4[0.3968] det_loss_cls_5[0.3506] det_loss_box_5[0.3979] det_loss_cls_dn_0[0.0885] det_loss_box_dn_0[0.2756] det_loss_cls_dn_1[0.0846] det_loss_box_dn_1[0.2341] det_loss_cls_dn_2[0.0837] det_loss_box_dn_2[0.2189] det_loss_cls_dn_3[0.0825] det_loss_box_dn_3[0.2164] det_loss_cls_dn_4[0.0809] det_loss_box_dn_4[0.2153] det_loss_cls_dn_5[0.0798] det_loss_box_dn_5[0.2124] loss_depth[0.0372] total_loss[7.0808]
Rank[0/8] 06/20/2025 06:06:23 INFO stats.py:314 | Epoch[120] Step[174] GlobalStep[46974] Training Speed: 7.83 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 15:47:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:06:23 INFO loss_tracker.py:84 | Epoch[120/239] Step[174] GlobalStep[46974/NA]: det_loss_cls_0[0.4343] det_loss_box_0[0.5629] det_loss_cls_1[0.4272] det_loss_box_1[0.4275] det_loss_cls_2[0.3855] det_loss_box_2[0.3920] det_loss_cls_3[0.3540] det_loss_box_3[0.3763] det_loss_cls_4[0.3270] det_loss_box_4[0.3751] det_loss_cls_5[0.3181] det_loss_box_5[0.3731] det_loss_cls_dn_0[0.0858] det_loss_box_dn_0[0.2813] det_loss_cls_dn_1[0.0818] det_loss_box_dn_1[0.2334] det_loss_cls_dn_2[0.0808] det_loss_box_dn_2[0.2141] det_loss_cls_dn_3[0.0795] det_loss_box_dn_3[0.2108] det_loss_cls_dn_4[0.0779] det_loss_box_dn_4[0.2090] det_loss_cls_dn_5[0.0774] det_loss_box_dn_5[0.2081] loss_depth[0.0382] total_loss[6.6311]
Rank[0/8] 06/20/2025 06:06:49 INFO stats.py:314 | Epoch[120] Step[199] GlobalStep[46999] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 15:46:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:06:49 INFO loss_tracker.py:84 | Epoch[120/239] Step[199] GlobalStep[46999/NA]: det_loss_cls_0[0.4595] det_loss_box_0[0.6265] det_loss_cls_1[0.4541] det_loss_box_1[0.5025] det_loss_cls_2[0.4264] det_loss_box_2[0.4648] det_loss_cls_3[0.3989] det_loss_box_3[0.4514] det_loss_cls_4[0.3793] det_loss_box_4[0.4368] det_loss_cls_5[0.3692] det_loss_box_5[0.4433] det_loss_cls_dn_0[0.1022] det_loss_box_dn_0[0.3595] det_loss_cls_dn_1[0.0988] det_loss_box_dn_1[0.3053] det_loss_cls_dn_2[0.0980] det_loss_box_dn_2[0.2881] det_loss_cls_dn_3[0.0971] det_loss_box_dn_3[0.2829] det_loss_cls_dn_4[0.0965] det_loss_box_dn_4[0.2804] det_loss_cls_dn_5[0.0960] det_loss_box_dn_5[0.2710] loss_depth[0.0379] total_loss[7.8261]
Rank[0/8] 06/20/2025 06:07:14 INFO stats.py:314 | Epoch[120] Step[224] GlobalStep[47024] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 15:45:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:07:14 INFO loss_tracker.py:84 | Epoch[120/239] Step[224] GlobalStep[47024/NA]: det_loss_cls_0[0.6734] det_loss_box_0[0.8246] det_loss_cls_1[0.6645] det_loss_box_1[0.6404] det_loss_cls_2[0.6221] det_loss_box_2[0.5778] det_loss_cls_3[0.5775] det_loss_box_3[0.5644] det_loss_cls_4[0.5563] det_loss_box_4[0.5481] det_loss_cls_5[0.5483] det_loss_box_5[0.5465] det_loss_cls_dn_0[0.1136] det_loss_box_dn_0[0.2832] det_loss_cls_dn_1[0.1106] det_loss_box_dn_1[0.2408] det_loss_cls_dn_2[0.1101] det_loss_box_dn_2[0.2240] det_loss_cls_dn_3[0.1091] det_loss_box_dn_3[0.2209] det_loss_cls_dn_4[0.1080] det_loss_box_dn_4[0.2186] det_loss_cls_dn_5[0.1072] det_loss_box_dn_5[0.2184] loss_depth[0.0363] total_loss[9.4444]
Rank[0/8] 06/20/2025 06:07:40 INFO stats.py:314 | Epoch[120] Step[249] GlobalStep[47049] Training Speed: 7.84 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 15:45:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:07:40 INFO loss_tracker.py:84 | Epoch[120/239] Step[249] GlobalStep[47049/NA]: det_loss_cls_0[0.4889] det_loss_box_0[0.6438] det_loss_cls_1[0.4662] det_loss_box_1[0.4583] det_loss_cls_2[0.4203] det_loss_box_2[0.4086] det_loss_cls_3[0.3869] det_loss_box_3[0.3950] det_loss_cls_4[0.3587] det_loss_box_4[0.3879] det_loss_cls_5[0.3501] det_loss_box_5[0.3853] det_loss_cls_dn_0[0.0981] det_loss_box_dn_0[0.2995] det_loss_cls_dn_1[0.0944] det_loss_box_dn_1[0.2457] det_loss_cls_dn_2[0.0941] det_loss_box_dn_2[0.2295] det_loss_cls_dn_3[0.0929] det_loss_box_dn_3[0.2252] det_loss_cls_dn_4[0.0924] det_loss_box_dn_4[0.2237] det_loss_cls_dn_5[0.0920] det_loss_box_dn_5[0.2222] loss_depth[0.0409] total_loss[7.2004]
Rank[0/8] 06/20/2025 06:08:06 INFO stats.py:314 | Epoch[120] Step[274] GlobalStep[47074] Training Speed: 6.22 samples/sec across all devices.	Average Step Time: 1.29 sec.	Estimated Remaining Time: 15:44:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:08:06 INFO loss_tracker.py:84 | Epoch[120/239] Step[274] GlobalStep[47074/NA]: det_loss_cls_0[0.4666] det_loss_box_0[0.6624] det_loss_cls_1[0.4553] det_loss_box_1[0.5008] det_loss_cls_2[0.4074] det_loss_box_2[0.4454] det_loss_cls_3[0.3841] det_loss_box_3[0.4322] det_loss_cls_4[0.3741] det_loss_box_4[0.4183] det_loss_cls_5[0.3568] det_loss_box_5[0.4233] det_loss_cls_dn_0[0.1001] det_loss_box_dn_0[0.3038] det_loss_cls_dn_1[0.0979] det_loss_box_dn_1[0.2516] det_loss_cls_dn_2[0.0969] det_loss_box_dn_2[0.2346] det_loss_cls_dn_3[0.0959] det_loss_box_dn_3[0.2293] det_loss_cls_dn_4[0.0951] det_loss_box_dn_4[0.2280] det_loss_cls_dn_5[0.0942] det_loss_box_dn_5[0.2257] loss_depth[0.0383] total_loss[7.4183]
Rank[0/8] 06/20/2025 06:08:31 INFO stats.py:314 | Epoch[120] Step[299] GlobalStep[47099] Training Speed: 8.07 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 15:44:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:08:31 INFO loss_tracker.py:84 | Epoch[120/239] Step[299] GlobalStep[47099/NA]: det_loss_cls_0[0.4528] det_loss_box_0[0.5835] det_loss_cls_1[0.4314] det_loss_box_1[0.4497] det_loss_cls_2[0.3883] det_loss_box_2[0.4011] det_loss_cls_3[0.3714] det_loss_box_3[0.3747] det_loss_cls_4[0.3454] det_loss_box_4[0.3598] det_loss_cls_5[0.3337] det_loss_box_5[0.3585] det_loss_cls_dn_0[0.0972] det_loss_box_dn_0[0.2870] det_loss_cls_dn_1[0.0931] det_loss_box_dn_1[0.2430] det_loss_cls_dn_2[0.0924] det_loss_box_dn_2[0.2280] det_loss_cls_dn_3[0.0908] det_loss_box_dn_3[0.2244] det_loss_cls_dn_4[0.0899] det_loss_box_dn_4[0.2226] det_loss_cls_dn_5[0.0887] det_loss_box_dn_5[0.2169] loss_depth[0.0400] total_loss[6.8643]
Rank[0/8] 06/20/2025 06:08:57 INFO stats.py:314 | Epoch[120] Step[324] GlobalStep[47124] Training Speed: 8.15 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 15:43:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:08:57 INFO loss_tracker.py:84 | Epoch[120/239] Step[324] GlobalStep[47124/NA]: det_loss_cls_0[0.4658] det_loss_box_0[0.6068] det_loss_cls_1[0.4534] det_loss_box_1[0.4703] det_loss_cls_2[0.4172] det_loss_box_2[0.4375] det_loss_cls_3[0.3849] det_loss_box_3[0.4191] det_loss_cls_4[0.3646] det_loss_box_4[0.4157] det_loss_cls_5[0.3541] det_loss_box_5[0.4135] det_loss_cls_dn_0[0.0875] det_loss_box_dn_0[0.2742] det_loss_cls_dn_1[0.0832] det_loss_box_dn_1[0.2264] det_loss_cls_dn_2[0.0821] det_loss_box_dn_2[0.2115] det_loss_cls_dn_3[0.0814] det_loss_box_dn_3[0.2063] det_loss_cls_dn_4[0.0809] det_loss_box_dn_4[0.2053] det_loss_cls_dn_5[0.0803] det_loss_box_dn_5[0.2041] loss_depth[0.0372] total_loss[7.0633]
Rank[0/8] 06/20/2025 06:09:22 INFO stats.py:314 | Epoch[120] Step[349] GlobalStep[47149] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 15:42:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:09:22 INFO loss_tracker.py:84 | Epoch[120/239] Step[349] GlobalStep[47149/NA]: det_loss_cls_0[0.3823] det_loss_box_0[0.5020] det_loss_cls_1[0.3859] det_loss_box_1[0.3972] det_loss_cls_2[0.3425] det_loss_box_2[0.3606] det_loss_cls_3[0.3156] det_loss_box_3[0.3543] det_loss_cls_4[0.3002] det_loss_box_4[0.3438] det_loss_cls_5[0.2886] det_loss_box_5[0.3482] det_loss_cls_dn_0[0.0894] det_loss_box_dn_0[0.3026] det_loss_cls_dn_1[0.0851] det_loss_box_dn_1[0.2582] det_loss_cls_dn_2[0.0844] det_loss_box_dn_2[0.2378] det_loss_cls_dn_3[0.0828] det_loss_box_dn_3[0.2345] det_loss_cls_dn_4[0.0817] det_loss_box_dn_4[0.2323] det_loss_cls_dn_5[0.0809] det_loss_box_dn_5[0.2314] loss_depth[0.0365] total_loss[6.3587]
Rank[0/8] 06/20/2025 06:09:48 INFO stats.py:314 | Epoch[120] Step[374] GlobalStep[47174] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 15:42:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:09:48 INFO loss_tracker.py:84 | Epoch[120/239] Step[374] GlobalStep[47174/NA]: det_loss_cls_0[0.5228] det_loss_box_0[0.6274] det_loss_cls_1[0.5236] det_loss_box_1[0.4695] det_loss_cls_2[0.4774] det_loss_box_2[0.4361] det_loss_cls_3[0.4370] det_loss_box_3[0.4194] det_loss_cls_4[0.4024] det_loss_box_4[0.4116] det_loss_cls_5[0.3850] det_loss_box_5[0.4144] det_loss_cls_dn_0[0.0960] det_loss_box_dn_0[0.2523] det_loss_cls_dn_1[0.0914] det_loss_box_dn_1[0.2193] det_loss_cls_dn_2[0.0906] det_loss_box_dn_2[0.2102] det_loss_cls_dn_3[0.0889] det_loss_box_dn_3[0.2073] det_loss_cls_dn_4[0.0880] det_loss_box_dn_4[0.2068] det_loss_cls_dn_5[0.0874] det_loss_box_dn_5[0.2052] loss_depth[0.0353] total_loss[7.4053]
Rank[0/8] 06/20/2025 06:10:03 INFO stats.py:394 | Epoch[120] completed. Training Speed: 7.73 samples/sec across all devices.	Epoch Time: 403.86 sec.	Average Epoch Time: 403.86 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 15:41:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:10:18 INFO stats.py:314 | Epoch[121] Step[9] GlobalStep[47199] Training Speed: 9.82 samples/sec across all devices.	Average Step Time: 0.81 sec.	Estimated Remaining Time: 15:41:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:10:34 INFO loss_tracker.py:84 | Epoch[121/239] Step[24] GlobalStep[47214/NA]: det_loss_cls_0[0.5861] det_loss_box_0[0.7974] det_loss_cls_1[0.5790] det_loss_box_1[0.5948] det_loss_cls_2[0.5436] det_loss_box_2[0.5326] det_loss_cls_3[0.5059] det_loss_box_3[0.5106] det_loss_cls_4[0.4771] det_loss_box_4[0.5021] det_loss_cls_5[0.4600] det_loss_box_5[0.5039] det_loss_cls_dn_0[0.1035] det_loss_box_dn_0[0.3123] det_loss_cls_dn_1[0.0996] det_loss_box_dn_1[0.2640] det_loss_cls_dn_2[0.0991] det_loss_box_dn_2[0.2478] det_loss_cls_dn_3[0.0982] det_loss_box_dn_3[0.2447] det_loss_cls_dn_4[0.0973] det_loss_box_dn_4[0.2471] det_loss_cls_dn_5[0.0965] det_loss_box_dn_5[0.2435] loss_depth[0.0391] total_loss[8.7858]
Rank[0/8] 06/20/2025 06:10:45 INFO stats.py:314 | Epoch[121] Step[34] GlobalStep[47224] Training Speed: 6.51 samples/sec across all devices.	Average Step Time: 1.23 sec.	Estimated Remaining Time: 15:41:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:11:00 INFO loss_tracker.py:84 | Epoch[121/239] Step[49] GlobalStep[47239/NA]: det_loss_cls_0[0.4468] det_loss_box_0[0.6012] det_loss_cls_1[0.4329] det_loss_box_1[0.4499] det_loss_cls_2[0.3808] det_loss_box_2[0.4090] det_loss_cls_3[0.3473] det_loss_box_3[0.4010] det_loss_cls_4[0.3251] det_loss_box_4[0.3942] det_loss_cls_5[0.3195] det_loss_box_5[0.3890] det_loss_cls_dn_0[0.0923] det_loss_box_dn_0[0.2738] det_loss_cls_dn_1[0.0890] det_loss_box_dn_1[0.2330] det_loss_cls_dn_2[0.0884] det_loss_box_dn_2[0.2173] det_loss_cls_dn_3[0.0867] det_loss_box_dn_3[0.2148] det_loss_cls_dn_4[0.0856] det_loss_box_dn_4[0.2136] det_loss_cls_dn_5[0.0850] det_loss_box_dn_5[0.2126] loss_depth[0.0354] total_loss[6.8242]
Rank[0/8] 06/20/2025 06:11:10 INFO stats.py:314 | Epoch[121] Step[59] GlobalStep[47249] Training Speed: 8.56 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 15:40:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:11:26 INFO loss_tracker.py:84 | Epoch[121/239] Step[74] GlobalStep[47264/NA]: det_loss_cls_0[0.5112] det_loss_box_0[0.6811] det_loss_cls_1[0.5000] det_loss_box_1[0.5153] det_loss_cls_2[0.4597] det_loss_box_2[0.4640] det_loss_cls_3[0.4261] det_loss_box_3[0.4533] det_loss_cls_4[0.4010] det_loss_box_4[0.4403] det_loss_cls_5[0.3846] det_loss_box_5[0.4391] det_loss_cls_dn_0[0.0943] det_loss_box_dn_0[0.2851] det_loss_cls_dn_1[0.0903] det_loss_box_dn_1[0.2409] det_loss_cls_dn_2[0.0906] det_loss_box_dn_2[0.2251] det_loss_cls_dn_3[0.0898] det_loss_box_dn_3[0.2221] det_loss_cls_dn_4[0.0890] det_loss_box_dn_4[0.2213] det_loss_cls_dn_5[0.0879] det_loss_box_dn_5[0.2211] loss_depth[0.0397] total_loss[7.6728]
Rank[0/8] 06/20/2025 06:11:36 INFO stats.py:314 | Epoch[121] Step[84] GlobalStep[47274] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 15:40:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:11:52 INFO loss_tracker.py:84 | Epoch[121/239] Step[99] GlobalStep[47289/NA]: det_loss_cls_0[0.4803] det_loss_box_0[0.5357] det_loss_cls_1[0.4630] det_loss_box_1[0.4300] det_loss_cls_2[0.4264] det_loss_box_2[0.3977] det_loss_cls_3[0.4002] det_loss_box_3[0.3804] det_loss_cls_4[0.3657] det_loss_box_4[0.3868] det_loss_cls_5[0.3511] det_loss_box_5[0.3872] det_loss_cls_dn_0[0.0844] det_loss_box_dn_0[0.2197] det_loss_cls_dn_1[0.0805] det_loss_box_dn_1[0.1875] det_loss_cls_dn_2[0.0798] det_loss_box_dn_2[0.1752] det_loss_cls_dn_3[0.0788] det_loss_box_dn_3[0.1720] det_loss_cls_dn_4[0.0780] det_loss_box_dn_4[0.1704] det_loss_cls_dn_5[0.0770] det_loss_box_dn_5[0.1701] loss_depth[0.0326] total_loss[6.6106]
Rank[0/8] 06/20/2025 06:12:02 INFO stats.py:314 | Epoch[121] Step[109] GlobalStep[47299] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 15:39:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:12:18 INFO loss_tracker.py:84 | Epoch[121/239] Step[124] GlobalStep[47314/NA]: det_loss_cls_0[0.4292] det_loss_box_0[0.6248] det_loss_cls_1[0.4261] det_loss_box_1[0.4923] det_loss_cls_2[0.3990] det_loss_box_2[0.4534] det_loss_cls_3[0.3702] det_loss_box_3[0.4380] det_loss_cls_4[0.3536] det_loss_box_4[0.4190] det_loss_cls_5[0.3383] det_loss_box_5[0.4258] det_loss_cls_dn_0[0.0903] det_loss_box_dn_0[0.3389] det_loss_cls_dn_1[0.0859] det_loss_box_dn_1[0.2938] det_loss_cls_dn_2[0.0851] det_loss_box_dn_2[0.2778] det_loss_cls_dn_3[0.0842] det_loss_box_dn_3[0.2732] det_loss_cls_dn_4[0.0837] det_loss_box_dn_4[0.2710] det_loss_cls_dn_5[0.0827] det_loss_box_dn_5[0.2704] loss_depth[0.0356] total_loss[7.4422]
Rank[0/8] 06/20/2025 06:12:28 INFO stats.py:314 | Epoch[121] Step[134] GlobalStep[47324] Training Speed: 8.72 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 15:38:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:12:43 INFO loss_tracker.py:84 | Epoch[121/239] Step[149] GlobalStep[47339/NA]: det_loss_cls_0[0.4164] det_loss_box_0[0.5666] det_loss_cls_1[0.4066] det_loss_box_1[0.4403] det_loss_cls_2[0.3714] det_loss_box_2[0.4115] det_loss_cls_3[0.3452] det_loss_box_3[0.3984] det_loss_cls_4[0.3228] det_loss_box_4[0.3842] det_loss_cls_5[0.3106] det_loss_box_5[0.3863] det_loss_cls_dn_0[0.0804] det_loss_box_dn_0[0.2907] det_loss_cls_dn_1[0.0770] det_loss_box_dn_1[0.2417] det_loss_cls_dn_2[0.0760] det_loss_box_dn_2[0.2247] det_loss_cls_dn_3[0.0747] det_loss_box_dn_3[0.2222] det_loss_cls_dn_4[0.0735] det_loss_box_dn_4[0.2191] det_loss_cls_dn_5[0.0721] det_loss_box_dn_5[0.2182] loss_depth[0.0358] total_loss[6.6665]
Rank[0/8] 06/20/2025 06:12:53 INFO stats.py:314 | Epoch[121] Step[159] GlobalStep[47349] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 15:38:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:13:09 INFO loss_tracker.py:84 | Epoch[121/239] Step[174] GlobalStep[47364/NA]: det_loss_cls_0[0.4443] det_loss_box_0[0.5827] det_loss_cls_1[0.4286] det_loss_box_1[0.4205] det_loss_cls_2[0.3879] det_loss_box_2[0.3861] det_loss_cls_3[0.3630] det_loss_box_3[0.3690] det_loss_cls_4[0.3413] det_loss_box_4[0.3564] det_loss_cls_5[0.3303] det_loss_box_5[0.3589] det_loss_cls_dn_0[0.0930] det_loss_box_dn_0[0.2776] det_loss_cls_dn_1[0.0877] det_loss_box_dn_1[0.2315] det_loss_cls_dn_2[0.0868] det_loss_box_dn_2[0.2147] det_loss_cls_dn_3[0.0856] det_loss_box_dn_3[0.2124] det_loss_cls_dn_4[0.0846] det_loss_box_dn_4[0.2108] det_loss_cls_dn_5[0.0836] det_loss_box_dn_5[0.2092] loss_depth[0.0355] total_loss[6.6818]
Rank[0/8] 06/20/2025 06:13:19 INFO stats.py:314 | Epoch[121] Step[184] GlobalStep[47374] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 15:37:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:13:34 INFO loss_tracker.py:84 | Epoch[121/239] Step[199] GlobalStep[47389/NA]: det_loss_cls_0[0.4557] det_loss_box_0[0.6201] det_loss_cls_1[0.4476] det_loss_box_1[0.4734] det_loss_cls_2[0.4105] det_loss_box_2[0.4302] det_loss_cls_3[0.3877] det_loss_box_3[0.4224] det_loss_cls_4[0.3661] det_loss_box_4[0.4128] det_loss_cls_5[0.3499] det_loss_box_5[0.4159] det_loss_cls_dn_0[0.0914] det_loss_box_dn_0[0.3018] det_loss_cls_dn_1[0.0872] det_loss_box_dn_1[0.2588] det_loss_cls_dn_2[0.0857] det_loss_box_dn_2[0.2405] det_loss_cls_dn_3[0.0847] det_loss_box_dn_3[0.2380] det_loss_cls_dn_4[0.0840] det_loss_box_dn_4[0.2358] det_loss_cls_dn_5[0.0836] det_loss_box_dn_5[0.2344] loss_depth[0.0388] total_loss[7.2570]
Rank[0/8] 06/20/2025 06:13:45 INFO stats.py:314 | Epoch[121] Step[209] GlobalStep[47399] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 15:37:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:14:00 INFO loss_tracker.py:84 | Epoch[121/239] Step[224] GlobalStep[47414/NA]: det_loss_cls_0[0.6513] det_loss_box_0[0.7528] det_loss_cls_1[0.6626] det_loss_box_1[0.5741] det_loss_cls_2[0.6059] det_loss_box_2[0.5233] det_loss_cls_3[0.5675] det_loss_box_3[0.5030] det_loss_cls_4[0.5215] det_loss_box_4[0.5074] det_loss_cls_5[0.5099] det_loss_box_5[0.5029] det_loss_cls_dn_0[0.1043] det_loss_box_dn_0[0.2329] det_loss_cls_dn_1[0.0996] det_loss_box_dn_1[0.1935] det_loss_cls_dn_2[0.0998] det_loss_box_dn_2[0.1787] det_loss_cls_dn_3[0.0993] det_loss_box_dn_3[0.1756] det_loss_cls_dn_4[0.0986] det_loss_box_dn_4[0.1740] det_loss_cls_dn_5[0.0978] det_loss_box_dn_5[0.1725] loss_depth[0.0372] total_loss[8.6461]
Rank[0/8] 06/20/2025 06:14:11 INFO stats.py:314 | Epoch[121] Step[234] GlobalStep[47424] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 15:36:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:14:26 INFO loss_tracker.py:84 | Epoch[121/239] Step[249] GlobalStep[47439/NA]: det_loss_cls_0[0.5017] det_loss_box_0[0.6532] det_loss_cls_1[0.4900] det_loss_box_1[0.4801] det_loss_cls_2[0.4486] det_loss_box_2[0.4399] det_loss_cls_3[0.4164] det_loss_box_3[0.4205] det_loss_cls_4[0.3921] det_loss_box_4[0.4105] det_loss_cls_5[0.3851] det_loss_box_5[0.4069] det_loss_cls_dn_0[0.1034] det_loss_box_dn_0[0.2948] det_loss_cls_dn_1[0.1006] det_loss_box_dn_1[0.2413] det_loss_cls_dn_2[0.0995] det_loss_box_dn_2[0.2268] det_loss_cls_dn_3[0.0981] det_loss_box_dn_3[0.2277] det_loss_cls_dn_4[0.0979] det_loss_box_dn_4[0.2264] det_loss_cls_dn_5[0.0963] det_loss_box_dn_5[0.2253] loss_depth[0.0390] total_loss[7.5221]
Rank[0/8] 06/20/2025 06:14:36 INFO stats.py:314 | Epoch[121] Step[259] GlobalStep[47449] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 15:35:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:14:52 INFO loss_tracker.py:84 | Epoch[121/239] Step[274] GlobalStep[47464/NA]: det_loss_cls_0[0.3939] det_loss_box_0[0.4802] det_loss_cls_1[0.3905] det_loss_box_1[0.3612] det_loss_cls_2[0.3460] det_loss_box_2[0.3259] det_loss_cls_3[0.3231] det_loss_box_3[0.3154] det_loss_cls_4[0.2958] det_loss_box_4[0.3101] det_loss_cls_5[0.2847] det_loss_box_5[0.3142] det_loss_cls_dn_0[0.0833] det_loss_box_dn_0[0.2776] det_loss_cls_dn_1[0.0787] det_loss_box_dn_1[0.2297] det_loss_cls_dn_2[0.0772] det_loss_box_dn_2[0.2159] det_loss_cls_dn_3[0.0761] det_loss_box_dn_3[0.2097] det_loss_cls_dn_4[0.0748] det_loss_box_dn_4[0.2086] det_loss_cls_dn_5[0.0736] det_loss_box_dn_5[0.2049] loss_depth[0.0340] total_loss[5.9851]
Rank[0/8] 06/20/2025 06:15:02 INFO stats.py:314 | Epoch[121] Step[284] GlobalStep[47474] Training Speed: 8.15 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 15:35:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:15:18 INFO loss_tracker.py:84 | Epoch[121/239] Step[299] GlobalStep[47489/NA]: det_loss_cls_0[0.6387] det_loss_box_0[0.7713] det_loss_cls_1[0.6305] det_loss_box_1[0.5969] det_loss_cls_2[0.5855] det_loss_box_2[0.5461] det_loss_cls_3[0.5547] det_loss_box_3[0.5184] det_loss_cls_4[0.5249] det_loss_box_4[0.5125] det_loss_cls_5[0.5158] det_loss_box_5[0.5116] det_loss_cls_dn_0[0.1149] det_loss_box_dn_0[0.2791] det_loss_cls_dn_1[0.1117] det_loss_box_dn_1[0.2361] det_loss_cls_dn_2[0.1103] det_loss_box_dn_2[0.2184] det_loss_cls_dn_3[0.1098] det_loss_box_dn_3[0.2142] det_loss_cls_dn_4[0.1092] det_loss_box_dn_4[0.2129] det_loss_cls_dn_5[0.1078] det_loss_box_dn_5[0.2113] loss_depth[0.0367] total_loss[8.9794]
Rank[0/8] 06/20/2025 06:15:28 INFO stats.py:314 | Epoch[121] Step[309] GlobalStep[47499] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 15:34:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:15:44 INFO loss_tracker.py:84 | Epoch[121/239] Step[324] GlobalStep[47514/NA]: det_loss_cls_0[0.4957] det_loss_box_0[0.6592] det_loss_cls_1[0.4949] det_loss_box_1[0.5075] det_loss_cls_2[0.4511] det_loss_box_2[0.4535] det_loss_cls_3[0.4163] det_loss_box_3[0.4363] det_loss_cls_4[0.3886] det_loss_box_4[0.4301] det_loss_cls_5[0.3711] det_loss_box_5[0.4349] det_loss_cls_dn_0[0.0852] det_loss_box_dn_0[0.2858] det_loss_cls_dn_1[0.0815] det_loss_box_dn_1[0.2293] det_loss_cls_dn_2[0.0801] det_loss_box_dn_2[0.2127] det_loss_cls_dn_3[0.0792] det_loss_box_dn_3[0.2066] det_loss_cls_dn_4[0.0783] det_loss_box_dn_4[0.2029] det_loss_cls_dn_5[0.0770] det_loss_box_dn_5[0.2009] loss_depth[0.0340] total_loss[7.3929]
Rank[0/8] 06/20/2025 06:15:54 INFO stats.py:314 | Epoch[121] Step[334] GlobalStep[47524] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 15:34:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:16:09 INFO loss_tracker.py:84 | Epoch[121/239] Step[349] GlobalStep[47539/NA]: det_loss_cls_0[0.5452] det_loss_box_0[0.7852] det_loss_cls_1[0.5454] det_loss_box_1[0.5921] det_loss_cls_2[0.5048] det_loss_box_2[0.5251] det_loss_cls_3[0.4723] det_loss_box_3[0.5038] det_loss_cls_4[0.4386] det_loss_box_4[0.4904] det_loss_cls_5[0.4141] det_loss_box_5[0.4976] det_loss_cls_dn_0[0.0985] det_loss_box_dn_0[0.2817] det_loss_cls_dn_1[0.0949] det_loss_box_dn_1[0.2457] det_loss_cls_dn_2[0.0944] det_loss_box_dn_2[0.2312] det_loss_cls_dn_3[0.0930] det_loss_box_dn_3[0.2288] det_loss_cls_dn_4[0.0918] det_loss_box_dn_4[0.2277] det_loss_cls_dn_5[0.0915] det_loss_box_dn_5[0.2238] loss_depth[0.0371] total_loss[8.3548]
Rank[0/8] 06/20/2025 06:16:20 INFO stats.py:314 | Epoch[121] Step[359] GlobalStep[47549] Training Speed: 7.76 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 15:33:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:16:35 INFO loss_tracker.py:84 | Epoch[121/239] Step[374] GlobalStep[47564/NA]: det_loss_cls_0[0.5496] det_loss_box_0[0.7359] det_loss_cls_1[0.5357] det_loss_box_1[0.5557] det_loss_cls_2[0.4858] det_loss_box_2[0.4970] det_loss_cls_3[0.4575] det_loss_box_3[0.4732] det_loss_cls_4[0.4181] det_loss_box_4[0.4670] det_loss_cls_5[0.4064] det_loss_box_5[0.4682] det_loss_cls_dn_0[0.0966] det_loss_box_dn_0[0.2816] det_loss_cls_dn_1[0.0933] det_loss_box_dn_1[0.2406] det_loss_cls_dn_2[0.0921] det_loss_box_dn_2[0.2244] det_loss_cls_dn_3[0.0912] det_loss_box_dn_3[0.2205] det_loss_cls_dn_4[0.0902] det_loss_box_dn_4[0.2190] det_loss_cls_dn_5[0.0894] det_loss_box_dn_5[0.2177] loss_depth[0.0377] total_loss[8.0445]
Rank[0/8] 06/20/2025 06:16:46 INFO stats.py:314 | Epoch[121] Step[384] GlobalStep[47574] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 15:33:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:16:51 INFO stats.py:394 | Epoch[121] completed. Training Speed: 7.65 samples/sec across all devices.	Epoch Time: 407.72 sec.	Average Epoch Time: 407.72 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 15:32:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:17:13 INFO stats.py:314 | Epoch[122] Step[19] GlobalStep[47599] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 15:32:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:17:18 INFO loss_tracker.py:84 | Epoch[122/239] Step[24] GlobalStep[47604/NA]: det_loss_cls_0[0.4668] det_loss_box_0[0.6415] det_loss_cls_1[0.4614] det_loss_box_1[0.4818] det_loss_cls_2[0.4206] det_loss_box_2[0.4359] det_loss_cls_3[0.3874] det_loss_box_3[0.4212] det_loss_cls_4[0.3679] det_loss_box_4[0.4113] det_loss_cls_5[0.3558] det_loss_box_5[0.4137] det_loss_cls_dn_0[0.1004] det_loss_box_dn_0[0.3141] det_loss_cls_dn_1[0.0975] det_loss_box_dn_1[0.2636] det_loss_cls_dn_2[0.0971] det_loss_box_dn_2[0.2448] det_loss_cls_dn_3[0.0956] det_loss_box_dn_3[0.2411] det_loss_cls_dn_4[0.0947] det_loss_box_dn_4[0.2387] det_loss_cls_dn_5[0.0934] det_loss_box_dn_5[0.2328] loss_depth[0.0390] total_loss[7.4183]
Rank[0/8] 06/20/2025 06:17:39 INFO stats.py:314 | Epoch[122] Step[44] GlobalStep[47624] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 15:31:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:17:44 INFO loss_tracker.py:84 | Epoch[122/239] Step[49] GlobalStep[47629/NA]: det_loss_cls_0[0.4519] det_loss_box_0[0.6162] det_loss_cls_1[0.4521] det_loss_box_1[0.4654] det_loss_cls_2[0.4145] det_loss_box_2[0.4179] det_loss_cls_3[0.3761] det_loss_box_3[0.4137] det_loss_cls_4[0.3374] det_loss_box_4[0.4086] det_loss_cls_5[0.3224] det_loss_box_5[0.4084] det_loss_cls_dn_0[0.0871] det_loss_box_dn_0[0.2832] det_loss_cls_dn_1[0.0854] det_loss_box_dn_1[0.2347] det_loss_cls_dn_2[0.0859] det_loss_box_dn_2[0.2170] det_loss_cls_dn_3[0.0858] det_loss_box_dn_3[0.2144] det_loss_cls_dn_4[0.0864] det_loss_box_dn_4[0.2127] det_loss_cls_dn_5[0.0859] det_loss_box_dn_5[0.2098] loss_depth[0.0382] total_loss[7.0108]
Rank[0/8] 06/20/2025 06:18:05 INFO stats.py:314 | Epoch[122] Step[69] GlobalStep[47649] Training Speed: 8.06 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 15:31:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:18:10 INFO loss_tracker.py:84 | Epoch[122/239] Step[74] GlobalStep[47654/NA]: det_loss_cls_0[0.5695] det_loss_box_0[0.7746] det_loss_cls_1[0.5636] det_loss_box_1[0.5996] det_loss_cls_2[0.5246] det_loss_box_2[0.5334] det_loss_cls_3[0.5013] det_loss_box_3[0.5124] det_loss_cls_4[0.4617] det_loss_box_4[0.5037] det_loss_cls_5[0.4482] det_loss_box_5[0.5023] det_loss_cls_dn_0[0.1034] det_loss_box_dn_0[0.3249] det_loss_cls_dn_1[0.0998] det_loss_box_dn_1[0.2744] det_loss_cls_dn_2[0.0986] det_loss_box_dn_2[0.2562] det_loss_cls_dn_3[0.0980] det_loss_box_dn_3[0.2516] det_loss_cls_dn_4[0.0981] det_loss_box_dn_4[0.2498] det_loss_cls_dn_5[0.0972] det_loss_box_dn_5[0.2484] loss_depth[0.0375] total_loss[8.7329]
Rank[0/8] 06/20/2025 06:18:31 INFO stats.py:314 | Epoch[122] Step[94] GlobalStep[47674] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 15:30:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:18:36 INFO loss_tracker.py:84 | Epoch[122/239] Step[99] GlobalStep[47679/NA]: det_loss_cls_0[0.5110] det_loss_box_0[0.6377] det_loss_cls_1[0.4989] det_loss_box_1[0.4815] det_loss_cls_2[0.4569] det_loss_box_2[0.4467] det_loss_cls_3[0.4262] det_loss_box_3[0.4334] det_loss_cls_4[0.3971] det_loss_box_4[0.4242] det_loss_cls_5[0.3859] det_loss_box_5[0.4227] det_loss_cls_dn_0[0.0944] det_loss_box_dn_0[0.2795] det_loss_cls_dn_1[0.0913] det_loss_box_dn_1[0.2338] det_loss_cls_dn_2[0.0908] det_loss_box_dn_2[0.2171] det_loss_cls_dn_3[0.0897] det_loss_box_dn_3[0.2145] det_loss_cls_dn_4[0.0885] det_loss_box_dn_4[0.2123] det_loss_cls_dn_5[0.0880] det_loss_box_dn_5[0.2104] loss_depth[0.0361] total_loss[7.4686]
Rank[0/8] 06/20/2025 06:18:57 INFO stats.py:314 | Epoch[122] Step[119] GlobalStep[47699] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 15:30:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:19:02 INFO loss_tracker.py:84 | Epoch[122/239] Step[124] GlobalStep[47704/NA]: det_loss_cls_0[0.5888] det_loss_box_0[0.8131] det_loss_cls_1[0.5803] det_loss_box_1[0.6494] det_loss_cls_2[0.5308] det_loss_box_2[0.5952] det_loss_cls_3[0.4985] det_loss_box_3[0.5748] det_loss_cls_4[0.4637] det_loss_box_4[0.5642] det_loss_cls_5[0.4502] det_loss_box_5[0.5659] det_loss_cls_dn_0[0.0989] det_loss_box_dn_0[0.3191] det_loss_cls_dn_1[0.0948] det_loss_box_dn_1[0.2717] det_loss_cls_dn_2[0.0938] det_loss_box_dn_2[0.2535] det_loss_cls_dn_3[0.0928] det_loss_box_dn_3[0.2487] det_loss_cls_dn_4[0.0914] det_loss_box_dn_4[0.2450] det_loss_cls_dn_5[0.0900] det_loss_box_dn_5[0.2423] loss_depth[0.0384] total_loss[9.0552]
Rank[0/8] 06/20/2025 06:19:23 INFO stats.py:314 | Epoch[122] Step[144] GlobalStep[47724] Training Speed: 7.75 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 15:29:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:19:28 INFO loss_tracker.py:84 | Epoch[122/239] Step[149] GlobalStep[47729/NA]: det_loss_cls_0[0.5881] det_loss_box_0[0.7147] det_loss_cls_1[0.5798] det_loss_box_1[0.5538] det_loss_cls_2[0.5277] det_loss_box_2[0.5216] det_loss_cls_3[0.4942] det_loss_box_3[0.5019] det_loss_cls_4[0.4590] det_loss_box_4[0.4942] det_loss_cls_5[0.4436] det_loss_box_5[0.4905] det_loss_cls_dn_0[0.0884] det_loss_box_dn_0[0.2630] det_loss_cls_dn_1[0.0847] det_loss_box_dn_1[0.2191] det_loss_cls_dn_2[0.0836] det_loss_box_dn_2[0.2048] det_loss_cls_dn_3[0.0826] det_loss_box_dn_3[0.2013] det_loss_cls_dn_4[0.0820] det_loss_box_dn_4[0.1995] det_loss_cls_dn_5[0.0810] det_loss_box_dn_5[0.1983] loss_depth[0.0369] total_loss[8.1945]
Rank[0/8] 06/20/2025 06:19:49 INFO stats.py:314 | Epoch[122] Step[169] GlobalStep[47749] Training Speed: 5.89 samples/sec across all devices.	Average Step Time: 1.36 sec.	Estimated Remaining Time: 15:29:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:19:54 INFO loss_tracker.py:84 | Epoch[122/239] Step[174] GlobalStep[47754/NA]: det_loss_cls_0[0.5303] det_loss_box_0[0.6288] det_loss_cls_1[0.5131] det_loss_box_1[0.4677] det_loss_cls_2[0.4565] det_loss_box_2[0.4381] det_loss_cls_3[0.4281] det_loss_box_3[0.4276] det_loss_cls_4[0.3962] det_loss_box_4[0.4194] det_loss_cls_5[0.3818] det_loss_box_5[0.4180] det_loss_cls_dn_0[0.0939] det_loss_box_dn_0[0.2884] det_loss_cls_dn_1[0.0902] det_loss_box_dn_1[0.2467] det_loss_cls_dn_2[0.0901] det_loss_box_dn_2[0.2333] det_loss_cls_dn_3[0.0890] det_loss_box_dn_3[0.2314] det_loss_cls_dn_4[0.0878] det_loss_box_dn_4[0.2305] det_loss_cls_dn_5[0.0871] det_loss_box_dn_5[0.2309] loss_depth[0.0384] total_loss[7.5432]
Rank[0/8] 06/20/2025 06:20:15 INFO stats.py:314 | Epoch[122] Step[194] GlobalStep[47774] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 15:28:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:20:20 INFO loss_tracker.py:84 | Epoch[122/239] Step[199] GlobalStep[47779/NA]: det_loss_cls_0[0.4520] det_loss_box_0[0.5662] det_loss_cls_1[0.4527] det_loss_box_1[0.4377] det_loss_cls_2[0.4077] det_loss_box_2[0.4032] det_loss_cls_3[0.3812] det_loss_box_3[0.3868] det_loss_cls_4[0.3582] det_loss_box_4[0.3791] det_loss_cls_5[0.3435] det_loss_box_5[0.3794] det_loss_cls_dn_0[0.0986] det_loss_box_dn_0[0.2532] det_loss_cls_dn_1[0.0941] det_loss_box_dn_1[0.2171] det_loss_cls_dn_2[0.0936] det_loss_box_dn_2[0.2069] det_loss_cls_dn_3[0.0920] det_loss_box_dn_3[0.2046] det_loss_cls_dn_4[0.0903] det_loss_box_dn_4[0.2031] det_loss_cls_dn_5[0.0896] det_loss_box_dn_5[0.2005] loss_depth[0.0355] total_loss[6.8269]
Rank[0/8] 06/20/2025 06:20:41 INFO stats.py:314 | Epoch[122] Step[219] GlobalStep[47799] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 15:27:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:20:46 INFO loss_tracker.py:84 | Epoch[122/239] Step[224] GlobalStep[47804/NA]: det_loss_cls_0[0.5371] det_loss_box_0[0.6506] det_loss_cls_1[0.5217] det_loss_box_1[0.5090] det_loss_cls_2[0.4749] det_loss_box_2[0.4634] det_loss_cls_3[0.4450] det_loss_box_3[0.4478] det_loss_cls_4[0.4170] det_loss_box_4[0.4473] det_loss_cls_5[0.4108] det_loss_box_5[0.4420] det_loss_cls_dn_0[0.1006] det_loss_box_dn_0[0.3165] det_loss_cls_dn_1[0.0970] det_loss_box_dn_1[0.2556] det_loss_cls_dn_2[0.0968] det_loss_box_dn_2[0.2324] det_loss_cls_dn_3[0.0958] det_loss_box_dn_3[0.2283] det_loss_cls_dn_4[0.0955] det_loss_box_dn_4[0.2277] det_loss_cls_dn_5[0.0947] det_loss_box_dn_5[0.2249] loss_depth[0.0399] total_loss[7.8724]
Rank[0/8] 06/20/2025 06:21:06 INFO stats.py:314 | Epoch[122] Step[244] GlobalStep[47824] Training Speed: 7.62 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 15:27:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:21:12 INFO loss_tracker.py:84 | Epoch[122/239] Step[249] GlobalStep[47829/NA]: det_loss_cls_0[0.6425] det_loss_box_0[0.8728] det_loss_cls_1[0.6134] det_loss_box_1[0.6710] det_loss_cls_2[0.5626] det_loss_box_2[0.6032] det_loss_cls_3[0.5405] det_loss_box_3[0.5876] det_loss_cls_4[0.5045] det_loss_box_4[0.5758] det_loss_cls_5[0.4933] det_loss_box_5[0.5724] det_loss_cls_dn_0[0.0995] det_loss_box_dn_0[0.2953] det_loss_cls_dn_1[0.0975] det_loss_box_dn_1[0.2527] det_loss_cls_dn_2[0.0970] det_loss_box_dn_2[0.2347] det_loss_cls_dn_3[0.0967] det_loss_box_dn_3[0.2310] det_loss_cls_dn_4[0.0954] det_loss_box_dn_4[0.2296] det_loss_cls_dn_5[0.0944] det_loss_box_dn_5[0.2268] loss_depth[0.0400] total_loss[9.3300]
Rank[0/8] 06/20/2025 06:21:33 INFO stats.py:314 | Epoch[122] Step[269] GlobalStep[47849] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 15:26:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:21:38 INFO loss_tracker.py:84 | Epoch[122/239] Step[274] GlobalStep[47854/NA]: det_loss_cls_0[0.4994] det_loss_box_0[0.6868] det_loss_cls_1[0.4834] det_loss_box_1[0.5261] det_loss_cls_2[0.4442] det_loss_box_2[0.4878] det_loss_cls_3[0.4202] det_loss_box_3[0.4844] det_loss_cls_4[0.3912] det_loss_box_4[0.4770] det_loss_cls_5[0.3791] det_loss_box_5[0.4739] det_loss_cls_dn_0[0.1001] det_loss_box_dn_0[0.3743] det_loss_cls_dn_1[0.0964] det_loss_box_dn_1[0.3101] det_loss_cls_dn_2[0.0965] det_loss_box_dn_2[0.2951] det_loss_cls_dn_3[0.0953] det_loss_box_dn_3[0.2896] det_loss_cls_dn_4[0.0940] det_loss_box_dn_4[0.2871] det_loss_cls_dn_5[0.0934] det_loss_box_dn_5[0.2811] loss_depth[0.0408] total_loss[8.2071]
Rank[0/8] 06/20/2025 06:21:58 INFO stats.py:314 | Epoch[122] Step[294] GlobalStep[47874] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 15:26:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:22:04 INFO loss_tracker.py:84 | Epoch[122/239] Step[299] GlobalStep[47879/NA]: det_loss_cls_0[0.3710] det_loss_box_0[0.5425] det_loss_cls_1[0.3570] det_loss_box_1[0.4293] det_loss_cls_2[0.3316] det_loss_box_2[0.3841] det_loss_cls_3[0.3021] det_loss_box_3[0.3771] det_loss_cls_4[0.2776] det_loss_box_4[0.3737] det_loss_cls_5[0.2686] det_loss_box_5[0.3768] det_loss_cls_dn_0[0.0853] det_loss_box_dn_0[0.3344] det_loss_cls_dn_1[0.0811] det_loss_box_dn_1[0.2835] det_loss_cls_dn_2[0.0797] det_loss_box_dn_2[0.2660] det_loss_cls_dn_3[0.0780] det_loss_box_dn_3[0.2621] det_loss_cls_dn_4[0.0773] det_loss_box_dn_4[0.2601] det_loss_cls_dn_5[0.0764] det_loss_box_dn_5[0.2580] loss_depth[0.0384] total_loss[6.5723]
Rank[0/8] 06/20/2025 06:22:24 INFO stats.py:314 | Epoch[122] Step[319] GlobalStep[47899] Training Speed: 7.85 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 15:25:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:22:29 INFO loss_tracker.py:84 | Epoch[122/239] Step[324] GlobalStep[47904/NA]: det_loss_cls_0[0.5847] det_loss_box_0[0.8128] det_loss_cls_1[0.5723] det_loss_box_1[0.6494] det_loss_cls_2[0.5244] det_loss_box_2[0.5918] det_loss_cls_3[0.4872] det_loss_box_3[0.5797] det_loss_cls_4[0.4607] det_loss_box_4[0.5673] det_loss_cls_5[0.4463] det_loss_box_5[0.5654] det_loss_cls_dn_0[0.0999] det_loss_box_dn_0[0.3154] det_loss_cls_dn_1[0.0962] det_loss_box_dn_1[0.2750] det_loss_cls_dn_2[0.0959] det_loss_box_dn_2[0.2565] det_loss_cls_dn_3[0.0956] det_loss_box_dn_3[0.2529] det_loss_cls_dn_4[0.0940] det_loss_box_dn_4[0.2519] det_loss_cls_dn_5[0.0935] det_loss_box_dn_5[0.2497] loss_depth[0.0380] total_loss[9.0565]
Rank[0/8] 06/20/2025 06:22:50 INFO stats.py:314 | Epoch[122] Step[344] GlobalStep[47924] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 15:25:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:22:55 INFO loss_tracker.py:84 | Epoch[122/239] Step[349] GlobalStep[47929/NA]: det_loss_cls_0[0.5919] det_loss_box_0[0.7394] det_loss_cls_1[0.5734] det_loss_box_1[0.5677] det_loss_cls_2[0.5267] det_loss_box_2[0.5133] det_loss_cls_3[0.4936] det_loss_box_3[0.4974] det_loss_cls_4[0.4632] det_loss_box_4[0.4907] det_loss_cls_5[0.4470] det_loss_box_5[0.4914] det_loss_cls_dn_0[0.0897] det_loss_box_dn_0[0.2543] det_loss_cls_dn_1[0.0866] det_loss_box_dn_1[0.2165] det_loss_cls_dn_2[0.0853] det_loss_box_dn_2[0.2003] det_loss_cls_dn_3[0.0845] det_loss_box_dn_3[0.1986] det_loss_cls_dn_4[0.0830] det_loss_box_dn_4[0.1978] det_loss_cls_dn_5[0.0821] det_loss_box_dn_5[0.1970] loss_depth[0.0336] total_loss[8.2049]
Rank[0/8] 06/20/2025 06:23:16 INFO stats.py:314 | Epoch[122] Step[369] GlobalStep[47949] Training Speed: 8.02 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 15:24:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:23:22 INFO loss_tracker.py:84 | Epoch[122/239] Step[374] GlobalStep[47954/NA]: det_loss_cls_0[0.4570] det_loss_box_0[0.5373] det_loss_cls_1[0.4612] det_loss_box_1[0.4072] det_loss_cls_2[0.4105] det_loss_box_2[0.3736] det_loss_cls_3[0.3744] det_loss_box_3[0.3668] det_loss_cls_4[0.3514] det_loss_box_4[0.3655] det_loss_cls_5[0.3395] det_loss_box_5[0.3673] det_loss_cls_dn_0[0.0970] det_loss_box_dn_0[0.2600] det_loss_cls_dn_1[0.0937] det_loss_box_dn_1[0.2151] det_loss_cls_dn_2[0.0932] det_loss_box_dn_2[0.1945] det_loss_cls_dn_3[0.0916] det_loss_box_dn_3[0.1923] det_loss_cls_dn_4[0.0904] det_loss_box_dn_4[0.1912] det_loss_cls_dn_5[0.0900] det_loss_box_dn_5[0.1903] loss_depth[0.0333] total_loss[6.6443]
Rank[0/8] 06/20/2025 06:23:37 INFO stats.py:394 | Epoch[122] completed. Training Speed: 7.68 samples/sec across all devices.	Epoch Time: 406.11 sec.	Average Epoch Time: 406.11 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 15:23:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:23:45 INFO stats.py:314 | Epoch[123] Step[4] GlobalStep[47974] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 15:23:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:24:07 INFO loss_tracker.py:84 | Epoch[123/239] Step[24] GlobalStep[47994/NA]: det_loss_cls_0[0.4564] det_loss_box_0[0.6001] det_loss_cls_1[0.4476] det_loss_box_1[0.4551] det_loss_cls_2[0.4095] det_loss_box_2[0.4080] det_loss_cls_3[0.3894] det_loss_box_3[0.3921] det_loss_cls_4[0.3620] det_loss_box_4[0.3892] det_loss_cls_5[0.3508] det_loss_box_5[0.3850] det_loss_cls_dn_0[0.0966] det_loss_box_dn_0[0.2786] det_loss_cls_dn_1[0.0937] det_loss_box_dn_1[0.2258] det_loss_cls_dn_2[0.0930] det_loss_box_dn_2[0.2072] det_loss_cls_dn_3[0.0921] det_loss_box_dn_3[0.2025] det_loss_cls_dn_4[0.0912] det_loss_box_dn_4[0.2013] det_loss_cls_dn_5[0.0906] det_loss_box_dn_5[0.2006] loss_depth[0.0359] total_loss[6.9542]
Rank[0/8] 06/20/2025 06:24:12 INFO stats.py:314 | Epoch[123] Step[29] GlobalStep[47999] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 15:23:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:24:33 INFO loss_tracker.py:84 | Epoch[123/239] Step[49] GlobalStep[48019/NA]: det_loss_cls_0[0.5639] det_loss_box_0[0.7512] det_loss_cls_1[0.5551] det_loss_box_1[0.5701] det_loss_cls_2[0.5186] det_loss_box_2[0.5316] det_loss_cls_3[0.4958] det_loss_box_3[0.5029] det_loss_cls_4[0.4621] det_loss_box_4[0.4949] det_loss_cls_5[0.4503] det_loss_box_5[0.4971] det_loss_cls_dn_0[0.1059] det_loss_box_dn_0[0.2811] det_loss_cls_dn_1[0.1015] det_loss_box_dn_1[0.2391] det_loss_cls_dn_2[0.1010] det_loss_box_dn_2[0.2249] det_loss_cls_dn_3[0.0998] det_loss_box_dn_3[0.2223] det_loss_cls_dn_4[0.0993] det_loss_box_dn_4[0.2202] det_loss_cls_dn_5[0.0989] det_loss_box_dn_5[0.2190] loss_depth[0.0374] total_loss[8.4440]
Rank[0/8] 06/20/2025 06:24:38 INFO stats.py:314 | Epoch[123] Step[54] GlobalStep[48024] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 15:22:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:24:59 INFO loss_tracker.py:84 | Epoch[123/239] Step[74] GlobalStep[48044/NA]: det_loss_cls_0[0.5096] det_loss_box_0[0.8340] det_loss_cls_1[0.5039] det_loss_box_1[0.6784] det_loss_cls_2[0.4644] det_loss_box_2[0.6310] det_loss_cls_3[0.4366] det_loss_box_3[0.6203] det_loss_cls_4[0.4148] det_loss_box_4[0.5893] det_loss_cls_5[0.4010] det_loss_box_5[0.5849] det_loss_cls_dn_0[0.0926] det_loss_box_dn_0[0.4042] det_loss_cls_dn_1[0.0889] det_loss_box_dn_1[0.3532] det_loss_cls_dn_2[0.0884] det_loss_box_dn_2[0.3359] det_loss_cls_dn_3[0.0872] det_loss_box_dn_3[0.3267] det_loss_cls_dn_4[0.0863] det_loss_box_dn_4[0.3261] det_loss_cls_dn_5[0.0853] det_loss_box_dn_5[0.3242] loss_depth[0.0351] total_loss[9.3023]
Rank[0/8] 06/20/2025 06:25:04 INFO stats.py:314 | Epoch[123] Step[79] GlobalStep[48049] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 15:22:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:25:24 INFO loss_tracker.py:84 | Epoch[123/239] Step[99] GlobalStep[48069/NA]: det_loss_cls_0[0.6523] det_loss_box_0[0.8826] det_loss_cls_1[0.6310] det_loss_box_1[0.6911] det_loss_cls_2[0.5805] det_loss_box_2[0.6240] det_loss_cls_3[0.5503] det_loss_box_3[0.6111] det_loss_cls_4[0.5169] det_loss_box_4[0.5929] det_loss_cls_5[0.4943] det_loss_box_5[0.5983] det_loss_cls_dn_0[0.0953] det_loss_box_dn_0[0.2678] det_loss_cls_dn_1[0.0910] det_loss_box_dn_1[0.2315] det_loss_cls_dn_2[0.0907] det_loss_box_dn_2[0.2200] det_loss_cls_dn_3[0.0899] det_loss_box_dn_3[0.2166] det_loss_cls_dn_4[0.0892] det_loss_box_dn_4[0.2160] det_loss_cls_dn_5[0.0882] det_loss_box_dn_5[0.2152] loss_depth[0.0360] total_loss[9.3728]
Rank[0/8] 06/20/2025 06:25:30 INFO stats.py:314 | Epoch[123] Step[104] GlobalStep[48074] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 15:21:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:25:51 INFO loss_tracker.py:84 | Epoch[123/239] Step[124] GlobalStep[48094/NA]: det_loss_cls_0[0.4924] det_loss_box_0[0.6890] det_loss_cls_1[0.4861] det_loss_box_1[0.5338] det_loss_cls_2[0.4354] det_loss_box_2[0.4983] det_loss_cls_3[0.4172] det_loss_box_3[0.4746] det_loss_cls_4[0.3899] det_loss_box_4[0.4588] det_loss_cls_5[0.3771] det_loss_box_5[0.4564] det_loss_cls_dn_0[0.0901] det_loss_box_dn_0[0.3247] det_loss_cls_dn_1[0.0861] det_loss_box_dn_1[0.2740] det_loss_cls_dn_2[0.0856] det_loss_box_dn_2[0.2569] det_loss_cls_dn_3[0.0848] det_loss_box_dn_3[0.2521] det_loss_cls_dn_4[0.0839] det_loss_box_dn_4[0.2494] det_loss_cls_dn_5[0.0833] det_loss_box_dn_5[0.2447] loss_depth[0.0378] total_loss[7.8623]
Rank[0/8] 06/20/2025 06:25:56 INFO stats.py:314 | Epoch[123] Step[129] GlobalStep[48099] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 15:21:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:26:17 INFO loss_tracker.py:84 | Epoch[123/239] Step[149] GlobalStep[48119/NA]: det_loss_cls_0[0.4509] det_loss_box_0[0.7081] det_loss_cls_1[0.4551] det_loss_box_1[0.5525] det_loss_cls_2[0.4257] det_loss_box_2[0.4996] det_loss_cls_3[0.4054] det_loss_box_3[0.4805] det_loss_cls_4[0.3780] det_loss_box_4[0.4683] det_loss_cls_5[0.3712] det_loss_box_5[0.4659] det_loss_cls_dn_0[0.0898] det_loss_box_dn_0[0.3069] det_loss_cls_dn_1[0.0846] det_loss_box_dn_1[0.2667] det_loss_cls_dn_2[0.0844] det_loss_box_dn_2[0.2514] det_loss_cls_dn_3[0.0833] det_loss_box_dn_3[0.2490] det_loss_cls_dn_4[0.0821] det_loss_box_dn_4[0.2476] det_loss_cls_dn_5[0.0812] det_loss_box_dn_5[0.2447] loss_depth[0.0432] total_loss[7.7762]
Rank[0/8] 06/20/2025 06:26:22 INFO stats.py:314 | Epoch[123] Step[154] GlobalStep[48124] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 15:20:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:26:42 INFO loss_tracker.py:84 | Epoch[123/239] Step[174] GlobalStep[48144/NA]: det_loss_cls_0[0.5190] det_loss_box_0[0.6893] det_loss_cls_1[0.4968] det_loss_box_1[0.5098] det_loss_cls_2[0.4531] det_loss_box_2[0.4625] det_loss_cls_3[0.4206] det_loss_box_3[0.4516] det_loss_cls_4[0.3969] det_loss_box_4[0.4354] det_loss_cls_5[0.3919] det_loss_box_5[0.4271] det_loss_cls_dn_0[0.1031] det_loss_box_dn_0[0.2663] det_loss_cls_dn_1[0.0992] det_loss_box_dn_1[0.2275] det_loss_cls_dn_2[0.0987] det_loss_box_dn_2[0.2104] det_loss_cls_dn_3[0.0979] det_loss_box_dn_3[0.2077] det_loss_cls_dn_4[0.0972] det_loss_box_dn_4[0.2059] det_loss_cls_dn_5[0.0965] det_loss_box_dn_5[0.2056] loss_depth[0.0382] total_loss[7.6078]
Rank[0/8] 06/20/2025 06:26:47 INFO stats.py:314 | Epoch[123] Step[179] GlobalStep[48149] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 15:19:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:27:08 INFO loss_tracker.py:84 | Epoch[123/239] Step[199] GlobalStep[48169/NA]: det_loss_cls_0[0.4547] det_loss_box_0[0.5575] det_loss_cls_1[0.4496] det_loss_box_1[0.4375] det_loss_cls_2[0.4100] det_loss_box_2[0.3958] det_loss_cls_3[0.3765] det_loss_box_3[0.3888] det_loss_cls_4[0.3514] det_loss_box_4[0.3859] det_loss_cls_5[0.3374] det_loss_box_5[0.3842] det_loss_cls_dn_0[0.0986] det_loss_box_dn_0[0.2824] det_loss_cls_dn_1[0.0942] det_loss_box_dn_1[0.2397] det_loss_cls_dn_2[0.0934] det_loss_box_dn_2[0.2177] det_loss_cls_dn_3[0.0922] det_loss_box_dn_3[0.2149] det_loss_cls_dn_4[0.0914] det_loss_box_dn_4[0.2137] det_loss_cls_dn_5[0.0902] det_loss_box_dn_5[0.2131] loss_depth[0.0377] total_loss[6.9085]
Rank[0/8] 06/20/2025 06:27:13 INFO stats.py:314 | Epoch[123] Step[204] GlobalStep[48174] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 15:19:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:27:34 INFO loss_tracker.py:84 | Epoch[123/239] Step[224] GlobalStep[48194/NA]: det_loss_cls_0[0.6143] det_loss_box_0[0.7797] det_loss_cls_1[0.5881] det_loss_box_1[0.6032] det_loss_cls_2[0.5480] det_loss_box_2[0.5373] det_loss_cls_3[0.5087] det_loss_box_3[0.5237] det_loss_cls_4[0.4707] det_loss_box_4[0.5179] det_loss_cls_5[0.4569] det_loss_box_5[0.5140] det_loss_cls_dn_0[0.0931] det_loss_box_dn_0[0.2701] det_loss_cls_dn_1[0.0894] det_loss_box_dn_1[0.2303] det_loss_cls_dn_2[0.0881] det_loss_box_dn_2[0.2142] det_loss_cls_dn_3[0.0867] det_loss_box_dn_3[0.2113] det_loss_cls_dn_4[0.0861] det_loss_box_dn_4[0.2098] det_loss_cls_dn_5[0.0854] det_loss_box_dn_5[0.2085] loss_depth[0.0385] total_loss[8.5740]
Rank[0/8] 06/20/2025 06:27:40 INFO stats.py:314 | Epoch[123] Step[229] GlobalStep[48199] Training Speed: 8.07 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 15:18:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:28:00 INFO loss_tracker.py:84 | Epoch[123/239] Step[249] GlobalStep[48219/NA]: det_loss_cls_0[0.5074] det_loss_box_0[0.6454] det_loss_cls_1[0.4901] det_loss_box_1[0.4777] det_loss_cls_2[0.4332] det_loss_box_2[0.4395] det_loss_cls_3[0.4044] det_loss_box_3[0.4223] det_loss_cls_4[0.3820] det_loss_box_4[0.4152] det_loss_cls_5[0.3646] det_loss_box_5[0.4172] det_loss_cls_dn_0[0.1027] det_loss_box_dn_0[0.2538] det_loss_cls_dn_1[0.0980] det_loss_box_dn_1[0.2125] det_loss_cls_dn_2[0.0984] det_loss_box_dn_2[0.1992] det_loss_cls_dn_3[0.0974] det_loss_box_dn_3[0.1955] det_loss_cls_dn_4[0.0963] det_loss_box_dn_4[0.1943] det_loss_cls_dn_5[0.0957] det_loss_box_dn_5[0.1926] loss_depth[0.0401] total_loss[7.2755]
Rank[0/8] 06/20/2025 06:28:05 INFO stats.py:314 | Epoch[123] Step[254] GlobalStep[48224] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 15:18:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:28:26 INFO loss_tracker.py:84 | Epoch[123/239] Step[274] GlobalStep[48244/NA]: det_loss_cls_0[0.5218] det_loss_box_0[0.6590] det_loss_cls_1[0.4992] det_loss_box_1[0.5258] det_loss_cls_2[0.4626] det_loss_box_2[0.4942] det_loss_cls_3[0.4359] det_loss_box_3[0.4815] det_loss_cls_4[0.4235] det_loss_box_4[0.4650] det_loss_cls_5[0.4110] det_loss_box_5[0.4697] det_loss_cls_dn_0[0.0963] det_loss_box_dn_0[0.2951] det_loss_cls_dn_1[0.0933] det_loss_box_dn_1[0.2465] det_loss_cls_dn_2[0.0924] det_loss_box_dn_2[0.2328] det_loss_cls_dn_3[0.0908] det_loss_box_dn_3[0.2291] det_loss_cls_dn_4[0.0900] det_loss_box_dn_4[0.2263] det_loss_cls_dn_5[0.0891] det_loss_box_dn_5[0.2244] loss_depth[0.0382] total_loss[7.8936]
Rank[0/8] 06/20/2025 06:28:31 INFO stats.py:314 | Epoch[123] Step[279] GlobalStep[48249] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 15:17:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:28:52 INFO loss_tracker.py:84 | Epoch[123/239] Step[299] GlobalStep[48269/NA]: det_loss_cls_0[0.5400] det_loss_box_0[0.6441] det_loss_cls_1[0.5238] det_loss_box_1[0.5039] det_loss_cls_2[0.4769] det_loss_box_2[0.4458] det_loss_cls_3[0.4409] det_loss_box_3[0.4283] det_loss_cls_4[0.4137] det_loss_box_4[0.4310] det_loss_cls_5[0.4077] det_loss_box_5[0.4239] det_loss_cls_dn_0[0.1029] det_loss_box_dn_0[0.2768] det_loss_cls_dn_1[0.1007] det_loss_box_dn_1[0.2369] det_loss_cls_dn_2[0.1001] det_loss_box_dn_2[0.2213] det_loss_cls_dn_3[0.0991] det_loss_box_dn_3[0.2186] det_loss_cls_dn_4[0.0981] det_loss_box_dn_4[0.2173] det_loss_cls_dn_5[0.0973] det_loss_box_dn_5[0.2166] loss_depth[0.0355] total_loss[7.7010]
Rank[0/8] 06/20/2025 06:28:57 INFO stats.py:314 | Epoch[123] Step[304] GlobalStep[48274] Training Speed: 8.69 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 15:17:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:29:18 INFO loss_tracker.py:84 | Epoch[123/239] Step[324] GlobalStep[48294/NA]: det_loss_cls_0[0.5216] det_loss_box_0[0.6842] det_loss_cls_1[0.5081] det_loss_box_1[0.5369] det_loss_cls_2[0.4591] det_loss_box_2[0.4918] det_loss_cls_3[0.4301] det_loss_box_3[0.4767] det_loss_cls_4[0.4014] det_loss_box_4[0.4691] det_loss_cls_5[0.3879] det_loss_box_5[0.4736] det_loss_cls_dn_0[0.0962] det_loss_box_dn_0[0.3033] det_loss_cls_dn_1[0.0930] det_loss_box_dn_1[0.2575] det_loss_cls_dn_2[0.0929] det_loss_box_dn_2[0.2397] det_loss_cls_dn_3[0.0922] det_loss_box_dn_3[0.2357] det_loss_cls_dn_4[0.0908] det_loss_box_dn_4[0.2340] det_loss_cls_dn_5[0.0902] det_loss_box_dn_5[0.2328] loss_depth[0.0343] total_loss[7.9331]
Rank[0/8] 06/20/2025 06:29:23 INFO stats.py:314 | Epoch[123] Step[329] GlobalStep[48299] Training Speed: 7.77 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 15:16:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:29:43 INFO loss_tracker.py:84 | Epoch[123/239] Step[349] GlobalStep[48319/NA]: det_loss_cls_0[0.4266] det_loss_box_0[0.5562] det_loss_cls_1[0.4251] det_loss_box_1[0.4255] det_loss_cls_2[0.3799] det_loss_box_2[0.3950] det_loss_cls_3[0.3520] det_loss_box_3[0.3833] det_loss_cls_4[0.3295] det_loss_box_4[0.3685] det_loss_cls_5[0.3201] det_loss_box_5[0.3659] det_loss_cls_dn_0[0.0903] det_loss_box_dn_0[0.2843] det_loss_cls_dn_1[0.0870] det_loss_box_dn_1[0.2409] det_loss_cls_dn_2[0.0864] det_loss_box_dn_2[0.2268] det_loss_cls_dn_3[0.0851] det_loss_box_dn_3[0.2225] det_loss_cls_dn_4[0.0844] det_loss_box_dn_4[0.2208] det_loss_cls_dn_5[0.0834] det_loss_box_dn_5[0.2175] loss_depth[0.0379] total_loss[6.6945]
Rank[0/8] 06/20/2025 06:29:49 INFO stats.py:314 | Epoch[123] Step[354] GlobalStep[48324] Training Speed: 6.57 samples/sec across all devices.	Average Step Time: 1.22 sec.	Estimated Remaining Time: 15:15:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:30:09 INFO loss_tracker.py:84 | Epoch[123/239] Step[374] GlobalStep[48344/NA]: det_loss_cls_0[0.5219] det_loss_box_0[0.7055] det_loss_cls_1[0.5106] det_loss_box_1[0.5327] det_loss_cls_2[0.4676] det_loss_box_2[0.4739] det_loss_cls_3[0.4281] det_loss_box_3[0.4608] det_loss_cls_4[0.4015] det_loss_box_4[0.4504] det_loss_cls_5[0.3899] det_loss_box_5[0.4519] det_loss_cls_dn_0[0.0937] det_loss_box_dn_0[0.2851] det_loss_cls_dn_1[0.0893] det_loss_box_dn_1[0.2493] det_loss_cls_dn_2[0.0881] det_loss_box_dn_2[0.2346] det_loss_cls_dn_3[0.0876] det_loss_box_dn_3[0.2311] det_loss_cls_dn_4[0.0861] det_loss_box_dn_4[0.2301] det_loss_cls_dn_5[0.0852] det_loss_box_dn_5[0.2255] loss_depth[0.0372] total_loss[7.8179]
Rank[0/8] 06/20/2025 06:30:15 INFO stats.py:314 | Epoch[123] Step[379] GlobalStep[48349] Training Speed: 6.22 samples/sec across all devices.	Average Step Time: 1.29 sec.	Estimated Remaining Time: 15:15:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:30:25 INFO stats.py:394 | Epoch[123] completed. Training Speed: 7.65 samples/sec across all devices.	Epoch Time: 408.08 sec.	Average Epoch Time: 408.08 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 15:15:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:30:42 INFO stats.py:314 | Epoch[124] Step[14] GlobalStep[48374] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 15:14:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:30:52 INFO loss_tracker.py:84 | Epoch[124/239] Step[24] GlobalStep[48384/NA]: det_loss_cls_0[0.4864] det_loss_box_0[0.6219] det_loss_cls_1[0.4877] det_loss_box_1[0.4823] det_loss_cls_2[0.4337] det_loss_box_2[0.4346] det_loss_cls_3[0.4027] det_loss_box_3[0.4297] det_loss_cls_4[0.3774] det_loss_box_4[0.4201] det_loss_cls_5[0.3635] det_loss_box_5[0.4230] det_loss_cls_dn_0[0.1003] det_loss_box_dn_0[0.2786] det_loss_cls_dn_1[0.0965] det_loss_box_dn_1[0.2397] det_loss_cls_dn_2[0.0956] det_loss_box_dn_2[0.2231] det_loss_cls_dn_3[0.0943] det_loss_box_dn_3[0.2199] det_loss_cls_dn_4[0.0931] det_loss_box_dn_4[0.2176] det_loss_cls_dn_5[0.0922] det_loss_box_dn_5[0.2165] loss_depth[0.0387] total_loss[7.3691]
Rank[0/8] 06/20/2025 06:31:08 INFO stats.py:314 | Epoch[124] Step[39] GlobalStep[48399] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 15:14:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:31:19 INFO loss_tracker.py:84 | Epoch[124/239] Step[49] GlobalStep[48409/NA]: det_loss_cls_0[0.6346] det_loss_box_0[0.8488] det_loss_cls_1[0.6029] det_loss_box_1[0.6666] det_loss_cls_2[0.5596] det_loss_box_2[0.6018] det_loss_cls_3[0.5310] det_loss_box_3[0.5756] det_loss_cls_4[0.4855] det_loss_box_4[0.5764] det_loss_cls_5[0.4763] det_loss_box_5[0.5767] det_loss_cls_dn_0[0.0922] det_loss_box_dn_0[0.2823] det_loss_cls_dn_1[0.0883] det_loss_box_dn_1[0.2332] det_loss_cls_dn_2[0.0872] det_loss_box_dn_2[0.2142] det_loss_cls_dn_3[0.0862] det_loss_box_dn_3[0.2099] det_loss_cls_dn_4[0.0859] det_loss_box_dn_4[0.2078] det_loss_cls_dn_5[0.0846] det_loss_box_dn_5[0.2061] loss_depth[0.0379] total_loss[9.0517]
Rank[0/8] 06/20/2025 06:31:34 INFO stats.py:314 | Epoch[124] Step[64] GlobalStep[48424] Training Speed: 8.62 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 15:13:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:31:45 INFO loss_tracker.py:84 | Epoch[124/239] Step[74] GlobalStep[48434/NA]: det_loss_cls_0[0.4222] det_loss_box_0[0.5947] det_loss_cls_1[0.4075] det_loss_box_1[0.4668] det_loss_cls_2[0.3763] det_loss_box_2[0.4131] det_loss_cls_3[0.3509] det_loss_box_3[0.4040] det_loss_cls_4[0.3225] det_loss_box_4[0.3966] det_loss_cls_5[0.3177] det_loss_box_5[0.3930] det_loss_cls_dn_0[0.0867] det_loss_box_dn_0[0.3778] det_loss_cls_dn_1[0.0829] det_loss_box_dn_1[0.3183] det_loss_cls_dn_2[0.0828] det_loss_box_dn_2[0.3006] det_loss_cls_dn_3[0.0802] det_loss_box_dn_3[0.2963] det_loss_cls_dn_4[0.0791] det_loss_box_dn_4[0.2947] det_loss_cls_dn_5[0.0785] det_loss_box_dn_5[0.2908] loss_depth[0.0378] total_loss[7.2719]
Rank[0/8] 06/20/2025 06:32:00 INFO stats.py:314 | Epoch[124] Step[89] GlobalStep[48449] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 15:13:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:32:10 INFO loss_tracker.py:84 | Epoch[124/239] Step[99] GlobalStep[48459/NA]: det_loss_cls_0[0.5883] det_loss_box_0[0.7471] det_loss_cls_1[0.5738] det_loss_box_1[0.5777] det_loss_cls_2[0.5304] det_loss_box_2[0.5175] det_loss_cls_3[0.5032] det_loss_box_3[0.5085] det_loss_cls_4[0.4754] det_loss_box_4[0.4928] det_loss_cls_5[0.4537] det_loss_box_5[0.4967] det_loss_cls_dn_0[0.1026] det_loss_box_dn_0[0.2646] det_loss_cls_dn_1[0.0990] det_loss_box_dn_1[0.2263] det_loss_cls_dn_2[0.0987] det_loss_box_dn_2[0.2133] det_loss_cls_dn_3[0.0978] det_loss_box_dn_3[0.2110] det_loss_cls_dn_4[0.0965] det_loss_box_dn_4[0.2098] det_loss_cls_dn_5[0.0958] det_loss_box_dn_5[0.2087] loss_depth[0.0366] total_loss[8.4258]
Rank[0/8] 06/20/2025 06:32:25 INFO stats.py:314 | Epoch[124] Step[114] GlobalStep[48474] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 15:12:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:32:36 INFO loss_tracker.py:84 | Epoch[124/239] Step[124] GlobalStep[48484/NA]: det_loss_cls_0[0.4675] det_loss_box_0[0.6720] det_loss_cls_1[0.4639] det_loss_box_1[0.5227] det_loss_cls_2[0.4183] det_loss_box_2[0.4728] det_loss_cls_3[0.3987] det_loss_box_3[0.4541] det_loss_cls_4[0.3757] det_loss_box_4[0.4423] det_loss_cls_5[0.3603] det_loss_box_5[0.4434] det_loss_cls_dn_0[0.0935] det_loss_box_dn_0[0.2723] det_loss_cls_dn_1[0.0905] det_loss_box_dn_1[0.2291] det_loss_cls_dn_2[0.0898] det_loss_box_dn_2[0.2152] det_loss_cls_dn_3[0.0890] det_loss_box_dn_3[0.2121] det_loss_cls_dn_4[0.0879] det_loss_box_dn_4[0.2101] det_loss_cls_dn_5[0.0871] det_loss_box_dn_5[0.2092] loss_depth[0.0369] total_loss[7.4143]
Rank[0/8] 06/20/2025 06:32:51 INFO stats.py:314 | Epoch[124] Step[139] GlobalStep[48499] Training Speed: 6.47 samples/sec across all devices.	Average Step Time: 1.24 sec.	Estimated Remaining Time: 15:11:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:33:02 INFO loss_tracker.py:84 | Epoch[124/239] Step[149] GlobalStep[48509/NA]: det_loss_cls_0[0.4889] det_loss_box_0[0.5900] det_loss_cls_1[0.4745] det_loss_box_1[0.4334] det_loss_cls_2[0.4349] det_loss_box_2[0.4005] det_loss_cls_3[0.4021] det_loss_box_3[0.3899] det_loss_cls_4[0.3760] det_loss_box_4[0.3807] det_loss_cls_5[0.3639] det_loss_box_5[0.3763] det_loss_cls_dn_0[0.0983] det_loss_box_dn_0[0.2893] det_loss_cls_dn_1[0.0936] det_loss_box_dn_1[0.2309] det_loss_cls_dn_2[0.0928] det_loss_box_dn_2[0.2128] det_loss_cls_dn_3[0.0927] det_loss_box_dn_3[0.2097] det_loss_cls_dn_4[0.0919] det_loss_box_dn_4[0.2061] det_loss_cls_dn_5[0.0913] det_loss_box_dn_5[0.2036] loss_depth[0.0388] total_loss[7.0631]
Rank[0/8] 06/20/2025 06:33:17 INFO stats.py:314 | Epoch[124] Step[164] GlobalStep[48524] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 15:11:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:33:28 INFO loss_tracker.py:84 | Epoch[124/239] Step[174] GlobalStep[48534/NA]: det_loss_cls_0[0.5499] det_loss_box_0[0.8782] det_loss_cls_1[0.5444] det_loss_box_1[0.6956] det_loss_cls_2[0.5027] det_loss_box_2[0.6131] det_loss_cls_3[0.4756] det_loss_box_3[0.5939] det_loss_cls_4[0.4432] det_loss_box_4[0.5843] det_loss_cls_5[0.4265] det_loss_box_5[0.5812] det_loss_cls_dn_0[0.1048] det_loss_box_dn_0[0.4142] det_loss_cls_dn_1[0.1001] det_loss_box_dn_1[0.3720] det_loss_cls_dn_2[0.0982] det_loss_box_dn_2[0.3560] det_loss_cls_dn_3[0.0969] det_loss_box_dn_3[0.3530] det_loss_cls_dn_4[0.0953] det_loss_box_dn_4[0.3514] det_loss_cls_dn_5[0.0938] det_loss_box_dn_5[0.3453] loss_depth[0.0404] total_loss[9.7100]
Rank[0/8] 06/20/2025 06:33:43 INFO stats.py:314 | Epoch[124] Step[189] GlobalStep[48549] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 15:10:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:33:54 INFO loss_tracker.py:84 | Epoch[124/239] Step[199] GlobalStep[48559/NA]: det_loss_cls_0[0.4453] det_loss_box_0[0.5609] det_loss_cls_1[0.4457] det_loss_box_1[0.4397] det_loss_cls_2[0.3917] det_loss_box_2[0.3923] det_loss_cls_3[0.3550] det_loss_box_3[0.3807] det_loss_cls_4[0.3269] det_loss_box_4[0.3756] det_loss_cls_5[0.3130] det_loss_box_5[0.3744] det_loss_cls_dn_0[0.0953] det_loss_box_dn_0[0.2433] det_loss_cls_dn_1[0.0925] det_loss_box_dn_1[0.1961] det_loss_cls_dn_2[0.0919] det_loss_box_dn_2[0.1777] det_loss_cls_dn_3[0.0910] det_loss_box_dn_3[0.1742] det_loss_cls_dn_4[0.0900] det_loss_box_dn_4[0.1726] det_loss_cls_dn_5[0.0891] det_loss_box_dn_5[0.1719] loss_depth[0.0382] total_loss[6.5250]
Rank[0/8] 06/20/2025 06:34:09 INFO stats.py:314 | Epoch[124] Step[214] GlobalStep[48574] Training Speed: 7.85 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 15:10:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:34:20 INFO loss_tracker.py:84 | Epoch[124/239] Step[224] GlobalStep[48584/NA]: det_loss_cls_0[0.5112] det_loss_box_0[0.6913] det_loss_cls_1[0.5000] det_loss_box_1[0.5367] det_loss_cls_2[0.4609] det_loss_box_2[0.4998] det_loss_cls_3[0.4313] det_loss_box_3[0.4841] det_loss_cls_4[0.4084] det_loss_box_4[0.4659] det_loss_cls_5[0.3877] det_loss_box_5[0.4707] det_loss_cls_dn_0[0.0960] det_loss_box_dn_0[0.3605] det_loss_cls_dn_1[0.0922] det_loss_box_dn_1[0.3016] det_loss_cls_dn_2[0.0908] det_loss_box_dn_2[0.2849] det_loss_cls_dn_3[0.0899] det_loss_box_dn_3[0.2808] det_loss_cls_dn_4[0.0888] det_loss_box_dn_4[0.2773] det_loss_cls_dn_5[0.0874] det_loss_box_dn_5[0.2738] loss_depth[0.0384] total_loss[8.2101]
Rank[0/8] 06/20/2025 06:34:35 INFO stats.py:314 | Epoch[124] Step[239] GlobalStep[48599] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 15:09:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:34:45 INFO loss_tracker.py:84 | Epoch[124/239] Step[249] GlobalStep[48609/NA]: det_loss_cls_0[0.4746] det_loss_box_0[0.6421] det_loss_cls_1[0.4780] det_loss_box_1[0.4966] det_loss_cls_2[0.4364] det_loss_box_2[0.4403] det_loss_cls_3[0.4034] det_loss_box_3[0.4327] det_loss_cls_4[0.3778] det_loss_box_4[0.4250] det_loss_cls_5[0.3589] det_loss_box_5[0.4328] det_loss_cls_dn_0[0.1016] det_loss_box_dn_0[0.2792] det_loss_cls_dn_1[0.0973] det_loss_box_dn_1[0.2369] det_loss_cls_dn_2[0.0965] det_loss_box_dn_2[0.2201] det_loss_cls_dn_3[0.0954] det_loss_box_dn_3[0.2159] det_loss_cls_dn_4[0.0942] det_loss_box_dn_4[0.2142] det_loss_cls_dn_5[0.0937] det_loss_box_dn_5[0.2122] loss_depth[0.0387] total_loss[7.3946]
Rank[0/8] 06/20/2025 06:35:01 INFO stats.py:314 | Epoch[124] Step[264] GlobalStep[48624] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 15:08:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:35:11 INFO loss_tracker.py:84 | Epoch[124/239] Step[274] GlobalStep[48634/NA]: det_loss_cls_0[0.5076] det_loss_box_0[0.7292] det_loss_cls_1[0.4878] det_loss_box_1[0.5912] det_loss_cls_2[0.4455] det_loss_box_2[0.5473] det_loss_cls_3[0.4321] det_loss_box_3[0.5206] det_loss_cls_4[0.4136] det_loss_box_4[0.5132] det_loss_cls_5[0.4039] det_loss_box_5[0.5106] det_loss_cls_dn_0[0.0926] det_loss_box_dn_0[0.4482] det_loss_cls_dn_1[0.0885] det_loss_box_dn_1[0.3876] det_loss_cls_dn_2[0.0879] det_loss_box_dn_2[0.3629] det_loss_cls_dn_3[0.0869] det_loss_box_dn_3[0.3589] det_loss_cls_dn_4[0.0861] det_loss_box_dn_4[0.3557] det_loss_cls_dn_5[0.0852] det_loss_box_dn_5[0.3538] loss_depth[0.0399] total_loss[8.9370]
Rank[0/8] 06/20/2025 06:35:26 INFO stats.py:314 | Epoch[124] Step[289] GlobalStep[48649] Training Speed: 6.86 samples/sec across all devices.	Average Step Time: 1.17 sec.	Estimated Remaining Time: 15:08:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:35:36 INFO loss_tracker.py:84 | Epoch[124/239] Step[299] GlobalStep[48659/NA]: det_loss_cls_0[0.5396] det_loss_box_0[0.7123] det_loss_cls_1[0.5365] det_loss_box_1[0.5568] det_loss_cls_2[0.5028] det_loss_box_2[0.5103] det_loss_cls_3[0.4813] det_loss_box_3[0.4917] det_loss_cls_4[0.4543] det_loss_box_4[0.4803] det_loss_cls_5[0.4432] det_loss_box_5[0.4805] det_loss_cls_dn_0[0.0972] det_loss_box_dn_0[0.3030] det_loss_cls_dn_1[0.0943] det_loss_box_dn_1[0.2544] det_loss_cls_dn_2[0.0936] det_loss_box_dn_2[0.2384] det_loss_cls_dn_3[0.0927] det_loss_box_dn_3[0.2348] det_loss_cls_dn_4[0.0911] det_loss_box_dn_4[0.2323] det_loss_cls_dn_5[0.0902] det_loss_box_dn_5[0.2309] loss_depth[0.0364] total_loss[8.2792]
Rank[0/8] 06/20/2025 06:35:53 INFO stats.py:314 | Epoch[124] Step[314] GlobalStep[48674] Training Speed: 7.83 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 15:07:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:36:02 INFO loss_tracker.py:84 | Epoch[124/239] Step[324] GlobalStep[48684/NA]: det_loss_cls_0[0.4999] det_loss_box_0[0.7262] det_loss_cls_1[0.4851] det_loss_box_1[0.5757] det_loss_cls_2[0.4604] det_loss_box_2[0.5062] det_loss_cls_3[0.4301] det_loss_box_3[0.4857] det_loss_cls_4[0.4100] det_loss_box_4[0.4714] det_loss_cls_5[0.3959] det_loss_box_5[0.4723] det_loss_cls_dn_0[0.0928] det_loss_box_dn_0[0.3263] det_loss_cls_dn_1[0.0885] det_loss_box_dn_1[0.2835] det_loss_cls_dn_2[0.0880] det_loss_box_dn_2[0.2659] det_loss_cls_dn_3[0.0871] det_loss_box_dn_3[0.2605] det_loss_cls_dn_4[0.0860] det_loss_box_dn_4[0.2586] det_loss_cls_dn_5[0.0847] det_loss_box_dn_5[0.2547] loss_depth[0.0372] total_loss[8.1329]
Rank[0/8] 06/20/2025 06:36:18 INFO stats.py:314 | Epoch[124] Step[339] GlobalStep[48699] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 15:07:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:36:28 INFO loss_tracker.py:84 | Epoch[124/239] Step[349] GlobalStep[48709/NA]: det_loss_cls_0[0.5181] det_loss_box_0[0.7488] det_loss_cls_1[0.4962] det_loss_box_1[0.5556] det_loss_cls_2[0.4565] det_loss_box_2[0.5010] det_loss_cls_3[0.4206] det_loss_box_3[0.4811] det_loss_cls_4[0.3992] det_loss_box_4[0.4797] det_loss_cls_5[0.3835] det_loss_box_5[0.4788] det_loss_cls_dn_0[0.0960] det_loss_box_dn_0[0.3333] det_loss_cls_dn_1[0.0921] det_loss_box_dn_1[0.2827] det_loss_cls_dn_2[0.0919] det_loss_box_dn_2[0.2606] det_loss_cls_dn_3[0.0911] det_loss_box_dn_3[0.2554] det_loss_cls_dn_4[0.0898] det_loss_box_dn_4[0.2528] det_loss_cls_dn_5[0.0889] det_loss_box_dn_5[0.2520] loss_depth[0.0422] total_loss[8.1479]
Rank[0/8] 06/20/2025 06:36:43 INFO stats.py:314 | Epoch[124] Step[364] GlobalStep[48724] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 15:06:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:36:54 INFO loss_tracker.py:84 | Epoch[124/239] Step[374] GlobalStep[48734/NA]: det_loss_cls_0[0.5834] det_loss_box_0[0.7992] det_loss_cls_1[0.5634] det_loss_box_1[0.6311] det_loss_cls_2[0.5178] det_loss_box_2[0.5731] det_loss_cls_3[0.4782] det_loss_box_3[0.5591] det_loss_cls_4[0.4551] det_loss_box_4[0.5329] det_loss_cls_5[0.4285] det_loss_box_5[0.5404] det_loss_cls_dn_0[0.0966] det_loss_box_dn_0[0.2987] det_loss_cls_dn_1[0.0926] det_loss_box_dn_1[0.2573] det_loss_cls_dn_2[0.0922] det_loss_box_dn_2[0.2411] det_loss_cls_dn_3[0.0914] det_loss_box_dn_3[0.2373] det_loss_cls_dn_4[0.0900] det_loss_box_dn_4[0.2358] det_loss_cls_dn_5[0.0882] det_loss_box_dn_5[0.2334] loss_depth[0.0387] total_loss[8.7554]
Rank[0/8] 06/20/2025 06:37:09 INFO stats.py:314 | Epoch[124] Step[389] GlobalStep[48749] Training Speed: 8.01 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 15:06:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:37:09 INFO stats.py:394 | Epoch[124] completed. Training Speed: 7.72 samples/sec across all devices.	Epoch Time: 403.92 sec.	Average Epoch Time: 403.92 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 15:06:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:37:37 INFO stats.py:314 | Epoch[125] Step[24] GlobalStep[48774] Training Speed: 8.06 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 15:05:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:37:37 INFO loss_tracker.py:84 | Epoch[125/239] Step[24] GlobalStep[48774/NA]: det_loss_cls_0[0.5421] det_loss_box_0[0.6848] det_loss_cls_1[0.5385] det_loss_box_1[0.5469] det_loss_cls_2[0.4959] det_loss_box_2[0.5087] det_loss_cls_3[0.4730] det_loss_box_3[0.4861] det_loss_cls_4[0.4498] det_loss_box_4[0.4694] det_loss_cls_5[0.4387] det_loss_box_5[0.4682] det_loss_cls_dn_0[0.1009] det_loss_box_dn_0[0.3071] det_loss_cls_dn_1[0.0974] det_loss_box_dn_1[0.2591] det_loss_cls_dn_2[0.0967] det_loss_box_dn_2[0.2435] det_loss_cls_dn_3[0.0954] det_loss_box_dn_3[0.2369] det_loss_cls_dn_4[0.0945] det_loss_box_dn_4[0.2350] det_loss_cls_dn_5[0.0935] det_loss_box_dn_5[0.2340] loss_depth[0.0368] total_loss[8.2327]
Rank[0/8] 06/20/2025 06:38:02 INFO stats.py:314 | Epoch[125] Step[49] GlobalStep[48799] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 15:04:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:38:02 INFO loss_tracker.py:84 | Epoch[125/239] Step[49] GlobalStep[48799/NA]: det_loss_cls_0[0.5641] det_loss_box_0[0.7327] det_loss_cls_1[0.5494] det_loss_box_1[0.5280] det_loss_cls_2[0.4947] det_loss_box_2[0.4801] det_loss_cls_3[0.4602] det_loss_box_3[0.4686] det_loss_cls_4[0.4342] det_loss_box_4[0.4448] det_loss_cls_5[0.4152] det_loss_box_5[0.4497] det_loss_cls_dn_0[0.0991] det_loss_box_dn_0[0.2787] det_loss_cls_dn_1[0.0958] det_loss_box_dn_1[0.2278] det_loss_cls_dn_2[0.0952] det_loss_box_dn_2[0.2068] det_loss_cls_dn_3[0.0947] det_loss_box_dn_3[0.2040] det_loss_cls_dn_4[0.0937] det_loss_box_dn_4[0.2025] det_loss_cls_dn_5[0.0929] det_loss_box_dn_5[0.2014] loss_depth[0.0379] total_loss[7.9524]
Rank[0/8] 06/20/2025 06:38:28 INFO stats.py:314 | Epoch[125] Step[74] GlobalStep[48824] Training Speed: 8.67 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 15:04:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:38:28 INFO loss_tracker.py:84 | Epoch[125/239] Step[74] GlobalStep[48824/NA]: det_loss_cls_0[0.5308] det_loss_box_0[0.7176] det_loss_cls_1[0.5252] det_loss_box_1[0.5352] det_loss_cls_2[0.4670] det_loss_box_2[0.4933] det_loss_cls_3[0.4405] det_loss_box_3[0.4766] det_loss_cls_4[0.4117] det_loss_box_4[0.4650] det_loss_cls_5[0.4001] det_loss_box_5[0.4636] det_loss_cls_dn_0[0.1027] det_loss_box_dn_0[0.3505] det_loss_cls_dn_1[0.0977] det_loss_box_dn_1[0.2967] det_loss_cls_dn_2[0.0965] det_loss_box_dn_2[0.2787] det_loss_cls_dn_3[0.0954] det_loss_box_dn_3[0.2714] det_loss_cls_dn_4[0.0940] det_loss_box_dn_4[0.2709] det_loss_cls_dn_5[0.0936] det_loss_box_dn_5[0.2700] loss_depth[0.0388] total_loss[8.2831]
Rank[0/8] 06/20/2025 06:38:54 INFO stats.py:314 | Epoch[125] Step[99] GlobalStep[48849] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 15:03:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:38:54 INFO loss_tracker.py:84 | Epoch[125/239] Step[99] GlobalStep[48849/NA]: det_loss_cls_0[0.5424] det_loss_box_0[0.6960] det_loss_cls_1[0.5275] det_loss_box_1[0.5261] det_loss_cls_2[0.4838] det_loss_box_2[0.4729] det_loss_cls_3[0.4534] det_loss_box_3[0.4533] det_loss_cls_4[0.4276] det_loss_box_4[0.4360] det_loss_cls_5[0.4082] det_loss_box_5[0.4370] det_loss_cls_dn_0[0.0970] det_loss_box_dn_0[0.2669] det_loss_cls_dn_1[0.0939] det_loss_box_dn_1[0.2234] det_loss_cls_dn_2[0.0932] det_loss_box_dn_2[0.2061] det_loss_cls_dn_3[0.0919] det_loss_box_dn_3[0.2024] det_loss_cls_dn_4[0.0914] det_loss_box_dn_4[0.2003] det_loss_cls_dn_5[0.0906] det_loss_box_dn_5[0.1984] loss_depth[0.0383] total_loss[7.7580]
Rank[0/8] 06/20/2025 06:39:20 INFO stats.py:314 | Epoch[125] Step[124] GlobalStep[48874] Training Speed: 7.91 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 15:03:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:39:20 INFO loss_tracker.py:84 | Epoch[125/239] Step[124] GlobalStep[48874/NA]: det_loss_cls_0[0.6077] det_loss_box_0[0.7969] det_loss_cls_1[0.5904] det_loss_box_1[0.5937] det_loss_cls_2[0.5427] det_loss_box_2[0.5440] det_loss_cls_3[0.5088] det_loss_box_3[0.5370] det_loss_cls_4[0.4799] det_loss_box_4[0.5206] det_loss_cls_5[0.4678] det_loss_box_5[0.5196] det_loss_cls_dn_0[0.0980] det_loss_box_dn_0[0.2531] det_loss_cls_dn_1[0.0956] det_loss_box_dn_1[0.2128] det_loss_cls_dn_2[0.0957] det_loss_box_dn_2[0.1970] det_loss_cls_dn_3[0.0953] det_loss_box_dn_3[0.1943] det_loss_cls_dn_4[0.0939] det_loss_box_dn_4[0.1928] det_loss_cls_dn_5[0.0929] det_loss_box_dn_5[0.1919] loss_depth[0.0369] total_loss[8.5590]
Rank[0/8] 06/20/2025 06:39:45 INFO stats.py:314 | Epoch[125] Step[149] GlobalStep[48899] Training Speed: 7.55 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 15:02:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:39:45 INFO loss_tracker.py:84 | Epoch[125/239] Step[149] GlobalStep[48899/NA]: det_loss_cls_0[0.6061] det_loss_box_0[0.8024] det_loss_cls_1[0.5878] det_loss_box_1[0.6370] det_loss_cls_2[0.5579] det_loss_box_2[0.5688] det_loss_cls_3[0.5305] det_loss_box_3[0.5493] det_loss_cls_4[0.4877] det_loss_box_4[0.5419] det_loss_cls_5[0.4719] det_loss_box_5[0.5397] det_loss_cls_dn_0[0.1047] det_loss_box_dn_0[0.3192] det_loss_cls_dn_1[0.1021] det_loss_box_dn_1[0.2710] det_loss_cls_dn_2[0.1012] det_loss_box_dn_2[0.2517] det_loss_cls_dn_3[0.1003] det_loss_box_dn_3[0.2478] det_loss_cls_dn_4[0.0989] det_loss_box_dn_4[0.2466] det_loss_cls_dn_5[0.0981] det_loss_box_dn_5[0.2460] loss_depth[0.0382] total_loss[9.1067]
Rank[0/8] 06/20/2025 06:40:11 INFO stats.py:314 | Epoch[125] Step[174] GlobalStep[48924] Training Speed: 8.56 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 15:02:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:40:11 INFO loss_tracker.py:84 | Epoch[125/239] Step[174] GlobalStep[48924/NA]: det_loss_cls_0[0.4073] det_loss_box_0[0.5206] det_loss_cls_1[0.3970] det_loss_box_1[0.4211] det_loss_cls_2[0.3703] det_loss_box_2[0.3853] det_loss_cls_3[0.3399] det_loss_box_3[0.3825] det_loss_cls_4[0.3231] det_loss_box_4[0.3786] det_loss_cls_5[0.3155] det_loss_box_5[0.3781] det_loss_cls_dn_0[0.0914] det_loss_box_dn_0[0.3234] det_loss_cls_dn_1[0.0871] det_loss_box_dn_1[0.2755] det_loss_cls_dn_2[0.0867] det_loss_box_dn_2[0.2599] det_loss_cls_dn_3[0.0862] det_loss_box_dn_3[0.2562] det_loss_cls_dn_4[0.0848] det_loss_box_dn_4[0.2545] det_loss_cls_dn_5[0.0838] det_loss_box_dn_5[0.2520] loss_depth[0.0360] total_loss[6.7966]
Rank[0/8] 06/20/2025 06:40:37 INFO stats.py:314 | Epoch[125] Step[199] GlobalStep[48949] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 15:01:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:40:37 INFO loss_tracker.py:84 | Epoch[125/239] Step[199] GlobalStep[48949/NA]: det_loss_cls_0[0.4488] det_loss_box_0[0.5729] det_loss_cls_1[0.4348] det_loss_box_1[0.4330] det_loss_cls_2[0.3963] det_loss_box_2[0.4052] det_loss_cls_3[0.3562] det_loss_box_3[0.3948] det_loss_cls_4[0.3352] det_loss_box_4[0.3873] det_loss_cls_5[0.3241] det_loss_box_5[0.3880] det_loss_cls_dn_0[0.0988] det_loss_box_dn_0[0.2908] det_loss_cls_dn_1[0.0947] det_loss_box_dn_1[0.2523] det_loss_cls_dn_2[0.0939] det_loss_box_dn_2[0.2377] det_loss_cls_dn_3[0.0928] det_loss_box_dn_3[0.2348] det_loss_cls_dn_4[0.0921] det_loss_box_dn_4[0.2346] det_loss_cls_dn_5[0.0907] det_loss_box_dn_5[0.2335] loss_depth[0.0361] total_loss[6.9596]
Rank[0/8] 06/20/2025 06:41:03 INFO stats.py:314 | Epoch[125] Step[224] GlobalStep[48974] Training Speed: 7.81 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 15:00:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:41:03 INFO loss_tracker.py:84 | Epoch[125/239] Step[224] GlobalStep[48974/NA]: det_loss_cls_0[0.5837] det_loss_box_0[0.7731] det_loss_cls_1[0.5708] det_loss_box_1[0.5953] det_loss_cls_2[0.5309] det_loss_box_2[0.5371] det_loss_cls_3[0.4891] det_loss_box_3[0.5184] det_loss_cls_4[0.4550] det_loss_box_4[0.5069] det_loss_cls_5[0.4333] det_loss_box_5[0.5103] det_loss_cls_dn_0[0.0964] det_loss_box_dn_0[0.3013] det_loss_cls_dn_1[0.0924] det_loss_box_dn_1[0.2578] det_loss_cls_dn_2[0.0920] det_loss_box_dn_2[0.2392] det_loss_cls_dn_3[0.0908] det_loss_box_dn_3[0.2371] det_loss_cls_dn_4[0.0899] det_loss_box_dn_4[0.2369] det_loss_cls_dn_5[0.0891] det_loss_box_dn_5[0.2365] loss_depth[0.0381] total_loss[8.6016]
Rank[0/8] 06/20/2025 06:41:29 INFO stats.py:314 | Epoch[125] Step[249] GlobalStep[48999] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 15:00:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:41:29 INFO loss_tracker.py:84 | Epoch[125/239] Step[249] GlobalStep[48999/NA]: det_loss_cls_0[0.4678] det_loss_box_0[0.5862] det_loss_cls_1[0.4586] det_loss_box_1[0.4620] det_loss_cls_2[0.4248] det_loss_box_2[0.4174] det_loss_cls_3[0.3986] det_loss_box_3[0.4092] det_loss_cls_4[0.3657] det_loss_box_4[0.4092] det_loss_cls_5[0.3557] det_loss_box_5[0.4077] det_loss_cls_dn_0[0.0947] det_loss_box_dn_0[0.2997] det_loss_cls_dn_1[0.0906] det_loss_box_dn_1[0.2528] det_loss_cls_dn_2[0.0893] det_loss_box_dn_2[0.2370] det_loss_cls_dn_3[0.0882] det_loss_box_dn_3[0.2330] det_loss_cls_dn_4[0.0866] det_loss_box_dn_4[0.2316] det_loss_cls_dn_5[0.0861] det_loss_box_dn_5[0.2292] loss_depth[0.0400] total_loss[7.2216]
Rank[0/8] 06/20/2025 06:41:55 INFO stats.py:314 | Epoch[125] Step[274] GlobalStep[49024] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 14:59:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:41:55 INFO loss_tracker.py:84 | Epoch[125/239] Step[274] GlobalStep[49024/NA]: det_loss_cls_0[0.5185] det_loss_box_0[0.6980] det_loss_cls_1[0.5090] det_loss_box_1[0.5428] det_loss_cls_2[0.4722] det_loss_box_2[0.4899] det_loss_cls_3[0.4528] det_loss_box_3[0.4671] det_loss_cls_4[0.4206] det_loss_box_4[0.4602] det_loss_cls_5[0.4065] det_loss_box_5[0.4574] det_loss_cls_dn_0[0.0944] det_loss_box_dn_0[0.2711] det_loss_cls_dn_1[0.0908] det_loss_box_dn_1[0.2274] det_loss_cls_dn_2[0.0896] det_loss_box_dn_2[0.2095] det_loss_cls_dn_3[0.0885] det_loss_box_dn_3[0.2069] det_loss_cls_dn_4[0.0875] det_loss_box_dn_4[0.2058] det_loss_cls_dn_5[0.0864] det_loss_box_dn_5[0.2045] loss_depth[0.0383] total_loss[7.7957]
Rank[0/8] 06/20/2025 06:42:20 INFO stats.py:314 | Epoch[125] Step[299] GlobalStep[49049] Training Speed: 8.22 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 14:59:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:42:20 INFO loss_tracker.py:84 | Epoch[125/239] Step[299] GlobalStep[49049/NA]: det_loss_cls_0[0.4669] det_loss_box_0[0.6660] det_loss_cls_1[0.4464] det_loss_box_1[0.5243] det_loss_cls_2[0.4136] det_loss_box_2[0.4685] det_loss_cls_3[0.3844] det_loss_box_3[0.4444] det_loss_cls_4[0.3586] det_loss_box_4[0.4310] det_loss_cls_5[0.3423] det_loss_box_5[0.4336] det_loss_cls_dn_0[0.0832] det_loss_box_dn_0[0.2949] det_loss_cls_dn_1[0.0785] det_loss_box_dn_1[0.2478] det_loss_cls_dn_2[0.0773] det_loss_box_dn_2[0.2270] det_loss_cls_dn_3[0.0764] det_loss_box_dn_3[0.2230] det_loss_cls_dn_4[0.0757] det_loss_box_dn_4[0.2205] det_loss_cls_dn_5[0.0746] det_loss_box_dn_5[0.2192] loss_depth[0.0377] total_loss[7.3158]
Rank[0/8] 06/20/2025 06:42:46 INFO stats.py:314 | Epoch[125] Step[324] GlobalStep[49074] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 14:58:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:42:46 INFO loss_tracker.py:84 | Epoch[125/239] Step[324] GlobalStep[49074/NA]: det_loss_cls_0[0.4411] det_loss_box_0[0.5378] det_loss_cls_1[0.4347] det_loss_box_1[0.4037] det_loss_cls_2[0.3922] det_loss_box_2[0.3662] det_loss_cls_3[0.3557] det_loss_box_3[0.3552] det_loss_cls_4[0.3251] det_loss_box_4[0.3506] det_loss_cls_5[0.3120] det_loss_box_5[0.3527] det_loss_cls_dn_0[0.0994] det_loss_box_dn_0[0.2555] det_loss_cls_dn_1[0.0951] det_loss_box_dn_1[0.2070] det_loss_cls_dn_2[0.0946] det_loss_box_dn_2[0.1893] det_loss_cls_dn_3[0.0938] det_loss_box_dn_3[0.1870] det_loss_cls_dn_4[0.0925] det_loss_box_dn_4[0.1841] det_loss_cls_dn_5[0.0918] det_loss_box_dn_5[0.1831] loss_depth[0.0381] total_loss[6.4386]
Rank[0/8] 06/20/2025 06:43:12 INFO stats.py:314 | Epoch[125] Step[349] GlobalStep[49099] Training Speed: 12.43 samples/sec across all devices.	Average Step Time: 0.64 sec.	Estimated Remaining Time: 14:58:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:43:12 INFO loss_tracker.py:84 | Epoch[125/239] Step[349] GlobalStep[49099/NA]: det_loss_cls_0[0.4846] det_loss_box_0[0.6163] det_loss_cls_1[0.4693] det_loss_box_1[0.4911] det_loss_cls_2[0.4313] det_loss_box_2[0.4485] det_loss_cls_3[0.4080] det_loss_box_3[0.4318] det_loss_cls_4[0.3749] det_loss_box_4[0.4262] det_loss_cls_5[0.3641] det_loss_box_5[0.4234] det_loss_cls_dn_0[0.0919] det_loss_box_dn_0[0.3251] det_loss_cls_dn_1[0.0889] det_loss_box_dn_1[0.2744] det_loss_cls_dn_2[0.0885] det_loss_box_dn_2[0.2542] det_loss_cls_dn_3[0.0877] det_loss_box_dn_3[0.2502] det_loss_cls_dn_4[0.0866] det_loss_box_dn_4[0.2494] det_loss_cls_dn_5[0.0855] det_loss_box_dn_5[0.2477] loss_depth[0.0382] total_loss[7.5379]
Rank[0/8] 06/20/2025 06:43:38 INFO stats.py:314 | Epoch[125] Step[374] GlobalStep[49124] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 14:57:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:43:38 INFO loss_tracker.py:84 | Epoch[125/239] Step[374] GlobalStep[49124/NA]: det_loss_cls_0[0.4764] det_loss_box_0[0.5952] det_loss_cls_1[0.4635] det_loss_box_1[0.4631] det_loss_cls_2[0.4353] det_loss_box_2[0.4242] det_loss_cls_3[0.4053] det_loss_box_3[0.4159] det_loss_cls_4[0.3849] det_loss_box_4[0.4069] det_loss_cls_5[0.3602] det_loss_box_5[0.4161] det_loss_cls_dn_0[0.1066] det_loss_box_dn_0[0.3620] det_loss_cls_dn_1[0.1025] det_loss_box_dn_1[0.3068] det_loss_cls_dn_2[0.1014] det_loss_box_dn_2[0.2895] det_loss_cls_dn_3[0.1010] det_loss_box_dn_3[0.2866] det_loss_cls_dn_4[0.1003] det_loss_box_dn_4[0.2854] det_loss_cls_dn_5[0.0996] det_loss_box_dn_5[0.2793] loss_depth[0.0377] total_loss[7.7056]
Rank[0/8] 06/20/2025 06:43:53 INFO stats.py:394 | Epoch[125] completed. Training Speed: 7.72 samples/sec across all devices.	Epoch Time: 403.97 sec.	Average Epoch Time: 403.97 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 14:57:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:44:05 INFO stats.py:314 | Epoch[126] Step[9] GlobalStep[49149] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 14:56:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:44:20 INFO loss_tracker.py:84 | Epoch[126/239] Step[24] GlobalStep[49164/NA]: det_loss_cls_0[0.4262] det_loss_box_0[0.6295] det_loss_cls_1[0.4154] det_loss_box_1[0.4893] det_loss_cls_2[0.3884] det_loss_box_2[0.4401] det_loss_cls_3[0.3587] det_loss_box_3[0.4264] det_loss_cls_4[0.3352] det_loss_box_4[0.4153] det_loss_cls_5[0.3227] det_loss_box_5[0.4166] det_loss_cls_dn_0[0.0917] det_loss_box_dn_0[0.3803] det_loss_cls_dn_1[0.0886] det_loss_box_dn_1[0.3245] det_loss_cls_dn_2[0.0874] det_loss_box_dn_2[0.3029] det_loss_cls_dn_3[0.0864] det_loss_box_dn_3[0.3008] det_loss_cls_dn_4[0.0857] det_loss_box_dn_4[0.2973] det_loss_cls_dn_5[0.0848] det_loss_box_dn_5[0.2907] loss_depth[0.0368] total_loss[7.5216]
Rank[0/8] 06/20/2025 06:44:30 INFO stats.py:314 | Epoch[126] Step[34] GlobalStep[49174] Training Speed: 8.12 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 14:56:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:44:46 INFO loss_tracker.py:84 | Epoch[126/239] Step[49] GlobalStep[49189/NA]: det_loss_cls_0[0.3909] det_loss_box_0[0.5526] det_loss_cls_1[0.3913] det_loss_box_1[0.4271] det_loss_cls_2[0.3484] det_loss_box_2[0.4005] det_loss_cls_3[0.3193] det_loss_box_3[0.3957] det_loss_cls_4[0.3037] det_loss_box_4[0.3856] det_loss_cls_5[0.2961] det_loss_box_5[0.3904] det_loss_cls_dn_0[0.0902] det_loss_box_dn_0[0.3194] det_loss_cls_dn_1[0.0874] det_loss_box_dn_1[0.2730] det_loss_cls_dn_2[0.0860] det_loss_box_dn_2[0.2609] det_loss_cls_dn_3[0.0852] det_loss_box_dn_3[0.2579] det_loss_cls_dn_4[0.0846] det_loss_box_dn_4[0.2574] det_loss_cls_dn_5[0.0834] det_loss_box_dn_5[0.2562] loss_depth[0.0373] total_loss[6.7803]
Rank[0/8] 06/20/2025 06:44:56 INFO stats.py:314 | Epoch[126] Step[59] GlobalStep[49199] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 14:55:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:45:11 INFO loss_tracker.py:84 | Epoch[126/239] Step[74] GlobalStep[49214/NA]: det_loss_cls_0[0.4373] det_loss_box_0[0.5931] det_loss_cls_1[0.4270] det_loss_box_1[0.4561] det_loss_cls_2[0.3839] det_loss_box_2[0.4193] det_loss_cls_3[0.3580] det_loss_box_3[0.4043] det_loss_cls_4[0.3430] det_loss_box_4[0.3864] det_loss_cls_5[0.3303] det_loss_box_5[0.3835] det_loss_cls_dn_0[0.0951] det_loss_box_dn_0[0.3059] det_loss_cls_dn_1[0.0916] det_loss_box_dn_1[0.2577] det_loss_cls_dn_2[0.0906] det_loss_box_dn_2[0.2407] det_loss_cls_dn_3[0.0890] det_loss_box_dn_3[0.2371] det_loss_cls_dn_4[0.0888] det_loss_box_dn_4[0.2341] det_loss_cls_dn_5[0.0881] det_loss_box_dn_5[0.2308] loss_depth[0.0371] total_loss[7.0090]
Rank[0/8] 06/20/2025 06:45:22 INFO stats.py:314 | Epoch[126] Step[84] GlobalStep[49224] Training Speed: 7.86 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 14:55:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:45:37 INFO loss_tracker.py:84 | Epoch[126/239] Step[99] GlobalStep[49239/NA]: det_loss_cls_0[0.5578] det_loss_box_0[0.7450] det_loss_cls_1[0.5485] det_loss_box_1[0.5633] det_loss_cls_2[0.5031] det_loss_box_2[0.5164] det_loss_cls_3[0.4759] det_loss_box_3[0.4943] det_loss_cls_4[0.4471] det_loss_box_4[0.4879] det_loss_cls_5[0.4312] det_loss_box_5[0.4866] det_loss_cls_dn_0[0.0930] det_loss_box_dn_0[0.3060] det_loss_cls_dn_1[0.0888] det_loss_box_dn_1[0.2592] det_loss_cls_dn_2[0.0889] det_loss_box_dn_2[0.2414] det_loss_cls_dn_3[0.0880] det_loss_box_dn_3[0.2373] det_loss_cls_dn_4[0.0866] det_loss_box_dn_4[0.2351] det_loss_cls_dn_5[0.0854] det_loss_box_dn_5[0.2430] loss_depth[0.0388] total_loss[8.3487]
Rank[0/8] 06/20/2025 06:45:48 INFO stats.py:314 | Epoch[126] Step[109] GlobalStep[49249] Training Speed: 7.86 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 14:54:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:46:03 INFO loss_tracker.py:84 | Epoch[126/239] Step[124] GlobalStep[49264/NA]: det_loss_cls_0[0.6194] det_loss_box_0[0.8220] det_loss_cls_1[0.6234] det_loss_box_1[0.6202] det_loss_cls_2[0.5711] det_loss_box_2[0.5674] det_loss_cls_3[0.5343] det_loss_box_3[0.5566] det_loss_cls_4[0.5058] det_loss_box_4[0.5428] det_loss_cls_5[0.4818] det_loss_box_5[0.5499] det_loss_cls_dn_0[0.1026] det_loss_box_dn_0[0.2535] det_loss_cls_dn_1[0.0995] det_loss_box_dn_1[0.2162] det_loss_cls_dn_2[0.0988] det_loss_box_dn_2[0.2021] det_loss_cls_dn_3[0.0979] det_loss_box_dn_3[0.1990] det_loss_cls_dn_4[0.0974] det_loss_box_dn_4[0.1974] det_loss_cls_dn_5[0.0966] det_loss_box_dn_5[0.1970] loss_depth[0.0372] total_loss[8.8897]
Rank[0/8] 06/20/2025 06:46:13 INFO stats.py:314 | Epoch[126] Step[134] GlobalStep[49274] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 14:54:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:46:28 INFO loss_tracker.py:84 | Epoch[126/239] Step[149] GlobalStep[49289/NA]: det_loss_cls_0[0.3437] det_loss_box_0[0.3907] det_loss_cls_1[0.3450] det_loss_box_1[0.2965] det_loss_cls_2[0.2980] det_loss_box_2[0.2694] det_loss_cls_3[0.2649] det_loss_box_3[0.2691] det_loss_cls_4[0.2595] det_loss_box_4[0.2556] det_loss_cls_5[0.2409] det_loss_box_5[0.2608] det_loss_cls_dn_0[0.0911] det_loss_box_dn_0[0.2548] det_loss_cls_dn_1[0.0878] det_loss_box_dn_1[0.2084] det_loss_cls_dn_2[0.0877] det_loss_box_dn_2[0.1934] det_loss_cls_dn_3[0.0870] det_loss_box_dn_3[0.1903] det_loss_cls_dn_4[0.0861] det_loss_box_dn_4[0.1892] det_loss_cls_dn_5[0.0858] det_loss_box_dn_5[0.1881] loss_depth[0.0362] total_loss[5.2800]
Rank[0/8] 06/20/2025 06:46:39 INFO stats.py:314 | Epoch[126] Step[159] GlobalStep[49299] Training Speed: 6.68 samples/sec across all devices.	Average Step Time: 1.20 sec.	Estimated Remaining Time: 14:53:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:46:54 INFO loss_tracker.py:84 | Epoch[126/239] Step[174] GlobalStep[49314/NA]: det_loss_cls_0[0.4752] det_loss_box_0[0.6447] det_loss_cls_1[0.4781] det_loss_box_1[0.5091] det_loss_cls_2[0.4361] det_loss_box_2[0.4696] det_loss_cls_3[0.4069] det_loss_box_3[0.4599] det_loss_cls_4[0.3803] det_loss_box_4[0.4443] det_loss_cls_5[0.3632] det_loss_box_5[0.4456] det_loss_cls_dn_0[0.0924] det_loss_box_dn_0[0.2996] det_loss_cls_dn_1[0.0881] det_loss_box_dn_1[0.2469] det_loss_cls_dn_2[0.0874] det_loss_box_dn_2[0.2320] det_loss_cls_dn_3[0.0863] det_loss_box_dn_3[0.2276] det_loss_cls_dn_4[0.0862] det_loss_box_dn_4[0.2263] det_loss_cls_dn_5[0.0850] det_loss_box_dn_5[0.2236] loss_depth[0.0377] total_loss[7.5322]
Rank[0/8] 06/20/2025 06:47:04 INFO stats.py:314 | Epoch[126] Step[184] GlobalStep[49324] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 14:52:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:47:20 INFO loss_tracker.py:84 | Epoch[126/239] Step[199] GlobalStep[49339/NA]: det_loss_cls_0[0.5912] det_loss_box_0[0.6886] det_loss_cls_1[0.5715] det_loss_box_1[0.5354] det_loss_cls_2[0.5252] det_loss_box_2[0.4819] det_loss_cls_3[0.4920] det_loss_box_3[0.4698] det_loss_cls_4[0.4596] det_loss_box_4[0.4569] det_loss_cls_5[0.4410] det_loss_box_5[0.4627] det_loss_cls_dn_0[0.1121] det_loss_box_dn_0[0.2623] det_loss_cls_dn_1[0.1094] det_loss_box_dn_1[0.2261] det_loss_cls_dn_2[0.1088] det_loss_box_dn_2[0.2126] det_loss_cls_dn_3[0.1079] det_loss_box_dn_3[0.2093] det_loss_cls_dn_4[0.1068] det_loss_box_dn_4[0.2081] det_loss_cls_dn_5[0.1062] det_loss_box_dn_5[0.2072] loss_depth[0.0357] total_loss[8.1883]
Rank[0/8] 06/20/2025 06:47:30 INFO stats.py:314 | Epoch[126] Step[209] GlobalStep[49349] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 14:52:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:47:45 INFO loss_tracker.py:84 | Epoch[126/239] Step[224] GlobalStep[49364/NA]: det_loss_cls_0[0.5826] det_loss_box_0[0.7766] det_loss_cls_1[0.5734] det_loss_box_1[0.6136] det_loss_cls_2[0.5335] det_loss_box_2[0.5448] det_loss_cls_3[0.4846] det_loss_box_3[0.5431] det_loss_cls_4[0.4521] det_loss_box_4[0.5342] det_loss_cls_5[0.4406] det_loss_box_5[0.5377] det_loss_cls_dn_0[0.0964] det_loss_box_dn_0[0.2672] det_loss_cls_dn_1[0.0933] det_loss_box_dn_1[0.2245] det_loss_cls_dn_2[0.0930] det_loss_box_dn_2[0.2101] det_loss_cls_dn_3[0.0919] det_loss_box_dn_3[0.2078] det_loss_cls_dn_4[0.0909] det_loss_box_dn_4[0.2065] det_loss_cls_dn_5[0.0900] det_loss_box_dn_5[0.2045] loss_depth[0.0393] total_loss[8.5322]
Rank[0/8] 06/20/2025 06:47:56 INFO stats.py:314 | Epoch[126] Step[234] GlobalStep[49374] Training Speed: 7.73 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 14:51:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:48:12 INFO loss_tracker.py:84 | Epoch[126/239] Step[249] GlobalStep[49389/NA]: det_loss_cls_0[0.4788] det_loss_box_0[0.7319] det_loss_cls_1[0.4633] det_loss_box_1[0.5726] det_loss_cls_2[0.4229] det_loss_box_2[0.5242] det_loss_cls_3[0.3936] det_loss_box_3[0.5122] det_loss_cls_4[0.3687] det_loss_box_4[0.4969] det_loss_cls_5[0.3547] det_loss_box_5[0.5000] det_loss_cls_dn_0[0.0931] det_loss_box_dn_0[0.4460] det_loss_cls_dn_1[0.0897] det_loss_box_dn_1[0.3933] det_loss_cls_dn_2[0.0891] det_loss_box_dn_2[0.3766] det_loss_cls_dn_3[0.0878] det_loss_box_dn_3[0.3707] det_loss_cls_dn_4[0.0871] det_loss_box_dn_4[0.3649] det_loss_cls_dn_5[0.0856] det_loss_box_dn_5[0.3620] loss_depth[0.0401] total_loss[8.7060]
Rank[0/8] 06/20/2025 06:48:22 INFO stats.py:314 | Epoch[126] Step[259] GlobalStep[49399] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 14:51:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:48:37 INFO loss_tracker.py:84 | Epoch[126/239] Step[274] GlobalStep[49414/NA]: det_loss_cls_0[0.5151] det_loss_box_0[0.6356] det_loss_cls_1[0.5049] det_loss_box_1[0.4670] det_loss_cls_2[0.4477] det_loss_box_2[0.4360] det_loss_cls_3[0.4132] det_loss_box_3[0.4234] det_loss_cls_4[0.3895] det_loss_box_4[0.4150] det_loss_cls_5[0.3711] det_loss_box_5[0.4137] det_loss_cls_dn_0[0.0972] det_loss_box_dn_0[0.2574] det_loss_cls_dn_1[0.0933] det_loss_box_dn_1[0.2147] det_loss_cls_dn_2[0.0923] det_loss_box_dn_2[0.2017] det_loss_cls_dn_3[0.0916] det_loss_box_dn_3[0.1995] det_loss_cls_dn_4[0.0902] det_loss_box_dn_4[0.1979] det_loss_cls_dn_5[0.0895] det_loss_box_dn_5[0.1979] loss_depth[0.0380] total_loss[7.2933]
Rank[0/8] 06/20/2025 06:48:47 INFO stats.py:314 | Epoch[126] Step[284] GlobalStep[49424] Training Speed: 8.76 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 14:50:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:49:03 INFO loss_tracker.py:84 | Epoch[126/239] Step[299] GlobalStep[49439/NA]: det_loss_cls_0[0.5552] det_loss_box_0[0.7201] det_loss_cls_1[0.5613] det_loss_box_1[0.5561] det_loss_cls_2[0.5085] det_loss_box_2[0.5092] det_loss_cls_3[0.4694] det_loss_box_3[0.4857] det_loss_cls_4[0.4369] det_loss_box_4[0.4764] det_loss_cls_5[0.4241] det_loss_box_5[0.4754] det_loss_cls_dn_0[0.0914] det_loss_box_dn_0[0.2614] det_loss_cls_dn_1[0.0871] det_loss_box_dn_1[0.2203] det_loss_cls_dn_2[0.0856] det_loss_box_dn_2[0.2061] det_loss_cls_dn_3[0.0845] det_loss_box_dn_3[0.2026] det_loss_cls_dn_4[0.0830] det_loss_box_dn_4[0.2025] det_loss_cls_dn_5[0.0819] det_loss_box_dn_5[0.2007] loss_depth[0.0377] total_loss[8.0231]
Rank[0/8] 06/20/2025 06:49:14 INFO stats.py:314 | Epoch[126] Step[309] GlobalStep[49449] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 14:50:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:49:29 INFO loss_tracker.py:84 | Epoch[126/239] Step[324] GlobalStep[49464/NA]: det_loss_cls_0[0.5246] det_loss_box_0[0.6106] det_loss_cls_1[0.4966] det_loss_box_1[0.4763] det_loss_cls_2[0.4505] det_loss_box_2[0.4220] det_loss_cls_3[0.4271] det_loss_box_3[0.4017] det_loss_cls_4[0.4014] det_loss_box_4[0.3870] det_loss_cls_5[0.3812] det_loss_box_5[0.3919] det_loss_cls_dn_0[0.1004] det_loss_box_dn_0[0.2974] det_loss_cls_dn_1[0.0969] det_loss_box_dn_1[0.2545] det_loss_cls_dn_2[0.0967] det_loss_box_dn_2[0.2350] det_loss_cls_dn_3[0.0957] det_loss_box_dn_3[0.2312] det_loss_cls_dn_4[0.0950] det_loss_box_dn_4[0.2277] det_loss_cls_dn_5[0.0941] det_loss_box_dn_5[0.2250] loss_depth[0.0386] total_loss[7.4590]
Rank[0/8] 06/20/2025 06:49:39 INFO stats.py:314 | Epoch[126] Step[334] GlobalStep[49474] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 14:49:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:49:55 INFO loss_tracker.py:84 | Epoch[126/239] Step[349] GlobalStep[49489/NA]: det_loss_cls_0[0.5567] det_loss_box_0[0.7351] det_loss_cls_1[0.5455] det_loss_box_1[0.5701] det_loss_cls_2[0.5024] det_loss_box_2[0.5198] det_loss_cls_3[0.4737] det_loss_box_3[0.5041] det_loss_cls_4[0.4526] det_loss_box_4[0.4889] det_loss_cls_5[0.4315] det_loss_box_5[0.5007] det_loss_cls_dn_0[0.1005] det_loss_box_dn_0[0.2746] det_loss_cls_dn_1[0.0975] det_loss_box_dn_1[0.2306] det_loss_cls_dn_2[0.0967] det_loss_box_dn_2[0.2139] det_loss_cls_dn_3[0.0951] det_loss_box_dn_3[0.2114] det_loss_cls_dn_4[0.0942] det_loss_box_dn_4[0.2106] det_loss_cls_dn_5[0.0927] det_loss_box_dn_5[0.2078] loss_depth[0.0381] total_loss[8.2447]
Rank[0/8] 06/20/2025 06:50:06 INFO stats.py:314 | Epoch[126] Step[359] GlobalStep[49499] Training Speed: 6.38 samples/sec across all devices.	Average Step Time: 1.25 sec.	Estimated Remaining Time: 14:48:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:50:21 INFO loss_tracker.py:84 | Epoch[126/239] Step[374] GlobalStep[49514/NA]: det_loss_cls_0[0.5670] det_loss_box_0[0.7858] det_loss_cls_1[0.5406] det_loss_box_1[0.5983] det_loss_cls_2[0.4965] det_loss_box_2[0.5579] det_loss_cls_3[0.4601] det_loss_box_3[0.5451] det_loss_cls_4[0.4311] det_loss_box_4[0.5377] det_loss_cls_5[0.4197] det_loss_box_5[0.5313] det_loss_cls_dn_0[0.0910] det_loss_box_dn_0[0.2938] det_loss_cls_dn_1[0.0875] det_loss_box_dn_1[0.2468] det_loss_cls_dn_2[0.0866] det_loss_box_dn_2[0.2300] det_loss_cls_dn_3[0.0851] det_loss_box_dn_3[0.2265] det_loss_cls_dn_4[0.0840] det_loss_box_dn_4[0.2248] det_loss_cls_dn_5[0.0831] det_loss_box_dn_5[0.2209] loss_depth[0.0434] total_loss[8.4743]
Rank[0/8] 06/20/2025 06:50:31 INFO stats.py:314 | Epoch[126] Step[384] GlobalStep[49524] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 14:48:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:50:36 INFO stats.py:394 | Epoch[126] completed. Training Speed: 7.74 samples/sec across all devices.	Epoch Time: 403.14 sec.	Average Epoch Time: 403.14 sec.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 14:48:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:50:57 INFO stats.py:314 | Epoch[127] Step[19] GlobalStep[49549] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 14:47:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:51:03 INFO loss_tracker.py:84 | Epoch[127/239] Step[24] GlobalStep[49554/NA]: det_loss_cls_0[0.5024] det_loss_box_0[0.6330] det_loss_cls_1[0.4899] det_loss_box_1[0.4980] det_loss_cls_2[0.4473] det_loss_box_2[0.4557] det_loss_cls_3[0.4169] det_loss_box_3[0.4375] det_loss_cls_4[0.3900] det_loss_box_4[0.4359] det_loss_cls_5[0.3778] det_loss_box_5[0.4318] det_loss_cls_dn_0[0.0947] det_loss_box_dn_0[0.2677] det_loss_cls_dn_1[0.0906] det_loss_box_dn_1[0.2288] det_loss_cls_dn_2[0.0889] det_loss_box_dn_2[0.2156] det_loss_cls_dn_3[0.0885] det_loss_box_dn_3[0.2130] det_loss_cls_dn_4[0.0875] det_loss_box_dn_4[0.2119] det_loss_cls_dn_5[0.0867] det_loss_box_dn_5[0.2079] loss_depth[0.0372] total_loss[7.4352]
Rank[0/8] 06/20/2025 06:51:24 INFO stats.py:314 | Epoch[127] Step[44] GlobalStep[49574] Training Speed: 6.77 samples/sec across all devices.	Average Step Time: 1.18 sec.	Estimated Remaining Time: 14:47:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:51:29 INFO loss_tracker.py:84 | Epoch[127/239] Step[49] GlobalStep[49579/NA]: det_loss_cls_0[0.4982] det_loss_box_0[0.7310] det_loss_cls_1[0.4865] det_loss_box_1[0.5916] det_loss_cls_2[0.4552] det_loss_box_2[0.5202] det_loss_cls_3[0.4305] det_loss_box_3[0.4878] det_loss_cls_4[0.3972] det_loss_box_4[0.4716] det_loss_cls_5[0.3742] det_loss_box_5[0.4770] det_loss_cls_dn_0[0.0871] det_loss_box_dn_0[0.2746] det_loss_cls_dn_1[0.0824] det_loss_box_dn_1[0.2336] det_loss_cls_dn_2[0.0822] det_loss_box_dn_2[0.2151] det_loss_cls_dn_3[0.0808] det_loss_box_dn_3[0.2099] det_loss_cls_dn_4[0.0799] det_loss_box_dn_4[0.2075] det_loss_cls_dn_5[0.0785] det_loss_box_dn_5[0.2077] loss_depth[0.0366] total_loss[7.7970]
Rank[0/8] 06/20/2025 06:51:50 INFO stats.py:314 | Epoch[127] Step[69] GlobalStep[49599] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 14:46:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:51:55 INFO loss_tracker.py:84 | Epoch[127/239] Step[74] GlobalStep[49604/NA]: det_loss_cls_0[0.5148] det_loss_box_0[0.6835] det_loss_cls_1[0.5181] det_loss_box_1[0.5286] det_loss_cls_2[0.4738] det_loss_box_2[0.4917] det_loss_cls_3[0.4411] det_loss_box_3[0.4748] det_loss_cls_4[0.4145] det_loss_box_4[0.4689] det_loss_cls_5[0.3958] det_loss_box_5[0.4683] det_loss_cls_dn_0[0.0968] det_loss_box_dn_0[0.2662] det_loss_cls_dn_1[0.0932] det_loss_box_dn_1[0.2261] det_loss_cls_dn_2[0.0928] det_loss_box_dn_2[0.2098] det_loss_cls_dn_3[0.0919] det_loss_box_dn_3[0.2058] det_loss_cls_dn_4[0.0913] det_loss_box_dn_4[0.2052] det_loss_cls_dn_5[0.0905] det_loss_box_dn_5[0.2045] loss_depth[0.0387] total_loss[7.7868]
Rank[0/8] 06/20/2025 06:52:15 INFO stats.py:314 | Epoch[127] Step[94] GlobalStep[49624] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 14:46:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:52:21 INFO loss_tracker.py:84 | Epoch[127/239] Step[99] GlobalStep[49629/NA]: det_loss_cls_0[0.3688] det_loss_box_0[0.4784] det_loss_cls_1[0.3586] det_loss_box_1[0.3754] det_loss_cls_2[0.3227] det_loss_box_2[0.3437] det_loss_cls_3[0.3024] det_loss_box_3[0.3302] det_loss_cls_4[0.2747] det_loss_box_4[0.3261] det_loss_cls_5[0.2628] det_loss_box_5[0.3258] det_loss_cls_dn_0[0.0888] det_loss_box_dn_0[0.2877] det_loss_cls_dn_1[0.0861] det_loss_box_dn_1[0.2391] det_loss_cls_dn_2[0.0859] det_loss_box_dn_2[0.2246] det_loss_cls_dn_3[0.0853] det_loss_box_dn_3[0.2246] det_loss_cls_dn_4[0.0849] det_loss_box_dn_4[0.2201] det_loss_cls_dn_5[0.0841] det_loss_box_dn_5[0.2203] loss_depth[0.0357] total_loss[6.0367]
Rank[0/8] 06/20/2025 06:52:41 INFO stats.py:314 | Epoch[127] Step[119] GlobalStep[49649] Training Speed: 7.75 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 14:45:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:52:46 INFO loss_tracker.py:84 | Epoch[127/239] Step[124] GlobalStep[49654/NA]: det_loss_cls_0[0.4938] det_loss_box_0[0.6380] det_loss_cls_1[0.4739] det_loss_box_1[0.4765] det_loss_cls_2[0.4296] det_loss_box_2[0.4265] det_loss_cls_3[0.4022] det_loss_box_3[0.4039] det_loss_cls_4[0.3811] det_loss_box_4[0.3980] det_loss_cls_5[0.3660] det_loss_box_5[0.4006] det_loss_cls_dn_0[0.0857] det_loss_box_dn_0[0.2440] det_loss_cls_dn_1[0.0831] det_loss_box_dn_1[0.2053] det_loss_cls_dn_2[0.0823] det_loss_box_dn_2[0.1921] det_loss_cls_dn_3[0.0812] det_loss_box_dn_3[0.1898] det_loss_cls_dn_4[0.0804] det_loss_box_dn_4[0.1887] det_loss_cls_dn_5[0.0798] det_loss_box_dn_5[0.1867] loss_depth[0.0410] total_loss[7.0301]
Rank[0/8] 06/20/2025 06:53:07 INFO stats.py:314 | Epoch[127] Step[144] GlobalStep[49674] Training Speed: 7.49 samples/sec across all devices.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 14:45:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:53:12 INFO loss_tracker.py:84 | Epoch[127/239] Step[149] GlobalStep[49679/NA]: det_loss_cls_0[0.4247] det_loss_box_0[0.5190] det_loss_cls_1[0.4124] det_loss_box_1[0.4332] det_loss_cls_2[0.3747] det_loss_box_2[0.3964] det_loss_cls_3[0.3524] det_loss_box_3[0.3785] det_loss_cls_4[0.3303] det_loss_box_4[0.3685] det_loss_cls_5[0.3201] det_loss_box_5[0.3654] det_loss_cls_dn_0[0.0969] det_loss_box_dn_0[0.2480] det_loss_cls_dn_1[0.0939] det_loss_box_dn_1[0.2081] det_loss_cls_dn_2[0.0922] det_loss_box_dn_2[0.1934] det_loss_cls_dn_3[0.0915] det_loss_box_dn_3[0.1887] det_loss_cls_dn_4[0.0905] det_loss_box_dn_4[0.1866] det_loss_cls_dn_5[0.0898] det_loss_box_dn_5[0.1852] loss_depth[0.0364] total_loss[6.4770]
Rank[0/8] 06/20/2025 06:53:33 INFO stats.py:314 | Epoch[127] Step[169] GlobalStep[49699] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 14:44:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:53:38 INFO loss_tracker.py:84 | Epoch[127/239] Step[174] GlobalStep[49704/NA]: det_loss_cls_0[0.4284] det_loss_box_0[0.5438] det_loss_cls_1[0.4197] det_loss_box_1[0.4153] det_loss_cls_2[0.3769] det_loss_box_2[0.3770] det_loss_cls_3[0.3475] det_loss_box_3[0.3665] det_loss_cls_4[0.3229] det_loss_box_4[0.3512] det_loss_cls_5[0.3084] det_loss_box_5[0.3544] det_loss_cls_dn_0[0.0937] det_loss_box_dn_0[0.2783] det_loss_cls_dn_1[0.0904] det_loss_box_dn_1[0.2328] det_loss_cls_dn_2[0.0900] det_loss_box_dn_2[0.2146] det_loss_cls_dn_3[0.0888] det_loss_box_dn_3[0.2108] det_loss_cls_dn_4[0.0881] det_loss_box_dn_4[0.2095] det_loss_cls_dn_5[0.0870] det_loss_box_dn_5[0.2078] loss_depth[0.0358] total_loss[6.5394]
Rank[0/8] 06/20/2025 06:53:58 INFO stats.py:314 | Epoch[127] Step[194] GlobalStep[49724] Training Speed: 13.01 samples/sec across all devices.	Average Step Time: 0.62 sec.	Estimated Remaining Time: 14:43:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:54:04 INFO loss_tracker.py:84 | Epoch[127/239] Step[199] GlobalStep[49729/NA]: det_loss_cls_0[0.4857] det_loss_box_0[0.6366] det_loss_cls_1[0.4855] det_loss_box_1[0.4783] det_loss_cls_2[0.4501] det_loss_box_2[0.4283] det_loss_cls_3[0.4237] det_loss_box_3[0.4146] det_loss_cls_4[0.3974] det_loss_box_4[0.4036] det_loss_cls_5[0.3842] det_loss_box_5[0.4039] det_loss_cls_dn_0[0.0981] det_loss_box_dn_0[0.2815] det_loss_cls_dn_1[0.0939] det_loss_box_dn_1[0.2299] det_loss_cls_dn_2[0.0936] det_loss_box_dn_2[0.2107] det_loss_cls_dn_3[0.0920] det_loss_box_dn_3[0.2078] det_loss_cls_dn_4[0.0911] det_loss_box_dn_4[0.2067] det_loss_cls_dn_5[0.0899] det_loss_box_dn_5[0.2066] loss_depth[0.0379] total_loss[7.3317]
Rank[0/8] 06/20/2025 06:54:25 INFO stats.py:314 | Epoch[127] Step[219] GlobalStep[49749] Training Speed: 6.92 samples/sec across all devices.	Average Step Time: 1.16 sec.	Estimated Remaining Time: 14:43:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:54:30 INFO loss_tracker.py:84 | Epoch[127/239] Step[224] GlobalStep[49754/NA]: det_loss_cls_0[0.4949] det_loss_box_0[0.6575] det_loss_cls_1[0.4849] det_loss_box_1[0.5222] det_loss_cls_2[0.4433] det_loss_box_2[0.4668] det_loss_cls_3[0.4241] det_loss_box_3[0.4452] det_loss_cls_4[0.4024] det_loss_box_4[0.4400] det_loss_cls_5[0.3917] det_loss_box_5[0.4420] det_loss_cls_dn_0[0.0970] det_loss_box_dn_0[0.2972] det_loss_cls_dn_1[0.0937] det_loss_box_dn_1[0.2517] det_loss_cls_dn_2[0.0934] det_loss_box_dn_2[0.2354] det_loss_cls_dn_3[0.0921] det_loss_box_dn_3[0.2322] det_loss_cls_dn_4[0.0908] det_loss_box_dn_4[0.2307] det_loss_cls_dn_5[0.0902] det_loss_box_dn_5[0.2274] loss_depth[0.0388] total_loss[7.6858]
Rank[0/8] 06/20/2025 06:54:51 INFO stats.py:314 | Epoch[127] Step[244] GlobalStep[49774] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 14:42:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:54:56 INFO loss_tracker.py:84 | Epoch[127/239] Step[249] GlobalStep[49779/NA]: det_loss_cls_0[0.5951] det_loss_box_0[0.7913] det_loss_cls_1[0.5832] det_loss_box_1[0.6121] det_loss_cls_2[0.5404] det_loss_box_2[0.5709] det_loss_cls_3[0.5107] det_loss_box_3[0.5404] det_loss_cls_4[0.4942] det_loss_box_4[0.5250] det_loss_cls_5[0.4770] det_loss_box_5[0.5248] det_loss_cls_dn_0[0.1048] det_loss_box_dn_0[0.3220] det_loss_cls_dn_1[0.1009] det_loss_box_dn_1[0.2766] det_loss_cls_dn_2[0.1003] det_loss_box_dn_2[0.2526] det_loss_cls_dn_3[0.0993] det_loss_box_dn_3[0.2483] det_loss_cls_dn_4[0.0985] det_loss_box_dn_4[0.2455] det_loss_cls_dn_5[0.0976] det_loss_box_dn_5[0.2436] loss_depth[0.0383] total_loss[8.9934]
Rank[0/8] 06/20/2025 06:55:16 INFO stats.py:314 | Epoch[127] Step[269] GlobalStep[49799] Training Speed: 8.80 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 14:42:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:55:21 INFO loss_tracker.py:84 | Epoch[127/239] Step[274] GlobalStep[49804/NA]: det_loss_cls_0[0.4149] det_loss_box_0[0.5142] det_loss_cls_1[0.4004] det_loss_box_1[0.4071] det_loss_cls_2[0.3530] det_loss_box_2[0.3775] det_loss_cls_3[0.3389] det_loss_box_3[0.3634] det_loss_cls_4[0.3155] det_loss_box_4[0.3596] det_loss_cls_5[0.3023] det_loss_box_5[0.3594] det_loss_cls_dn_0[0.0929] det_loss_box_dn_0[0.3037] det_loss_cls_dn_1[0.0900] det_loss_box_dn_1[0.2544] det_loss_cls_dn_2[0.0893] det_loss_box_dn_2[0.2351] det_loss_cls_dn_3[0.0881] det_loss_box_dn_3[0.2312] det_loss_cls_dn_4[0.0875] det_loss_box_dn_4[0.2302] det_loss_cls_dn_5[0.0867] det_loss_box_dn_5[0.2270] loss_depth[0.0355] total_loss[6.5580]
Rank[0/8] 06/20/2025 06:55:42 INFO stats.py:314 | Epoch[127] Step[294] GlobalStep[49824] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 14:41:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:55:47 INFO loss_tracker.py:84 | Epoch[127/239] Step[299] GlobalStep[49829/NA]: det_loss_cls_0[0.5063] det_loss_box_0[0.6855] det_loss_cls_1[0.5124] det_loss_box_1[0.5196] det_loss_cls_2[0.4596] det_loss_box_2[0.4486] det_loss_cls_3[0.4193] det_loss_box_3[0.4314] det_loss_cls_4[0.3953] det_loss_box_4[0.4194] det_loss_cls_5[0.3809] det_loss_box_5[0.4198] det_loss_cls_dn_0[0.1028] det_loss_box_dn_0[0.2698] det_loss_cls_dn_1[0.0999] det_loss_box_dn_1[0.2225] det_loss_cls_dn_2[0.0991] det_loss_box_dn_2[0.2053] det_loss_cls_dn_3[0.0985] det_loss_box_dn_3[0.2009] det_loss_cls_dn_4[0.0969] det_loss_box_dn_4[0.1997] det_loss_cls_dn_5[0.0962] det_loss_box_dn_5[0.1984] loss_depth[0.0366] total_loss[7.5249]
Rank[0/8] 06/20/2025 06:56:09 INFO stats.py:314 | Epoch[127] Step[319] GlobalStep[49849] Training Speed: 8.68 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 14:41:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:56:14 INFO loss_tracker.py:84 | Epoch[127/239] Step[324] GlobalStep[49854/NA]: det_loss_cls_0[0.3548] det_loss_box_0[0.4817] det_loss_cls_1[0.3503] det_loss_box_1[0.3798] det_loss_cls_2[0.3163] det_loss_box_2[0.3455] det_loss_cls_3[0.2914] det_loss_box_3[0.3395] det_loss_cls_4[0.2757] det_loss_box_4[0.3314] det_loss_cls_5[0.2634] det_loss_box_5[0.3326] det_loss_cls_dn_0[0.0819] det_loss_box_dn_0[0.2805] det_loss_cls_dn_1[0.0780] det_loss_box_dn_1[0.2259] det_loss_cls_dn_2[0.0768] det_loss_box_dn_2[0.2040] det_loss_cls_dn_3[0.0763] det_loss_box_dn_3[0.1990] det_loss_cls_dn_4[0.0748] det_loss_box_dn_4[0.1976] det_loss_cls_dn_5[0.0739] det_loss_box_dn_5[0.1964] loss_depth[0.0379] total_loss[5.8652]
Rank[0/8] 06/20/2025 06:56:34 INFO stats.py:314 | Epoch[127] Step[344] GlobalStep[49874] Training Speed: 8.56 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 14:40:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:56:39 INFO loss_tracker.py:84 | Epoch[127/239] Step[349] GlobalStep[49879/NA]: det_loss_cls_0[0.6633] det_loss_box_0[0.9129] det_loss_cls_1[0.6556] det_loss_box_1[0.6872] det_loss_cls_2[0.6122] det_loss_box_2[0.6208] det_loss_cls_3[0.5842] det_loss_box_3[0.5956] det_loss_cls_4[0.5654] det_loss_box_4[0.5756] det_loss_cls_5[0.5491] det_loss_box_5[0.5804] det_loss_cls_dn_0[0.1039] det_loss_box_dn_0[0.3191] det_loss_cls_dn_1[0.1012] det_loss_box_dn_1[0.2735] det_loss_cls_dn_2[0.1009] det_loss_box_dn_2[0.2552] det_loss_cls_dn_3[0.0998] det_loss_box_dn_3[0.2511] det_loss_cls_dn_4[0.0995] det_loss_box_dn_4[0.2484] det_loss_cls_dn_5[0.0987] det_loss_box_dn_5[0.2463] loss_depth[0.0425] total_loss[9.8424]
Rank[0/8] 06/20/2025 06:57:00 INFO stats.py:314 | Epoch[127] Step[369] GlobalStep[49899] Training Speed: 8.08 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 14:39:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:57:05 INFO loss_tracker.py:84 | Epoch[127/239] Step[374] GlobalStep[49904/NA]: det_loss_cls_0[0.5215] det_loss_box_0[0.6160] det_loss_cls_1[0.5043] det_loss_box_1[0.4842] det_loss_cls_2[0.4665] det_loss_box_2[0.4459] det_loss_cls_3[0.4376] det_loss_box_3[0.4324] det_loss_cls_4[0.4200] det_loss_box_4[0.4205] det_loss_cls_5[0.4032] det_loss_box_5[0.4243] det_loss_cls_dn_0[0.0885] det_loss_box_dn_0[0.3032] det_loss_cls_dn_1[0.0847] det_loss_box_dn_1[0.2570] det_loss_cls_dn_2[0.0833] det_loss_box_dn_2[0.2401] det_loss_cls_dn_3[0.0822] det_loss_box_dn_3[0.2363] det_loss_cls_dn_4[0.0816] det_loss_box_dn_4[0.2324] det_loss_cls_dn_5[0.0805] det_loss_box_dn_5[0.2294] loss_depth[0.0374] total_loss[7.6131]
Rank[0/8] 06/20/2025 06:57:20 INFO stats.py:394 | Epoch[127] completed. Training Speed: 7.72 samples/sec across all devices.	Epoch Time: 404.02 sec.	Average Epoch Time: 404.02 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 14:39:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:57:29 INFO stats.py:314 | Epoch[128] Step[4] GlobalStep[49924] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 14:39:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:57:50 INFO loss_tracker.py:84 | Epoch[128/239] Step[24] GlobalStep[49944/NA]: det_loss_cls_0[0.4554] det_loss_box_0[0.6054] det_loss_cls_1[0.4519] det_loss_box_1[0.4660] det_loss_cls_2[0.4029] det_loss_box_2[0.4149] det_loss_cls_3[0.3788] det_loss_box_3[0.4067] det_loss_cls_4[0.3639] det_loss_box_4[0.3924] det_loss_cls_5[0.3540] det_loss_box_5[0.3932] det_loss_cls_dn_0[0.0925] det_loss_box_dn_0[0.3071] det_loss_cls_dn_1[0.0885] det_loss_box_dn_1[0.2568] det_loss_cls_dn_2[0.0882] det_loss_box_dn_2[0.2402] det_loss_cls_dn_3[0.0867] det_loss_box_dn_3[0.2357] det_loss_cls_dn_4[0.0864] det_loss_box_dn_4[0.2345] det_loss_cls_dn_5[0.0852] det_loss_box_dn_5[0.2338] loss_depth[0.0379] total_loss[7.1589]
Rank[0/8] 06/20/2025 06:57:55 INFO stats.py:314 | Epoch[128] Step[29] GlobalStep[49949] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 14:38:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:58:15 INFO loss_tracker.py:84 | Epoch[128/239] Step[49] GlobalStep[49969/NA]: det_loss_cls_0[0.5640] det_loss_box_0[0.7878] det_loss_cls_1[0.5527] det_loss_box_1[0.6431] det_loss_cls_2[0.5187] det_loss_box_2[0.5884] det_loss_cls_3[0.4954] det_loss_box_3[0.5628] det_loss_cls_4[0.4670] det_loss_box_4[0.5498] det_loss_cls_5[0.4517] det_loss_box_5[0.5461] det_loss_cls_dn_0[0.1007] det_loss_box_dn_0[0.3207] det_loss_cls_dn_1[0.0963] det_loss_box_dn_1[0.2765] det_loss_cls_dn_2[0.0958] det_loss_box_dn_2[0.2597] det_loss_cls_dn_3[0.0944] det_loss_box_dn_3[0.2554] det_loss_cls_dn_4[0.0937] det_loss_box_dn_4[0.2552] det_loss_cls_dn_5[0.0929] det_loss_box_dn_5[0.2534] loss_depth[0.0334] total_loss[8.9553]
Rank[0/8] 06/20/2025 06:58:20 INFO stats.py:314 | Epoch[128] Step[54] GlobalStep[49974] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 14:38:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:58:40 INFO loss_tracker.py:84 | Epoch[128/239] Step[74] GlobalStep[49994/NA]: det_loss_cls_0[0.4562] det_loss_box_0[0.6988] det_loss_cls_1[0.4506] det_loss_box_1[0.5506] det_loss_cls_2[0.4083] det_loss_box_2[0.5015] det_loss_cls_3[0.3869] det_loss_box_3[0.4801] det_loss_cls_4[0.3602] det_loss_box_4[0.4848] det_loss_cls_5[0.3490] det_loss_box_5[0.4853] det_loss_cls_dn_0[0.0872] det_loss_box_dn_0[0.3378] det_loss_cls_dn_1[0.0837] det_loss_box_dn_1[0.2857] det_loss_cls_dn_2[0.0826] det_loss_box_dn_2[0.2668] det_loss_cls_dn_3[0.0820] det_loss_box_dn_3[0.2628] det_loss_cls_dn_4[0.0812] det_loss_box_dn_4[0.2608] det_loss_cls_dn_5[0.0801] det_loss_box_dn_5[0.2597] loss_depth[0.0376] total_loss[7.8204]
Rank[0/8] 06/20/2025 06:58:46 INFO stats.py:314 | Epoch[128] Step[79] GlobalStep[49999] Training Speed: 6.79 samples/sec across all devices.	Average Step Time: 1.18 sec.	Estimated Remaining Time: 14:37:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:59:07 INFO loss_tracker.py:84 | Epoch[128/239] Step[99] GlobalStep[50019/NA]: det_loss_cls_0[0.5316] det_loss_box_0[0.6803] det_loss_cls_1[0.5160] det_loss_box_1[0.5095] det_loss_cls_2[0.4517] det_loss_box_2[0.4708] det_loss_cls_3[0.4260] det_loss_box_3[0.4459] det_loss_cls_4[0.3924] det_loss_box_4[0.4422] det_loss_cls_5[0.3800] det_loss_box_5[0.4417] det_loss_cls_dn_0[0.0855] det_loss_box_dn_0[0.2561] det_loss_cls_dn_1[0.0816] det_loss_box_dn_1[0.2102] det_loss_cls_dn_2[0.0809] det_loss_box_dn_2[0.1959] det_loss_cls_dn_3[0.0799] det_loss_box_dn_3[0.1913] det_loss_cls_dn_4[0.0786] det_loss_box_dn_4[0.1899] det_loss_cls_dn_5[0.0782] det_loss_box_dn_5[0.1896] loss_depth[0.0403] total_loss[7.4461]
Rank[0/8] 06/20/2025 06:59:12 INFO stats.py:314 | Epoch[128] Step[104] GlobalStep[50024] Training Speed: 8.64 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 14:37:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:59:32 INFO loss_tracker.py:84 | Epoch[128/239] Step[124] GlobalStep[50044/NA]: det_loss_cls_0[0.4863] det_loss_box_0[0.6319] det_loss_cls_1[0.4820] det_loss_box_1[0.4979] det_loss_cls_2[0.4244] det_loss_box_2[0.4672] det_loss_cls_3[0.3951] det_loss_box_3[0.4468] det_loss_cls_4[0.3686] det_loss_box_4[0.4322] det_loss_cls_5[0.3583] det_loss_box_5[0.4302] det_loss_cls_dn_0[0.0992] det_loss_box_dn_0[0.2962] det_loss_cls_dn_1[0.0957] det_loss_box_dn_1[0.2647] det_loss_cls_dn_2[0.0950] det_loss_box_dn_2[0.2489] det_loss_cls_dn_3[0.0933] det_loss_box_dn_3[0.2459] det_loss_cls_dn_4[0.0924] det_loss_box_dn_4[0.2447] det_loss_cls_dn_5[0.0911] det_loss_box_dn_5[0.2435] loss_depth[0.0354] total_loss[7.5668]
Rank[0/8] 06/20/2025 06:59:37 INFO stats.py:314 | Epoch[128] Step[129] GlobalStep[50049] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 14:36:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 06:59:58 INFO loss_tracker.py:84 | Epoch[128/239] Step[149] GlobalStep[50069/NA]: det_loss_cls_0[0.5785] det_loss_box_0[0.8228] det_loss_cls_1[0.5686] det_loss_box_1[0.6151] det_loss_cls_2[0.5251] det_loss_box_2[0.5465] det_loss_cls_3[0.4956] det_loss_box_3[0.5292] det_loss_cls_4[0.4761] det_loss_box_4[0.5147] det_loss_cls_5[0.4633] det_loss_box_5[0.5163] det_loss_cls_dn_0[0.0963] det_loss_box_dn_0[0.3339] det_loss_cls_dn_1[0.0923] det_loss_box_dn_1[0.2807] det_loss_cls_dn_2[0.0917] det_loss_box_dn_2[0.2608] det_loss_cls_dn_3[0.0901] det_loss_box_dn_3[0.2572] det_loss_cls_dn_4[0.0895] det_loss_box_dn_4[0.2550] det_loss_cls_dn_5[0.0890] det_loss_box_dn_5[0.2490] loss_depth[0.0435] total_loss[8.8808]
Rank[0/8] 06/20/2025 07:00:03 INFO stats.py:314 | Epoch[128] Step[154] GlobalStep[50074] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 14:35:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:00:24 INFO loss_tracker.py:84 | Epoch[128/239] Step[174] GlobalStep[50094/NA]: det_loss_cls_0[0.5949] det_loss_box_0[0.7610] det_loss_cls_1[0.5873] det_loss_box_1[0.5770] det_loss_cls_2[0.5372] det_loss_box_2[0.5280] det_loss_cls_3[0.5026] det_loss_box_3[0.5133] det_loss_cls_4[0.4714] det_loss_box_4[0.4990] det_loss_cls_5[0.4556] det_loss_box_5[0.4988] det_loss_cls_dn_0[0.1058] det_loss_box_dn_0[0.2400] det_loss_cls_dn_1[0.1018] det_loss_box_dn_1[0.2017] det_loss_cls_dn_2[0.1022] det_loss_box_dn_2[0.1911] det_loss_cls_dn_3[0.1007] det_loss_box_dn_3[0.1872] det_loss_cls_dn_4[0.0998] det_loss_box_dn_4[0.1857] det_loss_cls_dn_5[0.0997] det_loss_box_dn_5[0.1836] loss_depth[0.0371] total_loss[8.3624]
Rank[0/8] 06/20/2025 07:00:29 INFO stats.py:314 | Epoch[128] Step[179] GlobalStep[50099] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 14:35:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:00:50 INFO loss_tracker.py:84 | Epoch[128/239] Step[199] GlobalStep[50119/NA]: det_loss_cls_0[0.4479] det_loss_box_0[0.5779] det_loss_cls_1[0.4361] det_loss_box_1[0.4460] det_loss_cls_2[0.4041] det_loss_box_2[0.4124] det_loss_cls_3[0.3651] det_loss_box_3[0.4012] det_loss_cls_4[0.3421] det_loss_box_4[0.3836] det_loss_cls_5[0.3292] det_loss_box_5[0.3873] det_loss_cls_dn_0[0.0896] det_loss_box_dn_0[0.2844] det_loss_cls_dn_1[0.0858] det_loss_box_dn_1[0.2402] det_loss_cls_dn_2[0.0860] det_loss_box_dn_2[0.2263] det_loss_cls_dn_3[0.0846] det_loss_box_dn_3[0.2236] det_loss_cls_dn_4[0.0836] det_loss_box_dn_4[0.2213] det_loss_cls_dn_5[0.0833] det_loss_box_dn_5[0.2202] loss_depth[0.0347] total_loss[6.8964]
Rank[0/8] 06/20/2025 07:00:55 INFO stats.py:314 | Epoch[128] Step[204] GlobalStep[50124] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 14:34:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:01:15 INFO loss_tracker.py:84 | Epoch[128/239] Step[224] GlobalStep[50144/NA]: det_loss_cls_0[0.4666] det_loss_box_0[0.6455] det_loss_cls_1[0.4538] det_loss_box_1[0.5098] det_loss_cls_2[0.4189] det_loss_box_2[0.4545] det_loss_cls_3[0.3848] det_loss_box_3[0.4430] det_loss_cls_4[0.3695] det_loss_box_4[0.4217] det_loss_cls_5[0.3551] det_loss_box_5[0.4239] det_loss_cls_dn_0[0.0957] det_loss_box_dn_0[0.3532] det_loss_cls_dn_1[0.0920] det_loss_box_dn_1[0.2986] det_loss_cls_dn_2[0.0905] det_loss_box_dn_2[0.2779] det_loss_cls_dn_3[0.0899] det_loss_box_dn_3[0.2726] det_loss_cls_dn_4[0.0892] det_loss_box_dn_4[0.2701] det_loss_cls_dn_5[0.0881] det_loss_box_dn_5[0.2672] loss_depth[0.0407] total_loss[7.6727]
Rank[0/8] 06/20/2025 07:01:21 INFO stats.py:314 | Epoch[128] Step[229] GlobalStep[50149] Training Speed: 6.73 samples/sec across all devices.	Average Step Time: 1.19 sec.	Estimated Remaining Time: 14:34:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:01:42 INFO loss_tracker.py:84 | Epoch[128/239] Step[249] GlobalStep[50169/NA]: det_loss_cls_0[0.4808] det_loss_box_0[0.6805] det_loss_cls_1[0.4826] det_loss_box_1[0.5635] det_loss_cls_2[0.4349] det_loss_box_2[0.5039] det_loss_cls_3[0.4104] det_loss_box_3[0.4828] det_loss_cls_4[0.3873] det_loss_box_4[0.4727] det_loss_cls_5[0.3726] det_loss_box_5[0.4730] det_loss_cls_dn_0[0.1012] det_loss_box_dn_0[0.3191] det_loss_cls_dn_1[0.0977] det_loss_box_dn_1[0.2638] det_loss_cls_dn_2[0.0980] det_loss_box_dn_2[0.2446] det_loss_cls_dn_3[0.0966] det_loss_box_dn_3[0.2420] det_loss_cls_dn_4[0.0954] det_loss_box_dn_4[0.2406] det_loss_cls_dn_5[0.0946] det_loss_box_dn_5[0.2335] loss_depth[0.0378] total_loss[7.9098]
Rank[0/8] 06/20/2025 07:01:47 INFO stats.py:314 | Epoch[128] Step[254] GlobalStep[50174] Training Speed: 9.83 samples/sec across all devices.	Average Step Time: 0.81 sec.	Estimated Remaining Time: 14:33:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:02:07 INFO loss_tracker.py:84 | Epoch[128/239] Step[274] GlobalStep[50194/NA]: det_loss_cls_0[0.4903] det_loss_box_0[0.6183] det_loss_cls_1[0.4684] det_loss_box_1[0.4761] det_loss_cls_2[0.4347] det_loss_box_2[0.4355] det_loss_cls_3[0.4116] det_loss_box_3[0.4264] det_loss_cls_4[0.3803] det_loss_box_4[0.4187] det_loss_cls_5[0.3736] det_loss_box_5[0.4187] det_loss_cls_dn_0[0.0919] det_loss_box_dn_0[0.2656] det_loss_cls_dn_1[0.0883] det_loss_box_dn_1[0.2230] det_loss_cls_dn_2[0.0881] det_loss_box_dn_2[0.2080] det_loss_cls_dn_3[0.0869] det_loss_box_dn_3[0.2061] det_loss_cls_dn_4[0.0861] det_loss_box_dn_4[0.2044] det_loss_cls_dn_5[0.0856] det_loss_box_dn_5[0.2038] loss_depth[0.0368] total_loss[7.2272]
Rank[0/8] 06/20/2025 07:02:13 INFO stats.py:314 | Epoch[128] Step[279] GlobalStep[50199] Training Speed: 8.03 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 14:33:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:02:33 INFO loss_tracker.py:84 | Epoch[128/239] Step[299] GlobalStep[50219/NA]: det_loss_cls_0[0.3778] det_loss_box_0[0.4853] det_loss_cls_1[0.3768] det_loss_box_1[0.3683] det_loss_cls_2[0.3365] det_loss_box_2[0.3412] det_loss_cls_3[0.3199] det_loss_box_3[0.3267] det_loss_cls_4[0.2921] det_loss_box_4[0.3161] det_loss_cls_5[0.2789] det_loss_box_5[0.3158] det_loss_cls_dn_0[0.0877] det_loss_box_dn_0[0.3213] det_loss_cls_dn_1[0.0832] det_loss_box_dn_1[0.2760] det_loss_cls_dn_2[0.0830] det_loss_box_dn_2[0.2630] det_loss_cls_dn_3[0.0821] det_loss_box_dn_3[0.2551] det_loss_cls_dn_4[0.0812] det_loss_box_dn_4[0.2539] det_loss_cls_dn_5[0.0803] det_loss_box_dn_5[0.2530] loss_depth[0.0364] total_loss[6.2917]
Rank[0/8] 06/20/2025 07:02:38 INFO stats.py:314 | Epoch[128] Step[304] GlobalStep[50224] Training Speed: 10.26 samples/sec across all devices.	Average Step Time: 0.78 sec.	Estimated Remaining Time: 14:32:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:02:59 INFO loss_tracker.py:84 | Epoch[128/239] Step[324] GlobalStep[50244/NA]: det_loss_cls_0[0.4168] det_loss_box_0[0.5444] det_loss_cls_1[0.4006] det_loss_box_1[0.4179] det_loss_cls_2[0.3517] det_loss_box_2[0.3877] det_loss_cls_3[0.3277] det_loss_box_3[0.3703] det_loss_cls_4[0.3069] det_loss_box_4[0.3663] det_loss_cls_5[0.2969] det_loss_box_5[0.3634] det_loss_cls_dn_0[0.0946] det_loss_box_dn_0[0.2958] det_loss_cls_dn_1[0.0911] det_loss_box_dn_1[0.2490] det_loss_cls_dn_2[0.0905] det_loss_box_dn_2[0.2325] det_loss_cls_dn_3[0.0891] det_loss_box_dn_3[0.2278] det_loss_cls_dn_4[0.0885] det_loss_box_dn_4[0.2260] det_loss_cls_dn_5[0.0878] det_loss_box_dn_5[0.2260] loss_depth[0.0367] total_loss[6.5859]
Rank[0/8] 06/20/2025 07:03:05 INFO stats.py:314 | Epoch[128] Step[329] GlobalStep[50249] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 14:32:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:03:25 INFO loss_tracker.py:84 | Epoch[128/239] Step[349] GlobalStep[50269/NA]: det_loss_cls_0[0.4348] det_loss_box_0[0.5818] det_loss_cls_1[0.4225] det_loss_box_1[0.4477] det_loss_cls_2[0.3696] det_loss_box_2[0.4045] det_loss_cls_3[0.3389] det_loss_box_3[0.3907] det_loss_cls_4[0.3153] det_loss_box_4[0.3772] det_loss_cls_5[0.2969] det_loss_box_5[0.3820] det_loss_cls_dn_0[0.0867] det_loss_box_dn_0[0.2881] det_loss_cls_dn_1[0.0816] det_loss_box_dn_1[0.2455] det_loss_cls_dn_2[0.0810] det_loss_box_dn_2[0.2301] det_loss_cls_dn_3[0.0794] det_loss_box_dn_3[0.2276] det_loss_cls_dn_4[0.0787] det_loss_box_dn_4[0.2258] det_loss_cls_dn_5[0.0778] det_loss_box_dn_5[0.2228] loss_depth[0.0364] total_loss[6.7234]
Rank[0/8] 06/20/2025 07:03:30 INFO stats.py:314 | Epoch[128] Step[354] GlobalStep[50274] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 14:31:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:03:51 INFO loss_tracker.py:84 | Epoch[128/239] Step[374] GlobalStep[50294/NA]: det_loss_cls_0[0.4389] det_loss_box_0[0.5631] det_loss_cls_1[0.4217] det_loss_box_1[0.4380] det_loss_cls_2[0.3804] det_loss_box_2[0.4017] det_loss_cls_3[0.3584] det_loss_box_3[0.3868] det_loss_cls_4[0.3273] det_loss_box_4[0.3790] det_loss_cls_5[0.3231] det_loss_box_5[0.3731] det_loss_cls_dn_0[0.0934] det_loss_box_dn_0[0.3073] det_loss_cls_dn_1[0.0900] det_loss_box_dn_1[0.2589] det_loss_cls_dn_2[0.0893] det_loss_box_dn_2[0.2406] det_loss_cls_dn_3[0.0879] det_loss_box_dn_3[0.2373] det_loss_cls_dn_4[0.0869] det_loss_box_dn_4[0.2360] det_loss_cls_dn_5[0.0861] det_loss_box_dn_5[0.2327] loss_depth[0.0396] total_loss[6.8777]
Rank[0/8] 06/20/2025 07:03:56 INFO stats.py:314 | Epoch[128] Step[379] GlobalStep[50299] Training Speed: 8.66 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 14:30:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:04:06 INFO stats.py:394 | Epoch[128] completed. Training Speed: 7.68 samples/sec across all devices.	Epoch Time: 406.18 sec.	Average Epoch Time: 406.18 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 14:30:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:04:25 INFO stats.py:314 | Epoch[129] Step[14] GlobalStep[50324] Training Speed: 5.55 samples/sec across all devices.	Average Step Time: 1.44 sec.	Estimated Remaining Time: 14:30:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:04:36 INFO loss_tracker.py:84 | Epoch[129/239] Step[24] GlobalStep[50334/NA]: det_loss_cls_0[0.5523] det_loss_box_0[0.7582] det_loss_cls_1[0.5382] det_loss_box_1[0.5646] det_loss_cls_2[0.4967] det_loss_box_2[0.5074] det_loss_cls_3[0.4626] det_loss_box_3[0.4969] det_loss_cls_4[0.4364] det_loss_box_4[0.4826] det_loss_cls_5[0.4236] det_loss_box_5[0.4807] det_loss_cls_dn_0[0.1003] det_loss_box_dn_0[0.2648] det_loss_cls_dn_1[0.0957] det_loss_box_dn_1[0.2244] det_loss_cls_dn_2[0.0948] det_loss_box_dn_2[0.2091] det_loss_cls_dn_3[0.0934] det_loss_box_dn_3[0.2055] det_loss_cls_dn_4[0.0927] det_loss_box_dn_4[0.2037] det_loss_cls_dn_5[0.0915] det_loss_box_dn_5[0.2016] loss_depth[0.0386] total_loss[8.1162]
Rank[0/8] 06/20/2025 07:04:51 INFO stats.py:314 | Epoch[129] Step[39] GlobalStep[50349] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 14:29:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:05:02 INFO loss_tracker.py:84 | Epoch[129/239] Step[49] GlobalStep[50359/NA]: det_loss_cls_0[0.5635] det_loss_box_0[0.7318] det_loss_cls_1[0.5344] det_loss_box_1[0.5728] det_loss_cls_2[0.4946] det_loss_box_2[0.5241] det_loss_cls_3[0.4684] det_loss_box_3[0.4978] det_loss_cls_4[0.4450] det_loss_box_4[0.4912] det_loss_cls_5[0.4269] det_loss_box_5[0.4891] det_loss_cls_dn_0[0.1017] det_loss_box_dn_0[0.2969] det_loss_cls_dn_1[0.0974] det_loss_box_dn_1[0.2557] det_loss_cls_dn_2[0.0970] det_loss_box_dn_2[0.2358] det_loss_cls_dn_3[0.0958] det_loss_box_dn_3[0.2342] det_loss_cls_dn_4[0.0947] det_loss_box_dn_4[0.2321] det_loss_cls_dn_5[0.0943] det_loss_box_dn_5[0.2320] loss_depth[0.0382] total_loss[8.3453]
Rank[0/8] 06/20/2025 07:05:17 INFO stats.py:314 | Epoch[129] Step[64] GlobalStep[50374] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 14:29:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:05:28 INFO loss_tracker.py:84 | Epoch[129/239] Step[74] GlobalStep[50384/NA]: det_loss_cls_0[0.4667] det_loss_box_0[0.6617] det_loss_cls_1[0.4612] det_loss_box_1[0.4726] det_loss_cls_2[0.4107] det_loss_box_2[0.4248] det_loss_cls_3[0.3895] det_loss_box_3[0.4093] det_loss_cls_4[0.3635] det_loss_box_4[0.4006] det_loss_cls_5[0.3527] det_loss_box_5[0.4005] det_loss_cls_dn_0[0.0991] det_loss_box_dn_0[0.2882] det_loss_cls_dn_1[0.0956] det_loss_box_dn_1[0.2344] det_loss_cls_dn_2[0.0957] det_loss_box_dn_2[0.2143] det_loss_cls_dn_3[0.0942] det_loss_box_dn_3[0.2109] det_loss_cls_dn_4[0.0931] det_loss_box_dn_4[0.2081] det_loss_cls_dn_5[0.0929] det_loss_box_dn_5[0.2064] loss_depth[0.0380] total_loss[7.1845]
Rank[0/8] 06/20/2025 07:05:43 INFO stats.py:314 | Epoch[129] Step[89] GlobalStep[50399] Training Speed: 8.17 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 14:28:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:05:53 INFO loss_tracker.py:84 | Epoch[129/239] Step[99] GlobalStep[50409/NA]: det_loss_cls_0[0.4797] det_loss_box_0[0.6248] det_loss_cls_1[0.4716] det_loss_box_1[0.4881] det_loss_cls_2[0.4294] det_loss_box_2[0.4450] det_loss_cls_3[0.4023] det_loss_box_3[0.4272] det_loss_cls_4[0.3725] det_loss_box_4[0.4114] det_loss_cls_5[0.3548] det_loss_box_5[0.4071] det_loss_cls_dn_0[0.0879] det_loss_box_dn_0[0.3018] det_loss_cls_dn_1[0.0837] det_loss_box_dn_1[0.2578] det_loss_cls_dn_2[0.0833] det_loss_box_dn_2[0.2376] det_loss_cls_dn_3[0.0821] det_loss_box_dn_3[0.2322] det_loss_cls_dn_4[0.0814] det_loss_box_dn_4[0.2315] det_loss_cls_dn_5[0.0798] det_loss_box_dn_5[0.2260] loss_depth[0.0367] total_loss[7.3355]
Rank[0/8] 06/20/2025 07:06:09 INFO stats.py:314 | Epoch[129] Step[114] GlobalStep[50424] Training Speed: 8.34 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 14:28:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:06:19 INFO loss_tracker.py:84 | Epoch[129/239] Step[124] GlobalStep[50434/NA]: det_loss_cls_0[0.5065] det_loss_box_0[0.7019] det_loss_cls_1[0.4927] det_loss_box_1[0.5401] det_loss_cls_2[0.4505] det_loss_box_2[0.4924] det_loss_cls_3[0.4197] det_loss_box_3[0.4788] det_loss_cls_4[0.4044] det_loss_box_4[0.4664] det_loss_cls_5[0.3869] det_loss_box_5[0.4671] det_loss_cls_dn_0[0.0911] det_loss_box_dn_0[0.2803] det_loss_cls_dn_1[0.0881] det_loss_box_dn_1[0.2318] det_loss_cls_dn_2[0.0871] det_loss_box_dn_2[0.2141] det_loss_cls_dn_3[0.0866] det_loss_box_dn_3[0.2093] det_loss_cls_dn_4[0.0865] det_loss_box_dn_4[0.2079] det_loss_cls_dn_5[0.0850] det_loss_box_dn_5[0.2076] loss_depth[0.0372] total_loss[7.7202]
Rank[0/8] 06/20/2025 07:06:34 INFO stats.py:314 | Epoch[129] Step[139] GlobalStep[50449] Training Speed: 7.15 samples/sec across all devices.	Average Step Time: 1.12 sec.	Estimated Remaining Time: 14:27:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:06:45 INFO loss_tracker.py:84 | Epoch[129/239] Step[149] GlobalStep[50459/NA]: det_loss_cls_0[0.5277] det_loss_box_0[0.7179] det_loss_cls_1[0.4984] det_loss_box_1[0.5435] det_loss_cls_2[0.4593] det_loss_box_2[0.5020] det_loss_cls_3[0.4314] det_loss_box_3[0.4888] det_loss_cls_4[0.4085] det_loss_box_4[0.4815] det_loss_cls_5[0.3929] det_loss_box_5[0.4858] det_loss_cls_dn_0[0.0962] det_loss_box_dn_0[0.2956] det_loss_cls_dn_1[0.0923] det_loss_box_dn_1[0.2495] det_loss_cls_dn_2[0.0912] det_loss_box_dn_2[0.2338] det_loss_cls_dn_3[0.0903] det_loss_box_dn_3[0.2305] det_loss_cls_dn_4[0.0887] det_loss_box_dn_4[0.2282] det_loss_cls_dn_5[0.0876] det_loss_box_dn_5[0.2277] loss_depth[0.0361] total_loss[7.9854]
Rank[0/8] 06/20/2025 07:07:01 INFO stats.py:314 | Epoch[129] Step[164] GlobalStep[50474] Training Speed: 6.79 samples/sec across all devices.	Average Step Time: 1.18 sec.	Estimated Remaining Time: 14:26:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:07:11 INFO loss_tracker.py:84 | Epoch[129/239] Step[174] GlobalStep[50484/NA]: det_loss_cls_0[0.5567] det_loss_box_0[0.7204] det_loss_cls_1[0.5429] det_loss_box_1[0.5881] det_loss_cls_2[0.5002] det_loss_box_2[0.5229] det_loss_cls_3[0.4787] det_loss_box_3[0.5021] det_loss_cls_4[0.4451] det_loss_box_4[0.4902] det_loss_cls_5[0.4302] det_loss_box_5[0.4933] det_loss_cls_dn_0[0.1024] det_loss_box_dn_0[0.3127] det_loss_cls_dn_1[0.0974] det_loss_box_dn_1[0.2631] det_loss_cls_dn_2[0.0972] det_loss_box_dn_2[0.2429] det_loss_cls_dn_3[0.0962] det_loss_box_dn_3[0.2379] det_loss_cls_dn_4[0.0950] det_loss_box_dn_4[0.2350] det_loss_cls_dn_5[0.0945] det_loss_box_dn_5[0.2318] loss_depth[0.0334] total_loss[8.4104]
Rank[0/8] 06/20/2025 07:07:26 INFO stats.py:314 | Epoch[129] Step[189] GlobalStep[50499] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 14:26:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:07:37 INFO loss_tracker.py:84 | Epoch[129/239] Step[199] GlobalStep[50509/NA]: det_loss_cls_0[0.5884] det_loss_box_0[0.7420] det_loss_cls_1[0.5824] det_loss_box_1[0.5745] det_loss_cls_2[0.5464] det_loss_box_2[0.5232] det_loss_cls_3[0.5183] det_loss_box_3[0.5027] det_loss_cls_4[0.4818] det_loss_box_4[0.5020] det_loss_cls_5[0.4701] det_loss_box_5[0.4981] det_loss_cls_dn_0[0.1144] det_loss_box_dn_0[0.3205] det_loss_cls_dn_1[0.1104] det_loss_box_dn_1[0.2720] det_loss_cls_dn_2[0.1107] det_loss_box_dn_2[0.2536] det_loss_cls_dn_3[0.1097] det_loss_box_dn_3[0.2517] det_loss_cls_dn_4[0.1088] det_loss_box_dn_4[0.2502] det_loss_cls_dn_5[0.1079] det_loss_box_dn_5[0.2496] loss_depth[0.0393] total_loss[8.8289]
Rank[0/8] 06/20/2025 07:07:52 INFO stats.py:314 | Epoch[129] Step[214] GlobalStep[50524] Training Speed: 7.96 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 14:25:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:08:03 INFO loss_tracker.py:84 | Epoch[129/239] Step[224] GlobalStep[50534/NA]: det_loss_cls_0[0.4431] det_loss_box_0[0.5835] det_loss_cls_1[0.4182] det_loss_box_1[0.4467] det_loss_cls_2[0.3915] det_loss_box_2[0.3928] det_loss_cls_3[0.3592] det_loss_box_3[0.3741] det_loss_cls_4[0.3298] det_loss_box_4[0.3737] det_loss_cls_5[0.3171] det_loss_box_5[0.3687] det_loss_cls_dn_0[0.0908] det_loss_box_dn_0[0.3742] det_loss_cls_dn_1[0.0867] det_loss_box_dn_1[0.3207] det_loss_cls_dn_2[0.0865] det_loss_box_dn_2[0.2889] det_loss_cls_dn_3[0.0847] det_loss_box_dn_3[0.2845] det_loss_cls_dn_4[0.0838] det_loss_box_dn_4[0.2830] det_loss_cls_dn_5[0.0826] det_loss_box_dn_5[0.2813] loss_depth[0.0404] total_loss[7.1864]
Rank[0/8] 06/20/2025 07:08:18 INFO stats.py:314 | Epoch[129] Step[239] GlobalStep[50549] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 14:25:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:08:28 INFO loss_tracker.py:84 | Epoch[129/239] Step[249] GlobalStep[50559/NA]: det_loss_cls_0[0.4083] det_loss_box_0[0.5526] det_loss_cls_1[0.3923] det_loss_box_1[0.4057] det_loss_cls_2[0.3586] det_loss_box_2[0.3712] det_loss_cls_3[0.3346] det_loss_box_3[0.3519] det_loss_cls_4[0.3135] det_loss_box_4[0.3477] det_loss_cls_5[0.3039] det_loss_box_5[0.3497] det_loss_cls_dn_0[0.0834] det_loss_box_dn_0[0.3058] det_loss_cls_dn_1[0.0791] det_loss_box_dn_1[0.2457] det_loss_cls_dn_2[0.0771] det_loss_box_dn_2[0.2308] det_loss_cls_dn_3[0.0763] det_loss_box_dn_3[0.2287] det_loss_cls_dn_4[0.0753] det_loss_box_dn_4[0.2265] det_loss_cls_dn_5[0.0744] det_loss_box_dn_5[0.2254] loss_depth[0.0384] total_loss[6.4568]
Rank[0/8] 06/20/2025 07:08:44 INFO stats.py:314 | Epoch[129] Step[264] GlobalStep[50574] Training Speed: 6.93 samples/sec across all devices.	Average Step Time: 1.15 sec.	Estimated Remaining Time: 14:24:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:08:55 INFO loss_tracker.py:84 | Epoch[129/239] Step[274] GlobalStep[50584/NA]: det_loss_cls_0[0.4449] det_loss_box_0[0.5861] det_loss_cls_1[0.4516] det_loss_box_1[0.4514] det_loss_cls_2[0.4135] det_loss_box_2[0.4045] det_loss_cls_3[0.3865] det_loss_box_3[0.3872] det_loss_cls_4[0.3573] det_loss_box_4[0.3881] det_loss_cls_5[0.3490] det_loss_box_5[0.3891] det_loss_cls_dn_0[0.0887] det_loss_box_dn_0[0.3000] det_loss_cls_dn_1[0.0852] det_loss_box_dn_1[0.2501] det_loss_cls_dn_2[0.0842] det_loss_box_dn_2[0.2319] det_loss_cls_dn_3[0.0835] det_loss_box_dn_3[0.2287] det_loss_cls_dn_4[0.0827] det_loss_box_dn_4[0.2262] det_loss_cls_dn_5[0.0816] det_loss_box_dn_5[0.2243] loss_depth[0.0342] total_loss[7.0105]
Rank[0/8] 06/20/2025 07:09:10 INFO stats.py:314 | Epoch[129] Step[289] GlobalStep[50599] Training Speed: 8.19 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 14:24:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:09:20 INFO loss_tracker.py:84 | Epoch[129/239] Step[299] GlobalStep[50609/NA]: det_loss_cls_0[0.3958] det_loss_box_0[0.4528] det_loss_cls_1[0.3936] det_loss_box_1[0.3548] det_loss_cls_2[0.3555] det_loss_box_2[0.3167] det_loss_cls_3[0.3250] det_loss_box_3[0.3032] det_loss_cls_4[0.3040] det_loss_box_4[0.2999] det_loss_cls_5[0.2931] det_loss_box_5[0.3038] det_loss_cls_dn_0[0.0856] det_loss_box_dn_0[0.2292] det_loss_cls_dn_1[0.0816] det_loss_box_dn_1[0.1924] det_loss_cls_dn_2[0.0804] det_loss_box_dn_2[0.1756] det_loss_cls_dn_3[0.0790] det_loss_box_dn_3[0.1738] det_loss_cls_dn_4[0.0784] det_loss_box_dn_4[0.1726] det_loss_cls_dn_5[0.0776] det_loss_box_dn_5[0.1714] loss_depth[0.0353] total_loss[5.7312]
Rank[0/8] 06/20/2025 07:09:36 INFO stats.py:314 | Epoch[129] Step[314] GlobalStep[50624] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 14:23:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:09:46 INFO loss_tracker.py:84 | Epoch[129/239] Step[324] GlobalStep[50634/NA]: det_loss_cls_0[0.4741] det_loss_box_0[0.6313] det_loss_cls_1[0.4869] det_loss_box_1[0.4675] det_loss_cls_2[0.4318] det_loss_box_2[0.4324] det_loss_cls_3[0.4132] det_loss_box_3[0.4181] det_loss_cls_4[0.3862] det_loss_box_4[0.4116] det_loss_cls_5[0.3766] det_loss_box_5[0.4117] det_loss_cls_dn_0[0.0966] det_loss_box_dn_0[0.2767] det_loss_cls_dn_1[0.0940] det_loss_box_dn_1[0.2355] det_loss_cls_dn_2[0.0945] det_loss_box_dn_2[0.2212] det_loss_cls_dn_3[0.0930] det_loss_box_dn_3[0.2170] det_loss_cls_dn_4[0.0923] det_loss_box_dn_4[0.2154] det_loss_cls_dn_5[0.0921] det_loss_box_dn_5[0.2129] loss_depth[0.0388] total_loss[7.3214]
Rank[0/8] 06/20/2025 07:10:01 INFO stats.py:314 | Epoch[129] Step[339] GlobalStep[50649] Training Speed: 8.12 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 14:23:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:10:12 INFO loss_tracker.py:84 | Epoch[129/239] Step[349] GlobalStep[50659/NA]: det_loss_cls_0[0.5954] det_loss_box_0[0.8287] det_loss_cls_1[0.5827] det_loss_box_1[0.6183] det_loss_cls_2[0.5311] det_loss_box_2[0.5695] det_loss_cls_3[0.4964] det_loss_box_3[0.5421] det_loss_cls_4[0.4632] det_loss_box_4[0.5301] det_loss_cls_5[0.4501] det_loss_box_5[0.5310] det_loss_cls_dn_0[0.0892] det_loss_box_dn_0[0.2381] det_loss_cls_dn_1[0.0846] det_loss_box_dn_1[0.2010] det_loss_cls_dn_2[0.0840] det_loss_box_dn_2[0.1882] det_loss_cls_dn_3[0.0829] det_loss_box_dn_3[0.1857] det_loss_cls_dn_4[0.0817] det_loss_box_dn_4[0.1842] det_loss_cls_dn_5[0.0805] det_loss_box_dn_5[0.1838] loss_depth[0.0380] total_loss[8.4603]
Rank[0/8] 06/20/2025 07:10:28 INFO stats.py:314 | Epoch[129] Step[364] GlobalStep[50674] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 14:22:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:10:38 INFO loss_tracker.py:84 | Epoch[129/239] Step[374] GlobalStep[50684/NA]: det_loss_cls_0[0.4167] det_loss_box_0[0.5348] det_loss_cls_1[0.4093] det_loss_box_1[0.3983] det_loss_cls_2[0.3609] det_loss_box_2[0.3527] det_loss_cls_3[0.3401] det_loss_box_3[0.3381] det_loss_cls_4[0.3097] det_loss_box_4[0.3405] det_loss_cls_5[0.2969] det_loss_box_5[0.3398] det_loss_cls_dn_0[0.0825] det_loss_box_dn_0[0.2606] det_loss_cls_dn_1[0.0797] det_loss_box_dn_1[0.2115] det_loss_cls_dn_2[0.0788] det_loss_box_dn_2[0.1955] det_loss_cls_dn_3[0.0772] det_loss_box_dn_3[0.1930] det_loss_cls_dn_4[0.0763] det_loss_box_dn_4[0.1911] det_loss_cls_dn_5[0.0756] det_loss_box_dn_5[0.1899] loss_depth[0.0406] total_loss[6.1903]
Rank[0/8] 06/20/2025 07:10:53 INFO stats.py:314 | Epoch[129] Step[389] GlobalStep[50699] Training Speed: 8.64 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 14:21:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:10:53 INFO trainer.py:268 | 
==================================================BEGIN EVAL==================================================
Rank[0/8] 06/20/2025 07:11:48 INFO trainer.py:275 | eval: 10
Rank[0/8] 06/20/2025 07:11:55 INFO trainer.py:275 | eval: 20
Rank[0/8] 06/20/2025 07:12:03 INFO trainer.py:275 | eval: 30
Rank[0/8] 06/20/2025 07:12:16 INFO trainer.py:275 | eval: 40
Rank[0/8] 06/20/2025 07:12:23 INFO trainer.py:275 | eval: 50
Rank[0/8] 06/20/2025 07:12:29 INFO trainer.py:275 | eval: 60
Rank[0/8] 06/20/2025 07:12:36 INFO trainer.py:275 | eval: 70
Rank[0/8] 06/20/2025 07:12:47 INFO trainer.py:275 | eval: 80
Rank[0/8] 06/20/2025 07:12:57 INFO trainer.py:275 | eval: 90
Rank[0/8] 06/20/2025 07:13:05 INFO trainer.py:275 | eval: 100
Rank[0/8] 06/20/2025 07:13:11 INFO metrics.py:428 | number of results: 824
Rank[0/8] 06/20/2025 07:17:20 INFO metrics.py:332 | eval results of overall:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| picture             | 0.2383  | 0.4343  | 0.0169  | 0.0991  |
| sink                | 0.6443  | 0.8477  | 0.2142  | 0.4321  |
| bin                 | 0.6168  | 0.8268  | 0.3631  | 0.5307  |
| toilet              | 0.8537  | 0.8978  | 0.6697  | 0.7883  |
| curtain             | 0.5431  | 0.8478  | 0.1043  | 0.4090  |
| toilet paper        | 0.3552  | 0.5591  | 0.0412  | 0.1732  |
| rail                | 0.0247  | 0.3281  | 0.0069  | 0.0312  |
| bathtub             | 0.8185  | 0.8824  | 0.5380  | 0.6765  |
| doorframe           | 0.2849  | 0.7426  | 0.0697  | 0.3678  |
| bar                 | 0.0719  | 0.3636  | 0.0008  | 0.0303  |
| door                | 0.3083  | 0.6675  | 0.0343  | 0.2567  |
| towel               | 0.3617  | 0.6613  | 0.0782  | 0.2323  |
| bag                 | 0.3003  | 0.6649  | 0.0540  | 0.2595  |
| tissue box          | 0.1321  | 0.4242  | 0.0816  | 0.1515  |
| cup                 | 0.0735  | 0.3051  | 0.0060  | 0.0339  |
| vanity              | 0.5283  | 0.8222  | 0.5025  | 0.7111  |
| bottle              | 0.0586  | 0.3454  | 0.0008  | 0.0446  |
| stopcock            | 0.2099  | 0.5172  | 0.0000  | 0.0000  |
| mirror              | 0.1204  | 0.5598  | 0.0095  | 0.1413  |
| switch              | 0.0034  | 0.1447  | 0.0000  | 0.0000  |
| shower              | 0.0234  | 0.2333  | 0.0038  | 0.1000  |
| socket              | 0.0091  | 0.2124  | 0.0001  | 0.0133  |
| tissue              | 0.0082  | 0.3077  | 0.0000  | 0.0000  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hair dryer          | 0.0130  | 0.4286  | 0.0000  | 0.0000  |
| ledge               | 0.0710  | 0.6667  | 0.0000  | 0.0000  |
| clothes             | 0.1686  | 0.5955  | 0.0274  | 0.2134  |
| frame               | 0.0722  | 0.5918  | 0.0009  | 0.0612  |
| plunger             | 0.8179  | 1.0000  | 0.3009  | 0.6000  |
| cabinet             | 0.2854  | 0.7452  | 0.1631  | 0.5125  |
| mat                 | 0.0508  | 0.3243  | 0.0000  | 0.0270  |
| backpack            | 0.5545  | 0.8226  | 0.2740  | 0.5806  |
| basket              | 0.2456  | 0.6261  | 0.1355  | 0.3043  |
| hanger              | 0.0007  | 0.1667  | 0.0000  | 0.0000  |
| window              | 0.2011  | 0.6572  | 0.0300  | 0.2410  |
| shelf               | 0.3018  | 0.7086  | 0.1109  | 0.4227  |
| blanket             | 0.1868  | 0.7900  | 0.0603  | 0.4500  |
| box                 | 0.1691  | 0.5256  | 0.0383  | 0.1809  |
| microwave           | 0.5806  | 0.7143  | 0.2684  | 0.4082  |
| vent                | 0.0099  | 0.1667  | 0.0000  | 0.0000  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radiator            | 0.6637  | 0.8000  | 0.1487  | 0.3778  |
| paper               | 0.0268  | 0.2426  | 0.0003  | 0.0444  |
| bed                 | 0.7384  | 0.9231  | 0.5528  | 0.8757  |
| candle              | 0.0139  | 0.0556  | 0.0000  | 0.0000  |
| stool               | 0.3092  | 0.6975  | 0.1828  | 0.4454  |
| plant               | 0.3301  | 0.6236  | 0.0725  | 0.2633  |
| lamp                | 0.3062  | 0.6090  | 0.0674  | 0.2388  |
| light               | 0.0308  | 0.2714  | 0.0006  | 0.0449  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sunglasses          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clock               | 0.2242  | 0.5106  | 0.0020  | 0.0851  |
| pillow              | 0.3262  | 0.6611  | 0.0361  | 0.2008  |
| case                | 0.0979  | 0.3729  | 0.0321  | 0.1412  |
| conducting wire     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mouse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coil                | 0.1667  | 0.1667  | 0.0417  | 0.1667  |
| soap dish           | 0.6456  | 0.7333  | 0.1846  | 0.3333  |
| scale               | 0.1529  | 0.3636  | 0.0303  | 0.0909  |
| doorway             | 0.0003  | 0.1111  | 0.0003  | 0.1111  |
| carpet              | 0.0599  | 0.3810  | 0.0022  | 0.1190  |
| laptop              | 0.4622  | 0.6957  | 0.1778  | 0.3913  |
| hamper              | 0.3745  | 0.5455  | 0.3507  | 0.4545  |
| chair               | 0.6123  | 0.8384  | 0.4150  | 0.6327  |
| computer            | 0.2976  | 0.6557  | 0.0049  | 0.0984  |
| blackboard          | 0.6041  | 0.8049  | 0.2584  | 0.4512  |
| monitor             | 0.5012  | 0.6902  | 0.0924  | 0.2663  |
| keyboard            | 0.0196  | 0.1395  | 0.0000  | 0.0000  |
| desk                | 0.4981  | 0.9382  | 0.3192  | 0.7303  |
| table               | 0.5050  | 0.8730  | 0.3767  | 0.7241  |
| plug                | 0.0644  | 0.1579  | 0.0000  | 0.0000  |
| windowsill          | 0.0678  | 0.5870  | 0.0030  | 0.1522  |
| telephone           | 0.3494  | 0.6000  | 0.0487  | 0.2333  |
| wardrobe            | 0.4485  | 0.7831  | 0.2722  | 0.6145  |
| couch               | 0.4263  | 0.8760  | 0.2978  | 0.7479  |
| blinds              | 0.1382  | 0.4600  | 0.0051  | 0.1800  |
| jacket              | 0.5860  | 0.7917  | 0.2328  | 0.5000  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| furniture           | 0.0018  | 0.3333  | 0.0004  | 0.1667  |
| book                | 0.0633  | 0.4525  | 0.0037  | 0.0992  |
| tube                | 0.0018  | 0.0769  | 0.0000  | 0.0000  |
| window frame        | 0.0333  | 0.5354  | 0.0095  | 0.2626  |
| can                 | 0.0042  | 0.0833  | 0.0042  | 0.0833  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pipe                | 0.0364  | 0.2143  | 0.0000  | 0.0000  |
| stand               | 0.5553  | 0.8720  | 0.3768  | 0.6341  |
| cushion             | 0.0858  | 0.5294  | 0.0032  | 0.1176  |
| decoration          | 0.0790  | 0.4286  | 0.0068  | 0.0857  |
| paper cutter        | 0.8056  | 0.8333  | 0.1684  | 0.3333  |
| dresser             | 0.6297  | 0.9074  | 0.5032  | 0.7593  |
| toy                 | 0.0943  | 0.4242  | 0.0122  | 0.1364  |
| shoe                | 0.1711  | 0.4019  | 0.0027  | 0.0748  |
| notebook            | 0.0006  | 0.0769  | 0.0000  | 0.0000  |
| tv                  | 0.5745  | 0.7723  | 0.1143  | 0.3465  |
| rack                | 0.0715  | 0.5192  | 0.0263  | 0.3269  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| remote control      | 0.0847  | 0.2222  | 0.0096  | 0.0741  |
| container           | 0.0090  | 0.2712  | 0.0040  | 0.1017  |
| calendar            | 0.0157  | 0.3333  | 0.0000  | 0.0000  |
| cart                | 0.0016  | 0.2500  | 0.0000  | 0.0000  |
| board               | 0.1927  | 0.6613  | 0.0256  | 0.2581  |
| printer             | 0.5031  | 0.7959  | 0.4053  | 0.6735  |
| guitar              | 0.1072  | 0.4167  | 0.0093  | 0.0833  |
| refrigerator        | 0.6388  | 0.8600  | 0.3731  | 0.6400  |
| ottoman             | 0.1200  | 0.7500  | 0.0867  | 0.5278  |
| coffee maker        | 0.3624  | 0.6552  | 0.1468  | 0.2759  |
| bench               | 0.3365  | 0.7547  | 0.2425  | 0.5660  |
| luggage             | 0.6852  | 1.0000  | 0.6852  | 1.0000  |
| tray                | 0.0005  | 0.1429  | 0.0000  | 0.0000  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| purse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| counter             | 0.1769  | 0.6881  | 0.0165  | 0.2936  |
| stall               | 0.2477  | 0.7647  | 0.1143  | 0.5294  |
| drawer              | 0.2516  | 0.6667  | 0.0658  | 0.4444  |
| faucet              | 0.0327  | 0.2766  | 0.0000  | 0.0000  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| letter              | 0.1980  | 0.7500  | 0.0000  | 0.0000  |
| broom               | 0.0130  | 0.3571  | 0.0000  | 0.0000  |
| fireplace           | 0.1600  | 0.4643  | 0.0022  | 0.0714  |
| balcony             | 0.5345  | 1.0000  | 0.5000  | 0.5000  |
| grab bar            | 0.0041  | 0.2500  | 0.0000  | 0.0000  |
| soap dispenser      | 0.3515  | 0.5556  | 0.0136  | 0.0741  |
| dispenser           | 0.4705  | 0.7273  | 0.1429  | 0.2955  |
| mailbox             | 0.2086  | 1.0000  | 0.0481  | 0.6000  |
| pillar              | 0.3488  | 0.6977  | 0.0929  | 0.3953  |
| glass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| partition           | 0.3013  | 0.6667  | 0.1667  | 0.1667  |
| column              | 0.0188  | 0.3947  | 0.0039  | 0.1316  |
| stairs              | 0.0588  | 0.4306  | 0.0052  | 0.1806  |
| holder              | 0.1481  | 0.2222  | 0.0000  | 0.0000  |
| copier              | 0.8761  | 0.9167  | 0.7305  | 0.8333  |
| fire extinguisher   | 0.3649  | 0.6000  | 0.3333  | 0.4000  |
| screen              | 0.0047  | 0.4000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| poster              | 0.0371  | 0.1212  | 0.0019  | 0.0152  |
| projector           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cube                | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| stove               | 0.4940  | 0.8611  | 0.2799  | 0.5833  |
| air conditioner     | 0.5500  | 0.6000  | 0.0333  | 0.2000  |
| tablet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| person              | 0.1461  | 0.5000  | 0.0030  | 0.3333  |
| file                | 0.0930  | 0.3529  | 0.0557  | 0.1471  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hat                 | 0.0260  | 0.2222  | 0.0000  | 0.0000  |
| fan                 | 0.6276  | 0.7969  | 0.1713  | 0.4375  |
| roll                | 0.0918  | 0.5000  | 0.0011  | 0.2000  |
| heater              | 0.4773  | 0.7931  | 0.1835  | 0.4138  |
| statue              | 0.1010  | 0.5263  | 0.0213  | 0.1053  |
| headphones          | 0.0026  | 0.2000  | 0.0026  | 0.2000  |
| ladder              | 0.1547  | 0.4706  | 0.0778  | 0.2353  |
| ball                | 0.4462  | 0.4762  | 0.1675  | 0.3810  |
| bucket              | 0.2524  | 0.6410  | 0.0663  | 0.2308  |
| basin               | 0.0034  | 0.1765  | 0.0001  | 0.0588  |
| bicycle             | 0.3750  | 0.6667  | 0.0893  | 0.6667  |
| crate               | 0.4857  | 0.5714  | 0.3143  | 0.4286  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vacuum cleaner      | 0.7564  | 0.8571  | 0.1905  | 0.2857  |
| handle              | 0.0003  | 0.2500  | 0.0000  | 0.0000  |
| piano               | 0.2667  | 0.4000  | 0.2667  | 0.4000  |
| toothpaste          | 0.0080  | 0.2000  | 0.0000  | 0.0000  |
| soap                | 0.0157  | 0.3333  | 0.0000  | 0.0000  |
| headboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crib                | 0.3684  | 0.6667  | 0.0702  | 0.6667  |
| barricade           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchen island      | 0.1860  | 0.5714  | 0.1655  | 0.4286  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sign                | 0.0624  | 0.5333  | 0.0009  | 0.0667  |
| machine             | 0.5863  | 0.8947  | 0.3705  | 0.4737  |
| water cooler        | 0.6429  | 1.0000  | 0.5000  | 0.5000  |
| kettle              | 0.1347  | 0.5556  | 0.0307  | 0.2778  |
| dish rack           | 0.5656  | 1.0000  | 0.0065  | 0.2000  |
| oven                | 0.4349  | 0.8214  | 0.1029  | 0.3571  |
| toaster             | 0.4067  | 0.5000  | 0.0000  | 0.0000  |
| teapot              | 0.1222  | 0.2222  | 0.1111  | 0.1111  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.1297  | 0.5000  | 0.1038  | 0.2000  |
| plate               | 0.0277  | 0.3654  | 0.0001  | 0.0192  |
| range hood          | 0.4422  | 0.8571  | 0.0804  | 0.2143  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flowerpot           | 0.0706  | 0.3922  | 0.0044  | 0.1373  |
| bowl                | 0.0934  | 0.3768  | 0.0303  | 0.1159  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| arch                | 0.0001  | 0.0333  | 0.0001  | 0.0333  |
| steps               | 0.0012  | 0.2000  | 0.0000  | 0.0000  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garbage             | 0.3709  | 0.7500  | 0.1728  | 0.5000  |
| commode             | 0.4759  | 0.9038  | 0.3597  | 0.7692  |
| folder              | 0.1404  | 0.3571  | 0.0026  | 0.0714  |
| player              | 0.2500  | 0.2500  | 0.2500  | 0.2500  |
| magazine            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vase                | 0.1281  | 0.3600  | 0.0271  | 0.1200  |
| humidifier          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| device              | 0.1090  | 0.3636  | 0.0000  | 0.0000  |
| jalousie            | 0.3333  | 1.0000  | 0.0000  | 0.0000  |
| jar                 | 0.0909  | 0.3077  | 0.0769  | 0.0769  |
| umbrella            | 0.0167  | 0.2000  | 0.0105  | 0.2000  |
| chandelier          | 0.1546  | 0.3810  | 0.0277  | 0.1905  |
| brush               | 0.2063  | 0.3571  | 0.0000  | 0.0000  |
| clothes dryer       | 0.3349  | 0.8000  | 0.2026  | 0.4667  |
| fruit               | 0.2155  | 0.3571  | 0.0810  | 0.1429  |
| shampoo             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.4435  | 0.7857  | 0.1493  | 0.5000  |
| glove               | 0.5000  | 0.6667  | 0.3333  | 0.3333  |
| beam                | 0.0002  | 0.0909  | 0.0000  | 0.0000  |
| toiletry            | 0.1153  | 0.3478  | 0.0000  | 0.0000  |
| flush               | 0.8333  | 1.0000  | 0.0000  | 0.0000  |
| beanbag             | 1.0000  | 1.0000  | 0.8333  | 0.8333  |
| pan                 | 0.2282  | 0.5882  | 0.0151  | 0.1176  |
| bread               | 0.2143  | 0.4000  | 0.0000  | 0.0000  |
| pot                 | 0.0996  | 0.4074  | 0.0335  | 0.1111  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sack                | 0.6608  | 1.0000  | 0.2262  | 0.4286  |
| kitchenware         | 0.2091  | 0.4130  | 0.0349  | 0.1087  |
| pack                | 0.0323  | 0.6667  | 0.0061  | 0.3333  |
| spoon               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| food                | 0.0021  | 0.0909  | 0.0000  | 0.0000  |
| radio               | 0.2500  | 0.2500  | 0.2500  | 0.2500  |
| price tag           | 0.0948  | 0.4286  | 0.0000  | 0.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drum                | 0.0556  | 0.2500  | 0.0000  | 0.0000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| napkins             | 0.2037  | 0.6667  | 0.0000  | 0.0000  |
| candlestick         | 0.3333  | 0.3333  | 0.0000  | 0.0000  |
| treadmill           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet               | 0.5000  | 0.6667  | 0.5000  | 0.6667  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell            | 0.4444  | 0.6667  | 0.0032  | 0.3333  |
| mop                 | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| roof                | 0.0003  | 0.1250  | 0.0000  | 0.0000  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| countertop          | 0.0200  | 0.2500  | 0.0000  | 0.0357  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| thermostat          | 0.0833  | 0.1667  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| footstool           | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| album               | 0.0312  | 1.0000  | 0.0000  | 0.0000  |
| washbasin           | 0.0021  | 0.1333  | 0.0021  | 0.1333  |
| map                 | 0.0556  | 0.5000  | 0.0556  | 0.5000  |
| wine                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0048  | 0.1818  | 0.0019  | 0.0909  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.2941  | 0.7000  | 0.0083  | 0.2500  |
| garage door         | 0.4222  | 0.6667  | 0.2393  | 0.5000  |
| adhesive tape       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool table          | 0.1667  | 0.3333  | 0.1667  | 0.3333  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| helmets             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| menu                | 0.3214  | 0.4286  | 0.0000  | 0.0000  |
| salt                | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| cosmetics           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cap                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sponge              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water heater        | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| overall             | 0.2141  | 0.4284  | 0.0888  | 0.1951  |
| head                | 0.2820  | 0.5910  | 0.1175  | 0.2970  |
| common              | 0.1866  | 0.3959  | 0.0762  | 0.1692  |
| tail                | 0.1704  | 0.2850  | 0.0713  | 0.1117  |
| small               | 0.0671  | 0.2115  | 0.0090  | 0.0300  |
| medium              | 0.2228  | 0.4670  | 0.0963  | 0.2203  |
| large               | 0.1225  | 0.5280  | 0.0573  | 0.3089  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 07:17:20 INFO metrics.py:437 | number of scannet results: 250
Rank[0/8] 06/20/2025 07:18:36 INFO metrics.py:332 | eval results of scannet:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| picture             | 0.3273  | 0.5149  | 0.0365  | 0.1231  |
| sink                | 0.7862  | 0.9318  | 0.2996  | 0.4773  |
| bin                 | 0.6429  | 0.8255  | 0.4222  | 0.5574  |
| toilet              | 0.9297  | 0.9643  | 0.7530  | 0.8571  |
| curtain             | 0.6552  | 0.8850  | 0.1329  | 0.4513  |
| toilet paper        | 0.4235  | 0.5570  | 0.0378  | 0.1139  |
| rail                | 0.1283  | 0.6667  | 0.0741  | 0.3333  |
| bathtub             | 0.9405  | 0.9688  | 0.6597  | 0.7812  |
| doorframe           | 0.3053  | 0.7817  | 0.0478  | 0.3028  |
| bar                 | 0.0642  | 0.4545  | 0.0000  | 0.0000  |
| door                | 0.4686  | 0.7993  | 0.0773  | 0.3779  |
| towel               | 0.4424  | 0.7355  | 0.0938  | 0.2149  |
| bag                 | 0.3342  | 0.6575  | 0.0876  | 0.2877  |
| tissue box          | 0.1815  | 0.4091  | 0.1189  | 0.1818  |
| cup                 | 0.1700  | 0.3333  | 0.0300  | 0.0435  |
| vanity              | 0.6722  | 0.9333  | 0.6518  | 0.8333  |
| bottle              | 0.0757  | 0.3579  | 0.0011  | 0.0421  |
| stopcock            | 0.2100  | 0.5172  | 0.0000  | 0.0000  |
| mirror              | 0.2096  | 0.6506  | 0.0125  | 0.1325  |
| switch              | 0.0106  | 0.2025  | 0.0000  | 0.0000  |
| shower              | 0.0334  | 0.2143  | 0.0007  | 0.0714  |
| socket              | 0.0110  | 0.2282  | 0.0001  | 0.0146  |
| tissue              | 0.0074  | 0.2222  | 0.0000  | 0.0000  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hair dryer          | 0.0140  | 0.4286  | 0.0000  | 0.0000  |
| ledge               | 0.0710  | 0.6667  | 0.0000  | 0.0000  |
| clothes             | 0.1994  | 0.5948  | 0.0388  | 0.2288  |
| frame               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plunger             | 0.8179  | 1.0000  | 0.3009  | 0.6000  |
| cabinet             | 0.3782  | 0.7738  | 0.2025  | 0.5238  |
| mat                 | 0.0749  | 0.4000  | 0.0000  | 0.0000  |
| backpack            | 0.6323  | 0.8929  | 0.3649  | 0.7143  |
| basket              | 0.0742  | 0.4231  | 0.0637  | 0.2308  |
| window              | 0.4157  | 0.8642  | 0.0784  | 0.4074  |
| shelf               | 0.5210  | 0.8235  | 0.2478  | 0.6691  |
| blanket             | 0.1145  | 0.5417  | 0.0455  | 0.3750  |
| box                 | 0.1419  | 0.4484  | 0.0306  | 0.1349  |
| microwave           | 0.5812  | 0.7000  | 0.2484  | 0.4500  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radiator            | 0.7754  | 0.8793  | 0.1886  | 0.4483  |
| paper               | 0.0268  | 0.2226  | 0.0002  | 0.0264  |
| bed                 | 0.8526  | 1.0000  | 0.7637  | 0.9825  |
| stool               | 0.3659  | 0.7273  | 0.1922  | 0.4773  |
| plant               | 0.3232  | 0.5714  | 0.0548  | 0.1849  |
| lamp                | 0.4587  | 0.7143  | 0.2189  | 0.3571  |
| light               | 0.0446  | 0.4138  | 0.0000  | 0.0000  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clock               | 0.3138  | 0.6400  | 0.0068  | 0.1200  |
| pillow              | 0.4016  | 0.6597  | 0.1654  | 0.3472  |
| case                | 0.6417  | 0.8947  | 0.2504  | 0.5789  |
| mouse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dish           | 0.7879  | 0.8333  | 0.2328  | 0.4167  |
| scale               | 0.2553  | 0.5000  | 0.2500  | 0.2500  |
| carpet              | 0.0091  | 0.1000  | 0.0000  | 0.0000  |
| laptop              | 0.4749  | 0.6000  | 0.1009  | 0.2667  |
| hamper              | 0.4119  | 0.6000  | 0.3857  | 0.5000  |
| chair               | 0.6535  | 0.8539  | 0.4212  | 0.6302  |
| computer            | 0.2912  | 0.6667  | 0.0093  | 0.1026  |
| blackboard          | 0.6614  | 0.8261  | 0.2905  | 0.4493  |
| monitor             | 0.5649  | 0.7071  | 0.1490  | 0.3214  |
| keyboard            | 0.0324  | 0.1207  | 0.0000  | 0.0000  |
| desk                | 0.6127  | 0.9873  | 0.3929  | 0.7468  |
| table               | 0.6132  | 0.9346  | 0.4978  | 0.7808  |
| plug                | 0.0000  | 0.1111  | 0.0000  | 0.0000  |
| windowsill          | 0.0333  | 0.8333  | 0.0005  | 0.1667  |
| telephone           | 0.4790  | 0.7188  | 0.0797  | 0.3125  |
| wardrobe            | 0.6815  | 1.0000  | 0.3308  | 0.5000  |
| couch               | 0.6129  | 1.0000  | 0.4725  | 0.8625  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jacket              | 0.6132  | 0.8261  | 0.2433  | 0.5217  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| book                | 0.0541  | 0.4571  | 0.0028  | 0.1167  |
| tube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pipe                | 0.0001  | 0.1429  | 0.0000  | 0.0000  |
| stand               | 0.7647  | 0.9245  | 0.5519  | 0.7170  |
| cushion             | 0.0016  | 0.2500  | 0.0000  | 0.0000  |
| decoration          | 0.3055  | 0.6667  | 0.0033  | 0.1250  |
| paper cutter        | 0.7600  | 0.8000  | 0.0013  | 0.2000  |
| dresser             | 0.7496  | 0.9730  | 0.6107  | 0.8108  |
| toy                 | 0.0510  | 0.2941  | 0.0002  | 0.0588  |
| shoe                | 0.2254  | 0.4000  | 0.0087  | 0.1500  |
| notebook            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tv                  | 0.7786  | 0.8696  | 0.3397  | 0.5217  |
| rack                | 0.0020  | 0.2727  | 0.0018  | 0.1818  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| container           | 0.0032  | 0.2174  | 0.0010  | 0.0435  |
| calendar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cart                | 0.0038  | 0.5000  | 0.0000  | 0.0000  |
| board               | 0.1694  | 0.6875  | 0.0465  | 0.4375  |
| printer             | 0.5611  | 0.8182  | 0.5521  | 0.7576  |
| guitar              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| refrigerator        | 0.8911  | 0.9500  | 0.5477  | 0.8000  |
| ottoman             | 0.0566  | 0.8571  | 0.0097  | 0.2857  |
| coffee maker        | 0.6083  | 0.8235  | 0.2892  | 0.4706  |
| bench               | 0.7056  | 1.0000  | 0.6800  | 0.8000  |
| tray                | 0.0020  | 0.3333  | 0.0000  | 0.0000  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| purse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| counter             | 0.2852  | 0.8571  | 0.0622  | 0.4857  |
| stall               | 0.2480  | 0.7647  | 0.1145  | 0.5294  |
| drawer              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| faucet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| broom               | 0.0198  | 0.4167  | 0.0000  | 0.0000  |
| grab bar            | 0.0041  | 0.2500  | 0.0000  | 0.0000  |
| soap dispenser      | 0.4286  | 0.6190  | 0.0183  | 0.0952  |
| dispenser           | 0.6690  | 0.7949  | 0.1725  | 0.3077  |
| mailbox             | 0.2086  | 1.0000  | 0.0481  | 0.6000  |
| pillar              | 0.0241  | 0.6000  | 0.0000  | 0.0000  |
| stairs              | 0.1069  | 0.8000  | 0.0202  | 0.8000  |
| holder              | 0.1000  | 0.2000  | 0.0000  | 0.0000  |
| copier              | 0.8761  | 0.9167  | 0.7305  | 0.8333  |
| fire extinguisher   | 0.0208  | 0.3333  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| poster              | 0.0476  | 1.0000  | 0.0000  | 0.0000  |
| stove               | 0.9475  | 1.0000  | 0.9475  | 1.0000  |
| person              | 0.1461  | 0.5000  | 0.0030  | 0.3333  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fan                 | 0.6649  | 0.8750  | 0.1667  | 0.3750  |
| headphones          | 0.0054  | 0.2000  | 0.0054  | 0.2000  |
| ladder              | 0.0900  | 0.4000  | 0.0900  | 0.4000  |
| ball                | 0.4028  | 0.4167  | 0.1126  | 0.2500  |
| bucket              | 0.0626  | 0.6429  | 0.0126  | 0.1429  |
| basin               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 0.5556  | 0.6667  | 0.1222  | 0.6667  |
| crate               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vacuum cleaner      | 1.0000  | 1.0000  | 0.6667  | 0.6667  |
| toothpaste          | 0.0080  | 0.2000  | 0.0000  | 0.0000  |
| barricade           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sign                | 0.0591  | 0.5000  | 0.0013  | 0.0714  |
| machine             | 0.6541  | 0.9375  | 0.3763  | 0.4375  |
| water cooler        | 0.6429  | 1.0000  | 0.5000  | 0.5000  |
| kettle              | 0.1705  | 0.5000  | 0.0000  | 0.0000  |
| dish rack           | 0.6193  | 1.0000  | 0.0132  | 0.2500  |
| oven                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster             | 0.0167  | 0.2500  | 0.0000  | 0.0000  |
| teapot              | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.0053  | 0.3333  | 0.0000  | 0.0000  |
| plate               | 0.0613  | 0.6000  | 0.0400  | 0.2000  |
| range hood          | 0.7143  | 1.0000  | 0.1600  | 0.4000  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flowerpot           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bowl                | 0.0302  | 0.3333  | 0.0022  | 0.1111  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pot                 | 0.1000  | 0.5000  | 0.0000  | 0.0000  |
| map                 | 0.1111  | 1.0000  | 0.1111  | 1.0000  |
| washing machine     | 0.3966  | 0.8333  | 0.1792  | 0.5000  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.1213  | 1.0000  | 0.0625  | 0.5000  |
| speaker             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| scannet             | 0.2570  | 0.4817  | 0.1188  | 0.2408  |
| head                | 0.3429  | 0.6154  | 0.1637  | 0.3272  |
| common              | 0.1999  | 0.3860  | 0.0824  | 0.1605  |
| tail                | 0.0795  | 0.2225  | 0.0417  | 0.1174  |
| small               | 0.0488  | 0.1938  | 0.0064  | 0.0229  |
| medium              | 0.3070  | 0.5721  | 0.1377  | 0.2845  |
| large               | 0.2519  | 0.7685  | 0.1264  | 0.4782  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 07:18:36 INFO metrics.py:437 | number of 3rscan results: 235
Rank[0/8] 06/20/2025 07:19:47 INFO metrics.py:332 | eval results of 3rscan:
+-------------------+---------+---------+---------+---------+
| classes           | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+-------------------+---------+---------+---------+---------+
| monitor           | 0.4792  | 0.6500  | 0.0230  | 0.1000  |
| chair             | 0.6966  | 0.8484  | 0.5256  | 0.6852  |
| keyboard          | 0.0155  | 0.2083  | 0.0000  | 0.0000  |
| desk              | 0.4423  | 0.9250  | 0.3097  | 0.7375  |
| cup               | 0.0119  | 0.3077  | 0.0000  | 0.0256  |
| case              | 0.0488  | 0.3357  | 0.0173  | 0.0929  |
| mouse             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bottle            | 0.0392  | 0.2645  | 0.0002  | 0.0248  |
| bin               | 0.7051  | 0.8902  | 0.3335  | 0.5366  |
| plug              | 0.1481  | 0.2222  | 0.0000  | 0.0000  |
| computer          | 0.3360  | 0.6667  | 0.0004  | 0.0952  |
| paper             | 0.0485  | 0.3286  | 0.0027  | 0.1143  |
| pen               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bag               | 0.3061  | 0.6869  | 0.0522  | 0.2626  |
| table             | 0.5173  | 0.8829  | 0.3431  | 0.7297  |
| window            | 0.1858  | 0.6918  | 0.0231  | 0.2260  |
| wardrobe          | 0.4846  | 0.8125  | 0.3122  | 0.6875  |
| box               | 0.2054  | 0.5468  | 0.0486  | 0.1917  |
| backpack          | 0.5104  | 0.7879  | 0.2120  | 0.4848  |
| socket            | 0.0005  | 0.0625  | 0.0000  | 0.0000  |
| stand             | 0.4617  | 0.8852  | 0.2902  | 0.5574  |
| book              | 0.1018  | 0.5089  | 0.0145  | 0.0769  |
| garbage           | 0.3722  | 0.7500  | 0.1736  | 0.5000  |
| radiator          | 0.4405  | 0.6552  | 0.0994  | 0.2759  |
| conducting wire   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plate             | 0.0359  | 0.3333  | 0.0000  | 0.0000  |
| cabinet           | 0.3713  | 0.8291  | 0.2120  | 0.6111  |
| blinds            | 0.1807  | 0.5641  | 0.0067  | 0.2051  |
| curtain           | 0.5463  | 0.8651  | 0.1327  | 0.4206  |
| telephone         | 0.2208  | 0.4583  | 0.0223  | 0.1667  |
| windowsill        | 0.0939  | 0.5641  | 0.0044  | 0.1538  |
| shelf             | 0.2542  | 0.7102  | 0.0767  | 0.3428  |
| frame             | 0.1661  | 0.8000  | 0.0021  | 0.0667  |
| paper cutter      | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| notebook          | 0.0008  | 0.0909  | 0.0000  | 0.0000  |
| plant             | 0.3686  | 0.5891  | 0.0856  | 0.2426  |
| file              | 0.1134  | 0.4286  | 0.0677  | 0.1786  |
| couch             | 0.4390  | 0.8286  | 0.2960  | 0.7143  |
| commode           | 0.4773  | 0.9038  | 0.3607  | 0.7692  |
| picture           | 0.2191  | 0.4187  | 0.0122  | 0.0976  |
| cushion           | 0.1552  | 0.7667  | 0.0060  | 0.1667  |
| stairs            | 0.2457  | 0.7857  | 0.0248  | 0.3571  |
| door              | 0.3555  | 0.7279  | 0.0469  | 0.2794  |
| wire              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| furniture         | 0.0290  | 0.6667  | 0.0072  | 0.3333  |
| switch            | 0.0009  | 0.0755  | 0.0000  | 0.0000  |
| clock             | 0.1357  | 0.3684  | 0.0000  | 0.0000  |
| air conditioner   | 0.6875  | 0.7500  | 0.0417  | 0.2500  |
| bar               | 0.6667  | 0.6667  | 0.0476  | 0.3333  |
| bed               | 0.8390  | 0.9636  | 0.6179  | 0.8727  |
| tv                | 0.6005  | 0.8857  | 0.0326  | 0.2286  |
| lamp              | 0.2467  | 0.5188  | 0.0140  | 0.1313  |
| stool             | 0.4062  | 0.7963  | 0.2549  | 0.5185  |
| pillow            | 0.4501  | 0.7109  | 0.0233  | 0.1797  |
| candle            | 0.0167  | 0.0667  | 0.0000  | 0.0000  |
| ottoman           | 0.3700  | 1.0000  | 0.3475  | 0.9091  |
| bowl              | 0.2063  | 0.4444  | 0.0756  | 0.1111  |
| blanket           | 0.2692  | 0.9538  | 0.0842  | 0.5077  |
| sunglasses        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| laptop            | 0.6708  | 0.8333  | 0.4611  | 0.6667  |
| light             | 0.0663  | 0.4205  | 0.0006  | 0.0341  |
| basket            | 0.4026  | 0.7656  | 0.2379  | 0.4062  |
| rack              | 0.2057  | 0.7333  | 0.0537  | 0.4667  |
| folder            | 0.1404  | 0.3571  | 0.0026  | 0.0714  |
| bench             | 0.4288  | 0.8571  | 0.2903  | 0.7143  |
| board             | 0.3178  | 0.6667  | 0.0419  | 0.0833  |
| pad               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes           | 0.1953  | 0.6602  | 0.0281  | 0.1942  |
| teapot            | 0.2500  | 0.2500  | 0.2500  | 0.2500  |
| carpet            | 0.0938  | 0.5536  | 0.0036  | 0.1786  |
| poster            | 0.0446  | 0.1111  | 0.0023  | 0.0159  |
| tablet            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| doorframe         | 0.1918  | 0.7000  | 0.0059  | 0.1400  |
| decoration        | 0.1288  | 0.4688  | 0.0335  | 0.0938  |
| sign              | 0.2682  | 1.0000  | 0.0000  | 0.0000  |
| flowerpot         | 0.1268  | 0.4583  | 0.0047  | 0.0833  |
| player            | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| fireplace         | 0.0667  | 0.2000  | 0.0000  | 0.0000  |
| sink              | 0.6452  | 0.8393  | 0.1812  | 0.3571  |
| towel             | 0.2569  | 0.6456  | 0.0713  | 0.2025  |
| printer           | 0.5996  | 0.8889  | 0.3161  | 0.6667  |
| magazine          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vase              | 0.2249  | 0.3500  | 0.0506  | 0.1000  |
| mirror            | 0.1318  | 0.5556  | 0.0164  | 0.1481  |
| drawer            | 0.5104  | 1.0000  | 0.1370  | 0.6667  |
| container         | 0.0756  | 0.4444  | 0.0575  | 0.2222  |
| rail              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| counter           | 0.2412  | 0.7561  | 0.0080  | 0.2927  |
| blackboard        | 0.3702  | 0.7500  | 0.0932  | 0.5000  |
| heater            | 0.4802  | 0.7895  | 0.1835  | 0.4035  |
| humidifier        | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| fan               | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| device            | 0.1090  | 0.3636  | 0.0000  | 0.0000  |
| roll              | 0.0935  | 0.5000  | 0.0012  | 0.2000  |
| jalousie          | 0.3333  | 1.0000  | 0.0000  | 0.0000  |
| ball              | 0.6000  | 0.6000  | 0.2803  | 0.6000  |
| balcony           | 0.5345  | 1.0000  | 0.5000  | 0.5000  |
| window frame      | 0.0060  | 0.2500  | 0.0003  | 0.0833  |
| jar               | 0.1516  | 0.3750  | 0.1250  | 0.1250  |
| holder            | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| umbrella          | 0.0833  | 1.0000  | 0.0526  | 1.0000  |
| chandelier        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shoe              | 0.1809  | 0.4762  | 0.0018  | 0.0238  |
| remote control    | 0.1270  | 0.3000  | 0.0173  | 0.1000  |
| brush             | 0.4427  | 0.6250  | 0.0000  | 0.0000  |
| clothes dryer     | 0.4283  | 0.8000  | 0.2516  | 0.4000  |
| coffee maker      | 0.1180  | 0.5556  | 0.0000  | 0.0000  |
| scale             | 0.3369  | 0.6667  | 0.0000  | 0.0000  |
| hanger            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toilet paper      | 0.0340  | 0.4167  | 0.0060  | 0.0833  |
| toy               | 0.2201  | 0.6923  | 0.0440  | 0.2308  |
| fruit             | 0.6000  | 0.6000  | 0.2000  | 0.2000  |
| calendar          | 0.0229  | 0.4000  | 0.0000  | 0.0000  |
| refrigerator      | 0.5750  | 0.8667  | 0.4548  | 0.6667  |
| basin             | 0.0057  | 0.1333  | 0.0000  | 0.0000  |
| guitar            | 0.1667  | 0.5000  | 0.1667  | 0.5000  |
| tissue box        | 0.0556  | 0.3333  | 0.0556  | 0.3333  |
| dresser           | 1.0000  | 1.0000  | 0.5000  | 0.5000  |
| bathtub           | 0.9265  | 1.0000  | 0.4021  | 0.6667  |
| statue            | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| toilet            | 0.8355  | 0.8824  | 0.6765  | 0.7059  |
| shampoo           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shower            | 0.1266  | 0.5000  | 0.1250  | 0.2500  |
| faucet            | 0.1547  | 1.0000  | 0.0000  | 0.0000  |
| pipe              | 0.2581  | 0.7500  | 0.0000  | 0.0000  |
| washing machine   | 0.5223  | 0.8571  | 0.1761  | 0.5000  |
| glove             | 0.5000  | 0.6667  | 0.3333  | 0.3333  |
| beam              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dispenser         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue            | 0.0741  | 0.6667  | 0.0000  | 0.0000  |
| toiletry          | 0.0476  | 1.0000  | 0.0000  | 0.0000  |
| soap              | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| flush             | 0.8333  | 1.0000  | 0.0000  | 0.0000  |
| tube              | 0.0065  | 0.1429  | 0.0000  | 0.0000  |
| beanbag           | 1.0000  | 1.0000  | 0.8333  | 0.8333  |
| letter            | 0.1980  | 0.7500  | 0.0000  | 0.0000  |
| can               | 0.0063  | 0.1250  | 0.0063  | 0.1250  |
| column            | 0.0052  | 0.5000  | 0.0000  | 0.0000  |
| pillar            | 0.4383  | 0.5882  | 0.1835  | 0.5294  |
| ladder            | 0.4048  | 1.0000  | 0.1667  | 0.3333  |
| microwave         | 0.9051  | 1.0000  | 0.4541  | 0.5882  |
| pan               | 0.2942  | 0.5833  | 0.0216  | 0.1667  |
| stove             | 0.3782  | 0.9412  | 0.0310  | 0.4118  |
| range hood        | 0.4061  | 0.8000  | 0.0667  | 0.2000  |
| bread             | 0.2143  | 0.4000  | 0.0000  | 0.0000  |
| oven              | 0.6313  | 1.0000  | 0.1529  | 0.4737  |
| toaster           | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| pot               | 0.3211  | 0.5000  | 0.1667  | 0.1667  |
| shovel            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sack              | 0.6608  | 1.0000  | 0.2262  | 0.4286  |
| dishwasher        | 0.7067  | 1.0000  | 0.3333  | 0.3333  |
| kettle            | 0.1816  | 0.6667  | 0.0472  | 0.4167  |
| mat               | 0.0370  | 0.3333  | 0.0000  | 0.0000  |
| kitchenware       | 0.2197  | 0.4318  | 0.0366  | 0.1136  |
| pack              | 0.0387  | 0.7692  | 0.0074  | 0.3846  |
| spoon             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket            | 0.7029  | 0.8000  | 0.1743  | 0.3333  |
| knife             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| food              | 0.0025  | 0.1000  | 0.0000  | 0.0000  |
| partition         | 0.3615  | 0.8000  | 0.2000  | 0.2000  |
| tray              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crate             | 0.6800  | 0.8000  | 0.4400  | 0.6000  |
| radio             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| price tag         | 0.0948  | 0.4286  | 0.0000  | 0.0000  |
| crib              | 0.3684  | 0.6667  | 0.0702  | 0.6667  |
| cube              | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| piano             | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| broom             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dispenser    | 0.0933  | 0.4000  | 0.0000  | 0.0000  |
| hook              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| napkins           | 0.2037  | 0.6667  | 0.0000  | 0.0000  |
| candlestick       | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| bicycle           | 0.8333  | 1.0000  | 0.2333  | 1.0000  |
| treadmill         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| screen            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| glass             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mop               | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| soap dish         | 0.0455  | 0.5000  | 0.0000  | 0.0000  |
| hat               | 0.1905  | 0.2857  | 0.0000  | 0.0000  |
| coil              | 0.1667  | 0.1667  | 0.0417  | 0.1667  |
| tool              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mattress          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| helmets           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker           | 0.0026  | 0.0833  | 0.0026  | 0.0833  |
| vacuum cleaner    | 0.6875  | 0.7500  | 0.0000  | 0.0000  |
| menu              | 0.3214  | 0.4286  | 0.0000  | 0.0000  |
| washbasin         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| salt              | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| machine           | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| map               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| luggage           | 0.6905  | 1.0000  | 0.6905  | 1.0000  |
| album             | 0.0714  | 1.0000  | 0.0000  | 0.0000  |
| cosmetics         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sponge            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| seasoning         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| footstool         | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| water heater      | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| dish rack         | 0.5000  | 1.0000  | 0.0000  | 0.0000  |
| scissors          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| -------           | ------- | ------- | ------- | ------- |
| 3rscan            | 0.3158  | 0.5535  | 0.1273  | 0.2401  |
| head              | 0.3091  | 0.6235  | 0.1221  | 0.2911  |
| common            | 0.3325  | 0.5522  | 0.1314  | 0.2305  |
| tail              | 0.3051  | 0.4212  | 0.1314  | 0.1559  |
| small             | 0.1035  | 0.2816  | 0.0105  | 0.0361  |
| medium            | 0.3290  | 0.5908  | 0.1438  | 0.2758  |
| large             | 0.2268  | 0.7545  | 0.0969  | 0.4431  |
+-------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 07:19:47 INFO metrics.py:437 | number of matterport3d results: 339
Rank[0/8] 06/20/2025 07:21:09 INFO metrics.py:332 | eval results of matterport3d:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| mirror              | 0.0699  | 0.4595  | 0.0095  | 0.1486  |
| picture             | 0.2032  | 0.3913  | 0.0083  | 0.0845  |
| doorframe           | 0.2991  | 0.7335  | 0.1037  | 0.4222  |
| sink                | 0.5062  | 0.7778  | 0.1801  | 0.4343  |
| cabinet             | 0.1397  | 0.6314  | 0.0881  | 0.4025  |
| door                | 0.1990  | 0.5431  | 0.0119  | 0.1540  |
| plant               | 0.2926  | 0.7411  | 0.1053  | 0.3839  |
| light               | 0.0252  | 0.2222  | 0.0014  | 0.0513  |
| towel               | 0.3914  | 0.5909  | 0.0872  | 0.2727  |
| switch              | 0.0002  | 0.1000  | 0.0000  | 0.0000  |
| vent                | 0.0158  | 0.1667  | 0.0000  | 0.0000  |
| lamp                | 0.3082  | 0.6703  | 0.1060  | 0.3187  |
| countertop          | 0.0200  | 0.2500  | 0.0000  | 0.0357  |
| decoration          | 0.0140  | 0.3452  | 0.0007  | 0.0714  |
| window              | 0.1202  | 0.5500  | 0.0157  | 0.1722  |
| counter             | 0.0147  | 0.4242  | 0.0007  | 0.0909  |
| bathtub             | 0.6059  | 0.7407  | 0.4475  | 0.5556  |
| dresser             | 0.2432  | 0.7333  | 0.2184  | 0.6667  |
| drawer              | 0.0028  | 0.3750  | 0.0010  | 0.2500  |
| shelf               | 0.1688  | 0.5912  | 0.0600  | 0.3431  |
| bottle              | 0.0750  | 0.5000  | 0.0092  | 0.1042  |
| handle              | 0.0003  | 0.2500  | 0.0000  | 0.0000  |
| toilet              | 0.7958  | 0.8438  | 0.6078  | 0.7500  |
| table               | 0.3406  | 0.7564  | 0.2713  | 0.6218  |
| box                 | 0.0653  | 0.7115  | 0.0157  | 0.3077  |
| desk                | 0.3577  | 0.7895  | 0.1203  | 0.6316  |
| flowerpot           | 0.0327  | 0.3600  | 0.0069  | 0.2000  |
| case                | 0.0001  | 0.1111  | 0.0000  | 0.0556  |
| vanity              | 0.3055  | 0.6000  | 0.2639  | 0.4667  |
| frame               | 0.0011  | 0.2941  | 0.0001  | 0.0588  |
| clothes             | 0.1347  | 0.4828  | 0.0270  | 0.2069  |
| vase                | 0.0893  | 0.3667  | 0.0165  | 0.1333  |
| blanket             | 0.0135  | 0.3636  | 0.0035  | 0.2727  |
| curtain             | 0.4079  | 0.7812  | 0.0726  | 0.3438  |
| wardrobe            | 0.1127  | 0.4545  | 0.0032  | 0.2727  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| statue              | 0.0710  | 0.5294  | 0.0637  | 0.1176  |
| pillow              | 0.1839  | 0.6215  | 0.0144  | 0.1514  |
| tissue              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bench               | 0.2335  | 0.5500  | 0.1629  | 0.3000  |
| roof                | 0.0003  | 0.1250  | 0.0000  | 0.0000  |
| plate               | 0.0191  | 0.3571  | 0.0000  | 0.0000  |
| window frame        | 0.0407  | 0.5747  | 0.0123  | 0.2874  |
| paper               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stand               | 0.5281  | 0.8000  | 0.4123  | 0.6400  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| chair               | 0.4413  | 0.7687  | 0.2450  | 0.5476  |
| bed                 | 0.6189  | 0.8070  | 0.2894  | 0.7719  |
| doorway             | 0.0003  | 0.1111  | 0.0003  | 0.1111  |
| basket              | 0.0574  | 0.4800  | 0.0024  | 0.1200  |
| monitor             | 0.2083  | 0.5000  | 0.0000  | 0.0000  |
| couch               | 0.3091  | 0.8043  | 0.1766  | 0.6739  |
| tv                  | 0.4826  | 0.6279  | 0.1698  | 0.3488  |
| stairs              | 0.0301  | 0.3019  | 0.0034  | 0.0755  |
| screen              | 0.0059  | 0.5000  | 0.0000  | 0.0000  |
| ottoman             | 0.1065  | 0.5556  | 0.0471  | 0.3889  |
| cushion             | 0.0010  | 0.1765  | 0.0001  | 0.0588  |
| book                | 0.0870  | 0.3622  | 0.0052  | 0.0709  |
| stool               | 0.0153  | 0.3810  | 0.0078  | 0.1905  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| telephone           | 0.2750  | 0.5000  | 0.0000  | 0.0000  |
| blinds              | 0.0009  | 0.1000  | 0.0009  | 0.1000  |
| keyboard            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| board               | 0.1863  | 0.5000  | 0.0000  | 0.0000  |
| toy                 | 0.1335  | 0.3889  | 0.0338  | 0.1389  |
| fireplace           | 0.2132  | 0.5217  | 0.0033  | 0.0870  |
| rail                | 0.0252  | 0.3148  | 0.0000  | 0.0000  |
| clock               | 0.3333  | 0.3333  | 0.0011  | 0.3333  |
| bowl                | 0.0317  | 0.3333  | 0.0017  | 0.1212  |
| bin                 | 0.3342  | 0.7073  | 0.1049  | 0.3659  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mat                 | 0.0754  | 0.2917  | 0.0001  | 0.0417  |
| column              | 0.0212  | 0.3889  | 0.0044  | 0.1389  |
| piano               | 0.1667  | 0.5000  | 0.1667  | 0.5000  |
| fruit               | 0.0833  | 0.2500  | 0.0250  | 0.1250  |
| pillar              | 0.3816  | 0.8095  | 0.1016  | 0.3810  |
| carpet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| steps               | 0.0012  | 0.2000  | 0.0000  | 0.0000  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| thermostat          | 0.0833  | 0.1667  | 0.0000  | 0.0000  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beam                | 0.0003  | 0.0952  | 0.0000  | 0.0000  |
| dispenser           | 0.0132  | 0.5000  | 0.0132  | 0.5000  |
| shower              | 0.0042  | 0.1667  | 0.0001  | 0.0833  |
| soap dispenser      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toilet paper        | 0.3266  | 0.6111  | 0.1375  | 0.3333  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| windowsill          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| socket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.5159  | 0.6250  | 0.3244  | 0.5000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| footstool           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pot                 | 0.0080  | 0.3077  | 0.0002  | 0.0769  |
| cup                 | 0.0003  | 0.1000  | 0.0000  | 0.0000  |
| backpack            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washbasin           | 0.0022  | 0.1429  | 0.0022  | 0.1429  |
| hanger              | 0.0041  | 0.2000  | 0.0000  | 0.0000  |
| faucet              | 0.0477  | 0.2778  | 0.0000  | 0.0000  |
| pipe                | 0.0004  | 0.1000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.3333  | 0.6667  | 0.3333  | 0.6667  |
| holder              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket              | 0.0119  | 0.4000  | 0.0064  | 0.2000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchen island      | 0.1874  | 0.5714  | 0.1657  | 0.4286  |
| heater              | 0.3333  | 1.0000  | 0.3333  | 1.0000  |
| radiator            | 0.4444  | 0.6667  | 0.0000  | 0.0000  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fan                 | 0.6917  | 0.7963  | 0.2379  | 0.4630  |
| teapot              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jar                 | 0.0063  | 0.3333  | 0.0000  | 0.0000  |
| printer             | 0.4603  | 0.5714  | 0.2857  | 0.2857  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stove               | 0.1535  | 0.6000  | 0.1296  | 0.5000  |
| basin               | 0.0032  | 1.0000  | 0.0032  | 1.0000  |
| wine                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| refrigerator        | 0.2637  | 0.7333  | 0.0844  | 0.4000  |
| toaster             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scale               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue box          | 0.0712  | 0.5000  | 0.0000  | 0.0000  |
| pan                 | 0.2515  | 0.6000  | 0.0000  | 0.0000  |
| rack                | 0.0004  | 0.1818  | 0.0003  | 0.0909  |
| bag                 | 0.1308  | 0.5385  | 0.0022  | 0.0769  |
| speaker             | 0.0098  | 0.3750  | 0.0039  | 0.1250  |
| mouse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball                | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| food                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.3367  | 0.7778  | 0.0104  | 0.2778  |
| microwave           | 0.1154  | 0.3333  | 0.0833  | 0.0833  |
| kettle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| chandelier          | 0.2018  | 0.4211  | 0.0411  | 0.2105  |
| fire extinguisher   | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| garage door         | 0.4309  | 0.6667  | 0.2485  | 0.5000  |
| ladder              | 0.1785  | 0.3333  | 0.0556  | 0.1111  |
| laptop              | 0.3167  | 1.0000  | 0.0333  | 0.5000  |
| toiletry            | 0.1194  | 0.3182  | 0.0000  | 0.0000  |
| soap                | 0.0005  | 0.2000  | 0.0000  | 0.0000  |
| machine             | 0.0043  | 0.5000  | 0.0043  | 0.5000  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dish           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| arch                | 0.0001  | 0.0333  | 0.0001  | 0.0333  |
| container           | 0.0354  | 0.2593  | 0.0103  | 0.1111  |
| bar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.2718  | 0.7500  | 0.0000  | 0.0000  |
| jacket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| computer            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| oven                | 0.2284  | 0.8000  | 0.1000  | 0.2000  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shoe                | 0.0736  | 0.2800  | 0.0010  | 0.0400  |
| kitchenware         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| guitar              | 0.1810  | 0.5714  | 0.0000  | 0.0000  |
| bicycle             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| partition           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool table          | 0.1667  | 0.3333  | 0.1667  | 0.3333  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet               | 0.2500  | 0.5000  | 0.2500  | 0.5000  |
| magazine            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hamper              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blackboard          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drum                | 0.0625  | 0.2500  | 0.0000  | 0.0000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| player              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pack                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.0147  | 0.2500  | 0.0147  | 0.2500  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coffee maker        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| air conditioner     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell            | 0.1667  | 0.5000  | 0.0076  | 0.5000  |
| candlestick         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| matterport3d        | 0.1152  | 0.3125  | 0.0520  | 0.1584  |
| head                | 0.1709  | 0.4477  | 0.0683  | 0.2250  |
| common              | 0.0702  | 0.2216  | 0.0388  | 0.1076  |
| tail                | 0.0437  | 0.0922  | 0.0308  | 0.0653  |
| small               | 0.0473  | 0.1509  | 0.0143  | 0.0417  |
| medium              | 0.1071  | 0.3303  | 0.0466  | 0.1590  |
| large               | 0.0883  | 0.4275  | 0.0370  | 0.2529  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 07:21:09 INFO metrics.py:437 | number of arkit results: 0
Rank[0/8] 06/20/2025 07:21:10 INFO stats.py:394 | Epoch[129] completed. Training Speed: 3.05 samples/sec across all devices.	Epoch Time: 1023.28 sec.	Average Epoch Time: 1023.28 sec.	Average Step Time: 2.62 sec.	Estimated Remaining Time: 14:30:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:21:10 WARNING accelerator.py:3099 | Deleting 1 checkpoints to make room for new checkpoint.
Rank[0/8] 06/20/2025 07:21:11 INFO accelerator.py:3111 | Saving current state to /job_data/checkpoints/checkpoint_12
Rank[2/8] 06/20/2025 07:21:11 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 129 to /job_data/checkpoints/checkpoint_12
Rank[4/8] 06/20/2025 07:21:11 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 129 to /job_data/checkpoints/checkpoint_12
Rank[3/8] 06/20/2025 07:21:11 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 129 to /job_data/checkpoints/checkpoint_12
Rank[7/8] 06/20/2025 07:21:11 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 129 to /job_data/checkpoints/checkpoint_12
Rank[5/8] 06/20/2025 07:21:11 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 129 to /job_data/checkpoints/checkpoint_12
Rank[6/8] 06/20/2025 07:21:11 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 129 to /job_data/checkpoints/checkpoint_12
Rank[1/8] 06/20/2025 07:21:11 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 129 to /job_data/checkpoints/checkpoint_12
Rank[0/8] 06/20/2025 07:21:13 INFO checkpointing.py:106 | Model weights saved in /job_data/checkpoints/checkpoint_12/model.safetensors
Rank[0/8] 06/20/2025 07:21:16 INFO checkpointing.py:113 | Optimizer state saved in /job_data/checkpoints/checkpoint_12/optimizer.bin
Rank[0/8] 06/20/2025 07:21:16 INFO checkpointing.py:120 | Scheduler state saved in /job_data/checkpoints/checkpoint_12/scheduler.bin
Rank[0/8] 06/20/2025 07:21:16 INFO checkpointing.py:137 | Sampler state for dataloader 0 saved in /job_data/checkpoints/checkpoint_12/sampler.bin
Rank[0/8] 06/20/2025 07:21:16 INFO checkpointing.py:137 | Sampler state for dataloader 1 saved in /job_data/checkpoints/checkpoint_12/sampler_1.bin
Rank[0/8] 06/20/2025 07:21:16 INFO checkpointing.py:164 | Random states saved in /job_data/checkpoints/checkpoint_12/random_states_0.pkl
Rank[0/8] 06/20/2025 07:21:16 INFO checkpointing.py:300 | Saving the state of TrainerProgressState to /job_data/checkpoints/checkpoint_12/custom_checkpoint_0.pkl
Rank[0/8] 06/20/2025 07:21:16 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 129 to /job_data/checkpoints/checkpoint_12
Rank[0/8] 06/20/2025 07:21:45 INFO stats.py:314 | Epoch[130] Step[24] GlobalStep[50724] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 14:30:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:21:45 INFO loss_tracker.py:84 | Epoch[130/239] Step[24] GlobalStep[50724/NA]: det_loss_cls_0[0.4984] det_loss_box_0[0.6323] det_loss_cls_1[0.4829] det_loss_box_1[0.4761] det_loss_cls_2[0.4244] det_loss_box_2[0.4294] det_loss_cls_3[0.3965] det_loss_box_3[0.4064] det_loss_cls_4[0.3663] det_loss_box_4[0.4009] det_loss_cls_5[0.3530] det_loss_box_5[0.4027] det_loss_cls_dn_0[0.0853] det_loss_box_dn_0[0.2446] det_loss_cls_dn_1[0.0814] det_loss_box_dn_1[0.1984] det_loss_cls_dn_2[0.0810] det_loss_box_dn_2[0.1807] det_loss_cls_dn_3[0.0800] det_loss_box_dn_3[0.1757] det_loss_cls_dn_4[0.0790] det_loss_box_dn_4[0.1746] det_loss_cls_dn_5[0.0779] det_loss_box_dn_5[0.1737] loss_depth[0.0373] total_loss[6.9387]
Rank[0/8] 06/20/2025 07:22:11 INFO stats.py:314 | Epoch[130] Step[49] GlobalStep[50749] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 14:29:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:22:11 INFO loss_tracker.py:84 | Epoch[130/239] Step[49] GlobalStep[50749/NA]: det_loss_cls_0[0.4736] det_loss_box_0[0.5854] det_loss_cls_1[0.4568] det_loss_box_1[0.4415] det_loss_cls_2[0.4160] det_loss_box_2[0.4133] det_loss_cls_3[0.3836] det_loss_box_3[0.4014] det_loss_cls_4[0.3595] det_loss_box_4[0.3917] det_loss_cls_5[0.3420] det_loss_box_5[0.3939] det_loss_cls_dn_0[0.0991] det_loss_box_dn_0[0.2807] det_loss_cls_dn_1[0.0949] det_loss_box_dn_1[0.2336] det_loss_cls_dn_2[0.0939] det_loss_box_dn_2[0.2164] det_loss_cls_dn_3[0.0926] det_loss_box_dn_3[0.2115] det_loss_cls_dn_4[0.0915] det_loss_box_dn_4[0.2093] det_loss_cls_dn_5[0.0909] det_loss_box_dn_5[0.2087] loss_depth[0.0363] total_loss[7.0180]
Rank[0/8] 06/20/2025 07:22:37 INFO stats.py:314 | Epoch[130] Step[74] GlobalStep[50774] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 14:29:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:22:37 INFO loss_tracker.py:84 | Epoch[130/239] Step[74] GlobalStep[50774/NA]: det_loss_cls_0[0.3934] det_loss_box_0[0.5500] det_loss_cls_1[0.3880] det_loss_box_1[0.4261] det_loss_cls_2[0.3558] det_loss_box_2[0.3808] det_loss_cls_3[0.3226] det_loss_box_3[0.3813] det_loss_cls_4[0.3049] det_loss_box_4[0.3755] det_loss_cls_5[0.2949] det_loss_box_5[0.3765] det_loss_cls_dn_0[0.0979] det_loss_box_dn_0[0.3353] det_loss_cls_dn_1[0.0934] det_loss_box_dn_1[0.2853] det_loss_cls_dn_2[0.0934] det_loss_box_dn_2[0.2681] det_loss_cls_dn_3[0.0917] det_loss_box_dn_3[0.2641] det_loss_cls_dn_4[0.0917] det_loss_box_dn_4[0.2624] det_loss_cls_dn_5[0.0910] det_loss_box_dn_5[0.2589] loss_depth[0.0414] total_loss[6.8246]
Rank[0/8] 06/20/2025 07:23:03 INFO stats.py:314 | Epoch[130] Step[99] GlobalStep[50799] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 14:28:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:23:03 INFO loss_tracker.py:84 | Epoch[130/239] Step[99] GlobalStep[50799/NA]: det_loss_cls_0[0.5183] det_loss_box_0[0.6985] det_loss_cls_1[0.4966] det_loss_box_1[0.5221] det_loss_cls_2[0.4554] det_loss_box_2[0.4577] det_loss_cls_3[0.4240] det_loss_box_3[0.4433] det_loss_cls_4[0.3984] det_loss_box_4[0.4315] det_loss_cls_5[0.3849] det_loss_box_5[0.4349] det_loss_cls_dn_0[0.0935] det_loss_box_dn_0[0.2483] det_loss_cls_dn_1[0.0909] det_loss_box_dn_1[0.2093] det_loss_cls_dn_2[0.0905] det_loss_box_dn_2[0.1939] det_loss_cls_dn_3[0.0892] det_loss_box_dn_3[0.1917] det_loss_cls_dn_4[0.0880] det_loss_box_dn_4[0.1898] det_loss_cls_dn_5[0.0874] det_loss_box_dn_5[0.1894] loss_depth[0.0388] total_loss[7.4664]
Rank[0/8] 06/20/2025 07:23:29 INFO stats.py:314 | Epoch[130] Step[124] GlobalStep[50824] Training Speed: 6.20 samples/sec across all devices.	Average Step Time: 1.29 sec.	Estimated Remaining Time: 14:27:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:23:29 INFO loss_tracker.py:84 | Epoch[130/239] Step[124] GlobalStep[50824/NA]: det_loss_cls_0[0.5107] det_loss_box_0[0.6899] det_loss_cls_1[0.5076] det_loss_box_1[0.5254] det_loss_cls_2[0.4647] det_loss_box_2[0.4716] det_loss_cls_3[0.4188] det_loss_box_3[0.4547] det_loss_cls_4[0.3921] det_loss_box_4[0.4406] det_loss_cls_5[0.3760] det_loss_box_5[0.4410] det_loss_cls_dn_0[0.0987] det_loss_box_dn_0[0.2685] det_loss_cls_dn_1[0.0946] det_loss_box_dn_1[0.2285] det_loss_cls_dn_2[0.0946] det_loss_box_dn_2[0.2095] det_loss_cls_dn_3[0.0931] det_loss_box_dn_3[0.2060] det_loss_cls_dn_4[0.0920] det_loss_box_dn_4[0.2050] det_loss_cls_dn_5[0.0912] det_loss_box_dn_5[0.2045] loss_depth[0.0364] total_loss[7.6155]
Rank[0/8] 06/20/2025 07:23:55 INFO stats.py:314 | Epoch[130] Step[149] GlobalStep[50849] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 14:27:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:23:55 INFO loss_tracker.py:84 | Epoch[130/239] Step[149] GlobalStep[50849/NA]: det_loss_cls_0[0.5384] det_loss_box_0[0.6643] det_loss_cls_1[0.5153] det_loss_box_1[0.5329] det_loss_cls_2[0.4671] det_loss_box_2[0.4942] det_loss_cls_3[0.4468] det_loss_box_3[0.4756] det_loss_cls_4[0.4256] det_loss_box_4[0.4573] det_loss_cls_5[0.4142] det_loss_box_5[0.4588] det_loss_cls_dn_0[0.1054] det_loss_box_dn_0[0.2771] det_loss_cls_dn_1[0.1023] det_loss_box_dn_1[0.2405] det_loss_cls_dn_2[0.1019] det_loss_box_dn_2[0.2271] det_loss_cls_dn_3[0.1008] det_loss_box_dn_3[0.2227] det_loss_cls_dn_4[0.0994] det_loss_box_dn_4[0.2203] det_loss_cls_dn_5[0.0987] det_loss_box_dn_5[0.2166] loss_depth[0.0361] total_loss[7.9395]
Rank[0/8] 06/20/2025 07:24:20 INFO stats.py:314 | Epoch[130] Step[174] GlobalStep[50874] Training Speed: 8.62 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 14:26:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:24:20 INFO loss_tracker.py:84 | Epoch[130/239] Step[174] GlobalStep[50874/NA]: det_loss_cls_0[0.4825] det_loss_box_0[0.6199] det_loss_cls_1[0.4656] det_loss_box_1[0.4836] det_loss_cls_2[0.4203] det_loss_box_2[0.4295] det_loss_cls_3[0.3909] det_loss_box_3[0.4143] det_loss_cls_4[0.3571] det_loss_box_4[0.4080] det_loss_cls_5[0.3437] det_loss_box_5[0.4106] det_loss_cls_dn_0[0.0929] det_loss_box_dn_0[0.2615] det_loss_cls_dn_1[0.0886] det_loss_box_dn_1[0.2130] det_loss_cls_dn_2[0.0879] det_loss_box_dn_2[0.1931] det_loss_cls_dn_3[0.0866] det_loss_box_dn_3[0.1898] det_loss_cls_dn_4[0.0853] det_loss_box_dn_4[0.1873] det_loss_cls_dn_5[0.0849] det_loss_box_dn_5[0.1862] loss_depth[0.0361] total_loss[7.0189]
Rank[0/8] 06/20/2025 07:24:46 INFO stats.py:314 | Epoch[130] Step[199] GlobalStep[50899] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 14:26:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:24:46 INFO loss_tracker.py:84 | Epoch[130/239] Step[199] GlobalStep[50899/NA]: det_loss_cls_0[0.4145] det_loss_box_0[0.5609] det_loss_cls_1[0.4075] det_loss_box_1[0.4448] det_loss_cls_2[0.3782] det_loss_box_2[0.3945] det_loss_cls_3[0.3594] det_loss_box_3[0.3757] det_loss_cls_4[0.3372] det_loss_box_4[0.3696] det_loss_cls_5[0.3254] det_loss_box_5[0.3710] det_loss_cls_dn_0[0.1012] det_loss_box_dn_0[0.3124] det_loss_cls_dn_1[0.0965] det_loss_box_dn_1[0.2730] det_loss_cls_dn_2[0.0964] det_loss_box_dn_2[0.2535] det_loss_cls_dn_3[0.0944] det_loss_box_dn_3[0.2478] det_loss_cls_dn_4[0.0937] det_loss_box_dn_4[0.2443] det_loss_cls_dn_5[0.0923] det_loss_box_dn_5[0.2423] loss_depth[0.0345] total_loss[6.9210]
Rank[0/8] 06/20/2025 07:25:12 INFO stats.py:314 | Epoch[130] Step[224] GlobalStep[50924] Training Speed: 7.95 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 14:25:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:25:12 INFO loss_tracker.py:84 | Epoch[130/239] Step[224] GlobalStep[50924/NA]: det_loss_cls_0[0.4849] det_loss_box_0[0.6231] det_loss_cls_1[0.4650] det_loss_box_1[0.4827] det_loss_cls_2[0.4140] det_loss_box_2[0.4387] det_loss_cls_3[0.3879] det_loss_box_3[0.4120] det_loss_cls_4[0.3622] det_loss_box_4[0.4054] det_loss_cls_5[0.3434] det_loss_box_5[0.4058] det_loss_cls_dn_0[0.0976] det_loss_box_dn_0[0.2650] det_loss_cls_dn_1[0.0951] det_loss_box_dn_1[0.2210] det_loss_cls_dn_2[0.0944] det_loss_box_dn_2[0.2048] det_loss_cls_dn_3[0.0926] det_loss_box_dn_3[0.2015] det_loss_cls_dn_4[0.0915] det_loss_box_dn_4[0.2000] det_loss_cls_dn_5[0.0907] det_loss_box_dn_5[0.1988] loss_depth[0.0380] total_loss[7.1162]
Rank[0/8] 06/20/2025 07:25:38 INFO stats.py:314 | Epoch[130] Step[249] GlobalStep[50949] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 14:25:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:25:38 INFO loss_tracker.py:84 | Epoch[130/239] Step[249] GlobalStep[50949/NA]: det_loss_cls_0[0.4998] det_loss_box_0[0.6452] det_loss_cls_1[0.4880] det_loss_box_1[0.4957] det_loss_cls_2[0.4336] det_loss_box_2[0.4488] det_loss_cls_3[0.3997] det_loss_box_3[0.4370] det_loss_cls_4[0.3706] det_loss_box_4[0.4269] det_loss_cls_5[0.3564] det_loss_box_5[0.4300] det_loss_cls_dn_0[0.0974] det_loss_box_dn_0[0.3268] det_loss_cls_dn_1[0.0934] det_loss_box_dn_1[0.2774] det_loss_cls_dn_2[0.0921] det_loss_box_dn_2[0.2563] det_loss_cls_dn_3[0.0918] det_loss_box_dn_3[0.2537] det_loss_cls_dn_4[0.0907] det_loss_box_dn_4[0.2510] det_loss_cls_dn_5[0.0898] det_loss_box_dn_5[0.2484] loss_depth[0.0380] total_loss[7.6385]
Rank[0/8] 06/20/2025 07:26:04 INFO stats.py:314 | Epoch[130] Step[274] GlobalStep[50974] Training Speed: 8.19 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 14:24:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:26:04 INFO loss_tracker.py:84 | Epoch[130/239] Step[274] GlobalStep[50974/NA]: det_loss_cls_0[0.5772] det_loss_box_0[0.7500] det_loss_cls_1[0.5550] det_loss_box_1[0.5570] det_loss_cls_2[0.5018] det_loss_box_2[0.5026] det_loss_cls_3[0.4724] det_loss_box_3[0.4831] det_loss_cls_4[0.4419] det_loss_box_4[0.4664] det_loss_cls_5[0.4169] det_loss_box_5[0.4684] det_loss_cls_dn_0[0.0969] det_loss_box_dn_0[0.2636] det_loss_cls_dn_1[0.0940] det_loss_box_dn_1[0.2240] det_loss_cls_dn_2[0.0934] det_loss_box_dn_2[0.2084] det_loss_cls_dn_3[0.0920] det_loss_box_dn_3[0.2057] det_loss_cls_dn_4[0.0914] det_loss_box_dn_4[0.2047] det_loss_cls_dn_5[0.0907] det_loss_box_dn_5[0.2040] loss_depth[0.0375] total_loss[8.0989]
Rank[0/8] 06/20/2025 07:26:30 INFO stats.py:314 | Epoch[130] Step[299] GlobalStep[50999] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 14:23:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:26:30 INFO loss_tracker.py:84 | Epoch[130/239] Step[299] GlobalStep[50999/NA]: det_loss_cls_0[0.4813] det_loss_box_0[0.6456] det_loss_cls_1[0.4737] det_loss_box_1[0.5257] det_loss_cls_2[0.4402] det_loss_box_2[0.4802] det_loss_cls_3[0.4162] det_loss_box_3[0.4679] det_loss_cls_4[0.3894] det_loss_box_4[0.4559] det_loss_cls_5[0.3699] det_loss_box_5[0.4620] det_loss_cls_dn_0[0.0962] det_loss_box_dn_0[0.3171] det_loss_cls_dn_1[0.0932] det_loss_box_dn_1[0.2660] det_loss_cls_dn_2[0.0920] det_loss_box_dn_2[0.2472] det_loss_cls_dn_3[0.0913] det_loss_box_dn_3[0.2409] det_loss_cls_dn_4[0.0900] det_loss_box_dn_4[0.2394] det_loss_cls_dn_5[0.0893] det_loss_box_dn_5[0.2354] loss_depth[0.0362] total_loss[7.7422]
Rank[0/8] 06/20/2025 07:26:56 INFO stats.py:314 | Epoch[130] Step[324] GlobalStep[51024] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 14:23:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:26:56 INFO loss_tracker.py:84 | Epoch[130/239] Step[324] GlobalStep[51024/NA]: det_loss_cls_0[0.4290] det_loss_box_0[0.5338] det_loss_cls_1[0.4247] det_loss_box_1[0.4140] det_loss_cls_2[0.3762] det_loss_box_2[0.3911] det_loss_cls_3[0.3611] det_loss_box_3[0.3714] det_loss_cls_4[0.3391] det_loss_box_4[0.3653] det_loss_cls_5[0.3224] det_loss_box_5[0.3682] det_loss_cls_dn_0[0.0952] det_loss_box_dn_0[0.3254] det_loss_cls_dn_1[0.0910] det_loss_box_dn_1[0.2717] det_loss_cls_dn_2[0.0894] det_loss_box_dn_2[0.2518] det_loss_cls_dn_3[0.0886] det_loss_box_dn_3[0.2473] det_loss_cls_dn_4[0.0872] det_loss_box_dn_4[0.2434] det_loss_cls_dn_5[0.0861] det_loss_box_dn_5[0.2388] loss_depth[0.0366] total_loss[6.8490]
Rank[0/8] 06/20/2025 07:27:22 INFO stats.py:314 | Epoch[130] Step[349] GlobalStep[51049] Training Speed: 7.38 samples/sec across all devices.	Average Step Time: 1.08 sec.	Estimated Remaining Time: 14:22:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:27:22 INFO loss_tracker.py:84 | Epoch[130/239] Step[349] GlobalStep[51049/NA]: det_loss_cls_0[0.5308] det_loss_box_0[0.7099] det_loss_cls_1[0.5249] det_loss_box_1[0.5594] det_loss_cls_2[0.4898] det_loss_box_2[0.5080] det_loss_cls_3[0.4643] det_loss_box_3[0.4892] det_loss_cls_4[0.4245] det_loss_box_4[0.4816] det_loss_cls_5[0.4070] det_loss_box_5[0.4820] det_loss_cls_dn_0[0.0977] det_loss_box_dn_0[0.3528] det_loss_cls_dn_1[0.0932] det_loss_box_dn_1[0.3040] det_loss_cls_dn_2[0.0923] det_loss_box_dn_2[0.2893] det_loss_cls_dn_3[0.0916] det_loss_box_dn_3[0.2841] det_loss_cls_dn_4[0.0901] det_loss_box_dn_4[0.2832] det_loss_cls_dn_5[0.0892] det_loss_box_dn_5[0.2777] loss_depth[0.0356] total_loss[8.4522]
Rank[0/8] 06/20/2025 07:27:48 INFO stats.py:314 | Epoch[130] Step[374] GlobalStep[51074] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 14:22:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:27:48 INFO loss_tracker.py:84 | Epoch[130/239] Step[374] GlobalStep[51074/NA]: det_loss_cls_0[0.5701] det_loss_box_0[0.7731] det_loss_cls_1[0.5579] det_loss_box_1[0.6086] det_loss_cls_2[0.5140] det_loss_box_2[0.5587] det_loss_cls_3[0.4754] det_loss_box_3[0.5514] det_loss_cls_4[0.4406] det_loss_box_4[0.5349] det_loss_cls_5[0.4209] det_loss_box_5[0.5453] det_loss_cls_dn_0[0.1054] det_loss_box_dn_0[0.3049] det_loss_cls_dn_1[0.1010] det_loss_box_dn_1[0.2581] det_loss_cls_dn_2[0.1007] det_loss_box_dn_2[0.2399] det_loss_cls_dn_3[0.0998] det_loss_box_dn_3[0.2352] det_loss_cls_dn_4[0.0987] det_loss_box_dn_4[0.2334] det_loss_cls_dn_5[0.0979] det_loss_box_dn_5[0.2312] loss_depth[0.0369] total_loss[8.6941]
Rank[0/8] 06/20/2025 07:28:03 INFO stats.py:394 | Epoch[130] completed. Training Speed: 7.66 samples/sec across all devices.	Epoch Time: 407.29 sec.	Average Epoch Time: 407.29 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 14:21:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:28:17 INFO stats.py:314 | Epoch[131] Step[9] GlobalStep[51099] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 14:21:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:28:32 INFO loss_tracker.py:84 | Epoch[131/239] Step[24] GlobalStep[51114/NA]: det_loss_cls_0[0.4982] det_loss_box_0[0.6905] det_loss_cls_1[0.4923] det_loss_box_1[0.5286] det_loss_cls_2[0.4507] det_loss_box_2[0.4786] det_loss_cls_3[0.4165] det_loss_box_3[0.4677] det_loss_cls_4[0.3870] det_loss_box_4[0.4570] det_loss_cls_5[0.3713] det_loss_box_5[0.4571] det_loss_cls_dn_0[0.0898] det_loss_box_dn_0[0.2982] det_loss_cls_dn_1[0.0861] det_loss_box_dn_1[0.2556] det_loss_cls_dn_2[0.0850] det_loss_box_dn_2[0.2411] det_loss_cls_dn_3[0.0841] det_loss_box_dn_3[0.2364] det_loss_cls_dn_4[0.0831] det_loss_box_dn_4[0.2350] det_loss_cls_dn_5[0.0826] det_loss_box_dn_5[0.2325] loss_depth[0.0347] total_loss[7.7398]
Rank[0/8] 06/20/2025 07:28:42 INFO stats.py:314 | Epoch[131] Step[34] GlobalStep[51124] Training Speed: 8.02 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 14:21:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:28:58 INFO loss_tracker.py:84 | Epoch[131/239] Step[49] GlobalStep[51139/NA]: det_loss_cls_0[0.4607] det_loss_box_0[0.5973] det_loss_cls_1[0.4412] det_loss_box_1[0.4605] det_loss_cls_2[0.3937] det_loss_box_2[0.4191] det_loss_cls_3[0.3687] det_loss_box_3[0.4061] det_loss_cls_4[0.3492] det_loss_box_4[0.3934] det_loss_cls_5[0.3385] det_loss_box_5[0.3896] det_loss_cls_dn_0[0.1012] det_loss_box_dn_0[0.3310] det_loss_cls_dn_1[0.0955] det_loss_box_dn_1[0.2864] det_loss_cls_dn_2[0.0952] det_loss_box_dn_2[0.2696] det_loss_cls_dn_3[0.0947] det_loss_box_dn_3[0.2653] det_loss_cls_dn_4[0.0930] det_loss_box_dn_4[0.2628] det_loss_cls_dn_5[0.0921] det_loss_box_dn_5[0.2567] loss_depth[0.0380] total_loss[7.2997]
Rank[0/8] 06/20/2025 07:29:08 INFO stats.py:314 | Epoch[131] Step[59] GlobalStep[51149] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 14:20:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:29:24 INFO loss_tracker.py:84 | Epoch[131/239] Step[74] GlobalStep[51164/NA]: det_loss_cls_0[0.5316] det_loss_box_0[0.7685] det_loss_cls_1[0.5249] det_loss_box_1[0.5924] det_loss_cls_2[0.4833] det_loss_box_2[0.5281] det_loss_cls_3[0.4478] det_loss_box_3[0.5085] det_loss_cls_4[0.4167] det_loss_box_4[0.4938] det_loss_cls_5[0.3967] det_loss_box_5[0.4949] det_loss_cls_dn_0[0.0973] det_loss_box_dn_0[0.3186] det_loss_cls_dn_1[0.0929] det_loss_box_dn_1[0.2643] det_loss_cls_dn_2[0.0922] det_loss_box_dn_2[0.2424] det_loss_cls_dn_3[0.0918] det_loss_box_dn_3[0.2383] det_loss_cls_dn_4[0.0902] det_loss_box_dn_4[0.2371] det_loss_cls_dn_5[0.0898] det_loss_box_dn_5[0.2376] loss_depth[0.0403] total_loss[8.3199]
Rank[0/8] 06/20/2025 07:29:34 INFO stats.py:314 | Epoch[131] Step[84] GlobalStep[51174] Training Speed: 11.80 samples/sec across all devices.	Average Step Time: 0.68 sec.	Estimated Remaining Time: 14:19:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:29:50 INFO loss_tracker.py:84 | Epoch[131/239] Step[99] GlobalStep[51189/NA]: det_loss_cls_0[0.4471] det_loss_box_0[0.6409] det_loss_cls_1[0.4302] det_loss_box_1[0.5111] det_loss_cls_2[0.3854] det_loss_box_2[0.4654] det_loss_cls_3[0.3573] det_loss_box_3[0.4481] det_loss_cls_4[0.3324] det_loss_box_4[0.4439] det_loss_cls_5[0.3169] det_loss_box_5[0.4419] det_loss_cls_dn_0[0.0915] det_loss_box_dn_0[0.3326] det_loss_cls_dn_1[0.0876] det_loss_box_dn_1[0.2685] det_loss_cls_dn_2[0.0863] det_loss_box_dn_2[0.2459] det_loss_cls_dn_3[0.0857] det_loss_box_dn_3[0.2428] det_loss_cls_dn_4[0.0853] det_loss_box_dn_4[0.2387] det_loss_cls_dn_5[0.0842] det_loss_box_dn_5[0.2370] loss_depth[0.0370] total_loss[7.3437]
Rank[0/8] 06/20/2025 07:30:00 INFO stats.py:314 | Epoch[131] Step[109] GlobalStep[51199] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 14:19:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:30:15 INFO loss_tracker.py:84 | Epoch[131/239] Step[124] GlobalStep[51214/NA]: det_loss_cls_0[0.5479] det_loss_box_0[0.8148] det_loss_cls_1[0.5381] det_loss_box_1[0.6474] det_loss_cls_2[0.4848] det_loss_box_2[0.6037] det_loss_cls_3[0.4429] det_loss_box_3[0.5955] det_loss_cls_4[0.4186] det_loss_box_4[0.5775] det_loss_cls_5[0.3988] det_loss_box_5[0.5800] det_loss_cls_dn_0[0.0921] det_loss_box_dn_0[0.3194] det_loss_cls_dn_1[0.0870] det_loss_box_dn_1[0.2747] det_loss_cls_dn_2[0.0858] det_loss_box_dn_2[0.2601] det_loss_cls_dn_3[0.0849] det_loss_box_dn_3[0.2576] det_loss_cls_dn_4[0.0840] det_loss_box_dn_4[0.2561] det_loss_cls_dn_5[0.0830] det_loss_box_dn_5[0.2543] loss_depth[0.0401] total_loss[8.8292]
Rank[0/8] 06/20/2025 07:30:26 INFO stats.py:314 | Epoch[131] Step[134] GlobalStep[51224] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 14:18:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:30:41 INFO loss_tracker.py:84 | Epoch[131/239] Step[149] GlobalStep[51239/NA]: det_loss_cls_0[0.6384] det_loss_box_0[0.8364] det_loss_cls_1[0.6417] det_loss_box_1[0.6458] det_loss_cls_2[0.5892] det_loss_box_2[0.5930] det_loss_cls_3[0.5620] det_loss_box_3[0.5652] det_loss_cls_4[0.5294] det_loss_box_4[0.5482] det_loss_cls_5[0.5002] det_loss_box_5[0.5481] det_loss_cls_dn_0[0.0951] det_loss_box_dn_0[0.2986] det_loss_cls_dn_1[0.0916] det_loss_box_dn_1[0.2588] det_loss_cls_dn_2[0.0910] det_loss_box_dn_2[0.2422] det_loss_cls_dn_3[0.0898] det_loss_box_dn_3[0.2383] det_loss_cls_dn_4[0.0887] det_loss_box_dn_4[0.2363] det_loss_cls_dn_5[0.0879] det_loss_box_dn_5[0.2349] loss_depth[0.0366] total_loss[9.2873]
Rank[0/8] 06/20/2025 07:30:51 INFO stats.py:314 | Epoch[131] Step[159] GlobalStep[51249] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 14:18:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:31:07 INFO loss_tracker.py:84 | Epoch[131/239] Step[174] GlobalStep[51264/NA]: det_loss_cls_0[0.3955] det_loss_box_0[0.5586] det_loss_cls_1[0.4000] det_loss_box_1[0.4449] det_loss_cls_2[0.3470] det_loss_box_2[0.4025] det_loss_cls_3[0.3144] det_loss_box_3[0.3908] det_loss_cls_4[0.2950] det_loss_box_4[0.3861] det_loss_cls_5[0.2819] det_loss_box_5[0.3896] det_loss_cls_dn_0[0.0889] det_loss_box_dn_0[0.3232] det_loss_cls_dn_1[0.0837] det_loss_box_dn_1[0.2698] det_loss_cls_dn_2[0.0825] det_loss_box_dn_2[0.2530] det_loss_cls_dn_3[0.0820] det_loss_box_dn_3[0.2518] det_loss_cls_dn_4[0.0811] det_loss_box_dn_4[0.2482] det_loss_cls_dn_5[0.0803] det_loss_box_dn_5[0.2443] loss_depth[0.0409] total_loss[6.7359]
Rank[0/8] 06/20/2025 07:31:17 INFO stats.py:314 | Epoch[131] Step[184] GlobalStep[51274] Training Speed: 8.66 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 14:17:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:31:33 INFO loss_tracker.py:84 | Epoch[131/239] Step[199] GlobalStep[51289/NA]: det_loss_cls_0[0.3688] det_loss_box_0[0.5333] det_loss_cls_1[0.3667] det_loss_box_1[0.4239] det_loss_cls_2[0.3296] det_loss_box_2[0.3809] det_loss_cls_3[0.3092] det_loss_box_3[0.3668] det_loss_cls_4[0.2920] det_loss_box_4[0.3622] det_loss_cls_5[0.2824] det_loss_box_5[0.3611] det_loss_cls_dn_0[0.0856] det_loss_box_dn_0[0.4935] det_loss_cls_dn_1[0.0813] det_loss_box_dn_1[0.4261] det_loss_cls_dn_2[0.0808] det_loss_box_dn_2[0.4071] det_loss_cls_dn_3[0.0801] det_loss_box_dn_3[0.4004] det_loss_cls_dn_4[0.0784] det_loss_box_dn_4[0.3986] det_loss_cls_dn_5[0.0771] det_loss_box_dn_5[0.3968] loss_depth[0.0382] total_loss[7.4211]
Rank[0/8] 06/20/2025 07:31:43 INFO stats.py:314 | Epoch[131] Step[209] GlobalStep[51299] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 14:17:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:31:58 INFO loss_tracker.py:84 | Epoch[131/239] Step[224] GlobalStep[51314/NA]: det_loss_cls_0[0.4746] det_loss_box_0[0.6181] det_loss_cls_1[0.4555] det_loss_box_1[0.4810] det_loss_cls_2[0.4109] det_loss_box_2[0.4342] det_loss_cls_3[0.3932] det_loss_box_3[0.4211] det_loss_cls_4[0.3689] det_loss_box_4[0.4050] det_loss_cls_5[0.3514] det_loss_box_5[0.4034] det_loss_cls_dn_0[0.0903] det_loss_box_dn_0[0.2572] det_loss_cls_dn_1[0.0864] det_loss_box_dn_1[0.2132] det_loss_cls_dn_2[0.0849] det_loss_box_dn_2[0.1979] det_loss_cls_dn_3[0.0839] det_loss_box_dn_3[0.1946] det_loss_cls_dn_4[0.0831] det_loss_box_dn_4[0.1931] det_loss_cls_dn_5[0.0829] det_loss_box_dn_5[0.1916] loss_depth[0.0339] total_loss[7.0104]
Rank[0/8] 06/20/2025 07:32:09 INFO stats.py:314 | Epoch[131] Step[234] GlobalStep[51324] Training Speed: 8.15 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 14:16:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:32:25 INFO loss_tracker.py:84 | Epoch[131/239] Step[249] GlobalStep[51339/NA]: det_loss_cls_0[0.5449] det_loss_box_0[0.7198] det_loss_cls_1[0.5167] det_loss_box_1[0.5645] det_loss_cls_2[0.4819] det_loss_box_2[0.5139] det_loss_cls_3[0.4533] det_loss_box_3[0.4905] det_loss_cls_4[0.4212] det_loss_box_4[0.4878] det_loss_cls_5[0.4091] det_loss_box_5[0.4852] det_loss_cls_dn_0[0.1099] det_loss_box_dn_0[0.3359] det_loss_cls_dn_1[0.1063] det_loss_box_dn_1[0.2795] det_loss_cls_dn_2[0.1059] det_loss_box_dn_2[0.2614] det_loss_cls_dn_3[0.1046] det_loss_box_dn_3[0.2569] det_loss_cls_dn_4[0.1037] det_loss_box_dn_4[0.2552] det_loss_cls_dn_5[0.1025] det_loss_box_dn_5[0.2537] loss_depth[0.0391] total_loss[8.4032]
Rank[0/8] 06/20/2025 07:32:36 INFO stats.py:314 | Epoch[131] Step[259] GlobalStep[51349] Training Speed: 7.96 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 14:15:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:32:50 INFO loss_tracker.py:84 | Epoch[131/239] Step[274] GlobalStep[51364/NA]: det_loss_cls_0[0.5187] det_loss_box_0[0.6480] det_loss_cls_1[0.5193] det_loss_box_1[0.4949] det_loss_cls_2[0.4702] det_loss_box_2[0.4583] det_loss_cls_3[0.4409] det_loss_box_3[0.4439] det_loss_cls_4[0.4212] det_loss_box_4[0.4296] det_loss_cls_5[0.4117] det_loss_box_5[0.4316] det_loss_cls_dn_0[0.1072] det_loss_box_dn_0[0.2703] det_loss_cls_dn_1[0.1045] det_loss_box_dn_1[0.2348] det_loss_cls_dn_2[0.1035] det_loss_box_dn_2[0.2198] det_loss_cls_dn_3[0.1031] det_loss_box_dn_3[0.2168] det_loss_cls_dn_4[0.1027] det_loss_box_dn_4[0.2138] det_loss_cls_dn_5[0.1026] det_loss_box_dn_5[0.2127] loss_depth[0.0363] total_loss[7.7164]
Rank[0/8] 06/20/2025 07:33:01 INFO stats.py:314 | Epoch[131] Step[284] GlobalStep[51374] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 14:15:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:33:16 INFO loss_tracker.py:84 | Epoch[131/239] Step[299] GlobalStep[51389/NA]: det_loss_cls_0[0.3953] det_loss_box_0[0.4982] det_loss_cls_1[0.3832] det_loss_box_1[0.3805] det_loss_cls_2[0.3315] det_loss_box_2[0.3469] det_loss_cls_3[0.3154] det_loss_box_3[0.3384] det_loss_cls_4[0.2855] det_loss_box_4[0.3375] det_loss_cls_5[0.2687] det_loss_box_5[0.3410] det_loss_cls_dn_0[0.0829] det_loss_box_dn_0[0.2573] det_loss_cls_dn_1[0.0790] det_loss_box_dn_1[0.2154] det_loss_cls_dn_2[0.0780] det_loss_box_dn_2[0.1975] det_loss_cls_dn_3[0.0770] det_loss_box_dn_3[0.1949] det_loss_cls_dn_4[0.0765] det_loss_box_dn_4[0.1934] det_loss_cls_dn_5[0.0753] det_loss_box_dn_5[0.1888] loss_depth[0.0360] total_loss[5.9740]
Rank[0/8] 06/20/2025 07:33:26 INFO stats.py:314 | Epoch[131] Step[309] GlobalStep[51399] Training Speed: 7.70 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 14:14:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:33:42 INFO loss_tracker.py:84 | Epoch[131/239] Step[324] GlobalStep[51414/NA]: det_loss_cls_0[0.5134] det_loss_box_0[0.5801] det_loss_cls_1[0.4941] det_loss_box_1[0.4420] det_loss_cls_2[0.4478] det_loss_box_2[0.4021] det_loss_cls_3[0.4250] det_loss_box_3[0.3860] det_loss_cls_4[0.4037] det_loss_box_4[0.3727] det_loss_cls_5[0.3935] det_loss_box_5[0.3747] det_loss_cls_dn_0[0.0918] det_loss_box_dn_0[0.2563] det_loss_cls_dn_1[0.0885] det_loss_box_dn_1[0.2141] det_loss_cls_dn_2[0.0877] det_loss_box_dn_2[0.1979] det_loss_cls_dn_3[0.0866] det_loss_box_dn_3[0.1952] det_loss_cls_dn_4[0.0855] det_loss_box_dn_4[0.1947] det_loss_cls_dn_5[0.0848] det_loss_box_dn_5[0.1909] loss_depth[0.0354] total_loss[7.0444]
Rank[0/8] 06/20/2025 07:33:53 INFO stats.py:314 | Epoch[131] Step[334] GlobalStep[51424] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 14:14:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:34:08 INFO loss_tracker.py:84 | Epoch[131/239] Step[349] GlobalStep[51439/NA]: det_loss_cls_0[0.4938] det_loss_box_0[0.5978] det_loss_cls_1[0.4676] det_loss_box_1[0.4618] det_loss_cls_2[0.4332] det_loss_box_2[0.4253] det_loss_cls_3[0.4091] det_loss_box_3[0.4124] det_loss_cls_4[0.3866] det_loss_box_4[0.4113] det_loss_cls_5[0.3729] det_loss_box_5[0.4104] det_loss_cls_dn_0[0.0990] det_loss_box_dn_0[0.2808] det_loss_cls_dn_1[0.0945] det_loss_box_dn_1[0.2424] det_loss_cls_dn_2[0.0935] det_loss_box_dn_2[0.2250] det_loss_cls_dn_3[0.0926] det_loss_box_dn_3[0.2220] det_loss_cls_dn_4[0.0919] det_loss_box_dn_4[0.2210] det_loss_cls_dn_5[0.0911] det_loss_box_dn_5[0.2202] loss_depth[0.0391] total_loss[7.2953]
Rank[0/8] 06/20/2025 07:34:18 INFO stats.py:314 | Epoch[131] Step[359] GlobalStep[51449] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 14:13:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:34:34 INFO loss_tracker.py:84 | Epoch[131/239] Step[374] GlobalStep[51464/NA]: det_loss_cls_0[0.4965] det_loss_box_0[0.6750] det_loss_cls_1[0.4792] det_loss_box_1[0.5334] det_loss_cls_2[0.4403] det_loss_box_2[0.4841] det_loss_cls_3[0.4146] det_loss_box_3[0.4494] det_loss_cls_4[0.3987] det_loss_box_4[0.4379] det_loss_cls_5[0.3875] det_loss_box_5[0.4341] det_loss_cls_dn_0[0.0898] det_loss_box_dn_0[0.3096] det_loss_cls_dn_1[0.0869] det_loss_box_dn_1[0.2693] det_loss_cls_dn_2[0.0862] det_loss_box_dn_2[0.2485] det_loss_cls_dn_3[0.0859] det_loss_box_dn_3[0.2438] det_loss_cls_dn_4[0.0848] det_loss_box_dn_4[0.2410] det_loss_cls_dn_5[0.0849] det_loss_box_dn_5[0.2352] loss_depth[0.0367] total_loss[7.7333]
Rank[0/8] 06/20/2025 07:34:44 INFO stats.py:314 | Epoch[131] Step[384] GlobalStep[51474] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 14:13:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:34:49 INFO stats.py:394 | Epoch[131] completed. Training Speed: 7.69 samples/sec across all devices.	Epoch Time: 405.95 sec.	Average Epoch Time: 405.95 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 14:13:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:35:11 INFO stats.py:314 | Epoch[132] Step[19] GlobalStep[51499] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 14:12:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:35:16 INFO loss_tracker.py:84 | Epoch[132/239] Step[24] GlobalStep[51504/NA]: det_loss_cls_0[0.5200] det_loss_box_0[0.6971] det_loss_cls_1[0.5028] det_loss_box_1[0.5465] det_loss_cls_2[0.4519] det_loss_box_2[0.4981] det_loss_cls_3[0.4230] det_loss_box_3[0.4741] det_loss_cls_4[0.3926] det_loss_box_4[0.4678] det_loss_cls_5[0.3756] det_loss_box_5[0.4680] det_loss_cls_dn_0[0.0955] det_loss_box_dn_0[0.3006] det_loss_cls_dn_1[0.0921] det_loss_box_dn_1[0.2533] det_loss_cls_dn_2[0.0913] det_loss_box_dn_2[0.2375] det_loss_cls_dn_3[0.0905] det_loss_box_dn_3[0.2350] det_loss_cls_dn_4[0.0899] det_loss_box_dn_4[0.2326] det_loss_cls_dn_5[0.0894] det_loss_box_dn_5[0.2309] loss_depth[0.0384] total_loss[7.8946]
Rank[0/8] 06/20/2025 07:35:37 INFO stats.py:314 | Epoch[132] Step[44] GlobalStep[51524] Training Speed: 8.63 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 14:12:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:35:42 INFO loss_tracker.py:84 | Epoch[132/239] Step[49] GlobalStep[51529/NA]: det_loss_cls_0[0.5769] det_loss_box_0[0.7579] det_loss_cls_1[0.5479] det_loss_box_1[0.6209] det_loss_cls_2[0.5059] det_loss_box_2[0.5722] det_loss_cls_3[0.4892] det_loss_box_3[0.5500] det_loss_cls_4[0.4646] det_loss_box_4[0.5281] det_loss_cls_5[0.4484] det_loss_box_5[0.5250] det_loss_cls_dn_0[0.0963] det_loss_box_dn_0[0.2789] det_loss_cls_dn_1[0.0934] det_loss_box_dn_1[0.2442] det_loss_cls_dn_2[0.0936] det_loss_box_dn_2[0.2312] det_loss_cls_dn_3[0.0921] det_loss_box_dn_3[0.2284] det_loss_cls_dn_4[0.0918] det_loss_box_dn_4[0.2274] det_loss_cls_dn_5[0.0909] det_loss_box_dn_5[0.2225] loss_depth[0.0380] total_loss[8.6155]
Rank[0/8] 06/20/2025 07:36:03 INFO stats.py:314 | Epoch[132] Step[69] GlobalStep[51549] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 14:11:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:36:08 INFO loss_tracker.py:84 | Epoch[132/239] Step[74] GlobalStep[51554/NA]: det_loss_cls_0[0.4960] det_loss_box_0[0.7031] det_loss_cls_1[0.4723] det_loss_box_1[0.5506] det_loss_cls_2[0.4184] det_loss_box_2[0.5040] det_loss_cls_3[0.3907] det_loss_box_3[0.4905] det_loss_cls_4[0.3786] det_loss_box_4[0.4748] det_loss_cls_5[0.3605] det_loss_box_5[0.4776] det_loss_cls_dn_0[0.0987] det_loss_box_dn_0[0.2762] det_loss_cls_dn_1[0.0942] det_loss_box_dn_1[0.2349] det_loss_cls_dn_2[0.0933] det_loss_box_dn_2[0.2217] det_loss_cls_dn_3[0.0922] det_loss_box_dn_3[0.2192] det_loss_cls_dn_4[0.0908] det_loss_box_dn_4[0.2177] det_loss_cls_dn_5[0.0897] det_loss_box_dn_5[0.2168] loss_depth[0.0366] total_loss[7.6993]
Rank[0/8] 06/20/2025 07:36:30 INFO stats.py:314 | Epoch[132] Step[94] GlobalStep[51574] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 14:10:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:36:35 INFO loss_tracker.py:84 | Epoch[132/239] Step[99] GlobalStep[51579/NA]: det_loss_cls_0[0.4761] det_loss_box_0[0.6099] det_loss_cls_1[0.4609] det_loss_box_1[0.4461] det_loss_cls_2[0.4112] det_loss_box_2[0.4040] det_loss_cls_3[0.3686] det_loss_box_3[0.3891] det_loss_cls_4[0.3342] det_loss_box_4[0.3923] det_loss_cls_5[0.3261] det_loss_box_5[0.3808] det_loss_cls_dn_0[0.0881] det_loss_box_dn_0[0.2566] det_loss_cls_dn_1[0.0841] det_loss_box_dn_1[0.2152] det_loss_cls_dn_2[0.0834] det_loss_box_dn_2[0.1991] det_loss_cls_dn_3[0.0830] det_loss_box_dn_3[0.1961] det_loss_cls_dn_4[0.0817] det_loss_box_dn_4[0.1929] det_loss_cls_dn_5[0.0815] det_loss_box_dn_5[0.1915] loss_depth[0.0358] total_loss[6.7884]
Rank[0/8] 06/20/2025 07:36:55 INFO stats.py:314 | Epoch[132] Step[119] GlobalStep[51599] Training Speed: 7.82 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 14:10:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:37:01 INFO loss_tracker.py:84 | Epoch[132/239] Step[124] GlobalStep[51604/NA]: det_loss_cls_0[0.5122] det_loss_box_0[0.6696] det_loss_cls_1[0.4917] det_loss_box_1[0.4916] det_loss_cls_2[0.4406] det_loss_box_2[0.4442] det_loss_cls_3[0.4203] det_loss_box_3[0.4286] det_loss_cls_4[0.3999] det_loss_box_4[0.4152] det_loss_cls_5[0.3904] det_loss_box_5[0.4159] det_loss_cls_dn_0[0.0952] det_loss_box_dn_0[0.2680] det_loss_cls_dn_1[0.0924] det_loss_box_dn_1[0.2248] det_loss_cls_dn_2[0.0918] det_loss_box_dn_2[0.2078] det_loss_cls_dn_3[0.0910] det_loss_box_dn_3[0.2027] det_loss_cls_dn_4[0.0902] det_loss_box_dn_4[0.2005] det_loss_cls_dn_5[0.0898] det_loss_box_dn_5[0.1991] loss_depth[0.0398] total_loss[7.4134]
Rank[0/8] 06/20/2025 07:37:21 INFO stats.py:314 | Epoch[132] Step[144] GlobalStep[51624] Training Speed: 10.62 samples/sec across all devices.	Average Step Time: 0.75 sec.	Estimated Remaining Time: 14:09:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:37:26 INFO loss_tracker.py:84 | Epoch[132/239] Step[149] GlobalStep[51629/NA]: det_loss_cls_0[0.4320] det_loss_box_0[0.6283] det_loss_cls_1[0.4228] det_loss_box_1[0.4914] det_loss_cls_2[0.3834] det_loss_box_2[0.4440] det_loss_cls_3[0.3656] det_loss_box_3[0.4279] det_loss_cls_4[0.3459] det_loss_box_4[0.4051] det_loss_cls_5[0.3327] det_loss_box_5[0.4105] det_loss_cls_dn_0[0.0937] det_loss_box_dn_0[0.3083] det_loss_cls_dn_1[0.0892] det_loss_box_dn_1[0.2592] det_loss_cls_dn_2[0.0881] det_loss_box_dn_2[0.2426] det_loss_cls_dn_3[0.0873] det_loss_box_dn_3[0.2380] det_loss_cls_dn_4[0.0863] det_loss_box_dn_4[0.2352] det_loss_cls_dn_5[0.0854] det_loss_box_dn_5[0.2316] loss_depth[0.0354] total_loss[7.1698]
Rank[0/8] 06/20/2025 07:37:47 INFO stats.py:314 | Epoch[132] Step[169] GlobalStep[51649] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 14:09:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:37:52 INFO loss_tracker.py:84 | Epoch[132/239] Step[174] GlobalStep[51654/NA]: det_loss_cls_0[0.4429] det_loss_box_0[0.5501] det_loss_cls_1[0.4335] det_loss_box_1[0.4301] det_loss_cls_2[0.3987] det_loss_box_2[0.3918] det_loss_cls_3[0.3731] det_loss_box_3[0.3886] det_loss_cls_4[0.3470] det_loss_box_4[0.3889] det_loss_cls_5[0.3376] det_loss_box_5[0.3872] det_loss_cls_dn_0[0.0924] det_loss_box_dn_0[0.3543] det_loss_cls_dn_1[0.0892] det_loss_box_dn_1[0.2927] det_loss_cls_dn_2[0.0880] det_loss_box_dn_2[0.2722] det_loss_cls_dn_3[0.0871] det_loss_box_dn_3[0.2657] det_loss_cls_dn_4[0.0863] det_loss_box_dn_4[0.2619] det_loss_cls_dn_5[0.0852] det_loss_box_dn_5[0.2599] loss_depth[0.0387] total_loss[7.1429]
Rank[0/8] 06/20/2025 07:38:13 INFO stats.py:314 | Epoch[132] Step[194] GlobalStep[51674] Training Speed: 6.92 samples/sec across all devices.	Average Step Time: 1.16 sec.	Estimated Remaining Time: 14:08:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:38:18 INFO loss_tracker.py:84 | Epoch[132/239] Step[199] GlobalStep[51679/NA]: det_loss_cls_0[0.4592] det_loss_box_0[0.5824] det_loss_cls_1[0.4434] det_loss_box_1[0.4461] det_loss_cls_2[0.4000] det_loss_box_2[0.3976] det_loss_cls_3[0.3729] det_loss_box_3[0.3809] det_loss_cls_4[0.3486] det_loss_box_4[0.3751] det_loss_cls_5[0.3374] det_loss_box_5[0.3779] det_loss_cls_dn_0[0.0971] det_loss_box_dn_0[0.2705] det_loss_cls_dn_1[0.0936] det_loss_box_dn_1[0.2255] det_loss_cls_dn_2[0.0924] det_loss_box_dn_2[0.2089] det_loss_cls_dn_3[0.0914] det_loss_box_dn_3[0.2052] det_loss_cls_dn_4[0.0905] det_loss_box_dn_4[0.2040] det_loss_cls_dn_5[0.0897] det_loss_box_dn_5[0.2026] loss_depth[0.0365] total_loss[6.8294]
Rank[0/8] 06/20/2025 07:38:39 INFO stats.py:314 | Epoch[132] Step[219] GlobalStep[51699] Training Speed: 7.85 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 14:08:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:38:44 INFO loss_tracker.py:84 | Epoch[132/239] Step[224] GlobalStep[51704/NA]: det_loss_cls_0[0.5370] det_loss_box_0[0.6514] det_loss_cls_1[0.5138] det_loss_box_1[0.4844] det_loss_cls_2[0.4653] det_loss_box_2[0.4449] det_loss_cls_3[0.4378] det_loss_box_3[0.4239] det_loss_cls_4[0.4161] det_loss_box_4[0.4142] det_loss_cls_5[0.4060] det_loss_box_5[0.4150] det_loss_cls_dn_0[0.0916] det_loss_box_dn_0[0.2437] det_loss_cls_dn_1[0.0880] det_loss_box_dn_1[0.1980] det_loss_cls_dn_2[0.0870] det_loss_box_dn_2[0.1820] det_loss_cls_dn_3[0.0863] det_loss_box_dn_3[0.1769] det_loss_cls_dn_4[0.0855] det_loss_box_dn_4[0.1759] det_loss_cls_dn_5[0.0840] det_loss_box_dn_5[0.1751] loss_depth[0.0358] total_loss[7.3195]
Rank[0/8] 06/20/2025 07:39:05 INFO stats.py:314 | Epoch[132] Step[244] GlobalStep[51724] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 14:07:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:39:10 INFO loss_tracker.py:84 | Epoch[132/239] Step[249] GlobalStep[51729/NA]: det_loss_cls_0[0.6139] det_loss_box_0[0.7726] det_loss_cls_1[0.5957] det_loss_box_1[0.5866] det_loss_cls_2[0.5470] det_loss_box_2[0.5342] det_loss_cls_3[0.5064] det_loss_box_3[0.5171] det_loss_cls_4[0.4746] det_loss_box_4[0.5000] det_loss_cls_5[0.4631] det_loss_box_5[0.4961] det_loss_cls_dn_0[0.0920] det_loss_box_dn_0[0.2891] det_loss_cls_dn_1[0.0873] det_loss_box_dn_1[0.2515] det_loss_cls_dn_2[0.0874] det_loss_box_dn_2[0.2337] det_loss_cls_dn_3[0.0861] det_loss_box_dn_3[0.2298] det_loss_cls_dn_4[0.0851] det_loss_box_dn_4[0.2284] det_loss_cls_dn_5[0.0845] det_loss_box_dn_5[0.2254] loss_depth[0.0369] total_loss[8.6243]
Rank[0/8] 06/20/2025 07:39:31 INFO stats.py:314 | Epoch[132] Step[269] GlobalStep[51749] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 14:06:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:39:36 INFO loss_tracker.py:84 | Epoch[132/239] Step[274] GlobalStep[51754/NA]: det_loss_cls_0[0.5829] det_loss_box_0[0.8261] det_loss_cls_1[0.5757] det_loss_box_1[0.6392] det_loss_cls_2[0.5084] det_loss_box_2[0.5853] det_loss_cls_3[0.4677] det_loss_box_3[0.5727] det_loss_cls_4[0.4454] det_loss_box_4[0.5663] det_loss_cls_5[0.4241] det_loss_box_5[0.5722] det_loss_cls_dn_0[0.0935] det_loss_box_dn_0[0.3956] det_loss_cls_dn_1[0.0910] det_loss_box_dn_1[0.3283] det_loss_cls_dn_2[0.0912] det_loss_box_dn_2[0.3115] det_loss_cls_dn_3[0.0891] det_loss_box_dn_3[0.3036] det_loss_cls_dn_4[0.0879] det_loss_box_dn_4[0.3011] det_loss_cls_dn_5[0.0874] det_loss_box_dn_5[0.2987] loss_depth[0.0370] total_loss[9.2820]
Rank[0/8] 06/20/2025 07:39:57 INFO stats.py:314 | Epoch[132] Step[294] GlobalStep[51774] Training Speed: 6.04 samples/sec across all devices.	Average Step Time: 1.32 sec.	Estimated Remaining Time: 14:06:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:40:02 INFO loss_tracker.py:84 | Epoch[132/239] Step[299] GlobalStep[51779/NA]: det_loss_cls_0[0.6039] det_loss_box_0[0.8647] det_loss_cls_1[0.5629] det_loss_box_1[0.6695] det_loss_cls_2[0.5279] det_loss_box_2[0.6038] det_loss_cls_3[0.4983] det_loss_box_3[0.5791] det_loss_cls_4[0.4778] det_loss_box_4[0.5636] det_loss_cls_5[0.4516] det_loss_box_5[0.5729] det_loss_cls_dn_0[0.0920] det_loss_box_dn_0[0.3485] det_loss_cls_dn_1[0.0881] det_loss_box_dn_1[0.2946] det_loss_cls_dn_2[0.0867] det_loss_box_dn_2[0.2806] det_loss_cls_dn_3[0.0859] det_loss_box_dn_3[0.2762] det_loss_cls_dn_4[0.0848] det_loss_box_dn_4[0.2730] det_loss_cls_dn_5[0.0837] det_loss_box_dn_5[0.2790] loss_depth[0.0383] total_loss[9.2874]
Rank[0/8] 06/20/2025 07:40:23 INFO stats.py:314 | Epoch[132] Step[319] GlobalStep[51799] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 14:05:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:40:28 INFO loss_tracker.py:84 | Epoch[132/239] Step[324] GlobalStep[51804/NA]: det_loss_cls_0[0.5496] det_loss_box_0[0.6711] det_loss_cls_1[0.5394] det_loss_box_1[0.5261] det_loss_cls_2[0.4842] det_loss_box_2[0.4671] det_loss_cls_3[0.4637] det_loss_box_3[0.4481] det_loss_cls_4[0.4391] det_loss_box_4[0.4389] det_loss_cls_5[0.4260] det_loss_box_5[0.4421] det_loss_cls_dn_0[0.0916] det_loss_box_dn_0[0.2454] det_loss_cls_dn_1[0.0888] det_loss_box_dn_1[0.2116] det_loss_cls_dn_2[0.0882] det_loss_box_dn_2[0.1972] det_loss_cls_dn_3[0.0880] det_loss_box_dn_3[0.1944] det_loss_cls_dn_4[0.0874] det_loss_box_dn_4[0.1932] det_loss_cls_dn_5[0.0870] det_loss_box_dn_5[0.1918] loss_depth[0.0369] total_loss[7.6967]
Rank[0/8] 06/20/2025 07:40:49 INFO stats.py:314 | Epoch[132] Step[344] GlobalStep[51824] Training Speed: 7.97 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 14:05:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:40:54 INFO loss_tracker.py:84 | Epoch[132/239] Step[349] GlobalStep[51829/NA]: det_loss_cls_0[0.5646] det_loss_box_0[0.7944] det_loss_cls_1[0.5469] det_loss_box_1[0.6119] det_loss_cls_2[0.4862] det_loss_box_2[0.5671] det_loss_cls_3[0.4494] det_loss_box_3[0.5564] det_loss_cls_4[0.4195] det_loss_box_4[0.5443] det_loss_cls_5[0.3985] det_loss_box_5[0.5497] det_loss_cls_dn_0[0.0975] det_loss_box_dn_0[0.4682] det_loss_cls_dn_1[0.0936] det_loss_box_dn_1[0.4042] det_loss_cls_dn_2[0.0927] det_loss_box_dn_2[0.3909] det_loss_cls_dn_3[0.0915] det_loss_box_dn_3[0.3856] det_loss_cls_dn_4[0.0907] det_loss_box_dn_4[0.3885] det_loss_cls_dn_5[0.0889] det_loss_box_dn_5[0.3871] loss_depth[0.0367] total_loss[9.5050]
Rank[0/8] 06/20/2025 07:41:15 INFO stats.py:314 | Epoch[132] Step[369] GlobalStep[51849] Training Speed: 7.69 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 14:04:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:41:20 INFO loss_tracker.py:84 | Epoch[132/239] Step[374] GlobalStep[51854/NA]: det_loss_cls_0[0.5293] det_loss_box_0[0.7177] det_loss_cls_1[0.4994] det_loss_box_1[0.5607] det_loss_cls_2[0.4505] det_loss_box_2[0.5029] det_loss_cls_3[0.4229] det_loss_box_3[0.4826] det_loss_cls_4[0.4097] det_loss_box_4[0.4632] det_loss_cls_5[0.3951] det_loss_box_5[0.4642] det_loss_cls_dn_0[0.0995] det_loss_box_dn_0[0.3526] det_loss_cls_dn_1[0.0962] det_loss_box_dn_1[0.2984] det_loss_cls_dn_2[0.0957] det_loss_box_dn_2[0.2789] det_loss_cls_dn_3[0.0937] det_loss_box_dn_3[0.2711] det_loss_cls_dn_4[0.0927] det_loss_box_dn_4[0.2672] det_loss_cls_dn_5[0.0916] det_loss_box_dn_5[0.2616] loss_depth[0.0379] total_loss[8.2352]
Rank[0/8] 06/20/2025 07:41:36 INFO stats.py:394 | Epoch[132] completed. Training Speed: 7.67 samples/sec across all devices.	Epoch Time: 406.57 sec.	Average Epoch Time: 406.57 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 14:04:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:41:42 INFO stats.py:314 | Epoch[133] Step[4] GlobalStep[51874] Training Speed: 7.89 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 14:04:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:42:03 INFO loss_tracker.py:84 | Epoch[133/239] Step[24] GlobalStep[51894/NA]: det_loss_cls_0[0.5188] det_loss_box_0[0.6825] det_loss_cls_1[0.4976] det_loss_box_1[0.5272] det_loss_cls_2[0.4600] det_loss_box_2[0.4751] det_loss_cls_3[0.4362] det_loss_box_3[0.4605] det_loss_cls_4[0.4154] det_loss_box_4[0.4470] det_loss_cls_5[0.3984] det_loss_box_5[0.4493] det_loss_cls_dn_0[0.1019] det_loss_box_dn_0[0.2963] det_loss_cls_dn_1[0.0979] det_loss_box_dn_1[0.2488] det_loss_cls_dn_2[0.0971] det_loss_box_dn_2[0.2335] det_loss_cls_dn_3[0.0962] det_loss_box_dn_3[0.2305] det_loss_cls_dn_4[0.0950] det_loss_box_dn_4[0.2283] det_loss_cls_dn_5[0.0942] det_loss_box_dn_5[0.2266] loss_depth[0.0391] total_loss[7.8534]
Rank[0/8] 06/20/2025 07:42:08 INFO stats.py:314 | Epoch[133] Step[29] GlobalStep[51899] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 14:03:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:42:29 INFO loss_tracker.py:84 | Epoch[133/239] Step[49] GlobalStep[51919/NA]: det_loss_cls_0[0.4695] det_loss_box_0[0.6172] det_loss_cls_1[0.4640] det_loss_box_1[0.4852] det_loss_cls_2[0.4304] det_loss_box_2[0.4229] det_loss_cls_3[0.3981] det_loss_box_3[0.4141] det_loss_cls_4[0.3711] det_loss_box_4[0.4097] det_loss_cls_5[0.3600] det_loss_box_5[0.4118] det_loss_cls_dn_0[0.0958] det_loss_box_dn_0[0.2836] det_loss_cls_dn_1[0.0913] det_loss_box_dn_1[0.2356] det_loss_cls_dn_2[0.0901] det_loss_box_dn_2[0.2228] det_loss_cls_dn_3[0.0887] det_loss_box_dn_3[0.2179] det_loss_cls_dn_4[0.0875] det_loss_box_dn_4[0.2160] det_loss_cls_dn_5[0.0870] det_loss_box_dn_5[0.2143] loss_depth[0.0346] total_loss[7.2190]
Rank[0/8] 06/20/2025 07:42:34 INFO stats.py:314 | Epoch[133] Step[54] GlobalStep[51924] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 14:02:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:42:55 INFO loss_tracker.py:84 | Epoch[133/239] Step[74] GlobalStep[51944/NA]: det_loss_cls_0[0.5075] det_loss_box_0[0.7184] det_loss_cls_1[0.5108] det_loss_box_1[0.5709] det_loss_cls_2[0.4633] det_loss_box_2[0.5339] det_loss_cls_3[0.4290] det_loss_box_3[0.5047] det_loss_cls_4[0.4015] det_loss_box_4[0.4860] det_loss_cls_5[0.3819] det_loss_box_5[0.4881] det_loss_cls_dn_0[0.0929] det_loss_box_dn_0[0.2871] det_loss_cls_dn_1[0.0883] det_loss_box_dn_1[0.2541] det_loss_cls_dn_2[0.0882] det_loss_box_dn_2[0.2411] det_loss_cls_dn_3[0.0864] det_loss_box_dn_3[0.2385] det_loss_cls_dn_4[0.0861] det_loss_box_dn_4[0.2366] det_loss_cls_dn_5[0.0853] det_loss_box_dn_5[0.2341] loss_depth[0.0358] total_loss[8.0507]
Rank[0/8] 06/20/2025 07:43:00 INFO stats.py:314 | Epoch[133] Step[79] GlobalStep[51949] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 14:02:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:43:21 INFO loss_tracker.py:84 | Epoch[133/239] Step[99] GlobalStep[51969/NA]: det_loss_cls_0[0.5506] det_loss_box_0[0.7557] det_loss_cls_1[0.5308] det_loss_box_1[0.6094] det_loss_cls_2[0.4826] det_loss_box_2[0.5559] det_loss_cls_3[0.4484] det_loss_box_3[0.5340] det_loss_cls_4[0.4246] det_loss_box_4[0.5124] det_loss_cls_5[0.4043] det_loss_box_5[0.5178] det_loss_cls_dn_0[0.0929] det_loss_box_dn_0[0.2529] det_loss_cls_dn_1[0.0890] det_loss_box_dn_1[0.2181] det_loss_cls_dn_2[0.0882] det_loss_box_dn_2[0.2052] det_loss_cls_dn_3[0.0878] det_loss_box_dn_3[0.2028] det_loss_cls_dn_4[0.0868] det_loss_box_dn_4[0.2018] det_loss_cls_dn_5[0.0863] det_loss_box_dn_5[0.2010] loss_depth[0.0375] total_loss[8.1766]
Rank[0/8] 06/20/2025 07:43:27 INFO stats.py:314 | Epoch[133] Step[104] GlobalStep[51974] Training Speed: 8.64 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 14:01:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:43:48 INFO loss_tracker.py:84 | Epoch[133/239] Step[124] GlobalStep[51994/NA]: det_loss_cls_0[0.4789] det_loss_box_0[0.6589] det_loss_cls_1[0.4618] det_loss_box_1[0.5048] det_loss_cls_2[0.4246] det_loss_box_2[0.4517] det_loss_cls_3[0.3845] det_loss_box_3[0.4508] det_loss_cls_4[0.3629] det_loss_box_4[0.4308] det_loss_cls_5[0.3485] det_loss_box_5[0.4293] det_loss_cls_dn_0[0.0876] det_loss_box_dn_0[0.2928] det_loss_cls_dn_1[0.0831] det_loss_box_dn_1[0.2437] det_loss_cls_dn_2[0.0825] det_loss_box_dn_2[0.2254] det_loss_cls_dn_3[0.0816] det_loss_box_dn_3[0.2203] det_loss_cls_dn_4[0.0807] det_loss_box_dn_4[0.2174] det_loss_cls_dn_5[0.0791] det_loss_box_dn_5[0.2164] loss_depth[0.0369] total_loss[7.3348]
Rank[0/8] 06/20/2025 07:43:53 INFO stats.py:314 | Epoch[133] Step[129] GlobalStep[51999] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 14:01:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:44:14 INFO loss_tracker.py:84 | Epoch[133/239] Step[149] GlobalStep[52019/NA]: det_loss_cls_0[0.4665] det_loss_box_0[0.5883] det_loss_cls_1[0.4601] det_loss_box_1[0.4357] det_loss_cls_2[0.4054] det_loss_box_2[0.4046] det_loss_cls_3[0.3885] det_loss_box_3[0.3833] det_loss_cls_4[0.3589] det_loss_box_4[0.3730] det_loss_cls_5[0.3459] det_loss_box_5[0.3698] det_loss_cls_dn_0[0.1009] det_loss_box_dn_0[0.2661] det_loss_cls_dn_1[0.0963] det_loss_box_dn_1[0.2203] det_loss_cls_dn_2[0.0961] det_loss_box_dn_2[0.2043] det_loss_cls_dn_3[0.0936] det_loss_box_dn_3[0.2006] det_loss_cls_dn_4[0.0928] det_loss_box_dn_4[0.1988] det_loss_cls_dn_5[0.0916] det_loss_box_dn_5[0.1968] loss_depth[0.0359] total_loss[6.8740]
Rank[0/8] 06/20/2025 07:44:19 INFO stats.py:314 | Epoch[133] Step[154] GlobalStep[52024] Training Speed: 6.28 samples/sec across all devices.	Average Step Time: 1.27 sec.	Estimated Remaining Time: 14:00:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:44:40 INFO loss_tracker.py:84 | Epoch[133/239] Step[174] GlobalStep[52044/NA]: det_loss_cls_0[0.4297] det_loss_box_0[0.5400] det_loss_cls_1[0.4232] det_loss_box_1[0.4224] det_loss_cls_2[0.3803] det_loss_box_2[0.3972] det_loss_cls_3[0.3592] det_loss_box_3[0.3765] det_loss_cls_4[0.3346] det_loss_box_4[0.3679] det_loss_cls_5[0.3253] det_loss_box_5[0.3675] det_loss_cls_dn_0[0.0877] det_loss_box_dn_0[0.2865] det_loss_cls_dn_1[0.0835] det_loss_box_dn_1[0.2336] det_loss_cls_dn_2[0.0829] det_loss_box_dn_2[0.2109] det_loss_cls_dn_3[0.0823] det_loss_box_dn_3[0.2091] det_loss_cls_dn_4[0.0815] det_loss_box_dn_4[0.2065] det_loss_cls_dn_5[0.0813] det_loss_box_dn_5[0.2060] loss_depth[0.0357] total_loss[6.6112]
Rank[0/8] 06/20/2025 07:44:45 INFO stats.py:314 | Epoch[133] Step[179] GlobalStep[52049] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 14:00:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:45:06 INFO loss_tracker.py:84 | Epoch[133/239] Step[199] GlobalStep[52069/NA]: det_loss_cls_0[0.5608] det_loss_box_0[0.6909] det_loss_cls_1[0.5410] det_loss_box_1[0.5204] det_loss_cls_2[0.4916] det_loss_box_2[0.4645] det_loss_cls_3[0.4683] det_loss_box_3[0.4424] det_loss_cls_4[0.4426] det_loss_box_4[0.4327] det_loss_cls_5[0.4310] det_loss_box_5[0.4335] det_loss_cls_dn_0[0.0980] det_loss_box_dn_0[0.3720] det_loss_cls_dn_1[0.0942] det_loss_box_dn_1[0.3221] det_loss_cls_dn_2[0.0940] det_loss_box_dn_2[0.2991] det_loss_cls_dn_3[0.0924] det_loss_box_dn_3[0.2948] det_loss_cls_dn_4[0.0916] det_loss_box_dn_4[0.2917] det_loss_cls_dn_5[0.0906] det_loss_box_dn_5[0.2871] loss_depth[0.0364] total_loss[8.3839]
Rank[0/8] 06/20/2025 07:45:11 INFO stats.py:314 | Epoch[133] Step[204] GlobalStep[52074] Training Speed: 7.66 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 13:59:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:45:32 INFO loss_tracker.py:84 | Epoch[133/239] Step[224] GlobalStep[52094/NA]: det_loss_cls_0[0.5174] det_loss_box_0[0.6924] det_loss_cls_1[0.5100] det_loss_box_1[0.5134] det_loss_cls_2[0.4591] det_loss_box_2[0.4733] det_loss_cls_3[0.4223] det_loss_box_3[0.4530] det_loss_cls_4[0.3975] det_loss_box_4[0.4415] det_loss_cls_5[0.3799] det_loss_box_5[0.4424] det_loss_cls_dn_0[0.0923] det_loss_box_dn_0[0.3004] det_loss_cls_dn_1[0.0895] det_loss_box_dn_1[0.2476] det_loss_cls_dn_2[0.0886] det_loss_box_dn_2[0.2279] det_loss_cls_dn_3[0.0874] det_loss_box_dn_3[0.2246] det_loss_cls_dn_4[0.0861] det_loss_box_dn_4[0.2217] det_loss_cls_dn_5[0.0854] det_loss_box_dn_5[0.2220] loss_depth[0.0410] total_loss[7.7166]
Rank[0/8] 06/20/2025 07:45:37 INFO stats.py:314 | Epoch[133] Step[229] GlobalStep[52099] Training Speed: 8.17 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 13:59:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:45:58 INFO loss_tracker.py:84 | Epoch[133/239] Step[249] GlobalStep[52119/NA]: det_loss_cls_0[0.5827] det_loss_box_0[0.7887] det_loss_cls_1[0.5594] det_loss_box_1[0.5995] det_loss_cls_2[0.5182] det_loss_box_2[0.5402] det_loss_cls_3[0.4741] det_loss_box_3[0.5208] det_loss_cls_4[0.4538] det_loss_box_4[0.5092] det_loss_cls_5[0.4307] det_loss_box_5[0.5128] det_loss_cls_dn_0[0.1015] det_loss_box_dn_0[0.2761] det_loss_cls_dn_1[0.0988] det_loss_box_dn_1[0.2349] det_loss_cls_dn_2[0.0979] det_loss_box_dn_2[0.2230] det_loss_cls_dn_3[0.0966] det_loss_box_dn_3[0.2196] det_loss_cls_dn_4[0.0955] det_loss_box_dn_4[0.2179] det_loss_cls_dn_5[0.0949] det_loss_box_dn_5[0.2166] loss_depth[0.0404] total_loss[8.5040]
Rank[0/8] 06/20/2025 07:46:03 INFO stats.py:314 | Epoch[133] Step[254] GlobalStep[52124] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 13:58:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:46:24 INFO loss_tracker.py:84 | Epoch[133/239] Step[274] GlobalStep[52144/NA]: det_loss_cls_0[0.5612] det_loss_box_0[0.7500] det_loss_cls_1[0.5484] det_loss_box_1[0.5701] det_loss_cls_2[0.5040] det_loss_box_2[0.5091] det_loss_cls_3[0.4792] det_loss_box_3[0.4901] det_loss_cls_4[0.4619] det_loss_box_4[0.4630] det_loss_cls_5[0.4459] det_loss_box_5[0.4652] det_loss_cls_dn_0[0.0983] det_loss_box_dn_0[0.2837] det_loss_cls_dn_1[0.0951] det_loss_box_dn_1[0.2446] det_loss_cls_dn_2[0.0942] det_loss_box_dn_2[0.2315] det_loss_cls_dn_3[0.0937] det_loss_box_dn_3[0.2284] det_loss_cls_dn_4[0.0925] det_loss_box_dn_4[0.2259] det_loss_cls_dn_5[0.0914] det_loss_box_dn_5[0.2253] loss_depth[0.0345] total_loss[8.2872]
Rank[0/8] 06/20/2025 07:46:29 INFO stats.py:314 | Epoch[133] Step[279] GlobalStep[52149] Training Speed: 7.85 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 13:57:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:46:50 INFO loss_tracker.py:84 | Epoch[133/239] Step[299] GlobalStep[52169/NA]: det_loss_cls_0[0.4614] det_loss_box_0[0.6444] det_loss_cls_1[0.4418] det_loss_box_1[0.4924] det_loss_cls_2[0.4121] det_loss_box_2[0.4541] det_loss_cls_3[0.3957] det_loss_box_3[0.4373] det_loss_cls_4[0.3741] det_loss_box_4[0.4390] det_loss_cls_5[0.3637] det_loss_box_5[0.4378] det_loss_cls_dn_0[0.1002] det_loss_box_dn_0[0.3108] det_loss_cls_dn_1[0.0968] det_loss_box_dn_1[0.2654] det_loss_cls_dn_2[0.0957] det_loss_box_dn_2[0.2516] det_loss_cls_dn_3[0.0945] det_loss_box_dn_3[0.2488] det_loss_cls_dn_4[0.0937] det_loss_box_dn_4[0.2457] det_loss_cls_dn_5[0.0927] det_loss_box_dn_5[0.2426] loss_depth[0.0368] total_loss[7.5290]
Rank[0/8] 06/20/2025 07:46:55 INFO stats.py:314 | Epoch[133] Step[304] GlobalStep[52174] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 13:57:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:47:15 INFO loss_tracker.py:84 | Epoch[133/239] Step[324] GlobalStep[52194/NA]: det_loss_cls_0[0.5324] det_loss_box_0[0.6978] det_loss_cls_1[0.5266] det_loss_box_1[0.5609] det_loss_cls_2[0.4858] det_loss_box_2[0.4974] det_loss_cls_3[0.4543] det_loss_box_3[0.4820] det_loss_cls_4[0.4458] det_loss_box_4[0.4711] det_loss_cls_5[0.4276] det_loss_box_5[0.4719] det_loss_cls_dn_0[0.1022] det_loss_box_dn_0[0.2688] det_loss_cls_dn_1[0.0983] det_loss_box_dn_1[0.2313] det_loss_cls_dn_2[0.0986] det_loss_box_dn_2[0.2163] det_loss_cls_dn_3[0.0988] det_loss_box_dn_3[0.2127] det_loss_cls_dn_4[0.0988] det_loss_box_dn_4[0.2105] det_loss_cls_dn_5[0.0990] det_loss_box_dn_5[0.2090] loss_depth[0.0362] total_loss[8.0340]
Rank[0/8] 06/20/2025 07:47:21 INFO stats.py:314 | Epoch[133] Step[329] GlobalStep[52199] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 13:56:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:47:42 INFO loss_tracker.py:84 | Epoch[133/239] Step[349] GlobalStep[52219/NA]: det_loss_cls_0[0.6011] det_loss_box_0[0.8676] det_loss_cls_1[0.5784] det_loss_box_1[0.6505] det_loss_cls_2[0.5332] det_loss_box_2[0.5886] det_loss_cls_3[0.4914] det_loss_box_3[0.5687] det_loss_cls_4[0.4613] det_loss_box_4[0.5556] det_loss_cls_5[0.4502] det_loss_box_5[0.5518] det_loss_cls_dn_0[0.0984] det_loss_box_dn_0[0.3006] det_loss_cls_dn_1[0.0957] det_loss_box_dn_1[0.2497] det_loss_cls_dn_2[0.0947] det_loss_box_dn_2[0.2322] det_loss_cls_dn_3[0.0933] det_loss_box_dn_3[0.2280] det_loss_cls_dn_4[0.0920] det_loss_box_dn_4[0.2262] det_loss_cls_dn_5[0.0911] det_loss_box_dn_5[0.2249] loss_depth[0.0382] total_loss[8.9634]
Rank[0/8] 06/20/2025 07:47:47 INFO stats.py:314 | Epoch[133] Step[354] GlobalStep[52224] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 13:56:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:48:07 INFO loss_tracker.py:84 | Epoch[133/239] Step[374] GlobalStep[52244/NA]: det_loss_cls_0[0.4757] det_loss_box_0[0.6667] det_loss_cls_1[0.4525] det_loss_box_1[0.5350] det_loss_cls_2[0.4210] det_loss_box_2[0.4747] det_loss_cls_3[0.3956] det_loss_box_3[0.4586] det_loss_cls_4[0.3734] det_loss_box_4[0.4540] det_loss_cls_5[0.3629] det_loss_box_5[0.4495] det_loss_cls_dn_0[0.0935] det_loss_box_dn_0[0.3276] det_loss_cls_dn_1[0.0899] det_loss_box_dn_1[0.2870] det_loss_cls_dn_2[0.0890] det_loss_box_dn_2[0.2672] det_loss_cls_dn_3[0.0882] det_loss_box_dn_3[0.2625] det_loss_cls_dn_4[0.0875] det_loss_box_dn_4[0.2601] det_loss_cls_dn_5[0.0867] det_loss_box_dn_5[0.2574] loss_depth[0.0363] total_loss[7.7524]
Rank[0/8] 06/20/2025 07:48:12 INFO stats.py:314 | Epoch[133] Step[379] GlobalStep[52249] Training Speed: 8.67 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 13:55:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:48:22 INFO stats.py:394 | Epoch[133] completed. Training Speed: 7.68 samples/sec across all devices.	Epoch Time: 406.48 sec.	Average Epoch Time: 406.48 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 13:55:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:48:40 INFO stats.py:314 | Epoch[134] Step[14] GlobalStep[52274] Training Speed: 7.93 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 13:55:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:48:50 INFO loss_tracker.py:84 | Epoch[134/239] Step[24] GlobalStep[52284/NA]: det_loss_cls_0[0.5588] det_loss_box_0[0.6984] det_loss_cls_1[0.5443] det_loss_box_1[0.5470] det_loss_cls_2[0.5058] det_loss_box_2[0.4839] det_loss_cls_3[0.4761] det_loss_box_3[0.4671] det_loss_cls_4[0.4503] det_loss_box_4[0.4561] det_loss_cls_5[0.4373] det_loss_box_5[0.4577] det_loss_cls_dn_0[0.1015] det_loss_box_dn_0[0.2610] det_loss_cls_dn_1[0.0981] det_loss_box_dn_1[0.2210] det_loss_cls_dn_2[0.0978] det_loss_box_dn_2[0.2047] det_loss_cls_dn_3[0.0963] det_loss_box_dn_3[0.2014] det_loss_cls_dn_4[0.0956] det_loss_box_dn_4[0.1986] det_loss_cls_dn_5[0.0949] det_loss_box_dn_5[0.1980] loss_depth[0.0371] total_loss[7.9890]
Rank[0/8] 06/20/2025 07:49:06 INFO stats.py:314 | Epoch[134] Step[39] GlobalStep[52299] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 13:54:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:49:16 INFO loss_tracker.py:84 | Epoch[134/239] Step[49] GlobalStep[52309/NA]: det_loss_cls_0[0.4359] det_loss_box_0[0.5527] det_loss_cls_1[0.4334] det_loss_box_1[0.4122] det_loss_cls_2[0.3716] det_loss_box_2[0.3843] det_loss_cls_3[0.3440] det_loss_box_3[0.3726] det_loss_cls_4[0.3223] det_loss_box_4[0.3608] det_loss_cls_5[0.3123] det_loss_box_5[0.3601] det_loss_cls_dn_0[0.0935] det_loss_box_dn_0[0.2624] det_loss_cls_dn_1[0.0893] det_loss_box_dn_1[0.2126] det_loss_cls_dn_2[0.0884] det_loss_box_dn_2[0.1948] det_loss_cls_dn_3[0.0871] det_loss_box_dn_3[0.1912] det_loss_cls_dn_4[0.0867] det_loss_box_dn_4[0.1880] det_loss_cls_dn_5[0.0852] det_loss_box_dn_5[0.1873] loss_depth[0.0374] total_loss[6.4661]
Rank[0/8] 06/20/2025 07:49:32 INFO stats.py:314 | Epoch[134] Step[64] GlobalStep[52324] Training Speed: 7.95 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 13:54:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:49:42 INFO loss_tracker.py:84 | Epoch[134/239] Step[74] GlobalStep[52334/NA]: det_loss_cls_0[0.4732] det_loss_box_0[0.5702] det_loss_cls_1[0.4622] det_loss_box_1[0.4285] det_loss_cls_2[0.4249] det_loss_box_2[0.3932] det_loss_cls_3[0.3947] det_loss_box_3[0.3813] det_loss_cls_4[0.3720] det_loss_box_4[0.3758] det_loss_cls_5[0.3588] det_loss_box_5[0.3787] det_loss_cls_dn_0[0.1003] det_loss_box_dn_0[0.3554] det_loss_cls_dn_1[0.0964] det_loss_box_dn_1[0.2911] det_loss_cls_dn_2[0.0958] det_loss_box_dn_2[0.2709] det_loss_cls_dn_3[0.0946] det_loss_box_dn_3[0.2671] det_loss_cls_dn_4[0.0937] det_loss_box_dn_4[0.2633] det_loss_cls_dn_5[0.0929] det_loss_box_dn_5[0.2590] loss_depth[0.0368] total_loss[7.3306]
Rank[0/8] 06/20/2025 07:49:57 INFO stats.py:314 | Epoch[134] Step[89] GlobalStep[52349] Training Speed: 8.05 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 13:53:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:50:08 INFO loss_tracker.py:84 | Epoch[134/239] Step[99] GlobalStep[52359/NA]: det_loss_cls_0[0.5800] det_loss_box_0[0.7386] det_loss_cls_1[0.5586] det_loss_box_1[0.5844] det_loss_cls_2[0.5115] det_loss_box_2[0.5304] det_loss_cls_3[0.4752] det_loss_box_3[0.5072] det_loss_cls_4[0.4420] det_loss_box_4[0.4993] det_loss_cls_5[0.4266] det_loss_box_5[0.5006] det_loss_cls_dn_0[0.1113] det_loss_box_dn_0[0.3581] det_loss_cls_dn_1[0.1063] det_loss_box_dn_1[0.3171] det_loss_cls_dn_2[0.1062] det_loss_box_dn_2[0.3004] det_loss_cls_dn_3[0.1047] det_loss_box_dn_3[0.2961] det_loss_cls_dn_4[0.1031] det_loss_box_dn_4[0.2949] det_loss_cls_dn_5[0.1020] det_loss_box_dn_5[0.2931] loss_depth[0.0396] total_loss[8.8874]
Rank[0/8] 06/20/2025 07:50:24 INFO stats.py:314 | Epoch[134] Step[114] GlobalStep[52374] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 13:52:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:50:34 INFO loss_tracker.py:84 | Epoch[134/239] Step[124] GlobalStep[52384/NA]: det_loss_cls_0[0.3920] det_loss_box_0[0.5160] det_loss_cls_1[0.3730] det_loss_box_1[0.4076] det_loss_cls_2[0.3419] det_loss_box_2[0.3745] det_loss_cls_3[0.3229] det_loss_box_3[0.3581] det_loss_cls_4[0.3061] det_loss_box_4[0.3490] det_loss_cls_5[0.2953] det_loss_box_5[0.3520] det_loss_cls_dn_0[0.0899] det_loss_box_dn_0[0.3169] det_loss_cls_dn_1[0.0862] det_loss_box_dn_1[0.2673] det_loss_cls_dn_2[0.0864] det_loss_box_dn_2[0.2482] det_loss_cls_dn_3[0.0855] det_loss_box_dn_3[0.2457] det_loss_cls_dn_4[0.0849] det_loss_box_dn_4[0.2443] det_loss_cls_dn_5[0.0832] det_loss_box_dn_5[0.2429] loss_depth[0.0407] total_loss[6.5109]
Rank[0/8] 06/20/2025 07:50:50 INFO stats.py:314 | Epoch[134] Step[139] GlobalStep[52399] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 13:52:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:51:00 INFO loss_tracker.py:84 | Epoch[134/239] Step[149] GlobalStep[52409/NA]: det_loss_cls_0[0.5652] det_loss_box_0[0.7313] det_loss_cls_1[0.5658] det_loss_box_1[0.5682] det_loss_cls_2[0.5017] det_loss_box_2[0.5066] det_loss_cls_3[0.4737] det_loss_box_3[0.4790] det_loss_cls_4[0.4465] det_loss_box_4[0.4677] det_loss_cls_5[0.4314] det_loss_box_5[0.4696] det_loss_cls_dn_0[0.1017] det_loss_box_dn_0[0.2855] det_loss_cls_dn_1[0.0985] det_loss_box_dn_1[0.2430] det_loss_cls_dn_2[0.0984] det_loss_box_dn_2[0.2274] det_loss_cls_dn_3[0.0981] det_loss_box_dn_3[0.2238] det_loss_cls_dn_4[0.0972] det_loss_box_dn_4[0.2236] det_loss_cls_dn_5[0.0962] det_loss_box_dn_5[0.2222] loss_depth[0.0345] total_loss[8.2571]
Rank[0/8] 06/20/2025 07:51:15 INFO stats.py:314 | Epoch[134] Step[164] GlobalStep[52424] Training Speed: 7.98 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 13:51:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:51:25 INFO loss_tracker.py:84 | Epoch[134/239] Step[174] GlobalStep[52434/NA]: det_loss_cls_0[0.5194] det_loss_box_0[0.6844] det_loss_cls_1[0.5040] det_loss_box_1[0.5388] det_loss_cls_2[0.4613] det_loss_box_2[0.4777] det_loss_cls_3[0.4256] det_loss_box_3[0.4596] det_loss_cls_4[0.3925] det_loss_box_4[0.4505] det_loss_cls_5[0.3801] det_loss_box_5[0.4454] det_loss_cls_dn_0[0.0978] det_loss_box_dn_0[0.2569] det_loss_cls_dn_1[0.0946] det_loss_box_dn_1[0.2172] det_loss_cls_dn_2[0.0939] det_loss_box_dn_2[0.2014] det_loss_cls_dn_3[0.0922] det_loss_box_dn_3[0.1985] det_loss_cls_dn_4[0.0919] det_loss_box_dn_4[0.1970] det_loss_cls_dn_5[0.0905] det_loss_box_dn_5[0.1961] loss_depth[0.0372] total_loss[7.6046]
Rank[0/8] 06/20/2025 07:51:41 INFO stats.py:314 | Epoch[134] Step[189] GlobalStep[52449] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 13:51:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:51:51 INFO loss_tracker.py:84 | Epoch[134/239] Step[199] GlobalStep[52459/NA]: det_loss_cls_0[0.4119] det_loss_box_0[0.5010] det_loss_cls_1[0.4069] det_loss_box_1[0.3746] det_loss_cls_2[0.3590] det_loss_box_2[0.3415] det_loss_cls_3[0.3280] det_loss_box_3[0.3284] det_loss_cls_4[0.3080] det_loss_box_4[0.3305] det_loss_cls_5[0.2962] det_loss_box_5[0.3313] det_loss_cls_dn_0[0.0801] det_loss_box_dn_0[0.2695] det_loss_cls_dn_1[0.0767] det_loss_box_dn_1[0.2222] det_loss_cls_dn_2[0.0764] det_loss_box_dn_2[0.2044] det_loss_cls_dn_3[0.0755] det_loss_box_dn_3[0.2000] det_loss_cls_dn_4[0.0742] det_loss_box_dn_4[0.1975] det_loss_cls_dn_5[0.0740] det_loss_box_dn_5[0.1950] loss_depth[0.0378] total_loss[6.1009]
Rank[0/8] 06/20/2025 07:52:08 INFO stats.py:314 | Epoch[134] Step[214] GlobalStep[52474] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 13:50:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:52:18 INFO loss_tracker.py:84 | Epoch[134/239] Step[224] GlobalStep[52484/NA]: det_loss_cls_0[0.4560] det_loss_box_0[0.5388] det_loss_cls_1[0.4323] det_loss_box_1[0.4196] det_loss_cls_2[0.3852] det_loss_box_2[0.3777] det_loss_cls_3[0.3562] det_loss_box_3[0.3634] det_loss_cls_4[0.3353] det_loss_box_4[0.3617] det_loss_cls_5[0.3282] det_loss_box_5[0.3571] det_loss_cls_dn_0[0.1023] det_loss_box_dn_0[0.2942] det_loss_cls_dn_1[0.0988] det_loss_box_dn_1[0.2469] det_loss_cls_dn_2[0.0981] det_loss_box_dn_2[0.2309] det_loss_cls_dn_3[0.0970] det_loss_box_dn_3[0.2259] det_loss_cls_dn_4[0.0964] det_loss_box_dn_4[0.2253] det_loss_cls_dn_5[0.0955] det_loss_box_dn_5[0.2239] loss_depth[0.0374] total_loss[6.7843]
Rank[0/8] 06/20/2025 07:52:33 INFO stats.py:314 | Epoch[134] Step[239] GlobalStep[52499] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 13:50:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:52:43 INFO loss_tracker.py:84 | Epoch[134/239] Step[249] GlobalStep[52509/NA]: det_loss_cls_0[0.5739] det_loss_box_0[0.7299] det_loss_cls_1[0.5581] det_loss_box_1[0.5560] det_loss_cls_2[0.5086] det_loss_box_2[0.4929] det_loss_cls_3[0.4737] det_loss_box_3[0.4784] det_loss_cls_4[0.4519] det_loss_box_4[0.4643] det_loss_cls_5[0.4387] det_loss_box_5[0.4614] det_loss_cls_dn_0[0.0921] det_loss_box_dn_0[0.4047] det_loss_cls_dn_1[0.0882] det_loss_box_dn_1[0.3508] det_loss_cls_dn_2[0.0871] det_loss_box_dn_2[0.3294] det_loss_cls_dn_3[0.0859] det_loss_box_dn_3[0.3334] det_loss_cls_dn_4[0.0854] det_loss_box_dn_4[0.3257] det_loss_cls_dn_5[0.0841] det_loss_box_dn_5[0.3192] loss_depth[0.0385] total_loss[8.8122]
Rank[0/8] 06/20/2025 07:52:59 INFO stats.py:314 | Epoch[134] Step[264] GlobalStep[52524] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 13:49:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:53:09 INFO loss_tracker.py:84 | Epoch[134/239] Step[274] GlobalStep[52534/NA]: det_loss_cls_0[0.4946] det_loss_box_0[0.7111] det_loss_cls_1[0.4947] det_loss_box_1[0.5454] det_loss_cls_2[0.4491] det_loss_box_2[0.4897] det_loss_cls_3[0.4247] det_loss_box_3[0.4702] det_loss_cls_4[0.4024] det_loss_box_4[0.4690] det_loss_cls_5[0.3888] det_loss_box_5[0.4666] det_loss_cls_dn_0[0.1093] det_loss_box_dn_0[0.3427] det_loss_cls_dn_1[0.1067] det_loss_box_dn_1[0.2927] det_loss_cls_dn_2[0.1049] det_loss_box_dn_2[0.2747] det_loss_cls_dn_3[0.1056] det_loss_box_dn_3[0.2700] det_loss_cls_dn_4[0.1043] det_loss_box_dn_4[0.2685] det_loss_cls_dn_5[0.1031] det_loss_box_dn_5[0.2676] loss_depth[0.0379] total_loss[8.1942]
Rank[0/8] 06/20/2025 07:53:25 INFO stats.py:314 | Epoch[134] Step[289] GlobalStep[52549] Training Speed: 8.19 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 13:48:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:53:35 INFO loss_tracker.py:84 | Epoch[134/239] Step[299] GlobalStep[52559/NA]: det_loss_cls_0[0.4358] det_loss_box_0[0.5381] det_loss_cls_1[0.4268] det_loss_box_1[0.4160] det_loss_cls_2[0.3787] det_loss_box_2[0.3828] det_loss_cls_3[0.3575] det_loss_box_3[0.3668] det_loss_cls_4[0.3366] det_loss_box_4[0.3554] det_loss_cls_5[0.3244] det_loss_box_5[0.3544] det_loss_cls_dn_0[0.0981] det_loss_box_dn_0[0.2841] det_loss_cls_dn_1[0.0937] det_loss_box_dn_1[0.2402] det_loss_cls_dn_2[0.0936] det_loss_box_dn_2[0.2258] det_loss_cls_dn_3[0.0925] det_loss_box_dn_3[0.2222] det_loss_cls_dn_4[0.0919] det_loss_box_dn_4[0.2205] det_loss_cls_dn_5[0.0920] det_loss_box_dn_5[0.2193] loss_depth[0.0356] total_loss[6.6828]
Rank[0/8] 06/20/2025 07:53:51 INFO stats.py:314 | Epoch[134] Step[314] GlobalStep[52574] Training Speed: 10.87 samples/sec across all devices.	Average Step Time: 0.74 sec.	Estimated Remaining Time: 13:48:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:54:01 INFO loss_tracker.py:84 | Epoch[134/239] Step[324] GlobalStep[52584/NA]: det_loss_cls_0[0.4822] det_loss_box_0[0.7408] det_loss_cls_1[0.4731] det_loss_box_1[0.5923] det_loss_cls_2[0.4361] det_loss_box_2[0.5280] det_loss_cls_3[0.4107] det_loss_box_3[0.5152] det_loss_cls_4[0.3879] det_loss_box_4[0.5079] det_loss_cls_5[0.3721] det_loss_box_5[0.5153] det_loss_cls_dn_0[0.0903] det_loss_box_dn_0[0.3054] det_loss_cls_dn_1[0.0871] det_loss_box_dn_1[0.2645] det_loss_cls_dn_2[0.0858] det_loss_box_dn_2[0.2470] det_loss_cls_dn_3[0.0851] det_loss_box_dn_3[0.2452] det_loss_cls_dn_4[0.0840] det_loss_box_dn_4[0.2449] det_loss_cls_dn_5[0.0832] det_loss_box_dn_5[0.2440] loss_depth[0.0382] total_loss[8.0662]
Rank[0/8] 06/20/2025 07:54:17 INFO stats.py:314 | Epoch[134] Step[339] GlobalStep[52599] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 13:47:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:54:27 INFO loss_tracker.py:84 | Epoch[134/239] Step[349] GlobalStep[52609/NA]: det_loss_cls_0[0.5543] det_loss_box_0[0.7536] det_loss_cls_1[0.5354] det_loss_box_1[0.6019] det_loss_cls_2[0.4956] det_loss_box_2[0.5305] det_loss_cls_3[0.4636] det_loss_box_3[0.4994] det_loss_cls_4[0.4374] det_loss_box_4[0.4739] det_loss_cls_5[0.4194] det_loss_box_5[0.4769] det_loss_cls_dn_0[0.0944] det_loss_box_dn_0[0.2783] det_loss_cls_dn_1[0.0906] det_loss_box_dn_1[0.2253] det_loss_cls_dn_2[0.0898] det_loss_box_dn_2[0.2072] det_loss_cls_dn_3[0.0887] det_loss_box_dn_3[0.2031] det_loss_cls_dn_4[0.0877] det_loss_box_dn_4[0.2011] det_loss_cls_dn_5[0.0863] det_loss_box_dn_5[0.2002] loss_depth[0.0367] total_loss[8.1314]
Rank[0/8] 06/20/2025 07:54:43 INFO stats.py:314 | Epoch[134] Step[364] GlobalStep[52624] Training Speed: 7.91 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 13:47:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:54:53 INFO loss_tracker.py:84 | Epoch[134/239] Step[374] GlobalStep[52634/NA]: det_loss_cls_0[0.4685] det_loss_box_0[0.5986] det_loss_cls_1[0.4629] det_loss_box_1[0.4778] det_loss_cls_2[0.4236] det_loss_box_2[0.4369] det_loss_cls_3[0.3940] det_loss_box_3[0.4217] det_loss_cls_4[0.3574] det_loss_box_4[0.4227] det_loss_cls_5[0.3491] det_loss_box_5[0.4207] det_loss_cls_dn_0[0.0937] det_loss_box_dn_0[0.2716] det_loss_cls_dn_1[0.0902] det_loss_box_dn_1[0.2310] det_loss_cls_dn_2[0.0896] det_loss_box_dn_2[0.2181] det_loss_cls_dn_3[0.0888] det_loss_box_dn_3[0.2136] det_loss_cls_dn_4[0.0878] det_loss_box_dn_4[0.2108] det_loss_cls_dn_5[0.0865] det_loss_box_dn_5[0.2098] loss_depth[0.0368] total_loss[7.1622]
Rank[0/8] 06/20/2025 07:55:09 INFO stats.py:314 | Epoch[134] Step[389] GlobalStep[52649] Training Speed: 7.05 samples/sec across all devices.	Average Step Time: 1.13 sec.	Estimated Remaining Time: 13:46:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:55:09 INFO stats.py:394 | Epoch[134] completed. Training Speed: 7.67 samples/sec across all devices.	Epoch Time: 406.66 sec.	Average Epoch Time: 406.66 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 13:46:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:55:37 INFO stats.py:314 | Epoch[135] Step[24] GlobalStep[52674] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 13:46:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:55:37 INFO loss_tracker.py:84 | Epoch[135/239] Step[24] GlobalStep[52674/NA]: det_loss_cls_0[0.4563] det_loss_box_0[0.6383] det_loss_cls_1[0.4480] det_loss_box_1[0.4893] det_loss_cls_2[0.4037] det_loss_box_2[0.4444] det_loss_cls_3[0.3731] det_loss_box_3[0.4258] det_loss_cls_4[0.3469] det_loss_box_4[0.4189] det_loss_cls_5[0.3400] det_loss_box_5[0.4176] det_loss_cls_dn_0[0.1012] det_loss_box_dn_0[0.3276] det_loss_cls_dn_1[0.0978] det_loss_box_dn_1[0.2789] det_loss_cls_dn_2[0.0971] det_loss_box_dn_2[0.2630] det_loss_cls_dn_3[0.0963] det_loss_box_dn_3[0.2586] det_loss_cls_dn_4[0.0955] det_loss_box_dn_4[0.2571] det_loss_cls_dn_5[0.0944] det_loss_box_dn_5[0.2538] loss_depth[0.0366] total_loss[7.4601]
Rank[0/8] 06/20/2025 07:56:03 INFO stats.py:314 | Epoch[135] Step[49] GlobalStep[52699] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 13:45:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:56:03 INFO loss_tracker.py:84 | Epoch[135/239] Step[49] GlobalStep[52699/NA]: det_loss_cls_0[0.6168] det_loss_box_0[0.7432] det_loss_cls_1[0.6044] det_loss_box_1[0.5783] det_loss_cls_2[0.5643] det_loss_box_2[0.5425] det_loss_cls_3[0.5318] det_loss_box_3[0.5172] det_loss_cls_4[0.5139] det_loss_box_4[0.5068] det_loss_cls_5[0.5097] det_loss_box_5[0.5036] det_loss_cls_dn_0[0.1120] det_loss_box_dn_0[0.2828] det_loss_cls_dn_1[0.1090] det_loss_box_dn_1[0.2332] det_loss_cls_dn_2[0.1083] det_loss_box_dn_2[0.2126] det_loss_cls_dn_3[0.1076] det_loss_box_dn_3[0.2094] det_loss_cls_dn_4[0.1065] det_loss_box_dn_4[0.2067] det_loss_cls_dn_5[0.1056] det_loss_box_dn_5[0.2057] loss_depth[0.0386] total_loss[8.7704]
Rank[0/8] 06/20/2025 07:56:29 INFO stats.py:314 | Epoch[135] Step[74] GlobalStep[52724] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 13:45:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:56:29 INFO loss_tracker.py:84 | Epoch[135/239] Step[74] GlobalStep[52724/NA]: det_loss_cls_0[0.5158] det_loss_box_0[0.6820] det_loss_cls_1[0.5182] det_loss_box_1[0.5444] det_loss_cls_2[0.4709] det_loss_box_2[0.4925] det_loss_cls_3[0.4292] det_loss_box_3[0.4761] det_loss_cls_4[0.4094] det_loss_box_4[0.4595] det_loss_cls_5[0.4004] det_loss_box_5[0.4617] det_loss_cls_dn_0[0.1050] det_loss_box_dn_0[0.3460] det_loss_cls_dn_1[0.1015] det_loss_box_dn_1[0.2997] det_loss_cls_dn_2[0.1011] det_loss_box_dn_2[0.2847] det_loss_cls_dn_3[0.1006] det_loss_box_dn_3[0.2829] det_loss_cls_dn_4[0.0994] det_loss_box_dn_4[0.2804] det_loss_cls_dn_5[0.0987] det_loss_box_dn_5[0.2790] loss_depth[0.0411] total_loss[8.2800]
Rank[0/8] 06/20/2025 07:56:55 INFO stats.py:314 | Epoch[135] Step[99] GlobalStep[52749] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 13:44:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:56:55 INFO loss_tracker.py:84 | Epoch[135/239] Step[99] GlobalStep[52749/NA]: det_loss_cls_0[0.5260] det_loss_box_0[0.6592] det_loss_cls_1[0.5044] det_loss_box_1[0.5003] det_loss_cls_2[0.4567] det_loss_box_2[0.4445] det_loss_cls_3[0.4187] det_loss_box_3[0.4315] det_loss_cls_4[0.3991] det_loss_box_4[0.4297] det_loss_cls_5[0.3859] det_loss_box_5[0.4284] det_loss_cls_dn_0[0.0992] det_loss_box_dn_0[0.3536] det_loss_cls_dn_1[0.0954] det_loss_box_dn_1[0.2864] det_loss_cls_dn_2[0.0946] det_loss_box_dn_2[0.2681] det_loss_cls_dn_3[0.0936] det_loss_box_dn_3[0.2587] det_loss_cls_dn_4[0.0928] det_loss_box_dn_4[0.2565] det_loss_cls_dn_5[0.0920] det_loss_box_dn_5[0.2514] loss_depth[0.0387] total_loss[7.8655]
Rank[0/8] 06/20/2025 07:57:20 INFO stats.py:314 | Epoch[135] Step[124] GlobalStep[52774] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 13:43:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:57:20 INFO loss_tracker.py:84 | Epoch[135/239] Step[124] GlobalStep[52774/NA]: det_loss_cls_0[0.4528] det_loss_box_0[0.6017] det_loss_cls_1[0.4478] det_loss_box_1[0.4655] det_loss_cls_2[0.4088] det_loss_box_2[0.4215] det_loss_cls_3[0.3751] det_loss_box_3[0.4055] det_loss_cls_4[0.3553] det_loss_box_4[0.4047] det_loss_cls_5[0.3462] det_loss_box_5[0.4009] det_loss_cls_dn_0[0.0943] det_loss_box_dn_0[0.3017] det_loss_cls_dn_1[0.0904] det_loss_box_dn_1[0.2507] det_loss_cls_dn_2[0.0898] det_loss_box_dn_2[0.2324] det_loss_cls_dn_3[0.0888] det_loss_box_dn_3[0.2292] det_loss_cls_dn_4[0.0879] det_loss_box_dn_4[0.2289] det_loss_cls_dn_5[0.0871] det_loss_box_dn_5[0.2283] loss_depth[0.0367] total_loss[7.1320]
Rank[0/8] 06/20/2025 07:57:47 INFO stats.py:314 | Epoch[135] Step[149] GlobalStep[52799] Training Speed: 8.08 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 13:43:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:57:47 INFO loss_tracker.py:84 | Epoch[135/239] Step[149] GlobalStep[52799/NA]: det_loss_cls_0[0.5222] det_loss_box_0[0.7150] det_loss_cls_1[0.5088] det_loss_box_1[0.5631] det_loss_cls_2[0.4767] det_loss_box_2[0.5021] det_loss_cls_3[0.4362] det_loss_box_3[0.4987] det_loss_cls_4[0.4109] det_loss_box_4[0.4923] det_loss_cls_5[0.3964] det_loss_box_5[0.4943] det_loss_cls_dn_0[0.1006] det_loss_box_dn_0[0.3374] det_loss_cls_dn_1[0.0974] det_loss_box_dn_1[0.2930] det_loss_cls_dn_2[0.0972] det_loss_box_dn_2[0.2743] det_loss_cls_dn_3[0.0954] det_loss_box_dn_3[0.2714] det_loss_cls_dn_4[0.0939] det_loss_box_dn_4[0.2673] det_loss_cls_dn_5[0.0928] det_loss_box_dn_5[0.2633] loss_depth[0.0347] total_loss[8.3355]
Rank[0/8] 06/20/2025 07:58:13 INFO stats.py:314 | Epoch[135] Step[174] GlobalStep[52824] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 13:42:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:58:13 INFO loss_tracker.py:84 | Epoch[135/239] Step[174] GlobalStep[52824/NA]: det_loss_cls_0[0.4337] det_loss_box_0[0.5796] det_loss_cls_1[0.4322] det_loss_box_1[0.4509] det_loss_cls_2[0.3880] det_loss_box_2[0.4128] det_loss_cls_3[0.3570] det_loss_box_3[0.3975] det_loss_cls_4[0.3352] det_loss_box_4[0.3896] det_loss_cls_5[0.3144] det_loss_box_5[0.4006] det_loss_cls_dn_0[0.0883] det_loss_box_dn_0[0.2929] det_loss_cls_dn_1[0.0844] det_loss_box_dn_1[0.2524] det_loss_cls_dn_2[0.0841] det_loss_box_dn_2[0.2335] det_loss_cls_dn_3[0.0831] det_loss_box_dn_3[0.2290] det_loss_cls_dn_4[0.0817] det_loss_box_dn_4[0.2275] det_loss_cls_dn_5[0.0815] det_loss_box_dn_5[0.2266] loss_depth[0.0368] total_loss[6.8933]
Rank[0/8] 06/20/2025 07:58:38 INFO stats.py:314 | Epoch[135] Step[199] GlobalStep[52849] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 13:42:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:58:38 INFO loss_tracker.py:84 | Epoch[135/239] Step[199] GlobalStep[52849/NA]: det_loss_cls_0[0.5280] det_loss_box_0[0.7142] det_loss_cls_1[0.5066] det_loss_box_1[0.5507] det_loss_cls_2[0.4684] det_loss_box_2[0.5003] det_loss_cls_3[0.4350] det_loss_box_3[0.4832] det_loss_cls_4[0.4081] det_loss_box_4[0.4764] det_loss_cls_5[0.3940] det_loss_box_5[0.4790] det_loss_cls_dn_0[0.0934] det_loss_box_dn_0[0.3592] det_loss_cls_dn_1[0.0900] det_loss_box_dn_1[0.3137] det_loss_cls_dn_2[0.0892] det_loss_box_dn_2[0.2968] det_loss_cls_dn_3[0.0881] det_loss_box_dn_3[0.2925] det_loss_cls_dn_4[0.0873] det_loss_box_dn_4[0.2890] det_loss_cls_dn_5[0.0871] det_loss_box_dn_5[0.2843] loss_depth[0.0406] total_loss[8.3552]
Rank[0/8] 06/20/2025 07:59:05 INFO stats.py:314 | Epoch[135] Step[224] GlobalStep[52874] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 13:41:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:59:05 INFO loss_tracker.py:84 | Epoch[135/239] Step[224] GlobalStep[52874/NA]: det_loss_cls_0[0.5011] det_loss_box_0[0.6779] det_loss_cls_1[0.4960] det_loss_box_1[0.5117] det_loss_cls_2[0.4524] det_loss_box_2[0.4676] det_loss_cls_3[0.4285] det_loss_box_3[0.4528] det_loss_cls_4[0.4074] det_loss_box_4[0.4322] det_loss_cls_5[0.3884] det_loss_box_5[0.4413] det_loss_cls_dn_0[0.0958] det_loss_box_dn_0[0.3104] det_loss_cls_dn_1[0.0912] det_loss_box_dn_1[0.2651] det_loss_cls_dn_2[0.0909] det_loss_box_dn_2[0.2507] det_loss_cls_dn_3[0.0895] det_loss_box_dn_3[0.2472] det_loss_cls_dn_4[0.0887] det_loss_box_dn_4[0.2448] det_loss_cls_dn_5[0.0874] det_loss_box_dn_5[0.2431] loss_depth[0.0393] total_loss[7.8014]
Rank[0/8] 06/20/2025 07:59:31 INFO stats.py:314 | Epoch[135] Step[249] GlobalStep[52899] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 13:41:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:59:31 INFO loss_tracker.py:84 | Epoch[135/239] Step[249] GlobalStep[52899/NA]: det_loss_cls_0[0.5859] det_loss_box_0[0.7248] det_loss_cls_1[0.5860] det_loss_box_1[0.5716] det_loss_cls_2[0.5202] det_loss_box_2[0.5340] det_loss_cls_3[0.4862] det_loss_box_3[0.5217] det_loss_cls_4[0.4593] det_loss_box_4[0.5029] det_loss_cls_5[0.4437] det_loss_box_5[0.5045] det_loss_cls_dn_0[0.0975] det_loss_box_dn_0[0.2656] det_loss_cls_dn_1[0.0940] det_loss_box_dn_1[0.2337] det_loss_cls_dn_2[0.0929] det_loss_box_dn_2[0.2146] det_loss_cls_dn_3[0.0919] det_loss_box_dn_3[0.2127] det_loss_cls_dn_4[0.0909] det_loss_box_dn_4[0.2121] det_loss_cls_dn_5[0.0898] det_loss_box_dn_5[0.2111] loss_depth[0.0369] total_loss[8.3844]
Rank[0/8] 06/20/2025 07:59:56 INFO stats.py:314 | Epoch[135] Step[274] GlobalStep[52924] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 13:40:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 07:59:56 INFO loss_tracker.py:84 | Epoch[135/239] Step[274] GlobalStep[52924/NA]: det_loss_cls_0[0.5098] det_loss_box_0[0.5945] det_loss_cls_1[0.5091] det_loss_box_1[0.4490] det_loss_cls_2[0.4551] det_loss_box_2[0.4176] det_loss_cls_3[0.4258] det_loss_box_3[0.4049] det_loss_cls_4[0.3941] det_loss_box_4[0.3968] det_loss_cls_5[0.3766] det_loss_box_5[0.3994] det_loss_cls_dn_0[0.0995] det_loss_box_dn_0[0.2538] det_loss_cls_dn_1[0.0964] det_loss_box_dn_1[0.2080] det_loss_cls_dn_2[0.0964] det_loss_box_dn_2[0.1901] det_loss_cls_dn_3[0.0955] det_loss_box_dn_3[0.1870] det_loss_cls_dn_4[0.0942] det_loss_box_dn_4[0.1845] det_loss_cls_dn_5[0.0933] det_loss_box_dn_5[0.1836] loss_depth[0.0378] total_loss[7.1530]
Rank[0/8] 06/20/2025 08:00:22 INFO stats.py:314 | Epoch[135] Step[299] GlobalStep[52949] Training Speed: 6.26 samples/sec across all devices.	Average Step Time: 1.28 sec.	Estimated Remaining Time: 13:39:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:00:22 INFO loss_tracker.py:84 | Epoch[135/239] Step[299] GlobalStep[52949/NA]: det_loss_cls_0[0.6526] det_loss_box_0[0.8731] det_loss_cls_1[0.6409] det_loss_box_1[0.6583] det_loss_cls_2[0.5978] det_loss_box_2[0.5895] det_loss_cls_3[0.5572] det_loss_box_3[0.5796] det_loss_cls_4[0.5317] det_loss_box_4[0.5704] det_loss_cls_5[0.5116] det_loss_box_5[0.5747] det_loss_cls_dn_0[0.1027] det_loss_box_dn_0[0.2748] det_loss_cls_dn_1[0.0993] det_loss_box_dn_1[0.2326] det_loss_cls_dn_2[0.0985] det_loss_box_dn_2[0.2194] det_loss_cls_dn_3[0.0970] det_loss_box_dn_3[0.2154] det_loss_cls_dn_4[0.0961] det_loss_box_dn_4[0.2153] det_loss_cls_dn_5[0.0956] det_loss_box_dn_5[0.2130] loss_depth[0.0420] total_loss[9.3389]
Rank[0/8] 06/20/2025 08:00:48 INFO stats.py:314 | Epoch[135] Step[324] GlobalStep[52974] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 13:39:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:00:48 INFO loss_tracker.py:84 | Epoch[135/239] Step[324] GlobalStep[52974/NA]: det_loss_cls_0[0.5459] det_loss_box_0[0.6563] det_loss_cls_1[0.5386] det_loss_box_1[0.4814] det_loss_cls_2[0.4790] det_loss_box_2[0.4293] det_loss_cls_3[0.4437] det_loss_box_3[0.4156] det_loss_cls_4[0.4076] det_loss_box_4[0.4146] det_loss_cls_5[0.3956] det_loss_box_5[0.4117] det_loss_cls_dn_0[0.0908] det_loss_box_dn_0[0.2353] det_loss_cls_dn_1[0.0863] det_loss_box_dn_1[0.1950] det_loss_cls_dn_2[0.0856] det_loss_box_dn_2[0.1809] det_loss_cls_dn_3[0.0842] det_loss_box_dn_3[0.1783] det_loss_cls_dn_4[0.0832] det_loss_box_dn_4[0.1757] det_loss_cls_dn_5[0.0824] det_loss_box_dn_5[0.1746] loss_depth[0.0356] total_loss[7.3070]
Rank[0/8] 06/20/2025 08:01:14 INFO stats.py:314 | Epoch[135] Step[349] GlobalStep[52999] Training Speed: 8.07 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 13:38:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:01:14 INFO loss_tracker.py:84 | Epoch[135/239] Step[349] GlobalStep[52999/NA]: det_loss_cls_0[0.4371] det_loss_box_0[0.6440] det_loss_cls_1[0.4309] det_loss_box_1[0.5225] det_loss_cls_2[0.3927] det_loss_box_2[0.4704] det_loss_cls_3[0.3694] det_loss_box_3[0.4500] det_loss_cls_4[0.3564] det_loss_box_4[0.4351] det_loss_cls_5[0.3449] det_loss_box_5[0.4396] det_loss_cls_dn_0[0.0945] det_loss_box_dn_0[0.3045] det_loss_cls_dn_1[0.0914] det_loss_box_dn_1[0.2598] det_loss_cls_dn_2[0.0909] det_loss_box_dn_2[0.2421] det_loss_cls_dn_3[0.0901] det_loss_box_dn_3[0.2382] det_loss_cls_dn_4[0.0892] det_loss_box_dn_4[0.2371] det_loss_cls_dn_5[0.0881] det_loss_box_dn_5[0.2363] loss_depth[0.0397] total_loss[7.3950]
Rank[0/8] 06/20/2025 08:01:39 INFO stats.py:314 | Epoch[135] Step[374] GlobalStep[53024] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 13:38:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:01:39 INFO loss_tracker.py:84 | Epoch[135/239] Step[374] GlobalStep[53024/NA]: det_loss_cls_0[0.5266] det_loss_box_0[0.7255] det_loss_cls_1[0.5177] det_loss_box_1[0.5746] det_loss_cls_2[0.4796] det_loss_box_2[0.5278] det_loss_cls_3[0.4461] det_loss_box_3[0.5149] det_loss_cls_4[0.4226] det_loss_box_4[0.5022] det_loss_cls_5[0.4015] det_loss_box_5[0.5067] det_loss_cls_dn_0[0.0934] det_loss_box_dn_0[0.4168] det_loss_cls_dn_1[0.0900] det_loss_box_dn_1[0.3528] det_loss_cls_dn_2[0.0897] det_loss_box_dn_2[0.3272] det_loss_cls_dn_3[0.0886] det_loss_box_dn_3[0.3218] det_loss_cls_dn_4[0.0878] det_loss_box_dn_4[0.3168] det_loss_cls_dn_5[0.0868] det_loss_box_dn_5[0.3091] loss_depth[0.0389] total_loss[8.7659]
Rank[0/8] 06/20/2025 08:01:55 INFO stats.py:394 | Epoch[135] completed. Training Speed: 7.68 samples/sec across all devices.	Epoch Time: 406.30 sec.	Average Epoch Time: 406.30 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 13:37:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:02:07 INFO stats.py:314 | Epoch[136] Step[9] GlobalStep[53049] Training Speed: 6.44 samples/sec across all devices.	Average Step Time: 1.24 sec.	Estimated Remaining Time: 13:37:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:02:22 INFO loss_tracker.py:84 | Epoch[136/239] Step[24] GlobalStep[53064/NA]: det_loss_cls_0[0.4586] det_loss_box_0[0.6557] det_loss_cls_1[0.4529] det_loss_box_1[0.5111] det_loss_cls_2[0.4158] det_loss_box_2[0.4652] det_loss_cls_3[0.3812] det_loss_box_3[0.4567] det_loss_cls_4[0.3604] det_loss_box_4[0.4419] det_loss_cls_5[0.3476] det_loss_box_5[0.4430] det_loss_cls_dn_0[0.0972] det_loss_box_dn_0[0.4238] det_loss_cls_dn_1[0.0941] det_loss_box_dn_1[0.3742] det_loss_cls_dn_2[0.0937] det_loss_box_dn_2[0.3535] det_loss_cls_dn_3[0.0924] det_loss_box_dn_3[0.3529] det_loss_cls_dn_4[0.0912] det_loss_box_dn_4[0.3492] det_loss_cls_dn_5[0.0909] det_loss_box_dn_5[0.3490] loss_depth[0.0385] total_loss[8.1907]
Rank[0/8] 06/20/2025 08:02:32 INFO stats.py:314 | Epoch[136] Step[34] GlobalStep[53074] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 13:37:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:02:48 INFO loss_tracker.py:84 | Epoch[136/239] Step[49] GlobalStep[53089/NA]: det_loss_cls_0[0.5645] det_loss_box_0[0.7099] det_loss_cls_1[0.5449] det_loss_box_1[0.5570] det_loss_cls_2[0.4983] det_loss_box_2[0.5053] det_loss_cls_3[0.4625] det_loss_box_3[0.4859] det_loss_cls_4[0.4347] det_loss_box_4[0.4793] det_loss_cls_5[0.4135] det_loss_box_5[0.4789] det_loss_cls_dn_0[0.0956] det_loss_box_dn_0[0.2800] det_loss_cls_dn_1[0.0910] det_loss_box_dn_1[0.2425] det_loss_cls_dn_2[0.0901] det_loss_box_dn_2[0.2275] det_loss_cls_dn_3[0.0888] det_loss_box_dn_3[0.2240] det_loss_cls_dn_4[0.0880] det_loss_box_dn_4[0.2221] det_loss_cls_dn_5[0.0862] det_loss_box_dn_5[0.2195] loss_depth[0.0363] total_loss[8.1263]
Rank[0/8] 06/20/2025 08:02:58 INFO stats.py:314 | Epoch[136] Step[59] GlobalStep[53099] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 13:36:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:03:14 INFO loss_tracker.py:84 | Epoch[136/239] Step[74] GlobalStep[53114/NA]: det_loss_cls_0[0.4753] det_loss_box_0[0.6322] det_loss_cls_1[0.4647] det_loss_box_1[0.4794] det_loss_cls_2[0.4282] det_loss_box_2[0.4424] det_loss_cls_3[0.3967] det_loss_box_3[0.4268] det_loss_cls_4[0.3752] det_loss_box_4[0.4181] det_loss_cls_5[0.3577] det_loss_box_5[0.4238] det_loss_cls_dn_0[0.0931] det_loss_box_dn_0[0.2526] det_loss_cls_dn_1[0.0903] det_loss_box_dn_1[0.2093] det_loss_cls_dn_2[0.0898] det_loss_box_dn_2[0.1943] det_loss_cls_dn_3[0.0883] det_loss_box_dn_3[0.1912] det_loss_cls_dn_4[0.0870] det_loss_box_dn_4[0.1903] det_loss_cls_dn_5[0.0856] det_loss_box_dn_5[0.1898] loss_depth[0.0361] total_loss[7.1183]
Rank[0/8] 06/20/2025 08:03:24 INFO stats.py:314 | Epoch[136] Step[84] GlobalStep[53124] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 13:36:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:03:40 INFO loss_tracker.py:84 | Epoch[136/239] Step[99] GlobalStep[53139/NA]: det_loss_cls_0[0.5286] det_loss_box_0[0.6535] det_loss_cls_1[0.5163] det_loss_box_1[0.4876] det_loss_cls_2[0.4587] det_loss_box_2[0.4374] det_loss_cls_3[0.4289] det_loss_box_3[0.4309] det_loss_cls_4[0.3925] det_loss_box_4[0.4365] det_loss_cls_5[0.3768] det_loss_box_5[0.4373] det_loss_cls_dn_0[0.0984] det_loss_box_dn_0[0.2857] det_loss_cls_dn_1[0.0948] det_loss_box_dn_1[0.2395] det_loss_cls_dn_2[0.0942] det_loss_box_dn_2[0.2242] det_loss_cls_dn_3[0.0926] det_loss_box_dn_3[0.2198] det_loss_cls_dn_4[0.0924] det_loss_box_dn_4[0.2195] det_loss_cls_dn_5[0.0915] det_loss_box_dn_5[0.2174] loss_depth[0.0390] total_loss[7.5940]
Rank[0/8] 06/20/2025 08:03:50 INFO stats.py:314 | Epoch[136] Step[109] GlobalStep[53149] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 13:35:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:04:05 INFO loss_tracker.py:84 | Epoch[136/239] Step[124] GlobalStep[53164/NA]: det_loss_cls_0[0.5226] det_loss_box_0[0.6997] det_loss_cls_1[0.5277] det_loss_box_1[0.5367] det_loss_cls_2[0.4620] det_loss_box_2[0.4905] det_loss_cls_3[0.4329] det_loss_box_3[0.4620] det_loss_cls_4[0.4036] det_loss_box_4[0.4528] det_loss_cls_5[0.3867] det_loss_box_5[0.4520] det_loss_cls_dn_0[0.0896] det_loss_box_dn_0[0.3157] det_loss_cls_dn_1[0.0869] det_loss_box_dn_1[0.2751] det_loss_cls_dn_2[0.0859] det_loss_box_dn_2[0.2592] det_loss_cls_dn_3[0.0858] det_loss_box_dn_3[0.2572] det_loss_cls_dn_4[0.0848] det_loss_box_dn_4[0.2562] det_loss_cls_dn_5[0.0835] det_loss_box_dn_5[0.2559] loss_depth[0.0375] total_loss[8.0025]
Rank[0/8] 06/20/2025 08:04:16 INFO stats.py:314 | Epoch[136] Step[134] GlobalStep[53174] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 13:34:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:04:31 INFO loss_tracker.py:84 | Epoch[136/239] Step[149] GlobalStep[53189/NA]: det_loss_cls_0[0.4639] det_loss_box_0[0.5816] det_loss_cls_1[0.4492] det_loss_box_1[0.4593] det_loss_cls_2[0.4059] det_loss_box_2[0.4169] det_loss_cls_3[0.3876] det_loss_box_3[0.3946] det_loss_cls_4[0.3587] det_loss_box_4[0.3826] det_loss_cls_5[0.3420] det_loss_box_5[0.3922] det_loss_cls_dn_0[0.0977] det_loss_box_dn_0[0.2898] det_loss_cls_dn_1[0.0940] det_loss_box_dn_1[0.2434] det_loss_cls_dn_2[0.0925] det_loss_box_dn_2[0.2282] det_loss_cls_dn_3[0.0918] det_loss_box_dn_3[0.2244] det_loss_cls_dn_4[0.0905] det_loss_box_dn_4[0.2213] det_loss_cls_dn_5[0.0900] det_loss_box_dn_5[0.2201] loss_depth[0.0386] total_loss[7.0566]
Rank[0/8] 06/20/2025 08:04:41 INFO stats.py:314 | Epoch[136] Step[159] GlobalStep[53199] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 13:34:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:04:57 INFO loss_tracker.py:84 | Epoch[136/239] Step[174] GlobalStep[53214/NA]: det_loss_cls_0[0.4905] det_loss_box_0[0.6370] det_loss_cls_1[0.4893] det_loss_box_1[0.4924] det_loss_cls_2[0.4366] det_loss_box_2[0.4361] det_loss_cls_3[0.4158] det_loss_box_3[0.4142] det_loss_cls_4[0.3887] det_loss_box_4[0.4036] det_loss_cls_5[0.3810] det_loss_box_5[0.4012] det_loss_cls_dn_0[0.0972] det_loss_box_dn_0[0.2674] det_loss_cls_dn_1[0.0926] det_loss_box_dn_1[0.2225] det_loss_cls_dn_2[0.0913] det_loss_box_dn_2[0.2074] det_loss_cls_dn_3[0.0912] det_loss_box_dn_3[0.2041] det_loss_cls_dn_4[0.0895] det_loss_box_dn_4[0.2024] det_loss_cls_dn_5[0.0888] det_loss_box_dn_5[0.2008] loss_depth[0.0379] total_loss[7.2793]
Rank[0/8] 06/20/2025 08:05:07 INFO stats.py:314 | Epoch[136] Step[184] GlobalStep[53224] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 13:33:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:05:23 INFO loss_tracker.py:84 | Epoch[136/239] Step[199] GlobalStep[53239/NA]: det_loss_cls_0[0.5416] det_loss_box_0[0.7666] det_loss_cls_1[0.5233] det_loss_box_1[0.6037] det_loss_cls_2[0.4881] det_loss_box_2[0.5237] det_loss_cls_3[0.4482] det_loss_box_3[0.5086] det_loss_cls_4[0.4159] det_loss_box_4[0.5023] det_loss_cls_5[0.3996] det_loss_box_5[0.5029] det_loss_cls_dn_0[0.0958] det_loss_box_dn_0[0.2931] det_loss_cls_dn_1[0.0923] det_loss_box_dn_1[0.2443] det_loss_cls_dn_2[0.0920] det_loss_box_dn_2[0.2272] det_loss_cls_dn_3[0.0909] det_loss_box_dn_3[0.2219] det_loss_cls_dn_4[0.0896] det_loss_box_dn_4[0.2202] det_loss_cls_dn_5[0.0888] det_loss_box_dn_5[0.2194] loss_depth[0.0421] total_loss[8.2419]
Rank[0/8] 06/20/2025 08:05:33 INFO stats.py:314 | Epoch[136] Step[209] GlobalStep[53249] Training Speed: 8.05 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 13:33:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:05:48 INFO loss_tracker.py:84 | Epoch[136/239] Step[224] GlobalStep[53264/NA]: det_loss_cls_0[0.5234] det_loss_box_0[0.6972] det_loss_cls_1[0.5215] det_loss_box_1[0.5488] det_loss_cls_2[0.4808] det_loss_box_2[0.4996] det_loss_cls_3[0.4499] det_loss_box_3[0.4850] det_loss_cls_4[0.4131] det_loss_box_4[0.4806] det_loss_cls_5[0.4006] det_loss_box_5[0.4795] det_loss_cls_dn_0[0.0899] det_loss_box_dn_0[0.2797] det_loss_cls_dn_1[0.0850] det_loss_box_dn_1[0.2422] det_loss_cls_dn_2[0.0845] det_loss_box_dn_2[0.2269] det_loss_cls_dn_3[0.0827] det_loss_box_dn_3[0.2230] det_loss_cls_dn_4[0.0810] det_loss_box_dn_4[0.2222] det_loss_cls_dn_5[0.0796] det_loss_box_dn_5[0.2204] loss_depth[0.0369] total_loss[7.9340]
Rank[0/8] 06/20/2025 08:05:59 INFO stats.py:314 | Epoch[136] Step[234] GlobalStep[53274] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 13:32:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:06:14 INFO loss_tracker.py:84 | Epoch[136/239] Step[249] GlobalStep[53289/NA]: det_loss_cls_0[0.4537] det_loss_box_0[0.5580] det_loss_cls_1[0.4487] det_loss_box_1[0.4204] det_loss_cls_2[0.3987] det_loss_box_2[0.3883] det_loss_cls_3[0.3705] det_loss_box_3[0.3775] det_loss_cls_4[0.3407] det_loss_box_4[0.3742] det_loss_cls_5[0.3265] det_loss_box_5[0.3774] det_loss_cls_dn_0[0.0973] det_loss_box_dn_0[0.2920] det_loss_cls_dn_1[0.0945] det_loss_box_dn_1[0.2468] det_loss_cls_dn_2[0.0935] det_loss_box_dn_2[0.2326] det_loss_cls_dn_3[0.0927] det_loss_box_dn_3[0.2278] det_loss_cls_dn_4[0.0917] det_loss_box_dn_4[0.2250] det_loss_cls_dn_5[0.0909] det_loss_box_dn_5[0.2229] loss_depth[0.0391] total_loss[6.8816]
Rank[0/8] 06/20/2025 08:06:24 INFO stats.py:314 | Epoch[136] Step[259] GlobalStep[53299] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 13:32:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:06:40 INFO loss_tracker.py:84 | Epoch[136/239] Step[274] GlobalStep[53314/NA]: det_loss_cls_0[0.4686] det_loss_box_0[0.5928] det_loss_cls_1[0.4704] det_loss_box_1[0.4722] det_loss_cls_2[0.4328] det_loss_box_2[0.4331] det_loss_cls_3[0.4090] det_loss_box_3[0.4111] det_loss_cls_4[0.3850] det_loss_box_4[0.4061] det_loss_cls_5[0.3679] det_loss_box_5[0.4111] det_loss_cls_dn_0[0.0985] det_loss_box_dn_0[0.2765] det_loss_cls_dn_1[0.0941] det_loss_box_dn_1[0.2348] det_loss_cls_dn_2[0.0931] det_loss_box_dn_2[0.2189] det_loss_cls_dn_3[0.0931] det_loss_box_dn_3[0.2168] det_loss_cls_dn_4[0.0920] det_loss_box_dn_4[0.2149] det_loss_cls_dn_5[0.0911] det_loss_box_dn_5[0.2149] loss_depth[0.0369] total_loss[7.2357]
Rank[0/8] 06/20/2025 08:06:50 INFO stats.py:314 | Epoch[136] Step[284] GlobalStep[53324] Training Speed: 6.93 samples/sec across all devices.	Average Step Time: 1.15 sec.	Estimated Remaining Time: 13:31:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:07:06 INFO loss_tracker.py:84 | Epoch[136/239] Step[299] GlobalStep[53339/NA]: det_loss_cls_0[0.5239] det_loss_box_0[0.6780] det_loss_cls_1[0.5014] det_loss_box_1[0.5231] det_loss_cls_2[0.4696] det_loss_box_2[0.4530] det_loss_cls_3[0.4334] det_loss_box_3[0.4342] det_loss_cls_4[0.4121] det_loss_box_4[0.4268] det_loss_cls_5[0.3966] det_loss_box_5[0.4266] det_loss_cls_dn_0[0.0914] det_loss_box_dn_0[0.2616] det_loss_cls_dn_1[0.0882] det_loss_box_dn_1[0.2174] det_loss_cls_dn_2[0.0880] det_loss_box_dn_2[0.2001] det_loss_cls_dn_3[0.0865] det_loss_box_dn_3[0.1957] det_loss_cls_dn_4[0.0854] det_loss_box_dn_4[0.1943] det_loss_cls_dn_5[0.0846] det_loss_box_dn_5[0.1937] loss_depth[0.0352] total_loss[7.5007]
Rank[0/8] 06/20/2025 08:07:16 INFO stats.py:314 | Epoch[136] Step[309] GlobalStep[53349] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 13:31:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:07:31 INFO loss_tracker.py:84 | Epoch[136/239] Step[324] GlobalStep[53364/NA]: det_loss_cls_0[0.5335] det_loss_box_0[0.7179] det_loss_cls_1[0.5336] det_loss_box_1[0.5446] det_loss_cls_2[0.4822] det_loss_box_2[0.5004] det_loss_cls_3[0.4481] det_loss_box_3[0.4756] det_loss_cls_4[0.4112] det_loss_box_4[0.4598] det_loss_cls_5[0.3989] det_loss_box_5[0.4617] det_loss_cls_dn_0[0.0888] det_loss_box_dn_0[0.2573] det_loss_cls_dn_1[0.0853] det_loss_box_dn_1[0.2105] det_loss_cls_dn_2[0.0854] det_loss_box_dn_2[0.1953] det_loss_cls_dn_3[0.0844] det_loss_box_dn_3[0.1923] det_loss_cls_dn_4[0.0838] det_loss_box_dn_4[0.1905] det_loss_cls_dn_5[0.0829] det_loss_box_dn_5[0.1901] loss_depth[0.0342] total_loss[7.7483]
Rank[0/8] 06/20/2025 08:07:42 INFO stats.py:314 | Epoch[136] Step[334] GlobalStep[53374] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 13:30:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:07:57 INFO loss_tracker.py:84 | Epoch[136/239] Step[349] GlobalStep[53389/NA]: det_loss_cls_0[0.4659] det_loss_box_0[0.6294] det_loss_cls_1[0.4551] det_loss_box_1[0.4733] det_loss_cls_2[0.4161] det_loss_box_2[0.4371] det_loss_cls_3[0.3783] det_loss_box_3[0.4239] det_loss_cls_4[0.3542] det_loss_box_4[0.4133] det_loss_cls_5[0.3415] det_loss_box_5[0.4131] det_loss_cls_dn_0[0.0805] det_loss_box_dn_0[0.2982] det_loss_cls_dn_1[0.0753] det_loss_box_dn_1[0.2529] det_loss_cls_dn_2[0.0757] det_loss_box_dn_2[0.2316] det_loss_cls_dn_3[0.0736] det_loss_box_dn_3[0.2280] det_loss_cls_dn_4[0.0730] det_loss_box_dn_4[0.2245] det_loss_cls_dn_5[0.0717] det_loss_box_dn_5[0.2227] loss_depth[0.0360] total_loss[7.1450]
Rank[0/8] 06/20/2025 08:08:08 INFO stats.py:314 | Epoch[136] Step[359] GlobalStep[53399] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 13:29:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:08:23 INFO loss_tracker.py:84 | Epoch[136/239] Step[374] GlobalStep[53414/NA]: det_loss_cls_0[0.4460] det_loss_box_0[0.5957] det_loss_cls_1[0.4352] det_loss_box_1[0.4361] det_loss_cls_2[0.3920] det_loss_box_2[0.3993] det_loss_cls_3[0.3639] det_loss_box_3[0.3946] det_loss_cls_4[0.3387] det_loss_box_4[0.3875] det_loss_cls_5[0.3234] det_loss_box_5[0.3901] det_loss_cls_dn_0[0.0985] det_loss_box_dn_0[0.2778] det_loss_cls_dn_1[0.0941] det_loss_box_dn_1[0.2309] det_loss_cls_dn_2[0.0936] det_loss_box_dn_2[0.2152] det_loss_cls_dn_3[0.0926] det_loss_box_dn_3[0.2097] det_loss_cls_dn_4[0.0918] det_loss_box_dn_4[0.2084] det_loss_cls_dn_5[0.0908] det_loss_box_dn_5[0.2066] loss_depth[0.0348] total_loss[6.8474]
Rank[0/8] 06/20/2025 08:08:33 INFO stats.py:314 | Epoch[136] Step[384] GlobalStep[53424] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 13:29:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:08:38 INFO stats.py:394 | Epoch[136] completed. Training Speed: 7.73 samples/sec across all devices.	Epoch Time: 403.37 sec.	Average Epoch Time: 403.37 sec.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 13:29:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:09:00 INFO stats.py:314 | Epoch[137] Step[19] GlobalStep[53449] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 13:28:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:09:06 INFO loss_tracker.py:84 | Epoch[137/239] Step[24] GlobalStep[53454/NA]: det_loss_cls_0[0.4993] det_loss_box_0[0.7312] det_loss_cls_1[0.4849] det_loss_box_1[0.5779] det_loss_cls_2[0.4375] det_loss_box_2[0.5199] det_loss_cls_3[0.4187] det_loss_box_3[0.4953] det_loss_cls_4[0.3922] det_loss_box_4[0.4875] det_loss_cls_5[0.3762] det_loss_box_5[0.4938] det_loss_cls_dn_0[0.0931] det_loss_box_dn_0[0.3127] det_loss_cls_dn_1[0.0900] det_loss_box_dn_1[0.2641] det_loss_cls_dn_2[0.0892] det_loss_box_dn_2[0.2455] det_loss_cls_dn_3[0.0887] det_loss_box_dn_3[0.2417] det_loss_cls_dn_4[0.0874] det_loss_box_dn_4[0.2398] det_loss_cls_dn_5[0.0864] det_loss_box_dn_5[0.2358] loss_depth[0.0374] total_loss[8.0262]
Rank[0/8] 06/20/2025 08:09:26 INFO stats.py:314 | Epoch[137] Step[44] GlobalStep[53474] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 13:28:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:09:32 INFO loss_tracker.py:84 | Epoch[137/239] Step[49] GlobalStep[53479/NA]: det_loss_cls_0[0.5002] det_loss_box_0[0.6134] det_loss_cls_1[0.4823] det_loss_box_1[0.5097] det_loss_cls_2[0.4330] det_loss_box_2[0.4711] det_loss_cls_3[0.4028] det_loss_box_3[0.4489] det_loss_cls_4[0.3799] det_loss_box_4[0.4496] det_loss_cls_5[0.3671] det_loss_box_5[0.4473] det_loss_cls_dn_0[0.0870] det_loss_box_dn_0[0.3143] det_loss_cls_dn_1[0.0838] det_loss_box_dn_1[0.2634] det_loss_cls_dn_2[0.0827] det_loss_box_dn_2[0.2426] det_loss_cls_dn_3[0.0817] det_loss_box_dn_3[0.2379] det_loss_cls_dn_4[0.0813] det_loss_box_dn_4[0.2345] det_loss_cls_dn_5[0.0803] det_loss_box_dn_5[0.2320] loss_depth[0.0355] total_loss[7.5622]
Rank[0/8] 06/20/2025 08:09:52 INFO stats.py:314 | Epoch[137] Step[69] GlobalStep[53499] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 13:27:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:09:57 INFO loss_tracker.py:84 | Epoch[137/239] Step[74] GlobalStep[53504/NA]: det_loss_cls_0[0.4097] det_loss_box_0[0.4932] det_loss_cls_1[0.3883] det_loss_box_1[0.3811] det_loss_cls_2[0.3537] det_loss_box_2[0.3442] det_loss_cls_3[0.3165] det_loss_box_3[0.3434] det_loss_cls_4[0.2959] det_loss_box_4[0.3421] det_loss_cls_5[0.2819] det_loss_box_5[0.3415] det_loss_cls_dn_0[0.0822] det_loss_box_dn_0[0.2624] det_loss_cls_dn_1[0.0779] det_loss_box_dn_1[0.2229] det_loss_cls_dn_2[0.0773] det_loss_box_dn_2[0.2075] det_loss_cls_dn_3[0.0763] det_loss_box_dn_3[0.2043] det_loss_cls_dn_4[0.0755] det_loss_box_dn_4[0.2033] det_loss_cls_dn_5[0.0748] det_loss_box_dn_5[0.2015] loss_depth[0.0377] total_loss[6.0951]
Rank[0/8] 06/20/2025 08:10:19 INFO stats.py:314 | Epoch[137] Step[94] GlobalStep[53524] Training Speed: 7.84 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 13:27:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:10:24 INFO loss_tracker.py:84 | Epoch[137/239] Step[99] GlobalStep[53529/NA]: det_loss_cls_0[0.5068] det_loss_box_0[0.7157] det_loss_cls_1[0.4989] det_loss_box_1[0.5401] det_loss_cls_2[0.4521] det_loss_box_2[0.4915] det_loss_cls_3[0.4296] det_loss_box_3[0.4841] det_loss_cls_4[0.3992] det_loss_box_4[0.4703] det_loss_cls_5[0.3804] det_loss_box_5[0.4706] det_loss_cls_dn_0[0.0964] det_loss_box_dn_0[0.3201] det_loss_cls_dn_1[0.0935] det_loss_box_dn_1[0.2671] det_loss_cls_dn_2[0.0917] det_loss_box_dn_2[0.2472] det_loss_cls_dn_3[0.0904] det_loss_box_dn_3[0.2437] det_loss_cls_dn_4[0.0896] det_loss_box_dn_4[0.2433] det_loss_cls_dn_5[0.0888] det_loss_box_dn_5[0.2394] loss_depth[0.0388] total_loss[7.9894]
Rank[0/8] 06/20/2025 08:10:45 INFO stats.py:314 | Epoch[137] Step[119] GlobalStep[53549] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 13:26:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:10:50 INFO loss_tracker.py:84 | Epoch[137/239] Step[124] GlobalStep[53554/NA]: det_loss_cls_0[0.5887] det_loss_box_0[0.7234] det_loss_cls_1[0.5758] det_loss_box_1[0.5653] det_loss_cls_2[0.5328] det_loss_box_2[0.5062] det_loss_cls_3[0.4841] det_loss_box_3[0.4932] det_loss_cls_4[0.4552] det_loss_box_4[0.4785] det_loss_cls_5[0.4385] det_loss_box_5[0.4812] det_loss_cls_dn_0[0.1010] det_loss_box_dn_0[0.2683] det_loss_cls_dn_1[0.0971] det_loss_box_dn_1[0.2209] det_loss_cls_dn_2[0.0967] det_loss_box_dn_2[0.2064] det_loss_cls_dn_3[0.0953] det_loss_box_dn_3[0.2020] det_loss_cls_dn_4[0.0945] det_loss_box_dn_4[0.2005] det_loss_cls_dn_5[0.0935] det_loss_box_dn_5[0.2000] loss_depth[0.0388] total_loss[8.2378]
Rank[0/8] 06/20/2025 08:11:10 INFO stats.py:314 | Epoch[137] Step[144] GlobalStep[53574] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 13:26:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:11:16 INFO loss_tracker.py:84 | Epoch[137/239] Step[149] GlobalStep[53579/NA]: det_loss_cls_0[0.6755] det_loss_box_0[0.8782] det_loss_cls_1[0.6646] det_loss_box_1[0.6966] det_loss_cls_2[0.6003] det_loss_box_2[0.6394] det_loss_cls_3[0.5744] det_loss_box_3[0.6124] det_loss_cls_4[0.5373] det_loss_box_4[0.6054] det_loss_cls_5[0.5235] det_loss_box_5[0.6071] det_loss_cls_dn_0[0.1019] det_loss_box_dn_0[0.2772] det_loss_cls_dn_1[0.0990] det_loss_box_dn_1[0.2375] det_loss_cls_dn_2[0.0984] det_loss_box_dn_2[0.2234] det_loss_cls_dn_3[0.0971] det_loss_box_dn_3[0.2208] det_loss_cls_dn_4[0.0960] det_loss_box_dn_4[0.2197] det_loss_cls_dn_5[0.0952] det_loss_box_dn_5[0.2183] loss_depth[0.0379] total_loss[9.6369]
Rank[0/8] 06/20/2025 08:11:37 INFO stats.py:314 | Epoch[137] Step[169] GlobalStep[53599] Training Speed: 8.72 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 13:25:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:11:42 INFO loss_tracker.py:84 | Epoch[137/239] Step[174] GlobalStep[53604/NA]: det_loss_cls_0[0.3932] det_loss_box_0[0.5209] det_loss_cls_1[0.3772] det_loss_box_1[0.4013] det_loss_cls_2[0.3436] det_loss_box_2[0.3591] det_loss_cls_3[0.3181] det_loss_box_3[0.3449] det_loss_cls_4[0.2883] det_loss_box_4[0.3460] det_loss_cls_5[0.2756] det_loss_box_5[0.3460] det_loss_cls_dn_0[0.0920] det_loss_box_dn_0[0.2931] det_loss_cls_dn_1[0.0874] det_loss_box_dn_1[0.2414] det_loss_cls_dn_2[0.0859] det_loss_box_dn_2[0.2228] det_loss_cls_dn_3[0.0845] det_loss_box_dn_3[0.2201] det_loss_cls_dn_4[0.0835] det_loss_box_dn_4[0.2186] det_loss_cls_dn_5[0.0825] det_loss_box_dn_5[0.2174] loss_depth[0.0364] total_loss[6.2798]
Rank[0/8] 06/20/2025 08:12:02 INFO stats.py:314 | Epoch[137] Step[194] GlobalStep[53624] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 13:24:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:12:07 INFO loss_tracker.py:84 | Epoch[137/239] Step[199] GlobalStep[53629/NA]: det_loss_cls_0[0.3515] det_loss_box_0[0.4181] det_loss_cls_1[0.3470] det_loss_box_1[0.3076] det_loss_cls_2[0.2952] det_loss_box_2[0.2851] det_loss_cls_3[0.2694] det_loss_box_3[0.2751] det_loss_cls_4[0.2520] det_loss_box_4[0.2696] det_loss_cls_5[0.2426] det_loss_box_5[0.2689] det_loss_cls_dn_0[0.0904] det_loss_box_dn_0[0.2694] det_loss_cls_dn_1[0.0875] det_loss_box_dn_1[0.2327] det_loss_cls_dn_2[0.0869] det_loss_box_dn_2[0.2159] det_loss_cls_dn_3[0.0860] det_loss_box_dn_3[0.2110] det_loss_cls_dn_4[0.0838] det_loss_box_dn_4[0.2091] det_loss_cls_dn_5[0.0831] det_loss_box_dn_5[0.2015] loss_depth[0.0348] total_loss[5.4742]
Rank[0/8] 06/20/2025 08:12:28 INFO stats.py:314 | Epoch[137] Step[219] GlobalStep[53649] Training Speed: 8.15 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 13:24:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:12:33 INFO loss_tracker.py:84 | Epoch[137/239] Step[224] GlobalStep[53654/NA]: det_loss_cls_0[0.4517] det_loss_box_0[0.6418] det_loss_cls_1[0.4413] det_loss_box_1[0.5052] det_loss_cls_2[0.4023] det_loss_box_2[0.4877] det_loss_cls_3[0.3698] det_loss_box_3[0.4741] det_loss_cls_4[0.3464] det_loss_box_4[0.4628] det_loss_cls_5[0.3348] det_loss_box_5[0.4621] det_loss_cls_dn_0[0.0931] det_loss_box_dn_0[0.3136] det_loss_cls_dn_1[0.0900] det_loss_box_dn_1[0.2763] det_loss_cls_dn_2[0.0892] det_loss_box_dn_2[0.2580] det_loss_cls_dn_3[0.0884] det_loss_box_dn_3[0.2561] det_loss_cls_dn_4[0.0873] det_loss_box_dn_4[0.2539] det_loss_cls_dn_5[0.0866] det_loss_box_dn_5[0.2528] loss_depth[0.0369] total_loss[7.5622]
Rank[0/8] 06/20/2025 08:12:54 INFO stats.py:314 | Epoch[137] Step[244] GlobalStep[53674] Training Speed: 8.06 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 13:23:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:12:59 INFO loss_tracker.py:84 | Epoch[137/239] Step[249] GlobalStep[53679/NA]: det_loss_cls_0[0.5536] det_loss_box_0[0.7551] det_loss_cls_1[0.5438] det_loss_box_1[0.5995] det_loss_cls_2[0.5032] det_loss_box_2[0.5370] det_loss_cls_3[0.4808] det_loss_box_3[0.5157] det_loss_cls_4[0.4440] det_loss_box_4[0.5175] det_loss_cls_5[0.4253] det_loss_box_5[0.5206] det_loss_cls_dn_0[0.0874] det_loss_box_dn_0[0.3312] det_loss_cls_dn_1[0.0830] det_loss_box_dn_1[0.2892] det_loss_cls_dn_2[0.0822] det_loss_box_dn_2[0.2720] det_loss_cls_dn_3[0.0809] det_loss_box_dn_3[0.2697] det_loss_cls_dn_4[0.0798] det_loss_box_dn_4[0.2694] det_loss_cls_dn_5[0.0793] det_loss_box_dn_5[0.2678] loss_depth[0.0382] total_loss[8.6262]
Rank[0/8] 06/20/2025 08:13:19 INFO stats.py:314 | Epoch[137] Step[269] GlobalStep[53699] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 13:23:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:13:24 INFO loss_tracker.py:84 | Epoch[137/239] Step[274] GlobalStep[53704/NA]: det_loss_cls_0[0.5115] det_loss_box_0[0.6548] det_loss_cls_1[0.5026] det_loss_box_1[0.5235] det_loss_cls_2[0.4517] det_loss_box_2[0.4751] det_loss_cls_3[0.4302] det_loss_box_3[0.4608] det_loss_cls_4[0.3991] det_loss_box_4[0.4554] det_loss_cls_5[0.3905] det_loss_box_5[0.4510] det_loss_cls_dn_0[0.0943] det_loss_box_dn_0[0.2609] det_loss_cls_dn_1[0.0913] det_loss_box_dn_1[0.2201] det_loss_cls_dn_2[0.0907] det_loss_box_dn_2[0.2048] det_loss_cls_dn_3[0.0898] det_loss_box_dn_3[0.2006] det_loss_cls_dn_4[0.0883] det_loss_box_dn_4[0.2000] det_loss_cls_dn_5[0.0870] det_loss_box_dn_5[0.1992] loss_depth[0.0376] total_loss[7.5707]
Rank[0/8] 06/20/2025 08:13:45 INFO stats.py:314 | Epoch[137] Step[294] GlobalStep[53724] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 13:22:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:13:50 INFO loss_tracker.py:84 | Epoch[137/239] Step[299] GlobalStep[53729/NA]: det_loss_cls_0[0.5274] det_loss_box_0[0.7786] det_loss_cls_1[0.5100] det_loss_box_1[0.6402] det_loss_cls_2[0.4615] det_loss_box_2[0.5826] det_loss_cls_3[0.4325] det_loss_box_3[0.5645] det_loss_cls_4[0.3968] det_loss_box_4[0.5702] det_loss_cls_5[0.3905] det_loss_box_5[0.5630] det_loss_cls_dn_0[0.0954] det_loss_box_dn_0[0.3184] det_loss_cls_dn_1[0.0919] det_loss_box_dn_1[0.2827] det_loss_cls_dn_2[0.0914] det_loss_box_dn_2[0.2702] det_loss_cls_dn_3[0.0913] det_loss_box_dn_3[0.2666] det_loss_cls_dn_4[0.0899] det_loss_box_dn_4[0.2657] det_loss_cls_dn_5[0.0895] det_loss_box_dn_5[0.2650] loss_depth[0.0396] total_loss[8.6751]
Rank[0/8] 06/20/2025 08:14:11 INFO stats.py:314 | Epoch[137] Step[319] GlobalStep[53749] Training Speed: 8.12 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 13:22:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:14:16 INFO loss_tracker.py:84 | Epoch[137/239] Step[324] GlobalStep[53754/NA]: det_loss_cls_0[0.3630] det_loss_box_0[0.4683] det_loss_cls_1[0.3465] det_loss_box_1[0.3675] det_loss_cls_2[0.2967] det_loss_box_2[0.3401] det_loss_cls_3[0.2594] det_loss_box_3[0.3361] det_loss_cls_4[0.2411] det_loss_box_4[0.3319] det_loss_cls_5[0.2260] det_loss_box_5[0.3352] det_loss_cls_dn_0[0.0817] det_loss_box_dn_0[0.2951] det_loss_cls_dn_1[0.0774] det_loss_box_dn_1[0.2546] det_loss_cls_dn_2[0.0769] det_loss_box_dn_2[0.2413] det_loss_cls_dn_3[0.0757] det_loss_box_dn_3[0.2381] det_loss_cls_dn_4[0.0748] det_loss_box_dn_4[0.2357] det_loss_cls_dn_5[0.0736] det_loss_box_dn_5[0.2338] loss_depth[0.0368] total_loss[5.9073]
Rank[0/8] 06/20/2025 08:14:36 INFO stats.py:314 | Epoch[137] Step[344] GlobalStep[53774] Training Speed: 8.22 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 13:21:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:14:42 INFO loss_tracker.py:84 | Epoch[137/239] Step[349] GlobalStep[53779/NA]: det_loss_cls_0[0.5469] det_loss_box_0[0.6858] det_loss_cls_1[0.5281] det_loss_box_1[0.5520] det_loss_cls_2[0.4845] det_loss_box_2[0.5057] det_loss_cls_3[0.4491] det_loss_box_3[0.4922] det_loss_cls_4[0.4253] det_loss_box_4[0.4882] det_loss_cls_5[0.4056] det_loss_box_5[0.4892] det_loss_cls_dn_0[0.0839] det_loss_box_dn_0[0.2547] det_loss_cls_dn_1[0.0800] det_loss_box_dn_1[0.2118] det_loss_cls_dn_2[0.0785] det_loss_box_dn_2[0.2011] det_loss_cls_dn_3[0.0776] det_loss_box_dn_3[0.1979] det_loss_cls_dn_4[0.0767] det_loss_box_dn_4[0.1967] det_loss_cls_dn_5[0.0757] det_loss_box_dn_5[0.1961] loss_depth[0.0366] total_loss[7.8200]
Rank[0/8] 06/20/2025 08:15:02 INFO stats.py:314 | Epoch[137] Step[369] GlobalStep[53799] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 13:21:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:15:07 INFO loss_tracker.py:84 | Epoch[137/239] Step[374] GlobalStep[53804/NA]: det_loss_cls_0[0.5282] det_loss_box_0[0.6935] det_loss_cls_1[0.5023] det_loss_box_1[0.5285] det_loss_cls_2[0.4750] det_loss_box_2[0.4762] det_loss_cls_3[0.4420] det_loss_box_3[0.4628] det_loss_cls_4[0.4154] det_loss_box_4[0.4611] det_loss_cls_5[0.3983] det_loss_box_5[0.4625] det_loss_cls_dn_0[0.0892] det_loss_box_dn_0[0.2575] det_loss_cls_dn_1[0.0846] det_loss_box_dn_1[0.2192] det_loss_cls_dn_2[0.0840] det_loss_box_dn_2[0.2048] det_loss_cls_dn_3[0.0830] det_loss_box_dn_3[0.2024] det_loss_cls_dn_4[0.0819] det_loss_box_dn_4[0.2017] det_loss_cls_dn_5[0.0808] det_loss_box_dn_5[0.2008] loss_depth[0.0362] total_loss[7.6718]
Rank[0/8] 06/20/2025 08:15:22 INFO stats.py:394 | Epoch[137] completed. Training Speed: 7.72 samples/sec across all devices.	Epoch Time: 403.91 sec.	Average Epoch Time: 403.91 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 13:20:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:15:29 INFO stats.py:314 | Epoch[138] Step[4] GlobalStep[53824] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 13:20:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:15:51 INFO loss_tracker.py:84 | Epoch[138/239] Step[24] GlobalStep[53844/NA]: det_loss_cls_0[0.4580] det_loss_box_0[0.6430] det_loss_cls_1[0.4517] det_loss_box_1[0.5003] det_loss_cls_2[0.4082] det_loss_box_2[0.4441] det_loss_cls_3[0.3807] det_loss_box_3[0.4255] det_loss_cls_4[0.3598] det_loss_box_4[0.4126] det_loss_cls_5[0.3445] det_loss_box_5[0.4099] det_loss_cls_dn_0[0.0996] det_loss_box_dn_0[0.3121] det_loss_cls_dn_1[0.0959] det_loss_box_dn_1[0.2638] det_loss_cls_dn_2[0.0955] det_loss_box_dn_2[0.2466] det_loss_cls_dn_3[0.0945] det_loss_box_dn_3[0.2433] det_loss_cls_dn_4[0.0932] det_loss_box_dn_4[0.2410] det_loss_cls_dn_5[0.0921] det_loss_box_dn_5[0.2397] loss_depth[0.0381] total_loss[7.3939]
Rank[0/8] 06/20/2025 08:15:56 INFO stats.py:314 | Epoch[138] Step[29] GlobalStep[53849] Training Speed: 8.22 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 13:19:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:16:16 INFO loss_tracker.py:84 | Epoch[138/239] Step[49] GlobalStep[53869/NA]: det_loss_cls_0[0.4517] det_loss_box_0[0.6058] det_loss_cls_1[0.4467] det_loss_box_1[0.4748] det_loss_cls_2[0.4071] det_loss_box_2[0.4154] det_loss_cls_3[0.3762] det_loss_box_3[0.3989] det_loss_cls_4[0.3622] det_loss_box_4[0.3914] det_loss_cls_5[0.3394] det_loss_box_5[0.3965] det_loss_cls_dn_0[0.0950] det_loss_box_dn_0[0.2516] det_loss_cls_dn_1[0.0910] det_loss_box_dn_1[0.2075] det_loss_cls_dn_2[0.0909] det_loss_box_dn_2[0.1922] det_loss_cls_dn_3[0.0899] det_loss_box_dn_3[0.1896] det_loss_cls_dn_4[0.0889] det_loss_box_dn_4[0.1881] det_loss_cls_dn_5[0.0886] det_loss_box_dn_5[0.1872] loss_depth[0.0361] total_loss[6.8625]
Rank[0/8] 06/20/2025 08:16:21 INFO stats.py:314 | Epoch[138] Step[54] GlobalStep[53874] Training Speed: 7.77 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 13:19:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:16:42 INFO loss_tracker.py:84 | Epoch[138/239] Step[74] GlobalStep[53894/NA]: det_loss_cls_0[0.4792] det_loss_box_0[0.5984] det_loss_cls_1[0.4707] det_loss_box_1[0.4784] det_loss_cls_2[0.4446] det_loss_box_2[0.4255] det_loss_cls_3[0.4114] det_loss_box_3[0.4148] det_loss_cls_4[0.3823] det_loss_box_4[0.4000] det_loss_cls_5[0.3688] det_loss_box_5[0.3967] det_loss_cls_dn_0[0.0969] det_loss_box_dn_0[0.3129] det_loss_cls_dn_1[0.0934] det_loss_box_dn_1[0.2596] det_loss_cls_dn_2[0.0937] det_loss_box_dn_2[0.2441] det_loss_cls_dn_3[0.0924] det_loss_box_dn_3[0.2394] det_loss_cls_dn_4[0.0914] det_loss_box_dn_4[0.2377] det_loss_cls_dn_5[0.0908] det_loss_box_dn_5[0.2343] loss_depth[0.0351] total_loss[7.3924]
Rank[0/8] 06/20/2025 08:16:47 INFO stats.py:314 | Epoch[138] Step[79] GlobalStep[53899] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 13:18:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:17:08 INFO loss_tracker.py:84 | Epoch[138/239] Step[99] GlobalStep[53919/NA]: det_loss_cls_0[0.5622] det_loss_box_0[0.8320] det_loss_cls_1[0.5520] det_loss_box_1[0.6594] det_loss_cls_2[0.5043] det_loss_box_2[0.6029] det_loss_cls_3[0.4762] det_loss_box_3[0.5815] det_loss_cls_4[0.4550] det_loss_box_4[0.5611] det_loss_cls_5[0.4411] det_loss_box_5[0.5545] det_loss_cls_dn_0[0.0972] det_loss_box_dn_0[0.3158] det_loss_cls_dn_1[0.0929] det_loss_box_dn_1[0.2715] det_loss_cls_dn_2[0.0921] det_loss_box_dn_2[0.2562] det_loss_cls_dn_3[0.0906] det_loss_box_dn_3[0.2507] det_loss_cls_dn_4[0.0895] det_loss_box_dn_4[0.2501] det_loss_cls_dn_5[0.0884] det_loss_box_dn_5[0.2485] loss_depth[0.0377] total_loss[8.9634]
Rank[0/8] 06/20/2025 08:17:13 INFO stats.py:314 | Epoch[138] Step[104] GlobalStep[53924] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 13:18:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:17:33 INFO loss_tracker.py:84 | Epoch[138/239] Step[124] GlobalStep[53944/NA]: det_loss_cls_0[0.5507] det_loss_box_0[0.7580] det_loss_cls_1[0.5399] det_loss_box_1[0.5962] det_loss_cls_2[0.4959] det_loss_box_2[0.5397] det_loss_cls_3[0.4674] det_loss_box_3[0.5246] det_loss_cls_4[0.4483] det_loss_box_4[0.5051] det_loss_cls_5[0.4319] det_loss_box_5[0.5097] det_loss_cls_dn_0[0.1003] det_loss_box_dn_0[0.2747] det_loss_cls_dn_1[0.0971] det_loss_box_dn_1[0.2355] det_loss_cls_dn_2[0.0960] det_loss_box_dn_2[0.2199] det_loss_cls_dn_3[0.0944] det_loss_box_dn_3[0.2175] det_loss_cls_dn_4[0.0935] det_loss_box_dn_4[0.2165] det_loss_cls_dn_5[0.0922] det_loss_box_dn_5[0.2155] loss_depth[0.0405] total_loss[8.3613]
Rank[0/8] 06/20/2025 08:17:38 INFO stats.py:314 | Epoch[138] Step[129] GlobalStep[53949] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 13:17:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:17:59 INFO loss_tracker.py:84 | Epoch[138/239] Step[149] GlobalStep[53969/NA]: det_loss_cls_0[0.5388] det_loss_box_0[0.7694] det_loss_cls_1[0.5204] det_loss_box_1[0.6035] det_loss_cls_2[0.4824] det_loss_box_2[0.5574] det_loss_cls_3[0.4662] det_loss_box_3[0.5300] det_loss_cls_4[0.4394] det_loss_box_4[0.5168] det_loss_cls_5[0.4299] det_loss_box_5[0.5147] det_loss_cls_dn_0[0.1019] det_loss_box_dn_0[0.3150] det_loss_cls_dn_1[0.0972] det_loss_box_dn_1[0.2726] det_loss_cls_dn_2[0.0968] det_loss_box_dn_2[0.2587] det_loss_cls_dn_3[0.0955] det_loss_box_dn_3[0.2550] det_loss_cls_dn_4[0.0951] det_loss_box_dn_4[0.2538] det_loss_cls_dn_5[0.0945] det_loss_box_dn_5[0.2519] loss_depth[0.0379] total_loss[8.5946]
Rank[0/8] 06/20/2025 08:18:04 INFO stats.py:314 | Epoch[138] Step[154] GlobalStep[53974] Training Speed: 7.73 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 13:17:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:18:25 INFO loss_tracker.py:84 | Epoch[138/239] Step[174] GlobalStep[53994/NA]: det_loss_cls_0[0.5693] det_loss_box_0[0.7057] det_loss_cls_1[0.5498] det_loss_box_1[0.5458] det_loss_cls_2[0.4945] det_loss_box_2[0.4916] det_loss_cls_3[0.4635] det_loss_box_3[0.4711] det_loss_cls_4[0.4306] det_loss_box_4[0.4572] det_loss_cls_5[0.4096] det_loss_box_5[0.4574] det_loss_cls_dn_0[0.0848] det_loss_box_dn_0[0.2425] det_loss_cls_dn_1[0.0810] det_loss_box_dn_1[0.2011] det_loss_cls_dn_2[0.0802] det_loss_box_dn_2[0.1879] det_loss_cls_dn_3[0.0782] det_loss_box_dn_3[0.1841] det_loss_cls_dn_4[0.0769] det_loss_box_dn_4[0.1825] det_loss_cls_dn_5[0.0759] det_loss_box_dn_5[0.1812] loss_depth[0.0349] total_loss[7.7371]
Rank[0/8] 06/20/2025 08:18:30 INFO stats.py:314 | Epoch[138] Step[179] GlobalStep[53999] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 13:16:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:18:51 INFO loss_tracker.py:84 | Epoch[138/239] Step[199] GlobalStep[54019/NA]: det_loss_cls_0[0.5354] det_loss_box_0[0.7435] det_loss_cls_1[0.5085] det_loss_box_1[0.5746] det_loss_cls_2[0.4710] det_loss_box_2[0.5064] det_loss_cls_3[0.4412] det_loss_box_3[0.4833] det_loss_cls_4[0.4157] det_loss_box_4[0.4715] det_loss_cls_5[0.3993] det_loss_box_5[0.4684] det_loss_cls_dn_0[0.0963] det_loss_box_dn_0[0.2501] det_loss_cls_dn_1[0.0928] det_loss_box_dn_1[0.2124] det_loss_cls_dn_2[0.0925] det_loss_box_dn_2[0.1969] det_loss_cls_dn_3[0.0912] det_loss_box_dn_3[0.1925] det_loss_cls_dn_4[0.0907] det_loss_box_dn_4[0.1914] det_loss_cls_dn_5[0.0897] det_loss_box_dn_5[0.1899] loss_depth[0.0392] total_loss[7.8443]
Rank[0/8] 06/20/2025 08:18:56 INFO stats.py:314 | Epoch[138] Step[204] GlobalStep[54024] Training Speed: 8.17 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 13:16:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:19:17 INFO loss_tracker.py:84 | Epoch[138/239] Step[224] GlobalStep[54044/NA]: det_loss_cls_0[0.4837] det_loss_box_0[0.6738] det_loss_cls_1[0.4740] det_loss_box_1[0.5335] det_loss_cls_2[0.4475] det_loss_box_2[0.4613] det_loss_cls_3[0.4199] det_loss_box_3[0.4482] det_loss_cls_4[0.3949] det_loss_box_4[0.4352] det_loss_cls_5[0.3844] det_loss_box_5[0.4333] det_loss_cls_dn_0[0.0938] det_loss_box_dn_0[0.2919] det_loss_cls_dn_1[0.0914] det_loss_box_dn_1[0.2459] det_loss_cls_dn_2[0.0903] det_loss_box_dn_2[0.2303] det_loss_cls_dn_3[0.0890] det_loss_box_dn_3[0.2265] det_loss_cls_dn_4[0.0884] det_loss_box_dn_4[0.2243] det_loss_cls_dn_5[0.0876] det_loss_box_dn_5[0.2226] loss_depth[0.0376] total_loss[7.6095]
Rank[0/8] 06/20/2025 08:19:22 INFO stats.py:314 | Epoch[138] Step[229] GlobalStep[54049] Training Speed: 8.71 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 13:15:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:19:43 INFO loss_tracker.py:84 | Epoch[138/239] Step[249] GlobalStep[54069/NA]: det_loss_cls_0[0.6445] det_loss_box_0[0.8174] det_loss_cls_1[0.6306] det_loss_box_1[0.6332] det_loss_cls_2[0.5946] det_loss_box_2[0.5613] det_loss_cls_3[0.5713] det_loss_box_3[0.5310] det_loss_cls_4[0.5433] det_loss_box_4[0.5126] det_loss_cls_5[0.5249] det_loss_box_5[0.5162] det_loss_cls_dn_0[0.1098] det_loss_box_dn_0[0.2624] det_loss_cls_dn_1[0.1059] det_loss_box_dn_1[0.2271] det_loss_cls_dn_2[0.1053] det_loss_box_dn_2[0.2112] det_loss_cls_dn_3[0.1047] det_loss_box_dn_3[0.2079] det_loss_cls_dn_4[0.1032] det_loss_box_dn_4[0.2056] det_loss_cls_dn_5[0.1030] det_loss_box_dn_5[0.2051] loss_depth[0.0398] total_loss[9.0718]
Rank[0/8] 06/20/2025 08:19:48 INFO stats.py:314 | Epoch[138] Step[254] GlobalStep[54074] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 13:14:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:20:09 INFO loss_tracker.py:84 | Epoch[138/239] Step[274] GlobalStep[54094/NA]: det_loss_cls_0[0.4540] det_loss_box_0[0.5314] det_loss_cls_1[0.4400] det_loss_box_1[0.4116] det_loss_cls_2[0.4016] det_loss_box_2[0.3736] det_loss_cls_3[0.3648] det_loss_box_3[0.3591] det_loss_cls_4[0.3450] det_loss_box_4[0.3460] det_loss_cls_5[0.3284] det_loss_box_5[0.3489] det_loss_cls_dn_0[0.0881] det_loss_box_dn_0[0.2872] det_loss_cls_dn_1[0.0849] det_loss_box_dn_1[0.2280] det_loss_cls_dn_2[0.0847] det_loss_box_dn_2[0.2081] det_loss_cls_dn_3[0.0846] det_loss_box_dn_3[0.2046] det_loss_cls_dn_4[0.0840] det_loss_box_dn_4[0.2047] det_loss_cls_dn_5[0.0827] det_loss_box_dn_5[0.2028] loss_depth[0.0406] total_loss[6.5892]
Rank[0/8] 06/20/2025 08:20:14 INFO stats.py:314 | Epoch[138] Step[279] GlobalStep[54099] Training Speed: 8.68 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 13:14:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:20:35 INFO loss_tracker.py:84 | Epoch[138/239] Step[299] GlobalStep[54119/NA]: det_loss_cls_0[0.5163] det_loss_box_0[0.7132] det_loss_cls_1[0.5009] det_loss_box_1[0.5696] det_loss_cls_2[0.4648] det_loss_box_2[0.5093] det_loss_cls_3[0.4273] det_loss_box_3[0.4987] det_loss_cls_4[0.4119] det_loss_box_4[0.4802] det_loss_cls_5[0.3963] det_loss_box_5[0.4794] det_loss_cls_dn_0[0.0914] det_loss_box_dn_0[0.3453] det_loss_cls_dn_1[0.0866] det_loss_box_dn_1[0.2957] det_loss_cls_dn_2[0.0866] det_loss_box_dn_2[0.2790] det_loss_cls_dn_3[0.0853] det_loss_box_dn_3[0.2743] det_loss_cls_dn_4[0.0845] det_loss_box_dn_4[0.2717] det_loss_cls_dn_5[0.0833] det_loss_box_dn_5[0.2709] loss_depth[0.0406] total_loss[8.2632]
Rank[0/8] 06/20/2025 08:20:40 INFO stats.py:314 | Epoch[138] Step[304] GlobalStep[54124] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 13:13:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:21:00 INFO loss_tracker.py:84 | Epoch[138/239] Step[324] GlobalStep[54144/NA]: det_loss_cls_0[0.4653] det_loss_box_0[0.5226] det_loss_cls_1[0.4547] det_loss_box_1[0.3928] det_loss_cls_2[0.4132] det_loss_box_2[0.3513] det_loss_cls_3[0.3728] det_loss_box_3[0.3428] det_loss_cls_4[0.3457] det_loss_box_4[0.3352] det_loss_cls_5[0.3346] det_loss_box_5[0.3351] det_loss_cls_dn_0[0.0974] det_loss_box_dn_0[0.2453] det_loss_cls_dn_1[0.0941] det_loss_box_dn_1[0.1929] det_loss_cls_dn_2[0.0931] det_loss_box_dn_2[0.1755] det_loss_cls_dn_3[0.0925] det_loss_box_dn_3[0.1703] det_loss_cls_dn_4[0.0914] det_loss_box_dn_4[0.1682] det_loss_cls_dn_5[0.0905] det_loss_box_dn_5[0.1677] loss_depth[0.0340] total_loss[6.3790]
Rank[0/8] 06/20/2025 08:21:05 INFO stats.py:314 | Epoch[138] Step[329] GlobalStep[54149] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 13:13:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:21:26 INFO loss_tracker.py:84 | Epoch[138/239] Step[349] GlobalStep[54169/NA]: det_loss_cls_0[0.5296] det_loss_box_0[0.8211] det_loss_cls_1[0.5137] det_loss_box_1[0.6291] det_loss_cls_2[0.4794] det_loss_box_2[0.5736] det_loss_cls_3[0.4527] det_loss_box_3[0.5522] det_loss_cls_4[0.4272] det_loss_box_4[0.5357] det_loss_cls_5[0.3978] det_loss_box_5[0.5480] det_loss_cls_dn_0[0.0946] det_loss_box_dn_0[0.3319] det_loss_cls_dn_1[0.0911] det_loss_box_dn_1[0.2867] det_loss_cls_dn_2[0.0904] det_loss_box_dn_2[0.2713] det_loss_cls_dn_3[0.0897] det_loss_box_dn_3[0.2691] det_loss_cls_dn_4[0.0880] det_loss_box_dn_4[0.2668] det_loss_cls_dn_5[0.0868] det_loss_box_dn_5[0.2668] loss_depth[0.0375] total_loss[8.7310]
Rank[0/8] 06/20/2025 08:21:31 INFO stats.py:314 | Epoch[138] Step[354] GlobalStep[54174] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 13:12:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:21:52 INFO loss_tracker.py:84 | Epoch[138/239] Step[374] GlobalStep[54194/NA]: det_loss_cls_0[0.5261] det_loss_box_0[0.6908] det_loss_cls_1[0.5228] det_loss_box_1[0.5380] det_loss_cls_2[0.4815] det_loss_box_2[0.4674] det_loss_cls_3[0.4521] det_loss_box_3[0.4547] det_loss_cls_4[0.4180] det_loss_box_4[0.4466] det_loss_cls_5[0.4028] det_loss_box_5[0.4476] det_loss_cls_dn_0[0.0935] det_loss_box_dn_0[0.2745] det_loss_cls_dn_1[0.0903] det_loss_box_dn_1[0.2217] det_loss_cls_dn_2[0.0901] det_loss_box_dn_2[0.2060] det_loss_cls_dn_3[0.0895] det_loss_box_dn_3[0.2022] det_loss_cls_dn_4[0.0886] det_loss_box_dn_4[0.2003] det_loss_cls_dn_5[0.0880] det_loss_box_dn_5[0.1931] loss_depth[0.0366] total_loss[7.7230]
Rank[0/8] 06/20/2025 08:21:57 INFO stats.py:314 | Epoch[138] Step[379] GlobalStep[54199] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 13:12:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:22:07 INFO stats.py:394 | Epoch[138] completed. Training Speed: 7.71 samples/sec across all devices.	Epoch Time: 404.70 sec.	Average Epoch Time: 404.70 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 13:11:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:22:26 INFO stats.py:314 | Epoch[139] Step[14] GlobalStep[54224] Training Speed: 8.08 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 13:11:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:22:37 INFO loss_tracker.py:84 | Epoch[139/239] Step[24] GlobalStep[54234/NA]: det_loss_cls_0[0.5077] det_loss_box_0[0.6587] det_loss_cls_1[0.4999] det_loss_box_1[0.5112] det_loss_cls_2[0.4520] det_loss_box_2[0.4663] det_loss_cls_3[0.4166] det_loss_box_3[0.4522] det_loss_cls_4[0.3943] det_loss_box_4[0.4418] det_loss_cls_5[0.3779] det_loss_box_5[0.4423] det_loss_cls_dn_0[0.0981] det_loss_box_dn_0[0.2769] det_loss_cls_dn_1[0.0949] det_loss_box_dn_1[0.2383] det_loss_cls_dn_2[0.0938] det_loss_box_dn_2[0.2247] det_loss_cls_dn_3[0.0928] det_loss_box_dn_3[0.2219] det_loss_cls_dn_4[0.0917] det_loss_box_dn_4[0.2204] det_loss_cls_dn_5[0.0909] det_loss_box_dn_5[0.2193] loss_depth[0.0367] total_loss[7.6214]
Rank[0/8] 06/20/2025 08:22:52 INFO stats.py:314 | Epoch[139] Step[39] GlobalStep[54249] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 13:11:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:23:03 INFO loss_tracker.py:84 | Epoch[139/239] Step[49] GlobalStep[54259/NA]: det_loss_cls_0[0.5600] det_loss_box_0[0.8042] det_loss_cls_1[0.5489] det_loss_box_1[0.6043] det_loss_cls_2[0.5035] det_loss_box_2[0.5505] det_loss_cls_3[0.4651] det_loss_box_3[0.5287] det_loss_cls_4[0.4369] det_loss_box_4[0.5181] det_loss_cls_5[0.4243] det_loss_box_5[0.5146] det_loss_cls_dn_0[0.0922] det_loss_box_dn_0[0.2877] det_loss_cls_dn_1[0.0892] det_loss_box_dn_1[0.2457] det_loss_cls_dn_2[0.0896] det_loss_box_dn_2[0.2284] det_loss_cls_dn_3[0.0883] det_loss_box_dn_3[0.2246] det_loss_cls_dn_4[0.0876] det_loss_box_dn_4[0.2244] det_loss_cls_dn_5[0.0858] det_loss_box_dn_5[0.2233] loss_depth[0.0397] total_loss[8.4653]
Rank[0/8] 06/20/2025 08:23:18 INFO stats.py:314 | Epoch[139] Step[64] GlobalStep[54274] Training Speed: 7.81 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 13:10:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:23:29 INFO loss_tracker.py:84 | Epoch[139/239] Step[74] GlobalStep[54284/NA]: det_loss_cls_0[0.4795] det_loss_box_0[0.5988] det_loss_cls_1[0.4635] det_loss_box_1[0.4314] det_loss_cls_2[0.4084] det_loss_box_2[0.3987] det_loss_cls_3[0.3754] det_loss_box_3[0.3856] det_loss_cls_4[0.3551] det_loss_box_4[0.3783] det_loss_cls_5[0.3480] det_loss_box_5[0.3760] det_loss_cls_dn_0[0.0929] det_loss_box_dn_0[0.2643] det_loss_cls_dn_1[0.0893] det_loss_box_dn_1[0.2209] det_loss_cls_dn_2[0.0883] det_loss_box_dn_2[0.2032] det_loss_cls_dn_3[0.0873] det_loss_box_dn_3[0.1998] det_loss_cls_dn_4[0.0860] det_loss_box_dn_4[0.1984] det_loss_cls_dn_5[0.0852] det_loss_box_dn_5[0.1976] loss_depth[0.0369] total_loss[6.8489]
Rank[0/8] 06/20/2025 08:23:44 INFO stats.py:314 | Epoch[139] Step[89] GlobalStep[54299] Training Speed: 6.45 samples/sec across all devices.	Average Step Time: 1.24 sec.	Estimated Remaining Time: 13:09:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:23:55 INFO loss_tracker.py:84 | Epoch[139/239] Step[99] GlobalStep[54309/NA]: det_loss_cls_0[0.4357] det_loss_box_0[0.5689] det_loss_cls_1[0.4273] det_loss_box_1[0.4492] det_loss_cls_2[0.3886] det_loss_box_2[0.4096] det_loss_cls_3[0.3667] det_loss_box_3[0.3949] det_loss_cls_4[0.3469] det_loss_box_4[0.3860] det_loss_cls_5[0.3390] det_loss_box_5[0.3855] det_loss_cls_dn_0[0.1012] det_loss_box_dn_0[0.3338] det_loss_cls_dn_1[0.0972] det_loss_box_dn_1[0.2898] det_loss_cls_dn_2[0.0959] det_loss_box_dn_2[0.2694] det_loss_cls_dn_3[0.0953] det_loss_box_dn_3[0.2648] det_loss_cls_dn_4[0.0937] det_loss_box_dn_4[0.2636] det_loss_cls_dn_5[0.0931] det_loss_box_dn_5[0.2604] loss_depth[0.0370] total_loss[7.1937]
Rank[0/8] 06/20/2025 08:24:10 INFO stats.py:314 | Epoch[139] Step[114] GlobalStep[54324] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 13:09:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:24:20 INFO loss_tracker.py:84 | Epoch[139/239] Step[124] GlobalStep[54334/NA]: det_loss_cls_0[0.5333] det_loss_box_0[0.6368] det_loss_cls_1[0.5264] det_loss_box_1[0.4828] det_loss_cls_2[0.4734] det_loss_box_2[0.4410] det_loss_cls_3[0.4524] det_loss_box_3[0.4178] det_loss_cls_4[0.4185] det_loss_box_4[0.4119] det_loss_cls_5[0.4016] det_loss_box_5[0.4190] det_loss_cls_dn_0[0.0953] det_loss_box_dn_0[0.2716] det_loss_cls_dn_1[0.0926] det_loss_box_dn_1[0.2328] det_loss_cls_dn_2[0.0924] det_loss_box_dn_2[0.2146] det_loss_cls_dn_3[0.0925] det_loss_box_dn_3[0.2110] det_loss_cls_dn_4[0.0915] det_loss_box_dn_4[0.2086] det_loss_cls_dn_5[0.0908] det_loss_box_dn_5[0.2073] loss_depth[0.0373] total_loss[7.5532]
Rank[0/8] 06/20/2025 08:24:36 INFO stats.py:314 | Epoch[139] Step[139] GlobalStep[54349] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 13:08:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:24:46 INFO loss_tracker.py:84 | Epoch[139/239] Step[149] GlobalStep[54359/NA]: det_loss_cls_0[0.5041] det_loss_box_0[0.6692] det_loss_cls_1[0.5022] det_loss_box_1[0.5255] det_loss_cls_2[0.4602] det_loss_box_2[0.4716] det_loss_cls_3[0.4325] det_loss_box_3[0.4550] det_loss_cls_4[0.3969] det_loss_box_4[0.4482] det_loss_cls_5[0.3882] det_loss_box_5[0.4475] det_loss_cls_dn_0[0.1016] det_loss_box_dn_0[0.2776] det_loss_cls_dn_1[0.0974] det_loss_box_dn_1[0.2355] det_loss_cls_dn_2[0.0969] det_loss_box_dn_2[0.2209] det_loss_cls_dn_3[0.0961] det_loss_box_dn_3[0.2170] det_loss_cls_dn_4[0.0953] det_loss_box_dn_4[0.2153] det_loss_cls_dn_5[0.0942] det_loss_box_dn_5[0.2141] loss_depth[0.0360] total_loss[7.6990]
Rank[0/8] 06/20/2025 08:25:01 INFO stats.py:314 | Epoch[139] Step[164] GlobalStep[54374] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 13:08:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:25:12 INFO loss_tracker.py:84 | Epoch[139/239] Step[174] GlobalStep[54384/NA]: det_loss_cls_0[0.4751] det_loss_box_0[0.6450] det_loss_cls_1[0.4664] det_loss_box_1[0.5184] det_loss_cls_2[0.4272] det_loss_box_2[0.4514] det_loss_cls_3[0.3979] det_loss_box_3[0.4363] det_loss_cls_4[0.3744] det_loss_box_4[0.4242] det_loss_cls_5[0.3590] det_loss_box_5[0.4247] det_loss_cls_dn_0[0.0899] det_loss_box_dn_0[0.2871] det_loss_cls_dn_1[0.0863] det_loss_box_dn_1[0.2447] det_loss_cls_dn_2[0.0855] det_loss_box_dn_2[0.2293] det_loss_cls_dn_3[0.0846] det_loss_box_dn_3[0.2266] det_loss_cls_dn_4[0.0844] det_loss_box_dn_4[0.2245] det_loss_cls_dn_5[0.0832] det_loss_box_dn_5[0.2224] loss_depth[0.0343] total_loss[7.3827]
Rank[0/8] 06/20/2025 08:25:27 INFO stats.py:314 | Epoch[139] Step[189] GlobalStep[54399] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 13:07:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:25:38 INFO loss_tracker.py:84 | Epoch[139/239] Step[199] GlobalStep[54409/NA]: det_loss_cls_0[0.3906] det_loss_box_0[0.5450] det_loss_cls_1[0.3787] det_loss_box_1[0.4590] det_loss_cls_2[0.3461] det_loss_box_2[0.4238] det_loss_cls_3[0.3245] det_loss_box_3[0.4118] det_loss_cls_4[0.3114] det_loss_box_4[0.4077] det_loss_cls_5[0.2995] det_loss_box_5[0.4072] det_loss_cls_dn_0[0.0946] det_loss_box_dn_0[0.4071] det_loss_cls_dn_1[0.0917] det_loss_box_dn_1[0.3463] det_loss_cls_dn_2[0.0920] det_loss_box_dn_2[0.3189] det_loss_cls_dn_3[0.0905] det_loss_box_dn_3[0.3132] det_loss_cls_dn_4[0.0903] det_loss_box_dn_4[0.3114] det_loss_cls_dn_5[0.0893] det_loss_box_dn_5[0.3099] loss_depth[0.0358] total_loss[7.2961]
Rank[0/8] 06/20/2025 08:25:53 INFO stats.py:314 | Epoch[139] Step[214] GlobalStep[54424] Training Speed: 8.64 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 13:07:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:26:04 INFO loss_tracker.py:84 | Epoch[139/239] Step[224] GlobalStep[54434/NA]: det_loss_cls_0[0.6224] det_loss_box_0[0.8513] det_loss_cls_1[0.6022] det_loss_box_1[0.6533] det_loss_cls_2[0.5566] det_loss_box_2[0.5683] det_loss_cls_3[0.5216] det_loss_box_3[0.5448] det_loss_cls_4[0.4896] det_loss_box_4[0.5347] det_loss_cls_5[0.4819] det_loss_box_5[0.5252] det_loss_cls_dn_0[0.0982] det_loss_box_dn_0[0.2578] det_loss_cls_dn_1[0.0936] det_loss_box_dn_1[0.2170] det_loss_cls_dn_2[0.0933] det_loss_box_dn_2[0.2016] det_loss_cls_dn_3[0.0920] det_loss_box_dn_3[0.1994] det_loss_cls_dn_4[0.0913] det_loss_box_dn_4[0.1968] det_loss_cls_dn_5[0.0902] det_loss_box_dn_5[0.1963] loss_depth[0.0382] total_loss[8.8176]
Rank[0/8] 06/20/2025 08:26:19 INFO stats.py:314 | Epoch[139] Step[239] GlobalStep[54449] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 13:06:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:26:30 INFO loss_tracker.py:84 | Epoch[139/239] Step[249] GlobalStep[54459/NA]: det_loss_cls_0[0.5012] det_loss_box_0[0.6351] det_loss_cls_1[0.4805] det_loss_box_1[0.4936] det_loss_cls_2[0.4368] det_loss_box_2[0.4489] det_loss_cls_3[0.4066] det_loss_box_3[0.4270] det_loss_cls_4[0.3789] det_loss_box_4[0.4176] det_loss_cls_5[0.3673] det_loss_box_5[0.4162] det_loss_cls_dn_0[0.0903] det_loss_box_dn_0[0.2623] det_loss_cls_dn_1[0.0863] det_loss_box_dn_1[0.2179] det_loss_cls_dn_2[0.0857] det_loss_box_dn_2[0.2035] det_loss_cls_dn_3[0.0843] det_loss_box_dn_3[0.1995] det_loss_cls_dn_4[0.0831] det_loss_box_dn_4[0.1967] det_loss_cls_dn_5[0.0813] det_loss_box_dn_5[0.1957] loss_depth[0.0371] total_loss[7.2334]
Rank[0/8] 06/20/2025 08:26:45 INFO stats.py:314 | Epoch[139] Step[264] GlobalStep[54474] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 13:06:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:26:55 INFO loss_tracker.py:84 | Epoch[139/239] Step[274] GlobalStep[54484/NA]: det_loss_cls_0[0.5040] det_loss_box_0[0.6631] det_loss_cls_1[0.4965] det_loss_box_1[0.4885] det_loss_cls_2[0.4462] det_loss_box_2[0.4480] det_loss_cls_3[0.4136] det_loss_box_3[0.4286] det_loss_cls_4[0.3904] det_loss_box_4[0.4140] det_loss_cls_5[0.3773] det_loss_box_5[0.4162] det_loss_cls_dn_0[0.0993] det_loss_box_dn_0[0.3042] det_loss_cls_dn_1[0.0963] det_loss_box_dn_1[0.2608] det_loss_cls_dn_2[0.0958] det_loss_box_dn_2[0.2460] det_loss_cls_dn_3[0.0948] det_loss_box_dn_3[0.2421] det_loss_cls_dn_4[0.0939] det_loss_box_dn_4[0.2405] det_loss_cls_dn_5[0.0929] det_loss_box_dn_5[0.2388] loss_depth[0.0384] total_loss[7.6302]
Rank[0/8] 06/20/2025 08:27:11 INFO stats.py:314 | Epoch[139] Step[289] GlobalStep[54499] Training Speed: 8.07 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 13:05:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:27:21 INFO loss_tracker.py:84 | Epoch[139/239] Step[299] GlobalStep[54509/NA]: det_loss_cls_0[0.5227] det_loss_box_0[0.6479] det_loss_cls_1[0.5122] det_loss_box_1[0.5001] det_loss_cls_2[0.4699] det_loss_box_2[0.4610] det_loss_cls_3[0.4374] det_loss_box_3[0.4506] det_loss_cls_4[0.4256] det_loss_box_4[0.4279] det_loss_cls_5[0.4189] det_loss_box_5[0.4260] det_loss_cls_dn_0[0.1021] det_loss_box_dn_0[0.2965] det_loss_cls_dn_1[0.0989] det_loss_box_dn_1[0.2414] det_loss_cls_dn_2[0.0974] det_loss_box_dn_2[0.2209] det_loss_cls_dn_3[0.0961] det_loss_box_dn_3[0.2189] det_loss_cls_dn_4[0.0950] det_loss_box_dn_4[0.2164] det_loss_cls_dn_5[0.0945] det_loss_box_dn_5[0.2142] loss_depth[0.0380] total_loss[7.7308]
Rank[0/8] 06/20/2025 08:27:36 INFO stats.py:314 | Epoch[139] Step[314] GlobalStep[54524] Training Speed: 8.70 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 13:04:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:27:47 INFO loss_tracker.py:84 | Epoch[139/239] Step[324] GlobalStep[54534/NA]: det_loss_cls_0[0.5291] det_loss_box_0[0.6766] det_loss_cls_1[0.5245] det_loss_box_1[0.5192] det_loss_cls_2[0.4826] det_loss_box_2[0.4739] det_loss_cls_3[0.4497] det_loss_box_3[0.4659] det_loss_cls_4[0.4229] det_loss_box_4[0.4505] det_loss_cls_5[0.4124] det_loss_box_5[0.4469] det_loss_cls_dn_0[0.0966] det_loss_box_dn_0[0.2583] det_loss_cls_dn_1[0.0938] det_loss_box_dn_1[0.2128] det_loss_cls_dn_2[0.0928] det_loss_box_dn_2[0.1979] det_loss_cls_dn_3[0.0921] det_loss_box_dn_3[0.1952] det_loss_cls_dn_4[0.0912] det_loss_box_dn_4[0.1943] det_loss_cls_dn_5[0.0904] det_loss_box_dn_5[0.1935] loss_depth[0.0340] total_loss[7.6971]
Rank[0/8] 06/20/2025 08:28:02 INFO stats.py:314 | Epoch[139] Step[339] GlobalStep[54549] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 13:04:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:28:13 INFO loss_tracker.py:84 | Epoch[139/239] Step[349] GlobalStep[54559/NA]: det_loss_cls_0[0.4895] det_loss_box_0[0.7110] det_loss_cls_1[0.4761] det_loss_box_1[0.5573] det_loss_cls_2[0.4364] det_loss_box_2[0.5099] det_loss_cls_3[0.4145] det_loss_box_3[0.4886] det_loss_cls_4[0.3878] det_loss_box_4[0.4809] det_loss_cls_5[0.3684] det_loss_box_5[0.4786] det_loss_cls_dn_0[0.1020] det_loss_box_dn_0[0.3406] det_loss_cls_dn_1[0.0957] det_loss_box_dn_1[0.2893] det_loss_cls_dn_2[0.0950] det_loss_box_dn_2[0.2689] det_loss_cls_dn_3[0.0939] det_loss_box_dn_3[0.2640] det_loss_cls_dn_4[0.0928] det_loss_box_dn_4[0.2630] det_loss_cls_dn_5[0.0919] det_loss_box_dn_5[0.2614] loss_depth[0.0393] total_loss[8.0967]
Rank[0/8] 06/20/2025 08:28:28 INFO stats.py:314 | Epoch[139] Step[364] GlobalStep[54574] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 13:03:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:28:38 INFO loss_tracker.py:84 | Epoch[139/239] Step[374] GlobalStep[54584/NA]: det_loss_cls_0[0.4381] det_loss_box_0[0.6760] det_loss_cls_1[0.4392] det_loss_box_1[0.5179] det_loss_cls_2[0.3977] det_loss_box_2[0.4899] det_loss_cls_3[0.3634] det_loss_box_3[0.4658] det_loss_cls_4[0.3363] det_loss_box_4[0.4778] det_loss_cls_5[0.3280] det_loss_box_5[0.4731] det_loss_cls_dn_0[0.0938] det_loss_box_dn_0[0.3642] det_loss_cls_dn_1[0.0897] det_loss_box_dn_1[0.3173] det_loss_cls_dn_2[0.0896] det_loss_box_dn_2[0.2967] det_loss_cls_dn_3[0.0881] det_loss_box_dn_3[0.2953] det_loss_cls_dn_4[0.0876] det_loss_box_dn_4[0.2960] det_loss_cls_dn_5[0.0867] det_loss_box_dn_5[0.2934] loss_depth[0.0365] total_loss[7.8381]
Rank[0/8] 06/20/2025 08:28:54 INFO stats.py:314 | Epoch[139] Step[389] GlobalStep[54599] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 13:03:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:28:54 INFO trainer.py:268 | 
==================================================BEGIN EVAL==================================================
Rank[0/8] 06/20/2025 08:29:46 INFO trainer.py:275 | eval: 10
Rank[0/8] 06/20/2025 08:29:58 INFO trainer.py:275 | eval: 20
Rank[0/8] 06/20/2025 08:30:05 INFO trainer.py:275 | eval: 30
Rank[0/8] 06/20/2025 08:30:21 INFO trainer.py:275 | eval: 40
Rank[0/8] 06/20/2025 08:30:30 INFO trainer.py:275 | eval: 50
Rank[0/8] 06/20/2025 08:30:38 INFO trainer.py:275 | eval: 60
Rank[0/8] 06/20/2025 08:30:49 INFO trainer.py:275 | eval: 70
Rank[0/8] 06/20/2025 08:30:58 INFO trainer.py:275 | eval: 80
Rank[0/8] 06/20/2025 08:31:05 INFO trainer.py:275 | eval: 90
Rank[0/8] 06/20/2025 08:31:12 INFO trainer.py:275 | eval: 100
Rank[0/8] 06/20/2025 08:31:18 INFO metrics.py:428 | number of results: 824
Rank[0/8] 06/20/2025 08:35:22 INFO metrics.py:332 | eval results of overall:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| sink                | 0.6680  | 0.8230  | 0.1359  | 0.3704  |
| toilet              | 0.8446  | 0.8686  | 0.6508  | 0.7299  |
| bathtub             | 0.7973  | 0.8676  | 0.7163  | 0.7941  |
| picture             | 0.2041  | 0.4084  | 0.0214  | 0.0873  |
| curtain             | 0.4931  | 0.7552  | 0.0785  | 0.3403  |
| toilet paper        | 0.3731  | 0.5118  | 0.0761  | 0.1969  |
| bin                 | 0.6119  | 0.8212  | 0.3169  | 0.5196  |
| towel               | 0.3325  | 0.6645  | 0.0661  | 0.2452  |
| door                | 0.3251  | 0.6675  | 0.0423  | 0.2225  |
| stopcock            | 0.1489  | 0.3448  | 0.0055  | 0.0690  |
| doorframe           | 0.2866  | 0.6953  | 0.0539  | 0.3275  |
| rail                | 0.0134  | 0.2969  | 0.0015  | 0.0469  |
| vanity              | 0.4950  | 0.9111  | 0.4445  | 0.8000  |
| mirror              | 0.1124  | 0.5435  | 0.0082  | 0.1141  |
| bottle              | 0.0470  | 0.3231  | 0.0018  | 0.0724  |
| switch              | 0.0025  | 0.1316  | 0.0000  | 0.0000  |
| bar                 | 0.0270  | 0.1818  | 0.0002  | 0.0303  |
| ledge               | 0.0047  | 0.5000  | 0.0025  | 0.5000  |
| hair dryer          | 0.0714  | 0.1429  | 0.0000  | 0.0000  |
| socket              | 0.0137  | 0.1991  | 0.0000  | 0.0044  |
| cup                 | 0.1174  | 0.4576  | 0.0124  | 0.0763  |
| tissue box          | 0.1276  | 0.4848  | 0.0057  | 0.1212  |
| rod                 | 0.0005  | 0.0250  | 0.0000  | 0.0000  |
| shower              | 0.0536  | 0.2667  | 0.0044  | 0.1000  |
| light               | 0.0362  | 0.2500  | 0.0010  | 0.0363  |
| paper               | 0.0261  | 0.3284  | 0.0006  | 0.0621  |
| bag                 | 0.2388  | 0.6919  | 0.0416  | 0.2324  |
| backpack            | 0.5930  | 0.8065  | 0.2334  | 0.3710  |
| basket              | 0.2705  | 0.5826  | 0.0943  | 0.2783  |
| box                 | 0.1556  | 0.4705  | 0.0269  | 0.1429  |
| tissue              | 0.0025  | 0.1538  | 0.0021  | 0.0769  |
| decoration          | 0.0334  | 0.3714  | 0.0008  | 0.0500  |
| cabinet             | 0.2659  | 0.7493  | 0.1447  | 0.5111  |
| grab bar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dish           | 0.4417  | 0.7333  | 0.0889  | 0.1333  |
| mat                 | 0.0064  | 0.2432  | 0.0000  | 0.0270  |
| water cooler        | 0.6111  | 1.0000  | 0.5000  | 0.5000  |
| plunger             | 0.8103  | 1.0000  | 0.1888  | 0.8000  |
| soap                | 0.0233  | 0.6667  | 0.0013  | 0.3333  |
| faucet              | 0.0050  | 0.1915  | 0.0000  | 0.0213  |
| vent                | 0.0654  | 0.2917  | 0.0208  | 0.0417  |
| blanket             | 0.1503  | 0.7400  | 0.0147  | 0.2600  |
| couch               | 0.4001  | 0.8760  | 0.3078  | 0.7769  |
| window              | 0.1695  | 0.5494  | 0.0224  | 0.1692  |
| frame               | 0.1187  | 0.4490  | 0.0001  | 0.0612  |
| bed                 | 0.7652  | 0.9290  | 0.5766  | 0.8698  |
| lamp                | 0.3455  | 0.6090  | 0.0594  | 0.2119  |
| bucket              | 0.3425  | 0.5897  | 0.0729  | 0.2308  |
| purse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plant               | 0.3009  | 0.5958  | 0.1002  | 0.2725  |
| pillow              | 0.3483  | 0.6206  | 0.0464  | 0.1994  |
| table               | 0.4630  | 0.8464  | 0.3502  | 0.7147  |
| stool               | 0.2553  | 0.7143  | 0.1521  | 0.4622  |
| telephone           | 0.3706  | 0.5833  | 0.1165  | 0.2667  |
| computer            | 0.2787  | 0.6066  | 0.0077  | 0.1148  |
| keyboard            | 0.0406  | 0.1628  | 0.0000  | 0.0000  |
| blackboard          | 0.5199  | 0.7561  | 0.2208  | 0.3780  |
| monitor             | 0.5483  | 0.6848  | 0.1143  | 0.2989  |
| chair               | 0.5641  | 0.7363  | 0.4102  | 0.5769  |
| radiator            | 0.6225  | 0.7333  | 0.2273  | 0.3556  |
| mouse               | 0.0036  | 0.0811  | 0.0000  | 0.0000  |
| plug                | 0.0075  | 0.0526  | 0.0000  | 0.0000  |
| desk                | 0.4972  | 0.9101  | 0.3287  | 0.7303  |
| book                | 0.0363  | 0.2961  | 0.0012  | 0.0573  |
| stand               | 0.5079  | 0.8415  | 0.3697  | 0.6768  |
| shelf               | 0.2735  | 0.7050  | 0.1130  | 0.3921  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| notebook            | 0.0003  | 0.0769  | 0.0000  | 0.0000  |
| conducting wire     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jacket              | 0.5721  | 0.7500  | 0.1182  | 0.3750  |
| laptop              | 0.5386  | 0.6957  | 0.1080  | 0.3043  |
| remote control      | 0.0166  | 0.2963  | 0.0012  | 0.0741  |
| clothes             | 0.1754  | 0.5637  | 0.0253  | 0.1688  |
| fan                 | 0.5975  | 0.7969  | 0.2073  | 0.4531  |
| blinds              | 0.1419  | 0.4600  | 0.0061  | 0.2600  |
| tablet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pipe                | 0.0084  | 0.1429  | 0.0000  | 0.0000  |
| windowsill          | 0.0931  | 0.5217  | 0.0004  | 0.0435  |
| dresser             | 0.5599  | 0.9259  | 0.4316  | 0.7778  |
| paper cutter        | 0.7190  | 0.8333  | 0.1667  | 0.1667  |
| wardrobe            | 0.3968  | 0.7590  | 0.2010  | 0.5663  |
| cushion             | 0.1567  | 0.6275  | 0.0063  | 0.1373  |
| calendar            | 0.0002  | 0.1667  | 0.0000  | 0.0000  |
| toy                 | 0.1679  | 0.4242  | 0.0158  | 0.0909  |
| clock               | 0.3985  | 0.6596  | 0.0060  | 0.0851  |
| ottoman             | 0.0974  | 0.5833  | 0.0879  | 0.4722  |
| poster              | 0.0222  | 0.1364  | 0.0000  | 0.0000  |
| rack                | 0.0703  | 0.5000  | 0.0345  | 0.3462  |
| tv                  | 0.5761  | 0.7426  | 0.1411  | 0.3366  |
| case                | 0.0842  | 0.4237  | 0.0174  | 0.1186  |
| plate               | 0.0440  | 0.3654  | 0.0021  | 0.0577  |
| sign                | 0.0624  | 0.3667  | 0.0028  | 0.0333  |
| container           | 0.0069  | 0.1356  | 0.0009  | 0.0508  |
| board               | 0.2281  | 0.6613  | 0.0651  | 0.2903  |
| tray                | 0.0002  | 0.1429  | 0.0002  | 0.1429  |
| shoe                | 0.1348  | 0.3925  | 0.0028  | 0.0654  |
| bowl                | 0.1183  | 0.4348  | 0.0130  | 0.1304  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stairs              | 0.0797  | 0.4306  | 0.0088  | 0.1389  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bench               | 0.2519  | 0.6792  | 0.1823  | 0.3962  |
| guitar              | 0.2792  | 0.5000  | 0.0244  | 0.1667  |
| refrigerator        | 0.6367  | 0.7400  | 0.4273  | 0.5600  |
| luggage             | 0.4466  | 1.0000  | 0.4386  | 1.0000  |
| coffee maker        | 0.3455  | 0.5517  | 0.1265  | 0.2069  |
| kettle              | 0.1417  | 0.7222  | 0.0606  | 0.3889  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| microwave           | 0.6313  | 0.7347  | 0.2511  | 0.4286  |
| counter             | 0.1479  | 0.6697  | 0.0173  | 0.2661  |
| brush               | 0.1921  | 0.3571  | 0.0238  | 0.0714  |
| stall               | 0.0947  | 0.9412  | 0.0204  | 0.4706  |
| hamper              | 0.2928  | 0.5455  | 0.0866  | 0.2727  |
| ladder              | 0.1822  | 0.5882  | 0.0285  | 0.2353  |
| roof                | 0.0001  | 0.2917  | 0.0000  | 0.0417  |
| tube                | 0.0023  | 0.1538  | 0.0000  | 0.0000  |
| fireplace           | 0.1420  | 0.3571  | 0.0019  | 0.1429  |
| carpet              | 0.0850  | 0.4405  | 0.0018  | 0.0952  |
| chandelier          | 0.1625  | 0.4762  | 0.0029  | 0.0952  |
| basin               | 0.0007  | 0.1765  | 0.0000  | 0.0588  |
| soap dispenser      | 0.2763  | 0.4815  | 0.0439  | 0.1481  |
| dispenser           | 0.5476  | 0.7273  | 0.3606  | 0.4545  |
| holder              | 0.1122  | 0.2222  | 0.1111  | 0.1111  |
| hanger              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| copier              | 0.9167  | 0.9167  | 0.6106  | 0.7500  |
| glass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| doorway             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| window frame        | 0.0306  | 0.3535  | 0.0075  | 0.1313  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| projector           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| piano               | 0.3000  | 0.4000  | 0.3000  | 0.4000  |
| screen              | 0.0074  | 0.4000  | 0.0000  | 0.0000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pen                 | 0.0002  | 0.0667  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine             | 0.5045  | 0.8947  | 0.2925  | 0.7368  |
| sunglasses          | 0.0273  | 0.1538  | 0.0000  | 0.0000  |
| hat                 | 0.1393  | 0.3333  | 0.0000  | 0.0000  |
| person              | 0.2838  | 0.5000  | 0.0139  | 0.1667  |
| vacuum cleaner      | 0.7143  | 0.7143  | 0.1429  | 0.2857  |
| printer             | 0.5737  | 0.7551  | 0.3670  | 0.5918  |
| statue              | 0.1589  | 0.3684  | 0.0803  | 0.1579  |
| speaker             | 0.0090  | 0.0909  | 0.0007  | 0.0455  |
| ball                | 0.4316  | 0.4762  | 0.2143  | 0.2857  |
| can                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radio               | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| map                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 0.5136  | 1.0000  | 0.3633  | 0.8333  |
| fire extinguisher   | 0.5105  | 0.8000  | 0.2000  | 0.2000  |
| mailbox             | 0.0397  | 0.8000  | 0.0101  | 0.4000  |
| pot                 | 0.1117  | 0.4444  | 0.0018  | 0.0741  |
| toothpaste          | 0.0163  | 0.4000  | 0.0000  | 0.0000  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stove               | 0.4867  | 0.7778  | 0.3317  | 0.4722  |
| kitchenware         | 0.1455  | 0.5217  | 0.0233  | 0.1739  |
| washing machine     | 0.5244  | 0.9286  | 0.3361  | 0.7143  |
| teapot              | 0.0889  | 0.2222  | 0.0370  | 0.1111  |
| dish rack           | 0.0541  | 0.4000  | 0.0000  | 0.0000  |
| scale               | 0.1821  | 0.2727  | 0.0455  | 0.0909  |
| toaster             | 0.2445  | 0.5000  | 0.0333  | 0.1000  |
| pitcher             | 0.0470  | 0.6667  | 0.0470  | 0.6667  |
| pack                | 0.0348  | 0.5333  | 0.0009  | 0.0667  |
| clothes dryer       | 0.3442  | 0.7333  | 0.1835  | 0.6667  |
| oven                | 0.4558  | 0.8571  | 0.1348  | 0.3214  |
| flowerpot           | 0.0943  | 0.5882  | 0.0113  | 0.1961  |
| candle              | 0.0284  | 0.3333  | 0.0031  | 0.1667  |
| vase                | 0.1606  | 0.4600  | 0.0815  | 0.2400  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| magazine            | 0.0015  | 0.2000  | 0.0000  | 0.0000  |
| jar                 | 0.0073  | 0.3077  | 0.0004  | 0.0769  |
| dumbbell            | 0.2667  | 0.6667  | 0.0000  | 0.0000  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| file                | 0.0323  | 0.2941  | 0.0129  | 0.1471  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| air conditioner     | 0.7600  | 0.8000  | 0.0000  | 0.0000  |
| balcony             | 0.0417  | 0.5000  | 0.0000  | 0.0000  |
| footstool           | 0.0106  | 0.5000  | 0.0106  | 0.5000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| column              | 0.0314  | 0.2632  | 0.0015  | 0.1316  |
| folder              | 0.0111  | 0.2857  | 0.0022  | 0.0714  |
| garbage             | 0.6240  | 0.8750  | 0.4340  | 0.6250  |
| commode             | 0.4375  | 0.7885  | 0.3481  | 0.6346  |
| napkins             | 0.1905  | 0.6667  | 0.0000  | 0.0000  |
| player              | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| album               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| roll                | 0.1971  | 0.5000  | 0.0036  | 0.1000  |
| humidifier          | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| heater              | 0.5543  | 0.7586  | 0.0655  | 0.2241  |
| device              | 0.1309  | 0.2727  | 0.0182  | 0.0909  |
| drawer              | 0.1700  | 0.5556  | 0.1210  | 0.4444  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headphones          | 0.2887  | 0.6000  | 0.0000  | 0.0000  |
| fruit               | 0.1713  | 0.4286  | 0.0004  | 0.0714  |
| shampoo             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glove               | 0.8333  | 1.0000  | 0.0000  | 0.0000  |
| toiletry            | 0.0292  | 0.3478  | 0.0000  | 0.0000  |
| beam                | 0.0001  | 0.0909  | 0.0000  | 0.0000  |
| body loofah         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beanbag             | 1.0000  | 1.0000  | 0.8333  | 0.8333  |
| jalousie            | 0.8333  | 1.0000  | 0.0833  | 0.5000  |
| adhesive tape       | 0.1000  | 0.2000  | 0.0000  | 0.0000  |
| pillar              | 0.3430  | 0.6047  | 0.1556  | 0.3256  |
| pan                 | 0.1532  | 0.2941  | 0.0065  | 0.0588  |
| spoon               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sack                | 0.4143  | 0.8571  | 0.2155  | 0.5714  |
| food                | 0.0114  | 0.0909  | 0.0000  | 0.0000  |
| partition           | 0.1152  | 0.6667  | 0.0003  | 0.1667  |
| crib                | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| price tag           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drum                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| menu                | 0.6449  | 0.7143  | 0.0000  | 0.0000  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coil                | 0.0009  | 0.1667  | 0.0000  | 0.0000  |
| bread               | 0.0667  | 0.2000  | 0.0000  | 0.0000  |
| broom               | 0.0075  | 0.2857  | 0.0005  | 0.0714  |
| mop                 | 0.9167  | 1.0000  | 0.0000  | 0.0000  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| countertop          | 0.0396  | 0.3929  | 0.0035  | 0.1071  |
| handle              | 0.0002  | 0.2500  | 0.0000  | 0.0000  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| steps               | 0.0002  | 0.1000  | 0.0000  | 0.0000  |
| fence               | 0.0021  | 0.0833  | 0.0000  | 0.0000  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garage door         | 0.2545  | 0.8333  | 0.2333  | 0.3333  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washbasin           | 0.0047  | 0.3333  | 0.0008  | 0.1333  |
| thermostat          | 0.0012  | 0.1667  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchen island      | 0.1061  | 0.4286  | 0.0987  | 0.4286  |
| treadmill           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool table          | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| sponge              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| helmets             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| arch                | 0.0562  | 0.3000  | 0.0087  | 0.0667  |
| dishwasher          | 0.2250  | 0.3000  | 0.0500  | 0.1000  |
| crate               | 0.4286  | 0.4286  | 0.1429  | 0.2857  |
| mattress            | 0.0145  | 0.3333  | 0.0000  | 0.0000  |
| candlestick         | 0.3333  | 0.3333  | 0.1111  | 0.3333  |
| range hood          | 0.4061  | 0.7143  | 0.0446  | 0.1429  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| letter              | 0.5417  | 0.7500  | 0.0000  | 0.0000  |
| salt                | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| cube                | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| barricade           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| umbrella            | 0.2000  | 0.2000  | 0.0000  | 0.0000  |
| bidet               | 0.5556  | 0.6667  | 0.5556  | 0.6667  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water heater        | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| wine                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.2687  | 0.6000  | 0.0033  | 0.1000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flush               | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| cosmetics           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| overall             | 0.2032  | 0.4064  | 0.0799  | 0.1830  |
| head                | 0.2828  | 0.5747  | 0.1189  | 0.2806  |
| common              | 0.1761  | 0.3833  | 0.0635  | 0.1595  |
| tail                | 0.1460  | 0.2459  | 0.0554  | 0.1015  |
| small               | 0.0607  | 0.2081  | 0.0049  | 0.0392  |
| medium              | 0.2173  | 0.4476  | 0.0940  | 0.2129  |
| large               | 0.1155  | 0.5008  | 0.0541  | 0.2908  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 08:35:22 INFO metrics.py:437 | number of scannet results: 250
Rank[0/8] 06/20/2025 08:36:34 INFO metrics.py:332 | eval results of scannet:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| sink                | 0.7986  | 0.9091  | 0.1687  | 0.4205  |
| toilet              | 0.9159  | 0.9286  | 0.7291  | 0.7857  |
| bathtub             | 0.9292  | 0.9375  | 0.8697  | 0.8750  |
| picture             | 0.3218  | 0.5224  | 0.0654  | 0.1530  |
| curtain             | 0.6189  | 0.7788  | 0.1070  | 0.3717  |
| toilet paper        | 0.4496  | 0.5190  | 0.1140  | 0.2405  |
| bin                 | 0.6494  | 0.8213  | 0.3723  | 0.5404  |
| towel               | 0.3904  | 0.7025  | 0.0717  | 0.2810  |
| door                | 0.5138  | 0.7993  | 0.1107  | 0.3579  |
| stopcock            | 0.1492  | 0.3448  | 0.0055  | 0.0690  |
| doorframe           | 0.3999  | 0.7676  | 0.0592  | 0.3239  |
| rail                | 0.0785  | 0.6667  | 0.0159  | 0.3333  |
| vanity              | 0.6359  | 1.0000  | 0.5714  | 0.8667  |
| mirror              | 0.1047  | 0.5542  | 0.0074  | 0.1205  |
| bottle              | 0.0564  | 0.3053  | 0.0028  | 0.0789  |
| switch              | 0.0068  | 0.1772  | 0.0000  | 0.0000  |
| bar                 | 0.0231  | 0.1818  | 0.0000  | 0.0000  |
| ledge               | 0.0047  | 0.5000  | 0.0026  | 0.5000  |
| hair dryer          | 0.1429  | 0.1429  | 0.0000  | 0.0000  |
| socket              | 0.0166  | 0.2136  | 0.0000  | 0.0049  |
| cup                 | 0.1685  | 0.5507  | 0.0265  | 0.1159  |
| tissue box          | 0.1425  | 0.5000  | 0.0007  | 0.0909  |
| rod                 | 0.0016  | 0.0714  | 0.0000  | 0.0000  |
| shower              | 0.0714  | 0.2143  | 0.0048  | 0.0714  |
| light               | 0.0805  | 0.4483  | 0.0016  | 0.0690  |
| paper               | 0.0284  | 0.3170  | 0.0006  | 0.0604  |
| bag                 | 0.1955  | 0.6712  | 0.0825  | 0.2740  |
| backpack            | 0.6970  | 0.8571  | 0.4202  | 0.5357  |
| basket              | 0.0892  | 0.3077  | 0.0441  | 0.2308  |
| box                 | 0.1296  | 0.3770  | 0.0089  | 0.0754  |
| tissue              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| decoration          | 0.0433  | 0.1667  | 0.0000  | 0.0000  |
| cabinet             | 0.3407  | 0.7857  | 0.1503  | 0.5159  |
| grab bar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dish           | 0.5354  | 0.8333  | 0.1111  | 0.1667  |
| mat                 | 0.0158  | 0.3000  | 0.0000  | 0.0000  |
| water cooler        | 0.6111  | 1.0000  | 0.5000  | 0.5000  |
| plunger             | 0.8118  | 1.0000  | 0.1900  | 0.8000  |
| faucet              | 0.0011  | 0.1250  | 0.0000  | 0.0000  |
| blanket             | 0.1061  | 0.5417  | 0.0030  | 0.1250  |
| couch               | 0.5850  | 1.0000  | 0.4654  | 0.9375  |
| window              | 0.4078  | 0.8025  | 0.0790  | 0.3333  |
| frame               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bed                 | 0.9562  | 1.0000  | 0.8292  | 0.9649  |
| lamp                | 0.4490  | 0.7024  | 0.1694  | 0.3333  |
| bucket              | 0.2404  | 0.5714  | 0.0005  | 0.0714  |
| purse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plant               | 0.3080  | 0.5210  | 0.1136  | 0.2605  |
| pillow              | 0.4436  | 0.6319  | 0.1398  | 0.3472  |
| table               | 0.5735  | 0.9000  | 0.4443  | 0.7808  |
| stool               | 0.3034  | 0.7955  | 0.1572  | 0.4318  |
| telephone           | 0.4962  | 0.7188  | 0.1248  | 0.3125  |
| computer            | 0.2707  | 0.5897  | 0.0168  | 0.1026  |
| keyboard            | 0.0445  | 0.1724  | 0.0000  | 0.0000  |
| blackboard          | 0.5378  | 0.7681  | 0.2443  | 0.4058  |
| monitor             | 0.6031  | 0.7071  | 0.1755  | 0.3571  |
| chair               | 0.5996  | 0.7354  | 0.4127  | 0.5638  |
| radiator            | 0.6985  | 0.7759  | 0.3327  | 0.4655  |
| mouse               | 0.0107  | 0.1429  | 0.0000  | 0.0000  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| desk                | 0.6347  | 0.9747  | 0.4256  | 0.7975  |
| book                | 0.0324  | 0.2881  | 0.0013  | 0.0619  |
| stand               | 0.7538  | 0.9057  | 0.6547  | 0.7925  |
| shelf               | 0.4448  | 0.8750  | 0.2720  | 0.6544  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| notebook            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jacket              | 0.6064  | 0.7826  | 0.1262  | 0.3913  |
| laptop              | 0.5361  | 0.6000  | 0.1881  | 0.2667  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes             | 0.2147  | 0.5556  | 0.0223  | 0.1634  |
| fan                 | 0.7598  | 0.8750  | 0.3090  | 0.5000  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pipe                | 0.0012  | 0.1429  | 0.0000  | 0.0000  |
| windowsill          | 0.0022  | 0.3333  | 0.0000  | 0.0000  |
| dresser             | 0.6966  | 1.0000  | 0.5651  | 0.8649  |
| paper cutter        | 0.6333  | 0.8000  | 0.0000  | 0.0000  |
| wardrobe            | 0.6736  | 1.0000  | 0.3750  | 0.3750  |
| cushion             | 0.0556  | 0.5000  | 0.0139  | 0.2500  |
| calendar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toy                 | 0.0923  | 0.2941  | 0.0294  | 0.0588  |
| clock               | 0.5034  | 0.8000  | 0.0120  | 0.1200  |
| ottoman             | 0.0144  | 0.4286  | 0.0091  | 0.2857  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rack                | 0.0040  | 0.0909  | 0.0040  | 0.0909  |
| tv                  | 0.8349  | 0.9130  | 0.2031  | 0.3913  |
| case                | 0.5388  | 1.0000  | 0.1537  | 0.4211  |
| plate               | 0.0219  | 0.4000  | 0.0154  | 0.2000  |
| sign                | 0.0690  | 0.3929  | 0.0030  | 0.0357  |
| container           | 0.0028  | 0.0870  | 0.0016  | 0.0435  |
| board               | 0.1832  | 0.7500  | 0.0550  | 0.4688  |
| tray                | 0.0007  | 0.3333  | 0.0007  | 0.3333  |
| shoe                | 0.1337  | 0.4000  | 0.0004  | 0.0500  |
| bowl                | 0.0566  | 0.2222  | 0.0566  | 0.2222  |
| stairs              | 0.2182  | 0.8000  | 0.0292  | 0.4000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bench               | 0.7167  | 1.0000  | 0.5631  | 0.8000  |
| guitar              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| refrigerator        | 0.9000  | 0.9000  | 0.5991  | 0.6500  |
| coffee maker        | 0.5597  | 0.7059  | 0.2184  | 0.2941  |
| kettle              | 0.2027  | 1.0000  | 0.0439  | 0.7500  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| microwave           | 0.6624  | 0.7500  | 0.2732  | 0.5000  |
| counter             | 0.3082  | 0.8571  | 0.0945  | 0.5143  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stall               | 0.0952  | 0.9412  | 0.0205  | 0.4706  |
| hamper              | 0.3253  | 0.6000  | 0.0962  | 0.3000  |
| ladder              | 0.2462  | 0.6000  | 0.0286  | 0.2000  |
| tube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| carpet              | 0.0034  | 0.4000  | 0.0000  | 0.0000  |
| basin               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dispenser      | 0.3424  | 0.5238  | 0.0635  | 0.1905  |
| dispenser           | 0.6407  | 0.7436  | 0.4266  | 0.4872  |
| holder              | 0.2000  | 0.2000  | 0.2000  | 0.2000  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| copier              | 0.9167  | 0.9167  | 0.6106  | 0.7500  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pen                 | 0.0015  | 0.1111  | 0.0000  | 0.0000  |
| machine             | 0.5528  | 1.0000  | 0.3314  | 0.8125  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| person              | 0.2838  | 0.5000  | 0.0139  | 0.1667  |
| vacuum cleaner      | 0.6667  | 0.6667  | 0.6667  | 0.6667  |
| printer             | 0.6463  | 0.8182  | 0.5544  | 0.7273  |
| speaker             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball                | 0.4167  | 0.4167  | 0.1875  | 0.2500  |
| map                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 0.6667  | 1.0000  | 0.5286  | 1.0000  |
| fire extinguisher   | 0.3000  | 0.6667  | 0.0000  | 0.0000  |
| mailbox             | 0.0401  | 0.8000  | 0.0102  | 0.4000  |
| pot                 | 0.0294  | 0.5000  | 0.0000  | 0.0000  |
| toothpaste          | 0.0181  | 0.4000  | 0.0000  | 0.0000  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stove               | 0.9428  | 1.0000  | 0.9428  | 1.0000  |
| washing machine     | 0.5737  | 0.8333  | 0.5486  | 0.8333  |
| teapot              | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| dish rack           | 0.0935  | 0.5000  | 0.0000  | 0.0000  |
| scale               | 0.2537  | 0.5000  | 0.2500  | 0.2500  |
| toaster             | 0.0192  | 0.2500  | 0.0000  | 0.0000  |
| pitcher             | 0.0780  | 0.6667  | 0.0780  | 0.6667  |
| clothes dryer       | 0.8333  | 1.0000  | 0.8333  | 1.0000  |
| oven                | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| flowerpot           | 0.0301  | 1.0000  | 0.0000  | 0.0000  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.0833  | 0.3333  | 0.0000  | 0.0000  |
| crate               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| broom               | 0.0135  | 0.3333  | 0.0009  | 0.0833  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillar              | 0.2000  | 0.2000  | 0.2000  | 0.2000  |
| headphones          | 0.2994  | 0.6000  | 0.0000  | 0.0000  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.7829  | 1.0000  | 0.1000  | 0.2000  |
| drawer              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| scannet             | 0.2591  | 0.4608  | 0.1268  | 0.2421  |
| head                | 0.3446  | 0.5999  | 0.1755  | 0.3204  |
| common              | 0.2077  | 0.3601  | 0.0912  | 0.1793  |
| tail                | 0.0692  | 0.1935  | 0.0339  | 0.1065  |
| small               | 0.0494  | 0.1627  | 0.0067  | 0.0436  |
| medium              | 0.3106  | 0.5595  | 0.1558  | 0.2888  |
| large               | 0.2479  | 0.7788  | 0.1356  | 0.5257  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 08:36:34 INFO metrics.py:437 | number of 3rscan results: 235
Rank[0/8] 06/20/2025 08:37:42 INFO metrics.py:332 | eval results of 3rscan:
+-------------------+---------+---------+---------+---------+
| classes           | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+-------------------+---------+---------+---------+---------+
| monitor           | 0.5049  | 0.6250  | 0.0282  | 0.1000  |
| keyboard          | 0.0494  | 0.1667  | 0.0000  | 0.0000  |
| chair             | 0.6370  | 0.7543  | 0.5005  | 0.6238  |
| telephone         | 0.2803  | 0.4583  | 0.1277  | 0.2500  |
| bag               | 0.3081  | 0.7475  | 0.0366  | 0.2323  |
| mouse             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cup               | 0.0912  | 0.3333  | 0.0000  | 0.0256  |
| paper             | 0.0551  | 0.3857  | 0.0021  | 0.0714  |
| backpack          | 0.5217  | 0.7879  | 0.0935  | 0.2424  |
| desk              | 0.4303  | 0.9125  | 0.2821  | 0.7125  |
| plug              | 0.0370  | 0.1111  | 0.0000  | 0.0000  |
| case              | 0.0410  | 0.3714  | 0.0022  | 0.0857  |
| cabinet           | 0.3587  | 0.8120  | 0.2397  | 0.6368  |
| computer          | 0.3715  | 0.6667  | 0.0013  | 0.1429  |
| plate             | 0.0508  | 0.3636  | 0.0028  | 0.0303  |
| book              | 0.0537  | 0.3314  | 0.0025  | 0.0710  |
| pen               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bottle            | 0.0280  | 0.2562  | 0.0005  | 0.0331  |
| garbage           | 0.6456  | 0.8750  | 0.4479  | 0.6250  |
| stand             | 0.4157  | 0.8361  | 0.2142  | 0.5902  |
| socket            | 0.0015  | 0.0625  | 0.0000  | 0.0000  |
| conducting wire   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| table             | 0.4730  | 0.8468  | 0.3517  | 0.7342  |
| bin               | 0.7088  | 0.8659  | 0.2864  | 0.5244  |
| box               | 0.1949  | 0.5142  | 0.0474  | 0.1765  |
| window            | 0.1621  | 0.5959  | 0.0162  | 0.1301  |
| blinds            | 0.1852  | 0.5897  | 0.0082  | 0.3333  |
| wardrobe          | 0.4269  | 0.7969  | 0.2029  | 0.6406  |
| shelf             | 0.2693  | 0.7067  | 0.0833  | 0.3216  |
| stool             | 0.3523  | 0.7963  | 0.2294  | 0.5741  |
| curtain           | 0.5172  | 0.8016  | 0.0815  | 0.3492  |
| paper cutter      | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| notebook          | 0.0003  | 0.0909  | 0.0000  | 0.0000  |
| file              | 0.0395  | 0.3571  | 0.0157  | 0.1786  |
| plant             | 0.3427  | 0.5792  | 0.0941  | 0.2030  |
| toy               | 0.5107  | 0.7692  | 0.0192  | 0.0769  |
| picture           | 0.1613  | 0.3862  | 0.0082  | 0.0528  |
| cushion           | 0.2634  | 0.8000  | 0.0101  | 0.1667  |
| light             | 0.1082  | 0.4432  | 0.0119  | 0.0455  |
| door              | 0.2521  | 0.6176  | 0.0341  | 0.1985  |
| jar               | 0.0193  | 0.5000  | 0.0012  | 0.1250  |
| remote control    | 0.0277  | 0.4000  | 0.0020  | 0.1000  |
| pillow            | 0.3982  | 0.6445  | 0.0328  | 0.1602  |
| couch             | 0.3779  | 0.8429  | 0.3024  | 0.7000  |
| tablet            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| container         | 0.0204  | 0.2222  | 0.0033  | 0.1111  |
| sink              | 0.6435  | 0.7679  | 0.1415  | 0.2857  |
| tv                | 0.7048  | 0.8857  | 0.1387  | 0.3429  |
| lamp              | 0.2941  | 0.5188  | 0.0147  | 0.0938  |
| commode           | 0.4375  | 0.7885  | 0.3481  | 0.6346  |
| frame             | 0.2050  | 0.6667  | 0.0002  | 0.0667  |
| napkins           | 0.1905  | 0.6667  | 0.0000  | 0.0000  |
| candle            | 0.0390  | 0.3333  | 0.0039  | 0.1333  |
| teapot            | 0.0833  | 0.2500  | 0.0833  | 0.2500  |
| bed               | 0.8185  | 0.9818  | 0.5123  | 0.8727  |
| vase              | 0.3386  | 0.5500  | 0.1643  | 0.2000  |
| folder            | 0.0116  | 0.2857  | 0.0023  | 0.0714  |
| flowerpot         | 0.0839  | 0.4167  | 0.0013  | 0.0417  |
| bowl              | 0.1784  | 0.5185  | 0.0060  | 0.1111  |
| decoration        | 0.0944  | 0.5312  | 0.0046  | 0.0938  |
| clock             | 0.2973  | 0.4737  | 0.0000  | 0.0000  |
| blanket           | 0.2236  | 0.8769  | 0.0299  | 0.3385  |
| switch            | 0.0005  | 0.0755  | 0.0000  | 0.0000  |
| towel             | 0.2653  | 0.6835  | 0.0428  | 0.1646  |
| footstool         | 0.1000  | 1.0000  | 0.1000  | 1.0000  |
| doorframe         | 0.1594  | 0.7000  | 0.0380  | 0.2400  |
| poster            | 0.0734  | 0.1429  | 0.0000  | 0.0000  |
| carpet            | 0.1360  | 0.5536  | 0.0030  | 0.1429  |
| laptop            | 0.5675  | 0.8333  | 0.0362  | 0.3333  |
| clothes           | 0.2174  | 0.7087  | 0.0506  | 0.2039  |
| bench             | 0.2435  | 0.7500  | 0.2243  | 0.4643  |
| counter           | 0.1673  | 0.6829  | 0.0031  | 0.1951  |
| board             | 0.3580  | 0.6250  | 0.1250  | 0.1250  |
| kitchenware       | 0.1627  | 0.5455  | 0.0250  | 0.1818  |
| kettle            | 0.1678  | 0.7500  | 0.0813  | 0.3333  |
| player            | 0.3333  | 0.3333  | 0.0000  | 0.0000  |
| mat               | 0.0079  | 0.3333  | 0.0000  | 0.0000  |
| magazine          | 0.0019  | 0.2500  | 0.0000  | 0.0000  |
| album             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ottoman           | 0.3676  | 1.0000  | 0.3536  | 0.9091  |
| roll              | 0.2006  | 0.5000  | 0.0037  | 0.1000  |
| blackboard        | 0.4732  | 0.7500  | 0.1131  | 0.2500  |
| humidifier        | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| heater            | 0.5630  | 0.7544  | 0.0661  | 0.2105  |
| windowsill        | 0.1258  | 0.5641  | 0.0006  | 0.0513  |
| fan               | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| device            | 0.1309  | 0.2727  | 0.0182  | 0.0909  |
| radiator          | 0.5681  | 0.7241  | 0.0546  | 0.1724  |
| drawer            | 0.4762  | 0.7778  | 0.3508  | 0.6667  |
| pad               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| printer           | 0.6296  | 0.6667  | 0.1481  | 0.3333  |
| mirror            | 0.2273  | 0.5926  | 0.0230  | 0.1111  |
| chandelier        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shoe              | 0.2235  | 0.4524  | 0.0092  | 0.1190  |
| rack              | 0.2095  | 0.7000  | 0.1086  | 0.5333  |
| basket            | 0.4556  | 0.7812  | 0.1655  | 0.3594  |
| air conditioner   | 0.9500  | 1.0000  | 0.0000  | 0.0000  |
| window frame      | 0.0228  | 0.3333  | 0.0004  | 0.0833  |
| brush             | 0.3610  | 0.6250  | 0.0417  | 0.1250  |
| tissue box        | 0.1667  | 0.3333  | 0.1667  | 0.3333  |
| fruit             | 0.4592  | 0.8000  | 0.0015  | 0.2000  |
| scale             | 0.3333  | 0.3333  | 0.0000  | 0.0000  |
| luggage           | 0.5195  | 1.0000  | 0.5152  | 1.0000  |
| balcony           | 0.0417  | 0.5000  | 0.0000  | 0.0000  |
| guitar            | 1.0000  | 1.0000  | 0.5000  | 0.5000  |
| calendar          | 0.0005  | 0.2000  | 0.0000  | 0.0000  |
| microwave         | 0.8831  | 0.9412  | 0.3981  | 0.5294  |
| stairs            | 0.3222  | 0.6429  | 0.0185  | 0.2857  |
| ball              | 0.4667  | 0.6000  | 0.2000  | 0.2000  |
| refrigerator      | 0.6080  | 0.7333  | 0.5928  | 0.7333  |
| tray              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sunglasses        | 0.0273  | 0.1538  | 0.0000  | 0.0000  |
| hat               | 0.2503  | 0.4286  | 0.0000  | 0.0000  |
| rail              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dresser           | 0.6000  | 1.0000  | 0.0000  | 0.0000  |
| bar               | 0.4444  | 0.6667  | 0.0051  | 0.3333  |
| clothes dryer     | 0.4141  | 0.7000  | 0.1616  | 0.6000  |
| faucet            | 0.0333  | 0.3333  | 0.0000  | 0.0000  |
| bathtub           | 0.8447  | 0.8889  | 0.6970  | 0.7778  |
| statue            | 1.0000  | 1.0000  | 0.5000  | 0.5000  |
| shampoo           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toilet            | 0.8038  | 0.8235  | 0.4860  | 0.5294  |
| shower            | 0.1939  | 1.0000  | 0.0323  | 0.5000  |
| glove             | 0.8333  | 1.0000  | 0.0000  | 0.0000  |
| toiletry          | 0.3333  | 1.0000  | 0.0000  | 0.0000  |
| toilet paper      | 0.1064  | 0.4167  | 0.0003  | 0.0833  |
| washing machine   | 0.5696  | 1.0000  | 0.2952  | 0.7143  |
| beam              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basin             | 0.0021  | 0.1333  | 0.0000  | 0.0000  |
| bucket            | 0.7427  | 0.9333  | 0.2926  | 0.4667  |
| soap              | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| pipe              | 0.0192  | 0.2500  | 0.0000  | 0.0000  |
| tube              | 0.0080  | 0.2857  | 0.0000  | 0.0000  |
| beanbag           | 1.0000  | 1.0000  | 0.8333  | 0.8333  |
| jalousie          | 0.8333  | 1.0000  | 0.0833  | 0.5000  |
| adhesive tape     | 0.1250  | 0.2500  | 0.0000  | 0.0000  |
| can               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pack              | 0.0402  | 0.6154  | 0.0010  | 0.0769  |
| pillar            | 0.5029  | 0.6471  | 0.2605  | 0.4118  |
| stove             | 0.3506  | 0.8235  | 0.0939  | 0.4118  |
| pan               | 0.2188  | 0.4167  | 0.0093  | 0.0833  |
| oven              | 0.5999  | 1.0000  | 0.2038  | 0.4211  |
| spoon             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| knife             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pot               | 0.3694  | 0.5833  | 0.0119  | 0.0833  |
| coffee maker      | 0.0306  | 0.4444  | 0.0020  | 0.1111  |
| shovel            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sack              | 0.4143  | 0.8571  | 0.2155  | 0.5714  |
| food              | 0.0143  | 0.1000  | 0.0000  | 0.0000  |
| partition         | 0.1394  | 0.8000  | 0.0004  | 0.2000  |
| radio             | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| sign              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crib              | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| price tag         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wire              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fireplace         | 0.0500  | 0.2000  | 0.0017  | 0.2000  |
| hook              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| piano             | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| map               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| furniture         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| menu              | 0.6449  | 0.7143  | 0.0000  | 0.0000  |
| tool              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle           | 0.8333  | 1.0000  | 0.5625  | 1.0000  |
| coil              | 0.0009  | 0.1667  | 0.0000  | 0.0000  |
| column            | 0.0238  | 0.5000  | 0.0238  | 0.5000  |
| bread             | 0.0667  | 0.2000  | 0.0000  | 0.0000  |
| glass             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dispenser         | 0.3469  | 0.6667  | 0.0000  | 0.0000  |
| broom             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mop               | 0.9167  | 1.0000  | 0.0000  | 0.0000  |
| soap dish         | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| tissue            | 0.0934  | 0.6667  | 0.0833  | 0.3333  |
| ladder            | 0.5900  | 1.0000  | 0.3404  | 0.6667  |
| candlestick       | 1.0000  | 1.0000  | 0.3333  | 1.0000  |
| range hood        | 0.2500  | 0.4000  | 0.0250  | 0.2000  |
| mattress          | 0.0217  | 0.5000  | 0.0000  | 0.0000  |
| letter            | 0.5536  | 0.7500  | 0.0000  | 0.0000  |
| dumbbell          | 0.2500  | 1.0000  | 0.0000  | 0.0000  |
| speaker           | 0.0139  | 0.0833  | 0.0000  | 0.0000  |
| helmets           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vacuum cleaner    | 0.7500  | 0.7500  | 0.0000  | 0.0000  |
| hanger            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine           | 1.0000  | 1.0000  | 0.5000  | 1.0000  |
| screen            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washbasin         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| salt              | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| cube              | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| crate             | 0.6000  | 0.6000  | 0.2000  | 0.4000  |
| umbrella          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| soap dispenser    | 0.0760  | 0.4000  | 0.0000  | 0.0000  |
| sponge            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| holder            | 0.0400  | 1.0000  | 0.0000  | 0.0000  |
| flush             | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| bidet             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| water heater      | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| toaster           | 0.7056  | 1.0000  | 0.1250  | 0.2500  |
| scissors          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toothbrush        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| seasoning         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cosmetics         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dish rack         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| dishwasher        | 0.6667  | 0.6667  | 0.1667  | 0.3333  |
| -------           | ------- | ------- | ------- | ------- |
| 3rscan            | 0.3010  | 0.5225  | 0.1087  | 0.2214  |
| head              | 0.3127  | 0.6183  | 0.1076  | 0.2675  |
| common            | 0.3080  | 0.5028  | 0.1209  | 0.2198  |
| tail              | 0.2690  | 0.3667  | 0.0938  | 0.1356  |
| small             | 0.0957  | 0.2808  | 0.0104  | 0.0386  |
| medium            | 0.3162  | 0.5666  | 0.1259  | 0.2499  |
| large             | 0.2091  | 0.6977  | 0.0775  | 0.4178  |
+-------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 08:37:42 INFO metrics.py:437 | number of matterport3d results: 339
Rank[0/8] 06/20/2025 08:39:05 INFO metrics.py:332 | eval results of matterport3d:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| mirror              | 0.1258  | 0.5135  | 0.0098  | 0.1081  |
| sink                | 0.5664  | 0.7778  | 0.1313  | 0.3737  |
| towel               | 0.3683  | 0.6091  | 0.1124  | 0.2636  |
| picture             | 0.1617  | 0.3478  | 0.0035  | 0.0652  |
| doorframe           | 0.2959  | 0.6675  | 0.0683  | 0.3404  |
| plant               | 0.2590  | 0.7054  | 0.1017  | 0.4107  |
| bathtub             | 0.5774  | 0.7778  | 0.4896  | 0.7037  |
| light               | 0.0211  | 0.1852  | 0.0009  | 0.0313  |
| door                | 0.2331  | 0.5822  | 0.0082  | 0.1253  |
| countertop          | 0.0396  | 0.3929  | 0.0035  | 0.1071  |
| decoration          | 0.0181  | 0.3690  | 0.0002  | 0.0476  |
| blanket             | 0.0070  | 0.3636  | 0.0009  | 0.0909  |
| cabinet             | 0.1181  | 0.6483  | 0.0675  | 0.3814  |
| lamp                | 0.3762  | 0.6813  | 0.1371  | 0.3077  |
| flowerpot           | 0.1625  | 0.7200  | 0.0667  | 0.3600  |
| roof                | 0.0001  | 0.2917  | 0.0000  | 0.0417  |
| bottle              | 0.1112  | 0.5625  | 0.0103  | 0.1458  |
| vent                | 0.0677  | 0.2917  | 0.0208  | 0.0417  |
| case                | 0.0003  | 0.2222  | 0.0000  | 0.0556  |
| table               | 0.2783  | 0.7564  | 0.2150  | 0.5769  |
| drawer              | 0.0032  | 0.3750  | 0.0018  | 0.2500  |
| soap                | 0.0026  | 0.6000  | 0.0019  | 0.4000  |
| vase                | 0.0609  | 0.4000  | 0.0476  | 0.2667  |
| switch              | 0.0001  | 0.1000  | 0.0000  | 0.0000  |
| window              | 0.0775  | 0.4167  | 0.0056  | 0.1111  |
| toilet              | 0.7860  | 0.8281  | 0.6649  | 0.7344  |
| toiletry            | 0.0245  | 0.3182  | 0.0000  | 0.0000  |
| counter             | 0.0111  | 0.4545  | 0.0006  | 0.0909  |
| vanity              | 0.3110  | 0.7333  | 0.2972  | 0.6667  |
| plate               | 0.0903  | 0.3571  | 0.0004  | 0.0714  |
| toilet paper        | 0.2365  | 0.5278  | 0.0143  | 0.1389  |
| book                | 0.0442  | 0.2756  | 0.0008  | 0.0236  |
| cushion             | 0.0078  | 0.3529  | 0.0003  | 0.0588  |
| paper               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| doorway             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| handle              | 0.0002  | 0.2500  | 0.0000  | 0.0000  |
| pillow              | 0.2783  | 0.5962  | 0.0292  | 0.1640  |
| dresser             | 0.2304  | 0.7333  | 0.2075  | 0.6667  |
| faucet              | 0.0061  | 0.1944  | 0.0001  | 0.0278  |
| desk                | 0.2838  | 0.6316  | 0.2015  | 0.5263  |
| pot                 | 0.0420  | 0.3077  | 0.0002  | 0.0769  |
| shelf               | 0.1068  | 0.5328  | 0.0318  | 0.2774  |
| window frame        | 0.0331  | 0.3563  | 0.0089  | 0.1379  |
| basket              | 0.0713  | 0.3600  | 0.0032  | 0.1200  |
| shoe                | 0.0416  | 0.2800  | 0.0000  | 0.0000  |
| wardrobe            | 0.0254  | 0.3636  | 0.0065  | 0.2727  |
| frame               | 0.0001  | 0.1176  | 0.0000  | 0.0588  |
| bin                 | 0.3243  | 0.7317  | 0.1168  | 0.3902  |
| mat                 | 0.0091  | 0.2083  | 0.0001  | 0.0417  |
| shower              | 0.0002  | 0.0833  | 0.0000  | 0.0000  |
| cup                 | 0.0059  | 0.3000  | 0.0000  | 0.0000  |
| telephone           | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| tv                  | 0.3346  | 0.5349  | 0.1758  | 0.3023  |
| screen              | 0.0113  | 0.5000  | 0.0000  | 0.0000  |
| chair               | 0.4472  | 0.7075  | 0.3011  | 0.5374  |
| curtain             | 0.3223  | 0.6667  | 0.0792  | 0.2917  |
| bowl                | 0.1323  | 0.4242  | 0.0348  | 0.1212  |
| statue              | 0.0699  | 0.2941  | 0.0601  | 0.1176  |
| stand               | 0.4734  | 0.7800  | 0.3896  | 0.6600  |
| couch               | 0.2959  | 0.7935  | 0.2227  | 0.6957  |
| clock               | 0.3426  | 0.6667  | 0.3333  | 0.3333  |
| toy                 | 0.1188  | 0.3611  | 0.0404  | 0.1111  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| footstool           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stairs              | 0.0271  | 0.3396  | 0.0074  | 0.0755  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bed                 | 0.5002  | 0.8070  | 0.4001  | 0.7719  |
| stool               | 0.0178  | 0.3333  | 0.0142  | 0.2381  |
| ottoman             | 0.0459  | 0.3889  | 0.0341  | 0.2778  |
| candle              | 0.0115  | 0.3333  | 0.0060  | 0.3333  |
| bench               | 0.1892  | 0.5000  | 0.0785  | 0.2000  |
| board               | 0.1676  | 0.3333  | 0.0000  | 0.0000  |
| teapot              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| box                 | 0.0425  | 0.5385  | 0.0063  | 0.1731  |
| fruit               | 0.0509  | 0.2500  | 0.0000  | 0.0000  |
| carpet              | 0.0004  | 0.1111  | 0.0000  | 0.0000  |
| pillar              | 0.2911  | 0.6667  | 0.1264  | 0.2857  |
| column              | 0.0343  | 0.2500  | 0.0014  | 0.1111  |
| steps               | 0.0002  | 0.1000  | 0.0000  | 0.0000  |
| fence               | 0.0028  | 0.0833  | 0.0000  | 0.0000  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beam                | 0.0001  | 0.0952  | 0.0000  | 0.0000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rail                | 0.0290  | 0.2778  | 0.0001  | 0.0185  |
| garage door         | 0.2552  | 0.8333  | 0.2333  | 0.3333  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washbasin           | 0.0053  | 0.3571  | 0.0009  | 0.1429  |
| socket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| thermostat          | 0.0012  | 0.1667  | 0.0000  | 0.0000  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| windowsill          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.5083  | 0.8750  | 0.3502  | 0.6250  |
| holder              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue box          | 0.0854  | 0.5000  | 0.0006  | 0.1250  |
| fan                 | 0.6415  | 0.7963  | 0.2566  | 0.4630  |
| pipe                | 0.0333  | 0.1000  | 0.0000  | 0.0000  |
| dispenser           | 0.0060  | 0.5000  | 0.0060  | 0.5000  |
| soap dispenser      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hanger              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket              | 0.0083  | 0.1000  | 0.0083  | 0.1000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchen island      | 0.1061  | 0.4286  | 0.0987  | 0.4286  |
| piano               | 0.2500  | 0.5000  | 0.2500  | 0.5000  |
| heater              | 0.0208  | 1.0000  | 0.0208  | 1.0000  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fireplace           | 0.2150  | 0.3913  | 0.0026  | 0.1304  |
| refrigerator        | 0.2975  | 0.5333  | 0.0843  | 0.2667  |
| ball                | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| microwave           | 0.1640  | 0.4167  | 0.0952  | 0.1667  |
| monitor             | 0.3333  | 0.5000  | 0.0833  | 0.2500  |
| radiator            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scale               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basin               | 0.0006  | 1.0000  | 0.0006  | 1.0000  |
| pan                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| keyboard            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| container           | 0.0386  | 0.1481  | 0.0015  | 0.0370  |
| partition           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rack                | 0.0011  | 0.3636  | 0.0001  | 0.0909  |
| guitar              | 0.2500  | 0.5714  | 0.0089  | 0.1429  |
| bag                 | 0.0588  | 0.3846  | 0.0000  | 0.0000  |
| tissue              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool table          | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| speaker             | 0.0083  | 0.1250  | 0.0083  | 0.1250  |
| food                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| projector           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.1667  | 0.6667  | 0.1667  | 0.6667  |
| kitchenware         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes             | 0.0957  | 0.3276  | 0.0176  | 0.1207  |
| kettle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| chandelier          | 0.1899  | 0.5263  | 0.0037  | 0.1053  |
| ladder              | 0.0386  | 0.4444  | 0.0139  | 0.1111  |
| fire extinguisher   | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| dumbbell            | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| printer             | 0.4745  | 0.5714  | 0.2381  | 0.2857  |
| arch                | 0.0562  | 0.3000  | 0.0087  | 0.0667  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dish           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| computer            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| laptop              | 0.8333  | 1.0000  | 0.1667  | 0.5000  |
| oven                | 0.1739  | 0.8000  | 0.0111  | 0.2000  |
| mouse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stove               | 0.1598  | 0.5000  | 0.1000  | 0.1000  |
| coffee maker        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 0.0123  | 1.0000  | 0.0000  | 0.0000  |
| bidet               | 0.2500  | 0.5000  | 0.2500  | 0.5000  |
| backpack            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radio               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hamper              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.1437  | 0.7500  | 0.0000  | 0.0000  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| air conditioner     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water heater        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drum                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| can                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.3382  | 0.6667  | 0.0044  | 0.1111  |
| blackboard          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| matterport3d        | 0.1093  | 0.3034  | 0.0485  | 0.1435  |
| head                | 0.1600  | 0.4185  | 0.0723  | 0.2076  |
| common              | 0.0689  | 0.2324  | 0.0211  | 0.0886  |
| tail                | 0.0364  | 0.0870  | 0.0352  | 0.0614  |
| small               | 0.0448  | 0.1504  | 0.0131  | 0.0445  |
| medium              | 0.1043  | 0.3239  | 0.0468  | 0.1516  |
| large               | 0.0874  | 0.3996  | 0.0520  | 0.2314  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 08:39:05 INFO metrics.py:437 | number of arkit results: 0
Rank[0/8] 06/20/2025 08:39:06 INFO stats.py:394 | Epoch[139] completed. Training Speed: 3.06 samples/sec across all devices.	Epoch Time: 1018.85 sec.	Average Epoch Time: 1018.85 sec.	Average Step Time: 2.61 sec.	Estimated Remaining Time: 13:10:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:39:06 WARNING accelerator.py:3099 | Deleting 1 checkpoints to make room for new checkpoint.
Rank[0/8] 06/20/2025 08:39:07 INFO accelerator.py:3111 | Saving current state to /job_data/checkpoints/checkpoint_13
Rank[6/8] 06/20/2025 08:39:07 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 139 to /job_data/checkpoints/checkpoint_13
Rank[2/8] 06/20/2025 08:39:07 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 139 to /job_data/checkpoints/checkpoint_13
Rank[3/8] 06/20/2025 08:39:07 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 139 to /job_data/checkpoints/checkpoint_13
Rank[1/8] 06/20/2025 08:39:07 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 139 to /job_data/checkpoints/checkpoint_13
Rank[7/8] 06/20/2025 08:39:07 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 139 to /job_data/checkpoints/checkpoint_13
Rank[5/8] 06/20/2025 08:39:07 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 139 to /job_data/checkpoints/checkpoint_13
Rank[4/8] 06/20/2025 08:39:07 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 139 to /job_data/checkpoints/checkpoint_13
Rank[0/8] 06/20/2025 08:39:08 INFO checkpointing.py:106 | Model weights saved in /job_data/checkpoints/checkpoint_13/model.safetensors
Rank[0/8] 06/20/2025 08:39:11 INFO checkpointing.py:113 | Optimizer state saved in /job_data/checkpoints/checkpoint_13/optimizer.bin
Rank[0/8] 06/20/2025 08:39:11 INFO checkpointing.py:120 | Scheduler state saved in /job_data/checkpoints/checkpoint_13/scheduler.bin
Rank[0/8] 06/20/2025 08:39:11 INFO checkpointing.py:137 | Sampler state for dataloader 0 saved in /job_data/checkpoints/checkpoint_13/sampler.bin
Rank[0/8] 06/20/2025 08:39:11 INFO checkpointing.py:137 | Sampler state for dataloader 1 saved in /job_data/checkpoints/checkpoint_13/sampler_1.bin
Rank[0/8] 06/20/2025 08:39:11 INFO checkpointing.py:164 | Random states saved in /job_data/checkpoints/checkpoint_13/random_states_0.pkl
Rank[0/8] 06/20/2025 08:39:11 INFO checkpointing.py:300 | Saving the state of TrainerProgressState to /job_data/checkpoints/checkpoint_13/custom_checkpoint_0.pkl
Rank[0/8] 06/20/2025 08:39:11 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 139 to /job_data/checkpoints/checkpoint_13
Rank[0/8] 06/20/2025 08:39:40 INFO stats.py:314 | Epoch[140] Step[24] GlobalStep[54624] Training Speed: 8.59 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 13:10:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:39:41 INFO loss_tracker.py:84 | Epoch[140/239] Step[24] GlobalStep[54624/NA]: det_loss_cls_0[0.5752] det_loss_box_0[0.7500] det_loss_cls_1[0.5588] det_loss_box_1[0.5817] det_loss_cls_2[0.5126] det_loss_box_2[0.5164] det_loss_cls_3[0.4821] det_loss_box_3[0.4975] det_loss_cls_4[0.4581] det_loss_box_4[0.4911] det_loss_cls_5[0.4458] det_loss_box_5[0.4876] det_loss_cls_dn_0[0.0987] det_loss_box_dn_0[0.3005] det_loss_cls_dn_1[0.0956] det_loss_box_dn_1[0.2536] det_loss_cls_dn_2[0.0946] det_loss_box_dn_2[0.2368] det_loss_cls_dn_3[0.0937] det_loss_box_dn_3[0.2324] det_loss_cls_dn_4[0.0926] det_loss_box_dn_4[0.2310] det_loss_cls_dn_5[0.0919] det_loss_box_dn_5[0.2248] loss_depth[0.0377] total_loss[8.4410]
Rank[0/8] 06/20/2025 08:40:06 INFO stats.py:314 | Epoch[140] Step[49] GlobalStep[54649] Training Speed: 7.29 samples/sec across all devices.	Average Step Time: 1.10 sec.	Estimated Remaining Time: 13:09:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:40:06 INFO loss_tracker.py:84 | Epoch[140/239] Step[49] GlobalStep[54649/NA]: det_loss_cls_0[0.5346] det_loss_box_0[0.7044] det_loss_cls_1[0.5043] det_loss_box_1[0.5545] det_loss_cls_2[0.4557] det_loss_box_2[0.4969] det_loss_cls_3[0.4319] det_loss_box_3[0.4718] det_loss_cls_4[0.3939] det_loss_box_4[0.4535] det_loss_cls_5[0.3810] det_loss_box_5[0.4528] det_loss_cls_dn_0[0.0959] det_loss_box_dn_0[0.2698] det_loss_cls_dn_1[0.0929] det_loss_box_dn_1[0.2324] det_loss_cls_dn_2[0.0921] det_loss_box_dn_2[0.2182] det_loss_cls_dn_3[0.0916] det_loss_box_dn_3[0.2158] det_loss_cls_dn_4[0.0904] det_loss_box_dn_4[0.2138] det_loss_cls_dn_5[0.0898] det_loss_box_dn_5[0.2131] loss_depth[0.0368] total_loss[7.7878]
Rank[0/8] 06/20/2025 08:40:32 INFO stats.py:314 | Epoch[140] Step[74] GlobalStep[54674] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 13:09:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:40:32 INFO loss_tracker.py:84 | Epoch[140/239] Step[74] GlobalStep[54674/NA]: det_loss_cls_0[0.5770] det_loss_box_0[0.7480] det_loss_cls_1[0.5623] det_loss_box_1[0.5693] det_loss_cls_2[0.5180] det_loss_box_2[0.5318] det_loss_cls_3[0.4865] det_loss_box_3[0.5083] det_loss_cls_4[0.4599] det_loss_box_4[0.5001] det_loss_cls_5[0.4455] det_loss_box_5[0.5003] det_loss_cls_dn_0[0.0953] det_loss_box_dn_0[0.2559] det_loss_cls_dn_1[0.0923] det_loss_box_dn_1[0.2171] det_loss_cls_dn_2[0.0912] det_loss_box_dn_2[0.2014] det_loss_cls_dn_3[0.0901] det_loss_box_dn_3[0.1969] det_loss_cls_dn_4[0.0894] det_loss_box_dn_4[0.1942] det_loss_cls_dn_5[0.0882] det_loss_box_dn_5[0.1932] loss_depth[0.0343] total_loss[8.2467]
Rank[0/8] 06/20/2025 08:40:58 INFO stats.py:314 | Epoch[140] Step[99] GlobalStep[54699] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 13:08:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:40:58 INFO loss_tracker.py:84 | Epoch[140/239] Step[99] GlobalStep[54699/NA]: det_loss_cls_0[0.4873] det_loss_box_0[0.5909] det_loss_cls_1[0.4787] det_loss_box_1[0.4431] det_loss_cls_2[0.4248] det_loss_box_2[0.4105] det_loss_cls_3[0.3888] det_loss_box_3[0.3988] det_loss_cls_4[0.3648] det_loss_box_4[0.3910] det_loss_cls_5[0.3490] det_loss_box_5[0.3931] det_loss_cls_dn_0[0.0868] det_loss_box_dn_0[0.2471] det_loss_cls_dn_1[0.0835] det_loss_box_dn_1[0.2059] det_loss_cls_dn_2[0.0830] det_loss_box_dn_2[0.1923] det_loss_cls_dn_3[0.0821] det_loss_box_dn_3[0.1893] det_loss_cls_dn_4[0.0816] det_loss_box_dn_4[0.1889] det_loss_cls_dn_5[0.0809] det_loss_box_dn_5[0.1882] loss_depth[0.0374] total_loss[6.8678]
Rank[0/8] 06/20/2025 08:41:23 INFO stats.py:314 | Epoch[140] Step[124] GlobalStep[54724] Training Speed: 7.99 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 13:07:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:41:23 INFO loss_tracker.py:84 | Epoch[140/239] Step[124] GlobalStep[54724/NA]: det_loss_cls_0[0.5359] det_loss_box_0[0.7397] det_loss_cls_1[0.5274] det_loss_box_1[0.5767] det_loss_cls_2[0.4884] det_loss_box_2[0.5344] det_loss_cls_3[0.4551] det_loss_box_3[0.5099] det_loss_cls_4[0.4276] det_loss_box_4[0.4963] det_loss_cls_5[0.4128] det_loss_box_5[0.4934] det_loss_cls_dn_0[0.0948] det_loss_box_dn_0[0.2958] det_loss_cls_dn_1[0.0915] det_loss_box_dn_1[0.2526] det_loss_cls_dn_2[0.0898] det_loss_box_dn_2[0.2359] det_loss_cls_dn_3[0.0891] det_loss_box_dn_3[0.2328] det_loss_cls_dn_4[0.0880] det_loss_box_dn_4[0.2285] det_loss_cls_dn_5[0.0878] det_loss_box_dn_5[0.2263] loss_depth[0.0375] total_loss[8.2479]
Rank[0/8] 06/20/2025 08:41:49 INFO stats.py:314 | Epoch[140] Step[149] GlobalStep[54749] Training Speed: 6.32 samples/sec across all devices.	Average Step Time: 1.27 sec.	Estimated Remaining Time: 13:07:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:41:49 INFO loss_tracker.py:84 | Epoch[140/239] Step[149] GlobalStep[54749/NA]: det_loss_cls_0[0.4629] det_loss_box_0[0.6076] det_loss_cls_1[0.4501] det_loss_box_1[0.4468] det_loss_cls_2[0.4057] det_loss_box_2[0.4025] det_loss_cls_3[0.3798] det_loss_box_3[0.3785] det_loss_cls_4[0.3478] det_loss_box_4[0.3702] det_loss_cls_5[0.3354] det_loss_box_5[0.3681] det_loss_cls_dn_0[0.0897] det_loss_box_dn_0[0.3213] det_loss_cls_dn_1[0.0869] det_loss_box_dn_1[0.2640] det_loss_cls_dn_2[0.0867] det_loss_box_dn_2[0.2427] det_loss_cls_dn_3[0.0857] det_loss_box_dn_3[0.2372] det_loss_cls_dn_4[0.0846] det_loss_box_dn_4[0.2352] det_loss_cls_dn_5[0.0839] det_loss_box_dn_5[0.2327] loss_depth[0.0357] total_loss[7.0418]
Rank[0/8] 06/20/2025 08:42:15 INFO stats.py:314 | Epoch[140] Step[174] GlobalStep[54774] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 13:06:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:42:15 INFO loss_tracker.py:84 | Epoch[140/239] Step[174] GlobalStep[54774/NA]: det_loss_cls_0[0.4940] det_loss_box_0[0.6384] det_loss_cls_1[0.4906] det_loss_box_1[0.5059] det_loss_cls_2[0.4675] det_loss_box_2[0.4644] det_loss_cls_3[0.4375] det_loss_box_3[0.4486] det_loss_cls_4[0.4103] det_loss_box_4[0.4332] det_loss_cls_5[0.4024] det_loss_box_5[0.4242] det_loss_cls_dn_0[0.0931] det_loss_box_dn_0[0.3323] det_loss_cls_dn_1[0.0892] det_loss_box_dn_1[0.2822] det_loss_cls_dn_2[0.0894] det_loss_box_dn_2[0.2619] det_loss_cls_dn_3[0.0885] det_loss_box_dn_3[0.2569] det_loss_cls_dn_4[0.0879] det_loss_box_dn_4[0.2552] det_loss_cls_dn_5[0.0876] det_loss_box_dn_5[0.2528] loss_depth[0.0342] total_loss[7.8281]
Rank[0/8] 06/20/2025 08:42:41 INFO stats.py:314 | Epoch[140] Step[199] GlobalStep[54799] Training Speed: 6.77 samples/sec across all devices.	Average Step Time: 1.18 sec.	Estimated Remaining Time: 13:06:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:42:41 INFO loss_tracker.py:84 | Epoch[140/239] Step[199] GlobalStep[54799/NA]: det_loss_cls_0[0.3872] det_loss_box_0[0.5137] det_loss_cls_1[0.3761] det_loss_box_1[0.3941] det_loss_cls_2[0.3510] det_loss_box_2[0.3527] det_loss_cls_3[0.3186] det_loss_box_3[0.3410] det_loss_cls_4[0.3034] det_loss_box_4[0.3336] det_loss_cls_5[0.2983] det_loss_box_5[0.3283] det_loss_cls_dn_0[0.0902] det_loss_box_dn_0[0.3097] det_loss_cls_dn_1[0.0871] det_loss_box_dn_1[0.2595] det_loss_cls_dn_2[0.0860] det_loss_box_dn_2[0.2439] det_loss_cls_dn_3[0.0853] det_loss_box_dn_3[0.2385] det_loss_cls_dn_4[0.0844] det_loss_box_dn_4[0.2357] det_loss_cls_dn_5[0.0833] det_loss_box_dn_5[0.2345] loss_depth[0.0398] total_loss[6.3759]
Rank[0/8] 06/20/2025 08:43:06 INFO stats.py:314 | Epoch[140] Step[224] GlobalStep[54824] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 13:05:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:43:06 INFO loss_tracker.py:84 | Epoch[140/239] Step[224] GlobalStep[54824/NA]: det_loss_cls_0[0.6091] det_loss_box_0[0.8204] det_loss_cls_1[0.5808] det_loss_box_1[0.6575] det_loss_cls_2[0.5321] det_loss_box_2[0.5789] det_loss_cls_3[0.5009] det_loss_box_3[0.5595] det_loss_cls_4[0.4689] det_loss_box_4[0.5480] det_loss_cls_5[0.4492] det_loss_box_5[0.5567] det_loss_cls_dn_0[0.0960] det_loss_box_dn_0[0.2746] det_loss_cls_dn_1[0.0938] det_loss_box_dn_1[0.2279] det_loss_cls_dn_2[0.0929] det_loss_box_dn_2[0.2116] det_loss_cls_dn_3[0.0920] det_loss_box_dn_3[0.2083] det_loss_cls_dn_4[0.0909] det_loss_box_dn_4[0.2070] det_loss_cls_dn_5[0.0899] det_loss_box_dn_5[0.2061] loss_depth[0.0376] total_loss[8.7904]
Rank[0/8] 06/20/2025 08:43:32 INFO stats.py:314 | Epoch[140] Step[249] GlobalStep[54849] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 13:05:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:43:32 INFO loss_tracker.py:84 | Epoch[140/239] Step[249] GlobalStep[54849/NA]: det_loss_cls_0[0.4767] det_loss_box_0[0.5853] det_loss_cls_1[0.4750] det_loss_box_1[0.4476] det_loss_cls_2[0.4217] det_loss_box_2[0.4099] det_loss_cls_3[0.3798] det_loss_box_3[0.3955] det_loss_cls_4[0.3521] det_loss_box_4[0.3858] det_loss_cls_5[0.3433] det_loss_box_5[0.3884] det_loss_cls_dn_0[0.0891] det_loss_box_dn_0[0.2610] det_loss_cls_dn_1[0.0856] det_loss_box_dn_1[0.2138] det_loss_cls_dn_2[0.0845] det_loss_box_dn_2[0.1976] det_loss_cls_dn_3[0.0836] det_loss_box_dn_3[0.1942] det_loss_cls_dn_4[0.0822] det_loss_box_dn_4[0.1928] det_loss_cls_dn_5[0.0812] det_loss_box_dn_5[0.1925] loss_depth[0.0351] total_loss[6.8544]
Rank[0/8] 06/20/2025 08:43:58 INFO stats.py:314 | Epoch[140] Step[274] GlobalStep[54874] Training Speed: 7.80 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 13:04:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:43:58 INFO loss_tracker.py:84 | Epoch[140/239] Step[274] GlobalStep[54874/NA]: det_loss_cls_0[0.6213] det_loss_box_0[0.8350] det_loss_cls_1[0.6169] det_loss_box_1[0.6821] det_loss_cls_2[0.5768] det_loss_box_2[0.6258] det_loss_cls_3[0.5506] det_loss_box_3[0.6059] det_loss_cls_4[0.5251] det_loss_box_4[0.5962] det_loss_cls_5[0.5112] det_loss_box_5[0.5924] det_loss_cls_dn_0[0.1054] det_loss_box_dn_0[0.2689] det_loss_cls_dn_1[0.1025] det_loss_box_dn_1[0.2343] det_loss_cls_dn_2[0.1015] det_loss_box_dn_2[0.2214] det_loss_cls_dn_3[0.1006] det_loss_box_dn_3[0.2188] det_loss_cls_dn_4[0.0994] det_loss_box_dn_4[0.2173] det_loss_cls_dn_5[0.0991] det_loss_box_dn_5[0.2156] loss_depth[0.0373] total_loss[9.3614]
Rank[0/8] 06/20/2025 08:44:24 INFO stats.py:314 | Epoch[140] Step[299] GlobalStep[54899] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 13:03:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:44:24 INFO loss_tracker.py:84 | Epoch[140/239] Step[299] GlobalStep[54899/NA]: det_loss_cls_0[0.4500] det_loss_box_0[0.5587] det_loss_cls_1[0.4437] det_loss_box_1[0.4516] det_loss_cls_2[0.4079] det_loss_box_2[0.4051] det_loss_cls_3[0.3795] det_loss_box_3[0.3957] det_loss_cls_4[0.3530] det_loss_box_4[0.3899] det_loss_cls_5[0.3379] det_loss_box_5[0.3955] det_loss_cls_dn_0[0.0961] det_loss_box_dn_0[0.3147] det_loss_cls_dn_1[0.0928] det_loss_box_dn_1[0.2536] det_loss_cls_dn_2[0.0922] det_loss_box_dn_2[0.2308] det_loss_cls_dn_3[0.0919] det_loss_box_dn_3[0.2256] det_loss_cls_dn_4[0.0912] det_loss_box_dn_4[0.2224] det_loss_cls_dn_5[0.0906] det_loss_box_dn_5[0.2215] loss_depth[0.0378] total_loss[7.0297]
Rank[0/8] 06/20/2025 08:44:49 INFO stats.py:314 | Epoch[140] Step[324] GlobalStep[54924] Training Speed: 8.19 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 13:03:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:44:49 INFO loss_tracker.py:84 | Epoch[140/239] Step[324] GlobalStep[54924/NA]: det_loss_cls_0[0.5630] det_loss_box_0[0.7008] det_loss_cls_1[0.5420] det_loss_box_1[0.5476] det_loss_cls_2[0.5102] det_loss_box_2[0.4933] det_loss_cls_3[0.4789] det_loss_box_3[0.4766] det_loss_cls_4[0.4556] det_loss_box_4[0.4693] det_loss_cls_5[0.4439] det_loss_box_5[0.4723] det_loss_cls_dn_0[0.1022] det_loss_box_dn_0[0.2922] det_loss_cls_dn_1[0.0981] det_loss_box_dn_1[0.2527] det_loss_cls_dn_2[0.0980] det_loss_box_dn_2[0.2415] det_loss_cls_dn_3[0.0958] det_loss_box_dn_3[0.2369] det_loss_cls_dn_4[0.0956] det_loss_box_dn_4[0.2353] det_loss_cls_dn_5[0.0943] det_loss_box_dn_5[0.2330] loss_depth[0.0374] total_loss[8.2664]
Rank[0/8] 06/20/2025 08:45:15 INFO stats.py:314 | Epoch[140] Step[349] GlobalStep[54949] Training Speed: 7.91 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 13:02:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:45:15 INFO loss_tracker.py:84 | Epoch[140/239] Step[349] GlobalStep[54949/NA]: det_loss_cls_0[0.4987] det_loss_box_0[0.6075] det_loss_cls_1[0.4920] det_loss_box_1[0.4582] det_loss_cls_2[0.4343] det_loss_box_2[0.4290] det_loss_cls_3[0.3985] det_loss_box_3[0.4180] det_loss_cls_4[0.3794] det_loss_box_4[0.4106] det_loss_cls_5[0.3634] det_loss_box_5[0.4115] det_loss_cls_dn_0[0.0989] det_loss_box_dn_0[0.2869] det_loss_cls_dn_1[0.0952] det_loss_box_dn_1[0.2492] det_loss_cls_dn_2[0.0941] det_loss_box_dn_2[0.2360] det_loss_cls_dn_3[0.0932] det_loss_box_dn_3[0.2328] det_loss_cls_dn_4[0.0922] det_loss_box_dn_4[0.2312] det_loss_cls_dn_5[0.0914] det_loss_box_dn_5[0.2295] loss_depth[0.0378] total_loss[7.3694]
Rank[0/8] 06/20/2025 08:45:41 INFO stats.py:314 | Epoch[140] Step[374] GlobalStep[54974] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 13:02:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:45:41 INFO loss_tracker.py:84 | Epoch[140/239] Step[374] GlobalStep[54974/NA]: det_loss_cls_0[0.5205] det_loss_box_0[0.6768] det_loss_cls_1[0.5064] det_loss_box_1[0.5255] det_loss_cls_2[0.4705] det_loss_box_2[0.4727] det_loss_cls_3[0.4311] det_loss_box_3[0.4657] det_loss_cls_4[0.4059] det_loss_box_4[0.4589] det_loss_cls_5[0.3851] det_loss_box_5[0.4595] det_loss_cls_dn_0[0.0940] det_loss_box_dn_0[0.2919] det_loss_cls_dn_1[0.0891] det_loss_box_dn_1[0.2450] det_loss_cls_dn_2[0.0886] det_loss_box_dn_2[0.2328] det_loss_cls_dn_3[0.0875] det_loss_box_dn_3[0.2270] det_loss_cls_dn_4[0.0862] det_loss_box_dn_4[0.2253] det_loss_cls_dn_5[0.0851] det_loss_box_dn_5[0.2234] loss_depth[0.0374] total_loss[7.7919]
Rank[0/8] 06/20/2025 08:45:56 INFO stats.py:394 | Epoch[140] completed. Training Speed: 7.70 samples/sec across all devices.	Epoch Time: 404.98 sec.	Average Epoch Time: 404.98 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 13:01:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:46:09 INFO stats.py:314 | Epoch[141] Step[9] GlobalStep[54999] Training Speed: 7.63 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 13:01:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:46:24 INFO loss_tracker.py:84 | Epoch[141/239] Step[24] GlobalStep[55014/NA]: det_loss_cls_0[0.5363] det_loss_box_0[0.6789] det_loss_cls_1[0.5073] det_loss_box_1[0.5229] det_loss_cls_2[0.4679] det_loss_box_2[0.4688] det_loss_cls_3[0.4319] det_loss_box_3[0.4556] det_loss_cls_4[0.4091] det_loss_box_4[0.4448] det_loss_cls_5[0.3922] det_loss_box_5[0.4439] det_loss_cls_dn_0[0.0972] det_loss_box_dn_0[0.2819] det_loss_cls_dn_1[0.0943] det_loss_box_dn_1[0.2389] det_loss_cls_dn_2[0.0928] det_loss_box_dn_2[0.2251] det_loss_cls_dn_3[0.0913] det_loss_box_dn_3[0.2225] det_loss_cls_dn_4[0.0902] det_loss_box_dn_4[0.2207] det_loss_cls_dn_5[0.0896] det_loss_box_dn_5[0.2193] loss_depth[0.0372] total_loss[7.7606]
Rank[0/8] 06/20/2025 08:46:35 INFO stats.py:314 | Epoch[141] Step[34] GlobalStep[55024] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 13:01:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:46:50 INFO loss_tracker.py:84 | Epoch[141/239] Step[49] GlobalStep[55039/NA]: det_loss_cls_0[0.6656] det_loss_box_0[0.8739] det_loss_cls_1[0.6410] det_loss_box_1[0.6781] det_loss_cls_2[0.5901] det_loss_box_2[0.6071] det_loss_cls_3[0.5487] det_loss_box_3[0.5940] det_loss_cls_4[0.5281] det_loss_box_4[0.5727] det_loss_cls_5[0.5137] det_loss_box_5[0.5675] det_loss_cls_dn_0[0.1056] det_loss_box_dn_0[0.3156] det_loss_cls_dn_1[0.1024] det_loss_box_dn_1[0.2701] det_loss_cls_dn_2[0.1013] det_loss_box_dn_2[0.2567] det_loss_cls_dn_3[0.1001] det_loss_box_dn_3[0.2555] det_loss_cls_dn_4[0.0998] det_loss_box_dn_4[0.2529] det_loss_cls_dn_5[0.0993] det_loss_box_dn_5[0.2507] loss_depth[0.0367] total_loss[9.6272]
Rank[0/8] 06/20/2025 08:47:01 INFO stats.py:314 | Epoch[141] Step[59] GlobalStep[55049] Training Speed: 8.63 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 13:00:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:47:16 INFO loss_tracker.py:84 | Epoch[141/239] Step[74] GlobalStep[55064/NA]: det_loss_cls_0[0.5809] det_loss_box_0[0.6994] det_loss_cls_1[0.5598] det_loss_box_1[0.5606] det_loss_cls_2[0.5164] det_loss_box_2[0.5154] det_loss_cls_3[0.4825] det_loss_box_3[0.5036] det_loss_cls_4[0.4584] det_loss_box_4[0.4887] det_loss_cls_5[0.4403] det_loss_box_5[0.4918] det_loss_cls_dn_0[0.1039] det_loss_box_dn_0[0.2895] det_loss_cls_dn_1[0.0994] det_loss_box_dn_1[0.2453] det_loss_cls_dn_2[0.0986] det_loss_box_dn_2[0.2327] det_loss_cls_dn_3[0.0979] det_loss_box_dn_3[0.2283] det_loss_cls_dn_4[0.0971] det_loss_box_dn_4[0.2279] det_loss_cls_dn_5[0.0968] det_loss_box_dn_5[0.2263] loss_depth[0.0367] total_loss[8.3782]
Rank[0/8] 06/20/2025 08:47:27 INFO stats.py:314 | Epoch[141] Step[84] GlobalStep[55074] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 13:00:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:47:43 INFO loss_tracker.py:84 | Epoch[141/239] Step[99] GlobalStep[55089/NA]: det_loss_cls_0[0.4461] det_loss_box_0[0.5719] det_loss_cls_1[0.4394] det_loss_box_1[0.4314] det_loss_cls_2[0.3921] det_loss_box_2[0.3974] det_loss_cls_3[0.3608] det_loss_box_3[0.3845] det_loss_cls_4[0.3433] det_loss_box_4[0.3679] det_loss_cls_5[0.3347] det_loss_box_5[0.3678] det_loss_cls_dn_0[0.0978] det_loss_box_dn_0[0.2395] det_loss_cls_dn_1[0.0938] det_loss_box_dn_1[0.2008] det_loss_cls_dn_2[0.0927] det_loss_box_dn_2[0.1871] det_loss_cls_dn_3[0.0916] det_loss_box_dn_3[0.1828] det_loss_cls_dn_4[0.0906] det_loss_box_dn_4[0.1816] det_loss_cls_dn_5[0.0896] det_loss_box_dn_5[0.1808] loss_depth[0.0382] total_loss[6.6041]
Rank[0/8] 06/20/2025 08:47:53 INFO stats.py:314 | Epoch[141] Step[109] GlobalStep[55099] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 12:59:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:48:08 INFO loss_tracker.py:84 | Epoch[141/239] Step[124] GlobalStep[55114/NA]: det_loss_cls_0[0.5684] det_loss_box_0[0.7572] det_loss_cls_1[0.5458] det_loss_box_1[0.5767] det_loss_cls_2[0.5018] det_loss_box_2[0.5206] det_loss_cls_3[0.4709] det_loss_box_3[0.5012] det_loss_cls_4[0.4359] det_loss_box_4[0.4928] det_loss_cls_5[0.4276] det_loss_box_5[0.4884] det_loss_cls_dn_0[0.0939] det_loss_box_dn_0[0.2368] det_loss_cls_dn_1[0.0894] det_loss_box_dn_1[0.2016] det_loss_cls_dn_2[0.0887] det_loss_box_dn_2[0.1882] det_loss_cls_dn_3[0.0870] det_loss_box_dn_3[0.1847] det_loss_cls_dn_4[0.0862] det_loss_box_dn_4[0.1846] det_loss_cls_dn_5[0.0848] det_loss_box_dn_5[0.1837] loss_depth[0.0353] total_loss[8.0321]
Rank[0/8] 06/20/2025 08:48:19 INFO stats.py:314 | Epoch[141] Step[134] GlobalStep[55124] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 12:58:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:48:34 INFO loss_tracker.py:84 | Epoch[141/239] Step[149] GlobalStep[55139/NA]: det_loss_cls_0[0.5435] det_loss_box_0[0.7439] det_loss_cls_1[0.5219] det_loss_box_1[0.5886] det_loss_cls_2[0.4899] det_loss_box_2[0.5357] det_loss_cls_3[0.4656] det_loss_box_3[0.5138] det_loss_cls_4[0.4427] det_loss_box_4[0.5007] det_loss_cls_5[0.4294] det_loss_box_5[0.5079] det_loss_cls_dn_0[0.1081] det_loss_box_dn_0[0.3171] det_loss_cls_dn_1[0.1038] det_loss_box_dn_1[0.2718] det_loss_cls_dn_2[0.1038] det_loss_box_dn_2[0.2570] det_loss_cls_dn_3[0.1030] det_loss_box_dn_3[0.2538] det_loss_cls_dn_4[0.1018] det_loss_box_dn_4[0.2518] det_loss_cls_dn_5[0.1004] det_loss_box_dn_5[0.2500] loss_depth[0.0361] total_loss[8.5422]
Rank[0/8] 06/20/2025 08:48:45 INFO stats.py:314 | Epoch[141] Step[159] GlobalStep[55149] Training Speed: 6.01 samples/sec across all devices.	Average Step Time: 1.33 sec.	Estimated Remaining Time: 12:58:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:48:59 INFO loss_tracker.py:84 | Epoch[141/239] Step[174] GlobalStep[55164/NA]: det_loss_cls_0[0.4602] det_loss_box_0[0.5396] det_loss_cls_1[0.4381] det_loss_box_1[0.4173] det_loss_cls_2[0.4099] det_loss_box_2[0.3779] det_loss_cls_3[0.3792] det_loss_box_3[0.3608] det_loss_cls_4[0.3528] det_loss_box_4[0.3529] det_loss_cls_5[0.3421] det_loss_box_5[0.3544] det_loss_cls_dn_0[0.0900] det_loss_box_dn_0[0.2687] det_loss_cls_dn_1[0.0868] det_loss_box_dn_1[0.2247] det_loss_cls_dn_2[0.0855] det_loss_box_dn_2[0.2065] det_loss_cls_dn_3[0.0844] det_loss_box_dn_3[0.2033] det_loss_cls_dn_4[0.0833] det_loss_box_dn_4[0.2018] det_loss_cls_dn_5[0.0825] det_loss_box_dn_5[0.2005] loss_depth[0.0350] total_loss[6.6383]
Rank[0/8] 06/20/2025 08:49:10 INFO stats.py:314 | Epoch[141] Step[184] GlobalStep[55174] Training Speed: 6.29 samples/sec across all devices.	Average Step Time: 1.27 sec.	Estimated Remaining Time: 12:57:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:49:26 INFO loss_tracker.py:84 | Epoch[141/239] Step[199] GlobalStep[55189/NA]: det_loss_cls_0[0.5858] det_loss_box_0[0.7517] det_loss_cls_1[0.5769] det_loss_box_1[0.5721] det_loss_cls_2[0.5013] det_loss_box_2[0.5168] det_loss_cls_3[0.4589] det_loss_box_3[0.4999] det_loss_cls_4[0.4311] det_loss_box_4[0.4913] det_loss_cls_5[0.4171] det_loss_box_5[0.4930] det_loss_cls_dn_0[0.1010] det_loss_box_dn_0[0.2459] det_loss_cls_dn_1[0.0973] det_loss_box_dn_1[0.2054] det_loss_cls_dn_2[0.0972] det_loss_box_dn_2[0.1876] det_loss_cls_dn_3[0.0959] det_loss_box_dn_3[0.1859] det_loss_cls_dn_4[0.0947] det_loss_box_dn_4[0.1849] det_loss_cls_dn_5[0.0937] det_loss_box_dn_5[0.1839] loss_depth[0.0370] total_loss[8.1064]
Rank[0/8] 06/20/2025 08:49:36 INFO stats.py:314 | Epoch[141] Step[209] GlobalStep[55199] Training Speed: 8.06 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 12:57:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:49:51 INFO loss_tracker.py:84 | Epoch[141/239] Step[224] GlobalStep[55214/NA]: det_loss_cls_0[0.4758] det_loss_box_0[0.6267] det_loss_cls_1[0.4696] det_loss_box_1[0.4907] det_loss_cls_2[0.4254] det_loss_box_2[0.4596] det_loss_cls_3[0.3943] det_loss_box_3[0.4533] det_loss_cls_4[0.3724] det_loss_box_4[0.4454] det_loss_cls_5[0.3625] det_loss_box_5[0.4492] det_loss_cls_dn_0[0.1003] det_loss_box_dn_0[0.3130] det_loss_cls_dn_1[0.0972] det_loss_box_dn_1[0.2682] det_loss_cls_dn_2[0.0968] det_loss_box_dn_2[0.2537] det_loss_cls_dn_3[0.0955] det_loss_box_dn_3[0.2513] det_loss_cls_dn_4[0.0948] det_loss_box_dn_4[0.2482] det_loss_cls_dn_5[0.0941] det_loss_box_dn_5[0.2474] loss_depth[0.0393] total_loss[7.6246]
Rank[0/8] 06/20/2025 08:50:01 INFO stats.py:314 | Epoch[141] Step[234] GlobalStep[55224] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 12:56:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:50:17 INFO loss_tracker.py:84 | Epoch[141/239] Step[249] GlobalStep[55239/NA]: det_loss_cls_0[0.4517] det_loss_box_0[0.5806] det_loss_cls_1[0.4466] det_loss_box_1[0.4665] det_loss_cls_2[0.4052] det_loss_box_2[0.4224] det_loss_cls_3[0.3745] det_loss_box_3[0.4018] det_loss_cls_4[0.3473] det_loss_box_4[0.3888] det_loss_cls_5[0.3335] det_loss_box_5[0.3929] det_loss_cls_dn_0[0.0909] det_loss_box_dn_0[0.2784] det_loss_cls_dn_1[0.0883] det_loss_box_dn_1[0.2251] det_loss_cls_dn_2[0.0867] det_loss_box_dn_2[0.2097] det_loss_cls_dn_3[0.0856] det_loss_box_dn_3[0.2063] det_loss_cls_dn_4[0.0843] det_loss_box_dn_4[0.2035] det_loss_cls_dn_5[0.0832] det_loss_box_dn_5[0.2002] loss_depth[0.0396] total_loss[6.8939]
Rank[0/8] 06/20/2025 08:50:27 INFO stats.py:314 | Epoch[141] Step[259] GlobalStep[55249] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 12:56:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:50:43 INFO loss_tracker.py:84 | Epoch[141/239] Step[274] GlobalStep[55264/NA]: det_loss_cls_0[0.4996] det_loss_box_0[0.6813] det_loss_cls_1[0.4808] det_loss_box_1[0.5306] det_loss_cls_2[0.4363] det_loss_box_2[0.4768] det_loss_cls_3[0.4006] det_loss_box_3[0.4654] det_loss_cls_4[0.3732] det_loss_box_4[0.4554] det_loss_cls_5[0.3614] det_loss_box_5[0.4564] det_loss_cls_dn_0[0.0945] det_loss_box_dn_0[0.3308] det_loss_cls_dn_1[0.0916] det_loss_box_dn_1[0.2800] det_loss_cls_dn_2[0.0908] det_loss_box_dn_2[0.2572] det_loss_cls_dn_3[0.0899] det_loss_box_dn_3[0.2538] det_loss_cls_dn_4[0.0891] det_loss_box_dn_4[0.2496] det_loss_cls_dn_5[0.0882] det_loss_box_dn_5[0.2481] loss_depth[0.0368] total_loss[7.8180]
Rank[0/8] 06/20/2025 08:50:54 INFO stats.py:314 | Epoch[141] Step[284] GlobalStep[55274] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 12:55:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:51:09 INFO loss_tracker.py:84 | Epoch[141/239] Step[299] GlobalStep[55289/NA]: det_loss_cls_0[0.4521] det_loss_box_0[0.5853] det_loss_cls_1[0.4445] det_loss_box_1[0.4307] det_loss_cls_2[0.3960] det_loss_box_2[0.3816] det_loss_cls_3[0.3686] det_loss_box_3[0.3652] det_loss_cls_4[0.3398] det_loss_box_4[0.3587] det_loss_cls_5[0.3315] det_loss_box_5[0.3587] det_loss_cls_dn_0[0.1011] det_loss_box_dn_0[0.3092] det_loss_cls_dn_1[0.0974] det_loss_box_dn_1[0.2592] det_loss_cls_dn_2[0.0968] det_loss_box_dn_2[0.2403] det_loss_cls_dn_3[0.0962] det_loss_box_dn_3[0.2356] det_loss_cls_dn_4[0.0953] det_loss_box_dn_4[0.2335] det_loss_cls_dn_5[0.0950] det_loss_box_dn_5[0.2295] loss_depth[0.0387] total_loss[6.9404]
Rank[0/8] 06/20/2025 08:51:19 INFO stats.py:314 | Epoch[141] Step[309] GlobalStep[55299] Training Speed: 7.97 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 12:55:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:51:34 INFO loss_tracker.py:84 | Epoch[141/239] Step[324] GlobalStep[55314/NA]: det_loss_cls_0[0.4900] det_loss_box_0[0.6522] det_loss_cls_1[0.4727] det_loss_box_1[0.5000] det_loss_cls_2[0.4387] det_loss_box_2[0.4487] det_loss_cls_3[0.4053] det_loss_box_3[0.4472] det_loss_cls_4[0.3741] det_loss_box_4[0.4395] det_loss_cls_5[0.3568] det_loss_box_5[0.4445] det_loss_cls_dn_0[0.1007] det_loss_box_dn_0[0.2722] det_loss_cls_dn_1[0.0985] det_loss_box_dn_1[0.2284] det_loss_cls_dn_2[0.0974] det_loss_box_dn_2[0.2150] det_loss_cls_dn_3[0.0963] det_loss_box_dn_3[0.2126] det_loss_cls_dn_4[0.0952] det_loss_box_dn_4[0.2114] det_loss_cls_dn_5[0.0942] det_loss_box_dn_5[0.2096] loss_depth[0.0359] total_loss[7.4370]
Rank[0/8] 06/20/2025 08:51:45 INFO stats.py:314 | Epoch[141] Step[334] GlobalStep[55324] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 12:54:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:52:00 INFO loss_tracker.py:84 | Epoch[141/239] Step[349] GlobalStep[55339/NA]: det_loss_cls_0[0.5751] det_loss_box_0[0.7539] det_loss_cls_1[0.5731] det_loss_box_1[0.5815] det_loss_cls_2[0.5175] det_loss_box_2[0.5296] det_loss_cls_3[0.4934] det_loss_box_3[0.5072] det_loss_cls_4[0.4590] det_loss_box_4[0.4900] det_loss_cls_5[0.4467] det_loss_box_5[0.4870] det_loss_cls_dn_0[0.1020] det_loss_box_dn_0[0.2626] det_loss_cls_dn_1[0.0986] det_loss_box_dn_1[0.2175] det_loss_cls_dn_2[0.0978] det_loss_box_dn_2[0.2007] det_loss_cls_dn_3[0.0976] det_loss_box_dn_3[0.1982] det_loss_cls_dn_4[0.0962] det_loss_box_dn_4[0.1960] det_loss_cls_dn_5[0.0954] det_loss_box_dn_5[0.1942] loss_depth[0.0391] total_loss[8.3097]
Rank[0/8] 06/20/2025 08:52:10 INFO stats.py:314 | Epoch[141] Step[359] GlobalStep[55349] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 12:53:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:52:26 INFO loss_tracker.py:84 | Epoch[141/239] Step[374] GlobalStep[55364/NA]: det_loss_cls_0[0.5974] det_loss_box_0[0.8004] det_loss_cls_1[0.5831] det_loss_box_1[0.6092] det_loss_cls_2[0.5323] det_loss_box_2[0.5436] det_loss_cls_3[0.5059] det_loss_box_3[0.5371] det_loss_cls_4[0.4679] det_loss_box_4[0.5279] det_loss_cls_5[0.4534] det_loss_box_5[0.5296] det_loss_cls_dn_0[0.0962] det_loss_box_dn_0[0.3161] det_loss_cls_dn_1[0.0926] det_loss_box_dn_1[0.2727] det_loss_cls_dn_2[0.0921] det_loss_box_dn_2[0.2548] det_loss_cls_dn_3[0.0900] det_loss_box_dn_3[0.2530] det_loss_cls_dn_4[0.0888] det_loss_box_dn_4[0.2518] det_loss_cls_dn_5[0.0881] det_loss_box_dn_5[0.2496] loss_depth[0.0376] total_loss[8.8713]
Rank[0/8] 06/20/2025 08:52:36 INFO stats.py:314 | Epoch[141] Step[384] GlobalStep[55374] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 12:53:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:52:41 INFO stats.py:394 | Epoch[141] completed. Training Speed: 7.70 samples/sec across all devices.	Epoch Time: 404.97 sec.	Average Epoch Time: 404.97 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 12:53:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:53:03 INFO stats.py:314 | Epoch[142] Step[19] GlobalStep[55399] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 12:52:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:53:09 INFO loss_tracker.py:84 | Epoch[142/239] Step[24] GlobalStep[55404/NA]: det_loss_cls_0[0.5138] det_loss_box_0[0.6664] det_loss_cls_1[0.4986] det_loss_box_1[0.5091] det_loss_cls_2[0.4516] det_loss_box_2[0.4624] det_loss_cls_3[0.4242] det_loss_box_3[0.4366] det_loss_cls_4[0.4031] det_loss_box_4[0.4261] det_loss_cls_5[0.3839] det_loss_box_5[0.4324] det_loss_cls_dn_0[0.0987] det_loss_box_dn_0[0.3094] det_loss_cls_dn_1[0.0958] det_loss_box_dn_1[0.2637] det_loss_cls_dn_2[0.0952] det_loss_box_dn_2[0.2449] det_loss_cls_dn_3[0.0943] det_loss_box_dn_3[0.2416] det_loss_cls_dn_4[0.0933] det_loss_box_dn_4[0.2397] det_loss_cls_dn_5[0.0926] det_loss_box_dn_5[0.2363] loss_depth[0.0403] total_loss[7.7539]
Rank[0/8] 06/20/2025 08:53:29 INFO stats.py:314 | Epoch[142] Step[44] GlobalStep[55424] Training Speed: 8.06 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 12:52:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:53:35 INFO loss_tracker.py:84 | Epoch[142/239] Step[49] GlobalStep[55429/NA]: det_loss_cls_0[0.4786] det_loss_box_0[0.5819] det_loss_cls_1[0.4595] det_loss_box_1[0.4297] det_loss_cls_2[0.4193] det_loss_box_2[0.3893] det_loss_cls_3[0.3869] det_loss_box_3[0.3740] det_loss_cls_4[0.3631] det_loss_box_4[0.3648] det_loss_cls_5[0.3489] det_loss_box_5[0.3675] det_loss_cls_dn_0[0.0901] det_loss_box_dn_0[0.2662] det_loss_cls_dn_1[0.0869] det_loss_box_dn_1[0.2192] det_loss_cls_dn_2[0.0866] det_loss_box_dn_2[0.2016] det_loss_cls_dn_3[0.0855] det_loss_box_dn_3[0.1986] det_loss_cls_dn_4[0.0841] det_loss_box_dn_4[0.1979] det_loss_cls_dn_5[0.0836] det_loss_box_dn_5[0.1955] loss_depth[0.0371] total_loss[6.7964]
Rank[0/8] 06/20/2025 08:53:56 INFO stats.py:314 | Epoch[142] Step[69] GlobalStep[55449] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 12:51:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:54:01 INFO loss_tracker.py:84 | Epoch[142/239] Step[74] GlobalStep[55454/NA]: det_loss_cls_0[0.5304] det_loss_box_0[0.7094] det_loss_cls_1[0.5108] det_loss_box_1[0.5742] det_loss_cls_2[0.4648] det_loss_box_2[0.5290] det_loss_cls_3[0.4338] det_loss_box_3[0.4956] det_loss_cls_4[0.4031] det_loss_box_4[0.4925] det_loss_cls_5[0.3916] det_loss_box_5[0.4887] det_loss_cls_dn_0[0.0829] det_loss_box_dn_0[0.3751] det_loss_cls_dn_1[0.0791] det_loss_box_dn_1[0.3317] det_loss_cls_dn_2[0.0784] det_loss_box_dn_2[0.3043] det_loss_cls_dn_3[0.0772] det_loss_box_dn_3[0.2891] det_loss_cls_dn_4[0.0762] det_loss_box_dn_4[0.2816] det_loss_cls_dn_5[0.0750] det_loss_box_dn_5[0.2796] loss_depth[0.0364] total_loss[8.3905]
Rank[0/8] 06/20/2025 08:54:21 INFO stats.py:314 | Epoch[142] Step[94] GlobalStep[55474] Training Speed: 7.36 samples/sec across all devices.	Average Step Time: 1.09 sec.	Estimated Remaining Time: 12:51:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:54:27 INFO loss_tracker.py:84 | Epoch[142/239] Step[99] GlobalStep[55479/NA]: det_loss_cls_0[0.5147] det_loss_box_0[0.7168] det_loss_cls_1[0.4930] det_loss_box_1[0.5577] det_loss_cls_2[0.4468] det_loss_box_2[0.5204] det_loss_cls_3[0.4251] det_loss_box_3[0.5045] det_loss_cls_4[0.4090] det_loss_box_4[0.4824] det_loss_cls_5[0.3996] det_loss_box_5[0.4837] det_loss_cls_dn_0[0.1027] det_loss_box_dn_0[0.3577] det_loss_cls_dn_1[0.0985] det_loss_box_dn_1[0.2997] det_loss_cls_dn_2[0.0985] det_loss_box_dn_2[0.2819] det_loss_cls_dn_3[0.0987] det_loss_box_dn_3[0.2769] det_loss_cls_dn_4[0.0987] det_loss_box_dn_4[0.2753] det_loss_cls_dn_5[0.0976] det_loss_box_dn_5[0.2741] loss_depth[0.0356] total_loss[8.3498]
Rank[0/8] 06/20/2025 08:54:47 INFO stats.py:314 | Epoch[142] Step[119] GlobalStep[55499] Training Speed: 9.61 samples/sec across all devices.	Average Step Time: 0.83 sec.	Estimated Remaining Time: 12:50:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:54:52 INFO loss_tracker.py:84 | Epoch[142/239] Step[124] GlobalStep[55504/NA]: det_loss_cls_0[0.4314] det_loss_box_0[0.6227] det_loss_cls_1[0.4182] det_loss_box_1[0.4753] det_loss_cls_2[0.3730] det_loss_box_2[0.4307] det_loss_cls_3[0.3499] det_loss_box_3[0.4088] det_loss_cls_4[0.3280] det_loss_box_4[0.3949] det_loss_cls_5[0.3213] det_loss_box_5[0.3986] det_loss_cls_dn_0[0.0911] det_loss_box_dn_0[0.2907] det_loss_cls_dn_1[0.0868] det_loss_box_dn_1[0.2456] det_loss_cls_dn_2[0.0865] det_loss_box_dn_2[0.2294] det_loss_cls_dn_3[0.0860] det_loss_box_dn_3[0.2281] det_loss_cls_dn_4[0.0853] det_loss_box_dn_4[0.2276] det_loss_cls_dn_5[0.0846] det_loss_box_dn_5[0.2257] loss_depth[0.0368] total_loss[6.9570]
Rank[0/8] 06/20/2025 08:55:13 INFO stats.py:314 | Epoch[142] Step[144] GlobalStep[55524] Training Speed: 7.73 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 12:50:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:55:18 INFO loss_tracker.py:84 | Epoch[142/239] Step[149] GlobalStep[55529/NA]: det_loss_cls_0[0.4443] det_loss_box_0[0.6028] det_loss_cls_1[0.4453] det_loss_box_1[0.4486] det_loss_cls_2[0.3958] det_loss_box_2[0.4086] det_loss_cls_3[0.3595] det_loss_box_3[0.3995] det_loss_cls_4[0.3387] det_loss_box_4[0.3895] det_loss_cls_5[0.3309] det_loss_box_5[0.3839] det_loss_cls_dn_0[0.0934] det_loss_box_dn_0[0.2884] det_loss_cls_dn_1[0.0906] det_loss_box_dn_1[0.2378] det_loss_cls_dn_2[0.0896] det_loss_box_dn_2[0.2224] det_loss_cls_dn_3[0.0892] det_loss_box_dn_3[0.2195] det_loss_cls_dn_4[0.0886] det_loss_box_dn_4[0.2178] det_loss_cls_dn_5[0.0878] det_loss_box_dn_5[0.2106] loss_depth[0.0358] total_loss[6.9188]
Rank[0/8] 06/20/2025 08:55:38 INFO stats.py:314 | Epoch[142] Step[169] GlobalStep[55549] Training Speed: 8.08 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 12:49:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:55:43 INFO loss_tracker.py:84 | Epoch[142/239] Step[174] GlobalStep[55554/NA]: det_loss_cls_0[0.5259] det_loss_box_0[0.6708] det_loss_cls_1[0.4995] det_loss_box_1[0.5160] det_loss_cls_2[0.4553] det_loss_box_2[0.4629] det_loss_cls_3[0.4264] det_loss_box_3[0.4475] det_loss_cls_4[0.3973] det_loss_box_4[0.4353] det_loss_cls_5[0.3826] det_loss_box_5[0.4364] det_loss_cls_dn_0[0.0879] det_loss_box_dn_0[0.2661] det_loss_cls_dn_1[0.0853] det_loss_box_dn_1[0.2168] det_loss_cls_dn_2[0.0842] det_loss_box_dn_2[0.1987] det_loss_cls_dn_3[0.0835] det_loss_box_dn_3[0.1946] det_loss_cls_dn_4[0.0823] det_loss_box_dn_4[0.1928] det_loss_cls_dn_5[0.0821] det_loss_box_dn_5[0.1918] loss_depth[0.0347] total_loss[7.4569]
Rank[0/8] 06/20/2025 08:56:04 INFO stats.py:314 | Epoch[142] Step[194] GlobalStep[55574] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 12:48:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:56:09 INFO loss_tracker.py:84 | Epoch[142/239] Step[199] GlobalStep[55579/NA]: det_loss_cls_0[0.4673] det_loss_box_0[0.6139] det_loss_cls_1[0.4582] det_loss_box_1[0.4634] det_loss_cls_2[0.4200] det_loss_box_2[0.4230] det_loss_cls_3[0.3999] det_loss_box_3[0.3968] det_loss_cls_4[0.3731] det_loss_box_4[0.3888] det_loss_cls_5[0.3568] det_loss_box_5[0.3897] det_loss_cls_dn_0[0.1041] det_loss_box_dn_0[0.3408] det_loss_cls_dn_1[0.1005] det_loss_box_dn_1[0.2851] det_loss_cls_dn_2[0.0996] det_loss_box_dn_2[0.2663] det_loss_cls_dn_3[0.0979] det_loss_box_dn_3[0.2633] det_loss_cls_dn_4[0.0966] det_loss_box_dn_4[0.2634] det_loss_cls_dn_5[0.0954] det_loss_box_dn_5[0.2613] loss_depth[0.0408] total_loss[7.4659]
Rank[0/8] 06/20/2025 08:56:30 INFO stats.py:314 | Epoch[142] Step[219] GlobalStep[55599] Training Speed: 6.50 samples/sec across all devices.	Average Step Time: 1.23 sec.	Estimated Remaining Time: 12:48:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:56:35 INFO loss_tracker.py:84 | Epoch[142/239] Step[224] GlobalStep[55604/NA]: det_loss_cls_0[0.4987] det_loss_box_0[0.5864] det_loss_cls_1[0.4911] det_loss_box_1[0.4323] det_loss_cls_2[0.4464] det_loss_box_2[0.3809] det_loss_cls_3[0.4148] det_loss_box_3[0.3664] det_loss_cls_4[0.3874] det_loss_box_4[0.3538] det_loss_cls_5[0.3699] det_loss_box_5[0.3568] det_loss_cls_dn_0[0.0904] det_loss_box_dn_0[0.2285] det_loss_cls_dn_1[0.0866] det_loss_box_dn_1[0.1909] det_loss_cls_dn_2[0.0857] det_loss_box_dn_2[0.1723] det_loss_cls_dn_3[0.0846] det_loss_box_dn_3[0.1681] det_loss_cls_dn_4[0.0833] det_loss_box_dn_4[0.1654] det_loss_cls_dn_5[0.0828] det_loss_box_dn_5[0.1651] loss_depth[0.0322] total_loss[6.7209]
Rank[0/8] 06/20/2025 08:56:56 INFO stats.py:314 | Epoch[142] Step[244] GlobalStep[55624] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 12:47:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:57:01 INFO loss_tracker.py:84 | Epoch[142/239] Step[249] GlobalStep[55629/NA]: det_loss_cls_0[0.5136] det_loss_box_0[0.7639] det_loss_cls_1[0.4997] det_loss_box_1[0.6195] det_loss_cls_2[0.4569] det_loss_box_2[0.5566] det_loss_cls_3[0.4190] det_loss_box_3[0.5483] det_loss_cls_4[0.3925] det_loss_box_4[0.5263] det_loss_cls_5[0.3779] det_loss_box_5[0.5253] det_loss_cls_dn_0[0.0967] det_loss_box_dn_0[0.3491] det_loss_cls_dn_1[0.0929] det_loss_box_dn_1[0.3055] det_loss_cls_dn_2[0.0923] det_loss_box_dn_2[0.2918] det_loss_cls_dn_3[0.0918] det_loss_box_dn_3[0.2878] det_loss_cls_dn_4[0.0909] det_loss_box_dn_4[0.2866] det_loss_cls_dn_5[0.0900] det_loss_box_dn_5[0.2835] loss_depth[0.0380] total_loss[8.5966]
Rank[0/8] 06/20/2025 08:57:21 INFO stats.py:314 | Epoch[142] Step[269] GlobalStep[55649] Training Speed: 7.66 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 12:47:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:57:26 INFO loss_tracker.py:84 | Epoch[142/239] Step[274] GlobalStep[55654/NA]: det_loss_cls_0[0.5537] det_loss_box_0[0.7644] det_loss_cls_1[0.5427] det_loss_box_1[0.5791] det_loss_cls_2[0.4954] det_loss_box_2[0.5213] det_loss_cls_3[0.4630] det_loss_box_3[0.4992] det_loss_cls_4[0.4409] det_loss_box_4[0.4877] det_loss_cls_5[0.4202] det_loss_box_5[0.4935] det_loss_cls_dn_0[0.0984] det_loss_box_dn_0[0.2984] det_loss_cls_dn_1[0.0946] det_loss_box_dn_1[0.2453] det_loss_cls_dn_2[0.0939] det_loss_box_dn_2[0.2265] det_loss_cls_dn_3[0.0930] det_loss_box_dn_3[0.2218] det_loss_cls_dn_4[0.0920] det_loss_box_dn_4[0.2196] det_loss_cls_dn_5[0.0913] det_loss_box_dn_5[0.2157] loss_depth[0.0358] total_loss[8.2875]
Rank[0/8] 06/20/2025 08:57:47 INFO stats.py:314 | Epoch[142] Step[294] GlobalStep[55674] Training Speed: 8.07 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 12:46:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:57:52 INFO loss_tracker.py:84 | Epoch[142/239] Step[299] GlobalStep[55679/NA]: det_loss_cls_0[0.4723] det_loss_box_0[0.5723] det_loss_cls_1[0.4496] det_loss_box_1[0.4411] det_loss_cls_2[0.4091] det_loss_box_2[0.3823] det_loss_cls_3[0.3872] det_loss_box_3[0.3717] det_loss_cls_4[0.3597] det_loss_box_4[0.3632] det_loss_cls_5[0.3483] det_loss_box_5[0.3617] det_loss_cls_dn_0[0.0940] det_loss_box_dn_0[0.2710] det_loss_cls_dn_1[0.0905] det_loss_box_dn_1[0.2245] det_loss_cls_dn_2[0.0898] det_loss_box_dn_2[0.2055] det_loss_cls_dn_3[0.0891] det_loss_box_dn_3[0.2007] det_loss_cls_dn_4[0.0881] det_loss_box_dn_4[0.1980] det_loss_cls_dn_5[0.0867] det_loss_box_dn_5[0.1968] loss_depth[0.0348] total_loss[6.7881]
Rank[0/8] 06/20/2025 08:58:13 INFO stats.py:314 | Epoch[142] Step[319] GlobalStep[55699] Training Speed: 8.05 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 12:46:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:58:18 INFO loss_tracker.py:84 | Epoch[142/239] Step[324] GlobalStep[55704/NA]: det_loss_cls_0[0.5135] det_loss_box_0[0.6589] det_loss_cls_1[0.5027] det_loss_box_1[0.4939] det_loss_cls_2[0.4638] det_loss_box_2[0.4478] det_loss_cls_3[0.4352] det_loss_box_3[0.4207] det_loss_cls_4[0.4050] det_loss_box_4[0.4233] det_loss_cls_5[0.3875] det_loss_box_5[0.4228] det_loss_cls_dn_0[0.0861] det_loss_box_dn_0[0.2419] det_loss_cls_dn_1[0.0825] det_loss_box_dn_1[0.2059] det_loss_cls_dn_2[0.0817] det_loss_box_dn_2[0.1917] det_loss_cls_dn_3[0.0807] det_loss_box_dn_3[0.1882] det_loss_cls_dn_4[0.0798] det_loss_box_dn_4[0.1871] det_loss_cls_dn_5[0.0788] det_loss_box_dn_5[0.1858] loss_depth[0.0364] total_loss[7.3015]
Rank[0/8] 06/20/2025 08:58:38 INFO stats.py:314 | Epoch[142] Step[344] GlobalStep[55724] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 12:45:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:58:43 INFO loss_tracker.py:84 | Epoch[142/239] Step[349] GlobalStep[55729/NA]: det_loss_cls_0[0.4187] det_loss_box_0[0.5674] det_loss_cls_1[0.4173] det_loss_box_1[0.4379] det_loss_cls_2[0.3848] det_loss_box_2[0.3895] det_loss_cls_3[0.3559] det_loss_box_3[0.3825] det_loss_cls_4[0.3291] det_loss_box_4[0.3813] det_loss_cls_5[0.3137] det_loss_box_5[0.3829] det_loss_cls_dn_0[0.0933] det_loss_box_dn_0[0.3094] det_loss_cls_dn_1[0.0898] det_loss_box_dn_1[0.2571] det_loss_cls_dn_2[0.0891] det_loss_box_dn_2[0.2382] det_loss_cls_dn_3[0.0883] det_loss_box_dn_3[0.2352] det_loss_cls_dn_4[0.0878] det_loss_box_dn_4[0.2333] det_loss_cls_dn_5[0.0869] det_loss_box_dn_5[0.2283] loss_depth[0.0364] total_loss[6.8340]
Rank[0/8] 06/20/2025 08:59:04 INFO stats.py:314 | Epoch[142] Step[369] GlobalStep[55749] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 12:45:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:59:09 INFO loss_tracker.py:84 | Epoch[142/239] Step[374] GlobalStep[55754/NA]: det_loss_cls_0[0.4243] det_loss_box_0[0.5550] det_loss_cls_1[0.4192] det_loss_box_1[0.4480] det_loss_cls_2[0.3804] det_loss_box_2[0.4014] det_loss_cls_3[0.3391] det_loss_box_3[0.3932] det_loss_cls_4[0.3167] det_loss_box_4[0.3812] det_loss_cls_5[0.3038] det_loss_box_5[0.3804] det_loss_cls_dn_0[0.0865] det_loss_box_dn_0[0.2967] det_loss_cls_dn_1[0.0819] det_loss_box_dn_1[0.2478] det_loss_cls_dn_2[0.0809] det_loss_box_dn_2[0.2312] det_loss_cls_dn_3[0.0791] det_loss_box_dn_3[0.2292] det_loss_cls_dn_4[0.0781] det_loss_box_dn_4[0.2276] det_loss_cls_dn_5[0.0774] det_loss_box_dn_5[0.2261] loss_depth[0.0372] total_loss[6.7225]
Rank[0/8] 06/20/2025 08:59:24 INFO stats.py:394 | Epoch[142] completed. Training Speed: 7.74 samples/sec across all devices.	Epoch Time: 403.10 sec.	Average Epoch Time: 403.10 sec.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 12:44:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:59:31 INFO stats.py:314 | Epoch[143] Step[4] GlobalStep[55774] Training Speed: 7.32 samples/sec across all devices.	Average Step Time: 1.09 sec.	Estimated Remaining Time: 12:44:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 08:59:52 INFO loss_tracker.py:84 | Epoch[143/239] Step[24] GlobalStep[55794/NA]: det_loss_cls_0[0.4258] det_loss_box_0[0.5581] det_loss_cls_1[0.4176] det_loss_box_1[0.4382] det_loss_cls_2[0.3707] det_loss_box_2[0.3939] det_loss_cls_3[0.3395] det_loss_box_3[0.3875] det_loss_cls_4[0.3160] det_loss_box_4[0.3772] det_loss_cls_5[0.2969] det_loss_box_5[0.3815] det_loss_cls_dn_0[0.0852] det_loss_box_dn_0[0.2835] det_loss_cls_dn_1[0.0826] det_loss_box_dn_1[0.2297] det_loss_cls_dn_2[0.0812] det_loss_box_dn_2[0.2119] det_loss_cls_dn_3[0.0805] det_loss_box_dn_3[0.2083] det_loss_cls_dn_4[0.0790] det_loss_box_dn_4[0.2068] det_loss_cls_dn_5[0.0780] det_loss_box_dn_5[0.2050] loss_depth[0.0367] total_loss[6.5712]
Rank[0/8] 06/20/2025 08:59:57 INFO stats.py:314 | Epoch[143] Step[29] GlobalStep[55799] Training Speed: 8.84 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 12:43:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:00:18 INFO loss_tracker.py:84 | Epoch[143/239] Step[49] GlobalStep[55819/NA]: det_loss_cls_0[0.5351] det_loss_box_0[0.7051] det_loss_cls_1[0.5201] det_loss_box_1[0.5730] det_loss_cls_2[0.4664] det_loss_box_2[0.5322] det_loss_cls_3[0.4367] det_loss_box_3[0.5128] det_loss_cls_4[0.4024] det_loss_box_4[0.5022] det_loss_cls_5[0.3917] det_loss_box_5[0.4978] det_loss_cls_dn_0[0.0963] det_loss_box_dn_0[0.2941] det_loss_cls_dn_1[0.0934] det_loss_box_dn_1[0.2545] det_loss_cls_dn_2[0.0934] det_loss_box_dn_2[0.2388] det_loss_cls_dn_3[0.0922] det_loss_box_dn_3[0.2339] det_loss_cls_dn_4[0.0911] det_loss_box_dn_4[0.2319] det_loss_cls_dn_5[0.0898] det_loss_box_dn_5[0.2301] loss_depth[0.0393] total_loss[8.1544]
Rank[0/8] 06/20/2025 09:00:24 INFO stats.py:314 | Epoch[143] Step[54] GlobalStep[55824] Training Speed: 7.55 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 12:43:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:00:44 INFO loss_tracker.py:84 | Epoch[143/239] Step[74] GlobalStep[55844/NA]: det_loss_cls_0[0.4368] det_loss_box_0[0.5698] det_loss_cls_1[0.4255] det_loss_box_1[0.4691] det_loss_cls_2[0.3921] det_loss_box_2[0.4196] det_loss_cls_3[0.3575] det_loss_box_3[0.4128] det_loss_cls_4[0.3318] det_loss_box_4[0.3993] det_loss_cls_5[0.3222] det_loss_box_5[0.3974] det_loss_cls_dn_0[0.0914] det_loss_box_dn_0[0.2975] det_loss_cls_dn_1[0.0874] det_loss_box_dn_1[0.2546] det_loss_cls_dn_2[0.0871] det_loss_box_dn_2[0.2387] det_loss_cls_dn_3[0.0857] det_loss_box_dn_3[0.2333] det_loss_cls_dn_4[0.0843] det_loss_box_dn_4[0.2316] det_loss_cls_dn_5[0.0831] det_loss_box_dn_5[0.2287] loss_depth[0.0364] total_loss[6.9738]
Rank[0/8] 06/20/2025 09:00:49 INFO stats.py:314 | Epoch[143] Step[79] GlobalStep[55849] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 12:42:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:01:10 INFO loss_tracker.py:84 | Epoch[143/239] Step[99] GlobalStep[55869/NA]: det_loss_cls_0[0.5195] det_loss_box_0[0.6716] det_loss_cls_1[0.5111] det_loss_box_1[0.5138] det_loss_cls_2[0.4762] det_loss_box_2[0.4569] det_loss_cls_3[0.4376] det_loss_box_3[0.4404] det_loss_cls_4[0.4083] det_loss_box_4[0.4343] det_loss_cls_5[0.3953] det_loss_box_5[0.4367] det_loss_cls_dn_0[0.0872] det_loss_box_dn_0[0.2846] det_loss_cls_dn_1[0.0842] det_loss_box_dn_1[0.2336] det_loss_cls_dn_2[0.0842] det_loss_box_dn_2[0.2175] det_loss_cls_dn_3[0.0826] det_loss_box_dn_3[0.2154] det_loss_cls_dn_4[0.0819] det_loss_box_dn_4[0.2142] det_loss_cls_dn_5[0.0815] det_loss_box_dn_5[0.2129] loss_depth[0.0392] total_loss[7.6208]
Rank[0/8] 06/20/2025 09:01:15 INFO stats.py:314 | Epoch[143] Step[104] GlobalStep[55874] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 12:42:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:01:36 INFO loss_tracker.py:84 | Epoch[143/239] Step[124] GlobalStep[55894/NA]: det_loss_cls_0[0.5204] det_loss_box_0[0.6232] det_loss_cls_1[0.5226] det_loss_box_1[0.4693] det_loss_cls_2[0.4547] det_loss_box_2[0.4405] det_loss_cls_3[0.4271] det_loss_box_3[0.4253] det_loss_cls_4[0.3895] det_loss_box_4[0.4280] det_loss_cls_5[0.3790] det_loss_box_5[0.4252] det_loss_cls_dn_0[0.0966] det_loss_box_dn_0[0.2981] det_loss_cls_dn_1[0.0934] det_loss_box_dn_1[0.2488] det_loss_cls_dn_2[0.0931] det_loss_box_dn_2[0.2345] det_loss_cls_dn_3[0.0926] det_loss_box_dn_3[0.2322] det_loss_cls_dn_4[0.0919] det_loss_box_dn_4[0.2285] det_loss_cls_dn_5[0.0914] det_loss_box_dn_5[0.2281] loss_depth[0.0402] total_loss[7.5741]
Rank[0/8] 06/20/2025 09:01:41 INFO stats.py:314 | Epoch[143] Step[129] GlobalStep[55899] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 12:41:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:02:02 INFO loss_tracker.py:84 | Epoch[143/239] Step[149] GlobalStep[55919/NA]: det_loss_cls_0[0.5247] det_loss_box_0[0.7474] det_loss_cls_1[0.5072] det_loss_box_1[0.5774] det_loss_cls_2[0.4768] det_loss_box_2[0.5270] det_loss_cls_3[0.4503] det_loss_box_3[0.5050] det_loss_cls_4[0.4354] det_loss_box_4[0.4914] det_loss_cls_5[0.4218] det_loss_box_5[0.4914] det_loss_cls_dn_0[0.1012] det_loss_box_dn_0[0.2894] det_loss_cls_dn_1[0.0980] det_loss_box_dn_1[0.2461] det_loss_cls_dn_2[0.0969] det_loss_box_dn_2[0.2281] det_loss_cls_dn_3[0.0953] det_loss_box_dn_3[0.2237] det_loss_cls_dn_4[0.0945] det_loss_box_dn_4[0.2223] det_loss_cls_dn_5[0.0933] det_loss_box_dn_5[0.2205] loss_depth[0.0375] total_loss[8.2026]
Rank[0/8] 06/20/2025 09:02:07 INFO stats.py:314 | Epoch[143] Step[154] GlobalStep[55924] Training Speed: 8.58 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 12:41:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:02:28 INFO loss_tracker.py:84 | Epoch[143/239] Step[174] GlobalStep[55944/NA]: det_loss_cls_0[0.4684] det_loss_box_0[0.6327] det_loss_cls_1[0.4570] det_loss_box_1[0.4928] det_loss_cls_2[0.4049] det_loss_box_2[0.4437] det_loss_cls_3[0.3843] det_loss_box_3[0.4060] det_loss_cls_4[0.3578] det_loss_box_4[0.3975] det_loss_cls_5[0.3390] det_loss_box_5[0.4040] det_loss_cls_dn_0[0.0872] det_loss_box_dn_0[0.3125] det_loss_cls_dn_1[0.0841] det_loss_box_dn_1[0.2557] det_loss_cls_dn_2[0.0837] det_loss_box_dn_2[0.2333] det_loss_cls_dn_3[0.0819] det_loss_box_dn_3[0.2278] det_loss_cls_dn_4[0.0812] det_loss_box_dn_4[0.2235] det_loss_cls_dn_5[0.0797] det_loss_box_dn_5[0.2223] loss_depth[0.0379] total_loss[7.1986]
Rank[0/8] 06/20/2025 09:02:33 INFO stats.py:314 | Epoch[143] Step[179] GlobalStep[55949] Training Speed: 8.59 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 12:40:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:02:53 INFO loss_tracker.py:84 | Epoch[143/239] Step[199] GlobalStep[55969/NA]: det_loss_cls_0[0.5772] det_loss_box_0[0.7838] det_loss_cls_1[0.5773] det_loss_box_1[0.6314] det_loss_cls_2[0.5439] det_loss_box_2[0.5669] det_loss_cls_3[0.5082] det_loss_box_3[0.5559] det_loss_cls_4[0.4867] det_loss_box_4[0.5473] det_loss_cls_5[0.4730] det_loss_box_5[0.5423] det_loss_cls_dn_0[0.1005] det_loss_box_dn_0[0.3159] det_loss_cls_dn_1[0.0971] det_loss_box_dn_1[0.2662] det_loss_cls_dn_2[0.0961] det_loss_box_dn_2[0.2445] det_loss_cls_dn_3[0.0954] det_loss_box_dn_3[0.2365] det_loss_cls_dn_4[0.0945] det_loss_box_dn_4[0.2329] det_loss_cls_dn_5[0.0933] det_loss_box_dn_5[0.2303] loss_depth[0.0369] total_loss[8.9340]
Rank[0/8] 06/20/2025 09:02:59 INFO stats.py:314 | Epoch[143] Step[204] GlobalStep[55974] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 12:40:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:03:20 INFO loss_tracker.py:84 | Epoch[143/239] Step[224] GlobalStep[55994/NA]: det_loss_cls_0[0.3759] det_loss_box_0[0.5139] det_loss_cls_1[0.3651] det_loss_box_1[0.3907] det_loss_cls_2[0.3329] det_loss_box_2[0.3430] det_loss_cls_3[0.3040] det_loss_box_3[0.3321] det_loss_cls_4[0.2902] det_loss_box_4[0.3254] det_loss_cls_5[0.2740] det_loss_box_5[0.3295] det_loss_cls_dn_0[0.0994] det_loss_box_dn_0[0.3386] det_loss_cls_dn_1[0.0946] det_loss_box_dn_1[0.2855] det_loss_cls_dn_2[0.0942] det_loss_box_dn_2[0.2602] det_loss_cls_dn_3[0.0928] det_loss_box_dn_3[0.2562] det_loss_cls_dn_4[0.0916] det_loss_box_dn_4[0.2550] det_loss_cls_dn_5[0.0906] det_loss_box_dn_5[0.2538] loss_depth[0.0362] total_loss[6.4254]
Rank[0/8] 06/20/2025 09:03:25 INFO stats.py:314 | Epoch[143] Step[229] GlobalStep[55999] Training Speed: 7.64 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 12:39:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:03:46 INFO loss_tracker.py:84 | Epoch[143/239] Step[249] GlobalStep[56019/NA]: det_loss_cls_0[0.4445] det_loss_box_0[0.6363] det_loss_cls_1[0.4503] det_loss_box_1[0.4955] det_loss_cls_2[0.3956] det_loss_box_2[0.4462] det_loss_cls_3[0.3650] det_loss_box_3[0.4241] det_loss_cls_4[0.3442] det_loss_box_4[0.4137] det_loss_cls_5[0.3350] det_loss_box_5[0.4128] det_loss_cls_dn_0[0.0918] det_loss_box_dn_0[0.3111] det_loss_cls_dn_1[0.0878] det_loss_box_dn_1[0.2584] det_loss_cls_dn_2[0.0876] det_loss_box_dn_2[0.2388] det_loss_cls_dn_3[0.0866] det_loss_box_dn_3[0.2361] det_loss_cls_dn_4[0.0853] det_loss_box_dn_4[0.2343] det_loss_cls_dn_5[0.0843] det_loss_box_dn_5[0.2337] loss_depth[0.0345] total_loss[7.2334]
Rank[0/8] 06/20/2025 09:03:51 INFO stats.py:314 | Epoch[143] Step[254] GlobalStep[56024] Training Speed: 6.28 samples/sec across all devices.	Average Step Time: 1.27 sec.	Estimated Remaining Time: 12:38:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:04:11 INFO loss_tracker.py:84 | Epoch[143/239] Step[274] GlobalStep[56044/NA]: det_loss_cls_0[0.4868] det_loss_box_0[0.5901] det_loss_cls_1[0.4837] det_loss_box_1[0.4720] det_loss_cls_2[0.4461] det_loss_box_2[0.4309] det_loss_cls_3[0.4219] det_loss_box_3[0.4089] det_loss_cls_4[0.3932] det_loss_box_4[0.4080] det_loss_cls_5[0.3754] det_loss_box_5[0.4084] det_loss_cls_dn_0[0.1026] det_loss_box_dn_0[0.2742] det_loss_cls_dn_1[0.0994] det_loss_box_dn_1[0.2327] det_loss_cls_dn_2[0.0984] det_loss_box_dn_2[0.2158] det_loss_cls_dn_3[0.0975] det_loss_box_dn_3[0.2128] det_loss_cls_dn_4[0.0970] det_loss_box_dn_4[0.2117] det_loss_cls_dn_5[0.0959] det_loss_box_dn_5[0.2113] loss_depth[0.0336] total_loss[7.3082]
Rank[0/8] 06/20/2025 09:04:17 INFO stats.py:314 | Epoch[143] Step[279] GlobalStep[56049] Training Speed: 8.19 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 12:38:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:04:38 INFO loss_tracker.py:84 | Epoch[143/239] Step[299] GlobalStep[56069/NA]: det_loss_cls_0[0.3859] det_loss_box_0[0.4873] det_loss_cls_1[0.3731] det_loss_box_1[0.3742] det_loss_cls_2[0.3337] det_loss_box_2[0.3391] det_loss_cls_3[0.2951] det_loss_box_3[0.3363] det_loss_cls_4[0.2776] det_loss_box_4[0.3296] det_loss_cls_5[0.2667] det_loss_box_5[0.3325] det_loss_cls_dn_0[0.0957] det_loss_box_dn_0[0.3056] det_loss_cls_dn_1[0.0930] det_loss_box_dn_1[0.2602] det_loss_cls_dn_2[0.0931] det_loss_box_dn_2[0.2440] det_loss_cls_dn_3[0.0916] det_loss_box_dn_3[0.2412] det_loss_cls_dn_4[0.0914] det_loss_box_dn_4[0.2404] det_loss_cls_dn_5[0.0906] det_loss_box_dn_5[0.2383] loss_depth[0.0391] total_loss[6.2553]
Rank[0/8] 06/20/2025 09:04:43 INFO stats.py:314 | Epoch[143] Step[304] GlobalStep[56074] Training Speed: 7.97 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 12:37:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:05:03 INFO loss_tracker.py:84 | Epoch[143/239] Step[324] GlobalStep[56094/NA]: det_loss_cls_0[0.5051] det_loss_box_0[0.6024] det_loss_cls_1[0.4915] det_loss_box_1[0.4826] det_loss_cls_2[0.4330] det_loss_box_2[0.4302] det_loss_cls_3[0.4093] det_loss_box_3[0.4166] det_loss_cls_4[0.3879] det_loss_box_4[0.4059] det_loss_cls_5[0.3757] det_loss_box_5[0.4059] det_loss_cls_dn_0[0.0837] det_loss_box_dn_0[0.2455] det_loss_cls_dn_1[0.0790] det_loss_box_dn_1[0.2074] det_loss_cls_dn_2[0.0789] det_loss_box_dn_2[0.1939] det_loss_cls_dn_3[0.0770] det_loss_box_dn_3[0.1915] det_loss_cls_dn_4[0.0756] det_loss_box_dn_4[0.1900] det_loss_cls_dn_5[0.0747] det_loss_box_dn_5[0.1892] loss_depth[0.0368] total_loss[7.0690]
Rank[0/8] 06/20/2025 09:05:08 INFO stats.py:314 | Epoch[143] Step[329] GlobalStep[56099] Training Speed: 10.75 samples/sec across all devices.	Average Step Time: 0.74 sec.	Estimated Remaining Time: 12:37:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:05:29 INFO loss_tracker.py:84 | Epoch[143/239] Step[349] GlobalStep[56119/NA]: det_loss_cls_0[0.4800] det_loss_box_0[0.6179] det_loss_cls_1[0.4669] det_loss_box_1[0.4957] det_loss_cls_2[0.4217] det_loss_box_2[0.4358] det_loss_cls_3[0.4002] det_loss_box_3[0.4143] det_loss_cls_4[0.3654] det_loss_box_4[0.3990] det_loss_cls_5[0.3473] det_loss_box_5[0.4080] det_loss_cls_dn_0[0.0942] det_loss_box_dn_0[0.3180] det_loss_cls_dn_1[0.0905] det_loss_box_dn_1[0.2739] det_loss_cls_dn_2[0.0899] det_loss_box_dn_2[0.2527] det_loss_cls_dn_3[0.0882] det_loss_box_dn_3[0.2490] det_loss_cls_dn_4[0.0873] det_loss_box_dn_4[0.2475] det_loss_cls_dn_5[0.0862] det_loss_box_dn_5[0.2460] loss_depth[0.0345] total_loss[7.4102]
Rank[0/8] 06/20/2025 09:05:34 INFO stats.py:314 | Epoch[143] Step[354] GlobalStep[56124] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 12:36:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:05:55 INFO loss_tracker.py:84 | Epoch[143/239] Step[374] GlobalStep[56144/NA]: det_loss_cls_0[0.4868] det_loss_box_0[0.5969] det_loss_cls_1[0.4763] det_loss_box_1[0.4535] det_loss_cls_2[0.4291] det_loss_box_2[0.4147] det_loss_cls_3[0.4057] det_loss_box_3[0.4011] det_loss_cls_4[0.3753] det_loss_box_4[0.3873] det_loss_cls_5[0.3563] det_loss_box_5[0.3892] det_loss_cls_dn_0[0.1002] det_loss_box_dn_0[0.2908] det_loss_cls_dn_1[0.0962] det_loss_box_dn_1[0.2395] det_loss_cls_dn_2[0.0952] det_loss_box_dn_2[0.2209] det_loss_cls_dn_3[0.0934] det_loss_box_dn_3[0.2149] det_loss_cls_dn_4[0.0931] det_loss_box_dn_4[0.2125] det_loss_cls_dn_5[0.0911] det_loss_box_dn_5[0.2108] loss_depth[0.0382] total_loss[7.1688]
Rank[0/8] 06/20/2025 09:06:00 INFO stats.py:314 | Epoch[143] Step[379] GlobalStep[56149] Training Speed: 7.91 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 12:36:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:06:10 INFO stats.py:394 | Epoch[143] completed. Training Speed: 7.68 samples/sec across all devices.	Epoch Time: 406.10 sec.	Average Epoch Time: 406.10 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 12:35:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:06:28 INFO stats.py:314 | Epoch[144] Step[14] GlobalStep[56174] Training Speed: 8.71 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 12:35:38.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:06:38 INFO loss_tracker.py:84 | Epoch[144/239] Step[24] GlobalStep[56184/NA]: det_loss_cls_0[0.4480] det_loss_box_0[0.6395] det_loss_cls_1[0.4331] det_loss_box_1[0.4895] det_loss_cls_2[0.3923] det_loss_box_2[0.4504] det_loss_cls_3[0.3668] det_loss_box_3[0.4382] det_loss_cls_4[0.3499] det_loss_box_4[0.4294] det_loss_cls_5[0.3418] det_loss_box_5[0.4217] det_loss_cls_dn_0[0.0934] det_loss_box_dn_0[0.3190] det_loss_cls_dn_1[0.0900] det_loss_box_dn_1[0.2664] det_loss_cls_dn_2[0.0891] det_loss_box_dn_2[0.2432] det_loss_cls_dn_3[0.0875] det_loss_box_dn_3[0.2382] det_loss_cls_dn_4[0.0866] det_loss_box_dn_4[0.2343] det_loss_cls_dn_5[0.0856] det_loss_box_dn_5[0.2315] loss_depth[0.0366] total_loss[7.3021]
Rank[0/8] 06/20/2025 09:06:54 INFO stats.py:314 | Epoch[144] Step[39] GlobalStep[56199] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 12:35:05.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:07:04 INFO loss_tracker.py:84 | Epoch[144/239] Step[49] GlobalStep[56209/NA]: det_loss_cls_0[0.4651] det_loss_box_0[0.6251] det_loss_cls_1[0.4343] det_loss_box_1[0.4976] det_loss_cls_2[0.4065] det_loss_box_2[0.4475] det_loss_cls_3[0.3727] det_loss_box_3[0.4367] det_loss_cls_4[0.3476] det_loss_box_4[0.4231] det_loss_cls_5[0.3306] det_loss_box_5[0.4233] det_loss_cls_dn_0[0.0876] det_loss_box_dn_0[0.2880] det_loss_cls_dn_1[0.0836] det_loss_box_dn_1[0.2433] det_loss_cls_dn_2[0.0825] det_loss_box_dn_2[0.2287] det_loss_cls_dn_3[0.0803] det_loss_box_dn_3[0.2238] det_loss_cls_dn_4[0.0792] det_loss_box_dn_4[0.2216] det_loss_cls_dn_5[0.0784] det_loss_box_dn_5[0.2208] loss_depth[0.0409] total_loss[7.1689]
Rank[0/8] 06/20/2025 09:07:20 INFO stats.py:314 | Epoch[144] Step[64] GlobalStep[56224] Training Speed: 7.76 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 12:34:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:07:30 INFO loss_tracker.py:84 | Epoch[144/239] Step[74] GlobalStep[56234/NA]: det_loss_cls_0[0.4254] det_loss_box_0[0.6070] det_loss_cls_1[0.4055] det_loss_box_1[0.4783] det_loss_cls_2[0.3720] det_loss_box_2[0.4314] det_loss_cls_3[0.3542] det_loss_box_3[0.4136] det_loss_cls_4[0.3288] det_loss_box_4[0.4077] det_loss_cls_5[0.3105] det_loss_box_5[0.4150] det_loss_cls_dn_0[0.1003] det_loss_box_dn_0[0.3631] det_loss_cls_dn_1[0.0971] det_loss_box_dn_1[0.3144] det_loss_cls_dn_2[0.0967] det_loss_box_dn_2[0.2916] det_loss_cls_dn_3[0.0956] det_loss_box_dn_3[0.2851] det_loss_cls_dn_4[0.0946] det_loss_box_dn_4[0.2825] det_loss_cls_dn_5[0.0941] det_loss_box_dn_5[0.2810] loss_depth[0.0363] total_loss[7.3815]
Rank[0/8] 06/20/2025 09:07:46 INFO stats.py:314 | Epoch[144] Step[89] GlobalStep[56249] Training Speed: 7.82 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 12:33:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:07:56 INFO loss_tracker.py:84 | Epoch[144/239] Step[99] GlobalStep[56259/NA]: det_loss_cls_0[0.6226] det_loss_box_0[0.7978] det_loss_cls_1[0.6078] det_loss_box_1[0.6111] det_loss_cls_2[0.5638] det_loss_box_2[0.5490] det_loss_cls_3[0.5289] det_loss_box_3[0.5324] det_loss_cls_4[0.5097] det_loss_box_4[0.5249] det_loss_cls_5[0.4895] det_loss_box_5[0.5263] det_loss_cls_dn_0[0.1038] det_loss_box_dn_0[0.2635] det_loss_cls_dn_1[0.1003] det_loss_box_dn_1[0.2237] det_loss_cls_dn_2[0.1003] det_loss_box_dn_2[0.2121] det_loss_cls_dn_3[0.0993] det_loss_box_dn_3[0.2093] det_loss_cls_dn_4[0.0981] det_loss_box_dn_4[0.2084] det_loss_cls_dn_5[0.0967] det_loss_box_dn_5[0.2060] loss_depth[0.0379] total_loss[8.8235]
Rank[0/8] 06/20/2025 09:08:12 INFO stats.py:314 | Epoch[144] Step[114] GlobalStep[56274] Training Speed: 6.01 samples/sec across all devices.	Average Step Time: 1.33 sec.	Estimated Remaining Time: 12:33:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:08:22 INFO loss_tracker.py:84 | Epoch[144/239] Step[124] GlobalStep[56284/NA]: det_loss_cls_0[0.4719] det_loss_box_0[0.6429] det_loss_cls_1[0.4576] det_loss_box_1[0.4789] det_loss_cls_2[0.4247] det_loss_box_2[0.4340] det_loss_cls_3[0.3947] det_loss_box_3[0.4131] det_loss_cls_4[0.3680] det_loss_box_4[0.3961] det_loss_cls_5[0.3569] det_loss_box_5[0.4029] det_loss_cls_dn_0[0.0934] det_loss_box_dn_0[0.3060] det_loss_cls_dn_1[0.0895] det_loss_box_dn_1[0.2519] det_loss_cls_dn_2[0.0888] det_loss_box_dn_2[0.2339] det_loss_cls_dn_3[0.0873] det_loss_box_dn_3[0.2279] det_loss_cls_dn_4[0.0865] det_loss_box_dn_4[0.2256] det_loss_cls_dn_5[0.0855] det_loss_box_dn_5[0.2226] loss_depth[0.0390] total_loss[7.2796]
Rank[0/8] 06/20/2025 09:08:38 INFO stats.py:314 | Epoch[144] Step[139] GlobalStep[56299] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 12:32:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:08:48 INFO loss_tracker.py:84 | Epoch[144/239] Step[149] GlobalStep[56309/NA]: det_loss_cls_0[0.4546] det_loss_box_0[0.5901] det_loss_cls_1[0.4463] det_loss_box_1[0.4490] det_loss_cls_2[0.4013] det_loss_box_2[0.4055] det_loss_cls_3[0.3579] det_loss_box_3[0.3847] det_loss_cls_4[0.3369] det_loss_box_4[0.3752] det_loss_cls_5[0.3188] det_loss_box_5[0.3809] det_loss_cls_dn_0[0.0939] det_loss_box_dn_0[0.3049] det_loss_cls_dn_1[0.0903] det_loss_box_dn_1[0.2505] det_loss_cls_dn_2[0.0899] det_loss_box_dn_2[0.2326] det_loss_cls_dn_3[0.0889] det_loss_box_dn_3[0.2279] det_loss_cls_dn_4[0.0881] det_loss_box_dn_4[0.2258] det_loss_cls_dn_5[0.0878] det_loss_box_dn_5[0.2244] loss_depth[0.0363] total_loss[6.9427]
Rank[0/8] 06/20/2025 09:09:04 INFO stats.py:314 | Epoch[144] Step[164] GlobalStep[56324] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 12:32:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:09:14 INFO loss_tracker.py:84 | Epoch[144/239] Step[174] GlobalStep[56334/NA]: det_loss_cls_0[0.5721] det_loss_box_0[0.8331] det_loss_cls_1[0.5289] det_loss_box_1[0.6486] det_loss_cls_2[0.4930] det_loss_box_2[0.5725] det_loss_cls_3[0.4632] det_loss_box_3[0.5548] det_loss_cls_4[0.4390] det_loss_box_4[0.5430] det_loss_cls_5[0.4277] det_loss_box_5[0.5444] det_loss_cls_dn_0[0.0898] det_loss_box_dn_0[0.3208] det_loss_cls_dn_1[0.0858] det_loss_box_dn_1[0.2723] det_loss_cls_dn_2[0.0859] det_loss_box_dn_2[0.2587] det_loss_cls_dn_3[0.0848] det_loss_box_dn_3[0.2554] det_loss_cls_dn_4[0.0842] det_loss_box_dn_4[0.2548] det_loss_cls_dn_5[0.0830] det_loss_box_dn_5[0.2537] loss_depth[0.0387] total_loss[8.7884]
Rank[0/8] 06/20/2025 09:09:29 INFO stats.py:314 | Epoch[144] Step[189] GlobalStep[56349] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 12:31:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:09:40 INFO loss_tracker.py:84 | Epoch[144/239] Step[199] GlobalStep[56359/NA]: det_loss_cls_0[0.5727] det_loss_box_0[0.7666] det_loss_cls_1[0.5591] det_loss_box_1[0.5903] det_loss_cls_2[0.5281] det_loss_box_2[0.5412] det_loss_cls_3[0.4977] det_loss_box_3[0.5285] det_loss_cls_4[0.4623] det_loss_box_4[0.5184] det_loss_cls_5[0.4479] det_loss_box_5[0.5167] det_loss_cls_dn_0[0.0898] det_loss_box_dn_0[0.3064] det_loss_cls_dn_1[0.0857] det_loss_box_dn_1[0.2691] det_loss_cls_dn_2[0.0847] det_loss_box_dn_2[0.2562] det_loss_cls_dn_3[0.0833] det_loss_box_dn_3[0.2532] det_loss_cls_dn_4[0.0827] det_loss_box_dn_4[0.2504] det_loss_cls_dn_5[0.0820] det_loss_box_dn_5[0.2476] loss_depth[0.0402] total_loss[8.6607]
Rank[0/8] 06/20/2025 09:09:56 INFO stats.py:314 | Epoch[144] Step[214] GlobalStep[56374] Training Speed: 7.83 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 12:31:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:10:07 INFO loss_tracker.py:84 | Epoch[144/239] Step[224] GlobalStep[56384/NA]: det_loss_cls_0[0.5152] det_loss_box_0[0.6857] det_loss_cls_1[0.5121] det_loss_box_1[0.5078] det_loss_cls_2[0.4560] det_loss_box_2[0.4610] det_loss_cls_3[0.4312] det_loss_box_3[0.4386] det_loss_cls_4[0.3990] det_loss_box_4[0.4273] det_loss_cls_5[0.3842] det_loss_box_5[0.4278] det_loss_cls_dn_0[0.1041] det_loss_box_dn_0[0.2710] det_loss_cls_dn_1[0.1011] det_loss_box_dn_1[0.2242] det_loss_cls_dn_2[0.1010] det_loss_box_dn_2[0.2100] det_loss_cls_dn_3[0.0999] det_loss_box_dn_3[0.2065] det_loss_cls_dn_4[0.0991] det_loss_box_dn_4[0.2052] det_loss_cls_dn_5[0.0987] det_loss_box_dn_5[0.2043] loss_depth[0.0397] total_loss[7.6107]
Rank[0/8] 06/20/2025 09:10:22 INFO stats.py:314 | Epoch[144] Step[239] GlobalStep[56399] Training Speed: 8.05 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 12:30:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:10:32 INFO loss_tracker.py:84 | Epoch[144/239] Step[249] GlobalStep[56409/NA]: det_loss_cls_0[0.5193] det_loss_box_0[0.6930] det_loss_cls_1[0.5093] det_loss_box_1[0.5560] det_loss_cls_2[0.4750] det_loss_box_2[0.5077] det_loss_cls_3[0.4446] det_loss_box_3[0.4942] det_loss_cls_4[0.4168] det_loss_box_4[0.4830] det_loss_cls_5[0.3991] det_loss_box_5[0.4838] det_loss_cls_dn_0[0.0942] det_loss_box_dn_0[0.2759] det_loss_cls_dn_1[0.0903] det_loss_box_dn_1[0.2330] det_loss_cls_dn_2[0.0896] det_loss_box_dn_2[0.2189] det_loss_cls_dn_3[0.0877] det_loss_box_dn_3[0.2153] det_loss_cls_dn_4[0.0869] det_loss_box_dn_4[0.2136] det_loss_cls_dn_5[0.0862] det_loss_box_dn_5[0.2128] loss_depth[0.0393] total_loss[7.9257]
Rank[0/8] 06/20/2025 09:10:48 INFO stats.py:314 | Epoch[144] Step[264] GlobalStep[56424] Training Speed: 7.51 samples/sec across all devices.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 12:30:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:10:58 INFO loss_tracker.py:84 | Epoch[144/239] Step[274] GlobalStep[56434/NA]: det_loss_cls_0[0.4960] det_loss_box_0[0.6521] det_loss_cls_1[0.4840] det_loss_box_1[0.4837] det_loss_cls_2[0.4474] det_loss_box_2[0.4308] det_loss_cls_3[0.4158] det_loss_box_3[0.4157] det_loss_cls_4[0.3806] det_loss_box_4[0.4107] det_loss_cls_5[0.3691] det_loss_box_5[0.4093] det_loss_cls_dn_0[0.0997] det_loss_box_dn_0[0.2862] det_loss_cls_dn_1[0.0972] det_loss_box_dn_1[0.2410] det_loss_cls_dn_2[0.0971] det_loss_box_dn_2[0.2247] det_loss_cls_dn_3[0.0963] det_loss_box_dn_3[0.2209] det_loss_cls_dn_4[0.0958] det_loss_box_dn_4[0.2189] det_loss_cls_dn_5[0.0952] det_loss_box_dn_5[0.2186] loss_depth[0.0404] total_loss[7.4272]
Rank[0/8] 06/20/2025 09:11:14 INFO stats.py:314 | Epoch[144] Step[289] GlobalStep[56449] Training Speed: 7.79 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 12:29:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:11:24 INFO loss_tracker.py:84 | Epoch[144/239] Step[299] GlobalStep[56459/NA]: det_loss_cls_0[0.5257] det_loss_box_0[0.6574] det_loss_cls_1[0.5157] det_loss_box_1[0.5041] det_loss_cls_2[0.4622] det_loss_box_2[0.4509] det_loss_cls_3[0.4187] det_loss_box_3[0.4368] det_loss_cls_4[0.3864] det_loss_box_4[0.4232] det_loss_cls_5[0.3732] det_loss_box_5[0.4230] det_loss_cls_dn_0[0.0985] det_loss_box_dn_0[0.2647] det_loss_cls_dn_1[0.0943] det_loss_box_dn_1[0.2205] det_loss_cls_dn_2[0.0938] det_loss_box_dn_2[0.2060] det_loss_cls_dn_3[0.0930] det_loss_box_dn_3[0.2023] det_loss_cls_dn_4[0.0921] det_loss_box_dn_4[0.2007] det_loss_cls_dn_5[0.0910] det_loss_box_dn_5[0.1994] loss_depth[0.0375] total_loss[7.4710]
Rank[0/8] 06/20/2025 09:11:39 INFO stats.py:314 | Epoch[144] Step[314] GlobalStep[56474] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 12:29:00.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:11:50 INFO loss_tracker.py:84 | Epoch[144/239] Step[324] GlobalStep[56484/NA]: det_loss_cls_0[0.4434] det_loss_box_0[0.6957] det_loss_cls_1[0.4362] det_loss_box_1[0.5492] det_loss_cls_2[0.4006] det_loss_box_2[0.5081] det_loss_cls_3[0.3815] det_loss_box_3[0.4842] det_loss_cls_4[0.3588] det_loss_box_4[0.4614] det_loss_cls_5[0.3528] det_loss_box_5[0.4520] det_loss_cls_dn_0[0.0905] det_loss_box_dn_0[0.2995] det_loss_cls_dn_1[0.0868] det_loss_box_dn_1[0.2494] det_loss_cls_dn_2[0.0855] det_loss_box_dn_2[0.2293] det_loss_cls_dn_3[0.0843] det_loss_box_dn_3[0.2252] det_loss_cls_dn_4[0.0833] det_loss_box_dn_4[0.2229] det_loss_cls_dn_5[0.0825] det_loss_box_dn_5[0.2188] loss_depth[0.0380] total_loss[7.5199]
Rank[0/8] 06/20/2025 09:12:05 INFO stats.py:314 | Epoch[144] Step[339] GlobalStep[56499] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 12:28:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:12:16 INFO loss_tracker.py:84 | Epoch[144/239] Step[349] GlobalStep[56509/NA]: det_loss_cls_0[0.4468] det_loss_box_0[0.5902] det_loss_cls_1[0.4343] det_loss_box_1[0.4748] det_loss_cls_2[0.4047] det_loss_box_2[0.4271] det_loss_cls_3[0.3861] det_loss_box_3[0.4239] det_loss_cls_4[0.3688] det_loss_box_4[0.4088] det_loss_cls_5[0.3463] det_loss_box_5[0.4148] det_loss_cls_dn_0[0.0843] det_loss_box_dn_0[0.3133] det_loss_cls_dn_1[0.0812] det_loss_box_dn_1[0.2641] det_loss_cls_dn_2[0.0810] det_loss_box_dn_2[0.2497] det_loss_cls_dn_3[0.0797] det_loss_box_dn_3[0.2471] det_loss_cls_dn_4[0.0792] det_loss_box_dn_4[0.2466] det_loss_cls_dn_5[0.0779] det_loss_box_dn_5[0.2461] loss_depth[0.0402] total_loss[7.2170]
Rank[0/8] 06/20/2025 09:12:31 INFO stats.py:314 | Epoch[144] Step[364] GlobalStep[56524] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 12:27:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:12:41 INFO loss_tracker.py:84 | Epoch[144/239] Step[374] GlobalStep[56534/NA]: det_loss_cls_0[0.4890] det_loss_box_0[0.6361] det_loss_cls_1[0.4954] det_loss_box_1[0.5158] det_loss_cls_2[0.4657] det_loss_box_2[0.4495] det_loss_cls_3[0.4321] det_loss_box_3[0.4322] det_loss_cls_4[0.3965] det_loss_box_4[0.4254] det_loss_cls_5[0.3785] det_loss_box_5[0.4244] det_loss_cls_dn_0[0.0899] det_loss_box_dn_0[0.2595] det_loss_cls_dn_1[0.0866] det_loss_box_dn_1[0.2135] det_loss_cls_dn_2[0.0860] det_loss_box_dn_2[0.1989] det_loss_cls_dn_3[0.0850] det_loss_box_dn_3[0.1956] det_loss_cls_dn_4[0.0843] det_loss_box_dn_4[0.1933] det_loss_cls_dn_5[0.0833] det_loss_box_dn_5[0.1917] loss_depth[0.0354] total_loss[7.3436]
Rank[0/8] 06/20/2025 09:12:57 INFO stats.py:314 | Epoch[144] Step[389] GlobalStep[56549] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 12:27:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:12:57 INFO stats.py:394 | Epoch[144] completed. Training Speed: 7.67 samples/sec across all devices.	Epoch Time: 406.98 sec.	Average Epoch Time: 406.98 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 12:27:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:13:24 INFO stats.py:314 | Epoch[145] Step[24] GlobalStep[56574] Training Speed: 9.69 samples/sec across all devices.	Average Step Time: 0.83 sec.	Estimated Remaining Time: 12:26:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:13:24 INFO loss_tracker.py:84 | Epoch[145/239] Step[24] GlobalStep[56574/NA]: det_loss_cls_0[0.5339] det_loss_box_0[0.6497] det_loss_cls_1[0.5243] det_loss_box_1[0.4883] det_loss_cls_2[0.4764] det_loss_box_2[0.4438] det_loss_cls_3[0.4442] det_loss_box_3[0.4336] det_loss_cls_4[0.4137] det_loss_box_4[0.4223] det_loss_cls_5[0.3942] det_loss_box_5[0.4237] det_loss_cls_dn_0[0.1023] det_loss_box_dn_0[0.2501] det_loss_cls_dn_1[0.0987] det_loss_box_dn_1[0.2086] det_loss_cls_dn_2[0.0979] det_loss_box_dn_2[0.1955] det_loss_cls_dn_3[0.0971] det_loss_box_dn_3[0.1925] det_loss_cls_dn_4[0.0960] det_loss_box_dn_4[0.1920] det_loss_cls_dn_5[0.0950] det_loss_box_dn_5[0.1911] loss_depth[0.0385] total_loss[7.5034]
Rank[0/8] 06/20/2025 09:13:51 INFO stats.py:314 | Epoch[145] Step[49] GlobalStep[56599] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 12:26:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:13:51 INFO loss_tracker.py:84 | Epoch[145/239] Step[49] GlobalStep[56599/NA]: det_loss_cls_0[0.4727] det_loss_box_0[0.6014] det_loss_cls_1[0.4556] det_loss_box_1[0.4556] det_loss_cls_2[0.4191] det_loss_box_2[0.4027] det_loss_cls_3[0.3829] det_loss_box_3[0.3941] det_loss_cls_4[0.3468] det_loss_box_4[0.3945] det_loss_cls_5[0.3240] det_loss_box_5[0.3954] det_loss_cls_dn_0[0.0956] det_loss_box_dn_0[0.3029] det_loss_cls_dn_1[0.0919] det_loss_box_dn_1[0.2501] det_loss_cls_dn_2[0.0913] det_loss_box_dn_2[0.2337] det_loss_cls_dn_3[0.0900] det_loss_box_dn_3[0.2275] det_loss_cls_dn_4[0.0896] det_loss_box_dn_4[0.2271] det_loss_cls_dn_5[0.0882] det_loss_box_dn_5[0.2237] loss_depth[0.0399] total_loss[7.0963]
Rank[0/8] 06/20/2025 09:14:16 INFO stats.py:314 | Epoch[145] Step[74] GlobalStep[56624] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 12:25:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:14:16 INFO loss_tracker.py:84 | Epoch[145/239] Step[74] GlobalStep[56624/NA]: det_loss_cls_0[0.6692] det_loss_box_0[0.9401] det_loss_cls_1[0.6382] det_loss_box_1[0.7302] det_loss_cls_2[0.5889] det_loss_box_2[0.6676] det_loss_cls_3[0.5566] det_loss_box_3[0.6420] det_loss_cls_4[0.5332] det_loss_box_4[0.6267] det_loss_cls_5[0.5193] det_loss_box_5[0.6238] det_loss_cls_dn_0[0.1007] det_loss_box_dn_0[0.3258] det_loss_cls_dn_1[0.0976] det_loss_box_dn_1[0.2817] det_loss_cls_dn_2[0.0973] det_loss_box_dn_2[0.2677] det_loss_cls_dn_3[0.0960] det_loss_box_dn_3[0.2639] det_loss_cls_dn_4[0.0952] det_loss_box_dn_4[0.2612] det_loss_cls_dn_5[0.0947] det_loss_box_dn_5[0.2591] loss_depth[0.0402] total_loss[10.0170]
Rank[0/8] 06/20/2025 09:14:42 INFO stats.py:314 | Epoch[145] Step[99] GlobalStep[56649] Training Speed: 8.58 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 12:25:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:14:42 INFO loss_tracker.py:84 | Epoch[145/239] Step[99] GlobalStep[56649/NA]: det_loss_cls_0[0.4870] det_loss_box_0[0.5885] det_loss_cls_1[0.4921] det_loss_box_1[0.4206] det_loss_cls_2[0.4320] det_loss_box_2[0.3858] det_loss_cls_3[0.4050] det_loss_box_3[0.3743] det_loss_cls_4[0.3822] det_loss_box_4[0.3662] det_loss_cls_5[0.3709] det_loss_box_5[0.3670] det_loss_cls_dn_0[0.0992] det_loss_box_dn_0[0.2560] det_loss_cls_dn_1[0.0958] det_loss_box_dn_1[0.2128] det_loss_cls_dn_2[0.0946] det_loss_box_dn_2[0.1976] det_loss_cls_dn_3[0.0936] det_loss_box_dn_3[0.1934] det_loss_cls_dn_4[0.0926] det_loss_box_dn_4[0.1917] det_loss_cls_dn_5[0.0919] det_loss_box_dn_5[0.1917] loss_depth[0.0340] total_loss[6.9164]
Rank[0/8] 06/20/2025 09:15:09 INFO stats.py:314 | Epoch[145] Step[124] GlobalStep[56674] Training Speed: 7.78 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 12:24:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:15:09 INFO loss_tracker.py:84 | Epoch[145/239] Step[124] GlobalStep[56674/NA]: det_loss_cls_0[0.5297] det_loss_box_0[0.7287] det_loss_cls_1[0.5123] det_loss_box_1[0.5540] det_loss_cls_2[0.4567] det_loss_box_2[0.5061] det_loss_cls_3[0.4223] det_loss_box_3[0.4939] det_loss_cls_4[0.4005] det_loss_box_4[0.4774] det_loss_cls_5[0.3848] det_loss_box_5[0.4854] det_loss_cls_dn_0[0.0867] det_loss_box_dn_0[0.3341] det_loss_cls_dn_1[0.0825] det_loss_box_dn_1[0.2704] det_loss_cls_dn_2[0.0825] det_loss_box_dn_2[0.2543] det_loss_cls_dn_3[0.0808] det_loss_box_dn_3[0.2521] det_loss_cls_dn_4[0.0798] det_loss_box_dn_4[0.2493] det_loss_cls_dn_5[0.0790] det_loss_box_dn_5[0.2453] loss_depth[0.0397] total_loss[8.0882]
Rank[0/8] 06/20/2025 09:15:34 INFO stats.py:314 | Epoch[145] Step[149] GlobalStep[56699] Training Speed: 6.00 samples/sec across all devices.	Average Step Time: 1.33 sec.	Estimated Remaining Time: 12:24:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:15:34 INFO loss_tracker.py:84 | Epoch[145/239] Step[149] GlobalStep[56699/NA]: det_loss_cls_0[0.5144] det_loss_box_0[0.6312] det_loss_cls_1[0.5051] det_loss_box_1[0.4803] det_loss_cls_2[0.4491] det_loss_box_2[0.4292] det_loss_cls_3[0.4154] det_loss_box_3[0.4182] det_loss_cls_4[0.3850] det_loss_box_4[0.4190] det_loss_cls_5[0.3721] det_loss_box_5[0.4200] det_loss_cls_dn_0[0.0977] det_loss_box_dn_0[0.2877] det_loss_cls_dn_1[0.0927] det_loss_box_dn_1[0.2383] det_loss_cls_dn_2[0.0920] det_loss_box_dn_2[0.2209] det_loss_cls_dn_3[0.0907] det_loss_box_dn_3[0.2127] det_loss_cls_dn_4[0.0900] det_loss_box_dn_4[0.2114] det_loss_cls_dn_5[0.0884] det_loss_box_dn_5[0.2109] loss_depth[0.0353] total_loss[7.4076]
Rank[0/8] 06/20/2025 09:16:00 INFO stats.py:314 | Epoch[145] Step[174] GlobalStep[56724] Training Speed: 8.78 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 12:23:30.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:16:00 INFO loss_tracker.py:84 | Epoch[145/239] Step[174] GlobalStep[56724/NA]: det_loss_cls_0[0.4226] det_loss_box_0[0.5490] det_loss_cls_1[0.4183] det_loss_box_1[0.4044] det_loss_cls_2[0.3715] det_loss_box_2[0.3697] det_loss_cls_3[0.3400] det_loss_box_3[0.3517] det_loss_cls_4[0.3139] det_loss_box_4[0.3449] det_loss_cls_5[0.3030] det_loss_box_5[0.3441] det_loss_cls_dn_0[0.0913] det_loss_box_dn_0[0.2858] det_loss_cls_dn_1[0.0874] det_loss_box_dn_1[0.2362] det_loss_cls_dn_2[0.0872] det_loss_box_dn_2[0.2203] det_loss_cls_dn_3[0.0860] det_loss_box_dn_3[0.2172] det_loss_cls_dn_4[0.0854] det_loss_box_dn_4[0.2145] det_loss_cls_dn_5[0.0844] det_loss_box_dn_5[0.2132] loss_depth[0.0386] total_loss[6.4805]
Rank[0/8] 06/20/2025 09:16:26 INFO stats.py:314 | Epoch[145] Step[199] GlobalStep[56749] Training Speed: 7.87 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 12:22:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:16:26 INFO loss_tracker.py:84 | Epoch[145/239] Step[199] GlobalStep[56749/NA]: det_loss_cls_0[0.5253] det_loss_box_0[0.8161] det_loss_cls_1[0.5238] det_loss_box_1[0.6675] det_loss_cls_2[0.4917] det_loss_box_2[0.5946] det_loss_cls_3[0.4681] det_loss_box_3[0.5669] det_loss_cls_4[0.4410] det_loss_box_4[0.5629] det_loss_cls_5[0.4252] det_loss_box_5[0.5564] det_loss_cls_dn_0[0.0990] det_loss_box_dn_0[0.3058] det_loss_cls_dn_1[0.0953] det_loss_box_dn_1[0.2484] det_loss_cls_dn_2[0.0945] det_loss_box_dn_2[0.2241] det_loss_cls_dn_3[0.0930] det_loss_box_dn_3[0.2185] det_loss_cls_dn_4[0.0922] det_loss_box_dn_4[0.2158] det_loss_cls_dn_5[0.0913] det_loss_box_dn_5[0.2140] loss_depth[0.0380] total_loss[8.6694]
Rank[0/8] 06/20/2025 09:16:52 INFO stats.py:314 | Epoch[145] Step[224] GlobalStep[56774] Training Speed: 8.17 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 12:22:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:16:52 INFO loss_tracker.py:84 | Epoch[145/239] Step[224] GlobalStep[56774/NA]: det_loss_cls_0[0.4653] det_loss_box_0[0.6756] det_loss_cls_1[0.4646] det_loss_box_1[0.5260] det_loss_cls_2[0.4089] det_loss_box_2[0.4699] det_loss_cls_3[0.3804] det_loss_box_3[0.4584] det_loss_cls_4[0.3540] det_loss_box_4[0.4531] det_loss_cls_5[0.3364] det_loss_box_5[0.4561] det_loss_cls_dn_0[0.0930] det_loss_box_dn_0[0.3112] det_loss_cls_dn_1[0.0887] det_loss_box_dn_1[0.2576] det_loss_cls_dn_2[0.0874] det_loss_box_dn_2[0.2322] det_loss_cls_dn_3[0.0873] det_loss_box_dn_3[0.2280] det_loss_cls_dn_4[0.0860] det_loss_box_dn_4[0.2277] det_loss_cls_dn_5[0.0851] det_loss_box_dn_5[0.2268] loss_depth[0.0348] total_loss[7.4944]
Rank[0/8] 06/20/2025 09:17:18 INFO stats.py:314 | Epoch[145] Step[249] GlobalStep[56799] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 12:21:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:17:18 INFO loss_tracker.py:84 | Epoch[145/239] Step[249] GlobalStep[56799/NA]: det_loss_cls_0[0.4659] det_loss_box_0[0.6383] det_loss_cls_1[0.4441] det_loss_box_1[0.4722] det_loss_cls_2[0.4021] det_loss_box_2[0.4229] det_loss_cls_3[0.3664] det_loss_box_3[0.4023] det_loss_cls_4[0.3472] det_loss_box_4[0.3821] det_loss_cls_5[0.3278] det_loss_box_5[0.3879] det_loss_cls_dn_0[0.0928] det_loss_box_dn_0[0.2456] det_loss_cls_dn_1[0.0885] det_loss_box_dn_1[0.2026] det_loss_cls_dn_2[0.0884] det_loss_box_dn_2[0.1830] det_loss_cls_dn_3[0.0872] det_loss_box_dn_3[0.1787] det_loss_cls_dn_4[0.0864] det_loss_box_dn_4[0.1763] det_loss_cls_dn_5[0.0854] det_loss_box_dn_5[0.1758] loss_depth[0.0351] total_loss[6.7854]
Rank[0/8] 06/20/2025 09:17:44 INFO stats.py:314 | Epoch[145] Step[274] GlobalStep[56824] Training Speed: 7.68 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 12:21:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:17:44 INFO loss_tracker.py:84 | Epoch[145/239] Step[274] GlobalStep[56824/NA]: det_loss_cls_0[0.4369] det_loss_box_0[0.5544] det_loss_cls_1[0.4194] det_loss_box_1[0.4187] det_loss_cls_2[0.3830] det_loss_box_2[0.3807] det_loss_cls_3[0.3620] det_loss_box_3[0.3630] det_loss_cls_4[0.3338] det_loss_box_4[0.3599] det_loss_cls_5[0.3225] det_loss_box_5[0.3582] det_loss_cls_dn_0[0.0917] det_loss_box_dn_0[0.2633] det_loss_cls_dn_1[0.0879] det_loss_box_dn_1[0.2135] det_loss_cls_dn_2[0.0877] det_loss_box_dn_2[0.1940] det_loss_cls_dn_3[0.0860] det_loss_box_dn_3[0.1875] det_loss_cls_dn_4[0.0856] det_loss_box_dn_4[0.1855] det_loss_cls_dn_5[0.0845] det_loss_box_dn_5[0.1851] loss_depth[0.0382] total_loss[6.4830]
Rank[0/8] 06/20/2025 09:18:10 INFO stats.py:314 | Epoch[145] Step[299] GlobalStep[56849] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 12:20:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:18:10 INFO loss_tracker.py:84 | Epoch[145/239] Step[299] GlobalStep[56849/NA]: det_loss_cls_0[0.4676] det_loss_box_0[0.6368] det_loss_cls_1[0.4532] det_loss_box_1[0.4743] det_loss_cls_2[0.4177] det_loss_box_2[0.4387] det_loss_cls_3[0.3840] det_loss_box_3[0.4221] det_loss_cls_4[0.3611] det_loss_box_4[0.4226] det_loss_cls_5[0.3440] det_loss_box_5[0.4221] det_loss_cls_dn_0[0.0900] det_loss_box_dn_0[0.2675] det_loss_cls_dn_1[0.0869] det_loss_box_dn_1[0.2243] det_loss_cls_dn_2[0.0863] det_loss_box_dn_2[0.2085] det_loss_cls_dn_3[0.0855] det_loss_box_dn_3[0.2054] det_loss_cls_dn_4[0.0842] det_loss_box_dn_4[0.2045] det_loss_cls_dn_5[0.0836] det_loss_box_dn_5[0.2034] loss_depth[0.0351] total_loss[7.1094]
Rank[0/8] 06/20/2025 09:18:36 INFO stats.py:314 | Epoch[145] Step[324] GlobalStep[56874] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 12:20:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:18:36 INFO loss_tracker.py:84 | Epoch[145/239] Step[324] GlobalStep[56874/NA]: det_loss_cls_0[0.6543] det_loss_box_0[0.8523] det_loss_cls_1[0.6206] det_loss_box_1[0.6651] det_loss_cls_2[0.5793] det_loss_box_2[0.6016] det_loss_cls_3[0.5573] det_loss_box_3[0.5646] det_loss_cls_4[0.5368] det_loss_box_4[0.5451] det_loss_cls_5[0.5185] det_loss_box_5[0.5524] det_loss_cls_dn_0[0.1148] det_loss_box_dn_0[0.2889] det_loss_cls_dn_1[0.1116] det_loss_box_dn_1[0.2431] det_loss_cls_dn_2[0.1112] det_loss_box_dn_2[0.2274] det_loss_cls_dn_3[0.1100] det_loss_box_dn_3[0.2228] det_loss_cls_dn_4[0.1096] det_loss_box_dn_4[0.2209] det_loss_cls_dn_5[0.1088] det_loss_box_dn_5[0.2189] loss_depth[0.0378] total_loss[9.3735]
Rank[0/8] 06/20/2025 09:19:02 INFO stats.py:314 | Epoch[145] Step[349] GlobalStep[56899] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 12:19:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:19:02 INFO loss_tracker.py:84 | Epoch[145/239] Step[349] GlobalStep[56899/NA]: det_loss_cls_0[0.6032] det_loss_box_0[0.8040] det_loss_cls_1[0.5842] det_loss_box_1[0.6346] det_loss_cls_2[0.5462] det_loss_box_2[0.5818] det_loss_cls_3[0.5022] det_loss_box_3[0.5519] det_loss_cls_4[0.4772] det_loss_box_4[0.5330] det_loss_cls_5[0.4638] det_loss_box_5[0.5338] det_loss_cls_dn_0[0.0920] det_loss_box_dn_0[0.2980] det_loss_cls_dn_1[0.0895] det_loss_box_dn_1[0.2561] det_loss_cls_dn_2[0.0897] det_loss_box_dn_2[0.2426] det_loss_cls_dn_3[0.0881] det_loss_box_dn_3[0.2391] det_loss_cls_dn_4[0.0870] det_loss_box_dn_4[0.2373] det_loss_cls_dn_5[0.0864] det_loss_box_dn_5[0.2362] loss_depth[0.0346] total_loss[8.8926]
Rank[0/8] 06/20/2025 09:19:27 INFO stats.py:314 | Epoch[145] Step[374] GlobalStep[56924] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 12:19:06.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:19:27 INFO loss_tracker.py:84 | Epoch[145/239] Step[374] GlobalStep[56924/NA]: det_loss_cls_0[0.5348] det_loss_box_0[0.6719] det_loss_cls_1[0.5188] det_loss_box_1[0.5105] det_loss_cls_2[0.4848] det_loss_box_2[0.4652] det_loss_cls_3[0.4492] det_loss_box_3[0.4471] det_loss_cls_4[0.4217] det_loss_box_4[0.4438] det_loss_cls_5[0.4089] det_loss_box_5[0.4413] det_loss_cls_dn_0[0.1015] det_loss_box_dn_0[0.2806] det_loss_cls_dn_1[0.0976] det_loss_box_dn_1[0.2422] det_loss_cls_dn_2[0.0968] det_loss_box_dn_2[0.2271] det_loss_cls_dn_3[0.0956] det_loss_box_dn_3[0.2240] det_loss_cls_dn_4[0.0942] det_loss_box_dn_4[0.2221] det_loss_cls_dn_5[0.0937] det_loss_box_dn_5[0.2218] loss_depth[0.0368] total_loss[7.8324]
Rank[0/8] 06/20/2025 09:19:43 INFO stats.py:394 | Epoch[145] completed. Training Speed: 7.68 samples/sec across all devices.	Epoch Time: 406.03 sec.	Average Epoch Time: 406.03 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 12:18:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:19:54 INFO stats.py:314 | Epoch[146] Step[9] GlobalStep[56949] Training Speed: 7.72 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 12:18:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:20:10 INFO loss_tracker.py:84 | Epoch[146/239] Step[24] GlobalStep[56964/NA]: det_loss_cls_0[0.4823] det_loss_box_0[0.6317] det_loss_cls_1[0.4616] det_loss_box_1[0.4949] det_loss_cls_2[0.4229] det_loss_box_2[0.4401] det_loss_cls_3[0.3872] det_loss_box_3[0.4273] det_loss_cls_4[0.3703] det_loss_box_4[0.4160] det_loss_cls_5[0.3575] det_loss_box_5[0.4165] det_loss_cls_dn_0[0.0965] det_loss_box_dn_0[0.3234] det_loss_cls_dn_1[0.0921] det_loss_box_dn_1[0.2731] det_loss_cls_dn_2[0.0916] det_loss_box_dn_2[0.2556] det_loss_cls_dn_3[0.0909] det_loss_box_dn_3[0.2506] det_loss_cls_dn_4[0.0898] det_loss_box_dn_4[0.2487] det_loss_cls_dn_5[0.0890] det_loss_box_dn_5[0.2477] loss_depth[0.0391] total_loss[7.4965]
Rank[0/8] 06/20/2025 09:20:20 INFO stats.py:314 | Epoch[146] Step[34] GlobalStep[56974] Training Speed: 7.46 samples/sec across all devices.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 12:18:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:20:36 INFO loss_tracker.py:84 | Epoch[146/239] Step[49] GlobalStep[56989/NA]: det_loss_cls_0[0.4245] det_loss_box_0[0.5106] det_loss_cls_1[0.4211] det_loss_box_1[0.3912] det_loss_cls_2[0.3607] det_loss_box_2[0.3478] det_loss_cls_3[0.3292] det_loss_box_3[0.3418] det_loss_cls_4[0.2979] det_loss_box_4[0.3386] det_loss_cls_5[0.2817] det_loss_box_5[0.3356] det_loss_cls_dn_0[0.0791] det_loss_box_dn_0[0.2441] det_loss_cls_dn_1[0.0749] det_loss_box_dn_1[0.1968] det_loss_cls_dn_2[0.0739] det_loss_box_dn_2[0.1811] det_loss_cls_dn_3[0.0730] det_loss_box_dn_3[0.1766] det_loss_cls_dn_4[0.0723] det_loss_box_dn_4[0.1742] det_loss_cls_dn_5[0.0714] det_loss_box_dn_5[0.1725] loss_depth[0.0348] total_loss[6.0054]
Rank[0/8] 06/20/2025 09:20:46 INFO stats.py:314 | Epoch[146] Step[59] GlobalStep[56999] Training Speed: 7.61 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 12:17:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:21:02 INFO loss_tracker.py:84 | Epoch[146/239] Step[74] GlobalStep[57014/NA]: det_loss_cls_0[0.5256] det_loss_box_0[0.6848] det_loss_cls_1[0.5178] det_loss_box_1[0.5111] det_loss_cls_2[0.4619] det_loss_box_2[0.4569] det_loss_cls_3[0.4308] det_loss_box_3[0.4330] det_loss_cls_4[0.4013] det_loss_box_4[0.4216] det_loss_cls_5[0.3811] det_loss_box_5[0.4257] det_loss_cls_dn_0[0.0943] det_loss_box_dn_0[0.2368] det_loss_cls_dn_1[0.0912] det_loss_box_dn_1[0.1999] det_loss_cls_dn_2[0.0911] det_loss_box_dn_2[0.1836] det_loss_cls_dn_3[0.0903] det_loss_box_dn_3[0.1813] det_loss_cls_dn_4[0.0887] det_loss_box_dn_4[0.1797] det_loss_cls_dn_5[0.0878] det_loss_box_dn_5[0.1788] loss_depth[0.0393] total_loss[7.3946]
Rank[0/8] 06/20/2025 09:21:12 INFO stats.py:314 | Epoch[146] Step[84] GlobalStep[57024] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 12:16:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:21:28 INFO loss_tracker.py:84 | Epoch[146/239] Step[99] GlobalStep[57039/NA]: det_loss_cls_0[0.4832] det_loss_box_0[0.6505] det_loss_cls_1[0.4569] det_loss_box_1[0.4899] det_loss_cls_2[0.4092] det_loss_box_2[0.4422] det_loss_cls_3[0.3938] det_loss_box_3[0.4199] det_loss_cls_4[0.3698] det_loss_box_4[0.4129] det_loss_cls_5[0.3554] det_loss_box_5[0.4109] det_loss_cls_dn_0[0.0849] det_loss_box_dn_0[0.2957] det_loss_cls_dn_1[0.0817] det_loss_box_dn_1[0.2530] det_loss_cls_dn_2[0.0816] det_loss_box_dn_2[0.2277] det_loss_cls_dn_3[0.0810] det_loss_box_dn_3[0.2247] det_loss_cls_dn_4[0.0809] det_loss_box_dn_4[0.2226] det_loss_cls_dn_5[0.0796] det_loss_box_dn_5[0.2213] loss_depth[0.0386] total_loss[7.2680]
Rank[0/8] 06/20/2025 09:21:39 INFO stats.py:314 | Epoch[146] Step[109] GlobalStep[57049] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 12:16:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:21:54 INFO loss_tracker.py:84 | Epoch[146/239] Step[124] GlobalStep[57064/NA]: det_loss_cls_0[0.4719] det_loss_box_0[0.6004] det_loss_cls_1[0.4563] det_loss_box_1[0.4648] det_loss_cls_2[0.4112] det_loss_box_2[0.4076] det_loss_cls_3[0.3766] det_loss_box_3[0.3824] det_loss_cls_4[0.3564] det_loss_box_4[0.3724] det_loss_cls_5[0.3387] det_loss_box_5[0.3800] det_loss_cls_dn_0[0.0975] det_loss_box_dn_0[0.3044] det_loss_cls_dn_1[0.0944] det_loss_box_dn_1[0.2522] det_loss_cls_dn_2[0.0936] det_loss_box_dn_2[0.2326] det_loss_cls_dn_3[0.0924] det_loss_box_dn_3[0.2273] det_loss_cls_dn_4[0.0910] det_loss_box_dn_4[0.2247] det_loss_cls_dn_5[0.0898] det_loss_box_dn_5[0.2231] loss_depth[0.0372] total_loss[7.0790]
Rank[0/8] 06/20/2025 09:22:04 INFO stats.py:314 | Epoch[146] Step[134] GlobalStep[57074] Training Speed: 8.05 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 12:15:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:22:20 INFO loss_tracker.py:84 | Epoch[146/239] Step[149] GlobalStep[57089/NA]: det_loss_cls_0[0.4758] det_loss_box_0[0.6164] det_loss_cls_1[0.4615] det_loss_box_1[0.4566] det_loss_cls_2[0.4193] det_loss_box_2[0.4160] det_loss_cls_3[0.3970] det_loss_box_3[0.4092] det_loss_cls_4[0.3751] det_loss_box_4[0.4018] det_loss_cls_5[0.3627] det_loss_box_5[0.4023] det_loss_cls_dn_0[0.0994] det_loss_box_dn_0[0.3419] det_loss_cls_dn_1[0.0959] det_loss_box_dn_1[0.2848] det_loss_cls_dn_2[0.0951] det_loss_box_dn_2[0.2621] det_loss_cls_dn_3[0.0942] det_loss_box_dn_3[0.2559] det_loss_cls_dn_4[0.0931] det_loss_box_dn_4[0.2525] det_loss_cls_dn_5[0.0922] det_loss_box_dn_5[0.2463] loss_depth[0.0394] total_loss[7.4466]
Rank[0/8] 06/20/2025 09:22:30 INFO stats.py:314 | Epoch[146] Step[159] GlobalStep[57099] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 12:15:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:22:46 INFO loss_tracker.py:84 | Epoch[146/239] Step[174] GlobalStep[57114/NA]: det_loss_cls_0[0.4336] det_loss_box_0[0.5938] det_loss_cls_1[0.4307] det_loss_box_1[0.4622] det_loss_cls_2[0.4008] det_loss_box_2[0.4346] det_loss_cls_3[0.3692] det_loss_box_3[0.4271] det_loss_cls_4[0.3541] det_loss_box_4[0.4151] det_loss_cls_5[0.3459] det_loss_box_5[0.4129] det_loss_cls_dn_0[0.0845] det_loss_box_dn_0[0.3109] det_loss_cls_dn_1[0.0806] det_loss_box_dn_1[0.2721] det_loss_cls_dn_2[0.0799] det_loss_box_dn_2[0.2566] det_loss_cls_dn_3[0.0797] det_loss_box_dn_3[0.2550] det_loss_cls_dn_4[0.0795] det_loss_box_dn_4[0.2525] det_loss_cls_dn_5[0.0781] det_loss_box_dn_5[0.2521] loss_depth[0.0375] total_loss[7.1991]
Rank[0/8] 06/20/2025 09:22:57 INFO stats.py:314 | Epoch[146] Step[184] GlobalStep[57124] Training Speed: 6.65 samples/sec across all devices.	Average Step Time: 1.20 sec.	Estimated Remaining Time: 12:14:43.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:23:12 INFO loss_tracker.py:84 | Epoch[146/239] Step[199] GlobalStep[57139/NA]: det_loss_cls_0[0.5591] det_loss_box_0[0.7302] det_loss_cls_1[0.5480] det_loss_box_1[0.5588] det_loss_cls_2[0.5096] det_loss_box_2[0.4954] det_loss_cls_3[0.4648] det_loss_box_3[0.4991] det_loss_cls_4[0.4477] det_loss_box_4[0.4793] det_loss_cls_5[0.4320] det_loss_box_5[0.4797] det_loss_cls_dn_0[0.1104] det_loss_box_dn_0[0.2799] det_loss_cls_dn_1[0.1064] det_loss_box_dn_1[0.2339] det_loss_cls_dn_2[0.1059] det_loss_box_dn_2[0.2135] det_loss_cls_dn_3[0.1052] det_loss_box_dn_3[0.2096] det_loss_cls_dn_4[0.1043] det_loss_box_dn_4[0.2081] det_loss_cls_dn_5[0.1027] det_loss_box_dn_5[0.2060] loss_depth[0.0367] total_loss[8.2262]
Rank[0/8] 06/20/2025 09:23:22 INFO stats.py:314 | Epoch[146] Step[209] GlobalStep[57149] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 12:14:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:23:37 INFO loss_tracker.py:84 | Epoch[146/239] Step[224] GlobalStep[57164/NA]: det_loss_cls_0[0.5359] det_loss_box_0[0.7103] det_loss_cls_1[0.5114] det_loss_box_1[0.5605] det_loss_cls_2[0.4502] det_loss_box_2[0.4999] det_loss_cls_3[0.4093] det_loss_box_3[0.4881] det_loss_cls_4[0.3854] det_loss_box_4[0.4695] det_loss_cls_5[0.3625] det_loss_box_5[0.4745] det_loss_cls_dn_0[0.0911] det_loss_box_dn_0[0.2643] det_loss_cls_dn_1[0.0876] det_loss_box_dn_1[0.2209] det_loss_cls_dn_2[0.0866] det_loss_box_dn_2[0.2073] det_loss_cls_dn_3[0.0861] det_loss_box_dn_3[0.2037] det_loss_cls_dn_4[0.0847] det_loss_box_dn_4[0.2020] det_loss_cls_dn_5[0.0837] det_loss_box_dn_5[0.2007] loss_depth[0.0375] total_loss[7.7136]
Rank[0/8] 06/20/2025 09:23:48 INFO stats.py:314 | Epoch[146] Step[234] GlobalStep[57174] Training Speed: 8.06 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 12:13:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:24:03 INFO loss_tracker.py:84 | Epoch[146/239] Step[249] GlobalStep[57189/NA]: det_loss_cls_0[0.5214] det_loss_box_0[0.6883] det_loss_cls_1[0.5102] det_loss_box_1[0.5461] det_loss_cls_2[0.4523] det_loss_box_2[0.4900] det_loss_cls_3[0.4152] det_loss_box_3[0.4739] det_loss_cls_4[0.3821] det_loss_box_4[0.4556] det_loss_cls_5[0.3620] det_loss_box_5[0.4568] det_loss_cls_dn_0[0.0873] det_loss_box_dn_0[0.2788] det_loss_cls_dn_1[0.0833] det_loss_box_dn_1[0.2369] det_loss_cls_dn_2[0.0825] det_loss_box_dn_2[0.2222] det_loss_cls_dn_3[0.0808] det_loss_box_dn_3[0.2194] det_loss_cls_dn_4[0.0796] det_loss_box_dn_4[0.2196] det_loss_cls_dn_5[0.0790] det_loss_box_dn_5[0.2193] loss_depth[0.0368] total_loss[7.6794]
Rank[0/8] 06/20/2025 09:24:14 INFO stats.py:314 | Epoch[146] Step[259] GlobalStep[57199] Training Speed: 7.93 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 12:13:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:24:30 INFO loss_tracker.py:84 | Epoch[146/239] Step[274] GlobalStep[57214/NA]: det_loss_cls_0[0.4159] det_loss_box_0[0.5784] det_loss_cls_1[0.3951] det_loss_box_1[0.4613] det_loss_cls_2[0.3599] det_loss_box_2[0.4077] det_loss_cls_3[0.3322] det_loss_box_3[0.3974] det_loss_cls_4[0.3196] det_loss_box_4[0.3899] det_loss_cls_5[0.3152] det_loss_box_5[0.3837] det_loss_cls_dn_0[0.0941] det_loss_box_dn_0[0.3061] det_loss_cls_dn_1[0.0912] det_loss_box_dn_1[0.2562] det_loss_cls_dn_2[0.0905] det_loss_box_dn_2[0.2362] det_loss_cls_dn_3[0.0893] det_loss_box_dn_3[0.2332] det_loss_cls_dn_4[0.0884] det_loss_box_dn_4[0.2295] det_loss_cls_dn_5[0.0870] det_loss_box_dn_5[0.2287] loss_depth[0.0395] total_loss[6.8261]
Rank[0/8] 06/20/2025 09:24:40 INFO stats.py:314 | Epoch[146] Step[284] GlobalStep[57224] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 12:12:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:24:55 INFO loss_tracker.py:84 | Epoch[146/239] Step[299] GlobalStep[57239/NA]: det_loss_cls_0[0.5300] det_loss_box_0[0.7146] det_loss_cls_1[0.5364] det_loss_box_1[0.5689] det_loss_cls_2[0.4835] det_loss_box_2[0.5303] det_loss_cls_3[0.4623] det_loss_box_3[0.4997] det_loss_cls_4[0.4380] det_loss_box_4[0.5008] det_loss_cls_5[0.4233] det_loss_box_5[0.4958] det_loss_cls_dn_0[0.1006] det_loss_box_dn_0[0.3194] det_loss_cls_dn_1[0.0972] det_loss_box_dn_1[0.2785] det_loss_cls_dn_2[0.0966] det_loss_box_dn_2[0.2577] det_loss_cls_dn_3[0.0953] det_loss_box_dn_3[0.2549] det_loss_cls_dn_4[0.0940] det_loss_box_dn_4[0.2531] det_loss_cls_dn_5[0.0925] det_loss_box_dn_5[0.2518] loss_depth[0.0342] total_loss[8.4095]
Rank[0/8] 06/20/2025 09:25:05 INFO stats.py:314 | Epoch[146] Step[309] GlobalStep[57249] Training Speed: 7.56 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 12:11:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:25:21 INFO loss_tracker.py:84 | Epoch[146/239] Step[324] GlobalStep[57264/NA]: det_loss_cls_0[0.4219] det_loss_box_0[0.5481] det_loss_cls_1[0.4186] det_loss_box_1[0.4161] det_loss_cls_2[0.3721] det_loss_box_2[0.3821] det_loss_cls_3[0.3564] det_loss_box_3[0.3576] det_loss_cls_4[0.3366] det_loss_box_4[0.3491] det_loss_cls_5[0.3210] det_loss_box_5[0.3534] det_loss_cls_dn_0[0.0938] det_loss_box_dn_0[0.3647] det_loss_cls_dn_1[0.0901] det_loss_box_dn_1[0.3110] det_loss_cls_dn_2[0.0892] det_loss_box_dn_2[0.2838] det_loss_cls_dn_3[0.0880] det_loss_box_dn_3[0.2778] det_loss_cls_dn_4[0.0870] det_loss_box_dn_4[0.2750] det_loss_cls_dn_5[0.0859] det_loss_box_dn_5[0.2727] loss_depth[0.0365] total_loss[6.9885]
Rank[0/8] 06/20/2025 09:25:31 INFO stats.py:314 | Epoch[146] Step[334] GlobalStep[57274] Training Speed: 7.75 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 12:11:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:25:47 INFO loss_tracker.py:84 | Epoch[146/239] Step[349] GlobalStep[57289/NA]: det_loss_cls_0[0.5080] det_loss_box_0[0.7001] det_loss_cls_1[0.5046] det_loss_box_1[0.5075] det_loss_cls_2[0.4693] det_loss_box_2[0.4522] det_loss_cls_3[0.4346] det_loss_box_3[0.4330] det_loss_cls_4[0.4062] det_loss_box_4[0.4205] det_loss_cls_5[0.3961] det_loss_box_5[0.4256] det_loss_cls_dn_0[0.0920] det_loss_box_dn_0[0.3047] det_loss_cls_dn_1[0.0887] det_loss_box_dn_1[0.2596] det_loss_cls_dn_2[0.0885] det_loss_box_dn_2[0.2457] det_loss_cls_dn_3[0.0873] det_loss_box_dn_3[0.2388] det_loss_cls_dn_4[0.0863] det_loss_box_dn_4[0.2361] det_loss_cls_dn_5[0.0849] det_loss_box_dn_5[0.2329] loss_depth[0.0397] total_loss[7.7429]
Rank[0/8] 06/20/2025 09:25:58 INFO stats.py:314 | Epoch[146] Step[359] GlobalStep[57299] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 12:10:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:26:13 INFO loss_tracker.py:84 | Epoch[146/239] Step[374] GlobalStep[57314/NA]: det_loss_cls_0[0.4528] det_loss_box_0[0.6390] det_loss_cls_1[0.4300] det_loss_box_1[0.4957] det_loss_cls_2[0.3834] det_loss_box_2[0.4494] det_loss_cls_3[0.3548] det_loss_box_3[0.4400] det_loss_cls_4[0.3411] det_loss_box_4[0.4255] det_loss_cls_5[0.3234] det_loss_box_5[0.4258] det_loss_cls_dn_0[0.0881] det_loss_box_dn_0[0.2655] det_loss_cls_dn_1[0.0834] det_loss_box_dn_1[0.2220] det_loss_cls_dn_2[0.0825] det_loss_box_dn_2[0.2047] det_loss_cls_dn_3[0.0814] det_loss_box_dn_3[0.2005] det_loss_cls_dn_4[0.0800] det_loss_box_dn_4[0.2000] det_loss_cls_dn_5[0.0788] det_loss_box_dn_5[0.1980] loss_depth[0.0359] total_loss[6.9820]
Rank[0/8] 06/20/2025 09:26:23 INFO stats.py:314 | Epoch[146] Step[384] GlobalStep[57324] Training Speed: 8.07 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 12:10:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:26:28 INFO stats.py:394 | Epoch[146] completed. Training Speed: 7.71 samples/sec across all devices.	Epoch Time: 404.45 sec.	Average Epoch Time: 404.45 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 12:10:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:26:51 INFO stats.py:314 | Epoch[147] Step[19] GlobalStep[57349] Training Speed: 8.13 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 12:09:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:26:56 INFO loss_tracker.py:84 | Epoch[147/239] Step[24] GlobalStep[57354/NA]: det_loss_cls_0[0.5144] det_loss_box_0[0.7093] det_loss_cls_1[0.4931] det_loss_box_1[0.5284] det_loss_cls_2[0.4550] det_loss_box_2[0.4730] det_loss_cls_3[0.4254] det_loss_box_3[0.4507] det_loss_cls_4[0.4038] det_loss_box_4[0.4395] det_loss_cls_5[0.3852] det_loss_box_5[0.4466] det_loss_cls_dn_0[0.0930] det_loss_box_dn_0[0.2527] det_loss_cls_dn_1[0.0896] det_loss_box_dn_1[0.2124] det_loss_cls_dn_2[0.0890] det_loss_box_dn_2[0.1966] det_loss_cls_dn_3[0.0887] det_loss_box_dn_3[0.1939] det_loss_cls_dn_4[0.0871] det_loss_box_dn_4[0.1922] det_loss_cls_dn_5[0.0863] det_loss_box_dn_5[0.1917] loss_depth[0.0341] total_loss[7.5319]
Rank[0/8] 06/20/2025 09:27:17 INFO stats.py:314 | Epoch[147] Step[44] GlobalStep[57374] Training Speed: 6.61 samples/sec across all devices.	Average Step Time: 1.21 sec.	Estimated Remaining Time: 12:09:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:27:23 INFO loss_tracker.py:84 | Epoch[147/239] Step[49] GlobalStep[57379/NA]: det_loss_cls_0[0.5146] det_loss_box_0[0.7003] det_loss_cls_1[0.5130] det_loss_box_1[0.5241] det_loss_cls_2[0.4708] det_loss_box_2[0.4724] det_loss_cls_3[0.4400] det_loss_box_3[0.4600] det_loss_cls_4[0.4138] det_loss_box_4[0.4569] det_loss_cls_5[0.3945] det_loss_box_5[0.4627] det_loss_cls_dn_0[0.0931] det_loss_box_dn_0[0.2990] det_loss_cls_dn_1[0.0900] det_loss_box_dn_1[0.2519] det_loss_cls_dn_2[0.0899] det_loss_box_dn_2[0.2400] det_loss_cls_dn_3[0.0891] det_loss_box_dn_3[0.2351] det_loss_cls_dn_4[0.0879] det_loss_box_dn_4[0.2344] det_loss_cls_dn_5[0.0868] det_loss_box_dn_5[0.2320] loss_depth[0.0378] total_loss[7.8900]
Rank[0/8] 06/20/2025 09:27:43 INFO stats.py:314 | Epoch[147] Step[69] GlobalStep[57399] Training Speed: 7.80 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 12:08:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:27:48 INFO loss_tracker.py:84 | Epoch[147/239] Step[74] GlobalStep[57404/NA]: det_loss_cls_0[0.4396] det_loss_box_0[0.5590] det_loss_cls_1[0.4333] det_loss_box_1[0.4487] det_loss_cls_2[0.3866] det_loss_box_2[0.4177] det_loss_cls_3[0.3613] det_loss_box_3[0.4074] det_loss_cls_4[0.3399] det_loss_box_4[0.3965] det_loss_cls_5[0.3224] det_loss_box_5[0.3953] det_loss_cls_dn_0[0.0938] det_loss_box_dn_0[0.2910] det_loss_cls_dn_1[0.0899] det_loss_box_dn_1[0.2445] det_loss_cls_dn_2[0.0890] det_loss_box_dn_2[0.2272] det_loss_cls_dn_3[0.0882] det_loss_box_dn_3[0.2241] det_loss_cls_dn_4[0.0875] det_loss_box_dn_4[0.2223] det_loss_cls_dn_5[0.0863] det_loss_box_dn_5[0.2190] loss_depth[0.0366] total_loss[6.9072]
Rank[0/8] 06/20/2025 09:28:08 INFO stats.py:314 | Epoch[147] Step[94] GlobalStep[57424] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 12:08:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:28:14 INFO loss_tracker.py:84 | Epoch[147/239] Step[99] GlobalStep[57429/NA]: det_loss_cls_0[0.5143] det_loss_box_0[0.5942] det_loss_cls_1[0.4900] det_loss_box_1[0.4544] det_loss_cls_2[0.4420] det_loss_box_2[0.3995] det_loss_cls_3[0.4111] det_loss_box_3[0.3863] det_loss_cls_4[0.3746] det_loss_box_4[0.3875] det_loss_cls_5[0.3573] det_loss_box_5[0.3848] det_loss_cls_dn_0[0.0887] det_loss_box_dn_0[0.2280] det_loss_cls_dn_1[0.0843] det_loss_box_dn_1[0.1857] det_loss_cls_dn_2[0.0829] det_loss_box_dn_2[0.1694] det_loss_cls_dn_3[0.0822] det_loss_box_dn_3[0.1652] det_loss_cls_dn_4[0.0810] det_loss_box_dn_4[0.1636] det_loss_cls_dn_5[0.0796] det_loss_box_dn_5[0.1624] loss_depth[0.0359] total_loss[6.8049]
Rank[0/8] 06/20/2025 09:28:34 INFO stats.py:314 | Epoch[147] Step[119] GlobalStep[57449] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 12:07:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:28:40 INFO loss_tracker.py:84 | Epoch[147/239] Step[124] GlobalStep[57454/NA]: det_loss_cls_0[0.6056] det_loss_box_0[0.8242] det_loss_cls_1[0.6013] det_loss_box_1[0.6234] det_loss_cls_2[0.5386] det_loss_box_2[0.5597] det_loss_cls_3[0.5068] det_loss_box_3[0.5457] det_loss_cls_4[0.4797] det_loss_box_4[0.5370] det_loss_cls_5[0.4671] det_loss_box_5[0.5404] det_loss_cls_dn_0[0.1014] det_loss_box_dn_0[0.3093] det_loss_cls_dn_1[0.0973] det_loss_box_dn_1[0.2654] det_loss_cls_dn_2[0.0968] det_loss_box_dn_2[0.2515] det_loss_cls_dn_3[0.0959] det_loss_box_dn_3[0.2481] det_loss_cls_dn_4[0.0945] det_loss_box_dn_4[0.2472] det_loss_cls_dn_5[0.0934] det_loss_box_dn_5[0.2453] loss_depth[0.0344] total_loss[9.0099]
Rank[0/8] 06/20/2025 09:29:00 INFO stats.py:314 | Epoch[147] Step[144] GlobalStep[57474] Training Speed: 8.01 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 12:07:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:29:05 INFO loss_tracker.py:84 | Epoch[147/239] Step[149] GlobalStep[57479/NA]: det_loss_cls_0[0.5305] det_loss_box_0[0.6909] det_loss_cls_1[0.5168] det_loss_box_1[0.5354] det_loss_cls_2[0.4735] det_loss_box_2[0.4912] det_loss_cls_3[0.4478] det_loss_box_3[0.4679] det_loss_cls_4[0.4203] det_loss_box_4[0.4502] det_loss_cls_5[0.4183] det_loss_box_5[0.4479] det_loss_cls_dn_0[0.0981] det_loss_box_dn_0[0.3026] det_loss_cls_dn_1[0.0941] det_loss_box_dn_1[0.2563] det_loss_cls_dn_2[0.0935] det_loss_box_dn_2[0.2425] det_loss_cls_dn_3[0.0928] det_loss_box_dn_3[0.2361] det_loss_cls_dn_4[0.0907] det_loss_box_dn_4[0.2349] det_loss_cls_dn_5[0.0903] det_loss_box_dn_5[0.2298] loss_depth[0.0345] total_loss[7.9869]
Rank[0/8] 06/20/2025 09:29:25 INFO stats.py:314 | Epoch[147] Step[169] GlobalStep[57499] Training Speed: 8.17 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 12:06:30.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:29:31 INFO loss_tracker.py:84 | Epoch[147/239] Step[174] GlobalStep[57504/NA]: det_loss_cls_0[0.4792] det_loss_box_0[0.6263] det_loss_cls_1[0.4752] det_loss_box_1[0.4678] det_loss_cls_2[0.4260] det_loss_box_2[0.4220] det_loss_cls_3[0.3993] det_loss_box_3[0.4152] det_loss_cls_4[0.3635] det_loss_box_4[0.4082] det_loss_cls_5[0.3487] det_loss_box_5[0.4094] det_loss_cls_dn_0[0.0927] det_loss_box_dn_0[0.2704] det_loss_cls_dn_1[0.0888] det_loss_box_dn_1[0.2283] det_loss_cls_dn_2[0.0879] det_loss_box_dn_2[0.2145] det_loss_cls_dn_3[0.0871] det_loss_box_dn_3[0.2116] det_loss_cls_dn_4[0.0865] det_loss_box_dn_4[0.2100] det_loss_cls_dn_5[0.0855] det_loss_box_dn_5[0.2092] loss_depth[0.0369] total_loss[7.1502]
Rank[0/8] 06/20/2025 09:29:52 INFO stats.py:314 | Epoch[147] Step[194] GlobalStep[57524] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 12:05:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:29:57 INFO loss_tracker.py:84 | Epoch[147/239] Step[199] GlobalStep[57529/NA]: det_loss_cls_0[0.5509] det_loss_box_0[0.7196] det_loss_cls_1[0.5462] det_loss_box_1[0.5645] det_loss_cls_2[0.4834] det_loss_box_2[0.5134] det_loss_cls_3[0.4485] det_loss_box_3[0.4960] det_loss_cls_4[0.4186] det_loss_box_4[0.4916] det_loss_cls_5[0.4080] det_loss_box_5[0.4813] det_loss_cls_dn_0[0.1034] det_loss_box_dn_0[0.3185] det_loss_cls_dn_1[0.0993] det_loss_box_dn_1[0.2740] det_loss_cls_dn_2[0.0990] det_loss_box_dn_2[0.2461] det_loss_cls_dn_3[0.0987] det_loss_box_dn_3[0.2460] det_loss_cls_dn_4[0.0986] det_loss_box_dn_4[0.2432] det_loss_cls_dn_5[0.0979] det_loss_box_dn_5[0.2368] loss_depth[0.0365] total_loss[8.3198]
Rank[0/8] 06/20/2025 09:30:18 INFO stats.py:314 | Epoch[147] Step[219] GlobalStep[57549] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 12:05:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:30:23 INFO loss_tracker.py:84 | Epoch[147/239] Step[224] GlobalStep[57554/NA]: det_loss_cls_0[0.5282] det_loss_box_0[0.6994] det_loss_cls_1[0.5160] det_loss_box_1[0.5490] det_loss_cls_2[0.4697] det_loss_box_2[0.4941] det_loss_cls_3[0.4370] det_loss_box_3[0.4709] det_loss_cls_4[0.4048] det_loss_box_4[0.4698] det_loss_cls_5[0.3873] det_loss_box_5[0.4640] det_loss_cls_dn_0[0.0938] det_loss_box_dn_0[0.3971] det_loss_cls_dn_1[0.0900] det_loss_box_dn_1[0.3224] det_loss_cls_dn_2[0.0886] det_loss_box_dn_2[0.2951] det_loss_cls_dn_3[0.0872] det_loss_box_dn_3[0.2898] det_loss_cls_dn_4[0.0861] det_loss_box_dn_4[0.2907] det_loss_cls_dn_5[0.0856] det_loss_box_dn_5[0.2799] loss_depth[0.0385] total_loss[8.3349]
Rank[0/8] 06/20/2025 09:30:44 INFO stats.py:314 | Epoch[147] Step[244] GlobalStep[57574] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 12:04:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:30:49 INFO loss_tracker.py:84 | Epoch[147/239] Step[249] GlobalStep[57579/NA]: det_loss_cls_0[0.5285] det_loss_box_0[0.7081] det_loss_cls_1[0.5141] det_loss_box_1[0.5329] det_loss_cls_2[0.4815] det_loss_box_2[0.4917] det_loss_cls_3[0.4453] det_loss_box_3[0.4647] det_loss_cls_4[0.4162] det_loss_box_4[0.4598] det_loss_cls_5[0.3954] det_loss_box_5[0.4671] det_loss_cls_dn_0[0.0995] det_loss_box_dn_0[0.3041] det_loss_cls_dn_1[0.0961] det_loss_box_dn_1[0.2666] det_loss_cls_dn_2[0.0955] det_loss_box_dn_2[0.2513] det_loss_cls_dn_3[0.0940] det_loss_box_dn_3[0.2486] det_loss_cls_dn_4[0.0931] det_loss_box_dn_4[0.2471] det_loss_cls_dn_5[0.0921] det_loss_box_dn_5[0.2466] loss_depth[0.0385] total_loss[8.0783]
Rank[0/8] 06/20/2025 09:31:09 INFO stats.py:314 | Epoch[147] Step[269] GlobalStep[57599] Training Speed: 7.06 samples/sec across all devices.	Average Step Time: 1.13 sec.	Estimated Remaining Time: 12:04:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:31:14 INFO loss_tracker.py:84 | Epoch[147/239] Step[274] GlobalStep[57604/NA]: det_loss_cls_0[0.5926] det_loss_box_0[0.7139] det_loss_cls_1[0.5675] det_loss_box_1[0.5467] det_loss_cls_2[0.5134] det_loss_box_2[0.4937] det_loss_cls_3[0.4695] det_loss_box_3[0.4708] det_loss_cls_4[0.4448] det_loss_box_4[0.4597] det_loss_cls_5[0.4312] det_loss_box_5[0.4576] det_loss_cls_dn_0[0.0974] det_loss_box_dn_0[0.2575] det_loss_cls_dn_1[0.0931] det_loss_box_dn_1[0.2181] det_loss_cls_dn_2[0.0925] det_loss_box_dn_2[0.2023] det_loss_cls_dn_3[0.0914] det_loss_box_dn_3[0.1989] det_loss_cls_dn_4[0.0903] det_loss_box_dn_4[0.1975] det_loss_cls_dn_5[0.0894] det_loss_box_dn_5[0.1961] loss_depth[0.0390] total_loss[8.0251]
Rank[0/8] 06/20/2025 09:31:35 INFO stats.py:314 | Epoch[147] Step[294] GlobalStep[57624] Training Speed: 8.69 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 12:03:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:31:41 INFO loss_tracker.py:84 | Epoch[147/239] Step[299] GlobalStep[57629/NA]: det_loss_cls_0[0.5544] det_loss_box_0[0.6647] det_loss_cls_1[0.5445] det_loss_box_1[0.5271] det_loss_cls_2[0.5043] det_loss_box_2[0.4773] det_loss_cls_3[0.4690] det_loss_box_3[0.4605] det_loss_cls_4[0.4350] det_loss_box_4[0.4531] det_loss_cls_5[0.4170] det_loss_box_5[0.4584] det_loss_cls_dn_0[0.0965] det_loss_box_dn_0[0.2322] det_loss_cls_dn_1[0.0928] det_loss_box_dn_1[0.1937] det_loss_cls_dn_2[0.0924] det_loss_box_dn_2[0.1794] det_loss_cls_dn_3[0.0914] det_loss_box_dn_3[0.1769] det_loss_cls_dn_4[0.0903] det_loss_box_dn_4[0.1757] det_loss_cls_dn_5[0.0893] det_loss_box_dn_5[0.1745] loss_depth[0.0342] total_loss[7.6850]
Rank[0/8] 06/20/2025 09:32:02 INFO stats.py:314 | Epoch[147] Step[319] GlobalStep[57649] Training Speed: 8.13 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 12:03:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:32:07 INFO loss_tracker.py:84 | Epoch[147/239] Step[324] GlobalStep[57654/NA]: det_loss_cls_0[0.5070] det_loss_box_0[0.6702] det_loss_cls_1[0.4948] det_loss_box_1[0.5166] det_loss_cls_2[0.4540] det_loss_box_2[0.4597] det_loss_cls_3[0.4237] det_loss_box_3[0.4462] det_loss_cls_4[0.3946] det_loss_box_4[0.4346] det_loss_cls_5[0.3795] det_loss_box_5[0.4325] det_loss_cls_dn_0[0.0864] det_loss_box_dn_0[0.2702] det_loss_cls_dn_1[0.0828] det_loss_box_dn_1[0.2167] det_loss_cls_dn_2[0.0824] det_loss_box_dn_2[0.2018] det_loss_cls_dn_3[0.0809] det_loss_box_dn_3[0.1971] det_loss_cls_dn_4[0.0812] det_loss_box_dn_4[0.1943] det_loss_cls_dn_5[0.0795] det_loss_box_dn_5[0.1934] loss_depth[0.0383] total_loss[7.4183]
Rank[0/8] 06/20/2025 09:32:27 INFO stats.py:314 | Epoch[147] Step[344] GlobalStep[57674] Training Speed: 8.04 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 12:02:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:32:32 INFO loss_tracker.py:84 | Epoch[147/239] Step[349] GlobalStep[57679/NA]: det_loss_cls_0[0.6167] det_loss_box_0[0.7741] det_loss_cls_1[0.5962] det_loss_box_1[0.5615] det_loss_cls_2[0.5549] det_loss_box_2[0.5106] det_loss_cls_3[0.5200] det_loss_box_3[0.4920] det_loss_cls_4[0.4903] det_loss_box_4[0.4706] det_loss_cls_5[0.4791] det_loss_box_5[0.4648] det_loss_cls_dn_0[0.0948] det_loss_box_dn_0[0.2785] det_loss_cls_dn_1[0.0908] det_loss_box_dn_1[0.2240] det_loss_cls_dn_2[0.0901] det_loss_box_dn_2[0.2009] det_loss_cls_dn_3[0.0879] det_loss_box_dn_3[0.1945] det_loss_cls_dn_4[0.0873] det_loss_box_dn_4[0.1929] det_loss_cls_dn_5[0.0864] det_loss_box_dn_5[0.1919] loss_depth[0.0393] total_loss[8.3902]
Rank[0/8] 06/20/2025 09:32:53 INFO stats.py:314 | Epoch[147] Step[369] GlobalStep[57699] Training Speed: 8.66 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 12:02:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:32:58 INFO loss_tracker.py:84 | Epoch[147/239] Step[374] GlobalStep[57704/NA]: det_loss_cls_0[0.5430] det_loss_box_0[0.7477] det_loss_cls_1[0.5293] det_loss_box_1[0.5828] det_loss_cls_2[0.4823] det_loss_box_2[0.5210] det_loss_cls_3[0.4490] det_loss_box_3[0.5058] det_loss_cls_4[0.4276] det_loss_box_4[0.4918] det_loss_cls_5[0.4132] det_loss_box_5[0.4942] det_loss_cls_dn_0[0.0945] det_loss_box_dn_0[0.2903] det_loss_cls_dn_1[0.0898] det_loss_box_dn_1[0.2471] det_loss_cls_dn_2[0.0884] det_loss_box_dn_2[0.2288] det_loss_cls_dn_3[0.0878] det_loss_box_dn_3[0.2243] det_loss_cls_dn_4[0.0867] det_loss_box_dn_4[0.2220] det_loss_cls_dn_5[0.0860] det_loss_box_dn_5[0.2217] loss_depth[0.0376] total_loss[8.1929]
Rank[0/8] 06/20/2025 09:33:13 INFO stats.py:394 | Epoch[147] completed. Training Speed: 7.69 samples/sec across all devices.	Epoch Time: 405.65 sec.	Average Epoch Time: 405.65 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 12:01:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:33:20 INFO stats.py:314 | Epoch[148] Step[4] GlobalStep[57724] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 12:01:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:33:40 INFO loss_tracker.py:84 | Epoch[148/239] Step[24] GlobalStep[57744/NA]: det_loss_cls_0[0.4020] det_loss_box_0[0.5966] det_loss_cls_1[0.3939] det_loss_box_1[0.4624] det_loss_cls_2[0.3664] det_loss_box_2[0.4119] det_loss_cls_3[0.3420] det_loss_box_3[0.3929] det_loss_cls_4[0.3171] det_loss_box_4[0.3819] det_loss_cls_5[0.3071] det_loss_box_5[0.3811] det_loss_cls_dn_0[0.0900] det_loss_box_dn_0[0.3215] det_loss_cls_dn_1[0.0859] det_loss_box_dn_1[0.2669] det_loss_cls_dn_2[0.0849] det_loss_box_dn_2[0.2469] det_loss_cls_dn_3[0.0839] det_loss_box_dn_3[0.2424] det_loss_cls_dn_4[0.0831] det_loss_box_dn_4[0.2387] det_loss_cls_dn_5[0.0814] det_loss_box_dn_5[0.2370] loss_depth[0.0359] total_loss[6.8537]
Rank[0/8] 06/20/2025 09:33:46 INFO stats.py:314 | Epoch[148] Step[29] GlobalStep[57749] Training Speed: 8.13 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 12:01:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:34:06 INFO loss_tracker.py:84 | Epoch[148/239] Step[49] GlobalStep[57769/NA]: det_loss_cls_0[0.5333] det_loss_box_0[0.6947] det_loss_cls_1[0.5322] det_loss_box_1[0.5803] det_loss_cls_2[0.4853] det_loss_box_2[0.5256] det_loss_cls_3[0.4471] det_loss_box_3[0.5179] det_loss_cls_4[0.4242] det_loss_box_4[0.4967] det_loss_cls_5[0.4063] det_loss_box_5[0.4990] det_loss_cls_dn_0[0.1008] det_loss_box_dn_0[0.2768] det_loss_cls_dn_1[0.0975] det_loss_box_dn_1[0.2337] det_loss_cls_dn_2[0.0973] det_loss_box_dn_2[0.2188] det_loss_cls_dn_3[0.0964] det_loss_box_dn_3[0.2152] det_loss_cls_dn_4[0.0950] det_loss_box_dn_4[0.2142] det_loss_cls_dn_5[0.0938] det_loss_box_dn_5[0.2135] loss_depth[0.0361] total_loss[8.1314]
Rank[0/8] 06/20/2025 09:34:12 INFO stats.py:314 | Epoch[148] Step[54] GlobalStep[57774] Training Speed: 6.14 samples/sec across all devices.	Average Step Time: 1.30 sec.	Estimated Remaining Time: 12:00:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:34:32 INFO loss_tracker.py:84 | Epoch[148/239] Step[74] GlobalStep[57794/NA]: det_loss_cls_0[0.5167] det_loss_box_0[0.6847] det_loss_cls_1[0.5112] det_loss_box_1[0.5274] det_loss_cls_2[0.4574] det_loss_box_2[0.4608] det_loss_cls_3[0.4249] det_loss_box_3[0.4360] det_loss_cls_4[0.3846] det_loss_box_4[0.4436] det_loss_cls_5[0.3671] det_loss_box_5[0.4425] det_loss_cls_dn_0[0.0911] det_loss_box_dn_0[0.2778] det_loss_cls_dn_1[0.0879] det_loss_box_dn_1[0.2310] det_loss_cls_dn_2[0.0872] det_loss_box_dn_2[0.2165] det_loss_cls_dn_3[0.0859] det_loss_box_dn_3[0.2094] det_loss_cls_dn_4[0.0849] det_loss_box_dn_4[0.2076] det_loss_cls_dn_5[0.0837] det_loss_box_dn_5[0.2059] loss_depth[0.0352] total_loss[7.5609]
Rank[0/8] 06/20/2025 09:34:38 INFO stats.py:314 | Epoch[148] Step[79] GlobalStep[57799] Training Speed: 7.55 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 11:59:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:34:59 INFO loss_tracker.py:84 | Epoch[148/239] Step[99] GlobalStep[57819/NA]: det_loss_cls_0[0.5030] det_loss_box_0[0.6611] det_loss_cls_1[0.4828] det_loss_box_1[0.5209] det_loss_cls_2[0.4474] det_loss_box_2[0.4711] det_loss_cls_3[0.4242] det_loss_box_3[0.4536] det_loss_cls_4[0.4014] det_loss_box_4[0.4427] det_loss_cls_5[0.3823] det_loss_box_5[0.4386] det_loss_cls_dn_0[0.0938] det_loss_box_dn_0[0.3447] det_loss_cls_dn_1[0.0895] det_loss_box_dn_1[0.2804] det_loss_cls_dn_2[0.0898] det_loss_box_dn_2[0.2652] det_loss_cls_dn_3[0.0885] det_loss_box_dn_3[0.2599] det_loss_cls_dn_4[0.0871] det_loss_box_dn_4[0.2567] det_loss_cls_dn_5[0.0862] det_loss_box_dn_5[0.2519] loss_depth[0.0394] total_loss[7.8623]
Rank[0/8] 06/20/2025 09:35:04 INFO stats.py:314 | Epoch[148] Step[104] GlobalStep[57824] Training Speed: 7.96 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 11:59:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:35:24 INFO loss_tracker.py:84 | Epoch[148/239] Step[124] GlobalStep[57844/NA]: det_loss_cls_0[0.4337] det_loss_box_0[0.5580] det_loss_cls_1[0.4147] det_loss_box_1[0.4233] det_loss_cls_2[0.3670] det_loss_box_2[0.3736] det_loss_cls_3[0.3366] det_loss_box_3[0.3617] det_loss_cls_4[0.3170] det_loss_box_4[0.3507] det_loss_cls_5[0.3116] det_loss_box_5[0.3517] det_loss_cls_dn_0[0.0943] det_loss_box_dn_0[0.2699] det_loss_cls_dn_1[0.0908] det_loss_box_dn_1[0.2274] det_loss_cls_dn_2[0.0901] det_loss_box_dn_2[0.2086] det_loss_cls_dn_3[0.0886] det_loss_box_dn_3[0.2045] det_loss_cls_dn_4[0.0875] det_loss_box_dn_4[0.2026] det_loss_cls_dn_5[0.0862] det_loss_box_dn_5[0.2018] loss_depth[0.0358] total_loss[6.4877]
Rank[0/8] 06/20/2025 09:35:30 INFO stats.py:314 | Epoch[148] Step[129] GlobalStep[57849] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 11:58:51.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:35:51 INFO loss_tracker.py:84 | Epoch[148/239] Step[149] GlobalStep[57869/NA]: det_loss_cls_0[0.3757] det_loss_box_0[0.6013] det_loss_cls_1[0.3698] det_loss_box_1[0.4455] det_loss_cls_2[0.3380] det_loss_box_2[0.4009] det_loss_cls_3[0.3100] det_loss_box_3[0.3847] det_loss_cls_4[0.2919] det_loss_box_4[0.3773] det_loss_cls_5[0.2792] det_loss_box_5[0.3772] det_loss_cls_dn_0[0.0876] det_loss_box_dn_0[0.4063] det_loss_cls_dn_1[0.0845] det_loss_box_dn_1[0.3493] det_loss_cls_dn_2[0.0837] det_loss_box_dn_2[0.3302] det_loss_cls_dn_3[0.0825] det_loss_box_dn_3[0.3245] det_loss_cls_dn_4[0.0820] det_loss_box_dn_4[0.3224] det_loss_cls_dn_5[0.0810] det_loss_box_dn_5[0.3212] loss_depth[0.0384] total_loss[7.1453]
Rank[0/8] 06/20/2025 09:35:56 INFO stats.py:314 | Epoch[148] Step[154] GlobalStep[57874] Training Speed: 7.78 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 11:58:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:36:17 INFO loss_tracker.py:84 | Epoch[148/239] Step[174] GlobalStep[57894/NA]: det_loss_cls_0[0.6344] det_loss_box_0[0.7728] det_loss_cls_1[0.6140] det_loss_box_1[0.5777] det_loss_cls_2[0.5521] det_loss_box_2[0.5226] det_loss_cls_3[0.5140] det_loss_box_3[0.5065] det_loss_cls_4[0.4773] det_loss_box_4[0.5029] det_loss_cls_5[0.4717] det_loss_box_5[0.4953] det_loss_cls_dn_0[0.0984] det_loss_box_dn_0[0.2473] det_loss_cls_dn_1[0.0938] det_loss_box_dn_1[0.2062] det_loss_cls_dn_2[0.0924] det_loss_box_dn_2[0.1928] det_loss_cls_dn_3[0.0923] det_loss_box_dn_3[0.1900] det_loss_cls_dn_4[0.0913] det_loss_box_dn_4[0.1887] det_loss_cls_dn_5[0.0905] det_loss_box_dn_5[0.1874] loss_depth[0.0370] total_loss[8.4495]
Rank[0/8] 06/20/2025 09:36:22 INFO stats.py:314 | Epoch[148] Step[179] GlobalStep[57899] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 11:57:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:36:43 INFO loss_tracker.py:84 | Epoch[148/239] Step[199] GlobalStep[57919/NA]: det_loss_cls_0[0.5610] det_loss_box_0[0.7516] det_loss_cls_1[0.5450] det_loss_box_1[0.5848] det_loss_cls_2[0.5020] det_loss_box_2[0.5320] det_loss_cls_3[0.4768] det_loss_box_3[0.5088] det_loss_cls_4[0.4555] det_loss_box_4[0.4925] det_loss_cls_5[0.4425] det_loss_box_5[0.4941] det_loss_cls_dn_0[0.1088] det_loss_box_dn_0[0.3072] det_loss_cls_dn_1[0.1045] det_loss_box_dn_1[0.2627] det_loss_cls_dn_2[0.1041] det_loss_box_dn_2[0.2474] det_loss_cls_dn_3[0.1031] det_loss_box_dn_3[0.2439] det_loss_cls_dn_4[0.1021] det_loss_box_dn_4[0.2426] det_loss_cls_dn_5[0.1012] det_loss_box_dn_5[0.2406] loss_depth[0.0387] total_loss[8.5535]
Rank[0/8] 06/20/2025 09:36:48 INFO stats.py:314 | Epoch[148] Step[204] GlobalStep[57924] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 11:57:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:37:09 INFO loss_tracker.py:84 | Epoch[148/239] Step[224] GlobalStep[57944/NA]: det_loss_cls_0[0.4956] det_loss_box_0[0.6598] det_loss_cls_1[0.5002] det_loss_box_1[0.5140] det_loss_cls_2[0.4552] det_loss_box_2[0.4636] det_loss_cls_3[0.4312] det_loss_box_3[0.4427] det_loss_cls_4[0.4011] det_loss_box_4[0.4319] det_loss_cls_5[0.3755] det_loss_box_5[0.4422] det_loss_cls_dn_0[0.0976] det_loss_box_dn_0[0.2674] det_loss_cls_dn_1[0.0931] det_loss_box_dn_1[0.2294] det_loss_cls_dn_2[0.0918] det_loss_box_dn_2[0.2176] det_loss_cls_dn_3[0.0910] det_loss_box_dn_3[0.2138] det_loss_cls_dn_4[0.0895] det_loss_box_dn_4[0.2121] det_loss_cls_dn_5[0.0888] det_loss_box_dn_5[0.2112] loss_depth[0.0355] total_loss[7.5519]
Rank[0/8] 06/20/2025 09:37:14 INFO stats.py:314 | Epoch[148] Step[229] GlobalStep[57949] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 11:56:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:37:34 INFO loss_tracker.py:84 | Epoch[148/239] Step[249] GlobalStep[57969/NA]: det_loss_cls_0[0.5041] det_loss_box_0[0.7135] det_loss_cls_1[0.4916] det_loss_box_1[0.5706] det_loss_cls_2[0.4424] det_loss_box_2[0.5056] det_loss_cls_3[0.4100] det_loss_box_3[0.4717] det_loss_cls_4[0.3823] det_loss_box_4[0.4608] det_loss_cls_5[0.3644] det_loss_box_5[0.4681] det_loss_cls_dn_0[0.0993] det_loss_box_dn_0[0.2942] det_loss_cls_dn_1[0.0956] det_loss_box_dn_1[0.2487] det_loss_cls_dn_2[0.0947] det_loss_box_dn_2[0.2308] det_loss_cls_dn_3[0.0939] det_loss_box_dn_3[0.2272] det_loss_cls_dn_4[0.0929] det_loss_box_dn_4[0.2258] det_loss_cls_dn_5[0.0921] det_loss_box_dn_5[0.2241] loss_depth[0.0388] total_loss[7.8431]
Rank[0/8] 06/20/2025 09:37:40 INFO stats.py:314 | Epoch[148] Step[254] GlobalStep[57974] Training Speed: 8.13 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 11:56:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:38:00 INFO loss_tracker.py:84 | Epoch[148/239] Step[274] GlobalStep[57994/NA]: det_loss_cls_0[0.4899] det_loss_box_0[0.6246] det_loss_cls_1[0.4694] det_loss_box_1[0.4924] det_loss_cls_2[0.4225] det_loss_box_2[0.4543] det_loss_cls_3[0.3938] det_loss_box_3[0.4410] det_loss_cls_4[0.3734] det_loss_box_4[0.4295] det_loss_cls_5[0.3620] det_loss_box_5[0.4307] det_loss_cls_dn_0[0.1044] det_loss_box_dn_0[0.2931] det_loss_cls_dn_1[0.1013] det_loss_box_dn_1[0.2419] det_loss_cls_dn_2[0.1014] det_loss_box_dn_2[0.2270] det_loss_cls_dn_3[0.1004] det_loss_box_dn_3[0.2230] det_loss_cls_dn_4[0.0995] det_loss_box_dn_4[0.2217] det_loss_cls_dn_5[0.0985] det_loss_box_dn_5[0.2207] loss_depth[0.0402] total_loss[7.4566]
Rank[0/8] 06/20/2025 09:38:05 INFO stats.py:314 | Epoch[148] Step[279] GlobalStep[57999] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 11:55:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:38:26 INFO loss_tracker.py:84 | Epoch[148/239] Step[299] GlobalStep[58019/NA]: det_loss_cls_0[0.5371] det_loss_box_0[0.7322] det_loss_cls_1[0.5127] det_loss_box_1[0.5781] det_loss_cls_2[0.4739] det_loss_box_2[0.5253] det_loss_cls_3[0.4364] det_loss_box_3[0.5006] det_loss_cls_4[0.4091] det_loss_box_4[0.4912] det_loss_cls_5[0.3886] det_loss_box_5[0.4924] det_loss_cls_dn_0[0.1009] det_loss_box_dn_0[0.3674] det_loss_cls_dn_1[0.0974] det_loss_box_dn_1[0.3156] det_loss_cls_dn_2[0.0975] det_loss_box_dn_2[0.2989] det_loss_cls_dn_3[0.0957] det_loss_box_dn_3[0.2913] det_loss_cls_dn_4[0.0955] det_loss_box_dn_4[0.2889] det_loss_cls_dn_5[0.0943] det_loss_box_dn_5[0.2880] loss_depth[0.0345] total_loss[8.5434]
Rank[0/8] 06/20/2025 09:38:31 INFO stats.py:314 | Epoch[148] Step[304] GlobalStep[58024] Training Speed: 8.12 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 11:55:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:38:52 INFO loss_tracker.py:84 | Epoch[148/239] Step[324] GlobalStep[58044/NA]: det_loss_cls_0[0.5074] det_loss_box_0[0.6034] det_loss_cls_1[0.5063] det_loss_box_1[0.4584] det_loss_cls_2[0.4592] det_loss_box_2[0.4094] det_loss_cls_3[0.4171] det_loss_box_3[0.4061] det_loss_cls_4[0.3922] det_loss_box_4[0.3963] det_loss_cls_5[0.3815] det_loss_box_5[0.3975] det_loss_cls_dn_0[0.0952] det_loss_box_dn_0[0.2378] det_loss_cls_dn_1[0.0905] det_loss_box_dn_1[0.1989] det_loss_cls_dn_2[0.0902] det_loss_box_dn_2[0.1824] det_loss_cls_dn_3[0.0888] det_loss_box_dn_3[0.1791] det_loss_cls_dn_4[0.0880] det_loss_box_dn_4[0.1764] det_loss_cls_dn_5[0.0873] det_loss_box_dn_5[0.1750] loss_depth[0.0374] total_loss[7.0617]
Rank[0/8] 06/20/2025 09:38:57 INFO stats.py:314 | Epoch[148] Step[329] GlobalStep[58049] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 11:54:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:39:18 INFO loss_tracker.py:84 | Epoch[148/239] Step[349] GlobalStep[58069/NA]: det_loss_cls_0[0.4483] det_loss_box_0[0.5671] det_loss_cls_1[0.4367] det_loss_box_1[0.4128] det_loss_cls_2[0.3932] det_loss_box_2[0.3740] det_loss_cls_3[0.3613] det_loss_box_3[0.3624] det_loss_cls_4[0.3372] det_loss_box_4[0.3529] det_loss_cls_5[0.3206] det_loss_box_5[0.3548] det_loss_cls_dn_0[0.0934] det_loss_box_dn_0[0.2858] det_loss_cls_dn_1[0.0894] det_loss_box_dn_1[0.2361] det_loss_cls_dn_2[0.0888] det_loss_box_dn_2[0.2185] det_loss_cls_dn_3[0.0873] det_loss_box_dn_3[0.2140] det_loss_cls_dn_4[0.0870] det_loss_box_dn_4[0.2111] det_loss_cls_dn_5[0.0854] det_loss_box_dn_5[0.2091] loss_depth[0.0386] total_loss[6.6659]
Rank[0/8] 06/20/2025 09:39:23 INFO stats.py:314 | Epoch[148] Step[354] GlobalStep[58074] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 11:53:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:39:43 INFO loss_tracker.py:84 | Epoch[148/239] Step[374] GlobalStep[58094/NA]: det_loss_cls_0[0.6074] det_loss_box_0[0.7948] det_loss_cls_1[0.5957] det_loss_box_1[0.6463] det_loss_cls_2[0.5701] det_loss_box_2[0.5847] det_loss_cls_3[0.5512] det_loss_box_3[0.5723] det_loss_cls_4[0.5291] det_loss_box_4[0.5547] det_loss_cls_5[0.5171] det_loss_box_5[0.5511] det_loss_cls_dn_0[0.0939] det_loss_box_dn_0[0.3080] det_loss_cls_dn_1[0.0912] det_loss_box_dn_1[0.2580] det_loss_cls_dn_2[0.0910] det_loss_box_dn_2[0.2415] det_loss_cls_dn_3[0.0897] det_loss_box_dn_3[0.2373] det_loss_cls_dn_4[0.0888] det_loss_box_dn_4[0.2339] det_loss_cls_dn_5[0.0880] det_loss_box_dn_5[0.2319] loss_depth[0.0351] total_loss[9.1625]
Rank[0/8] 06/20/2025 09:39:48 INFO stats.py:314 | Epoch[148] Step[379] GlobalStep[58099] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 11:53:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:39:59 INFO stats.py:394 | Epoch[148] completed. Training Speed: 7.69 samples/sec across all devices.	Epoch Time: 405.55 sec.	Average Epoch Time: 405.55 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 11:53:10.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:40:18 INFO stats.py:314 | Epoch[149] Step[14] GlobalStep[58124] Training Speed: 7.77 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 11:52:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:40:28 INFO loss_tracker.py:84 | Epoch[149/239] Step[24] GlobalStep[58134/NA]: det_loss_cls_0[0.5689] det_loss_box_0[0.8481] det_loss_cls_1[0.5486] det_loss_box_1[0.6673] det_loss_cls_2[0.5063] det_loss_box_2[0.5944] det_loss_cls_3[0.4678] det_loss_box_3[0.5692] det_loss_cls_4[0.4394] det_loss_box_4[0.5598] det_loss_cls_5[0.4269] det_loss_box_5[0.5596] det_loss_cls_dn_0[0.0980] det_loss_box_dn_0[0.3135] det_loss_cls_dn_1[0.0942] det_loss_box_dn_1[0.2697] det_loss_cls_dn_2[0.0934] det_loss_box_dn_2[0.2513] det_loss_cls_dn_3[0.0924] det_loss_box_dn_3[0.2472] det_loss_cls_dn_4[0.0914] det_loss_box_dn_4[0.2460] det_loss_cls_dn_5[0.0903] det_loss_box_dn_5[0.2452] loss_depth[0.0388] total_loss[8.9279]
Rank[0/8] 06/20/2025 09:40:44 INFO stats.py:314 | Epoch[149] Step[39] GlobalStep[58149] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 11:52:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:40:55 INFO loss_tracker.py:84 | Epoch[149/239] Step[49] GlobalStep[58159/NA]: det_loss_cls_0[0.5626] det_loss_box_0[0.6574] det_loss_cls_1[0.5635] det_loss_box_1[0.5115] det_loss_cls_2[0.5251] det_loss_box_2[0.4613] det_loss_cls_3[0.4912] det_loss_box_3[0.4515] det_loss_cls_4[0.4692] det_loss_box_4[0.4481] det_loss_cls_5[0.4513] det_loss_box_5[0.4499] det_loss_cls_dn_0[0.0947] det_loss_box_dn_0[0.2521] det_loss_cls_dn_1[0.0918] det_loss_box_dn_1[0.2173] det_loss_cls_dn_2[0.0915] det_loss_box_dn_2[0.2025] det_loss_cls_dn_3[0.0907] det_loss_box_dn_3[0.2015] det_loss_cls_dn_4[0.0896] det_loss_box_dn_4[0.2004] det_loss_cls_dn_5[0.0884] det_loss_box_dn_5[0.1995] loss_depth[0.0354] total_loss[7.8980]
Rank[0/8] 06/20/2025 09:41:10 INFO stats.py:314 | Epoch[149] Step[64] GlobalStep[58174] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 11:51:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:41:20 INFO loss_tracker.py:84 | Epoch[149/239] Step[74] GlobalStep[58184/NA]: det_loss_cls_0[0.3755] det_loss_box_0[0.4838] det_loss_cls_1[0.3643] det_loss_box_1[0.3570] det_loss_cls_2[0.3161] det_loss_box_2[0.3258] det_loss_cls_3[0.2879] det_loss_box_3[0.3146] det_loss_cls_4[0.2734] det_loss_box_4[0.2989] det_loss_cls_5[0.2648] det_loss_box_5[0.3024] det_loss_cls_dn_0[0.0780] det_loss_box_dn_0[0.2860] det_loss_cls_dn_1[0.0736] det_loss_box_dn_1[0.2282] det_loss_cls_dn_2[0.0728] det_loss_box_dn_2[0.2111] det_loss_cls_dn_3[0.0713] det_loss_box_dn_3[0.2057] det_loss_cls_dn_4[0.0713] det_loss_box_dn_4[0.2040] det_loss_cls_dn_5[0.0695] det_loss_box_dn_5[0.2018] loss_depth[0.0364] total_loss[5.7743]
Rank[0/8] 06/20/2025 09:41:36 INFO stats.py:314 | Epoch[149] Step[89] GlobalStep[58199] Training Speed: 8.67 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 11:51:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:41:46 INFO loss_tracker.py:84 | Epoch[149/239] Step[99] GlobalStep[58209/NA]: det_loss_cls_0[0.4555] det_loss_box_0[0.5619] det_loss_cls_1[0.4447] det_loss_box_1[0.4400] det_loss_cls_2[0.4045] det_loss_box_2[0.3932] det_loss_cls_3[0.3743] det_loss_box_3[0.3829] det_loss_cls_4[0.3482] det_loss_box_4[0.3757] det_loss_cls_5[0.3334] det_loss_box_5[0.3800] det_loss_cls_dn_0[0.0932] det_loss_box_dn_0[0.2891] det_loss_cls_dn_1[0.0897] det_loss_box_dn_1[0.2334] det_loss_cls_dn_2[0.0895] det_loss_box_dn_2[0.2126] det_loss_cls_dn_3[0.0892] det_loss_box_dn_3[0.2073] det_loss_cls_dn_4[0.0893] det_loss_box_dn_4[0.2048] det_loss_cls_dn_5[0.0876] det_loss_box_dn_5[0.2039] loss_depth[0.0364] total_loss[6.8205]
Rank[0/8] 06/20/2025 09:42:02 INFO stats.py:314 | Epoch[149] Step[114] GlobalStep[58224] Training Speed: 7.89 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 11:50:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:42:12 INFO loss_tracker.py:84 | Epoch[149/239] Step[124] GlobalStep[58234/NA]: det_loss_cls_0[0.4941] det_loss_box_0[0.5944] det_loss_cls_1[0.4676] det_loss_box_1[0.4719] det_loss_cls_2[0.4289] det_loss_box_2[0.4112] det_loss_cls_3[0.3865] det_loss_box_3[0.3981] det_loss_cls_4[0.3579] det_loss_box_4[0.3816] det_loss_cls_5[0.3414] det_loss_box_5[0.3812] det_loss_cls_dn_0[0.0852] det_loss_box_dn_0[0.2694] det_loss_cls_dn_1[0.0816] det_loss_box_dn_1[0.2196] det_loss_cls_dn_2[0.0811] det_loss_box_dn_2[0.2019] det_loss_cls_dn_3[0.0790] det_loss_box_dn_3[0.1989] det_loss_cls_dn_4[0.0781] det_loss_box_dn_4[0.1972] det_loss_cls_dn_5[0.0773] det_loss_box_dn_5[0.1928] loss_depth[0.0356] total_loss[6.9127]
Rank[0/8] 06/20/2025 09:42:28 INFO stats.py:314 | Epoch[149] Step[139] GlobalStep[58249] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 11:50:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:42:38 INFO loss_tracker.py:84 | Epoch[149/239] Step[149] GlobalStep[58259/NA]: det_loss_cls_0[0.5889] det_loss_box_0[0.7509] det_loss_cls_1[0.5712] det_loss_box_1[0.5909] det_loss_cls_2[0.5187] det_loss_box_2[0.5295] det_loss_cls_3[0.4872] det_loss_box_3[0.5144] det_loss_cls_4[0.4601] det_loss_box_4[0.5131] det_loss_cls_5[0.4459] det_loss_box_5[0.5120] det_loss_cls_dn_0[0.1037] det_loss_box_dn_0[0.2822] det_loss_cls_dn_1[0.0998] det_loss_box_dn_1[0.2374] det_loss_cls_dn_2[0.0985] det_loss_box_dn_2[0.2181] det_loss_cls_dn_3[0.0980] det_loss_box_dn_3[0.2161] det_loss_cls_dn_4[0.0971] det_loss_box_dn_4[0.2149] det_loss_cls_dn_5[0.0959] det_loss_box_dn_5[0.2139] loss_depth[0.0350] total_loss[8.4934]
Rank[0/8] 06/20/2025 09:42:53 INFO stats.py:314 | Epoch[149] Step[164] GlobalStep[58274] Training Speed: 6.35 samples/sec across all devices.	Average Step Time: 1.26 sec.	Estimated Remaining Time: 11:49:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:43:04 INFO loss_tracker.py:84 | Epoch[149/239] Step[174] GlobalStep[58284/NA]: det_loss_cls_0[0.5465] det_loss_box_0[0.6935] det_loss_cls_1[0.5435] det_loss_box_1[0.5321] det_loss_cls_2[0.4976] det_loss_box_2[0.4579] det_loss_cls_3[0.4615] det_loss_box_3[0.4421] det_loss_cls_4[0.4356] det_loss_box_4[0.4383] det_loss_cls_5[0.4180] det_loss_box_5[0.4389] det_loss_cls_dn_0[0.1026] det_loss_box_dn_0[0.2377] det_loss_cls_dn_1[0.0998] det_loss_box_dn_1[0.1964] det_loss_cls_dn_2[0.0992] det_loss_box_dn_2[0.1829] det_loss_cls_dn_3[0.0976] det_loss_box_dn_3[0.1797] det_loss_cls_dn_4[0.0962] det_loss_box_dn_4[0.1785] det_loss_cls_dn_5[0.0958] det_loss_box_dn_5[0.1778] loss_depth[0.0378] total_loss[7.6876]
Rank[0/8] 06/20/2025 09:43:19 INFO stats.py:314 | Epoch[149] Step[189] GlobalStep[58299] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 11:49:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:43:29 INFO loss_tracker.py:84 | Epoch[149/239] Step[199] GlobalStep[58309/NA]: det_loss_cls_0[0.4057] det_loss_box_0[0.6013] det_loss_cls_1[0.3925] det_loss_box_1[0.4835] det_loss_cls_2[0.3631] det_loss_box_2[0.4298] det_loss_cls_3[0.3474] det_loss_box_3[0.4078] det_loss_cls_4[0.3252] det_loss_box_4[0.4040] det_loss_cls_5[0.3173] det_loss_box_5[0.4057] det_loss_cls_dn_0[0.0951] det_loss_box_dn_0[0.3505] det_loss_cls_dn_1[0.0902] det_loss_box_dn_1[0.3035] det_loss_cls_dn_2[0.0897] det_loss_box_dn_2[0.2813] det_loss_cls_dn_3[0.0884] det_loss_box_dn_3[0.2757] det_loss_cls_dn_4[0.0864] det_loss_box_dn_4[0.2741] det_loss_cls_dn_5[0.0853] det_loss_box_dn_5[0.2714] loss_depth[0.0347] total_loss[7.2094]
Rank[0/8] 06/20/2025 09:43:45 INFO stats.py:314 | Epoch[149] Step[214] GlobalStep[58324] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 11:48:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:43:55 INFO loss_tracker.py:84 | Epoch[149/239] Step[224] GlobalStep[58334/NA]: det_loss_cls_0[0.4550] det_loss_box_0[0.6131] det_loss_cls_1[0.4493] det_loss_box_1[0.4727] det_loss_cls_2[0.4243] det_loss_box_2[0.4194] det_loss_cls_3[0.3804] det_loss_box_3[0.4216] det_loss_cls_4[0.3643] det_loss_box_4[0.4105] det_loss_cls_5[0.3501] det_loss_box_5[0.4142] det_loss_cls_dn_0[0.0818] det_loss_box_dn_0[0.3190] det_loss_cls_dn_1[0.0777] det_loss_box_dn_1[0.2762] det_loss_cls_dn_2[0.0760] det_loss_box_dn_2[0.2621] det_loss_cls_dn_3[0.0751] det_loss_box_dn_3[0.2583] det_loss_cls_dn_4[0.0738] det_loss_box_dn_4[0.2566] det_loss_cls_dn_5[0.0727] det_loss_box_dn_5[0.2548] loss_depth[0.0362] total_loss[7.2954]
Rank[0/8] 06/20/2025 09:44:10 INFO stats.py:314 | Epoch[149] Step[239] GlobalStep[58349] Training Speed: 7.76 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 11:47:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:44:21 INFO loss_tracker.py:84 | Epoch[149/239] Step[249] GlobalStep[58359/NA]: det_loss_cls_0[0.4618] det_loss_box_0[0.5803] det_loss_cls_1[0.4544] det_loss_box_1[0.4522] det_loss_cls_2[0.4148] det_loss_box_2[0.4118] det_loss_cls_3[0.3933] det_loss_box_3[0.3934] det_loss_cls_4[0.3684] det_loss_box_4[0.3867] det_loss_cls_5[0.3575] det_loss_box_5[0.3847] det_loss_cls_dn_0[0.1023] det_loss_box_dn_0[0.2574] det_loss_cls_dn_1[0.0996] det_loss_box_dn_1[0.2119] det_loss_cls_dn_2[0.0985] det_loss_box_dn_2[0.1952] det_loss_cls_dn_3[0.0973] det_loss_box_dn_3[0.1925] det_loss_cls_dn_4[0.0969] det_loss_box_dn_4[0.1899] det_loss_cls_dn_5[0.0959] det_loss_box_dn_5[0.1893] loss_depth[0.0387] total_loss[6.9247]
Rank[0/8] 06/20/2025 09:44:36 INFO stats.py:314 | Epoch[149] Step[264] GlobalStep[58374] Training Speed: 7.92 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 11:47:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:44:47 INFO loss_tracker.py:84 | Epoch[149/239] Step[274] GlobalStep[58384/NA]: det_loss_cls_0[0.4514] det_loss_box_0[0.5577] det_loss_cls_1[0.4323] det_loss_box_1[0.4331] det_loss_cls_2[0.3825] det_loss_box_2[0.3875] det_loss_cls_3[0.3586] det_loss_box_3[0.3689] det_loss_cls_4[0.3251] det_loss_box_4[0.3637] det_loss_cls_5[0.3094] det_loss_box_5[0.3608] det_loss_cls_dn_0[0.0907] det_loss_box_dn_0[0.2789] det_loss_cls_dn_1[0.0870] det_loss_box_dn_1[0.2273] det_loss_cls_dn_2[0.0863] det_loss_box_dn_2[0.2126] det_loss_cls_dn_3[0.0849] det_loss_box_dn_3[0.2062] det_loss_cls_dn_4[0.0835] det_loss_box_dn_4[0.2055] det_loss_cls_dn_5[0.0822] det_loss_box_dn_5[0.2042] loss_depth[0.0348] total_loss[6.6153]
Rank[0/8] 06/20/2025 09:45:03 INFO stats.py:314 | Epoch[149] Step[289] GlobalStep[58399] Training Speed: 7.95 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 11:46:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:45:13 INFO loss_tracker.py:84 | Epoch[149/239] Step[299] GlobalStep[58409/NA]: det_loss_cls_0[0.4538] det_loss_box_0[0.6473] det_loss_cls_1[0.4451] det_loss_box_1[0.5257] det_loss_cls_2[0.4048] det_loss_box_2[0.4877] det_loss_cls_3[0.3752] det_loss_box_3[0.4734] det_loss_cls_4[0.3509] det_loss_box_4[0.4701] det_loss_cls_5[0.3391] det_loss_box_5[0.4695] det_loss_cls_dn_0[0.0917] det_loss_box_dn_0[0.3180] det_loss_cls_dn_1[0.0879] det_loss_box_dn_1[0.2741] det_loss_cls_dn_2[0.0876] det_loss_box_dn_2[0.2594] det_loss_cls_dn_3[0.0859] det_loss_box_dn_3[0.2555] det_loss_cls_dn_4[0.0847] det_loss_box_dn_4[0.2533] det_loss_cls_dn_5[0.0838] det_loss_box_dn_5[0.2511] loss_depth[0.0401] total_loss[7.6158]
Rank[0/8] 06/20/2025 09:45:28 INFO stats.py:314 | Epoch[149] Step[314] GlobalStep[58424] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 11:46:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:45:39 INFO loss_tracker.py:84 | Epoch[149/239] Step[324] GlobalStep[58434/NA]: det_loss_cls_0[0.4832] det_loss_box_0[0.6418] det_loss_cls_1[0.4901] det_loss_box_1[0.4911] det_loss_cls_2[0.4439] det_loss_box_2[0.4343] det_loss_cls_3[0.4126] det_loss_box_3[0.4216] det_loss_cls_4[0.3850] det_loss_box_4[0.4180] det_loss_cls_5[0.3647] det_loss_box_5[0.4217] det_loss_cls_dn_0[0.1047] det_loss_box_dn_0[0.3086] det_loss_cls_dn_1[0.1009] det_loss_box_dn_1[0.2640] det_loss_cls_dn_2[0.1001] det_loss_box_dn_2[0.2422] det_loss_cls_dn_3[0.0988] det_loss_box_dn_3[0.2383] det_loss_cls_dn_4[0.0980] det_loss_box_dn_4[0.2356] det_loss_cls_dn_5[0.0971] det_loss_box_dn_5[0.2333] loss_depth[0.0377] total_loss[7.5671]
Rank[0/8] 06/20/2025 09:45:54 INFO stats.py:314 | Epoch[149] Step[339] GlobalStep[58449] Training Speed: 8.06 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 11:45:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:46:04 INFO loss_tracker.py:84 | Epoch[149/239] Step[349] GlobalStep[58459/NA]: det_loss_cls_0[0.5624] det_loss_box_0[0.7347] det_loss_cls_1[0.5402] det_loss_box_1[0.5485] det_loss_cls_2[0.4740] det_loss_box_2[0.4884] det_loss_cls_3[0.4466] det_loss_box_3[0.4753] det_loss_cls_4[0.4260] det_loss_box_4[0.4581] det_loss_cls_5[0.4145] det_loss_box_5[0.4620] det_loss_cls_dn_0[0.1003] det_loss_box_dn_0[0.2642] det_loss_cls_dn_1[0.0964] det_loss_box_dn_1[0.2209] det_loss_cls_dn_2[0.0960] det_loss_box_dn_2[0.2002] det_loss_cls_dn_3[0.0944] det_loss_box_dn_3[0.1972] det_loss_cls_dn_4[0.0933] det_loss_box_dn_4[0.1950] det_loss_cls_dn_5[0.0920] det_loss_box_dn_5[0.1945] loss_depth[0.0385] total_loss[7.9137]
Rank[0/8] 06/20/2025 09:46:20 INFO stats.py:314 | Epoch[149] Step[364] GlobalStep[58474] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 11:45:15.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:46:30 INFO loss_tracker.py:84 | Epoch[149/239] Step[374] GlobalStep[58484/NA]: det_loss_cls_0[0.5353] det_loss_box_0[0.6927] det_loss_cls_1[0.5121] det_loss_box_1[0.5321] det_loss_cls_2[0.4664] det_loss_box_2[0.4758] det_loss_cls_3[0.4335] det_loss_box_3[0.4553] det_loss_cls_4[0.4070] det_loss_box_4[0.4465] det_loss_cls_5[0.3883] det_loss_box_5[0.4495] det_loss_cls_dn_0[0.0936] det_loss_box_dn_0[0.2605] det_loss_cls_dn_1[0.0904] det_loss_box_dn_1[0.2230] det_loss_cls_dn_2[0.0908] det_loss_box_dn_2[0.2073] det_loss_cls_dn_3[0.0898] det_loss_box_dn_3[0.2035] det_loss_cls_dn_4[0.0885] det_loss_box_dn_4[0.2023] det_loss_cls_dn_5[0.0876] det_loss_box_dn_5[0.2015] loss_depth[0.0378] total_loss[7.6712]
Rank[0/8] 06/20/2025 09:46:45 INFO stats.py:314 | Epoch[149] Step[389] GlobalStep[58499] Training Speed: 8.34 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 11:44:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:46:46 INFO trainer.py:268 | 
==================================================BEGIN EVAL==================================================
Rank[0/8] 06/20/2025 09:47:47 INFO trainer.py:275 | eval: 10
Rank[0/8] 06/20/2025 09:47:54 INFO trainer.py:275 | eval: 20
Rank[0/8] 06/20/2025 09:48:01 INFO trainer.py:275 | eval: 30
Rank[0/8] 06/20/2025 09:48:10 INFO trainer.py:275 | eval: 40
Rank[0/8] 06/20/2025 09:48:21 INFO trainer.py:275 | eval: 50
Rank[0/8] 06/20/2025 09:48:28 INFO trainer.py:275 | eval: 60
Rank[0/8] 06/20/2025 09:48:35 INFO trainer.py:275 | eval: 70
Rank[0/8] 06/20/2025 09:48:47 INFO trainer.py:275 | eval: 80
Rank[0/8] 06/20/2025 09:48:53 INFO trainer.py:275 | eval: 90
Rank[0/8] 06/20/2025 09:49:01 INFO trainer.py:275 | eval: 100
Rank[0/8] 06/20/2025 09:49:08 INFO metrics.py:428 | number of results: 824
Rank[0/8] 06/20/2025 09:53:22 INFO metrics.py:332 | eval results of overall:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| toilet              | 0.8558  | 0.8905  | 0.7067  | 0.7737  |
| bathtub             | 0.8384  | 0.8824  | 0.7175  | 0.7500  |
| picture             | 0.2096  | 0.4634  | 0.0106  | 0.0991  |
| sink                | 0.6579  | 0.8189  | 0.2320  | 0.4362  |
| curtain             | 0.4770  | 0.8269  | 0.1510  | 0.4597  |
| bin                 | 0.6442  | 0.8436  | 0.3661  | 0.5419  |
| towel               | 0.3540  | 0.5968  | 0.0812  | 0.2323  |
| toilet paper        | 0.2998  | 0.5118  | 0.0869  | 0.2520  |
| doorframe           | 0.2727  | 0.8074  | 0.0542  | 0.4396  |
| door                | 0.3172  | 0.7262  | 0.0358  | 0.2763  |
| mirror              | 0.1865  | 0.6793  | 0.0063  | 0.1685  |
| vanity              | 0.4906  | 0.8667  | 0.3750  | 0.7556  |
| stopcock            | 0.0965  | 0.4828  | 0.0153  | 0.0690  |
| bar                 | 0.0170  | 0.1818  | 0.0000  | 0.0000  |
| bottle              | 0.0577  | 0.4345  | 0.0044  | 0.1003  |
| switch              | 0.0053  | 0.0987  | 0.0000  | 0.0000  |
| bag                 | 0.2837  | 0.6811  | 0.0449  | 0.2432  |
| cup                 | 0.1116  | 0.3898  | 0.0055  | 0.0678  |
| socket              | 0.0074  | 0.1991  | 0.0000  | 0.0044  |
| box                 | 0.1472  | 0.5151  | 0.0247  | 0.1704  |
| hair dryer          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ledge               | 0.1492  | 0.8333  | 0.0152  | 0.1667  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue              | 0.0080  | 0.2308  | 0.0077  | 0.0769  |
| rail                | 0.0177  | 0.3906  | 0.0004  | 0.0469  |
| backpack            | 0.5589  | 0.8387  | 0.3225  | 0.5806  |
| soap dish           | 0.6364  | 0.6667  | 0.0062  | 0.1333  |
| cabinet             | 0.2672  | 0.7465  | 0.1437  | 0.5042  |
| shower              | 0.0584  | 0.2667  | 0.0051  | 0.1333  |
| tissue box          | 0.0499  | 0.4545  | 0.0136  | 0.2121  |
| microwave           | 0.5943  | 0.7347  | 0.2817  | 0.4286  |
| paper               | 0.0275  | 0.2959  | 0.0011  | 0.0503  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shelf               | 0.2627  | 0.6978  | 0.1018  | 0.4011  |
| bowl                | 0.1329  | 0.4493  | 0.0761  | 0.2029  |
| purse               | 0.0042  | 0.3333  | 0.0000  | 0.0000  |
| light               | 0.0339  | 0.2350  | 0.0012  | 0.0534  |
| counter             | 0.1492  | 0.6422  | 0.0514  | 0.3028  |
| hat                 | 0.1481  | 0.2222  | 0.0000  | 0.0000  |
| vent                | 0.0007  | 0.0833  | 0.0000  | 0.0000  |
| plant               | 0.3061  | 0.6628  | 0.0587  | 0.2956  |
| table               | 0.4587  | 0.8652  | 0.3600  | 0.7288  |
| lamp                | 0.3616  | 0.6627  | 0.0571  | 0.2418  |
| window              | 0.2118  | 0.6153  | 0.0334  | 0.2814  |
| faucet              | 0.0146  | 0.2340  | 0.0008  | 0.0426  |
| chair               | 0.6301  | 0.8384  | 0.4496  | 0.6516  |
| blackboard          | 0.5864  | 0.8171  | 0.2372  | 0.4878  |
| monitor             | 0.5623  | 0.7065  | 0.0621  | 0.2391  |
| keyboard            | 0.0667  | 0.2209  | 0.0000  | 0.0000  |
| radiator            | 0.7008  | 0.8222  | 0.3567  | 0.4444  |
| mouse               | 0.0002  | 0.0541  | 0.0000  | 0.0000  |
| computer            | 0.3677  | 0.6885  | 0.0292  | 0.0984  |
| desk                | 0.5187  | 0.9101  | 0.3412  | 0.7416  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plug                | 0.0267  | 0.1579  | 0.0000  | 0.0000  |
| telephone           | 0.4067  | 0.6167  | 0.1277  | 0.2667  |
| book                | 0.0475  | 0.3785  | 0.0038  | 0.0698  |
| laptop              | 0.4984  | 0.8261  | 0.0422  | 0.2174  |
| windowsill          | 0.0714  | 0.5000  | 0.0081  | 0.1957  |
| wardrobe            | 0.3407  | 0.7831  | 0.2301  | 0.5542  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes             | 0.1470  | 0.5064  | 0.0173  | 0.1752  |
| calendar            | 0.0012  | 0.1667  | 0.0000  | 0.0000  |
| remote control      | 0.0339  | 0.2593  | 0.0019  | 0.0741  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jacket              | 0.5538  | 0.8750  | 0.0757  | 0.2500  |
| clock               | 0.2671  | 0.5532  | 0.0132  | 0.1277  |
| file                | 0.1091  | 0.4412  | 0.0031  | 0.1176  |
| plate               | 0.0468  | 0.4038  | 0.0004  | 0.0769  |
| ball                | 0.4724  | 0.5714  | 0.3968  | 0.4762  |
| pipe                | 0.0027  | 0.2143  | 0.0000  | 0.0000  |
| bed                 | 0.8398  | 0.9349  | 0.6754  | 0.8698  |
| pillow              | 0.2783  | 0.5509  | 0.0400  | 0.1492  |
| stand               | 0.5706  | 0.8841  | 0.4375  | 0.7439  |
| beanbag             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| couch               | 0.4752  | 0.8347  | 0.3761  | 0.7397  |
| dresser             | 0.6437  | 0.9074  | 0.5381  | 0.8333  |
| blanket             | 0.2575  | 0.7300  | 0.0419  | 0.3700  |
| paper cutter        | 0.6752  | 0.8333  | 0.0417  | 0.1667  |
| tv                  | 0.6199  | 0.8416  | 0.1171  | 0.3366  |
| ottoman             | 0.1412  | 0.5278  | 0.1066  | 0.3889  |
| container           | 0.0016  | 0.0847  | 0.0004  | 0.0169  |
| stool               | 0.3395  | 0.7647  | 0.2138  | 0.5126  |
| case                | 0.0909  | 0.4463  | 0.0308  | 0.1412  |
| basket              | 0.2756  | 0.6435  | 0.1223  | 0.3565  |
| shoe                | 0.1932  | 0.4673  | 0.0143  | 0.1028  |
| toy                 | 0.1380  | 0.4091  | 0.0358  | 0.1364  |
| poster              | 0.0106  | 0.1667  | 0.0004  | 0.0152  |
| printer             | 0.4602  | 0.7143  | 0.2946  | 0.5510  |
| stairs              | 0.0789  | 0.4722  | 0.0065  | 0.1528  |
| decoration          | 0.0448  | 0.3571  | 0.0031  | 0.0571  |
| statue              | 0.1026  | 0.4211  | 0.0259  | 0.1579  |
| guitar              | 0.3042  | 0.5000  | 0.1019  | 0.1667  |
| coffee maker        | 0.4557  | 0.6552  | 0.0390  | 0.1724  |
| refrigerator        | 0.6392  | 0.8400  | 0.3373  | 0.5600  |
| luggage             | 0.5556  | 0.6667  | 0.5556  | 0.6667  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| person              | 0.4188  | 0.6667  | 0.0148  | 0.5000  |
| cosmetics           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| notebook            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| map                 | 0.0038  | 0.5000  | 0.0038  | 0.5000  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bench               | 0.2530  | 0.6604  | 0.1726  | 0.5472  |
| plunger             | 0.7693  | 1.0000  | 0.7600  | 0.8000  |
| soap                | 0.0158  | 0.3333  | 0.0003  | 0.1667  |
| tube                | 0.0053  | 0.2308  | 0.0002  | 0.0769  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sign                | 0.1114  | 0.4000  | 0.0030  | 0.0333  |
| ladder              | 0.2427  | 0.6471  | 0.0218  | 0.2353  |
| rack                | 0.0613  | 0.4615  | 0.0295  | 0.3077  |
| basin               | 0.0002  | 0.1176  | 0.0001  | 0.0588  |
| soap dispenser      | 0.3150  | 0.5556  | 0.1122  | 0.2963  |
| dispenser           | 0.4942  | 0.6364  | 0.1667  | 0.2273  |
| grab bar            | 0.0006  | 0.1250  | 0.0000  | 0.0000  |
| stall               | 0.2812  | 0.8235  | 0.1163  | 0.6471  |
| holder              | 0.0074  | 0.1111  | 0.0074  | 0.1111  |
| hanger              | 0.0008  | 0.1667  | 0.0000  | 0.0000  |
| partition           | 0.3208  | 0.6667  | 0.0000  | 0.0000  |
| toiletry            | 0.0279  | 0.2174  | 0.0001  | 0.0435  |
| window frame        | 0.0274  | 0.3333  | 0.0041  | 0.1515  |
| cushion             | 0.1720  | 0.4706  | 0.0115  | 0.1176  |
| pillar              | 0.4135  | 0.6977  | 0.1082  | 0.4651  |
| cart                | 0.0053  | 0.2500  | 0.0053  | 0.2500  |
| frame               | 0.0240  | 0.4694  | 0.0002  | 0.0408  |
| projector           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fireplace           | 0.0881  | 0.4286  | 0.0016  | 0.1071  |
| tablet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| screen              | 0.0190  | 0.4000  | 0.0000  | 0.0000  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fan                 | 0.6552  | 0.7969  | 0.1285  | 0.4531  |
| board               | 0.1577  | 0.5484  | 0.0639  | 0.1452  |
| can                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radio               | 0.0625  | 0.2500  | 0.0000  | 0.0000  |
| globe               | 0.0833  | 1.0000  | 0.0000  | 0.0000  |
| hamper              | 0.4068  | 0.5455  | 0.3545  | 0.4545  |
| carpet              | 0.1253  | 0.4881  | 0.0040  | 0.1071  |
| commode             | 0.5168  | 0.8654  | 0.4170  | 0.7115  |
| bicycle             | 0.3444  | 1.0000  | 0.2260  | 1.0000  |
| fire extinguisher   | 0.2072  | 0.6000  | 0.1000  | 0.2000  |
| copier              | 0.8837  | 0.9167  | 0.8837  | 0.9167  |
| toothpaste          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scale               | 0.1364  | 0.1818  | 0.0227  | 0.0909  |
| mat                 | 0.0283  | 0.3514  | 0.0002  | 0.0541  |
| water cooler        | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| kettle              | 0.2152  | 0.6111  | 0.0525  | 0.2778  |
| teapot              | 0.1389  | 0.2222  | 0.0139  | 0.1111  |
| toaster             | 0.5513  | 0.7000  | 0.0138  | 0.2000  |
| dish rack           | 0.1481  | 1.0000  | 0.0095  | 0.2000  |
| bucket              | 0.2252  | 0.5128  | 0.0741  | 0.2308  |
| mailbox             | 0.0538  | 1.0000  | 0.0020  | 0.2000  |
| kitchen island      | 0.2857  | 0.4286  | 0.2857  | 0.4286  |
| stove               | 0.5107  | 0.8333  | 0.2202  | 0.4722  |
| dishwasher          | 0.1921  | 0.4000  | 0.1500  | 0.2000  |
| oven                | 0.4670  | 0.7143  | 0.1312  | 0.3214  |
| washing machine     | 0.5076  | 0.7857  | 0.3603  | 0.6071  |
| jar                 | 0.1075  | 0.3846  | 0.0009  | 0.0769  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flowerpot           | 0.0499  | 0.6275  | 0.0039  | 0.1569  |
| vase                | 0.0983  | 0.3800  | 0.0303  | 0.1800  |
| pot                 | 0.0601  | 0.4815  | 0.0079  | 0.2593  |
| magazine            | 0.0066  | 0.6000  | 0.0010  | 0.2000  |
| crate               | 0.5584  | 0.7143  | 0.0940  | 0.4286  |
| steps               | 0.0005  | 0.3000  | 0.0000  | 0.0000  |
| piano               | 0.2800  | 0.4000  | 0.2571  | 0.4000  |
| player              | 0.2532  | 0.5000  | 0.0025  | 0.2500  |
| drum                | 0.0125  | 0.1250  | 0.0000  | 0.0000  |
| machine             | 0.6106  | 0.8947  | 0.3912  | 0.7895  |
| drawer              | 0.2649  | 0.6111  | 0.2642  | 0.5556  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell            | 0.4667  | 0.6667  | 0.0667  | 0.3333  |
| vacuum cleaner      | 0.6012  | 0.7143  | 0.0493  | 0.2857  |
| column              | 0.0163  | 0.3947  | 0.0059  | 0.1842  |
| garbage             | 0.3955  | 0.8750  | 0.1763  | 0.6250  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| folder              | 0.0411  | 0.2143  | 0.0032  | 0.0714  |
| blinds              | 0.1609  | 0.4400  | 0.0246  | 0.2400  |
| napkins             | 0.1667  | 0.3333  | 0.0000  | 0.0000  |
| price tag           | 0.0016  | 0.1429  | 0.0000  | 0.0000  |
| cube                | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candle              | 0.0752  | 0.2778  | 0.0051  | 0.0556  |
| humidifier          | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| heater              | 0.5126  | 0.8276  | 0.1108  | 0.3966  |
| roll                | 0.1100  | 0.2000  | 0.0000  | 0.0000  |
| umbrella            | 0.2000  | 0.2000  | 0.0000  | 0.0000  |
| device              | 0.0688  | 0.3636  | 0.0000  | 0.0000  |
| conducting wire     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| album               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| air conditioner     | 0.4000  | 0.4000  | 0.4000  | 0.4000  |
| doorway             | 0.0004  | 0.1111  | 0.0000  | 0.0000  |
| sunglasses          | 0.1120  | 0.5385  | 0.0080  | 0.1538  |
| shampoo             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.2377  | 0.8000  | 0.2174  | 0.6667  |
| brush               | 0.0734  | 0.3571  | 0.0000  | 0.0000  |
| beam                | 0.0074  | 0.1364  | 0.0000  | 0.0000  |
| flush               | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| body loofah         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pan                 | 0.2007  | 0.4118  | 0.0000  | 0.0000  |
| fruit               | 0.2754  | 0.5000  | 0.0765  | 0.1429  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| food                | 0.0028  | 0.0909  | 0.0028  | 0.0909  |
| jalousie            | 1.0000  | 1.0000  | 0.0116  | 0.5000  |
| balcony             | 0.0927  | 1.0000  | 0.0000  | 0.0000  |
| crib                | 0.3750  | 0.6667  | 0.3750  | 0.6667  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| chandelier          | 0.1900  | 0.4762  | 0.0041  | 0.0952  |
| speaker             | 0.0597  | 0.2727  | 0.0002  | 0.0455  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchenware         | 0.3111  | 0.5000  | 0.0320  | 0.1522  |
| broom               | 0.0265  | 0.5000  | 0.0000  | 0.0000  |
| mop                 | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| countertop          | 0.0279  | 0.2857  | 0.0001  | 0.0357  |
| roof                | 0.0002  | 0.1667  | 0.0000  | 0.0000  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| footstool           | 0.0417  | 0.5000  | 0.0417  | 0.5000  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washbasin           | 0.0039  | 0.4000  | 0.0012  | 0.1333  |
| garage door         | 0.3333  | 0.3333  | 0.1842  | 0.3333  |
| handle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headboard           | 0.0033  | 0.2500  | 0.0000  | 0.0000  |
| thermostat          | 0.0472  | 0.3333  | 0.0000  | 0.0000  |
| glass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| treadmill           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool table          | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.2864  | 0.7000  | 0.0010  | 0.1000  |
| arch                | 0.0549  | 0.3667  | 0.0340  | 0.1333  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.3620  | 0.8571  | 0.0436  | 0.2143  |
| candlestick         | 0.1667  | 0.3333  | 0.0000  | 0.0000  |
| pack                | 0.0219  | 0.4000  | 0.0009  | 0.0667  |
| spoon               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sack                | 0.3088  | 0.7143  | 0.1767  | 0.4286  |
| helmets             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bread               | 0.1000  | 0.2000  | 0.1000  | 0.2000  |
| bidet               | 0.5000  | 0.6667  | 0.5000  | 0.6667  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| menu                | 0.1976  | 0.5714  | 0.0130  | 0.1429  |
| salt                | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| barricade           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cap                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glove               | 0.5000  | 0.6667  | 0.0000  | 0.0000  |
| sponge              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wine                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coil                | 0.1032  | 0.3333  | 0.0238  | 0.1667  |
| headphones          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| letter              | 0.2048  | 0.7500  | 0.0000  | 0.0000  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water heater        | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| overall             | 0.2066  | 0.4198  | 0.0892  | 0.1991  |
| head                | 0.2888  | 0.5891  | 0.1258  | 0.3019  |
| common              | 0.1755  | 0.3939  | 0.0715  | 0.1812  |
| tail                | 0.1515  | 0.2633  | 0.0691  | 0.1068  |
| small               | 0.0590  | 0.1795  | 0.0058  | 0.0342  |
| medium              | 0.2134  | 0.4857  | 0.0983  | 0.2240  |
| large               | 0.1352  | 0.5268  | 0.0672  | 0.3420  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 09:53:22 INFO metrics.py:437 | number of scannet results: 250
Rank[0/8] 06/20/2025 09:54:39 INFO metrics.py:332 | eval results of scannet:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| toilet              | 0.9448  | 0.9643  | 0.7815  | 0.8214  |
| bathtub             | 0.9585  | 0.9688  | 0.8382  | 0.8438  |
| picture             | 0.2954  | 0.5373  | 0.0212  | 0.1306  |
| sink                | 0.8284  | 0.8750  | 0.3652  | 0.5341  |
| curtain             | 0.5995  | 0.8584  | 0.2331  | 0.5044  |
| bin                 | 0.6680  | 0.8340  | 0.3914  | 0.5489  |
| towel               | 0.4232  | 0.6446  | 0.0962  | 0.2479  |
| toilet paper        | 0.3269  | 0.4810  | 0.1078  | 0.2405  |
| doorframe           | 0.3073  | 0.8239  | 0.0636  | 0.4718  |
| door                | 0.4774  | 0.8528  | 0.1194  | 0.4548  |
| mirror              | 0.3034  | 0.7229  | 0.0109  | 0.1928  |
| vanity              | 0.6580  | 1.0000  | 0.4999  | 0.8667  |
| stopcock            | 0.0970  | 0.4828  | 0.0153  | 0.0690  |
| bar                 | 0.0156  | 0.1818  | 0.0000  | 0.0000  |
| bottle              | 0.0666  | 0.4421  | 0.0088  | 0.1105  |
| switch              | 0.0142  | 0.1266  | 0.0000  | 0.0000  |
| bag                 | 0.3006  | 0.6849  | 0.0749  | 0.2877  |
| cup                 | 0.1586  | 0.4348  | 0.0155  | 0.0870  |
| socket              | 0.0080  | 0.2136  | 0.0000  | 0.0049  |
| box                 | 0.1146  | 0.4286  | 0.0210  | 0.1389  |
| hair dryer          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ledge               | 0.1492  | 0.8333  | 0.0152  | 0.1667  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue              | 0.0002  | 0.1111  | 0.0000  | 0.0000  |
| rail                | 0.0590  | 0.5000  | 0.0038  | 0.1667  |
| backpack            | 0.6487  | 0.8929  | 0.4764  | 0.7500  |
| soap dish           | 0.7955  | 0.8333  | 0.0078  | 0.1667  |
| cabinet             | 0.3476  | 0.7778  | 0.1789  | 0.5278  |
| shower              | 0.0359  | 0.2143  | 0.0045  | 0.0714  |
| tissue box          | 0.1326  | 0.4091  | 0.0289  | 0.1364  |
| microwave           | 0.6126  | 0.7500  | 0.2573  | 0.4500  |
| paper               | 0.0294  | 0.2943  | 0.0015  | 0.0340  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shelf               | 0.4474  | 0.8382  | 0.2573  | 0.6176  |
| bowl                | 0.0417  | 0.2222  | 0.0417  | 0.2222  |
| purse               | 0.0062  | 0.3333  | 0.0000  | 0.0000  |
| light               | 0.1742  | 0.4828  | 0.0039  | 0.1034  |
| counter             | 0.3353  | 0.7714  | 0.1602  | 0.4571  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plant               | 0.2894  | 0.5630  | 0.0366  | 0.2101  |
| table               | 0.5328  | 0.9308  | 0.4347  | 0.7885  |
| lamp                | 0.4616  | 0.6786  | 0.1383  | 0.3333  |
| window              | 0.4976  | 0.8333  | 0.1250  | 0.5000  |
| faucet              | 0.0139  | 0.2500  | 0.0035  | 0.1250  |
| chair               | 0.6687  | 0.8386  | 0.4422  | 0.6210  |
| blackboard          | 0.5918  | 0.8261  | 0.2613  | 0.5072  |
| monitor             | 0.6130  | 0.7143  | 0.0980  | 0.2714  |
| keyboard            | 0.0739  | 0.2069  | 0.0000  | 0.0000  |
| radiator            | 0.8049  | 0.8793  | 0.5136  | 0.6034  |
| mouse               | 0.0007  | 0.0952  | 0.0000  | 0.0000  |
| computer            | 0.3953  | 0.6410  | 0.0531  | 0.1282  |
| desk                | 0.6406  | 0.9620  | 0.4240  | 0.7848  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plug                | 0.0003  | 0.1111  | 0.0000  | 0.0000  |
| telephone           | 0.5392  | 0.7500  | 0.1880  | 0.3438  |
| book                | 0.0463  | 0.3762  | 0.0044  | 0.0643  |
| laptop              | 0.5435  | 0.7333  | 0.0267  | 0.1333  |
| windowsill          | 0.0114  | 0.3333  | 0.0064  | 0.3333  |
| wardrobe            | 0.4098  | 0.7500  | 0.3750  | 0.3750  |
| clothes             | 0.1367  | 0.4902  | 0.0254  | 0.1830  |
| calendar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jacket              | 0.5807  | 0.9130  | 0.0794  | 0.2609  |
| clock               | 0.2775  | 0.6000  | 0.0376  | 0.1600  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plate               | 0.0105  | 0.4000  | 0.0011  | 0.2000  |
| ball                | 0.4217  | 0.5000  | 0.3167  | 0.3333  |
| pipe                | 0.0003  | 0.0714  | 0.0000  | 0.0000  |
| bed                 | 0.9827  | 1.0000  | 0.9034  | 0.9649  |
| pillow              | 0.4064  | 0.5694  | 0.1707  | 0.2986  |
| stand               | 0.8284  | 0.9245  | 0.7438  | 0.8679  |
| couch               | 0.7203  | 0.9500  | 0.5728  | 0.8750  |
| dresser             | 0.7654  | 0.9730  | 0.6825  | 0.9459  |
| blanket             | 0.1092  | 0.5000  | 0.0157  | 0.2083  |
| paper cutter        | 0.6889  | 0.8000  | 0.0000  | 0.0000  |
| tv                  | 0.8774  | 0.9565  | 0.1740  | 0.3913  |
| ottoman             | 0.0759  | 0.8571  | 0.0165  | 0.4286  |
| container           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stool               | 0.4056  | 0.8409  | 0.2337  | 0.5455  |
| case                | 0.6388  | 1.0000  | 0.3432  | 0.6842  |
| basket              | 0.0663  | 0.3462  | 0.0590  | 0.1923  |
| shoe                | 0.2751  | 0.5250  | 0.0110  | 0.0750  |
| toy                 | 0.0403  | 0.1765  | 0.0098  | 0.0588  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| printer             | 0.5000  | 0.7576  | 0.4198  | 0.6667  |
| stairs              | 0.3905  | 0.8000  | 0.0504  | 0.6000  |
| decoration          | 0.1146  | 0.2917  | 0.0104  | 0.0417  |
| guitar              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coffee maker        | 0.6442  | 0.8235  | 0.0537  | 0.2353  |
| refrigerator        | 0.8836  | 0.9000  | 0.4589  | 0.6500  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| person              | 0.4196  | 0.6667  | 0.0161  | 0.5000  |
| notebook            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| map                 | 0.0077  | 1.0000  | 0.0077  | 1.0000  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bench               | 0.5660  | 1.0000  | 0.5660  | 1.0000  |
| plunger             | 0.7694  | 1.0000  | 0.7600  | 0.8000  |
| tube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sign                | 0.1182  | 0.3929  | 0.0032  | 0.0357  |
| ladder              | 0.3600  | 0.6000  | 0.0000  | 0.0000  |
| rack                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basin               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dispenser      | 0.3594  | 0.5714  | 0.1415  | 0.3333  |
| dispenser           | 0.5672  | 0.6923  | 0.1891  | 0.2564  |
| grab bar            | 0.0006  | 0.1250  | 0.0000  | 0.0000  |
| stall               | 0.2814  | 0.8235  | 0.1164  | 0.6471  |
| holder              | 0.0250  | 0.2000  | 0.0250  | 0.2000  |
| cushion             | 0.1000  | 0.5000  | 0.0000  | 0.0000  |
| pillar              | 0.6000  | 0.6000  | 0.0015  | 0.2000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| frame               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fan                 | 0.7585  | 0.8750  | 0.1293  | 0.3750  |
| board               | 0.0671  | 0.4375  | 0.0016  | 0.1250  |
| hamper              | 0.4500  | 0.6000  | 0.3900  | 0.5000  |
| carpet              | 0.0196  | 0.3000  | 0.0003  | 0.1000  |
| bicycle             | 0.3667  | 1.0000  | 0.1598  | 1.0000  |
| fire extinguisher   | 0.0054  | 0.3333  | 0.0000  | 0.0000  |
| copier              | 0.8837  | 0.9167  | 0.8837  | 0.9167  |
| toothpaste          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scale               | 0.1250  | 0.2500  | 0.1250  | 0.2500  |
| mat                 | 0.0770  | 0.3000  | 0.0000  | 0.0000  |
| water cooler        | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| kettle              | 0.0825  | 0.5000  | 0.0625  | 0.2500  |
| teapot              | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| toaster             | 0.2250  | 0.7500  | 0.0250  | 0.2500  |
| dish rack           | 0.2000  | 1.0000  | 0.0156  | 0.2500  |
| bucket              | 0.0865  | 0.4286  | 0.0368  | 0.2857  |
| mailbox             | 0.0538  | 1.0000  | 0.0020  | 0.2000  |
| stove               | 0.9889  | 1.0000  | 0.6510  | 0.8889  |
| dishwasher          | 0.0476  | 0.3333  | 0.0000  | 0.0000  |
| oven                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.6111  | 1.0000  | 0.5606  | 1.0000  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flowerpot           | 0.0156  | 0.5000  | 0.0000  | 0.0000  |
| pot                 | 0.1667  | 0.5000  | 0.0000  | 0.0000  |
| crate               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine             | 0.6643  | 0.9375  | 0.4053  | 0.8125  |
| drawer              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vacuum cleaner      | 0.6667  | 0.6667  | 0.3333  | 0.3333  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.2917  | 1.0000  | 0.2917  | 1.0000  |
| broom               | 0.0478  | 0.5000  | 0.0000  | 0.0000  |
| speaker             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.5833  | 0.8000  | 0.1000  | 0.2000  |
| headphones          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| barricade           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.0417  | 0.5000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| scannet             | 0.2644  | 0.4695  | 0.1289  | 0.2570  |
| head                | 0.3562  | 0.6000  | 0.1758  | 0.3399  |
| common              | 0.1869  | 0.3516  | 0.0738  | 0.1696  |
| tail                | 0.1002  | 0.2560  | 0.0855  | 0.1548  |
| small               | 0.0420  | 0.1597  | 0.0081  | 0.0395  |
| medium              | 0.3025  | 0.5711  | 0.1486  | 0.3003  |
| large               | 0.2768  | 0.8028  | 0.1546  | 0.5555  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 09:54:39 INFO metrics.py:437 | number of 3rscan results: 235
Rank[0/8] 06/20/2025 09:55:52 INFO metrics.py:332 | eval results of 3rscan:
+-------------------+---------+---------+---------+---------+
| classes           | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+-------------------+---------+---------+---------+---------+
| chair             | 0.7043  | 0.8714  | 0.5447  | 0.7140  |
| monitor           | 0.5659  | 0.7000  | 0.0382  | 0.1500  |
| mouse             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| paper             | 0.0363  | 0.3143  | 0.0015  | 0.1143  |
| keyboard          | 0.0659  | 0.2917  | 0.0000  | 0.0000  |
| bag               | 0.3121  | 0.7172  | 0.0400  | 0.2424  |
| desk              | 0.4578  | 0.9000  | 0.2994  | 0.7125  |
| table             | 0.5233  | 0.8739  | 0.3876  | 0.7432  |
| cup               | 0.0754  | 0.3333  | 0.0001  | 0.0256  |
| case              | 0.0437  | 0.3857  | 0.0074  | 0.0714  |
| pen               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| backpack          | 0.5181  | 0.8182  | 0.2191  | 0.4545  |
| bottle            | 0.0371  | 0.3884  | 0.0005  | 0.0744  |
| plug              | 0.0741  | 0.2222  | 0.0000  | 0.0000  |
| garbage           | 0.4867  | 0.8750  | 0.2039  | 0.6250  |
| plate             | 0.0653  | 0.4242  | 0.0010  | 0.0606  |
| book              | 0.0558  | 0.4438  | 0.0063  | 0.1006  |
| bin               | 0.7233  | 0.9024  | 0.4001  | 0.5732  |
| cabinet           | 0.3425  | 0.8248  | 0.1981  | 0.6111  |
| computer          | 0.3803  | 0.8095  | 0.0001  | 0.0476  |
| window            | 0.2026  | 0.7123  | 0.0255  | 0.2603  |
| box               | 0.2017  | 0.5425  | 0.0352  | 0.1721  |
| wardrobe          | 0.3870  | 0.8438  | 0.2480  | 0.6094  |
| telephone         | 0.2746  | 0.4583  | 0.1031  | 0.2083  |
| tissue box        | 0.0152  | 0.3333  | 0.0152  | 0.3333  |
| calendar          | 0.0033  | 0.2000  | 0.0000  | 0.0000  |
| file              | 0.1737  | 0.5000  | 0.0048  | 0.1429  |
| stand             | 0.4615  | 0.9180  | 0.2841  | 0.6885  |
| shelf             | 0.2290  | 0.6996  | 0.0582  | 0.3534  |
| picture           | 0.1883  | 0.4390  | 0.0057  | 0.0772  |
| curtain           | 0.4916  | 0.8651  | 0.1308  | 0.5000  |
| blinds            | 0.2064  | 0.5641  | 0.0316  | 0.3077  |
| door              | 0.3372  | 0.7206  | 0.0253  | 0.2206  |
| plant             | 0.3514  | 0.6535  | 0.0673  | 0.2673  |
| socket            | 0.0057  | 0.0625  | 0.0000  | 0.0000  |
| notebook          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cushion           | 0.2808  | 0.6667  | 0.0196  | 0.1667  |
| pillow            | 0.3978  | 0.6250  | 0.0267  | 0.1328  |
| paper cutter      | 1.0000  | 1.0000  | 0.5000  | 1.0000  |
| tv                | 0.6339  | 0.9143  | 0.0982  | 0.3714  |
| commode           | 0.5191  | 0.8654  | 0.4186  | 0.7115  |
| lamp              | 0.3221  | 0.6250  | 0.0221  | 0.1375  |
| frame             | 0.0421  | 0.5667  | 0.0005  | 0.0667  |
| couch             | 0.4122  | 0.7857  | 0.3273  | 0.6571  |
| stool             | 0.4685  | 0.8519  | 0.3055  | 0.5926  |
| light             | 0.0424  | 0.3750  | 0.0023  | 0.0909  |
| bowl              | 0.1995  | 0.5556  | 0.1013  | 0.2222  |
| bed               | 0.9062  | 0.9818  | 0.6628  | 0.8727  |
| vase              | 0.1897  | 0.4000  | 0.0618  | 0.2000  |
| clock             | 0.2829  | 0.4737  | 0.0017  | 0.0526  |
| laptop            | 0.5236  | 1.0000  | 0.1310  | 0.5000  |
| flowerpot         | 0.0402  | 0.5417  | 0.0012  | 0.0833  |
| doorframe         | 0.1697  | 0.7400  | 0.0157  | 0.2800  |
| bench             | 0.3044  | 0.7857  | 0.2066  | 0.6071  |
| decoration        | 0.1142  | 0.4688  | 0.0111  | 0.0938  |
| remote control    | 0.0600  | 0.3500  | 0.0036  | 0.1000  |
| rack              | 0.1399  | 0.7000  | 0.0753  | 0.4667  |
| furniture         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bar               | 0.4074  | 0.6667  | 0.0000  | 0.0000  |
| toilet            | 0.8026  | 0.8235  | 0.6046  | 0.6471  |
| machine           | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| jar               | 0.1984  | 0.3750  | 0.0023  | 0.1250  |
| teapot            | 0.0833  | 0.2500  | 0.0833  | 0.2500  |
| board             | 0.3486  | 0.7083  | 0.1845  | 0.2083  |
| napkins           | 0.1667  | 0.3333  | 0.0000  | 0.0000  |
| blanket           | 0.3584  | 0.8308  | 0.0608  | 0.4615  |
| price tag         | 0.0016  | 0.1429  | 0.0000  | 0.0000  |
| poster            | 0.0406  | 0.1746  | 0.0023  | 0.0159  |
| folder            | 0.0411  | 0.2143  | 0.0032  | 0.0714  |
| ottoman           | 0.5033  | 1.0000  | 0.4321  | 0.8182  |
| player            | 0.3333  | 0.3333  | 0.0037  | 0.3333  |
| carpet            | 0.2026  | 0.6607  | 0.0067  | 0.1429  |
| magazine          | 0.0082  | 0.7500  | 0.0012  | 0.2500  |
| cube              | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| printer           | 0.6254  | 0.7778  | 0.1785  | 0.3333  |
| toy               | 0.4038  | 0.6154  | 0.1538  | 0.1538  |
| counter           | 0.1048  | 0.7073  | 0.0110  | 0.3415  |
| towel             | 0.2546  | 0.5696  | 0.0842  | 0.2278  |
| candle            | 0.0881  | 0.2667  | 0.0061  | 0.0667  |
| container         | 0.0233  | 0.2222  | 0.0185  | 0.1111  |
| sink              | 0.6228  | 0.8036  | 0.1479  | 0.3036  |
| pad               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blackboard        | 0.6444  | 0.8333  | 0.1680  | 0.4167  |
| humidifier        | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| windowsill        | 0.0958  | 0.5385  | 0.0101  | 0.1795  |
| fan               | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| heater            | 0.5306  | 0.8246  | 0.1136  | 0.3860  |
| radiator          | 0.5195  | 0.7586  | 0.0755  | 0.1724  |
| roll              | 0.1100  | 0.2000  | 0.0000  | 0.0000  |
| switch            | 0.0008  | 0.0377  | 0.0000  | 0.0000  |
| umbrella          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| device            | 0.0688  | 0.3636  | 0.0000  | 0.0000  |
| drawer            | 0.5357  | 0.8889  | 0.5357  | 0.8889  |
| conducting wire   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coffee maker      | 0.2980  | 0.5556  | 0.0370  | 0.1111  |
| tube              | 0.0221  | 0.4286  | 0.0006  | 0.1429  |
| ball              | 0.5200  | 0.6000  | 0.5200  | 0.6000  |
| mirror            | 0.1441  | 0.6296  | 0.0004  | 0.0370  |
| clothes           | 0.2344  | 0.6214  | 0.0202  | 0.1748  |
| shoe              | 0.2331  | 0.5476  | 0.0405  | 0.1667  |
| beanbag           | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| pot               | 0.1375  | 0.5833  | 0.0278  | 0.3333  |
| album             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| air conditioner   | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| dresser           | 1.0000  | 1.0000  | 0.5000  | 0.5000  |
| basket            | 0.4945  | 0.8750  | 0.2313  | 0.5000  |
| scale             | 0.3333  | 0.3333  | 0.0000  | 0.0000  |
| refrigerator      | 0.6359  | 0.8000  | 0.4906  | 0.6667  |
| tablet            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| microwave         | 0.9020  | 1.0000  | 0.5137  | 0.6471  |
| stairs            | 0.3312  | 0.7143  | 0.0507  | 0.3571  |
| rail              | 0.1250  | 0.2500  | 0.0000  | 0.0000  |
| sunglasses        | 0.1123  | 0.5385  | 0.0080  | 0.1538  |
| hat               | 0.2857  | 0.2857  | 0.0000  | 0.0000  |
| ladder            | 0.5667  | 1.0000  | 0.4667  | 0.6667  |
| bathtub           | 0.8765  | 0.8889  | 0.7531  | 0.7778  |
| shampoo           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer     | 0.3132  | 0.8000  | 0.2727  | 0.6000  |
| statue            | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| shower            | 0.6750  | 1.0000  | 0.0785  | 0.7500  |
| faucet            | 0.1717  | 0.6667  | 0.0303  | 0.3333  |
| brush             | 0.1378  | 0.6250  | 0.0000  | 0.0000  |
| washing machine   | 0.5298  | 0.6429  | 0.3600  | 0.4286  |
| soap dish         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beam              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toilet paper      | 0.1344  | 0.3333  | 0.0000  | 0.0000  |
| flush             | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| soap              | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| toiletry          | 0.0769  | 1.0000  | 0.0000  | 0.0000  |
| bucket            | 0.6657  | 0.7333  | 0.1763  | 0.2667  |
| tissue            | 0.1162  | 0.6667  | 0.1111  | 0.3333  |
| body loofah       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| can               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| column            | 0.0238  | 1.0000  | 0.0122  | 0.5000  |
| pillar            | 0.5624  | 0.7647  | 0.3989  | 0.5294  |
| oven              | 0.6715  | 1.0000  | 0.1968  | 0.4737  |
| pan               | 0.2869  | 0.5833  | 0.0000  | 0.0000  |
| stove             | 0.3430  | 0.8824  | 0.0541  | 0.3529  |
| kettle            | 0.2952  | 0.7500  | 0.0638  | 0.3333  |
| fruit             | 0.6364  | 0.8000  | 0.2182  | 0.4000  |
| shovel            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basin             | 0.0006  | 0.0667  | 0.0000  | 0.0000  |
| knife             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| food              | 0.0034  | 0.1000  | 0.0034  | 0.1000  |
| jalousie          | 1.0000  | 1.0000  | 0.0116  | 0.5000  |
| screen            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| balcony           | 0.0927  | 1.0000  | 0.0000  | 0.0000  |
| sign              | 0.0455  | 0.5000  | 0.0000  | 0.0000  |
| radio             | 0.2500  | 1.0000  | 0.0000  | 0.0000  |
| crib              | 0.3750  | 0.6667  | 0.3750  | 0.6667  |
| fireplace         | 0.0013  | 0.2000  | 0.0000  | 0.0000  |
| piano             | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| holder            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| chandelier        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mattress          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker           | 0.1167  | 0.2500  | 0.0000  | 0.0000  |
| map               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle           | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| pipe              | 0.0175  | 0.7500  | 0.0000  | 0.0000  |
| window frame      | 0.0641  | 0.3333  | 0.0000  | 0.0000  |
| guitar            | 1.0000  | 1.0000  | 0.5000  | 0.5000  |
| partition         | 0.3858  | 0.8000  | 0.0000  | 0.0000  |
| soap dispenser    | 0.3000  | 0.6000  | 0.0333  | 0.2000  |
| kitchenware       | 0.3254  | 0.5227  | 0.0335  | 0.1591  |
| dispenser         | 0.0667  | 0.3333  | 0.0000  | 0.0000  |
| broom             | 0.0050  | 0.5000  | 0.0000  | 0.0000  |
| mop               | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| mat               | 0.0278  | 0.3333  | 0.0278  | 0.3333  |
| candlestick       | 0.5000  | 1.0000  | 0.0000  | 0.0000  |
| range hood        | 0.3119  | 1.0000  | 0.0289  | 0.4000  |
| pack              | 0.0253  | 0.4615  | 0.0011  | 0.0769  |
| tool              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| spoon             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hook              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hanger            | 0.0052  | 0.5000  | 0.0000  | 0.0000  |
| sack              | 0.3088  | 0.7143  | 0.1767  | 0.4286  |
| cosmetics         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crate             | 0.7818  | 1.0000  | 0.1339  | 0.6000  |
| treadmill         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| helmets           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glass             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tray              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washbasin         | 0.1000  | 1.0000  | 0.0000  | 0.0000  |
| bread             | 0.1000  | 0.2000  | 0.1000  | 0.2000  |
| bidet             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| menu              | 0.1976  | 0.5714  | 0.0130  | 0.1429  |
| salt              | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| dishwasher        | 0.6667  | 0.6667  | 0.6667  | 0.6667  |
| glove             | 0.5000  | 0.6667  | 0.0000  | 0.0000  |
| luggage           | 0.5556  | 0.6667  | 0.5556  | 0.6667  |
| sponge            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wire              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coil              | 0.1032  | 0.3333  | 0.0238  | 0.1667  |
| toothbrush        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| letter            | 0.2048  | 0.7500  | 0.0000  | 0.0000  |
| scissors          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| footstool         | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| fire extinguisher | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| dish rack         | 0.0833  | 1.0000  | 0.0000  | 0.0000  |
| seasoning         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water heater      | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| vacuum cleaner    | 0.7500  | 0.7500  | 0.0192  | 0.2500  |
| toaster           | 1.0000  | 1.0000  | 0.0208  | 0.2500  |
| -------           | ------- | ------- | ------- | ------- |
| 3rscan            | 0.3069  | 0.5440  | 0.1290  | 0.2441  |
| head              | 0.3284  | 0.6417  | 0.1302  | 0.3091  |
| common            | 0.3298  | 0.5408  | 0.1611  | 0.2433  |
| tail              | 0.2367  | 0.3689  | 0.0838  | 0.1260  |
| small             | 0.0921  | 0.2435  | 0.0133  | 0.0440  |
| medium            | 0.3265  | 0.6187  | 0.1476  | 0.2814  |
| large             | 0.2419  | 0.7027  | 0.1002  | 0.4972  |
+-------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 09:55:52 INFO metrics.py:437 | number of matterport3d results: 339
Rank[0/8] 06/20/2025 09:57:18 INFO metrics.py:332 | eval results of matterport3d:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| door                | 0.2190  | 0.6292  | 0.0087  | 0.1567  |
| sink                | 0.5026  | 0.7778  | 0.1820  | 0.4242  |
| picture             | 0.1777  | 0.4300  | 0.0092  | 0.0918  |
| towel               | 0.3791  | 0.5636  | 0.0793  | 0.2182  |
| plant               | 0.2826  | 0.7857  | 0.0969  | 0.4375  |
| doorframe           | 0.3018  | 0.8100  | 0.0649  | 0.4485  |
| mirror              | 0.1137  | 0.6486  | 0.0080  | 0.1892  |
| light               | 0.0278  | 0.1795  | 0.0012  | 0.0399  |
| vent                | 0.0007  | 0.0833  | 0.0000  | 0.0000  |
| bathtub             | 0.6615  | 0.7778  | 0.5159  | 0.6296  |
| drawer              | 0.0012  | 0.3750  | 0.0006  | 0.2500  |
| toilet              | 0.8148  | 0.8438  | 0.7070  | 0.7656  |
| shelf               | 0.1534  | 0.5547  | 0.0356  | 0.2847  |
| blanket             | 0.1029  | 0.6364  | 0.0456  | 0.1818  |
| countertop          | 0.0279  | 0.2857  | 0.0001  | 0.0357  |
| case                | 0.0023  | 0.3333  | 0.0003  | 0.1111  |
| box                 | 0.0462  | 0.6923  | 0.0132  | 0.3077  |
| window              | 0.1066  | 0.4778  | 0.0101  | 0.1917  |
| cabinet             | 0.1370  | 0.6356  | 0.0717  | 0.3729  |
| table               | 0.2654  | 0.7436  | 0.2233  | 0.6090  |
| bottle              | 0.1144  | 0.5208  | 0.0097  | 0.1250  |
| switch              | 0.0001  | 0.1500  | 0.0000  | 0.0000  |
| lamp                | 0.3916  | 0.7143  | 0.1013  | 0.3407  |
| vase                | 0.0559  | 0.3667  | 0.0366  | 0.1667  |
| flowerpot           | 0.0924  | 0.7200  | 0.0286  | 0.2400  |
| pot                 | 0.0034  | 0.3846  | 0.0014  | 0.2308  |
| counter             | 0.0213  | 0.4242  | 0.0010  | 0.0909  |
| bench               | 0.2346  | 0.4000  | 0.1670  | 0.3500  |
| roof                | 0.0002  | 0.1667  | 0.0000  | 0.0000  |
| decoration          | 0.0132  | 0.3333  | 0.0002  | 0.0476  |
| toilet paper        | 0.3168  | 0.6389  | 0.0913  | 0.3611  |
| wardrobe            | 0.0194  | 0.4545  | 0.0075  | 0.3636  |
| dresser             | 0.3408  | 0.7333  | 0.2273  | 0.6000  |
| shower              | 0.0000  | 0.0833  | 0.0000  | 0.0000  |
| plate               | 0.0583  | 0.3571  | 0.0001  | 0.0714  |
| toiletry            | 0.0285  | 0.1818  | 0.0001  | 0.0455  |
| faucet              | 0.0110  | 0.1944  | 0.0000  | 0.0000  |
| stool               | 0.0115  | 0.3810  | 0.0030  | 0.2381  |
| mat                 | 0.0179  | 0.3750  | 0.0000  | 0.0417  |
| bed                 | 0.6205  | 0.8246  | 0.4376  | 0.7719  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.4828  | 0.8750  | 0.2973  | 0.6250  |
| statue              | 0.0766  | 0.4118  | 0.0738  | 0.1765  |
| clothes             | 0.0837  | 0.3448  | 0.0131  | 0.1552  |
| pillow              | 0.1045  | 0.4826  | 0.0040  | 0.0946  |
| doorway             | 0.0004  | 0.1111  | 0.0000  | 0.0000  |
| stand               | 0.3981  | 0.8000  | 0.3216  | 0.6800  |
| paper               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| curtain             | 0.3371  | 0.7396  | 0.0795  | 0.3542  |
| couch               | 0.3086  | 0.7717  | 0.2600  | 0.6848  |
| bowl                | 0.1454  | 0.4242  | 0.1019  | 0.1818  |
| chair               | 0.4685  | 0.7789  | 0.3365  | 0.6429  |
| tv                  | 0.4800  | 0.7209  | 0.1617  | 0.2791  |
| stairs              | 0.0287  | 0.3774  | 0.0011  | 0.0566  |
| book                | 0.0763  | 0.2992  | 0.0021  | 0.0472  |
| clock               | 0.3548  | 0.6667  | 0.0009  | 0.3333  |
| steps               | 0.0005  | 0.3000  | 0.0000  | 0.0000  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cushion             | 0.0107  | 0.1176  | 0.0027  | 0.0588  |
| ottoman             | 0.0099  | 0.1111  | 0.0099  | 0.1111  |
| screen              | 0.0555  | 0.5000  | 0.0000  | 0.0000  |
| frame               | 0.0032  | 0.3529  | 0.0000  | 0.0000  |
| desk                | 0.3216  | 0.7368  | 0.2942  | 0.6842  |
| footstool           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toy                 | 0.1189  | 0.4444  | 0.0075  | 0.1667  |
| fireplace           | 0.1430  | 0.4783  | 0.0028  | 0.1304  |
| cup                 | 0.0133  | 0.3000  | 0.0014  | 0.1000  |
| player              | 0.0714  | 1.0000  | 0.0000  | 0.0000  |
| ball                | 0.6875  | 0.7500  | 0.6875  | 0.7500  |
| backpack            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mouse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit               | 0.1447  | 0.3750  | 0.0000  | 0.0000  |
| column              | 0.0208  | 0.3611  | 0.0076  | 0.1667  |
| pillar              | 0.4359  | 0.6667  | 0.0417  | 0.4762  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| window frame        | 0.0243  | 0.3333  | 0.0053  | 0.1724  |
| washbasin           | 0.0041  | 0.3571  | 0.0013  | 0.1429  |
| carpet              | 0.0000  | 0.0556  | 0.0000  | 0.0000  |
| garage door         | 0.3333  | 0.3333  | 0.1842  | 0.3333  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bin                 | 0.3730  | 0.7805  | 0.1554  | 0.4390  |
| fan                 | 0.6663  | 0.7963  | 0.1734  | 0.4815  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vanity              | 0.1661  | 0.6000  | 0.1529  | 0.5333  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basin               | 0.0013  | 1.0000  | 0.0013  | 1.0000  |
| windowsill          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue box          | 0.1189  | 0.6250  | 0.0295  | 0.3750  |
| dispenser           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| handle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rail                | 0.0153  | 0.3889  | 0.0002  | 0.0370  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headboard           | 0.0033  | 0.2500  | 0.0000  | 0.0000  |
| thermostat          | 0.0472  | 0.3333  | 0.0000  | 0.0000  |
| telephone           | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| bucket              | 0.0061  | 0.3000  | 0.0012  | 0.1000  |
| soap dispenser      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basket              | 0.0534  | 0.3600  | 0.0032  | 0.1600  |
| container           | 0.0211  | 0.1111  | 0.0000  | 0.0000  |
| holder              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchen island      | 0.2857  | 0.4286  | 0.2857  | 0.4286  |
| heater              | 0.1111  | 1.0000  | 0.1111  | 1.0000  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jar                 | 0.0047  | 0.6667  | 0.0000  | 0.0000  |
| monitor             | 0.2250  | 0.5000  | 0.0000  | 0.0000  |
| computer            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| refrigerator        | 0.2383  | 0.8000  | 0.1123  | 0.3333  |
| piano               | 0.1667  | 0.5000  | 0.1250  | 0.5000  |
| radiator            | 0.1667  | 0.3333  | 0.0000  | 0.0000  |
| printer             | 0.4286  | 0.4286  | 0.2857  | 0.2857  |
| laptop              | 0.5000  | 1.0000  | 0.0000  | 0.0000  |
| microwave           | 0.1223  | 0.3333  | 0.0833  | 0.0833  |
| scale               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| board               | 0.0220  | 0.5000  | 0.0000  | 0.0000  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blackboard          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| guitar              | 0.2356  | 0.5714  | 0.0204  | 0.1429  |
| rack                | 0.0005  | 0.2727  | 0.0002  | 0.1818  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pipe                | 0.0020  | 0.2000  | 0.0000  | 0.0000  |
| pool table          | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0097  | 0.3750  | 0.0011  | 0.1250  |
| food                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| projector           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bag                 | 0.0738  | 0.3846  | 0.0000  | 0.0000  |
| clothes dryer       | 0.2222  | 0.6667  | 0.2222  | 0.6667  |
| excercise equipment | 0.3181  | 0.7222  | 0.0012  | 0.1111  |
| oven                | 0.0400  | 0.2000  | 0.0000  | 0.0000  |
| stove               | 0.2262  | 0.6000  | 0.1069  | 0.3000  |
| arch                | 0.0549  | 0.3667  | 0.0340  | 0.1333  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| teapot              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher   | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| ladder              | 0.1723  | 0.5556  | 0.0007  | 0.2222  |
| pan                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell            | 0.2500  | 0.5000  | 0.2500  | 0.5000  |
| hanger              | 0.0005  | 0.1000  | 0.0000  | 0.0000  |
| beam                | 0.0086  | 0.1429  | 0.0000  | 0.0000  |
| soap                | 0.0006  | 0.2000  | 0.0006  | 0.2000  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine             | 0.0086  | 0.5000  | 0.0086  | 0.5000  |
| soap dish           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| air conditioner     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| chandelier          | 0.2146  | 0.5263  | 0.0050  | 0.1053  |
| cart                | 0.0385  | 0.5000  | 0.0385  | 0.5000  |
| keyboard            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shoe                | 0.0387  | 0.2400  | 0.0001  | 0.0400  |
| globe               | 0.1429  | 1.0000  | 0.0000  | 0.0000  |
| range hood          | 0.0752  | 0.7500  | 0.0000  | 0.0000  |
| kitchenware         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| file                | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| bicycle             | 0.0526  | 1.0000  | 0.0526  | 1.0000  |
| partition           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| socket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet               | 0.1667  | 0.5000  | 0.1667  | 0.5000  |
| magazine            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.0357  | 0.2500  | 0.0000  | 0.0000  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hamper              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drum                | 0.0125  | 0.1250  | 0.0000  | 0.0000  |
| kettle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jacket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coffee maker        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candle              | 0.0167  | 0.3333  | 0.0000  | 0.0000  |
| can                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radio               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cap                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| matterport3d        | 0.1129  | 0.3320  | 0.0484  | 0.1571  |
| head                | 0.1658  | 0.4448  | 0.0696  | 0.2178  |
| common              | 0.0675  | 0.2553  | 0.0251  | 0.1160  |
| tail                | 0.0516  | 0.1501  | 0.0359  | 0.0591  |
| small               | 0.0497  | 0.1446  | 0.0030  | 0.0411  |
| medium              | 0.1037  | 0.3746  | 0.0451  | 0.1636  |
| large               | 0.1261  | 0.4161  | 0.0682  | 0.2441  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 09:57:18 INFO metrics.py:437 | number of arkit results: 0
Rank[0/8] 06/20/2025 09:57:18 INFO stats.py:394 | Epoch[149] completed. Training Speed: 3.00 samples/sec across all devices.	Epoch Time: 1039.68 sec.	Average Epoch Time: 1039.68 sec.	Average Step Time: 2.67 sec.	Estimated Remaining Time: 11:51:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:57:19 WARNING accelerator.py:3099 | Deleting 1 checkpoints to make room for new checkpoint.
Rank[0/8] 06/20/2025 09:57:19 INFO accelerator.py:3111 | Saving current state to /job_data/checkpoints/checkpoint_14
Rank[2/8] 06/20/2025 09:57:19 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 149 to /job_data/checkpoints/checkpoint_14
Rank[7/8] 06/20/2025 09:57:19 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 149 to /job_data/checkpoints/checkpoint_14
Rank[5/8] 06/20/2025 09:57:19 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 149 to /job_data/checkpoints/checkpoint_14
Rank[4/8] 06/20/2025 09:57:19 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 149 to /job_data/checkpoints/checkpoint_14
Rank[3/8] 06/20/2025 09:57:20 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 149 to /job_data/checkpoints/checkpoint_14
Rank[1/8] 06/20/2025 09:57:20 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 149 to /job_data/checkpoints/checkpoint_14
Rank[6/8] 06/20/2025 09:57:20 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 149 to /job_data/checkpoints/checkpoint_14
Rank[0/8] 06/20/2025 09:57:21 INFO checkpointing.py:106 | Model weights saved in /job_data/checkpoints/checkpoint_14/model.safetensors
Rank[0/8] 06/20/2025 09:57:23 INFO checkpointing.py:113 | Optimizer state saved in /job_data/checkpoints/checkpoint_14/optimizer.bin
Rank[0/8] 06/20/2025 09:57:24 INFO checkpointing.py:120 | Scheduler state saved in /job_data/checkpoints/checkpoint_14/scheduler.bin
Rank[0/8] 06/20/2025 09:57:24 INFO checkpointing.py:137 | Sampler state for dataloader 0 saved in /job_data/checkpoints/checkpoint_14/sampler.bin
Rank[0/8] 06/20/2025 09:57:24 INFO checkpointing.py:137 | Sampler state for dataloader 1 saved in /job_data/checkpoints/checkpoint_14/sampler_1.bin
Rank[0/8] 06/20/2025 09:57:24 INFO checkpointing.py:164 | Random states saved in /job_data/checkpoints/checkpoint_14/random_states_0.pkl
Rank[0/8] 06/20/2025 09:57:24 INFO checkpointing.py:300 | Saving the state of TrainerProgressState to /job_data/checkpoints/checkpoint_14/custom_checkpoint_0.pkl
Rank[0/8] 06/20/2025 09:57:24 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 149 to /job_data/checkpoints/checkpoint_14
Rank[0/8] 06/20/2025 09:57:51 INFO stats.py:314 | Epoch[150] Step[24] GlobalStep[58524] Training Speed: 8.00 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 11:50:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:57:51 INFO loss_tracker.py:84 | Epoch[150/239] Step[24] GlobalStep[58524/NA]: det_loss_cls_0[0.5212] det_loss_box_0[0.6952] det_loss_cls_1[0.5209] det_loss_box_1[0.5424] det_loss_cls_2[0.4769] det_loss_box_2[0.4917] det_loss_cls_3[0.4410] det_loss_box_3[0.4720] det_loss_cls_4[0.4171] det_loss_box_4[0.4618] det_loss_cls_5[0.4053] det_loss_box_5[0.4591] det_loss_cls_dn_0[0.0963] det_loss_box_dn_0[0.3137] det_loss_cls_dn_1[0.0923] det_loss_box_dn_1[0.2694] det_loss_cls_dn_2[0.0921] det_loss_box_dn_2[0.2540] det_loss_cls_dn_3[0.0909] det_loss_box_dn_3[0.2516] det_loss_cls_dn_4[0.0897] det_loss_box_dn_4[0.2490] det_loss_cls_dn_5[0.0888] det_loss_box_dn_5[0.2466] loss_depth[0.0381] total_loss[8.0773]
Rank[0/8] 06/20/2025 09:58:16 INFO stats.py:314 | Epoch[150] Step[49] GlobalStep[58549] Training Speed: 8.12 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 11:49:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:58:16 INFO loss_tracker.py:84 | Epoch[150/239] Step[49] GlobalStep[58549/NA]: det_loss_cls_0[0.4335] det_loss_box_0[0.5946] det_loss_cls_1[0.4338] det_loss_box_1[0.4515] det_loss_cls_2[0.3805] det_loss_box_2[0.4139] det_loss_cls_3[0.3491] det_loss_box_3[0.3909] det_loss_cls_4[0.3267] det_loss_box_4[0.3760] det_loss_cls_5[0.3096] det_loss_box_5[0.3783] det_loss_cls_dn_0[0.0939] det_loss_box_dn_0[0.3325] det_loss_cls_dn_1[0.0899] det_loss_box_dn_1[0.2862] det_loss_cls_dn_2[0.0890] det_loss_box_dn_2[0.2695] det_loss_cls_dn_3[0.0883] det_loss_box_dn_3[0.2649] det_loss_cls_dn_4[0.0879] det_loss_box_dn_4[0.2636] det_loss_cls_dn_5[0.0870] det_loss_box_dn_5[0.2605] loss_depth[0.0350] total_loss[7.0866]
Rank[0/8] 06/20/2025 09:58:42 INFO stats.py:314 | Epoch[150] Step[74] GlobalStep[58574] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 11:49:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:58:42 INFO loss_tracker.py:84 | Epoch[150/239] Step[74] GlobalStep[58574/NA]: det_loss_cls_0[0.4625] det_loss_box_0[0.6440] det_loss_cls_1[0.4588] det_loss_box_1[0.5127] det_loss_cls_2[0.4113] det_loss_box_2[0.4762] det_loss_cls_3[0.3821] det_loss_box_3[0.4711] det_loss_cls_4[0.3675] det_loss_box_4[0.4493] det_loss_cls_5[0.3575] det_loss_box_5[0.4495] det_loss_cls_dn_0[0.0857] det_loss_box_dn_0[0.2701] det_loss_cls_dn_1[0.0813] det_loss_box_dn_1[0.2271] det_loss_cls_dn_2[0.0815] det_loss_box_dn_2[0.2091] det_loss_cls_dn_3[0.0804] det_loss_box_dn_3[0.2057] det_loss_cls_dn_4[0.0797] det_loss_box_dn_4[0.2039] det_loss_cls_dn_5[0.0785] det_loss_box_dn_5[0.2026] loss_depth[0.0396] total_loss[7.2877]
Rank[0/8] 06/20/2025 09:59:08 INFO stats.py:314 | Epoch[150] Step[99] GlobalStep[58599] Training Speed: 8.34 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 11:48:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:59:08 INFO loss_tracker.py:84 | Epoch[150/239] Step[99] GlobalStep[58599/NA]: det_loss_cls_0[0.5543] det_loss_box_0[0.7186] det_loss_cls_1[0.5230] det_loss_box_1[0.5901] det_loss_cls_2[0.4810] det_loss_box_2[0.5481] det_loss_cls_3[0.4554] det_loss_box_3[0.5262] det_loss_cls_4[0.4321] det_loss_box_4[0.5149] det_loss_cls_5[0.4120] det_loss_box_5[0.5191] det_loss_cls_dn_0[0.0888] det_loss_box_dn_0[0.2768] det_loss_cls_dn_1[0.0849] det_loss_box_dn_1[0.2397] det_loss_cls_dn_2[0.0844] det_loss_box_dn_2[0.2263] det_loss_cls_dn_3[0.0837] det_loss_box_dn_3[0.2234] det_loss_cls_dn_4[0.0826] det_loss_box_dn_4[0.2222] det_loss_cls_dn_5[0.0817] det_loss_box_dn_5[0.2190] loss_depth[0.0383] total_loss[8.2267]
Rank[0/8] 06/20/2025 09:59:33 INFO stats.py:314 | Epoch[150] Step[124] GlobalStep[58624] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 11:48:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:59:33 INFO loss_tracker.py:84 | Epoch[150/239] Step[124] GlobalStep[58624/NA]: det_loss_cls_0[0.4841] det_loss_box_0[0.6801] det_loss_cls_1[0.4594] det_loss_box_1[0.5467] det_loss_cls_2[0.4223] det_loss_box_2[0.4905] det_loss_cls_3[0.4043] det_loss_box_3[0.4596] det_loss_cls_4[0.3817] det_loss_box_4[0.4565] det_loss_cls_5[0.3711] det_loss_box_5[0.4524] det_loss_cls_dn_0[0.0882] det_loss_box_dn_0[0.3004] det_loss_cls_dn_1[0.0855] det_loss_box_dn_1[0.2572] det_loss_cls_dn_2[0.0848] det_loss_box_dn_2[0.2429] det_loss_cls_dn_3[0.0830] det_loss_box_dn_3[0.2354] det_loss_cls_dn_4[0.0823] det_loss_box_dn_4[0.2337] det_loss_cls_dn_5[0.0815] det_loss_box_dn_5[0.2321] loss_depth[0.0422] total_loss[7.6579]
Rank[0/8] 06/20/2025 09:59:59 INFO stats.py:314 | Epoch[150] Step[149] GlobalStep[58649] Training Speed: 8.75 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 11:47:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 09:59:59 INFO loss_tracker.py:84 | Epoch[150/239] Step[149] GlobalStep[58649/NA]: det_loss_cls_0[0.4247] det_loss_box_0[0.5969] det_loss_cls_1[0.4065] det_loss_box_1[0.4733] det_loss_cls_2[0.3783] det_loss_box_2[0.4275] det_loss_cls_3[0.3587] det_loss_box_3[0.4098] det_loss_cls_4[0.3348] det_loss_box_4[0.4037] det_loss_cls_5[0.3241] det_loss_box_5[0.3991] det_loss_cls_dn_0[0.0908] det_loss_box_dn_0[0.3206] det_loss_cls_dn_1[0.0862] det_loss_box_dn_1[0.2733] det_loss_cls_dn_2[0.0851] det_loss_box_dn_2[0.2569] det_loss_cls_dn_3[0.0838] det_loss_box_dn_3[0.2527] det_loss_cls_dn_4[0.0827] det_loss_box_dn_4[0.2513] det_loss_cls_dn_5[0.0819] det_loss_box_dn_5[0.2496] loss_depth[0.0374] total_loss[7.0900]
Rank[0/8] 06/20/2025 10:00:24 INFO stats.py:314 | Epoch[150] Step[174] GlobalStep[58674] Training Speed: 8.34 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 11:47:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:00:24 INFO loss_tracker.py:84 | Epoch[150/239] Step[174] GlobalStep[58674/NA]: det_loss_cls_0[0.4576] det_loss_box_0[0.5522] det_loss_cls_1[0.4510] det_loss_box_1[0.4289] det_loss_cls_2[0.4050] det_loss_box_2[0.3866] det_loss_cls_3[0.3794] det_loss_box_3[0.3701] det_loss_cls_4[0.3532] det_loss_box_4[0.3678] det_loss_cls_5[0.3503] det_loss_box_5[0.3679] det_loss_cls_dn_0[0.0967] det_loss_box_dn_0[0.2691] det_loss_cls_dn_1[0.0921] det_loss_box_dn_1[0.2233] det_loss_cls_dn_2[0.0909] det_loss_box_dn_2[0.2067] det_loss_cls_dn_3[0.0901] det_loss_box_dn_3[0.2034] det_loss_cls_dn_4[0.0888] det_loss_box_dn_4[0.2028] det_loss_cls_dn_5[0.0876] det_loss_box_dn_5[0.2013] loss_depth[0.0376] total_loss[6.7603]
Rank[0/8] 06/20/2025 10:00:50 INFO stats.py:314 | Epoch[150] Step[199] GlobalStep[58699] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 11:46:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:00:50 INFO loss_tracker.py:84 | Epoch[150/239] Step[199] GlobalStep[58699/NA]: det_loss_cls_0[0.4149] det_loss_box_0[0.5486] det_loss_cls_1[0.4124] det_loss_box_1[0.4344] det_loss_cls_2[0.3677] det_loss_box_2[0.3951] det_loss_cls_3[0.3400] det_loss_box_3[0.3857] det_loss_cls_4[0.3274] det_loss_box_4[0.3670] det_loss_cls_5[0.3198] det_loss_box_5[0.3693] det_loss_cls_dn_0[0.0830] det_loss_box_dn_0[0.2583] det_loss_cls_dn_1[0.0798] det_loss_box_dn_1[0.2156] det_loss_cls_dn_2[0.0794] det_loss_box_dn_2[0.2018] det_loss_cls_dn_3[0.0785] det_loss_box_dn_3[0.1986] det_loss_cls_dn_4[0.0774] det_loss_box_dn_4[0.1970] det_loss_cls_dn_5[0.0765] det_loss_box_dn_5[0.1954] loss_depth[0.0393] total_loss[6.4628]
Rank[0/8] 06/20/2025 10:01:16 INFO stats.py:314 | Epoch[150] Step[224] GlobalStep[58724] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 11:46:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:01:16 INFO loss_tracker.py:84 | Epoch[150/239] Step[224] GlobalStep[58724/NA]: det_loss_cls_0[0.5308] det_loss_box_0[0.7843] det_loss_cls_1[0.5263] det_loss_box_1[0.5968] det_loss_cls_2[0.4851] det_loss_box_2[0.5308] det_loss_cls_3[0.4527] det_loss_box_3[0.5059] det_loss_cls_4[0.4295] det_loss_box_4[0.4795] det_loss_cls_5[0.4124] det_loss_box_5[0.4878] det_loss_cls_dn_0[0.0996] det_loss_box_dn_0[0.3191] det_loss_cls_dn_1[0.0966] det_loss_box_dn_1[0.2664] det_loss_cls_dn_2[0.0952] det_loss_box_dn_2[0.2530] det_loss_cls_dn_3[0.0945] det_loss_box_dn_3[0.2478] det_loss_cls_dn_4[0.0932] det_loss_box_dn_4[0.2452] det_loss_cls_dn_5[0.0924] det_loss_box_dn_5[0.2435] loss_depth[0.0374] total_loss[8.4058]
Rank[0/8] 06/20/2025 10:01:41 INFO stats.py:314 | Epoch[150] Step[249] GlobalStep[58749] Training Speed: 7.98 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 11:45:33.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:01:41 INFO loss_tracker.py:84 | Epoch[150/239] Step[249] GlobalStep[58749/NA]: det_loss_cls_0[0.5684] det_loss_box_0[0.8061] det_loss_cls_1[0.5497] det_loss_box_1[0.6693] det_loss_cls_2[0.5130] det_loss_box_2[0.6139] det_loss_cls_3[0.4924] det_loss_box_3[0.5758] det_loss_cls_4[0.4624] det_loss_box_4[0.5683] det_loss_cls_5[0.4495] det_loss_box_5[0.5653] det_loss_cls_dn_0[0.0953] det_loss_box_dn_0[0.3513] det_loss_cls_dn_1[0.0910] det_loss_box_dn_1[0.2970] det_loss_cls_dn_2[0.0902] det_loss_box_dn_2[0.2826] det_loss_cls_dn_3[0.0886] det_loss_box_dn_3[0.2789] det_loss_cls_dn_4[0.0865] det_loss_box_dn_4[0.2763] det_loss_cls_dn_5[0.0857] det_loss_box_dn_5[0.2740] loss_depth[0.0371] total_loss[9.1687]
Rank[0/8] 06/20/2025 10:02:07 INFO stats.py:314 | Epoch[150] Step[274] GlobalStep[58774] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 11:45:01.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:02:08 INFO loss_tracker.py:84 | Epoch[150/239] Step[274] GlobalStep[58774/NA]: det_loss_cls_0[0.5351] det_loss_box_0[0.8120] det_loss_cls_1[0.5115] det_loss_box_1[0.6338] det_loss_cls_2[0.4733] det_loss_box_2[0.5544] det_loss_cls_3[0.4395] det_loss_box_3[0.5240] det_loss_cls_4[0.4195] det_loss_box_4[0.4988] det_loss_cls_5[0.4041] det_loss_box_5[0.4970] det_loss_cls_dn_0[0.0936] det_loss_box_dn_0[0.3092] det_loss_cls_dn_1[0.0902] det_loss_box_dn_1[0.2590] det_loss_cls_dn_2[0.0895] det_loss_box_dn_2[0.2419] det_loss_cls_dn_3[0.0886] det_loss_box_dn_3[0.2392] det_loss_cls_dn_4[0.0877] det_loss_box_dn_4[0.2374] det_loss_cls_dn_5[0.0868] det_loss_box_dn_5[0.2362] loss_depth[0.0411] total_loss[8.4033]
Rank[0/8] 06/20/2025 10:02:33 INFO stats.py:314 | Epoch[150] Step[299] GlobalStep[58799] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 11:44:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:02:33 INFO loss_tracker.py:84 | Epoch[150/239] Step[299] GlobalStep[58799/NA]: det_loss_cls_0[0.6312] det_loss_box_0[0.8347] det_loss_cls_1[0.6228] det_loss_box_1[0.6672] det_loss_cls_2[0.5640] det_loss_box_2[0.5969] det_loss_cls_3[0.5333] det_loss_box_3[0.5669] det_loss_cls_4[0.4991] det_loss_box_4[0.5617] det_loss_cls_5[0.4772] det_loss_box_5[0.5639] det_loss_cls_dn_0[0.0922] det_loss_box_dn_0[0.2398] det_loss_cls_dn_1[0.0898] det_loss_box_dn_1[0.2031] det_loss_cls_dn_2[0.0887] det_loss_box_dn_2[0.1889] det_loss_cls_dn_3[0.0875] det_loss_box_dn_3[0.1842] det_loss_cls_dn_4[0.0872] det_loss_box_dn_4[0.1819] det_loss_cls_dn_5[0.0863] det_loss_box_dn_5[0.1810] loss_depth[0.0369] total_loss[8.8663]
Rank[0/8] 06/20/2025 10:02:58 INFO stats.py:314 | Epoch[150] Step[324] GlobalStep[58824] Training Speed: 7.95 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 11:43:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:02:58 INFO loss_tracker.py:84 | Epoch[150/239] Step[324] GlobalStep[58824/NA]: det_loss_cls_0[0.4560] det_loss_box_0[0.5724] det_loss_cls_1[0.4559] det_loss_box_1[0.4492] det_loss_cls_2[0.4022] det_loss_box_2[0.4127] det_loss_cls_3[0.3726] det_loss_box_3[0.4019] det_loss_cls_4[0.3442] det_loss_box_4[0.3937] det_loss_cls_5[0.3351] det_loss_box_5[0.3900] det_loss_cls_dn_0[0.0921] det_loss_box_dn_0[0.2849] det_loss_cls_dn_1[0.0891] det_loss_box_dn_1[0.2289] det_loss_cls_dn_2[0.0882] det_loss_box_dn_2[0.2103] det_loss_cls_dn_3[0.0876] det_loss_box_dn_3[0.2052] det_loss_cls_dn_4[0.0860] det_loss_box_dn_4[0.2023] det_loss_cls_dn_5[0.0846] det_loss_box_dn_5[0.2008] loss_depth[0.0372] total_loss[6.8831]
Rank[0/8] 06/20/2025 10:03:25 INFO stats.py:314 | Epoch[150] Step[349] GlobalStep[58849] Training Speed: 7.19 samples/sec across all devices.	Average Step Time: 1.11 sec.	Estimated Remaining Time: 11:43:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:03:25 INFO loss_tracker.py:84 | Epoch[150/239] Step[349] GlobalStep[58849/NA]: det_loss_cls_0[0.5107] det_loss_box_0[0.6063] det_loss_cls_1[0.4876] det_loss_box_1[0.4725] det_loss_cls_2[0.4310] det_loss_box_2[0.4271] det_loss_cls_3[0.3963] det_loss_box_3[0.4118] det_loss_cls_4[0.3717] det_loss_box_4[0.4128] det_loss_cls_5[0.3621] det_loss_box_5[0.4108] det_loss_cls_dn_0[0.0959] det_loss_box_dn_0[0.2976] det_loss_cls_dn_1[0.0929] det_loss_box_dn_1[0.2584] det_loss_cls_dn_2[0.0925] det_loss_box_dn_2[0.2441] det_loss_cls_dn_3[0.0910] det_loss_box_dn_3[0.2393] det_loss_cls_dn_4[0.0900] det_loss_box_dn_4[0.2378] det_loss_cls_dn_5[0.0893] det_loss_box_dn_5[0.2335] loss_depth[0.0383] total_loss[7.4013]
Rank[0/8] 06/20/2025 10:03:50 INFO stats.py:314 | Epoch[150] Step[374] GlobalStep[58874] Training Speed: 8.19 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 11:42:48.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:03:50 INFO loss_tracker.py:84 | Epoch[150/239] Step[374] GlobalStep[58874/NA]: det_loss_cls_0[0.6202] det_loss_box_0[0.8266] det_loss_cls_1[0.6159] det_loss_box_1[0.6790] det_loss_cls_2[0.5612] det_loss_box_2[0.6263] det_loss_cls_3[0.5305] det_loss_box_3[0.5954] det_loss_cls_4[0.4905] det_loss_box_4[0.5983] det_loss_cls_5[0.4704] det_loss_box_5[0.5983] det_loss_cls_dn_0[0.0960] det_loss_box_dn_0[0.2763] det_loss_cls_dn_1[0.0913] det_loss_box_dn_1[0.2364] det_loss_cls_dn_2[0.0911] det_loss_box_dn_2[0.2224] det_loss_cls_dn_3[0.0902] det_loss_box_dn_3[0.2201] det_loss_cls_dn_4[0.0888] det_loss_box_dn_4[0.2191] det_loss_cls_dn_5[0.0883] det_loss_box_dn_5[0.2134] loss_depth[0.0406] total_loss[9.1865]
Rank[0/8] 06/20/2025 10:04:05 INFO stats.py:394 | Epoch[150] completed. Training Speed: 7.77 samples/sec across all devices.	Epoch Time: 401.41 sec.	Average Epoch Time: 401.41 sec.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 11:42:28.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:04:18 INFO stats.py:314 | Epoch[151] Step[9] GlobalStep[58899] Training Speed: 8.64 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 11:42:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:04:34 INFO loss_tracker.py:84 | Epoch[151/239] Step[24] GlobalStep[58914/NA]: det_loss_cls_0[0.5759] det_loss_box_0[0.7950] det_loss_cls_1[0.5465] det_loss_box_1[0.6000] det_loss_cls_2[0.5000] det_loss_box_2[0.5285] det_loss_cls_3[0.4659] det_loss_box_3[0.5080] det_loss_cls_4[0.4434] det_loss_box_4[0.4936] det_loss_cls_5[0.4260] det_loss_box_5[0.4877] det_loss_cls_dn_0[0.0928] det_loss_box_dn_0[0.2817] det_loss_cls_dn_1[0.0891] det_loss_box_dn_1[0.2302] det_loss_cls_dn_2[0.0888] det_loss_box_dn_2[0.2120] det_loss_cls_dn_3[0.0885] det_loss_box_dn_3[0.2078] det_loss_cls_dn_4[0.0874] det_loss_box_dn_4[0.2062] det_loss_cls_dn_5[0.0861] det_loss_box_dn_5[0.2057] loss_depth[0.0402] total_loss[8.2868]
Rank[0/8] 06/20/2025 10:04:45 INFO stats.py:314 | Epoch[151] Step[34] GlobalStep[58924] Training Speed: 5.68 samples/sec across all devices.	Average Step Time: 1.41 sec.	Estimated Remaining Time: 11:41:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:05:00 INFO loss_tracker.py:84 | Epoch[151/239] Step[49] GlobalStep[58939/NA]: det_loss_cls_0[0.4719] det_loss_box_0[0.6706] det_loss_cls_1[0.4760] det_loss_box_1[0.5285] det_loss_cls_2[0.4318] det_loss_box_2[0.4822] det_loss_cls_3[0.3989] det_loss_box_3[0.4601] det_loss_cls_4[0.3753] det_loss_box_4[0.4552] det_loss_cls_5[0.3556] det_loss_box_5[0.4613] det_loss_cls_dn_0[0.0958] det_loss_box_dn_0[0.2983] det_loss_cls_dn_1[0.0924] det_loss_box_dn_1[0.2580] det_loss_cls_dn_2[0.0920] det_loss_box_dn_2[0.2460] det_loss_cls_dn_3[0.0909] det_loss_box_dn_3[0.2446] det_loss_cls_dn_4[0.0905] det_loss_box_dn_4[0.2448] det_loss_cls_dn_5[0.0897] det_loss_box_dn_5[0.2441] loss_depth[0.0399] total_loss[7.6946]
Rank[0/8] 06/20/2025 10:05:11 INFO stats.py:314 | Epoch[151] Step[59] GlobalStep[58949] Training Speed: 7.84 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 11:41:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:05:26 INFO loss_tracker.py:84 | Epoch[151/239] Step[74] GlobalStep[58964/NA]: det_loss_cls_0[0.4054] det_loss_box_0[0.6127] det_loss_cls_1[0.4003] det_loss_box_1[0.4770] det_loss_cls_2[0.3558] det_loss_box_2[0.4312] det_loss_cls_3[0.3269] det_loss_box_3[0.4140] det_loss_cls_4[0.3064] det_loss_box_4[0.3990] det_loss_cls_5[0.2923] det_loss_box_5[0.4012] det_loss_cls_dn_0[0.0956] det_loss_box_dn_0[0.3156] det_loss_cls_dn_1[0.0938] det_loss_box_dn_1[0.2626] det_loss_cls_dn_2[0.0927] det_loss_box_dn_2[0.2437] det_loss_cls_dn_3[0.0910] det_loss_box_dn_3[0.2394] det_loss_cls_dn_4[0.0899] det_loss_box_dn_4[0.2385] det_loss_cls_dn_5[0.0886] det_loss_box_dn_5[0.2365] loss_depth[0.0409] total_loss[6.9512]
Rank[0/8] 06/20/2025 10:05:36 INFO stats.py:314 | Epoch[151] Step[84] GlobalStep[58974] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 11:40:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:05:51 INFO loss_tracker.py:84 | Epoch[151/239] Step[99] GlobalStep[58989/NA]: det_loss_cls_0[0.4928] det_loss_box_0[0.6537] det_loss_cls_1[0.5015] det_loss_box_1[0.4904] det_loss_cls_2[0.4438] det_loss_box_2[0.4538] det_loss_cls_3[0.4076] det_loss_box_3[0.4298] det_loss_cls_4[0.3763] det_loss_box_4[0.4264] det_loss_cls_5[0.3704] det_loss_box_5[0.4221] det_loss_cls_dn_0[0.0959] det_loss_box_dn_0[0.3209] det_loss_cls_dn_1[0.0914] det_loss_box_dn_1[0.2806] det_loss_cls_dn_2[0.0913] det_loss_box_dn_2[0.2602] det_loss_cls_dn_3[0.0891] det_loss_box_dn_3[0.2568] det_loss_cls_dn_4[0.0882] det_loss_box_dn_4[0.2543] det_loss_cls_dn_5[0.0871] det_loss_box_dn_5[0.2474] loss_depth[0.0378] total_loss[7.6697]
Rank[0/8] 06/20/2025 10:06:01 INFO stats.py:314 | Epoch[151] Step[109] GlobalStep[58999] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 11:40:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:06:18 INFO loss_tracker.py:84 | Epoch[151/239] Step[124] GlobalStep[59014/NA]: det_loss_cls_0[0.5070] det_loss_box_0[0.6463] det_loss_cls_1[0.4948] det_loss_box_1[0.4718] det_loss_cls_2[0.4364] det_loss_box_2[0.4345] det_loss_cls_3[0.4098] det_loss_box_3[0.4186] det_loss_cls_4[0.3813] det_loss_box_4[0.4097] det_loss_cls_5[0.3725] det_loss_box_5[0.4166] det_loss_cls_dn_0[0.0920] det_loss_box_dn_0[0.2846] det_loss_cls_dn_1[0.0876] det_loss_box_dn_1[0.2375] det_loss_cls_dn_2[0.0875] det_loss_box_dn_2[0.2212] det_loss_cls_dn_3[0.0869] det_loss_box_dn_3[0.2147] det_loss_cls_dn_4[0.0856] det_loss_box_dn_4[0.2123] det_loss_cls_dn_5[0.0849] det_loss_box_dn_5[0.2093] loss_depth[0.0369] total_loss[7.3403]
Rank[0/8] 06/20/2025 10:06:28 INFO stats.py:314 | Epoch[151] Step[134] GlobalStep[59024] Training Speed: 8.06 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 11:39:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:06:43 INFO loss_tracker.py:84 | Epoch[151/239] Step[149] GlobalStep[59039/NA]: det_loss_cls_0[0.4854] det_loss_box_0[0.6683] det_loss_cls_1[0.4784] det_loss_box_1[0.5153] det_loss_cls_2[0.4291] det_loss_box_2[0.4611] det_loss_cls_3[0.3980] det_loss_box_3[0.4444] det_loss_cls_4[0.3765] det_loss_box_4[0.4325] det_loss_cls_5[0.3638] det_loss_box_5[0.4355] det_loss_cls_dn_0[0.1047] det_loss_box_dn_0[0.2976] det_loss_cls_dn_1[0.1012] det_loss_box_dn_1[0.2527] det_loss_cls_dn_2[0.1003] det_loss_box_dn_2[0.2369] det_loss_cls_dn_3[0.0996] det_loss_box_dn_3[0.2322] det_loss_cls_dn_4[0.0990] det_loss_box_dn_4[0.2304] det_loss_cls_dn_5[0.0978] det_loss_box_dn_5[0.2287] loss_depth[0.0340] total_loss[7.6036]
Rank[0/8] 06/20/2025 10:06:53 INFO stats.py:314 | Epoch[151] Step[159] GlobalStep[59049] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 11:38:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:07:09 INFO loss_tracker.py:84 | Epoch[151/239] Step[174] GlobalStep[59064/NA]: det_loss_cls_0[0.4912] det_loss_box_0[0.6562] det_loss_cls_1[0.4837] det_loss_box_1[0.5176] det_loss_cls_2[0.4346] det_loss_box_2[0.4748] det_loss_cls_3[0.3974] det_loss_box_3[0.4673] det_loss_cls_4[0.3687] det_loss_box_4[0.4644] det_loss_cls_5[0.3497] det_loss_box_5[0.4665] det_loss_cls_dn_0[0.0832] det_loss_box_dn_0[0.2462] det_loss_cls_dn_1[0.0801] det_loss_box_dn_1[0.2100] det_loss_cls_dn_2[0.0786] det_loss_box_dn_2[0.1957] det_loss_cls_dn_3[0.0773] det_loss_box_dn_3[0.1936] det_loss_cls_dn_4[0.0762] det_loss_box_dn_4[0.1914] det_loss_cls_dn_5[0.0748] det_loss_box_dn_5[0.1905] loss_depth[0.0341] total_loss[7.3037]
Rank[0/8] 06/20/2025 10:07:19 INFO stats.py:314 | Epoch[151] Step[184] GlobalStep[59074] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 11:38:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:07:35 INFO loss_tracker.py:84 | Epoch[151/239] Step[199] GlobalStep[59089/NA]: det_loss_cls_0[0.4087] det_loss_box_0[0.5055] det_loss_cls_1[0.3981] det_loss_box_1[0.3765] det_loss_cls_2[0.3429] det_loss_box_2[0.3454] det_loss_cls_3[0.3091] det_loss_box_3[0.3351] det_loss_cls_4[0.2913] det_loss_box_4[0.3254] det_loss_cls_5[0.2758] det_loss_box_5[0.3271] det_loss_cls_dn_0[0.0780] det_loss_box_dn_0[0.2398] det_loss_cls_dn_1[0.0747] det_loss_box_dn_1[0.1887] det_loss_cls_dn_2[0.0741] det_loss_box_dn_2[0.1742] det_loss_cls_dn_3[0.0733] det_loss_box_dn_3[0.1706] det_loss_cls_dn_4[0.0724] det_loss_box_dn_4[0.1702] det_loss_cls_dn_5[0.0717] det_loss_box_dn_5[0.1692] loss_depth[0.0384] total_loss[5.8362]
Rank[0/8] 06/20/2025 10:07:46 INFO stats.py:314 | Epoch[151] Step[209] GlobalStep[59099] Training Speed: 8.05 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 11:37:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:08:01 INFO loss_tracker.py:84 | Epoch[151/239] Step[224] GlobalStep[59114/NA]: det_loss_cls_0[0.5465] det_loss_box_0[0.7793] det_loss_cls_1[0.5180] det_loss_box_1[0.6266] det_loss_cls_2[0.4649] det_loss_box_2[0.5441] det_loss_cls_3[0.4334] det_loss_box_3[0.5338] det_loss_cls_4[0.4174] det_loss_box_4[0.5112] det_loss_cls_5[0.3985] det_loss_box_5[0.5119] det_loss_cls_dn_0[0.0945] det_loss_box_dn_0[0.3540] det_loss_cls_dn_1[0.0910] det_loss_box_dn_1[0.3040] det_loss_cls_dn_2[0.0907] det_loss_box_dn_2[0.2850] det_loss_cls_dn_3[0.0897] det_loss_box_dn_3[0.2815] det_loss_cls_dn_4[0.0888] det_loss_box_dn_4[0.2797] det_loss_cls_dn_5[0.0885] det_loss_box_dn_5[0.2772] loss_depth[0.0409] total_loss[8.6513]
Rank[0/8] 06/20/2025 10:08:11 INFO stats.py:314 | Epoch[151] Step[234] GlobalStep[59124] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 11:37:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:08:26 INFO loss_tracker.py:84 | Epoch[151/239] Step[249] GlobalStep[59139/NA]: det_loss_cls_0[0.5208] det_loss_box_0[0.6725] det_loss_cls_1[0.5115] det_loss_box_1[0.5331] det_loss_cls_2[0.4617] det_loss_box_2[0.4961] det_loss_cls_3[0.4277] det_loss_box_3[0.4750] det_loss_cls_4[0.4051] det_loss_box_4[0.4672] det_loss_cls_5[0.3901] det_loss_box_5[0.4714] det_loss_cls_dn_0[0.1040] det_loss_box_dn_0[0.3625] det_loss_cls_dn_1[0.0995] det_loss_box_dn_1[0.3107] det_loss_cls_dn_2[0.0986] det_loss_box_dn_2[0.2974] det_loss_cls_dn_3[0.0981] det_loss_box_dn_3[0.2900] det_loss_cls_dn_4[0.0970] det_loss_box_dn_4[0.2893] det_loss_cls_dn_5[0.0958] det_loss_box_dn_5[0.2877] loss_depth[0.0367] total_loss[8.2993]
Rank[0/8] 06/20/2025 10:08:37 INFO stats.py:314 | Epoch[151] Step[259] GlobalStep[59149] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 11:36:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:08:52 INFO loss_tracker.py:84 | Epoch[151/239] Step[274] GlobalStep[59164/NA]: det_loss_cls_0[0.5564] det_loss_box_0[0.7419] det_loss_cls_1[0.5350] det_loss_box_1[0.5850] det_loss_cls_2[0.4957] det_loss_box_2[0.5177] det_loss_cls_3[0.4589] det_loss_box_3[0.5000] det_loss_cls_4[0.4258] det_loss_box_4[0.4923] det_loss_cls_5[0.4068] det_loss_box_5[0.4886] det_loss_cls_dn_0[0.0880] det_loss_box_dn_0[0.2705] det_loss_cls_dn_1[0.0833] det_loss_box_dn_1[0.2290] det_loss_cls_dn_2[0.0823] det_loss_box_dn_2[0.2120] det_loss_cls_dn_3[0.0814] det_loss_box_dn_3[0.2089] det_loss_cls_dn_4[0.0803] det_loss_box_dn_4[0.2079] det_loss_cls_dn_5[0.0793] det_loss_box_dn_5[0.2061] loss_depth[0.0374] total_loss[8.0705]
Rank[0/8] 06/20/2025 10:09:02 INFO stats.py:314 | Epoch[151] Step[284] GlobalStep[59174] Training Speed: 7.83 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 11:36:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:09:18 INFO loss_tracker.py:84 | Epoch[151/239] Step[299] GlobalStep[59189/NA]: det_loss_cls_0[0.3954] det_loss_box_0[0.4723] det_loss_cls_1[0.3829] det_loss_box_1[0.3703] det_loss_cls_2[0.3433] det_loss_box_2[0.3420] det_loss_cls_3[0.3188] det_loss_box_3[0.3270] det_loss_cls_4[0.3011] det_loss_box_4[0.3191] det_loss_cls_5[0.2895] det_loss_box_5[0.3195] det_loss_cls_dn_0[0.0912] det_loss_box_dn_0[0.2787] det_loss_cls_dn_1[0.0871] det_loss_box_dn_1[0.2275] det_loss_cls_dn_2[0.0863] det_loss_box_dn_2[0.2125] det_loss_cls_dn_3[0.0850] det_loss_box_dn_3[0.2083] det_loss_cls_dn_4[0.0849] det_loss_box_dn_4[0.2077] det_loss_cls_dn_5[0.0845] det_loss_box_dn_5[0.2067] loss_depth[0.0385] total_loss[6.0800]
Rank[0/8] 06/20/2025 10:09:29 INFO stats.py:314 | Epoch[151] Step[309] GlobalStep[59199] Training Speed: 6.96 samples/sec across all devices.	Average Step Time: 1.15 sec.	Estimated Remaining Time: 11:35:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:09:44 INFO loss_tracker.py:84 | Epoch[151/239] Step[324] GlobalStep[59214/NA]: det_loss_cls_0[0.4507] det_loss_box_0[0.5769] det_loss_cls_1[0.4405] det_loss_box_1[0.4468] det_loss_cls_2[0.3912] det_loss_box_2[0.4102] det_loss_cls_3[0.3669] det_loss_box_3[0.3930] det_loss_cls_4[0.3421] det_loss_box_4[0.3860] det_loss_cls_5[0.3363] det_loss_box_5[0.3875] det_loss_cls_dn_0[0.1055] det_loss_box_dn_0[0.2918] det_loss_cls_dn_1[0.1019] det_loss_box_dn_1[0.2490] det_loss_cls_dn_2[0.1010] det_loss_box_dn_2[0.2304] det_loss_cls_dn_3[0.0996] det_loss_box_dn_3[0.2285] det_loss_cls_dn_4[0.0994] det_loss_box_dn_4[0.2275] det_loss_cls_dn_5[0.0982] det_loss_box_dn_5[0.2259] loss_depth[0.0349] total_loss[7.0218]
Rank[0/8] 06/20/2025 10:09:54 INFO stats.py:314 | Epoch[151] Step[334] GlobalStep[59224] Training Speed: 8.15 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 11:35:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:10:10 INFO loss_tracker.py:84 | Epoch[151/239] Step[349] GlobalStep[59239/NA]: det_loss_cls_0[0.5250] det_loss_box_0[0.6721] det_loss_cls_1[0.5104] det_loss_box_1[0.5254] det_loss_cls_2[0.4487] det_loss_box_2[0.4844] det_loss_cls_3[0.4198] det_loss_box_3[0.4706] det_loss_cls_4[0.3950] det_loss_box_4[0.4567] det_loss_cls_5[0.3837] det_loss_box_5[0.4569] det_loss_cls_dn_0[0.0888] det_loss_box_dn_0[0.2923] det_loss_cls_dn_1[0.0856] det_loss_box_dn_1[0.2386] det_loss_cls_dn_2[0.0848] det_loss_box_dn_2[0.2246] det_loss_cls_dn_3[0.0837] det_loss_box_dn_3[0.2198] det_loss_cls_dn_4[0.0828] det_loss_box_dn_4[0.2210] det_loss_cls_dn_5[0.0812] det_loss_box_dn_5[0.2189] loss_depth[0.0369] total_loss[7.7075]
Rank[0/8] 06/20/2025 10:10:20 INFO stats.py:314 | Epoch[151] Step[359] GlobalStep[59249] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 11:34:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:10:36 INFO loss_tracker.py:84 | Epoch[151/239] Step[374] GlobalStep[59264/NA]: det_loss_cls_0[0.5978] det_loss_box_0[0.8008] det_loss_cls_1[0.5765] det_loss_box_1[0.6098] det_loss_cls_2[0.5116] det_loss_box_2[0.5636] det_loss_cls_3[0.4813] det_loss_box_3[0.5355] det_loss_cls_4[0.4557] det_loss_box_4[0.5206] det_loss_cls_5[0.4471] det_loss_box_5[0.5178] det_loss_cls_dn_0[0.0956] det_loss_box_dn_0[0.2857] det_loss_cls_dn_1[0.0926] det_loss_box_dn_1[0.2430] det_loss_cls_dn_2[0.0912] det_loss_box_dn_2[0.2267] det_loss_cls_dn_3[0.0897] det_loss_box_dn_3[0.2236] det_loss_cls_dn_4[0.0884] det_loss_box_dn_4[0.2224] det_loss_cls_dn_5[0.0875] det_loss_box_dn_5[0.2210] loss_depth[0.0365] total_loss[8.6222]
Rank[0/8] 06/20/2025 10:10:46 INFO stats.py:314 | Epoch[151] Step[384] GlobalStep[59274] Training Speed: 8.65 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 11:34:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:10:52 INFO stats.py:394 | Epoch[151] completed. Training Speed: 7.67 samples/sec across all devices.	Epoch Time: 406.71 sec.	Average Epoch Time: 406.71 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 11:33:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:11:17 INFO stats.py:314 | Epoch[152] Step[19] GlobalStep[59299] Training Speed: 7.69 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 11:33:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:11:22 INFO loss_tracker.py:84 | Epoch[152/239] Step[24] GlobalStep[59304/NA]: det_loss_cls_0[0.4634] det_loss_box_0[0.6228] det_loss_cls_1[0.4578] det_loss_box_1[0.4665] det_loss_cls_2[0.4096] det_loss_box_2[0.4174] det_loss_cls_3[0.3804] det_loss_box_3[0.3978] det_loss_cls_4[0.3580] det_loss_box_4[0.3904] det_loss_cls_5[0.3461] det_loss_box_5[0.3871] det_loss_cls_dn_0[0.0895] det_loss_box_dn_0[0.3111] det_loss_cls_dn_1[0.0857] det_loss_box_dn_1[0.2602] det_loss_cls_dn_2[0.0852] det_loss_box_dn_2[0.2398] det_loss_cls_dn_3[0.0845] det_loss_box_dn_3[0.2359] det_loss_cls_dn_4[0.0840] det_loss_box_dn_4[0.2340] det_loss_cls_dn_5[0.0832] det_loss_box_dn_5[0.2299] loss_depth[0.0381] total_loss[7.1584]
Rank[0/8] 06/20/2025 10:11:43 INFO stats.py:314 | Epoch[152] Step[44] GlobalStep[59324] Training Speed: 7.23 samples/sec across all devices.	Average Step Time: 1.11 sec.	Estimated Remaining Time: 11:32:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:11:48 INFO loss_tracker.py:84 | Epoch[152/239] Step[49] GlobalStep[59329/NA]: det_loss_cls_0[0.5747] det_loss_box_0[0.7566] det_loss_cls_1[0.5711] det_loss_box_1[0.5792] det_loss_cls_2[0.5256] det_loss_box_2[0.5280] det_loss_cls_3[0.4877] det_loss_box_3[0.5216] det_loss_cls_4[0.4507] det_loss_box_4[0.5162] det_loss_cls_5[0.4473] det_loss_box_5[0.5054] det_loss_cls_dn_0[0.0957] det_loss_box_dn_0[0.3274] det_loss_cls_dn_1[0.0934] det_loss_box_dn_1[0.2860] det_loss_cls_dn_2[0.0937] det_loss_box_dn_2[0.2693] det_loss_cls_dn_3[0.0913] det_loss_box_dn_3[0.2643] det_loss_cls_dn_4[0.0908] det_loss_box_dn_4[0.2628] det_loss_cls_dn_5[0.0895] det_loss_box_dn_5[0.2617] loss_depth[0.0414] total_loss[8.7315]
Rank[0/8] 06/20/2025 10:12:09 INFO stats.py:314 | Epoch[152] Step[69] GlobalStep[59349] Training Speed: 7.99 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 11:32:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:12:14 INFO loss_tracker.py:84 | Epoch[152/239] Step[74] GlobalStep[59354/NA]: det_loss_cls_0[0.4926] det_loss_box_0[0.6663] det_loss_cls_1[0.4837] det_loss_box_1[0.5096] det_loss_cls_2[0.4454] det_loss_box_2[0.4501] det_loss_cls_3[0.4151] det_loss_box_3[0.4300] det_loss_cls_4[0.3907] det_loss_box_4[0.4233] det_loss_cls_5[0.3737] det_loss_box_5[0.4258] det_loss_cls_dn_0[0.1017] det_loss_box_dn_0[0.2788] det_loss_cls_dn_1[0.0992] det_loss_box_dn_1[0.2354] det_loss_cls_dn_2[0.0987] det_loss_box_dn_2[0.2186] det_loss_cls_dn_3[0.0980] det_loss_box_dn_3[0.2155] det_loss_cls_dn_4[0.0973] det_loss_box_dn_4[0.2143] det_loss_cls_dn_5[0.0963] det_loss_box_dn_5[0.2124] loss_depth[0.0372] total_loss[7.5094]
Rank[0/8] 06/20/2025 10:12:34 INFO stats.py:314 | Epoch[152] Step[94] GlobalStep[59374] Training Speed: 7.80 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 11:31:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:12:40 INFO loss_tracker.py:84 | Epoch[152/239] Step[99] GlobalStep[59379/NA]: det_loss_cls_0[0.4334] det_loss_box_0[0.5625] det_loss_cls_1[0.4084] det_loss_box_1[0.4304] det_loss_cls_2[0.3710] det_loss_box_2[0.3895] det_loss_cls_3[0.3513] det_loss_box_3[0.3697] det_loss_cls_4[0.3233] det_loss_box_4[0.3738] det_loss_cls_5[0.3100] det_loss_box_5[0.3742] det_loss_cls_dn_0[0.0920] det_loss_box_dn_0[0.2805] det_loss_cls_dn_1[0.0884] det_loss_box_dn_1[0.2331] det_loss_cls_dn_2[0.0876] det_loss_box_dn_2[0.2200] det_loss_cls_dn_3[0.0864] det_loss_box_dn_3[0.2160] det_loss_cls_dn_4[0.0855] det_loss_box_dn_4[0.2136] det_loss_cls_dn_5[0.0847] det_loss_box_dn_5[0.2116] loss_depth[0.0370] total_loss[6.6337]
Rank[0/8] 06/20/2025 10:13:00 INFO stats.py:314 | Epoch[152] Step[119] GlobalStep[59399] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 11:31:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:13:05 INFO loss_tracker.py:84 | Epoch[152/239] Step[124] GlobalStep[59404/NA]: det_loss_cls_0[0.5911] det_loss_box_0[0.7920] det_loss_cls_1[0.5872] det_loss_box_1[0.6374] det_loss_cls_2[0.5445] det_loss_box_2[0.5617] det_loss_cls_3[0.5107] det_loss_box_3[0.5397] det_loss_cls_4[0.4754] det_loss_box_4[0.5268] det_loss_cls_5[0.4528] det_loss_box_5[0.5248] det_loss_cls_dn_0[0.0874] det_loss_box_dn_0[0.2469] det_loss_cls_dn_1[0.0841] det_loss_box_dn_1[0.2088] det_loss_cls_dn_2[0.0831] det_loss_box_dn_2[0.1957] det_loss_cls_dn_3[0.0824] det_loss_box_dn_3[0.1958] det_loss_cls_dn_4[0.0817] det_loss_box_dn_4[0.1957] det_loss_cls_dn_5[0.0806] det_loss_box_dn_5[0.1955] loss_depth[0.0369] total_loss[8.5187]
Rank[0/8] 06/20/2025 10:13:26 INFO stats.py:314 | Epoch[152] Step[144] GlobalStep[59424] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 11:30:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:13:31 INFO loss_tracker.py:84 | Epoch[152/239] Step[149] GlobalStep[59429/NA]: det_loss_cls_0[0.4944] det_loss_box_0[0.5709] det_loss_cls_1[0.4780] det_loss_box_1[0.4597] det_loss_cls_2[0.4312] det_loss_box_2[0.4280] det_loss_cls_3[0.4103] det_loss_box_3[0.4162] det_loss_cls_4[0.3927] det_loss_box_4[0.4058] det_loss_cls_5[0.3855] det_loss_box_5[0.4068] det_loss_cls_dn_0[0.0978] det_loss_box_dn_0[0.2594] det_loss_cls_dn_1[0.0929] det_loss_box_dn_1[0.2205] det_loss_cls_dn_2[0.0928] det_loss_box_dn_2[0.2056] det_loss_cls_dn_3[0.0917] det_loss_box_dn_3[0.2029] det_loss_cls_dn_4[0.0916] det_loss_box_dn_4[0.2010] det_loss_cls_dn_5[0.0909] det_loss_box_dn_5[0.2000] loss_depth[0.0370] total_loss[7.1637]
Rank[0/8] 06/20/2025 10:13:52 INFO stats.py:314 | Epoch[152] Step[169] GlobalStep[59449] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 11:30:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:13:57 INFO loss_tracker.py:84 | Epoch[152/239] Step[174] GlobalStep[59454/NA]: det_loss_cls_0[0.4175] det_loss_box_0[0.5521] det_loss_cls_1[0.4010] det_loss_box_1[0.4330] det_loss_cls_2[0.3591] det_loss_box_2[0.4017] det_loss_cls_3[0.3295] det_loss_box_3[0.3916] det_loss_cls_4[0.3107] det_loss_box_4[0.3877] det_loss_cls_5[0.2985] det_loss_box_5[0.3837] det_loss_cls_dn_0[0.0929] det_loss_box_dn_0[0.3812] det_loss_cls_dn_1[0.0892] det_loss_box_dn_1[0.3316] det_loss_cls_dn_2[0.0887] det_loss_box_dn_2[0.3127] det_loss_cls_dn_3[0.0874] det_loss_box_dn_3[0.3070] det_loss_cls_dn_4[0.0868] det_loss_box_dn_4[0.3032] det_loss_cls_dn_5[0.0859] det_loss_box_dn_5[0.3014] loss_depth[0.0386] total_loss[7.1725]
Rank[0/8] 06/20/2025 10:14:17 INFO stats.py:314 | Epoch[152] Step[194] GlobalStep[59474] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 11:29:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:14:22 INFO loss_tracker.py:84 | Epoch[152/239] Step[199] GlobalStep[59479/NA]: det_loss_cls_0[0.4147] det_loss_box_0[0.6197] det_loss_cls_1[0.4026] det_loss_box_1[0.4751] det_loss_cls_2[0.3637] det_loss_box_2[0.4495] det_loss_cls_3[0.3386] det_loss_box_3[0.4396] det_loss_cls_4[0.3174] det_loss_box_4[0.4296] det_loss_cls_5[0.3035] det_loss_box_5[0.4328] det_loss_cls_dn_0[0.0881] det_loss_box_dn_0[0.3019] det_loss_cls_dn_1[0.0842] det_loss_box_dn_1[0.2463] det_loss_cls_dn_2[0.0840] det_loss_box_dn_2[0.2307] det_loss_cls_dn_3[0.0829] det_loss_box_dn_3[0.2245] det_loss_cls_dn_4[0.0815] det_loss_box_dn_4[0.2225] det_loss_cls_dn_5[0.0803] det_loss_box_dn_5[0.2226] loss_depth[0.0386] total_loss[6.9748]
Rank[0/8] 06/20/2025 10:14:43 INFO stats.py:314 | Epoch[152] Step[219] GlobalStep[59499] Training Speed: 6.39 samples/sec across all devices.	Average Step Time: 1.25 sec.	Estimated Remaining Time: 11:29:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:14:48 INFO loss_tracker.py:84 | Epoch[152/239] Step[224] GlobalStep[59504/NA]: det_loss_cls_0[0.5535] det_loss_box_0[0.6928] det_loss_cls_1[0.5447] det_loss_box_1[0.5274] det_loss_cls_2[0.4913] det_loss_box_2[0.4721] det_loss_cls_3[0.4553] det_loss_box_3[0.4491] det_loss_cls_4[0.4302] det_loss_box_4[0.4389] det_loss_cls_5[0.4213] det_loss_box_5[0.4317] det_loss_cls_dn_0[0.1012] det_loss_box_dn_0[0.2565] det_loss_cls_dn_1[0.0968] det_loss_box_dn_1[0.2128] det_loss_cls_dn_2[0.0961] det_loss_box_dn_2[0.1960] det_loss_cls_dn_3[0.0949] det_loss_box_dn_3[0.1918] det_loss_cls_dn_4[0.0939] det_loss_box_dn_4[0.1898] det_loss_cls_dn_5[0.0930] det_loss_box_dn_5[0.1890] loss_depth[0.0382] total_loss[7.7582]
Rank[0/8] 06/20/2025 10:15:08 INFO stats.py:314 | Epoch[152] Step[244] GlobalStep[59524] Training Speed: 7.90 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 11:28:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:15:13 INFO loss_tracker.py:84 | Epoch[152/239] Step[249] GlobalStep[59529/NA]: det_loss_cls_0[0.4937] det_loss_box_0[0.6761] det_loss_cls_1[0.4767] det_loss_box_1[0.5177] det_loss_cls_2[0.4277] det_loss_box_2[0.4555] det_loss_cls_3[0.3846] det_loss_box_3[0.4419] det_loss_cls_4[0.3608] det_loss_box_4[0.4365] det_loss_cls_5[0.3439] det_loss_box_5[0.4386] det_loss_cls_dn_0[0.0924] det_loss_box_dn_0[0.2648] det_loss_cls_dn_1[0.0889] det_loss_box_dn_1[0.2239] det_loss_cls_dn_2[0.0875] det_loss_box_dn_2[0.2076] det_loss_cls_dn_3[0.0862] det_loss_box_dn_3[0.2041] det_loss_cls_dn_4[0.0849] det_loss_box_dn_4[0.2024] det_loss_cls_dn_5[0.0841] det_loss_box_dn_5[0.2012] loss_depth[0.0409] total_loss[7.3226]
Rank[0/8] 06/20/2025 10:15:34 INFO stats.py:314 | Epoch[152] Step[269] GlobalStep[59549] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 11:28:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:15:39 INFO loss_tracker.py:84 | Epoch[152/239] Step[274] GlobalStep[59554/NA]: det_loss_cls_0[0.4639] det_loss_box_0[0.6452] det_loss_cls_1[0.4375] det_loss_box_1[0.4846] det_loss_cls_2[0.3948] det_loss_box_2[0.4420] det_loss_cls_3[0.3680] det_loss_box_3[0.4248] det_loss_cls_4[0.3393] det_loss_box_4[0.4153] det_loss_cls_5[0.3249] det_loss_box_5[0.4141] det_loss_cls_dn_0[0.0942] det_loss_box_dn_0[0.2922] det_loss_cls_dn_1[0.0919] det_loss_box_dn_1[0.2380] det_loss_cls_dn_2[0.0906] det_loss_box_dn_2[0.2192] det_loss_cls_dn_3[0.0895] det_loss_box_dn_3[0.2153] det_loss_cls_dn_4[0.0885] det_loss_box_dn_4[0.2134] det_loss_cls_dn_5[0.0875] det_loss_box_dn_5[0.2118] loss_depth[0.0391] total_loss[7.1258]
Rank[0/8] 06/20/2025 10:15:59 INFO stats.py:314 | Epoch[152] Step[294] GlobalStep[59574] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 11:27:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:16:05 INFO loss_tracker.py:84 | Epoch[152/239] Step[299] GlobalStep[59579/NA]: det_loss_cls_0[0.5730] det_loss_box_0[0.7759] det_loss_cls_1[0.5537] det_loss_box_1[0.6144] det_loss_cls_2[0.5069] det_loss_box_2[0.5724] det_loss_cls_3[0.4704] det_loss_box_3[0.5512] det_loss_cls_4[0.4591] det_loss_box_4[0.5147] det_loss_cls_5[0.4333] det_loss_box_5[0.5285] det_loss_cls_dn_0[0.1098] det_loss_box_dn_0[0.3097] det_loss_cls_dn_1[0.1062] det_loss_box_dn_1[0.2635] det_loss_cls_dn_2[0.1066] det_loss_box_dn_2[0.2458] det_loss_cls_dn_3[0.1065] det_loss_box_dn_3[0.2421] det_loss_cls_dn_4[0.1057] det_loss_box_dn_4[0.2407] det_loss_cls_dn_5[0.1048] det_loss_box_dn_5[0.2370] loss_depth[0.0383] total_loss[8.7700]
Rank[0/8] 06/20/2025 10:16:26 INFO stats.py:314 | Epoch[152] Step[319] GlobalStep[59599] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 11:26:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:16:31 INFO loss_tracker.py:84 | Epoch[152/239] Step[324] GlobalStep[59604/NA]: det_loss_cls_0[0.5389] det_loss_box_0[0.6599] det_loss_cls_1[0.5231] det_loss_box_1[0.4862] det_loss_cls_2[0.4743] det_loss_box_2[0.4368] det_loss_cls_3[0.4448] det_loss_box_3[0.4115] det_loss_cls_4[0.4157] det_loss_box_4[0.4018] det_loss_cls_5[0.4001] det_loss_box_5[0.4012] det_loss_cls_dn_0[0.0957] det_loss_box_dn_0[0.2359] det_loss_cls_dn_1[0.0919] det_loss_box_dn_1[0.1952] det_loss_cls_dn_2[0.0915] det_loss_box_dn_2[0.1770] det_loss_cls_dn_3[0.0907] det_loss_box_dn_3[0.1737] det_loss_cls_dn_4[0.0898] det_loss_box_dn_4[0.1719] det_loss_cls_dn_5[0.0887] det_loss_box_dn_5[0.1706] loss_depth[0.0375] total_loss[7.3047]
Rank[0/8] 06/20/2025 10:16:51 INFO stats.py:314 | Epoch[152] Step[344] GlobalStep[59624] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 11:26:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:16:56 INFO loss_tracker.py:84 | Epoch[152/239] Step[349] GlobalStep[59629/NA]: det_loss_cls_0[0.4780] det_loss_box_0[0.6058] det_loss_cls_1[0.4675] det_loss_box_1[0.4754] det_loss_cls_2[0.4180] det_loss_box_2[0.4436] det_loss_cls_3[0.3781] det_loss_box_3[0.4291] det_loss_cls_4[0.3535] det_loss_box_4[0.4248] det_loss_cls_5[0.3449] det_loss_box_5[0.4221] det_loss_cls_dn_0[0.0932] det_loss_box_dn_0[0.2864] det_loss_cls_dn_1[0.0901] det_loss_box_dn_1[0.2369] det_loss_cls_dn_2[0.0884] det_loss_box_dn_2[0.2207] det_loss_cls_dn_3[0.0869] det_loss_box_dn_3[0.2166] det_loss_cls_dn_4[0.0858] det_loss_box_dn_4[0.2143] det_loss_cls_dn_5[0.0845] det_loss_box_dn_5[0.2130] loss_depth[0.0356] total_loss[7.1931]
Rank[0/8] 06/20/2025 10:17:17 INFO stats.py:314 | Epoch[152] Step[369] GlobalStep[59649] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 11:25:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:17:22 INFO loss_tracker.py:84 | Epoch[152/239] Step[374] GlobalStep[59654/NA]: det_loss_cls_0[0.4794] det_loss_box_0[0.6224] det_loss_cls_1[0.4755] det_loss_box_1[0.4678] det_loss_cls_2[0.4253] det_loss_box_2[0.4177] det_loss_cls_3[0.4037] det_loss_box_3[0.3976] det_loss_cls_4[0.3840] det_loss_box_4[0.3965] det_loss_cls_5[0.3752] det_loss_box_5[0.3952] det_loss_cls_dn_0[0.1015] det_loss_box_dn_0[0.2700] det_loss_cls_dn_1[0.0984] det_loss_box_dn_1[0.2250] det_loss_cls_dn_2[0.0982] det_loss_box_dn_2[0.2048] det_loss_cls_dn_3[0.0971] det_loss_box_dn_3[0.2003] det_loss_cls_dn_4[0.0964] det_loss_box_dn_4[0.1993] det_loss_cls_dn_5[0.0951] det_loss_box_dn_5[0.1987] loss_depth[0.0348] total_loss[7.1599]
Rank[0/8] 06/20/2025 10:17:37 INFO stats.py:394 | Epoch[152] completed. Training Speed: 7.69 samples/sec across all devices.	Epoch Time: 405.61 sec.	Average Epoch Time: 405.61 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 11:25:24.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:17:44 INFO stats.py:314 | Epoch[153] Step[4] GlobalStep[59674] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 11:25:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:18:04 INFO loss_tracker.py:84 | Epoch[153/239] Step[24] GlobalStep[59694/NA]: det_loss_cls_0[0.5330] det_loss_box_0[0.6554] det_loss_cls_1[0.5066] det_loss_box_1[0.5087] det_loss_cls_2[0.4751] det_loss_box_2[0.4570] det_loss_cls_3[0.4478] det_loss_box_3[0.4357] det_loss_cls_4[0.4240] det_loss_box_4[0.4335] det_loss_cls_5[0.4159] det_loss_box_5[0.4329] det_loss_cls_dn_0[0.1022] det_loss_box_dn_0[0.2947] det_loss_cls_dn_1[0.0977] det_loss_box_dn_1[0.2485] det_loss_cls_dn_2[0.0967] det_loss_box_dn_2[0.2342] det_loss_cls_dn_3[0.0958] det_loss_box_dn_3[0.2296] det_loss_cls_dn_4[0.0953] det_loss_box_dn_4[0.2282] det_loss_cls_dn_5[0.0943] det_loss_box_dn_5[0.2273] loss_depth[0.0352] total_loss[7.8053]
Rank[0/8] 06/20/2025 10:18:10 INFO stats.py:314 | Epoch[153] Step[29] GlobalStep[59699] Training Speed: 7.92 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 11:24:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:18:30 INFO loss_tracker.py:84 | Epoch[153/239] Step[49] GlobalStep[59719/NA]: det_loss_cls_0[0.4076] det_loss_box_0[0.5024] det_loss_cls_1[0.3981] det_loss_box_1[0.3744] det_loss_cls_2[0.3426] det_loss_box_2[0.3449] det_loss_cls_3[0.3138] det_loss_box_3[0.3311] det_loss_cls_4[0.2872] det_loss_box_4[0.3263] det_loss_cls_5[0.2731] det_loss_box_5[0.3270] det_loss_cls_dn_0[0.0813] det_loss_box_dn_0[0.2320] det_loss_cls_dn_1[0.0788] det_loss_box_dn_1[0.1938] det_loss_cls_dn_2[0.0781] det_loss_box_dn_2[0.1812] det_loss_cls_dn_3[0.0779] det_loss_box_dn_3[0.1787] det_loss_cls_dn_4[0.0757] det_loss_box_dn_4[0.1778] det_loss_cls_dn_5[0.0753] det_loss_box_dn_5[0.1766] loss_depth[0.0379] total_loss[5.8735]
Rank[0/8] 06/20/2025 10:18:35 INFO stats.py:314 | Epoch[153] Step[54] GlobalStep[59724] Training Speed: 7.78 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 11:24:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:18:56 INFO loss_tracker.py:84 | Epoch[153/239] Step[74] GlobalStep[59744/NA]: det_loss_cls_0[0.4569] det_loss_box_0[0.6124] det_loss_cls_1[0.4393] det_loss_box_1[0.4694] det_loss_cls_2[0.4033] det_loss_box_2[0.4248] det_loss_cls_3[0.3652] det_loss_box_3[0.4137] det_loss_cls_4[0.3477] det_loss_box_4[0.4022] det_loss_cls_5[0.3266] det_loss_box_5[0.4054] det_loss_cls_dn_0[0.0890] det_loss_box_dn_0[0.2995] det_loss_cls_dn_1[0.0855] det_loss_box_dn_1[0.2453] det_loss_cls_dn_2[0.0845] det_loss_box_dn_2[0.2286] det_loss_cls_dn_3[0.0834] det_loss_box_dn_3[0.2242] det_loss_cls_dn_4[0.0826] det_loss_box_dn_4[0.2226] det_loss_cls_dn_5[0.0819] det_loss_box_dn_5[0.2130] loss_depth[0.0368] total_loss[7.0438]
Rank[0/8] 06/20/2025 10:19:02 INFO stats.py:314 | Epoch[153] Step[79] GlobalStep[59749] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 11:23:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:19:22 INFO loss_tracker.py:84 | Epoch[153/239] Step[99] GlobalStep[59769/NA]: det_loss_cls_0[0.4906] det_loss_box_0[0.6332] det_loss_cls_1[0.4716] det_loss_box_1[0.4792] det_loss_cls_2[0.4345] det_loss_box_2[0.4459] det_loss_cls_3[0.4104] det_loss_box_3[0.4304] det_loss_cls_4[0.3716] det_loss_box_4[0.4286] det_loss_cls_5[0.3555] det_loss_box_5[0.4234] det_loss_cls_dn_0[0.0891] det_loss_box_dn_0[0.2658] det_loss_cls_dn_1[0.0855] det_loss_box_dn_1[0.2187] det_loss_cls_dn_2[0.0851] det_loss_box_dn_2[0.1990] det_loss_cls_dn_3[0.0836] det_loss_box_dn_3[0.1948] det_loss_cls_dn_4[0.0823] det_loss_box_dn_4[0.1926] det_loss_cls_dn_5[0.0814] det_loss_box_dn_5[0.1908] loss_depth[0.0355] total_loss[7.1790]
Rank[0/8] 06/20/2025 10:19:27 INFO stats.py:314 | Epoch[153] Step[104] GlobalStep[59774] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 11:23:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:19:48 INFO loss_tracker.py:84 | Epoch[153/239] Step[124] GlobalStep[59794/NA]: det_loss_cls_0[0.4593] det_loss_box_0[0.6278] det_loss_cls_1[0.4530] det_loss_box_1[0.4789] det_loss_cls_2[0.4092] det_loss_box_2[0.4243] det_loss_cls_3[0.3719] det_loss_box_3[0.4103] det_loss_cls_4[0.3443] det_loss_box_4[0.4091] det_loss_cls_5[0.3233] det_loss_box_5[0.4133] det_loss_cls_dn_0[0.0888] det_loss_box_dn_0[0.2991] det_loss_cls_dn_1[0.0847] det_loss_box_dn_1[0.2478] det_loss_cls_dn_2[0.0842] det_loss_box_dn_2[0.2300] det_loss_cls_dn_3[0.0826] det_loss_box_dn_3[0.2260] det_loss_cls_dn_4[0.0815] det_loss_box_dn_4[0.2244] det_loss_cls_dn_5[0.0807] det_loss_box_dn_5[0.2232] loss_depth[0.0381] total_loss[7.1158]
Rank[0/8] 06/20/2025 10:19:53 INFO stats.py:314 | Epoch[153] Step[129] GlobalStep[59799] Training Speed: 7.80 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 11:22:34.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:20:14 INFO loss_tracker.py:84 | Epoch[153/239] Step[149] GlobalStep[59819/NA]: det_loss_cls_0[0.4059] det_loss_box_0[0.5276] det_loss_cls_1[0.3991] det_loss_box_1[0.4086] det_loss_cls_2[0.3583] det_loss_box_2[0.3690] det_loss_cls_3[0.3345] det_loss_box_3[0.3547] det_loss_cls_4[0.3074] det_loss_box_4[0.3500] det_loss_cls_5[0.2960] det_loss_box_5[0.3523] det_loss_cls_dn_0[0.0940] det_loss_box_dn_0[0.2822] det_loss_cls_dn_1[0.0902] det_loss_box_dn_1[0.2316] det_loss_cls_dn_2[0.0889] det_loss_box_dn_2[0.2130] det_loss_cls_dn_3[0.0873] det_loss_box_dn_3[0.2080] det_loss_cls_dn_4[0.0861] det_loss_box_dn_4[0.2064] det_loss_cls_dn_5[0.0855] det_loss_box_dn_5[0.2051] loss_depth[0.0378] total_loss[6.3798]
Rank[0/8] 06/20/2025 10:20:20 INFO stats.py:314 | Epoch[153] Step[154] GlobalStep[59824] Training Speed: 6.68 samples/sec across all devices.	Average Step Time: 1.20 sec.	Estimated Remaining Time: 11:22:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:20:40 INFO loss_tracker.py:84 | Epoch[153/239] Step[174] GlobalStep[59844/NA]: det_loss_cls_0[0.4611] det_loss_box_0[0.6327] det_loss_cls_1[0.4643] det_loss_box_1[0.4734] det_loss_cls_2[0.4335] det_loss_box_2[0.4434] det_loss_cls_3[0.4015] det_loss_box_3[0.4229] det_loss_cls_4[0.3765] det_loss_box_4[0.4117] det_loss_cls_5[0.3683] det_loss_box_5[0.4093] det_loss_cls_dn_0[0.0872] det_loss_box_dn_0[0.2905] det_loss_cls_dn_1[0.0839] det_loss_box_dn_1[0.2392] det_loss_cls_dn_2[0.0833] det_loss_box_dn_2[0.2269] det_loss_cls_dn_3[0.0821] det_loss_box_dn_3[0.2227] det_loss_cls_dn_4[0.0814] det_loss_box_dn_4[0.2210] det_loss_cls_dn_5[0.0804] det_loss_box_dn_5[0.2212] loss_depth[0.0372] total_loss[7.2555]
Rank[0/8] 06/20/2025 10:20:45 INFO stats.py:314 | Epoch[153] Step[179] GlobalStep[59849] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 11:21:29.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:21:06 INFO loss_tracker.py:84 | Epoch[153/239] Step[199] GlobalStep[59869/NA]: det_loss_cls_0[0.4934] det_loss_box_0[0.6406] det_loss_cls_1[0.4749] det_loss_box_1[0.4957] det_loss_cls_2[0.4383] det_loss_box_2[0.4268] det_loss_cls_3[0.4012] det_loss_box_3[0.4214] det_loss_cls_4[0.3844] det_loss_box_4[0.4112] det_loss_cls_5[0.3747] det_loss_box_5[0.4111] det_loss_cls_dn_0[0.1000] det_loss_box_dn_0[0.2845] det_loss_cls_dn_1[0.0960] det_loss_box_dn_1[0.2400] det_loss_cls_dn_2[0.0953] det_loss_box_dn_2[0.2206] det_loss_cls_dn_3[0.0946] det_loss_box_dn_3[0.2175] det_loss_cls_dn_4[0.0938] det_loss_box_dn_4[0.2158] det_loss_cls_dn_5[0.0927] det_loss_box_dn_5[0.2129] loss_depth[0.0398] total_loss[7.3773]
Rank[0/8] 06/20/2025 10:21:12 INFO stats.py:314 | Epoch[153] Step[204] GlobalStep[59874] Training Speed: 8.05 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 11:20:56.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:21:32 INFO loss_tracker.py:84 | Epoch[153/239] Step[224] GlobalStep[59894/NA]: det_loss_cls_0[0.5434] det_loss_box_0[0.8073] det_loss_cls_1[0.5353] det_loss_box_1[0.6581] det_loss_cls_2[0.5032] det_loss_box_2[0.5737] det_loss_cls_3[0.4702] det_loss_box_3[0.5556] det_loss_cls_4[0.4547] det_loss_box_4[0.5325] det_loss_cls_5[0.4403] det_loss_box_5[0.5352] det_loss_cls_dn_0[0.0931] det_loss_box_dn_0[0.3423] det_loss_cls_dn_1[0.0893] det_loss_box_dn_1[0.2994] det_loss_cls_dn_2[0.0880] det_loss_box_dn_2[0.2825] det_loss_cls_dn_3[0.0868] det_loss_box_dn_3[0.2778] det_loss_cls_dn_4[0.0871] det_loss_box_dn_4[0.2737] det_loss_cls_dn_5[0.0867] det_loss_box_dn_5[0.2709] loss_depth[0.0401] total_loss[8.9272]
Rank[0/8] 06/20/2025 10:21:37 INFO stats.py:314 | Epoch[153] Step[229] GlobalStep[59899] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 11:20:23.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:21:58 INFO loss_tracker.py:84 | Epoch[153/239] Step[249] GlobalStep[59919/NA]: det_loss_cls_0[0.4582] det_loss_box_0[0.5992] det_loss_cls_1[0.4442] det_loss_box_1[0.4428] det_loss_cls_2[0.4000] det_loss_box_2[0.4094] det_loss_cls_3[0.3618] det_loss_box_3[0.3901] det_loss_cls_4[0.3315] det_loss_box_4[0.3856] det_loss_cls_5[0.3168] det_loss_box_5[0.3845] det_loss_cls_dn_0[0.0830] det_loss_box_dn_0[0.2818] det_loss_cls_dn_1[0.0797] det_loss_box_dn_1[0.2342] det_loss_cls_dn_2[0.0794] det_loss_box_dn_2[0.2152] det_loss_cls_dn_3[0.0779] det_loss_box_dn_3[0.2099] det_loss_cls_dn_4[0.0771] det_loss_box_dn_4[0.2085] det_loss_cls_dn_5[0.0763] det_loss_box_dn_5[0.2075] loss_depth[0.0358] total_loss[6.7905]
Rank[0/8] 06/20/2025 10:22:03 INFO stats.py:314 | Epoch[153] Step[254] GlobalStep[59924] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 11:19:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:22:23 INFO loss_tracker.py:84 | Epoch[153/239] Step[274] GlobalStep[59944/NA]: det_loss_cls_0[0.5656] det_loss_box_0[0.6610] det_loss_cls_1[0.5511] det_loss_box_1[0.5209] det_loss_cls_2[0.5091] det_loss_box_2[0.4636] det_loss_cls_3[0.4784] det_loss_box_3[0.4468] det_loss_cls_4[0.4459] det_loss_box_4[0.4400] det_loss_cls_5[0.4343] det_loss_box_5[0.4372] det_loss_cls_dn_0[0.0980] det_loss_box_dn_0[0.2578] det_loss_cls_dn_1[0.0938] det_loss_box_dn_1[0.2171] det_loss_cls_dn_2[0.0938] det_loss_box_dn_2[0.2019] det_loss_cls_dn_3[0.0921] det_loss_box_dn_3[0.1977] det_loss_cls_dn_4[0.0916] det_loss_box_dn_4[0.1957] det_loss_cls_dn_5[0.0906] det_loss_box_dn_5[0.1979] loss_depth[0.0338] total_loss[7.8158]
Rank[0/8] 06/20/2025 10:22:28 INFO stats.py:314 | Epoch[153] Step[279] GlobalStep[59949] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 11:19:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:22:49 INFO loss_tracker.py:84 | Epoch[153/239] Step[299] GlobalStep[59969/NA]: det_loss_cls_0[0.4032] det_loss_box_0[0.5003] det_loss_cls_1[0.3939] det_loss_box_1[0.3858] det_loss_cls_2[0.3413] det_loss_box_2[0.3517] det_loss_cls_3[0.3200] det_loss_box_3[0.3397] det_loss_cls_4[0.2940] det_loss_box_4[0.3389] det_loss_cls_5[0.2891] det_loss_box_5[0.3336] det_loss_cls_dn_0[0.0894] det_loss_box_dn_0[0.2612] det_loss_cls_dn_1[0.0865] det_loss_box_dn_1[0.2091] det_loss_cls_dn_2[0.0846] det_loss_box_dn_2[0.1906] det_loss_cls_dn_3[0.0835] det_loss_box_dn_3[0.1865] det_loss_cls_dn_4[0.0831] det_loss_box_dn_4[0.1844] det_loss_cls_dn_5[0.0820] det_loss_box_dn_5[0.1844] loss_depth[0.0344] total_loss[6.0511]
Rank[0/8] 06/20/2025 10:22:54 INFO stats.py:314 | Epoch[153] Step[304] GlobalStep[59974] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 11:18:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:23:15 INFO loss_tracker.py:84 | Epoch[153/239] Step[324] GlobalStep[59994/NA]: det_loss_cls_0[0.4758] det_loss_box_0[0.6104] det_loss_cls_1[0.4585] det_loss_box_1[0.4329] det_loss_cls_2[0.4008] det_loss_box_2[0.3983] det_loss_cls_3[0.3703] det_loss_box_3[0.3924] det_loss_cls_4[0.3545] det_loss_box_4[0.3757] det_loss_cls_5[0.3448] det_loss_box_5[0.3736] det_loss_cls_dn_0[0.0920] det_loss_box_dn_0[0.2642] det_loss_cls_dn_1[0.0881] det_loss_box_dn_1[0.2108] det_loss_cls_dn_2[0.0873] det_loss_box_dn_2[0.1952] det_loss_cls_dn_3[0.0850] det_loss_box_dn_3[0.1903] det_loss_cls_dn_4[0.0843] det_loss_box_dn_4[0.1897] det_loss_cls_dn_5[0.0836] det_loss_box_dn_5[0.1883] loss_depth[0.0382] total_loss[6.7850]
Rank[0/8] 06/20/2025 10:23:21 INFO stats.py:314 | Epoch[153] Step[329] GlobalStep[59999] Training Speed: 7.85 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 11:18:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:23:41 INFO loss_tracker.py:84 | Epoch[153/239] Step[349] GlobalStep[60019/NA]: det_loss_cls_0[0.4950] det_loss_box_0[0.6352] det_loss_cls_1[0.4852] det_loss_box_1[0.4670] det_loss_cls_2[0.4408] det_loss_box_2[0.4280] det_loss_cls_3[0.4166] det_loss_box_3[0.4121] det_loss_cls_4[0.3913] det_loss_box_4[0.4075] det_loss_cls_5[0.3773] det_loss_box_5[0.4040] det_loss_cls_dn_0[0.0982] det_loss_box_dn_0[0.2748] det_loss_cls_dn_1[0.0952] det_loss_box_dn_1[0.2219] det_loss_cls_dn_2[0.0950] det_loss_box_dn_2[0.2035] det_loss_cls_dn_3[0.0938] det_loss_box_dn_3[0.1988] det_loss_cls_dn_4[0.0931] det_loss_box_dn_4[0.1981] det_loss_cls_dn_5[0.0923] det_loss_box_dn_5[0.1961] loss_depth[0.0361] total_loss[7.2568]
Rank[0/8] 06/20/2025 10:23:46 INFO stats.py:314 | Epoch[153] Step[354] GlobalStep[60024] Training Speed: 8.59 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 11:17:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:24:07 INFO loss_tracker.py:84 | Epoch[153/239] Step[374] GlobalStep[60044/NA]: det_loss_cls_0[0.3838] det_loss_box_0[0.5266] det_loss_cls_1[0.3780] det_loss_box_1[0.4072] det_loss_cls_2[0.3342] det_loss_box_2[0.3670] det_loss_cls_3[0.3126] det_loss_box_3[0.3505] det_loss_cls_4[0.2841] det_loss_box_4[0.3493] det_loss_cls_5[0.2681] det_loss_box_5[0.3504] det_loss_cls_dn_0[0.0935] det_loss_box_dn_0[0.3256] det_loss_cls_dn_1[0.0908] det_loss_box_dn_1[0.2580] det_loss_cls_dn_2[0.0898] det_loss_box_dn_2[0.2346] det_loss_cls_dn_3[0.0887] det_loss_box_dn_3[0.2304] det_loss_cls_dn_4[0.0883] det_loss_box_dn_4[0.2297] det_loss_cls_dn_5[0.0878] det_loss_box_dn_5[0.2289] loss_depth[0.0396] total_loss[6.3975]
Rank[0/8] 06/20/2025 10:24:12 INFO stats.py:314 | Epoch[153] Step[379] GlobalStep[60049] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 11:17:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:24:22 INFO stats.py:394 | Epoch[153] completed. Training Speed: 7.71 samples/sec across all devices.	Epoch Time: 404.61 sec.	Average Epoch Time: 404.61 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 11:16:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:24:40 INFO stats.py:314 | Epoch[154] Step[14] GlobalStep[60074] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 11:16:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:24:51 INFO loss_tracker.py:84 | Epoch[154/239] Step[24] GlobalStep[60084/NA]: det_loss_cls_0[0.4332] det_loss_box_0[0.5806] det_loss_cls_1[0.4216] det_loss_box_1[0.4574] det_loss_cls_2[0.3850] det_loss_box_2[0.4100] det_loss_cls_3[0.3561] det_loss_box_3[0.3964] det_loss_cls_4[0.3302] det_loss_box_4[0.3958] det_loss_cls_5[0.3188] det_loss_box_5[0.3936] det_loss_cls_dn_0[0.0874] det_loss_box_dn_0[0.3093] det_loss_cls_dn_1[0.0840] det_loss_box_dn_1[0.2623] det_loss_cls_dn_2[0.0829] det_loss_box_dn_2[0.2429] det_loss_cls_dn_3[0.0816] det_loss_box_dn_3[0.2384] det_loss_cls_dn_4[0.0808] det_loss_box_dn_4[0.2363] det_loss_cls_dn_5[0.0800] det_loss_box_dn_5[0.2341] loss_depth[0.0395] total_loss[6.9385]
Rank[0/8] 06/20/2025 10:25:06 INFO stats.py:314 | Epoch[154] Step[39] GlobalStep[60099] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 11:16:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:25:16 INFO loss_tracker.py:84 | Epoch[154/239] Step[49] GlobalStep[60109/NA]: det_loss_cls_0[0.4124] det_loss_box_0[0.5721] det_loss_cls_1[0.4055] det_loss_box_1[0.4380] det_loss_cls_2[0.3568] det_loss_box_2[0.3869] det_loss_cls_3[0.3241] det_loss_box_3[0.3769] det_loss_cls_4[0.3072] det_loss_box_4[0.3601] det_loss_cls_5[0.2938] det_loss_box_5[0.3599] det_loss_cls_dn_0[0.0945] det_loss_box_dn_0[0.2914] det_loss_cls_dn_1[0.0908] det_loss_box_dn_1[0.2461] det_loss_cls_dn_2[0.0902] det_loss_box_dn_2[0.2296] det_loss_cls_dn_3[0.0892] det_loss_box_dn_3[0.2264] det_loss_cls_dn_4[0.0881] det_loss_box_dn_4[0.2235] det_loss_cls_dn_5[0.0877] det_loss_box_dn_5[0.2224] loss_depth[0.0394] total_loss[6.6129]
Rank[0/8] 06/20/2025 10:25:32 INFO stats.py:314 | Epoch[154] Step[64] GlobalStep[60124] Training Speed: 6.16 samples/sec across all devices.	Average Step Time: 1.30 sec.	Estimated Remaining Time: 11:15:30.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:25:42 INFO loss_tracker.py:84 | Epoch[154/239] Step[74] GlobalStep[60134/NA]: det_loss_cls_0[0.6251] det_loss_box_0[0.8495] det_loss_cls_1[0.5933] det_loss_box_1[0.6578] det_loss_cls_2[0.5382] det_loss_box_2[0.5765] det_loss_cls_3[0.4879] det_loss_box_3[0.5486] det_loss_cls_4[0.4663] det_loss_box_4[0.5358] det_loss_cls_5[0.4465] det_loss_box_5[0.5380] det_loss_cls_dn_0[0.0975] det_loss_box_dn_0[0.2927] det_loss_cls_dn_1[0.0932] det_loss_box_dn_1[0.2462] det_loss_cls_dn_2[0.0920] det_loss_box_dn_2[0.2255] det_loss_cls_dn_3[0.0911] det_loss_box_dn_3[0.2215] det_loss_cls_dn_4[0.0902] det_loss_box_dn_4[0.2199] det_loss_cls_dn_5[0.0894] det_loss_box_dn_5[0.2184] loss_depth[0.0397] total_loss[8.8809]
Rank[0/8] 06/20/2025 10:25:58 INFO stats.py:314 | Epoch[154] Step[89] GlobalStep[60149] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 11:14:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:26:09 INFO loss_tracker.py:84 | Epoch[154/239] Step[99] GlobalStep[60159/NA]: det_loss_cls_0[0.4521] det_loss_box_0[0.5194] det_loss_cls_1[0.4245] det_loss_box_1[0.4025] det_loss_cls_2[0.3813] det_loss_box_2[0.3682] det_loss_cls_3[0.3557] det_loss_box_3[0.3446] det_loss_cls_4[0.3278] det_loss_box_4[0.3396] det_loss_cls_5[0.3142] det_loss_box_5[0.3415] det_loss_cls_dn_0[0.0943] det_loss_box_dn_0[0.2765] det_loss_cls_dn_1[0.0914] det_loss_box_dn_1[0.2312] det_loss_cls_dn_2[0.0910] det_loss_box_dn_2[0.2148] det_loss_cls_dn_3[0.0902] det_loss_box_dn_3[0.2110] det_loss_cls_dn_4[0.0890] det_loss_box_dn_4[0.2082] det_loss_cls_dn_5[0.0884] det_loss_box_dn_5[0.2068] loss_depth[0.0380] total_loss[6.5022]
Rank[0/8] 06/20/2025 10:26:24 INFO stats.py:314 | Epoch[154] Step[114] GlobalStep[60174] Training Speed: 8.62 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 11:14:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:26:34 INFO loss_tracker.py:84 | Epoch[154/239] Step[124] GlobalStep[60184/NA]: det_loss_cls_0[0.4945] det_loss_box_0[0.6305] det_loss_cls_1[0.4648] det_loss_box_1[0.4785] det_loss_cls_2[0.4405] det_loss_box_2[0.4315] det_loss_cls_3[0.4086] det_loss_box_3[0.4268] det_loss_cls_4[0.3957] det_loss_box_4[0.4138] det_loss_cls_5[0.3788] det_loss_box_5[0.4157] det_loss_cls_dn_0[0.1004] det_loss_box_dn_0[0.2817] det_loss_cls_dn_1[0.0963] det_loss_box_dn_1[0.2401] det_loss_cls_dn_2[0.0955] det_loss_box_dn_2[0.2260] det_loss_cls_dn_3[0.0942] det_loss_box_dn_3[0.2228] det_loss_cls_dn_4[0.0932] det_loss_box_dn_4[0.2204] det_loss_cls_dn_5[0.0923] det_loss_box_dn_5[0.2180] loss_depth[0.0356] total_loss[7.3961]
Rank[0/8] 06/20/2025 10:26:50 INFO stats.py:314 | Epoch[154] Step[139] GlobalStep[60199] Training Speed: 8.12 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 11:13:52.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:27:00 INFO loss_tracker.py:84 | Epoch[154/239] Step[149] GlobalStep[60209/NA]: det_loss_cls_0[0.6415] det_loss_box_0[0.7943] det_loss_cls_1[0.6359] det_loss_box_1[0.6355] det_loss_cls_2[0.5992] det_loss_box_2[0.5697] det_loss_cls_3[0.5642] det_loss_box_3[0.5461] det_loss_cls_4[0.5333] det_loss_box_4[0.5409] det_loss_cls_5[0.5214] det_loss_box_5[0.5340] det_loss_cls_dn_0[0.1067] det_loss_box_dn_0[0.2824] det_loss_cls_dn_1[0.1033] det_loss_box_dn_1[0.2408] det_loss_cls_dn_2[0.1028] det_loss_box_dn_2[0.2264] det_loss_cls_dn_3[0.1013] det_loss_box_dn_3[0.2227] det_loss_cls_dn_4[0.1002] det_loss_box_dn_4[0.2210] det_loss_cls_dn_5[0.0994] det_loss_box_dn_5[0.2183] loss_depth[0.0377] total_loss[9.1790]
Rank[0/8] 06/20/2025 10:27:16 INFO stats.py:314 | Epoch[154] Step[164] GlobalStep[60224] Training Speed: 6.51 samples/sec across all devices.	Average Step Time: 1.23 sec.	Estimated Remaining Time: 11:13:19.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:27:26 INFO loss_tracker.py:84 | Epoch[154/239] Step[174] GlobalStep[60234/NA]: det_loss_cls_0[0.4993] det_loss_box_0[0.6527] det_loss_cls_1[0.4967] det_loss_box_1[0.5194] det_loss_cls_2[0.4707] det_loss_box_2[0.4751] det_loss_cls_3[0.4343] det_loss_box_3[0.4533] det_loss_cls_4[0.4053] det_loss_box_4[0.4487] det_loss_cls_5[0.3889] det_loss_box_5[0.4490] det_loss_cls_dn_0[0.0968] det_loss_box_dn_0[0.2932] det_loss_cls_dn_1[0.0927] det_loss_box_dn_1[0.2540] det_loss_cls_dn_2[0.0933] det_loss_box_dn_2[0.2377] det_loss_cls_dn_3[0.0927] det_loss_box_dn_3[0.2342] det_loss_cls_dn_4[0.0910] det_loss_box_dn_4[0.2313] det_loss_cls_dn_5[0.0901] det_loss_box_dn_5[0.2291] loss_depth[0.0331] total_loss[7.7623]
Rank[0/8] 06/20/2025 10:27:42 INFO stats.py:314 | Epoch[154] Step[189] GlobalStep[60249] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 11:12:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:27:52 INFO loss_tracker.py:84 | Epoch[154/239] Step[199] GlobalStep[60259/NA]: det_loss_cls_0[0.5018] det_loss_box_0[0.6850] det_loss_cls_1[0.4699] det_loss_box_1[0.5080] det_loss_cls_2[0.4368] det_loss_box_2[0.4620] det_loss_cls_3[0.4054] det_loss_box_3[0.4422] det_loss_cls_4[0.3713] det_loss_box_4[0.4365] det_loss_cls_5[0.3575] det_loss_box_5[0.4366] det_loss_cls_dn_0[0.0973] det_loss_box_dn_0[0.2727] det_loss_cls_dn_1[0.0935] det_loss_box_dn_1[0.2309] det_loss_cls_dn_2[0.0930] det_loss_box_dn_2[0.2140] det_loss_cls_dn_3[0.0913] det_loss_box_dn_3[0.2116] det_loss_cls_dn_4[0.0900] det_loss_box_dn_4[0.2106] det_loss_cls_dn_5[0.0887] det_loss_box_dn_5[0.2089] loss_depth[0.0368] total_loss[7.4522]
Rank[0/8] 06/20/2025 10:28:07 INFO stats.py:314 | Epoch[154] Step[214] GlobalStep[60274] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 11:12:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:28:17 INFO loss_tracker.py:84 | Epoch[154/239] Step[224] GlobalStep[60284/NA]: det_loss_cls_0[0.5782] det_loss_box_0[0.7999] det_loss_cls_1[0.5498] det_loss_box_1[0.6047] det_loss_cls_2[0.5094] det_loss_box_2[0.5414] det_loss_cls_3[0.4800] det_loss_box_3[0.5167] det_loss_cls_4[0.4463] det_loss_box_4[0.5132] det_loss_cls_5[0.4350] det_loss_box_5[0.5118] det_loss_cls_dn_0[0.1013] det_loss_box_dn_0[0.3278] det_loss_cls_dn_1[0.0966] det_loss_box_dn_1[0.2833] det_loss_cls_dn_2[0.0957] det_loss_box_dn_2[0.2666] det_loss_cls_dn_3[0.0934] det_loss_box_dn_3[0.2613] det_loss_cls_dn_4[0.0925] det_loss_box_dn_4[0.2590] det_loss_cls_dn_5[0.0912] det_loss_box_dn_5[0.2578] loss_depth[0.0412] total_loss[8.7542]
Rank[0/8] 06/20/2025 10:28:32 INFO stats.py:314 | Epoch[154] Step[239] GlobalStep[60299] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 11:11:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:28:43 INFO loss_tracker.py:84 | Epoch[154/239] Step[249] GlobalStep[60309/NA]: det_loss_cls_0[0.4818] det_loss_box_0[0.6193] det_loss_cls_1[0.4594] det_loss_box_1[0.4817] det_loss_cls_2[0.4186] det_loss_box_2[0.4433] det_loss_cls_3[0.3914] det_loss_box_3[0.4332] det_loss_cls_4[0.3680] det_loss_box_4[0.4186] det_loss_cls_5[0.3546] det_loss_box_5[0.4198] det_loss_cls_dn_0[0.1051] det_loss_box_dn_0[0.2844] det_loss_cls_dn_1[0.1006] det_loss_box_dn_1[0.2384] det_loss_cls_dn_2[0.0998] det_loss_box_dn_2[0.2241] det_loss_cls_dn_3[0.0986] det_loss_box_dn_3[0.2186] det_loss_cls_dn_4[0.0975] det_loss_box_dn_4[0.2177] det_loss_cls_dn_5[0.0968] det_loss_box_dn_5[0.2165] loss_depth[0.0376] total_loss[7.3251]
Rank[0/8] 06/20/2025 10:28:59 INFO stats.py:314 | Epoch[154] Step[264] GlobalStep[60324] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 11:11:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:29:09 INFO loss_tracker.py:84 | Epoch[154/239] Step[274] GlobalStep[60334/NA]: det_loss_cls_0[0.5448] det_loss_box_0[0.7167] det_loss_cls_1[0.5276] det_loss_box_1[0.5267] det_loss_cls_2[0.4702] det_loss_box_2[0.4801] det_loss_cls_3[0.4377] det_loss_box_3[0.4582] det_loss_cls_4[0.4066] det_loss_box_4[0.4489] det_loss_cls_5[0.3920] det_loss_box_5[0.4466] det_loss_cls_dn_0[0.0957] det_loss_box_dn_0[0.2849] det_loss_cls_dn_1[0.0912] det_loss_box_dn_1[0.2400] det_loss_cls_dn_2[0.0908] det_loss_box_dn_2[0.2213] det_loss_cls_dn_3[0.0889] det_loss_box_dn_3[0.2166] det_loss_cls_dn_4[0.0878] det_loss_box_dn_4[0.2131] det_loss_cls_dn_5[0.0867] det_loss_box_dn_5[0.2125] loss_depth[0.0358] total_loss[7.8211]
Rank[0/8] 06/20/2025 10:29:24 INFO stats.py:314 | Epoch[154] Step[289] GlobalStep[60349] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 11:10:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:29:35 INFO loss_tracker.py:84 | Epoch[154/239] Step[299] GlobalStep[60359/NA]: det_loss_cls_0[0.4668] det_loss_box_0[0.6372] det_loss_cls_1[0.4464] det_loss_box_1[0.5035] det_loss_cls_2[0.4044] det_loss_box_2[0.4522] det_loss_cls_3[0.3826] det_loss_box_3[0.4381] det_loss_cls_4[0.3599] det_loss_box_4[0.4261] det_loss_cls_5[0.3469] det_loss_box_5[0.4238] det_loss_cls_dn_0[0.1009] det_loss_box_dn_0[0.3175] det_loss_cls_dn_1[0.0967] det_loss_box_dn_1[0.2685] det_loss_cls_dn_2[0.0962] det_loss_box_dn_2[0.2512] det_loss_cls_dn_3[0.0951] det_loss_box_dn_3[0.2475] det_loss_cls_dn_4[0.0944] det_loss_box_dn_4[0.2450] det_loss_cls_dn_5[0.0934] det_loss_box_dn_5[0.2436] loss_depth[0.0394] total_loss[7.4773]
Rank[0/8] 06/20/2025 10:29:50 INFO stats.py:314 | Epoch[154] Step[314] GlobalStep[60374] Training Speed: 7.90 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 11:10:02.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:30:01 INFO loss_tracker.py:84 | Epoch[154/239] Step[324] GlobalStep[60384/NA]: det_loss_cls_0[0.3790] det_loss_box_0[0.4488] det_loss_cls_1[0.3586] det_loss_box_1[0.3286] det_loss_cls_2[0.3152] det_loss_box_2[0.2870] det_loss_cls_3[0.2891] det_loss_box_3[0.2755] det_loss_cls_4[0.2579] det_loss_box_4[0.2698] det_loss_cls_5[0.2438] det_loss_box_5[0.2730] det_loss_cls_dn_0[0.1015] det_loss_box_dn_0[0.2779] det_loss_cls_dn_1[0.0981] det_loss_box_dn_1[0.2200] det_loss_cls_dn_2[0.0973] det_loss_box_dn_2[0.1953] det_loss_cls_dn_3[0.0965] det_loss_box_dn_3[0.1900] det_loss_cls_dn_4[0.0952] det_loss_box_dn_4[0.1875] det_loss_cls_dn_5[0.0943] det_loss_box_dn_5[0.1871] loss_depth[0.0389] total_loss[5.6059]
Rank[0/8] 06/20/2025 10:30:16 INFO stats.py:314 | Epoch[154] Step[339] GlobalStep[60399] Training Speed: 8.64 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 11:09:30.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:30:27 INFO loss_tracker.py:84 | Epoch[154/239] Step[349] GlobalStep[60409/NA]: det_loss_cls_0[0.5952] det_loss_box_0[0.7892] det_loss_cls_1[0.5851] det_loss_box_1[0.6416] det_loss_cls_2[0.5219] det_loss_box_2[0.5779] det_loss_cls_3[0.4993] det_loss_box_3[0.5485] det_loss_cls_4[0.4679] det_loss_box_4[0.5441] det_loss_cls_5[0.4471] det_loss_box_5[0.5444] det_loss_cls_dn_0[0.0943] det_loss_box_dn_0[0.2363] det_loss_cls_dn_1[0.0911] det_loss_box_dn_1[0.2024] det_loss_cls_dn_2[0.0905] det_loss_box_dn_2[0.1884] det_loss_cls_dn_3[0.0895] det_loss_box_dn_3[0.1852] det_loss_cls_dn_4[0.0887] det_loss_box_dn_4[0.1834] det_loss_cls_dn_5[0.0871] det_loss_box_dn_5[0.1826] loss_depth[0.0341] total_loss[8.5157]
Rank[0/8] 06/20/2025 10:30:42 INFO stats.py:314 | Epoch[154] Step[364] GlobalStep[60424] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 11:08:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:30:52 INFO loss_tracker.py:84 | Epoch[154/239] Step[374] GlobalStep[60434/NA]: det_loss_cls_0[0.3657] det_loss_box_0[0.5056] det_loss_cls_1[0.3558] det_loss_box_1[0.4050] det_loss_cls_2[0.3200] det_loss_box_2[0.3569] det_loss_cls_3[0.2881] det_loss_box_3[0.3442] det_loss_cls_4[0.2656] det_loss_box_4[0.3411] det_loss_cls_5[0.2592] det_loss_box_5[0.3396] det_loss_cls_dn_0[0.0815] det_loss_box_dn_0[0.3151] det_loss_cls_dn_1[0.0771] det_loss_box_dn_1[0.2578] det_loss_cls_dn_2[0.0761] det_loss_box_dn_2[0.2356] det_loss_cls_dn_3[0.0751] det_loss_box_dn_3[0.2283] det_loss_cls_dn_4[0.0735] det_loss_box_dn_4[0.2268] det_loss_cls_dn_5[0.0729] det_loss_box_dn_5[0.2277] loss_depth[0.0374] total_loss[6.1314]
Rank[0/8] 06/20/2025 10:31:08 INFO stats.py:314 | Epoch[154] Step[389] GlobalStep[60449] Training Speed: 8.66 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 11:08:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:31:08 INFO stats.py:394 | Epoch[154] completed. Training Speed: 7.69 samples/sec across all devices.	Epoch Time: 405.67 sec.	Average Epoch Time: 405.67 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 11:08:25.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:31:35 INFO stats.py:314 | Epoch[155] Step[24] GlobalStep[60474] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 11:07:53.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:31:35 INFO loss_tracker.py:84 | Epoch[155/239] Step[24] GlobalStep[60474/NA]: det_loss_cls_0[0.4739] det_loss_box_0[0.6347] det_loss_cls_1[0.4625] det_loss_box_1[0.5010] det_loss_cls_2[0.4264] det_loss_box_2[0.4597] det_loss_cls_3[0.3967] det_loss_box_3[0.4399] det_loss_cls_4[0.3718] det_loss_box_4[0.4319] det_loss_cls_5[0.3624] det_loss_box_5[0.4316] det_loss_cls_dn_0[0.0976] det_loss_box_dn_0[0.3127] det_loss_cls_dn_1[0.0936] det_loss_box_dn_1[0.2666] det_loss_cls_dn_2[0.0930] det_loss_box_dn_2[0.2477] det_loss_cls_dn_3[0.0920] det_loss_box_dn_3[0.2434] det_loss_cls_dn_4[0.0907] det_loss_box_dn_4[0.2405] det_loss_cls_dn_5[0.0900] det_loss_box_dn_5[0.2377] loss_depth[0.0356] total_loss[7.5336]
Rank[0/8] 06/20/2025 10:32:00 INFO stats.py:314 | Epoch[155] Step[49] GlobalStep[60499] Training Speed: 7.57 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 11:07:20.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:32:00 INFO loss_tracker.py:84 | Epoch[155/239] Step[49] GlobalStep[60499/NA]: det_loss_cls_0[0.4207] det_loss_box_0[0.5502] det_loss_cls_1[0.4000] det_loss_box_1[0.4192] det_loss_cls_2[0.3527] det_loss_box_2[0.3804] det_loss_cls_3[0.3118] det_loss_box_3[0.3804] det_loss_cls_4[0.2948] det_loss_box_4[0.3705] det_loss_cls_5[0.2825] det_loss_box_5[0.3618] det_loss_cls_dn_0[0.0861] det_loss_box_dn_0[0.3139] det_loss_cls_dn_1[0.0831] det_loss_box_dn_1[0.2495] det_loss_cls_dn_2[0.0829] det_loss_box_dn_2[0.2322] det_loss_cls_dn_3[0.0812] det_loss_box_dn_3[0.2270] det_loss_cls_dn_4[0.0799] det_loss_box_dn_4[0.2271] det_loss_cls_dn_5[0.0794] det_loss_box_dn_5[0.2249] loss_depth[0.0397] total_loss[6.5319]
Rank[0/8] 06/20/2025 10:32:26 INFO stats.py:314 | Epoch[155] Step[74] GlobalStep[60524] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 11:06:47.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:32:26 INFO loss_tracker.py:84 | Epoch[155/239] Step[74] GlobalStep[60524/NA]: det_loss_cls_0[0.4871] det_loss_box_0[0.6196] det_loss_cls_1[0.4816] det_loss_box_1[0.4786] det_loss_cls_2[0.4265] det_loss_box_2[0.4345] det_loss_cls_3[0.3894] det_loss_box_3[0.4168] det_loss_cls_4[0.3683] det_loss_box_4[0.4008] det_loss_cls_5[0.3582] det_loss_box_5[0.3991] det_loss_cls_dn_0[0.0942] det_loss_box_dn_0[0.2676] det_loss_cls_dn_1[0.0891] det_loss_box_dn_1[0.2282] det_loss_cls_dn_2[0.0890] det_loss_box_dn_2[0.2124] det_loss_cls_dn_3[0.0878] det_loss_box_dn_3[0.2067] det_loss_cls_dn_4[0.0864] det_loss_box_dn_4[0.2056] det_loss_cls_dn_5[0.0853] det_loss_box_dn_5[0.2048] loss_depth[0.0344] total_loss[7.1520]
Rank[0/8] 06/20/2025 10:32:51 INFO stats.py:314 | Epoch[155] Step[99] GlobalStep[60549] Training Speed: 8.69 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 11:06:14.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:32:51 INFO loss_tracker.py:84 | Epoch[155/239] Step[99] GlobalStep[60549/NA]: det_loss_cls_0[0.5549] det_loss_box_0[0.7775] det_loss_cls_1[0.5479] det_loss_box_1[0.6032] det_loss_cls_2[0.5104] det_loss_box_2[0.5377] det_loss_cls_3[0.4642] det_loss_box_3[0.5279] det_loss_cls_4[0.4350] det_loss_box_4[0.5140] det_loss_cls_5[0.4183] det_loss_box_5[0.5146] det_loss_cls_dn_0[0.0971] det_loss_box_dn_0[0.3245] det_loss_cls_dn_1[0.0937] det_loss_box_dn_1[0.2691] det_loss_cls_dn_2[0.0925] det_loss_box_dn_2[0.2541] det_loss_cls_dn_3[0.0913] det_loss_box_dn_3[0.2480] det_loss_cls_dn_4[0.0903] det_loss_box_dn_4[0.2462] det_loss_cls_dn_5[0.0895] det_loss_box_dn_5[0.2447] loss_depth[0.0404] total_loss[8.5869]
Rank[0/8] 06/20/2025 10:33:17 INFO stats.py:314 | Epoch[155] Step[124] GlobalStep[60574] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 11:05:42.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:33:17 INFO loss_tracker.py:84 | Epoch[155/239] Step[124] GlobalStep[60574/NA]: det_loss_cls_0[0.4031] det_loss_box_0[0.5032] det_loss_cls_1[0.4037] det_loss_box_1[0.3766] det_loss_cls_2[0.3638] det_loss_box_2[0.3296] det_loss_cls_3[0.3264] det_loss_box_3[0.3191] det_loss_cls_4[0.3036] det_loss_box_4[0.3149] det_loss_cls_5[0.2904] det_loss_box_5[0.3140] det_loss_cls_dn_0[0.0873] det_loss_box_dn_0[0.2824] det_loss_cls_dn_1[0.0838] det_loss_box_dn_1[0.2391] det_loss_cls_dn_2[0.0830] det_loss_box_dn_2[0.2254] det_loss_cls_dn_3[0.0816] det_loss_box_dn_3[0.2216] det_loss_cls_dn_4[0.0807] det_loss_box_dn_4[0.2197] det_loss_cls_dn_5[0.0799] det_loss_box_dn_5[0.2168] loss_depth[0.0388] total_loss[6.1886]
Rank[0/8] 06/20/2025 10:33:43 INFO stats.py:314 | Epoch[155] Step[149] GlobalStep[60599] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 11:05:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:33:43 INFO loss_tracker.py:84 | Epoch[155/239] Step[149] GlobalStep[60599/NA]: det_loss_cls_0[0.4664] det_loss_box_0[0.6338] det_loss_cls_1[0.4577] det_loss_box_1[0.4740] det_loss_cls_2[0.4026] det_loss_box_2[0.4239] det_loss_cls_3[0.3723] det_loss_box_3[0.4106] det_loss_cls_4[0.3462] det_loss_box_4[0.4023] det_loss_cls_5[0.3285] det_loss_box_5[0.3997] det_loss_cls_dn_0[0.0973] det_loss_box_dn_0[0.2879] det_loss_cls_dn_1[0.0941] det_loss_box_dn_1[0.2443] det_loss_cls_dn_2[0.0930] det_loss_box_dn_2[0.2249] det_loss_cls_dn_3[0.0922] det_loss_box_dn_3[0.2216] det_loss_cls_dn_4[0.0911] det_loss_box_dn_4[0.2200] det_loss_cls_dn_5[0.0907] det_loss_box_dn_5[0.2166] loss_depth[0.0397] total_loss[7.1314]
Rank[0/8] 06/20/2025 10:34:09 INFO stats.py:314 | Epoch[155] Step[174] GlobalStep[60624] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 11:04:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:34:09 INFO loss_tracker.py:84 | Epoch[155/239] Step[174] GlobalStep[60624/NA]: det_loss_cls_0[0.4782] det_loss_box_0[0.5928] det_loss_cls_1[0.4630] det_loss_box_1[0.4444] det_loss_cls_2[0.4083] det_loss_box_2[0.4074] det_loss_cls_3[0.3754] det_loss_box_3[0.4032] det_loss_cls_4[0.3627] det_loss_box_4[0.3836] det_loss_cls_5[0.3417] det_loss_box_5[0.3873] det_loss_cls_dn_0[0.0979] det_loss_box_dn_0[0.2942] det_loss_cls_dn_1[0.0955] det_loss_box_dn_1[0.2393] det_loss_cls_dn_2[0.0951] det_loss_box_dn_2[0.2196] det_loss_cls_dn_3[0.0934] det_loss_box_dn_3[0.2150] det_loss_cls_dn_4[0.0923] det_loss_box_dn_4[0.2118] det_loss_cls_dn_5[0.0915] det_loss_box_dn_5[0.2100] loss_depth[0.0374] total_loss[7.0409]
Rank[0/8] 06/20/2025 10:34:35 INFO stats.py:314 | Epoch[155] Step[199] GlobalStep[60649] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 11:04:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:34:36 INFO loss_tracker.py:84 | Epoch[155/239] Step[199] GlobalStep[60649/NA]: det_loss_cls_0[0.5398] det_loss_box_0[0.7046] det_loss_cls_1[0.5376] det_loss_box_1[0.5708] det_loss_cls_2[0.4968] det_loss_box_2[0.5238] det_loss_cls_3[0.4652] det_loss_box_3[0.5125] det_loss_cls_4[0.4437] det_loss_box_4[0.4942] det_loss_cls_5[0.4341] det_loss_box_5[0.4972] det_loss_cls_dn_0[0.0913] det_loss_box_dn_0[0.3192] det_loss_cls_dn_1[0.0887] det_loss_box_dn_1[0.2744] det_loss_cls_dn_2[0.0876] det_loss_box_dn_2[0.2580] det_loss_cls_dn_3[0.0872] det_loss_box_dn_3[0.2493] det_loss_cls_dn_4[0.0861] det_loss_box_dn_4[0.2464] det_loss_cls_dn_5[0.0851] det_loss_box_dn_5[0.2419] loss_depth[0.0351] total_loss[8.3706]
Rank[0/8] 06/20/2025 10:35:01 INFO stats.py:314 | Epoch[155] Step[224] GlobalStep[60674] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 11:03:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:35:01 INFO loss_tracker.py:84 | Epoch[155/239] Step[224] GlobalStep[60674/NA]: det_loss_cls_0[0.4516] det_loss_box_0[0.5719] det_loss_cls_1[0.4414] det_loss_box_1[0.4349] det_loss_cls_2[0.3932] det_loss_box_2[0.3881] det_loss_cls_3[0.3693] det_loss_box_3[0.3746] det_loss_cls_4[0.3507] det_loss_box_4[0.3593] det_loss_cls_5[0.3414] det_loss_box_5[0.3604] det_loss_cls_dn_0[0.1058] det_loss_box_dn_0[0.3048] det_loss_cls_dn_1[0.1018] det_loss_box_dn_1[0.2586] det_loss_cls_dn_2[0.1007] det_loss_box_dn_2[0.2421] det_loss_cls_dn_3[0.1000] det_loss_box_dn_3[0.2392] det_loss_cls_dn_4[0.0992] det_loss_box_dn_4[0.2382] det_loss_cls_dn_5[0.0987] det_loss_box_dn_5[0.2370] loss_depth[0.0401] total_loss[7.0031]
Rank[0/8] 06/20/2025 10:35:27 INFO stats.py:314 | Epoch[155] Step[249] GlobalStep[60699] Training Speed: 7.06 samples/sec across all devices.	Average Step Time: 1.13 sec.	Estimated Remaining Time: 11:02:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:35:27 INFO loss_tracker.py:84 | Epoch[155/239] Step[249] GlobalStep[60699/NA]: det_loss_cls_0[0.4541] det_loss_box_0[0.5333] det_loss_cls_1[0.4545] det_loss_box_1[0.3938] det_loss_cls_2[0.3973] det_loss_box_2[0.3604] det_loss_cls_3[0.3571] det_loss_box_3[0.3523] det_loss_cls_4[0.3344] det_loss_box_4[0.3429] det_loss_cls_5[0.3166] det_loss_box_5[0.3436] det_loss_cls_dn_0[0.0916] det_loss_box_dn_0[0.2418] det_loss_cls_dn_1[0.0877] det_loss_box_dn_1[0.2014] det_loss_cls_dn_2[0.0863] det_loss_box_dn_2[0.1858] det_loss_cls_dn_3[0.0850] det_loss_box_dn_3[0.1821] det_loss_cls_dn_4[0.0838] det_loss_box_dn_4[0.1813] det_loss_cls_dn_5[0.0825] det_loss_box_dn_5[0.1797] loss_depth[0.0352] total_loss[6.3646]
Rank[0/8] 06/20/2025 10:35:53 INFO stats.py:314 | Epoch[155] Step[274] GlobalStep[60724] Training Speed: 8.59 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 11:02:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:35:53 INFO loss_tracker.py:84 | Epoch[155/239] Step[274] GlobalStep[60724/NA]: det_loss_cls_0[0.3992] det_loss_box_0[0.5367] det_loss_cls_1[0.3792] det_loss_box_1[0.4274] det_loss_cls_2[0.3488] det_loss_box_2[0.3733] det_loss_cls_3[0.3284] det_loss_box_3[0.3618] det_loss_cls_4[0.3045] det_loss_box_4[0.3535] det_loss_cls_5[0.2941] det_loss_box_5[0.3474] det_loss_cls_dn_0[0.0930] det_loss_box_dn_0[0.3323] det_loss_cls_dn_1[0.0900] det_loss_box_dn_1[0.2812] det_loss_cls_dn_2[0.0893] det_loss_box_dn_2[0.2611] det_loss_cls_dn_3[0.0884] det_loss_box_dn_3[0.2571] det_loss_cls_dn_4[0.0876] det_loss_box_dn_4[0.2563] det_loss_cls_dn_5[0.0869] det_loss_box_dn_5[0.2541] loss_depth[0.0375] total_loss[6.6688]
Rank[0/8] 06/20/2025 10:36:19 INFO stats.py:314 | Epoch[155] Step[299] GlobalStep[60749] Training Speed: 8.56 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 11:01:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:36:19 INFO loss_tracker.py:84 | Epoch[155/239] Step[299] GlobalStep[60749/NA]: det_loss_cls_0[0.5199] det_loss_box_0[0.6437] det_loss_cls_1[0.4975] det_loss_box_1[0.5068] det_loss_cls_2[0.4582] det_loss_box_2[0.4600] det_loss_cls_3[0.4216] det_loss_box_3[0.4293] det_loss_cls_4[0.3917] det_loss_box_4[0.4218] det_loss_cls_5[0.3793] det_loss_box_5[0.4212] det_loss_cls_dn_0[0.0990] det_loss_box_dn_0[0.2546] det_loss_cls_dn_1[0.0953] det_loss_box_dn_1[0.2135] det_loss_cls_dn_2[0.0944] det_loss_box_dn_2[0.1940] det_loss_cls_dn_3[0.0935] det_loss_box_dn_3[0.1908] det_loss_cls_dn_4[0.0930] det_loss_box_dn_4[0.1901] det_loss_cls_dn_5[0.0919] det_loss_box_dn_5[0.1891] loss_depth[0.0370] total_loss[7.3872]
Rank[0/8] 06/20/2025 10:36:45 INFO stats.py:314 | Epoch[155] Step[324] GlobalStep[60774] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 11:01:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:36:45 INFO loss_tracker.py:84 | Epoch[155/239] Step[324] GlobalStep[60774/NA]: det_loss_cls_0[0.3884] det_loss_box_0[0.4710] det_loss_cls_1[0.3893] det_loss_box_1[0.3654] det_loss_cls_2[0.3499] det_loss_box_2[0.3433] det_loss_cls_3[0.3229] det_loss_box_3[0.3405] det_loss_cls_4[0.3008] det_loss_box_4[0.3403] det_loss_cls_5[0.2922] det_loss_box_5[0.3336] det_loss_cls_dn_0[0.0851] det_loss_box_dn_0[0.2466] det_loss_cls_dn_1[0.0821] det_loss_box_dn_1[0.2124] det_loss_cls_dn_2[0.0816] det_loss_box_dn_2[0.1993] det_loss_cls_dn_3[0.0798] det_loss_box_dn_3[0.1962] det_loss_cls_dn_4[0.0794] det_loss_box_dn_4[0.1948] det_loss_cls_dn_5[0.0783] det_loss_box_dn_5[0.1923] loss_depth[0.0319] total_loss[5.9973]
Rank[0/8] 06/20/2025 10:37:11 INFO stats.py:314 | Epoch[155] Step[349] GlobalStep[60799] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 11:00:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:37:11 INFO loss_tracker.py:84 | Epoch[155/239] Step[349] GlobalStep[60799/NA]: det_loss_cls_0[0.5500] det_loss_box_0[0.7171] det_loss_cls_1[0.5282] det_loss_box_1[0.5460] det_loss_cls_2[0.4709] det_loss_box_2[0.5024] det_loss_cls_3[0.4424] det_loss_box_3[0.4887] det_loss_cls_4[0.4131] det_loss_box_4[0.4749] det_loss_cls_5[0.3899] det_loss_box_5[0.4755] det_loss_cls_dn_0[0.0837] det_loss_box_dn_0[0.2938] det_loss_cls_dn_1[0.0792] det_loss_box_dn_1[0.2562] det_loss_cls_dn_2[0.0780] det_loss_box_dn_2[0.2368] det_loss_cls_dn_3[0.0767] det_loss_box_dn_3[0.2313] det_loss_cls_dn_4[0.0761] det_loss_box_dn_4[0.2289] det_loss_cls_dn_5[0.0755] det_loss_box_dn_5[0.2268] loss_depth[0.0390] total_loss[7.9812]
Rank[0/8] 06/20/2025 10:37:37 INFO stats.py:314 | Epoch[155] Step[374] GlobalStep[60824] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 11:00:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:37:37 INFO loss_tracker.py:84 | Epoch[155/239] Step[374] GlobalStep[60824/NA]: det_loss_cls_0[0.5457] det_loss_box_0[0.7244] det_loss_cls_1[0.5371] det_loss_box_1[0.5698] det_loss_cls_2[0.4798] det_loss_box_2[0.5143] det_loss_cls_3[0.4521] det_loss_box_3[0.4961] det_loss_cls_4[0.4331] det_loss_box_4[0.4817] det_loss_cls_5[0.4215] det_loss_box_5[0.4795] det_loss_cls_dn_0[0.1048] det_loss_box_dn_0[0.2981] det_loss_cls_dn_1[0.1011] det_loss_box_dn_1[0.2608] det_loss_cls_dn_2[0.1006] det_loss_box_dn_2[0.2422] det_loss_cls_dn_3[0.0994] det_loss_box_dn_3[0.2385] det_loss_cls_dn_4[0.0977] det_loss_box_dn_4[0.2368] det_loss_cls_dn_5[0.0969] det_loss_box_dn_5[0.2362] loss_depth[0.0342] total_loss[8.2824]
Rank[0/8] 06/20/2025 10:37:53 INFO stats.py:394 | Epoch[155] completed. Training Speed: 7.70 samples/sec across all devices.	Epoch Time: 405.03 sec.	Average Epoch Time: 405.03 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 10:59:57.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:38:07 INFO stats.py:314 | Epoch[156] Step[9] GlobalStep[60849] Training Speed: 14.09 samples/sec across all devices.	Average Step Time: 0.57 sec.	Estimated Remaining Time: 10:59:46.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:38:23 INFO loss_tracker.py:84 | Epoch[156/239] Step[24] GlobalStep[60864/NA]: det_loss_cls_0[0.5041] det_loss_box_0[0.6366] det_loss_cls_1[0.4975] det_loss_box_1[0.4830] det_loss_cls_2[0.4423] det_loss_box_2[0.4427] det_loss_cls_3[0.4097] det_loss_box_3[0.4232] det_loss_cls_4[0.3868] det_loss_box_4[0.4148] det_loss_cls_5[0.3736] det_loss_box_5[0.4179] det_loss_cls_dn_0[0.0958] det_loss_box_dn_0[0.2631] det_loss_cls_dn_1[0.0920] det_loss_box_dn_1[0.2219] det_loss_cls_dn_2[0.0911] det_loss_box_dn_2[0.2091] det_loss_cls_dn_3[0.0904] det_loss_box_dn_3[0.2064] det_loss_cls_dn_4[0.0895] det_loss_box_dn_4[0.2049] det_loss_cls_dn_5[0.0887] det_loss_box_dn_5[0.2027] loss_depth[0.0371] total_loss[7.3250]
Rank[0/8] 06/20/2025 10:38:33 INFO stats.py:314 | Epoch[156] Step[34] GlobalStep[60874] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 10:59:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:38:49 INFO loss_tracker.py:84 | Epoch[156/239] Step[49] GlobalStep[60889/NA]: det_loss_cls_0[0.4707] det_loss_box_0[0.5952] det_loss_cls_1[0.4574] det_loss_box_1[0.4780] det_loss_cls_2[0.4082] det_loss_box_2[0.4338] det_loss_cls_3[0.3916] det_loss_box_3[0.4126] det_loss_cls_4[0.3664] det_loss_box_4[0.4097] det_loss_cls_5[0.3569] det_loss_box_5[0.4044] det_loss_cls_dn_0[0.0948] det_loss_box_dn_0[0.3065] det_loss_cls_dn_1[0.0916] det_loss_box_dn_1[0.2618] det_loss_cls_dn_2[0.0904] det_loss_box_dn_2[0.2425] det_loss_cls_dn_3[0.0894] det_loss_box_dn_3[0.2411] det_loss_cls_dn_4[0.0884] det_loss_box_dn_4[0.2394] det_loss_cls_dn_5[0.0877] det_loss_box_dn_5[0.2375] loss_depth[0.0330] total_loss[7.2889]
Rank[0/8] 06/20/2025 10:39:00 INFO stats.py:314 | Epoch[156] Step[59] GlobalStep[60899] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 10:58:41.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:39:15 INFO loss_tracker.py:84 | Epoch[156/239] Step[74] GlobalStep[60914/NA]: det_loss_cls_0[0.4762] det_loss_box_0[0.6229] det_loss_cls_1[0.4674] det_loss_box_1[0.4721] det_loss_cls_2[0.4382] det_loss_box_2[0.4206] det_loss_cls_3[0.4051] det_loss_box_3[0.4006] det_loss_cls_4[0.3722] det_loss_box_4[0.3968] det_loss_cls_5[0.3654] det_loss_box_5[0.3929] det_loss_cls_dn_0[0.1015] det_loss_box_dn_0[0.3236] det_loss_cls_dn_1[0.0976] det_loss_box_dn_1[0.2585] det_loss_cls_dn_2[0.0962] det_loss_box_dn_2[0.2409] det_loss_cls_dn_3[0.0948] det_loss_box_dn_3[0.2339] det_loss_cls_dn_4[0.0941] det_loss_box_dn_4[0.2330] det_loss_cls_dn_5[0.0932] det_loss_box_dn_5[0.2313] loss_depth[0.0399] total_loss[7.3690]
Rank[0/8] 06/20/2025 10:39:26 INFO stats.py:314 | Epoch[156] Step[84] GlobalStep[60924] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 10:58:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:39:41 INFO loss_tracker.py:84 | Epoch[156/239] Step[99] GlobalStep[60939/NA]: det_loss_cls_0[0.4527] det_loss_box_0[0.5351] det_loss_cls_1[0.4270] det_loss_box_1[0.4226] det_loss_cls_2[0.3895] det_loss_box_2[0.3905] det_loss_cls_3[0.3532] det_loss_box_3[0.3765] det_loss_cls_4[0.3337] det_loss_box_4[0.3693] det_loss_cls_5[0.3180] det_loss_box_5[0.3748] det_loss_cls_dn_0[0.0885] det_loss_box_dn_0[0.2353] det_loss_cls_dn_1[0.0844] det_loss_box_dn_1[0.1899] det_loss_cls_dn_2[0.0833] det_loss_box_dn_2[0.1739] det_loss_cls_dn_3[0.0834] det_loss_box_dn_3[0.1709] det_loss_cls_dn_4[0.0812] det_loss_box_dn_4[0.1677] det_loss_cls_dn_5[0.0798] det_loss_box_dn_5[0.1665] loss_depth[0.0349] total_loss[6.3826]
Rank[0/8] 06/20/2025 10:39:51 INFO stats.py:314 | Epoch[156] Step[109] GlobalStep[60949] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 10:57:36.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:40:07 INFO loss_tracker.py:84 | Epoch[156/239] Step[124] GlobalStep[60964/NA]: det_loss_cls_0[0.5215] det_loss_box_0[0.6792] det_loss_cls_1[0.5050] det_loss_box_1[0.5161] det_loss_cls_2[0.4600] det_loss_box_2[0.4729] det_loss_cls_3[0.4276] det_loss_box_3[0.4708] det_loss_cls_4[0.4045] det_loss_box_4[0.4529] det_loss_cls_5[0.3865] det_loss_box_5[0.4525] det_loss_cls_dn_0[0.0917] det_loss_box_dn_0[0.2592] det_loss_cls_dn_1[0.0876] det_loss_box_dn_1[0.2182] det_loss_cls_dn_2[0.0867] det_loss_box_dn_2[0.2025] det_loss_cls_dn_3[0.0852] det_loss_box_dn_3[0.1993] det_loss_cls_dn_4[0.0846] det_loss_box_dn_4[0.1972] det_loss_cls_dn_5[0.0838] det_loss_box_dn_5[0.1958] loss_depth[0.0344] total_loss[7.5757]
Rank[0/8] 06/20/2025 10:40:17 INFO stats.py:314 | Epoch[156] Step[134] GlobalStep[60974] Training Speed: 6.38 samples/sec across all devices.	Average Step Time: 1.25 sec.	Estimated Remaining Time: 10:57:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:40:33 INFO loss_tracker.py:84 | Epoch[156/239] Step[149] GlobalStep[60989/NA]: det_loss_cls_0[0.7243] det_loss_box_0[1.0121] det_loss_cls_1[0.7055] det_loss_box_1[0.7632] det_loss_cls_2[0.6612] det_loss_box_2[0.6955] det_loss_cls_3[0.6248] det_loss_box_3[0.6660] det_loss_cls_4[0.5876] det_loss_box_4[0.6527] det_loss_cls_5[0.5762] det_loss_box_5[0.6472] det_loss_cls_dn_0[0.1061] det_loss_box_dn_0[0.2844] det_loss_cls_dn_1[0.1023] det_loss_box_dn_1[0.2433] det_loss_cls_dn_2[0.1022] det_loss_box_dn_2[0.2287] det_loss_cls_dn_3[0.1008] det_loss_box_dn_3[0.2247] det_loss_cls_dn_4[0.1002] det_loss_box_dn_4[0.2216] det_loss_cls_dn_5[0.0992] det_loss_box_dn_5[0.2189] loss_depth[0.0388] total_loss[10.3875]
Rank[0/8] 06/20/2025 10:40:43 INFO stats.py:314 | Epoch[156] Step[159] GlobalStep[60999] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 10:56:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:40:59 INFO loss_tracker.py:84 | Epoch[156/239] Step[174] GlobalStep[61014/NA]: det_loss_cls_0[0.5693] det_loss_box_0[0.7741] det_loss_cls_1[0.5633] det_loss_box_1[0.6069] det_loss_cls_2[0.4991] det_loss_box_2[0.5504] det_loss_cls_3[0.4542] det_loss_box_3[0.5364] det_loss_cls_4[0.4323] det_loss_box_4[0.5150] det_loss_cls_5[0.4193] det_loss_box_5[0.5208] det_loss_cls_dn_0[0.0805] det_loss_box_dn_0[0.2369] det_loss_cls_dn_1[0.0769] det_loss_box_dn_1[0.1942] det_loss_cls_dn_2[0.0762] det_loss_box_dn_2[0.1785] det_loss_cls_dn_3[0.0748] det_loss_box_dn_3[0.1744] det_loss_cls_dn_4[0.0744] det_loss_box_dn_4[0.1723] det_loss_cls_dn_5[0.0736] det_loss_box_dn_5[0.1659] loss_depth[0.0375] total_loss[8.0573]
Rank[0/8] 06/20/2025 10:41:09 INFO stats.py:314 | Epoch[156] Step[184] GlobalStep[61024] Training Speed: 8.06 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 10:55:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:41:25 INFO loss_tracker.py:84 | Epoch[156/239] Step[199] GlobalStep[61039/NA]: det_loss_cls_0[0.4242] det_loss_box_0[0.4746] det_loss_cls_1[0.4145] det_loss_box_1[0.3603] det_loss_cls_2[0.3625] det_loss_box_2[0.3278] det_loss_cls_3[0.3383] det_loss_box_3[0.3146] det_loss_cls_4[0.3114] det_loss_box_4[0.3078] det_loss_cls_5[0.3023] det_loss_box_5[0.3073] det_loss_cls_dn_0[0.0994] det_loss_box_dn_0[0.2604] det_loss_cls_dn_1[0.0962] det_loss_box_dn_1[0.2170] det_loss_cls_dn_2[0.0955] det_loss_box_dn_2[0.2007] det_loss_cls_dn_3[0.0946] det_loss_box_dn_3[0.1965] det_loss_cls_dn_4[0.0942] det_loss_box_dn_4[0.1942] det_loss_cls_dn_5[0.0933] det_loss_box_dn_5[0.1929] loss_depth[0.0375] total_loss[6.1181]
Rank[0/8] 06/20/2025 10:41:35 INFO stats.py:314 | Epoch[156] Step[209] GlobalStep[61049] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 10:55:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:41:51 INFO loss_tracker.py:84 | Epoch[156/239] Step[224] GlobalStep[61064/NA]: det_loss_cls_0[0.5097] det_loss_box_0[0.6155] det_loss_cls_1[0.5010] det_loss_box_1[0.4504] det_loss_cls_2[0.4358] det_loss_box_2[0.4118] det_loss_cls_3[0.4040] det_loss_box_3[0.3874] det_loss_cls_4[0.3784] det_loss_box_4[0.3821] det_loss_cls_5[0.3620] det_loss_box_5[0.3860] det_loss_cls_dn_0[0.0886] det_loss_box_dn_0[0.2930] det_loss_cls_dn_1[0.0858] det_loss_box_dn_1[0.2470] det_loss_cls_dn_2[0.0854] det_loss_box_dn_2[0.2307] det_loss_cls_dn_3[0.0841] det_loss_box_dn_3[0.2277] det_loss_cls_dn_4[0.0835] det_loss_box_dn_4[0.2246] det_loss_cls_dn_5[0.0821] det_loss_box_dn_5[0.2212] loss_depth[0.0391] total_loss[7.2173]
Rank[0/8] 06/20/2025 10:42:01 INFO stats.py:314 | Epoch[156] Step[234] GlobalStep[61074] Training Speed: 8.58 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 10:54:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:42:17 INFO loss_tracker.py:84 | Epoch[156/239] Step[249] GlobalStep[61089/NA]: det_loss_cls_0[0.4604] det_loss_box_0[0.5597] det_loss_cls_1[0.4583] det_loss_box_1[0.4050] det_loss_cls_2[0.3962] det_loss_box_2[0.3631] det_loss_cls_3[0.3666] det_loss_box_3[0.3423] det_loss_cls_4[0.3388] det_loss_box_4[0.3349] det_loss_cls_5[0.3249] det_loss_box_5[0.3382] det_loss_cls_dn_0[0.0913] det_loss_box_dn_0[0.2459] det_loss_cls_dn_1[0.0876] det_loss_box_dn_1[0.2017] det_loss_cls_dn_2[0.0868] det_loss_box_dn_2[0.1881] det_loss_cls_dn_3[0.0854] det_loss_box_dn_3[0.1842] det_loss_cls_dn_4[0.0848] det_loss_box_dn_4[0.1813] det_loss_cls_dn_5[0.0837] det_loss_box_dn_5[0.1800] loss_depth[0.0361] total_loss[6.4250]
Rank[0/8] 06/20/2025 10:42:27 INFO stats.py:314 | Epoch[156] Step[259] GlobalStep[61099] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 10:54:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:42:43 INFO loss_tracker.py:84 | Epoch[156/239] Step[274] GlobalStep[61114/NA]: det_loss_cls_0[0.4351] det_loss_box_0[0.5866] det_loss_cls_1[0.4356] det_loss_box_1[0.4537] det_loss_cls_2[0.3892] det_loss_box_2[0.4050] det_loss_cls_3[0.3656] det_loss_box_3[0.3940] det_loss_cls_4[0.3478] det_loss_box_4[0.3953] det_loss_cls_5[0.3405] det_loss_box_5[0.3934] det_loss_cls_dn_0[0.0991] det_loss_box_dn_0[0.3007] det_loss_cls_dn_1[0.0962] det_loss_box_dn_1[0.2499] det_loss_cls_dn_2[0.0959] det_loss_box_dn_2[0.2356] det_loss_cls_dn_3[0.0949] det_loss_box_dn_3[0.2311] det_loss_cls_dn_4[0.0943] det_loss_box_dn_4[0.2291] det_loss_cls_dn_5[0.0933] det_loss_box_dn_5[0.2282] loss_depth[0.0371] total_loss[7.0269]
Rank[0/8] 06/20/2025 10:42:53 INFO stats.py:314 | Epoch[156] Step[284] GlobalStep[61124] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 10:53:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:43:08 INFO loss_tracker.py:84 | Epoch[156/239] Step[299] GlobalStep[61139/NA]: det_loss_cls_0[0.6429] det_loss_box_0[0.9202] det_loss_cls_1[0.6431] det_loss_box_1[0.7107] det_loss_cls_2[0.5887] det_loss_box_2[0.6558] det_loss_cls_3[0.5520] det_loss_box_3[0.6354] det_loss_cls_4[0.5229] det_loss_box_4[0.6390] det_loss_cls_5[0.5051] det_loss_box_5[0.6334] det_loss_cls_dn_0[0.1035] det_loss_box_dn_0[0.2915] det_loss_cls_dn_1[0.0995] det_loss_box_dn_1[0.2510] det_loss_cls_dn_2[0.0987] det_loss_box_dn_2[0.2372] det_loss_cls_dn_3[0.0978] det_loss_box_dn_3[0.2341] det_loss_cls_dn_4[0.0965] det_loss_box_dn_4[0.2344] det_loss_cls_dn_5[0.0959] det_loss_box_dn_5[0.2336] loss_depth[0.0363] total_loss[9.7594]
Rank[0/8] 06/20/2025 10:43:19 INFO stats.py:314 | Epoch[156] Step[309] GlobalStep[61149] Training Speed: 7.97 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 10:53:16.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:43:34 INFO loss_tracker.py:84 | Epoch[156/239] Step[324] GlobalStep[61164/NA]: det_loss_cls_0[0.4228] det_loss_box_0[0.5436] det_loss_cls_1[0.4148] det_loss_box_1[0.4176] det_loss_cls_2[0.3730] det_loss_box_2[0.3790] det_loss_cls_3[0.3335] det_loss_box_3[0.3690] det_loss_cls_4[0.3141] det_loss_box_4[0.3558] det_loss_cls_5[0.3030] det_loss_box_5[0.3589] det_loss_cls_dn_0[0.0851] det_loss_box_dn_0[0.2435] det_loss_cls_dn_1[0.0808] det_loss_box_dn_1[0.2046] det_loss_cls_dn_2[0.0794] det_loss_box_dn_2[0.1901] det_loss_cls_dn_3[0.0778] det_loss_box_dn_3[0.1865] det_loss_cls_dn_4[0.0769] det_loss_box_dn_4[0.1849] det_loss_cls_dn_5[0.0760] det_loss_box_dn_5[0.1840] loss_depth[0.0361] total_loss[6.2908]
Rank[0/8] 06/20/2025 10:43:45 INFO stats.py:314 | Epoch[156] Step[334] GlobalStep[61174] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 10:52:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:44:00 INFO loss_tracker.py:84 | Epoch[156/239] Step[349] GlobalStep[61189/NA]: det_loss_cls_0[0.4405] det_loss_box_0[0.6205] det_loss_cls_1[0.4283] det_loss_box_1[0.4728] det_loss_cls_2[0.3953] det_loss_box_2[0.4262] det_loss_cls_3[0.3684] det_loss_box_3[0.4194] det_loss_cls_4[0.3499] det_loss_box_4[0.4085] det_loss_cls_5[0.3331] det_loss_box_5[0.4148] det_loss_cls_dn_0[0.0852] det_loss_box_dn_0[0.3006] det_loss_cls_dn_1[0.0817] det_loss_box_dn_1[0.2546] det_loss_cls_dn_2[0.0807] det_loss_box_dn_2[0.2376] det_loss_cls_dn_3[0.0790] det_loss_box_dn_3[0.2339] det_loss_cls_dn_4[0.0782] det_loss_box_dn_4[0.2336] det_loss_cls_dn_5[0.0772] det_loss_box_dn_5[0.2326] loss_depth[0.0352] total_loss[7.0880]
Rank[0/8] 06/20/2025 10:44:11 INFO stats.py:314 | Epoch[156] Step[359] GlobalStep[61199] Training Speed: 7.87 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 10:52:11.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:44:26 INFO loss_tracker.py:84 | Epoch[156/239] Step[374] GlobalStep[61214/NA]: det_loss_cls_0[0.5319] det_loss_box_0[0.6978] det_loss_cls_1[0.5156] det_loss_box_1[0.5369] det_loss_cls_2[0.4668] det_loss_box_2[0.4840] det_loss_cls_3[0.4324] det_loss_box_3[0.4650] det_loss_cls_4[0.4176] det_loss_box_4[0.4452] det_loss_cls_5[0.4049] det_loss_box_5[0.4455] det_loss_cls_dn_0[0.1027] det_loss_box_dn_0[0.2918] det_loss_cls_dn_1[0.0986] det_loss_box_dn_1[0.2463] det_loss_cls_dn_2[0.0978] det_loss_box_dn_2[0.2272] det_loss_cls_dn_3[0.0970] det_loss_box_dn_3[0.2243] det_loss_cls_dn_4[0.0958] det_loss_box_dn_4[0.2218] det_loss_cls_dn_5[0.0945] det_loss_box_dn_5[0.2200] loss_depth[0.0380] total_loss[7.8993]
Rank[0/8] 06/20/2025 10:44:37 INFO stats.py:314 | Epoch[156] Step[384] GlobalStep[61224] Training Speed: 7.40 samples/sec across all devices.	Average Step Time: 1.08 sec.	Estimated Remaining Time: 10:51:39.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:44:42 INFO stats.py:394 | Epoch[156] completed. Training Speed: 7.63 samples/sec across all devices.	Epoch Time: 409.07 sec.	Average Epoch Time: 409.07 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 10:51:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:45:07 INFO stats.py:314 | Epoch[157] Step[19] GlobalStep[61249] Training Speed: 8.07 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 10:51:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:45:13 INFO loss_tracker.py:84 | Epoch[157/239] Step[24] GlobalStep[61254/NA]: det_loss_cls_0[0.5051] det_loss_box_0[0.6889] det_loss_cls_1[0.4929] det_loss_box_1[0.5276] det_loss_cls_2[0.4471] det_loss_box_2[0.4756] det_loss_cls_3[0.4053] det_loss_box_3[0.4617] det_loss_cls_4[0.3861] det_loss_box_4[0.4532] det_loss_cls_5[0.3759] det_loss_box_5[0.4524] det_loss_cls_dn_0[0.0902] det_loss_box_dn_0[0.3058] det_loss_cls_dn_1[0.0862] det_loss_box_dn_1[0.2509] det_loss_cls_dn_2[0.0851] det_loss_box_dn_2[0.2343] det_loss_cls_dn_3[0.0837] det_loss_box_dn_3[0.2306] det_loss_cls_dn_4[0.0827] det_loss_box_dn_4[0.2281] det_loss_cls_dn_5[0.0820] det_loss_box_dn_5[0.2262] loss_depth[0.0383] total_loss[7.6961]
Rank[0/8] 06/20/2025 10:45:34 INFO stats.py:314 | Epoch[157] Step[44] GlobalStep[61274] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 10:50:37.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:45:39 INFO loss_tracker.py:84 | Epoch[157/239] Step[49] GlobalStep[61279/NA]: det_loss_cls_0[0.4205] det_loss_box_0[0.4946] det_loss_cls_1[0.4196] det_loss_box_1[0.3624] det_loss_cls_2[0.3635] det_loss_box_2[0.3385] det_loss_cls_3[0.3265] det_loss_box_3[0.3284] det_loss_cls_4[0.3087] det_loss_box_4[0.3262] det_loss_cls_5[0.2912] det_loss_box_5[0.3300] det_loss_cls_dn_0[0.0807] det_loss_box_dn_0[0.2358] det_loss_cls_dn_1[0.0775] det_loss_box_dn_1[0.1870] det_loss_cls_dn_2[0.0766] det_loss_box_dn_2[0.1725] det_loss_cls_dn_3[0.0751] det_loss_box_dn_3[0.1692] det_loss_cls_dn_4[0.0739] det_loss_box_dn_4[0.1668] det_loss_cls_dn_5[0.0730] det_loss_box_dn_5[0.1635] loss_depth[0.0370] total_loss[5.8986]
Rank[0/8] 06/20/2025 10:45:59 INFO stats.py:314 | Epoch[157] Step[69] GlobalStep[61299] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 10:50:04.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:46:05 INFO loss_tracker.py:84 | Epoch[157/239] Step[74] GlobalStep[61304/NA]: det_loss_cls_0[0.4950] det_loss_box_0[0.6479] det_loss_cls_1[0.4804] det_loss_box_1[0.4838] det_loss_cls_2[0.4310] det_loss_box_2[0.4377] det_loss_cls_3[0.3985] det_loss_box_3[0.4313] det_loss_cls_4[0.3793] det_loss_box_4[0.4210] det_loss_cls_5[0.3712] det_loss_box_5[0.4185] det_loss_cls_dn_0[0.1052] det_loss_box_dn_0[0.2947] det_loss_cls_dn_1[0.1022] det_loss_box_dn_1[0.2450] det_loss_cls_dn_2[0.1012] det_loss_box_dn_2[0.2305] det_loss_cls_dn_3[0.1003] det_loss_box_dn_3[0.2269] det_loss_cls_dn_4[0.0991] det_loss_box_dn_4[0.2257] det_loss_cls_dn_5[0.0983] det_loss_box_dn_5[0.2241] loss_depth[0.0358] total_loss[7.4846]
Rank[0/8] 06/20/2025 10:46:25 INFO stats.py:314 | Epoch[157] Step[94] GlobalStep[61324] Training Speed: 7.75 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 10:49:32.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:46:31 INFO loss_tracker.py:84 | Epoch[157/239] Step[99] GlobalStep[61329/NA]: det_loss_cls_0[0.4917] det_loss_box_0[0.6737] det_loss_cls_1[0.4798] det_loss_box_1[0.5002] det_loss_cls_2[0.4293] det_loss_box_2[0.4475] det_loss_cls_3[0.3984] det_loss_box_3[0.4243] det_loss_cls_4[0.3719] det_loss_box_4[0.4131] det_loss_cls_5[0.3575] det_loss_box_5[0.4154] det_loss_cls_dn_0[0.0897] det_loss_box_dn_0[0.2839] det_loss_cls_dn_1[0.0849] det_loss_box_dn_1[0.2311] det_loss_cls_dn_2[0.0833] det_loss_box_dn_2[0.2170] det_loss_cls_dn_3[0.0826] det_loss_box_dn_3[0.2115] det_loss_cls_dn_4[0.0818] det_loss_box_dn_4[0.2094] det_loss_cls_dn_5[0.0813] det_loss_box_dn_5[0.2071] loss_depth[0.0366] total_loss[7.3031]
Rank[0/8] 06/20/2025 10:46:51 INFO stats.py:314 | Epoch[157] Step[119] GlobalStep[61349] Training Speed: 7.63 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 10:48:59.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:46:56 INFO loss_tracker.py:84 | Epoch[157/239] Step[124] GlobalStep[61354/NA]: det_loss_cls_0[0.4716] det_loss_box_0[0.5671] det_loss_cls_1[0.4758] det_loss_box_1[0.4276] det_loss_cls_2[0.4098] det_loss_box_2[0.3911] det_loss_cls_3[0.3736] det_loss_box_3[0.3821] det_loss_cls_4[0.3569] det_loss_box_4[0.3748] det_loss_cls_5[0.3502] det_loss_box_5[0.3723] det_loss_cls_dn_0[0.0935] det_loss_box_dn_0[0.2519] det_loss_cls_dn_1[0.0893] det_loss_box_dn_1[0.2067] det_loss_cls_dn_2[0.0880] det_loss_box_dn_2[0.1958] det_loss_cls_dn_3[0.0863] det_loss_box_dn_3[0.1894] det_loss_cls_dn_4[0.0849] det_loss_box_dn_4[0.1882] det_loss_cls_dn_5[0.0841] det_loss_box_dn_5[0.1861] loss_depth[0.0343] total_loss[6.7315]
Rank[0/8] 06/20/2025 10:47:17 INFO stats.py:314 | Epoch[157] Step[144] GlobalStep[61374] Training Speed: 8.59 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 10:48:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:47:22 INFO loss_tracker.py:84 | Epoch[157/239] Step[149] GlobalStep[61379/NA]: det_loss_cls_0[0.4657] det_loss_box_0[0.6269] det_loss_cls_1[0.4674] det_loss_box_1[0.4890] det_loss_cls_2[0.4175] det_loss_box_2[0.4473] det_loss_cls_3[0.3884] det_loss_box_3[0.4389] det_loss_cls_4[0.3767] det_loss_box_4[0.4199] det_loss_cls_5[0.3653] det_loss_box_5[0.4169] det_loss_cls_dn_0[0.0913] det_loss_box_dn_0[0.2792] det_loss_cls_dn_1[0.0880] det_loss_box_dn_1[0.2357] det_loss_cls_dn_2[0.0876] det_loss_box_dn_2[0.2230] det_loss_cls_dn_3[0.0857] det_loss_box_dn_3[0.2208] det_loss_cls_dn_4[0.0849] det_loss_box_dn_4[0.2176] det_loss_cls_dn_5[0.0844] det_loss_box_dn_5[0.2171] loss_depth[0.0340] total_loss[7.2693]
Rank[0/8] 06/20/2025 10:47:43 INFO stats.py:314 | Epoch[157] Step[169] GlobalStep[61399] Training Speed: 8.67 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 10:47:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:47:48 INFO loss_tracker.py:84 | Epoch[157/239] Step[174] GlobalStep[61404/NA]: det_loss_cls_0[0.4795] det_loss_box_0[0.7195] det_loss_cls_1[0.4723] det_loss_box_1[0.5738] det_loss_cls_2[0.4252] det_loss_box_2[0.5410] det_loss_cls_3[0.3999] det_loss_box_3[0.5190] det_loss_cls_4[0.3798] det_loss_box_4[0.4782] det_loss_cls_5[0.3691] det_loss_box_5[0.4770] det_loss_cls_dn_0[0.0971] det_loss_box_dn_0[0.3233] det_loss_cls_dn_1[0.0942] det_loss_box_dn_1[0.2756] det_loss_cls_dn_2[0.0935] det_loss_box_dn_2[0.2612] det_loss_cls_dn_3[0.0929] det_loss_box_dn_3[0.2577] det_loss_cls_dn_4[0.0925] det_loss_box_dn_4[0.2581] det_loss_cls_dn_5[0.0915] det_loss_box_dn_5[0.2571] loss_depth[0.0390] total_loss[8.0680]
Rank[0/8] 06/20/2025 10:48:09 INFO stats.py:314 | Epoch[157] Step[194] GlobalStep[61424] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 10:47:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:48:14 INFO loss_tracker.py:84 | Epoch[157/239] Step[199] GlobalStep[61429/NA]: det_loss_cls_0[0.5996] det_loss_box_0[0.8529] det_loss_cls_1[0.5758] det_loss_box_1[0.6681] det_loss_cls_2[0.5112] det_loss_box_2[0.6145] det_loss_cls_3[0.4747] det_loss_box_3[0.5995] det_loss_cls_4[0.4515] det_loss_box_4[0.5970] det_loss_cls_5[0.4324] det_loss_box_5[0.5967] det_loss_cls_dn_0[0.0986] det_loss_box_dn_0[0.3854] det_loss_cls_dn_1[0.0948] det_loss_box_dn_1[0.3336] det_loss_cls_dn_2[0.0940] det_loss_box_dn_2[0.3194] det_loss_cls_dn_3[0.0930] det_loss_box_dn_3[0.3151] det_loss_cls_dn_4[0.0920] det_loss_box_dn_4[0.3160] det_loss_cls_dn_5[0.0915] det_loss_box_dn_5[0.3132] loss_depth[0.0383] total_loss[9.5589]
Rank[0/8] 06/20/2025 10:48:35 INFO stats.py:314 | Epoch[157] Step[219] GlobalStep[61449] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 10:46:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:48:40 INFO loss_tracker.py:84 | Epoch[157/239] Step[224] GlobalStep[61454/NA]: det_loss_cls_0[0.4842] det_loss_box_0[0.6903] det_loss_cls_1[0.4754] det_loss_box_1[0.4994] det_loss_cls_2[0.4247] det_loss_box_2[0.4386] det_loss_cls_3[0.3896] det_loss_box_3[0.4342] det_loss_cls_4[0.3716] det_loss_box_4[0.4197] det_loss_cls_5[0.3522] det_loss_box_5[0.4231] det_loss_cls_dn_0[0.0961] det_loss_box_dn_0[0.2945] det_loss_cls_dn_1[0.0937] det_loss_box_dn_1[0.2432] det_loss_cls_dn_2[0.0933] det_loss_box_dn_2[0.2256] det_loss_cls_dn_3[0.0920] det_loss_box_dn_3[0.2212] det_loss_cls_dn_4[0.0913] det_loss_box_dn_4[0.2191] det_loss_cls_dn_5[0.0902] det_loss_box_dn_5[0.2180] loss_depth[0.0395] total_loss[7.4208]
Rank[0/8] 06/20/2025 10:49:00 INFO stats.py:314 | Epoch[157] Step[244] GlobalStep[61474] Training Speed: 7.35 samples/sec across all devices.	Average Step Time: 1.09 sec.	Estimated Remaining Time: 10:46:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:49:06 INFO loss_tracker.py:84 | Epoch[157/239] Step[249] GlobalStep[61479/NA]: det_loss_cls_0[0.5422] det_loss_box_0[0.6486] det_loss_cls_1[0.5276] det_loss_box_1[0.5151] det_loss_cls_2[0.4752] det_loss_box_2[0.4605] det_loss_cls_3[0.4394] det_loss_box_3[0.4422] det_loss_cls_4[0.4103] det_loss_box_4[0.4278] det_loss_cls_5[0.3949] det_loss_box_5[0.4294] det_loss_cls_dn_0[0.0895] det_loss_box_dn_0[0.2513] det_loss_cls_dn_1[0.0862] det_loss_box_dn_1[0.2108] det_loss_cls_dn_2[0.0853] det_loss_box_dn_2[0.1980] det_loss_cls_dn_3[0.0839] det_loss_box_dn_3[0.1941] det_loss_cls_dn_4[0.0825] det_loss_box_dn_4[0.1914] det_loss_cls_dn_5[0.0810] det_loss_box_dn_5[0.1897] loss_depth[0.0350] total_loss[7.4920]
Rank[0/8] 06/20/2025 10:49:26 INFO stats.py:314 | Epoch[157] Step[269] GlobalStep[61499] Training Speed: 14.49 samples/sec across all devices.	Average Step Time: 0.55 sec.	Estimated Remaining Time: 10:45:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:49:31 INFO loss_tracker.py:84 | Epoch[157/239] Step[274] GlobalStep[61504/NA]: det_loss_cls_0[0.4642] det_loss_box_0[0.6043] det_loss_cls_1[0.4511] det_loss_box_1[0.4731] det_loss_cls_2[0.3985] det_loss_box_2[0.4298] det_loss_cls_3[0.3687] det_loss_box_3[0.4169] det_loss_cls_4[0.3435] det_loss_box_4[0.4030] det_loss_cls_5[0.3302] det_loss_box_5[0.4039] det_loss_cls_dn_0[0.0775] det_loss_box_dn_0[0.3400] det_loss_cls_dn_1[0.0744] det_loss_box_dn_1[0.2765] det_loss_cls_dn_2[0.0734] det_loss_box_dn_2[0.2512] det_loss_cls_dn_3[0.0724] det_loss_box_dn_3[0.2517] det_loss_cls_dn_4[0.0718] det_loss_box_dn_4[0.2470] det_loss_cls_dn_5[0.0706] det_loss_box_dn_5[0.2436] loss_depth[0.0380] total_loss[7.1752]
Rank[0/8] 06/20/2025 10:49:52 INFO stats.py:314 | Epoch[157] Step[294] GlobalStep[61524] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 10:45:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:49:57 INFO loss_tracker.py:84 | Epoch[157/239] Step[299] GlobalStep[61529/NA]: det_loss_cls_0[0.5054] det_loss_box_0[0.6575] det_loss_cls_1[0.5064] det_loss_box_1[0.5034] det_loss_cls_2[0.4619] det_loss_box_2[0.4473] det_loss_cls_3[0.4314] det_loss_box_3[0.4197] det_loss_cls_4[0.4033] det_loss_box_4[0.4098] det_loss_cls_5[0.3859] det_loss_box_5[0.4130] det_loss_cls_dn_0[0.1004] det_loss_box_dn_0[0.2873] det_loss_cls_dn_1[0.0983] det_loss_box_dn_1[0.2412] det_loss_cls_dn_2[0.0979] det_loss_box_dn_2[0.2233] det_loss_cls_dn_3[0.0971] det_loss_box_dn_3[0.2206] det_loss_cls_dn_4[0.0971] det_loss_box_dn_4[0.2184] det_loss_cls_dn_5[0.0959] det_loss_box_dn_5[0.2171] loss_depth[0.0377] total_loss[7.5769]
Rank[0/8] 06/20/2025 10:50:18 INFO stats.py:314 | Epoch[157] Step[319] GlobalStep[61549] Training Speed: 7.66 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 10:44:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:50:23 INFO loss_tracker.py:84 | Epoch[157/239] Step[324] GlobalStep[61554/NA]: det_loss_cls_0[0.6635] det_loss_box_0[0.9107] det_loss_cls_1[0.6458] det_loss_box_1[0.6908] det_loss_cls_2[0.5938] det_loss_box_2[0.6166] det_loss_cls_3[0.5511] det_loss_box_3[0.6059] det_loss_cls_4[0.5215] det_loss_box_4[0.5943] det_loss_cls_5[0.5066] det_loss_box_5[0.5947] det_loss_cls_dn_0[0.1060] det_loss_box_dn_0[0.3009] det_loss_cls_dn_1[0.1033] det_loss_box_dn_1[0.2583] det_loss_cls_dn_2[0.1032] det_loss_box_dn_2[0.2444] det_loss_cls_dn_3[0.1015] det_loss_box_dn_3[0.2392] det_loss_cls_dn_4[0.1006] det_loss_box_dn_4[0.2370] det_loss_cls_dn_5[0.1000] det_loss_box_dn_5[0.2366] loss_depth[0.0384] total_loss[9.6645]
Rank[0/8] 06/20/2025 10:50:44 INFO stats.py:314 | Epoch[157] Step[344] GlobalStep[61574] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 10:44:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:50:49 INFO loss_tracker.py:84 | Epoch[157/239] Step[349] GlobalStep[61579/NA]: det_loss_cls_0[0.4685] det_loss_box_0[0.5907] det_loss_cls_1[0.4700] det_loss_box_1[0.4556] det_loss_cls_2[0.4329] det_loss_box_2[0.4134] det_loss_cls_3[0.4109] det_loss_box_3[0.4007] det_loss_cls_4[0.3812] det_loss_box_4[0.4007] det_loss_cls_5[0.3638] det_loss_box_5[0.4098] det_loss_cls_dn_0[0.0958] det_loss_box_dn_0[0.2691] det_loss_cls_dn_1[0.0920] det_loss_box_dn_1[0.2296] det_loss_cls_dn_2[0.0912] det_loss_box_dn_2[0.2152] det_loss_cls_dn_3[0.0907] det_loss_box_dn_3[0.2116] det_loss_cls_dn_4[0.0896] det_loss_box_dn_4[0.2094] det_loss_cls_dn_5[0.0890] det_loss_box_dn_5[0.2080] loss_depth[0.0377] total_loss[7.1272]
Rank[0/8] 06/20/2025 10:51:10 INFO stats.py:314 | Epoch[157] Step[369] GlobalStep[61599] Training Speed: 8.34 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 10:43:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:51:15 INFO loss_tracker.py:84 | Epoch[157/239] Step[374] GlobalStep[61604/NA]: det_loss_cls_0[0.4543] det_loss_box_0[0.5952] det_loss_cls_1[0.4327] det_loss_box_1[0.4585] det_loss_cls_2[0.3919] det_loss_box_2[0.4207] det_loss_cls_3[0.3614] det_loss_box_3[0.4089] det_loss_cls_4[0.3357] det_loss_box_4[0.3973] det_loss_cls_5[0.3210] det_loss_box_5[0.4017] det_loss_cls_dn_0[0.0932] det_loss_box_dn_0[0.2645] det_loss_cls_dn_1[0.0892] det_loss_box_dn_1[0.2257] det_loss_cls_dn_2[0.0880] det_loss_box_dn_2[0.2096] det_loss_cls_dn_3[0.0871] det_loss_box_dn_3[0.2059] det_loss_cls_dn_4[0.0856] det_loss_box_dn_4[0.2032] det_loss_cls_dn_5[0.0846] det_loss_box_dn_5[0.2025] loss_depth[0.0371] total_loss[6.8557]
Rank[0/8] 06/20/2025 10:51:30 INFO stats.py:394 | Epoch[157] completed. Training Speed: 7.64 samples/sec across all devices.	Epoch Time: 408.41 sec.	Average Epoch Time: 408.41 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 10:43:09.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:51:37 INFO stats.py:314 | Epoch[158] Step[4] GlobalStep[61624] Training Speed: 6.59 samples/sec across all devices.	Average Step Time: 1.21 sec.	Estimated Remaining Time: 10:43:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:51:57 INFO loss_tracker.py:84 | Epoch[158/239] Step[24] GlobalStep[61644/NA]: det_loss_cls_0[0.4423] det_loss_box_0[0.5981] det_loss_cls_1[0.4343] det_loss_box_1[0.4599] det_loss_cls_2[0.3863] det_loss_box_2[0.4191] det_loss_cls_3[0.3596] det_loss_box_3[0.4099] det_loss_cls_4[0.3358] det_loss_box_4[0.4096] det_loss_cls_5[0.3242] det_loss_box_5[0.4109] det_loss_cls_dn_0[0.0893] det_loss_box_dn_0[0.3282] det_loss_cls_dn_1[0.0855] det_loss_box_dn_1[0.2736] det_loss_cls_dn_2[0.0839] det_loss_box_dn_2[0.2519] det_loss_cls_dn_3[0.0824] det_loss_box_dn_3[0.2438] det_loss_cls_dn_4[0.0811] det_loss_box_dn_4[0.2411] det_loss_cls_dn_5[0.0800] det_loss_box_dn_5[0.2381] loss_depth[0.0354] total_loss[7.1043]
Rank[0/8] 06/20/2025 10:52:03 INFO stats.py:314 | Epoch[158] Step[29] GlobalStep[61649] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 10:42:31.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:52:23 INFO loss_tracker.py:84 | Epoch[158/239] Step[49] GlobalStep[61669/NA]: det_loss_cls_0[0.4587] det_loss_box_0[0.5848] det_loss_cls_1[0.4460] det_loss_box_1[0.4519] det_loss_cls_2[0.4117] det_loss_box_2[0.4037] det_loss_cls_3[0.3835] det_loss_box_3[0.3889] det_loss_cls_4[0.3582] det_loss_box_4[0.3852] det_loss_cls_5[0.3448] det_loss_box_5[0.3810] det_loss_cls_dn_0[0.0975] det_loss_box_dn_0[0.2902] det_loss_cls_dn_1[0.0924] det_loss_box_dn_1[0.2430] det_loss_cls_dn_2[0.0917] det_loss_box_dn_2[0.2301] det_loss_cls_dn_3[0.0901] det_loss_box_dn_3[0.2261] det_loss_cls_dn_4[0.0895] det_loss_box_dn_4[0.2235] det_loss_cls_dn_5[0.0889] det_loss_box_dn_5[0.2229] loss_depth[0.0360] total_loss[7.0203]
Rank[0/8] 06/20/2025 10:52:28 INFO stats.py:314 | Epoch[158] Step[54] GlobalStep[61674] Training Speed: 6.21 samples/sec across all devices.	Average Step Time: 1.29 sec.	Estimated Remaining Time: 10:41:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:52:49 INFO loss_tracker.py:84 | Epoch[158/239] Step[74] GlobalStep[61694/NA]: det_loss_cls_0[0.4431] det_loss_box_0[0.5813] det_loss_cls_1[0.4341] det_loss_box_1[0.4449] det_loss_cls_2[0.3956] det_loss_box_2[0.4002] det_loss_cls_3[0.3733] det_loss_box_3[0.3856] det_loss_cls_4[0.3588] det_loss_box_4[0.3772] det_loss_cls_5[0.3450] det_loss_box_5[0.3812] det_loss_cls_dn_0[0.0975] det_loss_box_dn_0[0.2917] det_loss_cls_dn_1[0.0938] det_loss_box_dn_1[0.2452] det_loss_cls_dn_2[0.0934] det_loss_box_dn_2[0.2295] det_loss_cls_dn_3[0.0924] det_loss_box_dn_3[0.2268] det_loss_cls_dn_4[0.0916] det_loss_box_dn_4[0.2254] det_loss_cls_dn_5[0.0909] det_loss_box_dn_5[0.2245] loss_depth[0.0398] total_loss[6.9628]
Rank[0/8] 06/20/2025 10:52:54 INFO stats.py:314 | Epoch[158] Step[79] GlobalStep[61699] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 10:41:26.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:53:15 INFO loss_tracker.py:84 | Epoch[158/239] Step[99] GlobalStep[61719/NA]: det_loss_cls_0[0.5026] det_loss_box_0[0.6305] det_loss_cls_1[0.4907] det_loss_box_1[0.4786] det_loss_cls_2[0.4465] det_loss_box_2[0.4351] det_loss_cls_3[0.4104] det_loss_box_3[0.4212] det_loss_cls_4[0.3813] det_loss_box_4[0.4214] det_loss_cls_5[0.3699] det_loss_box_5[0.4212] det_loss_cls_dn_0[0.0946] det_loss_box_dn_0[0.2433] det_loss_cls_dn_1[0.0914] det_loss_box_dn_1[0.2042] det_loss_cls_dn_2[0.0898] det_loss_box_dn_2[0.1907] det_loss_cls_dn_3[0.0885] det_loss_box_dn_3[0.1885] det_loss_cls_dn_4[0.0881] det_loss_box_dn_4[0.1868] det_loss_cls_dn_5[0.0870] det_loss_box_dn_5[0.1868] loss_depth[0.0382] total_loss[7.1874]
Rank[0/8] 06/20/2025 10:53:20 INFO stats.py:314 | Epoch[158] Step[104] GlobalStep[61724] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 10:40:54.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:53:41 INFO loss_tracker.py:84 | Epoch[158/239] Step[124] GlobalStep[61744/NA]: det_loss_cls_0[0.4900] det_loss_box_0[0.6671] det_loss_cls_1[0.4735] det_loss_box_1[0.5119] det_loss_cls_2[0.4173] det_loss_box_2[0.4723] det_loss_cls_3[0.3830] det_loss_box_3[0.4624] det_loss_cls_4[0.3607] det_loss_box_4[0.4429] det_loss_cls_5[0.3437] det_loss_box_5[0.4437] det_loss_cls_dn_0[0.0978] det_loss_box_dn_0[0.3481] det_loss_cls_dn_1[0.0920] det_loss_box_dn_1[0.3056] det_loss_cls_dn_2[0.0905] det_loss_box_dn_2[0.2901] det_loss_cls_dn_3[0.0893] det_loss_box_dn_3[0.2862] det_loss_cls_dn_4[0.0879] det_loss_box_dn_4[0.2859] det_loss_cls_dn_5[0.0873] det_loss_box_dn_5[0.2819] loss_depth[0.0358] total_loss[7.8468]
Rank[0/8] 06/20/2025 10:53:46 INFO stats.py:314 | Epoch[158] Step[129] GlobalStep[61749] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 10:40:21.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:54:07 INFO loss_tracker.py:84 | Epoch[158/239] Step[149] GlobalStep[61769/NA]: det_loss_cls_0[0.4039] det_loss_box_0[0.4661] det_loss_cls_1[0.4066] det_loss_box_1[0.3574] det_loss_cls_2[0.3554] det_loss_box_2[0.3181] det_loss_cls_3[0.3321] det_loss_box_3[0.3069] det_loss_cls_4[0.3100] det_loss_box_4[0.3016] det_loss_cls_5[0.2927] det_loss_box_5[0.3086] det_loss_cls_dn_0[0.0971] det_loss_box_dn_0[0.2560] det_loss_cls_dn_1[0.0935] det_loss_box_dn_1[0.2106] det_loss_cls_dn_2[0.0928] det_loss_box_dn_2[0.1932] det_loss_cls_dn_3[0.0919] det_loss_box_dn_3[0.1880] det_loss_cls_dn_4[0.0910] det_loss_box_dn_4[0.1854] det_loss_cls_dn_5[0.0895] det_loss_box_dn_5[0.1835] loss_depth[0.0363] total_loss[5.9684]
Rank[0/8] 06/20/2025 10:54:12 INFO stats.py:314 | Epoch[158] Step[154] GlobalStep[61774] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 10:39:49.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:54:33 INFO loss_tracker.py:84 | Epoch[158/239] Step[174] GlobalStep[61794/NA]: det_loss_cls_0[0.5338] det_loss_box_0[0.6401] det_loss_cls_1[0.5117] det_loss_box_1[0.4850] det_loss_cls_2[0.4647] det_loss_box_2[0.4360] det_loss_cls_3[0.4289] det_loss_box_3[0.4254] det_loss_cls_4[0.4080] det_loss_box_4[0.4168] det_loss_cls_5[0.3843] det_loss_box_5[0.4220] det_loss_cls_dn_0[0.0932] det_loss_box_dn_0[0.2466] det_loss_cls_dn_1[0.0896] det_loss_box_dn_1[0.2008] det_loss_cls_dn_2[0.0886] det_loss_box_dn_2[0.1841] det_loss_cls_dn_3[0.0873] det_loss_box_dn_3[0.1792] det_loss_cls_dn_4[0.0864] det_loss_box_dn_4[0.1764] det_loss_cls_dn_5[0.0852] det_loss_box_dn_5[0.1745] loss_depth[0.0371] total_loss[7.2857]
Rank[0/8] 06/20/2025 10:54:38 INFO stats.py:314 | Epoch[158] Step[179] GlobalStep[61799] Training Speed: 8.67 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 10:39:17.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:54:59 INFO loss_tracker.py:84 | Epoch[158/239] Step[199] GlobalStep[61819/NA]: det_loss_cls_0[0.4599] det_loss_box_0[0.5571] det_loss_cls_1[0.4676] det_loss_box_1[0.4134] det_loss_cls_2[0.4178] det_loss_box_2[0.3785] det_loss_cls_3[0.3856] det_loss_box_3[0.3667] det_loss_cls_4[0.3639] det_loss_box_4[0.3562] det_loss_cls_5[0.3487] det_loss_box_5[0.3553] det_loss_cls_dn_0[0.0965] det_loss_box_dn_0[0.3295] det_loss_cls_dn_1[0.0937] det_loss_box_dn_1[0.2667] det_loss_cls_dn_2[0.0931] det_loss_box_dn_2[0.2485] det_loss_cls_dn_3[0.0913] det_loss_box_dn_3[0.2423] det_loss_cls_dn_4[0.0903] det_loss_box_dn_4[0.2416] det_loss_cls_dn_5[0.0889] det_loss_box_dn_5[0.2388] loss_depth[0.0388] total_loss[7.0308]
Rank[0/8] 06/20/2025 10:55:04 INFO stats.py:314 | Epoch[158] Step[204] GlobalStep[61824] Training Speed: 7.27 samples/sec across all devices.	Average Step Time: 1.10 sec.	Estimated Remaining Time: 10:38:44.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:55:25 INFO loss_tracker.py:84 | Epoch[158/239] Step[224] GlobalStep[61844/NA]: det_loss_cls_0[0.5356] det_loss_box_0[0.7073] det_loss_cls_1[0.5198] det_loss_box_1[0.5119] det_loss_cls_2[0.4696] det_loss_box_2[0.4663] det_loss_cls_3[0.4331] det_loss_box_3[0.4526] det_loss_cls_4[0.4073] det_loss_box_4[0.4444] det_loss_cls_5[0.3970] det_loss_box_5[0.4422] det_loss_cls_dn_0[0.0986] det_loss_box_dn_0[0.2697] det_loss_cls_dn_1[0.0956] det_loss_box_dn_1[0.2249] det_loss_cls_dn_2[0.0955] det_loss_box_dn_2[0.2052] det_loss_cls_dn_3[0.0942] det_loss_box_dn_3[0.2012] det_loss_cls_dn_4[0.0927] det_loss_box_dn_4[0.1981] det_loss_cls_dn_5[0.0917] det_loss_box_dn_5[0.1973] loss_depth[0.0416] total_loss[7.6932]
Rank[0/8] 06/20/2025 10:55:30 INFO stats.py:314 | Epoch[158] Step[229] GlobalStep[61849] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 10:38:12.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:55:50 INFO loss_tracker.py:84 | Epoch[158/239] Step[249] GlobalStep[61869/NA]: det_loss_cls_0[0.4752] det_loss_box_0[0.6500] det_loss_cls_1[0.4701] det_loss_box_1[0.4827] det_loss_cls_2[0.4201] det_loss_box_2[0.4389] det_loss_cls_3[0.3920] det_loss_box_3[0.4293] det_loss_cls_4[0.3739] det_loss_box_4[0.4172] det_loss_cls_5[0.3527] det_loss_box_5[0.4286] det_loss_cls_dn_0[0.0959] det_loss_box_dn_0[0.3163] det_loss_cls_dn_1[0.0917] det_loss_box_dn_1[0.2662] det_loss_cls_dn_2[0.0906] det_loss_box_dn_2[0.2465] det_loss_cls_dn_3[0.0889] det_loss_box_dn_3[0.2398] det_loss_cls_dn_4[0.0881] det_loss_box_dn_4[0.2382] det_loss_cls_dn_5[0.0870] det_loss_box_dn_5[0.2361] loss_depth[0.0416] total_loss[7.4575]
Rank[0/8] 06/20/2025 10:55:56 INFO stats.py:314 | Epoch[158] Step[254] GlobalStep[61874] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 10:37:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:56:17 INFO loss_tracker.py:84 | Epoch[158/239] Step[274] GlobalStep[61894/NA]: det_loss_cls_0[0.4854] det_loss_box_0[0.6330] det_loss_cls_1[0.4726] det_loss_box_1[0.4999] det_loss_cls_2[0.4209] det_loss_box_2[0.4620] det_loss_cls_3[0.3880] det_loss_box_3[0.4385] det_loss_cls_4[0.3582] det_loss_box_4[0.4321] det_loss_cls_5[0.3528] det_loss_box_5[0.4254] det_loss_cls_dn_0[0.0947] det_loss_box_dn_0[0.3066] det_loss_cls_dn_1[0.0914] det_loss_box_dn_1[0.2607] det_loss_cls_dn_2[0.0917] det_loss_box_dn_2[0.2420] det_loss_cls_dn_3[0.0904] det_loss_box_dn_3[0.2386] det_loss_cls_dn_4[0.0899] det_loss_box_dn_4[0.2358] det_loss_cls_dn_5[0.0889] det_loss_box_dn_5[0.2346] loss_depth[0.0361] total_loss[7.4702]
Rank[0/8] 06/20/2025 10:56:22 INFO stats.py:314 | Epoch[158] Step[279] GlobalStep[61899] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 10:37:07.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:56:42 INFO loss_tracker.py:84 | Epoch[158/239] Step[299] GlobalStep[61919/NA]: det_loss_cls_0[0.4788] det_loss_box_0[0.6914] det_loss_cls_1[0.4748] det_loss_box_1[0.5386] det_loss_cls_2[0.4376] det_loss_box_2[0.4670] det_loss_cls_3[0.3994] det_loss_box_3[0.4580] det_loss_cls_4[0.3839] det_loss_box_4[0.4305] det_loss_cls_5[0.3678] det_loss_box_5[0.4388] det_loss_cls_dn_0[0.0976] det_loss_box_dn_0[0.2946] det_loss_cls_dn_1[0.0944] det_loss_box_dn_1[0.2465] det_loss_cls_dn_2[0.0936] det_loss_box_dn_2[0.2277] det_loss_cls_dn_3[0.0925] det_loss_box_dn_3[0.2234] det_loss_cls_dn_4[0.0918] det_loss_box_dn_4[0.2213] det_loss_cls_dn_5[0.0905] det_loss_box_dn_5[0.2180] loss_depth[0.0416] total_loss[7.6000]
Rank[0/8] 06/20/2025 10:56:47 INFO stats.py:314 | Epoch[158] Step[304] GlobalStep[61924] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 10:36:35.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:57:08 INFO loss_tracker.py:84 | Epoch[158/239] Step[324] GlobalStep[61944/NA]: det_loss_cls_0[0.4864] det_loss_box_0[0.6745] det_loss_cls_1[0.4612] det_loss_box_1[0.5238] det_loss_cls_2[0.4310] det_loss_box_2[0.4462] det_loss_cls_3[0.3949] det_loss_box_3[0.4366] det_loss_cls_4[0.3665] det_loss_box_4[0.4310] det_loss_cls_5[0.3530] det_loss_box_5[0.4288] det_loss_cls_dn_0[0.0969] det_loss_box_dn_0[0.3103] det_loss_cls_dn_1[0.0927] det_loss_box_dn_1[0.2624] det_loss_cls_dn_2[0.0922] det_loss_box_dn_2[0.2445] det_loss_cls_dn_3[0.0903] det_loss_box_dn_3[0.2405] det_loss_cls_dn_4[0.0886] det_loss_box_dn_4[0.2393] det_loss_cls_dn_5[0.0873] det_loss_box_dn_5[0.2374] loss_depth[0.0373] total_loss[7.5536]
Rank[0/8] 06/20/2025 10:57:14 INFO stats.py:314 | Epoch[158] Step[329] GlobalStep[61949] Training Speed: 7.09 samples/sec across all devices.	Average Step Time: 1.13 sec.	Estimated Remaining Time: 10:36:03.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:57:34 INFO loss_tracker.py:84 | Epoch[158/239] Step[349] GlobalStep[61969/NA]: det_loss_cls_0[0.6215] det_loss_box_0[0.7876] det_loss_cls_1[0.6050] det_loss_box_1[0.6016] det_loss_cls_2[0.5632] det_loss_box_2[0.5488] det_loss_cls_3[0.5303] det_loss_box_3[0.5395] det_loss_cls_4[0.5119] det_loss_box_4[0.5233] det_loss_cls_5[0.4899] det_loss_box_5[0.5283] det_loss_cls_dn_0[0.1027] det_loss_box_dn_0[0.2675] det_loss_cls_dn_1[0.0996] det_loss_box_dn_1[0.2223] det_loss_cls_dn_2[0.0995] det_loss_box_dn_2[0.2064] det_loss_cls_dn_3[0.0977] det_loss_box_dn_3[0.2031] det_loss_cls_dn_4[0.0968] det_loss_box_dn_4[0.2021] det_loss_cls_dn_5[0.0961] det_loss_box_dn_5[0.1989] loss_depth[0.0394] total_loss[8.7830]
Rank[0/8] 06/20/2025 10:57:39 INFO stats.py:314 | Epoch[158] Step[354] GlobalStep[61974] Training Speed: 8.74 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 10:35:30.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:58:00 INFO loss_tracker.py:84 | Epoch[158/239] Step[374] GlobalStep[61994/NA]: det_loss_cls_0[0.4843] det_loss_box_0[0.6041] det_loss_cls_1[0.4642] det_loss_box_1[0.4813] det_loss_cls_2[0.4134] det_loss_box_2[0.4430] det_loss_cls_3[0.3854] det_loss_box_3[0.4312] det_loss_cls_4[0.3644] det_loss_box_4[0.4172] det_loss_cls_5[0.3549] det_loss_box_5[0.4159] det_loss_cls_dn_0[0.0961] det_loss_box_dn_0[0.2743] det_loss_cls_dn_1[0.0920] det_loss_box_dn_1[0.2242] det_loss_cls_dn_2[0.0906] det_loss_box_dn_2[0.2115] det_loss_cls_dn_3[0.0896] det_loss_box_dn_3[0.2090] det_loss_cls_dn_4[0.0882] det_loss_box_dn_4[0.2082] det_loss_cls_dn_5[0.0874] det_loss_box_dn_5[0.2064] loss_depth[0.0372] total_loss[7.1741]
Rank[0/8] 06/20/2025 10:58:05 INFO stats.py:314 | Epoch[158] Step[379] GlobalStep[61999] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 10:34:58.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:58:16 INFO stats.py:394 | Epoch[158] completed. Training Speed: 7.70 samples/sec across all devices.	Epoch Time: 405.45 sec.	Average Epoch Time: 405.45 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 10:34:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:58:33 INFO stats.py:314 | Epoch[159] Step[14] GlobalStep[62024] Training Speed: 7.97 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 10:34:27.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:58:44 INFO loss_tracker.py:84 | Epoch[159/239] Step[24] GlobalStep[62034/NA]: det_loss_cls_0[0.4858] det_loss_box_0[0.5982] det_loss_cls_1[0.4755] det_loss_box_1[0.4519] det_loss_cls_2[0.4264] det_loss_box_2[0.4130] det_loss_cls_3[0.3982] det_loss_box_3[0.3921] det_loss_cls_4[0.3724] det_loss_box_4[0.3944] det_loss_cls_5[0.3622] det_loss_box_5[0.3921] det_loss_cls_dn_0[0.0982] det_loss_box_dn_0[0.2924] det_loss_cls_dn_1[0.0944] det_loss_box_dn_1[0.2470] det_loss_cls_dn_2[0.0943] det_loss_box_dn_2[0.2296] det_loss_cls_dn_3[0.0934] det_loss_box_dn_3[0.2261] det_loss_cls_dn_4[0.0923] det_loss_box_dn_4[0.2250] det_loss_cls_dn_5[0.0917] det_loss_box_dn_5[0.2234] loss_depth[0.0389] total_loss[7.2090]
Rank[0/8] 06/20/2025 10:59:00 INFO stats.py:314 | Epoch[159] Step[39] GlobalStep[62049] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 10:33:55.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:59:10 INFO loss_tracker.py:84 | Epoch[159/239] Step[49] GlobalStep[62059/NA]: det_loss_cls_0[0.4979] det_loss_box_0[0.6234] det_loss_cls_1[0.4907] det_loss_box_1[0.4561] det_loss_cls_2[0.4407] det_loss_box_2[0.4148] det_loss_cls_3[0.4068] det_loss_box_3[0.3961] det_loss_cls_4[0.3770] det_loss_box_4[0.3867] det_loss_cls_5[0.3637] det_loss_box_5[0.3830] det_loss_cls_dn_0[0.0919] det_loss_box_dn_0[0.2666] det_loss_cls_dn_1[0.0876] det_loss_box_dn_1[0.2269] det_loss_cls_dn_2[0.0872] det_loss_box_dn_2[0.2101] det_loss_cls_dn_3[0.0857] det_loss_box_dn_3[0.2072] det_loss_cls_dn_4[0.0847] det_loss_box_dn_4[0.2048] det_loss_cls_dn_5[0.0836] det_loss_box_dn_5[0.2036] loss_depth[0.0359] total_loss[7.1126]
Rank[0/8] 06/20/2025 10:59:25 INFO stats.py:314 | Epoch[159] Step[64] GlobalStep[62074] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 10:33:22.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 10:59:35 INFO loss_tracker.py:84 | Epoch[159/239] Step[74] GlobalStep[62084/NA]: det_loss_cls_0[0.4375] det_loss_box_0[0.5550] det_loss_cls_1[0.4335] det_loss_box_1[0.4227] det_loss_cls_2[0.3881] det_loss_box_2[0.3854] det_loss_cls_3[0.3624] det_loss_box_3[0.3562] det_loss_cls_4[0.3414] det_loss_box_4[0.3449] det_loss_cls_5[0.3290] det_loss_box_5[0.3465] det_loss_cls_dn_0[0.1007] det_loss_box_dn_0[0.3161] det_loss_cls_dn_1[0.0971] det_loss_box_dn_1[0.2685] det_loss_cls_dn_2[0.0959] det_loss_box_dn_2[0.2494] det_loss_cls_dn_3[0.0942] det_loss_box_dn_3[0.2445] det_loss_cls_dn_4[0.0931] det_loss_box_dn_4[0.2402] det_loss_cls_dn_5[0.0927] det_loss_box_dn_5[0.2416] loss_depth[0.0379] total_loss[6.8745]
Rank[0/8] 06/20/2025 10:59:51 INFO stats.py:314 | Epoch[159] Step[89] GlobalStep[62099] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 10:32:50.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 11:00:01 INFO loss_tracker.py:84 | Epoch[159/239] Step[99] GlobalStep[62109/NA]: det_loss_cls_0[0.5120] det_loss_box_0[0.6853] det_loss_cls_1[0.4945] det_loss_box_1[0.5272] det_loss_cls_2[0.4559] det_loss_box_2[0.4815] det_loss_cls_3[0.4273] det_loss_box_3[0.4637] det_loss_cls_4[0.4072] det_loss_box_4[0.4575] det_loss_cls_5[0.3986] det_loss_box_5[0.4492] det_loss_cls_dn_0[0.0981] det_loss_box_dn_0[0.2971] det_loss_cls_dn_1[0.0950] det_loss_box_dn_1[0.2546] det_loss_cls_dn_2[0.0948] det_loss_box_dn_2[0.2338] det_loss_cls_dn_3[0.0931] det_loss_box_dn_3[0.2300] det_loss_cls_dn_4[0.0924] det_loss_box_dn_4[0.2293] det_loss_cls_dn_5[0.0917] det_loss_box_dn_5[0.2273] loss_depth[0.0385] total_loss[7.8356]
Rank[0/8] 06/20/2025 11:00:17 INFO stats.py:314 | Epoch[159] Step[114] GlobalStep[62124] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 10:32:18.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 11:00:27 INFO loss_tracker.py:84 | Epoch[159/239] Step[124] GlobalStep[62134/NA]: det_loss_cls_0[0.5349] det_loss_box_0[0.6949] det_loss_cls_1[0.5323] det_loss_box_1[0.5515] det_loss_cls_2[0.4891] det_loss_box_2[0.5170] det_loss_cls_3[0.4539] det_loss_box_3[0.5045] det_loss_cls_4[0.4317] det_loss_box_4[0.4997] det_loss_cls_5[0.4194] det_loss_box_5[0.4999] det_loss_cls_dn_0[0.1002] det_loss_box_dn_0[0.3970] det_loss_cls_dn_1[0.0970] det_loss_box_dn_1[0.3450] det_loss_cls_dn_2[0.0964] det_loss_box_dn_2[0.3294] det_loss_cls_dn_3[0.0958] det_loss_box_dn_3[0.3245] det_loss_cls_dn_4[0.0949] det_loss_box_dn_4[0.3203] det_loss_cls_dn_5[0.0934] det_loss_box_dn_5[0.3202] loss_depth[0.0379] total_loss[8.7808]
Rank[0/8] 06/20/2025 11:00:43 INFO stats.py:314 | Epoch[159] Step[139] GlobalStep[62149] Training Speed: 8.34 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 10:31:45.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 11:00:52 INFO loss_tracker.py:84 | Epoch[159/239] Step[149] GlobalStep[62159/NA]: det_loss_cls_0[0.4426] det_loss_box_0[0.6346] det_loss_cls_1[0.4294] det_loss_box_1[0.4635] det_loss_cls_2[0.3915] det_loss_box_2[0.4243] det_loss_cls_3[0.3637] det_loss_box_3[0.4168] det_loss_cls_4[0.3381] det_loss_box_4[0.4077] det_loss_cls_5[0.3294] det_loss_box_5[0.4096] det_loss_cls_dn_0[0.0864] det_loss_box_dn_0[0.2745] det_loss_cls_dn_1[0.0832] det_loss_box_dn_1[0.2311] det_loss_cls_dn_2[0.0820] det_loss_box_dn_2[0.2151] det_loss_cls_dn_3[0.0811] det_loss_box_dn_3[0.2123] det_loss_cls_dn_4[0.0802] det_loss_box_dn_4[0.2107] det_loss_cls_dn_5[0.0791] det_loss_box_dn_5[0.2101] loss_depth[0.0386] total_loss[6.9354]
Rank[0/8] 06/20/2025 11:01:08 INFO stats.py:314 | Epoch[159] Step[164] GlobalStep[62174] Training Speed: 8.15 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 10:31:13.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 11:01:18 INFO loss_tracker.py:84 | Epoch[159/239] Step[174] GlobalStep[62184/NA]: det_loss_cls_0[0.5138] det_loss_box_0[0.6548] det_loss_cls_1[0.5027] det_loss_box_1[0.5010] det_loss_cls_2[0.4658] det_loss_box_2[0.4640] det_loss_cls_3[0.4375] det_loss_box_3[0.4554] det_loss_cls_4[0.4177] det_loss_box_4[0.4444] det_loss_cls_5[0.4037] det_loss_box_5[0.4420] det_loss_cls_dn_0[0.1005] det_loss_box_dn_0[0.3119] det_loss_cls_dn_1[0.0975] det_loss_box_dn_1[0.2681] det_loss_cls_dn_2[0.0964] det_loss_box_dn_2[0.2507] det_loss_cls_dn_3[0.0947] det_loss_box_dn_3[0.2477] det_loss_cls_dn_4[0.0933] det_loss_box_dn_4[0.2457] det_loss_cls_dn_5[0.0925] det_loss_box_dn_5[0.2440] loss_depth[0.0380] total_loss[7.8837]
Rank[0/8] 06/20/2025 11:01:34 INFO stats.py:314 | Epoch[159] Step[189] GlobalStep[62199] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 10:30:40.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 11:01:44 INFO loss_tracker.py:84 | Epoch[159/239] Step[199] GlobalStep[62209/NA]: det_loss_cls_0[0.5614] det_loss_box_0[0.7809] det_loss_cls_1[0.5433] det_loss_box_1[0.6218] det_loss_cls_2[0.5110] det_loss_box_2[0.5777] det_loss_cls_3[0.4841] det_loss_box_3[0.5743] det_loss_cls_4[0.4546] det_loss_box_4[0.5803] det_loss_cls_5[0.4475] det_loss_box_5[0.5753] det_loss_cls_dn_0[0.0927] det_loss_box_dn_0[0.3243] det_loss_cls_dn_1[0.0900] det_loss_box_dn_1[0.2765] det_loss_cls_dn_2[0.0896] det_loss_box_dn_2[0.2602] det_loss_cls_dn_3[0.0879] det_loss_box_dn_3[0.2551] det_loss_cls_dn_4[0.0872] det_loss_box_dn_4[0.2514] det_loss_cls_dn_5[0.0862] det_loss_box_dn_5[0.2510] loss_depth[0.0380] total_loss[8.9021]
Rank[0/8] 06/20/2025 11:02:00 INFO stats.py:314 | Epoch[159] Step[214] GlobalStep[62224] Training Speed: 6.57 samples/sec across all devices.	Average Step Time: 1.22 sec.	Estimated Remaining Time: 10:30:08.	Learning Rate: 2.00000e-04.	
Rank[0/8] 06/20/2025 11:02:10 INFO loss_tracker.py:84 | Epoch[159/239] Step[224] GlobalStep[62234/NA]: det_loss_cls_0[0.5593] det_loss_box_0[0.7820] det_loss_cls_1[0.5530] det_loss_box_1[0.6475] det_loss_cls_2[0.5063] det_loss_box_2[0.5771] det_loss_cls_3[0.4855] det_loss_box_3[0.5495] det_loss_cls_4[0.4606] det_loss_box_4[0.5387] det_loss_cls_5[0.4440] det_loss_box_5[0.5408] det_loss_cls_dn_0[0.1028] det_loss_box_dn_0[0.3204] det_loss_cls_dn_1[0.0988] det_loss_box_dn_1[0.2834] det_loss_cls_dn_2[0.0975] det_loss_box_dn_2[0.2661] det_loss_cls_dn_3[0.0966] det_loss_box_dn_3[0.2617] det_loss_cls_dn_4[0.0954] det_loss_box_dn_4[0.2591] det_loss_cls_dn_5[0.0945] det_loss_box_dn_5[0.2575] loss_depth[0.0360] total_loss[8.9141]
Rank[0/8] 06/20/2025 11:02:25 INFO stats.py:314 | Epoch[159] Step[239] GlobalStep[62249] Training Speed: 8.22 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 10:29:36.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:02:35 INFO loss_tracker.py:84 | Epoch[159/239] Step[249] GlobalStep[62259/NA]: det_loss_cls_0[0.6172] det_loss_box_0[0.6813] det_loss_cls_1[0.6100] det_loss_box_1[0.5287] det_loss_cls_2[0.5391] det_loss_box_2[0.4813] det_loss_cls_3[0.5046] det_loss_box_3[0.4602] det_loss_cls_4[0.4722] det_loss_box_4[0.4522] det_loss_cls_5[0.4616] det_loss_box_5[0.4545] det_loss_cls_dn_0[0.1117] det_loss_box_dn_0[0.2254] det_loss_cls_dn_1[0.1081] det_loss_box_dn_1[0.1897] det_loss_cls_dn_2[0.1074] det_loss_box_dn_2[0.1780] det_loss_cls_dn_3[0.1064] det_loss_box_dn_3[0.1741] det_loss_cls_dn_4[0.1057] det_loss_box_dn_4[0.1721] det_loss_cls_dn_5[0.1051] det_loss_box_dn_5[0.1718] loss_depth[0.0383] total_loss[8.0565]
Rank[0/8] 06/20/2025 11:02:51 INFO stats.py:314 | Epoch[159] Step[264] GlobalStep[62274] Training Speed: 8.58 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 10:29:03.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:03:02 INFO loss_tracker.py:84 | Epoch[159/239] Step[274] GlobalStep[62284/NA]: det_loss_cls_0[0.3989] det_loss_box_0[0.5091] det_loss_cls_1[0.3929] det_loss_box_1[0.4204] det_loss_cls_2[0.3517] det_loss_box_2[0.3843] det_loss_cls_3[0.3247] det_loss_box_3[0.3740] det_loss_cls_4[0.3073] det_loss_box_4[0.3659] det_loss_cls_5[0.2912] det_loss_box_5[0.3664] det_loss_cls_dn_0[0.0790] det_loss_box_dn_0[0.2994] det_loss_cls_dn_1[0.0750] det_loss_box_dn_1[0.2430] det_loss_cls_dn_2[0.0733] det_loss_box_dn_2[0.2235] det_loss_cls_dn_3[0.0717] det_loss_box_dn_3[0.2183] det_loss_cls_dn_4[0.0703] det_loss_box_dn_4[0.2162] det_loss_cls_dn_5[0.0693] det_loss_box_dn_5[0.2147] loss_depth[0.0354] total_loss[6.3758]
Rank[0/8] 06/20/2025 11:03:17 INFO stats.py:314 | Epoch[159] Step[289] GlobalStep[62299] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 10:28:31.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:03:27 INFO loss_tracker.py:84 | Epoch[159/239] Step[299] GlobalStep[62309/NA]: det_loss_cls_0[0.5350] det_loss_box_0[0.6898] det_loss_cls_1[0.5226] det_loss_box_1[0.5353] det_loss_cls_2[0.4669] det_loss_box_2[0.4750] det_loss_cls_3[0.4353] det_loss_box_3[0.4562] det_loss_cls_4[0.4090] det_loss_box_4[0.4481] det_loss_cls_5[0.3969] det_loss_box_5[0.4467] det_loss_cls_dn_0[0.0999] det_loss_box_dn_0[0.2543] det_loss_cls_dn_1[0.0975] det_loss_box_dn_1[0.2108] det_loss_cls_dn_2[0.0962] det_loss_box_dn_2[0.1949] det_loss_cls_dn_3[0.0948] det_loss_box_dn_3[0.1913] det_loss_cls_dn_4[0.0937] det_loss_box_dn_4[0.1892] det_loss_cls_dn_5[0.0925] det_loss_box_dn_5[0.1882] loss_depth[0.0354] total_loss[7.6556]
Rank[0/8] 06/20/2025 11:03:42 INFO stats.py:314 | Epoch[159] Step[314] GlobalStep[62324] Training Speed: 8.58 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 10:27:59.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:03:52 INFO loss_tracker.py:84 | Epoch[159/239] Step[324] GlobalStep[62334/NA]: det_loss_cls_0[0.4958] det_loss_box_0[0.7574] det_loss_cls_1[0.4722] det_loss_box_1[0.6282] det_loss_cls_2[0.4417] det_loss_box_2[0.5877] det_loss_cls_3[0.4209] det_loss_box_3[0.5623] det_loss_cls_4[0.4073] det_loss_box_4[0.5396] det_loss_cls_5[0.3959] det_loss_box_5[0.5346] det_loss_cls_dn_0[0.0978] det_loss_box_dn_0[0.3459] det_loss_cls_dn_1[0.0940] det_loss_box_dn_1[0.2983] det_loss_cls_dn_2[0.0930] det_loss_box_dn_2[0.2781] det_loss_cls_dn_3[0.0911] det_loss_box_dn_3[0.2738] det_loss_cls_dn_4[0.0897] det_loss_box_dn_4[0.2729] det_loss_cls_dn_5[0.0891] det_loss_box_dn_5[0.2708] loss_depth[0.0399] total_loss[8.5781]
Rank[0/8] 06/20/2025 11:04:08 INFO stats.py:314 | Epoch[159] Step[339] GlobalStep[62349] Training Speed: 6.44 samples/sec across all devices.	Average Step Time: 1.24 sec.	Estimated Remaining Time: 10:27:26.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:04:19 INFO loss_tracker.py:84 | Epoch[159/239] Step[349] GlobalStep[62359/NA]: det_loss_cls_0[0.4561] det_loss_box_0[0.6013] det_loss_cls_1[0.4320] det_loss_box_1[0.4574] det_loss_cls_2[0.3833] det_loss_box_2[0.4165] det_loss_cls_3[0.3492] det_loss_box_3[0.3935] det_loss_cls_4[0.3224] det_loss_box_4[0.3931] det_loss_cls_5[0.3088] det_loss_box_5[0.3978] det_loss_cls_dn_0[0.0947] det_loss_box_dn_0[0.2708] det_loss_cls_dn_1[0.0919] det_loss_box_dn_1[0.2198] det_loss_cls_dn_2[0.0909] det_loss_box_dn_2[0.2039] det_loss_cls_dn_3[0.0895] det_loss_box_dn_3[0.2009] det_loss_cls_dn_4[0.0885] det_loss_box_dn_4[0.1978] det_loss_cls_dn_5[0.0874] det_loss_box_dn_5[0.1945] loss_depth[0.0376] total_loss[6.7796]
Rank[0/8] 06/20/2025 11:04:34 INFO stats.py:314 | Epoch[159] Step[364] GlobalStep[62374] Training Speed: 7.83 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 10:26:54.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:04:45 INFO loss_tracker.py:84 | Epoch[159/239] Step[374] GlobalStep[62384/NA]: det_loss_cls_0[0.5068] det_loss_box_0[0.6100] det_loss_cls_1[0.5152] det_loss_box_1[0.4592] det_loss_cls_2[0.4596] det_loss_box_2[0.4183] det_loss_cls_3[0.4179] det_loss_box_3[0.4109] det_loss_cls_4[0.3933] det_loss_box_4[0.4049] det_loss_cls_5[0.3828] det_loss_box_5[0.3996] det_loss_cls_dn_0[0.1034] det_loss_box_dn_0[0.3130] det_loss_cls_dn_1[0.1001] det_loss_box_dn_1[0.2609] det_loss_cls_dn_2[0.0991] det_loss_box_dn_2[0.2443] det_loss_cls_dn_3[0.0981] det_loss_box_dn_3[0.2409] det_loss_cls_dn_4[0.0975] det_loss_box_dn_4[0.2394] det_loss_cls_dn_5[0.0969] det_loss_box_dn_5[0.2396] loss_depth[0.0360] total_loss[7.5478]
Rank[0/8] 06/20/2025 11:05:00 INFO stats.py:314 | Epoch[159] Step[389] GlobalStep[62399] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 10:26:22.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:05:00 INFO trainer.py:268 | 
==================================================BEGIN EVAL==================================================
Rank[0/8] 06/20/2025 11:06:00 INFO trainer.py:275 | eval: 10
Rank[0/8] 06/20/2025 11:06:12 INFO trainer.py:275 | eval: 20
Rank[0/8] 06/20/2025 11:06:19 INFO trainer.py:275 | eval: 30
Rank[0/8] 06/20/2025 11:06:30 INFO trainer.py:275 | eval: 40
Rank[0/8] 06/20/2025 11:06:41 INFO trainer.py:275 | eval: 50
Rank[0/8] 06/20/2025 11:06:53 INFO trainer.py:275 | eval: 60
Rank[0/8] 06/20/2025 11:07:00 INFO trainer.py:275 | eval: 70
Rank[0/8] 06/20/2025 11:07:12 INFO trainer.py:275 | eval: 80
Rank[0/8] 06/20/2025 11:07:24 INFO trainer.py:275 | eval: 90
Rank[0/8] 06/20/2025 11:07:35 INFO trainer.py:275 | eval: 100
Rank[0/8] 06/20/2025 11:07:41 INFO metrics.py:428 | number of results: 824
Rank[0/8] 06/20/2025 11:11:56 INFO metrics.py:332 | eval results of overall:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| bathtub             | 0.8474  | 0.8971  | 0.6266  | 0.7206  |
| curtain             | 0.5524  | 0.8597  | 0.1311  | 0.4746  |
| toilet              | 0.8615  | 0.8905  | 0.6939  | 0.7664  |
| bin                 | 0.6563  | 0.8631  | 0.3947  | 0.6061  |
| sink                | 0.7117  | 0.8395  | 0.2339  | 0.4691  |
| picture             | 0.2399  | 0.4688  | 0.0173  | 0.1078  |
| toilet paper        | 0.3744  | 0.5906  | 0.0737  | 0.2362  |
| doorframe           | 0.2758  | 0.7250  | 0.0680  | 0.3835  |
| door                | 0.3670  | 0.7249  | 0.0492  | 0.3068  |
| vanity              | 0.5174  | 0.9111  | 0.4549  | 0.7778  |
| stopcock            | 0.1935  | 0.5517  | 0.0026  | 0.0690  |
| towel               | 0.3835  | 0.6903  | 0.0922  | 0.3032  |
| mirror              | 0.1832  | 0.6630  | 0.0044  | 0.1685  |
| bag                 | 0.2788  | 0.7081  | 0.0744  | 0.3243  |
| bar                 | 0.0432  | 0.2424  | 0.0002  | 0.0606  |
| rail                | 0.0278  | 0.4062  | 0.0002  | 0.0156  |
| bottle              | 0.0892  | 0.4206  | 0.0029  | 0.0891  |
| shower              | 0.0513  | 0.3000  | 0.0017  | 0.1000  |
| tissue box          | 0.1917  | 0.6364  | 0.1410  | 0.2727  |
| switch              | 0.0010  | 0.0855  | 0.0000  | 0.0000  |
| hair dryer          | 0.0007  | 0.1429  | 0.0000  | 0.0000  |
| cup                 | 0.1279  | 0.4492  | 0.0329  | 0.1271  |
| socket              | 0.0077  | 0.2212  | 0.0001  | 0.0133  |
| faucet              | 0.0199  | 0.2128  | 0.0000  | 0.0000  |
| rod                 | 0.0125  | 0.0500  | 0.0000  | 0.0000  |
| soap dish           | 0.4425  | 0.7333  | 0.0960  | 0.2000  |
| microwave           | 0.6414  | 0.7755  | 0.3054  | 0.4694  |
| tissue              | 0.0110  | 0.0769  | 0.0000  | 0.0000  |
| counter             | 0.1779  | 0.6697  | 0.0568  | 0.2844  |
| basket              | 0.2747  | 0.6174  | 0.1131  | 0.3304  |
| ledge               | 0.0168  | 0.3333  | 0.0015  | 0.1667  |
| box                 | 0.1759  | 0.5387  | 0.0417  | 0.1992  |
| light               | 0.0578  | 0.2991  | 0.0039  | 0.0556  |
| plant               | 0.3328  | 0.6905  | 0.0886  | 0.3256  |
| cabinet             | 0.2995  | 0.7562  | 0.1650  | 0.5069  |
| paper               | 0.0241  | 0.3018  | 0.0005  | 0.0562  |
| shelf               | 0.3549  | 0.7284  | 0.1515  | 0.4532  |
| pillow              | 0.3265  | 0.6262  | 0.0580  | 0.2008  |
| blanket             | 0.2267  | 0.7900  | 0.0601  | 0.3900  |
| frame               | 0.0853  | 0.5102  | 0.0009  | 0.0408  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes             | 0.1779  | 0.6529  | 0.0370  | 0.2675  |
| stool               | 0.2919  | 0.7395  | 0.1867  | 0.5462  |
| case                | 0.1166  | 0.4463  | 0.0431  | 0.1638  |
| vent                | 0.0200  | 0.1667  | 0.0000  | 0.0000  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plunger             | 0.8118  | 1.0000  | 0.4500  | 0.6000  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fence               | 0.0278  | 0.0833  | 0.0019  | 0.0833  |
| bed                 | 0.8049  | 0.9231  | 0.6804  | 0.8817  |
| window              | 0.2478  | 0.6257  | 0.0506  | 0.2365  |
| purse               | 0.0053  | 0.5000  | 0.0000  | 0.0000  |
| cosmetics           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| decoration          | 0.0617  | 0.4714  | 0.0044  | 0.1071  |
| lamp                | 0.3288  | 0.6299  | 0.0908  | 0.2687  |
| backpack            | 0.5890  | 0.8226  | 0.3236  | 0.5484  |
| remote control      | 0.0223  | 0.2222  | 0.0066  | 0.1111  |
| soap                | 0.0636  | 0.6667  | 0.0146  | 0.3333  |
| toaster             | 0.5837  | 0.9000  | 0.1579  | 0.4000  |
| chair               | 0.6339  | 0.8222  | 0.4774  | 0.6544  |
| monitor             | 0.5527  | 0.7283  | 0.1290  | 0.3098  |
| blackboard          | 0.5496  | 0.8049  | 0.2427  | 0.4268  |
| computer            | 0.3304  | 0.7213  | 0.0211  | 0.1475  |
| radiator            | 0.6997  | 0.7889  | 0.1696  | 0.3556  |
| table               | 0.5238  | 0.8715  | 0.4187  | 0.7476  |
| keyboard            | 0.0565  | 0.2442  | 0.0000  | 0.0000  |
| desk                | 0.5401  | 0.9213  | 0.3752  | 0.7584  |
| mouse               | 0.0079  | 0.1081  | 0.0000  | 0.0000  |
| plug                | 0.0344  | 0.1053  | 0.0000  | 0.0000  |
| telephone           | 0.3537  | 0.6500  | 0.0740  | 0.2167  |
| blinds              | 0.2240  | 0.4400  | 0.0495  | 0.2600  |
| book                | 0.0907  | 0.4818  | 0.0107  | 0.1257  |
| windowsill          | 0.0797  | 0.6522  | 0.0031  | 0.1087  |
| stand               | 0.6023  | 0.9024  | 0.4705  | 0.7500  |
| projector           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| couch               | 0.5057  | 0.8512  | 0.3968  | 0.7314  |
| laptop              | 0.4555  | 0.7391  | 0.1476  | 0.3478  |
| jacket              | 0.6138  | 0.9167  | 0.3694  | 0.6667  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tablet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stairs              | 0.0876  | 0.4306  | 0.0083  | 0.1250  |
| umbrella            | 0.2000  | 0.2000  | 0.2000  | 0.2000  |
| board               | 0.2042  | 0.7097  | 0.0262  | 0.2258  |
| bucket              | 0.2736  | 0.7179  | 0.1015  | 0.4615  |
| balcony             | 0.0756  | 1.0000  | 0.0032  | 0.5000  |
| pen                 | 0.0019  | 0.0667  | 0.0000  | 0.0000  |
| pipe                | 0.0008  | 0.1429  | 0.0000  | 0.0000  |
| dresser             | 0.6400  | 0.9259  | 0.5103  | 0.7963  |
| tv                  | 0.6367  | 0.8515  | 0.2047  | 0.4455  |
| rack                | 0.1428  | 0.5000  | 0.0735  | 0.4038  |
| wardrobe            | 0.4124  | 0.7590  | 0.2638  | 0.6145  |
| shoe                | 0.2065  | 0.4673  | 0.0110  | 0.1028  |
| toy                 | 0.1581  | 0.4394  | 0.0516  | 0.1364  |
| clock               | 0.3274  | 0.6596  | 0.0092  | 0.0851  |
| ottoman             | 0.1509  | 0.7222  | 0.1176  | 0.5833  |
| plate               | 0.0281  | 0.4038  | 0.0014  | 0.0769  |
| statue              | 0.0945  | 0.4211  | 0.0577  | 0.3158  |
| paper cutter        | 0.8333  | 0.8333  | 0.0000  | 0.0000  |
| calendar            | 0.0048  | 0.3333  | 0.0000  | 0.0000  |
| bench               | 0.2632  | 0.7170  | 0.2075  | 0.5849  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine             | 0.4254  | 0.9474  | 0.3318  | 0.6842  |
| carpet              | 0.1738  | 0.5357  | 0.0069  | 0.1548  |
| cushion             | 0.1942  | 0.5294  | 0.0509  | 0.1765  |
| fan                 | 0.5777  | 0.7969  | 0.1561  | 0.4375  |
| sign                | 0.0752  | 0.5333  | 0.0005  | 0.1000  |
| stove               | 0.5692  | 0.8333  | 0.3612  | 0.5000  |
| bicycle             | 0.7250  | 1.0000  | 0.5571  | 1.0000  |
| guitar              | 0.2944  | 0.5000  | 0.0833  | 0.0833  |
| refrigerator        | 0.6634  | 0.8800  | 0.4556  | 0.6200  |
| luggage             | 0.6989  | 1.0000  | 0.6667  | 0.6667  |
| person              | 0.3151  | 0.6667  | 0.0000  | 0.0000  |
| scale               | 0.1532  | 0.4545  | 0.0317  | 0.3636  |
| bowl                | 0.1348  | 0.4638  | 0.0484  | 0.1449  |
| notebook            | 0.0002  | 0.0769  | 0.0000  | 0.0000  |
| kettle              | 0.1182  | 0.6111  | 0.0413  | 0.3333  |
| coffee maker        | 0.4028  | 0.6207  | 0.1078  | 0.2414  |
| hanger              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| broom               | 0.0151  | 0.2143  | 0.0000  | 0.0000  |
| doorway             | 0.0011  | 0.3333  | 0.0001  | 0.1111  |
| hamper              | 0.4219  | 0.5455  | 0.4219  | 0.5455  |
| toothpaste          | 0.0011  | 0.2000  | 0.0000  | 0.0000  |
| hat                 | 0.1111  | 0.1111  | 0.0000  | 0.0000  |
| container           | 0.0042  | 0.1525  | 0.0015  | 0.0678  |
| tube                | 0.0041  | 0.2308  | 0.0000  | 0.0000  |
| mat                 | 0.0137  | 0.3243  | 0.0000  | 0.0000  |
| grab bar            | 0.0343  | 0.3750  | 0.0074  | 0.1250  |
| soap dispenser      | 0.4329  | 0.6296  | 0.0952  | 0.2593  |
| dispenser           | 0.4967  | 0.6591  | 0.3719  | 0.5000  |
| stall               | 0.2072  | 1.0000  | 0.1679  | 0.5294  |
| holder              | 0.1389  | 0.2222  | 0.0000  | 0.0000  |
| mailbox             | 0.0667  | 0.4000  | 0.0000  | 0.0000  |
| pillar              | 0.3539  | 0.7209  | 0.2088  | 0.4186  |
| column              | 0.0181  | 0.3421  | 0.0014  | 0.1842  |
| partition           | 0.2278  | 0.6667  | 0.0015  | 0.1667  |
| window frame        | 0.0663  | 0.4545  | 0.0057  | 0.1515  |
| cube                | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| alarm               | 0.0006  | 0.1250  | 0.0000  | 0.0000  |
| furniture           | 0.0010  | 0.1667  | 0.0000  | 0.0000  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| copier              | 0.9167  | 0.9167  | 0.9167  | 0.9167  |
| fire extinguisher   | 0.3082  | 0.6000  | 0.0538  | 0.4000  |
| cart                | 0.0833  | 0.2500  | 0.0833  | 0.2500  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball                | 0.4524  | 0.5238  | 0.3917  | 0.4762  |
| printer             | 0.5669  | 0.8163  | 0.3570  | 0.6122  |
| vacuum cleaner      | 0.8095  | 0.8571  | 0.1905  | 0.4286  |
| piano               | 0.3239  | 0.8000  | 0.3239  | 0.8000  |
| poster              | 0.0071  | 0.1364  | 0.0000  | 0.0000  |
| basin               | 0.0045  | 0.3529  | 0.0001  | 0.0588  |
| clothes dryer       | 0.4653  | 0.8667  | 0.2589  | 0.4667  |
| map                 | 0.0192  | 0.5000  | 0.0192  | 0.5000  |
| album               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| commode             | 0.4896  | 0.8654  | 0.3700  | 0.7692  |
| teapot              | 0.0044  | 0.1111  | 0.0044  | 0.1111  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fireplace           | 0.1849  | 0.5000  | 0.0027  | 0.1429  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water cooler        | 0.8333  | 1.0000  | 0.8333  | 1.0000  |
| dish rack           | 0.4071  | 0.8000  | 0.0500  | 0.2000  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pan                 | 0.2486  | 0.5294  | 0.0768  | 0.1765  |
| pot                 | 0.0435  | 0.4815  | 0.0132  | 0.1481  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| oven                | 0.4596  | 0.7857  | 0.1873  | 0.4643  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchenware         | 0.2581  | 0.5435  | 0.0293  | 0.1739  |
| flowerpot           | 0.0856  | 0.4902  | 0.0025  | 0.1373  |
| vase                | 0.1029  | 0.4400  | 0.0528  | 0.2400  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| price tag           | 0.1443  | 0.2857  | 0.0000  | 0.0000  |
| magazine            | 0.0281  | 0.6000  | 0.0036  | 0.2000  |
| dumbbell            | 0.6667  | 0.6667  | 0.3333  | 0.3333  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coil                | 0.1389  | 0.3333  | 0.0000  | 0.0000  |
| adhesive tape       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toiletry            | 0.1056  | 0.5652  | 0.0014  | 0.0870  |
| file                | 0.0980  | 0.4412  | 0.0015  | 0.1176  |
| speaker             | 0.0899  | 0.3182  | 0.0016  | 0.0909  |
| ladder              | 0.1012  | 0.4118  | 0.0526  | 0.1765  |
| humidifier          | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| air conditioner     | 0.6000  | 0.6000  | 0.6000  | 0.6000  |
| pool table          | 0.1667  | 0.3333  | 0.1667  | 0.3333  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garbage             | 0.3956  | 0.7500  | 0.2555  | 0.5000  |
| conducting wire     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candlestick         | 0.0230  | 0.6667  | 0.0000  | 0.0000  |
| napkins             | 0.1493  | 1.0000  | 0.0000  | 0.0000  |
| candle              | 0.0634  | 0.2778  | 0.0004  | 0.0556  |
| folder              | 0.0215  | 0.2857  | 0.0024  | 0.0714  |
| spoon               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jar                 | 0.1570  | 0.3077  | 0.0391  | 0.1538  |
| mattress            | 0.0667  | 0.3333  | 0.0000  | 0.0000  |
| player              | 0.2500  | 0.2500  | 0.2500  | 0.2500  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| roll                | 0.2311  | 0.4000  | 0.0000  | 0.0000  |
| heater              | 0.6087  | 0.8448  | 0.1608  | 0.2586  |
| device              | 0.0776  | 0.5455  | 0.0130  | 0.0909  |
| drawer              | 0.2353  | 0.6667  | 0.0229  | 0.5556  |
| brush               | 0.0782  | 0.3571  | 0.0000  | 0.0000  |
| headphones          | 0.2308  | 0.4000  | 0.2000  | 0.2000  |
| chandelier          | 0.1194  | 0.3333  | 0.0282  | 0.1429  |
| beanbag             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| sack                | 0.5010  | 0.8571  | 0.0833  | 0.2857  |
| sunglasses          | 0.0695  | 0.5385  | 0.0018  | 0.1538  |
| shampoo             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beam                | 0.0020  | 0.1364  | 0.0016  | 0.0909  |
| washing machine     | 0.4537  | 0.6429  | 0.2201  | 0.3214  |
| body loofah         | 0.1667  | 0.5000  | 0.0000  | 0.0000  |
| can                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit               | 0.2284  | 0.3571  | 0.0790  | 0.2143  |
| food                | 0.0909  | 0.0909  | 0.0000  | 0.0000  |
| jalousie            | 1.0000  | 1.0000  | 0.0132  | 0.5000  |
| crate               | 0.3740  | 0.7143  | 0.1880  | 0.4286  |
| screen              | 0.0170  | 0.4000  | 0.0022  | 0.2000  |
| radio               | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| crib                | 0.3542  | 0.6667  | 0.3542  | 0.6667  |
| pack                | 0.0354  | 0.6000  | 0.0094  | 0.2000  |
| drum                | 0.0104  | 0.1250  | 0.0000  | 0.0000  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| handle              | 0.0002  | 0.2500  | 0.0000  | 0.0000  |
| mop                 | 0.9167  | 1.0000  | 0.0000  | 0.0000  |
| flush               | 0.8333  | 1.0000  | 0.0000  | 0.0000  |
| roof                | 0.0009  | 0.2500  | 0.0000  | 0.0417  |
| countertop          | 0.0330  | 0.4643  | 0.0005  | 0.1429  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| footstool           | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| wine                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washbasin           | 0.0026  | 0.2000  | 0.0017  | 0.0667  |
| glass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| steps               | 0.0003  | 0.2000  | 0.0000  | 0.0000  |
| headboard           | 0.0156  | 0.1250  | 0.0000  | 0.0000  |
| treadmill           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| thermostat          | 0.0417  | 0.1667  | 0.0000  | 0.0000  |
| banister            | 0.0074  | 0.2000  | 0.0000  | 0.0000  |
| kitchen island      | 0.3469  | 0.4286  | 0.3469  | 0.4286  |
| salt                | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.4120  | 0.6500  | 0.1399  | 0.2500  |
| garage door         | 0.3611  | 0.5000  | 0.3611  | 0.5000  |
| arch                | 0.0007  | 0.1667  | 0.0002  | 0.1000  |
| dishwasher          | 0.2733  | 0.5000  | 0.2120  | 0.3000  |
| range hood          | 0.4155  | 0.7143  | 0.1696  | 0.2143  |
| barricade           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| menu                | 0.5714  | 0.5714  | 0.1502  | 0.2857  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet               | 0.6667  | 0.6667  | 0.6667  | 0.6667  |
| cap                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| helmets             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sponge              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glove               | 0.2407  | 0.6667  | 0.0370  | 0.3333  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| letter              | 0.4583  | 1.0000  | 0.0417  | 0.2500  |
| bread               | 0.1000  | 0.2000  | 0.0000  | 0.0000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water heater        | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| overall             | 0.2274  | 0.4495  | 0.1065  | 0.2189  |
| head                | 0.3053  | 0.6149  | 0.1395  | 0.3170  |
| common              | 0.2022  | 0.4277  | 0.1022  | 0.2074  |
| tail                | 0.1696  | 0.2907  | 0.0746  | 0.1230  |
| small               | 0.0730  | 0.2155  | 0.0129  | 0.0496  |
| medium              | 0.2405  | 0.5056  | 0.1171  | 0.2408  |
| large               | 0.1450  | 0.5524  | 0.0791  | 0.3484  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 11:11:56 INFO metrics.py:437 | number of scannet results: 250
Rank[0/8] 06/20/2025 11:13:12 INFO metrics.py:332 | eval results of scannet:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| bathtub             | 0.9538  | 0.9688  | 0.6790  | 0.7500  |
| curtain             | 0.6568  | 0.8938  | 0.1487  | 0.4867  |
| toilet              | 0.9425  | 0.9643  | 0.8121  | 0.8571  |
| bin                 | 0.6879  | 0.8723  | 0.4236  | 0.5957  |
| sink                | 0.8534  | 0.9091  | 0.3094  | 0.5227  |
| picture             | 0.3311  | 0.5187  | 0.0384  | 0.1418  |
| toilet paper        | 0.4301  | 0.5823  | 0.0949  | 0.2405  |
| doorframe           | 0.3551  | 0.7394  | 0.0763  | 0.3380  |
| door                | 0.5312  | 0.8428  | 0.1253  | 0.4716  |
| vanity              | 0.6724  | 1.0000  | 0.5942  | 0.8667  |
| stopcock            | 0.1935  | 0.5517  | 0.0026  | 0.0690  |
| towel               | 0.4454  | 0.7273  | 0.1338  | 0.3471  |
| mirror              | 0.2785  | 0.6747  | 0.0119  | 0.2169  |
| bag                 | 0.2598  | 0.7123  | 0.0902  | 0.3425  |
| bar                 | 0.0359  | 0.2273  | 0.0000  | 0.0000  |
| rail                | 0.0859  | 0.6667  | 0.0000  | 0.0000  |
| bottle              | 0.1161  | 0.4263  | 0.0058  | 0.1053  |
| shower              | 0.0483  | 0.2857  | 0.0003  | 0.0714  |
| tissue box          | 0.2224  | 0.5909  | 0.1778  | 0.2727  |
| switch              | 0.0020  | 0.0886  | 0.0000  | 0.0000  |
| hair dryer          | 0.0007  | 0.1429  | 0.0000  | 0.0000  |
| cup                 | 0.2229  | 0.4928  | 0.0969  | 0.1739  |
| socket              | 0.0084  | 0.2379  | 0.0001  | 0.0146  |
| faucet              | 0.0005  | 0.1250  | 0.0000  | 0.0000  |
| rod                 | 0.0359  | 0.1429  | 0.0000  | 0.0000  |
| soap dish           | 0.5320  | 0.8333  | 0.1207  | 0.2500  |
| microwave           | 0.7085  | 0.8000  | 0.3227  | 0.5000  |
| tissue              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| counter             | 0.4024  | 0.8571  | 0.2219  | 0.5143  |
| basket              | 0.0316  | 0.3077  | 0.0216  | 0.1538  |
| ledge               | 0.0168  | 0.3333  | 0.0015  | 0.1667  |
| box                 | 0.1413  | 0.4841  | 0.0373  | 0.1627  |
| light               | 0.1871  | 0.3793  | 0.0086  | 0.0345  |
| plant               | 0.3292  | 0.6555  | 0.0765  | 0.3193  |
| cabinet             | 0.4034  | 0.8095  | 0.2068  | 0.5317  |
| paper               | 0.0228  | 0.2943  | 0.0007  | 0.0528  |
| shelf               | 0.5799  | 0.8603  | 0.3312  | 0.6691  |
| pillow              | 0.4629  | 0.6458  | 0.2245  | 0.4236  |
| blanket             | 0.0744  | 0.5833  | 0.0115  | 0.2083  |
| frame               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes             | 0.1856  | 0.6797  | 0.0480  | 0.2745  |
| stool               | 0.3061  | 0.7955  | 0.2133  | 0.5909  |
| case                | 0.7467  | 0.9474  | 0.3521  | 0.6842  |
| plunger             | 0.8118  | 1.0000  | 0.4500  | 0.6000  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bed                 | 0.9550  | 1.0000  | 0.9139  | 0.9825  |
| window              | 0.4666  | 0.8210  | 0.1573  | 0.4012  |
| purse               | 0.0084  | 0.5000  | 0.0000  | 0.0000  |
| decoration          | 0.1919  | 0.6667  | 0.0079  | 0.2083  |
| lamp                | 0.4572  | 0.7381  | 0.1556  | 0.3690  |
| backpack            | 0.7171  | 0.8929  | 0.4662  | 0.6429  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster             | 0.7976  | 1.0000  | 0.1488  | 0.5000  |
| chair               | 0.6802  | 0.8427  | 0.4925  | 0.6547  |
| monitor             | 0.5878  | 0.7429  | 0.1537  | 0.3357  |
| blackboard          | 0.5686  | 0.8261  | 0.2480  | 0.4203  |
| computer            | 0.3243  | 0.6923  | 0.0401  | 0.1538  |
| radiator            | 0.8242  | 0.8793  | 0.2279  | 0.4310  |
| table               | 0.6120  | 0.9269  | 0.5107  | 0.8346  |
| keyboard            | 0.0746  | 0.2241  | 0.0000  | 0.0000  |
| desk                | 0.6834  | 0.9747  | 0.4969  | 0.7722  |
| mouse               | 0.0554  | 0.1905  | 0.0000  | 0.0000  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| telephone           | 0.4605  | 0.7188  | 0.1453  | 0.2812  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| book                | 0.1123  | 0.4929  | 0.0119  | 0.1357  |
| windowsill          | 0.0363  | 0.8333  | 0.0009  | 0.1667  |
| stand               | 0.8230  | 0.9245  | 0.7170  | 0.8491  |
| couch               | 0.7490  | 0.9500  | 0.6343  | 0.8750  |
| laptop              | 0.5272  | 0.7333  | 0.1219  | 0.2667  |
| jacket              | 0.6658  | 0.9565  | 0.3961  | 0.6957  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stairs              | 0.1011  | 0.8000  | 0.0015  | 0.4000  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| board               | 0.1852  | 0.7500  | 0.0173  | 0.3438  |
| bucket              | 0.3386  | 0.6429  | 0.1333  | 0.5000  |
| pen                 | 0.0111  | 0.1111  | 0.0000  | 0.0000  |
| pipe                | 0.0001  | 0.0714  | 0.0000  | 0.0000  |
| dresser             | 0.7550  | 0.9730  | 0.6804  | 0.9189  |
| tv                  | 0.9105  | 1.0000  | 0.2948  | 0.5652  |
| rack                | 0.0005  | 0.0909  | 0.0001  | 0.0909  |
| wardrobe            | 0.5330  | 1.0000  | 0.3983  | 0.6250  |
| shoe                | 0.2700  | 0.5500  | 0.0414  | 0.1500  |
| toy                 | 0.0832  | 0.2941  | 0.0196  | 0.0588  |
| clock               | 0.3226  | 0.6400  | 0.0282  | 0.1600  |
| ottoman             | 0.1150  | 1.0000  | 0.0449  | 0.5714  |
| plate               | 0.0216  | 0.4000  | 0.0000  | 0.0000  |
| paper cutter        | 0.8000  | 0.8000  | 0.0000  | 0.0000  |
| calendar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bench               | 0.7914  | 1.0000  | 0.7914  | 1.0000  |
| machine             | 0.4452  | 0.9375  | 0.3296  | 0.6250  |
| carpet              | 0.0258  | 0.4000  | 0.0000  | 0.0000  |
| cushion             | 0.0116  | 0.5000  | 0.0032  | 0.2500  |
| fan                 | 0.7521  | 0.8750  | 0.1607  | 0.3750  |
| sign                | 0.0744  | 0.5000  | 0.0007  | 0.1071  |
| stove               | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| bicycle             | 0.7333  | 1.0000  | 0.5476  | 1.0000  |
| guitar              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| refrigerator        | 0.8821  | 0.9500  | 0.7032  | 0.8500  |
| person              | 0.3167  | 0.6667  | 0.0000  | 0.0000  |
| scale               | 0.2576  | 1.0000  | 0.2576  | 1.0000  |
| bowl                | 0.1926  | 0.3333  | 0.0420  | 0.2222  |
| notebook            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kettle              | 0.1651  | 0.7500  | 0.0037  | 0.2500  |
| coffee maker        | 0.5412  | 0.7059  | 0.2144  | 0.3529  |
| broom               | 0.0285  | 0.2500  | 0.0000  | 0.0000  |
| hamper              | 0.4656  | 0.6000  | 0.4656  | 0.6000  |
| toothpaste          | 0.0013  | 0.2000  | 0.0000  | 0.0000  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| container           | 0.0005  | 0.0870  | 0.0001  | 0.0435  |
| tube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mat                 | 0.0696  | 0.4000  | 0.0000  | 0.0000  |
| grab bar            | 0.0348  | 0.3750  | 0.0074  | 0.1250  |
| soap dispenser      | 0.5306  | 0.7143  | 0.1282  | 0.3333  |
| dispenser           | 0.5776  | 0.6667  | 0.4375  | 0.5385  |
| stall               | 0.2101  | 1.0000  | 0.1689  | 0.5294  |
| holder              | 0.2000  | 0.2000  | 0.0000  | 0.0000  |
| mailbox             | 0.0667  | 0.4000  | 0.0000  | 0.0000  |
| pillar              | 0.2235  | 0.4000  | 0.2235  | 0.4000  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| copier              | 0.9167  | 0.9167  | 0.9167  | 0.9167  |
| fire extinguisher   | 0.0111  | 0.3333  | 0.0000  | 0.0000  |
| cart                | 0.1667  | 0.5000  | 0.1667  | 0.5000  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball                | 0.4167  | 0.4167  | 0.3333  | 0.3333  |
| printer             | 0.6207  | 0.8788  | 0.5160  | 0.7273  |
| vacuum cleaner      | 0.8667  | 1.0000  | 0.8667  | 1.0000  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basin               | 0.0055  | 1.0000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.8333  | 1.0000  | 0.8333  | 1.0000  |
| map                 | 0.0400  | 1.0000  | 0.0400  | 1.0000  |
| teapot              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water cooler        | 0.8333  | 1.0000  | 0.8333  | 1.0000  |
| dish rack           | 0.6534  | 1.0000  | 0.0833  | 0.2500  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pot                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| oven                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flowerpot           | 0.0333  | 0.5000  | 0.0333  | 0.5000  |
| adhesive tape       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ladder              | 0.1238  | 0.4000  | 0.0667  | 0.2000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.0667  | 0.3333  | 0.0000  | 0.0000  |
| crate               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.6667  | 0.8000  | 0.4000  | 0.4000  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.5884  | 0.8333  | 0.3333  | 0.5000  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| barricade           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headphones          | 0.2400  | 0.4000  | 0.2000  | 0.2000  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drawer              | 0.0667  | 1.0000  | 0.0667  | 1.0000  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| scannet             | 0.2798  | 0.4971  | 0.1546  | 0.2836  |
| head                | 0.3729  | 0.6364  | 0.1989  | 0.3648  |
| common              | 0.2133  | 0.4079  | 0.1279  | 0.2258  |
| tail                | 0.1089  | 0.2177  | 0.0616  | 0.1344  |
| small               | 0.0508  | 0.1879  | 0.0127  | 0.0429  |
| medium              | 0.3317  | 0.6028  | 0.1784  | 0.3344  |
| large               | 0.2583  | 0.7783  | 0.1784  | 0.5553  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 11:13:12 INFO metrics.py:437 | number of 3rscan results: 235
Rank[0/8] 06/20/2025 11:14:21 INFO metrics.py:332 | eval results of 3rscan:
+-------------------+---------+---------+---------+---------+
| classes           | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+-------------------+---------+---------+---------+---------+
| chair             | 0.7012  | 0.8426  | 0.5437  | 0.6871  |
| monitor           | 0.5708  | 0.7000  | 0.1087  | 0.2500  |
| keyboard          | 0.0798  | 0.3333  | 0.0000  | 0.0000  |
| bin               | 0.7253  | 0.9146  | 0.3996  | 0.6829  |
| desk              | 0.4648  | 0.9125  | 0.2937  | 0.7625  |
| cup               | 0.0534  | 0.4103  | 0.0001  | 0.0769  |
| mouse             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| computer          | 0.3726  | 0.8095  | 0.0145  | 0.1429  |
| bag               | 0.3426  | 0.7475  | 0.0906  | 0.3535  |
| telephone         | 0.2677  | 0.5833  | 0.0480  | 0.1667  |
| case              | 0.0509  | 0.4071  | 0.0084  | 0.0929  |
| paper             | 0.0648  | 0.3429  | 0.0004  | 0.0714  |
| backpack          | 0.5050  | 0.7879  | 0.2293  | 0.4848  |
| plug              | 0.0926  | 0.2222  | 0.0000  | 0.0000  |
| pen               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wardrobe          | 0.4529  | 0.7656  | 0.2766  | 0.6562  |
| plate             | 0.0349  | 0.4242  | 0.0011  | 0.0909  |
| stand             | 0.4958  | 0.9016  | 0.3258  | 0.6885  |
| table             | 0.5745  | 0.8874  | 0.4369  | 0.7568  |
| bottle            | 0.0673  | 0.3719  | 0.0008  | 0.0413  |
| book              | 0.1109  | 0.5030  | 0.0181  | 0.1124  |
| window            | 0.2716  | 0.6712  | 0.0369  | 0.2466  |
| file              | 0.1089  | 0.4643  | 0.0018  | 0.1429  |
| curtain           | 0.5884  | 0.8810  | 0.1524  | 0.4841  |
| cabinet           | 0.4121  | 0.8205  | 0.2623  | 0.6026  |
| garbage           | 0.3960  | 0.7500  | 0.2557  | 0.5000  |
| blinds            | 0.2896  | 0.5641  | 0.0639  | 0.3333  |
| socket            | 0.0022  | 0.0625  | 0.0000  | 0.0000  |
| conducting wire   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| notebook          | 0.0002  | 0.0909  | 0.0000  | 0.0000  |
| plant             | 0.3752  | 0.6535  | 0.0902  | 0.2426  |
| box               | 0.2262  | 0.5556  | 0.0518  | 0.2026  |
| cushion           | 0.3345  | 0.7667  | 0.0870  | 0.2333  |
| wire              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stool             | 0.4137  | 0.8704  | 0.2528  | 0.6296  |
| commode           | 0.4908  | 0.8654  | 0.3710  | 0.7692  |
| shelf             | 0.3231  | 0.7279  | 0.0993  | 0.3852  |
| sink              | 0.7424  | 0.8393  | 0.1769  | 0.3571  |
| pillow            | 0.4424  | 0.6992  | 0.0432  | 0.1914  |
| picture           | 0.1950  | 0.4350  | 0.0159  | 0.0772  |
| clock             | 0.4090  | 0.7368  | 0.0000  | 0.0000  |
| printer           | 0.7057  | 0.8889  | 0.1521  | 0.3333  |
| door              | 0.3410  | 0.7353  | 0.0584  | 0.3235  |
| frame             | 0.1547  | 0.7667  | 0.0016  | 0.0667  |
| laptop            | 0.3667  | 0.6667  | 0.2917  | 0.5000  |
| candlestick       | 0.0175  | 1.0000  | 0.0000  | 0.0000  |
| bucket            | 0.6208  | 0.9333  | 0.1932  | 0.5333  |
| couch             | 0.4212  | 0.8429  | 0.3022  | 0.6857  |
| switch            | 0.0007  | 0.0755  | 0.0000  | 0.0000  |
| clothes           | 0.2386  | 0.6990  | 0.0464  | 0.2913  |
| bed               | 0.8685  | 0.9818  | 0.7398  | 0.8909  |
| tv                | 0.6875  | 0.9429  | 0.2155  | 0.4857  |
| lamp              | 0.2909  | 0.5375  | 0.0622  | 0.1562  |
| napkins           | 0.1493  | 1.0000  | 0.0000  | 0.0000  |
| light             | 0.1235  | 0.5455  | 0.0153  | 0.0909  |
| price tag         | 0.1443  | 0.2857  | 0.0000  | 0.0000  |
| candle            | 0.0747  | 0.2667  | 0.0000  | 0.0000  |
| blanket           | 0.3528  | 0.8923  | 0.0994  | 0.4769  |
| decoration        | 0.1722  | 0.5000  | 0.0125  | 0.0625  |
| vase              | 0.2351  | 0.4500  | 0.1219  | 0.2000  |
| bar               | 0.5617  | 1.0000  | 0.0053  | 0.6667  |
| bowl              | 0.2353  | 0.5556  | 0.1117  | 0.1852  |
| carpet            | 0.2882  | 0.6786  | 0.0126  | 0.2321  |
| folder            | 0.0215  | 0.2857  | 0.0024  | 0.0714  |
| spoon             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rack              | 0.3305  | 0.7000  | 0.1599  | 0.5667  |
| jar               | 0.2567  | 0.5000  | 0.0640  | 0.2500  |
| remote control    | 0.0433  | 0.3000  | 0.0129  | 0.1500  |
| towel             | 0.3046  | 0.6835  | 0.0711  | 0.2658  |
| hook              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mattress          | 0.1000  | 0.5000  | 0.0000  | 0.0000  |
| board             | 0.2979  | 0.7083  | 0.0557  | 0.1250  |
| ottoman           | 0.4296  | 0.9091  | 0.3844  | 0.8182  |
| toilet paper      | 0.1380  | 0.4167  | 0.0000  | 0.0000  |
| flowerpot         | 0.1361  | 0.5000  | 0.0003  | 0.0417  |
| soap dispenser    | 0.0933  | 0.4000  | 0.0000  | 0.0000  |
| player            | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| teapot            | 0.0312  | 0.2500  | 0.0312  | 0.2500  |
| magazine          | 0.0352  | 0.7500  | 0.0045  | 0.2500  |
| cube              | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| toy               | 0.4117  | 0.6154  | 0.1663  | 0.3077  |
| tablet            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| roll              | 0.2335  | 0.4000  | 0.0000  | 0.0000  |
| container         | 0.0578  | 0.4444  | 0.0370  | 0.1111  |
| calendar          | 0.0084  | 0.4000  | 0.0000  | 0.0000  |
| humidifier        | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| blackboard        | 0.5528  | 0.7500  | 0.3171  | 0.5000  |
| heater            | 0.6138  | 0.8421  | 0.1637  | 0.2632  |
| windowsill        | 0.1028  | 0.6410  | 0.0042  | 0.1026  |
| fan               | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| doorframe         | 0.1102  | 0.6400  | 0.0152  | 0.1800  |
| device            | 0.0776  | 0.5455  | 0.0130  | 0.0909  |
| holder            | 0.1429  | 1.0000  | 0.0000  | 0.0000  |
| radiator          | 0.4801  | 0.6552  | 0.0707  | 0.2414  |
| drawer            | 0.4779  | 0.8889  | 0.0526  | 0.7778  |
| ball              | 0.5500  | 0.6000  | 0.5500  | 0.6000  |
| speaker           | 0.1487  | 0.3333  | 0.0025  | 0.0833  |
| tube              | 0.0246  | 0.4286  | 0.0000  | 0.0000  |
| umbrella          | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| brush             | 0.1396  | 0.6250  | 0.0000  | 0.0000  |
| pillar            | 0.5898  | 0.7647  | 0.3412  | 0.4706  |
| air conditioner   | 0.7500  | 0.7500  | 0.7500  | 0.7500  |
| mirror            | 0.2527  | 0.7037  | 0.0000  | 0.0370  |
| chandelier        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pot               | 0.1680  | 0.6667  | 0.0424  | 0.1667  |
| shoe              | 0.2642  | 0.5476  | 0.0057  | 0.0952  |
| tray              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| album             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hanger            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basket            | 0.5156  | 0.7656  | 0.2353  | 0.4219  |
| balcony           | 0.0756  | 1.0000  | 0.0032  | 0.5000  |
| beanbag           | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| stairs            | 0.4201  | 0.7143  | 0.0470  | 0.3571  |
| guitar            | 1.0000  | 1.0000  | 0.5000  | 0.5000  |
| rail              | 0.1293  | 0.5000  | 0.0000  | 0.0000  |
| dresser           | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| bicycle           | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| tissue box        | 0.3611  | 0.6667  | 0.3333  | 0.3333  |
| sack              | 0.5010  | 0.8571  | 0.0833  | 0.2857  |
| bench             | 0.3404  | 0.8571  | 0.2419  | 0.7500  |
| scale             | 0.3333  | 0.3333  | 0.0000  | 0.0000  |
| sunglasses        | 0.0695  | 0.5385  | 0.0018  | 0.1538  |
| hat               | 0.1429  | 0.1429  | 0.0000  | 0.0000  |
| furniture         | 0.0076  | 0.3333  | 0.0000  | 0.0000  |
| mat               | 0.0095  | 0.3333  | 0.0000  | 0.0000  |
| bathtub           | 1.0000  | 1.0000  | 0.8395  | 0.8889  |
| clothes dryer     | 0.5193  | 0.9000  | 0.1434  | 0.3000  |
| statue            | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| shampoo           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| faucet            | 0.3333  | 0.6667  | 0.0000  | 0.0000  |
| toilet            | 0.8038  | 0.8235  | 0.4902  | 0.5882  |
| shower            | 0.5104  | 1.0000  | 0.0491  | 0.5000  |
| tissue            | 0.1111  | 0.3333  | 0.0000  | 0.0000  |
| dispenser         | 0.0556  | 0.6667  | 0.0000  | 0.0000  |
| toiletry          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| cosmetics         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beam              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine   | 0.4153  | 0.5714  | 0.2083  | 0.2857  |
| soap              | 0.5000  | 1.0000  | 0.0000  | 0.0000  |
| sign              | 0.5192  | 1.0000  | 0.0000  | 0.0000  |
| body loofah       | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| coffee maker      | 0.2275  | 0.6667  | 0.0010  | 0.1111  |
| can               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| column            | 0.0179  | 1.0000  | 0.0079  | 0.5000  |
| knife             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| microwave         | 0.8910  | 1.0000  | 0.5156  | 0.6471  |
| oven              | 0.6128  | 0.9474  | 0.2893  | 0.6842  |
| refrigerator      | 0.6760  | 0.8667  | 0.5960  | 0.7333  |
| stove             | 0.4132  | 0.8824  | 0.0725  | 0.3529  |
| pan               | 0.3438  | 0.5833  | 0.1071  | 0.1667  |
| counter           | 0.1511  | 0.7073  | 0.0098  | 0.2195  |
| kettle            | 0.1549  | 0.6667  | 0.0638  | 0.4167  |
| fruit             | 0.6000  | 0.6000  | 0.2000  | 0.2000  |
| shovel            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchenware       | 0.3075  | 0.5682  | 0.0347  | 0.1818  |
| basin             | 0.0157  | 0.2667  | 0.0000  | 0.0000  |
| poster            | 0.0160  | 0.1429  | 0.0000  | 0.0000  |
| food              | 0.1000  | 0.1000  | 0.0000  | 0.0000  |
| jalousie          | 1.0000  | 1.0000  | 0.0132  | 0.5000  |
| partition         | 0.2733  | 0.8000  | 0.0018  | 0.2000  |
| vacuum cleaner    | 0.7500  | 0.7500  | 0.0000  | 0.0000  |
| crate             | 0.6659  | 1.0000  | 0.3107  | 0.6000  |
| screen            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| window frame      | 0.0767  | 0.5000  | 0.0015  | 0.0833  |
| radio             | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| crib              | 0.3548  | 0.6667  | 0.3548  | 0.6667  |
| pack              | 0.0417  | 0.6923  | 0.0109  | 0.2308  |
| broom             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| piano             | 0.5000  | 1.0000  | 0.5000  | 1.0000  |
| fireplace         | 0.1000  | 0.2000  | 0.0000  | 0.0000  |
| map               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pipe              | 0.0083  | 0.7500  | 0.0000  | 0.0000  |
| mop               | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| flush             | 0.8333  | 1.0000  | 0.0000  | 0.0000  |
| soap dish         | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| ladder            | 0.3250  | 1.0000  | 0.1667  | 0.3333  |
| coil              | 0.1389  | 0.3333  | 0.0000  | 0.0000  |
| pad               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| menu              | 0.5714  | 0.5714  | 0.1502  | 0.2857  |
| range hood        | 0.3273  | 0.6000  | 0.0500  | 0.2000  |
| dishwasher        | 0.9167  | 1.0000  | 0.6667  | 0.6667  |
| tool              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| toaster           | 0.8929  | 1.0000  | 0.3214  | 0.5000  |
| dumbbell          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| helmets           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| salt              | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| glass             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sponge            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glove             | 0.2407  | 0.6667  | 0.0370  | 0.3333  |
| treadmill         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| letter            | 0.4583  | 1.0000  | 0.0417  | 0.2500  |
| bread             | 0.1000  | 0.2000  | 0.0000  | 0.0000  |
| fire extinguisher | 1.0000  | 1.0000  | 0.0333  | 1.0000  |
| footstool         | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| luggage           | 0.7333  | 1.0000  | 0.6667  | 0.6667  |
| water heater      | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| machine           | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| paper cutter      | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| dish rack         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| seasoning         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toothbrush        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scissors          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washbasin         | 0.1111  | 1.0000  | 0.0000  | 0.0000  |
| -------           | ------- | ------- | ------- | ------- |
| 3rscan            | 0.3326  | 0.5818  | 0.1354  | 0.2522  |
| head              | 0.3502  | 0.6729  | 0.1385  | 0.3075  |
| common            | 0.3543  | 0.5767  | 0.1567  | 0.2491  |
| tail              | 0.2710  | 0.4215  | 0.1008  | 0.1546  |
| small             | 0.1034  | 0.2887  | 0.0144  | 0.0458  |
| medium            | 0.3564  | 0.6436  | 0.1590  | 0.2853  |
| large             | 0.2508  | 0.8037  | 0.0940  | 0.4825  |
+-------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 11:14:21 INFO metrics.py:437 | number of matterport3d results: 339
Rank[0/8] 06/20/2025 11:15:48 INFO metrics.py:332 | eval results of matterport3d:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| mirror              | 0.0984  | 0.6351  | 0.0029  | 0.1622  |
| picture             | 0.2138  | 0.4565  | 0.0096  | 0.1039  |
| doorframe           | 0.3129  | 0.7309  | 0.1005  | 0.4274  |
| light               | 0.0383  | 0.2308  | 0.0014  | 0.0484  |
| door                | 0.2708  | 0.6292  | 0.0108  | 0.1723  |
| sink                | 0.5442  | 0.7778  | 0.2233  | 0.4848  |
| plant               | 0.3004  | 0.7946  | 0.1375  | 0.4821  |
| towel               | 0.4154  | 0.6545  | 0.1003  | 0.2818  |
| blanket             | 0.0557  | 0.6364  | 0.0139  | 0.2727  |
| lamp                | 0.2821  | 0.6923  | 0.1181  | 0.3736  |
| drawer              | 0.0024  | 0.3750  | 0.0018  | 0.2500  |
| decoration          | 0.0102  | 0.4048  | 0.0023  | 0.0952  |
| window              | 0.1396  | 0.5194  | 0.0166  | 0.1583  |
| roof                | 0.0009  | 0.2500  | 0.0000  | 0.0417  |
| cabinet             | 0.1248  | 0.6356  | 0.0650  | 0.3856  |
| dresser             | 0.2592  | 0.8000  | 0.1594  | 0.6000  |
| case                | 0.0009  | 0.2222  | 0.0001  | 0.1667  |
| flowerpot           | 0.0603  | 0.4800  | 0.0114  | 0.2000  |
| toilet              | 0.8014  | 0.8438  | 0.6662  | 0.7344  |
| vase                | 0.0150  | 0.4333  | 0.0074  | 0.2667  |
| bottle              | 0.1055  | 0.5208  | 0.0049  | 0.1458  |
| handle              | 0.0002  | 0.2500  | 0.0000  | 0.0000  |
| countertop          | 0.0330  | 0.4643  | 0.0005  | 0.1429  |
| shelf               | 0.1856  | 0.5985  | 0.0926  | 0.3796  |
| wardrobe            | 0.1549  | 0.5455  | 0.1389  | 0.3636  |
| vent                | 0.0223  | 0.1667  | 0.0000  | 0.0000  |
| stand               | 0.4749  | 0.8800  | 0.4214  | 0.7200  |
| frame               | 0.0001  | 0.1176  | 0.0000  | 0.0000  |
| counter             | 0.0107  | 0.4242  | 0.0002  | 0.1212  |
| pot                 | 0.0032  | 0.3846  | 0.0012  | 0.1538  |
| box                 | 0.0560  | 0.6538  | 0.0182  | 0.3462  |
| shower              | 0.0016  | 0.0833  | 0.0000  | 0.0000  |
| switch              | 0.0001  | 0.1000  | 0.0000  | 0.0000  |
| window frame        | 0.0665  | 0.4483  | 0.0072  | 0.1609  |
| table               | 0.3188  | 0.7564  | 0.2664  | 0.5897  |
| faucet              | 0.0181  | 0.1944  | 0.0000  | 0.0000  |
| bench               | 0.1490  | 0.4500  | 0.1263  | 0.2500  |
| statue              | 0.0752  | 0.4118  | 0.0043  | 0.2941  |
| fan                 | 0.6471  | 0.7963  | 0.1998  | 0.4630  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bed                 | 0.6307  | 0.7895  | 0.3343  | 0.7719  |
| chair               | 0.4324  | 0.7177  | 0.3377  | 0.5952  |
| curtain             | 0.3958  | 0.7917  | 0.1355  | 0.4479  |
| carpet              | 0.0009  | 0.1667  | 0.0000  | 0.0000  |
| pillow              | 0.1461  | 0.5584  | 0.0072  | 0.1073  |
| bathtub             | 0.6579  | 0.7778  | 0.5357  | 0.6296  |
| washing machine     | 0.4675  | 0.6250  | 0.1667  | 0.2500  |
| paper               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| screen              | 0.0217  | 0.5000  | 0.0028  | 0.2500  |
| tv                  | 0.4713  | 0.6977  | 0.2067  | 0.3488  |
| couch               | 0.3919  | 0.7717  | 0.2941  | 0.6413  |
| plate               | 0.0808  | 0.3571  | 0.0714  | 0.0714  |
| telephone           | 0.2544  | 0.5000  | 0.0000  | 0.0000  |
| ottoman             | 0.0568  | 0.5000  | 0.0440  | 0.4444  |
| book                | 0.0723  | 0.4173  | 0.0155  | 0.1102  |
| stool               | 0.0157  | 0.2857  | 0.0129  | 0.2381  |
| alarm               | 0.0014  | 0.1667  | 0.0000  | 0.0000  |
| clock               | 0.0833  | 0.3333  | 0.0000  | 0.0000  |
| toy                 | 0.1353  | 0.4444  | 0.0374  | 0.1111  |
| stairs              | 0.0067  | 0.3208  | 0.0013  | 0.0377  |
| footstool           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| board               | 0.1904  | 0.5000  | 0.0000  | 0.0000  |
| bowl                | 0.0512  | 0.4242  | 0.0021  | 0.0909  |
| column              | 0.0279  | 0.3056  | 0.0016  | 0.1667  |
| pillar              | 0.2447  | 0.7619  | 0.1304  | 0.3810  |
| monitor             | 0.3333  | 0.5000  | 0.0000  | 0.0000  |
| stove               | 0.3338  | 0.6000  | 0.1614  | 0.3000  |
| cushion             | 0.0045  | 0.1176  | 0.0003  | 0.0588  |
| desk                | 0.2928  | 0.7368  | 0.2391  | 0.6842  |
| keyboard            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wine                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cup                 | 0.0059  | 0.3000  | 0.0000  | 0.0000  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit               | 0.0714  | 0.2500  | 0.0714  | 0.2500  |
| mat                 | 0.0045  | 0.2917  | 0.0000  | 0.0000  |
| doorway             | 0.0011  | 0.3333  | 0.0001  | 0.1111  |
| beam                | 0.0021  | 0.1429  | 0.0016  | 0.0952  |
| partition           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vanity              | 0.1699  | 0.7333  | 0.1529  | 0.6000  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dispenser           | 0.0060  | 0.5000  | 0.0060  | 0.5000  |
| toilet paper        | 0.3582  | 0.6667  | 0.0682  | 0.3056  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bin                 | 0.3548  | 0.7073  | 0.2197  | 0.5122  |
| windowsill          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| holder              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| socket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washbasin           | 0.0026  | 0.1429  | 0.0018  | 0.0714  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rail                | 0.0433  | 0.3704  | 0.0010  | 0.0185  |
| steps               | 0.0003  | 0.2000  | 0.0000  | 0.0000  |
| headboard           | 0.0156  | 0.1250  | 0.0000  | 0.0000  |
| teapot              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rack                | 0.0015  | 0.3636  | 0.0011  | 0.2727  |
| bar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candle              | 0.0159  | 0.3333  | 0.0159  | 0.3333  |
| thermostat          | 0.0417  | 0.1667  | 0.0000  | 0.0000  |
| banister            | 0.0074  | 0.2000  | 0.0000  | 0.0000  |
| soap dispenser      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| heater              | 0.3333  | 1.0000  | 0.0000  | 0.0000  |
| hanger              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.2619  | 0.6667  | 0.2619  | 0.6667  |
| tissue              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket              | 0.0150  | 0.5000  | 0.0051  | 0.3000  |
| toiletry            | 0.0794  | 0.5455  | 0.0018  | 0.0909  |
| clothes             | 0.0983  | 0.5000  | 0.0129  | 0.2069  |
| kitchen island      | 0.3469  | 0.4286  | 0.3469  | 0.4286  |
| piano               | 0.1667  | 0.5000  | 0.1667  | 0.5000  |
| refrigerator        | 0.3267  | 0.8000  | 0.0729  | 0.2000  |
| radiator            | 0.1667  | 0.3333  | 0.0000  | 0.0000  |
| air conditioner     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| microwave           | 0.1701  | 0.4167  | 0.0841  | 0.1667  |
| container           | 0.0088  | 0.1111  | 0.0030  | 0.0741  |
| fireplace           | 0.2598  | 0.5652  | 0.0039  | 0.1739  |
| backpack            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball                | 0.5026  | 0.7500  | 0.5022  | 0.7500  |
| basin               | 0.0014  | 1.0000  | 0.0014  | 1.0000  |
| scale               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pan                 | 0.0358  | 0.4000  | 0.0036  | 0.2000  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pipe                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kettle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool table          | 0.1667  | 0.3333  | 0.1667  | 0.3333  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue box          | 0.0990  | 0.7500  | 0.0123  | 0.2500  |
| basket              | 0.0627  | 0.5600  | 0.0085  | 0.2800  |
| soap                | 0.1034  | 0.6000  | 0.1012  | 0.4000  |
| fence               | 0.0833  | 0.0833  | 0.0038  | 0.0833  |
| speaker             | 0.0293  | 0.3750  | 0.0024  | 0.1250  |
| ladder              | 0.0556  | 0.2222  | 0.0074  | 0.1111  |
| food                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bag                 | 0.0277  | 0.3846  | 0.0000  | 0.0000  |
| kitchenware         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.4578  | 0.7222  | 0.1555  | 0.2778  |
| dumbbell            | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| chandelier          | 0.1393  | 0.3684  | 0.0327  | 0.1579  |
| fire extinguisher   | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| garage door         | 0.3611  | 0.5000  | 0.3611  | 0.5000  |
| printer             | 0.4286  | 0.4286  | 0.4286  | 0.4286  |
| blackboard          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dish           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine             | 0.0180  | 1.0000  | 0.0180  | 1.0000  |
| arch                | 0.0007  | 0.1667  | 0.0002  | 0.1000  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| computer            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| oven                | 0.1849  | 0.8000  | 0.0000  | 0.0000  |
| mouse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shoe                | 0.0861  | 0.2000  | 0.0007  | 0.0400  |
| laptop              | 0.4500  | 1.0000  | 0.1000  | 0.5000  |
| file                | 0.2667  | 0.5000  | 0.0000  | 0.0000  |
| guitar              | 0.2000  | 0.5714  | 0.0000  | 0.0000  |
| bicycle             | 0.2500  | 1.0000  | 0.0109  | 1.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hamper              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet               | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| magazine            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.4000  | 0.7500  | 0.0000  | 0.0000  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pack                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drum                | 0.0125  | 0.1250  | 0.0000  | 0.0000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster             | 0.0119  | 0.5000  | 0.0000  | 0.0000  |
| coffee maker        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jacket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.0357  | 0.2500  | 0.0357  | 0.2500  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candlestick         | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| matterport3d        | 0.1236  | 0.3434  | 0.0614  | 0.1781  |
| head                | 0.1681  | 0.4575  | 0.0751  | 0.2208  |
| common              | 0.0792  | 0.2635  | 0.0444  | 0.1569  |
| tail                | 0.0847  | 0.1415  | 0.0598  | 0.0766  |
| small               | 0.0393  | 0.1637  | 0.0178  | 0.0631  |
| medium              | 0.1183  | 0.3745  | 0.0542  | 0.1768  |
| large               | 0.1203  | 0.4612  | 0.0701  | 0.2910  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 11:15:48 INFO metrics.py:437 | number of arkit results: 0
Rank[0/8] 06/20/2025 11:15:48 INFO stats.py:394 | Epoch[159] completed. Training Speed: 2.96 samples/sec across all devices.	Epoch Time: 1052.70 sec.	Average Epoch Time: 1052.70 sec.	Average Step Time: 2.70 sec.	Estimated Remaining Time: 10:31:46.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:15:49 WARNING accelerator.py:3099 | Deleting 1 checkpoints to make room for new checkpoint.
Rank[0/8] 06/20/2025 11:15:50 INFO accelerator.py:3111 | Saving current state to /job_data/checkpoints/checkpoint_15
Rank[6/8] 06/20/2025 11:15:50 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 159 to /job_data/checkpoints/checkpoint_15
Rank[4/8] 06/20/2025 11:15:51 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 159 to /job_data/checkpoints/checkpoint_15
Rank[1/8] 06/20/2025 11:15:51 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 159 to /job_data/checkpoints/checkpoint_15
Rank[3/8] 06/20/2025 11:15:51 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 159 to /job_data/checkpoints/checkpoint_15
Rank[2/8] 06/20/2025 11:15:51 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 159 to /job_data/checkpoints/checkpoint_15
Rank[5/8] 06/20/2025 11:15:51 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 159 to /job_data/checkpoints/checkpoint_15
Rank[7/8] 06/20/2025 11:15:51 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 159 to /job_data/checkpoints/checkpoint_15
Rank[0/8] 06/20/2025 11:15:54 INFO checkpointing.py:106 | Model weights saved in /job_data/checkpoints/checkpoint_15/model.safetensors
Rank[0/8] 06/20/2025 11:15:59 INFO checkpointing.py:113 | Optimizer state saved in /job_data/checkpoints/checkpoint_15/optimizer.bin
Rank[0/8] 06/20/2025 11:15:59 INFO checkpointing.py:120 | Scheduler state saved in /job_data/checkpoints/checkpoint_15/scheduler.bin
Rank[0/8] 06/20/2025 11:15:59 INFO checkpointing.py:137 | Sampler state for dataloader 0 saved in /job_data/checkpoints/checkpoint_15/sampler.bin
Rank[0/8] 06/20/2025 11:15:59 INFO checkpointing.py:137 | Sampler state for dataloader 1 saved in /job_data/checkpoints/checkpoint_15/sampler_1.bin
Rank[0/8] 06/20/2025 11:16:00 INFO checkpointing.py:164 | Random states saved in /job_data/checkpoints/checkpoint_15/random_states_0.pkl
Rank[0/8] 06/20/2025 11:16:00 INFO checkpointing.py:300 | Saving the state of TrainerProgressState to /job_data/checkpoints/checkpoint_15/custom_checkpoint_0.pkl
Rank[0/8] 06/20/2025 11:16:01 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 159 to /job_data/checkpoints/checkpoint_15
Rank[0/8] 06/20/2025 11:16:31 INFO stats.py:314 | Epoch[160] Step[24] GlobalStep[62424] Training Speed: 8.73 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 10:31:22.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:16:31 INFO loss_tracker.py:84 | Epoch[160/239] Step[24] GlobalStep[62424/NA]: det_loss_cls_0[0.4735] det_loss_box_0[0.5850] det_loss_cls_1[0.4583] det_loss_box_1[0.4375] det_loss_cls_2[0.4034] det_loss_box_2[0.3887] det_loss_cls_3[0.3790] det_loss_box_3[0.3738] det_loss_cls_4[0.3551] det_loss_box_4[0.3740] det_loss_cls_5[0.3487] det_loss_box_5[0.3705] det_loss_cls_dn_0[0.0945] det_loss_box_dn_0[0.2655] det_loss_cls_dn_1[0.0908] det_loss_box_dn_1[0.2188] det_loss_cls_dn_2[0.0896] det_loss_box_dn_2[0.2026] det_loss_cls_dn_3[0.0882] det_loss_box_dn_3[0.1991] det_loss_cls_dn_4[0.0874] det_loss_box_dn_4[0.1976] det_loss_cls_dn_5[0.0865] det_loss_box_dn_5[0.1961] loss_depth[0.0375] total_loss[6.8013]
Rank[0/8] 06/20/2025 11:16:56 INFO stats.py:314 | Epoch[160] Step[49] GlobalStep[62449] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 10:30:49.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:16:56 INFO loss_tracker.py:84 | Epoch[160/239] Step[49] GlobalStep[62449/NA]: det_loss_cls_0[0.4501] det_loss_box_0[0.6106] det_loss_cls_1[0.4277] det_loss_box_1[0.4758] det_loss_cls_2[0.3889] det_loss_box_2[0.4350] det_loss_cls_3[0.3655] det_loss_box_3[0.4183] det_loss_cls_4[0.3523] det_loss_box_4[0.4013] det_loss_cls_5[0.3336] det_loss_box_5[0.4014] det_loss_cls_dn_0[0.0932] det_loss_box_dn_0[0.3002] det_loss_cls_dn_1[0.0901] det_loss_box_dn_1[0.2490] det_loss_cls_dn_2[0.0893] det_loss_box_dn_2[0.2303] det_loss_cls_dn_3[0.0877] det_loss_box_dn_3[0.2261] det_loss_cls_dn_4[0.0868] det_loss_box_dn_4[0.2233] det_loss_cls_dn_5[0.0861] det_loss_box_dn_5[0.2222] loss_depth[0.0383] total_loss[7.0831]
Rank[0/8] 06/20/2025 11:17:22 INFO stats.py:314 | Epoch[160] Step[74] GlobalStep[62474] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 10:30:16.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:17:22 INFO loss_tracker.py:84 | Epoch[160/239] Step[74] GlobalStep[62474/NA]: det_loss_cls_0[0.4878] det_loss_box_0[0.6015] det_loss_cls_1[0.4918] det_loss_box_1[0.4637] det_loss_cls_2[0.4306] det_loss_box_2[0.4317] det_loss_cls_3[0.3992] det_loss_box_3[0.4210] det_loss_cls_4[0.3682] det_loss_box_4[0.4101] det_loss_cls_5[0.3546] det_loss_box_5[0.4093] det_loss_cls_dn_0[0.0962] det_loss_box_dn_0[0.2802] det_loss_cls_dn_1[0.0932] det_loss_box_dn_1[0.2380] det_loss_cls_dn_2[0.0919] det_loss_box_dn_2[0.2216] det_loss_cls_dn_3[0.0907] det_loss_box_dn_3[0.2191] det_loss_cls_dn_4[0.0897] det_loss_box_dn_4[0.2167] det_loss_cls_dn_5[0.0887] det_loss_box_dn_5[0.2140] loss_depth[0.0367] total_loss[7.2460]
Rank[0/8] 06/20/2025 11:17:48 INFO stats.py:314 | Epoch[160] Step[99] GlobalStep[62499] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 10:29:44.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:17:48 INFO loss_tracker.py:84 | Epoch[160/239] Step[99] GlobalStep[62499/NA]: det_loss_cls_0[0.3848] det_loss_box_0[0.4821] det_loss_cls_1[0.3793] det_loss_box_1[0.3400] det_loss_cls_2[0.3266] det_loss_box_2[0.3087] det_loss_cls_3[0.2883] det_loss_box_3[0.3059] det_loss_cls_4[0.2703] det_loss_box_4[0.2981] det_loss_cls_5[0.2582] det_loss_box_5[0.2974] det_loss_cls_dn_0[0.0836] det_loss_box_dn_0[0.2388] det_loss_cls_dn_1[0.0792] det_loss_box_dn_1[0.1992] det_loss_cls_dn_2[0.0782] det_loss_box_dn_2[0.1837] det_loss_cls_dn_3[0.0772] det_loss_box_dn_3[0.1807] det_loss_cls_dn_4[0.0757] det_loss_box_dn_4[0.1779] det_loss_cls_dn_5[0.0748] det_loss_box_dn_5[0.1767] loss_depth[0.0356] total_loss[5.6009]
Rank[0/8] 06/20/2025 11:18:13 INFO stats.py:314 | Epoch[160] Step[124] GlobalStep[62524] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 10:29:11.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:18:13 INFO loss_tracker.py:84 | Epoch[160/239] Step[124] GlobalStep[62524/NA]: det_loss_cls_0[0.6226] det_loss_box_0[0.8346] det_loss_cls_1[0.6130] det_loss_box_1[0.6093] det_loss_cls_2[0.5446] det_loss_box_2[0.5499] det_loss_cls_3[0.5068] det_loss_box_3[0.5303] det_loss_cls_4[0.4797] det_loss_box_4[0.5128] det_loss_cls_5[0.4611] det_loss_box_5[0.5136] det_loss_cls_dn_0[0.1006] det_loss_box_dn_0[0.2880] det_loss_cls_dn_1[0.0980] det_loss_box_dn_1[0.2303] det_loss_cls_dn_2[0.0972] det_loss_box_dn_2[0.2071] det_loss_cls_dn_3[0.0957] det_loss_box_dn_3[0.2023] det_loss_cls_dn_4[0.0949] det_loss_box_dn_4[0.1998] det_loss_cls_dn_5[0.0940] det_loss_box_dn_5[0.1960] loss_depth[0.0354] total_loss[8.7176]
Rank[0/8] 06/20/2025 11:18:39 INFO stats.py:314 | Epoch[160] Step[149] GlobalStep[62549] Training Speed: 8.04 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 10:28:38.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:18:39 INFO loss_tracker.py:84 | Epoch[160/239] Step[149] GlobalStep[62549/NA]: det_loss_cls_0[0.4367] det_loss_box_0[0.5070] det_loss_cls_1[0.4226] det_loss_box_1[0.3698] det_loss_cls_2[0.3704] det_loss_box_2[0.3311] det_loss_cls_3[0.3363] det_loss_box_3[0.3241] det_loss_cls_4[0.3170] det_loss_box_4[0.3141] det_loss_cls_5[0.3025] det_loss_box_5[0.3144] det_loss_cls_dn_0[0.0917] det_loss_box_dn_0[0.2311] det_loss_cls_dn_1[0.0881] det_loss_box_dn_1[0.1840] det_loss_cls_dn_2[0.0873] det_loss_box_dn_2[0.1667] det_loss_cls_dn_3[0.0856] det_loss_box_dn_3[0.1619] det_loss_cls_dn_4[0.0848] det_loss_box_dn_4[0.1598] det_loss_cls_dn_5[0.0841] det_loss_box_dn_5[0.1588] loss_depth[0.0368] total_loss[5.9669]
Rank[0/8] 06/20/2025 11:19:05 INFO stats.py:314 | Epoch[160] Step[174] GlobalStep[62574] Training Speed: 7.78 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 10:28:06.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:19:05 INFO loss_tracker.py:84 | Epoch[160/239] Step[174] GlobalStep[62574/NA]: det_loss_cls_0[0.4256] det_loss_box_0[0.5224] det_loss_cls_1[0.4199] det_loss_box_1[0.4022] det_loss_cls_2[0.3775] det_loss_box_2[0.3766] det_loss_cls_3[0.3519] det_loss_box_3[0.3647] det_loss_cls_4[0.3248] det_loss_box_4[0.3611] det_loss_cls_5[0.3134] det_loss_box_5[0.3603] det_loss_cls_dn_0[0.0921] det_loss_box_dn_0[0.2887] det_loss_cls_dn_1[0.0878] det_loss_box_dn_1[0.2410] det_loss_cls_dn_2[0.0866] det_loss_box_dn_2[0.2218] det_loss_cls_dn_3[0.0855] det_loss_box_dn_3[0.2184] det_loss_cls_dn_4[0.0851] det_loss_box_dn_4[0.2151] det_loss_cls_dn_5[0.0841] det_loss_box_dn_5[0.2132] loss_depth[0.0350] total_loss[6.5550]
Rank[0/8] 06/20/2025 11:19:31 INFO stats.py:314 | Epoch[160] Step[199] GlobalStep[62599] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 10:27:33.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:19:31 INFO loss_tracker.py:84 | Epoch[160/239] Step[199] GlobalStep[62599/NA]: det_loss_cls_0[0.6080] det_loss_box_0[0.8094] det_loss_cls_1[0.5903] det_loss_box_1[0.6325] det_loss_cls_2[0.5355] det_loss_box_2[0.5776] det_loss_cls_3[0.4935] det_loss_box_3[0.5642] det_loss_cls_4[0.4675] det_loss_box_4[0.5492] det_loss_cls_5[0.4518] det_loss_box_5[0.5508] det_loss_cls_dn_0[0.1009] det_loss_box_dn_0[0.2896] det_loss_cls_dn_1[0.0970] det_loss_box_dn_1[0.2473] det_loss_cls_dn_2[0.0959] det_loss_box_dn_2[0.2296] det_loss_cls_dn_3[0.0940] det_loss_box_dn_3[0.2239] det_loss_cls_dn_4[0.0933] det_loss_box_dn_4[0.2213] det_loss_cls_dn_5[0.0926] det_loss_box_dn_5[0.2192] loss_depth[0.0367] total_loss[8.8716]
Rank[0/8] 06/20/2025 11:19:56 INFO stats.py:314 | Epoch[160] Step[224] GlobalStep[62624] Training Speed: 8.76 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 10:27:00.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:19:56 INFO loss_tracker.py:84 | Epoch[160/239] Step[224] GlobalStep[62624/NA]: det_loss_cls_0[0.4442] det_loss_box_0[0.6297] det_loss_cls_1[0.4277] det_loss_box_1[0.4827] det_loss_cls_2[0.3659] det_loss_box_2[0.4401] det_loss_cls_3[0.3303] det_loss_box_3[0.4287] det_loss_cls_4[0.3052] det_loss_box_4[0.4176] det_loss_cls_5[0.2891] det_loss_box_5[0.4206] det_loss_cls_dn_0[0.0868] det_loss_box_dn_0[0.3178] det_loss_cls_dn_1[0.0833] det_loss_box_dn_1[0.2619] det_loss_cls_dn_2[0.0817] det_loss_box_dn_2[0.2371] det_loss_cls_dn_3[0.0807] det_loss_box_dn_3[0.2327] det_loss_cls_dn_4[0.0796] det_loss_box_dn_4[0.2285] det_loss_cls_dn_5[0.0787] det_loss_box_dn_5[0.2280] loss_depth[0.0364] total_loss[7.0149]
Rank[0/8] 06/20/2025 11:20:23 INFO stats.py:314 | Epoch[160] Step[249] GlobalStep[62649] Training Speed: 6.44 samples/sec across all devices.	Average Step Time: 1.24 sec.	Estimated Remaining Time: 10:26:28.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:20:23 INFO loss_tracker.py:84 | Epoch[160/239] Step[249] GlobalStep[62649/NA]: det_loss_cls_0[0.4122] det_loss_box_0[0.5044] det_loss_cls_1[0.4008] det_loss_box_1[0.3841] det_loss_cls_2[0.3451] det_loss_box_2[0.3412] det_loss_cls_3[0.3165] det_loss_box_3[0.3249] det_loss_cls_4[0.2893] det_loss_box_4[0.3214] det_loss_cls_5[0.2811] det_loss_box_5[0.3153] det_loss_cls_dn_0[0.0845] det_loss_box_dn_0[0.2794] det_loss_cls_dn_1[0.0814] det_loss_box_dn_1[0.2332] det_loss_cls_dn_2[0.0798] det_loss_box_dn_2[0.2073] det_loss_cls_dn_3[0.0780] det_loss_box_dn_3[0.2033] det_loss_cls_dn_4[0.0776] det_loss_box_dn_4[0.2008] det_loss_cls_dn_5[0.0766] det_loss_box_dn_5[0.1982] loss_depth[0.0377] total_loss[6.0741]
Rank[0/8] 06/20/2025 11:20:49 INFO stats.py:314 | Epoch[160] Step[274] GlobalStep[62674] Training Speed: 8.05 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 10:25:55.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:20:49 INFO loss_tracker.py:84 | Epoch[160/239] Step[274] GlobalStep[62674/NA]: det_loss_cls_0[0.4045] det_loss_box_0[0.5497] det_loss_cls_1[0.4005] det_loss_box_1[0.4077] det_loss_cls_2[0.3484] det_loss_box_2[0.3813] det_loss_cls_3[0.3240] det_loss_box_3[0.3694] det_loss_cls_4[0.2965] det_loss_box_4[0.3685] det_loss_cls_5[0.2843] det_loss_box_5[0.3687] det_loss_cls_dn_0[0.0829] det_loss_box_dn_0[0.2787] det_loss_cls_dn_1[0.0791] det_loss_box_dn_1[0.2310] det_loss_cls_dn_2[0.0781] det_loss_box_dn_2[0.2150] det_loss_cls_dn_3[0.0773] det_loss_box_dn_3[0.2098] det_loss_cls_dn_4[0.0762] det_loss_box_dn_4[0.2082] det_loss_cls_dn_5[0.0757] det_loss_box_dn_5[0.2046] loss_depth[0.0360] total_loss[6.3562]
Rank[0/8] 06/20/2025 11:21:14 INFO stats.py:314 | Epoch[160] Step[299] GlobalStep[62699] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 10:25:23.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:21:14 INFO loss_tracker.py:84 | Epoch[160/239] Step[299] GlobalStep[62699/NA]: det_loss_cls_0[0.5544] det_loss_box_0[0.7497] det_loss_cls_1[0.5487] det_loss_box_1[0.6024] det_loss_cls_2[0.4922] det_loss_box_2[0.5548] det_loss_cls_3[0.4583] det_loss_box_3[0.5365] det_loss_cls_4[0.4297] det_loss_box_4[0.5226] det_loss_cls_5[0.4178] det_loss_box_5[0.5184] det_loss_cls_dn_0[0.0986] det_loss_box_dn_0[0.2737] det_loss_cls_dn_1[0.0952] det_loss_box_dn_1[0.2304] det_loss_cls_dn_2[0.0947] det_loss_box_dn_2[0.2190] det_loss_cls_dn_3[0.0933] det_loss_box_dn_3[0.2143] det_loss_cls_dn_4[0.0922] det_loss_box_dn_4[0.2121] det_loss_cls_dn_5[0.0912] det_loss_box_dn_5[0.2102] loss_depth[0.0356] total_loss[8.3462]
Rank[0/8] 06/20/2025 11:21:40 INFO stats.py:314 | Epoch[160] Step[324] GlobalStep[62724] Training Speed: 8.06 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 10:24:50.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:21:40 INFO loss_tracker.py:84 | Epoch[160/239] Step[324] GlobalStep[62724/NA]: det_loss_cls_0[0.5848] det_loss_box_0[0.6530] det_loss_cls_1[0.5622] det_loss_box_1[0.4902] det_loss_cls_2[0.5026] det_loss_box_2[0.4458] det_loss_cls_3[0.4660] det_loss_box_3[0.4285] det_loss_cls_4[0.4321] det_loss_box_4[0.4226] det_loss_cls_5[0.4191] det_loss_box_5[0.4193] det_loss_cls_dn_0[0.0975] det_loss_box_dn_0[0.2306] det_loss_cls_dn_1[0.0935] det_loss_box_dn_1[0.1906] det_loss_cls_dn_2[0.0928] det_loss_box_dn_2[0.1759] det_loss_cls_dn_3[0.0912] det_loss_box_dn_3[0.1718] det_loss_cls_dn_4[0.0903] det_loss_box_dn_4[0.1688] det_loss_cls_dn_5[0.0895] det_loss_box_dn_5[0.1678] loss_depth[0.0365] total_loss[7.5231]
Rank[0/8] 06/20/2025 11:22:06 INFO stats.py:314 | Epoch[160] Step[349] GlobalStep[62749] Training Speed: 8.07 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 10:24:18.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:22:06 INFO loss_tracker.py:84 | Epoch[160/239] Step[349] GlobalStep[62749/NA]: det_loss_cls_0[0.5122] det_loss_box_0[0.6724] det_loss_cls_1[0.5026] det_loss_box_1[0.5254] det_loss_cls_2[0.4482] det_loss_box_2[0.4780] det_loss_cls_3[0.4288] det_loss_box_3[0.4594] det_loss_cls_4[0.4117] det_loss_box_4[0.4515] det_loss_cls_5[0.3975] det_loss_box_5[0.4497] det_loss_cls_dn_0[0.1016] det_loss_box_dn_0[0.3023] det_loss_cls_dn_1[0.0971] det_loss_box_dn_1[0.2518] det_loss_cls_dn_2[0.0962] det_loss_box_dn_2[0.2375] det_loss_cls_dn_3[0.0953] det_loss_box_dn_3[0.2320] det_loss_cls_dn_4[0.0942] det_loss_box_dn_4[0.2311] det_loss_cls_dn_5[0.0931] det_loss_box_dn_5[0.2295] loss_depth[0.0358] total_loss[7.8350]
Rank[0/8] 06/20/2025 11:22:31 INFO stats.py:314 | Epoch[160] Step[374] GlobalStep[62774] Training Speed: 8.34 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 10:23:45.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:22:31 INFO loss_tracker.py:84 | Epoch[160/239] Step[374] GlobalStep[62774/NA]: det_loss_cls_0[0.4320] det_loss_box_0[0.5216] det_loss_cls_1[0.4180] det_loss_box_1[0.3916] det_loss_cls_2[0.3736] det_loss_box_2[0.3465] det_loss_cls_3[0.3348] det_loss_box_3[0.3285] det_loss_cls_4[0.3154] det_loss_box_4[0.3169] det_loss_cls_5[0.2950] det_loss_box_5[0.3236] det_loss_cls_dn_0[0.0855] det_loss_box_dn_0[0.2354] det_loss_cls_dn_1[0.0817] det_loss_box_dn_1[0.1902] det_loss_cls_dn_2[0.0802] det_loss_box_dn_2[0.1772] det_loss_cls_dn_3[0.0791] det_loss_box_dn_3[0.1733] det_loss_cls_dn_4[0.0782] det_loss_box_dn_4[0.1713] det_loss_cls_dn_5[0.0777] det_loss_box_dn_5[0.1699] loss_depth[0.0324] total_loss[6.0295]
Rank[0/8] 06/20/2025 11:22:46 INFO stats.py:394 | Epoch[160] completed. Training Speed: 7.69 samples/sec across all devices.	Epoch Time: 405.96 sec.	Average Epoch Time: 405.96 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 10:23:25.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:22:58 INFO stats.py:314 | Epoch[161] Step[9] GlobalStep[62799] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 10:23:13.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:23:14 INFO loss_tracker.py:84 | Epoch[161/239] Step[24] GlobalStep[62814/NA]: det_loss_cls_0[0.4175] det_loss_box_0[0.5572] det_loss_cls_1[0.4120] det_loss_box_1[0.4195] det_loss_cls_2[0.3618] det_loss_box_2[0.3719] det_loss_cls_3[0.3278] det_loss_box_3[0.3573] det_loss_cls_4[0.3099] det_loss_box_4[0.3473] det_loss_cls_5[0.2895] det_loss_box_5[0.3511] det_loss_cls_dn_0[0.0844] det_loss_box_dn_0[0.2931] det_loss_cls_dn_1[0.0812] det_loss_box_dn_1[0.2402] det_loss_cls_dn_2[0.0799] det_loss_box_dn_2[0.2226] det_loss_cls_dn_3[0.0789] det_loss_box_dn_3[0.2166] det_loss_cls_dn_4[0.0775] det_loss_box_dn_4[0.2135] det_loss_cls_dn_5[0.0770] det_loss_box_dn_5[0.2109] loss_depth[0.0375] total_loss[6.4360]
Rank[0/8] 06/20/2025 11:23:25 INFO stats.py:314 | Epoch[161] Step[34] GlobalStep[62824] Training Speed: 8.22 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 10:22:41.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:23:40 INFO loss_tracker.py:84 | Epoch[161/239] Step[49] GlobalStep[62839/NA]: det_loss_cls_0[0.5610] det_loss_box_0[0.7199] det_loss_cls_1[0.5497] det_loss_box_1[0.5586] det_loss_cls_2[0.4931] det_loss_box_2[0.4990] det_loss_cls_3[0.4591] det_loss_box_3[0.4816] det_loss_cls_4[0.4261] det_loss_box_4[0.4822] det_loss_cls_5[0.4095] det_loss_box_5[0.4821] det_loss_cls_dn_0[0.1022] det_loss_box_dn_0[0.2709] det_loss_cls_dn_1[0.0995] det_loss_box_dn_1[0.2214] det_loss_cls_dn_2[0.0991] det_loss_box_dn_2[0.2069] det_loss_cls_dn_3[0.0973] det_loss_box_dn_3[0.2029] det_loss_cls_dn_4[0.0969] det_loss_box_dn_4[0.2001] det_loss_cls_dn_5[0.0958] det_loss_box_dn_5[0.1993] loss_depth[0.0368] total_loss[8.0511]
Rank[0/8] 06/20/2025 11:23:50 INFO stats.py:314 | Epoch[161] Step[59] GlobalStep[62849] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 10:22:08.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:24:05 INFO loss_tracker.py:84 | Epoch[161/239] Step[74] GlobalStep[62864/NA]: det_loss_cls_0[0.4570] det_loss_box_0[0.5665] det_loss_cls_1[0.4489] det_loss_box_1[0.4004] det_loss_cls_2[0.3985] det_loss_box_2[0.3607] det_loss_cls_3[0.3718] det_loss_box_3[0.3451] det_loss_cls_4[0.3356] det_loss_box_4[0.3482] det_loss_cls_5[0.3151] det_loss_box_5[0.3534] det_loss_cls_dn_0[0.0818] det_loss_box_dn_0[0.3259] det_loss_cls_dn_1[0.0791] det_loss_box_dn_1[0.2626] det_loss_cls_dn_2[0.0779] det_loss_box_dn_2[0.2421] det_loss_cls_dn_3[0.0768] det_loss_box_dn_3[0.2352] det_loss_cls_dn_4[0.0756] det_loss_box_dn_4[0.2324] det_loss_cls_dn_5[0.0746] det_loss_box_dn_5[0.2306] loss_depth[0.0377] total_loss[6.7336]
Rank[0/8] 06/20/2025 11:24:15 INFO stats.py:314 | Epoch[161] Step[84] GlobalStep[62874] Training Speed: 8.17 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 10:21:35.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:24:31 INFO loss_tracker.py:84 | Epoch[161/239] Step[99] GlobalStep[62889/NA]: det_loss_cls_0[0.4892] det_loss_box_0[0.5870] det_loss_cls_1[0.4692] det_loss_box_1[0.4522] det_loss_cls_2[0.4248] det_loss_box_2[0.4178] det_loss_cls_3[0.4018] det_loss_box_3[0.3961] det_loss_cls_4[0.3767] det_loss_box_4[0.3883] det_loss_cls_5[0.3666] det_loss_box_5[0.3882] det_loss_cls_dn_0[0.1066] det_loss_box_dn_0[0.2775] det_loss_cls_dn_1[0.1043] det_loss_box_dn_1[0.2349] det_loss_cls_dn_2[0.1039] det_loss_box_dn_2[0.2161] det_loss_cls_dn_3[0.1023] det_loss_box_dn_3[0.2121] det_loss_cls_dn_4[0.1017] det_loss_box_dn_4[0.2091] det_loss_cls_dn_5[0.1011] det_loss_box_dn_5[0.2079] loss_depth[0.0365] total_loss[7.1720]
Rank[0/8] 06/20/2025 11:24:42 INFO stats.py:314 | Epoch[161] Step[109] GlobalStep[62899] Training Speed: 5.79 samples/sec across all devices.	Average Step Time: 1.38 sec.	Estimated Remaining Time: 10:21:03.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:24:58 INFO loss_tracker.py:84 | Epoch[161/239] Step[124] GlobalStep[62914/NA]: det_loss_cls_0[0.4967] det_loss_box_0[0.5940] det_loss_cls_1[0.4791] det_loss_box_1[0.4519] det_loss_cls_2[0.4157] det_loss_box_2[0.4151] det_loss_cls_3[0.3803] det_loss_box_3[0.3943] det_loss_cls_4[0.3471] det_loss_box_4[0.3860] det_loss_cls_5[0.3309] det_loss_box_5[0.3867] det_loss_cls_dn_0[0.0845] det_loss_box_dn_0[0.2616] det_loss_cls_dn_1[0.0810] det_loss_box_dn_1[0.2134] det_loss_cls_dn_2[0.0799] det_loss_box_dn_2[0.1950] det_loss_cls_dn_3[0.0784] det_loss_box_dn_3[0.1902] det_loss_cls_dn_4[0.0770] det_loss_box_dn_4[0.1874] det_loss_cls_dn_5[0.0759] det_loss_box_dn_5[0.1843] loss_depth[0.0335] total_loss[6.8198]
Rank[0/8] 06/20/2025 11:25:08 INFO stats.py:314 | Epoch[161] Step[134] GlobalStep[62924] Training Speed: 7.41 samples/sec across all devices.	Average Step Time: 1.08 sec.	Estimated Remaining Time: 10:20:30.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:25:23 INFO loss_tracker.py:84 | Epoch[161/239] Step[149] GlobalStep[62939/NA]: det_loss_cls_0[0.5140] det_loss_box_0[0.6577] det_loss_cls_1[0.5097] det_loss_box_1[0.4785] det_loss_cls_2[0.4409] det_loss_box_2[0.4478] det_loss_cls_3[0.3910] det_loss_box_3[0.4363] det_loss_cls_4[0.3675] det_loss_box_4[0.4210] det_loss_cls_5[0.3462] det_loss_box_5[0.4231] det_loss_cls_dn_0[0.0917] det_loss_box_dn_0[0.2543] det_loss_cls_dn_1[0.0885] det_loss_box_dn_1[0.2124] det_loss_cls_dn_2[0.0867] det_loss_box_dn_2[0.1965] det_loss_cls_dn_3[0.0847] det_loss_box_dn_3[0.1919] det_loss_cls_dn_4[0.0834] det_loss_box_dn_4[0.1902] det_loss_cls_dn_5[0.0826] det_loss_box_dn_5[0.1877] loss_depth[0.0359] total_loss[7.2201]
Rank[0/8] 06/20/2025 11:25:33 INFO stats.py:314 | Epoch[161] Step[159] GlobalStep[62949] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 10:19:57.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:25:49 INFO loss_tracker.py:84 | Epoch[161/239] Step[174] GlobalStep[62964/NA]: det_loss_cls_0[0.5448] det_loss_box_0[0.7096] det_loss_cls_1[0.5312] det_loss_box_1[0.5468] det_loss_cls_2[0.4885] det_loss_box_2[0.5088] det_loss_cls_3[0.4508] det_loss_box_3[0.5009] det_loss_cls_4[0.4232] det_loss_box_4[0.4835] det_loss_cls_5[0.4163] det_loss_box_5[0.4787] det_loss_cls_dn_0[0.1012] det_loss_box_dn_0[0.3053] det_loss_cls_dn_1[0.0972] det_loss_box_dn_1[0.2499] det_loss_cls_dn_2[0.0963] det_loss_box_dn_2[0.2323] det_loss_cls_dn_3[0.0949] det_loss_box_dn_3[0.2280] det_loss_cls_dn_4[0.0942] det_loss_box_dn_4[0.2261] det_loss_cls_dn_5[0.0930] det_loss_box_dn_5[0.2220] loss_depth[0.0383] total_loss[8.1619]
Rank[0/8] 06/20/2025 11:25:59 INFO stats.py:314 | Epoch[161] Step[184] GlobalStep[62974] Training Speed: 7.98 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 10:19:25.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:26:15 INFO loss_tracker.py:84 | Epoch[161/239] Step[199] GlobalStep[62989/NA]: det_loss_cls_0[0.4441] det_loss_box_0[0.4964] det_loss_cls_1[0.4360] det_loss_box_1[0.3533] det_loss_cls_2[0.3787] det_loss_box_2[0.3194] det_loss_cls_3[0.3400] det_loss_box_3[0.3145] det_loss_cls_4[0.3196] det_loss_box_4[0.2978] det_loss_cls_5[0.3045] det_loss_box_5[0.2983] det_loss_cls_dn_0[0.0919] det_loss_box_dn_0[0.2334] det_loss_cls_dn_1[0.0889] det_loss_box_dn_1[0.1892] det_loss_cls_dn_2[0.0884] det_loss_box_dn_2[0.1732] det_loss_cls_dn_3[0.0874] det_loss_box_dn_3[0.1689] det_loss_cls_dn_4[0.0869] det_loss_box_dn_4[0.1675] det_loss_cls_dn_5[0.0857] det_loss_box_dn_5[0.1662] loss_depth[0.0380] total_loss[5.9681]
Rank[0/8] 06/20/2025 11:26:26 INFO stats.py:314 | Epoch[161] Step[209] GlobalStep[62999] Training Speed: 7.67 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 10:18:53.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:26:41 INFO loss_tracker.py:84 | Epoch[161/239] Step[224] GlobalStep[63014/NA]: det_loss_cls_0[0.4119] det_loss_box_0[0.5276] det_loss_cls_1[0.3975] det_loss_box_1[0.3925] det_loss_cls_2[0.3542] det_loss_box_2[0.3534] det_loss_cls_3[0.3192] det_loss_box_3[0.3419] det_loss_cls_4[0.2960] det_loss_box_4[0.3378] det_loss_cls_5[0.2786] det_loss_box_5[0.3407] det_loss_cls_dn_0[0.0841] det_loss_box_dn_0[0.3049] det_loss_cls_dn_1[0.0794] det_loss_box_dn_1[0.2544] det_loss_cls_dn_2[0.0782] det_loss_box_dn_2[0.2371] det_loss_cls_dn_3[0.0773] det_loss_box_dn_3[0.2332] det_loss_cls_dn_4[0.0761] det_loss_box_dn_4[0.2312] det_loss_cls_dn_5[0.0754] det_loss_box_dn_5[0.2280] loss_depth[0.0368] total_loss[6.3476]
Rank[0/8] 06/20/2025 11:26:51 INFO stats.py:314 | Epoch[161] Step[234] GlobalStep[63024] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 10:18:20.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:27:06 INFO loss_tracker.py:84 | Epoch[161/239] Step[249] GlobalStep[63039/NA]: det_loss_cls_0[0.4809] det_loss_box_0[0.5616] det_loss_cls_1[0.4737] det_loss_box_1[0.4090] det_loss_cls_2[0.4256] det_loss_box_2[0.3613] det_loss_cls_3[0.3855] det_loss_box_3[0.3534] det_loss_cls_4[0.3561] det_loss_box_4[0.3473] det_loss_cls_5[0.3408] det_loss_box_5[0.3482] det_loss_cls_dn_0[0.0946] det_loss_box_dn_0[0.2774] det_loss_cls_dn_1[0.0917] det_loss_box_dn_1[0.2226] det_loss_cls_dn_2[0.0905] det_loss_box_dn_2[0.2024] det_loss_cls_dn_3[0.0897] det_loss_box_dn_3[0.1891] det_loss_cls_dn_4[0.0891] det_loss_box_dn_4[0.1879] det_loss_cls_dn_5[0.0884] det_loss_box_dn_5[0.1819] loss_depth[0.0386] total_loss[6.6873]
Rank[0/8] 06/20/2025 11:27:17 INFO stats.py:314 | Epoch[161] Step[259] GlobalStep[63049] Training Speed: 7.98 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 10:17:47.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:27:32 INFO loss_tracker.py:84 | Epoch[161/239] Step[274] GlobalStep[63064/NA]: det_loss_cls_0[0.4837] det_loss_box_0[0.5651] det_loss_cls_1[0.4618] det_loss_box_1[0.4286] det_loss_cls_2[0.4101] det_loss_box_2[0.3865] det_loss_cls_3[0.3718] det_loss_box_3[0.3674] det_loss_cls_4[0.3369] det_loss_box_4[0.3643] det_loss_cls_5[0.3299] det_loss_box_5[0.3592] det_loss_cls_dn_0[0.0884] det_loss_box_dn_0[0.2451] det_loss_cls_dn_1[0.0848] det_loss_box_dn_1[0.1997] det_loss_cls_dn_2[0.0830] det_loss_box_dn_2[0.1857] det_loss_cls_dn_3[0.0818] det_loss_box_dn_3[0.1809] det_loss_cls_dn_4[0.0806] det_loss_box_dn_4[0.1786] det_loss_cls_dn_5[0.0798] det_loss_box_dn_5[0.1753] loss_depth[0.0378] total_loss[6.5668]
Rank[0/8] 06/20/2025 11:27:44 INFO stats.py:314 | Epoch[161] Step[284] GlobalStep[63074] Training Speed: 6.56 samples/sec across all devices.	Average Step Time: 1.22 sec.	Estimated Remaining Time: 10:17:15.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:27:59 INFO loss_tracker.py:84 | Epoch[161/239] Step[299] GlobalStep[63089/NA]: det_loss_cls_0[0.3995] det_loss_box_0[0.5245] det_loss_cls_1[0.3868] det_loss_box_1[0.4137] det_loss_cls_2[0.3507] det_loss_box_2[0.3671] det_loss_cls_3[0.3288] det_loss_box_3[0.3492] det_loss_cls_4[0.3022] det_loss_box_4[0.3383] det_loss_cls_5[0.2961] det_loss_box_5[0.3360] det_loss_cls_dn_0[0.0892] det_loss_box_dn_0[0.3215] det_loss_cls_dn_1[0.0865] det_loss_box_dn_1[0.2634] det_loss_cls_dn_2[0.0854] det_loss_box_dn_2[0.2388] det_loss_cls_dn_3[0.0845] det_loss_box_dn_3[0.2338] det_loss_cls_dn_4[0.0837] det_loss_box_dn_4[0.2291] det_loss_cls_dn_5[0.0829] det_loss_box_dn_5[0.2264] loss_depth[0.0384] total_loss[6.4563]
Rank[0/8] 06/20/2025 11:28:09 INFO stats.py:314 | Epoch[161] Step[309] GlobalStep[63099] Training Speed: 8.08 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 10:16:43.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:28:25 INFO loss_tracker.py:84 | Epoch[161/239] Step[324] GlobalStep[63114/NA]: det_loss_cls_0[0.5035] det_loss_box_0[0.6601] det_loss_cls_1[0.4698] det_loss_box_1[0.5285] det_loss_cls_2[0.4280] det_loss_box_2[0.4706] det_loss_cls_3[0.3950] det_loss_box_3[0.4488] det_loss_cls_4[0.3820] det_loss_box_4[0.4361] det_loss_cls_5[0.3719] det_loss_box_5[0.4308] det_loss_cls_dn_0[0.0883] det_loss_box_dn_0[0.2891] det_loss_cls_dn_1[0.0853] det_loss_box_dn_1[0.2394] det_loss_cls_dn_2[0.0844] det_loss_box_dn_2[0.2161] det_loss_cls_dn_3[0.0834] det_loss_box_dn_3[0.2123] det_loss_cls_dn_4[0.0827] det_loss_box_dn_4[0.2091] det_loss_cls_dn_5[0.0820] det_loss_box_dn_5[0.2077] loss_depth[0.0372] total_loss[7.4422]
Rank[0/8] 06/20/2025 11:28:35 INFO stats.py:314 | Epoch[161] Step[334] GlobalStep[63124] Training Speed: 7.28 samples/sec across all devices.	Average Step Time: 1.10 sec.	Estimated Remaining Time: 10:16:10.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:28:50 INFO loss_tracker.py:84 | Epoch[161/239] Step[349] GlobalStep[63139/NA]: det_loss_cls_0[0.4901] det_loss_box_0[0.6532] det_loss_cls_1[0.4840] det_loss_box_1[0.4762] det_loss_cls_2[0.4128] det_loss_box_2[0.4285] det_loss_cls_3[0.3761] det_loss_box_3[0.4143] det_loss_cls_4[0.3590] det_loss_box_4[0.4038] det_loss_cls_5[0.3499] det_loss_box_5[0.4048] det_loss_cls_dn_0[0.0953] det_loss_box_dn_0[0.2875] det_loss_cls_dn_1[0.0917] det_loss_box_dn_1[0.2331] det_loss_cls_dn_2[0.0912] det_loss_box_dn_2[0.2126] det_loss_cls_dn_3[0.0901] det_loss_box_dn_3[0.2084] det_loss_cls_dn_4[0.0892] det_loss_box_dn_4[0.2058] det_loss_cls_dn_5[0.0881] det_loss_box_dn_5[0.2015] loss_depth[0.0372] total_loss[7.1848]
Rank[0/8] 06/20/2025 11:29:01 INFO stats.py:314 | Epoch[161] Step[359] GlobalStep[63149] Training Speed: 8.58 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 10:15:38.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:29:17 INFO loss_tracker.py:84 | Epoch[161/239] Step[374] GlobalStep[63164/NA]: det_loss_cls_0[0.5042] det_loss_box_0[0.5972] det_loss_cls_1[0.4879] det_loss_box_1[0.4723] det_loss_cls_2[0.4444] det_loss_box_2[0.4353] det_loss_cls_3[0.4113] det_loss_box_3[0.4273] det_loss_cls_4[0.3898] det_loss_box_4[0.4162] det_loss_cls_5[0.3824] det_loss_box_5[0.4125] det_loss_cls_dn_0[0.0873] det_loss_box_dn_0[0.3000] det_loss_cls_dn_1[0.0830] det_loss_box_dn_1[0.2538] det_loss_cls_dn_2[0.0820] det_loss_box_dn_2[0.2364] det_loss_cls_dn_3[0.0805] det_loss_box_dn_3[0.2322] det_loss_cls_dn_4[0.0796] det_loss_box_dn_4[0.2305] det_loss_cls_dn_5[0.0788] det_loss_box_dn_5[0.2254] loss_depth[0.0362] total_loss[7.3865]
Rank[0/8] 06/20/2025 11:29:27 INFO stats.py:314 | Epoch[161] Step[384] GlobalStep[63174] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 10:15:05.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:29:31 INFO stats.py:394 | Epoch[161] completed. Training Speed: 7.70 samples/sec across all devices.	Epoch Time: 404.98 sec.	Average Epoch Time: 404.98 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 10:14:59.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:29:53 INFO stats.py:314 | Epoch[162] Step[19] GlobalStep[63199] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 10:14:33.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:29:58 INFO loss_tracker.py:84 | Epoch[162/239] Step[24] GlobalStep[63204/NA]: det_loss_cls_0[0.4841] det_loss_box_0[0.6199] det_loss_cls_1[0.4697] det_loss_box_1[0.4527] det_loss_cls_2[0.4126] det_loss_box_2[0.4047] det_loss_cls_3[0.3769] det_loss_box_3[0.3889] det_loss_cls_4[0.3487] det_loss_box_4[0.3814] det_loss_cls_5[0.3355] det_loss_box_5[0.3845] det_loss_cls_dn_0[0.0917] det_loss_box_dn_0[0.2564] det_loss_cls_dn_1[0.0891] det_loss_box_dn_1[0.2111] det_loss_cls_dn_2[0.0884] det_loss_box_dn_2[0.1938] det_loss_cls_dn_3[0.0873] det_loss_box_dn_3[0.1900] det_loss_cls_dn_4[0.0865] det_loss_box_dn_4[0.1878] det_loss_cls_dn_5[0.0857] det_loss_box_dn_5[0.1854] loss_depth[0.0369] total_loss[6.8499]
Rank[0/8] 06/20/2025 11:30:19 INFO stats.py:314 | Epoch[162] Step[44] GlobalStep[63224] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 10:14:01.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:30:24 INFO loss_tracker.py:84 | Epoch[162/239] Step[49] GlobalStep[63229/NA]: det_loss_cls_0[0.4465] det_loss_box_0[0.5652] det_loss_cls_1[0.4398] det_loss_box_1[0.4166] det_loss_cls_2[0.3805] det_loss_box_2[0.3768] det_loss_cls_3[0.3443] det_loss_box_3[0.3634] det_loss_cls_4[0.3152] det_loss_box_4[0.3525] det_loss_cls_5[0.2999] det_loss_box_5[0.3527] det_loss_cls_dn_0[0.0878] det_loss_box_dn_0[0.2514] det_loss_cls_dn_1[0.0846] det_loss_box_dn_1[0.2016] det_loss_cls_dn_2[0.0837] det_loss_box_dn_2[0.1845] det_loss_cls_dn_3[0.0825] det_loss_box_dn_3[0.1785] det_loss_cls_dn_4[0.0817] det_loss_box_dn_4[0.1753] det_loss_cls_dn_5[0.0809] det_loss_box_dn_5[0.1735] loss_depth[0.0344] total_loss[6.3540]
Rank[0/8] 06/20/2025 11:30:45 INFO stats.py:314 | Epoch[162] Step[69] GlobalStep[63249] Training Speed: 7.73 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 10:13:28.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:30:51 INFO loss_tracker.py:84 | Epoch[162/239] Step[74] GlobalStep[63254/NA]: det_loss_cls_0[0.4387] det_loss_box_0[0.6591] det_loss_cls_1[0.4264] det_loss_box_1[0.5163] det_loss_cls_2[0.3913] det_loss_box_2[0.4948] det_loss_cls_3[0.3611] det_loss_box_3[0.4903] det_loss_cls_4[0.3392] det_loss_box_4[0.4852] det_loss_cls_5[0.3332] det_loss_box_5[0.4776] det_loss_cls_dn_0[0.0889] det_loss_box_dn_0[0.3618] det_loss_cls_dn_1[0.0862] det_loss_box_dn_1[0.3141] det_loss_cls_dn_2[0.0851] det_loss_box_dn_2[0.2977] det_loss_cls_dn_3[0.0836] det_loss_box_dn_3[0.2919] det_loss_cls_dn_4[0.0823] det_loss_box_dn_4[0.2896] det_loss_cls_dn_5[0.0816] det_loss_box_dn_5[0.2860] loss_depth[0.0365] total_loss[7.7982]
Rank[0/8] 06/20/2025 11:31:11 INFO stats.py:314 | Epoch[162] Step[94] GlobalStep[63274] Training Speed: 7.77 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 10:12:56.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:31:16 INFO loss_tracker.py:84 | Epoch[162/239] Step[99] GlobalStep[63279/NA]: det_loss_cls_0[0.5425] det_loss_box_0[0.7069] det_loss_cls_1[0.5220] det_loss_box_1[0.5275] det_loss_cls_2[0.4672] det_loss_box_2[0.4886] det_loss_cls_3[0.4384] det_loss_box_3[0.4728] det_loss_cls_4[0.4107] det_loss_box_4[0.4677] det_loss_cls_5[0.3925] det_loss_box_5[0.4680] det_loss_cls_dn_0[0.0914] det_loss_box_dn_0[0.2781] det_loss_cls_dn_1[0.0879] det_loss_box_dn_1[0.2336] det_loss_cls_dn_2[0.0869] det_loss_box_dn_2[0.2173] det_loss_cls_dn_3[0.0855] det_loss_box_dn_3[0.2125] det_loss_cls_dn_4[0.0846] det_loss_box_dn_4[0.2114] det_loss_cls_dn_5[0.0837] det_loss_box_dn_5[0.2078] loss_depth[0.0368] total_loss[7.8224]
Rank[0/8] 06/20/2025 11:31:37 INFO stats.py:314 | Epoch[162] Step[119] GlobalStep[63299] Training Speed: 8.19 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 10:12:23.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:31:42 INFO loss_tracker.py:84 | Epoch[162/239] Step[124] GlobalStep[63304/NA]: det_loss_cls_0[0.5553] det_loss_box_0[0.6709] det_loss_cls_1[0.5446] det_loss_box_1[0.5019] det_loss_cls_2[0.4808] det_loss_box_2[0.4530] det_loss_cls_3[0.4426] det_loss_box_3[0.4336] det_loss_cls_4[0.4105] det_loss_box_4[0.4215] det_loss_cls_5[0.3990] det_loss_box_5[0.4205] det_loss_cls_dn_0[0.0916] det_loss_box_dn_0[0.2538] det_loss_cls_dn_1[0.0869] det_loss_box_dn_1[0.2037] det_loss_cls_dn_2[0.0858] det_loss_box_dn_2[0.1852] det_loss_cls_dn_3[0.0849] det_loss_box_dn_3[0.1824] det_loss_cls_dn_4[0.0838] det_loss_box_dn_4[0.1804] det_loss_cls_dn_5[0.0830] det_loss_box_dn_5[0.1752] loss_depth[0.0348] total_loss[7.4657]
Rank[0/8] 06/20/2025 11:32:03 INFO stats.py:314 | Epoch[162] Step[144] GlobalStep[63324] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 10:11:51.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:32:09 INFO loss_tracker.py:84 | Epoch[162/239] Step[149] GlobalStep[63329/NA]: det_loss_cls_0[0.5122] det_loss_box_0[0.6177] det_loss_cls_1[0.4859] det_loss_box_1[0.4767] det_loss_cls_2[0.4309] det_loss_box_2[0.4309] det_loss_cls_3[0.4160] det_loss_box_3[0.4086] det_loss_cls_4[0.3854] det_loss_box_4[0.4080] det_loss_cls_5[0.3787] det_loss_box_5[0.4039] det_loss_cls_dn_0[0.0988] det_loss_box_dn_0[0.2747] det_loss_cls_dn_1[0.0962] det_loss_box_dn_1[0.2354] det_loss_cls_dn_2[0.0954] det_loss_box_dn_2[0.2200] det_loss_cls_dn_3[0.0939] det_loss_box_dn_3[0.2168] det_loss_cls_dn_4[0.0937] det_loss_box_dn_4[0.2156] det_loss_cls_dn_5[0.0922] det_loss_box_dn_5[0.2148] loss_depth[0.0336] total_loss[7.3362]
Rank[0/8] 06/20/2025 11:32:29 INFO stats.py:314 | Epoch[162] Step[169] GlobalStep[63349] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 10:11:19.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:32:34 INFO loss_tracker.py:84 | Epoch[162/239] Step[174] GlobalStep[63354/NA]: det_loss_cls_0[0.5641] det_loss_box_0[0.6756] det_loss_cls_1[0.5496] det_loss_box_1[0.5085] det_loss_cls_2[0.4909] det_loss_box_2[0.4528] det_loss_cls_3[0.4571] det_loss_box_3[0.4397] det_loss_cls_4[0.4314] det_loss_box_4[0.4358] det_loss_cls_5[0.4168] det_loss_box_5[0.4379] det_loss_cls_dn_0[0.1029] det_loss_box_dn_0[0.2866] det_loss_cls_dn_1[0.0996] det_loss_box_dn_1[0.2400] det_loss_cls_dn_2[0.0995] det_loss_box_dn_2[0.2222] det_loss_cls_dn_3[0.0981] det_loss_box_dn_3[0.2168] det_loss_cls_dn_4[0.0971] det_loss_box_dn_4[0.2152] det_loss_cls_dn_5[0.0961] det_loss_box_dn_5[0.2110] loss_depth[0.0382] total_loss[7.8835]
Rank[0/8] 06/20/2025 11:32:54 INFO stats.py:314 | Epoch[162] Step[194] GlobalStep[63374] Training Speed: 10.29 samples/sec across all devices.	Average Step Time: 0.78 sec.	Estimated Remaining Time: 10:10:46.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:33:00 INFO loss_tracker.py:84 | Epoch[162/239] Step[199] GlobalStep[63379/NA]: det_loss_cls_0[0.5517] det_loss_box_0[0.6963] det_loss_cls_1[0.5348] det_loss_box_1[0.5203] det_loss_cls_2[0.4882] det_loss_box_2[0.4700] det_loss_cls_3[0.4564] det_loss_box_3[0.4528] det_loss_cls_4[0.4272] det_loss_box_4[0.4420] det_loss_cls_5[0.4130] det_loss_box_5[0.4391] det_loss_cls_dn_0[0.1001] det_loss_box_dn_0[0.2748] det_loss_cls_dn_1[0.0970] det_loss_box_dn_1[0.2207] det_loss_cls_dn_2[0.0959] det_loss_box_dn_2[0.2009] det_loss_cls_dn_3[0.0950] det_loss_box_dn_3[0.1964] det_loss_cls_dn_4[0.0940] det_loss_box_dn_4[0.1947] det_loss_cls_dn_5[0.0929] det_loss_box_dn_5[0.1906] loss_depth[0.0378] total_loss[7.7827]
Rank[0/8] 06/20/2025 11:33:20 INFO stats.py:314 | Epoch[162] Step[219] GlobalStep[63399] Training Speed: 8.02 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 10:10:13.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:33:25 INFO loss_tracker.py:84 | Epoch[162/239] Step[224] GlobalStep[63404/NA]: det_loss_cls_0[0.5107] det_loss_box_0[0.7592] det_loss_cls_1[0.5110] det_loss_box_1[0.6136] det_loss_cls_2[0.4707] det_loss_box_2[0.5475] det_loss_cls_3[0.4517] det_loss_box_3[0.5176] det_loss_cls_4[0.4315] det_loss_box_4[0.5115] det_loss_cls_5[0.4204] det_loss_box_5[0.5129] det_loss_cls_dn_0[0.0944] det_loss_box_dn_0[0.2736] det_loss_cls_dn_1[0.0913] det_loss_box_dn_1[0.2341] det_loss_cls_dn_2[0.0899] det_loss_box_dn_2[0.2117] det_loss_cls_dn_3[0.0887] det_loss_box_dn_3[0.2088] det_loss_cls_dn_4[0.0886] det_loss_box_dn_4[0.2064] det_loss_cls_dn_5[0.0881] det_loss_box_dn_5[0.2042] loss_depth[0.0367] total_loss[8.1747]
Rank[0/8] 06/20/2025 11:33:46 INFO stats.py:314 | Epoch[162] Step[244] GlobalStep[63424] Training Speed: 8.74 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 10:09:41.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:33:51 INFO loss_tracker.py:84 | Epoch[162/239] Step[249] GlobalStep[63429/NA]: det_loss_cls_0[0.5680] det_loss_box_0[0.7726] det_loss_cls_1[0.5530] det_loss_box_1[0.5977] det_loss_cls_2[0.5045] det_loss_box_2[0.5371] det_loss_cls_3[0.4673] det_loss_box_3[0.5178] det_loss_cls_4[0.4346] det_loss_box_4[0.5124] det_loss_cls_5[0.4188] det_loss_box_5[0.5138] det_loss_cls_dn_0[0.0955] det_loss_box_dn_0[0.2554] det_loss_cls_dn_1[0.0921] det_loss_box_dn_1[0.2151] det_loss_cls_dn_2[0.0917] det_loss_box_dn_2[0.2010] det_loss_cls_dn_3[0.0903] det_loss_box_dn_3[0.1967] det_loss_cls_dn_4[0.0896] det_loss_box_dn_4[0.1949] det_loss_cls_dn_5[0.0888] det_loss_box_dn_5[0.1935] loss_depth[0.0365] total_loss[8.2387]
Rank[0/8] 06/20/2025 11:34:12 INFO stats.py:314 | Epoch[162] Step[269] GlobalStep[63449] Training Speed: 8.63 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 10:09:09.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:34:17 INFO loss_tracker.py:84 | Epoch[162/239] Step[274] GlobalStep[63454/NA]: det_loss_cls_0[0.4695] det_loss_box_0[0.5492] det_loss_cls_1[0.4556] det_loss_box_1[0.4084] det_loss_cls_2[0.4033] det_loss_box_2[0.3671] det_loss_cls_3[0.3620] det_loss_box_3[0.3537] det_loss_cls_4[0.3306] det_loss_box_4[0.3561] det_loss_cls_5[0.3236] det_loss_box_5[0.3551] det_loss_cls_dn_0[0.0869] det_loss_box_dn_0[0.2553] det_loss_cls_dn_1[0.0830] det_loss_box_dn_1[0.2080] det_loss_cls_dn_2[0.0827] det_loss_box_dn_2[0.1918] det_loss_cls_dn_3[0.0819] det_loss_box_dn_3[0.1888] det_loss_cls_dn_4[0.0811] det_loss_box_dn_4[0.1864] det_loss_cls_dn_5[0.0805] det_loss_box_dn_5[0.1809] loss_depth[0.0371] total_loss[6.4784]
Rank[0/8] 06/20/2025 11:34:37 INFO stats.py:314 | Epoch[162] Step[294] GlobalStep[63474] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 10:08:36.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:34:42 INFO loss_tracker.py:84 | Epoch[162/239] Step[299] GlobalStep[63479/NA]: det_loss_cls_0[0.4344] det_loss_box_0[0.5342] det_loss_cls_1[0.4147] det_loss_box_1[0.4142] det_loss_cls_2[0.3631] det_loss_box_2[0.3878] det_loss_cls_3[0.3312] det_loss_box_3[0.3780] det_loss_cls_4[0.3192] det_loss_box_4[0.3624] det_loss_cls_5[0.3067] det_loss_box_5[0.3631] det_loss_cls_dn_0[0.0825] det_loss_box_dn_0[0.2568] det_loss_cls_dn_1[0.0781] det_loss_box_dn_1[0.2087] det_loss_cls_dn_2[0.0768] det_loss_box_dn_2[0.1913] det_loss_cls_dn_3[0.0756] det_loss_box_dn_3[0.1863] det_loss_cls_dn_4[0.0746] det_loss_box_dn_4[0.1851] det_loss_cls_dn_5[0.0737] det_loss_box_dn_5[0.1842] loss_depth[0.0331] total_loss[6.3157]
Rank[0/8] 06/20/2025 11:35:04 INFO stats.py:314 | Epoch[162] Step[319] GlobalStep[63499] Training Speed: 6.66 samples/sec across all devices.	Average Step Time: 1.20 sec.	Estimated Remaining Time: 10:08:04.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:35:09 INFO loss_tracker.py:84 | Epoch[162/239] Step[324] GlobalStep[63504/NA]: det_loss_cls_0[0.5109] det_loss_box_0[0.8091] det_loss_cls_1[0.4944] det_loss_box_1[0.6184] det_loss_cls_2[0.4547] det_loss_box_2[0.5644] det_loss_cls_3[0.4180] det_loss_box_3[0.5511] det_loss_cls_4[0.3889] det_loss_box_4[0.5297] det_loss_cls_5[0.3790] det_loss_box_5[0.5275] det_loss_cls_dn_0[0.0832] det_loss_box_dn_0[0.2809] det_loss_cls_dn_1[0.0800] det_loss_box_dn_1[0.2337] det_loss_cls_dn_2[0.0793] det_loss_box_dn_2[0.2132] det_loss_cls_dn_3[0.0781] det_loss_box_dn_3[0.2092] det_loss_cls_dn_4[0.0775] det_loss_box_dn_4[0.2055] det_loss_cls_dn_5[0.0770] det_loss_box_dn_5[0.2009] loss_depth[0.0355] total_loss[8.1000]
Rank[0/8] 06/20/2025 11:35:29 INFO stats.py:314 | Epoch[162] Step[344] GlobalStep[63524] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 10:07:31.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:35:34 INFO loss_tracker.py:84 | Epoch[162/239] Step[349] GlobalStep[63529/NA]: det_loss_cls_0[0.5495] det_loss_box_0[0.6740] det_loss_cls_1[0.5434] det_loss_box_1[0.5289] det_loss_cls_2[0.4861] det_loss_box_2[0.4703] det_loss_cls_3[0.4514] det_loss_box_3[0.4472] det_loss_cls_4[0.4210] det_loss_box_4[0.4352] det_loss_cls_5[0.4065] det_loss_box_5[0.4355] det_loss_cls_dn_0[0.1040] det_loss_box_dn_0[0.3243] det_loss_cls_dn_1[0.1017] det_loss_box_dn_1[0.2625] det_loss_cls_dn_2[0.1004] det_loss_box_dn_2[0.2456] det_loss_cls_dn_3[0.0993] det_loss_box_dn_3[0.2383] det_loss_cls_dn_4[0.0985] det_loss_box_dn_4[0.2366] det_loss_cls_dn_5[0.0977] det_loss_box_dn_5[0.2334] loss_depth[0.0360] total_loss[8.0277]
Rank[0/8] 06/20/2025 11:35:55 INFO stats.py:314 | Epoch[162] Step[369] GlobalStep[63549] Training Speed: 7.91 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 10:06:59.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:36:00 INFO loss_tracker.py:84 | Epoch[162/239] Step[374] GlobalStep[63554/NA]: det_loss_cls_0[0.3880] det_loss_box_0[0.4898] det_loss_cls_1[0.3870] det_loss_box_1[0.3614] det_loss_cls_2[0.3371] det_loss_box_2[0.3370] det_loss_cls_3[0.3033] det_loss_box_3[0.3278] det_loss_cls_4[0.2724] det_loss_box_4[0.3246] det_loss_cls_5[0.2630] det_loss_box_5[0.3240] det_loss_cls_dn_0[0.0797] det_loss_box_dn_0[0.2782] det_loss_cls_dn_1[0.0757] det_loss_box_dn_1[0.2225] det_loss_cls_dn_2[0.0745] det_loss_box_dn_2[0.2058] det_loss_cls_dn_3[0.0737] det_loss_box_dn_3[0.2009] det_loss_cls_dn_4[0.0725] det_loss_box_dn_4[0.1987] det_loss_cls_dn_5[0.0720] det_loss_box_dn_5[0.1967] loss_depth[0.0382] total_loss[5.9043]
Rank[0/8] 06/20/2025 11:36:15 INFO stats.py:394 | Epoch[162] completed. Training Speed: 7.72 samples/sec across all devices.	Epoch Time: 404.02 sec.	Average Epoch Time: 404.02 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 10:06:33.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:36:22 INFO stats.py:314 | Epoch[163] Step[4] GlobalStep[63574] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 10:06:27.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:36:43 INFO loss_tracker.py:84 | Epoch[163/239] Step[24] GlobalStep[63594/NA]: det_loss_cls_0[0.4901] det_loss_box_0[0.6345] det_loss_cls_1[0.4770] det_loss_box_1[0.4692] det_loss_cls_2[0.4281] det_loss_box_2[0.4332] det_loss_cls_3[0.3935] det_loss_box_3[0.4208] det_loss_cls_4[0.3665] det_loss_box_4[0.4138] det_loss_cls_5[0.3500] det_loss_box_5[0.4155] det_loss_cls_dn_0[0.0923] det_loss_box_dn_0[0.2622] det_loss_cls_dn_1[0.0891] det_loss_box_dn_1[0.2163] det_loss_cls_dn_2[0.0882] det_loss_box_dn_2[0.2008] det_loss_cls_dn_3[0.0872] det_loss_box_dn_3[0.1966] det_loss_cls_dn_4[0.0861] det_loss_box_dn_4[0.1939] det_loss_cls_dn_5[0.0851] det_loss_box_dn_5[0.1920] loss_depth[0.0367] total_loss[7.1188]
Rank[0/8] 06/20/2025 11:36:48 INFO stats.py:314 | Epoch[163] Step[29] GlobalStep[63599] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 10:05:55.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:37:09 INFO loss_tracker.py:84 | Epoch[163/239] Step[49] GlobalStep[63619/NA]: det_loss_cls_0[0.4770] det_loss_box_0[0.5967] det_loss_cls_1[0.4530] det_loss_box_1[0.4462] det_loss_cls_2[0.4035] det_loss_box_2[0.4119] det_loss_cls_3[0.3725] det_loss_box_3[0.3981] det_loss_cls_4[0.3440] det_loss_box_4[0.3856] det_loss_cls_5[0.3367] det_loss_box_5[0.3860] det_loss_cls_dn_0[0.0930] det_loss_box_dn_0[0.2573] det_loss_cls_dn_1[0.0887] det_loss_box_dn_1[0.2131] det_loss_cls_dn_2[0.0882] det_loss_box_dn_2[0.1981] det_loss_cls_dn_3[0.0871] det_loss_box_dn_3[0.1940] det_loss_cls_dn_4[0.0861] det_loss_box_dn_4[0.1912] det_loss_cls_dn_5[0.0852] det_loss_box_dn_5[0.1907] loss_depth[0.0351] total_loss[6.8191]
Rank[0/8] 06/20/2025 11:37:14 INFO stats.py:314 | Epoch[163] Step[54] GlobalStep[63624] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 10:05:22.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:37:35 INFO loss_tracker.py:84 | Epoch[163/239] Step[74] GlobalStep[63644/NA]: det_loss_cls_0[0.5220] det_loss_box_0[0.6274] det_loss_cls_1[0.5066] det_loss_box_1[0.4715] det_loss_cls_2[0.4609] det_loss_box_2[0.4360] det_loss_cls_3[0.4331] det_loss_box_3[0.4280] det_loss_cls_4[0.4157] det_loss_box_4[0.4168] det_loss_cls_5[0.4057] det_loss_box_5[0.4131] det_loss_cls_dn_0[0.0998] det_loss_box_dn_0[0.3059] det_loss_cls_dn_1[0.0968] det_loss_box_dn_1[0.2649] det_loss_cls_dn_2[0.0961] det_loss_box_dn_2[0.2490] det_loss_cls_dn_3[0.0954] det_loss_box_dn_3[0.2451] det_loss_cls_dn_4[0.0953] det_loss_box_dn_4[0.2434] det_loss_cls_dn_5[0.0942] det_loss_box_dn_5[0.2420] loss_depth[0.0398] total_loss[7.7042]
Rank[0/8] 06/20/2025 11:37:40 INFO stats.py:314 | Epoch[163] Step[79] GlobalStep[63649] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 10:04:50.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:38:01 INFO loss_tracker.py:84 | Epoch[163/239] Step[99] GlobalStep[63669/NA]: det_loss_cls_0[0.4457] det_loss_box_0[0.6482] det_loss_cls_1[0.4326] det_loss_box_1[0.5193] det_loss_cls_2[0.3918] det_loss_box_2[0.4723] det_loss_cls_3[0.3604] det_loss_box_3[0.4723] det_loss_cls_4[0.3441] det_loss_box_4[0.4378] det_loss_cls_5[0.3261] det_loss_box_5[0.4414] det_loss_cls_dn_0[0.0866] det_loss_box_dn_0[0.2844] det_loss_cls_dn_1[0.0833] det_loss_box_dn_1[0.2430] det_loss_cls_dn_2[0.0822] det_loss_box_dn_2[0.2249] det_loss_cls_dn_3[0.0811] det_loss_box_dn_3[0.2229] det_loss_cls_dn_4[0.0799] det_loss_box_dn_4[0.2197] det_loss_cls_dn_5[0.0789] det_loss_box_dn_5[0.2185] loss_depth[0.0360] total_loss[7.2334]
Rank[0/8] 06/20/2025 11:38:06 INFO stats.py:314 | Epoch[163] Step[104] GlobalStep[63674] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 10:04:18.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:38:26 INFO loss_tracker.py:84 | Epoch[163/239] Step[124] GlobalStep[63694/NA]: det_loss_cls_0[0.4707] det_loss_box_0[0.5595] det_loss_cls_1[0.4513] det_loss_box_1[0.4358] det_loss_cls_2[0.3972] det_loss_box_2[0.3898] det_loss_cls_3[0.3662] det_loss_box_3[0.3749] det_loss_cls_4[0.3403] det_loss_box_4[0.3678] det_loss_cls_5[0.3372] det_loss_box_5[0.3647] det_loss_cls_dn_0[0.0922] det_loss_box_dn_0[0.2483] det_loss_cls_dn_1[0.0887] det_loss_box_dn_1[0.2003] det_loss_cls_dn_2[0.0879] det_loss_box_dn_2[0.1840] det_loss_cls_dn_3[0.0865] det_loss_box_dn_3[0.1797] det_loss_cls_dn_4[0.0854] det_loss_box_dn_4[0.1777] det_loss_cls_dn_5[0.0847] det_loss_box_dn_5[0.1771] loss_depth[0.0374] total_loss[6.5851]
Rank[0/8] 06/20/2025 11:38:32 INFO stats.py:314 | Epoch[163] Step[129] GlobalStep[63699] Training Speed: 7.56 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 10:03:45.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:38:52 INFO loss_tracker.py:84 | Epoch[163/239] Step[149] GlobalStep[63719/NA]: det_loss_cls_0[0.5628] det_loss_box_0[0.7860] det_loss_cls_1[0.5506] det_loss_box_1[0.6133] det_loss_cls_2[0.4869] det_loss_box_2[0.5592] det_loss_cls_3[0.4543] det_loss_box_3[0.5399] det_loss_cls_4[0.4258] det_loss_box_4[0.5157] det_loss_cls_5[0.4093] det_loss_box_5[0.5205] det_loss_cls_dn_0[0.0884] det_loss_box_dn_0[0.2651] det_loss_cls_dn_1[0.0841] det_loss_box_dn_1[0.2178] det_loss_cls_dn_2[0.0834] det_loss_box_dn_2[0.2053] det_loss_cls_dn_3[0.0816] det_loss_box_dn_3[0.2015] det_loss_cls_dn_4[0.0808] det_loss_box_dn_4[0.1994] det_loss_cls_dn_5[0.0799] det_loss_box_dn_5[0.1972] loss_depth[0.0345] total_loss[8.2434]
Rank[0/8] 06/20/2025 11:38:57 INFO stats.py:314 | Epoch[163] Step[154] GlobalStep[63724] Training Speed: 7.68 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 10:03:13.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:39:18 INFO loss_tracker.py:84 | Epoch[163/239] Step[174] GlobalStep[63744/NA]: det_loss_cls_0[0.3859] det_loss_box_0[0.5075] det_loss_cls_1[0.3658] det_loss_box_1[0.3766] det_loss_cls_2[0.3193] det_loss_box_2[0.3446] det_loss_cls_3[0.2825] det_loss_box_3[0.3396] det_loss_cls_4[0.2597] det_loss_box_4[0.3288] det_loss_cls_5[0.2483] det_loss_box_5[0.3316] det_loss_cls_dn_0[0.0896] det_loss_box_dn_0[0.2859] det_loss_cls_dn_1[0.0856] det_loss_box_dn_1[0.2384] det_loss_cls_dn_2[0.0838] det_loss_box_dn_2[0.2166] det_loss_cls_dn_3[0.0829] det_loss_box_dn_3[0.2132] det_loss_cls_dn_4[0.0817] det_loss_box_dn_4[0.2103] det_loss_cls_dn_5[0.0806] det_loss_box_dn_5[0.2065] loss_depth[0.0347] total_loss[6.0000]
Rank[0/8] 06/20/2025 11:39:23 INFO stats.py:314 | Epoch[163] Step[179] GlobalStep[63749] Training Speed: 7.32 samples/sec across all devices.	Average Step Time: 1.09 sec.	Estimated Remaining Time: 10:02:41.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:39:44 INFO loss_tracker.py:84 | Epoch[163/239] Step[199] GlobalStep[63769/NA]: det_loss_cls_0[0.5240] det_loss_box_0[0.6244] det_loss_cls_1[0.5210] det_loss_box_1[0.4680] det_loss_cls_2[0.4612] det_loss_box_2[0.4389] det_loss_cls_3[0.4296] det_loss_box_3[0.4238] det_loss_cls_4[0.4073] det_loss_box_4[0.4070] det_loss_cls_5[0.3916] det_loss_box_5[0.4088] det_loss_cls_dn_0[0.1042] det_loss_box_dn_0[0.2582] det_loss_cls_dn_1[0.1007] det_loss_box_dn_1[0.2134] det_loss_cls_dn_2[0.0999] det_loss_box_dn_2[0.2009] det_loss_cls_dn_3[0.0990] det_loss_box_dn_3[0.1967] det_loss_cls_dn_4[0.0980] det_loss_box_dn_4[0.1952] det_loss_cls_dn_5[0.0972] det_loss_box_dn_5[0.1938] loss_depth[0.0343] total_loss[7.3973]
Rank[0/8] 06/20/2025 11:39:49 INFO stats.py:314 | Epoch[163] Step[204] GlobalStep[63774] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 10:02:08.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:40:10 INFO loss_tracker.py:84 | Epoch[163/239] Step[224] GlobalStep[63794/NA]: det_loss_cls_0[0.3980] det_loss_box_0[0.5492] det_loss_cls_1[0.3825] det_loss_box_1[0.4421] det_loss_cls_2[0.3395] det_loss_box_2[0.3978] det_loss_cls_3[0.3225] det_loss_box_3[0.3837] det_loss_cls_4[0.3026] det_loss_box_4[0.3717] det_loss_cls_5[0.2937] det_loss_box_5[0.3712] det_loss_cls_dn_0[0.0793] det_loss_box_dn_0[0.3095] det_loss_cls_dn_1[0.0759] det_loss_box_dn_1[0.2624] det_loss_cls_dn_2[0.0751] det_loss_box_dn_2[0.2410] det_loss_cls_dn_3[0.0738] det_loss_box_dn_3[0.2327] det_loss_cls_dn_4[0.0726] det_loss_box_dn_4[0.2302] det_loss_cls_dn_5[0.0717] det_loss_box_dn_5[0.2268] loss_depth[0.0378] total_loss[6.5433]
Rank[0/8] 06/20/2025 11:40:15 INFO stats.py:314 | Epoch[163] Step[229] GlobalStep[63799] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 10:01:36.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:40:36 INFO loss_tracker.py:84 | Epoch[163/239] Step[249] GlobalStep[63819/NA]: det_loss_cls_0[0.3525] det_loss_box_0[0.4550] det_loss_cls_1[0.3431] det_loss_box_1[0.3663] det_loss_cls_2[0.2967] det_loss_box_2[0.3216] det_loss_cls_3[0.2722] det_loss_box_3[0.3075] det_loss_cls_4[0.2518] det_loss_box_4[0.2969] det_loss_cls_5[0.2437] det_loss_box_5[0.2956] det_loss_cls_dn_0[0.0872] det_loss_box_dn_0[0.2969] det_loss_cls_dn_1[0.0839] det_loss_box_dn_1[0.2412] det_loss_cls_dn_2[0.0828] det_loss_box_dn_2[0.2217] det_loss_cls_dn_3[0.0816] det_loss_box_dn_3[0.2170] det_loss_cls_dn_4[0.0805] det_loss_box_dn_4[0.2121] det_loss_cls_dn_5[0.0795] det_loss_box_dn_5[0.2113] loss_depth[0.0362] total_loss[5.7349]
Rank[0/8] 06/20/2025 11:40:41 INFO stats.py:314 | Epoch[163] Step[254] GlobalStep[63824] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 10:01:03.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:41:02 INFO loss_tracker.py:84 | Epoch[163/239] Step[274] GlobalStep[63844/NA]: det_loss_cls_0[0.5234] det_loss_box_0[0.6018] det_loss_cls_1[0.5057] det_loss_box_1[0.4529] det_loss_cls_2[0.4415] det_loss_box_2[0.4099] det_loss_cls_3[0.4025] det_loss_box_3[0.3990] det_loss_cls_4[0.3673] det_loss_box_4[0.3929] det_loss_cls_5[0.3547] det_loss_box_5[0.3898] det_loss_cls_dn_0[0.0903] det_loss_box_dn_0[0.2221] det_loss_cls_dn_1[0.0865] det_loss_box_dn_1[0.1846] det_loss_cls_dn_2[0.0854] det_loss_box_dn_2[0.1689] det_loss_cls_dn_3[0.0841] det_loss_box_dn_3[0.1666] det_loss_cls_dn_4[0.0830] det_loss_box_dn_4[0.1644] det_loss_cls_dn_5[0.0821] det_loss_box_dn_5[0.1637] loss_depth[0.0345] total_loss[6.8578]
Rank[0/8] 06/20/2025 11:41:07 INFO stats.py:314 | Epoch[163] Step[279] GlobalStep[63849] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 10:00:31.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:41:28 INFO loss_tracker.py:84 | Epoch[163/239] Step[299] GlobalStep[63869/NA]: det_loss_cls_0[0.4389] det_loss_box_0[0.5259] det_loss_cls_1[0.4241] det_loss_box_1[0.3798] det_loss_cls_2[0.3694] det_loss_box_2[0.3323] det_loss_cls_3[0.3312] det_loss_box_3[0.3185] det_loss_cls_4[0.3072] det_loss_box_4[0.3136] det_loss_cls_5[0.2925] det_loss_box_5[0.3147] det_loss_cls_dn_0[0.1001] det_loss_box_dn_0[0.2395] det_loss_cls_dn_1[0.0974] det_loss_box_dn_1[0.1920] det_loss_cls_dn_2[0.0966] det_loss_box_dn_2[0.1754] det_loss_cls_dn_3[0.0951] det_loss_box_dn_3[0.1716] det_loss_cls_dn_4[0.0941] det_loss_box_dn_4[0.1667] det_loss_cls_dn_5[0.0931] det_loss_box_dn_5[0.1659] loss_depth[0.0360] total_loss[6.0713]
Rank[0/8] 06/20/2025 11:41:33 INFO stats.py:314 | Epoch[163] Step[304] GlobalStep[63874] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 9:59:59.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:41:54 INFO loss_tracker.py:84 | Epoch[163/239] Step[324] GlobalStep[63894/NA]: det_loss_cls_0[0.4478] det_loss_box_0[0.5681] det_loss_cls_1[0.4390] det_loss_box_1[0.4284] det_loss_cls_2[0.3863] det_loss_box_2[0.3890] det_loss_cls_3[0.3506] det_loss_box_3[0.3869] det_loss_cls_4[0.3328] det_loss_box_4[0.3809] det_loss_cls_5[0.3149] det_loss_box_5[0.3818] det_loss_cls_dn_0[0.0899] det_loss_box_dn_0[0.2716] det_loss_cls_dn_1[0.0874] det_loss_box_dn_1[0.2323] det_loss_cls_dn_2[0.0860] det_loss_box_dn_2[0.2159] det_loss_cls_dn_3[0.0850] det_loss_box_dn_3[0.2126] det_loss_cls_dn_4[0.0841] det_loss_box_dn_4[0.2109] det_loss_cls_dn_5[0.0826] det_loss_box_dn_5[0.2095] loss_depth[0.0346] total_loss[6.7090]
Rank[0/8] 06/20/2025 11:41:59 INFO stats.py:314 | Epoch[163] Step[329] GlobalStep[63899] Training Speed: 8.59 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 9:59:27.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:42:20 INFO loss_tracker.py:84 | Epoch[163/239] Step[349] GlobalStep[63919/NA]: det_loss_cls_0[0.5177] det_loss_box_0[0.6607] det_loss_cls_1[0.5097] det_loss_box_1[0.5261] det_loss_cls_2[0.4493] det_loss_box_2[0.4796] det_loss_cls_3[0.4203] det_loss_box_3[0.4603] det_loss_cls_4[0.3811] det_loss_box_4[0.4551] det_loss_cls_5[0.3678] det_loss_box_5[0.4510] det_loss_cls_dn_0[0.0884] det_loss_box_dn_0[0.2350] det_loss_cls_dn_1[0.0856] det_loss_box_dn_1[0.1982] det_loss_cls_dn_2[0.0842] det_loss_box_dn_2[0.1837] det_loss_cls_dn_3[0.0835] det_loss_box_dn_3[0.1813] det_loss_cls_dn_4[0.0826] det_loss_box_dn_4[0.1799] det_loss_cls_dn_5[0.0817] det_loss_box_dn_5[0.1784] loss_depth[0.0336] total_loss[7.3747]
Rank[0/8] 06/20/2025 11:42:25 INFO stats.py:314 | Epoch[163] Step[354] GlobalStep[63924] Training Speed: 7.70 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 9:58:54.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:42:46 INFO loss_tracker.py:84 | Epoch[163/239] Step[374] GlobalStep[63944/NA]: det_loss_cls_0[0.4660] det_loss_box_0[0.5919] det_loss_cls_1[0.4541] det_loss_box_1[0.4592] det_loss_cls_2[0.4044] det_loss_box_2[0.4136] det_loss_cls_3[0.3795] det_loss_box_3[0.4009] det_loss_cls_4[0.3542] det_loss_box_4[0.3925] det_loss_cls_5[0.3434] det_loss_box_5[0.3913] det_loss_cls_dn_0[0.0997] det_loss_box_dn_0[0.3092] det_loss_cls_dn_1[0.0964] det_loss_box_dn_1[0.2598] det_loss_cls_dn_2[0.0955] det_loss_box_dn_2[0.2435] det_loss_cls_dn_3[0.0945] det_loss_box_dn_3[0.2405] det_loss_cls_dn_4[0.0945] det_loss_box_dn_4[0.2381] det_loss_cls_dn_5[0.0939] det_loss_box_dn_5[0.2365] loss_depth[0.0374] total_loss[7.1903]
Rank[0/8] 06/20/2025 11:42:51 INFO stats.py:314 | Epoch[163] Step[379] GlobalStep[63949] Training Speed: 8.70 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 9:58:22.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:43:01 INFO stats.py:394 | Epoch[163] completed. Training Speed: 7.69 samples/sec across all devices.	Epoch Time: 405.80 sec.	Average Epoch Time: 405.80 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 9:58:09.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:43:23 INFO stats.py:314 | Epoch[164] Step[14] GlobalStep[63974] Training Speed: 8.65 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 9:57:53.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:43:33 INFO loss_tracker.py:84 | Epoch[164/239] Step[24] GlobalStep[63984/NA]: det_loss_cls_0[0.5158] det_loss_box_0[0.6765] det_loss_cls_1[0.5084] det_loss_box_1[0.5457] det_loss_cls_2[0.4518] det_loss_box_2[0.4985] det_loss_cls_3[0.4199] det_loss_box_3[0.4802] det_loss_cls_4[0.3895] det_loss_box_4[0.4635] det_loss_cls_5[0.3771] det_loss_box_5[0.4646] det_loss_cls_dn_0[0.0902] det_loss_box_dn_0[0.3259] det_loss_cls_dn_1[0.0872] det_loss_box_dn_1[0.2685] det_loss_cls_dn_2[0.0863] det_loss_box_dn_2[0.2518] det_loss_cls_dn_3[0.0848] det_loss_box_dn_3[0.2438] det_loss_cls_dn_4[0.0835] det_loss_box_dn_4[0.2393] det_loss_cls_dn_5[0.0824] det_loss_box_dn_5[0.2361] loss_depth[0.0367] total_loss[7.9080]
Rank[0/8] 06/20/2025 11:43:48 INFO stats.py:314 | Epoch[164] Step[39] GlobalStep[63999] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 9:57:20.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:43:59 INFO loss_tracker.py:84 | Epoch[164/239] Step[49] GlobalStep[64009/NA]: det_loss_cls_0[0.5012] det_loss_box_0[0.5638] det_loss_cls_1[0.4732] det_loss_box_1[0.4306] det_loss_cls_2[0.4142] det_loss_box_2[0.3867] det_loss_cls_3[0.3818] det_loss_box_3[0.3784] det_loss_cls_4[0.3522] det_loss_box_4[0.3696] det_loss_cls_5[0.3346] det_loss_box_5[0.3738] det_loss_cls_dn_0[0.0944] det_loss_box_dn_0[0.2390] det_loss_cls_dn_1[0.0911] det_loss_box_dn_1[0.1957] det_loss_cls_dn_2[0.0898] det_loss_box_dn_2[0.1818] det_loss_cls_dn_3[0.0887] det_loss_box_dn_3[0.1768] det_loss_cls_dn_4[0.0876] det_loss_box_dn_4[0.1746] det_loss_cls_dn_5[0.0866] det_loss_box_dn_5[0.1723] loss_depth[0.0338] total_loss[6.6722]
Rank[0/8] 06/20/2025 11:44:14 INFO stats.py:314 | Epoch[164] Step[64] GlobalStep[64024] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 9:56:48.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:44:24 INFO loss_tracker.py:84 | Epoch[164/239] Step[74] GlobalStep[64034/NA]: det_loss_cls_0[0.5246] det_loss_box_0[0.6694] det_loss_cls_1[0.5127] det_loss_box_1[0.5203] det_loss_cls_2[0.4569] det_loss_box_2[0.4640] det_loss_cls_3[0.4173] det_loss_box_3[0.4461] det_loss_cls_4[0.3949] det_loss_box_4[0.4358] det_loss_cls_5[0.3761] det_loss_box_5[0.4408] det_loss_cls_dn_0[0.0896] det_loss_box_dn_0[0.2456] det_loss_cls_dn_1[0.0856] det_loss_box_dn_1[0.2027] det_loss_cls_dn_2[0.0846] det_loss_box_dn_2[0.1867] det_loss_cls_dn_3[0.0835] det_loss_box_dn_3[0.1839] det_loss_cls_dn_4[0.0819] det_loss_box_dn_4[0.1821] det_loss_cls_dn_5[0.0812] det_loss_box_dn_5[0.1808] loss_depth[0.0374] total_loss[7.3846]
Rank[0/8] 06/20/2025 11:44:40 INFO stats.py:314 | Epoch[164] Step[89] GlobalStep[64049] Training Speed: 8.66 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 9:56:16.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:44:50 INFO loss_tracker.py:84 | Epoch[164/239] Step[99] GlobalStep[64059/NA]: det_loss_cls_0[0.3937] det_loss_box_0[0.4916] det_loss_cls_1[0.3804] det_loss_box_1[0.3723] det_loss_cls_2[0.3289] det_loss_box_2[0.3452] det_loss_cls_3[0.2921] det_loss_box_3[0.3422] det_loss_cls_4[0.2767] det_loss_box_4[0.3249] det_loss_cls_5[0.2602] det_loss_box_5[0.3330] det_loss_cls_dn_0[0.0877] det_loss_box_dn_0[0.2837] det_loss_cls_dn_1[0.0833] det_loss_box_dn_1[0.2318] det_loss_cls_dn_2[0.0819] det_loss_box_dn_2[0.2129] det_loss_cls_dn_3[0.0805] det_loss_box_dn_3[0.2088] det_loss_cls_dn_4[0.0795] det_loss_box_dn_4[0.2048] det_loss_cls_dn_5[0.0783] det_loss_box_dn_5[0.2006] loss_depth[0.0350] total_loss[6.0100]
Rank[0/8] 06/20/2025 11:45:06 INFO stats.py:314 | Epoch[164] Step[114] GlobalStep[64074] Training Speed: 7.74 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 9:55:43.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:45:16 INFO loss_tracker.py:84 | Epoch[164/239] Step[124] GlobalStep[64084/NA]: det_loss_cls_0[0.5813] det_loss_box_0[0.6657] det_loss_cls_1[0.5576] det_loss_box_1[0.5063] det_loss_cls_2[0.4962] det_loss_box_2[0.4482] det_loss_cls_3[0.4587] det_loss_box_3[0.4288] det_loss_cls_4[0.4236] det_loss_box_4[0.4284] det_loss_cls_5[0.4090] det_loss_box_5[0.4317] det_loss_cls_dn_0[0.0957] det_loss_box_dn_0[0.2670] det_loss_cls_dn_1[0.0933] det_loss_box_dn_1[0.2231] det_loss_cls_dn_2[0.0926] det_loss_box_dn_2[0.2071] det_loss_cls_dn_3[0.0919] det_loss_box_dn_3[0.2033] det_loss_cls_dn_4[0.0911] det_loss_box_dn_4[0.2010] det_loss_cls_dn_5[0.0901] det_loss_box_dn_5[0.2003] loss_depth[0.0347] total_loss[7.7268]
Rank[0/8] 06/20/2025 11:45:31 INFO stats.py:314 | Epoch[164] Step[139] GlobalStep[64099] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 9:55:11.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:45:42 INFO loss_tracker.py:84 | Epoch[164/239] Step[149] GlobalStep[64109/NA]: det_loss_cls_0[0.4673] det_loss_box_0[0.5665] det_loss_cls_1[0.4539] det_loss_box_1[0.4320] det_loss_cls_2[0.3896] det_loss_box_2[0.3957] det_loss_cls_3[0.3556] det_loss_box_3[0.3822] det_loss_cls_4[0.3246] det_loss_box_4[0.3812] det_loss_cls_5[0.3158] det_loss_box_5[0.3791] det_loss_cls_dn_0[0.0831] det_loss_box_dn_0[0.2441] det_loss_cls_dn_1[0.0802] det_loss_box_dn_1[0.2021] det_loss_cls_dn_2[0.0788] det_loss_box_dn_2[0.1881] det_loss_cls_dn_3[0.0775] det_loss_box_dn_3[0.1849] det_loss_cls_dn_4[0.0769] det_loss_box_dn_4[0.1831] det_loss_cls_dn_5[0.0759] det_loss_box_dn_5[0.1819] loss_depth[0.0354] total_loss[6.5356]
Rank[0/8] 06/20/2025 11:45:58 INFO stats.py:314 | Epoch[164] Step[164] GlobalStep[64124] Training Speed: 6.17 samples/sec across all devices.	Average Step Time: 1.30 sec.	Estimated Remaining Time: 9:54:39.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:46:09 INFO loss_tracker.py:84 | Epoch[164/239] Step[174] GlobalStep[64134/NA]: det_loss_cls_0[0.3742] det_loss_box_0[0.4533] det_loss_cls_1[0.3623] det_loss_box_1[0.3337] det_loss_cls_2[0.3101] det_loss_box_2[0.2974] det_loss_cls_3[0.2861] det_loss_box_3[0.2833] det_loss_cls_4[0.2628] det_loss_box_4[0.2781] det_loss_cls_5[0.2483] det_loss_box_5[0.2864] det_loss_cls_dn_0[0.0893] det_loss_box_dn_0[0.3214] det_loss_cls_dn_1[0.0858] det_loss_box_dn_1[0.2555] det_loss_cls_dn_2[0.0846] det_loss_box_dn_2[0.2354] det_loss_cls_dn_3[0.0833] det_loss_box_dn_3[0.2282] det_loss_cls_dn_4[0.0823] det_loss_box_dn_4[0.2257] det_loss_cls_dn_5[0.0816] det_loss_box_dn_5[0.2205] loss_depth[0.0386] total_loss[5.8083]
Rank[0/8] 06/20/2025 11:46:24 INFO stats.py:314 | Epoch[164] Step[189] GlobalStep[64149] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 9:54:07.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:46:34 INFO loss_tracker.py:84 | Epoch[164/239] Step[199] GlobalStep[64159/NA]: det_loss_cls_0[0.5042] det_loss_box_0[0.6742] det_loss_cls_1[0.4815] det_loss_box_1[0.5220] det_loss_cls_2[0.4388] det_loss_box_2[0.4690] det_loss_cls_3[0.4169] det_loss_box_3[0.4477] det_loss_cls_4[0.3948] det_loss_box_4[0.4422] det_loss_cls_5[0.3887] det_loss_box_5[0.4404] det_loss_cls_dn_0[0.0994] det_loss_box_dn_0[0.3155] det_loss_cls_dn_1[0.0960] det_loss_box_dn_1[0.2583] det_loss_cls_dn_2[0.0951] det_loss_box_dn_2[0.2407] det_loss_cls_dn_3[0.0941] det_loss_box_dn_3[0.2362] det_loss_cls_dn_4[0.0932] det_loss_box_dn_4[0.2335] det_loss_cls_dn_5[0.0925] det_loss_box_dn_5[0.2318] loss_depth[0.0391] total_loss[7.7459]
Rank[0/8] 06/20/2025 11:46:50 INFO stats.py:314 | Epoch[164] Step[214] GlobalStep[64174] Training Speed: 8.56 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 9:53:34.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:47:00 INFO loss_tracker.py:84 | Epoch[164/239] Step[224] GlobalStep[64184/NA]: det_loss_cls_0[0.4381] det_loss_box_0[0.5202] det_loss_cls_1[0.4199] det_loss_box_1[0.3929] det_loss_cls_2[0.3748] det_loss_box_2[0.3571] det_loss_cls_3[0.3419] det_loss_box_3[0.3388] det_loss_cls_4[0.3122] det_loss_box_4[0.3284] det_loss_cls_5[0.2983] det_loss_box_5[0.3354] det_loss_cls_dn_0[0.0942] det_loss_box_dn_0[0.2534] det_loss_cls_dn_1[0.0903] det_loss_box_dn_1[0.2084] det_loss_cls_dn_2[0.0895] det_loss_box_dn_2[0.1939] det_loss_cls_dn_3[0.0876] det_loss_box_dn_3[0.1889] det_loss_cls_dn_4[0.0870] det_loss_box_dn_4[0.1866] det_loss_cls_dn_5[0.0858] det_loss_box_dn_5[0.1852] loss_depth[0.0370] total_loss[6.2460]
Rank[0/8] 06/20/2025 11:47:16 INFO stats.py:314 | Epoch[164] Step[239] GlobalStep[64199] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 9:53:02.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:47:27 INFO loss_tracker.py:84 | Epoch[164/239] Step[249] GlobalStep[64209/NA]: det_loss_cls_0[0.5811] det_loss_box_0[0.8132] det_loss_cls_1[0.5650] det_loss_box_1[0.6102] det_loss_cls_2[0.5108] det_loss_box_2[0.5247] det_loss_cls_3[0.4789] det_loss_box_3[0.5066] det_loss_cls_4[0.4494] det_loss_box_4[0.5025] det_loss_cls_5[0.4361] det_loss_box_5[0.5050] det_loss_cls_dn_0[0.0851] det_loss_box_dn_0[0.3304] det_loss_cls_dn_1[0.0805] det_loss_box_dn_1[0.2785] det_loss_cls_dn_2[0.0799] det_loss_box_dn_2[0.2610] det_loss_cls_dn_3[0.0789] det_loss_box_dn_3[0.2538] det_loss_cls_dn_4[0.0776] det_loss_box_dn_4[0.2505] det_loss_cls_dn_5[0.0771] det_loss_box_dn_5[0.2436] loss_depth[0.0362] total_loss[8.6167]
Rank[0/8] 06/20/2025 11:47:42 INFO stats.py:314 | Epoch[164] Step[264] GlobalStep[64224] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 9:52:30.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:47:52 INFO loss_tracker.py:84 | Epoch[164/239] Step[274] GlobalStep[64234/NA]: det_loss_cls_0[0.6214] det_loss_box_0[0.7742] det_loss_cls_1[0.6005] det_loss_box_1[0.6090] det_loss_cls_2[0.5470] det_loss_box_2[0.5561] det_loss_cls_3[0.5051] det_loss_box_3[0.5488] det_loss_cls_4[0.4741] det_loss_box_4[0.5401] det_loss_cls_5[0.4574] det_loss_box_5[0.5354] det_loss_cls_dn_0[0.1032] det_loss_box_dn_0[0.3092] det_loss_cls_dn_1[0.0996] det_loss_box_dn_1[0.2637] det_loss_cls_dn_2[0.0991] det_loss_box_dn_2[0.2492] det_loss_cls_dn_3[0.0975] det_loss_box_dn_3[0.2452] det_loss_cls_dn_4[0.0965] det_loss_box_dn_4[0.2432] det_loss_cls_dn_5[0.0956] det_loss_box_dn_5[0.2379] loss_depth[0.0395] total_loss[8.9487]
Rank[0/8] 06/20/2025 11:48:07 INFO stats.py:314 | Epoch[164] Step[289] GlobalStep[64249] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 9:51:58.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:48:18 INFO loss_tracker.py:84 | Epoch[164/239] Step[299] GlobalStep[64259/NA]: det_loss_cls_0[0.4113] det_loss_box_0[0.5178] det_loss_cls_1[0.4027] det_loss_box_1[0.3853] det_loss_cls_2[0.3517] det_loss_box_2[0.3497] det_loss_cls_3[0.3203] det_loss_box_3[0.3416] det_loss_cls_4[0.2991] det_loss_box_4[0.3369] det_loss_cls_5[0.2834] det_loss_box_5[0.3392] det_loss_cls_dn_0[0.0927] det_loss_box_dn_0[0.2629] det_loss_cls_dn_1[0.0887] det_loss_box_dn_1[0.2170] det_loss_cls_dn_2[0.0880] det_loss_box_dn_2[0.2020] det_loss_cls_dn_3[0.0866] det_loss_box_dn_3[0.1984] det_loss_cls_dn_4[0.0858] det_loss_box_dn_4[0.1973] det_loss_cls_dn_5[0.0852] det_loss_box_dn_5[0.1938] loss_depth[0.0376] total_loss[6.1750]
Rank[0/8] 06/20/2025 11:48:33 INFO stats.py:314 | Epoch[164] Step[314] GlobalStep[64274] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 9:51:25.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:48:44 INFO loss_tracker.py:84 | Epoch[164/239] Step[324] GlobalStep[64284/NA]: det_loss_cls_0[0.5230] det_loss_box_0[0.7937] det_loss_cls_1[0.5146] det_loss_box_1[0.6204] det_loss_cls_2[0.4546] det_loss_box_2[0.5645] det_loss_cls_3[0.4165] det_loss_box_3[0.5547] det_loss_cls_4[0.3899] det_loss_box_4[0.5377] det_loss_cls_5[0.3752] det_loss_box_5[0.5380] det_loss_cls_dn_0[0.0867] det_loss_box_dn_0[0.2376] det_loss_cls_dn_1[0.0829] det_loss_box_dn_1[0.1977] det_loss_cls_dn_2[0.0823] det_loss_box_dn_2[0.1838] det_loss_cls_dn_3[0.0816] det_loss_box_dn_3[0.1817] det_loss_cls_dn_4[0.0807] det_loss_box_dn_4[0.1800] det_loss_cls_dn_5[0.0799] det_loss_box_dn_5[0.1796] loss_depth[0.0355] total_loss[7.9727]
Rank[0/8] 06/20/2025 11:48:59 INFO stats.py:314 | Epoch[164] Step[339] GlobalStep[64299] Training Speed: 8.59 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 9:50:53.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:49:09 INFO loss_tracker.py:84 | Epoch[164/239] Step[349] GlobalStep[64309/NA]: det_loss_cls_0[0.5180] det_loss_box_0[0.6996] det_loss_cls_1[0.5084] det_loss_box_1[0.5296] det_loss_cls_2[0.4483] det_loss_box_2[0.4944] det_loss_cls_3[0.4139] det_loss_box_3[0.4771] det_loss_cls_4[0.3927] det_loss_box_4[0.4566] det_loss_cls_5[0.3792] det_loss_box_5[0.4572] det_loss_cls_dn_0[0.0807] det_loss_box_dn_0[0.2314] det_loss_cls_dn_1[0.0763] det_loss_box_dn_1[0.1892] det_loss_cls_dn_2[0.0747] det_loss_box_dn_2[0.1740] det_loss_cls_dn_3[0.0737] det_loss_box_dn_3[0.1702] det_loss_cls_dn_4[0.0729] det_loss_box_dn_4[0.1691] det_loss_cls_dn_5[0.0721] det_loss_box_dn_5[0.1681] loss_depth[0.0414] total_loss[7.3688]
Rank[0/8] 06/20/2025 11:49:25 INFO stats.py:314 | Epoch[164] Step[364] GlobalStep[64324] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 9:50:21.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:49:35 INFO loss_tracker.py:84 | Epoch[164/239] Step[374] GlobalStep[64334/NA]: det_loss_cls_0[0.4788] det_loss_box_0[0.6435] det_loss_cls_1[0.4630] det_loss_box_1[0.4878] det_loss_cls_2[0.4063] det_loss_box_2[0.4193] det_loss_cls_3[0.3722] det_loss_box_3[0.4023] det_loss_cls_4[0.3516] det_loss_box_4[0.3821] det_loss_cls_5[0.3413] det_loss_box_5[0.3861] det_loss_cls_dn_0[0.0975] det_loss_box_dn_0[0.2637] det_loss_cls_dn_1[0.0928] det_loss_box_dn_1[0.2150] det_loss_cls_dn_2[0.0914] det_loss_box_dn_2[0.1973] det_loss_cls_dn_3[0.0903] det_loss_box_dn_3[0.1926] det_loss_cls_dn_4[0.0894] det_loss_box_dn_4[0.1898] det_loss_cls_dn_5[0.0886] det_loss_box_dn_5[0.1878] loss_depth[0.0365] total_loss[6.9669]
Rank[0/8] 06/20/2025 11:49:50 INFO stats.py:314 | Epoch[164] Step[389] GlobalStep[64349] Training Speed: 8.91 samples/sec across all devices.	Average Step Time: 0.90 sec.	Estimated Remaining Time: 9:49:48.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:49:50 INFO stats.py:394 | Epoch[164] completed. Training Speed: 7.64 samples/sec across all devices.	Epoch Time: 408.58 sec.	Average Epoch Time: 408.58 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 9:49:48.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:50:19 INFO stats.py:314 | Epoch[165] Step[24] GlobalStep[64374] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 9:49:17.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:50:19 INFO loss_tracker.py:84 | Epoch[165/239] Step[24] GlobalStep[64374/NA]: det_loss_cls_0[0.4882] det_loss_box_0[0.6470] det_loss_cls_1[0.4721] det_loss_box_1[0.5036] det_loss_cls_2[0.4193] det_loss_box_2[0.4539] det_loss_cls_3[0.3881] det_loss_box_3[0.4360] det_loss_cls_4[0.3621] det_loss_box_4[0.4247] det_loss_cls_5[0.3500] det_loss_box_5[0.4238] det_loss_cls_dn_0[0.0954] det_loss_box_dn_0[0.2812] det_loss_cls_dn_1[0.0910] det_loss_box_dn_1[0.2363] det_loss_cls_dn_2[0.0900] det_loss_box_dn_2[0.2188] det_loss_cls_dn_3[0.0890] det_loss_box_dn_3[0.2149] det_loss_cls_dn_4[0.0880] det_loss_box_dn_4[0.2126] det_loss_cls_dn_5[0.0872] det_loss_box_dn_5[0.2111] loss_depth[0.0379] total_loss[7.3223]
Rank[0/8] 06/20/2025 11:50:44 INFO stats.py:314 | Epoch[165] Step[49] GlobalStep[64399] Training Speed: 7.99 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 9:48:45.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:50:44 INFO loss_tracker.py:84 | Epoch[165/239] Step[49] GlobalStep[64399/NA]: det_loss_cls_0[0.4760] det_loss_box_0[0.5774] det_loss_cls_1[0.4441] det_loss_box_1[0.4478] det_loss_cls_2[0.4039] det_loss_box_2[0.4105] det_loss_cls_3[0.3734] det_loss_box_3[0.3956] det_loss_cls_4[0.3535] det_loss_box_4[0.3808] det_loss_cls_5[0.3361] det_loss_box_5[0.3838] det_loss_cls_dn_0[0.0948] det_loss_box_dn_0[0.2854] det_loss_cls_dn_1[0.0905] det_loss_box_dn_1[0.2374] det_loss_cls_dn_2[0.0890] det_loss_box_dn_2[0.2207] det_loss_cls_dn_3[0.0872] det_loss_box_dn_3[0.2176] det_loss_cls_dn_4[0.0862] det_loss_box_dn_4[0.2150] det_loss_cls_dn_5[0.0850] det_loss_box_dn_5[0.2110] loss_depth[0.0354] total_loss[6.9377]
Rank[0/8] 06/20/2025 11:51:10 INFO stats.py:314 | Epoch[165] Step[74] GlobalStep[64424] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 9:48:13.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:51:10 INFO loss_tracker.py:84 | Epoch[165/239] Step[74] GlobalStep[64424/NA]: det_loss_cls_0[0.4710] det_loss_box_0[0.5660] det_loss_cls_1[0.4542] det_loss_box_1[0.4395] det_loss_cls_2[0.4022] det_loss_box_2[0.4104] det_loss_cls_3[0.3761] det_loss_box_3[0.3840] det_loss_cls_4[0.3605] det_loss_box_4[0.3677] det_loss_cls_5[0.3482] det_loss_box_5[0.3738] det_loss_cls_dn_0[0.0919] det_loss_box_dn_0[0.3063] det_loss_cls_dn_1[0.0881] det_loss_box_dn_1[0.2403] det_loss_cls_dn_2[0.0862] det_loss_box_dn_2[0.2232] det_loss_cls_dn_3[0.0849] det_loss_box_dn_3[0.2175] det_loss_cls_dn_4[0.0839] det_loss_box_dn_4[0.2151] det_loss_cls_dn_5[0.0827] det_loss_box_dn_5[0.2099] loss_depth[0.0364] total_loss[6.9198]
Rank[0/8] 06/20/2025 11:51:36 INFO stats.py:314 | Epoch[165] Step[99] GlobalStep[64449] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 9:47:40.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:51:36 INFO loss_tracker.py:84 | Epoch[165/239] Step[99] GlobalStep[64449/NA]: det_loss_cls_0[0.4728] det_loss_box_0[0.6087] det_loss_cls_1[0.4569] det_loss_box_1[0.4377] det_loss_cls_2[0.4070] det_loss_box_2[0.3970] det_loss_cls_3[0.3790] det_loss_box_3[0.3799] det_loss_cls_4[0.3521] det_loss_box_4[0.3757] det_loss_cls_5[0.3405] det_loss_box_5[0.3775] det_loss_cls_dn_0[0.1011] det_loss_box_dn_0[0.2912] det_loss_cls_dn_1[0.0972] det_loss_box_dn_1[0.2445] det_loss_cls_dn_2[0.0962] det_loss_box_dn_2[0.2272] det_loss_cls_dn_3[0.0951] det_loss_box_dn_3[0.2242] det_loss_cls_dn_4[0.0944] det_loss_box_dn_4[0.2205] det_loss_cls_dn_5[0.0933] det_loss_box_dn_5[0.2191] loss_depth[0.0381] total_loss[7.0272]
Rank[0/8] 06/20/2025 11:52:02 INFO stats.py:314 | Epoch[165] Step[124] GlobalStep[64474] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 9:47:08.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:52:02 INFO loss_tracker.py:84 | Epoch[165/239] Step[124] GlobalStep[64474/NA]: det_loss_cls_0[0.4651] det_loss_box_0[0.6043] det_loss_cls_1[0.4590] det_loss_box_1[0.4485] det_loss_cls_2[0.3971] det_loss_box_2[0.4094] det_loss_cls_3[0.3676] det_loss_box_3[0.4030] det_loss_cls_4[0.3412] det_loss_box_4[0.3979] det_loss_cls_5[0.3266] det_loss_box_5[0.3999] det_loss_cls_dn_0[0.0950] det_loss_box_dn_0[0.3277] det_loss_cls_dn_1[0.0910] det_loss_box_dn_1[0.2655] det_loss_cls_dn_2[0.0900] det_loss_box_dn_2[0.2432] det_loss_cls_dn_3[0.0887] det_loss_box_dn_3[0.2383] det_loss_cls_dn_4[0.0878] det_loss_box_dn_4[0.2352] det_loss_cls_dn_5[0.0868] det_loss_box_dn_5[0.2301] loss_depth[0.0348] total_loss[7.1334]
Rank[0/8] 06/20/2025 11:52:28 INFO stats.py:314 | Epoch[165] Step[149] GlobalStep[64499] Training Speed: 7.85 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 9:46:36.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:52:28 INFO loss_tracker.py:84 | Epoch[165/239] Step[149] GlobalStep[64499/NA]: det_loss_cls_0[0.4799] det_loss_box_0[0.5634] det_loss_cls_1[0.4803] det_loss_box_1[0.4232] det_loss_cls_2[0.4252] det_loss_box_2[0.3927] det_loss_cls_3[0.3999] det_loss_box_3[0.3711] det_loss_cls_4[0.3763] det_loss_box_4[0.3574] det_loss_cls_5[0.3643] det_loss_box_5[0.3619] det_loss_cls_dn_0[0.0957] det_loss_box_dn_0[0.2952] det_loss_cls_dn_1[0.0928] det_loss_box_dn_1[0.2428] det_loss_cls_dn_2[0.0918] det_loss_box_dn_2[0.2243] det_loss_cls_dn_3[0.0907] det_loss_box_dn_3[0.2184] det_loss_cls_dn_4[0.0898] det_loss_box_dn_4[0.2188] det_loss_cls_dn_5[0.0891] det_loss_box_dn_5[0.2157] loss_depth[0.0368] total_loss[6.9975]
Rank[0/8] 06/20/2025 11:52:54 INFO stats.py:314 | Epoch[165] Step[174] GlobalStep[64524] Training Speed: 7.80 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 9:46:04.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:52:54 INFO loss_tracker.py:84 | Epoch[165/239] Step[174] GlobalStep[64524/NA]: det_loss_cls_0[0.4269] det_loss_box_0[0.5627] det_loss_cls_1[0.4118] det_loss_box_1[0.4421] det_loss_cls_2[0.3591] det_loss_box_2[0.4023] det_loss_cls_3[0.3293] det_loss_box_3[0.3867] det_loss_cls_4[0.3117] det_loss_box_4[0.3667] det_loss_cls_5[0.2986] det_loss_box_5[0.3699] det_loss_cls_dn_0[0.0889] det_loss_box_dn_0[0.3768] det_loss_cls_dn_1[0.0857] det_loss_box_dn_1[0.3135] det_loss_cls_dn_2[0.0853] det_loss_box_dn_2[0.2896] det_loss_cls_dn_3[0.0836] det_loss_box_dn_3[0.2834] det_loss_cls_dn_4[0.0825] det_loss_box_dn_4[0.2807] det_loss_cls_dn_5[0.0815] det_loss_box_dn_5[0.2787] loss_depth[0.0383] total_loss[7.0360]
Rank[0/8] 06/20/2025 11:53:20 INFO stats.py:314 | Epoch[165] Step[199] GlobalStep[64549] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 9:45:32.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:53:20 INFO loss_tracker.py:84 | Epoch[165/239] Step[199] GlobalStep[64549/NA]: det_loss_cls_0[0.5117] det_loss_box_0[0.6443] det_loss_cls_1[0.5057] det_loss_box_1[0.5022] det_loss_cls_2[0.4481] det_loss_box_2[0.4496] det_loss_cls_3[0.4155] det_loss_box_3[0.4233] det_loss_cls_4[0.3890] det_loss_box_4[0.4110] det_loss_cls_5[0.3679] det_loss_box_5[0.4165] det_loss_cls_dn_0[0.0939] det_loss_box_dn_0[0.2665] det_loss_cls_dn_1[0.0903] det_loss_box_dn_1[0.2231] det_loss_cls_dn_2[0.0896] det_loss_box_dn_2[0.2064] det_loss_cls_dn_3[0.0884] det_loss_box_dn_3[0.2018] det_loss_cls_dn_4[0.0879] det_loss_box_dn_4[0.1990] det_loss_cls_dn_5[0.0874] det_loss_box_dn_5[0.1968] loss_depth[0.0358] total_loss[7.3517]
Rank[0/8] 06/20/2025 11:53:45 INFO stats.py:314 | Epoch[165] Step[224] GlobalStep[64574] Training Speed: 6.31 samples/sec across all devices.	Average Step Time: 1.27 sec.	Estimated Remaining Time: 9:44:59.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:53:45 INFO loss_tracker.py:84 | Epoch[165/239] Step[224] GlobalStep[64574/NA]: det_loss_cls_0[0.4517] det_loss_box_0[0.5675] det_loss_cls_1[0.4339] det_loss_box_1[0.4495] det_loss_cls_2[0.3813] det_loss_box_2[0.4071] det_loss_cls_3[0.3595] det_loss_box_3[0.3842] det_loss_cls_4[0.3347] det_loss_box_4[0.3766] det_loss_cls_5[0.3209] det_loss_box_5[0.3843] det_loss_cls_dn_0[0.0885] det_loss_box_dn_0[0.2676] det_loss_cls_dn_1[0.0857] det_loss_box_dn_1[0.2217] det_loss_cls_dn_2[0.0844] det_loss_box_dn_2[0.1996] det_loss_cls_dn_3[0.0828] det_loss_box_dn_3[0.1948] det_loss_cls_dn_4[0.0822] det_loss_box_dn_4[0.1925] det_loss_cls_dn_5[0.0814] det_loss_box_dn_5[0.1918] loss_depth[0.0343] total_loss[6.6585]
Rank[0/8] 06/20/2025 11:54:11 INFO stats.py:314 | Epoch[165] Step[249] GlobalStep[64599] Training Speed: 8.63 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 9:44:27.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:54:11 INFO loss_tracker.py:84 | Epoch[165/239] Step[249] GlobalStep[64599/NA]: det_loss_cls_0[0.4493] det_loss_box_0[0.6428] det_loss_cls_1[0.4368] det_loss_box_1[0.4962] det_loss_cls_2[0.3743] det_loss_box_2[0.4431] det_loss_cls_3[0.3443] det_loss_box_3[0.4226] det_loss_cls_4[0.3206] det_loss_box_4[0.4048] det_loss_cls_5[0.3052] det_loss_box_5[0.4044] det_loss_cls_dn_0[0.0882] det_loss_box_dn_0[0.4503] det_loss_cls_dn_1[0.0842] det_loss_box_dn_1[0.3712] det_loss_cls_dn_2[0.0831] det_loss_box_dn_2[0.3498] det_loss_cls_dn_3[0.0809] det_loss_box_dn_3[0.3424] det_loss_cls_dn_4[0.0803] det_loss_box_dn_4[0.3396] det_loss_cls_dn_5[0.0790] det_loss_box_dn_5[0.3346] loss_depth[0.0365] total_loss[7.7645]
Rank[0/8] 06/20/2025 11:54:38 INFO stats.py:314 | Epoch[165] Step[274] GlobalStep[64624] Training Speed: 6.45 samples/sec across all devices.	Average Step Time: 1.24 sec.	Estimated Remaining Time: 9:43:55.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:54:38 INFO loss_tracker.py:84 | Epoch[165/239] Step[274] GlobalStep[64624/NA]: det_loss_cls_0[0.5432] det_loss_box_0[0.5984] det_loss_cls_1[0.5176] det_loss_box_1[0.4458] det_loss_cls_2[0.4607] det_loss_box_2[0.4062] det_loss_cls_3[0.4216] det_loss_box_3[0.4038] det_loss_cls_4[0.3983] det_loss_box_4[0.3950] det_loss_cls_5[0.3803] det_loss_box_5[0.3969] det_loss_cls_dn_0[0.0933] det_loss_box_dn_0[0.2354] det_loss_cls_dn_1[0.0902] det_loss_box_dn_1[0.1908] det_loss_cls_dn_2[0.0893] det_loss_box_dn_2[0.1726] det_loss_cls_dn_3[0.0879] det_loss_box_dn_3[0.1688] det_loss_cls_dn_4[0.0867] det_loss_box_dn_4[0.1663] det_loss_cls_dn_5[0.0857] det_loss_box_dn_5[0.1652] loss_depth[0.0358] total_loss[7.0355]
Rank[0/8] 06/20/2025 11:55:03 INFO stats.py:314 | Epoch[165] Step[299] GlobalStep[64649] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 9:43:23.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:55:03 INFO loss_tracker.py:84 | Epoch[165/239] Step[299] GlobalStep[64649/NA]: det_loss_cls_0[0.5750] det_loss_box_0[0.8088] det_loss_cls_1[0.5626] det_loss_box_1[0.6124] det_loss_cls_2[0.5031] det_loss_box_2[0.5555] det_loss_cls_3[0.4727] det_loss_box_3[0.5269] det_loss_cls_4[0.4422] det_loss_box_4[0.5129] det_loss_cls_5[0.4210] det_loss_box_5[0.5236] det_loss_cls_dn_0[0.0897] det_loss_box_dn_0[0.2492] det_loss_cls_dn_1[0.0860] det_loss_box_dn_1[0.2053] det_loss_cls_dn_2[0.0845] det_loss_box_dn_2[0.1898] det_loss_cls_dn_3[0.0836] det_loss_box_dn_3[0.1862] det_loss_cls_dn_4[0.0830] det_loss_box_dn_4[0.1840] det_loss_cls_dn_5[0.0821] det_loss_box_dn_5[0.1828] loss_depth[0.0351] total_loss[8.2582]
Rank[0/8] 06/20/2025 11:55:28 INFO stats.py:314 | Epoch[165] Step[324] GlobalStep[64674] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 9:42:50.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:55:28 INFO loss_tracker.py:84 | Epoch[165/239] Step[324] GlobalStep[64674/NA]: det_loss_cls_0[0.3942] det_loss_box_0[0.4754] det_loss_cls_1[0.3959] det_loss_box_1[0.3562] det_loss_cls_2[0.3472] det_loss_box_2[0.3248] det_loss_cls_3[0.3177] det_loss_box_3[0.3140] det_loss_cls_4[0.2920] det_loss_box_4[0.3036] det_loss_cls_5[0.2781] det_loss_box_5[0.3072] det_loss_cls_dn_0[0.0916] det_loss_box_dn_0[0.2957] det_loss_cls_dn_1[0.0880] det_loss_box_dn_1[0.2389] det_loss_cls_dn_2[0.0873] det_loss_box_dn_2[0.2221] det_loss_cls_dn_3[0.0862] det_loss_box_dn_3[0.2169] det_loss_cls_dn_4[0.0853] det_loss_box_dn_4[0.2148] det_loss_cls_dn_5[0.0847] det_loss_box_dn_5[0.2113] loss_depth[0.0372] total_loss[6.0661]
Rank[0/8] 06/20/2025 11:55:54 INFO stats.py:314 | Epoch[165] Step[349] GlobalStep[64699] Training Speed: 6.26 samples/sec across all devices.	Average Step Time: 1.28 sec.	Estimated Remaining Time: 9:42:18.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:55:54 INFO loss_tracker.py:84 | Epoch[165/239] Step[349] GlobalStep[64699/NA]: det_loss_cls_0[0.5170] det_loss_box_0[0.6272] det_loss_cls_1[0.4825] det_loss_box_1[0.4757] det_loss_cls_2[0.4391] det_loss_box_2[0.4292] det_loss_cls_3[0.4027] det_loss_box_3[0.4179] det_loss_cls_4[0.3839] det_loss_box_4[0.4081] det_loss_cls_5[0.3707] det_loss_box_5[0.4075] det_loss_cls_dn_0[0.0918] det_loss_box_dn_0[0.2696] det_loss_cls_dn_1[0.0885] det_loss_box_dn_1[0.2249] det_loss_cls_dn_2[0.0875] det_loss_box_dn_2[0.2108] det_loss_cls_dn_3[0.0868] det_loss_box_dn_3[0.2050] det_loss_cls_dn_4[0.0862] det_loss_box_dn_4[0.2020] det_loss_cls_dn_5[0.0853] det_loss_box_dn_5[0.1980] loss_depth[0.0366] total_loss[7.2345]
Rank[0/8] 06/20/2025 11:56:20 INFO stats.py:314 | Epoch[165] Step[374] GlobalStep[64724] Training Speed: 8.19 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 9:41:46.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:56:20 INFO loss_tracker.py:84 | Epoch[165/239] Step[374] GlobalStep[64724/NA]: det_loss_cls_0[0.5129] det_loss_box_0[0.6524] det_loss_cls_1[0.4986] det_loss_box_1[0.5348] det_loss_cls_2[0.4598] det_loss_box_2[0.4720] det_loss_cls_3[0.4300] det_loss_box_3[0.4537] det_loss_cls_4[0.4046] det_loss_box_4[0.4474] det_loss_cls_5[0.3966] det_loss_box_5[0.4437] det_loss_cls_dn_0[0.0953] det_loss_box_dn_0[0.3268] det_loss_cls_dn_1[0.0920] det_loss_box_dn_1[0.2696] det_loss_cls_dn_2[0.0912] det_loss_box_dn_2[0.2494] det_loss_cls_dn_3[0.0902] det_loss_box_dn_3[0.2406] det_loss_cls_dn_4[0.0897] det_loss_box_dn_4[0.2373] det_loss_cls_dn_5[0.0888] det_loss_box_dn_5[0.2315] loss_depth[0.0322] total_loss[7.8410]
Rank[0/8] 06/20/2025 11:56:35 INFO stats.py:394 | Epoch[165] completed. Training Speed: 7.70 samples/sec across all devices.	Epoch Time: 405.31 sec.	Average Epoch Time: 405.31 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 9:41:27.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:56:47 INFO stats.py:314 | Epoch[166] Step[9] GlobalStep[64749] Training Speed: 6.62 samples/sec across all devices.	Average Step Time: 1.21 sec.	Estimated Remaining Time: 9:41:14.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:57:03 INFO loss_tracker.py:84 | Epoch[166/239] Step[24] GlobalStep[64764/NA]: det_loss_cls_0[0.5384] det_loss_box_0[0.7248] det_loss_cls_1[0.5226] det_loss_box_1[0.5406] det_loss_cls_2[0.4707] det_loss_box_2[0.4855] det_loss_cls_3[0.4372] det_loss_box_3[0.4643] det_loss_cls_4[0.4069] det_loss_box_4[0.4646] det_loss_cls_5[0.3912] det_loss_box_5[0.4658] det_loss_cls_dn_0[0.1013] det_loss_box_dn_0[0.2457] det_loss_cls_dn_1[0.0972] det_loss_box_dn_1[0.2063] det_loss_cls_dn_2[0.0969] det_loss_box_dn_2[0.1919] det_loss_cls_dn_3[0.0957] det_loss_box_dn_3[0.1892] det_loss_cls_dn_4[0.0950] det_loss_box_dn_4[0.1874] det_loss_cls_dn_5[0.0942] det_loss_box_dn_5[0.1860] loss_depth[0.0372] total_loss[7.7366]
Rank[0/8] 06/20/2025 11:57:13 INFO stats.py:314 | Epoch[166] Step[34] GlobalStep[64774] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 9:40:42.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:57:29 INFO loss_tracker.py:84 | Epoch[166/239] Step[49] GlobalStep[64789/NA]: det_loss_cls_0[0.5480] det_loss_box_0[0.7284] det_loss_cls_1[0.5321] det_loss_box_1[0.5438] det_loss_cls_2[0.4591] det_loss_box_2[0.4896] det_loss_cls_3[0.4169] det_loss_box_3[0.4698] det_loss_cls_4[0.3926] det_loss_box_4[0.4556] det_loss_cls_5[0.3790] det_loss_box_5[0.4547] det_loss_cls_dn_0[0.0844] det_loss_box_dn_0[0.2226] det_loss_cls_dn_1[0.0803] det_loss_box_dn_1[0.1861] det_loss_cls_dn_2[0.0785] det_loss_box_dn_2[0.1732] det_loss_cls_dn_3[0.0774] det_loss_box_dn_3[0.1699] det_loss_cls_dn_4[0.0763] det_loss_box_dn_4[0.1677] det_loss_cls_dn_5[0.0756] det_loss_box_dn_5[0.1670] loss_depth[0.0378] total_loss[7.4666]
Rank[0/8] 06/20/2025 11:57:39 INFO stats.py:314 | Epoch[166] Step[59] GlobalStep[64799] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 9:40:10.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:57:54 INFO loss_tracker.py:84 | Epoch[166/239] Step[74] GlobalStep[64814/NA]: det_loss_cls_0[0.5252] det_loss_box_0[0.6587] det_loss_cls_1[0.5161] det_loss_box_1[0.4656] det_loss_cls_2[0.4611] det_loss_box_2[0.4255] det_loss_cls_3[0.4273] det_loss_box_3[0.4167] det_loss_cls_4[0.4050] det_loss_box_4[0.4069] det_loss_cls_5[0.3898] det_loss_box_5[0.4089] det_loss_cls_dn_0[0.1006] det_loss_box_dn_0[0.2378] det_loss_cls_dn_1[0.0977] det_loss_box_dn_1[0.1958] det_loss_cls_dn_2[0.0969] det_loss_box_dn_2[0.1777] det_loss_cls_dn_3[0.0956] det_loss_box_dn_3[0.1752] det_loss_cls_dn_4[0.0950] det_loss_box_dn_4[0.1728] det_loss_cls_dn_5[0.0944] det_loss_box_dn_5[0.1718] loss_depth[0.0355] total_loss[7.2539]
Rank[0/8] 06/20/2025 11:58:04 INFO stats.py:314 | Epoch[166] Step[84] GlobalStep[64824] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 9:39:38.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:58:20 INFO loss_tracker.py:84 | Epoch[166/239] Step[99] GlobalStep[64839/NA]: det_loss_cls_0[0.4674] det_loss_box_0[0.6284] det_loss_cls_1[0.4592] det_loss_box_1[0.4856] det_loss_cls_2[0.4134] det_loss_box_2[0.4450] det_loss_cls_3[0.3800] det_loss_box_3[0.4305] det_loss_cls_4[0.3600] det_loss_box_4[0.4193] det_loss_cls_5[0.3462] det_loss_box_5[0.4202] det_loss_cls_dn_0[0.0846] det_loss_box_dn_0[0.3060] det_loss_cls_dn_1[0.0816] det_loss_box_dn_1[0.2657] det_loss_cls_dn_2[0.0808] det_loss_box_dn_2[0.2501] det_loss_cls_dn_3[0.0800] det_loss_box_dn_3[0.2463] det_loss_cls_dn_4[0.0790] det_loss_box_dn_4[0.2435] det_loss_cls_dn_5[0.0782] det_loss_box_dn_5[0.2380] loss_depth[0.0372] total_loss[7.3263]
Rank[0/8] 06/20/2025 11:58:31 INFO stats.py:314 | Epoch[166] Step[109] GlobalStep[64849] Training Speed: 7.62 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 9:39:06.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:58:46 INFO loss_tracker.py:84 | Epoch[166/239] Step[124] GlobalStep[64864/NA]: det_loss_cls_0[0.4330] det_loss_box_0[0.5135] det_loss_cls_1[0.4115] det_loss_box_1[0.3744] det_loss_cls_2[0.3583] det_loss_box_2[0.3454] det_loss_cls_3[0.3254] det_loss_box_3[0.3356] det_loss_cls_4[0.3128] det_loss_box_4[0.3271] det_loss_cls_5[0.2998] det_loss_box_5[0.3265] det_loss_cls_dn_0[0.0927] det_loss_box_dn_0[0.2561] det_loss_cls_dn_1[0.0895] det_loss_box_dn_1[0.2073] det_loss_cls_dn_2[0.0884] det_loss_box_dn_2[0.1916] det_loss_cls_dn_3[0.0874] det_loss_box_dn_3[0.1864] det_loss_cls_dn_4[0.0867] det_loss_box_dn_4[0.1840] det_loss_cls_dn_5[0.0860] det_loss_box_dn_5[0.1827] loss_depth[0.0363] total_loss[6.1384]
Rank[0/8] 06/20/2025 11:58:57 INFO stats.py:314 | Epoch[166] Step[134] GlobalStep[64874] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 9:38:34.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:59:12 INFO loss_tracker.py:84 | Epoch[166/239] Step[149] GlobalStep[64889/NA]: det_loss_cls_0[0.4677] det_loss_box_0[0.5461] det_loss_cls_1[0.4527] det_loss_box_1[0.4008] det_loss_cls_2[0.3920] det_loss_box_2[0.3474] det_loss_cls_3[0.3625] det_loss_box_3[0.3423] det_loss_cls_4[0.3321] det_loss_box_4[0.3407] det_loss_cls_5[0.3138] det_loss_box_5[0.3481] det_loss_cls_dn_0[0.0987] det_loss_box_dn_0[0.2285] det_loss_cls_dn_1[0.0956] det_loss_box_dn_1[0.1834] det_loss_cls_dn_2[0.0948] det_loss_box_dn_2[0.1681] det_loss_cls_dn_3[0.0933] det_loss_box_dn_3[0.1640] det_loss_cls_dn_4[0.0924] det_loss_box_dn_4[0.1628] det_loss_cls_dn_5[0.0914] det_loss_box_dn_5[0.1617] loss_depth[0.0369] total_loss[6.3180]
Rank[0/8] 06/20/2025 11:59:22 INFO stats.py:314 | Epoch[166] Step[159] GlobalStep[64899] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 9:38:01.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 11:59:37 INFO loss_tracker.py:84 | Epoch[166/239] Step[174] GlobalStep[64914/NA]: det_loss_cls_0[0.3302] det_loss_box_0[0.4063] det_loss_cls_1[0.3121] det_loss_box_1[0.3044] det_loss_cls_2[0.2811] det_loss_box_2[0.2698] det_loss_cls_3[0.2575] det_loss_box_3[0.2614] det_loss_cls_4[0.2403] det_loss_box_4[0.2523] det_loss_cls_5[0.2285] det_loss_box_5[0.2536] det_loss_cls_dn_0[0.0866] det_loss_box_dn_0[0.2582] det_loss_cls_dn_1[0.0837] det_loss_box_dn_1[0.2159] det_loss_cls_dn_2[0.0828] det_loss_box_dn_2[0.1960] det_loss_cls_dn_3[0.0818] det_loss_box_dn_3[0.1925] det_loss_cls_dn_4[0.0808] det_loss_box_dn_4[0.1906] det_loss_cls_dn_5[0.0801] det_loss_box_dn_5[0.1888] loss_depth[0.0382] total_loss[5.1735]
Rank[0/8] 06/20/2025 11:59:48 INFO stats.py:314 | Epoch[166] Step[184] GlobalStep[64924] Training Speed: 7.77 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 9:37:29.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:00:03 INFO loss_tracker.py:84 | Epoch[166/239] Step[199] GlobalStep[64939/NA]: det_loss_cls_0[0.6045] det_loss_box_0[0.9855] det_loss_cls_1[0.5902] det_loss_box_1[0.7675] det_loss_cls_2[0.5443] det_loss_box_2[0.7025] det_loss_cls_3[0.5207] det_loss_box_3[0.6851] det_loss_cls_4[0.4990] det_loss_box_4[0.6757] det_loss_cls_5[0.4866] det_loss_box_5[0.6675] det_loss_cls_dn_0[0.1069] det_loss_box_dn_0[0.3095] det_loss_cls_dn_1[0.1036] det_loss_box_dn_1[0.2628] det_loss_cls_dn_2[0.1027] det_loss_box_dn_2[0.2487] det_loss_cls_dn_3[0.1015] det_loss_box_dn_3[0.2449] det_loss_cls_dn_4[0.1004] det_loss_box_dn_4[0.2428] det_loss_cls_dn_5[0.0998] det_loss_box_dn_5[0.2420] loss_depth[0.0387] total_loss[9.9332]
Rank[0/8] 06/20/2025 12:00:14 INFO stats.py:314 | Epoch[166] Step[209] GlobalStep[64949] Training Speed: 7.19 samples/sec across all devices.	Average Step Time: 1.11 sec.	Estimated Remaining Time: 9:36:57.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:00:30 INFO loss_tracker.py:84 | Epoch[166/239] Step[224] GlobalStep[64964/NA]: det_loss_cls_0[0.4833] det_loss_box_0[0.5928] det_loss_cls_1[0.4806] det_loss_box_1[0.4802] det_loss_cls_2[0.4249] det_loss_box_2[0.4380] det_loss_cls_3[0.3936] det_loss_box_3[0.4253] det_loss_cls_4[0.3648] det_loss_box_4[0.4154] det_loss_cls_5[0.3556] det_loss_box_5[0.4123] det_loss_cls_dn_0[0.0916] det_loss_box_dn_0[0.3119] det_loss_cls_dn_1[0.0880] det_loss_box_dn_1[0.2599] det_loss_cls_dn_2[0.0871] det_loss_box_dn_2[0.2322] det_loss_cls_dn_3[0.0853] det_loss_box_dn_3[0.2276] det_loss_cls_dn_4[0.0843] det_loss_box_dn_4[0.2224] det_loss_cls_dn_5[0.0834] det_loss_box_dn_5[0.2157] loss_depth[0.0336] total_loss[7.2899]
Rank[0/8] 06/20/2025 12:00:40 INFO stats.py:314 | Epoch[166] Step[234] GlobalStep[64974] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 9:36:25.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:00:54 INFO loss_tracker.py:84 | Epoch[166/239] Step[249] GlobalStep[64989/NA]: det_loss_cls_0[0.3927] det_loss_box_0[0.4798] det_loss_cls_1[0.3814] det_loss_box_1[0.3579] det_loss_cls_2[0.3385] det_loss_box_2[0.3264] det_loss_cls_3[0.3067] det_loss_box_3[0.3147] det_loss_cls_4[0.2849] det_loss_box_4[0.3107] det_loss_cls_5[0.2756] det_loss_box_5[0.3100] det_loss_cls_dn_0[0.0804] det_loss_box_dn_0[0.2551] det_loss_cls_dn_1[0.0769] det_loss_box_dn_1[0.2074] det_loss_cls_dn_2[0.0759] det_loss_box_dn_2[0.1928] det_loss_cls_dn_3[0.0748] det_loss_box_dn_3[0.1891] det_loss_cls_dn_4[0.0741] det_loss_box_dn_4[0.1876] det_loss_cls_dn_5[0.0735] det_loss_box_dn_5[0.1797] loss_depth[0.0350] total_loss[5.7816]
Rank[0/8] 06/20/2025 12:01:05 INFO stats.py:314 | Epoch[166] Step[259] GlobalStep[64999] Training Speed: 7.61 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 9:35:53.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:01:21 INFO loss_tracker.py:84 | Epoch[166/239] Step[274] GlobalStep[65014/NA]: det_loss_cls_0[0.4811] det_loss_box_0[0.6449] det_loss_cls_1[0.4642] det_loss_box_1[0.4894] det_loss_cls_2[0.4214] det_loss_box_2[0.4289] det_loss_cls_3[0.3830] det_loss_box_3[0.4168] det_loss_cls_4[0.3584] det_loss_box_4[0.4090] det_loss_cls_5[0.3470] det_loss_box_5[0.4036] det_loss_cls_dn_0[0.0881] det_loss_box_dn_0[0.2747] det_loss_cls_dn_1[0.0853] det_loss_box_dn_1[0.2217] det_loss_cls_dn_2[0.0837] det_loss_box_dn_2[0.2037] det_loss_cls_dn_3[0.0828] det_loss_box_dn_3[0.2007] det_loss_cls_dn_4[0.0818] det_loss_box_dn_4[0.1981] det_loss_cls_dn_5[0.0809] det_loss_box_dn_5[0.1917] loss_depth[0.0340] total_loss[7.0750]
Rank[0/8] 06/20/2025 12:01:31 INFO stats.py:314 | Epoch[166] Step[284] GlobalStep[65024] Training Speed: 7.68 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 9:35:21.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:01:47 INFO loss_tracker.py:84 | Epoch[166/239] Step[299] GlobalStep[65039/NA]: det_loss_cls_0[0.5194] det_loss_box_0[0.7018] det_loss_cls_1[0.4901] det_loss_box_1[0.5459] det_loss_cls_2[0.4349] det_loss_box_2[0.4863] det_loss_cls_3[0.4062] det_loss_box_3[0.4675] det_loss_cls_4[0.3815] det_loss_box_4[0.4608] det_loss_cls_5[0.3726] det_loss_box_5[0.4634] det_loss_cls_dn_0[0.0992] det_loss_box_dn_0[0.3128] det_loss_cls_dn_1[0.0960] det_loss_box_dn_1[0.2669] det_loss_cls_dn_2[0.0956] det_loss_box_dn_2[0.2526] det_loss_cls_dn_3[0.0944] det_loss_box_dn_3[0.2462] det_loss_cls_dn_4[0.0935] det_loss_box_dn_4[0.2446] det_loss_cls_dn_5[0.0923] det_loss_box_dn_5[0.2424] loss_depth[0.0340] total_loss[7.9008]
Rank[0/8] 06/20/2025 12:01:57 INFO stats.py:314 | Epoch[166] Step[309] GlobalStep[65049] Training Speed: 8.59 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 9:34:49.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:02:12 INFO loss_tracker.py:84 | Epoch[166/239] Step[324] GlobalStep[65064/NA]: det_loss_cls_0[0.4041] det_loss_box_0[0.4820] det_loss_cls_1[0.3974] det_loss_box_1[0.3564] det_loss_cls_2[0.3284] det_loss_box_2[0.3281] det_loss_cls_3[0.2951] det_loss_box_3[0.3214] det_loss_cls_4[0.2677] det_loss_box_4[0.3140] det_loss_cls_5[0.2576] det_loss_box_5[0.3129] det_loss_cls_dn_0[0.0873] det_loss_box_dn_0[0.2603] det_loss_cls_dn_1[0.0838] det_loss_box_dn_1[0.2177] det_loss_cls_dn_2[0.0831] det_loss_box_dn_2[0.2000] det_loss_cls_dn_3[0.0816] det_loss_box_dn_3[0.1954] det_loss_cls_dn_4[0.0810] det_loss_box_dn_4[0.1938] det_loss_cls_dn_5[0.0803] det_loss_box_dn_5[0.1913] loss_depth[0.0368] total_loss[5.8576]
Rank[0/8] 06/20/2025 12:02:23 INFO stats.py:314 | Epoch[166] Step[334] GlobalStep[65074] Training Speed: 8.73 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 9:34:16.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:02:38 INFO loss_tracker.py:84 | Epoch[166/239] Step[349] GlobalStep[65089/NA]: det_loss_cls_0[0.4474] det_loss_box_0[0.5097] det_loss_cls_1[0.4260] det_loss_box_1[0.3831] det_loss_cls_2[0.3704] det_loss_box_2[0.3438] det_loss_cls_3[0.3424] det_loss_box_3[0.3319] det_loss_cls_4[0.3221] det_loss_box_4[0.3223] det_loss_cls_5[0.3072] det_loss_box_5[0.3252] det_loss_cls_dn_0[0.0922] det_loss_box_dn_0[0.2648] det_loss_cls_dn_1[0.0876] det_loss_box_dn_1[0.2204] det_loss_cls_dn_2[0.0870] det_loss_box_dn_2[0.2018] det_loss_cls_dn_3[0.0864] det_loss_box_dn_3[0.1985] det_loss_cls_dn_4[0.0852] det_loss_box_dn_4[0.1960] det_loss_cls_dn_5[0.0848] det_loss_box_dn_5[0.1914] loss_depth[0.0340] total_loss[6.2618]
Rank[0/8] 06/20/2025 12:02:48 INFO stats.py:314 | Epoch[166] Step[359] GlobalStep[65099] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 9:33:44.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:03:03 INFO loss_tracker.py:84 | Epoch[166/239] Step[374] GlobalStep[65114/NA]: det_loss_cls_0[0.5063] det_loss_box_0[0.5710] det_loss_cls_1[0.4943] det_loss_box_1[0.4231] det_loss_cls_2[0.4283] det_loss_box_2[0.3783] det_loss_cls_3[0.3952] det_loss_box_3[0.3701] det_loss_cls_4[0.3733] det_loss_box_4[0.3615] det_loss_cls_5[0.3622] det_loss_box_5[0.3611] det_loss_cls_dn_0[0.1047] det_loss_box_dn_0[0.2479] det_loss_cls_dn_1[0.1005] det_loss_box_dn_1[0.2002] det_loss_cls_dn_2[0.0998] det_loss_box_dn_2[0.1827] det_loss_cls_dn_3[0.0991] det_loss_box_dn_3[0.1771] det_loss_cls_dn_4[0.0980] det_loss_box_dn_4[0.1749] det_loss_cls_dn_5[0.0973] det_loss_box_dn_5[0.1733] loss_depth[0.0357] total_loss[6.8160]
Rank[0/8] 06/20/2025 12:03:14 INFO stats.py:314 | Epoch[166] Step[384] GlobalStep[65124] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 9:33:12.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:03:19 INFO stats.py:394 | Epoch[166] completed. Training Speed: 7.73 samples/sec across all devices.	Epoch Time: 403.79 sec.	Average Epoch Time: 403.79 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 9:33:06.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:03:42 INFO stats.py:314 | Epoch[167] Step[19] GlobalStep[65149] Training Speed: 7.97 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 9:32:41.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:03:48 INFO loss_tracker.py:84 | Epoch[167/239] Step[24] GlobalStep[65154/NA]: det_loss_cls_0[0.4763] det_loss_box_0[0.6403] det_loss_cls_1[0.4697] det_loss_box_1[0.4853] det_loss_cls_2[0.4159] det_loss_box_2[0.4422] det_loss_cls_3[0.3797] det_loss_box_3[0.4280] det_loss_cls_4[0.3549] det_loss_box_4[0.4227] det_loss_cls_5[0.3462] det_loss_box_5[0.4193] det_loss_cls_dn_0[0.0895] det_loss_box_dn_0[0.2673] det_loss_cls_dn_1[0.0864] det_loss_box_dn_1[0.2148] det_loss_cls_dn_2[0.0860] det_loss_box_dn_2[0.1960] det_loss_cls_dn_3[0.0849] det_loss_box_dn_3[0.1924] det_loss_cls_dn_4[0.0845] det_loss_box_dn_4[0.1899] det_loss_cls_dn_5[0.0840] det_loss_box_dn_5[0.1886] loss_depth[0.0368] total_loss[7.0817]
Rank[0/8] 06/20/2025 12:04:08 INFO stats.py:314 | Epoch[167] Step[44] GlobalStep[65174] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 9:32:09.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:04:13 INFO loss_tracker.py:84 | Epoch[167/239] Step[49] GlobalStep[65179/NA]: det_loss_cls_0[0.5234] det_loss_box_0[0.6566] det_loss_cls_1[0.5088] det_loss_box_1[0.5074] det_loss_cls_2[0.4492] det_loss_box_2[0.4591] det_loss_cls_3[0.4101] det_loss_box_3[0.4477] det_loss_cls_4[0.3756] det_loss_box_4[0.4501] det_loss_cls_5[0.3646] det_loss_box_5[0.4491] det_loss_cls_dn_0[0.0968] det_loss_box_dn_0[0.2651] det_loss_cls_dn_1[0.0934] det_loss_box_dn_1[0.2184] det_loss_cls_dn_2[0.0922] det_loss_box_dn_2[0.2037] det_loss_cls_dn_3[0.0914] det_loss_box_dn_3[0.1997] det_loss_cls_dn_4[0.0907] det_loss_box_dn_4[0.1979] det_loss_cls_dn_5[0.0900] det_loss_box_dn_5[0.1946] loss_depth[0.0371] total_loss[7.4726]
Rank[0/8] 06/20/2025 12:04:34 INFO stats.py:314 | Epoch[167] Step[69] GlobalStep[65199] Training Speed: 8.69 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 9:31:37.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:04:39 INFO loss_tracker.py:84 | Epoch[167/239] Step[74] GlobalStep[65204/NA]: det_loss_cls_0[0.4942] det_loss_box_0[0.6574] det_loss_cls_1[0.4763] det_loss_box_1[0.4827] det_loss_cls_2[0.4286] det_loss_box_2[0.4366] det_loss_cls_3[0.3924] det_loss_box_3[0.4263] det_loss_cls_4[0.3670] det_loss_box_4[0.4221] det_loss_cls_5[0.3547] det_loss_box_5[0.4213] det_loss_cls_dn_0[0.0947] det_loss_box_dn_0[0.3259] det_loss_cls_dn_1[0.0919] det_loss_box_dn_1[0.2688] det_loss_cls_dn_2[0.0905] det_loss_box_dn_2[0.2481] det_loss_cls_dn_3[0.0893] det_loss_box_dn_3[0.2424] det_loss_cls_dn_4[0.0884] det_loss_box_dn_4[0.2393] det_loss_cls_dn_5[0.0875] det_loss_box_dn_5[0.2379] loss_depth[0.0360] total_loss[7.5004]
Rank[0/8] 06/20/2025 12:05:00 INFO stats.py:314 | Epoch[167] Step[94] GlobalStep[65224] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 9:31:05.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:05:05 INFO loss_tracker.py:84 | Epoch[167/239] Step[99] GlobalStep[65229/NA]: det_loss_cls_0[0.3956] det_loss_box_0[0.5836] det_loss_cls_1[0.3758] det_loss_box_1[0.4589] det_loss_cls_2[0.3432] det_loss_box_2[0.4301] det_loss_cls_3[0.3119] det_loss_box_3[0.4236] det_loss_cls_4[0.2909] det_loss_box_4[0.4114] det_loss_cls_5[0.2817] det_loss_box_5[0.4077] det_loss_cls_dn_0[0.0852] det_loss_box_dn_0[0.3332] det_loss_cls_dn_1[0.0821] det_loss_box_dn_1[0.2876] det_loss_cls_dn_2[0.0809] det_loss_box_dn_2[0.2616] det_loss_cls_dn_3[0.0800] det_loss_box_dn_3[0.2574] det_loss_cls_dn_4[0.0794] det_loss_box_dn_4[0.2536] det_loss_cls_dn_5[0.0782] det_loss_box_dn_5[0.2493] loss_depth[0.0384] total_loss[6.8813]
Rank[0/8] 06/20/2025 12:05:25 INFO stats.py:314 | Epoch[167] Step[119] GlobalStep[65249] Training Speed: 8.56 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 9:30:33.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:05:31 INFO loss_tracker.py:84 | Epoch[167/239] Step[124] GlobalStep[65254/NA]: det_loss_cls_0[0.5655] det_loss_box_0[0.6936] det_loss_cls_1[0.5459] det_loss_box_1[0.5342] det_loss_cls_2[0.4912] det_loss_box_2[0.4912] det_loss_cls_3[0.4535] det_loss_box_3[0.4835] det_loss_cls_4[0.4325] det_loss_box_4[0.4744] det_loss_cls_5[0.4243] det_loss_box_5[0.4701] det_loss_cls_dn_0[0.0960] det_loss_box_dn_0[0.3156] det_loss_cls_dn_1[0.0939] det_loss_box_dn_1[0.2636] det_loss_cls_dn_2[0.0929] det_loss_box_dn_2[0.2407] det_loss_cls_dn_3[0.0921] det_loss_box_dn_3[0.2381] det_loss_cls_dn_4[0.0912] det_loss_box_dn_4[0.2345] det_loss_cls_dn_5[0.0904] det_loss_box_dn_5[0.2299] loss_depth[0.0373] total_loss[8.1759]
Rank[0/8] 06/20/2025 12:05:52 INFO stats.py:314 | Epoch[167] Step[144] GlobalStep[65274] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 9:30:01.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:05:57 INFO loss_tracker.py:84 | Epoch[167/239] Step[149] GlobalStep[65279/NA]: det_loss_cls_0[0.5128] det_loss_box_0[0.6900] det_loss_cls_1[0.5111] det_loss_box_1[0.5121] det_loss_cls_2[0.4488] det_loss_box_2[0.4564] det_loss_cls_3[0.4054] det_loss_box_3[0.4468] det_loss_cls_4[0.3909] det_loss_box_4[0.4358] det_loss_cls_5[0.3794] det_loss_box_5[0.4302] det_loss_cls_dn_0[0.0826] det_loss_box_dn_0[0.2804] det_loss_cls_dn_1[0.0796] det_loss_box_dn_1[0.2266] det_loss_cls_dn_2[0.0788] det_loss_box_dn_2[0.2086] det_loss_cls_dn_3[0.0781] det_loss_box_dn_3[0.2044] det_loss_cls_dn_4[0.0768] det_loss_box_dn_4[0.2016] det_loss_cls_dn_5[0.0760] det_loss_box_dn_5[0.1995] loss_depth[0.0390] total_loss[7.4516]
Rank[0/8] 06/20/2025 12:06:17 INFO stats.py:314 | Epoch[167] Step[169] GlobalStep[65299] Training Speed: 8.17 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 9:29:29.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:06:22 INFO loss_tracker.py:84 | Epoch[167/239] Step[174] GlobalStep[65304/NA]: det_loss_cls_0[0.4945] det_loss_box_0[0.5788] det_loss_cls_1[0.4868] det_loss_box_1[0.4419] det_loss_cls_2[0.4325] det_loss_box_2[0.4089] det_loss_cls_3[0.3861] det_loss_box_3[0.3907] det_loss_cls_4[0.3639] det_loss_box_4[0.3741] det_loss_cls_5[0.3465] det_loss_box_5[0.3726] det_loss_cls_dn_0[0.0908] det_loss_box_dn_0[0.2306] det_loss_cls_dn_1[0.0867] det_loss_box_dn_1[0.1881] det_loss_cls_dn_2[0.0857] det_loss_box_dn_2[0.1733] det_loss_cls_dn_3[0.0847] det_loss_box_dn_3[0.1707] det_loss_cls_dn_4[0.0836] det_loss_box_dn_4[0.1686] det_loss_cls_dn_5[0.0825] det_loss_box_dn_5[0.1682] loss_depth[0.0329] total_loss[6.7238]
Rank[0/8] 06/20/2025 12:06:44 INFO stats.py:314 | Epoch[167] Step[194] GlobalStep[65324] Training Speed: 7.75 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 9:28:57.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:06:49 INFO loss_tracker.py:84 | Epoch[167/239] Step[199] GlobalStep[65329/NA]: det_loss_cls_0[0.3950] det_loss_box_0[0.4809] det_loss_cls_1[0.3915] det_loss_box_1[0.3626] det_loss_cls_2[0.3322] det_loss_box_2[0.3367] det_loss_cls_3[0.2981] det_loss_box_3[0.3294] det_loss_cls_4[0.2776] det_loss_box_4[0.3243] det_loss_cls_5[0.2662] det_loss_box_5[0.3221] det_loss_cls_dn_0[0.0894] det_loss_box_dn_0[0.2742] det_loss_cls_dn_1[0.0849] det_loss_box_dn_1[0.2268] det_loss_cls_dn_2[0.0840] det_loss_box_dn_2[0.2080] det_loss_cls_dn_3[0.0827] det_loss_box_dn_3[0.2034] det_loss_cls_dn_4[0.0818] det_loss_box_dn_4[0.2008] det_loss_cls_dn_5[0.0812] det_loss_box_dn_5[0.1988] loss_depth[0.0366] total_loss[5.9693]
Rank[0/8] 06/20/2025 12:07:09 INFO stats.py:314 | Epoch[167] Step[219] GlobalStep[65349] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 9:28:25.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:07:14 INFO loss_tracker.py:84 | Epoch[167/239] Step[224] GlobalStep[65354/NA]: det_loss_cls_0[0.4364] det_loss_box_0[0.5233] det_loss_cls_1[0.4191] det_loss_box_1[0.3910] det_loss_cls_2[0.3618] det_loss_box_2[0.3462] det_loss_cls_3[0.3173] det_loss_box_3[0.3370] det_loss_cls_4[0.2946] det_loss_box_4[0.3267] det_loss_cls_5[0.2804] det_loss_box_5[0.3277] det_loss_cls_dn_0[0.0877] det_loss_box_dn_0[0.2308] det_loss_cls_dn_1[0.0838] det_loss_box_dn_1[0.1867] det_loss_cls_dn_2[0.0825] det_loss_box_dn_2[0.1709] det_loss_cls_dn_3[0.0812] det_loss_box_dn_3[0.1679] det_loss_cls_dn_4[0.0800] det_loss_box_dn_4[0.1668] det_loss_cls_dn_5[0.0790] det_loss_box_dn_5[0.1650] loss_depth[0.0339] total_loss[5.9777]
Rank[0/8] 06/20/2025 12:07:35 INFO stats.py:314 | Epoch[167] Step[244] GlobalStep[65374] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 9:27:52.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:07:40 INFO loss_tracker.py:84 | Epoch[167/239] Step[249] GlobalStep[65379/NA]: det_loss_cls_0[0.5513] det_loss_box_0[0.6762] det_loss_cls_1[0.5527] det_loss_box_1[0.4980] det_loss_cls_2[0.5013] det_loss_box_2[0.4421] det_loss_cls_3[0.4649] det_loss_box_3[0.4297] det_loss_cls_4[0.4277] det_loss_box_4[0.4301] det_loss_cls_5[0.4114] det_loss_box_5[0.4322] det_loss_cls_dn_0[0.0980] det_loss_box_dn_0[0.2311] det_loss_cls_dn_1[0.0950] det_loss_box_dn_1[0.1896] det_loss_cls_dn_2[0.0938] det_loss_box_dn_2[0.1740] det_loss_cls_dn_3[0.0930] det_loss_box_dn_3[0.1714] det_loss_cls_dn_4[0.0918] det_loss_box_dn_4[0.1697] det_loss_cls_dn_5[0.0908] det_loss_box_dn_5[0.1692] loss_depth[0.0384] total_loss[7.5234]
Rank[0/8] 06/20/2025 12:08:01 INFO stats.py:314 | Epoch[167] Step[269] GlobalStep[65399] Training Speed: 6.60 samples/sec across all devices.	Average Step Time: 1.21 sec.	Estimated Remaining Time: 9:27:20.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:08:06 INFO loss_tracker.py:84 | Epoch[167/239] Step[274] GlobalStep[65404/NA]: det_loss_cls_0[0.4620] det_loss_box_0[0.6013] det_loss_cls_1[0.4634] det_loss_box_1[0.4509] det_loss_cls_2[0.3968] det_loss_box_2[0.4186] det_loss_cls_3[0.3618] det_loss_box_3[0.4044] det_loss_cls_4[0.3295] det_loss_box_4[0.3852] det_loss_cls_5[0.3188] det_loss_box_5[0.3834] det_loss_cls_dn_0[0.0870] det_loss_box_dn_0[0.3009] det_loss_cls_dn_1[0.0823] det_loss_box_dn_1[0.2519] det_loss_cls_dn_2[0.0812] det_loss_box_dn_2[0.2369] det_loss_cls_dn_3[0.0803] det_loss_box_dn_3[0.2300] det_loss_cls_dn_4[0.0793] det_loss_box_dn_4[0.2264] det_loss_cls_dn_5[0.0785] det_loss_box_dn_5[0.2224] loss_depth[0.0323] total_loss[6.9652]
Rank[0/8] 06/20/2025 12:08:27 INFO stats.py:314 | Epoch[167] Step[294] GlobalStep[65424] Training Speed: 7.86 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 9:26:48.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:08:32 INFO loss_tracker.py:84 | Epoch[167/239] Step[299] GlobalStep[65429/NA]: det_loss_cls_0[0.5041] det_loss_box_0[0.6826] det_loss_cls_1[0.4968] det_loss_box_1[0.5272] det_loss_cls_2[0.4385] det_loss_box_2[0.4765] det_loss_cls_3[0.3922] det_loss_box_3[0.4702] det_loss_cls_4[0.3687] det_loss_box_4[0.4555] det_loss_cls_5[0.3558] det_loss_box_5[0.4573] det_loss_cls_dn_0[0.0921] det_loss_box_dn_0[0.3126] det_loss_cls_dn_1[0.0890] det_loss_box_dn_1[0.2668] det_loss_cls_dn_2[0.0876] det_loss_box_dn_2[0.2458] det_loss_cls_dn_3[0.0858] det_loss_box_dn_3[0.2389] det_loss_cls_dn_4[0.0848] det_loss_box_dn_4[0.2367] det_loss_cls_dn_5[0.0838] det_loss_box_dn_5[0.2344] loss_depth[0.0336] total_loss[7.7172]
Rank[0/8] 06/20/2025 12:08:52 INFO stats.py:314 | Epoch[167] Step[319] GlobalStep[65449] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 9:26:16.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:08:57 INFO loss_tracker.py:84 | Epoch[167/239] Step[324] GlobalStep[65454/NA]: det_loss_cls_0[0.4323] det_loss_box_0[0.5284] det_loss_cls_1[0.4158] det_loss_box_1[0.3908] det_loss_cls_2[0.3701] det_loss_box_2[0.3572] det_loss_cls_3[0.3310] det_loss_box_3[0.3483] det_loss_cls_4[0.3065] det_loss_box_4[0.3459] det_loss_cls_5[0.2977] det_loss_box_5[0.3457] det_loss_cls_dn_0[0.0912] det_loss_box_dn_0[0.2390] det_loss_cls_dn_1[0.0873] det_loss_box_dn_1[0.1970] det_loss_cls_dn_2[0.0858] det_loss_box_dn_2[0.1769] det_loss_cls_dn_3[0.0850] det_loss_box_dn_3[0.1725] det_loss_cls_dn_4[0.0836] det_loss_box_dn_4[0.1715] det_loss_cls_dn_5[0.0828] det_loss_box_dn_5[0.1697] loss_depth[0.0376] total_loss[6.1494]
Rank[0/8] 06/20/2025 12:09:18 INFO stats.py:314 | Epoch[167] Step[344] GlobalStep[65474] Training Speed: 8.12 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 9:25:44.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:09:24 INFO loss_tracker.py:84 | Epoch[167/239] Step[349] GlobalStep[65479/NA]: det_loss_cls_0[0.4854] det_loss_box_0[0.5460] det_loss_cls_1[0.4730] det_loss_box_1[0.4185] det_loss_cls_2[0.3980] det_loss_box_2[0.3789] det_loss_cls_3[0.3573] det_loss_box_3[0.3714] det_loss_cls_4[0.3293] det_loss_box_4[0.3682] det_loss_cls_5[0.3130] det_loss_box_5[0.3692] det_loss_cls_dn_0[0.0855] det_loss_box_dn_0[0.2542] det_loss_cls_dn_1[0.0823] det_loss_box_dn_1[0.2096] det_loss_cls_dn_2[0.0815] det_loss_box_dn_2[0.1888] det_loss_cls_dn_3[0.0798] det_loss_box_dn_3[0.1858] det_loss_cls_dn_4[0.0786] det_loss_box_dn_4[0.1837] det_loss_cls_dn_5[0.0779] det_loss_box_dn_5[0.1826] loss_depth[0.0345] total_loss[6.5329]
Rank[0/8] 06/20/2025 12:09:45 INFO stats.py:314 | Epoch[167] Step[369] GlobalStep[65499] Training Speed: 7.08 samples/sec across all devices.	Average Step Time: 1.13 sec.	Estimated Remaining Time: 9:25:12.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:09:50 INFO loss_tracker.py:84 | Epoch[167/239] Step[374] GlobalStep[65504/NA]: det_loss_cls_0[0.4540] det_loss_box_0[0.5919] det_loss_cls_1[0.4462] det_loss_box_1[0.4554] det_loss_cls_2[0.3880] det_loss_box_2[0.4270] det_loss_cls_3[0.3556] det_loss_box_3[0.4066] det_loss_cls_4[0.3323] det_loss_box_4[0.4018] det_loss_cls_5[0.3197] det_loss_box_5[0.4020] det_loss_cls_dn_0[0.0943] det_loss_box_dn_0[0.2922] det_loss_cls_dn_1[0.0903] det_loss_box_dn_1[0.2377] det_loss_cls_dn_2[0.0888] det_loss_box_dn_2[0.2202] det_loss_cls_dn_3[0.0872] det_loss_box_dn_3[0.2154] det_loss_cls_dn_4[0.0856] det_loss_box_dn_4[0.2121] det_loss_cls_dn_5[0.0847] det_loss_box_dn_5[0.2109] loss_depth[0.0365] total_loss[6.9364]
Rank[0/8] 06/20/2025 12:10:05 INFO stats.py:394 | Epoch[167] completed. Training Speed: 7.69 samples/sec across all devices.	Epoch Time: 405.91 sec.	Average Epoch Time: 405.91 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 9:24:47.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:10:11 INFO stats.py:314 | Epoch[168] Step[4] GlobalStep[65524] Training Speed: 8.17 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 9:24:41.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:10:31 INFO loss_tracker.py:84 | Epoch[168/239] Step[24] GlobalStep[65544/NA]: det_loss_cls_0[0.4609] det_loss_box_0[0.5687] det_loss_cls_1[0.4424] det_loss_box_1[0.4168] det_loss_cls_2[0.3761] det_loss_box_2[0.3696] det_loss_cls_3[0.3405] det_loss_box_3[0.3513] det_loss_cls_4[0.3147] det_loss_box_4[0.3510] det_loss_cls_5[0.3061] det_loss_box_5[0.3512] det_loss_cls_dn_0[0.0848] det_loss_box_dn_0[0.2486] det_loss_cls_dn_1[0.0805] det_loss_box_dn_1[0.1977] det_loss_cls_dn_2[0.0790] det_loss_box_dn_2[0.1804] det_loss_cls_dn_3[0.0778] det_loss_box_dn_3[0.1766] det_loss_cls_dn_4[0.0768] det_loss_box_dn_4[0.1738] det_loss_cls_dn_5[0.0758] det_loss_box_dn_5[0.1704] loss_depth[0.0362] total_loss[6.3076]
Rank[0/8] 06/20/2025 12:10:37 INFO stats.py:314 | Epoch[168] Step[29] GlobalStep[65549] Training Speed: 8.69 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 9:24:08.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:10:58 INFO loss_tracker.py:84 | Epoch[168/239] Step[49] GlobalStep[65569/NA]: det_loss_cls_0[0.4798] det_loss_box_0[0.5677] det_loss_cls_1[0.4678] det_loss_box_1[0.4397] det_loss_cls_2[0.4207] det_loss_box_2[0.3968] det_loss_cls_3[0.3917] det_loss_box_3[0.3789] det_loss_cls_4[0.3676] det_loss_box_4[0.3673] det_loss_cls_5[0.3576] det_loss_box_5[0.3693] det_loss_cls_dn_0[0.0848] det_loss_box_dn_0[0.2597] det_loss_cls_dn_1[0.0819] det_loss_box_dn_1[0.2142] det_loss_cls_dn_2[0.0812] det_loss_box_dn_2[0.1980] det_loss_cls_dn_3[0.0807] det_loss_box_dn_3[0.1937] det_loss_cls_dn_4[0.0801] det_loss_box_dn_4[0.1913] det_loss_cls_dn_5[0.0793] det_loss_box_dn_5[0.1840] loss_depth[0.0369] total_loss[6.7707]
Rank[0/8] 06/20/2025 12:11:03 INFO stats.py:314 | Epoch[168] Step[54] GlobalStep[65574] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 9:23:37.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:11:24 INFO loss_tracker.py:84 | Epoch[168/239] Step[74] GlobalStep[65594/NA]: det_loss_cls_0[0.4092] det_loss_box_0[0.5213] det_loss_cls_1[0.3850] det_loss_box_1[0.3964] det_loss_cls_2[0.3294] det_loss_box_2[0.3648] det_loss_cls_3[0.3124] det_loss_box_3[0.3478] det_loss_cls_4[0.2910] det_loss_box_4[0.3382] det_loss_cls_5[0.2773] det_loss_box_5[0.3418] det_loss_cls_dn_0[0.0847] det_loss_box_dn_0[0.2505] det_loss_cls_dn_1[0.0810] det_loss_box_dn_1[0.2045] det_loss_cls_dn_2[0.0802] det_loss_box_dn_2[0.1890] det_loss_cls_dn_3[0.0789] det_loss_box_dn_3[0.1862] det_loss_cls_dn_4[0.0780] det_loss_box_dn_4[0.1843] det_loss_cls_dn_5[0.0771] det_loss_box_dn_5[0.1830] loss_depth[0.0348] total_loss[6.0268]
Rank[0/8] 06/20/2025 12:11:29 INFO stats.py:314 | Epoch[168] Step[79] GlobalStep[65599] Training Speed: 8.67 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 9:23:05.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:11:50 INFO loss_tracker.py:84 | Epoch[168/239] Step[99] GlobalStep[65619/NA]: det_loss_cls_0[0.5447] det_loss_box_0[0.6207] det_loss_cls_1[0.5212] det_loss_box_1[0.4617] det_loss_cls_2[0.4567] det_loss_box_2[0.4236] det_loss_cls_3[0.4204] det_loss_box_3[0.4088] det_loss_cls_4[0.4043] det_loss_box_4[0.3902] det_loss_cls_5[0.3823] det_loss_box_5[0.3951] det_loss_cls_dn_0[0.0801] det_loss_box_dn_0[0.2615] det_loss_cls_dn_1[0.0765] det_loss_box_dn_1[0.2053] det_loss_cls_dn_2[0.0751] det_loss_box_dn_2[0.1868] det_loss_cls_dn_3[0.0745] det_loss_box_dn_3[0.1819] det_loss_cls_dn_4[0.0735] det_loss_box_dn_4[0.1771] det_loss_cls_dn_5[0.0728] det_loss_box_dn_5[0.1732] loss_depth[0.0355] total_loss[7.1037]
Rank[0/8] 06/20/2025 12:11:55 INFO stats.py:314 | Epoch[168] Step[104] GlobalStep[65624] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 9:22:33.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:12:15 INFO loss_tracker.py:84 | Epoch[168/239] Step[124] GlobalStep[65644/NA]: det_loss_cls_0[0.3500] det_loss_box_0[0.4208] det_loss_cls_1[0.3402] det_loss_box_1[0.3128] det_loss_cls_2[0.2918] det_loss_box_2[0.2838] det_loss_cls_3[0.2650] det_loss_box_3[0.2793] det_loss_cls_4[0.2408] det_loss_box_4[0.2699] det_loss_cls_5[0.2289] det_loss_box_5[0.2700] det_loss_cls_dn_0[0.0820] det_loss_box_dn_0[0.2464] det_loss_cls_dn_1[0.0777] det_loss_box_dn_1[0.2007] det_loss_cls_dn_2[0.0767] det_loss_box_dn_2[0.1833] det_loss_cls_dn_3[0.0757] det_loss_box_dn_3[0.1793] det_loss_cls_dn_4[0.0750] det_loss_box_dn_4[0.1763] det_loss_cls_dn_5[0.0742] det_loss_box_dn_5[0.1742] loss_depth[0.0357] total_loss[5.2107]
Rank[0/8] 06/20/2025 12:12:20 INFO stats.py:314 | Epoch[168] Step[129] GlobalStep[65649] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 9:22:01.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:12:42 INFO loss_tracker.py:84 | Epoch[168/239] Step[149] GlobalStep[65669/NA]: det_loss_cls_0[0.4318] det_loss_box_0[0.5623] det_loss_cls_1[0.4315] det_loss_box_1[0.4215] det_loss_cls_2[0.3663] det_loss_box_2[0.3903] det_loss_cls_3[0.3365] det_loss_box_3[0.3789] det_loss_cls_4[0.3152] det_loss_box_4[0.3697] det_loss_cls_5[0.3039] det_loss_box_5[0.3712] det_loss_cls_dn_0[0.0904] det_loss_box_dn_0[0.2784] det_loss_cls_dn_1[0.0865] det_loss_box_dn_1[0.2350] det_loss_cls_dn_2[0.0851] det_loss_box_dn_2[0.2153] det_loss_cls_dn_3[0.0838] det_loss_box_dn_3[0.2123] det_loss_cls_dn_4[0.0826] det_loss_box_dn_4[0.2090] det_loss_cls_dn_5[0.0817] det_loss_box_dn_5[0.2069] loss_depth[0.0340] total_loss[6.5799]
Rank[0/8] 06/20/2025 12:12:47 INFO stats.py:314 | Epoch[168] Step[154] GlobalStep[65674] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 9:21:29.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:13:07 INFO loss_tracker.py:84 | Epoch[168/239] Step[174] GlobalStep[65694/NA]: det_loss_cls_0[0.4855] det_loss_box_0[0.5492] det_loss_cls_1[0.4653] det_loss_box_1[0.4194] det_loss_cls_2[0.3983] det_loss_box_2[0.3797] det_loss_cls_3[0.3592] det_loss_box_3[0.3734] det_loss_cls_4[0.3363] det_loss_box_4[0.3593] det_loss_cls_5[0.3259] det_loss_box_5[0.3564] det_loss_cls_dn_0[0.0964] det_loss_box_dn_0[0.2508] det_loss_cls_dn_1[0.0926] det_loss_box_dn_1[0.2064] det_loss_cls_dn_2[0.0913] det_loss_box_dn_2[0.1892] det_loss_cls_dn_3[0.0900] det_loss_box_dn_3[0.1853] det_loss_cls_dn_4[0.0890] det_loss_box_dn_4[0.1818] det_loss_cls_dn_5[0.0882] det_loss_box_dn_5[0.1796] loss_depth[0.0355] total_loss[6.5839]
Rank[0/8] 06/20/2025 12:13:12 INFO stats.py:314 | Epoch[168] Step[179] GlobalStep[65699] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 9:20:57.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:13:33 INFO loss_tracker.py:84 | Epoch[168/239] Step[199] GlobalStep[65719/NA]: det_loss_cls_0[0.4595] det_loss_box_0[0.6624] det_loss_cls_1[0.4417] det_loss_box_1[0.5189] det_loss_cls_2[0.4055] det_loss_box_2[0.4702] det_loss_cls_3[0.3791] det_loss_box_3[0.4510] det_loss_cls_4[0.3535] det_loss_box_4[0.4413] det_loss_cls_5[0.3399] det_loss_box_5[0.4411] det_loss_cls_dn_0[0.1063] det_loss_box_dn_0[0.3135] det_loss_cls_dn_1[0.1024] det_loss_box_dn_1[0.2664] det_loss_cls_dn_2[0.1017] det_loss_box_dn_2[0.2492] det_loss_cls_dn_3[0.1005] det_loss_box_dn_3[0.2437] det_loss_cls_dn_4[0.0997] det_loss_box_dn_4[0.2408] det_loss_cls_dn_5[0.0987] det_loss_box_dn_5[0.2383] loss_depth[0.0372] total_loss[7.5623]
Rank[0/8] 06/20/2025 12:13:38 INFO stats.py:314 | Epoch[168] Step[204] GlobalStep[65724] Training Speed: 7.97 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 9:20:25.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:13:59 INFO loss_tracker.py:84 | Epoch[168/239] Step[224] GlobalStep[65744/NA]: det_loss_cls_0[0.4795] det_loss_box_0[0.6292] det_loss_cls_1[0.4572] det_loss_box_1[0.4698] det_loss_cls_2[0.4013] det_loss_box_2[0.4118] det_loss_cls_3[0.3636] det_loss_box_3[0.3954] det_loss_cls_4[0.3383] det_loss_box_4[0.3888] det_loss_cls_5[0.3280] det_loss_box_5[0.3864] det_loss_cls_dn_0[0.0873] det_loss_box_dn_0[0.2386] det_loss_cls_dn_1[0.0829] det_loss_box_dn_1[0.1973] det_loss_cls_dn_2[0.0820] det_loss_box_dn_2[0.1809] det_loss_cls_dn_3[0.0808] det_loss_box_dn_3[0.1778] det_loss_cls_dn_4[0.0802] det_loss_box_dn_4[0.1753] det_loss_cls_dn_5[0.0792] det_loss_box_dn_5[0.1746] loss_depth[0.0357] total_loss[6.7218]
Rank[0/8] 06/20/2025 12:14:04 INFO stats.py:314 | Epoch[168] Step[229] GlobalStep[65749] Training Speed: 7.95 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 9:19:53.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:14:25 INFO loss_tracker.py:84 | Epoch[168/239] Step[249] GlobalStep[65769/NA]: det_loss_cls_0[0.4420] det_loss_box_0[0.5527] det_loss_cls_1[0.4378] det_loss_box_1[0.4251] det_loss_cls_2[0.3872] det_loss_box_2[0.3882] det_loss_cls_3[0.3563] det_loss_box_3[0.3770] det_loss_cls_4[0.3308] det_loss_box_4[0.3653] det_loss_cls_5[0.3150] det_loss_box_5[0.3611] det_loss_cls_dn_0[0.0913] det_loss_box_dn_0[0.2811] det_loss_cls_dn_1[0.0873] det_loss_box_dn_1[0.2380] det_loss_cls_dn_2[0.0871] det_loss_box_dn_2[0.2172] det_loss_cls_dn_3[0.0863] det_loss_box_dn_3[0.2127] det_loss_cls_dn_4[0.0853] det_loss_box_dn_4[0.2102] det_loss_cls_dn_5[0.0847] det_loss_box_dn_5[0.2074] loss_depth[0.0319] total_loss[6.6590]
Rank[0/8] 06/20/2025 12:14:30 INFO stats.py:314 | Epoch[168] Step[254] GlobalStep[65774] Training Speed: 7.49 samples/sec across all devices.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 9:19:21.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:14:50 INFO loss_tracker.py:84 | Epoch[168/239] Step[274] GlobalStep[65794/NA]: det_loss_cls_0[0.4328] det_loss_box_0[0.5188] det_loss_cls_1[0.4278] det_loss_box_1[0.3837] det_loss_cls_2[0.3645] det_loss_box_2[0.3455] det_loss_cls_3[0.3302] det_loss_box_3[0.3407] det_loss_cls_4[0.3086] det_loss_box_4[0.3347] det_loss_cls_5[0.2986] det_loss_box_5[0.3314] det_loss_cls_dn_0[0.0821] det_loss_box_dn_0[0.2618] det_loss_cls_dn_1[0.0791] det_loss_box_dn_1[0.2074] det_loss_cls_dn_2[0.0778] det_loss_box_dn_2[0.1836] det_loss_cls_dn_3[0.0771] det_loss_box_dn_3[0.1807] det_loss_cls_dn_4[0.0763] det_loss_box_dn_4[0.1771] det_loss_cls_dn_5[0.0751] det_loss_box_dn_5[0.1745] loss_depth[0.0356] total_loss[6.1057]
Rank[0/8] 06/20/2025 12:14:55 INFO stats.py:314 | Epoch[168] Step[279] GlobalStep[65799] Training Speed: 8.62 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 9:18:49.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:15:16 INFO loss_tracker.py:84 | Epoch[168/239] Step[299] GlobalStep[65819/NA]: det_loss_cls_0[0.6072] det_loss_box_0[0.7449] det_loss_cls_1[0.5826] det_loss_box_1[0.5585] det_loss_cls_2[0.5252] det_loss_box_2[0.4960] det_loss_cls_3[0.4919] det_loss_box_3[0.4761] det_loss_cls_4[0.4752] det_loss_box_4[0.4604] det_loss_cls_5[0.4563] det_loss_box_5[0.4577] det_loss_cls_dn_0[0.1030] det_loss_box_dn_0[0.2995] det_loss_cls_dn_1[0.0992] det_loss_box_dn_1[0.2365] det_loss_cls_dn_2[0.0986] det_loss_box_dn_2[0.2213] det_loss_cls_dn_3[0.0973] det_loss_box_dn_3[0.2141] det_loss_cls_dn_4[0.0966] det_loss_box_dn_4[0.2105] det_loss_cls_dn_5[0.0961] det_loss_box_dn_5[0.2081] loss_depth[0.0365] total_loss[8.3496]
Rank[0/8] 06/20/2025 12:15:21 INFO stats.py:314 | Epoch[168] Step[304] GlobalStep[65824] Training Speed: 7.92 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 9:18:17.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:15:42 INFO loss_tracker.py:84 | Epoch[168/239] Step[324] GlobalStep[65844/NA]: det_loss_cls_0[0.4778] det_loss_box_0[0.5706] det_loss_cls_1[0.4649] det_loss_box_1[0.4371] det_loss_cls_2[0.4106] det_loss_box_2[0.3883] det_loss_cls_3[0.3817] det_loss_box_3[0.3795] det_loss_cls_4[0.3547] det_loss_box_4[0.3757] det_loss_cls_5[0.3409] det_loss_box_5[0.3804] det_loss_cls_dn_0[0.1060] det_loss_box_dn_0[0.2840] det_loss_cls_dn_1[0.1021] det_loss_box_dn_1[0.2338] det_loss_cls_dn_2[0.1013] det_loss_box_dn_2[0.2176] det_loss_cls_dn_3[0.1005] det_loss_box_dn_3[0.2122] det_loss_cls_dn_4[0.0998] det_loss_box_dn_4[0.2101] det_loss_cls_dn_5[0.0988] det_loss_box_dn_5[0.2062] loss_depth[0.0359] total_loss[6.9708]
Rank[0/8] 06/20/2025 12:15:48 INFO stats.py:314 | Epoch[168] Step[329] GlobalStep[65849] Training Speed: 8.01 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 9:17:45.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:16:08 INFO loss_tracker.py:84 | Epoch[168/239] Step[349] GlobalStep[65869/NA]: det_loss_cls_0[0.4988] det_loss_box_0[0.6601] det_loss_cls_1[0.4850] det_loss_box_1[0.5142] det_loss_cls_2[0.4421] det_loss_box_2[0.4732] det_loss_cls_3[0.4147] det_loss_box_3[0.4606] det_loss_cls_4[0.3938] det_loss_box_4[0.4484] det_loss_cls_5[0.3858] det_loss_box_5[0.4438] det_loss_cls_dn_0[0.0942] det_loss_box_dn_0[0.2726] det_loss_cls_dn_1[0.0898] det_loss_box_dn_1[0.2292] det_loss_cls_dn_2[0.0892] det_loss_box_dn_2[0.2126] det_loss_cls_dn_3[0.0883] det_loss_box_dn_3[0.2070] det_loss_cls_dn_4[0.0872] det_loss_box_dn_4[0.2043] det_loss_cls_dn_5[0.0859] det_loss_box_dn_5[0.2011] loss_depth[0.0376] total_loss[7.5193]
Rank[0/8] 06/20/2025 12:16:13 INFO stats.py:314 | Epoch[168] Step[354] GlobalStep[65874] Training Speed: 8.34 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 9:17:13.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:16:34 INFO loss_tracker.py:84 | Epoch[168/239] Step[374] GlobalStep[65894/NA]: det_loss_cls_0[0.4166] det_loss_box_0[0.4238] det_loss_cls_1[0.3906] det_loss_box_1[0.3147] det_loss_cls_2[0.3336] det_loss_box_2[0.2876] det_loss_cls_3[0.3039] det_loss_box_3[0.2880] det_loss_cls_4[0.2850] det_loss_box_4[0.2776] det_loss_cls_5[0.2779] det_loss_box_5[0.2767] det_loss_cls_dn_0[0.0997] det_loss_box_dn_0[0.2393] det_loss_cls_dn_1[0.0952] det_loss_box_dn_1[0.1994] det_loss_cls_dn_2[0.0938] det_loss_box_dn_2[0.1851] det_loss_cls_dn_3[0.0928] det_loss_box_dn_3[0.1825] det_loss_cls_dn_4[0.0919] det_loss_box_dn_4[0.1810] det_loss_cls_dn_5[0.0911] det_loss_box_dn_5[0.1801] loss_depth[0.0335] total_loss[5.6417]
Rank[0/8] 06/20/2025 12:16:39 INFO stats.py:314 | Epoch[168] Step[379] GlobalStep[65899] Training Speed: 8.07 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 9:16:41.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:16:49 INFO stats.py:394 | Epoch[168] completed. Training Speed: 7.71 samples/sec across all devices.	Epoch Time: 404.43 sec.	Average Epoch Time: 404.43 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 9:16:28.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:17:06 INFO stats.py:314 | Epoch[169] Step[14] GlobalStep[65924] Training Speed: 7.75 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 9:16:10.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:17:17 INFO loss_tracker.py:84 | Epoch[169/239] Step[24] GlobalStep[65934/NA]: det_loss_cls_0[0.5040] det_loss_box_0[0.5964] det_loss_cls_1[0.4859] det_loss_box_1[0.4557] det_loss_cls_2[0.4254] det_loss_box_2[0.4106] det_loss_cls_3[0.3887] det_loss_box_3[0.3943] det_loss_cls_4[0.3652] det_loss_box_4[0.3823] det_loss_cls_5[0.3540] det_loss_box_5[0.3816] det_loss_cls_dn_0[0.0979] det_loss_box_dn_0[0.2577] det_loss_cls_dn_1[0.0936] det_loss_box_dn_1[0.2128] det_loss_cls_dn_2[0.0920] det_loss_box_dn_2[0.1959] det_loss_cls_dn_3[0.0910] det_loss_box_dn_3[0.1913] det_loss_cls_dn_4[0.0901] det_loss_box_dn_4[0.1881] det_loss_cls_dn_5[0.0891] det_loss_box_dn_5[0.1859] loss_depth[0.0361] total_loss[6.9658]
Rank[0/8] 06/20/2025 12:17:32 INFO stats.py:314 | Epoch[169] Step[39] GlobalStep[65949] Training Speed: 7.82 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 9:15:38.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:17:42 INFO loss_tracker.py:84 | Epoch[169/239] Step[49] GlobalStep[65959/NA]: det_loss_cls_0[0.4669] det_loss_box_0[0.5742] det_loss_cls_1[0.4477] det_loss_box_1[0.4317] det_loss_cls_2[0.4011] det_loss_box_2[0.3871] det_loss_cls_3[0.3676] det_loss_box_3[0.3773] det_loss_cls_4[0.3495] det_loss_box_4[0.3621] det_loss_cls_5[0.3316] det_loss_box_5[0.3708] det_loss_cls_dn_0[0.0864] det_loss_box_dn_0[0.2505] det_loss_cls_dn_1[0.0825] det_loss_box_dn_1[0.2009] det_loss_cls_dn_2[0.0812] det_loss_box_dn_2[0.1815] det_loss_cls_dn_3[0.0801] det_loss_box_dn_3[0.1773] det_loss_cls_dn_4[0.0792] det_loss_box_dn_4[0.1746] det_loss_cls_dn_5[0.0786] det_loss_box_dn_5[0.1726] loss_depth[0.0370] total_loss[6.5500]
Rank[0/8] 06/20/2025 12:17:58 INFO stats.py:314 | Epoch[169] Step[64] GlobalStep[65974] Training Speed: 7.64 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 9:15:06.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:18:08 INFO loss_tracker.py:84 | Epoch[169/239] Step[74] GlobalStep[65984/NA]: det_loss_cls_0[0.4927] det_loss_box_0[0.6684] det_loss_cls_1[0.4879] det_loss_box_1[0.5003] det_loss_cls_2[0.4299] det_loss_box_2[0.4588] det_loss_cls_3[0.3870] det_loss_box_3[0.4450] det_loss_cls_4[0.3651] det_loss_box_4[0.4300] det_loss_cls_5[0.3543] det_loss_box_5[0.4289] det_loss_cls_dn_0[0.0935] det_loss_box_dn_0[0.2925] det_loss_cls_dn_1[0.0890] det_loss_box_dn_1[0.2479] det_loss_cls_dn_2[0.0882] det_loss_box_dn_2[0.2310] det_loss_cls_dn_3[0.0869] det_loss_box_dn_3[0.2264] det_loss_cls_dn_4[0.0861] det_loss_box_dn_4[0.2246] det_loss_cls_dn_5[0.0853] det_loss_box_dn_5[0.2226] loss_depth[0.0377] total_loss[7.4601]
Rank[0/8] 06/20/2025 12:18:24 INFO stats.py:314 | Epoch[169] Step[89] GlobalStep[65999] Training Speed: 11.04 samples/sec across all devices.	Average Step Time: 0.72 sec.	Estimated Remaining Time: 9:14:34.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:18:35 INFO loss_tracker.py:84 | Epoch[169/239] Step[99] GlobalStep[66009/NA]: det_loss_cls_0[0.4726] det_loss_box_0[0.6107] det_loss_cls_1[0.4631] det_loss_box_1[0.4408] det_loss_cls_2[0.4083] det_loss_box_2[0.3937] det_loss_cls_3[0.3753] det_loss_box_3[0.3814] det_loss_cls_4[0.3517] det_loss_box_4[0.3744] det_loss_cls_5[0.3409] det_loss_box_5[0.3670] det_loss_cls_dn_0[0.0922] det_loss_box_dn_0[0.2465] det_loss_cls_dn_1[0.0885] det_loss_box_dn_1[0.1983] det_loss_cls_dn_2[0.0870] det_loss_box_dn_2[0.1778] det_loss_cls_dn_3[0.0864] det_loss_box_dn_3[0.1754] det_loss_cls_dn_4[0.0857] det_loss_box_dn_4[0.1729] det_loss_cls_dn_5[0.0849] det_loss_box_dn_5[0.1715] loss_depth[0.0391] total_loss[6.6861]
Rank[0/8] 06/20/2025 12:18:50 INFO stats.py:314 | Epoch[169] Step[114] GlobalStep[66024] Training Speed: 8.13 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 9:14:02.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:19:00 INFO loss_tracker.py:84 | Epoch[169/239] Step[124] GlobalStep[66034/NA]: det_loss_cls_0[0.5129] det_loss_box_0[0.5770] det_loss_cls_1[0.4944] det_loss_box_1[0.4125] det_loss_cls_2[0.4350] det_loss_box_2[0.3832] det_loss_cls_3[0.3886] det_loss_box_3[0.3793] det_loss_cls_4[0.3672] det_loss_box_4[0.3598] det_loss_cls_5[0.3541] det_loss_box_5[0.3579] det_loss_cls_dn_0[0.0981] det_loss_box_dn_0[0.2411] det_loss_cls_dn_1[0.0944] det_loss_box_dn_1[0.1957] det_loss_cls_dn_2[0.0935] det_loss_box_dn_2[0.1797] det_loss_cls_dn_3[0.0925] det_loss_box_dn_3[0.1768] det_loss_cls_dn_4[0.0913] det_loss_box_dn_4[0.1752] det_loss_cls_dn_5[0.0903] det_loss_box_dn_5[0.1747] loss_depth[0.0348] total_loss[6.7600]
Rank[0/8] 06/20/2025 12:19:15 INFO stats.py:314 | Epoch[169] Step[139] GlobalStep[66049] Training Speed: 13.03 samples/sec across all devices.	Average Step Time: 0.61 sec.	Estimated Remaining Time: 9:13:30.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:19:26 INFO loss_tracker.py:84 | Epoch[169/239] Step[149] GlobalStep[66059/NA]: det_loss_cls_0[0.3526] det_loss_box_0[0.4173] det_loss_cls_1[0.3316] det_loss_box_1[0.3179] det_loss_cls_2[0.2734] det_loss_box_2[0.2798] det_loss_cls_3[0.2453] det_loss_box_3[0.2757] det_loss_cls_4[0.2267] det_loss_box_4[0.2687] det_loss_cls_5[0.2168] det_loss_box_5[0.2697] det_loss_cls_dn_0[0.0783] det_loss_box_dn_0[0.2431] det_loss_cls_dn_1[0.0738] det_loss_box_dn_1[0.1904] det_loss_cls_dn_2[0.0730] det_loss_box_dn_2[0.1728] det_loss_cls_dn_3[0.0716] det_loss_box_dn_3[0.1693] det_loss_cls_dn_4[0.0707] det_loss_box_dn_4[0.1669] det_loss_cls_dn_5[0.0697] det_loss_box_dn_5[0.1657] loss_depth[0.0375] total_loss[5.0582]
Rank[0/8] 06/20/2025 12:19:41 INFO stats.py:314 | Epoch[169] Step[164] GlobalStep[66074] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 9:12:58.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:19:51 INFO loss_tracker.py:84 | Epoch[169/239] Step[174] GlobalStep[66084/NA]: det_loss_cls_0[0.4738] det_loss_box_0[0.5960] det_loss_cls_1[0.4508] det_loss_box_1[0.4630] det_loss_cls_2[0.4022] det_loss_box_2[0.4113] det_loss_cls_3[0.3621] det_loss_box_3[0.3936] det_loss_cls_4[0.3424] det_loss_box_4[0.3926] det_loss_cls_5[0.3278] det_loss_box_5[0.3939] det_loss_cls_dn_0[0.0857] det_loss_box_dn_0[0.2583] det_loss_cls_dn_1[0.0815] det_loss_box_dn_1[0.2230] det_loss_cls_dn_2[0.0809] det_loss_box_dn_2[0.2040] det_loss_cls_dn_3[0.0796] det_loss_box_dn_3[0.2005] det_loss_cls_dn_4[0.0786] det_loss_box_dn_4[0.1975] det_loss_cls_dn_5[0.0779] det_loss_box_dn_5[0.1962] loss_depth[0.0362] total_loss[6.8095]
Rank[0/8] 06/20/2025 12:20:07 INFO stats.py:314 | Epoch[169] Step[189] GlobalStep[66099] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 9:12:26.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:20:18 INFO loss_tracker.py:84 | Epoch[169/239] Step[199] GlobalStep[66109/NA]: det_loss_cls_0[0.4650] det_loss_box_0[0.6359] det_loss_cls_1[0.4313] det_loss_box_1[0.4847] det_loss_cls_2[0.3807] det_loss_box_2[0.4430] det_loss_cls_3[0.3482] det_loss_box_3[0.4306] det_loss_cls_4[0.3334] det_loss_box_4[0.4158] det_loss_cls_5[0.3151] det_loss_box_5[0.4236] det_loss_cls_dn_0[0.0885] det_loss_box_dn_0[0.3057] det_loss_cls_dn_1[0.0844] det_loss_box_dn_1[0.2544] det_loss_cls_dn_2[0.0837] det_loss_box_dn_2[0.2355] det_loss_cls_dn_3[0.0823] det_loss_box_dn_3[0.2304] det_loss_cls_dn_4[0.0809] det_loss_box_dn_4[0.2273] det_loss_cls_dn_5[0.0801] det_loss_box_dn_5[0.2255] loss_depth[0.0359] total_loss[7.1218]
Rank[0/8] 06/20/2025 12:20:33 INFO stats.py:314 | Epoch[169] Step[214] GlobalStep[66124] Training Speed: 7.90 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 9:11:54.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:20:43 INFO loss_tracker.py:84 | Epoch[169/239] Step[224] GlobalStep[66134/NA]: det_loss_cls_0[0.4675] det_loss_box_0[0.5417] det_loss_cls_1[0.4618] det_loss_box_1[0.4007] det_loss_cls_2[0.3987] det_loss_box_2[0.3631] det_loss_cls_3[0.3627] det_loss_box_3[0.3482] det_loss_cls_4[0.3398] det_loss_box_4[0.3432] det_loss_cls_5[0.3318] det_loss_box_5[0.3403] det_loss_cls_dn_0[0.0827] det_loss_box_dn_0[0.2591] det_loss_cls_dn_1[0.0806] det_loss_box_dn_1[0.2035] det_loss_cls_dn_2[0.0797] det_loss_box_dn_2[0.1872] det_loss_cls_dn_3[0.0791] det_loss_box_dn_3[0.1815] det_loss_cls_dn_4[0.0786] det_loss_box_dn_4[0.1782] det_loss_cls_dn_5[0.0780] det_loss_box_dn_5[0.1750] loss_depth[0.0373] total_loss[6.4001]
Rank[0/8] 06/20/2025 12:20:58 INFO stats.py:314 | Epoch[169] Step[239] GlobalStep[66149] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 9:11:22.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:21:09 INFO loss_tracker.py:84 | Epoch[169/239] Step[249] GlobalStep[66159/NA]: det_loss_cls_0[0.4106] det_loss_box_0[0.5546] det_loss_cls_1[0.3970] det_loss_box_1[0.4241] det_loss_cls_2[0.3511] det_loss_box_2[0.3958] det_loss_cls_3[0.3275] det_loss_box_3[0.3872] det_loss_cls_4[0.3068] det_loss_box_4[0.3738] det_loss_cls_5[0.2958] det_loss_box_5[0.3752] det_loss_cls_dn_0[0.0774] det_loss_box_dn_0[0.3195] det_loss_cls_dn_1[0.0724] det_loss_box_dn_1[0.2649] det_loss_cls_dn_2[0.0713] det_loss_box_dn_2[0.2468] det_loss_cls_dn_3[0.0701] det_loss_box_dn_3[0.2411] det_loss_cls_dn_4[0.0694] det_loss_box_dn_4[0.2386] det_loss_cls_dn_5[0.0684] det_loss_box_dn_5[0.2330] loss_depth[0.0356] total_loss[6.6079]
Rank[0/8] 06/20/2025 12:21:25 INFO stats.py:314 | Epoch[169] Step[264] GlobalStep[66174] Training Speed: 8.75 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 9:10:50.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:21:35 INFO loss_tracker.py:84 | Epoch[169/239] Step[274] GlobalStep[66184/NA]: det_loss_cls_0[0.5850] det_loss_box_0[0.8538] det_loss_cls_1[0.5749] det_loss_box_1[0.6521] det_loss_cls_2[0.5213] det_loss_box_2[0.5921] det_loss_cls_3[0.4847] det_loss_box_3[0.5738] det_loss_cls_4[0.4616] det_loss_box_4[0.5586] det_loss_cls_5[0.4461] det_loss_box_5[0.5616] det_loss_cls_dn_0[0.0904] det_loss_box_dn_0[0.2873] det_loss_cls_dn_1[0.0868] det_loss_box_dn_1[0.2410] det_loss_cls_dn_2[0.0861] det_loss_box_dn_2[0.2213] det_loss_cls_dn_3[0.0849] det_loss_box_dn_3[0.2166] det_loss_cls_dn_4[0.0840] det_loss_box_dn_4[0.2125] det_loss_cls_dn_5[0.0830] det_loss_box_dn_5[0.2117] loss_depth[0.0372] total_loss[8.8085]
Rank[0/8] 06/20/2025 12:21:50 INFO stats.py:314 | Epoch[169] Step[289] GlobalStep[66199] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 9:10:18.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:22:00 INFO loss_tracker.py:84 | Epoch[169/239] Step[299] GlobalStep[66209/NA]: det_loss_cls_0[0.5348] det_loss_box_0[0.6772] det_loss_cls_1[0.5213] det_loss_box_1[0.5351] det_loss_cls_2[0.4637] det_loss_box_2[0.4789] det_loss_cls_3[0.4300] det_loss_box_3[0.4630] det_loss_cls_4[0.4043] det_loss_box_4[0.4482] det_loss_cls_5[0.3931] det_loss_box_5[0.4483] det_loss_cls_dn_0[0.0937] det_loss_box_dn_0[0.2409] det_loss_cls_dn_1[0.0905] det_loss_box_dn_1[0.1973] det_loss_cls_dn_2[0.0896] det_loss_box_dn_2[0.1796] det_loss_cls_dn_3[0.0885] det_loss_box_dn_3[0.1762] det_loss_cls_dn_4[0.0874] det_loss_box_dn_4[0.1733] det_loss_cls_dn_5[0.0866] det_loss_box_dn_5[0.1721] loss_depth[0.0348] total_loss[7.5082]
Rank[0/8] 06/20/2025 12:22:16 INFO stats.py:314 | Epoch[169] Step[314] GlobalStep[66224] Training Speed: 7.68 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 9:09:46.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:22:26 INFO loss_tracker.py:84 | Epoch[169/239] Step[324] GlobalStep[66234/NA]: det_loss_cls_0[0.5659] det_loss_box_0[0.7004] det_loss_cls_1[0.5474] det_loss_box_1[0.5114] det_loss_cls_2[0.4719] det_loss_box_2[0.4805] det_loss_cls_3[0.4365] det_loss_box_3[0.4623] det_loss_cls_4[0.4022] det_loss_box_4[0.4484] det_loss_cls_5[0.3997] det_loss_box_5[0.4451] det_loss_cls_dn_0[0.0974] det_loss_box_dn_0[0.2641] det_loss_cls_dn_1[0.0937] det_loss_box_dn_1[0.2187] det_loss_cls_dn_2[0.0923] det_loss_box_dn_2[0.2033] det_loss_cls_dn_3[0.0915] det_loss_box_dn_3[0.2007] det_loss_cls_dn_4[0.0909] det_loss_box_dn_4[0.1989] det_loss_cls_dn_5[0.0901] det_loss_box_dn_5[0.1952] loss_depth[0.0371] total_loss[7.7456]
Rank[0/8] 06/20/2025 12:22:42 INFO stats.py:314 | Epoch[169] Step[339] GlobalStep[66249] Training Speed: 6.47 samples/sec across all devices.	Average Step Time: 1.24 sec.	Estimated Remaining Time: 9:09:15.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:22:53 INFO loss_tracker.py:84 | Epoch[169/239] Step[349] GlobalStep[66259/NA]: det_loss_cls_0[0.4252] det_loss_box_0[0.4308] det_loss_cls_1[0.4089] det_loss_box_1[0.3277] det_loss_cls_2[0.3475] det_loss_box_2[0.2929] det_loss_cls_3[0.3246] det_loss_box_3[0.2767] det_loss_cls_4[0.2990] det_loss_box_4[0.2745] det_loss_cls_5[0.2820] det_loss_box_5[0.2787] det_loss_cls_dn_0[0.0919] det_loss_box_dn_0[0.2272] det_loss_cls_dn_1[0.0893] det_loss_box_dn_1[0.1782] det_loss_cls_dn_2[0.0881] det_loss_box_dn_2[0.1609] det_loss_cls_dn_3[0.0871] det_loss_box_dn_3[0.1547] det_loss_cls_dn_4[0.0859] det_loss_box_dn_4[0.1506] det_loss_cls_dn_5[0.0850] det_loss_box_dn_5[0.1485] loss_depth[0.0387] total_loss[5.5546]
Rank[0/8] 06/20/2025 12:23:08 INFO stats.py:314 | Epoch[169] Step[364] GlobalStep[66274] Training Speed: 6.69 samples/sec across all devices.	Average Step Time: 1.20 sec.	Estimated Remaining Time: 9:08:43.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:23:18 INFO loss_tracker.py:84 | Epoch[169/239] Step[374] GlobalStep[66284/NA]: det_loss_cls_0[0.4397] det_loss_box_0[0.6041] det_loss_cls_1[0.4270] det_loss_box_1[0.4797] det_loss_cls_2[0.3888] det_loss_box_2[0.4316] det_loss_cls_3[0.3621] det_loss_box_3[0.4242] det_loss_cls_4[0.3438] det_loss_box_4[0.4130] det_loss_cls_5[0.3351] det_loss_box_5[0.4070] det_loss_cls_dn_0[0.1046] det_loss_box_dn_0[0.3935] det_loss_cls_dn_1[0.1016] det_loss_box_dn_1[0.3309] det_loss_cls_dn_2[0.1006] det_loss_box_dn_2[0.3063] det_loss_cls_dn_3[0.0989] det_loss_box_dn_3[0.3002] det_loss_cls_dn_4[0.0983] det_loss_box_dn_4[0.2964] det_loss_cls_dn_5[0.0971] det_loss_box_dn_5[0.2879] loss_depth[0.0367] total_loss[7.6090]
Rank[0/8] 06/20/2025 12:23:33 INFO stats.py:314 | Epoch[169] Step[389] GlobalStep[66299] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 9:08:10.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:23:33 INFO trainer.py:268 | 
==================================================BEGIN EVAL==================================================
Rank[0/8] 06/20/2025 12:24:30 INFO trainer.py:275 | eval: 10
Rank[0/8] 06/20/2025 12:24:42 INFO trainer.py:275 | eval: 20
Rank[0/8] 06/20/2025 12:24:53 INFO trainer.py:275 | eval: 30
Rank[0/8] 06/20/2025 12:25:05 INFO trainer.py:275 | eval: 40
Rank[0/8] 06/20/2025 12:25:14 INFO trainer.py:275 | eval: 50
Rank[0/8] 06/20/2025 12:25:24 INFO trainer.py:275 | eval: 60
Rank[0/8] 06/20/2025 12:25:32 INFO trainer.py:275 | eval: 70
Rank[0/8] 06/20/2025 12:25:41 INFO trainer.py:275 | eval: 80
Rank[0/8] 06/20/2025 12:25:51 INFO trainer.py:275 | eval: 90
Rank[0/8] 06/20/2025 12:25:58 INFO trainer.py:275 | eval: 100
Rank[0/8] 06/20/2025 12:26:03 INFO metrics.py:428 | number of results: 824
Rank[0/8] 06/20/2025 12:30:21 INFO metrics.py:332 | eval results of overall:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| bathtub             | 0.8759  | 0.9118  | 0.7673  | 0.8235  |
| bin                 | 0.6611  | 0.8520  | 0.4268  | 0.6397  |
| toilet              | 0.8623  | 0.8905  | 0.7562  | 0.7883  |
| toilet paper        | 0.3757  | 0.5669  | 0.1183  | 0.2913  |
| sink                | 0.7078  | 0.8642  | 0.2655  | 0.5185  |
| curtain             | 0.5593  | 0.8328  | 0.1441  | 0.4657  |
| picture             | 0.2637  | 0.4784  | 0.0257  | 0.1218  |
| doorframe           | 0.3277  | 0.7758  | 0.0759  | 0.4063  |
| towel               | 0.3871  | 0.6516  | 0.1110  | 0.2677  |
| door                | 0.3930  | 0.7152  | 0.0695  | 0.3276  |
| stopcock            | 0.1463  | 0.5172  | 0.0005  | 0.0345  |
| vanity              | 0.5664  | 0.9333  | 0.4395  | 0.8222  |
| bar                 | 0.0506  | 0.2424  | 0.0003  | 0.0303  |
| switch              | 0.0025  | 0.1316  | 0.0000  | 0.0000  |
| mirror              | 0.2380  | 0.6957  | 0.0165  | 0.1957  |
| bottle              | 0.0630  | 0.3593  | 0.0077  | 0.1003  |
| hair dryer          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rail                | 0.0356  | 0.4688  | 0.0032  | 0.0781  |
| bag                 | 0.2739  | 0.7189  | 0.0739  | 0.2919  |
| cup                 | 0.1423  | 0.3898  | 0.0208  | 0.0678  |
| socket              | 0.0177  | 0.2522  | 0.0004  | 0.0310  |
| tissue box          | 0.1563  | 0.5455  | 0.0971  | 0.3939  |
| microwave           | 0.6907  | 0.7755  | 0.3191  | 0.5102  |
| shower              | 0.0658  | 0.3000  | 0.0030  | 0.1000  |
| rod                 | 0.0250  | 0.0250  | 0.0000  | 0.0000  |
| light               | 0.0587  | 0.3120  | 0.0021  | 0.0406  |
| ledge               | 0.0259  | 0.6667  | 0.0013  | 0.3333  |
| tissue              | 0.0203  | 0.3077  | 0.0154  | 0.0769  |
| soap dish           | 0.6308  | 0.7333  | 0.1511  | 0.2000  |
| box                 | 0.1703  | 0.5242  | 0.0406  | 0.2136  |
| counter             | 0.2112  | 0.7064  | 0.0733  | 0.3119  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stool               | 0.3082  | 0.7815  | 0.2021  | 0.5630  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shelf               | 0.3417  | 0.7266  | 0.1587  | 0.4694  |
| vent                | 0.0123  | 0.2083  | 0.0000  | 0.0000  |
| plant               | 0.3234  | 0.6928  | 0.1084  | 0.3603  |
| paper               | 0.0287  | 0.2899  | 0.0007  | 0.0385  |
| faucet              | 0.0254  | 0.1915  | 0.0000  | 0.0000  |
| cabinet             | 0.3074  | 0.7535  | 0.1834  | 0.5512  |
| lamp                | 0.3677  | 0.6567  | 0.1037  | 0.3075  |
| bed                 | 0.8569  | 0.9349  | 0.7081  | 0.8639  |
| jar                 | 0.1096  | 0.3846  | 0.0824  | 0.1538  |
| purse               | 0.0008  | 0.1667  | 0.0000  | 0.0000  |
| cosmetics           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| couch               | 0.5217  | 0.8802  | 0.3987  | 0.7727  |
| toaster             | 0.4865  | 0.6000  | 0.1335  | 0.4000  |
| soap                | 0.1129  | 0.6667  | 0.0000  | 0.0000  |
| toothpaste          | 0.0111  | 0.4000  | 0.0000  | 0.0000  |
| basket              | 0.2788  | 0.5913  | 0.1320  | 0.3217  |
| notebook            | 0.0010  | 0.0769  | 0.0000  | 0.0000  |
| scale               | 0.2749  | 0.3636  | 0.0003  | 0.0909  |
| blanket             | 0.2853  | 0.7900  | 0.0466  | 0.4000  |
| hamper              | 0.3912  | 0.5455  | 0.2741  | 0.5455  |
| doorway             | 0.0002  | 0.1111  | 0.0000  | 0.0000  |
| frame               | 0.0911  | 0.5306  | 0.0086  | 0.1224  |
| mat                 | 0.0239  | 0.2432  | 0.0007  | 0.0270  |
| drawer              | 0.2821  | 0.6111  | 0.1675  | 0.5000  |
| shoe                | 0.2061  | 0.4206  | 0.0188  | 0.1215  |
| bowl                | 0.1744  | 0.4928  | 0.0735  | 0.2174  |
| decoration          | 0.0447  | 0.4714  | 0.0057  | 0.1071  |
| case                | 0.1048  | 0.4633  | 0.0457  | 0.1638  |
| chair               | 0.6234  | 0.8411  | 0.4742  | 0.6867  |
| radiator            | 0.7176  | 0.8000  | 0.2467  | 0.3889  |
| blackboard          | 0.6723  | 0.8293  | 0.2634  | 0.4634  |
| computer            | 0.3281  | 0.6721  | 0.0167  | 0.1311  |
| monitor             | 0.5576  | 0.7174  | 0.1266  | 0.3424  |
| keyboard            | 0.0695  | 0.2326  | 0.0000  | 0.0000  |
| desk                | 0.5509  | 0.9213  | 0.3898  | 0.7584  |
| mouse               | 0.0046  | 0.1081  | 0.0000  | 0.0000  |
| window              | 0.2687  | 0.6332  | 0.0570  | 0.2680  |
| table               | 0.5359  | 0.8809  | 0.4341  | 0.7618  |
| backpack            | 0.5849  | 0.8065  | 0.4130  | 0.6290  |
| plug                | 0.0440  | 0.1579  | 0.0000  | 0.0000  |
| telephone           | 0.4008  | 0.6667  | 0.1469  | 0.3000  |
| blinds              | 0.2217  | 0.4800  | 0.0088  | 0.2200  |
| book                | 0.0770  | 0.4399  | 0.0062  | 0.1145  |
| stand               | 0.5949  | 0.8963  | 0.4773  | 0.7500  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes             | 0.1999  | 0.5796  | 0.0487  | 0.2611  |
| jacket              | 0.6090  | 0.8750  | 0.2734  | 0.6250  |
| windowsill          | 0.1479  | 0.6957  | 0.0077  | 0.1522  |
| tablet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| projector           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| laptop              | 0.5126  | 0.7826  | 0.1300  | 0.3043  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plate               | 0.0309  | 0.3654  | 0.0045  | 0.1154  |
| pipe                | 0.0380  | 0.1429  | 0.0000  | 0.0000  |
| pillow              | 0.2904  | 0.6569  | 0.0450  | 0.2162  |
| dresser             | 0.6486  | 0.9074  | 0.5411  | 0.8148  |
| wardrobe            | 0.3825  | 0.7349  | 0.2768  | 0.5783  |
| tv                  | 0.6881  | 0.8614  | 0.2138  | 0.4158  |
| ottoman             | 0.1709  | 0.6667  | 0.1245  | 0.5000  |
| stairs              | 0.0848  | 0.4444  | 0.0094  | 0.1528  |
| remote control      | 0.0304  | 0.1852  | 0.0256  | 0.1111  |
| clock               | 0.3973  | 0.7234  | 0.0413  | 0.1915  |
| paper cutter        | 0.8377  | 1.0000  | 0.0000  | 0.0000  |
| calendar            | 0.0097  | 0.3333  | 0.0000  | 0.0000  |
| toy                 | 0.1635  | 0.3788  | 0.0682  | 0.1364  |
| rack                | 0.1251  | 0.5000  | 0.0574  | 0.3654  |
| poster              | 0.0152  | 0.1212  | 0.0000  | 0.0000  |
| container           | 0.0082  | 0.2712  | 0.0034  | 0.1525  |
| printer             | 0.5698  | 0.8163  | 0.3679  | 0.6327  |
| carpet              | 0.1916  | 0.5238  | 0.0108  | 0.1667  |
| cushion             | 0.2375  | 0.5882  | 0.0463  | 0.1961  |
| bench               | 0.3134  | 0.7170  | 0.2748  | 0.5849  |
| piano               | 0.2667  | 0.4000  | 0.2667  | 0.4000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stove               | 0.5966  | 0.8611  | 0.3430  | 0.5833  |
| machine             | 0.5094  | 0.8947  | 0.3573  | 0.6842  |
| bicycle             | 0.7000  | 1.0000  | 0.5794  | 1.0000  |
| guitar              | 0.2500  | 0.3333  | 0.0027  | 0.0833  |
| refrigerator        | 0.7167  | 0.8800  | 0.4863  | 0.6400  |
| luggage             | 0.6144  | 1.0000  | 0.6144  | 1.0000  |
| kettle              | 0.1844  | 0.6667  | 0.0423  | 0.2778  |
| person              | 0.2475  | 0.6667  | 0.0053  | 0.3333  |
| sunglasses          | 0.0645  | 0.3077  | 0.0024  | 0.1538  |
| coffee maker        | 0.3993  | 0.6207  | 0.1302  | 0.2069  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tray                | 0.0008  | 0.1429  | 0.0000  | 0.0000  |
| plunger             | 0.8000  | 0.8000  | 0.2189  | 0.8000  |
| broom               | 0.0064  | 0.2143  | 0.0000  | 0.0000  |
| stall               | 0.3472  | 1.0000  | 0.3113  | 0.5294  |
| mop                 | 0.8333  | 1.0000  | 0.2111  | 0.6667  |
| bucket              | 0.2100  | 0.7179  | 0.0666  | 0.3590  |
| grab bar            | 0.0294  | 0.2500  | 0.0000  | 0.0000  |
| countertop          | 0.0261  | 0.3929  | 0.0011  | 0.0714  |
| tube                | 0.0040  | 0.1538  | 0.0000  | 0.0000  |
| hat                 | 0.1358  | 0.2222  | 0.0002  | 0.1111  |
| board               | 0.1903  | 0.6935  | 0.0384  | 0.3710  |
| soap dispenser      | 0.3825  | 0.6296  | 0.0827  | 0.3333  |
| dispenser           | 0.5164  | 0.7273  | 0.3396  | 0.4773  |
| holder              | 0.2222  | 0.2222  | 0.1111  | 0.1111  |
| mailbox             | 0.0597  | 0.8000  | 0.0143  | 0.4000  |
| partition           | 0.4441  | 0.6667  | 0.1667  | 0.1667  |
| pillar              | 0.3545  | 0.7442  | 0.2455  | 0.4186  |
| column              | 0.0180  | 0.3684  | 0.0027  | 0.1316  |
| hanger              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washbasin           | 0.0074  | 0.2667  | 0.0043  | 0.1333  |
| alarm               | 0.0025  | 0.1250  | 0.0000  | 0.0000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headphones          | 0.2000  | 0.2000  | 0.2000  | 0.2000  |
| chandelier          | 0.0837  | 0.2381  | 0.0200  | 0.0952  |
| adhesive tape       | 0.0057  | 0.2000  | 0.0000  | 0.0000  |
| fire extinguisher   | 0.1776  | 0.6000  | 0.1600  | 0.4000  |
| window frame        | 0.0334  | 0.4242  | 0.0030  | 0.1919  |
| cube                | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fireplace           | 0.2061  | 0.4643  | 0.0106  | 0.2500  |
| copier              | 0.9097  | 0.9167  | 0.7602  | 0.9167  |
| fan                 | 0.6283  | 0.7969  | 0.1524  | 0.3594  |
| screen              | 0.0306  | 0.4000  | 0.0028  | 0.2000  |
| kitchen island      | 0.4360  | 0.5714  | 0.4360  | 0.5714  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball                | 0.4516  | 0.5238  | 0.3201  | 0.3810  |
| balcony             | 0.1190  | 1.0000  | 0.0357  | 0.5000  |
| statue              | 0.1646  | 0.5263  | 0.0853  | 0.3158  |
| vacuum cleaner      | 0.6143  | 0.7143  | 0.0571  | 0.2857  |
| humidifier          | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| basin               | 0.0328  | 0.2941  | 0.0005  | 0.1176  |
| map                 | 0.0200  | 0.5000  | 0.0200  | 0.5000  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water cooler        | 0.8333  | 1.0000  | 0.8333  | 1.0000  |
| sign                | 0.1121  | 0.3667  | 0.0001  | 0.0333  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crib                | 0.3548  | 0.6667  | 0.3548  | 0.6667  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool table          | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| barricade           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dish rack           | 0.4855  | 1.0000  | 0.0400  | 0.2000  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| teapot              | 0.0123  | 0.1111  | 0.0000  | 0.0000  |
| oven                | 0.4524  | 0.7857  | 0.1854  | 0.4286  |
| pot                 | 0.0796  | 0.5185  | 0.0159  | 0.1852  |
| brush               | 0.1163  | 0.2857  | 0.0000  | 0.0000  |
| fruit               | 0.2289  | 0.4286  | 0.0803  | 0.2857  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchenware         | 0.3162  | 0.5870  | 0.0253  | 0.1304  |
| drum                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mattress            | 0.1667  | 0.3333  | 0.0000  | 0.0000  |
| folder              | 0.0991  | 0.4286  | 0.0714  | 0.0714  |
| vase                | 0.1578  | 0.4400  | 0.0757  | 0.1800  |
| flowerpot           | 0.0473  | 0.4510  | 0.0012  | 0.1373  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| magazine            | 0.0137  | 0.6000  | 0.0013  | 0.2000  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| file                | 0.0687  | 0.2353  | 0.0297  | 0.0588  |
| speaker             | 0.0618  | 0.2273  | 0.0113  | 0.1818  |
| air conditioner     | 0.6000  | 0.6000  | 0.4000  | 0.4000  |
| headboard           | 0.0021  | 0.1250  | 0.0000  | 0.0000  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.3249  | 0.6500  | 0.0315  | 0.3000  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| steps               | 0.0009  | 0.3000  | 0.0000  | 0.0000  |
| garage door         | 0.3667  | 0.5000  | 0.0049  | 0.1667  |
| garbage             | 0.5406  | 0.7500  | 0.3229  | 0.5000  |
| conducting wire     | 0.0667  | 0.3333  | 0.0000  | 0.0000  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candlestick         | 0.0794  | 0.6667  | 0.0000  | 0.0000  |
| commode             | 0.5605  | 0.8654  | 0.4104  | 0.6923  |
| candle              | 0.0660  | 0.3333  | 0.0002  | 0.0556  |
| napkins             | 0.3704  | 0.6667  | 0.3333  | 0.3333  |
| price tag           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| spoon               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| player              | 0.2500  | 0.2500  | 0.2500  | 0.2500  |
| roll                | 0.2128  | 0.6000  | 0.0000  | 0.0000  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| heater              | 0.5730  | 0.8103  | 0.1940  | 0.4310  |
| device              | 0.2383  | 0.5455  | 0.0000  | 0.0000  |
| umbrella            | 0.2000  | 0.2000  | 0.2000  | 0.2000  |
| album               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| letter              | 0.4432  | 0.7500  | 0.0625  | 0.2500  |
| shampoo             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.4299  | 0.8667  | 0.3104  | 0.7333  |
| beam                | 0.0001  | 0.0909  | 0.0000  | 0.0455  |
| toiletry            | 0.0713  | 0.3913  | 0.0094  | 0.1304  |
| washing machine     | 0.5248  | 0.7500  | 0.3278  | 0.5000  |
| body loofah         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beanbag             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| can                 | 0.0306  | 0.1667  | 0.0069  | 0.0833  |
| pan                 | 0.2207  | 0.5882  | 0.0630  | 0.1765  |
| range hood          | 0.5702  | 0.8571  | 0.0476  | 0.1429  |
| pack                | 0.0329  | 0.6000  | 0.0107  | 0.2667  |
| food                | 0.0946  | 0.1818  | 0.0019  | 0.0909  |
| jalousie            | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| radio               | 0.2500  | 0.2500  | 0.2500  | 0.2500  |
| sponge              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ladder              | 0.1528  | 0.4706  | 0.1179  | 0.2353  |
| bread               | 0.1000  | 0.2000  | 0.0000  | 0.0000  |
| fence               | 0.0069  | 0.0833  | 0.0069  | 0.0833  |
| sack                | 0.5778  | 1.0000  | 0.2240  | 0.5714  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| arch                | 0.0094  | 0.1667  | 0.0004  | 0.1000  |
| flush               | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| coil                | 0.2778  | 0.3333  | 0.0000  | 0.0000  |
| roof                | 0.0029  | 0.2500  | 0.0000  | 0.0417  |
| handle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| footstool           | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| wine                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| treadmill           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| thermostat          | 0.1667  | 0.1667  | 0.0000  | 0.0000  |
| salt                | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.2706  | 0.5000  | 0.1056  | 0.2000  |
| glove               | 0.5556  | 0.6667  | 0.5556  | 0.6667  |
| crate               | 0.4025  | 0.5714  | 0.2215  | 0.4286  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet               | 0.4667  | 0.6667  | 0.4667  | 0.6667  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell            | 0.6667  | 0.6667  | 0.3333  | 0.3333  |
| helmets             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| menu                | 0.6024  | 0.7143  | 0.1429  | 0.1429  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water heater        | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| cap                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| overall             | 0.2378  | 0.4405  | 0.1128  | 0.2278  |
| head                | 0.3195  | 0.6177  | 0.1522  | 0.3338  |
| common              | 0.2095  | 0.4157  | 0.0994  | 0.2021  |
| tail                | 0.1801  | 0.2741  | 0.0847  | 0.1404  |
| small               | 0.0795  | 0.2331  | 0.0156  | 0.0582  |
| medium              | 0.2503  | 0.4922  | 0.1295  | 0.2607  |
| large               | 0.1544  | 0.5399  | 0.0809  | 0.3092  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 12:30:21 INFO metrics.py:437 | number of scannet results: 250
Rank[0/8] 06/20/2025 12:31:37 INFO metrics.py:332 | eval results of scannet:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| bathtub             | 0.9920  | 1.0000  | 0.8797  | 0.9062  |
| bin                 | 0.6817  | 0.8553  | 0.4547  | 0.6638  |
| toilet              | 0.9372  | 0.9643  | 0.8480  | 0.8571  |
| toilet paper        | 0.4211  | 0.5316  | 0.1574  | 0.3291  |
| sink                | 0.8531  | 0.9091  | 0.3360  | 0.5455  |
| curtain             | 0.6417  | 0.8407  | 0.1966  | 0.4690  |
| picture             | 0.3730  | 0.5746  | 0.0487  | 0.1530  |
| doorframe           | 0.4307  | 0.7958  | 0.0721  | 0.3803  |
| towel               | 0.4992  | 0.7273  | 0.1514  | 0.3223  |
| door                | 0.5741  | 0.8328  | 0.1717  | 0.5184  |
| stopcock            | 0.1463  | 0.5172  | 0.0005  | 0.0345  |
| vanity              | 0.6983  | 1.0000  | 0.5621  | 0.9333  |
| bar                 | 0.0556  | 0.2727  | 0.0000  | 0.0000  |
| switch              | 0.0049  | 0.1772  | 0.0000  | 0.0000  |
| mirror              | 0.3131  | 0.7229  | 0.0243  | 0.1446  |
| bottle              | 0.0877  | 0.3474  | 0.0069  | 0.0842  |
| hair dryer          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rail                | 0.0802  | 0.6667  | 0.0278  | 0.1667  |
| bag                 | 0.2236  | 0.7397  | 0.1066  | 0.3151  |
| cup                 | 0.2381  | 0.4203  | 0.0640  | 0.1159  |
| socket              | 0.0202  | 0.2718  | 0.0005  | 0.0340  |
| tissue box          | 0.2842  | 0.5455  | 0.2162  | 0.4091  |
| microwave           | 0.7572  | 0.8000  | 0.3223  | 0.5500  |
| shower              | 0.0602  | 0.3571  | 0.0001  | 0.0714  |
| rod                 | 0.0714  | 0.0714  | 0.0000  | 0.0000  |
| light               | 0.1555  | 0.4828  | 0.0000  | 0.0345  |
| ledge               | 0.0264  | 0.6667  | 0.0014  | 0.3333  |
| tissue              | 0.0002  | 0.1111  | 0.0000  | 0.0000  |
| soap dish           | 0.7464  | 0.8333  | 0.1889  | 0.2500  |
| box                 | 0.1334  | 0.4563  | 0.0376  | 0.1706  |
| counter             | 0.4849  | 0.8286  | 0.2786  | 0.5143  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stool               | 0.3067  | 0.8409  | 0.1901  | 0.5682  |
| shelf               | 0.5769  | 0.8309  | 0.3739  | 0.6838  |
| plant               | 0.3205  | 0.6555  | 0.1153  | 0.3782  |
| paper               | 0.0277  | 0.2868  | 0.0003  | 0.0302  |
| faucet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cabinet             | 0.3848  | 0.7857  | 0.2026  | 0.5476  |
| lamp                | 0.4929  | 0.7381  | 0.1960  | 0.4048  |
| bed                 | 0.9877  | 1.0000  | 0.9123  | 0.9649  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| purse               | 0.0017  | 0.1667  | 0.0000  | 0.0000  |
| couch               | 0.7297  | 0.9625  | 0.5866  | 0.8750  |
| toaster             | 0.3750  | 0.5000  | 0.3750  | 0.5000  |
| toothpaste          | 0.0118  | 0.4000  | 0.0000  | 0.0000  |
| basket              | 0.0644  | 0.2692  | 0.0625  | 0.1923  |
| notebook            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scale               | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| blanket             | 0.0964  | 0.5417  | 0.0042  | 0.2083  |
| hamper              | 0.4337  | 0.6000  | 0.3047  | 0.6000  |
| frame               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mat                 | 0.0353  | 0.2000  | 0.0000  | 0.0000  |
| drawer              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shoe                | 0.2112  | 0.4500  | 0.0387  | 0.1250  |
| bowl                | 0.2752  | 0.4444  | 0.0889  | 0.2222  |
| decoration          | 0.1361  | 0.5833  | 0.0017  | 0.1250  |
| case                | 0.7177  | 1.0000  | 0.5050  | 0.7895  |
| chair               | 0.6678  | 0.8590  | 0.4767  | 0.6721  |
| radiator            | 0.7940  | 0.8448  | 0.3649  | 0.5172  |
| blackboard          | 0.6937  | 0.8406  | 0.2723  | 0.4638  |
| computer            | 0.3308  | 0.6410  | 0.0354  | 0.1795  |
| monitor             | 0.6068  | 0.7286  | 0.1729  | 0.3857  |
| keyboard            | 0.1129  | 0.2759  | 0.0000  | 0.0000  |
| desk                | 0.6396  | 0.9747  | 0.4287  | 0.7722  |
| mouse               | 0.0132  | 0.1905  | 0.0000  | 0.0000  |
| window              | 0.5219  | 0.8210  | 0.1714  | 0.4383  |
| table               | 0.6358  | 0.9231  | 0.5334  | 0.8308  |
| backpack            | 0.7291  | 0.9286  | 0.6124  | 0.7500  |
| plug                | 0.0001  | 0.1111  | 0.0000  | 0.0000  |
| telephone           | 0.5261  | 0.7500  | 0.2360  | 0.4062  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| book                | 0.0877  | 0.4500  | 0.0073  | 0.1333  |
| stand               | 0.7866  | 0.9057  | 0.7187  | 0.8491  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes             | 0.2176  | 0.6013  | 0.0685  | 0.3007  |
| jacket              | 0.6417  | 0.9130  | 0.2886  | 0.6522  |
| windowsill          | 0.0188  | 0.8333  | 0.0014  | 0.1667  |
| laptop              | 0.5479  | 0.6667  | 0.0667  | 0.2000  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plate               | 0.0571  | 0.4000  | 0.0000  | 0.0000  |
| pipe                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillow              | 0.4329  | 0.6875  | 0.1876  | 0.4028  |
| dresser             | 0.7882  | 0.9459  | 0.7234  | 0.8919  |
| wardrobe            | 0.5250  | 0.6250  | 0.3750  | 0.3750  |
| tv                  | 0.8636  | 0.9130  | 0.3297  | 0.5217  |
| ottoman             | 0.1752  | 1.0000  | 0.0273  | 0.2857  |
| stairs              | 0.2084  | 1.0000  | 0.0231  | 0.6000  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clock               | 0.3940  | 0.7600  | 0.0684  | 0.2000  |
| paper cutter        | 0.8055  | 1.0000  | 0.0000  | 0.0000  |
| calendar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toy                 | 0.0688  | 0.2941  | 0.0588  | 0.1176  |
| rack                | 0.0032  | 0.0909  | 0.0000  | 0.0000  |
| poster              | 0.0833  | 1.0000  | 0.0000  | 0.0000  |
| container           | 0.0050  | 0.3043  | 0.0020  | 0.1739  |
| printer             | 0.6193  | 0.8788  | 0.5911  | 0.7879  |
| carpet              | 0.0709  | 0.5000  | 0.0000  | 0.0000  |
| cushion             | 0.0149  | 0.5000  | 0.0038  | 0.2500  |
| bench               | 0.5070  | 1.0000  | 0.5070  | 1.0000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stove               | 0.9889  | 1.0000  | 0.9889  | 1.0000  |
| machine             | 0.5408  | 1.0000  | 0.3584  | 0.7500  |
| bicycle             | 0.7576  | 1.0000  | 0.5909  | 1.0000  |
| guitar              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| refrigerator        | 0.9000  | 0.9000  | 0.7104  | 0.8000  |
| kettle              | 0.2611  | 0.5000  | 0.0000  | 0.0000  |
| person              | 0.2494  | 0.6667  | 0.0067  | 0.3333  |
| coffee maker        | 0.5101  | 0.6471  | 0.2289  | 0.3529  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plunger             | 0.8000  | 0.8000  | 0.2189  | 0.8000  |
| broom               | 0.0097  | 0.2500  | 0.0000  | 0.0000  |
| stall               | 0.3477  | 1.0000  | 0.3114  | 0.5294  |
| bucket              | 0.1826  | 0.7143  | 0.0353  | 0.2857  |
| grab bar            | 0.0294  | 0.2500  | 0.0000  | 0.0000  |
| tube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| board               | 0.1196  | 0.7812  | 0.0254  | 0.5625  |
| soap dispenser      | 0.3508  | 0.5714  | 0.0859  | 0.2857  |
| dispenser           | 0.6101  | 0.7179  | 0.3947  | 0.4872  |
| holder              | 0.2000  | 0.2000  | 0.2000  | 0.2000  |
| mailbox             | 0.0597  | 0.8000  | 0.0143  | 0.4000  |
| pillar              | 0.2093  | 0.4000  | 0.2000  | 0.2000  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headphones          | 0.2000  | 0.2000  | 0.2000  | 0.2000  |
| adhesive tape       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher   | 0.0196  | 0.3333  | 0.0000  | 0.0000  |
| copier              | 0.9097  | 0.9167  | 0.7602  | 0.9167  |
| fan                 | 0.8472  | 0.8750  | 0.1000  | 0.2500  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball                | 0.4167  | 0.4167  | 0.2292  | 0.2500  |
| vacuum cleaner      | 0.6667  | 0.6667  | 0.6667  | 0.6667  |
| basin               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| map                 | 0.0455  | 1.0000  | 0.0455  | 1.0000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water cooler        | 0.8333  | 1.0000  | 0.8333  | 1.0000  |
| sign                | 0.0984  | 0.3214  | 0.0001  | 0.0357  |
| barricade           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dish rack           | 0.6625  | 1.0000  | 0.0625  | 0.2500  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| teapot              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| oven                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pot                 | 0.0278  | 0.5000  | 0.0000  | 0.0000  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flowerpot           | 0.0208  | 0.5000  | 0.0208  | 0.5000  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.0625  | 0.5000  | 0.0000  | 0.0000  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.0476  | 0.3333  | 0.0000  | 0.0000  |
| crate               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.7485  | 1.0000  | 0.6667  | 0.6667  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ladder              | 0.1364  | 0.4000  | 0.1000  | 0.2000  |
| clothes dryer       | 0.5000  | 1.0000  | 0.5000  | 1.0000  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.8450  | 1.0000  | 0.1800  | 0.4000  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| scannet             | 0.2778  | 0.4797  | 0.1527  | 0.2737  |
| head                | 0.3801  | 0.6424  | 0.2129  | 0.3726  |
| common              | 0.2097  | 0.3605  | 0.1041  | 0.1817  |
| tail                | 0.0866  | 0.2000  | 0.0595  | 0.1480  |
| small               | 0.0555  | 0.1973  | 0.0139  | 0.0384  |
| medium              | 0.3284  | 0.5651  | 0.1824  | 0.3330  |
| large               | 0.2957  | 0.8129  | 0.1786  | 0.5373  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 12:31:37 INFO metrics.py:437 | number of 3rscan results: 235
Rank[0/8] 06/20/2025 12:32:48 INFO metrics.py:332 | eval results of 3rscan:
+-------------------+---------+---------+---------+---------+
| classes           | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+-------------------+---------+---------+---------+---------+
| chair             | 0.6988  | 0.8407  | 0.5656  | 0.7294  |
| monitor           | 0.5466  | 0.7000  | 0.0730  | 0.2250  |
| keyboard          | 0.0179  | 0.1667  | 0.0000  | 0.0000  |
| mouse             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| desk              | 0.5326  | 0.9125  | 0.4068  | 0.7625  |
| cup               | 0.0805  | 0.3590  | 0.0000  | 0.0000  |
| case              | 0.0409  | 0.4143  | 0.0038  | 0.0929  |
| bin               | 0.7421  | 0.9024  | 0.4608  | 0.6585  |
| computer          | 0.3690  | 0.7619  | 0.0002  | 0.0476  |
| paper             | 0.0642  | 0.3143  | 0.0050  | 0.0714  |
| bag               | 0.3624  | 0.7475  | 0.0846  | 0.3030  |
| plate             | 0.0345  | 0.3333  | 0.0045  | 0.1212  |
| stand             | 0.5324  | 0.9344  | 0.3541  | 0.6557  |
| pen               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| backpack          | 0.4845  | 0.7273  | 0.2591  | 0.5455  |
| wardrobe          | 0.4340  | 0.7969  | 0.3142  | 0.6406  |
| bottle            | 0.0279  | 0.2975  | 0.0031  | 0.0826  |
| plug              | 0.1111  | 0.2222  | 0.0000  | 0.0000  |
| curtain           | 0.5925  | 0.8810  | 0.1582  | 0.5079  |
| garbage           | 0.5406  | 0.7500  | 0.3229  | 0.5000  |
| window            | 0.2437  | 0.7055  | 0.0380  | 0.2329  |
| table             | 0.5963  | 0.9054  | 0.4581  | 0.7703  |
| socket            | 0.0017  | 0.0625  | 0.0000  | 0.0000  |
| cabinet           | 0.4575  | 0.8376  | 0.3011  | 0.6581  |
| book              | 0.1047  | 0.4852  | 0.0063  | 0.0828  |
| telephone         | 0.3117  | 0.5833  | 0.0693  | 0.2083  |
| conducting wire   | 0.0667  | 0.3333  | 0.0000  | 0.0000  |
| blinds            | 0.2847  | 0.6154  | 0.0115  | 0.2821  |
| box               | 0.2169  | 0.5468  | 0.0499  | 0.2200  |
| notebook          | 0.0012  | 0.0909  | 0.0000  | 0.0000  |
| sink              | 0.6984  | 0.8393  | 0.2327  | 0.4107  |
| plant             | 0.3453  | 0.6535  | 0.0932  | 0.2970  |
| cushion           | 0.3997  | 0.8667  | 0.0776  | 0.2333  |
| clock             | 0.4939  | 0.6842  | 0.0265  | 0.1579  |
| file              | 0.0834  | 0.2857  | 0.0361  | 0.0714  |
| picture           | 0.2112  | 0.4512  | 0.0177  | 0.0935  |
| shelf             | 0.2986  | 0.7385  | 0.1080  | 0.4134  |
| wire              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| laptop            | 0.5032  | 1.0000  | 0.3833  | 0.6667  |
| candlestick       | 0.1429  | 1.0000  | 0.0000  | 0.0000  |
| light             | 0.1357  | 0.5114  | 0.0028  | 0.0795  |
| switch            | 0.0013  | 0.0566  | 0.0000  | 0.0000  |
| frame             | 0.1566  | 0.7667  | 0.0142  | 0.1333  |
| printer           | 0.7553  | 0.8889  | 0.1536  | 0.3333  |
| pillow            | 0.4317  | 0.7422  | 0.0358  | 0.2109  |
| tissue box        | 0.0833  | 0.3333  | 0.0833  | 0.3333  |
| door              | 0.3971  | 0.7353  | 0.0801  | 0.3088  |
| towel             | 0.2702  | 0.6076  | 0.0806  | 0.1899  |
| pad               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket            | 0.6137  | 0.9333  | 0.2049  | 0.5333  |
| bed               | 0.9152  | 0.9818  | 0.8068  | 0.8909  |
| couch             | 0.5202  | 0.8571  | 0.4183  | 0.8000  |
| tv                | 0.7798  | 0.9714  | 0.2120  | 0.4571  |
| lamp              | 0.3141  | 0.5750  | 0.0498  | 0.1625  |
| commode           | 0.5629  | 0.8654  | 0.4121  | 0.6923  |
| stool             | 0.4662  | 0.9074  | 0.3159  | 0.6667  |
| blanket           | 0.4119  | 0.9077  | 0.0780  | 0.4923  |
| candle            | 0.0793  | 0.3333  | 0.0000  | 0.0000  |
| napkins           | 0.3704  | 0.6667  | 0.3333  | 0.3333  |
| decoration        | 0.1364  | 0.5312  | 0.0253  | 0.1250  |
| bowl              | 0.2738  | 0.6296  | 0.1334  | 0.2222  |
| price tag         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vase              | 0.3375  | 0.5000  | 0.1833  | 0.2000  |
| folder            | 0.0991  | 0.4286  | 0.0714  | 0.0714  |
| carpet            | 0.3037  | 0.6607  | 0.0195  | 0.2500  |
| tablet            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rack              | 0.3131  | 0.7000  | 0.1398  | 0.5667  |
| clothes           | 0.2592  | 0.6796  | 0.0553  | 0.2816  |
| remote control    | 0.0453  | 0.2500  | 0.0375  | 0.1500  |
| bench             | 0.4589  | 0.8571  | 0.4009  | 0.6786  |
| jar               | 0.1794  | 0.5000  | 0.1350  | 0.2500  |
| spoon             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flowerpot         | 0.0683  | 0.5000  | 0.0002  | 0.0417  |
| bar               | 0.5556  | 0.6667  | 0.0098  | 0.3333  |
| doorframe         | 0.1530  | 0.6800  | 0.0224  | 0.1800  |
| teapot            | 0.0357  | 0.2500  | 0.0000  | 0.0000  |
| ottoman           | 0.5705  | 0.9091  | 0.5283  | 0.8182  |
| player            | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| cube              | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| toy               | 0.4747  | 0.6154  | 0.1795  | 0.3077  |
| counter           | 0.1910  | 0.8049  | 0.0212  | 0.2927  |
| magazine          | 0.0172  | 0.7500  | 0.0016  | 0.2500  |
| calendar          | 0.0160  | 0.4000  | 0.0000  | 0.0000  |
| roll              | 0.2136  | 0.6000  | 0.0000  | 0.0000  |
| container         | 0.1728  | 0.5556  | 0.1192  | 0.3333  |
| mirror            | 0.3754  | 0.7037  | 0.0342  | 0.2222  |
| poster            | 0.0241  | 0.1111  | 0.0000  | 0.0000  |
| hook              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blackboard        | 0.7014  | 0.8333  | 0.3142  | 0.5000  |
| humidifier        | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| heater            | 0.5781  | 0.8070  | 0.1947  | 0.4211  |
| windowsill        | 0.1873  | 0.6923  | 0.0097  | 0.1538  |
| fan               | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| device            | 0.2383  | 0.5455  | 0.0000  | 0.0000  |
| ball              | 0.5200  | 0.6000  | 0.5200  | 0.6000  |
| board             | 0.3662  | 0.5833  | 0.0881  | 0.2083  |
| drawer            | 0.6483  | 1.0000  | 0.3833  | 0.7778  |
| brush             | 0.2083  | 0.3750  | 0.0000  | 0.0000  |
| kettle            | 0.2404  | 0.8333  | 0.0669  | 0.4167  |
| radiator          | 0.6366  | 0.7586  | 0.0408  | 0.1724  |
| tube              | 0.0151  | 0.2857  | 0.0000  | 0.0000  |
| speaker           | 0.0944  | 0.1667  | 0.0067  | 0.1667  |
| umbrella          | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| shoe              | 0.2933  | 0.5000  | 0.0200  | 0.1667  |
| pot               | 0.2731  | 0.5833  | 0.0521  | 0.1667  |
| hanger            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stairs            | 0.3139  | 0.7143  | 0.0266  | 0.4286  |
| air conditioner   | 0.7500  | 0.7500  | 0.5000  | 0.5000  |
| adhesive tape     | 0.0114  | 0.2500  | 0.0000  | 0.0000  |
| coffee maker      | 0.2529  | 0.7778  | 0.0000  | 0.0000  |
| cosmetics         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basket            | 0.5032  | 0.7656  | 0.2480  | 0.4375  |
| rail              | 0.1875  | 0.5000  | 0.0000  | 0.0000  |
| dresser           | 0.6667  | 1.0000  | 0.0000  | 0.0000  |
| guitar            | 1.0000  | 1.0000  | 0.0385  | 0.5000  |
| album             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| window frame      | 0.1021  | 0.4167  | 0.0010  | 0.0833  |
| microwave         | 0.9287  | 1.0000  | 0.5348  | 0.7059  |
| luggage           | 0.6556  | 1.0000  | 0.6556  | 1.0000  |
| balcony           | 0.1190  | 1.0000  | 0.0357  | 0.5000  |
| fruit             | 0.6200  | 0.8000  | 0.2100  | 0.4000  |
| refrigerator      | 0.6902  | 0.8667  | 0.6746  | 0.7333  |
| bicycle           | 0.8333  | 1.0000  | 0.8333  | 1.0000  |
| basin             | 0.0518  | 0.2667  | 0.0022  | 0.0667  |
| sunglasses        | 0.0646  | 0.3077  | 0.0025  | 0.1538  |
| hat               | 0.2143  | 0.2857  | 0.0009  | 0.1429  |
| letter            | 0.4432  | 0.7500  | 0.0625  | 0.2500  |
| paper cutter      | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| furniture         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bathtub           | 1.0000  | 1.0000  | 0.8765  | 0.8889  |
| shampoo           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scale             | 0.6771  | 1.0000  | 0.0021  | 0.3333  |
| statue            | 1.0000  | 1.0000  | 0.5000  | 0.5000  |
| clothes dryer     | 0.4774  | 0.9000  | 0.3068  | 0.7000  |
| toilet            | 0.8105  | 0.8235  | 0.6787  | 0.7059  |
| faucet            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shower            | 0.4238  | 1.0000  | 0.0674  | 0.5000  |
| toilet paper      | 0.1893  | 0.5833  | 0.0000  | 0.0000  |
| tissue            | 0.1778  | 0.6667  | 0.1111  | 0.3333  |
| beam              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toiletry          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| soap              | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| pipe              | 0.2756  | 1.0000  | 0.0000  | 0.0000  |
| soap dish         | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| washing machine   | 0.5661  | 0.6429  | 0.3644  | 0.4286  |
| dispenser         | 0.1087  | 1.0000  | 0.0278  | 0.3333  |
| body loofah       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beanbag           | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| can               | 0.0458  | 0.2500  | 0.0104  | 0.1250  |
| column            | 0.0560  | 1.0000  | 0.0385  | 0.5000  |
| pillar            | 0.6165  | 0.7647  | 0.4860  | 0.5882  |
| knife             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pan               | 0.3118  | 0.6667  | 0.0889  | 0.1667  |
| oven              | 0.6010  | 0.9474  | 0.3009  | 0.6316  |
| stove             | 0.4994  | 0.9412  | 0.0541  | 0.4706  |
| shovel            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchenware       | 0.3428  | 0.6136  | 0.0270  | 0.1364  |
| range hood        | 0.5927  | 0.8000  | 0.0000  | 0.0000  |
| pack              | 0.0380  | 0.6923  | 0.0124  | 0.3077  |
| food              | 0.1074  | 0.2000  | 0.0037  | 0.1000  |
| jalousie          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| partition         | 0.5329  | 0.8000  | 0.2000  | 0.2000  |
| radio             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| sign              | 0.5357  | 1.0000  | 0.0000  | 0.0000  |
| crib              | 0.3556  | 0.6667  | 0.3556  | 0.6667  |
| sponge            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fireplace         | 0.1000  | 0.2000  | 0.0500  | 0.2000  |
| ladder            | 0.4152  | 1.0000  | 0.3939  | 0.6667  |
| bread             | 0.1000  | 0.2000  | 0.0000  | 0.0000  |
| piano             | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| sack              | 0.5778  | 1.0000  | 0.2240  | 0.5714  |
| mattress          | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| holder            | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| broom             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tray              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| chandelier        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| map               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| screen            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glass             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dispenser    | 0.9000  | 1.0000  | 0.2600  | 0.6000  |
| mat               | 0.1111  | 0.3333  | 0.0278  | 0.3333  |
| mop               | 0.9167  | 1.0000  | 0.2500  | 0.6667  |
| flush             | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| coil              | 0.2778  | 0.3333  | 0.0000  | 0.0000  |
| tool              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crate             | 0.6571  | 0.8000  | 0.3762  | 0.6000  |
| machine           | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| dumbbell          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| helmets           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vacuum cleaner    | 0.7500  | 0.7500  | 0.0000  | 0.0000  |
| bidet             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| treadmill         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glove             | 0.5556  | 0.6667  | 0.5556  | 0.6667  |
| washbasin         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher        | 0.9167  | 1.0000  | 0.3333  | 0.3333  |
| menu              | 0.6024  | 0.7143  | 0.1429  | 0.1429  |
| salt              | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| water heater      | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| dish rack         | 0.2000  | 1.0000  | 0.0000  | 0.0000  |
| footstool         | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| toaster           | 1.0000  | 1.0000  | 0.2000  | 0.5000  |
| fire extinguisher | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| toothbrush        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| seasoning         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scissors          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------           | ------- | ------- | ------- | ------- |
| 3rscan            | 0.3657  | 0.5869  | 0.1602  | 0.2788  |
| head              | 0.3675  | 0.6723  | 0.1585  | 0.3354  |
| common            | 0.4154  | 0.6042  | 0.1733  | 0.2625  |
| tail              | 0.2953  | 0.4067  | 0.1456  | 0.1966  |
| small             | 0.1281  | 0.3180  | 0.0233  | 0.0828  |
| medium            | 0.3887  | 0.6525  | 0.1808  | 0.3096  |
| large             | 0.2716  | 0.7701  | 0.1230  | 0.4442  |
+-------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 12:32:48 INFO metrics.py:437 | number of matterport3d results: 339
Rank[0/8] 06/20/2025 12:34:17 INFO metrics.py:332 | eval results of matterport3d:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| picture             | 0.2340  | 0.4324  | 0.0210  | 0.1184  |
| mirror              | 0.1655  | 0.6622  | 0.0189  | 0.2432  |
| doorframe           | 0.3463  | 0.7810  | 0.1164  | 0.4459  |
| door                | 0.2677  | 0.6162  | 0.0175  | 0.1854  |
| light               | 0.0361  | 0.2479  | 0.0039  | 0.0313  |
| sink                | 0.5410  | 0.8384  | 0.2507  | 0.5556  |
| towel               | 0.4046  | 0.6000  | 0.1241  | 0.2636  |
| plant               | 0.3388  | 0.8036  | 0.1645  | 0.4554  |
| drawer              | 0.0011  | 0.2500  | 0.0011  | 0.2500  |
| blanket             | 0.0679  | 0.6364  | 0.0507  | 0.2727  |
| decoration          | 0.0127  | 0.4167  | 0.0010  | 0.0952  |
| bathtub             | 0.7013  | 0.7778  | 0.5755  | 0.7037  |
| toilet              | 0.8109  | 0.8438  | 0.7103  | 0.7500  |
| roof                | 0.0029  | 0.2500  | 0.0000  | 0.0417  |
| lamp                | 0.3761  | 0.7253  | 0.2047  | 0.4725  |
| vase                | 0.0238  | 0.4000  | 0.0029  | 0.1667  |
| case                | 0.0019  | 0.2778  | 0.0001  | 0.0556  |
| cabinet             | 0.1338  | 0.6356  | 0.0811  | 0.4492  |
| bottle              | 0.0959  | 0.5625  | 0.0510  | 0.2083  |
| switch              | 0.0003  | 0.1500  | 0.0000  | 0.0000  |
| stand               | 0.4421  | 0.8400  | 0.3982  | 0.7600  |
| window              | 0.1789  | 0.5194  | 0.0258  | 0.2056  |
| handle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vent                | 0.0142  | 0.2083  | 0.0000  | 0.0000  |
| counter             | 0.0131  | 0.4545  | 0.0009  | 0.1212  |
| flowerpot           | 0.0427  | 0.4000  | 0.0037  | 0.2000  |
| countertop          | 0.0261  | 0.3929  | 0.0011  | 0.0714  |
| dresser             | 0.2592  | 0.8000  | 0.2031  | 0.7333  |
| plate               | 0.0659  | 0.4286  | 0.0122  | 0.1429  |
| shelf               | 0.1752  | 0.5985  | 0.0659  | 0.3723  |
| frame               | 0.0011  | 0.1765  | 0.0006  | 0.1176  |
| table               | 0.3515  | 0.7756  | 0.3058  | 0.6346  |
| bed                 | 0.6678  | 0.8246  | 0.3499  | 0.7368  |
| soap                | 0.0246  | 0.6000  | 0.0000  | 0.0000  |
| toiletry            | 0.0464  | 0.3636  | 0.0116  | 0.1364  |
| pot                 | 0.0043  | 0.4615  | 0.0011  | 0.2308  |
| bench               | 0.1906  | 0.4500  | 0.1598  | 0.3500  |
| window frame        | 0.0304  | 0.4253  | 0.0037  | 0.2069  |
| statue              | 0.0592  | 0.4706  | 0.0465  | 0.2941  |
| shower              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mat                 | 0.0091  | 0.2500  | 0.0000  | 0.0000  |
| washbasin           | 0.0080  | 0.2857  | 0.0046  | 0.1429  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| box                 | 0.0444  | 0.6538  | 0.0154  | 0.3654  |
| book                | 0.0769  | 0.3465  | 0.0197  | 0.0945  |
| carpet              | 0.0008  | 0.1111  | 0.0000  | 0.0000  |
| chair               | 0.4628  | 0.7823  | 0.3597  | 0.6599  |
| curtain             | 0.4276  | 0.7604  | 0.0757  | 0.4062  |
| wardrobe            | 0.1013  | 0.4545  | 0.0995  | 0.3636  |
| rack                | 0.0010  | 0.3636  | 0.0004  | 0.1818  |
| vanity              | 0.2902  | 0.8000  | 0.2138  | 0.6000  |
| paper               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| couch               | 0.3509  | 0.8261  | 0.2232  | 0.6630  |
| tv                  | 0.5203  | 0.7442  | 0.1930  | 0.3256  |
| screen              | 0.0432  | 0.5000  | 0.0037  | 0.2500  |
| ottoman             | 0.0245  | 0.3889  | 0.0241  | 0.3889  |
| pillow              | 0.0943  | 0.5741  | 0.0075  | 0.1356  |
| alarm               | 0.0043  | 0.1667  | 0.0000  | 0.0000  |
| bowl                | 0.0985  | 0.3939  | 0.0572  | 0.2121  |
| stool               | 0.0326  | 0.3333  | 0.0092  | 0.2857  |
| clock               | 0.0735  | 0.6667  | 0.0034  | 0.3333  |
| telephone           | 0.2513  | 0.5000  | 0.0000  | 0.0000  |
| stairs              | 0.0305  | 0.3208  | 0.0193  | 0.0377  |
| footstool           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toy                 | 0.1156  | 0.3333  | 0.0478  | 0.0833  |
| desk                | 0.3483  | 0.7368  | 0.2869  | 0.6842  |
| cushion             | 0.0095  | 0.1176  | 0.0011  | 0.1176  |
| column              | 0.0189  | 0.3333  | 0.0028  | 0.1111  |
| steps               | 0.0009  | 0.3000  | 0.0000  | 0.0000  |
| cup                 | 0.0025  | 0.3000  | 0.0000  | 0.0000  |
| jar                 | 0.0033  | 0.3333  | 0.0000  | 0.0000  |
| wine                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillar              | 0.2609  | 0.8095  | 0.1265  | 0.3333  |
| keyboard            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| board               | 0.1826  | 0.6667  | 0.0000  | 0.0000  |
| candle              | 0.0095  | 0.3333  | 0.0095  | 0.3333  |
| tray                | 0.0037  | 0.3333  | 0.0000  | 0.0000  |
| food                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stove               | 0.3314  | 0.6000  | 0.1494  | 0.4000  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit               | 0.0540  | 0.2500  | 0.0540  | 0.2500  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fence               | 0.0069  | 0.0833  | 0.0069  | 0.0833  |
| doorway             | 0.0002  | 0.1111  | 0.0000  | 0.0000  |
| faucet              | 0.0371  | 0.2500  | 0.0000  | 0.0000  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| socket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bin                 | 0.3953  | 0.7317  | 0.2290  | 0.4634  |
| washing machine     | 0.3700  | 0.7500  | 0.0272  | 0.5000  |
| dispenser           | 0.0094  | 0.5000  | 0.0094  | 0.5000  |
| windowsill          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| holder              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toilet paper        | 0.3103  | 0.6389  | 0.0730  | 0.3056  |
| fan                 | 0.6558  | 0.7963  | 0.2206  | 0.3889  |
| rail                | 0.0371  | 0.4444  | 0.0008  | 0.0741  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| teapot              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headboard           | 0.0021  | 0.1250  | 0.0000  | 0.0000  |
| bar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball                | 0.5056  | 0.7500  | 0.5000  | 0.5000  |
| thermostat          | 0.1667  | 0.1667  | 0.0000  | 0.0000  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dispenser      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket              | 0.0115  | 0.4000  | 0.0027  | 0.2000  |
| heater              | 0.2500  | 1.0000  | 0.2500  | 1.0000  |
| hanger              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| brush               | 0.0076  | 0.2500  | 0.0000  | 0.0000  |
| kitchen island      | 0.4360  | 0.5714  | 0.4360  | 0.5714  |
| piano               | 0.2500  | 0.5000  | 0.2500  | 0.5000  |
| air conditioner     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| monitor             | 0.3333  | 0.5000  | 0.0000  | 0.0000  |
| refrigerator        | 0.4558  | 0.8667  | 0.0816  | 0.3333  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| microwave           | 0.1651  | 0.4167  | 0.0841  | 0.1667  |
| container           | 0.0085  | 0.1481  | 0.0021  | 0.0741  |
| basin               | 0.0009  | 1.0000  | 0.0009  | 1.0000  |
| kettle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fireplace           | 0.3469  | 0.5217  | 0.0097  | 0.2609  |
| printer             | 0.4286  | 0.4286  | 0.2381  | 0.2857  |
| radiator            | 0.0152  | 0.3333  | 0.0000  | 0.0000  |
| scale               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pipe                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pan                 | 0.0138  | 0.4000  | 0.0034  | 0.2000  |
| tissue box          | 0.1384  | 0.6250  | 0.0160  | 0.3750  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool table          | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| basket              | 0.0480  | 0.4800  | 0.0029  | 0.1600  |
| guitar              | 0.1429  | 0.2857  | 0.0000  | 0.0000  |
| tissue              | 0.0058  | 1.0000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0478  | 0.3750  | 0.0455  | 0.2500  |
| bag                 | 0.0372  | 0.3846  | 0.0002  | 0.0769  |
| clothes dryer       | 0.3000  | 0.6667  | 0.3000  | 0.6667  |
| excercise equipment | 0.3609  | 0.6667  | 0.0381  | 0.3333  |
| clothes             | 0.1488  | 0.3448  | 0.0226  | 0.1207  |
| kitchenware         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| chandelier          | 0.1023  | 0.2632  | 0.0234  | 0.1053  |
| fire extinguisher   | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| garage door         | 0.3667  | 0.5000  | 0.0049  | 0.1667  |
| ladder              | 0.1864  | 0.3333  | 0.1111  | 0.1111  |
| beam                | 0.0001  | 0.0952  | 0.0000  | 0.0476  |
| arch                | 0.0094  | 0.1667  | 0.0004  | 0.1000  |
| soap dish           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| computer            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| oven                | 0.2520  | 0.8000  | 0.0000  | 0.0000  |
| mouse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shoe                | 0.0946  | 0.2400  | 0.0004  | 0.0400  |
| backpack            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| laptop              | 0.7500  | 1.0000  | 0.0000  | 0.0000  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 1.0000  | 1.0000  | 0.0179  | 1.0000  |
| partition           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell            | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| hamper              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet               | 0.2500  | 0.5000  | 0.2500  | 0.5000  |
| magazine            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.4643  | 0.7500  | 0.0000  | 0.0000  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pack                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blackboard          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drum                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| player              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jacket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coffee maker        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.0357  | 0.2500  | 0.0357  | 0.2500  |
| candlestick         | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| matterport3d        | 0.1290  | 0.3343  | 0.0604  | 0.1760  |
| head                | 0.1747  | 0.4630  | 0.0776  | 0.2419  |
| common              | 0.0889  | 0.2570  | 0.0462  | 0.1345  |
| tail                | 0.0789  | 0.0989  | 0.0394  | 0.0604  |
| small               | 0.0407  | 0.1622  | 0.0177  | 0.0587  |
| medium              | 0.1251  | 0.3697  | 0.0592  | 0.1857  |
| large               | 0.1127  | 0.4117  | 0.0543  | 0.2555  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 12:34:17 INFO metrics.py:437 | number of arkit results: 0
Rank[0/8] 06/20/2025 12:34:18 INFO stats.py:394 | Epoch[169] completed. Training Speed: 2.98 samples/sec across all devices.	Epoch Time: 1048.25 sec.	Average Epoch Time: 1048.25 sec.	Average Step Time: 2.69 sec.	Estimated Remaining Time: 9:12:36.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:34:18 WARNING accelerator.py:3099 | Deleting 1 checkpoints to make room for new checkpoint.
Rank[0/8] 06/20/2025 12:34:18 INFO accelerator.py:3111 | Saving current state to /job_data/checkpoints/checkpoint_16
Rank[7/8] 06/20/2025 12:34:19 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 169 to /job_data/checkpoints/checkpoint_16
Rank[1/8] 06/20/2025 12:34:19 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 169 to /job_data/checkpoints/checkpoint_16
Rank[2/8] 06/20/2025 12:34:19 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 169 to /job_data/checkpoints/checkpoint_16
Rank[3/8] 06/20/2025 12:34:19 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 169 to /job_data/checkpoints/checkpoint_16
Rank[6/8] 06/20/2025 12:34:19 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 169 to /job_data/checkpoints/checkpoint_16
Rank[5/8] 06/20/2025 12:34:19 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 169 to /job_data/checkpoints/checkpoint_16
Rank[4/8] 06/20/2025 12:34:19 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 169 to /job_data/checkpoints/checkpoint_16
Rank[0/8] 06/20/2025 12:34:20 INFO checkpointing.py:106 | Model weights saved in /job_data/checkpoints/checkpoint_16/model.safetensors
Rank[0/8] 06/20/2025 12:34:23 INFO checkpointing.py:113 | Optimizer state saved in /job_data/checkpoints/checkpoint_16/optimizer.bin
Rank[0/8] 06/20/2025 12:34:23 INFO checkpointing.py:120 | Scheduler state saved in /job_data/checkpoints/checkpoint_16/scheduler.bin
Rank[0/8] 06/20/2025 12:34:23 INFO checkpointing.py:137 | Sampler state for dataloader 0 saved in /job_data/checkpoints/checkpoint_16/sampler.bin
Rank[0/8] 06/20/2025 12:34:23 INFO checkpointing.py:137 | Sampler state for dataloader 1 saved in /job_data/checkpoints/checkpoint_16/sampler_1.bin
Rank[0/8] 06/20/2025 12:34:23 INFO checkpointing.py:164 | Random states saved in /job_data/checkpoints/checkpoint_16/random_states_0.pkl
Rank[0/8] 06/20/2025 12:34:23 INFO checkpointing.py:300 | Saving the state of TrainerProgressState to /job_data/checkpoints/checkpoint_16/custom_checkpoint_0.pkl
Rank[0/8] 06/20/2025 12:34:24 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 169 to /job_data/checkpoints/checkpoint_16
Rank[0/8] 06/20/2025 12:34:51 INFO stats.py:314 | Epoch[170] Step[24] GlobalStep[66324] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 9:12:07.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:34:51 INFO loss_tracker.py:84 | Epoch[170/239] Step[24] GlobalStep[66324/NA]: det_loss_cls_0[0.4648] det_loss_box_0[0.6612] det_loss_cls_1[0.4519] det_loss_box_1[0.4931] det_loss_cls_2[0.4068] det_loss_box_2[0.4507] det_loss_cls_3[0.3697] det_loss_box_3[0.4412] det_loss_cls_4[0.3513] det_loss_box_4[0.4301] det_loss_cls_5[0.3370] det_loss_box_5[0.4287] det_loss_cls_dn_0[0.0861] det_loss_box_dn_0[0.2831] det_loss_cls_dn_1[0.0826] det_loss_box_dn_1[0.2379] det_loss_cls_dn_2[0.0817] det_loss_box_dn_2[0.2222] det_loss_cls_dn_3[0.0809] det_loss_box_dn_3[0.2178] det_loss_cls_dn_4[0.0804] det_loss_box_dn_4[0.2140] det_loss_cls_dn_5[0.0792] det_loss_box_dn_5[0.2112] loss_depth[0.0366] total_loss[7.2000]
Rank[0/8] 06/20/2025 12:35:17 INFO stats.py:314 | Epoch[170] Step[49] GlobalStep[66349] Training Speed: 8.05 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 9:11:35.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:35:17 INFO loss_tracker.py:84 | Epoch[170/239] Step[49] GlobalStep[66349/NA]: det_loss_cls_0[0.4982] det_loss_box_0[0.6801] det_loss_cls_1[0.4659] det_loss_box_1[0.4962] det_loss_cls_2[0.4176] det_loss_box_2[0.4379] det_loss_cls_3[0.3809] det_loss_box_3[0.4240] det_loss_cls_4[0.3566] det_loss_box_4[0.4156] det_loss_cls_5[0.3441] det_loss_box_5[0.4143] det_loss_cls_dn_0[0.0902] det_loss_box_dn_0[0.2646] det_loss_cls_dn_1[0.0869] det_loss_box_dn_1[0.2220] det_loss_cls_dn_2[0.0861] det_loss_box_dn_2[0.2043] det_loss_cls_dn_3[0.0850] det_loss_box_dn_3[0.2002] det_loss_cls_dn_4[0.0843] det_loss_box_dn_4[0.1970] det_loss_cls_dn_5[0.0834] det_loss_box_dn_5[0.1958] loss_depth[0.0372] total_loss[7.1685]
Rank[0/8] 06/20/2025 12:35:43 INFO stats.py:314 | Epoch[170] Step[74] GlobalStep[66374] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 9:11:02.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:35:43 INFO loss_tracker.py:84 | Epoch[170/239] Step[74] GlobalStep[66374/NA]: det_loss_cls_0[0.3579] det_loss_box_0[0.4789] det_loss_cls_1[0.3390] det_loss_box_1[0.3735] det_loss_cls_2[0.2973] det_loss_box_2[0.3266] det_loss_cls_3[0.2695] det_loss_box_3[0.3155] det_loss_cls_4[0.2493] det_loss_box_4[0.3119] det_loss_cls_5[0.2358] det_loss_box_5[0.3136] det_loss_cls_dn_0[0.0756] det_loss_box_dn_0[0.2669] det_loss_cls_dn_1[0.0706] det_loss_box_dn_1[0.2163] det_loss_cls_dn_2[0.0695] det_loss_box_dn_2[0.1977] det_loss_cls_dn_3[0.0685] det_loss_box_dn_3[0.1933] det_loss_cls_dn_4[0.0679] det_loss_box_dn_4[0.1906] det_loss_cls_dn_5[0.0671] det_loss_box_dn_5[0.1873] loss_depth[0.0343] total_loss[5.5744]
Rank[0/8] 06/20/2025 12:36:09 INFO stats.py:314 | Epoch[170] Step[99] GlobalStep[66399] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 9:10:30.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:36:09 INFO loss_tracker.py:84 | Epoch[170/239] Step[99] GlobalStep[66399/NA]: det_loss_cls_0[0.4242] det_loss_box_0[0.5268] det_loss_cls_1[0.4145] det_loss_box_1[0.3959] det_loss_cls_2[0.3452] det_loss_box_2[0.3693] det_loss_cls_3[0.3013] det_loss_box_3[0.3664] det_loss_cls_4[0.2822] det_loss_box_4[0.3498] det_loss_cls_5[0.2658] det_loss_box_5[0.3495] det_loss_cls_dn_0[0.0759] det_loss_box_dn_0[0.2494] det_loss_cls_dn_1[0.0712] det_loss_box_dn_1[0.2034] det_loss_cls_dn_2[0.0705] det_loss_box_dn_2[0.1865] det_loss_cls_dn_3[0.0691] det_loss_box_dn_3[0.1802] det_loss_cls_dn_4[0.0685] det_loss_box_dn_4[0.1769] det_loss_cls_dn_5[0.0672] det_loss_box_dn_5[0.1736] loss_depth[0.0351] total_loss[6.0185]
Rank[0/8] 06/20/2025 12:36:35 INFO stats.py:314 | Epoch[170] Step[124] GlobalStep[66424] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 9:09:58.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:36:35 INFO loss_tracker.py:84 | Epoch[170/239] Step[124] GlobalStep[66424/NA]: det_loss_cls_0[0.5133] det_loss_box_0[0.6233] det_loss_cls_1[0.5047] det_loss_box_1[0.4821] det_loss_cls_2[0.4444] det_loss_box_2[0.4532] det_loss_cls_3[0.4006] det_loss_box_3[0.4375] det_loss_cls_4[0.3813] det_loss_box_4[0.4188] det_loss_cls_5[0.3656] det_loss_box_5[0.4157] det_loss_cls_dn_0[0.0930] det_loss_box_dn_0[0.3154] det_loss_cls_dn_1[0.0898] det_loss_box_dn_1[0.2632] det_loss_cls_dn_2[0.0884] det_loss_box_dn_2[0.2412] det_loss_cls_dn_3[0.0873] det_loss_box_dn_3[0.2366] det_loss_cls_dn_4[0.0868] det_loss_box_dn_4[0.2324] det_loss_cls_dn_5[0.0855] det_loss_box_dn_5[0.2298] loss_depth[0.0348] total_loss[7.5246]
Rank[0/8] 06/20/2025 12:37:00 INFO stats.py:314 | Epoch[170] Step[149] GlobalStep[66449] Training Speed: 8.56 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 9:09:26.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:37:00 INFO loss_tracker.py:84 | Epoch[170/239] Step[149] GlobalStep[66449/NA]: det_loss_cls_0[0.4153] det_loss_box_0[0.4918] det_loss_cls_1[0.3982] det_loss_box_1[0.3696] det_loss_cls_2[0.3534] det_loss_box_2[0.3308] det_loss_cls_3[0.3094] det_loss_box_3[0.3251] det_loss_cls_4[0.2925] det_loss_box_4[0.3117] det_loss_cls_5[0.2786] det_loss_box_5[0.3097] det_loss_cls_dn_0[0.0950] det_loss_box_dn_0[0.2712] det_loss_cls_dn_1[0.0920] det_loss_box_dn_1[0.2221] det_loss_cls_dn_2[0.0914] det_loss_box_dn_2[0.2080] det_loss_cls_dn_3[0.0903] det_loss_box_dn_3[0.2006] det_loss_cls_dn_4[0.0899] det_loss_box_dn_4[0.1957] det_loss_cls_dn_5[0.0884] det_loss_box_dn_5[0.1936] loss_depth[0.0364] total_loss[6.0606]
Rank[0/8] 06/20/2025 12:37:26 INFO stats.py:314 | Epoch[170] Step[174] GlobalStep[66474] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 9:08:54.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:37:26 INFO loss_tracker.py:84 | Epoch[170/239] Step[174] GlobalStep[66474/NA]: det_loss_cls_0[0.5165] det_loss_box_0[0.6597] det_loss_cls_1[0.4964] det_loss_box_1[0.5070] det_loss_cls_2[0.4528] det_loss_box_2[0.4545] det_loss_cls_3[0.4157] det_loss_box_3[0.4411] det_loss_cls_4[0.3878] det_loss_box_4[0.4277] det_loss_cls_5[0.3744] det_loss_box_5[0.4232] det_loss_cls_dn_0[0.0985] det_loss_box_dn_0[0.3100] det_loss_cls_dn_1[0.0946] det_loss_box_dn_1[0.2596] det_loss_cls_dn_2[0.0931] det_loss_box_dn_2[0.2391] det_loss_cls_dn_3[0.0922] det_loss_box_dn_3[0.2356] det_loss_cls_dn_4[0.0912] det_loss_box_dn_4[0.2341] det_loss_cls_dn_5[0.0903] det_loss_box_dn_5[0.2303] loss_depth[0.0366] total_loss[7.6617]
Rank[0/8] 06/20/2025 12:37:52 INFO stats.py:314 | Epoch[170] Step[199] GlobalStep[66499] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 9:08:22.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:37:53 INFO loss_tracker.py:84 | Epoch[170/239] Step[199] GlobalStep[66499/NA]: det_loss_cls_0[0.4454] det_loss_box_0[0.5726] det_loss_cls_1[0.4466] det_loss_box_1[0.4307] det_loss_cls_2[0.3885] det_loss_box_2[0.3968] det_loss_cls_3[0.3455] det_loss_box_3[0.3851] det_loss_cls_4[0.3185] det_loss_box_4[0.3705] det_loss_cls_5[0.3101] det_loss_box_5[0.3674] det_loss_cls_dn_0[0.0800] det_loss_box_dn_0[0.2248] det_loss_cls_dn_1[0.0755] det_loss_box_dn_1[0.1824] det_loss_cls_dn_2[0.0746] det_loss_box_dn_2[0.1680] det_loss_cls_dn_3[0.0732] det_loss_box_dn_3[0.1632] det_loss_cls_dn_4[0.0724] det_loss_box_dn_4[0.1615] det_loss_cls_dn_5[0.0713] det_loss_box_dn_5[0.1592] loss_depth[0.0353] total_loss[6.3193]
Rank[0/8] 06/20/2025 12:38:18 INFO stats.py:314 | Epoch[170] Step[224] GlobalStep[66524] Training Speed: 8.04 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 9:07:49.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:38:18 INFO loss_tracker.py:84 | Epoch[170/239] Step[224] GlobalStep[66524/NA]: det_loss_cls_0[0.4441] det_loss_box_0[0.5021] det_loss_cls_1[0.4405] det_loss_box_1[0.3722] det_loss_cls_2[0.3868] det_loss_box_2[0.3453] det_loss_cls_3[0.3428] det_loss_box_3[0.3377] det_loss_cls_4[0.3154] det_loss_box_4[0.3296] det_loss_cls_5[0.2974] det_loss_box_5[0.3341] det_loss_cls_dn_0[0.0911] det_loss_box_dn_0[0.2554] det_loss_cls_dn_1[0.0882] det_loss_box_dn_1[0.2032] det_loss_cls_dn_2[0.0873] det_loss_box_dn_2[0.1833] det_loss_cls_dn_3[0.0865] det_loss_box_dn_3[0.1799] det_loss_cls_dn_4[0.0853] det_loss_box_dn_4[0.1775] det_loss_cls_dn_5[0.0847] det_loss_box_dn_5[0.1763] loss_depth[0.0325] total_loss[6.1792]
Rank[0/8] 06/20/2025 12:38:44 INFO stats.py:314 | Epoch[170] Step[249] GlobalStep[66549] Training Speed: 7.55 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 9:07:17.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:38:44 INFO loss_tracker.py:84 | Epoch[170/239] Step[249] GlobalStep[66549/NA]: det_loss_cls_0[0.5033] det_loss_box_0[0.6663] det_loss_cls_1[0.4835] det_loss_box_1[0.4978] det_loss_cls_2[0.4281] det_loss_box_2[0.4579] det_loss_cls_3[0.3906] det_loss_box_3[0.4413] det_loss_cls_4[0.3576] det_loss_box_4[0.4311] det_loss_cls_5[0.3399] det_loss_box_5[0.4369] det_loss_cls_dn_0[0.0876] det_loss_box_dn_0[0.2305] det_loss_cls_dn_1[0.0842] det_loss_box_dn_1[0.1901] det_loss_cls_dn_2[0.0829] det_loss_box_dn_2[0.1753] det_loss_cls_dn_3[0.0821] det_loss_box_dn_3[0.1708] det_loss_cls_dn_4[0.0808] det_loss_box_dn_4[0.1681] det_loss_cls_dn_5[0.0799] det_loss_box_dn_5[0.1670] loss_depth[0.0369] total_loss[7.0707]
Rank[0/8] 06/20/2025 12:39:10 INFO stats.py:314 | Epoch[170] Step[274] GlobalStep[66574] Training Speed: 6.67 samples/sec across all devices.	Average Step Time: 1.20 sec.	Estimated Remaining Time: 9:06:45.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:39:10 INFO loss_tracker.py:84 | Epoch[170/239] Step[274] GlobalStep[66574/NA]: det_loss_cls_0[0.4727] det_loss_box_0[0.5767] det_loss_cls_1[0.4719] det_loss_box_1[0.4320] det_loss_cls_2[0.4099] det_loss_box_2[0.3903] det_loss_cls_3[0.3714] det_loss_box_3[0.3790] det_loss_cls_4[0.3402] det_loss_box_4[0.3726] det_loss_cls_5[0.3332] det_loss_box_5[0.3680] det_loss_cls_dn_0[0.0847] det_loss_box_dn_0[0.2409] det_loss_cls_dn_1[0.0807] det_loss_box_dn_1[0.1994] det_loss_cls_dn_2[0.0797] det_loss_box_dn_2[0.1834] det_loss_cls_dn_3[0.0779] det_loss_box_dn_3[0.1812] det_loss_cls_dn_4[0.0772] det_loss_box_dn_4[0.1795] det_loss_cls_dn_5[0.0761] det_loss_box_dn_5[0.1773] loss_depth[0.0373] total_loss[6.5931]
Rank[0/8] 06/20/2025 12:39:36 INFO stats.py:314 | Epoch[170] Step[299] GlobalStep[66599] Training Speed: 8.64 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 9:06:13.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:39:36 INFO loss_tracker.py:84 | Epoch[170/239] Step[299] GlobalStep[66599/NA]: det_loss_cls_0[0.5024] det_loss_box_0[0.6362] det_loss_cls_1[0.4874] det_loss_box_1[0.4756] det_loss_cls_2[0.4370] det_loss_box_2[0.4216] det_loss_cls_3[0.4112] det_loss_box_3[0.3999] det_loss_cls_4[0.3891] det_loss_box_4[0.3930] det_loss_cls_5[0.3754] det_loss_box_5[0.3916] det_loss_cls_dn_0[0.0995] det_loss_box_dn_0[0.2707] det_loss_cls_dn_1[0.0958] det_loss_box_dn_1[0.2193] det_loss_cls_dn_2[0.0951] det_loss_box_dn_2[0.1989] det_loss_cls_dn_3[0.0934] det_loss_box_dn_3[0.1945] det_loss_cls_dn_4[0.0923] det_loss_box_dn_4[0.1926] det_loss_cls_dn_5[0.0915] det_loss_box_dn_5[0.1901] loss_depth[0.0359] total_loss[7.1902]
Rank[0/8] 06/20/2025 12:40:02 INFO stats.py:314 | Epoch[170] Step[324] GlobalStep[66624] Training Speed: 6.48 samples/sec across all devices.	Average Step Time: 1.23 sec.	Estimated Remaining Time: 9:05:41.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:40:02 INFO loss_tracker.py:84 | Epoch[170/239] Step[324] GlobalStep[66624/NA]: det_loss_cls_0[0.5309] det_loss_box_0[0.6855] det_loss_cls_1[0.5067] det_loss_box_1[0.5203] det_loss_cls_2[0.4323] det_loss_box_2[0.4775] det_loss_cls_3[0.4009] det_loss_box_3[0.4536] det_loss_cls_4[0.3724] det_loss_box_4[0.4439] det_loss_cls_5[0.3567] det_loss_box_5[0.4439] det_loss_cls_dn_0[0.0897] det_loss_box_dn_0[0.2665] det_loss_cls_dn_1[0.0861] det_loss_box_dn_1[0.2219] det_loss_cls_dn_2[0.0857] det_loss_box_dn_2[0.2071] det_loss_cls_dn_3[0.0847] det_loss_box_dn_3[0.2039] det_loss_cls_dn_4[0.0838] det_loss_box_dn_4[0.2014] det_loss_cls_dn_5[0.0827] det_loss_box_dn_5[0.1997] loss_depth[0.0354] total_loss[7.4734]
Rank[0/8] 06/20/2025 12:40:28 INFO stats.py:314 | Epoch[170] Step[349] GlobalStep[66649] Training Speed: 5.83 samples/sec across all devices.	Average Step Time: 1.37 sec.	Estimated Remaining Time: 9:05:09.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:40:28 INFO loss_tracker.py:84 | Epoch[170/239] Step[349] GlobalStep[66649/NA]: det_loss_cls_0[0.4322] det_loss_box_0[0.5483] det_loss_cls_1[0.4398] det_loss_box_1[0.4088] det_loss_cls_2[0.3692] det_loss_box_2[0.3791] det_loss_cls_3[0.3386] det_loss_box_3[0.3698] det_loss_cls_4[0.3127] det_loss_box_4[0.3619] det_loss_cls_5[0.2960] det_loss_box_5[0.3665] det_loss_cls_dn_0[0.0751] det_loss_box_dn_0[0.2595] det_loss_cls_dn_1[0.0715] det_loss_box_dn_1[0.2063] det_loss_cls_dn_2[0.0703] det_loss_box_dn_2[0.1939] det_loss_cls_dn_3[0.0691] det_loss_box_dn_3[0.1896] det_loss_cls_dn_4[0.0682] det_loss_box_dn_4[0.1870] det_loss_cls_dn_5[0.0675] det_loss_box_dn_5[0.1779] loss_depth[0.0350] total_loss[6.2938]
Rank[0/8] 06/20/2025 12:40:54 INFO stats.py:314 | Epoch[170] Step[374] GlobalStep[66674] Training Speed: 7.72 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 9:04:36.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:40:54 INFO loss_tracker.py:84 | Epoch[170/239] Step[374] GlobalStep[66674/NA]: det_loss_cls_0[0.5115] det_loss_box_0[0.6112] det_loss_cls_1[0.5004] det_loss_box_1[0.4579] det_loss_cls_2[0.4317] det_loss_box_2[0.4183] det_loss_cls_3[0.4028] det_loss_box_3[0.4070] det_loss_cls_4[0.3752] det_loss_box_4[0.4028] det_loss_cls_5[0.3593] det_loss_box_5[0.4007] det_loss_cls_dn_0[0.0925] det_loss_box_dn_0[0.2558] det_loss_cls_dn_1[0.0888] det_loss_box_dn_1[0.2105] det_loss_cls_dn_2[0.0879] det_loss_box_dn_2[0.1928] det_loss_cls_dn_3[0.0865] det_loss_box_dn_3[0.1897] det_loss_cls_dn_4[0.0853] det_loss_box_dn_4[0.1877] det_loss_cls_dn_5[0.0842] det_loss_box_dn_5[0.1869] loss_depth[0.0345] total_loss[7.0620]
Rank[0/8] 06/20/2025 12:41:09 INFO stats.py:394 | Epoch[170] completed. Training Speed: 7.70 samples/sec across all devices.	Epoch Time: 405.46 sec.	Average Epoch Time: 405.46 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 9:04:17.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:41:21 INFO stats.py:314 | Epoch[171] Step[9] GlobalStep[66699] Training Speed: 8.02 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 9:04:05.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:41:36 INFO loss_tracker.py:84 | Epoch[171/239] Step[24] GlobalStep[66714/NA]: det_loss_cls_0[0.4071] det_loss_box_0[0.5287] det_loss_cls_1[0.3952] det_loss_box_1[0.4066] det_loss_cls_2[0.3513] det_loss_box_2[0.3779] det_loss_cls_3[0.3296] det_loss_box_3[0.3615] det_loss_cls_4[0.3042] det_loss_box_4[0.3531] det_loss_cls_5[0.2901] det_loss_box_5[0.3523] det_loss_cls_dn_0[0.0918] det_loss_box_dn_0[0.2760] det_loss_cls_dn_1[0.0884] det_loss_box_dn_1[0.2311] det_loss_cls_dn_2[0.0876] det_loss_box_dn_2[0.2125] det_loss_cls_dn_3[0.0867] det_loss_box_dn_3[0.2083] det_loss_cls_dn_4[0.0859] det_loss_box_dn_4[0.2059] det_loss_cls_dn_5[0.0849] det_loss_box_dn_5[0.2035] loss_depth[0.0361] total_loss[6.3563]
Rank[0/8] 06/20/2025 12:41:46 INFO stats.py:314 | Epoch[171] Step[34] GlobalStep[66724] Training Speed: 8.05 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 9:03:33.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:42:02 INFO loss_tracker.py:84 | Epoch[171/239] Step[49] GlobalStep[66739/NA]: det_loss_cls_0[0.4969] det_loss_box_0[0.6952] det_loss_cls_1[0.4984] det_loss_box_1[0.5366] det_loss_cls_2[0.4432] det_loss_box_2[0.4770] det_loss_cls_3[0.4183] det_loss_box_3[0.4560] det_loss_cls_4[0.3879] det_loss_box_4[0.4450] det_loss_cls_5[0.3735] det_loss_box_5[0.4425] det_loss_cls_dn_0[0.0882] det_loss_box_dn_0[0.3093] det_loss_cls_dn_1[0.0856] det_loss_box_dn_1[0.2569] det_loss_cls_dn_2[0.0840] det_loss_box_dn_2[0.2378] det_loss_cls_dn_3[0.0831] det_loss_box_dn_3[0.2341] det_loss_cls_dn_4[0.0819] det_loss_box_dn_4[0.2308] det_loss_cls_dn_5[0.0809] det_loss_box_dn_5[0.2285] loss_depth[0.0373] total_loss[7.7090]
Rank[0/8] 06/20/2025 12:42:13 INFO stats.py:314 | Epoch[171] Step[59] GlobalStep[66749] Training Speed: 7.84 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 9:03:01.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:42:28 INFO loss_tracker.py:84 | Epoch[171/239] Step[74] GlobalStep[66764/NA]: det_loss_cls_0[0.6347] det_loss_box_0[0.7570] det_loss_cls_1[0.6106] det_loss_box_1[0.5761] det_loss_cls_2[0.5572] det_loss_box_2[0.5225] det_loss_cls_3[0.5125] det_loss_box_3[0.5130] det_loss_cls_4[0.4887] det_loss_box_4[0.4989] det_loss_cls_5[0.4719] det_loss_box_5[0.5008] det_loss_cls_dn_0[0.1104] det_loss_box_dn_0[0.2489] det_loss_cls_dn_1[0.1065] det_loss_box_dn_1[0.2040] det_loss_cls_dn_2[0.1057] det_loss_box_dn_2[0.1898] det_loss_cls_dn_3[0.1048] det_loss_box_dn_3[0.1863] det_loss_cls_dn_4[0.1039] det_loss_box_dn_4[0.1846] det_loss_cls_dn_5[0.1032] det_loss_box_dn_5[0.1832] loss_depth[0.0342] total_loss[8.5091]
Rank[0/8] 06/20/2025 12:42:38 INFO stats.py:314 | Epoch[171] Step[84] GlobalStep[66774] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 9:02:28.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:42:53 INFO loss_tracker.py:84 | Epoch[171/239] Step[99] GlobalStep[66789/NA]: det_loss_cls_0[0.4300] det_loss_box_0[0.5559] det_loss_cls_1[0.4264] det_loss_box_1[0.4334] det_loss_cls_2[0.3885] det_loss_box_2[0.3945] det_loss_cls_3[0.3451] det_loss_box_3[0.3862] det_loss_cls_4[0.3304] det_loss_box_4[0.3757] det_loss_cls_5[0.3146] det_loss_box_5[0.3801] det_loss_cls_dn_0[0.0861] det_loss_box_dn_0[0.3077] det_loss_cls_dn_1[0.0827] det_loss_box_dn_1[0.2528] det_loss_cls_dn_2[0.0820] det_loss_box_dn_2[0.2282] det_loss_cls_dn_3[0.0805] det_loss_box_dn_3[0.2188] det_loss_cls_dn_4[0.0793] det_loss_box_dn_4[0.2169] det_loss_cls_dn_5[0.0781] det_loss_box_dn_5[0.2133] loss_depth[0.0372] total_loss[6.7247]
Rank[0/8] 06/20/2025 12:43:03 INFO stats.py:314 | Epoch[171] Step[109] GlobalStep[66799] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 9:01:56.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:43:19 INFO loss_tracker.py:84 | Epoch[171/239] Step[124] GlobalStep[66814/NA]: det_loss_cls_0[0.4208] det_loss_box_0[0.5175] det_loss_cls_1[0.4244] det_loss_box_1[0.3899] det_loss_cls_2[0.3682] det_loss_box_2[0.3540] det_loss_cls_3[0.3313] det_loss_box_3[0.3467] det_loss_cls_4[0.3030] det_loss_box_4[0.3380] det_loss_cls_5[0.2928] det_loss_box_5[0.3379] det_loss_cls_dn_0[0.0877] det_loss_box_dn_0[0.2334] det_loss_cls_dn_1[0.0848] det_loss_box_dn_1[0.1946] det_loss_cls_dn_2[0.0844] det_loss_box_dn_2[0.1815] det_loss_cls_dn_3[0.0833] det_loss_box_dn_3[0.1783] det_loss_cls_dn_4[0.0823] det_loss_box_dn_4[0.1760] det_loss_cls_dn_5[0.0816] det_loss_box_dn_5[0.1740] loss_depth[0.0331] total_loss[6.0997]
Rank[0/8] 06/20/2025 12:43:30 INFO stats.py:314 | Epoch[171] Step[134] GlobalStep[66824] Training Speed: 7.33 samples/sec across all devices.	Average Step Time: 1.09 sec.	Estimated Remaining Time: 9:01:24.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:43:45 INFO loss_tracker.py:84 | Epoch[171/239] Step[149] GlobalStep[66839/NA]: det_loss_cls_0[0.4940] det_loss_box_0[0.6823] det_loss_cls_1[0.4758] det_loss_box_1[0.5154] det_loss_cls_2[0.4198] det_loss_box_2[0.4625] det_loss_cls_3[0.3885] det_loss_box_3[0.4466] det_loss_cls_4[0.3610] det_loss_box_4[0.4370] det_loss_cls_5[0.3394] det_loss_box_5[0.4443] det_loss_cls_dn_0[0.0831] det_loss_box_dn_0[0.2905] det_loss_cls_dn_1[0.0791] det_loss_box_dn_1[0.2318] det_loss_cls_dn_2[0.0777] det_loss_box_dn_2[0.2117] det_loss_cls_dn_3[0.0773] det_loss_box_dn_3[0.2055] det_loss_cls_dn_4[0.0764] det_loss_box_dn_4[0.2030] det_loss_cls_dn_5[0.0757] det_loss_box_dn_5[0.1997] loss_depth[0.0377] total_loss[7.3158]
Rank[0/8] 06/20/2025 12:43:55 INFO stats.py:314 | Epoch[171] Step[159] GlobalStep[66849] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 9:00:52.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:44:11 INFO loss_tracker.py:84 | Epoch[171/239] Step[174] GlobalStep[66864/NA]: det_loss_cls_0[0.5927] det_loss_box_0[0.7725] det_loss_cls_1[0.5834] det_loss_box_1[0.5973] det_loss_cls_2[0.5105] det_loss_box_2[0.5454] det_loss_cls_3[0.4745] det_loss_box_3[0.5303] det_loss_cls_4[0.4491] det_loss_box_4[0.5138] det_loss_cls_5[0.4346] det_loss_box_5[0.5094] det_loss_cls_dn_0[0.0992] det_loss_box_dn_0[0.3100] det_loss_cls_dn_1[0.0955] det_loss_box_dn_1[0.2612] det_loss_cls_dn_2[0.0946] det_loss_box_dn_2[0.2398] det_loss_cls_dn_3[0.0934] det_loss_box_dn_3[0.2352] det_loss_cls_dn_4[0.0930] det_loss_box_dn_4[0.2322] det_loss_cls_dn_5[0.0923] det_loss_box_dn_5[0.2284] loss_depth[0.0362] total_loss[8.6244]
Rank[0/8] 06/20/2025 12:44:21 INFO stats.py:314 | Epoch[171] Step[184] GlobalStep[66874] Training Speed: 8.66 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 9:00:19.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:44:37 INFO loss_tracker.py:84 | Epoch[171/239] Step[199] GlobalStep[66889/NA]: det_loss_cls_0[0.5310] det_loss_box_0[0.7137] det_loss_cls_1[0.5085] det_loss_box_1[0.5180] det_loss_cls_2[0.4517] det_loss_box_2[0.4575] det_loss_cls_3[0.4210] det_loss_box_3[0.4432] det_loss_cls_4[0.3926] det_loss_box_4[0.4295] det_loss_cls_5[0.3795] det_loss_box_5[0.4304] det_loss_cls_dn_0[0.0932] det_loss_box_dn_0[0.2693] det_loss_cls_dn_1[0.0895] det_loss_box_dn_1[0.2198] det_loss_cls_dn_2[0.0884] det_loss_box_dn_2[0.2022] det_loss_cls_dn_3[0.0872] det_loss_box_dn_3[0.1980] det_loss_cls_dn_4[0.0862] det_loss_box_dn_4[0.1953] det_loss_cls_dn_5[0.0854] det_loss_box_dn_5[0.1946] loss_depth[0.0391] total_loss[7.5252]
Rank[0/8] 06/20/2025 12:44:47 INFO stats.py:314 | Epoch[171] Step[209] GlobalStep[66899] Training Speed: 6.73 samples/sec across all devices.	Average Step Time: 1.19 sec.	Estimated Remaining Time: 8:59:48.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:45:02 INFO loss_tracker.py:84 | Epoch[171/239] Step[224] GlobalStep[66914/NA]: det_loss_cls_0[0.4755] det_loss_box_0[0.5690] det_loss_cls_1[0.4703] det_loss_box_1[0.4405] det_loss_cls_2[0.4285] det_loss_box_2[0.3953] det_loss_cls_3[0.3970] det_loss_box_3[0.3846] det_loss_cls_4[0.3647] det_loss_box_4[0.3772] det_loss_cls_5[0.3535] det_loss_box_5[0.3716] det_loss_cls_dn_0[0.0891] det_loss_box_dn_0[0.2559] det_loss_cls_dn_1[0.0858] det_loss_box_dn_1[0.2119] det_loss_cls_dn_2[0.0844] det_loss_box_dn_2[0.1979] det_loss_cls_dn_3[0.0834] det_loss_box_dn_3[0.1940] det_loss_cls_dn_4[0.0826] det_loss_box_dn_4[0.1920] det_loss_cls_dn_5[0.0819] det_loss_box_dn_5[0.1918] loss_depth[0.0342] total_loss[6.8126]
Rank[0/8] 06/20/2025 12:45:13 INFO stats.py:314 | Epoch[171] Step[234] GlobalStep[66924] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 8:59:15.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:45:28 INFO loss_tracker.py:84 | Epoch[171/239] Step[249] GlobalStep[66939/NA]: det_loss_cls_0[0.4371] det_loss_box_0[0.5562] det_loss_cls_1[0.4173] det_loss_box_1[0.4188] det_loss_cls_2[0.3704] det_loss_box_2[0.3792] det_loss_cls_3[0.3334] det_loss_box_3[0.3713] det_loss_cls_4[0.3072] det_loss_box_4[0.3643] det_loss_cls_5[0.2937] det_loss_box_5[0.3691] det_loss_cls_dn_0[0.0871] det_loss_box_dn_0[0.2822] det_loss_cls_dn_1[0.0834] det_loss_box_dn_1[0.2259] det_loss_cls_dn_2[0.0826] det_loss_box_dn_2[0.2086] det_loss_cls_dn_3[0.0814] det_loss_box_dn_3[0.2031] det_loss_cls_dn_4[0.0803] det_loss_box_dn_4[0.2005] det_loss_cls_dn_5[0.0794] det_loss_box_dn_5[0.1987] loss_depth[0.0360] total_loss[6.4671]
Rank[0/8] 06/20/2025 12:45:38 INFO stats.py:314 | Epoch[171] Step[259] GlobalStep[66949] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 8:58:43.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:45:53 INFO loss_tracker.py:84 | Epoch[171/239] Step[274] GlobalStep[66964/NA]: det_loss_cls_0[0.5176] det_loss_box_0[0.6909] det_loss_cls_1[0.5043] det_loss_box_1[0.5321] det_loss_cls_2[0.4515] det_loss_box_2[0.4895] det_loss_cls_3[0.4134] det_loss_box_3[0.4796] det_loss_cls_4[0.3848] det_loss_box_4[0.4681] det_loss_cls_5[0.3642] det_loss_box_5[0.4715] det_loss_cls_dn_0[0.0965] det_loss_box_dn_0[0.2738] det_loss_cls_dn_1[0.0929] det_loss_box_dn_1[0.2348] det_loss_cls_dn_2[0.0921] det_loss_box_dn_2[0.2198] det_loss_cls_dn_3[0.0909] det_loss_box_dn_3[0.2143] det_loss_cls_dn_4[0.0903] det_loss_box_dn_4[0.2119] det_loss_cls_dn_5[0.0894] det_loss_box_dn_5[0.2096] loss_depth[0.0335] total_loss[7.7173]
Rank[0/8] 06/20/2025 12:46:04 INFO stats.py:314 | Epoch[171] Step[284] GlobalStep[66974] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 8:58:11.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:46:20 INFO loss_tracker.py:84 | Epoch[171/239] Step[299] GlobalStep[66989/NA]: det_loss_cls_0[0.4016] det_loss_box_0[0.4510] det_loss_cls_1[0.3876] det_loss_box_1[0.3283] det_loss_cls_2[0.3409] det_loss_box_2[0.2925] det_loss_cls_3[0.3032] det_loss_box_3[0.2803] det_loss_cls_4[0.2856] det_loss_box_4[0.2757] det_loss_cls_5[0.2737] det_loss_box_5[0.2819] det_loss_cls_dn_0[0.0907] det_loss_box_dn_0[0.2432] det_loss_cls_dn_1[0.0876] det_loss_box_dn_1[0.1889] det_loss_cls_dn_2[0.0865] det_loss_box_dn_2[0.1662] det_loss_cls_dn_3[0.0855] det_loss_box_dn_3[0.1640] det_loss_cls_dn_4[0.0850] det_loss_box_dn_4[0.1603] det_loss_cls_dn_5[0.0846] det_loss_box_dn_5[0.1594] loss_depth[0.0343] total_loss[5.5386]
Rank[0/8] 06/20/2025 12:46:30 INFO stats.py:314 | Epoch[171] Step[309] GlobalStep[66999] Training Speed: 6.35 samples/sec across all devices.	Average Step Time: 1.26 sec.	Estimated Remaining Time: 8:57:39.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:46:45 INFO loss_tracker.py:84 | Epoch[171/239] Step[324] GlobalStep[67014/NA]: det_loss_cls_0[0.3715] det_loss_box_0[0.4771] det_loss_cls_1[0.3625] det_loss_box_1[0.3633] det_loss_cls_2[0.3153] det_loss_box_2[0.3231] det_loss_cls_3[0.2786] det_loss_box_3[0.3071] det_loss_cls_4[0.2599] det_loss_box_4[0.2934] det_loss_cls_5[0.2435] det_loss_box_5[0.2982] det_loss_cls_dn_0[0.0905] det_loss_box_dn_0[0.2648] det_loss_cls_dn_1[0.0870] det_loss_box_dn_1[0.2116] det_loss_cls_dn_2[0.0856] det_loss_box_dn_2[0.1921] det_loss_cls_dn_3[0.0842] det_loss_box_dn_3[0.1885] det_loss_cls_dn_4[0.0833] det_loss_box_dn_4[0.1856] det_loss_cls_dn_5[0.0827] det_loss_box_dn_5[0.1849] loss_depth[0.0368] total_loss[5.6712]
Rank[0/8] 06/20/2025 12:46:55 INFO stats.py:314 | Epoch[171] Step[334] GlobalStep[67024] Training Speed: 8.65 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 8:57:07.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:47:11 INFO loss_tracker.py:84 | Epoch[171/239] Step[349] GlobalStep[67039/NA]: det_loss_cls_0[0.3836] det_loss_box_0[0.4750] det_loss_cls_1[0.3612] det_loss_box_1[0.3597] det_loss_cls_2[0.3061] det_loss_box_2[0.3265] det_loss_cls_3[0.2683] det_loss_box_3[0.3203] det_loss_cls_4[0.2406] det_loss_box_4[0.3153] det_loss_cls_5[0.2310] det_loss_box_5[0.3141] det_loss_cls_dn_0[0.0739] det_loss_box_dn_0[0.2672] det_loss_cls_dn_1[0.0706] det_loss_box_dn_1[0.2237] det_loss_cls_dn_2[0.0699] det_loss_box_dn_2[0.2072] det_loss_cls_dn_3[0.0689] det_loss_box_dn_3[0.2023] det_loss_cls_dn_4[0.0679] det_loss_box_dn_4[0.2005] det_loss_cls_dn_5[0.0670] det_loss_box_dn_5[0.1961] loss_depth[0.0335] total_loss[5.6505]
Rank[0/8] 06/20/2025 12:47:21 INFO stats.py:314 | Epoch[171] Step[359] GlobalStep[67049] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 8:56:35.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:47:36 INFO loss_tracker.py:84 | Epoch[171/239] Step[374] GlobalStep[67064/NA]: det_loss_cls_0[0.5483] det_loss_box_0[0.7506] det_loss_cls_1[0.5415] det_loss_box_1[0.5828] det_loss_cls_2[0.4790] det_loss_box_2[0.5410] det_loss_cls_3[0.4512] det_loss_box_3[0.5207] det_loss_cls_4[0.4273] det_loss_box_4[0.5057] det_loss_cls_5[0.4141] det_loss_box_5[0.5062] det_loss_cls_dn_0[0.0895] det_loss_box_dn_0[0.3168] det_loss_cls_dn_1[0.0848] det_loss_box_dn_1[0.2595] det_loss_cls_dn_2[0.0835] det_loss_box_dn_2[0.2424] det_loss_cls_dn_3[0.0819] det_loss_box_dn_3[0.2331] det_loss_cls_dn_4[0.0809] det_loss_box_dn_4[0.2302] det_loss_cls_dn_5[0.0798] det_loss_box_dn_5[0.2260] loss_depth[0.0328] total_loss[8.3098]
Rank[0/8] 06/20/2025 12:47:47 INFO stats.py:314 | Epoch[171] Step[384] GlobalStep[67074] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 8:56:03.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:47:52 INFO stats.py:394 | Epoch[171] completed. Training Speed: 7.74 samples/sec across all devices.	Epoch Time: 403.29 sec.	Average Epoch Time: 403.29 sec.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 8:55:56.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:48:15 INFO stats.py:314 | Epoch[172] Step[19] GlobalStep[67099] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 8:55:31.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:48:20 INFO loss_tracker.py:84 | Epoch[172/239] Step[24] GlobalStep[67104/NA]: det_loss_cls_0[0.5113] det_loss_box_0[0.5581] det_loss_cls_1[0.5010] det_loss_box_1[0.4271] det_loss_cls_2[0.4341] det_loss_box_2[0.3916] det_loss_cls_3[0.3988] det_loss_box_3[0.3790] det_loss_cls_4[0.3768] det_loss_box_4[0.3653] det_loss_cls_5[0.3587] det_loss_box_5[0.3681] det_loss_cls_dn_0[0.0952] det_loss_box_dn_0[0.2417] det_loss_cls_dn_1[0.0920] det_loss_box_dn_1[0.2039] det_loss_cls_dn_2[0.0920] det_loss_box_dn_2[0.1885] det_loss_cls_dn_3[0.0908] det_loss_box_dn_3[0.1861] det_loss_cls_dn_4[0.0902] det_loss_box_dn_4[0.1852] det_loss_cls_dn_5[0.0894] det_loss_box_dn_5[0.1806] loss_depth[0.0346] total_loss[6.8400]
Rank[0/8] 06/20/2025 12:48:40 INFO stats.py:314 | Epoch[172] Step[44] GlobalStep[67124] Training Speed: 7.99 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 8:54:59.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:48:46 INFO loss_tracker.py:84 | Epoch[172/239] Step[49] GlobalStep[67129/NA]: det_loss_cls_0[0.3661] det_loss_box_0[0.4447] det_loss_cls_1[0.3613] det_loss_box_1[0.3340] det_loss_cls_2[0.3056] det_loss_box_2[0.3051] det_loss_cls_3[0.2700] det_loss_box_3[0.2969] det_loss_cls_4[0.2507] det_loss_box_4[0.2887] det_loss_cls_5[0.2378] det_loss_box_5[0.2898] det_loss_cls_dn_0[0.0803] det_loss_box_dn_0[0.2582] det_loss_cls_dn_1[0.0763] det_loss_box_dn_1[0.2110] det_loss_cls_dn_2[0.0756] det_loss_box_dn_2[0.1937] det_loss_cls_dn_3[0.0744] det_loss_box_dn_3[0.1893] det_loss_cls_dn_4[0.0738] det_loss_box_dn_4[0.1868] det_loss_cls_dn_5[0.0727] det_loss_box_dn_5[0.1829] loss_depth[0.0343] total_loss[5.4598]
Rank[0/8] 06/20/2025 12:49:07 INFO stats.py:314 | Epoch[172] Step[69] GlobalStep[67149] Training Speed: 8.64 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 8:54:27.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:49:13 INFO loss_tracker.py:84 | Epoch[172/239] Step[74] GlobalStep[67154/NA]: det_loss_cls_0[0.4880] det_loss_box_0[0.6484] det_loss_cls_1[0.4681] det_loss_box_1[0.4833] det_loss_cls_2[0.4117] det_loss_box_2[0.4395] det_loss_cls_3[0.3879] det_loss_box_3[0.4220] det_loss_cls_4[0.3667] det_loss_box_4[0.4175] det_loss_cls_5[0.3549] det_loss_box_5[0.4168] det_loss_cls_dn_0[0.0995] det_loss_box_dn_0[0.3331] det_loss_cls_dn_1[0.0956] det_loss_box_dn_1[0.2691] det_loss_cls_dn_2[0.0946] det_loss_box_dn_2[0.2488] det_loss_cls_dn_3[0.0938] det_loss_box_dn_3[0.2399] det_loss_cls_dn_4[0.0928] det_loss_box_dn_4[0.2369] det_loss_cls_dn_5[0.0928] det_loss_box_dn_5[0.2345] loss_depth[0.0368] total_loss[7.4731]
Rank[0/8] 06/20/2025 12:49:33 INFO stats.py:314 | Epoch[172] Step[94] GlobalStep[67174] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 8:53:55.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:49:38 INFO loss_tracker.py:84 | Epoch[172/239] Step[99] GlobalStep[67179/NA]: det_loss_cls_0[0.3699] det_loss_box_0[0.4843] det_loss_cls_1[0.3576] det_loss_box_1[0.3737] det_loss_cls_2[0.3059] det_loss_box_2[0.3368] det_loss_cls_3[0.2806] det_loss_box_3[0.3191] det_loss_cls_4[0.2610] det_loss_box_4[0.3091] det_loss_cls_5[0.2492] det_loss_box_5[0.3110] det_loss_cls_dn_0[0.0789] det_loss_box_dn_0[0.2892] det_loss_cls_dn_1[0.0763] det_loss_box_dn_1[0.2376] det_loss_cls_dn_2[0.0752] det_loss_box_dn_2[0.2195] det_loss_cls_dn_3[0.0744] det_loss_box_dn_3[0.2146] det_loss_cls_dn_4[0.0740] det_loss_box_dn_4[0.2061] det_loss_cls_dn_5[0.0731] det_loss_box_dn_5[0.2001] loss_depth[0.0378] total_loss[5.8153]
Rank[0/8] 06/20/2025 12:49:58 INFO stats.py:314 | Epoch[172] Step[119] GlobalStep[67199] Training Speed: 13.59 samples/sec across all devices.	Average Step Time: 0.59 sec.	Estimated Remaining Time: 8:53:23.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:50:04 INFO loss_tracker.py:84 | Epoch[172/239] Step[124] GlobalStep[67204/NA]: det_loss_cls_0[0.4729] det_loss_box_0[0.5937] det_loss_cls_1[0.4577] det_loss_box_1[0.4402] det_loss_cls_2[0.3965] det_loss_box_2[0.3900] det_loss_cls_3[0.3634] det_loss_box_3[0.3810] det_loss_cls_4[0.3361] det_loss_box_4[0.3671] det_loss_cls_5[0.3217] det_loss_box_5[0.3680] det_loss_cls_dn_0[0.0849] det_loss_box_dn_0[0.2623] det_loss_cls_dn_1[0.0816] det_loss_box_dn_1[0.2156] det_loss_cls_dn_2[0.0808] det_loss_box_dn_2[0.1980] det_loss_cls_dn_3[0.0795] det_loss_box_dn_3[0.1935] det_loss_cls_dn_4[0.0784] det_loss_box_dn_4[0.1913] det_loss_cls_dn_5[0.0777] det_loss_box_dn_5[0.1873] loss_depth[0.0340] total_loss[6.6530]
Rank[0/8] 06/20/2025 12:50:25 INFO stats.py:314 | Epoch[172] Step[144] GlobalStep[67224] Training Speed: 7.14 samples/sec across all devices.	Average Step Time: 1.12 sec.	Estimated Remaining Time: 8:52:51.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:50:30 INFO loss_tracker.py:84 | Epoch[172/239] Step[149] GlobalStep[67229/NA]: det_loss_cls_0[0.4432] det_loss_box_0[0.5418] det_loss_cls_1[0.4355] det_loss_box_1[0.3746] det_loss_cls_2[0.3659] det_loss_box_2[0.3490] det_loss_cls_3[0.3229] det_loss_box_3[0.3438] det_loss_cls_4[0.2944] det_loss_box_4[0.3352] det_loss_cls_5[0.2763] det_loss_box_5[0.3338] det_loss_cls_dn_0[0.0775] det_loss_box_dn_0[0.2505] det_loss_cls_dn_1[0.0741] det_loss_box_dn_1[0.1994] det_loss_cls_dn_2[0.0733] det_loss_box_dn_2[0.1815] det_loss_cls_dn_3[0.0720] det_loss_box_dn_3[0.1784] det_loss_cls_dn_4[0.0710] det_loss_box_dn_4[0.1738] det_loss_cls_dn_5[0.0704] det_loss_box_dn_5[0.1706] loss_depth[0.0365] total_loss[6.0456]
Rank[0/8] 06/20/2025 12:50:51 INFO stats.py:314 | Epoch[172] Step[169] GlobalStep[67249] Training Speed: 7.75 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 8:52:19.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:50:56 INFO loss_tracker.py:84 | Epoch[172/239] Step[174] GlobalStep[67254/NA]: det_loss_cls_0[0.4117] det_loss_box_0[0.5378] det_loss_cls_1[0.3986] det_loss_box_1[0.3812] det_loss_cls_2[0.3452] det_loss_box_2[0.3416] det_loss_cls_3[0.3202] det_loss_box_3[0.3260] det_loss_cls_4[0.2955] det_loss_box_4[0.3224] det_loss_cls_5[0.2857] det_loss_box_5[0.3209] det_loss_cls_dn_0[0.0791] det_loss_box_dn_0[0.2504] det_loss_cls_dn_1[0.0766] det_loss_box_dn_1[0.1995] det_loss_cls_dn_2[0.0750] det_loss_box_dn_2[0.1790] det_loss_cls_dn_3[0.0742] det_loss_box_dn_3[0.1749] det_loss_cls_dn_4[0.0736] det_loss_box_dn_4[0.1718] det_loss_cls_dn_5[0.0729] det_loss_box_dn_5[0.1697] loss_depth[0.0345] total_loss[5.9181]
Rank[0/8] 06/20/2025 12:51:16 INFO stats.py:314 | Epoch[172] Step[194] GlobalStep[67274] Training Speed: 8.56 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 8:51:47.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:51:22 INFO loss_tracker.py:84 | Epoch[172/239] Step[199] GlobalStep[67279/NA]: det_loss_cls_0[0.5593] det_loss_box_0[0.7247] det_loss_cls_1[0.5512] det_loss_box_1[0.5639] det_loss_cls_2[0.4741] det_loss_box_2[0.5225] det_loss_cls_3[0.4377] det_loss_box_3[0.5088] det_loss_cls_4[0.4074] det_loss_box_4[0.4938] det_loss_cls_5[0.3984] det_loss_box_5[0.4910] det_loss_cls_dn_0[0.0904] det_loss_box_dn_0[0.2593] det_loss_cls_dn_1[0.0862] det_loss_box_dn_1[0.2172] det_loss_cls_dn_2[0.0850] det_loss_box_dn_2[0.2001] det_loss_cls_dn_3[0.0836] det_loss_box_dn_3[0.1951] det_loss_cls_dn_4[0.0822] det_loss_box_dn_4[0.1929] det_loss_cls_dn_5[0.0815] det_loss_box_dn_5[0.1915] loss_depth[0.0345] total_loss[7.9322]
Rank[0/8] 06/20/2025 12:51:42 INFO stats.py:314 | Epoch[172] Step[219] GlobalStep[67299] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 8:51:15.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:51:47 INFO loss_tracker.py:84 | Epoch[172/239] Step[224] GlobalStep[67304/NA]: det_loss_cls_0[0.5574] det_loss_box_0[0.6457] det_loss_cls_1[0.5435] det_loss_box_1[0.4828] det_loss_cls_2[0.4792] det_loss_box_2[0.4570] det_loss_cls_3[0.4451] det_loss_box_3[0.4355] det_loss_cls_4[0.4211] det_loss_box_4[0.4276] det_loss_cls_5[0.4032] det_loss_box_5[0.4300] det_loss_cls_dn_0[0.0891] det_loss_box_dn_0[0.2554] det_loss_cls_dn_1[0.0851] det_loss_box_dn_1[0.2089] det_loss_cls_dn_2[0.0841] det_loss_box_dn_2[0.1899] det_loss_cls_dn_3[0.0832] det_loss_box_dn_3[0.1868] det_loss_cls_dn_4[0.0821] det_loss_box_dn_4[0.1844] det_loss_cls_dn_5[0.0815] det_loss_box_dn_5[0.1832] loss_depth[0.0361] total_loss[7.4778]
Rank[0/8] 06/20/2025 12:52:08 INFO stats.py:314 | Epoch[172] Step[244] GlobalStep[67324] Training Speed: 6.48 samples/sec across all devices.	Average Step Time: 1.23 sec.	Estimated Remaining Time: 8:50:43.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:52:13 INFO loss_tracker.py:84 | Epoch[172/239] Step[249] GlobalStep[67329/NA]: det_loss_cls_0[0.4483] det_loss_box_0[0.5691] det_loss_cls_1[0.4453] det_loss_box_1[0.4359] det_loss_cls_2[0.3948] det_loss_box_2[0.4030] det_loss_cls_3[0.3641] det_loss_box_3[0.3917] det_loss_cls_4[0.3421] det_loss_box_4[0.3891] det_loss_cls_5[0.3254] det_loss_box_5[0.3851] det_loss_cls_dn_0[0.0950] det_loss_box_dn_0[0.2923] det_loss_cls_dn_1[0.0916] det_loss_box_dn_1[0.2441] det_loss_cls_dn_2[0.0910] det_loss_box_dn_2[0.2234] det_loss_cls_dn_3[0.0901] det_loss_box_dn_3[0.2189] det_loss_cls_dn_4[0.0890] det_loss_box_dn_4[0.2161] det_loss_cls_dn_5[0.0877] det_loss_box_dn_5[0.2112] loss_depth[0.0374] total_loss[6.8814]
Rank[0/8] 06/20/2025 12:52:34 INFO stats.py:314 | Epoch[172] Step[269] GlobalStep[67349] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 8:50:11.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:52:39 INFO loss_tracker.py:84 | Epoch[172/239] Step[274] GlobalStep[67354/NA]: det_loss_cls_0[0.4007] det_loss_box_0[0.5050] det_loss_cls_1[0.3851] det_loss_box_1[0.3665] det_loss_cls_2[0.3354] det_loss_box_2[0.3404] det_loss_cls_3[0.3087] det_loss_box_3[0.3214] det_loss_cls_4[0.2849] det_loss_box_4[0.3131] det_loss_cls_5[0.2710] det_loss_box_5[0.3151] det_loss_cls_dn_0[0.0856] det_loss_box_dn_0[0.3249] det_loss_cls_dn_1[0.0816] det_loss_box_dn_1[0.2699] det_loss_cls_dn_2[0.0803] det_loss_box_dn_2[0.2497] det_loss_cls_dn_3[0.0791] det_loss_box_dn_3[0.2409] det_loss_cls_dn_4[0.0783] det_loss_box_dn_4[0.2382] det_loss_cls_dn_5[0.0772] det_loss_box_dn_5[0.2321] loss_depth[0.0370] total_loss[6.2221]
Rank[0/8] 06/20/2025 12:52:59 INFO stats.py:314 | Epoch[172] Step[294] GlobalStep[67374] Training Speed: 8.58 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 8:49:39.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:53:05 INFO loss_tracker.py:84 | Epoch[172/239] Step[299] GlobalStep[67379/NA]: det_loss_cls_0[0.3999] det_loss_box_0[0.5397] det_loss_cls_1[0.3949] det_loss_box_1[0.4158] det_loss_cls_2[0.3487] det_loss_box_2[0.3830] det_loss_cls_3[0.3203] det_loss_box_3[0.3598] det_loss_cls_4[0.3010] det_loss_box_4[0.3514] det_loss_cls_5[0.2888] det_loss_box_5[0.3523] det_loss_cls_dn_0[0.0843] det_loss_box_dn_0[0.3040] det_loss_cls_dn_1[0.0804] det_loss_box_dn_1[0.2461] det_loss_cls_dn_2[0.0797] det_loss_box_dn_2[0.2271] det_loss_cls_dn_3[0.0787] det_loss_box_dn_3[0.2210] det_loss_cls_dn_4[0.0782] det_loss_box_dn_4[0.2151] det_loss_cls_dn_5[0.0775] det_loss_box_dn_5[0.2084] loss_depth[0.0361] total_loss[6.3925]
Rank[0/8] 06/20/2025 12:53:26 INFO stats.py:314 | Epoch[172] Step[319] GlobalStep[67399] Training Speed: 6.75 samples/sec across all devices.	Average Step Time: 1.19 sec.	Estimated Remaining Time: 8:49:07.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:53:32 INFO loss_tracker.py:84 | Epoch[172/239] Step[324] GlobalStep[67404/NA]: det_loss_cls_0[0.7067] det_loss_box_0[0.9253] det_loss_cls_1[0.6742] det_loss_box_1[0.7100] det_loss_cls_2[0.6094] det_loss_box_2[0.6275] det_loss_cls_3[0.5626] det_loss_box_3[0.6115] det_loss_cls_4[0.5332] det_loss_box_4[0.5931] det_loss_cls_5[0.5184] det_loss_box_5[0.5909] det_loss_cls_dn_0[0.0944] det_loss_box_dn_0[0.3068] det_loss_cls_dn_1[0.0903] det_loss_box_dn_1[0.2475] det_loss_cls_dn_2[0.0898] det_loss_box_dn_2[0.2269] det_loss_cls_dn_3[0.0889] det_loss_box_dn_3[0.2237] det_loss_cls_dn_4[0.0881] det_loss_box_dn_4[0.2211] det_loss_cls_dn_5[0.0873] det_loss_box_dn_5[0.2174] loss_depth[0.0365] total_loss[9.6816]
Rank[0/8] 06/20/2025 12:53:52 INFO stats.py:314 | Epoch[172] Step[344] GlobalStep[67424] Training Speed: 8.67 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 8:48:35.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:53:57 INFO loss_tracker.py:84 | Epoch[172/239] Step[349] GlobalStep[67429/NA]: det_loss_cls_0[0.4073] det_loss_box_0[0.5134] det_loss_cls_1[0.4072] det_loss_box_1[0.3749] det_loss_cls_2[0.3454] det_loss_box_2[0.3457] det_loss_cls_3[0.3071] det_loss_box_3[0.3374] det_loss_cls_4[0.2875] det_loss_box_4[0.3301] det_loss_cls_5[0.2742] det_loss_box_5[0.3302] det_loss_cls_dn_0[0.0863] det_loss_box_dn_0[0.2499] det_loss_cls_dn_1[0.0823] det_loss_box_dn_1[0.2025] det_loss_cls_dn_2[0.0811] det_loss_box_dn_2[0.1822] det_loss_cls_dn_3[0.0797] det_loss_box_dn_3[0.1795] det_loss_cls_dn_4[0.0787] det_loss_box_dn_4[0.1772] det_loss_cls_dn_5[0.0775] det_loss_box_dn_5[0.1743] loss_depth[0.0368] total_loss[5.9486]
Rank[0/8] 06/20/2025 12:54:17 INFO stats.py:314 | Epoch[172] Step[369] GlobalStep[67449] Training Speed: 8.22 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 8:48:03.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:54:22 INFO loss_tracker.py:84 | Epoch[172/239] Step[374] GlobalStep[67454/NA]: det_loss_cls_0[0.5208] det_loss_box_0[0.6279] det_loss_cls_1[0.5055] det_loss_box_1[0.4677] det_loss_cls_2[0.4330] det_loss_box_2[0.4295] det_loss_cls_3[0.3889] det_loss_box_3[0.4162] det_loss_cls_4[0.3638] det_loss_box_4[0.4122] det_loss_cls_5[0.3487] det_loss_box_5[0.4143] det_loss_cls_dn_0[0.0868] det_loss_box_dn_0[0.2001] det_loss_cls_dn_1[0.0837] det_loss_box_dn_1[0.1644] det_loss_cls_dn_2[0.0823] det_loss_box_dn_2[0.1508] det_loss_cls_dn_3[0.0811] det_loss_box_dn_3[0.1482] det_loss_cls_dn_4[0.0798] det_loss_box_dn_4[0.1472] det_loss_cls_dn_5[0.0789] det_loss_box_dn_5[0.1460] loss_depth[0.0340] total_loss[6.8116]
Rank[0/8] 06/20/2025 12:54:38 INFO stats.py:394 | Epoch[172] completed. Training Speed: 7.69 samples/sec across all devices.	Epoch Time: 405.93 sec.	Average Epoch Time: 405.93 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 8:47:37.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:54:45 INFO stats.py:314 | Epoch[173] Step[4] GlobalStep[67474] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 8:47:31.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:55:05 INFO loss_tracker.py:84 | Epoch[173/239] Step[24] GlobalStep[67494/NA]: det_loss_cls_0[0.4578] det_loss_box_0[0.5517] det_loss_cls_1[0.4499] det_loss_box_1[0.4128] det_loss_cls_2[0.3986] det_loss_box_2[0.3718] det_loss_cls_3[0.3691] det_loss_box_3[0.3528] det_loss_cls_4[0.3404] det_loss_box_4[0.3490] det_loss_cls_5[0.3307] det_loss_box_5[0.3494] det_loss_cls_dn_0[0.0910] det_loss_box_dn_0[0.3003] det_loss_cls_dn_1[0.0879] det_loss_box_dn_1[0.2417] det_loss_cls_dn_2[0.0871] det_loss_box_dn_2[0.2168] det_loss_cls_dn_3[0.0859] det_loss_box_dn_3[0.2121] det_loss_cls_dn_4[0.0851] det_loss_box_dn_4[0.2098] det_loss_cls_dn_5[0.0844] det_loss_box_dn_5[0.2043] loss_depth[0.0357] total_loss[6.6763]
Rank[0/8] 06/20/2025 12:55:10 INFO stats.py:314 | Epoch[173] Step[29] GlobalStep[67499] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 8:46:59.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:55:31 INFO loss_tracker.py:84 | Epoch[173/239] Step[49] GlobalStep[67519/NA]: det_loss_cls_0[0.5758] det_loss_box_0[0.7289] det_loss_cls_1[0.5600] det_loss_box_1[0.5510] det_loss_cls_2[0.4910] det_loss_box_2[0.5039] det_loss_cls_3[0.4500] det_loss_box_3[0.4803] det_loss_cls_4[0.4275] det_loss_box_4[0.4687] det_loss_cls_5[0.4024] det_loss_box_5[0.4753] det_loss_cls_dn_0[0.0868] det_loss_box_dn_0[0.2412] det_loss_cls_dn_1[0.0831] det_loss_box_dn_1[0.2020] det_loss_cls_dn_2[0.0816] det_loss_box_dn_2[0.1880] det_loss_cls_dn_3[0.0799] det_loss_box_dn_3[0.1855] det_loss_cls_dn_4[0.0788] det_loss_box_dn_4[0.1840] det_loss_cls_dn_5[0.0781] det_loss_box_dn_5[0.1827] loss_depth[0.0370] total_loss[7.8236]
Rank[0/8] 06/20/2025 12:55:36 INFO stats.py:314 | Epoch[173] Step[54] GlobalStep[67524] Training Speed: 7.85 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 8:46:27.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:55:56 INFO loss_tracker.py:84 | Epoch[173/239] Step[74] GlobalStep[67544/NA]: det_loss_cls_0[0.5915] det_loss_box_0[0.7762] det_loss_cls_1[0.5722] det_loss_box_1[0.6052] det_loss_cls_2[0.5176] det_loss_box_2[0.5476] det_loss_cls_3[0.4812] det_loss_box_3[0.5175] det_loss_cls_4[0.4486] det_loss_box_4[0.5038] det_loss_cls_5[0.4388] det_loss_box_5[0.4996] det_loss_cls_dn_0[0.0952] det_loss_box_dn_0[0.2498] det_loss_cls_dn_1[0.0907] det_loss_box_dn_1[0.2045] det_loss_cls_dn_2[0.0897] det_loss_box_dn_2[0.1905] det_loss_cls_dn_3[0.0889] det_loss_box_dn_3[0.1871] det_loss_cls_dn_4[0.0879] det_loss_box_dn_4[0.1843] det_loss_cls_dn_5[0.0867] det_loss_box_dn_5[0.1821] loss_depth[0.0352] total_loss[8.2723]
Rank[0/8] 06/20/2025 12:56:02 INFO stats.py:314 | Epoch[173] Step[79] GlobalStep[67549] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 8:45:55.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:56:22 INFO loss_tracker.py:84 | Epoch[173/239] Step[99] GlobalStep[67569/NA]: det_loss_cls_0[0.4641] det_loss_box_0[0.6768] det_loss_cls_1[0.4510] det_loss_box_1[0.5334] det_loss_cls_2[0.4068] det_loss_box_2[0.4803] det_loss_cls_3[0.3738] det_loss_box_3[0.4748] det_loss_cls_4[0.3496] det_loss_box_4[0.4651] det_loss_cls_5[0.3365] det_loss_box_5[0.4696] det_loss_cls_dn_0[0.0916] det_loss_box_dn_0[0.3701] det_loss_cls_dn_1[0.0869] det_loss_box_dn_1[0.3246] det_loss_cls_dn_2[0.0860] det_loss_box_dn_2[0.2987] det_loss_cls_dn_3[0.0847] det_loss_box_dn_3[0.2926] det_loss_cls_dn_4[0.0839] det_loss_box_dn_4[0.2890] det_loss_cls_dn_5[0.0834] det_loss_box_dn_5[0.2871] loss_depth[0.0379] total_loss[7.8985]
Rank[0/8] 06/20/2025 12:56:27 INFO stats.py:314 | Epoch[173] Step[104] GlobalStep[67574] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 8:45:23.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:56:49 INFO loss_tracker.py:84 | Epoch[173/239] Step[124] GlobalStep[67594/NA]: det_loss_cls_0[0.4857] det_loss_box_0[0.6184] det_loss_cls_1[0.4702] det_loss_box_1[0.4737] det_loss_cls_2[0.4239] det_loss_box_2[0.4257] det_loss_cls_3[0.3907] det_loss_box_3[0.4029] det_loss_cls_4[0.3687] det_loss_box_4[0.3892] det_loss_cls_5[0.3525] det_loss_box_5[0.3942] det_loss_cls_dn_0[0.0876] det_loss_box_dn_0[0.2486] det_loss_cls_dn_1[0.0836] det_loss_box_dn_1[0.2032] det_loss_cls_dn_2[0.0823] det_loss_box_dn_2[0.1843] det_loss_cls_dn_3[0.0816] det_loss_box_dn_3[0.1804] det_loss_cls_dn_4[0.0808] det_loss_box_dn_4[0.1784] det_loss_cls_dn_5[0.0801] det_loss_box_dn_5[0.1776] loss_depth[0.0365] total_loss[6.9011]
Rank[0/8] 06/20/2025 12:56:54 INFO stats.py:314 | Epoch[173] Step[129] GlobalStep[67599] Training Speed: 8.22 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 8:44:51.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:57:14 INFO loss_tracker.py:84 | Epoch[173/239] Step[149] GlobalStep[67619/NA]: det_loss_cls_0[0.4943] det_loss_box_0[0.6321] det_loss_cls_1[0.4734] det_loss_box_1[0.4852] det_loss_cls_2[0.4009] det_loss_box_2[0.4436] det_loss_cls_3[0.3702] det_loss_box_3[0.4276] det_loss_cls_4[0.3496] det_loss_box_4[0.4109] det_loss_cls_5[0.3343] det_loss_box_5[0.4085] det_loss_cls_dn_0[0.0919] det_loss_box_dn_0[0.2648] det_loss_cls_dn_1[0.0884] det_loss_box_dn_1[0.2165] det_loss_cls_dn_2[0.0873] det_loss_box_dn_2[0.1974] det_loss_cls_dn_3[0.0864] det_loss_box_dn_3[0.1926] det_loss_cls_dn_4[0.0855] det_loss_box_dn_4[0.1900] det_loss_cls_dn_5[0.0848] det_loss_box_dn_5[0.1870] loss_depth[0.0358] total_loss[7.0393]
Rank[0/8] 06/20/2025 12:57:19 INFO stats.py:314 | Epoch[173] Step[154] GlobalStep[67624] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 8:44:19.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:57:40 INFO loss_tracker.py:84 | Epoch[173/239] Step[174] GlobalStep[67644/NA]: det_loss_cls_0[0.5745] det_loss_box_0[0.7329] det_loss_cls_1[0.5462] det_loss_box_1[0.5465] det_loss_cls_2[0.4972] det_loss_box_2[0.4945] det_loss_cls_3[0.4699] det_loss_box_3[0.4766] det_loss_cls_4[0.4439] det_loss_box_4[0.4614] det_loss_cls_5[0.4386] det_loss_box_5[0.4620] det_loss_cls_dn_0[0.0976] det_loss_box_dn_0[0.3762] det_loss_cls_dn_1[0.0947] det_loss_box_dn_1[0.3015] det_loss_cls_dn_2[0.0938] det_loss_box_dn_2[0.2767] det_loss_cls_dn_3[0.0927] det_loss_box_dn_3[0.2714] det_loss_cls_dn_4[0.0913] det_loss_box_dn_4[0.2664] det_loss_cls_dn_5[0.0908] det_loss_box_dn_5[0.2650] loss_depth[0.0378] total_loss[8.5002]
Rank[0/8] 06/20/2025 12:57:45 INFO stats.py:314 | Epoch[173] Step[179] GlobalStep[67649] Training Speed: 8.05 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 8:43:47.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:58:06 INFO loss_tracker.py:84 | Epoch[173/239] Step[199] GlobalStep[67669/NA]: det_loss_cls_0[0.4915] det_loss_box_0[0.6035] det_loss_cls_1[0.4765] det_loss_box_1[0.4491] det_loss_cls_2[0.4146] det_loss_box_2[0.4184] det_loss_cls_3[0.3775] det_loss_box_3[0.3975] det_loss_cls_4[0.3515] det_loss_box_4[0.3855] det_loss_cls_5[0.3346] det_loss_box_5[0.3903] det_loss_cls_dn_0[0.0944] det_loss_box_dn_0[0.2499] det_loss_cls_dn_1[0.0897] det_loss_box_dn_1[0.2059] det_loss_cls_dn_2[0.0890] det_loss_box_dn_2[0.1908] det_loss_cls_dn_3[0.0879] det_loss_box_dn_3[0.1875] det_loss_cls_dn_4[0.0866] det_loss_box_dn_4[0.1863] det_loss_cls_dn_5[0.0856] det_loss_box_dn_5[0.1851] loss_depth[0.0337] total_loss[6.8630]
Rank[0/8] 06/20/2025 12:58:11 INFO stats.py:314 | Epoch[173] Step[204] GlobalStep[67674] Training Speed: 8.34 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 8:43:15.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:58:31 INFO loss_tracker.py:84 | Epoch[173/239] Step[224] GlobalStep[67694/NA]: det_loss_cls_0[0.4969] det_loss_box_0[0.6128] det_loss_cls_1[0.4819] det_loss_box_1[0.4466] det_loss_cls_2[0.4131] det_loss_box_2[0.4125] det_loss_cls_3[0.3795] det_loss_box_3[0.4037] det_loss_cls_4[0.3489] det_loss_box_4[0.4009] det_loss_cls_5[0.3318] det_loss_box_5[0.3995] det_loss_cls_dn_0[0.0907] det_loss_box_dn_0[0.2320] det_loss_cls_dn_1[0.0866] det_loss_box_dn_1[0.1916] det_loss_cls_dn_2[0.0853] det_loss_box_dn_2[0.1761] det_loss_cls_dn_3[0.0840] det_loss_box_dn_3[0.1726] det_loss_cls_dn_4[0.0831] det_loss_box_dn_4[0.1709] det_loss_cls_dn_5[0.0825] det_loss_box_dn_5[0.1684] loss_depth[0.0332] total_loss[6.7849]
Rank[0/8] 06/20/2025 12:58:36 INFO stats.py:314 | Epoch[173] Step[229] GlobalStep[67699] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 8:42:43.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:58:57 INFO loss_tracker.py:84 | Epoch[173/239] Step[249] GlobalStep[67719/NA]: det_loss_cls_0[0.4755] det_loss_box_0[0.6323] det_loss_cls_1[0.4715] det_loss_box_1[0.4890] det_loss_cls_2[0.4123] det_loss_box_2[0.4467] det_loss_cls_3[0.3779] det_loss_box_3[0.4385] det_loss_cls_4[0.3568] det_loss_box_4[0.4165] det_loss_cls_5[0.3482] det_loss_box_5[0.4172] det_loss_cls_dn_0[0.0908] det_loss_box_dn_0[0.2960] det_loss_cls_dn_1[0.0864] det_loss_box_dn_1[0.2533] det_loss_cls_dn_2[0.0849] det_loss_box_dn_2[0.2356] det_loss_cls_dn_3[0.0838] det_loss_box_dn_3[0.2319] det_loss_cls_dn_4[0.0831] det_loss_box_dn_4[0.2293] det_loss_cls_dn_5[0.0823] det_loss_box_dn_5[0.2273] loss_depth[0.0341] total_loss[7.3012]
Rank[0/8] 06/20/2025 12:59:02 INFO stats.py:314 | Epoch[173] Step[254] GlobalStep[67724] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 8:42:11.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:59:23 INFO loss_tracker.py:84 | Epoch[173/239] Step[274] GlobalStep[67744/NA]: det_loss_cls_0[0.4857] det_loss_box_0[0.6142] det_loss_cls_1[0.4855] det_loss_box_1[0.4546] det_loss_cls_2[0.4244] det_loss_box_2[0.4060] det_loss_cls_3[0.4038] det_loss_box_3[0.3876] det_loss_cls_4[0.3723] det_loss_box_4[0.3860] det_loss_cls_5[0.3625] det_loss_box_5[0.3824] det_loss_cls_dn_0[0.0983] det_loss_box_dn_0[0.2504] det_loss_cls_dn_1[0.0958] det_loss_box_dn_1[0.2077] det_loss_cls_dn_2[0.0951] det_loss_box_dn_2[0.1934] det_loss_cls_dn_3[0.0936] det_loss_box_dn_3[0.1897] det_loss_cls_dn_4[0.0925] det_loss_box_dn_4[0.1877] det_loss_cls_dn_5[0.0919] det_loss_box_dn_5[0.1847] loss_depth[0.0349] total_loss[6.9810]
Rank[0/8] 06/20/2025 12:59:29 INFO stats.py:314 | Epoch[173] Step[279] GlobalStep[67749] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 8:41:39.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 12:59:49 INFO loss_tracker.py:84 | Epoch[173/239] Step[299] GlobalStep[67769/NA]: det_loss_cls_0[0.4265] det_loss_box_0[0.5218] det_loss_cls_1[0.4132] det_loss_box_1[0.3966] det_loss_cls_2[0.3527] det_loss_box_2[0.3748] det_loss_cls_3[0.3206] det_loss_box_3[0.3519] det_loss_cls_4[0.3002] det_loss_box_4[0.3504] det_loss_cls_5[0.2816] det_loss_box_5[0.3568] det_loss_cls_dn_0[0.0931] det_loss_box_dn_0[0.2752] det_loss_cls_dn_1[0.0905] det_loss_box_dn_1[0.2265] det_loss_cls_dn_2[0.0894] det_loss_box_dn_2[0.2089] det_loss_cls_dn_3[0.0889] det_loss_box_dn_3[0.2041] det_loss_cls_dn_4[0.0881] det_loss_box_dn_4[0.2013] det_loss_cls_dn_5[0.0874] det_loss_box_dn_5[0.1997] loss_depth[0.0334] total_loss[6.3338]
Rank[0/8] 06/20/2025 12:59:54 INFO stats.py:314 | Epoch[173] Step[304] GlobalStep[67774] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 8:41:07.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:00:14 INFO loss_tracker.py:84 | Epoch[173/239] Step[324] GlobalStep[67794/NA]: det_loss_cls_0[0.4343] det_loss_box_0[0.6262] det_loss_cls_1[0.4000] det_loss_box_1[0.4779] det_loss_cls_2[0.3673] det_loss_box_2[0.4202] det_loss_cls_3[0.3358] det_loss_box_3[0.4186] det_loss_cls_4[0.3275] det_loss_box_4[0.4000] det_loss_cls_5[0.3173] det_loss_box_5[0.3965] det_loss_cls_dn_0[0.0892] det_loss_box_dn_0[0.3457] det_loss_cls_dn_1[0.0847] det_loss_box_dn_1[0.2841] det_loss_cls_dn_2[0.0840] det_loss_box_dn_2[0.2639] det_loss_cls_dn_3[0.0826] det_loss_box_dn_3[0.2573] det_loss_cls_dn_4[0.0820] det_loss_box_dn_4[0.2542] det_loss_cls_dn_5[0.0812] det_loss_box_dn_5[0.2466] loss_depth[0.0379] total_loss[7.1148]
Rank[0/8] 06/20/2025 13:00:20 INFO stats.py:314 | Epoch[173] Step[329] GlobalStep[67799] Training Speed: 6.21 samples/sec across all devices.	Average Step Time: 1.29 sec.	Estimated Remaining Time: 8:40:35.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:00:40 INFO loss_tracker.py:84 | Epoch[173/239] Step[349] GlobalStep[67819/NA]: det_loss_cls_0[0.4660] det_loss_box_0[0.6329] det_loss_cls_1[0.4597] det_loss_box_1[0.4750] det_loss_cls_2[0.3948] det_loss_box_2[0.4446] det_loss_cls_3[0.3579] det_loss_box_3[0.4364] det_loss_cls_4[0.3391] det_loss_box_4[0.4208] det_loss_cls_5[0.3202] det_loss_box_5[0.4219] det_loss_cls_dn_0[0.0863] det_loss_box_dn_0[0.2782] det_loss_cls_dn_1[0.0827] det_loss_box_dn_1[0.2301] det_loss_cls_dn_2[0.0811] det_loss_box_dn_2[0.2129] det_loss_cls_dn_3[0.0803] det_loss_box_dn_3[0.2068] det_loss_cls_dn_4[0.0795] det_loss_box_dn_4[0.2039] det_loss_cls_dn_5[0.0786] det_loss_box_dn_5[0.2010] loss_depth[0.0349] total_loss[7.0255]
Rank[0/8] 06/20/2025 13:00:45 INFO stats.py:314 | Epoch[173] Step[354] GlobalStep[67824] Training Speed: 8.13 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 8:40:03.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:01:06 INFO loss_tracker.py:84 | Epoch[173/239] Step[374] GlobalStep[67844/NA]: det_loss_cls_0[0.5506] det_loss_box_0[0.5878] det_loss_cls_1[0.5350] det_loss_box_1[0.4286] det_loss_cls_2[0.4457] det_loss_box_2[0.3941] det_loss_cls_3[0.4137] det_loss_box_3[0.3770] det_loss_cls_4[0.3873] det_loss_box_4[0.3657] det_loss_cls_5[0.3714] det_loss_box_5[0.3659] det_loss_cls_dn_0[0.0964] det_loss_box_dn_0[0.2220] det_loss_cls_dn_1[0.0929] det_loss_box_dn_1[0.1782] det_loss_cls_dn_2[0.0921] det_loss_box_dn_2[0.1586] det_loss_cls_dn_3[0.0909] det_loss_box_dn_3[0.1554] det_loss_cls_dn_4[0.0898] det_loss_box_dn_4[0.1536] det_loss_cls_dn_5[0.0889] det_loss_box_dn_5[0.1521] loss_depth[0.0350] total_loss[6.8290]
Rank[0/8] 06/20/2025 13:01:11 INFO stats.py:314 | Epoch[173] Step[379] GlobalStep[67849] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 8:39:31.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:01:22 INFO stats.py:394 | Epoch[173] completed. Training Speed: 7.73 samples/sec across all devices.	Epoch Time: 403.57 sec.	Average Epoch Time: 403.57 sec.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 8:39:19.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:01:39 INFO stats.py:314 | Epoch[174] Step[14] GlobalStep[67874] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 8:39:00.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:01:49 INFO loss_tracker.py:84 | Epoch[174/239] Step[24] GlobalStep[67884/NA]: det_loss_cls_0[0.4330] det_loss_box_0[0.5082] det_loss_cls_1[0.4156] det_loss_box_1[0.3935] det_loss_cls_2[0.3693] det_loss_box_2[0.3592] det_loss_cls_3[0.3327] det_loss_box_3[0.3530] det_loss_cls_4[0.3077] det_loss_box_4[0.3435] det_loss_cls_5[0.2946] det_loss_box_5[0.3414] det_loss_cls_dn_0[0.0924] det_loss_box_dn_0[0.2606] det_loss_cls_dn_1[0.0894] det_loss_box_dn_1[0.2158] det_loss_cls_dn_2[0.0889] det_loss_box_dn_2[0.1986] det_loss_cls_dn_3[0.0881] det_loss_box_dn_3[0.1952] det_loss_cls_dn_4[0.0874] det_loss_box_dn_4[0.1927] det_loss_cls_dn_5[0.0866] det_loss_box_dn_5[0.1905] loss_depth[0.0361] total_loss[6.2739]
Rank[0/8] 06/20/2025 13:02:05 INFO stats.py:314 | Epoch[174] Step[39] GlobalStep[67899] Training Speed: 8.19 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 8:38:28.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:02:15 INFO loss_tracker.py:84 | Epoch[174/239] Step[49] GlobalStep[67909/NA]: det_loss_cls_0[0.4048] det_loss_box_0[0.5126] det_loss_cls_1[0.3929] det_loss_box_1[0.3842] det_loss_cls_2[0.3303] det_loss_box_2[0.3525] det_loss_cls_3[0.2920] det_loss_box_3[0.3395] det_loss_cls_4[0.2754] det_loss_box_4[0.3275] det_loss_cls_5[0.2678] det_loss_box_5[0.3216] det_loss_cls_dn_0[0.0938] det_loss_box_dn_0[0.3565] det_loss_cls_dn_1[0.0909] det_loss_box_dn_1[0.2902] det_loss_cls_dn_2[0.0899] det_loss_box_dn_2[0.2640] det_loss_cls_dn_3[0.0884] det_loss_box_dn_3[0.2543] det_loss_cls_dn_4[0.0873] det_loss_box_dn_4[0.2521] det_loss_cls_dn_5[0.0867] det_loss_box_dn_5[0.2361] loss_depth[0.0360] total_loss[6.4274]
Rank[0/8] 06/20/2025 13:02:31 INFO stats.py:314 | Epoch[174] Step[64] GlobalStep[67924] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 8:37:56.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:02:41 INFO loss_tracker.py:84 | Epoch[174/239] Step[74] GlobalStep[67934/NA]: det_loss_cls_0[0.4817] det_loss_box_0[0.5621] det_loss_cls_1[0.4695] det_loss_box_1[0.4126] det_loss_cls_2[0.4078] det_loss_box_2[0.3782] det_loss_cls_3[0.3643] det_loss_box_3[0.3678] det_loss_cls_4[0.3367] det_loss_box_4[0.3616] det_loss_cls_5[0.3264] det_loss_box_5[0.3616] det_loss_cls_dn_0[0.0877] det_loss_box_dn_0[0.2256] det_loss_cls_dn_1[0.0839] det_loss_box_dn_1[0.1819] det_loss_cls_dn_2[0.0829] det_loss_box_dn_2[0.1670] det_loss_cls_dn_3[0.0818] det_loss_box_dn_3[0.1666] det_loss_cls_dn_4[0.0812] det_loss_box_dn_4[0.1635] det_loss_cls_dn_5[0.0806] det_loss_box_dn_5[0.1621] loss_depth[0.0355] total_loss[6.4307]
Rank[0/8] 06/20/2025 13:02:57 INFO stats.py:314 | Epoch[174] Step[89] GlobalStep[67949] Training Speed: 7.87 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 8:37:24.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:03:07 INFO loss_tracker.py:84 | Epoch[174/239] Step[99] GlobalStep[67959/NA]: det_loss_cls_0[0.4915] det_loss_box_0[0.6524] det_loss_cls_1[0.4664] det_loss_box_1[0.5140] det_loss_cls_2[0.4052] det_loss_box_2[0.4710] det_loss_cls_3[0.3753] det_loss_box_3[0.4575] det_loss_cls_4[0.3488] det_loss_box_4[0.4412] det_loss_cls_5[0.3364] det_loss_box_5[0.4463] det_loss_cls_dn_0[0.0889] det_loss_box_dn_0[0.2836] det_loss_cls_dn_1[0.0860] det_loss_box_dn_1[0.2394] det_loss_cls_dn_2[0.0839] det_loss_box_dn_2[0.2227] det_loss_cls_dn_3[0.0822] det_loss_box_dn_3[0.2189] det_loss_cls_dn_4[0.0808] det_loss_box_dn_4[0.2180] det_loss_cls_dn_5[0.0803] det_loss_box_dn_5[0.2170] loss_depth[0.0340] total_loss[7.3418]
Rank[0/8] 06/20/2025 13:03:22 INFO stats.py:314 | Epoch[174] Step[114] GlobalStep[67974] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 8:36:52.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:03:33 INFO loss_tracker.py:84 | Epoch[174/239] Step[124] GlobalStep[67984/NA]: det_loss_cls_0[0.5200] det_loss_box_0[0.6338] det_loss_cls_1[0.4867] det_loss_box_1[0.5079] det_loss_cls_2[0.4280] det_loss_box_2[0.4606] det_loss_cls_3[0.3922] det_loss_box_3[0.4416] det_loss_cls_4[0.3632] det_loss_box_4[0.4385] det_loss_cls_5[0.3485] det_loss_box_5[0.4355] det_loss_cls_dn_0[0.0822] det_loss_box_dn_0[0.2762] det_loss_cls_dn_1[0.0786] det_loss_box_dn_1[0.2266] det_loss_cls_dn_2[0.0777] det_loss_box_dn_2[0.2085] det_loss_cls_dn_3[0.0768] det_loss_box_dn_3[0.2057] det_loss_cls_dn_4[0.0762] det_loss_box_dn_4[0.2044] det_loss_cls_dn_5[0.0755] det_loss_box_dn_5[0.1991] loss_depth[0.0369] total_loss[7.2808]
Rank[0/8] 06/20/2025 13:03:49 INFO stats.py:314 | Epoch[174] Step[139] GlobalStep[67999] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 8:36:21.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:03:59 INFO loss_tracker.py:84 | Epoch[174/239] Step[149] GlobalStep[68009/NA]: det_loss_cls_0[0.4202] det_loss_box_0[0.5446] det_loss_cls_1[0.4122] det_loss_box_1[0.4350] det_loss_cls_2[0.3562] det_loss_box_2[0.3820] det_loss_cls_3[0.3361] det_loss_box_3[0.3686] det_loss_cls_4[0.3116] det_loss_box_4[0.3574] det_loss_cls_5[0.3012] det_loss_box_5[0.3540] det_loss_cls_dn_0[0.0871] det_loss_box_dn_0[0.2747] det_loss_cls_dn_1[0.0835] det_loss_box_dn_1[0.2282] det_loss_cls_dn_2[0.0825] det_loss_box_dn_2[0.2135] det_loss_cls_dn_3[0.0809] det_loss_box_dn_3[0.2096] det_loss_cls_dn_4[0.0801] det_loss_box_dn_4[0.2058] det_loss_cls_dn_5[0.0794] det_loss_box_dn_5[0.2022] loss_depth[0.0354] total_loss[6.4422]
Rank[0/8] 06/20/2025 13:04:14 INFO stats.py:314 | Epoch[174] Step[164] GlobalStep[68024] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 8:35:49.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:04:25 INFO loss_tracker.py:84 | Epoch[174/239] Step[174] GlobalStep[68034/NA]: det_loss_cls_0[0.3702] det_loss_box_0[0.4753] det_loss_cls_1[0.3666] det_loss_box_1[0.3576] det_loss_cls_2[0.3205] det_loss_box_2[0.3096] det_loss_cls_3[0.2943] det_loss_box_3[0.2969] det_loss_cls_4[0.2724] det_loss_box_4[0.2856] det_loss_cls_5[0.2565] det_loss_box_5[0.2912] det_loss_cls_dn_0[0.0898] det_loss_box_dn_0[0.2730] det_loss_cls_dn_1[0.0866] det_loss_box_dn_1[0.2185] det_loss_cls_dn_2[0.0859] det_loss_box_dn_2[0.2025] det_loss_cls_dn_3[0.0851] det_loss_box_dn_3[0.1971] det_loss_cls_dn_4[0.0844] det_loss_box_dn_4[0.1941] det_loss_cls_dn_5[0.0830] det_loss_box_dn_5[0.1911] loss_depth[0.0369] total_loss[5.7245]
Rank[0/8] 06/20/2025 13:04:40 INFO stats.py:314 | Epoch[174] Step[189] GlobalStep[68049] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 8:35:17.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:04:51 INFO loss_tracker.py:84 | Epoch[174/239] Step[199] GlobalStep[68059/NA]: det_loss_cls_0[0.4282] det_loss_box_0[0.5497] det_loss_cls_1[0.4166] det_loss_box_1[0.4316] det_loss_cls_2[0.3701] det_loss_box_2[0.3802] det_loss_cls_3[0.3384] det_loss_box_3[0.3671] det_loss_cls_4[0.3138] det_loss_box_4[0.3587] det_loss_cls_5[0.2985] det_loss_box_5[0.3670] det_loss_cls_dn_0[0.0841] det_loss_box_dn_0[0.3473] det_loss_cls_dn_1[0.0809] det_loss_box_dn_1[0.2902] det_loss_cls_dn_2[0.0801] det_loss_box_dn_2[0.2715] det_loss_cls_dn_3[0.0791] det_loss_box_dn_3[0.2677] det_loss_cls_dn_4[0.0782] det_loss_box_dn_4[0.2642] det_loss_cls_dn_5[0.0775] det_loss_box_dn_5[0.2554] loss_depth[0.0374] total_loss[6.8335]
Rank[0/8] 06/20/2025 13:05:06 INFO stats.py:314 | Epoch[174] Step[214] GlobalStep[68074] Training Speed: 8.19 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 8:34:45.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:05:17 INFO loss_tracker.py:84 | Epoch[174/239] Step[224] GlobalStep[68084/NA]: det_loss_cls_0[0.4459] det_loss_box_0[0.5456] det_loss_cls_1[0.4270] det_loss_box_1[0.4279] det_loss_cls_2[0.3804] det_loss_box_2[0.3864] det_loss_cls_3[0.3506] det_loss_box_3[0.3746] det_loss_cls_4[0.3219] det_loss_box_4[0.3647] det_loss_cls_5[0.3139] det_loss_box_5[0.3636] det_loss_cls_dn_0[0.0935] det_loss_box_dn_0[0.2909] det_loss_cls_dn_1[0.0895] det_loss_box_dn_1[0.2391] det_loss_cls_dn_2[0.0883] det_loss_box_dn_2[0.2200] det_loss_cls_dn_3[0.0873] det_loss_box_dn_3[0.2131] det_loss_cls_dn_4[0.0860] det_loss_box_dn_4[0.2118] det_loss_cls_dn_5[0.0856] det_loss_box_dn_5[0.2101] loss_depth[0.0356] total_loss[6.6531]
Rank[0/8] 06/20/2025 13:05:32 INFO stats.py:314 | Epoch[174] Step[239] GlobalStep[68099] Training Speed: 8.03 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 8:34:13.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:05:42 INFO loss_tracker.py:84 | Epoch[174/239] Step[249] GlobalStep[68109/NA]: det_loss_cls_0[0.5199] det_loss_box_0[0.6566] det_loss_cls_1[0.5029] det_loss_box_1[0.5104] det_loss_cls_2[0.4410] det_loss_box_2[0.4702] det_loss_cls_3[0.4096] det_loss_box_3[0.4504] det_loss_cls_4[0.3925] det_loss_box_4[0.4383] det_loss_cls_5[0.3645] det_loss_box_5[0.4493] det_loss_cls_dn_0[0.0903] det_loss_box_dn_0[0.3204] det_loss_cls_dn_1[0.0868] det_loss_box_dn_1[0.2647] det_loss_cls_dn_2[0.0861] det_loss_box_dn_2[0.2469] det_loss_cls_dn_3[0.0842] det_loss_box_dn_3[0.2420] det_loss_cls_dn_4[0.0839] det_loss_box_dn_4[0.2386] det_loss_cls_dn_5[0.0829] det_loss_box_dn_5[0.2337] loss_depth[0.0375] total_loss[7.7035]
Rank[0/8] 06/20/2025 13:05:58 INFO stats.py:314 | Epoch[174] Step[264] GlobalStep[68124] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 8:33:41.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:06:08 INFO loss_tracker.py:84 | Epoch[174/239] Step[274] GlobalStep[68134/NA]: det_loss_cls_0[0.5012] det_loss_box_0[0.6272] det_loss_cls_1[0.4877] det_loss_box_1[0.4832] det_loss_cls_2[0.4193] det_loss_box_2[0.4318] det_loss_cls_3[0.3908] det_loss_box_3[0.4162] det_loss_cls_4[0.3557] det_loss_box_4[0.4093] det_loss_cls_5[0.3412] det_loss_box_5[0.4088] det_loss_cls_dn_0[0.0849] det_loss_box_dn_0[0.2369] det_loss_cls_dn_1[0.0811] det_loss_box_dn_1[0.1897] det_loss_cls_dn_2[0.0802] det_loss_box_dn_2[0.1749] det_loss_cls_dn_3[0.0792] det_loss_box_dn_3[0.1720] det_loss_cls_dn_4[0.0777] det_loss_box_dn_4[0.1707] det_loss_cls_dn_5[0.0767] det_loss_box_dn_5[0.1695] loss_depth[0.0351] total_loss[6.9009]
Rank[0/8] 06/20/2025 13:06:23 INFO stats.py:314 | Epoch[174] Step[289] GlobalStep[68149] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 8:33:09.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:06:34 INFO loss_tracker.py:84 | Epoch[174/239] Step[299] GlobalStep[68159/NA]: det_loss_cls_0[0.3644] det_loss_box_0[0.4671] det_loss_cls_1[0.3479] det_loss_box_1[0.3695] det_loss_cls_2[0.2974] det_loss_box_2[0.3266] det_loss_cls_3[0.2707] det_loss_box_3[0.3126] det_loss_cls_4[0.2527] det_loss_box_4[0.3009] det_loss_cls_5[0.2423] det_loss_box_5[0.3060] det_loss_cls_dn_0[0.0889] det_loss_box_dn_0[0.3083] det_loss_cls_dn_1[0.0839] det_loss_box_dn_1[0.2471] det_loss_cls_dn_2[0.0827] det_loss_box_dn_2[0.2244] det_loss_cls_dn_3[0.0815] det_loss_box_dn_3[0.2114] det_loss_cls_dn_4[0.0811] det_loss_box_dn_4[0.2081] det_loss_cls_dn_5[0.0799] det_loss_box_dn_5[0.2014] loss_depth[0.0363] total_loss[5.7931]
Rank[0/8] 06/20/2025 13:06:50 INFO stats.py:314 | Epoch[174] Step[314] GlobalStep[68174] Training Speed: 7.98 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 8:32:37.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:07:00 INFO loss_tracker.py:84 | Epoch[174/239] Step[324] GlobalStep[68184/NA]: det_loss_cls_0[0.4971] det_loss_box_0[0.6410] det_loss_cls_1[0.4903] det_loss_box_1[0.4659] det_loss_cls_2[0.4178] det_loss_box_2[0.4187] det_loss_cls_3[0.3855] det_loss_box_3[0.4042] det_loss_cls_4[0.3570] det_loss_box_4[0.3994] det_loss_cls_5[0.3424] det_loss_box_5[0.3988] det_loss_cls_dn_0[0.0916] det_loss_box_dn_0[0.2548] det_loss_cls_dn_1[0.0881] det_loss_box_dn_1[0.2081] det_loss_cls_dn_2[0.0869] det_loss_box_dn_2[0.1917] det_loss_cls_dn_3[0.0860] det_loss_box_dn_3[0.1866] det_loss_cls_dn_4[0.0847] det_loss_box_dn_4[0.1837] det_loss_cls_dn_5[0.0840] det_loss_box_dn_5[0.1803] loss_depth[0.0348] total_loss[6.9795]
Rank[0/8] 06/20/2025 13:07:16 INFO stats.py:314 | Epoch[174] Step[339] GlobalStep[68199] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 8:32:06.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:07:26 INFO loss_tracker.py:84 | Epoch[174/239] Step[349] GlobalStep[68209/NA]: det_loss_cls_0[0.4957] det_loss_box_0[0.6723] det_loss_cls_1[0.4861] det_loss_box_1[0.5246] det_loss_cls_2[0.4302] det_loss_box_2[0.4771] det_loss_cls_3[0.3870] det_loss_box_3[0.4588] det_loss_cls_4[0.3698] det_loss_box_4[0.4381] det_loss_cls_5[0.3529] det_loss_box_5[0.4321] det_loss_cls_dn_0[0.0889] det_loss_box_dn_0[0.3182] det_loss_cls_dn_1[0.0840] det_loss_box_dn_1[0.2609] det_loss_cls_dn_2[0.0831] det_loss_box_dn_2[0.2437] det_loss_cls_dn_3[0.0815] det_loss_box_dn_3[0.2350] det_loss_cls_dn_4[0.0802] det_loss_box_dn_4[0.2325] det_loss_cls_dn_5[0.0790] det_loss_box_dn_5[0.2299] loss_depth[0.0384] total_loss[7.5797]
Rank[0/8] 06/20/2025 13:07:41 INFO stats.py:314 | Epoch[174] Step[364] GlobalStep[68224] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 8:31:34.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:07:52 INFO loss_tracker.py:84 | Epoch[174/239] Step[374] GlobalStep[68234/NA]: det_loss_cls_0[0.5348] det_loss_box_0[0.7615] det_loss_cls_1[0.5017] det_loss_box_1[0.5952] det_loss_cls_2[0.4609] det_loss_box_2[0.5386] det_loss_cls_3[0.4291] det_loss_box_3[0.5128] det_loss_cls_4[0.4086] det_loss_box_4[0.4986] det_loss_cls_5[0.3949] det_loss_box_5[0.5013] det_loss_cls_dn_0[0.0939] det_loss_box_dn_0[0.3109] det_loss_cls_dn_1[0.0897] det_loss_box_dn_1[0.2679] det_loss_cls_dn_2[0.0889] det_loss_box_dn_2[0.2554] det_loss_cls_dn_3[0.0881] det_loss_box_dn_3[0.2521] det_loss_cls_dn_4[0.0872] det_loss_box_dn_4[0.2497] det_loss_cls_dn_5[0.0860] det_loss_box_dn_5[0.2467] loss_depth[0.0365] total_loss[8.2912]
Rank[0/8] 06/20/2025 13:08:08 INFO stats.py:314 | Epoch[174] Step[389] GlobalStep[68249] Training Speed: 7.49 samples/sec across all devices.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 8:31:02.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:08:08 INFO stats.py:394 | Epoch[174] completed. Training Speed: 7.68 samples/sec across all devices.	Epoch Time: 406.17 sec.	Average Epoch Time: 406.17 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 8:31:02.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:08:35 INFO stats.py:314 | Epoch[175] Step[24] GlobalStep[68274] Training Speed: 7.54 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 8:30:31.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:08:35 INFO loss_tracker.py:84 | Epoch[175/239] Step[24] GlobalStep[68274/NA]: det_loss_cls_0[0.5356] det_loss_box_0[0.6936] det_loss_cls_1[0.5151] det_loss_box_1[0.5386] det_loss_cls_2[0.4441] det_loss_box_2[0.4921] det_loss_cls_3[0.4069] det_loss_box_3[0.4754] det_loss_cls_4[0.3816] det_loss_box_4[0.4646] det_loss_cls_5[0.3688] det_loss_box_5[0.4626] det_loss_cls_dn_0[0.0937] det_loss_box_dn_0[0.3348] det_loss_cls_dn_1[0.0893] det_loss_box_dn_1[0.2838] det_loss_cls_dn_2[0.0886] det_loss_box_dn_2[0.2614] det_loss_cls_dn_3[0.0877] det_loss_box_dn_3[0.2571] det_loss_cls_dn_4[0.0865] det_loss_box_dn_4[0.2529] det_loss_cls_dn_5[0.0855] det_loss_box_dn_5[0.2519] loss_depth[0.0348] total_loss[7.9869]
Rank[0/8] 06/20/2025 13:09:00 INFO stats.py:314 | Epoch[175] Step[49] GlobalStep[68299] Training Speed: 7.76 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 8:29:59.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:09:00 INFO loss_tracker.py:84 | Epoch[175/239] Step[49] GlobalStep[68299/NA]: det_loss_cls_0[0.4252] det_loss_box_0[0.5730] det_loss_cls_1[0.4253] det_loss_box_1[0.4444] det_loss_cls_2[0.3757] det_loss_box_2[0.4094] det_loss_cls_3[0.3464] det_loss_box_3[0.3871] det_loss_cls_4[0.3187] det_loss_box_4[0.3777] det_loss_cls_5[0.3097] det_loss_box_5[0.3756] det_loss_cls_dn_0[0.0834] det_loss_box_dn_0[0.2689] det_loss_cls_dn_1[0.0790] det_loss_box_dn_1[0.2206] det_loss_cls_dn_2[0.0779] det_loss_box_dn_2[0.2030] det_loss_cls_dn_3[0.0771] det_loss_box_dn_3[0.2013] det_loss_cls_dn_4[0.0762] det_loss_box_dn_4[0.1991] det_loss_cls_dn_5[0.0754] det_loss_box_dn_5[0.1980] loss_depth[0.0367] total_loss[6.5649]
Rank[0/8] 06/20/2025 13:09:27 INFO stats.py:314 | Epoch[175] Step[74] GlobalStep[68324] Training Speed: 8.07 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 8:29:27.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:09:27 INFO loss_tracker.py:84 | Epoch[175/239] Step[74] GlobalStep[68324/NA]: det_loss_cls_0[0.5696] det_loss_box_0[0.6937] det_loss_cls_1[0.5437] det_loss_box_1[0.5249] det_loss_cls_2[0.4811] det_loss_box_2[0.4709] det_loss_cls_3[0.4463] det_loss_box_3[0.4607] det_loss_cls_4[0.4133] det_loss_box_4[0.4491] det_loss_cls_5[0.4051] det_loss_box_5[0.4452] det_loss_cls_dn_0[0.1000] det_loss_box_dn_0[0.2842] det_loss_cls_dn_1[0.0961] det_loss_box_dn_1[0.2411] det_loss_cls_dn_2[0.0948] det_loss_box_dn_2[0.2266] det_loss_cls_dn_3[0.0941] det_loss_box_dn_3[0.2212] det_loss_cls_dn_4[0.0932] det_loss_box_dn_4[0.2193] det_loss_cls_dn_5[0.0924] det_loss_box_dn_5[0.2163] loss_depth[0.0370] total_loss[7.9199]
Rank[0/8] 06/20/2025 13:09:52 INFO stats.py:314 | Epoch[175] Step[99] GlobalStep[68349] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 8:28:55.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:09:52 INFO loss_tracker.py:84 | Epoch[175/239] Step[99] GlobalStep[68349/NA]: det_loss_cls_0[0.4944] det_loss_box_0[0.6310] det_loss_cls_1[0.4797] det_loss_box_1[0.4740] det_loss_cls_2[0.4250] det_loss_box_2[0.4354] det_loss_cls_3[0.3899] det_loss_box_3[0.4181] det_loss_cls_4[0.3641] det_loss_box_4[0.4098] det_loss_cls_5[0.3534] det_loss_box_5[0.4066] det_loss_cls_dn_0[0.0887] det_loss_box_dn_0[0.2450] det_loss_cls_dn_1[0.0844] det_loss_box_dn_1[0.2050] det_loss_cls_dn_2[0.0834] det_loss_box_dn_2[0.1909] det_loss_cls_dn_3[0.0827] det_loss_box_dn_3[0.1889] det_loss_cls_dn_4[0.0823] det_loss_box_dn_4[0.1869] det_loss_cls_dn_5[0.0815] det_loss_box_dn_5[0.1859] loss_depth[0.0362] total_loss[7.0231]
Rank[0/8] 06/20/2025 13:10:18 INFO stats.py:314 | Epoch[175] Step[124] GlobalStep[68374] Training Speed: 7.95 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 8:28:23.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:10:18 INFO loss_tracker.py:84 | Epoch[175/239] Step[124] GlobalStep[68374/NA]: det_loss_cls_0[0.5197] det_loss_box_0[0.6439] det_loss_cls_1[0.5028] det_loss_box_1[0.4789] det_loss_cls_2[0.4563] det_loss_box_2[0.4282] det_loss_cls_3[0.4284] det_loss_box_3[0.4139] det_loss_cls_4[0.3970] det_loss_box_4[0.4023] det_loss_cls_5[0.3903] det_loss_box_5[0.4004] det_loss_cls_dn_0[0.0903] det_loss_box_dn_0[0.2466] det_loss_cls_dn_1[0.0871] det_loss_box_dn_1[0.2082] det_loss_cls_dn_2[0.0867] det_loss_box_dn_2[0.1914] det_loss_cls_dn_3[0.0851] det_loss_box_dn_3[0.1879] det_loss_cls_dn_4[0.0848] det_loss_box_dn_4[0.1856] det_loss_cls_dn_5[0.0840] det_loss_box_dn_5[0.1835] loss_depth[0.0356] total_loss[7.2188]
Rank[0/8] 06/20/2025 13:10:44 INFO stats.py:314 | Epoch[175] Step[149] GlobalStep[68399] Training Speed: 7.50 samples/sec across all devices.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 8:27:51.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:10:44 INFO loss_tracker.py:84 | Epoch[175/239] Step[149] GlobalStep[68399/NA]: det_loss_cls_0[0.4033] det_loss_box_0[0.5666] det_loss_cls_1[0.3828] det_loss_box_1[0.4326] det_loss_cls_2[0.3287] det_loss_box_2[0.3872] det_loss_cls_3[0.2976] det_loss_box_3[0.3716] det_loss_cls_4[0.2768] det_loss_box_4[0.3596] det_loss_cls_5[0.2648] det_loss_box_5[0.3632] det_loss_cls_dn_0[0.0789] det_loss_box_dn_0[0.3341] det_loss_cls_dn_1[0.0761] det_loss_box_dn_1[0.2778] det_loss_cls_dn_2[0.0752] det_loss_box_dn_2[0.2550] det_loss_cls_dn_3[0.0739] det_loss_box_dn_3[0.2505] det_loss_cls_dn_4[0.0730] det_loss_box_dn_4[0.2464] det_loss_cls_dn_5[0.0717] det_loss_box_dn_5[0.2439] loss_depth[0.0366] total_loss[6.5279]
Rank[0/8] 06/20/2025 13:11:10 INFO stats.py:314 | Epoch[175] Step[174] GlobalStep[68424] Training Speed: 8.22 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 8:27:20.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:11:10 INFO loss_tracker.py:84 | Epoch[175/239] Step[174] GlobalStep[68424/NA]: det_loss_cls_0[0.4218] det_loss_box_0[0.5500] det_loss_cls_1[0.4068] det_loss_box_1[0.4180] det_loss_cls_2[0.3515] det_loss_box_2[0.3804] det_loss_cls_3[0.3255] det_loss_box_3[0.3728] det_loss_cls_4[0.3084] det_loss_box_4[0.3520] det_loss_cls_5[0.2968] det_loss_box_5[0.3537] det_loss_cls_dn_0[0.0922] det_loss_box_dn_0[0.2787] det_loss_cls_dn_1[0.0883] det_loss_box_dn_1[0.2291] det_loss_cls_dn_2[0.0867] det_loss_box_dn_2[0.2099] det_loss_cls_dn_3[0.0861] det_loss_box_dn_3[0.2067] det_loss_cls_dn_4[0.0845] det_loss_box_dn_4[0.2044] det_loss_cls_dn_5[0.0840] det_loss_box_dn_5[0.2030] loss_depth[0.0385] total_loss[6.4298]
Rank[0/8] 06/20/2025 13:11:36 INFO stats.py:314 | Epoch[175] Step[199] GlobalStep[68449] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 8:26:48.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:11:36 INFO loss_tracker.py:84 | Epoch[175/239] Step[199] GlobalStep[68449/NA]: det_loss_cls_0[0.4522] det_loss_box_0[0.5209] det_loss_cls_1[0.4398] det_loss_box_1[0.3962] det_loss_cls_2[0.3833] det_loss_box_2[0.3538] det_loss_cls_3[0.3456] det_loss_box_3[0.3417] det_loss_cls_4[0.3265] det_loss_box_4[0.3331] det_loss_cls_5[0.3121] det_loss_box_5[0.3365] det_loss_cls_dn_0[0.0966] det_loss_box_dn_0[0.2420] det_loss_cls_dn_1[0.0933] det_loss_box_dn_1[0.2013] det_loss_cls_dn_2[0.0930] det_loss_box_dn_2[0.1840] det_loss_cls_dn_3[0.0916] det_loss_box_dn_3[0.1804] det_loss_cls_dn_4[0.0904] det_loss_box_dn_4[0.1788] det_loss_cls_dn_5[0.0898] det_loss_box_dn_5[0.1763] loss_depth[0.0343] total_loss[6.2935]
Rank[0/8] 06/20/2025 13:12:01 INFO stats.py:314 | Epoch[175] Step[224] GlobalStep[68474] Training Speed: 8.58 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 8:26:16.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:12:01 INFO loss_tracker.py:84 | Epoch[175/239] Step[224] GlobalStep[68474/NA]: det_loss_cls_0[0.5883] det_loss_box_0[0.6689] det_loss_cls_1[0.5655] det_loss_box_1[0.5245] det_loss_cls_2[0.5084] det_loss_box_2[0.4704] det_loss_cls_3[0.4670] det_loss_box_3[0.4571] det_loss_cls_4[0.4447] det_loss_box_4[0.4395] det_loss_cls_5[0.4319] det_loss_box_5[0.4364] det_loss_cls_dn_0[0.1020] det_loss_box_dn_0[0.2495] det_loss_cls_dn_1[0.0980] det_loss_box_dn_1[0.2060] det_loss_cls_dn_2[0.0973] det_loss_box_dn_2[0.1919] det_loss_cls_dn_3[0.0964] det_loss_box_dn_3[0.1886] det_loss_cls_dn_4[0.0956] det_loss_box_dn_4[0.1865] det_loss_cls_dn_5[0.0949] det_loss_box_dn_5[0.1853] loss_depth[0.0350] total_loss[7.8298]
Rank[0/8] 06/20/2025 13:12:28 INFO stats.py:314 | Epoch[175] Step[249] GlobalStep[68499] Training Speed: 8.58 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 8:25:44.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:12:28 INFO loss_tracker.py:84 | Epoch[175/239] Step[249] GlobalStep[68499/NA]: det_loss_cls_0[0.4550] det_loss_box_0[0.5821] det_loss_cls_1[0.4322] det_loss_box_1[0.4338] det_loss_cls_2[0.3899] det_loss_box_2[0.3995] det_loss_cls_3[0.3657] det_loss_box_3[0.3799] det_loss_cls_4[0.3403] det_loss_box_4[0.3851] det_loss_cls_5[0.3293] det_loss_box_5[0.3854] det_loss_cls_dn_0[0.0830] det_loss_box_dn_0[0.2677] det_loss_cls_dn_1[0.0800] det_loss_box_dn_1[0.2159] det_loss_cls_dn_2[0.0792] det_loss_box_dn_2[0.2007] det_loss_cls_dn_3[0.0781] det_loss_box_dn_3[0.1957] det_loss_cls_dn_4[0.0775] det_loss_box_dn_4[0.1939] det_loss_cls_dn_5[0.0769] det_loss_box_dn_5[0.1917] loss_depth[0.0378] total_loss[6.6565]
Rank[0/8] 06/20/2025 13:12:54 INFO stats.py:314 | Epoch[175] Step[274] GlobalStep[68524] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 8:25:12.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:12:54 INFO loss_tracker.py:84 | Epoch[175/239] Step[274] GlobalStep[68524/NA]: det_loss_cls_0[0.4528] det_loss_box_0[0.5453] det_loss_cls_1[0.4324] det_loss_box_1[0.4025] det_loss_cls_2[0.3777] det_loss_box_2[0.3745] det_loss_cls_3[0.3418] det_loss_box_3[0.3596] det_loss_cls_4[0.3242] det_loss_box_4[0.3511] det_loss_cls_5[0.3110] det_loss_box_5[0.3529] det_loss_cls_dn_0[0.0842] det_loss_box_dn_0[0.2376] det_loss_cls_dn_1[0.0800] det_loss_box_dn_1[0.1919] det_loss_cls_dn_2[0.0793] det_loss_box_dn_2[0.1749] det_loss_cls_dn_3[0.0786] det_loss_box_dn_3[0.1703] det_loss_cls_dn_4[0.0776] det_loss_box_dn_4[0.1677] det_loss_cls_dn_5[0.0767] det_loss_box_dn_5[0.1658] loss_depth[0.0343] total_loss[6.2447]
Rank[0/8] 06/20/2025 13:13:19 INFO stats.py:314 | Epoch[175] Step[299] GlobalStep[68549] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 8:24:40.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:13:19 INFO loss_tracker.py:84 | Epoch[175/239] Step[299] GlobalStep[68549/NA]: det_loss_cls_0[0.4052] det_loss_box_0[0.4984] det_loss_cls_1[0.3937] det_loss_box_1[0.3724] det_loss_cls_2[0.3417] det_loss_box_2[0.3432] det_loss_cls_3[0.3134] det_loss_box_3[0.3429] det_loss_cls_4[0.3016] det_loss_box_4[0.3279] det_loss_cls_5[0.2860] det_loss_box_5[0.3317] det_loss_cls_dn_0[0.0819] det_loss_box_dn_0[0.2850] det_loss_cls_dn_1[0.0779] det_loss_box_dn_1[0.2384] det_loss_cls_dn_2[0.0772] det_loss_box_dn_2[0.2135] det_loss_cls_dn_3[0.0764] det_loss_box_dn_3[0.2090] det_loss_cls_dn_4[0.0756] det_loss_box_dn_4[0.2059] det_loss_cls_dn_5[0.0746] det_loss_box_dn_5[0.1964] loss_depth[0.0368] total_loss[6.1067]
Rank[0/8] 06/20/2025 13:13:45 INFO stats.py:314 | Epoch[175] Step[324] GlobalStep[68574] Training Speed: 7.81 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 8:24:08.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:13:45 INFO loss_tracker.py:84 | Epoch[175/239] Step[324] GlobalStep[68574/NA]: det_loss_cls_0[0.4248] det_loss_box_0[0.5351] det_loss_cls_1[0.4142] det_loss_box_1[0.4131] det_loss_cls_2[0.3574] det_loss_box_2[0.3831] det_loss_cls_3[0.3260] det_loss_box_3[0.3737] det_loss_cls_4[0.3069] det_loss_box_4[0.3678] det_loss_cls_5[0.2923] det_loss_box_5[0.3695] det_loss_cls_dn_0[0.0875] det_loss_box_dn_0[0.2917] det_loss_cls_dn_1[0.0829] det_loss_box_dn_1[0.2449] det_loss_cls_dn_2[0.0820] det_loss_box_dn_2[0.2223] det_loss_cls_dn_3[0.0807] det_loss_box_dn_3[0.2167] det_loss_cls_dn_4[0.0800] det_loss_box_dn_4[0.2155] det_loss_cls_dn_5[0.0791] det_loss_box_dn_5[0.2131] loss_depth[0.0333] total_loss[6.4936]
Rank[0/8] 06/20/2025 13:14:11 INFO stats.py:314 | Epoch[175] Step[349] GlobalStep[68599] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 8:23:37.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:14:11 INFO loss_tracker.py:84 | Epoch[175/239] Step[349] GlobalStep[68599/NA]: det_loss_cls_0[0.4266] det_loss_box_0[0.5707] det_loss_cls_1[0.4225] det_loss_box_1[0.4139] det_loss_cls_2[0.3574] det_loss_box_2[0.3691] det_loss_cls_3[0.3283] det_loss_box_3[0.3610] det_loss_cls_4[0.3012] det_loss_box_4[0.3581] det_loss_cls_5[0.2911] det_loss_box_5[0.3595] det_loss_cls_dn_0[0.0895] det_loss_box_dn_0[0.2619] det_loss_cls_dn_1[0.0863] det_loss_box_dn_1[0.2159] det_loss_cls_dn_2[0.0855] det_loss_box_dn_2[0.1987] det_loss_cls_dn_3[0.0842] det_loss_box_dn_3[0.1950] det_loss_cls_dn_4[0.0830] det_loss_box_dn_4[0.1915] det_loss_cls_dn_5[0.0820] det_loss_box_dn_5[0.1849] loss_depth[0.0359] total_loss[6.3535]
Rank[0/8] 06/20/2025 13:14:36 INFO stats.py:314 | Epoch[175] Step[374] GlobalStep[68624] Training Speed: 7.19 samples/sec across all devices.	Average Step Time: 1.11 sec.	Estimated Remaining Time: 8:23:05.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:14:36 INFO loss_tracker.py:84 | Epoch[175/239] Step[374] GlobalStep[68624/NA]: det_loss_cls_0[0.4414] det_loss_box_0[0.5453] det_loss_cls_1[0.4406] det_loss_box_1[0.4069] det_loss_cls_2[0.3738] det_loss_box_2[0.3832] det_loss_cls_3[0.3376] det_loss_box_3[0.3721] det_loss_cls_4[0.3207] det_loss_box_4[0.3546] det_loss_cls_5[0.3015] det_loss_box_5[0.3611] det_loss_cls_dn_0[0.0970] det_loss_box_dn_0[0.2359] det_loss_cls_dn_1[0.0923] det_loss_box_dn_1[0.1942] det_loss_cls_dn_2[0.0918] det_loss_box_dn_2[0.1791] det_loss_cls_dn_3[0.0907] det_loss_box_dn_3[0.1741] det_loss_cls_dn_4[0.0901] det_loss_box_dn_4[0.1729] det_loss_cls_dn_5[0.0889] det_loss_box_dn_5[0.1704] loss_depth[0.0354] total_loss[6.3516]
Rank[0/8] 06/20/2025 13:14:51 INFO stats.py:394 | Epoch[175] completed. Training Speed: 7.73 samples/sec across all devices.	Epoch Time: 403.37 sec.	Average Epoch Time: 403.37 sec.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 8:22:45.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:15:05 INFO stats.py:314 | Epoch[176] Step[9] GlobalStep[68649] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 8:22:34.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:15:21 INFO loss_tracker.py:84 | Epoch[176/239] Step[24] GlobalStep[68664/NA]: det_loss_cls_0[0.4338] det_loss_box_0[0.5030] det_loss_cls_1[0.4163] det_loss_box_1[0.3657] det_loss_cls_2[0.3710] det_loss_box_2[0.3248] det_loss_cls_3[0.3397] det_loss_box_3[0.3158] det_loss_cls_4[0.3176] det_loss_box_4[0.3054] det_loss_cls_5[0.3026] det_loss_box_5[0.3090] det_loss_cls_dn_0[0.0979] det_loss_box_dn_0[0.2405] det_loss_cls_dn_1[0.0952] det_loss_box_dn_1[0.1937] det_loss_cls_dn_2[0.0941] det_loss_box_dn_2[0.1770] det_loss_cls_dn_3[0.0929] det_loss_box_dn_3[0.1736] det_loss_cls_dn_4[0.0920] det_loss_box_dn_4[0.1709] det_loss_cls_dn_5[0.0908] det_loss_box_dn_5[0.1697] loss_depth[0.0356] total_loss[6.0289]
Rank[0/8] 06/20/2025 13:15:31 INFO stats.py:314 | Epoch[176] Step[34] GlobalStep[68674] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 8:22:02.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:15:46 INFO loss_tracker.py:84 | Epoch[176/239] Step[49] GlobalStep[68689/NA]: det_loss_cls_0[0.4821] det_loss_box_0[0.6311] det_loss_cls_1[0.4587] det_loss_box_1[0.4677] det_loss_cls_2[0.3978] det_loss_box_2[0.4218] det_loss_cls_3[0.3637] det_loss_box_3[0.4107] det_loss_cls_4[0.3387] det_loss_box_4[0.4018] det_loss_cls_5[0.3202] det_loss_box_5[0.4066] det_loss_cls_dn_0[0.0889] det_loss_box_dn_0[0.3104] det_loss_cls_dn_1[0.0847] det_loss_box_dn_1[0.2533] det_loss_cls_dn_2[0.0837] det_loss_box_dn_2[0.2322] det_loss_cls_dn_3[0.0825] det_loss_box_dn_3[0.2266] det_loss_cls_dn_4[0.0819] det_loss_box_dn_4[0.2241] det_loss_cls_dn_5[0.0809] det_loss_box_dn_5[0.2221] loss_depth[0.0367] total_loss[7.1089]
Rank[0/8] 06/20/2025 13:15:56 INFO stats.py:314 | Epoch[176] Step[59] GlobalStep[68699] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 8:21:30.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:16:12 INFO loss_tracker.py:84 | Epoch[176/239] Step[74] GlobalStep[68714/NA]: det_loss_cls_0[0.4950] det_loss_box_0[0.6324] det_loss_cls_1[0.4801] det_loss_box_1[0.4624] det_loss_cls_2[0.4098] det_loss_box_2[0.4274] det_loss_cls_3[0.3798] det_loss_box_3[0.4036] det_loss_cls_4[0.3512] det_loss_box_4[0.4009] det_loss_cls_5[0.3369] det_loss_box_5[0.4026] det_loss_cls_dn_0[0.0848] det_loss_box_dn_0[0.2865] det_loss_cls_dn_1[0.0810] det_loss_box_dn_1[0.2363] det_loss_cls_dn_2[0.0802] det_loss_box_dn_2[0.2230] det_loss_cls_dn_3[0.0784] det_loss_box_dn_3[0.2189] det_loss_cls_dn_4[0.0771] det_loss_box_dn_4[0.2163] det_loss_cls_dn_5[0.0764] det_loss_box_dn_5[0.2134] loss_depth[0.0337] total_loss[7.0881]
Rank[0/8] 06/20/2025 13:16:22 INFO stats.py:314 | Epoch[176] Step[84] GlobalStep[68724] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 8:20:58.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:16:38 INFO loss_tracker.py:84 | Epoch[176/239] Step[99] GlobalStep[68739/NA]: det_loss_cls_0[0.4090] det_loss_box_0[0.5842] det_loss_cls_1[0.3947] det_loss_box_1[0.4396] det_loss_cls_2[0.3420] det_loss_box_2[0.4082] det_loss_cls_3[0.3261] det_loss_box_3[0.3882] det_loss_cls_4[0.3076] det_loss_box_4[0.3722] det_loss_cls_5[0.2994] det_loss_box_5[0.3684] det_loss_cls_dn_0[0.0895] det_loss_box_dn_0[0.3157] det_loss_cls_dn_1[0.0854] det_loss_box_dn_1[0.2589] det_loss_cls_dn_2[0.0843] det_loss_box_dn_2[0.2389] det_loss_cls_dn_3[0.0829] det_loss_box_dn_3[0.2343] det_loss_cls_dn_4[0.0811] det_loss_box_dn_4[0.2312] det_loss_cls_dn_5[0.0801] det_loss_box_dn_5[0.2281] loss_depth[0.0334] total_loss[6.6835]
Rank[0/8] 06/20/2025 13:16:48 INFO stats.py:314 | Epoch[176] Step[109] GlobalStep[68749] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 8:20:27.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:17:04 INFO loss_tracker.py:84 | Epoch[176/239] Step[124] GlobalStep[68764/NA]: det_loss_cls_0[0.5470] det_loss_box_0[0.6657] det_loss_cls_1[0.5341] det_loss_box_1[0.5281] det_loss_cls_2[0.4698] det_loss_box_2[0.4862] det_loss_cls_3[0.4257] det_loss_box_3[0.4695] det_loss_cls_4[0.4034] det_loss_box_4[0.4571] det_loss_cls_5[0.3926] det_loss_box_5[0.4587] det_loss_cls_dn_0[0.0982] det_loss_box_dn_0[0.2356] det_loss_cls_dn_1[0.0949] det_loss_box_dn_1[0.1952] det_loss_cls_dn_2[0.0944] det_loss_box_dn_2[0.1824] det_loss_cls_dn_3[0.0936] det_loss_box_dn_3[0.1793] det_loss_cls_dn_4[0.0931] det_loss_box_dn_4[0.1778] det_loss_cls_dn_5[0.0924] det_loss_box_dn_5[0.1769] loss_depth[0.0323] total_loss[7.5837]
Rank[0/8] 06/20/2025 13:17:14 INFO stats.py:314 | Epoch[176] Step[134] GlobalStep[68774] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 8:19:55.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:17:29 INFO loss_tracker.py:84 | Epoch[176/239] Step[149] GlobalStep[68789/NA]: det_loss_cls_0[0.5799] det_loss_box_0[0.7122] det_loss_cls_1[0.5590] det_loss_box_1[0.5302] det_loss_cls_2[0.4961] det_loss_box_2[0.4629] det_loss_cls_3[0.4678] det_loss_box_3[0.4375] det_loss_cls_4[0.4364] det_loss_box_4[0.4284] det_loss_cls_5[0.4247] det_loss_box_5[0.4284] det_loss_cls_dn_0[0.0929] det_loss_box_dn_0[0.2282] det_loss_cls_dn_1[0.0902] det_loss_box_dn_1[0.1799] det_loss_cls_dn_2[0.0891] det_loss_box_dn_2[0.1625] det_loss_cls_dn_3[0.0883] det_loss_box_dn_3[0.1582] det_loss_cls_dn_4[0.0875] det_loss_box_dn_4[0.1550] det_loss_cls_dn_5[0.0867] det_loss_box_dn_5[0.1542] loss_depth[0.0327] total_loss[7.5691]
Rank[0/8] 06/20/2025 13:17:39 INFO stats.py:314 | Epoch[176] Step[159] GlobalStep[68799] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 8:19:23.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:17:55 INFO loss_tracker.py:84 | Epoch[176/239] Step[174] GlobalStep[68814/NA]: det_loss_cls_0[0.4800] det_loss_box_0[0.5884] det_loss_cls_1[0.4721] det_loss_box_1[0.4367] det_loss_cls_2[0.4056] det_loss_box_2[0.3926] det_loss_cls_3[0.3689] det_loss_box_3[0.3768] det_loss_cls_4[0.3380] det_loss_box_4[0.3679] det_loss_cls_5[0.3205] det_loss_box_5[0.3716] det_loss_cls_dn_0[0.0863] det_loss_box_dn_0[0.2368] det_loss_cls_dn_1[0.0823] det_loss_box_dn_1[0.1876] det_loss_cls_dn_2[0.0812] det_loss_box_dn_2[0.1658] det_loss_cls_dn_3[0.0802] det_loss_box_dn_3[0.1621] det_loss_cls_dn_4[0.0790] det_loss_box_dn_4[0.1586] det_loss_cls_dn_5[0.0780] det_loss_box_dn_5[0.1563] loss_depth[0.0347] total_loss[6.5079]
Rank[0/8] 06/20/2025 13:18:05 INFO stats.py:314 | Epoch[176] Step[184] GlobalStep[68824] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 8:18:51.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:18:20 INFO loss_tracker.py:84 | Epoch[176/239] Step[199] GlobalStep[68839/NA]: det_loss_cls_0[0.5020] det_loss_box_0[0.6093] det_loss_cls_1[0.4890] det_loss_box_1[0.4608] det_loss_cls_2[0.4335] det_loss_box_2[0.4163] det_loss_cls_3[0.3970] det_loss_box_3[0.4090] det_loss_cls_4[0.3684] det_loss_box_4[0.3994] det_loss_cls_5[0.3511] det_loss_box_5[0.4041] det_loss_cls_dn_0[0.0918] det_loss_box_dn_0[0.2424] det_loss_cls_dn_1[0.0870] det_loss_box_dn_1[0.2020] det_loss_cls_dn_2[0.0860] det_loss_box_dn_2[0.1865] det_loss_cls_dn_3[0.0846] det_loss_box_dn_3[0.1837] det_loss_cls_dn_4[0.0837] det_loss_box_dn_4[0.1823] det_loss_cls_dn_5[0.0827] det_loss_box_dn_5[0.1807] loss_depth[0.0328] total_loss[6.9661]
Rank[0/8] 06/20/2025 13:18:31 INFO stats.py:314 | Epoch[176] Step[209] GlobalStep[68849] Training Speed: 7.18 samples/sec across all devices.	Average Step Time: 1.11 sec.	Estimated Remaining Time: 8:18:19.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:18:46 INFO loss_tracker.py:84 | Epoch[176/239] Step[224] GlobalStep[68864/NA]: det_loss_cls_0[0.6038] det_loss_box_0[0.7762] det_loss_cls_1[0.5864] det_loss_box_1[0.6050] det_loss_cls_2[0.5148] det_loss_box_2[0.5506] det_loss_cls_3[0.4850] det_loss_box_3[0.5338] det_loss_cls_4[0.4536] det_loss_box_4[0.5196] det_loss_cls_5[0.4345] det_loss_box_5[0.5177] det_loss_cls_dn_0[0.0799] det_loss_box_dn_0[0.2477] det_loss_cls_dn_1[0.0764] det_loss_box_dn_1[0.1979] det_loss_cls_dn_2[0.0754] det_loss_box_dn_2[0.1781] det_loss_cls_dn_3[0.0746] det_loss_box_dn_3[0.1744] det_loss_cls_dn_4[0.0733] det_loss_box_dn_4[0.1720] det_loss_cls_dn_5[0.0723] det_loss_box_dn_5[0.1704] loss_depth[0.0366] total_loss[8.2100]
Rank[0/8] 06/20/2025 13:18:56 INFO stats.py:314 | Epoch[176] Step[234] GlobalStep[68874] Training Speed: 8.05 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 8:17:47.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:19:11 INFO loss_tracker.py:84 | Epoch[176/239] Step[249] GlobalStep[68889/NA]: det_loss_cls_0[0.4646] det_loss_box_0[0.6428] det_loss_cls_1[0.4419] det_loss_box_1[0.4845] det_loss_cls_2[0.4008] det_loss_box_2[0.4419] det_loss_cls_3[0.3688] det_loss_box_3[0.4273] det_loss_cls_4[0.3446] det_loss_box_4[0.4110] det_loss_cls_5[0.3310] det_loss_box_5[0.4118] det_loss_cls_dn_0[0.0939] det_loss_box_dn_0[0.2855] det_loss_cls_dn_1[0.0890] det_loss_box_dn_1[0.2331] det_loss_cls_dn_2[0.0878] det_loss_box_dn_2[0.2177] det_loss_cls_dn_3[0.0857] det_loss_box_dn_3[0.2139] det_loss_cls_dn_4[0.0839] det_loss_box_dn_4[0.2115] det_loss_cls_dn_5[0.0827] det_loss_box_dn_5[0.2101] loss_depth[0.0360] total_loss[7.1018]
Rank[0/8] 06/20/2025 13:19:22 INFO stats.py:314 | Epoch[176] Step[259] GlobalStep[68899] Training Speed: 5.93 samples/sec across all devices.	Average Step Time: 1.35 sec.	Estimated Remaining Time: 8:17:16.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:19:38 INFO loss_tracker.py:84 | Epoch[176/239] Step[274] GlobalStep[68914/NA]: det_loss_cls_0[0.4172] det_loss_box_0[0.5379] det_loss_cls_1[0.4109] det_loss_box_1[0.3900] det_loss_cls_2[0.3493] det_loss_box_2[0.3447] det_loss_cls_3[0.3195] det_loss_box_3[0.3398] det_loss_cls_4[0.2942] det_loss_box_4[0.3345] det_loss_cls_5[0.2816] det_loss_box_5[0.3333] det_loss_cls_dn_0[0.0889] det_loss_box_dn_0[0.2687] det_loss_cls_dn_1[0.0845] det_loss_box_dn_1[0.2187] det_loss_cls_dn_2[0.0832] det_loss_box_dn_2[0.1991] det_loss_cls_dn_3[0.0822] det_loss_box_dn_3[0.1949] det_loss_cls_dn_4[0.0813] det_loss_box_dn_4[0.1925] det_loss_cls_dn_5[0.0807] det_loss_box_dn_5[0.1900] loss_depth[0.0389] total_loss[6.1565]
Rank[0/8] 06/20/2025 13:19:48 INFO stats.py:314 | Epoch[176] Step[284] GlobalStep[68924] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 8:16:44.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:20:04 INFO loss_tracker.py:84 | Epoch[176/239] Step[299] GlobalStep[68939/NA]: det_loss_cls_0[0.4564] det_loss_box_0[0.5423] det_loss_cls_1[0.4348] det_loss_box_1[0.4344] det_loss_cls_2[0.3624] det_loss_box_2[0.4129] det_loss_cls_3[0.3364] det_loss_box_3[0.3854] det_loss_cls_4[0.3148] det_loss_box_4[0.3750] det_loss_cls_5[0.3080] det_loss_box_5[0.3730] det_loss_cls_dn_0[0.0925] det_loss_box_dn_0[0.2441] det_loss_cls_dn_1[0.0893] det_loss_box_dn_1[0.2004] det_loss_cls_dn_2[0.0886] det_loss_box_dn_2[0.1838] det_loss_cls_dn_3[0.0877] det_loss_box_dn_3[0.1801] det_loss_cls_dn_4[0.0868] det_loss_box_dn_4[0.1778] det_loss_cls_dn_5[0.0861] det_loss_box_dn_5[0.1763] loss_depth[0.0355] total_loss[6.4649]
Rank[0/8] 06/20/2025 13:20:14 INFO stats.py:314 | Epoch[176] Step[309] GlobalStep[68949] Training Speed: 8.12 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 8:16:12.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:20:29 INFO loss_tracker.py:84 | Epoch[176/239] Step[324] GlobalStep[68964/NA]: det_loss_cls_0[0.5037] det_loss_box_0[0.6387] det_loss_cls_1[0.4886] det_loss_box_1[0.4897] det_loss_cls_2[0.4318] det_loss_box_2[0.4450] det_loss_cls_3[0.3932] det_loss_box_3[0.4284] det_loss_cls_4[0.3674] det_loss_box_4[0.4134] det_loss_cls_5[0.3520] det_loss_box_5[0.4179] det_loss_cls_dn_0[0.0864] det_loss_box_dn_0[0.2554] det_loss_cls_dn_1[0.0833] det_loss_box_dn_1[0.2086] det_loss_cls_dn_2[0.0821] det_loss_box_dn_2[0.1914] det_loss_cls_dn_3[0.0804] det_loss_box_dn_3[0.1855] det_loss_cls_dn_4[0.0793] det_loss_box_dn_4[0.1828] det_loss_cls_dn_5[0.0784] det_loss_box_dn_5[0.1820] loss_depth[0.0354] total_loss[7.1010]
Rank[0/8] 06/20/2025 13:20:39 INFO stats.py:314 | Epoch[176] Step[334] GlobalStep[68974] Training Speed: 8.77 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 8:15:40.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:20:55 INFO loss_tracker.py:84 | Epoch[176/239] Step[349] GlobalStep[68989/NA]: det_loss_cls_0[0.4179] det_loss_box_0[0.4873] det_loss_cls_1[0.4088] det_loss_box_1[0.3622] det_loss_cls_2[0.3515] det_loss_box_2[0.3396] det_loss_cls_3[0.3232] det_loss_box_3[0.3297] det_loss_cls_4[0.2993] det_loss_box_4[0.3193] det_loss_cls_5[0.2780] det_loss_box_5[0.3224] det_loss_cls_dn_0[0.0923] det_loss_box_dn_0[0.2668] det_loss_cls_dn_1[0.0892] det_loss_box_dn_1[0.2130] det_loss_cls_dn_2[0.0889] det_loss_box_dn_2[0.1937] det_loss_cls_dn_3[0.0882] det_loss_box_dn_3[0.1879] det_loss_cls_dn_4[0.0868] det_loss_box_dn_4[0.1853] det_loss_cls_dn_5[0.0860] det_loss_box_dn_5[0.1838] loss_depth[0.0358] total_loss[6.0366]
Rank[0/8] 06/20/2025 13:21:06 INFO stats.py:314 | Epoch[176] Step[359] GlobalStep[68999] Training Speed: 11.07 samples/sec across all devices.	Average Step Time: 0.72 sec.	Estimated Remaining Time: 8:15:09.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:21:21 INFO loss_tracker.py:84 | Epoch[176/239] Step[374] GlobalStep[69014/NA]: det_loss_cls_0[0.5299] det_loss_box_0[0.7494] det_loss_cls_1[0.5119] det_loss_box_1[0.5904] det_loss_cls_2[0.4762] det_loss_box_2[0.5558] det_loss_cls_3[0.4525] det_loss_box_3[0.5347] det_loss_cls_4[0.4271] det_loss_box_4[0.5131] det_loss_cls_5[0.4163] det_loss_box_5[0.5098] det_loss_cls_dn_0[0.0995] det_loss_box_dn_0[0.2950] det_loss_cls_dn_1[0.0953] det_loss_box_dn_1[0.2382] det_loss_cls_dn_2[0.0950] det_loss_box_dn_2[0.2214] det_loss_cls_dn_3[0.0940] det_loss_box_dn_3[0.2166] det_loss_cls_dn_4[0.0932] det_loss_box_dn_4[0.2142] det_loss_cls_dn_5[0.0920] det_loss_box_dn_5[0.2125] loss_depth[0.0376] total_loss[8.2718]
Rank[0/8] 06/20/2025 13:21:32 INFO stats.py:314 | Epoch[176] Step[384] GlobalStep[69024] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 8:14:37.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:21:36 INFO stats.py:394 | Epoch[176] completed. Training Speed: 7.70 samples/sec across all devices.	Epoch Time: 405.09 sec.	Average Epoch Time: 405.09 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 8:14:31.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:21:59 INFO stats.py:314 | Epoch[177] Step[19] GlobalStep[69049] Training Speed: 8.19 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 8:14:06.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:22:04 INFO loss_tracker.py:84 | Epoch[177/239] Step[24] GlobalStep[69054/NA]: det_loss_cls_0[0.4697] det_loss_box_0[0.5560] det_loss_cls_1[0.4496] det_loss_box_1[0.4097] det_loss_cls_2[0.3864] det_loss_box_2[0.3715] det_loss_cls_3[0.3569] det_loss_box_3[0.3576] det_loss_cls_4[0.3278] det_loss_box_4[0.3549] det_loss_cls_5[0.3124] det_loss_box_5[0.3589] det_loss_cls_dn_0[0.0935] det_loss_box_dn_0[0.2973] det_loss_cls_dn_1[0.0898] det_loss_box_dn_1[0.2426] det_loss_cls_dn_2[0.0892] det_loss_box_dn_2[0.2255] det_loss_cls_dn_3[0.0880] det_loss_box_dn_3[0.2205] det_loss_cls_dn_4[0.0872] det_loss_box_dn_4[0.2167] det_loss_cls_dn_5[0.0862] det_loss_box_dn_5[0.2155] loss_depth[0.0377] total_loss[6.7012]
Rank[0/8] 06/20/2025 13:22:25 INFO stats.py:314 | Epoch[177] Step[44] GlobalStep[69074] Training Speed: 7.43 samples/sec across all devices.	Average Step Time: 1.08 sec.	Estimated Remaining Time: 8:13:34.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:22:30 INFO loss_tracker.py:84 | Epoch[177/239] Step[49] GlobalStep[69079/NA]: det_loss_cls_0[0.4448] det_loss_box_0[0.5813] det_loss_cls_1[0.4403] det_loss_box_1[0.4447] det_loss_cls_2[0.3825] det_loss_box_2[0.3999] det_loss_cls_3[0.3503] det_loss_box_3[0.3908] det_loss_cls_4[0.3219] det_loss_box_4[0.3782] det_loss_cls_5[0.3086] det_loss_box_5[0.3758] det_loss_cls_dn_0[0.0963] det_loss_box_dn_0[0.2594] det_loss_cls_dn_1[0.0923] det_loss_box_dn_1[0.2177] det_loss_cls_dn_2[0.0912] det_loss_box_dn_2[0.2007] det_loss_cls_dn_3[0.0902] det_loss_box_dn_3[0.1985] det_loss_cls_dn_4[0.0888] det_loss_box_dn_4[0.1963] det_loss_cls_dn_5[0.0880] det_loss_box_dn_5[0.1955] loss_depth[0.0327] total_loss[6.6667]
Rank[0/8] 06/20/2025 13:22:50 INFO stats.py:314 | Epoch[177] Step[69] GlobalStep[69099] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 8:13:02.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:22:55 INFO loss_tracker.py:84 | Epoch[177/239] Step[74] GlobalStep[69104/NA]: det_loss_cls_0[0.4781] det_loss_box_0[0.7189] det_loss_cls_1[0.4593] det_loss_box_1[0.5435] det_loss_cls_2[0.4229] det_loss_box_2[0.4897] det_loss_cls_3[0.3959] det_loss_box_3[0.4697] det_loss_cls_4[0.3701] det_loss_box_4[0.4686] det_loss_cls_5[0.3616] det_loss_box_5[0.4611] det_loss_cls_dn_0[0.0915] det_loss_box_dn_0[0.3054] det_loss_cls_dn_1[0.0872] det_loss_box_dn_1[0.2580] det_loss_cls_dn_2[0.0863] det_loss_box_dn_2[0.2384] det_loss_cls_dn_3[0.0856] det_loss_box_dn_3[0.2337] det_loss_cls_dn_4[0.0846] det_loss_box_dn_4[0.2297] det_loss_cls_dn_5[0.0842] det_loss_box_dn_5[0.2275] loss_depth[0.0375] total_loss[7.6892]
Rank[0/8] 06/20/2025 13:23:16 INFO stats.py:314 | Epoch[177] Step[94] GlobalStep[69124] Training Speed: 7.61 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 8:12:30.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:23:21 INFO loss_tracker.py:84 | Epoch[177/239] Step[99] GlobalStep[69129/NA]: det_loss_cls_0[0.4873] det_loss_box_0[0.6083] det_loss_cls_1[0.4719] det_loss_box_1[0.4631] det_loss_cls_2[0.4163] det_loss_box_2[0.4070] det_loss_cls_3[0.3788] det_loss_box_3[0.3977] det_loss_cls_4[0.3497] det_loss_box_4[0.3896] det_loss_cls_5[0.3441] det_loss_box_5[0.3839] det_loss_cls_dn_0[0.0848] det_loss_box_dn_0[0.2822] det_loss_cls_dn_1[0.0812] det_loss_box_dn_1[0.2257] det_loss_cls_dn_2[0.0802] det_loss_box_dn_2[0.2063] det_loss_cls_dn_3[0.0789] det_loss_box_dn_3[0.2014] det_loss_cls_dn_4[0.0781] det_loss_box_dn_4[0.1986] det_loss_cls_dn_5[0.0772] det_loss_box_dn_5[0.1972] loss_depth[0.0371] total_loss[6.9268]
Rank[0/8] 06/20/2025 13:23:41 INFO stats.py:314 | Epoch[177] Step[119] GlobalStep[69149] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 8:11:59.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:23:46 INFO loss_tracker.py:84 | Epoch[177/239] Step[124] GlobalStep[69154/NA]: det_loss_cls_0[0.4072] det_loss_box_0[0.5231] det_loss_cls_1[0.3933] det_loss_box_1[0.3773] det_loss_cls_2[0.3402] det_loss_box_2[0.3418] det_loss_cls_3[0.3071] det_loss_box_3[0.3299] det_loss_cls_4[0.2791] det_loss_box_4[0.3189] det_loss_cls_5[0.2616] det_loss_box_5[0.3241] det_loss_cls_dn_0[0.0895] det_loss_box_dn_0[0.2406] det_loss_cls_dn_1[0.0857] det_loss_box_dn_1[0.2001] det_loss_cls_dn_2[0.0847] det_loss_box_dn_2[0.1828] det_loss_cls_dn_3[0.0832] det_loss_box_dn_3[0.1790] det_loss_cls_dn_4[0.0824] det_loss_box_dn_4[0.1768] det_loss_cls_dn_5[0.0815] det_loss_box_dn_5[0.1752] loss_depth[0.0333] total_loss[5.8980]
Rank[0/8] 06/20/2025 13:24:08 INFO stats.py:314 | Epoch[177] Step[144] GlobalStep[69174] Training Speed: 6.74 samples/sec across all devices.	Average Step Time: 1.19 sec.	Estimated Remaining Time: 8:11:27.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:24:13 INFO loss_tracker.py:84 | Epoch[177/239] Step[149] GlobalStep[69179/NA]: det_loss_cls_0[0.4659] det_loss_box_0[0.5779] det_loss_cls_1[0.4466] det_loss_box_1[0.4420] det_loss_cls_2[0.3845] det_loss_box_2[0.3927] det_loss_cls_3[0.3490] det_loss_box_3[0.3727] det_loss_cls_4[0.3187] det_loss_box_4[0.3653] det_loss_cls_5[0.3095] det_loss_box_5[0.3622] det_loss_cls_dn_0[0.0851] det_loss_box_dn_0[0.2450] det_loss_cls_dn_1[0.0823] det_loss_box_dn_1[0.1971] det_loss_cls_dn_2[0.0814] det_loss_box_dn_2[0.1809] det_loss_cls_dn_3[0.0802] det_loss_box_dn_3[0.1765] det_loss_cls_dn_4[0.0792] det_loss_box_dn_4[0.1739] det_loss_cls_dn_5[0.0786] det_loss_box_dn_5[0.1702] loss_depth[0.0349] total_loss[6.4521]
Rank[0/8] 06/20/2025 13:24:33 INFO stats.py:314 | Epoch[177] Step[169] GlobalStep[69199] Training Speed: 7.72 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 8:10:55.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:24:38 INFO loss_tracker.py:84 | Epoch[177/239] Step[174] GlobalStep[69204/NA]: det_loss_cls_0[0.4786] det_loss_box_0[0.5540] det_loss_cls_1[0.4601] det_loss_box_1[0.4206] det_loss_cls_2[0.4065] det_loss_box_2[0.3811] det_loss_cls_3[0.3731] det_loss_box_3[0.3704] det_loss_cls_4[0.3483] det_loss_box_4[0.3631] det_loss_cls_5[0.3380] det_loss_box_5[0.3627] det_loss_cls_dn_0[0.0975] det_loss_box_dn_0[0.2736] det_loss_cls_dn_1[0.0939] det_loss_box_dn_1[0.2192] det_loss_cls_dn_2[0.0935] det_loss_box_dn_2[0.2023] det_loss_cls_dn_3[0.0926] det_loss_box_dn_3[0.1959] det_loss_cls_dn_4[0.0924] det_loss_box_dn_4[0.1919] det_loss_cls_dn_5[0.0913] det_loss_box_dn_5[0.1901] loss_depth[0.0352] total_loss[6.7259]
Rank[0/8] 06/20/2025 13:24:59 INFO stats.py:314 | Epoch[177] Step[194] GlobalStep[69224] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 8:10:23.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:25:04 INFO loss_tracker.py:84 | Epoch[177/239] Step[199] GlobalStep[69229/NA]: det_loss_cls_0[0.5112] det_loss_box_0[0.6796] det_loss_cls_1[0.4917] det_loss_box_1[0.5337] det_loss_cls_2[0.4279] det_loss_box_2[0.4803] det_loss_cls_3[0.3903] det_loss_box_3[0.4638] det_loss_cls_4[0.3655] det_loss_box_4[0.4458] det_loss_cls_5[0.3545] det_loss_box_5[0.4465] det_loss_cls_dn_0[0.0839] det_loss_box_dn_0[0.2589] det_loss_cls_dn_1[0.0792] det_loss_box_dn_1[0.2155] det_loss_cls_dn_2[0.0785] det_loss_box_dn_2[0.2018] det_loss_cls_dn_3[0.0773] det_loss_box_dn_3[0.1971] det_loss_cls_dn_4[0.0764] det_loss_box_dn_4[0.1942] det_loss_cls_dn_5[0.0758] det_loss_box_dn_5[0.1890] loss_depth[0.0345] total_loss[7.3529]
Rank[0/8] 06/20/2025 13:25:25 INFO stats.py:314 | Epoch[177] Step[219] GlobalStep[69249] Training Speed: 8.67 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 8:09:52.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:25:30 INFO loss_tracker.py:84 | Epoch[177/239] Step[224] GlobalStep[69254/NA]: det_loss_cls_0[0.3953] det_loss_box_0[0.5510] det_loss_cls_1[0.3773] det_loss_box_1[0.4101] det_loss_cls_2[0.3284] det_loss_box_2[0.3673] det_loss_cls_3[0.2950] det_loss_box_3[0.3600] det_loss_cls_4[0.2733] det_loss_box_4[0.3434] det_loss_cls_5[0.2638] det_loss_box_5[0.3459] det_loss_cls_dn_0[0.0935] det_loss_box_dn_0[0.2833] det_loss_cls_dn_1[0.0897] det_loss_box_dn_1[0.2287] det_loss_cls_dn_2[0.0885] det_loss_box_dn_2[0.2066] det_loss_cls_dn_3[0.0876] det_loss_box_dn_3[0.2020] det_loss_cls_dn_4[0.0870] det_loss_box_dn_4[0.1981] det_loss_cls_dn_5[0.0863] det_loss_box_dn_5[0.1974] loss_depth[0.0388] total_loss[6.1985]
Rank[0/8] 06/20/2025 13:25:50 INFO stats.py:314 | Epoch[177] Step[244] GlobalStep[69274] Training Speed: 8.15 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 8:09:20.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:25:56 INFO loss_tracker.py:84 | Epoch[177/239] Step[249] GlobalStep[69279/NA]: det_loss_cls_0[0.6020] det_loss_box_0[0.7564] det_loss_cls_1[0.5803] det_loss_box_1[0.5562] det_loss_cls_2[0.5103] det_loss_box_2[0.5153] det_loss_cls_3[0.4756] det_loss_box_3[0.4827] det_loss_cls_4[0.4459] det_loss_box_4[0.4708] det_loss_cls_5[0.4310] det_loss_box_5[0.4630] det_loss_cls_dn_0[0.0940] det_loss_box_dn_0[0.2367] det_loss_cls_dn_1[0.0905] det_loss_box_dn_1[0.1965] det_loss_cls_dn_2[0.0895] det_loss_box_dn_2[0.1806] det_loss_cls_dn_3[0.0885] det_loss_box_dn_3[0.1774] det_loss_cls_dn_4[0.0877] det_loss_box_dn_4[0.1747] det_loss_cls_dn_5[0.0871] det_loss_box_dn_5[0.1734] loss_depth[0.0367] total_loss[8.0026]
Rank[0/8] 06/20/2025 13:26:16 INFO stats.py:314 | Epoch[177] Step[269] GlobalStep[69299] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 8:08:48.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:26:22 INFO loss_tracker.py:84 | Epoch[177/239] Step[274] GlobalStep[69304/NA]: det_loss_cls_0[0.4082] det_loss_box_0[0.5125] det_loss_cls_1[0.3963] det_loss_box_1[0.3862] det_loss_cls_2[0.3517] det_loss_box_2[0.3513] det_loss_cls_3[0.3225] det_loss_box_3[0.3494] det_loss_cls_4[0.3020] det_loss_box_4[0.3390] det_loss_cls_5[0.2925] det_loss_box_5[0.3386] det_loss_cls_dn_0[0.0841] det_loss_box_dn_0[0.2948] det_loss_cls_dn_1[0.0807] det_loss_box_dn_1[0.2418] det_loss_cls_dn_2[0.0794] det_loss_box_dn_2[0.2230] det_loss_cls_dn_3[0.0786] det_loss_box_dn_3[0.2185] det_loss_cls_dn_4[0.0777] det_loss_box_dn_4[0.2159] det_loss_cls_dn_5[0.0770] det_loss_box_dn_5[0.2109] loss_depth[0.0338] total_loss[6.2662]
Rank[0/8] 06/20/2025 13:26:42 INFO stats.py:314 | Epoch[177] Step[294] GlobalStep[69324] Training Speed: 12.39 samples/sec across all devices.	Average Step Time: 0.65 sec.	Estimated Remaining Time: 8:08:16.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:26:48 INFO loss_tracker.py:84 | Epoch[177/239] Step[299] GlobalStep[69329/NA]: det_loss_cls_0[0.4140] det_loss_box_0[0.5451] det_loss_cls_1[0.4071] det_loss_box_1[0.4024] det_loss_cls_2[0.3501] det_loss_box_2[0.3722] det_loss_cls_3[0.3112] det_loss_box_3[0.3739] det_loss_cls_4[0.2991] det_loss_box_4[0.3681] det_loss_cls_5[0.2855] det_loss_box_5[0.3682] det_loss_cls_dn_0[0.0764] det_loss_box_dn_0[0.2409] det_loss_cls_dn_1[0.0725] det_loss_box_dn_1[0.1972] det_loss_cls_dn_2[0.0718] det_loss_box_dn_2[0.1804] det_loss_cls_dn_3[0.0703] det_loss_box_dn_3[0.1750] det_loss_cls_dn_4[0.0695] det_loss_box_dn_4[0.1725] det_loss_cls_dn_5[0.0685] det_loss_box_dn_5[0.1684] loss_depth[0.0369] total_loss[6.0971]
Rank[0/8] 06/20/2025 13:27:08 INFO stats.py:314 | Epoch[177] Step[319] GlobalStep[69349] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 8:07:45.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:27:13 INFO loss_tracker.py:84 | Epoch[177/239] Step[324] GlobalStep[69354/NA]: det_loss_cls_0[0.4994] det_loss_box_0[0.6154] det_loss_cls_1[0.4742] det_loss_box_1[0.4516] det_loss_cls_2[0.4182] det_loss_box_2[0.4235] det_loss_cls_3[0.3886] det_loss_box_3[0.4127] det_loss_cls_4[0.3580] det_loss_box_4[0.3966] det_loss_cls_5[0.3481] det_loss_box_5[0.3964] det_loss_cls_dn_0[0.0912] det_loss_box_dn_0[0.2499] det_loss_cls_dn_1[0.0867] det_loss_box_dn_1[0.2074] det_loss_cls_dn_2[0.0858] det_loss_box_dn_2[0.1939] det_loss_cls_dn_3[0.0854] det_loss_box_dn_3[0.1910] det_loss_cls_dn_4[0.0843] det_loss_box_dn_4[0.1891] det_loss_cls_dn_5[0.0834] det_loss_box_dn_5[0.1879] loss_depth[0.0370] total_loss[6.9555]
Rank[0/8] 06/20/2025 13:27:34 INFO stats.py:314 | Epoch[177] Step[344] GlobalStep[69374] Training Speed: 7.93 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 8:07:13.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:27:39 INFO loss_tracker.py:84 | Epoch[177/239] Step[349] GlobalStep[69379/NA]: det_loss_cls_0[0.4314] det_loss_box_0[0.5357] det_loss_cls_1[0.4306] det_loss_box_1[0.3975] det_loss_cls_2[0.3797] det_loss_box_2[0.3550] det_loss_cls_3[0.3555] det_loss_box_3[0.3452] det_loss_cls_4[0.3260] det_loss_box_4[0.3331] det_loss_cls_5[0.3145] det_loss_box_5[0.3349] det_loss_cls_dn_0[0.0919] det_loss_box_dn_0[0.2697] det_loss_cls_dn_1[0.0887] det_loss_box_dn_1[0.2244] det_loss_cls_dn_2[0.0881] det_loss_box_dn_2[0.2043] det_loss_cls_dn_3[0.0870] det_loss_box_dn_3[0.2003] det_loss_cls_dn_4[0.0862] det_loss_box_dn_4[0.1980] det_loss_cls_dn_5[0.0860] det_loss_box_dn_5[0.1956] loss_depth[0.0356] total_loss[6.3950]
Rank[0/8] 06/20/2025 13:27:59 INFO stats.py:314 | Epoch[177] Step[369] GlobalStep[69399] Training Speed: 7.79 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 8:06:41.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:28:04 INFO loss_tracker.py:84 | Epoch[177/239] Step[374] GlobalStep[69404/NA]: det_loss_cls_0[0.5246] det_loss_box_0[0.7918] det_loss_cls_1[0.5066] det_loss_box_1[0.6032] det_loss_cls_2[0.4647] det_loss_box_2[0.5130] det_loss_cls_3[0.4418] det_loss_box_3[0.4844] det_loss_cls_4[0.4065] det_loss_box_4[0.4800] det_loss_cls_5[0.3900] det_loss_box_5[0.4812] det_loss_cls_dn_0[0.0919] det_loss_box_dn_0[0.3419] det_loss_cls_dn_1[0.0896] det_loss_box_dn_1[0.2819] det_loss_cls_dn_2[0.0888] det_loss_box_dn_2[0.2632] det_loss_cls_dn_3[0.0879] det_loss_box_dn_3[0.2573] det_loss_cls_dn_4[0.0870] det_loss_box_dn_4[0.2545] det_loss_cls_dn_5[0.0868] det_loss_box_dn_5[0.2495] loss_depth[0.0413] total_loss[8.3092]
Rank[0/8] 06/20/2025 13:28:20 INFO stats.py:394 | Epoch[177] completed. Training Speed: 7.73 samples/sec across all devices.	Epoch Time: 403.46 sec.	Average Epoch Time: 403.46 sec.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 8:06:16.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:28:27 INFO stats.py:314 | Epoch[178] Step[4] GlobalStep[69424] Training Speed: 7.59 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 8:06:10.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:28:47 INFO loss_tracker.py:84 | Epoch[178/239] Step[24] GlobalStep[69444/NA]: det_loss_cls_0[0.4655] det_loss_box_0[0.5973] det_loss_cls_1[0.4511] det_loss_box_1[0.4506] det_loss_cls_2[0.3940] det_loss_box_2[0.4070] det_loss_cls_3[0.3638] det_loss_box_3[0.3935] det_loss_cls_4[0.3382] det_loss_box_4[0.3814] det_loss_cls_5[0.3255] det_loss_box_5[0.3739] det_loss_cls_dn_0[0.0870] det_loss_box_dn_0[0.2937] det_loss_cls_dn_1[0.0830] det_loss_box_dn_1[0.2324] det_loss_cls_dn_2[0.0815] det_loss_box_dn_2[0.2095] det_loss_cls_dn_3[0.0803] det_loss_box_dn_3[0.2041] det_loss_cls_dn_4[0.0793] det_loss_box_dn_4[0.1997] det_loss_cls_dn_5[0.0784] det_loss_box_dn_5[0.1939] loss_depth[0.0352] total_loss[6.7997]
Rank[0/8] 06/20/2025 13:28:52 INFO stats.py:314 | Epoch[178] Step[29] GlobalStep[69449] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 8:05:38.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:29:12 INFO loss_tracker.py:84 | Epoch[178/239] Step[49] GlobalStep[69469/NA]: det_loss_cls_0[0.5169] det_loss_box_0[0.6250] det_loss_cls_1[0.4777] det_loss_box_1[0.4652] det_loss_cls_2[0.4173] det_loss_box_2[0.4226] det_loss_cls_3[0.3810] det_loss_box_3[0.3997] det_loss_cls_4[0.3593] det_loss_box_4[0.3949] det_loss_cls_5[0.3483] det_loss_box_5[0.3915] det_loss_cls_dn_0[0.0885] det_loss_box_dn_0[0.2359] det_loss_cls_dn_1[0.0840] det_loss_box_dn_1[0.1903] det_loss_cls_dn_2[0.0830] det_loss_box_dn_2[0.1767] det_loss_cls_dn_3[0.0820] det_loss_box_dn_3[0.1723] det_loss_cls_dn_4[0.0812] det_loss_box_dn_4[0.1708] det_loss_cls_dn_5[0.0800] det_loss_box_dn_5[0.1680] loss_depth[0.0377] total_loss[6.8496]
Rank[0/8] 06/20/2025 13:29:18 INFO stats.py:314 | Epoch[178] Step[54] GlobalStep[69474] Training Speed: 6.73 samples/sec across all devices.	Average Step Time: 1.19 sec.	Estimated Remaining Time: 8:05:07.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:29:38 INFO loss_tracker.py:84 | Epoch[178/239] Step[74] GlobalStep[69494/NA]: det_loss_cls_0[0.5495] det_loss_box_0[0.6612] det_loss_cls_1[0.5407] det_loss_box_1[0.5034] det_loss_cls_2[0.4834] det_loss_box_2[0.4562] det_loss_cls_3[0.4510] det_loss_box_3[0.4505] det_loss_cls_4[0.4226] det_loss_box_4[0.4467] det_loss_cls_5[0.4075] det_loss_box_5[0.4365] det_loss_cls_dn_0[0.1041] det_loss_box_dn_0[0.2679] det_loss_cls_dn_1[0.1013] det_loss_box_dn_1[0.2264] det_loss_cls_dn_2[0.1009] det_loss_box_dn_2[0.2139] det_loss_cls_dn_3[0.1007] det_loss_box_dn_3[0.2101] det_loss_cls_dn_4[0.1002] det_loss_box_dn_4[0.2088] det_loss_cls_dn_5[0.0994] det_loss_box_dn_5[0.2063] loss_depth[0.0384] total_loss[7.7875]
Rank[0/8] 06/20/2025 13:29:43 INFO stats.py:314 | Epoch[178] Step[79] GlobalStep[69499] Training Speed: 7.30 samples/sec across all devices.	Average Step Time: 1.10 sec.	Estimated Remaining Time: 8:04:35.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:30:04 INFO loss_tracker.py:84 | Epoch[178/239] Step[99] GlobalStep[69519/NA]: det_loss_cls_0[0.3964] det_loss_box_0[0.5779] det_loss_cls_1[0.3794] det_loss_box_1[0.4420] det_loss_cls_2[0.3406] det_loss_box_2[0.3837] det_loss_cls_3[0.3115] det_loss_box_3[0.3713] det_loss_cls_4[0.2924] det_loss_box_4[0.3660] det_loss_cls_5[0.2802] det_loss_box_5[0.3683] det_loss_cls_dn_0[0.0888] det_loss_box_dn_0[0.3110] det_loss_cls_dn_1[0.0849] det_loss_box_dn_1[0.2591] det_loss_cls_dn_2[0.0835] det_loss_box_dn_2[0.2339] det_loss_cls_dn_3[0.0820] det_loss_box_dn_3[0.2291] det_loss_cls_dn_4[0.0806] det_loss_box_dn_4[0.2260] det_loss_cls_dn_5[0.0797] det_loss_box_dn_5[0.2245] loss_depth[0.0363] total_loss[6.5289]
Rank[0/8] 06/20/2025 13:30:09 INFO stats.py:314 | Epoch[178] Step[104] GlobalStep[69524] Training Speed: 7.73 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 8:04:03.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:30:30 INFO loss_tracker.py:84 | Epoch[178/239] Step[124] GlobalStep[69544/NA]: det_loss_cls_0[0.4927] det_loss_box_0[0.6697] det_loss_cls_1[0.4610] det_loss_box_1[0.5206] det_loss_cls_2[0.4183] det_loss_box_2[0.4677] det_loss_cls_3[0.3873] det_loss_box_3[0.4547] det_loss_cls_4[0.3690] det_loss_box_4[0.4420] det_loss_cls_5[0.3536] det_loss_box_5[0.4467] det_loss_cls_dn_0[0.0835] det_loss_box_dn_0[0.2710] det_loss_cls_dn_1[0.0802] det_loss_box_dn_1[0.2224] det_loss_cls_dn_2[0.0798] det_loss_box_dn_2[0.2056] det_loss_cls_dn_3[0.0786] det_loss_box_dn_3[0.2032] det_loss_cls_dn_4[0.0776] det_loss_box_dn_4[0.2008] det_loss_cls_dn_5[0.0769] det_loss_box_dn_5[0.1975] loss_depth[0.0380] total_loss[7.2986]
Rank[0/8] 06/20/2025 13:30:35 INFO stats.py:314 | Epoch[178] Step[129] GlobalStep[69549] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 8:03:32.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:30:56 INFO loss_tracker.py:84 | Epoch[178/239] Step[149] GlobalStep[69569/NA]: det_loss_cls_0[0.4394] det_loss_box_0[0.5079] det_loss_cls_1[0.4358] det_loss_box_1[0.3661] det_loss_cls_2[0.3764] det_loss_box_2[0.3429] det_loss_cls_3[0.3527] det_loss_box_3[0.3251] det_loss_cls_4[0.3201] det_loss_box_4[0.3180] det_loss_cls_5[0.3071] det_loss_box_5[0.3218] det_loss_cls_dn_0[0.0835] det_loss_box_dn_0[0.2423] det_loss_cls_dn_1[0.0793] det_loss_box_dn_1[0.2030] det_loss_cls_dn_2[0.0783] det_loss_box_dn_2[0.1897] det_loss_cls_dn_3[0.0775] det_loss_box_dn_3[0.1851] det_loss_cls_dn_4[0.0766] det_loss_box_dn_4[0.1831] det_loss_cls_dn_5[0.0752] det_loss_box_dn_5[0.1790] loss_depth[0.0370] total_loss[6.1030]
Rank[0/8] 06/20/2025 13:31:01 INFO stats.py:314 | Epoch[178] Step[154] GlobalStep[69574] Training Speed: 6.72 samples/sec across all devices.	Average Step Time: 1.19 sec.	Estimated Remaining Time: 8:03:00.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:31:22 INFO loss_tracker.py:84 | Epoch[178/239] Step[174] GlobalStep[69594/NA]: det_loss_cls_0[0.4272] det_loss_box_0[0.5819] det_loss_cls_1[0.4237] det_loss_box_1[0.4452] det_loss_cls_2[0.3619] det_loss_box_2[0.4097] det_loss_cls_3[0.3290] det_loss_box_3[0.3981] det_loss_cls_4[0.3088] det_loss_box_4[0.3794] det_loss_cls_5[0.2974] det_loss_box_5[0.3788] det_loss_cls_dn_0[0.0865] det_loss_box_dn_0[0.2743] det_loss_cls_dn_1[0.0827] det_loss_box_dn_1[0.2238] det_loss_cls_dn_2[0.0813] det_loss_box_dn_2[0.2055] det_loss_cls_dn_3[0.0803] det_loss_box_dn_3[0.2010] det_loss_cls_dn_4[0.0793] det_loss_box_dn_4[0.1970] det_loss_cls_dn_5[0.0784] det_loss_box_dn_5[0.1950] loss_depth[0.0349] total_loss[6.5610]
Rank[0/8] 06/20/2025 13:31:27 INFO stats.py:314 | Epoch[178] Step[179] GlobalStep[69599] Training Speed: 8.72 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 8:02:29.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:31:48 INFO loss_tracker.py:84 | Epoch[178/239] Step[199] GlobalStep[69619/NA]: det_loss_cls_0[0.5467] det_loss_box_0[0.7623] det_loss_cls_1[0.5284] det_loss_box_1[0.5738] det_loss_cls_2[0.4758] det_loss_box_2[0.5339] det_loss_cls_3[0.4273] det_loss_box_3[0.5116] det_loss_cls_4[0.4039] det_loss_box_4[0.5090] det_loss_cls_5[0.3881] det_loss_box_5[0.5008] det_loss_cls_dn_0[0.0870] det_loss_box_dn_0[0.3247] det_loss_cls_dn_1[0.0831] det_loss_box_dn_1[0.2756] det_loss_cls_dn_2[0.0824] det_loss_box_dn_2[0.2574] det_loss_cls_dn_3[0.0814] det_loss_box_dn_3[0.2526] det_loss_cls_dn_4[0.0805] det_loss_box_dn_4[0.2514] det_loss_cls_dn_5[0.0802] det_loss_box_dn_5[0.2490] loss_depth[0.0374] total_loss[8.3045]
Rank[0/8] 06/20/2025 13:31:53 INFO stats.py:314 | Epoch[178] Step[204] GlobalStep[69624] Training Speed: 9.73 samples/sec across all devices.	Average Step Time: 0.82 sec.	Estimated Remaining Time: 8:01:57.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:32:14 INFO loss_tracker.py:84 | Epoch[178/239] Step[224] GlobalStep[69644/NA]: det_loss_cls_0[0.4482] det_loss_box_0[0.5567] det_loss_cls_1[0.4218] det_loss_box_1[0.4204] det_loss_cls_2[0.3685] det_loss_box_2[0.3761] det_loss_cls_3[0.3373] det_loss_box_3[0.3682] det_loss_cls_4[0.3167] det_loss_box_4[0.3550] det_loss_cls_5[0.2959] det_loss_box_5[0.3569] det_loss_cls_dn_0[0.0905] det_loss_box_dn_0[0.2714] det_loss_cls_dn_1[0.0872] det_loss_box_dn_1[0.2263] det_loss_cls_dn_2[0.0859] det_loss_box_dn_2[0.2089] det_loss_cls_dn_3[0.0844] det_loss_box_dn_3[0.2038] det_loss_cls_dn_4[0.0837] det_loss_box_dn_4[0.2009] det_loss_cls_dn_5[0.0829] det_loss_box_dn_5[0.1988] loss_depth[0.0348] total_loss[6.4813]
Rank[0/8] 06/20/2025 13:32:19 INFO stats.py:314 | Epoch[178] Step[229] GlobalStep[69649] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 8:01:25.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:32:40 INFO loss_tracker.py:84 | Epoch[178/239] Step[249] GlobalStep[69669/NA]: det_loss_cls_0[0.6441] det_loss_box_0[0.8375] det_loss_cls_1[0.6193] det_loss_box_1[0.6635] det_loss_cls_2[0.5505] det_loss_box_2[0.5995] det_loss_cls_3[0.5154] det_loss_box_3[0.5751] det_loss_cls_4[0.4843] det_loss_box_4[0.5598] det_loss_cls_5[0.4686] det_loss_box_5[0.5594] det_loss_cls_dn_0[0.0938] det_loss_box_dn_0[0.2504] det_loss_cls_dn_1[0.0895] det_loss_box_dn_1[0.2092] det_loss_cls_dn_2[0.0887] det_loss_box_dn_2[0.1969] det_loss_cls_dn_3[0.0875] det_loss_box_dn_3[0.1930] det_loss_cls_dn_4[0.0864] det_loss_box_dn_4[0.1908] det_loss_cls_dn_5[0.0851] det_loss_box_dn_5[0.1898] loss_depth[0.0321] total_loss[8.8703]
Rank[0/8] 06/20/2025 13:32:45 INFO stats.py:314 | Epoch[178] Step[254] GlobalStep[69674] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 8:00:54.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:33:06 INFO loss_tracker.py:84 | Epoch[178/239] Step[274] GlobalStep[69694/NA]: det_loss_cls_0[0.4994] det_loss_box_0[0.6883] det_loss_cls_1[0.4860] det_loss_box_1[0.5305] det_loss_cls_2[0.4412] det_loss_box_2[0.4771] det_loss_cls_3[0.4174] det_loss_box_3[0.4620] det_loss_cls_4[0.4002] det_loss_box_4[0.4390] det_loss_cls_5[0.3836] det_loss_box_5[0.4372] det_loss_cls_dn_0[0.0943] det_loss_box_dn_0[0.2782] det_loss_cls_dn_1[0.0911] det_loss_box_dn_1[0.2381] det_loss_cls_dn_2[0.0900] det_loss_box_dn_2[0.2201] det_loss_cls_dn_3[0.0882] det_loss_box_dn_3[0.2161] det_loss_cls_dn_4[0.0870] det_loss_box_dn_4[0.2132] det_loss_cls_dn_5[0.0859] det_loss_box_dn_5[0.2076] loss_depth[0.0335] total_loss[7.6053]
Rank[0/8] 06/20/2025 13:33:11 INFO stats.py:314 | Epoch[178] Step[279] GlobalStep[69699] Training Speed: 8.76 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 8:00:22.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:33:31 INFO loss_tracker.py:84 | Epoch[178/239] Step[299] GlobalStep[69719/NA]: det_loss_cls_0[0.3956] det_loss_box_0[0.4774] det_loss_cls_1[0.3962] det_loss_box_1[0.3429] det_loss_cls_2[0.3367] det_loss_box_2[0.2996] det_loss_cls_3[0.3047] det_loss_box_3[0.2868] det_loss_cls_4[0.2811] det_loss_box_4[0.2891] det_loss_cls_5[0.2716] det_loss_box_5[0.2924] det_loss_cls_dn_0[0.0930] det_loss_box_dn_0[0.2449] det_loss_cls_dn_1[0.0897] det_loss_box_dn_1[0.1934] det_loss_cls_dn_2[0.0891] det_loss_box_dn_2[0.1724] det_loss_cls_dn_3[0.0878] det_loss_box_dn_3[0.1668] det_loss_cls_dn_4[0.0873] det_loss_box_dn_4[0.1645] det_loss_cls_dn_5[0.0865] det_loss_box_dn_5[0.1623] loss_depth[0.0363] total_loss[5.6480]
Rank[0/8] 06/20/2025 13:33:37 INFO stats.py:314 | Epoch[178] Step[304] GlobalStep[69724] Training Speed: 7.97 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 7:59:51.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:33:58 INFO loss_tracker.py:84 | Epoch[178/239] Step[324] GlobalStep[69744/NA]: det_loss_cls_0[0.4834] det_loss_box_0[0.5669] det_loss_cls_1[0.4678] det_loss_box_1[0.4115] det_loss_cls_2[0.4244] det_loss_box_2[0.3672] det_loss_cls_3[0.3910] det_loss_box_3[0.3592] det_loss_cls_4[0.3587] det_loss_box_4[0.3500] det_loss_cls_5[0.3537] det_loss_box_5[0.3488] det_loss_cls_dn_0[0.0949] det_loss_box_dn_0[0.2597] det_loss_cls_dn_1[0.0921] det_loss_box_dn_1[0.2125] det_loss_cls_dn_2[0.0911] det_loss_box_dn_2[0.1939] det_loss_cls_dn_3[0.0903] det_loss_box_dn_3[0.1900] det_loss_cls_dn_4[0.0896] det_loss_box_dn_4[0.1875] det_loss_cls_dn_5[0.0892] det_loss_box_dn_5[0.1852] loss_depth[0.0353] total_loss[6.6937]
Rank[0/8] 06/20/2025 13:34:03 INFO stats.py:314 | Epoch[178] Step[329] GlobalStep[69749] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 7:59:19.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:34:24 INFO loss_tracker.py:84 | Epoch[178/239] Step[349] GlobalStep[69769/NA]: det_loss_cls_0[0.4185] det_loss_box_0[0.4854] det_loss_cls_1[0.4142] det_loss_box_1[0.3602] det_loss_cls_2[0.3413] det_loss_box_2[0.3237] det_loss_cls_3[0.3059] det_loss_box_3[0.3101] det_loss_cls_4[0.2785] det_loss_box_4[0.3074] det_loss_cls_5[0.2632] det_loss_box_5[0.3074] det_loss_cls_dn_0[0.0873] det_loss_box_dn_0[0.2252] det_loss_cls_dn_1[0.0840] det_loss_box_dn_1[0.1837] det_loss_cls_dn_2[0.0830] det_loss_box_dn_2[0.1659] det_loss_cls_dn_3[0.0814] det_loss_box_dn_3[0.1628] det_loss_cls_dn_4[0.0804] det_loss_box_dn_4[0.1601] det_loss_cls_dn_5[0.0792] det_loss_box_dn_5[0.1584] loss_depth[0.0348] total_loss[5.7022]
Rank[0/8] 06/20/2025 13:34:29 INFO stats.py:314 | Epoch[178] Step[354] GlobalStep[69774] Training Speed: 7.40 samples/sec across all devices.	Average Step Time: 1.08 sec.	Estimated Remaining Time: 7:58:48.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:34:50 INFO loss_tracker.py:84 | Epoch[178/239] Step[374] GlobalStep[69794/NA]: det_loss_cls_0[0.3868] det_loss_box_0[0.4986] det_loss_cls_1[0.3716] det_loss_box_1[0.3856] det_loss_cls_2[0.3288] det_loss_box_2[0.3428] det_loss_cls_3[0.2922] det_loss_box_3[0.3357] det_loss_cls_4[0.2728] det_loss_box_4[0.3273] det_loss_cls_5[0.2591] det_loss_box_5[0.3312] det_loss_cls_dn_0[0.0835] det_loss_box_dn_0[0.2978] det_loss_cls_dn_1[0.0794] det_loss_box_dn_1[0.2519] det_loss_cls_dn_2[0.0774] det_loss_box_dn_2[0.2325] det_loss_cls_dn_3[0.0759] det_loss_box_dn_3[0.2296] det_loss_cls_dn_4[0.0752] det_loss_box_dn_4[0.2279] det_loss_cls_dn_5[0.0743] det_loss_box_dn_5[0.2264] loss_depth[0.0341] total_loss[6.0984]
Rank[0/8] 06/20/2025 13:34:55 INFO stats.py:314 | Epoch[178] Step[379] GlobalStep[69799] Training Speed: 8.78 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 7:58:16.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:35:05 INFO stats.py:394 | Epoch[178] completed. Training Speed: 7.70 samples/sec across all devices.	Epoch Time: 405.36 sec.	Average Epoch Time: 405.36 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 7:58:03.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:35:25 INFO stats.py:314 | Epoch[179] Step[14] GlobalStep[69824] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 7:57:46.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:35:36 INFO loss_tracker.py:84 | Epoch[179/239] Step[24] GlobalStep[69834/NA]: det_loss_cls_0[0.4496] det_loss_box_0[0.5879] det_loss_cls_1[0.4396] det_loss_box_1[0.4362] det_loss_cls_2[0.3790] det_loss_box_2[0.4022] det_loss_cls_3[0.3402] det_loss_box_3[0.3920] det_loss_cls_4[0.3228] det_loss_box_4[0.3790] det_loss_cls_5[0.3060] det_loss_box_5[0.3808] det_loss_cls_dn_0[0.0890] det_loss_box_dn_0[0.2884] det_loss_cls_dn_1[0.0850] det_loss_box_dn_1[0.2414] det_loss_cls_dn_2[0.0839] det_loss_box_dn_2[0.2219] det_loss_cls_dn_3[0.0830] det_loss_box_dn_3[0.2174] det_loss_cls_dn_4[0.0823] det_loss_box_dn_4[0.2154] det_loss_cls_dn_5[0.0814] det_loss_box_dn_5[0.2108] loss_depth[0.0349] total_loss[6.7503]
Rank[0/8] 06/20/2025 13:35:52 INFO stats.py:314 | Epoch[179] Step[39] GlobalStep[69849] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 7:57:14.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:36:02 INFO loss_tracker.py:84 | Epoch[179/239] Step[49] GlobalStep[69859/NA]: det_loss_cls_0[0.5201] det_loss_box_0[0.6616] det_loss_cls_1[0.5063] det_loss_box_1[0.4939] det_loss_cls_2[0.4480] det_loss_box_2[0.4411] det_loss_cls_3[0.4200] det_loss_box_3[0.4327] det_loss_cls_4[0.3937] det_loss_box_4[0.4221] det_loss_cls_5[0.3744] det_loss_box_5[0.4232] det_loss_cls_dn_0[0.1017] det_loss_box_dn_0[0.2516] det_loss_cls_dn_1[0.0960] det_loss_box_dn_1[0.2105] det_loss_cls_dn_2[0.0945] det_loss_box_dn_2[0.1945] det_loss_cls_dn_3[0.0935] det_loss_box_dn_3[0.1902] det_loss_cls_dn_4[0.0924] det_loss_box_dn_4[0.1885] det_loss_cls_dn_5[0.0917] det_loss_box_dn_5[0.1864] loss_depth[0.0349] total_loss[7.3633]
Rank[0/8] 06/20/2025 13:36:17 INFO stats.py:314 | Epoch[179] Step[64] GlobalStep[69874] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 7:56:43.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:36:27 INFO loss_tracker.py:84 | Epoch[179/239] Step[74] GlobalStep[69884/NA]: det_loss_cls_0[0.5535] det_loss_box_0[0.6668] det_loss_cls_1[0.5434] det_loss_box_1[0.4946] det_loss_cls_2[0.4654] det_loss_box_2[0.4529] det_loss_cls_3[0.4197] det_loss_box_3[0.4394] det_loss_cls_4[0.3879] det_loss_box_4[0.4262] det_loss_cls_5[0.3706] det_loss_box_5[0.4247] det_loss_cls_dn_0[0.0927] det_loss_box_dn_0[0.2508] det_loss_cls_dn_1[0.0893] det_loss_box_dn_1[0.2067] det_loss_cls_dn_2[0.0878] det_loss_box_dn_2[0.1919] det_loss_cls_dn_3[0.0869] det_loss_box_dn_3[0.1875] det_loss_cls_dn_4[0.0860] det_loss_box_dn_4[0.1845] det_loss_cls_dn_5[0.0848] det_loss_box_dn_5[0.1832] loss_depth[0.0364] total_loss[7.4134]
Rank[0/8] 06/20/2025 13:36:43 INFO stats.py:314 | Epoch[179] Step[89] GlobalStep[69899] Training Speed: 7.99 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 7:56:11.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:36:53 INFO loss_tracker.py:84 | Epoch[179/239] Step[99] GlobalStep[69909/NA]: det_loss_cls_0[0.4462] det_loss_box_0[0.5821] det_loss_cls_1[0.4279] det_loss_box_1[0.4536] det_loss_cls_2[0.3797] det_loss_box_2[0.4093] det_loss_cls_3[0.3496] det_loss_box_3[0.3942] det_loss_cls_4[0.3281] det_loss_box_4[0.3870] det_loss_cls_5[0.3162] det_loss_box_5[0.3820] det_loss_cls_dn_0[0.0884] det_loss_box_dn_0[0.2895] det_loss_cls_dn_1[0.0846] det_loss_box_dn_1[0.2413] det_loss_cls_dn_2[0.0841] det_loss_box_dn_2[0.2238] det_loss_cls_dn_3[0.0826] det_loss_box_dn_3[0.2189] det_loss_cls_dn_4[0.0820] det_loss_box_dn_4[0.2149] det_loss_cls_dn_5[0.0815] det_loss_box_dn_5[0.2115] loss_depth[0.0363] total_loss[6.7952]
Rank[0/8] 06/20/2025 13:37:09 INFO stats.py:314 | Epoch[179] Step[114] GlobalStep[69924] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 7:55:39.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:37:20 INFO loss_tracker.py:84 | Epoch[179/239] Step[124] GlobalStep[69934/NA]: det_loss_cls_0[0.4202] det_loss_box_0[0.4679] det_loss_cls_1[0.3865] det_loss_box_1[0.3417] det_loss_cls_2[0.3391] det_loss_box_2[0.3145] det_loss_cls_3[0.3149] det_loss_box_3[0.3000] det_loss_cls_4[0.2909] det_loss_box_4[0.2961] det_loss_cls_5[0.2728] det_loss_box_5[0.3012] det_loss_cls_dn_0[0.0914] det_loss_box_dn_0[0.2899] det_loss_cls_dn_1[0.0872] det_loss_box_dn_1[0.2358] det_loss_cls_dn_2[0.0865] det_loss_box_dn_2[0.2093] det_loss_cls_dn_3[0.0850] det_loss_box_dn_3[0.2067] det_loss_cls_dn_4[0.0838] det_loss_box_dn_4[0.2050] det_loss_cls_dn_5[0.0831] det_loss_box_dn_5[0.2031] loss_depth[0.0367] total_loss[5.9491]
Rank[0/8] 06/20/2025 13:37:35 INFO stats.py:314 | Epoch[179] Step[139] GlobalStep[69949] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 7:55:08.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:37:45 INFO loss_tracker.py:84 | Epoch[179/239] Step[149] GlobalStep[69959/NA]: det_loss_cls_0[0.6070] det_loss_box_0[0.7475] det_loss_cls_1[0.5753] det_loss_box_1[0.5711] det_loss_cls_2[0.5234] det_loss_box_2[0.5080] det_loss_cls_3[0.5029] det_loss_box_3[0.4896] det_loss_cls_4[0.4743] det_loss_box_4[0.4658] det_loss_cls_5[0.4606] det_loss_box_5[0.4717] det_loss_cls_dn_0[0.0914] det_loss_box_dn_0[0.2632] det_loss_cls_dn_1[0.0892] det_loss_box_dn_1[0.2072] det_loss_cls_dn_2[0.0878] det_loss_box_dn_2[0.1868] det_loss_cls_dn_3[0.0866] det_loss_box_dn_3[0.1824] det_loss_cls_dn_4[0.0856] det_loss_box_dn_4[0.1769] det_loss_cls_dn_5[0.0844] det_loss_box_dn_5[0.1755] loss_depth[0.0365] total_loss[8.1509]
Rank[0/8] 06/20/2025 13:38:01 INFO stats.py:314 | Epoch[179] Step[164] GlobalStep[69974] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 7:54:36.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:38:11 INFO loss_tracker.py:84 | Epoch[179/239] Step[174] GlobalStep[69984/NA]: det_loss_cls_0[0.4431] det_loss_box_0[0.5623] det_loss_cls_1[0.4383] det_loss_box_1[0.4383] det_loss_cls_2[0.3825] det_loss_box_2[0.4032] det_loss_cls_3[0.3554] det_loss_box_3[0.3841] det_loss_cls_4[0.3360] det_loss_box_4[0.3647] det_loss_cls_5[0.3225] det_loss_box_5[0.3675] det_loss_cls_dn_0[0.0929] det_loss_box_dn_0[0.3053] det_loss_cls_dn_1[0.0893] det_loss_box_dn_1[0.2531] det_loss_cls_dn_2[0.0880] det_loss_box_dn_2[0.2347] det_loss_cls_dn_3[0.0867] det_loss_box_dn_3[0.2305] det_loss_cls_dn_4[0.0857] det_loss_box_dn_4[0.2286] det_loss_cls_dn_5[0.0854] det_loss_box_dn_5[0.2258] loss_depth[0.0359] total_loss[6.8399]
Rank[0/8] 06/20/2025 13:38:27 INFO stats.py:314 | Epoch[179] Step[189] GlobalStep[69999] Training Speed: 12.71 samples/sec across all devices.	Average Step Time: 0.63 sec.	Estimated Remaining Time: 7:54:05.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:38:38 INFO loss_tracker.py:84 | Epoch[179/239] Step[199] GlobalStep[70009/NA]: det_loss_cls_0[0.4614] det_loss_box_0[0.6436] det_loss_cls_1[0.4397] det_loss_box_1[0.4822] det_loss_cls_2[0.3821] det_loss_box_2[0.4461] det_loss_cls_3[0.3558] det_loss_box_3[0.4285] det_loss_cls_4[0.3354] det_loss_box_4[0.4148] det_loss_cls_5[0.3223] det_loss_box_5[0.4153] det_loss_cls_dn_0[0.0862] det_loss_box_dn_0[0.2920] det_loss_cls_dn_1[0.0816] det_loss_box_dn_1[0.2353] det_loss_cls_dn_2[0.0809] det_loss_box_dn_2[0.2122] det_loss_cls_dn_3[0.0799] det_loss_box_dn_3[0.2099] det_loss_cls_dn_4[0.0790] det_loss_box_dn_4[0.2063] det_loss_cls_dn_5[0.0781] det_loss_box_dn_5[0.2014] loss_depth[0.0366] total_loss[7.0066]
Rank[0/8] 06/20/2025 13:38:53 INFO stats.py:314 | Epoch[179] Step[214] GlobalStep[70024] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 7:53:33.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:39:04 INFO loss_tracker.py:84 | Epoch[179/239] Step[224] GlobalStep[70034/NA]: det_loss_cls_0[0.4019] det_loss_box_0[0.5280] det_loss_cls_1[0.3913] det_loss_box_1[0.4143] det_loss_cls_2[0.3215] det_loss_box_2[0.3647] det_loss_cls_3[0.2921] det_loss_box_3[0.3517] det_loss_cls_4[0.2806] det_loss_box_4[0.3376] det_loss_cls_5[0.2617] det_loss_box_5[0.3369] det_loss_cls_dn_0[0.0839] det_loss_box_dn_0[0.3258] det_loss_cls_dn_1[0.0811] det_loss_box_dn_1[0.2610] det_loss_cls_dn_2[0.0800] det_loss_box_dn_2[0.2417] det_loss_cls_dn_3[0.0794] det_loss_box_dn_3[0.2293] det_loss_cls_dn_4[0.0785] det_loss_box_dn_4[0.2266] det_loss_cls_dn_5[0.0776] det_loss_box_dn_5[0.2171] loss_depth[0.0353] total_loss[6.2999]
Rank[0/8] 06/20/2025 13:39:19 INFO stats.py:314 | Epoch[179] Step[239] GlobalStep[70049] Training Speed: 7.91 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 7:53:02.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:39:29 INFO loss_tracker.py:84 | Epoch[179/239] Step[249] GlobalStep[70059/NA]: det_loss_cls_0[0.3875] det_loss_box_0[0.4970] det_loss_cls_1[0.3792] det_loss_box_1[0.3897] det_loss_cls_2[0.3329] det_loss_box_2[0.3577] det_loss_cls_3[0.3096] det_loss_box_3[0.3431] det_loss_cls_4[0.2875] det_loss_box_4[0.3378] det_loss_cls_5[0.2769] det_loss_box_5[0.3375] det_loss_cls_dn_0[0.0777] det_loss_box_dn_0[0.2559] det_loss_cls_dn_1[0.0739] det_loss_box_dn_1[0.2065] det_loss_cls_dn_2[0.0723] det_loss_box_dn_2[0.1891] det_loss_cls_dn_3[0.0711] det_loss_box_dn_3[0.1848] det_loss_cls_dn_4[0.0703] det_loss_box_dn_4[0.1827] det_loss_cls_dn_5[0.0699] det_loss_box_dn_5[0.1788] loss_depth[0.0326] total_loss[5.9018]
Rank[0/8] 06/20/2025 13:39:45 INFO stats.py:314 | Epoch[179] Step[264] GlobalStep[70074] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 7:52:30.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:39:55 INFO loss_tracker.py:84 | Epoch[179/239] Step[274] GlobalStep[70084/NA]: det_loss_cls_0[0.4586] det_loss_box_0[0.5164] det_loss_cls_1[0.4459] det_loss_box_1[0.3812] det_loss_cls_2[0.3858] det_loss_box_2[0.3414] det_loss_cls_3[0.3498] det_loss_box_3[0.3316] det_loss_cls_4[0.3241] det_loss_box_4[0.3233] det_loss_cls_5[0.3152] det_loss_box_5[0.3231] det_loss_cls_dn_0[0.0954] det_loss_box_dn_0[0.2601] det_loss_cls_dn_1[0.0918] det_loss_box_dn_1[0.2161] det_loss_cls_dn_2[0.0907] det_loss_box_dn_2[0.1914] det_loss_cls_dn_3[0.0898] det_loss_box_dn_3[0.1869] det_loss_cls_dn_4[0.0888] det_loss_box_dn_4[0.1841] det_loss_cls_dn_5[0.0881] det_loss_box_dn_5[0.1801] loss_depth[0.0341] total_loss[6.2937]
Rank[0/8] 06/20/2025 13:40:11 INFO stats.py:314 | Epoch[179] Step[289] GlobalStep[70099] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 7:51:59.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:40:21 INFO loss_tracker.py:84 | Epoch[179/239] Step[299] GlobalStep[70109/NA]: det_loss_cls_0[0.4526] det_loss_box_0[0.6755] det_loss_cls_1[0.4206] det_loss_box_1[0.5330] det_loss_cls_2[0.3795] det_loss_box_2[0.4684] det_loss_cls_3[0.3522] det_loss_box_3[0.4505] det_loss_cls_4[0.3293] det_loss_box_4[0.4341] det_loss_cls_5[0.3210] det_loss_box_5[0.4312] det_loss_cls_dn_0[0.0962] det_loss_box_dn_0[0.2994] det_loss_cls_dn_1[0.0931] det_loss_box_dn_1[0.2443] det_loss_cls_dn_2[0.0919] det_loss_box_dn_2[0.2265] det_loss_cls_dn_3[0.0911] det_loss_box_dn_3[0.2218] det_loss_cls_dn_4[0.0905] det_loss_box_dn_4[0.2187] det_loss_cls_dn_5[0.0897] det_loss_box_dn_5[0.2179] loss_depth[0.0374] total_loss[7.2665]
Rank[0/8] 06/20/2025 13:40:37 INFO stats.py:314 | Epoch[179] Step[314] GlobalStep[70124] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 7:51:27.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:40:47 INFO loss_tracker.py:84 | Epoch[179/239] Step[324] GlobalStep[70134/NA]: det_loss_cls_0[0.4639] det_loss_box_0[0.5181] det_loss_cls_1[0.4450] det_loss_box_1[0.3813] det_loss_cls_2[0.3831] det_loss_box_2[0.3470] det_loss_cls_3[0.3479] det_loss_box_3[0.3376] det_loss_cls_4[0.3294] det_loss_box_4[0.3278] det_loss_cls_5[0.3178] det_loss_box_5[0.3281] det_loss_cls_dn_0[0.0906] det_loss_box_dn_0[0.2334] det_loss_cls_dn_1[0.0870] det_loss_box_dn_1[0.1814] det_loss_cls_dn_2[0.0866] det_loss_box_dn_2[0.1663] det_loss_cls_dn_3[0.0858] det_loss_box_dn_3[0.1631] det_loss_cls_dn_4[0.0851] det_loss_box_dn_4[0.1615] det_loss_cls_dn_5[0.0851] det_loss_box_dn_5[0.1587] loss_depth[0.0349] total_loss[6.1465]
Rank[0/8] 06/20/2025 13:41:03 INFO stats.py:314 | Epoch[179] Step[339] GlobalStep[70149] Training Speed: 8.63 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 7:50:56.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:41:13 INFO loss_tracker.py:84 | Epoch[179/239] Step[349] GlobalStep[70159/NA]: det_loss_cls_0[0.3923] det_loss_box_0[0.4498] det_loss_cls_1[0.3743] det_loss_box_1[0.3474] det_loss_cls_2[0.3393] det_loss_box_2[0.3181] det_loss_cls_3[0.3152] det_loss_box_3[0.3054] det_loss_cls_4[0.3014] det_loss_box_4[0.2973] det_loss_cls_5[0.2871] det_loss_box_5[0.3017] det_loss_cls_dn_0[0.0800] det_loss_box_dn_0[0.2831] det_loss_cls_dn_1[0.0752] det_loss_box_dn_1[0.2256] det_loss_cls_dn_2[0.0744] det_loss_box_dn_2[0.2051] det_loss_cls_dn_3[0.0733] det_loss_box_dn_3[0.1977] det_loss_cls_dn_4[0.0724] det_loss_box_dn_4[0.1928] det_loss_cls_dn_5[0.0715] det_loss_box_dn_5[0.1903] loss_depth[0.0379] total_loss[5.8087]
Rank[0/8] 06/20/2025 13:41:29 INFO stats.py:314 | Epoch[179] Step[364] GlobalStep[70174] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 7:50:24.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:41:39 INFO loss_tracker.py:84 | Epoch[179/239] Step[374] GlobalStep[70184/NA]: det_loss_cls_0[0.4293] det_loss_box_0[0.5221] det_loss_cls_1[0.4155] det_loss_box_1[0.3845] det_loss_cls_2[0.3585] det_loss_box_2[0.3482] det_loss_cls_3[0.3242] det_loss_box_3[0.3353] det_loss_cls_4[0.3037] det_loss_box_4[0.3231] det_loss_cls_5[0.2937] det_loss_box_5[0.3229] det_loss_cls_dn_0[0.0906] det_loss_box_dn_0[0.2783] det_loss_cls_dn_1[0.0867] det_loss_box_dn_1[0.2285] det_loss_cls_dn_2[0.0858] det_loss_box_dn_2[0.2079] det_loss_cls_dn_3[0.0854] det_loss_box_dn_3[0.2029] det_loss_cls_dn_4[0.0851] det_loss_box_dn_4[0.2007] det_loss_cls_dn_5[0.0836] det_loss_box_dn_5[0.1994] loss_depth[0.0374] total_loss[6.2332]
Rank[0/8] 06/20/2025 13:41:54 INFO stats.py:314 | Epoch[179] Step[389] GlobalStep[70199] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 7:49:53.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:41:55 INFO trainer.py:268 | 
==================================================BEGIN EVAL==================================================
Rank[0/8] 06/20/2025 13:42:54 INFO trainer.py:275 | eval: 10
Rank[0/8] 06/20/2025 13:43:08 INFO trainer.py:275 | eval: 20
Rank[0/8] 06/20/2025 13:43:15 INFO trainer.py:275 | eval: 30
Rank[0/8] 06/20/2025 13:43:22 INFO trainer.py:275 | eval: 40
Rank[0/8] 06/20/2025 13:43:31 INFO trainer.py:275 | eval: 50
Rank[0/8] 06/20/2025 13:43:39 INFO trainer.py:275 | eval: 60
Rank[0/8] 06/20/2025 13:43:47 INFO trainer.py:275 | eval: 70
Rank[0/8] 06/20/2025 13:43:58 INFO trainer.py:275 | eval: 80
Rank[0/8] 06/20/2025 13:44:07 INFO trainer.py:275 | eval: 90
Rank[0/8] 06/20/2025 13:44:14 INFO trainer.py:275 | eval: 100
Rank[0/8] 06/20/2025 13:44:19 INFO metrics.py:428 | number of results: 824
Rank[0/8] 06/20/2025 13:48:30 INFO metrics.py:332 | eval results of overall:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| bathtub             | 0.8543  | 0.8971  | 0.7440  | 0.7941  |
| curtain             | 0.5440  | 0.8388  | 0.1305  | 0.4537  |
| toilet              | 0.8578  | 0.8905  | 0.7384  | 0.7883  |
| bin                 | 0.6583  | 0.8827  | 0.4414  | 0.6508  |
| picture             | 0.2658  | 0.4881  | 0.0279  | 0.1228  |
| toilet paper        | 0.3872  | 0.5906  | 0.1376  | 0.3543  |
| sink                | 0.6838  | 0.8436  | 0.2710  | 0.5391  |
| doorframe           | 0.3144  | 0.7776  | 0.0910  | 0.4098  |
| door                | 0.3959  | 0.7384  | 0.0840  | 0.3178  |
| towel               | 0.3870  | 0.6581  | 0.1301  | 0.3000  |
| mirror              | 0.2286  | 0.6522  | 0.0097  | 0.1902  |
| switch              | 0.0026  | 0.1447  | 0.0000  | 0.0000  |
| stopcock            | 0.1554  | 0.5862  | 0.0004  | 0.0345  |
| bar                 | 0.0636  | 0.3030  | 0.0009  | 0.0303  |
| vanity              | 0.5812  | 0.9111  | 0.4654  | 0.8000  |
| bottle              | 0.0792  | 0.4123  | 0.0055  | 0.1086  |
| bag                 | 0.2718  | 0.7189  | 0.0600  | 0.3081  |
| hair dryer          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rail                | 0.0328  | 0.4531  | 0.0000  | 0.0312  |
| cup                 | 0.0991  | 0.3644  | 0.0252  | 0.0593  |
| socket              | 0.0189  | 0.2611  | 0.0001  | 0.0177  |
| tissue box          | 0.1294  | 0.5455  | 0.0740  | 0.3636  |
| microwave           | 0.6160  | 0.7551  | 0.2882  | 0.4898  |
| tissue              | 0.0181  | 0.3846  | 0.0154  | 0.0769  |
| ledge               | 0.0114  | 0.3333  | 0.0015  | 0.1667  |
| rod                 | 0.0006  | 0.0250  | 0.0000  | 0.0000  |
| light               | 0.0672  | 0.3162  | 0.0048  | 0.0556  |
| shower              | 0.0749  | 0.3000  | 0.0031  | 0.1000  |
| vent                | 0.0153  | 0.1667  | 0.0000  | 0.0000  |
| box                 | 0.1755  | 0.5151  | 0.0458  | 0.2045  |
| stool               | 0.3119  | 0.7983  | 0.2122  | 0.5966  |
| soap dish           | 0.4680  | 0.6667  | 0.1279  | 0.2667  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| paper               | 0.0316  | 0.3077  | 0.0007  | 0.0444  |
| mat                 | 0.0241  | 0.2703  | 0.0108  | 0.0541  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cabinet             | 0.3141  | 0.7673  | 0.1831  | 0.5416  |
| blanket             | 0.2525  | 0.8000  | 0.0599  | 0.3700  |
| shelf               | 0.3357  | 0.7158  | 0.1629  | 0.4478  |
| shoe                | 0.1784  | 0.4486  | 0.0369  | 0.1308  |
| carpet              | 0.1870  | 0.5000  | 0.0135  | 0.1310  |
| plant               | 0.3268  | 0.6882  | 0.0999  | 0.3187  |
| bed                 | 0.8487  | 0.9349  | 0.6804  | 0.8580  |
| notebook            | 0.0008  | 0.0769  | 0.0000  | 0.0000  |
| purse               | 0.0007  | 0.1667  | 0.0000  | 0.0000  |
| clothes             | 0.1926  | 0.6019  | 0.0521  | 0.2898  |
| basket              | 0.2997  | 0.5826  | 0.1347  | 0.3391  |
| counter             | 0.1885  | 0.6606  | 0.0634  | 0.3394  |
| case                | 0.1169  | 0.4633  | 0.0487  | 0.1469  |
| toothpaste          | 0.0014  | 0.2000  | 0.0000  | 0.0000  |
| faucet              | 0.0241  | 0.2128  | 0.0000  | 0.0000  |
| lamp                | 0.3466  | 0.6537  | 0.0986  | 0.3104  |
| soap                | 0.1710  | 0.6667  | 0.0013  | 0.1667  |
| backpack            | 0.5809  | 0.8548  | 0.3716  | 0.6290  |
| couch               | 0.5132  | 0.8430  | 0.4157  | 0.7397  |
| cosmetics           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scale               | 0.2787  | 0.3636  | 0.0000  | 0.0000  |
| tablet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| decoration          | 0.0537  | 0.4643  | 0.0084  | 0.1357  |
| laptop              | 0.4839  | 0.6957  | 0.1527  | 0.3043  |
| ottoman             | 0.1895  | 0.6389  | 0.1405  | 0.4722  |
| candle              | 0.0699  | 0.4444  | 0.0007  | 0.1667  |
| window              | 0.2627  | 0.6317  | 0.0526  | 0.2470  |
| doorway             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillow              | 0.2909  | 0.6179  | 0.0506  | 0.2050  |
| sunglasses          | 0.1232  | 0.4615  | 0.0051  | 0.1538  |
| chair               | 0.6192  | 0.8384  | 0.4798  | 0.6806  |
| radiator            | 0.7393  | 0.8222  | 0.1768  | 0.3556  |
| blackboard          | 0.6312  | 0.7805  | 0.2690  | 0.4512  |
| computer            | 0.3184  | 0.6393  | 0.0175  | 0.1311  |
| monitor             | 0.5468  | 0.7120  | 0.0907  | 0.3152  |
| keyboard            | 0.0692  | 0.2209  | 0.0003  | 0.0116  |
| desk                | 0.5712  | 0.9101  | 0.4116  | 0.7753  |
| mouse               | 0.0019  | 0.0541  | 0.0000  | 0.0000  |
| table               | 0.5287  | 0.8746  | 0.4244  | 0.7492  |
| plug                | 0.0352  | 0.1579  | 0.0000  | 0.0000  |
| telephone           | 0.3713  | 0.6667  | 0.1244  | 0.2833  |
| blinds              | 0.2241  | 0.4600  | 0.0273  | 0.1800  |
| book                | 0.0701  | 0.4036  | 0.0070  | 0.1173  |
| projector           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| windowsill          | 0.1107  | 0.5870  | 0.0221  | 0.1304  |
| jacket              | 0.6166  | 0.7917  | 0.3607  | 0.5833  |
| stand               | 0.5686  | 0.8902  | 0.4577  | 0.7317  |
| wardrobe            | 0.3922  | 0.7711  | 0.2872  | 0.6024  |
| umbrella            | 0.2000  | 0.2000  | 0.2000  | 0.2000  |
| board               | 0.1843  | 0.6290  | 0.0615  | 0.2903  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pipe                | 0.0015  | 0.1786  | 0.0000  | 0.0000  |
| dresser             | 0.6635  | 0.9074  | 0.5900  | 0.8148  |
| tv                  | 0.6221  | 0.8119  | 0.1983  | 0.4356  |
| container           | 0.0110  | 0.2712  | 0.0035  | 0.1186  |
| paper cutter        | 0.8382  | 1.0000  | 0.0333  | 0.1667  |
| stairs              | 0.0863  | 0.4583  | 0.0105  | 0.1528  |
| clock               | 0.4559  | 0.7021  | 0.0449  | 0.1915  |
| remote control      | 0.0690  | 0.2222  | 0.0037  | 0.0370  |
| toy                 | 0.1421  | 0.3939  | 0.0325  | 0.1364  |
| printer             | 0.6345  | 0.8367  | 0.3841  | 0.6122  |
| rack                | 0.1891  | 0.5000  | 0.0990  | 0.4808  |
| poster              | 0.0296  | 0.1667  | 0.0017  | 0.0152  |
| bench               | 0.3037  | 0.7547  | 0.2473  | 0.5660  |
| guitar              | 0.2730  | 0.5000  | 0.1042  | 0.1667  |
| refrigerator        | 0.7189  | 0.8800  | 0.4896  | 0.6400  |
| luggage             | 0.6032  | 1.0000  | 0.5556  | 0.6667  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine             | 0.5140  | 0.7895  | 0.3494  | 0.6842  |
| coffee maker        | 0.4809  | 0.5862  | 0.0848  | 0.1724  |
| kettle              | 0.2099  | 0.6667  | 0.0264  | 0.2222  |
| bowl                | 0.1626  | 0.4928  | 0.0698  | 0.2174  |
| hat                 | 0.1667  | 0.2222  | 0.0006  | 0.1111  |
| plate               | 0.0203  | 0.3077  | 0.0035  | 0.0769  |
| plunger             | 0.8000  | 0.8000  | 0.2097  | 0.6000  |
| stall               | 0.2612  | 1.0000  | 0.1710  | 0.8235  |
| frame               | 0.0749  | 0.5510  | 0.0008  | 0.0612  |
| hamper              | 0.5152  | 0.5455  | 0.5152  | 0.5455  |
| window frame        | 0.0295  | 0.3939  | 0.0026  | 0.1919  |
| countertop          | 0.0102  | 0.2500  | 0.0022  | 0.1071  |
| grab bar            | 0.0769  | 0.5000  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket              | 0.2480  | 0.7179  | 0.1148  | 0.4103  |
| soap dispenser      | 0.4820  | 0.6667  | 0.1474  | 0.3333  |
| dispenser           | 0.5369  | 0.7273  | 0.4095  | 0.5682  |
| mailbox             | 0.0468  | 0.8000  | 0.0229  | 0.4000  |
| holder              | 0.2234  | 0.3333  | 0.1111  | 0.1111  |
| partition           | 0.4444  | 0.6667  | 0.1667  | 0.1667  |
| pillar              | 0.3447  | 0.7674  | 0.2119  | 0.4884  |
| column              | 0.0151  | 0.3421  | 0.0025  | 0.1316  |
| hanger              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| alarm               | 0.0024  | 0.1250  | 0.0000  | 0.0000  |
| chandelier          | 0.0714  | 0.2381  | 0.0190  | 0.0952  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| spoon               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape       | 0.2000  | 0.2000  | 0.0000  | 0.0000  |
| furniture           | 0.0015  | 0.1667  | 0.0000  | 0.0000  |
| fireplace           | 0.1847  | 0.4643  | 0.0219  | 0.1786  |
| cube                | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| copier              | 0.9167  | 0.9167  | 0.9167  | 0.9167  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 0.7176  | 1.0000  | 0.5096  | 1.0000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchen island      | 0.3929  | 0.4286  | 0.3929  | 0.4286  |
| screen              | 0.0469  | 0.4000  | 0.0000  | 0.0000  |
| fan                 | 0.6138  | 0.7969  | 0.1622  | 0.3594  |
| person              | 0.2087  | 0.6667  | 0.0051  | 0.3333  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tube                | 0.0044  | 0.1538  | 0.0000  | 0.0000  |
| statue              | 0.1702  | 0.4211  | 0.0912  | 0.3158  |
| piano               | 0.2667  | 0.4000  | 0.2667  | 0.4000  |
| calendar            | 0.0095  | 0.3333  | 0.0000  | 0.0000  |
| humidifier          | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| vacuum cleaner      | 0.4769  | 0.7143  | 0.0179  | 0.2857  |
| ball                | 0.4476  | 0.5238  | 0.3973  | 0.5238  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| map                 | 0.0147  | 0.5000  | 0.0147  | 0.5000  |
| basin               | 0.0233  | 0.2353  | 0.0000  | 0.0000  |
| fire extinguisher   | 0.3090  | 0.6000  | 0.0582  | 0.4000  |
| roof                | 0.0029  | 0.2083  | 0.0001  | 0.0417  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water cooler        | 0.8333  | 1.0000  | 0.8333  | 1.0000  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crib                | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| garage door         | 0.3519  | 0.5000  | 0.1215  | 0.5000  |
| headboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool table          | 0.1667  | 0.3333  | 0.1667  | 0.3333  |
| barricade           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| heater              | 0.5752  | 0.7759  | 0.1887  | 0.4138  |
| sign                | 0.1298  | 0.5000  | 0.0002  | 0.0333  |
| dish rack           | 0.4344  | 1.0000  | 0.3600  | 0.6000  |
| teapot              | 0.0111  | 0.1111  | 0.0111  | 0.1111  |
| toaster             | 0.4167  | 0.6000  | 0.0911  | 0.4000  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| oven                | 0.4465  | 0.8214  | 0.2156  | 0.4643  |
| kitchenware         | 0.2415  | 0.5000  | 0.0314  | 0.1522  |
| pan                 | 0.2313  | 0.5294  | 0.0246  | 0.1765  |
| fruit               | 0.1573  | 0.2857  | 0.0727  | 0.1429  |
| pot                 | 0.1384  | 0.4444  | 0.0159  | 0.1111  |
| pack                | 0.0385  | 0.6667  | 0.0084  | 0.3333  |
| brush               | 0.1163  | 0.3571  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jar                 | 0.1014  | 0.3846  | 0.0824  | 0.1538  |
| cushion             | 0.2212  | 0.5490  | 0.0086  | 0.1961  |
| drum                | 0.0417  | 0.1250  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stove               | 0.5559  | 0.8056  | 0.3135  | 0.5000  |
| folder              | 0.1383  | 0.3571  | 0.1010  | 0.2143  |
| device              | 0.2049  | 0.5455  | 0.0000  | 0.0000  |
| crate               | 0.4728  | 0.7143  | 0.2921  | 0.4286  |
| mattress            | 0.0556  | 0.3333  | 0.0000  | 0.0000  |
| bread               | 0.2000  | 0.2000  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beanbag             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| bidet               | 0.4167  | 0.6667  | 0.4167  | 0.6667  |
| sack                | 0.4912  | 0.8571  | 0.1457  | 0.2857  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| balcony             | 0.2083  | 1.0000  | 0.0227  | 0.5000  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flowerpot           | 0.0361  | 0.4706  | 0.0021  | 0.1373  |
| vase                | 0.1492  | 0.4600  | 0.0508  | 0.2000  |
| range hood          | 0.4163  | 0.8571  | 0.0397  | 0.2857  |
| magazine            | 0.0150  | 0.6000  | 0.0096  | 0.4000  |
| file                | 0.0522  | 0.2059  | 0.0503  | 0.1471  |
| speaker             | 0.1238  | 0.3182  | 0.0032  | 0.0909  |
| air conditioner     | 0.6000  | 0.6000  | 0.4000  | 0.4000  |
| excercise equipment | 0.3570  | 0.6500  | 0.0415  | 0.4000  |
| glass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| can                 | 0.0196  | 0.1667  | 0.0049  | 0.0833  |
| garbage             | 0.6467  | 0.8750  | 0.2857  | 0.5000  |
| conducting wire     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| commode             | 0.5697  | 0.8846  | 0.4537  | 0.7308  |
| price tag           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| napkins             | 0.4444  | 0.6667  | 0.0000  | 0.0000  |
| player              | 0.2500  | 0.2500  | 0.2500  | 0.2500  |
| roll                | 0.2148  | 0.4000  | 0.0000  | 0.0000  |
| headphones          | 0.2061  | 0.4000  | 0.2000  | 0.2000  |
| drawer              | 0.3071  | 0.6667  | 0.2845  | 0.5000  |
| album               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coil                | 0.2222  | 0.3333  | 0.0000  | 0.0000  |
| letter              | 0.3571  | 0.7500  | 0.0432  | 0.5000  |
| clothes dryer       | 0.4068  | 0.8000  | 0.2635  | 0.6000  |
| shampoo             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toiletry            | 0.1275  | 0.3478  | 0.0017  | 0.0870  |
| beam                | 0.0002  | 0.1364  | 0.0000  | 0.0455  |
| washing machine     | 0.5212  | 0.8214  | 0.3159  | 0.5714  |
| body loofah         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candlestick         | 0.0167  | 0.3333  | 0.0000  | 0.0000  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| food                | 0.0933  | 0.1818  | 0.0012  | 0.0909  |
| jalousie            | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| radio               | 0.2500  | 0.2500  | 0.2500  | 0.2500  |
| sponge              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| broom               | 0.0262  | 0.2857  | 0.0000  | 0.0000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| salt                | 1.0000  | 1.0000  | 0.2500  | 0.5000  |
| glove               | 0.9167  | 1.0000  | 0.0000  | 0.0000  |
| arch                | 0.0342  | 0.1667  | 0.0004  | 0.0667  |
| mop                 | 0.8667  | 1.0000  | 0.1667  | 0.3333  |
| flush               | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| ladder              | 0.2018  | 0.4706  | 0.1500  | 0.2353  |
| handle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| steps               | 0.0014  | 0.3000  | 0.0001  | 0.2000  |
| footstool           | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washbasin           | 0.0105  | 0.3333  | 0.0102  | 0.2667  |
| thermostat          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| treadmill           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.2898  | 0.4000  | 0.1000  | 0.1000  |
| cap                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wine                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell            | 0.6667  | 0.6667  | 0.3375  | 0.6667  |
| helmets             | 0.0046  | 0.3333  | 0.0000  | 0.0000  |
| menu                | 0.6429  | 0.7143  | 0.1429  | 0.1429  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water heater        | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| overall             | 0.2375  | 0.4357  | 0.1119  | 0.2272  |
| head                | 0.3155  | 0.6111  | 0.1520  | 0.3313  |
| common              | 0.2118  | 0.4090  | 0.1077  | 0.2142  |
| tail                | 0.1810  | 0.2734  | 0.0727  | 0.1275  |
| small               | 0.0841  | 0.2329  | 0.0146  | 0.0600  |
| medium              | 0.2463  | 0.4875  | 0.1277  | 0.2610  |
| large               | 0.1515  | 0.5336  | 0.0843  | 0.3352  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 13:48:30 INFO metrics.py:437 | number of scannet results: 250
Rank[0/8] 06/20/2025 13:49:45 INFO metrics.py:332 | eval results of scannet:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| bathtub             | 0.9586  | 0.9688  | 0.8494  | 0.8750  |
| curtain             | 0.6307  | 0.8584  | 0.1509  | 0.4159  |
| toilet              | 0.9406  | 0.9643  | 0.7895  | 0.8214  |
| bin                 | 0.6852  | 0.8723  | 0.4608  | 0.6468  |
| picture             | 0.3440  | 0.5336  | 0.0658  | 0.1567  |
| toilet paper        | 0.4365  | 0.5696  | 0.1718  | 0.3671  |
| sink                | 0.8247  | 0.8750  | 0.3757  | 0.5795  |
| doorframe           | 0.3591  | 0.7746  | 0.1061  | 0.3944  |
| door                | 0.5905  | 0.8528  | 0.2167  | 0.5017  |
| towel               | 0.4997  | 0.7025  | 0.1666  | 0.3058  |
| mirror              | 0.3673  | 0.6988  | 0.0249  | 0.1687  |
| switch              | 0.0037  | 0.1899  | 0.0000  | 0.0000  |
| stopcock            | 0.1554  | 0.5862  | 0.0004  | 0.0345  |
| bar                 | 0.0797  | 0.3636  | 0.0000  | 0.0000  |
| vanity              | 0.7486  | 1.0000  | 0.6071  | 0.9000  |
| bottle              | 0.0998  | 0.4105  | 0.0095  | 0.1105  |
| bag                 | 0.1898  | 0.7397  | 0.0924  | 0.3151  |
| hair dryer          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rail                | 0.0820  | 0.6667  | 0.0000  | 0.0000  |
| cup                 | 0.2111  | 0.4783  | 0.0634  | 0.1014  |
| socket              | 0.0211  | 0.2816  | 0.0001  | 0.0194  |
| tissue box          | 0.2687  | 0.5455  | 0.2236  | 0.4091  |
| microwave           | 0.5689  | 0.7500  | 0.3434  | 0.6000  |
| tissue              | 0.0006  | 0.2222  | 0.0000  | 0.0000  |
| ledge               | 0.0119  | 0.3333  | 0.0016  | 0.1667  |
| rod                 | 0.0019  | 0.0714  | 0.0000  | 0.0000  |
| light               | 0.1639  | 0.4828  | 0.0352  | 0.0690  |
| shower              | 0.0410  | 0.2143  | 0.0003  | 0.0714  |
| box                 | 0.1253  | 0.4325  | 0.0407  | 0.1468  |
| stool               | 0.3421  | 0.8182  | 0.2161  | 0.5909  |
| soap dish           | 0.7778  | 0.8333  | 0.2063  | 0.3333  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| paper               | 0.0249  | 0.3094  | 0.0002  | 0.0340  |
| mat                 | 0.0591  | 0.3000  | 0.0250  | 0.1000  |
| cabinet             | 0.3730  | 0.7698  | 0.2023  | 0.5397  |
| blanket             | 0.0779  | 0.5417  | 0.0175  | 0.2500  |
| shelf               | 0.5601  | 0.8235  | 0.3819  | 0.6691  |
| shoe                | 0.1885  | 0.4750  | 0.0439  | 0.1500  |
| carpet              | 0.0474  | 0.5000  | 0.0000  | 0.0000  |
| plant               | 0.3204  | 0.6723  | 0.1004  | 0.3193  |
| bed                 | 0.9824  | 1.0000  | 0.9062  | 0.9649  |
| notebook            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| purse               | 0.0014  | 0.1667  | 0.0000  | 0.0000  |
| clothes             | 0.2019  | 0.6013  | 0.0896  | 0.3529  |
| basket              | 0.0866  | 0.3077  | 0.0840  | 0.1923  |
| counter             | 0.4549  | 0.8286  | 0.2226  | 0.5429  |
| case                | 0.6737  | 1.0000  | 0.4505  | 0.7368  |
| toothpaste          | 0.0019  | 0.2000  | 0.0000  | 0.0000  |
| faucet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| lamp                | 0.4843  | 0.7381  | 0.2384  | 0.5000  |
| backpack            | 0.7094  | 0.9286  | 0.5413  | 0.7500  |
| couch               | 0.7321  | 0.9625  | 0.6205  | 0.8750  |
| scale               | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| decoration          | 0.1954  | 0.6250  | 0.0081  | 0.2083  |
| laptop              | 0.5178  | 0.6000  | 0.1909  | 0.2667  |
| ottoman             | 0.2122  | 1.0000  | 0.0489  | 0.5714  |
| window              | 0.5353  | 0.8395  | 0.1696  | 0.4568  |
| pillow              | 0.4333  | 0.6458  | 0.2108  | 0.4236  |
| chair               | 0.6595  | 0.8447  | 0.4749  | 0.6588  |
| radiator            | 0.8126  | 0.8621  | 0.2422  | 0.4483  |
| blackboard          | 0.6475  | 0.7826  | 0.2872  | 0.4493  |
| computer            | 0.2833  | 0.5641  | 0.0407  | 0.1538  |
| monitor             | 0.5975  | 0.7286  | 0.1459  | 0.3643  |
| keyboard            | 0.1251  | 0.2586  | 0.0000  | 0.0000  |
| desk                | 0.6868  | 0.9747  | 0.4815  | 0.7848  |
| mouse               | 0.0063  | 0.0952  | 0.0000  | 0.0000  |
| table               | 0.6316  | 0.9308  | 0.4965  | 0.8115  |
| plug                | 0.0001  | 0.1111  | 0.0000  | 0.0000  |
| telephone           | 0.4859  | 0.7500  | 0.2034  | 0.3750  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| book                | 0.0789  | 0.4190  | 0.0077  | 0.1333  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| windowsill          | 0.0079  | 0.5000  | 0.0014  | 0.1667  |
| jacket              | 0.6456  | 0.8261  | 0.3775  | 0.6087  |
| stand               | 0.7570  | 0.9057  | 0.6542  | 0.7925  |
| wardrobe            | 0.4064  | 0.7500  | 0.3750  | 0.3750  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| board               | 0.1025  | 0.6562  | 0.0081  | 0.4062  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pipe                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dresser             | 0.7984  | 0.9730  | 0.7596  | 0.9189  |
| tv                  | 0.8675  | 0.9130  | 0.3410  | 0.5217  |
| container           | 0.0042  | 0.2609  | 0.0031  | 0.1739  |
| paper cutter        | 0.8057  | 1.0000  | 0.0500  | 0.2000  |
| stairs              | 0.0877  | 0.8000  | 0.0184  | 0.6000  |
| clock               | 0.4437  | 0.6800  | 0.0880  | 0.2000  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toy                 | 0.0672  | 0.2941  | 0.0588  | 0.1176  |
| printer             | 0.7079  | 0.9091  | 0.5850  | 0.7273  |
| rack                | 0.0010  | 0.0909  | 0.0010  | 0.0909  |
| poster              | 0.1000  | 1.0000  | 0.0000  | 0.0000  |
| bench               | 0.3580  | 1.0000  | 0.3580  | 1.0000  |
| guitar              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| refrigerator        | 0.8969  | 0.9500  | 0.7105  | 0.8000  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine             | 0.5429  | 0.8750  | 0.3435  | 0.7500  |
| coffee maker        | 0.6073  | 0.6471  | 0.1569  | 0.2353  |
| kettle              | 0.1348  | 0.5000  | 0.0000  | 0.0000  |
| bowl                | 0.3009  | 0.4444  | 0.0741  | 0.2222  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plate               | 0.0308  | 0.4000  | 0.0000  | 0.0000  |
| plunger             | 0.8000  | 0.8000  | 0.2097  | 0.6000  |
| stall               | 0.2614  | 1.0000  | 0.1711  | 0.8235  |
| frame               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hamper              | 0.5667  | 0.6000  | 0.5667  | 0.6000  |
| grab bar            | 0.0800  | 0.5000  | 0.0000  | 0.0000  |
| bucket              | 0.1524  | 0.7143  | 0.0868  | 0.4286  |
| soap dispenser      | 0.4673  | 0.6190  | 0.1579  | 0.2857  |
| dispenser           | 0.6104  | 0.7179  | 0.4945  | 0.6154  |
| mailbox             | 0.0468  | 0.8000  | 0.0229  | 0.4000  |
| holder              | 0.2000  | 0.2000  | 0.2000  | 0.2000  |
| pillar              | 0.2250  | 0.4000  | 0.2000  | 0.2000  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| copier              | 0.9167  | 0.9167  | 0.9167  | 0.9167  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 0.6667  | 1.0000  | 0.4630  | 1.0000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fan                 | 0.8472  | 0.8750  | 0.0625  | 0.1250  |
| person              | 0.2104  | 0.6667  | 0.0054  | 0.3333  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| calendar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vacuum cleaner      | 0.1481  | 0.6667  | 0.1481  | 0.6667  |
| ball                | 0.4167  | 0.4167  | 0.3356  | 0.4167  |
| map                 | 0.0357  | 1.0000  | 0.0357  | 1.0000  |
| basin               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher   | 0.0076  | 0.3333  | 0.0000  | 0.0000  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water cooler        | 0.8333  | 1.0000  | 0.8333  | 1.0000  |
| barricade           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sign                | 0.1102  | 0.4643  | 0.0003  | 0.0357  |
| dish rack           | 0.5995  | 1.0000  | 0.5625  | 0.7500  |
| teapot              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster             | 0.0909  | 0.5000  | 0.0909  | 0.5000  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| oven                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pot                 | 0.0500  | 0.5000  | 0.0000  | 0.0000  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cushion             | 0.0137  | 0.5000  | 0.0042  | 0.2500  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stove               | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| crate               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flowerpot           | 0.0167  | 0.5000  | 0.0167  | 0.5000  |
| range hood          | 0.7357  | 1.0000  | 0.1333  | 0.4000  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.6269  | 0.8333  | 0.6000  | 0.6667  |
| dishwasher          | 0.0556  | 0.3333  | 0.0000  | 0.0000  |
| ladder              | 0.1444  | 0.4000  | 0.1000  | 0.2000  |
| drawer              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.8333  | 1.0000  | 0.8333  | 1.0000  |
| broom               | 0.0493  | 0.3333  | 0.0000  | 0.0000  |
| headphones          | 0.2143  | 0.4000  | 0.2000  | 0.2000  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| scannet             | 0.2689  | 0.4719  | 0.1549  | 0.2800  |
| head                | 0.3693  | 0.6301  | 0.2123  | 0.3768  |
| common              | 0.2039  | 0.3536  | 0.1167  | 0.1974  |
| tail                | 0.0775  | 0.2053  | 0.0477  | 0.1400  |
| small               | 0.0554  | 0.1901  | 0.0161  | 0.0381  |
| medium              | 0.3101  | 0.5645  | 0.1791  | 0.3443  |
| large               | 0.2894  | 0.7948  | 0.1917  | 0.5552  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 13:49:45 INFO metrics.py:437 | number of 3rscan results: 235
Rank[0/8] 06/20/2025 13:50:52 INFO metrics.py:332 | eval results of 3rscan:
+-------------------+---------+---------+---------+---------+
| classes           | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+-------------------+---------+---------+---------+---------+
| chair             | 0.7189  | 0.8637  | 0.6011  | 0.7466  |
| monitor           | 0.5343  | 0.6750  | 0.0196  | 0.1500  |
| keyboard          | 0.0296  | 0.1667  | 0.0026  | 0.0417  |
| desk              | 0.5173  | 0.8875  | 0.3875  | 0.7875  |
| computer          | 0.3889  | 0.8095  | 0.0039  | 0.0952  |
| mouse             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cup               | 0.0120  | 0.2308  | 0.0000  | 0.0000  |
| bag               | 0.3761  | 0.7475  | 0.0640  | 0.3333  |
| case              | 0.0573  | 0.4286  | 0.0071  | 0.0786  |
| paper             | 0.0944  | 0.3143  | 0.0063  | 0.0857  |
| stand             | 0.5321  | 0.9344  | 0.4019  | 0.6721  |
| plate             | 0.0358  | 0.3030  | 0.0053  | 0.0909  |
| garbage           | 0.6470  | 0.8750  | 0.2857  | 0.5000  |
| bin               | 0.7506  | 0.9634  | 0.5162  | 0.7439  |
| pen               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plug              | 0.1111  | 0.2222  | 0.0000  | 0.0000  |
| curtain           | 0.5828  | 0.8889  | 0.1686  | 0.5000  |
| bottle            | 0.0467  | 0.3636  | 0.0021  | 0.0744  |
| wardrobe          | 0.4523  | 0.8281  | 0.3140  | 0.6719  |
| backpack          | 0.5027  | 0.8182  | 0.2559  | 0.5455  |
| book              | 0.1006  | 0.3964  | 0.0120  | 0.1124  |
| table             | 0.5799  | 0.8964  | 0.4720  | 0.7703  |
| window            | 0.2664  | 0.6986  | 0.0331  | 0.2123  |
| telephone         | 0.2846  | 0.5833  | 0.0555  | 0.2083  |
| cabinet           | 0.4851  | 0.8547  | 0.2992  | 0.6667  |
| box               | 0.2282  | 0.5512  | 0.0574  | 0.2157  |
| socket            | 0.0057  | 0.0625  | 0.0000  | 0.0000  |
| notebook          | 0.0009  | 0.0909  | 0.0000  | 0.0000  |
| sink              | 0.6927  | 0.8214  | 0.1738  | 0.4107  |
| conducting wire   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blinds            | 0.2876  | 0.5897  | 0.0351  | 0.2308  |
| plant             | 0.3702  | 0.6584  | 0.0963  | 0.2376  |
| file              | 0.0635  | 0.2500  | 0.0611  | 0.1786  |
| hat               | 0.2857  | 0.2857  | 0.0022  | 0.1429  |
| laptop            | 0.4667  | 0.8333  | 0.1270  | 0.5000  |
| tissue box        | 0.0556  | 0.3333  | 0.0556  | 0.3333  |
| clock             | 0.5294  | 0.7368  | 0.0089  | 0.1053  |
| picture           | 0.2255  | 0.4756  | 0.0120  | 0.0976  |
| shelf             | 0.2979  | 0.7279  | 0.1060  | 0.3993  |
| towel             | 0.3034  | 0.6456  | 0.1151  | 0.2278  |
| switch            | 0.0035  | 0.0943  | 0.0000  | 0.0000  |
| pillow            | 0.4145  | 0.6992  | 0.0368  | 0.1836  |
| cushion           | 0.3726  | 0.8000  | 0.0141  | 0.2333  |
| printer           | 0.7738  | 0.8889  | 0.2078  | 0.4444  |
| frame             | 0.1334  | 0.8333  | 0.0017  | 0.1000  |
| light             | 0.1544  | 0.5455  | 0.0193  | 0.0909  |
| bucket            | 0.6925  | 1.0000  | 0.3055  | 0.5333  |
| pad               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tv                | 0.7199  | 0.9429  | 0.2433  | 0.5429  |
| lamp              | 0.3056  | 0.5938  | 0.0537  | 0.1625  |
| commode           | 0.5705  | 0.8846  | 0.4542  | 0.7308  |
| couch             | 0.5134  | 0.8286  | 0.4085  | 0.7429  |
| stool             | 0.4634  | 0.9444  | 0.3254  | 0.7222  |
| door              | 0.3536  | 0.7353  | 0.0960  | 0.3088  |
| decoration        | 0.1338  | 0.4375  | 0.0402  | 0.0938  |
| blanket           | 0.3894  | 0.9077  | 0.1009  | 0.4462  |
| price tag         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candle            | 0.0843  | 0.4667  | 0.0006  | 0.1333  |
| bed               | 0.9156  | 0.9818  | 0.6962  | 0.8364  |
| bowl              | 0.2642  | 0.7037  | 0.1298  | 0.1852  |
| folder            | 0.1383  | 0.3571  | 0.1010  | 0.2143  |
| napkins           | 0.4444  | 0.6667  | 0.0000  | 0.0000  |
| vase              | 0.3339  | 0.5500  | 0.1195  | 0.2000  |
| clothes           | 0.2714  | 0.7184  | 0.0470  | 0.2427  |
| carpet            | 0.2903  | 0.6250  | 0.0235  | 0.1964  |
| tablet            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bench             | 0.4389  | 0.8571  | 0.3527  | 0.6429  |
| flowerpot         | 0.0437  | 0.5417  | 0.0006  | 0.0417  |
| rack              | 0.3862  | 0.7000  | 0.1962  | 0.6667  |
| jar               | 0.1651  | 0.5000  | 0.1346  | 0.2500  |
| teapot            | 0.0312  | 0.2500  | 0.0312  | 0.2500  |
| spoon             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ottoman           | 0.5589  | 0.9091  | 0.5137  | 0.8182  |
| player            | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| cube              | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| counter           | 0.1352  | 0.7317  | 0.0172  | 0.3171  |
| magazine          | 0.0187  | 0.7500  | 0.0120  | 0.5000  |
| toy               | 0.4318  | 0.6154  | 0.0769  | 0.2308  |
| container         | 0.2540  | 0.6667  | 0.1111  | 0.1111  |
| roll              | 0.2148  | 0.4000  | 0.0000  | 0.0000  |
| bar               | 0.5000  | 0.6667  | 0.0417  | 0.3333  |
| blackboard        | 0.6455  | 0.8333  | 0.2646  | 0.5000  |
| humidifier        | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| heater            | 0.5813  | 0.7719  | 0.1899  | 0.4035  |
| windowsill        | 0.1419  | 0.6154  | 0.0264  | 0.1282  |
| fan               | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| device            | 0.2049  | 0.5455  | 0.0000  | 0.0000  |
| doorframe         | 0.2096  | 0.7400  | 0.0898  | 0.3200  |
| ball              | 0.6000  | 0.6000  | 0.6000  | 0.6000  |
| radiator          | 0.6452  | 0.7931  | 0.0748  | 0.2069  |
| drawer            | 0.6977  | 1.0000  | 0.6547  | 0.8889  |
| speaker           | 0.2083  | 0.3333  | 0.0000  | 0.0000  |
| board             | 0.3390  | 0.6250  | 0.1594  | 0.2083  |
| brush             | 0.2018  | 0.5000  | 0.0000  | 0.0000  |
| umbrella          | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| kettle            | 0.2883  | 0.8333  | 0.0409  | 0.3333  |
| mirror            | 0.3804  | 0.6667  | 0.0151  | 0.2222  |
| shoe              | 0.2805  | 0.5476  | 0.0618  | 0.1905  |
| poster            | 0.0363  | 0.1587  | 0.0020  | 0.0159  |
| album             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pot               | 0.4369  | 0.5833  | 0.0560  | 0.2500  |
| hanger            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| remote control    | 0.0991  | 0.3000  | 0.0056  | 0.0500  |
| adhesive tape     | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| coffee maker      | 0.3525  | 0.6667  | 0.0058  | 0.1111  |
| basket            | 0.5214  | 0.7656  | 0.2480  | 0.4844  |
| coil              | 0.2222  | 0.3333  | 0.0000  | 0.0000  |
| cosmetics         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| air conditioner   | 0.7500  | 0.7500  | 0.5000  | 0.5000  |
| dresser           | 1.0000  | 1.0000  | 0.2500  | 0.5000  |
| rail              | 0.3333  | 0.5000  | 0.0000  | 0.0000  |
| guitar            | 1.0000  | 1.0000  | 0.5000  | 0.5000  |
| calendar          | 0.0156  | 0.4000  | 0.0000  | 0.0000  |
| window frame      | 0.1225  | 0.4167  | 0.0011  | 0.0833  |
| refrigerator      | 0.6757  | 0.8667  | 0.6524  | 0.7333  |
| microwave         | 0.9722  | 1.0000  | 0.3942  | 0.5882  |
| balcony           | 0.2083  | 1.0000  | 0.0227  | 0.5000  |
| fruit             | 0.5000  | 0.6000  | 0.2000  | 0.2000  |
| bicycle           | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| stairs            | 0.4520  | 0.7143  | 0.0493  | 0.3571  |
| pack              | 0.0445  | 0.7692  | 0.0097  | 0.3846  |
| tissue            | 0.1444  | 0.6667  | 0.1111  | 0.3333  |
| basin             | 0.0471  | 0.2667  | 0.0000  | 0.0000  |
| scale             | 0.6667  | 0.6667  | 0.0000  | 0.0000  |
| sunglasses        | 0.1235  | 0.4615  | 0.0051  | 0.1538  |
| letter            | 0.3571  | 0.7500  | 0.0432  | 0.5000  |
| paper cutter      | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| furniture         | 0.0123  | 0.3333  | 0.0000  | 0.0000  |
| bathtub           | 1.0000  | 1.0000  | 0.8765  | 0.8889  |
| clothes dryer     | 0.3894  | 0.8000  | 0.1779  | 0.5000  |
| statue            | 1.0000  | 1.0000  | 0.5000  | 0.5000  |
| shampoo           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toilet            | 0.8162  | 0.8235  | 0.7059  | 0.7059  |
| faucet            | 0.2222  | 0.6667  | 0.0000  | 0.0000  |
| shower            | 0.5938  | 1.0000  | 0.0648  | 0.5000  |
| toiletry          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| toilet paper      | 0.1997  | 0.5000  | 0.0056  | 0.0833  |
| soap              | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| beam              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dish         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tube              | 0.0230  | 0.2857  | 0.0000  | 0.0000  |
| pipe              | 0.0183  | 1.0000  | 0.0000  | 0.0000  |
| washing machine   | 0.5749  | 0.8571  | 0.3233  | 0.5000  |
| machine           | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| dispenser         | 0.2255  | 1.0000  | 0.0000  | 0.0000  |
| body loofah       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beanbag           | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| can               | 0.0294  | 0.2500  | 0.0074  | 0.1250  |
| column            | 0.0192  | 1.0000  | 0.0111  | 0.5000  |
| candlestick       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillar            | 0.6267  | 0.7647  | 0.4608  | 0.6471  |
| knife             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mat               | 0.1667  | 0.3333  | 0.1667  | 0.3333  |
| pan               | 0.3215  | 0.6667  | 0.0360  | 0.2500  |
| oven              | 0.5996  | 1.0000  | 0.3421  | 0.6842  |
| stove             | 0.4257  | 0.8235  | 0.0355  | 0.3529  |
| kitchenware       | 0.2617  | 0.5227  | 0.0344  | 0.1591  |
| shovel            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood        | 0.5000  | 0.8000  | 0.0083  | 0.4000  |
| food              | 0.1037  | 0.2000  | 0.0019  | 0.1000  |
| jalousie          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| partition         | 0.5333  | 0.8000  | 0.2000  | 0.2000  |
| radio             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| sign              | 0.6250  | 1.0000  | 0.0000  | 0.0000  |
| crib              | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| sponge            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| piano             | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| fireplace         | 0.1000  | 0.2000  | 0.0500  | 0.2000  |
| mattress          | 0.0833  | 0.5000  | 0.0000  | 0.0000  |
| tray              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| holder            | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| broom             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dispenser    | 0.9000  | 1.0000  | 0.2600  | 0.6000  |
| chandelier        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hook              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| map               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| screen            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| salt              | 1.0000  | 1.0000  | 0.2500  | 0.5000  |
| glove             | 0.9167  | 1.0000  | 0.0000  | 0.0000  |
| glass             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vacuum cleaner    | 0.7500  | 0.7500  | 0.0000  | 0.0000  |
| bread             | 0.2000  | 0.2000  | 0.0000  | 0.0000  |
| mop               | 1.0000  | 1.0000  | 0.1667  | 0.3333  |
| flush             | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| sack              | 0.4912  | 0.8571  | 0.1457  | 0.2857  |
| ladder            | 0.4242  | 1.0000  | 0.3524  | 0.6667  |
| tool              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wire              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crate             | 0.7246  | 1.0000  | 0.4214  | 0.6000  |
| helmets           | 0.0046  | 0.3333  | 0.0000  | 0.0000  |
| dumbbell          | 1.0000  | 1.0000  | 0.0526  | 1.0000  |
| bidet             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| treadmill         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher        | 1.0000  | 1.0000  | 0.3333  | 0.3333  |
| washbasin         | 0.0833  | 1.0000  | 0.0833  | 1.0000  |
| menu              | 0.6429  | 0.7143  | 0.1429  | 0.1429  |
| water heater      | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| seasoning         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| footstool         | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| fire extinguisher | 1.0000  | 1.0000  | 0.0667  | 1.0000  |
| luggage           | 0.6389  | 1.0000  | 0.5556  | 0.6667  |
| toaster           | 1.0000  | 1.0000  | 0.2000  | 0.5000  |
| scissors          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toothbrush        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dish rack         | 0.1111  | 1.0000  | 0.0000  | 0.0000  |
| -------           | ------- | ------- | ------- | ------- |
| 3rscan            | 0.3754  | 0.5897  | 0.1559  | 0.2838  |
| head              | 0.3844  | 0.6817  | 0.1581  | 0.3341  |
| common            | 0.4156  | 0.6101  | 0.1735  | 0.2951  |
| tail              | 0.3045  | 0.3931  | 0.1279  | 0.1762  |
| small             | 0.1370  | 0.3076  | 0.0193  | 0.0721  |
| medium            | 0.3925  | 0.6639  | 0.1775  | 0.3213  |
| large             | 0.2679  | 0.7897  | 0.1208  | 0.4582  |
+-------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 13:50:52 INFO metrics.py:437 | number of matterport3d results: 339
Rank[0/8] 06/20/2025 13:52:23 INFO metrics.py:332 | eval results of matterport3d:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| mirror              | 0.1127  | 0.5946  | 0.0031  | 0.2027  |
| picture             | 0.2481  | 0.4662  | 0.0181  | 0.1159  |
| door                | 0.2765  | 0.6501  | 0.0151  | 0.1775  |
| light               | 0.0418  | 0.2450  | 0.0011  | 0.0456  |
| doorframe           | 0.3484  | 0.7836  | 0.1078  | 0.4274  |
| sink                | 0.5273  | 0.8283  | 0.2872  | 0.5758  |
| blanket             | 0.1500  | 0.7273  | 0.0238  | 0.1818  |
| towel               | 0.3939  | 0.6182  | 0.1219  | 0.3455  |
| plant               | 0.3060  | 0.7589  | 0.1384  | 0.4643  |
| decoration          | 0.0103  | 0.4286  | 0.0012  | 0.1310  |
| drawer              | 0.0030  | 0.3750  | 0.0004  | 0.1250  |
| case                | 0.0004  | 0.1667  | 0.0000  | 0.0556  |
| cabinet             | 0.1333  | 0.6780  | 0.0802  | 0.4195  |
| vase                | 0.0149  | 0.4000  | 0.0045  | 0.2000  |
| lamp                | 0.3425  | 0.6813  | 0.1343  | 0.3956  |
| stand               | 0.3959  | 0.8200  | 0.3424  | 0.7400  |
| roof                | 0.0029  | 0.2083  | 0.0001  | 0.0417  |
| flowerpot           | 0.0434  | 0.4000  | 0.0061  | 0.2000  |
| window              | 0.1441  | 0.5111  | 0.0207  | 0.1667  |
| bottle              | 0.1171  | 0.5417  | 0.0081  | 0.1875  |
| counter             | 0.0209  | 0.3939  | 0.0015  | 0.1515  |
| toilet              | 0.7930  | 0.8438  | 0.7067  | 0.7812  |
| countertop          | 0.0102  | 0.2500  | 0.0022  | 0.1071  |
| table               | 0.3395  | 0.7500  | 0.2893  | 0.6154  |
| bench               | 0.1984  | 0.5500  | 0.1609  | 0.3500  |
| box                 | 0.0475  | 0.5962  | 0.0148  | 0.3846  |
| vent                | 0.0168  | 0.1667  | 0.0000  | 0.0000  |
| toiletry            | 0.1137  | 0.3182  | 0.0027  | 0.0909  |
| handle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plate               | 0.0045  | 0.2857  | 0.0030  | 0.0714  |
| statue              | 0.0906  | 0.3529  | 0.0802  | 0.2941  |
| bathtub             | 0.6701  | 0.7778  | 0.5665  | 0.6667  |
| shelf               | 0.1678  | 0.5839  | 0.0711  | 0.3285  |
| soap                | 0.0048  | 0.6000  | 0.0024  | 0.2000  |
| curtain             | 0.4024  | 0.7500  | 0.0793  | 0.4375  |
| pot                 | 0.0019  | 0.3077  | 0.0000  | 0.0000  |
| carpet              | 0.0019  | 0.1111  | 0.0000  | 0.0000  |
| chair               | 0.4394  | 0.7721  | 0.3297  | 0.6361  |
| cup                 | 0.0006  | 0.1000  | 0.0000  | 0.0000  |
| frame               | 0.0006  | 0.1176  | 0.0000  | 0.0000  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| book                | 0.0711  | 0.3622  | 0.0096  | 0.0709  |
| pillow              | 0.1052  | 0.5394  | 0.0125  | 0.1230  |
| mat                 | 0.0070  | 0.2500  | 0.0000  | 0.0000  |
| container           | 0.0082  | 0.1481  | 0.0020  | 0.0741  |
| toy                 | 0.0977  | 0.3611  | 0.0100  | 0.1111  |
| tissue              | 0.0047  | 1.0000  | 0.0000  | 0.0000  |
| dresser             | 0.1931  | 0.7333  | 0.1443  | 0.6000  |
| switch              | 0.0002  | 0.1000  | 0.0000  | 0.0000  |
| stool               | 0.0313  | 0.3810  | 0.0107  | 0.2857  |
| rack                | 0.0010  | 0.3636  | 0.0010  | 0.3636  |
| window frame        | 0.0218  | 0.3908  | 0.0030  | 0.2069  |
| couch               | 0.3389  | 0.7500  | 0.2676  | 0.6196  |
| paper               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| screen              | 0.0716  | 0.5000  | 0.0000  | 0.0000  |
| tv                  | 0.4111  | 0.6512  | 0.1249  | 0.3023  |
| bed                 | 0.6573  | 0.8246  | 0.3950  | 0.7719  |
| ottoman             | 0.0292  | 0.3333  | 0.0140  | 0.2222  |
| steps               | 0.0015  | 0.3000  | 0.0001  | 0.2000  |
| stairs              | 0.0164  | 0.3585  | 0.0021  | 0.0566  |
| alarm               | 0.0051  | 0.1667  | 0.0000  | 0.0000  |
| clock               | 0.1788  | 0.6667  | 0.1788  | 0.6667  |
| bowl                | 0.1219  | 0.3333  | 0.0963  | 0.2424  |
| telephone           | 0.2515  | 0.5000  | 0.0000  | 0.0000  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| footstool           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cushion             | 0.0046  | 0.1176  | 0.0008  | 0.1176  |
| desk                | 0.4219  | 0.7368  | 0.3115  | 0.6842  |
| column              | 0.0161  | 0.3056  | 0.0027  | 0.1111  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| food                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stove               | 0.2505  | 0.6000  | 0.1157  | 0.3000  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit               | 0.0250  | 0.1250  | 0.0250  | 0.1250  |
| pillar              | 0.2261  | 0.8571  | 0.0822  | 0.4286  |
| doorway             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shower              | 0.0077  | 0.1667  | 0.0000  | 0.0000  |
| washing machine     | 0.3694  | 0.7500  | 0.0517  | 0.6250  |
| bin                 | 0.3396  | 0.7805  | 0.2246  | 0.4878  |
| washbasin           | 0.0118  | 0.2857  | 0.0116  | 0.2143  |
| dispenser           | 0.0122  | 0.5000  | 0.0122  | 0.5000  |
| thermostat          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fan                 | 0.6250  | 0.7963  | 0.2416  | 0.4074  |
| vanity              | 0.2602  | 0.7333  | 0.2250  | 0.6000  |
| windowsill          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toilet paper        | 0.3121  | 0.6667  | 0.1042  | 0.4167  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| holder              | 0.0027  | 0.3333  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rail                | 0.0308  | 0.4259  | 0.0000  | 0.0370  |
| headboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| teapot              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball                | 0.3791  | 0.7500  | 0.3791  | 0.7500  |
| soap dispenser      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| faucet              | 0.0174  | 0.2222  | 0.0000  | 0.0000  |
| heater              | 0.1111  | 1.0000  | 0.1111  | 1.0000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hanger              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchen island      | 0.3929  | 0.4286  | 0.3929  | 0.4286  |
| piano               | 0.1667  | 0.5000  | 0.1667  | 0.5000  |
| monitor             | 0.3333  | 0.5000  | 0.0833  | 0.2500  |
| refrigerator        | 0.4962  | 0.8000  | 0.0836  | 0.3333  |
| jar                 | 0.0048  | 0.3333  | 0.0000  | 0.0000  |
| candle              | 0.0042  | 0.3333  | 0.0042  | 0.3333  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| air conditioner     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| microwave           | 0.1971  | 0.4167  | 0.1071  | 0.1667  |
| fireplace           | 0.3056  | 0.5217  | 0.0498  | 0.1739  |
| radiator            | 0.1667  | 0.3333  | 0.0000  | 0.0000  |
| scale               | 0.0278  | 0.2500  | 0.0000  | 0.0000  |
| wardrobe            | 0.1048  | 0.4545  | 0.1027  | 0.3636  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basin               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pipe                | 0.0001  | 0.1000  | 0.0000  | 0.0000  |
| pan                 | 0.0250  | 0.2000  | 0.0000  | 0.0000  |
| board               | 0.1738  | 0.5000  | 0.0000  | 0.0000  |
| bucket              | 0.0059  | 0.3000  | 0.0013  | 0.2000  |
| kettle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue box          | 0.1071  | 0.6250  | 0.0035  | 0.2500  |
| pool table          | 0.1667  | 0.3333  | 0.1667  | 0.3333  |
| guitar              | 0.2041  | 0.5714  | 0.0238  | 0.1429  |
| basket              | 0.0510  | 0.4000  | 0.0015  | 0.1200  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0316  | 0.3750  | 0.0295  | 0.2500  |
| keyboard            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| projector           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bag                 | 0.0348  | 0.3846  | 0.0006  | 0.0769  |
| clothes dryer       | 0.2667  | 0.6667  | 0.2667  | 0.6667  |
| excercise equipment | 0.4036  | 0.7222  | 0.0484  | 0.4444  |
| clothes             | 0.1059  | 0.3966  | 0.0369  | 0.2069  |
| kitchenware         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher   | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| ladder              | 0.1889  | 0.3333  | 0.1111  | 0.1111  |
| garage door         | 0.3519  | 0.5000  | 0.1215  | 0.5000  |
| printer             | 0.4286  | 0.4286  | 0.1905  | 0.2857  |
| arch                | 0.0342  | 0.1667  | 0.0004  | 0.0667  |
| soap dish           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beam                | 0.0003  | 0.1429  | 0.0000  | 0.0476  |
| brush               | 0.0068  | 0.2500  | 0.0000  | 0.0000  |
| chandelier          | 0.1166  | 0.2632  | 0.0325  | 0.1053  |
| computer            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| laptop              | 0.5000  | 1.0000  | 0.0000  | 0.0000  |
| oven                | 0.2921  | 0.8000  | 0.0000  | 0.0000  |
| socket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mouse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 0.3333  | 1.0000  | 0.0127  | 1.0000  |
| partition           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| backpack            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shoe                | 0.0261  | 0.2400  | 0.0000  | 0.0000  |
| dumbbell            | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| hamper              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet               | 0.2500  | 0.5000  | 0.2500  | 0.5000  |
| magazine            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.1284  | 0.7500  | 0.0000  | 0.0000  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blackboard          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drum                | 0.0417  | 0.1250  | 0.0000  | 0.0000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jacket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| player              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pack                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water heater        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coffee maker        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radio               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candlestick         | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| matterport3d        | 0.1141  | 0.3118  | 0.0563  | 0.1670  |
| head                | 0.1653  | 0.4493  | 0.0750  | 0.2420  |
| common              | 0.0742  | 0.2389  | 0.0437  | 0.1190  |
| tail                | 0.0549  | 0.0770  | 0.0300  | 0.0568  |
| small               | 0.0411  | 0.1768  | 0.0230  | 0.0765  |
| medium              | 0.1077  | 0.3351  | 0.0500  | 0.1685  |
| large               | 0.1103  | 0.4091  | 0.0648  | 0.2660  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 13:52:23 INFO metrics.py:437 | number of arkit results: 0
Rank[0/8] 06/20/2025 13:52:23 INFO stats.py:394 | Epoch[179] completed. Training Speed: 3.01 samples/sec across all devices.	Epoch Time: 1038.24 sec.	Average Epoch Time: 1038.24 sec.	Average Step Time: 2.66 sec.	Estimated Remaining Time: 7:53:22.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:52:24 WARNING accelerator.py:3099 | Deleting 1 checkpoints to make room for new checkpoint.
Rank[0/8] 06/20/2025 13:52:24 INFO accelerator.py:3111 | Saving current state to /job_data/checkpoints/checkpoint_17
Rank[7/8] 06/20/2025 13:52:25 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 179 to /job_data/checkpoints/checkpoint_17
Rank[1/8] 06/20/2025 13:52:25 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 179 to /job_data/checkpoints/checkpoint_17
Rank[6/8] 06/20/2025 13:52:25 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 179 to /job_data/checkpoints/checkpoint_17
Rank[4/8] 06/20/2025 13:52:25 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 179 to /job_data/checkpoints/checkpoint_17
Rank[3/8] 06/20/2025 13:52:25 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 179 to /job_data/checkpoints/checkpoint_17
Rank[2/8] 06/20/2025 13:52:25 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 179 to /job_data/checkpoints/checkpoint_17
Rank[5/8] 06/20/2025 13:52:25 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 179 to /job_data/checkpoints/checkpoint_17
Rank[0/8] 06/20/2025 13:52:26 INFO checkpointing.py:106 | Model weights saved in /job_data/checkpoints/checkpoint_17/model.safetensors
Rank[0/8] 06/20/2025 13:52:29 INFO checkpointing.py:113 | Optimizer state saved in /job_data/checkpoints/checkpoint_17/optimizer.bin
Rank[0/8] 06/20/2025 13:52:29 INFO checkpointing.py:120 | Scheduler state saved in /job_data/checkpoints/checkpoint_17/scheduler.bin
Rank[0/8] 06/20/2025 13:52:29 INFO checkpointing.py:137 | Sampler state for dataloader 0 saved in /job_data/checkpoints/checkpoint_17/sampler.bin
Rank[0/8] 06/20/2025 13:52:29 INFO checkpointing.py:137 | Sampler state for dataloader 1 saved in /job_data/checkpoints/checkpoint_17/sampler_1.bin
Rank[0/8] 06/20/2025 13:52:29 INFO checkpointing.py:164 | Random states saved in /job_data/checkpoints/checkpoint_17/random_states_0.pkl
Rank[0/8] 06/20/2025 13:52:29 INFO checkpointing.py:300 | Saving the state of TrainerProgressState to /job_data/checkpoints/checkpoint_17/custom_checkpoint_0.pkl
Rank[0/8] 06/20/2025 13:52:29 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 179 to /job_data/checkpoints/checkpoint_17
Rank[0/8] 06/20/2025 13:52:57 INFO stats.py:314 | Epoch[180] Step[24] GlobalStep[70224] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 7:52:53.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:52:57 INFO loss_tracker.py:84 | Epoch[180/239] Step[24] GlobalStep[70224/NA]: det_loss_cls_0[0.5017] det_loss_box_0[0.6275] det_loss_cls_1[0.4768] det_loss_box_1[0.4654] det_loss_cls_2[0.4168] det_loss_box_2[0.4193] det_loss_cls_3[0.3777] det_loss_box_3[0.4040] det_loss_cls_4[0.3546] det_loss_box_4[0.3968] det_loss_cls_5[0.3416] det_loss_box_5[0.3964] det_loss_cls_dn_0[0.0866] det_loss_box_dn_0[0.2575] det_loss_cls_dn_1[0.0826] det_loss_box_dn_1[0.2093] det_loss_cls_dn_2[0.0817] det_loss_box_dn_2[0.1932] det_loss_cls_dn_3[0.0803] det_loss_box_dn_3[0.1896] det_loss_cls_dn_4[0.0794] det_loss_box_dn_4[0.1871] det_loss_cls_dn_5[0.0784] det_loss_box_dn_5[0.1859] loss_depth[0.0364] total_loss[6.9266]
Rank[0/8] 06/20/2025 13:53:22 INFO stats.py:314 | Epoch[180] Step[49] GlobalStep[70249] Training Speed: 7.47 samples/sec across all devices.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 7:52:21.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:53:22 INFO loss_tracker.py:84 | Epoch[180/239] Step[49] GlobalStep[70249/NA]: det_loss_cls_0[0.4904] det_loss_box_0[0.6814] det_loss_cls_1[0.4740] det_loss_box_1[0.5398] det_loss_cls_2[0.4087] det_loss_box_2[0.4811] det_loss_cls_3[0.3870] det_loss_box_3[0.4554] det_loss_cls_4[0.3608] det_loss_box_4[0.4510] det_loss_cls_5[0.3465] det_loss_box_5[0.4509] det_loss_cls_dn_0[0.0956] det_loss_box_dn_0[0.3020] det_loss_cls_dn_1[0.0921] det_loss_box_dn_1[0.2540] det_loss_cls_dn_2[0.0917] det_loss_box_dn_2[0.2358] det_loss_cls_dn_3[0.0903] det_loss_box_dn_3[0.2317] det_loss_cls_dn_4[0.0899] det_loss_box_dn_4[0.2297] det_loss_cls_dn_5[0.0887] det_loss_box_dn_5[0.2285] loss_depth[0.0356] total_loss[7.5927]
Rank[0/8] 06/20/2025 13:53:48 INFO stats.py:314 | Epoch[180] Step[74] GlobalStep[70274] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 7:51:49.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:53:48 INFO loss_tracker.py:84 | Epoch[180/239] Step[74] GlobalStep[70274/NA]: det_loss_cls_0[0.3618] det_loss_box_0[0.4973] det_loss_cls_1[0.3476] det_loss_box_1[0.3695] det_loss_cls_2[0.3146] det_loss_box_2[0.3310] det_loss_cls_3[0.2916] det_loss_box_3[0.3185] det_loss_cls_4[0.2707] det_loss_box_4[0.3095] det_loss_cls_5[0.2641] det_loss_box_5[0.3094] det_loss_cls_dn_0[0.0936] det_loss_box_dn_0[0.2862] det_loss_cls_dn_1[0.0899] det_loss_box_dn_1[0.2323] det_loss_cls_dn_2[0.0893] det_loss_box_dn_2[0.2097] det_loss_cls_dn_3[0.0883] det_loss_box_dn_3[0.2068] det_loss_cls_dn_4[0.0879] det_loss_box_dn_4[0.2029] det_loss_cls_dn_5[0.0871] det_loss_box_dn_5[0.1989] loss_depth[0.0374] total_loss[5.8959]
Rank[0/8] 06/20/2025 13:54:14 INFO stats.py:314 | Epoch[180] Step[99] GlobalStep[70299] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 7:51:17.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:54:14 INFO loss_tracker.py:84 | Epoch[180/239] Step[99] GlobalStep[70299/NA]: det_loss_cls_0[0.4179] det_loss_box_0[0.5210] det_loss_cls_1[0.4142] det_loss_box_1[0.3949] det_loss_cls_2[0.3457] det_loss_box_2[0.3614] det_loss_cls_3[0.3124] det_loss_box_3[0.3508] det_loss_cls_4[0.2926] det_loss_box_4[0.3364] det_loss_cls_5[0.2790] det_loss_box_5[0.3417] det_loss_cls_dn_0[0.0788] det_loss_box_dn_0[0.2656] det_loss_cls_dn_1[0.0741] det_loss_box_dn_1[0.2173] det_loss_cls_dn_2[0.0736] det_loss_box_dn_2[0.2003] det_loss_cls_dn_3[0.0722] det_loss_box_dn_3[0.1958] det_loss_cls_dn_4[0.0712] det_loss_box_dn_4[0.1929] det_loss_cls_dn_5[0.0703] det_loss_box_dn_5[0.1888] loss_depth[0.0331] total_loss[6.1020]
Rank[0/8] 06/20/2025 13:54:40 INFO stats.py:314 | Epoch[180] Step[124] GlobalStep[70324] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 7:50:45.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:54:40 INFO loss_tracker.py:84 | Epoch[180/239] Step[124] GlobalStep[70324/NA]: det_loss_cls_0[0.4243] det_loss_box_0[0.6280] det_loss_cls_1[0.4184] det_loss_box_1[0.4878] det_loss_cls_2[0.3646] det_loss_box_2[0.4404] det_loss_cls_3[0.3332] det_loss_box_3[0.4313] det_loss_cls_4[0.3090] det_loss_box_4[0.4066] det_loss_cls_5[0.2981] det_loss_box_5[0.4070] det_loss_cls_dn_0[0.0805] det_loss_box_dn_0[0.2806] det_loss_cls_dn_1[0.0761] det_loss_box_dn_1[0.2314] det_loss_cls_dn_2[0.0756] det_loss_box_dn_2[0.2093] det_loss_cls_dn_3[0.0747] det_loss_box_dn_3[0.2047] det_loss_cls_dn_4[0.0738] det_loss_box_dn_4[0.2012] det_loss_cls_dn_5[0.0724] det_loss_box_dn_5[0.1988] loss_depth[0.0370] total_loss[6.7650]
Rank[0/8] 06/20/2025 13:55:05 INFO stats.py:314 | Epoch[180] Step[149] GlobalStep[70349] Training Speed: 5.68 samples/sec across all devices.	Average Step Time: 1.41 sec.	Estimated Remaining Time: 7:50:14.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:55:05 INFO loss_tracker.py:84 | Epoch[180/239] Step[149] GlobalStep[70349/NA]: det_loss_cls_0[0.3947] det_loss_box_0[0.5499] det_loss_cls_1[0.3785] det_loss_box_1[0.4419] det_loss_cls_2[0.3325] det_loss_box_2[0.4026] det_loss_cls_3[0.3134] det_loss_box_3[0.3860] det_loss_cls_4[0.2948] det_loss_box_4[0.3762] det_loss_cls_5[0.2866] det_loss_box_5[0.3754] det_loss_cls_dn_0[0.0919] det_loss_box_dn_0[0.4144] det_loss_cls_dn_1[0.0883] det_loss_box_dn_1[0.3438] det_loss_cls_dn_2[0.0872] det_loss_box_dn_2[0.3151] det_loss_cls_dn_3[0.0867] det_loss_box_dn_3[0.3116] det_loss_cls_dn_4[0.0859] det_loss_box_dn_4[0.3051] det_loss_cls_dn_5[0.0852] det_loss_box_dn_5[0.3010] loss_depth[0.0359] total_loss[7.0845]
Rank[0/8] 06/20/2025 13:55:31 INFO stats.py:314 | Epoch[180] Step[174] GlobalStep[70374] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 7:49:42.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:55:31 INFO loss_tracker.py:84 | Epoch[180/239] Step[174] GlobalStep[70374/NA]: det_loss_cls_0[0.4126] det_loss_box_0[0.5708] det_loss_cls_1[0.4130] det_loss_box_1[0.4373] det_loss_cls_2[0.3604] det_loss_box_2[0.4048] det_loss_cls_3[0.3336] det_loss_box_3[0.3839] det_loss_cls_4[0.3140] det_loss_box_4[0.3728] det_loss_cls_5[0.3063] det_loss_box_5[0.3594] det_loss_cls_dn_0[0.0869] det_loss_box_dn_0[0.3264] det_loss_cls_dn_1[0.0832] det_loss_box_dn_1[0.2632] det_loss_cls_dn_2[0.0820] det_loss_box_dn_2[0.2449] det_loss_cls_dn_3[0.0806] det_loss_box_dn_3[0.2404] det_loss_cls_dn_4[0.0797] det_loss_box_dn_4[0.2364] det_loss_cls_dn_5[0.0789] det_loss_box_dn_5[0.2261] loss_depth[0.0329] total_loss[6.7308]
Rank[0/8] 06/20/2025 13:55:57 INFO stats.py:314 | Epoch[180] Step[199] GlobalStep[70399] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 7:49:10.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:55:57 INFO loss_tracker.py:84 | Epoch[180/239] Step[199] GlobalStep[70399/NA]: det_loss_cls_0[0.3963] det_loss_box_0[0.4315] det_loss_cls_1[0.3845] det_loss_box_1[0.3096] det_loss_cls_2[0.3254] det_loss_box_2[0.2788] det_loss_cls_3[0.2908] det_loss_box_3[0.2689] det_loss_cls_4[0.2655] det_loss_box_4[0.2637] det_loss_cls_5[0.2566] det_loss_box_5[0.2621] det_loss_cls_dn_0[0.0880] det_loss_box_dn_0[0.2252] det_loss_cls_dn_1[0.0848] det_loss_box_dn_1[0.1799] det_loss_cls_dn_2[0.0844] det_loss_box_dn_2[0.1640] det_loss_cls_dn_3[0.0834] det_loss_box_dn_3[0.1593] det_loss_cls_dn_4[0.0823] det_loss_box_dn_4[0.1557] det_loss_cls_dn_5[0.0817] det_loss_box_dn_5[0.1547] loss_depth[0.0358] total_loss[5.3130]
Rank[0/8] 06/20/2025 13:56:22 INFO stats.py:314 | Epoch[180] Step[224] GlobalStep[70424] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 7:48:38.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:56:22 INFO loss_tracker.py:84 | Epoch[180/239] Step[224] GlobalStep[70424/NA]: det_loss_cls_0[0.5914] det_loss_box_0[0.7874] det_loss_cls_1[0.5594] det_loss_box_1[0.6241] det_loss_cls_2[0.5055] det_loss_box_2[0.5635] det_loss_cls_3[0.4675] det_loss_box_3[0.5399] det_loss_cls_4[0.4355] det_loss_box_4[0.5282] det_loss_cls_5[0.4181] det_loss_box_5[0.5364] det_loss_cls_dn_0[0.0969] det_loss_box_dn_0[0.2791] det_loss_cls_dn_1[0.0915] det_loss_box_dn_1[0.2318] det_loss_cls_dn_2[0.0908] det_loss_box_dn_2[0.2150] det_loss_cls_dn_3[0.0894] det_loss_box_dn_3[0.2106] det_loss_cls_dn_4[0.0886] det_loss_box_dn_4[0.2088] det_loss_cls_dn_5[0.0877] det_loss_box_dn_5[0.2077] loss_depth[0.0340] total_loss[8.4888]
Rank[0/8] 06/20/2025 13:56:49 INFO stats.py:314 | Epoch[180] Step[249] GlobalStep[70449] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 7:48:06.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:56:49 INFO loss_tracker.py:84 | Epoch[180/239] Step[249] GlobalStep[70449/NA]: det_loss_cls_0[0.4235] det_loss_box_0[0.5418] det_loss_cls_1[0.4049] det_loss_box_1[0.4069] det_loss_cls_2[0.3597] det_loss_box_2[0.3820] det_loss_cls_3[0.3339] det_loss_box_3[0.3643] det_loss_cls_4[0.3093] det_loss_box_4[0.3611] det_loss_cls_5[0.3048] det_loss_box_5[0.3521] det_loss_cls_dn_0[0.0881] det_loss_box_dn_0[0.3126] det_loss_cls_dn_1[0.0843] det_loss_box_dn_1[0.2642] det_loss_cls_dn_2[0.0834] det_loss_box_dn_2[0.2456] det_loss_cls_dn_3[0.0823] det_loss_box_dn_3[0.2329] det_loss_cls_dn_4[0.0816] det_loss_box_dn_4[0.2277] det_loss_cls_dn_5[0.0809] det_loss_box_dn_5[0.2187] loss_depth[0.0350] total_loss[6.5815]
Rank[0/8] 06/20/2025 13:57:14 INFO stats.py:314 | Epoch[180] Step[274] GlobalStep[70474] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 7:47:34.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:57:14 INFO loss_tracker.py:84 | Epoch[180/239] Step[274] GlobalStep[70474/NA]: det_loss_cls_0[0.5804] det_loss_box_0[0.7781] det_loss_cls_1[0.5538] det_loss_box_1[0.5541] det_loss_cls_2[0.4779] det_loss_box_2[0.5083] det_loss_cls_3[0.4509] det_loss_box_3[0.4848] det_loss_cls_4[0.4160] det_loss_box_4[0.4747] det_loss_cls_5[0.4019] det_loss_box_5[0.4753] det_loss_cls_dn_0[0.0923] det_loss_box_dn_0[0.2382] det_loss_cls_dn_1[0.0887] det_loss_box_dn_1[0.1983] det_loss_cls_dn_2[0.0882] det_loss_box_dn_2[0.1810] det_loss_cls_dn_3[0.0869] det_loss_box_dn_3[0.1777] det_loss_cls_dn_4[0.0859] det_loss_box_dn_4[0.1750] det_loss_cls_dn_5[0.0853] det_loss_box_dn_5[0.1735] loss_depth[0.0363] total_loss[7.8633]
Rank[0/8] 06/20/2025 13:57:40 INFO stats.py:314 | Epoch[180] Step[299] GlobalStep[70499] Training Speed: 8.13 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 7:47:03.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:57:40 INFO loss_tracker.py:84 | Epoch[180/239] Step[299] GlobalStep[70499/NA]: det_loss_cls_0[0.4600] det_loss_box_0[0.6066] det_loss_cls_1[0.4481] det_loss_box_1[0.4705] det_loss_cls_2[0.3985] det_loss_box_2[0.4183] det_loss_cls_3[0.3604] det_loss_box_3[0.4087] det_loss_cls_4[0.3424] det_loss_box_4[0.3982] det_loss_cls_5[0.3275] det_loss_box_5[0.4001] det_loss_cls_dn_0[0.0857] det_loss_box_dn_0[0.2515] det_loss_cls_dn_1[0.0802] det_loss_box_dn_1[0.2082] det_loss_cls_dn_2[0.0797] det_loss_box_dn_2[0.1918] det_loss_cls_dn_3[0.0787] det_loss_box_dn_3[0.1872] det_loss_cls_dn_4[0.0779] det_loss_box_dn_4[0.1852] det_loss_cls_dn_5[0.0772] det_loss_box_dn_5[0.1841] loss_depth[0.0350] total_loss[6.7618]
Rank[0/8] 06/20/2025 13:58:06 INFO stats.py:314 | Epoch[180] Step[324] GlobalStep[70524] Training Speed: 6.75 samples/sec across all devices.	Average Step Time: 1.18 sec.	Estimated Remaining Time: 7:46:31.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:58:06 INFO loss_tracker.py:84 | Epoch[180/239] Step[324] GlobalStep[70524/NA]: det_loss_cls_0[0.5662] det_loss_box_0[0.7271] det_loss_cls_1[0.5399] det_loss_box_1[0.5476] det_loss_cls_2[0.4741] det_loss_box_2[0.4935] det_loss_cls_3[0.4378] det_loss_box_3[0.4771] det_loss_cls_4[0.4127] det_loss_box_4[0.4660] det_loss_cls_5[0.4033] det_loss_box_5[0.4640] det_loss_cls_dn_0[0.1000] det_loss_box_dn_0[0.2653] det_loss_cls_dn_1[0.0968] det_loss_box_dn_1[0.2188] det_loss_cls_dn_2[0.0961] det_loss_box_dn_2[0.2008] det_loss_cls_dn_3[0.0956] det_loss_box_dn_3[0.1972] det_loss_cls_dn_4[0.0951] det_loss_box_dn_4[0.1953] det_loss_cls_dn_5[0.0941] det_loss_box_dn_5[0.1922] loss_depth[0.0380] total_loss[7.8949]
Rank[0/8] 06/20/2025 13:58:31 INFO stats.py:314 | Epoch[180] Step[349] GlobalStep[70549] Training Speed: 8.34 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 7:45:59.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:58:32 INFO loss_tracker.py:84 | Epoch[180/239] Step[349] GlobalStep[70549/NA]: det_loss_cls_0[0.4176] det_loss_box_0[0.5566] det_loss_cls_1[0.4009] det_loss_box_1[0.4175] det_loss_cls_2[0.3530] det_loss_box_2[0.3836] det_loss_cls_3[0.3213] det_loss_box_3[0.3836] det_loss_cls_4[0.3035] det_loss_box_4[0.3674] det_loss_cls_5[0.2913] det_loss_box_5[0.3667] det_loss_cls_dn_0[0.0935] det_loss_box_dn_0[0.2909] det_loss_cls_dn_1[0.0905] det_loss_box_dn_1[0.2375] det_loss_cls_dn_2[0.0890] det_loss_box_dn_2[0.2201] det_loss_cls_dn_3[0.0893] det_loss_box_dn_3[0.2153] det_loss_cls_dn_4[0.0881] det_loss_box_dn_4[0.2147] det_loss_cls_dn_5[0.0877] det_loss_box_dn_5[0.2050] loss_depth[0.0365] total_loss[6.5212]
Rank[0/8] 06/20/2025 13:58:58 INFO stats.py:314 | Epoch[180] Step[374] GlobalStep[70574] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 7:45:27.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:58:58 INFO loss_tracker.py:84 | Epoch[180/239] Step[374] GlobalStep[70574/NA]: det_loss_cls_0[0.3489] det_loss_box_0[0.4566] det_loss_cls_1[0.3489] det_loss_box_1[0.3463] det_loss_cls_2[0.2924] det_loss_box_2[0.3176] det_loss_cls_3[0.2600] det_loss_box_3[0.3008] det_loss_cls_4[0.2429] det_loss_box_4[0.2850] det_loss_cls_5[0.2261] det_loss_box_5[0.2936] det_loss_cls_dn_0[0.0781] det_loss_box_dn_0[0.3008] det_loss_cls_dn_1[0.0748] det_loss_box_dn_1[0.2369] det_loss_cls_dn_2[0.0733] det_loss_box_dn_2[0.2126] det_loss_cls_dn_3[0.0721] det_loss_box_dn_3[0.2039] det_loss_cls_dn_4[0.0711] det_loss_box_dn_4[0.1993] det_loss_cls_dn_5[0.0701] det_loss_box_dn_5[0.1941] loss_depth[0.0342] total_loss[5.5405]
Rank[0/8] 06/20/2025 13:59:13 INFO stats.py:394 | Epoch[180] completed. Training Speed: 7.72 samples/sec across all devices.	Epoch Time: 403.94 sec.	Average Epoch Time: 403.94 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 7:45:08.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:59:25 INFO stats.py:314 | Epoch[181] Step[9] GlobalStep[70599] Training Speed: 6.51 samples/sec across all devices.	Average Step Time: 1.23 sec.	Estimated Remaining Time: 7:44:56.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 13:59:41 INFO loss_tracker.py:84 | Epoch[181/239] Step[24] GlobalStep[70614/NA]: det_loss_cls_0[0.5304] det_loss_box_0[0.6119] det_loss_cls_1[0.5149] det_loss_box_1[0.4748] det_loss_cls_2[0.4536] det_loss_box_2[0.4360] det_loss_cls_3[0.4113] det_loss_box_3[0.4270] det_loss_cls_4[0.3807] det_loss_box_4[0.4181] det_loss_cls_5[0.3615] det_loss_box_5[0.4220] det_loss_cls_dn_0[0.0946] det_loss_box_dn_0[0.2494] det_loss_cls_dn_1[0.0911] det_loss_box_dn_1[0.2023] det_loss_cls_dn_2[0.0902] det_loss_box_dn_2[0.1846] det_loss_cls_dn_3[0.0889] det_loss_box_dn_3[0.1808] det_loss_cls_dn_4[0.0883] det_loss_box_dn_4[0.1791] det_loss_cls_dn_5[0.0872] det_loss_box_dn_5[0.1778] loss_depth[0.0354] total_loss[7.1920]
Rank[0/8] 06/20/2025 13:59:52 INFO stats.py:314 | Epoch[181] Step[34] GlobalStep[70624] Training Speed: 8.12 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 7:44:24.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:00:07 INFO loss_tracker.py:84 | Epoch[181/239] Step[49] GlobalStep[70639/NA]: det_loss_cls_0[0.5163] det_loss_box_0[0.6658] det_loss_cls_1[0.5148] det_loss_box_1[0.4963] det_loss_cls_2[0.4502] det_loss_box_2[0.4431] det_loss_cls_3[0.4077] det_loss_box_3[0.4329] det_loss_cls_4[0.3799] det_loss_box_4[0.4176] det_loss_cls_5[0.3637] det_loss_box_5[0.4161] det_loss_cls_dn_0[0.0907] det_loss_box_dn_0[0.2872] det_loss_cls_dn_1[0.0860] det_loss_box_dn_1[0.2324] det_loss_cls_dn_2[0.0850] det_loss_box_dn_2[0.2104] det_loss_cls_dn_3[0.0838] det_loss_box_dn_3[0.2068] det_loss_cls_dn_4[0.0833] det_loss_box_dn_4[0.2038] det_loss_cls_dn_5[0.0827] det_loss_box_dn_5[0.2020] loss_depth[0.0367] total_loss[7.3950]
Rank[0/8] 06/20/2025 14:00:18 INFO stats.py:314 | Epoch[181] Step[59] GlobalStep[70649] Training Speed: 7.93 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 7:43:53.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:00:33 INFO loss_tracker.py:84 | Epoch[181/239] Step[74] GlobalStep[70664/NA]: det_loss_cls_0[0.4249] det_loss_box_0[0.5870] det_loss_cls_1[0.4175] det_loss_box_1[0.4549] det_loss_cls_2[0.3767] det_loss_box_2[0.4036] det_loss_cls_3[0.3450] det_loss_box_3[0.3917] det_loss_cls_4[0.3200] det_loss_box_4[0.3791] det_loss_cls_5[0.3084] det_loss_box_5[0.3795] det_loss_cls_dn_0[0.0899] det_loss_box_dn_0[0.3181] det_loss_cls_dn_1[0.0853] det_loss_box_dn_1[0.2602] det_loss_cls_dn_2[0.0846] det_loss_box_dn_2[0.2378] det_loss_cls_dn_3[0.0831] det_loss_box_dn_3[0.2323] det_loss_cls_dn_4[0.0817] det_loss_box_dn_4[0.2289] det_loss_cls_dn_5[0.0808] det_loss_box_dn_5[0.2272] loss_depth[0.0342] total_loss[6.8322]
Rank[0/8] 06/20/2025 14:00:43 INFO stats.py:314 | Epoch[181] Step[84] GlobalStep[70674] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 7:43:21.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:00:59 INFO loss_tracker.py:84 | Epoch[181/239] Step[99] GlobalStep[70689/NA]: det_loss_cls_0[0.5665] det_loss_box_0[0.8428] det_loss_cls_1[0.5548] det_loss_box_1[0.6596] det_loss_cls_2[0.4924] det_loss_box_2[0.5909] det_loss_cls_3[0.4583] det_loss_box_3[0.5528] det_loss_cls_4[0.4333] det_loss_box_4[0.5351] det_loss_cls_5[0.4096] det_loss_box_5[0.5409] det_loss_cls_dn_0[0.0935] det_loss_box_dn_0[0.3337] det_loss_cls_dn_1[0.0897] det_loss_box_dn_1[0.2809] det_loss_cls_dn_2[0.0885] det_loss_box_dn_2[0.2630] det_loss_cls_dn_3[0.0877] det_loss_box_dn_3[0.2568] det_loss_cls_dn_4[0.0869] det_loss_box_dn_4[0.2530] det_loss_cls_dn_5[0.0858] det_loss_box_dn_5[0.2471] loss_depth[0.0352] total_loss[8.8390]
Rank[0/8] 06/20/2025 14:01:10 INFO stats.py:314 | Epoch[181] Step[109] GlobalStep[70699] Training Speed: 7.96 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 7:42:49.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:01:25 INFO loss_tracker.py:84 | Epoch[181/239] Step[124] GlobalStep[70714/NA]: det_loss_cls_0[0.4670] det_loss_box_0[0.5810] det_loss_cls_1[0.4555] det_loss_box_1[0.4438] det_loss_cls_2[0.3992] det_loss_box_2[0.4081] det_loss_cls_3[0.3733] det_loss_box_3[0.3900] det_loss_cls_4[0.3455] det_loss_box_4[0.3846] det_loss_cls_5[0.3405] det_loss_box_5[0.3728] det_loss_cls_dn_0[0.0846] det_loss_box_dn_0[0.2663] det_loss_cls_dn_1[0.0815] det_loss_box_dn_1[0.2110] det_loss_cls_dn_2[0.0805] det_loss_box_dn_2[0.1939] det_loss_cls_dn_3[0.0794] det_loss_box_dn_3[0.1884] det_loss_cls_dn_4[0.0790] det_loss_box_dn_4[0.1861] det_loss_cls_dn_5[0.0779] det_loss_box_dn_5[0.1831] loss_depth[0.0342] total_loss[6.7073]
Rank[0/8] 06/20/2025 14:01:36 INFO stats.py:314 | Epoch[181] Step[134] GlobalStep[70724] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 7:42:18.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:01:51 INFO loss_tracker.py:84 | Epoch[181/239] Step[149] GlobalStep[70739/NA]: det_loss_cls_0[0.3852] det_loss_box_0[0.5030] det_loss_cls_1[0.3643] det_loss_box_1[0.3725] det_loss_cls_2[0.3306] det_loss_box_2[0.3286] det_loss_cls_3[0.3067] det_loss_box_3[0.3174] det_loss_cls_4[0.2868] det_loss_box_4[0.3098] det_loss_cls_5[0.2798] det_loss_box_5[0.3070] det_loss_cls_dn_0[0.0849] det_loss_box_dn_0[0.2700] det_loss_cls_dn_1[0.0820] det_loss_box_dn_1[0.2178] det_loss_cls_dn_2[0.0803] det_loss_box_dn_2[0.1988] det_loss_cls_dn_3[0.0797] det_loss_box_dn_3[0.1946] det_loss_cls_dn_4[0.0781] det_loss_box_dn_4[0.1913] det_loss_cls_dn_5[0.0772] det_loss_box_dn_5[0.1876] loss_depth[0.0342] total_loss[5.8682]
Rank[0/8] 06/20/2025 14:02:01 INFO stats.py:314 | Epoch[181] Step[159] GlobalStep[70749] Training Speed: 7.89 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 7:41:46.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:02:17 INFO loss_tracker.py:84 | Epoch[181/239] Step[174] GlobalStep[70764/NA]: det_loss_cls_0[0.3763] det_loss_box_0[0.4418] det_loss_cls_1[0.3617] det_loss_box_1[0.3265] det_loss_cls_2[0.3092] det_loss_box_2[0.2998] det_loss_cls_3[0.2900] det_loss_box_3[0.2844] det_loss_cls_4[0.2711] det_loss_box_4[0.2777] det_loss_cls_5[0.2563] det_loss_box_5[0.2789] det_loss_cls_dn_0[0.0831] det_loss_box_dn_0[0.2451] det_loss_cls_dn_1[0.0793] det_loss_box_dn_1[0.1947] det_loss_cls_dn_2[0.0783] det_loss_box_dn_2[0.1773] det_loss_cls_dn_3[0.0771] det_loss_box_dn_3[0.1734] det_loss_cls_dn_4[0.0764] det_loss_box_dn_4[0.1726] det_loss_cls_dn_5[0.0756] det_loss_box_dn_5[0.1710] loss_depth[0.0347] total_loss[5.4123]
Rank[0/8] 06/20/2025 14:02:27 INFO stats.py:314 | Epoch[181] Step[184] GlobalStep[70774] Training Speed: 5.86 samples/sec across all devices.	Average Step Time: 1.36 sec.	Estimated Remaining Time: 7:41:14.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:02:43 INFO loss_tracker.py:84 | Epoch[181/239] Step[199] GlobalStep[70789/NA]: det_loss_cls_0[0.4565] det_loss_box_0[0.5489] det_loss_cls_1[0.4370] det_loss_box_1[0.4493] det_loss_cls_2[0.3813] det_loss_box_2[0.4082] det_loss_cls_3[0.3515] det_loss_box_3[0.3922] det_loss_cls_4[0.3223] det_loss_box_4[0.3853] det_loss_cls_5[0.3163] det_loss_box_5[0.3786] det_loss_cls_dn_0[0.0855] det_loss_box_dn_0[0.2826] det_loss_cls_dn_1[0.0830] det_loss_box_dn_1[0.2329] det_loss_cls_dn_2[0.0827] det_loss_box_dn_2[0.2162] det_loss_cls_dn_3[0.0817] det_loss_box_dn_3[0.2115] det_loss_cls_dn_4[0.0806] det_loss_box_dn_4[0.2109] det_loss_cls_dn_5[0.0795] det_loss_box_dn_5[0.2038] loss_depth[0.0357] total_loss[6.7140]
Rank[0/8] 06/20/2025 14:02:53 INFO stats.py:314 | Epoch[181] Step[209] GlobalStep[70799] Training Speed: 8.19 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 7:40:42.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:03:09 INFO loss_tracker.py:84 | Epoch[181/239] Step[224] GlobalStep[70814/NA]: det_loss_cls_0[0.4894] det_loss_box_0[0.6672] det_loss_cls_1[0.4689] det_loss_box_1[0.5073] det_loss_cls_2[0.4149] det_loss_box_2[0.4502] det_loss_cls_3[0.3743] det_loss_box_3[0.4430] det_loss_cls_4[0.3589] det_loss_box_4[0.4280] det_loss_cls_5[0.3374] det_loss_box_5[0.4311] det_loss_cls_dn_0[0.0892] det_loss_box_dn_0[0.2792] det_loss_cls_dn_1[0.0851] det_loss_box_dn_1[0.2246] det_loss_cls_dn_2[0.0843] det_loss_box_dn_2[0.2053] det_loss_cls_dn_3[0.0834] det_loss_box_dn_3[0.2020] det_loss_cls_dn_4[0.0830] det_loss_box_dn_4[0.1991] det_loss_cls_dn_5[0.0822] det_loss_box_dn_5[0.1954] loss_depth[0.0386] total_loss[7.2219]
Rank[0/8] 06/20/2025 14:03:19 INFO stats.py:314 | Epoch[181] Step[234] GlobalStep[70824] Training Speed: 7.62 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 7:40:11.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:03:35 INFO loss_tracker.py:84 | Epoch[181/239] Step[249] GlobalStep[70839/NA]: det_loss_cls_0[0.5658] det_loss_box_0[0.7648] det_loss_cls_1[0.5303] det_loss_box_1[0.5884] det_loss_cls_2[0.4628] det_loss_box_2[0.5296] det_loss_cls_3[0.4336] det_loss_box_3[0.5150] det_loss_cls_4[0.4062] det_loss_box_4[0.4962] det_loss_cls_5[0.3935] det_loss_box_5[0.4953] det_loss_cls_dn_0[0.0897] det_loss_box_dn_0[0.2567] det_loss_cls_dn_1[0.0861] det_loss_box_dn_1[0.2187] det_loss_cls_dn_2[0.0850] det_loss_box_dn_2[0.2042] det_loss_cls_dn_3[0.0843] det_loss_box_dn_3[0.2018] det_loss_cls_dn_4[0.0839] det_loss_box_dn_4[0.2005] det_loss_cls_dn_5[0.0828] det_loss_box_dn_5[0.1994] loss_depth[0.0370] total_loss[8.0117]
Rank[0/8] 06/20/2025 14:03:45 INFO stats.py:314 | Epoch[181] Step[259] GlobalStep[70849] Training Speed: 7.59 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 7:39:39.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:04:01 INFO loss_tracker.py:84 | Epoch[181/239] Step[274] GlobalStep[70864/NA]: det_loss_cls_0[0.4895] det_loss_box_0[0.6273] det_loss_cls_1[0.4653] det_loss_box_1[0.4717] det_loss_cls_2[0.4181] det_loss_box_2[0.4242] det_loss_cls_3[0.3820] det_loss_box_3[0.4165] det_loss_cls_4[0.3564] det_loss_box_4[0.4088] det_loss_cls_5[0.3453] det_loss_box_5[0.4096] det_loss_cls_dn_0[0.0955] det_loss_box_dn_0[0.3042] det_loss_cls_dn_1[0.0920] det_loss_box_dn_1[0.2522] det_loss_cls_dn_2[0.0914] det_loss_box_dn_2[0.2304] det_loss_cls_dn_3[0.0907] det_loss_box_dn_3[0.2253] det_loss_cls_dn_4[0.0899] det_loss_box_dn_4[0.2225] det_loss_cls_dn_5[0.0889] det_loss_box_dn_5[0.2210] loss_depth[0.0382] total_loss[7.2567]
Rank[0/8] 06/20/2025 14:04:11 INFO stats.py:314 | Epoch[181] Step[284] GlobalStep[70874] Training Speed: 8.05 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 7:39:07.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:04:26 INFO loss_tracker.py:84 | Epoch[181/239] Step[299] GlobalStep[70889/NA]: det_loss_cls_0[0.4606] det_loss_box_0[0.5964] det_loss_cls_1[0.4557] det_loss_box_1[0.4491] det_loss_cls_2[0.4131] det_loss_box_2[0.4040] det_loss_cls_3[0.3864] det_loss_box_3[0.3762] det_loss_cls_4[0.3652] det_loss_box_4[0.3689] det_loss_cls_5[0.3503] det_loss_box_5[0.3673] det_loss_cls_dn_0[0.0846] det_loss_box_dn_0[0.2864] det_loss_cls_dn_1[0.0807] det_loss_box_dn_1[0.2402] det_loss_cls_dn_2[0.0797] det_loss_box_dn_2[0.2259] det_loss_cls_dn_3[0.0786] det_loss_box_dn_3[0.2204] det_loss_cls_dn_4[0.0777] det_loss_box_dn_4[0.2181] det_loss_cls_dn_5[0.0767] det_loss_box_dn_5[0.2157] loss_depth[0.0361] total_loss[6.9141]
Rank[0/8] 06/20/2025 14:04:37 INFO stats.py:314 | Epoch[181] Step[309] GlobalStep[70899] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 7:38:36.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:04:52 INFO loss_tracker.py:84 | Epoch[181/239] Step[324] GlobalStep[70914/NA]: det_loss_cls_0[0.4442] det_loss_box_0[0.5911] det_loss_cls_1[0.4372] det_loss_box_1[0.4487] det_loss_cls_2[0.3754] det_loss_box_2[0.3979] det_loss_cls_3[0.3501] det_loss_box_3[0.3763] det_loss_cls_4[0.3158] det_loss_box_4[0.3767] det_loss_cls_5[0.3104] det_loss_box_5[0.3719] det_loss_cls_dn_0[0.0945] det_loss_box_dn_0[0.2662] det_loss_cls_dn_1[0.0906] det_loss_box_dn_1[0.2168] det_loss_cls_dn_2[0.0891] det_loss_box_dn_2[0.1982] det_loss_cls_dn_3[0.0884] det_loss_box_dn_3[0.1928] det_loss_cls_dn_4[0.0874] det_loss_box_dn_4[0.1925] det_loss_cls_dn_5[0.0865] det_loss_box_dn_5[0.1899] loss_depth[0.0332] total_loss[6.6220]
Rank[0/8] 06/20/2025 14:05:03 INFO stats.py:314 | Epoch[181] Step[334] GlobalStep[70924] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 7:38:04.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:05:18 INFO loss_tracker.py:84 | Epoch[181/239] Step[349] GlobalStep[70939/NA]: det_loss_cls_0[0.4549] det_loss_box_0[0.6276] det_loss_cls_1[0.4482] det_loss_box_1[0.4944] det_loss_cls_2[0.3832] det_loss_box_2[0.4575] det_loss_cls_3[0.3638] det_loss_box_3[0.4419] det_loss_cls_4[0.3464] det_loss_box_4[0.4257] det_loss_cls_5[0.3333] det_loss_box_5[0.4267] det_loss_cls_dn_0[0.0993] det_loss_box_dn_0[0.3051] det_loss_cls_dn_1[0.0961] det_loss_box_dn_1[0.2614] det_loss_cls_dn_2[0.0955] det_loss_box_dn_2[0.2435] det_loss_cls_dn_3[0.0946] det_loss_box_dn_3[0.2381] det_loss_cls_dn_4[0.0935] det_loss_box_dn_4[0.2366] det_loss_cls_dn_5[0.0925] det_loss_box_dn_5[0.2311] loss_depth[0.0341] total_loss[7.3248]
Rank[0/8] 06/20/2025 14:05:29 INFO stats.py:314 | Epoch[181] Step[359] GlobalStep[70949] Training Speed: 6.36 samples/sec across all devices.	Average Step Time: 1.26 sec.	Estimated Remaining Time: 7:37:32.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:05:44 INFO loss_tracker.py:84 | Epoch[181/239] Step[374] GlobalStep[70964/NA]: det_loss_cls_0[0.5470] det_loss_box_0[0.7399] det_loss_cls_1[0.5248] det_loss_box_1[0.5564] det_loss_cls_2[0.4606] det_loss_box_2[0.5144] det_loss_cls_3[0.4263] det_loss_box_3[0.4985] det_loss_cls_4[0.4034] det_loss_box_4[0.4872] det_loss_cls_5[0.3951] det_loss_box_5[0.4825] det_loss_cls_dn_0[0.0916] det_loss_box_dn_0[0.2884] det_loss_cls_dn_1[0.0889] det_loss_box_dn_1[0.2372] det_loss_cls_dn_2[0.0886] det_loss_box_dn_2[0.2188] det_loss_cls_dn_3[0.0877] det_loss_box_dn_3[0.2131] det_loss_cls_dn_4[0.0871] det_loss_box_dn_4[0.2099] det_loss_cls_dn_5[0.0862] det_loss_box_dn_5[0.2076] loss_depth[0.0367] total_loss[7.9777]
Rank[0/8] 06/20/2025 14:05:54 INFO stats.py:314 | Epoch[181] Step[384] GlobalStep[70974] Training Speed: 7.70 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 7:37:00.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:06:00 INFO stats.py:394 | Epoch[181] completed. Training Speed: 7.67 samples/sec across all devices.	Epoch Time: 407.00 sec.	Average Epoch Time: 407.00 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 7:36:54.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:06:22 INFO stats.py:314 | Epoch[182] Step[19] GlobalStep[70999] Training Speed: 8.69 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 7:36:29.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:06:27 INFO loss_tracker.py:84 | Epoch[182/239] Step[24] GlobalStep[71004/NA]: det_loss_cls_0[0.4245] det_loss_box_0[0.5354] det_loss_cls_1[0.4131] det_loss_box_1[0.4127] det_loss_cls_2[0.3637] det_loss_box_2[0.3720] det_loss_cls_3[0.3369] det_loss_box_3[0.3579] det_loss_cls_4[0.3142] det_loss_box_4[0.3455] det_loss_cls_5[0.3000] det_loss_box_5[0.3357] det_loss_cls_dn_0[0.0954] det_loss_box_dn_0[0.3211] det_loss_cls_dn_1[0.0913] det_loss_box_dn_1[0.2634] det_loss_cls_dn_2[0.0903] det_loss_box_dn_2[0.2436] det_loss_cls_dn_3[0.0893] det_loss_box_dn_3[0.2377] det_loss_cls_dn_4[0.0885] det_loss_box_dn_4[0.2346] det_loss_cls_dn_5[0.0875] det_loss_box_dn_5[0.2266] loss_depth[0.0351] total_loss[6.6161]
Rank[0/8] 06/20/2025 14:06:47 INFO stats.py:314 | Epoch[182] Step[44] GlobalStep[71024] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 7:35:57.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:06:53 INFO loss_tracker.py:84 | Epoch[182/239] Step[49] GlobalStep[71029/NA]: det_loss_cls_0[0.4527] det_loss_box_0[0.5499] det_loss_cls_1[0.4378] det_loss_box_1[0.4125] det_loss_cls_2[0.3875] det_loss_box_2[0.3696] det_loss_cls_3[0.3501] det_loss_box_3[0.3612] det_loss_cls_4[0.3332] det_loss_box_4[0.3453] det_loss_cls_5[0.3163] det_loss_box_5[0.3480] det_loss_cls_dn_0[0.0925] det_loss_box_dn_0[0.2745] det_loss_cls_dn_1[0.0887] det_loss_box_dn_1[0.2224] det_loss_cls_dn_2[0.0876] det_loss_box_dn_2[0.2017] det_loss_cls_dn_3[0.0860] det_loss_box_dn_3[0.1970] det_loss_cls_dn_4[0.0851] det_loss_box_dn_4[0.1933] det_loss_cls_dn_5[0.0844] det_loss_box_dn_5[0.1918] loss_depth[0.0370] total_loss[6.5059]
Rank[0/8] 06/20/2025 14:07:13 INFO stats.py:314 | Epoch[182] Step[69] GlobalStep[71049] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 7:35:25.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:07:18 INFO loss_tracker.py:84 | Epoch[182/239] Step[74] GlobalStep[71054/NA]: det_loss_cls_0[0.4359] det_loss_box_0[0.5367] det_loss_cls_1[0.4228] det_loss_box_1[0.4043] det_loss_cls_2[0.3641] det_loss_box_2[0.3642] det_loss_cls_3[0.3257] det_loss_box_3[0.3469] det_loss_cls_4[0.2965] det_loss_box_4[0.3476] det_loss_cls_5[0.2806] det_loss_box_5[0.3488] det_loss_cls_dn_0[0.0854] det_loss_box_dn_0[0.2319] det_loss_cls_dn_1[0.0809] det_loss_box_dn_1[0.1966] det_loss_cls_dn_2[0.0798] det_loss_box_dn_2[0.1788] det_loss_cls_dn_3[0.0786] det_loss_box_dn_3[0.1746] det_loss_cls_dn_4[0.0782] det_loss_box_dn_4[0.1730] det_loss_cls_dn_5[0.0771] det_loss_box_dn_5[0.1719] loss_depth[0.0344] total_loss[6.1153]
Rank[0/8] 06/20/2025 14:07:39 INFO stats.py:314 | Epoch[182] Step[94] GlobalStep[71074] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 7:34:54.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:07:44 INFO loss_tracker.py:84 | Epoch[182/239] Step[99] GlobalStep[71079/NA]: det_loss_cls_0[0.4411] det_loss_box_0[0.5473] det_loss_cls_1[0.4255] det_loss_box_1[0.4025] det_loss_cls_2[0.3726] det_loss_box_2[0.3584] det_loss_cls_3[0.3348] det_loss_box_3[0.3510] det_loss_cls_4[0.3138] det_loss_box_4[0.3398] det_loss_cls_5[0.2924] det_loss_box_5[0.3482] det_loss_cls_dn_0[0.0867] det_loss_box_dn_0[0.2324] det_loss_cls_dn_1[0.0844] det_loss_box_dn_1[0.1902] det_loss_cls_dn_2[0.0835] det_loss_box_dn_2[0.1759] det_loss_cls_dn_3[0.0828] det_loss_box_dn_3[0.1721] det_loss_cls_dn_4[0.0822] det_loss_box_dn_4[0.1704] det_loss_cls_dn_5[0.0817] det_loss_box_dn_5[0.1685] loss_depth[0.0375] total_loss[6.1756]
Rank[0/8] 06/20/2025 14:08:05 INFO stats.py:314 | Epoch[182] Step[119] GlobalStep[71099] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 7:34:22.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:08:09 INFO loss_tracker.py:84 | Epoch[182/239] Step[124] GlobalStep[71104/NA]: det_loss_cls_0[0.4885] det_loss_box_0[0.7288] det_loss_cls_1[0.4786] det_loss_box_1[0.5732] det_loss_cls_2[0.4333] det_loss_box_2[0.5278] det_loss_cls_3[0.4130] det_loss_box_3[0.5065] det_loss_cls_4[0.3839] det_loss_box_4[0.4997] det_loss_cls_5[0.3689] det_loss_box_5[0.5012] det_loss_cls_dn_0[0.0947] det_loss_box_dn_0[0.3349] det_loss_cls_dn_1[0.0926] det_loss_box_dn_1[0.2870] det_loss_cls_dn_2[0.0915] det_loss_box_dn_2[0.2656] det_loss_cls_dn_3[0.0901] det_loss_box_dn_3[0.2609] det_loss_cls_dn_4[0.0894] det_loss_box_dn_4[0.2570] det_loss_cls_dn_5[0.0888] det_loss_box_dn_5[0.2514] loss_depth[0.0371] total_loss[8.1442]
Rank[0/8] 06/20/2025 14:08:30 INFO stats.py:314 | Epoch[182] Step[144] GlobalStep[71124] Training Speed: 8.08 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 7:33:50.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:08:35 INFO loss_tracker.py:84 | Epoch[182/239] Step[149] GlobalStep[71129/NA]: det_loss_cls_0[0.5221] det_loss_box_0[0.6119] det_loss_cls_1[0.5032] det_loss_box_1[0.4478] det_loss_cls_2[0.4402] det_loss_box_2[0.4069] det_loss_cls_3[0.4055] det_loss_box_3[0.3973] det_loss_cls_4[0.3787] det_loss_box_4[0.3877] det_loss_cls_5[0.3654] det_loss_box_5[0.3881] det_loss_cls_dn_0[0.0945] det_loss_box_dn_0[0.2393] det_loss_cls_dn_1[0.0899] det_loss_box_dn_1[0.1965] det_loss_cls_dn_2[0.0890] det_loss_box_dn_2[0.1823] det_loss_cls_dn_3[0.0881] det_loss_box_dn_3[0.1769] det_loss_cls_dn_4[0.0873] det_loss_box_dn_4[0.1749] det_loss_cls_dn_5[0.0867] det_loss_box_dn_5[0.1729] loss_depth[0.0345] total_loss[6.9678]
Rank[0/8] 06/20/2025 14:08:57 INFO stats.py:314 | Epoch[182] Step[169] GlobalStep[71149] Training Speed: 7.77 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 7:33:19.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:09:01 INFO loss_tracker.py:84 | Epoch[182/239] Step[174] GlobalStep[71154/NA]: det_loss_cls_0[0.4326] det_loss_box_0[0.5589] det_loss_cls_1[0.4287] det_loss_box_1[0.4096] det_loss_cls_2[0.3720] det_loss_box_2[0.3690] det_loss_cls_3[0.3428] det_loss_box_3[0.3499] det_loss_cls_4[0.3211] det_loss_box_4[0.3400] det_loss_cls_5[0.3101] det_loss_box_5[0.3412] det_loss_cls_dn_0[0.0887] det_loss_box_dn_0[0.2748] det_loss_cls_dn_1[0.0848] det_loss_box_dn_1[0.2224] det_loss_cls_dn_2[0.0845] det_loss_box_dn_2[0.2037] det_loss_cls_dn_3[0.0835] det_loss_box_dn_3[0.1985] det_loss_cls_dn_4[0.0824] det_loss_box_dn_4[0.1951] det_loss_cls_dn_5[0.0813] det_loss_box_dn_5[0.1910] loss_depth[0.0377] total_loss[6.4044]
Rank[0/8] 06/20/2025 14:09:22 INFO stats.py:314 | Epoch[182] Step[194] GlobalStep[71174] Training Speed: 8.65 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 7:32:47.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:09:27 INFO loss_tracker.py:84 | Epoch[182/239] Step[199] GlobalStep[71179/NA]: det_loss_cls_0[0.3719] det_loss_box_0[0.5106] det_loss_cls_1[0.3597] det_loss_box_1[0.3864] det_loss_cls_2[0.3098] det_loss_box_2[0.3596] det_loss_cls_3[0.2789] det_loss_box_3[0.3573] det_loss_cls_4[0.2651] det_loss_box_4[0.3490] det_loss_cls_5[0.2543] det_loss_box_5[0.3491] det_loss_cls_dn_0[0.0857] det_loss_box_dn_0[0.4037] det_loss_cls_dn_1[0.0820] det_loss_box_dn_1[0.3381] det_loss_cls_dn_2[0.0813] det_loss_box_dn_2[0.3108] det_loss_cls_dn_3[0.0803] det_loss_box_dn_3[0.3065] det_loss_cls_dn_4[0.0794] det_loss_box_dn_4[0.3036] det_loss_cls_dn_5[0.0784] det_loss_box_dn_5[0.3009] loss_depth[0.0359] total_loss[6.6387]
Rank[0/8] 06/20/2025 14:09:48 INFO stats.py:314 | Epoch[182] Step[219] GlobalStep[71199] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 7:32:15.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:09:53 INFO loss_tracker.py:84 | Epoch[182/239] Step[224] GlobalStep[71204/NA]: det_loss_cls_0[0.4626] det_loss_box_0[0.5715] det_loss_cls_1[0.4471] det_loss_box_1[0.4316] det_loss_cls_2[0.3854] det_loss_box_2[0.4051] det_loss_cls_3[0.3507] det_loss_box_3[0.3864] det_loss_cls_4[0.3376] det_loss_box_4[0.3781] det_loss_cls_5[0.3234] det_loss_box_5[0.3859] det_loss_cls_dn_0[0.0844] det_loss_box_dn_0[0.2380] det_loss_cls_dn_1[0.0803] det_loss_box_dn_1[0.1938] det_loss_cls_dn_2[0.0794] det_loss_box_dn_2[0.1771] det_loss_cls_dn_3[0.0785] det_loss_box_dn_3[0.1744] det_loss_cls_dn_4[0.0779] det_loss_box_dn_4[0.1737] det_loss_cls_dn_5[0.0771] det_loss_box_dn_5[0.1717] loss_depth[0.0364] total_loss[6.5080]
Rank[0/8] 06/20/2025 14:10:14 INFO stats.py:314 | Epoch[182] Step[244] GlobalStep[71224] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 7:31:44.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:10:20 INFO loss_tracker.py:84 | Epoch[182/239] Step[249] GlobalStep[71229/NA]: det_loss_cls_0[0.4947] det_loss_box_0[0.5585] det_loss_cls_1[0.4836] det_loss_box_1[0.4130] det_loss_cls_2[0.4127] det_loss_box_2[0.3756] det_loss_cls_3[0.3681] det_loss_box_3[0.3710] det_loss_cls_4[0.3440] det_loss_box_4[0.3645] det_loss_cls_5[0.3330] det_loss_box_5[0.3607] det_loss_cls_dn_0[0.0832] det_loss_box_dn_0[0.2244] det_loss_cls_dn_1[0.0802] det_loss_box_dn_1[0.1785] det_loss_cls_dn_2[0.0796] det_loss_box_dn_2[0.1626] det_loss_cls_dn_3[0.0788] det_loss_box_dn_3[0.1593] det_loss_cls_dn_4[0.0781] det_loss_box_dn_4[0.1566] det_loss_cls_dn_5[0.0772] det_loss_box_dn_5[0.1554] loss_depth[0.0377] total_loss[6.4310]
Rank[0/8] 06/20/2025 14:10:40 INFO stats.py:314 | Epoch[182] Step[269] GlobalStep[71249] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 7:31:12.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:10:45 INFO loss_tracker.py:84 | Epoch[182/239] Step[274] GlobalStep[71254/NA]: det_loss_cls_0[0.4067] det_loss_box_0[0.5270] det_loss_cls_1[0.3982] det_loss_box_1[0.4141] det_loss_cls_2[0.3498] det_loss_box_2[0.3688] det_loss_cls_3[0.3100] det_loss_box_3[0.3573] det_loss_cls_4[0.2949] det_loss_box_4[0.3533] det_loss_cls_5[0.2873] det_loss_box_5[0.3516] det_loss_cls_dn_0[0.0874] det_loss_box_dn_0[0.3845] det_loss_cls_dn_1[0.0829] det_loss_box_dn_1[0.3303] det_loss_cls_dn_2[0.0814] det_loss_box_dn_2[0.3118] det_loss_cls_dn_3[0.0806] det_loss_box_dn_3[0.3061] det_loss_cls_dn_4[0.0799] det_loss_box_dn_4[0.3021] det_loss_cls_dn_5[0.0791] det_loss_box_dn_5[0.2906] loss_depth[0.0350] total_loss[6.8706]
Rank[0/8] 06/20/2025 14:11:06 INFO stats.py:314 | Epoch[182] Step[294] GlobalStep[71274] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 7:30:40.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:11:11 INFO loss_tracker.py:84 | Epoch[182/239] Step[299] GlobalStep[71279/NA]: det_loss_cls_0[0.5485] det_loss_box_0[0.6747] det_loss_cls_1[0.5358] det_loss_box_1[0.5168] det_loss_cls_2[0.4782] det_loss_box_2[0.4636] det_loss_cls_3[0.4389] det_loss_box_3[0.4528] det_loss_cls_4[0.4075] det_loss_box_4[0.4392] det_loss_cls_5[0.3999] det_loss_box_5[0.4397] det_loss_cls_dn_0[0.0980] det_loss_box_dn_0[0.2934] det_loss_cls_dn_1[0.0953] det_loss_box_dn_1[0.2403] det_loss_cls_dn_2[0.0946] det_loss_box_dn_2[0.2192] det_loss_cls_dn_3[0.0937] det_loss_box_dn_3[0.2144] det_loss_cls_dn_4[0.0931] det_loss_box_dn_4[0.2115] det_loss_cls_dn_5[0.0923] det_loss_box_dn_5[0.2098] loss_depth[0.0373] total_loss[7.7885]
Rank[0/8] 06/20/2025 14:11:32 INFO stats.py:314 | Epoch[182] Step[319] GlobalStep[71299] Training Speed: 7.98 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 7:30:09.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:11:37 INFO loss_tracker.py:84 | Epoch[182/239] Step[324] GlobalStep[71304/NA]: det_loss_cls_0[0.4516] det_loss_box_0[0.5382] det_loss_cls_1[0.4538] det_loss_box_1[0.4008] det_loss_cls_2[0.3911] det_loss_box_2[0.3663] det_loss_cls_3[0.3602] det_loss_box_3[0.3585] det_loss_cls_4[0.3395] det_loss_box_4[0.3504] det_loss_cls_5[0.3305] det_loss_box_5[0.3423] det_loss_cls_dn_0[0.0880] det_loss_box_dn_0[0.2309] det_loss_cls_dn_1[0.0850] det_loss_box_dn_1[0.1891] det_loss_cls_dn_2[0.0839] det_loss_box_dn_2[0.1717] det_loss_cls_dn_3[0.0828] det_loss_box_dn_3[0.1691] det_loss_cls_dn_4[0.0819] det_loss_box_dn_4[0.1670] det_loss_cls_dn_5[0.0814] det_loss_box_dn_5[0.1662] loss_depth[0.0360] total_loss[6.3160]
Rank[0/8] 06/20/2025 14:11:58 INFO stats.py:314 | Epoch[182] Step[344] GlobalStep[71324] Training Speed: 8.62 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 7:29:37.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:12:03 INFO loss_tracker.py:84 | Epoch[182/239] Step[349] GlobalStep[71329/NA]: det_loss_cls_0[0.4027] det_loss_box_0[0.4501] det_loss_cls_1[0.3766] det_loss_box_1[0.3367] det_loss_cls_2[0.3184] det_loss_box_2[0.3007] det_loss_cls_3[0.2957] det_loss_box_3[0.3013] det_loss_cls_4[0.2773] det_loss_box_4[0.2948] det_loss_cls_5[0.2702] det_loss_box_5[0.2975] det_loss_cls_dn_0[0.0955] det_loss_box_dn_0[0.2880] det_loss_cls_dn_1[0.0923] det_loss_box_dn_1[0.2310] det_loss_cls_dn_2[0.0913] det_loss_box_dn_2[0.2108] det_loss_cls_dn_3[0.0899] det_loss_box_dn_3[0.2029] det_loss_cls_dn_4[0.0890] det_loss_box_dn_4[0.1974] det_loss_cls_dn_5[0.0885] det_loss_box_dn_5[0.1941] loss_depth[0.0350] total_loss[5.8274]
Rank[0/8] 06/20/2025 14:12:23 INFO stats.py:314 | Epoch[182] Step[369] GlobalStep[71349] Training Speed: 7.97 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 7:29:05.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:12:29 INFO loss_tracker.py:84 | Epoch[182/239] Step[374] GlobalStep[71354/NA]: det_loss_cls_0[0.4796] det_loss_box_0[0.5980] det_loss_cls_1[0.4660] det_loss_box_1[0.4542] det_loss_cls_2[0.4081] det_loss_box_2[0.4115] det_loss_cls_3[0.3698] det_loss_box_3[0.4005] det_loss_cls_4[0.3454] det_loss_box_4[0.3890] det_loss_cls_5[0.3283] det_loss_box_5[0.3954] det_loss_cls_dn_0[0.0862] det_loss_box_dn_0[0.2563] det_loss_cls_dn_1[0.0837] det_loss_box_dn_1[0.2046] det_loss_cls_dn_2[0.0824] det_loss_box_dn_2[0.1884] det_loss_cls_dn_3[0.0816] det_loss_box_dn_3[0.1842] det_loss_cls_dn_4[0.0808] det_loss_box_dn_4[0.1822] det_loss_cls_dn_5[0.0804] det_loss_box_dn_5[0.1809] loss_depth[0.0348] total_loss[6.7723]
Rank[0/8] 06/20/2025 14:12:44 INFO stats.py:394 | Epoch[182] completed. Training Speed: 7.72 samples/sec across all devices.	Epoch Time: 403.89 sec.	Average Epoch Time: 403.89 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 7:28:40.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:12:51 INFO stats.py:314 | Epoch[183] Step[4] GlobalStep[71374] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 7:28:34.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:13:12 INFO loss_tracker.py:84 | Epoch[183/239] Step[24] GlobalStep[71394/NA]: det_loss_cls_0[0.4923] det_loss_box_0[0.6284] det_loss_cls_1[0.4785] det_loss_box_1[0.4680] det_loss_cls_2[0.4199] det_loss_box_2[0.4256] det_loss_cls_3[0.3883] det_loss_box_3[0.4083] det_loss_cls_4[0.3667] det_loss_box_4[0.3903] det_loss_cls_5[0.3530] det_loss_box_5[0.3899] det_loss_cls_dn_0[0.0881] det_loss_box_dn_0[0.2797] det_loss_cls_dn_1[0.0843] det_loss_box_dn_1[0.2349] det_loss_cls_dn_2[0.0835] det_loss_box_dn_2[0.2144] det_loss_cls_dn_3[0.0822] det_loss_box_dn_3[0.2103] det_loss_cls_dn_4[0.0812] det_loss_box_dn_4[0.2075] det_loss_cls_dn_5[0.0803] det_loss_box_dn_5[0.2049] loss_depth[0.0379] total_loss[7.0982]
Rank[0/8] 06/20/2025 14:13:17 INFO stats.py:314 | Epoch[183] Step[29] GlobalStep[71399] Training Speed: 8.76 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 7:28:03.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:13:38 INFO loss_tracker.py:84 | Epoch[183/239] Step[49] GlobalStep[71419/NA]: det_loss_cls_0[0.5452] det_loss_box_0[0.6706] det_loss_cls_1[0.5294] det_loss_box_1[0.5217] det_loss_cls_2[0.4640] det_loss_box_2[0.4764] det_loss_cls_3[0.4201] det_loss_box_3[0.4649] det_loss_cls_4[0.3978] det_loss_box_4[0.4473] det_loss_cls_5[0.3842] det_loss_box_5[0.4461] det_loss_cls_dn_0[0.0899] det_loss_box_dn_0[0.2924] det_loss_cls_dn_1[0.0855] det_loss_box_dn_1[0.2516] det_loss_cls_dn_2[0.0841] det_loss_box_dn_2[0.2352] det_loss_cls_dn_3[0.0831] det_loss_box_dn_3[0.2303] det_loss_cls_dn_4[0.0818] det_loss_box_dn_4[0.2290] det_loss_cls_dn_5[0.0808] det_loss_box_dn_5[0.2258] loss_depth[0.0356] total_loss[7.7729]
Rank[0/8] 06/20/2025 14:13:43 INFO stats.py:314 | Epoch[183] Step[54] GlobalStep[71424] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 7:27:31.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:14:03 INFO loss_tracker.py:84 | Epoch[183/239] Step[74] GlobalStep[71444/NA]: det_loss_cls_0[0.4641] det_loss_box_0[0.5881] det_loss_cls_1[0.4488] det_loss_box_1[0.4501] det_loss_cls_2[0.3888] det_loss_box_2[0.4040] det_loss_cls_3[0.3457] det_loss_box_3[0.3934] det_loss_cls_4[0.3196] det_loss_box_4[0.3844] det_loss_cls_5[0.3095] det_loss_box_5[0.3828] det_loss_cls_dn_0[0.0825] det_loss_box_dn_0[0.2613] det_loss_cls_dn_1[0.0786] det_loss_box_dn_1[0.2125] det_loss_cls_dn_2[0.0776] det_loss_box_dn_2[0.1917] det_loss_cls_dn_3[0.0762] det_loss_box_dn_3[0.1881] det_loss_cls_dn_4[0.0752] det_loss_box_dn_4[0.1864] det_loss_cls_dn_5[0.0740] det_loss_box_dn_5[0.1851] loss_depth[0.0351] total_loss[6.6035]
Rank[0/8] 06/20/2025 14:14:08 INFO stats.py:314 | Epoch[183] Step[79] GlobalStep[71449] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 7:26:59.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:14:29 INFO loss_tracker.py:84 | Epoch[183/239] Step[99] GlobalStep[71469/NA]: det_loss_cls_0[0.5000] det_loss_box_0[0.5836] det_loss_cls_1[0.4682] det_loss_box_1[0.4211] det_loss_cls_2[0.4039] det_loss_box_2[0.3818] det_loss_cls_3[0.3639] det_loss_box_3[0.3730] det_loss_cls_4[0.3370] det_loss_box_4[0.3691] det_loss_cls_5[0.3343] det_loss_box_5[0.3634] det_loss_cls_dn_0[0.0908] det_loss_box_dn_0[0.2299] det_loss_cls_dn_1[0.0874] det_loss_box_dn_1[0.1823] det_loss_cls_dn_2[0.0862] det_loss_box_dn_2[0.1637] det_loss_cls_dn_3[0.0844] det_loss_box_dn_3[0.1599] det_loss_cls_dn_4[0.0840] det_loss_box_dn_4[0.1573] det_loss_cls_dn_5[0.0832] det_loss_box_dn_5[0.1559] loss_depth[0.0347] total_loss[6.4989]
Rank[0/8] 06/20/2025 14:14:35 INFO stats.py:314 | Epoch[183] Step[104] GlobalStep[71474] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 7:26:28.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:14:56 INFO loss_tracker.py:84 | Epoch[183/239] Step[124] GlobalStep[71494/NA]: det_loss_cls_0[0.4421] det_loss_box_0[0.5607] det_loss_cls_1[0.4335] det_loss_box_1[0.4086] det_loss_cls_2[0.3807] det_loss_box_2[0.3704] det_loss_cls_3[0.3492] det_loss_box_3[0.3614] det_loss_cls_4[0.3240] det_loss_box_4[0.3513] det_loss_cls_5[0.3115] det_loss_box_5[0.3528] det_loss_cls_dn_0[0.0931] det_loss_box_dn_0[0.2824] det_loss_cls_dn_1[0.0888] det_loss_box_dn_1[0.2315] det_loss_cls_dn_2[0.0877] det_loss_box_dn_2[0.2135] det_loss_cls_dn_3[0.0863] det_loss_box_dn_3[0.2080] det_loss_cls_dn_4[0.0851] det_loss_box_dn_4[0.2063] det_loss_cls_dn_5[0.0840] det_loss_box_dn_5[0.2031] loss_depth[0.0367] total_loss[6.5529]
Rank[0/8] 06/20/2025 14:15:01 INFO stats.py:314 | Epoch[183] Step[129] GlobalStep[71499] Training Speed: 8.62 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 7:25:56.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:15:21 INFO loss_tracker.py:84 | Epoch[183/239] Step[149] GlobalStep[71519/NA]: det_loss_cls_0[0.3657] det_loss_box_0[0.4581] det_loss_cls_1[0.3523] det_loss_box_1[0.3533] det_loss_cls_2[0.3127] det_loss_box_2[0.3204] det_loss_cls_3[0.2895] det_loss_box_3[0.3132] det_loss_cls_4[0.2716] det_loss_box_4[0.2958] det_loss_cls_5[0.2555] det_loss_box_5[0.2990] det_loss_cls_dn_0[0.0877] det_loss_box_dn_0[0.2714] det_loss_cls_dn_1[0.0846] det_loss_box_dn_1[0.2164] det_loss_cls_dn_2[0.0842] det_loss_box_dn_2[0.2004] det_loss_cls_dn_3[0.0833] det_loss_box_dn_3[0.1951] det_loss_cls_dn_4[0.0818] det_loss_box_dn_4[0.1915] det_loss_cls_dn_5[0.0811] det_loss_box_dn_5[0.1882] loss_depth[0.0362] total_loss[5.6892]
Rank[0/8] 06/20/2025 14:15:26 INFO stats.py:314 | Epoch[183] Step[154] GlobalStep[71524] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 7:25:24.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:15:47 INFO loss_tracker.py:84 | Epoch[183/239] Step[174] GlobalStep[71544/NA]: det_loss_cls_0[0.6083] det_loss_box_0[0.6978] det_loss_cls_1[0.5876] det_loss_box_1[0.5240] det_loss_cls_2[0.5302] det_loss_box_2[0.4615] det_loss_cls_3[0.4926] det_loss_box_3[0.4478] det_loss_cls_4[0.4663] det_loss_box_4[0.4312] det_loss_cls_5[0.4481] det_loss_box_5[0.4319] det_loss_cls_dn_0[0.0985] det_loss_box_dn_0[0.2529] det_loss_cls_dn_1[0.0943] det_loss_box_dn_1[0.2039] det_loss_cls_dn_2[0.0933] det_loss_box_dn_2[0.1860] det_loss_cls_dn_3[0.0922] det_loss_box_dn_3[0.1820] det_loss_cls_dn_4[0.0912] det_loss_box_dn_4[0.1820] det_loss_cls_dn_5[0.0905] det_loss_box_dn_5[0.1795] loss_depth[0.0358] total_loss[7.9095]
Rank[0/8] 06/20/2025 14:15:52 INFO stats.py:314 | Epoch[183] Step[179] GlobalStep[71549] Training Speed: 8.71 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 7:24:53.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:16:13 INFO loss_tracker.py:84 | Epoch[183/239] Step[199] GlobalStep[71569/NA]: det_loss_cls_0[0.4039] det_loss_box_0[0.5020] det_loss_cls_1[0.3751] det_loss_box_1[0.3619] det_loss_cls_2[0.3106] det_loss_box_2[0.3346] det_loss_cls_3[0.2795] det_loss_box_3[0.3205] det_loss_cls_4[0.2587] det_loss_box_4[0.3185] det_loss_cls_5[0.2397] det_loss_box_5[0.3222] det_loss_cls_dn_0[0.0814] det_loss_box_dn_0[0.2795] det_loss_cls_dn_1[0.0782] det_loss_box_dn_1[0.2163] det_loss_cls_dn_2[0.0770] det_loss_box_dn_2[0.1968] det_loss_cls_dn_3[0.0754] det_loss_box_dn_3[0.1912] det_loss_cls_dn_4[0.0751] det_loss_box_dn_4[0.1873] det_loss_cls_dn_5[0.0742] det_loss_box_dn_5[0.1860] loss_depth[0.0350] total_loss[5.7806]
Rank[0/8] 06/20/2025 14:16:19 INFO stats.py:314 | Epoch[183] Step[204] GlobalStep[71574] Training Speed: 7.58 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 7:24:21.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:16:39 INFO loss_tracker.py:84 | Epoch[183/239] Step[224] GlobalStep[71594/NA]: det_loss_cls_0[0.4562] det_loss_box_0[0.6882] det_loss_cls_1[0.4542] det_loss_box_1[0.5491] det_loss_cls_2[0.4005] det_loss_box_2[0.4922] det_loss_cls_3[0.3677] det_loss_box_3[0.4852] det_loss_cls_4[0.3445] det_loss_box_4[0.4729] det_loss_cls_5[0.3351] det_loss_box_5[0.4728] det_loss_cls_dn_0[0.0723] det_loss_box_dn_0[0.2373] det_loss_cls_dn_1[0.0686] det_loss_box_dn_1[0.2032] det_loss_cls_dn_2[0.0672] det_loss_box_dn_2[0.1928] det_loss_cls_dn_3[0.0660] det_loss_box_dn_3[0.1891] det_loss_cls_dn_4[0.0650] det_loss_box_dn_4[0.1872] det_loss_cls_dn_5[0.0644] det_loss_box_dn_5[0.1857] loss_depth[0.0335] total_loss[7.1509]
Rank[0/8] 06/20/2025 14:16:44 INFO stats.py:314 | Epoch[183] Step[229] GlobalStep[71599] Training Speed: 8.70 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 7:23:50.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:17:05 INFO loss_tracker.py:84 | Epoch[183/239] Step[249] GlobalStep[71619/NA]: det_loss_cls_0[0.4424] det_loss_box_0[0.5355] det_loss_cls_1[0.4380] det_loss_box_1[0.4083] det_loss_cls_2[0.3741] det_loss_box_2[0.3690] det_loss_cls_3[0.3423] det_loss_box_3[0.3609] det_loss_cls_4[0.3249] det_loss_box_4[0.3511] det_loss_cls_5[0.3133] det_loss_box_5[0.3527] det_loss_cls_dn_0[0.0837] det_loss_box_dn_0[0.2624] det_loss_cls_dn_1[0.0802] det_loss_box_dn_1[0.2178] det_loss_cls_dn_2[0.0791] det_loss_box_dn_2[0.2003] det_loss_cls_dn_3[0.0780] det_loss_box_dn_3[0.1984] det_loss_cls_dn_4[0.0772] det_loss_box_dn_4[0.1959] det_loss_cls_dn_5[0.0766] det_loss_box_dn_5[0.1927] loss_depth[0.0342] total_loss[6.3890]
Rank[0/8] 06/20/2025 14:17:10 INFO stats.py:314 | Epoch[183] Step[254] GlobalStep[71624] Training Speed: 8.69 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 7:23:18.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:17:31 INFO loss_tracker.py:84 | Epoch[183/239] Step[274] GlobalStep[71644/NA]: det_loss_cls_0[0.3900] det_loss_box_0[0.4563] det_loss_cls_1[0.3857] det_loss_box_1[0.3363] det_loss_cls_2[0.3243] det_loss_box_2[0.3189] det_loss_cls_3[0.2975] det_loss_box_3[0.3105] det_loss_cls_4[0.2780] det_loss_box_4[0.2969] det_loss_cls_5[0.2660] det_loss_box_5[0.2999] det_loss_cls_dn_0[0.0953] det_loss_box_dn_0[0.2580] det_loss_cls_dn_1[0.0927] det_loss_box_dn_1[0.2091] det_loss_cls_dn_2[0.0920] det_loss_box_dn_2[0.1895] det_loss_cls_dn_3[0.0912] det_loss_box_dn_3[0.1836] det_loss_cls_dn_4[0.0904] det_loss_box_dn_4[0.1799] det_loss_cls_dn_5[0.0899] det_loss_box_dn_5[0.1783] loss_depth[0.0360] total_loss[5.7461]
Rank[0/8] 06/20/2025 14:17:36 INFO stats.py:314 | Epoch[183] Step[279] GlobalStep[71649] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 7:22:46.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:17:57 INFO loss_tracker.py:84 | Epoch[183/239] Step[299] GlobalStep[71669/NA]: det_loss_cls_0[0.5451] det_loss_box_0[0.6208] det_loss_cls_1[0.5170] det_loss_box_1[0.4544] det_loss_cls_2[0.4415] det_loss_box_2[0.4028] det_loss_cls_3[0.3997] det_loss_box_3[0.3959] det_loss_cls_4[0.3742] det_loss_box_4[0.3865] det_loss_cls_5[0.3596] det_loss_box_5[0.3871] det_loss_cls_dn_0[0.0931] det_loss_box_dn_0[0.2294] det_loss_cls_dn_1[0.0896] det_loss_box_dn_1[0.1845] det_loss_cls_dn_2[0.0887] det_loss_box_dn_2[0.1694] det_loss_cls_dn_3[0.0878] det_loss_box_dn_3[0.1641] det_loss_cls_dn_4[0.0872] det_loss_box_dn_4[0.1617] det_loss_cls_dn_5[0.0864] det_loss_box_dn_5[0.1568] loss_depth[0.0362] total_loss[6.9194]
Rank[0/8] 06/20/2025 14:18:02 INFO stats.py:314 | Epoch[183] Step[304] GlobalStep[71674] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 7:22:15.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:18:22 INFO loss_tracker.py:84 | Epoch[183/239] Step[324] GlobalStep[71694/NA]: det_loss_cls_0[0.4744] det_loss_box_0[0.5787] det_loss_cls_1[0.4774] det_loss_box_1[0.4340] det_loss_cls_2[0.3987] det_loss_box_2[0.3987] det_loss_cls_3[0.3730] det_loss_box_3[0.3819] det_loss_cls_4[0.3387] det_loss_box_4[0.3761] det_loss_cls_5[0.3222] det_loss_box_5[0.3751] det_loss_cls_dn_0[0.0789] det_loss_box_dn_0[0.2610] det_loss_cls_dn_1[0.0758] det_loss_box_dn_1[0.2152] det_loss_cls_dn_2[0.0750] det_loss_box_dn_2[0.1979] det_loss_cls_dn_3[0.0740] det_loss_box_dn_3[0.1943] det_loss_cls_dn_4[0.0733] det_loss_box_dn_4[0.1922] det_loss_cls_dn_5[0.0726] det_loss_box_dn_5[0.1912] loss_depth[0.0381] total_loss[6.6686]
Rank[0/8] 06/20/2025 14:18:28 INFO stats.py:314 | Epoch[183] Step[329] GlobalStep[71699] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 7:21:43.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:18:48 INFO loss_tracker.py:84 | Epoch[183/239] Step[349] GlobalStep[71719/NA]: det_loss_cls_0[0.5216] det_loss_box_0[0.7507] det_loss_cls_1[0.4965] det_loss_box_1[0.5442] det_loss_cls_2[0.4359] det_loss_box_2[0.4786] det_loss_cls_3[0.4071] det_loss_box_3[0.4580] det_loss_cls_4[0.3895] det_loss_box_4[0.4378] det_loss_cls_5[0.3735] det_loss_box_5[0.4371] det_loss_cls_dn_0[0.0974] det_loss_box_dn_0[0.2957] det_loss_cls_dn_1[0.0936] det_loss_box_dn_1[0.2443] det_loss_cls_dn_2[0.0930] det_loss_box_dn_2[0.2237] det_loss_cls_dn_3[0.0912] det_loss_box_dn_3[0.2172] det_loss_cls_dn_4[0.0904] det_loss_box_dn_4[0.2145] det_loss_cls_dn_5[0.0897] det_loss_box_dn_5[0.2119] loss_depth[0.0393] total_loss[7.7321]
Rank[0/8] 06/20/2025 14:18:53 INFO stats.py:314 | Epoch[183] Step[354] GlobalStep[71724] Training Speed: 8.34 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 7:21:12.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:19:14 INFO loss_tracker.py:84 | Epoch[183/239] Step[374] GlobalStep[71744/NA]: det_loss_cls_0[0.5319] det_loss_box_0[0.6139] det_loss_cls_1[0.5085] det_loss_box_1[0.4525] det_loss_cls_2[0.4413] det_loss_box_2[0.4203] det_loss_cls_3[0.4023] det_loss_box_3[0.4112] det_loss_cls_4[0.3780] det_loss_box_4[0.3992] det_loss_cls_5[0.3652] det_loss_box_5[0.3982] det_loss_cls_dn_0[0.0899] det_loss_box_dn_0[0.2561] det_loss_cls_dn_1[0.0868] det_loss_box_dn_1[0.2078] det_loss_cls_dn_2[0.0856] det_loss_box_dn_2[0.1912] det_loss_cls_dn_3[0.0845] det_loss_box_dn_3[0.1884] det_loss_cls_dn_4[0.0834] det_loss_box_dn_4[0.1862] det_loss_cls_dn_5[0.0826] det_loss_box_dn_5[0.1848] loss_depth[0.0340] total_loss[7.0837]
Rank[0/8] 06/20/2025 14:19:19 INFO stats.py:314 | Epoch[183] Step[379] GlobalStep[71749] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 7:20:40.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:19:29 INFO stats.py:394 | Epoch[183] completed. Training Speed: 7.70 samples/sec across all devices.	Epoch Time: 405.18 sec.	Average Epoch Time: 405.18 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 7:20:27.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:19:47 INFO stats.py:314 | Epoch[184] Step[14] GlobalStep[71774] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 7:20:09.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:19:57 INFO loss_tracker.py:84 | Epoch[184/239] Step[24] GlobalStep[71784/NA]: det_loss_cls_0[0.4097] det_loss_box_0[0.5046] det_loss_cls_1[0.4035] det_loss_box_1[0.3945] det_loss_cls_2[0.3529] det_loss_box_2[0.3604] det_loss_cls_3[0.3174] det_loss_box_3[0.3542] det_loss_cls_4[0.2977] det_loss_box_4[0.3438] det_loss_cls_5[0.2904] det_loss_box_5[0.3439] det_loss_cls_dn_0[0.0886] det_loss_box_dn_0[0.3197] det_loss_cls_dn_1[0.0842] det_loss_box_dn_1[0.2704] det_loss_cls_dn_2[0.0828] det_loss_box_dn_2[0.2508] det_loss_cls_dn_3[0.0822] det_loss_box_dn_3[0.2452] det_loss_cls_dn_4[0.0810] det_loss_box_dn_4[0.2431] det_loss_cls_dn_5[0.0803] det_loss_box_dn_5[0.2385] loss_depth[0.0361] total_loss[6.4757]
Rank[0/8] 06/20/2025 14:20:13 INFO stats.py:314 | Epoch[184] Step[39] GlobalStep[71799] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 7:19:37.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:20:23 INFO loss_tracker.py:84 | Epoch[184/239] Step[49] GlobalStep[71809/NA]: det_loss_cls_0[0.4408] det_loss_box_0[0.5532] det_loss_cls_1[0.4276] det_loss_box_1[0.4188] det_loss_cls_2[0.3713] det_loss_box_2[0.3817] det_loss_cls_3[0.3450] det_loss_box_3[0.3746] det_loss_cls_4[0.3194] det_loss_box_4[0.3647] det_loss_cls_5[0.3016] det_loss_box_5[0.3713] det_loss_cls_dn_0[0.0851] det_loss_box_dn_0[0.3152] det_loss_cls_dn_1[0.0813] det_loss_box_dn_1[0.2680] det_loss_cls_dn_2[0.0805] det_loss_box_dn_2[0.2547] det_loss_cls_dn_3[0.0795] det_loss_box_dn_3[0.2440] det_loss_cls_dn_4[0.0789] det_loss_box_dn_4[0.2405] det_loss_cls_dn_5[0.0778] det_loss_box_dn_5[0.2346] loss_depth[0.0346] total_loss[6.7448]
Rank[0/8] 06/20/2025 14:20:39 INFO stats.py:314 | Epoch[184] Step[64] GlobalStep[71824] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 7:19:06.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:20:49 INFO loss_tracker.py:84 | Epoch[184/239] Step[74] GlobalStep[71834/NA]: det_loss_cls_0[0.3988] det_loss_box_0[0.4920] det_loss_cls_1[0.3802] det_loss_box_1[0.3671] det_loss_cls_2[0.3198] det_loss_box_2[0.3419] det_loss_cls_3[0.2870] det_loss_box_3[0.3280] det_loss_cls_4[0.2656] det_loss_box_4[0.3205] det_loss_cls_5[0.2557] det_loss_box_5[0.3237] det_loss_cls_dn_0[0.0962] det_loss_box_dn_0[0.2510] det_loss_cls_dn_1[0.0909] det_loss_box_dn_1[0.2099] det_loss_cls_dn_2[0.0894] det_loss_box_dn_2[0.1938] det_loss_cls_dn_3[0.0885] det_loss_box_dn_3[0.1898] det_loss_cls_dn_4[0.0877] det_loss_box_dn_4[0.1863] det_loss_cls_dn_5[0.0867] det_loss_box_dn_5[0.1835] loss_depth[0.0367] total_loss[5.8707]
Rank[0/8] 06/20/2025 14:21:04 INFO stats.py:314 | Epoch[184] Step[89] GlobalStep[71849] Training Speed: 8.68 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 7:18:34.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:21:14 INFO loss_tracker.py:84 | Epoch[184/239] Step[99] GlobalStep[71859/NA]: det_loss_cls_0[0.5160] det_loss_box_0[0.6440] det_loss_cls_1[0.4925] det_loss_box_1[0.4904] det_loss_cls_2[0.4402] det_loss_box_2[0.4439] det_loss_cls_3[0.4146] det_loss_box_3[0.4177] det_loss_cls_4[0.3893] det_loss_box_4[0.4034] det_loss_cls_5[0.3740] det_loss_box_5[0.4061] det_loss_cls_dn_0[0.0924] det_loss_box_dn_0[0.2381] det_loss_cls_dn_1[0.0879] det_loss_box_dn_1[0.1962] det_loss_cls_dn_2[0.0871] det_loss_box_dn_2[0.1814] det_loss_cls_dn_3[0.0860] det_loss_box_dn_3[0.1778] det_loss_cls_dn_4[0.0850] det_loss_box_dn_4[0.1761] det_loss_cls_dn_5[0.0840] det_loss_box_dn_5[0.1743] loss_depth[0.0362] total_loss[7.1346]
Rank[0/8] 06/20/2025 14:21:29 INFO stats.py:314 | Epoch[184] Step[114] GlobalStep[71874] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 7:18:02.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:21:40 INFO loss_tracker.py:84 | Epoch[184/239] Step[124] GlobalStep[71884/NA]: det_loss_cls_0[0.4564] det_loss_box_0[0.5643] det_loss_cls_1[0.4384] det_loss_box_1[0.4128] det_loss_cls_2[0.3672] det_loss_box_2[0.3666] det_loss_cls_3[0.3294] det_loss_box_3[0.3589] det_loss_cls_4[0.3100] det_loss_box_4[0.3437] det_loss_cls_5[0.2924] det_loss_box_5[0.3470] det_loss_cls_dn_0[0.0752] det_loss_box_dn_0[0.2217] det_loss_cls_dn_1[0.0718] det_loss_box_dn_1[0.1778] det_loss_cls_dn_2[0.0711] det_loss_box_dn_2[0.1631] det_loss_cls_dn_3[0.0701] det_loss_box_dn_3[0.1596] det_loss_cls_dn_4[0.0695] det_loss_box_dn_4[0.1569] det_loss_cls_dn_5[0.0688] det_loss_box_dn_5[0.1557] loss_depth[0.0339] total_loss[6.0823]
Rank[0/8] 06/20/2025 14:21:55 INFO stats.py:314 | Epoch[184] Step[139] GlobalStep[71899] Training Speed: 12.24 samples/sec across all devices.	Average Step Time: 0.65 sec.	Estimated Remaining Time: 7:17:31.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:22:06 INFO loss_tracker.py:84 | Epoch[184/239] Step[149] GlobalStep[71909/NA]: det_loss_cls_0[0.3926] det_loss_box_0[0.4896] det_loss_cls_1[0.3832] det_loss_box_1[0.3730] det_loss_cls_2[0.3247] det_loss_box_2[0.3473] det_loss_cls_3[0.3003] det_loss_box_3[0.3374] det_loss_cls_4[0.2761] det_loss_box_4[0.3300] det_loss_cls_5[0.2646] det_loss_box_5[0.3276] det_loss_cls_dn_0[0.0809] det_loss_box_dn_0[0.2831] det_loss_cls_dn_1[0.0775] det_loss_box_dn_1[0.2378] det_loss_cls_dn_2[0.0763] det_loss_box_dn_2[0.2222] det_loss_cls_dn_3[0.0752] det_loss_box_dn_3[0.2171] det_loss_cls_dn_4[0.0744] det_loss_box_dn_4[0.2138] det_loss_cls_dn_5[0.0734] det_loss_box_dn_5[0.2090] loss_depth[0.0392] total_loss[6.0264]
Rank[0/8] 06/20/2025 14:22:22 INFO stats.py:314 | Epoch[184] Step[164] GlobalStep[71924] Training Speed: 7.99 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 7:16:59.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:22:32 INFO loss_tracker.py:84 | Epoch[184/239] Step[174] GlobalStep[71934/NA]: det_loss_cls_0[0.4084] det_loss_box_0[0.5027] det_loss_cls_1[0.3933] det_loss_box_1[0.3810] det_loss_cls_2[0.3331] det_loss_box_2[0.3422] det_loss_cls_3[0.2892] det_loss_box_3[0.3333] det_loss_cls_4[0.2658] det_loss_box_4[0.3233] det_loss_cls_5[0.2530] det_loss_box_5[0.3245] det_loss_cls_dn_0[0.0922] det_loss_box_dn_0[0.2342] det_loss_cls_dn_1[0.0879] det_loss_box_dn_1[0.1952] det_loss_cls_dn_2[0.0871] det_loss_box_dn_2[0.1780] det_loss_cls_dn_3[0.0857] det_loss_box_dn_3[0.1748] det_loss_cls_dn_4[0.0852] det_loss_box_dn_4[0.1723] det_loss_cls_dn_5[0.0842] det_loss_box_dn_5[0.1715] loss_depth[0.0326] total_loss[5.8304]
Rank[0/8] 06/20/2025 14:22:47 INFO stats.py:314 | Epoch[184] Step[189] GlobalStep[71949] Training Speed: 8.77 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 7:16:28.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:22:58 INFO loss_tracker.py:84 | Epoch[184/239] Step[199] GlobalStep[71959/NA]: det_loss_cls_0[0.4003] det_loss_box_0[0.5042] det_loss_cls_1[0.3874] det_loss_box_1[0.3967] det_loss_cls_2[0.3329] det_loss_box_2[0.3539] det_loss_cls_3[0.3069] det_loss_box_3[0.3462] det_loss_cls_4[0.2796] det_loss_box_4[0.3410] det_loss_cls_5[0.2703] det_loss_box_5[0.3379] det_loss_cls_dn_0[0.0943] det_loss_box_dn_0[0.2931] det_loss_cls_dn_1[0.0910] det_loss_box_dn_1[0.2359] det_loss_cls_dn_2[0.0902] det_loss_box_dn_2[0.2152] det_loss_cls_dn_3[0.0889] det_loss_box_dn_3[0.2101] det_loss_cls_dn_4[0.0882] det_loss_box_dn_4[0.2070] det_loss_cls_dn_5[0.0875] det_loss_box_dn_5[0.2055] loss_depth[0.0349] total_loss[6.1992]
Rank[0/8] 06/20/2025 14:23:14 INFO stats.py:314 | Epoch[184] Step[214] GlobalStep[71974] Training Speed: 7.00 samples/sec across all devices.	Average Step Time: 1.14 sec.	Estimated Remaining Time: 7:15:56.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:23:24 INFO loss_tracker.py:84 | Epoch[184/239] Step[224] GlobalStep[71984/NA]: det_loss_cls_0[0.4991] det_loss_box_0[0.6041] det_loss_cls_1[0.4925] det_loss_box_1[0.4502] det_loss_cls_2[0.4209] det_loss_box_2[0.4044] det_loss_cls_3[0.3924] det_loss_box_3[0.3888] det_loss_cls_4[0.3655] det_loss_box_4[0.3772] det_loss_cls_5[0.3579] det_loss_box_5[0.3741] det_loss_cls_dn_0[0.0891] det_loss_box_dn_0[0.2463] det_loss_cls_dn_1[0.0849] det_loss_box_dn_1[0.1972] det_loss_cls_dn_2[0.0836] det_loss_box_dn_2[0.1835] det_loss_cls_dn_3[0.0825] det_loss_box_dn_3[0.1783] det_loss_cls_dn_4[0.0814] det_loss_box_dn_4[0.1770] det_loss_cls_dn_5[0.0804] det_loss_box_dn_5[0.1751] loss_depth[0.0349] total_loss[6.8213]
Rank[0/8] 06/20/2025 14:23:40 INFO stats.py:314 | Epoch[184] Step[239] GlobalStep[71999] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 7:15:25.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:23:50 INFO loss_tracker.py:84 | Epoch[184/239] Step[249] GlobalStep[72009/NA]: det_loss_cls_0[0.4671] det_loss_box_0[0.5558] det_loss_cls_1[0.4517] det_loss_box_1[0.4285] det_loss_cls_2[0.3897] det_loss_box_2[0.4001] det_loss_cls_3[0.3574] det_loss_box_3[0.3832] det_loss_cls_4[0.3325] det_loss_box_4[0.3741] det_loss_cls_5[0.3143] det_loss_box_5[0.3755] det_loss_cls_dn_0[0.0871] det_loss_box_dn_0[0.2611] det_loss_cls_dn_1[0.0830] det_loss_box_dn_1[0.2205] det_loss_cls_dn_2[0.0822] det_loss_box_dn_2[0.2031] det_loss_cls_dn_3[0.0807] det_loss_box_dn_3[0.2007] det_loss_cls_dn_4[0.0796] det_loss_box_dn_4[0.1975] det_loss_cls_dn_5[0.0787] det_loss_box_dn_5[0.1961] loss_depth[0.0336] total_loss[6.6340]
Rank[0/8] 06/20/2025 14:24:05 INFO stats.py:314 | Epoch[184] Step[264] GlobalStep[72024] Training Speed: 7.78 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 7:14:53.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:24:15 INFO loss_tracker.py:84 | Epoch[184/239] Step[274] GlobalStep[72034/NA]: det_loss_cls_0[0.5111] det_loss_box_0[0.6385] det_loss_cls_1[0.5039] det_loss_box_1[0.4753] det_loss_cls_2[0.4463] det_loss_box_2[0.4354] det_loss_cls_3[0.4105] det_loss_box_3[0.4315] det_loss_cls_4[0.3943] det_loss_box_4[0.4127] det_loss_cls_5[0.3805] det_loss_box_5[0.4180] det_loss_cls_dn_0[0.0980] det_loss_box_dn_0[0.2481] det_loss_cls_dn_1[0.0945] det_loss_box_dn_1[0.2011] det_loss_cls_dn_2[0.0939] det_loss_box_dn_2[0.1851] det_loss_cls_dn_3[0.0926] det_loss_box_dn_3[0.1822] det_loss_cls_dn_4[0.0918] det_loss_box_dn_4[0.1790] det_loss_cls_dn_5[0.0910] det_loss_box_dn_5[0.1768] loss_depth[0.0339] total_loss[7.2261]
Rank[0/8] 06/20/2025 14:24:30 INFO stats.py:314 | Epoch[184] Step[289] GlobalStep[72049] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 7:14:21.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:24:41 INFO loss_tracker.py:84 | Epoch[184/239] Step[299] GlobalStep[72059/NA]: det_loss_cls_0[0.4701] det_loss_box_0[0.5487] det_loss_cls_1[0.4546] det_loss_box_1[0.4082] det_loss_cls_2[0.4087] det_loss_box_2[0.3719] det_loss_cls_3[0.3672] det_loss_box_3[0.3707] det_loss_cls_4[0.3394] det_loss_box_4[0.3653] det_loss_cls_5[0.3235] det_loss_box_5[0.3671] det_loss_cls_dn_0[0.0971] det_loss_box_dn_0[0.2599] det_loss_cls_dn_1[0.0934] det_loss_box_dn_1[0.2151] det_loss_cls_dn_2[0.0924] det_loss_box_dn_2[0.2016] det_loss_cls_dn_3[0.0911] det_loss_box_dn_3[0.1973] det_loss_cls_dn_4[0.0900] det_loss_box_dn_4[0.1941] det_loss_cls_dn_5[0.0889] det_loss_box_dn_5[0.1882] loss_depth[0.0333] total_loss[6.6380]
Rank[0/8] 06/20/2025 14:24:57 INFO stats.py:314 | Epoch[184] Step[314] GlobalStep[72074] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 7:13:50.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:25:07 INFO loss_tracker.py:84 | Epoch[184/239] Step[324] GlobalStep[72084/NA]: det_loss_cls_0[0.4620] det_loss_box_0[0.6026] det_loss_cls_1[0.4467] det_loss_box_1[0.4579] det_loss_cls_2[0.3980] det_loss_box_2[0.4109] det_loss_cls_3[0.3539] det_loss_box_3[0.3995] det_loss_cls_4[0.3321] det_loss_box_4[0.3929] det_loss_cls_5[0.3195] det_loss_box_5[0.3951] det_loss_cls_dn_0[0.0919] det_loss_box_dn_0[0.2393] det_loss_cls_dn_1[0.0887] det_loss_box_dn_1[0.1979] det_loss_cls_dn_2[0.0875] det_loss_box_dn_2[0.1816] det_loss_cls_dn_3[0.0865] det_loss_box_dn_3[0.1777] det_loss_cls_dn_4[0.0859] det_loss_box_dn_4[0.1748] det_loss_cls_dn_5[0.0852] det_loss_box_dn_5[0.1740] loss_depth[0.0353] total_loss[6.6776]
Rank[0/8] 06/20/2025 14:25:22 INFO stats.py:314 | Epoch[184] Step[339] GlobalStep[72099] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 7:13:19.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:25:32 INFO loss_tracker.py:84 | Epoch[184/239] Step[349] GlobalStep[72109/NA]: det_loss_cls_0[0.4852] det_loss_box_0[0.5858] det_loss_cls_1[0.4823] det_loss_box_1[0.4355] det_loss_cls_2[0.4149] det_loss_box_2[0.4026] det_loss_cls_3[0.3915] det_loss_box_3[0.3848] det_loss_cls_4[0.3640] det_loss_box_4[0.3788] det_loss_cls_5[0.3515] det_loss_box_5[0.3815] det_loss_cls_dn_0[0.0795] det_loss_box_dn_0[0.2288] det_loss_cls_dn_1[0.0759] det_loss_box_dn_1[0.1856] det_loss_cls_dn_2[0.0750] det_loss_box_dn_2[0.1686] det_loss_cls_dn_3[0.0740] det_loss_box_dn_3[0.1653] det_loss_cls_dn_4[0.0732] det_loss_box_dn_4[0.1623] det_loss_cls_dn_5[0.0725] det_loss_box_dn_5[0.1594] loss_depth[0.0334] total_loss[6.6119]
Rank[0/8] 06/20/2025 14:25:48 INFO stats.py:314 | Epoch[184] Step[364] GlobalStep[72124] Training Speed: 8.64 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 7:12:47.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:25:58 INFO loss_tracker.py:84 | Epoch[184/239] Step[374] GlobalStep[72134/NA]: det_loss_cls_0[0.4095] det_loss_box_0[0.5865] det_loss_cls_1[0.4039] det_loss_box_1[0.4556] det_loss_cls_2[0.3564] det_loss_box_2[0.4027] det_loss_cls_3[0.3264] det_loss_box_3[0.3891] det_loss_cls_4[0.3057] det_loss_box_4[0.3777] det_loss_cls_5[0.2948] det_loss_box_5[0.3773] det_loss_cls_dn_0[0.0921] det_loss_box_dn_0[0.3126] det_loss_cls_dn_1[0.0890] det_loss_box_dn_1[0.2582] det_loss_cls_dn_2[0.0876] det_loss_box_dn_2[0.2302] det_loss_cls_dn_3[0.0867] det_loss_box_dn_3[0.2262] det_loss_cls_dn_4[0.0857] det_loss_box_dn_4[0.2215] det_loss_cls_dn_5[0.0851] det_loss_box_dn_5[0.2196] loss_depth[0.0333] total_loss[6.7133]
Rank[0/8] 06/20/2025 14:26:13 INFO stats.py:314 | Epoch[184] Step[389] GlobalStep[72149] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 7:12:15.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:26:13 INFO stats.py:394 | Epoch[184] completed. Training Speed: 7.71 samples/sec across all devices.	Epoch Time: 404.50 sec.	Average Epoch Time: 404.50 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 7:12:15.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:26:42 INFO stats.py:314 | Epoch[185] Step[24] GlobalStep[72174] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 7:11:44.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:26:42 INFO loss_tracker.py:84 | Epoch[185/239] Step[24] GlobalStep[72174/NA]: det_loss_cls_0[0.5149] det_loss_box_0[0.6295] det_loss_cls_1[0.4984] det_loss_box_1[0.4898] det_loss_cls_2[0.4391] det_loss_box_2[0.4493] det_loss_cls_3[0.4098] det_loss_box_3[0.4307] det_loss_cls_4[0.3819] det_loss_box_4[0.4293] det_loss_cls_5[0.3648] det_loss_box_5[0.4297] det_loss_cls_dn_0[0.0873] det_loss_box_dn_0[0.2537] det_loss_cls_dn_1[0.0837] det_loss_box_dn_1[0.2083] det_loss_cls_dn_2[0.0826] det_loss_box_dn_2[0.1921] det_loss_cls_dn_3[0.0818] det_loss_box_dn_3[0.1828] det_loss_cls_dn_4[0.0807] det_loss_box_dn_4[0.1805] det_loss_cls_dn_5[0.0798] det_loss_box_dn_5[0.1773] loss_depth[0.0347] total_loss[7.1924]
Rank[0/8] 06/20/2025 14:27:07 INFO stats.py:314 | Epoch[185] Step[49] GlobalStep[72199] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 7:11:13.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:27:07 INFO loss_tracker.py:84 | Epoch[185/239] Step[49] GlobalStep[72199/NA]: det_loss_cls_0[0.4248] det_loss_box_0[0.5180] det_loss_cls_1[0.3894] det_loss_box_1[0.3962] det_loss_cls_2[0.3495] det_loss_box_2[0.3588] det_loss_cls_3[0.3188] det_loss_box_3[0.3424] det_loss_cls_4[0.2862] det_loss_box_4[0.3322] det_loss_cls_5[0.2762] det_loss_box_5[0.3317] det_loss_cls_dn_0[0.0822] det_loss_box_dn_0[0.2495] det_loss_cls_dn_1[0.0796] det_loss_box_dn_1[0.2029] det_loss_cls_dn_2[0.0782] det_loss_box_dn_2[0.1819] det_loss_cls_dn_3[0.0772] det_loss_box_dn_3[0.1775] det_loss_cls_dn_4[0.0762] det_loss_box_dn_4[0.1758] det_loss_cls_dn_5[0.0752] det_loss_box_dn_5[0.1744] loss_depth[0.0350] total_loss[5.9900]
Rank[0/8] 06/20/2025 14:27:33 INFO stats.py:314 | Epoch[185] Step[74] GlobalStep[72224] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 7:10:41.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:27:33 INFO loss_tracker.py:84 | Epoch[185/239] Step[74] GlobalStep[72224/NA]: det_loss_cls_0[0.4212] det_loss_box_0[0.5092] det_loss_cls_1[0.4267] det_loss_box_1[0.3866] det_loss_cls_2[0.3594] det_loss_box_2[0.3547] det_loss_cls_3[0.3182] det_loss_box_3[0.3402] det_loss_cls_4[0.2950] det_loss_box_4[0.3382] det_loss_cls_5[0.2852] det_loss_box_5[0.3364] det_loss_cls_dn_0[0.0838] det_loss_box_dn_0[0.2224] det_loss_cls_dn_1[0.0795] det_loss_box_dn_1[0.1843] det_loss_cls_dn_2[0.0785] det_loss_box_dn_2[0.1696] det_loss_cls_dn_3[0.0774] det_loss_box_dn_3[0.1660] det_loss_cls_dn_4[0.0765] det_loss_box_dn_4[0.1638] det_loss_cls_dn_5[0.0756] det_loss_box_dn_5[0.1612] loss_depth[0.0335] total_loss[5.9432]
Rank[0/8] 06/20/2025 14:28:00 INFO stats.py:314 | Epoch[185] Step[99] GlobalStep[72249] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 7:10:10.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:28:00 INFO loss_tracker.py:84 | Epoch[185/239] Step[99] GlobalStep[72249/NA]: det_loss_cls_0[0.5570] det_loss_box_0[0.8263] det_loss_cls_1[0.5442] det_loss_box_1[0.6594] det_loss_cls_2[0.5016] det_loss_box_2[0.5843] det_loss_cls_3[0.4650] det_loss_box_3[0.5618] det_loss_cls_4[0.4436] det_loss_box_4[0.5575] det_loss_cls_5[0.4261] det_loss_box_5[0.5609] det_loss_cls_dn_0[0.0895] det_loss_box_dn_0[0.3012] det_loss_cls_dn_1[0.0859] det_loss_box_dn_1[0.2589] det_loss_cls_dn_2[0.0851] det_loss_box_dn_2[0.2450] det_loss_cls_dn_3[0.0836] det_loss_box_dn_3[0.2401] det_loss_cls_dn_4[0.0825] det_loss_box_dn_4[0.2365] det_loss_cls_dn_5[0.0813] det_loss_box_dn_5[0.2348] loss_depth[0.0389] total_loss[8.7508]
Rank[0/8] 06/20/2025 14:28:25 INFO stats.py:314 | Epoch[185] Step[124] GlobalStep[72274] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 7:09:38.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:28:25 INFO loss_tracker.py:84 | Epoch[185/239] Step[124] GlobalStep[72274/NA]: det_loss_cls_0[0.5353] det_loss_box_0[0.6282] det_loss_cls_1[0.5134] det_loss_box_1[0.4777] det_loss_cls_2[0.4611] det_loss_box_2[0.4213] det_loss_cls_3[0.4207] det_loss_box_3[0.4135] det_loss_cls_4[0.3963] det_loss_box_4[0.4067] det_loss_cls_5[0.3783] det_loss_box_5[0.4164] det_loss_cls_dn_0[0.0981] det_loss_box_dn_0[0.2958] det_loss_cls_dn_1[0.0941] det_loss_box_dn_1[0.2334] det_loss_cls_dn_2[0.0929] det_loss_box_dn_2[0.2129] det_loss_cls_dn_3[0.0916] det_loss_box_dn_3[0.2023] det_loss_cls_dn_4[0.0907] det_loss_box_dn_4[0.1995] det_loss_cls_dn_5[0.0899] det_loss_box_dn_5[0.1991] loss_depth[0.0357] total_loss[7.4053]
Rank[0/8] 06/20/2025 14:28:51 INFO stats.py:314 | Epoch[185] Step[149] GlobalStep[72299] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 7:09:07.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:28:51 INFO loss_tracker.py:84 | Epoch[185/239] Step[149] GlobalStep[72299/NA]: det_loss_cls_0[0.3971] det_loss_box_0[0.4671] det_loss_cls_1[0.3826] det_loss_box_1[0.3613] det_loss_cls_2[0.3220] det_loss_box_2[0.3315] det_loss_cls_3[0.2917] det_loss_box_3[0.3181] det_loss_cls_4[0.2694] det_loss_box_4[0.3125] det_loss_cls_5[0.2628] det_loss_box_5[0.3094] det_loss_cls_dn_0[0.0846] det_loss_box_dn_0[0.2485] det_loss_cls_dn_1[0.0806] det_loss_box_dn_1[0.2084] det_loss_cls_dn_2[0.0798] det_loss_box_dn_2[0.1931] det_loss_cls_dn_3[0.0789] det_loss_box_dn_3[0.1895] det_loss_cls_dn_4[0.0780] det_loss_box_dn_4[0.1874] det_loss_cls_dn_5[0.0773] det_loss_box_dn_5[0.1857] loss_depth[0.0352] total_loss[5.7524]
Rank[0/8] 06/20/2025 14:29:17 INFO stats.py:314 | Epoch[185] Step[174] GlobalStep[72324] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 7:08:36.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:29:17 INFO loss_tracker.py:84 | Epoch[185/239] Step[174] GlobalStep[72324/NA]: det_loss_cls_0[0.4954] det_loss_box_0[0.6691] det_loss_cls_1[0.4705] det_loss_box_1[0.5123] det_loss_cls_2[0.4152] det_loss_box_2[0.4661] det_loss_cls_3[0.3864] det_loss_box_3[0.4437] det_loss_cls_4[0.3728] det_loss_box_4[0.4211] det_loss_cls_5[0.3602] det_loss_box_5[0.4203] det_loss_cls_dn_0[0.0912] det_loss_box_dn_0[0.3590] det_loss_cls_dn_1[0.0883] det_loss_box_dn_1[0.2949] det_loss_cls_dn_2[0.0878] det_loss_box_dn_2[0.2768] det_loss_cls_dn_3[0.0868] det_loss_box_dn_3[0.2727] det_loss_cls_dn_4[0.0861] det_loss_box_dn_4[0.2691] det_loss_cls_dn_5[0.0851] det_loss_box_dn_5[0.2624] loss_depth[0.0359] total_loss[7.7292]
Rank[0/8] 06/20/2025 14:29:43 INFO stats.py:314 | Epoch[185] Step[199] GlobalStep[72349] Training Speed: 8.68 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 7:08:04.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:29:43 INFO loss_tracker.py:84 | Epoch[185/239] Step[199] GlobalStep[72349/NA]: det_loss_cls_0[0.4728] det_loss_box_0[0.6058] det_loss_cls_1[0.4453] det_loss_box_1[0.4761] det_loss_cls_2[0.3988] det_loss_box_2[0.4263] det_loss_cls_3[0.3640] det_loss_box_3[0.4149] det_loss_cls_4[0.3460] det_loss_box_4[0.4026] det_loss_cls_5[0.3356] det_loss_box_5[0.4017] det_loss_cls_dn_0[0.0948] det_loss_box_dn_0[0.2711] det_loss_cls_dn_1[0.0912] det_loss_box_dn_1[0.2265] det_loss_cls_dn_2[0.0902] det_loss_box_dn_2[0.2107] det_loss_cls_dn_3[0.0892] det_loss_box_dn_3[0.2077] det_loss_cls_dn_4[0.0881] det_loss_box_dn_4[0.2063] det_loss_cls_dn_5[0.0873] det_loss_box_dn_5[0.2045] loss_depth[0.0329] total_loss[6.9903]
Rank[0/8] 06/20/2025 14:30:08 INFO stats.py:314 | Epoch[185] Step[224] GlobalStep[72374] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 7:07:32.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:30:08 INFO loss_tracker.py:84 | Epoch[185/239] Step[224] GlobalStep[72374/NA]: det_loss_cls_0[0.4038] det_loss_box_0[0.5080] det_loss_cls_1[0.3854] det_loss_box_1[0.3817] det_loss_cls_2[0.3271] det_loss_box_2[0.3519] det_loss_cls_3[0.2974] det_loss_box_3[0.3369] det_loss_cls_4[0.2849] det_loss_box_4[0.3245] det_loss_cls_5[0.2713] det_loss_box_5[0.3254] det_loss_cls_dn_0[0.0905] det_loss_box_dn_0[0.2617] det_loss_cls_dn_1[0.0870] det_loss_box_dn_1[0.2185] det_loss_cls_dn_2[0.0856] det_loss_box_dn_2[0.2014] det_loss_cls_dn_3[0.0842] det_loss_box_dn_3[0.1979] det_loss_cls_dn_4[0.0830] det_loss_box_dn_4[0.1955] det_loss_cls_dn_5[0.0819] det_loss_box_dn_5[0.1940] loss_depth[0.0339] total_loss[6.0135]
Rank[0/8] 06/20/2025 14:30:34 INFO stats.py:314 | Epoch[185] Step[249] GlobalStep[72399] Training Speed: 7.91 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 7:07:01.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:30:34 INFO loss_tracker.py:84 | Epoch[185/239] Step[249] GlobalStep[72399/NA]: det_loss_cls_0[0.4541] det_loss_box_0[0.5895] det_loss_cls_1[0.4318] det_loss_box_1[0.4212] det_loss_cls_2[0.3743] det_loss_box_2[0.3815] det_loss_cls_3[0.3489] det_loss_box_3[0.3577] det_loss_cls_4[0.3166] det_loss_box_4[0.3489] det_loss_cls_5[0.3081] det_loss_box_5[0.3461] det_loss_cls_dn_0[0.0950] det_loss_box_dn_0[0.2550] det_loss_cls_dn_1[0.0918] det_loss_box_dn_1[0.2107] det_loss_cls_dn_2[0.0907] det_loss_box_dn_2[0.1946] det_loss_cls_dn_3[0.0897] det_loss_box_dn_3[0.1901] det_loss_cls_dn_4[0.0885] det_loss_box_dn_4[0.1882] det_loss_cls_dn_5[0.0878] det_loss_box_dn_5[0.1838] loss_depth[0.0353] total_loss[6.4799]
Rank[0/8] 06/20/2025 14:31:00 INFO stats.py:314 | Epoch[185] Step[274] GlobalStep[72424] Training Speed: 7.73 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 7:06:29.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:31:00 INFO loss_tracker.py:84 | Epoch[185/239] Step[274] GlobalStep[72424/NA]: det_loss_cls_0[0.4921] det_loss_box_0[0.7078] det_loss_cls_1[0.4852] det_loss_box_1[0.5491] det_loss_cls_2[0.4214] det_loss_box_2[0.5008] det_loss_cls_3[0.3874] det_loss_box_3[0.4874] det_loss_cls_4[0.3632] det_loss_box_4[0.4719] det_loss_cls_5[0.3520] det_loss_box_5[0.4683] det_loss_cls_dn_0[0.0928] det_loss_box_dn_0[0.2856] det_loss_cls_dn_1[0.0894] det_loss_box_dn_1[0.2394] det_loss_cls_dn_2[0.0889] det_loss_box_dn_2[0.2202] det_loss_cls_dn_3[0.0882] det_loss_box_dn_3[0.2159] det_loss_cls_dn_4[0.0879] det_loss_box_dn_4[0.2153] det_loss_cls_dn_5[0.0871] det_loss_box_dn_5[0.2122] loss_depth[0.0372] total_loss[7.6466]
Rank[0/8] 06/20/2025 14:31:26 INFO stats.py:314 | Epoch[185] Step[299] GlobalStep[72449] Training Speed: 8.59 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 7:05:58.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:31:26 INFO loss_tracker.py:84 | Epoch[185/239] Step[299] GlobalStep[72449/NA]: det_loss_cls_0[0.3856] det_loss_box_0[0.4858] det_loss_cls_1[0.3698] det_loss_box_1[0.3854] det_loss_cls_2[0.3145] det_loss_box_2[0.3433] det_loss_cls_3[0.2959] det_loss_box_3[0.3308] det_loss_cls_4[0.2769] det_loss_box_4[0.3249] det_loss_cls_5[0.2655] det_loss_box_5[0.3241] det_loss_cls_dn_0[0.0847] det_loss_box_dn_0[0.2586] det_loss_cls_dn_1[0.0817] det_loss_box_dn_1[0.2114] det_loss_cls_dn_2[0.0806] det_loss_box_dn_2[0.1945] det_loss_cls_dn_3[0.0794] det_loss_box_dn_3[0.1890] det_loss_cls_dn_4[0.0786] det_loss_box_dn_4[0.1863] det_loss_cls_dn_5[0.0777] det_loss_box_dn_5[0.1825] loss_depth[0.0366] total_loss[5.8439]
Rank[0/8] 06/20/2025 14:31:52 INFO stats.py:314 | Epoch[185] Step[324] GlobalStep[72474] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 7:05:26.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:31:52 INFO loss_tracker.py:84 | Epoch[185/239] Step[324] GlobalStep[72474/NA]: det_loss_cls_0[0.4384] det_loss_box_0[0.6157] det_loss_cls_1[0.4375] det_loss_box_1[0.4510] det_loss_cls_2[0.3802] det_loss_box_2[0.4015] det_loss_cls_3[0.3486] det_loss_box_3[0.3850] det_loss_cls_4[0.3238] det_loss_box_4[0.3773] det_loss_cls_5[0.3143] det_loss_box_5[0.3771] det_loss_cls_dn_0[0.0912] det_loss_box_dn_0[0.2864] det_loss_cls_dn_1[0.0889] det_loss_box_dn_1[0.2337] det_loss_cls_dn_2[0.0874] det_loss_box_dn_2[0.2165] det_loss_cls_dn_3[0.0861] det_loss_box_dn_3[0.2117] det_loss_cls_dn_4[0.0858] det_loss_box_dn_4[0.2084] det_loss_cls_dn_5[0.0847] det_loss_box_dn_5[0.2066] loss_depth[0.0345] total_loss[6.7721]
Rank[0/8] 06/20/2025 14:32:18 INFO stats.py:314 | Epoch[185] Step[349] GlobalStep[72499] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 7:04:55.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:32:18 INFO loss_tracker.py:84 | Epoch[185/239] Step[349] GlobalStep[72499/NA]: det_loss_cls_0[0.5365] det_loss_box_0[0.8808] det_loss_cls_1[0.5086] det_loss_box_1[0.6614] det_loss_cls_2[0.4525] det_loss_box_2[0.6013] det_loss_cls_3[0.4281] det_loss_box_3[0.5836] det_loss_cls_4[0.4102] det_loss_box_4[0.5491] det_loss_cls_5[0.3953] det_loss_box_5[0.5580] det_loss_cls_dn_0[0.0972] det_loss_box_dn_0[0.3671] det_loss_cls_dn_1[0.0932] det_loss_box_dn_1[0.3164] det_loss_cls_dn_2[0.0922] det_loss_box_dn_2[0.2907] det_loss_cls_dn_3[0.0911] det_loss_box_dn_3[0.2872] det_loss_cls_dn_4[0.0902] det_loss_box_dn_4[0.2837] det_loss_cls_dn_5[0.0892] det_loss_box_dn_5[0.2817] loss_depth[0.0369] total_loss[8.9825]
Rank[0/8] 06/20/2025 14:32:43 INFO stats.py:314 | Epoch[185] Step[374] GlobalStep[72524] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 7:04:23.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:32:43 INFO loss_tracker.py:84 | Epoch[185/239] Step[374] GlobalStep[72524/NA]: det_loss_cls_0[0.5543] det_loss_box_0[0.7083] det_loss_cls_1[0.5501] det_loss_box_1[0.5549] det_loss_cls_2[0.4883] det_loss_box_2[0.5057] det_loss_cls_3[0.4559] det_loss_box_3[0.4879] det_loss_cls_4[0.4287] det_loss_box_4[0.4839] det_loss_cls_5[0.4145] det_loss_box_5[0.4856] det_loss_cls_dn_0[0.0915] det_loss_box_dn_0[0.2811] det_loss_cls_dn_1[0.0873] det_loss_box_dn_1[0.2316] det_loss_cls_dn_2[0.0863] det_loss_box_dn_2[0.2155] det_loss_cls_dn_3[0.0853] det_loss_box_dn_3[0.2128] det_loss_cls_dn_4[0.0846] det_loss_box_dn_4[0.2122] det_loss_cls_dn_5[0.0838] det_loss_box_dn_5[0.2097] loss_depth[0.0339] total_loss[8.0340]
Rank[0/8] 06/20/2025 14:32:59 INFO stats.py:394 | Epoch[185] completed. Training Speed: 7.70 samples/sec across all devices.	Epoch Time: 405.29 sec.	Average Epoch Time: 405.29 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 7:04:04.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:33:13 INFO stats.py:314 | Epoch[186] Step[9] GlobalStep[72549] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 7:03:53.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:33:29 INFO loss_tracker.py:84 | Epoch[186/239] Step[24] GlobalStep[72564/NA]: det_loss_cls_0[0.4178] det_loss_box_0[0.5272] det_loss_cls_1[0.4048] det_loss_box_1[0.3961] det_loss_cls_2[0.3478] det_loss_box_2[0.3629] det_loss_cls_3[0.3164] det_loss_box_3[0.3501] det_loss_cls_4[0.2879] det_loss_box_4[0.3463] det_loss_cls_5[0.2816] det_loss_box_5[0.3424] det_loss_cls_dn_0[0.0841] det_loss_box_dn_0[0.2742] det_loss_cls_dn_1[0.0805] det_loss_box_dn_1[0.2216] det_loss_cls_dn_2[0.0802] det_loss_box_dn_2[0.2012] det_loss_cls_dn_3[0.0788] det_loss_box_dn_3[0.1949] det_loss_cls_dn_4[0.0782] det_loss_box_dn_4[0.1925] det_loss_cls_dn_5[0.0772] det_loss_box_dn_5[0.1906] loss_depth[0.0368] total_loss[6.1722]
Rank[0/8] 06/20/2025 14:33:40 INFO stats.py:314 | Epoch[186] Step[34] GlobalStep[72574] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 7:03:22.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:33:55 INFO loss_tracker.py:84 | Epoch[186/239] Step[49] GlobalStep[72589/NA]: det_loss_cls_0[0.4195] det_loss_box_0[0.5325] det_loss_cls_1[0.4175] det_loss_box_1[0.4023] det_loss_cls_2[0.3518] det_loss_box_2[0.3679] det_loss_cls_3[0.3154] det_loss_box_3[0.3510] det_loss_cls_4[0.2922] det_loss_box_4[0.3448] det_loss_cls_5[0.2770] det_loss_box_5[0.3460] det_loss_cls_dn_0[0.0881] det_loss_box_dn_0[0.2536] det_loss_cls_dn_1[0.0849] det_loss_box_dn_1[0.2025] det_loss_cls_dn_2[0.0841] det_loss_box_dn_2[0.1847] det_loss_cls_dn_3[0.0837] det_loss_box_dn_3[0.1791] det_loss_cls_dn_4[0.0829] det_loss_box_dn_4[0.1774] det_loss_cls_dn_5[0.0821] det_loss_box_dn_5[0.1754] loss_depth[0.0357] total_loss[6.1320]
Rank[0/8] 06/20/2025 14:34:05 INFO stats.py:314 | Epoch[186] Step[59] GlobalStep[72599] Training Speed: 7.80 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 7:02:50.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:34:21 INFO loss_tracker.py:84 | Epoch[186/239] Step[74] GlobalStep[72614/NA]: det_loss_cls_0[0.4050] det_loss_box_0[0.4725] det_loss_cls_1[0.3936] det_loss_box_1[0.3491] det_loss_cls_2[0.3333] det_loss_box_2[0.3119] det_loss_cls_3[0.3027] det_loss_box_3[0.3049] det_loss_cls_4[0.2833] det_loss_box_4[0.2927] det_loss_cls_5[0.2756] det_loss_box_5[0.2933] det_loss_cls_dn_0[0.0906] det_loss_box_dn_0[0.2334] det_loss_cls_dn_1[0.0867] det_loss_box_dn_1[0.1854] det_loss_cls_dn_2[0.0863] det_loss_box_dn_2[0.1649] det_loss_cls_dn_3[0.0851] det_loss_box_dn_3[0.1607] det_loss_cls_dn_4[0.0843] det_loss_box_dn_4[0.1589] det_loss_cls_dn_5[0.0832] det_loss_box_dn_5[0.1571] loss_depth[0.0313] total_loss[5.6258]
Rank[0/8] 06/20/2025 14:34:31 INFO stats.py:314 | Epoch[186] Step[84] GlobalStep[72624] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 7:02:19.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:34:47 INFO loss_tracker.py:84 | Epoch[186/239] Step[99] GlobalStep[72639/NA]: det_loss_cls_0[0.4193] det_loss_box_0[0.4948] det_loss_cls_1[0.4034] det_loss_box_1[0.3620] det_loss_cls_2[0.3541] det_loss_box_2[0.3322] det_loss_cls_3[0.3160] det_loss_box_3[0.3243] det_loss_cls_4[0.3011] det_loss_box_4[0.3163] det_loss_cls_5[0.2968] det_loss_box_5[0.3131] det_loss_cls_dn_0[0.0952] det_loss_box_dn_0[0.3075] det_loss_cls_dn_1[0.0922] det_loss_box_dn_1[0.2505] det_loss_cls_dn_2[0.0909] det_loss_box_dn_2[0.2275] det_loss_cls_dn_3[0.0906] det_loss_box_dn_3[0.2237] det_loss_cls_dn_4[0.0902] det_loss_box_dn_4[0.2202] det_loss_cls_dn_5[0.0893] det_loss_box_dn_5[0.2168] loss_depth[0.0386] total_loss[6.2668]
Rank[0/8] 06/20/2025 14:34:57 INFO stats.py:314 | Epoch[186] Step[109] GlobalStep[72649] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 7:01:47.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:35:12 INFO loss_tracker.py:84 | Epoch[186/239] Step[124] GlobalStep[72664/NA]: det_loss_cls_0[0.4128] det_loss_box_0[0.5224] det_loss_cls_1[0.4015] det_loss_box_1[0.4064] det_loss_cls_2[0.3339] det_loss_box_2[0.3679] det_loss_cls_3[0.3095] det_loss_box_3[0.3582] det_loss_cls_4[0.2907] det_loss_box_4[0.3540] det_loss_cls_5[0.2766] det_loss_box_5[0.3519] det_loss_cls_dn_0[0.0892] det_loss_box_dn_0[0.2620] det_loss_cls_dn_1[0.0861] det_loss_box_dn_1[0.2147] det_loss_cls_dn_2[0.0851] det_loss_box_dn_2[0.1995] det_loss_cls_dn_3[0.0837] det_loss_box_dn_3[0.1963] det_loss_cls_dn_4[0.0830] det_loss_box_dn_4[0.1944] det_loss_cls_dn_5[0.0818] det_loss_box_dn_5[0.1928] loss_depth[0.0350] total_loss[6.1893]
Rank[0/8] 06/20/2025 14:35:23 INFO stats.py:314 | Epoch[186] Step[134] GlobalStep[72674] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 7:01:16.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:35:38 INFO loss_tracker.py:84 | Epoch[186/239] Step[149] GlobalStep[72689/NA]: det_loss_cls_0[0.4879] det_loss_box_0[0.5979] det_loss_cls_1[0.4885] det_loss_box_1[0.4578] det_loss_cls_2[0.4251] det_loss_box_2[0.4153] det_loss_cls_3[0.3910] det_loss_box_3[0.4016] det_loss_cls_4[0.3718] det_loss_box_4[0.3943] det_loss_cls_5[0.3580] det_loss_box_5[0.3944] det_loss_cls_dn_0[0.1023] det_loss_box_dn_0[0.2767] det_loss_cls_dn_1[0.0971] det_loss_box_dn_1[0.2319] det_loss_cls_dn_2[0.0963] det_loss_box_dn_2[0.2110] det_loss_cls_dn_3[0.0950] det_loss_box_dn_3[0.2073] det_loss_cls_dn_4[0.0940] det_loss_box_dn_4[0.2040] det_loss_cls_dn_5[0.0932] det_loss_box_dn_5[0.2017] loss_depth[0.0361] total_loss[7.1304]
Rank[0/8] 06/20/2025 14:35:48 INFO stats.py:314 | Epoch[186] Step[159] GlobalStep[72699] Training Speed: 6.47 samples/sec across all devices.	Average Step Time: 1.24 sec.	Estimated Remaining Time: 7:00:44.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:36:03 INFO loss_tracker.py:84 | Epoch[186/239] Step[174] GlobalStep[72714/NA]: det_loss_cls_0[0.4842] det_loss_box_0[0.6320] det_loss_cls_1[0.4741] det_loss_box_1[0.4611] det_loss_cls_2[0.4043] det_loss_box_2[0.4239] det_loss_cls_3[0.3805] det_loss_box_3[0.4045] det_loss_cls_4[0.3495] det_loss_box_4[0.3950] det_loss_cls_5[0.3379] det_loss_box_5[0.3949] det_loss_cls_dn_0[0.0931] det_loss_box_dn_0[0.2802] det_loss_cls_dn_1[0.0899] det_loss_box_dn_1[0.2257] det_loss_cls_dn_2[0.0892] det_loss_box_dn_2[0.2045] det_loss_cls_dn_3[0.0882] det_loss_box_dn_3[0.2005] det_loss_cls_dn_4[0.0877] det_loss_box_dn_4[0.1975] det_loss_cls_dn_5[0.0870] det_loss_box_dn_5[0.1943] loss_depth[0.0372] total_loss[7.0170]
Rank[0/8] 06/20/2025 14:36:14 INFO stats.py:314 | Epoch[186] Step[184] GlobalStep[72724] Training Speed: 6.96 samples/sec across all devices.	Average Step Time: 1.15 sec.	Estimated Remaining Time: 7:00:13.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:36:29 INFO loss_tracker.py:84 | Epoch[186/239] Step[199] GlobalStep[72739/NA]: det_loss_cls_0[0.4347] det_loss_box_0[0.6080] det_loss_cls_1[0.4175] det_loss_box_1[0.4754] det_loss_cls_2[0.3658] det_loss_box_2[0.4292] det_loss_cls_3[0.3391] det_loss_box_3[0.4155] det_loss_cls_4[0.3129] det_loss_box_4[0.4110] det_loss_cls_5[0.2952] det_loss_box_5[0.4104] det_loss_cls_dn_0[0.0807] det_loss_box_dn_0[0.2959] det_loss_cls_dn_1[0.0769] det_loss_box_dn_1[0.2493] det_loss_cls_dn_2[0.0759] det_loss_box_dn_2[0.2324] det_loss_cls_dn_3[0.0741] det_loss_box_dn_3[0.2286] det_loss_cls_dn_4[0.0734] det_loss_box_dn_4[0.2268] det_loss_cls_dn_5[0.0730] det_loss_box_dn_5[0.2224] loss_depth[0.0353] total_loss[6.8596]
Rank[0/8] 06/20/2025 14:36:39 INFO stats.py:314 | Epoch[186] Step[209] GlobalStep[72749] Training Speed: 9.95 samples/sec across all devices.	Average Step Time: 0.80 sec.	Estimated Remaining Time: 6:59:41.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:36:55 INFO loss_tracker.py:84 | Epoch[186/239] Step[224] GlobalStep[72764/NA]: det_loss_cls_0[0.5137] det_loss_box_0[0.6085] det_loss_cls_1[0.4958] det_loss_box_1[0.4672] det_loss_cls_2[0.4318] det_loss_box_2[0.4182] det_loss_cls_3[0.4051] det_loss_box_3[0.4022] det_loss_cls_4[0.3803] det_loss_box_4[0.3985] det_loss_cls_5[0.3674] det_loss_box_5[0.4005] det_loss_cls_dn_0[0.0866] det_loss_box_dn_0[0.2499] det_loss_cls_dn_1[0.0827] det_loss_box_dn_1[0.2064] det_loss_cls_dn_2[0.0822] det_loss_box_dn_2[0.1922] det_loss_cls_dn_3[0.0810] det_loss_box_dn_3[0.1877] det_loss_cls_dn_4[0.0803] det_loss_box_dn_4[0.1837] det_loss_cls_dn_5[0.0798] det_loss_box_dn_5[0.1814] loss_depth[0.0369] total_loss[7.0200]
Rank[0/8] 06/20/2025 14:37:05 INFO stats.py:314 | Epoch[186] Step[234] GlobalStep[72774] Training Speed: 8.00 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 6:59:10.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:37:21 INFO loss_tracker.py:84 | Epoch[186/239] Step[249] GlobalStep[72789/NA]: det_loss_cls_0[0.4242] det_loss_box_0[0.5529] det_loss_cls_1[0.4187] det_loss_box_1[0.4234] det_loss_cls_2[0.3644] det_loss_box_2[0.3909] det_loss_cls_3[0.3334] det_loss_box_3[0.3782] det_loss_cls_4[0.3111] det_loss_box_4[0.3705] det_loss_cls_5[0.3089] det_loss_box_5[0.3611] det_loss_cls_dn_0[0.0839] det_loss_box_dn_0[0.2752] det_loss_cls_dn_1[0.0804] det_loss_box_dn_1[0.2290] det_loss_cls_dn_2[0.0794] det_loss_box_dn_2[0.2155] det_loss_cls_dn_3[0.0791] det_loss_box_dn_3[0.2104] det_loss_cls_dn_4[0.0783] det_loss_box_dn_4[0.2081] det_loss_cls_dn_5[0.0775] det_loss_box_dn_5[0.2055] loss_depth[0.0341] total_loss[6.4940]
Rank[0/8] 06/20/2025 14:37:31 INFO stats.py:314 | Epoch[186] Step[259] GlobalStep[72799] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 6:58:38.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:37:47 INFO loss_tracker.py:84 | Epoch[186/239] Step[274] GlobalStep[72814/NA]: det_loss_cls_0[0.4934] det_loss_box_0[0.6590] det_loss_cls_1[0.4843] det_loss_box_1[0.5029] det_loss_cls_2[0.4272] det_loss_box_2[0.4479] det_loss_cls_3[0.3903] det_loss_box_3[0.4293] det_loss_cls_4[0.3594] det_loss_box_4[0.4193] det_loss_cls_5[0.3486] det_loss_box_5[0.4181] det_loss_cls_dn_0[0.0877] det_loss_box_dn_0[0.2731] det_loss_cls_dn_1[0.0836] det_loss_box_dn_1[0.2257] det_loss_cls_dn_2[0.0824] det_loss_box_dn_2[0.2093] det_loss_cls_dn_3[0.0811] det_loss_box_dn_3[0.2038] det_loss_cls_dn_4[0.0803] det_loss_box_dn_4[0.2032] det_loss_cls_dn_5[0.0794] det_loss_box_dn_5[0.2019] loss_depth[0.0372] total_loss[7.2285]
Rank[0/8] 06/20/2025 14:37:57 INFO stats.py:314 | Epoch[186] Step[284] GlobalStep[72824] Training Speed: 8.65 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 6:58:07.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:38:13 INFO loss_tracker.py:84 | Epoch[186/239] Step[299] GlobalStep[72839/NA]: det_loss_cls_0[0.5164] det_loss_box_0[0.6053] det_loss_cls_1[0.4967] det_loss_box_1[0.4624] det_loss_cls_2[0.4306] det_loss_box_2[0.4072] det_loss_cls_3[0.3928] det_loss_box_3[0.3935] det_loss_cls_4[0.3706] det_loss_box_4[0.3776] det_loss_cls_5[0.3578] det_loss_box_5[0.3801] det_loss_cls_dn_0[0.0957] det_loss_box_dn_0[0.2383] det_loss_cls_dn_1[0.0920] det_loss_box_dn_1[0.1959] det_loss_cls_dn_2[0.0908] det_loss_box_dn_2[0.1791] det_loss_cls_dn_3[0.0899] det_loss_box_dn_3[0.1733] det_loss_cls_dn_4[0.0891] det_loss_box_dn_4[0.1705] det_loss_cls_dn_5[0.0884] det_loss_box_dn_5[0.1689] loss_depth[0.0321] total_loss[6.8951]
Rank[0/8] 06/20/2025 14:38:23 INFO stats.py:314 | Epoch[186] Step[309] GlobalStep[72849] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 6:57:36.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:38:38 INFO loss_tracker.py:84 | Epoch[186/239] Step[324] GlobalStep[72864/NA]: det_loss_cls_0[0.3449] det_loss_box_0[0.4803] det_loss_cls_1[0.3420] det_loss_box_1[0.3787] det_loss_cls_2[0.2926] det_loss_box_2[0.3351] det_loss_cls_3[0.2646] det_loss_box_3[0.3261] det_loss_cls_4[0.2420] det_loss_box_4[0.3250] det_loss_cls_5[0.2376] det_loss_box_5[0.3228] det_loss_cls_dn_0[0.0748] det_loss_box_dn_0[0.2983] det_loss_cls_dn_1[0.0713] det_loss_box_dn_1[0.2597] det_loss_cls_dn_2[0.0700] det_loss_box_dn_2[0.2414] det_loss_cls_dn_3[0.0693] det_loss_box_dn_3[0.2340] det_loss_cls_dn_4[0.0682] det_loss_box_dn_4[0.2313] det_loss_cls_dn_5[0.0677] det_loss_box_dn_5[0.2239] loss_depth[0.0342] total_loss[5.8360]
Rank[0/8] 06/20/2025 14:38:49 INFO stats.py:314 | Epoch[186] Step[334] GlobalStep[72874] Training Speed: 7.56 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 6:57:04.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:39:04 INFO loss_tracker.py:84 | Epoch[186/239] Step[349] GlobalStep[72889/NA]: det_loss_cls_0[0.5620] det_loss_box_0[0.7075] det_loss_cls_1[0.5477] det_loss_box_1[0.5626] det_loss_cls_2[0.4812] det_loss_box_2[0.5261] det_loss_cls_3[0.4429] det_loss_box_3[0.5144] det_loss_cls_4[0.4262] det_loss_box_4[0.4938] det_loss_cls_5[0.4094] det_loss_box_5[0.4944] det_loss_cls_dn_0[0.0861] det_loss_box_dn_0[0.2604] det_loss_cls_dn_1[0.0814] det_loss_box_dn_1[0.2158] det_loss_cls_dn_2[0.0808] det_loss_box_dn_2[0.1984] det_loss_cls_dn_3[0.0791] det_loss_box_dn_3[0.1936] det_loss_cls_dn_4[0.0780] det_loss_box_dn_4[0.1918] det_loss_cls_dn_5[0.0771] det_loss_box_dn_5[0.1883] loss_depth[0.0367] total_loss[7.9356]
Rank[0/8] 06/20/2025 14:39:14 INFO stats.py:314 | Epoch[186] Step[359] GlobalStep[72899] Training Speed: 7.53 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 6:56:33.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:39:30 INFO loss_tracker.py:84 | Epoch[186/239] Step[374] GlobalStep[72914/NA]: det_loss_cls_0[0.5972] det_loss_box_0[0.7625] det_loss_cls_1[0.5951] det_loss_box_1[0.5560] det_loss_cls_2[0.5229] det_loss_box_2[0.5178] det_loss_cls_3[0.4768] det_loss_box_3[0.5005] det_loss_cls_4[0.4505] det_loss_box_4[0.4831] det_loss_cls_5[0.4346] det_loss_box_5[0.4807] det_loss_cls_dn_0[0.0918] det_loss_box_dn_0[0.2549] det_loss_cls_dn_1[0.0883] det_loss_box_dn_1[0.2173] det_loss_cls_dn_2[0.0880] det_loss_box_dn_2[0.2018] det_loss_cls_dn_3[0.0875] det_loss_box_dn_3[0.1993] det_loss_cls_dn_4[0.0869] det_loss_box_dn_4[0.1977] det_loss_cls_dn_5[0.0863] det_loss_box_dn_5[0.1969] loss_depth[0.0342] total_loss[8.2089]
Rank[0/8] 06/20/2025 14:39:40 INFO stats.py:314 | Epoch[186] Step[384] GlobalStep[72924] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 6:56:01.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:39:45 INFO stats.py:394 | Epoch[186] completed. Training Speed: 7.67 samples/sec across all devices.	Epoch Time: 406.71 sec.	Average Epoch Time: 406.71 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 6:55:55.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:40:08 INFO stats.py:314 | Epoch[187] Step[19] GlobalStep[72949] Training Speed: 8.81 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 6:55:30.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:40:13 INFO loss_tracker.py:84 | Epoch[187/239] Step[24] GlobalStep[72954/NA]: det_loss_cls_0[0.4082] det_loss_box_0[0.4843] det_loss_cls_1[0.3947] det_loss_box_1[0.3705] det_loss_cls_2[0.3368] det_loss_box_2[0.3415] det_loss_cls_3[0.3052] det_loss_box_3[0.3299] det_loss_cls_4[0.2885] det_loss_box_4[0.3146] det_loss_cls_5[0.2758] det_loss_box_5[0.3170] det_loss_cls_dn_0[0.0947] det_loss_box_dn_0[0.3060] det_loss_cls_dn_1[0.0904] det_loss_box_dn_1[0.2505] det_loss_cls_dn_2[0.0896] det_loss_box_dn_2[0.2266] det_loss_cls_dn_3[0.0883] det_loss_box_dn_3[0.2220] det_loss_cls_dn_4[0.0876] det_loss_box_dn_4[0.2180] det_loss_cls_dn_5[0.0870] det_loss_box_dn_5[0.2115] loss_depth[0.0363] total_loss[6.1752]
Rank[0/8] 06/20/2025 14:40:34 INFO stats.py:314 | Epoch[187] Step[44] GlobalStep[72974] Training Speed: 7.26 samples/sec across all devices.	Average Step Time: 1.10 sec.	Estimated Remaining Time: 6:54:59.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:40:40 INFO loss_tracker.py:84 | Epoch[187/239] Step[49] GlobalStep[72979/NA]: det_loss_cls_0[0.4674] det_loss_box_0[0.5726] det_loss_cls_1[0.4596] det_loss_box_1[0.4172] det_loss_cls_2[0.3972] det_loss_box_2[0.3730] det_loss_cls_3[0.3598] det_loss_box_3[0.3575] det_loss_cls_4[0.3375] det_loss_box_4[0.3548] det_loss_cls_5[0.3283] det_loss_box_5[0.3539] det_loss_cls_dn_0[0.0917] det_loss_box_dn_0[0.2513] det_loss_cls_dn_1[0.0881] det_loss_box_dn_1[0.2033] det_loss_cls_dn_2[0.0869] det_loss_box_dn_2[0.1873] det_loss_cls_dn_3[0.0855] det_loss_box_dn_3[0.1837] det_loss_cls_dn_4[0.0844] det_loss_box_dn_4[0.1820] det_loss_cls_dn_5[0.0834] det_loss_box_dn_5[0.1763] loss_depth[0.0351] total_loss[6.5179]
Rank[0/8] 06/20/2025 14:41:00 INFO stats.py:314 | Epoch[187] Step[69] GlobalStep[72999] Training Speed: 7.44 samples/sec across all devices.	Average Step Time: 1.08 sec.	Estimated Remaining Time: 6:54:28.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:41:06 INFO loss_tracker.py:84 | Epoch[187/239] Step[74] GlobalStep[73004/NA]: det_loss_cls_0[0.5130] det_loss_box_0[0.5676] det_loss_cls_1[0.5008] det_loss_box_1[0.4220] det_loss_cls_2[0.4252] det_loss_box_2[0.3798] det_loss_cls_3[0.3732] det_loss_box_3[0.3651] det_loss_cls_4[0.3457] det_loss_box_4[0.3585] det_loss_cls_5[0.3313] det_loss_box_5[0.3571] det_loss_cls_dn_0[0.0933] det_loss_box_dn_0[0.2426] det_loss_cls_dn_1[0.0902] det_loss_box_dn_1[0.1961] det_loss_cls_dn_2[0.0891] det_loss_box_dn_2[0.1790] det_loss_cls_dn_3[0.0879] det_loss_box_dn_3[0.1752] det_loss_cls_dn_4[0.0875] det_loss_box_dn_4[0.1715] det_loss_cls_dn_5[0.0862] det_loss_box_dn_5[0.1686] loss_depth[0.0335] total_loss[6.6401]
Rank[0/8] 06/20/2025 14:41:26 INFO stats.py:314 | Epoch[187] Step[94] GlobalStep[73024] Training Speed: 8.63 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 6:53:56.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:41:31 INFO loss_tracker.py:84 | Epoch[187/239] Step[99] GlobalStep[73029/NA]: det_loss_cls_0[0.4987] det_loss_box_0[0.6130] det_loss_cls_1[0.4696] det_loss_box_1[0.4589] det_loss_cls_2[0.3931] det_loss_box_2[0.4225] det_loss_cls_3[0.3669] det_loss_box_3[0.4015] det_loss_cls_4[0.3385] det_loss_box_4[0.3891] det_loss_cls_5[0.3289] det_loss_box_5[0.3855] det_loss_cls_dn_0[0.0889] det_loss_box_dn_0[0.2461] det_loss_cls_dn_1[0.0849] det_loss_box_dn_1[0.1977] det_loss_cls_dn_2[0.0841] det_loss_box_dn_2[0.1819] det_loss_cls_dn_3[0.0833] det_loss_box_dn_3[0.1775] det_loss_cls_dn_4[0.0825] det_loss_box_dn_4[0.1747] det_loss_cls_dn_5[0.0815] det_loss_box_dn_5[0.1718] loss_depth[0.0348] total_loss[6.7559]
Rank[0/8] 06/20/2025 14:41:52 INFO stats.py:314 | Epoch[187] Step[119] GlobalStep[73049] Training Speed: 7.71 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 6:53:25.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:41:57 INFO loss_tracker.py:84 | Epoch[187/239] Step[124] GlobalStep[73054/NA]: det_loss_cls_0[0.5268] det_loss_box_0[0.7694] det_loss_cls_1[0.5183] det_loss_box_1[0.5581] det_loss_cls_2[0.4657] det_loss_box_2[0.5020] det_loss_cls_3[0.4290] det_loss_box_3[0.4896] det_loss_cls_4[0.4069] det_loss_box_4[0.4745] det_loss_cls_5[0.3898] det_loss_box_5[0.4771] det_loss_cls_dn_0[0.0932] det_loss_box_dn_0[0.2963] det_loss_cls_dn_1[0.0897] det_loss_box_dn_1[0.2459] det_loss_cls_dn_2[0.0890] det_loss_box_dn_2[0.2250] det_loss_cls_dn_3[0.0877] det_loss_box_dn_3[0.2209] det_loss_cls_dn_4[0.0871] det_loss_box_dn_4[0.2177] det_loss_cls_dn_5[0.0861] det_loss_box_dn_5[0.2133] loss_depth[0.0377] total_loss[7.9968]
Rank[0/8] 06/20/2025 14:42:18 INFO stats.py:314 | Epoch[187] Step[144] GlobalStep[73074] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 6:52:54.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:42:23 INFO loss_tracker.py:84 | Epoch[187/239] Step[149] GlobalStep[73079/NA]: det_loss_cls_0[0.4128] det_loss_box_0[0.5221] det_loss_cls_1[0.4140] det_loss_box_1[0.3854] det_loss_cls_2[0.3571] det_loss_box_2[0.3540] det_loss_cls_3[0.3296] det_loss_box_3[0.3411] det_loss_cls_4[0.3026] det_loss_box_4[0.3369] det_loss_cls_5[0.2906] det_loss_box_5[0.3387] det_loss_cls_dn_0[0.0838] det_loss_box_dn_0[0.2394] det_loss_cls_dn_1[0.0801] det_loss_box_dn_1[0.1922] det_loss_cls_dn_2[0.0794] det_loss_box_dn_2[0.1718] det_loss_cls_dn_3[0.0786] det_loss_box_dn_3[0.1680] det_loss_cls_dn_4[0.0777] det_loss_box_dn_4[0.1663] det_loss_cls_dn_5[0.0770] det_loss_box_dn_5[0.1651] loss_depth[0.0341] total_loss[5.9984]
Rank[0/8] 06/20/2025 14:42:44 INFO stats.py:314 | Epoch[187] Step[169] GlobalStep[73099] Training Speed: 7.92 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 6:52:22.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:42:49 INFO loss_tracker.py:84 | Epoch[187/239] Step[174] GlobalStep[73104/NA]: det_loss_cls_0[0.5286] det_loss_box_0[0.6332] det_loss_cls_1[0.5142] det_loss_box_1[0.4790] det_loss_cls_2[0.4423] det_loss_box_2[0.4351] det_loss_cls_3[0.4076] det_loss_box_3[0.4200] det_loss_cls_4[0.3806] det_loss_box_4[0.4097] det_loss_cls_5[0.3609] det_loss_box_5[0.4136] det_loss_cls_dn_0[0.0876] det_loss_box_dn_0[0.2395] det_loss_cls_dn_1[0.0833] det_loss_box_dn_1[0.1958] det_loss_cls_dn_2[0.0826] det_loss_box_dn_2[0.1813] det_loss_cls_dn_3[0.0812] det_loss_box_dn_3[0.1752] det_loss_cls_dn_4[0.0802] det_loss_box_dn_4[0.1733] det_loss_cls_dn_5[0.0794] det_loss_box_dn_5[0.1720] loss_depth[0.0347] total_loss[7.0909]
Rank[0/8] 06/20/2025 14:43:10 INFO stats.py:314 | Epoch[187] Step[194] GlobalStep[73124] Training Speed: 7.93 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 6:51:51.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:43:15 INFO loss_tracker.py:84 | Epoch[187/239] Step[199] GlobalStep[73129/NA]: det_loss_cls_0[0.4962] det_loss_box_0[0.6484] det_loss_cls_1[0.4695] det_loss_box_1[0.4949] det_loss_cls_2[0.4039] det_loss_box_2[0.4429] det_loss_cls_3[0.3765] det_loss_box_3[0.4275] det_loss_cls_4[0.3532] det_loss_box_4[0.4147] det_loss_cls_5[0.3356] det_loss_box_5[0.4209] det_loss_cls_dn_0[0.0914] det_loss_box_dn_0[0.2933] det_loss_cls_dn_1[0.0870] det_loss_box_dn_1[0.2356] det_loss_cls_dn_2[0.0861] det_loss_box_dn_2[0.2166] det_loss_cls_dn_3[0.0853] det_loss_box_dn_3[0.2104] det_loss_cls_dn_4[0.0842] det_loss_box_dn_4[0.2081] det_loss_cls_dn_5[0.0836] det_loss_box_dn_5[0.2039] loss_depth[0.0353] total_loss[7.2051]
Rank[0/8] 06/20/2025 14:43:36 INFO stats.py:314 | Epoch[187] Step[219] GlobalStep[73149] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 6:51:20.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:43:41 INFO loss_tracker.py:84 | Epoch[187/239] Step[224] GlobalStep[73154/NA]: det_loss_cls_0[0.4959] det_loss_box_0[0.6312] det_loss_cls_1[0.4708] det_loss_box_1[0.4804] det_loss_cls_2[0.4158] det_loss_box_2[0.4302] det_loss_cls_3[0.3817] det_loss_box_3[0.4153] det_loss_cls_4[0.3625] det_loss_box_4[0.4014] det_loss_cls_5[0.3494] det_loss_box_5[0.4042] det_loss_cls_dn_0[0.0997] det_loss_box_dn_0[0.2981] det_loss_cls_dn_1[0.0962] det_loss_box_dn_1[0.2512] det_loss_cls_dn_2[0.0951] det_loss_box_dn_2[0.2335] det_loss_cls_dn_3[0.0940] det_loss_box_dn_3[0.2296] det_loss_cls_dn_4[0.0934] det_loss_box_dn_4[0.2268] det_loss_cls_dn_5[0.0927] det_loss_box_dn_5[0.2245] loss_depth[0.0353] total_loss[7.3089]
Rank[0/8] 06/20/2025 14:44:02 INFO stats.py:314 | Epoch[187] Step[244] GlobalStep[73174] Training Speed: 6.67 samples/sec across all devices.	Average Step Time: 1.20 sec.	Estimated Remaining Time: 6:50:48.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:44:07 INFO loss_tracker.py:84 | Epoch[187/239] Step[249] GlobalStep[73179/NA]: det_loss_cls_0[0.3484] det_loss_box_0[0.4630] det_loss_cls_1[0.3232] det_loss_box_1[0.3387] det_loss_cls_2[0.2683] det_loss_box_2[0.3053] det_loss_cls_3[0.2407] det_loss_box_3[0.2934] det_loss_cls_4[0.2209] det_loss_box_4[0.2879] det_loss_cls_5[0.2113] det_loss_box_5[0.2900] det_loss_cls_dn_0[0.0843] det_loss_box_dn_0[0.2923] det_loss_cls_dn_1[0.0806] det_loss_box_dn_1[0.2401] det_loss_cls_dn_2[0.0800] det_loss_box_dn_2[0.2194] det_loss_cls_dn_3[0.0786] det_loss_box_dn_3[0.2148] det_loss_cls_dn_4[0.0785] det_loss_box_dn_4[0.2136] det_loss_cls_dn_5[0.0772] det_loss_box_dn_5[0.2094] loss_depth[0.0391] total_loss[5.4988]
Rank[0/8] 06/20/2025 14:44:27 INFO stats.py:314 | Epoch[187] Step[269] GlobalStep[73199] Training Speed: 8.83 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 6:50:17.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:44:32 INFO loss_tracker.py:84 | Epoch[187/239] Step[274] GlobalStep[73204/NA]: det_loss_cls_0[0.5056] det_loss_box_0[0.8443] det_loss_cls_1[0.4878] det_loss_box_1[0.6691] det_loss_cls_2[0.4406] det_loss_box_2[0.6492] det_loss_cls_3[0.4112] det_loss_box_3[0.6308] det_loss_cls_4[0.3938] det_loss_box_4[0.6115] det_loss_cls_5[0.3873] det_loss_box_5[0.5925] det_loss_cls_dn_0[0.0950] det_loss_box_dn_0[0.4042] det_loss_cls_dn_1[0.0909] det_loss_box_dn_1[0.3472] det_loss_cls_dn_2[0.0899] det_loss_box_dn_2[0.3219] det_loss_cls_dn_3[0.0889] det_loss_box_dn_3[0.3153] det_loss_cls_dn_4[0.0877] det_loss_box_dn_4[0.3126] det_loss_cls_dn_5[0.0868] det_loss_box_dn_5[0.3078] loss_depth[0.0382] total_loss[9.2101]
Rank[0/8] 06/20/2025 14:44:53 INFO stats.py:314 | Epoch[187] Step[294] GlobalStep[73224] Training Speed: 5.19 samples/sec across all devices.	Average Step Time: 1.54 sec.	Estimated Remaining Time: 6:49:45.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:44:59 INFO loss_tracker.py:84 | Epoch[187/239] Step[299] GlobalStep[73229/NA]: det_loss_cls_0[0.4555] det_loss_box_0[0.5672] det_loss_cls_1[0.4279] det_loss_box_1[0.4309] det_loss_cls_2[0.3613] det_loss_box_2[0.3874] det_loss_cls_3[0.3299] det_loss_box_3[0.3826] det_loss_cls_4[0.3074] det_loss_box_4[0.3697] det_loss_cls_5[0.2948] det_loss_box_5[0.3676] det_loss_cls_dn_0[0.0885] det_loss_box_dn_0[0.2461] det_loss_cls_dn_1[0.0850] det_loss_box_dn_1[0.1957] det_loss_cls_dn_2[0.0839] det_loss_box_dn_2[0.1784] det_loss_cls_dn_3[0.0832] det_loss_box_dn_3[0.1761] det_loss_cls_dn_4[0.0822] det_loss_box_dn_4[0.1733] det_loss_cls_dn_5[0.0814] det_loss_box_dn_5[0.1724] loss_depth[0.0351] total_loss[6.3636]
Rank[0/8] 06/20/2025 14:45:19 INFO stats.py:314 | Epoch[187] Step[319] GlobalStep[73249] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 6:49:14.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:45:24 INFO loss_tracker.py:84 | Epoch[187/239] Step[324] GlobalStep[73254/NA]: det_loss_cls_0[0.5253] det_loss_box_0[0.6376] det_loss_cls_1[0.5241] det_loss_box_1[0.4557] det_loss_cls_2[0.4453] det_loss_box_2[0.4126] det_loss_cls_3[0.4099] det_loss_box_3[0.3956] det_loss_cls_4[0.3839] det_loss_box_4[0.3887] det_loss_cls_5[0.3725] det_loss_box_5[0.3850] det_loss_cls_dn_0[0.0893] det_loss_box_dn_0[0.2369] det_loss_cls_dn_1[0.0857] det_loss_box_dn_1[0.1948] det_loss_cls_dn_2[0.0847] det_loss_box_dn_2[0.1784] det_loss_cls_dn_3[0.0834] det_loss_box_dn_3[0.1728] det_loss_cls_dn_4[0.0826] det_loss_box_dn_4[0.1701] det_loss_cls_dn_5[0.0819] det_loss_box_dn_5[0.1667] loss_depth[0.0342] total_loss[6.9976]
Rank[0/8] 06/20/2025 14:45:44 INFO stats.py:314 | Epoch[187] Step[344] GlobalStep[73274] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 6:48:42.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:45:50 INFO loss_tracker.py:84 | Epoch[187/239] Step[349] GlobalStep[73279/NA]: det_loss_cls_0[0.4533] det_loss_box_0[0.5703] det_loss_cls_1[0.4274] det_loss_box_1[0.4253] det_loss_cls_2[0.3676] det_loss_box_2[0.3778] det_loss_cls_3[0.3405] det_loss_box_3[0.3622] det_loss_cls_4[0.3142] det_loss_box_4[0.3515] det_loss_cls_5[0.3042] det_loss_box_5[0.3484] det_loss_cls_dn_0[0.0775] det_loss_box_dn_0[0.2843] det_loss_cls_dn_1[0.0733] det_loss_box_dn_1[0.2260] det_loss_cls_dn_2[0.0725] det_loss_box_dn_2[0.2059] det_loss_cls_dn_3[0.0713] det_loss_box_dn_3[0.2021] det_loss_cls_dn_4[0.0707] det_loss_box_dn_4[0.1997] det_loss_cls_dn_5[0.0698] det_loss_box_dn_5[0.1982] loss_depth[0.0333] total_loss[6.4271]
Rank[0/8] 06/20/2025 14:46:10 INFO stats.py:314 | Epoch[187] Step[369] GlobalStep[73299] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 6:48:11.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:46:15 INFO loss_tracker.py:84 | Epoch[187/239] Step[374] GlobalStep[73304/NA]: det_loss_cls_0[0.4900] det_loss_box_0[0.5871] det_loss_cls_1[0.4596] det_loss_box_1[0.4539] det_loss_cls_2[0.3996] det_loss_box_2[0.3939] det_loss_cls_3[0.3657] det_loss_box_3[0.3823] det_loss_cls_4[0.3375] det_loss_box_4[0.3728] det_loss_cls_5[0.3194] det_loss_box_5[0.3770] det_loss_cls_dn_0[0.0839] det_loss_box_dn_0[0.2460] det_loss_cls_dn_1[0.0805] det_loss_box_dn_1[0.2002] det_loss_cls_dn_2[0.0793] det_loss_box_dn_2[0.1823] det_loss_cls_dn_3[0.0782] det_loss_box_dn_3[0.1787] det_loss_cls_dn_4[0.0776] det_loss_box_dn_4[0.1767] det_loss_cls_dn_5[0.0764] det_loss_box_dn_5[0.1746] loss_depth[0.0353] total_loss[6.6087]
Rank[0/8] 06/20/2025 14:46:31 INFO stats.py:394 | Epoch[187] completed. Training Speed: 7.70 samples/sec across all devices.	Epoch Time: 405.45 sec.	Average Epoch Time: 405.45 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 6:47:46.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:46:37 INFO stats.py:314 | Epoch[188] Step[4] GlobalStep[73324] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 6:47:40.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:46:58 INFO loss_tracker.py:84 | Epoch[188/239] Step[24] GlobalStep[73344/NA]: det_loss_cls_0[0.5021] det_loss_box_0[0.6236] det_loss_cls_1[0.4861] det_loss_box_1[0.4774] det_loss_cls_2[0.4206] det_loss_box_2[0.4361] det_loss_cls_3[0.3827] det_loss_box_3[0.4217] det_loss_cls_4[0.3553] det_loss_box_4[0.4124] det_loss_cls_5[0.3431] det_loss_box_5[0.4108] det_loss_cls_dn_0[0.0874] det_loss_box_dn_0[0.2460] det_loss_cls_dn_1[0.0835] det_loss_box_dn_1[0.2062] det_loss_cls_dn_2[0.0827] det_loss_box_dn_2[0.1922] det_loss_cls_dn_3[0.0810] det_loss_box_dn_3[0.1882] det_loss_cls_dn_4[0.0802] det_loss_box_dn_4[0.1854] det_loss_cls_dn_5[0.0795] det_loss_box_dn_5[0.1833] loss_depth[0.0351] total_loss[7.0026]
Rank[0/8] 06/20/2025 14:47:03 INFO stats.py:314 | Epoch[188] Step[29] GlobalStep[73349] Training Speed: 7.90 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 6:47:09.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:47:23 INFO loss_tracker.py:84 | Epoch[188/239] Step[49] GlobalStep[73369/NA]: det_loss_cls_0[0.5093] det_loss_box_0[0.5058] det_loss_cls_1[0.4861] det_loss_box_1[0.3806] det_loss_cls_2[0.4145] det_loss_box_2[0.3443] det_loss_cls_3[0.3793] det_loss_box_3[0.3329] det_loss_cls_4[0.3529] det_loss_box_4[0.3223] det_loss_cls_5[0.3405] det_loss_box_5[0.3222] det_loss_cls_dn_0[0.0823] det_loss_box_dn_0[0.2119] det_loss_cls_dn_1[0.0786] det_loss_box_dn_1[0.1642] det_loss_cls_dn_2[0.0777] det_loss_box_dn_2[0.1458] det_loss_cls_dn_3[0.0765] det_loss_box_dn_3[0.1414] det_loss_cls_dn_4[0.0755] det_loss_box_dn_4[0.1395] det_loss_cls_dn_5[0.0748] det_loss_box_dn_5[0.1383] loss_depth[0.0357] total_loss[6.1334]
Rank[0/8] 06/20/2025 14:47:29 INFO stats.py:314 | Epoch[188] Step[54] GlobalStep[73374] Training Speed: 7.86 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 6:46:37.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:47:50 INFO loss_tracker.py:84 | Epoch[188/239] Step[74] GlobalStep[73394/NA]: det_loss_cls_0[0.3586] det_loss_box_0[0.4389] det_loss_cls_1[0.3478] det_loss_box_1[0.3334] det_loss_cls_2[0.2985] det_loss_box_2[0.3065] det_loss_cls_3[0.2680] det_loss_box_3[0.3040] det_loss_cls_4[0.2463] det_loss_box_4[0.2980] det_loss_cls_5[0.2362] det_loss_box_5[0.2994] det_loss_cls_dn_0[0.0735] det_loss_box_dn_0[0.2737] det_loss_cls_dn_1[0.0682] det_loss_box_dn_1[0.2335] det_loss_cls_dn_2[0.0671] det_loss_box_dn_2[0.2168] det_loss_cls_dn_3[0.0659] det_loss_box_dn_3[0.2115] det_loss_cls_dn_4[0.0647] det_loss_box_dn_4[0.2095] det_loss_cls_dn_5[0.0633] det_loss_box_dn_5[0.2055] loss_depth[0.0340] total_loss[5.5227]
Rank[0/8] 06/20/2025 14:47:55 INFO stats.py:314 | Epoch[188] Step[79] GlobalStep[73399] Training Speed: 7.90 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 6:46:06.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:48:16 INFO loss_tracker.py:84 | Epoch[188/239] Step[99] GlobalStep[73419/NA]: det_loss_cls_0[0.5105] det_loss_box_0[0.6384] det_loss_cls_1[0.4902] det_loss_box_1[0.4936] det_loss_cls_2[0.4372] det_loss_box_2[0.4547] det_loss_cls_3[0.3974] det_loss_box_3[0.4418] det_loss_cls_4[0.3715] det_loss_box_4[0.4225] det_loss_cls_5[0.3564] det_loss_box_5[0.4225] det_loss_cls_dn_0[0.0885] det_loss_box_dn_0[0.2464] det_loss_cls_dn_1[0.0852] det_loss_box_dn_1[0.2062] det_loss_cls_dn_2[0.0844] det_loss_box_dn_2[0.1898] det_loss_cls_dn_3[0.0833] det_loss_box_dn_3[0.1864] det_loss_cls_dn_4[0.0825] det_loss_box_dn_4[0.1844] det_loss_cls_dn_5[0.0817] det_loss_box_dn_5[0.1843] loss_depth[0.0331] total_loss[7.1728]
Rank[0/8] 06/20/2025 14:48:21 INFO stats.py:314 | Epoch[188] Step[104] GlobalStep[73424] Training Speed: 8.34 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 6:45:35.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:48:41 INFO loss_tracker.py:84 | Epoch[188/239] Step[124] GlobalStep[73444/NA]: det_loss_cls_0[0.3320] det_loss_box_0[0.3991] det_loss_cls_1[0.3321] det_loss_box_1[0.3050] det_loss_cls_2[0.2540] det_loss_box_2[0.2876] det_loss_cls_3[0.2284] det_loss_box_3[0.2782] det_loss_cls_4[0.2115] det_loss_box_4[0.2716] det_loss_cls_5[0.2050] det_loss_box_5[0.2701] det_loss_cls_dn_0[0.0796] det_loss_box_dn_0[0.2568] det_loss_cls_dn_1[0.0757] det_loss_box_dn_1[0.2122] det_loss_cls_dn_2[0.0749] det_loss_box_dn_2[0.1925] det_loss_cls_dn_3[0.0740] det_loss_box_dn_3[0.1895] det_loss_cls_dn_4[0.0731] det_loss_box_dn_4[0.1870] det_loss_cls_dn_5[0.0721] det_loss_box_dn_5[0.1846] loss_depth[0.0354] total_loss[5.0821]
Rank[0/8] 06/20/2025 14:48:46 INFO stats.py:314 | Epoch[188] Step[129] GlobalStep[73449] Training Speed: 8.00 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 6:45:03.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:49:07 INFO loss_tracker.py:84 | Epoch[188/239] Step[149] GlobalStep[73469/NA]: det_loss_cls_0[0.3971] det_loss_box_0[0.4880] det_loss_cls_1[0.3837] det_loss_box_1[0.3657] det_loss_cls_2[0.3416] det_loss_box_2[0.3179] det_loss_cls_3[0.3074] det_loss_box_3[0.3106] det_loss_cls_4[0.2857] det_loss_box_4[0.3047] det_loss_cls_5[0.2737] det_loss_box_5[0.3056] det_loss_cls_dn_0[0.0914] det_loss_box_dn_0[0.2334] det_loss_cls_dn_1[0.0886] det_loss_box_dn_1[0.1884] det_loss_cls_dn_2[0.0876] det_loss_box_dn_2[0.1722] det_loss_cls_dn_3[0.0868] det_loss_box_dn_3[0.1688] det_loss_cls_dn_4[0.0859] det_loss_box_dn_4[0.1669] det_loss_cls_dn_5[0.0850] det_loss_box_dn_5[0.1657] loss_depth[0.0337] total_loss[5.7362]
Rank[0/8] 06/20/2025 14:49:12 INFO stats.py:314 | Epoch[188] Step[154] GlobalStep[73474] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 6:44:32.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:49:34 INFO loss_tracker.py:84 | Epoch[188/239] Step[174] GlobalStep[73494/NA]: det_loss_cls_0[0.5301] det_loss_box_0[0.6742] det_loss_cls_1[0.5139] det_loss_box_1[0.5181] det_loss_cls_2[0.4412] det_loss_box_2[0.4646] det_loss_cls_3[0.3973] det_loss_box_3[0.4500] det_loss_cls_4[0.3771] det_loss_box_4[0.4362] det_loss_cls_5[0.3606] det_loss_box_5[0.4373] det_loss_cls_dn_0[0.0949] det_loss_box_dn_0[0.3077] det_loss_cls_dn_1[0.0899] det_loss_box_dn_1[0.2583] det_loss_cls_dn_2[0.0889] det_loss_box_dn_2[0.2401] det_loss_cls_dn_3[0.0881] det_loss_box_dn_3[0.2371] det_loss_cls_dn_4[0.0871] det_loss_box_dn_4[0.2324] det_loss_cls_dn_5[0.0861] det_loss_box_dn_5[0.2272] loss_depth[0.0341] total_loss[7.6725]
Rank[0/8] 06/20/2025 14:49:39 INFO stats.py:314 | Epoch[188] Step[179] GlobalStep[73499] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 6:44:01.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:49:59 INFO loss_tracker.py:84 | Epoch[188/239] Step[199] GlobalStep[73519/NA]: det_loss_cls_0[0.5237] det_loss_box_0[0.6199] det_loss_cls_1[0.5096] det_loss_box_1[0.4647] det_loss_cls_2[0.4606] det_loss_box_2[0.4196] det_loss_cls_3[0.4263] det_loss_box_3[0.4126] det_loss_cls_4[0.4008] det_loss_box_4[0.3976] det_loss_cls_5[0.3847] det_loss_box_5[0.3955] det_loss_cls_dn_0[0.0875] det_loss_box_dn_0[0.2667] det_loss_cls_dn_1[0.0839] det_loss_box_dn_1[0.2220] det_loss_cls_dn_2[0.0833] det_loss_box_dn_2[0.2062] det_loss_cls_dn_3[0.0820] det_loss_box_dn_3[0.2012] det_loss_cls_dn_4[0.0811] det_loss_box_dn_4[0.1988] det_loss_cls_dn_5[0.0802] det_loss_box_dn_5[0.1971] loss_depth[0.0367] total_loss[7.2422]
Rank[0/8] 06/20/2025 14:50:04 INFO stats.py:314 | Epoch[188] Step[204] GlobalStep[73524] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 6:43:29.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:50:24 INFO loss_tracker.py:84 | Epoch[188/239] Step[224] GlobalStep[73544/NA]: det_loss_cls_0[0.5328] det_loss_box_0[0.6407] det_loss_cls_1[0.5143] det_loss_box_1[0.4961] det_loss_cls_2[0.4471] det_loss_box_2[0.4569] det_loss_cls_3[0.4096] det_loss_box_3[0.4404] det_loss_cls_4[0.3839] det_loss_box_4[0.4326] det_loss_cls_5[0.3693] det_loss_box_5[0.4294] det_loss_cls_dn_0[0.1002] det_loss_box_dn_0[0.2327] det_loss_cls_dn_1[0.0970] det_loss_box_dn_1[0.1905] det_loss_cls_dn_2[0.0964] det_loss_box_dn_2[0.1777] det_loss_cls_dn_3[0.0956] det_loss_box_dn_3[0.1751] det_loss_cls_dn_4[0.0949] det_loss_box_dn_4[0.1725] det_loss_cls_dn_5[0.0942] det_loss_box_dn_5[0.1711] loss_depth[0.0329] total_loss[7.2841]
Rank[0/8] 06/20/2025 14:50:30 INFO stats.py:314 | Epoch[188] Step[229] GlobalStep[73549] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 6:42:58.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:50:51 INFO loss_tracker.py:84 | Epoch[188/239] Step[249] GlobalStep[73569/NA]: det_loss_cls_0[0.4571] det_loss_box_0[0.5838] det_loss_cls_1[0.4573] det_loss_box_1[0.4342] det_loss_cls_2[0.3966] det_loss_box_2[0.3933] det_loss_cls_3[0.3559] det_loss_box_3[0.3876] det_loss_cls_4[0.3341] det_loss_box_4[0.3707] det_loss_cls_5[0.3283] det_loss_box_5[0.3712] det_loss_cls_dn_0[0.0851] det_loss_box_dn_0[0.2699] det_loss_cls_dn_1[0.0812] det_loss_box_dn_1[0.2257] det_loss_cls_dn_2[0.0805] det_loss_box_dn_2[0.2101] det_loss_cls_dn_3[0.0795] det_loss_box_dn_3[0.2068] det_loss_cls_dn_4[0.0789] det_loss_box_dn_4[0.2040] det_loss_cls_dn_5[0.0781] det_loss_box_dn_5[0.1996] loss_depth[0.0324] total_loss[6.7018]
Rank[0/8] 06/20/2025 14:50:56 INFO stats.py:314 | Epoch[188] Step[254] GlobalStep[73574] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 6:42:27.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:51:17 INFO loss_tracker.py:84 | Epoch[188/239] Step[274] GlobalStep[73594/NA]: det_loss_cls_0[0.4224] det_loss_box_0[0.5595] det_loss_cls_1[0.3972] det_loss_box_1[0.4421] det_loss_cls_2[0.3517] det_loss_box_2[0.4036] det_loss_cls_3[0.3229] det_loss_box_3[0.3961] det_loss_cls_4[0.3056] det_loss_box_4[0.3878] det_loss_cls_5[0.2915] det_loss_box_5[0.3894] det_loss_cls_dn_0[0.0897] det_loss_box_dn_0[0.2868] det_loss_cls_dn_1[0.0861] det_loss_box_dn_1[0.2397] det_loss_cls_dn_2[0.0849] det_loss_box_dn_2[0.2245] det_loss_cls_dn_3[0.0834] det_loss_box_dn_3[0.2197] det_loss_cls_dn_4[0.0826] det_loss_box_dn_4[0.2163] det_loss_cls_dn_5[0.0819] det_loss_box_dn_5[0.2126] loss_depth[0.0376] total_loss[6.6157]
Rank[0/8] 06/20/2025 14:51:22 INFO stats.py:314 | Epoch[188] Step[279] GlobalStep[73599] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 6:41:56.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:51:42 INFO loss_tracker.py:84 | Epoch[188/239] Step[299] GlobalStep[73619/NA]: det_loss_cls_0[0.3914] det_loss_box_0[0.5037] det_loss_cls_1[0.3926] det_loss_box_1[0.3780] det_loss_cls_2[0.3349] det_loss_box_2[0.3462] det_loss_cls_3[0.3083] det_loss_box_3[0.3365] det_loss_cls_4[0.2955] det_loss_box_4[0.3261] det_loss_cls_5[0.2837] det_loss_box_5[0.3267] det_loss_cls_dn_0[0.0914] det_loss_box_dn_0[0.3092] det_loss_cls_dn_1[0.0884] det_loss_box_dn_1[0.2438] det_loss_cls_dn_2[0.0875] det_loss_box_dn_2[0.2254] det_loss_cls_dn_3[0.0868] det_loss_box_dn_3[0.2167] det_loss_cls_dn_4[0.0861] det_loss_box_dn_4[0.2116] det_loss_cls_dn_5[0.0855] det_loss_box_dn_5[0.2053] loss_depth[0.0358] total_loss[6.1972]
Rank[0/8] 06/20/2025 14:51:47 INFO stats.py:314 | Epoch[188] Step[304] GlobalStep[73624] Training Speed: 7.45 samples/sec across all devices.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 6:41:24.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:52:09 INFO loss_tracker.py:84 | Epoch[188/239] Step[324] GlobalStep[73644/NA]: det_loss_cls_0[0.4425] det_loss_box_0[0.5669] det_loss_cls_1[0.4433] det_loss_box_1[0.4304] det_loss_cls_2[0.3816] det_loss_box_2[0.3949] det_loss_cls_3[0.3503] det_loss_box_3[0.3829] det_loss_cls_4[0.3291] det_loss_box_4[0.3715] det_loss_cls_5[0.3163] det_loss_box_5[0.3723] det_loss_cls_dn_0[0.0893] det_loss_box_dn_0[0.3631] det_loss_cls_dn_1[0.0864] det_loss_box_dn_1[0.2825] det_loss_cls_dn_2[0.0857] det_loss_box_dn_2[0.2540] det_loss_cls_dn_3[0.0849] det_loss_box_dn_3[0.2468] det_loss_cls_dn_4[0.0844] det_loss_box_dn_4[0.2429] det_loss_cls_dn_5[0.0833] det_loss_box_dn_5[0.2386] loss_depth[0.0358] total_loss[6.9598]
Rank[0/8] 06/20/2025 14:52:14 INFO stats.py:314 | Epoch[188] Step[329] GlobalStep[73649] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 6:40:53.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:52:35 INFO loss_tracker.py:84 | Epoch[188/239] Step[349] GlobalStep[73669/NA]: det_loss_cls_0[0.3645] det_loss_box_0[0.4999] det_loss_cls_1[0.3551] det_loss_box_1[0.3748] det_loss_cls_2[0.3043] det_loss_box_2[0.3334] det_loss_cls_3[0.2712] det_loss_box_3[0.3218] det_loss_cls_4[0.2597] det_loss_box_4[0.3065] det_loss_cls_5[0.2502] det_loss_box_5[0.3063] det_loss_cls_dn_0[0.0830] det_loss_box_dn_0[0.3059] det_loss_cls_dn_1[0.0801] det_loss_box_dn_1[0.2458] det_loss_cls_dn_2[0.0794] det_loss_box_dn_2[0.2262] det_loss_cls_dn_3[0.0776] det_loss_box_dn_3[0.2198] det_loss_cls_dn_4[0.0774] det_loss_box_dn_4[0.2202] det_loss_cls_dn_5[0.0765] det_loss_box_dn_5[0.2133] loss_depth[0.0362] total_loss[5.8891]
Rank[0/8] 06/20/2025 14:52:40 INFO stats.py:314 | Epoch[188] Step[354] GlobalStep[73674] Training Speed: 8.05 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 6:40:22.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:53:01 INFO loss_tracker.py:84 | Epoch[188/239] Step[374] GlobalStep[73694/NA]: det_loss_cls_0[0.4036] det_loss_box_0[0.4725] det_loss_cls_1[0.3972] det_loss_box_1[0.3488] det_loss_cls_2[0.3243] det_loss_box_2[0.3274] det_loss_cls_3[0.2891] det_loss_box_3[0.3141] det_loss_cls_4[0.2665] det_loss_box_4[0.2993] det_loss_cls_5[0.2532] det_loss_box_5[0.2995] det_loss_cls_dn_0[0.0753] det_loss_box_dn_0[0.2371] det_loss_cls_dn_1[0.0719] det_loss_box_dn_1[0.1844] det_loss_cls_dn_2[0.0715] det_loss_box_dn_2[0.1672] det_loss_cls_dn_3[0.0704] det_loss_box_dn_3[0.1614] det_loss_cls_dn_4[0.0698] det_loss_box_dn_4[0.1589] det_loss_cls_dn_5[0.0685] det_loss_box_dn_5[0.1579] loss_depth[0.0339] total_loss[5.5235]
Rank[0/8] 06/20/2025 14:53:05 INFO stats.py:314 | Epoch[188] Step[379] GlobalStep[73699] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 6:39:50.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:53:15 INFO stats.py:394 | Epoch[188] completed. Training Speed: 7.72 samples/sec across all devices.	Epoch Time: 404.40 sec.	Average Epoch Time: 404.40 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 6:39:38.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:53:34 INFO stats.py:314 | Epoch[189] Step[14] GlobalStep[73724] Training Speed: 6.47 samples/sec across all devices.	Average Step Time: 1.24 sec.	Estimated Remaining Time: 6:39:20.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:53:44 INFO loss_tracker.py:84 | Epoch[189/239] Step[24] GlobalStep[73734/NA]: det_loss_cls_0[0.4238] det_loss_box_0[0.5740] det_loss_cls_1[0.4088] det_loss_box_1[0.4320] det_loss_cls_2[0.3432] det_loss_box_2[0.3876] det_loss_cls_3[0.3170] det_loss_box_3[0.3688] det_loss_cls_4[0.2973] det_loss_box_4[0.3498] det_loss_cls_5[0.2862] det_loss_box_5[0.3476] det_loss_cls_dn_0[0.0792] det_loss_box_dn_0[0.2595] det_loss_cls_dn_1[0.0755] det_loss_box_dn_1[0.2066] det_loss_cls_dn_2[0.0745] det_loss_box_dn_2[0.1882] det_loss_cls_dn_3[0.0732] det_loss_box_dn_3[0.1836] det_loss_cls_dn_4[0.0720] det_loss_box_dn_4[0.1796] det_loss_cls_dn_5[0.0710] det_loss_box_dn_5[0.1763] loss_depth[0.0366] total_loss[6.2119]
Rank[0/8] 06/20/2025 14:54:00 INFO stats.py:314 | Epoch[189] Step[39] GlobalStep[73749] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 6:38:49.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:54:11 INFO loss_tracker.py:84 | Epoch[189/239] Step[49] GlobalStep[73759/NA]: det_loss_cls_0[0.3952] det_loss_box_0[0.5223] det_loss_cls_1[0.3856] det_loss_box_1[0.3963] det_loss_cls_2[0.3317] det_loss_box_2[0.3606] det_loss_cls_3[0.2867] det_loss_box_3[0.3583] det_loss_cls_4[0.2720] det_loss_box_4[0.3464] det_loss_cls_5[0.2575] det_loss_box_5[0.3487] det_loss_cls_dn_0[0.0836] det_loss_box_dn_0[0.2698] det_loss_cls_dn_1[0.0788] det_loss_box_dn_1[0.2159] det_loss_cls_dn_2[0.0773] det_loss_box_dn_2[0.1987] det_loss_cls_dn_3[0.0764] det_loss_box_dn_3[0.1944] det_loss_cls_dn_4[0.0755] det_loss_box_dn_4[0.1924] det_loss_cls_dn_5[0.0746] det_loss_box_dn_5[0.1908] loss_depth[0.0351] total_loss[6.0246]
Rank[0/8] 06/20/2025 14:54:26 INFO stats.py:314 | Epoch[189] Step[64] GlobalStep[73774] Training Speed: 7.66 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 6:38:17.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:54:36 INFO loss_tracker.py:84 | Epoch[189/239] Step[74] GlobalStep[73784/NA]: det_loss_cls_0[0.4661] det_loss_box_0[0.6325] det_loss_cls_1[0.4515] det_loss_box_1[0.4976] det_loss_cls_2[0.3977] det_loss_box_2[0.4466] det_loss_cls_3[0.3704] det_loss_box_3[0.4433] det_loss_cls_4[0.3490] det_loss_box_4[0.4335] det_loss_cls_5[0.3350] det_loss_box_5[0.4343] det_loss_cls_dn_0[0.0874] det_loss_box_dn_0[0.3286] det_loss_cls_dn_1[0.0842] det_loss_box_dn_1[0.2747] det_loss_cls_dn_2[0.0833] det_loss_box_dn_2[0.2573] det_loss_cls_dn_3[0.0829] det_loss_box_dn_3[0.2516] det_loss_cls_dn_4[0.0822] det_loss_box_dn_4[0.2494] det_loss_cls_dn_5[0.0811] det_loss_box_dn_5[0.2425] loss_depth[0.0354] total_loss[7.3982]
Rank[0/8] 06/20/2025 14:54:51 INFO stats.py:314 | Epoch[189] Step[89] GlobalStep[73799] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 6:37:46.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:55:02 INFO loss_tracker.py:84 | Epoch[189/239] Step[99] GlobalStep[73809/NA]: det_loss_cls_0[0.4815] det_loss_box_0[0.6079] det_loss_cls_1[0.4617] det_loss_box_1[0.4701] det_loss_cls_2[0.3983] det_loss_box_2[0.4304] det_loss_cls_3[0.3674] det_loss_box_3[0.4125] det_loss_cls_4[0.3407] det_loss_box_4[0.3991] det_loss_cls_5[0.3224] det_loss_box_5[0.4041] det_loss_cls_dn_0[0.0901] det_loss_box_dn_0[0.2618] det_loss_cls_dn_1[0.0864] det_loss_box_dn_1[0.2191] det_loss_cls_dn_2[0.0853] det_loss_box_dn_2[0.2023] det_loss_cls_dn_3[0.0844] det_loss_box_dn_3[0.1978] det_loss_cls_dn_4[0.0833] det_loss_box_dn_4[0.1961] det_loss_cls_dn_5[0.0822] det_loss_box_dn_5[0.1927] loss_depth[0.0356] total_loss[6.9131]
Rank[0/8] 06/20/2025 14:55:18 INFO stats.py:314 | Epoch[189] Step[114] GlobalStep[73824] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 6:37:15.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:55:29 INFO loss_tracker.py:84 | Epoch[189/239] Step[124] GlobalStep[73834/NA]: det_loss_cls_0[0.3401] det_loss_box_0[0.4446] det_loss_cls_1[0.3350] det_loss_box_1[0.3234] det_loss_cls_2[0.2746] det_loss_box_2[0.2894] det_loss_cls_3[0.2361] det_loss_box_3[0.2823] det_loss_cls_4[0.2175] det_loss_box_4[0.2765] det_loss_cls_5[0.2032] det_loss_box_5[0.2772] det_loss_cls_dn_0[0.0788] det_loss_box_dn_0[0.2840] det_loss_cls_dn_1[0.0757] det_loss_box_dn_1[0.2306] det_loss_cls_dn_2[0.0751] det_loss_box_dn_2[0.2124] det_loss_cls_dn_3[0.0741] det_loss_box_dn_3[0.2073] det_loss_cls_dn_4[0.0737] det_loss_box_dn_4[0.2030] det_loss_cls_dn_5[0.0732] det_loss_box_dn_5[0.1976] loss_depth[0.0372] total_loss[5.3225]
Rank[0/8] 06/20/2025 14:55:44 INFO stats.py:314 | Epoch[189] Step[139] GlobalStep[73849] Training Speed: 8.17 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 6:36:44.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:55:54 INFO loss_tracker.py:84 | Epoch[189/239] Step[149] GlobalStep[73859/NA]: det_loss_cls_0[0.5286] det_loss_box_0[0.7138] det_loss_cls_1[0.5060] det_loss_box_1[0.5359] det_loss_cls_2[0.4400] det_loss_box_2[0.4892] det_loss_cls_3[0.3990] det_loss_box_3[0.4609] det_loss_cls_4[0.3706] det_loss_box_4[0.4462] det_loss_cls_5[0.3564] det_loss_box_5[0.4441] det_loss_cls_dn_0[0.0880] det_loss_box_dn_0[0.2672] det_loss_cls_dn_1[0.0839] det_loss_box_dn_1[0.2224] det_loss_cls_dn_2[0.0833] det_loss_box_dn_2[0.2049] det_loss_cls_dn_3[0.0818] det_loss_box_dn_3[0.2007] det_loss_cls_dn_4[0.0809] det_loss_box_dn_4[0.1982] det_loss_cls_dn_5[0.0799] det_loss_box_dn_5[0.1962] loss_depth[0.0351] total_loss[7.5131]
Rank[0/8] 06/20/2025 14:56:09 INFO stats.py:314 | Epoch[189] Step[164] GlobalStep[73874] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 6:36:12.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:56:20 INFO loss_tracker.py:84 | Epoch[189/239] Step[174] GlobalStep[73884/NA]: det_loss_cls_0[0.3470] det_loss_box_0[0.4150] det_loss_cls_1[0.3382] det_loss_box_1[0.3176] det_loss_cls_2[0.2789] det_loss_box_2[0.2928] det_loss_cls_3[0.2482] det_loss_box_3[0.2814] det_loss_cls_4[0.2274] det_loss_box_4[0.2748] det_loss_cls_5[0.2189] det_loss_box_5[0.2770] det_loss_cls_dn_0[0.0915] det_loss_box_dn_0[0.2887] det_loss_cls_dn_1[0.0874] det_loss_box_dn_1[0.2235] det_loss_cls_dn_2[0.0864] det_loss_box_dn_2[0.2014] det_loss_cls_dn_3[0.0854] det_loss_box_dn_3[0.1931] det_loss_cls_dn_4[0.0841] det_loss_box_dn_4[0.1896] det_loss_cls_dn_5[0.0838] det_loss_box_dn_5[0.1867] loss_depth[0.0334] total_loss[5.3521]
Rank[0/8] 06/20/2025 14:56:35 INFO stats.py:314 | Epoch[189] Step[189] GlobalStep[73899] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 6:35:41.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:56:46 INFO loss_tracker.py:84 | Epoch[189/239] Step[199] GlobalStep[73909/NA]: det_loss_cls_0[0.4409] det_loss_box_0[0.5744] det_loss_cls_1[0.4464] det_loss_box_1[0.4480] det_loss_cls_2[0.3830] det_loss_box_2[0.4202] det_loss_cls_3[0.3395] det_loss_box_3[0.4001] det_loss_cls_4[0.3128] det_loss_box_4[0.3964] det_loss_cls_5[0.3073] det_loss_box_5[0.3900] det_loss_cls_dn_0[0.0898] det_loss_box_dn_0[0.2510] det_loss_cls_dn_1[0.0855] det_loss_box_dn_1[0.2089] det_loss_cls_dn_2[0.0845] det_loss_box_dn_2[0.1962] det_loss_cls_dn_3[0.0834] det_loss_box_dn_3[0.1921] det_loss_cls_dn_4[0.0826] det_loss_box_dn_4[0.1899] det_loss_cls_dn_5[0.0815] det_loss_box_dn_5[0.1884] loss_depth[0.0324] total_loss[6.6255]
Rank[0/8] 06/20/2025 14:57:02 INFO stats.py:314 | Epoch[189] Step[214] GlobalStep[73924] Training Speed: 8.13 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 6:35:10.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:57:12 INFO loss_tracker.py:84 | Epoch[189/239] Step[224] GlobalStep[73934/NA]: det_loss_cls_0[0.5225] det_loss_box_0[0.6916] det_loss_cls_1[0.4958] det_loss_box_1[0.5024] det_loss_cls_2[0.4348] det_loss_box_2[0.4497] det_loss_cls_3[0.3893] det_loss_box_3[0.4311] det_loss_cls_4[0.3609] det_loss_box_4[0.4193] det_loss_cls_5[0.3480] det_loss_box_5[0.4197] det_loss_cls_dn_0[0.0903] det_loss_box_dn_0[0.2481] det_loss_cls_dn_1[0.0865] det_loss_box_dn_1[0.1994] det_loss_cls_dn_2[0.0856] det_loss_box_dn_2[0.1806] det_loss_cls_dn_3[0.0848] det_loss_box_dn_3[0.1773] det_loss_cls_dn_4[0.0845] det_loss_box_dn_4[0.1748] det_loss_cls_dn_5[0.0839] det_loss_box_dn_5[0.1739] loss_depth[0.0379] total_loss[7.1727]
Rank[0/8] 06/20/2025 14:57:27 INFO stats.py:314 | Epoch[189] Step[239] GlobalStep[73949] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 6:34:38.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:57:38 INFO loss_tracker.py:84 | Epoch[189/239] Step[249] GlobalStep[73959/NA]: det_loss_cls_0[0.5233] det_loss_box_0[0.6232] det_loss_cls_1[0.5021] det_loss_box_1[0.4792] det_loss_cls_2[0.4330] det_loss_box_2[0.4322] det_loss_cls_3[0.3908] det_loss_box_3[0.4222] det_loss_cls_4[0.3668] det_loss_box_4[0.4105] det_loss_cls_5[0.3549] det_loss_box_5[0.4048] det_loss_cls_dn_0[0.0993] det_loss_box_dn_0[0.2447] det_loss_cls_dn_1[0.0966] det_loss_box_dn_1[0.2084] det_loss_cls_dn_2[0.0955] det_loss_box_dn_2[0.1905] det_loss_cls_dn_3[0.0941] det_loss_box_dn_3[0.1869] det_loss_cls_dn_4[0.0935] det_loss_box_dn_4[0.1849] det_loss_cls_dn_5[0.0925] det_loss_box_dn_5[0.1829] loss_depth[0.0338] total_loss[7.1466]
Rank[0/8] 06/20/2025 14:57:53 INFO stats.py:314 | Epoch[189] Step[264] GlobalStep[73974] Training Speed: 7.64 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 6:34:07.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:58:04 INFO loss_tracker.py:84 | Epoch[189/239] Step[274] GlobalStep[73984/NA]: det_loss_cls_0[0.4025] det_loss_box_0[0.4756] det_loss_cls_1[0.3770] det_loss_box_1[0.3540] det_loss_cls_2[0.3149] det_loss_box_2[0.3271] det_loss_cls_3[0.2793] det_loss_box_3[0.3211] det_loss_cls_4[0.2651] det_loss_box_4[0.3168] det_loss_cls_5[0.2541] det_loss_box_5[0.3183] det_loss_cls_dn_0[0.0887] det_loss_box_dn_0[0.3227] det_loss_cls_dn_1[0.0854] det_loss_box_dn_1[0.2647] det_loss_cls_dn_2[0.0837] det_loss_box_dn_2[0.2393] det_loss_cls_dn_3[0.0823] det_loss_box_dn_3[0.2349] det_loss_cls_dn_4[0.0812] det_loss_box_dn_4[0.2307] det_loss_cls_dn_5[0.0803] det_loss_box_dn_5[0.2247] loss_depth[0.0362] total_loss[6.0606]
Rank[0/8] 06/20/2025 14:58:19 INFO stats.py:314 | Epoch[189] Step[289] GlobalStep[73999] Training Speed: 6.38 samples/sec across all devices.	Average Step Time: 1.25 sec.	Estimated Remaining Time: 6:33:36.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:58:30 INFO loss_tracker.py:84 | Epoch[189/239] Step[299] GlobalStep[74009/NA]: det_loss_cls_0[0.5141] det_loss_box_0[0.6260] det_loss_cls_1[0.5091] det_loss_box_1[0.4848] det_loss_cls_2[0.4417] det_loss_box_2[0.4376] det_loss_cls_3[0.4090] det_loss_box_3[0.4259] det_loss_cls_4[0.3823] det_loss_box_4[0.4212] det_loss_cls_5[0.3681] det_loss_box_5[0.4213] det_loss_cls_dn_0[0.0901] det_loss_box_dn_0[0.2731] det_loss_cls_dn_1[0.0855] det_loss_box_dn_1[0.2253] det_loss_cls_dn_2[0.0843] det_loss_box_dn_2[0.2084] det_loss_cls_dn_3[0.0830] det_loss_box_dn_3[0.2041] det_loss_cls_dn_4[0.0823] det_loss_box_dn_4[0.2009] det_loss_cls_dn_5[0.0810] det_loss_box_dn_5[0.1996] loss_depth[0.0331] total_loss[7.2917]
Rank[0/8] 06/20/2025 14:58:45 INFO stats.py:314 | Epoch[189] Step[314] GlobalStep[74024] Training Speed: 7.91 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 6:33:05.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:58:55 INFO loss_tracker.py:84 | Epoch[189/239] Step[324] GlobalStep[74034/NA]: det_loss_cls_0[0.4748] det_loss_box_0[0.5659] det_loss_cls_1[0.4576] det_loss_box_1[0.4291] det_loss_cls_2[0.3882] det_loss_box_2[0.3847] det_loss_cls_3[0.3531] det_loss_box_3[0.3695] det_loss_cls_4[0.3253] det_loss_box_4[0.3594] det_loss_cls_5[0.3133] det_loss_box_5[0.3600] det_loss_cls_dn_0[0.0916] det_loss_box_dn_0[0.2334] det_loss_cls_dn_1[0.0868] det_loss_box_dn_1[0.1937] det_loss_cls_dn_2[0.0854] det_loss_box_dn_2[0.1793] det_loss_cls_dn_3[0.0840] det_loss_box_dn_3[0.1749] det_loss_cls_dn_4[0.0831] det_loss_box_dn_4[0.1717] det_loss_cls_dn_5[0.0819] det_loss_box_dn_5[0.1703] loss_depth[0.0324] total_loss[6.4493]
Rank[0/8] 06/20/2025 14:59:11 INFO stats.py:314 | Epoch[189] Step[339] GlobalStep[74049] Training Speed: 8.59 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 6:32:33.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:59:22 INFO loss_tracker.py:84 | Epoch[189/239] Step[349] GlobalStep[74059/NA]: det_loss_cls_0[0.3942] det_loss_box_0[0.4908] det_loss_cls_1[0.3776] det_loss_box_1[0.3636] det_loss_cls_2[0.3256] det_loss_box_2[0.3319] det_loss_cls_3[0.2971] det_loss_box_3[0.3226] det_loss_cls_4[0.2804] det_loss_box_4[0.3205] det_loss_cls_5[0.2695] det_loss_box_5[0.3214] det_loss_cls_dn_0[0.0924] det_loss_box_dn_0[0.3115] det_loss_cls_dn_1[0.0883] det_loss_box_dn_1[0.2350] det_loss_cls_dn_2[0.0872] det_loss_box_dn_2[0.2181] det_loss_cls_dn_3[0.0862] det_loss_box_dn_3[0.2068] det_loss_cls_dn_4[0.0849] det_loss_box_dn_4[0.2027] det_loss_cls_dn_5[0.0837] det_loss_box_dn_5[0.1972] loss_depth[0.0342] total_loss[6.0235]
Rank[0/8] 06/20/2025 14:59:37 INFO stats.py:314 | Epoch[189] Step[364] GlobalStep[74074] Training Speed: 8.67 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 6:32:02.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 14:59:47 INFO loss_tracker.py:84 | Epoch[189/239] Step[374] GlobalStep[74084/NA]: det_loss_cls_0[0.5486] det_loss_box_0[0.6533] det_loss_cls_1[0.5275] det_loss_box_1[0.5039] det_loss_cls_2[0.4726] det_loss_box_2[0.4515] det_loss_cls_3[0.4421] det_loss_box_3[0.4369] det_loss_cls_4[0.4181] det_loss_box_4[0.4291] det_loss_cls_5[0.4173] det_loss_box_5[0.4204] det_loss_cls_dn_0[0.0829] det_loss_box_dn_0[0.2025] det_loss_cls_dn_1[0.0789] det_loss_box_dn_1[0.1649] det_loss_cls_dn_2[0.0777] det_loss_box_dn_2[0.1515] det_loss_cls_dn_3[0.0765] det_loss_box_dn_3[0.1492] det_loss_cls_dn_4[0.0756] det_loss_box_dn_4[0.1477] det_loss_cls_dn_5[0.0747] det_loss_box_dn_5[0.1451] loss_depth[0.0351] total_loss[7.1836]
Rank[0/8] 06/20/2025 15:00:03 INFO stats.py:314 | Epoch[189] Step[389] GlobalStep[74099] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 6:31:31.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:00:03 INFO trainer.py:268 | 
==================================================BEGIN EVAL==================================================
Rank[0/8] 06/20/2025 15:01:05 INFO trainer.py:275 | eval: 10
Rank[0/8] 06/20/2025 15:01:15 INFO trainer.py:275 | eval: 20
Rank[0/8] 06/20/2025 15:01:25 INFO trainer.py:275 | eval: 30
Rank[0/8] 06/20/2025 15:01:32 INFO trainer.py:275 | eval: 40
Rank[0/8] 06/20/2025 15:01:39 INFO trainer.py:275 | eval: 50
Rank[0/8] 06/20/2025 15:01:45 INFO trainer.py:275 | eval: 60
Rank[0/8] 06/20/2025 15:01:57 INFO trainer.py:275 | eval: 70
Rank[0/8] 06/20/2025 15:02:05 INFO trainer.py:275 | eval: 80
Rank[0/8] 06/20/2025 15:02:14 INFO trainer.py:275 | eval: 90
Rank[0/8] 06/20/2025 15:02:21 INFO trainer.py:275 | eval: 100
Rank[0/8] 06/20/2025 15:02:27 INFO metrics.py:428 | number of results: 824
Rank[0/8] 06/20/2025 15:06:42 INFO metrics.py:332 | eval results of overall:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| bathtub             | 0.8601  | 0.9118  | 0.7275  | 0.8088  |
| curtain             | 0.5565  | 0.8209  | 0.1718  | 0.4896  |
| toilet              | 0.8639  | 0.8905  | 0.7482  | 0.8175  |
| toilet paper        | 0.4022  | 0.5906  | 0.1530  | 0.3386  |
| bin                 | 0.6562  | 0.8715  | 0.3989  | 0.6257  |
| picture             | 0.2535  | 0.4731  | 0.0299  | 0.1358  |
| sink                | 0.6941  | 0.8436  | 0.2397  | 0.5062  |
| doorframe           | 0.3251  | 0.7443  | 0.0908  | 0.3993  |
| door                | 0.4042  | 0.7237  | 0.0682  | 0.2910  |
| towel               | 0.4062  | 0.7032  | 0.1168  | 0.2903  |
| stopcock            | 0.1151  | 0.5172  | 0.0011  | 0.0690  |
| vanity              | 0.5923  | 0.8889  | 0.4972  | 0.7778  |
| switch              | 0.0016  | 0.1316  | 0.0000  | 0.0000  |
| bar                 | 0.1208  | 0.2727  | 0.0008  | 0.0303  |
| mirror              | 0.2058  | 0.6304  | 0.0144  | 0.1739  |
| bottle              | 0.0779  | 0.3983  | 0.0047  | 0.0891  |
| rail                | 0.0235  | 0.3594  | 0.0008  | 0.0781  |
| hair dryer          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| socket              | 0.0127  | 0.2389  | 0.0000  | 0.0133  |
| bag                 | 0.2583  | 0.7027  | 0.0557  | 0.3405  |
| microwave           | 0.6219  | 0.7347  | 0.3358  | 0.4898  |
| shower              | 0.0874  | 0.2667  | 0.0114  | 0.1000  |
| rod                 | 0.0125  | 0.0250  | 0.0000  | 0.0000  |
| tissue              | 0.0231  | 0.1538  | 0.0192  | 0.0769  |
| cup                 | 0.1128  | 0.3729  | 0.0025  | 0.0508  |
| light               | 0.0672  | 0.2863  | 0.0020  | 0.0534  |
| tissue box          | 0.1127  | 0.4242  | 0.0462  | 0.2424  |
| ledge               | 0.1801  | 0.5000  | 0.0045  | 0.1667  |
| vent                | 0.0141  | 0.2083  | 0.0000  | 0.0000  |
| box                 | 0.1622  | 0.5256  | 0.0389  | 0.2241  |
| door knob           | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| paper               | 0.0321  | 0.3373  | 0.0008  | 0.0592  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stool               | 0.3274  | 0.7479  | 0.2345  | 0.5714  |
| purse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plant               | 0.3308  | 0.6928  | 0.0992  | 0.3395  |
| basket              | 0.2821  | 0.5913  | 0.1473  | 0.3478  |
| lamp                | 0.3779  | 0.6687  | 0.1101  | 0.2955  |
| soap dish           | 0.5911  | 0.8000  | 0.1778  | 0.3333  |
| faucet              | 0.0157  | 0.2340  | 0.0000  | 0.0213  |
| backpack            | 0.5819  | 0.8387  | 0.3869  | 0.6290  |
| notebook            | 0.0008  | 0.0769  | 0.0000  | 0.0000  |
| shoe                | 0.2253  | 0.4579  | 0.0180  | 0.0935  |
| window              | 0.2549  | 0.6272  | 0.0493  | 0.2425  |
| soap                | 0.1747  | 0.6667  | 0.0031  | 0.1667  |
| laptop              | 0.4710  | 0.6522  | 0.1838  | 0.3913  |
| doorway             | 0.0010  | 0.1111  | 0.0000  | 0.0000  |
| shelf               | 0.3326  | 0.7338  | 0.1604  | 0.4478  |
| bowl                | 0.1536  | 0.5217  | 0.0418  | 0.2029  |
| blanket             | 0.2739  | 0.7700  | 0.0691  | 0.4200  |
| bed                 | 0.8640  | 0.9290  | 0.6891  | 0.8402  |
| tablet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mat                 | 0.0445  | 0.2973  | 0.0090  | 0.0270  |
| hat                 | 0.1313  | 0.2222  | 0.0000  | 0.0000  |
| couch               | 0.5223  | 0.8636  | 0.4068  | 0.7521  |
| bucket              | 0.2260  | 0.7436  | 0.0444  | 0.2821  |
| plate               | 0.0366  | 0.4038  | 0.0029  | 0.0577  |
| case                | 0.1195  | 0.4859  | 0.0393  | 0.1525  |
| holder              | 0.1705  | 0.3333  | 0.1111  | 0.1111  |
| fruit               | 0.1406  | 0.4286  | 0.0769  | 0.2143  |
| cosmetics           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sunglasses          | 0.0509  | 0.5385  | 0.0019  | 0.1538  |
| decoration          | 0.0323  | 0.4571  | 0.0012  | 0.0929  |
| toothpaste          | 0.0031  | 0.2000  | 0.0000  | 0.0000  |
| keyboard            | 0.0840  | 0.2326  | 0.0016  | 0.0233  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toy                 | 0.1475  | 0.4394  | 0.0642  | 0.1818  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mouse               | 0.0053  | 0.0811  | 0.0000  | 0.0000  |
| cabinet             | 0.3116  | 0.7645  | 0.1816  | 0.5485  |
| candle              | 0.1418  | 0.3333  | 0.0010  | 0.1111  |
| hamper              | 0.5065  | 0.5455  | 0.5065  | 0.5455  |
| ball                | 0.4495  | 0.5238  | 0.4021  | 0.4762  |
| jar                 | 0.1966  | 0.4615  | 0.0902  | 0.2308  |
| carpet              | 0.1704  | 0.5119  | 0.0042  | 0.1429  |
| printer             | 0.6080  | 0.7755  | 0.4185  | 0.6327  |
| scale               | 0.2786  | 0.4545  | 0.1515  | 0.1818  |
| chair               | 0.6045  | 0.8406  | 0.4660  | 0.6722  |
| radiator            | 0.7370  | 0.8111  | 0.1818  | 0.3667  |
| blackboard          | 0.6650  | 0.8171  | 0.2550  | 0.4268  |
| computer            | 0.3648  | 0.6721  | 0.0160  | 0.1148  |
| monitor             | 0.5415  | 0.7174  | 0.0921  | 0.3043  |
| desk                | 0.5931  | 0.9326  | 0.4050  | 0.7528  |
| table               | 0.5369  | 0.8668  | 0.4435  | 0.7476  |
| plug                | 0.0421  | 0.1053  | 0.0000  | 0.0000  |
| telephone           | 0.4124  | 0.6667  | 0.1388  | 0.3000  |
| blinds              | 0.2432  | 0.4600  | 0.0104  | 0.2200  |
| clothes             | 0.2016  | 0.5796  | 0.0490  | 0.2452  |
| book                | 0.0674  | 0.3939  | 0.0049  | 0.1075  |
| projector           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| umbrella            | 0.2000  | 0.2000  | 0.2000  | 0.2000  |
| windowsill          | 0.1368  | 0.6739  | 0.0277  | 0.1957  |
| jacket              | 0.6093  | 0.7917  | 0.3596  | 0.6250  |
| stand               | 0.5835  | 0.8841  | 0.4401  | 0.7439  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| can                 | 0.0176  | 0.1667  | 0.0093  | 0.0833  |
| stairs              | 0.0715  | 0.4722  | 0.0084  | 0.1389  |
| pipe                | 0.0015  | 0.1786  | 0.0004  | 0.0357  |
| pillow              | 0.2662  | 0.5927  | 0.0440  | 0.1869  |
| dresser             | 0.6527  | 0.9074  | 0.5685  | 0.8148  |
| wardrobe            | 0.4263  | 0.7831  | 0.2770  | 0.5904  |
| tv                  | 0.6226  | 0.8317  | 0.1426  | 0.3762  |
| paper cutter        | 0.8125  | 1.0000  | 0.0000  | 0.0000  |
| container           | 0.0151  | 0.2203  | 0.0082  | 0.0678  |
| rack                | 0.2159  | 0.5000  | 0.0887  | 0.3462  |
| ottoman             | 0.1673  | 0.6111  | 0.1208  | 0.4444  |
| cushion             | 0.2208  | 0.5686  | 0.0199  | 0.1569  |
| clock               | 0.3525  | 0.7021  | 0.0198  | 0.1702  |
| calendar            | 0.0133  | 0.3333  | 0.0000  | 0.0000  |
| poster              | 0.0193  | 0.1515  | 0.0001  | 0.0152  |
| frame               | 0.0789  | 0.4694  | 0.0012  | 0.1020  |
| bench               | 0.2800  | 0.7358  | 0.2033  | 0.5094  |
| remote control      | 0.0463  | 0.2222  | 0.0010  | 0.0741  |
| furniture           | 0.0010  | 0.1667  | 0.0000  | 0.0000  |
| guitar              | 0.2535  | 0.4167  | 0.1250  | 0.1667  |
| refrigerator        | 0.7394  | 0.9000  | 0.4843  | 0.6200  |
| luggage             | 0.6984  | 1.0000  | 0.6984  | 1.0000  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine             | 0.4859  | 0.7895  | 0.2787  | 0.6316  |
| coffee maker        | 0.4269  | 0.6207  | 0.1341  | 0.3103  |
| kettle              | 0.1653  | 0.6667  | 0.0502  | 0.3333  |
| plunger             | 0.8000  | 0.8000  | 0.4944  | 0.8000  |
| counter             | 0.2136  | 0.6972  | 0.0516  | 0.2752  |
| stall               | 0.2328  | 0.9412  | 0.1068  | 0.5882  |
| window frame        | 0.0581  | 0.4343  | 0.0035  | 0.1818  |
| countertop          | 0.0165  | 0.2500  | 0.0001  | 0.0357  |
| grab bar            | 0.0052  | 0.1250  | 0.0000  | 0.0000  |
| spoon               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dish rack           | 0.5643  | 0.8000  | 0.2857  | 0.6000  |
| treadmill           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.2993  | 0.5500  | 0.0157  | 0.2000  |
| copier              | 0.9097  | 0.9167  | 0.9097  | 0.9167  |
| soap dispenser      | 0.2384  | 0.6296  | 0.0265  | 0.1111  |
| dispenser           | 0.4956  | 0.6591  | 0.2776  | 0.4091  |
| mailbox             | 0.0542  | 0.8000  | 0.0067  | 0.2000  |
| hanger              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| partition           | 0.4157  | 0.6667  | 0.1667  | 0.1667  |
| column              | 0.0265  | 0.3684  | 0.0086  | 0.1579  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| chandelier          | 0.0621  | 0.1905  | 0.0135  | 0.0952  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headphones          | 0.2000  | 0.2000  | 0.0000  | 0.0000  |
| thermostat          | 0.1667  | 0.1667  | 0.0000  | 0.0000  |
| board               | 0.1625  | 0.6935  | 0.0493  | 0.2581  |
| teapot              | 0.0166  | 0.2222  | 0.0139  | 0.1111  |
| footstool           | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| kitchenware         | 0.2763  | 0.5217  | 0.0289  | 0.1304  |
| cube                | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| drum                | 0.0096  | 0.1250  | 0.0000  | 0.0000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 0.6647  | 1.0000  | 0.5003  | 0.8333  |
| kitchen island      | 0.2476  | 0.4286  | 0.2476  | 0.4286  |
| stove               | 0.5750  | 0.8611  | 0.3173  | 0.4722  |
| screen              | 0.0633  | 0.4000  | 0.0000  | 0.0000  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| balcony             | 0.2576  | 1.0000  | 0.0000  | 0.0000  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| person              | 0.3079  | 0.6667  | 0.0215  | 0.3333  |
| fan                 | 0.5940  | 0.7969  | 0.1620  | 0.4062  |
| tube                | 0.0051  | 0.3077  | 0.0000  | 0.0000  |
| statue              | 0.1145  | 0.4737  | 0.1108  | 0.2632  |
| piano               | 0.2615  | 0.6000  | 0.2615  | 0.6000  |
| vacuum cleaner      | 0.4618  | 0.7143  | 0.0130  | 0.2857  |
| basin               | 0.0209  | 0.2353  | 0.0000  | 0.0000  |
| map                 | 0.0417  | 0.5000  | 0.0417  | 0.5000  |
| fire extinguisher   | 0.3082  | 0.6000  | 0.3000  | 0.4000  |
| roof                | 0.0008  | 0.2083  | 0.0000  | 0.0000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crib                | 0.3611  | 0.6667  | 0.3611  | 0.6667  |
| mattress            | 0.0667  | 0.3333  | 0.0000  | 0.0000  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| arch                | 0.0348  | 0.1667  | 0.0008  | 0.0667  |
| barricade           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garage door         | 0.3690  | 0.5000  | 0.3333  | 0.3333  |
| pool table          | 0.1667  | 0.3333  | 0.1667  | 0.3333  |
| heater              | 0.6372  | 0.8276  | 0.1368  | 0.3276  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| commode             | 0.5300  | 0.8654  | 0.3993  | 0.7115  |
| sign                | 0.0618  | 0.3333  | 0.0000  | 0.0000  |
| pillar              | 0.3483  | 0.6977  | 0.1902  | 0.4419  |
| water cooler        | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| toaster             | 0.4051  | 0.5000  | 0.0270  | 0.2000  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pan                 | 0.2331  | 0.4706  | 0.0228  | 0.1176  |
| pot                 | 0.1491  | 0.4815  | 0.0037  | 0.0370  |
| oven                | 0.5652  | 0.8214  | 0.1244  | 0.2857  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| brush               | 0.1616  | 0.3571  | 0.0000  | 0.0000  |
| folder              | 0.1349  | 0.2857  | 0.0714  | 0.0714  |
| device              | 0.3395  | 0.5455  | 0.0000  | 0.0000  |
| magazine            | 0.0075  | 0.4000  | 0.0000  | 0.0000  |
| crate               | 0.3791  | 0.5714  | 0.1553  | 0.2857  |
| bread               | 0.2000  | 0.2000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pack                | 0.0271  | 0.6000  | 0.0074  | 0.2667  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garbage             | 0.7090  | 0.8750  | 0.4141  | 0.6250  |
| cap                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sack                | 0.6046  | 0.8571  | 0.1527  | 0.2857  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| food                | 0.0972  | 0.1818  | 0.0972  | 0.1818  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sponge              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| roll                | 0.2864  | 0.6000  | 0.0000  | 0.0000  |
| bidet               | 0.5000  | 0.6667  | 0.5000  | 0.6667  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| player              | 0.2500  | 0.2500  | 0.2500  | 0.2500  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glove               | 0.4444  | 0.6667  | 0.0556  | 0.3333  |
| flush               | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| wine                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radio               | 0.2500  | 0.2500  | 0.2500  | 0.2500  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vase                | 0.1705  | 0.4000  | 0.0723  | 0.2800  |
| flowerpot           | 0.0384  | 0.4118  | 0.0026  | 0.1373  |
| file                | 0.0646  | 0.2353  | 0.0299  | 0.0588  |
| speaker             | 0.0341  | 0.3182  | 0.0049  | 0.0909  |
| fireplace           | 0.2158  | 0.4286  | 0.0121  | 0.1786  |
| air conditioner     | 0.6000  | 0.6000  | 0.1000  | 0.2000  |
| conducting wire     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| price tag           | 0.1454  | 0.2857  | 0.0000  | 0.0000  |
| napkins             | 0.6667  | 0.6667  | 0.0000  | 0.0000  |
| humidifier          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| drawer              | 0.3152  | 0.6667  | 0.1995  | 0.5000  |
| album               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| letter              | 0.3214  | 0.7500  | 0.0000  | 0.0000  |
| clothes dryer       | 0.4307  | 0.8000  | 0.3397  | 0.7333  |
| shampoo             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beam                | 0.0002  | 0.0909  | 0.0000  | 0.0455  |
| toiletry            | 0.1333  | 0.3478  | 0.0000  | 0.0000  |
| washing machine     | 0.4597  | 0.7500  | 0.3326  | 0.5714  |
| body loofah         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beanbag             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| jalousie            | 1.0000  | 1.0000  | 0.5000  | 0.5000  |
| candlestick         | 0.0175  | 0.3333  | 0.0000  | 0.0000  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| broom               | 0.0222  | 0.2143  | 0.0000  | 0.0000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| steps               | 0.0027  | 0.3000  | 0.0000  | 0.0000  |
| salt                | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.2614  | 0.4000  | 0.2000  | 0.2000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water heater        | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| menu                | 0.6508  | 0.7143  | 0.1429  | 0.1429  |
| mop                 | 1.0000  | 1.0000  | 0.1667  | 0.3333  |
| range hood          | 0.3758  | 0.6429  | 0.1143  | 0.2857  |
| dumbbell            | 0.6667  | 0.6667  | 0.1667  | 0.3333  |
| ladder              | 0.1773  | 0.5882  | 0.1288  | 0.3529  |
| coil                | 0.0603  | 0.3333  | 0.0000  | 0.0000  |
| handle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washbasin           | 0.0133  | 0.3333  | 0.0047  | 0.2000  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| helmets             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| overall             | 0.2398  | 0.4314  | 0.1121  | 0.2145  |
| head                | 0.3175  | 0.6089  | 0.1464  | 0.3216  |
| common              | 0.2124  | 0.4024  | 0.1034  | 0.1957  |
| tail                | 0.1855  | 0.2694  | 0.0844  | 0.1181  |
| small               | 0.0867  | 0.2282  | 0.0122  | 0.0431  |
| medium              | 0.2531  | 0.4890  | 0.1312  | 0.2543  |
| large               | 0.1538  | 0.5336  | 0.0833  | 0.3220  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 15:06:42 INFO metrics.py:437 | number of scannet results: 250
Rank[0/8] 06/20/2025 15:07:57 INFO metrics.py:332 | eval results of scannet:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| bathtub             | 0.9753  | 1.0000  | 0.8461  | 0.8750  |
| curtain             | 0.6386  | 0.8584  | 0.2236  | 0.5044  |
| toilet              | 0.9471  | 0.9643  | 0.8614  | 0.9107  |
| toilet paper        | 0.4528  | 0.5570  | 0.1975  | 0.3418  |
| bin                 | 0.6920  | 0.8851  | 0.4431  | 0.6511  |
| picture             | 0.3559  | 0.5522  | 0.0635  | 0.1716  |
| sink                | 0.8267  | 0.8977  | 0.3266  | 0.6023  |
| doorframe           | 0.3906  | 0.7817  | 0.1133  | 0.4014  |
| door                | 0.5923  | 0.8528  | 0.1588  | 0.4314  |
| towel               | 0.5431  | 0.7686  | 0.1261  | 0.2893  |
| stopcock            | 0.1151  | 0.5172  | 0.0011  | 0.0690  |
| vanity              | 0.7699  | 0.9667  | 0.6587  | 0.8667  |
| switch              | 0.0030  | 0.1519  | 0.0000  | 0.0000  |
| bar                 | 0.1559  | 0.3182  | 0.0000  | 0.0000  |
| mirror              | 0.3314  | 0.6506  | 0.0406  | 0.1928  |
| bottle              | 0.1048  | 0.3947  | 0.0062  | 0.0947  |
| rail                | 0.0498  | 0.5000  | 0.0088  | 0.1667  |
| hair dryer          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| socket              | 0.0144  | 0.2573  | 0.0000  | 0.0146  |
| bag                 | 0.1734  | 0.7397  | 0.0756  | 0.3699  |
| microwave           | 0.6596  | 0.8000  | 0.3714  | 0.5500  |
| shower              | 0.0956  | 0.2857  | 0.0079  | 0.0714  |
| rod                 | 0.0357  | 0.0714  | 0.0000  | 0.0000  |
| tissue              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cup                 | 0.2332  | 0.4203  | 0.0082  | 0.0870  |
| light               | 0.1719  | 0.3793  | 0.0345  | 0.0345  |
| tissue box          | 0.2370  | 0.4091  | 0.1678  | 0.2727  |
| ledge               | 0.1806  | 0.5000  | 0.0046  | 0.1667  |
| box                 | 0.1180  | 0.4484  | 0.0299  | 0.1548  |
| paper               | 0.0322  | 0.3472  | 0.0007  | 0.0566  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stool               | 0.3561  | 0.7955  | 0.2379  | 0.5909  |
| purse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plant               | 0.3349  | 0.6807  | 0.0980  | 0.3445  |
| basket              | 0.0890  | 0.3077  | 0.0875  | 0.1923  |
| lamp                | 0.4653  | 0.7738  | 0.2688  | 0.4405  |
| soap dish           | 0.7585  | 0.9167  | 0.2550  | 0.4167  |
| faucet              | 0.0010  | 0.1250  | 0.0009  | 0.1250  |
| backpack            | 0.6921  | 0.9286  | 0.5741  | 0.7857  |
| notebook            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shoe                | 0.2578  | 0.5000  | 0.0394  | 0.1250  |
| window              | 0.5418  | 0.8519  | 0.1684  | 0.4383  |
| laptop              | 0.4459  | 0.5333  | 0.2215  | 0.3333  |
| shelf               | 0.5511  | 0.8235  | 0.3716  | 0.6618  |
| bowl                | 0.2481  | 0.4444  | 0.0688  | 0.2222  |
| blanket             | 0.0635  | 0.5000  | 0.0089  | 0.2500  |
| bed                 | 0.9933  | 1.0000  | 0.8705  | 0.9298  |
| mat                 | 0.1048  | 0.3000  | 0.0333  | 0.1000  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| couch               | 0.7465  | 0.9625  | 0.6084  | 0.8750  |
| bucket              | 0.1626  | 0.7143  | 0.0244  | 0.2143  |
| plate               | 0.0242  | 0.4000  | 0.0000  | 0.0000  |
| case                | 0.6987  | 1.0000  | 0.3407  | 0.6842  |
| holder              | 0.2143  | 0.4000  | 0.2000  | 0.2000  |
| fruit               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| decoration          | 0.0460  | 0.4583  | 0.0013  | 0.0833  |
| toothpaste          | 0.0036  | 0.2000  | 0.0000  | 0.0000  |
| keyboard            | 0.0984  | 0.2414  | 0.0038  | 0.0345  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toy                 | 0.0680  | 0.3529  | 0.0588  | 0.1176  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mouse               | 0.0162  | 0.1429  | 0.0000  | 0.0000  |
| cabinet             | 0.3941  | 0.7937  | 0.2101  | 0.5516  |
| hamper              | 0.5571  | 0.6000  | 0.5571  | 0.6000  |
| ball                | 0.4167  | 0.4167  | 0.3333  | 0.3333  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| carpet              | 0.0419  | 0.5000  | 0.0003  | 0.1000  |
| printer             | 0.6489  | 0.7879  | 0.6370  | 0.7273  |
| scale               | 0.2500  | 0.2500  | 0.2500  | 0.2500  |
| chair               | 0.6515  | 0.8601  | 0.4606  | 0.6568  |
| radiator            | 0.8132  | 0.8621  | 0.2556  | 0.4655  |
| blackboard          | 0.6768  | 0.8261  | 0.2788  | 0.4493  |
| computer            | 0.3389  | 0.6410  | 0.0412  | 0.1538  |
| monitor             | 0.5831  | 0.7286  | 0.1509  | 0.3571  |
| desk                | 0.7035  | 0.9747  | 0.4810  | 0.7595  |
| table               | 0.6274  | 0.9038  | 0.5385  | 0.8115  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| telephone           | 0.5595  | 0.7812  | 0.2279  | 0.3750  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes             | 0.2201  | 0.5948  | 0.0688  | 0.2810  |
| book                | 0.0627  | 0.3881  | 0.0066  | 0.1238  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| windowsill          | 0.0692  | 0.8333  | 0.0012  | 0.1667  |
| jacket              | 0.6387  | 0.8261  | 0.3776  | 0.6522  |
| stand               | 0.7496  | 0.8868  | 0.6289  | 0.8113  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stairs              | 0.0872  | 0.8000  | 0.0350  | 0.4000  |
| pipe                | 0.0001  | 0.0714  | 0.0000  | 0.0000  |
| pillow              | 0.4262  | 0.6597  | 0.1791  | 0.4375  |
| dresser             | 0.7784  | 0.9730  | 0.7316  | 0.9189  |
| wardrobe            | 0.4608  | 0.8750  | 0.3438  | 0.3750  |
| tv                  | 0.7993  | 0.9130  | 0.2747  | 0.4783  |
| paper cutter        | 0.8084  | 1.0000  | 0.0000  | 0.0000  |
| container           | 0.0056  | 0.2174  | 0.0006  | 0.0435  |
| rack                | 0.0005  | 0.0909  | 0.0005  | 0.0909  |
| ottoman             | 0.1706  | 1.0000  | 0.0344  | 0.4286  |
| cushion             | 0.0101  | 0.5000  | 0.0000  | 0.0000  |
| clock               | 0.3620  | 0.6800  | 0.0610  | 0.2000  |
| calendar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| poster              | 0.0833  | 1.0000  | 0.0000  | 0.0000  |
| frame               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bench               | 0.3836  | 1.0000  | 0.3593  | 0.8000  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| guitar              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| refrigerator        | 0.9000  | 0.9500  | 0.6923  | 0.8000  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine             | 0.5195  | 0.8750  | 0.2684  | 0.6875  |
| coffee maker        | 0.5711  | 0.7059  | 0.2502  | 0.4118  |
| kettle              | 0.1396  | 0.7500  | 0.1250  | 0.2500  |
| plunger             | 0.8000  | 0.8000  | 0.4944  | 0.8000  |
| counter             | 0.4866  | 0.8857  | 0.1843  | 0.4286  |
| stall               | 0.2331  | 0.9412  | 0.1069  | 0.5882  |
| grab bar            | 0.0052  | 0.1250  | 0.0000  | 0.0000  |
| dish rack           | 0.5625  | 0.7500  | 0.5625  | 0.7500  |
| excercise equipment | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| copier              | 0.9097  | 0.9167  | 0.9097  | 0.9167  |
| soap dispenser      | 0.2223  | 0.6190  | 0.0306  | 0.0952  |
| dispenser           | 0.5990  | 0.6923  | 0.3230  | 0.4359  |
| mailbox             | 0.0542  | 0.8000  | 0.0067  | 0.2000  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headphones          | 0.2000  | 0.2000  | 0.0000  | 0.0000  |
| board               | 0.0796  | 0.7500  | 0.0044  | 0.3438  |
| teapot              | 0.0312  | 0.2500  | 0.0000  | 0.0000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 0.6270  | 1.0000  | 0.5000  | 1.0000  |
| stove               | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| person              | 0.3083  | 0.6667  | 0.0215  | 0.3333  |
| fan                 | 0.8438  | 0.8750  | 0.0982  | 0.2500  |
| tube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vacuum cleaner      | 0.0833  | 0.6667  | 0.0238  | 0.3333  |
| basin               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| map                 | 0.0833  | 1.0000  | 0.0833  | 1.0000  |
| fire extinguisher   | 0.0071  | 0.3333  | 0.0000  | 0.0000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| barricade           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sign                | 0.0414  | 0.2857  | 0.0000  | 0.0000  |
| pillar              | 0.2000  | 0.2000  | 0.2000  | 0.2000  |
| water cooler        | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| toaster             | 0.0147  | 0.2500  | 0.0147  | 0.2500  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pot                 | 0.0625  | 0.5000  | 0.0000  | 0.0000  |
| oven                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crate               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flowerpot           | 0.0116  | 0.5000  | 0.0116  | 0.5000  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.0417  | 0.3333  | 0.0000  | 0.0000  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ladder              | 0.1500  | 0.4000  | 0.0000  | 0.0000  |
| drawer              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.6026  | 1.0000  | 0.5000  | 0.6667  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| broom               | 0.0341  | 0.2500  | 0.0000  | 0.0000  |
| range hood          | 0.7829  | 1.0000  | 0.3867  | 0.6000  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.4000  | 1.0000  | 0.4000  | 1.0000  |
| -------             | ------- | ------- | ------- | ------- |
| scannet             | 0.2658  | 0.4675  | 0.1518  | 0.2676  |
| head                | 0.3697  | 0.6329  | 0.2050  | 0.3617  |
| common              | 0.1931  | 0.3491  | 0.1111  | 0.1879  |
| tail                | 0.0798  | 0.1770  | 0.0640  | 0.1300  |
| small               | 0.0617  | 0.1904  | 0.0121  | 0.0368  |
| medium              | 0.3093  | 0.5617  | 0.1783  | 0.3325  |
| large               | 0.3115  | 0.8061  | 0.1936  | 0.5472  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 15:07:57 INFO metrics.py:437 | number of 3rscan results: 235
Rank[0/8] 06/20/2025 15:09:06 INFO metrics.py:332 | eval results of 3rscan:
+-------------------+---------+---------+---------+---------+
| classes           | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+-------------------+---------+---------+---------+---------+
| chair             | 0.6920  | 0.8407  | 0.5852  | 0.7294  |
| monitor           | 0.5588  | 0.7000  | 0.0226  | 0.1250  |
| keyboard          | 0.0856  | 0.2500  | 0.0000  | 0.0000  |
| mouse             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| desk              | 0.5583  | 0.9375  | 0.3818  | 0.7625  |
| cup               | 0.0351  | 0.3333  | 0.0000  | 0.0000  |
| case              | 0.0579  | 0.4357  | 0.0082  | 0.0857  |
| computer          | 0.4119  | 0.7619  | 0.0006  | 0.0476  |
| paper             | 0.0712  | 0.3143  | 0.0025  | 0.0714  |
| bag               | 0.3735  | 0.7172  | 0.0716  | 0.3333  |
| plug              | 0.1111  | 0.2222  | 0.0000  | 0.0000  |
| plate             | 0.0435  | 0.3939  | 0.0048  | 0.0606  |
| pen               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bin               | 0.7368  | 0.9024  | 0.3764  | 0.6463  |
| stand             | 0.5512  | 0.9344  | 0.3572  | 0.7049  |
| garbage           | 0.7277  | 0.8750  | 0.4360  | 0.6250  |
| curtain           | 0.6069  | 0.8968  | 0.1873  | 0.5079  |
| bottle            | 0.0747  | 0.3471  | 0.0029  | 0.0661  |
| backpack          | 0.5137  | 0.7879  | 0.2599  | 0.5152  |
| window            | 0.2485  | 0.7123  | 0.0250  | 0.2123  |
| wardrobe          | 0.4922  | 0.8281  | 0.3069  | 0.6562  |
| telephone         | 0.3022  | 0.5417  | 0.0510  | 0.2083  |
| table             | 0.5983  | 0.8919  | 0.4627  | 0.7477  |
| cabinet           | 0.4370  | 0.8376  | 0.2703  | 0.6581  |
| book              | 0.1101  | 0.4142  | 0.0039  | 0.0947  |
| box               | 0.2122  | 0.5577  | 0.0551  | 0.2440  |
| conducting wire   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blinds            | 0.3125  | 0.5897  | 0.0135  | 0.2821  |
| notebook          | 0.0010  | 0.0909  | 0.0000  | 0.0000  |
| socket            | 0.0004  | 0.0625  | 0.0000  | 0.0000  |
| plant             | 0.3642  | 0.6485  | 0.0956  | 0.2871  |
| picture           | 0.2007  | 0.4187  | 0.0178  | 0.1016  |
| sink              | 0.7282  | 0.8393  | 0.2000  | 0.3750  |
| clock             | 0.3957  | 0.7368  | 0.0001  | 0.0526  |
| hat               | 0.2143  | 0.2857  | 0.0000  | 0.0000  |
| shelf             | 0.3020  | 0.7491  | 0.1124  | 0.4028  |
| printer           | 0.7989  | 0.8889  | 0.2838  | 0.5556  |
| tissue box        | 0.0667  | 0.3333  | 0.0667  | 0.3333  |
| laptop            | 0.5333  | 0.8333  | 0.2667  | 0.5000  |
| basket            | 0.5013  | 0.7812  | 0.2793  | 0.4844  |
| light             | 0.1609  | 0.5114  | 0.0048  | 0.1136  |
| file              | 0.0785  | 0.2857  | 0.0363  | 0.0714  |
| jar               | 0.3192  | 0.6250  | 0.1458  | 0.2500  |
| pillow            | 0.3807  | 0.6680  | 0.0383  | 0.1680  |
| couch             | 0.5223  | 0.8571  | 0.3881  | 0.7571  |
| clothes           | 0.2817  | 0.6699  | 0.0638  | 0.2524  |
| bucket            | 0.6603  | 1.0000  | 0.1600  | 0.4000  |
| switch            | 0.0012  | 0.0943  | 0.0000  | 0.0000  |
| cushion           | 0.3738  | 0.8333  | 0.0339  | 0.2333  |
| wire              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| towel             | 0.3108  | 0.6962  | 0.1146  | 0.2658  |
| tv                | 0.7267  | 0.9429  | 0.1308  | 0.4000  |
| lamp              | 0.3436  | 0.5938  | 0.0483  | 0.1625  |
| commode           | 0.5312  | 0.8654  | 0.4001  | 0.7115  |
| frame             | 0.1488  | 0.7000  | 0.0026  | 0.1333  |
| stool             | 0.4787  | 0.8704  | 0.3544  | 0.6667  |
| decoration        | 0.1416  | 0.5000  | 0.0112  | 0.1250  |
| price tag         | 0.1454  | 0.2857  | 0.0000  | 0.0000  |
| door              | 0.3664  | 0.7132  | 0.0714  | 0.3088  |
| candle            | 0.1737  | 0.3333  | 0.0011  | 0.0667  |
| bed               | 0.9290  | 0.9818  | 0.7588  | 0.8545  |
| vase              | 0.3478  | 0.4500  | 0.1720  | 0.3000  |
| blanket           | 0.4181  | 0.8923  | 0.1148  | 0.5077  |
| bowl              | 0.2432  | 0.7037  | 0.0713  | 0.1852  |
| folder            | 0.1349  | 0.2857  | 0.0714  | 0.0714  |
| rack              | 0.4554  | 0.7333  | 0.1769  | 0.4667  |
| tablet            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| napkins           | 0.6667  | 0.6667  | 0.0000  | 0.0000  |
| carpet            | 0.2674  | 0.6429  | 0.0074  | 0.1964  |
| bench             | 0.3912  | 0.8214  | 0.2776  | 0.6071  |
| teapot            | 0.0417  | 0.2500  | 0.0417  | 0.2500  |
| remote control    | 0.0714  | 0.3000  | 0.0015  | 0.1000  |
| flowerpot         | 0.0492  | 0.3750  | 0.0005  | 0.0417  |
| bar               | 0.5556  | 0.6667  | 0.0238  | 0.3333  |
| spoon             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ottoman           | 0.5960  | 0.9091  | 0.5121  | 0.7273  |
| player            | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| cube              | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| counter           | 0.1664  | 0.7805  | 0.0078  | 0.2927  |
| toy               | 0.4422  | 0.6154  | 0.2061  | 0.3846  |
| magazine          | 0.0094  | 0.5000  | 0.0000  | 0.0000  |
| roll              | 0.2865  | 0.6000  | 0.0000  | 0.0000  |
| container         | 0.1943  | 0.6667  | 0.1481  | 0.2222  |
| rail              | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| crib              | 0.3611  | 0.6667  | 0.3611  | 0.6667  |
| poster            | 0.0237  | 0.1429  | 0.0001  | 0.0159  |
| blackboard        | 0.7239  | 0.8333  | 0.1667  | 0.3333  |
| humidifier        | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| heater            | 0.6444  | 0.8246  | 0.1376  | 0.3158  |
| windowsill        | 0.1588  | 0.6667  | 0.0338  | 0.2051  |
| fan               | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| device            | 0.3395  | 0.5455  | 0.0000  | 0.0000  |
| doorframe         | 0.2065  | 0.6800  | 0.0304  | 0.3000  |
| ball              | 0.6000  | 0.6000  | 0.6000  | 0.6000  |
| board             | 0.3342  | 0.6667  | 0.1259  | 0.2083  |
| speaker           | 0.0486  | 0.3333  | 0.0060  | 0.0833  |
| brush             | 0.2863  | 0.6250  | 0.0000  | 0.0000  |
| drawer            | 0.6895  | 0.8889  | 0.4253  | 0.6667  |
| umbrella          | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| pad               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radiator          | 0.6352  | 0.7586  | 0.0602  | 0.2069  |
| tube              | 0.0241  | 0.5714  | 0.0000  | 0.0000  |
| mirror            | 0.2549  | 0.7407  | 0.0003  | 0.1481  |
| shoe              | 0.3128  | 0.5238  | 0.0118  | 0.1190  |
| pot               | 0.4282  | 0.6667  | 0.0104  | 0.0833  |
| hanger            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stairs            | 0.3932  | 0.7143  | 0.0380  | 0.3571  |
| coffee maker      | 0.2735  | 0.6667  | 0.0122  | 0.2222  |
| bathtub           | 1.0000  | 1.0000  | 0.8642  | 0.8889  |
| air conditioner   | 0.7500  | 0.7500  | 0.1250  | 0.2500  |
| dresser           | 1.0000  | 1.0000  | 0.5000  | 0.5000  |
| guitar            | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| album             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| balcony           | 0.2576  | 1.0000  | 0.0000  | 0.0000  |
| window frame      | 0.2008  | 0.5000  | 0.0011  | 0.0833  |
| kettle            | 0.2291  | 0.7500  | 0.0644  | 0.4167  |
| calendar          | 0.0222  | 0.4000  | 0.0000  | 0.0000  |
| basin             | 0.0358  | 0.2667  | 0.0000  | 0.0000  |
| fruit             | 0.3686  | 0.6000  | 0.2000  | 0.2000  |
| refrigerator      | 0.7131  | 0.8667  | 0.6091  | 0.7333  |
| bicycle           | 1.0000  | 1.0000  | 0.8333  | 1.0000  |
| microwave         | 0.9170  | 1.0000  | 0.5055  | 0.6471  |
| piano             | 0.4167  | 0.6667  | 0.4167  | 0.6667  |
| cosmetics         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue            | 0.1556  | 0.6667  | 0.1111  | 0.3333  |
| sunglasses        | 0.0517  | 0.5385  | 0.0020  | 0.1538  |
| letter            | 0.3214  | 0.7500  | 0.0000  | 0.0000  |
| paper cutter      | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| food              | 0.1074  | 0.2000  | 0.1074  | 0.2000  |
| furniture         | 0.0069  | 0.3333  | 0.0000  | 0.0000  |
| scale             | 0.6894  | 1.0000  | 0.3333  | 0.3333  |
| clothes dryer     | 0.5277  | 0.8000  | 0.3782  | 0.7000  |
| statue            | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| shampoo           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toilet            | 0.8131  | 0.8235  | 0.5980  | 0.6471  |
| faucet            | 0.0556  | 0.3333  | 0.0000  | 0.0000  |
| shower            | 0.5919  | 1.0000  | 0.0874  | 0.5000  |
| toilet paper      | 0.2259  | 0.5000  | 0.0000  | 0.0000  |
| beam              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toiletry          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| soap              | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| pipe              | 0.0189  | 1.0000  | 0.0048  | 0.2500  |
| dispenser         | 0.0030  | 0.3333  | 0.0000  | 0.0000  |
| soap dish         | 0.1667  | 0.5000  | 0.0000  | 0.0000  |
| washing machine   | 0.4569  | 0.6429  | 0.3954  | 0.5000  |
| machine           | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| body loofah       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beanbag           | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| jalousie          | 1.0000  | 1.0000  | 0.5000  | 0.5000  |
| can               | 0.0264  | 0.2500  | 0.0139  | 0.1250  |
| column            | 0.0124  | 1.0000  | 0.0067  | 0.5000  |
| candlestick       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillar            | 0.6085  | 0.7647  | 0.4402  | 0.6471  |
| knife             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pack              | 0.0313  | 0.6923  | 0.0086  | 0.3077  |
| pan               | 0.3274  | 0.5833  | 0.0325  | 0.1667  |
| oven              | 0.7427  | 1.0000  | 0.1851  | 0.4211  |
| stove             | 0.4641  | 0.8824  | 0.0495  | 0.2941  |
| shovel            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchenware       | 0.2943  | 0.5455  | 0.0303  | 0.1364  |
| partition         | 0.4988  | 0.8000  | 0.2000  | 0.2000  |
| radio             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| sign              | 0.5909  | 1.0000  | 0.0000  | 0.0000  |
| sponge            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fireplace         | 0.2000  | 0.2000  | 0.0400  | 0.2000  |
| mattress          | 0.1000  | 0.5000  | 0.0000  | 0.0000  |
| broom             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tray              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| holder            | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| chandelier        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hook              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| map               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| screen            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glove             | 0.4444  | 0.6667  | 0.0556  | 0.3333  |
| salt              | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| bidet             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| vacuum cleaner    | 0.7500  | 0.7500  | 0.0089  | 0.2500  |
| seasoning         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crate             | 0.5471  | 0.8000  | 0.2267  | 0.4000  |
| dishwasher        | 0.9167  | 1.0000  | 0.6667  | 0.6667  |
| water heater      | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| menu              | 0.6508  | 0.7143  | 0.1429  | 0.1429  |
| mop               | 1.0000  | 1.0000  | 0.1667  | 0.3333  |
| range hood        | 0.4000  | 0.6000  | 0.0400  | 0.2000  |
| dish rack         | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| toaster           | 1.0000  | 1.0000  | 0.0625  | 0.2500  |
| bread             | 0.2000  | 0.2000  | 0.0000  | 0.0000  |
| flush             | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| dumbbell          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| sack              | 0.6046  | 0.8571  | 0.1527  | 0.2857  |
| footstool         | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| luggage           | 0.6984  | 1.0000  | 0.6984  | 1.0000  |
| tool              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| treadmill         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| soap dispenser    | 0.5033  | 0.8000  | 0.0400  | 0.2000  |
| glass             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mat               | 0.0667  | 0.3333  | 0.0000  | 0.0000  |
| ladder            | 0.4145  | 1.0000  | 0.3956  | 1.0000  |
| coil              | 0.0603  | 0.3333  | 0.0000  | 0.0000  |
| helmets           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washbasin         | 0.1000  | 1.0000  | 0.1000  | 1.0000  |
| scissors          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toothbrush        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------           | ------- | ------- | ------- | ------- |
| 3rscan            | 0.3738  | 0.5818  | 0.1613  | 0.2733  |
| head              | 0.3805  | 0.6662  | 0.1528  | 0.3199  |
| common            | 0.4207  | 0.6152  | 0.1935  | 0.2933  |
| tail              | 0.2982  | 0.3819  | 0.1334  | 0.1609  |
| small             | 0.1273  | 0.3056  | 0.0187  | 0.0503  |
| medium            | 0.3931  | 0.6519  | 0.1876  | 0.3191  |
| large             | 0.2806  | 0.7915  | 0.1152  | 0.4712  |
+-------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 15:09:06 INFO metrics.py:437 | number of matterport3d results: 339
Rank[0/8] 06/20/2025 15:10:36 INFO metrics.py:332 | eval results of matterport3d:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| picture             | 0.2313  | 0.4541  | 0.0190  | 0.1329  |
| mirror              | 0.0927  | 0.5676  | 0.0023  | 0.1622  |
| door                | 0.2893  | 0.6266  | 0.0171  | 0.1749  |
| towel               | 0.4144  | 0.6364  | 0.1398  | 0.3091  |
| doorframe           | 0.3521  | 0.7388  | 0.1059  | 0.4116  |
| sink                | 0.5441  | 0.7980  | 0.2551  | 0.4949  |
| decoration          | 0.0085  | 0.4405  | 0.0003  | 0.0833  |
| light               | 0.0399  | 0.2222  | 0.0011  | 0.0399  |
| cabinet             | 0.1401  | 0.6610  | 0.0864  | 0.4364  |
| blanket             | 0.1262  | 0.6364  | 0.0432  | 0.2727  |
| drawer              | 0.0026  | 0.5000  | 0.0014  | 0.3750  |
| plant               | 0.3232  | 0.7857  | 0.1403  | 0.4286  |
| bench               | 0.2039  | 0.5500  | 0.1495  | 0.3000  |
| lamp                | 0.3884  | 0.7033  | 0.1614  | 0.3956  |
| vase                | 0.0603  | 0.3667  | 0.0047  | 0.2667  |
| counter             | 0.0226  | 0.3939  | 0.0011  | 0.0909  |
| table               | 0.3433  | 0.7692  | 0.3059  | 0.6410  |
| case                | 0.0018  | 0.3333  | 0.0005  | 0.1111  |
| countertop          | 0.0165  | 0.2500  | 0.0001  | 0.0357  |
| window              | 0.1313  | 0.4917  | 0.0143  | 0.1667  |
| roof                | 0.0008  | 0.2083  | 0.0000  | 0.0000  |
| bathtub             | 0.6740  | 0.7778  | 0.5192  | 0.7037  |
| bottle              | 0.0746  | 0.5417  | 0.0123  | 0.1250  |
| dresser             | 0.2361  | 0.7333  | 0.1821  | 0.6000  |
| stand               | 0.4688  | 0.8200  | 0.4081  | 0.7200  |
| switch              | 0.0003  | 0.1500  | 0.0000  | 0.0000  |
| handle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flowerpot           | 0.0467  | 0.4400  | 0.0118  | 0.2000  |
| shelf               | 0.1750  | 0.6131  | 0.0573  | 0.3285  |
| frame               | 0.0003  | 0.1176  | 0.0002  | 0.0588  |
| toiletry            | 0.1022  | 0.3182  | 0.0000  | 0.0000  |
| toilet              | 0.7998  | 0.8438  | 0.7088  | 0.7812  |
| box                 | 0.0504  | 0.6154  | 0.0147  | 0.3846  |
| statue              | 0.0925  | 0.4706  | 0.0888  | 0.2353  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vent                | 0.0152  | 0.2083  | 0.0000  | 0.0000  |
| bed                 | 0.6585  | 0.8070  | 0.3956  | 0.7368  |
| desk                | 0.3279  | 0.7368  | 0.2248  | 0.6842  |
| plate               | 0.0470  | 0.4286  | 0.0002  | 0.0714  |
| pillow              | 0.0903  | 0.5016  | 0.0060  | 0.0883  |
| soap                | 0.0071  | 0.6000  | 0.0049  | 0.2000  |
| pot                 | 0.0017  | 0.3077  | 0.0000  | 0.0000  |
| couch               | 0.3476  | 0.7826  | 0.2723  | 0.6413  |
| toy                 | 0.0997  | 0.4167  | 0.0259  | 0.1389  |
| cup                 | 0.0012  | 0.2000  | 0.0000  | 0.0000  |
| vanity              | 0.2419  | 0.7333  | 0.2052  | 0.6000  |
| washbasin           | 0.0147  | 0.2857  | 0.0053  | 0.1429  |
| book                | 0.0798  | 0.3858  | 0.0158  | 0.0709  |
| window frame        | 0.0421  | 0.4253  | 0.0042  | 0.1954  |
| fan                 | 0.6073  | 0.7963  | 0.2205  | 0.4444  |
| paper               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| curtain             | 0.3904  | 0.6771  | 0.1337  | 0.4479  |
| tv                  | 0.4494  | 0.6977  | 0.1183  | 0.3023  |
| chair               | 0.4500  | 0.7755  | 0.3284  | 0.6224  |
| screen              | 0.1019  | 0.5000  | 0.0000  | 0.0000  |
| stairs              | 0.0145  | 0.3774  | 0.0037  | 0.0566  |
| bowl                | 0.1282  | 0.3939  | 0.0586  | 0.2121  |
| clock               | 0.1812  | 0.6667  | 0.1812  | 0.6667  |
| stool               | 0.0425  | 0.3333  | 0.0122  | 0.2857  |
| ottoman             | 0.0177  | 0.2778  | 0.0174  | 0.2778  |
| carpet              | 0.0004  | 0.1111  | 0.0000  | 0.0000  |
| steps               | 0.0028  | 0.3000  | 0.0000  | 0.0000  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| telephone           | 0.2575  | 0.5000  | 0.0011  | 0.2500  |
| cushion             | 0.0029  | 0.1176  | 0.0003  | 0.0588  |
| footstool           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| column              | 0.0278  | 0.3333  | 0.0089  | 0.1389  |
| bar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mat                 | 0.0247  | 0.2917  | 0.0000  | 0.0000  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit               | 0.1268  | 0.3750  | 0.1000  | 0.2500  |
| pillar              | 0.2428  | 0.7619  | 0.0559  | 0.3333  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| doorway             | 0.0010  | 0.1111  | 0.0000  | 0.0000  |
| door knob           | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| shower              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.3874  | 0.7500  | 0.0840  | 0.6250  |
| bin                 | 0.3203  | 0.7317  | 0.1860  | 0.4390  |
| dispenser           | 0.0143  | 0.5000  | 0.0143  | 0.5000  |
| rail                | 0.0316  | 0.3519  | 0.0000  | 0.0741  |
| windowsill          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| faucet              | 0.0264  | 0.2500  | 0.0000  | 0.0000  |
| thermostat          | 0.1667  | 0.1667  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toilet paper        | 0.3170  | 0.6944  | 0.1186  | 0.4444  |
| headboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball                | 0.4255  | 0.7500  | 0.4255  | 0.7500  |
| teapot              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dispenser      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| holder              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket              | 0.0052  | 0.4000  | 0.0022  | 0.2000  |
| kitchen island      | 0.2476  | 0.4286  | 0.2476  | 0.4286  |
| heater              | 0.1000  | 1.0000  | 0.1000  | 1.0000  |
| piano               | 0.2500  | 0.5000  | 0.2500  | 0.5000  |
| monitor             | 0.3333  | 0.5000  | 0.0031  | 0.2500  |
| jar                 | 0.0101  | 0.3333  | 0.0101  | 0.3333  |
| candle              | 0.0050  | 0.3333  | 0.0050  | 0.3333  |
| air conditioner     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| refrigerator        | 0.5055  | 0.8667  | 0.0974  | 0.2667  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radiator            | 0.1667  | 0.3333  | 0.0000  | 0.0000  |
| fireplace           | 0.3244  | 0.4783  | 0.0140  | 0.1739  |
| container           | 0.0042  | 0.0741  | 0.0009  | 0.0370  |
| scale               | 0.0017  | 0.2500  | 0.0000  | 0.0000  |
| wardrobe            | 0.1316  | 0.4545  | 0.1297  | 0.3636  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basin               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rack                | 0.0006  | 0.2727  | 0.0006  | 0.2727  |
| pan                 | 0.0080  | 0.2000  | 0.0000  | 0.0000  |
| pipe                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| board               | 0.1705  | 0.5000  | 0.0000  | 0.0000  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue box          | 0.1087  | 0.5000  | 0.0063  | 0.1250  |
| basket              | 0.0505  | 0.4000  | 0.0020  | 0.1600  |
| guitar              | 0.1369  | 0.4286  | 0.0000  | 0.0000  |
| pool table          | 0.1667  | 0.3333  | 0.1667  | 0.3333  |
| keyboard            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0257  | 0.3750  | 0.0057  | 0.1250  |
| food                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| projector           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bag                 | 0.0340  | 0.3846  | 0.0023  | 0.2308  |
| clothes dryer       | 0.2222  | 0.6667  | 0.2222  | 0.6667  |
| excercise equipment | 0.3418  | 0.6111  | 0.0185  | 0.2222  |
| clothes             | 0.1134  | 0.3793  | 0.0246  | 0.1379  |
| microwave           | 0.1548  | 0.2500  | 0.1071  | 0.1667  |
| kitchenware         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kettle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher   | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| ladder              | 0.1403  | 0.5556  | 0.1185  | 0.3333  |
| garage door         | 0.3690  | 0.5000  | 0.3333  | 0.3333  |
| printer             | 0.5238  | 0.5714  | 0.1905  | 0.2857  |
| arch                | 0.0348  | 0.1667  | 0.0008  | 0.0667  |
| soap dish           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beam                | 0.0002  | 0.0952  | 0.0000  | 0.0476  |
| machine             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| chandelier          | 0.0798  | 0.2105  | 0.0186  | 0.1053  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stove               | 0.2759  | 0.7000  | 0.1090  | 0.3000  |
| oven                | 0.2667  | 0.8000  | 0.0000  | 0.0000  |
| computer            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mouse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shoe                | 0.0704  | 0.2800  | 0.0000  | 0.0000  |
| laptop              | 0.8333  | 1.0000  | 0.0385  | 0.5000  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 0.3333  | 1.0000  | 0.0000  | 0.0000  |
| partition           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet               | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| socket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drum                | 0.0104  | 0.1250  | 0.0000  | 0.0000  |
| hamper              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| magazine            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hanger              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| backpack            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.0227  | 0.2500  | 0.0000  | 0.0000  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blackboard          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jacket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| player              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pack                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell            | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| candlestick         | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| water heater        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| can                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cap                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coffee maker        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| matterport3d        | 0.1191  | 0.3042  | 0.0577  | 0.1615  |
| head                | 0.1736  | 0.4503  | 0.0749  | 0.2463  |
| common              | 0.0714  | 0.2112  | 0.0440  | 0.1029  |
| tail                | 0.0691  | 0.0954  | 0.0390  | 0.0499  |
| small               | 0.0466  | 0.1726  | 0.0242  | 0.0730  |
| medium              | 0.1116  | 0.3294  | 0.0504  | 0.1658  |
| large               | 0.1051  | 0.4077  | 0.0590  | 0.2504  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 15:10:36 INFO metrics.py:437 | number of arkit results: 0
Rank[0/8] 06/20/2025 15:10:37 INFO stats.py:394 | Epoch[189] completed. Training Speed: 3.00 samples/sec across all devices.	Epoch Time: 1041.58 sec.	Average Epoch Time: 1041.58 sec.	Average Step Time: 2.67 sec.	Estimated Remaining Time: 6:34:18.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:10:37 WARNING accelerator.py:3099 | Deleting 1 checkpoints to make room for new checkpoint.
Rank[0/8] 06/20/2025 15:10:38 INFO accelerator.py:3111 | Saving current state to /job_data/checkpoints/checkpoint_18
Rank[1/8] 06/20/2025 15:10:38 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 189 to /job_data/checkpoints/checkpoint_18
Rank[2/8] 06/20/2025 15:10:38 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 189 to /job_data/checkpoints/checkpoint_18
Rank[5/8] 06/20/2025 15:10:38 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 189 to /job_data/checkpoints/checkpoint_18
Rank[4/8] 06/20/2025 15:10:38 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 189 to /job_data/checkpoints/checkpoint_18
Rank[7/8] 06/20/2025 15:10:38 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 189 to /job_data/checkpoints/checkpoint_18
Rank[6/8] 06/20/2025 15:10:38 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 189 to /job_data/checkpoints/checkpoint_18
Rank[3/8] 06/20/2025 15:10:38 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 189 to /job_data/checkpoints/checkpoint_18
Rank[0/8] 06/20/2025 15:10:39 INFO checkpointing.py:106 | Model weights saved in /job_data/checkpoints/checkpoint_18/model.safetensors
Rank[0/8] 06/20/2025 15:10:42 INFO checkpointing.py:113 | Optimizer state saved in /job_data/checkpoints/checkpoint_18/optimizer.bin
Rank[0/8] 06/20/2025 15:10:42 INFO checkpointing.py:120 | Scheduler state saved in /job_data/checkpoints/checkpoint_18/scheduler.bin
Rank[0/8] 06/20/2025 15:10:42 INFO checkpointing.py:137 | Sampler state for dataloader 0 saved in /job_data/checkpoints/checkpoint_18/sampler.bin
Rank[0/8] 06/20/2025 15:10:42 INFO checkpointing.py:137 | Sampler state for dataloader 1 saved in /job_data/checkpoints/checkpoint_18/sampler_1.bin
Rank[0/8] 06/20/2025 15:10:42 INFO checkpointing.py:164 | Random states saved in /job_data/checkpoints/checkpoint_18/random_states_0.pkl
Rank[0/8] 06/20/2025 15:10:42 INFO checkpointing.py:300 | Saving the state of TrainerProgressState to /job_data/checkpoints/checkpoint_18/custom_checkpoint_0.pkl
Rank[0/8] 06/20/2025 15:10:42 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 189 to /job_data/checkpoints/checkpoint_18
Rank[0/8] 06/20/2025 15:11:09 INFO stats.py:314 | Epoch[190] Step[24] GlobalStep[74124] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 6:33:48.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:11:09 INFO loss_tracker.py:84 | Epoch[190/239] Step[24] GlobalStep[74124/NA]: det_loss_cls_0[0.5685] det_loss_box_0[0.7380] det_loss_cls_1[0.5455] det_loss_box_1[0.5808] det_loss_cls_2[0.4827] det_loss_box_2[0.5222] det_loss_cls_3[0.4439] det_loss_box_3[0.5094] det_loss_cls_4[0.4228] det_loss_box_4[0.4956] det_loss_cls_5[0.4154] det_loss_box_5[0.4961] det_loss_cls_dn_0[0.1006] det_loss_box_dn_0[0.2671] det_loss_cls_dn_1[0.0971] det_loss_box_dn_1[0.2158] det_loss_cls_dn_2[0.0960] det_loss_box_dn_2[0.1985] det_loss_cls_dn_3[0.0948] det_loss_box_dn_3[0.1944] det_loss_cls_dn_4[0.0939] det_loss_box_dn_4[0.1921] det_loss_cls_dn_5[0.0932] det_loss_box_dn_5[0.1890] loss_depth[0.0377] total_loss[8.0912]
Rank[0/8] 06/20/2025 15:11:35 INFO stats.py:314 | Epoch[190] Step[49] GlobalStep[74149] Training Speed: 8.64 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 6:33:17.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:11:35 INFO loss_tracker.py:84 | Epoch[190/239] Step[49] GlobalStep[74149/NA]: det_loss_cls_0[0.4588] det_loss_box_0[0.5812] det_loss_cls_1[0.4527] det_loss_box_1[0.4317] det_loss_cls_2[0.3935] det_loss_box_2[0.3801] det_loss_cls_3[0.3541] det_loss_box_3[0.3714] det_loss_cls_4[0.3336] det_loss_box_4[0.3591] det_loss_cls_5[0.3196] det_loss_box_5[0.3639] det_loss_cls_dn_0[0.0918] det_loss_box_dn_0[0.2329] det_loss_cls_dn_1[0.0887] det_loss_box_dn_1[0.1952] det_loss_cls_dn_2[0.0869] det_loss_box_dn_2[0.1802] det_loss_cls_dn_3[0.0859] det_loss_box_dn_3[0.1761] det_loss_cls_dn_4[0.0847] det_loss_box_dn_4[0.1746] det_loss_cls_dn_5[0.0839] det_loss_box_dn_5[0.1737] loss_depth[0.0334] total_loss[6.4878]
Rank[0/8] 06/20/2025 15:12:00 INFO stats.py:314 | Epoch[190] Step[74] GlobalStep[74174] Training Speed: 7.80 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 6:32:45.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:12:00 INFO loss_tracker.py:84 | Epoch[190/239] Step[74] GlobalStep[74174/NA]: det_loss_cls_0[0.5047] det_loss_box_0[0.6095] det_loss_cls_1[0.4892] det_loss_box_1[0.4364] det_loss_cls_2[0.4318] det_loss_box_2[0.4027] det_loss_cls_3[0.3898] det_loss_box_3[0.3919] det_loss_cls_4[0.3695] det_loss_box_4[0.3797] det_loss_cls_5[0.3539] det_loss_box_5[0.3771] det_loss_cls_dn_0[0.0947] det_loss_box_dn_0[0.2102] det_loss_cls_dn_1[0.0905] det_loss_box_dn_1[0.1691] det_loss_cls_dn_2[0.0901] det_loss_box_dn_2[0.1540] det_loss_cls_dn_3[0.0889] det_loss_box_dn_3[0.1494] det_loss_cls_dn_4[0.0883] det_loss_box_dn_4[0.1477] det_loss_cls_dn_5[0.0875] det_loss_box_dn_5[0.1458] loss_depth[0.0353] total_loss[6.6877]
Rank[0/8] 06/20/2025 15:12:27 INFO stats.py:314 | Epoch[190] Step[99] GlobalStep[74199] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 6:32:14.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:12:27 INFO loss_tracker.py:84 | Epoch[190/239] Step[99] GlobalStep[74199/NA]: det_loss_cls_0[0.3993] det_loss_box_0[0.5975] det_loss_cls_1[0.3766] det_loss_box_1[0.4659] det_loss_cls_2[0.3191] det_loss_box_2[0.4226] det_loss_cls_3[0.2849] det_loss_box_3[0.4071] det_loss_cls_4[0.2703] det_loss_box_4[0.3902] det_loss_cls_5[0.2581] det_loss_box_5[0.3941] det_loss_cls_dn_0[0.0886] det_loss_box_dn_0[0.2860] det_loss_cls_dn_1[0.0849] det_loss_box_dn_1[0.2422] det_loss_cls_dn_2[0.0841] det_loss_box_dn_2[0.2242] det_loss_cls_dn_3[0.0837] det_loss_box_dn_3[0.2205] det_loss_cls_dn_4[0.0832] det_loss_box_dn_4[0.2177] det_loss_cls_dn_5[0.0822] det_loss_box_dn_5[0.2148] loss_depth[0.0367] total_loss[6.5344]
Rank[0/8] 06/20/2025 15:12:53 INFO stats.py:314 | Epoch[190] Step[124] GlobalStep[74224] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 6:31:42.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:12:53 INFO loss_tracker.py:84 | Epoch[190/239] Step[124] GlobalStep[74224/NA]: det_loss_cls_0[0.4497] det_loss_box_0[0.5247] det_loss_cls_1[0.4474] det_loss_box_1[0.3876] det_loss_cls_2[0.3939] det_loss_box_2[0.3442] det_loss_cls_3[0.3698] det_loss_box_3[0.3304] det_loss_cls_4[0.3525] det_loss_box_4[0.3152] det_loss_cls_5[0.3398] det_loss_box_5[0.3187] det_loss_cls_dn_0[0.0856] det_loss_box_dn_0[0.2834] det_loss_cls_dn_1[0.0833] det_loss_box_dn_1[0.2304] det_loss_cls_dn_2[0.0821] det_loss_box_dn_2[0.2092] det_loss_cls_dn_3[0.0808] det_loss_box_dn_3[0.2052] det_loss_cls_dn_4[0.0801] det_loss_box_dn_4[0.2008] det_loss_cls_dn_5[0.0795] det_loss_box_dn_5[0.1980] loss_depth[0.0404] total_loss[6.4328]
Rank[0/8] 06/20/2025 15:13:18 INFO stats.py:314 | Epoch[190] Step[149] GlobalStep[74249] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 6:31:11.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:13:18 INFO loss_tracker.py:84 | Epoch[190/239] Step[149] GlobalStep[74249/NA]: det_loss_cls_0[0.4852] det_loss_box_0[0.6505] det_loss_cls_1[0.4576] det_loss_box_1[0.4996] det_loss_cls_2[0.3971] det_loss_box_2[0.4550] det_loss_cls_3[0.3674] det_loss_box_3[0.4399] det_loss_cls_4[0.3546] det_loss_box_4[0.4200] det_loss_cls_5[0.3385] det_loss_box_5[0.4235] det_loss_cls_dn_0[0.0870] det_loss_box_dn_0[0.2701] det_loss_cls_dn_1[0.0827] det_loss_box_dn_1[0.2255] det_loss_cls_dn_2[0.0819] det_loss_box_dn_2[0.2079] det_loss_cls_dn_3[0.0806] det_loss_box_dn_3[0.1961] det_loss_cls_dn_4[0.0794] det_loss_box_dn_4[0.1930] det_loss_cls_dn_5[0.0787] det_loss_box_dn_5[0.1870] loss_depth[0.0346] total_loss[7.0936]
Rank[0/8] 06/20/2025 15:13:45 INFO stats.py:314 | Epoch[190] Step[174] GlobalStep[74274] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 6:30:39.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:13:45 INFO loss_tracker.py:84 | Epoch[190/239] Step[174] GlobalStep[74274/NA]: det_loss_cls_0[0.4473] det_loss_box_0[0.5198] det_loss_cls_1[0.4296] det_loss_box_1[0.3844] det_loss_cls_2[0.3828] det_loss_box_2[0.3453] det_loss_cls_3[0.3551] det_loss_box_3[0.3298] det_loss_cls_4[0.3377] det_loss_box_4[0.3152] det_loss_cls_5[0.3298] det_loss_box_5[0.3170] det_loss_cls_dn_0[0.0862] det_loss_box_dn_0[0.2611] det_loss_cls_dn_1[0.0826] det_loss_box_dn_1[0.2058] det_loss_cls_dn_2[0.0813] det_loss_box_dn_2[0.1859] det_loss_cls_dn_3[0.0797] det_loss_box_dn_3[0.1806] det_loss_cls_dn_4[0.0791] det_loss_box_dn_4[0.1782] det_loss_cls_dn_5[0.0786] det_loss_box_dn_5[0.1750] loss_depth[0.0365] total_loss[6.2042]
Rank[0/8] 06/20/2025 15:14:10 INFO stats.py:314 | Epoch[190] Step[199] GlobalStep[74299] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 6:30:07.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:14:10 INFO loss_tracker.py:84 | Epoch[190/239] Step[199] GlobalStep[74299/NA]: det_loss_cls_0[0.4184] det_loss_box_0[0.5017] det_loss_cls_1[0.4099] det_loss_box_1[0.3651] det_loss_cls_2[0.3594] det_loss_box_2[0.3369] det_loss_cls_3[0.3320] det_loss_box_3[0.3240] det_loss_cls_4[0.3101] det_loss_box_4[0.3152] det_loss_cls_5[0.2948] det_loss_box_5[0.3192] det_loss_cls_dn_0[0.0956] det_loss_box_dn_0[0.2537] det_loss_cls_dn_1[0.0918] det_loss_box_dn_1[0.2044] det_loss_cls_dn_2[0.0913] det_loss_box_dn_2[0.1873] det_loss_cls_dn_3[0.0907] det_loss_box_dn_3[0.1836] det_loss_cls_dn_4[0.0900] det_loss_box_dn_4[0.1804] det_loss_cls_dn_5[0.0894] det_loss_box_dn_5[0.1776] loss_depth[0.0369] total_loss[6.0594]
Rank[0/8] 06/20/2025 15:14:36 INFO stats.py:314 | Epoch[190] Step[224] GlobalStep[74324] Training Speed: 7.50 samples/sec across all devices.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 6:29:36.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:14:36 INFO loss_tracker.py:84 | Epoch[190/239] Step[224] GlobalStep[74324/NA]: det_loss_cls_0[0.4527] det_loss_box_0[0.6083] det_loss_cls_1[0.4183] det_loss_box_1[0.4467] det_loss_cls_2[0.3688] det_loss_box_2[0.3978] det_loss_cls_3[0.3505] det_loss_box_3[0.3772] det_loss_cls_4[0.3284] det_loss_box_4[0.3658] det_loss_cls_5[0.3164] det_loss_box_5[0.3657] det_loss_cls_dn_0[0.0897] det_loss_box_dn_0[0.2786] det_loss_cls_dn_1[0.0867] det_loss_box_dn_1[0.2316] det_loss_cls_dn_2[0.0860] det_loss_box_dn_2[0.2068] det_loss_cls_dn_3[0.0847] det_loss_box_dn_3[0.2020] det_loss_cls_dn_4[0.0838] det_loss_box_dn_4[0.1980] det_loss_cls_dn_5[0.0828] det_loss_box_dn_5[0.1958] loss_depth[0.0369] total_loss[6.6601]
Rank[0/8] 06/20/2025 15:15:01 INFO stats.py:314 | Epoch[190] Step[249] GlobalStep[74349] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 6:29:05.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:15:01 INFO loss_tracker.py:84 | Epoch[190/239] Step[249] GlobalStep[74349/NA]: det_loss_cls_0[0.4081] det_loss_box_0[0.5316] det_loss_cls_1[0.3937] det_loss_box_1[0.3810] det_loss_cls_2[0.3341] det_loss_box_2[0.3376] det_loss_cls_3[0.3042] det_loss_box_3[0.3299] det_loss_cls_4[0.2815] det_loss_box_4[0.3164] det_loss_cls_5[0.2751] det_loss_box_5[0.3118] det_loss_cls_dn_0[0.0839] det_loss_box_dn_0[0.2718] det_loss_cls_dn_1[0.0816] det_loss_box_dn_1[0.2125] det_loss_cls_dn_2[0.0809] det_loss_box_dn_2[0.1942] det_loss_cls_dn_3[0.0798] det_loss_box_dn_3[0.1862] det_loss_cls_dn_4[0.0790] det_loss_box_dn_4[0.1842] det_loss_cls_dn_5[0.0785] det_loss_box_dn_5[0.1811] loss_depth[0.0366] total_loss[5.9553]
Rank[0/8] 06/20/2025 15:15:27 INFO stats.py:314 | Epoch[190] Step[274] GlobalStep[74374] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 6:28:33.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:15:27 INFO loss_tracker.py:84 | Epoch[190/239] Step[274] GlobalStep[74374/NA]: det_loss_cls_0[0.3724] det_loss_box_0[0.4626] det_loss_cls_1[0.3542] det_loss_box_1[0.3489] det_loss_cls_2[0.3043] det_loss_box_2[0.3143] det_loss_cls_3[0.2824] det_loss_box_3[0.3043] det_loss_cls_4[0.2674] det_loss_box_4[0.3008] det_loss_cls_5[0.2579] det_loss_box_5[0.2979] det_loss_cls_dn_0[0.0804] det_loss_box_dn_0[0.2474] det_loss_cls_dn_1[0.0767] det_loss_box_dn_1[0.1877] det_loss_cls_dn_2[0.0759] det_loss_box_dn_2[0.1661] det_loss_cls_dn_3[0.0751] det_loss_box_dn_3[0.1618] det_loss_cls_dn_4[0.0741] det_loss_box_dn_4[0.1604] det_loss_cls_dn_5[0.0733] det_loss_box_dn_5[0.1570] loss_depth[0.0343] total_loss[5.4377]
Rank[0/8] 06/20/2025 15:15:53 INFO stats.py:314 | Epoch[190] Step[299] GlobalStep[74399] Training Speed: 6.06 samples/sec across all devices.	Average Step Time: 1.32 sec.	Estimated Remaining Time: 6:28:02.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:15:53 INFO loss_tracker.py:84 | Epoch[190/239] Step[299] GlobalStep[74399/NA]: det_loss_cls_0[0.5552] det_loss_box_0[0.7144] det_loss_cls_1[0.5168] det_loss_box_1[0.5598] det_loss_cls_2[0.4634] det_loss_box_2[0.4945] det_loss_cls_3[0.4342] det_loss_box_3[0.4796] det_loss_cls_4[0.4175] det_loss_box_4[0.4603] det_loss_cls_5[0.3978] det_loss_box_5[0.4618] det_loss_cls_dn_0[0.0981] det_loss_box_dn_0[0.2727] det_loss_cls_dn_1[0.0940] det_loss_box_dn_1[0.2328] det_loss_cls_dn_2[0.0932] det_loss_box_dn_2[0.2156] det_loss_cls_dn_3[0.0917] det_loss_box_dn_3[0.2106] det_loss_cls_dn_4[0.0909] det_loss_box_dn_4[0.2090] det_loss_cls_dn_5[0.0898] det_loss_box_dn_5[0.2058] loss_depth[0.0352] total_loss[7.8945]
Rank[0/8] 06/20/2025 15:16:19 INFO stats.py:314 | Epoch[190] Step[324] GlobalStep[74424] Training Speed: 8.22 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 6:27:30.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:16:19 INFO loss_tracker.py:84 | Epoch[190/239] Step[324] GlobalStep[74424/NA]: det_loss_cls_0[0.4138] det_loss_box_0[0.4689] det_loss_cls_1[0.3933] det_loss_box_1[0.3598] det_loss_cls_2[0.3394] det_loss_box_2[0.3221] det_loss_cls_3[0.3091] det_loss_box_3[0.3129] det_loss_cls_4[0.2946] det_loss_box_4[0.3058] det_loss_cls_5[0.2817] det_loss_box_5[0.3051] det_loss_cls_dn_0[0.0935] det_loss_box_dn_0[0.2406] det_loss_cls_dn_1[0.0898] det_loss_box_dn_1[0.1927] det_loss_cls_dn_2[0.0891] det_loss_box_dn_2[0.1736] det_loss_cls_dn_3[0.0878] det_loss_box_dn_3[0.1701] det_loss_cls_dn_4[0.0876] det_loss_box_dn_4[0.1677] det_loss_cls_dn_5[0.0869] det_loss_box_dn_5[0.1664] loss_depth[0.0340] total_loss[5.7862]
Rank[0/8] 06/20/2025 15:16:45 INFO stats.py:314 | Epoch[190] Step[349] GlobalStep[74449] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 6:26:59.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:16:45 INFO loss_tracker.py:84 | Epoch[190/239] Step[349] GlobalStep[74449/NA]: det_loss_cls_0[0.5885] det_loss_box_0[0.7525] det_loss_cls_1[0.5750] det_loss_box_1[0.5563] det_loss_cls_2[0.5056] det_loss_box_2[0.5133] det_loss_cls_3[0.4617] det_loss_box_3[0.5036] det_loss_cls_4[0.4333] det_loss_box_4[0.4858] det_loss_cls_5[0.4054] det_loss_box_5[0.4905] det_loss_cls_dn_0[0.0900] det_loss_box_dn_0[0.2936] det_loss_cls_dn_1[0.0867] det_loss_box_dn_1[0.2470] det_loss_cls_dn_2[0.0862] det_loss_box_dn_2[0.2318] det_loss_cls_dn_3[0.0848] det_loss_box_dn_3[0.2277] det_loss_cls_dn_4[0.0838] det_loss_box_dn_4[0.2248] det_loss_cls_dn_5[0.0831] det_loss_box_dn_5[0.2204] loss_depth[0.0377] total_loss[8.2691]
Rank[0/8] 06/20/2025 15:17:11 INFO stats.py:314 | Epoch[190] Step[374] GlobalStep[74474] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 6:26:27.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:17:11 INFO loss_tracker.py:84 | Epoch[190/239] Step[374] GlobalStep[74474/NA]: det_loss_cls_0[0.5556] det_loss_box_0[0.7015] det_loss_cls_1[0.5310] det_loss_box_1[0.5687] det_loss_cls_2[0.4619] det_loss_box_2[0.5102] det_loss_cls_3[0.4268] det_loss_box_3[0.4904] det_loss_cls_4[0.3999] det_loss_box_4[0.4772] det_loss_cls_5[0.3908] det_loss_box_5[0.4723] det_loss_cls_dn_0[0.0917] det_loss_box_dn_0[0.2646] det_loss_cls_dn_1[0.0884] det_loss_box_dn_1[0.2207] det_loss_cls_dn_2[0.0873] det_loss_box_dn_2[0.2039] det_loss_cls_dn_3[0.0859] det_loss_box_dn_3[0.2002] det_loss_cls_dn_4[0.0847] det_loss_box_dn_4[0.1966] det_loss_cls_dn_5[0.0836] det_loss_box_dn_5[0.1948] loss_depth[0.0370] total_loss[7.8255]
Rank[0/8] 06/20/2025 15:17:26 INFO stats.py:394 | Epoch[190] completed. Training Speed: 7.74 samples/sec across all devices.	Epoch Time: 403.21 sec.	Average Epoch Time: 403.21 sec.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 6:26:08.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:17:39 INFO stats.py:314 | Epoch[191] Step[9] GlobalStep[74499] Training Speed: 6.67 samples/sec across all devices.	Average Step Time: 1.20 sec.	Estimated Remaining Time: 6:25:56.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:17:55 INFO loss_tracker.py:84 | Epoch[191/239] Step[24] GlobalStep[74514/NA]: det_loss_cls_0[0.4875] det_loss_box_0[0.6548] det_loss_cls_1[0.4736] det_loss_box_1[0.5089] det_loss_cls_2[0.4164] det_loss_box_2[0.4589] det_loss_cls_3[0.3796] det_loss_box_3[0.4501] det_loss_cls_4[0.3611] det_loss_box_4[0.4356] det_loss_cls_5[0.3417] det_loss_box_5[0.4392] det_loss_cls_dn_0[0.0877] det_loss_box_dn_0[0.2668] det_loss_cls_dn_1[0.0843] det_loss_box_dn_1[0.2226] det_loss_cls_dn_2[0.0838] det_loss_box_dn_2[0.2068] det_loss_cls_dn_3[0.0830] det_loss_box_dn_3[0.2039] det_loss_cls_dn_4[0.0820] det_loss_box_dn_4[0.2016] det_loss_cls_dn_5[0.0811] det_loss_box_dn_5[0.2002] loss_depth[0.0358] total_loss[7.2472]
Rank[0/8] 06/20/2025 15:18:05 INFO stats.py:314 | Epoch[191] Step[34] GlobalStep[74524] Training Speed: 8.07 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 6:25:25.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:18:20 INFO loss_tracker.py:84 | Epoch[191/239] Step[49] GlobalStep[74539/NA]: det_loss_cls_0[0.3681] det_loss_box_0[0.4363] det_loss_cls_1[0.3678] det_loss_box_1[0.3273] det_loss_cls_2[0.3032] det_loss_box_2[0.2975] det_loss_cls_3[0.2766] det_loss_box_3[0.2916] det_loss_cls_4[0.2540] det_loss_box_4[0.2879] det_loss_cls_5[0.2420] det_loss_box_5[0.2892] det_loss_cls_dn_0[0.0846] det_loss_box_dn_0[0.2616] det_loss_cls_dn_1[0.0810] det_loss_box_dn_1[0.2097] det_loss_cls_dn_2[0.0801] det_loss_box_dn_2[0.1927] det_loss_cls_dn_3[0.0794] det_loss_box_dn_3[0.1893] det_loss_cls_dn_4[0.0788] det_loss_box_dn_4[0.1851] det_loss_cls_dn_5[0.0780] det_loss_box_dn_5[0.1808] loss_depth[0.0331] total_loss[5.4757]
Rank[0/8] 06/20/2025 15:18:31 INFO stats.py:314 | Epoch[191] Step[59] GlobalStep[74549] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 6:24:53.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:18:47 INFO loss_tracker.py:84 | Epoch[191/239] Step[74] GlobalStep[74564/NA]: det_loss_cls_0[0.4400] det_loss_box_0[0.5599] det_loss_cls_1[0.4344] det_loss_box_1[0.4237] det_loss_cls_2[0.3793] det_loss_box_2[0.3844] det_loss_cls_3[0.3418] det_loss_box_3[0.3733] det_loss_cls_4[0.3176] det_loss_box_4[0.3702] det_loss_cls_5[0.3095] det_loss_box_5[0.3649] det_loss_cls_dn_0[0.0937] det_loss_box_dn_0[0.2975] det_loss_cls_dn_1[0.0900] det_loss_box_dn_1[0.2528] det_loss_cls_dn_2[0.0898] det_loss_box_dn_2[0.2321] det_loss_cls_dn_3[0.0883] det_loss_box_dn_3[0.2285] det_loss_cls_dn_4[0.0876] det_loss_box_dn_4[0.2254] det_loss_cls_dn_5[0.0864] det_loss_box_dn_5[0.2232] loss_depth[0.0358] total_loss[6.7302]
Rank[0/8] 06/20/2025 15:18:57 INFO stats.py:314 | Epoch[191] Step[84] GlobalStep[74574] Training Speed: 7.65 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 6:24:22.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:19:12 INFO loss_tracker.py:84 | Epoch[191/239] Step[99] GlobalStep[74589/NA]: det_loss_cls_0[0.4831] det_loss_box_0[0.5870] det_loss_cls_1[0.4592] det_loss_box_1[0.4376] det_loss_cls_2[0.3879] det_loss_box_2[0.3874] det_loss_cls_3[0.3483] det_loss_box_3[0.3795] det_loss_cls_4[0.3226] det_loss_box_4[0.3708] det_loss_cls_5[0.3080] det_loss_box_5[0.3732] det_loss_cls_dn_0[0.0825] det_loss_box_dn_0[0.2898] det_loss_cls_dn_1[0.0787] det_loss_box_dn_1[0.2268] det_loss_cls_dn_2[0.0781] det_loss_box_dn_2[0.2074] det_loss_cls_dn_3[0.0766] det_loss_box_dn_3[0.2029] det_loss_cls_dn_4[0.0760] det_loss_box_dn_4[0.2000] det_loss_cls_dn_5[0.0754] det_loss_box_dn_5[0.1977] loss_depth[0.0381] total_loss[6.6745]
Rank[0/8] 06/20/2025 15:19:23 INFO stats.py:314 | Epoch[191] Step[109] GlobalStep[74599] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 6:23:51.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:19:38 INFO loss_tracker.py:84 | Epoch[191/239] Step[124] GlobalStep[74614/NA]: det_loss_cls_0[0.4439] det_loss_box_0[0.5501] det_loss_cls_1[0.4307] det_loss_box_1[0.4282] det_loss_cls_2[0.3715] det_loss_box_2[0.3750] det_loss_cls_3[0.3397] det_loss_box_3[0.3639] det_loss_cls_4[0.3186] det_loss_box_4[0.3536] det_loss_cls_5[0.3039] det_loss_box_5[0.3505] det_loss_cls_dn_0[0.0860] det_loss_box_dn_0[0.2621] det_loss_cls_dn_1[0.0820] det_loss_box_dn_1[0.2106] det_loss_cls_dn_2[0.0812] det_loss_box_dn_2[0.1912] det_loss_cls_dn_3[0.0801] det_loss_box_dn_3[0.1859] det_loss_cls_dn_4[0.0792] det_loss_box_dn_4[0.1837] det_loss_cls_dn_5[0.0785] det_loss_box_dn_5[0.1819] loss_depth[0.0357] total_loss[6.3677]
Rank[0/8] 06/20/2025 15:19:48 INFO stats.py:314 | Epoch[191] Step[134] GlobalStep[74624] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 6:23:19.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:20:04 INFO loss_tracker.py:84 | Epoch[191/239] Step[149] GlobalStep[74639/NA]: det_loss_cls_0[0.4376] det_loss_box_0[0.6355] det_loss_cls_1[0.4306] det_loss_box_1[0.4994] det_loss_cls_2[0.3756] det_loss_box_2[0.4418] det_loss_cls_3[0.3498] det_loss_box_3[0.4217] det_loss_cls_4[0.3307] det_loss_box_4[0.4124] det_loss_cls_5[0.3204] det_loss_box_5[0.4161] det_loss_cls_dn_0[0.0888] det_loss_box_dn_0[0.3482] det_loss_cls_dn_1[0.0857] det_loss_box_dn_1[0.2934] det_loss_cls_dn_2[0.0845] det_loss_box_dn_2[0.2746] det_loss_cls_dn_3[0.0837] det_loss_box_dn_3[0.2678] det_loss_cls_dn_4[0.0826] det_loss_box_dn_4[0.2650] det_loss_cls_dn_5[0.0820] det_loss_box_dn_5[0.2632] loss_depth[0.0370] total_loss[7.3281]
Rank[0/8] 06/20/2025 15:20:15 INFO stats.py:314 | Epoch[191] Step[159] GlobalStep[74649] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 6:22:48.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:20:30 INFO loss_tracker.py:84 | Epoch[191/239] Step[174] GlobalStep[74664/NA]: det_loss_cls_0[0.4186] det_loss_box_0[0.5120] det_loss_cls_1[0.4004] det_loss_box_1[0.3826] det_loss_cls_2[0.3366] det_loss_box_2[0.3398] det_loss_cls_3[0.2946] det_loss_box_3[0.3347] det_loss_cls_4[0.2723] det_loss_box_4[0.3251] det_loss_cls_5[0.2629] det_loss_box_5[0.3271] det_loss_cls_dn_0[0.0899] det_loss_box_dn_0[0.2382] det_loss_cls_dn_1[0.0852] det_loss_box_dn_1[0.1939] det_loss_cls_dn_2[0.0844] det_loss_box_dn_2[0.1767] det_loss_cls_dn_3[0.0838] det_loss_box_dn_3[0.1735] det_loss_cls_dn_4[0.0827] det_loss_box_dn_4[0.1714] det_loss_cls_dn_5[0.0819] det_loss_box_dn_5[0.1703] loss_depth[0.0343] total_loss[5.8728]
Rank[0/8] 06/20/2025 15:20:40 INFO stats.py:314 | Epoch[191] Step[184] GlobalStep[74674] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 6:22:16.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:20:56 INFO loss_tracker.py:84 | Epoch[191/239] Step[199] GlobalStep[74689/NA]: det_loss_cls_0[0.4080] det_loss_box_0[0.5477] det_loss_cls_1[0.3911] det_loss_box_1[0.3919] det_loss_cls_2[0.3269] det_loss_box_2[0.3554] det_loss_cls_3[0.2932] det_loss_box_3[0.3445] det_loss_cls_4[0.2714] det_loss_box_4[0.3300] det_loss_cls_5[0.2614] det_loss_box_5[0.3262] det_loss_cls_dn_0[0.0845] det_loss_box_dn_0[0.2467] det_loss_cls_dn_1[0.0815] det_loss_box_dn_1[0.2001] det_loss_cls_dn_2[0.0811] det_loss_box_dn_2[0.1826] det_loss_cls_dn_3[0.0801] det_loss_box_dn_3[0.1794] det_loss_cls_dn_4[0.0794] det_loss_box_dn_4[0.1757] det_loss_cls_dn_5[0.0785] det_loss_box_dn_5[0.1746] loss_depth[0.0384] total_loss[5.9304]
Rank[0/8] 06/20/2025 15:21:06 INFO stats.py:314 | Epoch[191] Step[209] GlobalStep[74699] Training Speed: 7.68 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 6:21:45.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:21:21 INFO loss_tracker.py:84 | Epoch[191/239] Step[224] GlobalStep[74714/NA]: det_loss_cls_0[0.3872] det_loss_box_0[0.4791] det_loss_cls_1[0.3828] det_loss_box_1[0.3362] det_loss_cls_2[0.3248] det_loss_box_2[0.3065] det_loss_cls_3[0.2879] det_loss_box_3[0.2936] det_loss_cls_4[0.2689] det_loss_box_4[0.2818] det_loss_cls_5[0.2567] det_loss_box_5[0.2823] det_loss_cls_dn_0[0.0926] det_loss_box_dn_0[0.2802] det_loss_cls_dn_1[0.0891] det_loss_box_dn_1[0.2202] det_loss_cls_dn_2[0.0887] det_loss_box_dn_2[0.2021] det_loss_cls_dn_3[0.0883] det_loss_box_dn_3[0.1975] det_loss_cls_dn_4[0.0880] det_loss_box_dn_4[0.1948] det_loss_cls_dn_5[0.0872] det_loss_box_dn_5[0.1904] loss_depth[0.0332] total_loss[5.7403]
Rank[0/8] 06/20/2025 15:21:32 INFO stats.py:314 | Epoch[191] Step[234] GlobalStep[74724] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 6:21:13.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:21:47 INFO loss_tracker.py:84 | Epoch[191/239] Step[249] GlobalStep[74739/NA]: det_loss_cls_0[0.4751] det_loss_box_0[0.6002] det_loss_cls_1[0.4581] det_loss_box_1[0.4447] det_loss_cls_2[0.4044] det_loss_box_2[0.3901] det_loss_cls_3[0.3728] det_loss_box_3[0.3801] det_loss_cls_4[0.3547] det_loss_box_4[0.3711] det_loss_cls_5[0.3436] det_loss_box_5[0.3717] det_loss_cls_dn_0[0.0981] det_loss_box_dn_0[0.2894] det_loss_cls_dn_1[0.0950] det_loss_box_dn_1[0.2374] det_loss_cls_dn_2[0.0945] det_loss_box_dn_2[0.2110] det_loss_cls_dn_3[0.0943] det_loss_box_dn_3[0.2044] det_loss_cls_dn_4[0.0939] det_loss_box_dn_4[0.2024] det_loss_cls_dn_5[0.0936] det_loss_box_dn_5[0.1976] loss_depth[0.0374] total_loss[6.9154]
Rank[0/8] 06/20/2025 15:21:57 INFO stats.py:314 | Epoch[191] Step[259] GlobalStep[74749] Training Speed: 8.74 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 6:20:42.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:22:12 INFO loss_tracker.py:84 | Epoch[191/239] Step[274] GlobalStep[74764/NA]: det_loss_cls_0[0.4805] det_loss_box_0[0.6028] det_loss_cls_1[0.4518] det_loss_box_1[0.4709] det_loss_cls_2[0.3937] det_loss_box_2[0.4096] det_loss_cls_3[0.3577] det_loss_box_3[0.4112] det_loss_cls_4[0.3356] det_loss_box_4[0.3977] det_loss_cls_5[0.3233] det_loss_box_5[0.3966] det_loss_cls_dn_0[0.0820] det_loss_box_dn_0[0.2694] det_loss_cls_dn_1[0.0780] det_loss_box_dn_1[0.2152] det_loss_cls_dn_2[0.0769] det_loss_box_dn_2[0.1947] det_loss_cls_dn_3[0.0759] det_loss_box_dn_3[0.1902] det_loss_cls_dn_4[0.0751] det_loss_box_dn_4[0.1879] det_loss_cls_dn_5[0.0745] det_loss_box_dn_5[0.1857] loss_depth[0.0321] total_loss[6.7688]
Rank[0/8] 06/20/2025 15:22:23 INFO stats.py:314 | Epoch[191] Step[284] GlobalStep[74774] Training Speed: 8.69 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 6:20:10.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:22:38 INFO loss_tracker.py:84 | Epoch[191/239] Step[299] GlobalStep[74789/NA]: det_loss_cls_0[0.4225] det_loss_box_0[0.5061] det_loss_cls_1[0.4089] det_loss_box_1[0.3905] det_loss_cls_2[0.3466] det_loss_box_2[0.3482] det_loss_cls_3[0.3024] det_loss_box_3[0.3466] det_loss_cls_4[0.2812] det_loss_box_4[0.3331] det_loss_cls_5[0.2647] det_loss_box_5[0.3367] det_loss_cls_dn_0[0.0801] det_loss_box_dn_0[0.2663] det_loss_cls_dn_1[0.0767] det_loss_box_dn_1[0.2132] det_loss_cls_dn_2[0.0758] det_loss_box_dn_2[0.1945] det_loss_cls_dn_3[0.0751] det_loss_box_dn_3[0.1907] det_loss_cls_dn_4[0.0741] det_loss_box_dn_4[0.1889] det_loss_cls_dn_5[0.0734] det_loss_box_dn_5[0.1865] loss_depth[0.0365] total_loss[6.0195]
Rank[0/8] 06/20/2025 15:22:49 INFO stats.py:314 | Epoch[191] Step[309] GlobalStep[74799] Training Speed: 8.34 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 6:19:39.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:23:05 INFO loss_tracker.py:84 | Epoch[191/239] Step[324] GlobalStep[74814/NA]: det_loss_cls_0[0.4500] det_loss_box_0[0.5949] det_loss_cls_1[0.4294] det_loss_box_1[0.4641] det_loss_cls_2[0.3814] det_loss_box_2[0.4126] det_loss_cls_3[0.3525] det_loss_box_3[0.3894] det_loss_cls_4[0.3283] det_loss_box_4[0.3770] det_loss_cls_5[0.3151] det_loss_box_5[0.3759] det_loss_cls_dn_0[0.0888] det_loss_box_dn_0[0.2971] det_loss_cls_dn_1[0.0853] det_loss_box_dn_1[0.2464] det_loss_cls_dn_2[0.0844] det_loss_box_dn_2[0.2208] det_loss_cls_dn_3[0.0836] det_loss_box_dn_3[0.2136] det_loss_cls_dn_4[0.0827] det_loss_box_dn_4[0.2108] det_loss_cls_dn_5[0.0819] det_loss_box_dn_5[0.2064] loss_depth[0.0375] total_loss[6.8099]
Rank[0/8] 06/20/2025 15:23:15 INFO stats.py:314 | Epoch[191] Step[334] GlobalStep[74824] Training Speed: 8.34 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 6:19:08.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:23:30 INFO loss_tracker.py:84 | Epoch[191/239] Step[349] GlobalStep[74839/NA]: det_loss_cls_0[0.4191] det_loss_box_0[0.5607] det_loss_cls_1[0.4063] det_loss_box_1[0.4233] det_loss_cls_2[0.3599] det_loss_box_2[0.3880] det_loss_cls_3[0.3329] det_loss_box_3[0.3755] det_loss_cls_4[0.3177] det_loss_box_4[0.3647] det_loss_cls_5[0.3075] det_loss_box_5[0.3620] det_loss_cls_dn_0[0.0902] det_loss_box_dn_0[0.2704] det_loss_cls_dn_1[0.0874] det_loss_box_dn_1[0.2321] det_loss_cls_dn_2[0.0867] det_loss_box_dn_2[0.2180] det_loss_cls_dn_3[0.0859] det_loss_box_dn_3[0.2136] det_loss_cls_dn_4[0.0853] det_loss_box_dn_4[0.2115] det_loss_cls_dn_5[0.0847] det_loss_box_dn_5[0.2092] loss_depth[0.0345] total_loss[6.5269]
Rank[0/8] 06/20/2025 15:23:40 INFO stats.py:314 | Epoch[191] Step[359] GlobalStep[74849] Training Speed: 8.73 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 6:18:36.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:23:55 INFO loss_tracker.py:84 | Epoch[191/239] Step[374] GlobalStep[74864/NA]: det_loss_cls_0[0.4102] det_loss_box_0[0.5688] det_loss_cls_1[0.3963] det_loss_box_1[0.4776] det_loss_cls_2[0.3480] det_loss_box_2[0.4253] det_loss_cls_3[0.3205] det_loss_box_3[0.4162] det_loss_cls_4[0.2989] det_loss_box_4[0.4118] det_loss_cls_5[0.2876] det_loss_box_5[0.4110] det_loss_cls_dn_0[0.0948] det_loss_box_dn_0[0.3389] det_loss_cls_dn_1[0.0902] det_loss_box_dn_1[0.2889] det_loss_cls_dn_2[0.0891] det_loss_box_dn_2[0.2683] det_loss_cls_dn_3[0.0878] det_loss_box_dn_3[0.2650] det_loss_cls_dn_4[0.0872] det_loss_box_dn_4[0.2618] det_loss_cls_dn_5[0.0864] det_loss_box_dn_5[0.2585] loss_depth[0.0330] total_loss[7.0218]
Rank[0/8] 06/20/2025 15:24:05 INFO stats.py:314 | Epoch[191] Step[384] GlobalStep[74874] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 6:18:05.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:24:11 INFO stats.py:394 | Epoch[191] completed. Training Speed: 7.70 samples/sec across all devices.	Epoch Time: 405.01 sec.	Average Epoch Time: 405.01 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 6:17:58.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:24:33 INFO stats.py:314 | Epoch[192] Step[19] GlobalStep[74899] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 6:17:34.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:24:38 INFO loss_tracker.py:84 | Epoch[192/239] Step[24] GlobalStep[74904/NA]: det_loss_cls_0[0.5016] det_loss_box_0[0.6231] det_loss_cls_1[0.4831] det_loss_box_1[0.4691] det_loss_cls_2[0.4207] det_loss_box_2[0.4215] det_loss_cls_3[0.3847] det_loss_box_3[0.4031] det_loss_cls_4[0.3627] det_loss_box_4[0.3926] det_loss_cls_5[0.3551] det_loss_box_5[0.3923] det_loss_cls_dn_0[0.0944] det_loss_box_dn_0[0.2607] det_loss_cls_dn_1[0.0907] det_loss_box_dn_1[0.2088] det_loss_cls_dn_2[0.0900] det_loss_box_dn_2[0.1903] det_loss_cls_dn_3[0.0889] det_loss_box_dn_3[0.1856] det_loss_cls_dn_4[0.0883] det_loss_box_dn_4[0.1824] det_loss_cls_dn_5[0.0875] det_loss_box_dn_5[0.1810] loss_depth[0.0358] total_loss[6.9943]
Rank[0/8] 06/20/2025 15:24:59 INFO stats.py:314 | Epoch[192] Step[44] GlobalStep[74924] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 6:17:02.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:25:04 INFO loss_tracker.py:84 | Epoch[192/239] Step[49] GlobalStep[74929/NA]: det_loss_cls_0[0.3401] det_loss_box_0[0.4589] det_loss_cls_1[0.3243] det_loss_box_1[0.3516] det_loss_cls_2[0.2747] det_loss_box_2[0.3142] det_loss_cls_3[0.2469] det_loss_box_3[0.3018] det_loss_cls_4[0.2322] det_loss_box_4[0.2944] det_loss_cls_5[0.2222] det_loss_box_5[0.2932] det_loss_cls_dn_0[0.0783] det_loss_box_dn_0[0.3145] det_loss_cls_dn_1[0.0736] det_loss_box_dn_1[0.2529] det_loss_cls_dn_2[0.0722] det_loss_box_dn_2[0.2254] det_loss_cls_dn_3[0.0708] det_loss_box_dn_3[0.2189] det_loss_cls_dn_4[0.0703] det_loss_box_dn_4[0.2159] det_loss_cls_dn_5[0.0694] det_loss_box_dn_5[0.2073] loss_depth[0.0370] total_loss[5.5610]
Rank[0/8] 06/20/2025 15:25:24 INFO stats.py:314 | Epoch[192] Step[69] GlobalStep[74949] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 6:16:31.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:25:29 INFO loss_tracker.py:84 | Epoch[192/239] Step[74] GlobalStep[74954/NA]: det_loss_cls_0[0.4092] det_loss_box_0[0.5958] det_loss_cls_1[0.4065] det_loss_box_1[0.4501] det_loss_cls_2[0.3482] det_loss_box_2[0.4127] det_loss_cls_3[0.3122] det_loss_box_3[0.4043] det_loss_cls_4[0.2952] det_loss_box_4[0.3889] det_loss_cls_5[0.2822] det_loss_box_5[0.3885] det_loss_cls_dn_0[0.0881] det_loss_box_dn_0[0.3177] det_loss_cls_dn_1[0.0844] det_loss_box_dn_1[0.2660] det_loss_cls_dn_2[0.0840] det_loss_box_dn_2[0.2449] det_loss_cls_dn_3[0.0826] det_loss_box_dn_3[0.2390] det_loss_cls_dn_4[0.0819] det_loss_box_dn_4[0.2360] det_loss_cls_dn_5[0.0810] det_loss_box_dn_5[0.2304] loss_depth[0.0396] total_loss[6.7693]
Rank[0/8] 06/20/2025 15:25:51 INFO stats.py:314 | Epoch[192] Step[94] GlobalStep[74974] Training Speed: 7.98 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 6:16:00.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:25:56 INFO loss_tracker.py:84 | Epoch[192/239] Step[99] GlobalStep[74979/NA]: det_loss_cls_0[0.4814] det_loss_box_0[0.6993] det_loss_cls_1[0.4698] det_loss_box_1[0.5211] det_loss_cls_2[0.4083] det_loss_box_2[0.4685] det_loss_cls_3[0.3697] det_loss_box_3[0.4584] det_loss_cls_4[0.3517] det_loss_box_4[0.4416] det_loss_cls_5[0.3279] det_loss_box_5[0.4429] det_loss_cls_dn_0[0.0854] det_loss_box_dn_0[0.4027] det_loss_cls_dn_1[0.0826] det_loss_box_dn_1[0.3353] det_loss_cls_dn_2[0.0813] det_loss_box_dn_2[0.3110] det_loss_cls_dn_3[0.0805] det_loss_box_dn_3[0.3040] det_loss_cls_dn_4[0.0798] det_loss_box_dn_4[0.3000] det_loss_cls_dn_5[0.0791] det_loss_box_dn_5[0.2931] loss_depth[0.0381] total_loss[7.9137]
Rank[0/8] 06/20/2025 15:26:17 INFO stats.py:314 | Epoch[192] Step[119] GlobalStep[74999] Training Speed: 8.34 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 6:15:28.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:26:22 INFO loss_tracker.py:84 | Epoch[192/239] Step[124] GlobalStep[75004/NA]: det_loss_cls_0[0.4964] det_loss_box_0[0.6591] det_loss_cls_1[0.4754] det_loss_box_1[0.5026] det_loss_cls_2[0.4208] det_loss_box_2[0.4388] det_loss_cls_3[0.3827] det_loss_box_3[0.4253] det_loss_cls_4[0.3555] det_loss_box_4[0.4187] det_loss_cls_5[0.3420] det_loss_box_5[0.4181] det_loss_cls_dn_0[0.0828] det_loss_box_dn_0[0.2437] det_loss_cls_dn_1[0.0796] det_loss_box_dn_1[0.1922] det_loss_cls_dn_2[0.0785] det_loss_box_dn_2[0.1739] det_loss_cls_dn_3[0.0774] det_loss_box_dn_3[0.1709] det_loss_cls_dn_4[0.0767] det_loss_box_dn_4[0.1686] det_loss_cls_dn_5[0.0759] det_loss_box_dn_5[0.1674] loss_depth[0.0357] total_loss[6.9588]
Rank[0/8] 06/20/2025 15:26:42 INFO stats.py:314 | Epoch[192] Step[144] GlobalStep[75024] Training Speed: 7.84 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 6:14:57.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:26:47 INFO loss_tracker.py:84 | Epoch[192/239] Step[149] GlobalStep[75029/NA]: det_loss_cls_0[0.5240] det_loss_box_0[0.6606] det_loss_cls_1[0.5197] det_loss_box_1[0.5101] det_loss_cls_2[0.4730] det_loss_box_2[0.4577] det_loss_cls_3[0.4412] det_loss_box_3[0.4480] det_loss_cls_4[0.4183] det_loss_box_4[0.4359] det_loss_cls_5[0.4110] det_loss_box_5[0.4372] det_loss_cls_dn_0[0.1007] det_loss_box_dn_0[0.3659] det_loss_cls_dn_1[0.0975] det_loss_box_dn_1[0.2923] det_loss_cls_dn_2[0.0970] det_loss_box_dn_2[0.2719] det_loss_cls_dn_3[0.0960] det_loss_box_dn_3[0.2612] det_loss_cls_dn_4[0.0952] det_loss_box_dn_4[0.2558] det_loss_cls_dn_5[0.0942] det_loss_box_dn_5[0.2467] loss_depth[0.0360] total_loss[8.0470]
Rank[0/8] 06/20/2025 15:27:08 INFO stats.py:314 | Epoch[192] Step[169] GlobalStep[75049] Training Speed: 8.00 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 6:14:25.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:27:13 INFO loss_tracker.py:84 | Epoch[192/239] Step[174] GlobalStep[75054/NA]: det_loss_cls_0[0.4263] det_loss_box_0[0.5525] det_loss_cls_1[0.4133] det_loss_box_1[0.4247] det_loss_cls_2[0.3666] det_loss_box_2[0.3698] det_loss_cls_3[0.3380] det_loss_box_3[0.3588] det_loss_cls_4[0.3213] det_loss_box_4[0.3486] det_loss_cls_5[0.3043] det_loss_box_5[0.3574] det_loss_cls_dn_0[0.0932] det_loss_box_dn_0[0.2733] det_loss_cls_dn_1[0.0897] det_loss_box_dn_1[0.2282] det_loss_cls_dn_2[0.0887] det_loss_box_dn_2[0.2126] det_loss_cls_dn_3[0.0879] det_loss_box_dn_3[0.2080] det_loss_cls_dn_4[0.0873] det_loss_box_dn_4[0.2047] det_loss_cls_dn_5[0.0866] det_loss_box_dn_5[0.2017] loss_depth[0.0391] total_loss[6.4825]
Rank[0/8] 06/20/2025 15:27:34 INFO stats.py:314 | Epoch[192] Step[194] GlobalStep[75074] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 6:13:54.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:27:39 INFO loss_tracker.py:84 | Epoch[192/239] Step[199] GlobalStep[75079/NA]: det_loss_cls_0[0.4082] det_loss_box_0[0.4449] det_loss_cls_1[0.4035] det_loss_box_1[0.3249] det_loss_cls_2[0.3398] det_loss_box_2[0.2930] det_loss_cls_3[0.3061] det_loss_box_3[0.2837] det_loss_cls_4[0.2816] det_loss_box_4[0.2798] det_loss_cls_5[0.2695] det_loss_box_5[0.2789] det_loss_cls_dn_0[0.0903] det_loss_box_dn_0[0.2338] det_loss_cls_dn_1[0.0871] det_loss_box_dn_1[0.1941] det_loss_cls_dn_2[0.0861] det_loss_box_dn_2[0.1817] det_loss_cls_dn_3[0.0849] det_loss_box_dn_3[0.1786] det_loss_cls_dn_4[0.0845] det_loss_box_dn_4[0.1770] det_loss_cls_dn_5[0.0834] det_loss_box_dn_5[0.1734] loss_depth[0.0356] total_loss[5.6044]
Rank[0/8] 06/20/2025 15:28:00 INFO stats.py:314 | Epoch[192] Step[219] GlobalStep[75099] Training Speed: 7.67 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 6:13:23.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:28:05 INFO loss_tracker.py:84 | Epoch[192/239] Step[224] GlobalStep[75104/NA]: det_loss_cls_0[0.4252] det_loss_box_0[0.4882] det_loss_cls_1[0.4172] det_loss_box_1[0.3677] det_loss_cls_2[0.3526] det_loss_box_2[0.3305] det_loss_cls_3[0.3182] det_loss_box_3[0.3205] det_loss_cls_4[0.2948] det_loss_box_4[0.3092] det_loss_cls_5[0.2829] det_loss_box_5[0.3108] det_loss_cls_dn_0[0.0896] det_loss_box_dn_0[0.2375] det_loss_cls_dn_1[0.0853] det_loss_box_dn_1[0.1920] det_loss_cls_dn_2[0.0837] det_loss_box_dn_2[0.1728] det_loss_cls_dn_3[0.0826] det_loss_box_dn_3[0.1685] det_loss_cls_dn_4[0.0814] det_loss_box_dn_4[0.1654] det_loss_cls_dn_5[0.0808] det_loss_box_dn_5[0.1618] loss_depth[0.0361] total_loss[5.8552]
Rank[0/8] 06/20/2025 15:28:26 INFO stats.py:314 | Epoch[192] Step[244] GlobalStep[75124] Training Speed: 6.43 samples/sec across all devices.	Average Step Time: 1.24 sec.	Estimated Remaining Time: 6:12:51.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:28:31 INFO loss_tracker.py:84 | Epoch[192/239] Step[249] GlobalStep[75129/NA]: det_loss_cls_0[0.5084] det_loss_box_0[0.6859] det_loss_cls_1[0.4948] det_loss_box_1[0.5627] det_loss_cls_2[0.4456] det_loss_box_2[0.5023] det_loss_cls_3[0.4129] det_loss_box_3[0.4845] det_loss_cls_4[0.3875] det_loss_box_4[0.4757] det_loss_cls_5[0.3786] det_loss_box_5[0.4747] det_loss_cls_dn_0[0.1008] det_loss_box_dn_0[0.3224] det_loss_cls_dn_1[0.0970] det_loss_box_dn_1[0.2717] det_loss_cls_dn_2[0.0968] det_loss_box_dn_2[0.2568] det_loss_cls_dn_3[0.0951] det_loss_box_dn_3[0.2502] det_loss_cls_dn_4[0.0942] det_loss_box_dn_4[0.2484] det_loss_cls_dn_5[0.0934] det_loss_box_dn_5[0.2418] loss_depth[0.0345] total_loss[8.0167]
Rank[0/8] 06/20/2025 15:28:52 INFO stats.py:314 | Epoch[192] Step[269] GlobalStep[75149] Training Speed: 8.66 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 6:12:20.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:28:57 INFO loss_tracker.py:84 | Epoch[192/239] Step[274] GlobalStep[75154/NA]: det_loss_cls_0[0.4422] det_loss_box_0[0.5472] det_loss_cls_1[0.4437] det_loss_box_1[0.4076] det_loss_cls_2[0.3896] det_loss_box_2[0.3598] det_loss_cls_3[0.3518] det_loss_box_3[0.3407] det_loss_cls_4[0.3285] det_loss_box_4[0.3337] det_loss_cls_5[0.3135] det_loss_box_5[0.3343] det_loss_cls_dn_0[0.0840] det_loss_box_dn_0[0.2193] det_loss_cls_dn_1[0.0813] det_loss_box_dn_1[0.1703] det_loss_cls_dn_2[0.0805] det_loss_box_dn_2[0.1565] det_loss_cls_dn_3[0.0794] det_loss_box_dn_3[0.1535] det_loss_cls_dn_4[0.0789] det_loss_box_dn_4[0.1528] det_loss_cls_dn_5[0.0779] det_loss_box_dn_5[0.1509] loss_depth[0.0328] total_loss[6.1108]
Rank[0/8] 06/20/2025 15:29:18 INFO stats.py:314 | Epoch[192] Step[294] GlobalStep[75174] Training Speed: 8.76 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 6:11:49.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:29:23 INFO loss_tracker.py:84 | Epoch[192/239] Step[299] GlobalStep[75179/NA]: det_loss_cls_0[0.4423] det_loss_box_0[0.5523] det_loss_cls_1[0.4199] det_loss_box_1[0.3956] det_loss_cls_2[0.3649] det_loss_box_2[0.3637] det_loss_cls_3[0.3233] det_loss_box_3[0.3545] det_loss_cls_4[0.3024] det_loss_box_4[0.3412] det_loss_cls_5[0.2903] det_loss_box_5[0.3367] det_loss_cls_dn_0[0.0846] det_loss_box_dn_0[0.2426] det_loss_cls_dn_1[0.0806] det_loss_box_dn_1[0.1948] det_loss_cls_dn_2[0.0802] det_loss_box_dn_2[0.1771] det_loss_cls_dn_3[0.0792] det_loss_box_dn_3[0.1712] det_loss_cls_dn_4[0.0783] det_loss_box_dn_4[0.1682] det_loss_cls_dn_5[0.0777] det_loss_box_dn_5[0.1650] loss_depth[0.0357] total_loss[6.1222]
Rank[0/8] 06/20/2025 15:29:43 INFO stats.py:314 | Epoch[192] Step[319] GlobalStep[75199] Training Speed: 6.72 samples/sec across all devices.	Average Step Time: 1.19 sec.	Estimated Remaining Time: 6:11:17.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:29:49 INFO loss_tracker.py:84 | Epoch[192/239] Step[324] GlobalStep[75204/NA]: det_loss_cls_0[0.4441] det_loss_box_0[0.5687] det_loss_cls_1[0.4197] det_loss_box_1[0.4296] det_loss_cls_2[0.3676] det_loss_box_2[0.3842] det_loss_cls_3[0.3310] det_loss_box_3[0.3719] det_loss_cls_4[0.3150] det_loss_box_4[0.3642] det_loss_cls_5[0.3037] det_loss_box_5[0.3673] det_loss_cls_dn_0[0.0952] det_loss_box_dn_0[0.2888] det_loss_cls_dn_1[0.0911] det_loss_box_dn_1[0.2376] det_loss_cls_dn_2[0.0906] det_loss_box_dn_2[0.2218] det_loss_cls_dn_3[0.0894] det_loss_box_dn_3[0.2156] det_loss_cls_dn_4[0.0889] det_loss_box_dn_4[0.2119] det_loss_cls_dn_5[0.0879] det_loss_box_dn_5[0.2096] loss_depth[0.0361] total_loss[6.6316]
Rank[0/8] 06/20/2025 15:30:09 INFO stats.py:314 | Epoch[192] Step[344] GlobalStep[75224] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 6:10:46.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:30:15 INFO loss_tracker.py:84 | Epoch[192/239] Step[349] GlobalStep[75229/NA]: det_loss_cls_0[0.4226] det_loss_box_0[0.5160] det_loss_cls_1[0.3929] det_loss_box_1[0.3865] det_loss_cls_2[0.3462] det_loss_box_2[0.3434] det_loss_cls_3[0.3194] det_loss_box_3[0.3376] det_loss_cls_4[0.3000] det_loss_box_4[0.3262] det_loss_cls_5[0.2865] det_loss_box_5[0.3303] det_loss_cls_dn_0[0.0843] det_loss_box_dn_0[0.2577] det_loss_cls_dn_1[0.0810] det_loss_box_dn_1[0.2107] det_loss_cls_dn_2[0.0804] det_loss_box_dn_2[0.1921] det_loss_cls_dn_3[0.0789] det_loss_box_dn_3[0.1872] det_loss_cls_dn_4[0.0782] det_loss_box_dn_4[0.1850] det_loss_cls_dn_5[0.0771] det_loss_box_dn_5[0.1830] loss_depth[0.0386] total_loss[6.0418]
Rank[0/8] 06/20/2025 15:30:35 INFO stats.py:314 | Epoch[192] Step[369] GlobalStep[75249] Training Speed: 7.56 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 6:10:15.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:30:40 INFO loss_tracker.py:84 | Epoch[192/239] Step[374] GlobalStep[75254/NA]: det_loss_cls_0[0.5317] det_loss_box_0[0.6188] det_loss_cls_1[0.5131] det_loss_box_1[0.4679] det_loss_cls_2[0.4444] det_loss_box_2[0.4260] det_loss_cls_3[0.4066] det_loss_box_3[0.4035] det_loss_cls_4[0.3785] det_loss_box_4[0.4020] det_loss_cls_5[0.3596] det_loss_box_5[0.4067] det_loss_cls_dn_0[0.0812] det_loss_box_dn_0[0.2272] det_loss_cls_dn_1[0.0768] det_loss_box_dn_1[0.1880] det_loss_cls_dn_2[0.0761] det_loss_box_dn_2[0.1734] det_loss_cls_dn_3[0.0745] det_loss_box_dn_3[0.1683] det_loss_cls_dn_4[0.0739] det_loss_box_dn_4[0.1660] det_loss_cls_dn_5[0.0733] det_loss_box_dn_5[0.1649] loss_depth[0.0327] total_loss[6.9349]
Rank[0/8] 06/20/2025 15:30:55 INFO stats.py:394 | Epoch[192] completed. Training Speed: 7.71 samples/sec across all devices.	Epoch Time: 404.77 sec.	Average Epoch Time: 404.77 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 6:09:49.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:31:02 INFO stats.py:314 | Epoch[193] Step[4] GlobalStep[75274] Training Speed: 7.90 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 6:09:43.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:31:23 INFO loss_tracker.py:84 | Epoch[193/239] Step[24] GlobalStep[75294/NA]: det_loss_cls_0[0.4515] det_loss_box_0[0.5562] det_loss_cls_1[0.4261] det_loss_box_1[0.4304] det_loss_cls_2[0.3720] det_loss_box_2[0.3770] det_loss_cls_3[0.3374] det_loss_box_3[0.3674] det_loss_cls_4[0.3213] det_loss_box_4[0.3582] det_loss_cls_5[0.3124] det_loss_box_5[0.3584] det_loss_cls_dn_0[0.0961] det_loss_box_dn_0[0.2836] det_loss_cls_dn_1[0.0931] det_loss_box_dn_1[0.2339] det_loss_cls_dn_2[0.0929] det_loss_box_dn_2[0.2171] det_loss_cls_dn_3[0.0915] det_loss_box_dn_3[0.2128] det_loss_cls_dn_4[0.0907] det_loss_box_dn_4[0.2104] det_loss_cls_dn_5[0.0898] det_loss_box_dn_5[0.2084] loss_depth[0.0357] total_loss[6.6241]
Rank[0/8] 06/20/2025 15:31:28 INFO stats.py:314 | Epoch[193] Step[29] GlobalStep[75299] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 6:09:12.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:31:49 INFO loss_tracker.py:84 | Epoch[193/239] Step[49] GlobalStep[75319/NA]: det_loss_cls_0[0.4651] det_loss_box_0[0.5879] det_loss_cls_1[0.4381] det_loss_box_1[0.4372] det_loss_cls_2[0.3884] det_loss_box_2[0.3904] det_loss_cls_3[0.3522] det_loss_box_3[0.3680] det_loss_cls_4[0.3305] det_loss_box_4[0.3657] det_loss_cls_5[0.3176] det_loss_box_5[0.3644] det_loss_cls_dn_0[0.0894] det_loss_box_dn_0[0.2754] det_loss_cls_dn_1[0.0856] det_loss_box_dn_1[0.2280] det_loss_cls_dn_2[0.0849] det_loss_box_dn_2[0.2125] det_loss_cls_dn_3[0.0836] det_loss_box_dn_3[0.2090] det_loss_cls_dn_4[0.0825] det_loss_box_dn_4[0.2060] det_loss_cls_dn_5[0.0817] det_loss_box_dn_5[0.2011] loss_depth[0.0376] total_loss[6.6824]
Rank[0/8] 06/20/2025 15:31:54 INFO stats.py:314 | Epoch[193] Step[54] GlobalStep[75324] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 6:08:41.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:32:14 INFO loss_tracker.py:84 | Epoch[193/239] Step[74] GlobalStep[75344/NA]: det_loss_cls_0[0.4457] det_loss_box_0[0.5736] det_loss_cls_1[0.4174] det_loss_box_1[0.4309] det_loss_cls_2[0.3650] det_loss_box_2[0.3831] det_loss_cls_3[0.3314] det_loss_box_3[0.3703] det_loss_cls_4[0.3064] det_loss_box_4[0.3631] det_loss_cls_5[0.2942] det_loss_box_5[0.3581] det_loss_cls_dn_0[0.0832] det_loss_box_dn_0[0.2328] det_loss_cls_dn_1[0.0795] det_loss_box_dn_1[0.1931] det_loss_cls_dn_2[0.0781] det_loss_box_dn_2[0.1793] det_loss_cls_dn_3[0.0770] det_loss_box_dn_3[0.1753] det_loss_cls_dn_4[0.0754] det_loss_box_dn_4[0.1728] det_loss_cls_dn_5[0.0747] det_loss_box_dn_5[0.1718] loss_depth[0.0342] total_loss[6.2662]
Rank[0/8] 06/20/2025 15:32:19 INFO stats.py:314 | Epoch[193] Step[79] GlobalStep[75349] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 6:08:09.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:32:40 INFO loss_tracker.py:84 | Epoch[193/239] Step[99] GlobalStep[75369/NA]: det_loss_cls_0[0.3607] det_loss_box_0[0.4778] det_loss_cls_1[0.3512] det_loss_box_1[0.3562] det_loss_cls_2[0.2890] det_loss_box_2[0.3276] det_loss_cls_3[0.2493] det_loss_box_3[0.3181] det_loss_cls_4[0.2287] det_loss_box_4[0.3060] det_loss_cls_5[0.2154] det_loss_box_5[0.3079] det_loss_cls_dn_0[0.0813] det_loss_box_dn_0[0.2551] det_loss_cls_dn_1[0.0776] det_loss_box_dn_1[0.2078] det_loss_cls_dn_2[0.0764] det_loss_box_dn_2[0.1916] det_loss_cls_dn_3[0.0753] det_loss_box_dn_3[0.1875] det_loss_cls_dn_4[0.0743] det_loss_box_dn_4[0.1849] det_loss_cls_dn_5[0.0735] det_loss_box_dn_5[0.1830] loss_depth[0.0365] total_loss[5.4928]
Rank[0/8] 06/20/2025 15:32:45 INFO stats.py:314 | Epoch[193] Step[104] GlobalStep[75374] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 6:07:38.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:33:05 INFO loss_tracker.py:84 | Epoch[193/239] Step[124] GlobalStep[75394/NA]: det_loss_cls_0[0.3497] det_loss_box_0[0.4106] det_loss_cls_1[0.3369] det_loss_box_1[0.3340] det_loss_cls_2[0.2934] det_loss_box_2[0.3030] det_loss_cls_3[0.2661] det_loss_box_3[0.2889] det_loss_cls_4[0.2462] det_loss_box_4[0.2836] det_loss_cls_5[0.2367] det_loss_box_5[0.2839] det_loss_cls_dn_0[0.0800] det_loss_box_dn_0[0.2342] det_loss_cls_dn_1[0.0768] det_loss_box_dn_1[0.1951] det_loss_cls_dn_2[0.0758] det_loss_box_dn_2[0.1745] det_loss_cls_dn_3[0.0749] det_loss_box_dn_3[0.1712] det_loss_cls_dn_4[0.0743] det_loss_box_dn_4[0.1691] det_loss_cls_dn_5[0.0736] det_loss_box_dn_5[0.1676] loss_depth[0.0332] total_loss[5.2336]
Rank[0/8] 06/20/2025 15:33:11 INFO stats.py:314 | Epoch[193] Step[129] GlobalStep[75399] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 6:07:07.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:33:31 INFO loss_tracker.py:84 | Epoch[193/239] Step[149] GlobalStep[75419/NA]: det_loss_cls_0[0.5728] det_loss_box_0[0.7507] det_loss_cls_1[0.5487] det_loss_box_1[0.5616] det_loss_cls_2[0.5092] det_loss_box_2[0.4866] det_loss_cls_3[0.4695] det_loss_box_3[0.4642] det_loss_cls_4[0.4473] det_loss_box_4[0.4512] det_loss_cls_5[0.4297] det_loss_box_5[0.4510] det_loss_cls_dn_0[0.1020] det_loss_box_dn_0[0.2663] det_loss_cls_dn_1[0.0990] det_loss_box_dn_1[0.2166] det_loss_cls_dn_2[0.0981] det_loss_box_dn_2[0.1991] det_loss_cls_dn_3[0.0970] det_loss_box_dn_3[0.1953] det_loss_cls_dn_4[0.0964] det_loss_box_dn_4[0.1931] det_loss_cls_dn_5[0.0959] det_loss_box_dn_5[0.1915] loss_depth[0.0359] total_loss[8.0289]
Rank[0/8] 06/20/2025 15:33:36 INFO stats.py:314 | Epoch[193] Step[154] GlobalStep[75424] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 6:06:35.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:33:57 INFO loss_tracker.py:84 | Epoch[193/239] Step[174] GlobalStep[75444/NA]: det_loss_cls_0[0.5725] det_loss_box_0[0.7426] det_loss_cls_1[0.5583] det_loss_box_1[0.5454] det_loss_cls_2[0.4879] det_loss_box_2[0.5072] det_loss_cls_3[0.4466] det_loss_box_3[0.4924] det_loss_cls_4[0.4236] det_loss_box_4[0.4758] det_loss_cls_5[0.4049] det_loss_box_5[0.4721] det_loss_cls_dn_0[0.0959] det_loss_box_dn_0[0.2853] det_loss_cls_dn_1[0.0910] det_loss_box_dn_1[0.2402] det_loss_cls_dn_2[0.0901] det_loss_box_dn_2[0.2251] det_loss_cls_dn_3[0.0889] det_loss_box_dn_3[0.2189] det_loss_cls_dn_4[0.0881] det_loss_box_dn_4[0.2169] det_loss_cls_dn_5[0.0871] det_loss_box_dn_5[0.2135] loss_depth[0.0369] total_loss[8.1072]
Rank[0/8] 06/20/2025 15:34:02 INFO stats.py:314 | Epoch[193] Step[179] GlobalStep[75449] Training Speed: 8.04 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 6:06:04.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:34:23 INFO loss_tracker.py:84 | Epoch[193/239] Step[199] GlobalStep[75469/NA]: det_loss_cls_0[0.5114] det_loss_box_0[0.6033] det_loss_cls_1[0.5145] det_loss_box_1[0.4509] det_loss_cls_2[0.4475] det_loss_box_2[0.4013] det_loss_cls_3[0.4070] det_loss_box_3[0.3967] det_loss_cls_4[0.3728] det_loss_box_4[0.3863] det_loss_cls_5[0.3629] det_loss_box_5[0.3864] det_loss_cls_dn_0[0.0970] det_loss_box_dn_0[0.2420] det_loss_cls_dn_1[0.0941] det_loss_box_dn_1[0.1925] det_loss_cls_dn_2[0.0928] det_loss_box_dn_2[0.1737] det_loss_cls_dn_3[0.0920] det_loss_box_dn_3[0.1681] det_loss_cls_dn_4[0.0914] det_loss_box_dn_4[0.1651] det_loss_cls_dn_5[0.0907] det_loss_box_dn_5[0.1632] loss_depth[0.0350] total_loss[6.9387]
Rank[0/8] 06/20/2025 15:34:29 INFO stats.py:314 | Epoch[193] Step[204] GlobalStep[75474] Training Speed: 8.74 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 6:05:33.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:34:49 INFO loss_tracker.py:84 | Epoch[193/239] Step[224] GlobalStep[75494/NA]: det_loss_cls_0[0.4300] det_loss_box_0[0.5228] det_loss_cls_1[0.3985] det_loss_box_1[0.4032] det_loss_cls_2[0.3482] det_loss_box_2[0.3605] det_loss_cls_3[0.3208] det_loss_box_3[0.3422] det_loss_cls_4[0.2945] det_loss_box_4[0.3401] det_loss_cls_5[0.2857] det_loss_box_5[0.3398] det_loss_cls_dn_0[0.0951] det_loss_box_dn_0[0.2867] det_loss_cls_dn_1[0.0906] det_loss_box_dn_1[0.2359] det_loss_cls_dn_2[0.0894] det_loss_box_dn_2[0.2174] det_loss_cls_dn_3[0.0878] det_loss_box_dn_3[0.2127] det_loss_cls_dn_4[0.0870] det_loss_box_dn_4[0.2087] det_loss_cls_dn_5[0.0862] det_loss_box_dn_5[0.2072] loss_depth[0.0361] total_loss[6.3271]
Rank[0/8] 06/20/2025 15:34:54 INFO stats.py:314 | Epoch[193] Step[229] GlobalStep[75499] Training Speed: 8.70 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 6:05:02.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:35:15 INFO loss_tracker.py:84 | Epoch[193/239] Step[249] GlobalStep[75519/NA]: det_loss_cls_0[0.4101] det_loss_box_0[0.5168] det_loss_cls_1[0.3870] det_loss_box_1[0.3912] det_loss_cls_2[0.3179] det_loss_box_2[0.3512] det_loss_cls_3[0.2878] det_loss_box_3[0.3391] det_loss_cls_4[0.2634] det_loss_box_4[0.3283] det_loss_cls_5[0.2508] det_loss_box_5[0.3309] det_loss_cls_dn_0[0.0757] det_loss_box_dn_0[0.2692] det_loss_cls_dn_1[0.0713] det_loss_box_dn_1[0.2221] det_loss_cls_dn_2[0.0703] det_loss_box_dn_2[0.2056] det_loss_cls_dn_3[0.0687] det_loss_box_dn_3[0.1947] det_loss_cls_dn_4[0.0675] det_loss_box_dn_4[0.1920] det_loss_cls_dn_5[0.0667] det_loss_box_dn_5[0.1875] loss_depth[0.0333] total_loss[5.8993]
Rank[0/8] 06/20/2025 15:35:20 INFO stats.py:314 | Epoch[193] Step[254] GlobalStep[75524] Training Speed: 8.69 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 6:04:30.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:35:41 INFO loss_tracker.py:84 | Epoch[193/239] Step[274] GlobalStep[75544/NA]: det_loss_cls_0[0.3603] det_loss_box_0[0.4235] det_loss_cls_1[0.3542] det_loss_box_1[0.3145] det_loss_cls_2[0.2971] det_loss_box_2[0.2814] det_loss_cls_3[0.2672] det_loss_box_3[0.2819] det_loss_cls_4[0.2429] det_loss_box_4[0.2754] det_loss_cls_5[0.2320] det_loss_box_5[0.2757] det_loss_cls_dn_0[0.0865] det_loss_box_dn_0[0.2271] det_loss_cls_dn_1[0.0834] det_loss_box_dn_1[0.1779] det_loss_cls_dn_2[0.0827] det_loss_box_dn_2[0.1587] det_loss_cls_dn_3[0.0815] det_loss_box_dn_3[0.1560] det_loss_cls_dn_4[0.0804] det_loss_box_dn_4[0.1548] det_loss_cls_dn_5[0.0795] det_loss_box_dn_5[0.1524] loss_depth[0.0342] total_loss[5.1613]
Rank[0/8] 06/20/2025 15:35:46 INFO stats.py:314 | Epoch[193] Step[279] GlobalStep[75549] Training Speed: 8.72 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 6:03:59.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:36:07 INFO loss_tracker.py:84 | Epoch[193/239] Step[299] GlobalStep[75569/NA]: det_loss_cls_0[0.3997] det_loss_box_0[0.4730] det_loss_cls_1[0.3916] det_loss_box_1[0.3692] det_loss_cls_2[0.3324] det_loss_box_2[0.3379] det_loss_cls_3[0.3026] det_loss_box_3[0.3265] det_loss_cls_4[0.2892] det_loss_box_4[0.3176] det_loss_cls_5[0.2725] det_loss_box_5[0.3225] det_loss_cls_dn_0[0.0840] det_loss_box_dn_0[0.2776] det_loss_cls_dn_1[0.0791] det_loss_box_dn_1[0.2345] det_loss_cls_dn_2[0.0787] det_loss_box_dn_2[0.2182] det_loss_cls_dn_3[0.0777] det_loss_box_dn_3[0.2120] det_loss_cls_dn_4[0.0765] det_loss_box_dn_4[0.2115] det_loss_cls_dn_5[0.0758] det_loss_box_dn_5[0.2084] loss_depth[0.0355] total_loss[6.0041]
Rank[0/8] 06/20/2025 15:36:12 INFO stats.py:314 | Epoch[193] Step[304] GlobalStep[75574] Training Speed: 8.02 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 6:03:28.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:36:32 INFO loss_tracker.py:84 | Epoch[193/239] Step[324] GlobalStep[75594/NA]: det_loss_cls_0[0.5358] det_loss_box_0[0.6361] det_loss_cls_1[0.5308] det_loss_box_1[0.5043] det_loss_cls_2[0.4599] det_loss_box_2[0.4648] det_loss_cls_3[0.4279] det_loss_box_3[0.4419] det_loss_cls_4[0.3989] det_loss_box_4[0.4330] det_loss_cls_5[0.3859] det_loss_box_5[0.4327] det_loss_cls_dn_0[0.0901] det_loss_box_dn_0[0.2354] det_loss_cls_dn_1[0.0862] det_loss_box_dn_1[0.1961] det_loss_cls_dn_2[0.0853] det_loss_box_dn_2[0.1840] det_loss_cls_dn_3[0.0842] det_loss_box_dn_3[0.1796] det_loss_cls_dn_4[0.0834] det_loss_box_dn_4[0.1775] det_loss_cls_dn_5[0.0828] det_loss_box_dn_5[0.1735] loss_depth[0.0345] total_loss[7.3445]
Rank[0/8] 06/20/2025 15:36:37 INFO stats.py:314 | Epoch[193] Step[329] GlobalStep[75599] Training Speed: 7.97 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 6:02:56.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:36:58 INFO loss_tracker.py:84 | Epoch[193/239] Step[349] GlobalStep[75619/NA]: det_loss_cls_0[0.4287] det_loss_box_0[0.5227] det_loss_cls_1[0.4164] det_loss_box_1[0.3897] det_loss_cls_2[0.3623] det_loss_box_2[0.3725] det_loss_cls_3[0.3358] det_loss_box_3[0.3608] det_loss_cls_4[0.3151] det_loss_box_4[0.3459] det_loss_cls_5[0.3100] det_loss_box_5[0.3384] det_loss_cls_dn_0[0.1006] det_loss_box_dn_0[0.3021] det_loss_cls_dn_1[0.0962] det_loss_box_dn_1[0.2450] det_loss_cls_dn_2[0.0956] det_loss_box_dn_2[0.2250] det_loss_cls_dn_3[0.0947] det_loss_box_dn_3[0.2205] det_loss_cls_dn_4[0.0939] det_loss_box_dn_4[0.2157] det_loss_cls_dn_5[0.0933] det_loss_box_dn_5[0.2094] loss_depth[0.0331] total_loss[6.5236]
Rank[0/8] 06/20/2025 15:37:03 INFO stats.py:314 | Epoch[193] Step[354] GlobalStep[75624] Training Speed: 7.68 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 6:02:25.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:37:25 INFO loss_tracker.py:84 | Epoch[193/239] Step[374] GlobalStep[75644/NA]: det_loss_cls_0[0.3748] det_loss_box_0[0.4883] det_loss_cls_1[0.3669] det_loss_box_1[0.3910] det_loss_cls_2[0.3251] det_loss_box_2[0.3573] det_loss_cls_3[0.2968] det_loss_box_3[0.3505] det_loss_cls_4[0.2759] det_loss_box_4[0.3476] det_loss_cls_5[0.2692] det_loss_box_5[0.3460] det_loss_cls_dn_0[0.0773] det_loss_box_dn_0[0.2807] det_loss_cls_dn_1[0.0741] det_loss_box_dn_1[0.2395] det_loss_cls_dn_2[0.0728] det_loss_box_dn_2[0.2198] det_loss_cls_dn_3[0.0719] det_loss_box_dn_3[0.2178] det_loss_cls_dn_4[0.0712] det_loss_box_dn_4[0.2161] det_loss_cls_dn_5[0.0703] det_loss_box_dn_5[0.2133] loss_depth[0.0335] total_loss[6.0477]
Rank[0/8] 06/20/2025 15:37:29 INFO stats.py:314 | Epoch[193] Step[379] GlobalStep[75649] Training Speed: 8.69 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 6:01:54.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:37:40 INFO stats.py:394 | Epoch[193] completed. Training Speed: 7.72 samples/sec across all devices.	Epoch Time: 404.32 sec.	Average Epoch Time: 404.32 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 6:01:41.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:37:57 INFO stats.py:314 | Epoch[194] Step[14] GlobalStep[75674] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 6:01:23.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:38:08 INFO loss_tracker.py:84 | Epoch[194/239] Step[24] GlobalStep[75684/NA]: det_loss_cls_0[0.4922] det_loss_box_0[0.5930] det_loss_cls_1[0.4771] det_loss_box_1[0.4600] det_loss_cls_2[0.4112] det_loss_box_2[0.4226] det_loss_cls_3[0.3741] det_loss_box_3[0.4053] det_loss_cls_4[0.3492] det_loss_box_4[0.3973] det_loss_cls_5[0.3373] det_loss_box_5[0.3960] det_loss_cls_dn_0[0.0908] det_loss_box_dn_0[0.2936] det_loss_cls_dn_1[0.0875] det_loss_box_dn_1[0.2423] det_loss_cls_dn_2[0.0861] det_loss_box_dn_2[0.2264] det_loss_cls_dn_3[0.0852] det_loss_box_dn_3[0.2193] det_loss_cls_dn_4[0.0844] det_loss_box_dn_4[0.2165] det_loss_cls_dn_5[0.0838] det_loss_box_dn_5[0.2133] loss_depth[0.0341] total_loss[7.0786]
Rank[0/8] 06/20/2025 15:38:23 INFO stats.py:314 | Epoch[194] Step[39] GlobalStep[75699] Training Speed: 8.19 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 6:00:52.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:38:33 INFO loss_tracker.py:84 | Epoch[194/239] Step[49] GlobalStep[75709/NA]: det_loss_cls_0[0.4540] det_loss_box_0[0.5964] det_loss_cls_1[0.4449] det_loss_box_1[0.4473] det_loss_cls_2[0.3886] det_loss_box_2[0.4087] det_loss_cls_3[0.3591] det_loss_box_3[0.3977] det_loss_cls_4[0.3304] det_loss_box_4[0.3826] det_loss_cls_5[0.3236] det_loss_box_5[0.3810] det_loss_cls_dn_0[0.0888] det_loss_box_dn_0[0.2768] det_loss_cls_dn_1[0.0846] det_loss_box_dn_1[0.2298] det_loss_cls_dn_2[0.0837] det_loss_box_dn_2[0.2099] det_loss_cls_dn_3[0.0824] det_loss_box_dn_3[0.2040] det_loss_cls_dn_4[0.0817] det_loss_box_dn_4[0.2008] det_loss_cls_dn_5[0.0809] det_loss_box_dn_5[0.1981] loss_depth[0.0349] total_loss[6.7708]
Rank[0/8] 06/20/2025 15:38:49 INFO stats.py:314 | Epoch[194] Step[64] GlobalStep[75724] Training Speed: 6.32 samples/sec across all devices.	Average Step Time: 1.27 sec.	Estimated Remaining Time: 6:00:20.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:38:59 INFO loss_tracker.py:84 | Epoch[194/239] Step[74] GlobalStep[75734/NA]: det_loss_cls_0[0.4651] det_loss_box_0[0.6002] det_loss_cls_1[0.4670] det_loss_box_1[0.4441] det_loss_cls_2[0.3955] det_loss_box_2[0.4073] det_loss_cls_3[0.3535] det_loss_box_3[0.3965] det_loss_cls_4[0.3337] det_loss_box_4[0.3803] det_loss_cls_5[0.3202] det_loss_box_5[0.3807] det_loss_cls_dn_0[0.0793] det_loss_box_dn_0[0.2346] det_loss_cls_dn_1[0.0754] det_loss_box_dn_1[0.1917] det_loss_cls_dn_2[0.0743] det_loss_box_dn_2[0.1754] det_loss_cls_dn_3[0.0730] det_loss_box_dn_3[0.1719] det_loss_cls_dn_4[0.0720] det_loss_box_dn_4[0.1704] det_loss_cls_dn_5[0.0710] det_loss_box_dn_5[0.1690] loss_depth[0.0353] total_loss[6.5373]
Rank[0/8] 06/20/2025 15:39:15 INFO stats.py:314 | Epoch[194] Step[89] GlobalStep[75749] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 5:59:49.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:39:25 INFO loss_tracker.py:84 | Epoch[194/239] Step[99] GlobalStep[75759/NA]: det_loss_cls_0[0.4863] det_loss_box_0[0.6071] det_loss_cls_1[0.4631] det_loss_box_1[0.4478] det_loss_cls_2[0.3982] det_loss_box_2[0.4001] det_loss_cls_3[0.3669] det_loss_box_3[0.3882] det_loss_cls_4[0.3365] det_loss_box_4[0.3813] det_loss_cls_5[0.3300] det_loss_box_5[0.3733] det_loss_cls_dn_0[0.0904] det_loss_box_dn_0[0.2604] det_loss_cls_dn_1[0.0881] det_loss_box_dn_1[0.2121] det_loss_cls_dn_2[0.0874] det_loss_box_dn_2[0.1939] det_loss_cls_dn_3[0.0861] det_loss_box_dn_3[0.1912] det_loss_cls_dn_4[0.0851] det_loss_box_dn_4[0.1888] det_loss_cls_dn_5[0.0842] det_loss_box_dn_5[0.1842] loss_depth[0.0369] total_loss[6.7677]
Rank[0/8] 06/20/2025 15:39:40 INFO stats.py:314 | Epoch[194] Step[114] GlobalStep[75774] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 5:59:18.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:39:51 INFO loss_tracker.py:84 | Epoch[194/239] Step[124] GlobalStep[75784/NA]: det_loss_cls_0[0.3116] det_loss_box_0[0.3938] det_loss_cls_1[0.3027] det_loss_box_1[0.2908] det_loss_cls_2[0.2549] det_loss_box_2[0.2677] det_loss_cls_3[0.2348] det_loss_box_3[0.2635] det_loss_cls_4[0.2122] det_loss_box_4[0.2627] det_loss_cls_5[0.2079] det_loss_box_5[0.2612] det_loss_cls_dn_0[0.0799] det_loss_box_dn_0[0.2996] det_loss_cls_dn_1[0.0763] det_loss_box_dn_1[0.2379] det_loss_cls_dn_2[0.0758] det_loss_box_dn_2[0.2154] det_loss_cls_dn_3[0.0745] det_loss_box_dn_3[0.2091] det_loss_cls_dn_4[0.0737] det_loss_box_dn_4[0.2060] det_loss_cls_dn_5[0.0729] det_loss_box_dn_5[0.2012] loss_depth[0.0411] total_loss[5.1271]
Rank[0/8] 06/20/2025 15:40:07 INFO stats.py:314 | Epoch[194] Step[139] GlobalStep[75799] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 5:58:47.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:40:17 INFO loss_tracker.py:84 | Epoch[194/239] Step[149] GlobalStep[75809/NA]: det_loss_cls_0[0.4232] det_loss_box_0[0.6454] det_loss_cls_1[0.4222] det_loss_box_1[0.5270] det_loss_cls_2[0.3602] det_loss_box_2[0.5071] det_loss_cls_3[0.3334] det_loss_box_3[0.4961] det_loss_cls_4[0.3199] det_loss_box_4[0.4663] det_loss_cls_5[0.3108] det_loss_box_5[0.4651] det_loss_cls_dn_0[0.0840] det_loss_box_dn_0[0.4598] det_loss_cls_dn_1[0.0802] det_loss_box_dn_1[0.3894] det_loss_cls_dn_2[0.0793] det_loss_box_dn_2[0.3546] det_loss_cls_dn_3[0.0783] det_loss_box_dn_3[0.3480] det_loss_cls_dn_4[0.0774] det_loss_box_dn_4[0.3449] det_loss_cls_dn_5[0.0767] det_loss_box_dn_5[0.3421] loss_depth[0.0346] total_loss[8.0260]
Rank[0/8] 06/20/2025 15:40:32 INFO stats.py:314 | Epoch[194] Step[164] GlobalStep[75824] Training Speed: 8.07 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 5:58:15.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:40:43 INFO loss_tracker.py:84 | Epoch[194/239] Step[174] GlobalStep[75834/NA]: det_loss_cls_0[0.4446] det_loss_box_0[0.5172] det_loss_cls_1[0.4309] det_loss_box_1[0.3942] det_loss_cls_2[0.3750] det_loss_box_2[0.3593] det_loss_cls_3[0.3425] det_loss_box_3[0.3520] det_loss_cls_4[0.3204] det_loss_box_4[0.3425] det_loss_cls_5[0.3124] det_loss_box_5[0.3431] det_loss_cls_dn_0[0.0831] det_loss_box_dn_0[0.2146] det_loss_cls_dn_1[0.0799] det_loss_box_dn_1[0.1711] det_loss_cls_dn_2[0.0796] det_loss_box_dn_2[0.1568] det_loss_cls_dn_3[0.0782] det_loss_box_dn_3[0.1541] det_loss_cls_dn_4[0.0779] det_loss_box_dn_4[0.1515] det_loss_cls_dn_5[0.0769] det_loss_box_dn_5[0.1500] loss_depth[0.0344] total_loss[6.0423]
Rank[0/8] 06/20/2025 15:40:58 INFO stats.py:314 | Epoch[194] Step[189] GlobalStep[75849] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 5:57:44.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:41:08 INFO loss_tracker.py:84 | Epoch[194/239] Step[199] GlobalStep[75859/NA]: det_loss_cls_0[0.4185] det_loss_box_0[0.5641] det_loss_cls_1[0.4249] det_loss_box_1[0.4127] det_loss_cls_2[0.3578] det_loss_box_2[0.3801] det_loss_cls_3[0.3226] det_loss_box_3[0.3753] det_loss_cls_4[0.2976] det_loss_box_4[0.3682] det_loss_cls_5[0.2844] det_loss_box_5[0.3705] det_loss_cls_dn_0[0.0855] det_loss_box_dn_0[0.2764] det_loss_cls_dn_1[0.0815] det_loss_box_dn_1[0.2400] det_loss_cls_dn_2[0.0804] det_loss_box_dn_2[0.2194] det_loss_cls_dn_3[0.0791] det_loss_box_dn_3[0.2078] det_loss_cls_dn_4[0.0777] det_loss_box_dn_4[0.2058] det_loss_cls_dn_5[0.0768] det_loss_box_dn_5[0.2003] loss_depth[0.0332] total_loss[6.4407]
Rank[0/8] 06/20/2025 15:41:24 INFO stats.py:314 | Epoch[194] Step[214] GlobalStep[75874] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 5:57:13.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:41:34 INFO loss_tracker.py:84 | Epoch[194/239] Step[224] GlobalStep[75884/NA]: det_loss_cls_0[0.4284] det_loss_box_0[0.5089] det_loss_cls_1[0.4101] det_loss_box_1[0.3868] det_loss_cls_2[0.3512] det_loss_box_2[0.3467] det_loss_cls_3[0.3177] det_loss_box_3[0.3360] det_loss_cls_4[0.3085] det_loss_box_4[0.3261] det_loss_cls_5[0.2988] det_loss_box_5[0.3222] det_loss_cls_dn_0[0.0871] det_loss_box_dn_0[0.2269] det_loss_cls_dn_1[0.0838] det_loss_box_dn_1[0.1796] det_loss_cls_dn_2[0.0828] det_loss_box_dn_2[0.1613] det_loss_cls_dn_3[0.0819] det_loss_box_dn_3[0.1578] det_loss_cls_dn_4[0.0815] det_loss_box_dn_4[0.1554] det_loss_cls_dn_5[0.0808] det_loss_box_dn_5[0.1536] loss_depth[0.0344] total_loss[5.9082]
Rank[0/8] 06/20/2025 15:41:50 INFO stats.py:314 | Epoch[194] Step[239] GlobalStep[75899] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 5:56:42.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:42:00 INFO loss_tracker.py:84 | Epoch[194/239] Step[249] GlobalStep[75909/NA]: det_loss_cls_0[0.4617] det_loss_box_0[0.5817] det_loss_cls_1[0.4509] det_loss_box_1[0.4124] det_loss_cls_2[0.3850] det_loss_box_2[0.3755] det_loss_cls_3[0.3503] det_loss_box_3[0.3562] det_loss_cls_4[0.3361] det_loss_box_4[0.3320] det_loss_cls_5[0.3296] det_loss_box_5[0.3291] det_loss_cls_dn_0[0.0938] det_loss_box_dn_0[0.2468] det_loss_cls_dn_1[0.0915] det_loss_box_dn_1[0.1999] det_loss_cls_dn_2[0.0907] det_loss_box_dn_2[0.1815] det_loss_cls_dn_3[0.0902] det_loss_box_dn_3[0.1780] det_loss_cls_dn_4[0.0892] det_loss_box_dn_4[0.1752] det_loss_cls_dn_5[0.0886] det_loss_box_dn_5[0.1737] loss_depth[0.0366] total_loss[6.4361]
Rank[0/8] 06/20/2025 15:42:15 INFO stats.py:314 | Epoch[194] Step[264] GlobalStep[75924] Training Speed: 8.73 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 5:56:10.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:42:26 INFO loss_tracker.py:84 | Epoch[194/239] Step[274] GlobalStep[75934/NA]: det_loss_cls_0[0.5252] det_loss_box_0[0.6473] det_loss_cls_1[0.5182] det_loss_box_1[0.4708] det_loss_cls_2[0.4670] det_loss_box_2[0.4355] det_loss_cls_3[0.4408] det_loss_box_3[0.4116] det_loss_cls_4[0.4135] det_loss_box_4[0.4109] det_loss_cls_5[0.4089] det_loss_box_5[0.4026] det_loss_cls_dn_0[0.0954] det_loss_box_dn_0[0.2436] det_loss_cls_dn_1[0.0915] det_loss_box_dn_1[0.1979] det_loss_cls_dn_2[0.0909] det_loss_box_dn_2[0.1799] det_loss_cls_dn_3[0.0900] det_loss_box_dn_3[0.1758] det_loss_cls_dn_4[0.0890] det_loss_box_dn_4[0.1739] det_loss_cls_dn_5[0.0882] det_loss_box_dn_5[0.1714] loss_depth[0.0361] total_loss[7.2758]
Rank[0/8] 06/20/2025 15:42:41 INFO stats.py:314 | Epoch[194] Step[289] GlobalStep[75949] Training Speed: 6.46 samples/sec across all devices.	Average Step Time: 1.24 sec.	Estimated Remaining Time: 5:55:39.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:42:51 INFO loss_tracker.py:84 | Epoch[194/239] Step[299] GlobalStep[75959/NA]: det_loss_cls_0[0.4482] det_loss_box_0[0.5571] det_loss_cls_1[0.4347] det_loss_box_1[0.4218] det_loss_cls_2[0.3780] det_loss_box_2[0.3743] det_loss_cls_3[0.3530] det_loss_box_3[0.3592] det_loss_cls_4[0.3232] det_loss_box_4[0.3474] det_loss_cls_5[0.3097] det_loss_box_5[0.3477] det_loss_cls_dn_0[0.0895] det_loss_box_dn_0[0.3212] det_loss_cls_dn_1[0.0858] det_loss_box_dn_1[0.2510] det_loss_cls_dn_2[0.0847] det_loss_box_dn_2[0.2311] det_loss_cls_dn_3[0.0841] det_loss_box_dn_3[0.2219] det_loss_cls_dn_4[0.0832] det_loss_box_dn_4[0.2183] det_loss_cls_dn_5[0.0824] det_loss_box_dn_5[0.2128] loss_depth[0.0369] total_loss[6.6571]
Rank[0/8] 06/20/2025 15:43:07 INFO stats.py:314 | Epoch[194] Step[314] GlobalStep[75974] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 5:55:08.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:43:17 INFO loss_tracker.py:84 | Epoch[194/239] Step[324] GlobalStep[75984/NA]: det_loss_cls_0[0.4612] det_loss_box_0[0.5475] det_loss_cls_1[0.4461] det_loss_box_1[0.4053] det_loss_cls_2[0.3751] det_loss_box_2[0.3738] det_loss_cls_3[0.3445] det_loss_box_3[0.3555] det_loss_cls_4[0.3227] det_loss_box_4[0.3451] det_loss_cls_5[0.3118] det_loss_box_5[0.3440] det_loss_cls_dn_0[0.0870] det_loss_box_dn_0[0.3030] det_loss_cls_dn_1[0.0833] det_loss_box_dn_1[0.2525] det_loss_cls_dn_2[0.0832] det_loss_box_dn_2[0.2332] det_loss_cls_dn_3[0.0825] det_loss_box_dn_3[0.2276] det_loss_cls_dn_4[0.0817] det_loss_box_dn_4[0.2244] det_loss_cls_dn_5[0.0811] det_loss_box_dn_5[0.2182] loss_depth[0.0351] total_loss[6.6254]
Rank[0/8] 06/20/2025 15:43:33 INFO stats.py:314 | Epoch[194] Step[339] GlobalStep[75999] Training Speed: 8.12 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 5:54:36.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:43:43 INFO loss_tracker.py:84 | Epoch[194/239] Step[349] GlobalStep[76009/NA]: det_loss_cls_0[0.4461] det_loss_box_0[0.6199] det_loss_cls_1[0.4324] det_loss_box_1[0.4850] det_loss_cls_2[0.3768] det_loss_box_2[0.4228] det_loss_cls_3[0.3378] det_loss_box_3[0.4206] det_loss_cls_4[0.3218] det_loss_box_4[0.4084] det_loss_cls_5[0.3060] det_loss_box_5[0.4054] det_loss_cls_dn_0[0.0926] det_loss_box_dn_0[0.2692] det_loss_cls_dn_1[0.0881] det_loss_box_dn_1[0.2267] det_loss_cls_dn_2[0.0870] det_loss_box_dn_2[0.2059] det_loss_cls_dn_3[0.0855] det_loss_box_dn_3[0.2027] det_loss_cls_dn_4[0.0845] det_loss_box_dn_4[0.2003] det_loss_cls_dn_5[0.0836] det_loss_box_dn_5[0.1985] loss_depth[0.0331] total_loss[6.8407]
Rank[0/8] 06/20/2025 15:43:58 INFO stats.py:314 | Epoch[194] Step[364] GlobalStep[76024] Training Speed: 8.58 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 5:54:05.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:44:09 INFO loss_tracker.py:84 | Epoch[194/239] Step[374] GlobalStep[76034/NA]: det_loss_cls_0[0.4368] det_loss_box_0[0.5306] det_loss_cls_1[0.4125] det_loss_box_1[0.3898] det_loss_cls_2[0.3461] det_loss_box_2[0.3529] det_loss_cls_3[0.3206] det_loss_box_3[0.3365] det_loss_cls_4[0.3060] det_loss_box_4[0.3295] det_loss_cls_5[0.2905] det_loss_box_5[0.3317] det_loss_cls_dn_0[0.0985] det_loss_box_dn_0[0.2720] det_loss_cls_dn_1[0.0950] det_loss_box_dn_1[0.2181] det_loss_cls_dn_2[0.0938] det_loss_box_dn_2[0.2025] det_loss_cls_dn_3[0.0931] det_loss_box_dn_3[0.1980] det_loss_cls_dn_4[0.0927] det_loss_box_dn_4[0.1955] det_loss_cls_dn_5[0.0918] det_loss_box_dn_5[0.1915] loss_depth[0.0381] total_loss[6.2641]
Rank[0/8] 06/20/2025 15:44:24 INFO stats.py:314 | Epoch[194] Step[389] GlobalStep[76049] Training Speed: 6.65 samples/sec across all devices.	Average Step Time: 1.20 sec.	Estimated Remaining Time: 5:53:34.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:44:24 INFO stats.py:394 | Epoch[194] completed. Training Speed: 7.72 samples/sec across all devices.	Epoch Time: 404.16 sec.	Average Epoch Time: 404.16 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 5:53:34.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:44:51 INFO stats.py:314 | Epoch[195] Step[24] GlobalStep[76074] Training Speed: 7.71 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 5:53:03.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:44:51 INFO loss_tracker.py:84 | Epoch[195/239] Step[24] GlobalStep[76074/NA]: det_loss_cls_0[0.4801] det_loss_box_0[0.5879] det_loss_cls_1[0.4617] det_loss_box_1[0.4464] det_loss_cls_2[0.4050] det_loss_box_2[0.4047] det_loss_cls_3[0.3736] det_loss_box_3[0.3827] det_loss_cls_4[0.3485] det_loss_box_4[0.3705] det_loss_cls_5[0.3339] det_loss_box_5[0.3714] det_loss_cls_dn_0[0.0881] det_loss_box_dn_0[0.2594] det_loss_cls_dn_1[0.0843] det_loss_box_dn_1[0.2067] det_loss_cls_dn_2[0.0835] det_loss_box_dn_2[0.1828] det_loss_cls_dn_3[0.0823] det_loss_box_dn_3[0.1781] det_loss_cls_dn_4[0.0811] det_loss_box_dn_4[0.1756] det_loss_cls_dn_5[0.0804] det_loss_box_dn_5[0.1736] loss_depth[0.0341] total_loss[6.6764]
Rank[0/8] 06/20/2025 15:45:17 INFO stats.py:314 | Epoch[195] Step[49] GlobalStep[76099] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 5:52:32.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:45:17 INFO loss_tracker.py:84 | Epoch[195/239] Step[49] GlobalStep[76099/NA]: det_loss_cls_0[0.4332] det_loss_box_0[0.5453] det_loss_cls_1[0.4058] det_loss_box_1[0.3865] det_loss_cls_2[0.3596] det_loss_box_2[0.3583] det_loss_cls_3[0.3387] det_loss_box_3[0.3396] det_loss_cls_4[0.3125] det_loss_box_4[0.3358] det_loss_cls_5[0.2945] det_loss_box_5[0.3398] det_loss_cls_dn_0[0.0882] det_loss_box_dn_0[0.2642] det_loss_cls_dn_1[0.0840] det_loss_box_dn_1[0.2140] det_loss_cls_dn_2[0.0824] det_loss_box_dn_2[0.1928] det_loss_cls_dn_3[0.0817] det_loss_box_dn_3[0.1897] det_loss_cls_dn_4[0.0808] det_loss_box_dn_4[0.1867] det_loss_cls_dn_5[0.0796] det_loss_box_dn_5[0.1854] loss_depth[0.0325] total_loss[6.2116]
Rank[0/8] 06/20/2025 15:45:43 INFO stats.py:314 | Epoch[195] Step[74] GlobalStep[76124] Training Speed: 7.93 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 5:52:01.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:45:43 INFO loss_tracker.py:84 | Epoch[195/239] Step[74] GlobalStep[76124/NA]: det_loss_cls_0[0.4462] det_loss_box_0[0.5019] det_loss_cls_1[0.4324] det_loss_box_1[0.3743] det_loss_cls_2[0.3749] det_loss_box_2[0.3422] det_loss_cls_3[0.3378] det_loss_box_3[0.3326] det_loss_cls_4[0.3186] det_loss_box_4[0.3259] det_loss_cls_5[0.3094] det_loss_box_5[0.3269] det_loss_cls_dn_0[0.0980] det_loss_box_dn_0[0.2682] det_loss_cls_dn_1[0.0942] det_loss_box_dn_1[0.2185] det_loss_cls_dn_2[0.0935] det_loss_box_dn_2[0.2024] det_loss_cls_dn_3[0.0924] det_loss_box_dn_3[0.1956] det_loss_cls_dn_4[0.0909] det_loss_box_dn_4[0.1932] det_loss_cls_dn_5[0.0905] det_loss_box_dn_5[0.1860] loss_depth[0.0333] total_loss[6.2796]
Rank[0/8] 06/20/2025 15:46:09 INFO stats.py:314 | Epoch[195] Step[99] GlobalStep[76149] Training Speed: 7.79 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 5:51:29.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:46:09 INFO loss_tracker.py:84 | Epoch[195/239] Step[99] GlobalStep[76149/NA]: det_loss_cls_0[0.4892] det_loss_box_0[0.5875] det_loss_cls_1[0.4819] det_loss_box_1[0.4483] det_loss_cls_2[0.4293] det_loss_box_2[0.4067] det_loss_cls_3[0.3967] det_loss_box_3[0.3921] det_loss_cls_4[0.3695] det_loss_box_4[0.3885] det_loss_cls_5[0.3632] det_loss_box_5[0.3853] det_loss_cls_dn_0[0.0957] det_loss_box_dn_0[0.2618] det_loss_cls_dn_1[0.0935] det_loss_box_dn_1[0.2196] det_loss_cls_dn_2[0.0926] det_loss_box_dn_2[0.2039] det_loss_cls_dn_3[0.0919] det_loss_box_dn_3[0.2002] det_loss_cls_dn_4[0.0916] det_loss_box_dn_4[0.1982] det_loss_cls_dn_5[0.0909] det_loss_box_dn_5[0.1946] loss_depth[0.0364] total_loss[7.0094]
Rank[0/8] 06/20/2025 15:46:35 INFO stats.py:314 | Epoch[195] Step[124] GlobalStep[76174] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 5:50:58.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:46:35 INFO loss_tracker.py:84 | Epoch[195/239] Step[124] GlobalStep[76174/NA]: det_loss_cls_0[0.5561] det_loss_box_0[0.7837] det_loss_cls_1[0.5351] det_loss_box_1[0.6102] det_loss_cls_2[0.4758] det_loss_box_2[0.5531] det_loss_cls_3[0.4419] det_loss_box_3[0.5308] det_loss_cls_4[0.4157] det_loss_box_4[0.5203] det_loss_cls_5[0.4028] det_loss_box_5[0.5193] det_loss_cls_dn_0[0.0932] det_loss_box_dn_0[0.2925] det_loss_cls_dn_1[0.0892] det_loss_box_dn_1[0.2445] det_loss_cls_dn_2[0.0884] det_loss_box_dn_2[0.2257] det_loss_cls_dn_3[0.0868] det_loss_box_dn_3[0.2205] det_loss_cls_dn_4[0.0864] det_loss_box_dn_4[0.2177] det_loss_cls_dn_5[0.0853] det_loss_box_dn_5[0.2167] loss_depth[0.0372] total_loss[8.3287]
Rank[0/8] 06/20/2025 15:47:01 INFO stats.py:314 | Epoch[195] Step[149] GlobalStep[76199] Training Speed: 7.98 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 5:50:27.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:47:01 INFO loss_tracker.py:84 | Epoch[195/239] Step[149] GlobalStep[76199/NA]: det_loss_cls_0[0.5396] det_loss_box_0[0.6802] det_loss_cls_1[0.5218] det_loss_box_1[0.5167] det_loss_cls_2[0.4489] det_loss_box_2[0.4741] det_loss_cls_3[0.4064] det_loss_box_3[0.4631] det_loss_cls_4[0.3756] det_loss_box_4[0.4487] det_loss_cls_5[0.3579] det_loss_box_5[0.4535] det_loss_cls_dn_0[0.0862] det_loss_box_dn_0[0.2615] det_loss_cls_dn_1[0.0825] det_loss_box_dn_1[0.2193] det_loss_cls_dn_2[0.0812] det_loss_box_dn_2[0.2022] det_loss_cls_dn_3[0.0801] det_loss_box_dn_3[0.1990] det_loss_cls_dn_4[0.0787] det_loss_box_dn_4[0.1977] det_loss_cls_dn_5[0.0778] det_loss_box_dn_5[0.1958] loss_depth[0.0389] total_loss[7.4872]
Rank[0/8] 06/20/2025 15:47:27 INFO stats.py:314 | Epoch[195] Step[174] GlobalStep[76224] Training Speed: 7.51 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 5:49:56.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:47:27 INFO loss_tracker.py:84 | Epoch[195/239] Step[174] GlobalStep[76224/NA]: det_loss_cls_0[0.3956] det_loss_box_0[0.4656] det_loss_cls_1[0.3959] det_loss_box_1[0.3373] det_loss_cls_2[0.3287] det_loss_box_2[0.3128] det_loss_cls_3[0.2916] det_loss_box_3[0.3024] det_loss_cls_4[0.2619] det_loss_box_4[0.3067] det_loss_cls_5[0.2498] det_loss_box_5[0.3025] det_loss_cls_dn_0[0.0885] det_loss_box_dn_0[0.2458] det_loss_cls_dn_1[0.0840] det_loss_box_dn_1[0.2030] det_loss_cls_dn_2[0.0826] det_loss_box_dn_2[0.1825] det_loss_cls_dn_3[0.0816] det_loss_box_dn_3[0.1756] det_loss_cls_dn_4[0.0809] det_loss_box_dn_4[0.1729] det_loss_cls_dn_5[0.0798] det_loss_box_dn_5[0.1697] loss_depth[0.0344] total_loss[5.6320]
Rank[0/8] 06/20/2025 15:47:53 INFO stats.py:314 | Epoch[195] Step[199] GlobalStep[76249] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 5:49:25.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:47:53 INFO loss_tracker.py:84 | Epoch[195/239] Step[199] GlobalStep[76249/NA]: det_loss_cls_0[0.4250] det_loss_box_0[0.5542] det_loss_cls_1[0.4148] det_loss_box_1[0.4149] det_loss_cls_2[0.3696] det_loss_box_2[0.3723] det_loss_cls_3[0.3321] det_loss_box_3[0.3682] det_loss_cls_4[0.3130] det_loss_box_4[0.3478] det_loss_cls_5[0.3027] det_loss_box_5[0.3520] det_loss_cls_dn_0[0.0822] det_loss_box_dn_0[0.2471] det_loss_cls_dn_1[0.0789] det_loss_box_dn_1[0.1941] det_loss_cls_dn_2[0.0783] det_loss_box_dn_2[0.1740] det_loss_cls_dn_3[0.0774] det_loss_box_dn_3[0.1694] det_loss_cls_dn_4[0.0767] det_loss_box_dn_4[0.1669] det_loss_cls_dn_5[0.0764] det_loss_box_dn_5[0.1655] loss_depth[0.0350] total_loss[6.1886]
Rank[0/8] 06/20/2025 15:48:19 INFO stats.py:314 | Epoch[195] Step[224] GlobalStep[76274] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 5:48:53.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:48:19 INFO loss_tracker.py:84 | Epoch[195/239] Step[224] GlobalStep[76274/NA]: det_loss_cls_0[0.4117] det_loss_box_0[0.4985] det_loss_cls_1[0.4159] det_loss_box_1[0.3797] det_loss_cls_2[0.3490] det_loss_box_2[0.3445] det_loss_cls_3[0.3183] det_loss_box_3[0.3359] det_loss_cls_4[0.2976] det_loss_box_4[0.3292] det_loss_cls_5[0.2850] det_loss_box_5[0.3319] det_loss_cls_dn_0[0.0959] det_loss_box_dn_0[0.2493] det_loss_cls_dn_1[0.0920] det_loss_box_dn_1[0.2004] det_loss_cls_dn_2[0.0912] det_loss_box_dn_2[0.1831] det_loss_cls_dn_3[0.0905] det_loss_box_dn_3[0.1789] det_loss_cls_dn_4[0.0896] det_loss_box_dn_4[0.1756] det_loss_cls_dn_5[0.0888] det_loss_box_dn_5[0.1745] loss_depth[0.0348] total_loss[6.0419]
Rank[0/8] 06/20/2025 15:48:45 INFO stats.py:314 | Epoch[195] Step[249] GlobalStep[76299] Training Speed: 7.66 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 5:48:22.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:48:45 INFO loss_tracker.py:84 | Epoch[195/239] Step[249] GlobalStep[76299/NA]: det_loss_cls_0[0.4264] det_loss_box_0[0.5122] det_loss_cls_1[0.4095] det_loss_box_1[0.3923] det_loss_cls_2[0.3566] det_loss_box_2[0.3514] det_loss_cls_3[0.3249] det_loss_box_3[0.3388] det_loss_cls_4[0.3046] det_loss_box_4[0.3396] det_loss_cls_5[0.2939] det_loss_box_5[0.3398] det_loss_cls_dn_0[0.0933] det_loss_box_dn_0[0.3036] det_loss_cls_dn_1[0.0884] det_loss_box_dn_1[0.2524] det_loss_cls_dn_2[0.0874] det_loss_box_dn_2[0.2337] det_loss_cls_dn_3[0.0866] det_loss_box_dn_3[0.2290] det_loss_cls_dn_4[0.0859] det_loss_box_dn_4[0.2259] det_loss_cls_dn_5[0.0851] det_loss_box_dn_5[0.2231] loss_depth[0.0343] total_loss[6.4190]
Rank[0/8] 06/20/2025 15:49:11 INFO stats.py:314 | Epoch[195] Step[274] GlobalStep[76324] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 5:47:51.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:49:11 INFO loss_tracker.py:84 | Epoch[195/239] Step[274] GlobalStep[76324/NA]: det_loss_cls_0[0.5397] det_loss_box_0[0.7217] det_loss_cls_1[0.5297] det_loss_box_1[0.5523] det_loss_cls_2[0.4801] det_loss_box_2[0.4990] det_loss_cls_3[0.4457] det_loss_box_3[0.4829] det_loss_cls_4[0.4186] det_loss_box_4[0.4738] det_loss_cls_5[0.4064] det_loss_box_5[0.4712] det_loss_cls_dn_0[0.0909] det_loss_box_dn_0[0.2646] det_loss_cls_dn_1[0.0880] det_loss_box_dn_1[0.2228] det_loss_cls_dn_2[0.0871] det_loss_box_dn_2[0.2053] det_loss_cls_dn_3[0.0858] det_loss_box_dn_3[0.2031] det_loss_cls_dn_4[0.0851] det_loss_box_dn_4[0.2009] det_loss_cls_dn_5[0.0843] det_loss_box_dn_5[0.1987] loss_depth[0.0356] total_loss[7.8732]
Rank[0/8] 06/20/2025 15:49:36 INFO stats.py:314 | Epoch[195] Step[299] GlobalStep[76349] Training Speed: 8.03 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 5:47:20.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:49:36 INFO loss_tracker.py:84 | Epoch[195/239] Step[299] GlobalStep[76349/NA]: det_loss_cls_0[0.4813] det_loss_box_0[0.5714] det_loss_cls_1[0.4809] det_loss_box_1[0.4354] det_loss_cls_2[0.4157] det_loss_box_2[0.3976] det_loss_cls_3[0.3864] det_loss_box_3[0.3800] det_loss_cls_4[0.3558] det_loss_box_4[0.3727] det_loss_cls_5[0.3357] det_loss_box_5[0.3763] det_loss_cls_dn_0[0.0958] det_loss_box_dn_0[0.2826] det_loss_cls_dn_1[0.0920] det_loss_box_dn_1[0.2207] det_loss_cls_dn_2[0.0913] det_loss_box_dn_2[0.2019] det_loss_cls_dn_3[0.0903] det_loss_box_dn_3[0.1928] det_loss_cls_dn_4[0.0889] det_loss_box_dn_4[0.1908] det_loss_cls_dn_5[0.0881] det_loss_box_dn_5[0.1825] loss_depth[0.0315] total_loss[6.8385]
Rank[0/8] 06/20/2025 15:50:02 INFO stats.py:314 | Epoch[195] Step[324] GlobalStep[76374] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 5:46:49.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:50:02 INFO loss_tracker.py:84 | Epoch[195/239] Step[324] GlobalStep[76374/NA]: det_loss_cls_0[0.3761] det_loss_box_0[0.4764] det_loss_cls_1[0.3708] det_loss_box_1[0.3589] det_loss_cls_2[0.3161] det_loss_box_2[0.3151] det_loss_cls_3[0.2849] det_loss_box_3[0.3074] det_loss_cls_4[0.2580] det_loss_box_4[0.3042] det_loss_cls_5[0.2489] det_loss_box_5[0.3033] det_loss_cls_dn_0[0.0839] det_loss_box_dn_0[0.2475] det_loss_cls_dn_1[0.0799] det_loss_box_dn_1[0.2002] det_loss_cls_dn_2[0.0790] det_loss_box_dn_2[0.1834] det_loss_cls_dn_3[0.0775] det_loss_box_dn_3[0.1800] det_loss_cls_dn_4[0.0762] det_loss_box_dn_4[0.1778] det_loss_cls_dn_5[0.0751] det_loss_box_dn_5[0.1759] loss_depth[0.0336] total_loss[5.5901]
Rank[0/8] 06/20/2025 15:50:28 INFO stats.py:314 | Epoch[195] Step[349] GlobalStep[76399] Training Speed: 8.04 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 5:46:17.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:50:28 INFO loss_tracker.py:84 | Epoch[195/239] Step[349] GlobalStep[76399/NA]: det_loss_cls_0[0.4273] det_loss_box_0[0.5457] det_loss_cls_1[0.4181] det_loss_box_1[0.4081] det_loss_cls_2[0.3541] det_loss_box_2[0.3653] det_loss_cls_3[0.3299] det_loss_box_3[0.3559] det_loss_cls_4[0.3054] det_loss_box_4[0.3472] det_loss_cls_5[0.2874] det_loss_box_5[0.3501] det_loss_cls_dn_0[0.0897] det_loss_box_dn_0[0.2707] det_loss_cls_dn_1[0.0850] det_loss_box_dn_1[0.2311] det_loss_cls_dn_2[0.0842] det_loss_box_dn_2[0.2139] det_loss_cls_dn_3[0.0830] det_loss_box_dn_3[0.2089] det_loss_cls_dn_4[0.0820] det_loss_box_dn_4[0.2071] det_loss_cls_dn_5[0.0809] det_loss_box_dn_5[0.2029] loss_depth[0.0353] total_loss[6.3690]
Rank[0/8] 06/20/2025 15:50:53 INFO stats.py:314 | Epoch[195] Step[374] GlobalStep[76424] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 5:45:46.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:50:53 INFO loss_tracker.py:84 | Epoch[195/239] Step[374] GlobalStep[76424/NA]: det_loss_cls_0[0.4001] det_loss_box_0[0.4921] det_loss_cls_1[0.3902] det_loss_box_1[0.3584] det_loss_cls_2[0.3343] det_loss_box_2[0.3147] det_loss_cls_3[0.3031] det_loss_box_3[0.3117] det_loss_cls_4[0.2900] det_loss_box_4[0.2991] det_loss_cls_5[0.2764] det_loss_box_5[0.3009] det_loss_cls_dn_0[0.1011] det_loss_box_dn_0[0.2867] det_loss_cls_dn_1[0.0986] det_loss_box_dn_1[0.2362] det_loss_cls_dn_2[0.0974] det_loss_box_dn_2[0.2140] det_loss_cls_dn_3[0.0962] det_loss_box_dn_3[0.2086] det_loss_cls_dn_4[0.0953] det_loss_box_dn_4[0.2051] det_loss_cls_dn_5[0.0947] det_loss_box_dn_5[0.2024] loss_depth[0.0379] total_loss[6.0450]
Rank[0/8] 06/20/2025 15:51:09 INFO stats.py:394 | Epoch[195] completed. Training Speed: 7.70 samples/sec across all devices.	Epoch Time: 405.01 sec.	Average Epoch Time: 405.01 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 5:45:28.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:51:20 INFO stats.py:314 | Epoch[196] Step[9] GlobalStep[76449] Training Speed: 8.67 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 5:45:15.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:51:36 INFO loss_tracker.py:84 | Epoch[196/239] Step[24] GlobalStep[76464/NA]: det_loss_cls_0[0.4622] det_loss_box_0[0.5989] det_loss_cls_1[0.4488] det_loss_box_1[0.4832] det_loss_cls_2[0.3947] det_loss_box_2[0.4453] det_loss_cls_3[0.3613] det_loss_box_3[0.4268] det_loss_cls_4[0.3421] det_loss_box_4[0.4216] det_loss_cls_5[0.3294] det_loss_box_5[0.4223] det_loss_cls_dn_0[0.0933] det_loss_box_dn_0[0.2939] det_loss_cls_dn_1[0.0897] det_loss_box_dn_1[0.2422] det_loss_cls_dn_2[0.0892] det_loss_box_dn_2[0.2225] det_loss_cls_dn_3[0.0877] det_loss_box_dn_3[0.2174] det_loss_cls_dn_4[0.0871] det_loss_box_dn_4[0.2140] det_loss_cls_dn_5[0.0863] det_loss_box_dn_5[0.2111] loss_depth[0.0354] total_loss[7.1065]
Rank[0/8] 06/20/2025 15:51:47 INFO stats.py:314 | Epoch[196] Step[34] GlobalStep[76474] Training Speed: 7.82 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 5:44:44.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:52:02 INFO loss_tracker.py:84 | Epoch[196/239] Step[49] GlobalStep[76489/NA]: det_loss_cls_0[0.4869] det_loss_box_0[0.6527] det_loss_cls_1[0.4805] det_loss_box_1[0.4968] det_loss_cls_2[0.4196] det_loss_box_2[0.4546] det_loss_cls_3[0.3905] det_loss_box_3[0.4381] det_loss_cls_4[0.3718] det_loss_box_4[0.4228] det_loss_cls_5[0.3619] det_loss_box_5[0.4218] det_loss_cls_dn_0[0.0950] det_loss_box_dn_0[0.2599] det_loss_cls_dn_1[0.0920] det_loss_box_dn_1[0.2033] det_loss_cls_dn_2[0.0909] det_loss_box_dn_2[0.1855] det_loss_cls_dn_3[0.0898] det_loss_box_dn_3[0.1799] det_loss_cls_dn_4[0.0886] det_loss_box_dn_4[0.1774] det_loss_cls_dn_5[0.0878] det_loss_box_dn_5[0.1766] loss_depth[0.0374] total_loss[7.1619]
Rank[0/8] 06/20/2025 15:52:13 INFO stats.py:314 | Epoch[196] Step[59] GlobalStep[76499] Training Speed: 6.69 samples/sec across all devices.	Average Step Time: 1.20 sec.	Estimated Remaining Time: 5:44:13.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:52:28 INFO loss_tracker.py:84 | Epoch[196/239] Step[74] GlobalStep[76514/NA]: det_loss_cls_0[0.5176] det_loss_box_0[0.7313] det_loss_cls_1[0.5089] det_loss_box_1[0.5571] det_loss_cls_2[0.4556] det_loss_box_2[0.5036] det_loss_cls_3[0.4287] det_loss_box_3[0.4857] det_loss_cls_4[0.4069] det_loss_box_4[0.4722] det_loss_cls_5[0.3918] det_loss_box_5[0.4704] det_loss_cls_dn_0[0.0990] det_loss_box_dn_0[0.3903] det_loss_cls_dn_1[0.0951] det_loss_box_dn_1[0.3144] det_loss_cls_dn_2[0.0939] det_loss_box_dn_2[0.2913] det_loss_cls_dn_3[0.0927] det_loss_box_dn_3[0.2793] det_loss_cls_dn_4[0.0918] det_loss_box_dn_4[0.2744] det_loss_cls_dn_5[0.0909] det_loss_box_dn_5[0.2707] loss_depth[0.0372] total_loss[8.3508]
Rank[0/8] 06/20/2025 15:52:38 INFO stats.py:314 | Epoch[196] Step[84] GlobalStep[76524] Training Speed: 8.12 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 5:43:42.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:52:54 INFO loss_tracker.py:84 | Epoch[196/239] Step[99] GlobalStep[76539/NA]: det_loss_cls_0[0.4802] det_loss_box_0[0.6110] det_loss_cls_1[0.4619] det_loss_box_1[0.4500] det_loss_cls_2[0.3879] det_loss_box_2[0.4105] det_loss_cls_3[0.3541] det_loss_box_3[0.3976] det_loss_cls_4[0.3304] det_loss_box_4[0.3847] det_loss_cls_5[0.3162] det_loss_box_5[0.3884] det_loss_cls_dn_0[0.0937] det_loss_box_dn_0[0.2680] det_loss_cls_dn_1[0.0906] det_loss_box_dn_1[0.2140] det_loss_cls_dn_2[0.0895] det_loss_box_dn_2[0.1963] det_loss_cls_dn_3[0.0885] det_loss_box_dn_3[0.1912] det_loss_cls_dn_4[0.0877] det_loss_box_dn_4[0.1875] det_loss_cls_dn_5[0.0872] det_loss_box_dn_5[0.1830] loss_depth[0.0339] total_loss[6.7839]
Rank[0/8] 06/20/2025 15:53:04 INFO stats.py:314 | Epoch[196] Step[109] GlobalStep[76549] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 5:43:11.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:53:20 INFO loss_tracker.py:84 | Epoch[196/239] Step[124] GlobalStep[76564/NA]: det_loss_cls_0[0.4033] det_loss_box_0[0.5239] det_loss_cls_1[0.3900] det_loss_box_1[0.3907] det_loss_cls_2[0.3282] det_loss_box_2[0.3501] det_loss_cls_3[0.2927] det_loss_box_3[0.3436] det_loss_cls_4[0.2728] det_loss_box_4[0.3329] det_loss_cls_5[0.2578] det_loss_box_5[0.3343] det_loss_cls_dn_0[0.0770] det_loss_box_dn_0[0.2483] det_loss_cls_dn_1[0.0733] det_loss_box_dn_1[0.2013] det_loss_cls_dn_2[0.0717] det_loss_box_dn_2[0.1825] det_loss_cls_dn_3[0.0703] det_loss_box_dn_3[0.1790] det_loss_cls_dn_4[0.0692] det_loss_box_dn_4[0.1768] det_loss_cls_dn_5[0.0682] det_loss_box_dn_5[0.1751] loss_depth[0.0372] total_loss[5.8500]
Rank[0/8] 06/20/2025 15:53:30 INFO stats.py:314 | Epoch[196] Step[134] GlobalStep[76574] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 5:42:40.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:53:46 INFO loss_tracker.py:84 | Epoch[196/239] Step[149] GlobalStep[76589/NA]: det_loss_cls_0[0.4997] det_loss_box_0[0.5972] det_loss_cls_1[0.4838] det_loss_box_1[0.4593] det_loss_cls_2[0.4303] det_loss_box_2[0.4192] det_loss_cls_3[0.3829] det_loss_box_3[0.4207] det_loss_cls_4[0.3598] det_loss_box_4[0.4106] det_loss_cls_5[0.3434] det_loss_box_5[0.4154] det_loss_cls_dn_0[0.0887] det_loss_box_dn_0[0.2555] det_loss_cls_dn_1[0.0851] det_loss_box_dn_1[0.2098] det_loss_cls_dn_2[0.0841] det_loss_box_dn_2[0.1893] det_loss_cls_dn_3[0.0829] det_loss_box_dn_3[0.1863] det_loss_cls_dn_4[0.0825] det_loss_box_dn_4[0.1841] det_loss_cls_dn_5[0.0816] det_loss_box_dn_5[0.1832] loss_depth[0.0342] total_loss[6.9697]
Rank[0/8] 06/20/2025 15:53:56 INFO stats.py:314 | Epoch[196] Step[159] GlobalStep[76599] Training Speed: 8.04 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 5:42:09.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:54:12 INFO loss_tracker.py:84 | Epoch[196/239] Step[174] GlobalStep[76614/NA]: det_loss_cls_0[0.5212] det_loss_box_0[0.6560] det_loss_cls_1[0.5045] det_loss_box_1[0.4948] det_loss_cls_2[0.4332] det_loss_box_2[0.4513] det_loss_cls_3[0.3959] det_loss_box_3[0.4423] det_loss_cls_4[0.3750] det_loss_box_4[0.4260] det_loss_cls_5[0.3651] det_loss_box_5[0.4194] det_loss_cls_dn_0[0.0925] det_loss_box_dn_0[0.2789] det_loss_cls_dn_1[0.0895] det_loss_box_dn_1[0.2205] det_loss_cls_dn_2[0.0883] det_loss_box_dn_2[0.2019] det_loss_cls_dn_3[0.0872] det_loss_box_dn_3[0.1980] det_loss_cls_dn_4[0.0860] det_loss_box_dn_4[0.1947] det_loss_cls_dn_5[0.0855] det_loss_box_dn_5[0.1895] loss_depth[0.0342] total_loss[7.3313]
Rank[0/8] 06/20/2025 15:54:23 INFO stats.py:314 | Epoch[196] Step[184] GlobalStep[76624] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 5:41:37.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:54:38 INFO loss_tracker.py:84 | Epoch[196/239] Step[199] GlobalStep[76639/NA]: det_loss_cls_0[0.3413] det_loss_box_0[0.4028] det_loss_cls_1[0.3313] det_loss_box_1[0.2968] det_loss_cls_2[0.2736] det_loss_box_2[0.2742] det_loss_cls_3[0.2469] det_loss_box_3[0.2699] det_loss_cls_4[0.2301] det_loss_box_4[0.2636] det_loss_cls_5[0.2166] det_loss_box_5[0.2660] det_loss_cls_dn_0[0.0737] det_loss_box_dn_0[0.2500] det_loss_cls_dn_1[0.0696] det_loss_box_dn_1[0.1987] det_loss_cls_dn_2[0.0688] det_loss_box_dn_2[0.1755] det_loss_cls_dn_3[0.0674] det_loss_box_dn_3[0.1719] det_loss_cls_dn_4[0.0662] det_loss_box_dn_4[0.1688] det_loss_cls_dn_5[0.0655] det_loss_box_dn_5[0.1653] loss_depth[0.0319] total_loss[4.9864]
Rank[0/8] 06/20/2025 15:54:48 INFO stats.py:314 | Epoch[196] Step[209] GlobalStep[76649] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 5:41:06.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:55:04 INFO loss_tracker.py:84 | Epoch[196/239] Step[224] GlobalStep[76664/NA]: det_loss_cls_0[0.3595] det_loss_box_0[0.4656] det_loss_cls_1[0.3500] det_loss_box_1[0.3639] det_loss_cls_2[0.3048] det_loss_box_2[0.3312] det_loss_cls_3[0.2783] det_loss_box_3[0.3182] det_loss_cls_4[0.2634] det_loss_box_4[0.3044] det_loss_cls_5[0.2494] det_loss_box_5[0.3114] det_loss_cls_dn_0[0.0898] det_loss_box_dn_0[0.2766] det_loss_cls_dn_1[0.0865] det_loss_box_dn_1[0.2297] det_loss_cls_dn_2[0.0848] det_loss_box_dn_2[0.2106] det_loss_cls_dn_3[0.0836] det_loss_box_dn_3[0.2048] det_loss_cls_dn_4[0.0827] det_loss_box_dn_4[0.2016] det_loss_cls_dn_5[0.0818] det_loss_box_dn_5[0.1981] loss_depth[0.0352] total_loss[5.7660]
Rank[0/8] 06/20/2025 15:55:15 INFO stats.py:314 | Epoch[196] Step[234] GlobalStep[76674] Training Speed: 7.98 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 5:40:35.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:55:30 INFO loss_tracker.py:84 | Epoch[196/239] Step[249] GlobalStep[76689/NA]: det_loss_cls_0[0.3479] det_loss_box_0[0.4198] det_loss_cls_1[0.3383] det_loss_box_1[0.3069] det_loss_cls_2[0.2864] det_loss_box_2[0.2792] det_loss_cls_3[0.2560] det_loss_box_3[0.2694] det_loss_cls_4[0.2346] det_loss_box_4[0.2610] det_loss_cls_5[0.2300] det_loss_box_5[0.2599] det_loss_cls_dn_0[0.0868] det_loss_box_dn_0[0.2323] det_loss_cls_dn_1[0.0843] det_loss_box_dn_1[0.1865] det_loss_cls_dn_2[0.0839] det_loss_box_dn_2[0.1703] det_loss_cls_dn_3[0.0827] det_loss_box_dn_3[0.1672] det_loss_cls_dn_4[0.0817] det_loss_box_dn_4[0.1647] det_loss_cls_dn_5[0.0809] det_loss_box_dn_5[0.1637] loss_depth[0.0373] total_loss[5.1115]
Rank[0/8] 06/20/2025 15:55:40 INFO stats.py:314 | Epoch[196] Step[259] GlobalStep[76699] Training Speed: 6.76 samples/sec across all devices.	Average Step Time: 1.18 sec.	Estimated Remaining Time: 5:40:04.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:55:56 INFO loss_tracker.py:84 | Epoch[196/239] Step[274] GlobalStep[76714/NA]: det_loss_cls_0[0.4937] det_loss_box_0[0.6852] det_loss_cls_1[0.4722] det_loss_box_1[0.5323] det_loss_cls_2[0.4208] det_loss_box_2[0.4893] det_loss_cls_3[0.3912] det_loss_box_3[0.4796] det_loss_cls_4[0.3685] det_loss_box_4[0.4647] det_loss_cls_5[0.3542] det_loss_box_5[0.4597] det_loss_cls_dn_0[0.0928] det_loss_box_dn_0[0.3050] det_loss_cls_dn_1[0.0899] det_loss_box_dn_1[0.2579] det_loss_cls_dn_2[0.0888] det_loss_box_dn_2[0.2389] det_loss_cls_dn_3[0.0877] det_loss_box_dn_3[0.2347] det_loss_cls_dn_4[0.0867] det_loss_box_dn_4[0.2316] det_loss_cls_dn_5[0.0863] det_loss_box_dn_5[0.2305] loss_depth[0.0332] total_loss[7.6754]
Rank[0/8] 06/20/2025 15:56:06 INFO stats.py:314 | Epoch[196] Step[284] GlobalStep[76724] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 5:39:33.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:56:22 INFO loss_tracker.py:84 | Epoch[196/239] Step[299] GlobalStep[76739/NA]: det_loss_cls_0[0.6058] det_loss_box_0[0.7996] det_loss_cls_1[0.5849] det_loss_box_1[0.6015] det_loss_cls_2[0.5187] det_loss_box_2[0.5350] det_loss_cls_3[0.4815] det_loss_box_3[0.5169] det_loss_cls_4[0.4584] det_loss_box_4[0.5017] det_loss_cls_5[0.4384] det_loss_box_5[0.5040] det_loss_cls_dn_0[0.0986] det_loss_box_dn_0[0.2546] det_loss_cls_dn_1[0.0952] det_loss_box_dn_1[0.2120] det_loss_cls_dn_2[0.0942] det_loss_box_dn_2[0.1953] det_loss_cls_dn_3[0.0929] det_loss_box_dn_3[0.1928] det_loss_cls_dn_4[0.0917] det_loss_box_dn_4[0.1909] det_loss_cls_dn_5[0.0908] det_loss_box_dn_5[0.1895] loss_depth[0.0366] total_loss[8.3814]
Rank[0/8] 06/20/2025 15:56:33 INFO stats.py:314 | Epoch[196] Step[309] GlobalStep[76749] Training Speed: 7.19 samples/sec across all devices.	Average Step Time: 1.11 sec.	Estimated Remaining Time: 5:39:02.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:56:48 INFO loss_tracker.py:84 | Epoch[196/239] Step[324] GlobalStep[76764/NA]: det_loss_cls_0[0.4295] det_loss_box_0[0.5128] det_loss_cls_1[0.4210] det_loss_box_1[0.3727] det_loss_cls_2[0.3449] det_loss_box_2[0.3402] det_loss_cls_3[0.3136] det_loss_box_3[0.3309] det_loss_cls_4[0.2868] det_loss_box_4[0.3277] det_loss_cls_5[0.2746] det_loss_box_5[0.3266] det_loss_cls_dn_0[0.0884] det_loss_box_dn_0[0.2681] det_loss_cls_dn_1[0.0841] det_loss_box_dn_1[0.2217] det_loss_cls_dn_2[0.0836] det_loss_box_dn_2[0.2059] det_loss_cls_dn_3[0.0825] det_loss_box_dn_3[0.2010] det_loss_cls_dn_4[0.0816] det_loss_box_dn_4[0.1989] det_loss_cls_dn_5[0.0810] det_loss_box_dn_5[0.1955] loss_depth[0.0338] total_loss[6.1074]
Rank[0/8] 06/20/2025 15:56:58 INFO stats.py:314 | Epoch[196] Step[334] GlobalStep[76774] Training Speed: 8.06 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 5:38:31.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:57:13 INFO loss_tracker.py:84 | Epoch[196/239] Step[349] GlobalStep[76789/NA]: det_loss_cls_0[0.3888] det_loss_box_0[0.4411] det_loss_cls_1[0.3836] det_loss_box_1[0.3357] det_loss_cls_2[0.3283] det_loss_box_2[0.3036] det_loss_cls_3[0.2975] det_loss_box_3[0.2993] det_loss_cls_4[0.2812] det_loss_box_4[0.2899] det_loss_cls_5[0.2741] det_loss_box_5[0.2894] det_loss_cls_dn_0[0.0891] det_loss_box_dn_0[0.2681] det_loss_cls_dn_1[0.0853] det_loss_box_dn_1[0.2159] det_loss_cls_dn_2[0.0843] det_loss_box_dn_2[0.1961] det_loss_cls_dn_3[0.0831] det_loss_box_dn_3[0.1909] det_loss_cls_dn_4[0.0826] det_loss_box_dn_4[0.1898] det_loss_cls_dn_5[0.0824] det_loss_box_dn_5[0.1877] loss_depth[0.0349] total_loss[5.7026]
Rank[0/8] 06/20/2025 15:57:23 INFO stats.py:314 | Epoch[196] Step[359] GlobalStep[76799] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 5:37:59.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:57:39 INFO loss_tracker.py:84 | Epoch[196/239] Step[374] GlobalStep[76814/NA]: det_loss_cls_0[0.4803] det_loss_box_0[0.6707] det_loss_cls_1[0.4638] det_loss_box_1[0.5041] det_loss_cls_2[0.3941] det_loss_box_2[0.4743] det_loss_cls_3[0.3648] det_loss_box_3[0.4572] det_loss_cls_4[0.3468] det_loss_box_4[0.4332] det_loss_cls_5[0.3313] det_loss_box_5[0.4320] det_loss_cls_dn_0[0.0906] det_loss_box_dn_0[0.3139] det_loss_cls_dn_1[0.0869] det_loss_box_dn_1[0.2572] det_loss_cls_dn_2[0.0860] det_loss_box_dn_2[0.2320] det_loss_cls_dn_3[0.0846] det_loss_box_dn_3[0.2278] det_loss_cls_dn_4[0.0840] det_loss_box_dn_4[0.2259] det_loss_cls_dn_5[0.0834] det_loss_box_dn_5[0.2239] loss_depth[0.0385] total_loss[7.3872]
Rank[0/8] 06/20/2025 15:57:49 INFO stats.py:314 | Epoch[196] Step[384] GlobalStep[76824] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 5:37:28.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:57:54 INFO stats.py:394 | Epoch[196] completed. Training Speed: 7.70 samples/sec across all devices.	Epoch Time: 405.25 sec.	Average Epoch Time: 405.25 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 5:37:22.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:58:17 INFO stats.py:314 | Epoch[197] Step[19] GlobalStep[76849] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 5:36:58.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:58:22 INFO loss_tracker.py:84 | Epoch[197/239] Step[24] GlobalStep[76854/NA]: det_loss_cls_0[0.4372] det_loss_box_0[0.5431] det_loss_cls_1[0.4304] det_loss_box_1[0.4278] det_loss_cls_2[0.3646] det_loss_box_2[0.3842] det_loss_cls_3[0.3328] det_loss_box_3[0.3712] det_loss_cls_4[0.3162] det_loss_box_4[0.3613] det_loss_cls_5[0.3046] det_loss_box_5[0.3626] det_loss_cls_dn_0[0.0881] det_loss_box_dn_0[0.2551] det_loss_cls_dn_1[0.0847] det_loss_box_dn_1[0.2094] det_loss_cls_dn_2[0.0837] det_loss_box_dn_2[0.1917] det_loss_cls_dn_3[0.0829] det_loss_box_dn_3[0.1874] det_loss_cls_dn_4[0.0821] det_loss_box_dn_4[0.1843] det_loss_cls_dn_5[0.0812] det_loss_box_dn_5[0.1835] loss_depth[0.0349] total_loss[6.3852]
Rank[0/8] 06/20/2025 15:58:43 INFO stats.py:314 | Epoch[197] Step[44] GlobalStep[76874] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 5:36:26.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:58:48 INFO loss_tracker.py:84 | Epoch[197/239] Step[49] GlobalStep[76879/NA]: det_loss_cls_0[0.4331] det_loss_box_0[0.5338] det_loss_cls_1[0.4158] det_loss_box_1[0.4116] det_loss_cls_2[0.3485] det_loss_box_2[0.3671] det_loss_cls_3[0.3181] det_loss_box_3[0.3523] det_loss_cls_4[0.2999] det_loss_box_4[0.3389] det_loss_cls_5[0.2833] det_loss_box_5[0.3408] det_loss_cls_dn_0[0.0840] det_loss_box_dn_0[0.2683] det_loss_cls_dn_1[0.0801] det_loss_box_dn_1[0.2237] det_loss_cls_dn_2[0.0792] det_loss_box_dn_2[0.2026] det_loss_cls_dn_3[0.0784] det_loss_box_dn_3[0.1974] det_loss_cls_dn_4[0.0773] det_loss_box_dn_4[0.1959] det_loss_cls_dn_5[0.0767] det_loss_box_dn_5[0.1927] loss_depth[0.0341] total_loss[6.2336]
Rank[0/8] 06/20/2025 15:59:08 INFO stats.py:314 | Epoch[197] Step[69] GlobalStep[76899] Training Speed: 7.73 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 5:35:55.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:59:14 INFO loss_tracker.py:84 | Epoch[197/239] Step[74] GlobalStep[76904/NA]: det_loss_cls_0[0.4684] det_loss_box_0[0.6027] det_loss_cls_1[0.4588] det_loss_box_1[0.4832] det_loss_cls_2[0.3947] det_loss_box_2[0.4464] det_loss_cls_3[0.3714] det_loss_box_3[0.4324] det_loss_cls_4[0.3456] det_loss_box_4[0.4269] det_loss_cls_5[0.3401] det_loss_box_5[0.4215] det_loss_cls_dn_0[0.0898] det_loss_box_dn_0[0.2635] det_loss_cls_dn_1[0.0865] det_loss_box_dn_1[0.2149] det_loss_cls_dn_2[0.0852] det_loss_box_dn_2[0.1976] det_loss_cls_dn_3[0.0847] det_loss_box_dn_3[0.1933] det_loss_cls_dn_4[0.0839] det_loss_box_dn_4[0.1917] det_loss_cls_dn_5[0.0834] det_loss_box_dn_5[0.1910] loss_depth[0.0344] total_loss[6.9917]
Rank[0/8] 06/20/2025 15:59:34 INFO stats.py:314 | Epoch[197] Step[94] GlobalStep[76924] Training Speed: 8.19 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 5:35:24.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 15:59:40 INFO loss_tracker.py:84 | Epoch[197/239] Step[99] GlobalStep[76929/NA]: det_loss_cls_0[0.4351] det_loss_box_0[0.5247] det_loss_cls_1[0.4132] det_loss_box_1[0.4106] det_loss_cls_2[0.3587] det_loss_box_2[0.3718] det_loss_cls_3[0.3288] det_loss_box_3[0.3533] det_loss_cls_4[0.3042] det_loss_box_4[0.3423] det_loss_cls_5[0.2913] det_loss_box_5[0.3402] det_loss_cls_dn_0[0.0758] det_loss_box_dn_0[0.2564] det_loss_cls_dn_1[0.0717] det_loss_box_dn_1[0.2054] det_loss_cls_dn_2[0.0709] det_loss_box_dn_2[0.1888] det_loss_cls_dn_3[0.0700] det_loss_box_dn_3[0.1848] det_loss_cls_dn_4[0.0686] det_loss_box_dn_4[0.1821] det_loss_cls_dn_5[0.0674] det_loss_box_dn_5[0.1788] loss_depth[0.0338] total_loss[6.1289]
Rank[0/8] 06/20/2025 16:00:00 INFO stats.py:314 | Epoch[197] Step[119] GlobalStep[76949] Training Speed: 6.70 samples/sec across all devices.	Average Step Time: 1.19 sec.	Estimated Remaining Time: 5:34:53.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:00:06 INFO loss_tracker.py:84 | Epoch[197/239] Step[124] GlobalStep[76954/NA]: det_loss_cls_0[0.4394] det_loss_box_0[0.5216] det_loss_cls_1[0.4203] det_loss_box_1[0.3930] det_loss_cls_2[0.3760] det_loss_box_2[0.3437] det_loss_cls_3[0.3365] det_loss_box_3[0.3339] det_loss_cls_4[0.3116] det_loss_box_4[0.3250] det_loss_cls_5[0.3014] det_loss_box_5[0.3253] det_loss_cls_dn_0[0.0868] det_loss_box_dn_0[0.2580] det_loss_cls_dn_1[0.0832] det_loss_box_dn_1[0.2050] det_loss_cls_dn_2[0.0822] det_loss_box_dn_2[0.1858] det_loss_cls_dn_3[0.0818] det_loss_box_dn_3[0.1830] det_loss_cls_dn_4[0.0802] det_loss_box_dn_4[0.1806] det_loss_cls_dn_5[0.0795] det_loss_box_dn_5[0.1786] loss_depth[0.0347] total_loss[6.1471]
Rank[0/8] 06/20/2025 16:00:26 INFO stats.py:314 | Epoch[197] Step[144] GlobalStep[76974] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 5:34:22.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:00:31 INFO loss_tracker.py:84 | Epoch[197/239] Step[149] GlobalStep[76979/NA]: det_loss_cls_0[0.3986] det_loss_box_0[0.4909] det_loss_cls_1[0.3919] det_loss_box_1[0.3828] det_loss_cls_2[0.3325] det_loss_box_2[0.3490] det_loss_cls_3[0.3030] det_loss_box_3[0.3424] det_loss_cls_4[0.2863] det_loss_box_4[0.3233] det_loss_cls_5[0.2752] det_loss_box_5[0.3267] det_loss_cls_dn_0[0.0929] det_loss_box_dn_0[0.3066] det_loss_cls_dn_1[0.0894] det_loss_box_dn_1[0.2426] det_loss_cls_dn_2[0.0887] det_loss_box_dn_2[0.2179] det_loss_cls_dn_3[0.0879] det_loss_box_dn_3[0.2139] det_loss_cls_dn_4[0.0872] det_loss_box_dn_4[0.2100] det_loss_cls_dn_5[0.0865] det_loss_box_dn_5[0.2086] loss_depth[0.0366] total_loss[6.1716]
Rank[0/8] 06/20/2025 16:00:52 INFO stats.py:314 | Epoch[197] Step[169] GlobalStep[76999] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 5:33:51.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:00:57 INFO loss_tracker.py:84 | Epoch[197/239] Step[174] GlobalStep[77004/NA]: det_loss_cls_0[0.4342] det_loss_box_0[0.5412] det_loss_cls_1[0.4153] det_loss_box_1[0.4226] det_loss_cls_2[0.3670] det_loss_box_2[0.3872] det_loss_cls_3[0.3479] det_loss_box_3[0.3680] det_loss_cls_4[0.3231] det_loss_box_4[0.3641] det_loss_cls_5[0.3143] det_loss_box_5[0.3476] det_loss_cls_dn_0[0.0877] det_loss_box_dn_0[0.2801] det_loss_cls_dn_1[0.0839] det_loss_box_dn_1[0.2196] det_loss_cls_dn_2[0.0832] det_loss_box_dn_2[0.2011] det_loss_cls_dn_3[0.0821] det_loss_box_dn_3[0.1945] det_loss_cls_dn_4[0.0812] det_loss_box_dn_4[0.1921] det_loss_cls_dn_5[0.0801] det_loss_box_dn_5[0.1831] loss_depth[0.0355] total_loss[6.4369]
Rank[0/8] 06/20/2025 16:01:18 INFO stats.py:314 | Epoch[197] Step[194] GlobalStep[77024] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 5:33:20.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:01:23 INFO loss_tracker.py:84 | Epoch[197/239] Step[199] GlobalStep[77029/NA]: det_loss_cls_0[0.4857] det_loss_box_0[0.6726] det_loss_cls_1[0.4636] det_loss_box_1[0.5075] det_loss_cls_2[0.4126] det_loss_box_2[0.4655] det_loss_cls_3[0.3865] det_loss_box_3[0.4473] det_loss_cls_4[0.3590] det_loss_box_4[0.4416] det_loss_cls_5[0.3456] det_loss_box_5[0.4408] det_loss_cls_dn_0[0.0900] det_loss_box_dn_0[0.2932] det_loss_cls_dn_1[0.0857] det_loss_box_dn_1[0.2412] det_loss_cls_dn_2[0.0852] det_loss_box_dn_2[0.2252] det_loss_cls_dn_3[0.0837] det_loss_box_dn_3[0.2188] det_loss_cls_dn_4[0.0832] det_loss_box_dn_4[0.2165] det_loss_cls_dn_5[0.0818] det_loss_box_dn_5[0.2135] loss_depth[0.0376] total_loss[7.3839]
Rank[0/8] 06/20/2025 16:01:44 INFO stats.py:314 | Epoch[197] Step[219] GlobalStep[77049] Training Speed: 7.87 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 5:32:49.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:01:48 INFO loss_tracker.py:84 | Epoch[197/239] Step[224] GlobalStep[77054/NA]: det_loss_cls_0[0.3889] det_loss_box_0[0.4349] det_loss_cls_1[0.3653] det_loss_box_1[0.3357] det_loss_cls_2[0.3157] det_loss_box_2[0.3089] det_loss_cls_3[0.2947] det_loss_box_3[0.3004] det_loss_cls_4[0.2792] det_loss_box_4[0.2860] det_loss_cls_5[0.2649] det_loss_box_5[0.2886] det_loss_cls_dn_0[0.0882] det_loss_box_dn_0[0.2558] det_loss_cls_dn_1[0.0833] det_loss_box_dn_1[0.2081] det_loss_cls_dn_2[0.0821] det_loss_box_dn_2[0.1913] det_loss_cls_dn_3[0.0813] det_loss_box_dn_3[0.1866] det_loss_cls_dn_4[0.0800] det_loss_box_dn_4[0.1844] det_loss_cls_dn_5[0.0790] det_loss_box_dn_5[0.1824] loss_depth[0.0343] total_loss[5.5999]
Rank[0/8] 06/20/2025 16:02:10 INFO stats.py:314 | Epoch[197] Step[244] GlobalStep[77074] Training Speed: 7.29 samples/sec across all devices.	Average Step Time: 1.10 sec.	Estimated Remaining Time: 5:32:18.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:02:15 INFO loss_tracker.py:84 | Epoch[197/239] Step[249] GlobalStep[77079/NA]: det_loss_cls_0[0.4703] det_loss_box_0[0.5557] det_loss_cls_1[0.4550] det_loss_box_1[0.3982] det_loss_cls_2[0.3879] det_loss_box_2[0.3516] det_loss_cls_3[0.3590] det_loss_box_3[0.3438] det_loss_cls_4[0.3264] det_loss_box_4[0.3335] det_loss_cls_5[0.3122] det_loss_box_5[0.3344] det_loss_cls_dn_0[0.0812] det_loss_box_dn_0[0.2176] det_loss_cls_dn_1[0.0772] det_loss_box_dn_1[0.1777] det_loss_cls_dn_2[0.0765] det_loss_box_dn_2[0.1607] det_loss_cls_dn_3[0.0755] det_loss_box_dn_3[0.1580] det_loss_cls_dn_4[0.0750] det_loss_box_dn_4[0.1563] det_loss_cls_dn_5[0.0742] det_loss_box_dn_5[0.1546] loss_depth[0.0377] total_loss[6.1501]
Rank[0/8] 06/20/2025 16:02:35 INFO stats.py:314 | Epoch[197] Step[269] GlobalStep[77099] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 5:31:47.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:02:41 INFO loss_tracker.py:84 | Epoch[197/239] Step[274] GlobalStep[77104/NA]: det_loss_cls_0[0.4596] det_loss_box_0[0.5808] det_loss_cls_1[0.4524] det_loss_box_1[0.4188] det_loss_cls_2[0.3817] det_loss_box_2[0.3862] det_loss_cls_3[0.3493] det_loss_box_3[0.3743] det_loss_cls_4[0.3256] det_loss_box_4[0.3729] det_loss_cls_5[0.3141] det_loss_box_5[0.3748] det_loss_cls_dn_0[0.0895] det_loss_box_dn_0[0.2553] det_loss_cls_dn_1[0.0865] det_loss_box_dn_1[0.2047] det_loss_cls_dn_2[0.0847] det_loss_box_dn_2[0.1890] det_loss_cls_dn_3[0.0832] det_loss_box_dn_3[0.1858] det_loss_cls_dn_4[0.0819] det_loss_box_dn_4[0.1839] det_loss_cls_dn_5[0.0813] det_loss_box_dn_5[0.1817] loss_depth[0.0338] total_loss[6.5320]
Rank[0/8] 06/20/2025 16:03:01 INFO stats.py:314 | Epoch[197] Step[294] GlobalStep[77124] Training Speed: 8.84 samples/sec across all devices.	Average Step Time: 0.90 sec.	Estimated Remaining Time: 5:31:15.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:03:06 INFO loss_tracker.py:84 | Epoch[197/239] Step[299] GlobalStep[77129/NA]: det_loss_cls_0[0.4680] det_loss_box_0[0.6681] det_loss_cls_1[0.4465] det_loss_box_1[0.5130] det_loss_cls_2[0.3836] det_loss_box_2[0.4746] det_loss_cls_3[0.3578] det_loss_box_3[0.4443] det_loss_cls_4[0.3371] det_loss_box_4[0.4291] det_loss_cls_5[0.3251] det_loss_box_5[0.4182] det_loss_cls_dn_0[0.0822] det_loss_box_dn_0[0.4005] det_loss_cls_dn_1[0.0784] det_loss_box_dn_1[0.3292] det_loss_cls_dn_2[0.0778] det_loss_box_dn_2[0.3009] det_loss_cls_dn_3[0.0763] det_loss_box_dn_3[0.2915] det_loss_cls_dn_4[0.0753] det_loss_box_dn_4[0.2866] det_loss_cls_dn_5[0.0744] det_loss_box_dn_5[0.2761] loss_depth[0.0378] total_loss[7.6525]
Rank[0/8] 06/20/2025 16:03:27 INFO stats.py:314 | Epoch[197] Step[319] GlobalStep[77149] Training Speed: 6.18 samples/sec across all devices.	Average Step Time: 1.29 sec.	Estimated Remaining Time: 5:30:44.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:03:32 INFO loss_tracker.py:84 | Epoch[197/239] Step[324] GlobalStep[77154/NA]: det_loss_cls_0[0.4791] det_loss_box_0[0.6110] det_loss_cls_1[0.4635] det_loss_box_1[0.4691] det_loss_cls_2[0.4021] det_loss_box_2[0.4209] det_loss_cls_3[0.3690] det_loss_box_3[0.3991] det_loss_cls_4[0.3487] det_loss_box_4[0.3822] det_loss_cls_5[0.3374] det_loss_box_5[0.3846] det_loss_cls_dn_0[0.0926] det_loss_box_dn_0[0.2431] det_loss_cls_dn_1[0.0886] det_loss_box_dn_1[0.1949] det_loss_cls_dn_2[0.0878] det_loss_box_dn_2[0.1796] det_loss_cls_dn_3[0.0865] det_loss_box_dn_3[0.1759] det_loss_cls_dn_4[0.0861] det_loss_box_dn_4[0.1732] det_loss_cls_dn_5[0.0852] det_loss_box_dn_5[0.1727] loss_depth[0.0353] total_loss[6.7682]
Rank[0/8] 06/20/2025 16:03:53 INFO stats.py:314 | Epoch[197] Step[344] GlobalStep[77174] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 5:30:13.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:03:58 INFO loss_tracker.py:84 | Epoch[197/239] Step[349] GlobalStep[77179/NA]: det_loss_cls_0[0.4629] det_loss_box_0[0.5521] det_loss_cls_1[0.4473] det_loss_box_1[0.4213] det_loss_cls_2[0.3746] det_loss_box_2[0.3785] det_loss_cls_3[0.3402] det_loss_box_3[0.3700] det_loss_cls_4[0.3118] det_loss_box_4[0.3670] det_loss_cls_5[0.2995] det_loss_box_5[0.3706] det_loss_cls_dn_0[0.0838] det_loss_box_dn_0[0.2297] det_loss_cls_dn_1[0.0803] det_loss_box_dn_1[0.1849] det_loss_cls_dn_2[0.0800] det_loss_box_dn_2[0.1706] det_loss_cls_dn_3[0.0787] det_loss_box_dn_3[0.1672] det_loss_cls_dn_4[0.0778] det_loss_box_dn_4[0.1658] det_loss_cls_dn_5[0.0770] det_loss_box_dn_5[0.1644] loss_depth[0.0354] total_loss[6.2915]
Rank[0/8] 06/20/2025 16:04:19 INFO stats.py:314 | Epoch[197] Step[369] GlobalStep[77199] Training Speed: 7.80 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 5:29:42.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:04:24 INFO loss_tracker.py:84 | Epoch[197/239] Step[374] GlobalStep[77204/NA]: det_loss_cls_0[0.4891] det_loss_box_0[0.6259] det_loss_cls_1[0.4818] det_loss_box_1[0.4797] det_loss_cls_2[0.4224] det_loss_box_2[0.4376] det_loss_cls_3[0.4052] det_loss_box_3[0.4140] det_loss_cls_4[0.3793] det_loss_box_4[0.4070] det_loss_cls_5[0.3693] det_loss_box_5[0.4039] det_loss_cls_dn_0[0.0934] det_loss_box_dn_0[0.2297] det_loss_cls_dn_1[0.0902] det_loss_box_dn_1[0.1889] det_loss_cls_dn_2[0.0897] det_loss_box_dn_2[0.1732] det_loss_cls_dn_3[0.0884] det_loss_box_dn_3[0.1688] det_loss_cls_dn_4[0.0869] det_loss_box_dn_4[0.1671] det_loss_cls_dn_5[0.0860] det_loss_box_dn_5[0.1662] loss_depth[0.0348] total_loss[6.9786]
Rank[0/8] 06/20/2025 16:04:39 INFO stats.py:394 | Epoch[197] completed. Training Speed: 7.70 samples/sec across all devices.	Epoch Time: 405.15 sec.	Average Epoch Time: 405.15 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 5:29:17.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:04:46 INFO stats.py:314 | Epoch[198] Step[4] GlobalStep[77224] Training Speed: 7.69 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 5:29:12.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:05:07 INFO loss_tracker.py:84 | Epoch[198/239] Step[24] GlobalStep[77244/NA]: det_loss_cls_0[0.4223] det_loss_box_0[0.4860] det_loss_cls_1[0.4157] det_loss_box_1[0.3720] det_loss_cls_2[0.3450] det_loss_box_2[0.3472] det_loss_cls_3[0.3153] det_loss_box_3[0.3336] det_loss_cls_4[0.2924] det_loss_box_4[0.3202] det_loss_cls_5[0.2753] det_loss_box_5[0.3249] det_loss_cls_dn_0[0.0864] det_loss_box_dn_0[0.2289] det_loss_cls_dn_1[0.0832] det_loss_box_dn_1[0.1803] det_loss_cls_dn_2[0.0825] det_loss_box_dn_2[0.1632] det_loss_cls_dn_3[0.0815] det_loss_box_dn_3[0.1581] det_loss_cls_dn_4[0.0808] det_loss_box_dn_4[0.1554] det_loss_cls_dn_5[0.0803] det_loss_box_dn_5[0.1521] loss_depth[0.0353] total_loss[5.8179]
Rank[0/8] 06/20/2025 16:05:12 INFO stats.py:314 | Epoch[198] Step[29] GlobalStep[77249] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 5:28:41.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:05:33 INFO loss_tracker.py:84 | Epoch[198/239] Step[49] GlobalStep[77269/NA]: det_loss_cls_0[0.3700] det_loss_box_0[0.4973] det_loss_cls_1[0.3542] det_loss_box_1[0.3866] det_loss_cls_2[0.3006] det_loss_box_2[0.3487] det_loss_cls_3[0.2701] det_loss_box_3[0.3432] det_loss_cls_4[0.2527] det_loss_box_4[0.3365] det_loss_cls_5[0.2427] det_loss_box_5[0.3383] det_loss_cls_dn_0[0.0859] det_loss_box_dn_0[0.4001] det_loss_cls_dn_1[0.0825] det_loss_box_dn_1[0.3278] det_loss_cls_dn_2[0.0819] det_loss_box_dn_2[0.3023] det_loss_cls_dn_3[0.0811] det_loss_box_dn_3[0.2977] det_loss_cls_dn_4[0.0800] det_loss_box_dn_4[0.2936] det_loss_cls_dn_5[0.0790] det_loss_box_dn_5[0.2915] loss_depth[0.0353] total_loss[6.4795]
Rank[0/8] 06/20/2025 16:05:38 INFO stats.py:314 | Epoch[198] Step[54] GlobalStep[77274] Training Speed: 7.72 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 5:28:09.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:05:59 INFO loss_tracker.py:84 | Epoch[198/239] Step[74] GlobalStep[77294/NA]: det_loss_cls_0[0.4148] det_loss_box_0[0.5158] det_loss_cls_1[0.4116] det_loss_box_1[0.3865] det_loss_cls_2[0.3577] det_loss_box_2[0.3575] det_loss_cls_3[0.3312] det_loss_box_3[0.3372] det_loss_cls_4[0.3116] det_loss_box_4[0.3240] det_loss_cls_5[0.2995] det_loss_box_5[0.3240] det_loss_cls_dn_0[0.0929] det_loss_box_dn_0[0.2441] det_loss_cls_dn_1[0.0879] det_loss_box_dn_1[0.1951] det_loss_cls_dn_2[0.0872] det_loss_box_dn_2[0.1770] det_loss_cls_dn_3[0.0860] det_loss_box_dn_3[0.1740] det_loss_cls_dn_4[0.0854] det_loss_box_dn_4[0.1704] det_loss_cls_dn_5[0.0845] det_loss_box_dn_5[0.1690] loss_depth[0.0333] total_loss[6.0582]
Rank[0/8] 06/20/2025 16:06:04 INFO stats.py:314 | Epoch[198] Step[79] GlobalStep[77299] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 5:27:38.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:06:24 INFO loss_tracker.py:84 | Epoch[198/239] Step[99] GlobalStep[77319/NA]: det_loss_cls_0[0.5413] det_loss_box_0[0.6810] det_loss_cls_1[0.5246] det_loss_box_1[0.5032] det_loss_cls_2[0.4645] det_loss_box_2[0.4463] det_loss_cls_3[0.4348] det_loss_box_3[0.4198] det_loss_cls_4[0.4042] det_loss_box_4[0.4153] det_loss_cls_5[0.3942] det_loss_box_5[0.4101] det_loss_cls_dn_0[0.0992] det_loss_box_dn_0[0.3043] det_loss_cls_dn_1[0.0953] det_loss_box_dn_1[0.2432] det_loss_cls_dn_2[0.0946] det_loss_box_dn_2[0.2176] det_loss_cls_dn_3[0.0934] det_loss_box_dn_3[0.2112] det_loss_cls_dn_4[0.0923] det_loss_box_dn_4[0.2077] det_loss_cls_dn_5[0.0914] det_loss_box_dn_5[0.2028] loss_depth[0.0362] total_loss[7.6283]
Rank[0/8] 06/20/2025 16:06:29 INFO stats.py:314 | Epoch[198] Step[104] GlobalStep[77324] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 5:27:07.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:06:50 INFO loss_tracker.py:84 | Epoch[198/239] Step[124] GlobalStep[77344/NA]: det_loss_cls_0[0.4134] det_loss_box_0[0.5255] det_loss_cls_1[0.4016] det_loss_box_1[0.3730] det_loss_cls_2[0.3484] det_loss_box_2[0.3304] det_loss_cls_3[0.3161] det_loss_box_3[0.3153] det_loss_cls_4[0.2952] det_loss_box_4[0.3123] det_loss_cls_5[0.2762] det_loss_box_5[0.3168] det_loss_cls_dn_0[0.0853] det_loss_box_dn_0[0.2595] det_loss_cls_dn_1[0.0804] det_loss_box_dn_1[0.2070] det_loss_cls_dn_2[0.0794] det_loss_box_dn_2[0.1819] det_loss_cls_dn_3[0.0790] det_loss_box_dn_3[0.1766] det_loss_cls_dn_4[0.0783] det_loss_box_dn_4[0.1747] det_loss_cls_dn_5[0.0779] det_loss_box_dn_5[0.1726] loss_depth[0.0347] total_loss[5.9116]
Rank[0/8] 06/20/2025 16:06:55 INFO stats.py:314 | Epoch[198] Step[129] GlobalStep[77349] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 5:26:36.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:07:16 INFO loss_tracker.py:84 | Epoch[198/239] Step[149] GlobalStep[77369/NA]: det_loss_cls_0[0.4143] det_loss_box_0[0.4842] det_loss_cls_1[0.4022] det_loss_box_1[0.3763] det_loss_cls_2[0.3447] det_loss_box_2[0.3373] det_loss_cls_3[0.3126] det_loss_box_3[0.3319] det_loss_cls_4[0.2892] det_loss_box_4[0.3251] det_loss_cls_5[0.2839] det_loss_box_5[0.3204] det_loss_cls_dn_0[0.0853] det_loss_box_dn_0[0.2294] det_loss_cls_dn_1[0.0824] det_loss_box_dn_1[0.1837] det_loss_cls_dn_2[0.0816] det_loss_box_dn_2[0.1664] det_loss_cls_dn_3[0.0801] det_loss_box_dn_3[0.1615] det_loss_cls_dn_4[0.0793] det_loss_box_dn_4[0.1588] det_loss_cls_dn_5[0.0786] det_loss_box_dn_5[0.1575] loss_depth[0.0342] total_loss[5.8008]
Rank[0/8] 06/20/2025 16:07:21 INFO stats.py:314 | Epoch[198] Step[154] GlobalStep[77374] Training Speed: 8.12 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 5:26:05.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:07:41 INFO loss_tracker.py:84 | Epoch[198/239] Step[174] GlobalStep[77394/NA]: det_loss_cls_0[0.5822] det_loss_box_0[0.7805] det_loss_cls_1[0.5748] det_loss_box_1[0.5991] det_loss_cls_2[0.5118] det_loss_box_2[0.5602] det_loss_cls_3[0.4761] det_loss_box_3[0.5399] det_loss_cls_4[0.4402] det_loss_box_4[0.5193] det_loss_cls_5[0.4234] det_loss_box_5[0.5196] det_loss_cls_dn_0[0.0871] det_loss_box_dn_0[0.2697] det_loss_cls_dn_1[0.0826] det_loss_box_dn_1[0.2266] det_loss_cls_dn_2[0.0815] det_loss_box_dn_2[0.2113] det_loss_cls_dn_3[0.0803] det_loss_box_dn_3[0.2072] det_loss_cls_dn_4[0.0794] det_loss_box_dn_4[0.2045] det_loss_cls_dn_5[0.0786] det_loss_box_dn_5[0.2026] loss_depth[0.0352] total_loss[8.3738]
Rank[0/8] 06/20/2025 16:07:46 INFO stats.py:314 | Epoch[198] Step[179] GlobalStep[77399] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 5:25:34.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:08:08 INFO loss_tracker.py:84 | Epoch[198/239] Step[199] GlobalStep[77419/NA]: det_loss_cls_0[0.4211] det_loss_box_0[0.5087] det_loss_cls_1[0.4142] det_loss_box_1[0.3755] det_loss_cls_2[0.3525] det_loss_box_2[0.3542] det_loss_cls_3[0.3141] det_loss_box_3[0.3423] det_loss_cls_4[0.2933] det_loss_box_4[0.3367] det_loss_cls_5[0.2807] det_loss_box_5[0.3351] det_loss_cls_dn_0[0.0846] det_loss_box_dn_0[0.2222] det_loss_cls_dn_1[0.0798] det_loss_box_dn_1[0.1776] det_loss_cls_dn_2[0.0788] det_loss_box_dn_2[0.1608] det_loss_cls_dn_3[0.0775] det_loss_box_dn_3[0.1576] det_loss_cls_dn_4[0.0765] det_loss_box_dn_4[0.1566] det_loss_cls_dn_5[0.0758] det_loss_box_dn_5[0.1550] loss_depth[0.0331] total_loss[5.8645]
Rank[0/8] 06/20/2025 16:08:13 INFO stats.py:314 | Epoch[198] Step[204] GlobalStep[77424] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 5:25:03.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:08:34 INFO loss_tracker.py:84 | Epoch[198/239] Step[224] GlobalStep[77444/NA]: det_loss_cls_0[0.4332] det_loss_box_0[0.5743] det_loss_cls_1[0.4210] det_loss_box_1[0.4392] det_loss_cls_2[0.3690] det_loss_box_2[0.3998] det_loss_cls_3[0.3281] det_loss_box_3[0.3867] det_loss_cls_4[0.3120] det_loss_box_4[0.3671] det_loss_cls_5[0.2947] det_loss_box_5[0.3702] det_loss_cls_dn_0[0.0854] det_loss_box_dn_0[0.2915] det_loss_cls_dn_1[0.0810] det_loss_box_dn_1[0.2440] det_loss_cls_dn_2[0.0799] det_loss_box_dn_2[0.2270] det_loss_cls_dn_3[0.0789] det_loss_box_dn_3[0.2245] det_loss_cls_dn_4[0.0783] det_loss_box_dn_4[0.2224] det_loss_cls_dn_5[0.0769] det_loss_box_dn_5[0.2210] loss_depth[0.0362] total_loss[6.6422]
Rank[0/8] 06/20/2025 16:08:39 INFO stats.py:314 | Epoch[198] Step[229] GlobalStep[77449] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 5:24:32.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:08:59 INFO loss_tracker.py:84 | Epoch[198/239] Step[249] GlobalStep[77469/NA]: det_loss_cls_0[0.4838] det_loss_box_0[0.6564] det_loss_cls_1[0.4823] det_loss_box_1[0.4838] det_loss_cls_2[0.4177] det_loss_box_2[0.4357] det_loss_cls_3[0.3905] det_loss_box_3[0.4246] det_loss_cls_4[0.3670] det_loss_box_4[0.4096] det_loss_cls_5[0.3509] det_loss_box_5[0.4127] det_loss_cls_dn_0[0.0947] det_loss_box_dn_0[0.2553] det_loss_cls_dn_1[0.0914] det_loss_box_dn_1[0.2144] det_loss_cls_dn_2[0.0904] det_loss_box_dn_2[0.1907] det_loss_cls_dn_3[0.0896] det_loss_box_dn_3[0.1872] det_loss_cls_dn_4[0.0886] det_loss_box_dn_4[0.1849] det_loss_cls_dn_5[0.0879] det_loss_box_dn_5[0.1832] loss_depth[0.0370] total_loss[7.1103]
Rank[0/8] 06/20/2025 16:09:05 INFO stats.py:314 | Epoch[198] Step[254] GlobalStep[77474] Training Speed: 7.78 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 5:24:01.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:09:25 INFO loss_tracker.py:84 | Epoch[198/239] Step[274] GlobalStep[77494/NA]: det_loss_cls_0[0.4985] det_loss_box_0[0.5767] det_loss_cls_1[0.4726] det_loss_box_1[0.4322] det_loss_cls_2[0.4172] det_loss_box_2[0.3895] det_loss_cls_3[0.3862] det_loss_box_3[0.3762] det_loss_cls_4[0.3566] det_loss_box_4[0.3646] det_loss_cls_5[0.3425] det_loss_box_5[0.3663] det_loss_cls_dn_0[0.0858] det_loss_box_dn_0[0.2126] det_loss_cls_dn_1[0.0828] det_loss_box_dn_1[0.1738] det_loss_cls_dn_2[0.0817] det_loss_box_dn_2[0.1601] det_loss_cls_dn_3[0.0806] det_loss_box_dn_3[0.1570] det_loss_cls_dn_4[0.0804] det_loss_box_dn_4[0.1558] det_loss_cls_dn_5[0.0797] det_loss_box_dn_5[0.1536] loss_depth[0.0349] total_loss[6.5179]
Rank[0/8] 06/20/2025 16:09:31 INFO stats.py:314 | Epoch[198] Step[279] GlobalStep[77499] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 5:23:30.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:09:52 INFO loss_tracker.py:84 | Epoch[198/239] Step[299] GlobalStep[77519/NA]: det_loss_cls_0[0.5073] det_loss_box_0[0.5862] det_loss_cls_1[0.4958] det_loss_box_1[0.4538] det_loss_cls_2[0.4403] det_loss_box_2[0.3953] det_loss_cls_3[0.4065] det_loss_box_3[0.3821] det_loss_cls_4[0.3782] det_loss_box_4[0.3776] det_loss_cls_5[0.3612] det_loss_box_5[0.3796] det_loss_cls_dn_0[0.0927] det_loss_box_dn_0[0.2353] det_loss_cls_dn_1[0.0888] det_loss_box_dn_1[0.1911] det_loss_cls_dn_2[0.0875] det_loss_box_dn_2[0.1722] det_loss_cls_dn_3[0.0858] det_loss_box_dn_3[0.1680] det_loss_cls_dn_4[0.0850] det_loss_box_dn_4[0.1658] det_loss_cls_dn_5[0.0840] det_loss_box_dn_5[0.1642] loss_depth[0.0336] total_loss[6.8181]
Rank[0/8] 06/20/2025 16:09:57 INFO stats.py:314 | Epoch[198] Step[304] GlobalStep[77524] Training Speed: 8.58 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 5:22:59.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:10:18 INFO loss_tracker.py:84 | Epoch[198/239] Step[324] GlobalStep[77544/NA]: det_loss_cls_0[0.4907] det_loss_box_0[0.5678] det_loss_cls_1[0.4744] det_loss_box_1[0.4154] det_loss_cls_2[0.4100] det_loss_box_2[0.3771] det_loss_cls_3[0.3787] det_loss_box_3[0.3623] det_loss_cls_4[0.3564] det_loss_box_4[0.3538] det_loss_cls_5[0.3383] det_loss_box_5[0.3589] det_loss_cls_dn_0[0.0904] det_loss_box_dn_0[0.2673] det_loss_cls_dn_1[0.0868] det_loss_box_dn_1[0.2185] det_loss_cls_dn_2[0.0858] det_loss_box_dn_2[0.1960] det_loss_cls_dn_3[0.0851] det_loss_box_dn_3[0.1899] det_loss_cls_dn_4[0.0844] det_loss_box_dn_4[0.1875] det_loss_cls_dn_5[0.0836] det_loss_box_dn_5[0.1807] loss_depth[0.0334] total_loss[6.6732]
Rank[0/8] 06/20/2025 16:10:23 INFO stats.py:314 | Epoch[198] Step[329] GlobalStep[77549] Training Speed: 8.68 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 5:22:28.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:10:43 INFO loss_tracker.py:84 | Epoch[198/239] Step[349] GlobalStep[77569/NA]: det_loss_cls_0[0.4875] det_loss_box_0[0.6572] det_loss_cls_1[0.4697] det_loss_box_1[0.4841] det_loss_cls_2[0.4045] det_loss_box_2[0.4254] det_loss_cls_3[0.3809] det_loss_box_3[0.4121] det_loss_cls_4[0.3625] det_loss_box_4[0.3973] det_loss_cls_5[0.3482] det_loss_box_5[0.3984] det_loss_cls_dn_0[0.0974] det_loss_box_dn_0[0.2688] det_loss_cls_dn_1[0.0942] det_loss_box_dn_1[0.2208] det_loss_cls_dn_2[0.0935] det_loss_box_dn_2[0.2029] det_loss_cls_dn_3[0.0922] det_loss_box_dn_3[0.1984] det_loss_cls_dn_4[0.0916] det_loss_box_dn_4[0.1968] det_loss_cls_dn_5[0.0906] det_loss_box_dn_5[0.1962] loss_depth[0.0358] total_loss[7.1067]
Rank[0/8] 06/20/2025 16:10:48 INFO stats.py:314 | Epoch[198] Step[354] GlobalStep[77574] Training Speed: 6.11 samples/sec across all devices.	Average Step Time: 1.31 sec.	Estimated Remaining Time: 5:21:57.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:11:09 INFO loss_tracker.py:84 | Epoch[198/239] Step[374] GlobalStep[77594/NA]: det_loss_cls_0[0.4615] det_loss_box_0[0.6166] det_loss_cls_1[0.4430] det_loss_box_1[0.4761] det_loss_cls_2[0.4073] det_loss_box_2[0.4240] det_loss_cls_3[0.3640] det_loss_box_3[0.4135] det_loss_cls_4[0.3448] det_loss_box_4[0.4002] det_loss_cls_5[0.3317] det_loss_box_5[0.4038] det_loss_cls_dn_0[0.0902] det_loss_box_dn_0[0.2891] det_loss_cls_dn_1[0.0871] det_loss_box_dn_1[0.2403] det_loss_cls_dn_2[0.0863] det_loss_box_dn_2[0.2177] det_loss_cls_dn_3[0.0851] det_loss_box_dn_3[0.2130] det_loss_cls_dn_4[0.0838] det_loss_box_dn_4[0.2102] det_loss_cls_dn_5[0.0828] det_loss_box_dn_5[0.2091] loss_depth[0.0358] total_loss[7.0172]
Rank[0/8] 06/20/2025 16:11:14 INFO stats.py:314 | Epoch[198] Step[379] GlobalStep[77599] Training Speed: 6.63 samples/sec across all devices.	Average Step Time: 1.21 sec.	Estimated Remaining Time: 5:21:26.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:11:24 INFO stats.py:394 | Epoch[198] completed. Training Speed: 7.70 samples/sec across all devices.	Epoch Time: 405.03 sec.	Average Epoch Time: 405.03 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 5:21:13.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:11:42 INFO stats.py:314 | Epoch[199] Step[14] GlobalStep[77624] Training Speed: 7.91 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 5:20:55.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:11:53 INFO loss_tracker.py:84 | Epoch[199/239] Step[24] GlobalStep[77634/NA]: det_loss_cls_0[0.4327] det_loss_box_0[0.5695] det_loss_cls_1[0.4230] det_loss_box_1[0.4239] det_loss_cls_2[0.3667] det_loss_box_2[0.3791] det_loss_cls_3[0.3384] det_loss_box_3[0.3727] det_loss_cls_4[0.3166] det_loss_box_4[0.3615] det_loss_cls_5[0.3064] det_loss_box_5[0.3611] det_loss_cls_dn_0[0.0894] det_loss_box_dn_0[0.2547] det_loss_cls_dn_1[0.0863] det_loss_box_dn_1[0.2092] det_loss_cls_dn_2[0.0853] det_loss_box_dn_2[0.1922] det_loss_cls_dn_3[0.0847] det_loss_box_dn_3[0.1880] det_loss_cls_dn_4[0.0836] det_loss_box_dn_4[0.1850] det_loss_cls_dn_5[0.0830] det_loss_box_dn_5[0.1817] loss_depth[0.0356] total_loss[6.4104]
Rank[0/8] 06/20/2025 16:12:08 INFO stats.py:314 | Epoch[199] Step[39] GlobalStep[77649] Training Speed: 8.08 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 5:20:24.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:12:19 INFO loss_tracker.py:84 | Epoch[199/239] Step[49] GlobalStep[77659/NA]: det_loss_cls_0[0.4674] det_loss_box_0[0.5772] det_loss_cls_1[0.4706] det_loss_box_1[0.4372] det_loss_cls_2[0.4082] det_loss_box_2[0.4112] det_loss_cls_3[0.3703] det_loss_box_3[0.3980] det_loss_cls_4[0.3516] det_loss_box_4[0.3892] det_loss_cls_5[0.3407] det_loss_box_5[0.3914] det_loss_cls_dn_0[0.0897] det_loss_box_dn_0[0.2623] det_loss_cls_dn_1[0.0853] det_loss_box_dn_1[0.2132] det_loss_cls_dn_2[0.0844] det_loss_box_dn_2[0.1975] det_loss_cls_dn_3[0.0832] det_loss_box_dn_3[0.1930] det_loss_cls_dn_4[0.0821] det_loss_box_dn_4[0.1912] det_loss_cls_dn_5[0.0812] det_loss_box_dn_5[0.1899] loss_depth[0.0355] total_loss[6.8015]
Rank[0/8] 06/20/2025 16:12:34 INFO stats.py:314 | Epoch[199] Step[64] GlobalStep[77674] Training Speed: 7.89 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 5:19:53.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:12:45 INFO loss_tracker.py:84 | Epoch[199/239] Step[74] GlobalStep[77684/NA]: det_loss_cls_0[0.5165] det_loss_box_0[0.7066] det_loss_cls_1[0.5019] det_loss_box_1[0.5550] det_loss_cls_2[0.4520] det_loss_box_2[0.5106] det_loss_cls_3[0.4175] det_loss_box_3[0.4958] det_loss_cls_4[0.3920] det_loss_box_4[0.4804] det_loss_cls_5[0.3844] det_loss_box_5[0.4775] det_loss_cls_dn_0[0.0978] det_loss_box_dn_0[0.3866] det_loss_cls_dn_1[0.0934] det_loss_box_dn_1[0.3189] det_loss_cls_dn_2[0.0928] det_loss_box_dn_2[0.2917] det_loss_cls_dn_3[0.0920] det_loss_box_dn_3[0.2893] det_loss_cls_dn_4[0.0912] det_loss_box_dn_4[0.2839] det_loss_cls_dn_5[0.0907] det_loss_box_dn_5[0.2799] loss_depth[0.0359] total_loss[8.3341]
Rank[0/8] 06/20/2025 16:13:01 INFO stats.py:314 | Epoch[199] Step[89] GlobalStep[77699] Training Speed: 7.84 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 5:19:23.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:13:11 INFO loss_tracker.py:84 | Epoch[199/239] Step[99] GlobalStep[77709/NA]: det_loss_cls_0[0.5108] det_loss_box_0[0.6025] det_loss_cls_1[0.4919] det_loss_box_1[0.4474] det_loss_cls_2[0.4319] det_loss_box_2[0.4030] det_loss_cls_3[0.3943] det_loss_box_3[0.3960] det_loss_cls_4[0.3705] det_loss_box_4[0.3842] det_loss_cls_5[0.3519] det_loss_box_5[0.3874] det_loss_cls_dn_0[0.0990] det_loss_box_dn_0[0.2348] det_loss_cls_dn_1[0.0958] det_loss_box_dn_1[0.1926] det_loss_cls_dn_2[0.0949] det_loss_box_dn_2[0.1751] det_loss_cls_dn_3[0.0939] det_loss_box_dn_3[0.1714] det_loss_cls_dn_4[0.0929] det_loss_box_dn_4[0.1701] det_loss_cls_dn_5[0.0920] det_loss_box_dn_5[0.1685] loss_depth[0.0355] total_loss[6.8884]
Rank[0/8] 06/20/2025 16:13:26 INFO stats.py:314 | Epoch[199] Step[114] GlobalStep[77724] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 5:18:51.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:13:36 INFO loss_tracker.py:84 | Epoch[199/239] Step[124] GlobalStep[77734/NA]: det_loss_cls_0[0.4217] det_loss_box_0[0.5038] det_loss_cls_1[0.4079] det_loss_box_1[0.3805] det_loss_cls_2[0.3422] det_loss_box_2[0.3385] det_loss_cls_3[0.3213] det_loss_box_3[0.3274] det_loss_cls_4[0.3055] det_loss_box_4[0.3167] det_loss_cls_5[0.2921] det_loss_box_5[0.3185] det_loss_cls_dn_0[0.0877] det_loss_box_dn_0[0.2604] det_loss_cls_dn_1[0.0842] det_loss_box_dn_1[0.2066] det_loss_cls_dn_2[0.0828] det_loss_box_dn_2[0.1841] det_loss_cls_dn_3[0.0822] det_loss_box_dn_3[0.1812] det_loss_cls_dn_4[0.0820] det_loss_box_dn_4[0.1789] det_loss_cls_dn_5[0.0814] det_loss_box_dn_5[0.1728] loss_depth[0.0360] total_loss[5.9964]
Rank[0/8] 06/20/2025 16:13:52 INFO stats.py:314 | Epoch[199] Step[139] GlobalStep[77749] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 5:18:20.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:14:03 INFO loss_tracker.py:84 | Epoch[199/239] Step[149] GlobalStep[77759/NA]: det_loss_cls_0[0.4438] det_loss_box_0[0.5476] det_loss_cls_1[0.4348] det_loss_box_1[0.4128] det_loss_cls_2[0.3573] det_loss_box_2[0.3750] det_loss_cls_3[0.3218] det_loss_box_3[0.3620] det_loss_cls_4[0.2963] det_loss_box_4[0.3526] det_loss_cls_5[0.2797] det_loss_box_5[0.3561] det_loss_cls_dn_0[0.0872] det_loss_box_dn_0[0.3040] det_loss_cls_dn_1[0.0843] det_loss_box_dn_1[0.2400] det_loss_cls_dn_2[0.0832] det_loss_box_dn_2[0.2193] det_loss_cls_dn_3[0.0826] det_loss_box_dn_3[0.2160] det_loss_cls_dn_4[0.0819] det_loss_box_dn_4[0.2143] det_loss_cls_dn_5[0.0816] det_loss_box_dn_5[0.2035] loss_depth[0.0346] total_loss[6.4723]
Rank[0/8] 06/20/2025 16:14:18 INFO stats.py:314 | Epoch[199] Step[164] GlobalStep[77774] Training Speed: 7.66 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 5:17:50.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:14:29 INFO loss_tracker.py:84 | Epoch[199/239] Step[174] GlobalStep[77784/NA]: det_loss_cls_0[0.4513] det_loss_box_0[0.5217] det_loss_cls_1[0.4366] det_loss_box_1[0.3808] det_loss_cls_2[0.3783] det_loss_box_2[0.3511] det_loss_cls_3[0.3510] det_loss_box_3[0.3324] det_loss_cls_4[0.3294] det_loss_box_4[0.3260] det_loss_cls_5[0.3210] det_loss_box_5[0.3233] det_loss_cls_dn_0[0.0978] det_loss_box_dn_0[0.2388] det_loss_cls_dn_1[0.0940] det_loss_box_dn_1[0.1969] det_loss_cls_dn_2[0.0930] det_loss_box_dn_2[0.1809] det_loss_cls_dn_3[0.0920] det_loss_box_dn_3[0.1774] det_loss_cls_dn_4[0.0908] det_loss_box_dn_4[0.1755] det_loss_cls_dn_5[0.0900] det_loss_box_dn_5[0.1740] loss_depth[0.0357] total_loss[6.2400]
Rank[0/8] 06/20/2025 16:14:44 INFO stats.py:314 | Epoch[199] Step[189] GlobalStep[77799] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 5:17:18.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:14:54 INFO loss_tracker.py:84 | Epoch[199/239] Step[199] GlobalStep[77809/NA]: det_loss_cls_0[0.4506] det_loss_box_0[0.5375] det_loss_cls_1[0.4402] det_loss_box_1[0.4077] det_loss_cls_2[0.3790] det_loss_box_2[0.3798] det_loss_cls_3[0.3465] det_loss_box_3[0.3704] det_loss_cls_4[0.3257] det_loss_box_4[0.3606] det_loss_cls_5[0.3128] det_loss_box_5[0.3602] det_loss_cls_dn_0[0.0950] det_loss_box_dn_0[0.2445] det_loss_cls_dn_1[0.0915] det_loss_box_dn_1[0.1991] det_loss_cls_dn_2[0.0905] det_loss_box_dn_2[0.1805] det_loss_cls_dn_3[0.0891] det_loss_box_dn_3[0.1755] det_loss_cls_dn_4[0.0878] det_loss_box_dn_4[0.1729] det_loss_cls_dn_5[0.0869] det_loss_box_dn_5[0.1709] loss_depth[0.0351] total_loss[6.3899]
Rank[0/8] 06/20/2025 16:15:10 INFO stats.py:314 | Epoch[199] Step[214] GlobalStep[77824] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 5:16:47.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:15:21 INFO loss_tracker.py:84 | Epoch[199/239] Step[224] GlobalStep[77834/NA]: det_loss_cls_0[0.4616] det_loss_box_0[0.6400] det_loss_cls_1[0.4549] det_loss_box_1[0.4992] det_loss_cls_2[0.3967] det_loss_box_2[0.4580] det_loss_cls_3[0.3682] det_loss_box_3[0.4540] det_loss_cls_4[0.3458] det_loss_box_4[0.4355] det_loss_cls_5[0.3360] det_loss_box_5[0.4323] det_loss_cls_dn_0[0.0858] det_loss_box_dn_0[0.3600] det_loss_cls_dn_1[0.0824] det_loss_box_dn_1[0.2916] det_loss_cls_dn_2[0.0816] det_loss_box_dn_2[0.2658] det_loss_cls_dn_3[0.0805] det_loss_box_dn_3[0.2590] det_loss_cls_dn_4[0.0796] det_loss_box_dn_4[0.2531] det_loss_cls_dn_5[0.0788] det_loss_box_dn_5[0.2496] loss_depth[0.0367] total_loss[7.4869]
Rank[0/8] 06/20/2025 16:15:36 INFO stats.py:314 | Epoch[199] Step[239] GlobalStep[77849] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 5:16:17.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:15:46 INFO loss_tracker.py:84 | Epoch[199/239] Step[249] GlobalStep[77859/NA]: det_loss_cls_0[0.4830] det_loss_box_0[0.6357] det_loss_cls_1[0.4648] det_loss_box_1[0.4964] det_loss_cls_2[0.4023] det_loss_box_2[0.4519] det_loss_cls_3[0.3649] det_loss_box_3[0.4340] det_loss_cls_4[0.3423] det_loss_box_4[0.4225] det_loss_cls_5[0.3235] det_loss_box_5[0.4242] det_loss_cls_dn_0[0.0888] det_loss_box_dn_0[0.2750] det_loss_cls_dn_1[0.0849] det_loss_box_dn_1[0.2242] det_loss_cls_dn_2[0.0840] det_loss_box_dn_2[0.2063] det_loss_cls_dn_3[0.0824] det_loss_box_dn_3[0.2028] det_loss_cls_dn_4[0.0814] det_loss_box_dn_4[0.2012] det_loss_cls_dn_5[0.0804] det_loss_box_dn_5[0.1988] loss_depth[0.0342] total_loss[7.0901]
Rank[0/8] 06/20/2025 16:16:02 INFO stats.py:314 | Epoch[199] Step[264] GlobalStep[77874] Training Speed: 6.77 samples/sec across all devices.	Average Step Time: 1.18 sec.	Estimated Remaining Time: 5:15:46.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:16:12 INFO loss_tracker.py:84 | Epoch[199/239] Step[274] GlobalStep[77884/NA]: det_loss_cls_0[0.4555] det_loss_box_0[0.5360] det_loss_cls_1[0.4474] det_loss_box_1[0.3992] det_loss_cls_2[0.3745] det_loss_box_2[0.3608] det_loss_cls_3[0.3374] det_loss_box_3[0.3535] det_loss_cls_4[0.3163] det_loss_box_4[0.3417] det_loss_cls_5[0.3059] det_loss_box_5[0.3353] det_loss_cls_dn_0[0.0930] det_loss_box_dn_0[0.2544] det_loss_cls_dn_1[0.0886] det_loss_box_dn_1[0.2034] det_loss_cls_dn_2[0.0879] det_loss_box_dn_2[0.1881] det_loss_cls_dn_3[0.0872] det_loss_box_dn_3[0.1832] det_loss_cls_dn_4[0.0865] det_loss_box_dn_4[0.1813] det_loss_cls_dn_5[0.0856] det_loss_box_dn_5[0.1772] loss_depth[0.0340] total_loss[6.3143]
Rank[0/8] 06/20/2025 16:16:28 INFO stats.py:314 | Epoch[199] Step[289] GlobalStep[77899] Training Speed: 8.06 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 5:15:15.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:16:38 INFO loss_tracker.py:84 | Epoch[199/239] Step[299] GlobalStep[77909/NA]: det_loss_cls_0[0.5675] det_loss_box_0[0.8318] det_loss_cls_1[0.5404] det_loss_box_1[0.6592] det_loss_cls_2[0.4908] det_loss_box_2[0.5980] det_loss_cls_3[0.4532] det_loss_box_3[0.5829] det_loss_cls_4[0.4385] det_loss_box_4[0.5481] det_loss_cls_5[0.4186] det_loss_box_5[0.5559] det_loss_cls_dn_0[0.0965] det_loss_box_dn_0[0.2987] det_loss_cls_dn_1[0.0929] det_loss_box_dn_1[0.2538] det_loss_cls_dn_2[0.0916] det_loss_box_dn_2[0.2371] det_loss_cls_dn_3[0.0902] det_loss_box_dn_3[0.2331] det_loss_cls_dn_4[0.0895] det_loss_box_dn_4[0.2313] det_loss_cls_dn_5[0.0885] det_loss_box_dn_5[0.2295] loss_depth[0.0352] total_loss[8.7529]
Rank[0/8] 06/20/2025 16:16:53 INFO stats.py:314 | Epoch[199] Step[314] GlobalStep[77924] Training Speed: 8.58 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 5:14:44.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:17:04 INFO loss_tracker.py:84 | Epoch[199/239] Step[324] GlobalStep[77934/NA]: det_loss_cls_0[0.3691] det_loss_box_0[0.4120] det_loss_cls_1[0.3437] det_loss_box_1[0.3146] det_loss_cls_2[0.2973] det_loss_box_2[0.2875] det_loss_cls_3[0.2790] det_loss_box_3[0.2755] det_loss_cls_4[0.2542] det_loss_box_4[0.2766] det_loss_cls_5[0.2466] det_loss_box_5[0.2801] det_loss_cls_dn_0[0.0886] det_loss_box_dn_0[0.2527] det_loss_cls_dn_1[0.0855] det_loss_box_dn_1[0.2040] det_loss_cls_dn_2[0.0842] det_loss_box_dn_2[0.1867] det_loss_cls_dn_3[0.0833] det_loss_box_dn_3[0.1837] det_loss_cls_dn_4[0.0824] det_loss_box_dn_4[0.1815] det_loss_cls_dn_5[0.0816] det_loss_box_dn_5[0.1803] loss_depth[0.0377] total_loss[5.3685]
Rank[0/8] 06/20/2025 16:17:19 INFO stats.py:314 | Epoch[199] Step[339] GlobalStep[77949] Training Speed: 6.47 samples/sec across all devices.	Average Step Time: 1.24 sec.	Estimated Remaining Time: 5:14:13.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:17:30 INFO loss_tracker.py:84 | Epoch[199/239] Step[349] GlobalStep[77959/NA]: det_loss_cls_0[0.4918] det_loss_box_0[0.6379] det_loss_cls_1[0.4795] det_loss_box_1[0.4965] det_loss_cls_2[0.4100] det_loss_box_2[0.4586] det_loss_cls_3[0.3715] det_loss_box_3[0.4429] det_loss_cls_4[0.3509] det_loss_box_4[0.4338] det_loss_cls_5[0.3423] det_loss_box_5[0.4316] det_loss_cls_dn_0[0.0826] det_loss_box_dn_0[0.2339] det_loss_cls_dn_1[0.0790] det_loss_box_dn_1[0.1926] det_loss_cls_dn_2[0.0784] det_loss_box_dn_2[0.1759] det_loss_cls_dn_3[0.0769] det_loss_box_dn_3[0.1726] det_loss_cls_dn_4[0.0758] det_loss_box_dn_4[0.1713] det_loss_cls_dn_5[0.0748] det_loss_box_dn_5[0.1696] loss_depth[0.0367] total_loss[6.9675]
Rank[0/8] 06/20/2025 16:17:45 INFO stats.py:314 | Epoch[199] Step[364] GlobalStep[77974] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 5:13:42.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:17:55 INFO loss_tracker.py:84 | Epoch[199/239] Step[374] GlobalStep[77984/NA]: det_loss_cls_0[0.5007] det_loss_box_0[0.6105] det_loss_cls_1[0.4859] det_loss_box_1[0.4530] det_loss_cls_2[0.4218] det_loss_box_2[0.4042] det_loss_cls_3[0.3814] det_loss_box_3[0.3897] det_loss_cls_4[0.3554] det_loss_box_4[0.3850] det_loss_cls_5[0.3390] det_loss_box_5[0.3875] det_loss_cls_dn_0[0.0923] det_loss_box_dn_0[0.2571] det_loss_cls_dn_1[0.0887] det_loss_box_dn_1[0.2168] det_loss_cls_dn_2[0.0881] det_loss_box_dn_2[0.2000] det_loss_cls_dn_3[0.0871] det_loss_box_dn_3[0.1963] det_loss_cls_dn_4[0.0867] det_loss_box_dn_4[0.1947] det_loss_cls_dn_5[0.0859] det_loss_box_dn_5[0.1905] loss_depth[0.0334] total_loss[6.9317]
Rank[0/8] 06/20/2025 16:18:11 INFO stats.py:314 | Epoch[199] Step[389] GlobalStep[77999] Training Speed: 8.78 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 5:13:11.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:18:11 INFO trainer.py:268 | 
==================================================BEGIN EVAL==================================================
Rank[0/8] 06/20/2025 16:19:09 INFO trainer.py:275 | eval: 10
Rank[0/8] 06/20/2025 16:19:17 INFO trainer.py:275 | eval: 20
Rank[0/8] 06/20/2025 16:19:28 INFO trainer.py:275 | eval: 30
Rank[0/8] 06/20/2025 16:19:43 INFO trainer.py:275 | eval: 40
Rank[0/8] 06/20/2025 16:19:55 INFO trainer.py:275 | eval: 50
Rank[0/8] 06/20/2025 16:20:03 INFO trainer.py:275 | eval: 60
Rank[0/8] 06/20/2025 16:20:12 INFO trainer.py:275 | eval: 70
Rank[0/8] 06/20/2025 16:20:19 INFO trainer.py:275 | eval: 80
Rank[0/8] 06/20/2025 16:20:29 INFO trainer.py:275 | eval: 90
Rank[0/8] 06/20/2025 16:20:37 INFO trainer.py:275 | eval: 100
Rank[0/8] 06/20/2025 16:20:42 INFO metrics.py:428 | number of results: 824
Rank[0/8] 06/20/2025 16:24:54 INFO metrics.py:332 | eval results of overall:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| bathtub             | 0.8441  | 0.8971  | 0.7199  | 0.7941  |
| toilet              | 0.8682  | 0.8978  | 0.7831  | 0.8102  |
| curtain             | 0.5576  | 0.8269  | 0.1510  | 0.4507  |
| sink                | 0.7000  | 0.8519  | 0.2992  | 0.5350  |
| toilet paper        | 0.3765  | 0.5748  | 0.1718  | 0.3858  |
| bin                 | 0.6625  | 0.8715  | 0.4033  | 0.6425  |
| doorframe           | 0.3243  | 0.7461  | 0.0893  | 0.3975  |
| picture             | 0.2637  | 0.5086  | 0.0295  | 0.1444  |
| door                | 0.4130  | 0.7164  | 0.0737  | 0.3166  |
| towel               | 0.4035  | 0.6677  | 0.1124  | 0.2968  |
| vanity              | 0.5839  | 0.9111  | 0.5289  | 0.8444  |
| stopcock            | 0.0920  | 0.4828  | 0.0004  | 0.0345  |
| mirror              | 0.2208  | 0.6848  | 0.0172  | 0.1902  |
| switch              | 0.0024  | 0.1382  | 0.0000  | 0.0132  |
| bar                 | 0.1122  | 0.2727  | 0.0001  | 0.0303  |
| bottle              | 0.0579  | 0.4067  | 0.0030  | 0.0891  |
| bag                 | 0.2721  | 0.7135  | 0.0714  | 0.3351  |
| cup                 | 0.1276  | 0.4068  | 0.0186  | 0.0763  |
| socket              | 0.0110  | 0.2301  | 0.0001  | 0.0133  |
| shower              | 0.0515  | 0.3000  | 0.0033  | 0.1333  |
| rail                | 0.0249  | 0.4375  | 0.0013  | 0.0469  |
| hair dryer          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| microwave           | 0.6364  | 0.7347  | 0.3500  | 0.5306  |
| rod                 | 0.0250  | 0.0250  | 0.0000  | 0.0000  |
| tissue              | 0.0213  | 0.2308  | 0.0000  | 0.0000  |
| tissue box          | 0.1134  | 0.4545  | 0.0279  | 0.1818  |
| light               | 0.0584  | 0.2991  | 0.0055  | 0.0748  |
| box                 | 0.1690  | 0.5269  | 0.0464  | 0.2110  |
| ledge               | 0.2050  | 0.5000  | 0.0185  | 0.1667  |
| paper               | 0.0318  | 0.3432  | 0.0009  | 0.0769  |
| basket              | 0.2874  | 0.5826  | 0.1207  | 0.3565  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stool               | 0.3396  | 0.7395  | 0.2339  | 0.5630  |
| vent                | 0.0090  | 0.1667  | 0.0000  | 0.0000  |
| door knob           | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| plant               | 0.3380  | 0.6836  | 0.0938  | 0.2979  |
| shelf               | 0.3320  | 0.7230  | 0.1514  | 0.4353  |
| faucet              | 0.0439  | 0.3191  | 0.0000  | 0.0000  |
| cabinet             | 0.3123  | 0.7479  | 0.1881  | 0.5471  |
| blanket             | 0.2869  | 0.8000  | 0.0689  | 0.4300  |
| lamp                | 0.3587  | 0.6657  | 0.0862  | 0.2955  |
| soap dish           | 0.5118  | 0.6667  | 0.2778  | 0.3333  |
| shoe                | 0.2306  | 0.4486  | 0.0216  | 0.0935  |
| doorway             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bed                 | 0.8528  | 0.9231  | 0.6652  | 0.8580  |
| frame               | 0.0831  | 0.6122  | 0.0079  | 0.1429  |
| tablet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap                | 0.1733  | 0.3333  | 0.0033  | 0.1667  |
| toothpaste          | 0.0034  | 0.2000  | 0.0000  | 0.0000  |
| mat                 | 0.0479  | 0.3243  | 0.0087  | 0.0811  |
| scale               | 0.1818  | 0.1818  | 0.1818  | 0.1818  |
| purse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candle              | 0.0849  | 0.3889  | 0.0016  | 0.1111  |
| case                | 0.1091  | 0.4633  | 0.0472  | 0.1864  |
| notebook            | 0.0146  | 0.1538  | 0.0000  | 0.0000  |
| bowl                | 0.1894  | 0.5217  | 0.0460  | 0.2029  |
| jar                 | 0.1756  | 0.4615  | 0.0897  | 0.2308  |
| hamper              | 0.5227  | 0.5455  | 0.5227  | 0.5455  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| couch               | 0.5014  | 0.8554  | 0.4204  | 0.7686  |
| drawer              | 0.2863  | 0.5556  | 0.1790  | 0.4444  |
| stall               | 0.2710  | 1.0000  | 0.2338  | 0.5882  |
| wardrobe            | 0.4397  | 0.7952  | 0.2759  | 0.5783  |
| cosmetics           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plunger             | 0.8000  | 0.8000  | 0.4500  | 0.6000  |
| decoration          | 0.0432  | 0.4857  | 0.0072  | 0.1071  |
| sunglasses          | 0.0241  | 0.2308  | 0.0078  | 0.2308  |
| paper cutter        | 0.8333  | 0.8333  | 0.0333  | 0.1667  |
| keyboard            | 0.1045  | 0.3023  | 0.0003  | 0.0116  |
| counter             | 0.2291  | 0.7248  | 0.0823  | 0.3028  |
| laptop              | 0.4506  | 0.6957  | 0.1945  | 0.3913  |
| chair               | 0.5990  | 0.8356  | 0.4800  | 0.6973  |
| blackboard          | 0.6234  | 0.8049  | 0.3011  | 0.5000  |
| radiator            | 0.7376  | 0.8222  | 0.2317  | 0.4333  |
| computer            | 0.3623  | 0.6557  | 0.0136  | 0.1148  |
| monitor             | 0.5706  | 0.7609  | 0.1162  | 0.3261  |
| desk                | 0.5673  | 0.9270  | 0.3928  | 0.7697  |
| table               | 0.5254  | 0.8621  | 0.4364  | 0.7492  |
| window              | 0.2745  | 0.6512  | 0.0521  | 0.2814  |
| mouse               | 0.0030  | 0.0811  | 0.0000  | 0.0000  |
| plug                | 0.0302  | 0.1579  | 0.0000  | 0.0000  |
| backpack            | 0.5837  | 0.8871  | 0.3921  | 0.5968  |
| blinds              | 0.2851  | 0.5000  | 0.0187  | 0.3000  |
| telephone           | 0.4033  | 0.6333  | 0.1488  | 0.3000  |
| book                | 0.0668  | 0.4330  | 0.0074  | 0.1006  |
| stand               | 0.5839  | 0.8902  | 0.4833  | 0.7622  |
| clothes             | 0.2039  | 0.6115  | 0.0594  | 0.2643  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| windowsill          | 0.1229  | 0.5870  | 0.0517  | 0.2826  |
| projector           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| furniture           | 0.0016  | 0.1667  | 0.0000  | 0.0000  |
| jacket              | 0.6042  | 0.7917  | 0.1753  | 0.4583  |
| pipe                | 0.0368  | 0.1429  | 0.0004  | 0.0714  |
| pillow              | 0.2939  | 0.6416  | 0.0461  | 0.2190  |
| dresser             | 0.6412  | 0.9074  | 0.5555  | 0.8148  |
| tv                  | 0.6365  | 0.8416  | 0.1717  | 0.4059  |
| container           | 0.0109  | 0.2542  | 0.0031  | 0.1017  |
| ottoman             | 0.1833  | 0.6389  | 0.1599  | 0.5556  |
| calendar            | 0.0103  | 0.3333  | 0.0000  | 0.0000  |
| toy                 | 0.1472  | 0.4697  | 0.0450  | 0.1212  |
| stairs              | 0.0837  | 0.4722  | 0.0044  | 0.1528  |
| clock               | 0.3922  | 0.6809  | 0.0312  | 0.1915  |
| printer             | 0.5726  | 0.7755  | 0.4434  | 0.6735  |
| rack                | 0.1809  | 0.5577  | 0.0827  | 0.4615  |
| poster              | 0.0214  | 0.1970  | 0.0000  | 0.0000  |
| cushion             | 0.2297  | 0.5882  | 0.0256  | 0.1765  |
| carpet              | 0.1825  | 0.5476  | 0.0134  | 0.1667  |
| guitar              | 0.2618  | 0.5000  | 0.0833  | 0.0833  |
| refrigerator        | 0.7309  | 0.9000  | 0.5053  | 0.6200  |
| luggage             | 0.5806  | 1.0000  | 0.5556  | 0.6667  |
| bench               | 0.3019  | 0.7358  | 0.2443  | 0.5660  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coffee maker        | 0.4063  | 0.5517  | 0.0736  | 0.1724  |
| grab bar            | 0.0750  | 0.5000  | 0.0000  | 0.0000  |
| window frame        | 0.0347  | 0.4646  | 0.0020  | 0.1818  |
| glass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| screen              | 0.2222  | 0.4000  | 0.0000  | 0.0000  |
| board               | 0.1853  | 0.6935  | 0.0477  | 0.3065  |
| pillar              | 0.3585  | 0.6977  | 0.2182  | 0.4884  |
| countertop          | 0.0252  | 0.2500  | 0.0007  | 0.1071  |
| soap dispenser      | 0.4016  | 0.6296  | 0.0638  | 0.2963  |
| dispenser           | 0.5075  | 0.6364  | 0.3227  | 0.4318  |
| mailbox             | 0.1185  | 0.8000  | 0.0296  | 0.4000  |
| partition           | 0.3889  | 0.6667  | 0.1667  | 0.1667  |
| column              | 0.0130  | 0.3421  | 0.0028  | 0.1316  |
| hanger              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| chandelier          | 0.0159  | 0.0476  | 0.0000  | 0.0000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape       | 0.0125  | 0.2000  | 0.0000  | 0.0000  |
| headphones          | 0.2250  | 0.4000  | 0.2000  | 0.2000  |
| spoon               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| thermostat          | 0.1667  | 0.1667  | 0.0000  | 0.0000  |
| teapot              | 0.0101  | 0.1111  | 0.0101  | 0.1111  |
| holder              | 0.1429  | 0.2222  | 0.1111  | 0.1111  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| footstool           | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster             | 0.4085  | 0.5000  | 0.0194  | 0.2000  |
| kitchenware         | 0.2832  | 0.5217  | 0.0405  | 0.1522  |
| copier              | 0.9097  | 0.9167  | 0.9097  | 0.9167  |
| cube                | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| machine             | 0.5239  | 0.8421  | 0.4269  | 0.7368  |
| alarm               | 0.0060  | 0.1250  | 0.0000  | 0.0000  |
| fan                 | 0.6456  | 0.8125  | 0.1714  | 0.3750  |
| bicycle             | 0.6096  | 1.0000  | 0.3697  | 0.8333  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchen island      | 0.2286  | 0.4286  | 0.2286  | 0.4286  |
| drum                | 0.0250  | 0.1250  | 0.0000  | 0.0000  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crib                | 0.3846  | 0.6667  | 0.3846  | 0.6667  |
| stove               | 0.5660  | 0.8333  | 0.3572  | 0.5556  |
| pen                 | 0.0002  | 0.0667  | 0.0000  | 0.0000  |
| plate               | 0.0486  | 0.5385  | 0.0104  | 0.1731  |
| hat                 | 0.1122  | 0.2222  | 0.0000  | 0.0000  |
| tube                | 0.0110  | 0.1538  | 0.0000  | 0.0000  |
| statue              | 0.1046  | 0.3684  | 0.0657  | 0.2632  |
| person              | 0.2181  | 0.6667  | 0.0030  | 0.1667  |
| piano               | 0.2571  | 0.4000  | 0.2571  | 0.4000  |
| ball                | 0.4493  | 0.5238  | 0.4042  | 0.4286  |
| basin               | 0.0357  | 0.2353  | 0.0003  | 0.0588  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| map                 | 0.0455  | 0.5000  | 0.0455  | 0.5000  |
| fire extinguisher   | 0.3091  | 0.6000  | 0.3000  | 0.4000  |
| roof                | 0.0016  | 0.2917  | 0.0000  | 0.0000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| arch                | 0.0387  | 0.2000  | 0.0032  | 0.1000  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garage door         | 0.3490  | 0.5000  | 0.3490  | 0.5000  |
| pool table          | 0.1667  | 0.3333  | 0.1667  | 0.3333  |
| mattress            | 0.1667  | 0.3333  | 0.0000  | 0.0000  |
| barricade           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headboard           | 0.0089  | 0.1250  | 0.0000  | 0.0000  |
| heater              | 0.6085  | 0.8448  | 0.2086  | 0.4138  |
| excercise equipment | 0.3944  | 0.6500  | 0.0209  | 0.2500  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| commode             | 0.5358  | 0.8269  | 0.4438  | 0.6731  |
| beanbag             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| treadmill           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet               | 0.5556  | 0.6667  | 0.5556  | 0.6667  |
| sign                | 0.0766  | 0.3667  | 0.0029  | 0.1000  |
| water cooler        | 0.8333  | 1.0000  | 0.5588  | 1.0000  |
| dish rack           | 0.6656  | 1.0000  | 0.3600  | 0.6000  |
| kettle              | 0.1886  | 0.5556  | 0.0367  | 0.2778  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket              | 0.1891  | 0.7436  | 0.0501  | 0.3333  |
| oven                | 0.5717  | 0.8214  | 0.1482  | 0.3571  |
| pot                 | 0.1559  | 0.5185  | 0.0530  | 0.1481  |
| pan                 | 0.2068  | 0.4706  | 0.0882  | 0.1176  |
| fruit               | 0.1412  | 0.2857  | 0.0736  | 0.1429  |
| brush               | 0.1311  | 0.3571  | 0.0102  | 0.0714  |
| pack                | 0.0265  | 0.6667  | 0.0057  | 0.3333  |
| folder              | 0.1442  | 0.3571  | 0.0714  | 0.0714  |
| bread               | 0.1000  | 0.2000  | 0.0000  | 0.0000  |
| device              | 0.3277  | 0.6364  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crate               | 0.3929  | 0.5714  | 0.2143  | 0.2857  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flush               | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| food                | 0.0953  | 0.2727  | 0.0035  | 0.1818  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garbage             | 0.7416  | 0.8750  | 0.3839  | 0.5000  |
| cap                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radio               | 0.2500  | 0.2500  | 0.2500  | 0.2500  |
| glove               | 0.4444  | 0.6667  | 0.1111  | 0.3333  |
| sack                | 0.6803  | 0.8571  | 0.1633  | 0.2857  |
| can                 | 0.0258  | 0.1667  | 0.0258  | 0.1667  |
| sponge              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| magazine            | 0.0111  | 0.6000  | 0.0012  | 0.2000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| player              | 0.2500  | 0.2500  | 0.2500  | 0.2500  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wine                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| napkins             | 0.4444  | 0.6667  | 0.0000  | 0.0000  |
| washing machine     | 0.5082  | 0.8214  | 0.3361  | 0.6429  |
| roll                | 0.1915  | 0.5000  | 0.0000  | 0.0000  |
| balcony             | 0.2000  | 1.0000  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell            | 0.6667  | 0.6667  | 0.2018  | 0.6667  |
| broom               | 0.0288  | 0.2857  | 0.0024  | 0.0714  |
| salt                | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| price tag           | 0.0764  | 0.2857  | 0.0000  | 0.0000  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vacuum cleaner      | 0.6071  | 0.7143  | 0.0714  | 0.2857  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| helmets             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toiletry            | 0.1579  | 0.3478  | 0.0006  | 0.0435  |
| fireplace           | 0.1913  | 0.4643  | 0.0080  | 0.1786  |
| vase                | 0.1331  | 0.4200  | 0.0554  | 0.2400  |
| flowerpot           | 0.0765  | 0.3922  | 0.0017  | 0.1176  |
| range hood          | 0.3934  | 0.7857  | 0.1234  | 0.2857  |
| ladder              | 0.1488  | 0.5882  | 0.0480  | 0.1765  |
| file                | 0.0418  | 0.0882  | 0.0412  | 0.0588  |
| speaker             | 0.0980  | 0.2727  | 0.0066  | 0.1364  |
| air conditioner     | 0.6000  | 0.6000  | 0.4000  | 0.4000  |
| steps               | 0.0009  | 0.4000  | 0.0000  | 0.0000  |
| letter              | 0.5000  | 1.0000  | 0.2500  | 0.7500  |
| conducting wire     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| remote control      | 0.0397  | 0.1852  | 0.0109  | 0.0741  |
| humidifier          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| umbrella            | 0.2000  | 0.2000  | 0.2000  | 0.2000  |
| album               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.4420  | 0.8000  | 0.3415  | 0.7333  |
| shampoo             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beam                | 0.0000  | 0.0455  | 0.0000  | 0.0000  |
| body loofah         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jalousie            | 1.0000  | 1.0000  | 0.5000  | 0.5000  |
| candlestick         | 0.0559  | 0.6667  | 0.0000  | 0.0000  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.2648  | 0.4000  | 0.2000  | 0.2000  |
| water heater        | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| mop                 | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| menu                | 0.6071  | 0.7143  | 0.0000  | 0.0000  |
| handle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coil                | 0.2778  | 0.3333  | 0.0000  | 0.0000  |
| washbasin           | 0.0114  | 0.2667  | 0.0112  | 0.2000  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| overall             | 0.2435  | 0.4390  | 0.1156  | 0.2238  |
| head                | 0.3196  | 0.6211  | 0.1550  | 0.3393  |
| common              | 0.2163  | 0.3906  | 0.1105  | 0.2003  |
| tail                | 0.1910  | 0.2940  | 0.0781  | 0.1238  |
| small               | 0.0885  | 0.2320  | 0.0153  | 0.0668  |
| medium              | 0.2540  | 0.4942  | 0.1324  | 0.2577  |
| large               | 0.1530  | 0.5311  | 0.0790  | 0.3147  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 16:24:54 INFO metrics.py:437 | number of scannet results: 250
Rank[0/8] 06/20/2025 16:26:09 INFO metrics.py:332 | eval results of scannet:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| bathtub             | 0.9378  | 0.9688  | 0.8136  | 0.8438  |
| toilet              | 0.9478  | 0.9643  | 0.8296  | 0.8393  |
| curtain             | 0.6430  | 0.8407  | 0.1689  | 0.4159  |
| sink                | 0.8550  | 0.8977  | 0.3863  | 0.5568  |
| toilet paper        | 0.4439  | 0.5823  | 0.2193  | 0.4051  |
| bin                 | 0.6920  | 0.8723  | 0.4385  | 0.6511  |
| doorframe           | 0.4424  | 0.8099  | 0.0985  | 0.3662  |
| picture             | 0.3610  | 0.5672  | 0.0617  | 0.1791  |
| door                | 0.6103  | 0.8395  | 0.1564  | 0.4548  |
| towel               | 0.5028  | 0.7273  | 0.1413  | 0.3388  |
| vanity              | 0.7535  | 1.0000  | 0.7003  | 0.9667  |
| stopcock            | 0.0920  | 0.4828  | 0.0004  | 0.0345  |
| mirror              | 0.3023  | 0.7108  | 0.0227  | 0.1807  |
| switch              | 0.0063  | 0.1899  | 0.0000  | 0.0127  |
| bar                 | 0.1457  | 0.3182  | 0.0000  | 0.0000  |
| bottle              | 0.0701  | 0.4053  | 0.0043  | 0.1053  |
| bag                 | 0.2149  | 0.7123  | 0.0844  | 0.3699  |
| cup                 | 0.2411  | 0.4783  | 0.0548  | 0.1014  |
| socket              | 0.0119  | 0.2476  | 0.0001  | 0.0146  |
| shower              | 0.0575  | 0.2857  | 0.0013  | 0.0714  |
| rail                | 0.0784  | 0.6667  | 0.0196  | 0.3333  |
| hair dryer          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| microwave           | 0.6654  | 0.8000  | 0.3764  | 0.6000  |
| rod                 | 0.0714  | 0.0714  | 0.0000  | 0.0000  |
| tissue              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue box          | 0.2304  | 0.4545  | 0.1000  | 0.1818  |
| light               | 0.1745  | 0.4828  | 0.0361  | 0.1034  |
| box                 | 0.1260  | 0.4484  | 0.0292  | 0.1508  |
| ledge               | 0.2051  | 0.5000  | 0.0185  | 0.1667  |
| paper               | 0.0287  | 0.3396  | 0.0005  | 0.0679  |
| basket              | 0.1047  | 0.2308  | 0.1044  | 0.1923  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stool               | 0.3501  | 0.8182  | 0.2598  | 0.6136  |
| plant               | 0.3206  | 0.6387  | 0.1003  | 0.2941  |
| shelf               | 0.5553  | 0.8088  | 0.3532  | 0.6691  |
| faucet              | 0.0011  | 0.1250  | 0.0000  | 0.0000  |
| cabinet             | 0.3663  | 0.7619  | 0.2024  | 0.5397  |
| blanket             | 0.0507  | 0.5833  | 0.0060  | 0.2500  |
| lamp                | 0.4419  | 0.7500  | 0.2360  | 0.4167  |
| soap dish           | 0.7364  | 0.8333  | 0.3472  | 0.4167  |
| shoe                | 0.2796  | 0.4750  | 0.0282  | 0.1000  |
| bed                 | 0.9891  | 1.0000  | 0.8629  | 0.9474  |
| frame               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toothpaste          | 0.0041  | 0.2000  | 0.0000  | 0.0000  |
| mat                 | 0.1073  | 0.3000  | 0.0250  | 0.1000  |
| scale               | 0.2500  | 0.2500  | 0.2500  | 0.2500  |
| purse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| case                | 0.7207  | 1.0000  | 0.5351  | 0.8421  |
| notebook            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bowl                | 0.2273  | 0.4444  | 0.0617  | 0.2222  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hamper              | 0.5857  | 0.6000  | 0.5857  | 0.6000  |
| couch               | 0.7251  | 0.9625  | 0.6340  | 0.9125  |
| drawer              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stall               | 0.2713  | 1.0000  | 0.2339  | 0.5882  |
| wardrobe            | 0.4962  | 0.8750  | 0.3750  | 0.3750  |
| plunger             | 0.8000  | 0.8000  | 0.4500  | 0.6000  |
| decoration          | 0.1021  | 0.4583  | 0.0094  | 0.1250  |
| paper cutter        | 0.8000  | 0.8000  | 0.0500  | 0.2000  |
| keyboard            | 0.1517  | 0.3276  | 0.0007  | 0.0172  |
| counter             | 0.5481  | 0.8857  | 0.2570  | 0.4857  |
| laptop              | 0.4624  | 0.6000  | 0.2615  | 0.3333  |
| chair               | 0.6430  | 0.8437  | 0.4821  | 0.6823  |
| blackboard          | 0.6352  | 0.8116  | 0.2974  | 0.4783  |
| radiator            | 0.8357  | 0.8966  | 0.3092  | 0.5345  |
| computer            | 0.3638  | 0.6154  | 0.0420  | 0.1282  |
| monitor             | 0.6088  | 0.7643  | 0.1774  | 0.3857  |
| desk                | 0.6733  | 0.9873  | 0.4466  | 0.7595  |
| table               | 0.6135  | 0.9000  | 0.5470  | 0.8154  |
| window              | 0.5297  | 0.8519  | 0.1683  | 0.4753  |
| mouse               | 0.0088  | 0.1429  | 0.0000  | 0.0000  |
| plug                | 0.0001  | 0.1111  | 0.0000  | 0.0000  |
| backpack            | 0.7024  | 0.9643  | 0.5749  | 0.7143  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| telephone           | 0.5487  | 0.7812  | 0.2624  | 0.4375  |
| book                | 0.0672  | 0.4405  | 0.0079  | 0.1071  |
| stand               | 0.7734  | 0.9245  | 0.7112  | 0.8679  |
| clothes             | 0.2193  | 0.6340  | 0.0801  | 0.3203  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| windowsill          | 0.0188  | 0.5000  | 0.0019  | 0.1667  |
| jacket              | 0.6340  | 0.8261  | 0.1843  | 0.4783  |
| pipe                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillow              | 0.4466  | 0.6944  | 0.2158  | 0.4514  |
| dresser             | 0.7679  | 0.9730  | 0.7645  | 0.9459  |
| tv                  | 0.8367  | 0.9130  | 0.3766  | 0.5217  |
| container           | 0.0050  | 0.2609  | 0.0008  | 0.0870  |
| ottoman             | 0.1356  | 1.0000  | 0.0675  | 0.7143  |
| calendar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toy                 | 0.0695  | 0.3529  | 0.0301  | 0.1176  |
| stairs              | 0.1750  | 0.8000  | 0.0055  | 0.4000  |
| clock               | 0.4099  | 0.6800  | 0.0822  | 0.2400  |
| printer             | 0.5956  | 0.7879  | 0.5868  | 0.7576  |
| rack                | 0.0016  | 0.1818  | 0.0016  | 0.1818  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cushion             | 0.0530  | 0.7500  | 0.0208  | 0.2500  |
| carpet              | 0.0340  | 0.5000  | 0.0005  | 0.1000  |
| guitar              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| refrigerator        | 0.9086  | 0.9500  | 0.7347  | 0.8500  |
| bench               | 0.4376  | 1.0000  | 0.4376  | 1.0000  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coffee maker        | 0.5013  | 0.5882  | 0.1345  | 0.2353  |
| grab bar            | 0.0767  | 0.5000  | 0.0000  | 0.0000  |
| board               | 0.1112  | 0.7500  | 0.0100  | 0.4375  |
| pillar              | 0.0167  | 0.2000  | 0.0167  | 0.2000  |
| soap dispenser      | 0.4270  | 0.6190  | 0.0856  | 0.3333  |
| dispenser           | 0.5991  | 0.6667  | 0.3711  | 0.4615  |
| mailbox             | 0.1185  | 0.8000  | 0.0296  | 0.4000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headphones          | 0.2500  | 0.4000  | 0.2000  | 0.2000  |
| teapot              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| holder              | 0.2000  | 0.2000  | 0.2000  | 0.2000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster             | 0.0500  | 0.2500  | 0.0000  | 0.0000  |
| copier              | 0.9097  | 0.9167  | 0.9097  | 0.9167  |
| machine             | 0.5645  | 0.9375  | 0.4465  | 0.8125  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fan                 | 0.8281  | 0.8750  | 0.0982  | 0.2500  |
| bicycle             | 0.7778  | 1.0000  | 0.3841  | 1.0000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stove               | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| pen                 | 0.0014  | 0.1111  | 0.0000  | 0.0000  |
| plate               | 0.0444  | 0.4000  | 0.0049  | 0.2000  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| person              | 0.2181  | 0.6667  | 0.0030  | 0.1667  |
| ball                | 0.4167  | 0.4167  | 0.3333  | 0.3333  |
| basin               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| map                 | 0.0909  | 1.0000  | 0.0909  | 1.0000  |
| fire extinguisher   | 0.0068  | 0.3333  | 0.0000  | 0.0000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| barricade           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sign                | 0.0681  | 0.3214  | 0.0002  | 0.0714  |
| water cooler        | 0.8333  | 1.0000  | 0.5588  | 1.0000  |
| dish rack           | 0.6213  | 1.0000  | 0.5625  | 0.7500  |
| kettle              | 0.1250  | 0.2500  | 0.0833  | 0.2500  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket              | 0.2031  | 0.7143  | 0.0408  | 0.2857  |
| oven                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pot                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crate               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.6711  | 1.0000  | 0.5972  | 0.8333  |
| broom               | 0.0447  | 0.3333  | 0.0033  | 0.0833  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vacuum cleaner      | 0.6667  | 0.6667  | 0.6667  | 0.6667  |
| flowerpot           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.7357  | 1.0000  | 0.3667  | 0.6000  |
| ladder              | 0.1000  | 0.4000  | 0.0000  | 0.0000  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.0370  | 0.3333  | 0.0000  | 0.0000  |
| clothes dryer       | 0.4000  | 1.0000  | 0.4000  | 1.0000  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| scannet             | 0.2717  | 0.4587  | 0.1588  | 0.2804  |
| head                | 0.3718  | 0.6291  | 0.2134  | 0.3802  |
| common              | 0.2066  | 0.3206  | 0.1276  | 0.2007  |
| tail                | 0.0807  | 0.1953  | 0.0453  | 0.1233  |
| small               | 0.0631  | 0.1725  | 0.0163  | 0.0471  |
| medium              | 0.3189  | 0.5578  | 0.1910  | 0.3416  |
| large               | 0.3017  | 0.7711  | 0.1837  | 0.5401  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 16:26:09 INFO metrics.py:437 | number of 3rscan results: 235
Rank[0/8] 06/20/2025 16:27:16 INFO metrics.py:332 | eval results of 3rscan:
+-------------------+---------+---------+---------+---------+
| classes           | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+-------------------+---------+---------+---------+---------+
| chair             | 0.7108  | 0.8580  | 0.6093  | 0.7486  |
| monitor           | 0.6272  | 0.7750  | 0.0295  | 0.1250  |
| keyboard          | 0.0913  | 0.2917  | 0.0000  | 0.0000  |
| desk              | 0.5236  | 0.9125  | 0.3894  | 0.8125  |
| cup               | 0.0559  | 0.3590  | 0.0011  | 0.0513  |
| mouse             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| computer          | 0.3750  | 0.7619  | 0.0031  | 0.0952  |
| case              | 0.0492  | 0.4214  | 0.0036  | 0.1071  |
| paper             | 0.0838  | 0.3714  | 0.0072  | 0.1143  |
| stand             | 0.5735  | 0.9180  | 0.4243  | 0.7213  |
| bag               | 0.3732  | 0.7576  | 0.0842  | 0.3232  |
| plate             | 0.0653  | 0.5455  | 0.0170  | 0.1818  |
| plug              | 0.1111  | 0.2222  | 0.0000  | 0.0000  |
| garbage           | 0.7428  | 0.8750  | 0.3839  | 0.5000  |
| bin               | 0.7624  | 0.9146  | 0.4432  | 0.7317  |
| bottle            | 0.0488  | 0.3471  | 0.0004  | 0.0331  |
| pen               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| curtain           | 0.6211  | 0.8889  | 0.1956  | 0.5159  |
| wardrobe          | 0.5079  | 0.8438  | 0.3025  | 0.6406  |
| window            | 0.2754  | 0.7192  | 0.0210  | 0.2534  |
| backpack          | 0.5128  | 0.8485  | 0.2707  | 0.5152  |
| table             | 0.5924  | 0.8829  | 0.4596  | 0.7613  |
| book              | 0.1001  | 0.4379  | 0.0109  | 0.0947  |
| cabinet           | 0.4684  | 0.8333  | 0.3062  | 0.6667  |
| sink              | 0.6762  | 0.8393  | 0.2235  | 0.4643  |
| box               | 0.2182  | 0.5556  | 0.0701  | 0.2266  |
| socket            | 0.0156  | 0.0625  | 0.0000  | 0.0000  |
| telephone         | 0.3080  | 0.4583  | 0.0469  | 0.1250  |
| blinds            | 0.3661  | 0.6410  | 0.0243  | 0.3846  |
| plant             | 0.3852  | 0.6634  | 0.0786  | 0.2376  |
| notebook          | 0.0173  | 0.1818  | 0.0000  | 0.0000  |
| shelf             | 0.3114  | 0.7456  | 0.1068  | 0.3816  |
| clock             | 0.4517  | 0.6842  | 0.0053  | 0.0526  |
| picture           | 0.2284  | 0.4756  | 0.0232  | 0.1138  |
| conducting wire   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| couch             | 0.4955  | 0.8286  | 0.4165  | 0.7571  |
| laptop            | 0.4667  | 0.8333  | 0.1833  | 0.5000  |
| frame             | 0.1540  | 0.8333  | 0.0144  | 0.2000  |
| file              | 0.0507  | 0.1071  | 0.0500  | 0.0714  |
| tissue box        | 0.0667  | 0.3333  | 0.0667  | 0.3333  |
| pillow            | 0.4312  | 0.7539  | 0.0373  | 0.2109  |
| towel             | 0.3238  | 0.6709  | 0.1135  | 0.2532  |
| switch            | 0.0004  | 0.0377  | 0.0000  | 0.0000  |
| printer           | 0.8005  | 0.8889  | 0.3556  | 0.5556  |
| toy               | 0.4681  | 0.6923  | 0.1410  | 0.2308  |
| bucket            | 0.6383  | 1.0000  | 0.1927  | 0.4667  |
| light             | 0.1415  | 0.4886  | 0.0224  | 0.1250  |
| bed               | 0.9336  | 0.9636  | 0.7286  | 0.8545  |
| tv                | 0.7682  | 0.9714  | 0.1713  | 0.4286  |
| commode           | 0.5366  | 0.8269  | 0.4442  | 0.6731  |
| lamp              | 0.3476  | 0.6125  | 0.0402  | 0.1812  |
| stool             | 0.4930  | 0.8148  | 0.3158  | 0.6111  |
| candle            | 0.1047  | 0.4000  | 0.0018  | 0.0667  |
| blanket           | 0.4517  | 0.8923  | 0.1207  | 0.5231  |
| decoration        | 0.1338  | 0.5000  | 0.0352  | 0.0938  |
| door              | 0.4023  | 0.7059  | 0.0928  | 0.3235  |
| bowl              | 0.2990  | 0.7037  | 0.0861  | 0.2222  |
| cushion           | 0.3866  | 0.8667  | 0.0435  | 0.2667  |
| clothes           | 0.2918  | 0.7282  | 0.0760  | 0.2621  |
| price tag         | 0.0764  | 0.2857  | 0.0000  | 0.0000  |
| folder            | 0.1442  | 0.3571  | 0.0714  | 0.0714  |
| carpet            | 0.2950  | 0.6964  | 0.0231  | 0.2321  |
| tablet            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vase              | 0.2933  | 0.4000  | 0.1309  | 0.2500  |
| rack              | 0.4165  | 0.7667  | 0.1841  | 0.6000  |
| napkins           | 0.4444  | 0.6667  | 0.0000  | 0.0000  |
| bench             | 0.4105  | 0.8214  | 0.3424  | 0.6786  |
| flowerpot         | 0.1320  | 0.4583  | 0.0003  | 0.0417  |
| ottoman           | 0.6189  | 0.9091  | 0.5881  | 0.8182  |
| remote control    | 0.0608  | 0.2500  | 0.0178  | 0.1000  |
| jar               | 0.2899  | 0.6250  | 0.1479  | 0.3750  |
| teapot            | 0.0357  | 0.2500  | 0.0357  | 0.2500  |
| board             | 0.3751  | 0.6667  | 0.1277  | 0.2083  |
| player            | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| counter           | 0.1532  | 0.7805  | 0.0306  | 0.2439  |
| magazine          | 0.0139  | 0.7500  | 0.0015  | 0.2500  |
| cube              | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| roll              | 0.1916  | 0.5000  | 0.0000  | 0.0000  |
| poster            | 0.0314  | 0.2063  | 0.0000  | 0.0000  |
| container         | 0.1383  | 0.5556  | 0.0471  | 0.2222  |
| bar               | 0.5000  | 0.6667  | 0.0028  | 0.3333  |
| blackboard        | 0.7134  | 0.8333  | 0.4628  | 0.6667  |
| heater            | 0.6139  | 0.8421  | 0.2093  | 0.4035  |
| humidifier        | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| windowsill        | 0.1504  | 0.6154  | 0.0635  | 0.3077  |
| fan               | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| device            | 0.3277  | 0.6364  | 0.0000  | 0.0000  |
| doorframe         | 0.2046  | 0.6600  | 0.0137  | 0.2400  |
| ball              | 0.6205  | 0.8000  | 0.6000  | 0.6000  |
| brush             | 0.2213  | 0.5000  | 0.0179  | 0.1250  |
| drawer            | 0.6946  | 0.8889  | 0.4136  | 0.6667  |
| speaker           | 0.1667  | 0.3333  | 0.0064  | 0.0833  |
| umbrella          | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| radiator          | 0.5777  | 0.7241  | 0.1118  | 0.2759  |
| tube              | 0.0301  | 0.2857  | 0.0000  | 0.0000  |
| pad               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tool              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shoe              | 0.2981  | 0.5000  | 0.0352  | 0.1429  |
| mirror            | 0.3494  | 0.7037  | 0.0064  | 0.1852  |
| pot               | 0.4661  | 0.6667  | 0.1417  | 0.2500  |
| hanger            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coffee maker      | 0.3065  | 0.6667  | 0.0048  | 0.1111  |
| stairs            | 0.3871  | 0.7143  | 0.0284  | 0.4286  |
| bathtub           | 0.9889  | 1.0000  | 0.8528  | 0.8889  |
| wire              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basket            | 0.5058  | 0.7812  | 0.2163  | 0.4844  |
| calendar          | 0.0163  | 0.4000  | 0.0000  | 0.0000  |
| album             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rail              | 0.0132  | 0.2500  | 0.0000  | 0.0000  |
| air conditioner   | 0.7500  | 0.7500  | 0.5000  | 0.5000  |
| guitar            | 1.0000  | 1.0000  | 0.5000  | 0.5000  |
| dresser           | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| kettle            | 0.2619  | 0.7500  | 0.0448  | 0.3333  |
| balcony           | 0.2000  | 1.0000  | 0.0000  | 0.0000  |
| refrigerator      | 0.6858  | 0.8667  | 0.6515  | 0.7333  |
| window frame      | 0.0804  | 0.5000  | 0.0009  | 0.0833  |
| bicycle           | 0.8333  | 1.0000  | 0.8333  | 1.0000  |
| microwave         | 0.9708  | 1.0000  | 0.5490  | 0.7647  |
| basin             | 0.0546  | 0.2667  | 0.0011  | 0.0667  |
| tissue            | 0.1503  | 0.6667  | 0.0000  | 0.0000  |
| scale             | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| piano             | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| fireplace         | 0.2000  | 0.2000  | 0.0333  | 0.2000  |
| crate             | 0.6143  | 0.8000  | 0.3000  | 0.4000  |
| cosmetics         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sunglasses        | 0.0242  | 0.2308  | 0.0081  | 0.2308  |
| hat               | 0.1466  | 0.2857  | 0.0000  | 0.0000  |
| letter            | 0.5000  | 1.0000  | 0.2500  | 0.7500  |
| paper cutter      | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| furniture         | 0.0093  | 0.3333  | 0.0000  | 0.0000  |
| clothes dryer     | 0.5493  | 0.8000  | 0.3925  | 0.7000  |
| shampoo           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| statue            | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| toilet            | 0.8346  | 0.8824  | 0.7014  | 0.7059  |
| faucet            | 0.6111  | 1.0000  | 0.0000  | 0.0000  |
| shower            | 0.2833  | 1.0000  | 0.0576  | 0.5000  |
| beam              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toilet paper      | 0.0922  | 0.2500  | 0.0038  | 0.0833  |
| toiletry          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| soap              | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| washing machine   | 0.5472  | 0.7857  | 0.4501  | 0.6429  |
| dispenser         | 0.0033  | 0.3333  | 0.0000  | 0.0000  |
| machine           | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| body loofah       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beanbag           | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| jalousie          | 1.0000  | 1.0000  | 0.5000  | 0.5000  |
| adhesive tape     | 0.0192  | 0.2500  | 0.0000  | 0.0000  |
| can               | 0.0387  | 0.2500  | 0.0387  | 0.2500  |
| column            | 0.0126  | 1.0000  | 0.0088  | 0.5000  |
| candlestick       | 0.0417  | 1.0000  | 0.0000  | 0.0000  |
| pillar            | 0.6309  | 0.7647  | 0.4595  | 0.7059  |
| knife             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| spoon             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pipe              | 0.2654  | 0.7500  | 0.0053  | 0.5000  |
| oven              | 0.7681  | 1.0000  | 0.2229  | 0.5263  |
| pan               | 0.2948  | 0.6667  | 0.1250  | 0.1667  |
| stove             | 0.4429  | 0.8824  | 0.0919  | 0.4706  |
| shovel            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit             | 0.4000  | 0.6000  | 0.2000  | 0.2000  |
| kitchenware       | 0.3022  | 0.5455  | 0.0427  | 0.1591  |
| food              | 0.1061  | 0.3000  | 0.0048  | 0.2000  |
| partition         | 0.4667  | 0.8000  | 0.2000  | 0.2000  |
| radio             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| sign              | 0.3026  | 1.0000  | 0.2500  | 0.5000  |
| crib              | 0.3889  | 0.6667  | 0.3889  | 0.6667  |
| sponge            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| mattress          | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| treadmill         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bread             | 0.1000  | 0.2000  | 0.0000  | 0.0000  |
| broom             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tray              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| holder            | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| chandelier        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hook              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| map               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| screen            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pack              | 0.0306  | 0.7692  | 0.0065  | 0.3846  |
| glove             | 0.4444  | 0.6667  | 0.1111  | 0.3333  |
| salt              | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| seasoning         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flush             | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| dishwasher        | 0.9167  | 1.0000  | 0.6667  | 0.6667  |
| dish rack         | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| vacuum cleaner    | 0.7500  | 0.7500  | 0.0000  | 0.0000  |
| sack              | 0.6803  | 0.8571  | 0.1633  | 0.2857  |
| toaster           | 1.0000  | 1.0000  | 0.0545  | 0.5000  |
| water heater      | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| mop               | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| dumbbell          | 1.0000  | 1.0000  | 0.2000  | 1.0000  |
| range hood        | 0.3050  | 0.6000  | 0.0400  | 0.2000  |
| luggage           | 0.5956  | 1.0000  | 0.5556  | 0.6667  |
| helmets           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| menu              | 0.6071  | 0.7143  | 0.0000  | 0.0000  |
| footstool         | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| fire extinguisher | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| toothbrush        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dispenser    | 0.4444  | 0.8000  | 0.0222  | 0.2000  |
| ladder            | 0.4631  | 1.0000  | 0.4286  | 0.6667  |
| glass             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dish         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mat               | 0.0833  | 0.3333  | 0.0833  | 0.3333  |
| coil              | 0.2778  | 0.3333  | 0.0000  | 0.0000  |
| washbasin         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scissors          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------           | ------- | ------- | ------- | ------- |
| 3rscan            | 0.3734  | 0.5837  | 0.1652  | 0.2839  |
| head              | 0.3850  | 0.6778  | 0.1621  | 0.3434  |
| common            | 0.4055  | 0.5698  | 0.1947  | 0.2856  |
| tail              | 0.3087  | 0.4297  | 0.1313  | 0.1724  |
| small             | 0.1289  | 0.3096  | 0.0216  | 0.0803  |
| medium            | 0.3894  | 0.6502  | 0.1894  | 0.3194  |
| large             | 0.2918  | 0.7895  | 0.1294  | 0.4405  |
+-------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 16:27:16 INFO metrics.py:437 | number of matterport3d results: 339
Rank[0/8] 06/20/2025 16:28:47 INFO metrics.py:332 | eval results of matterport3d:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| picture             | 0.2318  | 0.4903  | 0.0182  | 0.1401  |
| mirror              | 0.1366  | 0.6486  | 0.0287  | 0.2027  |
| door                | 0.2733  | 0.6240  | 0.0209  | 0.2063  |
| towel               | 0.4101  | 0.6000  | 0.1166  | 0.2818  |
| doorframe           | 0.3108  | 0.7335  | 0.1205  | 0.4301  |
| sink                | 0.5547  | 0.8182  | 0.2924  | 0.5556  |
| light               | 0.0323  | 0.2365  | 0.0010  | 0.0598  |
| drawer              | 0.0006  | 0.2500  | 0.0006  | 0.2500  |
| blanket             | 0.1988  | 0.7273  | 0.0272  | 0.2727  |
| decoration          | 0.0150  | 0.4881  | 0.0009  | 0.1071  |
| plant               | 0.3308  | 0.7679  | 0.1542  | 0.4107  |
| toilet              | 0.8028  | 0.8438  | 0.7812  | 0.8125  |
| case                | 0.0006  | 0.2222  | 0.0003  | 0.1111  |
| roof                | 0.0016  | 0.2917  | 0.0000  | 0.0000  |
| bottle              | 0.0890  | 0.5625  | 0.0171  | 0.1667  |
| lamp                | 0.3380  | 0.6813  | 0.1200  | 0.3846  |
| cabinet             | 0.1345  | 0.6483  | 0.0840  | 0.4364  |
| bench               | 0.2318  | 0.5500  | 0.1681  | 0.3000  |
| window              | 0.1663  | 0.5333  | 0.0264  | 0.2056  |
| counter             | 0.0209  | 0.4848  | 0.0015  | 0.1818  |
| bathtub             | 0.6742  | 0.7778  | 0.5229  | 0.7037  |
| stand               | 0.4398  | 0.8200  | 0.3852  | 0.7000  |
| table               | 0.3253  | 0.7692  | 0.2781  | 0.6218  |
| box                 | 0.0480  | 0.6538  | 0.0123  | 0.3654  |
| vase                | 0.0198  | 0.4333  | 0.0036  | 0.2333  |
| handle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| switch              | 0.0003  | 0.2000  | 0.0000  | 0.0500  |
| shelf               | 0.1265  | 0.5912  | 0.0405  | 0.3139  |
| dresser             | 0.2495  | 0.7333  | 0.2023  | 0.6000  |
| frame               | 0.0006  | 0.2941  | 0.0000  | 0.0588  |
| countertop          | 0.0252  | 0.2500  | 0.0007  | 0.1071  |
| flowerpot           | 0.0299  | 0.3600  | 0.0058  | 0.2000  |
| plate               | 0.0481  | 0.5714  | 0.0035  | 0.1429  |
| washing machine     | 0.4903  | 0.7500  | 0.0832  | 0.5000  |
| pillow              | 0.1006  | 0.5268  | 0.0051  | 0.1199  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| desk                | 0.3937  | 0.7368  | 0.2736  | 0.6316  |
| vent                | 0.0106  | 0.1667  | 0.0000  | 0.0000  |
| bed                 | 0.6194  | 0.8070  | 0.3943  | 0.7719  |
| washbasin           | 0.0125  | 0.2857  | 0.0123  | 0.2143  |
| stool               | 0.0650  | 0.3810  | 0.0642  | 0.3333  |
| statue              | 0.0592  | 0.3529  | 0.0114  | 0.2353  |
| soap                | 0.0056  | 0.2000  | 0.0056  | 0.2000  |
| book                | 0.0792  | 0.4016  | 0.0133  | 0.0866  |
| couch               | 0.3257  | 0.7826  | 0.2573  | 0.6522  |
| mat                 | 0.0255  | 0.3333  | 0.0002  | 0.0417  |
| toy                 | 0.0929  | 0.4444  | 0.0360  | 0.0833  |
| chair               | 0.4297  | 0.7687  | 0.3177  | 0.6565  |
| pot                 | 0.0033  | 0.4615  | 0.0004  | 0.0769  |
| window frame        | 0.0320  | 0.4598  | 0.0023  | 0.1954  |
| paper               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| curtain             | 0.3786  | 0.7292  | 0.1042  | 0.4062  |
| tv                  | 0.4165  | 0.6977  | 0.1242  | 0.3256  |
| bowl                | 0.1207  | 0.3939  | 0.0403  | 0.1818  |
| stairs              | 0.0141  | 0.3774  | 0.0028  | 0.0566  |
| ottoman             | 0.0280  | 0.3333  | 0.0280  | 0.3333  |
| clock               | 0.1735  | 0.6667  | 0.1735  | 0.6667  |
| steps               | 0.0010  | 0.4000  | 0.0000  | 0.0000  |
| alarm               | 0.0104  | 0.1667  | 0.0000  | 0.0000  |
| screen              | 0.2778  | 0.5000  | 0.0000  | 0.0000  |
| telephone           | 0.2562  | 0.5000  | 0.0031  | 0.2500  |
| carpet              | 0.0003  | 0.1111  | 0.0000  | 0.0000  |
| cushion             | 0.0024  | 0.0588  | 0.0000  | 0.0000  |
| cup                 | 0.0003  | 0.1000  | 0.0000  | 0.0000  |
| footstool           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fan                 | 0.6652  | 0.8148  | 0.2096  | 0.4074  |
| rack                | 0.0015  | 0.3636  | 0.0015  | 0.3636  |
| column              | 0.0134  | 0.3056  | 0.0029  | 0.1111  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit               | 0.0417  | 0.1250  | 0.0417  | 0.1250  |
| pillar              | 0.2410  | 0.7619  | 0.1174  | 0.3810  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| doorway             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shower              | 0.0006  | 0.0833  | 0.0006  | 0.0833  |
| door knob           | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| bin                 | 0.3446  | 0.7805  | 0.1482  | 0.4146  |
| faucet              | 0.0208  | 0.3056  | 0.0000  | 0.0000  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vanity              | 0.2182  | 0.7333  | 0.1631  | 0.6000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toilet paper        | 0.3011  | 0.6667  | 0.1312  | 0.4444  |
| rail                | 0.0254  | 0.4259  | 0.0000  | 0.0185  |
| headboard           | 0.0089  | 0.1250  | 0.0000  | 0.0000  |
| windowsill          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball                | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| thermostat          | 0.1667  | 0.1667  | 0.0000  | 0.0000  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dispenser           | 0.0114  | 0.5000  | 0.0114  | 0.5000  |
| hanger              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dispenser      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| holder              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| brush               | 0.0167  | 0.2500  | 0.0000  | 0.0000  |
| kitchen island      | 0.2286  | 0.4286  | 0.2286  | 0.4286  |
| heater              | 0.3333  | 1.0000  | 0.3333  | 1.0000  |
| monitor             | 0.3333  | 0.5000  | 0.1250  | 0.2500  |
| jar                 | 0.0145  | 0.3333  | 0.0000  | 0.0000  |
| piano               | 0.1667  | 0.5000  | 0.1667  | 0.5000  |
| candle              | 0.0067  | 0.3333  | 0.0067  | 0.3333  |
| teapot              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| refrigerator        | 0.4945  | 0.8667  | 0.0877  | 0.2000  |
| air conditioner     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| board               | 0.1716  | 0.5000  | 0.0000  | 0.0000  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radiator            | 0.1667  | 0.3333  | 0.0000  | 0.0000  |
| scale               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wardrobe            | 0.0606  | 0.4545  | 0.0580  | 0.3636  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basin               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pipe                | 0.0001  | 0.1000  | 0.0000  | 0.0000  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pan                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket              | 0.0065  | 0.4000  | 0.0020  | 0.2000  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue box          | 0.1455  | 0.5000  | 0.0063  | 0.1250  |
| basket              | 0.0537  | 0.4400  | 0.0065  | 0.2000  |
| pool table          | 0.1667  | 0.3333  | 0.1667  | 0.3333  |
| tissue              | 0.0046  | 1.0000  | 0.0000  | 0.0000  |
| guitar              | 0.1677  | 0.5714  | 0.0000  | 0.0000  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0172  | 0.2500  | 0.0172  | 0.2500  |
| keyboard            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| food                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bag                 | 0.0386  | 0.3846  | 0.0109  | 0.2308  |
| clothes dryer       | 0.2667  | 0.6667  | 0.2667  | 0.6667  |
| excercise equipment | 0.4430  | 0.7222  | 0.0240  | 0.2778  |
| clothes             | 0.0887  | 0.3448  | 0.0249  | 0.1207  |
| kitchenware         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| microwave           | 0.1211  | 0.2500  | 0.0833  | 0.0833  |
| kettle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher   | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| ladder              | 0.1220  | 0.5556  | 0.0185  | 0.1111  |
| garage door         | 0.3490  | 0.5000  | 0.3490  | 0.5000  |
| printer             | 0.4921  | 0.5714  | 0.4286  | 0.4286  |
| arch                | 0.0387  | 0.2000  | 0.0032  | 0.1000  |
| toiletry            | 0.1240  | 0.3182  | 0.0009  | 0.0455  |
| soap dish           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fireplace           | 0.3377  | 0.5217  | 0.0081  | 0.1739  |
| beam                | 0.0000  | 0.0476  | 0.0000  | 0.0000  |
| container           | 0.0119  | 0.1481  | 0.0030  | 0.0741  |
| machine             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| chandelier          | 0.0175  | 0.0526  | 0.0000  | 0.0000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| computer            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stove               | 0.2178  | 0.6000  | 0.1174  | 0.3000  |
| oven                | 0.2109  | 0.8000  | 0.0000  | 0.0000  |
| socket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mouse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shoe                | 0.0669  | 0.3200  | 0.0000  | 0.0000  |
| laptop              | 0.8333  | 1.0000  | 0.0333  | 0.5000  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| partition           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet               | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| backpack            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hamper              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| magazine            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.1875  | 0.7500  | 0.0000  | 0.0000  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drum                | 0.0250  | 0.1250  | 0.0000  | 0.0000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jacket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pack                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water heater        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| player              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell            | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| candlestick         | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| can                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| matterport3d        | 0.1280  | 0.3216  | 0.0614  | 0.1651  |
| head                | 0.1767  | 0.4694  | 0.0830  | 0.2548  |
| common              | 0.0868  | 0.2212  | 0.0410  | 0.0924  |
| tail                | 0.0754  | 0.1053  | 0.0429  | 0.0608  |
| small               | 0.0482  | 0.1749  | 0.0249  | 0.0775  |
| medium              | 0.1225  | 0.3601  | 0.0543  | 0.1750  |
| large               | 0.1050  | 0.3968  | 0.0532  | 0.2468  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 16:28:47 INFO metrics.py:437 | number of arkit results: 0
Rank[0/8] 06/20/2025 16:28:47 INFO stats.py:394 | Epoch[199] completed. Training Speed: 2.99 samples/sec across all devices.	Epoch Time: 1043.03 sec.	Average Epoch Time: 1043.03 sec.	Average Step Time: 2.67 sec.	Estimated Remaining Time: 5:15:18.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:28:48 WARNING accelerator.py:3099 | Deleting 1 checkpoints to make room for new checkpoint.
Rank[0/8] 06/20/2025 16:28:49 INFO accelerator.py:3111 | Saving current state to /job_data/checkpoints/checkpoint_19
Rank[1/8] 06/20/2025 16:28:49 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 199 to /job_data/checkpoints/checkpoint_19
Rank[3/8] 06/20/2025 16:28:49 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 199 to /job_data/checkpoints/checkpoint_19
Rank[4/8] 06/20/2025 16:28:49 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 199 to /job_data/checkpoints/checkpoint_19
Rank[6/8] 06/20/2025 16:28:49 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 199 to /job_data/checkpoints/checkpoint_19
Rank[5/8] 06/20/2025 16:28:49 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 199 to /job_data/checkpoints/checkpoint_19
Rank[7/8] 06/20/2025 16:28:49 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 199 to /job_data/checkpoints/checkpoint_19
Rank[2/8] 06/20/2025 16:28:49 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 199 to /job_data/checkpoints/checkpoint_19
Rank[0/8] 06/20/2025 16:28:50 INFO checkpointing.py:106 | Model weights saved in /job_data/checkpoints/checkpoint_19/model.safetensors
Rank[0/8] 06/20/2025 16:28:53 INFO checkpointing.py:113 | Optimizer state saved in /job_data/checkpoints/checkpoint_19/optimizer.bin
Rank[0/8] 06/20/2025 16:28:53 INFO checkpointing.py:120 | Scheduler state saved in /job_data/checkpoints/checkpoint_19/scheduler.bin
Rank[0/8] 06/20/2025 16:28:53 INFO checkpointing.py:137 | Sampler state for dataloader 0 saved in /job_data/checkpoints/checkpoint_19/sampler.bin
Rank[0/8] 06/20/2025 16:28:53 INFO checkpointing.py:137 | Sampler state for dataloader 1 saved in /job_data/checkpoints/checkpoint_19/sampler_1.bin
Rank[0/8] 06/20/2025 16:28:53 INFO checkpointing.py:164 | Random states saved in /job_data/checkpoints/checkpoint_19/random_states_0.pkl
Rank[0/8] 06/20/2025 16:28:53 INFO checkpointing.py:300 | Saving the state of TrainerProgressState to /job_data/checkpoints/checkpoint_19/custom_checkpoint_0.pkl
Rank[0/8] 06/20/2025 16:28:53 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 199 to /job_data/checkpoints/checkpoint_19
Rank[0/8] 06/20/2025 16:29:22 INFO stats.py:314 | Epoch[200] Step[24] GlobalStep[78024] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 5:14:49.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:29:22 INFO loss_tracker.py:84 | Epoch[200/239] Step[24] GlobalStep[78024/NA]: det_loss_cls_0[0.5402] det_loss_box_0[0.6436] det_loss_cls_1[0.5103] det_loss_box_1[0.4883] det_loss_cls_2[0.4427] det_loss_box_2[0.4408] det_loss_cls_3[0.4057] det_loss_box_3[0.4299] det_loss_cls_4[0.3834] det_loss_box_4[0.4170] det_loss_cls_5[0.3699] det_loss_box_5[0.4157] det_loss_cls_dn_0[0.0935] det_loss_box_dn_0[0.3006] det_loss_cls_dn_1[0.0899] det_loss_box_dn_1[0.2430] det_loss_cls_dn_2[0.0893] det_loss_box_dn_2[0.2229] det_loss_cls_dn_3[0.0885] det_loss_box_dn_3[0.2204] det_loss_cls_dn_4[0.0879] det_loss_box_dn_4[0.2162] det_loss_cls_dn_5[0.0872] det_loss_box_dn_5[0.2147] loss_depth[0.0367] total_loss[7.4782]
Rank[0/8] 06/20/2025 16:29:49 INFO stats.py:314 | Epoch[200] Step[49] GlobalStep[78049] Training Speed: 8.13 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 5:14:17.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:29:49 INFO loss_tracker.py:84 | Epoch[200/239] Step[49] GlobalStep[78049/NA]: det_loss_cls_0[0.4627] det_loss_box_0[0.5728] det_loss_cls_1[0.4498] det_loss_box_1[0.4259] det_loss_cls_2[0.3996] det_loss_box_2[0.3902] det_loss_cls_3[0.3630] det_loss_box_3[0.3760] det_loss_cls_4[0.3461] det_loss_box_4[0.3652] det_loss_cls_5[0.3374] det_loss_box_5[0.3645] det_loss_cls_dn_0[0.0905] det_loss_box_dn_0[0.3228] det_loss_cls_dn_1[0.0860] det_loss_box_dn_1[0.2631] det_loss_cls_dn_2[0.0853] det_loss_box_dn_2[0.2455] det_loss_cls_dn_3[0.0843] det_loss_box_dn_3[0.2401] det_loss_cls_dn_4[0.0834] det_loss_box_dn_4[0.2352] det_loss_cls_dn_5[0.0824] det_loss_box_dn_5[0.2270] loss_depth[0.0374] total_loss[6.9364]
Rank[0/8] 06/20/2025 16:30:14 INFO stats.py:314 | Epoch[200] Step[74] GlobalStep[78074] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 5:13:46.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:30:14 INFO loss_tracker.py:84 | Epoch[200/239] Step[74] GlobalStep[78074/NA]: det_loss_cls_0[0.5503] det_loss_box_0[0.7341] det_loss_cls_1[0.5357] det_loss_box_1[0.5416] det_loss_cls_2[0.4786] det_loss_box_2[0.4824] det_loss_cls_3[0.4444] det_loss_box_3[0.4702] det_loss_cls_4[0.4175] det_loss_box_4[0.4536] det_loss_cls_5[0.4004] det_loss_box_5[0.4602] det_loss_cls_dn_0[0.0967] det_loss_box_dn_0[0.3221] det_loss_cls_dn_1[0.0924] det_loss_box_dn_1[0.2758] det_loss_cls_dn_2[0.0921] det_loss_box_dn_2[0.2607] det_loss_cls_dn_3[0.0910] det_loss_box_dn_3[0.2568] det_loss_cls_dn_4[0.0906] det_loss_box_dn_4[0.2535] det_loss_cls_dn_5[0.0893] det_loss_box_dn_5[0.2505] loss_depth[0.0353] total_loss[8.1758]
Rank[0/8] 06/20/2025 16:30:40 INFO stats.py:314 | Epoch[200] Step[99] GlobalStep[78099] Training Speed: 8.22 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 5:13:15.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:30:40 INFO loss_tracker.py:84 | Epoch[200/239] Step[99] GlobalStep[78099/NA]: det_loss_cls_0[0.5409] det_loss_box_0[0.7197] det_loss_cls_1[0.5332] det_loss_box_1[0.5398] det_loss_cls_2[0.4618] det_loss_box_2[0.4889] det_loss_cls_3[0.4154] det_loss_box_3[0.4700] det_loss_cls_4[0.3881] det_loss_box_4[0.4588] det_loss_cls_5[0.3706] det_loss_box_5[0.4591] det_loss_cls_dn_0[0.0877] det_loss_box_dn_0[0.2591] det_loss_cls_dn_1[0.0839] det_loss_box_dn_1[0.2128] det_loss_cls_dn_2[0.0824] det_loss_box_dn_2[0.1904] det_loss_cls_dn_3[0.0814] det_loss_box_dn_3[0.1858] det_loss_cls_dn_4[0.0804] det_loss_box_dn_4[0.1829] det_loss_cls_dn_5[0.0798] det_loss_box_dn_5[0.1820] loss_depth[0.0370] total_loss[7.5919]
Rank[0/8] 06/20/2025 16:31:06 INFO stats.py:314 | Epoch[200] Step[124] GlobalStep[78124] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 5:12:44.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:31:06 INFO loss_tracker.py:84 | Epoch[200/239] Step[124] GlobalStep[78124/NA]: det_loss_cls_0[0.4190] det_loss_box_0[0.5376] det_loss_cls_1[0.4043] det_loss_box_1[0.3982] det_loss_cls_2[0.3434] det_loss_box_2[0.3535] det_loss_cls_3[0.3200] det_loss_box_3[0.3386] det_loss_cls_4[0.3041] det_loss_box_4[0.3271] det_loss_cls_5[0.2955] det_loss_box_5[0.3168] det_loss_cls_dn_0[0.0840] det_loss_box_dn_0[0.2740] det_loss_cls_dn_1[0.0803] det_loss_box_dn_1[0.2137] det_loss_cls_dn_2[0.0790] det_loss_box_dn_2[0.1970] det_loss_cls_dn_3[0.0782] det_loss_box_dn_3[0.1933] det_loss_cls_dn_4[0.0775] det_loss_box_dn_4[0.1912] det_loss_cls_dn_5[0.0767] det_loss_box_dn_5[0.1850] loss_depth[0.0345] total_loss[6.1224]
Rank[0/8] 06/20/2025 16:31:32 INFO stats.py:314 | Epoch[200] Step[149] GlobalStep[78149] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 5:12:13.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:31:32 INFO loss_tracker.py:84 | Epoch[200/239] Step[149] GlobalStep[78149/NA]: det_loss_cls_0[0.4794] det_loss_box_0[0.5886] det_loss_cls_1[0.4812] det_loss_box_1[0.4259] det_loss_cls_2[0.4140] det_loss_box_2[0.3838] det_loss_cls_3[0.3759] det_loss_box_3[0.3668] det_loss_cls_4[0.3544] det_loss_box_4[0.3611] det_loss_cls_5[0.3305] det_loss_box_5[0.3671] det_loss_cls_dn_0[0.0882] det_loss_box_dn_0[0.2359] det_loss_cls_dn_1[0.0841] det_loss_box_dn_1[0.1899] det_loss_cls_dn_2[0.0831] det_loss_box_dn_2[0.1745] det_loss_cls_dn_3[0.0815] det_loss_box_dn_3[0.1704] det_loss_cls_dn_4[0.0805] det_loss_box_dn_4[0.1668] det_loss_cls_dn_5[0.0797] det_loss_box_dn_5[0.1632] loss_depth[0.0338] total_loss[6.5603]
Rank[0/8] 06/20/2025 16:31:57 INFO stats.py:314 | Epoch[200] Step[174] GlobalStep[78174] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 5:11:41.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:31:57 INFO loss_tracker.py:84 | Epoch[200/239] Step[174] GlobalStep[78174/NA]: det_loss_cls_0[0.4637] det_loss_box_0[0.5351] det_loss_cls_1[0.4492] det_loss_box_1[0.4032] det_loss_cls_2[0.3896] det_loss_box_2[0.3675] det_loss_cls_3[0.3629] det_loss_box_3[0.3569] det_loss_cls_4[0.3422] det_loss_box_4[0.3424] det_loss_cls_5[0.3354] det_loss_box_5[0.3403] det_loss_cls_dn_0[0.0958] det_loss_box_dn_0[0.2538] det_loss_cls_dn_1[0.0920] det_loss_box_dn_1[0.2068] det_loss_cls_dn_2[0.0906] det_loss_box_dn_2[0.1875] det_loss_cls_dn_3[0.0895] det_loss_box_dn_3[0.1834] det_loss_cls_dn_4[0.0890] det_loss_box_dn_4[0.1801] det_loss_cls_dn_5[0.0880] det_loss_box_dn_5[0.1791] loss_depth[0.0341] total_loss[6.4582]
Rank[0/8] 06/20/2025 16:32:23 INFO stats.py:314 | Epoch[200] Step[199] GlobalStep[78199] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 5:11:10.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:32:23 INFO loss_tracker.py:84 | Epoch[200/239] Step[199] GlobalStep[78199/NA]: det_loss_cls_0[0.2988] det_loss_box_0[0.3819] det_loss_cls_1[0.2856] det_loss_box_1[0.2808] det_loss_cls_2[0.2312] det_loss_box_2[0.2583] det_loss_cls_3[0.2133] det_loss_box_3[0.2487] det_loss_cls_4[0.1981] det_loss_box_4[0.2472] det_loss_cls_5[0.1873] det_loss_box_5[0.2496] det_loss_cls_dn_0[0.0824] det_loss_box_dn_0[0.2687] det_loss_cls_dn_1[0.0792] det_loss_box_dn_1[0.2163] det_loss_cls_dn_2[0.0786] det_loss_box_dn_2[0.2015] det_loss_cls_dn_3[0.0779] det_loss_box_dn_3[0.1957] det_loss_cls_dn_4[0.0770] det_loss_box_dn_4[0.1936] det_loss_cls_dn_5[0.0766] det_loss_box_dn_5[0.1880] loss_depth[0.0337] total_loss[4.8503]
Rank[0/8] 06/20/2025 16:32:49 INFO stats.py:314 | Epoch[200] Step[224] GlobalStep[78224] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 5:10:39.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:32:49 INFO loss_tracker.py:84 | Epoch[200/239] Step[224] GlobalStep[78224/NA]: det_loss_cls_0[0.4244] det_loss_box_0[0.5513] det_loss_cls_1[0.3992] det_loss_box_1[0.4240] det_loss_cls_2[0.3496] det_loss_box_2[0.3772] det_loss_cls_3[0.3326] det_loss_box_3[0.3556] det_loss_cls_4[0.3142] det_loss_box_4[0.3485] det_loss_cls_5[0.3009] det_loss_box_5[0.3505] det_loss_cls_dn_0[0.0963] det_loss_box_dn_0[0.3289] det_loss_cls_dn_1[0.0934] det_loss_box_dn_1[0.2599] det_loss_cls_dn_2[0.0923] det_loss_box_dn_2[0.2399] det_loss_cls_dn_3[0.0911] det_loss_box_dn_3[0.2375] det_loss_cls_dn_4[0.0906] det_loss_box_dn_4[0.2337] det_loss_cls_dn_5[0.0901] det_loss_box_dn_5[0.2303] loss_depth[0.0370] total_loss[6.6490]
Rank[0/8] 06/20/2025 16:33:15 INFO stats.py:314 | Epoch[200] Step[249] GlobalStep[78249] Training Speed: 8.15 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 5:10:08.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:33:15 INFO loss_tracker.py:84 | Epoch[200/239] Step[249] GlobalStep[78249/NA]: det_loss_cls_0[0.5722] det_loss_box_0[0.6878] det_loss_cls_1[0.5575] det_loss_box_1[0.5252] det_loss_cls_2[0.4876] det_loss_box_2[0.4850] det_loss_cls_3[0.4516] det_loss_box_3[0.4721] det_loss_cls_4[0.4200] det_loss_box_4[0.4596] det_loss_cls_5[0.4010] det_loss_box_5[0.4670] det_loss_cls_dn_0[0.0873] det_loss_box_dn_0[0.2741] det_loss_cls_dn_1[0.0827] det_loss_box_dn_1[0.2226] det_loss_cls_dn_2[0.0819] det_loss_box_dn_2[0.2055] det_loss_cls_dn_3[0.0811] det_loss_box_dn_3[0.1995] det_loss_cls_dn_4[0.0799] det_loss_box_dn_4[0.1970] det_loss_cls_dn_5[0.0791] det_loss_box_dn_5[0.1944] loss_depth[0.0343] total_loss[7.8058]
Rank[0/8] 06/20/2025 16:33:41 INFO stats.py:314 | Epoch[200] Step[274] GlobalStep[78274] Training Speed: 6.13 samples/sec across all devices.	Average Step Time: 1.30 sec.	Estimated Remaining Time: 5:09:37.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:33:41 INFO loss_tracker.py:84 | Epoch[200/239] Step[274] GlobalStep[78274/NA]: det_loss_cls_0[0.3356] det_loss_box_0[0.3979] det_loss_cls_1[0.3334] det_loss_box_1[0.3062] det_loss_cls_2[0.2765] det_loss_box_2[0.2713] det_loss_cls_3[0.2530] det_loss_box_3[0.2636] det_loss_cls_4[0.2361] det_loss_box_4[0.2491] det_loss_cls_5[0.2293] det_loss_box_5[0.2465] det_loss_cls_dn_0[0.0885] det_loss_box_dn_0[0.2228] det_loss_cls_dn_1[0.0846] det_loss_box_dn_1[0.1779] det_loss_cls_dn_2[0.0837] det_loss_box_dn_2[0.1606] det_loss_cls_dn_3[0.0821] det_loss_box_dn_3[0.1564] det_loss_cls_dn_4[0.0809] det_loss_box_dn_4[0.1540] det_loss_cls_dn_5[0.0800] det_loss_box_dn_5[0.1525] loss_depth[0.0331] total_loss[4.9558]
Rank[0/8] 06/20/2025 16:34:07 INFO stats.py:314 | Epoch[200] Step[299] GlobalStep[78299] Training Speed: 7.92 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 5:09:06.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:34:07 INFO loss_tracker.py:84 | Epoch[200/239] Step[299] GlobalStep[78299/NA]: det_loss_cls_0[0.4295] det_loss_box_0[0.5630] det_loss_cls_1[0.4187] det_loss_box_1[0.4206] det_loss_cls_2[0.3740] det_loss_box_2[0.3855] det_loss_cls_3[0.3513] det_loss_box_3[0.3666] det_loss_cls_4[0.3237] det_loss_box_4[0.3636] det_loss_cls_5[0.3156] det_loss_box_5[0.3635] det_loss_cls_dn_0[0.0913] det_loss_box_dn_0[0.2620] det_loss_cls_dn_1[0.0882] det_loss_box_dn_1[0.2173] det_loss_cls_dn_2[0.0873] det_loss_box_dn_2[0.1985] det_loss_cls_dn_3[0.0862] det_loss_box_dn_3[0.1940] det_loss_cls_dn_4[0.0856] det_loss_box_dn_4[0.1922] det_loss_cls_dn_5[0.0848] det_loss_box_dn_5[0.1910] loss_depth[0.0377] total_loss[6.4916]
Rank[0/8] 06/20/2025 16:34:32 INFO stats.py:314 | Epoch[200] Step[324] GlobalStep[78324] Training Speed: 6.86 samples/sec across all devices.	Average Step Time: 1.17 sec.	Estimated Remaining Time: 5:08:34.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:34:32 INFO loss_tracker.py:84 | Epoch[200/239] Step[324] GlobalStep[78324/NA]: det_loss_cls_0[0.4609] det_loss_box_0[0.5561] det_loss_cls_1[0.4537] det_loss_box_1[0.4079] det_loss_cls_2[0.3950] det_loss_box_2[0.3683] det_loss_cls_3[0.3632] det_loss_box_3[0.3571] det_loss_cls_4[0.3412] det_loss_box_4[0.3458] det_loss_cls_5[0.3295] det_loss_box_5[0.3434] det_loss_cls_dn_0[0.0936] det_loss_box_dn_0[0.2311] det_loss_cls_dn_1[0.0892] det_loss_box_dn_1[0.1894] det_loss_cls_dn_2[0.0879] det_loss_box_dn_2[0.1723] det_loss_cls_dn_3[0.0874] det_loss_box_dn_3[0.1694] det_loss_cls_dn_4[0.0862] det_loss_box_dn_4[0.1672] det_loss_cls_dn_5[0.0854] det_loss_box_dn_5[0.1635] loss_depth[0.0358] total_loss[6.3803]
Rank[0/8] 06/20/2025 16:34:58 INFO stats.py:314 | Epoch[200] Step[349] GlobalStep[78349] Training Speed: 6.69 samples/sec across all devices.	Average Step Time: 1.20 sec.	Estimated Remaining Time: 5:08:03.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:34:58 INFO loss_tracker.py:84 | Epoch[200/239] Step[349] GlobalStep[78349/NA]: det_loss_cls_0[0.4211] det_loss_box_0[0.5518] det_loss_cls_1[0.4118] det_loss_box_1[0.4205] det_loss_cls_2[0.3552] det_loss_box_2[0.3884] det_loss_cls_3[0.3165] det_loss_box_3[0.3878] det_loss_cls_4[0.2903] det_loss_box_4[0.3785] det_loss_cls_5[0.2809] det_loss_box_5[0.3750] det_loss_cls_dn_0[0.0807] det_loss_box_dn_0[0.2735] det_loss_cls_dn_1[0.0776] det_loss_box_dn_1[0.2209] det_loss_cls_dn_2[0.0765] det_loss_box_dn_2[0.2050] det_loss_cls_dn_3[0.0753] det_loss_box_dn_3[0.1998] det_loss_cls_dn_4[0.0748] det_loss_box_dn_4[0.1961] det_loss_cls_dn_5[0.0735] det_loss_box_dn_5[0.1945] loss_depth[0.0352] total_loss[6.3609]
Rank[0/8] 06/20/2025 16:35:24 INFO stats.py:314 | Epoch[200] Step[374] GlobalStep[78374] Training Speed: 6.98 samples/sec across all devices.	Average Step Time: 1.15 sec.	Estimated Remaining Time: 5:07:32.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:35:24 INFO loss_tracker.py:84 | Epoch[200/239] Step[374] GlobalStep[78374/NA]: det_loss_cls_0[0.4818] det_loss_box_0[0.6396] det_loss_cls_1[0.4606] det_loss_box_1[0.4828] det_loss_cls_2[0.4158] det_loss_box_2[0.4295] det_loss_cls_3[0.3956] det_loss_box_3[0.4087] det_loss_cls_4[0.3643] det_loss_box_4[0.3976] det_loss_cls_5[0.3530] det_loss_box_5[0.3916] det_loss_cls_dn_0[0.0925] det_loss_box_dn_0[0.2864] det_loss_cls_dn_1[0.0891] det_loss_box_dn_1[0.2355] det_loss_cls_dn_2[0.0877] det_loss_box_dn_2[0.2152] det_loss_cls_dn_3[0.0866] det_loss_box_dn_3[0.2095] det_loss_cls_dn_4[0.0856] det_loss_box_dn_4[0.2059] det_loss_cls_dn_5[0.0846] det_loss_box_dn_5[0.1997] loss_depth[0.0353] total_loss[7.1347]
Rank[0/8] 06/20/2025 16:35:39 INFO stats.py:394 | Epoch[200] completed. Training Speed: 7.68 samples/sec across all devices.	Epoch Time: 406.11 sec.	Average Epoch Time: 406.11 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 5:07:13.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:35:51 INFO stats.py:314 | Epoch[201] Step[9] GlobalStep[78399] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 5:07:01.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:36:06 INFO loss_tracker.py:84 | Epoch[201/239] Step[24] GlobalStep[78414/NA]: det_loss_cls_0[0.4664] det_loss_box_0[0.5688] det_loss_cls_1[0.4556] det_loss_box_1[0.4223] det_loss_cls_2[0.3891] det_loss_box_2[0.3880] det_loss_cls_3[0.3579] det_loss_box_3[0.3660] det_loss_cls_4[0.3349] det_loss_box_4[0.3585] det_loss_cls_5[0.3264] det_loss_box_5[0.3573] det_loss_cls_dn_0[0.0921] det_loss_box_dn_0[0.2479] det_loss_cls_dn_1[0.0881] det_loss_box_dn_1[0.2038] det_loss_cls_dn_2[0.0874] det_loss_box_dn_2[0.1879] det_loss_cls_dn_3[0.0867] det_loss_box_dn_3[0.1834] det_loss_cls_dn_4[0.0861] det_loss_box_dn_4[0.1811] det_loss_cls_dn_5[0.0850] det_loss_box_dn_5[0.1790] loss_depth[0.0361] total_loss[6.5357]
Rank[0/8] 06/20/2025 16:36:17 INFO stats.py:314 | Epoch[201] Step[34] GlobalStep[78424] Training Speed: 7.03 samples/sec across all devices.	Average Step Time: 1.14 sec.	Estimated Remaining Time: 5:06:30.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:36:33 INFO loss_tracker.py:84 | Epoch[201/239] Step[49] GlobalStep[78439/NA]: det_loss_cls_0[0.4720] det_loss_box_0[0.5813] det_loss_cls_1[0.4542] det_loss_box_1[0.4443] det_loss_cls_2[0.3972] det_loss_box_2[0.4021] det_loss_cls_3[0.3766] det_loss_box_3[0.3834] det_loss_cls_4[0.3573] det_loss_box_4[0.3756] det_loss_cls_5[0.3432] det_loss_box_5[0.3746] det_loss_cls_dn_0[0.0934] det_loss_box_dn_0[0.2302] det_loss_cls_dn_1[0.0906] det_loss_box_dn_1[0.1850] det_loss_cls_dn_2[0.0899] det_loss_box_dn_2[0.1671] det_loss_cls_dn_3[0.0889] det_loss_box_dn_3[0.1644] det_loss_cls_dn_4[0.0882] det_loss_box_dn_4[0.1632] det_loss_cls_dn_5[0.0873] det_loss_box_dn_5[0.1620] loss_depth[0.0381] total_loss[6.6102]
Rank[0/8] 06/20/2025 16:36:43 INFO stats.py:314 | Epoch[201] Step[59] GlobalStep[78449] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 5:05:59.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:36:58 INFO loss_tracker.py:84 | Epoch[201/239] Step[74] GlobalStep[78464/NA]: det_loss_cls_0[0.5225] det_loss_box_0[0.6027] det_loss_cls_1[0.5073] det_loss_box_1[0.4547] det_loss_cls_2[0.4564] det_loss_box_2[0.4179] det_loss_cls_3[0.4320] det_loss_box_3[0.3949] det_loss_cls_4[0.4010] det_loss_box_4[0.3822] det_loss_cls_5[0.3925] det_loss_box_5[0.3830] det_loss_cls_dn_0[0.1031] det_loss_box_dn_0[0.2463] det_loss_cls_dn_1[0.0986] det_loss_box_dn_1[0.2010] det_loss_cls_dn_2[0.0980] det_loss_box_dn_2[0.1848] det_loss_cls_dn_3[0.0968] det_loss_box_dn_3[0.1814] det_loss_cls_dn_4[0.0962] det_loss_box_dn_4[0.1797] det_loss_cls_dn_5[0.0954] det_loss_box_dn_5[0.1778] loss_depth[0.0354] total_loss[7.1418]
Rank[0/8] 06/20/2025 16:37:09 INFO stats.py:314 | Epoch[201] Step[84] GlobalStep[78474] Training Speed: 8.19 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 5:05:28.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:37:24 INFO loss_tracker.py:84 | Epoch[201/239] Step[99] GlobalStep[78489/NA]: det_loss_cls_0[0.3942] det_loss_box_0[0.4652] det_loss_cls_1[0.3831] det_loss_box_1[0.3472] det_loss_cls_2[0.3108] det_loss_box_2[0.3167] det_loss_cls_3[0.2769] det_loss_box_3[0.3045] det_loss_cls_4[0.2541] det_loss_box_4[0.3023] det_loss_cls_5[0.2451] det_loss_box_5[0.3017] det_loss_cls_dn_0[0.0789] det_loss_box_dn_0[0.2290] det_loss_cls_dn_1[0.0751] det_loss_box_dn_1[0.1853] det_loss_cls_dn_2[0.0743] det_loss_box_dn_2[0.1674] det_loss_cls_dn_3[0.0733] det_loss_box_dn_3[0.1622] det_loss_cls_dn_4[0.0724] det_loss_box_dn_4[0.1595] det_loss_cls_dn_5[0.0717] det_loss_box_dn_5[0.1581] loss_depth[0.0327] total_loss[5.4417]
Rank[0/8] 06/20/2025 16:37:35 INFO stats.py:314 | Epoch[201] Step[109] GlobalStep[78499] Training Speed: 8.04 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 5:04:56.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:37:50 INFO loss_tracker.py:84 | Epoch[201/239] Step[124] GlobalStep[78514/NA]: det_loss_cls_0[0.4770] det_loss_box_0[0.6016] det_loss_cls_1[0.4580] det_loss_box_1[0.4459] det_loss_cls_2[0.3876] det_loss_box_2[0.3977] det_loss_cls_3[0.3611] det_loss_box_3[0.3787] det_loss_cls_4[0.3392] det_loss_box_4[0.3696] det_loss_cls_5[0.3271] det_loss_box_5[0.3724] det_loss_cls_dn_0[0.0857] det_loss_box_dn_0[0.2512] det_loss_cls_dn_1[0.0825] det_loss_box_dn_1[0.2056] det_loss_cls_dn_2[0.0819] det_loss_box_dn_2[0.1866] det_loss_cls_dn_3[0.0808] det_loss_box_dn_3[0.1826] det_loss_cls_dn_4[0.0803] det_loss_box_dn_4[0.1802] det_loss_cls_dn_5[0.0790] det_loss_box_dn_5[0.1778] loss_depth[0.0371] total_loss[6.6272]
Rank[0/8] 06/20/2025 16:38:00 INFO stats.py:314 | Epoch[201] Step[134] GlobalStep[78524] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 5:04:25.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:38:16 INFO loss_tracker.py:84 | Epoch[201/239] Step[149] GlobalStep[78539/NA]: det_loss_cls_0[0.3920] det_loss_box_0[0.4652] det_loss_cls_1[0.3819] det_loss_box_1[0.3420] det_loss_cls_2[0.3326] det_loss_box_2[0.3108] det_loss_cls_3[0.3096] det_loss_box_3[0.3018] det_loss_cls_4[0.2785] det_loss_box_4[0.3015] det_loss_cls_5[0.2636] det_loss_box_5[0.3017] det_loss_cls_dn_0[0.0952] det_loss_box_dn_0[0.2357] det_loss_cls_dn_1[0.0916] det_loss_box_dn_1[0.1894] det_loss_cls_dn_2[0.0907] det_loss_box_dn_2[0.1734] det_loss_cls_dn_3[0.0894] det_loss_box_dn_3[0.1704] det_loss_cls_dn_4[0.0879] det_loss_box_dn_4[0.1682] det_loss_cls_dn_5[0.0869] det_loss_box_dn_5[0.1671] loss_depth[0.0339] total_loss[5.6612]
Rank[0/8] 06/20/2025 16:38:27 INFO stats.py:314 | Epoch[201] Step[159] GlobalStep[78549] Training Speed: 7.78 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 5:03:54.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:38:42 INFO loss_tracker.py:84 | Epoch[201/239] Step[174] GlobalStep[78564/NA]: det_loss_cls_0[0.4463] det_loss_box_0[0.5153] det_loss_cls_1[0.4209] det_loss_box_1[0.3891] det_loss_cls_2[0.3754] det_loss_box_2[0.3528] det_loss_cls_3[0.3434] det_loss_box_3[0.3454] det_loss_cls_4[0.3204] det_loss_box_4[0.3359] det_loss_cls_5[0.3078] det_loss_box_5[0.3359] det_loss_cls_dn_0[0.0890] det_loss_box_dn_0[0.3061] det_loss_cls_dn_1[0.0848] det_loss_box_dn_1[0.2438] det_loss_cls_dn_2[0.0836] det_loss_box_dn_2[0.2233] det_loss_cls_dn_3[0.0825] det_loss_box_dn_3[0.2160] det_loss_cls_dn_4[0.0818] det_loss_box_dn_4[0.2130] det_loss_cls_dn_5[0.0808] det_loss_box_dn_5[0.2077] loss_depth[0.0369] total_loss[6.4381]
Rank[0/8] 06/20/2025 16:38:52 INFO stats.py:314 | Epoch[201] Step[184] GlobalStep[78574] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 5:03:23.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:39:08 INFO loss_tracker.py:84 | Epoch[201/239] Step[199] GlobalStep[78589/NA]: det_loss_cls_0[0.4380] det_loss_box_0[0.5673] det_loss_cls_1[0.4235] det_loss_box_1[0.4450] det_loss_cls_2[0.3747] det_loss_box_2[0.4071] det_loss_cls_3[0.3354] det_loss_box_3[0.3899] det_loss_cls_4[0.3168] det_loss_box_4[0.3717] det_loss_cls_5[0.3113] det_loss_box_5[0.3671] det_loss_cls_dn_0[0.0950] det_loss_box_dn_0[0.3191] det_loss_cls_dn_1[0.0910] det_loss_box_dn_1[0.2674] det_loss_cls_dn_2[0.0909] det_loss_box_dn_2[0.2490] det_loss_cls_dn_3[0.0898] det_loss_box_dn_3[0.2473] det_loss_cls_dn_4[0.0892] det_loss_box_dn_4[0.2429] det_loss_cls_dn_5[0.0887] det_loss_box_dn_5[0.2404] loss_depth[0.0360] total_loss[6.8944]
Rank[0/8] 06/20/2025 16:39:18 INFO stats.py:314 | Epoch[201] Step[209] GlobalStep[78599] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 5:02:52.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:39:34 INFO loss_tracker.py:84 | Epoch[201/239] Step[224] GlobalStep[78614/NA]: det_loss_cls_0[0.3654] det_loss_box_0[0.4222] det_loss_cls_1[0.3512] det_loss_box_1[0.3299] det_loss_cls_2[0.2980] det_loss_box_2[0.3067] det_loss_cls_3[0.2777] det_loss_box_3[0.2973] det_loss_cls_4[0.2565] det_loss_box_4[0.2911] det_loss_cls_5[0.2515] det_loss_box_5[0.2843] det_loss_cls_dn_0[0.0884] det_loss_box_dn_0[0.2380] det_loss_cls_dn_1[0.0851] det_loss_box_dn_1[0.1934] det_loss_cls_dn_2[0.0849] det_loss_box_dn_2[0.1791] det_loss_cls_dn_3[0.0840] det_loss_box_dn_3[0.1749] det_loss_cls_dn_4[0.0832] det_loss_box_dn_4[0.1730] det_loss_cls_dn_5[0.0827] det_loss_box_dn_5[0.1712] loss_depth[0.0349] total_loss[5.4043]
Rank[0/8] 06/20/2025 16:39:45 INFO stats.py:314 | Epoch[201] Step[234] GlobalStep[78624] Training Speed: 8.12 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 5:02:21.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:40:00 INFO loss_tracker.py:84 | Epoch[201/239] Step[249] GlobalStep[78639/NA]: det_loss_cls_0[0.4643] det_loss_box_0[0.5877] det_loss_cls_1[0.4535] det_loss_box_1[0.4334] det_loss_cls_2[0.3929] det_loss_box_2[0.3973] det_loss_cls_3[0.3655] det_loss_box_3[0.3857] det_loss_cls_4[0.3451] det_loss_box_4[0.3771] det_loss_cls_5[0.3289] det_loss_box_5[0.3795] det_loss_cls_dn_0[0.0880] det_loss_box_dn_0[0.2888] det_loss_cls_dn_1[0.0850] det_loss_box_dn_1[0.2388] det_loss_cls_dn_2[0.0839] det_loss_box_dn_2[0.2152] det_loss_cls_dn_3[0.0828] det_loss_box_dn_3[0.2126] det_loss_cls_dn_4[0.0825] det_loss_box_dn_4[0.2110] det_loss_cls_dn_5[0.0815] det_loss_box_dn_5[0.2083] loss_depth[0.0353] total_loss[6.8246]
Rank[0/8] 06/20/2025 16:40:11 INFO stats.py:314 | Epoch[201] Step[259] GlobalStep[78649] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 5:01:50.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:40:26 INFO loss_tracker.py:84 | Epoch[201/239] Step[274] GlobalStep[78664/NA]: det_loss_cls_0[0.4911] det_loss_box_0[0.6068] det_loss_cls_1[0.4769] det_loss_box_1[0.4585] det_loss_cls_2[0.4242] det_loss_box_2[0.4164] det_loss_cls_3[0.3913] det_loss_box_3[0.3986] det_loss_cls_4[0.3676] det_loss_box_4[0.3943] det_loss_cls_5[0.3484] det_loss_box_5[0.3974] det_loss_cls_dn_0[0.0903] det_loss_box_dn_0[0.2711] det_loss_cls_dn_1[0.0876] det_loss_box_dn_1[0.2254] det_loss_cls_dn_2[0.0866] det_loss_box_dn_2[0.2088] det_loss_cls_dn_3[0.0861] det_loss_box_dn_3[0.2036] det_loss_cls_dn_4[0.0853] det_loss_box_dn_4[0.2009] det_loss_cls_dn_5[0.0846] det_loss_box_dn_5[0.1993] loss_depth[0.0352] total_loss[7.0364]
Rank[0/8] 06/20/2025 16:40:36 INFO stats.py:314 | Epoch[201] Step[284] GlobalStep[78674] Training Speed: 7.61 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 5:01:19.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:40:52 INFO loss_tracker.py:84 | Epoch[201/239] Step[299] GlobalStep[78689/NA]: det_loss_cls_0[0.5816] det_loss_box_0[0.7290] det_loss_cls_1[0.5714] det_loss_box_1[0.5539] det_loss_cls_2[0.5044] det_loss_box_2[0.5005] det_loss_cls_3[0.4671] det_loss_box_3[0.4782] det_loss_cls_4[0.4333] det_loss_box_4[0.4683] det_loss_cls_5[0.4131] det_loss_box_5[0.4692] det_loss_cls_dn_0[0.0943] det_loss_box_dn_0[0.2574] det_loss_cls_dn_1[0.0902] det_loss_box_dn_1[0.2157] det_loss_cls_dn_2[0.0894] det_loss_box_dn_2[0.2007] det_loss_cls_dn_3[0.0886] det_loss_box_dn_3[0.1971] det_loss_cls_dn_4[0.0876] det_loss_box_dn_4[0.1951] det_loss_cls_dn_5[0.0866] det_loss_box_dn_5[0.1927] loss_depth[0.0346] total_loss[8.0002]
Rank[0/8] 06/20/2025 16:41:02 INFO stats.py:314 | Epoch[201] Step[309] GlobalStep[78699] Training Speed: 8.66 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 5:00:47.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:41:18 INFO loss_tracker.py:84 | Epoch[201/239] Step[324] GlobalStep[78714/NA]: det_loss_cls_0[0.5645] det_loss_box_0[0.7347] det_loss_cls_1[0.5499] det_loss_box_1[0.5775] det_loss_cls_2[0.4946] det_loss_box_2[0.5286] det_loss_cls_3[0.4598] det_loss_box_3[0.5076] det_loss_cls_4[0.4381] det_loss_box_4[0.4876] det_loss_cls_5[0.4284] det_loss_box_5[0.4812] det_loss_cls_dn_0[0.1005] det_loss_box_dn_0[0.2369] det_loss_cls_dn_1[0.0972] det_loss_box_dn_1[0.1997] det_loss_cls_dn_2[0.0956] det_loss_box_dn_2[0.1844] det_loss_cls_dn_3[0.0944] det_loss_box_dn_3[0.1804] det_loss_cls_dn_4[0.0941] det_loss_box_dn_4[0.1785] det_loss_cls_dn_5[0.0933] det_loss_box_dn_5[0.1771] loss_depth[0.0352] total_loss[8.0199]
Rank[0/8] 06/20/2025 16:41:28 INFO stats.py:314 | Epoch[201] Step[334] GlobalStep[78724] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 5:00:16.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:41:43 INFO loss_tracker.py:84 | Epoch[201/239] Step[349] GlobalStep[78739/NA]: det_loss_cls_0[0.3735] det_loss_box_0[0.4480] det_loss_cls_1[0.3671] det_loss_box_1[0.3419] det_loss_cls_2[0.3239] det_loss_box_2[0.3171] det_loss_cls_3[0.2942] det_loss_box_3[0.3076] det_loss_cls_4[0.2732] det_loss_box_4[0.3017] det_loss_cls_5[0.2637] det_loss_box_5[0.2937] det_loss_cls_dn_0[0.0956] det_loss_box_dn_0[0.2753] det_loss_cls_dn_1[0.0909] det_loss_box_dn_1[0.2275] det_loss_cls_dn_2[0.0909] det_loss_box_dn_2[0.2094] det_loss_cls_dn_3[0.0900] det_loss_box_dn_3[0.2035] det_loss_cls_dn_4[0.0893] det_loss_box_dn_4[0.2016] det_loss_cls_dn_5[0.0888] det_loss_box_dn_5[0.1953] loss_depth[0.0350] total_loss[5.7986]
Rank[0/8] 06/20/2025 16:41:53 INFO stats.py:314 | Epoch[201] Step[359] GlobalStep[78749] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 4:59:45.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:42:09 INFO loss_tracker.py:84 | Epoch[201/239] Step[374] GlobalStep[78764/NA]: det_loss_cls_0[0.3654] det_loss_box_0[0.4599] det_loss_cls_1[0.3598] det_loss_box_1[0.3443] det_loss_cls_2[0.3146] det_loss_box_2[0.3152] det_loss_cls_3[0.2872] det_loss_box_3[0.3047] det_loss_cls_4[0.2634] det_loss_box_4[0.3005] det_loss_cls_5[0.2535] det_loss_box_5[0.3007] det_loss_cls_dn_0[0.0824] det_loss_box_dn_0[0.3011] det_loss_cls_dn_1[0.0793] det_loss_box_dn_1[0.2401] det_loss_cls_dn_2[0.0777] det_loss_box_dn_2[0.2155] det_loss_cls_dn_3[0.0772] det_loss_box_dn_3[0.2120] det_loss_cls_dn_4[0.0758] det_loss_box_dn_4[0.2075] det_loss_cls_dn_5[0.0754] det_loss_box_dn_5[0.2054] loss_depth[0.0342] total_loss[5.7529]
Rank[0/8] 06/20/2025 16:42:19 INFO stats.py:314 | Epoch[201] Step[384] GlobalStep[78774] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 4:59:14.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:42:24 INFO stats.py:394 | Epoch[201] completed. Training Speed: 7.71 samples/sec across all devices.	Epoch Time: 404.92 sec.	Average Epoch Time: 404.92 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 4:59:08.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:42:50 INFO stats.py:314 | Epoch[202] Step[19] GlobalStep[78799] Training Speed: 8.13 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 4:58:44.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:42:55 INFO loss_tracker.py:84 | Epoch[202/239] Step[24] GlobalStep[78804/NA]: det_loss_cls_0[0.4526] det_loss_box_0[0.5367] det_loss_cls_1[0.4463] det_loss_box_1[0.3949] det_loss_cls_2[0.3716] det_loss_box_2[0.3642] det_loss_cls_3[0.3400] det_loss_box_3[0.3492] det_loss_cls_4[0.3190] det_loss_box_4[0.3330] det_loss_cls_5[0.3025] det_loss_box_5[0.3361] det_loss_cls_dn_0[0.0842] det_loss_box_dn_0[0.2692] det_loss_cls_dn_1[0.0808] det_loss_box_dn_1[0.2138] det_loss_cls_dn_2[0.0799] det_loss_box_dn_2[0.1963] det_loss_cls_dn_3[0.0790] det_loss_box_dn_3[0.1915] det_loss_cls_dn_4[0.0780] det_loss_box_dn_4[0.1894] det_loss_cls_dn_5[0.0773] det_loss_box_dn_5[0.1874] loss_depth[0.0370] total_loss[6.3097]
Rank[0/8] 06/20/2025 16:43:16 INFO stats.py:314 | Epoch[202] Step[44] GlobalStep[78824] Training Speed: 7.86 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 4:58:13.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:43:21 INFO loss_tracker.py:84 | Epoch[202/239] Step[49] GlobalStep[78829/NA]: det_loss_cls_0[0.5296] det_loss_box_0[0.6752] det_loss_cls_1[0.5040] det_loss_box_1[0.5044] det_loss_cls_2[0.4478] det_loss_box_2[0.4464] det_loss_cls_3[0.4176] det_loss_box_3[0.4407] det_loss_cls_4[0.3977] det_loss_box_4[0.4335] det_loss_cls_5[0.3850] det_loss_box_5[0.4326] det_loss_cls_dn_0[0.0888] det_loss_box_dn_0[0.2341] det_loss_cls_dn_1[0.0858] det_loss_box_dn_1[0.1914] det_loss_cls_dn_2[0.0848] det_loss_box_dn_2[0.1742] det_loss_cls_dn_3[0.0827] det_loss_box_dn_3[0.1717] det_loss_cls_dn_4[0.0820] det_loss_box_dn_4[0.1685] det_loss_cls_dn_5[0.0813] det_loss_box_dn_5[0.1674] loss_depth[0.0358] total_loss[7.2629]
Rank[0/8] 06/20/2025 16:43:41 INFO stats.py:314 | Epoch[202] Step[69] GlobalStep[78849] Training Speed: 6.92 samples/sec across all devices.	Average Step Time: 1.16 sec.	Estimated Remaining Time: 4:57:42.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:43:46 INFO loss_tracker.py:84 | Epoch[202/239] Step[74] GlobalStep[78854/NA]: det_loss_cls_0[0.4063] det_loss_box_0[0.6134] det_loss_cls_1[0.3921] det_loss_box_1[0.4485] det_loss_cls_2[0.3465] det_loss_box_2[0.4018] det_loss_cls_3[0.3104] det_loss_box_3[0.3956] det_loss_cls_4[0.2937] det_loss_box_4[0.3840] det_loss_cls_5[0.2869] det_loss_box_5[0.3649] det_loss_cls_dn_0[0.0859] det_loss_box_dn_0[0.2894] det_loss_cls_dn_1[0.0818] det_loss_box_dn_1[0.2303] det_loss_cls_dn_2[0.0814] det_loss_box_dn_2[0.2094] det_loss_cls_dn_3[0.0801] det_loss_box_dn_3[0.2039] det_loss_cls_dn_4[0.0793] det_loss_box_dn_4[0.2022] det_loss_cls_dn_5[0.0785] det_loss_box_dn_5[0.1963] loss_depth[0.0353] total_loss[6.4978]
Rank[0/8] 06/20/2025 16:44:07 INFO stats.py:314 | Epoch[202] Step[94] GlobalStep[78874] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 4:57:10.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:44:12 INFO loss_tracker.py:84 | Epoch[202/239] Step[99] GlobalStep[78879/NA]: det_loss_cls_0[0.4835] det_loss_box_0[0.5639] det_loss_cls_1[0.4592] det_loss_box_1[0.4154] det_loss_cls_2[0.3988] det_loss_box_2[0.3747] det_loss_cls_3[0.3559] det_loss_box_3[0.3627] det_loss_cls_4[0.3305] det_loss_box_4[0.3530] det_loss_cls_5[0.3196] det_loss_box_5[0.3503] det_loss_cls_dn_0[0.0888] det_loss_box_dn_0[0.2617] det_loss_cls_dn_1[0.0846] det_loss_box_dn_1[0.2064] det_loss_cls_dn_2[0.0833] det_loss_box_dn_2[0.1865] det_loss_cls_dn_3[0.0822] det_loss_box_dn_3[0.1813] det_loss_cls_dn_4[0.0810] det_loss_box_dn_4[0.1785] det_loss_cls_dn_5[0.0804] det_loss_box_dn_5[0.1754] loss_depth[0.0363] total_loss[6.4938]
Rank[0/8] 06/20/2025 16:44:33 INFO stats.py:314 | Epoch[202] Step[119] GlobalStep[78899] Training Speed: 7.70 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 4:56:39.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:44:38 INFO loss_tracker.py:84 | Epoch[202/239] Step[124] GlobalStep[78904/NA]: det_loss_cls_0[0.4328] det_loss_box_0[0.5344] det_loss_cls_1[0.4137] det_loss_box_1[0.4009] det_loss_cls_2[0.3572] det_loss_box_2[0.3731] det_loss_cls_3[0.3240] det_loss_box_3[0.3656] det_loss_cls_4[0.3115] det_loss_box_4[0.3522] det_loss_cls_5[0.2994] det_loss_box_5[0.3506] det_loss_cls_dn_0[0.0949] det_loss_box_dn_0[0.2440] det_loss_cls_dn_1[0.0925] det_loss_box_dn_1[0.1936] det_loss_cls_dn_2[0.0917] det_loss_box_dn_2[0.1759] det_loss_cls_dn_3[0.0904] det_loss_box_dn_3[0.1728] det_loss_cls_dn_4[0.0894] det_loss_box_dn_4[0.1698] det_loss_cls_dn_5[0.0887] det_loss_box_dn_5[0.1672] loss_depth[0.0326] total_loss[6.2187]
Rank[0/8] 06/20/2025 16:44:59 INFO stats.py:314 | Epoch[202] Step[144] GlobalStep[78924] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 4:56:08.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:45:04 INFO loss_tracker.py:84 | Epoch[202/239] Step[149] GlobalStep[78929/NA]: det_loss_cls_0[0.4497] det_loss_box_0[0.5182] det_loss_cls_1[0.4302] det_loss_box_1[0.3921] det_loss_cls_2[0.3772] det_loss_box_2[0.3521] det_loss_cls_3[0.3398] det_loss_box_3[0.3445] det_loss_cls_4[0.3194] det_loss_box_4[0.3313] det_loss_cls_5[0.3072] det_loss_box_5[0.3290] det_loss_cls_dn_0[0.0936] det_loss_box_dn_0[0.2230] det_loss_cls_dn_1[0.0911] det_loss_box_dn_1[0.1787] det_loss_cls_dn_2[0.0908] det_loss_box_dn_2[0.1628] det_loss_cls_dn_3[0.0903] det_loss_box_dn_3[0.1606] det_loss_cls_dn_4[0.0898] det_loss_box_dn_4[0.1590] det_loss_cls_dn_5[0.0889] det_loss_box_dn_5[0.1586] loss_depth[0.0353] total_loss[6.1134]
Rank[0/8] 06/20/2025 16:45:25 INFO stats.py:314 | Epoch[202] Step[169] GlobalStep[78949] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 4:55:37.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:45:31 INFO loss_tracker.py:84 | Epoch[202/239] Step[174] GlobalStep[78954/NA]: det_loss_cls_0[0.4679] det_loss_box_0[0.5642] det_loss_cls_1[0.4567] det_loss_box_1[0.4371] det_loss_cls_2[0.4028] det_loss_box_2[0.3956] det_loss_cls_3[0.3678] det_loss_box_3[0.3908] det_loss_cls_4[0.3457] det_loss_box_4[0.3827] det_loss_cls_5[0.3274] det_loss_box_5[0.3872] det_loss_cls_dn_0[0.0979] det_loss_box_dn_0[0.2604] det_loss_cls_dn_1[0.0947] det_loss_box_dn_1[0.2220] det_loss_cls_dn_2[0.0937] det_loss_box_dn_2[0.2057] det_loss_cls_dn_3[0.0927] det_loss_box_dn_3[0.2001] det_loss_cls_dn_4[0.0918] det_loss_box_dn_4[0.1985] det_loss_cls_dn_5[0.0908] det_loss_box_dn_5[0.1965] loss_depth[0.0345] total_loss[6.8052]
Rank[0/8] 06/20/2025 16:45:51 INFO stats.py:314 | Epoch[202] Step[194] GlobalStep[78974] Training Speed: 8.13 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 4:55:06.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:45:56 INFO loss_tracker.py:84 | Epoch[202/239] Step[199] GlobalStep[78979/NA]: det_loss_cls_0[0.4355] det_loss_box_0[0.5133] det_loss_cls_1[0.4243] det_loss_box_1[0.3967] det_loss_cls_2[0.3626] det_loss_box_2[0.3693] det_loss_cls_3[0.3393] det_loss_box_3[0.3529] det_loss_cls_4[0.3126] det_loss_box_4[0.3424] det_loss_cls_5[0.3071] det_loss_box_5[0.3367] det_loss_cls_dn_0[0.0973] det_loss_box_dn_0[0.2483] det_loss_cls_dn_1[0.0945] det_loss_box_dn_1[0.2034] det_loss_cls_dn_2[0.0938] det_loss_box_dn_2[0.1850] det_loss_cls_dn_3[0.0926] det_loss_box_dn_3[0.1827] det_loss_cls_dn_4[0.0918] det_loss_box_dn_4[0.1808] det_loss_cls_dn_5[0.0910] det_loss_box_dn_5[0.1796] loss_depth[0.0354] total_loss[6.2688]
Rank[0/8] 06/20/2025 16:46:17 INFO stats.py:314 | Epoch[202] Step[219] GlobalStep[78999] Training Speed: 8.79 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 4:54:35.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:46:21 INFO loss_tracker.py:84 | Epoch[202/239] Step[224] GlobalStep[79004/NA]: det_loss_cls_0[0.4508] det_loss_box_0[0.5714] det_loss_cls_1[0.4327] det_loss_box_1[0.4149] det_loss_cls_2[0.3741] det_loss_box_2[0.3687] det_loss_cls_3[0.3503] det_loss_box_3[0.3485] det_loss_cls_4[0.3288] det_loss_box_4[0.3389] det_loss_cls_5[0.3107] det_loss_box_5[0.3409] det_loss_cls_dn_0[0.0832] det_loss_box_dn_0[0.2395] det_loss_cls_dn_1[0.0798] det_loss_box_dn_1[0.1923] det_loss_cls_dn_2[0.0788] det_loss_box_dn_2[0.1761] det_loss_cls_dn_3[0.0782] det_loss_box_dn_3[0.1707] det_loss_cls_dn_4[0.0773] det_loss_box_dn_4[0.1668] det_loss_cls_dn_5[0.0764] det_loss_box_dn_5[0.1656] loss_depth[0.0364] total_loss[6.2520]
Rank[0/8] 06/20/2025 16:46:43 INFO stats.py:314 | Epoch[202] Step[244] GlobalStep[79024] Training Speed: 8.62 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 4:54:04.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:46:48 INFO loss_tracker.py:84 | Epoch[202/239] Step[249] GlobalStep[79029/NA]: det_loss_cls_0[0.3926] det_loss_box_0[0.4761] det_loss_cls_1[0.3845] det_loss_box_1[0.3471] det_loss_cls_2[0.3324] det_loss_box_2[0.3222] det_loss_cls_3[0.2933] det_loss_box_3[0.3091] det_loss_cls_4[0.2707] det_loss_box_4[0.3043] det_loss_cls_5[0.2665] det_loss_box_5[0.3003] det_loss_cls_dn_0[0.0810] det_loss_box_dn_0[0.2409] det_loss_cls_dn_1[0.0782] det_loss_box_dn_1[0.1951] det_loss_cls_dn_2[0.0774] det_loss_box_dn_2[0.1794] det_loss_cls_dn_3[0.0765] det_loss_box_dn_3[0.1753] det_loss_cls_dn_4[0.0757] det_loss_box_dn_4[0.1724] det_loss_cls_dn_5[0.0750] det_loss_box_dn_5[0.1697] loss_depth[0.0345] total_loss[5.6302]
Rank[0/8] 06/20/2025 16:47:08 INFO stats.py:314 | Epoch[202] Step[269] GlobalStep[79049] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 4:53:33.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:47:14 INFO loss_tracker.py:84 | Epoch[202/239] Step[274] GlobalStep[79054/NA]: det_loss_cls_0[0.4765] det_loss_box_0[0.5869] det_loss_cls_1[0.4663] det_loss_box_1[0.4354] det_loss_cls_2[0.3962] det_loss_box_2[0.3981] det_loss_cls_3[0.3656] det_loss_box_3[0.3827] det_loss_cls_4[0.3405] det_loss_box_4[0.3698] det_loss_cls_5[0.3271] det_loss_box_5[0.3737] det_loss_cls_dn_0[0.0842] det_loss_box_dn_0[0.2174] det_loss_cls_dn_1[0.0811] det_loss_box_dn_1[0.1798] det_loss_cls_dn_2[0.0805] det_loss_box_dn_2[0.1628] det_loss_cls_dn_3[0.0800] det_loss_box_dn_3[0.1599] det_loss_cls_dn_4[0.0789] det_loss_box_dn_4[0.1574] det_loss_cls_dn_5[0.0782] det_loss_box_dn_5[0.1543] loss_depth[0.0335] total_loss[6.4669]
Rank[0/8] 06/20/2025 16:47:34 INFO stats.py:314 | Epoch[202] Step[294] GlobalStep[79074] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 4:53:02.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:47:39 INFO loss_tracker.py:84 | Epoch[202/239] Step[299] GlobalStep[79079/NA]: det_loss_cls_0[0.4505] det_loss_box_0[0.5063] det_loss_cls_1[0.4454] det_loss_box_1[0.3649] det_loss_cls_2[0.3891] det_loss_box_2[0.3464] det_loss_cls_3[0.3515] det_loss_box_3[0.3342] det_loss_cls_4[0.3204] det_loss_box_4[0.3292] det_loss_cls_5[0.3092] det_loss_box_5[0.3303] det_loss_cls_dn_0[0.0905] det_loss_box_dn_0[0.2549] det_loss_cls_dn_1[0.0871] det_loss_box_dn_1[0.1997] det_loss_cls_dn_2[0.0862] det_loss_box_dn_2[0.1794] det_loss_cls_dn_3[0.0847] det_loss_box_dn_3[0.1737] det_loss_cls_dn_4[0.0837] det_loss_box_dn_4[0.1725] det_loss_cls_dn_5[0.0832] det_loss_box_dn_5[0.1684] loss_depth[0.0330] total_loss[6.1743]
Rank[0/8] 06/20/2025 16:48:00 INFO stats.py:314 | Epoch[202] Step[319] GlobalStep[79099] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 4:52:31.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:48:06 INFO loss_tracker.py:84 | Epoch[202/239] Step[324] GlobalStep[79104/NA]: det_loss_cls_0[0.5169] det_loss_box_0[0.6324] det_loss_cls_1[0.4881] det_loss_box_1[0.4936] det_loss_cls_2[0.4286] det_loss_box_2[0.4443] det_loss_cls_3[0.3820] det_loss_box_3[0.4386] det_loss_cls_4[0.3637] det_loss_box_4[0.4169] det_loss_cls_5[0.3460] det_loss_box_5[0.4189] det_loss_cls_dn_0[0.0876] det_loss_box_dn_0[0.2724] det_loss_cls_dn_1[0.0846] det_loss_box_dn_1[0.2170] det_loss_cls_dn_2[0.0832] det_loss_box_dn_2[0.1973] det_loss_cls_dn_3[0.0819] det_loss_box_dn_3[0.1936] det_loss_cls_dn_4[0.0811] det_loss_box_dn_4[0.1922] det_loss_cls_dn_5[0.0801] det_loss_box_dn_5[0.1909] loss_depth[0.0344] total_loss[7.1664]
Rank[0/8] 06/20/2025 16:48:26 INFO stats.py:314 | Epoch[202] Step[344] GlobalStep[79124] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 4:52:00.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:48:31 INFO loss_tracker.py:84 | Epoch[202/239] Step[349] GlobalStep[79129/NA]: det_loss_cls_0[0.4523] det_loss_box_0[0.5847] det_loss_cls_1[0.4464] det_loss_box_1[0.4578] det_loss_cls_2[0.3901] det_loss_box_2[0.4203] det_loss_cls_3[0.3542] det_loss_box_3[0.4068] det_loss_cls_4[0.3287] det_loss_box_4[0.3973] det_loss_cls_5[0.3126] det_loss_box_5[0.3999] det_loss_cls_dn_0[0.0931] det_loss_box_dn_0[0.2677] det_loss_cls_dn_1[0.0896] det_loss_box_dn_1[0.2184] det_loss_cls_dn_2[0.0877] det_loss_box_dn_2[0.1999] det_loss_cls_dn_3[0.0864] det_loss_box_dn_3[0.1961] det_loss_cls_dn_4[0.0853] det_loss_box_dn_4[0.1938] det_loss_cls_dn_5[0.0845] det_loss_box_dn_5[0.1912] loss_depth[0.0337] total_loss[6.7785]
Rank[0/8] 06/20/2025 16:48:52 INFO stats.py:314 | Epoch[202] Step[369] GlobalStep[79149] Training Speed: 8.76 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 4:51:29.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:48:58 INFO loss_tracker.py:84 | Epoch[202/239] Step[374] GlobalStep[79154/NA]: det_loss_cls_0[0.3843] det_loss_box_0[0.4754] det_loss_cls_1[0.3701] det_loss_box_1[0.3621] det_loss_cls_2[0.3182] det_loss_box_2[0.3322] det_loss_cls_3[0.2820] det_loss_box_3[0.3255] det_loss_cls_4[0.2618] det_loss_box_4[0.3178] det_loss_cls_5[0.2566] det_loss_box_5[0.3137] det_loss_cls_dn_0[0.0883] det_loss_box_dn_0[0.2906] det_loss_cls_dn_1[0.0849] det_loss_box_dn_1[0.2273] det_loss_cls_dn_2[0.0838] det_loss_box_dn_2[0.2049] det_loss_cls_dn_3[0.0829] det_loss_box_dn_3[0.2005] det_loss_cls_dn_4[0.0821] det_loss_box_dn_4[0.1961] det_loss_cls_dn_5[0.0805] det_loss_box_dn_5[0.1922] loss_depth[0.0370] total_loss[5.8506]
Rank[0/8] 06/20/2025 16:49:13 INFO stats.py:394 | Epoch[202] completed. Training Speed: 7.64 samples/sec across all devices.	Epoch Time: 408.46 sec.	Average Epoch Time: 408.46 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 4:51:04.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:49:19 INFO stats.py:314 | Epoch[203] Step[4] GlobalStep[79174] Training Speed: 8.22 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 4:50:58.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:49:41 INFO loss_tracker.py:84 | Epoch[203/239] Step[24] GlobalStep[79194/NA]: det_loss_cls_0[0.4203] det_loss_box_0[0.5544] det_loss_cls_1[0.4066] det_loss_box_1[0.4152] det_loss_cls_2[0.3594] det_loss_box_2[0.3806] det_loss_cls_3[0.3278] det_loss_box_3[0.3675] det_loss_cls_4[0.3066] det_loss_box_4[0.3619] det_loss_cls_5[0.2947] det_loss_box_5[0.3609] det_loss_cls_dn_0[0.0853] det_loss_box_dn_0[0.3135] det_loss_cls_dn_1[0.0825] det_loss_box_dn_1[0.2417] det_loss_cls_dn_2[0.0816] det_loss_box_dn_2[0.2231] det_loss_cls_dn_3[0.0807] det_loss_box_dn_3[0.2147] det_loss_cls_dn_4[0.0802] det_loss_box_dn_4[0.2126] det_loss_cls_dn_5[0.0795] det_loss_box_dn_5[0.2010] loss_depth[0.0377] total_loss[6.4904]
Rank[0/8] 06/20/2025 16:49:46 INFO stats.py:314 | Epoch[203] Step[29] GlobalStep[79199] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 4:50:27.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:50:07 INFO loss_tracker.py:84 | Epoch[203/239] Step[49] GlobalStep[79219/NA]: det_loss_cls_0[0.3874] det_loss_box_0[0.4723] det_loss_cls_1[0.3757] det_loss_box_1[0.3571] det_loss_cls_2[0.3284] det_loss_box_2[0.3241] det_loss_cls_3[0.3010] det_loss_box_3[0.3139] det_loss_cls_4[0.2848] det_loss_box_4[0.3091] det_loss_cls_5[0.2752] det_loss_box_5[0.3031] det_loss_cls_dn_0[0.0913] det_loss_box_dn_0[0.2781] det_loss_cls_dn_1[0.0871] det_loss_box_dn_1[0.2262] det_loss_cls_dn_2[0.0864] det_loss_box_dn_2[0.2087] det_loss_cls_dn_3[0.0856] det_loss_box_dn_3[0.2040] det_loss_cls_dn_4[0.0853] det_loss_box_dn_4[0.2009] det_loss_cls_dn_5[0.0845] det_loss_box_dn_5[0.1959] loss_depth[0.0346] total_loss[5.9008]
Rank[0/8] 06/20/2025 16:50:12 INFO stats.py:314 | Epoch[203] Step[54] GlobalStep[79224] Training Speed: 8.08 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 4:49:56.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:50:33 INFO loss_tracker.py:84 | Epoch[203/239] Step[74] GlobalStep[79244/NA]: det_loss_cls_0[0.3836] det_loss_box_0[0.4658] det_loss_cls_1[0.3599] det_loss_box_1[0.3585] det_loss_cls_2[0.3046] det_loss_box_2[0.3316] det_loss_cls_3[0.2831] det_loss_box_3[0.3216] det_loss_cls_4[0.2666] det_loss_box_4[0.3216] det_loss_cls_5[0.2602] det_loss_box_5[0.3143] det_loss_cls_dn_0[0.0950] det_loss_box_dn_0[0.2691] det_loss_cls_dn_1[0.0909] det_loss_box_dn_1[0.2209] det_loss_cls_dn_2[0.0907] det_loss_box_dn_2[0.1995] det_loss_cls_dn_3[0.0899] det_loss_box_dn_3[0.1949] det_loss_cls_dn_4[0.0891] det_loss_box_dn_4[0.1929] det_loss_cls_dn_5[0.0885] det_loss_box_dn_5[0.1906] loss_depth[0.0371] total_loss[5.8203]
Rank[0/8] 06/20/2025 16:50:38 INFO stats.py:314 | Epoch[203] Step[79] GlobalStep[79249] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 4:49:25.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:50:59 INFO loss_tracker.py:84 | Epoch[203/239] Step[99] GlobalStep[79269/NA]: det_loss_cls_0[0.5198] det_loss_box_0[0.6874] det_loss_cls_1[0.5079] det_loss_box_1[0.5336] det_loss_cls_2[0.4581] det_loss_box_2[0.4898] det_loss_cls_3[0.4148] det_loss_box_3[0.4708] det_loss_cls_4[0.3802] det_loss_box_4[0.4611] det_loss_cls_5[0.3712] det_loss_box_5[0.4565] det_loss_cls_dn_0[0.1061] det_loss_box_dn_0[0.3298] det_loss_cls_dn_1[0.1032] det_loss_box_dn_1[0.2802] det_loss_cls_dn_2[0.1027] det_loss_box_dn_2[0.2592] det_loss_cls_dn_3[0.1019] det_loss_box_dn_3[0.2552] det_loss_cls_dn_4[0.1012] det_loss_box_dn_4[0.2518] det_loss_cls_dn_5[0.1005] det_loss_box_dn_5[0.2457] loss_depth[0.0378] total_loss[8.0266]
Rank[0/8] 06/20/2025 16:51:05 INFO stats.py:314 | Epoch[203] Step[104] GlobalStep[79274] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 4:48:54.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:51:25 INFO loss_tracker.py:84 | Epoch[203/239] Step[124] GlobalStep[79294/NA]: det_loss_cls_0[0.4363] det_loss_box_0[0.5281] det_loss_cls_1[0.4327] det_loss_box_1[0.4016] det_loss_cls_2[0.3803] det_loss_box_2[0.3647] det_loss_cls_3[0.3381] det_loss_box_3[0.3556] det_loss_cls_4[0.3089] det_loss_box_4[0.3509] det_loss_cls_5[0.2988] det_loss_box_5[0.3480] det_loss_cls_dn_0[0.0792] det_loss_box_dn_0[0.2252] det_loss_cls_dn_1[0.0762] det_loss_box_dn_1[0.1835] det_loss_cls_dn_2[0.0749] det_loss_box_dn_2[0.1646] det_loss_cls_dn_3[0.0737] det_loss_box_dn_3[0.1589] det_loss_cls_dn_4[0.0731] det_loss_box_dn_4[0.1572] det_loss_cls_dn_5[0.0720] det_loss_box_dn_5[0.1536] loss_depth[0.0341] total_loss[6.0701]
Rank[0/8] 06/20/2025 16:51:30 INFO stats.py:314 | Epoch[203] Step[129] GlobalStep[79299] Training Speed: 7.86 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 4:48:23.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:51:50 INFO loss_tracker.py:84 | Epoch[203/239] Step[149] GlobalStep[79319/NA]: det_loss_cls_0[0.4894] det_loss_box_0[0.6497] det_loss_cls_1[0.4907] det_loss_box_1[0.4865] det_loss_cls_2[0.4110] det_loss_box_2[0.4392] det_loss_cls_3[0.3721] det_loss_box_3[0.4209] det_loss_cls_4[0.3497] det_loss_box_4[0.4049] det_loss_cls_5[0.3411] det_loss_box_5[0.4046] det_loss_cls_dn_0[0.0933] det_loss_box_dn_0[0.2466] det_loss_cls_dn_1[0.0904] det_loss_box_dn_1[0.2021] det_loss_cls_dn_2[0.0895] det_loss_box_dn_2[0.1863] det_loss_cls_dn_3[0.0889] det_loss_box_dn_3[0.1837] det_loss_cls_dn_4[0.0878] det_loss_box_dn_4[0.1812] det_loss_cls_dn_5[0.0868] det_loss_box_dn_5[0.1794] loss_depth[0.0356] total_loss[7.0114]
Rank[0/8] 06/20/2025 16:51:55 INFO stats.py:314 | Epoch[203] Step[154] GlobalStep[79324] Training Speed: 14.56 samples/sec across all devices.	Average Step Time: 0.55 sec.	Estimated Remaining Time: 4:47:52.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:52:16 INFO loss_tracker.py:84 | Epoch[203/239] Step[174] GlobalStep[79344/NA]: det_loss_cls_0[0.3398] det_loss_box_0[0.4465] det_loss_cls_1[0.3349] det_loss_box_1[0.3544] det_loss_cls_2[0.2964] det_loss_box_2[0.3165] det_loss_cls_3[0.2741] det_loss_box_3[0.3095] det_loss_cls_4[0.2590] det_loss_box_4[0.2982] det_loss_cls_5[0.2440] det_loss_box_5[0.2979] det_loss_cls_dn_0[0.0787] det_loss_box_dn_0[0.3042] det_loss_cls_dn_1[0.0751] det_loss_box_dn_1[0.2406] det_loss_cls_dn_2[0.0741] det_loss_box_dn_2[0.2226] det_loss_cls_dn_3[0.0729] det_loss_box_dn_3[0.2185] det_loss_cls_dn_4[0.0723] det_loss_box_dn_4[0.2164] det_loss_cls_dn_5[0.0712] det_loss_box_dn_5[0.2137] loss_depth[0.0359] total_loss[5.6674]
Rank[0/8] 06/20/2025 16:52:21 INFO stats.py:314 | Epoch[203] Step[179] GlobalStep[79349] Training Speed: 8.19 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 4:47:21.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:52:42 INFO loss_tracker.py:84 | Epoch[203/239] Step[199] GlobalStep[79369/NA]: det_loss_cls_0[0.4181] det_loss_box_0[0.5590] det_loss_cls_1[0.3978] det_loss_box_1[0.4284] det_loss_cls_2[0.3533] det_loss_box_2[0.3887] det_loss_cls_3[0.3261] det_loss_box_3[0.3715] det_loss_cls_4[0.2994] det_loss_box_4[0.3646] det_loss_cls_5[0.2868] det_loss_box_5[0.3624] det_loss_cls_dn_0[0.0899] det_loss_box_dn_0[0.3343] det_loss_cls_dn_1[0.0863] det_loss_box_dn_1[0.2707] det_loss_cls_dn_2[0.0853] det_loss_box_dn_2[0.2494] det_loss_cls_dn_3[0.0843] det_loss_box_dn_3[0.2434] det_loss_cls_dn_4[0.0835] det_loss_box_dn_4[0.2389] det_loss_cls_dn_5[0.0828] det_loss_box_dn_5[0.2323] loss_depth[0.0353] total_loss[6.6724]
Rank[0/8] 06/20/2025 16:52:47 INFO stats.py:314 | Epoch[203] Step[204] GlobalStep[79374] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 4:46:50.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:53:08 INFO loss_tracker.py:84 | Epoch[203/239] Step[224] GlobalStep[79394/NA]: det_loss_cls_0[0.5399] det_loss_box_0[0.6929] det_loss_cls_1[0.5304] det_loss_box_1[0.5137] det_loss_cls_2[0.4624] det_loss_box_2[0.4602] det_loss_cls_3[0.4264] det_loss_box_3[0.4521] det_loss_cls_4[0.4032] det_loss_box_4[0.4426] det_loss_cls_5[0.3875] det_loss_box_5[0.4439] det_loss_cls_dn_0[0.0894] det_loss_box_dn_0[0.3717] det_loss_cls_dn_1[0.0866] det_loss_box_dn_1[0.3107] det_loss_cls_dn_2[0.0864] det_loss_box_dn_2[0.2895] det_loss_cls_dn_3[0.0850] det_loss_box_dn_3[0.2797] det_loss_cls_dn_4[0.0842] det_loss_box_dn_4[0.2782] det_loss_cls_dn_5[0.0836] det_loss_box_dn_5[0.2700] loss_depth[0.0335] total_loss[8.1037]
Rank[0/8] 06/20/2025 16:53:13 INFO stats.py:314 | Epoch[203] Step[229] GlobalStep[79399] Training Speed: 8.07 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 4:46:19.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:53:33 INFO loss_tracker.py:84 | Epoch[203/239] Step[249] GlobalStep[79419/NA]: det_loss_cls_0[0.4728] det_loss_box_0[0.5833] det_loss_cls_1[0.4600] det_loss_box_1[0.4429] det_loss_cls_2[0.4108] det_loss_box_2[0.3897] det_loss_cls_3[0.3768] det_loss_box_3[0.3841] det_loss_cls_4[0.3571] det_loss_box_4[0.3730] det_loss_cls_5[0.3464] det_loss_box_5[0.3704] det_loss_cls_dn_0[0.0808] det_loss_box_dn_0[0.2585] det_loss_cls_dn_1[0.0771] det_loss_box_dn_1[0.2093] det_loss_cls_dn_2[0.0761] det_loss_box_dn_2[0.1940] det_loss_cls_dn_3[0.0753] det_loss_box_dn_3[0.1919] det_loss_cls_dn_4[0.0743] det_loss_box_dn_4[0.1910] det_loss_cls_dn_5[0.0734] det_loss_box_dn_5[0.1905] loss_depth[0.0348] total_loss[6.6943]
Rank[0/8] 06/20/2025 16:53:38 INFO stats.py:314 | Epoch[203] Step[254] GlobalStep[79424] Training Speed: 8.13 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 4:45:48.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:54:00 INFO loss_tracker.py:84 | Epoch[203/239] Step[274] GlobalStep[79444/NA]: det_loss_cls_0[0.3928] det_loss_box_0[0.5119] det_loss_cls_1[0.3864] det_loss_box_1[0.3863] det_loss_cls_2[0.3463] det_loss_box_2[0.3418] det_loss_cls_3[0.3105] det_loss_box_3[0.3249] det_loss_cls_4[0.2859] det_loss_box_4[0.3135] det_loss_cls_5[0.2729] det_loss_box_5[0.3130] det_loss_cls_dn_0[0.0793] det_loss_box_dn_0[0.2743] det_loss_cls_dn_1[0.0764] det_loss_box_dn_1[0.2203] det_loss_cls_dn_2[0.0751] det_loss_box_dn_2[0.2017] det_loss_cls_dn_3[0.0741] det_loss_box_dn_3[0.1944] det_loss_cls_dn_4[0.0731] det_loss_box_dn_4[0.1921] det_loss_cls_dn_5[0.0727] det_loss_box_dn_5[0.1866] loss_depth[0.0369] total_loss[5.9432]
Rank[0/8] 06/20/2025 16:54:05 INFO stats.py:314 | Epoch[203] Step[279] GlobalStep[79449] Training Speed: 8.56 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 4:45:17.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:54:26 INFO loss_tracker.py:84 | Epoch[203/239] Step[299] GlobalStep[79469/NA]: det_loss_cls_0[0.4543] det_loss_box_0[0.5670] det_loss_cls_1[0.4437] det_loss_box_1[0.4317] det_loss_cls_2[0.3698] det_loss_box_2[0.3861] det_loss_cls_3[0.3345] det_loss_box_3[0.3634] det_loss_cls_4[0.3059] det_loss_box_4[0.3488] det_loss_cls_5[0.2974] det_loss_box_5[0.3457] det_loss_cls_dn_0[0.0828] det_loss_box_dn_0[0.2526] det_loss_cls_dn_1[0.0793] det_loss_box_dn_1[0.2013] det_loss_cls_dn_2[0.0784] det_loss_box_dn_2[0.1830] det_loss_cls_dn_3[0.0775] det_loss_box_dn_3[0.1794] det_loss_cls_dn_4[0.0766] det_loss_box_dn_4[0.1774] det_loss_cls_dn_5[0.0758] det_loss_box_dn_5[0.1764] loss_depth[0.0339] total_loss[6.3228]
Rank[0/8] 06/20/2025 16:54:31 INFO stats.py:314 | Epoch[203] Step[304] GlobalStep[79474] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 4:44:46.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:54:51 INFO loss_tracker.py:84 | Epoch[203/239] Step[324] GlobalStep[79494/NA]: det_loss_cls_0[0.4784] det_loss_box_0[0.5287] det_loss_cls_1[0.4630] det_loss_box_1[0.3786] det_loss_cls_2[0.3886] det_loss_box_2[0.3571] det_loss_cls_3[0.3507] det_loss_box_3[0.3495] det_loss_cls_4[0.3299] det_loss_box_4[0.3446] det_loss_cls_5[0.3139] det_loss_box_5[0.3476] det_loss_cls_dn_0[0.0918] det_loss_box_dn_0[0.2203] det_loss_cls_dn_1[0.0884] det_loss_box_dn_1[0.1752] det_loss_cls_dn_2[0.0879] det_loss_box_dn_2[0.1606] det_loss_cls_dn_3[0.0868] det_loss_box_dn_3[0.1557] det_loss_cls_dn_4[0.0856] det_loss_box_dn_4[0.1527] det_loss_cls_dn_5[0.0849] det_loss_box_dn_5[0.1518] loss_depth[0.0345] total_loss[6.2067]
Rank[0/8] 06/20/2025 16:54:56 INFO stats.py:314 | Epoch[203] Step[329] GlobalStep[79499] Training Speed: 6.78 samples/sec across all devices.	Average Step Time: 1.18 sec.	Estimated Remaining Time: 4:44:15.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:55:17 INFO loss_tracker.py:84 | Epoch[203/239] Step[349] GlobalStep[79519/NA]: det_loss_cls_0[0.5527] det_loss_box_0[0.6173] det_loss_cls_1[0.5425] det_loss_box_1[0.4691] det_loss_cls_2[0.4756] det_loss_box_2[0.4214] det_loss_cls_3[0.4251] det_loss_box_3[0.4210] det_loss_cls_4[0.4015] det_loss_box_4[0.4099] det_loss_cls_5[0.3807] det_loss_box_5[0.4130] det_loss_cls_dn_0[0.0857] det_loss_box_dn_0[0.2186] det_loss_cls_dn_1[0.0827] det_loss_box_dn_1[0.1780] det_loss_cls_dn_2[0.0816] det_loss_box_dn_2[0.1641] det_loss_cls_dn_3[0.0807] det_loss_box_dn_3[0.1607] det_loss_cls_dn_4[0.0795] det_loss_box_dn_4[0.1566] det_loss_cls_dn_5[0.0792] det_loss_box_dn_5[0.1545] loss_depth[0.0343] total_loss[7.0858]
Rank[0/8] 06/20/2025 16:55:23 INFO stats.py:314 | Epoch[203] Step[354] GlobalStep[79524] Training Speed: 8.00 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 4:43:44.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:55:43 INFO loss_tracker.py:84 | Epoch[203/239] Step[374] GlobalStep[79544/NA]: det_loss_cls_0[0.5086] det_loss_box_0[0.6350] det_loss_cls_1[0.4996] det_loss_box_1[0.4755] det_loss_cls_2[0.4356] det_loss_box_2[0.4264] det_loss_cls_3[0.4048] det_loss_box_3[0.4084] det_loss_cls_4[0.3757] det_loss_box_4[0.4017] det_loss_cls_5[0.3644] det_loss_box_5[0.3964] det_loss_cls_dn_0[0.1006] det_loss_box_dn_0[0.2687] det_loss_cls_dn_1[0.0965] det_loss_box_dn_1[0.2168] det_loss_cls_dn_2[0.0953] det_loss_box_dn_2[0.1997] det_loss_cls_dn_3[0.0938] det_loss_box_dn_3[0.1956] det_loss_cls_dn_4[0.0929] det_loss_box_dn_4[0.1935] det_loss_cls_dn_5[0.0921] det_loss_box_dn_5[0.1887] loss_depth[0.0352] total_loss[7.2017]
Rank[0/8] 06/20/2025 16:55:48 INFO stats.py:314 | Epoch[203] Step[379] GlobalStep[79549] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 4:43:13.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:55:58 INFO stats.py:394 | Epoch[203] completed. Training Speed: 7.70 samples/sec across all devices.	Epoch Time: 405.31 sec.	Average Epoch Time: 405.31 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 4:43:00.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:56:16 INFO stats.py:314 | Epoch[204] Step[14] GlobalStep[79574] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 4:42:42.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:56:26 INFO loss_tracker.py:84 | Epoch[204/239] Step[24] GlobalStep[79584/NA]: det_loss_cls_0[0.3637] det_loss_box_0[0.4663] det_loss_cls_1[0.3490] det_loss_box_1[0.3465] det_loss_cls_2[0.2886] det_loss_box_2[0.3120] det_loss_cls_3[0.2589] det_loss_box_3[0.2941] det_loss_cls_4[0.2386] det_loss_box_4[0.2855] det_loss_cls_5[0.2214] det_loss_box_5[0.2873] det_loss_cls_dn_0[0.0779] det_loss_box_dn_0[0.2856] det_loss_cls_dn_1[0.0738] det_loss_box_dn_1[0.2284] det_loss_cls_dn_2[0.0730] det_loss_box_dn_2[0.2040] det_loss_cls_dn_3[0.0719] det_loss_box_dn_3[0.1987] det_loss_cls_dn_4[0.0710] det_loss_box_dn_4[0.1952] det_loss_cls_dn_5[0.0704] det_loss_box_dn_5[0.1909] loss_depth[0.0347] total_loss[5.4874]
Rank[0/8] 06/20/2025 16:56:42 INFO stats.py:314 | Epoch[204] Step[39] GlobalStep[79599] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 4:42:11.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:56:53 INFO loss_tracker.py:84 | Epoch[204/239] Step[49] GlobalStep[79609/NA]: det_loss_cls_0[0.4312] det_loss_box_0[0.5692] det_loss_cls_1[0.4157] det_loss_box_1[0.4232] det_loss_cls_2[0.3607] det_loss_box_2[0.3869] det_loss_cls_3[0.3303] det_loss_box_3[0.3677] det_loss_cls_4[0.3089] det_loss_box_4[0.3557] det_loss_cls_5[0.2983] det_loss_box_5[0.3578] det_loss_cls_dn_0[0.0767] det_loss_box_dn_0[0.3218] det_loss_cls_dn_1[0.0720] det_loss_box_dn_1[0.2510] det_loss_cls_dn_2[0.0701] det_loss_box_dn_2[0.2286] det_loss_cls_dn_3[0.0690] det_loss_box_dn_3[0.2218] det_loss_cls_dn_4[0.0684] det_loss_box_dn_4[0.2176] det_loss_cls_dn_5[0.0676] det_loss_box_dn_5[0.2124] loss_depth[0.0356] total_loss[6.5181]
Rank[0/8] 06/20/2025 16:57:08 INFO stats.py:314 | Epoch[204] Step[64] GlobalStep[79624] Training Speed: 8.04 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 4:41:40.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:57:18 INFO loss_tracker.py:84 | Epoch[204/239] Step[74] GlobalStep[79634/NA]: det_loss_cls_0[0.4645] det_loss_box_0[0.5907] det_loss_cls_1[0.4440] det_loss_box_1[0.4396] det_loss_cls_2[0.3895] det_loss_box_2[0.4002] det_loss_cls_3[0.3559] det_loss_box_3[0.3840] det_loss_cls_4[0.3286] det_loss_box_4[0.3834] det_loss_cls_5[0.3149] det_loss_box_5[0.3853] det_loss_cls_dn_0[0.0921] det_loss_box_dn_0[0.2924] det_loss_cls_dn_1[0.0876] det_loss_box_dn_1[0.2462] det_loss_cls_dn_2[0.0866] det_loss_box_dn_2[0.2251] det_loss_cls_dn_3[0.0860] det_loss_box_dn_3[0.2199] det_loss_cls_dn_4[0.0849] det_loss_box_dn_4[0.2173] det_loss_cls_dn_5[0.0841] det_loss_box_dn_5[0.2145] loss_depth[0.0374] total_loss[6.8546]
Rank[0/8] 06/20/2025 16:57:34 INFO stats.py:314 | Epoch[204] Step[89] GlobalStep[79649] Training Speed: 8.12 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 4:41:09.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:57:44 INFO loss_tracker.py:84 | Epoch[204/239] Step[99] GlobalStep[79659/NA]: det_loss_cls_0[0.4802] det_loss_box_0[0.5890] det_loss_cls_1[0.4674] det_loss_box_1[0.4367] det_loss_cls_2[0.4201] det_loss_box_2[0.4050] det_loss_cls_3[0.3852] det_loss_box_3[0.3958] det_loss_cls_4[0.3603] det_loss_box_4[0.3889] det_loss_cls_5[0.3501] det_loss_box_5[0.3885] det_loss_cls_dn_0[0.0937] det_loss_box_dn_0[0.2556] det_loss_cls_dn_1[0.0897] det_loss_box_dn_1[0.2117] det_loss_cls_dn_2[0.0889] det_loss_box_dn_2[0.1970] det_loss_cls_dn_3[0.0876] det_loss_box_dn_3[0.1915] det_loss_cls_dn_4[0.0870] det_loss_box_dn_4[0.1899] det_loss_cls_dn_5[0.0861] det_loss_box_dn_5[0.1864] loss_depth[0.0324] total_loss[6.8647]
Rank[0/8] 06/20/2025 16:58:00 INFO stats.py:314 | Epoch[204] Step[114] GlobalStep[79674] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 4:40:38.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:58:10 INFO loss_tracker.py:84 | Epoch[204/239] Step[124] GlobalStep[79684/NA]: det_loss_cls_0[0.5064] det_loss_box_0[0.5674] det_loss_cls_1[0.4916] det_loss_box_1[0.4194] det_loss_cls_2[0.4342] det_loss_box_2[0.3851] det_loss_cls_3[0.3987] det_loss_box_3[0.3710] det_loss_cls_4[0.3810] det_loss_box_4[0.3584] det_loss_cls_5[0.3635] det_loss_box_5[0.3614] det_loss_cls_dn_0[0.0982] det_loss_box_dn_0[0.2190] det_loss_cls_dn_1[0.0948] det_loss_box_dn_1[0.1790] det_loss_cls_dn_2[0.0943] det_loss_box_dn_2[0.1618] det_loss_cls_dn_3[0.0940] det_loss_box_dn_3[0.1583] det_loss_cls_dn_4[0.0932] det_loss_box_dn_4[0.1562] det_loss_cls_dn_5[0.0930] det_loss_box_dn_5[0.1550] loss_depth[0.0356] total_loss[6.6706]
Rank[0/8] 06/20/2025 16:58:26 INFO stats.py:314 | Epoch[204] Step[139] GlobalStep[79699] Training Speed: 13.96 samples/sec across all devices.	Average Step Time: 0.57 sec.	Estimated Remaining Time: 4:40:07.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:58:36 INFO loss_tracker.py:84 | Epoch[204/239] Step[149] GlobalStep[79709/NA]: det_loss_cls_0[0.5548] det_loss_box_0[0.6834] det_loss_cls_1[0.5458] det_loss_box_1[0.5148] det_loss_cls_2[0.4780] det_loss_box_2[0.4685] det_loss_cls_3[0.4374] det_loss_box_3[0.4495] det_loss_cls_4[0.4191] det_loss_box_4[0.4326] det_loss_cls_5[0.3974] det_loss_box_5[0.4342] det_loss_cls_dn_0[0.0922] det_loss_box_dn_0[0.2503] det_loss_cls_dn_1[0.0895] det_loss_box_dn_1[0.2095] det_loss_cls_dn_2[0.0883] det_loss_box_dn_2[0.1939] det_loss_cls_dn_3[0.0875] det_loss_box_dn_3[0.1902] det_loss_cls_dn_4[0.0868] det_loss_box_dn_4[0.1877] det_loss_cls_dn_5[0.0857] det_loss_box_dn_5[0.1862] loss_depth[0.0340] total_loss[7.5974]
Rank[0/8] 06/20/2025 16:58:52 INFO stats.py:314 | Epoch[204] Step[164] GlobalStep[79724] Training Speed: 7.53 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 4:39:36.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:59:02 INFO loss_tracker.py:84 | Epoch[204/239] Step[174] GlobalStep[79734/NA]: det_loss_cls_0[0.4466] det_loss_box_0[0.5344] det_loss_cls_1[0.4321] det_loss_box_1[0.4074] det_loss_cls_2[0.3733] det_loss_box_2[0.3731] det_loss_cls_3[0.3430] det_loss_box_3[0.3599] det_loss_cls_4[0.3147] det_loss_box_4[0.3496] det_loss_cls_5[0.2994] det_loss_box_5[0.3534] det_loss_cls_dn_0[0.0942] det_loss_box_dn_0[0.2606] det_loss_cls_dn_1[0.0898] det_loss_box_dn_1[0.2198] det_loss_cls_dn_2[0.0890] det_loss_box_dn_2[0.2057] det_loss_cls_dn_3[0.0878] det_loss_box_dn_3[0.2029] det_loss_cls_dn_4[0.0870] det_loss_box_dn_4[0.2011] det_loss_cls_dn_5[0.0860] det_loss_box_dn_5[0.1986] loss_depth[0.0353] total_loss[6.4447]
Rank[0/8] 06/20/2025 16:59:18 INFO stats.py:314 | Epoch[204] Step[189] GlobalStep[79749] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 4:39:05.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:59:29 INFO loss_tracker.py:84 | Epoch[204/239] Step[199] GlobalStep[79759/NA]: det_loss_cls_0[0.4763] det_loss_box_0[0.6389] det_loss_cls_1[0.4681] det_loss_box_1[0.4858] det_loss_cls_2[0.4185] det_loss_box_2[0.4436] det_loss_cls_3[0.3896] det_loss_box_3[0.4253] det_loss_cls_4[0.3615] det_loss_box_4[0.4192] det_loss_cls_5[0.3505] det_loss_box_5[0.4193] det_loss_cls_dn_0[0.0896] det_loss_box_dn_0[0.2687] det_loss_cls_dn_1[0.0862] det_loss_box_dn_1[0.2196] det_loss_cls_dn_2[0.0856] det_loss_box_dn_2[0.2013] det_loss_cls_dn_3[0.0848] det_loss_box_dn_3[0.1977] det_loss_cls_dn_4[0.0848] det_loss_box_dn_4[0.1964] det_loss_cls_dn_5[0.0842] det_loss_box_dn_5[0.1957] loss_depth[0.0347] total_loss[7.1258]
Rank[0/8] 06/20/2025 16:59:44 INFO stats.py:314 | Epoch[204] Step[214] GlobalStep[79774] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 4:38:34.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 16:59:55 INFO loss_tracker.py:84 | Epoch[204/239] Step[224] GlobalStep[79784/NA]: det_loss_cls_0[0.4767] det_loss_box_0[0.6560] det_loss_cls_1[0.4735] det_loss_box_1[0.4886] det_loss_cls_2[0.4068] det_loss_box_2[0.4368] det_loss_cls_3[0.3679] det_loss_box_3[0.4258] det_loss_cls_4[0.3290] det_loss_box_4[0.4288] det_loss_cls_5[0.3186] det_loss_box_5[0.4225] det_loss_cls_dn_0[0.0799] det_loss_box_dn_0[0.2701] det_loss_cls_dn_1[0.0751] det_loss_box_dn_1[0.2283] det_loss_cls_dn_2[0.0743] det_loss_box_dn_2[0.2036] det_loss_cls_dn_3[0.0730] det_loss_box_dn_3[0.1990] det_loss_cls_dn_4[0.0722] det_loss_box_dn_4[0.1970] det_loss_cls_dn_5[0.0709] det_loss_box_dn_5[0.1915] loss_depth[0.0336] total_loss[6.9992]
Rank[0/8] 06/20/2025 17:00:10 INFO stats.py:314 | Epoch[204] Step[239] GlobalStep[79799] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 4:38:03.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:00:20 INFO loss_tracker.py:84 | Epoch[204/239] Step[249] GlobalStep[79809/NA]: det_loss_cls_0[0.3965] det_loss_box_0[0.4552] det_loss_cls_1[0.3935] det_loss_box_1[0.3370] det_loss_cls_2[0.3280] det_loss_box_2[0.3076] det_loss_cls_3[0.2927] det_loss_box_3[0.3004] det_loss_cls_4[0.2764] det_loss_box_4[0.2944] det_loss_cls_5[0.2675] det_loss_box_5[0.2955] det_loss_cls_dn_0[0.0946] det_loss_box_dn_0[0.2423] det_loss_cls_dn_1[0.0917] det_loss_box_dn_1[0.1959] det_loss_cls_dn_2[0.0905] det_loss_box_dn_2[0.1805] det_loss_cls_dn_3[0.0895] det_loss_box_dn_3[0.1764] det_loss_cls_dn_4[0.0883] det_loss_box_dn_4[0.1727] det_loss_cls_dn_5[0.0873] det_loss_box_dn_5[0.1719] loss_depth[0.0317] total_loss[5.6579]
Rank[0/8] 06/20/2025 17:00:35 INFO stats.py:314 | Epoch[204] Step[264] GlobalStep[79824] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 4:37:32.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:00:46 INFO loss_tracker.py:84 | Epoch[204/239] Step[274] GlobalStep[79834/NA]: det_loss_cls_0[0.4662] det_loss_box_0[0.6128] det_loss_cls_1[0.4619] det_loss_box_1[0.4793] det_loss_cls_2[0.4045] det_loss_box_2[0.4363] det_loss_cls_3[0.3679] det_loss_box_3[0.4326] det_loss_cls_4[0.3462] det_loss_box_4[0.4244] det_loss_cls_5[0.3320] det_loss_box_5[0.4227] det_loss_cls_dn_0[0.0892] det_loss_box_dn_0[0.2581] det_loss_cls_dn_1[0.0848] det_loss_box_dn_1[0.2093] det_loss_cls_dn_2[0.0839] det_loss_box_dn_2[0.1919] det_loss_cls_dn_3[0.0826] det_loss_box_dn_3[0.1874] det_loss_cls_dn_4[0.0817] det_loss_box_dn_4[0.1848] det_loss_cls_dn_5[0.0813] det_loss_box_dn_5[0.1836] loss_depth[0.0335] total_loss[6.9389]
Rank[0/8] 06/20/2025 17:01:01 INFO stats.py:314 | Epoch[204] Step[289] GlobalStep[79849] Training Speed: 8.58 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 4:37:01.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:01:12 INFO loss_tracker.py:84 | Epoch[204/239] Step[299] GlobalStep[79859/NA]: det_loss_cls_0[0.5380] det_loss_box_0[0.6576] det_loss_cls_1[0.5192] det_loss_box_1[0.4799] det_loss_cls_2[0.4517] det_loss_box_2[0.4494] det_loss_cls_3[0.4100] det_loss_box_3[0.4277] det_loss_cls_4[0.3747] det_loss_box_4[0.4202] det_loss_cls_5[0.3583] det_loss_box_5[0.4224] det_loss_cls_dn_0[0.0909] det_loss_box_dn_0[0.2716] det_loss_cls_dn_1[0.0860] det_loss_box_dn_1[0.2285] det_loss_cls_dn_2[0.0844] det_loss_box_dn_2[0.2098] det_loss_cls_dn_3[0.0830] det_loss_box_dn_3[0.2043] det_loss_cls_dn_4[0.0825] det_loss_box_dn_4[0.2021] det_loss_cls_dn_5[0.0816] det_loss_box_dn_5[0.1982] loss_depth[0.0349] total_loss[7.3668]
Rank[0/8] 06/20/2025 17:01:28 INFO stats.py:314 | Epoch[204] Step[314] GlobalStep[79874] Training Speed: 8.56 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 4:36:30.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:01:38 INFO loss_tracker.py:84 | Epoch[204/239] Step[324] GlobalStep[79884/NA]: det_loss_cls_0[0.4533] det_loss_box_0[0.5905] det_loss_cls_1[0.4317] det_loss_box_1[0.4472] det_loss_cls_2[0.3700] det_loss_box_2[0.4027] det_loss_cls_3[0.3457] det_loss_box_3[0.3838] det_loss_cls_4[0.3163] det_loss_box_4[0.3733] det_loss_cls_5[0.3058] det_loss_box_5[0.3684] det_loss_cls_dn_0[0.0796] det_loss_box_dn_0[0.2939] det_loss_cls_dn_1[0.0757] det_loss_box_dn_1[0.2473] det_loss_cls_dn_2[0.0754] det_loss_box_dn_2[0.2332] det_loss_cls_dn_3[0.0745] det_loss_box_dn_3[0.2294] det_loss_cls_dn_4[0.0737] det_loss_box_dn_4[0.2263] det_loss_cls_dn_5[0.0729] det_loss_box_dn_5[0.2236] loss_depth[0.0366] total_loss[6.7310]
Rank[0/8] 06/20/2025 17:01:53 INFO stats.py:314 | Epoch[204] Step[339] GlobalStep[79899] Training Speed: 6.15 samples/sec across all devices.	Average Step Time: 1.30 sec.	Estimated Remaining Time: 4:35:59.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:02:04 INFO loss_tracker.py:84 | Epoch[204/239] Step[349] GlobalStep[79909/NA]: det_loss_cls_0[0.4169] det_loss_box_0[0.5239] det_loss_cls_1[0.4088] det_loss_box_1[0.4005] det_loss_cls_2[0.3575] det_loss_box_2[0.3519] det_loss_cls_3[0.3249] det_loss_box_3[0.3397] det_loss_cls_4[0.3024] det_loss_box_4[0.3372] det_loss_cls_5[0.2943] det_loss_box_5[0.3316] det_loss_cls_dn_0[0.0902] det_loss_box_dn_0[0.2933] det_loss_cls_dn_1[0.0872] det_loss_box_dn_1[0.2409] det_loss_cls_dn_2[0.0868] det_loss_box_dn_2[0.2206] det_loss_cls_dn_3[0.0858] det_loss_box_dn_3[0.2174] det_loss_cls_dn_4[0.0850] det_loss_box_dn_4[0.2133] det_loss_cls_dn_5[0.0842] det_loss_box_dn_5[0.2107] loss_depth[0.0345] total_loss[6.3395]
Rank[0/8] 06/20/2025 17:02:19 INFO stats.py:314 | Epoch[204] Step[364] GlobalStep[79924] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 4:35:28.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:02:30 INFO loss_tracker.py:84 | Epoch[204/239] Step[374] GlobalStep[79934/NA]: det_loss_cls_0[0.4751] det_loss_box_0[0.6102] det_loss_cls_1[0.4561] det_loss_box_1[0.4585] det_loss_cls_2[0.4028] det_loss_box_2[0.4027] det_loss_cls_3[0.3729] det_loss_box_3[0.3889] det_loss_cls_4[0.3489] det_loss_box_4[0.3819] det_loss_cls_5[0.3412] det_loss_box_5[0.3831] det_loss_cls_dn_0[0.0963] det_loss_box_dn_0[0.2837] det_loss_cls_dn_1[0.0927] det_loss_box_dn_1[0.2225] det_loss_cls_dn_2[0.0915] det_loss_box_dn_2[0.2037] det_loss_cls_dn_3[0.0903] det_loss_box_dn_3[0.1998] det_loss_cls_dn_4[0.0897] det_loss_box_dn_4[0.1972] det_loss_cls_dn_5[0.0889] det_loss_box_dn_5[0.1922] loss_depth[0.0328] total_loss[6.9038]
Rank[0/8] 06/20/2025 17:02:46 INFO stats.py:314 | Epoch[204] Step[389] GlobalStep[79949] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 4:34:57.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:02:46 INFO stats.py:394 | Epoch[204] completed. Training Speed: 7.66 samples/sec across all devices.	Epoch Time: 407.52 sec.	Average Epoch Time: 407.52 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 4:34:57.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:03:14 INFO stats.py:314 | Epoch[205] Step[24] GlobalStep[79974] Training Speed: 7.44 samples/sec across all devices.	Average Step Time: 1.08 sec.	Estimated Remaining Time: 4:34:27.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:03:14 INFO loss_tracker.py:84 | Epoch[205/239] Step[24] GlobalStep[79974/NA]: det_loss_cls_0[0.4682] det_loss_box_0[0.5255] det_loss_cls_1[0.4480] det_loss_box_1[0.4011] det_loss_cls_2[0.3920] det_loss_box_2[0.3645] det_loss_cls_3[0.3560] det_loss_box_3[0.3518] det_loss_cls_4[0.3319] det_loss_box_4[0.3394] det_loss_cls_5[0.3155] det_loss_box_5[0.3369] det_loss_cls_dn_0[0.0924] det_loss_box_dn_0[0.2385] det_loss_cls_dn_1[0.0888] det_loss_box_dn_1[0.1946] det_loss_cls_dn_2[0.0877] det_loss_box_dn_2[0.1773] det_loss_cls_dn_3[0.0868] det_loss_box_dn_3[0.1746] det_loss_cls_dn_4[0.0862] det_loss_box_dn_4[0.1723] det_loss_cls_dn_5[0.0855] det_loss_box_dn_5[0.1697] loss_depth[0.0345] total_loss[6.3197]
Rank[0/8] 06/20/2025 17:03:40 INFO stats.py:314 | Epoch[205] Step[49] GlobalStep[79999] Training Speed: 8.03 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 4:33:56.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:03:40 INFO loss_tracker.py:84 | Epoch[205/239] Step[49] GlobalStep[79999/NA]: det_loss_cls_0[0.4561] det_loss_box_0[0.5742] det_loss_cls_1[0.4523] det_loss_box_1[0.4367] det_loss_cls_2[0.4000] det_loss_box_2[0.3977] det_loss_cls_3[0.3735] det_loss_box_3[0.3923] det_loss_cls_4[0.3435] det_loss_box_4[0.3866] det_loss_cls_5[0.3263] det_loss_box_5[0.3893] det_loss_cls_dn_0[0.0925] det_loss_box_dn_0[0.2424] det_loss_cls_dn_1[0.0874] det_loss_box_dn_1[0.1981] det_loss_cls_dn_2[0.0868] det_loss_box_dn_2[0.1835] det_loss_cls_dn_3[0.0857] det_loss_box_dn_3[0.1789] det_loss_cls_dn_4[0.0846] det_loss_box_dn_4[0.1770] det_loss_cls_dn_5[0.0835] det_loss_box_dn_5[0.1756] loss_depth[0.0344] total_loss[6.6387]
Rank[0/8] 06/20/2025 17:04:06 INFO stats.py:314 | Epoch[205] Step[74] GlobalStep[80024] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 4:33:25.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:04:06 INFO loss_tracker.py:84 | Epoch[205/239] Step[74] GlobalStep[80024/NA]: det_loss_cls_0[0.4546] det_loss_box_0[0.5708] det_loss_cls_1[0.4522] det_loss_box_1[0.4090] det_loss_cls_2[0.3926] det_loss_box_2[0.3626] det_loss_cls_3[0.3621] det_loss_box_3[0.3465] det_loss_cls_4[0.3442] det_loss_box_4[0.3405] det_loss_cls_5[0.3283] det_loss_box_5[0.3424] det_loss_cls_dn_0[0.0934] det_loss_box_dn_0[0.2449] det_loss_cls_dn_1[0.0902] det_loss_box_dn_1[0.1994] det_loss_cls_dn_2[0.0896] det_loss_box_dn_2[0.1824] det_loss_cls_dn_3[0.0887] det_loss_box_dn_3[0.1787] det_loss_cls_dn_4[0.0876] det_loss_box_dn_4[0.1757] det_loss_cls_dn_5[0.0868] det_loss_box_dn_5[0.1749] loss_depth[0.0316] total_loss[6.4298]
Rank[0/8] 06/20/2025 17:04:32 INFO stats.py:314 | Epoch[205] Step[99] GlobalStep[80049] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 4:32:54.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:04:32 INFO loss_tracker.py:84 | Epoch[205/239] Step[99] GlobalStep[80049/NA]: det_loss_cls_0[0.4451] det_loss_box_0[0.4874] det_loss_cls_1[0.4376] det_loss_box_1[0.3386] det_loss_cls_2[0.3597] det_loss_box_2[0.3117] det_loss_cls_3[0.3310] det_loss_box_3[0.3000] det_loss_cls_4[0.3063] det_loss_box_4[0.2909] det_loss_cls_5[0.2939] det_loss_box_5[0.2919] det_loss_cls_dn_0[0.0862] det_loss_box_dn_0[0.2156] det_loss_cls_dn_1[0.0832] det_loss_box_dn_1[0.1570] det_loss_cls_dn_2[0.0821] det_loss_box_dn_2[0.1418] det_loss_cls_dn_3[0.0817] det_loss_box_dn_3[0.1386] det_loss_cls_dn_4[0.0811] det_loss_box_dn_4[0.1350] det_loss_cls_dn_5[0.0806] det_loss_box_dn_5[0.1332] loss_depth[0.0349] total_loss[5.6451]
Rank[0/8] 06/20/2025 17:04:57 INFO stats.py:314 | Epoch[205] Step[124] GlobalStep[80074] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 4:32:23.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:04:57 INFO loss_tracker.py:84 | Epoch[205/239] Step[124] GlobalStep[80074/NA]: det_loss_cls_0[0.4238] det_loss_box_0[0.4706] det_loss_cls_1[0.4115] det_loss_box_1[0.3339] det_loss_cls_2[0.3447] det_loss_box_2[0.2963] det_loss_cls_3[0.3080] det_loss_box_3[0.2905] det_loss_cls_4[0.2909] det_loss_box_4[0.2888] det_loss_cls_5[0.2846] det_loss_box_5[0.2872] det_loss_cls_dn_0[0.0815] det_loss_box_dn_0[0.2273] det_loss_cls_dn_1[0.0784] det_loss_box_dn_1[0.1787] det_loss_cls_dn_2[0.0773] det_loss_box_dn_2[0.1597] det_loss_cls_dn_3[0.0760] det_loss_box_dn_3[0.1556] det_loss_cls_dn_4[0.0752] det_loss_box_dn_4[0.1521] det_loss_cls_dn_5[0.0744] det_loss_box_dn_5[0.1507] loss_depth[0.0367] total_loss[5.5544]
Rank[0/8] 06/20/2025 17:05:23 INFO stats.py:314 | Epoch[205] Step[149] GlobalStep[80099] Training Speed: 7.84 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 4:31:52.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:05:23 INFO loss_tracker.py:84 | Epoch[205/239] Step[149] GlobalStep[80099/NA]: det_loss_cls_0[0.4089] det_loss_box_0[0.5511] det_loss_cls_1[0.3873] det_loss_box_1[0.4257] det_loss_cls_2[0.3346] det_loss_box_2[0.3917] det_loss_cls_3[0.3096] det_loss_box_3[0.3745] det_loss_cls_4[0.2914] det_loss_box_4[0.3687] det_loss_cls_5[0.2825] det_loss_box_5[0.3688] det_loss_cls_dn_0[0.0807] det_loss_box_dn_0[0.2684] det_loss_cls_dn_1[0.0756] det_loss_box_dn_1[0.2189] det_loss_cls_dn_2[0.0751] det_loss_box_dn_2[0.2021] det_loss_cls_dn_3[0.0742] det_loss_box_dn_3[0.1977] det_loss_cls_dn_4[0.0730] det_loss_box_dn_4[0.1961] det_loss_cls_dn_5[0.0721] det_loss_box_dn_5[0.1929] loss_depth[0.0361] total_loss[6.2577]
Rank[0/8] 06/20/2025 17:05:50 INFO stats.py:314 | Epoch[205] Step[174] GlobalStep[80124] Training Speed: 6.48 samples/sec across all devices.	Average Step Time: 1.23 sec.	Estimated Remaining Time: 4:31:21.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:05:50 INFO loss_tracker.py:84 | Epoch[205/239] Step[174] GlobalStep[80124/NA]: det_loss_cls_0[0.4071] det_loss_box_0[0.4962] det_loss_cls_1[0.3914] det_loss_box_1[0.3543] det_loss_cls_2[0.3182] det_loss_box_2[0.3231] det_loss_cls_3[0.2896] det_loss_box_3[0.3050] det_loss_cls_4[0.2683] det_loss_box_4[0.2951] det_loss_cls_5[0.2600] det_loss_box_5[0.2960] det_loss_cls_dn_0[0.0925] det_loss_box_dn_0[0.2366] det_loss_cls_dn_1[0.0882] det_loss_box_dn_1[0.1892] det_loss_cls_dn_2[0.0868] det_loss_box_dn_2[0.1765] det_loss_cls_dn_3[0.0855] det_loss_box_dn_3[0.1705] det_loss_cls_dn_4[0.0845] det_loss_box_dn_4[0.1683] det_loss_cls_dn_5[0.0836] det_loss_box_dn_5[0.1643] loss_depth[0.0352] total_loss[5.6662]
Rank[0/8] 06/20/2025 17:06:16 INFO stats.py:314 | Epoch[205] Step[199] GlobalStep[80149] Training Speed: 7.36 samples/sec across all devices.	Average Step Time: 1.09 sec.	Estimated Remaining Time: 4:30:50.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:06:16 INFO loss_tracker.py:84 | Epoch[205/239] Step[199] GlobalStep[80149/NA]: det_loss_cls_0[0.4457] det_loss_box_0[0.5691] det_loss_cls_1[0.4350] det_loss_box_1[0.4334] det_loss_cls_2[0.3845] det_loss_box_2[0.4089] det_loss_cls_3[0.3583] det_loss_box_3[0.3969] det_loss_cls_4[0.3358] det_loss_box_4[0.3926] det_loss_cls_5[0.3202] det_loss_box_5[0.3917] det_loss_cls_dn_0[0.0851] det_loss_box_dn_0[0.2965] det_loss_cls_dn_1[0.0823] det_loss_box_dn_1[0.2393] det_loss_cls_dn_2[0.0813] det_loss_box_dn_2[0.2149] det_loss_cls_dn_3[0.0807] det_loss_box_dn_3[0.2109] det_loss_cls_dn_4[0.0804] det_loss_box_dn_4[0.2100] det_loss_cls_dn_5[0.0797] det_loss_box_dn_5[0.2087] loss_depth[0.0308] total_loss[6.7729]
Rank[0/8] 06/20/2025 17:06:41 INFO stats.py:314 | Epoch[205] Step[224] GlobalStep[80174] Training Speed: 11.88 samples/sec across all devices.	Average Step Time: 0.67 sec.	Estimated Remaining Time: 4:30:19.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:06:42 INFO loss_tracker.py:84 | Epoch[205/239] Step[224] GlobalStep[80174/NA]: det_loss_cls_0[0.5703] det_loss_box_0[0.6910] det_loss_cls_1[0.5440] det_loss_box_1[0.5039] det_loss_cls_2[0.4790] det_loss_box_2[0.4582] det_loss_cls_3[0.4337] det_loss_box_3[0.4497] det_loss_cls_4[0.3967] det_loss_box_4[0.4386] det_loss_cls_5[0.3854] det_loss_box_5[0.4400] det_loss_cls_dn_0[0.0822] det_loss_box_dn_0[0.2261] det_loss_cls_dn_1[0.0782] det_loss_box_dn_1[0.1861] det_loss_cls_dn_2[0.0777] det_loss_box_dn_2[0.1710] det_loss_cls_dn_3[0.0759] det_loss_box_dn_3[0.1678] det_loss_cls_dn_4[0.0745] det_loss_box_dn_4[0.1663] det_loss_cls_dn_5[0.0737] det_loss_box_dn_5[0.1657] loss_depth[0.0311] total_loss[7.3667]
Rank[0/8] 06/20/2025 17:07:07 INFO stats.py:314 | Epoch[205] Step[249] GlobalStep[80199] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 4:29:49.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:07:07 INFO loss_tracker.py:84 | Epoch[205/239] Step[249] GlobalStep[80199/NA]: det_loss_cls_0[0.4384] det_loss_box_0[0.4862] det_loss_cls_1[0.4138] det_loss_box_1[0.3800] det_loss_cls_2[0.3553] det_loss_box_2[0.3437] det_loss_cls_3[0.3353] det_loss_box_3[0.3283] det_loss_cls_4[0.3020] det_loss_box_4[0.3213] det_loss_cls_5[0.2937] det_loss_box_5[0.3222] det_loss_cls_dn_0[0.0922] det_loss_box_dn_0[0.2390] det_loss_cls_dn_1[0.0883] det_loss_box_dn_1[0.1968] det_loss_cls_dn_2[0.0877] det_loss_box_dn_2[0.1815] det_loss_cls_dn_3[0.0865] det_loss_box_dn_3[0.1799] det_loss_cls_dn_4[0.0863] det_loss_box_dn_4[0.1767] det_loss_cls_dn_5[0.0850] det_loss_box_dn_5[0.1753] loss_depth[0.0351] total_loss[6.0303]
Rank[0/8] 06/20/2025 17:07:33 INFO stats.py:314 | Epoch[205] Step[274] GlobalStep[80224] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 4:29:18.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:07:33 INFO loss_tracker.py:84 | Epoch[205/239] Step[274] GlobalStep[80224/NA]: det_loss_cls_0[0.4894] det_loss_box_0[0.6513] det_loss_cls_1[0.4593] det_loss_box_1[0.4901] det_loss_cls_2[0.3984] det_loss_box_2[0.4294] det_loss_cls_3[0.3646] det_loss_box_3[0.4205] det_loss_cls_4[0.3491] det_loss_box_4[0.4089] det_loss_cls_5[0.3289] det_loss_box_5[0.4112] det_loss_cls_dn_0[0.0804] det_loss_box_dn_0[0.2751] det_loss_cls_dn_1[0.0755] det_loss_box_dn_1[0.2158] det_loss_cls_dn_2[0.0745] det_loss_box_dn_2[0.1950] det_loss_cls_dn_3[0.0734] det_loss_box_dn_3[0.1867] det_loss_cls_dn_4[0.0723] det_loss_box_dn_4[0.1834] det_loss_cls_dn_5[0.0717] det_loss_box_dn_5[0.1813] loss_depth[0.0374] total_loss[6.9239]
Rank[0/8] 06/20/2025 17:07:59 INFO stats.py:314 | Epoch[205] Step[299] GlobalStep[80249] Training Speed: 7.91 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 4:28:47.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:07:59 INFO loss_tracker.py:84 | Epoch[205/239] Step[299] GlobalStep[80249/NA]: det_loss_cls_0[0.4231] det_loss_box_0[0.5186] det_loss_cls_1[0.4041] det_loss_box_1[0.3680] det_loss_cls_2[0.3198] det_loss_box_2[0.3417] det_loss_cls_3[0.2796] det_loss_box_3[0.3272] det_loss_cls_4[0.2662] det_loss_box_4[0.3118] det_loss_cls_5[0.2558] det_loss_box_5[0.3098] det_loss_cls_dn_0[0.0866] det_loss_box_dn_0[0.3075] det_loss_cls_dn_1[0.0826] det_loss_box_dn_1[0.2387] det_loss_cls_dn_2[0.0813] det_loss_box_dn_2[0.2174] det_loss_cls_dn_3[0.0807] det_loss_box_dn_3[0.2127] det_loss_cls_dn_4[0.0796] det_loss_box_dn_4[0.2080] det_loss_cls_dn_5[0.0788] det_loss_box_dn_5[0.2059] loss_depth[0.0329] total_loss[6.0387]
Rank[0/8] 06/20/2025 17:08:25 INFO stats.py:314 | Epoch[205] Step[324] GlobalStep[80274] Training Speed: 7.97 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 4:28:16.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:08:25 INFO loss_tracker.py:84 | Epoch[205/239] Step[324] GlobalStep[80274/NA]: det_loss_cls_0[0.5041] det_loss_box_0[0.5856] det_loss_cls_1[0.4721] det_loss_box_1[0.4342] det_loss_cls_2[0.4092] det_loss_box_2[0.3978] det_loss_cls_3[0.3810] det_loss_box_3[0.3728] det_loss_cls_4[0.3552] det_loss_box_4[0.3654] det_loss_cls_5[0.3372] det_loss_box_5[0.3708] det_loss_cls_dn_0[0.0888] det_loss_box_dn_0[0.2691] det_loss_cls_dn_1[0.0852] det_loss_box_dn_1[0.2165] det_loss_cls_dn_2[0.0839] det_loss_box_dn_2[0.2011] det_loss_cls_dn_3[0.0827] det_loss_box_dn_3[0.1967] det_loss_cls_dn_4[0.0814] det_loss_box_dn_4[0.1951] det_loss_cls_dn_5[0.0803] det_loss_box_dn_5[0.1917] loss_depth[0.0349] total_loss[6.7927]
Rank[0/8] 06/20/2025 17:08:51 INFO stats.py:314 | Epoch[205] Step[349] GlobalStep[80299] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 4:27:45.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:08:51 INFO loss_tracker.py:84 | Epoch[205/239] Step[349] GlobalStep[80299/NA]: det_loss_cls_0[0.5273] det_loss_box_0[0.6966] det_loss_cls_1[0.5027] det_loss_box_1[0.5145] det_loss_cls_2[0.4372] det_loss_box_2[0.4649] det_loss_cls_3[0.4055] det_loss_box_3[0.4511] det_loss_cls_4[0.3846] det_loss_box_4[0.4360] det_loss_cls_5[0.3651] det_loss_box_5[0.4378] det_loss_cls_dn_0[0.0976] det_loss_box_dn_0[0.2661] det_loss_cls_dn_1[0.0944] det_loss_box_dn_1[0.2224] det_loss_cls_dn_2[0.0939] det_loss_box_dn_2[0.2080] det_loss_cls_dn_3[0.0930] det_loss_box_dn_3[0.2038] det_loss_cls_dn_4[0.0919] det_loss_box_dn_4[0.2018] det_loss_cls_dn_5[0.0909] det_loss_box_dn_5[0.1971] loss_depth[0.0377] total_loss[7.5221]
Rank[0/8] 06/20/2025 17:09:16 INFO stats.py:314 | Epoch[205] Step[374] GlobalStep[80324] Training Speed: 8.15 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 4:27:14.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:09:16 INFO loss_tracker.py:84 | Epoch[205/239] Step[374] GlobalStep[80324/NA]: det_loss_cls_0[0.3501] det_loss_box_0[0.4655] det_loss_cls_1[0.3375] det_loss_box_1[0.3406] det_loss_cls_2[0.2841] det_loss_box_2[0.3112] det_loss_cls_3[0.2584] det_loss_box_3[0.3022] det_loss_cls_4[0.2401] det_loss_box_4[0.2905] det_loss_cls_5[0.2247] det_loss_box_5[0.2941] det_loss_cls_dn_0[0.0750] det_loss_box_dn_0[0.3069] det_loss_cls_dn_1[0.0700] det_loss_box_dn_1[0.2551] det_loss_cls_dn_2[0.0689] det_loss_box_dn_2[0.2345] det_loss_cls_dn_3[0.0679] det_loss_box_dn_3[0.2248] det_loss_cls_dn_4[0.0672] det_loss_box_dn_4[0.2225] det_loss_cls_dn_5[0.0658] det_loss_box_dn_5[0.2137] loss_depth[0.0341] total_loss[5.6053]
Rank[0/8] 06/20/2025 17:09:32 INFO stats.py:394 | Epoch[205] completed. Training Speed: 7.69 samples/sec across all devices.	Epoch Time: 405.88 sec.	Average Epoch Time: 405.88 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 4:26:55.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:09:46 INFO stats.py:314 | Epoch[206] Step[9] GlobalStep[80349] Training Speed: 8.22 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 4:26:43.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:10:01 INFO loss_tracker.py:84 | Epoch[206/239] Step[24] GlobalStep[80364/NA]: det_loss_cls_0[0.3666] det_loss_box_0[0.4552] det_loss_cls_1[0.3571] det_loss_box_1[0.3435] det_loss_cls_2[0.3099] det_loss_box_2[0.3196] det_loss_cls_3[0.2822] det_loss_box_3[0.3059] det_loss_cls_4[0.2634] det_loss_box_4[0.2981] det_loss_cls_5[0.2536] det_loss_box_5[0.2970] det_loss_cls_dn_0[0.0796] det_loss_box_dn_0[0.2515] det_loss_cls_dn_1[0.0761] det_loss_box_dn_1[0.2000] det_loss_cls_dn_2[0.0751] det_loss_box_dn_2[0.1819] det_loss_cls_dn_3[0.0738] det_loss_box_dn_3[0.1769] det_loss_cls_dn_4[0.0733] det_loss_box_dn_4[0.1738] det_loss_cls_dn_5[0.0727] det_loss_box_dn_5[0.1725] loss_depth[0.0339] total_loss[5.4934]
Rank[0/8] 06/20/2025 17:10:12 INFO stats.py:314 | Epoch[206] Step[34] GlobalStep[80374] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 4:26:13.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:10:27 INFO loss_tracker.py:84 | Epoch[206/239] Step[49] GlobalStep[80389/NA]: det_loss_cls_0[0.4556] det_loss_box_0[0.5759] det_loss_cls_1[0.4267] det_loss_box_1[0.4561] det_loss_cls_2[0.3749] det_loss_box_2[0.4074] det_loss_cls_3[0.3513] det_loss_box_3[0.3876] det_loss_cls_4[0.3271] det_loss_box_4[0.3786] det_loss_cls_5[0.3145] det_loss_box_5[0.3767] det_loss_cls_dn_0[0.0917] det_loss_box_dn_0[0.3652] det_loss_cls_dn_1[0.0887] det_loss_box_dn_1[0.3134] det_loss_cls_dn_2[0.0877] det_loss_box_dn_2[0.2893] det_loss_cls_dn_3[0.0864] det_loss_box_dn_3[0.2830] det_loss_cls_dn_4[0.0857] det_loss_box_dn_4[0.2828] det_loss_cls_dn_5[0.0847] det_loss_box_dn_5[0.2791] loss_depth[0.0376] total_loss[7.2075]
Rank[0/8] 06/20/2025 17:10:38 INFO stats.py:314 | Epoch[206] Step[59] GlobalStep[80399] Training Speed: 8.70 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 4:25:42.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:10:53 INFO loss_tracker.py:84 | Epoch[206/239] Step[74] GlobalStep[80414/NA]: det_loss_cls_0[0.3881] det_loss_box_0[0.4596] det_loss_cls_1[0.3770] det_loss_box_1[0.3280] det_loss_cls_2[0.3109] det_loss_box_2[0.3006] det_loss_cls_3[0.2782] det_loss_box_3[0.2934] det_loss_cls_4[0.2556] det_loss_box_4[0.2841] det_loss_cls_5[0.2494] det_loss_box_5[0.2817] det_loss_cls_dn_0[0.0898] det_loss_box_dn_0[0.2575] det_loss_cls_dn_1[0.0852] det_loss_box_dn_1[0.2019] det_loss_cls_dn_2[0.0838] det_loss_box_dn_2[0.1837] det_loss_cls_dn_3[0.0828] det_loss_box_dn_3[0.1766] det_loss_cls_dn_4[0.0819] det_loss_box_dn_4[0.1745] det_loss_cls_dn_5[0.0812] det_loss_box_dn_5[0.1721] loss_depth[0.0343] total_loss[5.5117]
Rank[0/8] 06/20/2025 17:11:03 INFO stats.py:314 | Epoch[206] Step[84] GlobalStep[80424] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 4:25:11.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:11:18 INFO loss_tracker.py:84 | Epoch[206/239] Step[99] GlobalStep[80439/NA]: det_loss_cls_0[0.5190] det_loss_box_0[0.6511] det_loss_cls_1[0.5033] det_loss_box_1[0.4779] det_loss_cls_2[0.4363] det_loss_box_2[0.4388] det_loss_cls_3[0.4025] det_loss_box_3[0.4301] det_loss_cls_4[0.3765] det_loss_box_4[0.4259] det_loss_cls_5[0.3581] det_loss_box_5[0.4293] det_loss_cls_dn_0[0.1002] det_loss_box_dn_0[0.2997] det_loss_cls_dn_1[0.0967] det_loss_box_dn_1[0.2478] det_loss_cls_dn_2[0.0960] det_loss_box_dn_2[0.2262] det_loss_cls_dn_3[0.0952] det_loss_box_dn_3[0.2212] det_loss_cls_dn_4[0.0941] det_loss_box_dn_4[0.2184] det_loss_cls_dn_5[0.0929] det_loss_box_dn_5[0.2168] loss_depth[0.0359] total_loss[7.4899]
Rank[0/8] 06/20/2025 17:11:28 INFO stats.py:314 | Epoch[206] Step[109] GlobalStep[80449] Training Speed: 7.91 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 4:24:40.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:11:44 INFO loss_tracker.py:84 | Epoch[206/239] Step[124] GlobalStep[80464/NA]: det_loss_cls_0[0.4755] det_loss_box_0[0.5003] det_loss_cls_1[0.4526] det_loss_box_1[0.3804] det_loss_cls_2[0.3965] det_loss_box_2[0.3453] det_loss_cls_3[0.3620] det_loss_box_3[0.3373] det_loss_cls_4[0.3427] det_loss_box_4[0.3217] det_loss_cls_5[0.3274] det_loss_box_5[0.3234] det_loss_cls_dn_0[0.0890] det_loss_box_dn_0[0.2449] det_loss_cls_dn_1[0.0856] det_loss_box_dn_1[0.1965] det_loss_cls_dn_2[0.0847] det_loss_box_dn_2[0.1776] det_loss_cls_dn_3[0.0841] det_loss_box_dn_3[0.1716] det_loss_cls_dn_4[0.0837] det_loss_box_dn_4[0.1696] det_loss_cls_dn_5[0.0829] det_loss_box_dn_5[0.1666] loss_depth[0.0344] total_loss[6.2360]
Rank[0/8] 06/20/2025 17:11:55 INFO stats.py:314 | Epoch[206] Step[134] GlobalStep[80474] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 4:24:09.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:12:10 INFO loss_tracker.py:84 | Epoch[206/239] Step[149] GlobalStep[80489/NA]: det_loss_cls_0[0.3325] det_loss_box_0[0.4613] det_loss_cls_1[0.3257] det_loss_box_1[0.3592] det_loss_cls_2[0.2838] det_loss_box_2[0.3218] det_loss_cls_3[0.2544] det_loss_box_3[0.3065] det_loss_cls_4[0.2397] det_loss_box_4[0.2959] det_loss_cls_5[0.2328] det_loss_box_5[0.2944] det_loss_cls_dn_0[0.0869] det_loss_box_dn_0[0.2917] det_loss_cls_dn_1[0.0830] det_loss_box_dn_1[0.2348] det_loss_cls_dn_2[0.0823] det_loss_box_dn_2[0.2133] det_loss_cls_dn_3[0.0813] det_loss_box_dn_3[0.2086] det_loss_cls_dn_4[0.0803] det_loss_box_dn_4[0.2049] det_loss_cls_dn_5[0.0796] det_loss_box_dn_5[0.2016] loss_depth[0.0357] total_loss[5.5922]
Rank[0/8] 06/20/2025 17:12:20 INFO stats.py:314 | Epoch[206] Step[159] GlobalStep[80499] Training Speed: 7.46 samples/sec across all devices.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 4:23:38.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:12:36 INFO loss_tracker.py:84 | Epoch[206/239] Step[174] GlobalStep[80514/NA]: det_loss_cls_0[0.4257] det_loss_box_0[0.5708] det_loss_cls_1[0.4133] det_loss_box_1[0.4535] det_loss_cls_2[0.3566] det_loss_box_2[0.4137] det_loss_cls_3[0.3277] det_loss_box_3[0.3949] det_loss_cls_4[0.3149] det_loss_box_4[0.3848] det_loss_cls_5[0.2980] det_loss_box_5[0.3858] det_loss_cls_dn_0[0.0920] det_loss_box_dn_0[0.2727] det_loss_cls_dn_1[0.0886] det_loss_box_dn_1[0.2224] det_loss_cls_dn_2[0.0878] det_loss_box_dn_2[0.2059] det_loss_cls_dn_3[0.0867] det_loss_box_dn_3[0.2008] det_loss_cls_dn_4[0.0856] det_loss_box_dn_4[0.1974] det_loss_cls_dn_5[0.0846] det_loss_box_dn_5[0.1965] loss_depth[0.0355] total_loss[6.5962]
Rank[0/8] 06/20/2025 17:12:46 INFO stats.py:314 | Epoch[206] Step[184] GlobalStep[80524] Training Speed: 7.71 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 4:23:07.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:13:01 INFO loss_tracker.py:84 | Epoch[206/239] Step[199] GlobalStep[80539/NA]: det_loss_cls_0[0.5765] det_loss_box_0[0.7623] det_loss_cls_1[0.5555] det_loss_box_1[0.5956] det_loss_cls_2[0.4849] det_loss_box_2[0.5351] det_loss_cls_3[0.4562] det_loss_box_3[0.5032] det_loss_cls_4[0.4356] det_loss_box_4[0.4826] det_loss_cls_5[0.4172] det_loss_box_5[0.4901] det_loss_cls_dn_0[0.0967] det_loss_box_dn_0[0.2726] det_loss_cls_dn_1[0.0939] det_loss_box_dn_1[0.2260] det_loss_cls_dn_2[0.0931] det_loss_box_dn_2[0.2080] det_loss_cls_dn_3[0.0920] det_loss_box_dn_3[0.2028] det_loss_cls_dn_4[0.0915] det_loss_box_dn_4[0.2013] det_loss_cls_dn_5[0.0905] det_loss_box_dn_5[0.2000] loss_depth[0.0385] total_loss[8.2017]
Rank[0/8] 06/20/2025 17:13:12 INFO stats.py:314 | Epoch[206] Step[209] GlobalStep[80549] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 4:22:36.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:13:28 INFO loss_tracker.py:84 | Epoch[206/239] Step[224] GlobalStep[80564/NA]: det_loss_cls_0[0.4898] det_loss_box_0[0.6183] det_loss_cls_1[0.4896] det_loss_box_1[0.4510] det_loss_cls_2[0.4354] det_loss_box_2[0.4046] det_loss_cls_3[0.3996] det_loss_box_3[0.3967] det_loss_cls_4[0.3808] det_loss_box_4[0.3827] det_loss_cls_5[0.3713] det_loss_box_5[0.3784] det_loss_cls_dn_0[0.0959] det_loss_box_dn_0[0.2577] det_loss_cls_dn_1[0.0925] det_loss_box_dn_1[0.2106] det_loss_cls_dn_2[0.0919] det_loss_box_dn_2[0.1931] det_loss_cls_dn_3[0.0907] det_loss_box_dn_3[0.1883] det_loss_cls_dn_4[0.0900] det_loss_box_dn_4[0.1859] det_loss_cls_dn_5[0.0891] det_loss_box_dn_5[0.1849] loss_depth[0.0345] total_loss[7.0032]
Rank[0/8] 06/20/2025 17:13:38 INFO stats.py:314 | Epoch[206] Step[234] GlobalStep[80574] Training Speed: 7.84 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 4:22:05.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:13:54 INFO loss_tracker.py:84 | Epoch[206/239] Step[249] GlobalStep[80589/NA]: det_loss_cls_0[0.4521] det_loss_box_0[0.5281] det_loss_cls_1[0.4506] det_loss_box_1[0.3897] det_loss_cls_2[0.3770] det_loss_box_2[0.3676] det_loss_cls_3[0.3359] det_loss_box_3[0.3480] det_loss_cls_4[0.3072] det_loss_box_4[0.3384] det_loss_cls_5[0.2958] det_loss_box_5[0.3403] det_loss_cls_dn_0[0.0824] det_loss_box_dn_0[0.2446] det_loss_cls_dn_1[0.0783] det_loss_box_dn_1[0.2039] det_loss_cls_dn_2[0.0770] det_loss_box_dn_2[0.1903] det_loss_cls_dn_3[0.0760] det_loss_box_dn_3[0.1851] det_loss_cls_dn_4[0.0751] det_loss_box_dn_4[0.1831] det_loss_cls_dn_5[0.0743] det_loss_box_dn_5[0.1797] loss_depth[0.0337] total_loss[6.2142]
Rank[0/8] 06/20/2025 17:14:04 INFO stats.py:314 | Epoch[206] Step[259] GlobalStep[80599] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 4:21:34.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:14:20 INFO loss_tracker.py:84 | Epoch[206/239] Step[274] GlobalStep[80614/NA]: det_loss_cls_0[0.4996] det_loss_box_0[0.6473] det_loss_cls_1[0.4734] det_loss_box_1[0.5013] det_loss_cls_2[0.4225] det_loss_box_2[0.4495] det_loss_cls_3[0.3862] det_loss_box_3[0.4444] det_loss_cls_4[0.3645] det_loss_box_4[0.4251] det_loss_cls_5[0.3470] det_loss_box_5[0.4259] det_loss_cls_dn_0[0.0903] det_loss_box_dn_0[0.2734] det_loss_cls_dn_1[0.0872] det_loss_box_dn_1[0.2238] det_loss_cls_dn_2[0.0866] det_loss_box_dn_2[0.2053] det_loss_cls_dn_3[0.0859] det_loss_box_dn_3[0.2014] det_loss_cls_dn_4[0.0853] det_loss_box_dn_4[0.1997] det_loss_cls_dn_5[0.0847] det_loss_box_dn_5[0.1982] loss_depth[0.0364] total_loss[7.2452]
Rank[0/8] 06/20/2025 17:14:30 INFO stats.py:314 | Epoch[206] Step[284] GlobalStep[80624] Training Speed: 6.12 samples/sec across all devices.	Average Step Time: 1.31 sec.	Estimated Remaining Time: 4:21:04.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:14:46 INFO loss_tracker.py:84 | Epoch[206/239] Step[299] GlobalStep[80639/NA]: det_loss_cls_0[0.5273] det_loss_box_0[0.6868] det_loss_cls_1[0.5075] det_loss_box_1[0.5161] det_loss_cls_2[0.4466] det_loss_box_2[0.4737] det_loss_cls_3[0.4120] det_loss_box_3[0.4464] det_loss_cls_4[0.3883] det_loss_box_4[0.4307] det_loss_cls_5[0.3726] det_loss_box_5[0.4296] det_loss_cls_dn_0[0.0833] det_loss_box_dn_0[0.2573] det_loss_cls_dn_1[0.0796] det_loss_box_dn_1[0.2182] det_loss_cls_dn_2[0.0785] det_loss_box_dn_2[0.1999] det_loss_cls_dn_3[0.0776] det_loss_box_dn_3[0.1962] det_loss_cls_dn_4[0.0768] det_loss_box_dn_4[0.1937] det_loss_cls_dn_5[0.0757] det_loss_box_dn_5[0.1918] loss_depth[0.0332] total_loss[7.3995]
Rank[0/8] 06/20/2025 17:14:56 INFO stats.py:314 | Epoch[206] Step[309] GlobalStep[80649] Training Speed: 8.05 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 4:20:33.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:15:11 INFO loss_tracker.py:84 | Epoch[206/239] Step[324] GlobalStep[80664/NA]: det_loss_cls_0[0.4140] det_loss_box_0[0.5179] det_loss_cls_1[0.3904] det_loss_box_1[0.4130] det_loss_cls_2[0.3347] det_loss_box_2[0.3744] det_loss_cls_3[0.3033] det_loss_box_3[0.3602] det_loss_cls_4[0.2788] det_loss_box_4[0.3570] det_loss_cls_5[0.2657] det_loss_box_5[0.3547] det_loss_cls_dn_0[0.0892] det_loss_box_dn_0[0.2658] det_loss_cls_dn_1[0.0865] det_loss_box_dn_1[0.2179] det_loss_cls_dn_2[0.0853] det_loss_box_dn_2[0.1956] det_loss_cls_dn_3[0.0843] det_loss_box_dn_3[0.1911] det_loss_cls_dn_4[0.0834] det_loss_box_dn_4[0.1877] det_loss_cls_dn_5[0.0823] det_loss_box_dn_5[0.1837] loss_depth[0.0373] total_loss[6.1545]
Rank[0/8] 06/20/2025 17:15:21 INFO stats.py:314 | Epoch[206] Step[334] GlobalStep[80674] Training Speed: 8.72 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 4:20:02.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:15:36 INFO loss_tracker.py:84 | Epoch[206/239] Step[349] GlobalStep[80689/NA]: det_loss_cls_0[0.5103] det_loss_box_0[0.6383] det_loss_cls_1[0.5075] det_loss_box_1[0.4676] det_loss_cls_2[0.4414] det_loss_box_2[0.4286] det_loss_cls_3[0.4028] det_loss_box_3[0.4185] det_loss_cls_4[0.3805] det_loss_box_4[0.4095] det_loss_cls_5[0.3668] det_loss_box_5[0.4052] det_loss_cls_dn_0[0.0947] det_loss_box_dn_0[0.2475] det_loss_cls_dn_1[0.0912] det_loss_box_dn_1[0.2007] det_loss_cls_dn_2[0.0909] det_loss_box_dn_2[0.1843] det_loss_cls_dn_3[0.0900] det_loss_box_dn_3[0.1786] det_loss_cls_dn_4[0.0894] det_loss_box_dn_4[0.1771] det_loss_cls_dn_5[0.0885] det_loss_box_dn_5[0.1750] loss_depth[0.0338] total_loss[7.1188]
Rank[0/8] 06/20/2025 17:15:47 INFO stats.py:314 | Epoch[206] Step[359] GlobalStep[80699] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 4:19:31.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:16:03 INFO loss_tracker.py:84 | Epoch[206/239] Step[374] GlobalStep[80714/NA]: det_loss_cls_0[0.4255] det_loss_box_0[0.5307] det_loss_cls_1[0.3962] det_loss_box_1[0.3994] det_loss_cls_2[0.3438] det_loss_box_2[0.3600] det_loss_cls_3[0.3128] det_loss_box_3[0.3523] det_loss_cls_4[0.2969] det_loss_box_4[0.3366] det_loss_cls_5[0.2889] det_loss_box_5[0.3363] det_loss_cls_dn_0[0.0922] det_loss_box_dn_0[0.2929] det_loss_cls_dn_1[0.0892] det_loss_box_dn_1[0.2416] det_loss_cls_dn_2[0.0887] det_loss_box_dn_2[0.2178] det_loss_cls_dn_3[0.0875] det_loss_box_dn_3[0.2136] det_loss_cls_dn_4[0.0870] det_loss_box_dn_4[0.2103] det_loss_cls_dn_5[0.0863] det_loss_box_dn_5[0.2066] loss_depth[0.0360] total_loss[6.3289]
Rank[0/8] 06/20/2025 17:16:13 INFO stats.py:314 | Epoch[206] Step[384] GlobalStep[80724] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 4:19:00.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:16:18 INFO stats.py:394 | Epoch[206] completed. Training Speed: 7.67 samples/sec across all devices.	Epoch Time: 406.56 sec.	Average Epoch Time: 406.56 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 4:18:54.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:16:41 INFO stats.py:314 | Epoch[207] Step[19] GlobalStep[80749] Training Speed: 7.93 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 4:18:30.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:16:47 INFO loss_tracker.py:84 | Epoch[207/239] Step[24] GlobalStep[80754/NA]: det_loss_cls_0[0.5323] det_loss_box_0[0.6479] det_loss_cls_1[0.5191] det_loss_box_1[0.5024] det_loss_cls_2[0.4467] det_loss_box_2[0.4546] det_loss_cls_3[0.4114] det_loss_box_3[0.4375] det_loss_cls_4[0.3824] det_loss_box_4[0.4239] det_loss_cls_5[0.3686] det_loss_box_5[0.4225] det_loss_cls_dn_0[0.0890] det_loss_box_dn_0[0.2673] det_loss_cls_dn_1[0.0856] det_loss_box_dn_1[0.2205] det_loss_cls_dn_2[0.0847] det_loss_box_dn_2[0.2031] det_loss_cls_dn_3[0.0836] det_loss_box_dn_3[0.1978] det_loss_cls_dn_4[0.0827] det_loss_box_dn_4[0.1966] det_loss_cls_dn_5[0.0820] det_loss_box_dn_5[0.1948] loss_depth[0.0369] total_loss[7.3742]
Rank[0/8] 06/20/2025 17:17:08 INFO stats.py:314 | Epoch[207] Step[44] GlobalStep[80774] Training Speed: 7.60 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 4:17:59.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:17:13 INFO loss_tracker.py:84 | Epoch[207/239] Step[49] GlobalStep[80779/NA]: det_loss_cls_0[0.3341] det_loss_box_0[0.4317] det_loss_cls_1[0.3295] det_loss_box_1[0.3285] det_loss_cls_2[0.2779] det_loss_box_2[0.2977] det_loss_cls_3[0.2492] det_loss_box_3[0.2914] det_loss_cls_4[0.2276] det_loss_box_4[0.2865] det_loss_cls_5[0.2172] det_loss_box_5[0.2873] det_loss_cls_dn_0[0.0823] det_loss_box_dn_0[0.3014] det_loss_cls_dn_1[0.0783] det_loss_box_dn_1[0.2479] det_loss_cls_dn_2[0.0771] det_loss_box_dn_2[0.2280] det_loss_cls_dn_3[0.0759] det_loss_box_dn_3[0.2231] det_loss_cls_dn_4[0.0754] det_loss_box_dn_4[0.2188] det_loss_cls_dn_5[0.0745] det_loss_box_dn_5[0.2142] loss_depth[0.0334] total_loss[5.4889]
Rank[0/8] 06/20/2025 17:17:34 INFO stats.py:314 | Epoch[207] Step[69] GlobalStep[80799] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 4:17:28.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:17:39 INFO loss_tracker.py:84 | Epoch[207/239] Step[74] GlobalStep[80804/NA]: det_loss_cls_0[0.4805] det_loss_box_0[0.5764] det_loss_cls_1[0.4697] det_loss_box_1[0.4372] det_loss_cls_2[0.4076] det_loss_box_2[0.3845] det_loss_cls_3[0.3643] det_loss_box_3[0.3755] det_loss_cls_4[0.3455] det_loss_box_4[0.3569] det_loss_cls_5[0.3324] det_loss_box_5[0.3578] det_loss_cls_dn_0[0.0988] det_loss_box_dn_0[0.2469] det_loss_cls_dn_1[0.0951] det_loss_box_dn_1[0.2027] det_loss_cls_dn_2[0.0940] det_loss_box_dn_2[0.1846] det_loss_cls_dn_3[0.0926] det_loss_box_dn_3[0.1790] det_loss_cls_dn_4[0.0919] det_loss_box_dn_4[0.1773] det_loss_cls_dn_5[0.0904] det_loss_box_dn_5[0.1752] loss_depth[0.0369] total_loss[6.6536]
Rank[0/8] 06/20/2025 17:17:59 INFO stats.py:314 | Epoch[207] Step[94] GlobalStep[80824] Training Speed: 12.51 samples/sec across all devices.	Average Step Time: 0.64 sec.	Estimated Remaining Time: 4:16:57.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:18:04 INFO loss_tracker.py:84 | Epoch[207/239] Step[99] GlobalStep[80829/NA]: det_loss_cls_0[0.4637] det_loss_box_0[0.5792] det_loss_cls_1[0.4498] det_loss_box_1[0.4250] det_loss_cls_2[0.3775] det_loss_box_2[0.3837] det_loss_cls_3[0.3411] det_loss_box_3[0.3696] det_loss_cls_4[0.3194] det_loss_box_4[0.3607] det_loss_cls_5[0.3044] det_loss_box_5[0.3613] det_loss_cls_dn_0[0.0911] det_loss_box_dn_0[0.2701] det_loss_cls_dn_1[0.0860] det_loss_box_dn_1[0.2176] det_loss_cls_dn_2[0.0846] det_loss_box_dn_2[0.2001] det_loss_cls_dn_3[0.0836] det_loss_box_dn_3[0.1940] det_loss_cls_dn_4[0.0827] det_loss_box_dn_4[0.1921] det_loss_cls_dn_5[0.0822] det_loss_box_dn_5[0.1871] loss_depth[0.0347] total_loss[6.5416]
Rank[0/8] 06/20/2025 17:18:25 INFO stats.py:314 | Epoch[207] Step[119] GlobalStep[80849] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 4:16:26.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:18:30 INFO loss_tracker.py:84 | Epoch[207/239] Step[124] GlobalStep[80854/NA]: det_loss_cls_0[0.5212] det_loss_box_0[0.6964] det_loss_cls_1[0.5033] det_loss_box_1[0.5229] det_loss_cls_2[0.4262] det_loss_box_2[0.4911] det_loss_cls_3[0.3915] det_loss_box_3[0.4701] det_loss_cls_4[0.3718] det_loss_box_4[0.4528] det_loss_cls_5[0.3493] det_loss_box_5[0.4599] det_loss_cls_dn_0[0.0918] det_loss_box_dn_0[0.2875] det_loss_cls_dn_1[0.0885] det_loss_box_dn_1[0.2386] det_loss_cls_dn_2[0.0880] det_loss_box_dn_2[0.2242] det_loss_cls_dn_3[0.0870] det_loss_box_dn_3[0.2175] det_loss_cls_dn_4[0.0862] det_loss_box_dn_4[0.2149] det_loss_cls_dn_5[0.0857] det_loss_box_dn_5[0.2112] loss_depth[0.0367] total_loss[7.6143]
Rank[0/8] 06/20/2025 17:18:51 INFO stats.py:314 | Epoch[207] Step[144] GlobalStep[80874] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 4:15:55.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:18:57 INFO loss_tracker.py:84 | Epoch[207/239] Step[149] GlobalStep[80879/NA]: det_loss_cls_0[0.5231] det_loss_box_0[0.6546] det_loss_cls_1[0.5101] det_loss_box_1[0.4874] det_loss_cls_2[0.4419] det_loss_box_2[0.4409] det_loss_cls_3[0.3988] det_loss_box_3[0.4237] det_loss_cls_4[0.3747] det_loss_box_4[0.4150] det_loss_cls_5[0.3623] det_loss_box_5[0.4138] det_loss_cls_dn_0[0.0900] det_loss_box_dn_0[0.2545] det_loss_cls_dn_1[0.0855] det_loss_box_dn_1[0.2023] det_loss_cls_dn_2[0.0844] det_loss_box_dn_2[0.1871] det_loss_cls_dn_3[0.0830] det_loss_box_dn_3[0.1821] det_loss_cls_dn_4[0.0819] det_loss_box_dn_4[0.1802] det_loss_cls_dn_5[0.0809] det_loss_box_dn_5[0.1797] loss_depth[0.0366] total_loss[7.1746]
Rank[0/8] 06/20/2025 17:19:17 INFO stats.py:314 | Epoch[207] Step[169] GlobalStep[80899] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 4:15:25.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:19:22 INFO loss_tracker.py:84 | Epoch[207/239] Step[174] GlobalStep[80904/NA]: det_loss_cls_0[0.4297] det_loss_box_0[0.5279] det_loss_cls_1[0.4055] det_loss_box_1[0.4028] det_loss_cls_2[0.3380] det_loss_box_2[0.3669] det_loss_cls_3[0.3125] det_loss_box_3[0.3488] det_loss_cls_4[0.2933] det_loss_box_4[0.3357] det_loss_cls_5[0.2821] det_loss_box_5[0.3268] det_loss_cls_dn_0[0.0828] det_loss_box_dn_0[0.3177] det_loss_cls_dn_1[0.0792] det_loss_box_dn_1[0.2498] det_loss_cls_dn_2[0.0780] det_loss_box_dn_2[0.2205] det_loss_cls_dn_3[0.0773] det_loss_box_dn_3[0.2134] det_loss_cls_dn_4[0.0766] det_loss_box_dn_4[0.2101] det_loss_cls_dn_5[0.0759] det_loss_box_dn_5[0.2026] loss_depth[0.0360] total_loss[6.2898]
Rank[0/8] 06/20/2025 17:19:43 INFO stats.py:314 | Epoch[207] Step[194] GlobalStep[80924] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 4:14:54.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:19:49 INFO loss_tracker.py:84 | Epoch[207/239] Step[199] GlobalStep[80929/NA]: det_loss_cls_0[0.4393] det_loss_box_0[0.6437] det_loss_cls_1[0.4248] det_loss_box_1[0.5030] det_loss_cls_2[0.3696] det_loss_box_2[0.4640] det_loss_cls_3[0.3373] det_loss_box_3[0.4564] det_loss_cls_4[0.3172] det_loss_box_4[0.4385] det_loss_cls_5[0.3020] det_loss_box_5[0.4387] det_loss_cls_dn_0[0.0917] det_loss_box_dn_0[0.3811] det_loss_cls_dn_1[0.0874] det_loss_box_dn_1[0.3134] det_loss_cls_dn_2[0.0870] det_loss_box_dn_2[0.2878] det_loss_cls_dn_3[0.0858] det_loss_box_dn_3[0.2815] det_loss_cls_dn_4[0.0849] det_loss_box_dn_4[0.2783] det_loss_cls_dn_5[0.0838] det_loss_box_dn_5[0.2749] loss_depth[0.0346] total_loss[7.5069]
Rank[0/8] 06/20/2025 17:20:09 INFO stats.py:314 | Epoch[207] Step[219] GlobalStep[80949] Training Speed: 8.01 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 4:14:23.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:20:14 INFO loss_tracker.py:84 | Epoch[207/239] Step[224] GlobalStep[80954/NA]: det_loss_cls_0[0.4321] det_loss_box_0[0.5540] det_loss_cls_1[0.4200] det_loss_box_1[0.4248] det_loss_cls_2[0.3589] det_loss_box_2[0.4079] det_loss_cls_3[0.3255] det_loss_box_3[0.3962] det_loss_cls_4[0.3122] det_loss_box_4[0.3877] det_loss_cls_5[0.2986] det_loss_box_5[0.3897] det_loss_cls_dn_0[0.0847] det_loss_box_dn_0[0.2840] det_loss_cls_dn_1[0.0807] det_loss_box_dn_1[0.2335] det_loss_cls_dn_2[0.0795] det_loss_box_dn_2[0.2172] det_loss_cls_dn_3[0.0783] det_loss_box_dn_3[0.2117] det_loss_cls_dn_4[0.0773] det_loss_box_dn_4[0.2080] det_loss_cls_dn_5[0.0765] det_loss_box_dn_5[0.2053] loss_depth[0.0352] total_loss[6.5796]
Rank[0/8] 06/20/2025 17:20:36 INFO stats.py:314 | Epoch[207] Step[244] GlobalStep[80974] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 4:13:52.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:20:41 INFO loss_tracker.py:84 | Epoch[207/239] Step[249] GlobalStep[80979/NA]: det_loss_cls_0[0.4163] det_loss_box_0[0.5516] det_loss_cls_1[0.4066] det_loss_box_1[0.4183] det_loss_cls_2[0.3475] det_loss_box_2[0.3816] det_loss_cls_3[0.3254] det_loss_box_3[0.3612] det_loss_cls_4[0.3151] det_loss_box_4[0.3471] det_loss_cls_5[0.2968] det_loss_box_5[0.3535] det_loss_cls_dn_0[0.0836] det_loss_box_dn_0[0.2986] det_loss_cls_dn_1[0.0812] det_loss_box_dn_1[0.2359] det_loss_cls_dn_2[0.0803] det_loss_box_dn_2[0.2103] det_loss_cls_dn_3[0.0796] det_loss_box_dn_3[0.2059] det_loss_cls_dn_4[0.0784] det_loss_box_dn_4[0.2035] det_loss_cls_dn_5[0.0777] det_loss_box_dn_5[0.2007] loss_depth[0.0339] total_loss[6.3909]
Rank[0/8] 06/20/2025 17:21:01 INFO stats.py:314 | Epoch[207] Step[269] GlobalStep[80999] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 4:13:21.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:21:06 INFO loss_tracker.py:84 | Epoch[207/239] Step[274] GlobalStep[81004/NA]: det_loss_cls_0[0.5868] det_loss_box_0[0.6868] det_loss_cls_1[0.5657] det_loss_box_1[0.5092] det_loss_cls_2[0.4953] det_loss_box_2[0.4605] det_loss_cls_3[0.4422] det_loss_box_3[0.4476] det_loss_cls_4[0.4241] det_loss_box_4[0.4312] det_loss_cls_5[0.4070] det_loss_box_5[0.4324] det_loss_cls_dn_0[0.0889] det_loss_box_dn_0[0.2130] det_loss_cls_dn_1[0.0849] det_loss_box_dn_1[0.1730] det_loss_cls_dn_2[0.0844] det_loss_box_dn_2[0.1592] det_loss_cls_dn_3[0.0832] det_loss_box_dn_3[0.1576] det_loss_cls_dn_4[0.0820] det_loss_box_dn_4[0.1563] det_loss_cls_dn_5[0.0811] det_loss_box_dn_5[0.1561] loss_depth[0.0365] total_loss[7.4452]
Rank[0/8] 06/20/2025 17:21:27 INFO stats.py:314 | Epoch[207] Step[294] GlobalStep[81024] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 4:12:50.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:21:31 INFO loss_tracker.py:84 | Epoch[207/239] Step[299] GlobalStep[81029/NA]: det_loss_cls_0[0.4805] det_loss_box_0[0.5428] det_loss_cls_1[0.4602] det_loss_box_1[0.4153] det_loss_cls_2[0.3971] det_loss_box_2[0.3809] det_loss_cls_3[0.3583] det_loss_box_3[0.3688] det_loss_cls_4[0.3339] det_loss_box_4[0.3575] det_loss_cls_5[0.3212] det_loss_box_5[0.3592] det_loss_cls_dn_0[0.0971] det_loss_box_dn_0[0.2358] det_loss_cls_dn_1[0.0930] det_loss_box_dn_1[0.1937] det_loss_cls_dn_2[0.0921] det_loss_box_dn_2[0.1791] det_loss_cls_dn_3[0.0909] det_loss_box_dn_3[0.1739] det_loss_cls_dn_4[0.0898] det_loss_box_dn_4[0.1720] det_loss_cls_dn_5[0.0890] det_loss_box_dn_5[0.1692] loss_depth[0.0351] total_loss[6.4867]
Rank[0/8] 06/20/2025 17:21:53 INFO stats.py:314 | Epoch[207] Step[319] GlobalStep[81049] Training Speed: 8.07 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 4:12:20.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:21:58 INFO loss_tracker.py:84 | Epoch[207/239] Step[324] GlobalStep[81054/NA]: det_loss_cls_0[0.4664] det_loss_box_0[0.6355] det_loss_cls_1[0.4481] det_loss_box_1[0.4926] det_loss_cls_2[0.3908] det_loss_box_2[0.4451] det_loss_cls_3[0.3503] det_loss_box_3[0.4286] det_loss_cls_4[0.3349] det_loss_box_4[0.4149] det_loss_cls_5[0.3205] det_loss_box_5[0.4181] det_loss_cls_dn_0[0.0906] det_loss_box_dn_0[0.2918] det_loss_cls_dn_1[0.0872] det_loss_box_dn_1[0.2426] det_loss_cls_dn_2[0.0862] det_loss_box_dn_2[0.2245] det_loss_cls_dn_3[0.0846] det_loss_box_dn_3[0.2192] det_loss_cls_dn_4[0.0842] det_loss_box_dn_4[0.2176] det_loss_cls_dn_5[0.0834] det_loss_box_dn_5[0.2132] loss_depth[0.0354] total_loss[7.1062]
Rank[0/8] 06/20/2025 17:22:18 INFO stats.py:314 | Epoch[207] Step[344] GlobalStep[81074] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 4:11:49.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:22:24 INFO loss_tracker.py:84 | Epoch[207/239] Step[349] GlobalStep[81079/NA]: det_loss_cls_0[0.5914] det_loss_box_0[0.7323] det_loss_cls_1[0.5846] det_loss_box_1[0.5465] det_loss_cls_2[0.5223] det_loss_box_2[0.4973] det_loss_cls_3[0.4851] det_loss_box_3[0.4856] det_loss_cls_4[0.4626] det_loss_box_4[0.4737] det_loss_cls_5[0.4455] det_loss_box_5[0.4757] det_loss_cls_dn_0[0.1051] det_loss_box_dn_0[0.3095] det_loss_cls_dn_1[0.1017] det_loss_box_dn_1[0.2413] det_loss_cls_dn_2[0.1010] det_loss_box_dn_2[0.2229] det_loss_cls_dn_3[0.0996] det_loss_box_dn_3[0.2167] det_loss_cls_dn_4[0.0988] det_loss_box_dn_4[0.2144] det_loss_cls_dn_5[0.0979] det_loss_box_dn_5[0.2097] loss_depth[0.0342] total_loss[8.3554]
Rank[0/8] 06/20/2025 17:22:44 INFO stats.py:314 | Epoch[207] Step[369] GlobalStep[81099] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 4:11:18.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:22:49 INFO loss_tracker.py:84 | Epoch[207/239] Step[374] GlobalStep[81104/NA]: det_loss_cls_0[0.4495] det_loss_box_0[0.5505] det_loss_cls_1[0.4407] det_loss_box_1[0.4059] det_loss_cls_2[0.3916] det_loss_box_2[0.3687] det_loss_cls_3[0.3584] det_loss_box_3[0.3611] det_loss_cls_4[0.3405] det_loss_box_4[0.3460] det_loss_cls_5[0.3226] det_loss_box_5[0.3506] det_loss_cls_dn_0[0.0945] det_loss_box_dn_0[0.2694] det_loss_cls_dn_1[0.0914] det_loss_box_dn_1[0.2236] det_loss_cls_dn_2[0.0904] det_loss_box_dn_2[0.2049] det_loss_cls_dn_3[0.0889] det_loss_box_dn_3[0.1995] det_loss_cls_dn_4[0.0879] det_loss_box_dn_4[0.1955] det_loss_cls_dn_5[0.0872] det_loss_box_dn_5[0.1944] loss_depth[0.0336] total_loss[6.5472]
Rank[0/8] 06/20/2025 17:23:05 INFO stats.py:394 | Epoch[207] completed. Training Speed: 7.67 samples/sec across all devices.	Epoch Time: 406.52 sec.	Average Epoch Time: 406.52 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 4:10:53.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:23:13 INFO stats.py:314 | Epoch[208] Step[4] GlobalStep[81124] Training Speed: 6.66 samples/sec across all devices.	Average Step Time: 1.20 sec.	Estimated Remaining Time: 4:10:48.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:23:33 INFO loss_tracker.py:84 | Epoch[208/239] Step[24] GlobalStep[81144/NA]: det_loss_cls_0[0.4192] det_loss_box_0[0.5337] det_loss_cls_1[0.4077] det_loss_box_1[0.3930] det_loss_cls_2[0.3468] det_loss_box_2[0.3631] det_loss_cls_3[0.3166] det_loss_box_3[0.3503] det_loss_cls_4[0.2996] det_loss_box_4[0.3390] det_loss_cls_5[0.2870] det_loss_box_5[0.3364] det_loss_cls_dn_0[0.0880] det_loss_box_dn_0[0.2638] det_loss_cls_dn_1[0.0839] det_loss_box_dn_1[0.2129] det_loss_cls_dn_2[0.0830] det_loss_box_dn_2[0.1933] det_loss_cls_dn_3[0.0819] det_loss_box_dn_3[0.1897] det_loss_cls_dn_4[0.0811] det_loss_box_dn_4[0.1876] det_loss_cls_dn_5[0.0803] det_loss_box_dn_5[0.1822] loss_depth[0.0349] total_loss[6.1549]
Rank[0/8] 06/20/2025 17:23:38 INFO stats.py:314 | Epoch[208] Step[29] GlobalStep[81149] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 4:10:17.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:23:59 INFO loss_tracker.py:84 | Epoch[208/239] Step[49] GlobalStep[81169/NA]: det_loss_cls_0[0.4456] det_loss_box_0[0.5434] det_loss_cls_1[0.4366] det_loss_box_1[0.4119] det_loss_cls_2[0.3794] det_loss_box_2[0.3711] det_loss_cls_3[0.3470] det_loss_box_3[0.3678] det_loss_cls_4[0.3326] det_loss_box_4[0.3573] det_loss_cls_5[0.3248] det_loss_box_5[0.3573] det_loss_cls_dn_0[0.0905] det_loss_box_dn_0[0.2557] det_loss_cls_dn_1[0.0880] det_loss_box_dn_1[0.2095] det_loss_cls_dn_2[0.0873] det_loss_box_dn_2[0.1929] det_loss_cls_dn_3[0.0863] det_loss_box_dn_3[0.1912] det_loss_cls_dn_4[0.0856] det_loss_box_dn_4[0.1878] det_loss_cls_dn_5[0.0847] det_loss_box_dn_5[0.1866] loss_depth[0.0339] total_loss[6.4547]
Rank[0/8] 06/20/2025 17:24:04 INFO stats.py:314 | Epoch[208] Step[54] GlobalStep[81174] Training Speed: 8.67 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 4:09:46.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:24:25 INFO loss_tracker.py:84 | Epoch[208/239] Step[74] GlobalStep[81194/NA]: det_loss_cls_0[0.4301] det_loss_box_0[0.4937] det_loss_cls_1[0.4127] det_loss_box_1[0.3672] det_loss_cls_2[0.3605] det_loss_box_2[0.3324] det_loss_cls_3[0.3233] det_loss_box_3[0.3210] det_loss_cls_4[0.3024] det_loss_box_4[0.3120] det_loss_cls_5[0.2903] det_loss_box_5[0.3142] det_loss_cls_dn_0[0.0923] det_loss_box_dn_0[0.2434] det_loss_cls_dn_1[0.0896] det_loss_box_dn_1[0.1965] det_loss_cls_dn_2[0.0885] det_loss_box_dn_2[0.1764] det_loss_cls_dn_3[0.0875] det_loss_box_dn_3[0.1723] det_loss_cls_dn_4[0.0868] det_loss_box_dn_4[0.1709] det_loss_cls_dn_5[0.0860] det_loss_box_dn_5[0.1699] loss_depth[0.0376] total_loss[5.9576]
Rank[0/8] 06/20/2025 17:24:30 INFO stats.py:314 | Epoch[208] Step[79] GlobalStep[81199] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 4:09:15.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:24:51 INFO loss_tracker.py:84 | Epoch[208/239] Step[99] GlobalStep[81219/NA]: det_loss_cls_0[0.5078] det_loss_box_0[0.7201] det_loss_cls_1[0.4835] det_loss_box_1[0.5407] det_loss_cls_2[0.4233] det_loss_box_2[0.4900] det_loss_cls_3[0.3940] det_loss_box_3[0.4684] det_loss_cls_4[0.3685] det_loss_box_4[0.4604] det_loss_cls_5[0.3523] det_loss_box_5[0.4568] det_loss_cls_dn_0[0.0921] det_loss_box_dn_0[0.3141] det_loss_cls_dn_1[0.0868] det_loss_box_dn_1[0.2642] det_loss_cls_dn_2[0.0858] det_loss_box_dn_2[0.2463] det_loss_cls_dn_3[0.0840] det_loss_box_dn_3[0.2384] det_loss_cls_dn_4[0.0832] det_loss_box_dn_4[0.2350] det_loss_cls_dn_5[0.0823] det_loss_box_dn_5[0.2300] loss_depth[0.0345] total_loss[7.7425]
Rank[0/8] 06/20/2025 17:24:56 INFO stats.py:314 | Epoch[208] Step[104] GlobalStep[81224] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 4:08:44.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:25:17 INFO loss_tracker.py:84 | Epoch[208/239] Step[124] GlobalStep[81244/NA]: det_loss_cls_0[0.4245] det_loss_box_0[0.5139] det_loss_cls_1[0.4077] det_loss_box_1[0.3871] det_loss_cls_2[0.3542] det_loss_box_2[0.3505] det_loss_cls_3[0.3236] det_loss_box_3[0.3386] det_loss_cls_4[0.3040] det_loss_box_4[0.3344] det_loss_cls_5[0.2939] det_loss_box_5[0.3343] det_loss_cls_dn_0[0.0872] det_loss_box_dn_0[0.2534] det_loss_cls_dn_1[0.0838] det_loss_box_dn_1[0.2099] det_loss_cls_dn_2[0.0828] det_loss_box_dn_2[0.1945] det_loss_cls_dn_3[0.0819] det_loss_box_dn_3[0.1903] det_loss_cls_dn_4[0.0818] det_loss_box_dn_4[0.1879] det_loss_cls_dn_5[0.0811] det_loss_box_dn_5[0.1861] loss_depth[0.0371] total_loss[6.1245]
Rank[0/8] 06/20/2025 17:25:22 INFO stats.py:314 | Epoch[208] Step[129] GlobalStep[81249] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 4:08:13.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:25:42 INFO loss_tracker.py:84 | Epoch[208/239] Step[149] GlobalStep[81269/NA]: det_loss_cls_0[0.5381] det_loss_box_0[0.6217] det_loss_cls_1[0.5202] det_loss_box_1[0.4491] det_loss_cls_2[0.4562] det_loss_box_2[0.3995] det_loss_cls_3[0.4239] det_loss_box_3[0.3797] det_loss_cls_4[0.4066] det_loss_box_4[0.3749] det_loss_cls_5[0.3871] det_loss_box_5[0.3747] det_loss_cls_dn_0[0.1026] det_loss_box_dn_0[0.2343] det_loss_cls_dn_1[0.0980] det_loss_box_dn_1[0.1907] det_loss_cls_dn_2[0.0971] det_loss_box_dn_2[0.1733] det_loss_cls_dn_3[0.0959] det_loss_box_dn_3[0.1693] det_loss_cls_dn_4[0.0952] det_loss_box_dn_4[0.1689] det_loss_cls_dn_5[0.0941] det_loss_box_dn_5[0.1686] loss_depth[0.0341] total_loss[7.0538]
Rank[0/8] 06/20/2025 17:25:48 INFO stats.py:314 | Epoch[208] Step[154] GlobalStep[81274] Training Speed: 6.98 samples/sec across all devices.	Average Step Time: 1.15 sec.	Estimated Remaining Time: 4:07:43.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:26:09 INFO loss_tracker.py:84 | Epoch[208/239] Step[174] GlobalStep[81294/NA]: det_loss_cls_0[0.3785] det_loss_box_0[0.4220] det_loss_cls_1[0.3727] det_loss_box_1[0.3212] det_loss_cls_2[0.3164] det_loss_box_2[0.2871] det_loss_cls_3[0.2895] det_loss_box_3[0.2787] det_loss_cls_4[0.2699] det_loss_box_4[0.2759] det_loss_cls_5[0.2585] det_loss_box_5[0.2771] det_loss_cls_dn_0[0.0919] det_loss_box_dn_0[0.2344] det_loss_cls_dn_1[0.0876] det_loss_box_dn_1[0.1819] det_loss_cls_dn_2[0.0872] det_loss_box_dn_2[0.1649] det_loss_cls_dn_3[0.0860] det_loss_box_dn_3[0.1595] det_loss_cls_dn_4[0.0856] det_loss_box_dn_4[0.1580] det_loss_cls_dn_5[0.0847] det_loss_box_dn_5[0.1568] loss_depth[0.0344] total_loss[5.3603]
Rank[0/8] 06/20/2025 17:26:14 INFO stats.py:314 | Epoch[208] Step[179] GlobalStep[81299] Training Speed: 8.58 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 4:07:12.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:26:34 INFO loss_tracker.py:84 | Epoch[208/239] Step[199] GlobalStep[81319/NA]: det_loss_cls_0[0.5303] det_loss_box_0[0.6648] det_loss_cls_1[0.5106] det_loss_box_1[0.5093] det_loss_cls_2[0.4428] det_loss_box_2[0.4705] det_loss_cls_3[0.4060] det_loss_box_3[0.4616] det_loss_cls_4[0.3791] det_loss_box_4[0.4592] det_loss_cls_5[0.3647] det_loss_box_5[0.4561] det_loss_cls_dn_0[0.0954] det_loss_box_dn_0[0.2725] det_loss_cls_dn_1[0.0909] det_loss_box_dn_1[0.2261] det_loss_cls_dn_2[0.0895] det_loss_box_dn_2[0.2034] det_loss_cls_dn_3[0.0878] det_loss_box_dn_3[0.1981] det_loss_cls_dn_4[0.0871] det_loss_box_dn_4[0.1959] det_loss_cls_dn_5[0.0863] det_loss_box_dn_5[0.1941] loss_depth[0.0326] total_loss[7.5149]
Rank[0/8] 06/20/2025 17:26:40 INFO stats.py:314 | Epoch[208] Step[204] GlobalStep[81324] Training Speed: 7.63 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 4:06:41.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:27:00 INFO loss_tracker.py:84 | Epoch[208/239] Step[224] GlobalStep[81344/NA]: det_loss_cls_0[0.5153] det_loss_box_0[0.6556] det_loss_cls_1[0.5021] det_loss_box_1[0.4824] det_loss_cls_2[0.4391] det_loss_box_2[0.4367] det_loss_cls_3[0.4098] det_loss_box_3[0.4284] det_loss_cls_4[0.3888] det_loss_box_4[0.4159] det_loss_cls_5[0.3839] det_loss_box_5[0.4101] det_loss_cls_dn_0[0.0961] det_loss_box_dn_0[0.2805] det_loss_cls_dn_1[0.0932] det_loss_box_dn_1[0.2347] det_loss_cls_dn_2[0.0926] det_loss_box_dn_2[0.2179] det_loss_cls_dn_3[0.0917] det_loss_box_dn_3[0.2140] det_loss_cls_dn_4[0.0906] det_loss_box_dn_4[0.2123] det_loss_cls_dn_5[0.0899] det_loss_box_dn_5[0.2075] loss_depth[0.0341] total_loss[7.4231]
Rank[0/8] 06/20/2025 17:27:05 INFO stats.py:314 | Epoch[208] Step[229] GlobalStep[81349] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 4:06:10.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:27:26 INFO loss_tracker.py:84 | Epoch[208/239] Step[249] GlobalStep[81369/NA]: det_loss_cls_0[0.4241] det_loss_box_0[0.5579] det_loss_cls_1[0.4152] det_loss_box_1[0.4239] det_loss_cls_2[0.3483] det_loss_box_2[0.3737] det_loss_cls_3[0.3101] det_loss_box_3[0.3486] det_loss_cls_4[0.2856] det_loss_box_4[0.3421] det_loss_cls_5[0.2745] det_loss_box_5[0.3390] det_loss_cls_dn_0[0.0843] det_loss_box_dn_0[0.2430] det_loss_cls_dn_1[0.0800] det_loss_box_dn_1[0.1993] det_loss_cls_dn_2[0.0791] det_loss_box_dn_2[0.1824] det_loss_cls_dn_3[0.0778] det_loss_box_dn_3[0.1781] det_loss_cls_dn_4[0.0767] det_loss_box_dn_4[0.1757] det_loss_cls_dn_5[0.0760] det_loss_box_dn_5[0.1739] loss_depth[0.0358] total_loss[6.1052]
Rank[0/8] 06/20/2025 17:27:31 INFO stats.py:314 | Epoch[208] Step[254] GlobalStep[81374] Training Speed: 6.99 samples/sec across all devices.	Average Step Time: 1.14 sec.	Estimated Remaining Time: 4:05:39.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:27:52 INFO loss_tracker.py:84 | Epoch[208/239] Step[274] GlobalStep[81394/NA]: det_loss_cls_0[0.4853] det_loss_box_0[0.5981] det_loss_cls_1[0.4594] det_loss_box_1[0.4455] det_loss_cls_2[0.3933] det_loss_box_2[0.4085] det_loss_cls_3[0.3585] det_loss_box_3[0.3876] det_loss_cls_4[0.3422] det_loss_box_4[0.3778] det_loss_cls_5[0.3312] det_loss_box_5[0.3760] det_loss_cls_dn_0[0.0791] det_loss_box_dn_0[0.2770] det_loss_cls_dn_1[0.0759] det_loss_box_dn_1[0.2198] det_loss_cls_dn_2[0.0751] det_loss_box_dn_2[0.1991] det_loss_cls_dn_3[0.0740] det_loss_box_dn_3[0.1938] det_loss_cls_dn_4[0.0732] det_loss_box_dn_4[0.1900] det_loss_cls_dn_5[0.0722] det_loss_box_dn_5[0.1875] loss_depth[0.0357] total_loss[6.7158]
Rank[0/8] 06/20/2025 17:27:57 INFO stats.py:314 | Epoch[208] Step[279] GlobalStep[81399] Training Speed: 8.00 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 4:05:09.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:28:17 INFO loss_tracker.py:84 | Epoch[208/239] Step[299] GlobalStep[81419/NA]: det_loss_cls_0[0.5048] det_loss_box_0[0.5863] det_loss_cls_1[0.4867] det_loss_box_1[0.4350] det_loss_cls_2[0.4324] det_loss_box_2[0.4050] det_loss_cls_3[0.4065] det_loss_box_3[0.3815] det_loss_cls_4[0.3767] det_loss_box_4[0.3792] det_loss_cls_5[0.3628] det_loss_box_5[0.3826] det_loss_cls_dn_0[0.0899] det_loss_box_dn_0[0.2891] det_loss_cls_dn_1[0.0863] det_loss_box_dn_1[0.2335] det_loss_cls_dn_2[0.0852] det_loss_box_dn_2[0.2150] det_loss_cls_dn_3[0.0845] det_loss_box_dn_3[0.2033] det_loss_cls_dn_4[0.0838] det_loss_box_dn_4[0.2021] det_loss_cls_dn_5[0.0832] det_loss_box_dn_5[0.1970] loss_depth[0.0345] total_loss[7.0267]
Rank[0/8] 06/20/2025 17:28:23 INFO stats.py:314 | Epoch[208] Step[304] GlobalStep[81424] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 4:04:38.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:28:43 INFO loss_tracker.py:84 | Epoch[208/239] Step[324] GlobalStep[81444/NA]: det_loss_cls_0[0.5563] det_loss_box_0[0.6429] det_loss_cls_1[0.5422] det_loss_box_1[0.5088] det_loss_cls_2[0.4820] det_loss_box_2[0.4543] det_loss_cls_3[0.4525] det_loss_box_3[0.4381] det_loss_cls_4[0.4223] det_loss_box_4[0.4253] det_loss_cls_5[0.4123] det_loss_box_5[0.4260] det_loss_cls_dn_0[0.0896] det_loss_box_dn_0[0.2721] det_loss_cls_dn_1[0.0857] det_loss_box_dn_1[0.2294] det_loss_cls_dn_2[0.0846] det_loss_box_dn_2[0.2123] det_loss_cls_dn_3[0.0834] det_loss_box_dn_3[0.2078] det_loss_cls_dn_4[0.0826] det_loss_box_dn_4[0.2052] det_loss_cls_dn_5[0.0819] det_loss_box_dn_5[0.2015] loss_depth[0.0351] total_loss[7.6345]
Rank[0/8] 06/20/2025 17:28:49 INFO stats.py:314 | Epoch[208] Step[329] GlobalStep[81449] Training Speed: 6.28 samples/sec across all devices.	Average Step Time: 1.27 sec.	Estimated Remaining Time: 4:04:07.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:29:10 INFO loss_tracker.py:84 | Epoch[208/239] Step[349] GlobalStep[81469/NA]: det_loss_cls_0[0.4518] det_loss_box_0[0.5645] det_loss_cls_1[0.4486] det_loss_box_1[0.4028] det_loss_cls_2[0.3859] det_loss_box_2[0.3580] det_loss_cls_3[0.3555] det_loss_box_3[0.3545] det_loss_cls_4[0.3366] det_loss_box_4[0.3390] det_loss_cls_5[0.3238] det_loss_box_5[0.3400] det_loss_cls_dn_0[0.0924] det_loss_box_dn_0[0.2353] det_loss_cls_dn_1[0.0893] det_loss_box_dn_1[0.1887] det_loss_cls_dn_2[0.0883] det_loss_box_dn_2[0.1739] det_loss_cls_dn_3[0.0874] det_loss_box_dn_3[0.1710] det_loss_cls_dn_4[0.0866] det_loss_box_dn_4[0.1693] det_loss_cls_dn_5[0.0859] det_loss_box_dn_5[0.1673] loss_depth[0.0359] total_loss[6.3326]
Rank[0/8] 06/20/2025 17:29:15 INFO stats.py:314 | Epoch[208] Step[354] GlobalStep[81474] Training Speed: 7.66 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 4:03:37.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:29:36 INFO loss_tracker.py:84 | Epoch[208/239] Step[374] GlobalStep[81494/NA]: det_loss_cls_0[0.6249] det_loss_box_0[0.7197] det_loss_cls_1[0.6125] det_loss_box_1[0.5157] det_loss_cls_2[0.5251] det_loss_box_2[0.4659] det_loss_cls_3[0.4691] det_loss_box_3[0.4572] det_loss_cls_4[0.4397] det_loss_box_4[0.4464] det_loss_cls_5[0.4261] det_loss_box_5[0.4462] det_loss_cls_dn_0[0.0939] det_loss_box_dn_0[0.2275] det_loss_cls_dn_1[0.0901] det_loss_box_dn_1[0.1850] det_loss_cls_dn_2[0.0891] det_loss_box_dn_2[0.1702] det_loss_cls_dn_3[0.0880] det_loss_box_dn_3[0.1669] det_loss_cls_dn_4[0.0871] det_loss_box_dn_4[0.1657] det_loss_cls_dn_5[0.0862] det_loss_box_dn_5[0.1637] loss_depth[0.0352] total_loss[7.7971]
Rank[0/8] 06/20/2025 17:29:41 INFO stats.py:314 | Epoch[208] Step[379] GlobalStep[81499] Training Speed: 7.98 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 4:03:06.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:29:51 INFO stats.py:394 | Epoch[208] completed. Training Speed: 7.68 samples/sec across all devices.	Epoch Time: 406.49 sec.	Average Epoch Time: 406.49 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 4:02:53.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:30:09 INFO stats.py:314 | Epoch[209] Step[14] GlobalStep[81524] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 4:02:35.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:30:19 INFO loss_tracker.py:84 | Epoch[209/239] Step[24] GlobalStep[81534/NA]: det_loss_cls_0[0.4351] det_loss_box_0[0.5418] det_loss_cls_1[0.4221] det_loss_box_1[0.4134] det_loss_cls_2[0.3619] det_loss_box_2[0.3703] det_loss_cls_3[0.3337] det_loss_box_3[0.3615] det_loss_cls_4[0.3115] det_loss_box_4[0.3485] det_loss_cls_5[0.3004] det_loss_box_5[0.3486] det_loss_cls_dn_0[0.0854] det_loss_box_dn_0[0.2594] det_loss_cls_dn_1[0.0829] det_loss_box_dn_1[0.2068] det_loss_cls_dn_2[0.0819] det_loss_box_dn_2[0.1880] det_loss_cls_dn_3[0.0811] det_loss_box_dn_3[0.1836] det_loss_cls_dn_4[0.0803] det_loss_box_dn_4[0.1806] det_loss_cls_dn_5[0.0801] det_loss_box_dn_5[0.1783] loss_depth[0.0341] total_loss[6.2711]
Rank[0/8] 06/20/2025 17:30:35 INFO stats.py:314 | Epoch[209] Step[39] GlobalStep[81549] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 4:02:05.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:30:46 INFO loss_tracker.py:84 | Epoch[209/239] Step[49] GlobalStep[81559/NA]: det_loss_cls_0[0.3713] det_loss_box_0[0.4373] det_loss_cls_1[0.3611] det_loss_box_1[0.3156] det_loss_cls_2[0.2944] det_loss_box_2[0.2881] det_loss_cls_3[0.2585] det_loss_box_3[0.2796] det_loss_cls_4[0.2506] det_loss_box_4[0.2638] det_loss_cls_5[0.2410] det_loss_box_5[0.2671] det_loss_cls_dn_0[0.0897] det_loss_box_dn_0[0.2610] det_loss_cls_dn_1[0.0860] det_loss_box_dn_1[0.1990] det_loss_cls_dn_2[0.0854] det_loss_box_dn_2[0.1808] det_loss_cls_dn_3[0.0844] det_loss_box_dn_3[0.1774] det_loss_cls_dn_4[0.0834] det_loss_box_dn_4[0.1759] det_loss_cls_dn_5[0.0828] det_loss_box_dn_5[0.1716] loss_depth[0.0365] total_loss[5.3424]
Rank[0/8] 06/20/2025 17:31:01 INFO stats.py:314 | Epoch[209] Step[64] GlobalStep[81574] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 4:01:34.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:31:11 INFO loss_tracker.py:84 | Epoch[209/239] Step[74] GlobalStep[81584/NA]: det_loss_cls_0[0.3798] det_loss_box_0[0.4891] det_loss_cls_1[0.3720] det_loss_box_1[0.3544] det_loss_cls_2[0.3204] det_loss_box_2[0.3233] det_loss_cls_3[0.2850] det_loss_box_3[0.3165] det_loss_cls_4[0.2646] det_loss_box_4[0.3106] det_loss_cls_5[0.2525] det_loss_box_5[0.3135] det_loss_cls_dn_0[0.0806] det_loss_box_dn_0[0.2931] det_loss_cls_dn_1[0.0763] det_loss_box_dn_1[0.2395] det_loss_cls_dn_2[0.0759] det_loss_box_dn_2[0.2188] det_loss_cls_dn_3[0.0750] det_loss_box_dn_3[0.2132] det_loss_cls_dn_4[0.0746] det_loss_box_dn_4[0.2094] det_loss_cls_dn_5[0.0737] det_loss_box_dn_5[0.2085] loss_depth[0.0398] total_loss[5.8601]
Rank[0/8] 06/20/2025 17:31:27 INFO stats.py:314 | Epoch[209] Step[89] GlobalStep[81599] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 4:01:03.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:31:37 INFO loss_tracker.py:84 | Epoch[209/239] Step[99] GlobalStep[81609/NA]: det_loss_cls_0[0.3434] det_loss_box_0[0.4488] det_loss_cls_1[0.3373] det_loss_box_1[0.3274] det_loss_cls_2[0.2861] det_loss_box_2[0.3028] det_loss_cls_3[0.2518] det_loss_box_3[0.3022] det_loss_cls_4[0.2329] det_loss_box_4[0.2912] det_loss_cls_5[0.2185] det_loss_box_5[0.2929] det_loss_cls_dn_0[0.0680] det_loss_box_dn_0[0.2945] det_loss_cls_dn_1[0.0642] det_loss_box_dn_1[0.2416] det_loss_cls_dn_2[0.0621] det_loss_box_dn_2[0.2196] det_loss_cls_dn_3[0.0608] det_loss_box_dn_3[0.2115] det_loss_cls_dn_4[0.0598] det_loss_box_dn_4[0.2077] det_loss_cls_dn_5[0.0588] det_loss_box_dn_5[0.1981] loss_depth[0.0374] total_loss[5.4195]
Rank[0/8] 06/20/2025 17:31:53 INFO stats.py:314 | Epoch[209] Step[114] GlobalStep[81624] Training Speed: 7.98 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 4:00:32.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:32:04 INFO loss_tracker.py:84 | Epoch[209/239] Step[124] GlobalStep[81634/NA]: det_loss_cls_0[0.5528] det_loss_box_0[0.6632] det_loss_cls_1[0.5224] det_loss_box_1[0.4767] det_loss_cls_2[0.4586] det_loss_box_2[0.4387] det_loss_cls_3[0.4223] det_loss_box_3[0.4292] det_loss_cls_4[0.4009] det_loss_box_4[0.4178] det_loss_cls_5[0.3906] det_loss_box_5[0.4067] det_loss_cls_dn_0[0.0986] det_loss_box_dn_0[0.2721] det_loss_cls_dn_1[0.0961] det_loss_box_dn_1[0.2144] det_loss_cls_dn_2[0.0949] det_loss_box_dn_2[0.1945] det_loss_cls_dn_3[0.0945] det_loss_box_dn_3[0.1908] det_loss_cls_dn_4[0.0936] det_loss_box_dn_4[0.1884] det_loss_cls_dn_5[0.0930] det_loss_box_dn_5[0.1832] loss_depth[0.0355] total_loss[7.4294]
Rank[0/8] 06/20/2025 17:32:19 INFO stats.py:314 | Epoch[209] Step[139] GlobalStep[81649] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 4:00:02.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:32:29 INFO loss_tracker.py:84 | Epoch[209/239] Step[149] GlobalStep[81659/NA]: det_loss_cls_0[0.4945] det_loss_box_0[0.6018] det_loss_cls_1[0.4760] det_loss_box_1[0.4329] det_loss_cls_2[0.4125] det_loss_box_2[0.3819] det_loss_cls_3[0.3754] det_loss_box_3[0.3674] det_loss_cls_4[0.3464] det_loss_box_4[0.3629] det_loss_cls_5[0.3319] det_loss_box_5[0.3642] det_loss_cls_dn_0[0.0826] det_loss_box_dn_0[0.2321] det_loss_cls_dn_1[0.0786] det_loss_box_dn_1[0.1841] det_loss_cls_dn_2[0.0773] det_loss_box_dn_2[0.1671] det_loss_cls_dn_3[0.0763] det_loss_box_dn_3[0.1625] det_loss_cls_dn_4[0.0750] det_loss_box_dn_4[0.1607] det_loss_cls_dn_5[0.0743] det_loss_box_dn_5[0.1589] loss_depth[0.0354] total_loss[6.5126]
Rank[0/8] 06/20/2025 17:32:45 INFO stats.py:314 | Epoch[209] Step[164] GlobalStep[81674] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 3:59:31.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:32:55 INFO loss_tracker.py:84 | Epoch[209/239] Step[174] GlobalStep[81684/NA]: det_loss_cls_0[0.5983] det_loss_box_0[0.7657] det_loss_cls_1[0.5902] det_loss_box_1[0.5846] det_loss_cls_2[0.5331] det_loss_box_2[0.5376] det_loss_cls_3[0.5039] det_loss_box_3[0.5137] det_loss_cls_4[0.4720] det_loss_box_4[0.5046] det_loss_cls_5[0.4606] det_loss_box_5[0.5032] det_loss_cls_dn_0[0.0921] det_loss_box_dn_0[0.2880] det_loss_cls_dn_1[0.0887] det_loss_box_dn_1[0.2417] det_loss_cls_dn_2[0.0879] det_loss_box_dn_2[0.2242] det_loss_cls_dn_3[0.0870] det_loss_box_dn_3[0.2181] det_loss_cls_dn_4[0.0857] det_loss_box_dn_4[0.2158] det_loss_cls_dn_5[0.0849] det_loss_box_dn_5[0.2117] loss_depth[0.0376] total_loss[8.5309]
Rank[0/8] 06/20/2025 17:33:10 INFO stats.py:314 | Epoch[209] Step[189] GlobalStep[81699] Training Speed: 8.07 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 3:59:00.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:33:21 INFO loss_tracker.py:84 | Epoch[209/239] Step[199] GlobalStep[81709/NA]: det_loss_cls_0[0.4340] det_loss_box_0[0.5341] det_loss_cls_1[0.4221] det_loss_box_1[0.3817] det_loss_cls_2[0.3535] det_loss_box_2[0.3482] det_loss_cls_3[0.3204] det_loss_box_3[0.3319] det_loss_cls_4[0.2891] det_loss_box_4[0.3310] det_loss_cls_5[0.2748] det_loss_box_5[0.3328] det_loss_cls_dn_0[0.0884] det_loss_box_dn_0[0.2607] det_loss_cls_dn_1[0.0848] det_loss_box_dn_1[0.2061] det_loss_cls_dn_2[0.0837] det_loss_box_dn_2[0.1876] det_loss_cls_dn_3[0.0830] det_loss_box_dn_3[0.1820] det_loss_cls_dn_4[0.0823] det_loss_box_dn_4[0.1789] det_loss_cls_dn_5[0.0812] det_loss_box_dn_5[0.1764] loss_depth[0.0336] total_loss[6.0825]
Rank[0/8] 06/20/2025 17:33:37 INFO stats.py:314 | Epoch[209] Step[214] GlobalStep[81724] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 3:58:29.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:33:47 INFO loss_tracker.py:84 | Epoch[209/239] Step[224] GlobalStep[81734/NA]: det_loss_cls_0[0.5274] det_loss_box_0[0.6710] det_loss_cls_1[0.5174] det_loss_box_1[0.5164] det_loss_cls_2[0.4428] det_loss_box_2[0.4653] det_loss_cls_3[0.4061] det_loss_box_3[0.4566] det_loss_cls_4[0.3831] det_loss_box_4[0.4418] det_loss_cls_5[0.3700] det_loss_box_5[0.4365] det_loss_cls_dn_0[0.0948] det_loss_box_dn_0[0.3195] det_loss_cls_dn_1[0.0913] det_loss_box_dn_1[0.2680] det_loss_cls_dn_2[0.0903] det_loss_box_dn_2[0.2510] det_loss_cls_dn_3[0.0892] det_loss_box_dn_3[0.2456] det_loss_cls_dn_4[0.0882] det_loss_box_dn_4[0.2429] det_loss_cls_dn_5[0.0873] det_loss_box_dn_5[0.2357] loss_depth[0.0338] total_loss[7.7723]
Rank[0/8] 06/20/2025 17:34:03 INFO stats.py:314 | Epoch[209] Step[239] GlobalStep[81749] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 3:57:59.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:34:13 INFO loss_tracker.py:84 | Epoch[209/239] Step[249] GlobalStep[81759/NA]: det_loss_cls_0[0.4506] det_loss_box_0[0.5986] det_loss_cls_1[0.4375] det_loss_box_1[0.4447] det_loss_cls_2[0.3805] det_loss_box_2[0.3953] det_loss_cls_3[0.3498] det_loss_box_3[0.3841] det_loss_cls_4[0.3267] det_loss_box_4[0.3745] det_loss_cls_5[0.3170] det_loss_box_5[0.3737] det_loss_cls_dn_0[0.0816] det_loss_box_dn_0[0.2675] det_loss_cls_dn_1[0.0782] det_loss_box_dn_1[0.2115] det_loss_cls_dn_2[0.0770] det_loss_box_dn_2[0.1896] det_loss_cls_dn_3[0.0764] det_loss_box_dn_3[0.1831] det_loss_cls_dn_4[0.0753] det_loss_box_dn_4[0.1811] det_loss_cls_dn_5[0.0748] det_loss_box_dn_5[0.1805] loss_depth[0.0354] total_loss[6.5448]
Rank[0/8] 06/20/2025 17:34:28 INFO stats.py:314 | Epoch[209] Step[264] GlobalStep[81774] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 3:57:28.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:34:38 INFO loss_tracker.py:84 | Epoch[209/239] Step[274] GlobalStep[81784/NA]: det_loss_cls_0[0.4111] det_loss_box_0[0.5175] det_loss_cls_1[0.4026] det_loss_box_1[0.3852] det_loss_cls_2[0.3399] det_loss_box_2[0.3515] det_loss_cls_3[0.3075] det_loss_box_3[0.3407] det_loss_cls_4[0.2934] det_loss_box_4[0.3296] det_loss_cls_5[0.2777] det_loss_box_5[0.3354] det_loss_cls_dn_0[0.0889] det_loss_box_dn_0[0.2692] det_loss_cls_dn_1[0.0863] det_loss_box_dn_1[0.2232] det_loss_cls_dn_2[0.0853] det_loss_box_dn_2[0.2051] det_loss_cls_dn_3[0.0843] det_loss_box_dn_3[0.2019] det_loss_cls_dn_4[0.0836] det_loss_box_dn_4[0.2002] det_loss_cls_dn_5[0.0832] det_loss_box_dn_5[0.1989] loss_depth[0.0347] total_loss[6.1369]
Rank[0/8] 06/20/2025 17:34:55 INFO stats.py:314 | Epoch[209] Step[289] GlobalStep[81799] Training Speed: 7.55 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 3:56:57.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:35:05 INFO loss_tracker.py:84 | Epoch[209/239] Step[299] GlobalStep[81809/NA]: det_loss_cls_0[0.4040] det_loss_box_0[0.4962] det_loss_cls_1[0.3996] det_loss_box_1[0.3644] det_loss_cls_2[0.3464] det_loss_box_2[0.3274] det_loss_cls_3[0.3105] det_loss_box_3[0.3276] det_loss_cls_4[0.2859] det_loss_box_4[0.3266] det_loss_cls_5[0.2725] det_loss_box_5[0.3274] det_loss_cls_dn_0[0.0859] det_loss_box_dn_0[0.2735] det_loss_cls_dn_1[0.0836] det_loss_box_dn_1[0.2198] det_loss_cls_dn_2[0.0830] det_loss_box_dn_2[0.1991] det_loss_cls_dn_3[0.0816] det_loss_box_dn_3[0.1943] det_loss_cls_dn_4[0.0809] det_loss_box_dn_4[0.1913] det_loss_cls_dn_5[0.0799] det_loss_box_dn_5[0.1859] loss_depth[0.0335] total_loss[5.9810]
Rank[0/8] 06/20/2025 17:35:20 INFO stats.py:314 | Epoch[209] Step[314] GlobalStep[81824] Training Speed: 7.83 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 3:56:26.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:35:30 INFO loss_tracker.py:84 | Epoch[209/239] Step[324] GlobalStep[81834/NA]: det_loss_cls_0[0.5065] det_loss_box_0[0.6888] det_loss_cls_1[0.4836] det_loss_box_1[0.4974] det_loss_cls_2[0.4190] det_loss_box_2[0.4421] det_loss_cls_3[0.3843] det_loss_box_3[0.4296] det_loss_cls_4[0.3640] det_loss_box_4[0.4166] det_loss_cls_5[0.3532] det_loss_box_5[0.4124] det_loss_cls_dn_0[0.0963] det_loss_box_dn_0[0.2858] det_loss_cls_dn_1[0.0936] det_loss_box_dn_1[0.2416] det_loss_cls_dn_2[0.0925] det_loss_box_dn_2[0.2220] det_loss_cls_dn_3[0.0913] det_loss_box_dn_3[0.2186] det_loss_cls_dn_4[0.0905] det_loss_box_dn_4[0.2150] det_loss_cls_dn_5[0.0896] det_loss_box_dn_5[0.2091] loss_depth[0.0387] total_loss[7.3822]
Rank[0/8] 06/20/2025 17:35:45 INFO stats.py:314 | Epoch[209] Step[339] GlobalStep[81849] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 3:55:56.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:35:56 INFO loss_tracker.py:84 | Epoch[209/239] Step[349] GlobalStep[81859/NA]: det_loss_cls_0[0.3933] det_loss_box_0[0.5291] det_loss_cls_1[0.3894] det_loss_box_1[0.4067] det_loss_cls_2[0.3214] det_loss_box_2[0.3779] det_loss_cls_3[0.2880] det_loss_box_3[0.3669] det_loss_cls_4[0.2717] det_loss_box_4[0.3511] det_loss_cls_5[0.2573] det_loss_box_5[0.3456] det_loss_cls_dn_0[0.0822] det_loss_box_dn_0[0.3263] det_loss_cls_dn_1[0.0785] det_loss_box_dn_1[0.2688] det_loss_cls_dn_2[0.0774] det_loss_box_dn_2[0.2502] det_loss_cls_dn_3[0.0764] det_loss_box_dn_3[0.2466] det_loss_cls_dn_4[0.0750] det_loss_box_dn_4[0.2409] det_loss_cls_dn_5[0.0738] det_loss_box_dn_5[0.2368] loss_depth[0.0354] total_loss[6.3667]
Rank[0/8] 06/20/2025 17:36:12 INFO stats.py:314 | Epoch[209] Step[364] GlobalStep[81874] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 3:55:25.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:36:22 INFO loss_tracker.py:84 | Epoch[209/239] Step[374] GlobalStep[81884/NA]: det_loss_cls_0[0.5097] det_loss_box_0[0.6957] det_loss_cls_1[0.5058] det_loss_box_1[0.5148] det_loss_cls_2[0.4357] det_loss_box_2[0.4534] det_loss_cls_3[0.4045] det_loss_box_3[0.4407] det_loss_cls_4[0.3750] det_loss_box_4[0.4320] det_loss_cls_5[0.3689] det_loss_box_5[0.4289] det_loss_cls_dn_0[0.0781] det_loss_box_dn_0[0.2633] det_loss_cls_dn_1[0.0748] det_loss_box_dn_1[0.2205] det_loss_cls_dn_2[0.0738] det_loss_box_dn_2[0.2035] det_loss_cls_dn_3[0.0730] det_loss_box_dn_3[0.1969] det_loss_cls_dn_4[0.0725] det_loss_box_dn_4[0.1961] det_loss_cls_dn_5[0.0718] det_loss_box_dn_5[0.1929] loss_depth[0.0341] total_loss[7.3165]
Rank[0/8] 06/20/2025 17:36:37 INFO stats.py:314 | Epoch[209] Step[389] GlobalStep[81899] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 3:54:54.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:36:37 INFO trainer.py:268 | 
==================================================BEGIN EVAL==================================================
Rank[0/8] 06/20/2025 17:37:34 INFO trainer.py:275 | eval: 10
Rank[0/8] 06/20/2025 17:37:43 INFO trainer.py:275 | eval: 20
Rank[0/8] 06/20/2025 17:37:51 INFO trainer.py:275 | eval: 30
Rank[0/8] 06/20/2025 17:38:01 INFO trainer.py:275 | eval: 40
Rank[0/8] 06/20/2025 17:38:08 INFO trainer.py:275 | eval: 50
Rank[0/8] 06/20/2025 17:38:15 INFO trainer.py:275 | eval: 60
Rank[0/8] 06/20/2025 17:38:24 INFO trainer.py:275 | eval: 70
Rank[0/8] 06/20/2025 17:38:33 INFO trainer.py:275 | eval: 80
Rank[0/8] 06/20/2025 17:38:43 INFO trainer.py:275 | eval: 90
Rank[0/8] 06/20/2025 17:38:51 INFO trainer.py:275 | eval: 100
Rank[0/8] 06/20/2025 17:38:57 INFO metrics.py:428 | number of results: 824
Rank[0/8] 06/20/2025 17:43:09 INFO metrics.py:332 | eval results of overall:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| bathtub             | 0.8535  | 0.8971  | 0.7511  | 0.8235  |
| curtain             | 0.5440  | 0.8358  | 0.1327  | 0.4119  |
| toilet              | 0.8700  | 0.8978  | 0.7513  | 0.8175  |
| toilet paper        | 0.3900  | 0.5984  | 0.1180  | 0.3228  |
| sink                | 0.7022  | 0.8601  | 0.2813  | 0.5185  |
| bin                 | 0.6435  | 0.8603  | 0.4156  | 0.6341  |
| picture             | 0.2600  | 0.4978  | 0.0361  | 0.1358  |
| doorframe           | 0.3124  | 0.7320  | 0.0753  | 0.3818  |
| door                | 0.4126  | 0.7139  | 0.0710  | 0.2946  |
| towel               | 0.3923  | 0.6774  | 0.1107  | 0.3129  |
| vanity              | 0.5947  | 0.8667  | 0.4988  | 0.7778  |
| mirror              | 0.2888  | 0.6902  | 0.0257  | 0.1957  |
| stopcock            | 0.1098  | 0.5517  | 0.0005  | 0.0345  |
| switch              | 0.0024  | 0.1447  | 0.0000  | 0.0066  |
| bar                 | 0.0691  | 0.2424  | 0.0072  | 0.0606  |
| bottle              | 0.0691  | 0.3955  | 0.0031  | 0.0808  |
| rail                | 0.0250  | 0.4062  | 0.0037  | 0.0938  |
| bag                 | 0.2776  | 0.7027  | 0.0747  | 0.3405  |
| hair dryer          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| socket              | 0.0147  | 0.2611  | 0.0000  | 0.0088  |
| cup                 | 0.1083  | 0.3983  | 0.0112  | 0.0593  |
| tissue              | 0.0455  | 0.1538  | 0.0455  | 0.1538  |
| ledge               | 0.2063  | 0.5000  | 0.0185  | 0.1667  |
| light               | 0.0681  | 0.3162  | 0.0015  | 0.0449  |
| shower              | 0.0927  | 0.4000  | 0.0107  | 0.0667  |
| rod                 | 0.0250  | 0.0250  | 0.0000  | 0.0000  |
| microwave           | 0.6081  | 0.7143  | 0.2383  | 0.4490  |
| basket              | 0.3014  | 0.5652  | 0.1291  | 0.3304  |
| door knob           | 0.1667  | 0.5000  | 0.0000  | 0.0000  |
| box                 | 0.1678  | 0.5046  | 0.0450  | 0.2176  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stool               | 0.3375  | 0.7647  | 0.2486  | 0.5966  |
| vent                | 0.0174  | 0.2500  | 0.0000  | 0.0000  |
| tissue box          | 0.1151  | 0.4848  | 0.0486  | 0.3333  |
| mat                 | 0.0165  | 0.2703  | 0.0078  | 0.0811  |
| shoe                | 0.2406  | 0.5327  | 0.0379  | 0.1215  |
| faucet              | 0.0402  | 0.2766  | 0.0000  | 0.0000  |
| paper               | 0.0274  | 0.3343  | 0.0004  | 0.0562  |
| blanket             | 0.2923  | 0.7800  | 0.0586  | 0.3700  |
| case                | 0.1013  | 0.4520  | 0.0652  | 0.1921  |
| carpet              | 0.1811  | 0.5000  | 0.0092  | 0.1429  |
| backpack            | 0.5460  | 0.8226  | 0.3554  | 0.5484  |
| cabinet             | 0.3081  | 0.7521  | 0.1825  | 0.5319  |
| plant               | 0.3218  | 0.7090  | 0.1050  | 0.3557  |
| soap dish           | 0.5613  | 0.6667  | 0.2889  | 0.3333  |
| soap                | 0.0077  | 0.6667  | 0.0000  | 0.0000  |
| doorway             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| decoration          | 0.0475  | 0.4643  | 0.0067  | 0.1214  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| lamp                | 0.3831  | 0.6657  | 0.1209  | 0.3224  |
| notebook            | 0.0015  | 0.0769  | 0.0000  | 0.0000  |
| keyboard            | 0.0786  | 0.2442  | 0.0008  | 0.0116  |
| purse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toothpaste          | 0.0010  | 0.2000  | 0.0000  | 0.0000  |
| shelf               | 0.3339  | 0.7302  | 0.1587  | 0.4335  |
| bed                 | 0.8638  | 0.9231  | 0.6971  | 0.8225  |
| tablet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes             | 0.2031  | 0.5955  | 0.0476  | 0.2484  |
| drawer              | 0.3504  | 0.6667  | 0.2226  | 0.5000  |
| frame               | 0.0657  | 0.5306  | 0.0084  | 0.1020  |
| sunglasses          | 0.0561  | 0.4615  | 0.0014  | 0.0769  |
| mouse               | 0.0018  | 0.0811  | 0.0000  | 0.0000  |
| candle              | 0.0700  | 0.2778  | 0.0017  | 0.1667  |
| book                | 0.0742  | 0.4595  | 0.0058  | 0.1103  |
| wardrobe            | 0.4288  | 0.7711  | 0.2728  | 0.5422  |
| scale               | 0.2742  | 0.3636  | 0.0000  | 0.0000  |
| hamper              | 0.4924  | 0.5455  | 0.4924  | 0.5455  |
| laptop              | 0.4304  | 0.7391  | 0.1053  | 0.2174  |
| jar                 | 0.1387  | 0.4615  | 0.0791  | 0.1538  |
| stall               | 0.3567  | 1.0000  | 0.2186  | 0.6471  |
| couch               | 0.5568  | 0.8595  | 0.4703  | 0.7727  |
| window frame        | 0.0209  | 0.3737  | 0.0015  | 0.1515  |
| fruit               | 0.1587  | 0.3571  | 0.0841  | 0.2143  |
| chair               | 0.5991  | 0.8411  | 0.4624  | 0.6795  |
| blackboard          | 0.6907  | 0.8293  | 0.1890  | 0.4146  |
| radiator            | 0.7216  | 0.8111  | 0.2179  | 0.3667  |
| computer            | 0.3567  | 0.6557  | 0.0208  | 0.1803  |
| monitor             | 0.5569  | 0.7174  | 0.1222  | 0.3315  |
| desk                | 0.5894  | 0.9213  | 0.4145  | 0.7528  |
| window              | 0.2783  | 0.6347  | 0.0521  | 0.2485  |
| table               | 0.5228  | 0.8699  | 0.4277  | 0.7524  |
| plug                | 0.0308  | 0.1579  | 0.0000  | 0.0000  |
| telephone           | 0.4327  | 0.7000  | 0.1342  | 0.3000  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stand               | 0.5760  | 0.8780  | 0.4646  | 0.7561  |
| windowsill          | 0.1376  | 0.6304  | 0.0398  | 0.2174  |
| blinds              | 0.2889  | 0.4800  | 0.0283  | 0.2000  |
| jacket              | 0.6034  | 0.7917  | 0.2684  | 0.5000  |
| furniture           | 0.0060  | 0.1667  | 0.0000  | 0.0000  |
| counter             | 0.2137  | 0.6972  | 0.0726  | 0.2936  |
| projector           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plate               | 0.0345  | 0.4038  | 0.0017  | 0.0962  |
| pipe                | 0.0378  | 0.1071  | 0.0008  | 0.0357  |
| pillow              | 0.2685  | 0.6513  | 0.0411  | 0.2190  |
| dresser             | 0.6537  | 0.8704  | 0.5766  | 0.7963  |
| tv                  | 0.6838  | 0.8515  | 0.2598  | 0.4851  |
| ottoman             | 0.1983  | 0.6944  | 0.1326  | 0.5556  |
| paper cutter        | 0.7554  | 1.0000  | 0.2222  | 0.3333  |
| container           | 0.0077  | 0.2373  | 0.0019  | 0.1186  |
| stairs              | 0.0863  | 0.4583  | 0.0105  | 0.1944  |
| remote control      | 0.0279  | 0.1852  | 0.0153  | 0.1111  |
| cushion             | 0.2294  | 0.5490  | 0.0305  | 0.1765  |
| toy                 | 0.1568  | 0.4545  | 0.0335  | 0.1212  |
| printer             | 0.5227  | 0.7755  | 0.3430  | 0.6735  |
| clock               | 0.3416  | 0.6170  | 0.0430  | 0.1277  |
| calendar            | 0.0190  | 0.3333  | 0.0000  | 0.0000  |
| poster              | 0.0374  | 0.1970  | 0.0013  | 0.0152  |
| rack                | 0.2125  | 0.5385  | 0.0961  | 0.4038  |
| guitar              | 0.2368  | 0.4167  | 0.0833  | 0.0833  |
| refrigerator        | 0.7312  | 0.9000  | 0.4728  | 0.6000  |
| luggage             | 0.7121  | 1.0000  | 0.6667  | 0.6667  |
| bench               | 0.2944  | 0.7547  | 0.2488  | 0.5849  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine             | 0.4543  | 0.7895  | 0.3885  | 0.6316  |
| kettle              | 0.2596  | 0.7222  | 0.0421  | 0.2222  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coffee maker        | 0.4498  | 0.5517  | 0.1732  | 0.2414  |
| plunger             | 0.5619  | 0.8000  | 0.5619  | 0.8000  |
| screen              | 0.0767  | 0.4000  | 0.0087  | 0.2000  |
| spoon               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| countertop          | 0.0133  | 0.2500  | 0.0004  | 0.0714  |
| dish rack           | 0.6956  | 1.0000  | 0.3600  | 0.6000  |
| chandelier          | 0.0508  | 0.1905  | 0.0127  | 0.0952  |
| headphones          | 0.4300  | 0.6000  | 0.2000  | 0.2000  |
| grab bar            | 0.0536  | 0.3750  | 0.0000  | 0.0000  |
| kitchen island      | 0.2234  | 0.4286  | 0.1693  | 0.4286  |
| excercise equipment | 0.4415  | 0.7000  | 0.0205  | 0.3000  |
| adhesive tape       | 0.2000  | 0.2000  | 0.0000  | 0.0000  |
| copier              | 0.8963  | 0.9167  | 0.8782  | 0.9167  |
| pillar              | 0.3757  | 0.6977  | 0.2433  | 0.4884  |
| thermostat          | 0.0556  | 0.1667  | 0.0000  | 0.0000  |
| bidet               | 0.4667  | 0.6667  | 0.4667  | 0.6667  |
| partition           | 0.5167  | 0.6667  | 0.0833  | 0.1667  |
| crib                | 0.3889  | 0.6667  | 0.3889  | 0.6667  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garage door         | 0.3476  | 0.5000  | 0.3476  | 0.5000  |
| glass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| teapot              | 0.0090  | 0.2222  | 0.0000  | 0.0000  |
| soap dispenser      | 0.4911  | 0.7778  | 0.0820  | 0.2963  |
| dispenser           | 0.5242  | 0.6364  | 0.3889  | 0.5000  |
| mailbox             | 0.0674  | 0.8000  | 0.0040  | 0.2000  |
| holder              | 0.1389  | 0.2222  | 0.1111  | 0.1111  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| column              | 0.0072  | 0.2632  | 0.0003  | 0.1053  |
| hanger              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| alarm               | 0.0043  | 0.1250  | 0.0000  | 0.0000  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchenware         | 0.2405  | 0.4783  | 0.0386  | 0.1522  |
| toaster             | 0.4000  | 0.4000  | 0.0500  | 0.1000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| footstool           | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| board               | 0.2213  | 0.6774  | 0.0522  | 0.3065  |
| body loofah         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cube                | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| drum                | 0.0345  | 0.2500  | 0.0000  | 0.0000  |
| bread               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 0.5996  | 1.0000  | 0.4500  | 0.8333  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mattress            | 0.1111  | 0.3333  | 0.0000  | 0.0000  |
| balcony             | 0.2935  | 1.0000  | 0.2500  | 0.5000  |
| handle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool table          | 0.1667  | 0.3333  | 0.1667  | 0.3333  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.5356  | 0.7143  | 0.3003  | 0.4286  |
| hat                 | 0.1134  | 0.3333  | 0.0006  | 0.1111  |
| statue              | 0.1021  | 0.4737  | 0.0888  | 0.2632  |
| tube                | 0.0128  | 0.1538  | 0.0000  | 0.0000  |
| fan                 | 0.6222  | 0.7812  | 0.2340  | 0.4531  |
| person              | 0.1738  | 0.3333  | 0.0025  | 0.1667  |
| piano               | 0.3000  | 0.4000  | 0.3000  | 0.4000  |
| ball                | 0.4475  | 0.5714  | 0.3445  | 0.4286  |
| basin               | 0.0076  | 0.1765  | 0.0008  | 0.0588  |
| album               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher   | 0.4073  | 0.6000  | 0.4000  | 0.4000  |
| roof                | 0.0015  | 0.2083  | 0.0000  | 0.0000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket              | 0.2085  | 0.7436  | 0.1017  | 0.4103  |
| bowl                | 0.1322  | 0.4783  | 0.0617  | 0.2174  |
| cosmetics           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| arch                | 0.0127  | 0.1333  | 0.0007  | 0.0667  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| heater              | 0.6551  | 0.8103  | 0.1680  | 0.4138  |
| barricade           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| commode             | 0.4960  | 0.8077  | 0.3859  | 0.6731  |
| beanbag             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| treadmill           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jalousie            | 1.0000  | 1.0000  | 0.5000  | 0.5000  |
| humidifier          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| candlestick         | 0.0340  | 0.6667  | 0.0000  | 0.0000  |
| glove               | 0.8333  | 1.0000  | 0.1111  | 0.3333  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sign                | 0.0950  | 0.3333  | 0.0000  | 0.0333  |
| water cooler        | 0.8333  | 1.0000  | 0.5122  | 1.0000  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| oven                | 0.5789  | 0.8214  | 0.1171  | 0.3571  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pot                 | 0.2123  | 0.5556  | 0.0326  | 0.2222  |
| brush               | 0.1235  | 0.3571  | 0.0000  | 0.0000  |
| pan                 | 0.1869  | 0.5882  | 0.0311  | 0.1765  |
| stove               | 0.5472  | 0.8333  | 0.3551  | 0.5833  |
| folder              | 0.1340  | 0.3571  | 0.0248  | 0.1429  |
| crate               | 0.5102  | 0.5714  | 0.2857  | 0.2857  |
| device              | 0.3913  | 0.6364  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| food                | 0.0909  | 0.0909  | 0.0000  | 0.0000  |
| pack                | 0.0594  | 0.6000  | 0.0015  | 0.1333  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garbage             | 0.6919  | 0.8750  | 0.4167  | 0.5000  |
| flush               | 0.8333  | 1.0000  | 0.0000  | 0.0000  |
| sponge              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sack                | 0.6667  | 0.8571  | 0.3175  | 0.5714  |
| cap                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| magazine            | 0.0107  | 0.6000  | 0.0009  | 0.2000  |
| can                 | 0.1111  | 0.1667  | 0.0000  | 0.0000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radio               | 0.2500  | 0.2500  | 0.2500  | 0.2500  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| napkins             | 0.5556  | 0.6667  | 0.0000  | 0.0000  |
| wine                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| map                 | 0.0500  | 0.5000  | 0.0500  | 0.5000  |
| ladder              | 0.2063  | 0.5294  | 0.0788  | 0.1765  |
| roll                | 0.1950  | 0.5000  | 0.0000  | 0.0000  |
| dumbbell            | 0.6667  | 0.6667  | 0.1667  | 0.3333  |
| salt                | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| player              | 0.2500  | 0.2500  | 0.2500  | 0.2500  |
| vacuum cleaner      | 0.5649  | 0.7143  | 0.0519  | 0.2857  |
| broom               | 0.0312  | 0.3571  | 0.0000  | 0.0000  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toiletry            | 0.1167  | 0.3043  | 0.0005  | 0.0435  |
| price tag           | 0.1531  | 0.2857  | 0.0000  | 0.0000  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fireplace           | 0.2152  | 0.4643  | 0.0314  | 0.2500  |
| steps               | 0.0011  | 0.3000  | 0.0000  | 0.0000  |
| vase                | 0.1598  | 0.4000  | 0.0761  | 0.2400  |
| flowerpot           | 0.0319  | 0.3725  | 0.0007  | 0.0784  |
| range hood          | 0.5368  | 0.8571  | 0.0321  | 0.1429  |
| file                | 0.0546  | 0.2647  | 0.0513  | 0.1471  |
| speaker             | 0.0862  | 0.2727  | 0.0006  | 0.0455  |
| air conditioner     | 0.6000  | 0.6000  | 0.4000  | 0.4000  |
| conducting wire     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| umbrella            | 0.2000  | 0.2000  | 0.2000  | 0.2000  |
| letter              | 0.7750  | 1.0000  | 0.0147  | 0.2500  |
| shampoo             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.4557  | 0.8667  | 0.3682  | 0.7333  |
| beam                | 0.0000  | 0.0455  | 0.0000  | 0.0455  |
| dishwasher          | 0.2375  | 0.3000  | 0.2000  | 0.2000  |
| mop                 | 1.0000  | 1.0000  | 0.1667  | 0.3333  |
| water heater        | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| helmets             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| menu                | 0.6429  | 0.7143  | 0.0000  | 0.0000  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coil                | 0.2500  | 0.3333  | 0.0417  | 0.1667  |
| washbasin           | 0.0081  | 0.2667  | 0.0079  | 0.2000  |
| -------             | ------- | ------- | ------- | ------- |
| overall             | 0.2480  | 0.4397  | 0.1167  | 0.2203  |
| head                | 0.3200  | 0.6116  | 0.1522  | 0.3307  |
| common              | 0.2219  | 0.4006  | 0.1149  | 0.1969  |
| tail                | 0.1984  | 0.2953  | 0.0799  | 0.1258  |
| small               | 0.0970  | 0.2468  | 0.0147  | 0.0513  |
| medium              | 0.2545  | 0.4844  | 0.1346  | 0.2582  |
| large               | 0.1572  | 0.5189  | 0.0825  | 0.3255  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 17:43:09 INFO metrics.py:437 | number of scannet results: 250
Rank[0/8] 06/20/2025 17:44:23 INFO metrics.py:332 | eval results of scannet:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| bathtub             | 0.9446  | 0.9688  | 0.8491  | 0.8750  |
| curtain             | 0.6036  | 0.8584  | 0.1590  | 0.3982  |
| toilet              | 0.9438  | 0.9643  | 0.8374  | 0.8929  |
| toilet paper        | 0.4332  | 0.5696  | 0.1459  | 0.3418  |
| sink                | 0.8504  | 0.9205  | 0.3924  | 0.5795  |
| bin                 | 0.6706  | 0.8681  | 0.4569  | 0.6596  |
| picture             | 0.3507  | 0.5410  | 0.0667  | 0.1604  |
| doorframe           | 0.4014  | 0.7746  | 0.0745  | 0.3662  |
| door                | 0.6085  | 0.8428  | 0.1532  | 0.4348  |
| towel               | 0.5070  | 0.7273  | 0.1282  | 0.3058  |
| vanity              | 0.7848  | 1.0000  | 0.6736  | 0.9333  |
| mirror              | 0.4339  | 0.7470  | 0.0517  | 0.1566  |
| stopcock            | 0.1098  | 0.5517  | 0.0005  | 0.0345  |
| switch              | 0.0053  | 0.2152  | 0.0000  | 0.0127  |
| bar                 | 0.0797  | 0.2727  | 0.0091  | 0.0455  |
| bottle              | 0.0940  | 0.3789  | 0.0068  | 0.0895  |
| rail                | 0.1078  | 0.6667  | 0.0392  | 0.3333  |
| bag                 | 0.1955  | 0.6575  | 0.0789  | 0.3562  |
| hair dryer          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| socket              | 0.0162  | 0.2816  | 0.0000  | 0.0097  |
| cup                 | 0.2132  | 0.4493  | 0.0365  | 0.1014  |
| tissue              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ledge               | 0.2064  | 0.5000  | 0.0185  | 0.1667  |
| light               | 0.1697  | 0.4828  | 0.0345  | 0.0345  |
| shower              | 0.1081  | 0.4286  | 0.0143  | 0.0714  |
| rod                 | 0.0714  | 0.0714  | 0.0000  | 0.0000  |
| microwave           | 0.6416  | 0.7500  | 0.1831  | 0.4500  |
| basket              | 0.0963  | 0.2692  | 0.0952  | 0.1923  |
| box                 | 0.1268  | 0.4286  | 0.0270  | 0.1587  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stool               | 0.3450  | 0.8409  | 0.2419  | 0.6364  |
| tissue box          | 0.2034  | 0.5000  | 0.1664  | 0.4091  |
| mat                 | 0.0456  | 0.3000  | 0.0250  | 0.1000  |
| shoe                | 0.2624  | 0.5500  | 0.0487  | 0.1750  |
| faucet              | 0.0016  | 0.1250  | 0.0000  | 0.0000  |
| paper               | 0.0242  | 0.3208  | 0.0003  | 0.0528  |
| blanket             | 0.0723  | 0.5417  | 0.0079  | 0.2917  |
| case                | 0.7218  | 1.0000  | 0.5723  | 0.8947  |
| carpet              | 0.0085  | 0.3000  | 0.0000  | 0.0000  |
| backpack            | 0.6473  | 0.9286  | 0.5485  | 0.7143  |
| cabinet             | 0.3723  | 0.7540  | 0.1999  | 0.4960  |
| plant               | 0.2992  | 0.6975  | 0.0911  | 0.3277  |
| soap dish           | 0.6822  | 0.7500  | 0.3611  | 0.4167  |
| decoration          | 0.0940  | 0.5000  | 0.0016  | 0.1250  |
| lamp                | 0.4672  | 0.7143  | 0.2731  | 0.4881  |
| notebook            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| keyboard            | 0.1148  | 0.2759  | 0.0025  | 0.0172  |
| purse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toothpaste          | 0.0015  | 0.2000  | 0.0000  | 0.0000  |
| shelf               | 0.5273  | 0.8015  | 0.3687  | 0.6618  |
| bed                 | 0.9942  | 1.0000  | 0.9122  | 0.9298  |
| clothes             | 0.1995  | 0.5817  | 0.0709  | 0.2680  |
| drawer              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| frame               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mouse               | 0.0058  | 0.1429  | 0.0000  | 0.0000  |
| book                | 0.0796  | 0.4500  | 0.0061  | 0.1214  |
| wardrobe            | 0.4546  | 0.8750  | 0.3750  | 0.3750  |
| scale               | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| hamper              | 0.5417  | 0.6000  | 0.5417  | 0.6000  |
| laptop              | 0.5003  | 0.6667  | 0.1444  | 0.2000  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stall               | 0.3575  | 1.0000  | 0.2190  | 0.6471  |
| couch               | 0.7768  | 0.9625  | 0.6861  | 0.9000  |
| fruit               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| chair               | 0.6475  | 0.8580  | 0.4610  | 0.6639  |
| blackboard          | 0.7074  | 0.8406  | 0.1690  | 0.3768  |
| radiator            | 0.7968  | 0.8793  | 0.3030  | 0.4483  |
| computer            | 0.3474  | 0.6154  | 0.0266  | 0.2051  |
| monitor             | 0.5952  | 0.7214  | 0.2039  | 0.4000  |
| desk                | 0.7049  | 0.9747  | 0.4897  | 0.7595  |
| window              | 0.5313  | 0.8272  | 0.1586  | 0.4444  |
| table               | 0.6027  | 0.9038  | 0.4921  | 0.8000  |
| plug                | 0.0001  | 0.1111  | 0.0000  | 0.0000  |
| telephone           | 0.5745  | 0.8125  | 0.2199  | 0.3750  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stand               | 0.7555  | 0.8868  | 0.6670  | 0.8113  |
| windowsill          | 0.0058  | 0.5000  | 0.0011  | 0.1667  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jacket              | 0.6324  | 0.8261  | 0.2816  | 0.5217  |
| counter             | 0.5576  | 0.8857  | 0.2441  | 0.5143  |
| plate               | 0.0154  | 0.2000  | 0.0000  | 0.0000  |
| pipe                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillow              | 0.4063  | 0.7014  | 0.1757  | 0.4514  |
| dresser             | 0.8006  | 0.9459  | 0.7909  | 0.9189  |
| tv                  | 0.8572  | 0.9565  | 0.3814  | 0.5217  |
| ottoman             | 0.2330  | 1.0000  | 0.0222  | 0.4286  |
| paper cutter        | 0.7067  | 1.0000  | 0.1000  | 0.2000  |
| container           | 0.0051  | 0.2609  | 0.0011  | 0.1304  |
| stairs              | 0.1723  | 0.8000  | 0.0413  | 0.8000  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cushion             | 0.0122  | 0.5000  | 0.0040  | 0.2500  |
| toy                 | 0.0694  | 0.3529  | 0.0588  | 0.1176  |
| printer             | 0.5463  | 0.8182  | 0.5407  | 0.7879  |
| clock               | 0.4500  | 0.6400  | 0.1000  | 0.1200  |
| calendar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| poster              | 0.0667  | 1.0000  | 0.0000  | 0.0000  |
| rack                | 0.0022  | 0.1818  | 0.0005  | 0.0909  |
| guitar              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| refrigerator        | 0.8836  | 0.9500  | 0.7014  | 0.8000  |
| bench               | 0.5799  | 1.0000  | 0.3944  | 0.8000  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine             | 0.4952  | 0.8750  | 0.4022  | 0.6875  |
| kettle              | 0.3429  | 0.7500  | 0.0000  | 0.0000  |
| coffee maker        | 0.6012  | 0.6471  | 0.2892  | 0.3529  |
| plunger             | 0.5619  | 0.8000  | 0.5619  | 0.8000  |
| dish rack           | 0.7292  | 1.0000  | 0.5625  | 0.7500  |
| headphones          | 0.4462  | 0.6000  | 0.2000  | 0.2000  |
| grab bar            | 0.0536  | 0.3750  | 0.0000  | 0.0000  |
| excercise equipment | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| copier              | 0.8963  | 0.9167  | 0.8782  | 0.9167  |
| pillar              | 0.0500  | 0.2000  | 0.0500  | 0.2000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| teapot              | 0.0139  | 0.2500  | 0.0000  | 0.0000  |
| soap dispenser      | 0.5229  | 0.8095  | 0.0970  | 0.2857  |
| dispenser           | 0.6129  | 0.6923  | 0.4477  | 0.5385  |
| mailbox             | 0.0674  | 0.8000  | 0.0040  | 0.2000  |
| holder              | 0.2000  | 0.2000  | 0.2000  | 0.2000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| board               | 0.1357  | 0.7188  | 0.0128  | 0.4062  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 0.7222  | 1.0000  | 0.4911  | 1.0000  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.6204  | 1.0000  | 0.5309  | 0.8333  |
| hat                 | 0.0058  | 1.0000  | 0.0000  | 0.0000  |
| tube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fan                 | 0.7937  | 0.8750  | 0.1607  | 0.2500  |
| person              | 0.1738  | 0.3333  | 0.0025  | 0.1667  |
| ball                | 0.4028  | 0.4167  | 0.2500  | 0.2500  |
| basin               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher   | 0.0054  | 0.3333  | 0.0000  | 0.0000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket              | 0.1559  | 0.7143  | 0.0516  | 0.3571  |
| bowl                | 0.2439  | 0.4444  | 0.0635  | 0.2222  |
| barricade           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sign                | 0.0809  | 0.2857  | 0.0001  | 0.0357  |
| water cooler        | 0.8333  | 1.0000  | 0.5122  | 1.0000  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| oven                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pot                 | 0.0500  | 0.5000  | 0.0500  | 0.5000  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stove               | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| crate               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| map                 | 0.1000  | 1.0000  | 0.1000  | 1.0000  |
| ladder              | 0.1364  | 0.4000  | 0.1000  | 0.2000  |
| vacuum cleaner      | 0.6667  | 0.6667  | 0.6667  | 0.6667  |
| broom               | 0.0459  | 0.4167  | 0.0000  | 0.0000  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flowerpot           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.7083  | 1.0000  | 0.1333  | 0.4000  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.4000  | 1.0000  | 0.4000  | 1.0000  |
| dishwasher          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| scannet             | 0.2725  | 0.4714  | 0.1554  | 0.2747  |
| head                | 0.3722  | 0.6283  | 0.2094  | 0.3768  |
| common              | 0.2150  | 0.3646  | 0.1228  | 0.1907  |
| tail                | 0.0665  | 0.1837  | 0.0471  | 0.1200  |
| small               | 0.0678  | 0.2085  | 0.0149  | 0.0397  |
| medium              | 0.3189  | 0.5583  | 0.1904  | 0.3442  |
| large               | 0.2930  | 0.7838  | 0.1834  | 0.5189  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 17:44:23 INFO metrics.py:437 | number of 3rscan results: 235
Rank[0/8] 06/20/2025 17:45:30 INFO metrics.py:332 | eval results of 3rscan:
+-------------------+---------+---------+---------+---------+
| classes           | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+-------------------+---------+---------+---------+---------+
| chair             | 0.7028  | 0.8484  | 0.5856  | 0.7294  |
| monitor           | 0.6039  | 0.7250  | 0.0395  | 0.1250  |
| keyboard          | 0.0467  | 0.2083  | 0.0000  | 0.0000  |
| mouse             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| desk              | 0.5327  | 0.9125  | 0.3845  | 0.7625  |
| computer          | 0.3872  | 0.7143  | 0.0198  | 0.1429  |
| case              | 0.0401  | 0.4000  | 0.0167  | 0.1071  |
| cup               | 0.0341  | 0.3590  | 0.0000  | 0.0000  |
| paper             | 0.0776  | 0.4000  | 0.0021  | 0.0714  |
| plate             | 0.0574  | 0.3939  | 0.0043  | 0.1212  |
| stand             | 0.5430  | 0.9180  | 0.3839  | 0.7377  |
| garbage           | 0.7052  | 0.8750  | 0.4250  | 0.5000  |
| bag               | 0.3922  | 0.7778  | 0.0971  | 0.3636  |
| bottle            | 0.0439  | 0.3471  | 0.0007  | 0.0248  |
| plug              | 0.0741  | 0.2222  | 0.0000  | 0.0000  |
| pen               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| curtain           | 0.6270  | 0.8968  | 0.1734  | 0.4683  |
| book              | 0.1145  | 0.4911  | 0.0097  | 0.1065  |
| wardrobe          | 0.4940  | 0.8125  | 0.2977  | 0.6094  |
| bin               | 0.7488  | 0.9146  | 0.4295  | 0.6707  |
| window            | 0.2957  | 0.7055  | 0.0327  | 0.1781  |
| table             | 0.5929  | 0.9009  | 0.4812  | 0.7748  |
| cabinet           | 0.4685  | 0.8547  | 0.2988  | 0.6581  |
| backpack          | 0.4970  | 0.7576  | 0.2337  | 0.4242  |
| box               | 0.2134  | 0.5251  | 0.0670  | 0.2266  |
| blinds            | 0.3704  | 0.6154  | 0.0362  | 0.2564  |
| telephone         | 0.3434  | 0.5833  | 0.0763  | 0.2083  |
| conducting wire   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| socket            | 0.0625  | 0.0625  | 0.0000  | 0.0000  |
| plant             | 0.3750  | 0.6584  | 0.1120  | 0.3168  |
| notebook          | 0.0017  | 0.0909  | 0.0000  | 0.0000  |
| sink              | 0.7038  | 0.8393  | 0.1731  | 0.3571  |
| clock             | 0.3052  | 0.5789  | 0.0105  | 0.0526  |
| laptop            | 0.4591  | 0.8333  | 0.1250  | 0.3333  |
| shelf             | 0.3140  | 0.7527  | 0.1036  | 0.3781  |
| picture           | 0.2255  | 0.4797  | 0.0263  | 0.1016  |
| couch             | 0.5181  | 0.8571  | 0.4361  | 0.7714  |
| file              | 0.0663  | 0.3214  | 0.0622  | 0.1786  |
| tissue box        | 0.0833  | 0.3333  | 0.0833  | 0.3333  |
| switch            | 0.0022  | 0.0566  | 0.0000  | 0.0000  |
| pad               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| light             | 0.1499  | 0.5455  | 0.0036  | 0.0795  |
| pillow            | 0.4086  | 0.7500  | 0.0391  | 0.2227  |
| printer           | 0.7965  | 0.8889  | 0.1926  | 0.5556  |
| jar               | 0.2240  | 0.6250  | 0.1293  | 0.2500  |
| towel             | 0.3479  | 0.7215  | 0.0914  | 0.2658  |
| candlestick       | 0.0058  | 1.0000  | 0.0000  | 0.0000  |
| tv                | 0.7527  | 0.9429  | 0.3182  | 0.6000  |
| commode           | 0.4965  | 0.8077  | 0.3862  | 0.6731  |
| lamp              | 0.3677  | 0.6062  | 0.0570  | 0.1875  |
| frame             | 0.1335  | 0.8000  | 0.0152  | 0.1667  |
| blanket           | 0.4228  | 0.8923  | 0.1000  | 0.4154  |
| stool             | 0.5113  | 0.8704  | 0.3762  | 0.6852  |
| decoration        | 0.1887  | 0.5938  | 0.0275  | 0.0938  |
| candle            | 0.0883  | 0.2667  | 0.0018  | 0.1333  |
| door              | 0.3767  | 0.7132  | 0.1102  | 0.3382  |
| price tag         | 0.1531  | 0.2857  | 0.0000  | 0.0000  |
| bowl              | 0.2146  | 0.6667  | 0.1299  | 0.2593  |
| bed               | 0.9206  | 0.9636  | 0.7664  | 0.8909  |
| folder            | 0.1340  | 0.3571  | 0.0248  | 0.1429  |
| clothes           | 0.3072  | 0.7476  | 0.0547  | 0.2816  |
| carpet            | 0.3011  | 0.6786  | 0.0152  | 0.2143  |
| bench             | 0.4080  | 0.8571  | 0.3634  | 0.7500  |
| cushion           | 0.3896  | 0.8000  | 0.0519  | 0.2333  |
| remote control    | 0.0449  | 0.2500  | 0.0256  | 0.1500  |
| vase              | 0.3245  | 0.4500  | 0.1786  | 0.2000  |
| tablet            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ottoman           | 0.6193  | 0.9091  | 0.5558  | 0.8182  |
| rack              | 0.4217  | 0.7333  | 0.1904  | 0.5667  |
| flowerpot         | 0.0485  | 0.3750  | 0.0003  | 0.0417  |
| teapot            | 0.0250  | 0.2500  | 0.0000  | 0.0000  |
| bar               | 0.5556  | 0.6667  | 0.0167  | 0.3333  |
| napkins           | 0.5556  | 0.6667  | 0.0000  | 0.0000  |
| player            | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| counter           | 0.1430  | 0.7561  | 0.0294  | 0.2439  |
| magazine          | 0.0134  | 0.7500  | 0.0012  | 0.2500  |
| cube              | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| roll              | 0.1951  | 0.5000  | 0.0000  | 0.0000  |
| toy               | 0.4808  | 0.5385  | 0.0865  | 0.2308  |
| poster            | 0.0522  | 0.1905  | 0.0018  | 0.0159  |
| blackboard        | 0.7301  | 0.8333  | 0.4776  | 0.6667  |
| humidifier        | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| heater            | 0.6586  | 0.8070  | 0.1670  | 0.4035  |
| windowsill        | 0.1706  | 0.6667  | 0.0481  | 0.2308  |
| fan               | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| device            | 0.3913  | 0.6364  | 0.0000  | 0.0000  |
| ball              | 0.6444  | 0.8000  | 0.6000  | 0.6000  |
| doorframe         | 0.1514  | 0.6800  | 0.0167  | 0.2000  |
| board             | 0.3945  | 0.6667  | 0.1358  | 0.2083  |
| umbrella          | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| brush             | 0.2140  | 0.5000  | 0.0000  | 0.0000  |
| radiator          | 0.6134  | 0.7241  | 0.1019  | 0.2414  |
| drawer            | 0.7690  | 1.0000  | 0.5014  | 0.8889  |
| speaker           | 0.1306  | 0.3333  | 0.0017  | 0.0833  |
| kettle            | 0.3450  | 0.8333  | 0.0646  | 0.3333  |
| tube              | 0.0381  | 0.2857  | 0.0000  | 0.0000  |
| shoe              | 0.3353  | 0.6667  | 0.0600  | 0.1429  |
| mirror            | 0.3726  | 0.7037  | 0.0935  | 0.2963  |
| pot               | 0.5375  | 0.5833  | 0.0750  | 0.1667  |
| bucket            | 0.6330  | 1.0000  | 0.3308  | 0.6000  |
| hanger            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape     | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| stairs            | 0.4424  | 0.7143  | 0.0626  | 0.4286  |
| coffee maker      | 0.2747  | 0.5556  | 0.0053  | 0.1111  |
| air conditioner   | 0.7500  | 0.7500  | 0.5000  | 0.5000  |
| cosmetics         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basket            | 0.5291  | 0.7500  | 0.2429  | 0.4688  |
| rail              | 0.0006  | 0.2500  | 0.0000  | 0.0000  |
| calendar          | 0.0296  | 0.4000  | 0.0000  | 0.0000  |
| dresser           | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| album             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| microwave         | 0.9053  | 1.0000  | 0.4784  | 0.7059  |
| guitar            | 1.0000  | 1.0000  | 0.5000  | 0.5000  |
| refrigerator      | 0.7370  | 0.8667  | 0.6034  | 0.6667  |
| balcony           | 0.2935  | 1.0000  | 0.2500  | 0.5000  |
| container         | 0.0761  | 0.4444  | 0.0191  | 0.2222  |
| tissue            | 0.2619  | 0.6667  | 0.2619  | 0.6667  |
| machine           | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| bicycle           | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| scale             | 0.6667  | 0.6667  | 0.0000  | 0.0000  |
| sunglasses        | 0.0574  | 0.4615  | 0.0014  | 0.0769  |
| hat               | 0.1480  | 0.2857  | 0.0020  | 0.1429  |
| letter            | 0.7750  | 1.0000  | 0.0147  | 0.2500  |
| paper cutter      | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| furniture         | 0.0370  | 0.3333  | 0.0000  | 0.0000  |
| bathtub           | 1.0000  | 1.0000  | 0.8642  | 0.8889  |
| statue            | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| shampoo           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer     | 0.5787  | 0.9000  | 0.4581  | 0.7000  |
| toilet            | 0.8652  | 0.8824  | 0.5688  | 0.6471  |
| faucet            | 0.2619  | 0.6667  | 0.0000  | 0.0000  |
| shower            | 0.4595  | 1.0000  | 0.0625  | 0.2500  |
| toilet paper      | 0.2337  | 0.5000  | 0.0046  | 0.0833  |
| beam              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toiletry          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| soap              | 0.0086  | 1.0000  | 0.0000  | 0.0000  |
| washing machine   | 0.5430  | 0.6429  | 0.3571  | 0.3571  |
| dispenser         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| body loofah       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beanbag           | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| jalousie          | 1.0000  | 1.0000  | 0.5000  | 0.5000  |
| basin             | 0.0214  | 0.2000  | 0.0024  | 0.0667  |
| can               | 0.1667  | 0.2500  | 0.0000  | 0.0000  |
| column            | 0.0248  | 1.0000  | 0.0248  | 1.0000  |
| pillar            | 0.6455  | 0.7647  | 0.4663  | 0.6471  |
| pipe              | 0.2700  | 0.7500  | 0.0069  | 0.2500  |
| knife             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fireplace         | 0.2000  | 0.2000  | 0.0500  | 0.2000  |
| partition         | 0.6200  | 0.8000  | 0.1000  | 0.2000  |
| glass             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| screen            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| spoon             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pan               | 0.2589  | 0.6667  | 0.0445  | 0.2500  |
| oven              | 0.7610  | 1.0000  | 0.1764  | 0.5263  |
| stove             | 0.4094  | 0.8824  | 0.0845  | 0.4118  |
| fruit             | 0.4033  | 0.8000  | 0.2100  | 0.4000  |
| kitchenware       | 0.2612  | 0.5000  | 0.0414  | 0.1591  |
| shovel            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| food              | 0.1000  | 0.1000  | 0.0000  | 0.0000  |
| radio             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| sign              | 0.5526  | 1.0000  | 0.0000  | 0.0000  |
| crib              | 0.3939  | 0.6667  | 0.3939  | 0.6667  |
| piano             | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| mattress          | 0.1667  | 0.5000  | 0.0000  | 0.0000  |
| sponge            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| treadmill         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ladder            | 0.4600  | 1.0000  | 0.3333  | 0.3333  |
| holder            | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| tray              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dish         | 0.1667  | 0.5000  | 0.0000  | 0.0000  |
| chandelier        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hook              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| map               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glove             | 0.8333  | 1.0000  | 0.1111  | 0.3333  |
| seasoning         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| salt              | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| flush             | 0.8333  | 1.0000  | 0.0000  | 0.0000  |
| dishwasher        | 0.8095  | 1.0000  | 0.6667  | 0.6667  |
| vacuum cleaner    | 0.7500  | 0.7500  | 0.0000  | 0.0000  |
| dish rack         | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| mop               | 1.0000  | 1.0000  | 0.1667  | 0.3333  |
| pack              | 0.0686  | 0.6923  | 0.0017  | 0.1538  |
| broom             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bread             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| water heater      | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| sack              | 0.6667  | 0.8571  | 0.3175  | 0.5714  |
| crate             | 0.7600  | 0.8000  | 0.4000  | 0.4000  |
| toaster           | 1.0000  | 1.0000  | 0.1250  | 0.2500  |
| range hood        | 0.7200  | 0.8000  | 0.0000  | 0.0000  |
| dumbbell          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| helmets           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tool              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| luggage           | 0.7436  | 1.0000  | 0.6667  | 0.6667  |
| menu              | 0.6429  | 0.7143  | 0.0000  | 0.0000  |
| footstool         | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| wire              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dispenser    | 0.5422  | 0.8000  | 0.0889  | 0.4000  |
| toothbrush        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scissors          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| window frame      | 0.0905  | 0.3333  | 0.0000  | 0.0000  |
| mat               | 0.1667  | 0.3333  | 0.1111  | 0.3333  |
| coil              | 0.2500  | 0.3333  | 0.0417  | 0.1667  |
| washbasin         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------           | ------- | ------- | ------- | ------- |
| 3rscan            | 0.3826  | 0.5844  | 0.1697  | 0.2764  |
| head              | 0.3845  | 0.6637  | 0.1600  | 0.3334  |
| common            | 0.4154  | 0.5892  | 0.2074  | 0.2759  |
| tail              | 0.3351  | 0.4324  | 0.1368  | 0.1724  |
| small             | 0.1325  | 0.3074  | 0.0171  | 0.0646  |
| medium            | 0.4029  | 0.6479  | 0.1968  | 0.3180  |
| large             | 0.2960  | 0.7645  | 0.1284  | 0.4470  |
+-------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 17:45:30 INFO metrics.py:437 | number of matterport3d results: 339
Rank[0/8] 06/20/2025 17:47:00 INFO metrics.py:332 | eval results of matterport3d:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| mirror              | 0.1681  | 0.6216  | 0.0079  | 0.2027  |
| picture             | 0.2392  | 0.4807  | 0.0280  | 0.1401  |
| doorframe           | 0.3278  | 0.7230  | 0.1025  | 0.4116  |
| door                | 0.2912  | 0.6136  | 0.0136  | 0.1697  |
| light               | 0.0452  | 0.2450  | 0.0008  | 0.0370  |
| sink                | 0.5539  | 0.8182  | 0.2902  | 0.5556  |
| towel               | 0.3713  | 0.5909  | 0.1477  | 0.3545  |
| drawer              | 0.0022  | 0.3750  | 0.0003  | 0.1250  |
| blanket             | 0.2091  | 0.6364  | 0.0811  | 0.2727  |
| plant               | 0.3195  | 0.8125  | 0.1343  | 0.4554  |
| lamp                | 0.3580  | 0.7253  | 0.2035  | 0.4066  |
| decoration          | 0.0109  | 0.4048  | 0.0012  | 0.1310  |
| case                | 0.0005  | 0.2778  | 0.0001  | 0.1111  |
| toilet              | 0.8055  | 0.8438  | 0.7604  | 0.7969  |
| roof                | 0.0015  | 0.2083  | 0.0000  | 0.0000  |
| cabinet             | 0.1230  | 0.6483  | 0.0712  | 0.4449  |
| counter             | 0.0163  | 0.4242  | 0.0021  | 0.1212  |
| bench               | 0.2256  | 0.5500  | 0.1688  | 0.3000  |
| table               | 0.3510  | 0.7692  | 0.3077  | 0.6410  |
| window              | 0.1686  | 0.5194  | 0.0293  | 0.1889  |
| vase                | 0.0566  | 0.3667  | 0.0048  | 0.2667  |
| bottle              | 0.0938  | 0.5833  | 0.0141  | 0.1875  |
| stand               | 0.4607  | 0.8200  | 0.4074  | 0.7200  |
| shelf               | 0.1578  | 0.6131  | 0.0586  | 0.3212  |
| box                 | 0.0534  | 0.6923  | 0.0130  | 0.4231  |
| frame               | 0.0001  | 0.1176  | 0.0000  | 0.0000  |
| plate               | 0.0071  | 0.5000  | 0.0001  | 0.0714  |
| pillow              | 0.0889  | 0.5489  | 0.0077  | 0.1104  |
| switch              | 0.0001  | 0.1000  | 0.0000  | 0.0000  |
| carpet              | 0.0004  | 0.0556  | 0.0000  | 0.0000  |
| countertop          | 0.0133  | 0.2500  | 0.0004  | 0.0714  |
| bathtub             | 0.6789  | 0.7778  | 0.5679  | 0.7407  |
| flowerpot           | 0.0309  | 0.4000  | 0.0022  | 0.1200  |
| mat                 | 0.0059  | 0.2500  | 0.0003  | 0.0417  |
| curtain             | 0.3879  | 0.7292  | 0.0772  | 0.3542  |
| vent                | 0.0207  | 0.2500  | 0.0000  | 0.0000  |
| statue              | 0.0719  | 0.4706  | 0.0557  | 0.2353  |
| handle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dresser             | 0.2370  | 0.6667  | 0.2027  | 0.6000  |
| window frame        | 0.0152  | 0.3793  | 0.0019  | 0.1724  |
| soap                | 0.0093  | 0.6000  | 0.0000  | 0.0000  |
| stool               | 0.0357  | 0.3333  | 0.0345  | 0.2857  |
| book                | 0.0833  | 0.4488  | 0.0126  | 0.0787  |
| desk                | 0.4144  | 0.7368  | 0.2924  | 0.6842  |
| paper               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| couch               | 0.4063  | 0.7717  | 0.3247  | 0.6630  |
| chair               | 0.4487  | 0.7721  | 0.3422  | 0.6429  |
| tv                  | 0.5599  | 0.7209  | 0.2135  | 0.3721  |
| bed                 | 0.6611  | 0.8070  | 0.3370  | 0.6491  |
| screen              | 0.1190  | 0.5000  | 0.0119  | 0.2500  |
| stairs              | 0.0050  | 0.3585  | 0.0023  | 0.0755  |
| ottoman             | 0.0306  | 0.4444  | 0.0301  | 0.4444  |
| bowl                | 0.1476  | 0.3333  | 0.0428  | 0.1818  |
| alarm               | 0.0076  | 0.1667  | 0.0000  | 0.0000  |
| steps               | 0.0012  | 0.3000  | 0.0000  | 0.0000  |
| clock               | 0.1857  | 0.6667  | 0.1857  | 0.6667  |
| toy                 | 0.1038  | 0.4722  | 0.0111  | 0.0833  |
| telephone           | 0.2567  | 0.5000  | 0.0033  | 0.2500  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fan                 | 0.6298  | 0.7778  | 0.2981  | 0.5000  |
| cushion             | 0.0024  | 0.1176  | 0.0002  | 0.0588  |
| footstool           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cup                 | 0.0009  | 0.2000  | 0.0000  | 0.0000  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wine                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jar                 | 0.0101  | 0.3333  | 0.0000  | 0.0000  |
| pot                 | 0.0035  | 0.5385  | 0.0008  | 0.2308  |
| candle              | 0.0104  | 0.3333  | 0.0104  | 0.3333  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| board               | 0.1806  | 0.5000  | 0.0008  | 0.1667  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit               | 0.0417  | 0.1250  | 0.0417  | 0.1250  |
| pillar              | 0.2647  | 0.7619  | 0.1579  | 0.4286  |
| column              | 0.0074  | 0.2222  | 0.0002  | 0.0556  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| doorway             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| door knob           | 0.1667  | 0.5000  | 0.0000  | 0.0000  |
| washbasin           | 0.0092  | 0.2857  | 0.0089  | 0.2143  |
| shower              | 0.0125  | 0.1667  | 0.0000  | 0.0000  |
| washing machine     | 0.4981  | 0.6250  | 0.0389  | 0.2500  |
| faucet              | 0.0355  | 0.2778  | 0.0000  | 0.0000  |
| bin                 | 0.3347  | 0.7073  | 0.1934  | 0.4146  |
| dispenser           | 0.0119  | 0.5000  | 0.0119  | 0.5000  |
| vanity              | 0.1942  | 0.6000  | 0.1476  | 0.4667  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toilet paper        | 0.3027  | 0.6944  | 0.1009  | 0.3611  |
| rail                | 0.0301  | 0.3889  | 0.0000  | 0.0741  |
| headboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| windowsill          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| teapot              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball                | 0.5056  | 0.7500  | 0.5056  | 0.7500  |
| socket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| thermostat          | 0.0556  | 0.1667  | 0.0000  | 0.0000  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dispenser      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| holder              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hanger              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet               | 0.2500  | 0.5000  | 0.2500  | 0.5000  |
| brush               | 0.0049  | 0.2500  | 0.0000  | 0.0000  |
| kitchen island      | 0.2234  | 0.4286  | 0.1693  | 0.4286  |
| heater              | 0.3333  | 1.0000  | 0.3333  | 1.0000  |
| monitor             | 0.3333  | 0.5000  | 0.0000  | 0.0000  |
| piano               | 0.2500  | 0.5000  | 0.2500  | 0.5000  |
| air conditioner     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| refrigerator        | 0.4945  | 0.8667  | 0.0802  | 0.2667  |
| rack                | 0.0016  | 0.3636  | 0.0009  | 0.2727  |
| radiator            | 0.1667  | 0.3333  | 0.0000  | 0.0000  |
| scale               | 0.0035  | 0.2500  | 0.0000  | 0.0000  |
| pan                 | 0.0143  | 0.4000  | 0.0000  | 0.0000  |
| basin               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wardrobe            | 0.0532  | 0.4545  | 0.0517  | 0.2727  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mouse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| keyboard            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue box          | 0.2029  | 0.5000  | 0.0054  | 0.1250  |
| pool table          | 0.1667  | 0.3333  | 0.1667  | 0.3333  |
| basket              | 0.0544  | 0.4000  | 0.0030  | 0.1200  |
| pipe                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shoe                | 0.0521  | 0.2800  | 0.0000  | 0.0000  |
| tissue              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0477  | 0.2500  | 0.0000  | 0.0000  |
| food                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| projector           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bag                 | 0.0459  | 0.3846  | 0.0025  | 0.0769  |
| clothes dryer       | 0.3333  | 0.6667  | 0.2222  | 0.6667  |
| clothes             | 0.1211  | 0.3621  | 0.0167  | 0.1379  |
| excercise equipment | 0.4925  | 0.7778  | 0.0237  | 0.3333  |
| kitchenware         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kettle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| chandelier          | 0.0684  | 0.2105  | 0.0171  | 0.1053  |
| fire extinguisher   | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| ladder              | 0.1977  | 0.4444  | 0.0004  | 0.1111  |
| bucket              | 0.0057  | 0.4000  | 0.0031  | 0.2000  |
| garage door         | 0.3476  | 0.5000  | 0.3476  | 0.5000  |
| printer             | 0.4286  | 0.4286  | 0.2381  | 0.2857  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toiletry            | 0.0758  | 0.2727  | 0.0008  | 0.0455  |
| soap dish           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fireplace           | 0.3361  | 0.5217  | 0.0347  | 0.2609  |
| beam                | 0.0000  | 0.0476  | 0.0000  | 0.0476  |
| arch                | 0.0127  | 0.1333  | 0.0007  | 0.0667  |
| container           | 0.0094  | 0.1481  | 0.0024  | 0.0741  |
| machine             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| computer            | 0.0026  | 1.0000  | 0.0000  | 0.0000  |
| stove               | 0.2956  | 0.6000  | 0.1476  | 0.5000  |
| oven                | 0.3381  | 0.8000  | 0.0000  | 0.0000  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| laptop              | 0.8333  | 1.0000  | 0.0000  | 0.0000  |
| guitar              | 0.1178  | 0.4286  | 0.0000  | 0.0000  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| microwave           | 0.1062  | 0.2500  | 0.0833  | 0.0833  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 0.1429  | 1.0000  | 0.0000  | 0.0000  |
| partition           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| backpack            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| player              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell            | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| hamper              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| magazine            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.0957  | 0.7500  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pack                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drum                | 0.0387  | 0.2500  | 0.0000  | 0.0000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jacket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candlestick         | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| water heater        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| can                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coffee maker        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| matterport3d        | 0.1229  | 0.3191  | 0.0596  | 0.1579  |
| head                | 0.1824  | 0.4707  | 0.0831  | 0.2432  |
| common              | 0.0732  | 0.2138  | 0.0410  | 0.0900  |
| tail                | 0.0603  | 0.1096  | 0.0322  | 0.0608  |
| small               | 0.0464  | 0.1916  | 0.0240  | 0.0763  |
| medium              | 0.1143  | 0.3369  | 0.0512  | 0.1603  |
| large               | 0.1149  | 0.3975  | 0.0634  | 0.2628  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 17:47:00 INFO metrics.py:437 | number of arkit results: 0
Rank[0/8] 06/20/2025 17:47:01 INFO stats.py:394 | Epoch[209] completed. Training Speed: 3.03 samples/sec across all devices.	Epoch Time: 1029.55 sec.	Average Epoch Time: 1029.55 sec.	Average Step Time: 2.64 sec.	Estimated Remaining Time: 3:56:23.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:47:01 WARNING accelerator.py:3099 | Deleting 1 checkpoints to make room for new checkpoint.
Rank[0/8] 06/20/2025 17:47:01 INFO accelerator.py:3111 | Saving current state to /job_data/checkpoints/checkpoint_20
Rank[1/8] 06/20/2025 17:47:02 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 209 to /job_data/checkpoints/checkpoint_20
Rank[7/8] 06/20/2025 17:47:02 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 209 to /job_data/checkpoints/checkpoint_20
Rank[3/8] 06/20/2025 17:47:02 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 209 to /job_data/checkpoints/checkpoint_20
Rank[5/8] 06/20/2025 17:47:02 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 209 to /job_data/checkpoints/checkpoint_20
Rank[2/8] 06/20/2025 17:47:02 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 209 to /job_data/checkpoints/checkpoint_20
Rank[4/8] 06/20/2025 17:47:02 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 209 to /job_data/checkpoints/checkpoint_20
Rank[6/8] 06/20/2025 17:47:02 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 209 to /job_data/checkpoints/checkpoint_20
Rank[0/8] 06/20/2025 17:47:03 INFO checkpointing.py:106 | Model weights saved in /job_data/checkpoints/checkpoint_20/model.safetensors
Rank[0/8] 06/20/2025 17:47:06 INFO checkpointing.py:113 | Optimizer state saved in /job_data/checkpoints/checkpoint_20/optimizer.bin
Rank[0/8] 06/20/2025 17:47:06 INFO checkpointing.py:120 | Scheduler state saved in /job_data/checkpoints/checkpoint_20/scheduler.bin
Rank[0/8] 06/20/2025 17:47:06 INFO checkpointing.py:137 | Sampler state for dataloader 0 saved in /job_data/checkpoints/checkpoint_20/sampler.bin
Rank[0/8] 06/20/2025 17:47:06 INFO checkpointing.py:137 | Sampler state for dataloader 1 saved in /job_data/checkpoints/checkpoint_20/sampler_1.bin
Rank[0/8] 06/20/2025 17:47:06 INFO checkpointing.py:164 | Random states saved in /job_data/checkpoints/checkpoint_20/random_states_0.pkl
Rank[0/8] 06/20/2025 17:47:06 INFO checkpointing.py:300 | Saving the state of TrainerProgressState to /job_data/checkpoints/checkpoint_20/custom_checkpoint_0.pkl
Rank[0/8] 06/20/2025 17:47:06 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 209 to /job_data/checkpoints/checkpoint_20
Rank[0/8] 06/20/2025 17:47:33 INFO stats.py:314 | Epoch[210] Step[24] GlobalStep[81924] Training Speed: 8.13 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 3:55:53.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:47:33 INFO loss_tracker.py:84 | Epoch[210/239] Step[24] GlobalStep[81924/NA]: det_loss_cls_0[0.4636] det_loss_box_0[0.5609] det_loss_cls_1[0.4505] det_loss_box_1[0.4240] det_loss_cls_2[0.3823] det_loss_box_2[0.3759] det_loss_cls_3[0.3455] det_loss_box_3[0.3642] det_loss_cls_4[0.3202] det_loss_box_4[0.3533] det_loss_cls_5[0.3069] det_loss_box_5[0.3529] det_loss_cls_dn_0[0.0836] det_loss_box_dn_0[0.2342] det_loss_cls_dn_1[0.0801] det_loss_box_dn_1[0.1921] det_loss_cls_dn_2[0.0793] det_loss_box_dn_2[0.1766] det_loss_cls_dn_3[0.0783] det_loss_box_dn_3[0.1730] det_loss_cls_dn_4[0.0777] det_loss_box_dn_4[0.1715] det_loss_cls_dn_5[0.0768] det_loss_box_dn_5[0.1702] loss_depth[0.0358] total_loss[6.3291]
Rank[0/8] 06/20/2025 17:48:00 INFO stats.py:314 | Epoch[210] Step[49] GlobalStep[81949] Training Speed: 8.10 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 3:55:22.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:48:00 INFO loss_tracker.py:84 | Epoch[210/239] Step[49] GlobalStep[81949/NA]: det_loss_cls_0[0.4787] det_loss_box_0[0.6247] det_loss_cls_1[0.4657] det_loss_box_1[0.4778] det_loss_cls_2[0.4136] det_loss_box_2[0.4261] det_loss_cls_3[0.3765] det_loss_box_3[0.4027] det_loss_cls_4[0.3530] det_loss_box_4[0.3927] det_loss_cls_5[0.3474] det_loss_box_5[0.3906] det_loss_cls_dn_0[0.1005] det_loss_box_dn_0[0.2776] det_loss_cls_dn_1[0.0968] det_loss_box_dn_1[0.2282] det_loss_cls_dn_2[0.0956] det_loss_box_dn_2[0.2065] det_loss_cls_dn_3[0.0943] det_loss_box_dn_3[0.2013] det_loss_cls_dn_4[0.0934] det_loss_box_dn_4[0.1980] det_loss_cls_dn_5[0.0925] det_loss_box_dn_5[0.1954] loss_depth[0.0366] total_loss[7.0662]
Rank[0/8] 06/20/2025 17:48:25 INFO stats.py:314 | Epoch[210] Step[74] GlobalStep[81974] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 3:54:51.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:48:25 INFO loss_tracker.py:84 | Epoch[210/239] Step[74] GlobalStep[81974/NA]: det_loss_cls_0[0.4412] det_loss_box_0[0.5204] det_loss_cls_1[0.4327] det_loss_box_1[0.4039] det_loss_cls_2[0.3769] det_loss_box_2[0.3752] det_loss_cls_3[0.3473] det_loss_box_3[0.3680] det_loss_cls_4[0.3243] det_loss_box_4[0.3606] det_loss_cls_5[0.3048] det_loss_box_5[0.3661] det_loss_cls_dn_0[0.0976] det_loss_box_dn_0[0.3203] det_loss_cls_dn_1[0.0948] det_loss_box_dn_1[0.2656] det_loss_cls_dn_2[0.0940] det_loss_box_dn_2[0.2457] det_loss_cls_dn_3[0.0931] det_loss_box_dn_3[0.2392] det_loss_cls_dn_4[0.0923] det_loss_box_dn_4[0.2345] det_loss_cls_dn_5[0.0915] det_loss_box_dn_5[0.2275] loss_depth[0.0336] total_loss[6.7511]
Rank[0/8] 06/20/2025 17:48:50 INFO stats.py:314 | Epoch[210] Step[99] GlobalStep[81999] Training Speed: 8.65 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 3:54:20.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:48:50 INFO loss_tracker.py:84 | Epoch[210/239] Step[99] GlobalStep[81999/NA]: det_loss_cls_0[0.5115] det_loss_box_0[0.6234] det_loss_cls_1[0.4941] det_loss_box_1[0.4704] det_loss_cls_2[0.4292] det_loss_box_2[0.4251] det_loss_cls_3[0.3910] det_loss_box_3[0.4235] det_loss_cls_4[0.3786] det_loss_box_4[0.4072] det_loss_cls_5[0.3715] det_loss_box_5[0.4027] det_loss_cls_dn_0[0.0930] det_loss_box_dn_0[0.2583] det_loss_cls_dn_1[0.0895] det_loss_box_dn_1[0.2145] det_loss_cls_dn_2[0.0892] det_loss_box_dn_2[0.1982] det_loss_cls_dn_3[0.0884] det_loss_box_dn_3[0.1951] det_loss_cls_dn_4[0.0878] det_loss_box_dn_4[0.1925] det_loss_cls_dn_5[0.0873] det_loss_box_dn_5[0.1882] loss_depth[0.0369] total_loss[7.1473]
Rank[0/8] 06/20/2025 17:49:17 INFO stats.py:314 | Epoch[210] Step[124] GlobalStep[82024] Training Speed: 8.08 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 3:53:49.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:49:17 INFO loss_tracker.py:84 | Epoch[210/239] Step[124] GlobalStep[82024/NA]: det_loss_cls_0[0.3391] det_loss_box_0[0.3989] det_loss_cls_1[0.3163] det_loss_box_1[0.2903] det_loss_cls_2[0.2518] det_loss_box_2[0.2637] det_loss_cls_3[0.2223] det_loss_box_3[0.2508] det_loss_cls_4[0.2029] det_loss_box_4[0.2466] det_loss_cls_5[0.1896] det_loss_box_5[0.2491] det_loss_cls_dn_0[0.0743] det_loss_box_dn_0[0.2387] det_loss_cls_dn_1[0.0716] det_loss_box_dn_1[0.1886] det_loss_cls_dn_2[0.0706] det_loss_box_dn_2[0.1710] det_loss_cls_dn_3[0.0701] det_loss_box_dn_3[0.1667] det_loss_cls_dn_4[0.0693] det_loss_box_dn_4[0.1656] det_loss_cls_dn_5[0.0686] det_loss_box_dn_5[0.1641] loss_depth[0.0339] total_loss[4.7745]
Rank[0/8] 06/20/2025 17:49:42 INFO stats.py:314 | Epoch[210] Step[149] GlobalStep[82049] Training Speed: 7.99 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 3:53:18.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:49:42 INFO loss_tracker.py:84 | Epoch[210/239] Step[149] GlobalStep[82049/NA]: det_loss_cls_0[0.3109] det_loss_box_0[0.3735] det_loss_cls_1[0.2976] det_loss_box_1[0.2872] det_loss_cls_2[0.2375] det_loss_box_2[0.2691] det_loss_cls_3[0.2162] det_loss_box_3[0.2540] det_loss_cls_4[0.1992] det_loss_box_4[0.2409] det_loss_cls_5[0.1913] det_loss_box_5[0.2411] det_loss_cls_dn_0[0.0763] det_loss_box_dn_0[0.2306] det_loss_cls_dn_1[0.0737] det_loss_box_dn_1[0.1960] det_loss_cls_dn_2[0.0720] det_loss_box_dn_2[0.1815] det_loss_cls_dn_3[0.0711] det_loss_box_dn_3[0.1792] det_loss_cls_dn_4[0.0703] det_loss_box_dn_4[0.1773] det_loss_cls_dn_5[0.0693] det_loss_box_dn_5[0.1767] loss_depth[0.0332] total_loss[4.7258]
Rank[0/8] 06/20/2025 17:50:08 INFO stats.py:314 | Epoch[210] Step[174] GlobalStep[82074] Training Speed: 8.84 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 3:52:48.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:50:08 INFO loss_tracker.py:84 | Epoch[210/239] Step[174] GlobalStep[82074/NA]: det_loss_cls_0[0.5138] det_loss_box_0[0.6716] det_loss_cls_1[0.4893] det_loss_box_1[0.5208] det_loss_cls_2[0.4403] det_loss_box_2[0.4810] det_loss_cls_3[0.4041] det_loss_box_3[0.4585] det_loss_cls_4[0.3828] det_loss_box_4[0.4553] det_loss_cls_5[0.3671] det_loss_box_5[0.4572] det_loss_cls_dn_0[0.0883] det_loss_box_dn_0[0.2906] det_loss_cls_dn_1[0.0844] det_loss_box_dn_1[0.2417] det_loss_cls_dn_2[0.0839] det_loss_box_dn_2[0.2278] det_loss_cls_dn_3[0.0825] det_loss_box_dn_3[0.2234] det_loss_cls_dn_4[0.0815] det_loss_box_dn_4[0.2217] det_loss_cls_dn_5[0.0803] det_loss_box_dn_5[0.2186] loss_depth[0.0374] total_loss[7.6040]
Rank[0/8] 06/20/2025 17:50:34 INFO stats.py:314 | Epoch[210] Step[199] GlobalStep[82099] Training Speed: 7.73 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 3:52:17.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:50:34 INFO loss_tracker.py:84 | Epoch[210/239] Step[199] GlobalStep[82099/NA]: det_loss_cls_0[0.4028] det_loss_box_0[0.4993] det_loss_cls_1[0.3947] det_loss_box_1[0.3763] det_loss_cls_2[0.3366] det_loss_box_2[0.3372] det_loss_cls_3[0.2941] det_loss_box_3[0.3292] det_loss_cls_4[0.2760] det_loss_box_4[0.3206] det_loss_cls_5[0.2649] det_loss_box_5[0.3205] det_loss_cls_dn_0[0.0865] det_loss_box_dn_0[0.2860] det_loss_cls_dn_1[0.0837] det_loss_box_dn_1[0.2234] det_loss_cls_dn_2[0.0829] det_loss_box_dn_2[0.1990] det_loss_cls_dn_3[0.0813] det_loss_box_dn_3[0.1921] det_loss_cls_dn_4[0.0811] det_loss_box_dn_4[0.1874] det_loss_cls_dn_5[0.0802] det_loss_box_dn_5[0.1859] loss_depth[0.0328] total_loss[5.9546]
Rank[0/8] 06/20/2025 17:51:00 INFO stats.py:314 | Epoch[210] Step[224] GlobalStep[82124] Training Speed: 7.85 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 3:51:46.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:51:00 INFO loss_tracker.py:84 | Epoch[210/239] Step[224] GlobalStep[82124/NA]: det_loss_cls_0[0.5052] det_loss_box_0[0.6433] det_loss_cls_1[0.5002] det_loss_box_1[0.4611] det_loss_cls_2[0.4139] det_loss_box_2[0.4236] det_loss_cls_3[0.3803] det_loss_box_3[0.4100] det_loss_cls_4[0.3590] det_loss_box_4[0.3900] det_loss_cls_5[0.3444] det_loss_box_5[0.3943] det_loss_cls_dn_0[0.0857] det_loss_box_dn_0[0.2374] det_loss_cls_dn_1[0.0822] det_loss_box_dn_1[0.1859] det_loss_cls_dn_2[0.0815] det_loss_box_dn_2[0.1682] det_loss_cls_dn_3[0.0804] det_loss_box_dn_3[0.1641] det_loss_cls_dn_4[0.0795] det_loss_box_dn_4[0.1619] det_loss_cls_dn_5[0.0786] det_loss_box_dn_5[0.1607] loss_depth[0.0355] total_loss[6.8270]
Rank[0/8] 06/20/2025 17:51:25 INFO stats.py:314 | Epoch[210] Step[249] GlobalStep[82149] Training Speed: 8.22 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 3:51:15.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:51:25 INFO loss_tracker.py:84 | Epoch[210/239] Step[249] GlobalStep[82149/NA]: det_loss_cls_0[0.4833] det_loss_box_0[0.6615] det_loss_cls_1[0.4637] det_loss_box_1[0.5037] det_loss_cls_2[0.4133] det_loss_box_2[0.4476] det_loss_cls_3[0.3880] det_loss_box_3[0.4218] det_loss_cls_4[0.3591] det_loss_box_4[0.4143] det_loss_cls_5[0.3497] det_loss_box_5[0.4117] det_loss_cls_dn_0[0.0858] det_loss_box_dn_0[0.2863] det_loss_cls_dn_1[0.0824] det_loss_box_dn_1[0.2390] det_loss_cls_dn_2[0.0814] det_loss_box_dn_2[0.2190] det_loss_cls_dn_3[0.0803] det_loss_box_dn_3[0.2144] det_loss_cls_dn_4[0.0797] det_loss_box_dn_4[0.2109] det_loss_cls_dn_5[0.0788] det_loss_box_dn_5[0.2080] loss_depth[0.0382] total_loss[7.2218]
Rank[0/8] 06/20/2025 17:51:51 INFO stats.py:314 | Epoch[210] Step[274] GlobalStep[82174] Training Speed: 8.56 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 3:50:44.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:51:51 INFO loss_tracker.py:84 | Epoch[210/239] Step[274] GlobalStep[82174/NA]: det_loss_cls_0[0.4454] det_loss_box_0[0.5617] det_loss_cls_1[0.4391] det_loss_box_1[0.4341] det_loss_cls_2[0.3714] det_loss_box_2[0.3979] det_loss_cls_3[0.3436] det_loss_box_3[0.3845] det_loss_cls_4[0.3199] det_loss_box_4[0.3775] det_loss_cls_5[0.3046] det_loss_box_5[0.3730] det_loss_cls_dn_0[0.0894] det_loss_box_dn_0[0.2992] det_loss_cls_dn_1[0.0854] det_loss_box_dn_1[0.2540] det_loss_cls_dn_2[0.0847] det_loss_box_dn_2[0.2381] det_loss_cls_dn_3[0.0834] det_loss_box_dn_3[0.2330] det_loss_cls_dn_4[0.0823] det_loss_box_dn_4[0.2305] det_loss_cls_dn_5[0.0814] det_loss_box_dn_5[0.2268] loss_depth[0.0352] total_loss[6.7762]
Rank[0/8] 06/20/2025 17:52:18 INFO stats.py:314 | Epoch[210] Step[299] GlobalStep[82199] Training Speed: 8.00 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 3:50:13.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:52:18 INFO loss_tracker.py:84 | Epoch[210/239] Step[299] GlobalStep[82199/NA]: det_loss_cls_0[0.4381] det_loss_box_0[0.4697] det_loss_cls_1[0.4424] det_loss_box_1[0.3518] det_loss_cls_2[0.3713] det_loss_box_2[0.3305] det_loss_cls_3[0.3377] det_loss_box_3[0.3201] det_loss_cls_4[0.3148] det_loss_box_4[0.3080] det_loss_cls_5[0.3003] det_loss_box_5[0.3048] det_loss_cls_dn_0[0.0884] det_loss_box_dn_0[0.2980] det_loss_cls_dn_1[0.0863] det_loss_box_dn_1[0.2420] det_loss_cls_dn_2[0.0851] det_loss_box_dn_2[0.2227] det_loss_cls_dn_3[0.0847] det_loss_box_dn_3[0.2166] det_loss_cls_dn_4[0.0839] det_loss_box_dn_4[0.2136] det_loss_cls_dn_5[0.0829] det_loss_box_dn_5[0.2087] loss_depth[0.0361] total_loss[6.2385]
Rank[0/8] 06/20/2025 17:52:43 INFO stats.py:314 | Epoch[210] Step[324] GlobalStep[82224] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 3:49:42.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:52:43 INFO loss_tracker.py:84 | Epoch[210/239] Step[324] GlobalStep[82224/NA]: det_loss_cls_0[0.4931] det_loss_box_0[0.5920] det_loss_cls_1[0.4885] det_loss_box_1[0.4291] det_loss_cls_2[0.4202] det_loss_box_2[0.3867] det_loss_cls_3[0.3764] det_loss_box_3[0.3812] det_loss_cls_4[0.3549] det_loss_box_4[0.3778] det_loss_cls_5[0.3412] det_loss_box_5[0.3781] det_loss_cls_dn_0[0.0967] det_loss_box_dn_0[0.2521] det_loss_cls_dn_1[0.0937] det_loss_box_dn_1[0.2101] det_loss_cls_dn_2[0.0929] det_loss_box_dn_2[0.1937] det_loss_cls_dn_3[0.0920] det_loss_box_dn_3[0.1889] det_loss_cls_dn_4[0.0912] det_loss_box_dn_4[0.1869] det_loss_cls_dn_5[0.0904] det_loss_box_dn_5[0.1859] loss_depth[0.0353] total_loss[6.8293]
Rank[0/8] 06/20/2025 17:53:09 INFO stats.py:314 | Epoch[210] Step[349] GlobalStep[82249] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 3:49:11.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:53:09 INFO loss_tracker.py:84 | Epoch[210/239] Step[349] GlobalStep[82249/NA]: det_loss_cls_0[0.3548] det_loss_box_0[0.4717] det_loss_cls_1[0.3463] det_loss_box_1[0.3460] det_loss_cls_2[0.2995] det_loss_box_2[0.3111] det_loss_cls_3[0.2639] det_loss_box_3[0.3032] det_loss_cls_4[0.2440] det_loss_box_4[0.2931] det_loss_cls_5[0.2363] det_loss_box_5[0.2947] det_loss_cls_dn_0[0.0780] det_loss_box_dn_0[0.3104] det_loss_cls_dn_1[0.0752] det_loss_box_dn_1[0.2397] det_loss_cls_dn_2[0.0734] det_loss_box_dn_2[0.2190] det_loss_cls_dn_3[0.0725] det_loss_box_dn_3[0.2168] det_loss_cls_dn_4[0.0715] det_loss_box_dn_4[0.2117] det_loss_cls_dn_5[0.0710] det_loss_box_dn_5[0.2041] loss_depth[0.0378] total_loss[5.6458]
Rank[0/8] 06/20/2025 17:53:36 INFO stats.py:314 | Epoch[210] Step[374] GlobalStep[82274] Training Speed: 7.78 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 3:48:40.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:53:36 INFO loss_tracker.py:84 | Epoch[210/239] Step[374] GlobalStep[82274/NA]: det_loss_cls_0[0.3845] det_loss_box_0[0.4671] det_loss_cls_1[0.3880] det_loss_box_1[0.3576] det_loss_cls_2[0.3346] det_loss_box_2[0.3294] det_loss_cls_3[0.3152] det_loss_box_3[0.3255] det_loss_cls_4[0.2983] det_loss_box_4[0.3165] det_loss_cls_5[0.2893] det_loss_box_5[0.3139] det_loss_cls_dn_0[0.0800] det_loss_box_dn_0[0.2799] det_loss_cls_dn_1[0.0765] det_loss_box_dn_1[0.2284] det_loss_cls_dn_2[0.0754] det_loss_box_dn_2[0.2132] det_loss_cls_dn_3[0.0742] det_loss_box_dn_3[0.2080] det_loss_cls_dn_4[0.0734] det_loss_box_dn_4[0.2043] det_loss_cls_dn_5[0.0724] det_loss_box_dn_5[0.1941] loss_depth[0.0345] total_loss[5.9341]
Rank[0/8] 06/20/2025 17:53:51 INFO stats.py:394 | Epoch[210] completed. Training Speed: 7.71 samples/sec across all devices.	Epoch Time: 404.85 sec.	Average Epoch Time: 404.85 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 3:48:22.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:54:02 INFO stats.py:314 | Epoch[211] Step[9] GlobalStep[82299] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 3:48:10.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:54:18 INFO loss_tracker.py:84 | Epoch[211/239] Step[24] GlobalStep[82314/NA]: det_loss_cls_0[0.3794] det_loss_box_0[0.4778] det_loss_cls_1[0.3710] det_loss_box_1[0.3585] det_loss_cls_2[0.3131] det_loss_box_2[0.3205] det_loss_cls_3[0.2879] det_loss_box_3[0.3084] det_loss_cls_4[0.2662] det_loss_box_4[0.3022] det_loss_cls_5[0.2549] det_loss_box_5[0.3041] det_loss_cls_dn_0[0.0855] det_loss_box_dn_0[0.2607] det_loss_cls_dn_1[0.0828] det_loss_box_dn_1[0.2064] det_loss_cls_dn_2[0.0820] det_loss_box_dn_2[0.1879] det_loss_cls_dn_3[0.0811] det_loss_box_dn_3[0.1841] det_loss_cls_dn_4[0.0803] det_loss_box_dn_4[0.1824] det_loss_cls_dn_5[0.0795] det_loss_box_dn_5[0.1805] loss_depth[0.0382] total_loss[5.6753]
Rank[0/8] 06/20/2025 17:54:28 INFO stats.py:314 | Epoch[211] Step[34] GlobalStep[82324] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 3:47:39.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:54:44 INFO loss_tracker.py:84 | Epoch[211/239] Step[49] GlobalStep[82339/NA]: det_loss_cls_0[0.5727] det_loss_box_0[0.7222] det_loss_cls_1[0.5654] det_loss_box_1[0.5547] det_loss_cls_2[0.5012] det_loss_box_2[0.4964] det_loss_cls_3[0.4620] det_loss_box_3[0.4687] det_loss_cls_4[0.4330] det_loss_box_4[0.4672] det_loss_cls_5[0.4209] det_loss_box_5[0.4662] det_loss_cls_dn_0[0.0901] det_loss_box_dn_0[0.2326] det_loss_cls_dn_1[0.0862] det_loss_box_dn_1[0.1873] det_loss_cls_dn_2[0.0854] det_loss_box_dn_2[0.1673] det_loss_cls_dn_3[0.0848] det_loss_box_dn_3[0.1634] det_loss_cls_dn_4[0.0843] det_loss_box_dn_4[0.1607] det_loss_cls_dn_5[0.0833] det_loss_box_dn_5[0.1578] loss_depth[0.0338] total_loss[7.7475]
Rank[0/8] 06/20/2025 17:54:55 INFO stats.py:314 | Epoch[211] Step[59] GlobalStep[82349] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 3:47:08.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:55:10 INFO loss_tracker.py:84 | Epoch[211/239] Step[74] GlobalStep[82364/NA]: det_loss_cls_0[0.4377] det_loss_box_0[0.5683] det_loss_cls_1[0.4154] det_loss_box_1[0.4305] det_loss_cls_2[0.3527] det_loss_box_2[0.3834] det_loss_cls_3[0.3272] det_loss_box_3[0.3710] det_loss_cls_4[0.3007] det_loss_box_4[0.3613] det_loss_cls_5[0.2869] det_loss_box_5[0.3645] det_loss_cls_dn_0[0.0974] det_loss_box_dn_0[0.2540] det_loss_cls_dn_1[0.0925] det_loss_box_dn_1[0.2108] det_loss_cls_dn_2[0.0913] det_loss_box_dn_2[0.1941] det_loss_cls_dn_3[0.0903] det_loss_box_dn_3[0.1876] det_loss_cls_dn_4[0.0891] det_loss_box_dn_4[0.1854] det_loss_cls_dn_5[0.0888] det_loss_box_dn_5[0.1824] loss_depth[0.0355] total_loss[6.3990]
Rank[0/8] 06/20/2025 17:55:20 INFO stats.py:314 | Epoch[211] Step[84] GlobalStep[82374] Training Speed: 8.22 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 3:46:37.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:55:35 INFO loss_tracker.py:84 | Epoch[211/239] Step[99] GlobalStep[82389/NA]: det_loss_cls_0[0.5356] det_loss_box_0[0.6893] det_loss_cls_1[0.5166] det_loss_box_1[0.5258] det_loss_cls_2[0.4616] det_loss_box_2[0.4702] det_loss_cls_3[0.4275] det_loss_box_3[0.4705] det_loss_cls_4[0.4089] det_loss_box_4[0.4523] det_loss_cls_5[0.3908] det_loss_box_5[0.4535] det_loss_cls_dn_0[0.0863] det_loss_box_dn_0[0.2452] det_loss_cls_dn_1[0.0828] det_loss_box_dn_1[0.2006] det_loss_cls_dn_2[0.0821] det_loss_box_dn_2[0.1857] det_loss_cls_dn_3[0.0812] det_loss_box_dn_3[0.1826] det_loss_cls_dn_4[0.0802] det_loss_box_dn_4[0.1800] det_loss_cls_dn_5[0.0797] det_loss_box_dn_5[0.1787] loss_depth[0.0369] total_loss[7.5045]
Rank[0/8] 06/20/2025 17:55:46 INFO stats.py:314 | Epoch[211] Step[109] GlobalStep[82399] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 3:46:06.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:56:01 INFO loss_tracker.py:84 | Epoch[211/239] Step[124] GlobalStep[82414/NA]: det_loss_cls_0[0.4925] det_loss_box_0[0.6656] det_loss_cls_1[0.4794] det_loss_box_1[0.4911] det_loss_cls_2[0.4127] det_loss_box_2[0.4483] det_loss_cls_3[0.3827] det_loss_box_3[0.4298] det_loss_cls_4[0.3548] det_loss_box_4[0.4138] det_loss_cls_5[0.3427] det_loss_box_5[0.4164] det_loss_cls_dn_0[0.0829] det_loss_box_dn_0[0.3116] det_loss_cls_dn_1[0.0789] det_loss_box_dn_1[0.2517] det_loss_cls_dn_2[0.0779] det_loss_box_dn_2[0.2326] det_loss_cls_dn_3[0.0769] det_loss_box_dn_3[0.2279] det_loss_cls_dn_4[0.0766] det_loss_box_dn_4[0.2250] det_loss_cls_dn_5[0.0757] det_loss_box_dn_5[0.2202] loss_depth[0.0347] total_loss[7.3025]
Rank[0/8] 06/20/2025 17:56:12 INFO stats.py:314 | Epoch[211] Step[134] GlobalStep[82424] Training Speed: 8.67 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 3:45:35.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:56:28 INFO loss_tracker.py:84 | Epoch[211/239] Step[149] GlobalStep[82439/NA]: det_loss_cls_0[0.4480] det_loss_box_0[0.5199] det_loss_cls_1[0.4405] det_loss_box_1[0.4026] det_loss_cls_2[0.3633] det_loss_box_2[0.3674] det_loss_cls_3[0.3301] det_loss_box_3[0.3523] det_loss_cls_4[0.3158] det_loss_box_4[0.3412] det_loss_cls_5[0.3011] det_loss_box_5[0.3415] det_loss_cls_dn_0[0.0828] det_loss_box_dn_0[0.2215] det_loss_cls_dn_1[0.0782] det_loss_box_dn_1[0.1757] det_loss_cls_dn_2[0.0778] det_loss_box_dn_2[0.1595] det_loss_cls_dn_3[0.0770] det_loss_box_dn_3[0.1559] det_loss_cls_dn_4[0.0761] det_loss_box_dn_4[0.1543] det_loss_cls_dn_5[0.0755] det_loss_box_dn_5[0.1533] loss_depth[0.0333] total_loss[6.0446]
Rank[0/8] 06/20/2025 17:56:38 INFO stats.py:314 | Epoch[211] Step[159] GlobalStep[82449] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 3:45:04.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:56:53 INFO loss_tracker.py:84 | Epoch[211/239] Step[174] GlobalStep[82464/NA]: det_loss_cls_0[0.4228] det_loss_box_0[0.5982] det_loss_cls_1[0.4234] det_loss_box_1[0.4917] det_loss_cls_2[0.3679] det_loss_box_2[0.4330] det_loss_cls_3[0.3498] det_loss_box_3[0.4215] det_loss_cls_4[0.3199] det_loss_box_4[0.4175] det_loss_cls_5[0.3054] det_loss_box_5[0.4143] det_loss_cls_dn_0[0.0889] det_loss_box_dn_0[0.3088] det_loss_cls_dn_1[0.0853] det_loss_box_dn_1[0.2676] det_loss_cls_dn_2[0.0840] det_loss_box_dn_2[0.2504] det_loss_cls_dn_3[0.0825] det_loss_box_dn_3[0.2459] det_loss_cls_dn_4[0.0821] det_loss_box_dn_4[0.2436] det_loss_cls_dn_5[0.0809] det_loss_box_dn_5[0.2409] loss_depth[0.0343] total_loss[7.0605]
Rank[0/8] 06/20/2025 17:57:04 INFO stats.py:314 | Epoch[211] Step[184] GlobalStep[82474] Training Speed: 8.74 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 3:44:33.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:57:19 INFO loss_tracker.py:84 | Epoch[211/239] Step[199] GlobalStep[82489/NA]: det_loss_cls_0[0.4050] det_loss_box_0[0.5096] det_loss_cls_1[0.3852] det_loss_box_1[0.3809] det_loss_cls_2[0.3247] det_loss_box_2[0.3418] det_loss_cls_3[0.2984] det_loss_box_3[0.3242] det_loss_cls_4[0.2727] det_loss_box_4[0.3193] det_loss_cls_5[0.2654] det_loss_box_5[0.3175] det_loss_cls_dn_0[0.0864] det_loss_box_dn_0[0.2522] det_loss_cls_dn_1[0.0826] det_loss_box_dn_1[0.2064] det_loss_cls_dn_2[0.0816] det_loss_box_dn_2[0.1877] det_loss_cls_dn_3[0.0804] det_loss_box_dn_3[0.1856] det_loss_cls_dn_4[0.0794] det_loss_box_dn_4[0.1841] det_loss_cls_dn_5[0.0786] det_loss_box_dn_5[0.1824] loss_depth[0.0360] total_loss[5.8679]
Rank[0/8] 06/20/2025 17:57:29 INFO stats.py:314 | Epoch[211] Step[209] GlobalStep[82499] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 3:44:03.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:57:46 INFO loss_tracker.py:84 | Epoch[211/239] Step[224] GlobalStep[82514/NA]: det_loss_cls_0[0.4650] det_loss_box_0[0.5588] det_loss_cls_1[0.4550] det_loss_box_1[0.4111] det_loss_cls_2[0.4025] det_loss_box_2[0.3740] det_loss_cls_3[0.3740] det_loss_box_3[0.3674] det_loss_cls_4[0.3520] det_loss_box_4[0.3560] det_loss_cls_5[0.3402] det_loss_box_5[0.3568] det_loss_cls_dn_0[0.0942] det_loss_box_dn_0[0.2602] det_loss_cls_dn_1[0.0902] det_loss_box_dn_1[0.2115] det_loss_cls_dn_2[0.0892] det_loss_box_dn_2[0.1921] det_loss_cls_dn_3[0.0884] det_loss_box_dn_3[0.1863] det_loss_cls_dn_4[0.0874] det_loss_box_dn_4[0.1847] det_loss_cls_dn_5[0.0864] det_loss_box_dn_5[0.1831] loss_depth[0.0361] total_loss[6.6029]
Rank[0/8] 06/20/2025 17:57:56 INFO stats.py:314 | Epoch[211] Step[234] GlobalStep[82524] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 3:43:32.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:58:11 INFO loss_tracker.py:84 | Epoch[211/239] Step[249] GlobalStep[82539/NA]: det_loss_cls_0[0.4959] det_loss_box_0[0.5929] det_loss_cls_1[0.4904] det_loss_box_1[0.4403] det_loss_cls_2[0.4347] det_loss_box_2[0.4007] det_loss_cls_3[0.4001] det_loss_box_3[0.3926] det_loss_cls_4[0.3798] det_loss_box_4[0.3833] det_loss_cls_5[0.3688] det_loss_box_5[0.3853] det_loss_cls_dn_0[0.0879] det_loss_box_dn_0[0.2540] det_loss_cls_dn_1[0.0849] det_loss_box_dn_1[0.2146] det_loss_cls_dn_2[0.0844] det_loss_box_dn_2[0.1982] det_loss_cls_dn_3[0.0836] det_loss_box_dn_3[0.1951] det_loss_cls_dn_4[0.0827] det_loss_box_dn_4[0.1926] det_loss_cls_dn_5[0.0818] det_loss_box_dn_5[0.1896] loss_depth[0.0392] total_loss[6.9534]
Rank[0/8] 06/20/2025 17:58:21 INFO stats.py:314 | Epoch[211] Step[259] GlobalStep[82549] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 3:43:01.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:58:37 INFO loss_tracker.py:84 | Epoch[211/239] Step[274] GlobalStep[82564/NA]: det_loss_cls_0[0.4993] det_loss_box_0[0.6723] det_loss_cls_1[0.4735] det_loss_box_1[0.4989] det_loss_cls_2[0.4066] det_loss_box_2[0.4486] det_loss_cls_3[0.3697] det_loss_box_3[0.4349] det_loss_cls_4[0.3531] det_loss_box_4[0.4106] det_loss_cls_5[0.3402] det_loss_box_5[0.4089] det_loss_cls_dn_0[0.0874] det_loss_box_dn_0[0.2479] det_loss_cls_dn_1[0.0837] det_loss_box_dn_1[0.2040] det_loss_cls_dn_2[0.0832] det_loss_box_dn_2[0.1843] det_loss_cls_dn_3[0.0817] det_loss_box_dn_3[0.1805] det_loss_cls_dn_4[0.0809] det_loss_box_dn_4[0.1784] det_loss_cls_dn_5[0.0801] det_loss_box_dn_5[0.1771] loss_depth[0.0371] total_loss[7.0231]
Rank[0/8] 06/20/2025 17:58:47 INFO stats.py:314 | Epoch[211] Step[284] GlobalStep[82574] Training Speed: 7.80 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 3:42:30.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:59:03 INFO loss_tracker.py:84 | Epoch[211/239] Step[299] GlobalStep[82589/NA]: det_loss_cls_0[0.5412] det_loss_box_0[0.6381] det_loss_cls_1[0.5356] det_loss_box_1[0.4568] det_loss_cls_2[0.4618] det_loss_box_2[0.4199] det_loss_cls_3[0.4194] det_loss_box_3[0.4071] det_loss_cls_4[0.3906] det_loss_box_4[0.3958] det_loss_cls_5[0.3790] det_loss_box_5[0.3960] det_loss_cls_dn_0[0.0911] det_loss_box_dn_0[0.2166] det_loss_cls_dn_1[0.0876] det_loss_box_dn_1[0.1765] det_loss_cls_dn_2[0.0868] det_loss_box_dn_2[0.1616] det_loss_cls_dn_3[0.0855] det_loss_box_dn_3[0.1581] det_loss_cls_dn_4[0.0847] det_loss_box_dn_4[0.1557] det_loss_cls_dn_5[0.0837] det_loss_box_dn_5[0.1521] loss_depth[0.0360] total_loss[7.0173]
Rank[0/8] 06/20/2025 17:59:14 INFO stats.py:314 | Epoch[211] Step[309] GlobalStep[82599] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 3:41:59.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:59:29 INFO loss_tracker.py:84 | Epoch[211/239] Step[324] GlobalStep[82614/NA]: det_loss_cls_0[0.4877] det_loss_box_0[0.5844] det_loss_cls_1[0.4764] det_loss_box_1[0.4494] det_loss_cls_2[0.4156] det_loss_box_2[0.4157] det_loss_cls_3[0.3888] det_loss_box_3[0.4095] det_loss_cls_4[0.3586] det_loss_box_4[0.4002] det_loss_cls_5[0.3422] det_loss_box_5[0.4044] det_loss_cls_dn_0[0.0939] det_loss_box_dn_0[0.2563] det_loss_cls_dn_1[0.0905] det_loss_box_dn_1[0.2057] det_loss_cls_dn_2[0.0894] det_loss_box_dn_2[0.1848] det_loss_cls_dn_3[0.0886] det_loss_box_dn_3[0.1806] det_loss_cls_dn_4[0.0878] det_loss_box_dn_4[0.1786] det_loss_cls_dn_5[0.0872] det_loss_box_dn_5[0.1768] loss_depth[0.0343] total_loss[6.8873]
Rank[0/8] 06/20/2025 17:59:39 INFO stats.py:314 | Epoch[211] Step[334] GlobalStep[82624] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 3:41:28.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 17:59:54 INFO loss_tracker.py:84 | Epoch[211/239] Step[349] GlobalStep[82639/NA]: det_loss_cls_0[0.4396] det_loss_box_0[0.5744] det_loss_cls_1[0.4283] det_loss_box_1[0.4312] det_loss_cls_2[0.3759] det_loss_box_2[0.3998] det_loss_cls_3[0.3417] det_loss_box_3[0.3918] det_loss_cls_4[0.3203] det_loss_box_4[0.3725] det_loss_cls_5[0.3120] det_loss_box_5[0.3696] det_loss_cls_dn_0[0.0914] det_loss_box_dn_0[0.2710] det_loss_cls_dn_1[0.0892] det_loss_box_dn_1[0.2225] det_loss_cls_dn_2[0.0885] det_loss_box_dn_2[0.2069] det_loss_cls_dn_3[0.0874] det_loss_box_dn_3[0.2012] det_loss_cls_dn_4[0.0866] det_loss_box_dn_4[0.2001] det_loss_cls_dn_5[0.0858] det_loss_box_dn_5[0.1978] loss_depth[0.0376] total_loss[6.6229]
Rank[0/8] 06/20/2025 18:00:05 INFO stats.py:314 | Epoch[211] Step[359] GlobalStep[82649] Training Speed: 7.91 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 3:40:57.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:00:21 INFO loss_tracker.py:84 | Epoch[211/239] Step[374] GlobalStep[82664/NA]: det_loss_cls_0[0.4936] det_loss_box_0[0.6453] det_loss_cls_1[0.4858] det_loss_box_1[0.4643] det_loss_cls_2[0.4219] det_loss_box_2[0.4181] det_loss_cls_3[0.3823] det_loss_box_3[0.4053] det_loss_cls_4[0.3601] det_loss_box_4[0.3905] det_loss_cls_5[0.3508] det_loss_box_5[0.3886] det_loss_cls_dn_0[0.0962] det_loss_box_dn_0[0.2536] det_loss_cls_dn_1[0.0931] det_loss_box_dn_1[0.2119] det_loss_cls_dn_2[0.0921] det_loss_box_dn_2[0.1951] det_loss_cls_dn_3[0.0904] det_loss_box_dn_3[0.1912] det_loss_cls_dn_4[0.0897] det_loss_box_dn_4[0.1897] det_loss_cls_dn_5[0.0889] det_loss_box_dn_5[0.1878] loss_depth[0.0337] total_loss[7.0200]
Rank[0/8] 06/20/2025 18:00:31 INFO stats.py:314 | Epoch[211] Step[384] GlobalStep[82674] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 3:40:27.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:00:36 INFO stats.py:394 | Epoch[211] completed. Training Speed: 7.70 samples/sec across all devices.	Epoch Time: 405.23 sec.	Average Epoch Time: 405.23 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 3:40:20.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:00:59 INFO stats.py:314 | Epoch[212] Step[19] GlobalStep[82699] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 3:39:56.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:01:04 INFO loss_tracker.py:84 | Epoch[212/239] Step[24] GlobalStep[82704/NA]: det_loss_cls_0[0.5802] det_loss_box_0[0.7384] det_loss_cls_1[0.5719] det_loss_box_1[0.5395] det_loss_cls_2[0.5083] det_loss_box_2[0.4840] det_loss_cls_3[0.4637] det_loss_box_3[0.4668] det_loss_cls_4[0.4462] det_loss_box_4[0.4506] det_loss_cls_5[0.4272] det_loss_box_5[0.4554] det_loss_cls_dn_0[0.0967] det_loss_box_dn_0[0.2607] det_loss_cls_dn_1[0.0936] det_loss_box_dn_1[0.2189] det_loss_cls_dn_2[0.0927] det_loss_box_dn_2[0.2028] det_loss_cls_dn_3[0.0916] det_loss_box_dn_3[0.1990] det_loss_cls_dn_4[0.0906] det_loss_box_dn_4[0.1967] det_loss_cls_dn_5[0.0901] det_loss_box_dn_5[0.1941] loss_depth[0.0373] total_loss[7.9970]
Rank[0/8] 06/20/2025 18:01:24 INFO stats.py:314 | Epoch[212] Step[44] GlobalStep[82724] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 3:39:25.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:01:29 INFO loss_tracker.py:84 | Epoch[212/239] Step[49] GlobalStep[82729/NA]: det_loss_cls_0[0.5638] det_loss_box_0[0.6976] det_loss_cls_1[0.5526] det_loss_box_1[0.5111] det_loss_cls_2[0.4934] det_loss_box_2[0.4571] det_loss_cls_3[0.4459] det_loss_box_3[0.4509] det_loss_cls_4[0.4218] det_loss_box_4[0.4329] det_loss_cls_5[0.3996] det_loss_box_5[0.4354] det_loss_cls_dn_0[0.0923] det_loss_box_dn_0[0.2625] det_loss_cls_dn_1[0.0896] det_loss_box_dn_1[0.2155] det_loss_cls_dn_2[0.0886] det_loss_box_dn_2[0.1966] det_loss_cls_dn_3[0.0873] det_loss_box_dn_3[0.1931] det_loss_cls_dn_4[0.0866] det_loss_box_dn_4[0.1899] det_loss_cls_dn_5[0.0861] det_loss_box_dn_5[0.1868] loss_depth[0.0384] total_loss[7.6751]
Rank[0/8] 06/20/2025 18:01:51 INFO stats.py:314 | Epoch[212] Step[69] GlobalStep[82749] Training Speed: 8.78 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 3:38:54.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:01:57 INFO loss_tracker.py:84 | Epoch[212/239] Step[74] GlobalStep[82754/NA]: det_loss_cls_0[0.4911] det_loss_box_0[0.5841] det_loss_cls_1[0.4836] det_loss_box_1[0.4515] det_loss_cls_2[0.4268] det_loss_box_2[0.4017] det_loss_cls_3[0.3909] det_loss_box_3[0.3843] det_loss_cls_4[0.3656] det_loss_box_4[0.3819] det_loss_cls_5[0.3577] det_loss_box_5[0.3824] det_loss_cls_dn_0[0.0910] det_loss_box_dn_0[0.2642] det_loss_cls_dn_1[0.0871] det_loss_box_dn_1[0.2134] det_loss_cls_dn_2[0.0865] det_loss_box_dn_2[0.1896] det_loss_cls_dn_3[0.0845] det_loss_box_dn_3[0.1829] det_loss_cls_dn_4[0.0835] det_loss_box_dn_4[0.1794] det_loss_cls_dn_5[0.0825] det_loss_box_dn_5[0.1735] loss_depth[0.0329] total_loss[6.8524]
Rank[0/8] 06/20/2025 18:02:17 INFO stats.py:314 | Epoch[212] Step[94] GlobalStep[82774] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 3:38:24.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:02:22 INFO loss_tracker.py:84 | Epoch[212/239] Step[99] GlobalStep[82779/NA]: det_loss_cls_0[0.5538] det_loss_box_0[0.5837] det_loss_cls_1[0.5450] det_loss_box_1[0.4346] det_loss_cls_2[0.4655] det_loss_box_2[0.3960] det_loss_cls_3[0.4309] det_loss_box_3[0.3849] det_loss_cls_4[0.4068] det_loss_box_4[0.3688] det_loss_cls_5[0.3917] det_loss_box_5[0.3691] det_loss_cls_dn_0[0.0855] det_loss_box_dn_0[0.2304] det_loss_cls_dn_1[0.0811] det_loss_box_dn_1[0.1905] det_loss_cls_dn_2[0.0795] det_loss_box_dn_2[0.1756] det_loss_cls_dn_3[0.0784] det_loss_box_dn_3[0.1722] det_loss_cls_dn_4[0.0777] det_loss_box_dn_4[0.1705] det_loss_cls_dn_5[0.0770] det_loss_box_dn_5[0.1680] loss_depth[0.0347] total_loss[6.9518]
Rank[0/8] 06/20/2025 18:02:42 INFO stats.py:314 | Epoch[212] Step[119] GlobalStep[82799] Training Speed: 8.68 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 3:37:53.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:02:47 INFO loss_tracker.py:84 | Epoch[212/239] Step[124] GlobalStep[82804/NA]: det_loss_cls_0[0.3831] det_loss_box_0[0.5401] det_loss_cls_1[0.3633] det_loss_box_1[0.4398] det_loss_cls_2[0.3200] det_loss_box_2[0.4079] det_loss_cls_3[0.3032] det_loss_box_3[0.3792] det_loss_cls_4[0.2797] det_loss_box_4[0.3742] det_loss_cls_5[0.2677] det_loss_box_5[0.3750] det_loss_cls_dn_0[0.0789] det_loss_box_dn_0[0.2829] det_loss_cls_dn_1[0.0753] det_loss_box_dn_1[0.2426] det_loss_cls_dn_2[0.0741] det_loss_box_dn_2[0.2267] det_loss_cls_dn_3[0.0727] det_loss_box_dn_3[0.2215] det_loss_cls_dn_4[0.0721] det_loss_box_dn_4[0.2193] det_loss_cls_dn_5[0.0714] det_loss_box_dn_5[0.2163] loss_depth[0.0364] total_loss[6.3234]
Rank[0/8] 06/20/2025 18:03:08 INFO stats.py:314 | Epoch[212] Step[144] GlobalStep[82824] Training Speed: 8.34 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 3:37:22.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:03:13 INFO loss_tracker.py:84 | Epoch[212/239] Step[149] GlobalStep[82829/NA]: det_loss_cls_0[0.5575] det_loss_box_0[0.6936] det_loss_cls_1[0.5468] det_loss_box_1[0.5151] det_loss_cls_2[0.4799] det_loss_box_2[0.4662] det_loss_cls_3[0.4401] det_loss_box_3[0.4591] det_loss_cls_4[0.4036] det_loss_box_4[0.4523] det_loss_cls_5[0.3910] det_loss_box_5[0.4506] det_loss_cls_dn_0[0.0958] det_loss_box_dn_0[0.2659] det_loss_cls_dn_1[0.0920] det_loss_box_dn_1[0.2168] det_loss_cls_dn_2[0.0909] det_loss_box_dn_2[0.1996] det_loss_cls_dn_3[0.0896] det_loss_box_dn_3[0.1940] det_loss_cls_dn_4[0.0888] det_loss_box_dn_4[0.1912] det_loss_cls_dn_5[0.0882] det_loss_box_dn_5[0.1877] loss_depth[0.0362] total_loss[7.6925]
Rank[0/8] 06/20/2025 18:03:35 INFO stats.py:314 | Epoch[212] Step[169] GlobalStep[82849] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 3:36:51.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:03:40 INFO loss_tracker.py:84 | Epoch[212/239] Step[174] GlobalStep[82854/NA]: det_loss_cls_0[0.4386] det_loss_box_0[0.5541] det_loss_cls_1[0.4351] det_loss_box_1[0.4136] det_loss_cls_2[0.3706] det_loss_box_2[0.3642] det_loss_cls_3[0.3326] det_loss_box_3[0.3490] det_loss_cls_4[0.3085] det_loss_box_4[0.3407] det_loss_cls_5[0.2989] det_loss_box_5[0.3374] det_loss_cls_dn_0[0.0889] det_loss_box_dn_0[0.2330] det_loss_cls_dn_1[0.0855] det_loss_box_dn_1[0.1851] det_loss_cls_dn_2[0.0847] det_loss_box_dn_2[0.1666] det_loss_cls_dn_3[0.0837] det_loss_box_dn_3[0.1626] det_loss_cls_dn_4[0.0830] det_loss_box_dn_4[0.1610] det_loss_cls_dn_5[0.0819] det_loss_box_dn_5[0.1595] loss_depth[0.0338] total_loss[6.1526]
Rank[0/8] 06/20/2025 18:04:01 INFO stats.py:314 | Epoch[212] Step[194] GlobalStep[82874] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 3:36:20.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:04:06 INFO loss_tracker.py:84 | Epoch[212/239] Step[199] GlobalStep[82879/NA]: det_loss_cls_0[0.4412] det_loss_box_0[0.6102] det_loss_cls_1[0.4363] det_loss_box_1[0.4569] det_loss_cls_2[0.3866] det_loss_box_2[0.4178] det_loss_cls_3[0.3645] det_loss_box_3[0.3980] det_loss_cls_4[0.3429] det_loss_box_4[0.3893] det_loss_cls_5[0.3323] det_loss_box_5[0.3883] det_loss_cls_dn_0[0.0870] det_loss_box_dn_0[0.2972] det_loss_cls_dn_1[0.0829] det_loss_box_dn_1[0.2534] det_loss_cls_dn_2[0.0826] det_loss_box_dn_2[0.2339] det_loss_cls_dn_3[0.0814] det_loss_box_dn_3[0.2282] det_loss_cls_dn_4[0.0806] det_loss_box_dn_4[0.2246] det_loss_cls_dn_5[0.0800] det_loss_box_dn_5[0.2203] loss_depth[0.0349] total_loss[6.9516]
Rank[0/8] 06/20/2025 18:04:26 INFO stats.py:314 | Epoch[212] Step[219] GlobalStep[82899] Training Speed: 7.90 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 3:35:49.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:04:31 INFO loss_tracker.py:84 | Epoch[212/239] Step[224] GlobalStep[82904/NA]: det_loss_cls_0[0.5034] det_loss_box_0[0.5762] det_loss_cls_1[0.4737] det_loss_box_1[0.4289] det_loss_cls_2[0.4171] det_loss_box_2[0.3911] det_loss_cls_3[0.3857] det_loss_box_3[0.3784] det_loss_cls_4[0.3603] det_loss_box_4[0.3682] det_loss_cls_5[0.3500] det_loss_box_5[0.3669] det_loss_cls_dn_0[0.0935] det_loss_box_dn_0[0.2574] det_loss_cls_dn_1[0.0895] det_loss_box_dn_1[0.2057] det_loss_cls_dn_2[0.0888] det_loss_box_dn_2[0.1906] det_loss_cls_dn_3[0.0875] det_loss_box_dn_3[0.1861] det_loss_cls_dn_4[0.0868] det_loss_box_dn_4[0.1844] det_loss_cls_dn_5[0.0858] det_loss_box_dn_5[0.1788] loss_depth[0.0342] total_loss[6.7690]
Rank[0/8] 06/20/2025 18:04:52 INFO stats.py:314 | Epoch[212] Step[244] GlobalStep[82924] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 3:35:19.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:04:58 INFO loss_tracker.py:84 | Epoch[212/239] Step[249] GlobalStep[82929/NA]: det_loss_cls_0[0.3797] det_loss_box_0[0.4566] det_loss_cls_1[0.3710] det_loss_box_1[0.3406] det_loss_cls_2[0.3168] det_loss_box_2[0.3111] det_loss_cls_3[0.2842] det_loss_box_3[0.3072] det_loss_cls_4[0.2723] det_loss_box_4[0.2971] det_loss_cls_5[0.2583] det_loss_box_5[0.3026] det_loss_cls_dn_0[0.0806] det_loss_box_dn_0[0.2731] det_loss_cls_dn_1[0.0777] det_loss_box_dn_1[0.2190] det_loss_cls_dn_2[0.0765] det_loss_box_dn_2[0.2006] det_loss_cls_dn_3[0.0758] det_loss_box_dn_3[0.1947] det_loss_cls_dn_4[0.0749] det_loss_box_dn_4[0.1912] det_loss_cls_dn_5[0.0743] det_loss_box_dn_5[0.1787] loss_depth[0.0359] total_loss[5.6504]
Rank[0/8] 06/20/2025 18:05:18 INFO stats.py:314 | Epoch[212] Step[269] GlobalStep[82949] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 3:34:48.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:05:24 INFO loss_tracker.py:84 | Epoch[212/239] Step[274] GlobalStep[82954/NA]: det_loss_cls_0[0.4305] det_loss_box_0[0.5348] det_loss_cls_1[0.4216] det_loss_box_1[0.4089] det_loss_cls_2[0.3574] det_loss_box_2[0.3676] det_loss_cls_3[0.3246] det_loss_box_3[0.3529] det_loss_cls_4[0.3009] det_loss_box_4[0.3435] det_loss_cls_5[0.2893] det_loss_box_5[0.3445] det_loss_cls_dn_0[0.0813] det_loss_box_dn_0[0.2284] det_loss_cls_dn_1[0.0786] det_loss_box_dn_1[0.1855] det_loss_cls_dn_2[0.0779] det_loss_box_dn_2[0.1726] det_loss_cls_dn_3[0.0767] det_loss_box_dn_3[0.1693] det_loss_cls_dn_4[0.0759] det_loss_box_dn_4[0.1667] det_loss_cls_dn_5[0.0749] det_loss_box_dn_5[0.1652] loss_depth[0.0346] total_loss[6.0640]
Rank[0/8] 06/20/2025 18:05:44 INFO stats.py:314 | Epoch[212] Step[294] GlobalStep[82974] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 3:34:17.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:05:49 INFO loss_tracker.py:84 | Epoch[212/239] Step[299] GlobalStep[82979/NA]: det_loss_cls_0[0.5051] det_loss_box_0[0.6321] det_loss_cls_1[0.4807] det_loss_box_1[0.4920] det_loss_cls_2[0.4313] det_loss_box_2[0.4541] det_loss_cls_3[0.3939] det_loss_box_3[0.4337] det_loss_cls_4[0.3719] det_loss_box_4[0.4174] det_loss_cls_5[0.3514] det_loss_box_5[0.4200] det_loss_cls_dn_0[0.0888] det_loss_box_dn_0[0.2722] det_loss_cls_dn_1[0.0849] det_loss_box_dn_1[0.2256] det_loss_cls_dn_2[0.0838] det_loss_box_dn_2[0.2075] det_loss_cls_dn_3[0.0822] det_loss_box_dn_3[0.2037] det_loss_cls_dn_4[0.0813] det_loss_box_dn_4[0.2013] det_loss_cls_dn_5[0.0803] det_loss_box_dn_5[0.2001] loss_depth[0.0368] total_loss[7.2321]
Rank[0/8] 06/20/2025 18:06:10 INFO stats.py:314 | Epoch[212] Step[319] GlobalStep[82999] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 3:33:46.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:06:15 INFO loss_tracker.py:84 | Epoch[212/239] Step[324] GlobalStep[83004/NA]: det_loss_cls_0[0.3451] det_loss_box_0[0.4275] det_loss_cls_1[0.3429] det_loss_box_1[0.3145] det_loss_cls_2[0.2904] det_loss_box_2[0.2820] det_loss_cls_3[0.2761] det_loss_box_3[0.2671] det_loss_cls_4[0.2524] det_loss_box_4[0.2611] det_loss_cls_5[0.2375] det_loss_box_5[0.2617] det_loss_cls_dn_0[0.0862] det_loss_box_dn_0[0.2412] det_loss_cls_dn_1[0.0833] det_loss_box_dn_1[0.1901] det_loss_cls_dn_2[0.0823] det_loss_box_dn_2[0.1688] det_loss_cls_dn_3[0.0811] det_loss_box_dn_3[0.1642] det_loss_cls_dn_4[0.0804] det_loss_box_dn_4[0.1621] det_loss_cls_dn_5[0.0800] det_loss_box_dn_5[0.1610] loss_depth[0.0327] total_loss[5.1719]
Rank[0/8] 06/20/2025 18:06:36 INFO stats.py:314 | Epoch[212] Step[344] GlobalStep[83024] Training Speed: 8.64 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 3:33:15.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:06:41 INFO loss_tracker.py:84 | Epoch[212/239] Step[349] GlobalStep[83029/NA]: det_loss_cls_0[0.6594] det_loss_box_0[0.7815] det_loss_cls_1[0.6497] det_loss_box_1[0.5819] det_loss_cls_2[0.5745] det_loss_box_2[0.5283] det_loss_cls_3[0.5331] det_loss_box_3[0.5111] det_loss_cls_4[0.4989] det_loss_box_4[0.5107] det_loss_cls_5[0.4916] det_loss_box_5[0.5093] det_loss_cls_dn_0[0.1034] det_loss_box_dn_0[0.2175] det_loss_cls_dn_1[0.0997] det_loss_box_dn_1[0.1780] det_loss_cls_dn_2[0.0986] det_loss_box_dn_2[0.1653] det_loss_cls_dn_3[0.0976] det_loss_box_dn_3[0.1624] det_loss_cls_dn_4[0.0972] det_loss_box_dn_4[0.1610] det_loss_cls_dn_5[0.0963] det_loss_box_dn_5[0.1596] loss_depth[0.0363] total_loss[8.5027]
Rank[0/8] 06/20/2025 18:07:02 INFO stats.py:314 | Epoch[212] Step[369] GlobalStep[83049] Training Speed: 7.91 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 3:32:44.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:07:07 INFO loss_tracker.py:84 | Epoch[212/239] Step[374] GlobalStep[83054/NA]: det_loss_cls_0[0.5997] det_loss_box_0[0.7861] det_loss_cls_1[0.5746] det_loss_box_1[0.5959] det_loss_cls_2[0.5228] det_loss_box_2[0.5362] det_loss_cls_3[0.4917] det_loss_box_3[0.5166] det_loss_cls_4[0.4595] det_loss_box_4[0.4995] det_loss_cls_5[0.4536] det_loss_box_5[0.4974] det_loss_cls_dn_0[0.0959] det_loss_box_dn_0[0.2671] det_loss_cls_dn_1[0.0922] det_loss_box_dn_1[0.2178] det_loss_cls_dn_2[0.0908] det_loss_box_dn_2[0.2019] det_loss_cls_dn_3[0.0901] det_loss_box_dn_3[0.1981] det_loss_cls_dn_4[0.0891] det_loss_box_dn_4[0.1962] det_loss_cls_dn_5[0.0885] det_loss_box_dn_5[0.1950] loss_depth[0.0336] total_loss[8.3896]
Rank[0/8] 06/20/2025 18:07:22 INFO stats.py:394 | Epoch[212] completed. Training Speed: 7.69 samples/sec across all devices.	Epoch Time: 405.51 sec.	Average Epoch Time: 405.51 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 3:32:20.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:07:30 INFO stats.py:314 | Epoch[213] Step[4] GlobalStep[83074] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 3:32:14.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:07:50 INFO loss_tracker.py:84 | Epoch[213/239] Step[24] GlobalStep[83094/NA]: det_loss_cls_0[0.4698] det_loss_box_0[0.5761] det_loss_cls_1[0.4623] det_loss_box_1[0.4369] det_loss_cls_2[0.4032] det_loss_box_2[0.3908] det_loss_cls_3[0.3682] det_loss_box_3[0.3775] det_loss_cls_4[0.3412] det_loss_box_4[0.3663] det_loss_cls_5[0.3261] det_loss_box_5[0.3682] det_loss_cls_dn_0[0.0946] det_loss_box_dn_0[0.2525] det_loss_cls_dn_1[0.0911] det_loss_box_dn_1[0.2070] det_loss_cls_dn_2[0.0901] det_loss_box_dn_2[0.1934] det_loss_cls_dn_3[0.0893] det_loss_box_dn_3[0.1893] det_loss_cls_dn_4[0.0885] det_loss_box_dn_4[0.1873] det_loss_cls_dn_5[0.0880] det_loss_box_dn_5[0.1862] loss_depth[0.0375] total_loss[6.6811]
Rank[0/8] 06/20/2025 18:07:56 INFO stats.py:314 | Epoch[213] Step[29] GlobalStep[83099] Training Speed: 7.96 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 3:31:43.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:08:17 INFO loss_tracker.py:84 | Epoch[213/239] Step[49] GlobalStep[83119/NA]: det_loss_cls_0[0.5272] det_loss_box_0[0.6764] det_loss_cls_1[0.5138] det_loss_box_1[0.4991] det_loss_cls_2[0.4527] det_loss_box_2[0.4494] det_loss_cls_3[0.4181] det_loss_box_3[0.4326] det_loss_cls_4[0.3907] det_loss_box_4[0.4207] det_loss_cls_5[0.3776] det_loss_box_5[0.4238] det_loss_cls_dn_0[0.0845] det_loss_box_dn_0[0.2349] det_loss_cls_dn_1[0.0808] det_loss_box_dn_1[0.1893] det_loss_cls_dn_2[0.0801] det_loss_box_dn_2[0.1715] det_loss_cls_dn_3[0.0783] det_loss_box_dn_3[0.1672] det_loss_cls_dn_4[0.0776] det_loss_box_dn_4[0.1659] det_loss_cls_dn_5[0.0766] det_loss_box_dn_5[0.1640] loss_depth[0.0339] total_loss[7.1867]
Rank[0/8] 06/20/2025 18:08:22 INFO stats.py:314 | Epoch[213] Step[54] GlobalStep[83124] Training Speed: 7.95 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 3:31:12.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:08:42 INFO loss_tracker.py:84 | Epoch[213/239] Step[74] GlobalStep[83144/NA]: det_loss_cls_0[0.3628] det_loss_box_0[0.4433] det_loss_cls_1[0.3559] det_loss_box_1[0.3219] det_loss_cls_2[0.2966] det_loss_box_2[0.2952] det_loss_cls_3[0.2616] det_loss_box_3[0.2902] det_loss_cls_4[0.2457] det_loss_box_4[0.2846] det_loss_cls_5[0.2355] det_loss_box_5[0.2885] det_loss_cls_dn_0[0.0771] det_loss_box_dn_0[0.2821] det_loss_cls_dn_1[0.0733] det_loss_box_dn_1[0.2279] det_loss_cls_dn_2[0.0724] det_loss_box_dn_2[0.2073] det_loss_cls_dn_3[0.0722] det_loss_box_dn_3[0.2026] det_loss_cls_dn_4[0.0716] det_loss_box_dn_4[0.1998] det_loss_cls_dn_5[0.0708] det_loss_box_dn_5[0.1951] loss_depth[0.0350] total_loss[5.4691]
Rank[0/8] 06/20/2025 18:08:47 INFO stats.py:314 | Epoch[213] Step[79] GlobalStep[83149] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 3:30:42.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:09:08 INFO loss_tracker.py:84 | Epoch[213/239] Step[99] GlobalStep[83169/NA]: det_loss_cls_0[0.3712] det_loss_box_0[0.4399] det_loss_cls_1[0.3685] det_loss_box_1[0.3355] det_loss_cls_2[0.3074] det_loss_box_2[0.3145] det_loss_cls_3[0.2765] det_loss_box_3[0.3084] det_loss_cls_4[0.2567] det_loss_box_4[0.3020] det_loss_cls_5[0.2378] det_loss_box_5[0.3071] det_loss_cls_dn_0[0.0825] det_loss_box_dn_0[0.2504] det_loss_cls_dn_1[0.0789] det_loss_box_dn_1[0.2114] det_loss_cls_dn_2[0.0778] det_loss_box_dn_2[0.1943] det_loss_cls_dn_3[0.0770] det_loss_box_dn_3[0.1911] det_loss_cls_dn_4[0.0764] det_loss_box_dn_4[0.1887] det_loss_cls_dn_5[0.0756] det_loss_box_dn_5[0.1867] loss_depth[0.0324] total_loss[5.5488]
Rank[0/8] 06/20/2025 18:09:13 INFO stats.py:314 | Epoch[213] Step[104] GlobalStep[83174] Training Speed: 8.67 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 3:30:11.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:09:34 INFO loss_tracker.py:84 | Epoch[213/239] Step[124] GlobalStep[83194/NA]: det_loss_cls_0[0.4700] det_loss_box_0[0.6191] det_loss_cls_1[0.4414] det_loss_box_1[0.4665] det_loss_cls_2[0.3826] det_loss_box_2[0.4364] det_loss_cls_3[0.3564] det_loss_box_3[0.4087] det_loss_cls_4[0.3389] det_loss_box_4[0.3926] det_loss_cls_5[0.3225] det_loss_box_5[0.3915] det_loss_cls_dn_0[0.0881] det_loss_box_dn_0[0.2758] det_loss_cls_dn_1[0.0856] det_loss_box_dn_1[0.2259] det_loss_cls_dn_2[0.0845] det_loss_box_dn_2[0.2093] det_loss_cls_dn_3[0.0831] det_loss_box_dn_3[0.2056] det_loss_cls_dn_4[0.0826] det_loss_box_dn_4[0.2032] det_loss_cls_dn_5[0.0815] det_loss_box_dn_5[0.1998] loss_depth[0.0349] total_loss[6.8863]
Rank[0/8] 06/20/2025 18:09:39 INFO stats.py:314 | Epoch[213] Step[129] GlobalStep[83199] Training Speed: 8.00 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 3:29:40.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:10:00 INFO loss_tracker.py:84 | Epoch[213/239] Step[149] GlobalStep[83219/NA]: det_loss_cls_0[0.4147] det_loss_box_0[0.5389] det_loss_cls_1[0.4121] det_loss_box_1[0.4005] det_loss_cls_2[0.3596] det_loss_box_2[0.3646] det_loss_cls_3[0.3295] det_loss_box_3[0.3479] det_loss_cls_4[0.3036] det_loss_box_4[0.3445] det_loss_cls_5[0.2957] det_loss_box_5[0.3419] det_loss_cls_dn_0[0.0859] det_loss_box_dn_0[0.2369] det_loss_cls_dn_1[0.0830] det_loss_box_dn_1[0.1895] det_loss_cls_dn_2[0.0823] det_loss_box_dn_2[0.1730] det_loss_cls_dn_3[0.0810] det_loss_box_dn_3[0.1688] det_loss_cls_dn_4[0.0799] det_loss_box_dn_4[0.1665] det_loss_cls_dn_5[0.0792] det_loss_box_dn_5[0.1647] loss_depth[0.0332] total_loss[6.0774]
Rank[0/8] 06/20/2025 18:10:05 INFO stats.py:314 | Epoch[213] Step[154] GlobalStep[83224] Training Speed: 8.00 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 3:29:09.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:10:25 INFO loss_tracker.py:84 | Epoch[213/239] Step[174] GlobalStep[83244/NA]: det_loss_cls_0[0.4945] det_loss_box_0[0.6514] det_loss_cls_1[0.4797] det_loss_box_1[0.5057] det_loss_cls_2[0.4263] det_loss_box_2[0.4521] det_loss_cls_3[0.3989] det_loss_box_3[0.4374] det_loss_cls_4[0.3752] det_loss_box_4[0.4343] det_loss_cls_5[0.3635] det_loss_box_5[0.4306] det_loss_cls_dn_0[0.0967] det_loss_box_dn_0[0.2626] det_loss_cls_dn_1[0.0920] det_loss_box_dn_1[0.2191] det_loss_cls_dn_2[0.0906] det_loss_box_dn_2[0.2067] det_loss_cls_dn_3[0.0893] det_loss_box_dn_3[0.2027] det_loss_cls_dn_4[0.0885] det_loss_box_dn_4[0.2009] det_loss_cls_dn_5[0.0873] det_loss_box_dn_5[0.1985] loss_depth[0.0364] total_loss[7.3212]
Rank[0/8] 06/20/2025 18:10:31 INFO stats.py:314 | Epoch[213] Step[179] GlobalStep[83249] Training Speed: 8.59 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 3:28:38.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:10:52 INFO loss_tracker.py:84 | Epoch[213/239] Step[199] GlobalStep[83269/NA]: det_loss_cls_0[0.4051] det_loss_box_0[0.5262] det_loss_cls_1[0.3924] det_loss_box_1[0.3969] det_loss_cls_2[0.3363] det_loss_box_2[0.3546] det_loss_cls_3[0.3103] det_loss_box_3[0.3315] det_loss_cls_4[0.2886] det_loss_box_4[0.3267] det_loss_cls_5[0.2751] det_loss_box_5[0.3235] det_loss_cls_dn_0[0.0804] det_loss_box_dn_0[0.2646] det_loss_cls_dn_1[0.0764] det_loss_box_dn_1[0.2036] det_loss_cls_dn_2[0.0755] det_loss_box_dn_2[0.1848] det_loss_cls_dn_3[0.0737] det_loss_box_dn_3[0.1790] det_loss_cls_dn_4[0.0733] det_loss_box_dn_4[0.1756] det_loss_cls_dn_5[0.0722] det_loss_box_dn_5[0.1735] loss_depth[0.0357] total_loss[5.9354]
Rank[0/8] 06/20/2025 18:10:57 INFO stats.py:314 | Epoch[213] Step[204] GlobalStep[83274] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 3:28:08.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:11:18 INFO loss_tracker.py:84 | Epoch[213/239] Step[224] GlobalStep[83294/NA]: det_loss_cls_0[0.4392] det_loss_box_0[0.5232] det_loss_cls_1[0.4205] det_loss_box_1[0.3908] det_loss_cls_2[0.3684] det_loss_box_2[0.3472] det_loss_cls_3[0.3334] det_loss_box_3[0.3326] det_loss_cls_4[0.3083] det_loss_box_4[0.3276] det_loss_cls_5[0.2998] det_loss_box_5[0.3289] det_loss_cls_dn_0[0.0888] det_loss_box_dn_0[0.2809] det_loss_cls_dn_1[0.0846] det_loss_box_dn_1[0.2201] det_loss_cls_dn_2[0.0844] det_loss_box_dn_2[0.1989] det_loss_cls_dn_3[0.0841] det_loss_box_dn_3[0.1940] det_loss_cls_dn_4[0.0834] det_loss_box_dn_4[0.1918] det_loss_cls_dn_5[0.0827] det_loss_box_dn_5[0.1899] loss_depth[0.0365] total_loss[6.2401]
Rank[0/8] 06/20/2025 18:11:23 INFO stats.py:314 | Epoch[213] Step[229] GlobalStep[83299] Training Speed: 8.17 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 3:27:37.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:11:43 INFO loss_tracker.py:84 | Epoch[213/239] Step[249] GlobalStep[83319/NA]: det_loss_cls_0[0.4033] det_loss_box_0[0.4938] det_loss_cls_1[0.4006] det_loss_box_1[0.3712] det_loss_cls_2[0.3410] det_loss_box_2[0.3241] det_loss_cls_3[0.3055] det_loss_box_3[0.3146] det_loss_cls_4[0.2877] det_loss_box_4[0.3110] det_loss_cls_5[0.2745] det_loss_box_5[0.3108] det_loss_cls_dn_0[0.0860] det_loss_box_dn_0[0.2628] det_loss_cls_dn_1[0.0833] det_loss_box_dn_1[0.2066] det_loss_cls_dn_2[0.0829] det_loss_box_dn_2[0.1852] det_loss_cls_dn_3[0.0817] det_loss_box_dn_3[0.1801] det_loss_cls_dn_4[0.0807] det_loss_box_dn_4[0.1770] det_loss_cls_dn_5[0.0798] det_loss_box_dn_5[0.1725] loss_depth[0.0354] total_loss[5.8522]
Rank[0/8] 06/20/2025 18:11:49 INFO stats.py:314 | Epoch[213] Step[254] GlobalStep[83324] Training Speed: 6.32 samples/sec across all devices.	Average Step Time: 1.27 sec.	Estimated Remaining Time: 3:27:06.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:12:10 INFO loss_tracker.py:84 | Epoch[213/239] Step[274] GlobalStep[83344/NA]: det_loss_cls_0[0.4574] det_loss_box_0[0.5464] det_loss_cls_1[0.4619] det_loss_box_1[0.4159] det_loss_cls_2[0.3892] det_loss_box_2[0.3881] det_loss_cls_3[0.3544] det_loss_box_3[0.3739] det_loss_cls_4[0.3326] det_loss_box_4[0.3593] det_loss_cls_5[0.3256] det_loss_box_5[0.3586] det_loss_cls_dn_0[0.0931] det_loss_box_dn_0[0.2414] det_loss_cls_dn_1[0.0896] det_loss_box_dn_1[0.1972] det_loss_cls_dn_2[0.0885] det_loss_box_dn_2[0.1811] det_loss_cls_dn_3[0.0872] det_loss_box_dn_3[0.1784] det_loss_cls_dn_4[0.0864] det_loss_box_dn_4[0.1766] det_loss_cls_dn_5[0.0856] det_loss_box_dn_5[0.1758] loss_depth[0.0339] total_loss[6.4780]
Rank[0/8] 06/20/2025 18:12:15 INFO stats.py:314 | Epoch[213] Step[279] GlobalStep[83349] Training Speed: 7.67 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 3:26:35.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:12:35 INFO loss_tracker.py:84 | Epoch[213/239] Step[299] GlobalStep[83369/NA]: det_loss_cls_0[0.3935] det_loss_box_0[0.4905] det_loss_cls_1[0.3793] det_loss_box_1[0.3790] det_loss_cls_2[0.3229] det_loss_box_2[0.3500] det_loss_cls_3[0.2910] det_loss_box_3[0.3434] det_loss_cls_4[0.2695] det_loss_box_4[0.3463] det_loss_cls_5[0.2608] det_loss_box_5[0.3474] det_loss_cls_dn_0[0.0831] det_loss_box_dn_0[0.2727] det_loss_cls_dn_1[0.0793] det_loss_box_dn_1[0.2313] det_loss_cls_dn_2[0.0784] det_loss_box_dn_2[0.2146] det_loss_cls_dn_3[0.0778] det_loss_box_dn_3[0.2023] det_loss_cls_dn_4[0.0769] det_loss_box_dn_4[0.2004] det_loss_cls_dn_5[0.0760] det_loss_box_dn_5[0.1925] loss_depth[0.0375] total_loss[5.9964]
Rank[0/8] 06/20/2025 18:12:40 INFO stats.py:314 | Epoch[213] Step[304] GlobalStep[83374] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 3:26:05.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:13:01 INFO loss_tracker.py:84 | Epoch[213/239] Step[324] GlobalStep[83394/NA]: det_loss_cls_0[0.3853] det_loss_box_0[0.4642] det_loss_cls_1[0.3697] det_loss_box_1[0.3420] det_loss_cls_2[0.3138] det_loss_box_2[0.3206] det_loss_cls_3[0.2866] det_loss_box_3[0.3023] det_loss_cls_4[0.2662] det_loss_box_4[0.2955] det_loss_cls_5[0.2557] det_loss_box_5[0.2983] det_loss_cls_dn_0[0.0887] det_loss_box_dn_0[0.2828] det_loss_cls_dn_1[0.0851] det_loss_box_dn_1[0.2299] det_loss_cls_dn_2[0.0846] det_loss_box_dn_2[0.2105] det_loss_cls_dn_3[0.0825] det_loss_box_dn_3[0.2064] det_loss_cls_dn_4[0.0819] det_loss_box_dn_4[0.2038] det_loss_cls_dn_5[0.0808] det_loss_box_dn_5[0.2026] loss_depth[0.0336] total_loss[5.7736]
Rank[0/8] 06/20/2025 18:13:06 INFO stats.py:314 | Epoch[213] Step[329] GlobalStep[83399] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 3:25:34.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:13:27 INFO loss_tracker.py:84 | Epoch[213/239] Step[349] GlobalStep[83419/NA]: det_loss_cls_0[0.4003] det_loss_box_0[0.4989] det_loss_cls_1[0.3996] det_loss_box_1[0.3664] det_loss_cls_2[0.3523] det_loss_box_2[0.3235] det_loss_cls_3[0.3234] det_loss_box_3[0.3067] det_loss_cls_4[0.3075] det_loss_box_4[0.2955] det_loss_cls_5[0.2987] det_loss_box_5[0.2942] det_loss_cls_dn_0[0.0951] det_loss_box_dn_0[0.2869] det_loss_cls_dn_1[0.0913] det_loss_box_dn_1[0.2280] det_loss_cls_dn_2[0.0900] det_loss_box_dn_2[0.2085] det_loss_cls_dn_3[0.0893] det_loss_box_dn_3[0.2017] det_loss_cls_dn_4[0.0886] det_loss_box_dn_4[0.1990] det_loss_cls_dn_5[0.0879] det_loss_box_dn_5[0.1949] loss_depth[0.0356] total_loss[6.0641]
Rank[0/8] 06/20/2025 18:13:33 INFO stats.py:314 | Epoch[213] Step[354] GlobalStep[83424] Training Speed: 7.46 samples/sec across all devices.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 3:25:03.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:13:53 INFO loss_tracker.py:84 | Epoch[213/239] Step[374] GlobalStep[83444/NA]: det_loss_cls_0[0.4829] det_loss_box_0[0.5977] det_loss_cls_1[0.4623] det_loss_box_1[0.4265] det_loss_cls_2[0.4058] det_loss_box_2[0.3778] det_loss_cls_3[0.3674] det_loss_box_3[0.3679] det_loss_cls_4[0.3360] det_loss_box_4[0.3622] det_loss_cls_5[0.3273] det_loss_box_5[0.3625] det_loss_cls_dn_0[0.0931] det_loss_box_dn_0[0.2441] det_loss_cls_dn_1[0.0901] det_loss_box_dn_1[0.1996] det_loss_cls_dn_2[0.0890] det_loss_box_dn_2[0.1819] det_loss_cls_dn_3[0.0876] det_loss_box_dn_3[0.1772] det_loss_cls_dn_4[0.0866] det_loss_box_dn_4[0.1750] det_loss_cls_dn_5[0.0857] det_loss_box_dn_5[0.1735] loss_depth[0.0359] total_loss[6.5956]
Rank[0/8] 06/20/2025 18:13:59 INFO stats.py:314 | Epoch[213] Step[379] GlobalStep[83449] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 3:24:32.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:14:08 INFO stats.py:394 | Epoch[213] completed. Training Speed: 7.67 samples/sec across all devices.	Epoch Time: 406.72 sec.	Average Epoch Time: 406.72 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 3:24:20.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:14:25 INFO stats.py:314 | Epoch[214] Step[14] GlobalStep[83474] Training Speed: 8.03 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 3:24:02.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:14:35 INFO loss_tracker.py:84 | Epoch[214/239] Step[24] GlobalStep[83484/NA]: det_loss_cls_0[0.4077] det_loss_box_0[0.4986] det_loss_cls_1[0.3987] det_loss_box_1[0.3744] det_loss_cls_2[0.3277] det_loss_box_2[0.3476] det_loss_cls_3[0.2946] det_loss_box_3[0.3335] det_loss_cls_4[0.2773] det_loss_box_4[0.3243] det_loss_cls_5[0.2606] det_loss_box_5[0.3259] det_loss_cls_dn_0[0.0932] det_loss_box_dn_0[0.2978] det_loss_cls_dn_1[0.0893] det_loss_box_dn_1[0.2314] det_loss_cls_dn_2[0.0884] det_loss_box_dn_2[0.2130] det_loss_cls_dn_3[0.0874] det_loss_box_dn_3[0.2070] det_loss_cls_dn_4[0.0870] det_loss_box_dn_4[0.2027] det_loss_cls_dn_5[0.0862] det_loss_box_dn_5[0.1982] loss_depth[0.0363] total_loss[6.0886]
Rank[0/8] 06/20/2025 18:14:51 INFO stats.py:314 | Epoch[214] Step[39] GlobalStep[83499] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 3:23:31.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:15:02 INFO loss_tracker.py:84 | Epoch[214/239] Step[49] GlobalStep[83509/NA]: det_loss_cls_0[0.4467] det_loss_box_0[0.5747] det_loss_cls_1[0.4369] det_loss_box_1[0.4500] det_loss_cls_2[0.3779] det_loss_box_2[0.4091] det_loss_cls_3[0.3482] det_loss_box_3[0.4001] det_loss_cls_4[0.3298] det_loss_box_4[0.3814] det_loss_cls_5[0.3191] det_loss_box_5[0.3806] det_loss_cls_dn_0[0.0871] det_loss_box_dn_0[0.2477] det_loss_cls_dn_1[0.0833] det_loss_box_dn_1[0.2076] det_loss_cls_dn_2[0.0820] det_loss_box_dn_2[0.1931] det_loss_cls_dn_3[0.0808] det_loss_box_dn_3[0.1888] det_loss_cls_dn_4[0.0797] det_loss_box_dn_4[0.1878] det_loss_cls_dn_5[0.0789] det_loss_box_dn_5[0.1854] loss_depth[0.0339] total_loss[6.5906]
Rank[0/8] 06/20/2025 18:15:17 INFO stats.py:314 | Epoch[214] Step[64] GlobalStep[83524] Training Speed: 6.04 samples/sec across all devices.	Average Step Time: 1.32 sec.	Estimated Remaining Time: 3:23:00.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:15:28 INFO loss_tracker.py:84 | Epoch[214/239] Step[74] GlobalStep[83534/NA]: det_loss_cls_0[0.5649] det_loss_box_0[0.6790] det_loss_cls_1[0.5623] det_loss_box_1[0.5187] det_loss_cls_2[0.5048] det_loss_box_2[0.4697] det_loss_cls_3[0.4673] det_loss_box_3[0.4543] det_loss_cls_4[0.4428] det_loss_box_4[0.4387] det_loss_cls_5[0.4191] det_loss_box_5[0.4388] det_loss_cls_dn_0[0.0871] det_loss_box_dn_0[0.2125] det_loss_cls_dn_1[0.0828] det_loss_box_dn_1[0.1721] det_loss_cls_dn_2[0.0822] det_loss_box_dn_2[0.1581] det_loss_cls_dn_3[0.0814] det_loss_box_dn_3[0.1538] det_loss_cls_dn_4[0.0806] det_loss_box_dn_4[0.1516] det_loss_cls_dn_5[0.0800] det_loss_box_dn_5[0.1488] loss_depth[0.0320] total_loss[7.4833]
Rank[0/8] 06/20/2025 18:15:43 INFO stats.py:314 | Epoch[214] Step[89] GlobalStep[83549] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 3:22:29.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:15:53 INFO loss_tracker.py:84 | Epoch[214/239] Step[99] GlobalStep[83559/NA]: det_loss_cls_0[0.4542] det_loss_box_0[0.5772] det_loss_cls_1[0.4350] det_loss_box_1[0.4417] det_loss_cls_2[0.3861] det_loss_box_2[0.4040] det_loss_cls_3[0.3559] det_loss_box_3[0.3880] det_loss_cls_4[0.3368] det_loss_box_4[0.3736] det_loss_cls_5[0.3255] det_loss_box_5[0.3716] det_loss_cls_dn_0[0.0864] det_loss_box_dn_0[0.2762] det_loss_cls_dn_1[0.0832] det_loss_box_dn_1[0.2177] det_loss_cls_dn_2[0.0823] det_loss_box_dn_2[0.1989] det_loss_cls_dn_3[0.0813] det_loss_box_dn_3[0.1924] det_loss_cls_dn_4[0.0807] det_loss_box_dn_4[0.1895] det_loss_cls_dn_5[0.0800] det_loss_box_dn_5[0.1866] loss_depth[0.0324] total_loss[6.6371]
Rank[0/8] 06/20/2025 18:16:08 INFO stats.py:314 | Epoch[214] Step[114] GlobalStep[83574] Training Speed: 8.68 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 3:21:59.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:16:19 INFO loss_tracker.py:84 | Epoch[214/239] Step[124] GlobalStep[83584/NA]: det_loss_cls_0[0.4236] det_loss_box_0[0.5149] det_loss_cls_1[0.4132] det_loss_box_1[0.3963] det_loss_cls_2[0.3584] det_loss_box_2[0.3628] det_loss_cls_3[0.3204] det_loss_box_3[0.3438] det_loss_cls_4[0.2945] det_loss_box_4[0.3349] det_loss_cls_5[0.2855] det_loss_box_5[0.3346] det_loss_cls_dn_0[0.0910] det_loss_box_dn_0[0.2355] det_loss_cls_dn_1[0.0883] det_loss_box_dn_1[0.1845] det_loss_cls_dn_2[0.0877] det_loss_box_dn_2[0.1643] det_loss_cls_dn_3[0.0866] det_loss_box_dn_3[0.1606] det_loss_cls_dn_4[0.0864] det_loss_box_dn_4[0.1581] det_loss_cls_dn_5[0.0858] det_loss_box_dn_5[0.1563] loss_depth[0.0336] total_loss[6.0015]
Rank[0/8] 06/20/2025 18:16:35 INFO stats.py:314 | Epoch[214] Step[139] GlobalStep[83599] Training Speed: 8.71 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 3:21:28.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:16:45 INFO loss_tracker.py:84 | Epoch[214/239] Step[149] GlobalStep[83609/NA]: det_loss_cls_0[0.5006] det_loss_box_0[0.6055] det_loss_cls_1[0.4746] det_loss_box_1[0.4474] det_loss_cls_2[0.4037] det_loss_box_2[0.3992] det_loss_cls_3[0.3536] det_loss_box_3[0.3847] det_loss_cls_4[0.3279] det_loss_box_4[0.3784] det_loss_cls_5[0.3108] det_loss_box_5[0.3772] det_loss_cls_dn_0[0.0861] det_loss_box_dn_0[0.2372] det_loss_cls_dn_1[0.0829] det_loss_box_dn_1[0.1924] det_loss_cls_dn_2[0.0819] det_loss_box_dn_2[0.1756] det_loss_cls_dn_3[0.0812] det_loss_box_dn_3[0.1710] det_loss_cls_dn_4[0.0807] det_loss_box_dn_4[0.1686] det_loss_cls_dn_5[0.0799] det_loss_box_dn_5[0.1658] loss_depth[0.0369] total_loss[6.6038]
Rank[0/8] 06/20/2025 18:17:00 INFO stats.py:314 | Epoch[214] Step[164] GlobalStep[83624] Training Speed: 8.08 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 3:20:57.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:17:10 INFO loss_tracker.py:84 | Epoch[214/239] Step[174] GlobalStep[83634/NA]: det_loss_cls_0[0.4762] det_loss_box_0[0.6405] det_loss_cls_1[0.4666] det_loss_box_1[0.5171] det_loss_cls_2[0.4218] det_loss_box_2[0.4712] det_loss_cls_3[0.3978] det_loss_box_3[0.4472] det_loss_cls_4[0.3803] det_loss_box_4[0.4232] det_loss_cls_5[0.3674] det_loss_box_5[0.4231] det_loss_cls_dn_0[0.1057] det_loss_box_dn_0[0.3176] det_loss_cls_dn_1[0.1011] det_loss_box_dn_1[0.2693] det_loss_cls_dn_2[0.1003] det_loss_box_dn_2[0.2511] det_loss_cls_dn_3[0.0991] det_loss_box_dn_3[0.2451] det_loss_cls_dn_4[0.0986] det_loss_box_dn_4[0.2424] det_loss_cls_dn_5[0.0976] det_loss_box_dn_5[0.2379] loss_depth[0.0352] total_loss[7.6333]
Rank[0/8] 06/20/2025 18:17:25 INFO stats.py:314 | Epoch[214] Step[189] GlobalStep[83649] Training Speed: 7.85 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 3:20:26.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:17:36 INFO loss_tracker.py:84 | Epoch[214/239] Step[199] GlobalStep[83659/NA]: det_loss_cls_0[0.5929] det_loss_box_0[0.7333] det_loss_cls_1[0.5809] det_loss_box_1[0.5663] det_loss_cls_2[0.5096] det_loss_box_2[0.5064] det_loss_cls_3[0.4776] det_loss_box_3[0.4944] det_loss_cls_4[0.4519] det_loss_box_4[0.4742] det_loss_cls_5[0.4357] det_loss_box_5[0.4630] det_loss_cls_dn_0[0.0966] det_loss_box_dn_0[0.2863] det_loss_cls_dn_1[0.0938] det_loss_box_dn_1[0.2322] det_loss_cls_dn_2[0.0935] det_loss_box_dn_2[0.2144] det_loss_cls_dn_3[0.0927] det_loss_box_dn_3[0.2103] det_loss_cls_dn_4[0.0918] det_loss_box_dn_4[0.2077] det_loss_cls_dn_5[0.0914] det_loss_box_dn_5[0.2022] loss_depth[0.0349] total_loss[8.2342]
Rank[0/8] 06/20/2025 18:17:52 INFO stats.py:314 | Epoch[214] Step[214] GlobalStep[83674] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 3:19:56.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:18:02 INFO loss_tracker.py:84 | Epoch[214/239] Step[224] GlobalStep[83684/NA]: det_loss_cls_0[0.5885] det_loss_box_0[0.7261] det_loss_cls_1[0.5959] det_loss_box_1[0.5330] det_loss_cls_2[0.5258] det_loss_box_2[0.4887] det_loss_cls_3[0.4826] det_loss_box_3[0.4787] det_loss_cls_4[0.4454] det_loss_box_4[0.4628] det_loss_cls_5[0.4254] det_loss_box_5[0.4629] det_loss_cls_dn_0[0.0961] det_loss_box_dn_0[0.2169] det_loss_cls_dn_1[0.0920] det_loss_box_dn_1[0.1760] det_loss_cls_dn_2[0.0910] det_loss_box_dn_2[0.1594] det_loss_cls_dn_3[0.0894] det_loss_box_dn_3[0.1566] det_loss_cls_dn_4[0.0885] det_loss_box_dn_4[0.1537] det_loss_cls_dn_5[0.0875] det_loss_box_dn_5[0.1517] loss_depth[0.0331] total_loss[7.8077]
Rank[0/8] 06/20/2025 18:18:18 INFO stats.py:314 | Epoch[214] Step[239] GlobalStep[83699] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 3:19:25.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:18:28 INFO loss_tracker.py:84 | Epoch[214/239] Step[249] GlobalStep[83709/NA]: det_loss_cls_0[0.5403] det_loss_box_0[0.6625] det_loss_cls_1[0.5262] det_loss_box_1[0.5221] det_loss_cls_2[0.4526] det_loss_box_2[0.4909] det_loss_cls_3[0.4148] det_loss_box_3[0.4701] det_loss_cls_4[0.3895] det_loss_box_4[0.4645] det_loss_cls_5[0.3770] det_loss_box_5[0.4611] det_loss_cls_dn_0[0.0919] det_loss_box_dn_0[0.2358] det_loss_cls_dn_1[0.0866] det_loss_box_dn_1[0.2030] det_loss_cls_dn_2[0.0854] det_loss_box_dn_2[0.1869] det_loss_cls_dn_3[0.0844] det_loss_box_dn_3[0.1810] det_loss_cls_dn_4[0.0840] det_loss_box_dn_4[0.1796] det_loss_cls_dn_5[0.0832] det_loss_box_dn_5[0.1778] loss_depth[0.0353] total_loss[7.4862]
Rank[0/8] 06/20/2025 18:18:43 INFO stats.py:314 | Epoch[214] Step[264] GlobalStep[83724] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 3:18:54.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:18:54 INFO loss_tracker.py:84 | Epoch[214/239] Step[274] GlobalStep[83734/NA]: det_loss_cls_0[0.4545] det_loss_box_0[0.6199] det_loss_cls_1[0.4478] det_loss_box_1[0.4534] det_loss_cls_2[0.3967] det_loss_box_2[0.4156] det_loss_cls_3[0.3584] det_loss_box_3[0.4041] det_loss_cls_4[0.3371] det_loss_box_4[0.3857] det_loss_cls_5[0.3247] det_loss_box_5[0.3856] det_loss_cls_dn_0[0.0854] det_loss_box_dn_0[0.2491] det_loss_cls_dn_1[0.0827] det_loss_box_dn_1[0.1999] det_loss_cls_dn_2[0.0816] det_loss_box_dn_2[0.1851] det_loss_cls_dn_3[0.0810] det_loss_box_dn_3[0.1806] det_loss_cls_dn_4[0.0804] det_loss_box_dn_4[0.1772] det_loss_cls_dn_5[0.0797] det_loss_box_dn_5[0.1748] loss_depth[0.0346] total_loss[6.6756]
Rank[0/8] 06/20/2025 18:19:10 INFO stats.py:314 | Epoch[214] Step[289] GlobalStep[83749] Training Speed: 6.92 samples/sec across all devices.	Average Step Time: 1.16 sec.	Estimated Remaining Time: 3:18:24.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:19:20 INFO loss_tracker.py:84 | Epoch[214/239] Step[299] GlobalStep[83759/NA]: det_loss_cls_0[0.4092] det_loss_box_0[0.4641] det_loss_cls_1[0.3987] det_loss_box_1[0.3485] det_loss_cls_2[0.3411] det_loss_box_2[0.3185] det_loss_cls_3[0.3081] det_loss_box_3[0.3132] det_loss_cls_4[0.2864] det_loss_box_4[0.3047] det_loss_cls_5[0.2712] det_loss_box_5[0.3083] det_loss_cls_dn_0[0.0808] det_loss_box_dn_0[0.2356] det_loss_cls_dn_1[0.0781] det_loss_box_dn_1[0.1858] det_loss_cls_dn_2[0.0769] det_loss_box_dn_2[0.1687] det_loss_cls_dn_3[0.0757] det_loss_box_dn_3[0.1635] det_loss_cls_dn_4[0.0748] det_loss_box_dn_4[0.1610] det_loss_cls_dn_5[0.0739] det_loss_box_dn_5[0.1589] loss_depth[0.0313] total_loss[5.6369]
Rank[0/8] 06/20/2025 18:19:36 INFO stats.py:314 | Epoch[214] Step[314] GlobalStep[83774] Training Speed: 8.58 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 3:17:53.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:19:46 INFO loss_tracker.py:84 | Epoch[214/239] Step[324] GlobalStep[83784/NA]: det_loss_cls_0[0.5350] det_loss_box_0[0.6231] det_loss_cls_1[0.5242] det_loss_box_1[0.4684] det_loss_cls_2[0.4576] det_loss_box_2[0.4284] det_loss_cls_3[0.4153] det_loss_box_3[0.4101] det_loss_cls_4[0.3852] det_loss_box_4[0.3955] det_loss_cls_5[0.3761] det_loss_box_5[0.3929] det_loss_cls_dn_0[0.0837] det_loss_box_dn_0[0.2117] det_loss_cls_dn_1[0.0803] det_loss_box_dn_1[0.1717] det_loss_cls_dn_2[0.0793] det_loss_box_dn_2[0.1565] det_loss_cls_dn_3[0.0782] det_loss_box_dn_3[0.1536] det_loss_cls_dn_4[0.0774] det_loss_box_dn_4[0.1519] det_loss_cls_dn_5[0.0766] det_loss_box_dn_5[0.1509] loss_depth[0.0345] total_loss[6.9178]
Rank[0/8] 06/20/2025 18:20:01 INFO stats.py:314 | Epoch[214] Step[339] GlobalStep[83799] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 3:17:22.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:20:11 INFO loss_tracker.py:84 | Epoch[214/239] Step[349] GlobalStep[83809/NA]: det_loss_cls_0[0.3213] det_loss_box_0[0.4021] det_loss_cls_1[0.3016] det_loss_box_1[0.3111] det_loss_cls_2[0.2533] det_loss_box_2[0.2802] det_loss_cls_3[0.2284] det_loss_box_3[0.2661] det_loss_cls_4[0.2090] det_loss_box_4[0.2596] det_loss_cls_5[0.1972] det_loss_box_5[0.2590] det_loss_cls_dn_0[0.0712] det_loss_box_dn_0[0.2559] det_loss_cls_dn_1[0.0665] det_loss_box_dn_1[0.2009] det_loss_cls_dn_2[0.0652] det_loss_box_dn_2[0.1807] det_loss_cls_dn_3[0.0641] det_loss_box_dn_3[0.1757] det_loss_cls_dn_4[0.0632] det_loss_box_dn_4[0.1727] det_loss_cls_dn_5[0.0623] det_loss_box_dn_5[0.1692] loss_depth[0.0360] total_loss[4.8725]
Rank[0/8] 06/20/2025 18:20:27 INFO stats.py:314 | Epoch[214] Step[364] GlobalStep[83824] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 3:16:51.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:20:38 INFO loss_tracker.py:84 | Epoch[214/239] Step[374] GlobalStep[83834/NA]: det_loss_cls_0[0.3468] det_loss_box_0[0.3954] det_loss_cls_1[0.3354] det_loss_box_1[0.2964] det_loss_cls_2[0.2783] det_loss_box_2[0.2746] det_loss_cls_3[0.2577] det_loss_box_3[0.2583] det_loss_cls_4[0.2340] det_loss_box_4[0.2584] det_loss_cls_5[0.2244] det_loss_box_5[0.2565] det_loss_cls_dn_0[0.0864] det_loss_box_dn_0[0.2515] det_loss_cls_dn_1[0.0830] det_loss_box_dn_1[0.2000] det_loss_cls_dn_2[0.0828] det_loss_box_dn_2[0.1780] det_loss_cls_dn_3[0.0815] det_loss_box_dn_3[0.1737] det_loss_cls_dn_4[0.0804] det_loss_box_dn_4[0.1717] det_loss_cls_dn_5[0.0793] det_loss_box_dn_5[0.1684] loss_depth[0.0318] total_loss[5.0847]
Rank[0/8] 06/20/2025 18:20:53 INFO stats.py:314 | Epoch[214] Step[389] GlobalStep[83849] Training Speed: 8.08 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 3:16:21.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:20:53 INFO stats.py:394 | Epoch[214] completed. Training Speed: 7.71 samples/sec across all devices.	Epoch Time: 404.70 sec.	Average Epoch Time: 404.70 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 3:16:21.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:21:20 INFO stats.py:314 | Epoch[215] Step[24] GlobalStep[83874] Training Speed: 7.90 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 3:15:50.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:21:20 INFO loss_tracker.py:84 | Epoch[215/239] Step[24] GlobalStep[83874/NA]: det_loss_cls_0[0.4608] det_loss_box_0[0.5977] det_loss_cls_1[0.4455] det_loss_box_1[0.4565] det_loss_cls_2[0.3951] det_loss_box_2[0.3990] det_loss_cls_3[0.3606] det_loss_box_3[0.3916] det_loss_cls_4[0.3394] det_loss_box_4[0.3785] det_loss_cls_5[0.3277] det_loss_box_5[0.3780] det_loss_cls_dn_0[0.0989] det_loss_box_dn_0[0.2619] det_loss_cls_dn_1[0.0957] det_loss_box_dn_1[0.2135] det_loss_cls_dn_2[0.0951] det_loss_box_dn_2[0.1971] det_loss_cls_dn_3[0.0938] det_loss_box_dn_3[0.1931] det_loss_cls_dn_4[0.0928] det_loss_box_dn_4[0.1897] det_loss_cls_dn_5[0.0918] det_loss_box_dn_5[0.1878] loss_depth[0.0348] total_loss[6.7763]
Rank[0/8] 06/20/2025 18:21:45 INFO stats.py:314 | Epoch[215] Step[49] GlobalStep[83899] Training Speed: 6.70 samples/sec across all devices.	Average Step Time: 1.19 sec.	Estimated Remaining Time: 3:15:19.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:21:45 INFO loss_tracker.py:84 | Epoch[215/239] Step[49] GlobalStep[83899/NA]: det_loss_cls_0[0.3848] det_loss_box_0[0.4889] det_loss_cls_1[0.3696] det_loss_box_1[0.3810] det_loss_cls_2[0.3042] det_loss_box_2[0.3382] det_loss_cls_3[0.2798] det_loss_box_3[0.3253] det_loss_cls_4[0.2589] det_loss_box_4[0.3156] det_loss_cls_5[0.2447] det_loss_box_5[0.3209] det_loss_cls_dn_0[0.0873] det_loss_box_dn_0[0.2698] det_loss_cls_dn_1[0.0851] det_loss_box_dn_1[0.2282] det_loss_cls_dn_2[0.0844] det_loss_box_dn_2[0.2080] det_loss_cls_dn_3[0.0834] det_loss_box_dn_3[0.2057] det_loss_cls_dn_4[0.0824] det_loss_box_dn_4[0.2038] det_loss_cls_dn_5[0.0814] det_loss_box_dn_5[0.2007] loss_depth[0.0317] total_loss[5.8639]
Rank[0/8] 06/20/2025 18:22:11 INFO stats.py:314 | Epoch[215] Step[74] GlobalStep[83924] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 3:14:49.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:22:11 INFO loss_tracker.py:84 | Epoch[215/239] Step[74] GlobalStep[83924/NA]: det_loss_cls_0[0.5145] det_loss_box_0[0.6381] det_loss_cls_1[0.4957] det_loss_box_1[0.4671] det_loss_cls_2[0.4408] det_loss_box_2[0.4113] det_loss_cls_3[0.4131] det_loss_box_3[0.3950] det_loss_cls_4[0.3856] det_loss_box_4[0.3922] det_loss_cls_5[0.3788] det_loss_box_5[0.3893] det_loss_cls_dn_0[0.1023] det_loss_box_dn_0[0.2489] det_loss_cls_dn_1[0.0987] det_loss_box_dn_1[0.2026] det_loss_cls_dn_2[0.0975] det_loss_box_dn_2[0.1861] det_loss_cls_dn_3[0.0963] det_loss_box_dn_3[0.1818] det_loss_cls_dn_4[0.0959] det_loss_box_dn_4[0.1801] det_loss_cls_dn_5[0.0954] det_loss_box_dn_5[0.1784] loss_depth[0.0340] total_loss[7.1198]
Rank[0/8] 06/20/2025 18:22:37 INFO stats.py:314 | Epoch[215] Step[99] GlobalStep[83949] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 3:14:18.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:22:37 INFO loss_tracker.py:84 | Epoch[215/239] Step[99] GlobalStep[83949/NA]: det_loss_cls_0[0.3393] det_loss_box_0[0.4157] det_loss_cls_1[0.3331] det_loss_box_1[0.3005] det_loss_cls_2[0.2792] det_loss_box_2[0.2738] det_loss_cls_3[0.2549] det_loss_box_3[0.2628] det_loss_cls_4[0.2303] det_loss_box_4[0.2603] det_loss_cls_5[0.2197] det_loss_box_5[0.2603] det_loss_cls_dn_0[0.0805] det_loss_box_dn_0[0.2080] det_loss_cls_dn_1[0.0775] det_loss_box_dn_1[0.1680] det_loss_cls_dn_2[0.0775] det_loss_box_dn_2[0.1527] det_loss_cls_dn_3[0.0763] det_loss_box_dn_3[0.1487] det_loss_cls_dn_4[0.0758] det_loss_box_dn_4[0.1474] det_loss_cls_dn_5[0.0746] det_loss_box_dn_5[0.1459] loss_depth[0.0340] total_loss[4.8969]
Rank[0/8] 06/20/2025 18:23:02 INFO stats.py:314 | Epoch[215] Step[124] GlobalStep[83974] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 3:13:47.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:23:02 INFO loss_tracker.py:84 | Epoch[215/239] Step[124] GlobalStep[83974/NA]: det_loss_cls_0[0.5243] det_loss_box_0[0.6055] det_loss_cls_1[0.5183] det_loss_box_1[0.4557] det_loss_cls_2[0.4538] det_loss_box_2[0.4145] det_loss_cls_3[0.4142] det_loss_box_3[0.3908] det_loss_cls_4[0.3828] det_loss_box_4[0.3847] det_loss_cls_5[0.3696] det_loss_box_5[0.3852] det_loss_cls_dn_0[0.1003] det_loss_box_dn_0[0.2604] det_loss_cls_dn_1[0.0960] det_loss_box_dn_1[0.2191] det_loss_cls_dn_2[0.0952] det_loss_box_dn_2[0.2010] det_loss_cls_dn_3[0.0936] det_loss_box_dn_3[0.1971] det_loss_cls_dn_4[0.0927] det_loss_box_dn_4[0.1944] det_loss_cls_dn_5[0.0918] det_loss_box_dn_5[0.1926] loss_depth[0.0353] total_loss[7.1691]
Rank[0/8] 06/20/2025 18:23:28 INFO stats.py:314 | Epoch[215] Step[149] GlobalStep[83999] Training Speed: 7.10 samples/sec across all devices.	Average Step Time: 1.13 sec.	Estimated Remaining Time: 3:13:16.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:23:28 INFO loss_tracker.py:84 | Epoch[215/239] Step[149] GlobalStep[83999/NA]: det_loss_cls_0[0.5228] det_loss_box_0[0.5853] det_loss_cls_1[0.4956] det_loss_box_1[0.4278] det_loss_cls_2[0.4250] det_loss_box_2[0.3850] det_loss_cls_3[0.3808] det_loss_box_3[0.3774] det_loss_cls_4[0.3562] det_loss_box_4[0.3684] det_loss_cls_5[0.3434] det_loss_box_5[0.3710] det_loss_cls_dn_0[0.0923] det_loss_box_dn_0[0.2515] det_loss_cls_dn_1[0.0888] det_loss_box_dn_1[0.2098] det_loss_cls_dn_2[0.0882] det_loss_box_dn_2[0.1954] det_loss_cls_dn_3[0.0879] det_loss_box_dn_3[0.1907] det_loss_cls_dn_4[0.0870] det_loss_box_dn_4[0.1881] det_loss_cls_dn_5[0.0860] det_loss_box_dn_5[0.1857] loss_depth[0.0321] total_loss[6.8221]
Rank[0/8] 06/20/2025 18:23:54 INFO stats.py:314 | Epoch[215] Step[174] GlobalStep[84024] Training Speed: 6.51 samples/sec across all devices.	Average Step Time: 1.23 sec.	Estimated Remaining Time: 3:12:46.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:23:54 INFO loss_tracker.py:84 | Epoch[215/239] Step[174] GlobalStep[84024/NA]: det_loss_cls_0[0.5790] det_loss_box_0[0.8325] det_loss_cls_1[0.5711] det_loss_box_1[0.6227] det_loss_cls_2[0.5123] det_loss_box_2[0.5449] det_loss_cls_3[0.4734] det_loss_box_3[0.5286] det_loss_cls_4[0.4531] det_loss_box_4[0.4987] det_loss_cls_5[0.4389] det_loss_box_5[0.4954] det_loss_cls_dn_0[0.0923] det_loss_box_dn_0[0.2521] det_loss_cls_dn_1[0.0878] det_loss_box_dn_1[0.2114] det_loss_cls_dn_2[0.0871] det_loss_box_dn_2[0.1974] det_loss_cls_dn_3[0.0861] det_loss_box_dn_3[0.1926] det_loss_cls_dn_4[0.0854] det_loss_box_dn_4[0.1912] det_loss_cls_dn_5[0.0843] det_loss_box_dn_5[0.1901] loss_depth[0.0357] total_loss[8.3441]
Rank[0/8] 06/20/2025 18:24:19 INFO stats.py:314 | Epoch[215] Step[199] GlobalStep[84049] Training Speed: 6.30 samples/sec across all devices.	Average Step Time: 1.27 sec.	Estimated Remaining Time: 3:12:15.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:24:19 INFO loss_tracker.py:84 | Epoch[215/239] Step[199] GlobalStep[84049/NA]: det_loss_cls_0[0.4492] det_loss_box_0[0.5378] det_loss_cls_1[0.4352] det_loss_box_1[0.3960] det_loss_cls_2[0.3684] det_loss_box_2[0.3438] det_loss_cls_3[0.3359] det_loss_box_3[0.3303] det_loss_cls_4[0.3168] det_loss_box_4[0.3148] det_loss_cls_5[0.2963] det_loss_box_5[0.3207] det_loss_cls_dn_0[0.0938] det_loss_box_dn_0[0.2394] det_loss_cls_dn_1[0.0904] det_loss_box_dn_1[0.1979] det_loss_cls_dn_2[0.0898] det_loss_box_dn_2[0.1801] det_loss_cls_dn_3[0.0884] det_loss_box_dn_3[0.1763] det_loss_cls_dn_4[0.0877] det_loss_box_dn_4[0.1730] det_loss_cls_dn_5[0.0867] det_loss_box_dn_5[0.1705] loss_depth[0.0341] total_loss[6.1535]
Rank[0/8] 06/20/2025 18:24:44 INFO stats.py:314 | Epoch[215] Step[224] GlobalStep[84074] Training Speed: 8.66 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 3:11:44.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:24:44 INFO loss_tracker.py:84 | Epoch[215/239] Step[224] GlobalStep[84074/NA]: det_loss_cls_0[0.5183] det_loss_box_0[0.6839] det_loss_cls_1[0.5068] det_loss_box_1[0.5303] det_loss_cls_2[0.4291] det_loss_box_2[0.4728] det_loss_cls_3[0.3968] det_loss_box_3[0.4481] det_loss_cls_4[0.3741] det_loss_box_4[0.4416] det_loss_cls_5[0.3629] det_loss_box_5[0.4404] det_loss_cls_dn_0[0.0943] det_loss_box_dn_0[0.2694] det_loss_cls_dn_1[0.0905] det_loss_box_dn_1[0.2206] det_loss_cls_dn_2[0.0899] det_loss_box_dn_2[0.2011] det_loss_cls_dn_3[0.0886] det_loss_box_dn_3[0.1986] det_loss_cls_dn_4[0.0872] det_loss_box_dn_4[0.1959] det_loss_cls_dn_5[0.0860] det_loss_box_dn_5[0.1945] loss_depth[0.0365] total_loss[7.4581]
Rank[0/8] 06/20/2025 18:25:10 INFO stats.py:314 | Epoch[215] Step[249] GlobalStep[84099] Training Speed: 8.69 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 3:11:14.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:25:10 INFO loss_tracker.py:84 | Epoch[215/239] Step[249] GlobalStep[84099/NA]: det_loss_cls_0[0.4194] det_loss_box_0[0.5093] det_loss_cls_1[0.4081] det_loss_box_1[0.3971] det_loss_cls_2[0.3568] det_loss_box_2[0.3544] det_loss_cls_3[0.3303] det_loss_box_3[0.3421] det_loss_cls_4[0.3032] det_loss_box_4[0.3408] det_loss_cls_5[0.2944] det_loss_box_5[0.3410] det_loss_cls_dn_0[0.0847] det_loss_box_dn_0[0.2493] det_loss_cls_dn_1[0.0811] det_loss_box_dn_1[0.2020] det_loss_cls_dn_2[0.0800] det_loss_box_dn_2[0.1860] det_loss_cls_dn_3[0.0792] det_loss_box_dn_3[0.1825] det_loss_cls_dn_4[0.0787] det_loss_box_dn_4[0.1806] det_loss_cls_dn_5[0.0779] det_loss_box_dn_5[0.1788] loss_depth[0.0329] total_loss[6.0906]
Rank[0/8] 06/20/2025 18:25:36 INFO stats.py:314 | Epoch[215] Step[274] GlobalStep[84124] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 3:10:43.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:25:36 INFO loss_tracker.py:84 | Epoch[215/239] Step[274] GlobalStep[84124/NA]: det_loss_cls_0[0.5429] det_loss_box_0[0.6286] det_loss_cls_1[0.5160] det_loss_box_1[0.4826] det_loss_cls_2[0.4575] det_loss_box_2[0.4467] det_loss_cls_3[0.4200] det_loss_box_3[0.4386] det_loss_cls_4[0.3938] det_loss_box_4[0.4290] det_loss_cls_5[0.3840] det_loss_box_5[0.4292] det_loss_cls_dn_0[0.0878] det_loss_box_dn_0[0.2525] det_loss_cls_dn_1[0.0835] det_loss_box_dn_1[0.2103] det_loss_cls_dn_2[0.0827] det_loss_box_dn_2[0.1936] det_loss_cls_dn_3[0.0813] det_loss_box_dn_3[0.1899] det_loss_cls_dn_4[0.0806] det_loss_box_dn_4[0.1880] det_loss_cls_dn_5[0.0797] det_loss_box_dn_5[0.1859] loss_depth[0.0331] total_loss[7.3178]
Rank[0/8] 06/20/2025 18:26:02 INFO stats.py:314 | Epoch[215] Step[299] GlobalStep[84149] Training Speed: 7.44 samples/sec across all devices.	Average Step Time: 1.08 sec.	Estimated Remaining Time: 3:10:12.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:26:02 INFO loss_tracker.py:84 | Epoch[215/239] Step[299] GlobalStep[84149/NA]: det_loss_cls_0[0.3891] det_loss_box_0[0.4701] det_loss_cls_1[0.3823] det_loss_box_1[0.3480] det_loss_cls_2[0.3134] det_loss_box_2[0.3266] det_loss_cls_3[0.2846] det_loss_box_3[0.3193] det_loss_cls_4[0.2582] det_loss_box_4[0.3081] det_loss_cls_5[0.2471] det_loss_box_5[0.3076] det_loss_cls_dn_0[0.0884] det_loss_box_dn_0[0.2588] det_loss_cls_dn_1[0.0837] det_loss_box_dn_1[0.2105] det_loss_cls_dn_2[0.0831] det_loss_box_dn_2[0.1914] det_loss_cls_dn_3[0.0818] det_loss_box_dn_3[0.1880] det_loss_cls_dn_4[0.0814] det_loss_box_dn_4[0.1853] det_loss_cls_dn_5[0.0806] det_loss_box_dn_5[0.1835] loss_depth[0.0323] total_loss[5.7032]
Rank[0/8] 06/20/2025 18:26:28 INFO stats.py:314 | Epoch[215] Step[324] GlobalStep[84174] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 3:09:41.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:26:28 INFO loss_tracker.py:84 | Epoch[215/239] Step[324] GlobalStep[84174/NA]: det_loss_cls_0[0.4532] det_loss_box_0[0.5633] det_loss_cls_1[0.4369] det_loss_box_1[0.4276] det_loss_cls_2[0.3686] det_loss_box_2[0.3876] det_loss_cls_3[0.3276] det_loss_box_3[0.3846] det_loss_cls_4[0.3029] det_loss_box_4[0.3723] det_loss_cls_5[0.2896] det_loss_box_5[0.3743] det_loss_cls_dn_0[0.0811] det_loss_box_dn_0[0.2423] det_loss_cls_dn_1[0.0776] det_loss_box_dn_1[0.2026] det_loss_cls_dn_2[0.0763] det_loss_box_dn_2[0.1861] det_loss_cls_dn_3[0.0752] det_loss_box_dn_3[0.1837] det_loss_cls_dn_4[0.0741] det_loss_box_dn_4[0.1824] det_loss_cls_dn_5[0.0736] det_loss_box_dn_5[0.1809] loss_depth[0.0343] total_loss[6.3588]
Rank[0/8] 06/20/2025 18:26:54 INFO stats.py:314 | Epoch[215] Step[349] GlobalStep[84199] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 3:09:11.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:26:54 INFO loss_tracker.py:84 | Epoch[215/239] Step[349] GlobalStep[84199/NA]: det_loss_cls_0[0.3683] det_loss_box_0[0.4499] det_loss_cls_1[0.3678] det_loss_box_1[0.3545] det_loss_cls_2[0.3224] det_loss_box_2[0.3218] det_loss_cls_3[0.3002] det_loss_box_3[0.3067] det_loss_cls_4[0.2771] det_loss_box_4[0.2952] det_loss_cls_5[0.2724] det_loss_box_5[0.2933] det_loss_cls_dn_0[0.0812] det_loss_box_dn_0[0.2804] det_loss_cls_dn_1[0.0776] det_loss_box_dn_1[0.2200] det_loss_cls_dn_2[0.0770] det_loss_box_dn_2[0.1983] det_loss_cls_dn_3[0.0759] det_loss_box_dn_3[0.1939] det_loss_cls_dn_4[0.0750] det_loss_box_dn_4[0.1908] det_loss_cls_dn_5[0.0742] det_loss_box_dn_5[0.1890] loss_depth[0.0341] total_loss[5.6972]
Rank[0/8] 06/20/2025 18:27:20 INFO stats.py:314 | Epoch[215] Step[374] GlobalStep[84224] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 3:08:40.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:27:20 INFO loss_tracker.py:84 | Epoch[215/239] Step[374] GlobalStep[84224/NA]: det_loss_cls_0[0.5450] det_loss_box_0[0.6997] det_loss_cls_1[0.5251] det_loss_box_1[0.5289] det_loss_cls_2[0.4503] det_loss_box_2[0.4924] det_loss_cls_3[0.4143] det_loss_box_3[0.4682] det_loss_cls_4[0.3926] det_loss_box_4[0.4465] det_loss_cls_5[0.3802] det_loss_box_5[0.4466] det_loss_cls_dn_0[0.0940] det_loss_box_dn_0[0.2653] det_loss_cls_dn_1[0.0907] det_loss_box_dn_1[0.2178] det_loss_cls_dn_2[0.0899] det_loss_box_dn_2[0.1996] det_loss_cls_dn_3[0.0886] det_loss_box_dn_3[0.1954] det_loss_cls_dn_4[0.0881] det_loss_box_dn_4[0.1936] det_loss_cls_dn_5[0.0872] det_loss_box_dn_5[0.1920] loss_depth[0.0346] total_loss[7.6269]
Rank[0/8] 06/20/2025 18:27:35 INFO stats.py:394 | Epoch[215] completed. Training Speed: 7.76 samples/sec across all devices.	Epoch Time: 402.13 sec.	Average Epoch Time: 402.13 sec.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 3:08:22.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:27:48 INFO stats.py:314 | Epoch[216] Step[9] GlobalStep[84249] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 3:08:10.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:28:04 INFO loss_tracker.py:84 | Epoch[216/239] Step[24] GlobalStep[84264/NA]: det_loss_cls_0[0.4476] det_loss_box_0[0.5678] det_loss_cls_1[0.4248] det_loss_box_1[0.4315] det_loss_cls_2[0.3740] det_loss_box_2[0.3903] det_loss_cls_3[0.3360] det_loss_box_3[0.3810] det_loss_cls_4[0.3138] det_loss_box_4[0.3708] det_loss_cls_5[0.3052] det_loss_box_5[0.3668] det_loss_cls_dn_0[0.0892] det_loss_box_dn_0[0.2667] det_loss_cls_dn_1[0.0859] det_loss_box_dn_1[0.2231] det_loss_cls_dn_2[0.0847] det_loss_box_dn_2[0.2051] det_loss_cls_dn_3[0.0831] det_loss_box_dn_3[0.1959] det_loss_cls_dn_4[0.0824] det_loss_box_dn_4[0.1937] det_loss_cls_dn_5[0.0816] det_loss_box_dn_5[0.1891] loss_depth[0.0362] total_loss[6.5265]
Rank[0/8] 06/20/2025 18:28:14 INFO stats.py:314 | Epoch[216] Step[34] GlobalStep[84274] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 3:07:39.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:28:29 INFO loss_tracker.py:84 | Epoch[216/239] Step[49] GlobalStep[84289/NA]: det_loss_cls_0[0.4559] det_loss_box_0[0.5328] det_loss_cls_1[0.4514] det_loss_box_1[0.3969] det_loss_cls_2[0.3925] det_loss_box_2[0.3481] det_loss_cls_3[0.3535] det_loss_box_3[0.3404] det_loss_cls_4[0.3330] det_loss_box_4[0.3306] det_loss_cls_5[0.3188] det_loss_box_5[0.3267] det_loss_cls_dn_0[0.0928] det_loss_box_dn_0[0.2333] det_loss_cls_dn_1[0.0896] det_loss_box_dn_1[0.1857] det_loss_cls_dn_2[0.0892] det_loss_box_dn_2[0.1714] det_loss_cls_dn_3[0.0888] det_loss_box_dn_3[0.1673] det_loss_cls_dn_4[0.0876] det_loss_box_dn_4[0.1652] det_loss_cls_dn_5[0.0871] det_loss_box_dn_5[0.1628] loss_depth[0.0329] total_loss[6.2344]
Rank[0/8] 06/20/2025 18:28:40 INFO stats.py:314 | Epoch[216] Step[59] GlobalStep[84299] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 3:07:08.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:28:55 INFO loss_tracker.py:84 | Epoch[216/239] Step[74] GlobalStep[84314/NA]: det_loss_cls_0[0.4175] det_loss_box_0[0.5161] det_loss_cls_1[0.4174] det_loss_box_1[0.3959] det_loss_cls_2[0.3486] det_loss_box_2[0.3629] det_loss_cls_3[0.3234] det_loss_box_3[0.3455] det_loss_cls_4[0.2961] det_loss_box_4[0.3422] det_loss_cls_5[0.2882] det_loss_box_5[0.3416] det_loss_cls_dn_0[0.0939] det_loss_box_dn_0[0.2391] det_loss_cls_dn_1[0.0904] det_loss_box_dn_1[0.1940] det_loss_cls_dn_2[0.0895] det_loss_box_dn_2[0.1765] det_loss_cls_dn_3[0.0890] det_loss_box_dn_3[0.1738] det_loss_cls_dn_4[0.0882] det_loss_box_dn_4[0.1710] det_loss_cls_dn_5[0.0876] det_loss_box_dn_5[0.1695] loss_depth[0.0339] total_loss[6.0917]
Rank[0/8] 06/20/2025 18:29:06 INFO stats.py:314 | Epoch[216] Step[84] GlobalStep[84324] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 3:06:38.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:29:22 INFO loss_tracker.py:84 | Epoch[216/239] Step[99] GlobalStep[84339/NA]: det_loss_cls_0[0.4149] det_loss_box_0[0.5352] det_loss_cls_1[0.4005] det_loss_box_1[0.3943] det_loss_cls_2[0.3509] det_loss_box_2[0.3637] det_loss_cls_3[0.3171] det_loss_box_3[0.3503] det_loss_cls_4[0.2992] det_loss_box_4[0.3415] det_loss_cls_5[0.2860] det_loss_box_5[0.3437] det_loss_cls_dn_0[0.0883] det_loss_box_dn_0[0.2511] det_loss_cls_dn_1[0.0848] det_loss_box_dn_1[0.2006] det_loss_cls_dn_2[0.0839] det_loss_box_dn_2[0.1822] det_loss_cls_dn_3[0.0827] det_loss_box_dn_3[0.1779] det_loss_cls_dn_4[0.0816] det_loss_box_dn_4[0.1748] det_loss_cls_dn_5[0.0809] det_loss_box_dn_5[0.1737] loss_depth[0.0344] total_loss[6.0944]
Rank[0/8] 06/20/2025 18:29:32 INFO stats.py:314 | Epoch[216] Step[109] GlobalStep[84349] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 3:06:07.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:29:47 INFO loss_tracker.py:84 | Epoch[216/239] Step[124] GlobalStep[84364/NA]: det_loss_cls_0[0.4669] det_loss_box_0[0.5926] det_loss_cls_1[0.4519] det_loss_box_1[0.4447] det_loss_cls_2[0.3865] det_loss_box_2[0.4128] det_loss_cls_3[0.3539] det_loss_box_3[0.4051] det_loss_cls_4[0.3320] det_loss_box_4[0.3894] det_loss_cls_5[0.3178] det_loss_box_5[0.3894] det_loss_cls_dn_0[0.0872] det_loss_box_dn_0[0.2762] det_loss_cls_dn_1[0.0838] det_loss_box_dn_1[0.2289] det_loss_cls_dn_2[0.0827] det_loss_box_dn_2[0.2110] det_loss_cls_dn_3[0.0820] det_loss_box_dn_3[0.2054] det_loss_cls_dn_4[0.0810] det_loss_box_dn_4[0.2014] det_loss_cls_dn_5[0.0800] det_loss_box_dn_5[0.1984] loss_depth[0.0341] total_loss[6.7953]
Rank[0/8] 06/20/2025 18:29:58 INFO stats.py:314 | Epoch[216] Step[134] GlobalStep[84374] Training Speed: 7.98 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 3:05:36.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:30:13 INFO loss_tracker.py:84 | Epoch[216/239] Step[149] GlobalStep[84389/NA]: det_loss_cls_0[0.3828] det_loss_box_0[0.4298] det_loss_cls_1[0.3660] det_loss_box_1[0.3354] det_loss_cls_2[0.3156] det_loss_box_2[0.3118] det_loss_cls_3[0.2848] det_loss_box_3[0.3015] det_loss_cls_4[0.2630] det_loss_box_4[0.2925] det_loss_cls_5[0.2502] det_loss_box_5[0.2947] det_loss_cls_dn_0[0.0849] det_loss_box_dn_0[0.2660] det_loss_cls_dn_1[0.0817] det_loss_box_dn_1[0.2150] det_loss_cls_dn_2[0.0808] det_loss_box_dn_2[0.1966] det_loss_cls_dn_3[0.0798] det_loss_box_dn_3[0.1926] det_loss_cls_dn_4[0.0795] det_loss_box_dn_4[0.1894] det_loss_cls_dn_5[0.0789] det_loss_box_dn_5[0.1844] loss_depth[0.0354] total_loss[5.5931]
Rank[0/8] 06/20/2025 18:30:24 INFO stats.py:314 | Epoch[216] Step[159] GlobalStep[84399] Training Speed: 6.86 samples/sec across all devices.	Average Step Time: 1.17 sec.	Estimated Remaining Time: 3:05:06.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:30:39 INFO loss_tracker.py:84 | Epoch[216/239] Step[174] GlobalStep[84414/NA]: det_loss_cls_0[0.3554] det_loss_box_0[0.4395] det_loss_cls_1[0.3422] det_loss_box_1[0.3416] det_loss_cls_2[0.3011] det_loss_box_2[0.3082] det_loss_cls_3[0.2729] det_loss_box_3[0.2986] det_loss_cls_4[0.2559] det_loss_box_4[0.2921] det_loss_cls_5[0.2452] det_loss_box_5[0.2903] det_loss_cls_dn_0[0.0843] det_loss_box_dn_0[0.2846] det_loss_cls_dn_1[0.0810] det_loss_box_dn_1[0.2252] det_loss_cls_dn_2[0.0800] det_loss_box_dn_2[0.2039] det_loss_cls_dn_3[0.0789] det_loss_box_dn_3[0.1994] det_loss_cls_dn_4[0.0783] det_loss_box_dn_4[0.1971] det_loss_cls_dn_5[0.0775] det_loss_box_dn_5[0.1913] loss_depth[0.0353] total_loss[5.5600]
Rank[0/8] 06/20/2025 18:30:49 INFO stats.py:314 | Epoch[216] Step[184] GlobalStep[84424] Training Speed: 6.33 samples/sec across all devices.	Average Step Time: 1.26 sec.	Estimated Remaining Time: 3:04:35.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:31:05 INFO loss_tracker.py:84 | Epoch[216/239] Step[199] GlobalStep[84439/NA]: det_loss_cls_0[0.4024] det_loss_box_0[0.5140] det_loss_cls_1[0.3924] det_loss_box_1[0.3724] det_loss_cls_2[0.3253] det_loss_box_2[0.3381] det_loss_cls_3[0.2907] det_loss_box_3[0.3336] det_loss_cls_4[0.2806] det_loss_box_4[0.3162] det_loss_cls_5[0.2716] det_loss_box_5[0.3166] det_loss_cls_dn_0[0.0747] det_loss_box_dn_0[0.2481] det_loss_cls_dn_1[0.0714] det_loss_box_dn_1[0.1960] det_loss_cls_dn_2[0.0703] det_loss_box_dn_2[0.1764] det_loss_cls_dn_3[0.0693] det_loss_box_dn_3[0.1723] det_loss_cls_dn_4[0.0686] det_loss_box_dn_4[0.1706] det_loss_cls_dn_5[0.0676] det_loss_box_dn_5[0.1691] loss_depth[0.0362] total_loss[5.7444]
Rank[0/8] 06/20/2025 18:31:16 INFO stats.py:314 | Epoch[216] Step[209] GlobalStep[84449] Training Speed: 8.19 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 3:04:05.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:31:31 INFO loss_tracker.py:84 | Epoch[216/239] Step[224] GlobalStep[84464/NA]: det_loss_cls_0[0.5153] det_loss_box_0[0.6602] det_loss_cls_1[0.4988] det_loss_box_1[0.4896] det_loss_cls_2[0.4292] det_loss_box_2[0.4472] det_loss_cls_3[0.3953] det_loss_box_3[0.4263] det_loss_cls_4[0.3677] det_loss_box_4[0.4211] det_loss_cls_5[0.3513] det_loss_box_5[0.4212] det_loss_cls_dn_0[0.0890] det_loss_box_dn_0[0.2541] det_loss_cls_dn_1[0.0844] det_loss_box_dn_1[0.2104] det_loss_cls_dn_2[0.0837] det_loss_box_dn_2[0.1954] det_loss_cls_dn_3[0.0828] det_loss_box_dn_3[0.1925] det_loss_cls_dn_4[0.0820] det_loss_box_dn_4[0.1907] det_loss_cls_dn_5[0.0812] det_loss_box_dn_5[0.1888] loss_depth[0.0348] total_loss[7.1932]
Rank[0/8] 06/20/2025 18:31:42 INFO stats.py:314 | Epoch[216] Step[234] GlobalStep[84474] Training Speed: 6.88 samples/sec across all devices.	Average Step Time: 1.16 sec.	Estimated Remaining Time: 3:03:34.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:31:57 INFO loss_tracker.py:84 | Epoch[216/239] Step[249] GlobalStep[84489/NA]: det_loss_cls_0[0.6768] det_loss_box_0[0.8872] det_loss_cls_1[0.6534] det_loss_box_1[0.6620] det_loss_cls_2[0.6032] det_loss_box_2[0.5762] det_loss_cls_3[0.5692] det_loss_box_3[0.5465] det_loss_cls_4[0.5191] det_loss_box_4[0.5267] det_loss_cls_5[0.4991] det_loss_box_5[0.5234] det_loss_cls_dn_0[0.0994] det_loss_box_dn_0[0.2600] det_loss_cls_dn_1[0.0961] det_loss_box_dn_1[0.2159] det_loss_cls_dn_2[0.0950] det_loss_box_dn_2[0.2008] det_loss_cls_dn_3[0.0939] det_loss_box_dn_3[0.1985] det_loss_cls_dn_4[0.0932] det_loss_box_dn_4[0.1953] det_loss_cls_dn_5[0.0924] det_loss_box_dn_5[0.1937] loss_depth[0.0389] total_loss[9.1159]
Rank[0/8] 06/20/2025 18:32:08 INFO stats.py:314 | Epoch[216] Step[259] GlobalStep[84499] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 3:03:03.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:32:23 INFO loss_tracker.py:84 | Epoch[216/239] Step[274] GlobalStep[84514/NA]: det_loss_cls_0[0.4032] det_loss_box_0[0.4276] det_loss_cls_1[0.4019] det_loss_box_1[0.3276] det_loss_cls_2[0.3411] det_loss_box_2[0.2991] det_loss_cls_3[0.3167] det_loss_box_3[0.2866] det_loss_cls_4[0.2968] det_loss_box_4[0.2798] det_loss_cls_5[0.2797] det_loss_box_5[0.2806] det_loss_cls_dn_0[0.0865] det_loss_box_dn_0[0.2886] det_loss_cls_dn_1[0.0827] det_loss_box_dn_1[0.2373] det_loss_cls_dn_2[0.0813] det_loss_box_dn_2[0.2185] det_loss_cls_dn_3[0.0803] det_loss_box_dn_3[0.2143] det_loss_cls_dn_4[0.0790] det_loss_box_dn_4[0.2098] det_loss_cls_dn_5[0.0779] det_loss_box_dn_5[0.2070] loss_depth[0.0312] total_loss[5.8351]
Rank[0/8] 06/20/2025 18:32:34 INFO stats.py:314 | Epoch[216] Step[284] GlobalStep[84524] Training Speed: 6.48 samples/sec across all devices.	Average Step Time: 1.23 sec.	Estimated Remaining Time: 3:02:33.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:32:49 INFO loss_tracker.py:84 | Epoch[216/239] Step[299] GlobalStep[84539/NA]: det_loss_cls_0[0.4650] det_loss_box_0[0.5742] det_loss_cls_1[0.4546] det_loss_box_1[0.4138] det_loss_cls_2[0.3884] det_loss_box_2[0.3678] det_loss_cls_3[0.3490] det_loss_box_3[0.3609] det_loss_cls_4[0.3341] det_loss_box_4[0.3478] det_loss_cls_5[0.3197] det_loss_box_5[0.3509] det_loss_cls_dn_0[0.0825] det_loss_box_dn_0[0.2754] det_loss_cls_dn_1[0.0789] det_loss_box_dn_1[0.2287] det_loss_cls_dn_2[0.0781] det_loss_box_dn_2[0.1983] det_loss_cls_dn_3[0.0765] det_loss_box_dn_3[0.1900] det_loss_cls_dn_4[0.0758] det_loss_box_dn_4[0.1868] det_loss_cls_dn_5[0.0749] det_loss_box_dn_5[0.1773] loss_depth[0.0353] total_loss[6.4846]
Rank[0/8] 06/20/2025 18:33:00 INFO stats.py:314 | Epoch[216] Step[309] GlobalStep[84549] Training Speed: 8.69 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 3:02:02.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:33:15 INFO loss_tracker.py:84 | Epoch[216/239] Step[324] GlobalStep[84564/NA]: det_loss_cls_0[0.4475] det_loss_box_0[0.5090] det_loss_cls_1[0.4348] det_loss_box_1[0.3892] det_loss_cls_2[0.3686] det_loss_box_2[0.3495] det_loss_cls_3[0.3264] det_loss_box_3[0.3440] det_loss_cls_4[0.2998] det_loss_box_4[0.3336] det_loss_cls_5[0.2847] det_loss_box_5[0.3339] det_loss_cls_dn_0[0.0839] det_loss_box_dn_0[0.2364] det_loss_cls_dn_1[0.0805] det_loss_box_dn_1[0.1852] det_loss_cls_dn_2[0.0790] det_loss_box_dn_2[0.1666] det_loss_cls_dn_3[0.0782] det_loss_box_dn_3[0.1622] det_loss_cls_dn_4[0.0777] det_loss_box_dn_4[0.1599] det_loss_cls_dn_5[0.0769] det_loss_box_dn_5[0.1587] loss_depth[0.0352] total_loss[6.0013]
Rank[0/8] 06/20/2025 18:33:25 INFO stats.py:314 | Epoch[216] Step[334] GlobalStep[84574] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 3:01:31.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:33:41 INFO loss_tracker.py:84 | Epoch[216/239] Step[349] GlobalStep[84589/NA]: det_loss_cls_0[0.4278] det_loss_box_0[0.5442] det_loss_cls_1[0.4141] det_loss_box_1[0.4141] det_loss_cls_2[0.3536] det_loss_box_2[0.3683] det_loss_cls_3[0.3208] det_loss_box_3[0.3483] det_loss_cls_4[0.2935] det_loss_box_4[0.3386] det_loss_cls_5[0.2795] det_loss_box_5[0.3425] det_loss_cls_dn_0[0.0921] det_loss_box_dn_0[0.2505] det_loss_cls_dn_1[0.0883] det_loss_box_dn_1[0.2015] det_loss_cls_dn_2[0.0873] det_loss_box_dn_2[0.1840] det_loss_cls_dn_3[0.0866] det_loss_box_dn_3[0.1790] det_loss_cls_dn_4[0.0857] det_loss_box_dn_4[0.1763] det_loss_cls_dn_5[0.0851] det_loss_box_dn_5[0.1756] loss_depth[0.0332] total_loss[6.1707]
Rank[0/8] 06/20/2025 18:33:51 INFO stats.py:314 | Epoch[216] Step[359] GlobalStep[84599] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 3:01:01.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:34:07 INFO loss_tracker.py:84 | Epoch[216/239] Step[374] GlobalStep[84614/NA]: det_loss_cls_0[0.4019] det_loss_box_0[0.4736] det_loss_cls_1[0.3930] det_loss_box_1[0.3488] det_loss_cls_2[0.3276] det_loss_box_2[0.3246] det_loss_cls_3[0.2996] det_loss_box_3[0.3129] det_loss_cls_4[0.2801] det_loss_box_4[0.3020] det_loss_cls_5[0.2695] det_loss_box_5[0.3019] det_loss_cls_dn_0[0.0853] det_loss_box_dn_0[0.2447] det_loss_cls_dn_1[0.0808] det_loss_box_dn_1[0.2022] det_loss_cls_dn_2[0.0805] det_loss_box_dn_2[0.1853] det_loss_cls_dn_3[0.0796] det_loss_box_dn_3[0.1812] det_loss_cls_dn_4[0.0789] det_loss_box_dn_4[0.1798] det_loss_cls_dn_5[0.0785] det_loss_box_dn_5[0.1759] loss_depth[0.0346] total_loss[5.7229]
Rank[0/8] 06/20/2025 18:34:18 INFO stats.py:314 | Epoch[216] Step[384] GlobalStep[84624] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 3:00:30.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:34:23 INFO stats.py:394 | Epoch[216] completed. Training Speed: 7.65 samples/sec across all devices.	Epoch Time: 407.67 sec.	Average Epoch Time: 407.67 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 3:00:24.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:34:47 INFO stats.py:314 | Epoch[217] Step[19] GlobalStep[84649] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 3:00:00.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:34:52 INFO loss_tracker.py:84 | Epoch[217/239] Step[24] GlobalStep[84654/NA]: det_loss_cls_0[0.5243] det_loss_box_0[0.6073] det_loss_cls_1[0.5129] det_loss_box_1[0.4361] det_loss_cls_2[0.4308] det_loss_box_2[0.3990] det_loss_cls_3[0.3912] det_loss_box_3[0.3845] det_loss_cls_4[0.3719] det_loss_box_4[0.3728] det_loss_cls_5[0.3595] det_loss_box_5[0.3729] det_loss_cls_dn_0[0.0857] det_loss_box_dn_0[0.2290] det_loss_cls_dn_1[0.0826] det_loss_box_dn_1[0.1796] det_loss_cls_dn_2[0.0814] det_loss_box_dn_2[0.1616] det_loss_cls_dn_3[0.0804] det_loss_box_dn_3[0.1573] det_loss_cls_dn_4[0.0796] det_loss_box_dn_4[0.1558] det_loss_cls_dn_5[0.0789] det_loss_box_dn_5[0.1524] loss_depth[0.0364] total_loss[6.7240]
Rank[0/8] 06/20/2025 18:35:13 INFO stats.py:314 | Epoch[217] Step[44] GlobalStep[84674] Training Speed: 8.66 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 2:59:29.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:35:18 INFO loss_tracker.py:84 | Epoch[217/239] Step[49] GlobalStep[84679/NA]: det_loss_cls_0[0.4748] det_loss_box_0[0.6122] det_loss_cls_1[0.4545] det_loss_box_1[0.4601] det_loss_cls_2[0.3956] det_loss_box_2[0.4054] det_loss_cls_3[0.3675] det_loss_box_3[0.3877] det_loss_cls_4[0.3460] det_loss_box_4[0.3790] det_loss_cls_5[0.3301] det_loss_box_5[0.3763] det_loss_cls_dn_0[0.0949] det_loss_box_dn_0[0.4169] det_loss_cls_dn_1[0.0900] det_loss_box_dn_1[0.3164] det_loss_cls_dn_2[0.0893] det_loss_box_dn_2[0.2842] det_loss_cls_dn_3[0.0881] det_loss_box_dn_3[0.2727] det_loss_cls_dn_4[0.0874] det_loss_box_dn_4[0.2654] det_loss_cls_dn_5[0.0865] det_loss_box_dn_5[0.2616] loss_depth[0.0374] total_loss[7.3797]
Rank[0/8] 06/20/2025 18:35:39 INFO stats.py:314 | Epoch[217] Step[69] GlobalStep[84699] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 2:58:59.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:35:44 INFO loss_tracker.py:84 | Epoch[217/239] Step[74] GlobalStep[84704/NA]: det_loss_cls_0[0.4694] det_loss_box_0[0.6203] det_loss_cls_1[0.4538] det_loss_box_1[0.4714] det_loss_cls_2[0.3937] det_loss_box_2[0.4340] det_loss_cls_3[0.3648] det_loss_box_3[0.4204] det_loss_cls_4[0.3411] det_loss_box_4[0.4075] det_loss_cls_5[0.3291] det_loss_box_5[0.4048] det_loss_cls_dn_0[0.0920] det_loss_box_dn_0[0.2629] det_loss_cls_dn_1[0.0888] det_loss_box_dn_1[0.2091] det_loss_cls_dn_2[0.0881] det_loss_box_dn_2[0.1894] det_loss_cls_dn_3[0.0873] det_loss_box_dn_3[0.1858] det_loss_cls_dn_4[0.0863] det_loss_box_dn_4[0.1839] det_loss_cls_dn_5[0.0854] det_loss_box_dn_5[0.1826] loss_depth[0.0352] total_loss[6.8870]
Rank[0/8] 06/20/2025 18:36:05 INFO stats.py:314 | Epoch[217] Step[94] GlobalStep[84724] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 2:58:28.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:36:10 INFO loss_tracker.py:84 | Epoch[217/239] Step[99] GlobalStep[84729/NA]: det_loss_cls_0[0.4922] det_loss_box_0[0.5738] det_loss_cls_1[0.4727] det_loss_box_1[0.4395] det_loss_cls_2[0.4060] det_loss_box_2[0.3963] det_loss_cls_3[0.3709] det_loss_box_3[0.3826] det_loss_cls_4[0.3530] det_loss_box_4[0.3670] det_loss_cls_5[0.3382] det_loss_box_5[0.3716] det_loss_cls_dn_0[0.0862] det_loss_box_dn_0[0.2338] det_loss_cls_dn_1[0.0835] det_loss_box_dn_1[0.1830] det_loss_cls_dn_2[0.0832] det_loss_box_dn_2[0.1656] det_loss_cls_dn_3[0.0825] det_loss_box_dn_3[0.1592] det_loss_cls_dn_4[0.0817] det_loss_box_dn_4[0.1573] det_loss_cls_dn_5[0.0811] det_loss_box_dn_5[0.1554] loss_depth[0.0333] total_loss[6.5498]
Rank[0/8] 06/20/2025 18:36:30 INFO stats.py:314 | Epoch[217] Step[119] GlobalStep[84749] Training Speed: 9.22 samples/sec across all devices.	Average Step Time: 0.87 sec.	Estimated Remaining Time: 2:57:57.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:36:36 INFO loss_tracker.py:84 | Epoch[217/239] Step[124] GlobalStep[84754/NA]: det_loss_cls_0[0.4258] det_loss_box_0[0.4973] det_loss_cls_1[0.4106] det_loss_box_1[0.3938] det_loss_cls_2[0.3452] det_loss_box_2[0.3612] det_loss_cls_3[0.3061] det_loss_box_3[0.3465] det_loss_cls_4[0.2887] det_loss_box_4[0.3312] det_loss_cls_5[0.2704] det_loss_box_5[0.3327] det_loss_cls_dn_0[0.0944] det_loss_box_dn_0[0.2775] det_loss_cls_dn_1[0.0908] det_loss_box_dn_1[0.2199] det_loss_cls_dn_2[0.0901] det_loss_box_dn_2[0.1994] det_loss_cls_dn_3[0.0892] det_loss_box_dn_3[0.1949] det_loss_cls_dn_4[0.0884] det_loss_box_dn_4[0.1926] det_loss_cls_dn_5[0.0877] det_loss_box_dn_5[0.1897] loss_depth[0.0344] total_loss[6.1583]
Rank[0/8] 06/20/2025 18:36:56 INFO stats.py:314 | Epoch[217] Step[144] GlobalStep[84774] Training Speed: 8.66 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 2:57:27.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:37:01 INFO loss_tracker.py:84 | Epoch[217/239] Step[149] GlobalStep[84779/NA]: det_loss_cls_0[0.5353] det_loss_box_0[0.6799] det_loss_cls_1[0.5145] det_loss_box_1[0.5106] det_loss_cls_2[0.4491] det_loss_box_2[0.4581] det_loss_cls_3[0.4187] det_loss_box_3[0.4369] det_loss_cls_4[0.3909] det_loss_box_4[0.4219] det_loss_cls_5[0.3736] det_loss_box_5[0.4210] det_loss_cls_dn_0[0.0905] det_loss_box_dn_0[0.3091] det_loss_cls_dn_1[0.0874] det_loss_box_dn_1[0.2375] det_loss_cls_dn_2[0.0868] det_loss_box_dn_2[0.2146] det_loss_cls_dn_3[0.0860] det_loss_box_dn_3[0.2025] det_loss_cls_dn_4[0.0855] det_loss_box_dn_4[0.1974] det_loss_cls_dn_5[0.0847] det_loss_box_dn_5[0.1913] loss_depth[0.0360] total_loss[7.5197]
Rank[0/8] 06/20/2025 18:37:22 INFO stats.py:314 | Epoch[217] Step[169] GlobalStep[84799] Training Speed: 7.99 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 2:56:56.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:37:27 INFO loss_tracker.py:84 | Epoch[217/239] Step[174] GlobalStep[84804/NA]: det_loss_cls_0[0.4772] det_loss_box_0[0.6234] det_loss_cls_1[0.4649] det_loss_box_1[0.4758] det_loss_cls_2[0.4229] det_loss_box_2[0.4316] det_loss_cls_3[0.3998] det_loss_box_3[0.4118] det_loss_cls_4[0.3781] det_loss_box_4[0.4011] det_loss_cls_5[0.3639] det_loss_box_5[0.4016] det_loss_cls_dn_0[0.0994] det_loss_box_dn_0[0.3423] det_loss_cls_dn_1[0.0966] det_loss_box_dn_1[0.2789] det_loss_cls_dn_2[0.0961] det_loss_box_dn_2[0.2587] det_loss_cls_dn_3[0.0954] det_loss_box_dn_3[0.2463] det_loss_cls_dn_4[0.0939] det_loss_box_dn_4[0.2450] det_loss_cls_dn_5[0.0931] det_loss_box_dn_5[0.2426] loss_depth[0.0379] total_loss[7.4782]
Rank[0/8] 06/20/2025 18:37:48 INFO stats.py:314 | Epoch[217] Step[194] GlobalStep[84824] Training Speed: 7.53 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 2:56:26.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:37:53 INFO loss_tracker.py:84 | Epoch[217/239] Step[199] GlobalStep[84829/NA]: det_loss_cls_0[0.5655] det_loss_box_0[0.6523] det_loss_cls_1[0.5542] det_loss_box_1[0.4695] det_loss_cls_2[0.4747] det_loss_box_2[0.4296] det_loss_cls_3[0.4385] det_loss_box_3[0.4136] det_loss_cls_4[0.4100] det_loss_box_4[0.4093] det_loss_cls_5[0.3962] det_loss_box_5[0.4115] det_loss_cls_dn_0[0.0970] det_loss_box_dn_0[0.2193] det_loss_cls_dn_1[0.0941] det_loss_box_dn_1[0.1776] det_loss_cls_dn_2[0.0930] det_loss_box_dn_2[0.1610] det_loss_cls_dn_3[0.0920] det_loss_box_dn_3[0.1576] det_loss_cls_dn_4[0.0915] det_loss_box_dn_4[0.1555] det_loss_cls_dn_5[0.0906] det_loss_box_dn_5[0.1546] loss_depth[0.0340] total_loss[7.2429]
Rank[0/8] 06/20/2025 18:38:14 INFO stats.py:314 | Epoch[217] Step[219] GlobalStep[84849] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 2:55:55.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:38:19 INFO loss_tracker.py:84 | Epoch[217/239] Step[224] GlobalStep[84854/NA]: det_loss_cls_0[0.5667] det_loss_box_0[0.7026] det_loss_cls_1[0.5455] det_loss_box_1[0.5160] det_loss_cls_2[0.4756] det_loss_box_2[0.4641] det_loss_cls_3[0.4358] det_loss_box_3[0.4513] det_loss_cls_4[0.4066] det_loss_box_4[0.4476] det_loss_cls_5[0.3842] det_loss_box_5[0.4533] det_loss_cls_dn_0[0.0891] det_loss_box_dn_0[0.2518] det_loss_cls_dn_1[0.0842] det_loss_box_dn_1[0.2047] det_loss_cls_dn_2[0.0836] det_loss_box_dn_2[0.1866] det_loss_cls_dn_3[0.0826] det_loss_box_dn_3[0.1835] det_loss_cls_dn_4[0.0818] det_loss_box_dn_4[0.1807] det_loss_cls_dn_5[0.0809] det_loss_box_dn_5[0.1800] loss_depth[0.0355] total_loss[7.5742]
Rank[0/8] 06/20/2025 18:38:40 INFO stats.py:314 | Epoch[217] Step[244] GlobalStep[84874] Training Speed: 7.34 samples/sec across all devices.	Average Step Time: 1.09 sec.	Estimated Remaining Time: 2:55:25.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:38:45 INFO loss_tracker.py:84 | Epoch[217/239] Step[249] GlobalStep[84879/NA]: det_loss_cls_0[0.5361] det_loss_box_0[0.6470] det_loss_cls_1[0.5184] det_loss_box_1[0.4926] det_loss_cls_2[0.4501] det_loss_box_2[0.4398] det_loss_cls_3[0.4153] det_loss_box_3[0.4270] det_loss_cls_4[0.3835] det_loss_box_4[0.4128] det_loss_cls_5[0.3645] det_loss_box_5[0.4168] det_loss_cls_dn_0[0.0903] det_loss_box_dn_0[0.2293] det_loss_cls_dn_1[0.0869] det_loss_box_dn_1[0.1903] det_loss_cls_dn_2[0.0858] det_loss_box_dn_2[0.1765] det_loss_cls_dn_3[0.0845] det_loss_box_dn_3[0.1723] det_loss_cls_dn_4[0.0836] det_loss_box_dn_4[0.1707] det_loss_cls_dn_5[0.0829] det_loss_box_dn_5[0.1693] loss_depth[0.0344] total_loss[7.1608]
Rank[0/8] 06/20/2025 18:39:06 INFO stats.py:314 | Epoch[217] Step[269] GlobalStep[84899] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 2:54:54.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:39:11 INFO loss_tracker.py:84 | Epoch[217/239] Step[274] GlobalStep[84904/NA]: det_loss_cls_0[0.3715] det_loss_box_0[0.5308] det_loss_cls_1[0.3706] det_loss_box_1[0.4060] det_loss_cls_2[0.3201] det_loss_box_2[0.3736] det_loss_cls_3[0.2974] det_loss_box_3[0.3611] det_loss_cls_4[0.2735] det_loss_box_4[0.3476] det_loss_cls_5[0.2629] det_loss_box_5[0.3439] det_loss_cls_dn_0[0.0896] det_loss_box_dn_0[0.3929] det_loss_cls_dn_1[0.0839] det_loss_box_dn_1[0.3214] det_loss_cls_dn_2[0.0829] det_loss_box_dn_2[0.2965] det_loss_cls_dn_3[0.0817] det_loss_box_dn_3[0.2895] det_loss_cls_dn_4[0.0811] det_loss_box_dn_4[0.2881] det_loss_cls_dn_5[0.0803] det_loss_box_dn_5[0.2815] loss_depth[0.0361] total_loss[6.6644]
Rank[0/8] 06/20/2025 18:39:32 INFO stats.py:314 | Epoch[217] Step[294] GlobalStep[84924] Training Speed: 7.71 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 2:54:23.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:39:37 INFO loss_tracker.py:84 | Epoch[217/239] Step[299] GlobalStep[84929/NA]: det_loss_cls_0[0.4260] det_loss_box_0[0.5600] det_loss_cls_1[0.4157] det_loss_box_1[0.4316] det_loss_cls_2[0.3578] det_loss_box_2[0.3790] det_loss_cls_3[0.3366] det_loss_box_3[0.3608] det_loss_cls_4[0.3141] det_loss_box_4[0.3497] det_loss_cls_5[0.3005] det_loss_box_5[0.3432] det_loss_cls_dn_0[0.0846] det_loss_box_dn_0[0.2548] det_loss_cls_dn_1[0.0818] det_loss_box_dn_1[0.2063] det_loss_cls_dn_2[0.0816] det_loss_box_dn_2[0.1870] det_loss_cls_dn_3[0.0806] det_loss_box_dn_3[0.1809] det_loss_cls_dn_4[0.0802] det_loss_box_dn_4[0.1794] det_loss_cls_dn_5[0.0790] det_loss_box_dn_5[0.1754] loss_depth[0.0346] total_loss[6.2812]
Rank[0/8] 06/20/2025 18:39:58 INFO stats.py:314 | Epoch[217] Step[319] GlobalStep[84949] Training Speed: 7.41 samples/sec across all devices.	Average Step Time: 1.08 sec.	Estimated Remaining Time: 2:53:53.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:40:03 INFO loss_tracker.py:84 | Epoch[217/239] Step[324] GlobalStep[84954/NA]: det_loss_cls_0[0.3964] det_loss_box_0[0.4906] det_loss_cls_1[0.3900] det_loss_box_1[0.3680] det_loss_cls_2[0.3239] det_loss_box_2[0.3425] det_loss_cls_3[0.2983] det_loss_box_3[0.3306] det_loss_cls_4[0.2823] det_loss_box_4[0.3144] det_loss_cls_5[0.2678] det_loss_box_5[0.3168] det_loss_cls_dn_0[0.0846] det_loss_box_dn_0[0.2883] det_loss_cls_dn_1[0.0819] det_loss_box_dn_1[0.2264] det_loss_cls_dn_2[0.0801] det_loss_box_dn_2[0.2043] det_loss_cls_dn_3[0.0792] det_loss_box_dn_3[0.1994] det_loss_cls_dn_4[0.0781] det_loss_box_dn_4[0.1970] det_loss_cls_dn_5[0.0772] det_loss_box_dn_5[0.1949] loss_depth[0.0372] total_loss[5.9502]
Rank[0/8] 06/20/2025 18:40:23 INFO stats.py:314 | Epoch[217] Step[344] GlobalStep[84974] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 2:53:22.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:40:28 INFO loss_tracker.py:84 | Epoch[217/239] Step[349] GlobalStep[84979/NA]: det_loss_cls_0[0.4663] det_loss_box_0[0.5528] det_loss_cls_1[0.4360] det_loss_box_1[0.4196] det_loss_cls_2[0.3817] det_loss_box_2[0.3784] det_loss_cls_3[0.3533] det_loss_box_3[0.3586] det_loss_cls_4[0.3269] det_loss_box_4[0.3492] det_loss_cls_5[0.3140] det_loss_box_5[0.3460] det_loss_cls_dn_0[0.0935] det_loss_box_dn_0[0.2429] det_loss_cls_dn_1[0.0895] det_loss_box_dn_1[0.1962] det_loss_cls_dn_2[0.0882] det_loss_box_dn_2[0.1771] det_loss_cls_dn_3[0.0874] det_loss_box_dn_3[0.1743] det_loss_cls_dn_4[0.0866] det_loss_box_dn_4[0.1730] det_loss_cls_dn_5[0.0858] det_loss_box_dn_5[0.1723] loss_depth[0.0330] total_loss[6.3826]
Rank[0/8] 06/20/2025 18:40:49 INFO stats.py:314 | Epoch[217] Step[369] GlobalStep[84999] Training Speed: 7.61 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 2:52:52.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:40:54 INFO loss_tracker.py:84 | Epoch[217/239] Step[374] GlobalStep[85004/NA]: det_loss_cls_0[0.4535] det_loss_box_0[0.6173] det_loss_cls_1[0.4341] det_loss_box_1[0.4743] det_loss_cls_2[0.3867] det_loss_box_2[0.4297] det_loss_cls_3[0.3565] det_loss_box_3[0.4166] det_loss_cls_4[0.3328] det_loss_box_4[0.4091] det_loss_cls_5[0.3198] det_loss_box_5[0.4083] det_loss_cls_dn_0[0.0931] det_loss_box_dn_0[0.2993] det_loss_cls_dn_1[0.0895] det_loss_box_dn_1[0.2447] det_loss_cls_dn_2[0.0884] det_loss_box_dn_2[0.2289] det_loss_cls_dn_3[0.0878] det_loss_box_dn_3[0.2261] det_loss_cls_dn_4[0.0871] det_loss_box_dn_4[0.2247] det_loss_cls_dn_5[0.0866] det_loss_box_dn_5[0.2231] loss_depth[0.0386] total_loss[7.0566]
Rank[0/8] 06/20/2025 18:41:10 INFO stats.py:394 | Epoch[217] completed. Training Speed: 7.66 samples/sec across all devices.	Epoch Time: 407.06 sec.	Average Epoch Time: 407.06 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 2:52:27.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:41:17 INFO stats.py:314 | Epoch[218] Step[4] GlobalStep[85024] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 2:52:21.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:41:37 INFO loss_tracker.py:84 | Epoch[218/239] Step[24] GlobalStep[85044/NA]: det_loss_cls_0[0.4707] det_loss_box_0[0.6061] det_loss_cls_1[0.4574] det_loss_box_1[0.4571] det_loss_cls_2[0.3911] det_loss_box_2[0.4148] det_loss_cls_3[0.3637] det_loss_box_3[0.4000] det_loss_cls_4[0.3443] det_loss_box_4[0.3887] det_loss_cls_5[0.3318] det_loss_box_5[0.3904] det_loss_cls_dn_0[0.0878] det_loss_box_dn_0[0.3013] det_loss_cls_dn_1[0.0842] det_loss_box_dn_1[0.2447] det_loss_cls_dn_2[0.0832] det_loss_box_dn_2[0.2253] det_loss_cls_dn_3[0.0822] det_loss_box_dn_3[0.2193] det_loss_cls_dn_4[0.0814] det_loss_box_dn_4[0.2165] det_loss_cls_dn_5[0.0805] det_loss_box_dn_5[0.2138] loss_depth[0.0361] total_loss[6.9724]
Rank[0/8] 06/20/2025 18:41:43 INFO stats.py:314 | Epoch[218] Step[29] GlobalStep[85049] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 2:51:51.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:42:03 INFO loss_tracker.py:84 | Epoch[218/239] Step[49] GlobalStep[85069/NA]: det_loss_cls_0[0.4044] det_loss_box_0[0.5334] det_loss_cls_1[0.3982] det_loss_box_1[0.3986] det_loss_cls_2[0.3433] det_loss_box_2[0.3596] det_loss_cls_3[0.3135] det_loss_box_3[0.3526] det_loss_cls_4[0.2987] det_loss_box_4[0.3373] det_loss_cls_5[0.2847] det_loss_box_5[0.3402] det_loss_cls_dn_0[0.0880] det_loss_box_dn_0[0.3047] det_loss_cls_dn_1[0.0837] det_loss_box_dn_1[0.2445] det_loss_cls_dn_2[0.0825] det_loss_box_dn_2[0.2193] det_loss_cls_dn_3[0.0814] det_loss_box_dn_3[0.2121] det_loss_cls_dn_4[0.0804] det_loss_box_dn_4[0.2095] det_loss_cls_dn_5[0.0795] det_loss_box_dn_5[0.2080] loss_depth[0.0376] total_loss[6.2956]
Rank[0/8] 06/20/2025 18:42:08 INFO stats.py:314 | Epoch[218] Step[54] GlobalStep[85074] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 2:51:20.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:42:29 INFO loss_tracker.py:84 | Epoch[218/239] Step[74] GlobalStep[85094/NA]: det_loss_cls_0[0.4142] det_loss_box_0[0.5285] det_loss_cls_1[0.4067] det_loss_box_1[0.3964] det_loss_cls_2[0.3496] det_loss_box_2[0.3543] det_loss_cls_3[0.3152] det_loss_box_3[0.3462] det_loss_cls_4[0.2932] det_loss_box_4[0.3399] det_loss_cls_5[0.2771] det_loss_box_5[0.3425] det_loss_cls_dn_0[0.0823] det_loss_box_dn_0[0.2755] det_loss_cls_dn_1[0.0784] det_loss_box_dn_1[0.2305] det_loss_cls_dn_2[0.0772] det_loss_box_dn_2[0.2155] det_loss_cls_dn_3[0.0764] det_loss_box_dn_3[0.2101] det_loss_cls_dn_4[0.0753] det_loss_box_dn_4[0.2079] det_loss_cls_dn_5[0.0746] det_loss_box_dn_5[0.2045] loss_depth[0.0329] total_loss[6.2050]
Rank[0/8] 06/20/2025 18:42:34 INFO stats.py:314 | Epoch[218] Step[79] GlobalStep[85099] Training Speed: 7.72 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 2:50:49.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:42:55 INFO loss_tracker.py:84 | Epoch[218/239] Step[99] GlobalStep[85119/NA]: det_loss_cls_0[0.3400] det_loss_box_0[0.4127] det_loss_cls_1[0.3347] det_loss_box_1[0.3121] det_loss_cls_2[0.2770] det_loss_box_2[0.2907] det_loss_cls_3[0.2526] det_loss_box_3[0.2831] det_loss_cls_4[0.2291] det_loss_box_4[0.2794] det_loss_cls_5[0.2152] det_loss_box_5[0.2818] det_loss_cls_dn_0[0.0791] det_loss_box_dn_0[0.3236] det_loss_cls_dn_1[0.0756] det_loss_box_dn_1[0.2637] det_loss_cls_dn_2[0.0741] det_loss_box_dn_2[0.2433] det_loss_cls_dn_3[0.0733] det_loss_box_dn_3[0.2347] det_loss_cls_dn_4[0.0724] det_loss_box_dn_4[0.2331] det_loss_cls_dn_5[0.0718] det_loss_box_dn_5[0.2284] loss_depth[0.0331] total_loss[5.5147]
Rank[0/8] 06/20/2025 18:43:00 INFO stats.py:314 | Epoch[218] Step[104] GlobalStep[85124] Training Speed: 7.35 samples/sec across all devices.	Average Step Time: 1.09 sec.	Estimated Remaining Time: 2:50:19.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:43:21 INFO loss_tracker.py:84 | Epoch[218/239] Step[124] GlobalStep[85144/NA]: det_loss_cls_0[0.4513] det_loss_box_0[0.5295] det_loss_cls_1[0.4454] det_loss_box_1[0.4127] det_loss_cls_2[0.3800] det_loss_box_2[0.3819] det_loss_cls_3[0.3425] det_loss_box_3[0.3730] det_loss_cls_4[0.3235] det_loss_box_4[0.3564] det_loss_cls_5[0.3109] det_loss_box_5[0.3545] det_loss_cls_dn_0[0.0861] det_loss_box_dn_0[0.2404] det_loss_cls_dn_1[0.0826] det_loss_box_dn_1[0.1938] det_loss_cls_dn_2[0.0819] det_loss_box_dn_2[0.1778] det_loss_cls_dn_3[0.0809] det_loss_box_dn_3[0.1750] det_loss_cls_dn_4[0.0800] det_loss_box_dn_4[0.1728] det_loss_cls_dn_5[0.0791] det_loss_box_dn_5[0.1710] loss_depth[0.0335] total_loss[6.3164]
Rank[0/8] 06/20/2025 18:43:26 INFO stats.py:314 | Epoch[218] Step[129] GlobalStep[85149] Training Speed: 7.73 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 2:49:48.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:43:47 INFO loss_tracker.py:84 | Epoch[218/239] Step[149] GlobalStep[85169/NA]: det_loss_cls_0[0.4566] det_loss_box_0[0.6013] det_loss_cls_1[0.4456] det_loss_box_1[0.4598] det_loss_cls_2[0.3844] det_loss_box_2[0.4277] det_loss_cls_3[0.3535] det_loss_box_3[0.4188] det_loss_cls_4[0.3390] det_loss_box_4[0.4090] det_loss_cls_5[0.3236] det_loss_box_5[0.4120] det_loss_cls_dn_0[0.0908] det_loss_box_dn_0[0.2359] det_loss_cls_dn_1[0.0880] det_loss_box_dn_1[0.1951] det_loss_cls_dn_2[0.0874] det_loss_box_dn_2[0.1788] det_loss_cls_dn_3[0.0864] det_loss_box_dn_3[0.1756] det_loss_cls_dn_4[0.0858] det_loss_box_dn_4[0.1735] det_loss_cls_dn_5[0.0852] det_loss_box_dn_5[0.1713] loss_depth[0.0342] total_loss[6.7192]
Rank[0/8] 06/20/2025 18:43:52 INFO stats.py:314 | Epoch[218] Step[154] GlobalStep[85174] Training Speed: 5.80 samples/sec across all devices.	Average Step Time: 1.38 sec.	Estimated Remaining Time: 2:49:18.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:44:13 INFO loss_tracker.py:84 | Epoch[218/239] Step[174] GlobalStep[85194/NA]: det_loss_cls_0[0.5132] det_loss_box_0[0.6055] det_loss_cls_1[0.5069] det_loss_box_1[0.4398] det_loss_cls_2[0.4408] det_loss_box_2[0.4077] det_loss_cls_3[0.4115] det_loss_box_3[0.3909] det_loss_cls_4[0.3880] det_loss_box_4[0.3847] det_loss_cls_5[0.3756] det_loss_box_5[0.3843] det_loss_cls_dn_0[0.0906] det_loss_box_dn_0[0.2351] det_loss_cls_dn_1[0.0867] det_loss_box_dn_1[0.1904] det_loss_cls_dn_2[0.0861] det_loss_box_dn_2[0.1730] det_loss_cls_dn_3[0.0849] det_loss_box_dn_3[0.1700] det_loss_cls_dn_4[0.0841] det_loss_box_dn_4[0.1682] det_loss_cls_dn_5[0.0833] det_loss_box_dn_5[0.1664] loss_depth[0.0335] total_loss[6.9012]
Rank[0/8] 06/20/2025 18:44:18 INFO stats.py:314 | Epoch[218] Step[179] GlobalStep[85199] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 2:48:47.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:44:39 INFO loss_tracker.py:84 | Epoch[218/239] Step[199] GlobalStep[85219/NA]: det_loss_cls_0[0.3919] det_loss_box_0[0.5187] det_loss_cls_1[0.3815] det_loss_box_1[0.3918] det_loss_cls_2[0.3219] det_loss_box_2[0.3562] det_loss_cls_3[0.2972] det_loss_box_3[0.3441] det_loss_cls_4[0.2736] det_loss_box_4[0.3371] det_loss_cls_5[0.2672] det_loss_box_5[0.3357] det_loss_cls_dn_0[0.0822] det_loss_box_dn_0[0.2705] det_loss_cls_dn_1[0.0791] det_loss_box_dn_1[0.2268] det_loss_cls_dn_2[0.0789] det_loss_box_dn_2[0.2109] det_loss_cls_dn_3[0.0780] det_loss_box_dn_3[0.2059] det_loss_cls_dn_4[0.0768] det_loss_box_dn_4[0.2048] det_loss_cls_dn_5[0.0760] det_loss_box_dn_5[0.1938] loss_depth[0.0363] total_loss[6.0371]
Rank[0/8] 06/20/2025 18:44:44 INFO stats.py:314 | Epoch[218] Step[204] GlobalStep[85224] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 2:48:17.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:45:04 INFO loss_tracker.py:84 | Epoch[218/239] Step[224] GlobalStep[85244/NA]: det_loss_cls_0[0.4792] det_loss_box_0[0.6348] det_loss_cls_1[0.4689] det_loss_box_1[0.4686] det_loss_cls_2[0.4169] det_loss_box_2[0.4167] det_loss_cls_3[0.3851] det_loss_box_3[0.4005] det_loss_cls_4[0.3586] det_loss_box_4[0.3929] det_loss_cls_5[0.3456] det_loss_box_5[0.3903] det_loss_cls_dn_0[0.0961] det_loss_box_dn_0[0.2530] det_loss_cls_dn_1[0.0931] det_loss_box_dn_1[0.2048] det_loss_cls_dn_2[0.0924] det_loss_box_dn_2[0.1906] det_loss_cls_dn_3[0.0913] det_loss_box_dn_3[0.1864] det_loss_cls_dn_4[0.0910] det_loss_box_dn_4[0.1845] det_loss_cls_dn_5[0.0902] det_loss_box_dn_5[0.1814] loss_depth[0.0362] total_loss[6.9491]
Rank[0/8] 06/20/2025 18:45:10 INFO stats.py:314 | Epoch[218] Step[229] GlobalStep[85249] Training Speed: 6.84 samples/sec across all devices.	Average Step Time: 1.17 sec.	Estimated Remaining Time: 2:47:46.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:45:31 INFO loss_tracker.py:84 | Epoch[218/239] Step[249] GlobalStep[85269/NA]: det_loss_cls_0[0.4073] det_loss_box_0[0.5311] det_loss_cls_1[0.3878] det_loss_box_1[0.4125] det_loss_cls_2[0.3332] det_loss_box_2[0.3712] det_loss_cls_3[0.3077] det_loss_box_3[0.3508] det_loss_cls_4[0.2875] det_loss_box_4[0.3407] det_loss_cls_5[0.2732] det_loss_box_5[0.3419] det_loss_cls_dn_0[0.0886] det_loss_box_dn_0[0.3280] det_loss_cls_dn_1[0.0851] det_loss_box_dn_1[0.2453] det_loss_cls_dn_2[0.0843] det_loss_box_dn_2[0.2198] det_loss_cls_dn_3[0.0830] det_loss_box_dn_3[0.2083] det_loss_cls_dn_4[0.0817] det_loss_box_dn_4[0.2042] det_loss_cls_dn_5[0.0809] det_loss_box_dn_5[0.1973] loss_depth[0.0394] total_loss[6.2909]
Rank[0/8] 06/20/2025 18:45:36 INFO stats.py:314 | Epoch[218] Step[254] GlobalStep[85274] Training Speed: 7.13 samples/sec across all devices.	Average Step Time: 1.12 sec.	Estimated Remaining Time: 2:47:16.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:45:56 INFO loss_tracker.py:84 | Epoch[218/239] Step[274] GlobalStep[85294/NA]: det_loss_cls_0[0.4942] det_loss_box_0[0.6359] det_loss_cls_1[0.4704] det_loss_box_1[0.4887] det_loss_cls_2[0.4135] det_loss_box_2[0.4477] det_loss_cls_3[0.3812] det_loss_box_3[0.4181] det_loss_cls_4[0.3529] det_loss_box_4[0.4030] det_loss_cls_5[0.3374] det_loss_box_5[0.4002] det_loss_cls_dn_0[0.0920] det_loss_box_dn_0[0.2534] det_loss_cls_dn_1[0.0871] det_loss_box_dn_1[0.2089] det_loss_cls_dn_2[0.0860] det_loss_box_dn_2[0.1941] det_loss_cls_dn_3[0.0849] det_loss_box_dn_3[0.1899] det_loss_cls_dn_4[0.0839] det_loss_box_dn_4[0.1880] det_loss_cls_dn_5[0.0829] det_loss_box_dn_5[0.1872] loss_depth[0.0340] total_loss[7.0154]
Rank[0/8] 06/20/2025 18:46:01 INFO stats.py:314 | Epoch[218] Step[279] GlobalStep[85299] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 2:46:45.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:46:22 INFO loss_tracker.py:84 | Epoch[218/239] Step[299] GlobalStep[85319/NA]: det_loss_cls_0[0.4112] det_loss_box_0[0.5007] det_loss_cls_1[0.4001] det_loss_box_1[0.3701] det_loss_cls_2[0.3430] det_loss_box_2[0.3308] det_loss_cls_3[0.3102] det_loss_box_3[0.3182] det_loss_cls_4[0.2844] det_loss_box_4[0.3112] det_loss_cls_5[0.2659] det_loss_box_5[0.3159] det_loss_cls_dn_0[0.0809] det_loss_box_dn_0[0.2358] det_loss_cls_dn_1[0.0779] det_loss_box_dn_1[0.1877] det_loss_cls_dn_2[0.0767] det_loss_box_dn_2[0.1729] det_loss_cls_dn_3[0.0755] det_loss_box_dn_3[0.1676] det_loss_cls_dn_4[0.0746] det_loss_box_dn_4[0.1649] det_loss_cls_dn_5[0.0739] det_loss_box_dn_5[0.1637] loss_depth[0.0359] total_loss[5.7498]
Rank[0/8] 06/20/2025 18:46:27 INFO stats.py:314 | Epoch[218] Step[304] GlobalStep[85324] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 2:46:14.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:46:48 INFO loss_tracker.py:84 | Epoch[218/239] Step[324] GlobalStep[85344/NA]: det_loss_cls_0[0.3272] det_loss_box_0[0.4417] det_loss_cls_1[0.3158] det_loss_box_1[0.3273] det_loss_cls_2[0.2619] det_loss_box_2[0.2936] det_loss_cls_3[0.2341] det_loss_box_3[0.2818] det_loss_cls_4[0.2133] det_loss_box_4[0.2690] det_loss_cls_5[0.2018] det_loss_box_5[0.2699] det_loss_cls_dn_0[0.0772] det_loss_box_dn_0[0.2708] det_loss_cls_dn_1[0.0727] det_loss_box_dn_1[0.2190] det_loss_cls_dn_2[0.0715] det_loss_box_dn_2[0.2018] det_loss_cls_dn_3[0.0704] det_loss_box_dn_3[0.1974] det_loss_cls_dn_4[0.0697] det_loss_box_dn_4[0.1939] det_loss_cls_dn_5[0.0689] det_loss_box_dn_5[0.1920] loss_depth[0.0350] total_loss[5.1778]
Rank[0/8] 06/20/2025 18:46:53 INFO stats.py:314 | Epoch[218] Step[329] GlobalStep[85349] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 2:45:44.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:47:14 INFO loss_tracker.py:84 | Epoch[218/239] Step[349] GlobalStep[85369/NA]: det_loss_cls_0[0.4577] det_loss_box_0[0.5418] det_loss_cls_1[0.4391] det_loss_box_1[0.3991] det_loss_cls_2[0.3841] det_loss_box_2[0.3671] det_loss_cls_3[0.3453] det_loss_box_3[0.3590] det_loss_cls_4[0.3263] det_loss_box_4[0.3451] det_loss_cls_5[0.3185] det_loss_box_5[0.3394] det_loss_cls_dn_0[0.0965] det_loss_box_dn_0[0.2808] det_loss_cls_dn_1[0.0945] det_loss_box_dn_1[0.2300] det_loss_cls_dn_2[0.0939] det_loss_box_dn_2[0.2117] det_loss_cls_dn_3[0.0929] det_loss_box_dn_3[0.2056] det_loss_cls_dn_4[0.0920] det_loss_box_dn_4[0.2009] det_loss_cls_dn_5[0.0907] det_loss_box_dn_5[0.1976] loss_depth[0.0341] total_loss[6.5438]
Rank[0/8] 06/20/2025 18:47:19 INFO stats.py:314 | Epoch[218] Step[354] GlobalStep[85374] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 2:45:13.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:47:40 INFO loss_tracker.py:84 | Epoch[218/239] Step[374] GlobalStep[85394/NA]: det_loss_cls_0[0.4048] det_loss_box_0[0.4847] det_loss_cls_1[0.3951] det_loss_box_1[0.3459] det_loss_cls_2[0.3354] det_loss_box_2[0.3090] det_loss_cls_3[0.2895] det_loss_box_3[0.3002] det_loss_cls_4[0.2628] det_loss_box_4[0.2981] det_loss_cls_5[0.2555] det_loss_box_5[0.2970] det_loss_cls_dn_0[0.0809] det_loss_box_dn_0[0.2552] det_loss_cls_dn_1[0.0780] det_loss_box_dn_1[0.2017] det_loss_cls_dn_2[0.0769] det_loss_box_dn_2[0.1803] det_loss_cls_dn_3[0.0758] det_loss_box_dn_3[0.1760] det_loss_cls_dn_4[0.0753] det_loss_box_dn_4[0.1735] det_loss_cls_dn_5[0.0748] det_loss_box_dn_5[0.1716] loss_depth[0.0358] total_loss[5.6340]
Rank[0/8] 06/20/2025 18:47:45 INFO stats.py:314 | Epoch[218] Step[379] GlobalStep[85399] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 2:44:43.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:47:55 INFO stats.py:394 | Epoch[218] completed. Training Speed: 7.71 samples/sec across all devices.	Epoch Time: 404.82 sec.	Average Epoch Time: 404.82 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 2:44:30.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:48:12 INFO stats.py:314 | Epoch[219] Step[14] GlobalStep[85424] Training Speed: 7.83 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 2:44:12.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:48:22 INFO loss_tracker.py:84 | Epoch[219/239] Step[24] GlobalStep[85434/NA]: det_loss_cls_0[0.4752] det_loss_box_0[0.5561] det_loss_cls_1[0.4552] det_loss_box_1[0.4301] det_loss_cls_2[0.3895] det_loss_box_2[0.3986] det_loss_cls_3[0.3598] det_loss_box_3[0.3861] det_loss_cls_4[0.3388] det_loss_box_4[0.3671] det_loss_cls_5[0.3226] det_loss_box_5[0.3715] det_loss_cls_dn_0[0.0934] det_loss_box_dn_0[0.2655] det_loss_cls_dn_1[0.0891] det_loss_box_dn_1[0.2209] det_loss_cls_dn_2[0.0882] det_loss_box_dn_2[0.2009] det_loss_cls_dn_3[0.0872] det_loss_box_dn_3[0.1961] det_loss_cls_dn_4[0.0864] det_loss_box_dn_4[0.1938] det_loss_cls_dn_5[0.0857] det_loss_box_dn_5[0.1925] loss_depth[0.0338] total_loss[6.6840]
Rank[0/8] 06/20/2025 18:48:38 INFO stats.py:314 | Epoch[219] Step[39] GlobalStep[85449] Training Speed: 7.89 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 2:43:42.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:48:48 INFO loss_tracker.py:84 | Epoch[219/239] Step[49] GlobalStep[85459/NA]: det_loss_cls_0[0.5159] det_loss_box_0[0.6281] det_loss_cls_1[0.4884] det_loss_box_1[0.4721] det_loss_cls_2[0.4224] det_loss_box_2[0.4393] det_loss_cls_3[0.3940] det_loss_box_3[0.4201] det_loss_cls_4[0.3556] det_loss_box_4[0.4196] det_loss_cls_5[0.3401] det_loss_box_5[0.4201] det_loss_cls_dn_0[0.1000] det_loss_box_dn_0[0.2462] det_loss_cls_dn_1[0.0954] det_loss_box_dn_1[0.2080] det_loss_cls_dn_2[0.0942] det_loss_box_dn_2[0.1923] det_loss_cls_dn_3[0.0935] det_loss_box_dn_3[0.1888] det_loss_cls_dn_4[0.0930] det_loss_box_dn_4[0.1874] det_loss_cls_dn_5[0.0923] det_loss_box_dn_5[0.1862] loss_depth[0.0349] total_loss[7.1277]
Rank[0/8] 06/20/2025 18:49:04 INFO stats.py:314 | Epoch[219] Step[64] GlobalStep[85474] Training Speed: 7.99 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 2:43:11.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:49:14 INFO loss_tracker.py:84 | Epoch[219/239] Step[74] GlobalStep[85484/NA]: det_loss_cls_0[0.5506] det_loss_box_0[0.6520] det_loss_cls_1[0.5210] det_loss_box_1[0.4824] det_loss_cls_2[0.4443] det_loss_box_2[0.4337] det_loss_cls_3[0.4046] det_loss_box_3[0.4230] det_loss_cls_4[0.3778] det_loss_box_4[0.4146] det_loss_cls_5[0.3640] det_loss_box_5[0.4131] det_loss_cls_dn_0[0.0917] det_loss_box_dn_0[0.2283] det_loss_cls_dn_1[0.0876] det_loss_box_dn_1[0.1880] det_loss_cls_dn_2[0.0867] det_loss_box_dn_2[0.1755] det_loss_cls_dn_3[0.0858] det_loss_box_dn_3[0.1728] det_loss_cls_dn_4[0.0842] det_loss_box_dn_4[0.1712] det_loss_cls_dn_5[0.0833] det_loss_box_dn_5[0.1702] loss_depth[0.0343] total_loss[7.1408]
Rank[0/8] 06/20/2025 18:49:30 INFO stats.py:314 | Epoch[219] Step[89] GlobalStep[85499] Training Speed: 8.63 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 2:42:41.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:49:41 INFO loss_tracker.py:84 | Epoch[219/239] Step[99] GlobalStep[85509/NA]: det_loss_cls_0[0.4370] det_loss_box_0[0.5106] det_loss_cls_1[0.4173] det_loss_box_1[0.3793] det_loss_cls_2[0.3483] det_loss_box_2[0.3427] det_loss_cls_3[0.3134] det_loss_box_3[0.3295] det_loss_cls_4[0.3012] det_loss_box_4[0.3184] det_loss_cls_5[0.2853] det_loss_box_5[0.3200] det_loss_cls_dn_0[0.0732] det_loss_box_dn_0[0.2475] det_loss_cls_dn_1[0.0686] det_loss_box_dn_1[0.1905] det_loss_cls_dn_2[0.0681] det_loss_box_dn_2[0.1687] det_loss_cls_dn_3[0.0668] det_loss_box_dn_3[0.1618] det_loss_cls_dn_4[0.0660] det_loss_box_dn_4[0.1580] det_loss_cls_dn_5[0.0652] det_loss_box_dn_5[0.1512] loss_depth[0.0311] total_loss[5.8196]
Rank[0/8] 06/20/2025 18:49:56 INFO stats.py:314 | Epoch[219] Step[114] GlobalStep[85524] Training Speed: 8.63 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 2:42:10.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:50:06 INFO loss_tracker.py:84 | Epoch[219/239] Step[124] GlobalStep[85534/NA]: det_loss_cls_0[0.4554] det_loss_box_0[0.6531] det_loss_cls_1[0.4383] det_loss_box_1[0.5223] det_loss_cls_2[0.3891] det_loss_box_2[0.4662] det_loss_cls_3[0.3613] det_loss_box_3[0.4506] det_loss_cls_4[0.3419] det_loss_box_4[0.4402] det_loss_cls_5[0.3167] det_loss_box_5[0.4506] det_loss_cls_dn_0[0.0887] det_loss_box_dn_0[0.3170] det_loss_cls_dn_1[0.0857] det_loss_box_dn_1[0.2724] det_loss_cls_dn_2[0.0850] det_loss_box_dn_2[0.2503] det_loss_cls_dn_3[0.0840] det_loss_box_dn_3[0.2460] det_loss_cls_dn_4[0.0828] det_loss_box_dn_4[0.2434] det_loss_cls_dn_5[0.0824] det_loss_box_dn_5[0.2408] loss_depth[0.0356] total_loss[7.3997]
Rank[0/8] 06/20/2025 18:50:22 INFO stats.py:314 | Epoch[219] Step[139] GlobalStep[85549] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 2:41:40.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:50:32 INFO loss_tracker.py:84 | Epoch[219/239] Step[149] GlobalStep[85559/NA]: det_loss_cls_0[0.4953] det_loss_box_0[0.6161] det_loss_cls_1[0.4814] det_loss_box_1[0.4588] det_loss_cls_2[0.4160] det_loss_box_2[0.4224] det_loss_cls_3[0.3738] det_loss_box_3[0.4141] det_loss_cls_4[0.3442] det_loss_box_4[0.4008] det_loss_cls_5[0.3299] det_loss_box_5[0.4004] det_loss_cls_dn_0[0.0865] det_loss_box_dn_0[0.2415] det_loss_cls_dn_1[0.0836] det_loss_box_dn_1[0.1979] det_loss_cls_dn_2[0.0831] det_loss_box_dn_2[0.1802] det_loss_cls_dn_3[0.0816] det_loss_box_dn_3[0.1771] det_loss_cls_dn_4[0.0808] det_loss_box_dn_4[0.1742] det_loss_cls_dn_5[0.0801] det_loss_box_dn_5[0.1719] loss_depth[0.0341] total_loss[6.8258]
Rank[0/8] 06/20/2025 18:50:48 INFO stats.py:314 | Epoch[219] Step[164] GlobalStep[85574] Training Speed: 6.10 samples/sec across all devices.	Average Step Time: 1.31 sec.	Estimated Remaining Time: 2:41:09.	Learning Rate: 2.00000e-05.	
Rank[0/8] 06/20/2025 18:50:58 INFO loss_tracker.py:84 | Epoch[219/239] Step[174] GlobalStep[85584/NA]: det_loss_cls_0[0.4041] det_loss_box_0[0.5014] det_loss_cls_1[0.4016] det_loss_box_1[0.3849] det_loss_cls_2[0.3483] det_loss_box_2[0.3455] det_loss_cls_3[0.3170] det_loss_box_3[0.3434] det_loss_cls_4[0.2998] det_loss_box_4[0.3416] det_loss_cls_5[0.2898] det_loss_box_5[0.3400] det_loss_cls_dn_0[0.0828] det_loss_box_dn_0[0.2313] det_loss_cls_dn_1[0.0789] det_loss_box_dn_1[0.1943] det_loss_cls_dn_2[0.0778] det_loss_box_dn_2[0.1796] det_loss_cls_dn_3[0.0767] det_loss_box_dn_3[0.1769] det_loss_cls_dn_4[0.0757] det_loss_box_dn_4[0.1764] det_loss_cls_dn_5[0.0748] det_loss_box_dn_5[0.1749] loss_depth[0.0351] total_loss[5.9528]
Rank[0/8] 06/20/2025 18:51:13 INFO stats.py:314 | Epoch[219] Step[189] GlobalStep[85599] Training Speed: 7.78 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 2:40:39.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 18:51:24 INFO loss_tracker.py:84 | Epoch[219/239] Step[199] GlobalStep[85609/NA]: det_loss_cls_0[0.4608] det_loss_box_0[0.5450] det_loss_cls_1[0.4446] det_loss_box_1[0.4320] det_loss_cls_2[0.3779] det_loss_box_2[0.3895] det_loss_cls_3[0.3502] det_loss_box_3[0.3719] det_loss_cls_4[0.3299] det_loss_box_4[0.3589] det_loss_cls_5[0.3167] det_loss_box_5[0.3613] det_loss_cls_dn_0[0.0911] det_loss_box_dn_0[0.2702] det_loss_cls_dn_1[0.0871] det_loss_box_dn_1[0.2287] det_loss_cls_dn_2[0.0862] det_loss_box_dn_2[0.2036] det_loss_cls_dn_3[0.0854] det_loss_box_dn_3[0.1997] det_loss_cls_dn_4[0.0846] det_loss_box_dn_4[0.1962] det_loss_cls_dn_5[0.0836] det_loss_box_dn_5[0.1917] loss_depth[0.0372] total_loss[6.5841]
Rank[0/8] 06/20/2025 18:51:39 INFO stats.py:314 | Epoch[219] Step[214] GlobalStep[85624] Training Speed: 7.65 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 2:40:08.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 18:51:50 INFO loss_tracker.py:84 | Epoch[219/239] Step[224] GlobalStep[85634/NA]: det_loss_cls_0[0.4437] det_loss_box_0[0.5794] det_loss_cls_1[0.4320] det_loss_box_1[0.4198] det_loss_cls_2[0.3678] det_loss_box_2[0.3840] det_loss_cls_3[0.3391] det_loss_box_3[0.3731] det_loss_cls_4[0.3242] det_loss_box_4[0.3594] det_loss_cls_5[0.3013] det_loss_box_5[0.3651] det_loss_cls_dn_0[0.0898] det_loss_box_dn_0[0.2494] det_loss_cls_dn_1[0.0850] det_loss_box_dn_1[0.2027] det_loss_cls_dn_2[0.0837] det_loss_box_dn_2[0.1843] det_loss_cls_dn_3[0.0820] det_loss_box_dn_3[0.1787] det_loss_cls_dn_4[0.0812] det_loss_box_dn_4[0.1763] det_loss_cls_dn_5[0.0803] det_loss_box_dn_5[0.1752] loss_depth[0.0369] total_loss[6.3943]
Rank[0/8] 06/20/2025 18:52:05 INFO stats.py:314 | Epoch[219] Step[239] GlobalStep[85649] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 2:39:38.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 18:52:15 INFO loss_tracker.py:84 | Epoch[219/239] Step[249] GlobalStep[85659/NA]: det_loss_cls_0[0.6292] det_loss_box_0[0.8420] det_loss_cls_1[0.6081] det_loss_box_1[0.6210] det_loss_cls_2[0.5387] det_loss_box_2[0.5582] det_loss_cls_3[0.5060] det_loss_box_3[0.5377] det_loss_cls_4[0.4750] det_loss_box_4[0.5144] det_loss_cls_5[0.4642] det_loss_box_5[0.5095] det_loss_cls_dn_0[0.0927] det_loss_box_dn_0[0.2685] det_loss_cls_dn_1[0.0898] det_loss_box_dn_1[0.2219] det_loss_cls_dn_2[0.0882] det_loss_box_dn_2[0.2063] det_loss_cls_dn_3[0.0871] det_loss_box_dn_3[0.2036] det_loss_cls_dn_4[0.0864] det_loss_box_dn_4[0.2009] det_loss_cls_dn_5[0.0854] det_loss_box_dn_5[0.1987] loss_depth[0.0367] total_loss[8.6701]
Rank[0/8] 06/20/2025 18:52:31 INFO stats.py:314 | Epoch[219] Step[264] GlobalStep[85674] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 2:39:07.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 18:52:41 INFO loss_tracker.py:84 | Epoch[219/239] Step[274] GlobalStep[85684/NA]: det_loss_cls_0[0.5328] det_loss_box_0[0.6071] det_loss_cls_1[0.5124] det_loss_box_1[0.4456] det_loss_cls_2[0.4593] det_loss_box_2[0.3951] det_loss_cls_3[0.4238] det_loss_box_3[0.3904] det_loss_cls_4[0.4053] det_loss_box_4[0.3798] det_loss_cls_5[0.3889] det_loss_box_5[0.3813] det_loss_cls_dn_0[0.0999] det_loss_box_dn_0[0.2329] det_loss_cls_dn_1[0.0962] det_loss_box_dn_1[0.1884] det_loss_cls_dn_2[0.0947] det_loss_box_dn_2[0.1708] det_loss_cls_dn_3[0.0937] det_loss_box_dn_3[0.1669] det_loss_cls_dn_4[0.0930] det_loss_box_dn_4[0.1645] det_loss_cls_dn_5[0.0921] det_loss_box_dn_5[0.1630] loss_depth[0.0354] total_loss[7.0132]
Rank[0/8] 06/20/2025 18:52:57 INFO stats.py:314 | Epoch[219] Step[289] GlobalStep[85699] Training Speed: 6.03 samples/sec across all devices.	Average Step Time: 1.33 sec.	Estimated Remaining Time: 2:38:37.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 18:53:08 INFO loss_tracker.py:84 | Epoch[219/239] Step[299] GlobalStep[85709/NA]: det_loss_cls_0[0.4522] det_loss_box_0[0.5987] det_loss_cls_1[0.4410] det_loss_box_1[0.4578] det_loss_cls_2[0.3948] det_loss_box_2[0.4063] det_loss_cls_3[0.3602] det_loss_box_3[0.3950] det_loss_cls_4[0.3357] det_loss_box_4[0.3898] det_loss_cls_5[0.3228] det_loss_box_5[0.3898] det_loss_cls_dn_0[0.0897] det_loss_box_dn_0[0.2822] det_loss_cls_dn_1[0.0864] det_loss_box_dn_1[0.2318] det_loss_cls_dn_2[0.0852] det_loss_box_dn_2[0.2129] det_loss_cls_dn_3[0.0836] det_loss_box_dn_3[0.2072] det_loss_cls_dn_4[0.0830] det_loss_box_dn_4[0.2048] det_loss_cls_dn_5[0.0820] det_loss_box_dn_5[0.2024] loss_depth[0.0369] total_loss[6.8322]
Rank[0/8] 06/20/2025 18:53:23 INFO stats.py:314 | Epoch[219] Step[314] GlobalStep[85724] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 2:38:06.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 18:53:33 INFO loss_tracker.py:84 | Epoch[219/239] Step[324] GlobalStep[85734/NA]: det_loss_cls_0[0.4759] det_loss_box_0[0.5437] det_loss_cls_1[0.4627] det_loss_box_1[0.4141] det_loss_cls_2[0.3940] det_loss_box_2[0.3793] det_loss_cls_3[0.3510] det_loss_box_3[0.3737] det_loss_cls_4[0.3383] det_loss_box_4[0.3594] det_loss_cls_5[0.3163] det_loss_box_5[0.3636] det_loss_cls_dn_0[0.0885] det_loss_box_dn_0[0.2497] det_loss_cls_dn_1[0.0848] det_loss_box_dn_1[0.2025] det_loss_cls_dn_2[0.0842] det_loss_box_dn_2[0.1846] det_loss_cls_dn_3[0.0833] det_loss_box_dn_3[0.1790] det_loss_cls_dn_4[0.0822] det_loss_box_dn_4[0.1768] det_loss_cls_dn_5[0.0816] det_loss_box_dn_5[0.1730] loss_depth[0.0343] total_loss[6.4765]
Rank[0/8] 06/20/2025 18:53:49 INFO stats.py:314 | Epoch[219] Step[339] GlobalStep[85749] Training Speed: 7.89 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 2:37:36.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 18:53:59 INFO loss_tracker.py:84 | Epoch[219/239] Step[349] GlobalStep[85759/NA]: det_loss_cls_0[0.5109] det_loss_box_0[0.6213] det_loss_cls_1[0.4889] det_loss_box_1[0.4828] det_loss_cls_2[0.4193] det_loss_box_2[0.4315] det_loss_cls_3[0.3890] det_loss_box_3[0.4136] det_loss_cls_4[0.3625] det_loss_box_4[0.4067] det_loss_cls_5[0.3478] det_loss_box_5[0.4045] det_loss_cls_dn_0[0.0991] det_loss_box_dn_0[0.2988] det_loss_cls_dn_1[0.0962] det_loss_box_dn_1[0.2523] det_loss_cls_dn_2[0.0952] det_loss_box_dn_2[0.2340] det_loss_cls_dn_3[0.0948] det_loss_box_dn_3[0.2308] det_loss_cls_dn_4[0.0942] det_loss_box_dn_4[0.2282] det_loss_cls_dn_5[0.0932] det_loss_box_dn_5[0.2229] loss_depth[0.0345] total_loss[7.3530]
Rank[0/8] 06/20/2025 18:54:15 INFO stats.py:314 | Epoch[219] Step[364] GlobalStep[85774] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 2:37:05.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 18:54:25 INFO loss_tracker.py:84 | Epoch[219/239] Step[374] GlobalStep[85784/NA]: det_loss_cls_0[0.4698] det_loss_box_0[0.5157] det_loss_cls_1[0.4566] det_loss_box_1[0.3761] det_loss_cls_2[0.3860] det_loss_box_2[0.3350] det_loss_cls_3[0.3588] det_loss_box_3[0.3167] det_loss_cls_4[0.3403] det_loss_box_4[0.3080] det_loss_cls_5[0.3295] det_loss_box_5[0.3068] det_loss_cls_dn_0[0.0944] det_loss_box_dn_0[0.2567] det_loss_cls_dn_1[0.0915] det_loss_box_dn_1[0.2059] det_loss_cls_dn_2[0.0907] det_loss_box_dn_2[0.1902] det_loss_cls_dn_3[0.0894] det_loss_box_dn_3[0.1851] det_loss_cls_dn_4[0.0889] det_loss_box_dn_4[0.1808] det_loss_cls_dn_5[0.0882] det_loss_box_dn_5[0.1758] loss_depth[0.0353] total_loss[6.2722]
Rank[0/8] 06/20/2025 18:54:40 INFO stats.py:314 | Epoch[219] Step[389] GlobalStep[85799] Training Speed: 7.95 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 2:36:35.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 18:54:41 INFO trainer.py:268 | 
==================================================BEGIN EVAL==================================================
Rank[0/8] 06/20/2025 18:55:40 INFO trainer.py:275 | eval: 10
Rank[0/8] 06/20/2025 18:55:50 INFO trainer.py:275 | eval: 20
Rank[0/8] 06/20/2025 18:55:58 INFO trainer.py:275 | eval: 30
Rank[0/8] 06/20/2025 18:56:11 INFO trainer.py:275 | eval: 40
Rank[0/8] 06/20/2025 18:56:21 INFO trainer.py:275 | eval: 50
Rank[0/8] 06/20/2025 18:56:31 INFO trainer.py:275 | eval: 60
Rank[0/8] 06/20/2025 18:56:43 INFO trainer.py:275 | eval: 70
Rank[0/8] 06/20/2025 18:56:53 INFO trainer.py:275 | eval: 80
Rank[0/8] 06/20/2025 18:57:02 INFO trainer.py:275 | eval: 90
Rank[0/8] 06/20/2025 18:57:12 INFO trainer.py:275 | eval: 100
Rank[0/8] 06/20/2025 18:57:18 INFO metrics.py:428 | number of results: 824
Rank[0/8] 06/20/2025 19:01:35 INFO metrics.py:332 | eval results of overall:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| bathtub             | 0.8397  | 0.8676  | 0.7075  | 0.7647  |
| curtain             | 0.5557  | 0.8239  | 0.1492  | 0.4687  |
| toilet paper        | 0.3747  | 0.6299  | 0.2021  | 0.3780  |
| toilet              | 0.8687  | 0.8978  | 0.7476  | 0.8029  |
| sink                | 0.6943  | 0.8724  | 0.2555  | 0.5062  |
| picture             | 0.2662  | 0.4903  | 0.0335  | 0.1466  |
| door                | 0.4231  | 0.7237  | 0.0752  | 0.3056  |
| bin                 | 0.6457  | 0.8603  | 0.4317  | 0.6480  |
| doorframe           | 0.3125  | 0.7373  | 0.0880  | 0.3888  |
| vanity              | 0.5495  | 0.8444  | 0.4746  | 0.7556  |
| towel               | 0.4223  | 0.6839  | 0.1379  | 0.3290  |
| bar                 | 0.0870  | 0.2727  | 0.0157  | 0.0606  |
| mirror              | 0.2510  | 0.6739  | 0.0228  | 0.2174  |
| bag                 | 0.2847  | 0.7243  | 0.1040  | 0.3405  |
| stopcock            | 0.0833  | 0.4483  | 0.0006  | 0.0345  |
| switch              | 0.0034  | 0.1316  | 0.0000  | 0.0000  |
| bottle              | 0.0784  | 0.3955  | 0.0066  | 0.1031  |
| socket              | 0.0184  | 0.2611  | 0.0000  | 0.0088  |
| hair dryer          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cup                 | 0.1283  | 0.3983  | 0.0049  | 0.0763  |
| rail                | 0.0216  | 0.3906  | 0.0010  | 0.0625  |
| ledge               | 0.1935  | 0.5000  | 0.0004  | 0.1667  |
| microwave           | 0.6376  | 0.7551  | 0.3167  | 0.4694  |
| shower              | 0.0983  | 0.3000  | 0.0052  | 0.1333  |
| rod                 | 0.0250  | 0.0250  | 0.0000  | 0.0000  |
| tissue              | 0.0233  | 0.1538  | 0.0233  | 0.1538  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| light               | 0.0615  | 0.3098  | 0.0015  | 0.0598  |
| tissue box          | 0.1846  | 0.5455  | 0.0561  | 0.3030  |
| basket              | 0.2937  | 0.5652  | 0.1517  | 0.3739  |
| vent                | 0.0135  | 0.2083  | 0.0000  | 0.0000  |
| box                 | 0.1716  | 0.5216  | 0.0444  | 0.2058  |
| plant               | 0.3281  | 0.6975  | 0.0966  | 0.3118  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cabinet             | 0.2967  | 0.7521  | 0.1807  | 0.5485  |
| stool               | 0.3162  | 0.7059  | 0.2200  | 0.5798  |
| lamp                | 0.3745  | 0.6776  | 0.1061  | 0.3134  |
| mat                 | 0.0480  | 0.3514  | 0.0039  | 0.0541  |
| doorway             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shoe                | 0.2055  | 0.4766  | 0.0289  | 0.1121  |
| tablet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| paper               | 0.0345  | 0.3402  | 0.0011  | 0.0740  |
| bed                 | 0.8592  | 0.9290  | 0.6750  | 0.8284  |
| blanket             | 0.2923  | 0.7500  | 0.0523  | 0.3500  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drawer              | 0.3359  | 0.6111  | 0.3067  | 0.5000  |
| faucet              | 0.0379  | 0.2128  | 0.0027  | 0.0426  |
| case                | 0.1026  | 0.4633  | 0.0629  | 0.2034  |
| wardrobe            | 0.4144  | 0.8193  | 0.3083  | 0.5783  |
| carpet              | 0.1605  | 0.5357  | 0.0096  | 0.1429  |
| glass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap                | 0.1783  | 0.5000  | 0.0052  | 0.1667  |
| decoration          | 0.0360  | 0.4071  | 0.0052  | 0.1286  |
| arch                | 0.0179  | 0.1000  | 0.0006  | 0.0667  |
| plunger             | 0.6800  | 0.8000  | 0.3600  | 0.6000  |
| clothes             | 0.2024  | 0.5541  | 0.0476  | 0.2484  |
| frame               | 0.1356  | 0.5714  | 0.0008  | 0.1020  |
| scale               | 0.1577  | 0.3636  | 0.0914  | 0.1818  |
| partition           | 0.4535  | 0.6667  | 0.1667  | 0.1667  |
| couch               | 0.5222  | 0.8719  | 0.4157  | 0.7769  |
| soap dish           | 0.5647  | 0.7333  | 0.0754  | 0.2000  |
| sunglasses          | 0.0623  | 0.3077  | 0.0046  | 0.1538  |
| stall               | 0.3254  | 1.0000  | 0.2199  | 0.5294  |
| backpack            | 0.5908  | 0.8548  | 0.3654  | 0.5645  |
| screen              | 0.1000  | 0.2000  | 0.0000  | 0.0000  |
| balcony             | 0.2500  | 0.5000  | 0.0208  | 0.5000  |
| blinds              | 0.2439  | 0.4800  | 0.0354  | 0.2600  |
| counter             | 0.2285  | 0.6881  | 0.0521  | 0.3303  |
| toothpaste          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dresser             | 0.6341  | 0.8889  | 0.5601  | 0.8148  |
| window              | 0.2793  | 0.6602  | 0.0608  | 0.2650  |
| pillar              | 0.3739  | 0.6744  | 0.2569  | 0.4884  |
| hanger              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| chair               | 0.6103  | 0.8579  | 0.4686  | 0.6951  |
| radiator            | 0.6915  | 0.8111  | 0.1524  | 0.3000  |
| blackboard          | 0.6481  | 0.8049  | 0.2667  | 0.4512  |
| computer            | 0.3455  | 0.6066  | 0.0217  | 0.1475  |
| keyboard            | 0.1189  | 0.2791  | 0.0009  | 0.0233  |
| monitor             | 0.5887  | 0.7065  | 0.1616  | 0.3750  |
| desk                | 0.5743  | 0.9213  | 0.3960  | 0.7640  |
| mouse               | 0.0052  | 0.1081  | 0.0000  | 0.0000  |
| table               | 0.5253  | 0.8793  | 0.4265  | 0.7602  |
| plug                | 0.0301  | 0.1053  | 0.0000  | 0.0000  |
| telephone           | 0.4063  | 0.6667  | 0.1153  | 0.3000  |
| book                | 0.0703  | 0.4399  | 0.0062  | 0.1131  |
| shelf               | 0.3290  | 0.7194  | 0.1448  | 0.4335  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| windowsill          | 0.1302  | 0.5870  | 0.0230  | 0.2391  |
| stand               | 0.5938  | 0.8963  | 0.4561  | 0.7439  |
| jacket              | 0.6221  | 0.8333  | 0.2469  | 0.5417  |
| umbrella            | 0.2000  | 0.2000  | 0.2000  | 0.2000  |
| projector           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pipe                | 0.0384  | 0.2143  | 0.0000  | 0.0000  |
| pillow              | 0.2731  | 0.6290  | 0.0497  | 0.2315  |
| tv                  | 0.6716  | 0.8515  | 0.1729  | 0.4059  |
| paper cutter        | 0.7857  | 0.8333  | 0.0000  | 0.0000  |
| ottoman             | 0.1907  | 0.6944  | 0.1473  | 0.6111  |
| container           | 0.0116  | 0.1864  | 0.0072  | 0.0847  |
| remote control      | 0.0701  | 0.2222  | 0.0165  | 0.0741  |
| clock               | 0.5472  | 0.8085  | 0.0430  | 0.1489  |
| rack                | 0.1949  | 0.4808  | 0.1207  | 0.4615  |
| printer             | 0.5563  | 0.7755  | 0.3824  | 0.6531  |
| toy                 | 0.1470  | 0.5000  | 0.0632  | 0.1667  |
| guitar              | 0.2382  | 0.4167  | 0.0833  | 0.0833  |
| refrigerator        | 0.7256  | 0.8800  | 0.4102  | 0.5800  |
| luggage             | 0.5956  | 1.0000  | 0.5556  | 0.6667  |
| purse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bench               | 0.3161  | 0.7547  | 0.2443  | 0.5472  |
| laptop              | 0.4729  | 0.7391  | 0.1381  | 0.2609  |
| notebook            | 0.0006  | 0.0769  | 0.0000  | 0.0000  |
| kettle              | 0.2292  | 0.6667  | 0.0791  | 0.2778  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plate               | 0.0387  | 0.4423  | 0.0055  | 0.0962  |
| jar                 | 0.1016  | 0.3846  | 0.0786  | 0.1538  |
| coffee maker        | 0.3936  | 0.5862  | 0.0416  | 0.1379  |
| brush               | 0.1721  | 0.4286  | 0.0102  | 0.0714  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| window frame        | 0.0299  | 0.4242  | 0.0033  | 0.1616  |
| countertop          | 0.0147  | 0.2857  | 0.0001  | 0.0357  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hamper              | 0.4866  | 0.5455  | 0.4866  | 0.5455  |
| roof                | 0.0028  | 0.3333  | 0.0000  | 0.0417  |
| grab bar            | 0.0952  | 0.5000  | 0.0000  | 0.0000  |
| crib                | 0.3750  | 0.6667  | 0.3750  | 0.6667  |
| kitchen island      | 0.2062  | 0.4286  | 0.2062  | 0.4286  |
| hat                 | 0.1111  | 0.1111  | 0.0000  | 0.0000  |
| bowl                | 0.1308  | 0.5217  | 0.0441  | 0.2029  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dispenser      | 0.4062  | 0.7407  | 0.0680  | 0.3333  |
| dispenser           | 0.4731  | 0.5909  | 0.3394  | 0.4545  |
| stairs              | 0.0702  | 0.4583  | 0.0069  | 0.1944  |
| column              | 0.0202  | 0.3684  | 0.0015  | 0.0789  |
| chandelier          | 0.0124  | 0.0952  | 0.0000  | 0.0000  |
| alarm               | 0.0069  | 0.1250  | 0.0000  | 0.0000  |
| mailbox             | 0.0975  | 1.0000  | 0.0302  | 0.6000  |
| holder              | 0.2222  | 0.2222  | 0.1111  | 0.1111  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape       | 0.2000  | 0.2000  | 0.0000  | 0.0000  |
| headphones          | 0.3833  | 0.6000  | 0.2000  | 0.2000  |
| thermostat          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| copier              | 0.9028  | 0.9167  | 0.9028  | 0.9167  |
| spoon               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| teapot              | 0.0159  | 0.1111  | 0.0000  | 0.0000  |
| poster              | 0.0135  | 0.1667  | 0.0025  | 0.0152  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchenware         | 0.2680  | 0.5217  | 0.0218  | 0.1522  |
| toaster             | 0.4158  | 0.6000  | 0.0333  | 0.1000  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drum                | 0.0208  | 0.1250  | 0.0000  | 0.0000  |
| cube                | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| board               | 0.1632  | 0.6129  | 0.0526  | 0.2581  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 0.7482  | 1.0000  | 0.4454  | 0.8333  |
| mattress            | 0.1111  | 0.3333  | 0.0000  | 0.0000  |
| piano               | 0.3000  | 0.4000  | 0.3000  | 0.4000  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| magazine            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fireplace           | 0.1848  | 0.4286  | 0.0278  | 0.1786  |
| fan                 | 0.6779  | 0.7969  | 0.2682  | 0.4844  |
| pool table          | 0.1667  | 0.3333  | 0.1667  | 0.3333  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tube                | 0.0099  | 0.1538  | 0.0000  | 0.0000  |
| statue              | 0.1785  | 0.5263  | 0.0970  | 0.2105  |
| person              | 0.1392  | 0.6667  | 0.0093  | 0.1667  |
| calendar            | 0.0362  | 0.3333  | 0.0000  | 0.0000  |
| vacuum cleaner      | 0.5202  | 0.7143  | 0.0317  | 0.2857  |
| ball                | 0.4479  | 0.5238  | 0.3187  | 0.4286  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basin               | 0.0024  | 0.1765  | 0.0005  | 0.0588  |
| fire extinguisher   | 0.3000  | 0.4000  | 0.3000  | 0.4000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cosmetics           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garage door         | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| barricade           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| heater              | 0.6494  | 0.8448  | 0.1735  | 0.3966  |
| excercise equipment | 0.3435  | 0.6000  | 0.0137  | 0.2000  |
| commode             | 0.5400  | 0.8462  | 0.3459  | 0.6731  |
| beanbag             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| treadmill           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet               | 0.4444  | 0.6667  | 0.4444  | 0.6667  |
| footstool           | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| crate               | 0.5714  | 0.5714  | 0.2143  | 0.2857  |
| jalousie            | 1.0000  | 1.0000  | 0.5000  | 0.5000  |
| candlestick         | 0.0698  | 0.6667  | 0.0000  | 0.0000  |
| humidifier          | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sign                | 0.0720  | 0.3333  | 0.0002  | 0.0667  |
| water cooler        | 0.7500  | 1.0000  | 0.7500  | 1.0000  |
| dish rack           | 0.7650  | 1.0000  | 0.3600  | 0.6000  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| oven                | 0.5560  | 0.8214  | 0.1170  | 0.4286  |
| bucket              | 0.2155  | 0.7692  | 0.1005  | 0.4359  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pot                 | 0.2138  | 0.5185  | 0.0294  | 0.1852  |
| fruit               | 0.1406  | 0.4286  | 0.0876  | 0.2143  |
| pack                | 0.0541  | 0.7333  | 0.0147  | 0.2667  |
| stove               | 0.5703  | 0.8611  | 0.2837  | 0.4722  |
| pan                 | 0.2054  | 0.6471  | 0.0213  | 0.1176  |
| device              | 0.2835  | 0.5455  | 0.0004  | 0.0909  |
| bread               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cushion             | 0.2354  | 0.5294  | 0.0267  | 0.1961  |
| folder              | 0.0785  | 0.2857  | 0.0143  | 0.0714  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| food                | 0.0909  | 0.0909  | 0.0000  | 0.0000  |
| garbage             | 0.7592  | 0.8750  | 0.6149  | 0.7500  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flush               | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| sponge              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glove               | 0.8333  | 1.0000  | 0.1667  | 0.3333  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sack                | 0.5139  | 0.8571  | 0.2614  | 0.7143  |
| player              | 0.2500  | 0.2500  | 0.2500  | 0.2500  |
| radio               | 0.2500  | 0.2500  | 0.2500  | 0.2500  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cap                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| can                 | 0.0833  | 0.1667  | 0.0278  | 0.0833  |
| map                 | 0.0385  | 0.5000  | 0.0385  | 0.5000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wine                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.5168  | 0.7500  | 0.2709  | 0.3929  |
| ladder              | 0.1833  | 0.4706  | 0.0830  | 0.1765  |
| salt                | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| napkins             | 0.4444  | 0.6667  | 0.1667  | 0.3333  |
| dumbbell            | 0.5556  | 0.6667  | 0.1158  | 0.6667  |
| menu                | 0.6508  | 0.7143  | 0.0714  | 0.1429  |
| roll                | 0.2903  | 0.5000  | 0.0000  | 0.0000  |
| toiletry            | 0.0864  | 0.2609  | 0.0040  | 0.0435  |
| broom               | 0.0701  | 0.4286  | 0.0000  | 0.0000  |
| steps               | 0.0005  | 0.2000  | 0.0001  | 0.1000  |
| flowerpot           | 0.0743  | 0.3725  | 0.0007  | 0.0980  |
| range hood          | 0.4532  | 0.7143  | 0.0857  | 0.2857  |
| vase                | 0.1818  | 0.4200  | 0.0803  | 0.2400  |
| file                | 0.0634  | 0.2353  | 0.0302  | 0.0882  |
| speaker             | 0.1466  | 0.3636  | 0.0076  | 0.0909  |
| air conditioner     | 0.8000  | 0.8000  | 0.4000  | 0.4000  |
| clothes dryer       | 0.3920  | 0.8667  | 0.3465  | 0.6667  |
| machine             | 0.6200  | 0.8421  | 0.4183  | 0.6842  |
| conducting wire     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| price tag           | 0.0026  | 0.1429  | 0.0000  | 0.0000  |
| candle              | 0.1123  | 0.3889  | 0.0019  | 0.1667  |
| album               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| letter              | 0.7054  | 1.0000  | 0.0625  | 0.2500  |
| shampoo             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beam                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| body loofah         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.2600  | 0.3000  | 0.2000  | 0.2000  |
| water heater        | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| mop                 | 0.9167  | 1.0000  | 0.1667  | 0.3333  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| handle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| helmets             | 0.0061  | 0.3333  | 0.0061  | 0.3333  |
| coil                | 0.2778  | 0.3333  | 0.0556  | 0.1667  |
| washbasin           | 0.0072  | 0.1333  | 0.0039  | 0.0667  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| overall             | 0.2449  | 0.4287  | 0.1150  | 0.2246  |
| head                | 0.3249  | 0.6153  | 0.1509  | 0.3328  |
| common              | 0.2215  | 0.3879  | 0.1094  | 0.1984  |
| tail                | 0.1839  | 0.2701  | 0.0818  | 0.1357  |
| small               | 0.0946  | 0.2273  | 0.0138  | 0.0583  |
| medium              | 0.2502  | 0.4761  | 0.1330  | 0.2574  |
| large               | 0.1570  | 0.5146  | 0.0822  | 0.3255  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 19:01:35 INFO metrics.py:437 | number of scannet results: 250
Rank[0/8] 06/20/2025 19:02:51 INFO metrics.py:332 | eval results of scannet:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| bathtub             | 0.9375  | 0.9375  | 0.8750  | 0.8750  |
| curtain             | 0.6197  | 0.8319  | 0.1363  | 0.4071  |
| toilet paper        | 0.4440  | 0.6456  | 0.2613  | 0.4051  |
| toilet              | 0.9294  | 0.9643  | 0.8141  | 0.8750  |
| sink                | 0.8427  | 0.9091  | 0.3298  | 0.5341  |
| picture             | 0.3501  | 0.5448  | 0.0734  | 0.1903  |
| door                | 0.6106  | 0.8528  | 0.1542  | 0.4415  |
| bin                 | 0.6787  | 0.8681  | 0.4477  | 0.6340  |
| doorframe           | 0.3966  | 0.7535  | 0.0814  | 0.3451  |
| vanity              | 0.7222  | 0.9667  | 0.6379  | 0.9000  |
| towel               | 0.5886  | 0.7769  | 0.1629  | 0.3306  |
| bar                 | 0.1072  | 0.3182  | 0.0227  | 0.0455  |
| mirror              | 0.3759  | 0.7108  | 0.0254  | 0.1687  |
| bag                 | 0.2294  | 0.6986  | 0.1393  | 0.3288  |
| stopcock            | 0.0833  | 0.4483  | 0.0006  | 0.0345  |
| switch              | 0.0058  | 0.1772  | 0.0000  | 0.0000  |
| bottle              | 0.1003  | 0.3895  | 0.0087  | 0.1158  |
| socket              | 0.0201  | 0.2816  | 0.0000  | 0.0097  |
| hair dryer          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cup                 | 0.2141  | 0.4638  | 0.0132  | 0.1014  |
| rail                | 0.0702  | 0.6667  | 0.0175  | 0.3333  |
| ledge               | 0.1935  | 0.5000  | 0.0004  | 0.1667  |
| microwave           | 0.6594  | 0.8000  | 0.2790  | 0.4500  |
| shower              | 0.0985  | 0.2857  | 0.0038  | 0.0714  |
| rod                 | 0.0714  | 0.0714  | 0.0000  | 0.0000  |
| tissue              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| light               | 0.1311  | 0.4828  | 0.0096  | 0.1379  |
| tissue box          | 0.2578  | 0.5455  | 0.1796  | 0.3636  |
| basket              | 0.1018  | 0.2308  | 0.1014  | 0.1923  |
| box                 | 0.1259  | 0.4484  | 0.0330  | 0.1508  |
| plant               | 0.3021  | 0.6639  | 0.1019  | 0.3109  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cabinet             | 0.3487  | 0.7579  | 0.1826  | 0.5159  |
| stool               | 0.3191  | 0.7045  | 0.2416  | 0.5682  |
| lamp                | 0.4740  | 0.7976  | 0.2623  | 0.4762  |
| mat                 | 0.1082  | 0.4000  | 0.0000  | 0.0000  |
| shoe                | 0.2208  | 0.5750  | 0.0364  | 0.1500  |
| paper               | 0.0289  | 0.3283  | 0.0006  | 0.0642  |
| bed                 | 0.9898  | 1.0000  | 0.8922  | 0.9474  |
| blanket             | 0.0609  | 0.5000  | 0.0158  | 0.2500  |
| drawer              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| faucet              | 0.0010  | 0.1250  | 0.0010  | 0.1250  |
| case                | 0.6696  | 1.0000  | 0.5661  | 0.8947  |
| wardrobe            | 0.4852  | 0.8750  | 0.3750  | 0.3750  |
| carpet              | 0.0108  | 0.4000  | 0.0000  | 0.0000  |
| decoration          | 0.0395  | 0.2917  | 0.0003  | 0.0417  |
| plunger             | 0.6800  | 0.8000  | 0.3600  | 0.6000  |
| clothes             | 0.1976  | 0.5294  | 0.0740  | 0.2941  |
| frame               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scale               | 0.2500  | 0.2500  | 0.0061  | 0.2500  |
| couch               | 0.7319  | 0.9625  | 0.6091  | 0.9125  |
| soap dish           | 0.7481  | 0.8333  | 0.1062  | 0.2500  |
| stall               | 0.3258  | 1.0000  | 0.2200  | 0.5294  |
| backpack            | 0.7304  | 0.9286  | 0.6017  | 0.7143  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| counter             | 0.5191  | 0.8571  | 0.1781  | 0.5429  |
| toothpaste          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dresser             | 0.7659  | 0.9730  | 0.7627  | 0.9459  |
| window              | 0.5316  | 0.8395  | 0.1900  | 0.4259  |
| pillar              | 0.0074  | 0.2000  | 0.0000  | 0.0000  |
| chair               | 0.6573  | 0.8764  | 0.4661  | 0.6844  |
| radiator            | 0.7985  | 0.8966  | 0.2125  | 0.3621  |
| blackboard          | 0.6670  | 0.8116  | 0.3018  | 0.4783  |
| computer            | 0.3543  | 0.6154  | 0.0273  | 0.1538  |
| keyboard            | 0.1222  | 0.2586  | 0.0013  | 0.0172  |
| monitor             | 0.6349  | 0.7214  | 0.2265  | 0.4357  |
| desk                | 0.6780  | 0.9747  | 0.4741  | 0.7975  |
| mouse               | 0.0234  | 0.1905  | 0.0000  | 0.0000  |
| table               | 0.6071  | 0.9308  | 0.4950  | 0.8038  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| telephone           | 0.5307  | 0.7812  | 0.1949  | 0.3750  |
| book                | 0.0697  | 0.4429  | 0.0057  | 0.1190  |
| shelf               | 0.5190  | 0.7941  | 0.3232  | 0.6250  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| windowsill          | 0.0882  | 0.5000  | 0.0014  | 0.1667  |
| stand               | 0.7683  | 0.8868  | 0.6393  | 0.7925  |
| jacket              | 0.6510  | 0.8696  | 0.2584  | 0.5652  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pipe                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillow              | 0.4071  | 0.6528  | 0.1709  | 0.4583  |
| tv                  | 0.8110  | 0.9130  | 0.2785  | 0.4348  |
| paper cutter        | 0.7600  | 0.8000  | 0.0000  | 0.0000  |
| ottoman             | 0.1945  | 1.0000  | 0.0402  | 0.7143  |
| container           | 0.0024  | 0.1739  | 0.0005  | 0.0870  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clock               | 0.4933  | 0.7600  | 0.0687  | 0.1600  |
| rack                | 0.0005  | 0.0909  | 0.0005  | 0.0909  |
| printer             | 0.5898  | 0.8182  | 0.5828  | 0.7879  |
| toy                 | 0.0885  | 0.4118  | 0.0784  | 0.1176  |
| guitar              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| refrigerator        | 0.8957  | 0.9500  | 0.5257  | 0.7000  |
| purse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bench               | 0.6422  | 1.0000  | 0.6422  | 1.0000  |
| laptop              | 0.5143  | 0.6000  | 0.1576  | 0.2667  |
| notebook            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kettle              | 0.1875  | 0.5000  | 0.1250  | 0.2500  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plate               | 0.0077  | 0.2000  | 0.0000  | 0.0000  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coffee maker        | 0.4923  | 0.5882  | 0.0756  | 0.1765  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hamper              | 0.5375  | 0.6000  | 0.5375  | 0.6000  |
| grab bar            | 0.0952  | 0.5000  | 0.0000  | 0.0000  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bowl                | 0.2848  | 0.4444  | 0.0889  | 0.2222  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dispenser      | 0.3638  | 0.7143  | 0.0800  | 0.3333  |
| dispenser           | 0.5683  | 0.6410  | 0.4000  | 0.4872  |
| stairs              | 0.2690  | 0.8000  | 0.0543  | 0.8000  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mailbox             | 0.0975  | 1.0000  | 0.0302  | 0.6000  |
| holder              | 0.2000  | 0.2000  | 0.2000  | 0.2000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headphones          | 0.4000  | 0.6000  | 0.2000  | 0.2000  |
| copier              | 0.9028  | 0.9167  | 0.9028  | 0.9167  |
| teapot              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster             | 0.0833  | 0.5000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| board               | 0.0850  | 0.5938  | 0.0057  | 0.3438  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 1.0000  | 1.0000  | 0.4686  | 1.0000  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fan                 | 0.8281  | 0.8750  | 0.1696  | 0.3750  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| person              | 0.1392  | 0.6667  | 0.0093  | 0.1667  |
| calendar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vacuum cleaner      | 0.3333  | 0.6667  | 0.3333  | 0.6667  |
| ball                | 0.4167  | 0.4167  | 0.2292  | 0.2500  |
| basin               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| barricade           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crate               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sign                | 0.0553  | 0.2857  | 0.0004  | 0.0714  |
| water cooler        | 0.7500  | 1.0000  | 0.7500  | 1.0000  |
| dish rack           | 0.8000  | 1.0000  | 0.5625  | 0.7500  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| oven                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket              | 0.1605  | 0.7143  | 0.0424  | 0.3571  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pot                 | 0.0833  | 0.5000  | 0.0000  | 0.0000  |
| fruit               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stove               | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| cushion             | 0.0213  | 0.5000  | 0.0061  | 0.2500  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| map                 | 0.0833  | 1.0000  | 0.0833  | 1.0000  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.7014  | 1.0000  | 0.6333  | 0.6667  |
| ladder              | 0.1138  | 0.4000  | 0.1000  | 0.2000  |
| broom               | 0.1096  | 0.5000  | 0.0000  | 0.0000  |
| flowerpot           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.7583  | 1.0000  | 0.3000  | 0.6000  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.3333  | 1.0000  | 0.3333  | 1.0000  |
| machine             | 0.6869  | 0.9375  | 0.4376  | 0.7500  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| scannet             | 0.2729  | 0.4554  | 0.1511  | 0.2741  |
| head                | 0.3750  | 0.6226  | 0.2090  | 0.3708  |
| common              | 0.2077  | 0.3134  | 0.1098  | 0.1942  |
| tail                | 0.0760  | 0.2120  | 0.0489  | 0.1280  |
| small               | 0.0653  | 0.1652  | 0.0146  | 0.0424  |
| medium              | 0.3230  | 0.5558  | 0.1875  | 0.3296  |
| large               | 0.3027  | 0.7874  | 0.1725  | 0.5619  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 19:02:51 INFO metrics.py:437 | number of 3rscan results: 235
Rank[0/8] 06/20/2025 19:03:59 INFO metrics.py:332 | eval results of 3rscan:
+-------------------+---------+---------+---------+---------+
| classes           | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+-------------------+---------+---------+---------+---------+
| chair             | 0.7120  | 0.8599  | 0.5992  | 0.7505  |
| monitor           | 0.5987  | 0.6750  | 0.1091  | 0.2000  |
| keyboard          | 0.1783  | 0.3750  | 0.0021  | 0.0417  |
| mouse             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| desk              | 0.5334  | 0.9125  | 0.3651  | 0.7625  |
| cup               | 0.0717  | 0.3333  | 0.0007  | 0.0513  |
| paper             | 0.1129  | 0.4000  | 0.0065  | 0.1143  |
| computer          | 0.3535  | 0.6190  | 0.0171  | 0.1429  |
| case              | 0.0413  | 0.4143  | 0.0099  | 0.1286  |
| plate             | 0.0516  | 0.4848  | 0.0105  | 0.0909  |
| bag               | 0.3759  | 0.7778  | 0.1127  | 0.3737  |
| garbage           | 0.7608  | 0.8750  | 0.6165  | 0.7500  |
| curtain           | 0.6244  | 0.8810  | 0.2178  | 0.5556  |
| pen               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stand             | 0.5728  | 0.9508  | 0.3760  | 0.7049  |
| plug              | 0.1111  | 0.2222  | 0.0000  | 0.0000  |
| bottle            | 0.0640  | 0.3554  | 0.0019  | 0.0661  |
| wardrobe          | 0.4749  | 0.8594  | 0.3468  | 0.6562  |
| window            | 0.3082  | 0.7534  | 0.0345  | 0.2329  |
| backpack          | 0.4973  | 0.8182  | 0.1940  | 0.4545  |
| table             | 0.6024  | 0.8919  | 0.4807  | 0.7658  |
| bin               | 0.7403  | 0.8902  | 0.5071  | 0.7561  |
| book              | 0.1202  | 0.4793  | 0.0127  | 0.1243  |
| cabinet           | 0.4501  | 0.8376  | 0.3052  | 0.6709  |
| box               | 0.2144  | 0.5512  | 0.0578  | 0.2179  |
| socket            | 0.0125  | 0.0625  | 0.0000  | 0.0000  |
| telephone         | 0.3383  | 0.5417  | 0.0487  | 0.2083  |
| notebook          | 0.0008  | 0.0909  | 0.0000  | 0.0000  |
| sink              | 0.6706  | 0.8214  | 0.1813  | 0.3929  |
| blinds            | 0.3128  | 0.6154  | 0.0456  | 0.3333  |
| file              | 0.0770  | 0.2857  | 0.0367  | 0.1071  |
| plant             | 0.3803  | 0.6584  | 0.0918  | 0.2871  |
| picture           | 0.2410  | 0.4675  | 0.0213  | 0.1057  |
| shelf             | 0.3240  | 0.7527  | 0.1072  | 0.3922  |
| clock             | 0.6837  | 0.8947  | 0.0132  | 0.0526  |
| light             | 0.1418  | 0.5341  | 0.0021  | 0.0795  |
| laptop            | 0.5212  | 1.0000  | 0.2000  | 0.3333  |
| jar               | 0.1694  | 0.5000  | 0.1284  | 0.2500  |
| switch            | 0.0028  | 0.0755  | 0.0000  | 0.0000  |
| couch             | 0.5343  | 0.8714  | 0.4166  | 0.8000  |
| tissue box        | 0.4667  | 0.6667  | 0.0667  | 0.3333  |
| calendar          | 0.0600  | 0.4000  | 0.0000  | 0.0000  |
| printer           | 0.8127  | 0.8889  | 0.3633  | 0.5556  |
| conducting wire   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| towel             | 0.3391  | 0.6582  | 0.1216  | 0.2911  |
| pillow            | 0.4187  | 0.6992  | 0.0533  | 0.2109  |
| frame             | 0.2369  | 0.8667  | 0.0015  | 0.1333  |
| tv                | 0.7454  | 0.9714  | 0.1963  | 0.4857  |
| commode           | 0.5407  | 0.8462  | 0.3463  | 0.6731  |
| lamp              | 0.3386  | 0.5938  | 0.0464  | 0.1875  |
| price tag         | 0.0026  | 0.1429  | 0.0000  | 0.0000  |
| decoration        | 0.1470  | 0.5000  | 0.0246  | 0.1562  |
| candle            | 0.1414  | 0.4000  | 0.0019  | 0.1333  |
| bed               | 0.9171  | 0.9818  | 0.7422  | 0.8727  |
| bowl              | 0.2105  | 0.7037  | 0.0880  | 0.2222  |
| cushion           | 0.3979  | 0.7667  | 0.0448  | 0.2333  |
| stool             | 0.4988  | 0.8704  | 0.3356  | 0.7037  |
| folder            | 0.0785  | 0.2857  | 0.0143  | 0.0714  |
| blanket           | 0.4620  | 0.8462  | 0.0850  | 0.4154  |
| door              | 0.3953  | 0.7206  | 0.0870  | 0.3456  |
| tablet            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| carpet            | 0.2674  | 0.6786  | 0.0168  | 0.2143  |
| vase              | 0.3306  | 0.4500  | 0.1833  | 0.2000  |
| remote control    | 0.1064  | 0.3000  | 0.0250  | 0.1000  |
| bench             | 0.4399  | 0.8929  | 0.3396  | 0.6429  |
| napkins           | 0.4444  | 0.6667  | 0.1667  | 0.3333  |
| rack              | 0.4188  | 0.7000  | 0.2447  | 0.6667  |
| clothes           | 0.3216  | 0.6893  | 0.0490  | 0.2621  |
| flowerpot         | 0.1378  | 0.4583  | 0.0002  | 0.0417  |
| bucket            | 0.6659  | 1.0000  | 0.3702  | 0.6667  |
| bar               | 0.5000  | 0.6667  | 0.0078  | 0.3333  |
| ottoman           | 0.6287  | 1.0000  | 0.5959  | 0.9091  |
| teapot            | 0.0625  | 0.2500  | 0.0000  | 0.0000  |
| player            | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| magazine          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toy               | 0.4494  | 0.6923  | 0.2183  | 0.3077  |
| cube              | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| counter           | 0.1830  | 0.7317  | 0.0186  | 0.3171  |
| roll              | 0.2904  | 0.5000  | 0.0000  | 0.0000  |
| poster            | 0.0191  | 0.1746  | 0.0032  | 0.0159  |
| blackboard        | 0.6755  | 0.8333  | 0.1132  | 0.3333  |
| heater            | 0.6511  | 0.8421  | 0.1719  | 0.3860  |
| humidifier        | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| windowsill        | 0.1520  | 0.6154  | 0.0307  | 0.2564  |
| device            | 0.2835  | 0.5455  | 0.0004  | 0.0909  |
| fan               | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| ball              | 0.5200  | 0.6000  | 0.5200  | 0.6000  |
| board             | 0.3309  | 0.6667  | 0.1327  | 0.2083  |
| umbrella          | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| doorframe         | 0.2121  | 0.6800  | 0.0269  | 0.2600  |
| container         | 0.2073  | 0.5556  | 0.1667  | 0.2222  |
| pad               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker           | 0.2282  | 0.4167  | 0.0064  | 0.0833  |
| brush             | 0.3035  | 0.6250  | 0.0179  | 0.1250  |
| drawer            | 0.7587  | 1.0000  | 0.6675  | 0.8889  |
| kettle            | 0.3161  | 0.8333  | 0.1056  | 0.3333  |
| mirror            | 0.4492  | 0.7037  | 0.0801  | 0.2963  |
| shoe              | 0.2999  | 0.5000  | 0.0421  | 0.1429  |
| pot               | 0.5751  | 0.6667  | 0.1042  | 0.2500  |
| album             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hanger            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape     | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| air conditioner   | 1.0000  | 1.0000  | 0.5000  | 0.5000  |
| stairs            | 0.3743  | 0.7143  | 0.0333  | 0.3571  |
| basket            | 0.5118  | 0.7812  | 0.2943  | 0.5469  |
| radiator          | 0.5069  | 0.6897  | 0.0601  | 0.2069  |
| rail              | 0.0334  | 0.5000  | 0.0000  | 0.0000  |
| dresser           | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| balcony           | 0.2500  | 0.5000  | 0.0208  | 0.5000  |
| refrigerator      | 0.7150  | 0.8667  | 0.6345  | 0.7333  |
| guitar            | 1.0000  | 1.0000  | 0.5000  | 0.5000  |
| bicycle           | 0.8333  | 1.0000  | 0.8333  | 1.0000  |
| basin             | 0.0074  | 0.2000  | 0.0014  | 0.0667  |
| window frame      | 0.1354  | 0.5000  | 0.0010  | 0.0833  |
| fruit             | 0.3500  | 0.6000  | 0.2000  | 0.2000  |
| tissue            | 0.1778  | 0.6667  | 0.1778  | 0.6667  |
| microwave         | 0.9508  | 1.0000  | 0.5526  | 0.7059  |
| sunglasses        | 0.0627  | 0.3077  | 0.0050  | 0.1538  |
| hat               | 0.1429  | 0.1429  | 0.0000  | 0.0000  |
| letter            | 0.7054  | 1.0000  | 0.0625  | 0.2500  |
| furniture         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| piano             | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| crib              | 0.3810  | 0.6667  | 0.3810  | 0.6667  |
| fireplace         | 0.2000  | 0.2000  | 0.1000  | 0.2000  |
| bathtub           | 1.0000  | 1.0000  | 0.5556  | 0.6667  |
| scale             | 0.3556  | 1.0000  | 0.3333  | 0.3333  |
| clothes dryer     | 0.5047  | 0.9000  | 0.4366  | 0.6000  |
| shampoo           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| statue            | 1.0000  | 1.0000  | 0.5000  | 0.5000  |
| toilet            | 0.8660  | 0.8824  | 0.7523  | 0.7647  |
| shower            | 0.6010  | 1.0000  | 0.0863  | 0.7500  |
| faucet            | 0.4667  | 0.6667  | 0.0667  | 0.3333  |
| toilet paper      | 0.1014  | 0.3333  | 0.0056  | 0.0833  |
| beam              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tube              | 0.0408  | 0.2857  | 0.0000  | 0.0000  |
| washing machine   | 0.5123  | 0.6429  | 0.2317  | 0.3571  |
| dispenser         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap              | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| toiletry          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| body loofah       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beanbag           | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| cosmetics         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jalousie          | 1.0000  | 1.0000  | 0.5000  | 0.5000  |
| can               | 0.1250  | 0.2500  | 0.0417  | 0.1250  |
| column            | 0.0289  | 1.0000  | 0.0000  | 0.0000  |
| knife             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candlestick       | 0.1429  | 1.0000  | 0.0000  | 0.0000  |
| spoon             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pack              | 0.0625  | 0.8462  | 0.0169  | 0.3077  |
| oven              | 0.7531  | 1.0000  | 0.1776  | 0.5789  |
| pan               | 0.2710  | 0.6667  | 0.0305  | 0.1667  |
| stove             | 0.4785  | 0.9412  | 0.0167  | 0.2353  |
| coffee maker      | 0.2460  | 0.7778  | 0.0034  | 0.1111  |
| shovel            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchenware       | 0.3035  | 0.5455  | 0.0246  | 0.1591  |
| sack              | 0.5139  | 0.8571  | 0.2614  | 0.7143  |
| range hood        | 0.4750  | 0.6000  | 0.0250  | 0.2000  |
| food              | 0.1000  | 0.1000  | 0.0000  | 0.0000  |
| pillar            | 0.5993  | 0.7059  | 0.4904  | 0.5882  |
| partition         | 0.5442  | 0.8000  | 0.2000  | 0.2000  |
| radio             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| sign              | 0.5476  | 1.0000  | 0.0000  | 0.0000  |
| mattress          | 0.1667  | 0.5000  | 0.0000  | 0.0000  |
| bidet             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| sponge            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| treadmill         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| holder            | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| broom             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tray              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| chandelier        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hook              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| salt              | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| glove             | 0.8333  | 1.0000  | 0.1667  | 0.3333  |
| dishwasher        | 0.8667  | 1.0000  | 0.6667  | 0.6667  |
| seasoning         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crate             | 0.8000  | 0.8000  | 0.3000  | 0.4000  |
| vacuum cleaner    | 0.7500  | 0.7500  | 0.0000  | 0.0000  |
| dish rack         | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| flush             | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| menu              | 0.6508  | 0.7143  | 0.0714  | 0.1429  |
| water heater      | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| mop               | 1.0000  | 1.0000  | 0.1667  | 0.3333  |
| bread             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| screen            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tool              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster           | 1.0000  | 1.0000  | 0.0833  | 0.2500  |
| wire              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| footstool         | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| luggage           | 0.6270  | 1.0000  | 0.5556  | 0.6667  |
| dumbbell          | 1.0000  | 1.0000  | 0.0500  | 1.0000  |
| map               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glass             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pipe              | 0.2809  | 1.0000  | 0.0000  | 0.0000  |
| ladder            | 0.4089  | 1.0000  | 0.3333  | 0.3333  |
| soap dish         | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| mat               | 0.1667  | 0.3333  | 0.1667  | 0.3333  |
| helmets           | 0.0093  | 0.3333  | 0.0093  | 0.3333  |
| coil              | 0.2778  | 0.3333  | 0.0556  | 0.1667  |
| machine           | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| washbasin         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| paper cutter      | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| fire extinguisher | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| soap dispenser    | 0.8433  | 1.0000  | 0.0733  | 0.4000  |
| toothbrush        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scissors          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------           | ------- | ------- | ------- | ------- |
| 3rscan            | 0.3927  | 0.5876  | 0.1668  | 0.2834  |
| head              | 0.4016  | 0.6806  | 0.1600  | 0.3362  |
| common            | 0.4367  | 0.6009  | 0.1967  | 0.2822  |
| tail              | 0.3169  | 0.3987  | 0.1391  | 0.1883  |
| small             | 0.1526  | 0.3329  | 0.0230  | 0.0782  |
| medium            | 0.4033  | 0.6427  | 0.1899  | 0.3193  |
| large             | 0.2939  | 0.7966  | 0.1313  | 0.4704  |
+-------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 19:03:59 INFO metrics.py:437 | number of matterport3d results: 339
Rank[0/8] 06/20/2025 19:05:32 INFO metrics.py:332 | eval results of matterport3d:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| picture             | 0.2390  | 0.4686  | 0.0221  | 0.1425  |
| mirror              | 0.1230  | 0.6216  | 0.0224  | 0.2432  |
| door                | 0.2920  | 0.6240  | 0.0211  | 0.1854  |
| light               | 0.0393  | 0.2393  | 0.0013  | 0.0484  |
| doorframe           | 0.3072  | 0.7388  | 0.1155  | 0.4222  |
| towel               | 0.3846  | 0.6000  | 0.1597  | 0.3545  |
| sink                | 0.5659  | 0.8687  | 0.2565  | 0.5455  |
| drawer              | 0.0011  | 0.2500  | 0.0005  | 0.1250  |
| plant               | 0.3281  | 0.8036  | 0.1333  | 0.3571  |
| blanket             | 0.1074  | 0.7273  | 0.0202  | 0.1818  |
| decoration          | 0.0126  | 0.4048  | 0.0010  | 0.1429  |
| lamp                | 0.3764  | 0.7143  | 0.1689  | 0.3846  |
| case                | 0.0005  | 0.2778  | 0.0000  | 0.0556  |
| window              | 0.1613  | 0.5417  | 0.0281  | 0.2056  |
| vase                | 0.0883  | 0.4000  | 0.0088  | 0.2667  |
| table               | 0.3422  | 0.7756  | 0.2972  | 0.6795  |
| bottle              | 0.0933  | 0.5208  | 0.0295  | 0.1458  |
| counter             | 0.0229  | 0.4545  | 0.0005  | 0.1212  |
| roof                | 0.0028  | 0.3333  | 0.0000  | 0.0417  |
| cabinet             | 0.1301  | 0.6610  | 0.0815  | 0.4619  |
| stand               | 0.4480  | 0.8400  | 0.4167  | 0.7400  |
| toilet              | 0.8166  | 0.8438  | 0.6908  | 0.7500  |
| vent                | 0.0167  | 0.2083  | 0.0000  | 0.0000  |
| box                 | 0.0651  | 0.6154  | 0.0161  | 0.3654  |
| pillow              | 0.0850  | 0.5615  | 0.0091  | 0.1451  |
| switch              | 0.0002  | 0.1000  | 0.0000  | 0.0000  |
| plate               | 0.0437  | 0.4286  | 0.0011  | 0.1429  |
| flowerpot           | 0.0270  | 0.3200  | 0.0021  | 0.1600  |
| statue              | 0.0975  | 0.4706  | 0.0887  | 0.1765  |
| shelf               | 0.1349  | 0.5766  | 0.0485  | 0.3285  |
| bench               | 0.2305  | 0.5000  | 0.1721  | 0.3000  |
| pot                 | 0.0023  | 0.3846  | 0.0003  | 0.1538  |
| countertop          | 0.0147  | 0.2857  | 0.0001  | 0.0357  |
| frame               | 0.0003  | 0.1176  | 0.0001  | 0.0588  |
| tissue              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| carpet              | 0.0010  | 0.1667  | 0.0000  | 0.0000  |
| mat                 | 0.0105  | 0.3333  | 0.0000  | 0.0417  |
| chair               | 0.4500  | 0.7925  | 0.3165  | 0.6327  |
| rack                | 0.0007  | 0.2727  | 0.0007  | 0.2727  |
| curtain             | 0.4049  | 0.7396  | 0.1258  | 0.4271  |
| bathtub             | 0.6561  | 0.7407  | 0.4975  | 0.6667  |
| cup                 | 0.0010  | 0.2000  | 0.0000  | 0.0000  |
| handle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| paper               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| couch               | 0.3256  | 0.7935  | 0.2533  | 0.6413  |
| tv                  | 0.5512  | 0.7209  | 0.1254  | 0.3256  |
| bed                 | 0.6494  | 0.8070  | 0.3454  | 0.6667  |
| bowl                | 0.1243  | 0.3939  | 0.0259  | 0.1818  |
| book                | 0.0790  | 0.3780  | 0.0109  | 0.0787  |
| stool               | 0.0158  | 0.2857  | 0.0087  | 0.2857  |
| ottoman             | 0.0308  | 0.3889  | 0.0308  | 0.3889  |
| alarm               | 0.0104  | 0.1667  | 0.0000  | 0.0000  |
| stairs              | 0.0127  | 0.3585  | 0.0028  | 0.0943  |
| toy                 | 0.0968  | 0.4722  | 0.0132  | 0.1389  |
| telephone           | 0.2548  | 0.5000  | 0.0024  | 0.2500  |
| clock               | 0.3563  | 0.6667  | 0.3563  | 0.6667  |
| desk                | 0.3704  | 0.7368  | 0.2570  | 0.6316  |
| fan                 | 0.6976  | 0.7963  | 0.3275  | 0.5185  |
| steps               | 0.0005  | 0.2000  | 0.0001  | 0.1000  |
| screen              | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| dresser             | 0.2055  | 0.6667  | 0.1768  | 0.6000  |
| jar                 | 0.0033  | 0.3333  | 0.0000  | 0.0000  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wine                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| footstool           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cushion             | 0.0029  | 0.1176  | 0.0008  | 0.1176  |
| doorway             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candle              | 0.0085  | 0.3333  | 0.0085  | 0.3333  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit               | 0.1389  | 0.3750  | 0.1250  | 0.2500  |
| pillar              | 0.2945  | 0.7619  | 0.1705  | 0.5238  |
| column              | 0.0209  | 0.3333  | 0.0016  | 0.0833  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| window frame        | 0.0228  | 0.4138  | 0.0044  | 0.1724  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shower              | 0.0004  | 0.0833  | 0.0000  | 0.0000  |
| washbasin           | 0.0081  | 0.1429  | 0.0045  | 0.0714  |
| bin                 | 0.3356  | 0.7561  | 0.2367  | 0.5122  |
| washing machine     | 0.3911  | 0.7500  | 0.0556  | 0.2500  |
| dispenser           | 0.0172  | 0.5000  | 0.0172  | 0.5000  |
| faucet              | 0.0121  | 0.1944  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toilet paper        | 0.2999  | 0.6944  | 0.1162  | 0.4167  |
| rail                | 0.0322  | 0.3519  | 0.0000  | 0.0370  |
| headboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| windowsill          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball                | 0.5064  | 0.7500  | 0.5064  | 0.7500  |
| teapot              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| backpack            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| thermostat          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dispenser      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| holder              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hanger              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchen island      | 0.2062  | 0.4286  | 0.2062  | 0.4286  |
| heater              | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| piano               | 0.2500  | 0.5000  | 0.2500  | 0.5000  |
| monitor             | 0.3333  | 0.5000  | 0.0000  | 0.0000  |
| air conditioner     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| refrigerator        | 0.4637  | 0.8000  | 0.0937  | 0.2667  |
| radiator            | 0.1667  | 0.3333  | 0.0000  | 0.0000  |
| scale               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pan                 | 0.0521  | 0.6000  | 0.0000  | 0.0000  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wardrobe            | 0.0726  | 0.5455  | 0.0701  | 0.2727  |
| basin               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| board               | 0.1761  | 0.5000  | 0.0000  | 0.0000  |
| keyboard            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket              | 0.0153  | 0.5000  | 0.0039  | 0.2000  |
| mouse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pipe                | 0.0004  | 0.2000  | 0.0000  | 0.0000  |
| tissue box          | 0.1609  | 0.5000  | 0.0125  | 0.1250  |
| pool table          | 0.1667  | 0.3333  | 0.1667  | 0.3333  |
| soap                | 0.0111  | 0.4000  | 0.0095  | 0.2000  |
| basket              | 0.0688  | 0.3600  | 0.0036  | 0.1200  |
| shoe                | 0.0350  | 0.2800  | 0.0000  | 0.0000  |
| guitar              | 0.1451  | 0.4286  | 0.0000  | 0.0000  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0471  | 0.3750  | 0.0179  | 0.1250  |
| food                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bag                 | 0.0603  | 0.4615  | 0.0156  | 0.1538  |
| clothes dryer       | 0.2667  | 0.6667  | 0.2667  | 0.6667  |
| clothes             | 0.1257  | 0.3793  | 0.0147  | 0.1034  |
| excercise equipment | 0.3931  | 0.6667  | 0.0168  | 0.2222  |
| kitchenware         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kettle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| microwave           | 0.1566  | 0.3333  | 0.1071  | 0.1667  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher   | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| ladder              | 0.1667  | 0.3333  | 0.0093  | 0.1111  |
| garage door         | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| printer             | 0.4286  | 0.4286  | 0.0476  | 0.1429  |
| toiletry            | 0.0565  | 0.2273  | 0.0065  | 0.0455  |
| brush               | 0.0013  | 0.2500  | 0.0000  | 0.0000  |
| vanity              | 0.1980  | 0.6000  | 0.1619  | 0.4667  |
| soap dish           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fireplace           | 0.3296  | 0.4783  | 0.0310  | 0.1739  |
| beam                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| arch                | 0.0179  | 0.1000  | 0.0006  | 0.0667  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| container           | 0.0026  | 0.0741  | 0.0006  | 0.0370  |
| machine             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| chandelier          | 0.0227  | 0.1053  | 0.0000  | 0.0000  |
| computer            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stove               | 0.2481  | 0.6000  | 0.1452  | 0.4000  |
| oven                | 0.1726  | 0.8000  | 0.0018  | 0.2000  |
| socket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| laptop              | 0.5000  | 1.0000  | 0.0000  | 0.0000  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 0.1111  | 1.0000  | 0.0000  | 0.0000  |
| partition           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet               | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| drum                | 0.0208  | 0.1250  | 0.0000  | 0.0000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell            | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| hamper              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| magazine            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.0769  | 0.5000  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pack                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| player              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jacket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coffee maker        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| can                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candlestick         | 0.1667  | 0.5000  | 0.0000  | 0.0000  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| matterport3d        | 0.1215  | 0.3056  | 0.0645  | 0.1565  |
| head                | 0.1842  | 0.4602  | 0.0877  | 0.2409  |
| common              | 0.0707  | 0.2004  | 0.0436  | 0.0893  |
| tail                | 0.0498  | 0.0793  | 0.0431  | 0.0570  |
| small               | 0.0484  | 0.1540  | 0.0308  | 0.0747  |
| medium              | 0.1165  | 0.3388  | 0.0554  | 0.1607  |
| large               | 0.0961  | 0.3742  | 0.0561  | 0.2375  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 19:05:32 INFO metrics.py:437 | number of arkit results: 0
Rank[0/8] 06/20/2025 19:05:33 INFO stats.py:394 | Epoch[219] completed. Training Speed: 2.95 samples/sec across all devices.	Epoch Time: 1057.88 sec.	Average Epoch Time: 1057.88 sec.	Average Step Time: 2.71 sec.	Estimated Remaining Time: 2:37:34.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:05:33 WARNING accelerator.py:3099 | Deleting 1 checkpoints to make room for new checkpoint.
Rank[0/8] 06/20/2025 19:05:34 INFO accelerator.py:3111 | Saving current state to /job_data/checkpoints/checkpoint_21
Rank[3/8] 06/20/2025 19:05:34 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 219 to /job_data/checkpoints/checkpoint_21
Rank[5/8] 06/20/2025 19:05:34 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 219 to /job_data/checkpoints/checkpoint_21
Rank[4/8] 06/20/2025 19:05:34 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 219 to /job_data/checkpoints/checkpoint_21
Rank[1/8] 06/20/2025 19:05:34 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 219 to /job_data/checkpoints/checkpoint_21
Rank[7/8] 06/20/2025 19:05:34 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 219 to /job_data/checkpoints/checkpoint_21
Rank[6/8] 06/20/2025 19:05:34 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 219 to /job_data/checkpoints/checkpoint_21
Rank[2/8] 06/20/2025 19:05:34 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 219 to /job_data/checkpoints/checkpoint_21
Rank[0/8] 06/20/2025 19:05:36 INFO checkpointing.py:106 | Model weights saved in /job_data/checkpoints/checkpoint_21/model.safetensors
Rank[0/8] 06/20/2025 19:05:38 INFO checkpointing.py:113 | Optimizer state saved in /job_data/checkpoints/checkpoint_21/optimizer.bin
Rank[0/8] 06/20/2025 19:05:38 INFO checkpointing.py:120 | Scheduler state saved in /job_data/checkpoints/checkpoint_21/scheduler.bin
Rank[0/8] 06/20/2025 19:05:38 INFO checkpointing.py:137 | Sampler state for dataloader 0 saved in /job_data/checkpoints/checkpoint_21/sampler.bin
Rank[0/8] 06/20/2025 19:05:38 INFO checkpointing.py:137 | Sampler state for dataloader 1 saved in /job_data/checkpoints/checkpoint_21/sampler_1.bin
Rank[0/8] 06/20/2025 19:05:38 INFO checkpointing.py:164 | Random states saved in /job_data/checkpoints/checkpoint_21/random_states_0.pkl
Rank[0/8] 06/20/2025 19:05:38 INFO checkpointing.py:300 | Saving the state of TrainerProgressState to /job_data/checkpoints/checkpoint_21/custom_checkpoint_0.pkl
Rank[0/8] 06/20/2025 19:05:38 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 219 to /job_data/checkpoints/checkpoint_21
Rank[0/8] 06/20/2025 19:06:08 INFO stats.py:314 | Epoch[220] Step[24] GlobalStep[85824] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 2:37:04.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:06:08 INFO loss_tracker.py:84 | Epoch[220/239] Step[24] GlobalStep[85824/NA]: det_loss_cls_0[0.5190] det_loss_box_0[0.6263] det_loss_cls_1[0.4987] det_loss_box_1[0.4828] det_loss_cls_2[0.4402] det_loss_box_2[0.4327] det_loss_cls_3[0.4014] det_loss_box_3[0.4258] det_loss_cls_4[0.3838] det_loss_box_4[0.4123] det_loss_cls_5[0.3755] det_loss_box_5[0.4126] det_loss_cls_dn_0[0.0918] det_loss_box_dn_0[0.2585] det_loss_cls_dn_1[0.0882] det_loss_box_dn_1[0.2145] det_loss_cls_dn_2[0.0877] det_loss_box_dn_2[0.2001] det_loss_cls_dn_3[0.0866] det_loss_box_dn_3[0.1967] det_loss_cls_dn_4[0.0860] det_loss_box_dn_4[0.1946] det_loss_cls_dn_5[0.0852] det_loss_box_dn_5[0.1936] loss_depth[0.0351] total_loss[7.2296]
Rank[0/8] 06/20/2025 19:06:33 INFO stats.py:314 | Epoch[220] Step[49] GlobalStep[85849] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 2:36:33.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:06:33 INFO loss_tracker.py:84 | Epoch[220/239] Step[49] GlobalStep[85849/NA]: det_loss_cls_0[0.4895] det_loss_box_0[0.5810] det_loss_cls_1[0.4761] det_loss_box_1[0.4202] det_loss_cls_2[0.3991] det_loss_box_2[0.3836] det_loss_cls_3[0.3642] det_loss_box_3[0.3648] det_loss_cls_4[0.3477] det_loss_box_4[0.3531] det_loss_cls_5[0.3333] det_loss_box_5[0.3520] det_loss_cls_dn_0[0.0911] det_loss_box_dn_0[0.2420] det_loss_cls_dn_1[0.0879] det_loss_box_dn_1[0.1923] det_loss_cls_dn_2[0.0866] det_loss_box_dn_2[0.1764] det_loss_cls_dn_3[0.0851] det_loss_box_dn_3[0.1733] det_loss_cls_dn_4[0.0846] det_loss_box_dn_4[0.1704] det_loss_cls_dn_5[0.0836] det_loss_box_dn_5[0.1690] loss_depth[0.0336] total_loss[6.5407]
Rank[0/8] 06/20/2025 19:06:59 INFO stats.py:314 | Epoch[220] Step[74] GlobalStep[85874] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 2:36:03.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:06:59 INFO loss_tracker.py:84 | Epoch[220/239] Step[74] GlobalStep[85874/NA]: det_loss_cls_0[0.4563] det_loss_box_0[0.5409] det_loss_cls_1[0.4436] det_loss_box_1[0.3960] det_loss_cls_2[0.3913] det_loss_box_2[0.3474] det_loss_cls_3[0.3576] det_loss_box_3[0.3377] det_loss_cls_4[0.3384] det_loss_box_4[0.3196] det_loss_cls_5[0.3286] det_loss_box_5[0.3201] det_loss_cls_dn_0[0.0974] det_loss_box_dn_0[0.2522] det_loss_cls_dn_1[0.0935] det_loss_box_dn_1[0.2115] det_loss_cls_dn_2[0.0930] det_loss_box_dn_2[0.1976] det_loss_cls_dn_3[0.0924] det_loss_box_dn_3[0.1930] det_loss_cls_dn_4[0.0910] det_loss_box_dn_4[0.1910] det_loss_cls_dn_5[0.0903] det_loss_box_dn_5[0.1893] loss_depth[0.0350] total_loss[6.4047]
Rank[0/8] 06/20/2025 19:07:25 INFO stats.py:314 | Epoch[220] Step[99] GlobalStep[85899] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 2:35:32.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:07:25 INFO loss_tracker.py:84 | Epoch[220/239] Step[99] GlobalStep[85899/NA]: det_loss_cls_0[0.4727] det_loss_box_0[0.5703] det_loss_cls_1[0.4572] det_loss_box_1[0.4124] det_loss_cls_2[0.3891] det_loss_box_2[0.3658] det_loss_cls_3[0.3560] det_loss_box_3[0.3563] det_loss_cls_4[0.3371] det_loss_box_4[0.3406] det_loss_cls_5[0.3257] det_loss_box_5[0.3396] det_loss_cls_dn_0[0.0877] det_loss_box_dn_0[0.2471] det_loss_cls_dn_1[0.0837] det_loss_box_dn_1[0.2049] det_loss_cls_dn_2[0.0826] det_loss_box_dn_2[0.1889] det_loss_cls_dn_3[0.0819] det_loss_box_dn_3[0.1869] det_loss_cls_dn_4[0.0811] det_loss_box_dn_4[0.1848] det_loss_cls_dn_5[0.0802] det_loss_box_dn_5[0.1813] loss_depth[0.0345] total_loss[6.4482]
Rank[0/8] 06/20/2025 19:07:51 INFO stats.py:314 | Epoch[220] Step[124] GlobalStep[85924] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 2:35:01.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:07:51 INFO loss_tracker.py:84 | Epoch[220/239] Step[124] GlobalStep[85924/NA]: det_loss_cls_0[0.4395] det_loss_box_0[0.5908] det_loss_cls_1[0.4285] det_loss_box_1[0.4587] det_loss_cls_2[0.3645] det_loss_box_2[0.4015] det_loss_cls_3[0.3280] det_loss_box_3[0.3933] det_loss_cls_4[0.3024] det_loss_box_4[0.3809] det_loss_cls_5[0.2945] det_loss_box_5[0.3765] det_loss_cls_dn_0[0.0845] det_loss_box_dn_0[0.2618] det_loss_cls_dn_1[0.0811] det_loss_box_dn_1[0.2218] det_loss_cls_dn_2[0.0800] det_loss_box_dn_2[0.2062] det_loss_cls_dn_3[0.0793] det_loss_box_dn_3[0.2035] det_loss_cls_dn_4[0.0782] det_loss_box_dn_4[0.2018] det_loss_cls_dn_5[0.0776] det_loss_box_dn_5[0.2006] loss_depth[0.0344] total_loss[6.5698]
Rank[0/8] 06/20/2025 19:08:16 INFO stats.py:314 | Epoch[220] Step[149] GlobalStep[85949] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 2:34:30.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:08:16 INFO loss_tracker.py:84 | Epoch[220/239] Step[149] GlobalStep[85949/NA]: det_loss_cls_0[0.3783] det_loss_box_0[0.4303] det_loss_cls_1[0.3599] det_loss_box_1[0.3102] det_loss_cls_2[0.3085] det_loss_box_2[0.2806] det_loss_cls_3[0.2735] det_loss_box_3[0.2752] det_loss_cls_4[0.2545] det_loss_box_4[0.2611] det_loss_cls_5[0.2426] det_loss_box_5[0.2644] det_loss_cls_dn_0[0.0850] det_loss_box_dn_0[0.2443] det_loss_cls_dn_1[0.0812] det_loss_box_dn_1[0.1931] det_loss_cls_dn_2[0.0804] det_loss_box_dn_2[0.1767] det_loss_cls_dn_3[0.0789] det_loss_box_dn_3[0.1705] det_loss_cls_dn_4[0.0780] det_loss_box_dn_4[0.1659] det_loss_cls_dn_5[0.0773] det_loss_box_dn_5[0.1651] loss_depth[0.0338] total_loss[5.2693]
Rank[0/8] 06/20/2025 19:08:42 INFO stats.py:314 | Epoch[220] Step[174] GlobalStep[85974] Training Speed: 8.12 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 2:34:00.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:08:42 INFO loss_tracker.py:84 | Epoch[220/239] Step[174] GlobalStep[85974/NA]: det_loss_cls_0[0.5305] det_loss_box_0[0.7072] det_loss_cls_1[0.5184] det_loss_box_1[0.5500] det_loss_cls_2[0.4594] det_loss_box_2[0.4956] det_loss_cls_3[0.4259] det_loss_box_3[0.4754] det_loss_cls_4[0.4015] det_loss_box_4[0.4540] det_loss_cls_5[0.3792] det_loss_box_5[0.4531] det_loss_cls_dn_0[0.0963] det_loss_box_dn_0[0.2964] det_loss_cls_dn_1[0.0924] det_loss_box_dn_1[0.2292] det_loss_cls_dn_2[0.0909] det_loss_box_dn_2[0.2088] det_loss_cls_dn_3[0.0900] det_loss_box_dn_3[0.2022] det_loss_cls_dn_4[0.0892] det_loss_box_dn_4[0.1990] det_loss_cls_dn_5[0.0883] det_loss_box_dn_5[0.1931] loss_depth[0.0339] total_loss[7.7599]
Rank[0/8] 06/20/2025 19:09:08 INFO stats.py:314 | Epoch[220] Step[199] GlobalStep[85999] Training Speed: 8.06 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 2:33:29.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:09:08 INFO loss_tracker.py:84 | Epoch[220/239] Step[199] GlobalStep[85999/NA]: det_loss_cls_0[0.3889] det_loss_box_0[0.6120] det_loss_cls_1[0.3784] det_loss_box_1[0.4383] det_loss_cls_2[0.3273] det_loss_box_2[0.3994] det_loss_cls_3[0.3059] det_loss_box_3[0.3925] det_loss_cls_4[0.2866] det_loss_box_4[0.3832] det_loss_cls_5[0.2719] det_loss_box_5[0.3904] det_loss_cls_dn_0[0.0945] det_loss_box_dn_0[0.3713] det_loss_cls_dn_1[0.0917] det_loss_box_dn_1[0.3036] det_loss_cls_dn_2[0.0910] det_loss_box_dn_2[0.2790] det_loss_cls_dn_3[0.0896] det_loss_box_dn_3[0.2699] det_loss_cls_dn_4[0.0888] det_loss_box_dn_4[0.2686] det_loss_cls_dn_5[0.0883] det_loss_box_dn_5[0.2630] loss_depth[0.0343] total_loss[6.9086]
Rank[0/8] 06/20/2025 19:09:34 INFO stats.py:314 | Epoch[220] Step[224] GlobalStep[86024] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 2:32:58.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:09:34 INFO loss_tracker.py:84 | Epoch[220/239] Step[224] GlobalStep[86024/NA]: det_loss_cls_0[0.3693] det_loss_box_0[0.5138] det_loss_cls_1[0.3602] det_loss_box_1[0.3727] det_loss_cls_2[0.3078] det_loss_box_2[0.3283] det_loss_cls_3[0.2838] det_loss_box_3[0.3196] det_loss_cls_4[0.2677] det_loss_box_4[0.3068] det_loss_cls_5[0.2566] det_loss_box_5[0.3086] det_loss_cls_dn_0[0.0900] det_loss_box_dn_0[0.3271] det_loss_cls_dn_1[0.0872] det_loss_box_dn_1[0.2709] det_loss_cls_dn_2[0.0864] det_loss_box_dn_2[0.2509] det_loss_cls_dn_3[0.0854] det_loss_box_dn_3[0.2446] det_loss_cls_dn_4[0.0845] det_loss_box_dn_4[0.2408] det_loss_cls_dn_5[0.0836] det_loss_box_dn_5[0.2351] loss_depth[0.0365] total_loss[6.1182]
Rank[0/8] 06/20/2025 19:09:59 INFO stats.py:314 | Epoch[220] Step[249] GlobalStep[86049] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 2:32:28.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:09:59 INFO loss_tracker.py:84 | Epoch[220/239] Step[249] GlobalStep[86049/NA]: det_loss_cls_0[0.4481] det_loss_box_0[0.5629] det_loss_cls_1[0.4355] det_loss_box_1[0.4159] det_loss_cls_2[0.3819] det_loss_box_2[0.3782] det_loss_cls_3[0.3578] det_loss_box_3[0.3503] det_loss_cls_4[0.3278] det_loss_box_4[0.3408] det_loss_cls_5[0.3148] det_loss_box_5[0.3426] det_loss_cls_dn_0[0.0929] det_loss_box_dn_0[0.2637] det_loss_cls_dn_1[0.0897] det_loss_box_dn_1[0.2176] det_loss_cls_dn_2[0.0881] det_loss_box_dn_2[0.2012] det_loss_cls_dn_3[0.0866] det_loss_box_dn_3[0.1985] det_loss_cls_dn_4[0.0856] det_loss_box_dn_4[0.1970] det_loss_cls_dn_5[0.0846] det_loss_box_dn_5[0.1958] loss_depth[0.0347] total_loss[6.4926]
Rank[0/8] 06/20/2025 19:10:25 INFO stats.py:314 | Epoch[220] Step[274] GlobalStep[86074] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 2:31:57.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:10:25 INFO loss_tracker.py:84 | Epoch[220/239] Step[274] GlobalStep[86074/NA]: det_loss_cls_0[0.4577] det_loss_box_0[0.5805] det_loss_cls_1[0.4512] det_loss_box_1[0.4546] det_loss_cls_2[0.3865] det_loss_box_2[0.4117] det_loss_cls_3[0.3549] det_loss_box_3[0.3990] det_loss_cls_4[0.3364] det_loss_box_4[0.3812] det_loss_cls_5[0.3151] det_loss_box_5[0.3840] det_loss_cls_dn_0[0.0898] det_loss_box_dn_0[0.2617] det_loss_cls_dn_1[0.0849] det_loss_box_dn_1[0.2194] det_loss_cls_dn_2[0.0844] det_loss_box_dn_2[0.2013] det_loss_cls_dn_3[0.0829] det_loss_box_dn_3[0.1966] det_loss_cls_dn_4[0.0824] det_loss_box_dn_4[0.1948] det_loss_cls_dn_5[0.0813] det_loss_box_dn_5[0.1933] loss_depth[0.0364] total_loss[6.7220]
Rank[0/8] 06/20/2025 19:10:51 INFO stats.py:314 | Epoch[220] Step[299] GlobalStep[86099] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 2:31:26.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:10:51 INFO loss_tracker.py:84 | Epoch[220/239] Step[299] GlobalStep[86099/NA]: det_loss_cls_0[0.4707] det_loss_box_0[0.5436] det_loss_cls_1[0.4475] det_loss_box_1[0.3983] det_loss_cls_2[0.3920] det_loss_box_2[0.3615] det_loss_cls_3[0.3553] det_loss_box_3[0.3534] det_loss_cls_4[0.3338] det_loss_box_4[0.3436] det_loss_cls_5[0.3145] det_loss_box_5[0.3482] det_loss_cls_dn_0[0.0839] det_loss_box_dn_0[0.2181] det_loss_cls_dn_1[0.0805] det_loss_box_dn_1[0.1680] det_loss_cls_dn_2[0.0794] det_loss_box_dn_2[0.1493] det_loss_cls_dn_3[0.0786] det_loss_box_dn_3[0.1447] det_loss_cls_dn_4[0.0784] det_loss_box_dn_4[0.1423] det_loss_cls_dn_5[0.0773] det_loss_box_dn_5[0.1404] loss_depth[0.0358] total_loss[6.1391]
Rank[0/8] 06/20/2025 19:11:17 INFO stats.py:314 | Epoch[220] Step[324] GlobalStep[86124] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 2:30:56.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:11:17 INFO loss_tracker.py:84 | Epoch[220/239] Step[324] GlobalStep[86124/NA]: det_loss_cls_0[0.4578] det_loss_box_0[0.6131] det_loss_cls_1[0.4471] det_loss_box_1[0.4386] det_loss_cls_2[0.3885] det_loss_box_2[0.3976] det_loss_cls_3[0.3448] det_loss_box_3[0.3892] det_loss_cls_4[0.3162] det_loss_box_4[0.3818] det_loss_cls_5[0.3012] det_loss_box_5[0.3889] det_loss_cls_dn_0[0.0786] det_loss_box_dn_0[0.2804] det_loss_cls_dn_1[0.0746] det_loss_box_dn_1[0.2265] det_loss_cls_dn_2[0.0735] det_loss_box_dn_2[0.2065] det_loss_cls_dn_3[0.0721] det_loss_box_dn_3[0.1997] det_loss_cls_dn_4[0.0716] det_loss_box_dn_4[0.1980] det_loss_cls_dn_5[0.0711] det_loss_box_dn_5[0.1901] loss_depth[0.0352] total_loss[6.6427]
Rank[0/8] 06/20/2025 19:11:43 INFO stats.py:314 | Epoch[220] Step[349] GlobalStep[86149] Training Speed: 8.04 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 2:30:25.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:11:43 INFO loss_tracker.py:84 | Epoch[220/239] Step[349] GlobalStep[86149/NA]: det_loss_cls_0[0.4540] det_loss_box_0[0.5886] det_loss_cls_1[0.4335] det_loss_box_1[0.4472] det_loss_cls_2[0.3781] det_loss_box_2[0.3997] det_loss_cls_3[0.3525] det_loss_box_3[0.3942] det_loss_cls_4[0.3319] det_loss_box_4[0.3792] det_loss_cls_5[0.3136] det_loss_box_5[0.3837] det_loss_cls_dn_0[0.0884] det_loss_box_dn_0[0.2869] det_loss_cls_dn_1[0.0860] det_loss_box_dn_1[0.2384] det_loss_cls_dn_2[0.0850] det_loss_box_dn_2[0.2219] det_loss_cls_dn_3[0.0839] det_loss_box_dn_3[0.2193] det_loss_cls_dn_4[0.0835] det_loss_box_dn_4[0.2166] det_loss_cls_dn_5[0.0829] det_loss_box_dn_5[0.2134] loss_depth[0.0365] total_loss[6.7989]
Rank[0/8] 06/20/2025 19:12:09 INFO stats.py:314 | Epoch[220] Step[374] GlobalStep[86174] Training Speed: 8.07 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 2:29:54.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:12:09 INFO loss_tracker.py:84 | Epoch[220/239] Step[374] GlobalStep[86174/NA]: det_loss_cls_0[0.4788] det_loss_box_0[0.5706] det_loss_cls_1[0.4609] det_loss_box_1[0.4174] det_loss_cls_2[0.3842] det_loss_box_2[0.3796] det_loss_cls_3[0.3460] det_loss_box_3[0.3708] det_loss_cls_4[0.3227] det_loss_box_4[0.3658] det_loss_cls_5[0.3089] det_loss_box_5[0.3690] det_loss_cls_dn_0[0.0788] det_loss_box_dn_0[0.2195] det_loss_cls_dn_1[0.0757] det_loss_box_dn_1[0.1774] det_loss_cls_dn_2[0.0748] det_loss_box_dn_2[0.1613] det_loss_cls_dn_3[0.0740] det_loss_box_dn_3[0.1563] det_loss_cls_dn_4[0.0731] det_loss_box_dn_4[0.1533] det_loss_cls_dn_5[0.0724] det_loss_box_dn_5[0.1508] loss_depth[0.0345] total_loss[6.2768]
Rank[0/8] 06/20/2025 19:12:24 INFO stats.py:394 | Epoch[220] completed. Training Speed: 7.69 samples/sec across all devices.	Epoch Time: 405.57 sec.	Average Epoch Time: 405.57 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 2:29:36.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:12:36 INFO stats.py:314 | Epoch[221] Step[9] GlobalStep[86199] Training Speed: 6.00 samples/sec across all devices.	Average Step Time: 1.33 sec.	Estimated Remaining Time: 2:29:24.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:12:52 INFO loss_tracker.py:84 | Epoch[221/239] Step[24] GlobalStep[86214/NA]: det_loss_cls_0[0.4923] det_loss_box_0[0.5821] det_loss_cls_1[0.4747] det_loss_box_1[0.4377] det_loss_cls_2[0.4122] det_loss_box_2[0.3992] det_loss_cls_3[0.3749] det_loss_box_3[0.3893] det_loss_cls_4[0.3482] det_loss_box_4[0.3831] det_loss_cls_5[0.3343] det_loss_box_5[0.3811] det_loss_cls_dn_0[0.0861] det_loss_box_dn_0[0.2627] det_loss_cls_dn_1[0.0827] det_loss_box_dn_1[0.2161] det_loss_cls_dn_2[0.0815] det_loss_box_dn_2[0.1989] det_loss_cls_dn_3[0.0804] det_loss_box_dn_3[0.1958] det_loss_cls_dn_4[0.0794] det_loss_box_dn_4[0.1934] det_loss_cls_dn_5[0.0788] det_loss_box_dn_5[0.1912] loss_depth[0.0324] total_loss[6.7884]
Rank[0/8] 06/20/2025 19:13:02 INFO stats.py:314 | Epoch[221] Step[34] GlobalStep[86224] Training Speed: 8.12 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 2:28:53.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:13:18 INFO loss_tracker.py:84 | Epoch[221/239] Step[49] GlobalStep[86239/NA]: det_loss_cls_0[0.4517] det_loss_box_0[0.5309] det_loss_cls_1[0.4308] det_loss_box_1[0.3865] det_loss_cls_2[0.3749] det_loss_box_2[0.3562] det_loss_cls_3[0.3411] det_loss_box_3[0.3381] det_loss_cls_4[0.3118] det_loss_box_4[0.3390] det_loss_cls_5[0.3011] det_loss_box_5[0.3371] det_loss_cls_dn_0[0.0853] det_loss_box_dn_0[0.2474] det_loss_cls_dn_1[0.0833] det_loss_box_dn_1[0.1950] det_loss_cls_dn_2[0.0822] det_loss_box_dn_2[0.1783] det_loss_cls_dn_3[0.0810] det_loss_box_dn_3[0.1724] det_loss_cls_dn_4[0.0806] det_loss_box_dn_4[0.1705] det_loss_cls_dn_5[0.0798] det_loss_box_dn_5[0.1686] loss_depth[0.0365] total_loss[6.1600]
Rank[0/8] 06/20/2025 19:13:29 INFO stats.py:314 | Epoch[221] Step[59] GlobalStep[86249] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 2:28:22.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:13:44 INFO loss_tracker.py:84 | Epoch[221/239] Step[74] GlobalStep[86264/NA]: det_loss_cls_0[0.4815] det_loss_box_0[0.5519] det_loss_cls_1[0.4678] det_loss_box_1[0.4182] det_loss_cls_2[0.4163] det_loss_box_2[0.3794] det_loss_cls_3[0.3842] det_loss_box_3[0.3639] det_loss_cls_4[0.3500] det_loss_box_4[0.3571] det_loss_cls_5[0.3368] det_loss_box_5[0.3571] det_loss_cls_dn_0[0.0855] det_loss_box_dn_0[0.3182] det_loss_cls_dn_1[0.0819] det_loss_box_dn_1[0.2634] det_loss_cls_dn_2[0.0806] det_loss_box_dn_2[0.2309] det_loss_cls_dn_3[0.0800] det_loss_box_dn_3[0.2257] det_loss_cls_dn_4[0.0793] det_loss_box_dn_4[0.2232] det_loss_cls_dn_5[0.0786] det_loss_box_dn_5[0.2192] loss_depth[0.0326] total_loss[6.8632]
Rank[0/8] 06/20/2025 19:13:55 INFO stats.py:314 | Epoch[221] Step[84] GlobalStep[86274] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 2:27:52.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:14:11 INFO loss_tracker.py:84 | Epoch[221/239] Step[99] GlobalStep[86289/NA]: det_loss_cls_0[0.5017] det_loss_box_0[0.6422] det_loss_cls_1[0.4907] det_loss_box_1[0.4812] det_loss_cls_2[0.4293] det_loss_box_2[0.4433] det_loss_cls_3[0.4026] det_loss_box_3[0.4295] det_loss_cls_4[0.3800] det_loss_box_4[0.4242] det_loss_cls_5[0.3618] det_loss_box_5[0.4261] det_loss_cls_dn_0[0.0840] det_loss_box_dn_0[0.2740] det_loss_cls_dn_1[0.0797] det_loss_box_dn_1[0.2282] det_loss_cls_dn_2[0.0786] det_loss_box_dn_2[0.2092] det_loss_cls_dn_3[0.0772] det_loss_box_dn_3[0.2053] det_loss_cls_dn_4[0.0766] det_loss_box_dn_4[0.2027] det_loss_cls_dn_5[0.0758] det_loss_box_dn_5[0.1988] loss_depth[0.0361] total_loss[7.2387]
Rank[0/8] 06/20/2025 19:14:21 INFO stats.py:314 | Epoch[221] Step[109] GlobalStep[86299] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 2:27:21.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:14:36 INFO loss_tracker.py:84 | Epoch[221/239] Step[124] GlobalStep[86314/NA]: det_loss_cls_0[0.5014] det_loss_box_0[0.5720] det_loss_cls_1[0.4772] det_loss_box_1[0.4445] det_loss_cls_2[0.4116] det_loss_box_2[0.4135] det_loss_cls_3[0.3858] det_loss_box_3[0.3907] det_loss_cls_4[0.3708] det_loss_box_4[0.3770] det_loss_cls_5[0.3562] det_loss_box_5[0.3792] det_loss_cls_dn_0[0.0911] det_loss_box_dn_0[0.2490] det_loss_cls_dn_1[0.0878] det_loss_box_dn_1[0.2075] det_loss_cls_dn_2[0.0869] det_loss_box_dn_2[0.1921] det_loss_cls_dn_3[0.0860] det_loss_box_dn_3[0.1851] det_loss_cls_dn_4[0.0851] det_loss_box_dn_4[0.1840] det_loss_cls_dn_5[0.0848] det_loss_box_dn_5[0.1775] loss_depth[0.0377] total_loss[6.8347]
Rank[0/8] 06/20/2025 19:14:47 INFO stats.py:314 | Epoch[221] Step[134] GlobalStep[86324] Training Speed: 7.86 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 2:26:51.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:15:02 INFO loss_tracker.py:84 | Epoch[221/239] Step[149] GlobalStep[86339/NA]: det_loss_cls_0[0.3653] det_loss_box_0[0.5000] det_loss_cls_1[0.3641] det_loss_box_1[0.3779] det_loss_cls_2[0.3155] det_loss_box_2[0.3463] det_loss_cls_3[0.2812] det_loss_box_3[0.3414] det_loss_cls_4[0.2696] det_loss_box_4[0.3326] det_loss_cls_5[0.2605] det_loss_box_5[0.3302] det_loss_cls_dn_0[0.0765] det_loss_box_dn_0[0.2576] det_loss_cls_dn_1[0.0726] det_loss_box_dn_1[0.2142] det_loss_cls_dn_2[0.0719] det_loss_box_dn_2[0.1978] det_loss_cls_dn_3[0.0713] det_loss_box_dn_3[0.1938] det_loss_cls_dn_4[0.0707] det_loss_box_dn_4[0.1916] det_loss_cls_dn_5[0.0701] det_loss_box_dn_5[0.1866] loss_depth[0.0341] total_loss[5.7934]
Rank[0/8] 06/20/2025 19:15:12 INFO stats.py:314 | Epoch[221] Step[159] GlobalStep[86349] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 2:26:20.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:15:28 INFO loss_tracker.py:84 | Epoch[221/239] Step[174] GlobalStep[86364/NA]: det_loss_cls_0[0.4287] det_loss_box_0[0.5883] det_loss_cls_1[0.4196] det_loss_box_1[0.4449] det_loss_cls_2[0.3616] det_loss_box_2[0.3958] det_loss_cls_3[0.3307] det_loss_box_3[0.3780] det_loss_cls_4[0.3132] det_loss_box_4[0.3676] det_loss_cls_5[0.3030] det_loss_box_5[0.3637] det_loss_cls_dn_0[0.0841] det_loss_box_dn_0[0.2606] det_loss_cls_dn_1[0.0803] det_loss_box_dn_1[0.2048] det_loss_cls_dn_2[0.0798] det_loss_box_dn_2[0.1862] det_loss_cls_dn_3[0.0792] det_loss_box_dn_3[0.1830] det_loss_cls_dn_4[0.0782] det_loss_box_dn_4[0.1790] det_loss_cls_dn_5[0.0776] det_loss_box_dn_5[0.1774] loss_depth[0.0370] total_loss[6.4023]
Rank[0/8] 06/20/2025 19:15:38 INFO stats.py:314 | Epoch[221] Step[184] GlobalStep[86374] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 2:25:49.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:15:54 INFO loss_tracker.py:84 | Epoch[221/239] Step[199] GlobalStep[86389/NA]: det_loss_cls_0[0.4176] det_loss_box_0[0.5575] det_loss_cls_1[0.4066] det_loss_box_1[0.4125] det_loss_cls_2[0.3257] det_loss_box_2[0.3645] det_loss_cls_3[0.2970] det_loss_box_3[0.3474] det_loss_cls_4[0.2702] det_loss_box_4[0.3424] det_loss_cls_5[0.2552] det_loss_box_5[0.3385] det_loss_cls_dn_0[0.0792] det_loss_box_dn_0[0.2975] det_loss_cls_dn_1[0.0747] det_loss_box_dn_1[0.2435] det_loss_cls_dn_2[0.0734] det_loss_box_dn_2[0.2293] det_loss_cls_dn_3[0.0723] det_loss_box_dn_3[0.2229] det_loss_cls_dn_4[0.0714] det_loss_box_dn_4[0.2216] det_loss_cls_dn_5[0.0706] det_loss_box_dn_5[0.2138] loss_depth[0.0364] total_loss[6.2417]
Rank[0/8] 06/20/2025 19:16:05 INFO stats.py:314 | Epoch[221] Step[209] GlobalStep[86399] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 2:25:19.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:16:20 INFO loss_tracker.py:84 | Epoch[221/239] Step[224] GlobalStep[86414/NA]: det_loss_cls_0[0.4539] det_loss_box_0[0.5808] det_loss_cls_1[0.4358] det_loss_box_1[0.4262] det_loss_cls_2[0.3588] det_loss_box_2[0.3974] det_loss_cls_3[0.3334] det_loss_box_3[0.3834] det_loss_cls_4[0.3084] det_loss_box_4[0.3718] det_loss_cls_5[0.2928] det_loss_box_5[0.3739] det_loss_cls_dn_0[0.0878] det_loss_box_dn_0[0.2755] det_loss_cls_dn_1[0.0846] det_loss_box_dn_1[0.2216] det_loss_cls_dn_2[0.0837] det_loss_box_dn_2[0.2034] det_loss_cls_dn_3[0.0827] det_loss_box_dn_3[0.1971] det_loss_cls_dn_4[0.0819] det_loss_box_dn_4[0.1950] det_loss_cls_dn_5[0.0812] det_loss_box_dn_5[0.1924] loss_depth[0.0352] total_loss[6.5386]
Rank[0/8] 06/20/2025 19:16:30 INFO stats.py:314 | Epoch[221] Step[234] GlobalStep[86424] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 2:24:48.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:16:46 INFO loss_tracker.py:84 | Epoch[221/239] Step[249] GlobalStep[86439/NA]: det_loss_cls_0[0.4235] det_loss_box_0[0.6078] det_loss_cls_1[0.4156] det_loss_box_1[0.4513] det_loss_cls_2[0.3541] det_loss_box_2[0.4141] det_loss_cls_3[0.3359] det_loss_box_3[0.3868] det_loss_cls_4[0.3113] det_loss_box_4[0.3901] det_loss_cls_5[0.2969] det_loss_box_5[0.3923] det_loss_cls_dn_0[0.0962] det_loss_box_dn_0[0.4414] det_loss_cls_dn_1[0.0919] det_loss_box_dn_1[0.3560] det_loss_cls_dn_2[0.0917] det_loss_box_dn_2[0.3290] det_loss_cls_dn_3[0.0901] det_loss_box_dn_3[0.3143] det_loss_cls_dn_4[0.0889] det_loss_box_dn_4[0.3118] det_loss_cls_dn_5[0.0881] det_loss_box_dn_5[0.3067] loss_depth[0.0374] total_loss[7.4233]
Rank[0/8] 06/20/2025 19:16:56 INFO stats.py:314 | Epoch[221] Step[259] GlobalStep[86449] Training Speed: 8.69 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 2:24:17.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:17:12 INFO loss_tracker.py:84 | Epoch[221/239] Step[274] GlobalStep[86464/NA]: det_loss_cls_0[0.4438] det_loss_box_0[0.5727] det_loss_cls_1[0.4318] det_loss_box_1[0.4524] det_loss_cls_2[0.3803] det_loss_box_2[0.4133] det_loss_cls_3[0.3539] det_loss_box_3[0.4053] det_loss_cls_4[0.3313] det_loss_box_4[0.3996] det_loss_cls_5[0.3203] det_loss_box_5[0.4042] det_loss_cls_dn_0[0.0883] det_loss_box_dn_0[0.2800] det_loss_cls_dn_1[0.0845] det_loss_box_dn_1[0.2292] det_loss_cls_dn_2[0.0835] det_loss_box_dn_2[0.2038] det_loss_cls_dn_3[0.0827] det_loss_box_dn_3[0.1989] det_loss_cls_dn_4[0.0819] det_loss_box_dn_4[0.1970] det_loss_cls_dn_5[0.0810] det_loss_box_dn_5[0.1938] loss_depth[0.0373] total_loss[6.7507]
Rank[0/8] 06/20/2025 19:17:22 INFO stats.py:314 | Epoch[221] Step[284] GlobalStep[86474] Training Speed: 8.06 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 2:23:47.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:17:38 INFO loss_tracker.py:84 | Epoch[221/239] Step[299] GlobalStep[86489/NA]: det_loss_cls_0[0.4126] det_loss_box_0[0.4847] det_loss_cls_1[0.3907] det_loss_box_1[0.3558] det_loss_cls_2[0.3295] det_loss_box_2[0.3274] det_loss_cls_3[0.2937] det_loss_box_3[0.3203] det_loss_cls_4[0.2743] det_loss_box_4[0.3095] det_loss_cls_5[0.2630] det_loss_box_5[0.3105] det_loss_cls_dn_0[0.0810] det_loss_box_dn_0[0.2118] det_loss_cls_dn_1[0.0783] det_loss_box_dn_1[0.1666] det_loss_cls_dn_2[0.0777] det_loss_box_dn_2[0.1493] det_loss_cls_dn_3[0.0768] det_loss_box_dn_3[0.1461] det_loss_cls_dn_4[0.0761] det_loss_box_dn_4[0.1444] det_loss_cls_dn_5[0.0754] det_loss_box_dn_5[0.1427] loss_depth[0.0339] total_loss[5.5320]
Rank[0/8] 06/20/2025 19:17:48 INFO stats.py:314 | Epoch[221] Step[309] GlobalStep[86499] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 2:23:16.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:18:03 INFO loss_tracker.py:84 | Epoch[221/239] Step[324] GlobalStep[86514/NA]: det_loss_cls_0[0.4741] det_loss_box_0[0.5580] det_loss_cls_1[0.4602] det_loss_box_1[0.4109] det_loss_cls_2[0.4065] det_loss_box_2[0.3674] det_loss_cls_3[0.3686] det_loss_box_3[0.3560] det_loss_cls_4[0.3464] det_loss_box_4[0.3479] det_loss_cls_5[0.3373] det_loss_box_5[0.3480] det_loss_cls_dn_0[0.1000] det_loss_box_dn_0[0.3353] det_loss_cls_dn_1[0.0961] det_loss_box_dn_1[0.2647] det_loss_cls_dn_2[0.0954] det_loss_box_dn_2[0.2420] det_loss_cls_dn_3[0.0942] det_loss_box_dn_3[0.2353] det_loss_cls_dn_4[0.0935] det_loss_box_dn_4[0.2306] det_loss_cls_dn_5[0.0928] det_loss_box_dn_5[0.2291] loss_depth[0.0354] total_loss[6.9256]
Rank[0/8] 06/20/2025 19:18:13 INFO stats.py:314 | Epoch[221] Step[334] GlobalStep[86524] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 2:22:45.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:18:29 INFO loss_tracker.py:84 | Epoch[221/239] Step[349] GlobalStep[86539/NA]: det_loss_cls_0[0.5254] det_loss_box_0[0.6109] det_loss_cls_1[0.5191] det_loss_box_1[0.4502] det_loss_cls_2[0.4421] det_loss_box_2[0.4108] det_loss_cls_3[0.4052] det_loss_box_3[0.4026] det_loss_cls_4[0.3801] det_loss_box_4[0.3961] det_loss_cls_5[0.3697] det_loss_box_5[0.3948] det_loss_cls_dn_0[0.0931] det_loss_box_dn_0[0.2220] det_loss_cls_dn_1[0.0884] det_loss_box_dn_1[0.1796] det_loss_cls_dn_2[0.0875] det_loss_box_dn_2[0.1652] det_loss_cls_dn_3[0.0870] det_loss_box_dn_3[0.1618] det_loss_cls_dn_4[0.0867] det_loss_box_dn_4[0.1595] det_loss_cls_dn_5[0.0859] det_loss_box_dn_5[0.1581] loss_depth[0.0352] total_loss[6.9171]
Rank[0/8] 06/20/2025 19:18:40 INFO stats.py:314 | Epoch[221] Step[359] GlobalStep[86549] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 2:22:15.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:18:56 INFO loss_tracker.py:84 | Epoch[221/239] Step[374] GlobalStep[86564/NA]: det_loss_cls_0[0.4704] det_loss_box_0[0.5136] det_loss_cls_1[0.4432] det_loss_box_1[0.4024] det_loss_cls_2[0.3772] det_loss_box_2[0.3775] det_loss_cls_3[0.3463] det_loss_box_3[0.3608] det_loss_cls_4[0.3170] det_loss_box_4[0.3554] det_loss_cls_5[0.3016] det_loss_box_5[0.3551] det_loss_cls_dn_0[0.0782] det_loss_box_dn_0[0.2414] det_loss_cls_dn_1[0.0739] det_loss_box_dn_1[0.1963] det_loss_cls_dn_2[0.0729] det_loss_box_dn_2[0.1761] det_loss_cls_dn_3[0.0722] det_loss_box_dn_3[0.1733] det_loss_cls_dn_4[0.0715] det_loss_box_dn_4[0.1710] det_loss_cls_dn_5[0.0707] det_loss_box_dn_5[0.1703] loss_depth[0.0344] total_loss[6.2228]
Rank[0/8] 06/20/2025 19:19:05 INFO stats.py:314 | Epoch[221] Step[384] GlobalStep[86574] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 2:21:44.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:19:10 INFO stats.py:394 | Epoch[221] completed. Training Speed: 7.68 samples/sec across all devices.	Epoch Time: 406.34 sec.	Average Epoch Time: 406.34 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 2:21:38.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:19:32 INFO stats.py:314 | Epoch[222] Step[19] GlobalStep[86599] Training Speed: 7.40 samples/sec across all devices.	Average Step Time: 1.08 sec.	Estimated Remaining Time: 2:21:14.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:19:38 INFO loss_tracker.py:84 | Epoch[222/239] Step[24] GlobalStep[86604/NA]: det_loss_cls_0[0.3947] det_loss_box_0[0.5148] det_loss_cls_1[0.3779] det_loss_box_1[0.3960] det_loss_cls_2[0.3228] det_loss_box_2[0.3573] det_loss_cls_3[0.2906] det_loss_box_3[0.3472] det_loss_cls_4[0.2734] det_loss_box_4[0.3330] det_loss_cls_5[0.2616] det_loss_box_5[0.3318] det_loss_cls_dn_0[0.0845] det_loss_box_dn_0[0.2916] det_loss_cls_dn_1[0.0813] det_loss_box_dn_1[0.2379] det_loss_cls_dn_2[0.0803] det_loss_box_dn_2[0.2164] det_loss_cls_dn_3[0.0793] det_loss_box_dn_3[0.2128] det_loss_cls_dn_4[0.0786] det_loss_box_dn_4[0.2101] det_loss_cls_dn_5[0.0779] det_loss_box_dn_5[0.2057] loss_depth[0.0355] total_loss[6.0929]
Rank[0/8] 06/20/2025 19:19:59 INFO stats.py:314 | Epoch[222] Step[44] GlobalStep[86624] Training Speed: 8.61 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 2:20:43.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:20:04 INFO loss_tracker.py:84 | Epoch[222/239] Step[49] GlobalStep[86629/NA]: det_loss_cls_0[0.5231] det_loss_box_0[0.6049] det_loss_cls_1[0.5090] det_loss_box_1[0.4671] det_loss_cls_2[0.4531] det_loss_box_2[0.4263] det_loss_cls_3[0.4142] det_loss_box_3[0.4202] det_loss_cls_4[0.3948] det_loss_box_4[0.4053] det_loss_cls_5[0.3790] det_loss_box_5[0.4077] det_loss_cls_dn_0[0.0909] det_loss_box_dn_0[0.2299] det_loss_cls_dn_1[0.0868] det_loss_box_dn_1[0.1895] det_loss_cls_dn_2[0.0861] det_loss_box_dn_2[0.1735] det_loss_cls_dn_3[0.0848] det_loss_box_dn_3[0.1702] det_loss_cls_dn_4[0.0841] det_loss_box_dn_4[0.1677] det_loss_cls_dn_5[0.0832] det_loss_box_dn_5[0.1653] loss_depth[0.0341] total_loss[7.0507]
Rank[0/8] 06/20/2025 19:20:24 INFO stats.py:314 | Epoch[222] Step[69] GlobalStep[86649] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 2:20:12.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:20:30 INFO loss_tracker.py:84 | Epoch[222/239] Step[74] GlobalStep[86654/NA]: det_loss_cls_0[0.5044] det_loss_box_0[0.6431] det_loss_cls_1[0.4740] det_loss_box_1[0.4861] det_loss_cls_2[0.4026] det_loss_box_2[0.4461] det_loss_cls_3[0.3820] det_loss_box_3[0.4219] det_loss_cls_4[0.3487] det_loss_box_4[0.4166] det_loss_cls_5[0.3318] det_loss_box_5[0.4168] det_loss_cls_dn_0[0.0900] det_loss_box_dn_0[0.2559] det_loss_cls_dn_1[0.0864] det_loss_box_dn_1[0.2110] det_loss_cls_dn_2[0.0854] det_loss_box_dn_2[0.1937] det_loss_cls_dn_3[0.0836] det_loss_box_dn_3[0.1901] det_loss_cls_dn_4[0.0831] det_loss_box_dn_4[0.1873] det_loss_cls_dn_5[0.0818] det_loss_box_dn_5[0.1861] loss_depth[0.0348] total_loss[7.0433]
Rank[0/8] 06/20/2025 19:20:50 INFO stats.py:314 | Epoch[222] Step[94] GlobalStep[86674] Training Speed: 8.57 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 2:19:42.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:20:55 INFO loss_tracker.py:84 | Epoch[222/239] Step[99] GlobalStep[86679/NA]: det_loss_cls_0[0.4631] det_loss_box_0[0.5168] det_loss_cls_1[0.4626] det_loss_box_1[0.3841] det_loss_cls_2[0.3917] det_loss_box_2[0.3529] det_loss_cls_3[0.3580] det_loss_box_3[0.3356] det_loss_cls_4[0.3249] det_loss_box_4[0.3319] det_loss_cls_5[0.3177] det_loss_box_5[0.3302] det_loss_cls_dn_0[0.0934] det_loss_box_dn_0[0.2478] det_loss_cls_dn_1[0.0899] det_loss_box_dn_1[0.1975] det_loss_cls_dn_2[0.0890] det_loss_box_dn_2[0.1804] det_loss_cls_dn_3[0.0877] det_loss_box_dn_3[0.1767] det_loss_cls_dn_4[0.0868] det_loss_box_dn_4[0.1743] det_loss_cls_dn_5[0.0859] det_loss_box_dn_5[0.1730] loss_depth[0.0343] total_loss[6.2862]
Rank[0/8] 06/20/2025 19:21:16 INFO stats.py:314 | Epoch[222] Step[119] GlobalStep[86699] Training Speed: 7.88 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 2:19:11.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:21:21 INFO loss_tracker.py:84 | Epoch[222/239] Step[124] GlobalStep[86704/NA]: det_loss_cls_0[0.3994] det_loss_box_0[0.5164] det_loss_cls_1[0.3830] det_loss_box_1[0.3742] det_loss_cls_2[0.3280] det_loss_box_2[0.3228] det_loss_cls_3[0.2951] det_loss_box_3[0.3134] det_loss_cls_4[0.2711] det_loss_box_4[0.3122] det_loss_cls_5[0.2650] det_loss_box_5[0.3117] det_loss_cls_dn_0[0.0826] det_loss_box_dn_0[0.2427] det_loss_cls_dn_1[0.0790] det_loss_box_dn_1[0.1918] det_loss_cls_dn_2[0.0781] det_loss_box_dn_2[0.1727] det_loss_cls_dn_3[0.0772] det_loss_box_dn_3[0.1705] det_loss_cls_dn_4[0.0764] det_loss_box_dn_4[0.1681] det_loss_cls_dn_5[0.0757] det_loss_box_dn_5[0.1663] loss_depth[0.0377] total_loss[5.7112]
Rank[0/8] 06/20/2025 19:21:42 INFO stats.py:314 | Epoch[222] Step[144] GlobalStep[86724] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 2:18:41.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:21:47 INFO loss_tracker.py:84 | Epoch[222/239] Step[149] GlobalStep[86729/NA]: det_loss_cls_0[0.4112] det_loss_box_0[0.4771] det_loss_cls_1[0.3978] det_loss_box_1[0.3579] det_loss_cls_2[0.3392] det_loss_box_2[0.3240] det_loss_cls_3[0.3118] det_loss_box_3[0.3164] det_loss_cls_4[0.2981] det_loss_box_4[0.3030] det_loss_cls_5[0.2847] det_loss_box_5[0.3072] det_loss_cls_dn_0[0.0932] det_loss_box_dn_0[0.2663] det_loss_cls_dn_1[0.0897] det_loss_box_dn_1[0.2096] det_loss_cls_dn_2[0.0888] det_loss_box_dn_2[0.1916] det_loss_cls_dn_3[0.0874] det_loss_box_dn_3[0.1867] det_loss_cls_dn_4[0.0865] det_loss_box_dn_4[0.1841] det_loss_cls_dn_5[0.0859] det_loss_box_dn_5[0.1809] loss_depth[0.0326] total_loss[5.9119]
Rank[0/8] 06/20/2025 19:22:08 INFO stats.py:314 | Epoch[222] Step[169] GlobalStep[86749] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 2:18:10.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:22:12 INFO loss_tracker.py:84 | Epoch[222/239] Step[174] GlobalStep[86754/NA]: det_loss_cls_0[0.4746] det_loss_box_0[0.5547] det_loss_cls_1[0.4478] det_loss_box_1[0.4151] det_loss_cls_2[0.3738] det_loss_box_2[0.3872] det_loss_cls_3[0.3464] det_loss_box_3[0.3750] det_loss_cls_4[0.3163] det_loss_box_4[0.3667] det_loss_cls_5[0.3045] det_loss_box_5[0.3679] det_loss_cls_dn_0[0.0832] det_loss_box_dn_0[0.2611] det_loss_cls_dn_1[0.0798] det_loss_box_dn_1[0.2175] det_loss_cls_dn_2[0.0802] det_loss_box_dn_2[0.2013] det_loss_cls_dn_3[0.0784] det_loss_box_dn_3[0.1951] det_loss_cls_dn_4[0.0779] det_loss_box_dn_4[0.1913] det_loss_cls_dn_5[0.0780] det_loss_box_dn_5[0.1821] loss_depth[0.0340] total_loss[6.4899]
Rank[0/8] 06/20/2025 19:22:33 INFO stats.py:314 | Epoch[222] Step[194] GlobalStep[86774] Training Speed: 6.89 samples/sec across all devices.	Average Step Time: 1.16 sec.	Estimated Remaining Time: 2:17:39.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:22:39 INFO loss_tracker.py:84 | Epoch[222/239] Step[199] GlobalStep[86779/NA]: det_loss_cls_0[0.4111] det_loss_box_0[0.5235] det_loss_cls_1[0.4103] det_loss_box_1[0.3977] det_loss_cls_2[0.3452] det_loss_box_2[0.3492] det_loss_cls_3[0.3075] det_loss_box_3[0.3419] det_loss_cls_4[0.2899] det_loss_box_4[0.3335] det_loss_cls_5[0.2799] det_loss_box_5[0.3309] det_loss_cls_dn_0[0.0853] det_loss_box_dn_0[0.2610] det_loss_cls_dn_1[0.0811] det_loss_box_dn_1[0.2230] det_loss_cls_dn_2[0.0803] det_loss_box_dn_2[0.2035] det_loss_cls_dn_3[0.0796] det_loss_box_dn_3[0.1983] det_loss_cls_dn_4[0.0787] det_loss_box_dn_4[0.1961] det_loss_cls_dn_5[0.0782] det_loss_box_dn_5[0.1929] loss_depth[0.0331] total_loss[6.1117]
Rank[0/8] 06/20/2025 19:22:59 INFO stats.py:314 | Epoch[222] Step[219] GlobalStep[86799] Training Speed: 7.35 samples/sec across all devices.	Average Step Time: 1.09 sec.	Estimated Remaining Time: 2:17:09.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:23:04 INFO loss_tracker.py:84 | Epoch[222/239] Step[224] GlobalStep[86804/NA]: det_loss_cls_0[0.3960] det_loss_box_0[0.4979] det_loss_cls_1[0.3859] det_loss_box_1[0.3801] det_loss_cls_2[0.3361] det_loss_box_2[0.3320] det_loss_cls_3[0.3019] det_loss_box_3[0.3342] det_loss_cls_4[0.2858] det_loss_box_4[0.3218] det_loss_cls_5[0.2752] det_loss_box_5[0.3216] det_loss_cls_dn_0[0.0852] det_loss_box_dn_0[0.2878] det_loss_cls_dn_1[0.0820] det_loss_box_dn_1[0.2340] det_loss_cls_dn_2[0.0817] det_loss_box_dn_2[0.2139] det_loss_cls_dn_3[0.0815] det_loss_box_dn_3[0.2095] det_loss_cls_dn_4[0.0806] det_loss_box_dn_4[0.2076] det_loss_cls_dn_5[0.0797] det_loss_box_dn_5[0.2044] loss_depth[0.0332] total_loss[6.0496]
Rank[0/8] 06/20/2025 19:23:25 INFO stats.py:314 | Epoch[222] Step[244] GlobalStep[86824] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 2:16:38.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:23:30 INFO loss_tracker.py:84 | Epoch[222/239] Step[249] GlobalStep[86829/NA]: det_loss_cls_0[0.4384] det_loss_box_0[0.5675] det_loss_cls_1[0.4253] det_loss_box_1[0.4296] det_loss_cls_2[0.3637] det_loss_box_2[0.3988] det_loss_cls_3[0.3356] det_loss_box_3[0.3858] det_loss_cls_4[0.3163] det_loss_box_4[0.3733] det_loss_cls_5[0.3052] det_loss_box_5[0.3737] det_loss_cls_dn_0[0.0911] det_loss_box_dn_0[0.2737] det_loss_cls_dn_1[0.0877] det_loss_box_dn_1[0.2225] det_loss_cls_dn_2[0.0865] det_loss_box_dn_2[0.2039] det_loss_cls_dn_3[0.0858] det_loss_box_dn_3[0.1979] det_loss_cls_dn_4[0.0851] det_loss_box_dn_4[0.1956] det_loss_cls_dn_5[0.0845] det_loss_box_dn_5[0.1924] loss_depth[0.0357] total_loss[6.5555]
Rank[0/8] 06/20/2025 19:23:51 INFO stats.py:314 | Epoch[222] Step[269] GlobalStep[86849] Training Speed: 6.03 samples/sec across all devices.	Average Step Time: 1.33 sec.	Estimated Remaining Time: 2:16:08.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:23:56 INFO loss_tracker.py:84 | Epoch[222/239] Step[274] GlobalStep[86854/NA]: det_loss_cls_0[0.4831] det_loss_box_0[0.5883] det_loss_cls_1[0.4713] det_loss_box_1[0.4449] det_loss_cls_2[0.4114] det_loss_box_2[0.4065] det_loss_cls_3[0.3756] det_loss_box_3[0.3932] det_loss_cls_4[0.3527] det_loss_box_4[0.3807] det_loss_cls_5[0.3387] det_loss_box_5[0.3795] det_loss_cls_dn_0[0.0923] det_loss_box_dn_0[0.2558] det_loss_cls_dn_1[0.0881] det_loss_box_dn_1[0.2123] det_loss_cls_dn_2[0.0869] det_loss_box_dn_2[0.1961] det_loss_cls_dn_3[0.0855] det_loss_box_dn_3[0.1917] det_loss_cls_dn_4[0.0847] det_loss_box_dn_4[0.1896] det_loss_cls_dn_5[0.0840] det_loss_box_dn_5[0.1885] loss_depth[0.0347] total_loss[6.8159]
Rank[0/8] 06/20/2025 19:24:17 INFO stats.py:314 | Epoch[222] Step[294] GlobalStep[86874] Training Speed: 7.88 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 2:15:37.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:24:22 INFO loss_tracker.py:84 | Epoch[222/239] Step[299] GlobalStep[86879/NA]: det_loss_cls_0[0.4357] det_loss_box_0[0.5319] det_loss_cls_1[0.4239] det_loss_box_1[0.4140] det_loss_cls_2[0.3766] det_loss_box_2[0.3654] det_loss_cls_3[0.3420] det_loss_box_3[0.3512] det_loss_cls_4[0.3179] det_loss_box_4[0.3402] det_loss_cls_5[0.3015] det_loss_box_5[0.3401] det_loss_cls_dn_0[0.0893] det_loss_box_dn_0[0.2383] det_loss_cls_dn_1[0.0861] det_loss_box_dn_1[0.1944] det_loss_cls_dn_2[0.0855] det_loss_box_dn_2[0.1754] det_loss_cls_dn_3[0.0842] det_loss_box_dn_3[0.1713] det_loss_cls_dn_4[0.0834] det_loss_box_dn_4[0.1689] det_loss_cls_dn_5[0.0817] det_loss_box_dn_5[0.1673] loss_depth[0.0325] total_loss[6.1987]
Rank[0/8] 06/20/2025 19:24:42 INFO stats.py:314 | Epoch[222] Step[319] GlobalStep[86899] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 2:15:06.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:24:47 INFO loss_tracker.py:84 | Epoch[222/239] Step[324] GlobalStep[86904/NA]: det_loss_cls_0[0.5351] det_loss_box_0[0.6141] det_loss_cls_1[0.5228] det_loss_box_1[0.4498] det_loss_cls_2[0.4426] det_loss_box_2[0.4050] det_loss_cls_3[0.4030] det_loss_box_3[0.3904] det_loss_cls_4[0.3695] det_loss_box_4[0.3840] det_loss_cls_5[0.3495] det_loss_box_5[0.3842] det_loss_cls_dn_0[0.0868] det_loss_box_dn_0[0.2263] det_loss_cls_dn_1[0.0831] det_loss_box_dn_1[0.1837] det_loss_cls_dn_2[0.0817] det_loss_box_dn_2[0.1637] det_loss_cls_dn_3[0.0812] det_loss_box_dn_3[0.1598] det_loss_cls_dn_4[0.0800] det_loss_box_dn_4[0.1583] det_loss_cls_dn_5[0.0794] det_loss_box_dn_5[0.1575] loss_depth[0.0338] total_loss[6.8252]
Rank[0/8] 06/20/2025 19:25:08 INFO stats.py:314 | Epoch[222] Step[344] GlobalStep[86924] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 2:14:36.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:25:13 INFO loss_tracker.py:84 | Epoch[222/239] Step[349] GlobalStep[86929/NA]: det_loss_cls_0[0.6429] det_loss_box_0[0.7400] det_loss_cls_1[0.6344] det_loss_box_1[0.5387] det_loss_cls_2[0.5732] det_loss_box_2[0.4890] det_loss_cls_3[0.5344] det_loss_box_3[0.4697] det_loss_cls_4[0.5050] det_loss_box_4[0.4635] det_loss_cls_5[0.4879] det_loss_box_5[0.4621] det_loss_cls_dn_0[0.1060] det_loss_box_dn_0[0.2259] det_loss_cls_dn_1[0.1018] det_loss_box_dn_1[0.1911] det_loss_cls_dn_2[0.1011] det_loss_box_dn_2[0.1740] det_loss_cls_dn_3[0.0998] det_loss_box_dn_3[0.1700] det_loss_cls_dn_4[0.0988] det_loss_box_dn_4[0.1679] det_loss_cls_dn_5[0.0977] det_loss_box_dn_5[0.1651] loss_depth[0.0350] total_loss[8.2753]
Rank[0/8] 06/20/2025 19:25:34 INFO stats.py:314 | Epoch[222] Step[369] GlobalStep[86949] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 2:14:05.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:25:39 INFO loss_tracker.py:84 | Epoch[222/239] Step[374] GlobalStep[86954/NA]: det_loss_cls_0[0.4103] det_loss_box_0[0.4731] det_loss_cls_1[0.4029] det_loss_box_1[0.3565] det_loss_cls_2[0.3366] det_loss_box_2[0.3268] det_loss_cls_3[0.3014] det_loss_box_3[0.3137] det_loss_cls_4[0.2841] det_loss_box_4[0.3099] det_loss_cls_5[0.2746] det_loss_box_5[0.3083] det_loss_cls_dn_0[0.0772] det_loss_box_dn_0[0.3046] det_loss_cls_dn_1[0.0732] det_loss_box_dn_1[0.2397] det_loss_cls_dn_2[0.0719] det_loss_box_dn_2[0.2209] det_loss_cls_dn_3[0.0710] det_loss_box_dn_3[0.2050] det_loss_cls_dn_4[0.0697] det_loss_box_dn_4[0.2019] det_loss_cls_dn_5[0.0688] det_loss_box_dn_5[0.1955] loss_depth[0.0320] total_loss[5.9296]
Rank[0/8] 06/20/2025 19:25:55 INFO stats.py:394 | Epoch[222] completed. Training Speed: 7.72 samples/sec across all devices.	Epoch Time: 404.31 sec.	Average Epoch Time: 404.31 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 2:13:41.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:26:09 INFO stats.py:314 | Epoch[223] Step[4] GlobalStep[86974] Training Speed: 7.61 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 2:13:35.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:26:30 INFO loss_tracker.py:84 | Epoch[223/239] Step[24] GlobalStep[86994/NA]: det_loss_cls_0[0.5016] det_loss_box_0[0.6460] det_loss_cls_1[0.4887] det_loss_box_1[0.4860] det_loss_cls_2[0.4299] det_loss_box_2[0.4314] det_loss_cls_3[0.3954] det_loss_box_3[0.4182] det_loss_cls_4[0.3771] det_loss_box_4[0.4038] det_loss_cls_5[0.3644] det_loss_box_5[0.4066] det_loss_cls_dn_0[0.0917] det_loss_box_dn_0[0.2554] det_loss_cls_dn_1[0.0879] det_loss_box_dn_1[0.2071] det_loss_cls_dn_2[0.0871] det_loss_box_dn_2[0.1882] det_loss_cls_dn_3[0.0859] det_loss_box_dn_3[0.1840] det_loss_cls_dn_4[0.0854] det_loss_box_dn_4[0.1821] det_loss_cls_dn_5[0.0849] det_loss_box_dn_5[0.1793] loss_depth[0.0360] total_loss[7.1042]
Rank[0/8] 06/20/2025 19:26:35 INFO stats.py:314 | Epoch[223] Step[29] GlobalStep[86999] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 2:13:05.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:26:56 INFO loss_tracker.py:84 | Epoch[223/239] Step[49] GlobalStep[87019/NA]: det_loss_cls_0[0.4750] det_loss_box_0[0.6001] det_loss_cls_1[0.4536] det_loss_box_1[0.4376] det_loss_cls_2[0.3985] det_loss_box_2[0.3951] det_loss_cls_3[0.3746] det_loss_box_3[0.3808] det_loss_cls_4[0.3439] det_loss_box_4[0.3718] det_loss_cls_5[0.3284] det_loss_box_5[0.3743] det_loss_cls_dn_0[0.0839] det_loss_box_dn_0[0.2588] det_loss_cls_dn_1[0.0813] det_loss_box_dn_1[0.2042] det_loss_cls_dn_2[0.0800] det_loss_box_dn_2[0.1862] det_loss_cls_dn_3[0.0795] det_loss_box_dn_3[0.1808] det_loss_cls_dn_4[0.0788] det_loss_box_dn_4[0.1788] det_loss_cls_dn_5[0.0780] det_loss_box_dn_5[0.1765] loss_depth[0.0372] total_loss[6.6379]
Rank[0/8] 06/20/2025 19:27:01 INFO stats.py:314 | Epoch[223] Step[54] GlobalStep[87024] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 2:12:34.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:27:22 INFO loss_tracker.py:84 | Epoch[223/239] Step[74] GlobalStep[87044/NA]: det_loss_cls_0[0.4074] det_loss_box_0[0.5271] det_loss_cls_1[0.4016] det_loss_box_1[0.4019] det_loss_cls_2[0.3461] det_loss_box_2[0.3624] det_loss_cls_3[0.3154] det_loss_box_3[0.3570] det_loss_cls_4[0.3028] det_loss_box_4[0.3387] det_loss_cls_5[0.2895] det_loss_box_5[0.3404] det_loss_cls_dn_0[0.0942] det_loss_box_dn_0[0.2612] det_loss_cls_dn_1[0.0894] det_loss_box_dn_1[0.2119] det_loss_cls_dn_2[0.0883] det_loss_box_dn_2[0.1910] det_loss_cls_dn_3[0.0873] det_loss_box_dn_3[0.1882] det_loss_cls_dn_4[0.0867] det_loss_box_dn_4[0.1855] det_loss_cls_dn_5[0.0858] det_loss_box_dn_5[0.1832] loss_depth[0.0352] total_loss[6.1783]
Rank[0/8] 06/20/2025 19:27:27 INFO stats.py:314 | Epoch[223] Step[79] GlobalStep[87049] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 2:12:04.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:27:48 INFO loss_tracker.py:84 | Epoch[223/239] Step[99] GlobalStep[87069/NA]: det_loss_cls_0[0.3697] det_loss_box_0[0.4237] det_loss_cls_1[0.3467] det_loss_box_1[0.2943] det_loss_cls_2[0.2905] det_loss_box_2[0.2520] det_loss_cls_3[0.2606] det_loss_box_3[0.2477] det_loss_cls_4[0.2417] det_loss_box_4[0.2420] det_loss_cls_5[0.2298] det_loss_box_5[0.2451] det_loss_cls_dn_0[0.0823] det_loss_box_dn_0[0.2472] det_loss_cls_dn_1[0.0781] det_loss_box_dn_1[0.1860] det_loss_cls_dn_2[0.0773] det_loss_box_dn_2[0.1628] det_loss_cls_dn_3[0.0760] det_loss_box_dn_3[0.1582] det_loss_cls_dn_4[0.0754] det_loss_box_dn_4[0.1550] det_loss_cls_dn_5[0.0750] det_loss_box_dn_5[0.1531] loss_depth[0.0335] total_loss[5.0038]
Rank[0/8] 06/20/2025 19:27:54 INFO stats.py:314 | Epoch[223] Step[104] GlobalStep[87074] Training Speed: 7.82 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 2:11:33.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:28:14 INFO loss_tracker.py:84 | Epoch[223/239] Step[124] GlobalStep[87094/NA]: det_loss_cls_0[0.5207] det_loss_box_0[0.6615] det_loss_cls_1[0.5104] det_loss_box_1[0.4870] det_loss_cls_2[0.4362] det_loss_box_2[0.4415] det_loss_cls_3[0.3996] det_loss_box_3[0.4139] det_loss_cls_4[0.3759] det_loss_box_4[0.3970] det_loss_cls_5[0.3634] det_loss_box_5[0.3951] det_loss_cls_dn_0[0.0894] det_loss_box_dn_0[0.2920] det_loss_cls_dn_1[0.0866] det_loss_box_dn_1[0.2331] det_loss_cls_dn_2[0.0863] det_loss_box_dn_2[0.2112] det_loss_cls_dn_3[0.0848] det_loss_box_dn_3[0.2052] det_loss_cls_dn_4[0.0839] det_loss_box_dn_4[0.2004] det_loss_cls_dn_5[0.0831] det_loss_box_dn_5[0.1948] loss_depth[0.0359] total_loss[7.2889]
Rank[0/8] 06/20/2025 19:28:19 INFO stats.py:314 | Epoch[223] Step[129] GlobalStep[87099] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 2:11:02.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:28:40 INFO loss_tracker.py:84 | Epoch[223/239] Step[149] GlobalStep[87119/NA]: det_loss_cls_0[0.3712] det_loss_box_0[0.4705] det_loss_cls_1[0.3524] det_loss_box_1[0.3448] det_loss_cls_2[0.3018] det_loss_box_2[0.3057] det_loss_cls_3[0.2785] det_loss_box_3[0.3005] det_loss_cls_4[0.2576] det_loss_box_4[0.2948] det_loss_cls_5[0.2464] det_loss_box_5[0.2960] det_loss_cls_dn_0[0.0812] det_loss_box_dn_0[0.2652] det_loss_cls_dn_1[0.0771] det_loss_box_dn_1[0.2101] det_loss_cls_dn_2[0.0763] det_loss_box_dn_2[0.1935] det_loss_cls_dn_3[0.0754] det_loss_box_dn_3[0.1889] det_loss_cls_dn_4[0.0747] det_loss_box_dn_4[0.1848] det_loss_cls_dn_5[0.0738] det_loss_box_dn_5[0.1807] loss_depth[0.0384] total_loss[5.5403]
Rank[0/8] 06/20/2025 19:28:45 INFO stats.py:314 | Epoch[223] Step[154] GlobalStep[87124] Training Speed: 7.75 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 2:10:32.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:29:06 INFO loss_tracker.py:84 | Epoch[223/239] Step[174] GlobalStep[87144/NA]: det_loss_cls_0[0.4319] det_loss_box_0[0.5292] det_loss_cls_1[0.4149] det_loss_box_1[0.4141] det_loss_cls_2[0.3614] det_loss_box_2[0.3784] det_loss_cls_3[0.3337] det_loss_box_3[0.3713] det_loss_cls_4[0.3124] det_loss_box_4[0.3586] det_loss_cls_5[0.3043] det_loss_box_5[0.3564] det_loss_cls_dn_0[0.0879] det_loss_box_dn_0[0.2626] det_loss_cls_dn_1[0.0830] det_loss_box_dn_1[0.2126] det_loss_cls_dn_2[0.0826] det_loss_box_dn_2[0.1970] det_loss_cls_dn_3[0.0816] det_loss_box_dn_3[0.1898] det_loss_cls_dn_4[0.0808] det_loss_box_dn_4[0.1877] det_loss_cls_dn_5[0.0803] det_loss_box_dn_5[0.1869] loss_depth[0.0354] total_loss[6.3349]
Rank[0/8] 06/20/2025 19:29:11 INFO stats.py:314 | Epoch[223] Step[179] GlobalStep[87149] Training Speed: 8.14 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 2:10:01.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:29:32 INFO loss_tracker.py:84 | Epoch[223/239] Step[199] GlobalStep[87169/NA]: det_loss_cls_0[0.4959] det_loss_box_0[0.5905] det_loss_cls_1[0.4778] det_loss_box_1[0.4508] det_loss_cls_2[0.4212] det_loss_box_2[0.4117] det_loss_cls_3[0.3966] det_loss_box_3[0.3950] det_loss_cls_4[0.3697] det_loss_box_4[0.3817] det_loss_cls_5[0.3524] det_loss_box_5[0.3857] det_loss_cls_dn_0[0.0991] det_loss_box_dn_0[0.2786] det_loss_cls_dn_1[0.0960] det_loss_box_dn_1[0.2302] det_loss_cls_dn_2[0.0956] det_loss_box_dn_2[0.2157] det_loss_cls_dn_3[0.0943] det_loss_box_dn_3[0.2091] det_loss_cls_dn_4[0.0930] det_loss_box_dn_4[0.2075] det_loss_cls_dn_5[0.0923] det_loss_box_dn_5[0.2021] loss_depth[0.0348] total_loss[7.0772]
Rank[0/8] 06/20/2025 19:29:37 INFO stats.py:314 | Epoch[223] Step[204] GlobalStep[87174] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 2:09:31.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:29:57 INFO loss_tracker.py:84 | Epoch[223/239] Step[224] GlobalStep[87194/NA]: det_loss_cls_0[0.3753] det_loss_box_0[0.4702] det_loss_cls_1[0.3630] det_loss_box_1[0.3582] det_loss_cls_2[0.3136] det_loss_box_2[0.3237] det_loss_cls_3[0.2815] det_loss_box_3[0.3066] det_loss_cls_4[0.2687] det_loss_box_4[0.2920] det_loss_cls_5[0.2480] det_loss_box_5[0.2987] det_loss_cls_dn_0[0.0827] det_loss_box_dn_0[0.2884] det_loss_cls_dn_1[0.0789] det_loss_box_dn_1[0.2412] det_loss_cls_dn_2[0.0779] det_loss_box_dn_2[0.2211] det_loss_cls_dn_3[0.0770] det_loss_box_dn_3[0.2154] det_loss_cls_dn_4[0.0763] det_loss_box_dn_4[0.2116] det_loss_cls_dn_5[0.0754] det_loss_box_dn_5[0.2071] loss_depth[0.0330] total_loss[5.7855]
Rank[0/8] 06/20/2025 19:30:03 INFO stats.py:314 | Epoch[223] Step[229] GlobalStep[87199] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 2:09:00.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:30:23 INFO loss_tracker.py:84 | Epoch[223/239] Step[249] GlobalStep[87219/NA]: det_loss_cls_0[0.4697] det_loss_box_0[0.6000] det_loss_cls_1[0.4583] det_loss_box_1[0.4419] det_loss_cls_2[0.3844] det_loss_box_2[0.3942] det_loss_cls_3[0.3415] det_loss_box_3[0.3834] det_loss_cls_4[0.3203] det_loss_box_4[0.3684] det_loss_cls_5[0.3047] det_loss_box_5[0.3711] det_loss_cls_dn_0[0.0803] det_loss_box_dn_0[0.2147] det_loss_cls_dn_1[0.0769] det_loss_box_dn_1[0.1732] det_loss_cls_dn_2[0.0762] det_loss_box_dn_2[0.1598] det_loss_cls_dn_3[0.0754] det_loss_box_dn_3[0.1573] det_loss_cls_dn_4[0.0746] det_loss_box_dn_4[0.1544] det_loss_cls_dn_5[0.0737] det_loss_box_dn_5[0.1522] loss_depth[0.0339] total_loss[6.3406]
Rank[0/8] 06/20/2025 19:30:29 INFO stats.py:314 | Epoch[223] Step[254] GlobalStep[87224] Training Speed: 7.98 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 2:08:30.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:30:50 INFO loss_tracker.py:84 | Epoch[223/239] Step[274] GlobalStep[87244/NA]: det_loss_cls_0[0.5251] det_loss_box_0[0.6523] det_loss_cls_1[0.5128] det_loss_box_1[0.4904] det_loss_cls_2[0.4432] det_loss_box_2[0.4404] det_loss_cls_3[0.4141] det_loss_box_3[0.4184] det_loss_cls_4[0.3859] det_loss_box_4[0.4136] det_loss_cls_5[0.3732] det_loss_box_5[0.4153] det_loss_cls_dn_0[0.0830] det_loss_box_dn_0[0.2820] det_loss_cls_dn_1[0.0795] det_loss_box_dn_1[0.2357] det_loss_cls_dn_2[0.0787] det_loss_box_dn_2[0.2190] det_loss_cls_dn_3[0.0779] det_loss_box_dn_3[0.2149] det_loss_cls_dn_4[0.0773] det_loss_box_dn_4[0.2122] det_loss_cls_dn_5[0.0764] det_loss_box_dn_5[0.2077] loss_depth[0.0356] total_loss[7.3645]
Rank[0/8] 06/20/2025 19:30:55 INFO stats.py:314 | Epoch[223] Step[279] GlobalStep[87249] Training Speed: 8.07 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 2:07:59.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:31:15 INFO loss_tracker.py:84 | Epoch[223/239] Step[299] GlobalStep[87269/NA]: det_loss_cls_0[0.5698] det_loss_box_0[0.6685] det_loss_cls_1[0.5598] det_loss_box_1[0.5093] det_loss_cls_2[0.4938] det_loss_box_2[0.4566] det_loss_cls_3[0.4590] det_loss_box_3[0.4431] det_loss_cls_4[0.4367] det_loss_box_4[0.4350] det_loss_cls_5[0.4162] det_loss_box_5[0.4411] det_loss_cls_dn_0[0.0876] det_loss_box_dn_0[0.2267] det_loss_cls_dn_1[0.0839] det_loss_box_dn_1[0.1894] det_loss_cls_dn_2[0.0834] det_loss_box_dn_2[0.1755] det_loss_cls_dn_3[0.0822] det_loss_box_dn_3[0.1725] det_loss_cls_dn_4[0.0813] det_loss_box_dn_4[0.1707] det_loss_cls_dn_5[0.0808] det_loss_box_dn_5[0.1686] loss_depth[0.0343] total_loss[7.5261]
Rank[0/8] 06/20/2025 19:31:20 INFO stats.py:314 | Epoch[223] Step[304] GlobalStep[87274] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 2:07:29.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:31:41 INFO loss_tracker.py:84 | Epoch[223/239] Step[324] GlobalStep[87294/NA]: det_loss_cls_0[0.5478] det_loss_box_0[0.6801] det_loss_cls_1[0.5257] det_loss_box_1[0.5195] det_loss_cls_2[0.4627] det_loss_box_2[0.4700] det_loss_cls_3[0.4277] det_loss_box_3[0.4580] det_loss_cls_4[0.4068] det_loss_box_4[0.4414] det_loss_cls_5[0.3965] det_loss_box_5[0.4355] det_loss_cls_dn_0[0.0878] det_loss_box_dn_0[0.2517] det_loss_cls_dn_1[0.0834] det_loss_box_dn_1[0.2089] det_loss_cls_dn_2[0.0825] det_loss_box_dn_2[0.1892] det_loss_cls_dn_3[0.0818] det_loss_box_dn_3[0.1853] det_loss_cls_dn_4[0.0811] det_loss_box_dn_4[0.1824] det_loss_cls_dn_5[0.0804] det_loss_box_dn_5[0.1780] loss_depth[0.0349] total_loss[7.4991]
Rank[0/8] 06/20/2025 19:31:46 INFO stats.py:314 | Epoch[223] Step[329] GlobalStep[87299] Training Speed: 7.80 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 2:06:58.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:32:07 INFO loss_tracker.py:84 | Epoch[223/239] Step[349] GlobalStep[87319/NA]: det_loss_cls_0[0.3940] det_loss_box_0[0.4876] det_loss_cls_1[0.3870] det_loss_box_1[0.3709] det_loss_cls_2[0.3373] det_loss_box_2[0.3315] det_loss_cls_3[0.3055] det_loss_box_3[0.3212] det_loss_cls_4[0.2861] det_loss_box_4[0.3098] det_loss_cls_5[0.2757] det_loss_box_5[0.3046] det_loss_cls_dn_0[0.0871] det_loss_box_dn_0[0.2523] det_loss_cls_dn_1[0.0837] det_loss_box_dn_1[0.2056] det_loss_cls_dn_2[0.0829] det_loss_box_dn_2[0.1885] det_loss_cls_dn_3[0.0817] det_loss_box_dn_3[0.1826] det_loss_cls_dn_4[0.0810] det_loss_box_dn_4[0.1808] det_loss_cls_dn_5[0.0804] det_loss_box_dn_5[0.1783] loss_depth[0.0361] total_loss[5.8323]
Rank[0/8] 06/20/2025 19:32:12 INFO stats.py:314 | Epoch[223] Step[354] GlobalStep[87324] Training Speed: 7.83 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 2:06:28.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:32:33 INFO loss_tracker.py:84 | Epoch[223/239] Step[374] GlobalStep[87344/NA]: det_loss_cls_0[0.5668] det_loss_box_0[0.7283] det_loss_cls_1[0.5414] det_loss_box_1[0.5358] det_loss_cls_2[0.4798] det_loss_box_2[0.5039] det_loss_cls_3[0.4450] det_loss_box_3[0.4784] det_loss_cls_4[0.4226] det_loss_box_4[0.4612] det_loss_cls_5[0.4133] det_loss_box_5[0.4550] det_loss_cls_dn_0[0.0982] det_loss_box_dn_0[0.2720] det_loss_cls_dn_1[0.0962] det_loss_box_dn_1[0.2293] det_loss_cls_dn_2[0.0956] det_loss_box_dn_2[0.2157] det_loss_cls_dn_3[0.0944] det_loss_box_dn_3[0.2130] det_loss_cls_dn_4[0.0937] det_loss_box_dn_4[0.2106] det_loss_cls_dn_5[0.0928] det_loss_box_dn_5[0.2094] loss_depth[0.0393] total_loss[7.9921]
Rank[0/8] 06/20/2025 19:32:38 INFO stats.py:314 | Epoch[223] Step[379] GlobalStep[87349] Training Speed: 8.74 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 2:05:57.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:32:48 INFO stats.py:394 | Epoch[223] completed. Training Speed: 7.55 samples/sec across all devices.	Epoch Time: 413.25 sec.	Average Epoch Time: 413.25 sec.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 2:05:45.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:33:05 INFO stats.py:314 | Epoch[224] Step[14] GlobalStep[87374] Training Speed: 7.63 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 2:05:27.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:33:16 INFO loss_tracker.py:84 | Epoch[224/239] Step[24] GlobalStep[87384/NA]: det_loss_cls_0[0.4404] det_loss_box_0[0.5656] det_loss_cls_1[0.4270] det_loss_box_1[0.4349] det_loss_cls_2[0.3728] det_loss_box_2[0.3966] det_loss_cls_3[0.3313] det_loss_box_3[0.3867] det_loss_cls_4[0.3061] det_loss_box_4[0.3750] det_loss_cls_5[0.2939] det_loss_box_5[0.3760] det_loss_cls_dn_0[0.0861] det_loss_box_dn_0[0.2574] det_loss_cls_dn_1[0.0827] det_loss_box_dn_1[0.2114] det_loss_cls_dn_2[0.0818] det_loss_box_dn_2[0.1951] det_loss_cls_dn_3[0.0808] det_loss_box_dn_3[0.1893] det_loss_cls_dn_4[0.0801] det_loss_box_dn_4[0.1875] det_loss_cls_dn_5[0.0793] det_loss_box_dn_5[0.1860] loss_depth[0.0335] total_loss[6.4575]
Rank[0/8] 06/20/2025 19:33:31 INFO stats.py:314 | Epoch[224] Step[39] GlobalStep[87399] Training Speed: 8.34 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 2:04:56.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:33:42 INFO loss_tracker.py:84 | Epoch[224/239] Step[49] GlobalStep[87409/NA]: det_loss_cls_0[0.4408] det_loss_box_0[0.5211] det_loss_cls_1[0.4336] det_loss_box_1[0.3999] det_loss_cls_2[0.3713] det_loss_box_2[0.3622] det_loss_cls_3[0.3259] det_loss_box_3[0.3554] det_loss_cls_4[0.2991] det_loss_box_4[0.3419] det_loss_cls_5[0.2870] det_loss_box_5[0.3417] det_loss_cls_dn_0[0.0845] det_loss_box_dn_0[0.2168] det_loss_cls_dn_1[0.0802] det_loss_box_dn_1[0.1770] det_loss_cls_dn_2[0.0800] det_loss_box_dn_2[0.1605] det_loss_cls_dn_3[0.0788] det_loss_box_dn_3[0.1565] det_loss_cls_dn_4[0.0780] det_loss_box_dn_4[0.1548] det_loss_cls_dn_5[0.0773] det_loss_box_dn_5[0.1519] loss_depth[0.0329] total_loss[6.0091]
Rank[0/8] 06/20/2025 19:33:57 INFO stats.py:314 | Epoch[224] Step[64] GlobalStep[87424] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 2:04:25.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:34:08 INFO loss_tracker.py:84 | Epoch[224/239] Step[74] GlobalStep[87434/NA]: det_loss_cls_0[0.3720] det_loss_box_0[0.4545] det_loss_cls_1[0.3586] det_loss_box_1[0.3454] det_loss_cls_2[0.3121] det_loss_box_2[0.3095] det_loss_cls_3[0.2836] det_loss_box_3[0.3041] det_loss_cls_4[0.2679] det_loss_box_4[0.2959] det_loss_cls_5[0.2523] det_loss_box_5[0.3002] det_loss_cls_dn_0[0.0803] det_loss_box_dn_0[0.2522] det_loss_cls_dn_1[0.0770] det_loss_box_dn_1[0.2025] det_loss_cls_dn_2[0.0760] det_loss_box_dn_2[0.1841] det_loss_cls_dn_3[0.0752] det_loss_box_dn_3[0.1808] det_loss_cls_dn_4[0.0744] det_loss_box_dn_4[0.1788] det_loss_cls_dn_5[0.0737] det_loss_box_dn_5[0.1765] loss_depth[0.0309] total_loss[5.5186]
Rank[0/8] 06/20/2025 19:34:23 INFO stats.py:314 | Epoch[224] Step[89] GlobalStep[87449] Training Speed: 7.83 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 2:03:55.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:34:33 INFO loss_tracker.py:84 | Epoch[224/239] Step[99] GlobalStep[87459/NA]: det_loss_cls_0[0.4430] det_loss_box_0[0.5272] det_loss_cls_1[0.4273] det_loss_box_1[0.3763] det_loss_cls_2[0.3659] det_loss_box_2[0.3297] det_loss_cls_3[0.3297] det_loss_box_3[0.3197] det_loss_cls_4[0.3075] det_loss_box_4[0.3150] det_loss_cls_5[0.2944] det_loss_box_5[0.3197] det_loss_cls_dn_0[0.0895] det_loss_box_dn_0[0.2916] det_loss_cls_dn_1[0.0858] det_loss_box_dn_1[0.2254] det_loss_cls_dn_2[0.0848] det_loss_box_dn_2[0.2053] det_loss_cls_dn_3[0.0841] det_loss_box_dn_3[0.1958] det_loss_cls_dn_4[0.0834] det_loss_box_dn_4[0.1940] det_loss_cls_dn_5[0.0827] det_loss_box_dn_5[0.1866] loss_depth[0.0379] total_loss[6.2026]
Rank[0/8] 06/20/2025 19:34:49 INFO stats.py:314 | Epoch[224] Step[114] GlobalStep[87474] Training Speed: 7.87 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 2:03:24.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:34:59 INFO loss_tracker.py:84 | Epoch[224/239] Step[124] GlobalStep[87484/NA]: det_loss_cls_0[0.4347] det_loss_box_0[0.5129] det_loss_cls_1[0.4350] det_loss_box_1[0.3594] det_loss_cls_2[0.3583] det_loss_box_2[0.3290] det_loss_cls_3[0.3275] det_loss_box_3[0.3209] det_loss_cls_4[0.3040] det_loss_box_4[0.3125] det_loss_cls_5[0.2912] det_loss_box_5[0.3135] det_loss_cls_dn_0[0.0829] det_loss_box_dn_0[0.1967] det_loss_cls_dn_1[0.0797] det_loss_box_dn_1[0.1535] det_loss_cls_dn_2[0.0788] det_loss_box_dn_2[0.1401] det_loss_cls_dn_3[0.0784] det_loss_box_dn_3[0.1371] det_loss_cls_dn_4[0.0776] det_loss_box_dn_4[0.1358] det_loss_cls_dn_5[0.0769] det_loss_box_dn_5[0.1348] loss_depth[0.0356] total_loss[5.7065]
Rank[0/8] 06/20/2025 19:35:15 INFO stats.py:314 | Epoch[224] Step[139] GlobalStep[87499] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 2:02:54.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:35:25 INFO loss_tracker.py:84 | Epoch[224/239] Step[149] GlobalStep[87509/NA]: det_loss_cls_0[0.4689] det_loss_box_0[0.6331] det_loss_cls_1[0.4648] det_loss_box_1[0.4738] det_loss_cls_2[0.4025] det_loss_box_2[0.4277] det_loss_cls_3[0.3672] det_loss_box_3[0.4121] det_loss_cls_4[0.3387] det_loss_box_4[0.4020] det_loss_cls_5[0.3294] det_loss_box_5[0.3970] det_loss_cls_dn_0[0.0866] det_loss_box_dn_0[0.2752] det_loss_cls_dn_1[0.0830] det_loss_box_dn_1[0.2235] det_loss_cls_dn_2[0.0819] det_loss_box_dn_2[0.2060] det_loss_cls_dn_3[0.0805] det_loss_box_dn_3[0.2022] det_loss_cls_dn_4[0.0795] det_loss_box_dn_4[0.2006] det_loss_cls_dn_5[0.0787] det_loss_box_dn_5[0.1970] loss_depth[0.0344] total_loss[6.9463]
Rank[0/8] 06/20/2025 19:35:41 INFO stats.py:314 | Epoch[224] Step[164] GlobalStep[87524] Training Speed: 7.61 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 2:02:23.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:35:51 INFO loss_tracker.py:84 | Epoch[224/239] Step[174] GlobalStep[87534/NA]: det_loss_cls_0[0.4529] det_loss_box_0[0.5241] det_loss_cls_1[0.4379] det_loss_box_1[0.3908] det_loss_cls_2[0.3805] det_loss_box_2[0.3542] det_loss_cls_3[0.3554] det_loss_box_3[0.3407] det_loss_cls_4[0.3260] det_loss_box_4[0.3317] det_loss_cls_5[0.3167] det_loss_box_5[0.3288] det_loss_cls_dn_0[0.0825] det_loss_box_dn_0[0.2612] det_loss_cls_dn_1[0.0794] det_loss_box_dn_1[0.2178] det_loss_cls_dn_2[0.0786] det_loss_box_dn_2[0.2004] det_loss_cls_dn_3[0.0775] det_loss_box_dn_3[0.1964] det_loss_cls_dn_4[0.0770] det_loss_box_dn_4[0.1942] det_loss_cls_dn_5[0.0759] det_loss_box_dn_5[0.1905] loss_depth[0.0363] total_loss[6.3072]
Rank[0/8] 06/20/2025 19:36:07 INFO stats.py:314 | Epoch[224] Step[189] GlobalStep[87549] Training Speed: 8.56 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 2:01:53.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:36:17 INFO loss_tracker.py:84 | Epoch[224/239] Step[199] GlobalStep[87559/NA]: det_loss_cls_0[0.3758] det_loss_box_0[0.4292] det_loss_cls_1[0.3635] det_loss_box_1[0.3312] det_loss_cls_2[0.2931] det_loss_box_2[0.3120] det_loss_cls_3[0.2734] det_loss_box_3[0.2998] det_loss_cls_4[0.2531] det_loss_box_4[0.2899] det_loss_cls_5[0.2416] det_loss_box_5[0.2927] det_loss_cls_dn_0[0.0844] det_loss_box_dn_0[0.2190] det_loss_cls_dn_1[0.0811] det_loss_box_dn_1[0.1753] det_loss_cls_dn_2[0.0800] det_loss_box_dn_2[0.1597] det_loss_cls_dn_3[0.0790] det_loss_box_dn_3[0.1550] det_loss_cls_dn_4[0.0785] det_loss_box_dn_4[0.1532] det_loss_cls_dn_5[0.0778] det_loss_box_dn_5[0.1513] loss_depth[0.0342] total_loss[5.2838]
Rank[0/8] 06/20/2025 19:36:33 INFO stats.py:314 | Epoch[224] Step[214] GlobalStep[87574] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 2:01:22.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:36:43 INFO loss_tracker.py:84 | Epoch[224/239] Step[224] GlobalStep[87584/NA]: det_loss_cls_0[0.4337] det_loss_box_0[0.5385] det_loss_cls_1[0.4259] det_loss_box_1[0.4099] det_loss_cls_2[0.3602] det_loss_box_2[0.3768] det_loss_cls_3[0.3222] det_loss_box_3[0.3629] det_loss_cls_4[0.2954] det_loss_box_4[0.3509] det_loss_cls_5[0.2802] det_loss_box_5[0.3543] det_loss_cls_dn_0[0.0921] det_loss_box_dn_0[0.2918] det_loss_cls_dn_1[0.0874] det_loss_box_dn_1[0.2456] det_loss_cls_dn_2[0.0863] det_loss_box_dn_2[0.2279] det_loss_cls_dn_3[0.0846] det_loss_box_dn_3[0.2170] det_loss_cls_dn_4[0.0834] det_loss_box_dn_4[0.2155] det_loss_cls_dn_5[0.0825] det_loss_box_dn_5[0.2098] loss_depth[0.0334] total_loss[6.4683]
Rank[0/8] 06/20/2025 19:36:58 INFO stats.py:314 | Epoch[224] Step[239] GlobalStep[87599] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 2:00:52.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:37:09 INFO loss_tracker.py:84 | Epoch[224/239] Step[249] GlobalStep[87609/NA]: det_loss_cls_0[0.5055] det_loss_box_0[0.5820] det_loss_cls_1[0.4946] det_loss_box_1[0.4273] det_loss_cls_2[0.4312] det_loss_box_2[0.3961] det_loss_cls_3[0.3971] det_loss_box_3[0.3784] det_loss_cls_4[0.3705] det_loss_box_4[0.3698] det_loss_cls_5[0.3583] det_loss_box_5[0.3675] det_loss_cls_dn_0[0.1026] det_loss_box_dn_0[0.2698] det_loss_cls_dn_1[0.0992] det_loss_box_dn_1[0.2211] det_loss_cls_dn_2[0.0989] det_loss_box_dn_2[0.2045] det_loss_cls_dn_3[0.0978] det_loss_box_dn_3[0.1998] det_loss_cls_dn_4[0.0972] det_loss_box_dn_4[0.1973] det_loss_cls_dn_5[0.0966] det_loss_box_dn_5[0.1955] loss_depth[0.0349] total_loss[6.9937]
Rank[0/8] 06/20/2025 19:37:24 INFO stats.py:314 | Epoch[224] Step[264] GlobalStep[87624] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 2:00:21.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:37:35 INFO loss_tracker.py:84 | Epoch[224/239] Step[274] GlobalStep[87634/NA]: det_loss_cls_0[0.4799] det_loss_box_0[0.5298] det_loss_cls_1[0.4665] det_loss_box_1[0.3826] det_loss_cls_2[0.3915] det_loss_box_2[0.3560] det_loss_cls_3[0.3631] det_loss_box_3[0.3314] det_loss_cls_4[0.3294] det_loss_box_4[0.3284] det_loss_cls_5[0.3218] det_loss_box_5[0.3262] det_loss_cls_dn_0[0.0963] det_loss_box_dn_0[0.3020] det_loss_cls_dn_1[0.0932] det_loss_box_dn_1[0.2433] det_loss_cls_dn_2[0.0926] det_loss_box_dn_2[0.2225] det_loss_cls_dn_3[0.0916] det_loss_box_dn_3[0.2166] det_loss_cls_dn_4[0.0908] det_loss_box_dn_4[0.2137] det_loss_cls_dn_5[0.0901] det_loss_box_dn_5[0.2044] loss_depth[0.0361] total_loss[6.5998]
Rank[0/8] 06/20/2025 19:37:50 INFO stats.py:314 | Epoch[224] Step[289] GlobalStep[87649] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1:59:51.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:38:01 INFO loss_tracker.py:84 | Epoch[224/239] Step[299] GlobalStep[87659/NA]: det_loss_cls_0[0.4305] det_loss_box_0[0.5285] det_loss_cls_1[0.4149] det_loss_box_1[0.3936] det_loss_cls_2[0.3535] det_loss_box_2[0.3415] det_loss_cls_3[0.3238] det_loss_box_3[0.3232] det_loss_cls_4[0.3052] det_loss_box_4[0.3223] det_loss_cls_5[0.2937] det_loss_box_5[0.3201] det_loss_cls_dn_0[0.0762] det_loss_box_dn_0[0.2765] det_loss_cls_dn_1[0.0719] det_loss_box_dn_1[0.2067] det_loss_cls_dn_2[0.0709] det_loss_box_dn_2[0.1870] det_loss_cls_dn_3[0.0697] det_loss_box_dn_3[0.1763] det_loss_cls_dn_4[0.0689] det_loss_box_dn_4[0.1740] det_loss_cls_dn_5[0.0683] det_loss_box_dn_5[0.1701] loss_depth[0.0374] total_loss[6.0047]
Rank[0/8] 06/20/2025 19:38:17 INFO stats.py:314 | Epoch[224] Step[314] GlobalStep[87674] Training Speed: 6.84 samples/sec across all devices.	Average Step Time: 1.17 sec.	Estimated Remaining Time: 1:59:20.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:38:27 INFO loss_tracker.py:84 | Epoch[224/239] Step[324] GlobalStep[87684/NA]: det_loss_cls_0[0.3951] det_loss_box_0[0.5392] det_loss_cls_1[0.3946] det_loss_box_1[0.3934] det_loss_cls_2[0.3292] det_loss_box_2[0.3672] det_loss_cls_3[0.2978] det_loss_box_3[0.3606] det_loss_cls_4[0.2806] det_loss_box_4[0.3473] det_loss_cls_5[0.2686] det_loss_box_5[0.3459] det_loss_cls_dn_0[0.0713] det_loss_box_dn_0[0.2391] det_loss_cls_dn_1[0.0676] det_loss_box_dn_1[0.1987] det_loss_cls_dn_2[0.0667] det_loss_box_dn_2[0.1852] det_loss_cls_dn_3[0.0657] det_loss_box_dn_3[0.1826] det_loss_cls_dn_4[0.0653] det_loss_box_dn_4[0.1821] det_loss_cls_dn_5[0.0645] det_loss_box_dn_5[0.1785] loss_depth[0.0378] total_loss[5.9249]
Rank[0/8] 06/20/2025 19:38:42 INFO stats.py:314 | Epoch[224] Step[339] GlobalStep[87699] Training Speed: 11.04 samples/sec across all devices.	Average Step Time: 0.72 sec.	Estimated Remaining Time: 1:58:50.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:38:53 INFO loss_tracker.py:84 | Epoch[224/239] Step[349] GlobalStep[87709/NA]: det_loss_cls_0[0.5052] det_loss_box_0[0.6181] det_loss_cls_1[0.4915] det_loss_box_1[0.4753] det_loss_cls_2[0.4232] det_loss_box_2[0.4290] det_loss_cls_3[0.3900] det_loss_box_3[0.4199] det_loss_cls_4[0.3574] det_loss_box_4[0.4059] det_loss_cls_5[0.3462] det_loss_box_5[0.4038] det_loss_cls_dn_0[0.0838] det_loss_box_dn_0[0.2613] det_loss_cls_dn_1[0.0801] det_loss_box_dn_1[0.2063] det_loss_cls_dn_2[0.0792] det_loss_box_dn_2[0.1868] det_loss_cls_dn_3[0.0776] det_loss_box_dn_3[0.1816] det_loss_cls_dn_4[0.0769] det_loss_box_dn_4[0.1781] det_loss_cls_dn_5[0.0761] det_loss_box_dn_5[0.1745] loss_depth[0.0331] total_loss[6.9608]
Rank[0/8] 06/20/2025 19:39:08 INFO stats.py:314 | Epoch[224] Step[364] GlobalStep[87724] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1:58:19.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:39:18 INFO loss_tracker.py:84 | Epoch[224/239] Step[374] GlobalStep[87734/NA]: det_loss_cls_0[0.4994] det_loss_box_0[0.6064] det_loss_cls_1[0.4885] det_loss_box_1[0.4386] det_loss_cls_2[0.4230] det_loss_box_2[0.4043] det_loss_cls_3[0.3869] det_loss_box_3[0.3931] det_loss_cls_4[0.3496] det_loss_box_4[0.3852] det_loss_cls_5[0.3426] det_loss_box_5[0.3833] det_loss_cls_dn_0[0.0922] det_loss_box_dn_0[0.2348] det_loss_cls_dn_1[0.0885] det_loss_box_dn_1[0.1918] det_loss_cls_dn_2[0.0873] det_loss_box_dn_2[0.1754] det_loss_cls_dn_3[0.0861] det_loss_box_dn_3[0.1724] det_loss_cls_dn_4[0.0853] det_loss_box_dn_4[0.1703] det_loss_cls_dn_5[0.0845] det_loss_box_dn_5[0.1683] loss_depth[0.0338] total_loss[6.7716]
Rank[0/8] 06/20/2025 19:39:34 INFO stats.py:314 | Epoch[224] Step[389] GlobalStep[87749] Training Speed: 8.75 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 1:57:49.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:39:34 INFO stats.py:394 | Epoch[224] completed. Training Speed: 7.69 samples/sec across all devices.	Epoch Time: 405.84 sec.	Average Epoch Time: 405.84 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1:57:49.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:40:01 INFO stats.py:314 | Epoch[225] Step[24] GlobalStep[87774] Training Speed: 8.50 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1:57:18.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:40:01 INFO loss_tracker.py:84 | Epoch[225/239] Step[24] GlobalStep[87774/NA]: det_loss_cls_0[0.5135] det_loss_box_0[0.6376] det_loss_cls_1[0.5060] det_loss_box_1[0.4773] det_loss_cls_2[0.4454] det_loss_box_2[0.4365] det_loss_cls_3[0.4139] det_loss_box_3[0.4271] det_loss_cls_4[0.3857] det_loss_box_4[0.4150] det_loss_cls_5[0.3711] det_loss_box_5[0.4111] det_loss_cls_dn_0[0.0860] det_loss_box_dn_0[0.2488] det_loss_cls_dn_1[0.0825] det_loss_box_dn_1[0.2055] det_loss_cls_dn_2[0.0814] det_loss_box_dn_2[0.1906] det_loss_cls_dn_3[0.0802] det_loss_box_dn_3[0.1862] det_loss_cls_dn_4[0.0793] det_loss_box_dn_4[0.1834] det_loss_cls_dn_5[0.0785] det_loss_box_dn_5[0.1797] loss_depth[0.0323] total_loss[7.1546]
Rank[0/8] 06/20/2025 19:40:27 INFO stats.py:314 | Epoch[225] Step[49] GlobalStep[87799] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1:56:48.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:40:27 INFO loss_tracker.py:84 | Epoch[225/239] Step[49] GlobalStep[87799/NA]: det_loss_cls_0[0.3925] det_loss_box_0[0.4811] det_loss_cls_1[0.3832] det_loss_box_1[0.3699] det_loss_cls_2[0.3202] det_loss_box_2[0.3320] det_loss_cls_3[0.2818] det_loss_box_3[0.3249] det_loss_cls_4[0.2678] det_loss_box_4[0.3096] det_loss_cls_5[0.2521] det_loss_box_5[0.3097] det_loss_cls_dn_0[0.0830] det_loss_box_dn_0[0.2405] det_loss_cls_dn_1[0.0786] det_loss_box_dn_1[0.1917] det_loss_cls_dn_2[0.0774] det_loss_box_dn_2[0.1713] det_loss_cls_dn_3[0.0762] det_loss_box_dn_3[0.1670] det_loss_cls_dn_4[0.0754] det_loss_box_dn_4[0.1635] det_loss_cls_dn_5[0.0745] det_loss_box_dn_5[0.1628] loss_depth[0.0342] total_loss[5.6208]
Rank[0/8] 06/20/2025 19:40:53 INFO stats.py:314 | Epoch[225] Step[74] GlobalStep[87824] Training Speed: 8.00 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1:56:17.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:40:53 INFO loss_tracker.py:84 | Epoch[225/239] Step[74] GlobalStep[87824/NA]: det_loss_cls_0[0.4973] det_loss_box_0[0.6985] det_loss_cls_1[0.4736] det_loss_box_1[0.5379] det_loss_cls_2[0.4193] det_loss_box_2[0.4665] det_loss_cls_3[0.3895] det_loss_box_3[0.4453] det_loss_cls_4[0.3698] det_loss_box_4[0.4418] det_loss_cls_5[0.3538] det_loss_box_5[0.4436] det_loss_cls_dn_0[0.0948] det_loss_box_dn_0[0.3764] det_loss_cls_dn_1[0.0892] det_loss_box_dn_1[0.3153] det_loss_cls_dn_2[0.0882] det_loss_box_dn_2[0.2942] det_loss_cls_dn_3[0.0873] det_loss_box_dn_3[0.2865] det_loss_cls_dn_4[0.0868] det_loss_box_dn_4[0.2816] det_loss_cls_dn_5[0.0860] det_loss_box_dn_5[0.2759] loss_depth[0.0378] total_loss[7.9369]
Rank[0/8] 06/20/2025 19:41:18 INFO stats.py:314 | Epoch[225] Step[99] GlobalStep[87849] Training Speed: 7.99 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1:55:47.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:41:18 INFO loss_tracker.py:84 | Epoch[225/239] Step[99] GlobalStep[87849/NA]: det_loss_cls_0[0.3524] det_loss_box_0[0.4122] det_loss_cls_1[0.3372] det_loss_box_1[0.3117] det_loss_cls_2[0.2869] det_loss_box_2[0.2885] det_loss_cls_3[0.2517] det_loss_box_3[0.2739] det_loss_cls_4[0.2383] det_loss_box_4[0.2599] det_loss_cls_5[0.2235] det_loss_box_5[0.2639] det_loss_cls_dn_0[0.0798] det_loss_box_dn_0[0.2375] det_loss_cls_dn_1[0.0762] det_loss_box_dn_1[0.1891] det_loss_cls_dn_2[0.0753] det_loss_box_dn_2[0.1734] det_loss_cls_dn_3[0.0742] det_loss_box_dn_3[0.1697] det_loss_cls_dn_4[0.0737] det_loss_box_dn_4[0.1679] det_loss_cls_dn_5[0.0727] det_loss_box_dn_5[0.1663] loss_depth[0.0364] total_loss[5.0925]
Rank[0/8] 06/20/2025 19:41:44 INFO stats.py:314 | Epoch[225] Step[124] GlobalStep[87874] Training Speed: 8.64 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1:55:16.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:41:44 INFO loss_tracker.py:84 | Epoch[225/239] Step[124] GlobalStep[87874/NA]: det_loss_cls_0[0.4390] det_loss_box_0[0.5426] det_loss_cls_1[0.4152] det_loss_box_1[0.4238] det_loss_cls_2[0.3637] det_loss_box_2[0.3771] det_loss_cls_3[0.3378] det_loss_box_3[0.3630] det_loss_cls_4[0.3211] det_loss_box_4[0.3581] det_loss_cls_5[0.3045] det_loss_box_5[0.3590] det_loss_cls_dn_0[0.0909] det_loss_box_dn_0[0.2641] det_loss_cls_dn_1[0.0865] det_loss_box_dn_1[0.2271] det_loss_cls_dn_2[0.0857] det_loss_box_dn_2[0.2105] det_loss_cls_dn_3[0.0843] det_loss_box_dn_3[0.2048] det_loss_cls_dn_4[0.0833] det_loss_box_dn_4[0.2017] det_loss_cls_dn_5[0.0823] det_loss_box_dn_5[0.1982] loss_depth[0.0334] total_loss[6.4577]
Rank[0/8] 06/20/2025 19:42:11 INFO stats.py:314 | Epoch[225] Step[149] GlobalStep[87899] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1:54:46.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:42:11 INFO loss_tracker.py:84 | Epoch[225/239] Step[149] GlobalStep[87899/NA]: det_loss_cls_0[0.5000] det_loss_box_0[0.6101] det_loss_cls_1[0.4907] det_loss_box_1[0.4824] det_loss_cls_2[0.4257] det_loss_box_2[0.4470] det_loss_cls_3[0.3898] det_loss_box_3[0.4321] det_loss_cls_4[0.3641] det_loss_box_4[0.4165] det_loss_cls_5[0.3499] det_loss_box_5[0.4160] det_loss_cls_dn_0[0.0914] det_loss_box_dn_0[0.2461] det_loss_cls_dn_1[0.0877] det_loss_box_dn_1[0.2070] det_loss_cls_dn_2[0.0870] det_loss_box_dn_2[0.1918] det_loss_cls_dn_3[0.0861] det_loss_box_dn_3[0.1882] det_loss_cls_dn_4[0.0851] det_loss_box_dn_4[0.1869] det_loss_cls_dn_5[0.0842] det_loss_box_dn_5[0.1847] loss_depth[0.0307] total_loss[7.0813]
Rank[0/8] 06/20/2025 19:42:36 INFO stats.py:314 | Epoch[225] Step[174] GlobalStep[87924] Training Speed: 11.27 samples/sec across all devices.	Average Step Time: 0.71 sec.	Estimated Remaining Time: 1:54:15.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:42:37 INFO loss_tracker.py:84 | Epoch[225/239] Step[174] GlobalStep[87924/NA]: det_loss_cls_0[0.3579] det_loss_box_0[0.4249] det_loss_cls_1[0.3460] det_loss_box_1[0.3227] det_loss_cls_2[0.2891] det_loss_box_2[0.3017] det_loss_cls_3[0.2651] det_loss_box_3[0.2851] det_loss_cls_4[0.2430] det_loss_box_4[0.2806] det_loss_cls_5[0.2303] det_loss_box_5[0.2801] det_loss_cls_dn_0[0.0777] det_loss_box_dn_0[0.2411] det_loss_cls_dn_1[0.0747] det_loss_box_dn_1[0.1983] det_loss_cls_dn_2[0.0746] det_loss_box_dn_2[0.1798] det_loss_cls_dn_3[0.0735] det_loss_box_dn_3[0.1749] det_loss_cls_dn_4[0.0726] det_loss_box_dn_4[0.1739] det_loss_cls_dn_5[0.0721] det_loss_box_dn_5[0.1700] loss_depth[0.0342] total_loss[5.2440]
Rank[0/8] 06/20/2025 19:43:02 INFO stats.py:314 | Epoch[225] Step[199] GlobalStep[87949] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1:53:45.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:43:02 INFO loss_tracker.py:84 | Epoch[225/239] Step[199] GlobalStep[87949/NA]: det_loss_cls_0[0.3793] det_loss_box_0[0.5180] det_loss_cls_1[0.3654] det_loss_box_1[0.3767] det_loss_cls_2[0.3190] det_loss_box_2[0.3284] det_loss_cls_3[0.2870] det_loss_box_3[0.3203] det_loss_cls_4[0.2658] det_loss_box_4[0.3101] det_loss_cls_5[0.2613] det_loss_box_5[0.3087] det_loss_cls_dn_0[0.0863] det_loss_box_dn_0[0.2743] det_loss_cls_dn_1[0.0829] det_loss_box_dn_1[0.2182] det_loss_cls_dn_2[0.0817] det_loss_box_dn_2[0.1920] det_loss_cls_dn_3[0.0803] det_loss_box_dn_3[0.1882] det_loss_cls_dn_4[0.0795] det_loss_box_dn_4[0.1856] det_loss_cls_dn_5[0.0788] det_loss_box_dn_5[0.1833] loss_depth[0.0366] total_loss[5.8078]
Rank[0/8] 06/20/2025 19:43:29 INFO stats.py:314 | Epoch[225] Step[224] GlobalStep[87974] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1:53:14.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:43:29 INFO loss_tracker.py:84 | Epoch[225/239] Step[224] GlobalStep[87974/NA]: det_loss_cls_0[0.3945] det_loss_box_0[0.4745] det_loss_cls_1[0.3804] det_loss_box_1[0.3500] det_loss_cls_2[0.3120] det_loss_box_2[0.3270] det_loss_cls_3[0.2929] det_loss_box_3[0.3020] det_loss_cls_4[0.2625] det_loss_box_4[0.3043] det_loss_cls_5[0.2495] det_loss_box_5[0.3033] det_loss_cls_dn_0[0.0801] det_loss_box_dn_0[0.3129] det_loss_cls_dn_1[0.0756] det_loss_box_dn_1[0.2475] det_loss_cls_dn_2[0.0744] det_loss_box_dn_2[0.2219] det_loss_cls_dn_3[0.0738] det_loss_box_dn_3[0.2167] det_loss_cls_dn_4[0.0731] det_loss_box_dn_4[0.2140] det_loss_cls_dn_5[0.0723] det_loss_box_dn_5[0.2032] loss_depth[0.0346] total_loss[5.8530]
Rank[0/8] 06/20/2025 19:43:55 INFO stats.py:314 | Epoch[225] Step[249] GlobalStep[87999] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1:52:44.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:43:55 INFO loss_tracker.py:84 | Epoch[225/239] Step[249] GlobalStep[87999/NA]: det_loss_cls_0[0.5400] det_loss_box_0[0.6060] det_loss_cls_1[0.5349] det_loss_box_1[0.4768] det_loss_cls_2[0.4676] det_loss_box_2[0.4344] det_loss_cls_3[0.4338] det_loss_box_3[0.4255] det_loss_cls_4[0.4070] det_loss_box_4[0.4161] det_loss_cls_5[0.3905] det_loss_box_5[0.4168] det_loss_cls_dn_0[0.1073] det_loss_box_dn_0[0.2201] det_loss_cls_dn_1[0.1050] det_loss_box_dn_1[0.1835] det_loss_cls_dn_2[0.1038] det_loss_box_dn_2[0.1704] det_loss_cls_dn_3[0.1035] det_loss_box_dn_3[0.1674] det_loss_cls_dn_4[0.1030] det_loss_box_dn_4[0.1645] det_loss_cls_dn_5[0.1026] det_loss_box_dn_5[0.1630] loss_depth[0.0336] total_loss[7.2773]
Rank[0/8] 06/20/2025 19:44:21 INFO stats.py:314 | Epoch[225] Step[274] GlobalStep[88024] Training Speed: 8.11 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1:52:14.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:44:21 INFO loss_tracker.py:84 | Epoch[225/239] Step[274] GlobalStep[88024/NA]: det_loss_cls_0[0.3925] det_loss_box_0[0.4835] det_loss_cls_1[0.3681] det_loss_box_1[0.3483] det_loss_cls_2[0.3189] det_loss_box_2[0.3146] det_loss_cls_3[0.2968] det_loss_box_3[0.2980] det_loss_cls_4[0.2835] det_loss_box_4[0.2930] det_loss_cls_5[0.2677] det_loss_box_5[0.2985] det_loss_cls_dn_0[0.0812] det_loss_box_dn_0[0.2696] det_loss_cls_dn_1[0.0783] det_loss_box_dn_1[0.2041] det_loss_cls_dn_2[0.0776] det_loss_box_dn_2[0.1782] det_loss_cls_dn_3[0.0766] det_loss_box_dn_3[0.1718] det_loss_cls_dn_4[0.0761] det_loss_box_dn_4[0.1717] det_loss_cls_dn_5[0.0757] det_loss_box_dn_5[0.1660] loss_depth[0.0383] total_loss[5.6285]
Rank[0/8] 06/20/2025 19:44:48 INFO stats.py:314 | Epoch[225] Step[299] GlobalStep[88049] Training Speed: 7.61 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1:51:43.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:44:48 INFO loss_tracker.py:84 | Epoch[225/239] Step[299] GlobalStep[88049/NA]: det_loss_cls_0[0.4430] det_loss_box_0[0.5312] det_loss_cls_1[0.4279] det_loss_box_1[0.3820] det_loss_cls_2[0.3516] det_loss_box_2[0.3567] det_loss_cls_3[0.3162] det_loss_box_3[0.3472] det_loss_cls_4[0.2993] det_loss_box_4[0.3318] det_loss_cls_5[0.2812] det_loss_box_5[0.3353] det_loss_cls_dn_0[0.0745] det_loss_box_dn_0[0.2435] det_loss_cls_dn_1[0.0705] det_loss_box_dn_1[0.1969] det_loss_cls_dn_2[0.0703] det_loss_box_dn_2[0.1813] det_loss_cls_dn_3[0.0691] det_loss_box_dn_3[0.1693] det_loss_cls_dn_4[0.0680] det_loss_box_dn_4[0.1654] det_loss_cls_dn_5[0.0676] det_loss_box_dn_5[0.1605] loss_depth[0.0367] total_loss[5.9769]
Rank[0/8] 06/20/2025 19:45:13 INFO stats.py:314 | Epoch[225] Step[324] GlobalStep[88074] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1:51:13.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:45:13 INFO loss_tracker.py:84 | Epoch[225/239] Step[324] GlobalStep[88074/NA]: det_loss_cls_0[0.5207] det_loss_box_0[0.6380] det_loss_cls_1[0.5015] det_loss_box_1[0.4909] det_loss_cls_2[0.4419] det_loss_box_2[0.4454] det_loss_cls_3[0.4081] det_loss_box_3[0.4354] det_loss_cls_4[0.3791] det_loss_box_4[0.4172] det_loss_cls_5[0.3661] det_loss_box_5[0.4174] det_loss_cls_dn_0[0.0913] det_loss_box_dn_0[0.2505] det_loss_cls_dn_1[0.0871] det_loss_box_dn_1[0.2056] det_loss_cls_dn_2[0.0857] det_loss_box_dn_2[0.1926] det_loss_cls_dn_3[0.0840] det_loss_box_dn_3[0.1887] det_loss_cls_dn_4[0.0831] det_loss_box_dn_4[0.1872] det_loss_cls_dn_5[0.0825] det_loss_box_dn_5[0.1832] loss_depth[0.0349] total_loss[7.2181]
Rank[0/8] 06/20/2025 19:45:39 INFO stats.py:314 | Epoch[225] Step[349] GlobalStep[88099] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1:50:42.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:45:39 INFO loss_tracker.py:84 | Epoch[225/239] Step[349] GlobalStep[88099/NA]: det_loss_cls_0[0.5497] det_loss_box_0[0.6679] det_loss_cls_1[0.5395] det_loss_box_1[0.4833] det_loss_cls_2[0.4753] det_loss_box_2[0.4300] det_loss_cls_3[0.4522] det_loss_box_3[0.4206] det_loss_cls_4[0.4313] det_loss_box_4[0.4132] det_loss_cls_5[0.4188] det_loss_box_5[0.4113] det_loss_cls_dn_0[0.0881] det_loss_box_dn_0[0.2610] det_loss_cls_dn_1[0.0852] det_loss_box_dn_1[0.2184] det_loss_cls_dn_2[0.0845] det_loss_box_dn_2[0.2020] det_loss_cls_dn_3[0.0837] det_loss_box_dn_3[0.1941] det_loss_cls_dn_4[0.0828] det_loss_box_dn_4[0.1912] det_loss_cls_dn_5[0.0821] det_loss_box_dn_5[0.1877] loss_depth[0.0343] total_loss[7.4883]
Rank[0/8] 06/20/2025 19:46:05 INFO stats.py:314 | Epoch[225] Step[374] GlobalStep[88124] Training Speed: 8.19 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1:50:12.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:46:05 INFO loss_tracker.py:84 | Epoch[225/239] Step[374] GlobalStep[88124/NA]: det_loss_cls_0[0.4382] det_loss_box_0[0.5730] det_loss_cls_1[0.4143] det_loss_box_1[0.4100] det_loss_cls_2[0.3479] det_loss_box_2[0.3712] det_loss_cls_3[0.3165] det_loss_box_3[0.3667] det_loss_cls_4[0.2933] det_loss_box_4[0.3497] det_loss_cls_5[0.2860] det_loss_box_5[0.3467] det_loss_cls_dn_0[0.0912] det_loss_box_dn_0[0.2672] det_loss_cls_dn_1[0.0883] det_loss_box_dn_1[0.2148] det_loss_cls_dn_2[0.0873] det_loss_box_dn_2[0.1979] det_loss_cls_dn_3[0.0862] det_loss_box_dn_3[0.1942] det_loss_cls_dn_4[0.0853] det_loss_box_dn_4[0.1904] det_loss_cls_dn_5[0.0841] det_loss_box_dn_5[0.1881] loss_depth[0.0346] total_loss[6.3229]
Rank[0/8] 06/20/2025 19:46:20 INFO stats.py:394 | Epoch[225] completed. Training Speed: 7.68 samples/sec across all devices.	Epoch Time: 406.35 sec.	Average Epoch Time: 406.35 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1:49:53.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:46:31 INFO stats.py:314 | Epoch[226] Step[9] GlobalStep[88149] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1:49:41.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:46:47 INFO loss_tracker.py:84 | Epoch[226/239] Step[24] GlobalStep[88164/NA]: det_loss_cls_0[0.4354] det_loss_box_0[0.5161] det_loss_cls_1[0.4160] det_loss_box_1[0.3897] det_loss_cls_2[0.3533] det_loss_box_2[0.3509] det_loss_cls_3[0.3218] det_loss_box_3[0.3388] det_loss_cls_4[0.2981] det_loss_box_4[0.3308] det_loss_cls_5[0.2849] det_loss_box_5[0.3310] det_loss_cls_dn_0[0.0884] det_loss_box_dn_0[0.2573] det_loss_cls_dn_1[0.0848] det_loss_box_dn_1[0.2071] det_loss_cls_dn_2[0.0840] det_loss_box_dn_2[0.1873] det_loss_cls_dn_3[0.0827] det_loss_box_dn_3[0.1817] det_loss_cls_dn_4[0.0820] det_loss_box_dn_4[0.1787] det_loss_cls_dn_5[0.0810] det_loss_box_dn_5[0.1759] loss_depth[0.0346] total_loss[6.0924]
Rank[0/8] 06/20/2025 19:46:57 INFO stats.py:314 | Epoch[226] Step[34] GlobalStep[88174] Training Speed: 8.13 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1:49:11.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:47:13 INFO loss_tracker.py:84 | Epoch[226/239] Step[49] GlobalStep[88189/NA]: det_loss_cls_0[0.4588] det_loss_box_0[0.5630] det_loss_cls_1[0.4359] det_loss_box_1[0.4329] det_loss_cls_2[0.3861] det_loss_box_2[0.3899] det_loss_cls_3[0.3625] det_loss_box_3[0.3756] det_loss_cls_4[0.3484] det_loss_box_4[0.3569] det_loss_cls_5[0.3317] det_loss_box_5[0.3603] det_loss_cls_dn_0[0.0888] det_loss_box_dn_0[0.2499] det_loss_cls_dn_1[0.0854] det_loss_box_dn_1[0.2093] det_loss_cls_dn_2[0.0842] det_loss_box_dn_2[0.1953] det_loss_cls_dn_3[0.0831] det_loss_box_dn_3[0.1892] det_loss_cls_dn_4[0.0825] det_loss_box_dn_4[0.1871] det_loss_cls_dn_5[0.0820] det_loss_box_dn_5[0.1843] loss_depth[0.0362] total_loss[6.5594]
Rank[0/8] 06/20/2025 19:47:24 INFO stats.py:314 | Epoch[226] Step[59] GlobalStep[88199] Training Speed: 6.09 samples/sec across all devices.	Average Step Time: 1.31 sec.	Estimated Remaining Time: 1:48:40.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:47:39 INFO loss_tracker.py:84 | Epoch[226/239] Step[74] GlobalStep[88214/NA]: det_loss_cls_0[0.5285] det_loss_box_0[0.6577] det_loss_cls_1[0.5096] det_loss_box_1[0.5033] det_loss_cls_2[0.4454] det_loss_box_2[0.4547] det_loss_cls_3[0.4008] det_loss_box_3[0.4519] det_loss_cls_4[0.3752] det_loss_box_4[0.4344] det_loss_cls_5[0.3595] det_loss_box_5[0.4381] det_loss_cls_dn_0[0.0854] det_loss_box_dn_0[0.2323] det_loss_cls_dn_1[0.0820] det_loss_box_dn_1[0.1893] det_loss_cls_dn_2[0.0814] det_loss_box_dn_2[0.1738] det_loss_cls_dn_3[0.0805] det_loss_box_dn_3[0.1700] det_loss_cls_dn_4[0.0801] det_loss_box_dn_4[0.1680] det_loss_cls_dn_5[0.0790] det_loss_box_dn_5[0.1661] loss_depth[0.0348] total_loss[7.1817]
Rank[0/8] 06/20/2025 19:47:49 INFO stats.py:314 | Epoch[226] Step[84] GlobalStep[88224] Training Speed: 8.63 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1:48:10.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:48:05 INFO loss_tracker.py:84 | Epoch[226/239] Step[99] GlobalStep[88239/NA]: det_loss_cls_0[0.4580] det_loss_box_0[0.5667] det_loss_cls_1[0.4496] det_loss_box_1[0.4233] det_loss_cls_2[0.3759] det_loss_box_2[0.4010] det_loss_cls_3[0.3361] det_loss_box_3[0.3852] det_loss_cls_4[0.3141] det_loss_box_4[0.3738] det_loss_cls_5[0.3073] det_loss_box_5[0.3714] det_loss_cls_dn_0[0.0845] det_loss_box_dn_0[0.2776] det_loss_cls_dn_1[0.0809] det_loss_box_dn_1[0.2144] det_loss_cls_dn_2[0.0797] det_loss_box_dn_2[0.1973] det_loss_cls_dn_3[0.0787] det_loss_box_dn_3[0.1899] det_loss_cls_dn_4[0.0778] det_loss_box_dn_4[0.1866] det_loss_cls_dn_5[0.0767] det_loss_box_dn_5[0.1841] loss_depth[0.0344] total_loss[6.5249]
Rank[0/8] 06/20/2025 19:48:15 INFO stats.py:314 | Epoch[226] Step[109] GlobalStep[88249] Training Speed: 8.15 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1:47:39.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:48:30 INFO loss_tracker.py:84 | Epoch[226/239] Step[124] GlobalStep[88264/NA]: det_loss_cls_0[0.3934] det_loss_box_0[0.4773] det_loss_cls_1[0.3893] det_loss_box_1[0.3495] det_loss_cls_2[0.3329] det_loss_box_2[0.3223] det_loss_cls_3[0.3076] det_loss_box_3[0.3071] det_loss_cls_4[0.2867] det_loss_box_4[0.3003] det_loss_cls_5[0.2724] det_loss_box_5[0.3019] det_loss_cls_dn_0[0.0852] det_loss_box_dn_0[0.2365] det_loss_cls_dn_1[0.0819] det_loss_box_dn_1[0.1932] det_loss_cls_dn_2[0.0810] det_loss_box_dn_2[0.1757] det_loss_cls_dn_3[0.0799] det_loss_box_dn_3[0.1723] det_loss_cls_dn_4[0.0793] det_loss_box_dn_4[0.1700] det_loss_cls_dn_5[0.0785] det_loss_box_dn_5[0.1688] loss_depth[0.0351] total_loss[5.6781]
Rank[0/8] 06/20/2025 19:48:41 INFO stats.py:314 | Epoch[226] Step[134] GlobalStep[88274] Training Speed: 6.96 samples/sec across all devices.	Average Step Time: 1.15 sec.	Estimated Remaining Time: 1:47:09.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:48:56 INFO loss_tracker.py:84 | Epoch[226/239] Step[149] GlobalStep[88289/NA]: det_loss_cls_0[0.3694] det_loss_box_0[0.4436] det_loss_cls_1[0.3616] det_loss_box_1[0.3329] det_loss_cls_2[0.3031] det_loss_box_2[0.3051] det_loss_cls_3[0.2753] det_loss_box_3[0.2937] det_loss_cls_4[0.2540] det_loss_box_4[0.2871] det_loss_cls_5[0.2417] det_loss_box_5[0.2862] det_loss_cls_dn_0[0.0792] det_loss_box_dn_0[0.2263] det_loss_cls_dn_1[0.0762] det_loss_box_dn_1[0.1866] det_loss_cls_dn_2[0.0754] det_loss_box_dn_2[0.1707] det_loss_cls_dn_3[0.0746] det_loss_box_dn_3[0.1663] det_loss_cls_dn_4[0.0741] det_loss_box_dn_4[0.1646] det_loss_cls_dn_5[0.0733] det_loss_box_dn_5[0.1614] loss_depth[0.0393] total_loss[5.3216]
Rank[0/8] 06/20/2025 19:49:07 INFO stats.py:314 | Epoch[226] Step[159] GlobalStep[88299] Training Speed: 7.70 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1:46:39.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:49:22 INFO loss_tracker.py:84 | Epoch[226/239] Step[174] GlobalStep[88314/NA]: det_loss_cls_0[0.4872] det_loss_box_0[0.6365] det_loss_cls_1[0.4652] det_loss_box_1[0.4709] det_loss_cls_2[0.4112] det_loss_box_2[0.4364] det_loss_cls_3[0.3729] det_loss_box_3[0.4182] det_loss_cls_4[0.3456] det_loss_box_4[0.4107] det_loss_cls_5[0.3316] det_loss_box_5[0.4094] det_loss_cls_dn_0[0.0923] det_loss_box_dn_0[0.2491] det_loss_cls_dn_1[0.0889] det_loss_box_dn_1[0.2089] det_loss_cls_dn_2[0.0878] det_loss_box_dn_2[0.1931] det_loss_cls_dn_3[0.0865] det_loss_box_dn_3[0.1893] det_loss_cls_dn_4[0.0858] det_loss_box_dn_4[0.1876] det_loss_cls_dn_5[0.0850] det_loss_box_dn_5[0.1861] loss_depth[0.0342] total_loss[6.9703]
Rank[0/8] 06/20/2025 19:49:32 INFO stats.py:314 | Epoch[226] Step[184] GlobalStep[88324] Training Speed: 8.19 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1:46:08.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:49:48 INFO loss_tracker.py:84 | Epoch[226/239] Step[199] GlobalStep[88339/NA]: det_loss_cls_0[0.6237] det_loss_box_0[0.8135] det_loss_cls_1[0.5857] det_loss_box_1[0.5979] det_loss_cls_2[0.5213] det_loss_box_2[0.5481] det_loss_cls_3[0.4882] det_loss_box_3[0.5260] det_loss_cls_4[0.4522] det_loss_box_4[0.5124] det_loss_cls_5[0.4302] det_loss_box_5[0.5169] det_loss_cls_dn_0[0.0969] det_loss_box_dn_0[0.2983] det_loss_cls_dn_1[0.0928] det_loss_box_dn_1[0.2514] det_loss_cls_dn_2[0.0924] det_loss_box_dn_2[0.2393] det_loss_cls_dn_3[0.0912] det_loss_box_dn_3[0.2345] det_loss_cls_dn_4[0.0903] det_loss_box_dn_4[0.2320] det_loss_cls_dn_5[0.0896] det_loss_box_dn_5[0.2275] loss_depth[0.0384] total_loss[8.6908]
Rank[0/8] 06/20/2025 19:49:58 INFO stats.py:314 | Epoch[226] Step[209] GlobalStep[88349] Training Speed: 7.69 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1:45:38.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:50:14 INFO loss_tracker.py:84 | Epoch[226/239] Step[224] GlobalStep[88364/NA]: det_loss_cls_0[0.4382] det_loss_box_0[0.5912] det_loss_cls_1[0.4316] det_loss_box_1[0.4437] det_loss_cls_2[0.3784] det_loss_box_2[0.4183] det_loss_cls_3[0.3480] det_loss_box_3[0.4027] det_loss_cls_4[0.3279] det_loss_box_4[0.3926] det_loss_cls_5[0.3140] det_loss_box_5[0.3940] det_loss_cls_dn_0[0.0898] det_loss_box_dn_0[0.2778] det_loss_cls_dn_1[0.0865] det_loss_box_dn_1[0.2355] det_loss_cls_dn_2[0.0854] det_loss_box_dn_2[0.2214] det_loss_cls_dn_3[0.0839] det_loss_box_dn_3[0.2177] det_loss_cls_dn_4[0.0834] det_loss_box_dn_4[0.2162] det_loss_cls_dn_5[0.0827] det_loss_box_dn_5[0.2130] loss_depth[0.0358] total_loss[6.8095]
Rank[0/8] 06/20/2025 19:50:24 INFO stats.py:314 | Epoch[226] Step[234] GlobalStep[88374] Training Speed: 7.86 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1:45:07.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:50:40 INFO loss_tracker.py:84 | Epoch[226/239] Step[249] GlobalStep[88389/NA]: det_loss_cls_0[0.5297] det_loss_box_0[0.6599] det_loss_cls_1[0.5272] det_loss_box_1[0.5135] det_loss_cls_2[0.4603] det_loss_box_2[0.4704] det_loss_cls_3[0.4286] det_loss_box_3[0.4524] det_loss_cls_4[0.4097] det_loss_box_4[0.4361] det_loss_cls_5[0.3955] det_loss_box_5[0.4321] det_loss_cls_dn_0[0.0980] det_loss_box_dn_0[0.3001] det_loss_cls_dn_1[0.0932] det_loss_box_dn_1[0.2498] det_loss_cls_dn_2[0.0919] det_loss_box_dn_2[0.2275] det_loss_cls_dn_3[0.0904] det_loss_box_dn_3[0.2223] det_loss_cls_dn_4[0.0894] det_loss_box_dn_4[0.2197] det_loss_cls_dn_5[0.0884] det_loss_box_dn_5[0.2144] loss_depth[0.0344] total_loss[7.7350]
Rank[0/8] 06/20/2025 19:50:50 INFO stats.py:314 | Epoch[226] Step[259] GlobalStep[88399] Training Speed: 7.68 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1:44:37.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:51:05 INFO loss_tracker.py:84 | Epoch[226/239] Step[274] GlobalStep[88414/NA]: det_loss_cls_0[0.4168] det_loss_box_0[0.4475] det_loss_cls_1[0.3883] det_loss_box_1[0.3330] det_loss_cls_2[0.3348] det_loss_box_2[0.3038] det_loss_cls_3[0.3036] det_loss_box_3[0.2977] det_loss_cls_4[0.2883] det_loss_box_4[0.2935] det_loss_cls_5[0.2772] det_loss_box_5[0.2943] det_loss_cls_dn_0[0.0808] det_loss_box_dn_0[0.2213] det_loss_cls_dn_1[0.0776] det_loss_box_dn_1[0.1831] det_loss_cls_dn_2[0.0772] det_loss_box_dn_2[0.1696] det_loss_cls_dn_3[0.0757] det_loss_box_dn_3[0.1657] det_loss_cls_dn_4[0.0752] det_loss_box_dn_4[0.1641] det_loss_cls_dn_5[0.0747] det_loss_box_dn_5[0.1632] loss_depth[0.0341] total_loss[5.5413]
Rank[0/8] 06/20/2025 19:51:16 INFO stats.py:314 | Epoch[226] Step[284] GlobalStep[88424] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1:44:06.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:51:32 INFO loss_tracker.py:84 | Epoch[226/239] Step[299] GlobalStep[88439/NA]: det_loss_cls_0[0.4158] det_loss_box_0[0.5703] det_loss_cls_1[0.3963] det_loss_box_1[0.4355] det_loss_cls_2[0.3381] det_loss_box_2[0.4025] det_loss_cls_3[0.3157] det_loss_box_3[0.3859] det_loss_cls_4[0.2948] det_loss_box_4[0.3794] det_loss_cls_5[0.2784] det_loss_box_5[0.3833] det_loss_cls_dn_0[0.0833] det_loss_box_dn_0[0.2644] det_loss_cls_dn_1[0.0798] det_loss_box_dn_1[0.2018] det_loss_cls_dn_2[0.0787] det_loss_box_dn_2[0.1845] det_loss_cls_dn_3[0.0777] det_loss_box_dn_3[0.1806] det_loss_cls_dn_4[0.0769] det_loss_box_dn_4[0.1790] det_loss_cls_dn_5[0.0761] det_loss_box_dn_5[0.1757] loss_depth[0.0383] total_loss[6.2927]
Rank[0/8] 06/20/2025 19:51:42 INFO stats.py:314 | Epoch[226] Step[309] GlobalStep[88449] Training Speed: 8.58 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1:43:36.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:51:57 INFO loss_tracker.py:84 | Epoch[226/239] Step[324] GlobalStep[88464/NA]: det_loss_cls_0[0.4316] det_loss_box_0[0.5294] det_loss_cls_1[0.4134] det_loss_box_1[0.4182] det_loss_cls_2[0.3649] det_loss_box_2[0.3886] det_loss_cls_3[0.3522] det_loss_box_3[0.3768] det_loss_cls_4[0.3307] det_loss_box_4[0.3661] det_loss_cls_5[0.3224] det_loss_box_5[0.3611] det_loss_cls_dn_0[0.0855] det_loss_box_dn_0[0.2863] det_loss_cls_dn_1[0.0808] det_loss_box_dn_1[0.2301] det_loss_cls_dn_2[0.0797] det_loss_box_dn_2[0.2079] det_loss_cls_dn_3[0.0788] det_loss_box_dn_3[0.2023] det_loss_cls_dn_4[0.0777] det_loss_box_dn_4[0.1977] det_loss_cls_dn_5[0.0773] det_loss_box_dn_5[0.1893] loss_depth[0.0360] total_loss[6.4849]
Rank[0/8] 06/20/2025 19:52:08 INFO stats.py:314 | Epoch[226] Step[334] GlobalStep[88474] Training Speed: 8.17 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1:43:06.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:52:23 INFO loss_tracker.py:84 | Epoch[226/239] Step[349] GlobalStep[88489/NA]: det_loss_cls_0[0.5221] det_loss_box_0[0.6402] det_loss_cls_1[0.5106] det_loss_box_1[0.4963] det_loss_cls_2[0.4402] det_loss_box_2[0.4544] det_loss_cls_3[0.4137] det_loss_box_3[0.4434] det_loss_cls_4[0.3922] det_loss_box_4[0.4242] det_loss_cls_5[0.3688] det_loss_box_5[0.4324] det_loss_cls_dn_0[0.0952] det_loss_box_dn_0[0.2725] det_loss_cls_dn_1[0.0914] det_loss_box_dn_1[0.2324] det_loss_cls_dn_2[0.0902] det_loss_box_dn_2[0.2166] det_loss_cls_dn_3[0.0889] det_loss_box_dn_3[0.2117] det_loss_cls_dn_4[0.0880] det_loss_box_dn_4[0.2082] det_loss_cls_dn_5[0.0872] det_loss_box_dn_5[0.2049] loss_depth[0.0341] total_loss[7.4599]
Rank[0/8] 06/20/2025 19:52:34 INFO stats.py:314 | Epoch[226] Step[359] GlobalStep[88499] Training Speed: 8.15 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1:42:35.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:52:49 INFO loss_tracker.py:84 | Epoch[226/239] Step[374] GlobalStep[88514/NA]: det_loss_cls_0[0.4433] det_loss_box_0[0.5489] det_loss_cls_1[0.4303] det_loss_box_1[0.4157] det_loss_cls_2[0.3689] det_loss_box_2[0.3740] det_loss_cls_3[0.3393] det_loss_box_3[0.3612] det_loss_cls_4[0.3242] det_loss_box_4[0.3465] det_loss_cls_5[0.3169] det_loss_box_5[0.3437] det_loss_cls_dn_0[0.0932] det_loss_box_dn_0[0.3004] det_loss_cls_dn_1[0.0907] det_loss_box_dn_1[0.2392] det_loss_cls_dn_2[0.0904] det_loss_box_dn_2[0.2121] det_loss_cls_dn_3[0.0895] det_loss_box_dn_3[0.2070] det_loss_cls_dn_4[0.0887] det_loss_box_dn_4[0.2043] det_loss_cls_dn_5[0.0880] det_loss_box_dn_5[0.2007] loss_depth[0.0350] total_loss[6.5522]
Rank[0/8] 06/20/2025 19:52:59 INFO stats.py:314 | Epoch[226] Step[384] GlobalStep[88524] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1:42:05.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:53:04 INFO stats.py:394 | Epoch[226] completed. Training Speed: 7.72 samples/sec across all devices.	Epoch Time: 404.03 sec.	Average Epoch Time: 404.03 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1:41:59.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:53:27 INFO stats.py:314 | Epoch[227] Step[19] GlobalStep[88549] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1:41:34.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:53:32 INFO loss_tracker.py:84 | Epoch[227/239] Step[24] GlobalStep[88554/NA]: det_loss_cls_0[0.3891] det_loss_box_0[0.4618] det_loss_cls_1[0.3702] det_loss_box_1[0.3393] det_loss_cls_2[0.3028] det_loss_box_2[0.3170] det_loss_cls_3[0.2779] det_loss_box_3[0.3046] det_loss_cls_4[0.2523] det_loss_box_4[0.3029] det_loss_cls_5[0.2453] det_loss_box_5[0.3022] det_loss_cls_dn_0[0.0853] det_loss_box_dn_0[0.2654] det_loss_cls_dn_1[0.0825] det_loss_box_dn_1[0.2106] det_loss_cls_dn_2[0.0813] det_loss_box_dn_2[0.1880] det_loss_cls_dn_3[0.0804] det_loss_box_dn_3[0.1852] det_loss_cls_dn_4[0.0792] det_loss_box_dn_4[0.1844] det_loss_cls_dn_5[0.0785] det_loss_box_dn_5[0.1826] loss_depth[0.0341] total_loss[5.6029]
Rank[0/8] 06/20/2025 19:53:53 INFO stats.py:314 | Epoch[227] Step[44] GlobalStep[88574] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1:41:04.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:53:59 INFO loss_tracker.py:84 | Epoch[227/239] Step[49] GlobalStep[88579/NA]: det_loss_cls_0[0.4060] det_loss_box_0[0.4780] det_loss_cls_1[0.3918] det_loss_box_1[0.3602] det_loss_cls_2[0.3189] det_loss_box_2[0.3241] det_loss_cls_3[0.2852] det_loss_box_3[0.3123] det_loss_cls_4[0.2660] det_loss_box_4[0.3046] det_loss_cls_5[0.2512] det_loss_box_5[0.3098] det_loss_cls_dn_0[0.0800] det_loss_box_dn_0[0.2478] det_loss_cls_dn_1[0.0759] det_loss_box_dn_1[0.1959] det_loss_cls_dn_2[0.0751] det_loss_box_dn_2[0.1807] det_loss_cls_dn_3[0.0739] det_loss_box_dn_3[0.1768] det_loss_cls_dn_4[0.0735] det_loss_box_dn_4[0.1736] det_loss_cls_dn_5[0.0726] det_loss_box_dn_5[0.1710] loss_depth[0.0344] total_loss[5.6392]
Rank[0/8] 06/20/2025 19:54:18 INFO stats.py:314 | Epoch[227] Step[69] GlobalStep[88599] Training Speed: 11.43 samples/sec across all devices.	Average Step Time: 0.70 sec.	Estimated Remaining Time: 1:40:34.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:54:24 INFO loss_tracker.py:84 | Epoch[227/239] Step[74] GlobalStep[88604/NA]: det_loss_cls_0[0.3954] det_loss_box_0[0.5371] det_loss_cls_1[0.3825] det_loss_box_1[0.4073] det_loss_cls_2[0.3196] det_loss_box_2[0.3639] det_loss_cls_3[0.2960] det_loss_box_3[0.3566] det_loss_cls_4[0.2781] det_loss_box_4[0.3523] det_loss_cls_5[0.2728] det_loss_box_5[0.3520] det_loss_cls_dn_0[0.0802] det_loss_box_dn_0[0.2932] det_loss_cls_dn_1[0.0763] det_loss_box_dn_1[0.2366] det_loss_cls_dn_2[0.0751] det_loss_box_dn_2[0.2172] det_loss_cls_dn_3[0.0742] det_loss_box_dn_3[0.2110] det_loss_cls_dn_4[0.0734] det_loss_box_dn_4[0.2077] det_loss_cls_dn_5[0.0723] det_loss_box_dn_5[0.2055] loss_depth[0.0374] total_loss[6.1738]
Rank[0/8] 06/20/2025 19:54:45 INFO stats.py:314 | Epoch[227] Step[94] GlobalStep[88624] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1:40:03.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:54:50 INFO loss_tracker.py:84 | Epoch[227/239] Step[99] GlobalStep[88629/NA]: det_loss_cls_0[0.4999] det_loss_box_0[0.6656] det_loss_cls_1[0.4886] det_loss_box_1[0.5128] det_loss_cls_2[0.4357] det_loss_box_2[0.4560] det_loss_cls_3[0.3893] det_loss_box_3[0.4446] det_loss_cls_4[0.3587] det_loss_box_4[0.4265] det_loss_cls_5[0.3449] det_loss_box_5[0.4258] det_loss_cls_dn_0[0.0910] det_loss_box_dn_0[0.2524] det_loss_cls_dn_1[0.0868] det_loss_box_dn_1[0.2124] det_loss_cls_dn_2[0.0857] det_loss_box_dn_2[0.1978] det_loss_cls_dn_3[0.0845] det_loss_box_dn_3[0.1946] det_loss_cls_dn_4[0.0836] det_loss_box_dn_4[0.1914] det_loss_cls_dn_5[0.0824] det_loss_box_dn_5[0.1893] loss_depth[0.0333] total_loss[7.2334]
Rank[0/8] 06/20/2025 19:55:11 INFO stats.py:314 | Epoch[227] Step[119] GlobalStep[88649] Training Speed: 6.14 samples/sec across all devices.	Average Step Time: 1.30 sec.	Estimated Remaining Time: 1:39:33.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:55:16 INFO loss_tracker.py:84 | Epoch[227/239] Step[124] GlobalStep[88654/NA]: det_loss_cls_0[0.5234] det_loss_box_0[0.6392] det_loss_cls_1[0.4943] det_loss_box_1[0.4956] det_loss_cls_2[0.4366] det_loss_box_2[0.4457] det_loss_cls_3[0.4006] det_loss_box_3[0.4315] det_loss_cls_4[0.3778] det_loss_box_4[0.4225] det_loss_cls_5[0.3661] det_loss_box_5[0.4209] det_loss_cls_dn_0[0.0847] det_loss_box_dn_0[0.2727] det_loss_cls_dn_1[0.0811] det_loss_box_dn_1[0.2186] det_loss_cls_dn_2[0.0798] det_loss_box_dn_2[0.2002] det_loss_cls_dn_3[0.0784] det_loss_box_dn_3[0.1924] det_loss_cls_dn_4[0.0776] det_loss_box_dn_4[0.1889] det_loss_cls_dn_5[0.0767] det_loss_box_dn_5[0.1823] loss_depth[0.0340] total_loss[7.2215]
Rank[0/8] 06/20/2025 19:55:37 INFO stats.py:314 | Epoch[227] Step[144] GlobalStep[88674] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1:39:02.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:55:42 INFO loss_tracker.py:84 | Epoch[227/239] Step[149] GlobalStep[88679/NA]: det_loss_cls_0[0.3921] det_loss_box_0[0.5246] det_loss_cls_1[0.3844] det_loss_box_1[0.3895] det_loss_cls_2[0.3244] det_loss_box_2[0.3589] det_loss_cls_3[0.2995] det_loss_box_3[0.3404] det_loss_cls_4[0.2724] det_loss_box_4[0.3393] det_loss_cls_5[0.2602] det_loss_box_5[0.3406] det_loss_cls_dn_0[0.0926] det_loss_box_dn_0[0.2740] det_loss_cls_dn_1[0.0889] det_loss_box_dn_1[0.2208] det_loss_cls_dn_2[0.0871] det_loss_box_dn_2[0.1941] det_loss_cls_dn_3[0.0862] det_loss_box_dn_3[0.1886] det_loss_cls_dn_4[0.0853] det_loss_box_dn_4[0.1858] det_loss_cls_dn_5[0.0849] det_loss_box_dn_5[0.1847] loss_depth[0.0371] total_loss[6.0363]
Rank[0/8] 06/20/2025 19:56:02 INFO stats.py:314 | Epoch[227] Step[169] GlobalStep[88699] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1:38:32.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:56:08 INFO loss_tracker.py:84 | Epoch[227/239] Step[174] GlobalStep[88704/NA]: det_loss_cls_0[0.3959] det_loss_box_0[0.4741] det_loss_cls_1[0.3874] det_loss_box_1[0.3624] det_loss_cls_2[0.3189] det_loss_box_2[0.3368] det_loss_cls_3[0.2888] det_loss_box_3[0.3252] det_loss_cls_4[0.2712] det_loss_box_4[0.3127] det_loss_cls_5[0.2556] det_loss_box_5[0.3140] det_loss_cls_dn_0[0.0941] det_loss_box_dn_0[0.2920] det_loss_cls_dn_1[0.0899] det_loss_box_dn_1[0.2470] det_loss_cls_dn_2[0.0896] det_loss_box_dn_2[0.2272] det_loss_cls_dn_3[0.0881] det_loss_box_dn_3[0.2230] det_loss_cls_dn_4[0.0873] det_loss_box_dn_4[0.2209] det_loss_cls_dn_5[0.0863] det_loss_box_dn_5[0.2150] loss_depth[0.0359] total_loss[6.0393]
Rank[0/8] 06/20/2025 19:56:29 INFO stats.py:314 | Epoch[227] Step[194] GlobalStep[88724] Training Speed: 6.47 samples/sec across all devices.	Average Step Time: 1.24 sec.	Estimated Remaining Time: 1:38:02.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:56:34 INFO loss_tracker.py:84 | Epoch[227/239] Step[199] GlobalStep[88729/NA]: det_loss_cls_0[0.4265] det_loss_box_0[0.5227] det_loss_cls_1[0.4089] det_loss_box_1[0.3826] det_loss_cls_2[0.3651] det_loss_box_2[0.3420] det_loss_cls_3[0.3320] det_loss_box_3[0.3289] det_loss_cls_4[0.3154] det_loss_box_4[0.3199] det_loss_cls_5[0.3031] det_loss_box_5[0.3209] det_loss_cls_dn_0[0.0898] det_loss_box_dn_0[0.2397] det_loss_cls_dn_1[0.0872] det_loss_box_dn_1[0.1921] det_loss_cls_dn_2[0.0866] det_loss_box_dn_2[0.1745] det_loss_cls_dn_3[0.0859] det_loss_box_dn_3[0.1718] det_loss_cls_dn_4[0.0854] det_loss_box_dn_4[0.1704] det_loss_cls_dn_5[0.0849] det_loss_box_dn_5[0.1697] loss_depth[0.0360] total_loss[6.0423]
Rank[0/8] 06/20/2025 19:56:55 INFO stats.py:314 | Epoch[227] Step[219] GlobalStep[88749] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1:37:31.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:57:00 INFO loss_tracker.py:84 | Epoch[227/239] Step[224] GlobalStep[88754/NA]: det_loss_cls_0[0.3907] det_loss_box_0[0.4724] det_loss_cls_1[0.3765] det_loss_box_1[0.3614] det_loss_cls_2[0.3005] det_loss_box_2[0.3277] det_loss_cls_3[0.2665] det_loss_box_3[0.3174] det_loss_cls_4[0.2388] det_loss_box_4[0.3175] det_loss_cls_5[0.2302] det_loss_box_5[0.3163] det_loss_cls_dn_0[0.0737] det_loss_box_dn_0[0.2430] det_loss_cls_dn_1[0.0702] det_loss_box_dn_1[0.1950] det_loss_cls_dn_2[0.0692] det_loss_box_dn_2[0.1775] det_loss_cls_dn_3[0.0681] det_loss_box_dn_3[0.1737] det_loss_cls_dn_4[0.0672] det_loss_box_dn_4[0.1721] det_loss_cls_dn_5[0.0664] det_loss_box_dn_5[0.1704] loss_depth[0.0359] total_loss[5.4982]
Rank[0/8] 06/20/2025 19:57:20 INFO stats.py:314 | Epoch[227] Step[244] GlobalStep[88774] Training Speed: 8.00 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1:37:01.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:57:26 INFO loss_tracker.py:84 | Epoch[227/239] Step[249] GlobalStep[88779/NA]: det_loss_cls_0[0.4140] det_loss_box_0[0.5504] det_loss_cls_1[0.4053] det_loss_box_1[0.4071] det_loss_cls_2[0.3506] det_loss_box_2[0.3708] det_loss_cls_3[0.3217] det_loss_box_3[0.3572] det_loss_cls_4[0.3022] det_loss_box_4[0.3484] det_loss_cls_5[0.2932] det_loss_box_5[0.3464] det_loss_cls_dn_0[0.0884] det_loss_box_dn_0[0.2522] det_loss_cls_dn_1[0.0843] det_loss_box_dn_1[0.2034] det_loss_cls_dn_2[0.0832] det_loss_box_dn_2[0.1898] det_loss_cls_dn_3[0.0820] det_loss_box_dn_3[0.1865] det_loss_cls_dn_4[0.0810] det_loss_box_dn_4[0.1840] det_loss_cls_dn_5[0.0802] det_loss_box_dn_5[0.1830] loss_depth[0.0357] total_loss[6.2009]
Rank[0/8] 06/20/2025 19:57:46 INFO stats.py:314 | Epoch[227] Step[269] GlobalStep[88799] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1:36:30.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:57:52 INFO loss_tracker.py:84 | Epoch[227/239] Step[274] GlobalStep[88804/NA]: det_loss_cls_0[0.5184] det_loss_box_0[0.6433] det_loss_cls_1[0.4924] det_loss_box_1[0.4737] det_loss_cls_2[0.4320] det_loss_box_2[0.4199] det_loss_cls_3[0.3987] det_loss_box_3[0.4144] det_loss_cls_4[0.3787] det_loss_box_4[0.4017] det_loss_cls_5[0.3679] det_loss_box_5[0.3987] det_loss_cls_dn_0[0.0875] det_loss_box_dn_0[0.2335] det_loss_cls_dn_1[0.0848] det_loss_box_dn_1[0.1850] det_loss_cls_dn_2[0.0847] det_loss_box_dn_2[0.1644] det_loss_cls_dn_3[0.0838] det_loss_box_dn_3[0.1619] det_loss_cls_dn_4[0.0831] det_loss_box_dn_4[0.1601] det_loss_cls_dn_5[0.0820] det_loss_box_dn_5[0.1596] loss_depth[0.0323] total_loss[6.9425]
Rank[0/8] 06/20/2025 19:58:13 INFO stats.py:314 | Epoch[227] Step[294] GlobalStep[88824] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1:36:00.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:58:18 INFO loss_tracker.py:84 | Epoch[227/239] Step[299] GlobalStep[88829/NA]: det_loss_cls_0[0.3546] det_loss_box_0[0.4478] det_loss_cls_1[0.3501] det_loss_box_1[0.3545] det_loss_cls_2[0.2746] det_loss_box_2[0.3364] det_loss_cls_3[0.2510] det_loss_box_3[0.3265] det_loss_cls_4[0.2339] det_loss_box_4[0.3190] det_loss_cls_5[0.2278] det_loss_box_5[0.3178] det_loss_cls_dn_0[0.0752] det_loss_box_dn_0[0.2962] det_loss_cls_dn_1[0.0718] det_loss_box_dn_1[0.2423] det_loss_cls_dn_2[0.0708] det_loss_box_dn_2[0.2214] det_loss_cls_dn_3[0.0699] det_loss_box_dn_3[0.2162] det_loss_cls_dn_4[0.0691] det_loss_box_dn_4[0.2121] det_loss_cls_dn_5[0.0687] det_loss_box_dn_5[0.2094] loss_depth[0.0317] total_loss[5.6488]
Rank[0/8] 06/20/2025 19:58:39 INFO stats.py:314 | Epoch[227] Step[319] GlobalStep[88849] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1:35:30.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:58:44 INFO loss_tracker.py:84 | Epoch[227/239] Step[324] GlobalStep[88854/NA]: det_loss_cls_0[0.4964] det_loss_box_0[0.6164] det_loss_cls_1[0.4816] det_loss_box_1[0.4756] det_loss_cls_2[0.4211] det_loss_box_2[0.4333] det_loss_cls_3[0.3918] det_loss_box_3[0.4231] det_loss_cls_4[0.3730] det_loss_box_4[0.4062] det_loss_cls_5[0.3542] det_loss_box_5[0.4112] det_loss_cls_dn_0[0.0913] det_loss_box_dn_0[0.2405] det_loss_cls_dn_1[0.0877] det_loss_box_dn_1[0.1890] det_loss_cls_dn_2[0.0868] det_loss_box_dn_2[0.1697] det_loss_cls_dn_3[0.0855] det_loss_box_dn_3[0.1662] det_loss_cls_dn_4[0.0848] det_loss_box_dn_4[0.1636] det_loss_cls_dn_5[0.0842] det_loss_box_dn_5[0.1625] loss_depth[0.0357] total_loss[6.9313]
Rank[0/8] 06/20/2025 19:59:04 INFO stats.py:314 | Epoch[227] Step[344] GlobalStep[88874] Training Speed: 7.86 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1:34:59.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:59:10 INFO loss_tracker.py:84 | Epoch[227/239] Step[349] GlobalStep[88879/NA]: det_loss_cls_0[0.4091] det_loss_box_0[0.5882] det_loss_cls_1[0.3952] det_loss_box_1[0.4482] det_loss_cls_2[0.3359] det_loss_box_2[0.4193] det_loss_cls_3[0.3013] det_loss_box_3[0.4077] det_loss_cls_4[0.2756] det_loss_box_4[0.3949] det_loss_cls_5[0.2607] det_loss_box_5[0.3964] det_loss_cls_dn_0[0.0905] det_loss_box_dn_0[0.3904] det_loss_cls_dn_1[0.0870] det_loss_box_dn_1[0.3116] det_loss_cls_dn_2[0.0869] det_loss_box_dn_2[0.2805] det_loss_cls_dn_3[0.0859] det_loss_box_dn_3[0.2751] det_loss_cls_dn_4[0.0854] det_loss_box_dn_4[0.2720] det_loss_cls_dn_5[0.0843] det_loss_box_dn_5[0.2699] loss_depth[0.0349] total_loss[6.9868]
Rank[0/8] 06/20/2025 19:59:30 INFO stats.py:314 | Epoch[227] Step[369] GlobalStep[88899] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1:34:29.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:59:35 INFO loss_tracker.py:84 | Epoch[227/239] Step[374] GlobalStep[88904/NA]: det_loss_cls_0[0.3901] det_loss_box_0[0.4992] det_loss_cls_1[0.3817] det_loss_box_1[0.3891] det_loss_cls_2[0.3372] det_loss_box_2[0.3630] det_loss_cls_3[0.3107] det_loss_box_3[0.3454] det_loss_cls_4[0.2940] det_loss_box_4[0.3347] det_loss_cls_5[0.2798] det_loss_box_5[0.3411] det_loss_cls_dn_0[0.0973] det_loss_box_dn_0[0.2902] det_loss_cls_dn_1[0.0934] det_loss_box_dn_1[0.2363] det_loss_cls_dn_2[0.0925] det_loss_box_dn_2[0.2124] det_loss_cls_dn_3[0.0914] det_loss_box_dn_3[0.2069] det_loss_cls_dn_4[0.0906] det_loss_box_dn_4[0.2048] det_loss_cls_dn_5[0.0896] det_loss_box_dn_5[0.2031] loss_depth[0.0359] total_loss[6.2104]
Rank[0/8] 06/20/2025 19:59:51 INFO stats.py:394 | Epoch[227] completed. Training Speed: 7.68 samples/sec across all devices.	Epoch Time: 406.51 sec.	Average Epoch Time: 406.51 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1:34:05.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 19:59:57 INFO stats.py:314 | Epoch[228] Step[4] GlobalStep[88924] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1:33:59.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:00:18 INFO loss_tracker.py:84 | Epoch[228/239] Step[24] GlobalStep[88944/NA]: det_loss_cls_0[0.3972] det_loss_box_0[0.4964] det_loss_cls_1[0.3837] det_loss_box_1[0.3732] det_loss_cls_2[0.3288] det_loss_box_2[0.3371] det_loss_cls_3[0.2905] det_loss_box_3[0.3290] det_loss_cls_4[0.2714] det_loss_box_4[0.3243] det_loss_cls_5[0.2631] det_loss_box_5[0.3265] det_loss_cls_dn_0[0.0872] det_loss_box_dn_0[0.2519] det_loss_cls_dn_1[0.0834] det_loss_box_dn_1[0.2051] det_loss_cls_dn_2[0.0830] det_loss_box_dn_2[0.1909] det_loss_cls_dn_3[0.0818] det_loss_box_dn_3[0.1874] det_loss_cls_dn_4[0.0812] det_loss_box_dn_4[0.1853] det_loss_cls_dn_5[0.0802] det_loss_box_dn_5[0.1816] loss_depth[0.0355] total_loss[5.8558]
Rank[0/8] 06/20/2025 20:00:23 INFO stats.py:314 | Epoch[228] Step[29] GlobalStep[88949] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1:33:28.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:00:43 INFO loss_tracker.py:84 | Epoch[228/239] Step[49] GlobalStep[88969/NA]: det_loss_cls_0[0.4952] det_loss_box_0[0.6779] det_loss_cls_1[0.4642] det_loss_box_1[0.5353] det_loss_cls_2[0.4219] det_loss_box_2[0.4804] det_loss_cls_3[0.3971] det_loss_box_3[0.4672] det_loss_cls_4[0.3788] det_loss_box_4[0.4545] det_loss_cls_5[0.3643] det_loss_box_5[0.4549] det_loss_cls_dn_0[0.0916] det_loss_box_dn_0[0.2934] det_loss_cls_dn_1[0.0883] det_loss_box_dn_1[0.2470] det_loss_cls_dn_2[0.0870] det_loss_box_dn_2[0.2330] det_loss_cls_dn_3[0.0860] det_loss_box_dn_3[0.2286] det_loss_cls_dn_4[0.0855] det_loss_box_dn_4[0.2267] det_loss_cls_dn_5[0.0848] det_loss_box_dn_5[0.2253] loss_depth[0.0356] total_loss[7.6043]
Rank[0/8] 06/20/2025 20:00:49 INFO stats.py:314 | Epoch[228] Step[54] GlobalStep[88974] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1:32:58.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:01:10 INFO loss_tracker.py:84 | Epoch[228/239] Step[74] GlobalStep[88994/NA]: det_loss_cls_0[0.4154] det_loss_box_0[0.5266] det_loss_cls_1[0.3948] det_loss_box_1[0.3858] det_loss_cls_2[0.3247] det_loss_box_2[0.3509] det_loss_cls_3[0.2944] det_loss_box_3[0.3427] det_loss_cls_4[0.2778] det_loss_box_4[0.3365] det_loss_cls_5[0.2641] det_loss_box_5[0.3380] det_loss_cls_dn_0[0.0880] det_loss_box_dn_0[0.2498] det_loss_cls_dn_1[0.0838] det_loss_box_dn_1[0.2055] det_loss_cls_dn_2[0.0832] det_loss_box_dn_2[0.1913] det_loss_cls_dn_3[0.0819] det_loss_box_dn_3[0.1879] det_loss_cls_dn_4[0.0810] det_loss_box_dn_4[0.1866] det_loss_cls_dn_5[0.0804] det_loss_box_dn_5[0.1848] loss_depth[0.0336] total_loss[5.9894]
Rank[0/8] 06/20/2025 20:01:15 INFO stats.py:314 | Epoch[228] Step[79] GlobalStep[88999] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1:32:27.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:01:35 INFO loss_tracker.py:84 | Epoch[228/239] Step[99] GlobalStep[89019/NA]: det_loss_cls_0[0.5337] det_loss_box_0[0.6162] det_loss_cls_1[0.5217] det_loss_box_1[0.4613] det_loss_cls_2[0.4452] det_loss_box_2[0.4179] det_loss_cls_3[0.3982] det_loss_box_3[0.4025] det_loss_cls_4[0.3759] det_loss_box_4[0.3901] det_loss_cls_5[0.3636] det_loss_box_5[0.3912] det_loss_cls_dn_0[0.0941] det_loss_box_dn_0[0.2279] det_loss_cls_dn_1[0.0907] det_loss_box_dn_1[0.1811] det_loss_cls_dn_2[0.0904] det_loss_box_dn_2[0.1645] det_loss_cls_dn_3[0.0892] det_loss_box_dn_3[0.1612] det_loss_cls_dn_4[0.0882] det_loss_box_dn_4[0.1588] det_loss_cls_dn_5[0.0875] det_loss_box_dn_5[0.1580] loss_depth[0.0375] total_loss[6.9469]
Rank[0/8] 06/20/2025 20:01:40 INFO stats.py:314 | Epoch[228] Step[104] GlobalStep[89024] Training Speed: 7.77 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 1:31:57.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:02:01 INFO loss_tracker.py:84 | Epoch[228/239] Step[124] GlobalStep[89044/NA]: det_loss_cls_0[0.4010] det_loss_box_0[0.4897] det_loss_cls_1[0.3937] det_loss_box_1[0.3506] det_loss_cls_2[0.3290] det_loss_box_2[0.3172] det_loss_cls_3[0.3003] det_loss_box_3[0.3008] det_loss_cls_4[0.2750] det_loss_box_4[0.2943] det_loss_cls_5[0.2652] det_loss_box_5[0.2944] det_loss_cls_dn_0[0.0878] det_loss_box_dn_0[0.3007] det_loss_cls_dn_1[0.0840] det_loss_box_dn_1[0.2423] det_loss_cls_dn_2[0.0827] det_loss_box_dn_2[0.2229] det_loss_cls_dn_3[0.0816] det_loss_box_dn_3[0.2106] det_loss_cls_dn_4[0.0809] det_loss_box_dn_4[0.2063] det_loss_cls_dn_5[0.0800] det_loss_box_dn_5[0.1975] loss_depth[0.0359] total_loss[5.9244]
Rank[0/8] 06/20/2025 20:02:06 INFO stats.py:314 | Epoch[228] Step[129] GlobalStep[89049] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1:31:27.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:02:27 INFO loss_tracker.py:84 | Epoch[228/239] Step[149] GlobalStep[89069/NA]: det_loss_cls_0[0.4507] det_loss_box_0[0.6649] det_loss_cls_1[0.4285] det_loss_box_1[0.5032] det_loss_cls_2[0.3713] det_loss_box_2[0.4530] det_loss_cls_3[0.3370] det_loss_box_3[0.4380] det_loss_cls_4[0.3093] det_loss_box_4[0.4355] det_loss_cls_5[0.2931] det_loss_box_5[0.4422] det_loss_cls_dn_0[0.0875] det_loss_box_dn_0[0.3649] det_loss_cls_dn_1[0.0835] det_loss_box_dn_1[0.2935] det_loss_cls_dn_2[0.0832] det_loss_box_dn_2[0.2681] det_loss_cls_dn_3[0.0821] det_loss_box_dn_3[0.2588] det_loss_cls_dn_4[0.0813] det_loss_box_dn_4[0.2559] det_loss_cls_dn_5[0.0803] det_loss_box_dn_5[0.2527] loss_depth[0.0370] total_loss[7.3559]
Rank[0/8] 06/20/2025 20:02:32 INFO stats.py:314 | Epoch[228] Step[154] GlobalStep[89074] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1:30:56.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:02:52 INFO loss_tracker.py:84 | Epoch[228/239] Step[174] GlobalStep[89094/NA]: det_loss_cls_0[0.4151] det_loss_box_0[0.5132] det_loss_cls_1[0.4086] det_loss_box_1[0.3827] det_loss_cls_2[0.3549] det_loss_box_2[0.3476] det_loss_cls_3[0.3231] det_loss_box_3[0.3313] det_loss_cls_4[0.2895] det_loss_box_4[0.3280] det_loss_cls_5[0.2783] det_loss_box_5[0.3336] det_loss_cls_dn_0[0.0907] det_loss_box_dn_0[0.2843] det_loss_cls_dn_1[0.0863] det_loss_box_dn_1[0.2415] det_loss_cls_dn_2[0.0845] det_loss_box_dn_2[0.2251] det_loss_cls_dn_3[0.0837] det_loss_box_dn_3[0.2198] det_loss_cls_dn_4[0.0829] det_loss_box_dn_4[0.2170] det_loss_cls_dn_5[0.0819] det_loss_box_dn_5[0.2142] loss_depth[0.0335] total_loss[6.2514]
Rank[0/8] 06/20/2025 20:02:58 INFO stats.py:314 | Epoch[228] Step[179] GlobalStep[89099] Training Speed: 8.22 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1:30:26.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:03:18 INFO loss_tracker.py:84 | Epoch[228/239] Step[199] GlobalStep[89119/NA]: det_loss_cls_0[0.5458] det_loss_box_0[0.6607] det_loss_cls_1[0.5398] det_loss_box_1[0.4576] det_loss_cls_2[0.4552] det_loss_box_2[0.4084] det_loss_cls_3[0.4040] det_loss_box_3[0.3944] det_loss_cls_4[0.3817] det_loss_box_4[0.3820] det_loss_cls_5[0.3651] det_loss_box_5[0.3839] det_loss_cls_dn_0[0.0922] det_loss_box_dn_0[0.2199] det_loss_cls_dn_1[0.0883] det_loss_box_dn_1[0.1810] det_loss_cls_dn_2[0.0869] det_loss_box_dn_2[0.1644] det_loss_cls_dn_3[0.0856] det_loss_box_dn_3[0.1608] det_loss_cls_dn_4[0.0849] det_loss_box_dn_4[0.1580] det_loss_cls_dn_5[0.0842] det_loss_box_dn_5[0.1574] loss_depth[0.0358] total_loss[6.9778]
Rank[0/8] 06/20/2025 20:03:24 INFO stats.py:314 | Epoch[228] Step[204] GlobalStep[89124] Training Speed: 5.48 samples/sec across all devices.	Average Step Time: 1.46 sec.	Estimated Remaining Time: 1:29:56.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:03:44 INFO loss_tracker.py:84 | Epoch[228/239] Step[224] GlobalStep[89144/NA]: det_loss_cls_0[0.5028] det_loss_box_0[0.5487] det_loss_cls_1[0.4805] det_loss_box_1[0.4100] det_loss_cls_2[0.4181] det_loss_box_2[0.3661] det_loss_cls_3[0.3891] det_loss_box_3[0.3473] det_loss_cls_4[0.3608] det_loss_box_4[0.3437] det_loss_cls_5[0.3474] det_loss_box_5[0.3387] det_loss_cls_dn_0[0.0930] det_loss_box_dn_0[0.2138] det_loss_cls_dn_1[0.0905] det_loss_box_dn_1[0.1678] det_loss_cls_dn_2[0.0898] det_loss_box_dn_2[0.1521] det_loss_cls_dn_3[0.0887] det_loss_box_dn_3[0.1505] det_loss_cls_dn_4[0.0880] det_loss_box_dn_4[0.1485] det_loss_cls_dn_5[0.0874] det_loss_box_dn_5[0.1473] loss_depth[0.0359] total_loss[6.4064]
Rank[0/8] 06/20/2025 20:03:50 INFO stats.py:314 | Epoch[228] Step[229] GlobalStep[89149] Training Speed: 6.74 samples/sec across all devices.	Average Step Time: 1.19 sec.	Estimated Remaining Time: 1:29:25.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:04:10 INFO loss_tracker.py:84 | Epoch[228/239] Step[249] GlobalStep[89169/NA]: det_loss_cls_0[0.4698] det_loss_box_0[0.5889] det_loss_cls_1[0.4473] det_loss_box_1[0.4446] det_loss_cls_2[0.3927] det_loss_box_2[0.3985] det_loss_cls_3[0.3583] det_loss_box_3[0.3873] det_loss_cls_4[0.3364] det_loss_box_4[0.3775] det_loss_cls_5[0.3251] det_loss_box_5[0.3795] det_loss_cls_dn_0[0.0840] det_loss_box_dn_0[0.2396] det_loss_cls_dn_1[0.0793] det_loss_box_dn_1[0.1941] det_loss_cls_dn_2[0.0784] det_loss_box_dn_2[0.1769] det_loss_cls_dn_3[0.0770] det_loss_box_dn_3[0.1721] det_loss_cls_dn_4[0.0765] det_loss_box_dn_4[0.1702] det_loss_cls_dn_5[0.0754] det_loss_box_dn_5[0.1681] loss_depth[0.0342] total_loss[6.5317]
Rank[0/8] 06/20/2025 20:04:15 INFO stats.py:314 | Epoch[228] Step[254] GlobalStep[89174] Training Speed: 7.63 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1:28:55.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:04:36 INFO loss_tracker.py:84 | Epoch[228/239] Step[274] GlobalStep[89194/NA]: det_loss_cls_0[0.5080] det_loss_box_0[0.6407] det_loss_cls_1[0.4905] det_loss_box_1[0.4926] det_loss_cls_2[0.4341] det_loss_box_2[0.4442] det_loss_cls_3[0.4017] det_loss_box_3[0.4292] det_loss_cls_4[0.3799] det_loss_box_4[0.4111] det_loss_cls_5[0.3652] det_loss_box_5[0.4107] det_loss_cls_dn_0[0.0971] det_loss_box_dn_0[0.2734] det_loss_cls_dn_1[0.0929] det_loss_box_dn_1[0.2234] det_loss_cls_dn_2[0.0925] det_loss_box_dn_2[0.2066] det_loss_cls_dn_3[0.0914] det_loss_box_dn_3[0.2033] det_loss_cls_dn_4[0.0907] det_loss_box_dn_4[0.2006] det_loss_cls_dn_5[0.0899] det_loss_box_dn_5[0.1990] loss_depth[0.0355] total_loss[7.3042]
Rank[0/8] 06/20/2025 20:04:41 INFO stats.py:314 | Epoch[228] Step[279] GlobalStep[89199] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1:28:24.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:05:02 INFO loss_tracker.py:84 | Epoch[228/239] Step[299] GlobalStep[89219/NA]: det_loss_cls_0[0.3907] det_loss_box_0[0.4557] det_loss_cls_1[0.3738] det_loss_box_1[0.3376] det_loss_cls_2[0.3096] det_loss_box_2[0.3095] det_loss_cls_3[0.2830] det_loss_box_3[0.3040] det_loss_cls_4[0.2586] det_loss_box_4[0.3010] det_loss_cls_5[0.2516] det_loss_box_5[0.2981] det_loss_cls_dn_0[0.0847] det_loss_box_dn_0[0.2455] det_loss_cls_dn_1[0.0812] det_loss_box_dn_1[0.1961] det_loss_cls_dn_2[0.0805] det_loss_box_dn_2[0.1787] det_loss_cls_dn_3[0.0795] det_loss_box_dn_3[0.1765] det_loss_cls_dn_4[0.0787] det_loss_box_dn_4[0.1744] det_loss_cls_dn_5[0.0778] det_loss_box_dn_5[0.1722] loss_depth[0.0339] total_loss[5.5327]
Rank[0/8] 06/20/2025 20:05:07 INFO stats.py:314 | Epoch[228] Step[304] GlobalStep[89224] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1:27:54.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:05:28 INFO loss_tracker.py:84 | Epoch[228/239] Step[324] GlobalStep[89244/NA]: det_loss_cls_0[0.3156] det_loss_box_0[0.3982] det_loss_cls_1[0.3123] det_loss_box_1[0.2791] det_loss_cls_2[0.2421] det_loss_box_2[0.2525] det_loss_cls_3[0.2177] det_loss_box_3[0.2388] det_loss_cls_4[0.2065] det_loss_box_4[0.2322] det_loss_cls_5[0.1983] det_loss_box_5[0.2331] det_loss_cls_dn_0[0.0795] det_loss_box_dn_0[0.2614] det_loss_cls_dn_1[0.0767] det_loss_box_dn_1[0.2137] det_loss_cls_dn_2[0.0759] det_loss_box_dn_2[0.1949] det_loss_cls_dn_3[0.0749] det_loss_box_dn_3[0.1920] det_loss_cls_dn_4[0.0743] det_loss_box_dn_4[0.1902] det_loss_cls_dn_5[0.0737] det_loss_box_dn_5[0.1872] loss_depth[0.0371] total_loss[4.8579]
Rank[0/8] 06/20/2025 20:05:33 INFO stats.py:314 | Epoch[228] Step[329] GlobalStep[89249] Training Speed: 8.05 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1:27:24.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:05:54 INFO loss_tracker.py:84 | Epoch[228/239] Step[349] GlobalStep[89269/NA]: det_loss_cls_0[0.4747] det_loss_box_0[0.5232] det_loss_cls_1[0.4573] det_loss_box_1[0.3839] det_loss_cls_2[0.3787] det_loss_box_2[0.3511] det_loss_cls_3[0.3442] det_loss_box_3[0.3371] det_loss_cls_4[0.3140] det_loss_box_4[0.3352] det_loss_cls_5[0.3034] det_loss_box_5[0.3329] det_loss_cls_dn_0[0.0914] det_loss_box_dn_0[0.2306] det_loss_cls_dn_1[0.0872] det_loss_box_dn_1[0.1845] det_loss_cls_dn_2[0.0866] det_loss_box_dn_2[0.1686] det_loss_cls_dn_3[0.0854] det_loss_box_dn_3[0.1642] det_loss_cls_dn_4[0.0848] det_loss_box_dn_4[0.1619] det_loss_cls_dn_5[0.0839] det_loss_box_dn_5[0.1596] loss_depth[0.0341] total_loss[6.1584]
Rank[0/8] 06/20/2025 20:05:59 INFO stats.py:314 | Epoch[228] Step[354] GlobalStep[89274] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1:26:53.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:06:20 INFO loss_tracker.py:84 | Epoch[228/239] Step[374] GlobalStep[89294/NA]: det_loss_cls_0[0.4295] det_loss_box_0[0.6264] det_loss_cls_1[0.4256] det_loss_box_1[0.4773] det_loss_cls_2[0.3729] det_loss_box_2[0.4366] det_loss_cls_3[0.3335] det_loss_box_3[0.4182] det_loss_cls_4[0.3159] det_loss_box_4[0.4021] det_loss_cls_5[0.3002] det_loss_box_5[0.4081] det_loss_cls_dn_0[0.0866] det_loss_box_dn_0[0.2785] det_loss_cls_dn_1[0.0825] det_loss_box_dn_1[0.2300] det_loss_cls_dn_2[0.0818] det_loss_box_dn_2[0.2125] det_loss_cls_dn_3[0.0805] det_loss_box_dn_3[0.2081] det_loss_cls_dn_4[0.0796] det_loss_box_dn_4[0.2047] det_loss_cls_dn_5[0.0788] det_loss_box_dn_5[0.2030] loss_depth[0.0364] total_loss[6.8096]
Rank[0/8] 06/20/2025 20:06:24 INFO stats.py:314 | Epoch[228] Step[379] GlobalStep[89299] Training Speed: 8.84 samples/sec across all devices.	Average Step Time: 0.90 sec.	Estimated Remaining Time: 1:26:23.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:06:35 INFO stats.py:394 | Epoch[228] completed. Training Speed: 7.72 samples/sec across all devices.	Epoch Time: 404.09 sec.	Average Epoch Time: 404.09 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1:26:11.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:06:51 INFO stats.py:314 | Epoch[229] Step[14] GlobalStep[89324] Training Speed: 8.49 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1:25:53.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:07:02 INFO loss_tracker.py:84 | Epoch[229/239] Step[24] GlobalStep[89334/NA]: det_loss_cls_0[0.4133] det_loss_box_0[0.5148] det_loss_cls_1[0.4126] det_loss_box_1[0.3835] det_loss_cls_2[0.3394] det_loss_box_2[0.3529] det_loss_cls_3[0.3031] det_loss_box_3[0.3448] det_loss_cls_4[0.2810] det_loss_box_4[0.3298] det_loss_cls_5[0.2651] det_loss_box_5[0.3349] det_loss_cls_dn_0[0.0827] det_loss_box_dn_0[0.2292] det_loss_cls_dn_1[0.0794] det_loss_box_dn_1[0.1861] det_loss_cls_dn_2[0.0787] det_loss_box_dn_2[0.1703] det_loss_cls_dn_3[0.0776] det_loss_box_dn_3[0.1672] det_loss_cls_dn_4[0.0768] det_loss_box_dn_4[0.1652] det_loss_cls_dn_5[0.0761] det_loss_box_dn_5[0.1638] loss_depth[0.0328] total_loss[5.8611]
Rank[0/8] 06/20/2025 20:07:17 INFO stats.py:314 | Epoch[229] Step[39] GlobalStep[89349] Training Speed: 6.95 samples/sec across all devices.	Average Step Time: 1.15 sec.	Estimated Remaining Time: 1:25:22.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:07:27 INFO loss_tracker.py:84 | Epoch[229/239] Step[49] GlobalStep[89359/NA]: det_loss_cls_0[0.4704] det_loss_box_0[0.5242] det_loss_cls_1[0.4584] det_loss_box_1[0.4117] det_loss_cls_2[0.3726] det_loss_box_2[0.3851] det_loss_cls_3[0.3478] det_loss_box_3[0.3678] det_loss_cls_4[0.3286] det_loss_box_4[0.3613] det_loss_cls_5[0.3105] det_loss_box_5[0.3643] det_loss_cls_dn_0[0.0909] det_loss_box_dn_0[0.2345] det_loss_cls_dn_1[0.0870] det_loss_box_dn_1[0.1916] det_loss_cls_dn_2[0.0867] det_loss_box_dn_2[0.1729] det_loss_cls_dn_3[0.0857] det_loss_box_dn_3[0.1705] det_loss_cls_dn_4[0.0850] det_loss_box_dn_4[0.1676] det_loss_cls_dn_5[0.0847] det_loss_box_dn_5[0.1649] loss_depth[0.0371] total_loss[6.3617]
Rank[0/8] 06/20/2025 20:07:43 INFO stats.py:314 | Epoch[229] Step[64] GlobalStep[89374] Training Speed: 6.19 samples/sec across all devices.	Average Step Time: 1.29 sec.	Estimated Remaining Time: 1:24:52.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:07:53 INFO loss_tracker.py:84 | Epoch[229/239] Step[74] GlobalStep[89384/NA]: det_loss_cls_0[0.4075] det_loss_box_0[0.5494] det_loss_cls_1[0.4040] det_loss_box_1[0.4191] det_loss_cls_2[0.3518] det_loss_box_2[0.3828] det_loss_cls_3[0.3310] det_loss_box_3[0.3700] det_loss_cls_4[0.3144] det_loss_box_4[0.3558] det_loss_cls_5[0.3013] det_loss_box_5[0.3532] det_loss_cls_dn_0[0.0899] det_loss_box_dn_0[0.3203] det_loss_cls_dn_1[0.0859] det_loss_box_dn_1[0.2709] det_loss_cls_dn_2[0.0851] det_loss_box_dn_2[0.2497] det_loss_cls_dn_3[0.0846] det_loss_box_dn_3[0.2425] det_loss_cls_dn_4[0.0839] det_loss_box_dn_4[0.2379] det_loss_cls_dn_5[0.0830] det_loss_box_dn_5[0.2321] loss_depth[0.0356] total_loss[6.6418]
Rank[0/8] 06/20/2025 20:08:09 INFO stats.py:314 | Epoch[229] Step[89] GlobalStep[89399] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1:24:22.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:08:19 INFO loss_tracker.py:84 | Epoch[229/239] Step[99] GlobalStep[89409/NA]: det_loss_cls_0[0.4669] det_loss_box_0[0.5559] det_loss_cls_1[0.4482] det_loss_box_1[0.3928] det_loss_cls_2[0.3851] det_loss_box_2[0.3457] det_loss_cls_3[0.3477] det_loss_box_3[0.3375] det_loss_cls_4[0.3226] det_loss_box_4[0.3313] det_loss_cls_5[0.3085] det_loss_box_5[0.3346] det_loss_cls_dn_0[0.0965] det_loss_box_dn_0[0.2549] det_loss_cls_dn_1[0.0932] det_loss_box_dn_1[0.2034] det_loss_cls_dn_2[0.0921] det_loss_box_dn_2[0.1827] det_loss_cls_dn_3[0.0915] det_loss_box_dn_3[0.1774] det_loss_cls_dn_4[0.0910] det_loss_box_dn_4[0.1746] det_loss_cls_dn_5[0.0904] det_loss_box_dn_5[0.1706] loss_depth[0.0366] total_loss[6.3316]
Rank[0/8] 06/20/2025 20:08:34 INFO stats.py:314 | Epoch[229] Step[114] GlobalStep[89424] Training Speed: 7.81 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1:23:51.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:08:44 INFO loss_tracker.py:84 | Epoch[229/239] Step[124] GlobalStep[89434/NA]: det_loss_cls_0[0.4053] det_loss_box_0[0.4686] det_loss_cls_1[0.3984] det_loss_box_1[0.3553] det_loss_cls_2[0.3416] det_loss_box_2[0.3282] det_loss_cls_3[0.3173] det_loss_box_3[0.3131] det_loss_cls_4[0.2967] det_loss_box_4[0.3002] det_loss_cls_5[0.2827] det_loss_box_5[0.3005] det_loss_cls_dn_0[0.0855] det_loss_box_dn_0[0.2201] det_loss_cls_dn_1[0.0829] det_loss_box_dn_1[0.1725] det_loss_cls_dn_2[0.0820] det_loss_box_dn_2[0.1551] det_loss_cls_dn_3[0.0812] det_loss_box_dn_3[0.1530] det_loss_cls_dn_4[0.0803] det_loss_box_dn_4[0.1506] det_loss_cls_dn_5[0.0796] det_loss_box_dn_5[0.1487] loss_depth[0.0341] total_loss[5.6336]
Rank[0/8] 06/20/2025 20:09:00 INFO stats.py:314 | Epoch[229] Step[139] GlobalStep[89449] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1:23:21.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:09:11 INFO loss_tracker.py:84 | Epoch[229/239] Step[149] GlobalStep[89459/NA]: det_loss_cls_0[0.5087] det_loss_box_0[0.6334] det_loss_cls_1[0.5010] det_loss_box_1[0.4979] det_loss_cls_2[0.4287] det_loss_box_2[0.4755] det_loss_cls_3[0.4011] det_loss_box_3[0.4688] det_loss_cls_4[0.3813] det_loss_box_4[0.4552] det_loss_cls_5[0.3726] det_loss_box_5[0.4540] det_loss_cls_dn_0[0.0886] det_loss_box_dn_0[0.2982] det_loss_cls_dn_1[0.0846] det_loss_box_dn_1[0.2485] det_loss_cls_dn_2[0.0837] det_loss_box_dn_2[0.2328] det_loss_cls_dn_3[0.0824] det_loss_box_dn_3[0.2292] det_loss_cls_dn_4[0.0817] det_loss_box_dn_4[0.2253] det_loss_cls_dn_5[0.0809] det_loss_box_dn_5[0.2170] loss_depth[0.0360] total_loss[7.5672]
Rank[0/8] 06/20/2025 20:09:27 INFO stats.py:314 | Epoch[229] Step[164] GlobalStep[89474] Training Speed: 8.01 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1:22:51.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:09:37 INFO loss_tracker.py:84 | Epoch[229/239] Step[174] GlobalStep[89484/NA]: det_loss_cls_0[0.3833] det_loss_box_0[0.4603] det_loss_cls_1[0.3751] det_loss_box_1[0.3435] det_loss_cls_2[0.3179] det_loss_box_2[0.3089] det_loss_cls_3[0.2882] det_loss_box_3[0.3055] det_loss_cls_4[0.2686] det_loss_box_4[0.2948] det_loss_cls_5[0.2563] det_loss_box_5[0.2952] det_loss_cls_dn_0[0.0869] det_loss_box_dn_0[0.2508] det_loss_cls_dn_1[0.0842] det_loss_box_dn_1[0.1990] det_loss_cls_dn_2[0.0839] det_loss_box_dn_2[0.1810] det_loss_cls_dn_3[0.0828] det_loss_box_dn_3[0.1764] det_loss_cls_dn_4[0.0820] det_loss_box_dn_4[0.1749] det_loss_cls_dn_5[0.0814] det_loss_box_dn_5[0.1706] loss_depth[0.0367] total_loss[5.5883]
Rank[0/8] 06/20/2025 20:09:52 INFO stats.py:314 | Epoch[229] Step[189] GlobalStep[89499] Training Speed: 8.74 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 1:22:20.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:10:03 INFO loss_tracker.py:84 | Epoch[229/239] Step[199] GlobalStep[89509/NA]: det_loss_cls_0[0.5888] det_loss_box_0[0.7564] det_loss_cls_1[0.5673] det_loss_box_1[0.5644] det_loss_cls_2[0.4985] det_loss_box_2[0.5007] det_loss_cls_3[0.4619] det_loss_box_3[0.4720] det_loss_cls_4[0.4306] det_loss_box_4[0.4588] det_loss_cls_5[0.4139] det_loss_box_5[0.4615] det_loss_cls_dn_0[0.0975] det_loss_box_dn_0[0.2711] det_loss_cls_dn_1[0.0936] det_loss_box_dn_1[0.2251] det_loss_cls_dn_2[0.0923] det_loss_box_dn_2[0.2072] det_loss_cls_dn_3[0.0909] det_loss_box_dn_3[0.2025] det_loss_cls_dn_4[0.0904] det_loss_box_dn_4[0.1988] det_loss_cls_dn_5[0.0894] det_loss_box_dn_5[0.1958] loss_depth[0.0364] total_loss[8.0656]
Rank[0/8] 06/20/2025 20:10:18 INFO stats.py:314 | Epoch[229] Step[214] GlobalStep[89524] Training Speed: 6.78 samples/sec across all devices.	Average Step Time: 1.18 sec.	Estimated Remaining Time: 1:21:50.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:10:28 INFO loss_tracker.py:84 | Epoch[229/239] Step[224] GlobalStep[89534/NA]: det_loss_cls_0[0.4038] det_loss_box_0[0.4289] det_loss_cls_1[0.4037] det_loss_box_1[0.3155] det_loss_cls_2[0.3329] det_loss_box_2[0.2910] det_loss_cls_3[0.3100] det_loss_box_3[0.2774] det_loss_cls_4[0.2860] det_loss_box_4[0.2758] det_loss_cls_5[0.2810] det_loss_box_5[0.2724] det_loss_cls_dn_0[0.0896] det_loss_box_dn_0[0.2368] det_loss_cls_dn_1[0.0855] det_loss_box_dn_1[0.1886] det_loss_cls_dn_2[0.0844] det_loss_box_dn_2[0.1711] det_loss_cls_dn_3[0.0834] det_loss_box_dn_3[0.1680] det_loss_cls_dn_4[0.0829] det_loss_box_dn_4[0.1655] det_loss_cls_dn_5[0.0821] det_loss_box_dn_5[0.1630] loss_depth[0.0335] total_loss[5.5127]
Rank[0/8] 06/20/2025 20:10:44 INFO stats.py:314 | Epoch[229] Step[239] GlobalStep[89549] Training Speed: 8.79 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 1:21:20.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:10:54 INFO loss_tracker.py:84 | Epoch[229/239] Step[249] GlobalStep[89559/NA]: det_loss_cls_0[0.3281] det_loss_box_0[0.4535] det_loss_cls_1[0.3157] det_loss_box_1[0.3587] det_loss_cls_2[0.2588] det_loss_box_2[0.3310] det_loss_cls_3[0.2347] det_loss_box_3[0.3185] det_loss_cls_4[0.2170] det_loss_box_4[0.3107] det_loss_cls_5[0.2099] det_loss_box_5[0.3121] det_loss_cls_dn_0[0.0816] det_loss_box_dn_0[0.3145] det_loss_cls_dn_1[0.0783] det_loss_box_dn_1[0.2566] det_loss_cls_dn_2[0.0777] det_loss_box_dn_2[0.2334] det_loss_cls_dn_3[0.0762] det_loss_box_dn_3[0.2294] det_loss_cls_dn_4[0.0757] det_loss_box_dn_4[0.2261] det_loss_cls_dn_5[0.0746] det_loss_box_dn_5[0.2242] loss_depth[0.0334] total_loss[5.6305]
Rank[0/8] 06/20/2025 20:11:10 INFO stats.py:314 | Epoch[229] Step[264] GlobalStep[89574] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1:20:49.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:11:20 INFO loss_tracker.py:84 | Epoch[229/239] Step[274] GlobalStep[89584/NA]: det_loss_cls_0[0.5449] det_loss_box_0[0.6618] det_loss_cls_1[0.5263] det_loss_box_1[0.4918] det_loss_cls_2[0.4548] det_loss_box_2[0.4544] det_loss_cls_3[0.4167] det_loss_box_3[0.4421] det_loss_cls_4[0.3905] det_loss_box_4[0.4323] det_loss_cls_5[0.3699] det_loss_box_5[0.4370] det_loss_cls_dn_0[0.0978] det_loss_box_dn_0[0.2431] det_loss_cls_dn_1[0.0936] det_loss_box_dn_1[0.2001] det_loss_cls_dn_2[0.0931] det_loss_box_dn_2[0.1876] det_loss_cls_dn_3[0.0921] det_loss_box_dn_3[0.1835] det_loss_cls_dn_4[0.0914] det_loss_box_dn_4[0.1807] det_loss_cls_dn_5[0.0906] det_loss_box_dn_5[0.1795] loss_depth[0.0338] total_loss[7.3891]
Rank[0/8] 06/20/2025 20:11:35 INFO stats.py:314 | Epoch[229] Step[289] GlobalStep[89599] Training Speed: 8.17 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1:20:19.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:11:45 INFO loss_tracker.py:84 | Epoch[229/239] Step[299] GlobalStep[89609/NA]: det_loss_cls_0[0.3344] det_loss_box_0[0.4388] det_loss_cls_1[0.3370] det_loss_box_1[0.3278] det_loss_cls_2[0.2729] det_loss_box_2[0.3082] det_loss_cls_3[0.2461] det_loss_box_3[0.2941] det_loss_cls_4[0.2316] det_loss_box_4[0.2893] det_loss_cls_5[0.2169] det_loss_box_5[0.2911] det_loss_cls_dn_0[0.0843] det_loss_box_dn_0[0.2949] det_loss_cls_dn_1[0.0808] det_loss_box_dn_1[0.2380] det_loss_cls_dn_2[0.0799] det_loss_box_dn_2[0.2149] det_loss_cls_dn_3[0.0790] det_loss_box_dn_3[0.2103] det_loss_cls_dn_4[0.0784] det_loss_box_dn_4[0.2072] det_loss_cls_dn_5[0.0777] det_loss_box_dn_5[0.2058] loss_depth[0.0361] total_loss[5.4753]
Rank[0/8] 06/20/2025 20:12:01 INFO stats.py:314 | Epoch[229] Step[314] GlobalStep[89624] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1:19:49.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:12:11 INFO loss_tracker.py:84 | Epoch[229/239] Step[324] GlobalStep[89634/NA]: det_loss_cls_0[0.5676] det_loss_box_0[0.7145] det_loss_cls_1[0.5471] det_loss_box_1[0.5430] det_loss_cls_2[0.4563] det_loss_box_2[0.4849] det_loss_cls_3[0.4201] det_loss_box_3[0.4687] det_loss_cls_4[0.3840] det_loss_box_4[0.4572] det_loss_cls_5[0.3641] det_loss_box_5[0.4559] det_loss_cls_dn_0[0.0885] det_loss_box_dn_0[0.2398] det_loss_cls_dn_1[0.0841] det_loss_box_dn_1[0.1992] det_loss_cls_dn_2[0.0833] det_loss_box_dn_2[0.1817] det_loss_cls_dn_3[0.0819] det_loss_box_dn_3[0.1790] det_loss_cls_dn_4[0.0812] det_loss_box_dn_4[0.1775] det_loss_cls_dn_5[0.0804] det_loss_box_dn_5[0.1763] loss_depth[0.0371] total_loss[7.5534]
Rank[0/8] 06/20/2025 20:12:27 INFO stats.py:314 | Epoch[229] Step[339] GlobalStep[89649] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1:19:19.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:12:38 INFO loss_tracker.py:84 | Epoch[229/239] Step[349] GlobalStep[89659/NA]: det_loss_cls_0[0.5062] det_loss_box_0[0.5979] det_loss_cls_1[0.4964] det_loss_box_1[0.4413] det_loss_cls_2[0.4303] det_loss_box_2[0.3965] det_loss_cls_3[0.3963] det_loss_box_3[0.3886] det_loss_cls_4[0.3715] det_loss_box_4[0.3836] det_loss_cls_5[0.3591] det_loss_box_5[0.3826] det_loss_cls_dn_0[0.0896] det_loss_box_dn_0[0.2156] det_loss_cls_dn_1[0.0857] det_loss_box_dn_1[0.1796] det_loss_cls_dn_2[0.0847] det_loss_box_dn_2[0.1658] det_loss_cls_dn_3[0.0834] det_loss_box_dn_3[0.1632] det_loss_cls_dn_4[0.0823] det_loss_box_dn_4[0.1609] det_loss_cls_dn_5[0.0816] det_loss_box_dn_5[0.1598] loss_depth[0.0339] total_loss[6.7363]
Rank[0/8] 06/20/2025 20:12:53 INFO stats.py:314 | Epoch[229] Step[364] GlobalStep[89674] Training Speed: 8.64 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1:18:48.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:13:03 INFO loss_tracker.py:84 | Epoch[229/239] Step[374] GlobalStep[89684/NA]: det_loss_cls_0[0.5085] det_loss_box_0[0.6172] det_loss_cls_1[0.4888] det_loss_box_1[0.4584] det_loss_cls_2[0.4353] det_loss_box_2[0.4171] det_loss_cls_3[0.3932] det_loss_box_3[0.4075] det_loss_cls_4[0.3713] det_loss_box_4[0.3984] det_loss_cls_5[0.3576] det_loss_box_5[0.4008] det_loss_cls_dn_0[0.0879] det_loss_box_dn_0[0.2161] det_loss_cls_dn_1[0.0844] det_loss_box_dn_1[0.1789] det_loss_cls_dn_2[0.0831] det_loss_box_dn_2[0.1661] det_loss_cls_dn_3[0.0821] det_loss_box_dn_3[0.1641] det_loss_cls_dn_4[0.0811] det_loss_box_dn_4[0.1614] det_loss_cls_dn_5[0.0803] det_loss_box_dn_5[0.1607] loss_depth[0.0334] total_loss[6.8339]
Rank[0/8] 06/20/2025 20:13:18 INFO stats.py:314 | Epoch[229] Step[389] GlobalStep[89699] Training Speed: 8.77 samples/sec across all devices.	Average Step Time: 0.91 sec.	Estimated Remaining Time: 1:18:18.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:13:19 INFO trainer.py:268 | 
==================================================BEGIN EVAL==================================================
Rank[0/8] 06/20/2025 20:14:22 INFO trainer.py:275 | eval: 10
Rank[0/8] 06/20/2025 20:14:32 INFO trainer.py:275 | eval: 20
Rank[0/8] 06/20/2025 20:14:45 INFO trainer.py:275 | eval: 30
Rank[0/8] 06/20/2025 20:14:52 INFO trainer.py:275 | eval: 40
Rank[0/8] 06/20/2025 20:15:03 INFO trainer.py:275 | eval: 50
Rank[0/8] 06/20/2025 20:15:11 INFO trainer.py:275 | eval: 60
Rank[0/8] 06/20/2025 20:15:23 INFO trainer.py:275 | eval: 70
Rank[0/8] 06/20/2025 20:15:30 INFO trainer.py:275 | eval: 80
Rank[0/8] 06/20/2025 20:15:42 INFO trainer.py:275 | eval: 90
Rank[0/8] 06/20/2025 20:15:49 INFO trainer.py:275 | eval: 100
Rank[0/8] 06/20/2025 20:15:54 INFO metrics.py:428 | number of results: 824
Rank[0/8] 06/20/2025 20:20:08 INFO metrics.py:332 | eval results of overall:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| bathtub             | 0.8505  | 0.8971  | 0.7411  | 0.7794  |
| curtain             | 0.5592  | 0.8299  | 0.1494  | 0.4925  |
| toilet              | 0.8650  | 0.8978  | 0.7632  | 0.8175  |
| sink                | 0.6933  | 0.8683  | 0.2770  | 0.4979  |
| toilet paper        | 0.3834  | 0.6142  | 0.2020  | 0.3701  |
| picture             | 0.2700  | 0.5011  | 0.0279  | 0.1304  |
| bin                 | 0.6406  | 0.8799  | 0.4131  | 0.6369  |
| door                | 0.4164  | 0.7274  | 0.0771  | 0.3007  |
| doorframe           | 0.3147  | 0.7320  | 0.0915  | 0.3940  |
| vanity              | 0.5466  | 0.8667  | 0.4866  | 0.8000  |
| towel               | 0.4147  | 0.6871  | 0.1381  | 0.3323  |
| mirror              | 0.2658  | 0.6522  | 0.0290  | 0.1793  |
| bar                 | 0.1107  | 0.2727  | 0.0086  | 0.0606  |
| stopcock            | 0.1200  | 0.4828  | 0.0005  | 0.0345  |
| switch              | 0.0022  | 0.1184  | 0.0000  | 0.0000  |
| bottle              | 0.0718  | 0.3900  | 0.0033  | 0.0864  |
| bag                 | 0.2739  | 0.6919  | 0.0901  | 0.3730  |
| socket              | 0.0150  | 0.2832  | 0.0001  | 0.0133  |
| hair dryer          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rail                | 0.0306  | 0.4219  | 0.0029  | 0.0781  |
| cup                 | 0.1170  | 0.3390  | 0.0052  | 0.0508  |
| shower              | 0.0939  | 0.3000  | 0.0026  | 0.0667  |
| microwave           | 0.6310  | 0.7347  | 0.3699  | 0.5102  |
| ledge               | 0.0133  | 0.3333  | 0.0002  | 0.1667  |
| rod                 | 0.0250  | 0.0250  | 0.0000  | 0.0000  |
| tissue              | 0.0308  | 0.1538  | 0.0308  | 0.1538  |
| tissue box          | 0.1844  | 0.5758  | 0.0591  | 0.3333  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| light               | 0.0700  | 0.3077  | 0.0020  | 0.0620  |
| vent                | 0.0150  | 0.2083  | 0.0000  | 0.0000  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| box                 | 0.1689  | 0.5085  | 0.0512  | 0.2136  |
| basket              | 0.2949  | 0.6000  | 0.1463  | 0.3739  |
| mat                 | 0.0444  | 0.2973  | 0.0254  | 0.1081  |
| lamp                | 0.3755  | 0.6776  | 0.1169  | 0.3134  |
| shoe                | 0.2094  | 0.4299  | 0.0280  | 0.1215  |
| plant               | 0.3271  | 0.6952  | 0.1113  | 0.3210  |
| blanket             | 0.2787  | 0.7500  | 0.0570  | 0.4100  |
| doorway             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cabinet             | 0.3097  | 0.7548  | 0.1894  | 0.5443  |
| case                | 0.1041  | 0.4463  | 0.0502  | 0.1921  |
| stool               | 0.3176  | 0.7563  | 0.2292  | 0.5798  |
| tablet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shelf               | 0.3454  | 0.7230  | 0.1533  | 0.4335  |
| faucet              | 0.0389  | 0.1915  | 0.0001  | 0.0426  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bed                 | 0.8579  | 0.9349  | 0.6923  | 0.8580  |
| carpet              | 0.1659  | 0.5119  | 0.0186  | 0.1786  |
| soap dish           | 0.5536  | 0.7333  | 0.0754  | 0.2000  |
| paper               | 0.0315  | 0.3195  | 0.0010  | 0.0680  |
| backpack            | 0.5885  | 0.8387  | 0.3633  | 0.5484  |
| drawer              | 0.3376  | 0.6111  | 0.3100  | 0.5000  |
| glass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes             | 0.2015  | 0.5541  | 0.0470  | 0.2389  |
| wardrobe            | 0.4320  | 0.7952  | 0.2946  | 0.5663  |
| decoration          | 0.0368  | 0.3857  | 0.0044  | 0.0786  |
| soap                | 0.1772  | 0.5000  | 0.0654  | 0.3333  |
| couch               | 0.5327  | 0.8760  | 0.4189  | 0.7603  |
| partition           | 0.5324  | 0.6667  | 0.0839  | 0.3333  |
| arch                | 0.0346  | 0.1333  | 0.0005  | 0.0333  |
| stall               | 0.3617  | 1.0000  | 0.2290  | 0.5882  |
| toothpaste          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sunglasses          | 0.0667  | 0.4615  | 0.0034  | 0.1538  |
| frame               | 0.1164  | 0.5918  | 0.0023  | 0.1020  |
| dresser             | 0.6055  | 0.8889  | 0.5907  | 0.8333  |
| screen              | 0.2000  | 0.2000  | 0.0000  | 0.0000  |
| plunger             | 0.8000  | 0.8000  | 0.4500  | 0.6000  |
| counter             | 0.2501  | 0.7156  | 0.0782  | 0.3211  |
| balcony             | 0.3750  | 1.0000  | 0.0714  | 0.5000  |
| scale               | 0.2020  | 0.4545  | 0.0921  | 0.1818  |
| pillar              | 0.3674  | 0.6744  | 0.2584  | 0.4419  |
| window              | 0.2733  | 0.6542  | 0.0549  | 0.2575  |
| blinds              | 0.2292  | 0.4800  | 0.0320  | 0.2400  |
| hamper              | 0.4735  | 0.5455  | 0.4735  | 0.5455  |
| beam                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| window frame        | 0.0353  | 0.4141  | 0.0025  | 0.1616  |
| keyboard            | 0.1011  | 0.2558  | 0.0012  | 0.0233  |
| chair               | 0.5988  | 0.8428  | 0.4714  | 0.6968  |
| blackboard          | 0.6580  | 0.8171  | 0.2413  | 0.4512  |
| radiator            | 0.7267  | 0.8000  | 0.1863  | 0.3444  |
| computer            | 0.3336  | 0.5902  | 0.0262  | 0.1639  |
| monitor             | 0.5599  | 0.7228  | 0.1281  | 0.3424  |
| desk                | 0.5766  | 0.9101  | 0.4266  | 0.7416  |
| mouse               | 0.0024  | 0.1081  | 0.0000  | 0.0000  |
| table               | 0.5268  | 0.8730  | 0.4373  | 0.7696  |
| plug                | 0.0175  | 0.1053  | 0.0000  | 0.0000  |
| telephone           | 0.4146  | 0.7167  | 0.1314  | 0.3167  |
| book                | 0.0687  | 0.4260  | 0.0062  | 0.1089  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| windowsill          | 0.1791  | 0.6522  | 0.0224  | 0.2174  |
| stand               | 0.5806  | 0.8780  | 0.4636  | 0.7500  |
| jacket              | 0.6162  | 0.7917  | 0.2211  | 0.5000  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| projector           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| umbrella            | 0.2000  | 0.2000  | 0.2000  | 0.2000  |
| pipe                | 0.0400  | 0.1786  | 0.0000  | 0.0000  |
| pillow              | 0.2774  | 0.6290  | 0.0429  | 0.2176  |
| tv                  | 0.6697  | 0.8416  | 0.1737  | 0.4158  |
| paper cutter        | 0.8056  | 0.8333  | 0.0556  | 0.1667  |
| container           | 0.0109  | 0.2203  | 0.0061  | 0.0847  |
| ottoman             | 0.1827  | 0.6667  | 0.1570  | 0.6111  |
| remote control      | 0.0421  | 0.2222  | 0.0111  | 0.0741  |
| clock               | 0.4741  | 0.7447  | 0.0537  | 0.1702  |
| printer             | 0.5527  | 0.7755  | 0.3750  | 0.6735  |
| laptop              | 0.4830  | 0.7391  | 0.2503  | 0.4348  |
| guitar              | 0.2500  | 0.3333  | 0.0833  | 0.0833  |
| refrigerator        | 0.7441  | 0.8600  | 0.4440  | 0.5600  |
| luggage             | 0.5940  | 1.0000  | 0.5556  | 0.6667  |
| purse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bench               | 0.3026  | 0.7358  | 0.2688  | 0.5660  |
| notebook            | 0.0009  | 0.0769  | 0.0000  | 0.0000  |
| kettle              | 0.2321  | 0.7222  | 0.0491  | 0.2778  |
| coffee maker        | 0.4088  | 0.6207  | 0.0404  | 0.1379  |
| brush               | 0.1678  | 0.4286  | 0.0105  | 0.1429  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| countertop          | 0.0166  | 0.2857  | 0.0014  | 0.1071  |
| grab bar            | 0.0671  | 0.5000  | 0.0000  | 0.0000  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dish rack           | 0.7233  | 1.0000  | 0.3600  | 0.6000  |
| roof                | 0.0027  | 0.3333  | 0.0002  | 0.0833  |
| toy                 | 0.1526  | 0.4545  | 0.0689  | 0.1667  |
| rack                | 0.1827  | 0.5385  | 0.1124  | 0.4423  |
| hat                 | 0.1118  | 0.2222  | 0.0000  | 0.0000  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bowl                | 0.1046  | 0.4783  | 0.0471  | 0.1739  |
| soap dispenser      | 0.3800  | 0.7037  | 0.0876  | 0.3704  |
| dispenser           | 0.4756  | 0.5909  | 0.3385  | 0.4318  |
| holder              | 0.1852  | 0.2222  | 0.1111  | 0.1111  |
| mailbox             | 0.1032  | 0.8000  | 0.0118  | 0.2000  |
| chandelier          | 0.0095  | 0.0476  | 0.0000  | 0.0000  |
| stairs              | 0.0820  | 0.4722  | 0.0064  | 0.1667  |
| hanger              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| alarm               | 0.0066  | 0.1250  | 0.0000  | 0.0000  |
| column              | 0.0166  | 0.3684  | 0.0020  | 0.1579  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape       | 0.2000  | 0.2000  | 0.0000  | 0.0000  |
| headphones          | 0.4500  | 0.6000  | 0.2000  | 0.2000  |
| thermostat          | 0.0833  | 0.1667  | 0.0000  | 0.0000  |
| spoon               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| teapot              | 0.0137  | 0.2222  | 0.0000  | 0.0000  |
| copier              | 0.8958  | 0.9167  | 0.8958  | 0.9167  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchenware         | 0.2724  | 0.5435  | 0.0207  | 0.1087  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster             | 0.4233  | 0.7000  | 0.0317  | 0.3000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| poster              | 0.0166  | 0.1818  | 0.0000  | 0.0000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drum                | 0.0104  | 0.1250  | 0.0000  | 0.0000  |
| cube                | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bread               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| board               | 0.1842  | 0.6290  | 0.0539  | 0.2742  |
| bicycle             | 0.6511  | 1.0000  | 0.5375  | 0.8333  |
| fan                 | 0.6619  | 0.8125  | 0.2807  | 0.4688  |
| crib                | 0.3939  | 0.6667  | 0.3939  | 0.6667  |
| kitchen island      | 0.2517  | 0.5714  | 0.2395  | 0.4286  |
| mattress            | 0.0667  | 0.3333  | 0.0000  | 0.0000  |
| piano               | 0.3130  | 0.6000  | 0.3130  | 0.6000  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| magazine            | 0.0025  | 0.2000  | 0.0000  | 0.0000  |
| fireplace           | 0.2004  | 0.4643  | 0.0183  | 0.2143  |
| pool table          | 0.1667  | 0.3333  | 0.1667  | 0.3333  |
| headboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garage door         | 0.2852  | 0.5000  | 0.2852  | 0.5000  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plate               | 0.0306  | 0.4423  | 0.0016  | 0.0769  |
| tube                | 0.0095  | 0.1538  | 0.0000  | 0.0000  |
| statue              | 0.1910  | 0.5263  | 0.1013  | 0.2105  |
| person              | 0.1850  | 0.5000  | 0.0079  | 0.1667  |
| calendar            | 0.0344  | 0.3333  | 0.0000  | 0.0000  |
| vacuum cleaner      | 0.5112  | 0.7143  | 0.0245  | 0.2857  |
| ball                | 0.4508  | 0.4762  | 0.3345  | 0.3810  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basin               | 0.0197  | 0.2353  | 0.0005  | 0.0588  |
| fire extinguisher   | 0.3000  | 0.4000  | 0.3000  | 0.4000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cosmetics           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| barricade           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| heater              | 0.6536  | 0.8276  | 0.1704  | 0.4310  |
| excercise equipment | 0.3492  | 0.6000  | 0.0112  | 0.1500  |
| commode             | 0.5308  | 0.8654  | 0.3823  | 0.7308  |
| beanbag             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| treadmill           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet               | 0.4444  | 0.6667  | 0.4444  | 0.6667  |
| footstool           | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| crate               | 0.5102  | 0.5714  | 0.2381  | 0.2857  |
| jalousie            | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| candlestick         | 0.0952  | 0.6667  | 0.0000  | 0.0000  |
| humidifier          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sign                | 0.0580  | 0.3667  | 0.0001  | 0.0333  |
| water cooler        | 0.7500  | 1.0000  | 0.7500  | 1.0000  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket              | 0.2166  | 0.7179  | 0.0643  | 0.3590  |
| oven                | 0.5307  | 0.7857  | 0.1222  | 0.3929  |
| pot                 | 0.2115  | 0.4815  | 0.0281  | 0.1481  |
| fruit               | 0.1278  | 0.4286  | 0.0809  | 0.2143  |
| device              | 0.2840  | 0.6364  | 0.0000  | 0.0000  |
| folder              | 0.0418  | 0.2857  | 0.0238  | 0.0714  |
| cushion             | 0.2498  | 0.5098  | 0.0403  | 0.1961  |
| pan                 | 0.1975  | 0.5882  | 0.0300  | 0.1765  |
| stove               | 0.6027  | 0.8333  | 0.3219  | 0.5278  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| food                | 0.0909  | 0.0909  | 0.0000  | 0.0000  |
| pack                | 0.0502  | 0.6667  | 0.0171  | 0.4000  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garbage             | 0.7580  | 0.8750  | 0.7500  | 0.7500  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flush               | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sponge              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| radio               | 0.2500  | 0.2500  | 0.2500  | 0.2500  |
| glove               | 1.0000  | 1.0000  | 0.1111  | 0.3333  |
| sack                | 0.5703  | 1.0000  | 0.3106  | 0.7143  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| player              | 0.2500  | 0.2500  | 0.2500  | 0.2500  |
| cap                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| can                 | 0.0750  | 0.1667  | 0.0417  | 0.0833  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| map                 | 0.0385  | 0.5000  | 0.0385  | 0.5000  |
| wine                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| washing machine     | 0.5204  | 0.7500  | 0.2900  | 0.3571  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ladder              | 0.1933  | 0.4706  | 0.0784  | 0.1176  |
| salt                | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| napkins             | 0.3000  | 0.6667  | 0.1667  | 0.3333  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| roll                | 0.1417  | 0.4000  | 0.0000  | 0.0000  |
| dumbbell            | 0.6667  | 0.6667  | 0.1667  | 0.3333  |
| menu                | 0.6310  | 0.7143  | 0.0714  | 0.1429  |
| toiletry            | 0.1516  | 0.2609  | 0.0031  | 0.0435  |
| broom               | 0.0462  | 0.3571  | 0.0000  | 0.0000  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| price tag           | 0.0054  | 0.2857  | 0.0000  | 0.0000  |
| steps               | 0.0006  | 0.3000  | 0.0000  | 0.0000  |
| vase                | 0.1785  | 0.5000  | 0.0546  | 0.2200  |
| flowerpot           | 0.0610  | 0.3137  | 0.0023  | 0.0980  |
| range hood          | 0.4592  | 0.7857  | 0.0584  | 0.2143  |
| file                | 0.0632  | 0.2353  | 0.0300  | 0.0882  |
| speaker             | 0.1162  | 0.3182  | 0.0114  | 0.0909  |
| air conditioner     | 0.7333  | 0.8000  | 0.4000  | 0.4000  |
| machine             | 0.5863  | 0.8421  | 0.4174  | 0.6842  |
| conducting wire     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jar                 | 0.1032  | 0.3846  | 0.0801  | 0.2308  |
| candle              | 0.1578  | 0.4444  | 0.0017  | 0.1111  |
| album               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| letter              | 0.6262  | 1.0000  | 0.0500  | 0.2500  |
| clothes dryer       | 0.3998  | 0.8667  | 0.3914  | 0.8000  |
| shampoo             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| body loofah         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.2756  | 0.5000  | 0.0500  | 0.1000  |
| water heater        | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| mop                 | 1.0000  | 1.0000  | 0.1667  | 0.3333  |
| handle              | 0.0010  | 0.2500  | 0.0000  | 0.0000  |
| helmets             | 0.0095  | 0.3333  | 0.0095  | 0.3333  |
| coil                | 0.2778  | 0.3333  | 0.0556  | 0.1667  |
| washbasin           | 0.0144  | 0.2000  | 0.0095  | 0.0667  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| overall             | 0.2477  | 0.4377  | 0.1162  | 0.2246  |
| head                | 0.3240  | 0.6119  | 0.1564  | 0.3343  |
| common              | 0.2208  | 0.3981  | 0.1130  | 0.2018  |
| tail                | 0.1944  | 0.2913  | 0.0756  | 0.1301  |
| small               | 0.0875  | 0.2250  | 0.0140  | 0.0558  |
| medium              | 0.2546  | 0.4895  | 0.1321  | 0.2574  |
| large               | 0.1591  | 0.5170  | 0.0857  | 0.3235  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 20:20:08 INFO metrics.py:437 | number of scannet results: 250
Rank[0/8] 06/20/2025 20:21:24 INFO metrics.py:332 | eval results of scannet:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| bathtub             | 0.9600  | 0.9688  | 0.8750  | 0.8750  |
| curtain             | 0.6358  | 0.8496  | 0.1381  | 0.4690  |
| toilet              | 0.9402  | 0.9643  | 0.8159  | 0.8571  |
| sink                | 0.8429  | 0.9205  | 0.3758  | 0.5227  |
| toilet paper        | 0.4527  | 0.6203  | 0.2831  | 0.4177  |
| picture             | 0.3579  | 0.5560  | 0.0543  | 0.1530  |
| bin                 | 0.6669  | 0.8766  | 0.4336  | 0.6213  |
| door                | 0.6229  | 0.8428  | 0.1628  | 0.4214  |
| doorframe           | 0.3832  | 0.7394  | 0.1073  | 0.3592  |
| vanity              | 0.7342  | 1.0000  | 0.6649  | 0.9667  |
| towel               | 0.5361  | 0.7521  | 0.1747  | 0.3388  |
| mirror              | 0.4094  | 0.6988  | 0.0331  | 0.1566  |
| bar                 | 0.1360  | 0.3182  | 0.0114  | 0.0455  |
| stopcock            | 0.1200  | 0.4828  | 0.0005  | 0.0345  |
| switch              | 0.0059  | 0.1772  | 0.0000  | 0.0000  |
| bottle              | 0.0970  | 0.3895  | 0.0034  | 0.0947  |
| bag                 | 0.2098  | 0.6575  | 0.1166  | 0.4247  |
| socket              | 0.0166  | 0.3058  | 0.0001  | 0.0146  |
| hair dryer          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rail                | 0.0725  | 0.6667  | 0.0567  | 0.6667  |
| cup                 | 0.2088  | 0.4058  | 0.0164  | 0.0870  |
| shower              | 0.1074  | 0.2857  | 0.0002  | 0.0714  |
| microwave           | 0.6524  | 0.7500  | 0.3555  | 0.5000  |
| ledge               | 0.0133  | 0.3333  | 0.0002  | 0.1667  |
| rod                 | 0.0714  | 0.0714  | 0.0000  | 0.0000  |
| tissue              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue box          | 0.2437  | 0.5455  | 0.1778  | 0.3636  |
| light               | 0.1655  | 0.4483  | 0.0134  | 0.1034  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| box                 | 0.1206  | 0.4127  | 0.0403  | 0.1508  |
| basket              | 0.1010  | 0.2308  | 0.0987  | 0.1538  |
| mat                 | 0.1132  | 0.4000  | 0.1000  | 0.2000  |
| lamp                | 0.4588  | 0.7500  | 0.2925  | 0.4286  |
| shoe                | 0.2288  | 0.4750  | 0.0345  | 0.1750  |
| plant               | 0.2953  | 0.6387  | 0.1251  | 0.3193  |
| blanket             | 0.0764  | 0.5000  | 0.0182  | 0.2500  |
| cabinet             | 0.3594  | 0.7619  | 0.1900  | 0.5238  |
| case                | 0.6590  | 1.0000  | 0.4655  | 0.7895  |
| stool               | 0.3159  | 0.7727  | 0.2348  | 0.6136  |
| shelf               | 0.5373  | 0.8015  | 0.3595  | 0.6397  |
| faucet              | 0.0009  | 0.1250  | 0.0009  | 0.1250  |
| bed                 | 0.9914  | 1.0000  | 0.9200  | 0.9825  |
| carpet              | 0.0243  | 0.4000  | 0.0000  | 0.0000  |
| soap dish           | 0.7302  | 0.8333  | 0.1062  | 0.2500  |
| paper               | 0.0303  | 0.3132  | 0.0006  | 0.0566  |
| backpack            | 0.7169  | 0.9286  | 0.5330  | 0.6786  |
| drawer              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes             | 0.2108  | 0.5294  | 0.0719  | 0.2745  |
| wardrobe            | 0.4608  | 0.8750  | 0.3750  | 0.3750  |
| decoration          | 0.0332  | 0.2500  | 0.0000  | 0.0000  |
| couch               | 0.7525  | 0.9750  | 0.5976  | 0.8625  |
| stall               | 0.3620  | 1.0000  | 0.2291  | 0.5882  |
| toothpaste          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| frame               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dresser             | 0.7729  | 0.9730  | 0.7673  | 0.9459  |
| plunger             | 0.8000  | 0.8000  | 0.4500  | 0.6000  |
| counter             | 0.5560  | 0.8571  | 0.2493  | 0.5143  |
| scale               | 0.2500  | 0.2500  | 0.0167  | 0.2500  |
| pillar              | 0.0063  | 0.2000  | 0.0000  | 0.0000  |
| window              | 0.5263  | 0.8395  | 0.1656  | 0.4753  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hamper              | 0.5233  | 0.6000  | 0.5233  | 0.6000  |
| keyboard            | 0.0983  | 0.2241  | 0.0011  | 0.0172  |
| chair               | 0.6451  | 0.8519  | 0.4745  | 0.6823  |
| blackboard          | 0.6767  | 0.8261  | 0.2681  | 0.4638  |
| radiator            | 0.7969  | 0.8448  | 0.2245  | 0.3793  |
| computer            | 0.3336  | 0.5897  | 0.0403  | 0.1795  |
| monitor             | 0.6027  | 0.7214  | 0.2035  | 0.4000  |
| desk                | 0.6891  | 0.9620  | 0.5028  | 0.7468  |
| mouse               | 0.0063  | 0.1905  | 0.0000  | 0.0000  |
| table               | 0.6123  | 0.9154  | 0.5265  | 0.8192  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| telephone           | 0.5419  | 0.7812  | 0.2293  | 0.4062  |
| book                | 0.0689  | 0.4262  | 0.0058  | 0.1071  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| windowsill          | 0.1751  | 0.6667  | 0.0015  | 0.1667  |
| stand               | 0.7665  | 0.8868  | 0.7057  | 0.8302  |
| jacket              | 0.6450  | 0.8261  | 0.2315  | 0.5217  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pipe                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillow              | 0.4093  | 0.6597  | 0.1445  | 0.4306  |
| tv                  | 0.8306  | 0.8696  | 0.1522  | 0.3478  |
| paper cutter        | 0.7600  | 0.8000  | 0.0000  | 0.0000  |
| container           | 0.0036  | 0.2174  | 0.0011  | 0.0870  |
| ottoman             | 0.2113  | 1.0000  | 0.0800  | 0.7143  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clock               | 0.4256  | 0.7200  | 0.1057  | 0.2000  |
| printer             | 0.5817  | 0.8182  | 0.5763  | 0.7879  |
| laptop              | 0.5083  | 0.6000  | 0.3148  | 0.4000  |
| guitar              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| refrigerator        | 0.9037  | 0.9500  | 0.6495  | 0.7500  |
| purse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bench               | 0.6424  | 1.0000  | 0.6280  | 1.0000  |
| notebook            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kettle              | 0.2039  | 0.7500  | 0.0000  | 0.0000  |
| coffee maker        | 0.5204  | 0.6471  | 0.0756  | 0.1765  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| grab bar            | 0.0671  | 0.5000  | 0.0000  | 0.0000  |
| dish rack           | 0.7292  | 1.0000  | 0.5625  | 0.7500  |
| toy                 | 0.0883  | 0.3529  | 0.0784  | 0.1176  |
| rack                | 0.0006  | 0.0909  | 0.0006  | 0.0909  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bowl                | 0.3079  | 0.4444  | 0.0889  | 0.2222  |
| soap dispenser      | 0.3982  | 0.7143  | 0.1169  | 0.4286  |
| dispenser           | 0.5704  | 0.6410  | 0.3930  | 0.4615  |
| holder              | 0.2000  | 0.2000  | 0.2000  | 0.2000  |
| mailbox             | 0.1032  | 0.8000  | 0.0118  | 0.2000  |
| stairs              | 0.2053  | 0.8000  | 0.0345  | 0.6000  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headphones          | 0.4667  | 0.6000  | 0.2000  | 0.2000  |
| teapot              | 0.0417  | 0.2500  | 0.0000  | 0.0000  |
| copier              | 0.8958  | 0.9167  | 0.8958  | 0.9167  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster             | 0.1324  | 0.7500  | 0.0588  | 0.5000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| poster              | 0.0175  | 1.0000  | 0.0000  | 0.0000  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| board               | 0.1102  | 0.6250  | 0.0105  | 0.3750  |
| bicycle             | 0.7667  | 1.0000  | 0.5909  | 1.0000  |
| fan                 | 0.7839  | 0.8750  | 0.2321  | 0.3750  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pen                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plate               | 0.0067  | 0.2000  | 0.0000  | 0.0000  |
| tube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| person              | 0.1850  | 0.5000  | 0.0079  | 0.1667  |
| calendar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vacuum cleaner      | 0.4444  | 0.6667  | 0.4444  | 0.6667  |
| ball                | 0.4167  | 0.4167  | 0.2500  | 0.2500  |
| basin               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| barricade           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crate               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sign                | 0.0480  | 0.3214  | 0.0001  | 0.0357  |
| water cooler        | 0.7500  | 1.0000  | 0.7500  | 1.0000  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket              | 0.1355  | 0.6429  | 0.0172  | 0.2143  |
| oven                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pot                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cushion             | 0.0128  | 0.5000  | 0.0040  | 0.2500  |
| stove               | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| map                 | 0.0833  | 1.0000  | 0.0833  | 1.0000  |
| washing machine     | 0.7084  | 1.0000  | 0.6333  | 0.6667  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ladder              | 0.1200  | 0.4000  | 0.1000  | 0.2000  |
| broom               | 0.0706  | 0.4167  | 0.0000  | 0.0000  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| flowerpot           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.7357  | 1.0000  | 0.1333  | 0.4000  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| machine             | 0.6419  | 0.9375  | 0.4340  | 0.7500  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.0079  | 0.3333  | 0.0000  | 0.0000  |
| clothes dryer       | 0.4167  | 1.0000  | 0.4167  | 1.0000  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| scannet             | 0.2728  | 0.4591  | 0.1559  | 0.2740  |
| head                | 0.3738  | 0.6110  | 0.2162  | 0.3731  |
| common              | 0.2072  | 0.3423  | 0.1109  | 0.1886  |
| tail                | 0.0803  | 0.2107  | 0.0542  | 0.1320  |
| small               | 0.0547  | 0.1607  | 0.0143  | 0.0413  |
| medium              | 0.3252  | 0.5572  | 0.1919  | 0.3341  |
| large               | 0.3016  | 0.8016  | 0.1868  | 0.5748  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 20:21:24 INFO metrics.py:437 | number of 3rscan results: 235
Rank[0/8] 06/20/2025 20:22:31 INFO metrics.py:332 | eval results of 3rscan:
+-------------------+---------+---------+---------+---------+
| classes           | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+-------------------+---------+---------+---------+---------+
| chair             | 0.7006  | 0.8599  | 0.5827  | 0.7390  |
| monitor           | 0.5838  | 0.7500  | 0.0443  | 0.1750  |
| keyboard          | 0.1475  | 0.3750  | 0.0026  | 0.0417  |
| mouse             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| desk              | 0.5256  | 0.9000  | 0.3917  | 0.7625  |
| cup               | 0.0506  | 0.2821  | 0.0000  | 0.0000  |
| case              | 0.0457  | 0.4000  | 0.0077  | 0.1214  |
| computer          | 0.3550  | 0.6190  | 0.0169  | 0.1429  |
| paper             | 0.0722  | 0.3571  | 0.0054  | 0.1143  |
| plate             | 0.0528  | 0.4848  | 0.0033  | 0.0909  |
| stand             | 0.5653  | 0.9180  | 0.3524  | 0.6885  |
| bag               | 0.3758  | 0.7475  | 0.0999  | 0.3636  |
| garbage           | 0.7590  | 0.8750  | 0.7500  | 0.7500  |
| bottle            | 0.0505  | 0.3223  | 0.0009  | 0.0413  |
| pen               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| window            | 0.3103  | 0.7329  | 0.0325  | 0.2192  |
| wardrobe          | 0.5087  | 0.8281  | 0.3323  | 0.6406  |
| plug              | 0.0688  | 0.2222  | 0.0000  | 0.0000  |
| curtain           | 0.6145  | 0.8810  | 0.2132  | 0.5317  |
| bin               | 0.7546  | 0.9512  | 0.4901  | 0.7805  |
| table             | 0.6036  | 0.8964  | 0.4746  | 0.7883  |
| cabinet           | 0.4704  | 0.8333  | 0.3266  | 0.6752  |
| backpack          | 0.4991  | 0.7879  | 0.2441  | 0.4545  |
| book              | 0.1145  | 0.4734  | 0.0105  | 0.1183  |
| socket            | 0.0089  | 0.0625  | 0.0000  | 0.0000  |
| box               | 0.2161  | 0.5490  | 0.0657  | 0.2331  |
| telephone         | 0.3464  | 0.6250  | 0.0689  | 0.2083  |
| sink              | 0.6836  | 0.8214  | 0.1470  | 0.3571  |
| blinds            | 0.2940  | 0.6154  | 0.0412  | 0.3077  |
| notebook          | 0.0012  | 0.0909  | 0.0000  | 0.0000  |
| plant             | 0.3872  | 0.6782  | 0.1036  | 0.2871  |
| file              | 0.0767  | 0.2857  | 0.0364  | 0.1071  |
| picture           | 0.2398  | 0.4837  | 0.0214  | 0.1179  |
| shelf             | 0.3372  | 0.7597  | 0.1065  | 0.3781  |
| clock             | 0.5882  | 0.7895  | 0.0105  | 0.0526  |
| couch             | 0.5430  | 0.8714  | 0.4416  | 0.8000  |
| light             | 0.1409  | 0.5000  | 0.0036  | 0.1023  |
| laptop            | 0.5284  | 1.0000  | 0.2958  | 0.5000  |
| switch            | 0.0005  | 0.0377  | 0.0000  | 0.0000  |
| conducting wire   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue box        | 0.4667  | 0.6667  | 0.0667  | 0.3333  |
| printer           | 0.7831  | 0.8889  | 0.3277  | 0.6667  |
| jar               | 0.1689  | 0.5000  | 0.1300  | 0.2500  |
| tablet            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tv                | 0.7479  | 0.9429  | 0.2659  | 0.5143  |
| commode           | 0.5317  | 0.8654  | 0.3829  | 0.7308  |
| lamp              | 0.3488  | 0.6125  | 0.0511  | 0.1812  |
| frame             | 0.2091  | 0.8667  | 0.0053  | 0.1667  |
| pillow            | 0.4279  | 0.7188  | 0.0507  | 0.2148  |
| decoration        | 0.1436  | 0.5000  | 0.0205  | 0.0938  |
| price tag         | 0.0054  | 0.2857  | 0.0000  | 0.0000  |
| bed               | 0.9152  | 0.9818  | 0.7425  | 0.8727  |
| candle            | 0.1980  | 0.4667  | 0.0020  | 0.0667  |
| bowl              | 0.1861  | 0.6667  | 0.1007  | 0.2222  |
| stool             | 0.5001  | 0.9074  | 0.3489  | 0.6852  |
| cushion           | 0.4227  | 0.7667  | 0.0689  | 0.3000  |
| folder            | 0.0418  | 0.2857  | 0.0238  | 0.0714  |
| blanket           | 0.4202  | 0.8462  | 0.0956  | 0.5077  |
| door              | 0.3922  | 0.7206  | 0.0819  | 0.3529  |
| carpet            | 0.2692  | 0.6607  | 0.0310  | 0.2679  |
| vase              | 0.3345  | 0.5000  | 0.1250  | 0.1500  |
| remote control    | 0.0610  | 0.3000  | 0.0171  | 0.1000  |
| napkins           | 0.3000  | 0.6667  | 0.1667  | 0.3333  |
| rack              | 0.3784  | 0.7333  | 0.2300  | 0.6000  |
| clothes           | 0.2966  | 0.7087  | 0.0518  | 0.2427  |
| bar               | 0.6667  | 0.6667  | 0.0196  | 0.3333  |
| bench             | 0.4200  | 0.8214  | 0.3928  | 0.6786  |
| towel             | 0.3486  | 0.6709  | 0.1114  | 0.3038  |
| bucket            | 0.6887  | 0.9333  | 0.2607  | 0.6000  |
| flowerpot         | 0.1100  | 0.3750  | 0.0012  | 0.0417  |
| teapot            | 0.0417  | 0.2500  | 0.0000  | 0.0000  |
| ottoman           | 0.6352  | 0.9091  | 0.6352  | 0.9091  |
| player            | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| magazine          | 0.0032  | 0.2500  | 0.0000  | 0.0000  |
| cube              | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| counter           | 0.2068  | 0.8049  | 0.0403  | 0.3171  |
| toy               | 0.5063  | 0.6923  | 0.2128  | 0.3077  |
| poster            | 0.0234  | 0.1746  | 0.0000  | 0.0000  |
| roll              | 0.1417  | 0.4000  | 0.0000  | 0.0000  |
| blackboard        | 0.6506  | 0.8333  | 0.1295  | 0.4167  |
| heater            | 0.6566  | 0.8246  | 0.1691  | 0.4211  |
| humidifier        | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| windowsill        | 0.1989  | 0.6667  | 0.0305  | 0.2308  |
| device            | 0.2840  | 0.6364  | 0.0000  | 0.0000  |
| fan               | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| doorframe         | 0.2547  | 0.7400  | 0.0220  | 0.2200  |
| ball              | 0.6000  | 0.6000  | 0.6000  | 0.6000  |
| board             | 0.3308  | 0.6667  | 0.1322  | 0.1667  |
| umbrella          | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| container         | 0.1856  | 0.6667  | 0.1481  | 0.2222  |
| brush             | 0.2871  | 0.6250  | 0.0179  | 0.1250  |
| pad               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker           | 0.1681  | 0.3333  | 0.0139  | 0.0833  |
| drawer            | 0.7726  | 1.0000  | 0.6761  | 0.8889  |
| radiator          | 0.6420  | 0.7586  | 0.1385  | 0.3103  |
| kettle            | 0.3267  | 0.8333  | 0.0746  | 0.4167  |
| mirror            | 0.3835  | 0.6667  | 0.1063  | 0.2593  |
| shoe              | 0.3014  | 0.5000  | 0.0487  | 0.1429  |
| pot               | 0.5439  | 0.6667  | 0.0655  | 0.1667  |
| hanger            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape     | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| basket            | 0.5145  | 0.7969  | 0.2824  | 0.5312  |
| stairs            | 0.4813  | 0.7143  | 0.0367  | 0.4286  |
| air conditioner   | 0.9167  | 1.0000  | 0.5000  | 0.5000  |
| rail              | 0.3500  | 0.5000  | 0.0000  | 0.0000  |
| calendar          | 0.0583  | 0.4000  | 0.0000  | 0.0000  |
| dresser           | 0.2647  | 1.0000  | 0.2500  | 0.5000  |
| refrigerator      | 0.7197  | 0.8667  | 0.6142  | 0.6667  |
| balcony           | 0.3750  | 1.0000  | 0.0714  | 0.5000  |
| guitar            | 1.0000  | 1.0000  | 0.5000  | 0.5000  |
| album             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| microwave         | 0.9388  | 1.0000  | 0.6282  | 0.7647  |
| basin             | 0.0357  | 0.2667  | 0.0014  | 0.0667  |
| bicycle           | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| tissue            | 0.2619  | 0.6667  | 0.2619  | 0.6667  |
| scale             | 0.3641  | 1.0000  | 0.3333  | 0.3333  |
| sunglasses        | 0.0679  | 0.4615  | 0.0037  | 0.1538  |
| hat               | 0.1458  | 0.2857  | 0.0000  | 0.0000  |
| letter            | 0.6262  | 1.0000  | 0.0500  | 0.2500  |
| furniture         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| piano             | 0.4444  | 0.6667  | 0.4444  | 0.6667  |
| crib              | 0.4000  | 0.6667  | 0.4000  | 0.6667  |
| bathtub           | 1.0000  | 1.0000  | 0.6728  | 0.7778  |
| clothes dryer     | 0.4442  | 0.9000  | 0.4297  | 0.8000  |
| statue            | 1.0000  | 1.0000  | 0.5000  | 0.5000  |
| shampoo           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toilet            | 0.8354  | 0.8824  | 0.7116  | 0.7647  |
| faucet            | 0.4667  | 0.6667  | 0.0000  | 0.0000  |
| shower            | 0.5731  | 1.0000  | 0.0833  | 0.2500  |
| toilet paper      | 0.1685  | 0.4167  | 0.0049  | 0.0833  |
| beam              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tube              | 0.0315  | 0.2857  | 0.0000  | 0.0000  |
| dispenser         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toiletry          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| washing machine   | 0.5091  | 0.6429  | 0.2440  | 0.2857  |
| soap              | 1.0000  | 1.0000  | 0.5000  | 1.0000  |
| body loofah       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beanbag           | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| jalousie          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| cosmetics         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| can               | 0.1125  | 0.2500  | 0.0625  | 0.1250  |
| column            | 0.0212  | 1.0000  | 0.0212  | 1.0000  |
| candlestick       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| knife             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillar            | 0.6066  | 0.7059  | 0.5302  | 0.6471  |
| spoon             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pack              | 0.0579  | 0.7692  | 0.0198  | 0.4615  |
| oven              | 0.7246  | 1.0000  | 0.1907  | 0.5789  |
| pan               | 0.2685  | 0.6667  | 0.0430  | 0.2500  |
| stove             | 0.5198  | 0.8824  | 0.0441  | 0.2941  |
| fruit             | 0.3444  | 0.6000  | 0.2000  | 0.2000  |
| shovel            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coffee maker      | 0.2730  | 0.7778  | 0.0017  | 0.1111  |
| kitchenware       | 0.3069  | 0.5682  | 0.0228  | 0.1136  |
| food              | 0.1000  | 0.1000  | 0.0000  | 0.0000  |
| partition         | 0.6389  | 0.8000  | 0.1007  | 0.4000  |
| radio             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| sign              | 0.5370  | 1.0000  | 0.0000  | 0.0000  |
| fireplace         | 0.2000  | 0.2000  | 0.0667  | 0.2000  |
| mattress          | 0.1000  | 0.5000  | 0.0000  | 0.0000  |
| bidet             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| treadmill         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sponge            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| screen            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| holder            | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| broom             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tray              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dish         | 0.2500  | 0.5000  | 0.0000  | 0.0000  |
| chandelier        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hook              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| glove             | 1.0000  | 1.0000  | 0.1111  | 0.3333  |
| salt              | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| seasoning         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher        | 0.9167  | 1.0000  | 0.1667  | 0.3333  |
| crate             | 0.7600  | 0.8000  | 0.3333  | 0.4000  |
| dish rack         | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| flush             | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| vacuum cleaner    | 0.7500  | 0.7500  | 0.0000  | 0.0000  |
| water heater      | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| mop               | 1.0000  | 1.0000  | 0.1667  | 0.3333  |
| bread             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| menu              | 0.6310  | 0.7143  | 0.0714  | 0.1429  |
| sack              | 0.5703  | 1.0000  | 0.3106  | 0.7143  |
| range hood        | 0.5400  | 0.8000  | 0.0400  | 0.2000  |
| toaster           | 1.0000  | 1.0000  | 0.0625  | 0.2500  |
| tool              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| footstool         | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| wire              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| window frame      | 0.1652  | 0.5000  | 0.0011  | 0.0833  |
| glass             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pipe              | 0.3000  | 1.0000  | 0.0000  | 0.0000  |
| ladder            | 0.4183  | 1.0000  | 0.3333  | 0.3333  |
| mat               | 0.1667  | 0.3333  | 0.0185  | 0.3333  |
| luggage           | 0.6222  | 1.0000  | 0.5556  | 0.6667  |
| helmets           | 0.0128  | 0.3333  | 0.0128  | 0.3333  |
| coil              | 0.2778  | 0.3333  | 0.0556  | 0.1667  |
| machine           | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| washbasin         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| map               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| paper cutter      | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| fire extinguisher | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| soap dispenser    | 0.4762  | 0.8000  | 0.0286  | 0.2000  |
| toothbrush        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scissors          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------           | ------- | ------- | ------- | ------- |
| 3rscan            | 0.3917  | 0.5929  | 0.1736  | 0.2925  |
| head              | 0.3969  | 0.6802  | 0.1646  | 0.3475  |
| common            | 0.4333  | 0.6107  | 0.2205  | 0.3009  |
| tail              | 0.3260  | 0.4086  | 0.1269  | 0.1803  |
| small             | 0.1464  | 0.3343  | 0.0263  | 0.0768  |
| medium            | 0.3987  | 0.6486  | 0.1944  | 0.3287  |
| large             | 0.2996  | 0.7684  | 0.1241  | 0.4520  |
+-------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 20:22:31 INFO metrics.py:437 | number of matterport3d results: 339
Rank[0/8] 06/20/2025 20:24:02 INFO metrics.py:332 | eval results of matterport3d:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| picture             | 0.2443  | 0.4758  | 0.0201  | 0.1232  |
| door                | 0.2729  | 0.6397  | 0.0217  | 0.1880  |
| mirror              | 0.1375  | 0.5946  | 0.0290  | 0.1757  |
| doorframe           | 0.3040  | 0.7282  | 0.1074  | 0.4301  |
| light               | 0.0488  | 0.2479  | 0.0016  | 0.0484  |
| sink                | 0.5574  | 0.8485  | 0.3109  | 0.5556  |
| towel               | 0.3975  | 0.6273  | 0.1579  | 0.3455  |
| drawer              | 0.0011  | 0.2500  | 0.0006  | 0.1250  |
| plant               | 0.3213  | 0.7857  | 0.1468  | 0.3839  |
| blanket             | 0.1346  | 0.7273  | 0.0158  | 0.1818  |
| decoration          | 0.0132  | 0.3810  | 0.0007  | 0.0952  |
| case                | 0.0003  | 0.2222  | 0.0001  | 0.1111  |
| table               | 0.3405  | 0.7692  | 0.3019  | 0.6603  |
| bottle              | 0.1030  | 0.5625  | 0.0325  | 0.1667  |
| counter             | 0.0210  | 0.4545  | 0.0007  | 0.1212  |
| lamp                | 0.3819  | 0.7253  | 0.1753  | 0.4396  |
| toilet              | 0.8084  | 0.8438  | 0.7505  | 0.7969  |
| roof                | 0.0027  | 0.3333  | 0.0002  | 0.0833  |
| vase                | 0.0992  | 0.5000  | 0.0066  | 0.2667  |
| window              | 0.1504  | 0.5389  | 0.0282  | 0.1750  |
| cabinet             | 0.1391  | 0.6695  | 0.0780  | 0.4364  |
| vent                | 0.0189  | 0.2083  | 0.0000  | 0.0000  |
| bench               | 0.2301  | 0.5500  | 0.1682  | 0.3000  |
| plate               | 0.0101  | 0.4286  | 0.0001  | 0.0714  |
| stand               | 0.4354  | 0.8200  | 0.4156  | 0.7400  |
| bathtub             | 0.6669  | 0.7778  | 0.5640  | 0.6667  |
| countertop          | 0.0166  | 0.2857  | 0.0014  | 0.1071  |
| shelf               | 0.1557  | 0.5693  | 0.0517  | 0.3431  |
| flowerpot           | 0.0252  | 0.2800  | 0.0073  | 0.1600  |
| pillow              | 0.0838  | 0.5426  | 0.0063  | 0.1230  |
| box                 | 0.0529  | 0.6154  | 0.0138  | 0.3462  |
| switch              | 0.0001  | 0.1000  | 0.0000  | 0.0000  |
| statue              | 0.0973  | 0.4706  | 0.0827  | 0.1765  |
| frame               | 0.0004  | 0.1765  | 0.0000  | 0.0000  |
| pot                 | 0.0021  | 0.3846  | 0.0003  | 0.1538  |
| handle              | 0.0010  | 0.2500  | 0.0000  | 0.0000  |
| cup                 | 0.0002  | 0.1000  | 0.0000  | 0.0000  |
| mat                 | 0.0073  | 0.2500  | 0.0001  | 0.0417  |
| carpet              | 0.0006  | 0.1111  | 0.0000  | 0.0000  |
| curtain             | 0.4047  | 0.7396  | 0.1303  | 0.4688  |
| dresser             | 0.2322  | 0.6667  | 0.1988  | 0.6000  |
| soap                | 0.0096  | 0.4000  | 0.0087  | 0.2000  |
| desk                | 0.3597  | 0.7368  | 0.3406  | 0.6316  |
| stool               | 0.0191  | 0.3333  | 0.0180  | 0.2381  |
| rack                | 0.0017  | 0.4545  | 0.0010  | 0.3636  |
| paper               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| couch               | 0.3324  | 0.7935  | 0.2701  | 0.6413  |
| chair               | 0.4341  | 0.7823  | 0.3396  | 0.6701  |
| tv                  | 0.5321  | 0.7442  | 0.1563  | 0.3721  |
| bowl                | 0.0808  | 0.3333  | 0.0174  | 0.1212  |
| bed                 | 0.6403  | 0.8246  | 0.3668  | 0.7193  |
| book                | 0.0791  | 0.3622  | 0.0113  | 0.1024  |
| stairs              | 0.0090  | 0.3774  | 0.0032  | 0.0566  |
| alarm               | 0.0098  | 0.1667  | 0.0000  | 0.0000  |
| ottoman             | 0.0331  | 0.3889  | 0.0331  | 0.3889  |
| clock               | 0.1957  | 0.6667  | 0.1957  | 0.6667  |
| screen              | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| telephone           | 0.2590  | 0.7500  | 0.0023  | 0.2500  |
| toy                 | 0.0755  | 0.4167  | 0.0391  | 0.1389  |
| steps               | 0.0007  | 0.3000  | 0.0000  | 0.0000  |
| fan                 | 0.6742  | 0.8148  | 0.3220  | 0.5000  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jar                 | 0.0045  | 0.3333  | 0.0045  | 0.3333  |
| cushion             | 0.0029  | 0.0588  | 0.0000  | 0.0000  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wine                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| doorway             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| footstool           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candle              | 0.0058  | 0.3333  | 0.0058  | 0.3333  |
| board               | 0.1801  | 0.5000  | 0.0009  | 0.1667  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit               | 0.0944  | 0.3750  | 0.0774  | 0.2500  |
| pillar              | 0.2837  | 0.7619  | 0.1612  | 0.3810  |
| column              | 0.0171  | 0.3333  | 0.0020  | 0.1111  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| window frame        | 0.0258  | 0.4023  | 0.0033  | 0.1724  |
| door knob           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shower              | 0.0030  | 0.0833  | 0.0000  | 0.0000  |
| washbasin           | 0.0159  | 0.2143  | 0.0102  | 0.0714  |
| washing machine     | 0.4386  | 0.7500  | 0.0714  | 0.2500  |
| dispenser           | 0.0179  | 0.5000  | 0.0179  | 0.5000  |
| bin                 | 0.3261  | 0.7561  | 0.1755  | 0.4390  |
| faucet              | 0.0112  | 0.1667  | 0.0002  | 0.0278  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toilet paper        | 0.2711  | 0.6667  | 0.0666  | 0.3611  |
| rail                | 0.0362  | 0.3889  | 0.0000  | 0.0185  |
| headboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| windowsill          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball                | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| backpack            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| teapot              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| thermostat          | 0.0833  | 0.1667  | 0.0000  | 0.0000  |
| soap dispenser      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| holder              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hanger              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| brush               | 0.0132  | 0.2500  | 0.0023  | 0.2500  |
| kitchen island      | 0.2517  | 0.5714  | 0.2395  | 0.4286  |
| heater              | 0.3333  | 1.0000  | 0.3333  | 1.0000  |
| piano               | 0.2500  | 0.5000  | 0.2500  | 0.5000  |
| monitor             | 0.3333  | 0.5000  | 0.0000  | 0.0000  |
| air conditioner     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| refrigerator        | 0.5321  | 0.7333  | 0.0768  | 0.2000  |
| bucket              | 0.0117  | 0.5000  | 0.0031  | 0.2000  |
| radiator            | 0.1667  | 0.3333  | 0.0000  | 0.0000  |
| scale               | 0.0500  | 0.2500  | 0.0000  | 0.0000  |
| wardrobe            | 0.0350  | 0.5455  | 0.0323  | 0.2727  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basin               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pan                 | 0.0325  | 0.4000  | 0.0000  | 0.0000  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pipe                | 0.0002  | 0.1000  | 0.0000  | 0.0000  |
| keyboard            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mouse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pool table          | 0.1667  | 0.3333  | 0.1667  | 0.3333  |
| tissue box          | 0.1465  | 0.6250  | 0.0164  | 0.2500  |
| tissue              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| basket              | 0.0580  | 0.4800  | 0.0073  | 0.2000  |
| shoe                | 0.0285  | 0.2400  | 0.0000  | 0.0000  |
| guitar              | 0.1190  | 0.2857  | 0.0000  | 0.0000  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0729  | 0.3750  | 0.0179  | 0.1250  |
| food                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| projector           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bag                 | 0.0577  | 0.4615  | 0.0165  | 0.1538  |
| clothes dryer       | 0.2222  | 0.6667  | 0.2222  | 0.6667  |
| clothes             | 0.1306  | 0.3448  | 0.0358  | 0.1379  |
| excercise equipment | 0.4022  | 0.6667  | 0.0140  | 0.1667  |
| kitchenware         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kettle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| microwave           | 0.1403  | 0.3333  | 0.1019  | 0.1667  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher   | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| ladder              | 0.1778  | 0.3333  | 0.0000  | 0.0000  |
| garage door         | 0.2852  | 0.5000  | 0.2852  | 0.5000  |
| printer             | 0.4286  | 0.4286  | 0.1429  | 0.1429  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toiletry            | 0.1174  | 0.2273  | 0.0051  | 0.0455  |
| vanity              | 0.1562  | 0.6000  | 0.1230  | 0.4667  |
| soap dish           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fireplace           | 0.3332  | 0.5217  | 0.0145  | 0.2174  |
| beam                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| arch                | 0.0346  | 0.1333  | 0.0005  | 0.0333  |
| container           | 0.0026  | 0.0741  | 0.0006  | 0.0370  |
| machine             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| chandelier          | 0.0175  | 0.0526  | 0.0000  | 0.0000  |
| computer            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stove               | 0.2808  | 0.6000  | 0.1461  | 0.5000  |
| oven                | 0.2753  | 0.6000  | 0.0000  | 0.0000  |
| socket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| laptop              | 0.7500  | 1.0000  | 0.0455  | 0.5000  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 0.2000  | 1.0000  | 0.0000  | 0.0000  |
| partition           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet               | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| drum                | 0.0104  | 0.1250  | 0.0000  | 0.0000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell            | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| hamper              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| magazine            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.0714  | 0.5000  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| player              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jacket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pack                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blackboard          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| can                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candlestick         | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| water heater        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coffee maker        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.0500  | 0.2500  | 0.0000  | 0.0000  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| matterport3d        | 0.1216  | 0.3062  | 0.0600  | 0.1570  |
| head                | 0.1748  | 0.4584  | 0.0822  | 0.2446  |
| common              | 0.0743  | 0.2012  | 0.0412  | 0.0891  |
| tail                | 0.0741  | 0.1031  | 0.0384  | 0.0568  |
| small               | 0.0437  | 0.1540  | 0.0239  | 0.0762  |
| medium              | 0.1158  | 0.3386  | 0.0519  | 0.1659  |
| large               | 0.1056  | 0.3711  | 0.0612  | 0.2280  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 20:24:02 INFO metrics.py:437 | number of arkit results: 0
Rank[0/8] 06/20/2025 20:24:02 INFO stats.py:394 | Epoch[229] completed. Training Speed: 2.98 samples/sec across all devices.	Epoch Time: 1047.63 sec.	Average Epoch Time: 1047.63 sec.	Average Step Time: 2.69 sec.	Estimated Remaining Time: 1:18:46.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:24:03 WARNING accelerator.py:3099 | Deleting 1 checkpoints to make room for new checkpoint.
Rank[0/8] 06/20/2025 20:24:04 INFO accelerator.py:3111 | Saving current state to /job_data/checkpoints/checkpoint_22
Rank[2/8] 06/20/2025 20:24:04 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 229 to /job_data/checkpoints/checkpoint_22
Rank[6/8] 06/20/2025 20:24:04 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 229 to /job_data/checkpoints/checkpoint_22
Rank[5/8] 06/20/2025 20:24:04 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 229 to /job_data/checkpoints/checkpoint_22
Rank[7/8] 06/20/2025 20:24:04 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 229 to /job_data/checkpoints/checkpoint_22
Rank[1/8] 06/20/2025 20:24:04 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 229 to /job_data/checkpoints/checkpoint_22
Rank[4/8] 06/20/2025 20:24:04 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 229 to /job_data/checkpoints/checkpoint_22
Rank[3/8] 06/20/2025 20:24:04 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 229 to /job_data/checkpoints/checkpoint_22
Rank[0/8] 06/20/2025 20:24:06 INFO checkpointing.py:106 | Model weights saved in /job_data/checkpoints/checkpoint_22/model.safetensors
Rank[0/8] 06/20/2025 20:24:08 INFO checkpointing.py:113 | Optimizer state saved in /job_data/checkpoints/checkpoint_22/optimizer.bin
Rank[0/8] 06/20/2025 20:24:08 INFO checkpointing.py:120 | Scheduler state saved in /job_data/checkpoints/checkpoint_22/scheduler.bin
Rank[0/8] 06/20/2025 20:24:08 INFO checkpointing.py:137 | Sampler state for dataloader 0 saved in /job_data/checkpoints/checkpoint_22/sampler.bin
Rank[0/8] 06/20/2025 20:24:08 INFO checkpointing.py:137 | Sampler state for dataloader 1 saved in /job_data/checkpoints/checkpoint_22/sampler_1.bin
Rank[0/8] 06/20/2025 20:24:08 INFO checkpointing.py:164 | Random states saved in /job_data/checkpoints/checkpoint_22/random_states_0.pkl
Rank[0/8] 06/20/2025 20:24:08 INFO checkpointing.py:300 | Saving the state of TrainerProgressState to /job_data/checkpoints/checkpoint_22/custom_checkpoint_0.pkl
Rank[0/8] 06/20/2025 20:24:08 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 229 to /job_data/checkpoints/checkpoint_22
Rank[0/8] 06/20/2025 20:24:35 INFO stats.py:314 | Epoch[230] Step[24] GlobalStep[89724] Training Speed: 8.00 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 1:18:16.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:24:35 INFO loss_tracker.py:84 | Epoch[230/239] Step[24] GlobalStep[89724/NA]: det_loss_cls_0[0.4687] det_loss_box_0[0.5829] det_loss_cls_1[0.4564] det_loss_box_1[0.4375] det_loss_cls_2[0.3927] det_loss_box_2[0.3942] det_loss_cls_3[0.3616] det_loss_box_3[0.3811] det_loss_cls_4[0.3442] det_loss_box_4[0.3713] det_loss_cls_5[0.3323] det_loss_box_5[0.3725] det_loss_cls_dn_0[0.0951] det_loss_box_dn_0[0.2536] det_loss_cls_dn_1[0.0914] det_loss_box_dn_1[0.2064] det_loss_cls_dn_2[0.0903] det_loss_box_dn_2[0.1898] det_loss_cls_dn_3[0.0892] det_loss_box_dn_3[0.1854] det_loss_cls_dn_4[0.0885] det_loss_box_dn_4[0.1835] det_loss_cls_dn_5[0.0880] det_loss_box_dn_5[0.1811] loss_depth[0.0351] total_loss[6.6729]
Rank[0/8] 06/20/2025 20:25:00 INFO stats.py:314 | Epoch[230] Step[49] GlobalStep[89749] Training Speed: 7.80 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 1:17:45.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:25:00 INFO loss_tracker.py:84 | Epoch[230/239] Step[49] GlobalStep[89749/NA]: det_loss_cls_0[0.5031] det_loss_box_0[0.6650] det_loss_cls_1[0.5038] det_loss_box_1[0.5070] det_loss_cls_2[0.4487] det_loss_box_2[0.4621] det_loss_cls_3[0.4108] det_loss_box_3[0.4452] det_loss_cls_4[0.3896] det_loss_box_4[0.4329] det_loss_cls_5[0.3832] det_loss_box_5[0.4263] det_loss_cls_dn_0[0.0941] det_loss_box_dn_0[0.2927] det_loss_cls_dn_1[0.0896] det_loss_box_dn_1[0.2369] det_loss_cls_dn_2[0.0884] det_loss_box_dn_2[0.2179] det_loss_cls_dn_3[0.0872] det_loss_box_dn_3[0.2107] det_loss_cls_dn_4[0.0862] det_loss_box_dn_4[0.2083] det_loss_cls_dn_5[0.0852] det_loss_box_dn_5[0.2028] loss_depth[0.0330] total_loss[7.5107]
Rank[0/8] 06/20/2025 20:25:26 INFO stats.py:314 | Epoch[230] Step[74] GlobalStep[89774] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1:17:15.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:25:26 INFO loss_tracker.py:84 | Epoch[230/239] Step[74] GlobalStep[89774/NA]: det_loss_cls_0[0.3596] det_loss_box_0[0.4196] det_loss_cls_1[0.3623] det_loss_box_1[0.3055] det_loss_cls_2[0.3074] det_loss_box_2[0.2816] det_loss_cls_3[0.2822] det_loss_box_3[0.2761] det_loss_cls_4[0.2646] det_loss_box_4[0.2673] det_loss_cls_5[0.2572] det_loss_box_5[0.2675] det_loss_cls_dn_0[0.0759] det_loss_box_dn_0[0.2620] det_loss_cls_dn_1[0.0720] det_loss_box_dn_1[0.2167] det_loss_cls_dn_2[0.0711] det_loss_box_dn_2[0.1989] det_loss_cls_dn_3[0.0702] det_loss_box_dn_3[0.1952] det_loss_cls_dn_4[0.0696] det_loss_box_dn_4[0.1927] det_loss_cls_dn_5[0.0689] det_loss_box_dn_5[0.1843] loss_depth[0.0363] total_loss[5.3647]
Rank[0/8] 06/20/2025 20:25:52 INFO stats.py:314 | Epoch[230] Step[99] GlobalStep[89799] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1:16:44.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:25:52 INFO loss_tracker.py:84 | Epoch[230/239] Step[99] GlobalStep[89799/NA]: det_loss_cls_0[0.4544] det_loss_box_0[0.5431] det_loss_cls_1[0.4328] det_loss_box_1[0.4010] det_loss_cls_2[0.3694] det_loss_box_2[0.3662] det_loss_cls_3[0.3343] det_loss_box_3[0.3457] det_loss_cls_4[0.3105] det_loss_box_4[0.3357] det_loss_cls_5[0.2950] det_loss_box_5[0.3381] det_loss_cls_dn_0[0.0858] det_loss_box_dn_0[0.2484] det_loss_cls_dn_1[0.0823] det_loss_box_dn_1[0.1979] det_loss_cls_dn_2[0.0814] det_loss_box_dn_2[0.1790] det_loss_cls_dn_3[0.0800] det_loss_box_dn_3[0.1743] det_loss_cls_dn_4[0.0790] det_loss_box_dn_4[0.1715] det_loss_cls_dn_5[0.0783] det_loss_box_dn_5[0.1688] loss_depth[0.0362] total_loss[6.1892]
Rank[0/8] 06/20/2025 20:26:18 INFO stats.py:314 | Epoch[230] Step[124] GlobalStep[89824] Training Speed: 7.60 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 1:16:14.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:26:18 INFO loss_tracker.py:84 | Epoch[230/239] Step[124] GlobalStep[89824/NA]: det_loss_cls_0[0.4272] det_loss_box_0[0.5304] det_loss_cls_1[0.4074] det_loss_box_1[0.3942] det_loss_cls_2[0.3411] det_loss_box_2[0.3539] det_loss_cls_3[0.3115] det_loss_box_3[0.3387] det_loss_cls_4[0.2934] det_loss_box_4[0.3331] det_loss_cls_5[0.2798] det_loss_box_5[0.3348] det_loss_cls_dn_0[0.0894] det_loss_box_dn_0[0.2867] det_loss_cls_dn_1[0.0852] det_loss_box_dn_1[0.2332] det_loss_cls_dn_2[0.0843] det_loss_box_dn_2[0.2114] det_loss_cls_dn_3[0.0834] det_loss_box_dn_3[0.2058] det_loss_cls_dn_4[0.0826] det_loss_box_dn_4[0.2054] det_loss_cls_dn_5[0.0820] det_loss_box_dn_5[0.2031] loss_depth[0.0366] total_loss[6.2345]
Rank[0/8] 06/20/2025 20:26:44 INFO stats.py:314 | Epoch[230] Step[149] GlobalStep[89849] Training Speed: 6.99 samples/sec across all devices.	Average Step Time: 1.15 sec.	Estimated Remaining Time: 1:15:43.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:26:44 INFO loss_tracker.py:84 | Epoch[230/239] Step[149] GlobalStep[89849/NA]: det_loss_cls_0[0.5200] det_loss_box_0[0.6134] det_loss_cls_1[0.5000] det_loss_box_1[0.4612] det_loss_cls_2[0.4323] det_loss_box_2[0.4177] det_loss_cls_3[0.3909] det_loss_box_3[0.4081] det_loss_cls_4[0.3647] det_loss_box_4[0.3992] det_loss_cls_5[0.3489] det_loss_box_5[0.4020] det_loss_cls_dn_0[0.0908] det_loss_box_dn_0[0.2626] det_loss_cls_dn_1[0.0868] det_loss_box_dn_1[0.2153] det_loss_cls_dn_2[0.0861] det_loss_box_dn_2[0.2002] det_loss_cls_dn_3[0.0848] det_loss_box_dn_3[0.1952] det_loss_cls_dn_4[0.0839] det_loss_box_dn_4[0.1929] det_loss_cls_dn_5[0.0830] det_loss_box_dn_5[0.1907] loss_depth[0.0339] total_loss[7.0644]
Rank[0/8] 06/20/2025 20:27:10 INFO stats.py:314 | Epoch[230] Step[174] GlobalStep[89874] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1:15:13.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:27:10 INFO loss_tracker.py:84 | Epoch[230/239] Step[174] GlobalStep[89874/NA]: det_loss_cls_0[0.4811] det_loss_box_0[0.6065] det_loss_cls_1[0.4657] det_loss_box_1[0.4370] det_loss_cls_2[0.3997] det_loss_box_2[0.4041] det_loss_cls_3[0.3614] det_loss_box_3[0.3892] det_loss_cls_4[0.3370] det_loss_box_4[0.3852] det_loss_cls_5[0.3268] det_loss_box_5[0.3860] det_loss_cls_dn_0[0.0914] det_loss_box_dn_0[0.3264] det_loss_cls_dn_1[0.0885] det_loss_box_dn_1[0.2780] det_loss_cls_dn_2[0.0874] det_loss_box_dn_2[0.2596] det_loss_cls_dn_3[0.0866] det_loss_box_dn_3[0.2448] det_loss_cls_dn_4[0.0862] det_loss_box_dn_4[0.2427] det_loss_cls_dn_5[0.0854] det_loss_box_dn_5[0.2343] loss_depth[0.0340] total_loss[7.1248]
Rank[0/8] 06/20/2025 20:27:36 INFO stats.py:314 | Epoch[230] Step[199] GlobalStep[89899] Training Speed: 8.26 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1:14:42.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:27:36 INFO loss_tracker.py:84 | Epoch[230/239] Step[199] GlobalStep[89899/NA]: det_loss_cls_0[0.4985] det_loss_box_0[0.5658] det_loss_cls_1[0.4681] det_loss_box_1[0.4237] det_loss_cls_2[0.4202] det_loss_box_2[0.3760] det_loss_cls_3[0.3850] det_loss_box_3[0.3628] det_loss_cls_4[0.3615] det_loss_box_4[0.3543] det_loss_cls_5[0.3505] det_loss_box_5[0.3506] det_loss_cls_dn_0[0.0934] det_loss_box_dn_0[0.2482] det_loss_cls_dn_1[0.0896] det_loss_box_dn_1[0.2027] det_loss_cls_dn_2[0.0887] det_loss_box_dn_2[0.1845] det_loss_cls_dn_3[0.0874] det_loss_box_dn_3[0.1801] det_loss_cls_dn_4[0.0871] det_loss_box_dn_4[0.1772] det_loss_cls_dn_5[0.0864] det_loss_box_dn_5[0.1772] loss_depth[0.0349] total_loss[6.6542]
Rank[0/8] 06/20/2025 20:28:02 INFO stats.py:314 | Epoch[230] Step[224] GlobalStep[89924] Training Speed: 7.87 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 1:14:12.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:28:02 INFO loss_tracker.py:84 | Epoch[230/239] Step[224] GlobalStep[89924/NA]: det_loss_cls_0[0.5838] det_loss_box_0[0.7204] det_loss_cls_1[0.5760] det_loss_box_1[0.5153] det_loss_cls_2[0.5140] det_loss_box_2[0.4709] det_loss_cls_3[0.4780] det_loss_box_3[0.4525] det_loss_cls_4[0.4575] det_loss_box_4[0.4349] det_loss_cls_5[0.4443] det_loss_box_5[0.4372] det_loss_cls_dn_0[0.1033] det_loss_box_dn_0[0.2693] det_loss_cls_dn_1[0.0992] det_loss_box_dn_1[0.2233] det_loss_cls_dn_2[0.0983] det_loss_box_dn_2[0.2100] det_loss_cls_dn_3[0.0972] det_loss_box_dn_3[0.2055] det_loss_cls_dn_4[0.0967] det_loss_box_dn_4[0.2029] det_loss_cls_dn_5[0.0956] det_loss_box_dn_5[0.2008] loss_depth[0.0340] total_loss[8.0211]
Rank[0/8] 06/20/2025 20:28:28 INFO stats.py:314 | Epoch[230] Step[249] GlobalStep[89949] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1:13:41.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:28:28 INFO loss_tracker.py:84 | Epoch[230/239] Step[249] GlobalStep[89949/NA]: det_loss_cls_0[0.3719] det_loss_box_0[0.4923] det_loss_cls_1[0.3545] det_loss_box_1[0.3366] det_loss_cls_2[0.2880] det_loss_box_2[0.3067] det_loss_cls_3[0.2611] det_loss_box_3[0.2927] det_loss_cls_4[0.2385] det_loss_box_4[0.2857] det_loss_cls_5[0.2267] det_loss_box_5[0.2850] det_loss_cls_dn_0[0.0834] det_loss_box_dn_0[0.2267] det_loss_cls_dn_1[0.0799] det_loss_box_dn_1[0.1767] det_loss_cls_dn_2[0.0795] det_loss_box_dn_2[0.1573] det_loss_cls_dn_3[0.0782] det_loss_box_dn_3[0.1541] det_loss_cls_dn_4[0.0775] det_loss_box_dn_4[0.1525] det_loss_cls_dn_5[0.0763] det_loss_box_dn_5[0.1517] loss_depth[0.0376] total_loss[5.2712]
Rank[0/8] 06/20/2025 20:28:54 INFO stats.py:314 | Epoch[230] Step[274] GlobalStep[89974] Training Speed: 7.49 samples/sec across all devices.	Average Step Time: 1.07 sec.	Estimated Remaining Time: 1:13:11.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:28:54 INFO loss_tracker.py:84 | Epoch[230/239] Step[274] GlobalStep[89974/NA]: det_loss_cls_0[0.4536] det_loss_box_0[0.5088] det_loss_cls_1[0.4402] det_loss_box_1[0.3766] det_loss_cls_2[0.3822] det_loss_box_2[0.3544] det_loss_cls_3[0.3601] det_loss_box_3[0.3391] det_loss_cls_4[0.3313] det_loss_box_4[0.3331] det_loss_cls_5[0.3213] det_loss_box_5[0.3316] det_loss_cls_dn_0[0.0915] det_loss_box_dn_0[0.2906] det_loss_cls_dn_1[0.0881] det_loss_box_dn_1[0.2355] det_loss_cls_dn_2[0.0869] det_loss_box_dn_2[0.2129] det_loss_cls_dn_3[0.0861] det_loss_box_dn_3[0.2070] det_loss_cls_dn_4[0.0849] det_loss_box_dn_4[0.2046] det_loss_cls_dn_5[0.0841] det_loss_box_dn_5[0.2032] loss_depth[0.0368] total_loss[6.4446]
Rank[0/8] 06/20/2025 20:29:20 INFO stats.py:314 | Epoch[230] Step[299] GlobalStep[89999] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1:12:40.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:29:20 INFO loss_tracker.py:84 | Epoch[230/239] Step[299] GlobalStep[89999/NA]: det_loss_cls_0[0.4240] det_loss_box_0[0.4970] det_loss_cls_1[0.4185] det_loss_box_1[0.3862] det_loss_cls_2[0.3610] det_loss_box_2[0.3347] det_loss_cls_3[0.3313] det_loss_box_3[0.3267] det_loss_cls_4[0.3089] det_loss_box_4[0.3146] det_loss_cls_5[0.2926] det_loss_box_5[0.3196] det_loss_cls_dn_0[0.0839] det_loss_box_dn_0[0.2113] det_loss_cls_dn_1[0.0809] det_loss_box_dn_1[0.1627] det_loss_cls_dn_2[0.0803] det_loss_box_dn_2[0.1460] det_loss_cls_dn_3[0.0793] det_loss_box_dn_3[0.1419] det_loss_cls_dn_4[0.0787] det_loss_box_dn_4[0.1400] det_loss_cls_dn_5[0.0779] det_loss_box_dn_5[0.1380] loss_depth[0.0343] total_loss[5.7703]
Rank[0/8] 06/20/2025 20:29:46 INFO stats.py:314 | Epoch[230] Step[324] GlobalStep[90024] Training Speed: 6.56 samples/sec across all devices.	Average Step Time: 1.22 sec.	Estimated Remaining Time: 1:12:10.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:29:46 INFO loss_tracker.py:84 | Epoch[230/239] Step[324] GlobalStep[90024/NA]: det_loss_cls_0[0.4935] det_loss_box_0[0.6336] det_loss_cls_1[0.4757] det_loss_box_1[0.4753] det_loss_cls_2[0.4183] det_loss_box_2[0.4292] det_loss_cls_3[0.3827] det_loss_box_3[0.4173] det_loss_cls_4[0.3516] det_loss_box_4[0.4050] det_loss_cls_5[0.3420] det_loss_box_5[0.4038] det_loss_cls_dn_0[0.0933] det_loss_box_dn_0[0.2597] det_loss_cls_dn_1[0.0881] det_loss_box_dn_1[0.2186] det_loss_cls_dn_2[0.0872] det_loss_box_dn_2[0.2050] det_loss_cls_dn_3[0.0858] det_loss_box_dn_3[0.2007] det_loss_cls_dn_4[0.0846] det_loss_box_dn_4[0.1968] det_loss_cls_dn_5[0.0837] det_loss_box_dn_5[0.1933] loss_depth[0.0363] total_loss[7.0610]
Rank[0/8] 06/20/2025 20:30:12 INFO stats.py:314 | Epoch[230] Step[349] GlobalStep[90049] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 1:11:40.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:30:12 INFO loss_tracker.py:84 | Epoch[230/239] Step[349] GlobalStep[90049/NA]: det_loss_cls_0[0.4036] det_loss_box_0[0.5395] det_loss_cls_1[0.3966] det_loss_box_1[0.4094] det_loss_cls_2[0.3364] det_loss_box_2[0.3750] det_loss_cls_3[0.2973] det_loss_box_3[0.3630] det_loss_cls_4[0.2838] det_loss_box_4[0.3401] det_loss_cls_5[0.2731] det_loss_box_5[0.3443] det_loss_cls_dn_0[0.0843] det_loss_box_dn_0[0.2756] det_loss_cls_dn_1[0.0809] det_loss_box_dn_1[0.2205] det_loss_cls_dn_2[0.0806] det_loss_box_dn_2[0.1998] det_loss_cls_dn_3[0.0789] det_loss_box_dn_3[0.1943] det_loss_cls_dn_4[0.0783] det_loss_box_dn_4[0.1925] det_loss_cls_dn_5[0.0772] det_loss_box_dn_5[0.1903] loss_depth[0.0370] total_loss[6.1522]
Rank[0/8] 06/20/2025 20:30:37 INFO stats.py:314 | Epoch[230] Step[374] GlobalStep[90074] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1:11:09.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:30:37 INFO loss_tracker.py:84 | Epoch[230/239] Step[374] GlobalStep[90074/NA]: det_loss_cls_0[0.4104] det_loss_box_0[0.4910] det_loss_cls_1[0.3934] det_loss_box_1[0.3510] det_loss_cls_2[0.3305] det_loss_box_2[0.3185] det_loss_cls_3[0.3009] det_loss_box_3[0.3086] det_loss_cls_4[0.2785] det_loss_box_4[0.3036] det_loss_cls_5[0.2725] det_loss_box_5[0.3007] det_loss_cls_dn_0[0.0884] det_loss_box_dn_0[0.2665] det_loss_cls_dn_1[0.0841] det_loss_box_dn_1[0.2165] det_loss_cls_dn_2[0.0835] det_loss_box_dn_2[0.1981] det_loss_cls_dn_3[0.0825] det_loss_box_dn_3[0.1919] det_loss_cls_dn_4[0.0821] det_loss_box_dn_4[0.1893] det_loss_cls_dn_5[0.0812] det_loss_box_dn_5[0.1860] loss_depth[0.0337] total_loss[5.8434]
Rank[0/8] 06/20/2025 20:30:52 INFO stats.py:394 | Epoch[230] completed. Training Speed: 7.73 samples/sec across all devices.	Epoch Time: 403.75 sec.	Average Epoch Time: 403.75 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1:10:51.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:31:07 INFO stats.py:314 | Epoch[231] Step[9] GlobalStep[90099] Training Speed: 8.64 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 1:10:39.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:31:23 INFO loss_tracker.py:84 | Epoch[231/239] Step[24] GlobalStep[90114/NA]: det_loss_cls_0[0.3994] det_loss_box_0[0.5040] det_loss_cls_1[0.3945] det_loss_box_1[0.3963] det_loss_cls_2[0.3343] det_loss_box_2[0.3674] det_loss_cls_3[0.3058] det_loss_box_3[0.3578] det_loss_cls_4[0.2883] det_loss_box_4[0.3452] det_loss_cls_5[0.2759] det_loss_box_5[0.3484] det_loss_cls_dn_0[0.0857] det_loss_box_dn_0[0.2612] det_loss_cls_dn_1[0.0829] det_loss_box_dn_1[0.2133] det_loss_cls_dn_2[0.0825] det_loss_box_dn_2[0.1959] det_loss_cls_dn_3[0.0813] det_loss_box_dn_3[0.1924] det_loss_cls_dn_4[0.0804] det_loss_box_dn_4[0.1895] det_loss_cls_dn_5[0.0793] det_loss_box_dn_5[0.1875] loss_depth[0.0344] total_loss[6.0837]
Rank[0/8] 06/20/2025 20:31:33 INFO stats.py:314 | Epoch[231] Step[34] GlobalStep[90124] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1:10:08.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:31:48 INFO loss_tracker.py:84 | Epoch[231/239] Step[49] GlobalStep[90139/NA]: det_loss_cls_0[0.5425] det_loss_box_0[0.6430] det_loss_cls_1[0.5094] det_loss_box_1[0.4850] det_loss_cls_2[0.4351] det_loss_box_2[0.4335] det_loss_cls_3[0.3978] det_loss_box_3[0.4226] det_loss_cls_4[0.3702] det_loss_box_4[0.4057] det_loss_cls_5[0.3483] det_loss_box_5[0.4076] det_loss_cls_dn_0[0.0877] det_loss_box_dn_0[0.2352] det_loss_cls_dn_1[0.0836] det_loss_box_dn_1[0.1928] det_loss_cls_dn_2[0.0824] det_loss_box_dn_2[0.1758] det_loss_cls_dn_3[0.0815] det_loss_box_dn_3[0.1712] det_loss_cls_dn_4[0.0807] det_loss_box_dn_4[0.1695] det_loss_cls_dn_5[0.0799] det_loss_box_dn_5[0.1668] loss_depth[0.0357] total_loss[7.0436]
Rank[0/8] 06/20/2025 20:31:58 INFO stats.py:314 | Epoch[231] Step[59] GlobalStep[90149] Training Speed: 8.05 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1:09:38.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:32:14 INFO loss_tracker.py:84 | Epoch[231/239] Step[74] GlobalStep[90164/NA]: det_loss_cls_0[0.4944] det_loss_box_0[0.6455] det_loss_cls_1[0.4894] det_loss_box_1[0.4912] det_loss_cls_2[0.4367] det_loss_box_2[0.4439] det_loss_cls_3[0.4097] det_loss_box_3[0.4341] det_loss_cls_4[0.3726] det_loss_box_4[0.4248] det_loss_cls_5[0.3649] det_loss_box_5[0.4194] det_loss_cls_dn_0[0.0970] det_loss_box_dn_0[0.3171] det_loss_cls_dn_1[0.0917] det_loss_box_dn_1[0.2582] det_loss_cls_dn_2[0.0908] det_loss_box_dn_2[0.2326] det_loss_cls_dn_3[0.0893] det_loss_box_dn_3[0.2245] det_loss_cls_dn_4[0.0892] det_loss_box_dn_4[0.2217] det_loss_cls_dn_5[0.0880] det_loss_box_dn_5[0.2167] loss_depth[0.0358] total_loss[7.4792]
Rank[0/8] 06/20/2025 20:32:24 INFO stats.py:314 | Epoch[231] Step[84] GlobalStep[90174] Training Speed: 10.96 samples/sec across all devices.	Average Step Time: 0.73 sec.	Estimated Remaining Time: 1:09:07.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:32:39 INFO loss_tracker.py:84 | Epoch[231/239] Step[99] GlobalStep[90189/NA]: det_loss_cls_0[0.4121] det_loss_box_0[0.5029] det_loss_cls_1[0.4026] det_loss_box_1[0.3885] det_loss_cls_2[0.3349] det_loss_box_2[0.3620] det_loss_cls_3[0.3026] det_loss_box_3[0.3451] det_loss_cls_4[0.2695] det_loss_box_4[0.3439] det_loss_cls_5[0.2570] det_loss_box_5[0.3444] det_loss_cls_dn_0[0.0834] det_loss_box_dn_0[0.2468] det_loss_cls_dn_1[0.0799] det_loss_box_dn_1[0.2046] det_loss_cls_dn_2[0.0791] det_loss_box_dn_2[0.1890] det_loss_cls_dn_3[0.0777] det_loss_box_dn_3[0.1856] det_loss_cls_dn_4[0.0773] det_loss_box_dn_4[0.1843] det_loss_cls_dn_5[0.0765] det_loss_box_dn_5[0.1837] loss_depth[0.0343] total_loss[5.9676]
Rank[0/8] 06/20/2025 20:32:50 INFO stats.py:314 | Epoch[231] Step[109] GlobalStep[90199] Training Speed: 8.47 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1:08:37.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:33:06 INFO loss_tracker.py:84 | Epoch[231/239] Step[124] GlobalStep[90214/NA]: det_loss_cls_0[0.3811] det_loss_box_0[0.4789] det_loss_cls_1[0.3605] det_loss_box_1[0.3759] det_loss_cls_2[0.2978] det_loss_box_2[0.3285] det_loss_cls_3[0.2652] det_loss_box_3[0.3202] det_loss_cls_4[0.2473] det_loss_box_4[0.3019] det_loss_cls_5[0.2380] det_loss_box_5[0.2996] det_loss_cls_dn_0[0.0865] det_loss_box_dn_0[0.2983] det_loss_cls_dn_1[0.0828] det_loss_box_dn_1[0.2443] det_loss_cls_dn_2[0.0820] det_loss_box_dn_2[0.2258] det_loss_cls_dn_3[0.0804] det_loss_box_dn_3[0.2203] det_loss_cls_dn_4[0.0801] det_loss_box_dn_4[0.2175] det_loss_cls_dn_5[0.0792] det_loss_box_dn_5[0.2113] loss_depth[0.0357] total_loss[5.8391]
Rank[0/8] 06/20/2025 20:33:17 INFO stats.py:314 | Epoch[231] Step[134] GlobalStep[90224] Training Speed: 7.77 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 1:08:07.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:33:32 INFO loss_tracker.py:84 | Epoch[231/239] Step[149] GlobalStep[90239/NA]: det_loss_cls_0[0.4319] det_loss_box_0[0.5711] det_loss_cls_1[0.4170] det_loss_box_1[0.4167] det_loss_cls_2[0.3581] det_loss_box_2[0.3597] det_loss_cls_3[0.3229] det_loss_box_3[0.3478] det_loss_cls_4[0.2887] det_loss_box_4[0.3463] det_loss_cls_5[0.2747] det_loss_box_5[0.3465] det_loss_cls_dn_0[0.0789] det_loss_box_dn_0[0.2738] det_loss_cls_dn_1[0.0760] det_loss_box_dn_1[0.2204] det_loss_cls_dn_2[0.0752] det_loss_box_dn_2[0.2010] det_loss_cls_dn_3[0.0739] det_loss_box_dn_3[0.1967] det_loss_cls_dn_4[0.0730] det_loss_box_dn_4[0.1929] det_loss_cls_dn_5[0.0720] det_loss_box_dn_5[0.1906] loss_depth[0.0349] total_loss[6.2405]
Rank[0/8] 06/20/2025 20:33:42 INFO stats.py:314 | Epoch[231] Step[159] GlobalStep[90249] Training Speed: 8.24 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1:07:36.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:33:57 INFO loss_tracker.py:84 | Epoch[231/239] Step[174] GlobalStep[90264/NA]: det_loss_cls_0[0.4540] det_loss_box_0[0.5775] det_loss_cls_1[0.4506] det_loss_box_1[0.4535] det_loss_cls_2[0.3927] det_loss_box_2[0.4231] det_loss_cls_3[0.3570] det_loss_box_3[0.4126] det_loss_cls_4[0.3408] det_loss_box_4[0.4042] det_loss_cls_5[0.3300] det_loss_box_5[0.4047] det_loss_cls_dn_0[0.0836] det_loss_box_dn_0[0.2544] det_loss_cls_dn_1[0.0806] det_loss_box_dn_1[0.2115] det_loss_cls_dn_2[0.0798] det_loss_box_dn_2[0.1948] det_loss_cls_dn_3[0.0789] det_loss_box_dn_3[0.1914] det_loss_cls_dn_4[0.0782] det_loss_box_dn_4[0.1891] det_loss_cls_dn_5[0.0777] det_loss_box_dn_5[0.1880] loss_depth[0.0336] total_loss[6.7422]
Rank[0/8] 06/20/2025 20:34:08 INFO stats.py:314 | Epoch[231] Step[184] GlobalStep[90274] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1:07:06.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:34:23 INFO loss_tracker.py:84 | Epoch[231/239] Step[199] GlobalStep[90289/NA]: det_loss_cls_0[0.5207] det_loss_box_0[0.6278] det_loss_cls_1[0.5086] det_loss_box_1[0.4661] det_loss_cls_2[0.4217] det_loss_box_2[0.4306] det_loss_cls_3[0.3803] det_loss_box_3[0.4185] det_loss_cls_4[0.3597] det_loss_box_4[0.4034] det_loss_cls_5[0.3379] det_loss_box_5[0.4063] det_loss_cls_dn_0[0.0942] det_loss_box_dn_0[0.2597] det_loss_cls_dn_1[0.0896] det_loss_box_dn_1[0.2083] det_loss_cls_dn_2[0.0884] det_loss_box_dn_2[0.1914] det_loss_cls_dn_3[0.0873] det_loss_box_dn_3[0.1854] det_loss_cls_dn_4[0.0864] det_loss_box_dn_4[0.1837] det_loss_cls_dn_5[0.0853] det_loss_box_dn_5[0.1807] loss_depth[0.0328] total_loss[7.0548]
Rank[0/8] 06/20/2025 20:34:34 INFO stats.py:314 | Epoch[231] Step[209] GlobalStep[90299] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 1:06:35.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:34:50 INFO loss_tracker.py:84 | Epoch[231/239] Step[224] GlobalStep[90314/NA]: det_loss_cls_0[0.4666] det_loss_box_0[0.5408] det_loss_cls_1[0.4519] det_loss_box_1[0.4029] det_loss_cls_2[0.3873] det_loss_box_2[0.3644] det_loss_cls_3[0.3486] det_loss_box_3[0.3563] det_loss_cls_4[0.3310] det_loss_box_4[0.3490] det_loss_cls_5[0.3124] det_loss_box_5[0.3542] det_loss_cls_dn_0[0.0826] det_loss_box_dn_0[0.2611] det_loss_cls_dn_1[0.0797] det_loss_box_dn_1[0.2048] det_loss_cls_dn_2[0.0784] det_loss_box_dn_2[0.1826] det_loss_cls_dn_3[0.0773] det_loss_box_dn_3[0.1778] det_loss_cls_dn_4[0.0769] det_loss_box_dn_4[0.1756] det_loss_cls_dn_5[0.0760] det_loss_box_dn_5[0.1719] loss_depth[0.0362] total_loss[6.3461]
Rank[0/8] 06/20/2025 20:35:00 INFO stats.py:314 | Epoch[231] Step[234] GlobalStep[90324] Training Speed: 7.92 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 1:06:05.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:35:15 INFO loss_tracker.py:84 | Epoch[231/239] Step[249] GlobalStep[90339/NA]: det_loss_cls_0[0.4970] det_loss_box_0[0.5996] det_loss_cls_1[0.4838] det_loss_box_1[0.4567] det_loss_cls_2[0.4170] det_loss_box_2[0.4160] det_loss_cls_3[0.3837] det_loss_box_3[0.4072] det_loss_cls_4[0.3609] det_loss_box_4[0.3889] det_loss_cls_5[0.3503] det_loss_box_5[0.3844] det_loss_cls_dn_0[0.0948] det_loss_box_dn_0[0.2444] det_loss_cls_dn_1[0.0910] det_loss_box_dn_1[0.1986] det_loss_cls_dn_2[0.0895] det_loss_box_dn_2[0.1814] det_loss_cls_dn_3[0.0883] det_loss_box_dn_3[0.1773] det_loss_cls_dn_4[0.0873] det_loss_box_dn_4[0.1749] det_loss_cls_dn_5[0.0862] det_loss_box_dn_5[0.1736] loss_depth[0.0342] total_loss[6.8669]
Rank[0/8] 06/20/2025 20:35:26 INFO stats.py:314 | Epoch[231] Step[259] GlobalStep[90349] Training Speed: 8.08 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 1:05:34.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:35:41 INFO loss_tracker.py:84 | Epoch[231/239] Step[274] GlobalStep[90364/NA]: det_loss_cls_0[0.4136] det_loss_box_0[0.4780] det_loss_cls_1[0.4105] det_loss_box_1[0.3572] det_loss_cls_2[0.3403] det_loss_box_2[0.3268] det_loss_cls_3[0.3097] det_loss_box_3[0.3192] det_loss_cls_4[0.2854] det_loss_box_4[0.3145] det_loss_cls_5[0.2783] det_loss_box_5[0.3099] det_loss_cls_dn_0[0.0834] det_loss_box_dn_0[0.2631] det_loss_cls_dn_1[0.0807] det_loss_box_dn_1[0.2186] det_loss_cls_dn_2[0.0797] det_loss_box_dn_2[0.2039] det_loss_cls_dn_3[0.0788] det_loss_box_dn_3[0.1994] det_loss_cls_dn_4[0.0780] det_loss_box_dn_4[0.1976] det_loss_cls_dn_5[0.0773] det_loss_box_dn_5[0.1942] loss_depth[0.0336] total_loss[5.9315]
Rank[0/8] 06/20/2025 20:35:51 INFO stats.py:314 | Epoch[231] Step[284] GlobalStep[90374] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1:05:04.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:36:07 INFO loss_tracker.py:84 | Epoch[231/239] Step[299] GlobalStep[90389/NA]: det_loss_cls_0[0.4903] det_loss_box_0[0.5704] det_loss_cls_1[0.4677] det_loss_box_1[0.4149] det_loss_cls_2[0.3997] det_loss_box_2[0.3773] det_loss_cls_3[0.3643] det_loss_box_3[0.3679] det_loss_cls_4[0.3398] det_loss_box_4[0.3598] det_loss_cls_5[0.3291] det_loss_box_5[0.3603] det_loss_cls_dn_0[0.0884] det_loss_box_dn_0[0.2314] det_loss_cls_dn_1[0.0848] det_loss_box_dn_1[0.1912] det_loss_cls_dn_2[0.0837] det_loss_box_dn_2[0.1742] det_loss_cls_dn_3[0.0824] det_loss_box_dn_3[0.1711] det_loss_cls_dn_4[0.0815] det_loss_box_dn_4[0.1693] det_loss_cls_dn_5[0.0808] det_loss_box_dn_5[0.1682] loss_depth[0.0337] total_loss[6.4820]
Rank[0/8] 06/20/2025 20:36:17 INFO stats.py:314 | Epoch[231] Step[309] GlobalStep[90399] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1:04:34.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:36:32 INFO loss_tracker.py:84 | Epoch[231/239] Step[324] GlobalStep[90414/NA]: det_loss_cls_0[0.6049] det_loss_box_0[0.7676] det_loss_cls_1[0.5877] det_loss_box_1[0.5511] det_loss_cls_2[0.5252] det_loss_box_2[0.4904] det_loss_cls_3[0.4868] det_loss_box_3[0.4714] det_loss_cls_4[0.4650] det_loss_box_4[0.4505] det_loss_cls_5[0.4505] det_loss_box_5[0.4552] det_loss_cls_dn_0[0.0984] det_loss_box_dn_0[0.2323] det_loss_cls_dn_1[0.0945] det_loss_box_dn_1[0.1887] det_loss_cls_dn_2[0.0935] det_loss_box_dn_2[0.1703] det_loss_cls_dn_3[0.0928] det_loss_box_dn_3[0.1669] det_loss_cls_dn_4[0.0919] det_loss_box_dn_4[0.1648] det_loss_cls_dn_5[0.0914] det_loss_box_dn_5[0.1631] loss_depth[0.0349] total_loss[7.9896]
Rank[0/8] 06/20/2025 20:36:42 INFO stats.py:314 | Epoch[231] Step[334] GlobalStep[90424] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1:04:03.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:36:58 INFO loss_tracker.py:84 | Epoch[231/239] Step[349] GlobalStep[90439/NA]: det_loss_cls_0[0.6570] det_loss_box_0[0.8964] det_loss_cls_1[0.6371] det_loss_box_1[0.6814] det_loss_cls_2[0.5627] det_loss_box_2[0.6286] det_loss_cls_3[0.5211] det_loss_box_3[0.5975] det_loss_cls_4[0.4944] det_loss_box_4[0.5790] det_loss_cls_5[0.4820] det_loss_box_5[0.5803] det_loss_cls_dn_0[0.1063] det_loss_box_dn_0[0.2707] det_loss_cls_dn_1[0.1026] det_loss_box_dn_1[0.2274] det_loss_cls_dn_2[0.1017] det_loss_box_dn_2[0.2159] det_loss_cls_dn_3[0.1007] det_loss_box_dn_3[0.2124] det_loss_cls_dn_4[0.1001] det_loss_box_dn_4[0.2103] det_loss_cls_dn_5[0.0994] det_loss_box_dn_5[0.2096] loss_depth[0.0356] total_loss[9.3100]
Rank[0/8] 06/20/2025 20:37:08 INFO stats.py:314 | Epoch[231] Step[359] GlobalStep[90449] Training Speed: 8.66 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 1:03:33.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:37:24 INFO loss_tracker.py:84 | Epoch[231/239] Step[374] GlobalStep[90464/NA]: det_loss_cls_0[0.5320] det_loss_box_0[0.6425] det_loss_cls_1[0.5179] det_loss_box_1[0.4940] det_loss_cls_2[0.4598] det_loss_box_2[0.4437] det_loss_cls_3[0.4174] det_loss_box_3[0.4339] det_loss_cls_4[0.3955] det_loss_box_4[0.4191] det_loss_cls_5[0.3760] det_loss_box_5[0.4228] det_loss_cls_dn_0[0.0838] det_loss_box_dn_0[0.2115] det_loss_cls_dn_1[0.0800] det_loss_box_dn_1[0.1721] det_loss_cls_dn_2[0.0796] det_loss_box_dn_2[0.1590] det_loss_cls_dn_3[0.0783] det_loss_box_dn_3[0.1557] det_loss_cls_dn_4[0.0774] det_loss_box_dn_4[0.1537] det_loss_cls_dn_5[0.0768] det_loss_box_dn_5[0.1500] loss_depth[0.0352] total_loss[7.0677]
Rank[0/8] 06/20/2025 20:37:34 INFO stats.py:314 | Epoch[231] Step[384] GlobalStep[90474] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1:03:02.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:37:39 INFO stats.py:394 | Epoch[231] completed. Training Speed: 7.66 samples/sec across all devices.	Epoch Time: 407.21 sec.	Average Epoch Time: 407.21 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 1:02:56.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:38:03 INFO stats.py:314 | Epoch[232] Step[19] GlobalStep[90499] Training Speed: 6.65 samples/sec across all devices.	Average Step Time: 1.20 sec.	Estimated Remaining Time: 1:02:32.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:38:08 INFO loss_tracker.py:84 | Epoch[232/239] Step[24] GlobalStep[90504/NA]: det_loss_cls_0[0.4496] det_loss_box_0[0.5745] det_loss_cls_1[0.4405] det_loss_box_1[0.4256] det_loss_cls_2[0.3731] det_loss_box_2[0.3871] det_loss_cls_3[0.3352] det_loss_box_3[0.3782] det_loss_cls_4[0.3113] det_loss_box_4[0.3739] det_loss_cls_5[0.2971] det_loss_box_5[0.3722] det_loss_cls_dn_0[0.0870] det_loss_box_dn_0[0.2890] det_loss_cls_dn_1[0.0830] det_loss_box_dn_1[0.2315] det_loss_cls_dn_2[0.0816] det_loss_box_dn_2[0.2113] det_loss_cls_dn_3[0.0806] det_loss_box_dn_3[0.2030] det_loss_cls_dn_4[0.0798] det_loss_box_dn_4[0.2009] det_loss_cls_dn_5[0.0789] det_loss_box_dn_5[0.1967] loss_depth[0.0361] total_loss[6.5779]
Rank[0/8] 06/20/2025 20:38:29 INFO stats.py:314 | Epoch[232] Step[44] GlobalStep[90524] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1:02:02.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:38:34 INFO loss_tracker.py:84 | Epoch[232/239] Step[49] GlobalStep[90529/NA]: det_loss_cls_0[0.5592] det_loss_box_0[0.7919] det_loss_cls_1[0.5411] det_loss_box_1[0.6071] det_loss_cls_2[0.4873] det_loss_box_2[0.5475] det_loss_cls_3[0.4662] det_loss_box_3[0.5321] det_loss_cls_4[0.4387] det_loss_box_4[0.5165] det_loss_cls_5[0.4144] det_loss_box_5[0.5176] det_loss_cls_dn_0[0.1006] det_loss_box_dn_0[0.3227] det_loss_cls_dn_1[0.0970] det_loss_box_dn_1[0.2769] det_loss_cls_dn_2[0.0965] det_loss_box_dn_2[0.2578] det_loss_cls_dn_3[0.0952] det_loss_box_dn_3[0.2464] det_loss_cls_dn_4[0.0946] det_loss_box_dn_4[0.2442] det_loss_cls_dn_5[0.0932] det_loss_box_dn_5[0.2391] loss_depth[0.0336] total_loss[8.6173]
Rank[0/8] 06/20/2025 20:38:55 INFO stats.py:314 | Epoch[232] Step[69] GlobalStep[90549] Training Speed: 7.35 samples/sec across all devices.	Average Step Time: 1.09 sec.	Estimated Remaining Time: 1:01:31.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:39:00 INFO loss_tracker.py:84 | Epoch[232/239] Step[74] GlobalStep[90554/NA]: det_loss_cls_0[0.3518] det_loss_box_0[0.4731] det_loss_cls_1[0.3448] det_loss_box_1[0.3586] det_loss_cls_2[0.2855] det_loss_box_2[0.3359] det_loss_cls_3[0.2538] det_loss_box_3[0.3274] det_loss_cls_4[0.2379] det_loss_box_4[0.3127] det_loss_cls_5[0.2262] det_loss_box_5[0.3121] det_loss_cls_dn_0[0.0773] det_loss_box_dn_0[0.2608] det_loss_cls_dn_1[0.0732] det_loss_box_dn_1[0.2134] det_loss_cls_dn_2[0.0718] det_loss_box_dn_2[0.1949] det_loss_cls_dn_3[0.0708] det_loss_box_dn_3[0.1887] det_loss_cls_dn_4[0.0697] det_loss_box_dn_4[0.1862] det_loss_cls_dn_5[0.0689] det_loss_box_dn_5[0.1805] loss_depth[0.0318] total_loss[5.5074]
Rank[0/8] 06/20/2025 20:39:21 INFO stats.py:314 | Epoch[232] Step[94] GlobalStep[90574] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 1:01:01.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:39:26 INFO loss_tracker.py:84 | Epoch[232/239] Step[99] GlobalStep[90579/NA]: det_loss_cls_0[0.3791] det_loss_box_0[0.4667] det_loss_cls_1[0.3730] det_loss_box_1[0.3347] det_loss_cls_2[0.3042] det_loss_box_2[0.3023] det_loss_cls_3[0.2810] det_loss_box_3[0.2901] det_loss_cls_4[0.2638] det_loss_box_4[0.2922] det_loss_cls_5[0.2547] det_loss_box_5[0.2919] det_loss_cls_dn_0[0.0831] det_loss_box_dn_0[0.2679] det_loss_cls_dn_1[0.0803] det_loss_box_dn_1[0.2142] det_loss_cls_dn_2[0.0795] det_loss_box_dn_2[0.1887] det_loss_cls_dn_3[0.0783] det_loss_box_dn_3[0.1861] det_loss_cls_dn_4[0.0777] det_loss_box_dn_4[0.1827] det_loss_cls_dn_5[0.0770] det_loss_box_dn_5[0.1781] loss_depth[0.0348] total_loss[5.5621]
Rank[0/8] 06/20/2025 20:39:47 INFO stats.py:314 | Epoch[232] Step[119] GlobalStep[90599] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 1:00:30.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:39:52 INFO loss_tracker.py:84 | Epoch[232/239] Step[124] GlobalStep[90604/NA]: det_loss_cls_0[0.4218] det_loss_box_0[0.5260] det_loss_cls_1[0.3899] det_loss_box_1[0.3918] det_loss_cls_2[0.3370] det_loss_box_2[0.3508] det_loss_cls_3[0.3139] det_loss_box_3[0.3319] det_loss_cls_4[0.2940] det_loss_box_4[0.3277] det_loss_cls_5[0.2871] det_loss_box_5[0.3257] det_loss_cls_dn_0[0.0824] det_loss_box_dn_0[0.2764] det_loss_cls_dn_1[0.0789] det_loss_box_dn_1[0.2185] det_loss_cls_dn_2[0.0783] det_loss_box_dn_2[0.1983] det_loss_cls_dn_3[0.0771] det_loss_box_dn_3[0.1935] det_loss_cls_dn_4[0.0764] det_loss_box_dn_4[0.1903] det_loss_cls_dn_5[0.0758] det_loss_box_dn_5[0.1866] loss_depth[0.0369] total_loss[6.0672]
Rank[0/8] 06/20/2025 20:40:13 INFO stats.py:314 | Epoch[232] Step[144] GlobalStep[90624] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 1:00:00.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:40:18 INFO loss_tracker.py:84 | Epoch[232/239] Step[149] GlobalStep[90629/NA]: det_loss_cls_0[0.5171] det_loss_box_0[0.6212] det_loss_cls_1[0.5005] det_loss_box_1[0.4616] det_loss_cls_2[0.4297] det_loss_box_2[0.4220] det_loss_cls_3[0.3891] det_loss_box_3[0.4021] det_loss_cls_4[0.3628] det_loss_box_4[0.3875] det_loss_cls_5[0.3556] det_loss_box_5[0.3822] det_loss_cls_dn_0[0.0982] det_loss_box_dn_0[0.2955] det_loss_cls_dn_1[0.0948] det_loss_box_dn_1[0.2377] det_loss_cls_dn_2[0.0937] det_loss_box_dn_2[0.2138] det_loss_cls_dn_3[0.0926] det_loss_box_dn_3[0.2089] det_loss_cls_dn_4[0.0914] det_loss_box_dn_4[0.2052] det_loss_cls_dn_5[0.0906] det_loss_box_dn_5[0.1982] loss_depth[0.0353] total_loss[7.1871]
Rank[0/8] 06/20/2025 20:40:38 INFO stats.py:314 | Epoch[232] Step[169] GlobalStep[90649] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 0:59:30.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:40:43 INFO loss_tracker.py:84 | Epoch[232/239] Step[174] GlobalStep[90654/NA]: det_loss_cls_0[0.3638] det_loss_box_0[0.4636] det_loss_cls_1[0.3602] det_loss_box_1[0.3489] det_loss_cls_2[0.2978] det_loss_box_2[0.3260] det_loss_cls_3[0.2744] det_loss_box_3[0.3166] det_loss_cls_4[0.2580] det_loss_box_4[0.3029] det_loss_cls_5[0.2496] det_loss_box_5[0.3046] det_loss_cls_dn_0[0.0787] det_loss_box_dn_0[0.2687] det_loss_cls_dn_1[0.0764] det_loss_box_dn_1[0.2264] det_loss_cls_dn_2[0.0762] det_loss_box_dn_2[0.2096] det_loss_cls_dn_3[0.0751] det_loss_box_dn_3[0.2043] det_loss_cls_dn_4[0.0742] det_loss_box_dn_4[0.2018] det_loss_cls_dn_5[0.0734] det_loss_box_dn_5[0.1996] loss_depth[0.0357] total_loss[5.6664]
Rank[0/8] 06/20/2025 20:41:04 INFO stats.py:314 | Epoch[232] Step[194] GlobalStep[90674] Training Speed: 6.62 samples/sec across all devices.	Average Step Time: 1.21 sec.	Estimated Remaining Time: 0:58:59.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:41:10 INFO loss_tracker.py:84 | Epoch[232/239] Step[199] GlobalStep[90679/NA]: det_loss_cls_0[0.4913] det_loss_box_0[0.5501] det_loss_cls_1[0.4858] det_loss_box_1[0.4112] det_loss_cls_2[0.4138] det_loss_box_2[0.3838] det_loss_cls_3[0.3705] det_loss_box_3[0.3810] det_loss_cls_4[0.3428] det_loss_box_4[0.3709] det_loss_cls_5[0.3268] det_loss_box_5[0.3724] det_loss_cls_dn_0[0.0844] det_loss_box_dn_0[0.2440] det_loss_cls_dn_1[0.0799] det_loss_box_dn_1[0.1960] det_loss_cls_dn_2[0.0793] det_loss_box_dn_2[0.1775] det_loss_cls_dn_3[0.0779] det_loss_box_dn_3[0.1710] det_loss_cls_dn_4[0.0770] det_loss_box_dn_4[0.1684] det_loss_cls_dn_5[0.0764] det_loss_box_dn_5[0.1663] loss_depth[0.0328] total_loss[6.5311]
Rank[0/8] 06/20/2025 20:41:31 INFO stats.py:314 | Epoch[232] Step[219] GlobalStep[90699] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 0:58:29.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:41:36 INFO loss_tracker.py:84 | Epoch[232/239] Step[224] GlobalStep[90704/NA]: det_loss_cls_0[0.4081] det_loss_box_0[0.4834] det_loss_cls_1[0.3962] det_loss_box_1[0.3710] det_loss_cls_2[0.3380] det_loss_box_2[0.3366] det_loss_cls_3[0.3037] det_loss_box_3[0.3324] det_loss_cls_4[0.2856] det_loss_box_4[0.3169] det_loss_cls_5[0.2777] det_loss_box_5[0.3182] det_loss_cls_dn_0[0.0846] det_loss_box_dn_0[0.2595] det_loss_cls_dn_1[0.0808] det_loss_box_dn_1[0.2106] det_loss_cls_dn_2[0.0798] det_loss_box_dn_2[0.1962] det_loss_cls_dn_3[0.0792] det_loss_box_dn_3[0.1893] det_loss_cls_dn_4[0.0787] det_loss_box_dn_4[0.1869] det_loss_cls_dn_5[0.0780] det_loss_box_dn_5[0.1837] loss_depth[0.0330] total_loss[5.9083]
Rank[0/8] 06/20/2025 20:41:57 INFO stats.py:314 | Epoch[232] Step[244] GlobalStep[90724] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 0:57:58.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:42:02 INFO loss_tracker.py:84 | Epoch[232/239] Step[249] GlobalStep[90729/NA]: det_loss_cls_0[0.4986] det_loss_box_0[0.6652] det_loss_cls_1[0.4848] det_loss_box_1[0.5040] det_loss_cls_2[0.4105] det_loss_box_2[0.4708] det_loss_cls_3[0.3631] det_loss_box_3[0.4511] det_loss_cls_4[0.3384] det_loss_box_4[0.4344] det_loss_cls_5[0.3194] det_loss_box_5[0.4387] det_loss_cls_dn_0[0.0842] det_loss_box_dn_0[0.2574] det_loss_cls_dn_1[0.0802] det_loss_box_dn_1[0.2061] det_loss_cls_dn_2[0.0792] det_loss_box_dn_2[0.1865] det_loss_cls_dn_3[0.0779] det_loss_box_dn_3[0.1821] det_loss_cls_dn_4[0.0771] det_loss_box_dn_4[0.1795] det_loss_cls_dn_5[0.0762] det_loss_box_dn_5[0.1783] loss_depth[0.0353] total_loss[7.0794]
Rank[0/8] 06/20/2025 20:42:23 INFO stats.py:314 | Epoch[232] Step[269] GlobalStep[90749] Training Speed: 8.33 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 0:57:28.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:42:28 INFO loss_tracker.py:84 | Epoch[232/239] Step[274] GlobalStep[90754/NA]: det_loss_cls_0[0.4499] det_loss_box_0[0.4898] det_loss_cls_1[0.4327] det_loss_box_1[0.3543] det_loss_cls_2[0.3579] det_loss_box_2[0.3285] det_loss_cls_3[0.3214] det_loss_box_3[0.3209] det_loss_cls_4[0.2966] det_loss_box_4[0.3157] det_loss_cls_5[0.2814] det_loss_box_5[0.3175] det_loss_cls_dn_0[0.0883] det_loss_box_dn_0[0.2103] det_loss_cls_dn_1[0.0845] det_loss_box_dn_1[0.1686] det_loss_cls_dn_2[0.0841] det_loss_box_dn_2[0.1516] det_loss_cls_dn_3[0.0830] det_loss_box_dn_3[0.1489] det_loss_cls_dn_4[0.0819] det_loss_box_dn_4[0.1468] det_loss_cls_dn_5[0.0811] det_loss_box_dn_5[0.1450] loss_depth[0.0343] total_loss[5.7749]
Rank[0/8] 06/20/2025 20:42:48 INFO stats.py:314 | Epoch[232] Step[294] GlobalStep[90774] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 0:56:58.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:42:54 INFO loss_tracker.py:84 | Epoch[232/239] Step[299] GlobalStep[90779/NA]: det_loss_cls_0[0.5310] det_loss_box_0[0.6333] det_loss_cls_1[0.5231] det_loss_box_1[0.4637] det_loss_cls_2[0.4398] det_loss_box_2[0.4409] det_loss_cls_3[0.4041] det_loss_box_3[0.4254] det_loss_cls_4[0.3787] det_loss_box_4[0.4068] det_loss_cls_5[0.3673] det_loss_box_5[0.4060] det_loss_cls_dn_0[0.0976] det_loss_box_dn_0[0.2819] det_loss_cls_dn_1[0.0939] det_loss_box_dn_1[0.2385] det_loss_cls_dn_2[0.0930] det_loss_box_dn_2[0.2214] det_loss_cls_dn_3[0.0922] det_loss_box_dn_3[0.2168] det_loss_cls_dn_4[0.0914] det_loss_box_dn_4[0.2141] det_loss_cls_dn_5[0.0903] det_loss_box_dn_5[0.2114] loss_depth[0.0350] total_loss[7.3975]
Rank[0/8] 06/20/2025 20:43:15 INFO stats.py:314 | Epoch[232] Step[319] GlobalStep[90799] Training Speed: 7.64 samples/sec across all devices.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 0:56:27.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:43:20 INFO loss_tracker.py:84 | Epoch[232/239] Step[324] GlobalStep[90804/NA]: det_loss_cls_0[0.4100] det_loss_box_0[0.5147] det_loss_cls_1[0.3912] det_loss_box_1[0.3800] det_loss_cls_2[0.3339] det_loss_box_2[0.3370] det_loss_cls_3[0.3019] det_loss_box_3[0.3320] det_loss_cls_4[0.2774] det_loss_box_4[0.3238] det_loss_cls_5[0.2629] det_loss_box_5[0.3240] det_loss_cls_dn_0[0.0884] det_loss_box_dn_0[0.2461] det_loss_cls_dn_1[0.0848] det_loss_box_dn_1[0.2008] det_loss_cls_dn_2[0.0837] det_loss_box_dn_2[0.1819] det_loss_cls_dn_3[0.0827] det_loss_box_dn_3[0.1782] det_loss_cls_dn_4[0.0821] det_loss_box_dn_4[0.1762] det_loss_cls_dn_5[0.0810] det_loss_box_dn_5[0.1747] loss_depth[0.0374] total_loss[5.8870]
Rank[0/8] 06/20/2025 20:43:40 INFO stats.py:314 | Epoch[232] Step[344] GlobalStep[90824] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 0:55:57.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:43:46 INFO loss_tracker.py:84 | Epoch[232/239] Step[349] GlobalStep[90829/NA]: det_loss_cls_0[0.4247] det_loss_box_0[0.5162] det_loss_cls_1[0.4147] det_loss_box_1[0.3847] det_loss_cls_2[0.3501] det_loss_box_2[0.3448] det_loss_cls_3[0.3224] det_loss_box_3[0.3366] det_loss_cls_4[0.3042] det_loss_box_4[0.3193] det_loss_cls_5[0.2953] det_loss_box_5[0.3148] det_loss_cls_dn_0[0.0836] det_loss_box_dn_0[0.2654] det_loss_cls_dn_1[0.0808] det_loss_box_dn_1[0.2166] det_loss_cls_dn_2[0.0797] det_loss_box_dn_2[0.1984] det_loss_cls_dn_3[0.0789] det_loss_box_dn_3[0.1941] det_loss_cls_dn_4[0.0779] det_loss_box_dn_4[0.1911] det_loss_cls_dn_5[0.0774] det_loss_box_dn_5[0.1887] loss_depth[0.0364] total_loss[6.0968]
Rank[0/8] 06/20/2025 20:44:06 INFO stats.py:314 | Epoch[232] Step[369] GlobalStep[90849] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 0:55:26.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:44:11 INFO loss_tracker.py:84 | Epoch[232/239] Step[374] GlobalStep[90854/NA]: det_loss_cls_0[0.4950] det_loss_box_0[0.6298] det_loss_cls_1[0.4748] det_loss_box_1[0.4787] det_loss_cls_2[0.4133] det_loss_box_2[0.4392] det_loss_cls_3[0.3793] det_loss_box_3[0.4245] det_loss_cls_4[0.3585] det_loss_box_4[0.4078] det_loss_cls_5[0.3478] det_loss_box_5[0.4054] det_loss_cls_dn_0[0.0890] det_loss_box_dn_0[0.2510] det_loss_cls_dn_1[0.0855] det_loss_box_dn_1[0.2056] det_loss_cls_dn_2[0.0840] det_loss_box_dn_2[0.1890] det_loss_cls_dn_3[0.0830] det_loss_box_dn_3[0.1845] det_loss_cls_dn_4[0.0817] det_loss_box_dn_4[0.1823] det_loss_cls_dn_5[0.0809] det_loss_box_dn_5[0.1797] loss_depth[0.0376] total_loss[6.9878]
Rank[0/8] 06/20/2025 20:44:26 INFO stats.py:394 | Epoch[232] completed. Training Speed: 7.67 samples/sec across all devices.	Epoch Time: 406.93 sec.	Average Epoch Time: 406.93 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 0:55:02.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:44:33 INFO stats.py:314 | Epoch[233] Step[4] GlobalStep[90874] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 0:54:56.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:44:53 INFO loss_tracker.py:84 | Epoch[233/239] Step[24] GlobalStep[90894/NA]: det_loss_cls_0[0.3766] det_loss_box_0[0.4883] det_loss_cls_1[0.3659] det_loss_box_1[0.3720] det_loss_cls_2[0.3194] det_loss_box_2[0.3339] det_loss_cls_3[0.2955] det_loss_box_3[0.3244] det_loss_cls_4[0.2740] det_loss_box_4[0.3146] det_loss_cls_5[0.2623] det_loss_box_5[0.3139] det_loss_cls_dn_0[0.0923] det_loss_box_dn_0[0.3145] det_loss_cls_dn_1[0.0892] det_loss_box_dn_1[0.2537] det_loss_cls_dn_2[0.0884] det_loss_box_dn_2[0.2316] det_loss_cls_dn_3[0.0872] det_loss_box_dn_3[0.2263] det_loss_cls_dn_4[0.0859] det_loss_box_dn_4[0.2226] det_loss_cls_dn_5[0.0852] det_loss_box_dn_5[0.2208] loss_depth[0.0362] total_loss[6.0748]
Rank[0/8] 06/20/2025 20:44:59 INFO stats.py:314 | Epoch[233] Step[29] GlobalStep[90899] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 0:54:26.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:45:19 INFO loss_tracker.py:84 | Epoch[233/239] Step[49] GlobalStep[90919/NA]: det_loss_cls_0[0.4336] det_loss_box_0[0.5500] det_loss_cls_1[0.4192] det_loss_box_1[0.4393] det_loss_cls_2[0.3514] det_loss_box_2[0.4064] det_loss_cls_3[0.3266] det_loss_box_3[0.3876] det_loss_cls_4[0.3082] det_loss_box_4[0.3831] det_loss_cls_5[0.3004] det_loss_box_5[0.3866] det_loss_cls_dn_0[0.0941] det_loss_box_dn_0[0.2731] det_loss_cls_dn_1[0.0915] det_loss_box_dn_1[0.2243] det_loss_cls_dn_2[0.0907] det_loss_box_dn_2[0.2105] det_loss_cls_dn_3[0.0901] det_loss_box_dn_3[0.2060] det_loss_cls_dn_4[0.0894] det_loss_box_dn_4[0.2038] det_loss_cls_dn_5[0.0888] det_loss_box_dn_5[0.2022] loss_depth[0.0355] total_loss[6.5923]
Rank[0/8] 06/20/2025 20:45:25 INFO stats.py:314 | Epoch[233] Step[54] GlobalStep[90924] Training Speed: 7.79 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 0:53:55.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:45:45 INFO loss_tracker.py:84 | Epoch[233/239] Step[74] GlobalStep[90944/NA]: det_loss_cls_0[0.4325] det_loss_box_0[0.5370] det_loss_cls_1[0.4061] det_loss_box_1[0.3894] det_loss_cls_2[0.3480] det_loss_box_2[0.3583] det_loss_cls_3[0.3218] det_loss_box_3[0.3521] det_loss_cls_4[0.3000] det_loss_box_4[0.3424] det_loss_cls_5[0.2885] det_loss_box_5[0.3467] det_loss_cls_dn_0[0.0804] det_loss_box_dn_0[0.2600] det_loss_cls_dn_1[0.0763] det_loss_box_dn_1[0.2033] det_loss_cls_dn_2[0.0759] det_loss_box_dn_2[0.1855] det_loss_cls_dn_3[0.0744] det_loss_box_dn_3[0.1788] det_loss_cls_dn_4[0.0738] det_loss_box_dn_4[0.1773] det_loss_cls_dn_5[0.0731] det_loss_box_dn_5[0.1738] loss_depth[0.0329] total_loss[6.0883]
Rank[0/8] 06/20/2025 20:45:51 INFO stats.py:314 | Epoch[233] Step[79] GlobalStep[90949] Training Speed: 7.81 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 0:53:25.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:46:11 INFO loss_tracker.py:84 | Epoch[233/239] Step[99] GlobalStep[90969/NA]: det_loss_cls_0[0.4980] det_loss_box_0[0.6642] det_loss_cls_1[0.4739] det_loss_box_1[0.5020] det_loss_cls_2[0.4141] det_loss_box_2[0.4566] det_loss_cls_3[0.3880] det_loss_box_3[0.4409] det_loss_cls_4[0.3600] det_loss_box_4[0.4307] det_loss_cls_5[0.3429] det_loss_box_5[0.4353] det_loss_cls_dn_0[0.0848] det_loss_box_dn_0[0.2458] det_loss_cls_dn_1[0.0816] det_loss_box_dn_1[0.2009] det_loss_cls_dn_2[0.0809] det_loss_box_dn_2[0.1843] det_loss_cls_dn_3[0.0799] det_loss_box_dn_3[0.1821] det_loss_cls_dn_4[0.0796] det_loss_box_dn_4[0.1793] det_loss_cls_dn_5[0.0790] det_loss_box_dn_5[0.1768] loss_depth[0.0357] total_loss[7.0973]
Rank[0/8] 06/20/2025 20:46:16 INFO stats.py:314 | Epoch[233] Step[104] GlobalStep[90974] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 0:52:55.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:46:37 INFO loss_tracker.py:84 | Epoch[233/239] Step[124] GlobalStep[90994/NA]: det_loss_cls_0[0.4318] det_loss_box_0[0.4850] det_loss_cls_1[0.4196] det_loss_box_1[0.3512] det_loss_cls_2[0.3517] det_loss_box_2[0.3205] det_loss_cls_3[0.3183] det_loss_box_3[0.3117] det_loss_cls_4[0.2954] det_loss_box_4[0.3079] det_loss_cls_5[0.2800] det_loss_box_5[0.3079] det_loss_cls_dn_0[0.0854] det_loss_box_dn_0[0.2111] det_loss_cls_dn_1[0.0808] det_loss_box_dn_1[0.1643] det_loss_cls_dn_2[0.0793] det_loss_box_dn_2[0.1503] det_loss_cls_dn_3[0.0782] det_loss_box_dn_3[0.1480] det_loss_cls_dn_4[0.0779] det_loss_box_dn_4[0.1461] det_loss_cls_dn_5[0.0769] det_loss_box_dn_5[0.1448] loss_depth[0.0329] total_loss[5.6569]
Rank[0/8] 06/20/2025 20:46:42 INFO stats.py:314 | Epoch[233] Step[129] GlobalStep[90999] Training Speed: 8.52 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 0:52:24.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:47:02 INFO loss_tracker.py:84 | Epoch[233/239] Step[149] GlobalStep[91019/NA]: det_loss_cls_0[0.4329] det_loss_box_0[0.5491] det_loss_cls_1[0.4281] det_loss_box_1[0.4181] det_loss_cls_2[0.3601] det_loss_box_2[0.3898] det_loss_cls_3[0.3226] det_loss_box_3[0.3870] det_loss_cls_4[0.3068] det_loss_box_4[0.3758] det_loss_cls_5[0.2935] det_loss_box_5[0.3761] det_loss_cls_dn_0[0.0900] det_loss_box_dn_0[0.2761] det_loss_cls_dn_1[0.0865] det_loss_box_dn_1[0.2344] det_loss_cls_dn_2[0.0856] det_loss_box_dn_2[0.2193] det_loss_cls_dn_3[0.0852] det_loss_box_dn_3[0.2146] det_loss_cls_dn_4[0.0842] det_loss_box_dn_4[0.2137] det_loss_cls_dn_5[0.0835] det_loss_box_dn_5[0.2113] loss_depth[0.0364] total_loss[6.5606]
Rank[0/8] 06/20/2025 20:47:08 INFO stats.py:314 | Epoch[233] Step[154] GlobalStep[91024] Training Speed: 8.74 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 0:51:54.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:47:28 INFO loss_tracker.py:84 | Epoch[233/239] Step[174] GlobalStep[91044/NA]: det_loss_cls_0[0.4445] det_loss_box_0[0.5781] det_loss_cls_1[0.4360] det_loss_box_1[0.4419] det_loss_cls_2[0.3894] det_loss_box_2[0.3974] det_loss_cls_3[0.3555] det_loss_box_3[0.3836] det_loss_cls_4[0.3374] det_loss_box_4[0.3688] det_loss_cls_5[0.3255] det_loss_box_5[0.3719] det_loss_cls_dn_0[0.0899] det_loss_box_dn_0[0.2625] det_loss_cls_dn_1[0.0862] det_loss_box_dn_1[0.2175] det_loss_cls_dn_2[0.0850] det_loss_box_dn_2[0.1970] det_loss_cls_dn_3[0.0839] det_loss_box_dn_3[0.1940] det_loss_cls_dn_4[0.0832] det_loss_box_dn_4[0.1919] det_loss_cls_dn_5[0.0824] det_loss_box_dn_5[0.1889] loss_depth[0.0365] total_loss[6.6288]
Rank[0/8] 06/20/2025 20:47:33 INFO stats.py:314 | Epoch[233] Step[179] GlobalStep[91049] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 0:51:24.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:47:54 INFO loss_tracker.py:84 | Epoch[233/239] Step[199] GlobalStep[91069/NA]: det_loss_cls_0[0.3802] det_loss_box_0[0.4663] det_loss_cls_1[0.3746] det_loss_box_1[0.3480] det_loss_cls_2[0.3096] det_loss_box_2[0.3095] det_loss_cls_3[0.2820] det_loss_box_3[0.3041] det_loss_cls_4[0.2680] det_loss_box_4[0.2936] det_loss_cls_5[0.2508] det_loss_box_5[0.3002] det_loss_cls_dn_0[0.0784] det_loss_box_dn_0[0.3144] det_loss_cls_dn_1[0.0747] det_loss_box_dn_1[0.2552] det_loss_cls_dn_2[0.0730] det_loss_box_dn_2[0.2304] det_loss_cls_dn_3[0.0719] det_loss_box_dn_3[0.2231] det_loss_cls_dn_4[0.0714] det_loss_box_dn_4[0.2188] det_loss_cls_dn_5[0.0707] det_loss_box_dn_5[0.2118] loss_depth[0.0360] total_loss[5.8164]
Rank[0/8] 06/20/2025 20:47:59 INFO stats.py:314 | Epoch[233] Step[204] GlobalStep[91074] Training Speed: 6.53 samples/sec across all devices.	Average Step Time: 1.23 sec.	Estimated Remaining Time: 0:50:53.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:48:20 INFO loss_tracker.py:84 | Epoch[233/239] Step[224] GlobalStep[91094/NA]: det_loss_cls_0[0.4409] det_loss_box_0[0.5546] det_loss_cls_1[0.4329] det_loss_box_1[0.4056] det_loss_cls_2[0.3667] det_loss_box_2[0.3805] det_loss_cls_3[0.3355] det_loss_box_3[0.3694] det_loss_cls_4[0.3180] det_loss_box_4[0.3604] det_loss_cls_5[0.3121] det_loss_box_5[0.3553] det_loss_cls_dn_0[0.0948] det_loss_box_dn_0[0.2560] det_loss_cls_dn_1[0.0920] det_loss_box_dn_1[0.2051] det_loss_cls_dn_2[0.0910] det_loss_box_dn_2[0.1846] det_loss_cls_dn_3[0.0900] det_loss_box_dn_3[0.1812] det_loss_cls_dn_4[0.0894] det_loss_box_dn_4[0.1780] det_loss_cls_dn_5[0.0883] det_loss_box_dn_5[0.1757] loss_depth[0.0375] total_loss[6.3955]
Rank[0/8] 06/20/2025 20:48:25 INFO stats.py:314 | Epoch[233] Step[229] GlobalStep[91099] Training Speed: 6.88 samples/sec across all devices.	Average Step Time: 1.16 sec.	Estimated Remaining Time: 0:50:23.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:48:46 INFO loss_tracker.py:84 | Epoch[233/239] Step[249] GlobalStep[91119/NA]: det_loss_cls_0[0.4292] det_loss_box_0[0.4759] det_loss_cls_1[0.4065] det_loss_box_1[0.3508] det_loss_cls_2[0.3522] det_loss_box_2[0.3303] det_loss_cls_3[0.3254] det_loss_box_3[0.3176] det_loss_cls_4[0.2952] det_loss_box_4[0.3109] det_loss_cls_5[0.2821] det_loss_box_5[0.3154] det_loss_cls_dn_0[0.0950] det_loss_box_dn_0[0.2200] det_loss_cls_dn_1[0.0919] det_loss_box_dn_1[0.1815] det_loss_cls_dn_2[0.0910] det_loss_box_dn_2[0.1641] det_loss_cls_dn_3[0.0898] det_loss_box_dn_3[0.1613] det_loss_cls_dn_4[0.0881] det_loss_box_dn_4[0.1597] det_loss_cls_dn_5[0.0874] det_loss_box_dn_5[0.1586] loss_depth[0.0343] total_loss[5.8141]
Rank[0/8] 06/20/2025 20:48:51 INFO stats.py:314 | Epoch[233] Step[254] GlobalStep[91124] Training Speed: 7.58 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 0:49:52.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:49:12 INFO loss_tracker.py:84 | Epoch[233/239] Step[274] GlobalStep[91144/NA]: det_loss_cls_0[0.4361] det_loss_box_0[0.5271] det_loss_cls_1[0.4164] det_loss_box_1[0.4034] det_loss_cls_2[0.3411] det_loss_box_2[0.3719] det_loss_cls_3[0.3091] det_loss_box_3[0.3532] det_loss_cls_4[0.2877] det_loss_box_4[0.3458] det_loss_cls_5[0.2700] det_loss_box_5[0.3455] det_loss_cls_dn_0[0.0895] det_loss_box_dn_0[0.2802] det_loss_cls_dn_1[0.0856] det_loss_box_dn_1[0.2341] det_loss_cls_dn_2[0.0854] det_loss_box_dn_2[0.2152] det_loss_cls_dn_3[0.0845] det_loss_box_dn_3[0.2106] det_loss_cls_dn_4[0.0835] det_loss_box_dn_4[0.2080] det_loss_cls_dn_5[0.0827] det_loss_box_dn_5[0.2021] loss_depth[0.0345] total_loss[6.3031]
Rank[0/8] 06/20/2025 20:49:17 INFO stats.py:314 | Epoch[233] Step[279] GlobalStep[91149] Training Speed: 8.41 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 0:49:22.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:49:38 INFO loss_tracker.py:84 | Epoch[233/239] Step[299] GlobalStep[91169/NA]: det_loss_cls_0[0.3969] det_loss_box_0[0.5093] det_loss_cls_1[0.3867] det_loss_box_1[0.3875] det_loss_cls_2[0.3246] det_loss_box_2[0.3562] det_loss_cls_3[0.2930] det_loss_box_3[0.3442] det_loss_cls_4[0.2712] det_loss_box_4[0.3361] det_loss_cls_5[0.2613] det_loss_box_5[0.3331] det_loss_cls_dn_0[0.0804] det_loss_box_dn_0[0.3054] det_loss_cls_dn_1[0.0770] det_loss_box_dn_1[0.2509] det_loss_cls_dn_2[0.0766] det_loss_box_dn_2[0.2249] det_loss_cls_dn_3[0.0751] det_loss_box_dn_3[0.2197] det_loss_cls_dn_4[0.0746] det_loss_box_dn_4[0.2177] det_loss_cls_dn_5[0.0737] det_loss_box_dn_5[0.2159] loss_depth[0.0319] total_loss[6.1240]
Rank[0/8] 06/20/2025 20:49:43 INFO stats.py:314 | Epoch[233] Step[304] GlobalStep[91174] Training Speed: 8.07 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 0:48:52.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:50:03 INFO loss_tracker.py:84 | Epoch[233/239] Step[324] GlobalStep[91194/NA]: det_loss_cls_0[0.4906] det_loss_box_0[0.6280] det_loss_cls_1[0.4698] det_loss_box_1[0.4638] det_loss_cls_2[0.4155] det_loss_box_2[0.4102] det_loss_cls_3[0.3861] det_loss_box_3[0.3938] det_loss_cls_4[0.3611] det_loss_box_4[0.3865] det_loss_cls_5[0.3431] det_loss_box_5[0.3912] det_loss_cls_dn_0[0.0756] det_loss_box_dn_0[0.2500] det_loss_cls_dn_1[0.0727] det_loss_box_dn_1[0.1986] det_loss_cls_dn_2[0.0722] det_loss_box_dn_2[0.1780] det_loss_cls_dn_3[0.0712] det_loss_box_dn_3[0.1732] det_loss_cls_dn_4[0.0703] det_loss_box_dn_4[0.1700] det_loss_cls_dn_5[0.0694] det_loss_box_dn_5[0.1676] loss_depth[0.0345] total_loss[6.7431]
Rank[0/8] 06/20/2025 20:50:09 INFO stats.py:314 | Epoch[233] Step[329] GlobalStep[91199] Training Speed: 7.89 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 0:48:21.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:50:30 INFO loss_tracker.py:84 | Epoch[233/239] Step[349] GlobalStep[91219/NA]: det_loss_cls_0[0.4397] det_loss_box_0[0.5020] det_loss_cls_1[0.4207] det_loss_box_1[0.3733] det_loss_cls_2[0.3550] det_loss_box_2[0.3332] det_loss_cls_3[0.3196] det_loss_box_3[0.3249] det_loss_cls_4[0.2913] det_loss_box_4[0.3186] det_loss_cls_5[0.2787] det_loss_box_5[0.3150] det_loss_cls_dn_0[0.0874] det_loss_box_dn_0[0.2226] det_loss_cls_dn_1[0.0837] det_loss_box_dn_1[0.1723] det_loss_cls_dn_2[0.0825] det_loss_box_dn_2[0.1557] det_loss_cls_dn_3[0.0810] det_loss_box_dn_3[0.1514] det_loss_cls_dn_4[0.0807] det_loss_box_dn_4[0.1477] det_loss_cls_dn_5[0.0801] det_loss_box_dn_5[0.1444] loss_depth[0.0336] total_loss[5.7952]
Rank[0/8] 06/20/2025 20:50:35 INFO stats.py:314 | Epoch[233] Step[354] GlobalStep[91224] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 0:47:51.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:50:55 INFO loss_tracker.py:84 | Epoch[233/239] Step[374] GlobalStep[91244/NA]: det_loss_cls_0[0.3858] det_loss_box_0[0.4628] det_loss_cls_1[0.3858] det_loss_box_1[0.3546] det_loss_cls_2[0.3229] det_loss_box_2[0.3377] det_loss_cls_3[0.3039] det_loss_box_3[0.3157] det_loss_cls_4[0.2774] det_loss_box_4[0.3114] det_loss_cls_5[0.2621] det_loss_box_5[0.3166] det_loss_cls_dn_0[0.0853] det_loss_box_dn_0[0.3003] det_loss_cls_dn_1[0.0809] det_loss_box_dn_1[0.2452] det_loss_cls_dn_2[0.0795] det_loss_box_dn_2[0.2266] det_loss_cls_dn_3[0.0786] det_loss_box_dn_3[0.2224] det_loss_cls_dn_4[0.0778] det_loss_box_dn_4[0.2168] det_loss_cls_dn_5[0.0769] det_loss_box_dn_5[0.2108] loss_depth[0.0364] total_loss[5.9742]
Rank[0/8] 06/20/2025 20:51:00 INFO stats.py:314 | Epoch[233] Step[379] GlobalStep[91249] Training Speed: 7.85 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 0:47:21.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:51:10 INFO stats.py:394 | Epoch[233] completed. Training Speed: 7.73 samples/sec across all devices.	Epoch Time: 403.75 sec.	Average Epoch Time: 403.75 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 0:47:09.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:51:27 INFO stats.py:314 | Epoch[234] Step[14] GlobalStep[91274] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 0:46:50.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:51:38 INFO loss_tracker.py:84 | Epoch[234/239] Step[24] GlobalStep[91284/NA]: det_loss_cls_0[0.4054] det_loss_box_0[0.4889] det_loss_cls_1[0.3953] det_loss_box_1[0.3694] det_loss_cls_2[0.3401] det_loss_box_2[0.3280] det_loss_cls_3[0.3061] det_loss_box_3[0.3202] det_loss_cls_4[0.2843] det_loss_box_4[0.3150] det_loss_cls_5[0.2729] det_loss_box_5[0.3128] det_loss_cls_dn_0[0.0827] det_loss_box_dn_0[0.2942] det_loss_cls_dn_1[0.0794] det_loss_box_dn_1[0.2349] det_loss_cls_dn_2[0.0784] det_loss_box_dn_2[0.2150] det_loss_cls_dn_3[0.0774] det_loss_box_dn_3[0.2068] det_loss_cls_dn_4[0.0767] det_loss_box_dn_4[0.2041] det_loss_cls_dn_5[0.0758] det_loss_box_dn_5[0.1988] loss_depth[0.0348] total_loss[5.9973]
Rank[0/8] 06/20/2025 20:51:53 INFO stats.py:314 | Epoch[234] Step[39] GlobalStep[91299] Training Speed: 8.02 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 0:46:20.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:52:04 INFO loss_tracker.py:84 | Epoch[234/239] Step[49] GlobalStep[91309/NA]: det_loss_cls_0[0.4458] det_loss_box_0[0.4987] det_loss_cls_1[0.4303] det_loss_box_1[0.3719] det_loss_cls_2[0.3637] det_loss_box_2[0.3446] det_loss_cls_3[0.3310] det_loss_box_3[0.3319] det_loss_cls_4[0.3127] det_loss_box_4[0.3177] det_loss_cls_5[0.2939] det_loss_box_5[0.3226] det_loss_cls_dn_0[0.0860] det_loss_box_dn_0[0.2503] det_loss_cls_dn_1[0.0828] det_loss_box_dn_1[0.2002] det_loss_cls_dn_2[0.0819] det_loss_box_dn_2[0.1821] det_loss_cls_dn_3[0.0812] det_loss_box_dn_3[0.1772] det_loss_cls_dn_4[0.0807] det_loss_box_dn_4[0.1738] det_loss_cls_dn_5[0.0799] det_loss_box_dn_5[0.1718] loss_depth[0.0334] total_loss[6.0463]
Rank[0/8] 06/20/2025 20:52:20 INFO stats.py:314 | Epoch[234] Step[64] GlobalStep[91324] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 0:45:50.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:52:30 INFO loss_tracker.py:84 | Epoch[234/239] Step[74] GlobalStep[91334/NA]: det_loss_cls_0[0.5006] det_loss_box_0[0.6675] det_loss_cls_1[0.4931] det_loss_box_1[0.4990] det_loss_cls_2[0.4482] det_loss_box_2[0.4296] det_loss_cls_3[0.4110] det_loss_box_3[0.4183] det_loss_cls_4[0.3879] det_loss_box_4[0.4125] det_loss_cls_5[0.3759] det_loss_box_5[0.4146] det_loss_cls_dn_0[0.0987] det_loss_box_dn_0[0.2891] det_loss_cls_dn_1[0.0952] det_loss_box_dn_1[0.2377] det_loss_cls_dn_2[0.0937] det_loss_box_dn_2[0.2146] det_loss_cls_dn_3[0.0925] det_loss_box_dn_3[0.2094] det_loss_cls_dn_4[0.0920] det_loss_box_dn_4[0.2064] det_loss_cls_dn_5[0.0911] det_loss_box_dn_5[0.2044] loss_depth[0.0357] total_loss[7.4188]
Rank[0/8] 06/20/2025 20:52:45 INFO stats.py:314 | Epoch[234] Step[89] GlobalStep[91349] Training Speed: 7.77 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 0:45:19.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:52:56 INFO loss_tracker.py:84 | Epoch[234/239] Step[99] GlobalStep[91359/NA]: det_loss_cls_0[0.3791] det_loss_box_0[0.4710] det_loss_cls_1[0.3610] det_loss_box_1[0.3404] det_loss_cls_2[0.3062] det_loss_box_2[0.3050] det_loss_cls_3[0.2769] det_loss_box_3[0.2959] det_loss_cls_4[0.2562] det_loss_box_4[0.2863] det_loss_cls_5[0.2466] det_loss_box_5[0.2866] det_loss_cls_dn_0[0.0971] det_loss_box_dn_0[0.2746] det_loss_cls_dn_1[0.0936] det_loss_box_dn_1[0.2198] det_loss_cls_dn_2[0.0935] det_loss_box_dn_2[0.1979] det_loss_cls_dn_3[0.0927] det_loss_box_dn_3[0.1927] det_loss_cls_dn_4[0.0922] det_loss_box_dn_4[0.1902] det_loss_cls_dn_5[0.0920] det_loss_box_dn_5[0.1849] loss_depth[0.0348] total_loss[5.6671]
Rank[0/8] 06/20/2025 20:53:12 INFO stats.py:314 | Epoch[234] Step[114] GlobalStep[91374] Training Speed: 6.07 samples/sec across all devices.	Average Step Time: 1.32 sec.	Estimated Remaining Time: 0:44:49.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:53:22 INFO loss_tracker.py:84 | Epoch[234/239] Step[124] GlobalStep[91384/NA]: det_loss_cls_0[0.3737] det_loss_box_0[0.4213] det_loss_cls_1[0.3648] det_loss_box_1[0.3005] det_loss_cls_2[0.3114] det_loss_box_2[0.2769] det_loss_cls_3[0.2794] det_loss_box_3[0.2659] det_loss_cls_4[0.2557] det_loss_box_4[0.2640] det_loss_cls_5[0.2440] det_loss_box_5[0.2630] det_loss_cls_dn_0[0.0885] det_loss_box_dn_0[0.2271] det_loss_cls_dn_1[0.0834] det_loss_box_dn_1[0.1752] det_loss_cls_dn_2[0.0821] det_loss_box_dn_2[0.1569] det_loss_cls_dn_3[0.0812] det_loss_box_dn_3[0.1522] det_loss_cls_dn_4[0.0807] det_loss_box_dn_4[0.1484] det_loss_cls_dn_5[0.0799] det_loss_box_dn_5[0.1452] loss_depth[0.0345] total_loss[5.1561]
Rank[0/8] 06/20/2025 20:53:38 INFO stats.py:314 | Epoch[234] Step[139] GlobalStep[91399] Training Speed: 7.56 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 0:44:19.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:53:49 INFO loss_tracker.py:84 | Epoch[234/239] Step[149] GlobalStep[91409/NA]: det_loss_cls_0[0.4230] det_loss_box_0[0.6157] det_loss_cls_1[0.4271] det_loss_box_1[0.4608] det_loss_cls_2[0.3784] det_loss_box_2[0.4265] det_loss_cls_3[0.3527] det_loss_box_3[0.4107] det_loss_cls_4[0.3353] det_loss_box_4[0.3981] det_loss_cls_5[0.3213] det_loss_box_5[0.4012] det_loss_cls_dn_0[0.0781] det_loss_box_dn_0[0.2819] det_loss_cls_dn_1[0.0747] det_loss_box_dn_1[0.2278] det_loss_cls_dn_2[0.0739] det_loss_box_dn_2[0.2092] det_loss_cls_dn_3[0.0731] det_loss_box_dn_3[0.2024] det_loss_cls_dn_4[0.0725] det_loss_box_dn_4[0.1984] det_loss_cls_dn_5[0.0719] det_loss_box_dn_5[0.1929] loss_depth[0.0362] total_loss[6.7438]
Rank[0/8] 06/20/2025 20:54:04 INFO stats.py:314 | Epoch[234] Step[164] GlobalStep[91424] Training Speed: 7.71 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 0:43:49.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:54:15 INFO loss_tracker.py:84 | Epoch[234/239] Step[174] GlobalStep[91434/NA]: det_loss_cls_0[0.4285] det_loss_box_0[0.5135] det_loss_cls_1[0.4144] det_loss_box_1[0.3825] det_loss_cls_2[0.3545] det_loss_box_2[0.3563] det_loss_cls_3[0.3213] det_loss_box_3[0.3413] det_loss_cls_4[0.3057] det_loss_box_4[0.3338] det_loss_cls_5[0.2956] det_loss_box_5[0.3343] det_loss_cls_dn_0[0.0831] det_loss_box_dn_0[0.2854] det_loss_cls_dn_1[0.0797] det_loss_box_dn_1[0.2288] det_loss_cls_dn_2[0.0782] det_loss_box_dn_2[0.2088] det_loss_cls_dn_3[0.0771] det_loss_box_dn_3[0.2040] det_loss_cls_dn_4[0.0762] det_loss_box_dn_4[0.2011] det_loss_cls_dn_5[0.0757] det_loss_box_dn_5[0.1939] loss_depth[0.0350] total_loss[6.2088]
Rank[0/8] 06/20/2025 20:54:30 INFO stats.py:314 | Epoch[234] Step[189] GlobalStep[91449] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 0:43:18.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:54:41 INFO loss_tracker.py:84 | Epoch[234/239] Step[199] GlobalStep[91459/NA]: det_loss_cls_0[0.4989] det_loss_box_0[0.5417] det_loss_cls_1[0.4839] det_loss_box_1[0.3991] det_loss_cls_2[0.4278] det_loss_box_2[0.3716] det_loss_cls_3[0.3847] det_loss_box_3[0.3705] det_loss_cls_4[0.3613] det_loss_box_4[0.3652] det_loss_cls_5[0.3480] det_loss_box_5[0.3635] det_loss_cls_dn_0[0.0847] det_loss_box_dn_0[0.2346] det_loss_cls_dn_1[0.0806] det_loss_box_dn_1[0.1913] det_loss_cls_dn_2[0.0796] det_loss_box_dn_2[0.1732] det_loss_cls_dn_3[0.0787] det_loss_box_dn_3[0.1703] det_loss_cls_dn_4[0.0776] det_loss_box_dn_4[0.1691] det_loss_cls_dn_5[0.0767] det_loss_box_dn_5[0.1673] loss_depth[0.0354] total_loss[6.5352]
Rank[0/8] 06/20/2025 20:54:57 INFO stats.py:314 | Epoch[234] Step[214] GlobalStep[91474] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 0:42:48.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:55:07 INFO loss_tracker.py:84 | Epoch[234/239] Step[224] GlobalStep[91484/NA]: det_loss_cls_0[0.3575] det_loss_box_0[0.4428] det_loss_cls_1[0.3423] det_loss_box_1[0.3305] det_loss_cls_2[0.2879] det_loss_box_2[0.2932] det_loss_cls_3[0.2549] det_loss_box_3[0.2874] det_loss_cls_4[0.2435] det_loss_box_4[0.2740] det_loss_cls_5[0.2274] det_loss_box_5[0.2750] det_loss_cls_dn_0[0.0851] det_loss_box_dn_0[0.2421] det_loss_cls_dn_1[0.0820] det_loss_box_dn_1[0.1954] det_loss_cls_dn_2[0.0812] det_loss_box_dn_2[0.1766] det_loss_cls_dn_3[0.0810] det_loss_box_dn_3[0.1726] det_loss_cls_dn_4[0.0806] det_loss_box_dn_4[0.1698] det_loss_cls_dn_5[0.0803] det_loss_box_dn_5[0.1688] loss_depth[0.0359] total_loss[5.2678]
Rank[0/8] 06/20/2025 20:55:22 INFO stats.py:314 | Epoch[234] Step[239] GlobalStep[91499] Training Speed: 7.97 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 0:42:18.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:55:32 INFO loss_tracker.py:84 | Epoch[234/239] Step[249] GlobalStep[91509/NA]: det_loss_cls_0[0.4396] det_loss_box_0[0.5291] det_loss_cls_1[0.4206] det_loss_box_1[0.3884] det_loss_cls_2[0.3499] det_loss_box_2[0.3575] det_loss_cls_3[0.3111] det_loss_box_3[0.3486] det_loss_cls_4[0.2900] det_loss_box_4[0.3367] det_loss_cls_5[0.2743] det_loss_box_5[0.3393] det_loss_cls_dn_0[0.0905] det_loss_box_dn_0[0.2420] det_loss_cls_dn_1[0.0877] det_loss_box_dn_1[0.2019] det_loss_cls_dn_2[0.0872] det_loss_box_dn_2[0.1882] det_loss_cls_dn_3[0.0860] det_loss_box_dn_3[0.1827] det_loss_cls_dn_4[0.0852] det_loss_box_dn_4[0.1797] det_loss_cls_dn_5[0.0846] det_loss_box_dn_5[0.1773] loss_depth[0.0338] total_loss[6.1119]
Rank[0/8] 06/20/2025 20:55:48 INFO stats.py:314 | Epoch[234] Step[264] GlobalStep[91524] Training Speed: 8.46 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 0:41:47.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:55:59 INFO loss_tracker.py:84 | Epoch[234/239] Step[274] GlobalStep[91534/NA]: det_loss_cls_0[0.5765] det_loss_box_0[0.7053] det_loss_cls_1[0.5596] det_loss_box_1[0.5252] det_loss_cls_2[0.4867] det_loss_box_2[0.4499] det_loss_cls_3[0.4437] det_loss_box_3[0.4328] det_loss_cls_4[0.4197] det_loss_box_4[0.4176] det_loss_cls_5[0.4067] det_loss_box_5[0.4171] det_loss_cls_dn_0[0.0947] det_loss_box_dn_0[0.2445] det_loss_cls_dn_1[0.0916] det_loss_box_dn_1[0.2049] det_loss_cls_dn_2[0.0910] det_loss_box_dn_2[0.1836] det_loss_cls_dn_3[0.0900] det_loss_box_dn_3[0.1789] det_loss_cls_dn_4[0.0899] det_loss_box_dn_4[0.1763] det_loss_cls_dn_5[0.0890] det_loss_box_dn_5[0.1736] loss_depth[0.0382] total_loss[7.5869]
Rank[0/8] 06/20/2025 20:56:15 INFO stats.py:314 | Epoch[234] Step[289] GlobalStep[91549] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 0:41:17.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:56:25 INFO loss_tracker.py:84 | Epoch[234/239] Step[299] GlobalStep[91559/NA]: det_loss_cls_0[0.5813] det_loss_box_0[0.6555] det_loss_cls_1[0.5824] det_loss_box_1[0.4801] det_loss_cls_2[0.5022] det_loss_box_2[0.4348] det_loss_cls_3[0.4518] det_loss_box_3[0.4222] det_loss_cls_4[0.4286] det_loss_box_4[0.4108] det_loss_cls_5[0.4104] det_loss_box_5[0.4106] det_loss_cls_dn_0[0.0979] det_loss_box_dn_0[0.2419] det_loss_cls_dn_1[0.0952] det_loss_box_dn_1[0.1989] det_loss_cls_dn_2[0.0948] det_loss_box_dn_2[0.1836] det_loss_cls_dn_3[0.0935] det_loss_box_dn_3[0.1788] det_loss_cls_dn_4[0.0932] det_loss_box_dn_4[0.1760] det_loss_cls_dn_5[0.0924] det_loss_box_dn_5[0.1743] loss_depth[0.0368] total_loss[7.5280]
Rank[0/8] 06/20/2025 20:56:40 INFO stats.py:314 | Epoch[234] Step[314] GlobalStep[91574] Training Speed: 8.40 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 0:40:47.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:56:50 INFO loss_tracker.py:84 | Epoch[234/239] Step[324] GlobalStep[91584/NA]: det_loss_cls_0[0.5346] det_loss_box_0[0.7581] det_loss_cls_1[0.5432] det_loss_box_1[0.5700] det_loss_cls_2[0.4630] det_loss_box_2[0.5273] det_loss_cls_3[0.4275] det_loss_box_3[0.5047] det_loss_cls_4[0.4020] det_loss_box_4[0.4930] det_loss_cls_5[0.3880] det_loss_box_5[0.4910] det_loss_cls_dn_0[0.0794] det_loss_box_dn_0[0.2960] det_loss_cls_dn_1[0.0754] det_loss_box_dn_1[0.2478] det_loss_cls_dn_2[0.0745] det_loss_box_dn_2[0.2275] det_loss_cls_dn_3[0.0731] det_loss_box_dn_3[0.2231] det_loss_cls_dn_4[0.0724] det_loss_box_dn_4[0.2220] det_loss_cls_dn_5[0.0721] det_loss_box_dn_5[0.2198] loss_depth[0.0363] total_loss[8.0218]
Rank[0/8] 06/20/2025 20:57:06 INFO stats.py:314 | Epoch[234] Step[339] GlobalStep[91599] Training Speed: 7.98 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 0:40:16.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:57:16 INFO loss_tracker.py:84 | Epoch[234/239] Step[349] GlobalStep[91609/NA]: det_loss_cls_0[0.5096] det_loss_box_0[0.6632] det_loss_cls_1[0.5047] det_loss_box_1[0.4906] det_loss_cls_2[0.4442] det_loss_box_2[0.4454] det_loss_cls_3[0.4049] det_loss_box_3[0.4401] det_loss_cls_4[0.3859] det_loss_box_4[0.4249] det_loss_cls_5[0.3787] det_loss_box_5[0.4198] det_loss_cls_dn_0[0.0944] det_loss_box_dn_0[0.2764] det_loss_cls_dn_1[0.0923] det_loss_box_dn_1[0.2269] det_loss_cls_dn_2[0.0918] det_loss_box_dn_2[0.2110] det_loss_cls_dn_3[0.0911] det_loss_box_dn_3[0.2071] det_loss_cls_dn_4[0.0903] det_loss_box_dn_4[0.2046] det_loss_cls_dn_5[0.0897] det_loss_box_dn_5[0.2018] loss_depth[0.0369] total_loss[7.4264]
Rank[0/8] 06/20/2025 20:57:32 INFO stats.py:314 | Epoch[234] Step[364] GlobalStep[91624] Training Speed: 6.69 samples/sec across all devices.	Average Step Time: 1.20 sec.	Estimated Remaining Time: 0:39:46.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:57:42 INFO loss_tracker.py:84 | Epoch[234/239] Step[374] GlobalStep[91634/NA]: det_loss_cls_0[0.4626] det_loss_box_0[0.4951] det_loss_cls_1[0.4513] det_loss_box_1[0.3682] det_loss_cls_2[0.3882] det_loss_box_2[0.3376] det_loss_cls_3[0.3485] det_loss_box_3[0.3296] det_loss_cls_4[0.3197] det_loss_box_4[0.3243] det_loss_cls_5[0.3084] det_loss_box_5[0.3222] det_loss_cls_dn_0[0.0852] det_loss_box_dn_0[0.2209] det_loss_cls_dn_1[0.0817] det_loss_box_dn_1[0.1710] det_loss_cls_dn_2[0.0810] det_loss_box_dn_2[0.1557] det_loss_cls_dn_3[0.0800] det_loss_box_dn_3[0.1523] det_loss_cls_dn_4[0.0787] det_loss_box_dn_4[0.1506] det_loss_cls_dn_5[0.0780] det_loss_box_dn_5[0.1494] loss_depth[0.0319] total_loss[5.9724]
Rank[0/8] 06/20/2025 20:57:58 INFO stats.py:314 | Epoch[234] Step[389] GlobalStep[91649] Training Speed: 8.20 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 0:39:16.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:57:58 INFO stats.py:394 | Epoch[234] completed. Training Speed: 7.65 samples/sec across all devices.	Epoch Time: 407.61 sec.	Average Epoch Time: 407.61 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 0:39:16.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:58:27 INFO stats.py:314 | Epoch[235] Step[24] GlobalStep[91674] Training Speed: 8.44 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 0:38:45.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:58:27 INFO loss_tracker.py:84 | Epoch[235/239] Step[24] GlobalStep[91674/NA]: det_loss_cls_0[0.4643] det_loss_box_0[0.5720] det_loss_cls_1[0.4445] det_loss_box_1[0.4290] det_loss_cls_2[0.3861] det_loss_box_2[0.3912] det_loss_cls_3[0.3537] det_loss_box_3[0.3855] det_loss_cls_4[0.3331] det_loss_box_4[0.3719] det_loss_cls_5[0.3181] det_loss_box_5[0.3699] det_loss_cls_dn_0[0.0841] det_loss_box_dn_0[0.2954] det_loss_cls_dn_1[0.0802] det_loss_box_dn_1[0.2296] det_loss_cls_dn_2[0.0793] det_loss_box_dn_2[0.2088] det_loss_cls_dn_3[0.0786] det_loss_box_dn_3[0.2024] det_loss_cls_dn_4[0.0779] det_loss_box_dn_4[0.1991] det_loss_cls_dn_5[0.0772] det_loss_box_dn_5[0.1926] loss_depth[0.0357] total_loss[6.6602]
Rank[0/8] 06/20/2025 20:58:52 INFO stats.py:314 | Epoch[235] Step[49] GlobalStep[91699] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 0:38:15.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:58:52 INFO loss_tracker.py:84 | Epoch[235/239] Step[49] GlobalStep[91699/NA]: det_loss_cls_0[0.4533] det_loss_box_0[0.5518] det_loss_cls_1[0.4468] det_loss_box_1[0.4090] det_loss_cls_2[0.3658] det_loss_box_2[0.3758] det_loss_cls_3[0.3443] det_loss_box_3[0.3636] det_loss_cls_4[0.3164] det_loss_box_4[0.3550] det_loss_cls_5[0.2981] det_loss_box_5[0.3594] det_loss_cls_dn_0[0.0861] det_loss_box_dn_0[0.2446] det_loss_cls_dn_1[0.0812] det_loss_box_dn_1[0.1968] det_loss_cls_dn_2[0.0802] det_loss_box_dn_2[0.1758] det_loss_cls_dn_3[0.0789] det_loss_box_dn_3[0.1711] det_loss_cls_dn_4[0.0780] det_loss_box_dn_4[0.1687] det_loss_cls_dn_5[0.0773] det_loss_box_dn_5[0.1673] loss_depth[0.0351] total_loss[6.2807]
Rank[0/8] 06/20/2025 20:59:19 INFO stats.py:314 | Epoch[235] Step[74] GlobalStep[91724] Training Speed: 8.64 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 0:37:45.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:59:19 INFO loss_tracker.py:84 | Epoch[235/239] Step[74] GlobalStep[91724/NA]: det_loss_cls_0[0.4014] det_loss_box_0[0.4893] det_loss_cls_1[0.3843] det_loss_box_1[0.3707] det_loss_cls_2[0.3296] det_loss_box_2[0.3176] det_loss_cls_3[0.2953] det_loss_box_3[0.3170] det_loss_cls_4[0.2731] det_loss_box_4[0.3116] det_loss_cls_5[0.2652] det_loss_box_5[0.3123] det_loss_cls_dn_0[0.0831] det_loss_box_dn_0[0.2658] det_loss_cls_dn_1[0.0800] det_loss_box_dn_1[0.2072] det_loss_cls_dn_2[0.0791] det_loss_box_dn_2[0.1899] det_loss_cls_dn_3[0.0778] det_loss_box_dn_3[0.1838] det_loss_cls_dn_4[0.0771] det_loss_box_dn_4[0.1823] det_loss_cls_dn_5[0.0766] det_loss_box_dn_5[0.1801] loss_depth[0.0369] total_loss[5.7874]
Rank[0/8] 06/20/2025 20:59:44 INFO stats.py:314 | Epoch[235] Step[99] GlobalStep[91749] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 0:37:15.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 20:59:44 INFO loss_tracker.py:84 | Epoch[235/239] Step[99] GlobalStep[91749/NA]: det_loss_cls_0[0.3796] det_loss_box_0[0.4159] det_loss_cls_1[0.3693] det_loss_box_1[0.3158] det_loss_cls_2[0.3216] det_loss_box_2[0.2888] det_loss_cls_3[0.2916] det_loss_box_3[0.2830] det_loss_cls_4[0.2770] det_loss_box_4[0.2727] det_loss_cls_5[0.2619] det_loss_box_5[0.2727] det_loss_cls_dn_0[0.0922] det_loss_box_dn_0[0.2659] det_loss_cls_dn_1[0.0882] det_loss_box_dn_1[0.2046] det_loss_cls_dn_2[0.0875] det_loss_box_dn_2[0.1791] det_loss_cls_dn_3[0.0867] det_loss_box_dn_3[0.1736] det_loss_cls_dn_4[0.0867] det_loss_box_dn_4[0.1703] det_loss_cls_dn_5[0.0862] det_loss_box_dn_5[0.1683] loss_depth[0.0346] total_loss[5.4738]
Rank[0/8] 06/20/2025 21:00:10 INFO stats.py:314 | Epoch[235] Step[124] GlobalStep[91774] Training Speed: 7.30 samples/sec across all devices.	Average Step Time: 1.10 sec.	Estimated Remaining Time: 0:36:44.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:00:10 INFO loss_tracker.py:84 | Epoch[235/239] Step[124] GlobalStep[91774/NA]: det_loss_cls_0[0.4289] det_loss_box_0[0.5297] det_loss_cls_1[0.4076] det_loss_box_1[0.3937] det_loss_cls_2[0.3446] det_loss_box_2[0.3477] det_loss_cls_3[0.3112] det_loss_box_3[0.3366] det_loss_cls_4[0.2933] det_loss_box_4[0.3246] det_loss_cls_5[0.2780] det_loss_box_5[0.3282] det_loss_cls_dn_0[0.0830] det_loss_box_dn_0[0.2492] det_loss_cls_dn_1[0.0803] det_loss_box_dn_1[0.2067] det_loss_cls_dn_2[0.0792] det_loss_box_dn_2[0.1885] det_loss_cls_dn_3[0.0780] det_loss_box_dn_3[0.1851] det_loss_cls_dn_4[0.0770] det_loss_box_dn_4[0.1823] det_loss_cls_dn_5[0.0764] det_loss_box_dn_5[0.1804] loss_depth[0.0334] total_loss[6.0234]
Rank[0/8] 06/20/2025 21:00:36 INFO stats.py:314 | Epoch[235] Step[149] GlobalStep[91799] Training Speed: 8.32 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 0:36:14.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:00:36 INFO loss_tracker.py:84 | Epoch[235/239] Step[149] GlobalStep[91799/NA]: det_loss_cls_0[0.4478] det_loss_box_0[0.5744] det_loss_cls_1[0.4393] det_loss_box_1[0.4331] det_loss_cls_2[0.3937] det_loss_box_2[0.4033] det_loss_cls_3[0.3704] det_loss_box_3[0.3927] det_loss_cls_4[0.3545] det_loss_box_4[0.3803] det_loss_cls_5[0.3375] det_loss_box_5[0.3826] det_loss_cls_dn_0[0.0853] det_loss_box_dn_0[0.2426] det_loss_cls_dn_1[0.0817] det_loss_box_dn_1[0.2054] det_loss_cls_dn_2[0.0812] det_loss_box_dn_2[0.1907] det_loss_cls_dn_3[0.0799] det_loss_box_dn_3[0.1875] det_loss_cls_dn_4[0.0793] det_loss_box_dn_4[0.1849] det_loss_cls_dn_5[0.0784] det_loss_box_dn_5[0.1829] loss_depth[0.0354] total_loss[6.6247]
Rank[0/8] 06/20/2025 21:01:02 INFO stats.py:314 | Epoch[235] Step[174] GlobalStep[91824] Training Speed: 8.48 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 0:35:44.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:01:02 INFO loss_tracker.py:84 | Epoch[235/239] Step[174] GlobalStep[91824/NA]: det_loss_cls_0[0.4657] det_loss_box_0[0.5900] det_loss_cls_1[0.4587] det_loss_box_1[0.4420] det_loss_cls_2[0.3849] det_loss_box_2[0.4037] det_loss_cls_3[0.3594] det_loss_box_3[0.3876] det_loss_cls_4[0.3331] det_loss_box_4[0.3784] det_loss_cls_5[0.3246] det_loss_box_5[0.3720] det_loss_cls_dn_0[0.0912] det_loss_box_dn_0[0.2341] det_loss_cls_dn_1[0.0882] det_loss_box_dn_1[0.1864] det_loss_cls_dn_2[0.0867] det_loss_box_dn_2[0.1702] det_loss_cls_dn_3[0.0859] det_loss_box_dn_3[0.1680] det_loss_cls_dn_4[0.0853] det_loss_box_dn_4[0.1649] det_loss_cls_dn_5[0.0846] det_loss_box_dn_5[0.1638] loss_depth[0.0353] total_loss[6.5449]
Rank[0/8] 06/20/2025 21:01:28 INFO stats.py:314 | Epoch[235] Step[199] GlobalStep[91849] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 0:35:13.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:01:28 INFO loss_tracker.py:84 | Epoch[235/239] Step[199] GlobalStep[91849/NA]: det_loss_cls_0[0.4048] det_loss_box_0[0.4686] det_loss_cls_1[0.3841] det_loss_box_1[0.3523] det_loss_cls_2[0.3184] det_loss_box_2[0.3175] det_loss_cls_3[0.2800] det_loss_box_3[0.3121] det_loss_cls_4[0.2559] det_loss_box_4[0.3047] det_loss_cls_5[0.2490] det_loss_box_5[0.3041] det_loss_cls_dn_0[0.0897] det_loss_box_dn_0[0.2115] det_loss_cls_dn_1[0.0860] det_loss_box_dn_1[0.1700] det_loss_cls_dn_2[0.0853] det_loss_box_dn_2[0.1555] det_loss_cls_dn_3[0.0841] det_loss_box_dn_3[0.1527] det_loss_cls_dn_4[0.0838] det_loss_box_dn_4[0.1497] det_loss_cls_dn_5[0.0826] det_loss_box_dn_5[0.1474] loss_depth[0.0347] total_loss[5.4844]
Rank[0/8] 06/20/2025 21:01:54 INFO stats.py:314 | Epoch[235] Step[224] GlobalStep[91874] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 0:34:43.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:01:54 INFO loss_tracker.py:84 | Epoch[235/239] Step[224] GlobalStep[91874/NA]: det_loss_cls_0[0.5053] det_loss_box_0[0.6621] det_loss_cls_1[0.4920] det_loss_box_1[0.4897] det_loss_cls_2[0.4249] det_loss_box_2[0.4458] det_loss_cls_3[0.3905] det_loss_box_3[0.4313] det_loss_cls_4[0.3747] det_loss_box_4[0.4200] det_loss_cls_5[0.3525] det_loss_box_5[0.4242] det_loss_cls_dn_0[0.0904] det_loss_box_dn_0[0.2444] det_loss_cls_dn_1[0.0874] det_loss_box_dn_1[0.1967] det_loss_cls_dn_2[0.0863] det_loss_box_dn_2[0.1798] det_loss_cls_dn_3[0.0850] det_loss_box_dn_3[0.1760] det_loss_cls_dn_4[0.0846] det_loss_box_dn_4[0.1728] det_loss_cls_dn_5[0.0838] det_loss_box_dn_5[0.1717] loss_depth[0.0358] total_loss[7.1078]
Rank[0/8] 06/20/2025 21:02:20 INFO stats.py:314 | Epoch[235] Step[249] GlobalStep[91899] Training Speed: 7.79 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 0:34:13.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:02:20 INFO loss_tracker.py:84 | Epoch[235/239] Step[249] GlobalStep[91899/NA]: det_loss_cls_0[0.4659] det_loss_box_0[0.5533] det_loss_cls_1[0.4525] det_loss_box_1[0.4076] det_loss_cls_2[0.4004] det_loss_box_2[0.3742] det_loss_cls_3[0.3617] det_loss_box_3[0.3658] det_loss_cls_4[0.3334] det_loss_box_4[0.3542] det_loss_cls_5[0.3234] det_loss_box_5[0.3550] det_loss_cls_dn_0[0.0936] det_loss_box_dn_0[0.2450] det_loss_cls_dn_1[0.0895] det_loss_box_dn_1[0.2001] det_loss_cls_dn_2[0.0884] det_loss_box_dn_2[0.1827] det_loss_cls_dn_3[0.0878] det_loss_box_dn_3[0.1790] det_loss_cls_dn_4[0.0869] det_loss_box_dn_4[0.1766] det_loss_cls_dn_5[0.0864] det_loss_box_dn_5[0.1742] loss_depth[0.0363] total_loss[6.4742]
Rank[0/8] 06/20/2025 21:02:45 INFO stats.py:314 | Epoch[235] Step[274] GlobalStep[91924] Training Speed: 7.89 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 0:33:43.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:02:45 INFO loss_tracker.py:84 | Epoch[235/239] Step[274] GlobalStep[91924/NA]: det_loss_cls_0[0.4630] det_loss_box_0[0.5696] det_loss_cls_1[0.4373] det_loss_box_1[0.4206] det_loss_cls_2[0.3693] det_loss_box_2[0.3769] det_loss_cls_3[0.3419] det_loss_box_3[0.3621] det_loss_cls_4[0.3228] det_loss_box_4[0.3463] det_loss_cls_5[0.3074] det_loss_box_5[0.3458] det_loss_cls_dn_0[0.0866] det_loss_box_dn_0[0.2757] det_loss_cls_dn_1[0.0828] det_loss_box_dn_1[0.2214] det_loss_cls_dn_2[0.0821] det_loss_box_dn_2[0.1996] det_loss_cls_dn_3[0.0811] det_loss_box_dn_3[0.1930] det_loss_cls_dn_4[0.0801] det_loss_box_dn_4[0.1893] det_loss_cls_dn_5[0.0789] det_loss_box_dn_5[0.1850] loss_depth[0.0338] total_loss[6.4523]
Rank[0/8] 06/20/2025 21:03:11 INFO stats.py:314 | Epoch[235] Step[299] GlobalStep[91949] Training Speed: 7.28 samples/sec across all devices.	Average Step Time: 1.10 sec.	Estimated Remaining Time: 0:33:12.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:03:11 INFO loss_tracker.py:84 | Epoch[235/239] Step[299] GlobalStep[91949/NA]: det_loss_cls_0[0.5075] det_loss_box_0[0.6149] det_loss_cls_1[0.4963] det_loss_box_1[0.4472] det_loss_cls_2[0.4194] det_loss_box_2[0.4057] det_loss_cls_3[0.3807] det_loss_box_3[0.3913] det_loss_cls_4[0.3399] det_loss_box_4[0.3874] det_loss_cls_5[0.3312] det_loss_box_5[0.3844] det_loss_cls_dn_0[0.0850] det_loss_box_dn_0[0.2445] det_loss_cls_dn_1[0.0816] det_loss_box_dn_1[0.1982] det_loss_cls_dn_2[0.0810] det_loss_box_dn_2[0.1806] det_loss_cls_dn_3[0.0798] det_loss_box_dn_3[0.1776] det_loss_cls_dn_4[0.0792] det_loss_box_dn_4[0.1759] det_loss_cls_dn_5[0.0784] det_loss_box_dn_5[0.1753] loss_depth[0.0332] total_loss[6.7762]
Rank[0/8] 06/20/2025 21:03:37 INFO stats.py:314 | Epoch[235] Step[324] GlobalStep[91974] Training Speed: 7.90 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 0:32:42.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:03:37 INFO loss_tracker.py:84 | Epoch[235/239] Step[324] GlobalStep[91974/NA]: det_loss_cls_0[0.4711] det_loss_box_0[0.6284] det_loss_cls_1[0.4640] det_loss_box_1[0.4978] det_loss_cls_2[0.4109] det_loss_box_2[0.4327] det_loss_cls_3[0.3723] det_loss_box_3[0.4304] det_loss_cls_4[0.3595] det_loss_box_4[0.4173] det_loss_cls_5[0.3479] det_loss_box_5[0.4184] det_loss_cls_dn_0[0.0917] det_loss_box_dn_0[0.2746] det_loss_cls_dn_1[0.0872] det_loss_box_dn_1[0.2286] det_loss_cls_dn_2[0.0861] det_loss_box_dn_2[0.2129] det_loss_cls_dn_3[0.0848] det_loss_box_dn_3[0.2081] det_loss_cls_dn_4[0.0840] det_loss_box_dn_4[0.2036] det_loss_cls_dn_5[0.0828] det_loss_box_dn_5[0.2008] loss_depth[0.0369] total_loss[7.1327]
Rank[0/8] 06/20/2025 21:04:03 INFO stats.py:314 | Epoch[235] Step[349] GlobalStep[91999] Training Speed: 8.16 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 0:32:12.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:04:03 INFO loss_tracker.py:84 | Epoch[235/239] Step[349] GlobalStep[91999/NA]: det_loss_cls_0[0.5470] det_loss_box_0[0.6774] det_loss_cls_1[0.5315] det_loss_box_1[0.5081] det_loss_cls_2[0.4508] det_loss_box_2[0.4594] det_loss_cls_3[0.4024] det_loss_box_3[0.4347] det_loss_cls_4[0.3738] det_loss_box_4[0.4188] det_loss_cls_5[0.3600] det_loss_box_5[0.4176] det_loss_cls_dn_0[0.0904] det_loss_box_dn_0[0.2546] det_loss_cls_dn_1[0.0864] det_loss_box_dn_1[0.2101] det_loss_cls_dn_2[0.0858] det_loss_box_dn_2[0.1925] det_loss_cls_dn_3[0.0850] det_loss_box_dn_3[0.1889] det_loss_cls_dn_4[0.0842] det_loss_box_dn_4[0.1858] det_loss_cls_dn_5[0.0835] det_loss_box_dn_5[0.1843] loss_depth[0.0374] total_loss[7.3505]
Rank[0/8] 06/20/2025 21:04:28 INFO stats.py:314 | Epoch[235] Step[374] GlobalStep[92024] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 0:31:42.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:04:28 INFO loss_tracker.py:84 | Epoch[235/239] Step[374] GlobalStep[92024/NA]: det_loss_cls_0[0.4570] det_loss_box_0[0.6083] det_loss_cls_1[0.4395] det_loss_box_1[0.4538] det_loss_cls_2[0.3788] det_loss_box_2[0.4165] det_loss_cls_3[0.3516] det_loss_box_3[0.3950] det_loss_cls_4[0.3341] det_loss_box_4[0.3912] det_loss_cls_5[0.3231] det_loss_box_5[0.3886] det_loss_cls_dn_0[0.0938] det_loss_box_dn_0[0.2681] det_loss_cls_dn_1[0.0894] det_loss_box_dn_1[0.2185] det_loss_cls_dn_2[0.0880] det_loss_box_dn_2[0.2021] det_loss_cls_dn_3[0.0875] det_loss_box_dn_3[0.1986] det_loss_cls_dn_4[0.0870] det_loss_box_dn_4[0.1948] det_loss_cls_dn_5[0.0856] det_loss_box_dn_5[0.1935] loss_depth[0.0367] total_loss[6.7812]
Rank[0/8] 06/20/2025 21:04:44 INFO stats.py:394 | Epoch[235] completed. Training Speed: 7.69 samples/sec across all devices.	Epoch Time: 405.90 sec.	Average Epoch Time: 405.90 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 0:31:23.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:04:55 INFO stats.py:314 | Epoch[236] Step[9] GlobalStep[92049] Training Speed: 8.71 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 0:31:11.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:05:11 INFO loss_tracker.py:84 | Epoch[236/239] Step[24] GlobalStep[92064/NA]: det_loss_cls_0[0.3976] det_loss_box_0[0.5103] det_loss_cls_1[0.3829] det_loss_box_1[0.3981] det_loss_cls_2[0.3260] det_loss_box_2[0.3769] det_loss_cls_3[0.2979] det_loss_box_3[0.3629] det_loss_cls_4[0.2792] det_loss_box_4[0.3500] det_loss_cls_5[0.2666] det_loss_box_5[0.3543] det_loss_cls_dn_0[0.0866] det_loss_box_dn_0[0.2941] det_loss_cls_dn_1[0.0831] det_loss_box_dn_1[0.2418] det_loss_cls_dn_2[0.0822] det_loss_box_dn_2[0.2229] det_loss_cls_dn_3[0.0807] det_loss_box_dn_3[0.2162] det_loss_cls_dn_4[0.0803] det_loss_box_dn_4[0.2142] det_loss_cls_dn_5[0.0790] det_loss_box_dn_5[0.2099] loss_depth[0.0353] total_loss[6.2287]
Rank[0/8] 06/20/2025 21:05:21 INFO stats.py:314 | Epoch[236] Step[34] GlobalStep[92074] Training Speed: 8.72 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 0:30:41.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:05:37 INFO loss_tracker.py:84 | Epoch[236/239] Step[49] GlobalStep[92089/NA]: det_loss_cls_0[0.4848] det_loss_box_0[0.6037] det_loss_cls_1[0.4655] det_loss_box_1[0.4465] det_loss_cls_2[0.4025] det_loss_box_2[0.4099] det_loss_cls_3[0.3757] det_loss_box_3[0.3898] det_loss_cls_4[0.3578] det_loss_box_4[0.3774] det_loss_cls_5[0.3453] det_loss_box_5[0.3811] det_loss_cls_dn_0[0.0909] det_loss_box_dn_0[0.2839] det_loss_cls_dn_1[0.0868] det_loss_box_dn_1[0.2304] det_loss_cls_dn_2[0.0866] det_loss_box_dn_2[0.2100] det_loss_cls_dn_3[0.0853] det_loss_box_dn_3[0.2045] det_loss_cls_dn_4[0.0844] det_loss_box_dn_4[0.2013] det_loss_cls_dn_5[0.0835] det_loss_box_dn_5[0.2000] loss_depth[0.0357] total_loss[6.9233]
Rank[0/8] 06/20/2025 21:05:47 INFO stats.py:314 | Epoch[236] Step[59] GlobalStep[92099] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 0:30:11.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:06:02 INFO loss_tracker.py:84 | Epoch[236/239] Step[74] GlobalStep[92114/NA]: det_loss_cls_0[0.4818] det_loss_box_0[0.6027] det_loss_cls_1[0.4637] det_loss_box_1[0.4442] det_loss_cls_2[0.4021] det_loss_box_2[0.3935] det_loss_cls_3[0.3676] det_loss_box_3[0.3826] det_loss_cls_4[0.3408] det_loss_box_4[0.3688] det_loss_cls_5[0.3282] det_loss_box_5[0.3710] det_loss_cls_dn_0[0.0905] det_loss_box_dn_0[0.2255] det_loss_cls_dn_1[0.0872] det_loss_box_dn_1[0.1832] det_loss_cls_dn_2[0.0859] det_loss_box_dn_2[0.1653] det_loss_cls_dn_3[0.0850] det_loss_box_dn_3[0.1626] det_loss_cls_dn_4[0.0845] det_loss_box_dn_4[0.1616] det_loss_cls_dn_5[0.0835] det_loss_box_dn_5[0.1604] loss_depth[0.0385] total_loss[6.5608]
Rank[0/8] 06/20/2025 21:06:13 INFO stats.py:314 | Epoch[236] Step[84] GlobalStep[92124] Training Speed: 8.27 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 0:29:41.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:06:29 INFO loss_tracker.py:84 | Epoch[236/239] Step[99] GlobalStep[92139/NA]: det_loss_cls_0[0.4024] det_loss_box_0[0.4884] det_loss_cls_1[0.3984] det_loss_box_1[0.3634] det_loss_cls_2[0.3248] det_loss_box_2[0.3381] det_loss_cls_3[0.2966] det_loss_box_3[0.3265] det_loss_cls_4[0.2678] det_loss_box_4[0.3200] det_loss_cls_5[0.2632] det_loss_box_5[0.3107] det_loss_cls_dn_0[0.0851] det_loss_box_dn_0[0.2405] det_loss_cls_dn_1[0.0809] det_loss_box_dn_1[0.1897] det_loss_cls_dn_2[0.0802] det_loss_box_dn_2[0.1749] det_loss_cls_dn_3[0.0792] det_loss_box_dn_3[0.1711] det_loss_cls_dn_4[0.0782] det_loss_box_dn_4[0.1679] det_loss_cls_dn_5[0.0777] det_loss_box_dn_5[0.1666] loss_depth[0.0345] total_loss[5.7266]
Rank[0/8] 06/20/2025 21:06:39 INFO stats.py:314 | Epoch[236] Step[109] GlobalStep[92149] Training Speed: 8.59 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 0:29:10.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:06:55 INFO loss_tracker.py:84 | Epoch[236/239] Step[124] GlobalStep[92164/NA]: det_loss_cls_0[0.4034] det_loss_box_0[0.5341] det_loss_cls_1[0.3828] det_loss_box_1[0.4036] det_loss_cls_2[0.3207] det_loss_box_2[0.3723] det_loss_cls_3[0.2912] det_loss_box_3[0.3616] det_loss_cls_4[0.2742] det_loss_box_4[0.3532] det_loss_cls_5[0.2672] det_loss_box_5[0.3516] det_loss_cls_dn_0[0.0807] det_loss_box_dn_0[0.2622] det_loss_cls_dn_1[0.0775] det_loss_box_dn_1[0.2168] det_loss_cls_dn_2[0.0768] det_loss_box_dn_2[0.2048] det_loss_cls_dn_3[0.0756] det_loss_box_dn_3[0.1995] det_loss_cls_dn_4[0.0750] det_loss_box_dn_4[0.1967] det_loss_cls_dn_5[0.0741] det_loss_box_dn_5[0.1941] loss_depth[0.0362] total_loss[6.0862]
Rank[0/8] 06/20/2025 21:07:05 INFO stats.py:314 | Epoch[236] Step[134] GlobalStep[92174] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 0:28:40.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:07:20 INFO loss_tracker.py:84 | Epoch[236/239] Step[149] GlobalStep[92189/NA]: det_loss_cls_0[0.4088] det_loss_box_0[0.4903] det_loss_cls_1[0.3811] det_loss_box_1[0.3753] det_loss_cls_2[0.3300] det_loss_box_2[0.3360] det_loss_cls_3[0.2976] det_loss_box_3[0.3286] det_loss_cls_4[0.2813] det_loss_box_4[0.3164] det_loss_cls_5[0.2774] det_loss_box_5[0.3147] det_loss_cls_dn_0[0.0888] det_loss_box_dn_0[0.2740] det_loss_cls_dn_1[0.0862] det_loss_box_dn_1[0.2272] det_loss_cls_dn_2[0.0858] det_loss_box_dn_2[0.2093] det_loss_cls_dn_3[0.0843] det_loss_box_dn_3[0.2069] det_loss_cls_dn_4[0.0838] det_loss_box_dn_4[0.2034] det_loss_cls_dn_5[0.0827] det_loss_box_dn_5[0.2017] loss_depth[0.0345] total_loss[6.0058]
Rank[0/8] 06/20/2025 21:07:31 INFO stats.py:314 | Epoch[236] Step[159] GlobalStep[92199] Training Speed: 8.56 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 0:28:10.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:07:47 INFO loss_tracker.py:84 | Epoch[236/239] Step[174] GlobalStep[92214/NA]: det_loss_cls_0[0.4935] det_loss_box_0[0.6103] det_loss_cls_1[0.4836] det_loss_box_1[0.4868] det_loss_cls_2[0.4139] det_loss_box_2[0.4522] det_loss_cls_3[0.3875] det_loss_box_3[0.4382] det_loss_cls_4[0.3623] det_loss_box_4[0.4334] det_loss_cls_5[0.3491] det_loss_box_5[0.4289] det_loss_cls_dn_0[0.0910] det_loss_box_dn_0[0.3352] det_loss_cls_dn_1[0.0891] det_loss_box_dn_1[0.2731] det_loss_cls_dn_2[0.0883] det_loss_box_dn_2[0.2477] det_loss_cls_dn_3[0.0874] det_loss_box_dn_3[0.2420] det_loss_cls_dn_4[0.0869] det_loss_box_dn_4[0.2384] det_loss_cls_dn_5[0.0858] det_loss_box_dn_5[0.2355] loss_depth[0.0330] total_loss[7.4731]
Rank[0/8] 06/20/2025 21:07:57 INFO stats.py:314 | Epoch[236] Step[184] GlobalStep[92224] Training Speed: 8.63 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 0:27:40.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:08:12 INFO loss_tracker.py:84 | Epoch[236/239] Step[199] GlobalStep[92239/NA]: det_loss_cls_0[0.4241] det_loss_box_0[0.5271] det_loss_cls_1[0.4121] det_loss_box_1[0.3941] det_loss_cls_2[0.3483] det_loss_box_2[0.3726] det_loss_cls_3[0.3159] det_loss_box_3[0.3596] det_loss_cls_4[0.2936] det_loss_box_4[0.3467] det_loss_cls_5[0.2779] det_loss_box_5[0.3484] det_loss_cls_dn_0[0.0863] det_loss_box_dn_0[0.2449] det_loss_cls_dn_1[0.0826] det_loss_box_dn_1[0.1983] det_loss_cls_dn_2[0.0821] det_loss_box_dn_2[0.1817] det_loss_cls_dn_3[0.0814] det_loss_box_dn_3[0.1792] det_loss_cls_dn_4[0.0805] det_loss_box_dn_4[0.1756] det_loss_cls_dn_5[0.0796] det_loss_box_dn_5[0.1730] loss_depth[0.0380] total_loss[6.1036]
Rank[0/8] 06/20/2025 21:08:22 INFO stats.py:314 | Epoch[236] Step[209] GlobalStep[92249] Training Speed: 8.06 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 0:27:09.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:08:37 INFO loss_tracker.py:84 | Epoch[236/239] Step[224] GlobalStep[92264/NA]: det_loss_cls_0[0.4740] det_loss_box_0[0.6707] det_loss_cls_1[0.4614] det_loss_box_1[0.4679] det_loss_cls_2[0.4024] det_loss_box_2[0.4098] det_loss_cls_3[0.3637] det_loss_box_3[0.3984] det_loss_cls_4[0.3399] det_loss_box_4[0.3925] det_loss_cls_5[0.3260] det_loss_box_5[0.3962] det_loss_cls_dn_0[0.0953] det_loss_box_dn_0[0.2679] det_loss_cls_dn_1[0.0921] det_loss_box_dn_1[0.2218] det_loss_cls_dn_2[0.0912] det_loss_box_dn_2[0.2059] det_loss_cls_dn_3[0.0904] det_loss_box_dn_3[0.2014] det_loss_cls_dn_4[0.0895] det_loss_box_dn_4[0.1995] det_loss_cls_dn_5[0.0890] det_loss_box_dn_5[0.1978] loss_depth[0.0391] total_loss[6.9836]
Rank[0/8] 06/20/2025 21:08:47 INFO stats.py:314 | Epoch[236] Step[234] GlobalStep[92274] Training Speed: 8.45 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 0:26:39.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:09:04 INFO loss_tracker.py:84 | Epoch[236/239] Step[249] GlobalStep[92289/NA]: det_loss_cls_0[0.4121] det_loss_box_0[0.4986] det_loss_cls_1[0.3968] det_loss_box_1[0.3653] det_loss_cls_2[0.3188] det_loss_box_2[0.3346] det_loss_cls_3[0.2879] det_loss_box_3[0.3181] det_loss_cls_4[0.2609] det_loss_box_4[0.3144] det_loss_cls_5[0.2531] det_loss_box_5[0.3111] det_loss_cls_dn_0[0.0852] det_loss_box_dn_0[0.2300] det_loss_cls_dn_1[0.0817] det_loss_box_dn_1[0.1847] det_loss_cls_dn_2[0.0805] det_loss_box_dn_2[0.1612] det_loss_cls_dn_3[0.0795] det_loss_box_dn_3[0.1581] det_loss_cls_dn_4[0.0786] det_loss_box_dn_4[0.1558] det_loss_cls_dn_5[0.0777] det_loss_box_dn_5[0.1522] loss_depth[0.0361] total_loss[5.6327]
Rank[0/8] 06/20/2025 21:09:14 INFO stats.py:314 | Epoch[236] Step[259] GlobalStep[92299] Training Speed: 6.04 samples/sec across all devices.	Average Step Time: 1.32 sec.	Estimated Remaining Time: 0:26:09.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:09:30 INFO loss_tracker.py:84 | Epoch[236/239] Step[274] GlobalStep[92314/NA]: det_loss_cls_0[0.4792] det_loss_box_0[0.5940] det_loss_cls_1[0.4548] det_loss_box_1[0.4355] det_loss_cls_2[0.3958] det_loss_box_2[0.3864] det_loss_cls_3[0.3578] det_loss_box_3[0.3788] det_loss_cls_4[0.3376] det_loss_box_4[0.3664] det_loss_cls_5[0.3270] det_loss_box_5[0.3673] det_loss_cls_dn_0[0.0849] det_loss_box_dn_0[0.2414] det_loss_cls_dn_1[0.0803] det_loss_box_dn_1[0.2003] det_loss_cls_dn_2[0.0787] det_loss_box_dn_2[0.1839] det_loss_cls_dn_3[0.0775] det_loss_box_dn_3[0.1794] det_loss_cls_dn_4[0.0767] det_loss_box_dn_4[0.1762] det_loss_cls_dn_5[0.0761] det_loss_box_dn_5[0.1748] loss_depth[0.0347] total_loss[6.5457]
Rank[0/8] 06/20/2025 21:09:40 INFO stats.py:314 | Epoch[236] Step[284] GlobalStep[92324] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 0:25:39.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:09:55 INFO loss_tracker.py:84 | Epoch[236/239] Step[299] GlobalStep[92339/NA]: det_loss_cls_0[0.4335] det_loss_box_0[0.5404] det_loss_cls_1[0.4293] det_loss_box_1[0.4091] det_loss_cls_2[0.3813] det_loss_box_2[0.3576] det_loss_cls_3[0.3397] det_loss_box_3[0.3533] det_loss_cls_4[0.3184] det_loss_box_4[0.3447] det_loss_cls_5[0.3121] det_loss_box_5[0.3415] det_loss_cls_dn_0[0.0958] det_loss_box_dn_0[0.2685] det_loss_cls_dn_1[0.0925] det_loss_box_dn_1[0.2267] det_loss_cls_dn_2[0.0923] det_loss_box_dn_2[0.2110] det_loss_cls_dn_3[0.0912] det_loss_box_dn_3[0.2053] det_loss_cls_dn_4[0.0901] det_loss_box_dn_4[0.2025] det_loss_cls_dn_5[0.0895] det_loss_box_dn_5[0.1992] loss_depth[0.0346] total_loss[6.4601]
Rank[0/8] 06/20/2025 21:10:06 INFO stats.py:314 | Epoch[236] Step[309] GlobalStep[92349] Training Speed: 8.43 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 0:25:08.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:10:22 INFO loss_tracker.py:84 | Epoch[236/239] Step[324] GlobalStep[92364/NA]: det_loss_cls_0[0.3894] det_loss_box_0[0.5562] det_loss_cls_1[0.3718] det_loss_box_1[0.4227] det_loss_cls_2[0.3211] det_loss_box_2[0.3698] det_loss_cls_3[0.2937] det_loss_box_3[0.3600] det_loss_cls_4[0.2734] det_loss_box_4[0.3481] det_loss_cls_5[0.2643] det_loss_box_5[0.3474] det_loss_cls_dn_0[0.0875] det_loss_box_dn_0[0.2638] det_loss_cls_dn_1[0.0834] det_loss_box_dn_1[0.2162] det_loss_cls_dn_2[0.0821] det_loss_box_dn_2[0.1990] det_loss_cls_dn_3[0.0813] det_loss_box_dn_3[0.1937] det_loss_cls_dn_4[0.0802] det_loss_box_dn_4[0.1904] det_loss_cls_dn_5[0.0797] det_loss_box_dn_5[0.1877] loss_depth[0.0350] total_loss[6.0980]
Rank[0/8] 06/20/2025 21:10:32 INFO stats.py:314 | Epoch[236] Step[334] GlobalStep[92374] Training Speed: 8.18 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 0:24:38.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:10:48 INFO loss_tracker.py:84 | Epoch[236/239] Step[349] GlobalStep[92389/NA]: det_loss_cls_0[0.4573] det_loss_box_0[0.5961] det_loss_cls_1[0.4446] det_loss_box_1[0.4673] det_loss_cls_2[0.3838] det_loss_box_2[0.4303] det_loss_cls_3[0.3669] det_loss_box_3[0.4118] det_loss_cls_4[0.3354] det_loss_box_4[0.4096] det_loss_cls_5[0.3261] det_loss_box_5[0.4070] det_loss_cls_dn_0[0.0892] det_loss_box_dn_0[0.2663] det_loss_cls_dn_1[0.0850] det_loss_box_dn_1[0.2207] det_loss_cls_dn_2[0.0834] det_loss_box_dn_2[0.2021] det_loss_cls_dn_3[0.0823] det_loss_box_dn_3[0.1988] det_loss_cls_dn_4[0.0817] det_loss_box_dn_4[0.1968] det_loss_cls_dn_5[0.0810] det_loss_box_dn_5[0.1948] loss_depth[0.0376] total_loss[6.8560]
Rank[0/8] 06/20/2025 21:10:58 INFO stats.py:314 | Epoch[236] Step[359] GlobalStep[92399] Training Speed: 8.04 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 0:24:08.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:11:13 INFO loss_tracker.py:84 | Epoch[236/239] Step[374] GlobalStep[92414/NA]: det_loss_cls_0[0.3731] det_loss_box_0[0.4666] det_loss_cls_1[0.3677] det_loss_box_1[0.3415] det_loss_cls_2[0.3063] det_loss_box_2[0.3083] det_loss_cls_3[0.2722] det_loss_box_3[0.3034] det_loss_cls_4[0.2573] det_loss_box_4[0.2958] det_loss_cls_5[0.2468] det_loss_box_5[0.2964] det_loss_cls_dn_0[0.0879] det_loss_box_dn_0[0.3024] det_loss_cls_dn_1[0.0848] det_loss_box_dn_1[0.2466] det_loss_cls_dn_2[0.0840] det_loss_box_dn_2[0.2260] det_loss_cls_dn_3[0.0829] det_loss_box_dn_3[0.2217] det_loss_cls_dn_4[0.0826] det_loss_box_dn_4[0.2173] det_loss_cls_dn_5[0.0819] det_loss_box_dn_5[0.2099] loss_depth[0.0374] total_loss[5.8009]
Rank[0/8] 06/20/2025 21:11:24 INFO stats.py:314 | Epoch[236] Step[384] GlobalStep[92424] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 0:23:38.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:11:29 INFO stats.py:394 | Epoch[236] completed. Training Speed: 7.71 samples/sec across all devices.	Epoch Time: 404.87 sec.	Average Epoch Time: 404.87 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 0:23:32.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:11:52 INFO stats.py:314 | Epoch[237] Step[19] GlobalStep[92449] Training Speed: 8.29 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 0:23:07.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:11:58 INFO loss_tracker.py:84 | Epoch[237/239] Step[24] GlobalStep[92454/NA]: det_loss_cls_0[0.4628] det_loss_box_0[0.5525] det_loss_cls_1[0.4472] det_loss_box_1[0.4200] det_loss_cls_2[0.3892] det_loss_box_2[0.3836] det_loss_cls_3[0.3544] det_loss_box_3[0.3693] det_loss_cls_4[0.3356] det_loss_box_4[0.3578] det_loss_cls_5[0.3217] det_loss_box_5[0.3613] det_loss_cls_dn_0[0.0821] det_loss_box_dn_0[0.2667] det_loss_cls_dn_1[0.0782] det_loss_box_dn_1[0.2201] det_loss_cls_dn_2[0.0777] det_loss_box_dn_2[0.2055] det_loss_cls_dn_3[0.0764] det_loss_box_dn_3[0.2019] det_loss_cls_dn_4[0.0760] det_loss_box_dn_4[0.1996] det_loss_cls_dn_5[0.0754] det_loss_box_dn_5[0.1933] loss_depth[0.0334] total_loss[6.5416]
Rank[0/8] 06/20/2025 21:12:18 INFO stats.py:314 | Epoch[237] Step[44] GlobalStep[92474] Training Speed: 8.59 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 0:22:37.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:12:24 INFO loss_tracker.py:84 | Epoch[237/239] Step[49] GlobalStep[92479/NA]: det_loss_cls_0[0.4853] det_loss_box_0[0.5928] det_loss_cls_1[0.4715] det_loss_box_1[0.4617] det_loss_cls_2[0.4036] det_loss_box_2[0.4177] det_loss_cls_3[0.3732] det_loss_box_3[0.3965] det_loss_cls_4[0.3516] det_loss_box_4[0.3807] det_loss_cls_5[0.3331] det_loss_box_5[0.3863] det_loss_cls_dn_0[0.0979] det_loss_box_dn_0[0.2607] det_loss_cls_dn_1[0.0941] det_loss_box_dn_1[0.2166] det_loss_cls_dn_2[0.0940] det_loss_box_dn_2[0.1994] det_loss_cls_dn_3[0.0922] det_loss_box_dn_3[0.1950] det_loss_cls_dn_4[0.0917] det_loss_box_dn_4[0.1928] det_loss_cls_dn_5[0.0911] det_loss_box_dn_5[0.1913] loss_depth[0.0344] total_loss[6.9052]
Rank[0/8] 06/20/2025 21:12:44 INFO stats.py:314 | Epoch[237] Step[69] GlobalStep[92499] Training Speed: 7.57 samples/sec across all devices.	Average Step Time: 1.06 sec.	Estimated Remaining Time: 0:22:07.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:12:49 INFO loss_tracker.py:84 | Epoch[237/239] Step[74] GlobalStep[92504/NA]: det_loss_cls_0[0.5545] det_loss_box_0[0.7418] det_loss_cls_1[0.5239] det_loss_box_1[0.5607] det_loss_cls_2[0.4834] det_loss_box_2[0.5000] det_loss_cls_3[0.4570] det_loss_box_3[0.4826] det_loss_cls_4[0.4318] det_loss_box_4[0.4672] det_loss_cls_5[0.4221] det_loss_box_5[0.4666] det_loss_cls_dn_0[0.0965] det_loss_box_dn_0[0.3505] det_loss_cls_dn_1[0.0921] det_loss_box_dn_1[0.2822] det_loss_cls_dn_2[0.0911] det_loss_box_dn_2[0.2593] det_loss_cls_dn_3[0.0906] det_loss_box_dn_3[0.2488] det_loss_cls_dn_4[0.0899] det_loss_box_dn_4[0.2439] det_loss_cls_dn_5[0.0894] det_loss_box_dn_5[0.2362] loss_depth[0.0393] total_loss[8.3012]
Rank[0/8] 06/20/2025 21:13:10 INFO stats.py:314 | Epoch[237] Step[94] GlobalStep[92524] Training Speed: 6.00 samples/sec across all devices.	Average Step Time: 1.33 sec.	Estimated Remaining Time: 0:21:37.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:13:16 INFO loss_tracker.py:84 | Epoch[237/239] Step[99] GlobalStep[92529/NA]: det_loss_cls_0[0.4671] det_loss_box_0[0.5945] det_loss_cls_1[0.4487] det_loss_box_1[0.4473] det_loss_cls_2[0.3769] det_loss_box_2[0.4044] det_loss_cls_3[0.3442] det_loss_box_3[0.3909] det_loss_cls_4[0.3207] det_loss_box_4[0.3858] det_loss_cls_5[0.3094] det_loss_box_5[0.3887] det_loss_cls_dn_0[0.0835] det_loss_box_dn_0[0.2318] det_loss_cls_dn_1[0.0793] det_loss_box_dn_1[0.1929] det_loss_cls_dn_2[0.0791] det_loss_box_dn_2[0.1775] det_loss_cls_dn_3[0.0780] det_loss_box_dn_3[0.1741] det_loss_cls_dn_4[0.0773] det_loss_box_dn_4[0.1716] det_loss_cls_dn_5[0.0766] det_loss_box_dn_5[0.1707] loss_depth[0.0349] total_loss[6.5060]
Rank[0/8] 06/20/2025 21:13:36 INFO stats.py:314 | Epoch[237] Step[119] GlobalStep[92549] Training Speed: 8.62 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 0:21:07.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:13:41 INFO loss_tracker.py:84 | Epoch[237/239] Step[124] GlobalStep[92554/NA]: det_loss_cls_0[0.4801] det_loss_box_0[0.5744] det_loss_cls_1[0.4596] det_loss_box_1[0.4549] det_loss_cls_2[0.3931] det_loss_box_2[0.3937] det_loss_cls_3[0.3656] det_loss_box_3[0.3832] det_loss_cls_4[0.3310] det_loss_box_4[0.3785] det_loss_cls_5[0.3219] det_loss_box_5[0.3725] det_loss_cls_dn_0[0.0833] det_loss_box_dn_0[0.2550] det_loss_cls_dn_1[0.0793] det_loss_box_dn_1[0.2105] det_loss_cls_dn_2[0.0778] det_loss_box_dn_2[0.1946] det_loss_cls_dn_3[0.0766] det_loss_box_dn_3[0.1903] det_loss_cls_dn_4[0.0755] det_loss_box_dn_4[0.1882] det_loss_cls_dn_5[0.0743] det_loss_box_dn_5[0.1850] loss_depth[0.0337] total_loss[6.6326]
Rank[0/8] 06/20/2025 21:14:02 INFO stats.py:314 | Epoch[237] Step[144] GlobalStep[92574] Training Speed: 8.42 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 0:20:36.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:14:07 INFO loss_tracker.py:84 | Epoch[237/239] Step[149] GlobalStep[92579/NA]: det_loss_cls_0[0.5180] det_loss_box_0[0.6248] det_loss_cls_1[0.4969] det_loss_box_1[0.4637] det_loss_cls_2[0.4336] det_loss_box_2[0.4179] det_loss_cls_3[0.4027] det_loss_box_3[0.4009] det_loss_cls_4[0.3818] det_loss_box_4[0.3902] det_loss_cls_5[0.3684] det_loss_box_5[0.3899] det_loss_cls_dn_0[0.1017] det_loss_box_dn_0[0.2469] det_loss_cls_dn_1[0.0981] det_loss_box_dn_1[0.2020] det_loss_cls_dn_2[0.0973] det_loss_box_dn_2[0.1872] det_loss_cls_dn_3[0.0969] det_loss_box_dn_3[0.1833] det_loss_cls_dn_4[0.0961] det_loss_box_dn_4[0.1809] det_loss_cls_dn_5[0.0951] det_loss_box_dn_5[0.1785] loss_depth[0.0306] total_loss[7.0833]
Rank[0/8] 06/20/2025 21:14:27 INFO stats.py:314 | Epoch[237] Step[169] GlobalStep[92599] Training Speed: 8.22 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 0:20:06.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:14:32 INFO loss_tracker.py:84 | Epoch[237/239] Step[174] GlobalStep[92604/NA]: det_loss_cls_0[0.5008] det_loss_box_0[0.5880] det_loss_cls_1[0.4783] det_loss_box_1[0.4388] det_loss_cls_2[0.4084] det_loss_box_2[0.3927] det_loss_cls_3[0.3690] det_loss_box_3[0.3778] det_loss_cls_4[0.3486] det_loss_box_4[0.3604] det_loss_cls_5[0.3326] det_loss_box_5[0.3627] det_loss_cls_dn_0[0.0886] det_loss_box_dn_0[0.2555] det_loss_cls_dn_1[0.0848] det_loss_box_dn_1[0.2043] det_loss_cls_dn_2[0.0836] det_loss_box_dn_2[0.1866] det_loss_cls_dn_3[0.0824] det_loss_box_dn_3[0.1804] det_loss_cls_dn_4[0.0814] det_loss_box_dn_4[0.1784] det_loss_cls_dn_5[0.0808] det_loss_box_dn_5[0.1754] loss_depth[0.0349] total_loss[6.6751]
Rank[0/8] 06/20/2025 21:14:54 INFO stats.py:314 | Epoch[237] Step[194] GlobalStep[92624] Training Speed: 7.85 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 0:19:36.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:14:59 INFO loss_tracker.py:84 | Epoch[237/239] Step[199] GlobalStep[92629/NA]: det_loss_cls_0[0.5021] det_loss_box_0[0.5871] det_loss_cls_1[0.5129] det_loss_box_1[0.4248] det_loss_cls_2[0.4309] det_loss_box_2[0.3857] det_loss_cls_3[0.3939] det_loss_box_3[0.3707] det_loss_cls_4[0.3722] det_loss_box_4[0.3644] det_loss_cls_5[0.3577] det_loss_box_5[0.3626] det_loss_cls_dn_0[0.0867] det_loss_box_dn_0[0.2475] det_loss_cls_dn_1[0.0837] det_loss_box_dn_1[0.2029] det_loss_cls_dn_2[0.0828] det_loss_box_dn_2[0.1861] det_loss_cls_dn_3[0.0821] det_loss_box_dn_3[0.1822] det_loss_cls_dn_4[0.0814] det_loss_box_dn_4[0.1803] det_loss_cls_dn_5[0.0803] det_loss_box_dn_5[0.1740] loss_depth[0.0346] total_loss[6.7699]
Rank[0/8] 06/20/2025 21:15:19 INFO stats.py:314 | Epoch[237] Step[219] GlobalStep[92649] Training Speed: 8.66 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 0:19:06.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:15:25 INFO loss_tracker.py:84 | Epoch[237/239] Step[224] GlobalStep[92654/NA]: det_loss_cls_0[0.5394] det_loss_box_0[0.6818] det_loss_cls_1[0.5211] det_loss_box_1[0.5087] det_loss_cls_2[0.4412] det_loss_box_2[0.4622] det_loss_cls_3[0.4148] det_loss_box_3[0.4470] det_loss_cls_4[0.3856] det_loss_box_4[0.4331] det_loss_cls_5[0.3702] det_loss_box_5[0.4365] det_loss_cls_dn_0[0.0855] det_loss_box_dn_0[0.2372] det_loss_cls_dn_1[0.0819] det_loss_box_dn_1[0.1924] det_loss_cls_dn_2[0.0804] det_loss_box_dn_2[0.1753] det_loss_cls_dn_3[0.0793] det_loss_box_dn_3[0.1713] det_loss_cls_dn_4[0.0784] det_loss_box_dn_4[0.1687] det_loss_cls_dn_5[0.0776] det_loss_box_dn_5[0.1669] loss_depth[0.0342] total_loss[7.2706]
Rank[0/8] 06/20/2025 21:15:45 INFO stats.py:314 | Epoch[237] Step[244] GlobalStep[92674] Training Speed: 8.15 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 0:18:35.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:15:50 INFO loss_tracker.py:84 | Epoch[237/239] Step[249] GlobalStep[92679/NA]: det_loss_cls_0[0.3790] det_loss_box_0[0.4767] det_loss_cls_1[0.3731] det_loss_box_1[0.3648] det_loss_cls_2[0.3076] det_loss_box_2[0.3304] det_loss_cls_3[0.2767] det_loss_box_3[0.3249] det_loss_cls_4[0.2576] det_loss_box_4[0.3175] det_loss_cls_5[0.2462] det_loss_box_5[0.3141] det_loss_cls_dn_0[0.0814] det_loss_box_dn_0[0.2545] det_loss_cls_dn_1[0.0778] det_loss_box_dn_1[0.2041] det_loss_cls_dn_2[0.0767] det_loss_box_dn_2[0.1847] det_loss_cls_dn_3[0.0760] det_loss_box_dn_3[0.1798] det_loss_cls_dn_4[0.0753] det_loss_box_dn_4[0.1773] det_loss_cls_dn_5[0.0742] det_loss_box_dn_5[0.1753] loss_depth[0.0333] total_loss[5.6389]
Rank[0/8] 06/20/2025 21:16:11 INFO stats.py:314 | Epoch[237] Step[269] GlobalStep[92699] Training Speed: 6.51 samples/sec across all devices.	Average Step Time: 1.23 sec.	Estimated Remaining Time: 0:18:05.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:16:16 INFO loss_tracker.py:84 | Epoch[237/239] Step[274] GlobalStep[92704/NA]: det_loss_cls_0[0.4840] det_loss_box_0[0.6293] det_loss_cls_1[0.4710] det_loss_box_1[0.4782] det_loss_cls_2[0.4115] det_loss_box_2[0.4427] det_loss_cls_3[0.3858] det_loss_box_3[0.4298] det_loss_cls_4[0.3710] det_loss_box_4[0.4143] det_loss_cls_5[0.3614] det_loss_box_5[0.4164] det_loss_cls_dn_0[0.0934] det_loss_box_dn_0[0.2605] det_loss_cls_dn_1[0.0902] det_loss_box_dn_1[0.2191] det_loss_cls_dn_2[0.0897] det_loss_box_dn_2[0.2010] det_loss_cls_dn_3[0.0886] det_loss_box_dn_3[0.1967] det_loss_cls_dn_4[0.0880] det_loss_box_dn_4[0.1947] det_loss_cls_dn_5[0.0874] det_loss_box_dn_5[0.1921] loss_depth[0.0364] total_loss[7.1332]
Rank[0/8] 06/20/2025 21:16:36 INFO stats.py:314 | Epoch[237] Step[294] GlobalStep[92724] Training Speed: 8.35 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 0:17:35.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:16:42 INFO loss_tracker.py:84 | Epoch[237/239] Step[299] GlobalStep[92729/NA]: det_loss_cls_0[0.4992] det_loss_box_0[0.6394] det_loss_cls_1[0.4830] det_loss_box_1[0.5065] det_loss_cls_2[0.4170] det_loss_box_2[0.4552] det_loss_cls_3[0.3948] det_loss_box_3[0.4400] det_loss_cls_4[0.3713] det_loss_box_4[0.4333] det_loss_cls_5[0.3494] det_loss_box_5[0.4414] det_loss_cls_dn_0[0.0930] det_loss_box_dn_0[0.2668] det_loss_cls_dn_1[0.0885] det_loss_box_dn_1[0.2283] det_loss_cls_dn_2[0.0877] det_loss_box_dn_2[0.2106] det_loss_cls_dn_3[0.0862] det_loss_box_dn_3[0.2047] det_loss_cls_dn_4[0.0856] det_loss_box_dn_4[0.2031] det_loss_cls_dn_5[0.0846] det_loss_box_dn_5[0.1994] loss_depth[0.0328] total_loss[7.3018]
Rank[0/8] 06/20/2025 21:17:02 INFO stats.py:314 | Epoch[237] Step[319] GlobalStep[92749] Training Speed: 8.17 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 0:17:05.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:17:07 INFO loss_tracker.py:84 | Epoch[237/239] Step[324] GlobalStep[92754/NA]: det_loss_cls_0[0.4031] det_loss_box_0[0.4986] det_loss_cls_1[0.4026] det_loss_box_1[0.3750] det_loss_cls_2[0.3313] det_loss_box_2[0.3467] det_loss_cls_3[0.2996] det_loss_box_3[0.3372] det_loss_cls_4[0.2821] det_loss_box_4[0.3235] det_loss_cls_5[0.2678] det_loss_box_5[0.3243] det_loss_cls_dn_0[0.0894] det_loss_box_dn_0[0.2626] det_loss_cls_dn_1[0.0858] det_loss_box_dn_1[0.2174] det_loss_cls_dn_2[0.0850] det_loss_box_dn_2[0.1999] det_loss_cls_dn_3[0.0839] det_loss_box_dn_3[0.1970] det_loss_cls_dn_4[0.0833] det_loss_box_dn_4[0.1946] det_loss_cls_dn_5[0.0825] det_loss_box_dn_5[0.1913] loss_depth[0.0335] total_loss[5.9979]
Rank[0/8] 06/20/2025 21:17:28 INFO stats.py:314 | Epoch[237] Step[344] GlobalStep[92774] Training Speed: 7.81 samples/sec across all devices.	Average Step Time: 1.02 sec.	Estimated Remaining Time: 0:16:35.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:17:33 INFO loss_tracker.py:84 | Epoch[237/239] Step[349] GlobalStep[92779/NA]: det_loss_cls_0[0.3954] det_loss_box_0[0.5314] det_loss_cls_1[0.3957] det_loss_box_1[0.3780] det_loss_cls_2[0.3265] det_loss_box_2[0.3422] det_loss_cls_3[0.2937] det_loss_box_3[0.3342] det_loss_cls_4[0.2688] det_loss_box_4[0.3298] det_loss_cls_5[0.2624] det_loss_box_5[0.3268] det_loss_cls_dn_0[0.0728] det_loss_box_dn_0[0.2783] det_loss_cls_dn_1[0.0693] det_loss_box_dn_1[0.2231] det_loss_cls_dn_2[0.0685] det_loss_box_dn_2[0.2004] det_loss_cls_dn_3[0.0677] det_loss_box_dn_3[0.1956] det_loss_cls_dn_4[0.0669] det_loss_box_dn_4[0.1918] det_loss_cls_dn_5[0.0660] det_loss_box_dn_5[0.1893] loss_depth[0.0330] total_loss[5.9074]
Rank[0/8] 06/20/2025 21:17:54 INFO stats.py:314 | Epoch[237] Step[369] GlobalStep[92799] Training Speed: 8.25 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 0:16:04.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:18:00 INFO loss_tracker.py:84 | Epoch[237/239] Step[374] GlobalStep[92804/NA]: det_loss_cls_0[0.4580] det_loss_box_0[0.5402] det_loss_cls_1[0.4545] det_loss_box_1[0.4054] det_loss_cls_2[0.3803] det_loss_box_2[0.3600] det_loss_cls_3[0.3472] det_loss_box_3[0.3438] det_loss_cls_4[0.3210] det_loss_box_4[0.3404] det_loss_cls_5[0.3107] det_loss_box_5[0.3387] det_loss_cls_dn_0[0.0918] det_loss_box_dn_0[0.2406] det_loss_cls_dn_1[0.0891] det_loss_box_dn_1[0.2022] det_loss_cls_dn_2[0.0886] det_loss_box_dn_2[0.1832] det_loss_cls_dn_3[0.0877] det_loss_box_dn_3[0.1804] det_loss_cls_dn_4[0.0868] det_loss_box_dn_4[0.1781] det_loss_cls_dn_5[0.0860] det_loss_box_dn_5[0.1766] loss_depth[0.0346] total_loss[6.3261]
Rank[0/8] 06/20/2025 21:18:15 INFO stats.py:394 | Epoch[237] completed. Training Speed: 7.68 samples/sec across all devices.	Epoch Time: 406.38 sec.	Average Epoch Time: 406.38 sec.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 0:15:40.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:18:24 INFO stats.py:314 | Epoch[238] Step[4] GlobalStep[92824] Training Speed: 8.21 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 0:15:34.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:18:45 INFO loss_tracker.py:84 | Epoch[238/239] Step[24] GlobalStep[92844/NA]: det_loss_cls_0[0.5141] det_loss_box_0[0.6211] det_loss_cls_1[0.4935] det_loss_box_1[0.4884] det_loss_cls_2[0.4441] det_loss_box_2[0.4432] det_loss_cls_3[0.4100] det_loss_box_3[0.4303] det_loss_cls_4[0.3829] det_loss_box_4[0.4256] det_loss_cls_5[0.3707] det_loss_box_5[0.4198] det_loss_cls_dn_0[0.0954] det_loss_box_dn_0[0.2972] det_loss_cls_dn_1[0.0911] det_loss_box_dn_1[0.2472] det_loss_cls_dn_2[0.0903] det_loss_box_dn_2[0.2271] det_loss_cls_dn_3[0.0890] det_loss_box_dn_3[0.2244] det_loss_cls_dn_4[0.0882] det_loss_box_dn_4[0.2210] det_loss_cls_dn_5[0.0875] det_loss_box_dn_5[0.2179] loss_depth[0.0362] total_loss[7.4563]
Rank[0/8] 06/20/2025 21:18:50 INFO stats.py:314 | Epoch[238] Step[29] GlobalStep[92849] Training Speed: 8.54 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 0:15:04.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:19:11 INFO loss_tracker.py:84 | Epoch[238/239] Step[49] GlobalStep[92869/NA]: det_loss_cls_0[0.4006] det_loss_box_0[0.4901] det_loss_cls_1[0.3919] det_loss_box_1[0.3510] det_loss_cls_2[0.3255] det_loss_box_2[0.3164] det_loss_cls_3[0.2879] det_loss_box_3[0.3020] det_loss_cls_4[0.2680] det_loss_box_4[0.2959] det_loss_cls_5[0.2593] det_loss_box_5[0.2984] det_loss_cls_dn_0[0.0865] det_loss_box_dn_0[0.2578] det_loss_cls_dn_1[0.0830] det_loss_box_dn_1[0.2053] det_loss_cls_dn_2[0.0820] det_loss_box_dn_2[0.1835] det_loss_cls_dn_3[0.0811] det_loss_box_dn_3[0.1800] det_loss_cls_dn_4[0.0805] det_loss_box_dn_4[0.1769] det_loss_cls_dn_5[0.0799] det_loss_box_dn_5[0.1742] loss_depth[0.0380] total_loss[5.6956]
Rank[0/8] 06/20/2025 21:19:16 INFO stats.py:314 | Epoch[238] Step[54] GlobalStep[92874] Training Speed: 6.98 samples/sec across all devices.	Average Step Time: 1.15 sec.	Estimated Remaining Time: 0:14:34.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:19:37 INFO loss_tracker.py:84 | Epoch[238/239] Step[74] GlobalStep[92894/NA]: det_loss_cls_0[0.4248] det_loss_box_0[0.5402] det_loss_cls_1[0.4136] det_loss_box_1[0.3882] det_loss_cls_2[0.3585] det_loss_box_2[0.3431] det_loss_cls_3[0.3254] det_loss_box_3[0.3296] det_loss_cls_4[0.3029] det_loss_box_4[0.3234] det_loss_cls_5[0.2937] det_loss_box_5[0.3211] det_loss_cls_dn_0[0.0939] det_loss_box_dn_0[0.2631] det_loss_cls_dn_1[0.0897] det_loss_box_dn_1[0.2088] det_loss_cls_dn_2[0.0890] det_loss_box_dn_2[0.1880] det_loss_cls_dn_3[0.0878] det_loss_box_dn_3[0.1814] det_loss_cls_dn_4[0.0871] det_loss_box_dn_4[0.1761] det_loss_cls_dn_5[0.0859] det_loss_box_dn_5[0.1736] loss_depth[0.0363] total_loss[6.1251]
Rank[0/8] 06/20/2025 21:19:42 INFO stats.py:314 | Epoch[238] Step[79] GlobalStep[92899] Training Speed: 8.53 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 0:14:04.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:20:03 INFO loss_tracker.py:84 | Epoch[238/239] Step[99] GlobalStep[92919/NA]: det_loss_cls_0[0.3741] det_loss_box_0[0.4541] det_loss_cls_1[0.3755] det_loss_box_1[0.3293] det_loss_cls_2[0.3051] det_loss_box_2[0.2953] det_loss_cls_3[0.2751] det_loss_box_3[0.2807] det_loss_cls_4[0.2543] det_loss_box_4[0.2766] det_loss_cls_5[0.2429] det_loss_box_5[0.2778] det_loss_cls_dn_0[0.0898] det_loss_box_dn_0[0.2718] det_loss_cls_dn_1[0.0861] det_loss_box_dn_1[0.2159] det_loss_cls_dn_2[0.0849] det_loss_box_dn_2[0.1943] det_loss_cls_dn_3[0.0841] det_loss_box_dn_3[0.1899] det_loss_cls_dn_4[0.0833] det_loss_box_dn_4[0.1859] det_loss_cls_dn_5[0.0828] det_loss_box_dn_5[0.1838] loss_depth[0.0345] total_loss[5.5278]
Rank[0/8] 06/20/2025 21:20:08 INFO stats.py:314 | Epoch[238] Step[104] GlobalStep[92924] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 0:13:34.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:20:29 INFO loss_tracker.py:84 | Epoch[238/239] Step[124] GlobalStep[92944/NA]: det_loss_cls_0[0.4192] det_loss_box_0[0.5158] det_loss_cls_1[0.4133] det_loss_box_1[0.3867] det_loss_cls_2[0.3461] det_loss_box_2[0.3623] det_loss_cls_3[0.3139] det_loss_box_3[0.3515] det_loss_cls_4[0.2952] det_loss_box_4[0.3402] det_loss_cls_5[0.2826] det_loss_box_5[0.3394] det_loss_cls_dn_0[0.0869] det_loss_box_dn_0[0.2413] det_loss_cls_dn_1[0.0834] det_loss_box_dn_1[0.2011] det_loss_cls_dn_2[0.0826] det_loss_box_dn_2[0.1848] det_loss_cls_dn_3[0.0814] det_loss_box_dn_3[0.1800] det_loss_cls_dn_4[0.0809] det_loss_box_dn_4[0.1781] det_loss_cls_dn_5[0.0800] det_loss_box_dn_5[0.1760] loss_depth[0.0335] total_loss[6.0561]
Rank[0/8] 06/20/2025 21:20:34 INFO stats.py:314 | Epoch[238] Step[129] GlobalStep[92949] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 0:13:03.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:20:55 INFO loss_tracker.py:84 | Epoch[238/239] Step[149] GlobalStep[92969/NA]: det_loss_cls_0[0.6073] det_loss_box_0[0.7810] det_loss_cls_1[0.6016] det_loss_box_1[0.5938] det_loss_cls_2[0.5339] det_loss_box_2[0.5330] det_loss_cls_3[0.5079] det_loss_box_3[0.5138] det_loss_cls_4[0.4788] det_loss_box_4[0.4996] det_loss_cls_5[0.4667] det_loss_box_5[0.4952] det_loss_cls_dn_0[0.1045] det_loss_box_dn_0[0.2576] det_loss_cls_dn_1[0.1018] det_loss_box_dn_1[0.2114] det_loss_cls_dn_2[0.1013] det_loss_box_dn_2[0.1921] det_loss_cls_dn_3[0.1000] det_loss_box_dn_3[0.1877] det_loss_cls_dn_4[0.0990] det_loss_box_dn_4[0.1858] det_loss_cls_dn_5[0.0982] det_loss_box_dn_5[0.1836] loss_depth[0.0347] total_loss[8.4703]
Rank[0/8] 06/20/2025 21:21:00 INFO stats.py:314 | Epoch[238] Step[154] GlobalStep[92974] Training Speed: 7.75 samples/sec across all devices.	Average Step Time: 1.03 sec.	Estimated Remaining Time: 0:12:33.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:21:21 INFO loss_tracker.py:84 | Epoch[238/239] Step[174] GlobalStep[92994/NA]: det_loss_cls_0[0.3350] det_loss_box_0[0.4143] det_loss_cls_1[0.3075] det_loss_box_1[0.3036] det_loss_cls_2[0.2633] det_loss_box_2[0.2756] det_loss_cls_3[0.2329] det_loss_box_3[0.2673] det_loss_cls_4[0.2075] det_loss_box_4[0.2595] det_loss_cls_5[0.1996] det_loss_box_5[0.2581] det_loss_cls_dn_0[0.0854] det_loss_box_dn_0[0.2989] det_loss_cls_dn_1[0.0818] det_loss_box_dn_1[0.2325] det_loss_cls_dn_2[0.0814] det_loss_box_dn_2[0.2086] det_loss_cls_dn_3[0.0804] det_loss_box_dn_3[0.2040] det_loss_cls_dn_4[0.0793] det_loss_box_dn_4[0.2004] det_loss_cls_dn_5[0.0785] det_loss_box_dn_5[0.1983] loss_depth[0.0360] total_loss[5.1897]
Rank[0/8] 06/20/2025 21:21:26 INFO stats.py:314 | Epoch[238] Step[179] GlobalStep[92999] Training Speed: 8.13 samples/sec across all devices.	Average Step Time: 0.98 sec.	Estimated Remaining Time: 0:12:03.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:21:46 INFO loss_tracker.py:84 | Epoch[238/239] Step[199] GlobalStep[93019/NA]: det_loss_cls_0[0.4633] det_loss_box_0[0.5287] det_loss_cls_1[0.4445] det_loss_box_1[0.3981] det_loss_cls_2[0.3885] det_loss_box_2[0.3602] det_loss_cls_3[0.3620] det_loss_box_3[0.3480] det_loss_cls_4[0.3355] det_loss_box_4[0.3441] det_loss_cls_5[0.3295] det_loss_box_5[0.3405] det_loss_cls_dn_0[0.0977] det_loss_box_dn_0[0.2343] det_loss_cls_dn_1[0.0933] det_loss_box_dn_1[0.1888] det_loss_cls_dn_2[0.0926] det_loss_box_dn_2[0.1715] det_loss_cls_dn_3[0.0913] det_loss_box_dn_3[0.1677] det_loss_cls_dn_4[0.0905] det_loss_box_dn_4[0.1646] det_loss_cls_dn_5[0.0897] det_loss_box_dn_5[0.1632] loss_depth[0.0365] total_loss[6.3247]
Rank[0/8] 06/20/2025 21:21:52 INFO stats.py:314 | Epoch[238] Step[204] GlobalStep[93024] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 0:11:33.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:22:12 INFO loss_tracker.py:84 | Epoch[238/239] Step[224] GlobalStep[93044/NA]: det_loss_cls_0[0.4050] det_loss_box_0[0.5233] det_loss_cls_1[0.3809] det_loss_box_1[0.3873] det_loss_cls_2[0.3208] det_loss_box_2[0.3614] det_loss_cls_3[0.3005] det_loss_box_3[0.3516] det_loss_cls_4[0.2805] det_loss_box_4[0.3373] det_loss_cls_5[0.2716] det_loss_box_5[0.3386] det_loss_cls_dn_0[0.0798] det_loss_box_dn_0[0.2868] det_loss_cls_dn_1[0.0749] det_loss_box_dn_1[0.2399] det_loss_cls_dn_2[0.0739] det_loss_box_dn_2[0.2210] det_loss_cls_dn_3[0.0723] det_loss_box_dn_3[0.2147] det_loss_cls_dn_4[0.0711] det_loss_box_dn_4[0.2134] det_loss_cls_dn_5[0.0705] det_loss_box_dn_5[0.2074] loss_depth[0.0381] total_loss[6.1227]
Rank[0/8] 06/20/2025 21:22:18 INFO stats.py:314 | Epoch[238] Step[229] GlobalStep[93049] Training Speed: 8.36 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 0:11:03.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:22:39 INFO loss_tracker.py:84 | Epoch[238/239] Step[249] GlobalStep[93069/NA]: det_loss_cls_0[0.4222] det_loss_box_0[0.5138] det_loss_cls_1[0.4110] det_loss_box_1[0.3736] det_loss_cls_2[0.3423] det_loss_box_2[0.3411] det_loss_cls_3[0.3083] det_loss_box_3[0.3317] det_loss_cls_4[0.2862] det_loss_box_4[0.3254] det_loss_cls_5[0.2759] det_loss_box_5[0.3223] det_loss_cls_dn_0[0.0877] det_loss_box_dn_0[0.2235] det_loss_cls_dn_1[0.0840] det_loss_box_dn_1[0.1856] det_loss_cls_dn_2[0.0832] det_loss_box_dn_2[0.1670] det_loss_cls_dn_3[0.0822] det_loss_box_dn_3[0.1631] det_loss_cls_dn_4[0.0813] det_loss_box_dn_4[0.1606] det_loss_cls_dn_5[0.0800] det_loss_box_dn_5[0.1591] loss_depth[0.0344] total_loss[5.8453]
Rank[0/8] 06/20/2025 21:22:44 INFO stats.py:314 | Epoch[238] Step[254] GlobalStep[93074] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 0:10:33.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:23:04 INFO loss_tracker.py:84 | Epoch[238/239] Step[274] GlobalStep[93094/NA]: det_loss_cls_0[0.4113] det_loss_box_0[0.5248] det_loss_cls_1[0.3903] det_loss_box_1[0.4032] det_loss_cls_2[0.3336] det_loss_box_2[0.3630] det_loss_cls_3[0.2990] det_loss_box_3[0.3552] det_loss_cls_4[0.2800] det_loss_box_4[0.3473] det_loss_cls_5[0.2708] det_loss_box_5[0.3494] det_loss_cls_dn_0[0.0850] det_loss_box_dn_0[0.2562] det_loss_cls_dn_1[0.0817] det_loss_box_dn_1[0.2062] det_loss_cls_dn_2[0.0808] det_loss_box_dn_2[0.1899] det_loss_cls_dn_3[0.0796] det_loss_box_dn_3[0.1877] det_loss_cls_dn_4[0.0789] det_loss_box_dn_4[0.1850] det_loss_cls_dn_5[0.0778] det_loss_box_dn_5[0.1824] loss_depth[0.0341] total_loss[6.0532]
Rank[0/8] 06/20/2025 21:23:09 INFO stats.py:314 | Epoch[238] Step[279] GlobalStep[93099] Training Speed: 8.31 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 0:10:02.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:23:31 INFO loss_tracker.py:84 | Epoch[238/239] Step[299] GlobalStep[93119/NA]: det_loss_cls_0[0.4439] det_loss_box_0[0.6560] det_loss_cls_1[0.4185] det_loss_box_1[0.4945] det_loss_cls_2[0.3736] det_loss_box_2[0.4386] det_loss_cls_3[0.3459] det_loss_box_3[0.4205] det_loss_cls_4[0.3248] det_loss_box_4[0.4090] det_loss_cls_5[0.3060] det_loss_box_5[0.4165] det_loss_cls_dn_0[0.0882] det_loss_box_dn_0[0.3583] det_loss_cls_dn_1[0.0847] det_loss_box_dn_1[0.2875] det_loss_cls_dn_2[0.0843] det_loss_box_dn_2[0.2633] det_loss_cls_dn_3[0.0835] det_loss_box_dn_3[0.2529] det_loss_cls_dn_4[0.0832] det_loss_box_dn_4[0.2496] det_loss_cls_dn_5[0.0824] det_loss_box_dn_5[0.2417] loss_depth[0.0346] total_loss[7.2420]
Rank[0/8] 06/20/2025 21:23:36 INFO stats.py:314 | Epoch[238] Step[304] GlobalStep[93124] Training Speed: 7.94 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 0:09:32.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:23:57 INFO loss_tracker.py:84 | Epoch[238/239] Step[324] GlobalStep[93144/NA]: det_loss_cls_0[0.4323] det_loss_box_0[0.5319] det_loss_cls_1[0.4232] det_loss_box_1[0.4087] det_loss_cls_2[0.3627] det_loss_box_2[0.3734] det_loss_cls_3[0.3338] det_loss_box_3[0.3600] det_loss_cls_4[0.3143] det_loss_box_4[0.3484] det_loss_cls_5[0.3018] det_loss_box_5[0.3503] det_loss_cls_dn_0[0.0818] det_loss_box_dn_0[0.2194] det_loss_cls_dn_1[0.0787] det_loss_box_dn_1[0.1750] det_loss_cls_dn_2[0.0775] det_loss_box_dn_2[0.1618] det_loss_cls_dn_3[0.0763] det_loss_box_dn_3[0.1577] det_loss_cls_dn_4[0.0760] det_loss_box_dn_4[0.1553] det_loss_cls_dn_5[0.0746] det_loss_box_dn_5[0.1532] loss_depth[0.0335] total_loss[6.0616]
Rank[0/8] 06/20/2025 21:24:02 INFO stats.py:314 | Epoch[238] Step[329] GlobalStep[93149] Training Speed: 8.58 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 0:09:02.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:24:22 INFO loss_tracker.py:84 | Epoch[238/239] Step[349] GlobalStep[93169/NA]: det_loss_cls_0[0.4271] det_loss_box_0[0.5434] det_loss_cls_1[0.3958] det_loss_box_1[0.3944] det_loss_cls_2[0.3369] det_loss_box_2[0.3637] det_loss_cls_3[0.3058] det_loss_box_3[0.3522] det_loss_cls_4[0.2944] det_loss_box_4[0.3355] det_loss_cls_5[0.2824] det_loss_box_5[0.3391] det_loss_cls_dn_0[0.0894] det_loss_box_dn_0[0.2771] det_loss_cls_dn_1[0.0854] det_loss_box_dn_1[0.2239] det_loss_cls_dn_2[0.0844] det_loss_box_dn_2[0.2064] det_loss_cls_dn_3[0.0833] det_loss_box_dn_3[0.2017] det_loss_cls_dn_4[0.0825] det_loss_box_dn_4[0.1997] det_loss_cls_dn_5[0.0817] det_loss_box_dn_5[0.1963] loss_depth[0.0371] total_loss[6.2195]
Rank[0/8] 06/20/2025 21:24:28 INFO stats.py:314 | Epoch[238] Step[354] GlobalStep[93174] Training Speed: 8.34 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 0:08:32.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:24:48 INFO loss_tracker.py:84 | Epoch[238/239] Step[374] GlobalStep[93194/NA]: det_loss_cls_0[0.4989] det_loss_box_0[0.6911] det_loss_cls_1[0.4865] det_loss_box_1[0.5280] det_loss_cls_2[0.4297] det_loss_box_2[0.4849] det_loss_cls_3[0.4035] det_loss_box_3[0.4609] det_loss_cls_4[0.3765] det_loss_box_4[0.4423] det_loss_cls_5[0.3575] det_loss_box_5[0.4402] det_loss_cls_dn_0[0.0932] det_loss_box_dn_0[0.4464] det_loss_cls_dn_1[0.0889] det_loss_box_dn_1[0.3600] det_loss_cls_dn_2[0.0883] det_loss_box_dn_2[0.3356] det_loss_cls_dn_3[0.0872] det_loss_box_dn_3[0.3186] det_loss_cls_dn_4[0.0865] det_loss_box_dn_4[0.3144] det_loss_cls_dn_5[0.0859] det_loss_box_dn_5[0.3087] loss_depth[0.0379] total_loss[8.2516]
Rank[0/8] 06/20/2025 21:24:54 INFO stats.py:314 | Epoch[238] Step[379] GlobalStep[93199] Training Speed: 6.36 samples/sec across all devices.	Average Step Time: 1.26 sec.	Estimated Remaining Time: 0:08:02.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:25:04 INFO stats.py:394 | Epoch[238] completed. Training Speed: 7.63 samples/sec across all devices.	Epoch Time: 408.69 sec.	Average Epoch Time: 408.69 sec.	Average Step Time: 1.05 sec.	Estimated Remaining Time: 0:07:50.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:25:21 INFO stats.py:314 | Epoch[239] Step[14] GlobalStep[93224] Training Speed: 8.23 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 0:07:32.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:25:32 INFO loss_tracker.py:84 | Epoch[239/239] Step[24] GlobalStep[93234/NA]: det_loss_cls_0[0.4037] det_loss_box_0[0.5023] det_loss_cls_1[0.4058] det_loss_box_1[0.3663] det_loss_cls_2[0.3395] det_loss_box_2[0.3312] det_loss_cls_3[0.3109] det_loss_box_3[0.3158] det_loss_cls_4[0.2931] det_loss_box_4[0.3077] det_loss_cls_5[0.2826] det_loss_box_5[0.3039] det_loss_cls_dn_0[0.0853] det_loss_box_dn_0[0.2949] det_loss_cls_dn_1[0.0821] det_loss_box_dn_1[0.2423] det_loss_cls_dn_2[0.0813] det_loss_box_dn_2[0.2259] det_loss_cls_dn_3[0.0799] det_loss_box_dn_3[0.2188] det_loss_cls_dn_4[0.0795] det_loss_box_dn_4[0.2163] det_loss_cls_dn_5[0.0783] det_loss_box_dn_5[0.2135] loss_depth[0.0326] total_loss[6.0934]
Rank[0/8] 06/20/2025 21:25:47 INFO stats.py:314 | Epoch[239] Step[39] GlobalStep[93249] Training Speed: 7.91 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 0:07:01.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:25:58 INFO loss_tracker.py:84 | Epoch[239/239] Step[49] GlobalStep[93259/NA]: det_loss_cls_0[0.3604] det_loss_box_0[0.4471] det_loss_cls_1[0.3549] det_loss_box_1[0.3439] det_loss_cls_2[0.3027] det_loss_box_2[0.3114] det_loss_cls_3[0.2747] det_loss_box_3[0.3056] det_loss_cls_4[0.2612] det_loss_box_4[0.2966] det_loss_cls_5[0.2527] det_loss_box_5[0.2953] det_loss_cls_dn_0[0.0873] det_loss_box_dn_0[0.2502] det_loss_cls_dn_1[0.0828] det_loss_box_dn_1[0.1949] det_loss_cls_dn_2[0.0823] det_loss_box_dn_2[0.1746] det_loss_cls_dn_3[0.0815] det_loss_box_dn_3[0.1709] det_loss_cls_dn_4[0.0807] det_loss_box_dn_4[0.1706] det_loss_cls_dn_5[0.0800] det_loss_box_dn_5[0.1703] loss_depth[0.0332] total_loss[5.4659]
Rank[0/8] 06/20/2025 21:26:14 INFO stats.py:314 | Epoch[239] Step[64] GlobalStep[93274] Training Speed: 8.51 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 0:06:31.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:26:24 INFO loss_tracker.py:84 | Epoch[239/239] Step[74] GlobalStep[93284/NA]: det_loss_cls_0[0.4643] det_loss_box_0[0.5451] det_loss_cls_1[0.4560] det_loss_box_1[0.4110] det_loss_cls_2[0.3900] det_loss_box_2[0.3720] det_loss_cls_3[0.3511] det_loss_box_3[0.3601] det_loss_cls_4[0.3286] det_loss_box_4[0.3485] det_loss_cls_5[0.3163] det_loss_box_5[0.3488] det_loss_cls_dn_0[0.0871] det_loss_box_dn_0[0.2239] det_loss_cls_dn_1[0.0820] det_loss_box_dn_1[0.1813] det_loss_cls_dn_2[0.0806] det_loss_box_dn_2[0.1682] det_loss_cls_dn_3[0.0794] det_loss_box_dn_3[0.1638] det_loss_cls_dn_4[0.0783] det_loss_box_dn_4[0.1615] det_loss_cls_dn_5[0.0775] det_loss_box_dn_5[0.1596] loss_depth[0.0333] total_loss[6.2684]
Rank[0/8] 06/20/2025 21:26:39 INFO stats.py:314 | Epoch[239] Step[89] GlobalStep[93299] Training Speed: 8.72 samples/sec across all devices.	Average Step Time: 0.92 sec.	Estimated Remaining Time: 0:06:01.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:26:50 INFO loss_tracker.py:84 | Epoch[239/239] Step[99] GlobalStep[93309/NA]: det_loss_cls_0[0.5619] det_loss_box_0[0.6980] det_loss_cls_1[0.5449] det_loss_box_1[0.5307] det_loss_cls_2[0.4724] det_loss_box_2[0.4891] det_loss_cls_3[0.4389] det_loss_box_3[0.4626] det_loss_cls_4[0.4199] det_loss_box_4[0.4518] det_loss_cls_5[0.4005] det_loss_box_5[0.4538] det_loss_cls_dn_0[0.0954] det_loss_box_dn_0[0.2378] det_loss_cls_dn_1[0.0924] det_loss_box_dn_1[0.2001] det_loss_cls_dn_2[0.0918] det_loss_box_dn_2[0.1838] det_loss_cls_dn_3[0.0910] det_loss_box_dn_3[0.1816] det_loss_cls_dn_4[0.0900] det_loss_box_dn_4[0.1801] det_loss_cls_dn_5[0.0893] det_loss_box_dn_5[0.1774] loss_depth[0.0375] total_loss[7.6725]
Rank[0/8] 06/20/2025 21:27:05 INFO stats.py:314 | Epoch[239] Step[114] GlobalStep[93324] Training Speed: 8.28 samples/sec across all devices.	Average Step Time: 0.97 sec.	Estimated Remaining Time: 0:05:31.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:27:15 INFO loss_tracker.py:84 | Epoch[239/239] Step[124] GlobalStep[93334/NA]: det_loss_cls_0[0.4788] det_loss_box_0[0.5540] det_loss_cls_1[0.4721] det_loss_box_1[0.4118] det_loss_cls_2[0.4107] det_loss_box_2[0.3775] det_loss_cls_3[0.3659] det_loss_box_3[0.3696] det_loss_cls_4[0.3455] det_loss_box_4[0.3633] det_loss_cls_5[0.3367] det_loss_box_5[0.3577] det_loss_cls_dn_0[0.0952] det_loss_box_dn_0[0.2684] det_loss_cls_dn_1[0.0926] det_loss_box_dn_1[0.2166] det_loss_cls_dn_2[0.0919] det_loss_box_dn_2[0.1993] det_loss_cls_dn_3[0.0911] det_loss_box_dn_3[0.1943] det_loss_cls_dn_4[0.0901] det_loss_box_dn_4[0.1930] det_loss_cls_dn_5[0.0892] det_loss_box_dn_5[0.1874] loss_depth[0.0353] total_loss[6.6881]
Rank[0/8] 06/20/2025 21:27:31 INFO stats.py:314 | Epoch[239] Step[139] GlobalStep[93349] Training Speed: 8.60 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 0:05:01.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:27:42 INFO loss_tracker.py:84 | Epoch[239/239] Step[149] GlobalStep[93359/NA]: det_loss_cls_0[0.4710] det_loss_box_0[0.5647] det_loss_cls_1[0.4758] det_loss_box_1[0.4167] det_loss_cls_2[0.4031] det_loss_box_2[0.3720] det_loss_cls_3[0.3627] det_loss_box_3[0.3603] det_loss_cls_4[0.3415] det_loss_box_4[0.3477] det_loss_cls_5[0.3293] det_loss_box_5[0.3461] det_loss_cls_dn_0[0.0936] det_loss_box_dn_0[0.2395] det_loss_cls_dn_1[0.0900] det_loss_box_dn_1[0.1944] det_loss_cls_dn_2[0.0891] det_loss_box_dn_2[0.1768] det_loss_cls_dn_3[0.0878] det_loss_box_dn_3[0.1726] det_loss_cls_dn_4[0.0868] det_loss_box_dn_4[0.1711] det_loss_cls_dn_5[0.0860] det_loss_box_dn_5[0.1695] loss_depth[0.0367] total_loss[6.4846]
Rank[0/8] 06/20/2025 21:27:58 INFO stats.py:314 | Epoch[239] Step[164] GlobalStep[93374] Training Speed: 7.89 samples/sec across all devices.	Average Step Time: 1.01 sec.	Estimated Remaining Time: 0:04:31.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:28:08 INFO loss_tracker.py:84 | Epoch[239/239] Step[174] GlobalStep[93384/NA]: det_loss_cls_0[0.5249] det_loss_box_0[0.6658] det_loss_cls_1[0.5088] det_loss_box_1[0.5066] det_loss_cls_2[0.4353] det_loss_box_2[0.4675] det_loss_cls_3[0.3862] det_loss_box_3[0.4470] det_loss_cls_4[0.3664] det_loss_box_4[0.4344] det_loss_cls_5[0.3582] det_loss_box_5[0.4346] det_loss_cls_dn_0[0.0836] det_loss_box_dn_0[0.2712] det_loss_cls_dn_1[0.0794] det_loss_box_dn_1[0.2266] det_loss_cls_dn_2[0.0785] det_loss_box_dn_2[0.2089] det_loss_cls_dn_3[0.0775] det_loss_box_dn_3[0.2056] det_loss_cls_dn_4[0.0766] det_loss_box_dn_4[0.2033] det_loss_cls_dn_5[0.0758] det_loss_box_dn_5[0.2020] loss_depth[0.0365] total_loss[7.3614]
Rank[0/8] 06/20/2025 21:28:23 INFO stats.py:314 | Epoch[239] Step[189] GlobalStep[93399] Training Speed: 8.38 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 0:04:01.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:28:33 INFO loss_tracker.py:84 | Epoch[239/239] Step[199] GlobalStep[93409/NA]: det_loss_cls_0[0.4393] det_loss_box_0[0.5446] det_loss_cls_1[0.4234] det_loss_box_1[0.4014] det_loss_cls_2[0.3695] det_loss_box_2[0.3527] det_loss_cls_3[0.3405] det_loss_box_3[0.3432] det_loss_cls_4[0.3185] det_loss_box_4[0.3330] det_loss_cls_5[0.3116] det_loss_box_5[0.3314] det_loss_cls_dn_0[0.0941] det_loss_box_dn_0[0.2815] det_loss_cls_dn_1[0.0906] det_loss_box_dn_1[0.2171] det_loss_cls_dn_2[0.0898] det_loss_box_dn_2[0.1959] det_loss_cls_dn_3[0.0890] det_loss_box_dn_3[0.1862] det_loss_cls_dn_4[0.0881] det_loss_box_dn_4[0.1851] det_loss_cls_dn_5[0.0875] det_loss_box_dn_5[0.1816] loss_depth[0.0357] total_loss[6.3311]
Rank[0/8] 06/20/2025 21:28:49 INFO stats.py:314 | Epoch[239] Step[214] GlobalStep[93424] Training Speed: 8.09 samples/sec across all devices.	Average Step Time: 0.99 sec.	Estimated Remaining Time: 0:03:30.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:28:59 INFO loss_tracker.py:84 | Epoch[239/239] Step[224] GlobalStep[93434/NA]: det_loss_cls_0[0.4755] det_loss_box_0[0.5564] det_loss_cls_1[0.4684] det_loss_box_1[0.3997] det_loss_cls_2[0.3993] det_loss_box_2[0.3578] det_loss_cls_3[0.3578] det_loss_box_3[0.3335] det_loss_cls_4[0.3347] det_loss_box_4[0.3215] det_loss_cls_5[0.3246] det_loss_box_5[0.3200] det_loss_cls_dn_0[0.0912] det_loss_box_dn_0[0.2175] det_loss_cls_dn_1[0.0871] det_loss_box_dn_1[0.1735] det_loss_cls_dn_2[0.0861] det_loss_box_dn_2[0.1564] det_loss_cls_dn_3[0.0852] det_loss_box_dn_3[0.1533] det_loss_cls_dn_4[0.0839] det_loss_box_dn_4[0.1515] det_loss_cls_dn_5[0.0832] det_loss_box_dn_5[0.1506] loss_depth[0.0371] total_loss[6.2060]
Rank[0/8] 06/20/2025 21:29:15 INFO stats.py:314 | Epoch[239] Step[239] GlobalStep[93449] Training Speed: 7.68 samples/sec across all devices.	Average Step Time: 1.04 sec.	Estimated Remaining Time: 0:03:00.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:29:25 INFO loss_tracker.py:84 | Epoch[239/239] Step[249] GlobalStep[93459/NA]: det_loss_cls_0[0.4700] det_loss_box_0[0.5898] det_loss_cls_1[0.4519] det_loss_box_1[0.4260] det_loss_cls_2[0.3796] det_loss_box_2[0.3899] det_loss_cls_3[0.3402] det_loss_box_3[0.3797] det_loss_cls_4[0.3252] det_loss_box_4[0.3710] det_loss_cls_5[0.3101] det_loss_box_5[0.3729] det_loss_cls_dn_0[0.0869] det_loss_box_dn_0[0.2523] det_loss_cls_dn_1[0.0833] det_loss_box_dn_1[0.2046] det_loss_cls_dn_2[0.0820] det_loss_box_dn_2[0.1905] det_loss_cls_dn_3[0.0808] det_loss_box_dn_3[0.1866] det_loss_cls_dn_4[0.0799] det_loss_box_dn_4[0.1832] det_loss_cls_dn_5[0.0789] det_loss_box_dn_5[0.1816] loss_depth[0.0338] total_loss[6.5308]
Rank[0/8] 06/20/2025 21:29:41 INFO stats.py:314 | Epoch[239] Step[264] GlobalStep[93474] Training Speed: 8.30 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 0:02:30.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:29:51 INFO loss_tracker.py:84 | Epoch[239/239] Step[274] GlobalStep[93484/NA]: det_loss_cls_0[0.4088] det_loss_box_0[0.5097] det_loss_cls_1[0.3932] det_loss_box_1[0.3996] det_loss_cls_2[0.3365] det_loss_box_2[0.3608] det_loss_cls_3[0.3132] det_loss_box_3[0.3476] det_loss_cls_4[0.2952] det_loss_box_4[0.3473] det_loss_cls_5[0.2855] det_loss_box_5[0.3393] det_loss_cls_dn_0[0.0908] det_loss_box_dn_0[0.2895] det_loss_cls_dn_1[0.0876] det_loss_box_dn_1[0.2341] det_loss_cls_dn_2[0.0871] det_loss_box_dn_2[0.2126] det_loss_cls_dn_3[0.0860] det_loss_box_dn_3[0.2089] det_loss_cls_dn_4[0.0853] det_loss_box_dn_4[0.2066] det_loss_cls_dn_5[0.0844] det_loss_box_dn_5[0.2035] loss_depth[0.0366] total_loss[6.2498]
Rank[0/8] 06/20/2025 21:30:06 INFO stats.py:314 | Epoch[239] Step[289] GlobalStep[93499] Training Speed: 8.58 samples/sec across all devices.	Average Step Time: 0.93 sec.	Estimated Remaining Time: 0:02:00.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:30:17 INFO loss_tracker.py:84 | Epoch[239/239] Step[299] GlobalStep[93509/NA]: det_loss_cls_0[0.3189] det_loss_box_0[0.4121] det_loss_cls_1[0.3112] det_loss_box_1[0.3138] det_loss_cls_2[0.2610] det_loss_box_2[0.2858] det_loss_cls_3[0.2328] det_loss_box_3[0.2835] det_loss_cls_4[0.2158] det_loss_box_4[0.2698] det_loss_cls_5[0.2039] det_loss_box_5[0.2737] det_loss_cls_dn_0[0.0835] det_loss_box_dn_0[0.2854] det_loss_cls_dn_1[0.0792] det_loss_box_dn_1[0.2306] det_loss_cls_dn_2[0.0782] det_loss_box_dn_2[0.2122] det_loss_cls_dn_3[0.0769] det_loss_box_dn_3[0.2070] det_loss_cls_dn_4[0.0765] det_loss_box_dn_4[0.2039] det_loss_cls_dn_5[0.0755] det_loss_box_dn_5[0.2000] loss_depth[0.0360] total_loss[5.2270]
Rank[0/8] 06/20/2025 21:30:32 INFO stats.py:314 | Epoch[239] Step[314] GlobalStep[93524] Training Speed: 8.55 samples/sec across all devices.	Average Step Time: 0.94 sec.	Estimated Remaining Time: 0:01:30.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:30:43 INFO loss_tracker.py:84 | Epoch[239/239] Step[324] GlobalStep[93534/NA]: det_loss_cls_0[0.5140] det_loss_box_0[0.6680] det_loss_cls_1[0.4940] det_loss_box_1[0.5006] det_loss_cls_2[0.4274] det_loss_box_2[0.4472] det_loss_cls_3[0.3913] det_loss_box_3[0.4297] det_loss_cls_4[0.3657] det_loss_box_4[0.4266] det_loss_cls_5[0.3489] det_loss_box_5[0.4291] det_loss_cls_dn_0[0.0894] det_loss_box_dn_0[0.2893] det_loss_cls_dn_1[0.0858] det_loss_box_dn_1[0.2462] det_loss_cls_dn_2[0.0865] det_loss_box_dn_2[0.2313] det_loss_cls_dn_3[0.0848] det_loss_box_dn_3[0.2276] det_loss_cls_dn_4[0.0848] det_loss_box_dn_4[0.2249] det_loss_cls_dn_5[0.0842] det_loss_box_dn_5[0.2169] loss_depth[0.0349] total_loss[7.4291]
Rank[0/8] 06/20/2025 21:30:58 INFO stats.py:314 | Epoch[239] Step[339] GlobalStep[93549] Training Speed: 8.37 samples/sec across all devices.	Average Step Time: 0.96 sec.	Estimated Remaining Time: 0:01:00.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:31:09 INFO loss_tracker.py:84 | Epoch[239/239] Step[349] GlobalStep[93559/NA]: det_loss_cls_0[0.4382] det_loss_box_0[0.5817] det_loss_cls_1[0.4418] det_loss_box_1[0.4214] det_loss_cls_2[0.3741] det_loss_box_2[0.3855] det_loss_cls_3[0.3450] det_loss_box_3[0.3703] det_loss_cls_4[0.3236] det_loss_box_4[0.3596] det_loss_cls_5[0.3122] det_loss_box_5[0.3587] det_loss_cls_dn_0[0.0903] det_loss_box_dn_0[0.2584] det_loss_cls_dn_1[0.0867] det_loss_box_dn_1[0.2137] det_loss_cls_dn_2[0.0862] det_loss_box_dn_2[0.1988] det_loss_cls_dn_3[0.0854] det_loss_box_dn_3[0.1937] det_loss_cls_dn_4[0.0843] det_loss_box_dn_4[0.1908] det_loss_cls_dn_5[0.0835] det_loss_box_dn_5[0.1895] loss_depth[0.0333] total_loss[6.5069]
Rank[0/8] 06/20/2025 21:31:24 INFO stats.py:314 | Epoch[239] Step[364] GlobalStep[93574] Training Speed: 7.98 samples/sec across all devices.	Average Step Time: 1.00 sec.	Estimated Remaining Time: 0:00:30.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:31:34 INFO loss_tracker.py:84 | Epoch[239/239] Step[374] GlobalStep[93584/NA]: det_loss_cls_0[0.3898] det_loss_box_0[0.4545] det_loss_cls_1[0.3900] det_loss_box_1[0.3429] det_loss_cls_2[0.3266] det_loss_box_2[0.3151] det_loss_cls_3[0.3029] det_loss_box_3[0.3041] det_loss_cls_4[0.2816] det_loss_box_4[0.2930] det_loss_cls_5[0.2719] det_loss_box_5[0.2929] det_loss_cls_dn_0[0.0889] det_loss_box_dn_0[0.2344] det_loss_cls_dn_1[0.0864] det_loss_box_dn_1[0.1926] det_loss_cls_dn_2[0.0853] det_loss_box_dn_2[0.1804] det_loss_cls_dn_3[0.0843] det_loss_box_dn_3[0.1752] det_loss_cls_dn_4[0.0839] det_loss_box_dn_4[0.1747] det_loss_cls_dn_5[0.0831] det_loss_box_dn_5[0.1740] loss_depth[0.0330] total_loss[5.6414]
Rank[0/8] 06/20/2025 21:31:49 INFO stats.py:314 | Epoch[239] Step[389] GlobalStep[93599] Training Speed: 8.39 samples/sec across all devices.	Average Step Time: 0.95 sec.	Estimated Remaining Time: 0:00:00.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:31:49 INFO trainer.py:268 | 
==================================================BEGIN EVAL==================================================
Rank[0/8] 06/20/2025 21:32:48 INFO trainer.py:275 | eval: 10
Rank[0/8] 06/20/2025 21:33:00 INFO trainer.py:275 | eval: 20
Rank[0/8] 06/20/2025 21:33:11 INFO trainer.py:275 | eval: 30
Rank[0/8] 06/20/2025 21:33:20 INFO trainer.py:275 | eval: 40
Rank[0/8] 06/20/2025 21:33:32 INFO trainer.py:275 | eval: 50
Rank[0/8] 06/20/2025 21:33:39 INFO trainer.py:275 | eval: 60
Rank[0/8] 06/20/2025 21:33:46 INFO trainer.py:275 | eval: 70
Rank[0/8] 06/20/2025 21:33:53 INFO trainer.py:275 | eval: 80
Rank[0/8] 06/20/2025 21:34:00 INFO trainer.py:275 | eval: 90
Rank[0/8] 06/20/2025 21:34:08 INFO trainer.py:275 | eval: 100
Rank[0/8] 06/20/2025 21:34:13 INFO metrics.py:428 | number of results: 824
Rank[0/8] 06/20/2025 21:38:29 INFO metrics.py:332 | eval results of overall:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| bathtub             | 0.8424  | 0.8971  | 0.7390  | 0.7647  |
| curtain             | 0.5669  | 0.8358  | 0.1437  | 0.4687  |
| sink                | 0.6893  | 0.8560  | 0.2749  | 0.5103  |
| toilet              | 0.8625  | 0.8978  | 0.7911  | 0.8175  |
| toilet paper        | 0.3801  | 0.5827  | 0.1680  | 0.3386  |
| picture             | 0.2669  | 0.5032  | 0.0284  | 0.1390  |
| bin                 | 0.6521  | 0.8547  | 0.4037  | 0.6369  |
| doorframe           | 0.3193  | 0.7443  | 0.0867  | 0.3958  |
| door                | 0.4249  | 0.7249  | 0.0778  | 0.3154  |
| vanity              | 0.5632  | 0.8444  | 0.4895  | 0.7556  |
| towel               | 0.4202  | 0.6839  | 0.1417  | 0.3419  |
| mirror              | 0.2236  | 0.6196  | 0.0271  | 0.2174  |
| bar                 | 0.1381  | 0.3030  | 0.0083  | 0.0606  |
| stopcock            | 0.1189  | 0.5172  | 0.0007  | 0.0690  |
| switch              | 0.0030  | 0.1447  | 0.0000  | 0.0000  |
| bottle              | 0.0813  | 0.4123  | 0.0037  | 0.0808  |
| bag                 | 0.2741  | 0.6865  | 0.0838  | 0.3514  |
| socket              | 0.0147  | 0.2522  | 0.0001  | 0.0221  |
| hair dryer          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cup                 | 0.1421  | 0.4153  | 0.0143  | 0.0508  |
| rail                | 0.0317  | 0.4062  | 0.0030  | 0.0938  |
| shower              | 0.0967  | 0.3000  | 0.0033  | 0.0667  |
| ledge               | 0.1977  | 0.5000  | 0.0002  | 0.1667  |
| microwave           | 0.6303  | 0.7347  | 0.2999  | 0.4694  |
| rod                 | 0.0251  | 0.0500  | 0.0025  | 0.0250  |
| tissue              | 0.0321  | 0.1538  | 0.0321  | 0.1538  |
| light               | 0.0610  | 0.2991  | 0.0020  | 0.0577  |
| vent                | 0.0145  | 0.2083  | 0.0000  | 0.0000  |
| basket              | 0.3003  | 0.6087  | 0.1647  | 0.3565  |
| door knob           | 0.0833  | 0.5000  | 0.0000  | 0.0000  |
| tissue box          | 0.1402  | 0.5152  | 0.0390  | 0.3333  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| box                 | 0.1681  | 0.5138  | 0.0441  | 0.2163  |
| stool               | 0.3252  | 0.7395  | 0.2283  | 0.5714  |
| mat                 | 0.0457  | 0.2973  | 0.0155  | 0.0811  |
| lamp                | 0.3743  | 0.6716  | 0.1085  | 0.3254  |
| shoe                | 0.2285  | 0.5047  | 0.0242  | 0.1028  |
| case                | 0.1101  | 0.4633  | 0.0620  | 0.2034  |
| blanket             | 0.2698  | 0.7300  | 0.0686  | 0.3800  |
| cabinet             | 0.3040  | 0.7576  | 0.1868  | 0.5457  |
| doorway             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tablet              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| carpet              | 0.1635  | 0.5119  | 0.0147  | 0.1667  |
| faucet              | 0.0421  | 0.2766  | 0.0001  | 0.0426  |
| paper               | 0.0312  | 0.3166  | 0.0011  | 0.0740  |
| soap dish           | 0.5926  | 0.8000  | 0.1007  | 0.2667  |
| bed                 | 0.8557  | 0.9349  | 0.6823  | 0.8343  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| backpack            | 0.5581  | 0.8387  | 0.3627  | 0.5484  |
| shelf               | 0.3442  | 0.7302  | 0.1568  | 0.4335  |
| soap                | 0.1803  | 0.5000  | 0.0064  | 0.1667  |
| decoration          | 0.0429  | 0.4286  | 0.0041  | 0.0857  |
| toothpaste          | 0.0026  | 0.2000  | 0.0000  | 0.0000  |
| drawer              | 0.3392  | 0.6667  | 0.2930  | 0.5000  |
| clothes             | 0.1993  | 0.5764  | 0.0584  | 0.2611  |
| plant               | 0.3298  | 0.7021  | 0.1186  | 0.3372  |
| glass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stall               | 0.3321  | 1.0000  | 0.2066  | 0.6471  |
| sunglasses          | 0.0515  | 0.3077  | 0.0064  | 0.1538  |
| wardrobe            | 0.4352  | 0.7831  | 0.3126  | 0.5663  |
| couch               | 0.5353  | 0.8595  | 0.4120  | 0.7438  |
| dresser             | 0.5998  | 0.8889  | 0.5818  | 0.8148  |
| scale               | 0.2902  | 0.4545  | 0.0937  | 0.1818  |
| frame               | 0.1043  | 0.5714  | 0.0028  | 0.1633  |
| keyboard            | 0.1298  | 0.2791  | 0.0005  | 0.0116  |
| partition           | 0.5253  | 0.8333  | 0.0843  | 0.3333  |
| candle              | 0.1994  | 0.3889  | 0.0020  | 0.1111  |
| purse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| arch                | 0.0345  | 0.1333  | 0.0007  | 0.1000  |
| counter             | 0.2592  | 0.7615  | 0.0731  | 0.3303  |
| screen              | 0.1000  | 0.2000  | 0.0000  | 0.0000  |
| hamper              | 0.4965  | 0.5455  | 0.4965  | 0.5455  |
| mouse               | 0.0025  | 0.0811  | 0.0000  | 0.0000  |
| beam                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| chair               | 0.6007  | 0.8473  | 0.4636  | 0.6912  |
| blackboard          | 0.6704  | 0.8171  | 0.2030  | 0.4390  |
| radiator            | 0.7008  | 0.8000  | 0.1770  | 0.3444  |
| computer            | 0.3607  | 0.6230  | 0.0323  | 0.1967  |
| monitor             | 0.5751  | 0.7283  | 0.1149  | 0.3261  |
| desk                | 0.5809  | 0.9157  | 0.4211  | 0.7360  |
| window              | 0.2806  | 0.6437  | 0.0536  | 0.2560  |
| table               | 0.5230  | 0.8777  | 0.4368  | 0.7696  |
| plug                | 0.0234  | 0.1053  | 0.0000  | 0.0000  |
| telephone           | 0.4145  | 0.6833  | 0.1572  | 0.3333  |
| book                | 0.0655  | 0.4274  | 0.0054  | 0.0922  |
| blinds              | 0.2443  | 0.4800  | 0.0324  | 0.2600  |
| windowsill          | 0.1570  | 0.5870  | 0.0261  | 0.2174  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stand               | 0.5935  | 0.8841  | 0.4510  | 0.7561  |
| jacket              | 0.6116  | 0.7917  | 0.2315  | 0.4583  |
| projector           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| umbrella            | 0.2000  | 0.2000  | 0.2000  | 0.2000  |
| pipe                | 0.0382  | 0.1786  | 0.0002  | 0.0357  |
| pillow              | 0.2749  | 0.6402  | 0.0429  | 0.2273  |
| tv                  | 0.6826  | 0.8713  | 0.1822  | 0.3960  |
| paper cutter        | 0.7857  | 0.8333  | 0.1032  | 0.3333  |
| ottoman             | 0.1851  | 0.6389  | 0.1578  | 0.6111  |
| container           | 0.0103  | 0.2034  | 0.0058  | 0.1017  |
| remote control      | 0.0681  | 0.2222  | 0.0041  | 0.0370  |
| clock               | 0.5044  | 0.7447  | 0.0059  | 0.1277  |
| printer             | 0.5608  | 0.7959  | 0.4029  | 0.6735  |
| stairs              | 0.0898  | 0.4444  | 0.0114  | 0.1667  |
| guitar              | 0.2560  | 0.3333  | 0.0833  | 0.0833  |
| refrigerator        | 0.7392  | 0.8800  | 0.4842  | 0.5600  |
| luggage             | 0.5956  | 1.0000  | 0.5556  | 0.6667  |
| bench               | 0.3035  | 0.7547  | 0.2381  | 0.5849  |
| laptop              | 0.4640  | 0.7391  | 0.2236  | 0.3913  |
| notebook            | 0.0012  | 0.0769  | 0.0000  | 0.0000  |
| kettle              | 0.2142  | 0.7222  | 0.0450  | 0.2778  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coffee maker        | 0.4164  | 0.6207  | 0.0436  | 0.1724  |
| plunger             | 0.8000  | 0.8000  | 0.4500  | 0.6000  |
| brush               | 0.1654  | 0.4286  | 0.0102  | 0.0714  |
| window frame        | 0.0364  | 0.4343  | 0.0042  | 0.1818  |
| countertop          | 0.0329  | 0.2857  | 0.0003  | 0.0714  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillar              | 0.3762  | 0.6977  | 0.2623  | 0.4419  |
| dish rack           | 0.6988  | 1.0000  | 0.3600  | 0.6000  |
| grab bar            | 0.1542  | 0.7500  | 0.0000  | 0.0000  |
| spoon               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toy                 | 0.1600  | 0.4545  | 0.0600  | 0.1515  |
| rack                | 0.2061  | 0.5385  | 0.1135  | 0.4231  |
| bowl                | 0.1158  | 0.4928  | 0.0516  | 0.2029  |
| hat                 | 0.1111  | 0.1111  | 0.0016  | 0.1111  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dispenser      | 0.3718  | 0.7037  | 0.0525  | 0.2593  |
| dispenser           | 0.4912  | 0.6136  | 0.2822  | 0.3864  |
| chandelier          | 0.0159  | 0.0476  | 0.0000  | 0.0000  |
| mailbox             | 0.1080  | 1.0000  | 0.0032  | 0.2000  |
| holder              | 0.2222  | 0.2222  | 0.1111  | 0.1111  |
| alarm               | 0.0063  | 0.1250  | 0.0000  | 0.0000  |
| column              | 0.0114  | 0.3421  | 0.0006  | 0.0526  |
| hanger              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape       | 0.2000  | 0.2000  | 0.0000  | 0.0000  |
| headphones          | 0.4400  | 0.6000  | 0.2000  | 0.2000  |
| thermostat          | 0.1667  | 0.1667  | 0.0000  | 0.0000  |
| teapot              | 0.0139  | 0.1111  | 0.0139  | 0.1111  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kitchenware         | 0.2814  | 0.5652  | 0.0123  | 0.1087  |
| copier              | 0.9028  | 0.9167  | 0.9028  | 0.9167  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster             | 0.4259  | 0.7000  | 0.0358  | 0.2000  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| poster              | 0.0283  | 0.2273  | 0.0000  | 0.0000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| body loofah         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cap                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cube                | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| drum                | 0.0074  | 0.1250  | 0.0000  | 0.0000  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bread               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| board               | 0.1914  | 0.5806  | 0.0523  | 0.2419  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 0.7427  | 1.0000  | 0.7157  | 0.8333  |
| crib                | 0.5000  | 1.0000  | 0.5000  | 1.0000  |
| kitchen island      | 0.2877  | 0.5714  | 0.2738  | 0.4286  |
| mattress            | 0.1111  | 0.3333  | 0.0000  | 0.0000  |
| piano               | 0.2800  | 0.4000  | 0.2800  | 0.4000  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fan                 | 0.6563  | 0.7969  | 0.2053  | 0.4062  |
| magazine            | 0.0012  | 0.2000  | 0.0000  | 0.0000  |
| fireplace           | 0.1508  | 0.4643  | 0.0155  | 0.2143  |
| pool table          | 0.1667  | 0.3333  | 0.1667  | 0.3333  |
| headboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garage door         | 0.2869  | 0.5000  | 0.2858  | 0.5000  |
| balcony             | 0.3409  | 1.0000  | 0.0000  | 0.0000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| roof                | 0.0026  | 0.2917  | 0.0000  | 0.0000  |
| commode             | 0.5412  | 0.8269  | 0.4295  | 0.7308  |
| device              | 0.2970  | 0.5455  | 0.0000  | 0.0000  |
| pen                 | 0.0001  | 0.0667  | 0.0000  | 0.0000  |
| plate               | 0.0363  | 0.4808  | 0.0033  | 0.0962  |
| washing machine     | 0.5209  | 0.7500  | 0.3333  | 0.4286  |
| tube                | 0.0102  | 0.1538  | 0.0000  | 0.0000  |
| statue              | 0.1132  | 0.4737  | 0.1044  | 0.3158  |
| person              | 0.1823  | 0.5000  | 0.0056  | 0.1667  |
| calendar            | 0.0390  | 0.3333  | 0.0000  | 0.0000  |
| vacuum cleaner      | 0.4932  | 0.7143  | 0.0197  | 0.2857  |
| ball                | 0.4507  | 0.5238  | 0.3334  | 0.4286  |
| basin               | 0.0191  | 0.2353  | 0.0006  | 0.0588  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher   | 0.3000  | 0.4000  | 0.3000  | 0.4000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cosmetics           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| barricade           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| heater              | 0.6391  | 0.7931  | 0.1678  | 0.3793  |
| excercise equipment | 0.3818  | 0.6500  | 0.0180  | 0.2000  |
| beanbag             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| treadmill           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet               | 0.4286  | 0.6667  | 0.4286  | 0.6667  |
| footstool           | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| crate               | 0.5429  | 0.5714  | 0.1905  | 0.2857  |
| candlestick         | 0.1111  | 0.6667  | 0.0000  | 0.0000  |
| jalousie            | 1.0000  | 1.0000  | 0.5000  | 0.5000  |
| humidifier          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sign                | 0.0566  | 0.2333  | 0.0000  | 0.0000  |
| water cooler        | 0.7500  | 1.0000  | 0.7500  | 1.0000  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket              | 0.2121  | 0.7179  | 0.0936  | 0.3590  |
| oven                | 0.5108  | 0.8214  | 0.1763  | 0.3929  |
| pot                 | 0.2193  | 0.4444  | 0.0567  | 0.2593  |
| fruit               | 0.1287  | 0.4286  | 0.0811  | 0.2857  |
| jar                 | 0.1688  | 0.4615  | 0.0806  | 0.2308  |
| folder              | 0.0396  | 0.2857  | 0.0238  | 0.0714  |
| pan                 | 0.2029  | 0.5294  | 0.0204  | 0.1176  |
| stove               | 0.5804  | 0.8056  | 0.3099  | 0.5278  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pack                | 0.0484  | 0.7333  | 0.0121  | 0.4000  |
| food                | 0.0909  | 0.0909  | 0.0000  | 0.0000  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| garbage             | 0.7583  | 0.8750  | 0.6099  | 0.7500  |
| cushion             | 0.2497  | 0.5490  | 0.0257  | 0.2353  |
| flush               | 0.8333  | 1.0000  | 0.0000  | 0.0000  |
| glove               | 1.0000  | 1.0000  | 0.1667  | 0.3333  |
| radio               | 0.2500  | 0.2500  | 0.2500  | 0.2500  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sponge              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sack                | 0.5516  | 0.8571  | 0.3788  | 0.7143  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| player              | 0.2500  | 0.2500  | 0.2500  | 0.2500  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| map                 | 0.0333  | 0.5000  | 0.0333  | 0.5000  |
| can                 | 0.0429  | 0.1667  | 0.0278  | 0.0833  |
| wine                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| salt                | 1.0000  | 1.0000  | 0.0556  | 0.5000  |
| ladder              | 0.2068  | 0.4706  | 0.0595  | 0.1765  |
| napkins             | 0.2667  | 0.6667  | 0.1111  | 0.3333  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| roll                | 0.1851  | 0.5000  | 0.0000  | 0.0000  |
| menu                | 0.6508  | 0.7143  | 0.0476  | 0.1429  |
| dumbbell            | 0.6667  | 0.6667  | 0.1667  | 0.3333  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| broom               | 0.0331  | 0.3571  | 0.0000  | 0.0000  |
| toiletry            | 0.1402  | 0.3913  | 0.0020  | 0.0435  |
| price tag           | 0.1491  | 0.2857  | 0.0000  | 0.0000  |
| mop                 | 0.9167  | 1.0000  | 0.1667  | 0.3333  |
| helmets             | 0.0167  | 0.3333  | 0.0167  | 0.3333  |
| machine             | 0.5814  | 0.7895  | 0.4108  | 0.6316  |
| steps               | 0.0010  | 0.3000  | 0.0001  | 0.1000  |
| vase                | 0.1632  | 0.3800  | 0.0580  | 0.2000  |
| flowerpot           | 0.0537  | 0.3137  | 0.0005  | 0.0980  |
| range hood          | 0.4607  | 0.7857  | 0.0690  | 0.2143  |
| speaker             | 0.1152  | 0.3182  | 0.0103  | 0.1364  |
| air conditioner     | 0.6000  | 0.6000  | 0.4000  | 0.4000  |
| file                | 0.0634  | 0.2353  | 0.0161  | 0.1176  |
| clothes dryer       | 0.4359  | 0.8667  | 0.3818  | 0.8000  |
| conducting wire     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| album               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| letter              | 0.6262  | 1.0000  | 0.0500  | 0.2500  |
| shampoo             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.2600  | 0.3000  | 0.2000  | 0.2000  |
| water heater        | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| handle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coil                | 0.3333  | 0.3333  | 0.0000  | 0.0000  |
| washbasin           | 0.0154  | 0.2000  | 0.0153  | 0.1333  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| overall             | 0.2501  | 0.4404  | 0.1180  | 0.2278  |
| head                | 0.3253  | 0.6113  | 0.1541  | 0.3319  |
| common              | 0.2232  | 0.3896  | 0.1150  | 0.2070  |
| tail                | 0.1982  | 0.3104  | 0.0818  | 0.1370  |
| small               | 0.0953  | 0.2371  | 0.0138  | 0.0577  |
| medium              | 0.2558  | 0.4886  | 0.1349  | 0.2582  |
| large               | 0.1635  | 0.5100  | 0.0945  | 0.3356  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 21:38:29 INFO metrics.py:437 | number of scannet results: 250
Rank[0/8] 06/20/2025 21:39:44 INFO metrics.py:332 | eval results of scannet:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| bathtub             | 0.9391  | 0.9688  | 0.8750  | 0.8750  |
| curtain             | 0.6457  | 0.8496  | 0.1737  | 0.4425  |
| sink                | 0.8398  | 0.9091  | 0.3596  | 0.5568  |
| toilet              | 0.9351  | 0.9643  | 0.8497  | 0.8571  |
| toilet paper        | 0.4493  | 0.5823  | 0.2237  | 0.3671  |
| picture             | 0.3584  | 0.5634  | 0.0606  | 0.1754  |
| bin                 | 0.6749  | 0.8468  | 0.4159  | 0.6255  |
| doorframe           | 0.4023  | 0.7887  | 0.1028  | 0.3732  |
| door                | 0.6097  | 0.8361  | 0.1618  | 0.4582  |
| vanity              | 0.7496  | 0.9667  | 0.6649  | 0.9000  |
| towel               | 0.5392  | 0.7355  | 0.1831  | 0.3636  |
| mirror              | 0.3579  | 0.6386  | 0.0301  | 0.1807  |
| bar                 | 0.1643  | 0.3636  | 0.0114  | 0.0455  |
| stopcock            | 0.1189  | 0.5172  | 0.0007  | 0.0690  |
| switch              | 0.0063  | 0.1899  | 0.0000  | 0.0000  |
| bottle              | 0.1179  | 0.4211  | 0.0038  | 0.0895  |
| bag                 | 0.1992  | 0.6301  | 0.1159  | 0.3836  |
| socket              | 0.0163  | 0.2718  | 0.0001  | 0.0243  |
| hair dryer          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cup                 | 0.2232  | 0.4493  | 0.0310  | 0.0870  |
| rail                | 0.0823  | 0.6667  | 0.0526  | 0.6667  |
| shower              | 0.1074  | 0.2857  | 0.0005  | 0.0714  |
| ledge               | 0.1977  | 0.5000  | 0.0002  | 0.1667  |
| microwave           | 0.6533  | 0.7500  | 0.2417  | 0.4500  |
| rod                 | 0.0718  | 0.1429  | 0.0079  | 0.0714  |
| tissue              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| light               | 0.1532  | 0.5172  | 0.0010  | 0.0345  |
| basket              | 0.1058  | 0.2308  | 0.1049  | 0.1538  |
| tissue box          | 0.2517  | 0.5455  | 0.1143  | 0.3636  |
| toothbrush          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| box                 | 0.1193  | 0.4167  | 0.0252  | 0.1548  |
| stool               | 0.3297  | 0.7500  | 0.2320  | 0.6136  |
| mat                 | 0.1094  | 0.3000  | 0.0250  | 0.1000  |
| lamp                | 0.4785  | 0.7619  | 0.2801  | 0.4762  |
| shoe                | 0.2309  | 0.5000  | 0.0382  | 0.1500  |
| case                | 0.6688  | 1.0000  | 0.5614  | 0.8947  |
| blanket             | 0.0623  | 0.5000  | 0.0143  | 0.2500  |
| cabinet             | 0.3514  | 0.7579  | 0.1915  | 0.5119  |
| carpet              | 0.0096  | 0.4000  | 0.0000  | 0.0000  |
| faucet              | 0.0009  | 0.1250  | 0.0009  | 0.1250  |
| paper               | 0.0279  | 0.3094  | 0.0007  | 0.0717  |
| soap dish           | 0.7425  | 0.9167  | 0.1271  | 0.3333  |
| bed                 | 0.9799  | 1.0000  | 0.9225  | 0.9649  |
| backpack            | 0.6497  | 0.8929  | 0.5340  | 0.6786  |
| shelf               | 0.5388  | 0.8015  | 0.3571  | 0.6250  |
| decoration          | 0.0426  | 0.2917  | 0.0003  | 0.0417  |
| toothpaste          | 0.0032  | 0.2000  | 0.0000  | 0.0000  |
| drawer              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes             | 0.2059  | 0.5556  | 0.0796  | 0.3007  |
| plant               | 0.2966  | 0.6891  | 0.1242  | 0.3193  |
| stall               | 0.3326  | 1.0000  | 0.2069  | 0.6471  |
| wardrobe            | 0.4737  | 0.8750  | 0.3750  | 0.3750  |
| couch               | 0.7472  | 0.9625  | 0.5713  | 0.8500  |
| dresser             | 0.7616  | 0.9730  | 0.7558  | 0.9459  |
| scale               | 0.2500  | 0.2500  | 0.0312  | 0.2500  |
| frame               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| keyboard            | 0.1186  | 0.2414  | 0.0013  | 0.0172  |
| purse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| counter             | 0.5679  | 0.9143  | 0.2139  | 0.5143  |
| hamper              | 0.5500  | 0.6000  | 0.5500  | 0.6000  |
| mouse               | 0.0066  | 0.1429  | 0.0000  | 0.0000  |
| chair               | 0.6473  | 0.8590  | 0.4649  | 0.6762  |
| blackboard          | 0.6929  | 0.8261  | 0.2098  | 0.4493  |
| radiator            | 0.7952  | 0.8621  | 0.2225  | 0.3966  |
| computer            | 0.3782  | 0.6410  | 0.0451  | 0.2051  |
| monitor             | 0.6066  | 0.7286  | 0.1939  | 0.3929  |
| desk                | 0.6919  | 0.9747  | 0.4837  | 0.7342  |
| window              | 0.5334  | 0.8333  | 0.1867  | 0.4691  |
| table               | 0.6115  | 0.9115  | 0.5094  | 0.8038  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| telephone           | 0.5451  | 0.7812  | 0.1957  | 0.4062  |
| book                | 0.0636  | 0.4190  | 0.0054  | 0.0929  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| windowsill          | 0.1698  | 0.3333  | 0.0016  | 0.1667  |
| pad                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stand               | 0.8070  | 0.9057  | 0.6337  | 0.7925  |
| jacket              | 0.6404  | 0.8261  | 0.2426  | 0.4783  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pipe                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillow              | 0.4053  | 0.6944  | 0.1522  | 0.4444  |
| tv                  | 0.8287  | 0.8696  | 0.2337  | 0.3913  |
| paper cutter        | 0.7333  | 0.8000  | 0.0333  | 0.2000  |
| ottoman             | 0.2025  | 1.0000  | 0.0715  | 0.8571  |
| container           | 0.0044  | 0.2174  | 0.0018  | 0.1304  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clock               | 0.4966  | 0.7200  | 0.0148  | 0.1600  |
| printer             | 0.5828  | 0.8182  | 0.5656  | 0.7576  |
| stairs              | 0.1170  | 0.8000  | 0.0313  | 0.4000  |
| guitar              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| refrigerator        | 0.8992  | 0.9500  | 0.7292  | 0.8000  |
| bench               | 0.6533  | 1.0000  | 0.6533  | 1.0000  |
| laptop              | 0.4937  | 0.6000  | 0.2630  | 0.3333  |
| notebook            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| kettle              | 0.1786  | 0.7500  | 0.0625  | 0.2500  |
| scissors            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coffee maker        | 0.5216  | 0.6471  | 0.0756  | 0.1765  |
| plunger             | 0.8000  | 0.8000  | 0.4500  | 0.6000  |
| brush               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillar              | 0.0053  | 0.2000  | 0.0000  | 0.0000  |
| dish rack           | 0.7292  | 1.0000  | 0.5625  | 0.7500  |
| grab bar            | 0.1542  | 0.7500  | 0.0000  | 0.0000  |
| toy                 | 0.0883  | 0.3529  | 0.0784  | 0.1176  |
| rack                | 0.0050  | 0.1818  | 0.0002  | 0.0909  |
| bowl                | 0.2942  | 0.4444  | 0.1111  | 0.2222  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wrap                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dispenser      | 0.3205  | 0.6667  | 0.0676  | 0.2857  |
| dispenser           | 0.5888  | 0.6667  | 0.3355  | 0.4359  |
| mailbox             | 0.1080  | 1.0000  | 0.0032  | 0.2000  |
| holder              | 0.2000  | 0.2000  | 0.2000  | 0.2000  |
| alarm               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stapler             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| headphones          | 0.4545  | 0.6000  | 0.2000  | 0.2000  |
| teapot              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| copier              | 0.9028  | 0.9167  | 0.9028  | 0.9167  |
| toaster             | 0.1731  | 0.7500  | 0.0192  | 0.2500  |
| shovel              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| poster              | 0.0233  | 1.0000  | 0.0000  | 0.0000  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| board               | 0.0749  | 0.5625  | 0.0055  | 0.3125  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| mattress            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fan                 | 0.7760  | 0.8750  | 0.1607  | 0.2500  |
| pen                 | 0.0006  | 0.1111  | 0.0000  | 0.0000  |
| plate               | 0.0090  | 0.4000  | 0.0000  | 0.0000  |
| washing machine     | 0.7071  | 1.0000  | 0.6000  | 0.6667  |
| tube                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| person              | 0.1823  | 0.5000  | 0.0056  | 0.1667  |
| calendar            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| vacuum cleaner      | 0.3333  | 0.6667  | 0.3333  | 0.6667  |
| ball                | 0.4167  | 0.4167  | 0.2500  | 0.2500  |
| basin               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dvd                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| barricade           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| excercise equipment | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coat hanger         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| crate               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sign                | 0.0406  | 0.1786  | 0.0000  | 0.0000  |
| water cooler        | 0.7500  | 1.0000  | 0.7500  | 1.0000  |
| pitcher             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket              | 0.1295  | 0.6429  | 0.0141  | 0.2143  |
| oven                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pot                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stove               | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| structure           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cushion             | 0.0115  | 0.5000  | 0.0030  | 0.2500  |
| map                 | 0.0769  | 1.0000  | 0.0769  | 1.0000  |
| wire                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ladder              | 0.1250  | 0.4000  | 0.0250  | 0.2000  |
| label               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| broom               | 0.0473  | 0.4167  | 0.0000  | 0.0000  |
| machine             | 0.6352  | 0.8750  | 0.4253  | 0.6875  |
| flowerpot           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.7083  | 1.0000  | 0.3000  | 0.6000  |
| speaker             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes dryer       | 0.4500  | 1.0000  | 0.4500  | 1.0000  |
| eraser              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| scannet             | 0.2742  | 0.4612  | 0.1553  | 0.2737  |
| head                | 0.3734  | 0.6116  | 0.2093  | 0.3690  |
| common              | 0.2102  | 0.3359  | 0.1203  | 0.1984  |
| tail                | 0.0845  | 0.2367  | 0.0520  | 0.1220  |
| small               | 0.0670  | 0.1805  | 0.0122  | 0.0438  |
| medium              | 0.3240  | 0.5522  | 0.1876  | 0.3340  |
| large               | 0.3085  | 0.7841  | 0.2069  | 0.5568  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 21:39:44 INFO metrics.py:437 | number of 3rscan results: 235
Rank[0/8] 06/20/2025 21:40:52 INFO metrics.py:332 | eval results of 3rscan:
+-------------------+---------+---------+---------+---------+
| classes           | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+-------------------+---------+---------+---------+---------+
| chair             | 0.6999  | 0.8503  | 0.5809  | 0.7313  |
| monitor           | 0.6275  | 0.7500  | 0.0117  | 0.1000  |
| keyboard          | 0.1874  | 0.4167  | 0.0000  | 0.0000  |
| mouse             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| desk              | 0.5266  | 0.8875  | 0.3999  | 0.7500  |
| cup               | 0.0841  | 0.4103  | 0.0000  | 0.0000  |
| case              | 0.0508  | 0.4214  | 0.0098  | 0.1143  |
| computer          | 0.3618  | 0.6190  | 0.0166  | 0.1905  |
| paper             | 0.0833  | 0.3571  | 0.0052  | 0.0857  |
| stand             | 0.5591  | 0.9344  | 0.3742  | 0.7213  |
| plate             | 0.0491  | 0.4545  | 0.0066  | 0.1212  |
| bag               | 0.3757  | 0.7677  | 0.0924  | 0.3636  |
| garbage           | 0.7593  | 0.8750  | 0.6107  | 0.7500  |
| bottle            | 0.0542  | 0.3388  | 0.0011  | 0.0496  |
| pen               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| window            | 0.3071  | 0.7260  | 0.0281  | 0.1986  |
| wardrobe          | 0.5128  | 0.8281  | 0.3564  | 0.6406  |
| curtain           | 0.6155  | 0.9127  | 0.1725  | 0.5000  |
| plug              | 0.0889  | 0.2222  | 0.0000  | 0.0000  |
| table             | 0.5972  | 0.9054  | 0.4921  | 0.7973  |
| backpack          | 0.5014  | 0.8182  | 0.2471  | 0.4545  |
| bin               | 0.7507  | 0.9146  | 0.5096  | 0.7439  |
| cabinet           | 0.4709  | 0.8376  | 0.3189  | 0.6667  |
| book              | 0.1088  | 0.4852  | 0.0088  | 0.1006  |
| box               | 0.2163  | 0.5534  | 0.0642  | 0.2288  |
| socket            | 0.0104  | 0.0625  | 0.0000  | 0.0000  |
| blinds            | 0.3135  | 0.6154  | 0.0416  | 0.3333  |
| plant             | 0.3891  | 0.6584  | 0.1252  | 0.3218  |
| telephone         | 0.3346  | 0.5417  | 0.1726  | 0.2500  |
| notebook          | 0.0014  | 0.0909  | 0.0000  | 0.0000  |
| file              | 0.0770  | 0.2857  | 0.0195  | 0.1429  |
| sink              | 0.6952  | 0.8214  | 0.1877  | 0.3750  |
| picture           | 0.2273  | 0.4634  | 0.0179  | 0.0976  |
| clock             | 0.5906  | 0.7895  | 0.0000  | 0.0000  |
| shelf             | 0.3337  | 0.7562  | 0.1113  | 0.3993  |
| light             | 0.1145  | 0.4773  | 0.0048  | 0.0909  |
| couch             | 0.5487  | 0.8286  | 0.4396  | 0.7571  |
| switch            | 0.0017  | 0.0566  | 0.0000  | 0.0000  |
| laptop            | 0.5549  | 1.0000  | 0.3125  | 0.5000  |
| conducting wire   | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tablet            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tv                | 0.7554  | 0.9714  | 0.2932  | 0.5429  |
| commode           | 0.5419  | 0.8269  | 0.4301  | 0.7308  |
| lamp              | 0.3444  | 0.6062  | 0.0518  | 0.1875  |
| frame             | 0.1942  | 0.8333  | 0.0065  | 0.2333  |
| pillow            | 0.4264  | 0.7305  | 0.0482  | 0.2109  |
| decoration        | 0.1783  | 0.5000  | 0.0174  | 0.0625  |
| candle            | 0.2459  | 0.4000  | 0.0023  | 0.0667  |
| price tag         | 0.1491  | 0.2857  | 0.0000  | 0.0000  |
| bed               | 0.9134  | 0.9818  | 0.6955  | 0.8182  |
| bowl              | 0.1871  | 0.6296  | 0.1009  | 0.2222  |
| stool             | 0.5029  | 0.8889  | 0.3581  | 0.6852  |
| blanket           | 0.4049  | 0.8154  | 0.1097  | 0.4462  |
| folder            | 0.0396  | 0.2857  | 0.0238  | 0.0714  |
| cushion           | 0.4205  | 0.8000  | 0.0430  | 0.3000  |
| vase              | 0.3219  | 0.4000  | 0.1250  | 0.1500  |
| clothes           | 0.3004  | 0.7184  | 0.0681  | 0.2816  |
| door              | 0.4034  | 0.7059  | 0.0873  | 0.3309  |
| carpet            | 0.2731  | 0.6429  | 0.0255  | 0.2500  |
| remote control    | 0.1017  | 0.3000  | 0.0063  | 0.0500  |
| napkins           | 0.2667  | 0.6667  | 0.1111  | 0.3333  |
| rack              | 0.4428  | 0.7333  | 0.2373  | 0.6000  |
| flowerpot         | 0.0959  | 0.3750  | 0.0004  | 0.0833  |
| bar               | 0.6667  | 0.6667  | 0.0119  | 0.3333  |
| towel             | 0.3478  | 0.7215  | 0.1205  | 0.3165  |
| bucket            | 0.6688  | 1.0000  | 0.3583  | 0.6000  |
| bench             | 0.4278  | 0.8571  | 0.3974  | 0.7143  |
| jar               | 0.2750  | 0.6250  | 0.1302  | 0.2500  |
| teapot            | 0.0500  | 0.2500  | 0.0500  | 0.2500  |
| player            | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| ottoman           | 0.6518  | 0.9091  | 0.6518  | 0.9091  |
| magazine          | 0.0015  | 0.2500  | 0.0000  | 0.0000  |
| printer           | 0.8021  | 0.8889  | 0.3587  | 0.6667  |
| toy               | 0.5024  | 0.6923  | 0.2128  | 0.3077  |
| counter           | 0.2079  | 0.8293  | 0.0456  | 0.3415  |
| cube              | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| poster            | 0.0383  | 0.2222  | 0.0000  | 0.0000  |
| roll              | 0.1851  | 0.5000  | 0.0000  | 0.0000  |
| blackboard        | 0.6103  | 0.8333  | 0.2202  | 0.4167  |
| heater            | 0.6412  | 0.7895  | 0.1660  | 0.3684  |
| humidifier        | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| windowsill        | 0.1715  | 0.6410  | 0.0332  | 0.2308  |
| device            | 0.2970  | 0.5455  | 0.0000  | 0.0000  |
| fan               | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| doorframe         | 0.2622  | 0.7200  | 0.0222  | 0.2400  |
| ball              | 0.6000  | 0.6000  | 0.6000  | 0.6000  |
| board             | 0.3960  | 0.6667  | 0.1317  | 0.1667  |
| umbrella          | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| brush             | 0.2881  | 0.6250  | 0.0179  | 0.1250  |
| container         | 0.1908  | 0.5556  | 0.1556  | 0.2222  |
| speaker           | 0.1756  | 0.3333  | 0.0134  | 0.1667  |
| pad               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drawer            | 0.7870  | 1.0000  | 0.6501  | 0.8889  |
| radiator          | 0.5546  | 0.7241  | 0.1145  | 0.2759  |
| mirror            | 0.3645  | 0.6667  | 0.1209  | 0.2963  |
| shoe              | 0.3356  | 0.6190  | 0.0270  | 0.1190  |
| pot               | 0.5608  | 0.6667  | 0.1411  | 0.3333  |
| hanger            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| adhesive tape     | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| stairs            | 0.4739  | 0.7143  | 0.0647  | 0.4286  |
| basket            | 0.5341  | 0.8125  | 0.3236  | 0.5312  |
| air conditioner   | 0.7500  | 0.7500  | 0.5000  | 0.5000  |
| rail              | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| kettle            | 0.2992  | 0.8333  | 0.0619  | 0.3333  |
| tissue box        | 0.0667  | 0.3333  | 0.0667  | 0.3333  |
| calendar          | 0.0635  | 0.4000  | 0.0000  | 0.0000  |
| balcony           | 0.3409  | 1.0000  | 0.0000  | 0.0000  |
| microwave         | 0.9365  | 1.0000  | 0.5835  | 0.7059  |
| album             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dresser           | 0.2845  | 1.0000  | 0.2500  | 0.5000  |
| guitar            | 1.0000  | 1.0000  | 0.5000  | 0.5000  |
| basin             | 0.0370  | 0.2667  | 0.0019  | 0.0667  |
| tissue            | 0.2500  | 0.6667  | 0.2500  | 0.6667  |
| refrigerator      | 0.7096  | 0.8667  | 0.5506  | 0.6000  |
| window frame      | 0.1729  | 0.5833  | 0.0011  | 0.0833  |
| scale             | 0.6871  | 1.0000  | 0.3333  | 0.3333  |
| bicycle           | 0.8333  | 1.0000  | 0.8333  | 1.0000  |
| fruit             | 0.3533  | 0.6000  | 0.2033  | 0.4000  |
| sunglasses        | 0.0520  | 0.3077  | 0.0066  | 0.1538  |
| hat               | 0.1429  | 0.1429  | 0.0046  | 0.1429  |
| letter            | 0.6262  | 1.0000  | 0.0500  | 0.2500  |
| furniture         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bathtub           | 1.0000  | 1.0000  | 0.6914  | 0.7778  |
| clothes dryer     | 0.5398  | 0.9000  | 0.4636  | 0.8000  |
| statue            | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| shampoo           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toilet            | 0.8235  | 0.8824  | 0.7537  | 0.7647  |
| faucet            | 0.4667  | 0.6667  | 0.0000  | 0.0000  |
| shower            | 0.4992  | 1.0000  | 0.0833  | 0.2500  |
| toilet paper      | 0.1347  | 0.3333  | 0.0049  | 0.0833  |
| beam              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tube              | 0.0318  | 0.2857  | 0.0000  | 0.0000  |
| toiletry          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| dispenser         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap              | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| washing machine   | 0.5032  | 0.6429  | 0.3509  | 0.4286  |
| body loofah       | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| beanbag           | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| cosmetics         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jalousie          | 1.0000  | 1.0000  | 0.5000  | 0.5000  |
| can               | 0.0644  | 0.2500  | 0.0417  | 0.1250  |
| column            | 0.0226  | 1.0000  | 0.0000  | 0.0000  |
| candlestick       | 0.0909  | 1.0000  | 0.0000  | 0.0000  |
| knife             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| spoon             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pillar            | 0.6347  | 0.7647  | 0.5393  | 0.6471  |
| oven              | 0.7035  | 1.0000  | 0.2690  | 0.5789  |
| pan               | 0.2823  | 0.6667  | 0.0292  | 0.1667  |
| stove             | 0.4682  | 0.8235  | 0.0295  | 0.2941  |
| shovel            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coffee maker      | 0.3039  | 0.7778  | 0.0075  | 0.2222  |
| kitchenware       | 0.3158  | 0.5909  | 0.0138  | 0.1136  |
| food              | 0.1000  | 0.1000  | 0.0000  | 0.0000  |
| partition         | 0.6304  | 1.0000  | 0.1012  | 0.4000  |
| radio             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| sign              | 0.5435  | 1.0000  | 0.0000  | 0.0000  |
| crib              | 0.5152  | 1.0000  | 0.5152  | 1.0000  |
| piano             | 0.3333  | 0.3333  | 0.3333  | 0.3333  |
| fireplace         | 0.2000  | 0.2000  | 0.0667  | 0.2000  |
| bidet             | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| mattress          | 0.1667  | 0.5000  | 0.0000  | 0.0000  |
| treadmill         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell          | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| screen            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sponge            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| holder            | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| broom             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| chandelier        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hook              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| salt              | 1.0000  | 1.0000  | 0.0556  | 0.5000  |
| glove             | 1.0000  | 1.0000  | 0.1667  | 0.3333  |
| pack              | 0.0559  | 0.8462  | 0.0140  | 0.4615  |
| seasoning         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher        | 0.9167  | 1.0000  | 0.6667  | 0.6667  |
| crate             | 0.7600  | 0.8000  | 0.2667  | 0.4000  |
| dish rack         | 1.0000  | 1.0000  | 0.0000  | 0.0000  |
| flush             | 0.8333  | 1.0000  | 0.0000  | 0.0000  |
| vacuum cleaner    | 0.7500  | 0.7500  | 0.0000  | 0.0000  |
| menu              | 0.6508  | 0.7143  | 0.0476  | 0.1429  |
| water heater      | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| mop               | 1.0000  | 1.0000  | 0.1667  | 0.3333  |
| bread             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sack              | 0.5516  | 0.8571  | 0.3788  | 0.7143  |
| range hood        | 0.5771  | 0.8000  | 0.0000  | 0.0000  |
| toaster           | 1.0000  | 1.0000  | 0.0833  | 0.2500  |
| glass             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pipe              | 0.2747  | 1.0000  | 0.0025  | 0.2500  |
| wire              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tray              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ladder            | 0.4081  | 1.0000  | 0.3333  | 0.3333  |
| luggage           | 0.6222  | 1.0000  | 0.5556  | 0.6667  |
| soap dish         | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| mat               | 0.1667  | 0.3333  | 0.1667  | 0.3333  |
| helmets           | 0.0196  | 0.3333  | 0.0196  | 0.3333  |
| coil              | 0.3333  | 0.3333  | 0.0000  | 0.0000  |
| tool              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| soap dispenser    | 0.8048  | 1.0000  | 0.0333  | 0.2000  |
| machine           | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| washbasin         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| map               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| paper cutter      | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| footstool         | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| fire extinguisher | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| toothbrush        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| scissors          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------           | ------- | ------- | ------- | ------- |
| 3rscan            | 0.3922  | 0.5918  | 0.1781  | 0.2895  |
| head              | 0.3925  | 0.6733  | 0.1729  | 0.3363  |
| common            | 0.4376  | 0.5949  | 0.2150  | 0.2965  |
| tail              | 0.3304  | 0.4380  | 0.1377  | 0.1939  |
| small             | 0.1446  | 0.3313  | 0.0233  | 0.0725  |
| medium            | 0.4097  | 0.6530  | 0.2040  | 0.3292  |
| large             | 0.3028  | 0.7500  | 0.1305  | 0.4941  |
+-------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 21:40:52 INFO metrics.py:437 | number of matterport3d results: 339
Rank[0/8] 06/20/2025 21:42:23 INFO metrics.py:332 | eval results of matterport3d:
+---------------------+---------+---------+---------+---------+
| classes             | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+---------------------+---------+---------+---------+---------+
| picture             | 0.2463  | 0.4879  | 0.0196  | 0.1401  |
| mirror              | 0.0986  | 0.5811  | 0.0191  | 0.2297  |
| door                | 0.2977  | 0.6449  | 0.0219  | 0.1984  |
| doorframe           | 0.3098  | 0.7309  | 0.0996  | 0.4248  |
| light               | 0.0448  | 0.2365  | 0.0018  | 0.0513  |
| towel               | 0.4061  | 0.6000  | 0.1556  | 0.3364  |
| sink                | 0.5356  | 0.8283  | 0.2855  | 0.5455  |
| drawer              | 0.0014  | 0.3750  | 0.0006  | 0.1250  |
| plant               | 0.3321  | 0.7946  | 0.1368  | 0.3839  |
| blanket             | 0.2223  | 0.7273  | 0.0176  | 0.2727  |
| case                | 0.0003  | 0.2222  | 0.0002  | 0.1667  |
| lamp                | 0.3721  | 0.7033  | 0.1365  | 0.4286  |
| toilet              | 0.8119  | 0.8438  | 0.7438  | 0.7969  |
| vase                | 0.0867  | 0.3667  | 0.0161  | 0.2333  |
| roof                | 0.0026  | 0.2917  | 0.0000  | 0.0000  |
| window              | 0.1601  | 0.5250  | 0.0209  | 0.1833  |
| counter             | 0.0392  | 0.5152  | 0.0011  | 0.1212  |
| decoration          | 0.0124  | 0.4405  | 0.0009  | 0.1071  |
| table               | 0.3302  | 0.7821  | 0.2911  | 0.6731  |
| bench               | 0.2130  | 0.5500  | 0.0944  | 0.3000  |
| cabinet             | 0.1317  | 0.6780  | 0.0780  | 0.4619  |
| vent                | 0.0169  | 0.2083  | 0.0000  | 0.0000  |
| bathtub             | 0.6729  | 0.7778  | 0.5600  | 0.6296  |
| bottle              | 0.1178  | 0.5625  | 0.0349  | 0.1250  |
| shelf               | 0.1509  | 0.6058  | 0.0507  | 0.3139  |
| stand               | 0.4195  | 0.8000  | 0.4105  | 0.7600  |
| switch              | 0.0003  | 0.2000  | 0.0000  | 0.0000  |
| plate               | 0.0378  | 0.5714  | 0.0001  | 0.0714  |
| pillow              | 0.0853  | 0.5426  | 0.0064  | 0.1420  |
| box                 | 0.0531  | 0.6346  | 0.0151  | 0.4038  |
| countertop          | 0.0329  | 0.2857  | 0.0003  | 0.0714  |
| frame               | 0.0004  | 0.1765  | 0.0000  | 0.0588  |
| handle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| curtain             | 0.4077  | 0.7188  | 0.1005  | 0.4583  |
| carpet              | 0.0006  | 0.1667  | 0.0000  | 0.0000  |
| mat                 | 0.0076  | 0.2917  | 0.0000  | 0.0417  |
| cup                 | 0.0014  | 0.2000  | 0.0000  | 0.0000  |
| flowerpot           | 0.0231  | 0.2800  | 0.0008  | 0.1200  |
| paper               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| couch               | 0.3408  | 0.7935  | 0.2780  | 0.6413  |
| chair               | 0.4457  | 0.8027  | 0.3336  | 0.6701  |
| tv                  | 0.5665  | 0.7907  | 0.0803  | 0.2791  |
| bed                 | 0.6805  | 0.8246  | 0.3821  | 0.7193  |
| bowl                | 0.1148  | 0.3939  | 0.0311  | 0.1818  |
| statue              | 0.0905  | 0.4706  | 0.0807  | 0.2941  |
| book                | 0.0769  | 0.3780  | 0.0094  | 0.0787  |
| stool               | 0.0281  | 0.3333  | 0.0095  | 0.1905  |
| stairs              | 0.0102  | 0.3396  | 0.0037  | 0.0755  |
| ottoman             | 0.0249  | 0.3333  | 0.0249  | 0.3333  |
| clock               | 0.1842  | 0.6667  | 0.1842  | 0.6667  |
| telephone           | 0.2589  | 0.7500  | 0.0025  | 0.2500  |
| alarm               | 0.0104  | 0.1667  | 0.0000  | 0.0000  |
| screen              | 0.2500  | 0.2500  | 0.0000  | 0.0000  |
| toy                 | 0.0943  | 0.4167  | 0.0049  | 0.1111  |
| desk                | 0.3689  | 0.7895  | 0.3494  | 0.6842  |
| steps               | 0.0010  | 0.3000  | 0.0001  | 0.1000  |
| fan                 | 0.6820  | 0.7963  | 0.2540  | 0.4444  |
| dresser             | 0.2331  | 0.6667  | 0.1950  | 0.5333  |
| jar                 | 0.0069  | 0.3333  | 0.0069  | 0.3333  |
| tray                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| sheet               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| remote control      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pot                 | 0.0015  | 0.3077  | 0.0009  | 0.2308  |
| wine                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bar                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| footstool           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| cushion             | 0.0045  | 0.1176  | 0.0012  | 0.1176  |
| candle              | 0.0047  | 0.3333  | 0.0047  | 0.3333  |
| doorway             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| trophy              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fruit               | 0.0937  | 0.3750  | 0.0729  | 0.2500  |
| pillar              | 0.2912  | 0.7619  | 0.1640  | 0.3810  |
| column              | 0.0117  | 0.3056  | 0.0006  | 0.0556  |
| grass               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fence               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| window frame        | 0.0281  | 0.4138  | 0.0058  | 0.1954  |
| door knob           | 0.0833  | 0.5000  | 0.0000  | 0.0000  |
| shower              | 0.0014  | 0.0833  | 0.0000  | 0.0000  |
| washbasin           | 0.0168  | 0.2143  | 0.0167  | 0.1429  |
| bin                 | 0.3514  | 0.7805  | 0.1829  | 0.4878  |
| washing machine     | 0.4246  | 0.7500  | 0.0833  | 0.2500  |
| dispenser           | 0.0139  | 0.5000  | 0.0000  | 0.0000  |
| faucet              | 0.0177  | 0.2778  | 0.0002  | 0.0278  |
| baseboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toilet paper        | 0.2620  | 0.6667  | 0.0798  | 0.3611  |
| rail                | 0.0481  | 0.3889  | 0.0000  | 0.0370  |
| headboard           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| windowsill          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| rod                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| ball                | 0.4229  | 0.7500  | 0.4229  | 0.7500  |
| thermostat          | 0.1667  | 0.1667  | 0.0000  | 0.0000  |
| soap dispenser      | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| holder              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hanger              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| hook                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bucket              | 0.0082  | 0.4000  | 0.0033  | 0.2000  |
| soap                | 0.0120  | 0.4000  | 0.0111  | 0.2000  |
| brush               | 0.0081  | 0.2500  | 0.0000  | 0.0000  |
| kitchen island      | 0.2877  | 0.5714  | 0.2738  | 0.4286  |
| heater              | 0.5000  | 1.0000  | 0.5000  | 1.0000  |
| piano               | 0.1667  | 0.5000  | 0.1667  | 0.5000  |
| air conditioner     | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| teapot              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| refrigerator        | 0.5458  | 0.8000  | 0.1476  | 0.2000  |
| rack                | 0.0011  | 0.3636  | 0.0006  | 0.2727  |
| monitor             | 0.1964  | 0.5000  | 0.1250  | 0.2500  |
| radiator            | 0.1667  | 0.3333  | 0.0000  | 0.0000  |
| scale               | 0.0500  | 0.2500  | 0.0000  | 0.0000  |
| furniture           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wardrobe            | 0.0382  | 0.4545  | 0.0368  | 0.2727  |
| basin               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| blinds              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pan                 | 0.0182  | 0.2000  | 0.0000  | 0.0000  |
| hat                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| board               | 0.1786  | 0.3333  | 0.0033  | 0.1667  |
| pipe                | 0.0004  | 0.1000  | 0.0000  | 0.0000  |
| keyboard            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| tissue box          | 0.1532  | 0.5000  | 0.0172  | 0.2500  |
| pool table          | 0.1667  | 0.3333  | 0.1667  | 0.3333  |
| basket              | 0.0582  | 0.4800  | 0.0029  | 0.1200  |
| tissue              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| car                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| speaker             | 0.0661  | 0.3750  | 0.0156  | 0.1250  |
| food                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bag                 | 0.0549  | 0.3846  | 0.0015  | 0.0769  |
| clothes dryer       | 0.1905  | 0.6667  | 0.1574  | 0.6667  |
| kitchenware         | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| clothes             | 0.1222  | 0.3793  | 0.0411  | 0.1207  |
| excercise equipment | 0.4394  | 0.7222  | 0.0223  | 0.2222  |
| kettle              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| microwave           | 0.1415  | 0.3333  | 0.1042  | 0.1667  |
| banister            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fire extinguisher   | 1.0000  | 1.0000  | 1.0000  | 1.0000  |
| ladder              | 0.2222  | 0.3333  | 0.0003  | 0.1111  |
| garage door         | 0.2869  | 0.5000  | 0.2858  | 0.5000  |
| printer             | 0.4622  | 0.5714  | 0.2381  | 0.2857  |
| molding             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toiletry            | 0.1148  | 0.3636  | 0.0028  | 0.0455  |
| vanity              | 0.1732  | 0.6000  | 0.1286  | 0.4667  |
| soap dish           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| fireplace           | 0.2678  | 0.5217  | 0.0095  | 0.2174  |
| beam                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| arch                | 0.0345  | 0.1333  | 0.0007  | 0.1000  |
| container           | 0.0028  | 0.0741  | 0.0006  | 0.0370  |
| machine             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| chandelier          | 0.0263  | 0.0526  | 0.0000  | 0.0000  |
| computer            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stove               | 0.2909  | 0.6000  | 0.1340  | 0.5000  |
| oven                | 0.1758  | 0.8000  | 0.0000  | 0.0000  |
| socket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| mouse               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| shoe                | 0.0480  | 0.3200  | 0.0000  | 0.0000  |
| laptop              | 0.5000  | 1.0000  | 0.0556  | 0.5000  |
| guitar              | 0.1286  | 0.2857  | 0.0000  | 0.0000  |
| file                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| poster              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bicycle             | 0.1250  | 1.0000  | 0.0000  | 0.0000  |
| partition           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| bidet               | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| magazine            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| drum                | 0.0078  | 0.1250  | 0.0000  | 0.0000  |
| cart                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| backpack            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dumbbell            | 0.5000  | 0.5000  | 0.5000  | 0.5000  |
| hamper              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| range hood          | 0.0667  | 0.5000  | 0.0000  | 0.0000  |
| pool                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| plug                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| seasoning           | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| umbrella            | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| duct                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| support             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| jacket              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| stick               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| player              | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| wood                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| pack                | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| globe               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| toaster             | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| coffee maker        | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| dishwasher          | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| can                 | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| candlestick         | 0.5000  | 0.5000  | 0.0000  | 0.0000  |
| knife               | 0.0000  | 0.0000  | 0.0000  | 0.0000  |
| -------             | ------- | ------- | ------- | ------- |
| matterport3d        | 0.1217  | 0.3160  | 0.0611  | 0.1626  |
| head                | 0.1780  | 0.4664  | 0.0840  | 0.2455  |
| common              | 0.0687  | 0.2016  | 0.0380  | 0.0950  |
| tail                | 0.0740  | 0.1184  | 0.0454  | 0.0653  |
| small               | 0.0474  | 0.1585  | 0.0240  | 0.0659  |
| medium              | 0.1147  | 0.3538  | 0.0530  | 0.1710  |
| large               | 0.1046  | 0.3733  | 0.0574  | 0.2297  |
+---------------------+---------+---------+---------+---------+
Rank[0/8] 06/20/2025 21:42:23 INFO metrics.py:437 | number of arkit results: 0
Rank[0/8] 06/20/2025 21:42:24 INFO stats.py:394 | Epoch[239] completed. Training Speed: 3.00 samples/sec across all devices.	Epoch Time: 1040.05 sec.	Average Epoch Time: 1040.05 sec.	Average Step Time: 2.67 sec.	Estimated Remaining Time: 0:00:00.	Learning Rate: 2.00000e-06.	
Rank[0/8] 06/20/2025 21:42:24 WARNING accelerator.py:3099 | Deleting 1 checkpoints to make room for new checkpoint.
Rank[0/8] 06/20/2025 21:42:25 INFO accelerator.py:3111 | Saving current state to /job_data/checkpoints/checkpoint_23
Rank[1/8] 06/20/2025 21:42:25 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 239 to /job_data/checkpoints/checkpoint_23
Rank[5/8] 06/20/2025 21:42:25 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 239 to /job_data/checkpoints/checkpoint_23
Rank[4/8] 06/20/2025 21:42:25 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 239 to /job_data/checkpoints/checkpoint_23
Rank[6/8] 06/20/2025 21:42:25 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 239 to /job_data/checkpoints/checkpoint_23
Rank[3/8] 06/20/2025 21:42:25 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 239 to /job_data/checkpoints/checkpoint_23
Rank[7/8] 06/20/2025 21:42:25 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 239 to /job_data/checkpoints/checkpoint_23
Rank[2/8] 06/20/2025 21:42:25 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 239 to /job_data/checkpoints/checkpoint_23
Rank[0/8] 06/20/2025 21:42:26 INFO checkpointing.py:106 | Model weights saved in /job_data/checkpoints/checkpoint_23/model.safetensors
Rank[0/8] 06/20/2025 21:42:29 INFO checkpointing.py:113 | Optimizer state saved in /job_data/checkpoints/checkpoint_23/optimizer.bin
Rank[0/8] 06/20/2025 21:42:29 INFO checkpointing.py:120 | Scheduler state saved in /job_data/checkpoints/checkpoint_23/scheduler.bin
Rank[0/8] 06/20/2025 21:42:29 INFO checkpointing.py:137 | Sampler state for dataloader 0 saved in /job_data/checkpoints/checkpoint_23/sampler.bin
Rank[0/8] 06/20/2025 21:42:29 INFO checkpointing.py:137 | Sampler state for dataloader 1 saved in /job_data/checkpoints/checkpoint_23/sampler_1.bin
Rank[0/8] 06/20/2025 21:42:29 INFO checkpointing.py:164 | Random states saved in /job_data/checkpoints/checkpoint_23/random_states_0.pkl
Rank[0/8] 06/20/2025 21:42:29 INFO checkpointing.py:300 | Saving the state of TrainerProgressState to /job_data/checkpoints/checkpoint_23/custom_checkpoint_0.pkl
Rank[0/8] 06/20/2025 21:42:29 INFO checkpoint.py:137 | Save checkpoint at the end of epoch 239 to /job_data/checkpoints/checkpoint_23
Rank[0/8] 06/20/2025 21:42:29 INFO hook_based_trainer.py:396 | 
==================================================FINISH TRAINING==================================================