2025/05/11 14:13:16 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.10.16 (main, Dec 11 2024, 16:24:50) [GCC 11.2.0] CUDA available: True MUSA available: False numpy_random_seed: 1598545878 GPU 0,1,2,3,4,5,6,7: NVIDIA H800 CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 12.2, V12.2.91 GCC: gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 PyTorch: 2.3.1+cu121 PyTorch compiling details: PyTorch built with: - GCC 9.3 - C++ Version: 201703 - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v3.3.6 (Git Hash 86e6af5974177e513fd3fee58425e1063e7f1361) - OpenMP 201511 (a.k.a. OpenMP 4.5) - LAPACK is enabled (usually provided by MKL) - NNPACK is enabled - CPU capability usage: AVX512 - CUDA Runtime 12.1 - NVCC architecture flags: -gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 - CuDNN 8.9.2 - Magma 2.6.1 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=8.9.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.3.1, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, TorchVision: 0.18.1+cu121 OpenCV: 4.11.0 MMEngine: 0.10.7 Runtime environment: cudnn_benchmark: False mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: 1598545878 Distributed launcher: pytorch Distributed training: True GPU number: 8 ------------------------------------------------------------ 2025/05/11 14:13:18 - mmengine - INFO - Config: _decoder_layer_num = 8 _max_epoch = 400 _token_dim_ = 512 _warm_epoch = 0 backend_args = None class_names = [ 'cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window', 'bookshelf', 'picture', 'counter', 'desk', 'curtain', 'refrigerator', 'showercurtrain', 'toilet', 'sink', 'bathtub', 'garbagebin', ] custom_imports = dict( allow_failed_imports=False, imports=[ 'projects.VGGTDet.vggtdet', 'mmdet3d.evaluation.metrics.Indoor_NVS', ]) data_root = '/data/yang/codes/MVSDet/data/scannet/' dataset_type = 'MultiViewScanNetDataset' default_hooks = dict( checkpoint=dict( interval=2, max_keep_ckpts=1000, rule='greater', save_best=[ 'mAP_0.25', ], type='CheckpointHook'), logger=dict(interval=10, type='LoggerHook'), param_scheduler=dict(type='ParamSchedulerHook'), sampler_seed=dict(type='DistSamplerSeedHook'), timer=dict(type='IterTimerHook'), visualization=dict(type='Det3DVisualizationHook')) default_scope = 'mmdet3d' env_cfg = dict( cudnn_benchmark=False, dist_cfg=dict(backend='nccl'), mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) file_client_args = dict(backend='disk') find_unused_parameters = False input_modality = dict( use_camera=True, use_depth=False, use_lidar=False, use_neuralrecon_depth=False, use_ray=True) launcher = 'pytorch' load_from = None log_level = 'INFO' log_processor = dict(by_epoch=True, type='LogProcessor', window_size=50) metainfo = dict(CLASSES=[ 'cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window', 'bookshelf', 'picture', 'counter', 'desk', 'curtain', 'refrigerator', 'showercurtrain', 'toilet', 'sink', 'bathtub', 'garbagebin', ]) model = dict( N_rand=2048, N_samples=64, aabb=( [ -2.7, -2.7, -0.78, ], [ 3.7, 3.7, 1.78, ], ), atten_sample_ratio=10, backbone=dict( depth=50, frozen_stages=1, init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), norm_cfg=dict(requires_grad=False, type='BN'), norm_eval=True, num_stages=4, out_indices=( 0, 1, 2, 3, ), style='pytorch', type='mmdet.ResNet'), bbox_head=dict( if_project_frist_frame_back=True, if_v2_head=True, learn_center_diff=True, loss_weights=dict( center_loss=5.0, cls_loss=1.0, iou_loss=1.0, not_objness_loss=0.25, objness_loss=1.0, size_loss=1.0), matcher='one2more', matcher_cost_weights=dict(center=0.0, cls=1.0, giou=2.0, obj_ness=0.0), matcher_iou_thres=0.1, matcher_max_dynamic_samples=5, mlp_dropout=0.3, n_channels=512, n_classes=18, n_levels=8, n_reg_outs=6, prior_generator=dict( ranges=[ [ -3.2, -3.2, -1.28, 3.2, 3.2, 1.28, ], ], rotations=[ 0.0, ], type='AlignedAnchor3DRangeGenerator'), pts_assign_threshold=27, pts_center_threshold=18, type='VGGTDetHead', visualize_path='vis_dir/'), data_preprocessor=dict( bgr_to_rgb=True, mean=[ 123.675, 116.28, 103.53, ], pad_size_divisor=14, pad_value=1.0, std=[ 58.395, 57.12, 57.375, ], type='VGGTDetDataPreprocessor'), decoder_cfg=dict( dec_dim=512, dec_dropout=0.1, dec_ffn_dim=512, dec_nhead=4, dec_nlayers=8), depth_supervise=False, gs_cfg=dict( d_feature=256, dataset=dict(background_color=[ 0.0, 0.0, 0.0, ]), decoder=dict(name='splatting_cuda'), gaussian_adapter_cfg=dict( gaussian_scale_max=15.0, gaussian_scale_min=0.5, sh_degree=4), gaussians_per_pixel=3, num_monocular_samples=12, num_surfaces=1, opacity_mapping=dict(final=0.0, initial=0.0, warm_up=1), use_rgb_gaussian=True, use_transmittance=False), if_learnable_query=False, if_mix_precision=True, if_simpler_project=True, if_task_query=True, if_use_atten_fps=True, if_use_atten_sample=False, if_use_gt_query=False, if_use_pred_pc_query=True, lambda_dist=0.8, n_voxels=[ 40, 40, 16, ], near_far_range=[ 0.2, 5.0, ], neck=dict( in_channels=[ 256, 512, 1024, 2048, ], num_outs=4, out_channels=256, type='mmdet.FPN'), neck_3d=dict( in_channels=256, n_blocks=[ 1, 1, 1, ], out_channels=128, type='IndoorImVoxelNeck'), nerf_density=True, nerf_mode='image', nerf_sample_view=20, num_queries=256, prior_generator=dict( ranges=[ [ -3.2, -3.2, -1.28, 3.2, 3.2, 1.28, ], ], rotations=[ 0.0, ], type='AlignedAnchor3DRangeGenerator'), rgb_supervision=True, squeeze_scale=4, test_cfg=dict(iou_thr=0.25, nms_pre=1000, score_thr=0.01), test_only_last_layer=True, token_dim=512, topk=3, train_cfg=dict(), type='VGGTDet', use_multi_layers=True, use_nerf_mask=False, vis_dir=None, visualize_bbox=False, voxel_size=[ 0.16, 0.16, 0.2, ]) n_points = 100000 optim_wrapper = dict( clip_grad=dict(max_norm=35.0, norm_type=2), optimizer=dict(lr=0.00025, type='AdamW', weight_decay=0.0001), paramwise_cfg=dict( custom_keys=dict(backbone=dict(decay_mult=1.0, lr_mult=0.1))), type='OptimWrapper') param_scheduler = [ dict( T_max=399, begin=0, by_epoch=True, end=400, eta_min=1e-06, type='CosineAnnealingLR'), ] prior_generator = dict( ranges=[ [ -3.2, -3.2, -1.28, 3.2, 3.2, 1.28, ], ], rotations=[ 0.0, ], type='AlignedAnchor3DRangeGenerator') resume = True test_cfg = dict() test_collect_keys = [ 'img', 'lightpos', 'nerf_sizes', 'raydirs', 'gt_images', 'denorm_images', 'c2w', 'intrinsic', 'points', 'gt_bboxes_3d', 'gt_labels_3d', 'pose_matrix', 'axis_align_matrix', 'avg_distance', ] test_dataloader = dict( batch_size=1, dataset=dict( ann_file= '/data/yang/codes/MVSDet/data/scannet/scannet_infos_val_pts.pkl', box_type_3d='Depth', data_root='/data/yang/codes/MVSDet/data/scannet/', filter_empty_gt=True, metainfo=dict(CLASSES=[ 'cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window', 'bookshelf', 'picture', 'counter', 'desk', 'curtain', 'refrigerator', 'showercurtrain', 'toilet', 'sink', 'bathtub', 'garbagebin', ]), modality=dict( use_camera=True, use_depth=False, use_lidar=False, use_neuralrecon_depth=False, use_ray=True), pipeline=[ dict( backend_args=None, coord_type='DEPTH', data_root='/data/yang/codes/MVSDet/data/scannet/', load_dim=6, shift_height=False, type='LoadPointsFromFile', use_color=True, use_dim=[ 0, 1, 2, 3, 4, 5, ]), dict(type='LoadAnnotations3D'), dict(num_points=100000, type='PointSample'), dict( depth_range=[ 0.5, 5.5, ], loading='random', margin=10, mean=[ 123.675, 116.28, 103.53, ], n_images=81, nerf_target_views=1, std=[ 58.395, 57.12, 57.375, ], tgt_transforms=[ dict( file_client_args=dict(backend='disk'), type='LoadImageFromFile'), dict( interpolation='bicubic', keep_ratio=True, scale=( 448, 448, ), type='Resize'), ], transforms=[ dict( file_client_args=dict(backend='disk'), type='LoadImageFromFile'), dict( interpolation='bicubic', keep_ratio=True, scale=( 448, 448, ), type='Resize'), ], type='MultiViewPipeline_Tgt'), dict(coord_type='DEPTH', type='ProjectPCtoFirstFrameAndNorm'), dict( keys=[ 'img', 'lightpos', 'nerf_sizes', 'raydirs', 'gt_images', 'denorm_images', 'c2w', 'intrinsic', 'points', 'gt_bboxes_3d', 'gt_labels_3d', 'pose_matrix', 'axis_align_matrix', 'avg_distance', ], type='PackNeRFDetInputs'), ], test_mode=True, type='MultiViewScanNetDataset'), drop_last=False, num_workers=8, persistent_workers=True, sampler=dict(shuffle=False, type='DefaultSampler')) test_evaluator = [ dict(type='IndoorMetric'), ] test_pipeline = [ dict( backend_args=None, coord_type='DEPTH', data_root='/data/yang/codes/MVSDet/data/scannet/', load_dim=6, shift_height=False, type='LoadPointsFromFile', use_color=True, use_dim=[ 0, 1, 2, 3, 4, 5, ]), dict(type='LoadAnnotations3D'), dict(num_points=100000, type='PointSample'), dict( depth_range=[ 0.5, 5.5, ], loading='random', margin=10, mean=[ 123.675, 116.28, 103.53, ], n_images=81, nerf_target_views=1, std=[ 58.395, 57.12, 57.375, ], tgt_transforms=[ dict( file_client_args=dict(backend='disk'), type='LoadImageFromFile'), dict( interpolation='bicubic', keep_ratio=True, scale=( 448, 448, ), type='Resize'), ], transforms=[ dict( file_client_args=dict(backend='disk'), type='LoadImageFromFile'), dict( interpolation='bicubic', keep_ratio=True, scale=( 448, 448, ), type='Resize'), ], type='MultiViewPipeline_Tgt'), dict(coord_type='DEPTH', type='ProjectPCtoFirstFrameAndNorm'), dict( keys=[ 'img', 'lightpos', 'nerf_sizes', 'raydirs', 'gt_images', 'denorm_images', 'c2w', 'intrinsic', 'points', 'gt_bboxes_3d', 'gt_labels_3d', 'pose_matrix', 'axis_align_matrix', 'avg_distance', ], type='PackNeRFDetInputs'), ] train_cfg = dict(max_epochs=400, type='EpochBasedTrainLoop', val_interval=2) train_collect_keys = [ 'img', 'gt_bboxes_3d', 'gt_labels_3d', 'lightpos', 'nerf_sizes', 'raydirs', 'gt_images', 'denorm_images', 'c2w', 'intrinsic', 'points', 'pose_matrix', 'axis_align_matrix', 'avg_distance', ] train_dataloader = dict( batch_size=10, dataset=dict( dataset=dict( ann_file= '/data/yang/codes/MVSDet/data/scannet/scannet_infos_train_pts.pkl', box_type_3d='Depth', data_root='/data/yang/codes/MVSDet/data/scannet/', filter_empty_gt=True, metainfo=dict(CLASSES=[ 'cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window', 'bookshelf', 'picture', 'counter', 'desk', 'curtain', 'refrigerator', 'showercurtrain', 'toilet', 'sink', 'bathtub', 'garbagebin', ]), modality=dict( use_camera=True, use_depth=False, use_lidar=False, use_neuralrecon_depth=False, use_ray=True), pipeline=[ dict( backend_args=None, coord_type='DEPTH', data_root='/data/yang/codes/MVSDet/data/scannet/', load_dim=6, shift_height=False, type='LoadPointsFromFile', use_color=True, use_dim=[ 0, 1, 2, 3, 4, 5, ]), dict(type='LoadAnnotations3D'), dict(num_points=100000, type='PointSample'), dict( depth_range=[ 0.5, 5.5, ], loading='gap', margin=10, mean=[ 123.675, 116.28, 103.53, ], n_images=42, nerf_target_views=2, std=[ 58.395, 57.12, 57.375, ], tgt_transforms=[ dict( file_client_args=dict(backend='disk'), type='LoadImageFromFile'), dict( interpolation='bicubic', keep_ratio=True, scale=( 448, 448, ), type='Resize'), ], transforms=[ dict( file_client_args=dict(backend='disk'), type='LoadImageFromFile'), dict( interpolation='bicubic', keep_ratio=True, scale=( 448, 448, ), type='Resize'), ], type='MultiViewPipeline_Tgt'), dict(std=( 0.7, 0.7, 0.0, ), type='RandomShiftOrigin'), dict(coord_type='DEPTH', type='ProjectPCtoFirstFrameAndNorm'), dict( keys=[ 'img', 'gt_bboxes_3d', 'gt_labels_3d', 'lightpos', 'nerf_sizes', 'raydirs', 'gt_images', 'denorm_images', 'c2w', 'intrinsic', 'points', 'pose_matrix', 'axis_align_matrix', 'avg_distance', ], type='PackNeRFDetInputs'), ], test_mode=False, type='MultiViewScanNetDataset'), times=6, type='RepeatDataset'), num_workers=8, persistent_workers=True, sampler=dict(shuffle=True, type='DefaultSampler')) train_pipeline = [ dict( backend_args=None, coord_type='DEPTH', data_root='/data/yang/codes/MVSDet/data/scannet/', load_dim=6, shift_height=False, type='LoadPointsFromFile', use_color=True, use_dim=[ 0, 1, 2, 3, 4, 5, ]), dict(type='LoadAnnotations3D'), dict(num_points=100000, type='PointSample'), dict( depth_range=[ 0.5, 5.5, ], loading='gap', margin=10, mean=[ 123.675, 116.28, 103.53, ], n_images=42, nerf_target_views=2, std=[ 58.395, 57.12, 57.375, ], tgt_transforms=[ dict( file_client_args=dict(backend='disk'), type='LoadImageFromFile'), dict( interpolation='bicubic', keep_ratio=True, scale=( 448, 448, ), type='Resize'), ], transforms=[ dict( file_client_args=dict(backend='disk'), type='LoadImageFromFile'), dict( interpolation='bicubic', keep_ratio=True, scale=( 448, 448, ), type='Resize'), ], type='MultiViewPipeline_Tgt'), dict(std=( 0.7, 0.7, 0.0, ), type='RandomShiftOrigin'), dict(coord_type='DEPTH', type='ProjectPCtoFirstFrameAndNorm'), dict( keys=[ 'img', 'gt_bboxes_3d', 'gt_labels_3d', 'lightpos', 'nerf_sizes', 'raydirs', 'gt_images', 'denorm_images', 'c2w', 'intrinsic', 'points', 'pose_matrix', 'axis_align_matrix', 'avg_distance', ], type='PackNeRFDetInputs'), ] use_depth = False val_cfg = dict() val_dataloader = dict( batch_size=1, dataset=dict( ann_file= '/data/yang/codes/MVSDet/data/scannet/scannet_infos_val_pts.pkl', box_type_3d='Depth', data_root='/data/yang/codes/MVSDet/data/scannet/', filter_empty_gt=True, metainfo=dict(CLASSES=[ 'cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window', 'bookshelf', 'picture', 'counter', 'desk', 'curtain', 'refrigerator', 'showercurtrain', 'toilet', 'sink', 'bathtub', 'garbagebin', ]), modality=dict( use_camera=True, use_depth=False, use_lidar=False, use_neuralrecon_depth=False, use_ray=True), pipeline=[ dict( backend_args=None, coord_type='DEPTH', data_root='/data/yang/codes/MVSDet/data/scannet/', load_dim=6, shift_height=False, type='LoadPointsFromFile', use_color=True, use_dim=[ 0, 1, 2, 3, 4, 5, ]), dict(type='LoadAnnotations3D'), dict(num_points=100000, type='PointSample'), dict( depth_range=[ 0.5, 5.5, ], loading='random', margin=10, mean=[ 123.675, 116.28, 103.53, ], n_images=81, nerf_target_views=1, std=[ 58.395, 57.12, 57.375, ], tgt_transforms=[ dict( file_client_args=dict(backend='disk'), type='LoadImageFromFile'), dict( interpolation='bicubic', keep_ratio=True, scale=( 448, 448, ), type='Resize'), ], transforms=[ dict( file_client_args=dict(backend='disk'), type='LoadImageFromFile'), dict( interpolation='bicubic', keep_ratio=True, scale=( 448, 448, ), type='Resize'), ], type='MultiViewPipeline_Tgt'), dict(coord_type='DEPTH', type='ProjectPCtoFirstFrameAndNorm'), dict( keys=[ 'img', 'lightpos', 'nerf_sizes', 'raydirs', 'gt_images', 'denorm_images', 'c2w', 'intrinsic', 'points', 'gt_bboxes_3d', 'gt_labels_3d', 'pose_matrix', 'axis_align_matrix', 'avg_distance', ], type='PackNeRFDetInputs'), ], test_mode=True, type='MultiViewScanNetDataset'), drop_last=False, num_workers=8, persistent_workers=True, sampler=dict(shuffle=False, type='DefaultSampler')) val_evaluator = [ dict(type='IndoorMetric'), ] vis_backends = [ dict(type='LocalVisBackend'), dict( init_kwargs=dict( entity='3dv_team', group='baseline', name= '4layer_scannet_axis_no_norm_predpc_c2lr_400e_atten_fps_lmdis_08_one2more_matching_task_query', notes='debug', project='vggt_det'), type='WandbVisBackend'), ] visualizer = dict( name='visualizer', type='Det3DLocalVisualizer', vis_backends=[ dict(type='LocalVisBackend'), dict( init_kwargs=dict( entity='3dv_team', group='baseline', name= '4layer_scannet_axis_no_norm_predpc_c2lr_400e_atten_fps_lmdis_08_one2more_matching_task_query', notes='debug', project='vggt_det'), type='WandbVisBackend'), ]) work_dir = './work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName' 2025/05/11 14:14:02 - mmengine - INFO - Hooks will be executed in the following order: before_run: (VERY_HIGH ) RuntimeInfoHook (BELOW_NORMAL) LoggerHook -------------------- before_train: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (VERY_LOW ) CheckpointHook -------------------- before_train_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (NORMAL ) DistSamplerSeedHook -------------------- before_train_iter: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook -------------------- after_train_iter: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- after_train_epoch: (NORMAL ) IterTimerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- before_val: (VERY_HIGH ) RuntimeInfoHook -------------------- before_val_epoch: (NORMAL ) IterTimerHook -------------------- before_val_iter: (NORMAL ) IterTimerHook -------------------- after_val_iter: (NORMAL ) IterTimerHook (NORMAL ) Det3DVisualizationHook (BELOW_NORMAL) LoggerHook -------------------- after_val_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- after_val: (VERY_HIGH ) RuntimeInfoHook -------------------- after_train: (VERY_HIGH ) RuntimeInfoHook (VERY_LOW ) CheckpointHook -------------------- before_test: (VERY_HIGH ) RuntimeInfoHook -------------------- before_test_epoch: (NORMAL ) IterTimerHook -------------------- before_test_iter: (NORMAL ) IterTimerHook -------------------- after_test_iter: (NORMAL ) IterTimerHook (NORMAL ) Det3DVisualizationHook (BELOW_NORMAL) LoggerHook -------------------- after_test_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook -------------------- after_test: (VERY_HIGH ) RuntimeInfoHook -------------------- after_run: (BELOW_NORMAL) LoggerHook -------------------- 2025/05/11 14:14:47 - mmengine - INFO - ------------------------------ 2025/05/11 14:14:47 - mmengine - INFO - The length of training dataset: 1201 2025/05/11 14:14:47 - mmengine - INFO - The number of instances per category in the dataset: +----------------+--------+ | category | number | +----------------+--------+ | cabinet | 2854 | | bed | 614 | | chair | 8714 | | sofa | 812 | | table | 2542 | | door | 4052 | | window | 1856 | | bookshelf | 600 | | picture | 1322 | | counter | 432 | | desk | 1102 | | curtain | 584 | | refrigerator | 372 | | showercurtrain | 232 | | toilet | 402 | | sink | 780 | | bathtub | 226 | | garbagebin | 3970 | +----------------+--------+ 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.camera_token is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.register_token is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.cls_token is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.pos_embed is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.register_tokens is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.mask_token is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.patch_embed.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.patch_embed.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.0.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.0.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.0.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.0.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.0.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.0.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.0.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.0.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.0.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.0.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.0.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.0.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.0.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.0.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.1.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.1.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.1.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.1.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.1.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.1.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.1.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.1.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.1.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.1.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.1.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.1.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.1.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.1.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.2.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.2.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.2.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.2.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.2.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.2.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.2.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.2.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.2.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.2.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.2.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.2.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.2.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.2.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.3.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.3.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.3.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.3.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.3.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.3.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.3.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.3.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.3.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.3.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.3.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.3.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.3.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.3.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.4.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.4.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.4.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.4.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.4.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.4.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.4.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.4.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.4.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.4.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.4.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.4.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.4.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.4.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.5.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.5.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.5.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.5.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.5.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.5.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.5.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.5.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.5.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.5.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.5.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.5.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.5.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.5.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.6.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.6.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.6.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.6.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.6.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.6.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.6.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.6.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.6.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.6.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.6.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.6.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.6.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.6.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.7.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.7.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.7.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.7.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.7.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.7.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.7.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.7.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.7.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.7.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.7.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.7.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.7.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.7.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.8.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.8.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.8.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.8.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.8.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.8.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.8.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.8.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.8.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.8.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.8.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.8.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.8.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.8.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.9.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.9.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.9.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.9.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.9.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.9.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.9.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.9.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.9.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.9.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.9.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.9.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.9.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.9.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.10.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.10.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.10.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.10.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.10.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.10.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.10.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.10.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.10.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.10.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.10.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.10.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.10.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.10.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.11.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.11.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.11.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.11.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.11.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.11.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.11.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.11.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.11.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.11.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.11.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.11.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.11.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.11.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.12.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.12.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.12.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.12.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.12.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.12.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.12.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.12.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.12.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.12.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.12.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.12.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.12.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.12.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.13.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.13.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.13.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.13.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.13.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.13.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.13.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.13.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.13.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.13.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.13.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.13.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.13.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.13.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.14.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.14.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.14.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.14.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.14.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.14.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.14.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.14.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.14.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.14.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.14.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.14.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.14.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.14.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.15.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.15.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.15.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.15.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.15.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.15.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.15.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.15.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.15.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.15.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.15.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.15.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.15.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.15.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.16.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.16.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.16.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.16.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.16.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.16.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.16.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.16.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.16.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.16.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.16.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.16.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.16.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.16.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.17.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.17.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.17.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.17.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.17.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.17.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.17.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.17.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.17.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.17.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.17.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.17.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.17.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.17.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.18.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.18.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.18.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.18.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.18.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.18.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.18.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.18.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.18.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.18.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.18.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.18.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.18.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.18.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.19.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.19.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.19.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.19.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.19.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.19.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.19.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.19.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.19.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.19.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.19.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.19.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.19.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.19.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.20.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.20.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.20.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.20.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.20.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.20.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.20.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.20.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.20.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.20.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.20.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.20.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.20.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.20.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.21.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.21.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.21.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.21.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.21.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.21.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.21.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.21.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.21.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.21.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.21.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.21.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.21.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.21.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.22.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.22.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.22.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.22.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 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14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.22.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.22.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.22.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.23.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.23.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.23.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.23.attn.qkv.bias is skipped since its requires_grad=False 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requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.23.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.23.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.blocks.23.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.patch_embed.norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.0.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.0.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 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vggt_encoder.aggregator.frame_blocks.0.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.0.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.1.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.1.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.1.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.1.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.1.attn.q_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.1.attn.q_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.1.attn.k_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.1.attn.k_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.1.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.1.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.1.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.1.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.1.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.1.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.1.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.1.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.1.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.1.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.2.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.2.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.2.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.2.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.2.attn.q_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.2.attn.q_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.2.attn.k_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.2.attn.k_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.2.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.2.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.2.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.2.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.2.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.2.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.2.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.2.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.2.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.2.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.3.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.3.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.3.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.3.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.3.attn.q_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.3.attn.q_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.3.attn.k_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.3.attn.k_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.3.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.3.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.3.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.3.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.3.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.3.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.3.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.3.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.3.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.3.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.4.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.4.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.4.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.4.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.4.attn.q_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.4.attn.q_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.4.attn.k_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.4.attn.k_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.4.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.4.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.4.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.4.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.4.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.4.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.4.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.4.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.4.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.4.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.5.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.5.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.5.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.5.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.5.attn.q_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.5.attn.q_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.5.attn.k_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.5.attn.k_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.5.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.5.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.5.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.5.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.5.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.5.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.5.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.5.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.5.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.5.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.6.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.6.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.6.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.6.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.6.attn.q_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.6.attn.q_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.6.attn.k_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.6.attn.k_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.6.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.6.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.6.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.6.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.6.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.6.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.6.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.6.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.6.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.6.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.7.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.7.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.7.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.7.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.7.attn.q_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - 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vggt_encoder.aggregator.frame_blocks.7.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.7.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.7.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.7.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.7.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.7.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.8.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.8.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.8.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.8.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.8.attn.q_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.8.attn.q_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.8.attn.k_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.8.attn.k_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.8.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.8.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.8.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.8.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.8.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.8.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.8.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.8.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.8.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.8.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.9.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.9.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.9.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.9.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.9.attn.q_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.9.attn.q_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.9.attn.k_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.9.attn.k_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.9.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.9.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.9.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.9.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.9.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.9.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.9.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.9.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.9.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.9.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.10.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.10.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.10.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.10.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.10.attn.q_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.10.attn.q_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.10.attn.k_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.10.attn.k_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.10.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.10.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.10.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.10.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.10.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.10.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.10.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.10.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.10.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.10.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.11.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.11.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.11.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.11.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.11.attn.q_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.11.attn.q_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.11.attn.k_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.11.attn.k_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.11.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.11.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.11.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.11.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.11.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.11.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.11.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.11.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.11.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.11.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.12.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.12.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.12.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.12.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.12.attn.q_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.12.attn.q_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.12.attn.k_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.12.attn.k_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.12.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.12.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.12.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.12.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.12.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.12.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.12.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.12.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.12.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.12.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.13.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.13.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.13.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.13.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.13.attn.q_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.13.attn.q_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.13.attn.k_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.13.attn.k_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.13.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.13.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.13.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.13.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.13.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.13.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.13.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.13.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.13.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.13.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.14.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.14.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.14.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.14.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.14.attn.q_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.14.attn.q_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - 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vggt_encoder.aggregator.frame_blocks.19.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.19.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.19.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.19.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.19.attn.q_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.19.attn.q_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.19.attn.k_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - 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vggt_encoder.aggregator.frame_blocks.20.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.20.attn.q_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.20.attn.q_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.20.attn.k_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.20.attn.k_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.20.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.20.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - 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vggt_encoder.aggregator.frame_blocks.22.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.22.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.22.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.22.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.22.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.22.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.frame_blocks.22.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - 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vggt_encoder.aggregator.global_blocks.1.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.1.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.1.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.1.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.1.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.1.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.1.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - 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vggt_encoder.aggregator.global_blocks.2.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.2.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.2.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.2.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.3.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.3.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.3.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.3.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.3.attn.q_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.3.attn.q_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.3.attn.k_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.3.attn.k_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.3.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.3.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.3.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.3.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.3.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.3.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.3.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.3.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.3.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.3.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.4.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.4.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.4.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.4.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.4.attn.q_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.4.attn.q_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.4.attn.k_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.4.attn.k_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.4.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.4.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.4.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.4.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.4.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.4.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.4.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.4.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.4.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.4.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.5.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.5.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.5.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.5.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.5.attn.q_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.5.attn.q_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.5.attn.k_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.5.attn.k_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.5.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.5.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.5.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.5.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.5.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.5.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.5.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.5.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.5.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.5.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.6.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.6.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.6.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.6.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.6.attn.q_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.6.attn.q_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.6.attn.k_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.6.attn.k_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.6.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.6.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.6.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.6.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.6.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.6.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.6.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.6.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.6.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.6.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.7.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.7.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.7.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.7.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.7.attn.q_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.7.attn.q_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.7.attn.k_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.7.attn.k_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - 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vggt_encoder.aggregator.global_blocks.10.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.10.attn.q_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.10.attn.q_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.10.attn.k_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.10.attn.k_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.10.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.10.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.10.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.10.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.10.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.10.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.10.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.10.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.10.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - 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vggt_encoder.aggregator.global_blocks.12.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.12.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.12.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.12.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.12.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.12.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.12.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.12.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.12.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.13.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.13.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.13.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.13.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.13.attn.q_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - 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vggt_encoder.aggregator.global_blocks.13.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.13.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.13.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.13.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.13.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.13.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.14.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - 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vggt_encoder.aggregator.global_blocks.14.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.14.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.14.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.15.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.15.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.15.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.15.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - 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vggt_encoder.aggregator.global_blocks.15.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.15.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.15.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.15.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.15.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.15.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.15.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - 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vggt_encoder.aggregator.global_blocks.20.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.20.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.20.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.20.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.20.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.20.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.21.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - 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vggt_encoder.aggregator.global_blocks.22.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.22.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.22.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.22.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.22.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.22.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.22.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.23.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.23.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.23.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.23.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.23.attn.q_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.23.attn.q_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.23.attn.k_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - 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vggt_encoder.aggregator.global_blocks.23.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.23.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.23.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.aggregator.global_blocks.23.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.empty_pose_tokens is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.trunk.0.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.trunk.0.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.trunk.0.attn.qkv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.trunk.0.attn.qkv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.trunk.0.attn.proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.trunk.0.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.trunk.0.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.trunk.0.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.trunk.0.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.trunk.0.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - 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requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.trunk.2.attn.proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.trunk.2.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.trunk.2.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.trunk.2.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.trunk.2.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.trunk.2.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.trunk.2.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - 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requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.trunk.3.ls1.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.trunk.3.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.trunk.3.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.trunk.3.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.trunk.3.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.trunk.3.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.trunk.3.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.trunk.3.ls2.gamma is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.token_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.token_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.trunk_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.trunk_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.embed_pose.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.embed_pose.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.poseLN_modulation.1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.poseLN_modulation.1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.pose_branch.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.pose_branch.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.pose_branch.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.camera_head.pose_branch.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.projects.0.weight is skipped since its requires_grad=False 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requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.resize_layers.0.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.resize_layers.1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.resize_layers.1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.resize_layers.3.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.resize_layers.3.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.scratch.layer1_rn.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.scratch.layer2_rn.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - 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WARNING - vggt_encoder.point_head.scratch.refinenet1.resConfUnit1.conv2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.scratch.refinenet1.resConfUnit2.conv1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.scratch.refinenet1.resConfUnit2.conv1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.scratch.refinenet1.resConfUnit2.conv2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.scratch.refinenet1.resConfUnit2.conv2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.scratch.refinenet2.out_conv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.scratch.refinenet2.out_conv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.scratch.refinenet2.resConfUnit1.conv1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.scratch.refinenet2.resConfUnit1.conv1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.scratch.refinenet2.resConfUnit1.conv2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.scratch.refinenet2.resConfUnit1.conv2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.scratch.refinenet2.resConfUnit2.conv1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.scratch.refinenet2.resConfUnit2.conv1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - 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requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.scratch.refinenet3.resConfUnit1.conv2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.scratch.refinenet3.resConfUnit2.conv1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.scratch.refinenet3.resConfUnit2.conv1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.scratch.refinenet3.resConfUnit2.conv2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.scratch.refinenet3.resConfUnit2.conv2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.scratch.refinenet4.out_conv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.scratch.refinenet4.out_conv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.scratch.refinenet4.resConfUnit2.conv1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.scratch.refinenet4.resConfUnit2.conv1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.scratch.refinenet4.resConfUnit2.conv2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.scratch.refinenet4.resConfUnit2.conv2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.scratch.output_conv1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.scratch.output_conv1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.scratch.output_conv2.0.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.scratch.output_conv2.0.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.scratch.output_conv2.2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.point_head.scratch.output_conv2.2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.projects.0.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.projects.0.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.projects.1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.projects.1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.projects.2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.projects.2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.projects.3.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.projects.3.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.resize_layers.0.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.resize_layers.0.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.resize_layers.1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.resize_layers.1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.resize_layers.3.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.resize_layers.3.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.scratch.layer1_rn.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.scratch.layer2_rn.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.scratch.layer3_rn.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.scratch.layer4_rn.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.scratch.refinenet1.out_conv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.scratch.refinenet1.out_conv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.scratch.refinenet1.resConfUnit1.conv1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.scratch.refinenet1.resConfUnit1.conv1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.scratch.refinenet1.resConfUnit1.conv2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.scratch.refinenet1.resConfUnit1.conv2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.scratch.refinenet1.resConfUnit2.conv1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.scratch.refinenet1.resConfUnit2.conv1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.scratch.refinenet1.resConfUnit2.conv2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.scratch.refinenet1.resConfUnit2.conv2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.scratch.refinenet2.out_conv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.scratch.refinenet2.out_conv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.scratch.refinenet2.resConfUnit1.conv1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.scratch.refinenet2.resConfUnit1.conv1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.scratch.refinenet2.resConfUnit1.conv2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.scratch.refinenet2.resConfUnit1.conv2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.scratch.refinenet2.resConfUnit2.conv1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.scratch.refinenet2.resConfUnit2.conv1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.depth_head.scratch.refinenet2.resConfUnit2.conv2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - 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requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.layer3_rn.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.layer4_rn.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet1.out_conv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet1.out_conv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet1.resConfUnit1.conv1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet1.resConfUnit1.conv1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet1.resConfUnit1.conv2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet1.resConfUnit1.conv2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet1.resConfUnit2.conv1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet1.resConfUnit2.conv1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet1.resConfUnit2.conv2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet1.resConfUnit2.conv2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet2.out_conv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet2.out_conv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet2.resConfUnit1.conv1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet2.resConfUnit1.conv1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet2.resConfUnit1.conv2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet2.resConfUnit1.conv2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet2.resConfUnit2.conv1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet2.resConfUnit2.conv1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet2.resConfUnit2.conv2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet2.resConfUnit2.conv2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet3.out_conv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet3.out_conv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet3.resConfUnit1.conv1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet3.resConfUnit1.conv1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet3.resConfUnit1.conv2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet3.resConfUnit1.conv2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet3.resConfUnit2.conv1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet3.resConfUnit2.conv1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet3.resConfUnit2.conv2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet3.resConfUnit2.conv2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet4.out_conv.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet4.out_conv.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet4.resConfUnit2.conv1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.feature_extractor.scratch.refinenet4.resConfUnit2.conv1.bias 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mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.0.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.0.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.0.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.0.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.1.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.1.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.1.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.1.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.1.attn.in_proj_weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.1.attn.in_proj_bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.1.attn.out_proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.1.attn.out_proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.1.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.1.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.1.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.1.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.2.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.2.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 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requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.3.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.3.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.3.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.3.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.4.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.4.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.4.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.4.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.4.attn.in_proj_weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.4.attn.in_proj_bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.4.attn.out_proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.4.attn.out_proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.4.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.4.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.4.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.4.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.5.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.5.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.5.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.5.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.5.attn.in_proj_weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.5.attn.in_proj_bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.5.attn.out_proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - 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WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.0.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.0.norm_context.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.0.norm_context.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.0.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.0.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.0.cross_attn.in_proj_weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.0.cross_attn.in_proj_bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.0.cross_attn.out_proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.0.cross_attn.out_proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.0.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.0.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.0.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.0.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.1.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.1.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.1.norm_context.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.1.norm_context.bias is skipped since its requires_grad=False 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vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.1.cross_attn.out_proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.1.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.1.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.1.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.1.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.2.norm1.weight is skipped since its requires_grad=False 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skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.2.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.2.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.3.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.3.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.3.norm_context.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - 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its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.3.cross_attn.out_proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.3.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.3.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.3.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.3.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - 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14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.5.norm_context.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.5.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.5.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.5.cross_attn.in_proj_weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.5.cross_attn.in_proj_bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.5.cross_attn.out_proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.5.cross_attn.out_proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.5.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.5.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.5.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.5.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.0.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.0.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.0.norm_context.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.0.norm_context.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.0.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - 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vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.1.norm_context.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.1.norm_context.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.1.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.1.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.1.cross_attn.in_proj_weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.1.cross_attn.in_proj_bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.1.cross_attn.out_proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.1.cross_attn.out_proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.1.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.1.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.1.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.1.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.2.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.2.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.2.norm_context.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.2.norm_context.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.2.norm2.weight is skipped since its requires_grad=False 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vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.2.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.2.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.2.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.2.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.3.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.3.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.3.norm_context.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.3.norm_context.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.3.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.3.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.3.cross_attn.in_proj_weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.3.cross_attn.in_proj_bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.3.cross_attn.out_proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.3.cross_attn.out_proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.3.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.3.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.3.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.3.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.4.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.4.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.4.norm_context.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.4.norm_context.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.4.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.4.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.4.cross_attn.in_proj_weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.4.cross_attn.in_proj_bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.4.cross_attn.out_proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.4.cross_attn.out_proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.4.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.4.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.4.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.4.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.5.norm1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.5.norm1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.5.norm_context.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.5.norm_context.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.5.norm2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.5.norm2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.5.cross_attn.in_proj_weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.5.cross_attn.in_proj_bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.5.cross_attn.out_proj.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.5.cross_attn.out_proj.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.5.mlp.fc1.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.5.mlp.fc1.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.5.mlp.fc2.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.5.mlp.fc2.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.fmap_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.fmap_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.ffeat_norm.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.ffeat_norm.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.ffeat_updater.0.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.ffeat_updater.0.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.vis_predictor.0.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.vis_predictor.0.bias is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.conf_predictor.0.weight is skipped since its requires_grad=False 2025/05/11 14:14:47 - mmengine - WARNING - vggt_encoder.track_head.tracker.conf_predictor.0.bias is skipped since its requires_grad=False 2025/05/11 14:14:58 - mmengine - INFO - ------------------------------ 2025/05/11 14:14:58 - mmengine - INFO - The length of test dataset: 312 2025/05/11 14:14:58 - mmengine - INFO - The number of instances per category in the dataset: +----------------+--------+ | category | number | +----------------+--------+ | cabinet | 744 | | bed | 162 | | chair | 2736 | | sofa | 194 | | table | 700 | | door | 934 | | window | 564 | | bookshelf | 154 | | picture | 444 | | counter | 104 | | desk | 254 | | curtain | 134 | | refrigerator | 114 | | showercurtrain | 56 | | toilet | 116 | | sink | 196 | | bathtub | 62 | | garbagebin | 1060 | +----------------+--------+ 2025/05/11 14:14:58 - mmengine - WARNING - The prefix is not set in metric class IndoorMetric. Name of parameter - Initialization information task_query - torch.Size([1, 512]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.center_head.layers.0.weight - torch.Size([512, 512, 1]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.center_head.layers.1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.center_head.layers.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.center_head.layers.4.weight - torch.Size([256, 512, 1]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.center_head.layers.5.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.center_head.layers.5.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.center_head.layers.8.weight - torch.Size([128, 256, 1]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.center_head.layers.9.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.center_head.layers.9.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.center_head.layers.12.weight - torch.Size([64, 128, 1]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.center_head.layers.13.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.center_head.layers.13.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.center_head.layers.16.weight - torch.Size([3, 64, 1]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.center_head.layers.16.bias - torch.Size([3]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.size_head.layers.0.weight - torch.Size([512, 512, 1]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.size_head.layers.1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.size_head.layers.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.size_head.layers.4.weight - torch.Size([256, 512, 1]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.size_head.layers.5.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.size_head.layers.5.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.size_head.layers.8.weight - torch.Size([128, 256, 1]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.size_head.layers.9.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.size_head.layers.9.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.size_head.layers.12.weight - torch.Size([64, 128, 1]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.size_head.layers.13.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.size_head.layers.13.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.size_head.layers.16.weight - torch.Size([3, 64, 1]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.size_head.layers.16.bias - torch.Size([3]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.semcls_head.layers.0.weight - torch.Size([512, 512, 1]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.semcls_head.layers.1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.semcls_head.layers.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.semcls_head.layers.4.weight - torch.Size([256, 512, 1]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.semcls_head.layers.5.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.semcls_head.layers.5.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.semcls_head.layers.8.weight - torch.Size([128, 256, 1]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.semcls_head.layers.9.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.semcls_head.layers.9.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.semcls_head.layers.12.weight - torch.Size([64, 128, 1]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.semcls_head.layers.13.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.semcls_head.layers.13.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.semcls_head.layers.16.weight - torch.Size([19, 64, 1]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.semcls_head.layers.16.bias - torch.Size([19]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.scales.0.scale - torch.Size([]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.scales.1.scale - torch.Size([]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.scales.2.scale - torch.Size([]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.scales.3.scale - torch.Size([]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.scales.4.scale - torch.Size([]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.scales.5.scale - torch.Size([]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.scales.6.scale - torch.Size([]): The value is the same before and after calling `init_weights` of VGGTDet bbox_head.scales.7.scale - torch.Size([]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.camera_token - torch.Size([1, 2, 1, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.register_token - torch.Size([1, 2, 4, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.cls_token - torch.Size([1, 1, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.pos_embed - torch.Size([1, 1370, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.register_tokens - torch.Size([1, 4, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.mask_token - torch.Size([1, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.patch_embed.proj.weight - torch.Size([1024, 3, 14, 14]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.patch_embed.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.0.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.0.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.0.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.0.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.0.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.0.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.0.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.0.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.0.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.0.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.0.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.0.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.0.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.0.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.1.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.1.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.1.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.1.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.1.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.1.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.1.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.1.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.1.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.1.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.1.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.1.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.1.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.1.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.2.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.2.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.2.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.2.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.2.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.2.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.2.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.2.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.2.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.2.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.2.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.2.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.2.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.2.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.3.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.3.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.3.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.3.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.3.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.3.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.3.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.3.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.3.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.3.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.3.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.3.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.3.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.3.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.4.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.4.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.4.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.4.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.4.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.4.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.4.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.4.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.4.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.4.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.4.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.4.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.4.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.4.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.5.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.5.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.5.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.5.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.5.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.5.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.5.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.5.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.5.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.5.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.5.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.5.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.5.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.5.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.6.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.6.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.6.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.6.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.6.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.6.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.6.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.6.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.6.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.6.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.6.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.6.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.6.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.6.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.7.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.7.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.7.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.7.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.7.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.7.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.7.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.7.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.7.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.7.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.7.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.7.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.7.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.7.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.8.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.8.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.8.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.8.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.8.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.8.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.8.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.8.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.8.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.8.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.8.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.8.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.8.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.8.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.9.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.9.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.9.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.9.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.9.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.9.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.9.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.9.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.9.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.9.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.9.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.9.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.9.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.9.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.10.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.10.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.10.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.10.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.10.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.10.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.10.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.10.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.10.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.10.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.10.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.10.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.10.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.10.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.11.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.11.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.11.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.11.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.11.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.11.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.11.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.11.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.11.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.11.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.11.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.11.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.11.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.11.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.12.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.12.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.12.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.12.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.12.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.12.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.12.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.12.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.12.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.12.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.12.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.12.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.12.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.12.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.13.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.13.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.13.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.13.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.13.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.13.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.13.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.13.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.13.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.13.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.13.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.13.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.13.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.13.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.14.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.14.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.14.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.14.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.14.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.14.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.14.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.14.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.14.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.14.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.14.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.14.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.14.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.14.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.15.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.15.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.15.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.15.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.15.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.15.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.15.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.15.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.15.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.15.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.15.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.15.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.15.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.15.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.16.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.16.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.16.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.16.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.16.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.16.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.16.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.16.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.16.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.16.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.16.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.16.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.16.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.16.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.17.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.17.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.17.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.17.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.17.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.17.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.17.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.17.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.17.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.17.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.17.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.17.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.17.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.17.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.18.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.18.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.18.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.18.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.18.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.18.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.18.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.18.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.18.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.18.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.18.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.18.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.18.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.18.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.19.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.19.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.19.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.19.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.19.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.19.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.19.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.19.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.19.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.19.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.19.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.19.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.19.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.19.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.20.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.20.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.20.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.20.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.20.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.20.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.20.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.20.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.20.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.20.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.20.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.20.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.20.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.20.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.21.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.21.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.21.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.21.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.21.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.21.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.21.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.21.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.21.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.21.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.21.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.21.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.21.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.21.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.22.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.22.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.22.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.22.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.22.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.22.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.22.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.22.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.22.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.22.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.22.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.22.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.22.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.22.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.23.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.23.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.23.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.23.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.23.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.23.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.23.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.23.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.23.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.23.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.23.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.23.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.23.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.blocks.23.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.norm.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.patch_embed.norm.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.0.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.0.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.0.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.0.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.0.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.0.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.0.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.0.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.0.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.0.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.0.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.0.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.0.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.0.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.0.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.0.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.0.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.0.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.1.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.1.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.1.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.1.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.1.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.1.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.1.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.1.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.1.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.1.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.1.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.1.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.1.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.1.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.1.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.1.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.1.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.1.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.2.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.2.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.2.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.2.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.2.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.2.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.2.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.2.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.2.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.2.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.2.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.2.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.2.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.2.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.2.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.2.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.2.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.2.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.3.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.3.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.3.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.3.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.3.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.3.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.3.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.3.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.3.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.3.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.3.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.3.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.3.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.3.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.3.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.3.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.3.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.3.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.4.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.4.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.4.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.4.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.4.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.4.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.4.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.4.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.4.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.4.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.4.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.4.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.4.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.4.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.4.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.4.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.4.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.4.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.5.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.5.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.5.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.5.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.5.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.5.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.5.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.5.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.5.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.5.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.5.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.5.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.5.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.5.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.5.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.5.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.5.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.5.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.6.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.6.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.6.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.6.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.6.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.6.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.6.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.6.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.6.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.6.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.6.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.6.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.6.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.6.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.6.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.6.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.6.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.6.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.7.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.7.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.7.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.7.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.7.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.7.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.7.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.7.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.7.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.7.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.7.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.7.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.7.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.7.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.7.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.7.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.7.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.7.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.8.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.8.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.8.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.8.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.8.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.8.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.8.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.8.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.8.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.8.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.8.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.8.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.8.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.8.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.8.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.8.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.8.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.8.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.9.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.9.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.9.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.9.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.9.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.9.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.9.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.9.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.9.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.9.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.9.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.9.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.9.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.9.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.9.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.9.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.9.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.9.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.10.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.10.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.10.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.10.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.10.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.10.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.10.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.10.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.10.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.10.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.10.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.10.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.10.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.10.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.10.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.10.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.10.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.10.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.11.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.11.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.11.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.11.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.11.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.11.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.11.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.11.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.11.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.11.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.11.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.11.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.11.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.11.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.11.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.11.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.11.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.11.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.12.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.12.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.12.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.12.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.12.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.12.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.12.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.12.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.12.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.12.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.12.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.12.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.12.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.12.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.12.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.12.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.12.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.12.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.13.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.13.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.13.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.13.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.13.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.13.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.13.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.13.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.13.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.13.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.13.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.13.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.13.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.13.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.13.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.13.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.13.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.13.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.14.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.14.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.14.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.14.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.14.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.14.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.14.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.14.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.14.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.14.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.14.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.14.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.14.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.14.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.14.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.14.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.14.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.14.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.15.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.15.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.15.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.15.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.15.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.15.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.15.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.15.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.15.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.15.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.15.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.15.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.15.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.15.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.15.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.15.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.15.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.15.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.16.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.16.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.16.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.16.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.16.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.16.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.16.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.16.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.16.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.16.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.16.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.16.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.16.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.16.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.16.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.16.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.16.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.16.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.17.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.17.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.17.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.17.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.17.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.17.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.17.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.17.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.17.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.17.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.17.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.17.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.17.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.17.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.17.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.17.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.17.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.17.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.18.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.18.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.18.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.18.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.18.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.18.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.18.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.18.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.18.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.18.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.18.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.18.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.18.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.18.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.18.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.18.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.18.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.18.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.19.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.19.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.19.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.19.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.19.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.19.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.19.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.19.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.19.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.19.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.19.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.19.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.19.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.19.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.19.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.19.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.19.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.19.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.20.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.20.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.20.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.20.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.20.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.20.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.20.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.20.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.20.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.20.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.20.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.20.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.20.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.20.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.20.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.20.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.20.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.20.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.21.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.21.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.21.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.21.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.21.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.21.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.21.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.21.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.21.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.21.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.21.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.21.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.21.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.21.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.21.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.21.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.21.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.21.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.22.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.22.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.22.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.22.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.22.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.22.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.22.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.22.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.22.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.22.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.22.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.22.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.22.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.22.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.22.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.22.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.22.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.22.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.23.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.23.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.23.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.23.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.23.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.23.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.23.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.23.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.23.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.23.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.23.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.23.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.23.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.23.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.23.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.23.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.23.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.frame_blocks.23.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.0.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.0.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.0.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.0.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.0.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.0.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.0.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.0.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.0.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.0.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.0.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.0.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.0.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.0.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.0.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.0.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.0.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.0.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.1.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.1.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.1.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.1.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.1.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.1.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.1.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.1.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.1.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.1.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.1.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.1.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.1.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.1.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.1.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.1.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.1.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.1.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.2.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.2.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.2.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.2.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.2.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.2.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.2.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.2.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.2.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.2.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.2.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.2.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.2.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.2.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.2.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.2.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.2.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.2.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.3.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.3.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.3.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.3.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.3.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.3.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.3.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.3.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.3.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.3.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.3.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.3.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.3.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.3.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.3.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.3.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.3.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.3.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.4.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.4.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.4.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.4.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.4.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.4.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.4.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.4.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.4.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.4.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.4.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.4.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.4.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.4.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.4.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.4.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.4.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.4.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.5.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.5.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.5.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.5.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.5.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.5.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.5.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.5.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.5.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.5.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.5.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.5.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.5.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.5.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.5.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.5.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.5.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.5.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.6.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.6.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.6.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.6.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.6.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.6.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.6.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.6.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.6.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.6.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.6.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.6.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.6.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.6.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.6.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.6.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.6.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.6.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.7.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.7.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.7.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.7.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.7.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.7.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.7.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.7.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.7.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.7.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.7.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.7.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.7.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.7.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.7.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.7.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.7.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.7.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.8.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.8.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.8.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.8.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.8.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.8.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.8.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.8.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.8.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.8.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.8.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.8.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.8.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.8.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.8.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.8.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.8.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.8.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.9.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.9.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.9.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.9.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.9.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.9.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.9.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.9.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.9.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.9.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.9.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.9.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.9.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.9.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.9.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.9.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.9.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.9.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.10.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.10.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.10.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.10.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.10.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.10.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.10.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.10.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.10.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.10.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.10.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.10.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.10.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.10.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.10.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.10.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.10.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.10.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.11.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.11.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.11.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.11.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.11.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.11.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.11.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.11.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.11.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.11.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.11.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.11.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.11.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.11.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.11.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.11.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.11.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.11.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.12.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.12.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.12.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.12.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.12.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.12.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.12.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.12.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.12.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.12.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.12.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.12.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.12.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.12.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.12.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.12.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.12.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.12.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.13.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.13.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.13.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.13.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.13.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.13.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.13.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.13.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.13.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.13.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.13.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.13.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.13.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.13.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.13.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.13.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.13.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.13.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.14.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.14.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.14.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.14.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.14.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.14.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.14.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.14.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.14.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.14.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.14.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.14.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.14.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.14.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.14.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.14.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.14.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.14.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.15.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.15.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.15.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.15.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.15.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.15.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.15.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.15.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.15.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.15.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.15.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.15.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.15.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.15.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.15.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.15.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.15.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.15.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.16.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.16.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.16.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.16.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.16.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.16.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.16.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.16.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.16.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.16.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.16.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.16.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.16.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.16.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.16.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.16.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.16.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.16.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.17.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.17.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.17.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.17.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.17.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.17.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.17.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.17.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.17.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.17.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.17.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.17.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.17.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.17.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.17.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.17.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.17.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.17.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.18.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.18.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.18.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.18.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.18.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.18.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.18.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.18.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.18.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.18.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.18.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.18.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.18.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.18.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.18.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.18.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.18.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.18.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.19.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.19.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.19.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.19.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.19.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.19.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.19.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.19.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.19.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.19.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.19.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.19.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.19.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.19.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.19.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.19.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.19.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.19.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.20.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.20.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.20.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.20.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.20.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.20.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.20.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.20.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.20.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.20.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.20.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.20.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.20.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.20.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.20.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.20.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.20.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.20.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.21.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.21.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.21.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.21.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.21.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.21.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.21.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.21.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.21.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.21.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.21.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.21.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.21.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.21.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.21.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.21.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.21.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.21.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.22.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.22.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.22.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.22.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.22.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.22.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.22.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.22.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.22.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.22.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.22.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.22.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.22.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.22.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.22.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.22.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.22.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.22.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.23.norm1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.23.norm1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.23.attn.qkv.weight - torch.Size([3072, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.23.attn.qkv.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.23.attn.q_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.23.attn.q_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.23.attn.k_norm.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.23.attn.k_norm.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.23.attn.proj.weight - torch.Size([1024, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.23.attn.proj.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.23.ls1.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.23.norm2.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.23.norm2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.23.mlp.fc1.weight - torch.Size([4096, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.23.mlp.fc1.bias - torch.Size([4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.23.mlp.fc2.weight - torch.Size([1024, 4096]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.23.mlp.fc2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.aggregator.global_blocks.23.ls2.gamma - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.empty_pose_tokens - torch.Size([1, 1, 9]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.0.norm1.weight - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.0.norm1.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.0.attn.qkv.weight - torch.Size([6144, 2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.0.attn.qkv.bias - torch.Size([6144]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.0.attn.proj.weight - torch.Size([2048, 2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.0.attn.proj.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.0.ls1.gamma - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.0.norm2.weight - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.0.norm2.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.0.mlp.fc1.weight - torch.Size([8192, 2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.0.mlp.fc1.bias - torch.Size([8192]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.0.mlp.fc2.weight - torch.Size([2048, 8192]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.0.mlp.fc2.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.0.ls2.gamma - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.1.norm1.weight - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.1.norm1.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.1.attn.qkv.weight - torch.Size([6144, 2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.1.attn.qkv.bias - torch.Size([6144]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.1.attn.proj.weight - torch.Size([2048, 2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.1.attn.proj.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.1.ls1.gamma - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.1.norm2.weight - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.1.norm2.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.1.mlp.fc1.weight - torch.Size([8192, 2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.1.mlp.fc1.bias - torch.Size([8192]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.1.mlp.fc2.weight - torch.Size([2048, 8192]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.1.mlp.fc2.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.1.ls2.gamma - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.2.norm1.weight - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.2.norm1.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.2.attn.qkv.weight - torch.Size([6144, 2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.2.attn.qkv.bias - torch.Size([6144]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.2.attn.proj.weight - torch.Size([2048, 2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.2.attn.proj.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.2.ls1.gamma - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.2.norm2.weight - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.2.norm2.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.2.mlp.fc1.weight - torch.Size([8192, 2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.2.mlp.fc1.bias - torch.Size([8192]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.2.mlp.fc2.weight - torch.Size([2048, 8192]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.2.mlp.fc2.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.2.ls2.gamma - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.3.norm1.weight - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.3.norm1.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.3.attn.qkv.weight - torch.Size([6144, 2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.3.attn.qkv.bias - torch.Size([6144]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.3.attn.proj.weight - torch.Size([2048, 2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.3.attn.proj.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.3.ls1.gamma - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.3.norm2.weight - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.3.norm2.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.3.mlp.fc1.weight - torch.Size([8192, 2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.3.mlp.fc1.bias - torch.Size([8192]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.3.mlp.fc2.weight - torch.Size([2048, 8192]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.3.mlp.fc2.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk.3.ls2.gamma - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.token_norm.weight - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.token_norm.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk_norm.weight - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.trunk_norm.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.embed_pose.weight - torch.Size([2048, 9]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.embed_pose.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.poseLN_modulation.1.weight - torch.Size([6144, 2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.poseLN_modulation.1.bias - torch.Size([6144]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.pose_branch.fc1.weight - torch.Size([1024, 2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.pose_branch.fc1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.pose_branch.fc2.weight - torch.Size([9, 1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.camera_head.pose_branch.fc2.bias - torch.Size([9]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.norm.weight - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.norm.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.projects.0.weight - torch.Size([256, 2048, 1, 1]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.projects.0.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.projects.1.weight - torch.Size([512, 2048, 1, 1]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.projects.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.projects.2.weight - torch.Size([1024, 2048, 1, 1]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.projects.2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.projects.3.weight - torch.Size([1024, 2048, 1, 1]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.projects.3.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.resize_layers.0.weight - torch.Size([256, 256, 4, 4]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.resize_layers.0.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.resize_layers.1.weight - torch.Size([512, 512, 2, 2]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.resize_layers.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.resize_layers.3.weight - torch.Size([1024, 1024, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.resize_layers.3.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.layer1_rn.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.layer2_rn.weight - torch.Size([256, 512, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.layer3_rn.weight - torch.Size([256, 1024, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.layer4_rn.weight - torch.Size([256, 1024, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet1.out_conv.weight - torch.Size([256, 256, 1, 1]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet1.out_conv.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet1.resConfUnit1.conv1.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet1.resConfUnit1.conv1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet1.resConfUnit1.conv2.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet1.resConfUnit1.conv2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet1.resConfUnit2.conv1.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet1.resConfUnit2.conv1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet1.resConfUnit2.conv2.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet1.resConfUnit2.conv2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet2.out_conv.weight - torch.Size([256, 256, 1, 1]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet2.out_conv.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet2.resConfUnit1.conv1.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet2.resConfUnit1.conv1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet2.resConfUnit1.conv2.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet2.resConfUnit1.conv2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet2.resConfUnit2.conv1.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet2.resConfUnit2.conv1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet2.resConfUnit2.conv2.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet2.resConfUnit2.conv2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet3.out_conv.weight - torch.Size([256, 256, 1, 1]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet3.out_conv.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet3.resConfUnit1.conv1.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet3.resConfUnit1.conv1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet3.resConfUnit1.conv2.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet3.resConfUnit1.conv2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet3.resConfUnit2.conv1.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet3.resConfUnit2.conv1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet3.resConfUnit2.conv2.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet3.resConfUnit2.conv2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet4.out_conv.weight - torch.Size([256, 256, 1, 1]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet4.out_conv.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet4.resConfUnit2.conv1.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet4.resConfUnit2.conv1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet4.resConfUnit2.conv2.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.refinenet4.resConfUnit2.conv2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.output_conv1.weight - torch.Size([128, 256, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.output_conv1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.output_conv2.0.weight - torch.Size([32, 128, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.output_conv2.0.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.output_conv2.2.weight - torch.Size([4, 32, 1, 1]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.point_head.scratch.output_conv2.2.bias - torch.Size([4]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.norm.weight - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.norm.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.projects.0.weight - torch.Size([256, 2048, 1, 1]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.projects.0.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.projects.1.weight - torch.Size([512, 2048, 1, 1]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.projects.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.projects.2.weight - torch.Size([1024, 2048, 1, 1]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.projects.2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.projects.3.weight - torch.Size([1024, 2048, 1, 1]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.projects.3.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.resize_layers.0.weight - torch.Size([256, 256, 4, 4]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.resize_layers.0.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.resize_layers.1.weight - torch.Size([512, 512, 2, 2]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.resize_layers.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.resize_layers.3.weight - torch.Size([1024, 1024, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.resize_layers.3.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.layer1_rn.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.layer2_rn.weight - torch.Size([256, 512, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.layer3_rn.weight - torch.Size([256, 1024, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.layer4_rn.weight - torch.Size([256, 1024, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet1.out_conv.weight - torch.Size([256, 256, 1, 1]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet1.out_conv.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet1.resConfUnit1.conv1.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet1.resConfUnit1.conv1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet1.resConfUnit1.conv2.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet1.resConfUnit1.conv2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet1.resConfUnit2.conv1.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet1.resConfUnit2.conv1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet1.resConfUnit2.conv2.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet1.resConfUnit2.conv2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet2.out_conv.weight - torch.Size([256, 256, 1, 1]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet2.out_conv.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet2.resConfUnit1.conv1.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet2.resConfUnit1.conv1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet2.resConfUnit1.conv2.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet2.resConfUnit1.conv2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet2.resConfUnit2.conv1.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet2.resConfUnit2.conv1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet2.resConfUnit2.conv2.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet2.resConfUnit2.conv2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet3.out_conv.weight - torch.Size([256, 256, 1, 1]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet3.out_conv.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet3.resConfUnit1.conv1.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet3.resConfUnit1.conv1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet3.resConfUnit1.conv2.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet3.resConfUnit1.conv2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet3.resConfUnit2.conv1.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet3.resConfUnit2.conv1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet3.resConfUnit2.conv2.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet3.resConfUnit2.conv2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet4.out_conv.weight - torch.Size([256, 256, 1, 1]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet4.out_conv.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet4.resConfUnit2.conv1.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet4.resConfUnit2.conv1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet4.resConfUnit2.conv2.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.refinenet4.resConfUnit2.conv2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.output_conv1.weight - torch.Size([128, 256, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.output_conv1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.output_conv2.0.weight - torch.Size([32, 128, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.output_conv2.0.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.output_conv2.2.weight - torch.Size([2, 32, 1, 1]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.depth_head.scratch.output_conv2.2.bias - torch.Size([2]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.norm.weight - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.norm.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.projects.0.weight - torch.Size([256, 2048, 1, 1]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.projects.0.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.projects.1.weight - torch.Size([512, 2048, 1, 1]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.projects.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.projects.2.weight - torch.Size([1024, 2048, 1, 1]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.projects.2.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.projects.3.weight - torch.Size([1024, 2048, 1, 1]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.projects.3.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.resize_layers.0.weight - torch.Size([256, 256, 4, 4]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.resize_layers.0.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.resize_layers.1.weight - torch.Size([512, 512, 2, 2]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.resize_layers.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.resize_layers.3.weight - torch.Size([1024, 1024, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.resize_layers.3.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.layer1_rn.weight - torch.Size([128, 256, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.layer2_rn.weight - torch.Size([128, 512, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.layer3_rn.weight - torch.Size([128, 1024, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.layer4_rn.weight - torch.Size([128, 1024, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet1.out_conv.weight - torch.Size([128, 128, 1, 1]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet1.out_conv.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet1.resConfUnit1.conv1.weight - torch.Size([128, 128, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet1.resConfUnit1.conv1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet1.resConfUnit1.conv2.weight - torch.Size([128, 128, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet1.resConfUnit1.conv2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet1.resConfUnit2.conv1.weight - torch.Size([128, 128, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet1.resConfUnit2.conv1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet1.resConfUnit2.conv2.weight - torch.Size([128, 128, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet1.resConfUnit2.conv2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet2.out_conv.weight - torch.Size([128, 128, 1, 1]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet2.out_conv.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet2.resConfUnit1.conv1.weight - torch.Size([128, 128, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet2.resConfUnit1.conv1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet2.resConfUnit1.conv2.weight - torch.Size([128, 128, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet2.resConfUnit1.conv2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet2.resConfUnit2.conv1.weight - torch.Size([128, 128, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet2.resConfUnit2.conv1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet2.resConfUnit2.conv2.weight - torch.Size([128, 128, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet2.resConfUnit2.conv2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet3.out_conv.weight - torch.Size([128, 128, 1, 1]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet3.out_conv.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet3.resConfUnit1.conv1.weight - torch.Size([128, 128, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet3.resConfUnit1.conv1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet3.resConfUnit1.conv2.weight - torch.Size([128, 128, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet3.resConfUnit1.conv2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet3.resConfUnit2.conv1.weight - torch.Size([128, 128, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet3.resConfUnit2.conv1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet3.resConfUnit2.conv2.weight - torch.Size([128, 128, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet3.resConfUnit2.conv2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet4.out_conv.weight - torch.Size([128, 128, 1, 1]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet4.out_conv.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet4.resConfUnit2.conv1.weight - torch.Size([128, 128, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet4.resConfUnit2.conv1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet4.resConfUnit2.conv2.weight - torch.Size([128, 128, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.refinenet4.resConfUnit2.conv2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.output_conv1.weight - torch.Size([128, 128, 3, 3]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.feature_extractor.scratch.output_conv1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.query_ref_token - torch.Size([1, 2, 388]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.corr_mlp.fc1.weight - torch.Size([384, 567]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.corr_mlp.fc1.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.corr_mlp.fc2.weight - torch.Size([128, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.corr_mlp.fc2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.virual_tracks - torch.Size([1, 64, 1, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.input_norm.weight - torch.Size([388]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.input_norm.bias - torch.Size([388]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.input_transform.weight - torch.Size([384, 388]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.input_transform.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.output_norm.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.output_norm.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.flow_head.weight - torch.Size([130, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.flow_head.bias - torch.Size([130]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.0.norm1.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.0.norm1.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.0.norm2.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.0.norm2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.0.attn.in_proj_weight - torch.Size([1152, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.0.attn.in_proj_bias - torch.Size([1152]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.0.attn.out_proj.weight - torch.Size([384, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.0.attn.out_proj.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.0.mlp.fc1.weight - torch.Size([1536, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.0.mlp.fc1.bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.0.mlp.fc2.weight - torch.Size([384, 1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.0.mlp.fc2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.1.norm1.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.1.norm1.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.1.norm2.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.1.norm2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.1.attn.in_proj_weight - torch.Size([1152, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.1.attn.in_proj_bias - torch.Size([1152]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.1.attn.out_proj.weight - torch.Size([384, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.1.attn.out_proj.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.1.mlp.fc1.weight - torch.Size([1536, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.1.mlp.fc1.bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.1.mlp.fc2.weight - torch.Size([384, 1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.1.mlp.fc2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.2.norm1.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.2.norm1.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.2.norm2.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.2.norm2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.2.attn.in_proj_weight - torch.Size([1152, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.2.attn.in_proj_bias - torch.Size([1152]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.2.attn.out_proj.weight - torch.Size([384, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.2.attn.out_proj.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.2.mlp.fc1.weight - torch.Size([1536, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.2.mlp.fc1.bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.2.mlp.fc2.weight - torch.Size([384, 1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.2.mlp.fc2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.3.norm1.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.3.norm1.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.3.norm2.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.3.norm2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.3.attn.in_proj_weight - torch.Size([1152, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.3.attn.in_proj_bias - torch.Size([1152]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.3.attn.out_proj.weight - torch.Size([384, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.3.attn.out_proj.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.3.mlp.fc1.weight - torch.Size([1536, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.3.mlp.fc1.bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.3.mlp.fc2.weight - torch.Size([384, 1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.3.mlp.fc2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.4.norm1.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.4.norm1.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.4.norm2.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.4.norm2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.4.attn.in_proj_weight - torch.Size([1152, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.4.attn.in_proj_bias - torch.Size([1152]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.4.attn.out_proj.weight - torch.Size([384, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.4.attn.out_proj.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.4.mlp.fc1.weight - torch.Size([1536, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.4.mlp.fc1.bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.4.mlp.fc2.weight - torch.Size([384, 1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.4.mlp.fc2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.5.norm1.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.5.norm1.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.5.norm2.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.5.norm2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.5.attn.in_proj_weight - torch.Size([1152, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.5.attn.in_proj_bias - torch.Size([1152]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.5.attn.out_proj.weight - torch.Size([384, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.5.attn.out_proj.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.5.mlp.fc1.weight - torch.Size([1536, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.5.mlp.fc1.bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.5.mlp.fc2.weight - torch.Size([384, 1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.time_blocks.5.mlp.fc2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.0.norm1.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.0.norm1.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.0.norm2.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.0.norm2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.0.attn.in_proj_weight - torch.Size([1152, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.0.attn.in_proj_bias - torch.Size([1152]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.0.attn.out_proj.weight - torch.Size([384, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.0.attn.out_proj.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.0.mlp.fc1.weight - torch.Size([1536, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.0.mlp.fc1.bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.0.mlp.fc2.weight - torch.Size([384, 1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.0.mlp.fc2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.1.norm1.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.1.norm1.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.1.norm2.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.1.norm2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.1.attn.in_proj_weight - torch.Size([1152, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.1.attn.in_proj_bias - torch.Size([1152]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.1.attn.out_proj.weight - torch.Size([384, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.1.attn.out_proj.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.1.mlp.fc1.weight - torch.Size([1536, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.1.mlp.fc1.bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.1.mlp.fc2.weight - torch.Size([384, 1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.1.mlp.fc2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.2.norm1.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.2.norm1.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.2.norm2.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.2.norm2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.2.attn.in_proj_weight - torch.Size([1152, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.2.attn.in_proj_bias - torch.Size([1152]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.2.attn.out_proj.weight - torch.Size([384, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.2.attn.out_proj.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.2.mlp.fc1.weight - torch.Size([1536, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.2.mlp.fc1.bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.2.mlp.fc2.weight - torch.Size([384, 1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.2.mlp.fc2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.3.norm1.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.3.norm1.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.3.norm2.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.3.norm2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.3.attn.in_proj_weight - torch.Size([1152, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.3.attn.in_proj_bias - torch.Size([1152]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.3.attn.out_proj.weight - torch.Size([384, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.3.attn.out_proj.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.3.mlp.fc1.weight - torch.Size([1536, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.3.mlp.fc1.bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.3.mlp.fc2.weight - torch.Size([384, 1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.3.mlp.fc2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.4.norm1.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.4.norm1.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.4.norm2.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.4.norm2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.4.attn.in_proj_weight - torch.Size([1152, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.4.attn.in_proj_bias - torch.Size([1152]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.4.attn.out_proj.weight - torch.Size([384, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.4.attn.out_proj.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.4.mlp.fc1.weight - torch.Size([1536, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.4.mlp.fc1.bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.4.mlp.fc2.weight - torch.Size([384, 1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.4.mlp.fc2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.5.norm1.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.5.norm1.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.5.norm2.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.5.norm2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.5.attn.in_proj_weight - torch.Size([1152, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.5.attn.in_proj_bias - torch.Size([1152]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.5.attn.out_proj.weight - torch.Size([384, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.5.attn.out_proj.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.5.mlp.fc1.weight - torch.Size([1536, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.5.mlp.fc1.bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.5.mlp.fc2.weight - torch.Size([384, 1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual_blocks.5.mlp.fc2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.0.norm1.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.0.norm1.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.0.norm_context.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.0.norm_context.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.0.norm2.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.0.norm2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.0.cross_attn.in_proj_weight - torch.Size([1152, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.0.cross_attn.in_proj_bias - torch.Size([1152]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.0.cross_attn.out_proj.weight - torch.Size([384, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.0.cross_attn.out_proj.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.0.mlp.fc1.weight - torch.Size([1536, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.0.mlp.fc1.bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.0.mlp.fc2.weight - torch.Size([384, 1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.0.mlp.fc2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.1.norm1.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.1.norm1.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.1.norm_context.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.1.norm_context.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.1.norm2.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.1.norm2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.1.cross_attn.in_proj_weight - torch.Size([1152, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.1.cross_attn.in_proj_bias - torch.Size([1152]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.1.cross_attn.out_proj.weight - torch.Size([384, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.1.cross_attn.out_proj.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.1.mlp.fc1.weight - torch.Size([1536, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.1.mlp.fc1.bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.1.mlp.fc2.weight - torch.Size([384, 1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.1.mlp.fc2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.2.norm1.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.2.norm1.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.2.norm_context.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.2.norm_context.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.2.norm2.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.2.norm2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.2.cross_attn.in_proj_weight - torch.Size([1152, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.2.cross_attn.in_proj_bias - torch.Size([1152]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.2.cross_attn.out_proj.weight - torch.Size([384, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.2.cross_attn.out_proj.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.2.mlp.fc1.weight - torch.Size([1536, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.2.mlp.fc1.bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.2.mlp.fc2.weight - torch.Size([384, 1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.2.mlp.fc2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.3.norm1.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.3.norm1.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.3.norm_context.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.3.norm_context.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.3.norm2.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.3.norm2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.3.cross_attn.in_proj_weight - torch.Size([1152, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.3.cross_attn.in_proj_bias - torch.Size([1152]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.3.cross_attn.out_proj.weight - torch.Size([384, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.3.cross_attn.out_proj.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.3.mlp.fc1.weight - torch.Size([1536, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.3.mlp.fc1.bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.3.mlp.fc2.weight - torch.Size([384, 1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.3.mlp.fc2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.4.norm1.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.4.norm1.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.4.norm_context.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.4.norm_context.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.4.norm2.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.4.norm2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.4.cross_attn.in_proj_weight - torch.Size([1152, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.4.cross_attn.in_proj_bias - torch.Size([1152]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.4.cross_attn.out_proj.weight - torch.Size([384, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.4.cross_attn.out_proj.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.4.mlp.fc1.weight - torch.Size([1536, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.4.mlp.fc1.bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.4.mlp.fc2.weight - torch.Size([384, 1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.4.mlp.fc2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.5.norm1.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.5.norm1.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.5.norm_context.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.5.norm_context.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.5.norm2.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.5.norm2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.5.cross_attn.in_proj_weight - torch.Size([1152, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.5.cross_attn.in_proj_bias - torch.Size([1152]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.5.cross_attn.out_proj.weight - torch.Size([384, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.5.cross_attn.out_proj.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.5.mlp.fc1.weight - torch.Size([1536, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.5.mlp.fc1.bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.5.mlp.fc2.weight - torch.Size([384, 1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_point2virtual_blocks.5.mlp.fc2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.0.norm1.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.0.norm1.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.0.norm_context.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.0.norm_context.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.0.norm2.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.0.norm2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.0.cross_attn.in_proj_weight - torch.Size([1152, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.0.cross_attn.in_proj_bias - torch.Size([1152]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.0.cross_attn.out_proj.weight - torch.Size([384, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.0.cross_attn.out_proj.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.0.mlp.fc1.weight - torch.Size([1536, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.0.mlp.fc1.bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.0.mlp.fc2.weight - torch.Size([384, 1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.0.mlp.fc2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.1.norm1.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.1.norm1.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.1.norm_context.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.1.norm_context.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.1.norm2.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.1.norm2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.1.cross_attn.in_proj_weight - torch.Size([1152, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.1.cross_attn.in_proj_bias - torch.Size([1152]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.1.cross_attn.out_proj.weight - torch.Size([384, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.1.cross_attn.out_proj.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.1.mlp.fc1.weight - torch.Size([1536, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.1.mlp.fc1.bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.1.mlp.fc2.weight - torch.Size([384, 1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.1.mlp.fc2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.2.norm1.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.2.norm1.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.2.norm_context.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.2.norm_context.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.2.norm2.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.2.norm2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.2.cross_attn.in_proj_weight - torch.Size([1152, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.2.cross_attn.in_proj_bias - torch.Size([1152]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.2.cross_attn.out_proj.weight - torch.Size([384, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.2.cross_attn.out_proj.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.2.mlp.fc1.weight - torch.Size([1536, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.2.mlp.fc1.bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.2.mlp.fc2.weight - torch.Size([384, 1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.2.mlp.fc2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.3.norm1.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.3.norm1.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.3.norm_context.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.3.norm_context.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.3.norm2.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.3.norm2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.3.cross_attn.in_proj_weight - torch.Size([1152, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.3.cross_attn.in_proj_bias - torch.Size([1152]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.3.cross_attn.out_proj.weight - torch.Size([384, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.3.cross_attn.out_proj.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.3.mlp.fc1.weight - torch.Size([1536, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.3.mlp.fc1.bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.3.mlp.fc2.weight - torch.Size([384, 1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.3.mlp.fc2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.4.norm1.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.4.norm1.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.4.norm_context.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.4.norm_context.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.4.norm2.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.4.norm2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.4.cross_attn.in_proj_weight - torch.Size([1152, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.4.cross_attn.in_proj_bias - torch.Size([1152]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.4.cross_attn.out_proj.weight - torch.Size([384, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.4.cross_attn.out_proj.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.4.mlp.fc1.weight - torch.Size([1536, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.4.mlp.fc1.bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.4.mlp.fc2.weight - torch.Size([384, 1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.4.mlp.fc2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.5.norm1.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.5.norm1.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.5.norm_context.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.5.norm_context.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.5.norm2.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.5.norm2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.5.cross_attn.in_proj_weight - torch.Size([1152, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.5.cross_attn.in_proj_bias - torch.Size([1152]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.5.cross_attn.out_proj.weight - torch.Size([384, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.5.cross_attn.out_proj.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.5.mlp.fc1.weight - torch.Size([1536, 384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.5.mlp.fc1.bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.5.mlp.fc2.weight - torch.Size([384, 1536]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.updateformer.space_virtual2point_blocks.5.mlp.fc2.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.fmap_norm.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.fmap_norm.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.ffeat_norm.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.ffeat_norm.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.ffeat_updater.0.weight - torch.Size([128, 128]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.ffeat_updater.0.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.vis_predictor.0.weight - torch.Size([1, 128]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.vis_predictor.0.bias - torch.Size([1]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.conf_predictor.0.weight - torch.Size([1, 128]): The value is the same before and after calling `init_weights` of VGGTDet vggt_encoder.track_head.tracker.conf_predictor.0.bias - torch.Size([1]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.0.self_attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.0.self_attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.0.self_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.0.self_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.0.multihead_attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.0.multihead_attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.0.multihead_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.0.multihead_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.0.norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.0.norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.0.norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.0.norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.0.norm1_task.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.0.norm1_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.0.norm2_task.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.0.norm2_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.0.norm3.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.0.norm3.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.0.norm3_task.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.0.norm3_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.0.linear1.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.0.linear1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.0.linear2.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.0.linear2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.0.linear1_task.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.0.linear1_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.0.linear2_task.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.0.linear2_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.1.self_attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.1.self_attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.1.self_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.1.self_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.1.multihead_attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.1.multihead_attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.1.multihead_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.1.multihead_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.1.norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.1.norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.1.norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.1.norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.1.norm1_task.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.1.norm1_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.1.norm2_task.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.1.norm2_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.1.norm3.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.1.norm3.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.1.norm3_task.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.1.norm3_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.1.linear1.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.1.linear1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.1.linear2.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.1.linear2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.1.linear1_task.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.1.linear1_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.1.linear2_task.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.1.linear2_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.2.self_attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.2.self_attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.2.self_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.2.self_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.2.multihead_attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.2.multihead_attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.2.multihead_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.2.multihead_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.2.norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.2.norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.2.norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.2.norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.2.norm1_task.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.2.norm1_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.2.norm2_task.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.2.norm2_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.2.norm3.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.2.norm3.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.2.norm3_task.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.2.norm3_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.2.linear1.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.2.linear1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.2.linear2.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.2.linear2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.2.linear1_task.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.2.linear1_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.2.linear2_task.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.2.linear2_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.3.self_attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.3.self_attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.3.self_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.3.self_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.3.multihead_attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.3.multihead_attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.3.multihead_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.3.multihead_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.3.norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.3.norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.3.norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.3.norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.3.norm1_task.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.3.norm1_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.3.norm2_task.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.3.norm2_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.3.norm3.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.3.norm3.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.3.norm3_task.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.3.norm3_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.3.linear1.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.3.linear1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.3.linear2.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.3.linear2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.3.linear1_task.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.3.linear1_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.3.linear2_task.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.3.linear2_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.4.self_attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.4.self_attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.4.self_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.4.self_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.4.multihead_attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.4.multihead_attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.4.multihead_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.4.multihead_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.4.norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.4.norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.4.norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.4.norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.4.norm1_task.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.4.norm1_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.4.norm2_task.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.4.norm2_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.4.norm3.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.4.norm3.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.4.norm3_task.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.4.norm3_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.4.linear1.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.4.linear1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.4.linear2.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.4.linear2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.4.linear1_task.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.4.linear1_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.4.linear2_task.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.4.linear2_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.5.self_attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.5.self_attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.5.self_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.5.self_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.5.multihead_attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.5.multihead_attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.5.multihead_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.5.multihead_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.5.norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.5.norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.5.norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.5.norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.5.norm1_task.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.5.norm1_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.5.norm2_task.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.5.norm2_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.5.norm3.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.5.norm3.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.5.norm3_task.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.5.norm3_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.5.linear1.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.5.linear1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.5.linear2.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.5.linear2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.5.linear1_task.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.5.linear1_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.5.linear2_task.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.5.linear2_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.6.self_attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.6.self_attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.6.self_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.6.self_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.6.multihead_attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.6.multihead_attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.6.multihead_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.6.multihead_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.6.norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.6.norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.6.norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.6.norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.6.norm1_task.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.6.norm1_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.6.norm2_task.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.6.norm2_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.6.norm3.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.6.norm3.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.6.norm3_task.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.6.norm3_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.6.linear1.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.6.linear1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.6.linear2.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.6.linear2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.6.linear1_task.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.6.linear1_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.6.linear2_task.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.6.linear2_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.7.self_attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.7.self_attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.7.self_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.7.self_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.7.multihead_attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.7.multihead_attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.7.multihead_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.7.multihead_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.7.norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.7.norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.7.norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.7.norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.7.norm1_task.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.7.norm1_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.7.norm2_task.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.7.norm2_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.7.norm3.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.7.norm3.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.7.norm3_task.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.7.norm3_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.7.linear1.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.7.linear1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.7.linear2.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.7.linear2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.7.linear1_task.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.7.linear1_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.7.linear2_task.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.layers.7.linear2_task.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.norm.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.norm.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.task_head.layers.0.weight - torch.Size([512, 512, 1]): The value is the same before and after calling `init_weights` of VGGTDet decoder.task_head.layers.1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.task_head.layers.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet decoder.task_head.layers.4.weight - torch.Size([256, 512, 1]): The value is the same before and after calling `init_weights` of VGGTDet decoder.task_head.layers.5.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet decoder.task_head.layers.5.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of VGGTDet decoder.task_head.layers.8.weight - torch.Size([128, 256, 1]): The value is the same before and after calling `init_weights` of VGGTDet decoder.task_head.layers.9.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet decoder.task_head.layers.9.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of VGGTDet decoder.task_head.layers.12.weight - torch.Size([64, 128, 1]): The value is the same before and after calling `init_weights` of VGGTDet decoder.task_head.layers.13.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet decoder.task_head.layers.13.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of VGGTDet decoder.task_head.layers.16.weight - torch.Size([4, 64, 1]): The value is the same before and after calling `init_weights` of VGGTDet decoder.task_head.layers.16.bias - torch.Size([4]): The value is the same before and after calling `init_weights` of VGGTDet proj_feat_dim0.weight - torch.Size([512, 2048, 1, 1]): The value is the same before and after calling `init_weights` of VGGTDet proj_feat_dim0.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet proj_feat_dim1.weight - torch.Size([512, 2048, 1, 1]): The value is the same before and after calling `init_weights` of VGGTDet proj_feat_dim1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet proj_feat_dim2.weight - torch.Size([512, 2048, 1, 1]): The value is the same before and after calling `init_weights` of VGGTDet proj_feat_dim2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet proj_feat_dim3.weight - torch.Size([512, 2048, 1, 1]): The value is the same before and after calling `init_weights` of VGGTDet proj_feat_dim3.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet query_projection.layers.0.weight - torch.Size([512, 512, 1]): The value is the same before and after calling `init_weights` of VGGTDet query_projection.layers.0.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet query_projection.layers.2.weight - torch.Size([512, 512, 1]): The value is the same before and after calling `init_weights` of VGGTDet query_projection.layers.2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of VGGTDet 2025/05/11 14:15:01 - mmengine - INFO - Auto resumed from the latest checkpoint None. 2025/05/11 14:15:01 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io 2025/05/11 14:15:01 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. 2025/05/11 14:15:01 - mmengine - INFO - Checkpoints will be saved to /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName. 2025/05/11 14:17:31 - mmengine - INFO - Epoch(train) [1][10/91] base_lr: 2.5000e-04 lr: 2.5000e-04 eta: 6 days, 7:58:30 time: 15.0346 data_time: 5.0606 memory: 68702 grad_norm: 4.2646 loss: 6.1195 center_loss: 1.2989 size_loss: 0.6360 cls_loss: 3.1870 giou_loss: 0.9976 2025/05/11 14:19:07 - mmengine - INFO - Epoch(train) [1][20/91] base_lr: 2.5000e-04 lr: 2.5000e-04 eta: 5 days, 4:16:46 time: 12.2981 data_time: 2.8063 memory: 68703 grad_norm: 3.9240 loss: 5.9495 center_loss: 1.2402 size_loss: 0.5900 cls_loss: 3.1292 giou_loss: 0.9902 2025/05/11 14:20:43 - mmengine - INFO - Epoch(train) [1][30/91] base_lr: 2.5000e-04 lr: 2.5000e-04 eta: 4 days, 19:12:35 time: 11.4038 data_time: 2.0711 memory: 68702 grad_norm: 3.7343 loss: 5.8228 center_loss: 1.2049 size_loss: 0.5590 cls_loss: 3.0732 giou_loss: 0.9856 2025/05/11 14:22:19 - mmengine - INFO - Epoch(train) [1][40/91] base_lr: 2.5000e-04 lr: 2.5000e-04 eta: 4 days, 14:36:10 time: 10.9508 data_time: 1.7036 memory: 68700 grad_norm: 3.6119 loss: 5.7174 center_loss: 1.1784 size_loss: 0.5381 cls_loss: 3.0162 giou_loss: 0.9848 2025/05/11 14:23:55 - mmengine - INFO - Epoch(train) [1][50/91] base_lr: 2.5000e-04 lr: 2.5000e-04 eta: 4 days, 11:54:36 time: 10.6871 data_time: 1.4967 memory: 68702 grad_norm: 3.5100 loss: 5.6333 center_loss: 1.1649 size_loss: 0.5251 cls_loss: 2.9583 giou_loss: 0.9850 2025/05/11 14:25:31 - mmengine - INFO - Epoch(train) [1][60/91] base_lr: 2.5000e-04 lr: 2.5000e-04 eta: 4 days, 10:01:12 time: 9.5964 data_time: 0.6031 memory: 68702 grad_norm: 3.2437 loss: 5.4429 center_loss: 1.1259 size_loss: 0.4893 cls_loss: 2.8432 giou_loss: 0.9844 2025/05/11 14:27:07 - mmengine - INFO - Epoch(train) [1][70/91] base_lr: 2.5000e-04 lr: 2.5000e-04 eta: 4 days, 8:43:21 time: 9.6087 data_time: 0.6169 memory: 68702 grad_norm: 3.0896 loss: 5.2924 center_loss: 1.1070 size_loss: 0.4717 cls_loss: 2.7294 giou_loss: 0.9843 2025/05/11 14:28:43 - mmengine - INFO - Epoch(train) [1][80/91] base_lr: 2.5000e-04 lr: 2.5000e-04 eta: 4 days, 7:39:13 time: 9.5962 data_time: 0.6170 memory: 68701 grad_norm: 2.9631 loss: 5.1605 center_loss: 1.0973 size_loss: 0.4611 cls_loss: 2.6123 giou_loss: 0.9898 2025/05/11 14:30:20 - mmengine - INFO - Epoch(train) [1][90/91] base_lr: 2.5000e-04 lr: 2.5000e-04 eta: 4 days, 6:57:07 time: 9.6125 data_time: 0.6177 memory: 68702 grad_norm: 2.8409 loss: 5.0129 center_loss: 1.0768 size_loss: 0.4511 cls_loss: 2.4952 giou_loss: 0.9898 2025/05/11 14:30:22 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 14:32:50 - mmengine - INFO - Epoch(train) [2][10/91] base_lr: 2.5000e-04 lr: 2.5000e-04 eta: 4 days, 10:46:32 time: 10.5130 data_time: 1.4847 memory: 68703 grad_norm: 2.7829 loss: 4.8725 center_loss: 1.0679 size_loss: 0.4406 cls_loss: 2.3715 giou_loss: 0.9925 2025/05/11 14:34:27 - mmengine - INFO - Epoch(train) [2][20/91] base_lr: 2.5000e-04 lr: 2.5000e-04 eta: 4 days, 9:51:22 time: 10.5199 data_time: 1.4704 memory: 68702 grad_norm: 2.6913 loss: 4.7389 center_loss: 1.0545 size_loss: 0.4336 cls_loss: 2.2582 giou_loss: 0.9927 2025/05/11 14:36:02 - mmengine - INFO - Epoch(train) [2][30/91] base_lr: 2.5000e-04 lr: 2.5000e-04 eta: 4 days, 9:03:33 time: 10.5112 data_time: 1.4481 memory: 68702 grad_norm: 2.6046 loss: 4.6123 center_loss: 1.0414 size_loss: 0.4245 cls_loss: 2.1548 giou_loss: 0.9916 2025/05/11 14:37:39 - mmengine - INFO - Epoch(train) [2][40/91] base_lr: 2.5000e-04 lr: 2.5000e-04 eta: 4 days, 8:25:06 time: 10.5244 data_time: 1.4348 memory: 68702 grad_norm: 2.5106 loss: 4.5085 center_loss: 1.0367 size_loss: 0.4219 cls_loss: 2.0598 giou_loss: 0.9901 2025/05/11 14:39:15 - mmengine - INFO - Epoch(train) [2][50/91] base_lr: 2.5000e-04 lr: 2.5000e-04 eta: 4 days, 7:50:11 time: 10.6449 data_time: 1.4412 memory: 68702 grad_norm: 2.3582 loss: 4.4207 center_loss: 1.0428 size_loss: 0.4205 cls_loss: 1.9636 giou_loss: 0.9938 2025/05/11 14:40:51 - mmengine - INFO - Epoch(train) [2][60/91] base_lr: 2.5000e-04 lr: 2.5000e-04 eta: 4 days, 7:20:07 time: 9.6020 data_time: 0.5561 memory: 68703 grad_norm: 2.2745 loss: 4.3191 center_loss: 1.0286 size_loss: 0.4182 cls_loss: 1.8819 giou_loss: 0.9905 2025/05/11 14:42:27 - mmengine - INFO - Epoch(train) [2][70/91] base_lr: 2.5000e-04 lr: 2.5000e-04 eta: 4 days, 6:53:19 time: 9.5988 data_time: 0.5659 memory: 68703 grad_norm: 2.1805 loss: 4.2478 center_loss: 1.0292 size_loss: 0.4215 cls_loss: 1.8055 giou_loss: 0.9916 2025/05/11 14:44:02 - mmengine - INFO - Epoch(train) [2][80/91] base_lr: 2.5000e-04 lr: 2.5000e-04 eta: 4 days, 6:27:56 time: 9.5930 data_time: 0.5793 memory: 68702 grad_norm: 2.0885 loss: 4.1682 center_loss: 1.0246 size_loss: 0.4216 cls_loss: 1.7292 giou_loss: 0.9927 2025/05/11 14:45:36 - mmengine - INFO - Epoch(train) [2][90/91] base_lr: 2.5000e-04 lr: 2.5000e-04 eta: 4 days, 6:00:36 time: 9.5497 data_time: 0.5787 memory: 68700 grad_norm: 2.0081 loss: 4.0792 center_loss: 1.0087 size_loss: 0.4162 cls_loss: 1.6640 giou_loss: 0.9903 2025/05/11 14:45:38 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 14:45:38 - mmengine - INFO - Saving checkpoint at 2 epochs 2025/05/11 14:46:37 - mmengine - INFO - Epoch(val) [2][10/39] eta: 0:01:43 time: 3.5714 data_time: 0.9699 memory: 15952 2025/05/11 14:47:03 - mmengine - INFO - Epoch(val) [2][20/39] eta: 0:00:59 time: 3.1186 data_time: 0.5158 memory: 13407 2025/05/11 14:47:30 - mmengine - INFO - Epoch(val) [2][30/39] eta: 0:00:26 time: 2.9545 data_time: 0.3647 memory: 13407 2025/05/11 14:48:00 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | chair | 0.0069 | 0.3494 | 0.0000 | 0.0270 | | sofa | 0.0000 | 0.0206 | 0.0000 | 0.0000 | | table | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | garbagebin | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | bookshelf | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | picture | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | curtain | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | door | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | cabinet | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | refrigerator | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | counter | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | sink | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | window | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | desk | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | bed | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | toilet | 0.0000 | 0.0345 | 0.0000 | 0.0172 | | showercurtrain | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | bathtub | 0.0000 | 0.0000 | 0.0000 | 0.0000 | +----------------+---------+---------+---------+---------+ | Overall | 0.0004 | 0.0225 | 0.0000 | 0.0025 | +----------------+---------+---------+---------+---------+ 2025/05/11 14:48:00 - mmengine - INFO - Epoch(val) [2][39/39] chair_AP_0.25: 0.0069 sofa_AP_0.25: 0.0000 table_AP_0.25: 0.0000 garbagebin_AP_0.25: 0.0000 bookshelf_AP_0.25: 0.0000 picture_AP_0.25: 0.0000 curtain_AP_0.25: 0.0000 door_AP_0.25: 0.0000 cabinet_AP_0.25: 0.0000 refrigerator_AP_0.25: 0.0000 counter_AP_0.25: 0.0000 sink_AP_0.25: 0.0000 window_AP_0.25: 0.0000 desk_AP_0.25: 0.0000 bed_AP_0.25: 0.0000 toilet_AP_0.25: 0.0000 showercurtrain_AP_0.25: 0.0000 bathtub_AP_0.25: 0.0000 mAP_0.25: 0.0004 chair_rec_0.25: 0.3494 sofa_rec_0.25: 0.0206 table_rec_0.25: 0.0000 garbagebin_rec_0.25: 0.0000 bookshelf_rec_0.25: 0.0000 picture_rec_0.25: 0.0000 curtain_rec_0.25: 0.0000 door_rec_0.25: 0.0000 cabinet_rec_0.25: 0.0000 refrigerator_rec_0.25: 0.0000 counter_rec_0.25: 0.0000 sink_rec_0.25: 0.0000 window_rec_0.25: 0.0000 desk_rec_0.25: 0.0000 bed_rec_0.25: 0.0000 toilet_rec_0.25: 0.0345 showercurtrain_rec_0.25: 0.0000 bathtub_rec_0.25: 0.0000 mAR_0.25: 0.0225 chair_AP_0.50: 0.0000 sofa_AP_0.50: 0.0000 table_AP_0.50: 0.0000 garbagebin_AP_0.50: 0.0000 bookshelf_AP_0.50: 0.0000 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0000 door_AP_0.50: 0.0000 cabinet_AP_0.50: 0.0000 refrigerator_AP_0.50: 0.0000 counter_AP_0.50: 0.0000 sink_AP_0.50: 0.0000 window_AP_0.50: 0.0000 desk_AP_0.50: 0.0000 bed_AP_0.50: 0.0000 toilet_AP_0.50: 0.0000 showercurtrain_AP_0.50: 0.0000 bathtub_AP_0.50: 0.0000 mAP_0.50: 0.0000 chair_rec_0.50: 0.0270 sofa_rec_0.50: 0.0000 table_rec_0.50: 0.0000 garbagebin_rec_0.50: 0.0000 bookshelf_rec_0.50: 0.0000 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.0000 door_rec_0.50: 0.0000 cabinet_rec_0.50: 0.0000 refrigerator_rec_0.50: 0.0000 counter_rec_0.50: 0.0000 sink_rec_0.50: 0.0000 window_rec_0.50: 0.0000 desk_rec_0.50: 0.0000 bed_rec_0.50: 0.0000 toilet_rec_0.50: 0.0172 showercurtrain_rec_0.50: 0.0000 bathtub_rec_0.50: 0.0000 mAR_0.50: 0.0025 data_time: 0.2950 time: 2.8693 2025/05/11 14:48:21 - mmengine - INFO - The best checkpoint with 0.0004 mAP_0.25 at 2 epoch is saved to best_mAP_0.25_epoch_2.pth. 2025/05/11 14:51:12 - mmengine - INFO - Epoch(train) [3][10/91] base_lr: 2.4998e-04 lr: 2.4998e-04 eta: 4 days, 7:48:30 time: 10.3702 data_time: 1.5031 memory: 68703 grad_norm: 1.9696 loss: 4.0225 center_loss: 1.0109 size_loss: 0.4174 cls_loss: 1.6053 giou_loss: 0.9888 2025/05/11 14:52:48 - mmengine - INFO - Epoch(train) [3][20/91] base_lr: 2.4998e-04 lr: 2.4998e-04 eta: 4 days, 7:24:28 time: 10.3644 data_time: 1.4932 memory: 68702 grad_norm: 1.8841 loss: 3.9609 center_loss: 1.0093 size_loss: 0.4147 cls_loss: 1.5502 giou_loss: 0.9868 2025/05/11 14:54:23 - mmengine - INFO - Epoch(train) [3][30/91] base_lr: 2.4998e-04 lr: 2.4998e-04 eta: 4 days, 7:01:19 time: 10.3551 data_time: 1.4812 memory: 68702 grad_norm: 1.8030 loss: 3.9019 center_loss: 0.9983 size_loss: 0.4100 cls_loss: 1.5129 giou_loss: 0.9807 2025/05/11 14:56:00 - mmengine - INFO - Epoch(train) [3][40/91] base_lr: 2.4998e-04 lr: 2.4998e-04 eta: 4 days, 6:44:21 time: 10.3770 data_time: 1.4644 memory: 68702 grad_norm: 1.7251 loss: 3.8447 center_loss: 0.9855 size_loss: 0.4059 cls_loss: 1.4778 giou_loss: 0.9755 2025/05/11 14:57:37 - mmengine - INFO - Epoch(train) [3][50/91] base_lr: 2.4998e-04 lr: 2.4998e-04 eta: 4 days, 6:29:17 time: 10.5873 data_time: 1.4885 memory: 68702 grad_norm: 1.5967 loss: 3.8019 center_loss: 0.9849 size_loss: 0.4027 cls_loss: 1.4403 giou_loss: 0.9740 2025/05/11 14:59:13 - mmengine - INFO - Epoch(train) [3][60/91] base_lr: 2.4998e-04 lr: 2.4998e-04 eta: 4 days, 6:12:26 time: 9.6188 data_time: 0.5484 memory: 68703 grad_norm: 1.5211 loss: 3.7472 center_loss: 0.9686 size_loss: 0.3998 cls_loss: 1.4073 giou_loss: 0.9715 2025/05/11 15:00:49 - mmengine - INFO - Epoch(train) [3][70/91] base_lr: 2.4998e-04 lr: 2.4998e-04 eta: 4 days, 5:57:56 time: 9.6320 data_time: 0.5730 memory: 68702 grad_norm: 1.4465 loss: 3.7228 center_loss: 0.9687 size_loss: 0.3988 cls_loss: 1.3839 giou_loss: 0.9714 2025/05/11 15:02:25 - mmengine - INFO - Epoch(train) [3][80/91] base_lr: 2.4998e-04 lr: 2.4998e-04 eta: 4 days, 5:43:36 time: 9.6469 data_time: 0.5870 memory: 68703 grad_norm: 1.3734 loss: 3.6807 center_loss: 0.9604 size_loss: 0.3973 cls_loss: 1.3520 giou_loss: 0.9709 2025/05/11 15:03:59 - mmengine - INFO - Epoch(train) [3][90/91] base_lr: 2.4998e-04 lr: 2.4998e-04 eta: 4 days, 5:25:21 time: 9.5866 data_time: 0.5828 memory: 68702 grad_norm: 1.2977 loss: 3.6327 center_loss: 0.9536 size_loss: 0.3949 cls_loss: 1.3133 giou_loss: 0.9708 2025/05/11 15:04:01 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 15:06:31 - mmengine - INFO - Epoch(train) [4][10/91] base_lr: 2.4997e-04 lr: 2.4997e-04 eta: 4 days, 6:50:07 time: 10.4882 data_time: 1.5804 memory: 68702 grad_norm: 1.2581 loss: 3.5906 center_loss: 0.9401 size_loss: 0.3908 cls_loss: 1.2928 giou_loss: 0.9669 2025/05/11 15:08:08 - mmengine - INFO - Epoch(train) [4][20/91] base_lr: 2.4997e-04 lr: 2.4997e-04 eta: 4 days, 6:36:12 time: 10.5060 data_time: 1.5910 memory: 68702 grad_norm: 1.1834 loss: 3.5619 center_loss: 0.9392 size_loss: 0.3889 cls_loss: 1.2692 giou_loss: 0.9646 2025/05/11 15:09:44 - mmengine - INFO - Epoch(train) [4][30/91] base_lr: 2.4997e-04 lr: 2.4997e-04 eta: 4 days, 6:22:36 time: 10.5029 data_time: 1.5881 memory: 68702 grad_norm: 1.1200 loss: 3.5308 center_loss: 0.9329 size_loss: 0.3880 cls_loss: 1.2452 giou_loss: 0.9647 2025/05/11 15:11:20 - mmengine - INFO - Epoch(train) [4][40/91] base_lr: 2.4997e-04 lr: 2.4997e-04 eta: 4 days, 6:09:41 time: 10.5052 data_time: 1.5805 memory: 68702 grad_norm: 1.0571 loss: 3.5033 center_loss: 0.9322 size_loss: 0.3864 cls_loss: 1.2235 giou_loss: 0.9612 2025/05/11 15:12:57 - mmengine - INFO - Epoch(train) [4][50/91] base_lr: 2.4997e-04 lr: 2.4997e-04 eta: 4 days, 5:58:30 time: 10.7151 data_time: 1.6096 memory: 68702 grad_norm: 0.9533 loss: 3.5007 center_loss: 0.9432 size_loss: 0.3923 cls_loss: 1.2016 giou_loss: 0.9636 2025/05/11 15:14:34 - mmengine - INFO - Epoch(train) [4][60/91] base_lr: 2.4997e-04 lr: 2.4997e-04 eta: 4 days, 5:47:19 time: 9.6492 data_time: 0.6071 memory: 68703 grad_norm: 0.8935 loss: 3.4819 center_loss: 0.9441 size_loss: 0.3922 cls_loss: 1.1833 giou_loss: 0.9623 2025/05/11 15:16:10 - mmengine - INFO - Epoch(train) [4][70/91] base_lr: 2.4997e-04 lr: 2.4997e-04 eta: 4 days, 5:36:58 time: 9.6511 data_time: 0.6176 memory: 68703 grad_norm: 0.8438 loss: 3.4766 center_loss: 0.9511 size_loss: 0.3918 cls_loss: 1.1710 giou_loss: 0.9626 2025/05/11 15:17:47 - mmengine - INFO - Epoch(train) [4][80/91] base_lr: 2.4997e-04 lr: 2.4997e-04 eta: 4 days, 5:27:09 time: 9.6585 data_time: 0.6207 memory: 68702 grad_norm: 0.7855 loss: 3.4552 center_loss: 0.9440 size_loss: 0.3908 cls_loss: 1.1601 giou_loss: 0.9603 2025/05/11 15:19:24 - mmengine - INFO - Epoch(train) [4][90/91] base_lr: 2.4997e-04 lr: 2.4997e-04 eta: 4 days, 5:17:46 time: 9.6665 data_time: 0.6260 memory: 68703 grad_norm: 0.7434 loss: 3.4530 center_loss: 0.9498 size_loss: 0.3927 cls_loss: 1.1487 giou_loss: 0.9618 2025/05/11 15:19:26 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 15:19:26 - mmengine - INFO - Saving checkpoint at 4 epochs 2025/05/11 15:20:22 - mmengine - INFO - Epoch(val) [4][10/39] eta: 0:01:38 time: 2.9757 data_time: 0.4000 memory: 15952 2025/05/11 15:20:49 - mmengine - INFO - Epoch(val) [4][20/39] eta: 0:00:57 time: 2.7852 data_time: 0.2147 memory: 13407 2025/05/11 15:21:15 - mmengine - INFO - Epoch(val) [4][30/39] eta: 0:00:26 time: 2.7801 data_time: 0.2159 memory: 13407 2025/05/11 15:21:46 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | chair | 0.0119 | 0.4423 | 0.0001 | 0.0387 | | sofa | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | table | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | garbagebin | 0.0001 | 0.0245 | 0.0000 | 0.0019 | | bookshelf | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | picture | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | curtain | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | toilet | 0.0007 | 0.1207 | 0.0000 | 0.0000 | | door | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | cabinet | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | refrigerator | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | counter | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | sink | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | window | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | desk | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | bed | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | showercurtrain | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | bathtub | 0.0000 | 0.0000 | 0.0000 | 0.0000 | +----------------+---------+---------+---------+---------+ | Overall | 0.0007 | 0.0326 | 0.0000 | 0.0023 | +----------------+---------+---------+---------+---------+ 2025/05/11 15:21:46 - mmengine - INFO - Epoch(val) [4][39/39] chair_AP_0.25: 0.0119 sofa_AP_0.25: 0.0000 table_AP_0.25: 0.0000 garbagebin_AP_0.25: 0.0001 bookshelf_AP_0.25: 0.0000 picture_AP_0.25: 0.0000 curtain_AP_0.25: 0.0000 door_AP_0.25: 0.0000 cabinet_AP_0.25: 0.0000 refrigerator_AP_0.25: 0.0000 counter_AP_0.25: 0.0000 sink_AP_0.25: 0.0000 window_AP_0.25: 0.0000 desk_AP_0.25: 0.0000 bed_AP_0.25: 0.0000 toilet_AP_0.25: 0.0007 showercurtrain_AP_0.25: 0.0000 bathtub_AP_0.25: 0.0000 mAP_0.25: 0.0007 chair_rec_0.25: 0.4423 sofa_rec_0.25: 0.0000 table_rec_0.25: 0.0000 garbagebin_rec_0.25: 0.0245 bookshelf_rec_0.25: 0.0000 picture_rec_0.25: 0.0000 curtain_rec_0.25: 0.0000 door_rec_0.25: 0.0000 cabinet_rec_0.25: 0.0000 refrigerator_rec_0.25: 0.0000 counter_rec_0.25: 0.0000 sink_rec_0.25: 0.0000 window_rec_0.25: 0.0000 desk_rec_0.25: 0.0000 bed_rec_0.25: 0.0000 toilet_rec_0.25: 0.1207 showercurtrain_rec_0.25: 0.0000 bathtub_rec_0.25: 0.0000 mAR_0.25: 0.0326 chair_AP_0.50: 0.0001 sofa_AP_0.50: 0.0000 table_AP_0.50: 0.0000 garbagebin_AP_0.50: 0.0000 bookshelf_AP_0.50: 0.0000 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0000 door_AP_0.50: 0.0000 cabinet_AP_0.50: 0.0000 refrigerator_AP_0.50: 0.0000 counter_AP_0.50: 0.0000 sink_AP_0.50: 0.0000 window_AP_0.50: 0.0000 desk_AP_0.50: 0.0000 bed_AP_0.50: 0.0000 toilet_AP_0.50: 0.0000 showercurtrain_AP_0.50: 0.0000 bathtub_AP_0.50: 0.0000 mAP_0.50: 0.0000 chair_rec_0.50: 0.0387 sofa_rec_0.50: 0.0000 table_rec_0.50: 0.0000 garbagebin_rec_0.50: 0.0019 bookshelf_rec_0.50: 0.0000 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.0000 door_rec_0.50: 0.0000 cabinet_rec_0.50: 0.0000 refrigerator_rec_0.50: 0.0000 counter_rec_0.50: 0.0000 sink_rec_0.50: 0.0000 window_rec_0.50: 0.0000 desk_rec_0.50: 0.0000 bed_rec_0.50: 0.0000 toilet_rec_0.50: 0.0000 showercurtrain_rec_0.50: 0.0000 bathtub_rec_0.50: 0.0000 mAR_0.50: 0.0023 data_time: 0.2555 time: 2.8162 2025/05/11 15:21:46 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_2.pth is removed 2025/05/11 15:22:14 - mmengine - INFO - The best checkpoint with 0.0007 mAP_0.25 at 4 epoch is saved to best_mAP_0.25_epoch_4.pth. 2025/05/11 15:25:08 - mmengine - INFO - Epoch(train) [5][10/91] base_lr: 2.4994e-04 lr: 2.4994e-04 eta: 4 days, 6:10:56 time: 10.4507 data_time: 1.5464 memory: 68702 grad_norm: 0.7374 loss: 3.4213 center_loss: 0.9386 size_loss: 0.3850 cls_loss: 1.1404 giou_loss: 0.9573 2025/05/11 15:26:45 - mmengine - INFO - Epoch(train) [5][20/91] base_lr: 2.4994e-04 lr: 2.4994e-04 eta: 4 days, 6:02:10 time: 10.4692 data_time: 1.5594 memory: 68702 grad_norm: 0.7070 loss: 3.4272 center_loss: 0.9434 size_loss: 0.3878 cls_loss: 1.1363 giou_loss: 0.9597 2025/05/11 15:28:22 - mmengine - INFO - Epoch(train) [5][30/91] base_lr: 2.4994e-04 lr: 2.4994e-04 eta: 4 days, 5:52:58 time: 10.4831 data_time: 1.5604 memory: 68703 grad_norm: 0.6763 loss: 3.3966 center_loss: 0.9278 size_loss: 0.3851 cls_loss: 1.1252 giou_loss: 0.9584 2025/05/11 15:29:59 - mmengine - INFO - Epoch(train) [5][40/91] base_lr: 2.4994e-04 lr: 2.4994e-04 eta: 4 days, 5:44:05 time: 10.4855 data_time: 1.5556 memory: 68702 grad_norm: 0.6524 loss: 3.3904 center_loss: 0.9341 size_loss: 0.3848 cls_loss: 1.1112 giou_loss: 0.9602 2025/05/11 15:31:38 - mmengine - INFO - Epoch(train) [5][50/91] base_lr: 2.4994e-04 lr: 2.4994e-04 eta: 4 days, 5:37:15 time: 10.6676 data_time: 1.5679 memory: 68700 grad_norm: 0.5998 loss: 3.3630 center_loss: 0.9281 size_loss: 0.3807 cls_loss: 1.0952 giou_loss: 0.9590 2025/05/11 15:33:17 - mmengine - INFO - Epoch(train) [5][60/91] base_lr: 2.4994e-04 lr: 2.4994e-04 eta: 4 days, 5:31:44 time: 9.7768 data_time: 0.6700 memory: 68703 grad_norm: 0.5880 loss: 3.3592 center_loss: 0.9372 size_loss: 0.3838 cls_loss: 1.0787 giou_loss: 0.9594 2025/05/11 15:34:54 - mmengine - INFO - Epoch(train) [5][70/91] base_lr: 2.4994e-04 lr: 2.4994e-04 eta: 4 days, 5:24:20 time: 9.7742 data_time: 0.6615 memory: 68703 grad_norm: 0.5808 loss: 3.3452 center_loss: 0.9357 size_loss: 0.3809 cls_loss: 1.0731 giou_loss: 0.9554 2025/05/11 15:36:31 - mmengine - INFO - Epoch(train) [5][80/91] base_lr: 2.4994e-04 lr: 2.4994e-04 eta: 4 days, 5:16:29 time: 9.7718 data_time: 0.6587 memory: 68702 grad_norm: 0.5709 loss: 3.3477 center_loss: 0.9432 size_loss: 0.3815 cls_loss: 1.0676 giou_loss: 0.9553 2025/05/11 15:38:07 - mmengine - INFO - Epoch(train) [5][90/91] base_lr: 2.4994e-04 lr: 2.4994e-04 eta: 4 days, 5:08:22 time: 9.7617 data_time: 0.6538 memory: 68702 grad_norm: 0.5652 loss: 3.3299 center_loss: 0.9359 size_loss: 0.3785 cls_loss: 1.0646 giou_loss: 0.9508 2025/05/11 15:38:10 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 15:40:39 - mmengine - INFO - Epoch(train) [6][10/91] base_lr: 2.4990e-04 lr: 2.4990e-04 eta: 4 days, 5:57:33 time: 10.6074 data_time: 1.6166 memory: 68702 grad_norm: 0.5737 loss: 3.3491 center_loss: 0.9413 size_loss: 0.3809 cls_loss: 1.0730 giou_loss: 0.9539 2025/05/11 15:42:15 - mmengine - INFO - Epoch(train) [6][20/91] base_lr: 2.4990e-04 lr: 2.4990e-04 eta: 4 days, 5:49:02 time: 10.5802 data_time: 1.6043 memory: 68702 grad_norm: 0.5571 loss: 3.3363 center_loss: 0.9283 size_loss: 0.3764 cls_loss: 1.0800 giou_loss: 0.9516 2025/05/11 15:43:53 - mmengine - INFO - Epoch(train) [6][30/91] base_lr: 2.4990e-04 lr: 2.4990e-04 eta: 4 days, 5:41:24 time: 10.5794 data_time: 1.5868 memory: 68702 grad_norm: 0.5394 loss: 3.3432 center_loss: 0.9362 size_loss: 0.3756 cls_loss: 1.0753 giou_loss: 0.9562 2025/05/11 15:45:30 - mmengine - INFO - Epoch(train) [6][40/91] base_lr: 2.4990e-04 lr: 2.4990e-04 eta: 4 days, 5:34:35 time: 10.5882 data_time: 1.5697 memory: 68703 grad_norm: 0.5425 loss: 3.3424 center_loss: 0.9355 size_loss: 0.3786 cls_loss: 1.0713 giou_loss: 0.9570 2025/05/11 15:47:08 - mmengine - INFO - Epoch(train) [6][50/91] base_lr: 2.4990e-04 lr: 2.4990e-04 eta: 4 days, 5:28:31 time: 10.7760 data_time: 1.5746 memory: 68702 grad_norm: 0.5187 loss: 3.3417 center_loss: 0.9442 size_loss: 0.3815 cls_loss: 1.0566 giou_loss: 0.9594 2025/05/11 15:48:47 - mmengine - INFO - Epoch(train) [6][60/91] base_lr: 2.4990e-04 lr: 2.4990e-04 eta: 4 days, 5:22:37 time: 9.7593 data_time: 0.5925 memory: 68702 grad_norm: 0.5070 loss: 3.3403 center_loss: 0.9426 size_loss: 0.3830 cls_loss: 1.0561 giou_loss: 0.9585 2025/05/11 15:50:24 - mmengine - INFO - Epoch(train) [6][70/91] base_lr: 2.4990e-04 lr: 2.4990e-04 eta: 4 days, 5:15:37 time: 9.7655 data_time: 0.6029 memory: 68702 grad_norm: 0.4988 loss: 3.3390 center_loss: 0.9472 size_loss: 0.3857 cls_loss: 1.0480 giou_loss: 0.9580 2025/05/11 15:52:01 - mmengine - INFO - Epoch(train) [6][80/91] base_lr: 2.4990e-04 lr: 2.4990e-04 eta: 4 days, 5:09:02 time: 9.7655 data_time: 0.6056 memory: 68702 grad_norm: 0.4897 loss: 3.3307 center_loss: 0.9428 size_loss: 0.3863 cls_loss: 1.0476 giou_loss: 0.9540 2025/05/11 15:53:37 - mmengine - INFO - Epoch(train) [6][90/91] base_lr: 2.4990e-04 lr: 2.4990e-04 eta: 4 days, 5:01:27 time: 9.7350 data_time: 0.6124 memory: 68702 grad_norm: 0.4676 loss: 3.3296 center_loss: 0.9505 size_loss: 0.3856 cls_loss: 1.0364 giou_loss: 0.9571 2025/05/11 15:53:39 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 15:53:39 - mmengine - INFO - Saving checkpoint at 6 epochs 2025/05/11 15:54:36 - mmengine - INFO - Epoch(val) [6][10/39] eta: 0:01:36 time: 2.9152 data_time: 0.3624 memory: 15952 2025/05/11 15:55:03 - mmengine - INFO - Epoch(val) [6][20/39] eta: 0:00:56 time: 2.7696 data_time: 0.2268 memory: 13407 2025/05/11 15:55:29 - mmengine - INFO - Epoch(val) [6][30/39] eta: 0:00:25 time: 2.7569 data_time: 0.2256 memory: 13407 2025/05/11 15:55:58 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.0007 | 0.0094 | 0.0000 | 0.0000 | | door | 0.0000 | 0.0064 | 0.0000 | 0.0000 | | chair | 0.0154 | 0.4108 | 0.0001 | 0.0402 | | sofa | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | table | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | bookshelf | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | picture | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | curtain | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | cabinet | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | refrigerator | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | counter | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | sink | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | window | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | desk | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | bed | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | toilet | 0.0068 | 0.0517 | 0.0000 | 0.0000 | | showercurtrain | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | bathtub | 0.0000 | 0.0000 | 0.0000 | 0.0000 | +----------------+---------+---------+---------+---------+ | Overall | 0.0013 | 0.0266 | 0.0000 | 0.0022 | +----------------+---------+---------+---------+---------+ 2025/05/11 15:55:58 - mmengine - INFO - Epoch(val) [6][39/39] chair_AP_0.25: 0.0154 sofa_AP_0.25: 0.0000 table_AP_0.25: 0.0000 garbagebin_AP_0.25: 0.0007 bookshelf_AP_0.25: 0.0000 picture_AP_0.25: 0.0000 curtain_AP_0.25: 0.0000 door_AP_0.25: 0.0000 cabinet_AP_0.25: 0.0000 refrigerator_AP_0.25: 0.0000 counter_AP_0.25: 0.0000 sink_AP_0.25: 0.0000 window_AP_0.25: 0.0000 desk_AP_0.25: 0.0000 bed_AP_0.25: 0.0000 toilet_AP_0.25: 0.0068 showercurtrain_AP_0.25: 0.0000 bathtub_AP_0.25: 0.0000 mAP_0.25: 0.0013 chair_rec_0.25: 0.4108 sofa_rec_0.25: 0.0000 table_rec_0.25: 0.0000 garbagebin_rec_0.25: 0.0094 bookshelf_rec_0.25: 0.0000 picture_rec_0.25: 0.0000 curtain_rec_0.25: 0.0000 door_rec_0.25: 0.0064 cabinet_rec_0.25: 0.0000 refrigerator_rec_0.25: 0.0000 counter_rec_0.25: 0.0000 sink_rec_0.25: 0.0000 window_rec_0.25: 0.0000 desk_rec_0.25: 0.0000 bed_rec_0.25: 0.0000 toilet_rec_0.25: 0.0517 showercurtrain_rec_0.25: 0.0000 bathtub_rec_0.25: 0.0000 mAR_0.25: 0.0266 chair_AP_0.50: 0.0001 sofa_AP_0.50: 0.0000 table_AP_0.50: 0.0000 garbagebin_AP_0.50: 0.0000 bookshelf_AP_0.50: 0.0000 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0000 door_AP_0.50: 0.0000 cabinet_AP_0.50: 0.0000 refrigerator_AP_0.50: 0.0000 counter_AP_0.50: 0.0000 sink_AP_0.50: 0.0000 window_AP_0.50: 0.0000 desk_AP_0.50: 0.0000 bed_AP_0.50: 0.0000 toilet_AP_0.50: 0.0000 showercurtrain_AP_0.50: 0.0000 bathtub_AP_0.50: 0.0000 mAP_0.50: 0.0000 chair_rec_0.50: 0.0402 sofa_rec_0.50: 0.0000 table_rec_0.50: 0.0000 garbagebin_rec_0.50: 0.0000 bookshelf_rec_0.50: 0.0000 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.0000 door_rec_0.50: 0.0000 cabinet_rec_0.50: 0.0000 refrigerator_rec_0.50: 0.0000 counter_rec_0.50: 0.0000 sink_rec_0.50: 0.0000 window_rec_0.50: 0.0000 desk_rec_0.50: 0.0000 bed_rec_0.50: 0.0000 toilet_rec_0.50: 0.0000 showercurtrain_rec_0.50: 0.0000 bathtub_rec_0.50: 0.0000 mAR_0.50: 0.0022 data_time: 0.2645 time: 2.7715 2025/05/11 15:55:58 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_4.pth is removed 2025/05/11 15:56:19 - mmengine - INFO - The best checkpoint with 0.0013 mAP_0.25 at 6 epoch is saved to best_mAP_0.25_epoch_6.pth. 2025/05/11 15:59:11 - mmengine - INFO - Epoch(train) [7][10/91] base_lr: 2.4986e-04 lr: 2.4986e-04 eta: 4 days, 5:35:25 time: 10.4777 data_time: 1.5312 memory: 68700 grad_norm: 0.4874 loss: 3.3345 center_loss: 0.9519 size_loss: 0.3905 cls_loss: 1.0359 giou_loss: 0.9562 2025/05/11 16:00:49 - mmengine - INFO - Epoch(train) [7][20/91] base_lr: 2.4986e-04 lr: 2.4986e-04 eta: 4 days, 5:28:36 time: 10.4588 data_time: 1.5070 memory: 68700 grad_norm: 0.4823 loss: 3.3171 center_loss: 0.9476 size_loss: 0.3879 cls_loss: 1.0311 giou_loss: 0.9505 2025/05/11 16:02:25 - mmengine - INFO - Epoch(train) [7][30/91] base_lr: 2.4986e-04 lr: 2.4986e-04 eta: 4 days, 5:21:40 time: 10.4532 data_time: 1.4987 memory: 68701 grad_norm: 0.4734 loss: 3.3214 center_loss: 0.9494 size_loss: 0.3843 cls_loss: 1.0358 giou_loss: 0.9519 2025/05/11 16:04:02 - mmengine - INFO - Epoch(train) [7][40/91] base_lr: 2.4986e-04 lr: 2.4986e-04 eta: 4 days, 5:14:08 time: 10.4303 data_time: 1.4951 memory: 68702 grad_norm: 0.4820 loss: 3.3230 center_loss: 0.9557 size_loss: 0.3838 cls_loss: 1.0316 giou_loss: 0.9519 2025/05/11 16:05:39 - mmengine - INFO - Epoch(train) [7][50/91] base_lr: 2.4986e-04 lr: 2.4986e-04 eta: 4 days, 5:07:43 time: 10.6022 data_time: 1.5045 memory: 68702 grad_norm: 0.5015 loss: 3.3053 center_loss: 0.9395 size_loss: 0.3729 cls_loss: 1.0471 giou_loss: 0.9457 2025/05/11 16:07:15 - mmengine - INFO - Epoch(train) [7][60/91] base_lr: 2.4986e-04 lr: 2.4986e-04 eta: 4 days, 5:01:04 time: 9.6779 data_time: 0.5719 memory: 68702 grad_norm: 0.5208 loss: 3.3077 center_loss: 0.9522 size_loss: 0.3713 cls_loss: 1.0375 giou_loss: 0.9469 2025/05/11 16:08:52 - mmengine - INFO - Epoch(train) [7][70/91] base_lr: 2.4986e-04 lr: 2.4986e-04 eta: 4 days, 4:54:34 time: 9.6672 data_time: 0.5877 memory: 68703 grad_norm: 0.5307 loss: 3.3036 center_loss: 0.9453 size_loss: 0.3686 cls_loss: 1.0421 giou_loss: 0.9477 2025/05/11 16:10:30 - mmengine - INFO - Epoch(train) [7][80/91] base_lr: 2.4986e-04 lr: 2.4986e-04 eta: 4 days, 4:49:42 time: 9.6928 data_time: 0.6175 memory: 68702 grad_norm: 0.5579 loss: 3.2829 center_loss: 0.9380 size_loss: 0.3622 cls_loss: 1.0370 giou_loss: 0.9457 2025/05/11 16:12:07 - mmengine - INFO - Epoch(train) [7][90/91] base_lr: 2.4986e-04 lr: 2.4986e-04 eta: 4 days, 4:43:15 time: 9.6983 data_time: 0.6158 memory: 68702 grad_norm: 0.5545 loss: 3.2742 center_loss: 0.9303 size_loss: 0.3620 cls_loss: 1.0359 giou_loss: 0.9461 2025/05/11 16:12:08 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 16:14:33 - mmengine - INFO - Epoch(train) [8][10/91] base_lr: 2.4981e-04 lr: 2.4981e-04 eta: 4 days, 5:13:52 time: 10.4984 data_time: 1.5200 memory: 68700 grad_norm: 0.5262 loss: 3.2689 center_loss: 0.9249 size_loss: 0.3634 cls_loss: 1.0340 giou_loss: 0.9465 2025/05/11 16:16:10 - mmengine - INFO - Epoch(train) [8][20/91] base_lr: 2.4981e-04 lr: 2.4981e-04 eta: 4 days, 5:07:29 time: 10.4991 data_time: 1.5087 memory: 68703 grad_norm: 0.5110 loss: 3.2476 center_loss: 0.9117 size_loss: 0.3573 cls_loss: 1.0375 giou_loss: 0.9411 2025/05/11 16:17:47 - mmengine - INFO - Epoch(train) [8][30/91] base_lr: 2.4981e-04 lr: 2.4981e-04 eta: 4 days, 5:01:01 time: 10.5020 data_time: 1.4953 memory: 68702 grad_norm: 0.5090 loss: 3.2460 center_loss: 0.9088 size_loss: 0.3597 cls_loss: 1.0390 giou_loss: 0.9385 2025/05/11 16:19:24 - mmengine - INFO - Epoch(train) [8][40/91] base_lr: 2.4981e-04 lr: 2.4981e-04 eta: 4 days, 4:55:03 time: 10.4726 data_time: 1.4624 memory: 68702 grad_norm: 0.4934 loss: 3.2609 center_loss: 0.9182 size_loss: 0.3657 cls_loss: 1.0332 giou_loss: 0.9437 2025/05/11 16:21:01 - mmengine - INFO - Epoch(train) [8][50/91] base_lr: 2.4981e-04 lr: 2.4981e-04 eta: 4 days, 4:49:33 time: 10.6464 data_time: 1.4833 memory: 68702 grad_norm: 0.4597 loss: 3.2732 center_loss: 0.9154 size_loss: 0.3695 cls_loss: 1.0466 giou_loss: 0.9417 2025/05/11 16:22:38 - mmengine - INFO - Epoch(train) [8][60/91] base_lr: 2.4981e-04 lr: 2.4981e-04 eta: 4 days, 4:43:54 time: 9.6902 data_time: 0.5663 memory: 68702 grad_norm: 0.4519 loss: 3.2841 center_loss: 0.9217 size_loss: 0.3687 cls_loss: 1.0538 giou_loss: 0.9399 2025/05/11 16:24:15 - mmengine - INFO - Epoch(train) [8][70/91] base_lr: 2.4981e-04 lr: 2.4981e-04 eta: 4 days, 4:38:20 time: 9.6940 data_time: 0.5860 memory: 68702 grad_norm: 0.4444 loss: 3.3055 center_loss: 0.9283 size_loss: 0.3777 cls_loss: 1.0559 giou_loss: 0.9437 2025/05/11 16:25:51 - mmengine - INFO - Epoch(train) [8][80/91] base_lr: 2.4981e-04 lr: 2.4981e-04 eta: 4 days, 4:32:37 time: 9.6968 data_time: 0.5915 memory: 68702 grad_norm: 0.4616 loss: 3.3028 center_loss: 0.9310 size_loss: 0.3725 cls_loss: 1.0547 giou_loss: 0.9446 2025/05/11 16:27:27 - mmengine - INFO - Epoch(train) [8][90/91] base_lr: 2.4981e-04 lr: 2.4981e-04 eta: 4 days, 4:25:49 time: 9.6625 data_time: 0.5821 memory: 68702 grad_norm: 0.5023 loss: 3.2957 center_loss: 0.9328 size_loss: 0.3738 cls_loss: 1.0486 giou_loss: 0.9406 2025/05/11 16:27:29 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 16:27:29 - mmengine - INFO - Saving checkpoint at 8 epochs 2025/05/11 16:28:26 - mmengine - INFO - Epoch(val) [8][10/39] eta: 0:01:36 time: 2.8799 data_time: 0.3697 memory: 15952 2025/05/11 16:28:53 - mmengine - INFO - Epoch(val) [8][20/39] eta: 0:00:56 time: 2.7487 data_time: 0.2363 memory: 13407 2025/05/11 16:29:19 - mmengine - INFO - Epoch(val) [8][30/39] eta: 0:00:25 time: 2.7496 data_time: 0.2264 memory: 13407 2025/05/11 16:29:48 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | door | 0.0001 | 0.0278 | 0.0000 | 0.0021 | | chair | 0.0101 | 0.3114 | 0.0001 | 0.0270 | | sofa | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | table | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | bookshelf | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | picture | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | curtain | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | cabinet | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | refrigerator | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | counter | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | sink | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | toilet | 0.0004 | 0.0172 | 0.0000 | 0.0000 | | window | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | desk | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | bed | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | showercurtrain | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | bathtub | 0.0000 | 0.0000 | 0.0000 | 0.0000 | +----------------+---------+---------+---------+---------+ | Overall | 0.0006 | 0.0198 | 0.0000 | 0.0016 | +----------------+---------+---------+---------+---------+ 2025/05/11 16:29:49 - mmengine - INFO - Epoch(val) [8][39/39] chair_AP_0.25: 0.0101 sofa_AP_0.25: 0.0000 table_AP_0.25: 0.0000 garbagebin_AP_0.25: 0.0000 bookshelf_AP_0.25: 0.0000 picture_AP_0.25: 0.0000 curtain_AP_0.25: 0.0000 door_AP_0.25: 0.0001 cabinet_AP_0.25: 0.0000 refrigerator_AP_0.25: 0.0000 counter_AP_0.25: 0.0000 sink_AP_0.25: 0.0000 window_AP_0.25: 0.0000 desk_AP_0.25: 0.0000 bed_AP_0.25: 0.0000 toilet_AP_0.25: 0.0004 showercurtrain_AP_0.25: 0.0000 bathtub_AP_0.25: 0.0000 mAP_0.25: 0.0006 chair_rec_0.25: 0.3114 sofa_rec_0.25: 0.0000 table_rec_0.25: 0.0000 garbagebin_rec_0.25: 0.0000 bookshelf_rec_0.25: 0.0000 picture_rec_0.25: 0.0000 curtain_rec_0.25: 0.0000 door_rec_0.25: 0.0278 cabinet_rec_0.25: 0.0000 refrigerator_rec_0.25: 0.0000 counter_rec_0.25: 0.0000 sink_rec_0.25: 0.0000 window_rec_0.25: 0.0000 desk_rec_0.25: 0.0000 bed_rec_0.25: 0.0000 toilet_rec_0.25: 0.0172 showercurtrain_rec_0.25: 0.0000 bathtub_rec_0.25: 0.0000 mAR_0.25: 0.0198 chair_AP_0.50: 0.0001 sofa_AP_0.50: 0.0000 table_AP_0.50: 0.0000 garbagebin_AP_0.50: 0.0000 bookshelf_AP_0.50: 0.0000 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0000 door_AP_0.50: 0.0000 cabinet_AP_0.50: 0.0000 refrigerator_AP_0.50: 0.0000 counter_AP_0.50: 0.0000 sink_AP_0.50: 0.0000 window_AP_0.50: 0.0000 desk_AP_0.50: 0.0000 bed_AP_0.50: 0.0000 toilet_AP_0.50: 0.0000 showercurtrain_AP_0.50: 0.0000 bathtub_AP_0.50: 0.0000 mAP_0.50: 0.0000 chair_rec_0.50: 0.0270 sofa_rec_0.50: 0.0000 table_rec_0.50: 0.0000 garbagebin_rec_0.50: 0.0000 bookshelf_rec_0.50: 0.0000 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.0000 door_rec_0.50: 0.0021 cabinet_rec_0.50: 0.0000 refrigerator_rec_0.50: 0.0000 counter_rec_0.50: 0.0000 sink_rec_0.50: 0.0000 window_rec_0.50: 0.0000 desk_rec_0.50: 0.0000 bed_rec_0.50: 0.0000 toilet_rec_0.50: 0.0000 showercurtrain_rec_0.50: 0.0000 bathtub_rec_0.50: 0.0000 mAR_0.50: 0.0016 data_time: 0.2633 time: 2.7898 2025/05/11 16:32:14 - mmengine - INFO - Epoch(train) [9][10/91] base_lr: 2.4975e-04 lr: 2.4975e-04 eta: 4 days, 4:52:37 time: 10.4625 data_time: 1.4768 memory: 68702 grad_norm: 0.5294 loss: 3.2692 center_loss: 0.9307 size_loss: 0.3660 cls_loss: 1.0345 giou_loss: 0.9381 2025/05/11 16:33:50 - mmengine - INFO - Epoch(train) [9][20/91] base_lr: 2.4975e-04 lr: 2.4975e-04 eta: 4 days, 4:46:51 time: 10.4572 data_time: 1.4815 memory: 68701 grad_norm: 0.5510 loss: 3.2706 center_loss: 0.9292 size_loss: 0.3663 cls_loss: 1.0361 giou_loss: 0.9390 2025/05/11 16:35:28 - mmengine - INFO - Epoch(train) [9][30/91] base_lr: 2.4975e-04 lr: 2.4975e-04 eta: 4 days, 4:42:15 time: 10.4832 data_time: 1.4906 memory: 68702 grad_norm: 0.5623 loss: 3.2599 center_loss: 0.9316 size_loss: 0.3629 cls_loss: 1.0303 giou_loss: 0.9351 2025/05/11 16:37:05 - mmengine - INFO - Epoch(train) [9][40/91] base_lr: 2.4975e-04 lr: 2.4975e-04 eta: 4 days, 4:36:21 time: 10.4740 data_time: 1.4988 memory: 68702 grad_norm: 0.5497 loss: 3.2425 center_loss: 0.9302 size_loss: 0.3608 cls_loss: 1.0177 giou_loss: 0.9338 2025/05/11 16:38:43 - mmengine - INFO - Epoch(train) [9][50/91] base_lr: 2.4975e-04 lr: 2.4975e-04 eta: 4 days, 4:32:02 time: 10.6841 data_time: 1.5425 memory: 68702 grad_norm: 0.4785 loss: 3.2592 center_loss: 0.9280 size_loss: 0.3616 cls_loss: 1.0373 giou_loss: 0.9324 2025/05/11 16:40:20 - mmengine - INFO - Epoch(train) [9][60/91] base_lr: 2.4975e-04 lr: 2.4975e-04 eta: 4 days, 4:27:05 time: 9.7307 data_time: 0.6535 memory: 68703 grad_norm: 0.4840 loss: 3.2536 center_loss: 0.9238 size_loss: 0.3633 cls_loss: 1.0328 giou_loss: 0.9336 2025/05/11 16:41:57 - mmengine - INFO - Epoch(train) [9][70/91] base_lr: 2.4975e-04 lr: 2.4975e-04 eta: 4 days, 4:22:03 time: 9.7374 data_time: 0.6708 memory: 68703 grad_norm: 0.4812 loss: 3.2394 center_loss: 0.9168 size_loss: 0.3596 cls_loss: 1.0351 giou_loss: 0.9279 2025/05/11 16:43:34 - mmengine - INFO - Epoch(train) [9][80/91] base_lr: 2.4975e-04 lr: 2.4975e-04 eta: 4 days, 4:16:49 time: 9.7098 data_time: 0.6696 memory: 68701 grad_norm: 0.5044 loss: 3.2394 center_loss: 0.9134 size_loss: 0.3583 cls_loss: 1.0409 giou_loss: 0.9268 2025/05/11 16:45:11 - mmengine - INFO - Epoch(train) [9][90/91] base_lr: 2.4975e-04 lr: 2.4975e-04 eta: 4 days, 4:11:46 time: 9.7198 data_time: 0.6677 memory: 68702 grad_norm: 0.5077 loss: 3.2596 center_loss: 0.9155 size_loss: 0.3635 cls_loss: 1.0544 giou_loss: 0.9262 2025/05/11 16:45:13 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 16:47:43 - mmengine - INFO - Epoch(train) [10][10/91] base_lr: 2.4969e-04 lr: 2.4969e-04 eta: 4 days, 4:39:13 time: 10.6120 data_time: 1.5581 memory: 68702 grad_norm: 0.5374 loss: 3.2789 center_loss: 0.9387 size_loss: 0.3610 cls_loss: 1.0448 giou_loss: 0.9344 2025/05/11 16:49:20 - mmengine - INFO - Epoch(train) [10][20/91] base_lr: 2.4969e-04 lr: 2.4969e-04 eta: 4 days, 4:33:41 time: 10.5950 data_time: 1.5389 memory: 68702 grad_norm: 0.5603 loss: 3.2922 center_loss: 0.9482 size_loss: 0.3627 cls_loss: 1.0470 giou_loss: 0.9343 2025/05/11 16:50:56 - mmengine - INFO - Epoch(train) [10][30/91] base_lr: 2.4969e-04 lr: 2.4969e-04 eta: 4 days, 4:28:15 time: 10.5881 data_time: 1.5254 memory: 68702 grad_norm: 0.6386 loss: 3.2737 center_loss: 0.9540 size_loss: 0.3641 cls_loss: 1.0210 giou_loss: 0.9346 2025/05/11 16:52:33 - mmengine - INFO - Epoch(train) [10][40/91] base_lr: 2.4969e-04 lr: 2.4969e-04 eta: 4 days, 4:22:56 time: 10.5732 data_time: 1.5083 memory: 68702 grad_norm: 0.7985 loss: 3.2658 center_loss: 0.9497 size_loss: 0.3622 cls_loss: 1.0207 giou_loss: 0.9332 2025/05/11 16:54:10 - mmengine - INFO - Epoch(train) [10][50/91] base_lr: 2.4969e-04 lr: 2.4969e-04 eta: 4 days, 4:18:24 time: 10.7469 data_time: 1.5240 memory: 68703 grad_norm: 0.7752 loss: 3.2335 center_loss: 0.9353 size_loss: 0.3576 cls_loss: 1.0159 giou_loss: 0.9247 2025/05/11 16:55:46 - mmengine - INFO - Epoch(train) [10][60/91] base_lr: 2.4969e-04 lr: 2.4969e-04 eta: 4 days, 4:13:07 time: 9.6639 data_time: 0.6009 memory: 68702 grad_norm: 0.8464 loss: 3.2206 center_loss: 0.9274 size_loss: 0.3581 cls_loss: 1.0120 giou_loss: 0.9230 2025/05/11 16:57:23 - mmengine - INFO - Epoch(train) [10][70/91] base_lr: 2.4969e-04 lr: 2.4969e-04 eta: 4 days, 4:08:26 time: 9.6767 data_time: 0.6091 memory: 68702 grad_norm: 0.8416 loss: 3.2126 center_loss: 0.9134 size_loss: 0.3573 cls_loss: 1.0228 giou_loss: 0.9191 2025/05/11 16:59:00 - mmengine - INFO - Epoch(train) [10][80/91] base_lr: 2.4969e-04 lr: 2.4969e-04 eta: 4 days, 4:03:45 time: 9.6872 data_time: 0.6165 memory: 68701 grad_norm: 0.7667 loss: 3.2326 center_loss: 0.9120 size_loss: 0.3585 cls_loss: 1.0423 giou_loss: 0.9199 2025/05/11 17:00:37 - mmengine - INFO - Epoch(train) [10][90/91] base_lr: 2.4969e-04 lr: 2.4969e-04 eta: 4 days, 3:58:42 time: 9.6838 data_time: 0.6231 memory: 68702 grad_norm: 0.5862 loss: 3.2547 center_loss: 0.9194 size_loss: 0.3613 cls_loss: 1.0521 giou_loss: 0.9219 2025/05/11 17:00:39 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 17:00:39 - mmengine - INFO - Saving checkpoint at 10 epochs 2025/05/11 17:01:35 - mmengine - INFO - Epoch(val) [10][10/39] eta: 0:01:35 time: 2.8919 data_time: 0.3630 memory: 15952 2025/05/11 17:02:01 - mmengine - INFO - Epoch(val) [10][20/39] eta: 0:00:55 time: 2.7441 data_time: 0.2199 memory: 13407 2025/05/11 17:02:26 - mmengine - INFO - Epoch(val) [10][30/39] eta: 0:00:25 time: 2.7290 data_time: 0.2100 memory: 13407 2025/05/11 17:02:55 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.0000 | 0.0038 | 0.0000 | 0.0019 | | chair | 0.0292 | 0.3904 | 0.0002 | 0.0358 | | sofa | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | table | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | bookshelf | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | picture | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | curtain | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | bed | 0.0028 | 0.1358 | 0.0000 | 0.0000 | | door | 0.0001 | 0.0171 | 0.0000 | 0.0021 | | cabinet | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | refrigerator | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | counter | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | sink | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | window | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | desk | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | toilet | 0.0150 | 0.1207 | 0.0006 | 0.0345 | | showercurtrain | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | bathtub | 0.0000 | 0.0000 | 0.0000 | 0.0000 | +----------------+---------+---------+---------+---------+ | Overall | 0.0026 | 0.0371 | 0.0000 | 0.0041 | +----------------+---------+---------+---------+---------+ 2025/05/11 17:02:55 - mmengine - INFO - Epoch(val) [10][39/39] chair_AP_0.25: 0.0292 sofa_AP_0.25: 0.0000 table_AP_0.25: 0.0000 garbagebin_AP_0.25: 0.0000 bookshelf_AP_0.25: 0.0000 picture_AP_0.25: 0.0000 curtain_AP_0.25: 0.0000 door_AP_0.25: 0.0001 cabinet_AP_0.25: 0.0000 refrigerator_AP_0.25: 0.0000 counter_AP_0.25: 0.0000 sink_AP_0.25: 0.0000 window_AP_0.25: 0.0000 desk_AP_0.25: 0.0000 bed_AP_0.25: 0.0028 toilet_AP_0.25: 0.0150 showercurtrain_AP_0.25: 0.0000 bathtub_AP_0.25: 0.0000 mAP_0.25: 0.0026 chair_rec_0.25: 0.3904 sofa_rec_0.25: 0.0000 table_rec_0.25: 0.0000 garbagebin_rec_0.25: 0.0038 bookshelf_rec_0.25: 0.0000 picture_rec_0.25: 0.0000 curtain_rec_0.25: 0.0000 door_rec_0.25: 0.0171 cabinet_rec_0.25: 0.0000 refrigerator_rec_0.25: 0.0000 counter_rec_0.25: 0.0000 sink_rec_0.25: 0.0000 window_rec_0.25: 0.0000 desk_rec_0.25: 0.0000 bed_rec_0.25: 0.1358 toilet_rec_0.25: 0.1207 showercurtrain_rec_0.25: 0.0000 bathtub_rec_0.25: 0.0000 mAR_0.25: 0.0371 chair_AP_0.50: 0.0002 sofa_AP_0.50: 0.0000 table_AP_0.50: 0.0000 garbagebin_AP_0.50: 0.0000 bookshelf_AP_0.50: 0.0000 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0000 door_AP_0.50: 0.0000 cabinet_AP_0.50: 0.0000 refrigerator_AP_0.50: 0.0000 counter_AP_0.50: 0.0000 sink_AP_0.50: 0.0000 window_AP_0.50: 0.0000 desk_AP_0.50: 0.0000 bed_AP_0.50: 0.0000 toilet_AP_0.50: 0.0006 showercurtrain_AP_0.50: 0.0000 bathtub_AP_0.50: 0.0000 mAP_0.50: 0.0000 chair_rec_0.50: 0.0358 sofa_rec_0.50: 0.0000 table_rec_0.50: 0.0000 garbagebin_rec_0.50: 0.0019 bookshelf_rec_0.50: 0.0000 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.0000 door_rec_0.50: 0.0021 cabinet_rec_0.50: 0.0000 refrigerator_rec_0.50: 0.0000 counter_rec_0.50: 0.0000 sink_rec_0.50: 0.0000 window_rec_0.50: 0.0000 desk_rec_0.50: 0.0000 bed_rec_0.50: 0.0000 toilet_rec_0.50: 0.0345 showercurtrain_rec_0.50: 0.0000 bathtub_rec_0.50: 0.0000 mAR_0.50: 0.0041 data_time: 0.2451 time: 2.7475 2025/05/11 17:02:55 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_6.pth is removed 2025/05/11 17:03:20 - mmengine - INFO - The best checkpoint with 0.0026 mAP_0.25 at 10 epoch is saved to best_mAP_0.25_epoch_10.pth. 2025/05/11 17:06:12 - mmengine - INFO - Epoch(train) [11][10/91] base_lr: 2.4961e-04 lr: 2.4961e-04 eta: 4 days, 4:20:13 time: 10.4998 data_time: 1.5620 memory: 68700 grad_norm: 0.6253 loss: 3.2716 center_loss: 0.9151 size_loss: 0.3604 cls_loss: 1.0767 giou_loss: 0.9194 2025/05/11 17:07:47 - mmengine - INFO - Epoch(train) [11][20/91] base_lr: 2.4961e-04 lr: 2.4961e-04 eta: 4 days, 4:14:32 time: 10.4827 data_time: 1.5581 memory: 68703 grad_norm: 0.5736 loss: 3.2622 center_loss: 0.9091 size_loss: 0.3566 cls_loss: 1.0795 giou_loss: 0.9169 2025/05/11 17:09:23 - mmengine - INFO - Epoch(train) [11][30/91] base_lr: 2.4961e-04 lr: 2.4961e-04 eta: 4 days, 4:09:13 time: 10.4588 data_time: 1.5573 memory: 68702 grad_norm: 0.5782 loss: 3.2692 center_loss: 0.9203 size_loss: 0.3546 cls_loss: 1.0759 giou_loss: 0.9184 2025/05/11 17:10:59 - mmengine - INFO - Epoch(train) [11][40/91] base_lr: 2.4961e-04 lr: 2.4961e-04 eta: 4 days, 4:03:53 time: 10.4340 data_time: 1.5499 memory: 68702 grad_norm: 0.5849 loss: 3.2484 center_loss: 0.9167 size_loss: 0.3500 cls_loss: 1.0651 giou_loss: 0.9166 2025/05/11 17:12:35 - mmengine - INFO - Epoch(train) [11][50/91] base_lr: 2.4961e-04 lr: 2.4961e-04 eta: 4 days, 3:59:00 time: 10.5932 data_time: 1.5560 memory: 68702 grad_norm: 0.5543 loss: 3.2385 center_loss: 0.9216 size_loss: 0.3506 cls_loss: 1.0499 giou_loss: 0.9163 2025/05/11 17:14:13 - mmengine - INFO - Epoch(train) [11][60/91] base_lr: 2.4961e-04 lr: 2.4961e-04 eta: 4 days, 3:54:50 time: 9.6254 data_time: 0.6120 memory: 68702 grad_norm: 0.5448 loss: 3.2249 center_loss: 0.9232 size_loss: 0.3510 cls_loss: 1.0360 giou_loss: 0.9147 2025/05/11 17:15:49 - mmengine - INFO - Epoch(train) [11][70/91] base_lr: 2.4961e-04 lr: 2.4961e-04 eta: 4 days, 3:49:56 time: 9.6388 data_time: 0.6268 memory: 68700 grad_norm: 0.5482 loss: 3.2271 center_loss: 0.9222 size_loss: 0.3544 cls_loss: 1.0379 giou_loss: 0.9126 2025/05/11 17:17:25 - mmengine - INFO - Epoch(train) [11][80/91] base_lr: 2.4961e-04 lr: 2.4961e-04 eta: 4 days, 3:44:47 time: 9.6327 data_time: 0.6291 memory: 68702 grad_norm: 0.5506 loss: 3.2031 center_loss: 0.9114 size_loss: 0.3503 cls_loss: 1.0320 giou_loss: 0.9093 2025/05/11 17:19:00 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 17:19:00 - mmengine - INFO - Epoch(train) [11][90/91] base_lr: 2.4961e-04 lr: 2.4961e-04 eta: 4 days, 3:39:13 time: 9.6128 data_time: 0.6236 memory: 68702 grad_norm: 0.5604 loss: 3.2166 center_loss: 0.9133 size_loss: 0.3506 cls_loss: 1.0440 giou_loss: 0.9088 2025/05/11 17:19:02 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 17:21:29 - mmengine - INFO - Epoch(train) [12][10/91] base_lr: 2.4953e-04 lr: 2.4953e-04 eta: 4 days, 3:59:28 time: 10.4813 data_time: 1.5325 memory: 68701 grad_norm: 0.6287 loss: 3.2136 center_loss: 0.9085 size_loss: 0.3456 cls_loss: 1.0525 giou_loss: 0.9070 2025/05/11 17:23:06 - mmengine - INFO - Epoch(train) [12][20/91] base_lr: 2.4953e-04 lr: 2.4953e-04 eta: 4 days, 3:54:53 time: 10.4601 data_time: 1.5110 memory: 68702 grad_norm: 0.6509 loss: 3.2204 center_loss: 0.9127 size_loss: 0.3477 cls_loss: 1.0504 giou_loss: 0.9096 2025/05/11 17:24:43 - mmengine - INFO - Epoch(train) [12][30/91] base_lr: 2.4953e-04 lr: 2.4953e-04 eta: 4 days, 3:50:26 time: 10.4789 data_time: 1.5023 memory: 68702 grad_norm: 0.6840 loss: 3.2334 center_loss: 0.9152 size_loss: 0.3496 cls_loss: 1.0557 giou_loss: 0.9130 2025/05/11 17:26:18 - mmengine - INFO - Epoch(train) [12][40/91] base_lr: 2.4953e-04 lr: 2.4953e-04 eta: 4 days, 3:45:28 time: 10.4768 data_time: 1.4854 memory: 68702 grad_norm: 0.6920 loss: 3.2232 center_loss: 0.9176 size_loss: 0.3513 cls_loss: 1.0436 giou_loss: 0.9107 2025/05/11 17:27:55 - mmengine - INFO - Epoch(train) [12][50/91] base_lr: 2.4953e-04 lr: 2.4953e-04 eta: 4 days, 3:41:04 time: 10.6694 data_time: 1.4905 memory: 68702 grad_norm: 0.6666 loss: 3.2082 center_loss: 0.9158 size_loss: 0.3539 cls_loss: 1.0256 giou_loss: 0.9129 2025/05/11 17:29:33 - mmengine - INFO - Epoch(train) [12][60/91] base_lr: 2.4953e-04 lr: 2.4953e-04 eta: 4 days, 3:37:10 time: 9.6735 data_time: 0.5649 memory: 68702 grad_norm: 0.6673 loss: 3.2052 center_loss: 0.9145 size_loss: 0.3525 cls_loss: 1.0264 giou_loss: 0.9118 2025/05/11 17:31:09 - mmengine - INFO - Epoch(train) [12][70/91] base_lr: 2.4953e-04 lr: 2.4953e-04 eta: 4 days, 3:32:46 time: 9.6704 data_time: 0.5758 memory: 68702 grad_norm: 0.6829 loss: 3.2119 center_loss: 0.9174 size_loss: 0.3521 cls_loss: 1.0293 giou_loss: 0.9131 2025/05/11 17:32:45 - mmengine - INFO - Epoch(train) [12][80/91] base_lr: 2.4953e-04 lr: 2.4953e-04 eta: 4 days, 3:27:58 time: 9.6484 data_time: 0.5729 memory: 68702 grad_norm: 0.6661 loss: 3.2061 center_loss: 0.9169 size_loss: 0.3521 cls_loss: 1.0252 giou_loss: 0.9120 2025/05/11 17:34:20 - mmengine - INFO - Epoch(train) [12][90/91] base_lr: 2.4953e-04 lr: 2.4953e-04 eta: 4 days, 3:22:53 time: 9.6332 data_time: 0.5783 memory: 68702 grad_norm: 0.6717 loss: 3.1929 center_loss: 0.9116 size_loss: 0.3486 cls_loss: 1.0227 giou_loss: 0.9101 2025/05/11 17:34:22 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 17:34:22 - mmengine - INFO - Saving checkpoint at 12 epochs 2025/05/11 17:35:20 - mmengine - INFO - Epoch(val) [12][10/39] eta: 0:01:39 time: 2.8851 data_time: 0.3619 memory: 15952 2025/05/11 17:35:46 - mmengine - INFO - Epoch(val) [12][20/39] eta: 0:00:57 time: 2.7532 data_time: 0.2278 memory: 13407 2025/05/11 17:36:13 - mmengine - INFO - Epoch(val) [12][30/39] eta: 0:00:26 time: 2.7623 data_time: 0.2261 memory: 13407 2025/05/11 17:36:42 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | bed | 0.0269 | 0.3704 | 0.0031 | 0.0370 | | garbagebin | 0.0001 | 0.0415 | 0.0000 | 0.0000 | | door | 0.0002 | 0.0300 | 0.0000 | 0.0000 | | chair | 0.0286 | 0.4181 | 0.0005 | 0.0548 | | sofa | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | table | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | bookshelf | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | picture | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | curtain | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | cabinet | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | refrigerator | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | counter | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | sink | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | window | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | desk | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | toilet | 0.0131 | 0.4138 | 0.0003 | 0.0690 | | showercurtrain | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | bathtub | 0.0000 | 0.0000 | 0.0000 | 0.0000 | +----------------+---------+---------+---------+---------+ | Overall | 0.0038 | 0.0708 | 0.0002 | 0.0089 | +----------------+---------+---------+---------+---------+ 2025/05/11 17:36:42 - mmengine - INFO - Epoch(val) [12][39/39] chair_AP_0.25: 0.0286 sofa_AP_0.25: 0.0000 table_AP_0.25: 0.0000 garbagebin_AP_0.25: 0.0001 bookshelf_AP_0.25: 0.0000 picture_AP_0.25: 0.0000 curtain_AP_0.25: 0.0000 door_AP_0.25: 0.0002 cabinet_AP_0.25: 0.0000 refrigerator_AP_0.25: 0.0000 counter_AP_0.25: 0.0000 sink_AP_0.25: 0.0000 window_AP_0.25: 0.0000 desk_AP_0.25: 0.0000 bed_AP_0.25: 0.0269 toilet_AP_0.25: 0.0131 showercurtrain_AP_0.25: 0.0000 bathtub_AP_0.25: 0.0000 mAP_0.25: 0.0038 chair_rec_0.25: 0.4181 sofa_rec_0.25: 0.0000 table_rec_0.25: 0.0000 garbagebin_rec_0.25: 0.0415 bookshelf_rec_0.25: 0.0000 picture_rec_0.25: 0.0000 curtain_rec_0.25: 0.0000 door_rec_0.25: 0.0300 cabinet_rec_0.25: 0.0000 refrigerator_rec_0.25: 0.0000 counter_rec_0.25: 0.0000 sink_rec_0.25: 0.0000 window_rec_0.25: 0.0000 desk_rec_0.25: 0.0000 bed_rec_0.25: 0.3704 toilet_rec_0.25: 0.4138 showercurtrain_rec_0.25: 0.0000 bathtub_rec_0.25: 0.0000 mAR_0.25: 0.0708 chair_AP_0.50: 0.0005 sofa_AP_0.50: 0.0000 table_AP_0.50: 0.0000 garbagebin_AP_0.50: 0.0000 bookshelf_AP_0.50: 0.0000 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0000 door_AP_0.50: 0.0000 cabinet_AP_0.50: 0.0000 refrigerator_AP_0.50: 0.0000 counter_AP_0.50: 0.0000 sink_AP_0.50: 0.0000 window_AP_0.50: 0.0000 desk_AP_0.50: 0.0000 bed_AP_0.50: 0.0031 toilet_AP_0.50: 0.0003 showercurtrain_AP_0.50: 0.0000 bathtub_AP_0.50: 0.0000 mAP_0.50: 0.0002 chair_rec_0.50: 0.0548 sofa_rec_0.50: 0.0000 table_rec_0.50: 0.0000 garbagebin_rec_0.50: 0.0000 bookshelf_rec_0.50: 0.0000 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.0000 door_rec_0.50: 0.0000 cabinet_rec_0.50: 0.0000 refrigerator_rec_0.50: 0.0000 counter_rec_0.50: 0.0000 sink_rec_0.50: 0.0000 window_rec_0.50: 0.0000 desk_rec_0.50: 0.0000 bed_rec_0.50: 0.0370 toilet_rec_0.50: 0.0690 showercurtrain_rec_0.50: 0.0000 bathtub_rec_0.50: 0.0000 mAR_0.50: 0.0089 data_time: 0.2628 time: 2.8057 2025/05/11 17:36:42 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_10.pth is removed 2025/05/11 17:37:03 - mmengine - INFO - The best checkpoint with 0.0038 mAP_0.25 at 12 epoch is saved to best_mAP_0.25_epoch_12.pth. 2025/05/11 17:39:54 - mmengine - INFO - Epoch(train) [13][10/91] base_lr: 2.4944e-04 lr: 2.4944e-04 eta: 4 days, 3:40:07 time: 10.4491 data_time: 1.5236 memory: 68700 grad_norm: 0.7228 loss: 3.1703 center_loss: 0.9011 size_loss: 0.3386 cls_loss: 1.0265 giou_loss: 0.9041 2025/05/11 17:41:32 - mmengine - INFO - Epoch(train) [13][20/91] base_lr: 2.4944e-04 lr: 2.4944e-04 eta: 4 days, 3:36:03 time: 10.4409 data_time: 1.5117 memory: 68703 grad_norm: 0.7225 loss: 3.1708 center_loss: 0.9027 size_loss: 0.3402 cls_loss: 1.0241 giou_loss: 0.9037 2025/05/11 17:43:09 - mmengine - INFO - Epoch(train) [13][30/91] base_lr: 2.4944e-04 lr: 2.4944e-04 eta: 4 days, 3:31:56 time: 10.4544 data_time: 1.5259 memory: 68702 grad_norm: 0.7219 loss: 3.1678 center_loss: 0.9002 size_loss: 0.3384 cls_loss: 1.0264 giou_loss: 0.9028 2025/05/11 17:44:45 - mmengine - INFO - Epoch(train) [13][40/91] base_lr: 2.4944e-04 lr: 2.4944e-04 eta: 4 days, 3:27:23 time: 10.4594 data_time: 1.5188 memory: 68702 grad_norm: 0.7956 loss: 3.1696 center_loss: 0.8992 size_loss: 0.3365 cls_loss: 1.0338 giou_loss: 0.9001 2025/05/11 17:46:21 - mmengine - INFO - Epoch(train) [13][50/91] base_lr: 2.4944e-04 lr: 2.4944e-04 eta: 4 days, 3:23:11 time: 10.6409 data_time: 1.5183 memory: 68703 grad_norm: 0.7739 loss: 3.1978 center_loss: 0.9133 size_loss: 0.3447 cls_loss: 1.0350 giou_loss: 0.9047 2025/05/11 17:47:59 - mmengine - INFO - Epoch(train) [13][60/91] base_lr: 2.4944e-04 lr: 2.4944e-04 eta: 4 days, 3:19:42 time: 9.6967 data_time: 0.5640 memory: 68702 grad_norm: 0.7512 loss: 3.1976 center_loss: 0.9118 size_loss: 0.3469 cls_loss: 1.0370 giou_loss: 0.9019 2025/05/11 17:49:36 - mmengine - INFO - Epoch(train) [13][70/91] base_lr: 2.4944e-04 lr: 2.4944e-04 eta: 4 days, 3:15:42 time: 9.6911 data_time: 0.5717 memory: 68703 grad_norm: 0.8202 loss: 3.1913 center_loss: 0.9072 size_loss: 0.3462 cls_loss: 1.0389 giou_loss: 0.8990 2025/05/11 17:51:12 - mmengine - INFO - Epoch(train) [13][80/91] base_lr: 2.4944e-04 lr: 2.4944e-04 eta: 4 days, 3:11:02 time: 9.6607 data_time: 0.5566 memory: 68702 grad_norm: 0.8142 loss: 3.1753 center_loss: 0.9050 size_loss: 0.3444 cls_loss: 1.0300 giou_loss: 0.8959 2025/05/11 17:52:47 - mmengine - INFO - Epoch(train) [13][90/91] base_lr: 2.4944e-04 lr: 2.4944e-04 eta: 4 days, 3:06:10 time: 9.6385 data_time: 0.5599 memory: 68702 grad_norm: 0.7437 loss: 3.1791 center_loss: 0.9093 size_loss: 0.3446 cls_loss: 1.0274 giou_loss: 0.8977 2025/05/11 17:52:49 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 17:55:17 - mmengine - INFO - Epoch(train) [14][10/91] base_lr: 2.4935e-04 lr: 2.4935e-04 eta: 4 days, 3:23:39 time: 10.5244 data_time: 1.5071 memory: 68702 grad_norm: 0.7782 loss: 3.1821 center_loss: 0.9050 size_loss: 0.3421 cls_loss: 1.0386 giou_loss: 0.8963 2025/05/11 17:56:53 - mmengine - INFO - Epoch(train) [14][20/91] base_lr: 2.4935e-04 lr: 2.4935e-04 eta: 4 days, 3:19:04 time: 10.4796 data_time: 1.5056 memory: 68703 grad_norm: 0.7856 loss: 3.1924 center_loss: 0.9098 size_loss: 0.3438 cls_loss: 1.0433 giou_loss: 0.8955 2025/05/11 17:58:30 - mmengine - INFO - Epoch(train) [14][30/91] base_lr: 2.4935e-04 lr: 2.4935e-04 eta: 4 days, 3:15:10 time: 10.4908 data_time: 1.5179 memory: 68702 grad_norm: 0.7205 loss: 3.2128 center_loss: 0.9201 size_loss: 0.3450 cls_loss: 1.0478 giou_loss: 0.9000 2025/05/11 18:00:06 - mmengine - INFO - Epoch(train) [14][40/91] base_lr: 2.4935e-04 lr: 2.4935e-04 eta: 4 days, 3:10:53 time: 10.4999 data_time: 1.5231 memory: 68703 grad_norm: 0.7220 loss: 3.2087 center_loss: 0.9226 size_loss: 0.3469 cls_loss: 1.0382 giou_loss: 0.9010 2025/05/11 18:01:44 - mmengine - INFO - Epoch(train) [14][50/91] base_lr: 2.4935e-04 lr: 2.4935e-04 eta: 4 days, 3:07:20 time: 10.7041 data_time: 1.5462 memory: 68702 grad_norm: 0.6948 loss: 3.2101 center_loss: 0.9272 size_loss: 0.3471 cls_loss: 1.0303 giou_loss: 0.9055 2025/05/11 18:03:21 - mmengine - INFO - Epoch(train) [14][60/91] base_lr: 2.4935e-04 lr: 2.4935e-04 eta: 4 days, 3:03:23 time: 9.6631 data_time: 0.6053 memory: 68702 grad_norm: 0.6906 loss: 3.2365 center_loss: 0.9354 size_loss: 0.3461 cls_loss: 1.0444 giou_loss: 0.9105 2025/05/11 18:04:54 - mmengine - INFO - Epoch(train) [14][70/91] base_lr: 2.4935e-04 lr: 2.4935e-04 eta: 4 days, 2:58:09 time: 9.6260 data_time: 0.5855 memory: 68702 grad_norm: 0.6993 loss: 3.2135 center_loss: 0.9243 size_loss: 0.3425 cls_loss: 1.0351 giou_loss: 0.9115 2025/05/11 18:06:31 - mmengine - INFO - Epoch(train) [14][80/91] base_lr: 2.4935e-04 lr: 2.4935e-04 eta: 4 days, 2:54:00 time: 9.6065 data_time: 0.5882 memory: 68703 grad_norm: 0.7109 loss: 3.1871 center_loss: 0.9139 size_loss: 0.3383 cls_loss: 1.0267 giou_loss: 0.9083 2025/05/11 18:08:06 - mmengine - INFO - Epoch(train) [14][90/91] base_lr: 2.4935e-04 lr: 2.4935e-04 eta: 4 days, 2:49:25 time: 9.5845 data_time: 0.5909 memory: 68702 grad_norm: 0.7154 loss: 3.1773 center_loss: 0.9108 size_loss: 0.3377 cls_loss: 1.0239 giou_loss: 0.9048 2025/05/11 18:08:08 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 18:08:08 - mmengine - INFO - Saving checkpoint at 14 epochs 2025/05/11 18:09:05 - mmengine - INFO - Epoch(val) [14][10/39] eta: 0:01:36 time: 2.9100 data_time: 0.3630 memory: 15952 2025/05/11 18:09:31 - mmengine - INFO - Epoch(val) [14][20/39] eta: 0:00:56 time: 2.7579 data_time: 0.2227 memory: 13407 2025/05/11 18:09:57 - mmengine - INFO - Epoch(val) [14][30/39] eta: 0:00:25 time: 2.7497 data_time: 0.2103 memory: 13407 2025/05/11 18:10:26 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | bed | 0.1515 | 0.5556 | 0.0019 | 0.0494 | | garbagebin | 0.0003 | 0.0434 | 0.0000 | 0.0019 | | cabinet | 0.0003 | 0.0323 | 0.0000 | 0.0000 | | chair | 0.0314 | 0.3743 | 0.0005 | 0.0380 | | sofa | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | table | 0.0005 | 0.0343 | 0.0000 | 0.0000 | | bookshelf | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | picture | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | curtain | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | door | 0.0010 | 0.0899 | 0.0000 | 0.0021 | | refrigerator | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | counter | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | sink | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | window | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | desk | 0.0006 | 0.0079 | 0.0000 | 0.0000 | | toilet | 0.0160 | 0.2241 | 0.0005 | 0.0345 | | showercurtrain | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | bathtub | 0.0000 | 0.0000 | 0.0000 | 0.0000 | +----------------+---------+---------+---------+---------+ | Overall | 0.0112 | 0.0757 | 0.0002 | 0.0070 | +----------------+---------+---------+---------+---------+ 2025/05/11 18:10:26 - mmengine - INFO - Epoch(val) [14][39/39] chair_AP_0.25: 0.0314 sofa_AP_0.25: 0.0000 table_AP_0.25: 0.0005 garbagebin_AP_0.25: 0.0003 bookshelf_AP_0.25: 0.0000 picture_AP_0.25: 0.0000 curtain_AP_0.25: 0.0000 door_AP_0.25: 0.0010 cabinet_AP_0.25: 0.0003 refrigerator_AP_0.25: 0.0000 counter_AP_0.25: 0.0000 sink_AP_0.25: 0.0000 window_AP_0.25: 0.0000 desk_AP_0.25: 0.0006 bed_AP_0.25: 0.1515 toilet_AP_0.25: 0.0160 showercurtrain_AP_0.25: 0.0000 bathtub_AP_0.25: 0.0000 mAP_0.25: 0.0112 chair_rec_0.25: 0.3743 sofa_rec_0.25: 0.0000 table_rec_0.25: 0.0343 garbagebin_rec_0.25: 0.0434 bookshelf_rec_0.25: 0.0000 picture_rec_0.25: 0.0000 curtain_rec_0.25: 0.0000 door_rec_0.25: 0.0899 cabinet_rec_0.25: 0.0323 refrigerator_rec_0.25: 0.0000 counter_rec_0.25: 0.0000 sink_rec_0.25: 0.0000 window_rec_0.25: 0.0000 desk_rec_0.25: 0.0079 bed_rec_0.25: 0.5556 toilet_rec_0.25: 0.2241 showercurtrain_rec_0.25: 0.0000 bathtub_rec_0.25: 0.0000 mAR_0.25: 0.0757 chair_AP_0.50: 0.0005 sofa_AP_0.50: 0.0000 table_AP_0.50: 0.0000 garbagebin_AP_0.50: 0.0000 bookshelf_AP_0.50: 0.0000 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0000 door_AP_0.50: 0.0000 cabinet_AP_0.50: 0.0000 refrigerator_AP_0.50: 0.0000 counter_AP_0.50: 0.0000 sink_AP_0.50: 0.0000 window_AP_0.50: 0.0000 desk_AP_0.50: 0.0000 bed_AP_0.50: 0.0019 toilet_AP_0.50: 0.0005 showercurtrain_AP_0.50: 0.0000 bathtub_AP_0.50: 0.0000 mAP_0.50: 0.0002 chair_rec_0.50: 0.0380 sofa_rec_0.50: 0.0000 table_rec_0.50: 0.0000 garbagebin_rec_0.50: 0.0019 bookshelf_rec_0.50: 0.0000 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.0000 door_rec_0.50: 0.0021 cabinet_rec_0.50: 0.0000 refrigerator_rec_0.50: 0.0000 counter_rec_0.50: 0.0000 sink_rec_0.50: 0.0000 window_rec_0.50: 0.0000 desk_rec_0.50: 0.0000 bed_rec_0.50: 0.0494 toilet_rec_0.50: 0.0345 showercurtrain_rec_0.50: 0.0000 bathtub_rec_0.50: 0.0000 mAR_0.50: 0.0070 data_time: 0.2440 time: 2.7806 2025/05/11 18:10:26 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_12.pth is removed 2025/05/11 18:10:56 - mmengine - INFO - The best checkpoint with 0.0112 mAP_0.25 at 14 epoch is saved to best_mAP_0.25_epoch_14.pth. 2025/05/11 18:13:45 - mmengine - INFO - Epoch(train) [15][10/91] base_lr: 2.4924e-04 lr: 2.4924e-04 eta: 4 days, 3:03:17 time: 10.3599 data_time: 1.4865 memory: 68702 grad_norm: 0.7617 loss: 3.1647 center_loss: 0.8982 size_loss: 0.3414 cls_loss: 1.0258 giou_loss: 0.8993 2025/05/11 18:15:21 - mmengine - INFO - Epoch(train) [15][20/91] base_lr: 2.4924e-04 lr: 2.4924e-04 eta: 4 days, 2:59:08 time: 10.3449 data_time: 1.4955 memory: 68703 grad_norm: 0.7672 loss: 3.1135 center_loss: 0.8910 size_loss: 0.3381 cls_loss: 0.9923 giou_loss: 0.8922 2025/05/11 18:16:58 - mmengine - INFO - Epoch(train) [15][30/91] base_lr: 2.4924e-04 lr: 2.4924e-04 eta: 4 days, 2:55:26 time: 10.4137 data_time: 1.5107 memory: 68702 grad_norm: 0.7497 loss: 3.1041 center_loss: 0.8853 size_loss: 0.3372 cls_loss: 0.9944 giou_loss: 0.8872 2025/05/11 18:18:35 - mmengine - INFO - Epoch(train) [15][40/91] base_lr: 2.4924e-04 lr: 2.4924e-04 eta: 4 days, 2:51:34 time: 10.4199 data_time: 1.4924 memory: 68700 grad_norm: 0.7550 loss: 3.1033 center_loss: 0.8858 size_loss: 0.3380 cls_loss: 0.9935 giou_loss: 0.8860 2025/05/11 18:20:12 - mmengine - INFO - Epoch(train) [15][50/91] base_lr: 2.4924e-04 lr: 2.4924e-04 eta: 4 days, 2:48:06 time: 10.6205 data_time: 1.4961 memory: 68702 grad_norm: 0.7148 loss: 3.1143 center_loss: 0.8869 size_loss: 0.3371 cls_loss: 1.0021 giou_loss: 0.8883 2025/05/11 18:21:49 - mmengine - INFO - Epoch(train) [15][60/91] base_lr: 2.4924e-04 lr: 2.4924e-04 eta: 4 days, 2:44:17 time: 9.6736 data_time: 0.5937 memory: 68702 grad_norm: 0.6618 loss: 3.1187 center_loss: 0.8895 size_loss: 0.3345 cls_loss: 1.0076 giou_loss: 0.8871 2025/05/11 18:23:26 - mmengine - INFO - Epoch(train) [15][70/91] base_lr: 2.4924e-04 lr: 2.4924e-04 eta: 4 days, 2:40:36 time: 9.6867 data_time: 0.5852 memory: 68702 grad_norm: 0.6805 loss: 3.1133 center_loss: 0.8832 size_loss: 0.3344 cls_loss: 1.0106 giou_loss: 0.8850 2025/05/11 18:25:01 - mmengine - INFO - Epoch(train) [15][80/91] base_lr: 2.4924e-04 lr: 2.4924e-04 eta: 4 days, 2:36:25 time: 9.6577 data_time: 0.5941 memory: 68703 grad_norm: 0.7315 loss: 3.1179 center_loss: 0.8900 size_loss: 0.3338 cls_loss: 1.0067 giou_loss: 0.8874 2025/05/11 18:26:36 - mmengine - INFO - Epoch(train) [15][90/91] base_lr: 2.4924e-04 lr: 2.4924e-04 eta: 4 days, 2:32:12 time: 9.6329 data_time: 0.6063 memory: 68702 grad_norm: 0.7301 loss: 3.1252 center_loss: 0.8913 size_loss: 0.3362 cls_loss: 1.0099 giou_loss: 0.8877 2025/05/11 18:26:38 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 18:29:07 - mmengine - INFO - Epoch(train) [16][10/91] base_lr: 2.4913e-04 lr: 2.4913e-04 eta: 4 days, 2:46:46 time: 10.4931 data_time: 1.5377 memory: 68702 grad_norm: 0.8118 loss: 3.1051 center_loss: 0.8849 size_loss: 0.3342 cls_loss: 1.0023 giou_loss: 0.8838 2025/05/11 18:30:44 - mmengine - INFO - Epoch(train) [16][20/91] base_lr: 2.4913e-04 lr: 2.4913e-04 eta: 4 days, 2:43:13 time: 10.5032 data_time: 1.5591 memory: 68702 grad_norm: 0.8197 loss: 3.0962 center_loss: 0.8813 size_loss: 0.3325 cls_loss: 1.0004 giou_loss: 0.8820 2025/05/11 18:32:20 - mmengine - INFO - Epoch(train) [16][30/91] base_lr: 2.4913e-04 lr: 2.4913e-04 eta: 4 days, 2:39:30 time: 10.5063 data_time: 1.5465 memory: 68702 grad_norm: 0.8163 loss: 3.1131 center_loss: 0.8845 size_loss: 0.3322 cls_loss: 1.0142 giou_loss: 0.8822 2025/05/11 18:33:56 - mmengine - INFO - Epoch(train) [16][40/91] base_lr: 2.4913e-04 lr: 2.4913e-04 eta: 4 days, 2:35:33 time: 10.5104 data_time: 1.5432 memory: 68703 grad_norm: 0.7676 loss: 3.1160 center_loss: 0.8853 size_loss: 0.3309 cls_loss: 1.0194 giou_loss: 0.8806 2025/05/11 18:35:33 - mmengine - INFO - Epoch(train) [16][50/91] base_lr: 2.4913e-04 lr: 2.4913e-04 eta: 4 days, 2:32:01 time: 10.6953 data_time: 1.5455 memory: 68702 grad_norm: 0.7290 loss: 3.1191 center_loss: 0.8829 size_loss: 0.3296 cls_loss: 1.0245 giou_loss: 0.8821 2025/05/11 18:37:10 - mmengine - INFO - Epoch(train) [16][60/91] base_lr: 2.4913e-04 lr: 2.4913e-04 eta: 4 days, 2:28:19 time: 9.6643 data_time: 0.6159 memory: 68703 grad_norm: 0.7141 loss: 3.1025 center_loss: 0.8845 size_loss: 0.3284 cls_loss: 1.0079 giou_loss: 0.8817 2025/05/11 18:38:47 - mmengine - INFO - Epoch(train) [16][70/91] base_lr: 2.4913e-04 lr: 2.4913e-04 eta: 4 days, 2:24:47 time: 9.6584 data_time: 0.5885 memory: 68703 grad_norm: 0.7138 loss: 3.1053 center_loss: 0.8905 size_loss: 0.3301 cls_loss: 1.0030 giou_loss: 0.8817 2025/05/11 18:40:23 - mmengine - INFO - Epoch(train) [16][80/91] base_lr: 2.4913e-04 lr: 2.4913e-04 eta: 4 days, 2:20:58 time: 9.6464 data_time: 0.6006 memory: 68703 grad_norm: 0.7002 loss: 3.0939 center_loss: 0.8911 size_loss: 0.3287 cls_loss: 0.9921 giou_loss: 0.8820 2025/05/11 18:41:58 - mmengine - INFO - Epoch(train) [16][90/91] base_lr: 2.4913e-04 lr: 2.4913e-04 eta: 4 days, 2:16:52 time: 9.6311 data_time: 0.5996 memory: 68702 grad_norm: 0.6964 loss: 3.0678 center_loss: 0.8827 size_loss: 0.3251 cls_loss: 0.9810 giou_loss: 0.8789 2025/05/11 18:42:00 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 18:42:00 - mmengine - INFO - Saving checkpoint at 16 epochs 2025/05/11 18:42:58 - mmengine - INFO - Epoch(val) [16][10/39] eta: 0:01:42 time: 2.9294 data_time: 0.3855 memory: 15952 2025/05/11 18:43:24 - mmengine - INFO - Epoch(val) [16][20/39] eta: 0:00:58 time: 2.7869 data_time: 0.2488 memory: 13407 2025/05/11 18:43:51 - mmengine - INFO - Epoch(val) [16][30/39] eta: 0:00:26 time: 2.7974 data_time: 0.2551 memory: 13407 2025/05/11 18:44:20 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.0007 | 0.0340 | 0.0000 | 0.0000 | | cabinet | 0.0011 | 0.0753 | 0.0000 | 0.0054 | | chair | 0.0464 | 0.4759 | 0.0008 | 0.0482 | | sofa | 0.0021 | 0.0206 | 0.0000 | 0.0000 | | table | 0.0029 | 0.0486 | 0.0000 | 0.0029 | | bookshelf | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | picture | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | curtain | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | door | 0.0006 | 0.0471 | 0.0000 | 0.0000 | | refrigerator | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | counter | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | sink | 0.0102 | 0.0102 | 0.0000 | 0.0000 | | window | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | bed | 0.0566 | 0.4198 | 0.0003 | 0.0123 | | desk | 0.0075 | 0.0945 | 0.0000 | 0.0000 | | toilet | 0.0218 | 0.4828 | 0.0017 | 0.1034 | | showercurtrain | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | bathtub | 0.0000 | 0.0000 | 0.0000 | 0.0000 | +----------------+---------+---------+---------+---------+ | Overall | 0.0083 | 0.0949 | 0.0002 | 0.0096 | +----------------+---------+---------+---------+---------+ 2025/05/11 18:44:20 - mmengine - INFO - Epoch(val) [16][39/39] chair_AP_0.25: 0.0464 sofa_AP_0.25: 0.0021 table_AP_0.25: 0.0029 garbagebin_AP_0.25: 0.0007 bookshelf_AP_0.25: 0.0000 picture_AP_0.25: 0.0000 curtain_AP_0.25: 0.0000 door_AP_0.25: 0.0006 cabinet_AP_0.25: 0.0011 refrigerator_AP_0.25: 0.0000 counter_AP_0.25: 0.0000 sink_AP_0.25: 0.0102 window_AP_0.25: 0.0000 desk_AP_0.25: 0.0075 bed_AP_0.25: 0.0566 toilet_AP_0.25: 0.0218 showercurtrain_AP_0.25: 0.0000 bathtub_AP_0.25: 0.0000 mAP_0.25: 0.0083 chair_rec_0.25: 0.4759 sofa_rec_0.25: 0.0206 table_rec_0.25: 0.0486 garbagebin_rec_0.25: 0.0340 bookshelf_rec_0.25: 0.0000 picture_rec_0.25: 0.0000 curtain_rec_0.25: 0.0000 door_rec_0.25: 0.0471 cabinet_rec_0.25: 0.0753 refrigerator_rec_0.25: 0.0000 counter_rec_0.25: 0.0000 sink_rec_0.25: 0.0102 window_rec_0.25: 0.0000 desk_rec_0.25: 0.0945 bed_rec_0.25: 0.4198 toilet_rec_0.25: 0.4828 showercurtrain_rec_0.25: 0.0000 bathtub_rec_0.25: 0.0000 mAR_0.25: 0.0949 chair_AP_0.50: 0.0008 sofa_AP_0.50: 0.0000 table_AP_0.50: 0.0000 garbagebin_AP_0.50: 0.0000 bookshelf_AP_0.50: 0.0000 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0000 door_AP_0.50: 0.0000 cabinet_AP_0.50: 0.0000 refrigerator_AP_0.50: 0.0000 counter_AP_0.50: 0.0000 sink_AP_0.50: 0.0000 window_AP_0.50: 0.0000 desk_AP_0.50: 0.0000 bed_AP_0.50: 0.0003 toilet_AP_0.50: 0.0017 showercurtrain_AP_0.50: 0.0000 bathtub_AP_0.50: 0.0000 mAP_0.50: 0.0002 chair_rec_0.50: 0.0482 sofa_rec_0.50: 0.0000 table_rec_0.50: 0.0029 garbagebin_rec_0.50: 0.0000 bookshelf_rec_0.50: 0.0000 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.0000 door_rec_0.50: 0.0000 cabinet_rec_0.50: 0.0054 refrigerator_rec_0.50: 0.0000 counter_rec_0.50: 0.0000 sink_rec_0.50: 0.0000 window_rec_0.50: 0.0000 desk_rec_0.50: 0.0000 bed_rec_0.50: 0.0123 toilet_rec_0.50: 0.1034 showercurtrain_rec_0.50: 0.0000 bathtub_rec_0.50: 0.0000 mAR_0.50: 0.0096 data_time: 0.3004 time: 2.8360 2025/05/11 18:46:46 - mmengine - INFO - Epoch(train) [17][10/91] base_lr: 2.4901e-04 lr: 2.4901e-04 eta: 4 days, 2:29:29 time: 10.4562 data_time: 1.5579 memory: 68702 grad_norm: 0.7201 loss: 3.0617 center_loss: 0.8862 size_loss: 0.3255 cls_loss: 0.9715 giou_loss: 0.8785 2025/05/11 18:48:22 - mmengine - INFO - Epoch(train) [17][20/91] base_lr: 2.4901e-04 lr: 2.4901e-04 eta: 4 days, 2:25:46 time: 10.4475 data_time: 1.5523 memory: 68703 grad_norm: 0.7235 loss: 3.0644 center_loss: 0.8912 size_loss: 0.3251 cls_loss: 0.9678 giou_loss: 0.8804 2025/05/11 18:49:58 - mmengine - INFO - Epoch(train) [17][30/91] base_lr: 2.4901e-04 lr: 2.4901e-04 eta: 4 days, 2:22:00 time: 10.4453 data_time: 1.5429 memory: 68702 grad_norm: 0.7393 loss: 3.0865 center_loss: 0.8956 size_loss: 0.3295 cls_loss: 0.9747 giou_loss: 0.8867 2025/05/11 18:51:34 - mmengine - INFO - Epoch(train) [17][40/91] base_lr: 2.4901e-04 lr: 2.4901e-04 eta: 4 days, 2:18:10 time: 10.4386 data_time: 1.5389 memory: 68703 grad_norm: 0.7471 loss: 3.0883 center_loss: 0.8967 size_loss: 0.3332 cls_loss: 0.9680 giou_loss: 0.8905 2025/05/11 18:53:11 - mmengine - INFO - Epoch(train) [17][50/91] base_lr: 2.4901e-04 lr: 2.4901e-04 eta: 4 days, 2:14:38 time: 10.6178 data_time: 1.5493 memory: 68702 grad_norm: 0.7260 loss: 3.1268 center_loss: 0.9057 size_loss: 0.3410 cls_loss: 0.9862 giou_loss: 0.8939 2025/05/11 18:54:47 - mmengine - INFO - Epoch(train) [17][60/91] base_lr: 2.4901e-04 lr: 2.4901e-04 eta: 4 days, 2:11:04 time: 9.6310 data_time: 0.5787 memory: 68701 grad_norm: 0.7179 loss: 3.1478 center_loss: 0.9162 size_loss: 0.3412 cls_loss: 0.9941 giou_loss: 0.8964 2025/05/11 18:56:23 - mmengine - INFO - Epoch(train) [17][70/91] base_lr: 2.4901e-04 lr: 2.4901e-04 eta: 4 days, 2:07:26 time: 9.6277 data_time: 0.5856 memory: 68702 grad_norm: 0.7237 loss: 3.1866 center_loss: 0.9249 size_loss: 0.3443 cls_loss: 1.0183 giou_loss: 0.8991 2025/05/11 18:57:59 - mmengine - INFO - Epoch(train) [17][80/91] base_lr: 2.4901e-04 lr: 2.4901e-04 eta: 4 days, 2:03:39 time: 9.6190 data_time: 0.5925 memory: 68703 grad_norm: 0.7327 loss: 3.1498 center_loss: 0.9137 size_loss: 0.3366 cls_loss: 1.0047 giou_loss: 0.8948 2025/05/11 18:59:34 - mmengine - INFO - Epoch(train) [17][90/91] base_lr: 2.4901e-04 lr: 2.4901e-04 eta: 4 days, 1:59:39 time: 9.6030 data_time: 0.5900 memory: 68703 grad_norm: 0.7334 loss: 3.1285 center_loss: 0.9052 size_loss: 0.3298 cls_loss: 1.0051 giou_loss: 0.8883 2025/05/11 18:59:36 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 19:02:03 - mmengine - INFO - Epoch(train) [18][10/91] base_lr: 2.4889e-04 lr: 2.4889e-04 eta: 4 days, 2:11:55 time: 10.4607 data_time: 1.5315 memory: 68702 grad_norm: 0.8262 loss: 3.1029 center_loss: 0.8939 size_loss: 0.3237 cls_loss: 1.0039 giou_loss: 0.8813 2025/05/11 19:03:40 - mmengine - INFO - Epoch(train) [18][20/91] base_lr: 2.4889e-04 lr: 2.4889e-04 eta: 4 days, 2:08:28 time: 10.4652 data_time: 1.5184 memory: 68702 grad_norm: 0.8459 loss: 3.0785 center_loss: 0.8838 size_loss: 0.3211 cls_loss: 0.9930 giou_loss: 0.8806 2025/05/11 19:05:16 - mmengine - INFO - Epoch(train) [18][30/91] base_lr: 2.4889e-04 lr: 2.4889e-04 eta: 4 days, 2:04:48 time: 10.4665 data_time: 1.5140 memory: 68702 grad_norm: 0.8531 loss: 3.0559 center_loss: 0.8762 size_loss: 0.3179 cls_loss: 0.9852 giou_loss: 0.8766 2025/05/11 19:06:52 - mmengine - INFO - Epoch(train) [18][40/91] base_lr: 2.4889e-04 lr: 2.4889e-04 eta: 4 days, 2:01:03 time: 10.4625 data_time: 1.5152 memory: 68703 grad_norm: 0.8363 loss: 3.0501 center_loss: 0.8775 size_loss: 0.3140 cls_loss: 0.9846 giou_loss: 0.8740 2025/05/11 19:08:30 - mmengine - INFO - Epoch(train) [18][50/91] base_lr: 2.4889e-04 lr: 2.4889e-04 eta: 4 days, 1:58:10 time: 10.6760 data_time: 1.5334 memory: 68701 grad_norm: 0.8030 loss: 3.0626 center_loss: 0.8818 size_loss: 0.3152 cls_loss: 0.9886 giou_loss: 0.8770 2025/05/11 19:10:08 - mmengine - INFO - Epoch(train) [18][60/91] base_lr: 2.4889e-04 lr: 2.4889e-04 eta: 4 days, 1:55:18 time: 9.6928 data_time: 0.5939 memory: 68702 grad_norm: 0.7626 loss: 3.0818 center_loss: 0.8936 size_loss: 0.3181 cls_loss: 0.9903 giou_loss: 0.8799 2025/05/11 19:11:44 - mmengine - INFO - Epoch(train) [18][70/91] base_lr: 2.4889e-04 lr: 2.4889e-04 eta: 4 days, 1:51:39 time: 9.6762 data_time: 0.6169 memory: 68700 grad_norm: 0.7483 loss: 3.0831 center_loss: 0.8947 size_loss: 0.3220 cls_loss: 0.9901 giou_loss: 0.8763 2025/05/11 19:13:19 - mmengine - INFO - Epoch(train) [18][80/91] base_lr: 2.4889e-04 lr: 2.4889e-04 eta: 4 days, 1:47:51 time: 9.6624 data_time: 0.6276 memory: 68703 grad_norm: 0.7452 loss: 3.0859 center_loss: 0.9004 size_loss: 0.3227 cls_loss: 0.9840 giou_loss: 0.8788 2025/05/11 19:14:54 - mmengine - INFO - Epoch(train) [18][90/91] base_lr: 2.4889e-04 lr: 2.4889e-04 eta: 4 days, 1:43:56 time: 9.6457 data_time: 0.6267 memory: 68703 grad_norm: 0.7530 loss: 3.0954 center_loss: 0.8961 size_loss: 0.3261 cls_loss: 0.9968 giou_loss: 0.8765 2025/05/11 19:14:56 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 19:14:56 - mmengine - INFO - Saving checkpoint at 18 epochs 2025/05/11 19:15:54 - mmengine - INFO - Epoch(val) [18][10/39] eta: 0:01:38 time: 2.9491 data_time: 0.4150 memory: 15952 2025/05/11 19:16:20 - mmengine - INFO - Epoch(val) [18][20/39] eta: 0:00:56 time: 2.7633 data_time: 0.2416 memory: 13407 2025/05/11 19:16:45 - mmengine - INFO - Epoch(val) [18][30/39] eta: 0:00:25 time: 2.7641 data_time: 0.2401 memory: 13407 2025/05/11 19:17:14 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | sofa | 0.0481 | 0.2474 | 0.0001 | 0.0103 | | door | 0.0016 | 0.0942 | 0.0000 | 0.0000 | | garbagebin | 0.0009 | 0.0434 | 0.0000 | 0.0019 | | chair | 0.0568 | 0.4459 | 0.0005 | 0.0490 | | table | 0.0032 | 0.0629 | 0.0000 | 0.0029 | | bookshelf | 0.0258 | 0.0519 | 0.0000 | 0.0000 | | picture | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | curtain | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | window | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | cabinet | 0.0016 | 0.0699 | 0.0000 | 0.0027 | | refrigerator | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | counter | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | sink | 0.0089 | 0.0714 | 0.0000 | 0.0000 | | bed | 0.1687 | 0.6667 | 0.0007 | 0.0370 | | desk | 0.0364 | 0.2205 | 0.0002 | 0.0079 | | toilet | 0.0831 | 0.5172 | 0.0008 | 0.0690 | | showercurtrain | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | bathtub | 0.0000 | 0.0000 | 0.0000 | 0.0000 | +----------------+---------+---------+---------+---------+ | Overall | 0.0242 | 0.1384 | 0.0001 | 0.0100 | +----------------+---------+---------+---------+---------+ 2025/05/11 19:17:14 - mmengine - INFO - Epoch(val) [18][39/39] chair_AP_0.25: 0.0568 sofa_AP_0.25: 0.0481 table_AP_0.25: 0.0032 garbagebin_AP_0.25: 0.0009 bookshelf_AP_0.25: 0.0258 picture_AP_0.25: 0.0000 curtain_AP_0.25: 0.0000 door_AP_0.25: 0.0016 cabinet_AP_0.25: 0.0016 refrigerator_AP_0.25: 0.0000 counter_AP_0.25: 0.0000 sink_AP_0.25: 0.0089 window_AP_0.25: 0.0000 desk_AP_0.25: 0.0364 bed_AP_0.25: 0.1687 toilet_AP_0.25: 0.0831 showercurtrain_AP_0.25: 0.0000 bathtub_AP_0.25: 0.0000 mAP_0.25: 0.0242 chair_rec_0.25: 0.4459 sofa_rec_0.25: 0.2474 table_rec_0.25: 0.0629 garbagebin_rec_0.25: 0.0434 bookshelf_rec_0.25: 0.0519 picture_rec_0.25: 0.0000 curtain_rec_0.25: 0.0000 door_rec_0.25: 0.0942 cabinet_rec_0.25: 0.0699 refrigerator_rec_0.25: 0.0000 counter_rec_0.25: 0.0000 sink_rec_0.25: 0.0714 window_rec_0.25: 0.0000 desk_rec_0.25: 0.2205 bed_rec_0.25: 0.6667 toilet_rec_0.25: 0.5172 showercurtrain_rec_0.25: 0.0000 bathtub_rec_0.25: 0.0000 mAR_0.25: 0.1384 chair_AP_0.50: 0.0005 sofa_AP_0.50: 0.0001 table_AP_0.50: 0.0000 garbagebin_AP_0.50: 0.0000 bookshelf_AP_0.50: 0.0000 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0000 door_AP_0.50: 0.0000 cabinet_AP_0.50: 0.0000 refrigerator_AP_0.50: 0.0000 counter_AP_0.50: 0.0000 sink_AP_0.50: 0.0000 window_AP_0.50: 0.0000 desk_AP_0.50: 0.0002 bed_AP_0.50: 0.0007 toilet_AP_0.50: 0.0008 showercurtrain_AP_0.50: 0.0000 bathtub_AP_0.50: 0.0000 mAP_0.50: 0.0001 chair_rec_0.50: 0.0490 sofa_rec_0.50: 0.0103 table_rec_0.50: 0.0029 garbagebin_rec_0.50: 0.0019 bookshelf_rec_0.50: 0.0000 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.0000 door_rec_0.50: 0.0000 cabinet_rec_0.50: 0.0027 refrigerator_rec_0.50: 0.0000 counter_rec_0.50: 0.0000 sink_rec_0.50: 0.0000 window_rec_0.50: 0.0000 desk_rec_0.50: 0.0079 bed_rec_0.50: 0.0370 toilet_rec_0.50: 0.0690 showercurtrain_rec_0.50: 0.0000 bathtub_rec_0.50: 0.0000 mAR_0.50: 0.0100 data_time: 0.2733 time: 2.7855 2025/05/11 19:17:14 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_14.pth is removed 2025/05/11 19:17:36 - mmengine - INFO - The best checkpoint with 0.0242 mAP_0.25 at 18 epoch is saved to best_mAP_0.25_epoch_18.pth. 2025/05/11 19:20:28 - mmengine - INFO - Epoch(train) [19][10/91] base_lr: 2.4875e-04 lr: 2.4875e-04 eta: 4 days, 1:53:00 time: 10.3143 data_time: 1.4775 memory: 68702 grad_norm: 0.8042 loss: 3.1033 center_loss: 0.8963 size_loss: 0.3288 cls_loss: 1.0013 giou_loss: 0.8768 2025/05/11 19:22:05 - mmengine - INFO - Epoch(train) [19][20/91] base_lr: 2.4875e-04 lr: 2.4875e-04 eta: 4 days, 1:49:40 time: 10.3136 data_time: 1.4722 memory: 68703 grad_norm: 0.8148 loss: 3.0978 center_loss: 0.8947 size_loss: 0.3277 cls_loss: 0.9963 giou_loss: 0.8791 2025/05/11 19:23:42 - mmengine - INFO - Epoch(train) [19][30/91] base_lr: 2.4875e-04 lr: 2.4875e-04 eta: 4 days, 1:46:19 time: 10.3319 data_time: 1.4602 memory: 68702 grad_norm: 0.8375 loss: 3.0967 center_loss: 0.8985 size_loss: 0.3271 cls_loss: 0.9886 giou_loss: 0.8825 2025/05/11 19:25:18 - mmengine - INFO - Epoch(train) [19][40/91] base_lr: 2.4875e-04 lr: 2.4875e-04 eta: 4 days, 1:42:47 time: 10.3355 data_time: 1.4522 memory: 68702 grad_norm: 0.8257 loss: 3.0966 center_loss: 0.8926 size_loss: 0.3254 cls_loss: 0.9994 giou_loss: 0.8792 2025/05/11 19:26:54 - mmengine - INFO - Epoch(train) [19][50/91] base_lr: 2.4875e-04 lr: 2.4875e-04 eta: 4 days, 1:39:29 time: 10.5263 data_time: 1.4601 memory: 68703 grad_norm: 0.7662 loss: 3.0871 center_loss: 0.8952 size_loss: 0.3213 cls_loss: 0.9914 giou_loss: 0.8792 2025/05/11 19:28:31 - mmengine - INFO - Epoch(train) [19][60/91] base_lr: 2.4875e-04 lr: 2.4875e-04 eta: 4 days, 1:36:09 time: 9.6469 data_time: 0.6082 memory: 68702 grad_norm: 0.7472 loss: 3.0839 center_loss: 0.8981 size_loss: 0.3196 cls_loss: 0.9893 giou_loss: 0.8769 2025/05/11 19:30:08 - mmengine - INFO - Epoch(train) [19][70/91] base_lr: 2.4875e-04 lr: 2.4875e-04 eta: 4 days, 1:33:14 time: 9.6651 data_time: 0.6067 memory: 68702 grad_norm: 0.7494 loss: 3.0540 center_loss: 0.8832 size_loss: 0.3131 cls_loss: 0.9908 giou_loss: 0.8668 2025/05/11 19:31:44 - mmengine - INFO - Epoch(train) [19][80/91] base_lr: 2.4875e-04 lr: 2.4875e-04 eta: 4 days, 1:29:39 time: 9.6457 data_time: 0.6127 memory: 68702 grad_norm: 0.7375 loss: 3.0223 center_loss: 0.8766 size_loss: 0.3052 cls_loss: 0.9802 giou_loss: 0.8603 2025/05/11 19:33:19 - mmengine - INFO - Epoch(train) [19][90/91] base_lr: 2.4875e-04 lr: 2.4875e-04 eta: 4 days, 1:26:03 time: 9.6360 data_time: 0.6149 memory: 68703 grad_norm: 0.7429 loss: 3.0055 center_loss: 0.8731 size_loss: 0.3046 cls_loss: 0.9708 giou_loss: 0.8571 2025/05/11 19:33:21 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 19:35:45 - mmengine - INFO - Epoch(train) [20][10/91] base_lr: 2.4861e-04 lr: 2.4861e-04 eta: 4 days, 1:35:41 time: 10.4254 data_time: 1.4929 memory: 68702 grad_norm: 0.8044 loss: 3.0194 center_loss: 0.8768 size_loss: 0.3090 cls_loss: 0.9762 giou_loss: 0.8573 2025/05/11 19:37:21 - mmengine - INFO - Epoch(train) [20][20/91] base_lr: 2.4861e-04 lr: 2.4861e-04 eta: 4 days, 1:32:10 time: 10.4088 data_time: 1.4856 memory: 68703 grad_norm: 0.8209 loss: 3.0274 center_loss: 0.8724 size_loss: 0.3118 cls_loss: 0.9842 giou_loss: 0.8590 2025/05/11 19:38:58 - mmengine - INFO - Epoch(train) [20][30/91] base_lr: 2.4861e-04 lr: 2.4861e-04 eta: 4 days, 1:28:53 time: 10.3972 data_time: 1.4799 memory: 68703 grad_norm: 0.8343 loss: 3.0327 center_loss: 0.8702 size_loss: 0.3139 cls_loss: 0.9883 giou_loss: 0.8603 2025/05/11 19:40:34 - mmengine - INFO - Epoch(train) [20][40/91] base_lr: 2.4861e-04 lr: 2.4861e-04 eta: 4 days, 1:25:35 time: 10.4073 data_time: 1.4771 memory: 68702 grad_norm: 0.8577 loss: 3.0810 center_loss: 0.8838 size_loss: 0.3218 cls_loss: 1.0054 giou_loss: 0.8700 2025/05/11 19:42:11 - mmengine - INFO - Epoch(train) [20][50/91] base_lr: 2.4861e-04 lr: 2.4861e-04 eta: 4 days, 1:22:30 time: 10.5891 data_time: 1.4848 memory: 68703 grad_norm: 0.8310 loss: 3.0910 center_loss: 0.8908 size_loss: 0.3214 cls_loss: 1.0028 giou_loss: 0.8760 2025/05/11 19:43:48 - mmengine - INFO - Epoch(train) [20][60/91] base_lr: 2.4861e-04 lr: 2.4861e-04 eta: 4 days, 1:19:21 time: 9.6472 data_time: 0.6010 memory: 68703 grad_norm: 0.8451 loss: 3.0939 center_loss: 0.8902 size_loss: 0.3194 cls_loss: 1.0111 giou_loss: 0.8732 2025/05/11 19:45:24 - mmengine - INFO - Epoch(train) [20][70/91] base_lr: 2.4861e-04 lr: 2.4861e-04 eta: 4 days, 1:16:06 time: 9.6574 data_time: 0.6058 memory: 68702 grad_norm: 0.8565 loss: 3.0868 center_loss: 0.8930 size_loss: 0.3184 cls_loss: 1.0026 giou_loss: 0.8728 2025/05/11 19:47:00 - mmengine - INFO - Epoch(train) [20][80/91] base_lr: 2.4861e-04 lr: 2.4861e-04 eta: 4 days, 1:12:50 time: 9.6533 data_time: 0.6089 memory: 68701 grad_norm: 0.8556 loss: 3.1020 center_loss: 0.9019 size_loss: 0.3225 cls_loss: 1.0014 giou_loss: 0.8762 2025/05/11 19:48:35 - mmengine - INFO - Epoch(train) [20][90/91] base_lr: 2.4861e-04 lr: 2.4861e-04 eta: 4 days, 1:09:12 time: 9.6268 data_time: 0.6017 memory: 68703 grad_norm: 0.8460 loss: 3.1050 center_loss: 0.9046 size_loss: 0.3193 cls_loss: 1.0089 giou_loss: 0.8722 2025/05/11 19:48:37 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 19:48:37 - mmengine - INFO - Saving checkpoint at 20 epochs 2025/05/11 19:49:35 - mmengine - INFO - Epoch(val) [20][10/39] eta: 0:01:38 time: 2.9053 data_time: 0.3770 memory: 15952 2025/05/11 19:50:02 - mmengine - INFO - Epoch(val) [20][20/39] eta: 0:00:58 time: 2.7794 data_time: 0.2436 memory: 13407 2025/05/11 19:50:29 - mmengine - INFO - Epoch(val) [20][30/39] eta: 0:00:26 time: 2.7807 data_time: 0.2337 memory: 13407 2025/05/11 19:50:58 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | sofa | 0.0276 | 0.2371 | 0.0001 | 0.0103 | | table | 0.0027 | 0.0657 | 0.0000 | 0.0000 | | chair | 0.0481 | 0.3823 | 0.0004 | 0.0358 | | window | 0.0000 | 0.0035 | 0.0000 | 0.0000 | | garbagebin | 0.0067 | 0.0547 | 0.0001 | 0.0057 | | bookshelf | 0.0028 | 0.1299 | 0.0000 | 0.0130 | | picture | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | curtain | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | door | 0.0008 | 0.0771 | 0.0000 | 0.0043 | | cabinet | 0.0024 | 0.1075 | 0.0000 | 0.0081 | | refrigerator | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | counter | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | sink | 0.0179 | 0.1122 | 0.0009 | 0.0306 | | bed | 0.1840 | 0.6667 | 0.0087 | 0.0617 | | desk | 0.0372 | 0.3307 | 0.0001 | 0.0079 | | toilet | 0.0467 | 0.4828 | 0.0001 | 0.0172 | | showercurtrain | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | bathtub | 0.0000 | 0.0000 | 0.0000 | 0.0000 | +----------------+---------+---------+---------+---------+ | Overall | 0.0209 | 0.1472 | 0.0006 | 0.0108 | +----------------+---------+---------+---------+---------+ 2025/05/11 19:50:58 - mmengine - INFO - Epoch(val) [20][39/39] chair_AP_0.25: 0.0481 sofa_AP_0.25: 0.0276 table_AP_0.25: 0.0027 garbagebin_AP_0.25: 0.0067 bookshelf_AP_0.25: 0.0028 picture_AP_0.25: 0.0000 curtain_AP_0.25: 0.0000 door_AP_0.25: 0.0008 cabinet_AP_0.25: 0.0024 refrigerator_AP_0.25: 0.0000 counter_AP_0.25: 0.0000 sink_AP_0.25: 0.0179 window_AP_0.25: 0.0000 desk_AP_0.25: 0.0372 bed_AP_0.25: 0.1840 toilet_AP_0.25: 0.0467 showercurtrain_AP_0.25: 0.0000 bathtub_AP_0.25: 0.0000 mAP_0.25: 0.0209 chair_rec_0.25: 0.3823 sofa_rec_0.25: 0.2371 table_rec_0.25: 0.0657 garbagebin_rec_0.25: 0.0547 bookshelf_rec_0.25: 0.1299 picture_rec_0.25: 0.0000 curtain_rec_0.25: 0.0000 door_rec_0.25: 0.0771 cabinet_rec_0.25: 0.1075 refrigerator_rec_0.25: 0.0000 counter_rec_0.25: 0.0000 sink_rec_0.25: 0.1122 window_rec_0.25: 0.0035 desk_rec_0.25: 0.3307 bed_rec_0.25: 0.6667 toilet_rec_0.25: 0.4828 showercurtrain_rec_0.25: 0.0000 bathtub_rec_0.25: 0.0000 mAR_0.25: 0.1472 chair_AP_0.50: 0.0004 sofa_AP_0.50: 0.0001 table_AP_0.50: 0.0000 garbagebin_AP_0.50: 0.0001 bookshelf_AP_0.50: 0.0000 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0000 door_AP_0.50: 0.0000 cabinet_AP_0.50: 0.0000 refrigerator_AP_0.50: 0.0000 counter_AP_0.50: 0.0000 sink_AP_0.50: 0.0009 window_AP_0.50: 0.0000 desk_AP_0.50: 0.0001 bed_AP_0.50: 0.0087 toilet_AP_0.50: 0.0001 showercurtrain_AP_0.50: 0.0000 bathtub_AP_0.50: 0.0000 mAP_0.50: 0.0006 chair_rec_0.50: 0.0358 sofa_rec_0.50: 0.0103 table_rec_0.50: 0.0000 garbagebin_rec_0.50: 0.0057 bookshelf_rec_0.50: 0.0130 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.0000 door_rec_0.50: 0.0043 cabinet_rec_0.50: 0.0081 refrigerator_rec_0.50: 0.0000 counter_rec_0.50: 0.0000 sink_rec_0.50: 0.0306 window_rec_0.50: 0.0000 desk_rec_0.50: 0.0079 bed_rec_0.50: 0.0617 toilet_rec_0.50: 0.0172 showercurtrain_rec_0.50: 0.0000 bathtub_rec_0.50: 0.0000 mAR_0.50: 0.0108 data_time: 0.2753 time: 2.8316 2025/05/11 19:53:24 - mmengine - INFO - Epoch(train) [21][10/91] base_lr: 2.4846e-04 lr: 2.4846e-04 eta: 4 days, 1:18:45 time: 10.4417 data_time: 1.4768 memory: 68703 grad_norm: 0.9072 loss: 3.0983 center_loss: 0.9030 size_loss: 0.3213 cls_loss: 1.0025 giou_loss: 0.8714 2025/05/11 19:54:59 - mmengine - INFO - Epoch(train) [21][20/91] base_lr: 2.4846e-04 lr: 2.4846e-04 eta: 4 days, 1:15:20 time: 10.4235 data_time: 1.4601 memory: 68703 grad_norm: 0.9070 loss: 3.0719 center_loss: 0.8955 size_loss: 0.3220 cls_loss: 0.9860 giou_loss: 0.8685 2025/05/11 19:56:36 - mmengine - INFO - Epoch(train) [21][30/91] base_lr: 2.4846e-04 lr: 2.4846e-04 eta: 4 days, 1:12:08 time: 10.4306 data_time: 1.4517 memory: 68702 grad_norm: 0.9029 loss: 3.0698 center_loss: 0.8923 size_loss: 0.3187 cls_loss: 0.9885 giou_loss: 0.8704 2025/05/11 19:58:12 - mmengine - INFO - Epoch(train) [21][40/91] base_lr: 2.4846e-04 lr: 2.4846e-04 eta: 4 days, 1:08:59 time: 10.4331 data_time: 1.4438 memory: 68702 grad_norm: 0.8949 loss: 3.0694 center_loss: 0.8921 size_loss: 0.3194 cls_loss: 0.9886 giou_loss: 0.8693 2025/05/11 19:59:50 - mmengine - INFO - Epoch(train) [21][50/91] base_lr: 2.4846e-04 lr: 2.4846e-04 eta: 4 days, 1:06:02 time: 10.6273 data_time: 1.4535 memory: 68703 grad_norm: 0.8442 loss: 3.0456 center_loss: 0.8869 size_loss: 0.3151 cls_loss: 0.9764 giou_loss: 0.8670 2025/05/11 20:01:28 - mmengine - INFO - Epoch(train) [21][60/91] base_lr: 2.4846e-04 lr: 2.4846e-04 eta: 4 days, 1:03:21 time: 9.6791 data_time: 0.5579 memory: 68702 grad_norm: 0.8447 loss: 3.0388 center_loss: 0.8842 size_loss: 0.3137 cls_loss: 0.9774 giou_loss: 0.8634 2025/05/11 20:03:04 - mmengine - INFO - Epoch(train) [21][70/91] base_lr: 2.4846e-04 lr: 2.4846e-04 eta: 4 days, 1:00:17 time: 9.6962 data_time: 0.5710 memory: 68702 grad_norm: 0.8361 loss: 3.0659 center_loss: 0.8960 size_loss: 0.3153 cls_loss: 0.9886 giou_loss: 0.8660 2025/05/11 20:04:40 - mmengine - INFO - Epoch(train) [21][80/91] base_lr: 2.4846e-04 lr: 2.4846e-04 eta: 4 days, 0:57:00 time: 9.6864 data_time: 0.5791 memory: 68702 grad_norm: 0.8296 loss: 3.0655 center_loss: 0.9038 size_loss: 0.3139 cls_loss: 0.9837 giou_loss: 0.8642 2025/05/11 20:06:15 - mmengine - INFO - Epoch(train) [21][90/91] base_lr: 2.4846e-04 lr: 2.4846e-04 eta: 4 days, 0:53:23 time: 9.6490 data_time: 0.5784 memory: 68702 grad_norm: 0.8259 loss: 3.0467 center_loss: 0.8968 size_loss: 0.3100 cls_loss: 0.9787 giou_loss: 0.8611 2025/05/11 20:06:17 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 20:08:45 - mmengine - INFO - Epoch(train) [22][10/91] base_lr: 2.4830e-04 lr: 2.4830e-04 eta: 4 days, 1:03:05 time: 10.5121 data_time: 1.4045 memory: 68702 grad_norm: 0.8485 loss: 3.0146 center_loss: 0.8796 size_loss: 0.3074 cls_loss: 0.9733 giou_loss: 0.8543 2025/05/11 20:10:21 - mmengine - INFO - Epoch(train) [22][20/91] base_lr: 2.4830e-04 lr: 2.4830e-04 eta: 4 days, 0:59:47 time: 10.4683 data_time: 1.4115 memory: 68702 grad_norm: 0.8553 loss: 3.0159 center_loss: 0.8722 size_loss: 0.3084 cls_loss: 0.9800 giou_loss: 0.8553 2025/05/11 20:11:57 - mmengine - INFO - Epoch(train) [22][30/91] base_lr: 2.4830e-04 lr: 2.4830e-04 eta: 4 days, 0:56:32 time: 10.4618 data_time: 1.4141 memory: 68702 grad_norm: 0.8594 loss: 3.0186 center_loss: 0.8783 size_loss: 0.3121 cls_loss: 0.9689 giou_loss: 0.8593 2025/05/11 20:13:32 - mmengine - INFO - Epoch(train) [22][40/91] base_lr: 2.4830e-04 lr: 2.4830e-04 eta: 4 days, 0:53:10 time: 10.4492 data_time: 1.4131 memory: 68702 grad_norm: 0.8678 loss: 3.0036 center_loss: 0.8744 size_loss: 0.3105 cls_loss: 0.9642 giou_loss: 0.8545 2025/05/11 20:15:09 - mmengine - INFO - Epoch(train) [22][50/91] base_lr: 2.4830e-04 lr: 2.4830e-04 eta: 4 days, 0:50:06 time: 10.6390 data_time: 1.4356 memory: 68702 grad_norm: 0.8546 loss: 2.9915 center_loss: 0.8764 size_loss: 0.3068 cls_loss: 0.9541 giou_loss: 0.8543 2025/05/11 20:16:45 - mmengine - INFO - Epoch(train) [22][60/91] base_lr: 2.4830e-04 lr: 2.4830e-04 eta: 4 days, 0:46:52 time: 9.5998 data_time: 0.6099 memory: 68703 grad_norm: 0.8950 loss: 3.0028 center_loss: 0.8810 size_loss: 0.3089 cls_loss: 0.9593 giou_loss: 0.8535 2025/05/11 20:18:21 - mmengine - INFO - Epoch(train) [22][70/91] base_lr: 2.4830e-04 lr: 2.4830e-04 eta: 4 days, 0:43:51 time: 9.6133 data_time: 0.6227 memory: 68702 grad_norm: 0.9392 loss: 2.9997 center_loss: 0.8824 size_loss: 0.3043 cls_loss: 0.9602 giou_loss: 0.8529 2025/05/11 20:19:57 - mmengine - INFO - Epoch(train) [22][80/91] base_lr: 2.4830e-04 lr: 2.4830e-04 eta: 4 days, 0:40:32 time: 9.6023 data_time: 0.6247 memory: 68702 grad_norm: 0.9476 loss: 2.9951 center_loss: 0.8696 size_loss: 0.3002 cls_loss: 0.9771 giou_loss: 0.8482 2025/05/11 20:21:23 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 20:21:32 - mmengine - INFO - Epoch(train) [22][90/91] base_lr: 2.4830e-04 lr: 2.4830e-04 eta: 4 days, 0:37:08 time: 9.5940 data_time: 0.6209 memory: 68702 grad_norm: 0.9702 loss: 2.9980 center_loss: 0.8606 size_loss: 0.2994 cls_loss: 0.9919 giou_loss: 0.8462 2025/05/11 20:21:34 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 20:21:34 - mmengine - INFO - Saving checkpoint at 22 epochs 2025/05/11 20:22:32 - mmengine - INFO - Epoch(val) [22][10/39] eta: 0:01:40 time: 2.9558 data_time: 0.4036 memory: 15952 2025/05/11 20:22:58 - mmengine - INFO - Epoch(val) [22][20/39] eta: 0:00:57 time: 2.7948 data_time: 0.2623 memory: 13407 2025/05/11 20:23:24 - mmengine - INFO - Epoch(val) [22][30/39] eta: 0:00:25 time: 2.7632 data_time: 0.2428 memory: 13407 2025/05/11 20:23:52 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | chair | 0.0609 | 0.3911 | 0.0009 | 0.0468 | | door | 0.0023 | 0.1028 | 0.0000 | 0.0043 | | sofa | 0.0524 | 0.2062 | 0.0004 | 0.0103 | | garbagebin | 0.0062 | 0.0962 | 0.0000 | 0.0019 | | window | 0.0001 | 0.0142 | 0.0000 | 0.0000 | | table | 0.0050 | 0.0571 | 0.0000 | 0.0000 | | bookshelf | 0.0364 | 0.1948 | 0.0000 | 0.0000 | | picture | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | curtain | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | cabinet | 0.0070 | 0.1774 | 0.0000 | 0.0027 | | sink | 0.0308 | 0.2449 | 0.0008 | 0.0306 | | refrigerator | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | counter | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | bed | 0.3606 | 0.6543 | 0.0064 | 0.0988 | | desk | 0.0597 | 0.2835 | 0.0001 | 0.0157 | | toilet | 0.0900 | 0.5345 | 0.0033 | 0.1379 | | showercurtrain | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | bathtub | 0.0000 | 0.0000 | 0.0000 | 0.0000 | +----------------+---------+---------+---------+---------+ | Overall | 0.0395 | 0.1643 | 0.0007 | 0.0194 | +----------------+---------+---------+---------+---------+ 2025/05/11 20:23:52 - mmengine - INFO - Epoch(val) [22][39/39] chair_AP_0.25: 0.0609 sofa_AP_0.25: 0.0524 table_AP_0.25: 0.0050 garbagebin_AP_0.25: 0.0062 bookshelf_AP_0.25: 0.0364 picture_AP_0.25: 0.0000 curtain_AP_0.25: 0.0000 door_AP_0.25: 0.0023 cabinet_AP_0.25: 0.0070 refrigerator_AP_0.25: 0.0000 counter_AP_0.25: 0.0000 sink_AP_0.25: 0.0308 window_AP_0.25: 0.0001 desk_AP_0.25: 0.0597 bed_AP_0.25: 0.3606 toilet_AP_0.25: 0.0900 showercurtrain_AP_0.25: 0.0000 bathtub_AP_0.25: 0.0000 mAP_0.25: 0.0395 chair_rec_0.25: 0.3911 sofa_rec_0.25: 0.2062 table_rec_0.25: 0.0571 garbagebin_rec_0.25: 0.0962 bookshelf_rec_0.25: 0.1948 picture_rec_0.25: 0.0000 curtain_rec_0.25: 0.0000 door_rec_0.25: 0.1028 cabinet_rec_0.25: 0.1774 refrigerator_rec_0.25: 0.0000 counter_rec_0.25: 0.0000 sink_rec_0.25: 0.2449 window_rec_0.25: 0.0142 desk_rec_0.25: 0.2835 bed_rec_0.25: 0.6543 toilet_rec_0.25: 0.5345 showercurtrain_rec_0.25: 0.0000 bathtub_rec_0.25: 0.0000 mAR_0.25: 0.1643 chair_AP_0.50: 0.0009 sofa_AP_0.50: 0.0004 table_AP_0.50: 0.0000 garbagebin_AP_0.50: 0.0000 bookshelf_AP_0.50: 0.0000 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0000 door_AP_0.50: 0.0000 cabinet_AP_0.50: 0.0000 refrigerator_AP_0.50: 0.0000 counter_AP_0.50: 0.0000 sink_AP_0.50: 0.0008 window_AP_0.50: 0.0000 desk_AP_0.50: 0.0001 bed_AP_0.50: 0.0064 toilet_AP_0.50: 0.0033 showercurtrain_AP_0.50: 0.0000 bathtub_AP_0.50: 0.0000 mAP_0.50: 0.0007 chair_rec_0.50: 0.0468 sofa_rec_0.50: 0.0103 table_rec_0.50: 0.0000 garbagebin_rec_0.50: 0.0019 bookshelf_rec_0.50: 0.0000 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.0000 door_rec_0.50: 0.0043 cabinet_rec_0.50: 0.0027 refrigerator_rec_0.50: 0.0000 counter_rec_0.50: 0.0000 sink_rec_0.50: 0.0306 window_rec_0.50: 0.0000 desk_rec_0.50: 0.0157 bed_rec_0.50: 0.0988 toilet_rec_0.50: 0.1379 showercurtrain_rec_0.50: 0.0000 bathtub_rec_0.50: 0.0000 mAR_0.50: 0.0194 data_time: 0.2856 time: 2.7842 2025/05/11 20:23:52 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_18.pth is removed 2025/05/11 20:24:20 - mmengine - INFO - The best checkpoint with 0.0395 mAP_0.25 at 22 epoch is saved to best_mAP_0.25_epoch_22.pth. 2025/05/11 20:27:10 - mmengine - INFO - Epoch(train) [23][10/91] base_lr: 2.4814e-04 lr: 2.4814e-04 eta: 4 days, 0:45:06 time: 10.3871 data_time: 1.5330 memory: 68702 grad_norm: 1.0597 loss: 3.0064 center_loss: 0.8618 size_loss: 0.3000 cls_loss: 0.9979 giou_loss: 0.8468 2025/05/11 20:28:45 - mmengine - INFO - Epoch(train) [23][20/91] base_lr: 2.4814e-04 lr: 2.4814e-04 eta: 4 days, 0:41:49 time: 10.3798 data_time: 1.5257 memory: 68702 grad_norm: 1.0314 loss: 2.9884 center_loss: 0.8504 size_loss: 0.2941 cls_loss: 0.9990 giou_loss: 0.8448 2025/05/11 20:30:22 - mmengine - INFO - Epoch(train) [23][30/91] base_lr: 2.4814e-04 lr: 2.4814e-04 eta: 4 days, 0:38:50 time: 10.3851 data_time: 1.5260 memory: 68702 grad_norm: 1.0001 loss: 2.9764 center_loss: 0.8482 size_loss: 0.2928 cls_loss: 0.9925 giou_loss: 0.8428 2025/05/11 20:31:58 - mmengine - INFO - Epoch(train) [23][40/91] base_lr: 2.4814e-04 lr: 2.4814e-04 eta: 4 days, 0:35:37 time: 10.3813 data_time: 1.5170 memory: 68702 grad_norm: 1.0047 loss: 2.9629 center_loss: 0.8545 size_loss: 0.2924 cls_loss: 0.9751 giou_loss: 0.8409 2025/05/11 20:33:34 - mmengine - INFO - Epoch(train) [23][50/91] base_lr: 2.4814e-04 lr: 2.4814e-04 eta: 4 days, 0:32:36 time: 10.5649 data_time: 1.5338 memory: 68702 grad_norm: 0.9614 loss: 2.9176 center_loss: 0.8488 size_loss: 0.2891 cls_loss: 0.9386 giou_loss: 0.8411 2025/05/11 20:35:10 - mmengine - INFO - Epoch(train) [23][60/91] base_lr: 2.4814e-04 lr: 2.4814e-04 eta: 4 days, 0:29:31 time: 9.6134 data_time: 0.6129 memory: 68702 grad_norm: 0.9367 loss: 2.9233 center_loss: 0.8483 size_loss: 0.2915 cls_loss: 0.9428 giou_loss: 0.8407 2025/05/11 20:36:47 - mmengine - INFO - Epoch(train) [23][70/91] base_lr: 2.4814e-04 lr: 2.4814e-04 eta: 4 days, 0:26:30 time: 9.6283 data_time: 0.6227 memory: 68702 grad_norm: 0.9303 loss: 2.9298 center_loss: 0.8598 size_loss: 0.2963 cls_loss: 0.9320 giou_loss: 0.8418 2025/05/11 20:38:22 - mmengine - INFO - Epoch(train) [23][80/91] base_lr: 2.4814e-04 lr: 2.4814e-04 eta: 4 days, 0:23:18 time: 9.6067 data_time: 0.6125 memory: 68703 grad_norm: 0.9390 loss: 2.9537 center_loss: 0.8700 size_loss: 0.3028 cls_loss: 0.9345 giou_loss: 0.8464 2025/05/11 20:39:57 - mmengine - INFO - Epoch(train) [23][90/91] base_lr: 2.4814e-04 lr: 2.4814e-04 eta: 4 days, 0:19:49 time: 9.5822 data_time: 0.6083 memory: 68702 grad_norm: 0.9526 loss: 2.9587 center_loss: 0.8645 size_loss: 0.3018 cls_loss: 0.9448 giou_loss: 0.8476 2025/05/11 20:39:59 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 20:42:26 - mmengine - INFO - Epoch(train) [24][10/91] base_lr: 2.4796e-04 lr: 2.4796e-04 eta: 4 days, 0:28:18 time: 10.4459 data_time: 1.5278 memory: 68702 grad_norm: 1.0563 loss: 2.9684 center_loss: 0.8705 size_loss: 0.3028 cls_loss: 0.9473 giou_loss: 0.8477 2025/05/11 20:44:01 - mmengine - INFO - Epoch(train) [24][20/91] base_lr: 2.4796e-04 lr: 2.4796e-04 eta: 4 days, 0:25:01 time: 10.4280 data_time: 1.5010 memory: 68702 grad_norm: 1.0314 loss: 2.9656 center_loss: 0.8657 size_loss: 0.3010 cls_loss: 0.9549 giou_loss: 0.8439 2025/05/11 20:45:38 - mmengine - INFO - Epoch(train) [24][30/91] base_lr: 2.4796e-04 lr: 2.4796e-04 eta: 4 days, 0:22:02 time: 10.4329 data_time: 1.4958 memory: 68702 grad_norm: 1.0364 loss: 2.9427 center_loss: 0.8623 size_loss: 0.2961 cls_loss: 0.9434 giou_loss: 0.8411 2025/05/11 20:47:15 - mmengine - INFO - Epoch(train) [24][40/91] base_lr: 2.4796e-04 lr: 2.4796e-04 eta: 4 days, 0:19:16 time: 10.4609 data_time: 1.4934 memory: 68702 grad_norm: 1.0447 loss: 2.9195 center_loss: 0.8505 size_loss: 0.2909 cls_loss: 0.9423 giou_loss: 0.8358 2025/05/11 20:48:52 - mmengine - INFO - Epoch(train) [24][50/91] base_lr: 2.4796e-04 lr: 2.4796e-04 eta: 4 days, 0:16:26 time: 10.6613 data_time: 1.5104 memory: 68702 grad_norm: 1.0014 loss: 2.8953 center_loss: 0.8440 size_loss: 0.2889 cls_loss: 0.9312 giou_loss: 0.8311 2025/05/11 20:50:28 - mmengine - INFO - Epoch(train) [24][60/91] base_lr: 2.4796e-04 lr: 2.4796e-04 eta: 4 days, 0:13:27 time: 9.6419 data_time: 0.5744 memory: 68702 grad_norm: 0.9833 loss: 2.8838 center_loss: 0.8395 size_loss: 0.2858 cls_loss: 0.9307 giou_loss: 0.8279 2025/05/11 20:52:04 - mmengine - INFO - Epoch(train) [24][70/91] base_lr: 2.4796e-04 lr: 2.4796e-04 eta: 4 days, 0:10:30 time: 9.6633 data_time: 0.5867 memory: 68703 grad_norm: 1.0106 loss: 2.8596 center_loss: 0.8336 size_loss: 0.2832 cls_loss: 0.9180 giou_loss: 0.8248 2025/05/11 20:53:40 - mmengine - INFO - Epoch(train) [24][80/91] base_lr: 2.4796e-04 lr: 2.4796e-04 eta: 4 days, 0:07:24 time: 9.6491 data_time: 0.5910 memory: 68702 grad_norm: 1.0335 loss: 2.8843 center_loss: 0.8399 size_loss: 0.2870 cls_loss: 0.9291 giou_loss: 0.8283 2025/05/11 20:55:15 - mmengine - INFO - Epoch(train) [24][90/91] base_lr: 2.4796e-04 lr: 2.4796e-04 eta: 4 days, 0:04:04 time: 9.6006 data_time: 0.5827 memory: 68702 grad_norm: 1.0388 loss: 2.8739 center_loss: 0.8334 size_loss: 0.2853 cls_loss: 0.9310 giou_loss: 0.8243 2025/05/11 20:55:17 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 20:55:17 - mmengine - INFO - Saving checkpoint at 24 epochs 2025/05/11 20:56:15 - mmengine - INFO - Epoch(val) [24][10/39] eta: 0:01:37 time: 2.9005 data_time: 0.3977 memory: 15952 2025/05/11 20:56:41 - mmengine - INFO - Epoch(val) [24][20/39] eta: 0:00:56 time: 2.7260 data_time: 0.2307 memory: 13407 2025/05/11 20:57:07 - mmengine - INFO - Epoch(val) [24][30/39] eta: 0:00:25 time: 2.7296 data_time: 0.2287 memory: 13407 2025/05/11 20:57:35 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | sofa | 0.0876 | 0.4948 | 0.0014 | 0.0412 | | garbagebin | 0.0106 | 0.1245 | 0.0001 | 0.0075 | | cabinet | 0.0217 | 0.2070 | 0.0002 | 0.0215 | | chair | 0.0924 | 0.4693 | 0.0021 | 0.0636 | | table | 0.0220 | 0.2029 | 0.0043 | 0.0229 | | bookshelf | 0.0149 | 0.1429 | 0.0006 | 0.0390 | | picture | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | curtain | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | desk | 0.1230 | 0.6142 | 0.0053 | 0.0945 | | window | 0.0004 | 0.0355 | 0.0000 | 0.0000 | | door | 0.0074 | 0.0942 | 0.0000 | 0.0043 | | sink | 0.0351 | 0.2245 | 0.0104 | 0.0306 | | refrigerator | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | counter | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | bed | 0.2851 | 0.7407 | 0.0156 | 0.1605 | | bathtub | 0.0529 | 0.3226 | 0.0005 | 0.0323 | | toilet | 0.1684 | 0.6379 | 0.0019 | 0.0690 | | showercurtrain | 0.0000 | 0.0000 | 0.0000 | 0.0000 | +----------------+---------+---------+---------+---------+ | Overall | 0.0512 | 0.2395 | 0.0024 | 0.0326 | +----------------+---------+---------+---------+---------+ 2025/05/11 20:57:35 - mmengine - INFO - Epoch(val) [24][39/39] chair_AP_0.25: 0.0924 sofa_AP_0.25: 0.0876 table_AP_0.25: 0.0220 garbagebin_AP_0.25: 0.0106 bookshelf_AP_0.25: 0.0149 picture_AP_0.25: 0.0000 curtain_AP_0.25: 0.0000 door_AP_0.25: 0.0074 cabinet_AP_0.25: 0.0217 refrigerator_AP_0.25: 0.0000 counter_AP_0.25: 0.0000 sink_AP_0.25: 0.0351 window_AP_0.25: 0.0004 desk_AP_0.25: 0.1230 bed_AP_0.25: 0.2851 toilet_AP_0.25: 0.1684 showercurtrain_AP_0.25: 0.0000 bathtub_AP_0.25: 0.0529 mAP_0.25: 0.0512 chair_rec_0.25: 0.4693 sofa_rec_0.25: 0.4948 table_rec_0.25: 0.2029 garbagebin_rec_0.25: 0.1245 bookshelf_rec_0.25: 0.1429 picture_rec_0.25: 0.0000 curtain_rec_0.25: 0.0000 door_rec_0.25: 0.0942 cabinet_rec_0.25: 0.2070 refrigerator_rec_0.25: 0.0000 counter_rec_0.25: 0.0000 sink_rec_0.25: 0.2245 window_rec_0.25: 0.0355 desk_rec_0.25: 0.6142 bed_rec_0.25: 0.7407 toilet_rec_0.25: 0.6379 showercurtrain_rec_0.25: 0.0000 bathtub_rec_0.25: 0.3226 mAR_0.25: 0.2395 chair_AP_0.50: 0.0021 sofa_AP_0.50: 0.0014 table_AP_0.50: 0.0043 garbagebin_AP_0.50: 0.0001 bookshelf_AP_0.50: 0.0006 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0000 door_AP_0.50: 0.0000 cabinet_AP_0.50: 0.0002 refrigerator_AP_0.50: 0.0000 counter_AP_0.50: 0.0000 sink_AP_0.50: 0.0104 window_AP_0.50: 0.0000 desk_AP_0.50: 0.0053 bed_AP_0.50: 0.0156 toilet_AP_0.50: 0.0019 showercurtrain_AP_0.50: 0.0000 bathtub_AP_0.50: 0.0005 mAP_0.50: 0.0024 chair_rec_0.50: 0.0636 sofa_rec_0.50: 0.0412 table_rec_0.50: 0.0229 garbagebin_rec_0.50: 0.0075 bookshelf_rec_0.50: 0.0390 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.0000 door_rec_0.50: 0.0043 cabinet_rec_0.50: 0.0215 refrigerator_rec_0.50: 0.0000 counter_rec_0.50: 0.0000 sink_rec_0.50: 0.0306 window_rec_0.50: 0.0000 desk_rec_0.50: 0.0945 bed_rec_0.50: 0.1605 toilet_rec_0.50: 0.0690 showercurtrain_rec_0.50: 0.0000 bathtub_rec_0.50: 0.0323 mAR_0.50: 0.0326 data_time: 0.2679 time: 2.7681 2025/05/11 20:57:35 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_22.pth is removed 2025/05/11 20:58:02 - mmengine - INFO - The best checkpoint with 0.0512 mAP_0.25 at 24 epoch is saved to best_mAP_0.25_epoch_24.pth. 2025/05/11 21:00:56 - mmengine - INFO - Epoch(train) [25][10/91] base_lr: 2.4778e-04 lr: 2.4778e-04 eta: 4 days, 0:11:48 time: 10.4322 data_time: 1.5375 memory: 68702 grad_norm: 1.1519 loss: 2.8827 center_loss: 0.8298 size_loss: 0.2814 cls_loss: 0.9486 giou_loss: 0.8230 2025/05/11 21:02:31 - mmengine - INFO - Epoch(train) [25][20/91] base_lr: 2.4778e-04 lr: 2.4778e-04 eta: 4 days, 0:08:35 time: 10.4111 data_time: 1.5421 memory: 68702 grad_norm: 1.1385 loss: 2.8755 center_loss: 0.8262 size_loss: 0.2803 cls_loss: 0.9437 giou_loss: 0.8254 2025/05/11 21:04:07 - mmengine - INFO - Epoch(train) [25][30/91] base_lr: 2.4778e-04 lr: 2.4778e-04 eta: 4 days, 0:05:33 time: 10.4092 data_time: 1.5370 memory: 68700 grad_norm: 1.1198 loss: 2.8919 center_loss: 0.8305 size_loss: 0.2832 cls_loss: 0.9497 giou_loss: 0.8285 2025/05/11 21:05:44 - mmengine - INFO - Epoch(train) [25][40/91] base_lr: 2.4778e-04 lr: 2.4778e-04 eta: 4 days, 0:02:36 time: 10.4137 data_time: 1.5364 memory: 68702 grad_norm: 1.1277 loss: 2.8774 center_loss: 0.8235 size_loss: 0.2809 cls_loss: 0.9479 giou_loss: 0.8251 2025/05/11 21:07:20 - mmengine - INFO - Epoch(train) [25][50/91] base_lr: 2.4778e-04 lr: 2.4778e-04 eta: 3 days, 23:59:47 time: 10.6078 data_time: 1.5527 memory: 68702 grad_norm: 1.0559 loss: 2.8643 center_loss: 0.8259 size_loss: 0.2806 cls_loss: 0.9306 giou_loss: 0.8272 2025/05/11 21:08:57 - mmengine - INFO - Epoch(train) [25][60/91] base_lr: 2.4778e-04 lr: 2.4778e-04 eta: 3 days, 23:56:55 time: 9.6154 data_time: 0.5884 memory: 68702 grad_norm: 1.0843 loss: 2.8642 center_loss: 0.8307 size_loss: 0.2831 cls_loss: 0.9245 giou_loss: 0.8260 2025/05/11 21:10:33 - mmengine - INFO - Epoch(train) [25][70/91] base_lr: 2.4778e-04 lr: 2.4778e-04 eta: 3 days, 23:53:53 time: 9.6251 data_time: 0.5906 memory: 68703 grad_norm: 1.0912 loss: 2.8767 center_loss: 0.8393 size_loss: 0.2844 cls_loss: 0.9282 giou_loss: 0.8248 2025/05/11 21:12:09 - mmengine - INFO - Epoch(train) [25][80/91] base_lr: 2.4778e-04 lr: 2.4778e-04 eta: 3 days, 23:51:00 time: 9.6318 data_time: 0.5914 memory: 68702 grad_norm: 1.1378 loss: 2.8512 center_loss: 0.8289 size_loss: 0.2811 cls_loss: 0.9190 giou_loss: 0.8221 2025/05/11 21:13:44 - mmengine - INFO - Epoch(train) [25][90/91] base_lr: 2.4778e-04 lr: 2.4778e-04 eta: 3 days, 23:47:54 time: 9.6151 data_time: 0.5803 memory: 68702 grad_norm: 1.1808 loss: 2.8417 center_loss: 0.8240 size_loss: 0.2806 cls_loss: 0.9157 giou_loss: 0.8214 2025/05/11 21:13:46 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 21:16:15 - mmengine - INFO - Epoch(train) [26][10/91] base_lr: 2.4760e-04 lr: 2.4760e-04 eta: 3 days, 23:55:53 time: 10.4998 data_time: 1.5031 memory: 68702 grad_norm: 1.2433 loss: 2.8554 center_loss: 0.8351 size_loss: 0.2861 cls_loss: 0.9060 giou_loss: 0.8283 2025/05/11 21:17:52 - mmengine - INFO - Epoch(train) [26][20/91] base_lr: 2.4760e-04 lr: 2.4760e-04 eta: 3 days, 23:53:05 time: 10.5106 data_time: 1.5055 memory: 68702 grad_norm: 1.2212 loss: 2.8496 center_loss: 0.8298 size_loss: 0.2844 cls_loss: 0.9065 giou_loss: 0.8290 2025/05/11 21:19:28 - mmengine - INFO - Epoch(train) [26][30/91] base_lr: 2.4760e-04 lr: 2.4760e-04 eta: 3 days, 23:50:12 time: 10.5211 data_time: 1.5058 memory: 68703 grad_norm: 1.2309 loss: 2.8614 center_loss: 0.8362 size_loss: 0.2863 cls_loss: 0.9064 giou_loss: 0.8326 2025/05/11 21:21:04 - mmengine - INFO - Epoch(train) [26][40/91] base_lr: 2.4760e-04 lr: 2.4760e-04 eta: 3 days, 23:47:13 time: 10.5060 data_time: 1.4960 memory: 68700 grad_norm: 1.2039 loss: 2.8679 center_loss: 0.8452 size_loss: 0.2876 cls_loss: 0.9027 giou_loss: 0.8325 2025/05/11 21:22:41 - mmengine - INFO - Epoch(train) [26][50/91] base_lr: 2.4760e-04 lr: 2.4760e-04 eta: 3 days, 23:44:30 time: 10.6938 data_time: 1.5179 memory: 68702 grad_norm: 1.1262 loss: 2.8469 center_loss: 0.8385 size_loss: 0.2815 cls_loss: 0.9004 giou_loss: 0.8265 2025/05/11 21:24:18 - mmengine - INFO - Epoch(train) [26][60/91] base_lr: 2.4760e-04 lr: 2.4760e-04 eta: 3 days, 23:41:43 time: 9.6530 data_time: 0.5933 memory: 68703 grad_norm: 1.1397 loss: 2.8484 center_loss: 0.8347 size_loss: 0.2824 cls_loss: 0.9082 giou_loss: 0.8231 2025/05/11 21:25:56 - mmengine - INFO - Epoch(train) [26][70/91] base_lr: 2.4760e-04 lr: 2.4760e-04 eta: 3 days, 23:39:15 time: 9.6746 data_time: 0.6149 memory: 68702 grad_norm: 1.1551 loss: 2.8453 center_loss: 0.8394 size_loss: 0.2793 cls_loss: 0.9059 giou_loss: 0.8207 2025/05/11 21:27:31 - mmengine - INFO - Epoch(train) [26][80/91] base_lr: 2.4760e-04 lr: 2.4760e-04 eta: 3 days, 23:36:16 time: 9.6623 data_time: 0.6118 memory: 68702 grad_norm: 1.1747 loss: 2.8014 center_loss: 0.8175 size_loss: 0.2736 cls_loss: 0.8956 giou_loss: 0.8148 2025/05/11 21:29:06 - mmengine - INFO - Epoch(train) [26][90/91] base_lr: 2.4760e-04 lr: 2.4760e-04 eta: 3 days, 23:33:07 time: 9.6445 data_time: 0.6131 memory: 68702 grad_norm: 1.1809 loss: 2.7933 center_loss: 0.8112 size_loss: 0.2724 cls_loss: 0.8977 giou_loss: 0.8120 2025/05/11 21:29:08 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 21:29:08 - mmengine - INFO - Saving checkpoint at 26 epochs 2025/05/11 21:30:06 - mmengine - INFO - Epoch(val) [26][10/39] eta: 0:01:37 time: 2.8875 data_time: 0.3765 memory: 15952 2025/05/11 21:30:32 - mmengine - INFO - Epoch(val) [26][20/39] eta: 0:00:57 time: 2.7481 data_time: 0.2363 memory: 13407 2025/05/11 21:30:58 - mmengine - INFO - Epoch(val) [26][30/39] eta: 0:00:25 time: 2.7539 data_time: 0.2344 memory: 13407 2025/05/11 21:31:27 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | sofa | 0.1727 | 0.4845 | 0.0007 | 0.0309 | | chair | 0.1021 | 0.5387 | 0.0051 | 0.0811 | | garbagebin | 0.0198 | 0.1774 | 0.0006 | 0.0151 | | bookshelf | 0.0253 | 0.3117 | 0.0001 | 0.0130 | | door | 0.0167 | 0.1349 | 0.0001 | 0.0086 | | window | 0.0010 | 0.0709 | 0.0000 | 0.0000 | | table | 0.0875 | 0.2600 | 0.0011 | 0.0286 | | picture | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | curtain | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | cabinet | 0.0303 | 0.2339 | 0.0004 | 0.0188 | | sink | 0.0732 | 0.2959 | 0.0008 | 0.0306 | | refrigerator | 0.0240 | 0.1404 | 0.0162 | 0.0702 | | counter | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | bed | 0.4535 | 0.7778 | 0.0523 | 0.1728 | | desk | 0.1778 | 0.6142 | 0.0080 | 0.0630 | | bathtub | 0.2124 | 0.6129 | 0.0471 | 0.2258 | | showercurtrain | 0.0172 | 0.1429 | 0.0009 | 0.0357 | | toilet | 0.2840 | 0.7241 | 0.0173 | 0.1034 | +----------------+---------+---------+---------+---------+ | Overall | 0.0943 | 0.3067 | 0.0084 | 0.0499 | +----------------+---------+---------+---------+---------+ 2025/05/11 21:31:27 - mmengine - INFO - Epoch(val) [26][39/39] chair_AP_0.25: 0.1021 sofa_AP_0.25: 0.1727 table_AP_0.25: 0.0875 garbagebin_AP_0.25: 0.0198 bookshelf_AP_0.25: 0.0253 picture_AP_0.25: 0.0000 curtain_AP_0.25: 0.0000 door_AP_0.25: 0.0167 cabinet_AP_0.25: 0.0303 refrigerator_AP_0.25: 0.0240 counter_AP_0.25: 0.0000 sink_AP_0.25: 0.0732 window_AP_0.25: 0.0010 desk_AP_0.25: 0.1778 bed_AP_0.25: 0.4535 toilet_AP_0.25: 0.2840 showercurtrain_AP_0.25: 0.0172 bathtub_AP_0.25: 0.2124 mAP_0.25: 0.0943 chair_rec_0.25: 0.5387 sofa_rec_0.25: 0.4845 table_rec_0.25: 0.2600 garbagebin_rec_0.25: 0.1774 bookshelf_rec_0.25: 0.3117 picture_rec_0.25: 0.0000 curtain_rec_0.25: 0.0000 door_rec_0.25: 0.1349 cabinet_rec_0.25: 0.2339 refrigerator_rec_0.25: 0.1404 counter_rec_0.25: 0.0000 sink_rec_0.25: 0.2959 window_rec_0.25: 0.0709 desk_rec_0.25: 0.6142 bed_rec_0.25: 0.7778 toilet_rec_0.25: 0.7241 showercurtrain_rec_0.25: 0.1429 bathtub_rec_0.25: 0.6129 mAR_0.25: 0.3067 chair_AP_0.50: 0.0051 sofa_AP_0.50: 0.0007 table_AP_0.50: 0.0011 garbagebin_AP_0.50: 0.0006 bookshelf_AP_0.50: 0.0001 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0000 door_AP_0.50: 0.0001 cabinet_AP_0.50: 0.0004 refrigerator_AP_0.50: 0.0162 counter_AP_0.50: 0.0000 sink_AP_0.50: 0.0008 window_AP_0.50: 0.0000 desk_AP_0.50: 0.0080 bed_AP_0.50: 0.0523 toilet_AP_0.50: 0.0173 showercurtrain_AP_0.50: 0.0009 bathtub_AP_0.50: 0.0471 mAP_0.50: 0.0084 chair_rec_0.50: 0.0811 sofa_rec_0.50: 0.0309 table_rec_0.50: 0.0286 garbagebin_rec_0.50: 0.0151 bookshelf_rec_0.50: 0.0130 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.0000 door_rec_0.50: 0.0086 cabinet_rec_0.50: 0.0188 refrigerator_rec_0.50: 0.0702 counter_rec_0.50: 0.0000 sink_rec_0.50: 0.0306 window_rec_0.50: 0.0000 desk_rec_0.50: 0.0630 bed_rec_0.50: 0.1728 toilet_rec_0.50: 0.1034 showercurtrain_rec_0.50: 0.0357 bathtub_rec_0.50: 0.2258 mAR_0.50: 0.0499 data_time: 0.2744 time: 2.7972 2025/05/11 21:31:27 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_24.pth is removed 2025/05/11 21:31:48 - mmengine - INFO - The best checkpoint with 0.0943 mAP_0.25 at 26 epoch is saved to best_mAP_0.25_epoch_26.pth. 2025/05/11 21:34:38 - mmengine - INFO - Epoch(train) [27][10/91] base_lr: 2.4740e-04 lr: 2.4740e-04 eta: 3 days, 23:39:44 time: 10.4447 data_time: 1.5762 memory: 68703 grad_norm: 1.2099 loss: 2.7849 center_loss: 0.8102 size_loss: 0.2718 cls_loss: 0.8921 giou_loss: 0.8109 2025/05/11 21:36:15 - mmengine - INFO - Epoch(train) [27][20/91] base_lr: 2.4740e-04 lr: 2.4740e-04 eta: 3 days, 23:37:01 time: 10.4480 data_time: 1.5674 memory: 68703 grad_norm: 1.2182 loss: 2.7843 center_loss: 0.8151 size_loss: 0.2708 cls_loss: 0.8856 giou_loss: 0.8129 2025/05/11 21:37:52 - mmengine - INFO - Epoch(train) [27][30/91] base_lr: 2.4740e-04 lr: 2.4740e-04 eta: 3 days, 23:34:14 time: 10.4273 data_time: 1.5531 memory: 68703 grad_norm: 1.2084 loss: 2.7582 center_loss: 0.7995 size_loss: 0.2680 cls_loss: 0.8832 giou_loss: 0.8074 2025/05/11 21:39:28 - mmengine - INFO - Epoch(train) [27][40/91] base_lr: 2.4740e-04 lr: 2.4740e-04 eta: 3 days, 23:31:22 time: 10.4318 data_time: 1.5488 memory: 68703 grad_norm: 1.2110 loss: 2.7594 center_loss: 0.8054 size_loss: 0.2693 cls_loss: 0.8785 giou_loss: 0.8062 2025/05/11 21:41:05 - mmengine - INFO - Epoch(train) [27][50/91] base_lr: 2.4740e-04 lr: 2.4740e-04 eta: 3 days, 23:28:46 time: 10.6299 data_time: 1.5621 memory: 68702 grad_norm: 1.1810 loss: 2.7693 center_loss: 0.8119 size_loss: 0.2716 cls_loss: 0.8763 giou_loss: 0.8095 2025/05/11 21:42:42 - mmengine - INFO - Epoch(train) [27][60/91] base_lr: 2.4740e-04 lr: 2.4740e-04 eta: 3 days, 23:26:02 time: 9.6705 data_time: 0.5904 memory: 68702 grad_norm: 1.1787 loss: 2.7597 center_loss: 0.8060 size_loss: 0.2690 cls_loss: 0.8781 giou_loss: 0.8065 2025/05/11 21:44:18 - mmengine - INFO - Epoch(train) [27][70/91] base_lr: 2.4740e-04 lr: 2.4740e-04 eta: 3 days, 23:23:17 time: 9.6641 data_time: 0.6042 memory: 68702 grad_norm: 1.1605 loss: 2.7462 center_loss: 0.8000 size_loss: 0.2666 cls_loss: 0.8759 giou_loss: 0.8037 2025/05/11 21:45:54 - mmengine - INFO - Epoch(train) [27][80/91] base_lr: 2.4740e-04 lr: 2.4740e-04 eta: 3 days, 23:20:21 time: 9.6477 data_time: 0.5913 memory: 68702 grad_norm: 1.1755 loss: 2.7221 center_loss: 0.7929 size_loss: 0.2639 cls_loss: 0.8651 giou_loss: 0.8002 2025/05/11 21:47:29 - mmengine - INFO - Epoch(train) [27][90/91] base_lr: 2.4740e-04 lr: 2.4740e-04 eta: 3 days, 23:17:16 time: 9.6234 data_time: 0.5922 memory: 68702 grad_norm: 1.2598 loss: 2.7097 center_loss: 0.7896 size_loss: 0.2602 cls_loss: 0.8611 giou_loss: 0.7988 2025/05/11 21:47:31 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 21:50:02 - mmengine - INFO - Epoch(train) [28][10/91] base_lr: 2.4720e-04 lr: 2.4720e-04 eta: 3 days, 23:25:03 time: 10.5521 data_time: 1.5554 memory: 68702 grad_norm: 1.3469 loss: 2.6942 center_loss: 0.7860 size_loss: 0.2589 cls_loss: 0.8496 giou_loss: 0.7997 2025/05/11 21:51:38 - mmengine - INFO - Epoch(train) [28][20/91] base_lr: 2.4720e-04 lr: 2.4720e-04 eta: 3 days, 23:22:09 time: 10.5307 data_time: 1.5447 memory: 68702 grad_norm: 1.3590 loss: 2.7054 center_loss: 0.7854 size_loss: 0.2608 cls_loss: 0.8595 giou_loss: 0.7998 2025/05/11 21:53:15 - mmengine - INFO - Epoch(train) [28][30/91] base_lr: 2.4720e-04 lr: 2.4720e-04 eta: 3 days, 23:19:21 time: 10.5346 data_time: 1.5402 memory: 68702 grad_norm: 1.3741 loss: 2.7037 center_loss: 0.7879 size_loss: 0.2625 cls_loss: 0.8547 giou_loss: 0.7986 2025/05/11 21:54:50 - mmengine - INFO - Epoch(train) [28][40/91] base_lr: 2.4720e-04 lr: 2.4720e-04 eta: 3 days, 23:16:28 time: 10.5316 data_time: 1.5342 memory: 68703 grad_norm: 1.3567 loss: 2.7196 center_loss: 0.7901 size_loss: 0.2645 cls_loss: 0.8654 giou_loss: 0.7996 2025/05/11 21:56:28 - mmengine - INFO - Epoch(train) [28][50/91] base_lr: 2.4720e-04 lr: 2.4720e-04 eta: 3 days, 23:13:54 time: 10.7309 data_time: 1.5533 memory: 68702 grad_norm: 1.2888 loss: 2.7177 center_loss: 0.7906 size_loss: 0.2622 cls_loss: 0.8658 giou_loss: 0.7991 2025/05/11 21:58:04 - mmengine - INFO - Epoch(train) [28][60/91] base_lr: 2.4720e-04 lr: 2.4720e-04 eta: 3 days, 23:11:08 time: 9.6346 data_time: 0.5820 memory: 68702 grad_norm: 1.2572 loss: 2.7388 center_loss: 0.8024 size_loss: 0.2650 cls_loss: 0.8715 giou_loss: 0.7999 2025/05/11 21:59:41 - mmengine - INFO - Epoch(train) [28][70/91] base_lr: 2.4720e-04 lr: 2.4720e-04 eta: 3 days, 23:08:34 time: 9.6593 data_time: 0.5937 memory: 68703 grad_norm: 1.2842 loss: 2.7454 center_loss: 0.8106 size_loss: 0.2672 cls_loss: 0.8649 giou_loss: 0.8027 2025/05/11 22:01:17 - mmengine - INFO - Epoch(train) [28][80/91] base_lr: 2.4720e-04 lr: 2.4720e-04 eta: 3 days, 23:05:44 time: 9.6529 data_time: 0.5996 memory: 68702 grad_norm: 1.3068 loss: 2.7511 center_loss: 0.8152 size_loss: 0.2654 cls_loss: 0.8670 giou_loss: 0.8036 2025/05/11 22:02:53 - mmengine - INFO - Epoch(train) [28][90/91] base_lr: 2.4720e-04 lr: 2.4720e-04 eta: 3 days, 23:02:50 time: 9.6475 data_time: 0.5981 memory: 68700 grad_norm: 1.3224 loss: 2.7511 center_loss: 0.8215 size_loss: 0.2662 cls_loss: 0.8567 giou_loss: 0.8068 2025/05/11 22:02:55 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 22:02:55 - mmengine - INFO - Saving checkpoint at 28 epochs 2025/05/11 22:03:51 - mmengine - INFO - Epoch(val) [28][10/39] eta: 0:01:35 time: 2.8960 data_time: 0.3709 memory: 15952 2025/05/11 22:04:17 - mmengine - INFO - Epoch(val) [28][20/39] eta: 0:00:56 time: 2.7498 data_time: 0.2249 memory: 13407 2025/05/11 22:04:44 - mmengine - INFO - Epoch(val) [28][30/39] eta: 0:00:25 time: 2.7449 data_time: 0.2110 memory: 13407 2025/05/11 22:05:13 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | bookshelf | 0.0701 | 0.4156 | 0.0010 | 0.0390 | | chair | 0.1646 | 0.5855 | 0.0068 | 0.1067 | | sofa | 0.3419 | 0.5258 | 0.0196 | 0.1031 | | garbagebin | 0.0453 | 0.2358 | 0.0013 | 0.0245 | | window | 0.0034 | 0.1064 | 0.0000 | 0.0106 | | table | 0.1444 | 0.3400 | 0.0127 | 0.0571 | | picture | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | curtain | 0.0030 | 0.0299 | 0.0000 | 0.0000 | | door | 0.0286 | 0.2077 | 0.0003 | 0.0150 | | cabinet | 0.0628 | 0.2876 | 0.0023 | 0.0430 | | sink | 0.1793 | 0.4898 | 0.0143 | 0.1327 | | refrigerator | 0.1093 | 0.2456 | 0.0256 | 0.0877 | | counter | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | bed | 0.5445 | 0.7531 | 0.0863 | 0.2716 | | desk | 0.2903 | 0.6457 | 0.0251 | 0.1417 | | bathtub | 0.2444 | 0.6129 | 0.0439 | 0.2258 | | toilet | 0.3621 | 0.7759 | 0.0258 | 0.1379 | | showercurtrain | 0.1608 | 0.3214 | 0.0000 | 0.0000 | +----------------+---------+---------+---------+---------+ | Overall | 0.1530 | 0.3655 | 0.0147 | 0.0776 | +----------------+---------+---------+---------+---------+ 2025/05/11 22:05:13 - mmengine - INFO - Epoch(val) [28][39/39] chair_AP_0.25: 0.1646 sofa_AP_0.25: 0.3419 table_AP_0.25: 0.1444 garbagebin_AP_0.25: 0.0453 bookshelf_AP_0.25: 0.0701 picture_AP_0.25: 0.0000 curtain_AP_0.25: 0.0030 door_AP_0.25: 0.0286 cabinet_AP_0.25: 0.0628 refrigerator_AP_0.25: 0.1093 counter_AP_0.25: 0.0000 sink_AP_0.25: 0.1793 window_AP_0.25: 0.0034 desk_AP_0.25: 0.2903 bed_AP_0.25: 0.5445 toilet_AP_0.25: 0.3621 showercurtrain_AP_0.25: 0.1608 bathtub_AP_0.25: 0.2444 mAP_0.25: 0.1530 chair_rec_0.25: 0.5855 sofa_rec_0.25: 0.5258 table_rec_0.25: 0.3400 garbagebin_rec_0.25: 0.2358 bookshelf_rec_0.25: 0.4156 picture_rec_0.25: 0.0000 curtain_rec_0.25: 0.0299 door_rec_0.25: 0.2077 cabinet_rec_0.25: 0.2876 refrigerator_rec_0.25: 0.2456 counter_rec_0.25: 0.0000 sink_rec_0.25: 0.4898 window_rec_0.25: 0.1064 desk_rec_0.25: 0.6457 bed_rec_0.25: 0.7531 toilet_rec_0.25: 0.7759 showercurtrain_rec_0.25: 0.3214 bathtub_rec_0.25: 0.6129 mAR_0.25: 0.3655 chair_AP_0.50: 0.0068 sofa_AP_0.50: 0.0196 table_AP_0.50: 0.0127 garbagebin_AP_0.50: 0.0013 bookshelf_AP_0.50: 0.0010 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0000 door_AP_0.50: 0.0003 cabinet_AP_0.50: 0.0023 refrigerator_AP_0.50: 0.0256 counter_AP_0.50: 0.0000 sink_AP_0.50: 0.0143 window_AP_0.50: 0.0000 desk_AP_0.50: 0.0251 bed_AP_0.50: 0.0863 toilet_AP_0.50: 0.0258 showercurtrain_AP_0.50: 0.0000 bathtub_AP_0.50: 0.0439 mAP_0.50: 0.0147 chair_rec_0.50: 0.1067 sofa_rec_0.50: 0.1031 table_rec_0.50: 0.0571 garbagebin_rec_0.50: 0.0245 bookshelf_rec_0.50: 0.0390 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.0000 door_rec_0.50: 0.0150 cabinet_rec_0.50: 0.0430 refrigerator_rec_0.50: 0.0877 counter_rec_0.50: 0.0000 sink_rec_0.50: 0.1327 window_rec_0.50: 0.0106 desk_rec_0.50: 0.1417 bed_rec_0.50: 0.2716 toilet_rec_0.50: 0.1379 showercurtrain_rec_0.50: 0.0000 bathtub_rec_0.50: 0.2258 mAR_0.50: 0.0776 data_time: 0.2458 time: 2.7766 2025/05/11 22:05:13 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_26.pth is removed 2025/05/11 22:05:34 - mmengine - INFO - The best checkpoint with 0.1530 mAP_0.25 at 28 epoch is saved to best_mAP_0.25_epoch_28.pth. 2025/05/11 22:08:22 - mmengine - INFO - Epoch(train) [29][10/91] base_lr: 2.4699e-04 lr: 2.4699e-04 eta: 3 days, 23:08:28 time: 10.4113 data_time: 1.4958 memory: 68702 grad_norm: 1.3855 loss: 2.7446 center_loss: 0.8141 size_loss: 0.2679 cls_loss: 0.8572 giou_loss: 0.8054 2025/05/11 22:09:58 - mmengine - INFO - Epoch(train) [29][20/91] base_lr: 2.4699e-04 lr: 2.4699e-04 eta: 3 days, 23:05:41 time: 10.4050 data_time: 1.4897 memory: 68702 grad_norm: 1.4215 loss: 2.7284 center_loss: 0.8065 size_loss: 0.2649 cls_loss: 0.8543 giou_loss: 0.8027 2025/05/11 22:11:35 - mmengine - INFO - Epoch(train) [29][30/91] base_lr: 2.4699e-04 lr: 2.4699e-04 eta: 3 days, 23:03:01 time: 10.4039 data_time: 1.4782 memory: 68703 grad_norm: 1.4045 loss: 2.7159 center_loss: 0.7991 size_loss: 0.2632 cls_loss: 0.8549 giou_loss: 0.7987 2025/05/11 22:13:11 - mmengine - INFO - Epoch(train) [29][40/91] base_lr: 2.4699e-04 lr: 2.4699e-04 eta: 3 days, 23:00:13 time: 10.4013 data_time: 1.4705 memory: 68702 grad_norm: 1.3892 loss: 2.7101 center_loss: 0.7980 size_loss: 0.2634 cls_loss: 0.8503 giou_loss: 0.7985 2025/05/11 22:14:48 - mmengine - INFO - Epoch(train) [29][50/91] base_lr: 2.4699e-04 lr: 2.4699e-04 eta: 3 days, 22:57:42 time: 10.5877 data_time: 1.4810 memory: 68703 grad_norm: 1.3520 loss: 2.6900 center_loss: 0.7966 size_loss: 0.2611 cls_loss: 0.8363 giou_loss: 0.7960 2025/05/11 22:16:25 - mmengine - INFO - Epoch(train) [29][60/91] base_lr: 2.4699e-04 lr: 2.4699e-04 eta: 3 days, 22:55:13 time: 9.6736 data_time: 0.5692 memory: 68702 grad_norm: 1.3196 loss: 2.6585 center_loss: 0.7849 size_loss: 0.2562 cls_loss: 0.8292 giou_loss: 0.7881 2025/05/11 22:18:03 - mmengine - INFO - Epoch(train) [29][70/91] base_lr: 2.4699e-04 lr: 2.4699e-04 eta: 3 days, 22:52:42 time: 9.6950 data_time: 0.5746 memory: 68702 grad_norm: 1.2929 loss: 2.6296 center_loss: 0.7773 size_loss: 0.2536 cls_loss: 0.8157 giou_loss: 0.7830 2025/05/11 22:19:39 - mmengine - INFO - Epoch(train) [29][80/91] base_lr: 2.4699e-04 lr: 2.4699e-04 eta: 3 days, 22:49:55 time: 9.6814 data_time: 0.5804 memory: 68700 grad_norm: 1.3533 loss: 2.6204 center_loss: 0.7753 size_loss: 0.2492 cls_loss: 0.8146 giou_loss: 0.7815 2025/05/11 22:21:14 - mmengine - INFO - Epoch(train) [29][90/91] base_lr: 2.4699e-04 lr: 2.4699e-04 eta: 3 days, 22:46:59 time: 9.6638 data_time: 0.5783 memory: 68703 grad_norm: 1.4064 loss: 2.6117 center_loss: 0.7729 size_loss: 0.2491 cls_loss: 0.8115 giou_loss: 0.7782 2025/05/11 22:21:16 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 22:23:46 - mmengine - INFO - Epoch(train) [30][10/91] base_lr: 2.4677e-04 lr: 2.4677e-04 eta: 3 days, 22:53:51 time: 10.5704 data_time: 1.4546 memory: 68702 grad_norm: 1.4943 loss: 2.6115 center_loss: 0.7814 size_loss: 0.2503 cls_loss: 0.8022 giou_loss: 0.7776 2025/05/11 22:25:23 - mmengine - INFO - Epoch(train) [30][20/91] base_lr: 2.4677e-04 lr: 2.4677e-04 eta: 3 days, 22:51:09 time: 10.5475 data_time: 1.4672 memory: 68703 grad_norm: 1.4767 loss: 2.6383 center_loss: 0.7881 size_loss: 0.2541 cls_loss: 0.8129 giou_loss: 0.7833 2025/05/11 22:27:00 - mmengine - INFO - Epoch(train) [30][30/91] base_lr: 2.4677e-04 lr: 2.4677e-04 eta: 3 days, 22:48:34 time: 10.5467 data_time: 1.4646 memory: 68702 grad_norm: 1.4675 loss: 2.6272 center_loss: 0.7814 size_loss: 0.2531 cls_loss: 0.8112 giou_loss: 0.7816 2025/05/11 22:28:36 - mmengine - INFO - Epoch(train) [30][40/91] base_lr: 2.4677e-04 lr: 2.4677e-04 eta: 3 days, 22:45:53 time: 10.5511 data_time: 1.4657 memory: 68701 grad_norm: 1.4283 loss: 2.5999 center_loss: 0.7735 size_loss: 0.2512 cls_loss: 0.7975 giou_loss: 0.7776 2025/05/11 22:30:14 - mmengine - INFO - Epoch(train) [30][50/91] base_lr: 2.4677e-04 lr: 2.4677e-04 eta: 3 days, 22:43:26 time: 10.7520 data_time: 1.4806 memory: 68702 grad_norm: 1.3477 loss: 2.5610 center_loss: 0.7585 size_loss: 0.2478 cls_loss: 0.7818 giou_loss: 0.7729 2025/05/11 22:31:50 - mmengine - INFO - Epoch(train) [30][60/91] base_lr: 2.4677e-04 lr: 2.4677e-04 eta: 3 days, 22:40:50 time: 9.6818 data_time: 0.6048 memory: 68700 grad_norm: 1.3034 loss: 2.5586 center_loss: 0.7518 size_loss: 0.2478 cls_loss: 0.7900 giou_loss: 0.7691 2025/05/11 22:33:27 - mmengine - INFO - Epoch(train) [30][70/91] base_lr: 2.4677e-04 lr: 2.4677e-04 eta: 3 days, 22:38:16 time: 9.6918 data_time: 0.6019 memory: 68703 grad_norm: 1.3684 loss: 2.5380 center_loss: 0.7473 size_loss: 0.2431 cls_loss: 0.7783 giou_loss: 0.7694 2025/05/11 22:35:03 - mmengine - INFO - Epoch(train) [30][80/91] base_lr: 2.4677e-04 lr: 2.4677e-04 eta: 3 days, 22:35:32 time: 9.6731 data_time: 0.6101 memory: 68703 grad_norm: 1.4099 loss: 2.5461 center_loss: 0.7519 size_loss: 0.2400 cls_loss: 0.7829 giou_loss: 0.7713 2025/05/11 22:36:39 - mmengine - INFO - Epoch(train) [30][90/91] base_lr: 2.4677e-04 lr: 2.4677e-04 eta: 3 days, 22:32:41 time: 9.6517 data_time: 0.6126 memory: 68702 grad_norm: 1.4369 loss: 2.5564 center_loss: 0.7593 size_loss: 0.2427 cls_loss: 0.7792 giou_loss: 0.7751 2025/05/11 22:36:41 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 22:36:41 - mmengine - INFO - Saving checkpoint at 30 epochs 2025/05/11 22:37:40 - mmengine - INFO - Epoch(val) [30][10/39] eta: 0:01:37 time: 2.8931 data_time: 0.3619 memory: 15952 2025/05/11 22:38:06 - mmengine - INFO - Epoch(val) [30][20/39] eta: 0:00:56 time: 2.7528 data_time: 0.2255 memory: 13407 2025/05/11 22:38:32 - mmengine - INFO - Epoch(val) [30][30/39] eta: 0:00:25 time: 2.7461 data_time: 0.2243 memory: 13407 2025/05/11 22:39:00 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | sofa | 0.4040 | 0.7113 | 0.0349 | 0.1753 | | bookshelf | 0.0985 | 0.4156 | 0.0135 | 0.0390 | | chair | 0.1993 | 0.5702 | 0.0150 | 0.1148 | | garbagebin | 0.0516 | 0.2679 | 0.0012 | 0.0340 | | curtain | 0.0364 | 0.2090 | 0.0081 | 0.0597 | | window | 0.0096 | 0.1525 | 0.0000 | 0.0035 | | table | 0.1814 | 0.3943 | 0.0155 | 0.0943 | | picture | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | desk | 0.3701 | 0.7717 | 0.0241 | 0.1654 | | door | 0.0275 | 0.2099 | 0.0001 | 0.0128 | | cabinet | 0.0634 | 0.2796 | 0.0045 | 0.0349 | | sink | 0.1295 | 0.4082 | 0.0100 | 0.0714 | | refrigerator | 0.1021 | 0.2982 | 0.0165 | 0.0877 | | counter | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | bed | 0.5540 | 0.8148 | 0.1378 | 0.2963 | | bathtub | 0.2678 | 0.6129 | 0.0105 | 0.1290 | | toilet | 0.4447 | 0.7759 | 0.1134 | 0.2241 | | showercurtrain | 0.0560 | 0.2857 | 0.0000 | 0.0000 | +----------------+---------+---------+---------+---------+ | Overall | 0.1664 | 0.3988 | 0.0225 | 0.0857 | +----------------+---------+---------+---------+---------+ 2025/05/11 22:39:00 - mmengine - INFO - Epoch(val) [30][39/39] chair_AP_0.25: 0.1993 sofa_AP_0.25: 0.4040 table_AP_0.25: 0.1814 garbagebin_AP_0.25: 0.0516 bookshelf_AP_0.25: 0.0985 picture_AP_0.25: 0.0000 curtain_AP_0.25: 0.0364 door_AP_0.25: 0.0275 cabinet_AP_0.25: 0.0634 refrigerator_AP_0.25: 0.1021 counter_AP_0.25: 0.0000 sink_AP_0.25: 0.1295 window_AP_0.25: 0.0096 desk_AP_0.25: 0.3701 bed_AP_0.25: 0.5540 toilet_AP_0.25: 0.4447 showercurtrain_AP_0.25: 0.0560 bathtub_AP_0.25: 0.2678 mAP_0.25: 0.1664 chair_rec_0.25: 0.5702 sofa_rec_0.25: 0.7113 table_rec_0.25: 0.3943 garbagebin_rec_0.25: 0.2679 bookshelf_rec_0.25: 0.4156 picture_rec_0.25: 0.0000 curtain_rec_0.25: 0.2090 door_rec_0.25: 0.2099 cabinet_rec_0.25: 0.2796 refrigerator_rec_0.25: 0.2982 counter_rec_0.25: 0.0000 sink_rec_0.25: 0.4082 window_rec_0.25: 0.1525 desk_rec_0.25: 0.7717 bed_rec_0.25: 0.8148 toilet_rec_0.25: 0.7759 showercurtrain_rec_0.25: 0.2857 bathtub_rec_0.25: 0.6129 mAR_0.25: 0.3988 chair_AP_0.50: 0.0150 sofa_AP_0.50: 0.0349 table_AP_0.50: 0.0155 garbagebin_AP_0.50: 0.0012 bookshelf_AP_0.50: 0.0135 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0081 door_AP_0.50: 0.0001 cabinet_AP_0.50: 0.0045 refrigerator_AP_0.50: 0.0165 counter_AP_0.50: 0.0000 sink_AP_0.50: 0.0100 window_AP_0.50: 0.0000 desk_AP_0.50: 0.0241 bed_AP_0.50: 0.1378 toilet_AP_0.50: 0.1134 showercurtrain_AP_0.50: 0.0000 bathtub_AP_0.50: 0.0105 mAP_0.50: 0.0225 chair_rec_0.50: 0.1148 sofa_rec_0.50: 0.1753 table_rec_0.50: 0.0943 garbagebin_rec_0.50: 0.0340 bookshelf_rec_0.50: 0.0390 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.0597 door_rec_0.50: 0.0128 cabinet_rec_0.50: 0.0349 refrigerator_rec_0.50: 0.0877 counter_rec_0.50: 0.0000 sink_rec_0.50: 0.0714 window_rec_0.50: 0.0035 desk_rec_0.50: 0.1654 bed_rec_0.50: 0.2963 toilet_rec_0.50: 0.2241 showercurtrain_rec_0.50: 0.0000 bathtub_rec_0.50: 0.1290 mAR_0.50: 0.0857 data_time: 0.2599 time: 2.7685 2025/05/11 22:39:00 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_28.pth is removed 2025/05/11 22:39:26 - mmengine - INFO - The best checkpoint with 0.1664 mAP_0.25 at 30 epoch is saved to best_mAP_0.25_epoch_30.pth. 2025/05/11 22:42:20 - mmengine - INFO - Epoch(train) [31][10/91] base_lr: 2.4654e-04 lr: 2.4654e-04 eta: 3 days, 22:38:11 time: 10.4486 data_time: 1.5516 memory: 68703 grad_norm: 1.5072 loss: 2.6197 center_loss: 0.7918 size_loss: 0.2515 cls_loss: 0.7897 giou_loss: 0.7866 2025/05/11 22:43:58 - mmengine - INFO - Epoch(train) [31][20/91] base_lr: 2.4654e-04 lr: 2.4654e-04 eta: 3 days, 22:35:47 time: 10.4663 data_time: 1.5432 memory: 68702 grad_norm: 1.5040 loss: 2.6090 center_loss: 0.7884 size_loss: 0.2494 cls_loss: 0.7853 giou_loss: 0.7859 2025/05/11 22:45:34 - mmengine - INFO - Epoch(train) [31][30/91] base_lr: 2.4654e-04 lr: 2.4654e-04 eta: 3 days, 22:33:09 time: 10.4646 data_time: 1.5445 memory: 68702 grad_norm: 1.4625 loss: 2.5856 center_loss: 0.7770 size_loss: 0.2464 cls_loss: 0.7816 giou_loss: 0.7806 2025/05/11 22:47:12 - mmengine - INFO - Epoch(train) [31][40/91] base_lr: 2.4654e-04 lr: 2.4654e-04 eta: 3 days, 22:30:50 time: 10.5044 data_time: 1.5338 memory: 68702 grad_norm: 1.4518 loss: 2.5677 center_loss: 0.7733 size_loss: 0.2470 cls_loss: 0.7699 giou_loss: 0.7774 2025/05/11 22:48:50 - mmengine - INFO - Epoch(train) [31][50/91] base_lr: 2.4654e-04 lr: 2.4654e-04 eta: 3 days, 22:28:23 time: 10.6972 data_time: 1.5414 memory: 68703 grad_norm: 1.4140 loss: 2.5488 center_loss: 0.7629 size_loss: 0.2425 cls_loss: 0.7722 giou_loss: 0.7712 2025/05/11 22:50:27 - mmengine - INFO - Epoch(train) [31][60/91] base_lr: 2.4654e-04 lr: 2.4654e-04 eta: 3 days, 22:25:51 time: 9.7338 data_time: 0.5860 memory: 68702 grad_norm: 1.4238 loss: 2.5370 center_loss: 0.7531 size_loss: 0.2425 cls_loss: 0.7731 giou_loss: 0.7684 2025/05/11 22:52:04 - mmengine - INFO - Epoch(train) [31][70/91] base_lr: 2.4654e-04 lr: 2.4654e-04 eta: 3 days, 22:23:21 time: 9.7215 data_time: 0.5996 memory: 68702 grad_norm: 1.4515 loss: 2.5406 center_loss: 0.7531 size_loss: 0.2426 cls_loss: 0.7755 giou_loss: 0.7693 2025/05/11 22:53:40 - mmengine - INFO - Epoch(train) [31][80/91] base_lr: 2.4654e-04 lr: 2.4654e-04 eta: 3 days, 22:20:44 time: 9.7177 data_time: 0.5881 memory: 68700 grad_norm: 1.4806 loss: 2.5659 center_loss: 0.7669 size_loss: 0.2475 cls_loss: 0.7809 giou_loss: 0.7707 2025/05/11 22:55:16 - mmengine - INFO - Epoch(train) [31][90/91] base_lr: 2.4654e-04 lr: 2.4654e-04 eta: 3 days, 22:17:58 time: 9.6711 data_time: 0.5843 memory: 68700 grad_norm: 1.4580 loss: 2.5538 center_loss: 0.7617 size_loss: 0.2454 cls_loss: 0.7795 giou_loss: 0.7672 2025/05/11 22:55:18 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 22:57:44 - mmengine - INFO - Epoch(train) [32][10/91] base_lr: 2.4631e-04 lr: 2.4631e-04 eta: 3 days, 22:23:27 time: 10.4961 data_time: 1.4292 memory: 68702 grad_norm: 1.4787 loss: 2.5496 center_loss: 0.7517 size_loss: 0.2381 cls_loss: 0.7932 giou_loss: 0.7666 2025/05/11 22:59:21 - mmengine - INFO - Epoch(train) [32][20/91] base_lr: 2.4631e-04 lr: 2.4631e-04 eta: 3 days, 22:20:51 time: 10.4877 data_time: 1.4162 memory: 68702 grad_norm: 1.4470 loss: 2.5293 center_loss: 0.7470 size_loss: 0.2376 cls_loss: 0.7818 giou_loss: 0.7629 2025/05/11 23:00:57 - mmengine - INFO - Epoch(train) [32][30/91] base_lr: 2.4631e-04 lr: 2.4631e-04 eta: 3 days, 22:18:15 time: 10.4817 data_time: 1.4263 memory: 68703 grad_norm: 1.4410 loss: 2.5190 center_loss: 0.7459 size_loss: 0.2365 cls_loss: 0.7750 giou_loss: 0.7617 2025/05/11 23:02:34 - mmengine - INFO - Epoch(train) [32][40/91] base_lr: 2.4631e-04 lr: 2.4631e-04 eta: 3 days, 22:15:41 time: 10.4840 data_time: 1.4167 memory: 68702 grad_norm: 1.4250 loss: 2.4965 center_loss: 0.7332 size_loss: 0.2334 cls_loss: 0.7696 giou_loss: 0.7604 2025/05/11 23:04:11 - mmengine - INFO - Epoch(train) [32][50/91] base_lr: 2.4631e-04 lr: 2.4631e-04 eta: 3 days, 22:13:09 time: 10.6582 data_time: 1.4344 memory: 68702 grad_norm: 1.4261 loss: 2.5076 center_loss: 0.7453 size_loss: 0.2381 cls_loss: 0.7593 giou_loss: 0.7649 2025/05/11 23:05:48 - mmengine - INFO - Epoch(train) [32][60/91] base_lr: 2.4631e-04 lr: 2.4631e-04 eta: 3 days, 22:10:40 time: 9.6740 data_time: 0.5807 memory: 68703 grad_norm: 1.4048 loss: 2.5208 center_loss: 0.7507 size_loss: 0.2425 cls_loss: 0.7602 giou_loss: 0.7674 2025/05/11 23:07:25 - mmengine - INFO - Epoch(train) [32][70/91] base_lr: 2.4631e-04 lr: 2.4631e-04 eta: 3 days, 22:08:08 time: 9.6781 data_time: 0.5883 memory: 68702 grad_norm: 1.4384 loss: 2.5278 center_loss: 0.7592 size_loss: 0.2399 cls_loss: 0.7616 giou_loss: 0.7671 2025/05/11 23:09:01 - mmengine - INFO - Epoch(train) [32][80/91] base_lr: 2.4631e-04 lr: 2.4631e-04 eta: 3 days, 22:05:34 time: 9.6779 data_time: 0.5793 memory: 68702 grad_norm: 1.5191 loss: 2.5259 center_loss: 0.7563 size_loss: 0.2400 cls_loss: 0.7648 giou_loss: 0.7648 2025/05/11 23:10:37 - mmengine - INFO - Epoch(train) [32][90/91] base_lr: 2.4631e-04 lr: 2.4631e-04 eta: 3 days, 22:02:48 time: 9.6525 data_time: 0.5853 memory: 68700 grad_norm: 1.5769 loss: 2.5200 center_loss: 0.7558 size_loss: 0.2388 cls_loss: 0.7638 giou_loss: 0.7615 2025/05/11 23:10:39 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 23:10:39 - mmengine - INFO - Saving checkpoint at 32 epochs 2025/05/11 23:11:35 - mmengine - INFO - Epoch(val) [32][10/39] eta: 0:01:34 time: 2.8661 data_time: 0.3555 memory: 15952 2025/05/11 23:12:01 - mmengine - INFO - Epoch(val) [32][20/39] eta: 0:00:55 time: 2.7234 data_time: 0.2158 memory: 13407 2025/05/11 23:12:27 - mmengine - INFO - Epoch(val) [32][30/39] eta: 0:00:25 time: 2.7207 data_time: 0.2078 memory: 13407 2025/05/11 23:12:55 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | table | 0.2095 | 0.3829 | 0.0218 | 0.1057 | | garbagebin | 0.0500 | 0.2528 | 0.0009 | 0.0245 | | bookshelf | 0.0947 | 0.5584 | 0.0069 | 0.1039 | | sofa | 0.4479 | 0.6495 | 0.0503 | 0.1753 | | curtain | 0.0766 | 0.3582 | 0.0015 | 0.0448 | | chair | 0.1963 | 0.5563 | 0.0104 | 0.1118 | | picture | 0.0000 | 0.0045 | 0.0000 | 0.0000 | | window | 0.0322 | 0.2092 | 0.0019 | 0.0248 | | door | 0.0319 | 0.2355 | 0.0002 | 0.0236 | | desk | 0.3200 | 0.7008 | 0.0154 | 0.1260 | | cabinet | 0.0780 | 0.3333 | 0.0072 | 0.0699 | | refrigerator | 0.1309 | 0.3684 | 0.0381 | 0.1228 | | sink | 0.1568 | 0.4490 | 0.0137 | 0.0816 | | counter | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | bed | 0.5607 | 0.7407 | 0.1135 | 0.2593 | | bathtub | 0.2673 | 0.5806 | 0.0134 | 0.1613 | | showercurtrain | 0.1250 | 0.4286 | 0.0089 | 0.0357 | | toilet | 0.4043 | 0.7586 | 0.0853 | 0.2414 | +----------------+---------+---------+---------+---------+ | Overall | 0.1768 | 0.4204 | 0.0216 | 0.0951 | +----------------+---------+---------+---------+---------+ 2025/05/11 23:12:55 - mmengine - INFO - Epoch(val) [32][39/39] chair_AP_0.25: 0.1963 sofa_AP_0.25: 0.4479 table_AP_0.25: 0.2095 garbagebin_AP_0.25: 0.0500 bookshelf_AP_0.25: 0.0947 picture_AP_0.25: 0.0000 curtain_AP_0.25: 0.0766 door_AP_0.25: 0.0319 cabinet_AP_0.25: 0.0780 refrigerator_AP_0.25: 0.1309 counter_AP_0.25: 0.0000 sink_AP_0.25: 0.1568 window_AP_0.25: 0.0322 desk_AP_0.25: 0.3200 bed_AP_0.25: 0.5607 toilet_AP_0.25: 0.4043 showercurtrain_AP_0.25: 0.1250 bathtub_AP_0.25: 0.2673 mAP_0.25: 0.1768 chair_rec_0.25: 0.5563 sofa_rec_0.25: 0.6495 table_rec_0.25: 0.3829 garbagebin_rec_0.25: 0.2528 bookshelf_rec_0.25: 0.5584 picture_rec_0.25: 0.0045 curtain_rec_0.25: 0.3582 door_rec_0.25: 0.2355 cabinet_rec_0.25: 0.3333 refrigerator_rec_0.25: 0.3684 counter_rec_0.25: 0.0000 sink_rec_0.25: 0.4490 window_rec_0.25: 0.2092 desk_rec_0.25: 0.7008 bed_rec_0.25: 0.7407 toilet_rec_0.25: 0.7586 showercurtrain_rec_0.25: 0.4286 bathtub_rec_0.25: 0.5806 mAR_0.25: 0.4204 chair_AP_0.50: 0.0104 sofa_AP_0.50: 0.0503 table_AP_0.50: 0.0218 garbagebin_AP_0.50: 0.0009 bookshelf_AP_0.50: 0.0069 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0015 door_AP_0.50: 0.0002 cabinet_AP_0.50: 0.0072 refrigerator_AP_0.50: 0.0381 counter_AP_0.50: 0.0000 sink_AP_0.50: 0.0137 window_AP_0.50: 0.0019 desk_AP_0.50: 0.0154 bed_AP_0.50: 0.1135 toilet_AP_0.50: 0.0853 showercurtrain_AP_0.50: 0.0089 bathtub_AP_0.50: 0.0134 mAP_0.50: 0.0216 chair_rec_0.50: 0.1118 sofa_rec_0.50: 0.1753 table_rec_0.50: 0.1057 garbagebin_rec_0.50: 0.0245 bookshelf_rec_0.50: 0.1039 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.0448 door_rec_0.50: 0.0236 cabinet_rec_0.50: 0.0699 refrigerator_rec_0.50: 0.1228 counter_rec_0.50: 0.0000 sink_rec_0.50: 0.0816 window_rec_0.50: 0.0248 desk_rec_0.50: 0.1260 bed_rec_0.50: 0.2593 toilet_rec_0.50: 0.2414 showercurtrain_rec_0.50: 0.0357 bathtub_rec_0.50: 0.1613 mAR_0.50: 0.0951 data_time: 0.2426 time: 2.7547 2025/05/11 23:12:55 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_30.pth is removed 2025/05/11 23:13:22 - mmengine - INFO - The best checkpoint with 0.1768 mAP_0.25 at 32 epoch is saved to best_mAP_0.25_epoch_32.pth. 2025/05/11 23:16:16 - mmengine - INFO - Epoch(train) [33][10/91] base_lr: 2.4607e-04 lr: 2.4607e-04 eta: 3 days, 22:07:25 time: 10.4252 data_time: 1.5022 memory: 68700 grad_norm: 1.9775 loss: 2.5305 center_loss: 0.7675 size_loss: 0.2393 cls_loss: 0.7567 giou_loss: 0.7671 2025/05/11 23:17:53 - mmengine - INFO - Epoch(train) [33][20/91] base_lr: 2.4607e-04 lr: 2.4607e-04 eta: 3 days, 22:05:02 time: 10.4346 data_time: 1.5159 memory: 68702 grad_norm: 1.9558 loss: 2.5241 center_loss: 0.7636 size_loss: 0.2375 cls_loss: 0.7540 giou_loss: 0.7690 2025/05/11 23:19:31 - mmengine - INFO - Epoch(train) [33][30/91] base_lr: 2.4607e-04 lr: 2.4607e-04 eta: 3 days, 22:02:38 time: 10.4481 data_time: 1.5135 memory: 68702 grad_norm: 1.8778 loss: 2.5193 center_loss: 0.7552 size_loss: 0.2391 cls_loss: 0.7530 giou_loss: 0.7721 2025/05/11 23:21:08 - mmengine - INFO - Epoch(train) [33][40/91] base_lr: 2.4607e-04 lr: 2.4607e-04 eta: 3 days, 22:00:15 time: 10.4671 data_time: 1.5167 memory: 68702 grad_norm: 1.8161 loss: 2.5177 center_loss: 0.7567 size_loss: 0.2381 cls_loss: 0.7506 giou_loss: 0.7723 2025/05/11 23:22:47 - mmengine - INFO - Epoch(train) [33][50/91] base_lr: 2.4607e-04 lr: 2.4607e-04 eta: 3 days, 21:58:07 time: 10.6869 data_time: 1.5235 memory: 68702 grad_norm: 1.5227 loss: 2.5055 center_loss: 0.7552 size_loss: 0.2348 cls_loss: 0.7445 giou_loss: 0.7711 2025/05/11 23:24:25 - mmengine - INFO - Epoch(train) [33][60/91] base_lr: 2.4607e-04 lr: 2.4607e-04 eta: 3 days, 21:55:49 time: 9.7833 data_time: 0.6200 memory: 68702 grad_norm: 1.4344 loss: 2.4892 center_loss: 0.7481 size_loss: 0.2356 cls_loss: 0.7389 giou_loss: 0.7665 2025/05/11 23:26:02 - mmengine - INFO - Epoch(train) [33][70/91] base_lr: 2.4607e-04 lr: 2.4607e-04 eta: 3 days, 21:53:25 time: 9.7801 data_time: 0.6155 memory: 68702 grad_norm: 1.4099 loss: 2.4793 center_loss: 0.7395 size_loss: 0.2353 cls_loss: 0.7413 giou_loss: 0.7633 2025/05/11 23:27:39 - mmengine - INFO - Epoch(train) [33][80/91] base_lr: 2.4607e-04 lr: 2.4607e-04 eta: 3 days, 21:50:55 time: 9.7659 data_time: 0.6217 memory: 68700 grad_norm: 1.4153 loss: 2.4717 center_loss: 0.7411 size_loss: 0.2329 cls_loss: 0.7360 giou_loss: 0.7617 2025/05/11 23:28:56 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 23:29:15 - mmengine - INFO - Epoch(train) [33][90/91] base_lr: 2.4607e-04 lr: 2.4607e-04 eta: 3 days, 21:48:14 time: 9.7307 data_time: 0.6211 memory: 68702 grad_norm: 1.4685 loss: 2.4662 center_loss: 0.7418 size_loss: 0.2321 cls_loss: 0.7324 giou_loss: 0.7598 2025/05/11 23:29:17 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 23:31:44 - mmengine - INFO - Epoch(train) [34][10/91] base_lr: 2.4582e-04 lr: 2.4582e-04 eta: 3 days, 21:53:23 time: 10.5435 data_time: 1.5434 memory: 68702 grad_norm: 1.5817 loss: 2.4847 center_loss: 0.7459 size_loss: 0.2347 cls_loss: 0.7405 giou_loss: 0.7637 2025/05/11 23:33:20 - mmengine - INFO - Epoch(train) [34][20/91] base_lr: 2.4582e-04 lr: 2.4582e-04 eta: 3 days, 21:50:46 time: 10.5087 data_time: 1.5277 memory: 68702 grad_norm: 1.5792 loss: 2.5046 center_loss: 0.7567 size_loss: 0.2334 cls_loss: 0.7477 giou_loss: 0.7667 2025/05/11 23:34:57 - mmengine - INFO - Epoch(train) [34][30/91] base_lr: 2.4582e-04 lr: 2.4582e-04 eta: 3 days, 21:48:18 time: 10.5040 data_time: 1.5183 memory: 68702 grad_norm: 1.5913 loss: 2.4907 center_loss: 0.7589 size_loss: 0.2309 cls_loss: 0.7378 giou_loss: 0.7632 2025/05/11 23:36:34 - mmengine - INFO - Epoch(train) [34][40/91] base_lr: 2.4582e-04 lr: 2.4582e-04 eta: 3 days, 21:45:48 time: 10.5024 data_time: 1.5140 memory: 68703 grad_norm: 1.5910 loss: 2.4913 center_loss: 0.7555 size_loss: 0.2311 cls_loss: 0.7448 giou_loss: 0.7599 2025/05/11 23:38:11 - mmengine - INFO - Epoch(train) [34][50/91] base_lr: 2.4582e-04 lr: 2.4582e-04 eta: 3 days, 21:43:23 time: 10.6835 data_time: 1.5212 memory: 68702 grad_norm: 1.4064 loss: 2.4762 center_loss: 0.7476 size_loss: 0.2303 cls_loss: 0.7381 giou_loss: 0.7602 2025/05/11 23:39:48 - mmengine - INFO - Epoch(train) [34][60/91] base_lr: 2.4582e-04 lr: 2.4582e-04 eta: 3 days, 21:40:52 time: 9.6730 data_time: 0.5933 memory: 68702 grad_norm: 1.3867 loss: 2.4514 center_loss: 0.7382 size_loss: 0.2260 cls_loss: 0.7354 giou_loss: 0.7518 2025/05/11 23:41:24 - mmengine - INFO - Epoch(train) [34][70/91] base_lr: 2.4582e-04 lr: 2.4582e-04 eta: 3 days, 21:38:20 time: 9.6795 data_time: 0.5900 memory: 68702 grad_norm: 1.3963 loss: 2.4156 center_loss: 0.7193 size_loss: 0.2246 cls_loss: 0.7275 giou_loss: 0.7442 2025/05/11 23:43:00 - mmengine - INFO - Epoch(train) [34][80/91] base_lr: 2.4582e-04 lr: 2.4582e-04 eta: 3 days, 21:35:47 time: 9.6674 data_time: 0.6007 memory: 68703 grad_norm: 1.4287 loss: 2.4232 center_loss: 0.7187 size_loss: 0.2262 cls_loss: 0.7315 giou_loss: 0.7469 2025/05/11 23:44:36 - mmengine - INFO - Epoch(train) [34][90/91] base_lr: 2.4582e-04 lr: 2.4582e-04 eta: 3 days, 21:33:05 time: 9.6414 data_time: 0.6015 memory: 68703 grad_norm: 1.5079 loss: 2.4435 center_loss: 0.7300 size_loss: 0.2273 cls_loss: 0.7365 giou_loss: 0.7496 2025/05/11 23:44:38 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/11 23:44:38 - mmengine - INFO - Saving checkpoint at 34 epochs 2025/05/11 23:45:35 - mmengine - INFO - Epoch(val) [34][10/39] eta: 0:01:35 time: 2.8594 data_time: 0.3465 memory: 15952 2025/05/11 23:46:01 - mmengine - INFO - Epoch(val) [34][20/39] eta: 0:00:55 time: 2.7271 data_time: 0.2157 memory: 13407 2025/05/11 23:46:27 - mmengine - INFO - Epoch(val) [34][30/39] eta: 0:00:25 time: 2.7316 data_time: 0.2134 memory: 13407 2025/05/11 23:46:55 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | sofa | 0.5424 | 0.8041 | 0.0764 | 0.2371 | | garbagebin | 0.0676 | 0.2811 | 0.0029 | 0.0396 | | chair | 0.2456 | 0.5819 | 0.0180 | 0.1265 | | table | 0.2464 | 0.4000 | 0.0351 | 0.1257 | | curtain | 0.1387 | 0.3134 | 0.0513 | 0.1194 | | bookshelf | 0.1363 | 0.5584 | 0.0010 | 0.0260 | | picture | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | door | 0.0557 | 0.2698 | 0.0015 | 0.0407 | | desk | 0.4690 | 0.7480 | 0.0385 | 0.2205 | | bed | 0.7496 | 0.8642 | 0.1742 | 0.3210 | | window | 0.0463 | 0.1986 | 0.0004 | 0.0213 | | cabinet | 0.1018 | 0.3118 | 0.0037 | 0.0457 | | refrigerator | 0.2076 | 0.4561 | 0.0459 | 0.1579 | | sink | 0.2222 | 0.5102 | 0.0164 | 0.1224 | | counter | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | bathtub | 0.3366 | 0.6774 | 0.0398 | 0.1935 | | toilet | 0.5445 | 0.7586 | 0.1086 | 0.2414 | | showercurtrain | 0.1935 | 0.4643 | 0.0000 | 0.0000 | +----------------+---------+---------+---------+---------+ | Overall | 0.2391 | 0.4555 | 0.0341 | 0.1133 | +----------------+---------+---------+---------+---------+ 2025/05/11 23:46:55 - mmengine - INFO - Epoch(val) [34][39/39] chair_AP_0.25: 0.2456 sofa_AP_0.25: 0.5424 table_AP_0.25: 0.2464 garbagebin_AP_0.25: 0.0676 bookshelf_AP_0.25: 0.1363 picture_AP_0.25: 0.0000 curtain_AP_0.25: 0.1387 door_AP_0.25: 0.0557 cabinet_AP_0.25: 0.1018 refrigerator_AP_0.25: 0.2076 counter_AP_0.25: 0.0000 sink_AP_0.25: 0.2222 window_AP_0.25: 0.0463 desk_AP_0.25: 0.4690 bed_AP_0.25: 0.7496 toilet_AP_0.25: 0.5445 showercurtrain_AP_0.25: 0.1935 bathtub_AP_0.25: 0.3366 mAP_0.25: 0.2391 chair_rec_0.25: 0.5819 sofa_rec_0.25: 0.8041 table_rec_0.25: 0.4000 garbagebin_rec_0.25: 0.2811 bookshelf_rec_0.25: 0.5584 picture_rec_0.25: 0.0000 curtain_rec_0.25: 0.3134 door_rec_0.25: 0.2698 cabinet_rec_0.25: 0.3118 refrigerator_rec_0.25: 0.4561 counter_rec_0.25: 0.0000 sink_rec_0.25: 0.5102 window_rec_0.25: 0.1986 desk_rec_0.25: 0.7480 bed_rec_0.25: 0.8642 toilet_rec_0.25: 0.7586 showercurtrain_rec_0.25: 0.4643 bathtub_rec_0.25: 0.6774 mAR_0.25: 0.4555 chair_AP_0.50: 0.0180 sofa_AP_0.50: 0.0764 table_AP_0.50: 0.0351 garbagebin_AP_0.50: 0.0029 bookshelf_AP_0.50: 0.0010 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0513 door_AP_0.50: 0.0015 cabinet_AP_0.50: 0.0037 refrigerator_AP_0.50: 0.0459 counter_AP_0.50: 0.0000 sink_AP_0.50: 0.0164 window_AP_0.50: 0.0004 desk_AP_0.50: 0.0385 bed_AP_0.50: 0.1742 toilet_AP_0.50: 0.1086 showercurtrain_AP_0.50: 0.0000 bathtub_AP_0.50: 0.0398 mAP_0.50: 0.0341 chair_rec_0.50: 0.1265 sofa_rec_0.50: 0.2371 table_rec_0.50: 0.1257 garbagebin_rec_0.50: 0.0396 bookshelf_rec_0.50: 0.0260 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.1194 door_rec_0.50: 0.0407 cabinet_rec_0.50: 0.0457 refrigerator_rec_0.50: 0.1579 counter_rec_0.50: 0.0000 sink_rec_0.50: 0.1224 window_rec_0.50: 0.0213 desk_rec_0.50: 0.2205 bed_rec_0.50: 0.3210 toilet_rec_0.50: 0.2414 showercurtrain_rec_0.50: 0.0000 bathtub_rec_0.50: 0.1935 mAR_0.50: 0.1133 data_time: 0.2468 time: 2.7560 2025/05/11 23:46:55 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_32.pth is removed 2025/05/11 23:47:17 - mmengine - INFO - The best checkpoint with 0.2391 mAP_0.25 at 34 epoch is saved to best_mAP_0.25_epoch_34.pth. 2025/05/11 23:50:08 - mmengine - INFO - Epoch(train) [35][10/91] base_lr: 2.4557e-04 lr: 2.4557e-04 eta: 3 days, 21:37:11 time: 10.3983 data_time: 1.5259 memory: 68702 grad_norm: 1.8731 loss: 2.5051 center_loss: 0.7577 size_loss: 0.2334 cls_loss: 0.7535 giou_loss: 0.7605 2025/05/11 23:51:45 - mmengine - INFO - Epoch(train) [35][20/91] base_lr: 2.4557e-04 lr: 2.4557e-04 eta: 3 days, 21:34:43 time: 10.3995 data_time: 1.5411 memory: 68700 grad_norm: 1.8191 loss: 2.5446 center_loss: 0.7708 size_loss: 0.2372 cls_loss: 0.7659 giou_loss: 0.7707 2025/05/11 23:53:22 - mmengine - INFO - Epoch(train) [35][30/91] base_lr: 2.4557e-04 lr: 2.4557e-04 eta: 3 days, 21:32:18 time: 10.4130 data_time: 1.5401 memory: 68702 grad_norm: 1.7591 loss: 2.5707 center_loss: 0.7858 size_loss: 0.2388 cls_loss: 0.7702 giou_loss: 0.7759 2025/05/11 23:54:58 - mmengine - INFO - Epoch(train) [35][40/91] base_lr: 2.4557e-04 lr: 2.4557e-04 eta: 3 days, 21:29:48 time: 10.4174 data_time: 1.5361 memory: 68703 grad_norm: 1.6975 loss: 2.5490 center_loss: 0.7753 size_loss: 0.2380 cls_loss: 0.7646 giou_loss: 0.7711 2025/05/11 23:56:36 - mmengine - INFO - Epoch(train) [35][50/91] base_lr: 2.4557e-04 lr: 2.4557e-04 eta: 3 days, 21:27:23 time: 10.6027 data_time: 1.5464 memory: 68703 grad_norm: 1.4272 loss: 2.5322 center_loss: 0.7679 size_loss: 0.2370 cls_loss: 0.7579 giou_loss: 0.7694 2025/05/11 23:58:12 - mmengine - INFO - Epoch(train) [35][60/91] base_lr: 2.4557e-04 lr: 2.4557e-04 eta: 3 days, 21:24:53 time: 9.6827 data_time: 0.6143 memory: 68703 grad_norm: 1.3085 loss: 2.4951 center_loss: 0.7533 size_loss: 0.2328 cls_loss: 0.7462 giou_loss: 0.7628 2025/05/11 23:59:48 - mmengine - INFO - Epoch(train) [35][70/91] base_lr: 2.4557e-04 lr: 2.4557e-04 eta: 3 days, 21:22:22 time: 9.6727 data_time: 0.5974 memory: 68700 grad_norm: 1.3037 loss: 2.4702 center_loss: 0.7418 size_loss: 0.2308 cls_loss: 0.7405 giou_loss: 0.7572 2025/05/12 00:01:24 - mmengine - INFO - Epoch(train) [35][80/91] base_lr: 2.4557e-04 lr: 2.4557e-04 eta: 3 days, 21:19:40 time: 9.6364 data_time: 0.5928 memory: 68702 grad_norm: 1.3632 loss: 2.4413 center_loss: 0.7292 size_loss: 0.2296 cls_loss: 0.7304 giou_loss: 0.7522 2025/05/12 00:02:59 - mmengine - INFO - Epoch(train) [35][90/91] base_lr: 2.4557e-04 lr: 2.4557e-04 eta: 3 days, 21:16:56 time: 9.6086 data_time: 0.5847 memory: 68703 grad_norm: 1.3728 loss: 2.4421 center_loss: 0.7357 size_loss: 0.2281 cls_loss: 0.7258 giou_loss: 0.7525 2025/05/12 00:03:01 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 00:05:26 - mmengine - INFO - Epoch(train) [36][10/91] base_lr: 2.4530e-04 lr: 2.4530e-04 eta: 3 days, 21:21:18 time: 10.4178 data_time: 1.4413 memory: 68702 grad_norm: 1.4728 loss: 2.4311 center_loss: 0.7324 size_loss: 0.2289 cls_loss: 0.7154 giou_loss: 0.7545 2025/05/12 00:07:02 - mmengine - INFO - Epoch(train) [36][20/91] base_lr: 2.4530e-04 lr: 2.4530e-04 eta: 3 days, 21:18:41 time: 10.4023 data_time: 1.4323 memory: 68703 grad_norm: 1.4912 loss: 2.4281 center_loss: 0.7293 size_loss: 0.2294 cls_loss: 0.7172 giou_loss: 0.7521 2025/05/12 00:08:39 - mmengine - INFO - Epoch(train) [36][30/91] base_lr: 2.4530e-04 lr: 2.4530e-04 eta: 3 days, 21:16:19 time: 10.4254 data_time: 1.4307 memory: 68702 grad_norm: 1.4880 loss: 2.4163 center_loss: 0.7311 size_loss: 0.2277 cls_loss: 0.7061 giou_loss: 0.7514 2025/05/12 00:10:15 - mmengine - INFO - Epoch(train) [36][40/91] base_lr: 2.4530e-04 lr: 2.4530e-04 eta: 3 days, 21:13:45 time: 10.4297 data_time: 1.4314 memory: 68702 grad_norm: 1.4931 loss: 2.4104 center_loss: 0.7253 size_loss: 0.2277 cls_loss: 0.7085 giou_loss: 0.7489 2025/05/12 00:11:52 - mmengine - INFO - Epoch(train) [36][50/91] base_lr: 2.4530e-04 lr: 2.4530e-04 eta: 3 days, 21:11:23 time: 10.6274 data_time: 1.4508 memory: 68702 grad_norm: 1.4996 loss: 2.3974 center_loss: 0.7165 size_loss: 0.2233 cls_loss: 0.7133 giou_loss: 0.7443 2025/05/12 00:13:29 - mmengine - INFO - Epoch(train) [36][60/91] base_lr: 2.4530e-04 lr: 2.4530e-04 eta: 3 days, 21:08:59 time: 9.6650 data_time: 0.5963 memory: 68703 grad_norm: 1.4424 loss: 2.3956 center_loss: 0.7125 size_loss: 0.2226 cls_loss: 0.7168 giou_loss: 0.7437 2025/05/12 00:15:05 - mmengine - INFO - Epoch(train) [36][70/91] base_lr: 2.4530e-04 lr: 2.4530e-04 eta: 3 days, 21:06:27 time: 9.6718 data_time: 0.6093 memory: 68701 grad_norm: 1.4852 loss: 2.3716 center_loss: 0.7076 size_loss: 0.2201 cls_loss: 0.7047 giou_loss: 0.7392 2025/05/12 00:16:41 - mmengine - INFO - Epoch(train) [36][80/91] base_lr: 2.4530e-04 lr: 2.4530e-04 eta: 3 days, 21:03:52 time: 9.6430 data_time: 0.6212 memory: 68702 grad_norm: 1.5461 loss: 2.3941 center_loss: 0.7136 size_loss: 0.2214 cls_loss: 0.7166 giou_loss: 0.7427 2025/05/12 00:18:17 - mmengine - INFO - Epoch(train) [36][90/91] base_lr: 2.4530e-04 lr: 2.4530e-04 eta: 3 days, 21:01:12 time: 9.6283 data_time: 0.6246 memory: 68703 grad_norm: 1.5260 loss: 2.3941 center_loss: 0.7149 size_loss: 0.2200 cls_loss: 0.7185 giou_loss: 0.7407 2025/05/12 00:18:19 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 00:18:19 - mmengine - INFO - Saving checkpoint at 36 epochs 2025/05/12 00:19:15 - mmengine - INFO - Epoch(val) [36][10/39] eta: 0:01:36 time: 2.8701 data_time: 0.3543 memory: 15952 2025/05/12 00:19:41 - mmengine - INFO - Epoch(val) [36][20/39] eta: 0:00:56 time: 2.7448 data_time: 0.2277 memory: 13407 2025/05/12 00:20:07 - mmengine - INFO - Epoch(val) [36][30/39] eta: 0:00:25 time: 2.7519 data_time: 0.2277 memory: 13407 2025/05/12 00:20:36 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | sofa | 0.5564 | 0.7732 | 0.0515 | 0.2062 | | table | 0.3137 | 0.4714 | 0.0430 | 0.1429 | | garbagebin | 0.1044 | 0.3453 | 0.0064 | 0.0472 | | chair | 0.3119 | 0.6294 | 0.0281 | 0.1469 | | curtain | 0.0740 | 0.2687 | 0.0016 | 0.0448 | | picture | 0.0000 | 0.0090 | 0.0000 | 0.0045 | | bookshelf | 0.1661 | 0.5455 | 0.0157 | 0.1039 | | window | 0.0344 | 0.1879 | 0.0000 | 0.0071 | | desk | 0.5427 | 0.7717 | 0.1011 | 0.2598 | | cabinet | 0.1308 | 0.3548 | 0.0136 | 0.0780 | | door | 0.0603 | 0.3062 | 0.0019 | 0.0364 | | refrigerator | 0.2440 | 0.4912 | 0.0138 | 0.1404 | | sink | 0.2249 | 0.4592 | 0.0095 | 0.1122 | | counter | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | bed | 0.7696 | 0.8272 | 0.2379 | 0.3704 | | toilet | 0.5797 | 0.7759 | 0.1792 | 0.3448 | | bathtub | 0.4095 | 0.6774 | 0.0364 | 0.1613 | | showercurtrain | 0.0955 | 0.4286 | 0.0000 | 0.0000 | +----------------+---------+---------+---------+---------+ | Overall | 0.2566 | 0.4624 | 0.0411 | 0.1226 | +----------------+---------+---------+---------+---------+ 2025/05/12 00:20:36 - mmengine - INFO - Epoch(val) [36][39/39] chair_AP_0.25: 0.3119 sofa_AP_0.25: 0.5564 table_AP_0.25: 0.3137 garbagebin_AP_0.25: 0.1044 bookshelf_AP_0.25: 0.1661 picture_AP_0.25: 0.0000 curtain_AP_0.25: 0.0740 door_AP_0.25: 0.0603 cabinet_AP_0.25: 0.1308 refrigerator_AP_0.25: 0.2440 counter_AP_0.25: 0.0000 sink_AP_0.25: 0.2249 window_AP_0.25: 0.0344 desk_AP_0.25: 0.5427 bed_AP_0.25: 0.7696 toilet_AP_0.25: 0.5797 showercurtrain_AP_0.25: 0.0955 bathtub_AP_0.25: 0.4095 mAP_0.25: 0.2566 chair_rec_0.25: 0.6294 sofa_rec_0.25: 0.7732 table_rec_0.25: 0.4714 garbagebin_rec_0.25: 0.3453 bookshelf_rec_0.25: 0.5455 picture_rec_0.25: 0.0090 curtain_rec_0.25: 0.2687 door_rec_0.25: 0.3062 cabinet_rec_0.25: 0.3548 refrigerator_rec_0.25: 0.4912 counter_rec_0.25: 0.0000 sink_rec_0.25: 0.4592 window_rec_0.25: 0.1879 desk_rec_0.25: 0.7717 bed_rec_0.25: 0.8272 toilet_rec_0.25: 0.7759 showercurtrain_rec_0.25: 0.4286 bathtub_rec_0.25: 0.6774 mAR_0.25: 0.4624 chair_AP_0.50: 0.0281 sofa_AP_0.50: 0.0515 table_AP_0.50: 0.0430 garbagebin_AP_0.50: 0.0064 bookshelf_AP_0.50: 0.0157 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0016 door_AP_0.50: 0.0019 cabinet_AP_0.50: 0.0136 refrigerator_AP_0.50: 0.0138 counter_AP_0.50: 0.0000 sink_AP_0.50: 0.0095 window_AP_0.50: 0.0000 desk_AP_0.50: 0.1011 bed_AP_0.50: 0.2379 toilet_AP_0.50: 0.1792 showercurtrain_AP_0.50: 0.0000 bathtub_AP_0.50: 0.0364 mAP_0.50: 0.0411 chair_rec_0.50: 0.1469 sofa_rec_0.50: 0.2062 table_rec_0.50: 0.1429 garbagebin_rec_0.50: 0.0472 bookshelf_rec_0.50: 0.1039 picture_rec_0.50: 0.0045 curtain_rec_0.50: 0.0448 door_rec_0.50: 0.0364 cabinet_rec_0.50: 0.0780 refrigerator_rec_0.50: 0.1404 counter_rec_0.50: 0.0000 sink_rec_0.50: 0.1122 window_rec_0.50: 0.0071 desk_rec_0.50: 0.2598 bed_rec_0.50: 0.3704 toilet_rec_0.50: 0.3448 showercurtrain_rec_0.50: 0.0000 bathtub_rec_0.50: 0.1613 mAR_0.50: 0.1226 data_time: 0.2652 time: 2.7868 2025/05/12 00:20:36 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_34.pth is removed 2025/05/12 00:20:58 - mmengine - INFO - The best checkpoint with 0.2566 mAP_0.25 at 36 epoch is saved to best_mAP_0.25_epoch_36.pth. 2025/05/12 00:23:55 - mmengine - INFO - Epoch(train) [37][10/91] base_lr: 2.4503e-04 lr: 2.4503e-04 eta: 3 days, 21:05:20 time: 10.4292 data_time: 1.5718 memory: 68702 grad_norm: 1.5787 loss: 2.3750 center_loss: 0.7083 size_loss: 0.2169 cls_loss: 0.7126 giou_loss: 0.7373 2025/05/12 00:25:32 - mmengine - INFO - Epoch(train) [37][20/91] base_lr: 2.4503e-04 lr: 2.4503e-04 eta: 3 days, 21:02:52 time: 10.4225 data_time: 1.5715 memory: 68703 grad_norm: 1.6491 loss: 2.3974 center_loss: 0.7206 size_loss: 0.2194 cls_loss: 0.7180 giou_loss: 0.7394 2025/05/12 00:27:09 - mmengine - INFO - Epoch(train) [37][30/91] base_lr: 2.4503e-04 lr: 2.4503e-04 eta: 3 days, 21:00:27 time: 10.4373 data_time: 1.5688 memory: 68702 grad_norm: 1.6387 loss: 2.3831 center_loss: 0.7144 size_loss: 0.2172 cls_loss: 0.7162 giou_loss: 0.7354 2025/05/12 00:28:45 - mmengine - INFO - Epoch(train) [37][40/91] base_lr: 2.4503e-04 lr: 2.4503e-04 eta: 3 days, 20:57:54 time: 10.4360 data_time: 1.5610 memory: 68702 grad_norm: 1.5821 loss: 2.3957 center_loss: 0.7222 size_loss: 0.2205 cls_loss: 0.7170 giou_loss: 0.7361 2025/05/12 00:30:22 - mmengine - INFO - Epoch(train) [37][50/91] base_lr: 2.4503e-04 lr: 2.4503e-04 eta: 3 days, 20:55:34 time: 10.6287 data_time: 1.5628 memory: 68702 grad_norm: 1.5873 loss: 2.4261 center_loss: 0.7332 size_loss: 0.2239 cls_loss: 0.7268 giou_loss: 0.7423 2025/05/12 00:31:58 - mmengine - INFO - Epoch(train) [37][60/91] base_lr: 2.4503e-04 lr: 2.4503e-04 eta: 3 days, 20:53:06 time: 9.6594 data_time: 0.6121 memory: 68702 grad_norm: 1.5406 loss: 2.4344 center_loss: 0.7405 size_loss: 0.2233 cls_loss: 0.7305 giou_loss: 0.7401 2025/05/12 00:33:35 - mmengine - INFO - Epoch(train) [37][70/91] base_lr: 2.4503e-04 lr: 2.4503e-04 eta: 3 days, 20:50:38 time: 9.6576 data_time: 0.6075 memory: 68702 grad_norm: 1.4897 loss: 2.4020 center_loss: 0.7246 size_loss: 0.2197 cls_loss: 0.7209 giou_loss: 0.7368 2025/05/12 00:35:10 - mmengine - INFO - Epoch(train) [37][80/91] base_lr: 2.4503e-04 lr: 2.4503e-04 eta: 3 days, 20:48:04 time: 9.6359 data_time: 0.5986 memory: 68703 grad_norm: 1.4926 loss: 2.3892 center_loss: 0.7219 size_loss: 0.2169 cls_loss: 0.7127 giou_loss: 0.7378 2025/05/12 00:36:46 - mmengine - INFO - Epoch(train) [37][90/91] base_lr: 2.4503e-04 lr: 2.4503e-04 eta: 3 days, 20:45:26 time: 9.6215 data_time: 0.5907 memory: 68701 grad_norm: 1.5093 loss: 2.3605 center_loss: 0.7049 size_loss: 0.2126 cls_loss: 0.7081 giou_loss: 0.7348 2025/05/12 00:36:48 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 00:39:18 - mmengine - INFO - Epoch(train) [38][10/91] base_lr: 2.4475e-04 lr: 2.4475e-04 eta: 3 days, 20:50:13 time: 10.5214 data_time: 1.4489 memory: 68702 grad_norm: 1.6136 loss: 2.3516 center_loss: 0.7059 size_loss: 0.2153 cls_loss: 0.6968 giou_loss: 0.7335 2025/05/12 00:40:54 - mmengine - INFO - Epoch(train) [38][20/91] base_lr: 2.4475e-04 lr: 2.4475e-04 eta: 3 days, 20:47:45 time: 10.5222 data_time: 1.4499 memory: 68703 grad_norm: 1.5977 loss: 2.3618 center_loss: 0.7091 size_loss: 0.2194 cls_loss: 0.6947 giou_loss: 0.7387 2025/05/12 00:42:30 - mmengine - INFO - Epoch(train) [38][30/91] base_lr: 2.4475e-04 lr: 2.4475e-04 eta: 3 days, 20:45:12 time: 10.5158 data_time: 1.4581 memory: 68703 grad_norm: 1.5523 loss: 2.3587 center_loss: 0.7070 size_loss: 0.2188 cls_loss: 0.6971 giou_loss: 0.7358 2025/05/12 00:44:06 - mmengine - INFO - Epoch(train) [38][40/91] base_lr: 2.4475e-04 lr: 2.4475e-04 eta: 3 days, 20:42:37 time: 10.5117 data_time: 1.4613 memory: 68702 grad_norm: 1.5396 loss: 2.3769 center_loss: 0.7153 size_loss: 0.2203 cls_loss: 0.7038 giou_loss: 0.7375 2025/05/12 00:45:43 - mmengine - INFO - Epoch(train) [38][50/91] base_lr: 2.4475e-04 lr: 2.4475e-04 eta: 3 days, 20:40:16 time: 10.6958 data_time: 1.4855 memory: 68703 grad_norm: 1.5601 loss: 2.3782 center_loss: 0.7204 size_loss: 0.2189 cls_loss: 0.7019 giou_loss: 0.7370 2025/05/12 00:47:19 - mmengine - INFO - Epoch(train) [38][60/91] base_lr: 2.4475e-04 lr: 2.4475e-04 eta: 3 days, 20:37:50 time: 9.6286 data_time: 0.6318 memory: 68702 grad_norm: 1.5003 loss: 2.3689 center_loss: 0.7176 size_loss: 0.2149 cls_loss: 0.7010 giou_loss: 0.7354 2025/05/12 00:48:56 - mmengine - INFO - Epoch(train) [38][70/91] base_lr: 2.4475e-04 lr: 2.4475e-04 eta: 3 days, 20:35:24 time: 9.6278 data_time: 0.6290 memory: 68702 grad_norm: 1.5295 loss: 2.3667 center_loss: 0.7111 size_loss: 0.2152 cls_loss: 0.7043 giou_loss: 0.7362 2025/05/12 00:50:31 - mmengine - INFO - Epoch(train) [38][80/91] base_lr: 2.4475e-04 lr: 2.4475e-04 eta: 3 days, 20:32:50 time: 9.6241 data_time: 0.6384 memory: 68702 grad_norm: 1.5691 loss: 2.3910 center_loss: 0.7262 size_loss: 0.2176 cls_loss: 0.7059 giou_loss: 0.7413 2025/05/12 00:52:06 - mmengine - INFO - Epoch(train) [38][90/91] base_lr: 2.4475e-04 lr: 2.4475e-04 eta: 3 days, 20:30:06 time: 9.6014 data_time: 0.6316 memory: 68703 grad_norm: 1.5752 loss: 2.3967 center_loss: 0.7308 size_loss: 0.2193 cls_loss: 0.7041 giou_loss: 0.7425 2025/05/12 00:52:08 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 00:52:08 - mmengine - INFO - Saving checkpoint at 38 epochs 2025/05/12 00:53:05 - mmengine - INFO - Epoch(val) [38][10/39] eta: 0:01:37 time: 2.9033 data_time: 0.3716 memory: 15952 2025/05/12 00:53:32 - mmengine - INFO - Epoch(val) [38][20/39] eta: 0:00:57 time: 2.7715 data_time: 0.2321 memory: 13407 2025/05/12 00:53:58 - mmengine - INFO - Epoch(val) [38][30/39] eta: 0:00:26 time: 2.7728 data_time: 0.2211 memory: 13407 2025/05/12 00:54:27 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | sofa | 0.5867 | 0.7732 | 0.0778 | 0.2577 | | garbagebin | 0.1245 | 0.3170 | 0.0076 | 0.0396 | | curtain | 0.1185 | 0.3582 | 0.0222 | 0.0746 | | chair | 0.3206 | 0.6104 | 0.0350 | 0.1615 | | table | 0.3065 | 0.4629 | 0.0521 | 0.1657 | | bookshelf | 0.2828 | 0.6234 | 0.0269 | 0.1429 | | door | 0.0670 | 0.3319 | 0.0028 | 0.0471 | | picture | 0.0000 | 0.0090 | 0.0000 | 0.0000 | | desk | 0.5724 | 0.8425 | 0.0841 | 0.2362 | | window | 0.0325 | 0.2057 | 0.0013 | 0.0426 | | cabinet | 0.1175 | 0.3656 | 0.0131 | 0.0914 | | refrigerator | 0.2197 | 0.4737 | 0.0311 | 0.1404 | | sink | 0.2633 | 0.4898 | 0.0192 | 0.0816 | | counter | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | bed | 0.7391 | 0.8272 | 0.1384 | 0.2963 | | showercurtrain | 0.2405 | 0.4286 | 0.0119 | 0.0357 | | bathtub | 0.4442 | 0.6774 | 0.0836 | 0.2258 | | toilet | 0.6833 | 0.8621 | 0.2407 | 0.3621 | +----------------+---------+---------+---------+---------+ | Overall | 0.2844 | 0.4810 | 0.0471 | 0.1334 | +----------------+---------+---------+---------+---------+ 2025/05/12 00:54:27 - mmengine - INFO - Epoch(val) [38][39/39] chair_AP_0.25: 0.3206 sofa_AP_0.25: 0.5867 table_AP_0.25: 0.3065 garbagebin_AP_0.25: 0.1245 bookshelf_AP_0.25: 0.2828 picture_AP_0.25: 0.0000 curtain_AP_0.25: 0.1185 door_AP_0.25: 0.0670 cabinet_AP_0.25: 0.1175 refrigerator_AP_0.25: 0.2197 counter_AP_0.25: 0.0000 sink_AP_0.25: 0.2633 window_AP_0.25: 0.0325 desk_AP_0.25: 0.5724 bed_AP_0.25: 0.7391 toilet_AP_0.25: 0.6833 showercurtrain_AP_0.25: 0.2405 bathtub_AP_0.25: 0.4442 mAP_0.25: 0.2844 chair_rec_0.25: 0.6104 sofa_rec_0.25: 0.7732 table_rec_0.25: 0.4629 garbagebin_rec_0.25: 0.3170 bookshelf_rec_0.25: 0.6234 picture_rec_0.25: 0.0090 curtain_rec_0.25: 0.3582 door_rec_0.25: 0.3319 cabinet_rec_0.25: 0.3656 refrigerator_rec_0.25: 0.4737 counter_rec_0.25: 0.0000 sink_rec_0.25: 0.4898 window_rec_0.25: 0.2057 desk_rec_0.25: 0.8425 bed_rec_0.25: 0.8272 toilet_rec_0.25: 0.8621 showercurtrain_rec_0.25: 0.4286 bathtub_rec_0.25: 0.6774 mAR_0.25: 0.4810 chair_AP_0.50: 0.0350 sofa_AP_0.50: 0.0778 table_AP_0.50: 0.0521 garbagebin_AP_0.50: 0.0076 bookshelf_AP_0.50: 0.0269 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0222 door_AP_0.50: 0.0028 cabinet_AP_0.50: 0.0131 refrigerator_AP_0.50: 0.0311 counter_AP_0.50: 0.0000 sink_AP_0.50: 0.0192 window_AP_0.50: 0.0013 desk_AP_0.50: 0.0841 bed_AP_0.50: 0.1384 toilet_AP_0.50: 0.2407 showercurtrain_AP_0.50: 0.0119 bathtub_AP_0.50: 0.0836 mAP_0.50: 0.0471 chair_rec_0.50: 0.1615 sofa_rec_0.50: 0.2577 table_rec_0.50: 0.1657 garbagebin_rec_0.50: 0.0396 bookshelf_rec_0.50: 0.1429 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.0746 door_rec_0.50: 0.0471 cabinet_rec_0.50: 0.0914 refrigerator_rec_0.50: 0.1404 counter_rec_0.50: 0.0000 sink_rec_0.50: 0.0816 window_rec_0.50: 0.0426 desk_rec_0.50: 0.2362 bed_rec_0.50: 0.2963 toilet_rec_0.50: 0.3621 showercurtrain_rec_0.50: 0.0357 bathtub_rec_0.50: 0.2258 mAR_0.50: 0.1334 data_time: 0.2579 time: 2.8122 2025/05/12 00:54:27 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_36.pth is removed 2025/05/12 00:54:54 - mmengine - INFO - The best checkpoint with 0.2844 mAP_0.25 at 38 epoch is saved to best_mAP_0.25_epoch_38.pth. 2025/05/12 00:57:45 - mmengine - INFO - Epoch(train) [39][10/91] base_lr: 2.4447e-04 lr: 2.4447e-04 eta: 3 days, 20:33:28 time: 10.3500 data_time: 1.5265 memory: 68703 grad_norm: 1.6283 loss: 2.4021 center_loss: 0.7271 size_loss: 0.2220 cls_loss: 0.7093 giou_loss: 0.7438 2025/05/12 00:59:22 - mmengine - INFO - Epoch(train) [39][20/91] base_lr: 2.4447e-04 lr: 2.4447e-04 eta: 3 days, 20:31:08 time: 10.3639 data_time: 1.5065 memory: 68703 grad_norm: 1.5910 loss: 2.3771 center_loss: 0.7198 size_loss: 0.2217 cls_loss: 0.6955 giou_loss: 0.7401 2025/05/12 01:00:58 - mmengine - INFO - Epoch(train) [39][30/91] base_lr: 2.4447e-04 lr: 2.4447e-04 eta: 3 days, 20:28:35 time: 10.3505 data_time: 1.4994 memory: 68702 grad_norm: 1.5817 loss: 2.3512 center_loss: 0.7079 size_loss: 0.2188 cls_loss: 0.6914 giou_loss: 0.7331 2025/05/12 01:02:34 - mmengine - INFO - Epoch(train) [39][40/91] base_lr: 2.4447e-04 lr: 2.4447e-04 eta: 3 days, 20:26:04 time: 10.3531 data_time: 1.4829 memory: 68702 grad_norm: 1.5783 loss: 2.3357 center_loss: 0.6994 size_loss: 0.2170 cls_loss: 0.6892 giou_loss: 0.7300 2025/05/12 01:04:11 - mmengine - INFO - Epoch(train) [39][50/91] base_lr: 2.4447e-04 lr: 2.4447e-04 eta: 3 days, 20:23:46 time: 10.5585 data_time: 1.4857 memory: 68703 grad_norm: 1.5296 loss: 2.3348 center_loss: 0.6965 size_loss: 0.2156 cls_loss: 0.6921 giou_loss: 0.7307 2025/05/12 01:05:48 - mmengine - INFO - Epoch(train) [39][60/91] base_lr: 2.4447e-04 lr: 2.4447e-04 eta: 3 days, 20:21:26 time: 9.6550 data_time: 0.5740 memory: 68702 grad_norm: 1.4887 loss: 2.3314 center_loss: 0.6948 size_loss: 0.2146 cls_loss: 0.6934 giou_loss: 0.7286 2025/05/12 01:07:24 - mmengine - INFO - Epoch(train) [39][70/91] base_lr: 2.4447e-04 lr: 2.4447e-04 eta: 3 days, 20:18:58 time: 9.6345 data_time: 0.5872 memory: 68702 grad_norm: 1.5049 loss: 2.3312 center_loss: 0.6909 size_loss: 0.2131 cls_loss: 0.6991 giou_loss: 0.7282 2025/05/12 01:09:00 - mmengine - INFO - Epoch(train) [39][80/91] base_lr: 2.4447e-04 lr: 2.4447e-04 eta: 3 days, 20:16:31 time: 9.6461 data_time: 0.5843 memory: 68702 grad_norm: 1.5410 loss: 2.3422 center_loss: 0.7044 size_loss: 0.2154 cls_loss: 0.6935 giou_loss: 0.7289 2025/05/12 01:10:35 - mmengine - INFO - Epoch(train) [39][90/91] base_lr: 2.4447e-04 lr: 2.4447e-04 eta: 3 days, 20:13:51 time: 9.6246 data_time: 0.5886 memory: 68702 grad_norm: 1.5553 loss: 2.3480 center_loss: 0.7033 size_loss: 0.2161 cls_loss: 0.7026 giou_loss: 0.7261 2025/05/12 01:10:37 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 01:13:03 - mmengine - INFO - Epoch(train) [40][10/91] base_lr: 2.4418e-04 lr: 2.4418e-04 eta: 3 days, 20:17:38 time: 10.4399 data_time: 1.4719 memory: 68702 grad_norm: 1.6275 loss: 2.3695 center_loss: 0.7187 size_loss: 0.2223 cls_loss: 0.7013 giou_loss: 0.7272 2025/05/12 01:14:39 - mmengine - INFO - Epoch(train) [40][20/91] base_lr: 2.4418e-04 lr: 2.4418e-04 eta: 3 days, 20:15:07 time: 10.4175 data_time: 1.4691 memory: 68703 grad_norm: 1.6416 loss: 2.3841 center_loss: 0.7267 size_loss: 0.2220 cls_loss: 0.7074 giou_loss: 0.7280 2025/05/12 01:16:15 - mmengine - INFO - Epoch(train) [40][30/91] base_lr: 2.4418e-04 lr: 2.4418e-04 eta: 3 days, 20:12:41 time: 10.4221 data_time: 1.4714 memory: 68702 grad_norm: 1.6543 loss: 2.3929 center_loss: 0.7277 size_loss: 0.2220 cls_loss: 0.7170 giou_loss: 0.7263 2025/05/12 01:17:51 - mmengine - INFO - Epoch(train) [40][40/91] base_lr: 2.4418e-04 lr: 2.4418e-04 eta: 3 days, 20:10:11 time: 10.4137 data_time: 1.4682 memory: 68702 grad_norm: 1.6217 loss: 2.3808 center_loss: 0.7137 size_loss: 0.2184 cls_loss: 0.7222 giou_loss: 0.7265 2025/05/12 01:19:28 - mmengine - INFO - Epoch(train) [40][50/91] base_lr: 2.4418e-04 lr: 2.4418e-04 eta: 3 days, 20:07:51 time: 10.6074 data_time: 1.4817 memory: 68702 grad_norm: 1.6190 loss: 2.3285 center_loss: 0.6874 size_loss: 0.2077 cls_loss: 0.7161 giou_loss: 0.7173 2025/05/12 01:21:04 - mmengine - INFO - Epoch(train) [40][60/91] base_lr: 2.4418e-04 lr: 2.4418e-04 eta: 3 days, 20:05:25 time: 9.6167 data_time: 0.6034 memory: 68702 grad_norm: 1.5767 loss: 2.3323 center_loss: 0.6866 size_loss: 0.2092 cls_loss: 0.7195 giou_loss: 0.7169 2025/05/12 01:22:41 - mmengine - INFO - Epoch(train) [40][70/91] base_lr: 2.4418e-04 lr: 2.4418e-04 eta: 3 days, 20:03:12 time: 9.6538 data_time: 0.6065 memory: 68702 grad_norm: 1.5592 loss: 2.3129 center_loss: 0.6766 size_loss: 0.2094 cls_loss: 0.7136 giou_loss: 0.7133 2025/05/12 01:24:17 - mmengine - INFO - Epoch(train) [40][80/91] base_lr: 2.4418e-04 lr: 2.4418e-04 eta: 3 days, 20:00:44 time: 9.6465 data_time: 0.6017 memory: 68701 grad_norm: 1.5486 loss: 2.2969 center_loss: 0.6723 size_loss: 0.2068 cls_loss: 0.7072 giou_loss: 0.7106 2025/05/12 01:25:53 - mmengine - INFO - Epoch(train) [40][90/91] base_lr: 2.4418e-04 lr: 2.4418e-04 eta: 3 days, 19:58:10 time: 9.6344 data_time: 0.5954 memory: 68702 grad_norm: 1.5889 loss: 2.2867 center_loss: 0.6722 size_loss: 0.2056 cls_loss: 0.7009 giou_loss: 0.7080 2025/05/12 01:25:55 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 01:25:55 - mmengine - INFO - Saving checkpoint at 40 epochs 2025/05/12 01:26:52 - mmengine - INFO - Epoch(val) [40][10/39] eta: 0:01:35 time: 2.9097 data_time: 0.3582 memory: 15952 2025/05/12 01:27:18 - mmengine - INFO - Epoch(val) [40][20/39] eta: 0:00:56 time: 2.7609 data_time: 0.2171 memory: 13407 2025/05/12 01:27:45 - mmengine - INFO - Epoch(val) [40][30/39] eta: 0:00:25 time: 2.7654 data_time: 0.2229 memory: 13407 2025/05/12 01:28:12 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | sofa | 0.5325 | 0.7526 | 0.0474 | 0.2062 | | garbagebin | 0.1051 | 0.3509 | 0.0067 | 0.0623 | | chair | 0.3000 | 0.6323 | 0.0286 | 0.1491 | | table | 0.3046 | 0.4857 | 0.0490 | 0.1571 | | curtain | 0.1125 | 0.3582 | 0.0043 | 0.0597 | | bookshelf | 0.1435 | 0.5195 | 0.0110 | 0.0909 | | picture | 0.0007 | 0.0225 | 0.0000 | 0.0000 | | desk | 0.5757 | 0.8110 | 0.0535 | 0.2205 | | bed | 0.7759 | 0.8148 | 0.3247 | 0.4815 | | door | 0.0841 | 0.3191 | 0.0021 | 0.0471 | | window | 0.0506 | 0.2092 | 0.0011 | 0.0213 | | cabinet | 0.1698 | 0.4059 | 0.0195 | 0.0887 | | refrigerator | 0.2684 | 0.5263 | 0.0886 | 0.2456 | | sink | 0.2528 | 0.5306 | 0.0110 | 0.1020 | | counter | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | bathtub | 0.4026 | 0.6774 | 0.1239 | 0.2903 | | toilet | 0.6093 | 0.8621 | 0.1642 | 0.3103 | | showercurtrain | 0.1591 | 0.4643 | 0.0000 | 0.0000 | +----------------+---------+---------+---------+---------+ | Overall | 0.2693 | 0.4857 | 0.0520 | 0.1407 | +----------------+---------+---------+---------+---------+ 2025/05/12 01:28:12 - mmengine - INFO - Epoch(val) [40][39/39] chair_AP_0.25: 0.3000 sofa_AP_0.25: 0.5325 table_AP_0.25: 0.3046 garbagebin_AP_0.25: 0.1051 bookshelf_AP_0.25: 0.1435 picture_AP_0.25: 0.0007 curtain_AP_0.25: 0.1125 door_AP_0.25: 0.0841 cabinet_AP_0.25: 0.1698 refrigerator_AP_0.25: 0.2684 counter_AP_0.25: 0.0000 sink_AP_0.25: 0.2528 window_AP_0.25: 0.0506 desk_AP_0.25: 0.5757 bed_AP_0.25: 0.7759 toilet_AP_0.25: 0.6093 showercurtrain_AP_0.25: 0.1591 bathtub_AP_0.25: 0.4026 mAP_0.25: 0.2693 chair_rec_0.25: 0.6323 sofa_rec_0.25: 0.7526 table_rec_0.25: 0.4857 garbagebin_rec_0.25: 0.3509 bookshelf_rec_0.25: 0.5195 picture_rec_0.25: 0.0225 curtain_rec_0.25: 0.3582 door_rec_0.25: 0.3191 cabinet_rec_0.25: 0.4059 refrigerator_rec_0.25: 0.5263 counter_rec_0.25: 0.0000 sink_rec_0.25: 0.5306 window_rec_0.25: 0.2092 desk_rec_0.25: 0.8110 bed_rec_0.25: 0.8148 toilet_rec_0.25: 0.8621 showercurtrain_rec_0.25: 0.4643 bathtub_rec_0.25: 0.6774 mAR_0.25: 0.4857 chair_AP_0.50: 0.0286 sofa_AP_0.50: 0.0474 table_AP_0.50: 0.0490 garbagebin_AP_0.50: 0.0067 bookshelf_AP_0.50: 0.0110 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0043 door_AP_0.50: 0.0021 cabinet_AP_0.50: 0.0195 refrigerator_AP_0.50: 0.0886 counter_AP_0.50: 0.0000 sink_AP_0.50: 0.0110 window_AP_0.50: 0.0011 desk_AP_0.50: 0.0535 bed_AP_0.50: 0.3247 toilet_AP_0.50: 0.1642 showercurtrain_AP_0.50: 0.0000 bathtub_AP_0.50: 0.1239 mAP_0.50: 0.0520 chair_rec_0.50: 0.1491 sofa_rec_0.50: 0.2062 table_rec_0.50: 0.1571 garbagebin_rec_0.50: 0.0623 bookshelf_rec_0.50: 0.0909 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.0597 door_rec_0.50: 0.0471 cabinet_rec_0.50: 0.0887 refrigerator_rec_0.50: 0.2456 counter_rec_0.50: 0.0000 sink_rec_0.50: 0.1020 window_rec_0.50: 0.0213 desk_rec_0.50: 0.2205 bed_rec_0.50: 0.4815 toilet_rec_0.50: 0.3103 showercurtrain_rec_0.50: 0.0000 bathtub_rec_0.50: 0.2903 mAR_0.50: 0.1407 data_time: 0.2587 time: 2.7848 2025/05/12 01:30:39 - mmengine - INFO - Epoch(train) [41][10/91] base_lr: 2.4388e-04 lr: 2.4388e-04 eta: 3 days, 20:01:58 time: 10.4858 data_time: 1.5476 memory: 68703 grad_norm: 1.6939 loss: 2.2733 center_loss: 0.6664 size_loss: 0.2079 cls_loss: 0.6916 giou_loss: 0.7074 2025/05/12 01:32:15 - mmengine - INFO - Epoch(train) [41][20/91] base_lr: 2.4388e-04 lr: 2.4388e-04 eta: 3 days, 19:59:32 time: 10.4852 data_time: 1.5372 memory: 68700 grad_norm: 1.7333 loss: 2.2932 center_loss: 0.6702 size_loss: 0.2080 cls_loss: 0.7032 giou_loss: 0.7117 2025/05/12 01:33:51 - mmengine - INFO - Epoch(train) [41][30/91] base_lr: 2.4388e-04 lr: 2.4388e-04 eta: 3 days, 19:57:03 time: 10.4493 data_time: 1.5214 memory: 68703 grad_norm: 1.7019 loss: 2.2912 center_loss: 0.6683 size_loss: 0.2093 cls_loss: 0.7045 giou_loss: 0.7091 2025/05/12 01:35:27 - mmengine - INFO - Epoch(train) [41][40/91] base_lr: 2.4388e-04 lr: 2.4388e-04 eta: 3 days, 19:54:37 time: 10.4508 data_time: 1.5141 memory: 68700 grad_norm: 1.7213 loss: 2.2903 center_loss: 0.6624 size_loss: 0.2101 cls_loss: 0.7095 giou_loss: 0.7083 2025/05/12 01:37:04 - mmengine - INFO - Epoch(train) [41][50/91] base_lr: 2.4388e-04 lr: 2.4388e-04 eta: 3 days, 19:52:16 time: 10.6302 data_time: 1.5179 memory: 68702 grad_norm: 1.6177 loss: 2.2895 center_loss: 0.6627 size_loss: 0.2096 cls_loss: 0.7134 giou_loss: 0.7038 2025/05/12 01:38:40 - mmengine - INFO - Epoch(train) [41][60/91] base_lr: 2.4388e-04 lr: 2.4388e-04 eta: 3 days, 19:49:55 time: 9.6237 data_time: 0.5607 memory: 68702 grad_norm: 1.5157 loss: 2.2941 center_loss: 0.6618 size_loss: 0.2075 cls_loss: 0.7209 giou_loss: 0.7040 2025/05/12 01:40:16 - mmengine - INFO - Epoch(train) [41][70/91] base_lr: 2.4388e-04 lr: 2.4388e-04 eta: 3 days, 19:47:26 time: 9.6137 data_time: 0.5653 memory: 68702 grad_norm: 1.4928 loss: 2.2528 center_loss: 0.6500 size_loss: 0.2044 cls_loss: 0.7019 giou_loss: 0.6964 2025/05/12 01:41:52 - mmengine - INFO - Epoch(train) [41][80/91] base_lr: 2.4388e-04 lr: 2.4388e-04 eta: 3 days, 19:44:57 time: 9.6123 data_time: 0.5752 memory: 68703 grad_norm: 1.5150 loss: 2.2329 center_loss: 0.6410 size_loss: 0.1987 cls_loss: 0.7008 giou_loss: 0.6924 2025/05/12 01:43:26 - mmengine - INFO - Epoch(train) [41][90/91] base_lr: 2.4388e-04 lr: 2.4388e-04 eta: 3 days, 19:42:21 time: 9.5865 data_time: 0.5711 memory: 68702 grad_norm: 1.5524 loss: 2.2283 center_loss: 0.6418 size_loss: 0.1974 cls_loss: 0.6979 giou_loss: 0.6913 2025/05/12 01:43:28 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 01:45:59 - mmengine - INFO - Epoch(train) [42][10/91] base_lr: 2.4357e-04 lr: 2.4357e-04 eta: 3 days, 19:46:34 time: 10.5197 data_time: 1.5631 memory: 68702 grad_norm: 1.6937 loss: 2.2502 center_loss: 0.6473 size_loss: 0.1991 cls_loss: 0.7071 giou_loss: 0.6967 2025/05/12 01:47:36 - mmengine - INFO - Epoch(train) [42][20/91] base_lr: 2.4357e-04 lr: 2.4357e-04 eta: 3 days, 19:44:10 time: 10.5158 data_time: 1.5534 memory: 68702 grad_norm: 1.7123 loss: 2.2648 center_loss: 0.6523 size_loss: 0.2009 cls_loss: 0.7117 giou_loss: 0.7000 2025/05/12 01:49:12 - mmengine - INFO - Epoch(train) [42][30/91] base_lr: 2.4357e-04 lr: 2.4357e-04 eta: 3 days, 19:41:47 time: 10.5268 data_time: 1.5449 memory: 68703 grad_norm: 1.7549 loss: 2.2698 center_loss: 0.6502 size_loss: 0.2013 cls_loss: 0.7138 giou_loss: 0.7046 2025/05/12 01:50:48 - mmengine - INFO - Epoch(train) [42][40/91] base_lr: 2.4357e-04 lr: 2.4357e-04 eta: 3 days, 19:39:24 time: 10.5380 data_time: 1.5387 memory: 68703 grad_norm: 1.7541 loss: 2.2830 center_loss: 0.6596 size_loss: 0.2046 cls_loss: 0.7086 giou_loss: 0.7101 2025/05/12 01:52:26 - mmengine - INFO - Epoch(train) [42][50/91] base_lr: 2.4357e-04 lr: 2.4357e-04 eta: 3 days, 19:37:16 time: 10.7548 data_time: 1.5534 memory: 68702 grad_norm: 1.6297 loss: 2.2629 center_loss: 0.6521 size_loss: 0.2034 cls_loss: 0.6986 giou_loss: 0.7088 2025/05/12 01:54:03 - mmengine - INFO - Epoch(train) [42][60/91] base_lr: 2.4357e-04 lr: 2.4357e-04 eta: 3 days, 19:34:58 time: 9.6735 data_time: 0.5624 memory: 68702 grad_norm: 1.5296 loss: 2.2304 center_loss: 0.6375 size_loss: 0.1996 cls_loss: 0.6913 giou_loss: 0.7019 2025/05/12 01:55:40 - mmengine - INFO - Epoch(train) [42][70/91] base_lr: 2.4357e-04 lr: 2.4357e-04 eta: 3 days, 19:32:43 time: 9.6928 data_time: 0.5799 memory: 68702 grad_norm: 1.5331 loss: 2.2061 center_loss: 0.6293 size_loss: 0.1975 cls_loss: 0.6826 giou_loss: 0.6966 2025/05/12 01:57:16 - mmengine - INFO - Epoch(train) [42][80/91] base_lr: 2.4357e-04 lr: 2.4357e-04 eta: 3 days, 19:30:17 time: 9.6824 data_time: 0.5872 memory: 68703 grad_norm: 1.5190 loss: 2.2144 center_loss: 0.6382 size_loss: 0.1989 cls_loss: 0.6815 giou_loss: 0.6958 2025/05/12 01:58:51 - mmengine - INFO - Epoch(train) [42][90/91] base_lr: 2.4357e-04 lr: 2.4357e-04 eta: 3 days, 19:27:43 time: 9.6541 data_time: 0.5830 memory: 68700 grad_norm: 1.5398 loss: 2.1941 center_loss: 0.6253 size_loss: 0.1961 cls_loss: 0.6828 giou_loss: 0.6899 2025/05/12 01:58:53 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 01:58:53 - mmengine - INFO - Saving checkpoint at 42 epochs 2025/05/12 01:59:51 - mmengine - INFO - Epoch(val) [42][10/39] eta: 0:01:40 time: 2.9199 data_time: 0.3898 memory: 15952 2025/05/12 02:00:18 - mmengine - INFO - Epoch(val) [42][20/39] eta: 0:00:58 time: 2.7959 data_time: 0.2693 memory: 13407 2025/05/12 02:00:45 - mmengine - INFO - Epoch(val) [42][30/39] eta: 0:00:26 time: 2.8024 data_time: 0.2667 memory: 13407 2025/05/12 02:01:12 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | sofa | 0.5019 | 0.7526 | 0.0785 | 0.2577 | | garbagebin | 0.1222 | 0.3528 | 0.0064 | 0.0566 | | table | 0.3305 | 0.4771 | 0.0769 | 0.1943 | | curtain | 0.0681 | 0.2687 | 0.0002 | 0.0149 | | chair | 0.3792 | 0.6148 | 0.0606 | 0.1996 | | picture | 0.0001 | 0.0135 | 0.0000 | 0.0000 | | bookshelf | 0.1623 | 0.5584 | 0.0357 | 0.1429 | | desk | 0.5452 | 0.7953 | 0.1141 | 0.2756 | | door | 0.0931 | 0.3726 | 0.0043 | 0.0514 | | cabinet | 0.1576 | 0.3952 | 0.0221 | 0.1102 | | window | 0.0377 | 0.2092 | 0.0011 | 0.0319 | | refrigerator | 0.3599 | 0.5789 | 0.1106 | 0.2105 | | sink | 0.3119 | 0.5714 | 0.0554 | 0.1735 | | counter | 0.0346 | 0.0577 | 0.0000 | 0.0000 | | bed | 0.7742 | 0.8519 | 0.3015 | 0.4815 | | toilet | 0.6139 | 0.8793 | 0.2773 | 0.4310 | | bathtub | 0.4936 | 0.7097 | 0.0799 | 0.2581 | | showercurtrain | 0.1234 | 0.3929 | 0.0149 | 0.1071 | +----------------+---------+---------+---------+---------+ | Overall | 0.2839 | 0.4918 | 0.0689 | 0.1665 | +----------------+---------+---------+---------+---------+ 2025/05/12 02:01:12 - mmengine - INFO - Epoch(val) [42][39/39] chair_AP_0.25: 0.3792 sofa_AP_0.25: 0.5019 table_AP_0.25: 0.3305 garbagebin_AP_0.25: 0.1222 bookshelf_AP_0.25: 0.1623 picture_AP_0.25: 0.0001 curtain_AP_0.25: 0.0681 door_AP_0.25: 0.0931 cabinet_AP_0.25: 0.1576 refrigerator_AP_0.25: 0.3599 counter_AP_0.25: 0.0346 sink_AP_0.25: 0.3119 window_AP_0.25: 0.0377 desk_AP_0.25: 0.5452 bed_AP_0.25: 0.7742 toilet_AP_0.25: 0.6139 showercurtrain_AP_0.25: 0.1234 bathtub_AP_0.25: 0.4936 mAP_0.25: 0.2839 chair_rec_0.25: 0.6148 sofa_rec_0.25: 0.7526 table_rec_0.25: 0.4771 garbagebin_rec_0.25: 0.3528 bookshelf_rec_0.25: 0.5584 picture_rec_0.25: 0.0135 curtain_rec_0.25: 0.2687 door_rec_0.25: 0.3726 cabinet_rec_0.25: 0.3952 refrigerator_rec_0.25: 0.5789 counter_rec_0.25: 0.0577 sink_rec_0.25: 0.5714 window_rec_0.25: 0.2092 desk_rec_0.25: 0.7953 bed_rec_0.25: 0.8519 toilet_rec_0.25: 0.8793 showercurtrain_rec_0.25: 0.3929 bathtub_rec_0.25: 0.7097 mAR_0.25: 0.4918 chair_AP_0.50: 0.0606 sofa_AP_0.50: 0.0785 table_AP_0.50: 0.0769 garbagebin_AP_0.50: 0.0064 bookshelf_AP_0.50: 0.0357 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0002 door_AP_0.50: 0.0043 cabinet_AP_0.50: 0.0221 refrigerator_AP_0.50: 0.1106 counter_AP_0.50: 0.0000 sink_AP_0.50: 0.0554 window_AP_0.50: 0.0011 desk_AP_0.50: 0.1141 bed_AP_0.50: 0.3015 toilet_AP_0.50: 0.2773 showercurtrain_AP_0.50: 0.0149 bathtub_AP_0.50: 0.0799 mAP_0.50: 0.0689 chair_rec_0.50: 0.1996 sofa_rec_0.50: 0.2577 table_rec_0.50: 0.1943 garbagebin_rec_0.50: 0.0566 bookshelf_rec_0.50: 0.1429 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.0149 door_rec_0.50: 0.0514 cabinet_rec_0.50: 0.1102 refrigerator_rec_0.50: 0.2105 counter_rec_0.50: 0.0000 sink_rec_0.50: 0.1735 window_rec_0.50: 0.0319 desk_rec_0.50: 0.2756 bed_rec_0.50: 0.4815 toilet_rec_0.50: 0.4310 showercurtrain_rec_0.50: 0.1071 bathtub_rec_0.50: 0.2581 mAR_0.50: 0.1665 data_time: 0.3044 time: 2.8287 2025/05/12 02:03:40 - mmengine - INFO - Epoch(train) [43][10/91] base_lr: 2.4325e-04 lr: 2.4325e-04 eta: 3 days, 19:31:17 time: 10.4903 data_time: 1.4496 memory: 68703 grad_norm: 1.7834 loss: 2.1910 center_loss: 0.6184 size_loss: 0.1953 cls_loss: 0.6904 giou_loss: 0.6869 2025/05/12 02:05:16 - mmengine - INFO - Epoch(train) [43][20/91] base_lr: 2.4325e-04 lr: 2.4325e-04 eta: 3 days, 19:28:54 time: 10.4783 data_time: 1.4545 memory: 68702 grad_norm: 1.7994 loss: 2.2201 center_loss: 0.6352 size_loss: 0.2002 cls_loss: 0.6926 giou_loss: 0.6921 2025/05/12 02:06:52 - mmengine - INFO - Epoch(train) [43][30/91] base_lr: 2.4325e-04 lr: 2.4325e-04 eta: 3 days, 19:26:33 time: 10.4657 data_time: 1.4373 memory: 68702 grad_norm: 1.7686 loss: 2.2285 center_loss: 0.6388 size_loss: 0.2027 cls_loss: 0.6949 giou_loss: 0.6921 2025/05/12 02:08:29 - mmengine - INFO - Epoch(train) [43][40/91] base_lr: 2.4325e-04 lr: 2.4325e-04 eta: 3 days, 19:24:11 time: 10.4717 data_time: 1.4279 memory: 68700 grad_norm: 1.7703 loss: 2.2268 center_loss: 0.6410 size_loss: 0.2015 cls_loss: 0.6916 giou_loss: 0.6928 2025/05/12 02:10:06 - mmengine - INFO - Epoch(train) [43][50/91] base_lr: 2.4325e-04 lr: 2.4325e-04 eta: 3 days, 19:21:59 time: 10.6768 data_time: 1.4387 memory: 68703 grad_norm: 1.6502 loss: 2.2216 center_loss: 0.6425 size_loss: 0.2006 cls_loss: 0.6863 giou_loss: 0.6922 2025/05/12 02:11:43 - mmengine - INFO - Epoch(train) [43][60/91] base_lr: 2.4325e-04 lr: 2.4325e-04 eta: 3 days, 19:19:47 time: 9.6772 data_time: 0.5664 memory: 68702 grad_norm: 1.5494 loss: 2.2335 center_loss: 0.6494 size_loss: 0.2013 cls_loss: 0.6872 giou_loss: 0.6955 2025/05/12 02:13:20 - mmengine - INFO - Epoch(train) [43][70/91] base_lr: 2.4325e-04 lr: 2.4325e-04 eta: 3 days, 19:17:29 time: 9.6845 data_time: 0.5711 memory: 68703 grad_norm: 1.6029 loss: 2.2057 center_loss: 0.6354 size_loss: 0.1971 cls_loss: 0.6833 giou_loss: 0.6900 2025/05/12 02:14:56 - mmengine - INFO - Epoch(train) [43][80/91] base_lr: 2.4325e-04 lr: 2.4325e-04 eta: 3 days, 19:15:08 time: 9.6818 data_time: 0.5698 memory: 68702 grad_norm: 1.6184 loss: 2.1996 center_loss: 0.6300 size_loss: 0.1935 cls_loss: 0.6859 giou_loss: 0.6901 2025/05/12 02:16:32 - mmengine - INFO - Epoch(train) [43][90/91] base_lr: 2.4325e-04 lr: 2.4325e-04 eta: 3 days, 19:12:41 time: 9.6690 data_time: 0.5733 memory: 68702 grad_norm: 1.6300 loss: 2.1921 center_loss: 0.6237 size_loss: 0.1909 cls_loss: 0.6908 giou_loss: 0.6867 2025/05/12 02:16:34 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 02:19:00 - mmengine - INFO - Epoch(train) [44][10/91] base_lr: 2.4293e-04 lr: 2.4293e-04 eta: 3 days, 19:15:57 time: 10.4868 data_time: 1.5101 memory: 68702 grad_norm: 1.7077 loss: 2.2473 center_loss: 0.6433 size_loss: 0.1980 cls_loss: 0.7085 giou_loss: 0.6975 2025/05/12 02:20:37 - mmengine - INFO - Epoch(train) [44][20/91] base_lr: 2.4293e-04 lr: 2.4293e-04 eta: 3 days, 19:13:36 time: 10.4737 data_time: 1.5062 memory: 68702 grad_norm: 1.7743 loss: 2.2229 center_loss: 0.6293 size_loss: 0.1941 cls_loss: 0.7075 giou_loss: 0.6919 2025/05/12 02:22:13 - mmengine - INFO - Epoch(train) [44][30/91] base_lr: 2.4293e-04 lr: 2.4293e-04 eta: 3 days, 19:11:17 time: 10.4715 data_time: 1.4920 memory: 68702 grad_norm: 1.7295 loss: 2.2239 center_loss: 0.6299 size_loss: 0.1940 cls_loss: 0.7063 giou_loss: 0.6937 2025/05/12 02:23:49 - mmengine - INFO - Epoch(train) [44][40/91] base_lr: 2.4293e-04 lr: 2.4293e-04 eta: 3 days, 19:08:55 time: 10.4663 data_time: 1.4872 memory: 68702 grad_norm: 1.7244 loss: 2.2246 center_loss: 0.6326 size_loss: 0.1943 cls_loss: 0.7030 giou_loss: 0.6947 2025/05/12 02:25:26 - mmengine - INFO - Epoch(train) [44][50/91] base_lr: 2.4293e-04 lr: 2.4293e-04 eta: 3 days, 19:06:40 time: 10.6374 data_time: 1.4881 memory: 68702 grad_norm: 1.7389 loss: 2.1879 center_loss: 0.6210 size_loss: 0.1944 cls_loss: 0.6887 giou_loss: 0.6838 2025/05/12 02:27:03 - mmengine - INFO - Epoch(train) [44][60/91] base_lr: 2.4293e-04 lr: 2.4293e-04 eta: 3 days, 19:04:19 time: 9.6436 data_time: 0.5550 memory: 68703 grad_norm: 1.7202 loss: 2.1668 center_loss: 0.6145 size_loss: 0.1917 cls_loss: 0.6804 giou_loss: 0.6802 2025/05/12 02:28:39 - mmengine - INFO - Epoch(train) [44][70/91] base_lr: 2.4293e-04 lr: 2.4293e-04 eta: 3 days, 19:02:01 time: 9.6507 data_time: 0.5549 memory: 68702 grad_norm: 1.6708 loss: 2.1742 center_loss: 0.6224 size_loss: 0.1939 cls_loss: 0.6761 giou_loss: 0.6817 2025/05/12 02:30:15 - mmengine - INFO - Epoch(train) [44][80/91] base_lr: 2.4293e-04 lr: 2.4293e-04 eta: 3 days, 18:59:35 time: 9.6313 data_time: 0.5607 memory: 68703 grad_norm: 1.7199 loss: 2.1847 center_loss: 0.6278 size_loss: 0.1958 cls_loss: 0.6754 giou_loss: 0.6857 2025/05/12 02:31:21 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 02:31:50 - mmengine - INFO - Epoch(train) [44][90/91] base_lr: 2.4293e-04 lr: 2.4293e-04 eta: 3 days, 18:57:03 time: 9.6037 data_time: 0.5596 memory: 68702 grad_norm: 1.7359 loss: 2.2076 center_loss: 0.6329 size_loss: 0.1977 cls_loss: 0.6884 giou_loss: 0.6887 2025/05/12 02:31:52 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 02:31:52 - mmengine - INFO - Saving checkpoint at 44 epochs 2025/05/12 02:32:51 - mmengine - INFO - Epoch(val) [44][10/39] eta: 0:01:38 time: 2.9418 data_time: 0.4149 memory: 15952 2025/05/12 02:33:17 - mmengine - INFO - Epoch(val) [44][20/39] eta: 0:00:57 time: 2.7787 data_time: 0.2493 memory: 13407 2025/05/12 02:33:43 - mmengine - INFO - Epoch(val) [44][30/39] eta: 0:00:25 time: 2.7689 data_time: 0.2325 memory: 13407 2025/05/12 02:34:11 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | sofa | 0.5508 | 0.7423 | 0.0906 | 0.2577 | | garbagebin | 0.1162 | 0.3585 | 0.0039 | 0.0547 | | table | 0.3312 | 0.4657 | 0.0411 | 0.1400 | | curtain | 0.1540 | 0.3433 | 0.0017 | 0.0299 | | picture | 0.0062 | 0.0315 | 0.0000 | 0.0000 | | chair | 0.3872 | 0.6608 | 0.0434 | 0.1806 | | bookshelf | 0.1557 | 0.5325 | 0.0141 | 0.0909 | | door | 0.0978 | 0.3276 | 0.0062 | 0.0664 | | window | 0.0605 | 0.2376 | 0.0005 | 0.0213 | | cabinet | 0.1718 | 0.3844 | 0.0119 | 0.0833 | | refrigerator | 0.3304 | 0.4912 | 0.1551 | 0.2807 | | sink | 0.3012 | 0.4898 | 0.0705 | 0.1939 | | counter | 0.0481 | 0.0577 | 0.0000 | 0.0000 | | bed | 0.8011 | 0.8395 | 0.1979 | 0.3457 | | desk | 0.5925 | 0.7953 | 0.1308 | 0.3465 | | toilet | 0.6853 | 0.8448 | 0.2377 | 0.3276 | | bathtub | 0.5480 | 0.6774 | 0.1614 | 0.2903 | | showercurtrain | 0.1209 | 0.4286 | 0.0000 | 0.0000 | +----------------+---------+---------+---------+---------+ | Overall | 0.3033 | 0.4838 | 0.0648 | 0.1505 | +----------------+---------+---------+---------+---------+ 2025/05/12 02:34:11 - mmengine - INFO - Epoch(val) [44][39/39] chair_AP_0.25: 0.3872 sofa_AP_0.25: 0.5508 table_AP_0.25: 0.3312 garbagebin_AP_0.25: 0.1162 bookshelf_AP_0.25: 0.1557 picture_AP_0.25: 0.0062 curtain_AP_0.25: 0.1540 door_AP_0.25: 0.0978 cabinet_AP_0.25: 0.1718 refrigerator_AP_0.25: 0.3304 counter_AP_0.25: 0.0481 sink_AP_0.25: 0.3012 window_AP_0.25: 0.0605 desk_AP_0.25: 0.5925 bed_AP_0.25: 0.8011 toilet_AP_0.25: 0.6853 showercurtrain_AP_0.25: 0.1209 bathtub_AP_0.25: 0.5480 mAP_0.25: 0.3033 chair_rec_0.25: 0.6608 sofa_rec_0.25: 0.7423 table_rec_0.25: 0.4657 garbagebin_rec_0.25: 0.3585 bookshelf_rec_0.25: 0.5325 picture_rec_0.25: 0.0315 curtain_rec_0.25: 0.3433 door_rec_0.25: 0.3276 cabinet_rec_0.25: 0.3844 refrigerator_rec_0.25: 0.4912 counter_rec_0.25: 0.0577 sink_rec_0.25: 0.4898 window_rec_0.25: 0.2376 desk_rec_0.25: 0.7953 bed_rec_0.25: 0.8395 toilet_rec_0.25: 0.8448 showercurtrain_rec_0.25: 0.4286 bathtub_rec_0.25: 0.6774 mAR_0.25: 0.4838 chair_AP_0.50: 0.0434 sofa_AP_0.50: 0.0906 table_AP_0.50: 0.0411 garbagebin_AP_0.50: 0.0039 bookshelf_AP_0.50: 0.0141 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0017 door_AP_0.50: 0.0062 cabinet_AP_0.50: 0.0119 refrigerator_AP_0.50: 0.1551 counter_AP_0.50: 0.0000 sink_AP_0.50: 0.0705 window_AP_0.50: 0.0005 desk_AP_0.50: 0.1308 bed_AP_0.50: 0.1979 toilet_AP_0.50: 0.2377 showercurtrain_AP_0.50: 0.0000 bathtub_AP_0.50: 0.1614 mAP_0.50: 0.0648 chair_rec_0.50: 0.1806 sofa_rec_0.50: 0.2577 table_rec_0.50: 0.1400 garbagebin_rec_0.50: 0.0547 bookshelf_rec_0.50: 0.0909 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.0299 door_rec_0.50: 0.0664 cabinet_rec_0.50: 0.0833 refrigerator_rec_0.50: 0.2807 counter_rec_0.50: 0.0000 sink_rec_0.50: 0.1939 window_rec_0.50: 0.0213 desk_rec_0.50: 0.3465 bed_rec_0.50: 0.3457 toilet_rec_0.50: 0.3276 showercurtrain_rec_0.50: 0.0000 bathtub_rec_0.50: 0.2903 mAR_0.50: 0.1505 data_time: 0.2727 time: 2.8007 2025/05/12 02:34:11 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_38.pth is removed 2025/05/12 02:34:39 - mmengine - INFO - The best checkpoint with 0.3033 mAP_0.25 at 44 epoch is saved to best_mAP_0.25_epoch_44.pth. 2025/05/12 02:37:32 - mmengine - INFO - Epoch(train) [45][10/91] base_lr: 2.4260e-04 lr: 2.4260e-04 eta: 3 days, 18:59:38 time: 10.3520 data_time: 1.4705 memory: 68702 grad_norm: 1.8202 loss: 2.2069 center_loss: 0.6311 size_loss: 0.1972 cls_loss: 0.6886 giou_loss: 0.6900 2025/05/12 02:39:08 - mmengine - INFO - Epoch(train) [45][20/91] base_lr: 2.4260e-04 lr: 2.4260e-04 eta: 3 days, 18:57:16 time: 10.3555 data_time: 1.4830 memory: 68702 grad_norm: 1.8780 loss: 2.2207 center_loss: 0.6410 size_loss: 0.1988 cls_loss: 0.6889 giou_loss: 0.6920 2025/05/12 02:40:45 - mmengine - INFO - Epoch(train) [45][30/91] base_lr: 2.4260e-04 lr: 2.4260e-04 eta: 3 days, 18:55:04 time: 10.3679 data_time: 1.4959 memory: 68703 grad_norm: 1.8634 loss: 2.2064 center_loss: 0.6342 size_loss: 0.1974 cls_loss: 0.6837 giou_loss: 0.6910 2025/05/12 02:42:22 - mmengine - INFO - Epoch(train) [45][40/91] base_lr: 2.4260e-04 lr: 2.4260e-04 eta: 3 days, 18:52:47 time: 10.3850 data_time: 1.4955 memory: 68700 grad_norm: 1.8344 loss: 2.2241 center_loss: 0.6422 size_loss: 0.2005 cls_loss: 0.6894 giou_loss: 0.6920 2025/05/12 02:43:59 - mmengine - INFO - Epoch(train) [45][50/91] base_lr: 2.4260e-04 lr: 2.4260e-04 eta: 3 days, 18:50:38 time: 10.5937 data_time: 1.5144 memory: 68703 grad_norm: 1.7618 loss: 2.2061 center_loss: 0.6326 size_loss: 0.1986 cls_loss: 0.6885 giou_loss: 0.6864 2025/05/12 02:45:36 - mmengine - INFO - Epoch(train) [45][60/91] base_lr: 2.4260e-04 lr: 2.4260e-04 eta: 3 days, 18:48:26 time: 9.6952 data_time: 0.6217 memory: 68703 grad_norm: 1.8229 loss: 2.2126 center_loss: 0.6328 size_loss: 0.2009 cls_loss: 0.6908 giou_loss: 0.6881 2025/05/12 02:47:13 - mmengine - INFO - Epoch(train) [45][70/91] base_lr: 2.4260e-04 lr: 2.4260e-04 eta: 3 days, 18:46:07 time: 9.7008 data_time: 0.6212 memory: 68703 grad_norm: 1.7490 loss: 2.2019 center_loss: 0.6251 size_loss: 0.1993 cls_loss: 0.6918 giou_loss: 0.6857 2025/05/12 02:48:49 - mmengine - INFO - Epoch(train) [45][80/91] base_lr: 2.4260e-04 lr: 2.4260e-04 eta: 3 days, 18:43:47 time: 9.6774 data_time: 0.6236 memory: 68702 grad_norm: 1.7506 loss: 2.2156 center_loss: 0.6346 size_loss: 0.2004 cls_loss: 0.6936 giou_loss: 0.6869 2025/05/12 02:50:24 - mmengine - INFO - Epoch(train) [45][90/91] base_lr: 2.4260e-04 lr: 2.4260e-04 eta: 3 days, 18:41:18 time: 9.6466 data_time: 0.6222 memory: 68703 grad_norm: 1.7326 loss: 2.1950 center_loss: 0.6259 size_loss: 0.1966 cls_loss: 0.6886 giou_loss: 0.6838 2025/05/12 02:50:26 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 02:52:54 - mmengine - INFO - Epoch(train) [46][10/91] base_lr: 2.4227e-04 lr: 2.4227e-04 eta: 3 days, 18:44:32 time: 10.5003 data_time: 1.5548 memory: 68702 grad_norm: 1.8459 loss: 2.1953 center_loss: 0.6307 size_loss: 0.1987 cls_loss: 0.6801 giou_loss: 0.6858 2025/05/12 02:54:30 - mmengine - INFO - Epoch(train) [46][20/91] base_lr: 2.4227e-04 lr: 2.4227e-04 eta: 3 days, 18:42:13 time: 10.4844 data_time: 1.5349 memory: 68702 grad_norm: 1.8005 loss: 2.1987 center_loss: 0.6307 size_loss: 0.1963 cls_loss: 0.6859 giou_loss: 0.6859 2025/05/12 02:56:06 - mmengine - INFO - Epoch(train) [46][30/91] base_lr: 2.4227e-04 lr: 2.4227e-04 eta: 3 days, 18:39:51 time: 10.4780 data_time: 1.5438 memory: 68703 grad_norm: 1.8216 loss: 2.2044 center_loss: 0.6318 size_loss: 0.1965 cls_loss: 0.6876 giou_loss: 0.6885 2025/05/12 02:57:42 - mmengine - INFO - Epoch(train) [46][40/91] base_lr: 2.4227e-04 lr: 2.4227e-04 eta: 3 days, 18:37:33 time: 10.4774 data_time: 1.5370 memory: 68700 grad_norm: 1.8210 loss: 2.2039 center_loss: 0.6316 size_loss: 0.1959 cls_loss: 0.6892 giou_loss: 0.6872 2025/05/12 02:59:20 - mmengine - INFO - Epoch(train) [46][50/91] base_lr: 2.4227e-04 lr: 2.4227e-04 eta: 3 days, 18:35:27 time: 10.6888 data_time: 1.5683 memory: 68702 grad_norm: 1.7181 loss: 2.1826 center_loss: 0.6187 size_loss: 0.1926 cls_loss: 0.6886 giou_loss: 0.6827 2025/05/12 03:00:57 - mmengine - INFO - Epoch(train) [46][60/91] base_lr: 2.4227e-04 lr: 2.4227e-04 eta: 3 days, 18:33:12 time: 9.6663 data_time: 0.6354 memory: 68702 grad_norm: 1.7022 loss: 2.1870 center_loss: 0.6245 size_loss: 0.1903 cls_loss: 0.6900 giou_loss: 0.6821 2025/05/12 03:02:34 - mmengine - INFO - Epoch(train) [46][70/91] base_lr: 2.4227e-04 lr: 2.4227e-04 eta: 3 days, 18:30:57 time: 9.6756 data_time: 0.6423 memory: 68700 grad_norm: 1.6627 loss: 2.1794 center_loss: 0.6247 size_loss: 0.1900 cls_loss: 0.6840 giou_loss: 0.6808 2025/05/12 03:04:10 - mmengine - INFO - Epoch(train) [46][80/91] base_lr: 2.4227e-04 lr: 2.4227e-04 eta: 3 days, 18:28:37 time: 9.6757 data_time: 0.6297 memory: 68702 grad_norm: 1.6682 loss: 2.1628 center_loss: 0.6174 size_loss: 0.1861 cls_loss: 0.6840 giou_loss: 0.6753 2025/05/12 03:05:45 - mmengine - INFO - Epoch(train) [46][90/91] base_lr: 2.4227e-04 lr: 2.4227e-04 eta: 3 days, 18:26:08 time: 9.6447 data_time: 0.6217 memory: 68700 grad_norm: 1.7373 loss: 2.1531 center_loss: 0.6124 size_loss: 0.1850 cls_loss: 0.6830 giou_loss: 0.6726 2025/05/12 03:05:47 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 03:05:47 - mmengine - INFO - Saving checkpoint at 46 epochs 2025/05/12 03:06:45 - mmengine - INFO - Epoch(val) [46][10/39] eta: 0:01:39 time: 2.9273 data_time: 0.3995 memory: 15952 2025/05/12 03:07:11 - mmengine - INFO - Epoch(val) [46][20/39] eta: 0:00:57 time: 2.7732 data_time: 0.2440 memory: 13407 2025/05/12 03:07:37 - mmengine - INFO - Epoch(val) [46][30/39] eta: 0:00:26 time: 2.7704 data_time: 0.2421 memory: 13407 2025/05/12 03:08:04 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | sofa | 0.5789 | 0.7732 | 0.0434 | 0.2062 | | chair | 0.3479 | 0.5965 | 0.0397 | 0.1601 | | garbagebin | 0.1108 | 0.3208 | 0.0060 | 0.0642 | | curtain | 0.1569 | 0.4179 | 0.0227 | 0.1194 | | bookshelf | 0.2524 | 0.5974 | 0.0084 | 0.0909 | | picture | 0.0048 | 0.0225 | 0.0000 | 0.0000 | | door | 0.0980 | 0.3212 | 0.0022 | 0.0407 | | table | 0.3577 | 0.5200 | 0.0536 | 0.1571 | | bed | 0.8032 | 0.8642 | 0.2727 | 0.4444 | | cabinet | 0.1772 | 0.3898 | 0.0109 | 0.0699 | | window | 0.0349 | 0.2199 | 0.0016 | 0.0390 | | refrigerator | 0.3946 | 0.5614 | 0.2077 | 0.3158 | | sink | 0.2980 | 0.5816 | 0.0158 | 0.1122 | | counter | 0.0324 | 0.0962 | 0.0032 | 0.0192 | | desk | 0.5321 | 0.7874 | 0.0892 | 0.2441 | | toilet | 0.6730 | 0.8448 | 0.1862 | 0.3103 | | bathtub | 0.5632 | 0.7097 | 0.0465 | 0.1935 | | showercurtrain | 0.2049 | 0.4643 | 0.0000 | 0.0000 | +----------------+---------+---------+---------+---------+ | Overall | 0.3123 | 0.5049 | 0.0561 | 0.1437 | +----------------+---------+---------+---------+---------+ 2025/05/12 03:08:05 - mmengine - INFO - Epoch(val) [46][39/39] chair_AP_0.25: 0.3479 sofa_AP_0.25: 0.5789 table_AP_0.25: 0.3577 garbagebin_AP_0.25: 0.1108 bookshelf_AP_0.25: 0.2524 picture_AP_0.25: 0.0048 curtain_AP_0.25: 0.1569 door_AP_0.25: 0.0980 cabinet_AP_0.25: 0.1772 refrigerator_AP_0.25: 0.3946 counter_AP_0.25: 0.0324 sink_AP_0.25: 0.2980 window_AP_0.25: 0.0349 desk_AP_0.25: 0.5321 bed_AP_0.25: 0.8032 toilet_AP_0.25: 0.6730 showercurtrain_AP_0.25: 0.2049 bathtub_AP_0.25: 0.5632 mAP_0.25: 0.3123 chair_rec_0.25: 0.5965 sofa_rec_0.25: 0.7732 table_rec_0.25: 0.5200 garbagebin_rec_0.25: 0.3208 bookshelf_rec_0.25: 0.5974 picture_rec_0.25: 0.0225 curtain_rec_0.25: 0.4179 door_rec_0.25: 0.3212 cabinet_rec_0.25: 0.3898 refrigerator_rec_0.25: 0.5614 counter_rec_0.25: 0.0962 sink_rec_0.25: 0.5816 window_rec_0.25: 0.2199 desk_rec_0.25: 0.7874 bed_rec_0.25: 0.8642 toilet_rec_0.25: 0.8448 showercurtrain_rec_0.25: 0.4643 bathtub_rec_0.25: 0.7097 mAR_0.25: 0.5049 chair_AP_0.50: 0.0397 sofa_AP_0.50: 0.0434 table_AP_0.50: 0.0536 garbagebin_AP_0.50: 0.0060 bookshelf_AP_0.50: 0.0084 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0227 door_AP_0.50: 0.0022 cabinet_AP_0.50: 0.0109 refrigerator_AP_0.50: 0.2077 counter_AP_0.50: 0.0032 sink_AP_0.50: 0.0158 window_AP_0.50: 0.0016 desk_AP_0.50: 0.0892 bed_AP_0.50: 0.2727 toilet_AP_0.50: 0.1862 showercurtrain_AP_0.50: 0.0000 bathtub_AP_0.50: 0.0465 mAP_0.50: 0.0561 chair_rec_0.50: 0.1601 sofa_rec_0.50: 0.2062 table_rec_0.50: 0.1571 garbagebin_rec_0.50: 0.0642 bookshelf_rec_0.50: 0.0909 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.1194 door_rec_0.50: 0.0407 cabinet_rec_0.50: 0.0699 refrigerator_rec_0.50: 0.3158 counter_rec_0.50: 0.0192 sink_rec_0.50: 0.1122 window_rec_0.50: 0.0390 desk_rec_0.50: 0.2441 bed_rec_0.50: 0.4444 toilet_rec_0.50: 0.3103 showercurtrain_rec_0.50: 0.0000 bathtub_rec_0.50: 0.1935 mAR_0.50: 0.1437 data_time: 0.2840 time: 2.8014 2025/05/12 03:08:05 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_44.pth is removed 2025/05/12 03:08:31 - mmengine - INFO - The best checkpoint with 0.3123 mAP_0.25 at 46 epoch is saved to best_mAP_0.25_epoch_46.pth. 2025/05/12 03:11:23 - mmengine - INFO - Epoch(train) [47][10/91] base_lr: 2.4192e-04 lr: 2.4192e-04 eta: 3 days, 18:28:57 time: 10.4447 data_time: 1.5752 memory: 68703 grad_norm: 1.8717 loss: 2.1504 center_loss: 0.6140 size_loss: 0.1860 cls_loss: 0.6806 giou_loss: 0.6699 2025/05/12 03:13:00 - mmengine - INFO - Epoch(train) [47][20/91] base_lr: 2.4192e-04 lr: 2.4192e-04 eta: 3 days, 18:26:44 time: 10.4544 data_time: 1.5771 memory: 68702 grad_norm: 1.8282 loss: 2.1704 center_loss: 0.6198 size_loss: 0.1893 cls_loss: 0.6854 giou_loss: 0.6759 2025/05/12 03:14:36 - mmengine - INFO - Epoch(train) [47][30/91] base_lr: 2.4192e-04 lr: 2.4192e-04 eta: 3 days, 18:24:22 time: 10.4386 data_time: 1.5797 memory: 68703 grad_norm: 1.7960 loss: 2.1575 center_loss: 0.6122 size_loss: 0.1893 cls_loss: 0.6834 giou_loss: 0.6727 2025/05/12 03:16:12 - mmengine - INFO - Epoch(train) [47][40/91] base_lr: 2.4192e-04 lr: 2.4192e-04 eta: 3 days, 18:22:05 time: 10.4443 data_time: 1.5872 memory: 68703 grad_norm: 1.7528 loss: 2.1619 center_loss: 0.6155 size_loss: 0.1909 cls_loss: 0.6779 giou_loss: 0.6776 2025/05/12 03:17:49 - mmengine - INFO - Epoch(train) [47][50/91] base_lr: 2.4192e-04 lr: 2.4192e-04 eta: 3 days, 18:19:53 time: 10.6392 data_time: 1.6057 memory: 68702 grad_norm: 1.6292 loss: 2.1637 center_loss: 0.6122 size_loss: 0.1923 cls_loss: 0.6789 giou_loss: 0.6802 2025/05/12 03:19:26 - mmengine - INFO - Epoch(train) [47][60/91] base_lr: 2.4192e-04 lr: 2.4192e-04 eta: 3 days, 18:17:38 time: 9.6583 data_time: 0.6417 memory: 68702 grad_norm: 1.5378 loss: 2.1700 center_loss: 0.6186 size_loss: 0.1937 cls_loss: 0.6748 giou_loss: 0.6830 2025/05/12 03:21:02 - mmengine - INFO - Epoch(train) [47][70/91] base_lr: 2.4192e-04 lr: 2.4192e-04 eta: 3 days, 18:15:19 time: 9.6387 data_time: 0.6377 memory: 68702 grad_norm: 1.5743 loss: 2.1331 center_loss: 0.6000 size_loss: 0.1891 cls_loss: 0.6655 giou_loss: 0.6785 2025/05/12 03:22:38 - mmengine - INFO - Epoch(train) [47][80/91] base_lr: 2.4192e-04 lr: 2.4192e-04 eta: 3 days, 18:13:00 time: 9.6445 data_time: 0.6369 memory: 68703 grad_norm: 1.6261 loss: 2.1501 center_loss: 0.6086 size_loss: 0.1891 cls_loss: 0.6695 giou_loss: 0.6828 2025/05/12 03:24:13 - mmengine - INFO - Epoch(train) [47][90/91] base_lr: 2.4192e-04 lr: 2.4192e-04 eta: 3 days, 18:10:33 time: 9.6155 data_time: 0.6236 memory: 68702 grad_norm: 1.6659 loss: 2.1582 center_loss: 0.6129 size_loss: 0.1907 cls_loss: 0.6738 giou_loss: 0.6808 2025/05/12 03:24:15 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 03:26:46 - mmengine - INFO - Epoch(train) [48][10/91] base_lr: 2.4157e-04 lr: 2.4157e-04 eta: 3 days, 18:13:53 time: 10.5366 data_time: 1.5670 memory: 68702 grad_norm: 1.7501 loss: 2.1704 center_loss: 0.6172 size_loss: 0.1917 cls_loss: 0.6764 giou_loss: 0.6851 2025/05/12 03:28:22 - mmengine - INFO - Epoch(train) [48][20/91] base_lr: 2.4157e-04 lr: 2.4157e-04 eta: 3 days, 18:11:36 time: 10.5333 data_time: 1.5541 memory: 68701 grad_norm: 1.7599 loss: 2.1905 center_loss: 0.6238 size_loss: 0.1939 cls_loss: 0.6871 giou_loss: 0.6857 2025/05/12 03:29:59 - mmengine - INFO - Epoch(train) [48][30/91] base_lr: 2.4157e-04 lr: 2.4157e-04 eta: 3 days, 18:09:22 time: 10.5466 data_time: 1.5415 memory: 68702 grad_norm: 1.7732 loss: 2.2062 center_loss: 0.6292 size_loss: 0.1950 cls_loss: 0.6954 giou_loss: 0.6866 2025/05/12 03:31:36 - mmengine - INFO - Epoch(train) [48][40/91] base_lr: 2.4157e-04 lr: 2.4157e-04 eta: 3 days, 18:07:08 time: 10.5542 data_time: 1.5310 memory: 68702 grad_norm: 1.7128 loss: 2.1915 center_loss: 0.6256 size_loss: 0.1946 cls_loss: 0.6882 giou_loss: 0.6831 2025/05/12 03:33:13 - mmengine - INFO - Epoch(train) [48][50/91] base_lr: 2.4157e-04 lr: 2.4157e-04 eta: 3 days, 18:04:58 time: 10.7533 data_time: 1.5355 memory: 68700 grad_norm: 1.6572 loss: 2.1772 center_loss: 0.6168 size_loss: 0.1906 cls_loss: 0.6900 giou_loss: 0.6798 2025/05/12 03:34:50 - mmengine - INFO - Epoch(train) [48][60/91] base_lr: 2.4157e-04 lr: 2.4157e-04 eta: 3 days, 18:02:44 time: 9.6736 data_time: 0.5906 memory: 68702 grad_norm: 1.6633 loss: 2.1770 center_loss: 0.6213 size_loss: 0.1907 cls_loss: 0.6857 giou_loss: 0.6793 2025/05/12 03:36:26 - mmengine - INFO - Epoch(train) [48][70/91] base_lr: 2.4157e-04 lr: 2.4157e-04 eta: 3 days, 18:00:30 time: 9.6799 data_time: 0.5879 memory: 68702 grad_norm: 1.7395 loss: 2.1627 center_loss: 0.6114 size_loss: 0.1889 cls_loss: 0.6859 giou_loss: 0.6765 2025/05/12 03:38:02 - mmengine - INFO - Epoch(train) [48][80/91] base_lr: 2.4157e-04 lr: 2.4157e-04 eta: 3 days, 17:58:12 time: 9.6654 data_time: 0.5916 memory: 68702 grad_norm: 1.7743 loss: 2.1362 center_loss: 0.6000 size_loss: 0.1861 cls_loss: 0.6794 giou_loss: 0.6707 2025/05/12 03:39:37 - mmengine - INFO - Epoch(train) [48][90/91] base_lr: 2.4157e-04 lr: 2.4157e-04 eta: 3 days, 17:55:45 time: 9.6284 data_time: 0.5878 memory: 68703 grad_norm: 1.7615 loss: 2.1368 center_loss: 0.6002 size_loss: 0.1871 cls_loss: 0.6779 giou_loss: 0.6717 2025/05/12 03:39:39 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 03:39:39 - mmengine - INFO - Saving checkpoint at 48 epochs 2025/05/12 03:40:36 - mmengine - INFO - Epoch(val) [48][10/39] eta: 0:01:37 time: 2.9101 data_time: 0.3870 memory: 15952 2025/05/12 03:41:02 - mmengine - INFO - Epoch(val) [48][20/39] eta: 0:00:56 time: 2.7450 data_time: 0.2213 memory: 13407 2025/05/12 03:41:28 - mmengine - INFO - Epoch(val) [48][30/39] eta: 0:00:25 time: 2.7460 data_time: 0.2263 memory: 13407 2025/05/12 03:41:56 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.1500 | 0.3981 | 0.0103 | 0.0868 | | table | 0.3677 | 0.5029 | 0.1252 | 0.2457 | | sofa | 0.6221 | 0.8041 | 0.0956 | 0.2680 | | curtain | 0.1392 | 0.4328 | 0.0100 | 0.0597 | | chair | 0.4023 | 0.6579 | 0.0770 | 0.2449 | | picture | 0.0005 | 0.0315 | 0.0000 | 0.0000 | | door | 0.1080 | 0.3726 | 0.0064 | 0.0814 | | bookshelf | 0.2024 | 0.5584 | 0.0294 | 0.1429 | | cabinet | 0.2241 | 0.4355 | 0.0326 | 0.1290 | | window | 0.0671 | 0.2979 | 0.0034 | 0.0603 | | refrigerator | 0.4245 | 0.6667 | 0.1449 | 0.2807 | | sink | 0.4126 | 0.6224 | 0.0526 | 0.1429 | | counter | 0.0867 | 0.1154 | 0.0192 | 0.0192 | | desk | 0.6215 | 0.7795 | 0.1575 | 0.3543 | | bed | 0.7490 | 0.8272 | 0.3186 | 0.4568 | | bathtub | 0.4789 | 0.6774 | 0.1260 | 0.3226 | | toilet | 0.7132 | 0.8793 | 0.3816 | 0.5000 | | showercurtrain | 0.1752 | 0.4643 | 0.0040 | 0.0357 | +----------------+---------+---------+---------+---------+ | Overall | 0.3303 | 0.5291 | 0.0886 | 0.1906 | +----------------+---------+---------+---------+---------+ 2025/05/12 03:41:56 - mmengine - INFO - Epoch(val) [48][39/39] chair_AP_0.25: 0.4023 sofa_AP_0.25: 0.6221 table_AP_0.25: 0.3677 garbagebin_AP_0.25: 0.1500 bookshelf_AP_0.25: 0.2024 picture_AP_0.25: 0.0005 curtain_AP_0.25: 0.1392 door_AP_0.25: 0.1080 cabinet_AP_0.25: 0.2241 refrigerator_AP_0.25: 0.4245 counter_AP_0.25: 0.0867 sink_AP_0.25: 0.4126 window_AP_0.25: 0.0671 desk_AP_0.25: 0.6215 bed_AP_0.25: 0.7490 toilet_AP_0.25: 0.7132 showercurtrain_AP_0.25: 0.1752 bathtub_AP_0.25: 0.4789 mAP_0.25: 0.3303 chair_rec_0.25: 0.6579 sofa_rec_0.25: 0.8041 table_rec_0.25: 0.5029 garbagebin_rec_0.25: 0.3981 bookshelf_rec_0.25: 0.5584 picture_rec_0.25: 0.0315 curtain_rec_0.25: 0.4328 door_rec_0.25: 0.3726 cabinet_rec_0.25: 0.4355 refrigerator_rec_0.25: 0.6667 counter_rec_0.25: 0.1154 sink_rec_0.25: 0.6224 window_rec_0.25: 0.2979 desk_rec_0.25: 0.7795 bed_rec_0.25: 0.8272 toilet_rec_0.25: 0.8793 showercurtrain_rec_0.25: 0.4643 bathtub_rec_0.25: 0.6774 mAR_0.25: 0.5291 chair_AP_0.50: 0.0770 sofa_AP_0.50: 0.0956 table_AP_0.50: 0.1252 garbagebin_AP_0.50: 0.0103 bookshelf_AP_0.50: 0.0294 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0100 door_AP_0.50: 0.0064 cabinet_AP_0.50: 0.0326 refrigerator_AP_0.50: 0.1449 counter_AP_0.50: 0.0192 sink_AP_0.50: 0.0526 window_AP_0.50: 0.0034 desk_AP_0.50: 0.1575 bed_AP_0.50: 0.3186 toilet_AP_0.50: 0.3816 showercurtrain_AP_0.50: 0.0040 bathtub_AP_0.50: 0.1260 mAP_0.50: 0.0886 chair_rec_0.50: 0.2449 sofa_rec_0.50: 0.2680 table_rec_0.50: 0.2457 garbagebin_rec_0.50: 0.0868 bookshelf_rec_0.50: 0.1429 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.0597 door_rec_0.50: 0.0814 cabinet_rec_0.50: 0.1290 refrigerator_rec_0.50: 0.2807 counter_rec_0.50: 0.0192 sink_rec_0.50: 0.1429 window_rec_0.50: 0.0603 desk_rec_0.50: 0.3543 bed_rec_0.50: 0.4568 toilet_rec_0.50: 0.5000 showercurtrain_rec_0.50: 0.0357 bathtub_rec_0.50: 0.3226 mAR_0.50: 0.1906 data_time: 0.2630 time: 2.7730 2025/05/12 03:41:56 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_46.pth is removed 2025/05/12 03:42:18 - mmengine - INFO - The best checkpoint with 0.3303 mAP_0.25 at 48 epoch is saved to best_mAP_0.25_epoch_48.pth. 2025/05/12 03:45:09 - mmengine - INFO - Epoch(train) [49][10/91] base_lr: 2.4121e-04 lr: 2.4121e-04 eta: 3 days, 17:57:57 time: 10.3779 data_time: 1.4695 memory: 68702 grad_norm: 1.9110 loss: 2.1130 center_loss: 0.5935 size_loss: 0.1844 cls_loss: 0.6689 giou_loss: 0.6663 2025/05/12 03:46:46 - mmengine - INFO - Epoch(train) [49][20/91] base_lr: 2.4121e-04 lr: 2.4121e-04 eta: 3 days, 17:55:47 time: 10.3899 data_time: 1.4818 memory: 68703 grad_norm: 1.9282 loss: 2.1111 center_loss: 0.5935 size_loss: 0.1856 cls_loss: 0.6636 giou_loss: 0.6684 2025/05/12 03:48:23 - mmengine - INFO - Epoch(train) [49][30/91] base_lr: 2.4121e-04 lr: 2.4121e-04 eta: 3 days, 17:53:33 time: 10.3887 data_time: 1.4716 memory: 68702 grad_norm: 1.8034 loss: 2.1000 center_loss: 0.5900 size_loss: 0.1847 cls_loss: 0.6571 giou_loss: 0.6683 2025/05/12 03:49:59 - mmengine - INFO - Epoch(train) [49][40/91] base_lr: 2.4121e-04 lr: 2.4121e-04 eta: 3 days, 17:51:15 time: 10.3906 data_time: 1.4716 memory: 68702 grad_norm: 1.7177 loss: 2.1145 center_loss: 0.6000 size_loss: 0.1878 cls_loss: 0.6532 giou_loss: 0.6736 2025/05/12 03:51:36 - mmengine - INFO - Epoch(train) [49][50/91] base_lr: 2.4121e-04 lr: 2.4121e-04 eta: 3 days, 17:49:03 time: 10.5812 data_time: 1.4780 memory: 68703 grad_norm: 1.6903 loss: 2.1404 center_loss: 0.6110 size_loss: 0.1892 cls_loss: 0.6614 giou_loss: 0.6787 2025/05/12 03:53:13 - mmengine - INFO - Epoch(train) [49][60/91] base_lr: 2.4121e-04 lr: 2.4121e-04 eta: 3 days, 17:46:54 time: 9.6783 data_time: 0.6052 memory: 68701 grad_norm: 1.6201 loss: 2.1388 center_loss: 0.6143 size_loss: 0.1898 cls_loss: 0.6534 giou_loss: 0.6813 2025/05/12 03:54:49 - mmengine - INFO - Epoch(train) [49][70/91] base_lr: 2.4121e-04 lr: 2.4121e-04 eta: 3 days, 17:44:38 time: 9.6588 data_time: 0.5785 memory: 68702 grad_norm: 1.6850 loss: 2.1251 center_loss: 0.6085 size_loss: 0.1875 cls_loss: 0.6537 giou_loss: 0.6755 2025/05/12 03:56:25 - mmengine - INFO - Epoch(train) [49][80/91] base_lr: 2.4121e-04 lr: 2.4121e-04 eta: 3 days, 17:42:21 time: 9.6512 data_time: 0.5868 memory: 68700 grad_norm: 1.7490 loss: 2.1246 center_loss: 0.6084 size_loss: 0.1868 cls_loss: 0.6566 giou_loss: 0.6729 2025/05/12 03:58:00 - mmengine - INFO - Epoch(train) [49][90/91] base_lr: 2.4121e-04 lr: 2.4121e-04 eta: 3 days, 17:39:55 time: 9.6230 data_time: 0.5784 memory: 68702 grad_norm: 1.8001 loss: 2.1005 center_loss: 0.5954 size_loss: 0.1829 cls_loss: 0.6577 giou_loss: 0.6644 2025/05/12 03:58:02 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 04:00:34 - mmengine - INFO - Epoch(train) [50][10/91] base_lr: 2.4085e-04 lr: 2.4085e-04 eta: 3 days, 17:43:08 time: 10.5664 data_time: 1.5115 memory: 68702 grad_norm: 1.8275 loss: 2.0911 center_loss: 0.5865 size_loss: 0.1866 cls_loss: 0.6596 giou_loss: 0.6584 2025/05/12 04:02:10 - mmengine - INFO - Epoch(train) [50][20/91] base_lr: 2.4085e-04 lr: 2.4085e-04 eta: 3 days, 17:40:53 time: 10.5550 data_time: 1.5118 memory: 68702 grad_norm: 1.7978 loss: 2.1164 center_loss: 0.5961 size_loss: 0.1886 cls_loss: 0.6686 giou_loss: 0.6631 2025/05/12 04:03:46 - mmengine - INFO - Epoch(train) [50][30/91] base_lr: 2.4085e-04 lr: 2.4085e-04 eta: 3 days, 17:38:36 time: 10.5530 data_time: 1.5086 memory: 68703 grad_norm: 1.7036 loss: 2.1288 center_loss: 0.5986 size_loss: 0.1909 cls_loss: 0.6697 giou_loss: 0.6696 2025/05/12 04:05:23 - mmengine - INFO - Epoch(train) [50][40/91] base_lr: 2.4085e-04 lr: 2.4085e-04 eta: 3 days, 17:36:20 time: 10.5466 data_time: 1.5103 memory: 68703 grad_norm: 1.7726 loss: 2.1227 center_loss: 0.5997 size_loss: 0.1909 cls_loss: 0.6641 giou_loss: 0.6680 2025/05/12 04:07:00 - mmengine - INFO - Epoch(train) [50][50/91] base_lr: 2.4085e-04 lr: 2.4085e-04 eta: 3 days, 17:34:09 time: 10.7424 data_time: 1.5159 memory: 68702 grad_norm: 1.8074 loss: 2.1209 center_loss: 0.6028 size_loss: 0.1875 cls_loss: 0.6596 giou_loss: 0.6711 2025/05/12 04:08:36 - mmengine - INFO - Epoch(train) [50][60/91] base_lr: 2.4085e-04 lr: 2.4085e-04 eta: 3 days, 17:31:55 time: 9.6392 data_time: 0.5891 memory: 68702 grad_norm: 1.7717 loss: 2.1478 center_loss: 0.6146 size_loss: 0.1907 cls_loss: 0.6655 giou_loss: 0.6771 2025/05/12 04:10:12 - mmengine - INFO - Epoch(train) [50][70/91] base_lr: 2.4085e-04 lr: 2.4085e-04 eta: 3 days, 17:29:40 time: 9.6389 data_time: 0.5919 memory: 68702 grad_norm: 1.7513 loss: 2.1266 center_loss: 0.6017 size_loss: 0.1869 cls_loss: 0.6674 giou_loss: 0.6706 2025/05/12 04:11:48 - mmengine - INFO - Epoch(train) [50][80/91] base_lr: 2.4085e-04 lr: 2.4085e-04 eta: 3 days, 17:27:21 time: 9.6291 data_time: 0.5945 memory: 68702 grad_norm: 1.8022 loss: 2.1453 center_loss: 0.6134 size_loss: 0.1889 cls_loss: 0.6710 giou_loss: 0.6720 2025/05/12 04:13:22 - mmengine - INFO - Epoch(train) [50][90/91] base_lr: 2.4085e-04 lr: 2.4085e-04 eta: 3 days, 17:24:54 time: 9.5965 data_time: 0.5900 memory: 68702 grad_norm: 1.8203 loss: 2.1649 center_loss: 0.6176 size_loss: 0.1901 cls_loss: 0.6795 giou_loss: 0.6777 2025/05/12 04:13:24 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 04:13:24 - mmengine - INFO - Saving checkpoint at 50 epochs 2025/05/12 04:14:20 - mmengine - INFO - Epoch(val) [50][10/39] eta: 0:01:31 time: 2.8526 data_time: 0.3449 memory: 15952 2025/05/12 04:14:45 - mmengine - INFO - Epoch(val) [50][20/39] eta: 0:00:54 time: 2.6986 data_time: 0.2005 memory: 13407 2025/05/12 04:15:11 - mmengine - INFO - Epoch(val) [50][30/39] eta: 0:00:24 time: 2.6952 data_time: 0.2011 memory: 13407 2025/05/12 04:15:38 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | sofa | 0.5961 | 0.8041 | 0.1335 | 0.3505 | | garbagebin | 0.1794 | 0.4113 | 0.0106 | 0.0868 | | chair | 0.4287 | 0.6601 | 0.0703 | 0.2259 | | table | 0.3439 | 0.5086 | 0.0888 | 0.2057 | | curtain | 0.1918 | 0.4627 | 0.0331 | 0.1343 | | door | 0.1397 | 0.3790 | 0.0097 | 0.0707 | | bookshelf | 0.2556 | 0.6104 | 0.0366 | 0.1948 | | picture | 0.0008 | 0.0315 | 0.0001 | 0.0045 | | showercurtrain | 0.2282 | 0.5357 | 0.0083 | 0.0714 | | cabinet | 0.2252 | 0.4462 | 0.0479 | 0.1505 | | window | 0.0709 | 0.2518 | 0.0040 | 0.0390 | | refrigerator | 0.3453 | 0.5614 | 0.0926 | 0.2281 | | sink | 0.3524 | 0.5306 | 0.0430 | 0.1327 | | counter | 0.0582 | 0.1346 | 0.0247 | 0.0577 | | toilet | 0.7098 | 0.8448 | 0.2819 | 0.3966 | | bed | 0.8280 | 0.8642 | 0.3112 | 0.4815 | | desk | 0.6096 | 0.8425 | 0.1439 | 0.3465 | | bathtub | 0.5629 | 0.7097 | 0.1177 | 0.2581 | +----------------+---------+---------+---------+---------+ | Overall | 0.3404 | 0.5327 | 0.0810 | 0.1908 | +----------------+---------+---------+---------+---------+ 2025/05/12 04:15:38 - mmengine - INFO - Epoch(val) [50][39/39] chair_AP_0.25: 0.4287 sofa_AP_0.25: 0.5961 table_AP_0.25: 0.3439 garbagebin_AP_0.25: 0.1794 bookshelf_AP_0.25: 0.2556 picture_AP_0.25: 0.0008 curtain_AP_0.25: 0.1918 door_AP_0.25: 0.1397 cabinet_AP_0.25: 0.2252 refrigerator_AP_0.25: 0.3453 counter_AP_0.25: 0.0582 sink_AP_0.25: 0.3524 window_AP_0.25: 0.0709 desk_AP_0.25: 0.6096 bed_AP_0.25: 0.8280 toilet_AP_0.25: 0.7098 showercurtrain_AP_0.25: 0.2282 bathtub_AP_0.25: 0.5629 mAP_0.25: 0.3404 chair_rec_0.25: 0.6601 sofa_rec_0.25: 0.8041 table_rec_0.25: 0.5086 garbagebin_rec_0.25: 0.4113 bookshelf_rec_0.25: 0.6104 picture_rec_0.25: 0.0315 curtain_rec_0.25: 0.4627 door_rec_0.25: 0.3790 cabinet_rec_0.25: 0.4462 refrigerator_rec_0.25: 0.5614 counter_rec_0.25: 0.1346 sink_rec_0.25: 0.5306 window_rec_0.25: 0.2518 desk_rec_0.25: 0.8425 bed_rec_0.25: 0.8642 toilet_rec_0.25: 0.8448 showercurtrain_rec_0.25: 0.5357 bathtub_rec_0.25: 0.7097 mAR_0.25: 0.5327 chair_AP_0.50: 0.0703 sofa_AP_0.50: 0.1335 table_AP_0.50: 0.0888 garbagebin_AP_0.50: 0.0106 bookshelf_AP_0.50: 0.0366 picture_AP_0.50: 0.0001 curtain_AP_0.50: 0.0331 door_AP_0.50: 0.0097 cabinet_AP_0.50: 0.0479 refrigerator_AP_0.50: 0.0926 counter_AP_0.50: 0.0247 sink_AP_0.50: 0.0430 window_AP_0.50: 0.0040 desk_AP_0.50: 0.1439 bed_AP_0.50: 0.3112 toilet_AP_0.50: 0.2819 showercurtrain_AP_0.50: 0.0083 bathtub_AP_0.50: 0.1177 mAP_0.50: 0.0810 chair_rec_0.50: 0.2259 sofa_rec_0.50: 0.3505 table_rec_0.50: 0.2057 garbagebin_rec_0.50: 0.0868 bookshelf_rec_0.50: 0.1948 picture_rec_0.50: 0.0045 curtain_rec_0.50: 0.1343 door_rec_0.50: 0.0707 cabinet_rec_0.50: 0.1505 refrigerator_rec_0.50: 0.2281 counter_rec_0.50: 0.0577 sink_rec_0.50: 0.1327 window_rec_0.50: 0.0390 desk_rec_0.50: 0.3465 bed_rec_0.50: 0.4815 toilet_rec_0.50: 0.3966 showercurtrain_rec_0.50: 0.0714 bathtub_rec_0.50: 0.2581 mAR_0.50: 0.1908 data_time: 0.2246 time: 2.7131 2025/05/12 04:15:38 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_48.pth is removed 2025/05/12 04:16:00 - mmengine - INFO - The best checkpoint with 0.3404 mAP_0.25 at 50 epoch is saved to best_mAP_0.25_epoch_50.pth. 2025/05/12 04:18:55 - mmengine - INFO - Epoch(train) [51][10/91] base_lr: 2.4048e-04 lr: 2.4048e-04 eta: 3 days, 17:27:11 time: 10.4019 data_time: 1.5248 memory: 68702 grad_norm: 1.8203 loss: 2.1793 center_loss: 0.6193 size_loss: 0.1910 cls_loss: 0.6875 giou_loss: 0.6814 2025/05/12 04:20:32 - mmengine - INFO - Epoch(train) [51][20/91] base_lr: 2.4048e-04 lr: 2.4048e-04 eta: 3 days, 17:25:01 time: 10.4120 data_time: 1.5049 memory: 68702 grad_norm: 1.8079 loss: 2.1518 center_loss: 0.6069 size_loss: 0.1869 cls_loss: 0.6836 giou_loss: 0.6744 2025/05/12 04:22:09 - mmengine - INFO - Epoch(train) [51][30/91] base_lr: 2.4048e-04 lr: 2.4048e-04 eta: 3 days, 17:22:53 time: 10.4345 data_time: 1.4989 memory: 68700 grad_norm: 1.9006 loss: 2.1583 center_loss: 0.6144 size_loss: 0.1887 cls_loss: 0.6780 giou_loss: 0.6772 2025/05/12 04:23:46 - mmengine - INFO - Epoch(train) [51][40/91] base_lr: 2.4048e-04 lr: 2.4048e-04 eta: 3 days, 17:20:43 time: 10.4610 data_time: 1.4865 memory: 68703 grad_norm: 1.8090 loss: 2.1292 center_loss: 0.6019 size_loss: 0.1839 cls_loss: 0.6708 giou_loss: 0.6726 2025/05/12 04:25:23 - mmengine - INFO - Epoch(train) [51][50/91] base_lr: 2.4048e-04 lr: 2.4048e-04 eta: 3 days, 17:18:36 time: 10.6673 data_time: 1.4910 memory: 68702 grad_norm: 1.7228 loss: 2.1183 center_loss: 0.5987 size_loss: 0.1852 cls_loss: 0.6660 giou_loss: 0.6683 2025/05/12 04:27:00 - mmengine - INFO - Epoch(train) [51][60/91] base_lr: 2.4048e-04 lr: 2.4048e-04 eta: 3 days, 17:16:27 time: 9.7107 data_time: 0.5443 memory: 68703 grad_norm: 1.6531 loss: 2.1019 center_loss: 0.5945 size_loss: 0.1830 cls_loss: 0.6590 giou_loss: 0.6655 2025/05/12 04:28:38 - mmengine - INFO - Epoch(train) [51][70/91] base_lr: 2.4048e-04 lr: 2.4048e-04 eta: 3 days, 17:14:20 time: 9.7218 data_time: 0.5608 memory: 68703 grad_norm: 1.6533 loss: 2.1171 center_loss: 0.6052 size_loss: 0.1847 cls_loss: 0.6583 giou_loss: 0.6689 2025/05/12 04:30:15 - mmengine - INFO - Epoch(train) [51][80/91] base_lr: 2.4048e-04 lr: 2.4048e-04 eta: 3 days, 17:12:14 time: 9.7246 data_time: 0.5657 memory: 68702 grad_norm: 1.5354 loss: 2.0903 center_loss: 0.5927 size_loss: 0.1812 cls_loss: 0.6546 giou_loss: 0.6619 2025/05/12 04:31:51 - mmengine - INFO - Epoch(train) [51][90/91] base_lr: 2.4048e-04 lr: 2.4048e-04 eta: 3 days, 17:10:00 time: 9.7074 data_time: 0.5792 memory: 68702 grad_norm: 1.6147 loss: 2.0926 center_loss: 0.5902 size_loss: 0.1835 cls_loss: 0.6561 giou_loss: 0.6627 2025/05/12 04:31:54 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 04:34:19 - mmengine - INFO - Epoch(train) [52][10/91] base_lr: 2.4010e-04 lr: 2.4010e-04 eta: 3 days, 17:12:17 time: 10.5162 data_time: 1.4115 memory: 68702 grad_norm: 1.6962 loss: 2.1372 center_loss: 0.6154 size_loss: 0.1949 cls_loss: 0.6587 giou_loss: 0.6682 2025/05/12 04:35:56 - mmengine - INFO - Epoch(train) [52][20/91] base_lr: 2.4010e-04 lr: 2.4010e-04 eta: 3 days, 17:10:06 time: 10.5155 data_time: 1.4282 memory: 68702 grad_norm: 1.7752 loss: 2.1386 center_loss: 0.6168 size_loss: 0.1961 cls_loss: 0.6600 giou_loss: 0.6657 2025/05/12 04:37:33 - mmengine - INFO - Epoch(train) [52][30/91] base_lr: 2.4010e-04 lr: 2.4010e-04 eta: 3 days, 17:07:56 time: 10.5043 data_time: 1.4245 memory: 68702 grad_norm: 1.8208 loss: 2.1361 center_loss: 0.6156 size_loss: 0.1961 cls_loss: 0.6579 giou_loss: 0.6665 2025/05/12 04:39:10 - mmengine - INFO - Epoch(train) [52][40/91] base_lr: 2.4010e-04 lr: 2.4010e-04 eta: 3 days, 17:05:48 time: 10.4953 data_time: 1.4021 memory: 68702 grad_norm: 1.8904 loss: 2.1434 center_loss: 0.6219 size_loss: 0.1976 cls_loss: 0.6571 giou_loss: 0.6669 2025/05/12 04:40:47 - mmengine - INFO - Epoch(train) [52][50/91] base_lr: 2.4010e-04 lr: 2.4010e-04 eta: 3 days, 17:03:39 time: 10.6647 data_time: 1.4120 memory: 68702 grad_norm: 1.7818 loss: 2.0942 center_loss: 0.5956 size_loss: 0.1837 cls_loss: 0.6541 giou_loss: 0.6608 2025/05/12 04:42:24 - mmengine - INFO - Epoch(train) [52][60/91] base_lr: 2.4010e-04 lr: 2.4010e-04 eta: 3 days, 17:01:30 time: 9.6946 data_time: 0.5933 memory: 68703 grad_norm: 1.7305 loss: 2.0853 center_loss: 0.5915 size_loss: 0.1819 cls_loss: 0.6533 giou_loss: 0.6587 2025/05/12 04:44:01 - mmengine - INFO - Epoch(train) [52][70/91] base_lr: 2.4010e-04 lr: 2.4010e-04 eta: 3 days, 16:59:22 time: 9.7029 data_time: 0.5813 memory: 68702 grad_norm: 1.6627 loss: 2.0825 center_loss: 0.5855 size_loss: 0.1827 cls_loss: 0.6551 giou_loss: 0.6592 2025/05/12 04:45:38 - mmengine - INFO - Epoch(train) [52][80/91] base_lr: 2.4010e-04 lr: 2.4010e-04 eta: 3 days, 16:57:11 time: 9.6971 data_time: 0.5894 memory: 68703 grad_norm: 1.7372 loss: 2.0835 center_loss: 0.5855 size_loss: 0.1816 cls_loss: 0.6600 giou_loss: 0.6563 2025/05/12 04:47:14 - mmengine - INFO - Epoch(train) [52][90/91] base_lr: 2.4010e-04 lr: 2.4010e-04 eta: 3 days, 16:54:55 time: 9.6736 data_time: 0.5939 memory: 68702 grad_norm: 1.7344 loss: 2.0983 center_loss: 0.5898 size_loss: 0.1827 cls_loss: 0.6655 giou_loss: 0.6604 2025/05/12 04:47:16 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 04:47:16 - mmengine - INFO - Saving checkpoint at 52 epochs 2025/05/12 04:48:17 - mmengine - INFO - Epoch(val) [52][10/39] eta: 0:01:37 time: 2.8443 data_time: 0.3473 memory: 15952 2025/05/12 04:48:42 - mmengine - INFO - Epoch(val) [52][20/39] eta: 0:00:56 time: 2.7262 data_time: 0.2270 memory: 13407 2025/05/12 04:49:08 - mmengine - INFO - Epoch(val) [52][30/39] eta: 0:00:25 time: 2.7311 data_time: 0.2274 memory: 13407 2025/05/12 04:49:36 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | curtain | 0.1320 | 0.3881 | 0.0136 | 0.1045 | | table | 0.3827 | 0.5371 | 0.0932 | 0.2200 | | garbagebin | 0.1739 | 0.3811 | 0.0154 | 0.0943 | | sofa | 0.6123 | 0.7938 | 0.0812 | 0.2680 | | chair | 0.4475 | 0.6835 | 0.0730 | 0.2266 | | picture | 0.0016 | 0.0270 | 0.0000 | 0.0000 | | bookshelf | 0.1731 | 0.4935 | 0.0410 | 0.1818 | | door | 0.0981 | 0.3533 | 0.0039 | 0.0621 | | cabinet | 0.2009 | 0.4274 | 0.0341 | 0.1425 | | window | 0.0625 | 0.2553 | 0.0040 | 0.0496 | | refrigerator | 0.4435 | 0.6140 | 0.1688 | 0.2807 | | sink | 0.3750 | 0.5306 | 0.0328 | 0.1633 | | toilet | 0.7494 | 0.9483 | 0.2899 | 0.4655 | | counter | 0.0785 | 0.1346 | 0.0032 | 0.0192 | | bed | 0.7781 | 0.8395 | 0.3633 | 0.5062 | | desk | 0.6447 | 0.8583 | 0.1461 | 0.3150 | | bathtub | 0.7424 | 0.8065 | 0.1052 | 0.2903 | | showercurtrain | 0.2550 | 0.5714 | 0.0026 | 0.0357 | +----------------+---------+---------+---------+---------+ | Overall | 0.3528 | 0.5357 | 0.0817 | 0.1903 | +----------------+---------+---------+---------+---------+ 2025/05/12 04:49:36 - mmengine - INFO - Epoch(val) [52][39/39] chair_AP_0.25: 0.4475 sofa_AP_0.25: 0.6123 table_AP_0.25: 0.3827 garbagebin_AP_0.25: 0.1739 bookshelf_AP_0.25: 0.1731 picture_AP_0.25: 0.0016 curtain_AP_0.25: 0.1320 door_AP_0.25: 0.0981 cabinet_AP_0.25: 0.2009 refrigerator_AP_0.25: 0.4435 counter_AP_0.25: 0.0785 sink_AP_0.25: 0.3750 window_AP_0.25: 0.0625 desk_AP_0.25: 0.6447 bed_AP_0.25: 0.7781 toilet_AP_0.25: 0.7494 showercurtrain_AP_0.25: 0.2550 bathtub_AP_0.25: 0.7424 mAP_0.25: 0.3528 chair_rec_0.25: 0.6835 sofa_rec_0.25: 0.7938 table_rec_0.25: 0.5371 garbagebin_rec_0.25: 0.3811 bookshelf_rec_0.25: 0.4935 picture_rec_0.25: 0.0270 curtain_rec_0.25: 0.3881 door_rec_0.25: 0.3533 cabinet_rec_0.25: 0.4274 refrigerator_rec_0.25: 0.6140 counter_rec_0.25: 0.1346 sink_rec_0.25: 0.5306 window_rec_0.25: 0.2553 desk_rec_0.25: 0.8583 bed_rec_0.25: 0.8395 toilet_rec_0.25: 0.9483 showercurtrain_rec_0.25: 0.5714 bathtub_rec_0.25: 0.8065 mAR_0.25: 0.5357 chair_AP_0.50: 0.0730 sofa_AP_0.50: 0.0812 table_AP_0.50: 0.0932 garbagebin_AP_0.50: 0.0154 bookshelf_AP_0.50: 0.0410 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0136 door_AP_0.50: 0.0039 cabinet_AP_0.50: 0.0341 refrigerator_AP_0.50: 0.1688 counter_AP_0.50: 0.0032 sink_AP_0.50: 0.0328 window_AP_0.50: 0.0040 desk_AP_0.50: 0.1461 bed_AP_0.50: 0.3633 toilet_AP_0.50: 0.2899 showercurtrain_AP_0.50: 0.0026 bathtub_AP_0.50: 0.1052 mAP_0.50: 0.0817 chair_rec_0.50: 0.2266 sofa_rec_0.50: 0.2680 table_rec_0.50: 0.2200 garbagebin_rec_0.50: 0.0943 bookshelf_rec_0.50: 0.1818 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.1045 door_rec_0.50: 0.0621 cabinet_rec_0.50: 0.1425 refrigerator_rec_0.50: 0.2807 counter_rec_0.50: 0.0192 sink_rec_0.50: 0.1633 window_rec_0.50: 0.0496 desk_rec_0.50: 0.3150 bed_rec_0.50: 0.5062 toilet_rec_0.50: 0.4655 showercurtrain_rec_0.50: 0.0357 bathtub_rec_0.50: 0.2903 mAR_0.50: 0.1903 data_time: 0.2639 time: 2.7678 2025/05/12 04:49:36 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_50.pth is removed 2025/05/12 04:50:03 - mmengine - INFO - The best checkpoint with 0.3528 mAP_0.25 at 52 epoch is saved to best_mAP_0.25_epoch_52.pth. 2025/05/12 04:52:58 - mmengine - INFO - Epoch(train) [53][10/91] base_lr: 2.3971e-04 lr: 2.3971e-04 eta: 3 days, 16:57:00 time: 10.4723 data_time: 1.5348 memory: 68702 grad_norm: 1.7264 loss: 2.1307 center_loss: 0.6009 size_loss: 0.1856 cls_loss: 0.6785 giou_loss: 0.6658 2025/05/12 04:54:34 - mmengine - INFO - Epoch(train) [53][20/91] base_lr: 2.3971e-04 lr: 2.3971e-04 eta: 3 days, 16:54:47 time: 10.4537 data_time: 1.5173 memory: 68702 grad_norm: 1.7190 loss: 2.1269 center_loss: 0.5995 size_loss: 0.1839 cls_loss: 0.6761 giou_loss: 0.6674 2025/05/12 04:56:11 - mmengine - INFO - Epoch(train) [53][30/91] base_lr: 2.3971e-04 lr: 2.3971e-04 eta: 3 days, 16:52:35 time: 10.4455 data_time: 1.5137 memory: 68701 grad_norm: 1.7147 loss: 2.1254 center_loss: 0.6010 size_loss: 0.1819 cls_loss: 0.6752 giou_loss: 0.6672 2025/05/12 04:57:47 - mmengine - INFO - Epoch(train) [53][40/91] base_lr: 2.3971e-04 lr: 2.3971e-04 eta: 3 days, 16:50:23 time: 10.4402 data_time: 1.5102 memory: 68703 grad_norm: 1.6379 loss: 2.1164 center_loss: 0.5956 size_loss: 0.1829 cls_loss: 0.6711 giou_loss: 0.6668 2025/05/12 04:59:25 - mmengine - INFO - Epoch(train) [53][50/91] base_lr: 2.3971e-04 lr: 2.3971e-04 eta: 3 days, 16:48:18 time: 10.6265 data_time: 1.5238 memory: 68702 grad_norm: 1.5766 loss: 2.1173 center_loss: 0.5948 size_loss: 0.1826 cls_loss: 0.6735 giou_loss: 0.6664 2025/05/12 05:01:02 - mmengine - INFO - Epoch(train) [53][60/91] base_lr: 2.3971e-04 lr: 2.3971e-04 eta: 3 days, 16:46:11 time: 9.6798 data_time: 0.5893 memory: 68701 grad_norm: 1.5939 loss: 2.1077 center_loss: 0.5946 size_loss: 0.1829 cls_loss: 0.6664 giou_loss: 0.6638 2025/05/12 05:02:40 - mmengine - INFO - Epoch(train) [53][70/91] base_lr: 2.3971e-04 lr: 2.3971e-04 eta: 3 days, 16:44:14 time: 9.7284 data_time: 0.6171 memory: 68703 grad_norm: 1.5855 loss: 2.1048 center_loss: 0.5935 size_loss: 0.1841 cls_loss: 0.6659 giou_loss: 0.6613 2025/05/12 05:04:17 - mmengine - INFO - Epoch(train) [53][80/91] base_lr: 2.3971e-04 lr: 2.3971e-04 eta: 3 days, 16:42:06 time: 9.7377 data_time: 0.6465 memory: 68703 grad_norm: 1.6253 loss: 2.0875 center_loss: 0.5857 size_loss: 0.1844 cls_loss: 0.6585 giou_loss: 0.6590 2025/05/12 05:05:54 - mmengine - INFO - Epoch(train) [53][90/91] base_lr: 2.3971e-04 lr: 2.3971e-04 eta: 3 days, 16:39:57 time: 9.7466 data_time: 0.6673 memory: 68703 grad_norm: 1.6634 loss: 2.1061 center_loss: 0.5999 size_loss: 0.1848 cls_loss: 0.6587 giou_loss: 0.6626 2025/05/12 05:05:56 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 05:08:27 - mmengine - INFO - Epoch(train) [54][10/91] base_lr: 2.3932e-04 lr: 2.3932e-04 eta: 3 days, 16:42:39 time: 10.6628 data_time: 1.5758 memory: 68702 grad_norm: 1.9839 loss: 2.0965 center_loss: 0.5967 size_loss: 0.1815 cls_loss: 0.6566 giou_loss: 0.6617 2025/05/12 05:10:06 - mmengine - INFO - Epoch(train) [54][20/91] base_lr: 2.3932e-04 lr: 2.3932e-04 eta: 3 days, 16:40:44 time: 10.6978 data_time: 1.6052 memory: 68703 grad_norm: 2.0142 loss: 2.0900 center_loss: 0.5931 size_loss: 0.1815 cls_loss: 0.6554 giou_loss: 0.6600 2025/05/12 05:11:45 - mmengine - INFO - Epoch(train) [54][30/91] base_lr: 2.3932e-04 lr: 2.3932e-04 eta: 3 days, 16:38:45 time: 10.6918 data_time: 1.5974 memory: 68702 grad_norm: 2.0611 loss: 2.1098 center_loss: 0.6022 size_loss: 0.1838 cls_loss: 0.6589 giou_loss: 0.6649 2025/05/12 05:13:22 - mmengine - INFO - Epoch(train) [54][40/91] base_lr: 2.3932e-04 lr: 2.3932e-04 eta: 3 days, 16:36:41 time: 10.7042 data_time: 1.5841 memory: 68702 grad_norm: 2.0395 loss: 2.0924 center_loss: 0.5967 size_loss: 0.1802 cls_loss: 0.6562 giou_loss: 0.6593 2025/05/12 05:15:01 - mmengine - INFO - Epoch(train) [54][50/91] base_lr: 2.3932e-04 lr: 2.3932e-04 eta: 3 days, 16:34:43 time: 10.8891 data_time: 1.5918 memory: 68702 grad_norm: 1.9484 loss: 2.0829 center_loss: 0.5878 size_loss: 0.1798 cls_loss: 0.6569 giou_loss: 0.6584 2025/05/12 05:16:39 - mmengine - INFO - Epoch(train) [54][60/91] base_lr: 2.3932e-04 lr: 2.3932e-04 eta: 3 days, 16:32:41 time: 9.8323 data_time: 0.7005 memory: 68703 grad_norm: 1.7660 loss: 2.0927 center_loss: 0.5953 size_loss: 0.1808 cls_loss: 0.6556 giou_loss: 0.6611 2025/05/12 05:18:16 - mmengine - INFO - Epoch(train) [54][70/91] base_lr: 2.3932e-04 lr: 2.3932e-04 eta: 3 days, 16:30:36 time: 9.8000 data_time: 0.6746 memory: 68702 grad_norm: 1.7868 loss: 2.1023 center_loss: 0.6009 size_loss: 0.1801 cls_loss: 0.6581 giou_loss: 0.6633 2025/05/12 05:19:54 - mmengine - INFO - Epoch(train) [54][80/91] base_lr: 2.3932e-04 lr: 2.3932e-04 eta: 3 days, 16:28:31 time: 9.7785 data_time: 0.6797 memory: 68702 grad_norm: 1.7855 loss: 2.0872 center_loss: 0.5940 size_loss: 0.1788 cls_loss: 0.6542 giou_loss: 0.6602 2025/05/12 05:21:30 - mmengine - INFO - Epoch(train) [54][90/91] base_lr: 2.3932e-04 lr: 2.3932e-04 eta: 3 days, 16:26:21 time: 9.7581 data_time: 0.6840 memory: 68703 grad_norm: 1.8059 loss: 2.1233 center_loss: 0.6091 size_loss: 0.1842 cls_loss: 0.6617 giou_loss: 0.6683 2025/05/12 05:21:32 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 05:21:32 - mmengine - INFO - Saving checkpoint at 54 epochs 2025/05/12 05:22:29 - mmengine - INFO - Epoch(val) [54][10/39] eta: 0:01:35 time: 2.8749 data_time: 0.3641 memory: 15952 2025/05/12 05:22:56 - mmengine - INFO - Epoch(val) [54][20/39] eta: 0:00:56 time: 2.7305 data_time: 0.2220 memory: 13407 2025/05/12 05:23:22 - mmengine - INFO - Epoch(val) [54][30/39] eta: 0:00:25 time: 2.7403 data_time: 0.2248 memory: 13407 2025/05/12 05:23:50 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | sofa | 0.6490 | 0.8557 | 0.0772 | 0.2577 | | table | 0.3736 | 0.5143 | 0.1031 | 0.2400 | | garbagebin | 0.1589 | 0.3604 | 0.0091 | 0.0774 | | curtain | 0.1739 | 0.4030 | 0.0216 | 0.1194 | | chair | 0.4053 | 0.6652 | 0.0725 | 0.2310 | | picture | 0.0014 | 0.0450 | 0.0000 | 0.0000 | | bookshelf | 0.2107 | 0.5584 | 0.0234 | 0.1688 | | door | 0.0971 | 0.3383 | 0.0069 | 0.0771 | | window | 0.0829 | 0.3121 | 0.0074 | 0.0496 | | cabinet | 0.2058 | 0.4274 | 0.0253 | 0.1210 | | refrigerator | 0.3923 | 0.5965 | 0.1495 | 0.2456 | | sink | 0.4100 | 0.5918 | 0.0330 | 0.1531 | | counter | 0.0654 | 0.1346 | 0.0048 | 0.0192 | | desk | 0.5910 | 0.7717 | 0.1739 | 0.3858 | | bed | 0.7747 | 0.8272 | 0.2841 | 0.4691 | | toilet | 0.8396 | 0.9310 | 0.2718 | 0.4138 | | bathtub | 0.6289 | 0.7742 | 0.1378 | 0.2903 | | showercurtrain | 0.2734 | 0.5000 | 0.0010 | 0.0357 | +----------------+---------+---------+---------+---------+ | Overall | 0.3519 | 0.5337 | 0.0779 | 0.1864 | +----------------+---------+---------+---------+---------+ 2025/05/12 05:23:50 - mmengine - INFO - Epoch(val) [54][39/39] chair_AP_0.25: 0.4053 sofa_AP_0.25: 0.6490 table_AP_0.25: 0.3736 garbagebin_AP_0.25: 0.1589 bookshelf_AP_0.25: 0.2107 picture_AP_0.25: 0.0014 curtain_AP_0.25: 0.1739 door_AP_0.25: 0.0971 cabinet_AP_0.25: 0.2058 refrigerator_AP_0.25: 0.3923 counter_AP_0.25: 0.0654 sink_AP_0.25: 0.4100 window_AP_0.25: 0.0829 desk_AP_0.25: 0.5910 bed_AP_0.25: 0.7747 toilet_AP_0.25: 0.8396 showercurtrain_AP_0.25: 0.2734 bathtub_AP_0.25: 0.6289 mAP_0.25: 0.3519 chair_rec_0.25: 0.6652 sofa_rec_0.25: 0.8557 table_rec_0.25: 0.5143 garbagebin_rec_0.25: 0.3604 bookshelf_rec_0.25: 0.5584 picture_rec_0.25: 0.0450 curtain_rec_0.25: 0.4030 door_rec_0.25: 0.3383 cabinet_rec_0.25: 0.4274 refrigerator_rec_0.25: 0.5965 counter_rec_0.25: 0.1346 sink_rec_0.25: 0.5918 window_rec_0.25: 0.3121 desk_rec_0.25: 0.7717 bed_rec_0.25: 0.8272 toilet_rec_0.25: 0.9310 showercurtrain_rec_0.25: 0.5000 bathtub_rec_0.25: 0.7742 mAR_0.25: 0.5337 chair_AP_0.50: 0.0725 sofa_AP_0.50: 0.0772 table_AP_0.50: 0.1031 garbagebin_AP_0.50: 0.0091 bookshelf_AP_0.50: 0.0234 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0216 door_AP_0.50: 0.0069 cabinet_AP_0.50: 0.0253 refrigerator_AP_0.50: 0.1495 counter_AP_0.50: 0.0048 sink_AP_0.50: 0.0330 window_AP_0.50: 0.0074 desk_AP_0.50: 0.1739 bed_AP_0.50: 0.2841 toilet_AP_0.50: 0.2718 showercurtrain_AP_0.50: 0.0010 bathtub_AP_0.50: 0.1378 mAP_0.50: 0.0779 chair_rec_0.50: 0.2310 sofa_rec_0.50: 0.2577 table_rec_0.50: 0.2400 garbagebin_rec_0.50: 0.0774 bookshelf_rec_0.50: 0.1688 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.1194 door_rec_0.50: 0.0771 cabinet_rec_0.50: 0.1210 refrigerator_rec_0.50: 0.2456 counter_rec_0.50: 0.0192 sink_rec_0.50: 0.1531 window_rec_0.50: 0.0496 desk_rec_0.50: 0.3858 bed_rec_0.50: 0.4691 toilet_rec_0.50: 0.4138 showercurtrain_rec_0.50: 0.0357 bathtub_rec_0.50: 0.2903 mAR_0.50: 0.1864 data_time: 0.2635 time: 2.7828 2025/05/12 05:26:18 - mmengine - INFO - Epoch(train) [55][10/91] base_lr: 2.3892e-04 lr: 2.3892e-04 eta: 3 days, 16:28:37 time: 10.5943 data_time: 1.5999 memory: 68702 grad_norm: 1.7864 loss: 2.1645 center_loss: 0.6287 size_loss: 0.1884 cls_loss: 0.6714 giou_loss: 0.6760 2025/05/12 05:27:54 - mmengine - INFO - Epoch(train) [55][20/91] base_lr: 2.3892e-04 lr: 2.3892e-04 eta: 3 days, 16:26:26 time: 10.5650 data_time: 1.5787 memory: 68702 grad_norm: 1.7747 loss: 2.1705 center_loss: 0.6294 size_loss: 0.1901 cls_loss: 0.6712 giou_loss: 0.6798 2025/05/12 05:29:31 - mmengine - INFO - Epoch(train) [55][30/91] base_lr: 2.3892e-04 lr: 2.3892e-04 eta: 3 days, 16:24:18 time: 10.5516 data_time: 1.5694 memory: 68702 grad_norm: 1.7406 loss: 2.1639 center_loss: 0.6238 size_loss: 0.1894 cls_loss: 0.6737 giou_loss: 0.6769 2025/05/12 05:31:09 - mmengine - INFO - Epoch(train) [55][40/91] base_lr: 2.3892e-04 lr: 2.3892e-04 eta: 3 days, 16:22:17 time: 10.5643 data_time: 1.5769 memory: 68702 grad_norm: 1.7595 loss: 2.1747 center_loss: 0.6303 size_loss: 0.1904 cls_loss: 0.6762 giou_loss: 0.6779 2025/05/12 05:32:48 - mmengine - INFO - Epoch(train) [55][50/91] base_lr: 2.3892e-04 lr: 2.3892e-04 eta: 3 days, 16:20:19 time: 10.7568 data_time: 1.5922 memory: 68702 grad_norm: 1.6398 loss: 2.1252 center_loss: 0.6086 size_loss: 0.1841 cls_loss: 0.6654 giou_loss: 0.6670 2025/05/12 05:34:26 - mmengine - INFO - Epoch(train) [55][60/91] base_lr: 2.3892e-04 lr: 2.3892e-04 eta: 3 days, 16:18:18 time: 9.7613 data_time: 0.6737 memory: 68702 grad_norm: 1.6733 loss: 2.0772 center_loss: 0.5878 size_loss: 0.1790 cls_loss: 0.6514 giou_loss: 0.6589 2025/05/12 05:36:04 - mmengine - INFO - Epoch(train) [55][70/91] base_lr: 2.3892e-04 lr: 2.3892e-04 eta: 3 days, 16:16:18 time: 9.7948 data_time: 0.6911 memory: 68702 grad_norm: 1.7126 loss: 2.0630 center_loss: 0.5834 size_loss: 0.1769 cls_loss: 0.6505 giou_loss: 0.6521 2025/05/12 05:37:42 - mmengine - INFO - Epoch(train) [55][80/91] base_lr: 2.3892e-04 lr: 2.3892e-04 eta: 3 days, 16:14:15 time: 9.8081 data_time: 0.7064 memory: 68702 grad_norm: 1.6940 loss: 2.0604 center_loss: 0.5820 size_loss: 0.1764 cls_loss: 0.6484 giou_loss: 0.6536 2025/05/12 05:38:40 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 05:39:18 - mmengine - INFO - Epoch(train) [55][90/91] base_lr: 2.3892e-04 lr: 2.3892e-04 eta: 3 days, 16:12:04 time: 9.7771 data_time: 0.6849 memory: 68702 grad_norm: 1.6688 loss: 2.0344 center_loss: 0.5715 size_loss: 0.1735 cls_loss: 0.6383 giou_loss: 0.6510 2025/05/12 05:39:20 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 05:41:46 - mmengine - INFO - Epoch(train) [56][10/91] base_lr: 2.3851e-04 lr: 2.3851e-04 eta: 3 days, 16:14:04 time: 10.5769 data_time: 1.5906 memory: 68702 grad_norm: 1.8995 loss: 2.0303 center_loss: 0.5698 size_loss: 0.1745 cls_loss: 0.6370 giou_loss: 0.6489 2025/05/12 05:43:24 - mmengine - INFO - Epoch(train) [56][20/91] base_lr: 2.3851e-04 lr: 2.3851e-04 eta: 3 days, 16:11:59 time: 10.5653 data_time: 1.5745 memory: 68703 grad_norm: 1.9225 loss: 2.0408 center_loss: 0.5723 size_loss: 0.1759 cls_loss: 0.6390 giou_loss: 0.6536 2025/05/12 05:45:01 - mmengine - INFO - Epoch(train) [56][30/91] base_lr: 2.3851e-04 lr: 2.3851e-04 eta: 3 days, 16:09:53 time: 10.5410 data_time: 1.5567 memory: 68702 grad_norm: 1.8991 loss: 2.0231 center_loss: 0.5626 size_loss: 0.1753 cls_loss: 0.6342 giou_loss: 0.6511 2025/05/12 05:46:38 - mmengine - INFO - Epoch(train) [56][40/91] base_lr: 2.3851e-04 lr: 2.3851e-04 eta: 3 days, 16:07:44 time: 10.5225 data_time: 1.5349 memory: 68702 grad_norm: 1.9530 loss: 2.0266 center_loss: 0.5625 size_loss: 0.1771 cls_loss: 0.6355 giou_loss: 0.6515 2025/05/12 05:48:15 - mmengine - INFO - Epoch(train) [56][50/91] base_lr: 2.3851e-04 lr: 2.3851e-04 eta: 3 days, 16:05:41 time: 10.7025 data_time: 1.5501 memory: 68702 grad_norm: 2.0024 loss: 2.0626 center_loss: 0.5762 size_loss: 0.1801 cls_loss: 0.6469 giou_loss: 0.6594 2025/05/12 05:49:53 - mmengine - INFO - Epoch(train) [56][60/91] base_lr: 2.3851e-04 lr: 2.3851e-04 eta: 3 days, 16:03:37 time: 9.7274 data_time: 0.6270 memory: 68703 grad_norm: 1.9262 loss: 2.0709 center_loss: 0.5797 size_loss: 0.1812 cls_loss: 0.6496 giou_loss: 0.6605 2025/05/12 05:51:30 - mmengine - INFO - Epoch(train) [56][70/91] base_lr: 2.3851e-04 lr: 2.3851e-04 eta: 3 days, 16:01:29 time: 9.7185 data_time: 0.6305 memory: 68702 grad_norm: 1.9457 loss: 2.0782 center_loss: 0.5833 size_loss: 0.1832 cls_loss: 0.6523 giou_loss: 0.6594 2025/05/12 05:53:06 - mmengine - INFO - Epoch(train) [56][80/91] base_lr: 2.3851e-04 lr: 2.3851e-04 eta: 3 days, 15:59:20 time: 9.7073 data_time: 0.6316 memory: 68702 grad_norm: 1.9140 loss: 2.0967 center_loss: 0.5912 size_loss: 0.1841 cls_loss: 0.6592 giou_loss: 0.6621 2025/05/12 05:54:42 - mmengine - INFO - Epoch(train) [56][90/91] base_lr: 2.3851e-04 lr: 2.3851e-04 eta: 3 days, 15:57:06 time: 9.6891 data_time: 0.6298 memory: 68702 grad_norm: 1.8314 loss: 2.1125 center_loss: 0.6000 size_loss: 0.1845 cls_loss: 0.6625 giou_loss: 0.6655 2025/05/12 05:54:44 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 05:54:44 - mmengine - INFO - Saving checkpoint at 56 epochs 2025/05/12 05:55:42 - mmengine - INFO - Epoch(val) [56][10/39] eta: 0:01:34 time: 2.8762 data_time: 0.3550 memory: 15952 2025/05/12 05:56:08 - mmengine - INFO - Epoch(val) [56][20/39] eta: 0:00:55 time: 2.7382 data_time: 0.2183 memory: 13407 2025/05/12 05:56:34 - mmengine - INFO - Epoch(val) [56][30/39] eta: 0:00:25 time: 2.7326 data_time: 0.2077 memory: 13407 2025/05/12 05:57:01 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | sofa | 0.6338 | 0.8041 | 0.1250 | 0.3093 | | garbagebin | 0.1679 | 0.3604 | 0.0077 | 0.0660 | | table | 0.3917 | 0.5400 | 0.0804 | 0.2000 | | chair | 0.4526 | 0.6623 | 0.0890 | 0.2376 | | curtain | 0.2693 | 0.4925 | 0.0186 | 0.0746 | | bookshelf | 0.2613 | 0.5974 | 0.0222 | 0.1169 | | picture | 0.0027 | 0.0495 | 0.0001 | 0.0045 | | window | 0.1013 | 0.2801 | 0.0085 | 0.0567 | | cabinet | 0.2046 | 0.3683 | 0.0454 | 0.1237 | | door | 0.1227 | 0.3769 | 0.0058 | 0.0685 | | refrigerator | 0.5111 | 0.5965 | 0.2287 | 0.3158 | | sink | 0.3777 | 0.5816 | 0.0752 | 0.2245 | | counter | 0.0951 | 0.1538 | 0.0163 | 0.0577 | | bed | 0.8109 | 0.8642 | 0.3341 | 0.4815 | | desk | 0.5486 | 0.7717 | 0.1612 | 0.3780 | | toilet | 0.7360 | 0.8793 | 0.2627 | 0.4310 | | bathtub | 0.5190 | 0.7097 | 0.1497 | 0.3226 | | showercurtrain | 0.2363 | 0.5000 | 0.0000 | 0.0000 | +----------------+---------+---------+---------+---------+ | Overall | 0.3579 | 0.5327 | 0.0906 | 0.1927 | +----------------+---------+---------+---------+---------+ 2025/05/12 05:57:01 - mmengine - INFO - Epoch(val) [56][39/39] chair_AP_0.25: 0.4526 sofa_AP_0.25: 0.6338 table_AP_0.25: 0.3917 garbagebin_AP_0.25: 0.1679 bookshelf_AP_0.25: 0.2613 picture_AP_0.25: 0.0027 curtain_AP_0.25: 0.2693 door_AP_0.25: 0.1227 cabinet_AP_0.25: 0.2046 refrigerator_AP_0.25: 0.5111 counter_AP_0.25: 0.0951 sink_AP_0.25: 0.3777 window_AP_0.25: 0.1013 desk_AP_0.25: 0.5486 bed_AP_0.25: 0.8109 toilet_AP_0.25: 0.7360 showercurtrain_AP_0.25: 0.2363 bathtub_AP_0.25: 0.5190 mAP_0.25: 0.3579 chair_rec_0.25: 0.6623 sofa_rec_0.25: 0.8041 table_rec_0.25: 0.5400 garbagebin_rec_0.25: 0.3604 bookshelf_rec_0.25: 0.5974 picture_rec_0.25: 0.0495 curtain_rec_0.25: 0.4925 door_rec_0.25: 0.3769 cabinet_rec_0.25: 0.3683 refrigerator_rec_0.25: 0.5965 counter_rec_0.25: 0.1538 sink_rec_0.25: 0.5816 window_rec_0.25: 0.2801 desk_rec_0.25: 0.7717 bed_rec_0.25: 0.8642 toilet_rec_0.25: 0.8793 showercurtrain_rec_0.25: 0.5000 bathtub_rec_0.25: 0.7097 mAR_0.25: 0.5327 chair_AP_0.50: 0.0890 sofa_AP_0.50: 0.1250 table_AP_0.50: 0.0804 garbagebin_AP_0.50: 0.0077 bookshelf_AP_0.50: 0.0222 picture_AP_0.50: 0.0001 curtain_AP_0.50: 0.0186 door_AP_0.50: 0.0058 cabinet_AP_0.50: 0.0454 refrigerator_AP_0.50: 0.2287 counter_AP_0.50: 0.0163 sink_AP_0.50: 0.0752 window_AP_0.50: 0.0085 desk_AP_0.50: 0.1612 bed_AP_0.50: 0.3341 toilet_AP_0.50: 0.2627 showercurtrain_AP_0.50: 0.0000 bathtub_AP_0.50: 0.1497 mAP_0.50: 0.0906 chair_rec_0.50: 0.2376 sofa_rec_0.50: 0.3093 table_rec_0.50: 0.2000 garbagebin_rec_0.50: 0.0660 bookshelf_rec_0.50: 0.1169 picture_rec_0.50: 0.0045 curtain_rec_0.50: 0.0746 door_rec_0.50: 0.0685 cabinet_rec_0.50: 0.1237 refrigerator_rec_0.50: 0.3158 counter_rec_0.50: 0.0577 sink_rec_0.50: 0.2245 window_rec_0.50: 0.0567 desk_rec_0.50: 0.3780 bed_rec_0.50: 0.4815 toilet_rec_0.50: 0.4310 showercurtrain_rec_0.50: 0.0000 bathtub_rec_0.50: 0.3226 mAR_0.50: 0.1927 data_time: 0.2367 time: 2.7510 2025/05/12 05:57:01 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_52.pth is removed 2025/05/12 05:57:30 - mmengine - INFO - The best checkpoint with 0.3579 mAP_0.25 at 56 epoch is saved to best_mAP_0.25_epoch_56.pth. 2025/05/12 06:00:24 - mmengine - INFO - Epoch(train) [57][10/91] base_lr: 2.3809e-04 lr: 2.3809e-04 eta: 3 days, 15:58:43 time: 10.4478 data_time: 1.4889 memory: 68702 grad_norm: 1.8034 loss: 2.1152 center_loss: 0.6030 size_loss: 0.1854 cls_loss: 0.6629 giou_loss: 0.6639 2025/05/12 06:02:02 - mmengine - INFO - Epoch(train) [57][20/91] base_lr: 2.3809e-04 lr: 2.3809e-04 eta: 3 days, 15:56:41 time: 10.4549 data_time: 1.5005 memory: 68702 grad_norm: 1.7928 loss: 2.1397 center_loss: 0.6166 size_loss: 0.1876 cls_loss: 0.6678 giou_loss: 0.6677 2025/05/12 06:03:38 - mmengine - INFO - Epoch(train) [57][30/91] base_lr: 2.3809e-04 lr: 2.3809e-04 eta: 3 days, 15:54:32 time: 10.4503 data_time: 1.4949 memory: 68703 grad_norm: 1.8439 loss: 2.1373 center_loss: 0.6128 size_loss: 0.1863 cls_loss: 0.6710 giou_loss: 0.6672 2025/05/12 06:05:13 - mmengine - INFO - Epoch(train) [57][40/91] base_lr: 2.3809e-04 lr: 2.3809e-04 eta: 3 days, 15:52:11 time: 10.4052 data_time: 1.4753 memory: 68702 grad_norm: 1.8383 loss: 2.1136 center_loss: 0.6034 size_loss: 0.1844 cls_loss: 0.6634 giou_loss: 0.6624 2025/05/12 06:06:51 - mmengine - INFO - Epoch(train) [57][50/91] base_lr: 2.3809e-04 lr: 2.3809e-04 eta: 3 days, 15:50:09 time: 10.6001 data_time: 1.4810 memory: 68703 grad_norm: 2.1425 loss: 2.1096 center_loss: 0.6008 size_loss: 0.1846 cls_loss: 0.6633 giou_loss: 0.6609 2025/05/12 06:08:28 - mmengine - INFO - Epoch(train) [57][60/91] base_lr: 2.3809e-04 lr: 2.3809e-04 eta: 3 days, 15:48:01 time: 9.6725 data_time: 0.6102 memory: 68703 grad_norm: 2.0887 loss: 2.1010 center_loss: 0.5992 size_loss: 0.1842 cls_loss: 0.6572 giou_loss: 0.6604 2025/05/12 06:10:05 - mmengine - INFO - Epoch(train) [57][70/91] base_lr: 2.3809e-04 lr: 2.3809e-04 eta: 3 days, 15:45:56 time: 9.6608 data_time: 0.5988 memory: 68700 grad_norm: 2.0602 loss: 2.0928 center_loss: 0.5957 size_loss: 0.1841 cls_loss: 0.6535 giou_loss: 0.6595 2025/05/12 06:11:42 - mmengine - INFO - Epoch(train) [57][80/91] base_lr: 2.3809e-04 lr: 2.3809e-04 eta: 3 days, 15:43:50 time: 9.6670 data_time: 0.5935 memory: 68702 grad_norm: 1.9430 loss: 2.0982 center_loss: 0.6002 size_loss: 0.1840 cls_loss: 0.6498 giou_loss: 0.6642 2025/05/12 06:13:18 - mmengine - INFO - Epoch(train) [57][90/91] base_lr: 2.3809e-04 lr: 2.3809e-04 eta: 3 days, 15:41:37 time: 9.6911 data_time: 0.6009 memory: 68702 grad_norm: 1.9480 loss: 2.1224 center_loss: 0.6097 size_loss: 0.1862 cls_loss: 0.6566 giou_loss: 0.6700 2025/05/12 06:13:20 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 06:15:41 - mmengine - INFO - Epoch(train) [58][10/91] base_lr: 2.3767e-04 lr: 2.3767e-04 eta: 3 days, 15:43:00 time: 10.4180 data_time: 1.4556 memory: 68702 grad_norm: 1.6727 loss: 2.1257 center_loss: 0.6105 size_loss: 0.1852 cls_loss: 0.6537 giou_loss: 0.6763 2025/05/12 06:17:18 - mmengine - INFO - Epoch(train) [58][20/91] base_lr: 2.3767e-04 lr: 2.3767e-04 eta: 3 days, 15:40:51 time: 10.4085 data_time: 1.4430 memory: 68702 grad_norm: 1.7155 loss: 2.1000 center_loss: 0.5996 size_loss: 0.1825 cls_loss: 0.6464 giou_loss: 0.6714 2025/05/12 06:18:56 - mmengine - INFO - Epoch(train) [58][30/91] base_lr: 2.3767e-04 lr: 2.3767e-04 eta: 3 days, 15:38:49 time: 10.4230 data_time: 1.4475 memory: 68702 grad_norm: 1.7000 loss: 2.0879 center_loss: 0.5956 size_loss: 0.1809 cls_loss: 0.6443 giou_loss: 0.6672 2025/05/12 06:20:33 - mmengine - INFO - Epoch(train) [58][40/91] base_lr: 2.3767e-04 lr: 2.3767e-04 eta: 3 days, 15:36:45 time: 10.4237 data_time: 1.4566 memory: 68702 grad_norm: 1.7048 loss: 2.0789 center_loss: 0.5938 size_loss: 0.1830 cls_loss: 0.6376 giou_loss: 0.6644 2025/05/12 06:22:11 - mmengine - INFO - Epoch(train) [58][50/91] base_lr: 2.3767e-04 lr: 2.3767e-04 eta: 3 days, 15:34:47 time: 10.6321 data_time: 1.4782 memory: 68702 grad_norm: 1.6397 loss: 2.0842 center_loss: 0.5955 size_loss: 0.1830 cls_loss: 0.6423 giou_loss: 0.6635 2025/05/12 06:23:49 - mmengine - INFO - Epoch(train) [58][60/91] base_lr: 2.3767e-04 lr: 2.3767e-04 eta: 3 days, 15:32:44 time: 9.7504 data_time: 0.6237 memory: 68702 grad_norm: 1.6367 loss: 2.0764 center_loss: 0.5932 size_loss: 0.1835 cls_loss: 0.6423 giou_loss: 0.6574 2025/05/12 06:25:26 - mmengine - INFO - Epoch(train) [58][70/91] base_lr: 2.3767e-04 lr: 2.3767e-04 eta: 3 days, 15:30:42 time: 9.7722 data_time: 0.6324 memory: 68702 grad_norm: 1.5717 loss: 2.0882 center_loss: 0.5973 size_loss: 0.1838 cls_loss: 0.6461 giou_loss: 0.6610 2025/05/12 06:27:04 - mmengine - INFO - Epoch(train) [58][80/91] base_lr: 2.3767e-04 lr: 2.3767e-04 eta: 3 days, 15:28:37 time: 9.7597 data_time: 0.6352 memory: 68702 grad_norm: 1.6322 loss: 2.0829 center_loss: 0.5907 size_loss: 0.1816 cls_loss: 0.6479 giou_loss: 0.6627 2025/05/12 06:28:40 - mmengine - INFO - Epoch(train) [58][90/91] base_lr: 2.3767e-04 lr: 2.3767e-04 eta: 3 days, 15:26:27 time: 9.7395 data_time: 0.6198 memory: 68702 grad_norm: 1.5992 loss: 2.0729 center_loss: 0.5835 size_loss: 0.1779 cls_loss: 0.6519 giou_loss: 0.6596 2025/05/12 06:28:42 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 06:28:42 - mmengine - INFO - Saving checkpoint at 58 epochs 2025/05/12 06:29:37 - mmengine - INFO - Epoch(val) [58][10/39] eta: 0:01:33 time: 2.8475 data_time: 0.3337 memory: 15952 2025/05/12 06:30:03 - mmengine - INFO - Epoch(val) [58][20/39] eta: 0:00:55 time: 2.7115 data_time: 0.2044 memory: 13407 2025/05/12 06:30:29 - mmengine - INFO - Epoch(val) [58][30/39] eta: 0:00:25 time: 2.7216 data_time: 0.2135 memory: 13407 2025/05/12 06:30:57 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | sofa | 0.6781 | 0.8247 | 0.2012 | 0.4124 | | garbagebin | 0.1804 | 0.3868 | 0.0177 | 0.1038 | | table | 0.4449 | 0.5829 | 0.1261 | 0.2400 | | chair | 0.4640 | 0.6901 | 0.0949 | 0.2617 | | curtain | 0.1373 | 0.4030 | 0.0025 | 0.0448 | | bookshelf | 0.2665 | 0.6883 | 0.0381 | 0.1558 | | picture | 0.0047 | 0.0541 | 0.0003 | 0.0045 | | door | 0.1384 | 0.4154 | 0.0119 | 0.0878 | | cabinet | 0.2425 | 0.4113 | 0.0617 | 0.1425 | | window | 0.1253 | 0.3262 | 0.0057 | 0.0567 | | refrigerator | 0.4062 | 0.6140 | 0.2130 | 0.3684 | | sink | 0.4228 | 0.5816 | 0.0596 | 0.2041 | | counter | 0.0958 | 0.1538 | 0.0131 | 0.0385 | | toilet | 0.7347 | 0.9138 | 0.3195 | 0.4828 | | desk | 0.6671 | 0.8346 | 0.2137 | 0.4094 | | bed | 0.8194 | 0.8765 | 0.3705 | 0.5062 | | bathtub | 0.6004 | 0.7419 | 0.1935 | 0.3226 | | showercurtrain | 0.3838 | 0.5000 | 0.0164 | 0.1071 | +----------------+---------+---------+---------+---------+ | Overall | 0.3785 | 0.5555 | 0.1089 | 0.2194 | +----------------+---------+---------+---------+---------+ 2025/05/12 06:30:57 - mmengine - INFO - Epoch(val) [58][39/39] chair_AP_0.25: 0.4640 sofa_AP_0.25: 0.6781 table_AP_0.25: 0.4449 garbagebin_AP_0.25: 0.1804 bookshelf_AP_0.25: 0.2665 picture_AP_0.25: 0.0047 curtain_AP_0.25: 0.1373 door_AP_0.25: 0.1384 cabinet_AP_0.25: 0.2425 refrigerator_AP_0.25: 0.4062 counter_AP_0.25: 0.0958 sink_AP_0.25: 0.4228 window_AP_0.25: 0.1253 desk_AP_0.25: 0.6671 bed_AP_0.25: 0.8194 toilet_AP_0.25: 0.7347 showercurtrain_AP_0.25: 0.3838 bathtub_AP_0.25: 0.6004 mAP_0.25: 0.3785 chair_rec_0.25: 0.6901 sofa_rec_0.25: 0.8247 table_rec_0.25: 0.5829 garbagebin_rec_0.25: 0.3868 bookshelf_rec_0.25: 0.6883 picture_rec_0.25: 0.0541 curtain_rec_0.25: 0.4030 door_rec_0.25: 0.4154 cabinet_rec_0.25: 0.4113 refrigerator_rec_0.25: 0.6140 counter_rec_0.25: 0.1538 sink_rec_0.25: 0.5816 window_rec_0.25: 0.3262 desk_rec_0.25: 0.8346 bed_rec_0.25: 0.8765 toilet_rec_0.25: 0.9138 showercurtrain_rec_0.25: 0.5000 bathtub_rec_0.25: 0.7419 mAR_0.25: 0.5555 chair_AP_0.50: 0.0949 sofa_AP_0.50: 0.2012 table_AP_0.50: 0.1261 garbagebin_AP_0.50: 0.0177 bookshelf_AP_0.50: 0.0381 picture_AP_0.50: 0.0003 curtain_AP_0.50: 0.0025 door_AP_0.50: 0.0119 cabinet_AP_0.50: 0.0617 refrigerator_AP_0.50: 0.2130 counter_AP_0.50: 0.0131 sink_AP_0.50: 0.0596 window_AP_0.50: 0.0057 desk_AP_0.50: 0.2137 bed_AP_0.50: 0.3705 toilet_AP_0.50: 0.3195 showercurtrain_AP_0.50: 0.0164 bathtub_AP_0.50: 0.1935 mAP_0.50: 0.1089 chair_rec_0.50: 0.2617 sofa_rec_0.50: 0.4124 table_rec_0.50: 0.2400 garbagebin_rec_0.50: 0.1038 bookshelf_rec_0.50: 0.1558 picture_rec_0.50: 0.0045 curtain_rec_0.50: 0.0448 door_rec_0.50: 0.0878 cabinet_rec_0.50: 0.1425 refrigerator_rec_0.50: 0.3684 counter_rec_0.50: 0.0385 sink_rec_0.50: 0.2041 window_rec_0.50: 0.0567 desk_rec_0.50: 0.4094 bed_rec_0.50: 0.5062 toilet_rec_0.50: 0.4828 showercurtrain_rec_0.50: 0.1071 bathtub_rec_0.50: 0.3226 mAR_0.50: 0.2194 data_time: 0.2477 time: 2.7453 2025/05/12 06:30:57 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_56.pth is removed 2025/05/12 06:31:22 - mmengine - INFO - The best checkpoint with 0.3785 mAP_0.25 at 58 epoch is saved to best_mAP_0.25_epoch_58.pth. 2025/05/12 06:34:14 - mmengine - INFO - Epoch(train) [59][10/91] base_lr: 2.3724e-04 lr: 2.3724e-04 eta: 3 days, 15:28:10 time: 10.5293 data_time: 1.5662 memory: 68702 grad_norm: 1.6717 loss: 2.0493 center_loss: 0.5763 size_loss: 0.1759 cls_loss: 0.6416 giou_loss: 0.6556 2025/05/12 06:35:50 - mmengine - INFO - Epoch(train) [59][20/91] base_lr: 2.3724e-04 lr: 2.3724e-04 eta: 3 days, 15:26:01 time: 10.5095 data_time: 1.5621 memory: 68703 grad_norm: 1.6158 loss: 2.0437 center_loss: 0.5719 size_loss: 0.1740 cls_loss: 0.6409 giou_loss: 0.6569 2025/05/12 06:37:28 - mmengine - INFO - Epoch(train) [59][30/91] base_lr: 2.3724e-04 lr: 2.3724e-04 eta: 3 days, 15:23:56 time: 10.5003 data_time: 1.5583 memory: 68702 grad_norm: 1.6445 loss: 2.0456 center_loss: 0.5696 size_loss: 0.1747 cls_loss: 0.6456 giou_loss: 0.6557 2025/05/12 06:39:05 - mmengine - INFO - Epoch(train) [59][40/91] base_lr: 2.3724e-04 lr: 2.3724e-04 eta: 3 days, 15:21:52 time: 10.4991 data_time: 1.5570 memory: 68703 grad_norm: 1.7152 loss: 2.0309 center_loss: 0.5617 size_loss: 0.1737 cls_loss: 0.6434 giou_loss: 0.6520 2025/05/12 06:40:42 - mmengine - INFO - Epoch(train) [59][50/91] base_lr: 2.3724e-04 lr: 2.3724e-04 eta: 3 days, 15:19:48 time: 10.6770 data_time: 1.5676 memory: 68702 grad_norm: 1.7517 loss: 2.0450 center_loss: 0.5698 size_loss: 0.1748 cls_loss: 0.6469 giou_loss: 0.6535 2025/05/12 06:42:19 - mmengine - INFO - Epoch(train) [59][60/91] base_lr: 2.3724e-04 lr: 2.3724e-04 eta: 3 days, 15:17:43 time: 9.7062 data_time: 0.6014 memory: 68702 grad_norm: 1.7480 loss: 2.0504 center_loss: 0.5698 size_loss: 0.1770 cls_loss: 0.6483 giou_loss: 0.6553 2025/05/12 06:43:57 - mmengine - INFO - Epoch(train) [59][70/91] base_lr: 2.3724e-04 lr: 2.3724e-04 eta: 3 days, 15:15:41 time: 9.7263 data_time: 0.6158 memory: 68702 grad_norm: 1.8525 loss: 2.0525 center_loss: 0.5711 size_loss: 0.1773 cls_loss: 0.6508 giou_loss: 0.6532 2025/05/12 06:45:33 - mmengine - INFO - Epoch(train) [59][80/91] base_lr: 2.3724e-04 lr: 2.3724e-04 eta: 3 days, 15:13:32 time: 9.7125 data_time: 0.6120 memory: 68703 grad_norm: 1.7859 loss: 2.0356 center_loss: 0.5702 size_loss: 0.1771 cls_loss: 0.6384 giou_loss: 0.6500 2025/05/12 06:47:09 - mmengine - INFO - Epoch(train) [59][90/91] base_lr: 2.3724e-04 lr: 2.3724e-04 eta: 3 days, 15:11:22 time: 9.6898 data_time: 0.6084 memory: 68702 grad_norm: 1.6965 loss: 2.0436 center_loss: 0.5758 size_loss: 0.1781 cls_loss: 0.6361 giou_loss: 0.6536 2025/05/12 06:47:11 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 06:49:42 - mmengine - INFO - Epoch(train) [60][10/91] base_lr: 2.3681e-04 lr: 2.3681e-04 eta: 3 days, 15:13:32 time: 10.6100 data_time: 1.5969 memory: 68702 grad_norm: 1.7821 loss: 2.0563 center_loss: 0.5797 size_loss: 0.1803 cls_loss: 0.6394 giou_loss: 0.6568 2025/05/12 06:51:20 - mmengine - INFO - Epoch(train) [60][20/91] base_lr: 2.3681e-04 lr: 2.3681e-04 eta: 3 days, 15:11:27 time: 10.6084 data_time: 1.5814 memory: 68702 grad_norm: 1.8197 loss: 2.0677 center_loss: 0.5868 size_loss: 0.1808 cls_loss: 0.6430 giou_loss: 0.6571 2025/05/12 06:52:57 - mmengine - INFO - Epoch(train) [60][30/91] base_lr: 2.3681e-04 lr: 2.3681e-04 eta: 3 days, 15:09:24 time: 10.6106 data_time: 1.5808 memory: 68702 grad_norm: 1.9297 loss: 2.0700 center_loss: 0.5888 size_loss: 0.1804 cls_loss: 0.6439 giou_loss: 0.6568 2025/05/12 06:54:35 - mmengine - INFO - Epoch(train) [60][40/91] base_lr: 2.3681e-04 lr: 2.3681e-04 eta: 3 days, 15:07:22 time: 10.6272 data_time: 1.5761 memory: 68702 grad_norm: 1.9538 loss: 2.0888 center_loss: 0.5985 size_loss: 0.1792 cls_loss: 0.6511 giou_loss: 0.6599 2025/05/12 06:56:13 - mmengine - INFO - Epoch(train) [60][50/91] base_lr: 2.3681e-04 lr: 2.3681e-04 eta: 3 days, 15:05:25 time: 10.8330 data_time: 1.5903 memory: 68701 grad_norm: 1.8768 loss: 2.0591 center_loss: 0.5906 size_loss: 0.1750 cls_loss: 0.6402 giou_loss: 0.6533 2025/05/12 06:57:51 - mmengine - INFO - Epoch(train) [60][60/91] base_lr: 2.3681e-04 lr: 2.3681e-04 eta: 3 days, 15:03:24 time: 9.7678 data_time: 0.5889 memory: 68703 grad_norm: 1.7752 loss: 2.0645 center_loss: 0.5945 size_loss: 0.1744 cls_loss: 0.6429 giou_loss: 0.6527 2025/05/12 06:59:28 - mmengine - INFO - Epoch(train) [60][70/91] base_lr: 2.3681e-04 lr: 2.3681e-04 eta: 3 days, 15:01:20 time: 9.7662 data_time: 0.6022 memory: 68702 grad_norm: 1.6661 loss: 2.0416 center_loss: 0.5846 size_loss: 0.1707 cls_loss: 0.6359 giou_loss: 0.6504 2025/05/12 07:01:04 - mmengine - INFO - Epoch(train) [60][80/91] base_lr: 2.3681e-04 lr: 2.3681e-04 eta: 3 days, 14:59:12 time: 9.7489 data_time: 0.6010 memory: 68702 grad_norm: 1.5103 loss: 2.0377 center_loss: 0.5809 size_loss: 0.1723 cls_loss: 0.6356 giou_loss: 0.6488 2025/05/12 07:02:41 - mmengine - INFO - Epoch(train) [60][90/91] base_lr: 2.3681e-04 lr: 2.3681e-04 eta: 3 days, 14:57:02 time: 9.7208 data_time: 0.6025 memory: 68702 grad_norm: 1.6097 loss: 2.0165 center_loss: 0.5670 size_loss: 0.1724 cls_loss: 0.6338 giou_loss: 0.6433 2025/05/12 07:02:43 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 07:02:43 - mmengine - INFO - Saving checkpoint at 60 epochs 2025/05/12 07:03:38 - mmengine - INFO - Epoch(val) [60][10/39] eta: 0:01:34 time: 2.8492 data_time: 0.3509 memory: 15952 2025/05/12 07:04:04 - mmengine - INFO - Epoch(val) [60][20/39] eta: 0:00:55 time: 2.7178 data_time: 0.2234 memory: 13407 2025/05/12 07:04:30 - mmengine - INFO - Epoch(val) [60][30/39] eta: 0:00:25 time: 2.7327 data_time: 0.2317 memory: 13407 2025/05/12 07:04:57 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | sofa | 0.6519 | 0.7835 | 0.1423 | 0.3196 | | garbagebin | 0.1585 | 0.4170 | 0.0068 | 0.0811 | | chair | 0.4621 | 0.6586 | 0.0851 | 0.2200 | | table | 0.3887 | 0.5114 | 0.0780 | 0.2086 | | curtain | 0.1826 | 0.3582 | 0.0301 | 0.1194 | | bookshelf | 0.1926 | 0.5065 | 0.0450 | 0.1558 | | bed | 0.8215 | 0.8519 | 0.3363 | 0.4815 | | picture | 0.0035 | 0.0405 | 0.0000 | 0.0000 | | door | 0.1022 | 0.3769 | 0.0056 | 0.0857 | | cabinet | 0.2211 | 0.4113 | 0.0375 | 0.1290 | | window | 0.0842 | 0.2624 | 0.0063 | 0.0567 | | refrigerator | 0.4423 | 0.6140 | 0.1497 | 0.2982 | | sink | 0.3356 | 0.5204 | 0.0517 | 0.1837 | | counter | 0.1005 | 0.1346 | 0.0218 | 0.0385 | | desk | 0.6154 | 0.7953 | 0.1449 | 0.3307 | | toilet | 0.8301 | 0.9655 | 0.2891 | 0.4655 | | bathtub | 0.6699 | 0.7419 | 0.1097 | 0.2903 | | showercurtrain | 0.2944 | 0.4286 | 0.0119 | 0.0357 | +----------------+---------+---------+---------+---------+ | Overall | 0.3643 | 0.5210 | 0.0862 | 0.1945 | +----------------+---------+---------+---------+---------+ 2025/05/12 07:04:57 - mmengine - INFO - Epoch(val) [60][39/39] chair_AP_0.25: 0.4621 sofa_AP_0.25: 0.6519 table_AP_0.25: 0.3887 garbagebin_AP_0.25: 0.1585 bookshelf_AP_0.25: 0.1926 picture_AP_0.25: 0.0035 curtain_AP_0.25: 0.1826 door_AP_0.25: 0.1022 cabinet_AP_0.25: 0.2211 refrigerator_AP_0.25: 0.4423 counter_AP_0.25: 0.1005 sink_AP_0.25: 0.3356 window_AP_0.25: 0.0842 desk_AP_0.25: 0.6154 bed_AP_0.25: 0.8215 toilet_AP_0.25: 0.8301 showercurtrain_AP_0.25: 0.2944 bathtub_AP_0.25: 0.6699 mAP_0.25: 0.3643 chair_rec_0.25: 0.6586 sofa_rec_0.25: 0.7835 table_rec_0.25: 0.5114 garbagebin_rec_0.25: 0.4170 bookshelf_rec_0.25: 0.5065 picture_rec_0.25: 0.0405 curtain_rec_0.25: 0.3582 door_rec_0.25: 0.3769 cabinet_rec_0.25: 0.4113 refrigerator_rec_0.25: 0.6140 counter_rec_0.25: 0.1346 sink_rec_0.25: 0.5204 window_rec_0.25: 0.2624 desk_rec_0.25: 0.7953 bed_rec_0.25: 0.8519 toilet_rec_0.25: 0.9655 showercurtrain_rec_0.25: 0.4286 bathtub_rec_0.25: 0.7419 mAR_0.25: 0.5210 chair_AP_0.50: 0.0851 sofa_AP_0.50: 0.1423 table_AP_0.50: 0.0780 garbagebin_AP_0.50: 0.0068 bookshelf_AP_0.50: 0.0450 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0301 door_AP_0.50: 0.0056 cabinet_AP_0.50: 0.0375 refrigerator_AP_0.50: 0.1497 counter_AP_0.50: 0.0218 sink_AP_0.50: 0.0517 window_AP_0.50: 0.0063 desk_AP_0.50: 0.1449 bed_AP_0.50: 0.3363 toilet_AP_0.50: 0.2891 showercurtrain_AP_0.50: 0.0119 bathtub_AP_0.50: 0.1097 mAP_0.50: 0.0862 chair_rec_0.50: 0.2200 sofa_rec_0.50: 0.3196 table_rec_0.50: 0.2086 garbagebin_rec_0.50: 0.0811 bookshelf_rec_0.50: 0.1558 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.1194 door_rec_0.50: 0.0857 cabinet_rec_0.50: 0.1290 refrigerator_rec_0.50: 0.2982 counter_rec_0.50: 0.0385 sink_rec_0.50: 0.1837 window_rec_0.50: 0.0567 desk_rec_0.50: 0.3307 bed_rec_0.50: 0.4815 toilet_rec_0.50: 0.4655 showercurtrain_rec_0.50: 0.0357 bathtub_rec_0.50: 0.2903 mAR_0.50: 0.1945 data_time: 0.2598 time: 2.7501 2025/05/12 07:07:26 - mmengine - INFO - Epoch(train) [61][10/91] base_lr: 2.3636e-04 lr: 2.3636e-04 eta: 3 days, 14:58:53 time: 10.5653 data_time: 1.4458 memory: 68703 grad_norm: 1.7215 loss: 2.0260 center_loss: 0.5683 size_loss: 0.1740 cls_loss: 0.6384 giou_loss: 0.6453 2025/05/12 07:09:02 - mmengine - INFO - Epoch(train) [61][20/91] base_lr: 2.3636e-04 lr: 2.3636e-04 eta: 3 days, 14:56:47 time: 10.5469 data_time: 1.4479 memory: 68702 grad_norm: 1.8005 loss: 2.0156 center_loss: 0.5587 size_loss: 0.1719 cls_loss: 0.6409 giou_loss: 0.6442 2025/05/12 07:10:40 - mmengine - INFO - Epoch(train) [61][30/91] base_lr: 2.3636e-04 lr: 2.3636e-04 eta: 3 days, 14:54:43 time: 10.5514 data_time: 1.4363 memory: 68702 grad_norm: 1.8454 loss: 2.0231 center_loss: 0.5614 size_loss: 0.1735 cls_loss: 0.6438 giou_loss: 0.6444 2025/05/12 07:12:18 - mmengine - INFO - Epoch(train) [61][40/91] base_lr: 2.3636e-04 lr: 2.3636e-04 eta: 3 days, 14:52:45 time: 10.5802 data_time: 1.4292 memory: 68703 grad_norm: 1.8639 loss: 2.0144 center_loss: 0.5580 size_loss: 0.1704 cls_loss: 0.6440 giou_loss: 0.6420 2025/05/12 07:13:56 - mmengine - INFO - Epoch(train) [61][50/91] base_lr: 2.3636e-04 lr: 2.3636e-04 eta: 3 days, 14:50:46 time: 10.7745 data_time: 1.4344 memory: 68702 grad_norm: 1.7833 loss: 2.0159 center_loss: 0.5597 size_loss: 0.1691 cls_loss: 0.6437 giou_loss: 0.6435 2025/05/12 07:15:34 - mmengine - INFO - Epoch(train) [61][60/91] base_lr: 2.3636e-04 lr: 2.3636e-04 eta: 3 days, 14:48:45 time: 9.7624 data_time: 0.5908 memory: 68703 grad_norm: 1.8016 loss: 2.0199 center_loss: 0.5617 size_loss: 0.1697 cls_loss: 0.6438 giou_loss: 0.6447 2025/05/12 07:17:12 - mmengine - INFO - Epoch(train) [61][70/91] base_lr: 2.3636e-04 lr: 2.3636e-04 eta: 3 days, 14:46:46 time: 9.7821 data_time: 0.5858 memory: 68702 grad_norm: 1.7637 loss: 2.0178 center_loss: 0.5612 size_loss: 0.1696 cls_loss: 0.6434 giou_loss: 0.6436 2025/05/12 07:18:48 - mmengine - INFO - Epoch(train) [61][80/91] base_lr: 2.3636e-04 lr: 2.3636e-04 eta: 3 days, 14:44:40 time: 9.7745 data_time: 0.5906 memory: 68700 grad_norm: 1.7461 loss: 2.0183 center_loss: 0.5635 size_loss: 0.1702 cls_loss: 0.6413 giou_loss: 0.6432 2025/05/12 07:20:25 - mmengine - INFO - Epoch(train) [61][90/91] base_lr: 2.3636e-04 lr: 2.3636e-04 eta: 3 days, 14:42:34 time: 9.7440 data_time: 0.5912 memory: 68702 grad_norm: 1.7889 loss: 2.0237 center_loss: 0.5651 size_loss: 0.1722 cls_loss: 0.6393 giou_loss: 0.6471 2025/05/12 07:20:27 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 07:22:59 - mmengine - INFO - Epoch(train) [62][10/91] base_lr: 2.3591e-04 lr: 2.3591e-04 eta: 3 days, 14:44:37 time: 10.6593 data_time: 1.5012 memory: 68703 grad_norm: 1.8295 loss: 2.0471 center_loss: 0.5750 size_loss: 0.1763 cls_loss: 0.6417 giou_loss: 0.6541 2025/05/12 07:24:36 - mmengine - INFO - Epoch(train) [62][20/91] base_lr: 2.3591e-04 lr: 2.3591e-04 eta: 3 days, 14:42:32 time: 10.6489 data_time: 1.4871 memory: 68702 grad_norm: 1.7522 loss: 2.0561 center_loss: 0.5802 size_loss: 0.1787 cls_loss: 0.6434 giou_loss: 0.6538 2025/05/12 07:26:13 - mmengine - INFO - Epoch(train) [62][30/91] base_lr: 2.3591e-04 lr: 2.3591e-04 eta: 3 days, 14:40:29 time: 10.6363 data_time: 1.4810 memory: 68702 grad_norm: 1.8017 loss: 2.0640 center_loss: 0.5836 size_loss: 0.1810 cls_loss: 0.6412 giou_loss: 0.6582 2025/05/12 07:27:50 - mmengine - INFO - Epoch(train) [62][40/91] base_lr: 2.3591e-04 lr: 2.3591e-04 eta: 3 days, 14:38:26 time: 10.6426 data_time: 1.4853 memory: 68702 grad_norm: 1.8539 loss: 2.0434 center_loss: 0.5722 size_loss: 0.1766 cls_loss: 0.6402 giou_loss: 0.6545 2025/05/12 07:29:29 - mmengine - INFO - Epoch(train) [62][50/91] base_lr: 2.3591e-04 lr: 2.3591e-04 eta: 3 days, 14:36:29 time: 10.8292 data_time: 1.4971 memory: 68702 grad_norm: 1.7337 loss: 2.0399 center_loss: 0.5767 size_loss: 0.1772 cls_loss: 0.6357 giou_loss: 0.6503 2025/05/12 07:31:06 - mmengine - INFO - Epoch(train) [62][60/91] base_lr: 2.3591e-04 lr: 2.3591e-04 eta: 3 days, 14:34:25 time: 9.7384 data_time: 0.5778 memory: 68702 grad_norm: 1.6601 loss: 2.0225 center_loss: 0.5673 size_loss: 0.1743 cls_loss: 0.6332 giou_loss: 0.6477 2025/05/12 07:32:42 - mmengine - INFO - Epoch(train) [62][70/91] base_lr: 2.3591e-04 lr: 2.3591e-04 eta: 3 days, 14:32:18 time: 9.7297 data_time: 0.5901 memory: 68702 grad_norm: 1.6757 loss: 1.9926 center_loss: 0.5559 size_loss: 0.1698 cls_loss: 0.6251 giou_loss: 0.6418 2025/05/12 07:34:19 - mmengine - INFO - Epoch(train) [62][80/91] base_lr: 2.3591e-04 lr: 2.3591e-04 eta: 3 days, 14:30:14 time: 9.7245 data_time: 0.6019 memory: 68703 grad_norm: 1.6707 loss: 1.9801 center_loss: 0.5495 size_loss: 0.1680 cls_loss: 0.6277 giou_loss: 0.6350 2025/05/12 07:35:55 - mmengine - INFO - Epoch(train) [62][90/91] base_lr: 2.3591e-04 lr: 2.3591e-04 eta: 3 days, 14:28:01 time: 9.6877 data_time: 0.5923 memory: 68703 grad_norm: 1.5814 loss: 1.9903 center_loss: 0.5509 size_loss: 0.1696 cls_loss: 0.6335 giou_loss: 0.6363 2025/05/12 07:35:57 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 07:35:57 - mmengine - INFO - Saving checkpoint at 62 epochs 2025/05/12 07:36:54 - mmengine - INFO - Epoch(val) [62][10/39] eta: 0:01:35 time: 2.8621 data_time: 0.3661 memory: 15952 2025/05/12 07:37:20 - mmengine - INFO - Epoch(val) [62][20/39] eta: 0:00:56 time: 2.7406 data_time: 0.2391 memory: 13407 2025/05/12 07:37:47 - mmengine - INFO - Epoch(val) [62][30/39] eta: 0:00:25 time: 2.7477 data_time: 0.2382 memory: 13407 2025/05/12 07:38:13 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | chair | 0.4786 | 0.6820 | 0.0900 | 0.2449 | | garbagebin | 0.2063 | 0.4000 | 0.0195 | 0.0868 | | sofa | 0.6158 | 0.8351 | 0.0936 | 0.2680 | | table | 0.3808 | 0.5629 | 0.0846 | 0.2229 | | curtain | 0.2082 | 0.4030 | 0.0511 | 0.1343 | | bookshelf | 0.2710 | 0.6104 | 0.0468 | 0.2208 | | picture | 0.0019 | 0.0541 | 0.0000 | 0.0045 | | window | 0.1078 | 0.3121 | 0.0045 | 0.0567 | | desk | 0.5548 | 0.8189 | 0.1488 | 0.3701 | | door | 0.1056 | 0.3683 | 0.0053 | 0.0814 | | cabinet | 0.2447 | 0.4543 | 0.0531 | 0.1828 | | refrigerator | 0.4333 | 0.6842 | 0.1215 | 0.2807 | | sink | 0.3464 | 0.5714 | 0.0347 | 0.1531 | | counter | 0.0999 | 0.1538 | 0.0584 | 0.0769 | | bed | 0.7775 | 0.8148 | 0.2848 | 0.4321 | | toilet | 0.7574 | 0.9138 | 0.4270 | 0.5345 | | bathtub | 0.6585 | 0.7742 | 0.2022 | 0.3226 | | showercurtrain | 0.2796 | 0.5714 | 0.0087 | 0.0714 | +----------------+---------+---------+---------+---------+ | Overall | 0.3627 | 0.5547 | 0.0964 | 0.2080 | +----------------+---------+---------+---------+---------+ 2025/05/12 07:38:13 - mmengine - INFO - Epoch(val) [62][39/39] chair_AP_0.25: 0.4786 sofa_AP_0.25: 0.6158 table_AP_0.25: 0.3808 garbagebin_AP_0.25: 0.2063 bookshelf_AP_0.25: 0.2710 picture_AP_0.25: 0.0019 curtain_AP_0.25: 0.2082 door_AP_0.25: 0.1056 cabinet_AP_0.25: 0.2447 refrigerator_AP_0.25: 0.4333 counter_AP_0.25: 0.0999 sink_AP_0.25: 0.3464 window_AP_0.25: 0.1078 desk_AP_0.25: 0.5548 bed_AP_0.25: 0.7775 toilet_AP_0.25: 0.7574 showercurtrain_AP_0.25: 0.2796 bathtub_AP_0.25: 0.6585 mAP_0.25: 0.3627 chair_rec_0.25: 0.6820 sofa_rec_0.25: 0.8351 table_rec_0.25: 0.5629 garbagebin_rec_0.25: 0.4000 bookshelf_rec_0.25: 0.6104 picture_rec_0.25: 0.0541 curtain_rec_0.25: 0.4030 door_rec_0.25: 0.3683 cabinet_rec_0.25: 0.4543 refrigerator_rec_0.25: 0.6842 counter_rec_0.25: 0.1538 sink_rec_0.25: 0.5714 window_rec_0.25: 0.3121 desk_rec_0.25: 0.8189 bed_rec_0.25: 0.8148 toilet_rec_0.25: 0.9138 showercurtrain_rec_0.25: 0.5714 bathtub_rec_0.25: 0.7742 mAR_0.25: 0.5547 chair_AP_0.50: 0.0900 sofa_AP_0.50: 0.0936 table_AP_0.50: 0.0846 garbagebin_AP_0.50: 0.0195 bookshelf_AP_0.50: 0.0468 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0511 door_AP_0.50: 0.0053 cabinet_AP_0.50: 0.0531 refrigerator_AP_0.50: 0.1215 counter_AP_0.50: 0.0584 sink_AP_0.50: 0.0347 window_AP_0.50: 0.0045 desk_AP_0.50: 0.1488 bed_AP_0.50: 0.2848 toilet_AP_0.50: 0.4270 showercurtrain_AP_0.50: 0.0087 bathtub_AP_0.50: 0.2022 mAP_0.50: 0.0964 chair_rec_0.50: 0.2449 sofa_rec_0.50: 0.2680 table_rec_0.50: 0.2229 garbagebin_rec_0.50: 0.0868 bookshelf_rec_0.50: 0.2208 picture_rec_0.50: 0.0045 curtain_rec_0.50: 0.1343 door_rec_0.50: 0.0814 cabinet_rec_0.50: 0.1828 refrigerator_rec_0.50: 0.2807 counter_rec_0.50: 0.0769 sink_rec_0.50: 0.1531 window_rec_0.50: 0.0567 desk_rec_0.50: 0.3701 bed_rec_0.50: 0.4321 toilet_rec_0.50: 0.5345 showercurtrain_rec_0.50: 0.0714 bathtub_rec_0.50: 0.3226 mAR_0.50: 0.2080 data_time: 0.2680 time: 2.7767 2025/05/12 07:40:46 - mmengine - INFO - Epoch(train) [63][10/91] base_lr: 2.3546e-04 lr: 2.3546e-04 eta: 3 days, 14:30:04 time: 10.6159 data_time: 1.5490 memory: 68702 grad_norm: 1.7435 loss: 1.9762 center_loss: 0.5394 size_loss: 0.1665 cls_loss: 0.6363 giou_loss: 0.6341 2025/05/12 07:42:23 - mmengine - INFO - Epoch(train) [63][20/91] base_lr: 2.3546e-04 lr: 2.3546e-04 eta: 3 days, 14:27:59 time: 10.6145 data_time: 1.5509 memory: 68702 grad_norm: 1.7738 loss: 1.9792 center_loss: 0.5447 size_loss: 0.1685 cls_loss: 0.6303 giou_loss: 0.6357 2025/05/12 07:44:00 - mmengine - INFO - Epoch(train) [63][30/91] base_lr: 2.3546e-04 lr: 2.3546e-04 eta: 3 days, 14:25:53 time: 10.6126 data_time: 1.5439 memory: 68702 grad_norm: 1.8143 loss: 2.0023 center_loss: 0.5530 size_loss: 0.1705 cls_loss: 0.6347 giou_loss: 0.6441 2025/05/12 07:45:37 - mmengine - INFO - Epoch(train) [63][40/91] base_lr: 2.3546e-04 lr: 2.3546e-04 eta: 3 days, 14:23:49 time: 10.6155 data_time: 1.5446 memory: 68702 grad_norm: 1.9287 loss: 2.0081 center_loss: 0.5565 size_loss: 0.1707 cls_loss: 0.6336 giou_loss: 0.6474 2025/05/12 07:47:15 - mmengine - INFO - Epoch(train) [63][50/91] base_lr: 2.3546e-04 lr: 2.3546e-04 eta: 3 days, 14:21:51 time: 10.8250 data_time: 1.5598 memory: 68702 grad_norm: 1.9184 loss: 2.0054 center_loss: 0.5632 size_loss: 0.1714 cls_loss: 0.6243 giou_loss: 0.6466 2025/05/12 07:48:51 - mmengine - INFO - Epoch(train) [63][60/91] base_lr: 2.3546e-04 lr: 2.3546e-04 eta: 3 days, 14:19:45 time: 9.7086 data_time: 0.6019 memory: 68702 grad_norm: 1.9390 loss: 1.9909 center_loss: 0.5592 size_loss: 0.1704 cls_loss: 0.6200 giou_loss: 0.6413 2025/05/12 07:50:28 - mmengine - INFO - Epoch(train) [63][70/91] base_lr: 2.3546e-04 lr: 2.3546e-04 eta: 3 days, 14:17:38 time: 9.7011 data_time: 0.6085 memory: 68700 grad_norm: 1.9930 loss: 1.9931 center_loss: 0.5551 size_loss: 0.1689 cls_loss: 0.6291 giou_loss: 0.6400 2025/05/12 07:52:04 - mmengine - INFO - Epoch(train) [63][80/91] base_lr: 2.3546e-04 lr: 2.3546e-04 eta: 3 days, 14:15:31 time: 9.6950 data_time: 0.6110 memory: 68702 grad_norm: 1.9857 loss: 1.9865 center_loss: 0.5515 size_loss: 0.1690 cls_loss: 0.6303 giou_loss: 0.6357 2025/05/12 07:53:40 - mmengine - INFO - Epoch(train) [63][90/91] base_lr: 2.3546e-04 lr: 2.3546e-04 eta: 3 days, 14:13:19 time: 9.6611 data_time: 0.6061 memory: 68702 grad_norm: 1.8751 loss: 1.9780 center_loss: 0.5468 size_loss: 0.1680 cls_loss: 0.6290 giou_loss: 0.6341 2025/05/12 07:53:42 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 07:56:10 - mmengine - INFO - Epoch(train) [64][10/91] base_lr: 2.3499e-04 lr: 2.3499e-04 eta: 3 days, 14:14:55 time: 10.5114 data_time: 1.4491 memory: 68702 grad_norm: 1.8879 loss: 1.9977 center_loss: 0.5509 size_loss: 0.1691 cls_loss: 0.6414 giou_loss: 0.6363 2025/05/12 07:57:47 - mmengine - INFO - Epoch(train) [64][20/91] base_lr: 2.3499e-04 lr: 2.3499e-04 eta: 3 days, 14:12:51 time: 10.5208 data_time: 1.4516 memory: 68702 grad_norm: 1.9954 loss: 2.0204 center_loss: 0.5593 size_loss: 0.1711 cls_loss: 0.6482 giou_loss: 0.6418 2025/05/12 07:59:24 - mmengine - INFO - Epoch(train) [64][30/91] base_lr: 2.3499e-04 lr: 2.3499e-04 eta: 3 days, 14:10:45 time: 10.5228 data_time: 1.4469 memory: 68702 grad_norm: 1.9918 loss: 2.0274 center_loss: 0.5652 size_loss: 0.1723 cls_loss: 0.6459 giou_loss: 0.6439 2025/05/12 08:01:00 - mmengine - INFO - Epoch(train) [64][40/91] base_lr: 2.3499e-04 lr: 2.3499e-04 eta: 3 days, 14:08:40 time: 10.5286 data_time: 1.4355 memory: 68702 grad_norm: 1.9633 loss: 2.0345 center_loss: 0.5677 size_loss: 0.1727 cls_loss: 0.6479 giou_loss: 0.6463 2025/05/12 08:02:38 - mmengine - INFO - Epoch(train) [64][50/91] base_lr: 2.3499e-04 lr: 2.3499e-04 eta: 3 days, 14:06:39 time: 10.7239 data_time: 1.4392 memory: 68703 grad_norm: 1.9298 loss: 2.0336 center_loss: 0.5705 size_loss: 0.1739 cls_loss: 0.6412 giou_loss: 0.6481 2025/05/12 08:04:15 - mmengine - INFO - Epoch(train) [64][60/91] base_lr: 2.3499e-04 lr: 2.3499e-04 eta: 3 days, 14:04:35 time: 9.6997 data_time: 0.5932 memory: 68703 grad_norm: 1.9090 loss: 2.0430 center_loss: 0.5729 size_loss: 0.1757 cls_loss: 0.6439 giou_loss: 0.6505 2025/05/12 08:05:52 - mmengine - INFO - Epoch(train) [64][70/91] base_lr: 2.3499e-04 lr: 2.3499e-04 eta: 3 days, 14:02:31 time: 9.6990 data_time: 0.5766 memory: 68703 grad_norm: 1.6629 loss: 2.0224 center_loss: 0.5609 size_loss: 0.1721 cls_loss: 0.6447 giou_loss: 0.6447 2025/05/12 08:07:28 - mmengine - INFO - Epoch(train) [64][80/91] base_lr: 2.3499e-04 lr: 2.3499e-04 eta: 3 days, 14:00:25 time: 9.6966 data_time: 0.5688 memory: 68702 grad_norm: 1.5833 loss: 2.0168 center_loss: 0.5607 size_loss: 0.1721 cls_loss: 0.6425 giou_loss: 0.6416 2025/05/12 08:09:04 - mmengine - INFO - Epoch(train) [64][90/91] base_lr: 2.3499e-04 lr: 2.3499e-04 eta: 3 days, 13:58:16 time: 9.6751 data_time: 0.5752 memory: 68702 grad_norm: 1.6329 loss: 2.0161 center_loss: 0.5622 size_loss: 0.1729 cls_loss: 0.6368 giou_loss: 0.6442 2025/05/12 08:09:06 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 08:09:06 - mmengine - INFO - Saving checkpoint at 64 epochs 2025/05/12 08:10:02 - mmengine - INFO - Epoch(val) [64][10/39] eta: 0:01:38 time: 2.8980 data_time: 0.3802 memory: 15952 2025/05/12 08:10:28 - mmengine - INFO - Epoch(val) [64][20/39] eta: 0:00:57 time: 2.7650 data_time: 0.2459 memory: 13407 2025/05/12 08:10:54 - mmengine - INFO - Epoch(val) [64][30/39] eta: 0:00:25 time: 2.7517 data_time: 0.2338 memory: 13407 2025/05/12 08:11:21 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | table | 0.4428 | 0.5943 | 0.1088 | 0.2429 | | chair | 0.4841 | 0.6835 | 0.0857 | 0.2442 | | garbagebin | 0.1978 | 0.4113 | 0.0120 | 0.0887 | | sofa | 0.7051 | 0.8247 | 0.1206 | 0.3196 | | curtain | 0.2018 | 0.4627 | 0.0215 | 0.1045 | | picture | 0.0020 | 0.0360 | 0.0000 | 0.0000 | | bookshelf | 0.1891 | 0.5195 | 0.0072 | 0.0779 | | door | 0.1243 | 0.3940 | 0.0132 | 0.1028 | | cabinet | 0.2464 | 0.4435 | 0.0576 | 0.1774 | | window | 0.0775 | 0.3156 | 0.0046 | 0.0745 | | refrigerator | 0.4636 | 0.6316 | 0.1452 | 0.2105 | | sink | 0.4524 | 0.5918 | 0.0464 | 0.1633 | | counter | 0.1801 | 0.2500 | 0.0301 | 0.0769 | | desk | 0.6490 | 0.8346 | 0.2215 | 0.4331 | | bed | 0.7872 | 0.8519 | 0.2848 | 0.4815 | | toilet | 0.7687 | 0.8966 | 0.3800 | 0.4828 | | bathtub | 0.7072 | 0.8065 | 0.2158 | 0.3548 | | showercurtrain | 0.3923 | 0.6071 | 0.0000 | 0.0000 | +----------------+---------+---------+---------+---------+ | Overall | 0.3929 | 0.5642 | 0.0975 | 0.2020 | +----------------+---------+---------+---------+---------+ 2025/05/12 08:11:21 - mmengine - INFO - Epoch(val) [64][39/39] chair_AP_0.25: 0.4841 sofa_AP_0.25: 0.7051 table_AP_0.25: 0.4428 garbagebin_AP_0.25: 0.1978 bookshelf_AP_0.25: 0.1891 picture_AP_0.25: 0.0020 curtain_AP_0.25: 0.2018 door_AP_0.25: 0.1243 cabinet_AP_0.25: 0.2464 refrigerator_AP_0.25: 0.4636 counter_AP_0.25: 0.1801 sink_AP_0.25: 0.4524 window_AP_0.25: 0.0775 desk_AP_0.25: 0.6490 bed_AP_0.25: 0.7872 toilet_AP_0.25: 0.7687 showercurtrain_AP_0.25: 0.3923 bathtub_AP_0.25: 0.7072 mAP_0.25: 0.3929 chair_rec_0.25: 0.6835 sofa_rec_0.25: 0.8247 table_rec_0.25: 0.5943 garbagebin_rec_0.25: 0.4113 bookshelf_rec_0.25: 0.5195 picture_rec_0.25: 0.0360 curtain_rec_0.25: 0.4627 door_rec_0.25: 0.3940 cabinet_rec_0.25: 0.4435 refrigerator_rec_0.25: 0.6316 counter_rec_0.25: 0.2500 sink_rec_0.25: 0.5918 window_rec_0.25: 0.3156 desk_rec_0.25: 0.8346 bed_rec_0.25: 0.8519 toilet_rec_0.25: 0.8966 showercurtrain_rec_0.25: 0.6071 bathtub_rec_0.25: 0.8065 mAR_0.25: 0.5642 chair_AP_0.50: 0.0857 sofa_AP_0.50: 0.1206 table_AP_0.50: 0.1088 garbagebin_AP_0.50: 0.0120 bookshelf_AP_0.50: 0.0072 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0215 door_AP_0.50: 0.0132 cabinet_AP_0.50: 0.0576 refrigerator_AP_0.50: 0.1452 counter_AP_0.50: 0.0301 sink_AP_0.50: 0.0464 window_AP_0.50: 0.0046 desk_AP_0.50: 0.2215 bed_AP_0.50: 0.2848 toilet_AP_0.50: 0.3800 showercurtrain_AP_0.50: 0.0000 bathtub_AP_0.50: 0.2158 mAP_0.50: 0.0975 chair_rec_0.50: 0.2442 sofa_rec_0.50: 0.3196 table_rec_0.50: 0.2429 garbagebin_rec_0.50: 0.0887 bookshelf_rec_0.50: 0.0779 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.1045 door_rec_0.50: 0.1028 cabinet_rec_0.50: 0.1774 refrigerator_rec_0.50: 0.2105 counter_rec_0.50: 0.0769 sink_rec_0.50: 0.1633 window_rec_0.50: 0.0745 desk_rec_0.50: 0.4331 bed_rec_0.50: 0.4815 toilet_rec_0.50: 0.4828 showercurtrain_rec_0.50: 0.0000 bathtub_rec_0.50: 0.3548 mAR_0.50: 0.2020 data_time: 0.2736 time: 2.7871 2025/05/12 08:11:21 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_58.pth is removed 2025/05/12 08:11:44 - mmengine - INFO - The best checkpoint with 0.3929 mAP_0.25 at 64 epoch is saved to best_mAP_0.25_epoch_64.pth. 2025/05/12 08:14:37 - mmengine - INFO - Epoch(train) [65][10/91] base_lr: 2.3452e-04 lr: 2.3452e-04 eta: 3 days, 13:59:52 time: 10.5512 data_time: 1.5908 memory: 68702 grad_norm: 1.7996 loss: 2.0014 center_loss: 0.5598 size_loss: 0.1715 cls_loss: 0.6287 giou_loss: 0.6414 2025/05/12 08:16:14 - mmengine - INFO - Epoch(train) [65][20/91] base_lr: 2.3452e-04 lr: 2.3452e-04 eta: 3 days, 13:57:47 time: 10.5527 data_time: 1.5973 memory: 68703 grad_norm: 1.7792 loss: 1.9981 center_loss: 0.5596 size_loss: 0.1715 cls_loss: 0.6242 giou_loss: 0.6428 2025/05/12 08:17:51 - mmengine - INFO - Epoch(train) [65][30/91] base_lr: 2.3452e-04 lr: 2.3452e-04 eta: 3 days, 13:55:41 time: 10.5438 data_time: 1.6015 memory: 68702 grad_norm: 1.8176 loss: 2.0058 center_loss: 0.5629 size_loss: 0.1726 cls_loss: 0.6216 giou_loss: 0.6487 2025/05/12 08:19:27 - mmengine - INFO - Epoch(train) [65][40/91] base_lr: 2.3452e-04 lr: 2.3452e-04 eta: 3 days, 13:53:35 time: 10.5369 data_time: 1.5970 memory: 68702 grad_norm: 1.8203 loss: 1.9919 center_loss: 0.5616 size_loss: 0.1703 cls_loss: 0.6128 giou_loss: 0.6473 2025/05/12 08:21:04 - mmengine - INFO - Epoch(train) [65][50/91] base_lr: 2.3452e-04 lr: 2.3452e-04 eta: 3 days, 13:51:34 time: 10.7250 data_time: 1.6059 memory: 68703 grad_norm: 1.5532 loss: 1.9785 center_loss: 0.5520 size_loss: 0.1680 cls_loss: 0.6169 giou_loss: 0.6415 2025/05/12 08:22:43 - mmengine - INFO - Epoch(train) [65][60/91] base_lr: 2.3452e-04 lr: 2.3452e-04 eta: 3 days, 13:49:37 time: 9.7076 data_time: 0.5991 memory: 68703 grad_norm: 1.5379 loss: 1.9965 center_loss: 0.5603 size_loss: 0.1689 cls_loss: 0.6204 giou_loss: 0.6470 2025/05/12 08:24:21 - mmengine - INFO - Epoch(train) [65][70/91] base_lr: 2.3452e-04 lr: 2.3452e-04 eta: 3 days, 13:47:39 time: 9.7336 data_time: 0.5905 memory: 68702 grad_norm: 1.5659 loss: 2.0069 center_loss: 0.5682 size_loss: 0.1688 cls_loss: 0.6246 giou_loss: 0.6454 2025/05/12 08:25:58 - mmengine - INFO - Epoch(train) [65][80/91] base_lr: 2.3452e-04 lr: 2.3452e-04 eta: 3 days, 13:45:38 time: 9.7529 data_time: 0.5878 memory: 68702 grad_norm: 1.7332 loss: 2.0214 center_loss: 0.5787 size_loss: 0.1707 cls_loss: 0.6280 giou_loss: 0.6440 2025/05/12 08:27:35 - mmengine - INFO - Epoch(train) [65][90/91] base_lr: 2.3452e-04 lr: 2.3452e-04 eta: 3 days, 13:43:35 time: 9.7600 data_time: 0.5872 memory: 68702 grad_norm: 1.7393 loss: 2.0327 center_loss: 0.5788 size_loss: 0.1730 cls_loss: 0.6328 giou_loss: 0.6480 2025/05/12 08:27:37 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 08:30:06 - mmengine - INFO - Epoch(train) [66][10/91] base_lr: 2.3405e-04 lr: 2.3405e-04 eta: 3 days, 13:45:09 time: 10.6393 data_time: 1.4997 memory: 68702 grad_norm: 2.0509 loss: 2.0239 center_loss: 0.5737 size_loss: 0.1737 cls_loss: 0.6287 giou_loss: 0.6478 2025/05/12 08:31:43 - mmengine - INFO - Epoch(train) [66][20/91] base_lr: 2.3405e-04 lr: 2.3405e-04 eta: 3 days, 13:43:03 time: 10.6148 data_time: 1.4860 memory: 68702 grad_norm: 2.2348 loss: 2.0382 center_loss: 0.5776 size_loss: 0.1762 cls_loss: 0.6369 giou_loss: 0.6475 2025/05/12 08:33:20 - mmengine - INFO - Epoch(train) [66][30/91] base_lr: 2.3405e-04 lr: 2.3405e-04 eta: 3 days, 13:40:58 time: 10.5839 data_time: 1.4849 memory: 68703 grad_norm: 2.1966 loss: 2.0288 center_loss: 0.5736 size_loss: 0.1760 cls_loss: 0.6311 giou_loss: 0.6480 2025/05/12 08:34:57 - mmengine - INFO - Epoch(train) [66][40/91] base_lr: 2.3405e-04 lr: 2.3405e-04 eta: 3 days, 13:38:54 time: 10.5703 data_time: 1.4857 memory: 68703 grad_norm: 2.0504 loss: 2.0066 center_loss: 0.5593 size_loss: 0.1744 cls_loss: 0.6285 giou_loss: 0.6444 2025/05/12 08:36:35 - mmengine - INFO - Epoch(train) [66][50/91] base_lr: 2.3405e-04 lr: 2.3405e-04 eta: 3 days, 13:36:57 time: 10.7510 data_time: 1.4949 memory: 68702 grad_norm: 1.7794 loss: 2.0228 center_loss: 0.5655 size_loss: 0.1745 cls_loss: 0.6366 giou_loss: 0.6462 2025/05/12 08:38:12 - mmengine - INFO - Epoch(train) [66][60/91] base_lr: 2.3405e-04 lr: 2.3405e-04 eta: 3 days, 13:34:54 time: 9.7045 data_time: 0.5777 memory: 68702 grad_norm: 1.7678 loss: 2.0257 center_loss: 0.5695 size_loss: 0.1739 cls_loss: 0.6344 giou_loss: 0.6479 2025/05/12 08:39:49 - mmengine - INFO - Epoch(train) [66][70/91] base_lr: 2.3405e-04 lr: 2.3405e-04 eta: 3 days, 13:32:53 time: 9.7199 data_time: 0.5788 memory: 68701 grad_norm: 1.6873 loss: 2.0185 center_loss: 0.5688 size_loss: 0.1722 cls_loss: 0.6308 giou_loss: 0.6467 2025/05/12 08:41:26 - mmengine - INFO - Epoch(train) [66][80/91] base_lr: 2.3405e-04 lr: 2.3405e-04 eta: 3 days, 13:30:49 time: 9.7244 data_time: 0.5963 memory: 68702 grad_norm: 1.7366 loss: 2.0349 center_loss: 0.5779 size_loss: 0.1747 cls_loss: 0.6313 giou_loss: 0.6510 2025/05/12 08:42:14 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 08:43:01 - mmengine - INFO - Epoch(train) [66][90/91] base_lr: 2.3405e-04 lr: 2.3405e-04 eta: 3 days, 13:28:38 time: 9.6935 data_time: 0.5989 memory: 68703 grad_norm: 1.7614 loss: 2.0504 center_loss: 0.5840 size_loss: 0.1761 cls_loss: 0.6363 giou_loss: 0.6540 2025/05/12 08:43:03 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 08:43:03 - mmengine - INFO - Saving checkpoint at 66 epochs 2025/05/12 08:44:01 - mmengine - INFO - Epoch(val) [66][10/39] eta: 0:01:37 time: 2.9011 data_time: 0.3871 memory: 15952 2025/05/12 08:44:27 - mmengine - INFO - Epoch(val) [66][20/39] eta: 0:00:56 time: 2.7422 data_time: 0.2364 memory: 13407 2025/05/12 08:44:53 - mmengine - INFO - Epoch(val) [66][30/39] eta: 0:00:25 time: 2.7301 data_time: 0.2268 memory: 13407 2025/05/12 08:45:19 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.2205 | 0.4434 | 0.0204 | 0.1151 | | table | 0.3880 | 0.5114 | 0.1126 | 0.2257 | | curtain | 0.2060 | 0.4627 | 0.0667 | 0.1343 | | chair | 0.4858 | 0.6827 | 0.0992 | 0.2485 | | sofa | 0.6246 | 0.8247 | 0.1666 | 0.3711 | | picture | 0.0052 | 0.0541 | 0.0000 | 0.0000 | | bookshelf | 0.2791 | 0.5584 | 0.0368 | 0.1818 | | door | 0.1413 | 0.3897 | 0.0142 | 0.1028 | | window | 0.0756 | 0.2589 | 0.0038 | 0.0461 | | cabinet | 0.2255 | 0.4301 | 0.0725 | 0.1747 | | refrigerator | 0.3973 | 0.5965 | 0.1459 | 0.2632 | | sink | 0.4285 | 0.5918 | 0.0210 | 0.1020 | | counter | 0.1505 | 0.1923 | 0.0634 | 0.1154 | | bed | 0.8416 | 0.8765 | 0.3309 | 0.5062 | | desk | 0.6146 | 0.7953 | 0.2071 | 0.3780 | | toilet | 0.7105 | 0.8966 | 0.3568 | 0.5000 | | showercurtrain | 0.2689 | 0.5000 | 0.0357 | 0.0357 | | bathtub | 0.6046 | 0.7097 | 0.2330 | 0.3548 | +----------------+---------+---------+---------+---------+ | Overall | 0.3704 | 0.5430 | 0.1104 | 0.2142 | +----------------+---------+---------+---------+---------+ 2025/05/12 08:45:19 - mmengine - INFO - Epoch(val) [66][39/39] chair_AP_0.25: 0.4858 sofa_AP_0.25: 0.6246 table_AP_0.25: 0.3880 garbagebin_AP_0.25: 0.2205 bookshelf_AP_0.25: 0.2791 picture_AP_0.25: 0.0052 curtain_AP_0.25: 0.2060 door_AP_0.25: 0.1413 cabinet_AP_0.25: 0.2255 refrigerator_AP_0.25: 0.3973 counter_AP_0.25: 0.1505 sink_AP_0.25: 0.4285 window_AP_0.25: 0.0756 desk_AP_0.25: 0.6146 bed_AP_0.25: 0.8416 toilet_AP_0.25: 0.7105 showercurtrain_AP_0.25: 0.2689 bathtub_AP_0.25: 0.6046 mAP_0.25: 0.3704 chair_rec_0.25: 0.6827 sofa_rec_0.25: 0.8247 table_rec_0.25: 0.5114 garbagebin_rec_0.25: 0.4434 bookshelf_rec_0.25: 0.5584 picture_rec_0.25: 0.0541 curtain_rec_0.25: 0.4627 door_rec_0.25: 0.3897 cabinet_rec_0.25: 0.4301 refrigerator_rec_0.25: 0.5965 counter_rec_0.25: 0.1923 sink_rec_0.25: 0.5918 window_rec_0.25: 0.2589 desk_rec_0.25: 0.7953 bed_rec_0.25: 0.8765 toilet_rec_0.25: 0.8966 showercurtrain_rec_0.25: 0.5000 bathtub_rec_0.25: 0.7097 mAR_0.25: 0.5430 chair_AP_0.50: 0.0992 sofa_AP_0.50: 0.1666 table_AP_0.50: 0.1126 garbagebin_AP_0.50: 0.0204 bookshelf_AP_0.50: 0.0368 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0667 door_AP_0.50: 0.0142 cabinet_AP_0.50: 0.0725 refrigerator_AP_0.50: 0.1459 counter_AP_0.50: 0.0634 sink_AP_0.50: 0.0210 window_AP_0.50: 0.0038 desk_AP_0.50: 0.2071 bed_AP_0.50: 0.3309 toilet_AP_0.50: 0.3568 showercurtrain_AP_0.50: 0.0357 bathtub_AP_0.50: 0.2330 mAP_0.50: 0.1104 chair_rec_0.50: 0.2485 sofa_rec_0.50: 0.3711 table_rec_0.50: 0.2257 garbagebin_rec_0.50: 0.1151 bookshelf_rec_0.50: 0.1818 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.1343 door_rec_0.50: 0.1028 cabinet_rec_0.50: 0.1747 refrigerator_rec_0.50: 0.2632 counter_rec_0.50: 0.1154 sink_rec_0.50: 0.1020 window_rec_0.50: 0.0461 desk_rec_0.50: 0.3780 bed_rec_0.50: 0.5062 toilet_rec_0.50: 0.5000 showercurtrain_rec_0.50: 0.0357 bathtub_rec_0.50: 0.3548 mAR_0.50: 0.2142 data_time: 0.2658 time: 2.7644 2025/05/12 08:47:46 - mmengine - INFO - Epoch(train) [67][10/91] base_lr: 2.3356e-04 lr: 2.3356e-04 eta: 3 days, 13:29:56 time: 10.5143 data_time: 1.4628 memory: 68703 grad_norm: 1.8532 loss: 2.0543 center_loss: 0.5862 size_loss: 0.1775 cls_loss: 0.6336 giou_loss: 0.6570 2025/05/12 08:49:23 - mmengine - INFO - Epoch(train) [67][20/91] base_lr: 2.3356e-04 lr: 2.3356e-04 eta: 3 days, 13:27:53 time: 10.5172 data_time: 1.4699 memory: 68702 grad_norm: 1.9244 loss: 2.0357 center_loss: 0.5763 size_loss: 0.1756 cls_loss: 0.6332 giou_loss: 0.6505 2025/05/12 08:51:00 - mmengine - INFO - Epoch(train) [67][30/91] base_lr: 2.3356e-04 lr: 2.3356e-04 eta: 3 days, 13:25:49 time: 10.5025 data_time: 1.4818 memory: 68702 grad_norm: 1.8290 loss: 2.0346 center_loss: 0.5732 size_loss: 0.1740 cls_loss: 0.6368 giou_loss: 0.6506 2025/05/12 08:52:36 - mmengine - INFO - Epoch(train) [67][40/91] base_lr: 2.3356e-04 lr: 2.3356e-04 eta: 3 days, 13:23:43 time: 10.4914 data_time: 1.4620 memory: 68703 grad_norm: 1.7876 loss: 2.0160 center_loss: 0.5620 size_loss: 0.1715 cls_loss: 0.6361 giou_loss: 0.6464 2025/05/12 08:54:13 - mmengine - INFO - Epoch(train) [67][50/91] base_lr: 2.3356e-04 lr: 2.3356e-04 eta: 3 days, 13:21:40 time: 10.6762 data_time: 1.4766 memory: 68702 grad_norm: 1.7264 loss: 2.0005 center_loss: 0.5577 size_loss: 0.1696 cls_loss: 0.6350 giou_loss: 0.6382 2025/05/12 08:55:50 - mmengine - INFO - Epoch(train) [67][60/91] base_lr: 2.3356e-04 lr: 2.3356e-04 eta: 3 days, 13:19:36 time: 9.6777 data_time: 0.6074 memory: 68702 grad_norm: 1.7355 loss: 1.9958 center_loss: 0.5539 size_loss: 0.1674 cls_loss: 0.6350 giou_loss: 0.6395 2025/05/12 08:57:26 - mmengine - INFO - Epoch(train) [67][70/91] base_lr: 2.3356e-04 lr: 2.3356e-04 eta: 3 days, 13:17:30 time: 9.6640 data_time: 0.6028 memory: 68702 grad_norm: 1.6312 loss: 1.9784 center_loss: 0.5500 size_loss: 0.1652 cls_loss: 0.6270 giou_loss: 0.6362 2025/05/12 08:59:03 - mmengine - INFO - Epoch(train) [67][80/91] base_lr: 2.3356e-04 lr: 2.3356e-04 eta: 3 days, 13:15:25 time: 9.6573 data_time: 0.5971 memory: 68702 grad_norm: 1.6314 loss: 1.9580 center_loss: 0.5443 size_loss: 0.1640 cls_loss: 0.6179 giou_loss: 0.6318 2025/05/12 09:00:38 - mmengine - INFO - Epoch(train) [67][90/91] base_lr: 2.3356e-04 lr: 2.3356e-04 eta: 3 days, 13:13:15 time: 9.6407 data_time: 0.5925 memory: 68702 grad_norm: 1.7370 loss: 1.9522 center_loss: 0.5461 size_loss: 0.1626 cls_loss: 0.6122 giou_loss: 0.6313 2025/05/12 09:00:40 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 09:03:08 - mmengine - INFO - Epoch(train) [68][10/91] base_lr: 2.3307e-04 lr: 2.3307e-04 eta: 3 days, 13:14:33 time: 10.4995 data_time: 1.4299 memory: 68703 grad_norm: 1.7781 loss: 1.9566 center_loss: 0.5429 size_loss: 0.1642 cls_loss: 0.6162 giou_loss: 0.6333 2025/05/12 09:04:44 - mmengine - INFO - Epoch(train) [68][20/91] base_lr: 2.3307e-04 lr: 2.3307e-04 eta: 3 days, 13:12:27 time: 10.4908 data_time: 1.4183 memory: 68702 grad_norm: 1.8554 loss: 1.9596 center_loss: 0.5466 size_loss: 0.1662 cls_loss: 0.6151 giou_loss: 0.6317 2025/05/12 09:06:21 - mmengine - INFO - Epoch(train) [68][30/91] base_lr: 2.3307e-04 lr: 2.3307e-04 eta: 3 days, 13:10:23 time: 10.4995 data_time: 1.4060 memory: 68703 grad_norm: 1.8716 loss: 1.9690 center_loss: 0.5455 size_loss: 0.1669 cls_loss: 0.6231 giou_loss: 0.6336 2025/05/12 09:07:57 - mmengine - INFO - Epoch(train) [68][40/91] base_lr: 2.3307e-04 lr: 2.3307e-04 eta: 3 days, 13:08:18 time: 10.4956 data_time: 1.3979 memory: 68702 grad_norm: 1.7457 loss: 1.9725 center_loss: 0.5462 size_loss: 0.1658 cls_loss: 0.6276 giou_loss: 0.6329 2025/05/12 09:09:36 - mmengine - INFO - Epoch(train) [68][50/91] base_lr: 2.3307e-04 lr: 2.3307e-04 eta: 3 days, 13:06:21 time: 10.7013 data_time: 1.4220 memory: 68702 grad_norm: 1.7011 loss: 1.9869 center_loss: 0.5505 size_loss: 0.1668 cls_loss: 0.6335 giou_loss: 0.6362 2025/05/12 09:11:13 - mmengine - INFO - Epoch(train) [68][60/91] base_lr: 2.3307e-04 lr: 2.3307e-04 eta: 3 days, 13:04:22 time: 9.7038 data_time: 0.5757 memory: 68703 grad_norm: 1.7235 loss: 1.9897 center_loss: 0.5541 size_loss: 0.1667 cls_loss: 0.6322 giou_loss: 0.6367 2025/05/12 09:12:50 - mmengine - INFO - Epoch(train) [68][70/91] base_lr: 2.3307e-04 lr: 2.3307e-04 eta: 3 days, 13:02:19 time: 9.7147 data_time: 0.5847 memory: 68701 grad_norm: 1.9992 loss: 1.9918 center_loss: 0.5582 size_loss: 0.1678 cls_loss: 0.6295 giou_loss: 0.6364 2025/05/12 09:14:26 - mmengine - INFO - Epoch(train) [68][80/91] base_lr: 2.3307e-04 lr: 2.3307e-04 eta: 3 days, 13:00:14 time: 9.7058 data_time: 0.5954 memory: 68702 grad_norm: 2.0384 loss: 2.0061 center_loss: 0.5679 size_loss: 0.1697 cls_loss: 0.6254 giou_loss: 0.6430 2025/05/12 09:16:01 - mmengine - INFO - Epoch(train) [68][90/91] base_lr: 2.3307e-04 lr: 2.3307e-04 eta: 3 days, 12:58:01 time: 9.6745 data_time: 0.5876 memory: 68702 grad_norm: 2.0647 loss: 2.0181 center_loss: 0.5749 size_loss: 0.1743 cls_loss: 0.6234 giou_loss: 0.6455 2025/05/12 09:16:03 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 09:16:03 - mmengine - INFO - Saving checkpoint at 68 epochs 2025/05/12 09:17:02 - mmengine - INFO - Epoch(val) [68][10/39] eta: 0:01:36 time: 2.8759 data_time: 0.3723 memory: 15952 2025/05/12 09:17:28 - mmengine - INFO - Epoch(val) [68][20/39] eta: 0:00:56 time: 2.7292 data_time: 0.2210 memory: 13407 2025/05/12 09:17:54 - mmengine - INFO - Epoch(val) [68][30/39] eta: 0:00:25 time: 2.7370 data_time: 0.2202 memory: 13407 2025/05/12 09:18:21 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.1890 | 0.4113 | 0.0106 | 0.0774 | | sofa | 0.6548 | 0.7938 | 0.1319 | 0.2990 | | table | 0.4289 | 0.5857 | 0.1041 | 0.2400 | | chair | 0.4794 | 0.6893 | 0.0894 | 0.2493 | | curtain | 0.1673 | 0.4478 | 0.0039 | 0.0448 | | bookshelf | 0.3051 | 0.6104 | 0.0670 | 0.1429 | | picture | 0.0026 | 0.0721 | 0.0000 | 0.0000 | | door | 0.1425 | 0.4283 | 0.0086 | 0.0771 | | showercurtrain | 0.2688 | 0.5357 | 0.0202 | 0.0714 | | cabinet | 0.2574 | 0.4704 | 0.0493 | 0.1317 | | window | 0.0956 | 0.3333 | 0.0022 | 0.0461 | | refrigerator | 0.5222 | 0.6491 | 0.1343 | 0.2281 | | sink | 0.4803 | 0.6327 | 0.0988 | 0.2245 | | counter | 0.0631 | 0.1538 | 0.0027 | 0.0192 | | bed | 0.8183 | 0.8395 | 0.3133 | 0.5185 | | desk | 0.5957 | 0.7559 | 0.1552 | 0.3307 | | bathtub | 0.7502 | 0.8387 | 0.1727 | 0.3548 | | toilet | 0.7954 | 0.9310 | 0.3186 | 0.4310 | +----------------+---------+---------+---------+---------+ | Overall | 0.3898 | 0.5655 | 0.0935 | 0.1937 | +----------------+---------+---------+---------+---------+ 2025/05/12 09:18:21 - mmengine - INFO - Epoch(val) [68][39/39] chair_AP_0.25: 0.4794 sofa_AP_0.25: 0.6548 table_AP_0.25: 0.4289 garbagebin_AP_0.25: 0.1890 bookshelf_AP_0.25: 0.3051 picture_AP_0.25: 0.0026 curtain_AP_0.25: 0.1673 door_AP_0.25: 0.1425 cabinet_AP_0.25: 0.2574 refrigerator_AP_0.25: 0.5222 counter_AP_0.25: 0.0631 sink_AP_0.25: 0.4803 window_AP_0.25: 0.0956 desk_AP_0.25: 0.5957 bed_AP_0.25: 0.8183 toilet_AP_0.25: 0.7954 showercurtrain_AP_0.25: 0.2688 bathtub_AP_0.25: 0.7502 mAP_0.25: 0.3898 chair_rec_0.25: 0.6893 sofa_rec_0.25: 0.7938 table_rec_0.25: 0.5857 garbagebin_rec_0.25: 0.4113 bookshelf_rec_0.25: 0.6104 picture_rec_0.25: 0.0721 curtain_rec_0.25: 0.4478 door_rec_0.25: 0.4283 cabinet_rec_0.25: 0.4704 refrigerator_rec_0.25: 0.6491 counter_rec_0.25: 0.1538 sink_rec_0.25: 0.6327 window_rec_0.25: 0.3333 desk_rec_0.25: 0.7559 bed_rec_0.25: 0.8395 toilet_rec_0.25: 0.9310 showercurtrain_rec_0.25: 0.5357 bathtub_rec_0.25: 0.8387 mAR_0.25: 0.5655 chair_AP_0.50: 0.0894 sofa_AP_0.50: 0.1319 table_AP_0.50: 0.1041 garbagebin_AP_0.50: 0.0106 bookshelf_AP_0.50: 0.0670 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0039 door_AP_0.50: 0.0086 cabinet_AP_0.50: 0.0493 refrigerator_AP_0.50: 0.1343 counter_AP_0.50: 0.0027 sink_AP_0.50: 0.0988 window_AP_0.50: 0.0022 desk_AP_0.50: 0.1552 bed_AP_0.50: 0.3133 toilet_AP_0.50: 0.3186 showercurtrain_AP_0.50: 0.0202 bathtub_AP_0.50: 0.1727 mAP_0.50: 0.0935 chair_rec_0.50: 0.2493 sofa_rec_0.50: 0.2990 table_rec_0.50: 0.2400 garbagebin_rec_0.50: 0.0774 bookshelf_rec_0.50: 0.1429 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.0448 door_rec_0.50: 0.0771 cabinet_rec_0.50: 0.1317 refrigerator_rec_0.50: 0.2281 counter_rec_0.50: 0.0192 sink_rec_0.50: 0.2245 window_rec_0.50: 0.0461 desk_rec_0.50: 0.3307 bed_rec_0.50: 0.5185 toilet_rec_0.50: 0.4310 showercurtrain_rec_0.50: 0.0714 bathtub_rec_0.50: 0.3548 mAR_0.50: 0.1937 data_time: 0.2574 time: 2.7738 2025/05/12 09:20:53 - mmengine - INFO - Epoch(train) [69][10/91] base_lr: 2.3258e-04 lr: 2.3258e-04 eta: 3 days, 12:59:39 time: 10.6030 data_time: 1.4975 memory: 68702 grad_norm: 2.1819 loss: 2.0138 center_loss: 0.5747 size_loss: 0.1737 cls_loss: 0.6210 giou_loss: 0.6445 2025/05/12 09:22:30 - mmengine - INFO - Epoch(train) [69][20/91] base_lr: 2.3258e-04 lr: 2.3258e-04 eta: 3 days, 12:57:37 time: 10.5945 data_time: 1.4998 memory: 68702 grad_norm: 2.1889 loss: 2.0146 center_loss: 0.5715 size_loss: 0.1745 cls_loss: 0.6240 giou_loss: 0.6445 2025/05/12 09:24:07 - mmengine - INFO - Epoch(train) [69][30/91] base_lr: 2.3258e-04 lr: 2.3258e-04 eta: 3 days, 12:55:31 time: 10.5857 data_time: 1.4919 memory: 68700 grad_norm: 1.8688 loss: 2.0149 center_loss: 0.5667 size_loss: 0.1721 cls_loss: 0.6288 giou_loss: 0.6473 2025/05/12 09:25:43 - mmengine - INFO - Epoch(train) [69][40/91] base_lr: 2.3258e-04 lr: 2.3258e-04 eta: 3 days, 12:53:28 time: 10.5979 data_time: 1.4870 memory: 68700 grad_norm: 1.8675 loss: 2.0073 center_loss: 0.5658 size_loss: 0.1717 cls_loss: 0.6296 giou_loss: 0.6402 2025/05/12 09:27:21 - mmengine - INFO - Epoch(train) [69][50/91] base_lr: 2.3258e-04 lr: 2.3258e-04 eta: 3 days, 12:51:28 time: 10.7958 data_time: 1.4960 memory: 68703 grad_norm: 1.8807 loss: 2.0144 center_loss: 0.5648 size_loss: 0.1718 cls_loss: 0.6359 giou_loss: 0.6420 2025/05/12 09:28:57 - mmengine - INFO - Epoch(train) [69][60/91] base_lr: 2.3258e-04 lr: 2.3258e-04 eta: 3 days, 12:49:25 time: 9.6857 data_time: 0.5706 memory: 68703 grad_norm: 1.8360 loss: 2.0098 center_loss: 0.5631 size_loss: 0.1732 cls_loss: 0.6326 giou_loss: 0.6409 2025/05/12 09:30:34 - mmengine - INFO - Epoch(train) [69][70/91] base_lr: 2.3258e-04 lr: 2.3258e-04 eta: 3 days, 12:47:21 time: 9.6784 data_time: 0.5644 memory: 68702 grad_norm: 1.7912 loss: 1.9939 center_loss: 0.5589 size_loss: 0.1705 cls_loss: 0.6246 giou_loss: 0.6399 2025/05/12 09:32:10 - mmengine - INFO - Epoch(train) [69][80/91] base_lr: 2.3258e-04 lr: 2.3258e-04 eta: 3 days, 12:45:16 time: 9.6750 data_time: 0.5713 memory: 68703 grad_norm: 1.7207 loss: 1.9851 center_loss: 0.5567 size_loss: 0.1712 cls_loss: 0.6202 giou_loss: 0.6370 2025/05/12 09:33:46 - mmengine - INFO - Epoch(train) [69][90/91] base_lr: 2.3258e-04 lr: 2.3258e-04 eta: 3 days, 12:43:08 time: 9.6562 data_time: 0.5697 memory: 68702 grad_norm: 1.7019 loss: 1.9779 center_loss: 0.5533 size_loss: 0.1702 cls_loss: 0.6145 giou_loss: 0.6399 2025/05/12 09:33:48 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 09:36:16 - mmengine - INFO - Epoch(train) [70][10/91] base_lr: 2.3207e-04 lr: 2.3207e-04 eta: 3 days, 12:44:23 time: 10.5184 data_time: 1.4411 memory: 68702 grad_norm: 1.7205 loss: 1.9566 center_loss: 0.5491 size_loss: 0.1660 cls_loss: 0.6054 giou_loss: 0.6361 2025/05/12 09:37:53 - mmengine - INFO - Epoch(train) [70][20/91] base_lr: 2.3207e-04 lr: 2.3207e-04 eta: 3 days, 12:42:20 time: 10.5249 data_time: 1.4394 memory: 68702 grad_norm: 1.7076 loss: 1.9588 center_loss: 0.5517 size_loss: 0.1648 cls_loss: 0.6072 giou_loss: 0.6350 2025/05/12 09:39:31 - mmengine - INFO - Epoch(train) [70][30/91] base_lr: 2.3207e-04 lr: 2.3207e-04 eta: 3 days, 12:40:21 time: 10.5412 data_time: 1.4395 memory: 68703 grad_norm: 1.7457 loss: 1.9732 center_loss: 0.5559 size_loss: 0.1659 cls_loss: 0.6176 giou_loss: 0.6338 2025/05/12 09:41:07 - mmengine - INFO - Epoch(train) [70][40/91] base_lr: 2.3207e-04 lr: 2.3207e-04 eta: 3 days, 12:38:17 time: 10.5412 data_time: 1.4417 memory: 68702 grad_norm: 1.8507 loss: 2.0052 center_loss: 0.5729 size_loss: 0.1700 cls_loss: 0.6209 giou_loss: 0.6415 2025/05/12 09:42:45 - mmengine - INFO - Epoch(train) [70][50/91] base_lr: 2.3207e-04 lr: 2.3207e-04 eta: 3 days, 12:36:17 time: 10.7281 data_time: 1.4607 memory: 68702 grad_norm: 1.7411 loss: 2.0111 center_loss: 0.5766 size_loss: 0.1709 cls_loss: 0.6191 giou_loss: 0.6445 2025/05/12 09:44:21 - mmengine - INFO - Epoch(train) [70][60/91] base_lr: 2.3207e-04 lr: 2.3207e-04 eta: 3 days, 12:34:14 time: 9.6988 data_time: 0.5858 memory: 68702 grad_norm: 2.0262 loss: 2.0157 center_loss: 0.5717 size_loss: 0.1720 cls_loss: 0.6273 giou_loss: 0.6448 2025/05/12 09:45:58 - mmengine - INFO - Epoch(train) [70][70/91] base_lr: 2.3207e-04 lr: 2.3207e-04 eta: 3 days, 12:32:11 time: 9.6962 data_time: 0.5982 memory: 68703 grad_norm: 2.0652 loss: 2.0387 center_loss: 0.5810 size_loss: 0.1747 cls_loss: 0.6356 giou_loss: 0.6474 2025/05/12 09:47:34 - mmengine - INFO - Epoch(train) [70][80/91] base_lr: 2.3207e-04 lr: 2.3207e-04 eta: 3 days, 12:30:06 time: 9.6719 data_time: 0.6068 memory: 68702 grad_norm: 2.0139 loss: 2.0484 center_loss: 0.5831 size_loss: 0.1759 cls_loss: 0.6391 giou_loss: 0.6504 2025/05/12 09:49:10 - mmengine - INFO - Epoch(train) [70][90/91] base_lr: 2.3207e-04 lr: 2.3207e-04 eta: 3 days, 12:27:59 time: 9.6567 data_time: 0.6063 memory: 68702 grad_norm: 1.9418 loss: 2.0220 center_loss: 0.5673 size_loss: 0.1714 cls_loss: 0.6393 giou_loss: 0.6440 2025/05/12 09:49:12 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 09:49:12 - mmengine - INFO - Saving checkpoint at 70 epochs 2025/05/12 09:50:09 - mmengine - INFO - Epoch(val) [70][10/39] eta: 0:01:35 time: 2.8810 data_time: 0.3614 memory: 15952 2025/05/12 09:50:35 - mmengine - INFO - Epoch(val) [70][20/39] eta: 0:00:56 time: 2.7403 data_time: 0.2178 memory: 13407 2025/05/12 09:51:01 - mmengine - INFO - Epoch(val) [70][30/39] eta: 0:00:25 time: 2.7354 data_time: 0.2169 memory: 13407 2025/05/12 09:51:28 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | table | 0.4229 | 0.5771 | 0.1010 | 0.2371 | | garbagebin | 0.1764 | 0.3755 | 0.0141 | 0.0811 | | sofa | 0.6561 | 0.8454 | 0.1435 | 0.3711 | | chair | 0.4944 | 0.6871 | 0.0943 | 0.2449 | | curtain | 0.2358 | 0.4925 | 0.0016 | 0.0299 | | bookshelf | 0.2989 | 0.6494 | 0.0467 | 0.1688 | | picture | 0.0029 | 0.0225 | 0.0000 | 0.0000 | | window | 0.1191 | 0.3191 | 0.0068 | 0.0709 | | cabinet | 0.2362 | 0.4409 | 0.0398 | 0.1452 | | door | 0.1288 | 0.3769 | 0.0117 | 0.0878 | | counter | 0.1072 | 0.2115 | 0.0000 | 0.0000 | | refrigerator | 0.4049 | 0.5263 | 0.1054 | 0.2105 | | sink | 0.4711 | 0.6224 | 0.0576 | 0.1735 | | bed | 0.7984 | 0.8642 | 0.3820 | 0.5679 | | desk | 0.6753 | 0.8504 | 0.1501 | 0.3701 | | showercurtrain | 0.2815 | 0.5000 | 0.0714 | 0.0714 | | toilet | 0.7628 | 0.9138 | 0.2464 | 0.4138 | | bathtub | 0.6775 | 0.7419 | 0.3176 | 0.4194 | +----------------+---------+---------+---------+---------+ | Overall | 0.3861 | 0.5565 | 0.0994 | 0.2035 | +----------------+---------+---------+---------+---------+ 2025/05/12 09:51:29 - mmengine - INFO - Epoch(val) [70][39/39] chair_AP_0.25: 0.4944 sofa_AP_0.25: 0.6561 table_AP_0.25: 0.4229 garbagebin_AP_0.25: 0.1764 bookshelf_AP_0.25: 0.2989 picture_AP_0.25: 0.0029 curtain_AP_0.25: 0.2358 door_AP_0.25: 0.1288 cabinet_AP_0.25: 0.2362 refrigerator_AP_0.25: 0.4049 counter_AP_0.25: 0.1072 sink_AP_0.25: 0.4711 window_AP_0.25: 0.1191 desk_AP_0.25: 0.6753 bed_AP_0.25: 0.7984 toilet_AP_0.25: 0.7628 showercurtrain_AP_0.25: 0.2815 bathtub_AP_0.25: 0.6775 mAP_0.25: 0.3861 chair_rec_0.25: 0.6871 sofa_rec_0.25: 0.8454 table_rec_0.25: 0.5771 garbagebin_rec_0.25: 0.3755 bookshelf_rec_0.25: 0.6494 picture_rec_0.25: 0.0225 curtain_rec_0.25: 0.4925 door_rec_0.25: 0.3769 cabinet_rec_0.25: 0.4409 refrigerator_rec_0.25: 0.5263 counter_rec_0.25: 0.2115 sink_rec_0.25: 0.6224 window_rec_0.25: 0.3191 desk_rec_0.25: 0.8504 bed_rec_0.25: 0.8642 toilet_rec_0.25: 0.9138 showercurtrain_rec_0.25: 0.5000 bathtub_rec_0.25: 0.7419 mAR_0.25: 0.5565 chair_AP_0.50: 0.0943 sofa_AP_0.50: 0.1435 table_AP_0.50: 0.1010 garbagebin_AP_0.50: 0.0141 bookshelf_AP_0.50: 0.0467 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0016 door_AP_0.50: 0.0117 cabinet_AP_0.50: 0.0398 refrigerator_AP_0.50: 0.1054 counter_AP_0.50: 0.0000 sink_AP_0.50: 0.0576 window_AP_0.50: 0.0068 desk_AP_0.50: 0.1501 bed_AP_0.50: 0.3820 toilet_AP_0.50: 0.2464 showercurtrain_AP_0.50: 0.0714 bathtub_AP_0.50: 0.3176 mAP_0.50: 0.0994 chair_rec_0.50: 0.2449 sofa_rec_0.50: 0.3711 table_rec_0.50: 0.2371 garbagebin_rec_0.50: 0.0811 bookshelf_rec_0.50: 0.1688 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.0299 door_rec_0.50: 0.0878 cabinet_rec_0.50: 0.1452 refrigerator_rec_0.50: 0.2105 counter_rec_0.50: 0.0000 sink_rec_0.50: 0.1735 window_rec_0.50: 0.0709 desk_rec_0.50: 0.3701 bed_rec_0.50: 0.5679 toilet_rec_0.50: 0.4138 showercurtrain_rec_0.50: 0.0714 bathtub_rec_0.50: 0.4194 mAR_0.50: 0.2035 data_time: 0.2520 time: 2.7617 2025/05/12 09:53:55 - mmengine - INFO - Epoch(train) [71][10/91] base_lr: 2.3156e-04 lr: 2.3156e-04 eta: 3 days, 12:29:02 time: 10.4857 data_time: 1.4746 memory: 68702 grad_norm: 1.9926 loss: 2.0218 center_loss: 0.5656 size_loss: 0.1713 cls_loss: 0.6422 giou_loss: 0.6428 2025/05/12 09:55:32 - mmengine - INFO - Epoch(train) [71][20/91] base_lr: 2.3156e-04 lr: 2.3156e-04 eta: 3 days, 12:27:01 time: 10.4945 data_time: 1.4667 memory: 68702 grad_norm: 1.7702 loss: 2.0270 center_loss: 0.5710 size_loss: 0.1718 cls_loss: 0.6410 giou_loss: 0.6433 2025/05/12 09:57:10 - mmengine - INFO - Epoch(train) [71][30/91] base_lr: 2.3156e-04 lr: 2.3156e-04 eta: 3 days, 12:25:02 time: 10.5121 data_time: 1.4566 memory: 68702 grad_norm: 1.7392 loss: 2.0026 center_loss: 0.5599 size_loss: 0.1702 cls_loss: 0.6327 giou_loss: 0.6398 2025/05/12 09:58:47 - mmengine - INFO - Epoch(train) [71][40/91] base_lr: 2.3156e-04 lr: 2.3156e-04 eta: 3 days, 12:23:00 time: 10.5200 data_time: 1.4507 memory: 68702 grad_norm: 1.7521 loss: 1.9898 center_loss: 0.5574 size_loss: 0.1689 cls_loss: 0.6268 giou_loss: 0.6367 2025/05/12 10:00:25 - mmengine - INFO - Epoch(train) [71][50/91] base_lr: 2.3156e-04 lr: 2.3156e-04 eta: 3 days, 12:21:03 time: 10.7189 data_time: 1.4618 memory: 68702 grad_norm: 1.6271 loss: 1.9874 center_loss: 0.5545 size_loss: 0.1692 cls_loss: 0.6272 giou_loss: 0.6365 2025/05/12 10:02:02 - mmengine - INFO - Epoch(train) [71][60/91] base_lr: 2.3156e-04 lr: 2.3156e-04 eta: 3 days, 12:19:02 time: 9.7289 data_time: 0.5814 memory: 68703 grad_norm: 1.5991 loss: 1.9926 center_loss: 0.5588 size_loss: 0.1695 cls_loss: 0.6297 giou_loss: 0.6346 2025/05/12 10:03:39 - mmengine - INFO - Epoch(train) [71][70/91] base_lr: 2.3156e-04 lr: 2.3156e-04 eta: 3 days, 12:17:03 time: 9.7374 data_time: 0.5819 memory: 68702 grad_norm: 1.5077 loss: 1.9714 center_loss: 0.5534 size_loss: 0.1673 cls_loss: 0.6191 giou_loss: 0.6316 2025/05/12 10:05:16 - mmengine - INFO - Epoch(train) [71][80/91] base_lr: 2.3156e-04 lr: 2.3156e-04 eta: 3 days, 12:15:00 time: 9.7187 data_time: 0.5845 memory: 68703 grad_norm: 1.4852 loss: 1.9713 center_loss: 0.5523 size_loss: 0.1661 cls_loss: 0.6192 giou_loss: 0.6337 2025/05/12 10:06:52 - mmengine - INFO - Epoch(train) [71][90/91] base_lr: 2.3156e-04 lr: 2.3156e-04 eta: 3 days, 12:12:55 time: 9.7043 data_time: 0.5828 memory: 68702 grad_norm: 1.4786 loss: 1.9551 center_loss: 0.5430 size_loss: 0.1648 cls_loss: 0.6167 giou_loss: 0.6306 2025/05/12 10:06:54 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 10:09:22 - mmengine - INFO - Epoch(train) [72][10/91] base_lr: 2.3105e-04 lr: 2.3105e-04 eta: 3 days, 12:14:04 time: 10.5618 data_time: 1.4808 memory: 68702 grad_norm: 1.5827 loss: 1.9561 center_loss: 0.5428 size_loss: 0.1644 cls_loss: 0.6174 giou_loss: 0.6315 2025/05/12 10:10:59 - mmengine - INFO - Epoch(train) [72][20/91] base_lr: 2.3105e-04 lr: 2.3105e-04 eta: 3 days, 12:12:03 time: 10.5557 data_time: 1.4835 memory: 68702 grad_norm: 1.5862 loss: 1.9465 center_loss: 0.5405 size_loss: 0.1645 cls_loss: 0.6083 giou_loss: 0.6331 2025/05/12 10:12:37 - mmengine - INFO - Epoch(train) [72][30/91] base_lr: 2.3105e-04 lr: 2.3105e-04 eta: 3 days, 12:10:03 time: 10.5539 data_time: 1.4831 memory: 68700 grad_norm: 1.6294 loss: 1.9477 center_loss: 0.5383 size_loss: 0.1645 cls_loss: 0.6139 giou_loss: 0.6310 2025/05/12 10:14:14 - mmengine - INFO - Epoch(train) [72][40/91] base_lr: 2.3105e-04 lr: 2.3105e-04 eta: 3 days, 12:08:03 time: 10.5651 data_time: 1.4901 memory: 68702 grad_norm: 1.7772 loss: 1.9409 center_loss: 0.5355 size_loss: 0.1636 cls_loss: 0.6129 giou_loss: 0.6289 2025/05/12 10:15:51 - mmengine - INFO - Epoch(train) [72][50/91] base_lr: 2.3105e-04 lr: 2.3105e-04 eta: 3 days, 12:06:05 time: 10.7481 data_time: 1.5092 memory: 68702 grad_norm: 1.7421 loss: 1.9537 center_loss: 0.5425 size_loss: 0.1663 cls_loss: 0.6130 giou_loss: 0.6319 2025/05/12 10:17:29 - mmengine - INFO - Epoch(train) [72][60/91] base_lr: 2.3105e-04 lr: 2.3105e-04 eta: 3 days, 12:04:05 time: 9.7251 data_time: 0.5998 memory: 68703 grad_norm: 1.8325 loss: 1.9472 center_loss: 0.5388 size_loss: 0.1652 cls_loss: 0.6135 giou_loss: 0.6298 2025/05/12 10:19:06 - mmengine - INFO - Epoch(train) [72][70/91] base_lr: 2.3105e-04 lr: 2.3105e-04 eta: 3 days, 12:02:04 time: 9.7291 data_time: 0.6024 memory: 68702 grad_norm: 1.8208 loss: 1.9465 center_loss: 0.5398 size_loss: 0.1638 cls_loss: 0.6151 giou_loss: 0.6278 2025/05/12 10:20:42 - mmengine - INFO - Epoch(train) [72][80/91] base_lr: 2.3105e-04 lr: 2.3105e-04 eta: 3 days, 12:00:01 time: 9.7134 data_time: 0.6084 memory: 68703 grad_norm: 1.7739 loss: 1.9639 center_loss: 0.5527 size_loss: 0.1638 cls_loss: 0.6164 giou_loss: 0.6311 2025/05/12 10:22:18 - mmengine - INFO - Epoch(train) [72][90/91] base_lr: 2.3105e-04 lr: 2.3105e-04 eta: 3 days, 11:57:53 time: 9.6754 data_time: 0.6025 memory: 68703 grad_norm: 1.6537 loss: 1.9636 center_loss: 0.5524 size_loss: 0.1649 cls_loss: 0.6138 giou_loss: 0.6325 2025/05/12 10:22:20 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 10:22:20 - mmengine - INFO - Saving checkpoint at 72 epochs 2025/05/12 10:23:17 - mmengine - INFO - Epoch(val) [72][10/39] eta: 0:01:35 time: 2.8675 data_time: 0.3601 memory: 15952 2025/05/12 10:23:43 - mmengine - INFO - Epoch(val) [72][20/39] eta: 0:00:55 time: 2.7214 data_time: 0.2213 memory: 13407 2025/05/12 10:24:09 - mmengine - INFO - Epoch(val) [72][30/39] eta: 0:00:25 time: 2.7244 data_time: 0.2218 memory: 13407 2025/05/12 10:24:35 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | chair | 0.5222 | 0.6901 | 0.1049 | 0.2522 | | sofa | 0.6148 | 0.8247 | 0.1497 | 0.3402 | | bookshelf | 0.2880 | 0.5844 | 0.0775 | 0.2208 | | curtain | 0.2475 | 0.5224 | 0.0268 | 0.1194 | | garbagebin | 0.1770 | 0.3981 | 0.0098 | 0.0868 | | table | 0.4236 | 0.5571 | 0.1033 | 0.2257 | | picture | 0.0054 | 0.0450 | 0.0001 | 0.0090 | | bed | 0.7966 | 0.8519 | 0.3866 | 0.5062 | | door | 0.1660 | 0.4090 | 0.0146 | 0.0964 | | showercurtrain | 0.3858 | 0.7143 | 0.0031 | 0.0714 | | cabinet | 0.2441 | 0.4462 | 0.0568 | 0.1720 | | window | 0.0925 | 0.3085 | 0.0107 | 0.0745 | | counter | 0.1390 | 0.2308 | 0.0319 | 0.0769 | | refrigerator | 0.4467 | 0.5965 | 0.1598 | 0.2982 | | sink | 0.4142 | 0.6224 | 0.0822 | 0.2551 | | desk | 0.6248 | 0.8110 | 0.1665 | 0.4173 | | toilet | 0.6721 | 0.8966 | 0.2579 | 0.4483 | | bathtub | 0.6687 | 0.8065 | 0.1741 | 0.3548 | +----------------+---------+---------+---------+---------+ | Overall | 0.3849 | 0.5731 | 0.1009 | 0.2236 | +----------------+---------+---------+---------+---------+ 2025/05/12 10:24:35 - mmengine - INFO - Epoch(val) [72][39/39] chair_AP_0.25: 0.5222 sofa_AP_0.25: 0.6148 table_AP_0.25: 0.4236 garbagebin_AP_0.25: 0.1770 bookshelf_AP_0.25: 0.2880 picture_AP_0.25: 0.0054 curtain_AP_0.25: 0.2475 door_AP_0.25: 0.1660 cabinet_AP_0.25: 0.2441 refrigerator_AP_0.25: 0.4467 counter_AP_0.25: 0.1390 sink_AP_0.25: 0.4142 window_AP_0.25: 0.0925 desk_AP_0.25: 0.6248 bed_AP_0.25: 0.7966 toilet_AP_0.25: 0.6721 showercurtrain_AP_0.25: 0.3858 bathtub_AP_0.25: 0.6687 mAP_0.25: 0.3849 chair_rec_0.25: 0.6901 sofa_rec_0.25: 0.8247 table_rec_0.25: 0.5571 garbagebin_rec_0.25: 0.3981 bookshelf_rec_0.25: 0.5844 picture_rec_0.25: 0.0450 curtain_rec_0.25: 0.5224 door_rec_0.25: 0.4090 cabinet_rec_0.25: 0.4462 refrigerator_rec_0.25: 0.5965 counter_rec_0.25: 0.2308 sink_rec_0.25: 0.6224 window_rec_0.25: 0.3085 desk_rec_0.25: 0.8110 bed_rec_0.25: 0.8519 toilet_rec_0.25: 0.8966 showercurtrain_rec_0.25: 0.7143 bathtub_rec_0.25: 0.8065 mAR_0.25: 0.5731 chair_AP_0.50: 0.1049 sofa_AP_0.50: 0.1497 table_AP_0.50: 0.1033 garbagebin_AP_0.50: 0.0098 bookshelf_AP_0.50: 0.0775 picture_AP_0.50: 0.0001 curtain_AP_0.50: 0.0268 door_AP_0.50: 0.0146 cabinet_AP_0.50: 0.0568 refrigerator_AP_0.50: 0.1598 counter_AP_0.50: 0.0319 sink_AP_0.50: 0.0822 window_AP_0.50: 0.0107 desk_AP_0.50: 0.1665 bed_AP_0.50: 0.3866 toilet_AP_0.50: 0.2579 showercurtrain_AP_0.50: 0.0031 bathtub_AP_0.50: 0.1741 mAP_0.50: 0.1009 chair_rec_0.50: 0.2522 sofa_rec_0.50: 0.3402 table_rec_0.50: 0.2257 garbagebin_rec_0.50: 0.0868 bookshelf_rec_0.50: 0.2208 picture_rec_0.50: 0.0090 curtain_rec_0.50: 0.1194 door_rec_0.50: 0.0964 cabinet_rec_0.50: 0.1720 refrigerator_rec_0.50: 0.2982 counter_rec_0.50: 0.0769 sink_rec_0.50: 0.2551 window_rec_0.50: 0.0745 desk_rec_0.50: 0.4173 bed_rec_0.50: 0.5062 toilet_rec_0.50: 0.4483 showercurtrain_rec_0.50: 0.0714 bathtub_rec_0.50: 0.3548 mAR_0.50: 0.2236 data_time: 0.2557 time: 2.7491 2025/05/12 10:27:04 - mmengine - INFO - Epoch(train) [73][10/91] base_lr: 2.3052e-04 lr: 2.3052e-04 eta: 3 days, 11:59:02 time: 10.5541 data_time: 1.5449 memory: 68702 grad_norm: 1.7227 loss: 2.0039 center_loss: 0.5595 size_loss: 0.1657 cls_loss: 0.6376 giou_loss: 0.6410 2025/05/12 10:28:41 - mmengine - INFO - Epoch(train) [73][20/91] base_lr: 2.3052e-04 lr: 2.3052e-04 eta: 3 days, 11:56:59 time: 10.5424 data_time: 1.5483 memory: 68703 grad_norm: 1.6189 loss: 2.0001 center_loss: 0.5583 size_loss: 0.1660 cls_loss: 0.6377 giou_loss: 0.6381 2025/05/12 10:30:17 - mmengine - INFO - Epoch(train) [73][30/91] base_lr: 2.3052e-04 lr: 2.3052e-04 eta: 3 days, 11:54:56 time: 10.5277 data_time: 1.5358 memory: 68702 grad_norm: 1.6198 loss: 1.9904 center_loss: 0.5534 size_loss: 0.1647 cls_loss: 0.6357 giou_loss: 0.6367 2025/05/12 10:31:54 - mmengine - INFO - Epoch(train) [73][40/91] base_lr: 2.3052e-04 lr: 2.3052e-04 eta: 3 days, 11:52:54 time: 10.5313 data_time: 1.5383 memory: 68703 grad_norm: 1.6381 loss: 1.9830 center_loss: 0.5488 size_loss: 0.1663 cls_loss: 0.6315 giou_loss: 0.6364 2025/05/12 10:33:31 - mmengine - INFO - Epoch(train) [73][50/91] base_lr: 2.3052e-04 lr: 2.3052e-04 eta: 3 days, 11:50:55 time: 10.7253 data_time: 1.5472 memory: 68701 grad_norm: 1.6364 loss: 1.9696 center_loss: 0.5505 size_loss: 0.1667 cls_loss: 0.6193 giou_loss: 0.6331 2025/05/12 10:35:08 - mmengine - INFO - Epoch(train) [73][60/91] base_lr: 2.3052e-04 lr: 2.3052e-04 eta: 3 days, 11:48:55 time: 9.6862 data_time: 0.6036 memory: 68702 grad_norm: 1.6661 loss: 1.9435 center_loss: 0.5462 size_loss: 0.1636 cls_loss: 0.6067 giou_loss: 0.6269 2025/05/12 10:36:46 - mmengine - INFO - Epoch(train) [73][70/91] base_lr: 2.3052e-04 lr: 2.3052e-04 eta: 3 days, 11:46:55 time: 9.6990 data_time: 0.5991 memory: 68702 grad_norm: 1.6718 loss: 1.9708 center_loss: 0.5670 size_loss: 0.1642 cls_loss: 0.6085 giou_loss: 0.6311 2025/05/12 10:38:22 - mmengine - INFO - Epoch(train) [73][80/91] base_lr: 2.3052e-04 lr: 2.3052e-04 eta: 3 days, 11:44:53 time: 9.7024 data_time: 0.6132 memory: 68702 grad_norm: 1.7557 loss: 1.9926 center_loss: 0.5761 size_loss: 0.1679 cls_loss: 0.6124 giou_loss: 0.6362 2025/05/12 10:39:59 - mmengine - INFO - Epoch(train) [73][90/91] base_lr: 2.3052e-04 lr: 2.3052e-04 eta: 3 days, 11:42:49 time: 9.6907 data_time: 0.6045 memory: 68702 grad_norm: 1.7375 loss: 1.9863 center_loss: 0.5702 size_loss: 0.1668 cls_loss: 0.6140 giou_loss: 0.6353 2025/05/12 10:40:01 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 10:42:26 - mmengine - INFO - Epoch(train) [74][10/91] base_lr: 2.2999e-04 lr: 2.2999e-04 eta: 3 days, 11:43:38 time: 10.4906 data_time: 1.4166 memory: 68702 grad_norm: 1.7637 loss: 1.9832 center_loss: 0.5705 size_loss: 0.1647 cls_loss: 0.6152 giou_loss: 0.6328 2025/05/12 10:44:02 - mmengine - INFO - Epoch(train) [74][20/91] base_lr: 2.2999e-04 lr: 2.2999e-04 eta: 3 days, 11:41:35 time: 10.4865 data_time: 1.4134 memory: 68703 grad_norm: 1.7855 loss: 1.9929 center_loss: 0.5767 size_loss: 0.1670 cls_loss: 0.6128 giou_loss: 0.6364 2025/05/12 10:45:39 - mmengine - INFO - Epoch(train) [74][30/91] base_lr: 2.2999e-04 lr: 2.2999e-04 eta: 3 days, 11:39:33 time: 10.4756 data_time: 1.4176 memory: 68702 grad_norm: 1.7507 loss: 1.9691 center_loss: 0.5560 size_loss: 0.1668 cls_loss: 0.6139 giou_loss: 0.6324 2025/05/12 10:47:16 - mmengine - INFO - Epoch(train) [74][40/91] base_lr: 2.2999e-04 lr: 2.2999e-04 eta: 3 days, 11:37:31 time: 10.4719 data_time: 1.4147 memory: 68702 grad_norm: 1.6956 loss: 1.9660 center_loss: 0.5527 size_loss: 0.1661 cls_loss: 0.6162 giou_loss: 0.6310 2025/05/12 10:48:53 - mmengine - INFO - Epoch(train) [74][50/91] base_lr: 2.2999e-04 lr: 2.2999e-04 eta: 3 days, 11:35:33 time: 10.6531 data_time: 1.4349 memory: 68702 grad_norm: 1.6593 loss: 1.9636 center_loss: 0.5496 size_loss: 0.1654 cls_loss: 0.6194 giou_loss: 0.6292 2025/05/12 10:50:30 - mmengine - INFO - Epoch(train) [74][60/91] base_lr: 2.2999e-04 lr: 2.2999e-04 eta: 3 days, 11:33:33 time: 9.6903 data_time: 0.6138 memory: 68702 grad_norm: 1.6586 loss: 1.9628 center_loss: 0.5470 size_loss: 0.1666 cls_loss: 0.6184 giou_loss: 0.6307 2025/05/12 10:52:07 - mmengine - INFO - Epoch(train) [74][70/91] base_lr: 2.2999e-04 lr: 2.2999e-04 eta: 3 days, 11:31:31 time: 9.6933 data_time: 0.6262 memory: 68702 grad_norm: 1.6365 loss: 1.9493 center_loss: 0.5356 size_loss: 0.1636 cls_loss: 0.6221 giou_loss: 0.6279 2025/05/12 10:53:43 - mmengine - INFO - Epoch(train) [74][80/91] base_lr: 2.2999e-04 lr: 2.2999e-04 eta: 3 days, 11:29:29 time: 9.6862 data_time: 0.6211 memory: 68702 grad_norm: 1.6280 loss: 1.9502 center_loss: 0.5355 size_loss: 0.1627 cls_loss: 0.6240 giou_loss: 0.6280 2025/05/12 10:55:19 - mmengine - INFO - Epoch(train) [74][90/91] base_lr: 2.2999e-04 lr: 2.2999e-04 eta: 3 days, 11:27:22 time: 9.6620 data_time: 0.6215 memory: 68702 grad_norm: 1.5747 loss: 1.9703 center_loss: 0.5503 size_loss: 0.1649 cls_loss: 0.6225 giou_loss: 0.6326 2025/05/12 10:55:21 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 10:55:21 - mmengine - INFO - Saving checkpoint at 74 epochs 2025/05/12 10:56:21 - mmengine - INFO - Epoch(val) [74][10/39] eta: 0:01:43 time: 2.9135 data_time: 0.4136 memory: 15952 2025/05/12 10:56:47 - mmengine - INFO - Epoch(val) [74][20/39] eta: 0:00:58 time: 2.7793 data_time: 0.2704 memory: 13407 2025/05/12 10:57:13 - mmengine - INFO - Epoch(val) [74][30/39] eta: 0:00:26 time: 2.8001 data_time: 0.2849 memory: 13407 2025/05/12 10:57:40 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.1896 | 0.4170 | 0.0066 | 0.0811 | | table | 0.4282 | 0.5943 | 0.1297 | 0.2486 | | curtain | 0.2241 | 0.4776 | 0.0658 | 0.1642 | | sofa | 0.6598 | 0.8247 | 0.1230 | 0.3402 | | chair | 0.4881 | 0.6732 | 0.1175 | 0.2734 | | picture | 0.0024 | 0.0495 | 0.0000 | 0.0045 | | door | 0.1361 | 0.4325 | 0.0135 | 0.1156 | | bookshelf | 0.2343 | 0.5844 | 0.0327 | 0.2078 | | cabinet | 0.2204 | 0.4812 | 0.0385 | 0.1667 | | window | 0.1134 | 0.3121 | 0.0098 | 0.0674 | | refrigerator | 0.4254 | 0.6140 | 0.1588 | 0.2982 | | sink | 0.4006 | 0.5612 | 0.0730 | 0.2041 | | counter | 0.0513 | 0.1346 | 0.0128 | 0.0385 | | bed | 0.8049 | 0.8395 | 0.3765 | 0.5185 | | desk | 0.5912 | 0.8268 | 0.2150 | 0.4331 | | toilet | 0.8419 | 0.9310 | 0.3220 | 0.4310 | | bathtub | 0.5747 | 0.7097 | 0.2009 | 0.3548 | | showercurtrain | 0.2655 | 0.4643 | 0.1071 | 0.1071 | +----------------+---------+---------+---------+---------+ | Overall | 0.3696 | 0.5515 | 0.1113 | 0.2253 | +----------------+---------+---------+---------+---------+ 2025/05/12 10:57:40 - mmengine - INFO - Epoch(val) [74][39/39] chair_AP_0.25: 0.4881 sofa_AP_0.25: 0.6598 table_AP_0.25: 0.4282 garbagebin_AP_0.25: 0.1896 bookshelf_AP_0.25: 0.2343 picture_AP_0.25: 0.0024 curtain_AP_0.25: 0.2241 door_AP_0.25: 0.1361 cabinet_AP_0.25: 0.2204 refrigerator_AP_0.25: 0.4254 counter_AP_0.25: 0.0513 sink_AP_0.25: 0.4006 window_AP_0.25: 0.1134 desk_AP_0.25: 0.5912 bed_AP_0.25: 0.8049 toilet_AP_0.25: 0.8419 showercurtrain_AP_0.25: 0.2655 bathtub_AP_0.25: 0.5747 mAP_0.25: 0.3696 chair_rec_0.25: 0.6732 sofa_rec_0.25: 0.8247 table_rec_0.25: 0.5943 garbagebin_rec_0.25: 0.4170 bookshelf_rec_0.25: 0.5844 picture_rec_0.25: 0.0495 curtain_rec_0.25: 0.4776 door_rec_0.25: 0.4325 cabinet_rec_0.25: 0.4812 refrigerator_rec_0.25: 0.6140 counter_rec_0.25: 0.1346 sink_rec_0.25: 0.5612 window_rec_0.25: 0.3121 desk_rec_0.25: 0.8268 bed_rec_0.25: 0.8395 toilet_rec_0.25: 0.9310 showercurtrain_rec_0.25: 0.4643 bathtub_rec_0.25: 0.7097 mAR_0.25: 0.5515 chair_AP_0.50: 0.1175 sofa_AP_0.50: 0.1230 table_AP_0.50: 0.1297 garbagebin_AP_0.50: 0.0066 bookshelf_AP_0.50: 0.0327 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0658 door_AP_0.50: 0.0135 cabinet_AP_0.50: 0.0385 refrigerator_AP_0.50: 0.1588 counter_AP_0.50: 0.0128 sink_AP_0.50: 0.0730 window_AP_0.50: 0.0098 desk_AP_0.50: 0.2150 bed_AP_0.50: 0.3765 toilet_AP_0.50: 0.3220 showercurtrain_AP_0.50: 0.1071 bathtub_AP_0.50: 0.2009 mAP_0.50: 0.1113 chair_rec_0.50: 0.2734 sofa_rec_0.50: 0.3402 table_rec_0.50: 0.2486 garbagebin_rec_0.50: 0.0811 bookshelf_rec_0.50: 0.2078 picture_rec_0.50: 0.0045 curtain_rec_0.50: 0.1642 door_rec_0.50: 0.1156 cabinet_rec_0.50: 0.1667 refrigerator_rec_0.50: 0.2982 counter_rec_0.50: 0.0385 sink_rec_0.50: 0.2041 window_rec_0.50: 0.0674 desk_rec_0.50: 0.4331 bed_rec_0.50: 0.5185 toilet_rec_0.50: 0.4310 showercurtrain_rec_0.50: 0.1071 bathtub_rec_0.50: 0.3548 mAR_0.50: 0.2253 data_time: 0.3356 time: 2.8466 2025/05/12 11:00:11 - mmengine - INFO - Epoch(train) [75][10/91] base_lr: 2.2946e-04 lr: 2.2946e-04 eta: 3 days, 11:28:32 time: 10.5757 data_time: 1.4427 memory: 68702 grad_norm: 1.8845 loss: 2.0305 center_loss: 0.5672 size_loss: 0.1712 cls_loss: 0.6502 giou_loss: 0.6419 2025/05/12 11:01:48 - mmengine - INFO - Epoch(train) [75][20/91] base_lr: 2.2946e-04 lr: 2.2946e-04 eta: 3 days, 11:26:28 time: 10.5614 data_time: 1.4329 memory: 68702 grad_norm: 1.8261 loss: 2.0521 center_loss: 0.5745 size_loss: 0.1738 cls_loss: 0.6560 giou_loss: 0.6478 2025/05/12 11:03:24 - mmengine - INFO - Epoch(train) [75][30/91] base_lr: 2.2946e-04 lr: 2.2946e-04 eta: 3 days, 11:24:28 time: 10.5628 data_time: 1.4199 memory: 68702 grad_norm: 1.8172 loss: 2.0681 center_loss: 0.5788 size_loss: 0.1747 cls_loss: 0.6644 giou_loss: 0.6502 2025/05/12 11:05:01 - mmengine - INFO - Epoch(train) [75][40/91] base_lr: 2.2946e-04 lr: 2.2946e-04 eta: 3 days, 11:22:25 time: 10.5576 data_time: 1.4137 memory: 68703 grad_norm: 1.8097 loss: 2.0955 center_loss: 0.5925 size_loss: 0.1797 cls_loss: 0.6653 giou_loss: 0.6580 2025/05/12 11:06:38 - mmengine - INFO - Epoch(train) [75][50/91] base_lr: 2.2946e-04 lr: 2.2946e-04 eta: 3 days, 11:20:27 time: 10.7569 data_time: 1.4206 memory: 68701 grad_norm: 1.7632 loss: 2.0448 center_loss: 0.5754 size_loss: 0.1750 cls_loss: 0.6414 giou_loss: 0.6530 2025/05/12 11:08:16 - mmengine - INFO - Epoch(train) [75][60/91] base_lr: 2.2946e-04 lr: 2.2946e-04 eta: 3 days, 11:18:32 time: 9.7049 data_time: 0.5828 memory: 68702 grad_norm: 1.6804 loss: 2.0311 center_loss: 0.5699 size_loss: 0.1732 cls_loss: 0.6384 giou_loss: 0.6496 2025/05/12 11:09:54 - mmengine - INFO - Epoch(train) [75][70/91] base_lr: 2.2946e-04 lr: 2.2946e-04 eta: 3 days, 11:16:32 time: 9.7201 data_time: 0.5894 memory: 68703 grad_norm: 1.7290 loss: 2.0081 center_loss: 0.5658 size_loss: 0.1713 cls_loss: 0.6273 giou_loss: 0.6437 2025/05/12 11:11:30 - mmengine - INFO - Epoch(train) [75][80/91] base_lr: 2.2946e-04 lr: 2.2946e-04 eta: 3 days, 11:14:30 time: 9.7130 data_time: 0.5974 memory: 68703 grad_norm: 1.7363 loss: 1.9847 center_loss: 0.5595 size_loss: 0.1702 cls_loss: 0.6171 giou_loss: 0.6380 2025/05/12 11:13:06 - mmengine - INFO - Epoch(train) [75][90/91] base_lr: 2.2946e-04 lr: 2.2946e-04 eta: 3 days, 11:12:24 time: 9.6988 data_time: 0.6002 memory: 68702 grad_norm: 1.6941 loss: 1.9585 center_loss: 0.5481 size_loss: 0.1661 cls_loss: 0.6150 giou_loss: 0.6294 2025/05/12 11:13:08 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 11:15:39 - mmengine - INFO - Epoch(train) [76][10/91] base_lr: 2.2892e-04 lr: 2.2892e-04 eta: 3 days, 11:13:34 time: 10.6209 data_time: 1.5434 memory: 68702 grad_norm: 1.6343 loss: 1.9523 center_loss: 0.5456 size_loss: 0.1654 cls_loss: 0.6128 giou_loss: 0.6284 2025/05/12 11:17:15 - mmengine - INFO - Epoch(train) [76][20/91] base_lr: 2.2892e-04 lr: 2.2892e-04 eta: 3 days, 11:11:31 time: 10.5781 data_time: 1.5369 memory: 68702 grad_norm: 1.6772 loss: 1.9480 center_loss: 0.5421 size_loss: 0.1669 cls_loss: 0.6104 giou_loss: 0.6287 2025/05/12 11:18:53 - mmengine - INFO - Epoch(train) [76][30/91] base_lr: 2.2892e-04 lr: 2.2892e-04 eta: 3 days, 11:09:32 time: 10.5886 data_time: 1.5535 memory: 68703 grad_norm: 1.6816 loss: 1.9661 center_loss: 0.5489 size_loss: 0.1670 cls_loss: 0.6187 giou_loss: 0.6315 2025/05/12 11:20:30 - mmengine - INFO - Epoch(train) [76][40/91] base_lr: 2.2892e-04 lr: 2.2892e-04 eta: 3 days, 11:07:34 time: 10.6000 data_time: 1.5561 memory: 68702 grad_norm: 1.6613 loss: 1.9880 center_loss: 0.5607 size_loss: 0.1696 cls_loss: 0.6224 giou_loss: 0.6354 2025/05/12 11:22:08 - mmengine - INFO - Epoch(train) [76][50/91] base_lr: 2.2892e-04 lr: 2.2892e-04 eta: 3 days, 11:05:38 time: 10.8010 data_time: 1.5641 memory: 68703 grad_norm: 1.6816 loss: 2.0071 center_loss: 0.5740 size_loss: 0.1722 cls_loss: 0.6203 giou_loss: 0.6406 2025/05/12 11:23:46 - mmengine - INFO - Epoch(train) [76][60/91] base_lr: 2.2892e-04 lr: 2.2892e-04 eta: 3 days, 11:03:41 time: 9.7312 data_time: 0.6199 memory: 68703 grad_norm: 1.5997 loss: 2.0049 center_loss: 0.5676 size_loss: 0.1711 cls_loss: 0.6271 giou_loss: 0.6391 2025/05/12 11:25:22 - mmengine - INFO - Epoch(train) [76][70/91] base_lr: 2.2892e-04 lr: 2.2892e-04 eta: 3 days, 11:01:40 time: 9.7380 data_time: 0.6323 memory: 68702 grad_norm: 1.6103 loss: 1.9930 center_loss: 0.5647 size_loss: 0.1682 cls_loss: 0.6251 giou_loss: 0.6351 2025/05/12 11:27:00 - mmengine - INFO - Epoch(train) [76][80/91] base_lr: 2.2892e-04 lr: 2.2892e-04 eta: 3 days, 10:59:42 time: 9.7399 data_time: 0.6214 memory: 68703 grad_norm: 1.5363 loss: 1.9938 center_loss: 0.5593 size_loss: 0.1682 cls_loss: 0.6311 giou_loss: 0.6352 2025/05/12 11:28:35 - mmengine - INFO - Epoch(train) [76][90/91] base_lr: 2.2892e-04 lr: 2.2892e-04 eta: 3 days, 10:57:37 time: 9.7080 data_time: 0.6121 memory: 68702 grad_norm: 1.5082 loss: 1.9843 center_loss: 0.5532 size_loss: 0.1676 cls_loss: 0.6278 giou_loss: 0.6357 2025/05/12 11:28:37 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 11:28:37 - mmengine - INFO - Saving checkpoint at 76 epochs 2025/05/12 11:29:33 - mmengine - INFO - Epoch(val) [76][10/39] eta: 0:01:34 time: 2.9313 data_time: 0.4215 memory: 15952 2025/05/12 11:29:59 - mmengine - INFO - Epoch(val) [76][20/39] eta: 0:00:55 time: 2.7514 data_time: 0.2424 memory: 13407 2025/05/12 11:30:25 - mmengine - INFO - Epoch(val) [76][30/39] eta: 0:00:25 time: 2.7325 data_time: 0.2292 memory: 13407 2025/05/12 11:30:52 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | sofa | 0.6488 | 0.8144 | 0.1111 | 0.2990 | | garbagebin | 0.1715 | 0.3755 | 0.0169 | 0.0943 | | chair | 0.4620 | 0.6652 | 0.0855 | 0.2442 | | table | 0.4621 | 0.5743 | 0.1132 | 0.2171 | | curtain | 0.2293 | 0.4328 | 0.0318 | 0.1045 | | bookshelf | 0.3022 | 0.5974 | 0.0532 | 0.1818 | | door | 0.0963 | 0.3662 | 0.0099 | 0.0835 | | picture | 0.0133 | 0.0811 | 0.0000 | 0.0000 | | cabinet | 0.2509 | 0.4489 | 0.0348 | 0.1344 | | window | 0.1001 | 0.3121 | 0.0022 | 0.0355 | | refrigerator | 0.3746 | 0.5614 | 0.1170 | 0.2281 | | sink | 0.3983 | 0.5816 | 0.0309 | 0.1327 | | counter | 0.1291 | 0.2115 | 0.0184 | 0.0577 | | desk | 0.6473 | 0.8268 | 0.1815 | 0.4173 | | bed | 0.8263 | 0.8519 | 0.3940 | 0.5309 | | toilet | 0.7842 | 0.8793 | 0.1896 | 0.3276 | | bathtub | 0.7703 | 0.8387 | 0.1691 | 0.3226 | | showercurtrain | 0.3582 | 0.5714 | 0.0071 | 0.0357 | +----------------+---------+---------+---------+---------+ | Overall | 0.3903 | 0.5550 | 0.0870 | 0.1915 | +----------------+---------+---------+---------+---------+ 2025/05/12 11:30:52 - mmengine - INFO - Epoch(val) [76][39/39] chair_AP_0.25: 0.4620 sofa_AP_0.25: 0.6488 table_AP_0.25: 0.4621 garbagebin_AP_0.25: 0.1715 bookshelf_AP_0.25: 0.3022 picture_AP_0.25: 0.0133 curtain_AP_0.25: 0.2293 door_AP_0.25: 0.0963 cabinet_AP_0.25: 0.2509 refrigerator_AP_0.25: 0.3746 counter_AP_0.25: 0.1291 sink_AP_0.25: 0.3983 window_AP_0.25: 0.1001 desk_AP_0.25: 0.6473 bed_AP_0.25: 0.8263 toilet_AP_0.25: 0.7842 showercurtrain_AP_0.25: 0.3582 bathtub_AP_0.25: 0.7703 mAP_0.25: 0.3903 chair_rec_0.25: 0.6652 sofa_rec_0.25: 0.8144 table_rec_0.25: 0.5743 garbagebin_rec_0.25: 0.3755 bookshelf_rec_0.25: 0.5974 picture_rec_0.25: 0.0811 curtain_rec_0.25: 0.4328 door_rec_0.25: 0.3662 cabinet_rec_0.25: 0.4489 refrigerator_rec_0.25: 0.5614 counter_rec_0.25: 0.2115 sink_rec_0.25: 0.5816 window_rec_0.25: 0.3121 desk_rec_0.25: 0.8268 bed_rec_0.25: 0.8519 toilet_rec_0.25: 0.8793 showercurtrain_rec_0.25: 0.5714 bathtub_rec_0.25: 0.8387 mAR_0.25: 0.5550 chair_AP_0.50: 0.0855 sofa_AP_0.50: 0.1111 table_AP_0.50: 0.1132 garbagebin_AP_0.50: 0.0169 bookshelf_AP_0.50: 0.0532 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0318 door_AP_0.50: 0.0099 cabinet_AP_0.50: 0.0348 refrigerator_AP_0.50: 0.1170 counter_AP_0.50: 0.0184 sink_AP_0.50: 0.0309 window_AP_0.50: 0.0022 desk_AP_0.50: 0.1815 bed_AP_0.50: 0.3940 toilet_AP_0.50: 0.1896 showercurtrain_AP_0.50: 0.0071 bathtub_AP_0.50: 0.1691 mAP_0.50: 0.0870 chair_rec_0.50: 0.2442 sofa_rec_0.50: 0.2990 table_rec_0.50: 0.2171 garbagebin_rec_0.50: 0.0943 bookshelf_rec_0.50: 0.1818 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.1045 door_rec_0.50: 0.0835 cabinet_rec_0.50: 0.1344 refrigerator_rec_0.50: 0.2281 counter_rec_0.50: 0.0577 sink_rec_0.50: 0.1327 window_rec_0.50: 0.0355 desk_rec_0.50: 0.4173 bed_rec_0.50: 0.5309 toilet_rec_0.50: 0.3276 showercurtrain_rec_0.50: 0.0357 bathtub_rec_0.50: 0.3226 mAR_0.50: 0.1915 data_time: 0.2478 time: 2.7431 2025/05/12 11:33:20 - mmengine - INFO - Epoch(train) [77][10/91] base_lr: 2.2837e-04 lr: 2.2837e-04 eta: 3 days, 10:58:31 time: 10.5627 data_time: 1.5742 memory: 68700 grad_norm: 1.8329 loss: 1.9493 center_loss: 0.5373 size_loss: 0.1642 cls_loss: 0.6196 giou_loss: 0.6281 2025/05/12 11:34:57 - mmengine - INFO - Epoch(train) [77][20/91] base_lr: 2.2837e-04 lr: 2.2837e-04 eta: 3 days, 10:56:30 time: 10.5488 data_time: 1.5782 memory: 68702 grad_norm: 1.9215 loss: 1.9556 center_loss: 0.5442 size_loss: 0.1648 cls_loss: 0.6169 giou_loss: 0.6298 2025/05/12 11:36:33 - mmengine - INFO - Epoch(train) [77][30/91] base_lr: 2.2837e-04 lr: 2.2837e-04 eta: 3 days, 10:54:28 time: 10.5378 data_time: 1.5645 memory: 68703 grad_norm: 1.8633 loss: 2.0008 center_loss: 0.5638 size_loss: 0.1696 cls_loss: 0.6295 giou_loss: 0.6379 2025/05/12 11:38:10 - mmengine - INFO - Epoch(train) [77][40/91] base_lr: 2.2837e-04 lr: 2.2837e-04 eta: 3 days, 10:52:26 time: 10.5165 data_time: 1.5687 memory: 68702 grad_norm: 1.8679 loss: 1.9980 center_loss: 0.5670 size_loss: 0.1700 cls_loss: 0.6212 giou_loss: 0.6397 2025/05/12 11:39:47 - mmengine - INFO - Epoch(train) [77][50/91] base_lr: 2.2837e-04 lr: 2.2837e-04 eta: 3 days, 10:50:29 time: 10.7106 data_time: 1.5882 memory: 68703 grad_norm: 1.7760 loss: 1.9955 center_loss: 0.5669 size_loss: 0.1682 cls_loss: 0.6221 giou_loss: 0.6383 2025/05/12 11:41:24 - mmengine - INFO - Epoch(train) [77][60/91] base_lr: 2.2837e-04 lr: 2.2837e-04 eta: 3 days, 10:48:28 time: 9.6793 data_time: 0.6270 memory: 68702 grad_norm: 1.4970 loss: 2.0016 center_loss: 0.5670 size_loss: 0.1692 cls_loss: 0.6243 giou_loss: 0.6410 2025/05/12 11:43:01 - mmengine - INFO - Epoch(train) [77][70/91] base_lr: 2.2837e-04 lr: 2.2837e-04 eta: 3 days, 10:46:27 time: 9.6772 data_time: 0.6283 memory: 68702 grad_norm: 1.4624 loss: 1.9792 center_loss: 0.5588 size_loss: 0.1682 cls_loss: 0.6158 giou_loss: 0.6364 2025/05/12 11:44:37 - mmengine - INFO - Epoch(train) [77][80/91] base_lr: 2.2837e-04 lr: 2.2837e-04 eta: 3 days, 10:44:25 time: 9.6752 data_time: 0.6411 memory: 68703 grad_norm: 1.5968 loss: 1.9363 center_loss: 0.5369 size_loss: 0.1636 cls_loss: 0.6061 giou_loss: 0.6297 2025/05/12 11:45:16 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 11:46:13 - mmengine - INFO - Epoch(train) [77][90/91] base_lr: 2.2837e-04 lr: 2.2837e-04 eta: 3 days, 10:42:20 time: 9.6570 data_time: 0.6315 memory: 68700 grad_norm: 1.6202 loss: 1.9352 center_loss: 0.5344 size_loss: 0.1626 cls_loss: 0.6087 giou_loss: 0.6294 2025/05/12 11:46:15 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 11:48:40 - mmengine - INFO - Epoch(train) [78][10/91] base_lr: 2.2781e-04 lr: 2.2781e-04 eta: 3 days, 10:43:00 time: 10.4646 data_time: 1.5024 memory: 68702 grad_norm: 1.7800 loss: 1.9511 center_loss: 0.5389 size_loss: 0.1641 cls_loss: 0.6153 giou_loss: 0.6328 2025/05/12 11:50:17 - mmengine - INFO - Epoch(train) [78][20/91] base_lr: 2.2781e-04 lr: 2.2781e-04 eta: 3 days, 10:40:59 time: 10.4675 data_time: 1.4912 memory: 68703 grad_norm: 1.7785 loss: 1.9651 center_loss: 0.5461 size_loss: 0.1645 cls_loss: 0.6200 giou_loss: 0.6345 2025/05/12 11:51:55 - mmengine - INFO - Epoch(train) [78][30/91] base_lr: 2.2781e-04 lr: 2.2781e-04 eta: 3 days, 10:39:03 time: 10.4842 data_time: 1.4681 memory: 68702 grad_norm: 1.7409 loss: 1.9806 center_loss: 0.5446 size_loss: 0.1654 cls_loss: 0.6336 giou_loss: 0.6371 2025/05/12 11:53:32 - mmengine - INFO - Epoch(train) [78][40/91] base_lr: 2.2781e-04 lr: 2.2781e-04 eta: 3 days, 10:37:03 time: 10.4965 data_time: 1.4607 memory: 68702 grad_norm: 1.6670 loss: 1.9744 center_loss: 0.5450 size_loss: 0.1657 cls_loss: 0.6280 giou_loss: 0.6357 2025/05/12 11:55:10 - mmengine - INFO - Epoch(train) [78][50/91] base_lr: 2.2781e-04 lr: 2.2781e-04 eta: 3 days, 10:35:07 time: 10.6959 data_time: 1.4675 memory: 68702 grad_norm: 1.6115 loss: 1.9746 center_loss: 0.5475 size_loss: 0.1671 cls_loss: 0.6279 giou_loss: 0.6321 2025/05/12 11:56:47 - mmengine - INFO - Epoch(train) [78][60/91] base_lr: 2.2781e-04 lr: 2.2781e-04 eta: 3 days, 10:33:08 time: 9.7252 data_time: 0.5813 memory: 68702 grad_norm: 1.5104 loss: 1.9415 center_loss: 0.5361 size_loss: 0.1645 cls_loss: 0.6152 giou_loss: 0.6258 2025/05/12 11:58:24 - mmengine - INFO - Epoch(train) [78][70/91] base_lr: 2.2781e-04 lr: 2.2781e-04 eta: 3 days, 10:31:11 time: 9.7392 data_time: 0.5838 memory: 68702 grad_norm: 1.5082 loss: 1.9198 center_loss: 0.5281 size_loss: 0.1629 cls_loss: 0.6086 giou_loss: 0.6202 2025/05/12 12:00:01 - mmengine - INFO - Epoch(train) [78][80/91] base_lr: 2.2781e-04 lr: 2.2781e-04 eta: 3 days, 10:29:10 time: 9.7164 data_time: 0.5971 memory: 68702 grad_norm: 1.4974 loss: 1.9029 center_loss: 0.5261 size_loss: 0.1627 cls_loss: 0.5967 giou_loss: 0.6174 2025/05/12 12:01:36 - mmengine - INFO - Epoch(train) [78][90/91] base_lr: 2.2781e-04 lr: 2.2781e-04 eta: 3 days, 10:27:06 time: 9.6932 data_time: 0.5918 memory: 68703 grad_norm: 1.4379 loss: 1.9058 center_loss: 0.5249 size_loss: 0.1628 cls_loss: 0.5986 giou_loss: 0.6195 2025/05/12 12:01:38 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 12:01:38 - mmengine - INFO - Saving checkpoint at 78 epochs 2025/05/12 12:02:37 - mmengine - INFO - Epoch(val) [78][10/39] eta: 0:01:36 time: 2.8609 data_time: 0.3619 memory: 15952 2025/05/12 12:03:03 - mmengine - INFO - Epoch(val) [78][20/39] eta: 0:00:56 time: 2.7427 data_time: 0.2474 memory: 13407 2025/05/12 12:03:28 - mmengine - INFO - Epoch(val) [78][30/39] eta: 0:00:25 time: 2.7207 data_time: 0.2300 memory: 13407 2025/05/12 12:03:55 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.1985 | 0.4415 | 0.0105 | 0.0906 | | sofa | 0.6602 | 0.8144 | 0.2030 | 0.3711 | | table | 0.3996 | 0.5629 | 0.1136 | 0.2571 | | chair | 0.5297 | 0.7010 | 0.1045 | 0.2617 | | curtain | 0.2059 | 0.4627 | 0.0274 | 0.1343 | | bookshelf | 0.3284 | 0.6234 | 0.0343 | 0.1948 | | picture | 0.0130 | 0.0856 | 0.0000 | 0.0000 | | showercurtrain | 0.4160 | 0.6071 | 0.0158 | 0.1071 | | cabinet | 0.2488 | 0.4435 | 0.0398 | 0.1452 | | window | 0.1073 | 0.3191 | 0.0060 | 0.0745 | | door | 0.1282 | 0.3983 | 0.0144 | 0.1049 | | counter | 0.1049 | 0.1923 | 0.0000 | 0.0000 | | refrigerator | 0.4544 | 0.6316 | 0.1514 | 0.2807 | | sink | 0.4127 | 0.5612 | 0.0612 | 0.1735 | | desk | 0.6513 | 0.8189 | 0.1987 | 0.3937 | | bed | 0.8112 | 0.8395 | 0.3544 | 0.5185 | | toilet | 0.8099 | 0.8966 | 0.2799 | 0.3966 | | bathtub | 0.7009 | 0.8387 | 0.1770 | 0.3871 | +----------------+---------+---------+---------+---------+ | Overall | 0.3989 | 0.5688 | 0.0996 | 0.2162 | +----------------+---------+---------+---------+---------+ 2025/05/12 12:03:55 - mmengine - INFO - Epoch(val) [78][39/39] chair_AP_0.25: 0.5297 sofa_AP_0.25: 0.6602 table_AP_0.25: 0.3996 garbagebin_AP_0.25: 0.1985 bookshelf_AP_0.25: 0.3284 picture_AP_0.25: 0.0130 curtain_AP_0.25: 0.2059 door_AP_0.25: 0.1282 cabinet_AP_0.25: 0.2488 refrigerator_AP_0.25: 0.4544 counter_AP_0.25: 0.1049 sink_AP_0.25: 0.4127 window_AP_0.25: 0.1073 desk_AP_0.25: 0.6513 bed_AP_0.25: 0.8112 toilet_AP_0.25: 0.8099 showercurtrain_AP_0.25: 0.4160 bathtub_AP_0.25: 0.7009 mAP_0.25: 0.3989 chair_rec_0.25: 0.7010 sofa_rec_0.25: 0.8144 table_rec_0.25: 0.5629 garbagebin_rec_0.25: 0.4415 bookshelf_rec_0.25: 0.6234 picture_rec_0.25: 0.0856 curtain_rec_0.25: 0.4627 door_rec_0.25: 0.3983 cabinet_rec_0.25: 0.4435 refrigerator_rec_0.25: 0.6316 counter_rec_0.25: 0.1923 sink_rec_0.25: 0.5612 window_rec_0.25: 0.3191 desk_rec_0.25: 0.8189 bed_rec_0.25: 0.8395 toilet_rec_0.25: 0.8966 showercurtrain_rec_0.25: 0.6071 bathtub_rec_0.25: 0.8387 mAR_0.25: 0.5688 chair_AP_0.50: 0.1045 sofa_AP_0.50: 0.2030 table_AP_0.50: 0.1136 garbagebin_AP_0.50: 0.0105 bookshelf_AP_0.50: 0.0343 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0274 door_AP_0.50: 0.0144 cabinet_AP_0.50: 0.0398 refrigerator_AP_0.50: 0.1514 counter_AP_0.50: 0.0000 sink_AP_0.50: 0.0612 window_AP_0.50: 0.0060 desk_AP_0.50: 0.1987 bed_AP_0.50: 0.3544 toilet_AP_0.50: 0.2799 showercurtrain_AP_0.50: 0.0158 bathtub_AP_0.50: 0.1770 mAP_0.50: 0.0996 chair_rec_0.50: 0.2617 sofa_rec_0.50: 0.3711 table_rec_0.50: 0.2571 garbagebin_rec_0.50: 0.0906 bookshelf_rec_0.50: 0.1948 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.1343 door_rec_0.50: 0.1049 cabinet_rec_0.50: 0.1452 refrigerator_rec_0.50: 0.2807 counter_rec_0.50: 0.0000 sink_rec_0.50: 0.1735 window_rec_0.50: 0.0745 desk_rec_0.50: 0.3937 bed_rec_0.50: 0.5185 toilet_rec_0.50: 0.3966 showercurtrain_rec_0.50: 0.1071 bathtub_rec_0.50: 0.3871 mAR_0.50: 0.2162 data_time: 0.2701 time: 2.7562 2025/05/12 12:03:55 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_64.pth is removed 2025/05/12 12:04:21 - mmengine - INFO - The best checkpoint with 0.3989 mAP_0.25 at 78 epoch is saved to best_mAP_0.25_epoch_78.pth. 2025/05/12 12:07:16 - mmengine - INFO - Epoch(train) [79][10/91] base_lr: 2.2725e-04 lr: 2.2725e-04 eta: 3 days, 10:27:29 time: 10.4250 data_time: 1.4827 memory: 68703 grad_norm: 1.5670 loss: 1.9022 center_loss: 0.5230 size_loss: 0.1611 cls_loss: 0.5983 giou_loss: 0.6199 2025/05/12 12:08:53 - mmengine - INFO - Epoch(train) [79][20/91] base_lr: 2.2725e-04 lr: 2.2725e-04 eta: 3 days, 10:25:29 time: 10.4205 data_time: 1.4861 memory: 68702 grad_norm: 1.6870 loss: 1.9222 center_loss: 0.5280 size_loss: 0.1623 cls_loss: 0.6075 giou_loss: 0.6244 2025/05/12 12:10:29 - mmengine - INFO - Epoch(train) [79][30/91] base_lr: 2.2725e-04 lr: 2.2725e-04 eta: 3 days, 10:23:29 time: 10.4097 data_time: 1.4822 memory: 68702 grad_norm: 1.7972 loss: 1.9265 center_loss: 0.5308 size_loss: 0.1630 cls_loss: 0.6052 giou_loss: 0.6275 2025/05/12 12:12:06 - mmengine - INFO - Epoch(train) [79][40/91] base_lr: 2.2725e-04 lr: 2.2725e-04 eta: 3 days, 10:21:29 time: 10.4080 data_time: 1.4841 memory: 68702 grad_norm: 1.8390 loss: 1.9253 center_loss: 0.5311 size_loss: 0.1614 cls_loss: 0.6068 giou_loss: 0.6260 2025/05/12 12:13:44 - mmengine - INFO - Epoch(train) [79][50/91] base_lr: 2.2725e-04 lr: 2.2725e-04 eta: 3 days, 10:19:33 time: 10.6058 data_time: 1.5027 memory: 68702 grad_norm: 1.8077 loss: 1.9349 center_loss: 0.5381 size_loss: 0.1618 cls_loss: 0.6102 giou_loss: 0.6249 2025/05/12 12:15:21 - mmengine - INFO - Epoch(train) [79][60/91] base_lr: 2.2725e-04 lr: 2.2725e-04 eta: 3 days, 10:17:33 time: 9.6953 data_time: 0.6101 memory: 68702 grad_norm: 1.7479 loss: 1.9384 center_loss: 0.5376 size_loss: 0.1626 cls_loss: 0.6138 giou_loss: 0.6244 2025/05/12 12:16:59 - mmengine - INFO - Epoch(train) [79][70/91] base_lr: 2.2725e-04 lr: 2.2725e-04 eta: 3 days, 10:15:39 time: 9.7267 data_time: 0.6293 memory: 68703 grad_norm: 1.6539 loss: 1.9518 center_loss: 0.5454 size_loss: 0.1642 cls_loss: 0.6148 giou_loss: 0.6274 2025/05/12 12:18:36 - mmengine - INFO - Epoch(train) [79][80/91] base_lr: 2.2725e-04 lr: 2.2725e-04 eta: 3 days, 10:13:40 time: 9.7261 data_time: 0.6347 memory: 68702 grad_norm: 1.5552 loss: 1.9602 center_loss: 0.5467 size_loss: 0.1650 cls_loss: 0.6211 giou_loss: 0.6275 2025/05/12 12:20:12 - mmengine - INFO - Epoch(train) [79][90/91] base_lr: 2.2725e-04 lr: 2.2725e-04 eta: 3 days, 10:11:36 time: 9.7095 data_time: 0.6263 memory: 68702 grad_norm: 1.5674 loss: 1.9550 center_loss: 0.5428 size_loss: 0.1646 cls_loss: 0.6197 giou_loss: 0.6278 2025/05/12 12:20:14 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 12:22:40 - mmengine - INFO - Epoch(train) [80][10/91] base_lr: 2.2668e-04 lr: 2.2668e-04 eta: 3 days, 10:12:15 time: 10.5250 data_time: 1.5413 memory: 68703 grad_norm: 2.0656 loss: 1.9728 center_loss: 0.5542 size_loss: 0.1674 cls_loss: 0.6165 giou_loss: 0.6346 2025/05/12 12:24:17 - mmengine - INFO - Epoch(train) [80][20/91] base_lr: 2.2668e-04 lr: 2.2668e-04 eta: 3 days, 10:10:14 time: 10.5194 data_time: 1.5428 memory: 68702 grad_norm: 2.0653 loss: 2.0147 center_loss: 0.5738 size_loss: 0.1718 cls_loss: 0.6281 giou_loss: 0.6409 2025/05/12 12:25:54 - mmengine - INFO - Epoch(train) [80][30/91] base_lr: 2.2668e-04 lr: 2.2668e-04 eta: 3 days, 10:08:15 time: 10.5009 data_time: 1.5233 memory: 68700 grad_norm: 2.2036 loss: 2.0197 center_loss: 0.5753 size_loss: 0.1720 cls_loss: 0.6285 giou_loss: 0.6439 2025/05/12 12:27:31 - mmengine - INFO - Epoch(train) [80][40/91] base_lr: 2.2668e-04 lr: 2.2668e-04 eta: 3 days, 10:06:17 time: 10.5041 data_time: 1.5207 memory: 68703 grad_norm: 2.2701 loss: 2.0129 center_loss: 0.5760 size_loss: 0.1715 cls_loss: 0.6236 giou_loss: 0.6418 2025/05/12 12:29:09 - mmengine - INFO - Epoch(train) [80][50/91] base_lr: 2.2668e-04 lr: 2.2668e-04 eta: 3 days, 10:04:22 time: 10.6999 data_time: 1.5335 memory: 68703 grad_norm: 2.1813 loss: 1.9989 center_loss: 0.5651 size_loss: 0.1674 cls_loss: 0.6271 giou_loss: 0.6393 2025/05/12 12:30:46 - mmengine - INFO - Epoch(train) [80][60/91] base_lr: 2.2668e-04 lr: 2.2668e-04 eta: 3 days, 10:02:23 time: 9.7143 data_time: 0.6011 memory: 68702 grad_norm: 1.7771 loss: 1.9778 center_loss: 0.5539 size_loss: 0.1664 cls_loss: 0.6241 giou_loss: 0.6334 2025/05/12 12:32:23 - mmengine - INFO - Epoch(train) [80][70/91] base_lr: 2.2668e-04 lr: 2.2668e-04 eta: 3 days, 10:00:26 time: 9.7298 data_time: 0.6137 memory: 68702 grad_norm: 1.7345 loss: 1.9537 center_loss: 0.5404 size_loss: 0.1647 cls_loss: 0.6185 giou_loss: 0.6300 2025/05/12 12:34:00 - mmengine - INFO - Epoch(train) [80][80/91] base_lr: 2.2668e-04 lr: 2.2668e-04 eta: 3 days, 9:58:27 time: 9.7265 data_time: 0.6117 memory: 68702 grad_norm: 1.5989 loss: 1.9305 center_loss: 0.5310 size_loss: 0.1619 cls_loss: 0.6143 giou_loss: 0.6233 2025/05/12 12:35:36 - mmengine - INFO - Epoch(train) [80][90/91] base_lr: 2.2668e-04 lr: 2.2668e-04 eta: 3 days, 9:56:23 time: 9.6944 data_time: 0.6094 memory: 68702 grad_norm: 1.5329 loss: 1.9461 center_loss: 0.5362 size_loss: 0.1635 cls_loss: 0.6185 giou_loss: 0.6278 2025/05/12 12:35:38 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 12:35:38 - mmengine - INFO - Saving checkpoint at 80 epochs 2025/05/12 12:36:34 - mmengine - INFO - Epoch(val) [80][10/39] eta: 0:01:35 time: 2.8649 data_time: 0.3726 memory: 15952 2025/05/12 12:36:59 - mmengine - INFO - Epoch(val) [80][20/39] eta: 0:00:55 time: 2.7200 data_time: 0.2320 memory: 13407 2025/05/12 12:37:25 - mmengine - INFO - Epoch(val) [80][30/39] eta: 0:00:25 time: 2.7072 data_time: 0.2166 memory: 13407 2025/05/12 12:37:52 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.2484 | 0.4509 | 0.0094 | 0.0868 | | sofa | 0.6481 | 0.8454 | 0.1545 | 0.3505 | | chair | 0.5194 | 0.6944 | 0.1190 | 0.2690 | | table | 0.4637 | 0.5914 | 0.1217 | 0.2629 | | curtain | 0.2272 | 0.4627 | 0.0086 | 0.1045 | | bookshelf | 0.2751 | 0.5844 | 0.0828 | 0.2208 | | picture | 0.0075 | 0.0766 | 0.0007 | 0.0180 | | bed | 0.7980 | 0.8395 | 0.3064 | 0.5309 | | cabinet | 0.2728 | 0.4919 | 0.0406 | 0.1586 | | window | 0.0877 | 0.3085 | 0.0059 | 0.0638 | | door | 0.1393 | 0.4176 | 0.0096 | 0.0964 | | refrigerator | 0.5304 | 0.6491 | 0.1278 | 0.2632 | | sink | 0.4736 | 0.6327 | 0.0906 | 0.2041 | | counter | 0.1134 | 0.1923 | 0.0059 | 0.0385 | | desk | 0.6610 | 0.8268 | 0.2276 | 0.4409 | | toilet | 0.7876 | 0.9310 | 0.3624 | 0.5000 | | bathtub | 0.7327 | 0.8387 | 0.1717 | 0.3548 | | showercurtrain | 0.3137 | 0.6429 | 0.0016 | 0.0357 | +----------------+---------+---------+---------+---------+ | Overall | 0.4055 | 0.5820 | 0.1026 | 0.2222 | +----------------+---------+---------+---------+---------+ 2025/05/12 12:37:52 - mmengine - INFO - Epoch(val) [80][39/39] chair_AP_0.25: 0.5194 sofa_AP_0.25: 0.6481 table_AP_0.25: 0.4637 garbagebin_AP_0.25: 0.2484 bookshelf_AP_0.25: 0.2751 picture_AP_0.25: 0.0075 curtain_AP_0.25: 0.2272 door_AP_0.25: 0.1393 cabinet_AP_0.25: 0.2728 refrigerator_AP_0.25: 0.5304 counter_AP_0.25: 0.1134 sink_AP_0.25: 0.4736 window_AP_0.25: 0.0877 desk_AP_0.25: 0.6610 bed_AP_0.25: 0.7980 toilet_AP_0.25: 0.7876 showercurtrain_AP_0.25: 0.3137 bathtub_AP_0.25: 0.7327 mAP_0.25: 0.4055 chair_rec_0.25: 0.6944 sofa_rec_0.25: 0.8454 table_rec_0.25: 0.5914 garbagebin_rec_0.25: 0.4509 bookshelf_rec_0.25: 0.5844 picture_rec_0.25: 0.0766 curtain_rec_0.25: 0.4627 door_rec_0.25: 0.4176 cabinet_rec_0.25: 0.4919 refrigerator_rec_0.25: 0.6491 counter_rec_0.25: 0.1923 sink_rec_0.25: 0.6327 window_rec_0.25: 0.3085 desk_rec_0.25: 0.8268 bed_rec_0.25: 0.8395 toilet_rec_0.25: 0.9310 showercurtrain_rec_0.25: 0.6429 bathtub_rec_0.25: 0.8387 mAR_0.25: 0.5820 chair_AP_0.50: 0.1190 sofa_AP_0.50: 0.1545 table_AP_0.50: 0.1217 garbagebin_AP_0.50: 0.0094 bookshelf_AP_0.50: 0.0828 picture_AP_0.50: 0.0007 curtain_AP_0.50: 0.0086 door_AP_0.50: 0.0096 cabinet_AP_0.50: 0.0406 refrigerator_AP_0.50: 0.1278 counter_AP_0.50: 0.0059 sink_AP_0.50: 0.0906 window_AP_0.50: 0.0059 desk_AP_0.50: 0.2276 bed_AP_0.50: 0.3064 toilet_AP_0.50: 0.3624 showercurtrain_AP_0.50: 0.0016 bathtub_AP_0.50: 0.1717 mAP_0.50: 0.1026 chair_rec_0.50: 0.2690 sofa_rec_0.50: 0.3505 table_rec_0.50: 0.2629 garbagebin_rec_0.50: 0.0868 bookshelf_rec_0.50: 0.2208 picture_rec_0.50: 0.0180 curtain_rec_0.50: 0.1045 door_rec_0.50: 0.0964 cabinet_rec_0.50: 0.1586 refrigerator_rec_0.50: 0.2632 counter_rec_0.50: 0.0385 sink_rec_0.50: 0.2041 window_rec_0.50: 0.0638 desk_rec_0.50: 0.4409 bed_rec_0.50: 0.5309 toilet_rec_0.50: 0.5000 showercurtrain_rec_0.50: 0.0357 bathtub_rec_0.50: 0.3548 mAR_0.50: 0.2222 data_time: 0.2514 time: 2.7399 2025/05/12 12:37:52 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_78.pth is removed 2025/05/12 12:38:14 - mmengine - INFO - The best checkpoint with 0.4055 mAP_0.25 at 80 epoch is saved to best_mAP_0.25_epoch_80.pth. 2025/05/12 12:41:10 - mmengine - INFO - Epoch(train) [81][10/91] base_lr: 2.2611e-04 lr: 2.2611e-04 eta: 3 days, 9:57:12 time: 10.5808 data_time: 1.6407 memory: 68702 grad_norm: 1.5539 loss: 1.9440 center_loss: 0.5373 size_loss: 0.1645 cls_loss: 0.6139 giou_loss: 0.6282 2025/05/12 12:42:47 - mmengine - INFO - Epoch(train) [81][20/91] base_lr: 2.2611e-04 lr: 2.2611e-04 eta: 3 days, 9:55:11 time: 10.5656 data_time: 1.6275 memory: 68702 grad_norm: 1.5321 loss: 1.9570 center_loss: 0.5425 size_loss: 0.1648 cls_loss: 0.6168 giou_loss: 0.6329 2025/05/12 12:44:23 - mmengine - INFO - Epoch(train) [81][30/91] base_lr: 2.2611e-04 lr: 2.2611e-04 eta: 3 days, 9:53:11 time: 10.5514 data_time: 1.6167 memory: 68703 grad_norm: 1.5999 loss: 1.9417 center_loss: 0.5376 size_loss: 0.1610 cls_loss: 0.6125 giou_loss: 0.6306 2025/05/12 12:46:02 - mmengine - INFO - Epoch(train) [81][40/91] base_lr: 2.2611e-04 lr: 2.2611e-04 eta: 3 days, 9:51:19 time: 10.5811 data_time: 1.6336 memory: 68702 grad_norm: 1.6260 loss: 1.9394 center_loss: 0.5353 size_loss: 0.1605 cls_loss: 0.6128 giou_loss: 0.6307 2025/05/12 12:47:39 - mmengine - INFO - Epoch(train) [81][50/91] base_lr: 2.2611e-04 lr: 2.2611e-04 eta: 3 days, 9:49:21 time: 10.7668 data_time: 1.6432 memory: 68702 grad_norm: 1.6090 loss: 1.9434 center_loss: 0.5402 size_loss: 0.1597 cls_loss: 0.6149 giou_loss: 0.6285 2025/05/12 12:49:16 - mmengine - INFO - Epoch(train) [81][60/91] base_lr: 2.2611e-04 lr: 2.2611e-04 eta: 3 days, 9:47:22 time: 9.7122 data_time: 0.6097 memory: 68702 grad_norm: 1.6441 loss: 1.9291 center_loss: 0.5363 size_loss: 0.1577 cls_loss: 0.6099 giou_loss: 0.6251 2025/05/12 12:50:53 - mmengine - INFO - Epoch(train) [81][70/91] base_lr: 2.2611e-04 lr: 2.2611e-04 eta: 3 days, 9:45:24 time: 9.7282 data_time: 0.6230 memory: 68702 grad_norm: 1.6216 loss: 1.9139 center_loss: 0.5320 size_loss: 0.1583 cls_loss: 0.6033 giou_loss: 0.6203 2025/05/12 12:52:30 - mmengine - INFO - Epoch(train) [81][80/91] base_lr: 2.2611e-04 lr: 2.2611e-04 eta: 3 days, 9:43:24 time: 9.7270 data_time: 0.6361 memory: 68702 grad_norm: 1.5940 loss: 1.9152 center_loss: 0.5331 size_loss: 0.1600 cls_loss: 0.6027 giou_loss: 0.6193 2025/05/12 12:54:05 - mmengine - INFO - Epoch(train) [81][90/91] base_lr: 2.2611e-04 lr: 2.2611e-04 eta: 3 days, 9:41:20 time: 9.6681 data_time: 0.6121 memory: 68700 grad_norm: 1.6169 loss: 1.9228 center_loss: 0.5387 size_loss: 0.1620 cls_loss: 0.6011 giou_loss: 0.6210 2025/05/12 12:54:07 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 12:56:35 - mmengine - INFO - Epoch(train) [82][10/91] base_lr: 2.2553e-04 lr: 2.2553e-04 eta: 3 days, 9:41:58 time: 10.5159 data_time: 1.5418 memory: 68703 grad_norm: 1.6942 loss: 1.9361 center_loss: 0.5420 size_loss: 0.1647 cls_loss: 0.6070 giou_loss: 0.6223 2025/05/12 12:58:12 - mmengine - INFO - Epoch(train) [82][20/91] base_lr: 2.2553e-04 lr: 2.2553e-04 eta: 3 days, 9:40:01 time: 10.5270 data_time: 1.5391 memory: 68702 grad_norm: 1.7157 loss: 1.9646 center_loss: 0.5564 size_loss: 0.1663 cls_loss: 0.6155 giou_loss: 0.6264 2025/05/12 12:59:50 - mmengine - INFO - Epoch(train) [82][30/91] base_lr: 2.2553e-04 lr: 2.2553e-04 eta: 3 days, 9:38:05 time: 10.5340 data_time: 1.5184 memory: 68703 grad_norm: 1.7802 loss: 1.9722 center_loss: 0.5599 size_loss: 0.1658 cls_loss: 0.6169 giou_loss: 0.6296 2025/05/12 13:01:29 - mmengine - INFO - Epoch(train) [82][40/91] base_lr: 2.2553e-04 lr: 2.2553e-04 eta: 3 days, 9:36:14 time: 10.5780 data_time: 1.5126 memory: 68703 grad_norm: 1.7498 loss: 1.9820 center_loss: 0.5662 size_loss: 0.1670 cls_loss: 0.6149 giou_loss: 0.6339 2025/05/12 13:03:07 - mmengine - INFO - Epoch(train) [82][50/91] base_lr: 2.2553e-04 lr: 2.2553e-04 eta: 3 days, 9:34:21 time: 10.7890 data_time: 1.5176 memory: 68703 grad_norm: 1.6608 loss: 1.9512 center_loss: 0.5521 size_loss: 0.1635 cls_loss: 0.6071 giou_loss: 0.6285 2025/05/12 13:04:44 - mmengine - INFO - Epoch(train) [82][60/91] base_lr: 2.2553e-04 lr: 2.2553e-04 eta: 3 days, 9:32:24 time: 9.7922 data_time: 0.5874 memory: 68703 grad_norm: 1.6648 loss: 1.9269 center_loss: 0.5403 size_loss: 0.1606 cls_loss: 0.6016 giou_loss: 0.6244 2025/05/12 13:06:22 - mmengine - INFO - Epoch(train) [82][70/91] base_lr: 2.2553e-04 lr: 2.2553e-04 eta: 3 days, 9:30:29 time: 9.7978 data_time: 0.5807 memory: 68703 grad_norm: 1.7196 loss: 1.9164 center_loss: 0.5342 size_loss: 0.1613 cls_loss: 0.5999 giou_loss: 0.6210 2025/05/12 13:07:59 - mmengine - INFO - Epoch(train) [82][80/91] base_lr: 2.2553e-04 lr: 2.2553e-04 eta: 3 days, 9:28:31 time: 9.7884 data_time: 0.5949 memory: 68702 grad_norm: 1.7711 loss: 1.9204 center_loss: 0.5349 size_loss: 0.1620 cls_loss: 0.6033 giou_loss: 0.6203 2025/05/12 13:09:35 - mmengine - INFO - Epoch(train) [82][90/91] base_lr: 2.2553e-04 lr: 2.2553e-04 eta: 3 days, 9:26:29 time: 9.7318 data_time: 0.5934 memory: 68703 grad_norm: 1.8042 loss: 1.9181 center_loss: 0.5339 size_loss: 0.1629 cls_loss: 0.6039 giou_loss: 0.6175 2025/05/12 13:09:37 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 13:09:37 - mmengine - INFO - Saving checkpoint at 82 epochs 2025/05/12 13:10:35 - mmengine - INFO - Epoch(val) [82][10/39] eta: 0:01:37 time: 2.8635 data_time: 0.3705 memory: 15952 2025/05/12 13:11:01 - mmengine - INFO - Epoch(val) [82][20/39] eta: 0:00:56 time: 2.7246 data_time: 0.2314 memory: 13407 2025/05/12 13:11:27 - mmengine - INFO - Epoch(val) [82][30/39] eta: 0:00:25 time: 2.7353 data_time: 0.2311 memory: 13407 2025/05/12 13:11:54 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.1922 | 0.4566 | 0.0115 | 0.1075 | | table | 0.4241 | 0.5943 | 0.1331 | 0.2771 | | curtain | 0.1553 | 0.4925 | 0.0103 | 0.1045 | | chair | 0.4827 | 0.7142 | 0.0898 | 0.2683 | | sofa | 0.6655 | 0.8144 | 0.1324 | 0.2887 | | picture | 0.0048 | 0.0631 | 0.0000 | 0.0000 | | bookshelf | 0.2773 | 0.6104 | 0.0420 | 0.1818 | | door | 0.1030 | 0.3961 | 0.0076 | 0.0707 | | cabinet | 0.2637 | 0.4946 | 0.0363 | 0.1532 | | window | 0.0767 | 0.2943 | 0.0132 | 0.0567 | | counter | 0.0792 | 0.1923 | 0.0048 | 0.0385 | | refrigerator | 0.4226 | 0.5789 | 0.1258 | 0.2632 | | sink | 0.4357 | 0.6122 | 0.0577 | 0.1939 | | bed | 0.7728 | 0.8025 | 0.2353 | 0.4444 | | desk | 0.5882 | 0.8346 | 0.1540 | 0.4016 | | toilet | 0.8632 | 0.9655 | 0.3993 | 0.5172 | | bathtub | 0.7570 | 0.8387 | 0.2074 | 0.3871 | | showercurtrain | 0.3715 | 0.6071 | 0.0218 | 0.0714 | +----------------+---------+---------+---------+---------+ | Overall | 0.3853 | 0.5757 | 0.0935 | 0.2125 | +----------------+---------+---------+---------+---------+ 2025/05/12 13:11:54 - mmengine - INFO - Epoch(val) [82][39/39] chair_AP_0.25: 0.4827 sofa_AP_0.25: 0.6655 table_AP_0.25: 0.4241 garbagebin_AP_0.25: 0.1922 bookshelf_AP_0.25: 0.2773 picture_AP_0.25: 0.0048 curtain_AP_0.25: 0.1553 door_AP_0.25: 0.1030 cabinet_AP_0.25: 0.2637 refrigerator_AP_0.25: 0.4226 counter_AP_0.25: 0.0792 sink_AP_0.25: 0.4357 window_AP_0.25: 0.0767 desk_AP_0.25: 0.5882 bed_AP_0.25: 0.7728 toilet_AP_0.25: 0.8632 showercurtrain_AP_0.25: 0.3715 bathtub_AP_0.25: 0.7570 mAP_0.25: 0.3853 chair_rec_0.25: 0.7142 sofa_rec_0.25: 0.8144 table_rec_0.25: 0.5943 garbagebin_rec_0.25: 0.4566 bookshelf_rec_0.25: 0.6104 picture_rec_0.25: 0.0631 curtain_rec_0.25: 0.4925 door_rec_0.25: 0.3961 cabinet_rec_0.25: 0.4946 refrigerator_rec_0.25: 0.5789 counter_rec_0.25: 0.1923 sink_rec_0.25: 0.6122 window_rec_0.25: 0.2943 desk_rec_0.25: 0.8346 bed_rec_0.25: 0.8025 toilet_rec_0.25: 0.9655 showercurtrain_rec_0.25: 0.6071 bathtub_rec_0.25: 0.8387 mAR_0.25: 0.5757 chair_AP_0.50: 0.0898 sofa_AP_0.50: 0.1324 table_AP_0.50: 0.1331 garbagebin_AP_0.50: 0.0115 bookshelf_AP_0.50: 0.0420 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0103 door_AP_0.50: 0.0076 cabinet_AP_0.50: 0.0363 refrigerator_AP_0.50: 0.1258 counter_AP_0.50: 0.0048 sink_AP_0.50: 0.0577 window_AP_0.50: 0.0132 desk_AP_0.50: 0.1540 bed_AP_0.50: 0.2353 toilet_AP_0.50: 0.3993 showercurtrain_AP_0.50: 0.0218 bathtub_AP_0.50: 0.2074 mAP_0.50: 0.0935 chair_rec_0.50: 0.2683 sofa_rec_0.50: 0.2887 table_rec_0.50: 0.2771 garbagebin_rec_0.50: 0.1075 bookshelf_rec_0.50: 0.1818 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.1045 door_rec_0.50: 0.0707 cabinet_rec_0.50: 0.1532 refrigerator_rec_0.50: 0.2632 counter_rec_0.50: 0.0385 sink_rec_0.50: 0.1939 window_rec_0.50: 0.0567 desk_rec_0.50: 0.4016 bed_rec_0.50: 0.4444 toilet_rec_0.50: 0.5172 showercurtrain_rec_0.50: 0.0714 bathtub_rec_0.50: 0.3871 mAR_0.50: 0.2125 data_time: 0.2703 time: 2.7766 2025/05/12 13:14:19 - mmengine - INFO - Epoch(train) [83][10/91] base_lr: 2.2494e-04 lr: 2.2494e-04 eta: 3 days, 9:26:56 time: 10.5171 data_time: 1.4536 memory: 68703 grad_norm: 1.9297 loss: 1.9084 center_loss: 0.5281 size_loss: 0.1617 cls_loss: 0.6041 giou_loss: 0.6145 2025/05/12 13:15:55 - mmengine - INFO - Epoch(train) [83][20/91] base_lr: 2.2494e-04 lr: 2.2494e-04 eta: 3 days, 9:24:54 time: 10.4870 data_time: 1.4558 memory: 68703 grad_norm: 1.9437 loss: 1.9330 center_loss: 0.5410 size_loss: 0.1647 cls_loss: 0.6070 giou_loss: 0.6203 2025/05/12 13:17:32 - mmengine - INFO - Epoch(train) [83][30/91] base_lr: 2.2494e-04 lr: 2.2494e-04 eta: 3 days, 9:22:54 time: 10.4653 data_time: 1.4538 memory: 68703 grad_norm: 1.7766 loss: 1.9457 center_loss: 0.5462 size_loss: 0.1656 cls_loss: 0.6088 giou_loss: 0.6251 2025/05/12 13:19:09 - mmengine - INFO - Epoch(train) [83][40/91] base_lr: 2.2494e-04 lr: 2.2494e-04 eta: 3 days, 9:20:56 time: 10.4580 data_time: 1.4392 memory: 68702 grad_norm: 1.7790 loss: 1.9383 center_loss: 0.5459 size_loss: 0.1646 cls_loss: 0.6017 giou_loss: 0.6261 2025/05/12 13:20:46 - mmengine - INFO - Epoch(train) [83][50/91] base_lr: 2.2494e-04 lr: 2.2494e-04 eta: 3 days, 9:19:00 time: 10.6416 data_time: 1.4503 memory: 68702 grad_norm: 1.6866 loss: 1.9333 center_loss: 0.5387 size_loss: 0.1638 cls_loss: 0.6038 giou_loss: 0.6271 2025/05/12 13:22:23 - mmengine - INFO - Epoch(train) [83][60/91] base_lr: 2.2494e-04 lr: 2.2494e-04 eta: 3 days, 9:17:01 time: 9.6725 data_time: 0.5829 memory: 68703 grad_norm: 1.6234 loss: 1.9564 center_loss: 0.5508 size_loss: 0.1648 cls_loss: 0.6077 giou_loss: 0.6331 2025/05/12 13:23:59 - mmengine - INFO - Epoch(train) [83][70/91] base_lr: 2.2494e-04 lr: 2.2494e-04 eta: 3 days, 9:15:01 time: 9.6854 data_time: 0.5870 memory: 68703 grad_norm: 1.5759 loss: 1.9371 center_loss: 0.5435 size_loss: 0.1626 cls_loss: 0.6041 giou_loss: 0.6270 2025/05/12 13:25:36 - mmengine - INFO - Epoch(train) [83][80/91] base_lr: 2.2494e-04 lr: 2.2494e-04 eta: 3 days, 9:13:03 time: 9.6932 data_time: 0.5825 memory: 68703 grad_norm: 1.6692 loss: 1.9260 center_loss: 0.5400 size_loss: 0.1629 cls_loss: 0.6019 giou_loss: 0.6212 2025/05/12 13:27:12 - mmengine - INFO - Epoch(train) [83][90/91] base_lr: 2.2494e-04 lr: 2.2494e-04 eta: 3 days, 9:10:59 time: 9.6617 data_time: 0.5787 memory: 68702 grad_norm: 1.6150 loss: 1.9360 center_loss: 0.5439 size_loss: 0.1625 cls_loss: 0.6044 giou_loss: 0.6252 2025/05/12 13:27:14 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 13:29:42 - mmengine - INFO - Epoch(train) [84][10/91] base_lr: 2.2435e-04 lr: 2.2435e-04 eta: 3 days, 9:11:36 time: 10.5221 data_time: 1.5329 memory: 68703 grad_norm: 1.7109 loss: 1.9238 center_loss: 0.5408 size_loss: 0.1603 cls_loss: 0.5996 giou_loss: 0.6232 2025/05/12 13:31:18 - mmengine - INFO - Epoch(train) [84][20/91] base_lr: 2.2435e-04 lr: 2.2435e-04 eta: 3 days, 9:09:35 time: 10.5154 data_time: 1.5215 memory: 68703 grad_norm: 1.7072 loss: 1.9190 center_loss: 0.5349 size_loss: 0.1610 cls_loss: 0.6010 giou_loss: 0.6221 2025/05/12 13:32:55 - mmengine - INFO - Epoch(train) [84][30/91] base_lr: 2.2435e-04 lr: 2.2435e-04 eta: 3 days, 9:07:35 time: 10.5147 data_time: 1.5159 memory: 68702 grad_norm: 1.7870 loss: 1.9353 center_loss: 0.5429 size_loss: 0.1622 cls_loss: 0.6010 giou_loss: 0.6293 2025/05/12 13:34:32 - mmengine - INFO - Epoch(train) [84][40/91] base_lr: 2.2435e-04 lr: 2.2435e-04 eta: 3 days, 9:05:39 time: 10.5183 data_time: 1.5237 memory: 68702 grad_norm: 1.7314 loss: 1.9421 center_loss: 0.5478 size_loss: 0.1624 cls_loss: 0.5982 giou_loss: 0.6338 2025/05/12 13:36:10 - mmengine - INFO - Epoch(train) [84][50/91] base_lr: 2.2435e-04 lr: 2.2435e-04 eta: 3 days, 9:03:43 time: 10.7196 data_time: 1.5423 memory: 68702 grad_norm: 1.5985 loss: 1.9266 center_loss: 0.5399 size_loss: 0.1630 cls_loss: 0.5959 giou_loss: 0.6278 2025/05/12 13:37:46 - mmengine - INFO - Epoch(train) [84][60/91] base_lr: 2.2435e-04 lr: 2.2435e-04 eta: 3 days, 9:01:43 time: 9.6831 data_time: 0.5819 memory: 68703 grad_norm: 1.6136 loss: 1.9374 center_loss: 0.5502 size_loss: 0.1631 cls_loss: 0.5968 giou_loss: 0.6273 2025/05/12 13:39:24 - mmengine - INFO - Epoch(train) [84][70/91] base_lr: 2.2435e-04 lr: 2.2435e-04 eta: 3 days, 8:59:47 time: 9.7092 data_time: 0.6010 memory: 68702 grad_norm: 1.5873 loss: 1.9330 center_loss: 0.5506 size_loss: 0.1623 cls_loss: 0.5927 giou_loss: 0.6274 2025/05/12 13:41:00 - mmengine - INFO - Epoch(train) [84][80/91] base_lr: 2.2435e-04 lr: 2.2435e-04 eta: 3 days, 8:57:48 time: 9.7075 data_time: 0.6153 memory: 68702 grad_norm: 1.6526 loss: 1.9405 center_loss: 0.5556 size_loss: 0.1620 cls_loss: 0.5956 giou_loss: 0.6273 2025/05/12 13:42:36 - mmengine - INFO - Epoch(train) [84][90/91] base_lr: 2.2435e-04 lr: 2.2435e-04 eta: 3 days, 8:55:45 time: 9.6694 data_time: 0.6100 memory: 68700 grad_norm: 1.6723 loss: 1.9468 center_loss: 0.5571 size_loss: 0.1639 cls_loss: 0.5972 giou_loss: 0.6286 2025/05/12 13:42:37 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 13:42:37 - mmengine - INFO - Saving checkpoint at 84 epochs 2025/05/12 13:43:35 - mmengine - INFO - Epoch(val) [84][10/39] eta: 0:01:34 time: 2.8725 data_time: 0.3621 memory: 15952 2025/05/12 13:44:01 - mmengine - INFO - Epoch(val) [84][20/39] eta: 0:00:55 time: 2.7196 data_time: 0.2087 memory: 13407 2025/05/12 13:44:27 - mmengine - INFO - Epoch(val) [84][30/39] eta: 0:00:25 time: 2.7191 data_time: 0.2065 memory: 13407 2025/05/12 13:44:54 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | chair | 0.5119 | 0.6827 | 0.1079 | 0.2632 | | sofa | 0.7117 | 0.8660 | 0.1944 | 0.4021 | | bookshelf | 0.2641 | 0.6364 | 0.0331 | 0.1558 | | garbagebin | 0.1963 | 0.4472 | 0.0133 | 0.1057 | | table | 0.4548 | 0.6000 | 0.1438 | 0.2857 | | curtain | 0.2255 | 0.4328 | 0.0376 | 0.1194 | | picture | 0.0143 | 0.0901 | 0.0014 | 0.0135 | | door | 0.1474 | 0.4304 | 0.0116 | 0.1092 | | cabinet | 0.2505 | 0.4677 | 0.0371 | 0.1371 | | window | 0.1131 | 0.3440 | 0.0085 | 0.0603 | | refrigerator | 0.4511 | 0.6491 | 0.0602 | 0.2105 | | sink | 0.4043 | 0.5816 | 0.0461 | 0.1837 | | counter | 0.0802 | 0.1731 | 0.0064 | 0.0192 | | bed | 0.8152 | 0.8519 | 0.3526 | 0.5309 | | desk | 0.6793 | 0.8268 | 0.2329 | 0.4173 | | toilet | 0.7833 | 0.8793 | 0.4089 | 0.4828 | | bathtub | 0.6304 | 0.7742 | 0.1964 | 0.3548 | | showercurtrain | 0.4001 | 0.5714 | 0.1224 | 0.2143 | +----------------+---------+---------+---------+---------+ | Overall | 0.3963 | 0.5725 | 0.1119 | 0.2259 | +----------------+---------+---------+---------+---------+ 2025/05/12 13:44:54 - mmengine - INFO - Epoch(val) [84][39/39] chair_AP_0.25: 0.5119 sofa_AP_0.25: 0.7117 table_AP_0.25: 0.4548 garbagebin_AP_0.25: 0.1963 bookshelf_AP_0.25: 0.2641 picture_AP_0.25: 0.0143 curtain_AP_0.25: 0.2255 door_AP_0.25: 0.1474 cabinet_AP_0.25: 0.2505 refrigerator_AP_0.25: 0.4511 counter_AP_0.25: 0.0802 sink_AP_0.25: 0.4043 window_AP_0.25: 0.1131 desk_AP_0.25: 0.6793 bed_AP_0.25: 0.8152 toilet_AP_0.25: 0.7833 showercurtrain_AP_0.25: 0.4001 bathtub_AP_0.25: 0.6304 mAP_0.25: 0.3963 chair_rec_0.25: 0.6827 sofa_rec_0.25: 0.8660 table_rec_0.25: 0.6000 garbagebin_rec_0.25: 0.4472 bookshelf_rec_0.25: 0.6364 picture_rec_0.25: 0.0901 curtain_rec_0.25: 0.4328 door_rec_0.25: 0.4304 cabinet_rec_0.25: 0.4677 refrigerator_rec_0.25: 0.6491 counter_rec_0.25: 0.1731 sink_rec_0.25: 0.5816 window_rec_0.25: 0.3440 desk_rec_0.25: 0.8268 bed_rec_0.25: 0.8519 toilet_rec_0.25: 0.8793 showercurtrain_rec_0.25: 0.5714 bathtub_rec_0.25: 0.7742 mAR_0.25: 0.5725 chair_AP_0.50: 0.1079 sofa_AP_0.50: 0.1944 table_AP_0.50: 0.1438 garbagebin_AP_0.50: 0.0133 bookshelf_AP_0.50: 0.0331 picture_AP_0.50: 0.0014 curtain_AP_0.50: 0.0376 door_AP_0.50: 0.0116 cabinet_AP_0.50: 0.0371 refrigerator_AP_0.50: 0.0602 counter_AP_0.50: 0.0064 sink_AP_0.50: 0.0461 window_AP_0.50: 0.0085 desk_AP_0.50: 0.2329 bed_AP_0.50: 0.3526 toilet_AP_0.50: 0.4089 showercurtrain_AP_0.50: 0.1224 bathtub_AP_0.50: 0.1964 mAP_0.50: 0.1119 chair_rec_0.50: 0.2632 sofa_rec_0.50: 0.4021 table_rec_0.50: 0.2857 garbagebin_rec_0.50: 0.1057 bookshelf_rec_0.50: 0.1558 picture_rec_0.50: 0.0135 curtain_rec_0.50: 0.1194 door_rec_0.50: 0.1092 cabinet_rec_0.50: 0.1371 refrigerator_rec_0.50: 0.2105 counter_rec_0.50: 0.0192 sink_rec_0.50: 0.1837 window_rec_0.50: 0.0603 desk_rec_0.50: 0.4173 bed_rec_0.50: 0.5309 toilet_rec_0.50: 0.4828 showercurtrain_rec_0.50: 0.2143 bathtub_rec_0.50: 0.3548 mAR_0.50: 0.2259 data_time: 0.2402 time: 2.7451 2025/05/12 13:47:26 - mmengine - INFO - Epoch(train) [85][10/91] base_lr: 2.2375e-04 lr: 2.2375e-04 eta: 3 days, 8:56:33 time: 10.6079 data_time: 1.6742 memory: 68702 grad_norm: 1.7220 loss: 1.9597 center_loss: 0.5610 size_loss: 0.1657 cls_loss: 0.6013 giou_loss: 0.6318 2025/05/12 13:49:03 - mmengine - INFO - Epoch(train) [85][20/91] base_lr: 2.2375e-04 lr: 2.2375e-04 eta: 3 days, 8:54:36 time: 10.6218 data_time: 1.6800 memory: 68702 grad_norm: 1.7344 loss: 1.9587 center_loss: 0.5586 size_loss: 0.1655 cls_loss: 0.6046 giou_loss: 0.6299 2025/05/12 13:50:40 - mmengine - INFO - Epoch(train) [85][30/91] base_lr: 2.2375e-04 lr: 2.2375e-04 eta: 3 days, 8:52:37 time: 10.6012 data_time: 1.6607 memory: 68703 grad_norm: 1.9877 loss: 1.9557 center_loss: 0.5567 size_loss: 0.1659 cls_loss: 0.6045 giou_loss: 0.6285 2025/05/12 13:52:17 - mmengine - INFO - Epoch(train) [85][40/91] base_lr: 2.2375e-04 lr: 2.2375e-04 eta: 3 days, 8:50:40 time: 10.6111 data_time: 1.6530 memory: 68703 grad_norm: 1.8947 loss: 1.9679 center_loss: 0.5566 size_loss: 0.1678 cls_loss: 0.6142 giou_loss: 0.6293 2025/05/12 13:53:55 - mmengine - INFO - Epoch(train) [85][50/91] base_lr: 2.2375e-04 lr: 2.2375e-04 eta: 3 days, 8:48:46 time: 10.8234 data_time: 1.6752 memory: 68702 grad_norm: 1.8661 loss: 1.9466 center_loss: 0.5449 size_loss: 0.1625 cls_loss: 0.6124 giou_loss: 0.6268 2025/05/12 13:55:32 - mmengine - INFO - Epoch(train) [85][60/91] base_lr: 2.2375e-04 lr: 2.2375e-04 eta: 3 days, 8:46:49 time: 9.7233 data_time: 0.5959 memory: 68702 grad_norm: 1.8850 loss: 1.9322 center_loss: 0.5381 size_loss: 0.1609 cls_loss: 0.6079 giou_loss: 0.6253 2025/05/12 13:57:10 - mmengine - INFO - Epoch(train) [85][70/91] base_lr: 2.2375e-04 lr: 2.2375e-04 eta: 3 days, 8:44:54 time: 9.7329 data_time: 0.5907 memory: 68702 grad_norm: 1.8876 loss: 1.9457 center_loss: 0.5435 size_loss: 0.1624 cls_loss: 0.6092 giou_loss: 0.6306 2025/05/12 13:58:47 - mmengine - INFO - Epoch(train) [85][80/91] base_lr: 2.2375e-04 lr: 2.2375e-04 eta: 3 days, 8:42:58 time: 9.7444 data_time: 0.5936 memory: 68701 grad_norm: 1.6731 loss: 1.9711 center_loss: 0.5535 size_loss: 0.1661 cls_loss: 0.6166 giou_loss: 0.6349 2025/05/12 14:00:23 - mmengine - INFO - Epoch(train) [85][90/91] base_lr: 2.2375e-04 lr: 2.2375e-04 eta: 3 days, 8:40:55 time: 9.7169 data_time: 0.5889 memory: 68702 grad_norm: 1.6255 loss: 1.9350 center_loss: 0.5408 size_loss: 0.1630 cls_loss: 0.6016 giou_loss: 0.6296 2025/05/12 14:00:24 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 14:02:58 - mmengine - INFO - Epoch(train) [86][10/91] base_lr: 2.2314e-04 lr: 2.2314e-04 eta: 3 days, 8:41:47 time: 10.6754 data_time: 1.5474 memory: 68703 grad_norm: 1.7494 loss: 1.9385 center_loss: 0.5378 size_loss: 0.1643 cls_loss: 0.6076 giou_loss: 0.6287 2025/05/12 14:04:35 - mmengine - INFO - Epoch(train) [86][20/91] base_lr: 2.2314e-04 lr: 2.2314e-04 eta: 3 days, 8:39:48 time: 10.6656 data_time: 1.5355 memory: 68700 grad_norm: 1.7125 loss: 1.9449 center_loss: 0.5382 size_loss: 0.1661 cls_loss: 0.6110 giou_loss: 0.6297 2025/05/12 14:06:13 - mmengine - INFO - Epoch(train) [86][30/91] base_lr: 2.2314e-04 lr: 2.2314e-04 eta: 3 days, 8:37:54 time: 10.6667 data_time: 1.5263 memory: 68702 grad_norm: 1.7364 loss: 1.9398 center_loss: 0.5351 size_loss: 0.1645 cls_loss: 0.6123 giou_loss: 0.6279 2025/05/12 14:07:50 - mmengine - INFO - Epoch(train) [86][40/91] base_lr: 2.2314e-04 lr: 2.2314e-04 eta: 3 days, 8:35:57 time: 10.6616 data_time: 1.5224 memory: 68702 grad_norm: 1.7771 loss: 1.9296 center_loss: 0.5326 size_loss: 0.1626 cls_loss: 0.6078 giou_loss: 0.6265 2025/05/12 14:09:28 - mmengine - INFO - Epoch(train) [86][50/91] base_lr: 2.2314e-04 lr: 2.2314e-04 eta: 3 days, 8:34:02 time: 10.8590 data_time: 1.5396 memory: 68703 grad_norm: 1.8718 loss: 1.9326 center_loss: 0.5341 size_loss: 0.1617 cls_loss: 0.6107 giou_loss: 0.6260 2025/05/12 14:11:04 - mmengine - INFO - Epoch(train) [86][60/91] base_lr: 2.2314e-04 lr: 2.2314e-04 eta: 3 days, 8:32:04 time: 9.7253 data_time: 0.5597 memory: 68701 grad_norm: 1.7969 loss: 1.9363 center_loss: 0.5355 size_loss: 0.1612 cls_loss: 0.6146 giou_loss: 0.6249 2025/05/12 14:12:42 - mmengine - INFO - Epoch(train) [86][70/91] base_lr: 2.2314e-04 lr: 2.2314e-04 eta: 3 days, 8:30:08 time: 9.7348 data_time: 0.5706 memory: 68702 grad_norm: 1.9033 loss: 1.9388 center_loss: 0.5404 size_loss: 0.1604 cls_loss: 0.6126 giou_loss: 0.6254 2025/05/12 14:14:18 - mmengine - INFO - Epoch(train) [86][80/91] base_lr: 2.2314e-04 lr: 2.2314e-04 eta: 3 days, 8:28:09 time: 9.7100 data_time: 0.5750 memory: 68702 grad_norm: 1.8253 loss: 1.9339 center_loss: 0.5429 size_loss: 0.1608 cls_loss: 0.6055 giou_loss: 0.6247 2025/05/12 14:15:54 - mmengine - INFO - Epoch(train) [86][90/91] base_lr: 2.2314e-04 lr: 2.2314e-04 eta: 3 days, 8:26:07 time: 9.6778 data_time: 0.5708 memory: 68702 grad_norm: 1.7708 loss: 1.9309 center_loss: 0.5409 size_loss: 0.1602 cls_loss: 0.6070 giou_loss: 0.6228 2025/05/12 14:15:56 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 14:15:56 - mmengine - INFO - Saving checkpoint at 86 epochs 2025/05/12 14:16:53 - mmengine - INFO - Epoch(val) [86][10/39] eta: 0:01:35 time: 2.8556 data_time: 0.3462 memory: 15952 2025/05/12 14:17:19 - mmengine - INFO - Epoch(val) [86][20/39] eta: 0:00:55 time: 2.7253 data_time: 0.2154 memory: 13407 2025/05/12 14:17:45 - mmengine - INFO - Epoch(val) [86][30/39] eta: 0:00:25 time: 2.7254 data_time: 0.2142 memory: 13407 2025/05/12 14:18:12 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | chair | 0.5156 | 0.6879 | 0.1169 | 0.2690 | | sofa | 0.7244 | 0.8454 | 0.2130 | 0.4021 | | garbagebin | 0.2196 | 0.4340 | 0.0178 | 0.1208 | | table | 0.4325 | 0.5914 | 0.1171 | 0.2800 | | bookshelf | 0.3597 | 0.6753 | 0.0751 | 0.2987 | | curtain | 0.1400 | 0.3582 | 0.0147 | 0.1045 | | picture | 0.0163 | 0.0991 | 0.0001 | 0.0045 | | door | 0.1485 | 0.4368 | 0.0122 | 0.1049 | | cabinet | 0.2620 | 0.4892 | 0.0464 | 0.1613 | | window | 0.1190 | 0.3369 | 0.0176 | 0.0745 | | sink | 0.4935 | 0.6531 | 0.0945 | 0.2755 | | refrigerator | 0.3738 | 0.5614 | 0.1503 | 0.2807 | | counter | 0.1774 | 0.2308 | 0.0481 | 0.0962 | | desk | 0.7220 | 0.8819 | 0.2195 | 0.4331 | | bed | 0.8365 | 0.8642 | 0.4081 | 0.5309 | | toilet | 0.7950 | 0.8966 | 0.4312 | 0.5000 | | bathtub | 0.6576 | 0.7419 | 0.2667 | 0.4194 | | showercurtrain | 0.1761 | 0.5000 | 0.0018 | 0.0357 | +----------------+---------+---------+---------+---------+ | Overall | 0.3983 | 0.5713 | 0.1251 | 0.2440 | +----------------+---------+---------+---------+---------+ 2025/05/12 14:18:12 - mmengine - INFO - Epoch(val) [86][39/39] chair_AP_0.25: 0.5156 sofa_AP_0.25: 0.7244 table_AP_0.25: 0.4325 garbagebin_AP_0.25: 0.2196 bookshelf_AP_0.25: 0.3597 picture_AP_0.25: 0.0163 curtain_AP_0.25: 0.1400 door_AP_0.25: 0.1485 cabinet_AP_0.25: 0.2620 refrigerator_AP_0.25: 0.3738 counter_AP_0.25: 0.1774 sink_AP_0.25: 0.4935 window_AP_0.25: 0.1190 desk_AP_0.25: 0.7220 bed_AP_0.25: 0.8365 toilet_AP_0.25: 0.7950 showercurtrain_AP_0.25: 0.1761 bathtub_AP_0.25: 0.6576 mAP_0.25: 0.3983 chair_rec_0.25: 0.6879 sofa_rec_0.25: 0.8454 table_rec_0.25: 0.5914 garbagebin_rec_0.25: 0.4340 bookshelf_rec_0.25: 0.6753 picture_rec_0.25: 0.0991 curtain_rec_0.25: 0.3582 door_rec_0.25: 0.4368 cabinet_rec_0.25: 0.4892 refrigerator_rec_0.25: 0.5614 counter_rec_0.25: 0.2308 sink_rec_0.25: 0.6531 window_rec_0.25: 0.3369 desk_rec_0.25: 0.8819 bed_rec_0.25: 0.8642 toilet_rec_0.25: 0.8966 showercurtrain_rec_0.25: 0.5000 bathtub_rec_0.25: 0.7419 mAR_0.25: 0.5713 chair_AP_0.50: 0.1169 sofa_AP_0.50: 0.2130 table_AP_0.50: 0.1171 garbagebin_AP_0.50: 0.0178 bookshelf_AP_0.50: 0.0751 picture_AP_0.50: 0.0001 curtain_AP_0.50: 0.0147 door_AP_0.50: 0.0122 cabinet_AP_0.50: 0.0464 refrigerator_AP_0.50: 0.1503 counter_AP_0.50: 0.0481 sink_AP_0.50: 0.0945 window_AP_0.50: 0.0176 desk_AP_0.50: 0.2195 bed_AP_0.50: 0.4081 toilet_AP_0.50: 0.4312 showercurtrain_AP_0.50: 0.0018 bathtub_AP_0.50: 0.2667 mAP_0.50: 0.1251 chair_rec_0.50: 0.2690 sofa_rec_0.50: 0.4021 table_rec_0.50: 0.2800 garbagebin_rec_0.50: 0.1208 bookshelf_rec_0.50: 0.2987 picture_rec_0.50: 0.0045 curtain_rec_0.50: 0.1045 door_rec_0.50: 0.1049 cabinet_rec_0.50: 0.1613 refrigerator_rec_0.50: 0.2807 counter_rec_0.50: 0.0962 sink_rec_0.50: 0.2755 window_rec_0.50: 0.0745 desk_rec_0.50: 0.4331 bed_rec_0.50: 0.5309 toilet_rec_0.50: 0.5000 showercurtrain_rec_0.50: 0.0357 bathtub_rec_0.50: 0.4194 mAR_0.50: 0.2440 data_time: 0.2485 time: 2.7528 2025/05/12 14:20:39 - mmengine - INFO - Epoch(train) [87][10/91] base_lr: 2.2253e-04 lr: 2.2253e-04 eta: 3 days, 8:26:34 time: 10.5236 data_time: 1.4428 memory: 68701 grad_norm: 1.6385 loss: 1.9400 center_loss: 0.5455 size_loss: 0.1614 cls_loss: 0.6108 giou_loss: 0.6223 2025/05/12 14:22:15 - mmengine - INFO - Epoch(train) [87][20/91] base_lr: 2.2253e-04 lr: 2.2253e-04 eta: 3 days, 8:24:33 time: 10.5033 data_time: 1.4416 memory: 68702 grad_norm: 1.6131 loss: 1.9524 center_loss: 0.5537 size_loss: 0.1640 cls_loss: 0.6111 giou_loss: 0.6234 2025/05/12 14:23:54 - mmengine - INFO - Epoch(train) [87][30/91] base_lr: 2.2253e-04 lr: 2.2253e-04 eta: 3 days, 8:22:40 time: 10.5240 data_time: 1.4378 memory: 68700 grad_norm: 1.4992 loss: 1.9368 center_loss: 0.5457 size_loss: 0.1640 cls_loss: 0.6063 giou_loss: 0.6208 2025/05/12 14:25:31 - mmengine - INFO - Epoch(train) [87][40/91] base_lr: 2.2253e-04 lr: 2.2253e-04 eta: 3 days, 8:20:43 time: 10.5299 data_time: 1.4466 memory: 68703 grad_norm: 1.5305 loss: 1.9221 center_loss: 0.5392 size_loss: 0.1630 cls_loss: 0.6047 giou_loss: 0.6153 2025/05/12 14:27:08 - mmengine - INFO - Epoch(train) [87][50/91] base_lr: 2.2253e-04 lr: 2.2253e-04 eta: 3 days, 8:18:47 time: 10.7244 data_time: 1.4627 memory: 68700 grad_norm: 1.5851 loss: 1.9271 center_loss: 0.5410 size_loss: 0.1633 cls_loss: 0.6028 giou_loss: 0.6200 2025/05/12 14:28:44 - mmengine - INFO - Epoch(train) [87][60/91] base_lr: 2.2253e-04 lr: 2.2253e-04 eta: 3 days, 8:16:48 time: 9.7013 data_time: 0.5918 memory: 68703 grad_norm: 1.6410 loss: 1.9184 center_loss: 0.5434 size_loss: 0.1646 cls_loss: 0.5893 giou_loss: 0.6212 2025/05/12 14:30:21 - mmengine - INFO - Epoch(train) [87][70/91] base_lr: 2.2253e-04 lr: 2.2253e-04 eta: 3 days, 8:14:49 time: 9.7134 data_time: 0.6032 memory: 68702 grad_norm: 1.6702 loss: 1.8969 center_loss: 0.5345 size_loss: 0.1630 cls_loss: 0.5804 giou_loss: 0.6190 2025/05/12 14:31:57 - mmengine - INFO - Epoch(train) [87][80/91] base_lr: 2.2253e-04 lr: 2.2253e-04 eta: 3 days, 8:12:50 time: 9.6780 data_time: 0.6139 memory: 68700 grad_norm: 1.7320 loss: 1.9030 center_loss: 0.5334 size_loss: 0.1626 cls_loss: 0.5870 giou_loss: 0.6200 2025/05/12 14:33:33 - mmengine - INFO - Epoch(train) [87][90/91] base_lr: 2.2253e-04 lr: 2.2253e-04 eta: 3 days, 8:10:47 time: 9.6438 data_time: 0.6077 memory: 68702 grad_norm: 1.7624 loss: 1.9058 center_loss: 0.5320 size_loss: 0.1622 cls_loss: 0.5900 giou_loss: 0.6216 2025/05/12 14:33:35 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 14:36:03 - mmengine - INFO - Epoch(train) [88][10/91] base_lr: 2.2191e-04 lr: 2.2191e-04 eta: 3 days, 8:11:16 time: 10.5141 data_time: 1.4292 memory: 68703 grad_norm: 1.7719 loss: 1.8984 center_loss: 0.5289 size_loss: 0.1613 cls_loss: 0.5905 giou_loss: 0.6176 2025/05/12 14:37:39 - mmengine - INFO - Epoch(train) [88][20/91] base_lr: 2.2191e-04 lr: 2.2191e-04 eta: 3 days, 8:09:15 time: 10.5039 data_time: 1.4231 memory: 68702 grad_norm: 1.7265 loss: 1.8907 center_loss: 0.5224 size_loss: 0.1599 cls_loss: 0.5944 giou_loss: 0.6139 2025/05/12 14:39:16 - mmengine - INFO - Epoch(train) [88][30/91] base_lr: 2.2191e-04 lr: 2.2191e-04 eta: 3 days, 8:07:16 time: 10.5003 data_time: 1.4146 memory: 68702 grad_norm: 1.6487 loss: 1.8751 center_loss: 0.5161 size_loss: 0.1578 cls_loss: 0.5899 giou_loss: 0.6113 2025/05/12 14:40:52 - mmengine - INFO - Epoch(train) [88][40/91] base_lr: 2.2191e-04 lr: 2.2191e-04 eta: 3 days, 8:05:18 time: 10.5005 data_time: 1.4081 memory: 68703 grad_norm: 1.6358 loss: 1.8948 center_loss: 0.5254 size_loss: 0.1582 cls_loss: 0.5973 giou_loss: 0.6139 2025/05/12 14:42:30 - mmengine - INFO - Epoch(train) [88][50/91] base_lr: 2.2191e-04 lr: 2.2191e-04 eta: 3 days, 8:03:22 time: 10.7007 data_time: 1.4187 memory: 68703 grad_norm: 1.5865 loss: 1.8887 center_loss: 0.5277 size_loss: 0.1574 cls_loss: 0.5897 giou_loss: 0.6141 2025/05/12 14:44:06 - mmengine - INFO - Epoch(train) [88][60/91] base_lr: 2.2191e-04 lr: 2.2191e-04 eta: 3 days, 8:01:23 time: 9.6567 data_time: 0.5951 memory: 68702 grad_norm: 1.5536 loss: 1.8789 center_loss: 0.5179 size_loss: 0.1569 cls_loss: 0.5923 giou_loss: 0.6119 2025/05/12 14:45:42 - mmengine - INFO - Epoch(train) [88][70/91] base_lr: 2.2191e-04 lr: 2.2191e-04 eta: 3 days, 7:59:24 time: 9.6649 data_time: 0.5889 memory: 68703 grad_norm: 1.6132 loss: 1.8823 center_loss: 0.5173 size_loss: 0.1573 cls_loss: 0.5948 giou_loss: 0.6128 2025/05/12 14:47:19 - mmengine - INFO - Epoch(train) [88][80/91] base_lr: 2.2191e-04 lr: 2.2191e-04 eta: 3 days, 7:57:27 time: 9.6715 data_time: 0.5808 memory: 68702 grad_norm: 1.6573 loss: 1.9037 center_loss: 0.5232 size_loss: 0.1595 cls_loss: 0.6059 giou_loss: 0.6151 2025/05/12 14:47:48 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 14:48:55 - mmengine - INFO - Epoch(train) [88][90/91] base_lr: 2.2191e-04 lr: 2.2191e-04 eta: 3 days, 7:55:25 time: 9.6537 data_time: 0.5731 memory: 68702 grad_norm: 1.6503 loss: 1.8827 center_loss: 0.5168 size_loss: 0.1587 cls_loss: 0.5946 giou_loss: 0.6126 2025/05/12 14:48:57 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 14:48:57 - mmengine - INFO - Saving checkpoint at 88 epochs 2025/05/12 14:49:56 - mmengine - INFO - Epoch(val) [88][10/39] eta: 0:01:40 time: 2.8959 data_time: 0.3840 memory: 15952 2025/05/12 14:50:22 - mmengine - INFO - Epoch(val) [88][20/39] eta: 0:00:57 time: 2.7569 data_time: 0.2451 memory: 13407 2025/05/12 14:50:48 - mmengine - INFO - Epoch(val) [88][30/39] eta: 0:00:26 time: 2.7594 data_time: 0.2448 memory: 13407 2025/05/12 14:51:14 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.2100 | 0.4396 | 0.0108 | 0.0962 | | sofa | 0.7045 | 0.8557 | 0.1716 | 0.3711 | | table | 0.4159 | 0.5571 | 0.0956 | 0.2314 | | chair | 0.4749 | 0.6637 | 0.0997 | 0.2573 | | curtain | 0.2432 | 0.5224 | 0.0231 | 0.1194 | | bookshelf | 0.3143 | 0.6234 | 0.0573 | 0.1558 | | door | 0.1287 | 0.4069 | 0.0185 | 0.0878 | | picture | 0.0121 | 0.0991 | 0.0005 | 0.0090 | | cabinet | 0.2246 | 0.4812 | 0.0298 | 0.1478 | | window | 0.1115 | 0.3369 | 0.0099 | 0.0851 | | refrigerator | 0.3929 | 0.6316 | 0.1685 | 0.3158 | | sink | 0.4041 | 0.6633 | 0.0326 | 0.1020 | | counter | 0.1200 | 0.2500 | 0.0133 | 0.0962 | | desk | 0.6222 | 0.8268 | 0.1627 | 0.3780 | | toilet | 0.7955 | 0.8966 | 0.3992 | 0.5345 | | bed | 0.7941 | 0.8395 | 0.3085 | 0.4815 | | showercurtrain | 0.2772 | 0.5357 | 0.0366 | 0.1786 | | bathtub | 0.7195 | 0.8387 | 0.2257 | 0.4516 | +----------------+---------+---------+---------+---------+ | Overall | 0.3870 | 0.5816 | 0.1036 | 0.2277 | +----------------+---------+---------+---------+---------+ 2025/05/12 14:51:14 - mmengine - INFO - Epoch(val) [88][39/39] chair_AP_0.25: 0.4749 sofa_AP_0.25: 0.7045 table_AP_0.25: 0.4159 garbagebin_AP_0.25: 0.2100 bookshelf_AP_0.25: 0.3143 picture_AP_0.25: 0.0121 curtain_AP_0.25: 0.2432 door_AP_0.25: 0.1287 cabinet_AP_0.25: 0.2246 refrigerator_AP_0.25: 0.3929 counter_AP_0.25: 0.1200 sink_AP_0.25: 0.4041 window_AP_0.25: 0.1115 desk_AP_0.25: 0.6222 bed_AP_0.25: 0.7941 toilet_AP_0.25: 0.7955 showercurtrain_AP_0.25: 0.2772 bathtub_AP_0.25: 0.7195 mAP_0.25: 0.3870 chair_rec_0.25: 0.6637 sofa_rec_0.25: 0.8557 table_rec_0.25: 0.5571 garbagebin_rec_0.25: 0.4396 bookshelf_rec_0.25: 0.6234 picture_rec_0.25: 0.0991 curtain_rec_0.25: 0.5224 door_rec_0.25: 0.4069 cabinet_rec_0.25: 0.4812 refrigerator_rec_0.25: 0.6316 counter_rec_0.25: 0.2500 sink_rec_0.25: 0.6633 window_rec_0.25: 0.3369 desk_rec_0.25: 0.8268 bed_rec_0.25: 0.8395 toilet_rec_0.25: 0.8966 showercurtrain_rec_0.25: 0.5357 bathtub_rec_0.25: 0.8387 mAR_0.25: 0.5816 chair_AP_0.50: 0.0997 sofa_AP_0.50: 0.1716 table_AP_0.50: 0.0956 garbagebin_AP_0.50: 0.0108 bookshelf_AP_0.50: 0.0573 picture_AP_0.50: 0.0005 curtain_AP_0.50: 0.0231 door_AP_0.50: 0.0185 cabinet_AP_0.50: 0.0298 refrigerator_AP_0.50: 0.1685 counter_AP_0.50: 0.0133 sink_AP_0.50: 0.0326 window_AP_0.50: 0.0099 desk_AP_0.50: 0.1627 bed_AP_0.50: 0.3085 toilet_AP_0.50: 0.3992 showercurtrain_AP_0.50: 0.0366 bathtub_AP_0.50: 0.2257 mAP_0.50: 0.1036 chair_rec_0.50: 0.2573 sofa_rec_0.50: 0.3711 table_rec_0.50: 0.2314 garbagebin_rec_0.50: 0.0962 bookshelf_rec_0.50: 0.1558 picture_rec_0.50: 0.0090 curtain_rec_0.50: 0.1194 door_rec_0.50: 0.0878 cabinet_rec_0.50: 0.1478 refrigerator_rec_0.50: 0.3158 counter_rec_0.50: 0.0962 sink_rec_0.50: 0.1020 window_rec_0.50: 0.0851 desk_rec_0.50: 0.3780 bed_rec_0.50: 0.4815 toilet_rec_0.50: 0.5345 showercurtrain_rec_0.50: 0.1786 bathtub_rec_0.50: 0.4516 mAR_0.50: 0.2277 data_time: 0.2876 time: 2.8008 2025/05/12 14:53:40 - mmengine - INFO - Epoch(train) [89][10/91] base_lr: 2.2129e-04 lr: 2.2129e-04 eta: 3 days, 7:55:43 time: 10.4794 data_time: 1.4972 memory: 68702 grad_norm: 1.6661 loss: 1.8973 center_loss: 0.5243 size_loss: 0.1617 cls_loss: 0.5954 giou_loss: 0.6159 2025/05/12 14:55:18 - mmengine - INFO - Epoch(train) [89][20/91] base_lr: 2.2129e-04 lr: 2.2129e-04 eta: 3 days, 7:53:47 time: 10.4885 data_time: 1.4842 memory: 68703 grad_norm: 1.6728 loss: 1.8947 center_loss: 0.5240 size_loss: 0.1615 cls_loss: 0.5941 giou_loss: 0.6151 2025/05/12 14:56:55 - mmengine - INFO - Epoch(train) [89][30/91] base_lr: 2.2129e-04 lr: 2.2129e-04 eta: 3 days, 7:51:50 time: 10.5040 data_time: 1.4845 memory: 68702 grad_norm: 1.6566 loss: 1.8959 center_loss: 0.5238 size_loss: 0.1603 cls_loss: 0.5959 giou_loss: 0.6159 2025/05/12 14:58:31 - mmengine - INFO - Epoch(train) [89][40/91] base_lr: 2.2129e-04 lr: 2.2129e-04 eta: 3 days, 7:49:51 time: 10.4866 data_time: 1.4927 memory: 68703 grad_norm: 1.6929 loss: 1.8967 center_loss: 0.5312 size_loss: 0.1597 cls_loss: 0.5884 giou_loss: 0.6175 2025/05/12 15:00:09 - mmengine - INFO - Epoch(train) [89][50/91] base_lr: 2.2129e-04 lr: 2.2129e-04 eta: 3 days, 7:47:57 time: 10.6889 data_time: 1.5077 memory: 68702 grad_norm: 1.6192 loss: 1.9034 center_loss: 0.5302 size_loss: 0.1603 cls_loss: 0.5931 giou_loss: 0.6198 2025/05/12 15:01:46 - mmengine - INFO - Epoch(train) [89][60/91] base_lr: 2.2129e-04 lr: 2.2129e-04 eta: 3 days, 7:46:00 time: 9.7047 data_time: 0.5744 memory: 68700 grad_norm: 1.6097 loss: 1.9061 center_loss: 0.5315 size_loss: 0.1599 cls_loss: 0.5954 giou_loss: 0.6194 2025/05/12 15:03:23 - mmengine - INFO - Epoch(train) [89][70/91] base_lr: 2.2129e-04 lr: 2.2129e-04 eta: 3 days, 7:44:04 time: 9.7049 data_time: 0.5809 memory: 68703 grad_norm: 1.6381 loss: 1.9036 center_loss: 0.5344 size_loss: 0.1580 cls_loss: 0.5900 giou_loss: 0.6213 2025/05/12 15:04:59 - mmengine - INFO - Epoch(train) [89][80/91] base_lr: 2.2129e-04 lr: 2.2129e-04 eta: 3 days, 7:42:06 time: 9.6973 data_time: 0.5933 memory: 68703 grad_norm: 1.6729 loss: 1.8908 center_loss: 0.5317 size_loss: 0.1571 cls_loss: 0.5824 giou_loss: 0.6195 2025/05/12 15:06:35 - mmengine - INFO - Epoch(train) [89][90/91] base_lr: 2.2129e-04 lr: 2.2129e-04 eta: 3 days, 7:40:06 time: 9.6847 data_time: 0.5902 memory: 68702 grad_norm: 1.7392 loss: 1.8797 center_loss: 0.5266 size_loss: 0.1559 cls_loss: 0.5816 giou_loss: 0.6156 2025/05/12 15:06:37 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 15:09:04 - mmengine - INFO - Epoch(train) [90][10/91] base_lr: 2.2066e-04 lr: 2.2066e-04 eta: 3 days, 7:40:23 time: 10.5095 data_time: 1.4628 memory: 68702 grad_norm: 1.8726 loss: 1.8890 center_loss: 0.5310 size_loss: 0.1578 cls_loss: 0.5819 giou_loss: 0.6184 2025/05/12 15:10:41 - mmengine - INFO - Epoch(train) [90][20/91] base_lr: 2.2066e-04 lr: 2.2066e-04 eta: 3 days, 7:38:26 time: 10.5088 data_time: 1.4680 memory: 68702 grad_norm: 1.9027 loss: 1.8913 center_loss: 0.5282 size_loss: 0.1573 cls_loss: 0.5870 giou_loss: 0.6188 2025/05/12 15:12:18 - mmengine - INFO - Epoch(train) [90][30/91] base_lr: 2.2066e-04 lr: 2.2066e-04 eta: 3 days, 7:36:29 time: 10.5020 data_time: 1.4699 memory: 68702 grad_norm: 1.8661 loss: 1.9030 center_loss: 0.5313 size_loss: 0.1591 cls_loss: 0.5933 giou_loss: 0.6193 2025/05/12 15:13:55 - mmengine - INFO - Epoch(train) [90][40/91] base_lr: 2.2066e-04 lr: 2.2066e-04 eta: 3 days, 7:34:33 time: 10.5061 data_time: 1.4652 memory: 68702 grad_norm: 1.8788 loss: 1.9040 center_loss: 0.5310 size_loss: 0.1571 cls_loss: 0.5978 giou_loss: 0.6181 2025/05/12 15:15:32 - mmengine - INFO - Epoch(train) [90][50/91] base_lr: 2.2066e-04 lr: 2.2066e-04 eta: 3 days, 7:32:37 time: 10.6907 data_time: 1.4810 memory: 68702 grad_norm: 1.7195 loss: 1.9043 center_loss: 0.5313 size_loss: 0.1567 cls_loss: 0.5969 giou_loss: 0.6193 2025/05/12 15:17:09 - mmengine - INFO - Epoch(train) [90][60/91] base_lr: 2.2066e-04 lr: 2.2066e-04 eta: 3 days, 7:30:39 time: 9.6945 data_time: 0.6123 memory: 68702 grad_norm: 1.6340 loss: 1.8905 center_loss: 0.5238 size_loss: 0.1547 cls_loss: 0.5954 giou_loss: 0.6167 2025/05/12 15:18:46 - mmengine - INFO - Epoch(train) [90][70/91] base_lr: 2.2066e-04 lr: 2.2066e-04 eta: 3 days, 7:28:44 time: 9.7031 data_time: 0.6087 memory: 68701 grad_norm: 1.5523 loss: 1.8915 center_loss: 0.5276 size_loss: 0.1563 cls_loss: 0.5906 giou_loss: 0.6170 2025/05/12 15:20:22 - mmengine - INFO - Epoch(train) [90][80/91] base_lr: 2.2066e-04 lr: 2.2066e-04 eta: 3 days, 7:26:44 time: 9.6849 data_time: 0.6013 memory: 68702 grad_norm: 1.6638 loss: 1.8934 center_loss: 0.5310 size_loss: 0.1585 cls_loss: 0.5871 giou_loss: 0.6169 2025/05/12 15:21:58 - mmengine - INFO - Epoch(train) [90][90/91] base_lr: 2.2066e-04 lr: 2.2066e-04 eta: 3 days, 7:24:44 time: 9.6576 data_time: 0.5961 memory: 68702 grad_norm: 2.0169 loss: 1.9111 center_loss: 0.5367 size_loss: 0.1606 cls_loss: 0.5932 giou_loss: 0.6207 2025/05/12 15:21:59 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 15:21:59 - mmengine - INFO - Saving checkpoint at 90 epochs 2025/05/12 15:22:55 - mmengine - INFO - Epoch(val) [90][10/39] eta: 0:01:33 time: 2.8881 data_time: 0.3745 memory: 15952 2025/05/12 15:23:21 - mmengine - INFO - Epoch(val) [90][20/39] eta: 0:00:55 time: 2.7237 data_time: 0.2165 memory: 13407 2025/05/12 15:23:47 - mmengine - INFO - Epoch(val) [90][30/39] eta: 0:00:25 time: 2.7098 data_time: 0.2051 memory: 13407 2025/05/12 15:24:14 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | chair | 0.4822 | 0.6667 | 0.1057 | 0.2807 | | sofa | 0.7143 | 0.8351 | 0.1292 | 0.3299 | | garbagebin | 0.2085 | 0.4396 | 0.0152 | 0.1075 | | table | 0.4017 | 0.5486 | 0.0893 | 0.2171 | | bookshelf | 0.3275 | 0.5455 | 0.0838 | 0.2338 | | curtain | 0.1859 | 0.4328 | 0.0113 | 0.0597 | | picture | 0.0086 | 0.0676 | 0.0000 | 0.0000 | | door | 0.1411 | 0.4111 | 0.0118 | 0.1006 | | window | 0.1284 | 0.2979 | 0.0086 | 0.0674 | | cabinet | 0.2289 | 0.4651 | 0.0283 | 0.1559 | | refrigerator | 0.4299 | 0.6140 | 0.2150 | 0.3333 | | sink | 0.4105 | 0.5714 | 0.0949 | 0.2041 | | counter | 0.0987 | 0.2692 | 0.0043 | 0.0385 | | toilet | 0.7873 | 0.8966 | 0.3082 | 0.4138 | | desk | 0.6344 | 0.8346 | 0.1541 | 0.3858 | | bed | 0.7926 | 0.8148 | 0.3937 | 0.5556 | | bathtub | 0.6773 | 0.8065 | 0.1932 | 0.3226 | | showercurtrain | 0.2394 | 0.5000 | 0.0927 | 0.2143 | +----------------+---------+---------+---------+---------+ | Overall | 0.3832 | 0.5565 | 0.1077 | 0.2234 | +----------------+---------+---------+---------+---------+ 2025/05/12 15:24:14 - mmengine - INFO - Epoch(val) [90][39/39] chair_AP_0.25: 0.4822 sofa_AP_0.25: 0.7143 table_AP_0.25: 0.4017 garbagebin_AP_0.25: 0.2085 bookshelf_AP_0.25: 0.3275 picture_AP_0.25: 0.0086 curtain_AP_0.25: 0.1859 door_AP_0.25: 0.1411 cabinet_AP_0.25: 0.2289 refrigerator_AP_0.25: 0.4299 counter_AP_0.25: 0.0987 sink_AP_0.25: 0.4105 window_AP_0.25: 0.1284 desk_AP_0.25: 0.6344 bed_AP_0.25: 0.7926 toilet_AP_0.25: 0.7873 showercurtrain_AP_0.25: 0.2394 bathtub_AP_0.25: 0.6773 mAP_0.25: 0.3832 chair_rec_0.25: 0.6667 sofa_rec_0.25: 0.8351 table_rec_0.25: 0.5486 garbagebin_rec_0.25: 0.4396 bookshelf_rec_0.25: 0.5455 picture_rec_0.25: 0.0676 curtain_rec_0.25: 0.4328 door_rec_0.25: 0.4111 cabinet_rec_0.25: 0.4651 refrigerator_rec_0.25: 0.6140 counter_rec_0.25: 0.2692 sink_rec_0.25: 0.5714 window_rec_0.25: 0.2979 desk_rec_0.25: 0.8346 bed_rec_0.25: 0.8148 toilet_rec_0.25: 0.8966 showercurtrain_rec_0.25: 0.5000 bathtub_rec_0.25: 0.8065 mAR_0.25: 0.5565 chair_AP_0.50: 0.1057 sofa_AP_0.50: 0.1292 table_AP_0.50: 0.0893 garbagebin_AP_0.50: 0.0152 bookshelf_AP_0.50: 0.0838 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0113 door_AP_0.50: 0.0118 cabinet_AP_0.50: 0.0283 refrigerator_AP_0.50: 0.2150 counter_AP_0.50: 0.0043 sink_AP_0.50: 0.0949 window_AP_0.50: 0.0086 desk_AP_0.50: 0.1541 bed_AP_0.50: 0.3937 toilet_AP_0.50: 0.3082 showercurtrain_AP_0.50: 0.0927 bathtub_AP_0.50: 0.1932 mAP_0.50: 0.1077 chair_rec_0.50: 0.2807 sofa_rec_0.50: 0.3299 table_rec_0.50: 0.2171 garbagebin_rec_0.50: 0.1075 bookshelf_rec_0.50: 0.2338 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.0597 door_rec_0.50: 0.1006 cabinet_rec_0.50: 0.1559 refrigerator_rec_0.50: 0.3333 counter_rec_0.50: 0.0385 sink_rec_0.50: 0.2041 window_rec_0.50: 0.0674 desk_rec_0.50: 0.3858 bed_rec_0.50: 0.5556 toilet_rec_0.50: 0.4138 showercurtrain_rec_0.50: 0.2143 bathtub_rec_0.50: 0.3226 mAR_0.50: 0.2234 data_time: 0.2383 time: 2.7339 2025/05/12 15:26:39 - mmengine - INFO - Epoch(train) [91][10/91] base_lr: 2.2003e-04 lr: 2.2003e-04 eta: 3 days, 7:24:53 time: 10.4574 data_time: 1.4255 memory: 68702 grad_norm: 2.0351 loss: 1.9238 center_loss: 0.5384 size_loss: 0.1622 cls_loss: 0.6015 giou_loss: 0.6217 2025/05/12 15:28:15 - mmengine - INFO - Epoch(train) [91][20/91] base_lr: 2.2003e-04 lr: 2.2003e-04 eta: 3 days, 7:22:55 time: 10.4432 data_time: 1.4077 memory: 68702 grad_norm: 2.0576 loss: 1.9185 center_loss: 0.5374 size_loss: 0.1609 cls_loss: 0.5974 giou_loss: 0.6228 2025/05/12 15:29:52 - mmengine - INFO - Epoch(train) [91][30/91] base_lr: 2.2003e-04 lr: 2.2003e-04 eta: 3 days, 7:21:00 time: 10.4509 data_time: 1.4197 memory: 68703 grad_norm: 2.0613 loss: 1.9274 center_loss: 0.5417 size_loss: 0.1627 cls_loss: 0.5979 giou_loss: 0.6251 2025/05/12 15:31:30 - mmengine - INFO - Epoch(train) [91][40/91] base_lr: 2.2003e-04 lr: 2.2003e-04 eta: 3 days, 7:19:04 time: 10.4627 data_time: 1.4223 memory: 68702 grad_norm: 2.0037 loss: 1.9270 center_loss: 0.5364 size_loss: 0.1610 cls_loss: 0.6034 giou_loss: 0.6262 2025/05/12 15:33:07 - mmengine - INFO - Epoch(train) [91][50/91] base_lr: 2.2003e-04 lr: 2.2003e-04 eta: 3 days, 7:17:09 time: 10.6653 data_time: 1.4309 memory: 68702 grad_norm: 1.5499 loss: 1.9180 center_loss: 0.5340 size_loss: 0.1601 cls_loss: 0.6017 giou_loss: 0.6222 2025/05/12 15:34:44 - mmengine - INFO - Epoch(train) [91][60/91] base_lr: 2.2003e-04 lr: 2.2003e-04 eta: 3 days, 7:15:14 time: 9.7125 data_time: 0.5842 memory: 68702 grad_norm: 1.5942 loss: 1.9200 center_loss: 0.5359 size_loss: 0.1601 cls_loss: 0.5992 giou_loss: 0.6247 2025/05/12 15:36:21 - mmengine - INFO - Epoch(train) [91][70/91] base_lr: 2.2003e-04 lr: 2.2003e-04 eta: 3 days, 7:13:18 time: 9.7243 data_time: 0.5889 memory: 68702 grad_norm: 1.6971 loss: 1.9254 center_loss: 0.5387 size_loss: 0.1604 cls_loss: 0.6033 giou_loss: 0.6230 2025/05/12 15:37:59 - mmengine - INFO - Epoch(train) [91][80/91] base_lr: 2.2003e-04 lr: 2.2003e-04 eta: 3 days, 7:11:25 time: 9.7367 data_time: 0.5761 memory: 68703 grad_norm: 1.7538 loss: 1.9197 center_loss: 0.5371 size_loss: 0.1579 cls_loss: 0.6025 giou_loss: 0.6222 2025/05/12 15:39:35 - mmengine - INFO - Epoch(train) [91][90/91] base_lr: 2.2003e-04 lr: 2.2003e-04 eta: 3 days, 7:09:24 time: 9.7078 data_time: 0.5687 memory: 68702 grad_norm: 1.8463 loss: 1.9391 center_loss: 0.5499 size_loss: 0.1602 cls_loss: 0.6025 giou_loss: 0.6264 2025/05/12 15:39:37 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 15:42:04 - mmengine - INFO - Epoch(train) [92][10/91] base_lr: 2.1939e-04 lr: 2.1939e-04 eta: 3 days, 7:09:38 time: 10.5350 data_time: 1.4636 memory: 68703 grad_norm: 2.0182 loss: 1.9396 center_loss: 0.5489 size_loss: 0.1615 cls_loss: 0.6052 giou_loss: 0.6240 2025/05/12 15:43:41 - mmengine - INFO - Epoch(train) [92][20/91] base_lr: 2.1939e-04 lr: 2.1939e-04 eta: 3 days, 7:07:43 time: 10.5381 data_time: 1.4640 memory: 68702 grad_norm: 2.0691 loss: 1.9424 center_loss: 0.5516 size_loss: 0.1617 cls_loss: 0.6047 giou_loss: 0.6245 2025/05/12 15:45:18 - mmengine - INFO - Epoch(train) [92][30/91] base_lr: 2.1939e-04 lr: 2.1939e-04 eta: 3 days, 7:05:46 time: 10.5310 data_time: 1.4657 memory: 68703 grad_norm: 1.9818 loss: 1.9479 center_loss: 0.5527 size_loss: 0.1632 cls_loss: 0.6062 giou_loss: 0.6257 2025/05/12 15:46:55 - mmengine - INFO - Epoch(train) [92][40/91] base_lr: 2.1939e-04 lr: 2.1939e-04 eta: 3 days, 7:03:51 time: 10.5110 data_time: 1.4790 memory: 68702 grad_norm: 1.8860 loss: 1.9360 center_loss: 0.5486 size_loss: 0.1627 cls_loss: 0.6030 giou_loss: 0.6216 2025/05/12 15:48:32 - mmengine - INFO - Epoch(train) [92][50/91] base_lr: 2.1939e-04 lr: 2.1939e-04 eta: 3 days, 7:01:56 time: 10.7074 data_time: 1.4881 memory: 68700 grad_norm: 1.8154 loss: 1.9211 center_loss: 0.5456 size_loss: 0.1611 cls_loss: 0.5947 giou_loss: 0.6196 2025/05/12 15:50:09 - mmengine - INFO - Epoch(train) [92][60/91] base_lr: 2.1939e-04 lr: 2.1939e-04 eta: 3 days, 6:59:58 time: 9.7054 data_time: 0.5833 memory: 68702 grad_norm: 1.7410 loss: 1.9301 center_loss: 0.5490 size_loss: 0.1611 cls_loss: 0.5957 giou_loss: 0.6244 2025/05/12 15:51:46 - mmengine - INFO - Epoch(train) [92][70/91] base_lr: 2.1939e-04 lr: 2.1939e-04 eta: 3 days, 6:58:02 time: 9.6945 data_time: 0.5836 memory: 68702 grad_norm: 1.6852 loss: 1.9250 center_loss: 0.5473 size_loss: 0.1603 cls_loss: 0.5943 giou_loss: 0.6232 2025/05/12 15:53:22 - mmengine - INFO - Epoch(train) [92][80/91] base_lr: 2.1939e-04 lr: 2.1939e-04 eta: 3 days, 6:56:03 time: 9.6844 data_time: 0.5825 memory: 68703 grad_norm: 1.6574 loss: 1.9312 center_loss: 0.5525 size_loss: 0.1606 cls_loss: 0.5936 giou_loss: 0.6245 2025/05/12 15:54:57 - mmengine - INFO - Epoch(train) [92][90/91] base_lr: 2.1939e-04 lr: 2.1939e-04 eta: 3 days, 6:54:00 time: 9.6373 data_time: 0.5634 memory: 68702 grad_norm: 1.7621 loss: 1.9468 center_loss: 0.5536 size_loss: 0.1628 cls_loss: 0.6007 giou_loss: 0.6297 2025/05/12 15:54:59 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 15:54:59 - mmengine - INFO - Saving checkpoint at 92 epochs 2025/05/12 15:55:56 - mmengine - INFO - Epoch(val) [92][10/39] eta: 0:01:37 time: 2.8582 data_time: 0.3561 memory: 15952 2025/05/12 15:56:22 - mmengine - INFO - Epoch(val) [92][20/39] eta: 0:00:56 time: 2.7317 data_time: 0.2289 memory: 13407 2025/05/12 15:56:48 - mmengine - INFO - Epoch(val) [92][30/39] eta: 0:00:25 time: 2.7314 data_time: 0.2268 memory: 13407 2025/05/12 15:57:14 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | sofa | 0.6381 | 0.8144 | 0.1478 | 0.3711 | | garbagebin | 0.1933 | 0.4038 | 0.0209 | 0.1113 | | table | 0.4035 | 0.5629 | 0.0988 | 0.2371 | | chair | 0.4590 | 0.6776 | 0.0820 | 0.2573 | | curtain | 0.1970 | 0.4627 | 0.0227 | 0.1045 | | bookshelf | 0.2617 | 0.5065 | 0.0395 | 0.1818 | | picture | 0.0031 | 0.0631 | 0.0001 | 0.0090 | | door | 0.1152 | 0.3812 | 0.0071 | 0.1006 | | cabinet | 0.2128 | 0.4462 | 0.0290 | 0.1559 | | window | 0.1013 | 0.2979 | 0.0124 | 0.0780 | | refrigerator | 0.4096 | 0.5614 | 0.0845 | 0.2281 | | sink | 0.3656 | 0.5510 | 0.0265 | 0.1531 | | counter | 0.1093 | 0.2115 | 0.0108 | 0.0577 | | bed | 0.8172 | 0.8519 | 0.3532 | 0.5432 | | desk | 0.6455 | 0.8110 | 0.1625 | 0.3937 | | toilet | 0.7915 | 0.8966 | 0.4067 | 0.5345 | | bathtub | 0.7375 | 0.8065 | 0.2243 | 0.3548 | | showercurtrain | 0.2820 | 0.5714 | 0.0261 | 0.1786 | +----------------+---------+---------+---------+---------+ | Overall | 0.3746 | 0.5487 | 0.0975 | 0.2250 | +----------------+---------+---------+---------+---------+ 2025/05/12 15:57:14 - mmengine - INFO - Epoch(val) [92][39/39] chair_AP_0.25: 0.4590 sofa_AP_0.25: 0.6381 table_AP_0.25: 0.4035 garbagebin_AP_0.25: 0.1933 bookshelf_AP_0.25: 0.2617 picture_AP_0.25: 0.0031 curtain_AP_0.25: 0.1970 door_AP_0.25: 0.1152 cabinet_AP_0.25: 0.2128 refrigerator_AP_0.25: 0.4096 counter_AP_0.25: 0.1093 sink_AP_0.25: 0.3656 window_AP_0.25: 0.1013 desk_AP_0.25: 0.6455 bed_AP_0.25: 0.8172 toilet_AP_0.25: 0.7915 showercurtrain_AP_0.25: 0.2820 bathtub_AP_0.25: 0.7375 mAP_0.25: 0.3746 chair_rec_0.25: 0.6776 sofa_rec_0.25: 0.8144 table_rec_0.25: 0.5629 garbagebin_rec_0.25: 0.4038 bookshelf_rec_0.25: 0.5065 picture_rec_0.25: 0.0631 curtain_rec_0.25: 0.4627 door_rec_0.25: 0.3812 cabinet_rec_0.25: 0.4462 refrigerator_rec_0.25: 0.5614 counter_rec_0.25: 0.2115 sink_rec_0.25: 0.5510 window_rec_0.25: 0.2979 desk_rec_0.25: 0.8110 bed_rec_0.25: 0.8519 toilet_rec_0.25: 0.8966 showercurtrain_rec_0.25: 0.5714 bathtub_rec_0.25: 0.8065 mAR_0.25: 0.5487 chair_AP_0.50: 0.0820 sofa_AP_0.50: 0.1478 table_AP_0.50: 0.0988 garbagebin_AP_0.50: 0.0209 bookshelf_AP_0.50: 0.0395 picture_AP_0.50: 0.0001 curtain_AP_0.50: 0.0227 door_AP_0.50: 0.0071 cabinet_AP_0.50: 0.0290 refrigerator_AP_0.50: 0.0845 counter_AP_0.50: 0.0108 sink_AP_0.50: 0.0265 window_AP_0.50: 0.0124 desk_AP_0.50: 0.1625 bed_AP_0.50: 0.3532 toilet_AP_0.50: 0.4067 showercurtrain_AP_0.50: 0.0261 bathtub_AP_0.50: 0.2243 mAP_0.50: 0.0975 chair_rec_0.50: 0.2573 sofa_rec_0.50: 0.3711 table_rec_0.50: 0.2371 garbagebin_rec_0.50: 0.1113 bookshelf_rec_0.50: 0.1818 picture_rec_0.50: 0.0090 curtain_rec_0.50: 0.1045 door_rec_0.50: 0.1006 cabinet_rec_0.50: 0.1559 refrigerator_rec_0.50: 0.2281 counter_rec_0.50: 0.0577 sink_rec_0.50: 0.1531 window_rec_0.50: 0.0780 desk_rec_0.50: 0.3937 bed_rec_0.50: 0.5432 toilet_rec_0.50: 0.5345 showercurtrain_rec_0.50: 0.1786 bathtub_rec_0.50: 0.3548 mAR_0.50: 0.2250 data_time: 0.2649 time: 2.7663 2025/05/12 15:59:40 - mmengine - INFO - Epoch(train) [93][10/91] base_lr: 2.1874e-04 lr: 2.1874e-04 eta: 3 days, 6:54:10 time: 10.4524 data_time: 1.3794 memory: 68702 grad_norm: 1.9685 loss: 1.9647 center_loss: 0.5555 size_loss: 0.1649 cls_loss: 0.6105 giou_loss: 0.6338 2025/05/12 16:01:16 - mmengine - INFO - Epoch(train) [93][20/91] base_lr: 2.1874e-04 lr: 2.1874e-04 eta: 3 days, 6:52:11 time: 10.4437 data_time: 1.3816 memory: 68701 grad_norm: 1.9550 loss: 1.9803 center_loss: 0.5635 size_loss: 0.1655 cls_loss: 0.6152 giou_loss: 0.6361 2025/05/12 16:02:53 - mmengine - INFO - Epoch(train) [93][30/91] base_lr: 2.1874e-04 lr: 2.1874e-04 eta: 3 days, 6:50:15 time: 10.4468 data_time: 1.3771 memory: 68702 grad_norm: 1.9553 loss: 2.0091 center_loss: 0.5775 size_loss: 0.1694 cls_loss: 0.6217 giou_loss: 0.6406 2025/05/12 16:04:31 - mmengine - INFO - Epoch(train) [93][40/91] base_lr: 2.1874e-04 lr: 2.1874e-04 eta: 3 days, 6:48:20 time: 10.4640 data_time: 1.3702 memory: 68702 grad_norm: 1.9775 loss: 1.9922 center_loss: 0.5667 size_loss: 0.1667 cls_loss: 0.6206 giou_loss: 0.6382 2025/05/12 16:06:08 - mmengine - INFO - Epoch(train) [93][50/91] base_lr: 2.1874e-04 lr: 2.1874e-04 eta: 3 days, 6:46:24 time: 10.6653 data_time: 1.3879 memory: 68702 grad_norm: 1.6328 loss: 1.9907 center_loss: 0.5747 size_loss: 0.1631 cls_loss: 0.6176 giou_loss: 0.6353 2025/05/12 16:07:44 - mmengine - INFO - Epoch(train) [93][60/91] base_lr: 2.1874e-04 lr: 2.1874e-04 eta: 3 days, 6:44:27 time: 9.6801 data_time: 0.5771 memory: 68702 grad_norm: 1.5883 loss: 1.9624 center_loss: 0.5665 size_loss: 0.1611 cls_loss: 0.6069 giou_loss: 0.6279 2025/05/12 16:09:21 - mmengine - INFO - Epoch(train) [93][70/91] base_lr: 2.1874e-04 lr: 2.1874e-04 eta: 3 days, 6:42:31 time: 9.6944 data_time: 0.5854 memory: 68702 grad_norm: 1.5503 loss: 1.9306 center_loss: 0.5491 size_loss: 0.1591 cls_loss: 0.6031 giou_loss: 0.6194 2025/05/12 16:10:57 - mmengine - INFO - Epoch(train) [93][80/91] base_lr: 2.1874e-04 lr: 2.1874e-04 eta: 3 days, 6:40:32 time: 9.6759 data_time: 0.5858 memory: 68703 grad_norm: 1.5575 loss: 1.8926 center_loss: 0.5276 size_loss: 0.1546 cls_loss: 0.5976 giou_loss: 0.6129 2025/05/12 16:12:32 - mmengine - INFO - Epoch(train) [93][90/91] base_lr: 2.1874e-04 lr: 2.1874e-04 eta: 3 days, 6:38:30 time: 9.6356 data_time: 0.5864 memory: 68703 grad_norm: 1.6636 loss: 1.9077 center_loss: 0.5371 size_loss: 0.1558 cls_loss: 0.6004 giou_loss: 0.6144 2025/05/12 16:12:34 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 16:15:00 - mmengine - INFO - Epoch(train) [94][10/91] base_lr: 2.1809e-04 lr: 2.1809e-04 eta: 3 days, 6:38:38 time: 10.4521 data_time: 1.4418 memory: 68702 grad_norm: 1.7333 loss: 1.8875 center_loss: 0.5234 size_loss: 0.1579 cls_loss: 0.5952 giou_loss: 0.6109 2025/05/12 16:16:37 - mmengine - INFO - Epoch(train) [94][20/91] base_lr: 2.1809e-04 lr: 2.1809e-04 eta: 3 days, 6:36:42 time: 10.4654 data_time: 1.4284 memory: 68702 grad_norm: 1.7244 loss: 1.8810 center_loss: 0.5186 size_loss: 0.1569 cls_loss: 0.5966 giou_loss: 0.6089 2025/05/12 16:18:14 - mmengine - INFO - Epoch(train) [94][30/91] base_lr: 2.1809e-04 lr: 2.1809e-04 eta: 3 days, 6:34:47 time: 10.4676 data_time: 1.4246 memory: 68702 grad_norm: 1.7258 loss: 1.8965 center_loss: 0.5269 size_loss: 0.1576 cls_loss: 0.5974 giou_loss: 0.6146 2025/05/12 16:19:51 - mmengine - INFO - Epoch(train) [94][40/91] base_lr: 2.1809e-04 lr: 2.1809e-04 eta: 3 days, 6:32:50 time: 10.4771 data_time: 1.4252 memory: 68702 grad_norm: 1.6642 loss: 1.8975 center_loss: 0.5273 size_loss: 0.1579 cls_loss: 0.5999 giou_loss: 0.6125 2025/05/12 16:21:28 - mmengine - INFO - Epoch(train) [94][50/91] base_lr: 2.1809e-04 lr: 2.1809e-04 eta: 3 days, 6:30:56 time: 10.6797 data_time: 1.4322 memory: 68702 grad_norm: 1.5033 loss: 1.8826 center_loss: 0.5195 size_loss: 0.1556 cls_loss: 0.5968 giou_loss: 0.6108 2025/05/12 16:23:05 - mmengine - INFO - Epoch(train) [94][60/91] base_lr: 2.1809e-04 lr: 2.1809e-04 eta: 3 days, 6:29:00 time: 9.7035 data_time: 0.5627 memory: 68703 grad_norm: 1.4998 loss: 1.8662 center_loss: 0.5172 size_loss: 0.1531 cls_loss: 0.5895 giou_loss: 0.6065 2025/05/12 16:24:42 - mmengine - INFO - Epoch(train) [94][70/91] base_lr: 2.1809e-04 lr: 2.1809e-04 eta: 3 days, 6:27:05 time: 9.7040 data_time: 0.5699 memory: 68702 grad_norm: 1.5204 loss: 1.8809 center_loss: 0.5235 size_loss: 0.1546 cls_loss: 0.5914 giou_loss: 0.6113 2025/05/12 16:26:19 - mmengine - INFO - Epoch(train) [94][80/91] base_lr: 2.1809e-04 lr: 2.1809e-04 eta: 3 days, 6:25:08 time: 9.6927 data_time: 0.5668 memory: 68702 grad_norm: 1.5037 loss: 1.8779 center_loss: 0.5242 size_loss: 0.1559 cls_loss: 0.5881 giou_loss: 0.6097 2025/05/12 16:27:54 - mmengine - INFO - Epoch(train) [94][90/91] base_lr: 2.1809e-04 lr: 2.1809e-04 eta: 3 days, 6:23:08 time: 9.6739 data_time: 0.5621 memory: 68702 grad_norm: 1.5770 loss: 1.8867 center_loss: 0.5300 size_loss: 0.1575 cls_loss: 0.5845 giou_loss: 0.6147 2025/05/12 16:27:56 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 16:27:56 - mmengine - INFO - Saving checkpoint at 94 epochs 2025/05/12 16:28:54 - mmengine - INFO - Epoch(val) [94][10/39] eta: 0:01:35 time: 2.8714 data_time: 0.3648 memory: 15952 2025/05/12 16:29:20 - mmengine - INFO - Epoch(val) [94][20/39] eta: 0:00:55 time: 2.7177 data_time: 0.2141 memory: 13407 2025/05/12 16:29:45 - mmengine - INFO - Epoch(val) [94][30/39] eta: 0:00:25 time: 2.7160 data_time: 0.2127 memory: 13407 2025/05/12 16:30:12 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | sofa | 0.6258 | 0.8351 | 0.1403 | 0.3505 | | chair | 0.5138 | 0.6966 | 0.1204 | 0.2880 | | garbagebin | 0.1805 | 0.4113 | 0.0120 | 0.0906 | | table | 0.3840 | 0.5829 | 0.1010 | 0.2543 | | curtain | 0.2751 | 0.5373 | 0.0345 | 0.1642 | | bookshelf | 0.2456 | 0.6104 | 0.0459 | 0.2078 | | picture | 0.0130 | 0.0811 | 0.0023 | 0.0090 | | door | 0.1054 | 0.3919 | 0.0082 | 0.1049 | | cabinet | 0.2710 | 0.4839 | 0.0351 | 0.1505 | | window | 0.1363 | 0.3404 | 0.0104 | 0.0851 | | sink | 0.3917 | 0.5612 | 0.0411 | 0.1735 | | refrigerator | 0.4274 | 0.5789 | 0.1828 | 0.2982 | | counter | 0.1522 | 0.2115 | 0.0146 | 0.0769 | | desk | 0.6507 | 0.8504 | 0.2505 | 0.4882 | | bed | 0.8358 | 0.8642 | 0.3721 | 0.5062 | | toilet | 0.8761 | 0.9483 | 0.4676 | 0.5862 | | bathtub | 0.6819 | 0.8065 | 0.1725 | 0.3871 | | showercurtrain | 0.3242 | 0.6071 | 0.0310 | 0.1429 | +----------------+---------+---------+---------+---------+ | Overall | 0.3939 | 0.5777 | 0.1135 | 0.2424 | +----------------+---------+---------+---------+---------+ 2025/05/12 16:30:12 - mmengine - INFO - Epoch(val) [94][39/39] chair_AP_0.25: 0.5138 sofa_AP_0.25: 0.6258 table_AP_0.25: 0.3840 garbagebin_AP_0.25: 0.1805 bookshelf_AP_0.25: 0.2456 picture_AP_0.25: 0.0130 curtain_AP_0.25: 0.2751 door_AP_0.25: 0.1054 cabinet_AP_0.25: 0.2710 refrigerator_AP_0.25: 0.4274 counter_AP_0.25: 0.1522 sink_AP_0.25: 0.3917 window_AP_0.25: 0.1363 desk_AP_0.25: 0.6507 bed_AP_0.25: 0.8358 toilet_AP_0.25: 0.8761 showercurtrain_AP_0.25: 0.3242 bathtub_AP_0.25: 0.6819 mAP_0.25: 0.3939 chair_rec_0.25: 0.6966 sofa_rec_0.25: 0.8351 table_rec_0.25: 0.5829 garbagebin_rec_0.25: 0.4113 bookshelf_rec_0.25: 0.6104 picture_rec_0.25: 0.0811 curtain_rec_0.25: 0.5373 door_rec_0.25: 0.3919 cabinet_rec_0.25: 0.4839 refrigerator_rec_0.25: 0.5789 counter_rec_0.25: 0.2115 sink_rec_0.25: 0.5612 window_rec_0.25: 0.3404 desk_rec_0.25: 0.8504 bed_rec_0.25: 0.8642 toilet_rec_0.25: 0.9483 showercurtrain_rec_0.25: 0.6071 bathtub_rec_0.25: 0.8065 mAR_0.25: 0.5777 chair_AP_0.50: 0.1204 sofa_AP_0.50: 0.1403 table_AP_0.50: 0.1010 garbagebin_AP_0.50: 0.0120 bookshelf_AP_0.50: 0.0459 picture_AP_0.50: 0.0023 curtain_AP_0.50: 0.0345 door_AP_0.50: 0.0082 cabinet_AP_0.50: 0.0351 refrigerator_AP_0.50: 0.1828 counter_AP_0.50: 0.0146 sink_AP_0.50: 0.0411 window_AP_0.50: 0.0104 desk_AP_0.50: 0.2505 bed_AP_0.50: 0.3721 toilet_AP_0.50: 0.4676 showercurtrain_AP_0.50: 0.0310 bathtub_AP_0.50: 0.1725 mAP_0.50: 0.1135 chair_rec_0.50: 0.2880 sofa_rec_0.50: 0.3505 table_rec_0.50: 0.2543 garbagebin_rec_0.50: 0.0906 bookshelf_rec_0.50: 0.2078 picture_rec_0.50: 0.0090 curtain_rec_0.50: 0.1642 door_rec_0.50: 0.1049 cabinet_rec_0.50: 0.1505 refrigerator_rec_0.50: 0.2982 counter_rec_0.50: 0.0769 sink_rec_0.50: 0.1735 window_rec_0.50: 0.0851 desk_rec_0.50: 0.4882 bed_rec_0.50: 0.5062 toilet_rec_0.50: 0.5862 showercurtrain_rec_0.50: 0.1429 bathtub_rec_0.50: 0.3871 mAR_0.50: 0.2424 data_time: 0.2470 time: 2.7482 2025/05/12 16:32:41 - mmengine - INFO - Epoch(train) [95][10/91] base_lr: 2.1743e-04 lr: 2.1743e-04 eta: 3 days, 6:23:25 time: 10.5501 data_time: 1.3895 memory: 68702 grad_norm: 1.6389 loss: 1.8734 center_loss: 0.5223 size_loss: 0.1572 cls_loss: 0.5793 giou_loss: 0.6146 2025/05/12 16:34:18 - mmengine - INFO - Epoch(train) [95][20/91] base_lr: 2.1743e-04 lr: 2.1743e-04 eta: 3 days, 6:21:27 time: 10.5453 data_time: 1.3921 memory: 68702 grad_norm: 1.6219 loss: 1.8810 center_loss: 0.5204 size_loss: 0.1570 cls_loss: 0.5872 giou_loss: 0.6164 2025/05/12 16:35:55 - mmengine - INFO - Epoch(train) [95][30/91] base_lr: 2.1743e-04 lr: 2.1743e-04 eta: 3 days, 6:19:31 time: 10.5345 data_time: 1.3872 memory: 68702 grad_norm: 1.6635 loss: 1.8672 center_loss: 0.5128 size_loss: 0.1556 cls_loss: 0.5850 giou_loss: 0.6138 2025/05/12 16:37:31 - mmengine - INFO - Epoch(train) [95][40/91] base_lr: 2.1743e-04 lr: 2.1743e-04 eta: 3 days, 6:17:35 time: 10.5355 data_time: 1.3866 memory: 68702 grad_norm: 1.7105 loss: 1.8674 center_loss: 0.5093 size_loss: 0.1540 cls_loss: 0.5897 giou_loss: 0.6144 2025/05/12 16:39:08 - mmengine - INFO - Epoch(train) [95][50/91] base_lr: 2.1743e-04 lr: 2.1743e-04 eta: 3 days, 6:15:39 time: 10.7166 data_time: 1.3973 memory: 68703 grad_norm: 1.7174 loss: 1.8498 center_loss: 0.5055 size_loss: 0.1512 cls_loss: 0.5842 giou_loss: 0.6089 2025/05/12 16:40:45 - mmengine - INFO - Epoch(train) [95][60/91] base_lr: 2.1743e-04 lr: 2.1743e-04 eta: 3 days, 6:13:41 time: 9.6641 data_time: 0.5677 memory: 68702 grad_norm: 1.6654 loss: 1.8593 center_loss: 0.5067 size_loss: 0.1515 cls_loss: 0.5912 giou_loss: 0.6099 2025/05/12 16:42:21 - mmengine - INFO - Epoch(train) [95][70/91] base_lr: 2.1743e-04 lr: 2.1743e-04 eta: 3 days, 6:11:46 time: 9.6740 data_time: 0.5649 memory: 68702 grad_norm: 1.8098 loss: 1.8766 center_loss: 0.5143 size_loss: 0.1534 cls_loss: 0.5938 giou_loss: 0.6150 2025/05/12 16:43:58 - mmengine - INFO - Epoch(train) [95][80/91] base_lr: 2.1743e-04 lr: 2.1743e-04 eta: 3 days, 6:09:48 time: 9.6595 data_time: 0.5686 memory: 68702 grad_norm: 1.8009 loss: 1.8868 center_loss: 0.5171 size_loss: 0.1552 cls_loss: 0.5996 giou_loss: 0.6150 2025/05/12 16:45:33 - mmengine - INFO - Epoch(train) [95][90/91] base_lr: 2.1743e-04 lr: 2.1743e-04 eta: 3 days, 6:07:47 time: 9.6293 data_time: 0.5618 memory: 68702 grad_norm: 1.7306 loss: 1.8783 center_loss: 0.5155 size_loss: 0.1545 cls_loss: 0.5961 giou_loss: 0.6121 2025/05/12 16:45:35 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 16:48:03 - mmengine - INFO - Epoch(train) [96][10/91] base_lr: 2.1676e-04 lr: 2.1676e-04 eta: 3 days, 6:07:58 time: 10.4963 data_time: 1.5283 memory: 68702 grad_norm: 1.8270 loss: 1.9105 center_loss: 0.5256 size_loss: 0.1581 cls_loss: 0.6080 giou_loss: 0.6187 2025/05/12 16:49:39 - mmengine - INFO - Epoch(train) [96][20/91] base_lr: 2.1676e-04 lr: 2.1676e-04 eta: 3 days, 6:06:02 time: 10.5049 data_time: 1.5081 memory: 68702 grad_norm: 1.8690 loss: 1.9224 center_loss: 0.5325 size_loss: 0.1589 cls_loss: 0.6100 giou_loss: 0.6210 2025/05/12 16:51:17 - mmengine - INFO - Epoch(train) [96][30/91] base_lr: 2.1676e-04 lr: 2.1676e-04 eta: 3 days, 6:04:07 time: 10.5063 data_time: 1.5038 memory: 68702 grad_norm: 1.7362 loss: 1.9145 center_loss: 0.5285 size_loss: 0.1595 cls_loss: 0.6089 giou_loss: 0.6176 2025/05/12 16:52:54 - mmengine - INFO - Epoch(train) [96][40/91] base_lr: 2.1676e-04 lr: 2.1676e-04 eta: 3 days, 6:02:12 time: 10.5268 data_time: 1.5032 memory: 68701 grad_norm: 1.6799 loss: 1.9069 center_loss: 0.5281 size_loss: 0.1584 cls_loss: 0.6050 giou_loss: 0.6154 2025/05/12 16:54:31 - mmengine - INFO - Epoch(train) [96][50/91] base_lr: 2.1676e-04 lr: 2.1676e-04 eta: 3 days, 6:00:17 time: 10.7232 data_time: 1.5248 memory: 68702 grad_norm: 1.6790 loss: 1.9101 center_loss: 0.5299 size_loss: 0.1600 cls_loss: 0.6050 giou_loss: 0.6152 2025/05/12 16:56:08 - mmengine - INFO - Epoch(train) [96][60/91] base_lr: 2.1676e-04 lr: 2.1676e-04 eta: 3 days, 5:58:21 time: 9.6989 data_time: 0.5628 memory: 68703 grad_norm: 1.6024 loss: 1.8873 center_loss: 0.5197 size_loss: 0.1567 cls_loss: 0.6011 giou_loss: 0.6098 2025/05/12 16:57:45 - mmengine - INFO - Epoch(train) [96][70/91] base_lr: 2.1676e-04 lr: 2.1676e-04 eta: 3 days, 5:56:27 time: 9.7090 data_time: 0.5813 memory: 68702 grad_norm: 1.5957 loss: 1.8741 center_loss: 0.5147 size_loss: 0.1570 cls_loss: 0.5968 giou_loss: 0.6056 2025/05/12 16:59:21 - mmengine - INFO - Epoch(train) [96][80/91] base_lr: 2.1676e-04 lr: 2.1676e-04 eta: 3 days, 5:54:30 time: 9.6943 data_time: 0.5900 memory: 68702 grad_norm: 1.5711 loss: 1.8695 center_loss: 0.5161 size_loss: 0.1568 cls_loss: 0.5907 giou_loss: 0.6059 2025/05/12 17:00:56 - mmengine - INFO - Epoch(train) [96][90/91] base_lr: 2.1676e-04 lr: 2.1676e-04 eta: 3 days, 5:52:28 time: 9.6479 data_time: 0.5809 memory: 68702 grad_norm: 1.5778 loss: 1.8685 center_loss: 0.5111 size_loss: 0.1559 cls_loss: 0.5948 giou_loss: 0.6067 2025/05/12 17:00:58 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 17:00:58 - mmengine - INFO - Saving checkpoint at 96 epochs 2025/05/12 17:01:55 - mmengine - INFO - Epoch(val) [96][10/39] eta: 0:01:35 time: 2.8554 data_time: 0.3543 memory: 15952 2025/05/12 17:02:21 - mmengine - INFO - Epoch(val) [96][20/39] eta: 0:00:56 time: 2.7222 data_time: 0.2257 memory: 13407 2025/05/12 17:02:47 - mmengine - INFO - Epoch(val) [96][30/39] eta: 0:00:25 time: 2.7197 data_time: 0.2249 memory: 13407 2025/05/12 17:03:13 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | sofa | 0.7227 | 0.8866 | 0.1820 | 0.4021 | | garbagebin | 0.1928 | 0.4094 | 0.0183 | 0.1057 | | table | 0.4369 | 0.5714 | 0.1327 | 0.2600 | | chair | 0.5180 | 0.6915 | 0.1081 | 0.2610 | | curtain | 0.1399 | 0.4478 | 0.0063 | 0.0597 | | door | 0.1172 | 0.3769 | 0.0154 | 0.1135 | | picture | 0.0210 | 0.1081 | 0.0003 | 0.0090 | | window | 0.1533 | 0.3794 | 0.0140 | 0.0993 | | bookshelf | 0.2388 | 0.5844 | 0.0486 | 0.2208 | | cabinet | 0.2568 | 0.4731 | 0.0458 | 0.1747 | | refrigerator | 0.4284 | 0.5965 | 0.1619 | 0.3333 | | sink | 0.4511 | 0.6224 | 0.0517 | 0.1939 | | counter | 0.1524 | 0.2115 | 0.0282 | 0.0962 | | bed | 0.8306 | 0.8395 | 0.4062 | 0.5679 | | desk | 0.7021 | 0.8661 | 0.2523 | 0.4803 | | toilet | 0.7859 | 0.8793 | 0.3248 | 0.4138 | | bathtub | 0.6432 | 0.7742 | 0.0860 | 0.2581 | | showercurtrain | 0.3122 | 0.6071 | 0.0035 | 0.0714 | +----------------+---------+---------+---------+---------+ | Overall | 0.3946 | 0.5736 | 0.1048 | 0.2289 | +----------------+---------+---------+---------+---------+ 2025/05/12 17:03:13 - mmengine - INFO - Epoch(val) [96][39/39] chair_AP_0.25: 0.5180 sofa_AP_0.25: 0.7227 table_AP_0.25: 0.4369 garbagebin_AP_0.25: 0.1928 bookshelf_AP_0.25: 0.2388 picture_AP_0.25: 0.0210 curtain_AP_0.25: 0.1399 door_AP_0.25: 0.1172 cabinet_AP_0.25: 0.2568 refrigerator_AP_0.25: 0.4284 counter_AP_0.25: 0.1524 sink_AP_0.25: 0.4511 window_AP_0.25: 0.1533 desk_AP_0.25: 0.7021 bed_AP_0.25: 0.8306 toilet_AP_0.25: 0.7859 showercurtrain_AP_0.25: 0.3122 bathtub_AP_0.25: 0.6432 mAP_0.25: 0.3946 chair_rec_0.25: 0.6915 sofa_rec_0.25: 0.8866 table_rec_0.25: 0.5714 garbagebin_rec_0.25: 0.4094 bookshelf_rec_0.25: 0.5844 picture_rec_0.25: 0.1081 curtain_rec_0.25: 0.4478 door_rec_0.25: 0.3769 cabinet_rec_0.25: 0.4731 refrigerator_rec_0.25: 0.5965 counter_rec_0.25: 0.2115 sink_rec_0.25: 0.6224 window_rec_0.25: 0.3794 desk_rec_0.25: 0.8661 bed_rec_0.25: 0.8395 toilet_rec_0.25: 0.8793 showercurtrain_rec_0.25: 0.6071 bathtub_rec_0.25: 0.7742 mAR_0.25: 0.5736 chair_AP_0.50: 0.1081 sofa_AP_0.50: 0.1820 table_AP_0.50: 0.1327 garbagebin_AP_0.50: 0.0183 bookshelf_AP_0.50: 0.0486 picture_AP_0.50: 0.0003 curtain_AP_0.50: 0.0063 door_AP_0.50: 0.0154 cabinet_AP_0.50: 0.0458 refrigerator_AP_0.50: 0.1619 counter_AP_0.50: 0.0282 sink_AP_0.50: 0.0517 window_AP_0.50: 0.0140 desk_AP_0.50: 0.2523 bed_AP_0.50: 0.4062 toilet_AP_0.50: 0.3248 showercurtrain_AP_0.50: 0.0035 bathtub_AP_0.50: 0.0860 mAP_0.50: 0.1048 chair_rec_0.50: 0.2610 sofa_rec_0.50: 0.4021 table_rec_0.50: 0.2600 garbagebin_rec_0.50: 0.1057 bookshelf_rec_0.50: 0.2208 picture_rec_0.50: 0.0090 curtain_rec_0.50: 0.0597 door_rec_0.50: 0.1135 cabinet_rec_0.50: 0.1747 refrigerator_rec_0.50: 0.3333 counter_rec_0.50: 0.0962 sink_rec_0.50: 0.1939 window_rec_0.50: 0.0993 desk_rec_0.50: 0.4803 bed_rec_0.50: 0.5679 toilet_rec_0.50: 0.4138 showercurtrain_rec_0.50: 0.0714 bathtub_rec_0.50: 0.2581 mAR_0.50: 0.2289 data_time: 0.2638 time: 2.7516 2025/05/12 17:05:45 - mmengine - INFO - Epoch(train) [97][10/91] base_lr: 2.1610e-04 lr: 2.1610e-04 eta: 3 days, 5:52:48 time: 10.5737 data_time: 1.6087 memory: 68702 grad_norm: 1.6495 loss: 1.8780 center_loss: 0.5214 size_loss: 0.1562 cls_loss: 0.5881 giou_loss: 0.6123 2025/05/12 17:07:21 - mmengine - INFO - Epoch(train) [97][20/91] base_lr: 2.1610e-04 lr: 2.1610e-04 eta: 3 days, 5:50:50 time: 10.5604 data_time: 1.5899 memory: 68702 grad_norm: 1.8262 loss: 1.8892 center_loss: 0.5276 size_loss: 0.1567 cls_loss: 0.5906 giou_loss: 0.6143 2025/05/12 17:08:57 - mmengine - INFO - Epoch(train) [97][30/91] base_lr: 2.1610e-04 lr: 2.1610e-04 eta: 3 days, 5:48:52 time: 10.5370 data_time: 1.5802 memory: 68702 grad_norm: 1.8036 loss: 1.9042 center_loss: 0.5325 size_loss: 0.1578 cls_loss: 0.5936 giou_loss: 0.6204 2025/05/12 17:10:33 - mmengine - INFO - Epoch(train) [97][40/91] base_lr: 2.1610e-04 lr: 2.1610e-04 eta: 3 days, 5:46:54 time: 10.5285 data_time: 1.5770 memory: 68702 grad_norm: 1.8951 loss: 1.9070 center_loss: 0.5326 size_loss: 0.1561 cls_loss: 0.5976 giou_loss: 0.6208 2025/05/12 17:12:10 - mmengine - INFO - Epoch(train) [97][50/91] base_lr: 2.1610e-04 lr: 2.1610e-04 eta: 3 days, 5:44:59 time: 10.7331 data_time: 1.5974 memory: 68702 grad_norm: 1.8350 loss: 1.8894 center_loss: 0.5207 size_loss: 0.1547 cls_loss: 0.5965 giou_loss: 0.6175 2025/05/12 17:13:47 - mmengine - INFO - Epoch(train) [97][60/91] base_lr: 2.1610e-04 lr: 2.1610e-04 eta: 3 days, 5:43:03 time: 9.6391 data_time: 0.5658 memory: 68702 grad_norm: 1.8928 loss: 1.8900 center_loss: 0.5186 size_loss: 0.1537 cls_loss: 0.6008 giou_loss: 0.6169 2025/05/12 17:15:23 - mmengine - INFO - Epoch(train) [97][70/91] base_lr: 2.1610e-04 lr: 2.1610e-04 eta: 3 days, 5:41:06 time: 9.6423 data_time: 0.5780 memory: 68702 grad_norm: 1.7709 loss: 1.8984 center_loss: 0.5235 size_loss: 0.1564 cls_loss: 0.6004 giou_loss: 0.6181 2025/05/12 17:16:59 - mmengine - INFO - Epoch(train) [97][80/91] base_lr: 2.1610e-04 lr: 2.1610e-04 eta: 3 days, 5:39:07 time: 9.6386 data_time: 0.5807 memory: 68702 grad_norm: 1.7486 loss: 1.8769 center_loss: 0.5174 size_loss: 0.1558 cls_loss: 0.5898 giou_loss: 0.6140 2025/05/12 17:18:34 - mmengine - INFO - Epoch(train) [97][90/91] base_lr: 2.1610e-04 lr: 2.1610e-04 eta: 3 days, 5:37:05 time: 9.6110 data_time: 0.5690 memory: 68702 grad_norm: 1.7493 loss: 1.8828 center_loss: 0.5234 size_loss: 0.1590 cls_loss: 0.5850 giou_loss: 0.6154 2025/05/12 17:18:35 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 17:21:04 - mmengine - INFO - Epoch(train) [98][10/91] base_lr: 2.1542e-04 lr: 2.1542e-04 eta: 3 days, 5:37:14 time: 10.4782 data_time: 1.3684 memory: 68703 grad_norm: 1.7948 loss: 1.9114 center_loss: 0.5348 size_loss: 0.1632 cls_loss: 0.5937 giou_loss: 0.6198 2025/05/12 17:22:41 - mmengine - INFO - Epoch(train) [98][20/91] base_lr: 2.1542e-04 lr: 2.1542e-04 eta: 3 days, 5:35:20 time: 10.4916 data_time: 1.3724 memory: 68702 grad_norm: 1.7157 loss: 1.9190 center_loss: 0.5421 size_loss: 0.1644 cls_loss: 0.5910 giou_loss: 0.6215 2025/05/12 17:24:18 - mmengine - INFO - Epoch(train) [98][30/91] base_lr: 2.1542e-04 lr: 2.1542e-04 eta: 3 days, 5:33:24 time: 10.5044 data_time: 1.3719 memory: 68703 grad_norm: 1.6561 loss: 1.9057 center_loss: 0.5325 size_loss: 0.1610 cls_loss: 0.5921 giou_loss: 0.6202 2025/05/12 17:25:54 - mmengine - INFO - Epoch(train) [98][40/91] base_lr: 2.1542e-04 lr: 2.1542e-04 eta: 3 days, 5:31:28 time: 10.5134 data_time: 1.3627 memory: 68702 grad_norm: 1.6609 loss: 1.9002 center_loss: 0.5306 size_loss: 0.1593 cls_loss: 0.5926 giou_loss: 0.6177 2025/05/12 17:27:32 - mmengine - INFO - Epoch(train) [98][50/91] base_lr: 2.1542e-04 lr: 2.1542e-04 eta: 3 days, 5:29:34 time: 10.7215 data_time: 1.3729 memory: 68702 grad_norm: 1.5519 loss: 1.8781 center_loss: 0.5177 size_loss: 0.1548 cls_loss: 0.5922 giou_loss: 0.6134 2025/05/12 17:29:09 - mmengine - INFO - Epoch(train) [98][60/91] base_lr: 2.1542e-04 lr: 2.1542e-04 eta: 3 days, 5:27:39 time: 9.6929 data_time: 0.5636 memory: 68702 grad_norm: 1.5741 loss: 1.8705 center_loss: 0.5208 size_loss: 0.1535 cls_loss: 0.5858 giou_loss: 0.6104 2025/05/12 17:30:45 - mmengine - INFO - Epoch(train) [98][70/91] base_lr: 2.1542e-04 lr: 2.1542e-04 eta: 3 days, 5:25:42 time: 9.6830 data_time: 0.5566 memory: 68702 grad_norm: 1.7255 loss: 1.8729 center_loss: 0.5193 size_loss: 0.1541 cls_loss: 0.5892 giou_loss: 0.6103 2025/05/12 17:32:21 - mmengine - INFO - Epoch(train) [98][80/91] base_lr: 2.1542e-04 lr: 2.1542e-04 eta: 3 days, 5:23:45 time: 9.6660 data_time: 0.5514 memory: 68702 grad_norm: 1.7590 loss: 1.8732 center_loss: 0.5196 size_loss: 0.1535 cls_loss: 0.5904 giou_loss: 0.6096 2025/05/12 17:33:56 - mmengine - INFO - Epoch(train) [98][90/91] base_lr: 2.1542e-04 lr: 2.1542e-04 eta: 3 days, 5:21:43 time: 9.6345 data_time: 0.5447 memory: 68702 grad_norm: 1.7457 loss: 1.8757 center_loss: 0.5211 size_loss: 0.1529 cls_loss: 0.5929 giou_loss: 0.6089 2025/05/12 17:33:58 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 17:33:58 - mmengine - INFO - Saving checkpoint at 98 epochs 2025/05/12 17:34:55 - mmengine - INFO - Epoch(val) [98][10/39] eta: 0:01:35 time: 2.8593 data_time: 0.3659 memory: 15952 2025/05/12 17:35:21 - mmengine - INFO - Epoch(val) [98][20/39] eta: 0:00:56 time: 2.7240 data_time: 0.2259 memory: 13407 2025/05/12 17:35:46 - mmengine - INFO - Epoch(val) [98][30/39] eta: 0:00:25 time: 2.7180 data_time: 0.2166 memory: 13407 2025/05/12 17:36:13 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.1918 | 0.4075 | 0.0127 | 0.0943 | | sofa | 0.7151 | 0.8454 | 0.2040 | 0.3918 | | chair | 0.5584 | 0.7069 | 0.1304 | 0.2865 | | table | 0.4537 | 0.5943 | 0.1495 | 0.2686 | | curtain | 0.2307 | 0.5224 | 0.0062 | 0.0746 | | bookshelf | 0.2644 | 0.5974 | 0.0553 | 0.2338 | | picture | 0.0108 | 0.0856 | 0.0003 | 0.0135 | | cabinet | 0.2603 | 0.4704 | 0.0615 | 0.1828 | | window | 0.0968 | 0.3369 | 0.0115 | 0.0780 | | door | 0.1281 | 0.4026 | 0.0073 | 0.0792 | | refrigerator | 0.4622 | 0.5965 | 0.1368 | 0.2807 | | sink | 0.4534 | 0.6020 | 0.0713 | 0.2143 | | counter | 0.2522 | 0.3846 | 0.0209 | 0.0769 | | desk | 0.6522 | 0.8268 | 0.2344 | 0.4409 | | bed | 0.8247 | 0.8519 | 0.3518 | 0.5062 | | toilet | 0.8594 | 0.9138 | 0.4002 | 0.5172 | | bathtub | 0.6816 | 0.8710 | 0.2383 | 0.4194 | | showercurtrain | 0.2603 | 0.6071 | 0.0089 | 0.0357 | +----------------+---------+---------+---------+---------+ | Overall | 0.4087 | 0.5902 | 0.1167 | 0.2330 | +----------------+---------+---------+---------+---------+ 2025/05/12 17:36:13 - mmengine - INFO - Epoch(val) [98][39/39] chair_AP_0.25: 0.5584 sofa_AP_0.25: 0.7151 table_AP_0.25: 0.4537 garbagebin_AP_0.25: 0.1918 bookshelf_AP_0.25: 0.2644 picture_AP_0.25: 0.0108 curtain_AP_0.25: 0.2307 door_AP_0.25: 0.1281 cabinet_AP_0.25: 0.2603 refrigerator_AP_0.25: 0.4622 counter_AP_0.25: 0.2522 sink_AP_0.25: 0.4534 window_AP_0.25: 0.0968 desk_AP_0.25: 0.6522 bed_AP_0.25: 0.8247 toilet_AP_0.25: 0.8594 showercurtrain_AP_0.25: 0.2603 bathtub_AP_0.25: 0.6816 mAP_0.25: 0.4087 chair_rec_0.25: 0.7069 sofa_rec_0.25: 0.8454 table_rec_0.25: 0.5943 garbagebin_rec_0.25: 0.4075 bookshelf_rec_0.25: 0.5974 picture_rec_0.25: 0.0856 curtain_rec_0.25: 0.5224 door_rec_0.25: 0.4026 cabinet_rec_0.25: 0.4704 refrigerator_rec_0.25: 0.5965 counter_rec_0.25: 0.3846 sink_rec_0.25: 0.6020 window_rec_0.25: 0.3369 desk_rec_0.25: 0.8268 bed_rec_0.25: 0.8519 toilet_rec_0.25: 0.9138 showercurtrain_rec_0.25: 0.6071 bathtub_rec_0.25: 0.8710 mAR_0.25: 0.5902 chair_AP_0.50: 0.1304 sofa_AP_0.50: 0.2040 table_AP_0.50: 0.1495 garbagebin_AP_0.50: 0.0127 bookshelf_AP_0.50: 0.0553 picture_AP_0.50: 0.0003 curtain_AP_0.50: 0.0062 door_AP_0.50: 0.0073 cabinet_AP_0.50: 0.0615 refrigerator_AP_0.50: 0.1368 counter_AP_0.50: 0.0209 sink_AP_0.50: 0.0713 window_AP_0.50: 0.0115 desk_AP_0.50: 0.2344 bed_AP_0.50: 0.3518 toilet_AP_0.50: 0.4002 showercurtrain_AP_0.50: 0.0089 bathtub_AP_0.50: 0.2383 mAP_0.50: 0.1167 chair_rec_0.50: 0.2865 sofa_rec_0.50: 0.3918 table_rec_0.50: 0.2686 garbagebin_rec_0.50: 0.0943 bookshelf_rec_0.50: 0.2338 picture_rec_0.50: 0.0135 curtain_rec_0.50: 0.0746 door_rec_0.50: 0.0792 cabinet_rec_0.50: 0.1828 refrigerator_rec_0.50: 0.2807 counter_rec_0.50: 0.0769 sink_rec_0.50: 0.2143 window_rec_0.50: 0.0780 desk_rec_0.50: 0.4409 bed_rec_0.50: 0.5062 toilet_rec_0.50: 0.5172 showercurtrain_rec_0.50: 0.0357 bathtub_rec_0.50: 0.4194 mAR_0.50: 0.2330 data_time: 0.2526 time: 2.7549 2025/05/12 17:36:13 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_80.pth is removed 2025/05/12 17:36:40 - mmengine - INFO - The best checkpoint with 0.4087 mAP_0.25 at 98 epoch is saved to best_mAP_0.25_epoch_98.pth. 2025/05/12 17:39:34 - mmengine - INFO - Epoch(train) [99][10/91] base_lr: 2.1474e-04 lr: 2.1474e-04 eta: 3 days, 5:21:37 time: 10.4053 data_time: 1.4658 memory: 68702 grad_norm: 1.7302 loss: 1.8961 center_loss: 0.5349 size_loss: 0.1536 cls_loss: 0.5937 giou_loss: 0.6139 2025/05/12 17:41:10 - mmengine - INFO - Epoch(train) [99][20/91] base_lr: 2.1474e-04 lr: 2.1474e-04 eta: 3 days, 5:19:39 time: 10.3895 data_time: 1.4676 memory: 68702 grad_norm: 1.7611 loss: 1.8879 center_loss: 0.5264 size_loss: 0.1556 cls_loss: 0.5913 giou_loss: 0.6145 2025/05/12 17:42:47 - mmengine - INFO - Epoch(train) [99][30/91] base_lr: 2.1474e-04 lr: 2.1474e-04 eta: 3 days, 5:17:44 time: 10.3964 data_time: 1.4709 memory: 68702 grad_norm: 1.6573 loss: 1.8946 center_loss: 0.5344 size_loss: 0.1546 cls_loss: 0.5889 giou_loss: 0.6168 2025/05/12 17:44:24 - mmengine - INFO - Epoch(train) [99][40/91] base_lr: 2.1474e-04 lr: 2.1474e-04 eta: 3 days, 5:15:49 time: 10.4079 data_time: 1.4772 memory: 68702 grad_norm: 1.7309 loss: 1.8988 center_loss: 0.5404 size_loss: 0.1570 cls_loss: 0.5845 giou_loss: 0.6169 2025/05/12 17:46:01 - mmengine - INFO - Epoch(train) [99][50/91] base_lr: 2.1474e-04 lr: 2.1474e-04 eta: 3 days, 5:13:56 time: 10.6191 data_time: 1.5055 memory: 68702 grad_norm: 1.7135 loss: 1.8930 center_loss: 0.5363 size_loss: 0.1566 cls_loss: 0.5838 giou_loss: 0.6162 2025/05/12 17:47:38 - mmengine - INFO - Epoch(train) [99][60/91] base_lr: 2.1474e-04 lr: 2.1474e-04 eta: 3 days, 5:12:00 time: 9.6807 data_time: 0.5816 memory: 68702 grad_norm: 1.7154 loss: 1.8949 center_loss: 0.5336 size_loss: 0.1581 cls_loss: 0.5865 giou_loss: 0.6166 2025/05/12 17:49:15 - mmengine - INFO - Epoch(train) [99][70/91] base_lr: 2.1474e-04 lr: 2.1474e-04 eta: 3 days, 5:10:06 time: 9.7025 data_time: 0.5900 memory: 68702 grad_norm: 1.6538 loss: 1.9146 center_loss: 0.5402 size_loss: 0.1597 cls_loss: 0.5938 giou_loss: 0.6209 2025/05/12 17:50:52 - mmengine - INFO - Epoch(train) [99][80/91] base_lr: 2.1474e-04 lr: 2.1474e-04 eta: 3 days, 5:08:10 time: 9.6988 data_time: 0.6002 memory: 68702 grad_norm: 1.5991 loss: 1.8981 center_loss: 0.5298 size_loss: 0.1595 cls_loss: 0.5923 giou_loss: 0.6165 2025/05/12 17:51:11 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 17:52:27 - mmengine - INFO - Epoch(train) [99][90/91] base_lr: 2.1474e-04 lr: 2.1474e-04 eta: 3 days, 5:06:11 time: 9.6684 data_time: 0.5887 memory: 68703 grad_norm: 1.4659 loss: 1.8930 center_loss: 0.5275 size_loss: 0.1582 cls_loss: 0.5916 giou_loss: 0.6157 2025/05/12 17:52:29 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 17:54:56 - mmengine - INFO - Epoch(train) [100][10/91] base_lr: 2.1405e-04 lr: 2.1405e-04 eta: 3 days, 5:06:13 time: 10.5047 data_time: 1.4655 memory: 68702 grad_norm: 1.5028 loss: 1.8929 center_loss: 0.5251 size_loss: 0.1586 cls_loss: 0.5948 giou_loss: 0.6145 2025/05/12 17:56:33 - mmengine - INFO - Epoch(train) [100][20/91] base_lr: 2.1405e-04 lr: 2.1405e-04 eta: 3 days, 5:04:17 time: 10.5019 data_time: 1.4744 memory: 68703 grad_norm: 1.5038 loss: 1.8689 center_loss: 0.5143 size_loss: 0.1554 cls_loss: 0.5890 giou_loss: 0.6101 2025/05/12 17:58:09 - mmengine - INFO - Epoch(train) [100][30/91] base_lr: 2.1405e-04 lr: 2.1405e-04 eta: 3 days, 5:02:21 time: 10.4856 data_time: 1.4638 memory: 68702 grad_norm: 1.6298 loss: 1.8362 center_loss: 0.5010 size_loss: 0.1506 cls_loss: 0.5821 giou_loss: 0.6025 2025/05/12 17:59:45 - mmengine - INFO - Epoch(train) [100][40/91] base_lr: 2.1405e-04 lr: 2.1405e-04 eta: 3 days, 5:00:24 time: 10.4791 data_time: 1.4509 memory: 68702 grad_norm: 1.6253 loss: 1.8586 center_loss: 0.5117 size_loss: 0.1518 cls_loss: 0.5883 giou_loss: 0.6067 2025/05/12 18:01:23 - mmengine - INFO - Epoch(train) [100][50/91] base_lr: 2.1405e-04 lr: 2.1405e-04 eta: 3 days, 4:58:32 time: 10.6816 data_time: 1.4700 memory: 68702 grad_norm: 1.6297 loss: 1.8426 center_loss: 0.5081 size_loss: 0.1504 cls_loss: 0.5807 giou_loss: 0.6033 2025/05/12 18:03:01 - mmengine - INFO - Epoch(train) [100][60/91] base_lr: 2.1405e-04 lr: 2.1405e-04 eta: 3 days, 4:56:39 time: 9.6923 data_time: 0.5964 memory: 68702 grad_norm: 1.5824 loss: 1.8436 center_loss: 0.5053 size_loss: 0.1505 cls_loss: 0.5854 giou_loss: 0.6024 2025/05/12 18:04:37 - mmengine - INFO - Epoch(train) [100][70/91] base_lr: 2.1405e-04 lr: 2.1405e-04 eta: 3 days, 4:54:43 time: 9.6887 data_time: 0.5843 memory: 68703 grad_norm: 1.5697 loss: 1.8506 center_loss: 0.5164 size_loss: 0.1519 cls_loss: 0.5794 giou_loss: 0.6029 2025/05/12 18:06:13 - mmengine - INFO - Epoch(train) [100][80/91] base_lr: 2.1405e-04 lr: 2.1405e-04 eta: 3 days, 4:52:46 time: 9.6848 data_time: 0.5992 memory: 68702 grad_norm: 1.4635 loss: 1.8713 center_loss: 0.5276 size_loss: 0.1566 cls_loss: 0.5800 giou_loss: 0.6071 2025/05/12 18:07:49 - mmengine - INFO - Epoch(train) [100][90/91] base_lr: 2.1405e-04 lr: 2.1405e-04 eta: 3 days, 4:50:48 time: 9.6688 data_time: 0.5999 memory: 68702 grad_norm: 1.6634 loss: 1.8509 center_loss: 0.5153 size_loss: 0.1557 cls_loss: 0.5773 giou_loss: 0.6026 2025/05/12 18:07:51 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 18:07:51 - mmengine - INFO - Saving checkpoint at 100 epochs 2025/05/12 18:08:48 - mmengine - INFO - Epoch(val) [100][10/39] eta: 0:01:34 time: 2.8554 data_time: 0.3500 memory: 15952 2025/05/12 18:09:14 - mmengine - INFO - Epoch(val) [100][20/39] eta: 0:00:55 time: 2.7149 data_time: 0.2112 memory: 13407 2025/05/12 18:09:40 - mmengine - INFO - Epoch(val) [100][30/39] eta: 0:00:25 time: 2.7107 data_time: 0.2100 memory: 13407 2025/05/12 18:10:06 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.1899 | 0.4057 | 0.0162 | 0.0887 | | table | 0.4300 | 0.5886 | 0.1079 | 0.2457 | | chair | 0.5476 | 0.7047 | 0.1101 | 0.2734 | | curtain | 0.1182 | 0.4030 | 0.0193 | 0.0746 | | sofa | 0.7143 | 0.8247 | 0.1665 | 0.3093 | | picture | 0.0164 | 0.0856 | 0.0051 | 0.0090 | | bookshelf | 0.2382 | 0.5455 | 0.0436 | 0.2078 | | door | 0.1196 | 0.4047 | 0.0108 | 0.1071 | | cabinet | 0.2885 | 0.5134 | 0.0501 | 0.1667 | | window | 0.1385 | 0.3546 | 0.0162 | 0.0993 | | counter | 0.1486 | 0.3077 | 0.0104 | 0.0577 | | refrigerator | 0.3884 | 0.5614 | 0.1674 | 0.2982 | | sink | 0.4132 | 0.5816 | 0.0604 | 0.1939 | | desk | 0.7021 | 0.8346 | 0.2453 | 0.4646 | | bed | 0.8241 | 0.8519 | 0.3454 | 0.5062 | | bathtub | 0.8462 | 0.9032 | 0.2591 | 0.4516 | | showercurtrain | 0.3609 | 0.6071 | 0.0213 | 0.0714 | | toilet | 0.8099 | 0.9310 | 0.3698 | 0.4483 | +----------------+---------+---------+---------+---------+ | Overall | 0.4052 | 0.5783 | 0.1125 | 0.2263 | +----------------+---------+---------+---------+---------+ 2025/05/12 18:10:06 - mmengine - INFO - Epoch(val) [100][39/39] chair_AP_0.25: 0.5476 sofa_AP_0.25: 0.7143 table_AP_0.25: 0.4300 garbagebin_AP_0.25: 0.1899 bookshelf_AP_0.25: 0.2382 picture_AP_0.25: 0.0164 curtain_AP_0.25: 0.1182 door_AP_0.25: 0.1196 cabinet_AP_0.25: 0.2885 refrigerator_AP_0.25: 0.3884 counter_AP_0.25: 0.1486 sink_AP_0.25: 0.4132 window_AP_0.25: 0.1385 desk_AP_0.25: 0.7021 bed_AP_0.25: 0.8241 toilet_AP_0.25: 0.8099 showercurtrain_AP_0.25: 0.3609 bathtub_AP_0.25: 0.8462 mAP_0.25: 0.4052 chair_rec_0.25: 0.7047 sofa_rec_0.25: 0.8247 table_rec_0.25: 0.5886 garbagebin_rec_0.25: 0.4057 bookshelf_rec_0.25: 0.5455 picture_rec_0.25: 0.0856 curtain_rec_0.25: 0.4030 door_rec_0.25: 0.4047 cabinet_rec_0.25: 0.5134 refrigerator_rec_0.25: 0.5614 counter_rec_0.25: 0.3077 sink_rec_0.25: 0.5816 window_rec_0.25: 0.3546 desk_rec_0.25: 0.8346 bed_rec_0.25: 0.8519 toilet_rec_0.25: 0.9310 showercurtrain_rec_0.25: 0.6071 bathtub_rec_0.25: 0.9032 mAR_0.25: 0.5783 chair_AP_0.50: 0.1101 sofa_AP_0.50: 0.1665 table_AP_0.50: 0.1079 garbagebin_AP_0.50: 0.0162 bookshelf_AP_0.50: 0.0436 picture_AP_0.50: 0.0051 curtain_AP_0.50: 0.0193 door_AP_0.50: 0.0108 cabinet_AP_0.50: 0.0501 refrigerator_AP_0.50: 0.1674 counter_AP_0.50: 0.0104 sink_AP_0.50: 0.0604 window_AP_0.50: 0.0162 desk_AP_0.50: 0.2453 bed_AP_0.50: 0.3454 toilet_AP_0.50: 0.3698 showercurtrain_AP_0.50: 0.0213 bathtub_AP_0.50: 0.2591 mAP_0.50: 0.1125 chair_rec_0.50: 0.2734 sofa_rec_0.50: 0.3093 table_rec_0.50: 0.2457 garbagebin_rec_0.50: 0.0887 bookshelf_rec_0.50: 0.2078 picture_rec_0.50: 0.0090 curtain_rec_0.50: 0.0746 door_rec_0.50: 0.1071 cabinet_rec_0.50: 0.1667 refrigerator_rec_0.50: 0.2982 counter_rec_0.50: 0.0577 sink_rec_0.50: 0.1939 window_rec_0.50: 0.0993 desk_rec_0.50: 0.4646 bed_rec_0.50: 0.5062 toilet_rec_0.50: 0.4483 showercurtrain_rec_0.50: 0.0714 bathtub_rec_0.50: 0.4516 mAR_0.50: 0.2263 data_time: 0.2429 time: 2.7390 2025/05/12 18:12:33 - mmengine - INFO - Epoch(train) [101][10/91] base_lr: 2.1336e-04 lr: 2.1336e-04 eta: 3 days, 4:50:49 time: 10.4907 data_time: 1.5389 memory: 68702 grad_norm: 1.7378 loss: 1.8553 center_loss: 0.5176 size_loss: 0.1546 cls_loss: 0.5794 giou_loss: 0.6038 2025/05/12 18:14:10 - mmengine - INFO - Epoch(train) [101][20/91] base_lr: 2.1336e-04 lr: 2.1336e-04 eta: 3 days, 4:48:52 time: 10.4808 data_time: 1.5307 memory: 68702 grad_norm: 1.7997 loss: 1.8581 center_loss: 0.5208 size_loss: 0.1537 cls_loss: 0.5779 giou_loss: 0.6058 2025/05/12 18:15:42 - mmengine - INFO - Epoch(train) [101][30/91] base_lr: 2.1336e-04 lr: 2.1336e-04 eta: 3 days, 4:46:44 time: 10.3968 data_time: 1.5070 memory: 68702 grad_norm: 1.8379 loss: 1.8533 center_loss: 0.5116 size_loss: 0.1530 cls_loss: 0.5821 giou_loss: 0.6066 2025/05/12 18:17:19 - mmengine - INFO - Epoch(train) [101][40/91] base_lr: 2.1336e-04 lr: 2.1336e-04 eta: 3 days, 4:44:49 time: 10.4059 data_time: 1.5069 memory: 68702 grad_norm: 1.8493 loss: 1.8565 center_loss: 0.5135 size_loss: 0.1509 cls_loss: 0.5822 giou_loss: 0.6099 2025/05/12 18:18:56 - mmengine - INFO - Epoch(train) [101][50/91] base_lr: 2.1336e-04 lr: 2.1336e-04 eta: 3 days, 4:42:56 time: 10.5981 data_time: 1.5206 memory: 68703 grad_norm: 1.6029 loss: 1.8597 center_loss: 0.5143 size_loss: 0.1510 cls_loss: 0.5807 giou_loss: 0.6137 2025/05/12 18:20:33 - mmengine - INFO - Epoch(train) [101][60/91] base_lr: 2.1336e-04 lr: 2.1336e-04 eta: 3 days, 4:41:01 time: 9.5914 data_time: 0.5824 memory: 68702 grad_norm: 1.5968 loss: 1.8705 center_loss: 0.5230 size_loss: 0.1535 cls_loss: 0.5789 giou_loss: 0.6150 2025/05/12 18:22:09 - mmengine - INFO - Epoch(train) [101][70/91] base_lr: 2.1336e-04 lr: 2.1336e-04 eta: 3 days, 4:39:05 time: 9.5934 data_time: 0.5903 memory: 68702 grad_norm: 1.5118 loss: 1.8841 center_loss: 0.5274 size_loss: 0.1555 cls_loss: 0.5851 giou_loss: 0.6161 2025/05/12 18:23:46 - mmengine - INFO - Epoch(train) [101][80/91] base_lr: 2.1336e-04 lr: 2.1336e-04 eta: 3 days, 4:37:08 time: 9.6749 data_time: 0.6234 memory: 68702 grad_norm: 1.5153 loss: 1.8859 center_loss: 0.5286 size_loss: 0.1555 cls_loss: 0.5866 giou_loss: 0.6153 2025/05/12 18:25:21 - mmengine - INFO - Epoch(train) [101][90/91] base_lr: 2.1336e-04 lr: 2.1336e-04 eta: 3 days, 4:35:09 time: 9.6376 data_time: 0.6125 memory: 68703 grad_norm: 1.4766 loss: 1.8859 center_loss: 0.5305 size_loss: 0.1549 cls_loss: 0.5881 giou_loss: 0.6125 2025/05/12 18:25:23 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 18:27:49 - mmengine - INFO - Epoch(train) [102][10/91] base_lr: 2.1266e-04 lr: 2.1266e-04 eta: 3 days, 4:35:05 time: 10.4514 data_time: 1.4696 memory: 68703 grad_norm: 1.5569 loss: 1.8870 center_loss: 0.5337 size_loss: 0.1548 cls_loss: 0.5867 giou_loss: 0.6118 2025/05/12 18:29:25 - mmengine - INFO - Epoch(train) [102][20/91] base_lr: 2.1266e-04 lr: 2.1266e-04 eta: 3 days, 4:33:08 time: 10.4424 data_time: 1.4629 memory: 68703 grad_norm: 1.5226 loss: 1.8681 center_loss: 0.5221 size_loss: 0.1526 cls_loss: 0.5862 giou_loss: 0.6073 2025/05/12 18:31:02 - mmengine - INFO - Epoch(train) [102][30/91] base_lr: 2.1266e-04 lr: 2.1266e-04 eta: 3 days, 4:31:14 time: 10.4557 data_time: 1.4564 memory: 68702 grad_norm: 1.6112 loss: 1.8501 center_loss: 0.5151 size_loss: 0.1508 cls_loss: 0.5780 giou_loss: 0.6061 2025/05/12 18:32:39 - mmengine - INFO - Epoch(train) [102][40/91] base_lr: 2.1266e-04 lr: 2.1266e-04 eta: 3 days, 4:29:20 time: 10.4718 data_time: 1.4410 memory: 68702 grad_norm: 1.5286 loss: 1.8486 center_loss: 0.5125 size_loss: 0.1517 cls_loss: 0.5811 giou_loss: 0.6034 2025/05/12 18:34:16 - mmengine - INFO - Epoch(train) [102][50/91] base_lr: 2.1266e-04 lr: 2.1266e-04 eta: 3 days, 4:27:26 time: 10.6629 data_time: 1.4464 memory: 68700 grad_norm: 1.5806 loss: 1.8612 center_loss: 0.5147 size_loss: 0.1532 cls_loss: 0.5866 giou_loss: 0.6067 2025/05/12 18:35:53 - mmengine - INFO - Epoch(train) [102][60/91] base_lr: 2.1266e-04 lr: 2.1266e-04 eta: 3 days, 4:25:32 time: 9.6838 data_time: 0.5873 memory: 68703 grad_norm: 1.5279 loss: 1.8373 center_loss: 0.5046 size_loss: 0.1506 cls_loss: 0.5806 giou_loss: 0.6015 2025/05/12 18:37:30 - mmengine - INFO - Epoch(train) [102][70/91] base_lr: 2.1266e-04 lr: 2.1266e-04 eta: 3 days, 4:23:37 time: 9.7038 data_time: 0.6016 memory: 68702 grad_norm: 1.6089 loss: 1.8455 center_loss: 0.5065 size_loss: 0.1513 cls_loss: 0.5824 giou_loss: 0.6052 2025/05/12 18:39:06 - mmengine - INFO - Epoch(train) [102][80/91] base_lr: 2.1266e-04 lr: 2.1266e-04 eta: 3 days, 4:21:41 time: 9.6864 data_time: 0.5982 memory: 68703 grad_norm: 1.5941 loss: 1.8528 center_loss: 0.5089 size_loss: 0.1510 cls_loss: 0.5868 giou_loss: 0.6061 2025/05/12 18:40:41 - mmengine - INFO - Epoch(train) [102][90/91] base_lr: 2.1266e-04 lr: 2.1266e-04 eta: 3 days, 4:19:40 time: 9.6382 data_time: 0.5909 memory: 68703 grad_norm: 1.6141 loss: 1.8524 center_loss: 0.5094 size_loss: 0.1511 cls_loss: 0.5841 giou_loss: 0.6078 2025/05/12 18:40:43 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 18:40:43 - mmengine - INFO - Saving checkpoint at 102 epochs 2025/05/12 18:41:40 - mmengine - INFO - Epoch(val) [102][10/39] eta: 0:01:35 time: 2.8471 data_time: 0.3455 memory: 15952 2025/05/12 18:42:06 - mmengine - INFO - Epoch(val) [102][20/39] eta: 0:00:55 time: 2.7196 data_time: 0.2137 memory: 13407 2025/05/12 18:42:32 - mmengine - INFO - Epoch(val) [102][30/39] eta: 0:00:25 time: 2.7260 data_time: 0.2143 memory: 13407 2025/05/12 18:42:58 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.1922 | 0.4019 | 0.0122 | 0.0943 | | sofa | 0.6849 | 0.8247 | 0.1883 | 0.4021 | | chair | 0.5083 | 0.6857 | 0.1109 | 0.2756 | | table | 0.4389 | 0.5686 | 0.1115 | 0.2400 | | curtain | 0.2462 | 0.4776 | 0.0124 | 0.0746 | | picture | 0.0059 | 0.0901 | 0.0005 | 0.0135 | | bookshelf | 0.3308 | 0.6364 | 0.0287 | 0.1688 | | bed | 0.8154 | 0.8519 | 0.3639 | 0.5432 | | door | 0.0918 | 0.3790 | 0.0072 | 0.0814 | | cabinet | 0.2619 | 0.4704 | 0.0391 | 0.1640 | | window | 0.0850 | 0.2837 | 0.0091 | 0.0816 | | sink | 0.3742 | 0.5408 | 0.0222 | 0.1327 | | refrigerator | 0.4477 | 0.5789 | 0.0993 | 0.2456 | | counter | 0.1978 | 0.3654 | 0.0299 | 0.0769 | | toilet | 0.8437 | 0.9310 | 0.3377 | 0.4828 | | desk | 0.6827 | 0.8504 | 0.2239 | 0.4567 | | bathtub | 0.6417 | 0.8065 | 0.1478 | 0.3548 | | showercurtrain | 0.1393 | 0.3929 | 0.0084 | 0.0714 | +----------------+---------+---------+---------+---------+ | Overall | 0.3882 | 0.5631 | 0.0974 | 0.2200 | +----------------+---------+---------+---------+---------+ 2025/05/12 18:42:58 - mmengine - INFO - Epoch(val) [102][39/39] chair_AP_0.25: 0.5083 sofa_AP_0.25: 0.6849 table_AP_0.25: 0.4389 garbagebin_AP_0.25: 0.1922 bookshelf_AP_0.25: 0.3308 picture_AP_0.25: 0.0059 curtain_AP_0.25: 0.2462 door_AP_0.25: 0.0918 cabinet_AP_0.25: 0.2619 refrigerator_AP_0.25: 0.4477 counter_AP_0.25: 0.1978 sink_AP_0.25: 0.3742 window_AP_0.25: 0.0850 desk_AP_0.25: 0.6827 bed_AP_0.25: 0.8154 toilet_AP_0.25: 0.8437 showercurtrain_AP_0.25: 0.1393 bathtub_AP_0.25: 0.6417 mAP_0.25: 0.3882 chair_rec_0.25: 0.6857 sofa_rec_0.25: 0.8247 table_rec_0.25: 0.5686 garbagebin_rec_0.25: 0.4019 bookshelf_rec_0.25: 0.6364 picture_rec_0.25: 0.0901 curtain_rec_0.25: 0.4776 door_rec_0.25: 0.3790 cabinet_rec_0.25: 0.4704 refrigerator_rec_0.25: 0.5789 counter_rec_0.25: 0.3654 sink_rec_0.25: 0.5408 window_rec_0.25: 0.2837 desk_rec_0.25: 0.8504 bed_rec_0.25: 0.8519 toilet_rec_0.25: 0.9310 showercurtrain_rec_0.25: 0.3929 bathtub_rec_0.25: 0.8065 mAR_0.25: 0.5631 chair_AP_0.50: 0.1109 sofa_AP_0.50: 0.1883 table_AP_0.50: 0.1115 garbagebin_AP_0.50: 0.0122 bookshelf_AP_0.50: 0.0287 picture_AP_0.50: 0.0005 curtain_AP_0.50: 0.0124 door_AP_0.50: 0.0072 cabinet_AP_0.50: 0.0391 refrigerator_AP_0.50: 0.0993 counter_AP_0.50: 0.0299 sink_AP_0.50: 0.0222 window_AP_0.50: 0.0091 desk_AP_0.50: 0.2239 bed_AP_0.50: 0.3639 toilet_AP_0.50: 0.3377 showercurtrain_AP_0.50: 0.0084 bathtub_AP_0.50: 0.1478 mAP_0.50: 0.0974 chair_rec_0.50: 0.2756 sofa_rec_0.50: 0.4021 table_rec_0.50: 0.2400 garbagebin_rec_0.50: 0.0943 bookshelf_rec_0.50: 0.1688 picture_rec_0.50: 0.0135 curtain_rec_0.50: 0.0746 door_rec_0.50: 0.0814 cabinet_rec_0.50: 0.1640 refrigerator_rec_0.50: 0.2456 counter_rec_0.50: 0.0769 sink_rec_0.50: 0.1327 window_rec_0.50: 0.0816 desk_rec_0.50: 0.4567 bed_rec_0.50: 0.5432 toilet_rec_0.50: 0.4828 showercurtrain_rec_0.50: 0.0714 bathtub_rec_0.50: 0.3548 mAR_0.50: 0.2200 data_time: 0.2467 time: 2.7565 2025/05/12 18:45:22 - mmengine - INFO - Epoch(train) [103][10/91] base_lr: 2.1196e-04 lr: 2.1196e-04 eta: 3 days, 4:19:29 time: 10.4120 data_time: 1.4595 memory: 68702 grad_norm: 1.6915 loss: 1.8675 center_loss: 0.5168 size_loss: 0.1530 cls_loss: 0.5848 giou_loss: 0.6129 2025/05/12 18:46:58 - mmengine - INFO - Epoch(train) [103][20/91] base_lr: 2.1196e-04 lr: 2.1196e-04 eta: 3 days, 4:17:34 time: 10.4115 data_time: 1.4587 memory: 68702 grad_norm: 1.7857 loss: 1.8825 center_loss: 0.5219 size_loss: 0.1534 cls_loss: 0.5906 giou_loss: 0.6166 2025/05/12 18:48:35 - mmengine - INFO - Epoch(train) [103][30/91] base_lr: 2.1196e-04 lr: 2.1196e-04 eta: 3 days, 4:15:39 time: 10.4039 data_time: 1.4441 memory: 68702 grad_norm: 1.7716 loss: 1.9017 center_loss: 0.5309 size_loss: 0.1556 cls_loss: 0.5972 giou_loss: 0.6179 2025/05/12 18:50:12 - mmengine - INFO - Epoch(train) [103][40/91] base_lr: 2.1196e-04 lr: 2.1196e-04 eta: 3 days, 4:13:44 time: 10.4166 data_time: 1.4447 memory: 68702 grad_norm: 1.7167 loss: 1.9010 center_loss: 0.5313 size_loss: 0.1558 cls_loss: 0.5962 giou_loss: 0.6177 2025/05/12 18:51:50 - mmengine - INFO - Epoch(train) [103][50/91] base_lr: 2.1196e-04 lr: 2.1196e-04 eta: 3 days, 4:11:53 time: 10.6371 data_time: 1.4558 memory: 68702 grad_norm: 1.6726 loss: 1.8739 center_loss: 0.5175 size_loss: 0.1537 cls_loss: 0.5919 giou_loss: 0.6108 2025/05/12 18:53:28 - mmengine - INFO - Epoch(train) [103][60/91] base_lr: 2.1196e-04 lr: 2.1196e-04 eta: 3 days, 4:10:01 time: 9.7188 data_time: 0.5890 memory: 68702 grad_norm: 1.6090 loss: 1.8703 center_loss: 0.5130 size_loss: 0.1534 cls_loss: 0.5935 giou_loss: 0.6104 2025/05/12 18:55:05 - mmengine - INFO - Epoch(train) [103][70/91] base_lr: 2.1196e-04 lr: 2.1196e-04 eta: 3 days, 4:08:09 time: 9.7362 data_time: 0.5811 memory: 68702 grad_norm: 1.5670 loss: 1.8697 center_loss: 0.5104 size_loss: 0.1548 cls_loss: 0.5949 giou_loss: 0.6096 2025/05/12 18:56:43 - mmengine - INFO - Epoch(train) [103][80/91] base_lr: 2.1196e-04 lr: 2.1196e-04 eta: 3 days, 4:06:16 time: 9.7484 data_time: 0.5886 memory: 68702 grad_norm: 1.5942 loss: 1.8728 center_loss: 0.5125 size_loss: 0.1554 cls_loss: 0.5934 giou_loss: 0.6115 2025/05/12 18:58:18 - mmengine - INFO - Epoch(train) [103][90/91] base_lr: 2.1196e-04 lr: 2.1196e-04 eta: 3 days, 4:04:18 time: 9.7258 data_time: 0.5833 memory: 68702 grad_norm: 1.6058 loss: 1.8719 center_loss: 0.5133 size_loss: 0.1567 cls_loss: 0.5902 giou_loss: 0.6116 2025/05/12 18:58:20 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 19:00:49 - mmengine - INFO - Epoch(train) [104][10/91] base_lr: 2.1125e-04 lr: 2.1125e-04 eta: 3 days, 4:04:21 time: 10.5863 data_time: 1.4106 memory: 68702 grad_norm: 1.6445 loss: 1.8783 center_loss: 0.5161 size_loss: 0.1571 cls_loss: 0.5912 giou_loss: 0.6138 2025/05/12 19:02:27 - mmengine - INFO - Epoch(train) [104][20/91] base_lr: 2.1125e-04 lr: 2.1125e-04 eta: 3 days, 4:02:29 time: 10.5926 data_time: 1.4363 memory: 68703 grad_norm: 1.6803 loss: 1.8742 center_loss: 0.5173 size_loss: 0.1568 cls_loss: 0.5885 giou_loss: 0.6117 2025/05/12 19:04:04 - mmengine - INFO - Epoch(train) [104][30/91] base_lr: 2.1125e-04 lr: 2.1125e-04 eta: 3 days, 4:00:35 time: 10.5834 data_time: 1.4320 memory: 68703 grad_norm: 1.7059 loss: 1.8836 center_loss: 0.5196 size_loss: 0.1577 cls_loss: 0.5916 giou_loss: 0.6147 2025/05/12 19:05:41 - mmengine - INFO - Epoch(train) [104][40/91] base_lr: 2.1125e-04 lr: 2.1125e-04 eta: 3 days, 3:58:40 time: 10.5651 data_time: 1.4363 memory: 68702 grad_norm: 1.6547 loss: 1.8666 center_loss: 0.5151 size_loss: 0.1574 cls_loss: 0.5814 giou_loss: 0.6128 2025/05/12 19:07:18 - mmengine - INFO - Epoch(train) [104][50/91] base_lr: 2.1125e-04 lr: 2.1125e-04 eta: 3 days, 3:56:48 time: 10.7549 data_time: 1.4390 memory: 68702 grad_norm: 1.6325 loss: 1.8604 center_loss: 0.5110 size_loss: 0.1552 cls_loss: 0.5845 giou_loss: 0.6097 2025/05/12 19:08:55 - mmengine - INFO - Epoch(train) [104][60/91] base_lr: 2.1125e-04 lr: 2.1125e-04 eta: 3 days, 3:54:55 time: 9.7239 data_time: 0.6219 memory: 68702 grad_norm: 1.7459 loss: 1.8628 center_loss: 0.5176 size_loss: 0.1547 cls_loss: 0.5819 giou_loss: 0.6087 2025/05/12 19:10:33 - mmengine - INFO - Epoch(train) [104][70/91] base_lr: 2.1125e-04 lr: 2.1125e-04 eta: 3 days, 3:53:02 time: 9.7091 data_time: 0.5967 memory: 68703 grad_norm: 1.6948 loss: 1.8800 center_loss: 0.5246 size_loss: 0.1552 cls_loss: 0.5868 giou_loss: 0.6134 2025/05/12 19:12:09 - mmengine - INFO - Epoch(train) [104][80/91] base_lr: 2.1125e-04 lr: 2.1125e-04 eta: 3 days, 3:51:07 time: 9.7030 data_time: 0.6048 memory: 68702 grad_norm: 1.6238 loss: 1.8817 center_loss: 0.5255 size_loss: 0.1546 cls_loss: 0.5891 giou_loss: 0.6123 2025/05/12 19:13:45 - mmengine - INFO - Epoch(train) [104][90/91] base_lr: 2.1125e-04 lr: 2.1125e-04 eta: 3 days, 3:49:10 time: 9.6900 data_time: 0.5997 memory: 68703 grad_norm: 1.5929 loss: 1.8707 center_loss: 0.5169 size_loss: 0.1520 cls_loss: 0.5944 giou_loss: 0.6075 2025/05/12 19:13:47 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 19:13:47 - mmengine - INFO - Saving checkpoint at 104 epochs 2025/05/12 19:14:44 - mmengine - INFO - Epoch(val) [104][10/39] eta: 0:01:36 time: 2.8679 data_time: 0.3570 memory: 15952 2025/05/12 19:15:10 - mmengine - INFO - Epoch(val) [104][20/39] eta: 0:00:56 time: 2.7346 data_time: 0.2229 memory: 13407 2025/05/12 19:15:36 - mmengine - INFO - Epoch(val) [104][30/39] eta: 0:00:25 time: 2.7333 data_time: 0.2219 memory: 13407 2025/05/12 19:16:03 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.2085 | 0.4377 | 0.0221 | 0.1302 | | table | 0.4308 | 0.5886 | 0.1179 | 0.2457 | | chair | 0.5444 | 0.7178 | 0.1261 | 0.2961 | | sofa | 0.7051 | 0.8660 | 0.1084 | 0.3093 | | curtain | 0.2221 | 0.4925 | 0.0049 | 0.0597 | | bookshelf | 0.2847 | 0.6104 | 0.0698 | 0.2078 | | picture | 0.0095 | 0.1081 | 0.0004 | 0.0135 | | cabinet | 0.2726 | 0.5161 | 0.0397 | 0.1586 | | window | 0.1315 | 0.3794 | 0.0194 | 0.0745 | | door | 0.1492 | 0.4625 | 0.0106 | 0.1199 | | sink | 0.4806 | 0.6429 | 0.0703 | 0.2449 | | refrigerator | 0.4869 | 0.6491 | 0.2476 | 0.3158 | | counter | 0.0403 | 0.1538 | 0.0000 | 0.0000 | | desk | 0.6817 | 0.8740 | 0.2256 | 0.4567 | | bed | 0.8332 | 0.8642 | 0.3752 | 0.5432 | | toilet | 0.9126 | 0.9828 | 0.4482 | 0.5690 | | bathtub | 0.7917 | 0.8387 | 0.1686 | 0.3871 | | showercurtrain | 0.3397 | 0.6429 | 0.0235 | 0.1429 | +----------------+---------+---------+---------+---------+ | Overall | 0.4181 | 0.6015 | 0.1155 | 0.2375 | +----------------+---------+---------+---------+---------+ 2025/05/12 19:16:03 - mmengine - INFO - Epoch(val) [104][39/39] chair_AP_0.25: 0.5444 sofa_AP_0.25: 0.7051 table_AP_0.25: 0.4308 garbagebin_AP_0.25: 0.2085 bookshelf_AP_0.25: 0.2847 picture_AP_0.25: 0.0095 curtain_AP_0.25: 0.2221 door_AP_0.25: 0.1492 cabinet_AP_0.25: 0.2726 refrigerator_AP_0.25: 0.4869 counter_AP_0.25: 0.0403 sink_AP_0.25: 0.4806 window_AP_0.25: 0.1315 desk_AP_0.25: 0.6817 bed_AP_0.25: 0.8332 toilet_AP_0.25: 0.9126 showercurtrain_AP_0.25: 0.3397 bathtub_AP_0.25: 0.7917 mAP_0.25: 0.4181 chair_rec_0.25: 0.7178 sofa_rec_0.25: 0.8660 table_rec_0.25: 0.5886 garbagebin_rec_0.25: 0.4377 bookshelf_rec_0.25: 0.6104 picture_rec_0.25: 0.1081 curtain_rec_0.25: 0.4925 door_rec_0.25: 0.4625 cabinet_rec_0.25: 0.5161 refrigerator_rec_0.25: 0.6491 counter_rec_0.25: 0.1538 sink_rec_0.25: 0.6429 window_rec_0.25: 0.3794 desk_rec_0.25: 0.8740 bed_rec_0.25: 0.8642 toilet_rec_0.25: 0.9828 showercurtrain_rec_0.25: 0.6429 bathtub_rec_0.25: 0.8387 mAR_0.25: 0.6015 chair_AP_0.50: 0.1261 sofa_AP_0.50: 0.1084 table_AP_0.50: 0.1179 garbagebin_AP_0.50: 0.0221 bookshelf_AP_0.50: 0.0698 picture_AP_0.50: 0.0004 curtain_AP_0.50: 0.0049 door_AP_0.50: 0.0106 cabinet_AP_0.50: 0.0397 refrigerator_AP_0.50: 0.2476 counter_AP_0.50: 0.0000 sink_AP_0.50: 0.0703 window_AP_0.50: 0.0194 desk_AP_0.50: 0.2256 bed_AP_0.50: 0.3752 toilet_AP_0.50: 0.4482 showercurtrain_AP_0.50: 0.0235 bathtub_AP_0.50: 0.1686 mAP_0.50: 0.1155 chair_rec_0.50: 0.2961 sofa_rec_0.50: 0.3093 table_rec_0.50: 0.2457 garbagebin_rec_0.50: 0.1302 bookshelf_rec_0.50: 0.2078 picture_rec_0.50: 0.0135 curtain_rec_0.50: 0.0597 door_rec_0.50: 0.1199 cabinet_rec_0.50: 0.1586 refrigerator_rec_0.50: 0.3158 counter_rec_0.50: 0.0000 sink_rec_0.50: 0.2449 window_rec_0.50: 0.0745 desk_rec_0.50: 0.4567 bed_rec_0.50: 0.5432 toilet_rec_0.50: 0.5690 showercurtrain_rec_0.50: 0.1429 bathtub_rec_0.50: 0.3871 mAR_0.50: 0.2375 data_time: 0.2588 time: 2.7648 2025/05/12 19:16:03 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_98.pth is removed 2025/05/12 19:16:25 - mmengine - INFO - The best checkpoint with 0.4181 mAP_0.25 at 104 epoch is saved to best_mAP_0.25_epoch_104.pth. 2025/05/12 19:19:14 - mmengine - INFO - Epoch(train) [105][10/91] base_lr: 2.1054e-04 lr: 2.1054e-04 eta: 3 days, 3:48:54 time: 10.4476 data_time: 1.4786 memory: 68702 grad_norm: 1.6899 loss: 1.8713 center_loss: 0.5154 size_loss: 0.1533 cls_loss: 0.5938 giou_loss: 0.6087 2025/05/12 19:20:50 - mmengine - INFO - Epoch(train) [105][20/91] base_lr: 2.1054e-04 lr: 2.1054e-04 eta: 3 days, 3:46:59 time: 10.4334 data_time: 1.4793 memory: 68702 grad_norm: 1.6253 loss: 1.8651 center_loss: 0.5082 size_loss: 0.1548 cls_loss: 0.5938 giou_loss: 0.6083 2025/05/12 19:22:28 - mmengine - INFO - Epoch(train) [105][30/91] base_lr: 2.1054e-04 lr: 2.1054e-04 eta: 3 days, 3:45:06 time: 10.4329 data_time: 1.4783 memory: 68702 grad_norm: 1.6608 loss: 1.8567 center_loss: 0.5049 size_loss: 0.1538 cls_loss: 0.5913 giou_loss: 0.6067 2025/05/12 19:24:04 - mmengine - INFO - Epoch(train) [105][40/91] base_lr: 2.1054e-04 lr: 2.1054e-04 eta: 3 days, 3:43:12 time: 10.4350 data_time: 1.4838 memory: 68703 grad_norm: 1.6255 loss: 1.8680 center_loss: 0.5118 size_loss: 0.1553 cls_loss: 0.5923 giou_loss: 0.6087 2025/05/12 19:25:42 - mmengine - INFO - Epoch(train) [105][50/91] base_lr: 2.1054e-04 lr: 2.1054e-04 eta: 3 days, 3:41:19 time: 10.6196 data_time: 1.5019 memory: 68702 grad_norm: 1.5884 loss: 1.8574 center_loss: 0.5107 size_loss: 0.1545 cls_loss: 0.5822 giou_loss: 0.6099 2025/05/12 19:27:19 - mmengine - INFO - Epoch(train) [105][60/91] base_lr: 2.1054e-04 lr: 2.1054e-04 eta: 3 days, 3:39:26 time: 9.7014 data_time: 0.6289 memory: 68702 grad_norm: 1.5268 loss: 1.8511 center_loss: 0.5087 size_loss: 0.1532 cls_loss: 0.5811 giou_loss: 0.6082 2025/05/12 19:28:56 - mmengine - INFO - Epoch(train) [105][70/91] base_lr: 2.1054e-04 lr: 2.1054e-04 eta: 3 days, 3:37:33 time: 9.7157 data_time: 0.6272 memory: 68702 grad_norm: 1.5392 loss: 1.8343 center_loss: 0.5004 size_loss: 0.1508 cls_loss: 0.5800 giou_loss: 0.6031 2025/05/12 19:30:33 - mmengine - INFO - Epoch(train) [105][80/91] base_lr: 2.1054e-04 lr: 2.1054e-04 eta: 3 days, 3:35:38 time: 9.7015 data_time: 0.6294 memory: 68702 grad_norm: 1.5429 loss: 1.8406 center_loss: 0.5042 size_loss: 0.1530 cls_loss: 0.5790 giou_loss: 0.6044 2025/05/12 19:32:08 - mmengine - INFO - Epoch(train) [105][90/91] base_lr: 2.1054e-04 lr: 2.1054e-04 eta: 3 days, 3:33:40 time: 9.6711 data_time: 0.6211 memory: 68702 grad_norm: 1.5899 loss: 1.8217 center_loss: 0.4951 size_loss: 0.1508 cls_loss: 0.5751 giou_loss: 0.6007 2025/05/12 19:32:10 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 19:34:34 - mmengine - INFO - Epoch(train) [106][10/91] base_lr: 2.0982e-04 lr: 2.0982e-04 eta: 3 days, 3:33:24 time: 10.4408 data_time: 1.4990 memory: 68703 grad_norm: 1.7233 loss: 1.8961 center_loss: 0.5250 size_loss: 0.1593 cls_loss: 0.6013 giou_loss: 0.6104 2025/05/12 19:36:10 - mmengine - INFO - Epoch(train) [106][20/91] base_lr: 2.0982e-04 lr: 2.0982e-04 eta: 3 days, 3:31:30 time: 10.4334 data_time: 1.4978 memory: 68702 grad_norm: 1.7815 loss: 1.9032 center_loss: 0.5267 size_loss: 0.1601 cls_loss: 0.6044 giou_loss: 0.6121 2025/05/12 19:37:47 - mmengine - INFO - Epoch(train) [106][30/91] base_lr: 2.0982e-04 lr: 2.0982e-04 eta: 3 days, 3:29:36 time: 10.4272 data_time: 1.4840 memory: 68702 grad_norm: 1.7600 loss: 1.9057 center_loss: 0.5284 size_loss: 0.1591 cls_loss: 0.6053 giou_loss: 0.6130 2025/05/12 19:39:24 - mmengine - INFO - Epoch(train) [106][40/91] base_lr: 2.0982e-04 lr: 2.0982e-04 eta: 3 days, 3:27:42 time: 10.4311 data_time: 1.4820 memory: 68702 grad_norm: 1.7945 loss: 1.9064 center_loss: 0.5280 size_loss: 0.1578 cls_loss: 0.6081 giou_loss: 0.6125 2025/05/12 19:41:01 - mmengine - INFO - Epoch(train) [106][50/91] base_lr: 2.0982e-04 lr: 2.0982e-04 eta: 3 days, 3:25:49 time: 10.6218 data_time: 1.4968 memory: 68703 grad_norm: 1.7415 loss: 1.8790 center_loss: 0.5168 size_loss: 0.1538 cls_loss: 0.5994 giou_loss: 0.6089 2025/05/12 19:42:38 - mmengine - INFO - Epoch(train) [106][60/91] base_lr: 2.0982e-04 lr: 2.0982e-04 eta: 3 days, 3:23:55 time: 9.6898 data_time: 0.6128 memory: 68702 grad_norm: 1.7225 loss: 1.8788 center_loss: 0.5190 size_loss: 0.1540 cls_loss: 0.5950 giou_loss: 0.6109 2025/05/12 19:44:15 - mmengine - INFO - Epoch(train) [106][70/91] base_lr: 2.0982e-04 lr: 2.0982e-04 eta: 3 days, 3:22:01 time: 9.6938 data_time: 0.6084 memory: 68702 grad_norm: 1.6322 loss: 1.8834 center_loss: 0.5247 size_loss: 0.1536 cls_loss: 0.5925 giou_loss: 0.6126 2025/05/12 19:45:52 - mmengine - INFO - Epoch(train) [106][80/91] base_lr: 2.0982e-04 lr: 2.0982e-04 eta: 3 days, 3:20:08 time: 9.6968 data_time: 0.6163 memory: 68702 grad_norm: 1.7500 loss: 1.8985 center_loss: 0.5315 size_loss: 0.1553 cls_loss: 0.5963 giou_loss: 0.6154 2025/05/12 19:47:28 - mmengine - INFO - Epoch(train) [106][90/91] base_lr: 2.0982e-04 lr: 2.0982e-04 eta: 3 days, 3:18:11 time: 9.6708 data_time: 0.6099 memory: 68703 grad_norm: 1.6764 loss: 1.8911 center_loss: 0.5274 size_loss: 0.1549 cls_loss: 0.5953 giou_loss: 0.6136 2025/05/12 19:47:30 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 19:47:30 - mmengine - INFO - Saving checkpoint at 106 epochs 2025/05/12 19:48:27 - mmengine - INFO - Epoch(val) [106][10/39] eta: 0:01:38 time: 2.8914 data_time: 0.3856 memory: 15952 2025/05/12 19:48:53 - mmengine - INFO - Epoch(val) [106][20/39] eta: 0:00:56 time: 2.7450 data_time: 0.2412 memory: 13407 2025/05/12 19:49:19 - mmengine - INFO - Epoch(val) [106][30/39] eta: 0:00:25 time: 2.7499 data_time: 0.2525 memory: 13407 2025/05/12 19:49:45 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.2024 | 0.4132 | 0.0147 | 0.1019 | | chair | 0.4985 | 0.6849 | 0.0900 | 0.2566 | | sofa | 0.6864 | 0.8866 | 0.1831 | 0.4021 | | curtain | 0.1946 | 0.5373 | 0.0406 | 0.1642 | | table | 0.4536 | 0.5857 | 0.1223 | 0.2543 | | bookshelf | 0.2171 | 0.5714 | 0.0395 | 0.1818 | | bed | 0.8146 | 0.8272 | 0.3453 | 0.5432 | | picture | 0.0120 | 0.0676 | 0.0023 | 0.0045 | | window | 0.1189 | 0.3333 | 0.0065 | 0.0567 | | cabinet | 0.2870 | 0.4919 | 0.0560 | 0.1586 | | door | 0.1000 | 0.3940 | 0.0089 | 0.1156 | | refrigerator | 0.4888 | 0.6491 | 0.1165 | 0.2281 | | sink | 0.4424 | 0.5816 | 0.0504 | 0.1939 | | counter | 0.1447 | 0.3269 | 0.0140 | 0.0769 | | desk | 0.6342 | 0.8346 | 0.1737 | 0.4252 | | toilet | 0.8510 | 0.9310 | 0.4421 | 0.5172 | | bathtub | 0.6953 | 0.7742 | 0.2305 | 0.4194 | | showercurtrain | 0.3199 | 0.6071 | 0.0292 | 0.1429 | +----------------+---------+---------+---------+---------+ | Overall | 0.3979 | 0.5832 | 0.1092 | 0.2357 | +----------------+---------+---------+---------+---------+ 2025/05/12 19:49:45 - mmengine - INFO - Epoch(val) [106][39/39] chair_AP_0.25: 0.4985 sofa_AP_0.25: 0.6864 table_AP_0.25: 0.4536 garbagebin_AP_0.25: 0.2024 bookshelf_AP_0.25: 0.2171 picture_AP_0.25: 0.0120 curtain_AP_0.25: 0.1946 door_AP_0.25: 0.1000 cabinet_AP_0.25: 0.2870 refrigerator_AP_0.25: 0.4888 counter_AP_0.25: 0.1447 sink_AP_0.25: 0.4424 window_AP_0.25: 0.1189 desk_AP_0.25: 0.6342 bed_AP_0.25: 0.8146 toilet_AP_0.25: 0.8510 showercurtrain_AP_0.25: 0.3199 bathtub_AP_0.25: 0.6953 mAP_0.25: 0.3979 chair_rec_0.25: 0.6849 sofa_rec_0.25: 0.8866 table_rec_0.25: 0.5857 garbagebin_rec_0.25: 0.4132 bookshelf_rec_0.25: 0.5714 picture_rec_0.25: 0.0676 curtain_rec_0.25: 0.5373 door_rec_0.25: 0.3940 cabinet_rec_0.25: 0.4919 refrigerator_rec_0.25: 0.6491 counter_rec_0.25: 0.3269 sink_rec_0.25: 0.5816 window_rec_0.25: 0.3333 desk_rec_0.25: 0.8346 bed_rec_0.25: 0.8272 toilet_rec_0.25: 0.9310 showercurtrain_rec_0.25: 0.6071 bathtub_rec_0.25: 0.7742 mAR_0.25: 0.5832 chair_AP_0.50: 0.0900 sofa_AP_0.50: 0.1831 table_AP_0.50: 0.1223 garbagebin_AP_0.50: 0.0147 bookshelf_AP_0.50: 0.0395 picture_AP_0.50: 0.0023 curtain_AP_0.50: 0.0406 door_AP_0.50: 0.0089 cabinet_AP_0.50: 0.0560 refrigerator_AP_0.50: 0.1165 counter_AP_0.50: 0.0140 sink_AP_0.50: 0.0504 window_AP_0.50: 0.0065 desk_AP_0.50: 0.1737 bed_AP_0.50: 0.3453 toilet_AP_0.50: 0.4421 showercurtrain_AP_0.50: 0.0292 bathtub_AP_0.50: 0.2305 mAP_0.50: 0.1092 chair_rec_0.50: 0.2566 sofa_rec_0.50: 0.4021 table_rec_0.50: 0.2543 garbagebin_rec_0.50: 0.1019 bookshelf_rec_0.50: 0.1818 picture_rec_0.50: 0.0045 curtain_rec_0.50: 0.1642 door_rec_0.50: 0.1156 cabinet_rec_0.50: 0.1586 refrigerator_rec_0.50: 0.2281 counter_rec_0.50: 0.0769 sink_rec_0.50: 0.1939 window_rec_0.50: 0.0567 desk_rec_0.50: 0.4252 bed_rec_0.50: 0.5432 toilet_rec_0.50: 0.5172 showercurtrain_rec_0.50: 0.1429 bathtub_rec_0.50: 0.4194 mAR_0.50: 0.2357 data_time: 0.2965 time: 2.7832 2025/05/12 19:52:12 - mmengine - INFO - Epoch(train) [107][10/91] base_lr: 2.0910e-04 lr: 2.0910e-04 eta: 3 days, 3:18:03 time: 10.5135 data_time: 1.5704 memory: 68702 grad_norm: 1.6826 loss: 1.8860 center_loss: 0.5281 size_loss: 0.1563 cls_loss: 0.5894 giou_loss: 0.6121 2025/05/12 19:53:49 - mmengine - INFO - Epoch(train) [107][20/91] base_lr: 2.0910e-04 lr: 2.0910e-04 eta: 3 days, 3:16:09 time: 10.5058 data_time: 1.5671 memory: 68703 grad_norm: 1.6745 loss: 1.8910 center_loss: 0.5314 size_loss: 0.1576 cls_loss: 0.5881 giou_loss: 0.6138 2025/05/12 19:55:26 - mmengine - INFO - Epoch(train) [107][30/91] base_lr: 2.0910e-04 lr: 2.0910e-04 eta: 3 days, 3:14:15 time: 10.5046 data_time: 1.5613 memory: 68702 grad_norm: 1.7388 loss: 1.8872 center_loss: 0.5271 size_loss: 0.1583 cls_loss: 0.5889 giou_loss: 0.6130 2025/05/12 19:57:02 - mmengine - INFO - Epoch(train) [107][40/91] base_lr: 2.0910e-04 lr: 2.0910e-04 eta: 3 days, 3:12:19 time: 10.4880 data_time: 1.5578 memory: 68702 grad_norm: 1.6957 loss: 1.8906 center_loss: 0.5297 size_loss: 0.1580 cls_loss: 0.5868 giou_loss: 0.6160 2025/05/12 19:58:39 - mmengine - INFO - Epoch(train) [107][50/91] base_lr: 2.0910e-04 lr: 2.0910e-04 eta: 3 days, 3:10:25 time: 10.6693 data_time: 1.5703 memory: 68702 grad_norm: 1.6572 loss: 1.8930 center_loss: 0.5329 size_loss: 0.1585 cls_loss: 0.5866 giou_loss: 0.6150 2025/05/12 20:00:16 - mmengine - INFO - Epoch(train) [107][60/91] base_lr: 2.0910e-04 lr: 2.0910e-04 eta: 3 days, 3:08:32 time: 9.6628 data_time: 0.6041 memory: 68702 grad_norm: 1.6926 loss: 1.8844 center_loss: 0.5323 size_loss: 0.1572 cls_loss: 0.5805 giou_loss: 0.6144 2025/05/12 20:01:52 - mmengine - INFO - Epoch(train) [107][70/91] base_lr: 2.0910e-04 lr: 2.0910e-04 eta: 3 days, 3:06:38 time: 9.6668 data_time: 0.6059 memory: 68702 grad_norm: 1.6832 loss: 1.8646 center_loss: 0.5224 size_loss: 0.1546 cls_loss: 0.5765 giou_loss: 0.6112 2025/05/12 20:03:28 - mmengine - INFO - Epoch(train) [107][80/91] base_lr: 2.0910e-04 lr: 2.0910e-04 eta: 3 days, 3:04:42 time: 9.6520 data_time: 0.6141 memory: 68702 grad_norm: 1.6382 loss: 1.8433 center_loss: 0.5106 size_loss: 0.1519 cls_loss: 0.5742 giou_loss: 0.6066 2025/05/12 20:05:04 - mmengine - INFO - Epoch(train) [107][90/91] base_lr: 2.0910e-04 lr: 2.0910e-04 eta: 3 days, 3:02:44 time: 9.6363 data_time: 0.6089 memory: 68702 grad_norm: 1.5288 loss: 1.8297 center_loss: 0.5041 size_loss: 0.1513 cls_loss: 0.5727 giou_loss: 0.6016 2025/05/12 20:05:06 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 20:07:33 - mmengine - INFO - Epoch(train) [108][10/91] base_lr: 2.0837e-04 lr: 2.0837e-04 eta: 3 days, 3:02:36 time: 10.4856 data_time: 1.4485 memory: 68700 grad_norm: 1.7437 loss: 1.8748 center_loss: 0.5183 size_loss: 0.1553 cls_loss: 0.5915 giou_loss: 0.6096 2025/05/12 20:09:09 - mmengine - INFO - Epoch(train) [108][20/91] base_lr: 2.0837e-04 lr: 2.0837e-04 eta: 3 days, 3:00:41 time: 10.4795 data_time: 1.4508 memory: 68702 grad_norm: 1.7346 loss: 1.8630 center_loss: 0.5109 size_loss: 0.1538 cls_loss: 0.5921 giou_loss: 0.6062 2025/05/12 20:10:47 - mmengine - INFO - Epoch(train) [108][30/91] base_lr: 2.0837e-04 lr: 2.0837e-04 eta: 3 days, 2:58:50 time: 10.5059 data_time: 1.4400 memory: 68702 grad_norm: 1.7215 loss: 1.8824 center_loss: 0.5224 size_loss: 0.1556 cls_loss: 0.5962 giou_loss: 0.6082 2025/05/12 20:12:25 - mmengine - INFO - Epoch(train) [108][40/91] base_lr: 2.0837e-04 lr: 2.0837e-04 eta: 3 days, 2:56:59 time: 10.5444 data_time: 1.4191 memory: 68703 grad_norm: 1.6530 loss: 1.8876 center_loss: 0.5280 size_loss: 0.1568 cls_loss: 0.5958 giou_loss: 0.6071 2025/05/12 20:14:03 - mmengine - INFO - Epoch(train) [108][50/91] base_lr: 2.0837e-04 lr: 2.0837e-04 eta: 3 days, 2:55:09 time: 10.7510 data_time: 1.4273 memory: 68703 grad_norm: 1.5418 loss: 1.8709 center_loss: 0.5237 size_loss: 0.1566 cls_loss: 0.5832 giou_loss: 0.6074 2025/05/12 20:15:41 - mmengine - INFO - Epoch(train) [108][60/91] base_lr: 2.0837e-04 lr: 2.0837e-04 eta: 3 days, 2:53:18 time: 9.7600 data_time: 0.5717 memory: 68702 grad_norm: 1.4466 loss: 1.8631 center_loss: 0.5192 size_loss: 0.1561 cls_loss: 0.5822 giou_loss: 0.6055 2025/05/12 20:17:19 - mmengine - INFO - Epoch(train) [108][70/91] base_lr: 2.0837e-04 lr: 2.0837e-04 eta: 3 days, 2:51:27 time: 9.7887 data_time: 0.5615 memory: 68702 grad_norm: 1.4575 loss: 1.8763 center_loss: 0.5266 size_loss: 0.1566 cls_loss: 0.5840 giou_loss: 0.6091 2025/05/12 20:18:56 - mmengine - INFO - Epoch(train) [108][80/91] base_lr: 2.0837e-04 lr: 2.0837e-04 eta: 3 days, 2:49:35 time: 9.7790 data_time: 0.5548 memory: 68703 grad_norm: 1.4658 loss: 1.8419 center_loss: 0.5077 size_loss: 0.1525 cls_loss: 0.5785 giou_loss: 0.6032 2025/05/12 20:20:33 - mmengine - INFO - Epoch(train) [108][90/91] base_lr: 2.0837e-04 lr: 2.0837e-04 eta: 3 days, 2:47:41 time: 9.7549 data_time: 0.5545 memory: 68702 grad_norm: 1.5669 loss: 1.8487 center_loss: 0.5048 size_loss: 0.1530 cls_loss: 0.5839 giou_loss: 0.6070 2025/05/12 20:20:35 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 20:20:35 - mmengine - INFO - Saving checkpoint at 108 epochs 2025/05/12 20:21:30 - mmengine - INFO - Epoch(val) [108][10/39] eta: 0:01:34 time: 2.8762 data_time: 0.3896 memory: 15952 2025/05/12 20:21:56 - mmengine - INFO - Epoch(val) [108][20/39] eta: 0:00:55 time: 2.7093 data_time: 0.2258 memory: 13407 2025/05/12 20:22:22 - mmengine - INFO - Epoch(val) [108][30/39] eta: 0:00:25 time: 2.7087 data_time: 0.2263 memory: 13407 2025/05/12 20:22:48 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.1925 | 0.3774 | 0.0177 | 0.0981 | | sofa | 0.7336 | 0.8866 | 0.2132 | 0.4227 | | table | 0.4514 | 0.5914 | 0.1567 | 0.2971 | | chair | 0.5607 | 0.7222 | 0.1437 | 0.3107 | | bookshelf | 0.3300 | 0.6234 | 0.0704 | 0.1818 | | curtain | 0.2341 | 0.4478 | 0.0220 | 0.1194 | | picture | 0.0154 | 0.1036 | 0.0061 | 0.0270 | | door | 0.1382 | 0.4261 | 0.0136 | 0.1156 | | window | 0.1487 | 0.3652 | 0.0240 | 0.0957 | | cabinet | 0.2666 | 0.4839 | 0.0456 | 0.1909 | | sink | 0.5069 | 0.6224 | 0.1003 | 0.2245 | | refrigerator | 0.4723 | 0.6491 | 0.2063 | 0.3333 | | counter | 0.3083 | 0.4423 | 0.0274 | 0.1154 | | desk | 0.6891 | 0.8346 | 0.1755 | 0.4173 | | bed | 0.8210 | 0.8395 | 0.4328 | 0.5556 | | toilet | 0.8226 | 0.9655 | 0.4037 | 0.5000 | | bathtub | 0.7401 | 0.8710 | 0.1263 | 0.3548 | | showercurtrain | 0.3316 | 0.6429 | 0.0671 | 0.2143 | +----------------+---------+---------+---------+---------+ | Overall | 0.4313 | 0.6053 | 0.1251 | 0.2541 | +----------------+---------+---------+---------+---------+ 2025/05/12 20:22:48 - mmengine - INFO - Epoch(val) [108][39/39] chair_AP_0.25: 0.5607 sofa_AP_0.25: 0.7336 table_AP_0.25: 0.4514 garbagebin_AP_0.25: 0.1925 bookshelf_AP_0.25: 0.3300 picture_AP_0.25: 0.0154 curtain_AP_0.25: 0.2341 door_AP_0.25: 0.1382 cabinet_AP_0.25: 0.2666 refrigerator_AP_0.25: 0.4723 counter_AP_0.25: 0.3083 sink_AP_0.25: 0.5069 window_AP_0.25: 0.1487 desk_AP_0.25: 0.6891 bed_AP_0.25: 0.8210 toilet_AP_0.25: 0.8226 showercurtrain_AP_0.25: 0.3316 bathtub_AP_0.25: 0.7401 mAP_0.25: 0.4313 chair_rec_0.25: 0.7222 sofa_rec_0.25: 0.8866 table_rec_0.25: 0.5914 garbagebin_rec_0.25: 0.3774 bookshelf_rec_0.25: 0.6234 picture_rec_0.25: 0.1036 curtain_rec_0.25: 0.4478 door_rec_0.25: 0.4261 cabinet_rec_0.25: 0.4839 refrigerator_rec_0.25: 0.6491 counter_rec_0.25: 0.4423 sink_rec_0.25: 0.6224 window_rec_0.25: 0.3652 desk_rec_0.25: 0.8346 bed_rec_0.25: 0.8395 toilet_rec_0.25: 0.9655 showercurtrain_rec_0.25: 0.6429 bathtub_rec_0.25: 0.8710 mAR_0.25: 0.6053 chair_AP_0.50: 0.1437 sofa_AP_0.50: 0.2132 table_AP_0.50: 0.1567 garbagebin_AP_0.50: 0.0177 bookshelf_AP_0.50: 0.0704 picture_AP_0.50: 0.0061 curtain_AP_0.50: 0.0220 door_AP_0.50: 0.0136 cabinet_AP_0.50: 0.0456 refrigerator_AP_0.50: 0.2063 counter_AP_0.50: 0.0274 sink_AP_0.50: 0.1003 window_AP_0.50: 0.0240 desk_AP_0.50: 0.1755 bed_AP_0.50: 0.4328 toilet_AP_0.50: 0.4037 showercurtrain_AP_0.50: 0.0671 bathtub_AP_0.50: 0.1263 mAP_0.50: 0.1251 chair_rec_0.50: 0.3107 sofa_rec_0.50: 0.4227 table_rec_0.50: 0.2971 garbagebin_rec_0.50: 0.0981 bookshelf_rec_0.50: 0.1818 picture_rec_0.50: 0.0270 curtain_rec_0.50: 0.1194 door_rec_0.50: 0.1156 cabinet_rec_0.50: 0.1909 refrigerator_rec_0.50: 0.3333 counter_rec_0.50: 0.1154 sink_rec_0.50: 0.2245 window_rec_0.50: 0.0957 desk_rec_0.50: 0.4173 bed_rec_0.50: 0.5556 toilet_rec_0.50: 0.5000 showercurtrain_rec_0.50: 0.2143 bathtub_rec_0.50: 0.3548 mAR_0.50: 0.2541 data_time: 0.2462 time: 2.7252 2025/05/12 20:22:48 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_104.pth is removed 2025/05/12 20:23:09 - mmengine - INFO - The best checkpoint with 0.4313 mAP_0.25 at 108 epoch is saved to best_mAP_0.25_epoch_108.pth. 2025/05/12 20:26:01 - mmengine - INFO - Epoch(train) [109][10/91] base_lr: 2.0763e-04 lr: 2.0763e-04 eta: 3 days, 2:47:25 time: 10.5336 data_time: 1.4766 memory: 68702 grad_norm: 1.7332 loss: 1.8518 center_loss: 0.5145 size_loss: 0.1511 cls_loss: 0.5800 giou_loss: 0.6062 2025/05/12 20:27:39 - mmengine - INFO - Epoch(train) [109][20/91] base_lr: 2.0763e-04 lr: 2.0763e-04 eta: 3 days, 2:45:35 time: 10.5428 data_time: 1.4561 memory: 68702 grad_norm: 1.7638 loss: 1.8471 center_loss: 0.5096 size_loss: 0.1487 cls_loss: 0.5821 giou_loss: 0.6067 2025/05/12 20:29:18 - mmengine - INFO - Epoch(train) [109][30/91] base_lr: 2.0763e-04 lr: 2.0763e-04 eta: 3 days, 2:43:48 time: 10.5690 data_time: 1.4841 memory: 68702 grad_norm: 1.7169 loss: 1.8573 center_loss: 0.5145 size_loss: 0.1504 cls_loss: 0.5848 giou_loss: 0.6077 2025/05/12 20:30:57 - mmengine - INFO - Epoch(train) [109][40/91] base_lr: 2.0763e-04 lr: 2.0763e-04 eta: 3 days, 2:41:58 time: 10.5884 data_time: 1.4942 memory: 68702 grad_norm: 1.7533 loss: 1.8685 center_loss: 0.5214 size_loss: 0.1530 cls_loss: 0.5868 giou_loss: 0.6073 2025/05/12 20:32:35 - mmengine - INFO - Epoch(train) [109][50/91] base_lr: 2.0763e-04 lr: 2.0763e-04 eta: 3 days, 2:40:08 time: 10.7712 data_time: 1.5155 memory: 68702 grad_norm: 1.5749 loss: 1.8607 center_loss: 0.5215 size_loss: 0.1518 cls_loss: 0.5850 giou_loss: 0.6025 2025/05/12 20:34:13 - mmengine - INFO - Epoch(train) [109][60/91] base_lr: 2.0763e-04 lr: 2.0763e-04 eta: 3 days, 2:38:19 time: 9.8408 data_time: 0.5909 memory: 68703 grad_norm: 1.4845 loss: 1.8396 center_loss: 0.5046 size_loss: 0.1512 cls_loss: 0.5835 giou_loss: 0.6003 2025/05/12 20:35:51 - mmengine - INFO - Epoch(train) [109][70/91] base_lr: 2.0763e-04 lr: 2.0763e-04 eta: 3 days, 2:36:29 time: 9.8445 data_time: 0.6178 memory: 68702 grad_norm: 1.3671 loss: 1.8265 center_loss: 0.5009 size_loss: 0.1491 cls_loss: 0.5812 giou_loss: 0.5954 2025/05/12 20:37:29 - mmengine - INFO - Epoch(train) [109][80/91] base_lr: 2.0763e-04 lr: 2.0763e-04 eta: 3 days, 2:34:39 time: 9.8249 data_time: 0.6031 memory: 68702 grad_norm: 1.3701 loss: 1.8241 center_loss: 0.4998 size_loss: 0.1489 cls_loss: 0.5804 giou_loss: 0.5951 2025/05/12 20:39:07 - mmengine - INFO - Epoch(train) [109][90/91] base_lr: 2.0763e-04 lr: 2.0763e-04 eta: 3 days, 2:32:47 time: 9.8020 data_time: 0.6019 memory: 68702 grad_norm: 1.3639 loss: 1.8229 center_loss: 0.4989 size_loss: 0.1479 cls_loss: 0.5817 giou_loss: 0.5944 2025/05/12 20:39:09 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 20:41:35 - mmengine - INFO - Epoch(train) [110][10/91] base_lr: 2.0690e-04 lr: 2.0690e-04 eta: 3 days, 2:32:33 time: 10.5977 data_time: 1.5131 memory: 68702 grad_norm: 1.5041 loss: 1.8266 center_loss: 0.4949 size_loss: 0.1476 cls_loss: 0.5874 giou_loss: 0.5966 2025/05/12 20:43:13 - mmengine - INFO - Epoch(train) [110][20/91] base_lr: 2.0690e-04 lr: 2.0690e-04 eta: 3 days, 2:30:42 time: 10.5933 data_time: 1.5225 memory: 68702 grad_norm: 1.5598 loss: 1.8557 center_loss: 0.5136 size_loss: 0.1491 cls_loss: 0.5918 giou_loss: 0.6013 2025/05/12 20:44:51 - mmengine - INFO - Epoch(train) [110][30/91] base_lr: 2.0690e-04 lr: 2.0690e-04 eta: 3 days, 2:28:52 time: 10.5946 data_time: 1.5185 memory: 68703 grad_norm: 1.6447 loss: 1.8366 center_loss: 0.5048 size_loss: 0.1480 cls_loss: 0.5860 giou_loss: 0.5979 2025/05/12 20:46:28 - mmengine - INFO - Epoch(train) [110][40/91] base_lr: 2.0690e-04 lr: 2.0690e-04 eta: 3 days, 2:27:00 time: 10.5733 data_time: 1.5112 memory: 68701 grad_norm: 1.5715 loss: 1.8408 center_loss: 0.5067 size_loss: 0.1487 cls_loss: 0.5885 giou_loss: 0.5968 2025/05/12 20:48:05 - mmengine - INFO - Epoch(train) [110][50/91] base_lr: 2.0690e-04 lr: 2.0690e-04 eta: 3 days, 2:25:08 time: 10.7284 data_time: 1.5263 memory: 68702 grad_norm: 1.5353 loss: 1.8305 center_loss: 0.5055 size_loss: 0.1487 cls_loss: 0.5810 giou_loss: 0.5953 2025/05/12 20:49:42 - mmengine - INFO - Epoch(train) [110][60/91] base_lr: 2.0690e-04 lr: 2.0690e-04 eta: 3 days, 2:23:15 time: 9.7518 data_time: 0.6111 memory: 68702 grad_norm: 1.5084 loss: 1.8430 center_loss: 0.5127 size_loss: 0.1495 cls_loss: 0.5834 giou_loss: 0.5974 2025/05/12 20:51:19 - mmengine - INFO - Epoch(train) [110][70/91] base_lr: 2.0690e-04 lr: 2.0690e-04 eta: 3 days, 2:21:21 time: 9.7277 data_time: 0.6041 memory: 68702 grad_norm: 1.5315 loss: 1.8234 center_loss: 0.4971 size_loss: 0.1509 cls_loss: 0.5792 giou_loss: 0.5962 2025/05/12 20:52:55 - mmengine - INFO - Epoch(train) [110][80/91] base_lr: 2.0690e-04 lr: 2.0690e-04 eta: 3 days, 2:19:25 time: 9.6821 data_time: 0.6027 memory: 68702 grad_norm: 1.5028 loss: 1.8408 center_loss: 0.5048 size_loss: 0.1528 cls_loss: 0.5823 giou_loss: 0.6009 2025/05/12 20:53:05 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 20:54:30 - mmengine - INFO - Epoch(train) [110][90/91] base_lr: 2.0690e-04 lr: 2.0690e-04 eta: 3 days, 2:17:28 time: 9.6467 data_time: 0.5913 memory: 68702 grad_norm: 1.5760 loss: 1.8276 center_loss: 0.5009 size_loss: 0.1519 cls_loss: 0.5736 giou_loss: 0.6013 2025/05/12 20:54:32 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 20:54:32 - mmengine - INFO - Saving checkpoint at 110 epochs 2025/05/12 20:55:27 - mmengine - INFO - Epoch(val) [110][10/39] eta: 0:01:34 time: 2.8293 data_time: 0.3473 memory: 15952 2025/05/12 20:55:53 - mmengine - INFO - Epoch(val) [110][20/39] eta: 0:00:55 time: 2.6936 data_time: 0.2113 memory: 13407 2025/05/12 20:56:19 - mmengine - INFO - Epoch(val) [110][30/39] eta: 0:00:25 time: 2.6937 data_time: 0.2103 memory: 13407 2025/05/12 20:56:45 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | table | 0.4922 | 0.6143 | 0.1168 | 0.2400 | | garbagebin | 0.2087 | 0.4302 | 0.0176 | 0.1000 | | chair | 0.5730 | 0.7208 | 0.1435 | 0.3099 | | sofa | 0.6893 | 0.8866 | 0.1751 | 0.4124 | | curtain | 0.2325 | 0.5672 | 0.0353 | 0.0896 | | bookshelf | 0.3287 | 0.6104 | 0.0800 | 0.2338 | | picture | 0.0150 | 0.0901 | 0.0000 | 0.0045 | | door | 0.1343 | 0.4069 | 0.0119 | 0.0985 | | window | 0.1552 | 0.3688 | 0.0208 | 0.1028 | | cabinet | 0.2632 | 0.4892 | 0.0442 | 0.1559 | | counter | 0.2302 | 0.3269 | 0.0381 | 0.1154 | | refrigerator | 0.4903 | 0.6491 | 0.2062 | 0.3684 | | sink | 0.5016 | 0.6224 | 0.1129 | 0.2653 | | desk | 0.7109 | 0.8583 | 0.2518 | 0.4567 | | bed | 0.8353 | 0.8519 | 0.3317 | 0.5185 | | toilet | 0.8544 | 0.9138 | 0.4069 | 0.4828 | | bathtub | 0.6941 | 0.8065 | 0.1720 | 0.3548 | | showercurtrain | 0.3759 | 0.5714 | 0.0247 | 0.1071 | +----------------+---------+---------+---------+---------+ | Overall | 0.4325 | 0.5991 | 0.1216 | 0.2454 | +----------------+---------+---------+---------+---------+ 2025/05/12 20:56:45 - mmengine - INFO - Epoch(val) [110][39/39] chair_AP_0.25: 0.5730 sofa_AP_0.25: 0.6893 table_AP_0.25: 0.4922 garbagebin_AP_0.25: 0.2087 bookshelf_AP_0.25: 0.3287 picture_AP_0.25: 0.0150 curtain_AP_0.25: 0.2325 door_AP_0.25: 0.1343 cabinet_AP_0.25: 0.2632 refrigerator_AP_0.25: 0.4903 counter_AP_0.25: 0.2302 sink_AP_0.25: 0.5016 window_AP_0.25: 0.1552 desk_AP_0.25: 0.7109 bed_AP_0.25: 0.8353 toilet_AP_0.25: 0.8544 showercurtrain_AP_0.25: 0.3759 bathtub_AP_0.25: 0.6941 mAP_0.25: 0.4325 chair_rec_0.25: 0.7208 sofa_rec_0.25: 0.8866 table_rec_0.25: 0.6143 garbagebin_rec_0.25: 0.4302 bookshelf_rec_0.25: 0.6104 picture_rec_0.25: 0.0901 curtain_rec_0.25: 0.5672 door_rec_0.25: 0.4069 cabinet_rec_0.25: 0.4892 refrigerator_rec_0.25: 0.6491 counter_rec_0.25: 0.3269 sink_rec_0.25: 0.6224 window_rec_0.25: 0.3688 desk_rec_0.25: 0.8583 bed_rec_0.25: 0.8519 toilet_rec_0.25: 0.9138 showercurtrain_rec_0.25: 0.5714 bathtub_rec_0.25: 0.8065 mAR_0.25: 0.5991 chair_AP_0.50: 0.1435 sofa_AP_0.50: 0.1751 table_AP_0.50: 0.1168 garbagebin_AP_0.50: 0.0176 bookshelf_AP_0.50: 0.0800 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0353 door_AP_0.50: 0.0119 cabinet_AP_0.50: 0.0442 refrigerator_AP_0.50: 0.2062 counter_AP_0.50: 0.0381 sink_AP_0.50: 0.1129 window_AP_0.50: 0.0208 desk_AP_0.50: 0.2518 bed_AP_0.50: 0.3317 toilet_AP_0.50: 0.4069 showercurtrain_AP_0.50: 0.0247 bathtub_AP_0.50: 0.1720 mAP_0.50: 0.1216 chair_rec_0.50: 0.3099 sofa_rec_0.50: 0.4124 table_rec_0.50: 0.2400 garbagebin_rec_0.50: 0.1000 bookshelf_rec_0.50: 0.2338 picture_rec_0.50: 0.0045 curtain_rec_0.50: 0.0896 door_rec_0.50: 0.0985 cabinet_rec_0.50: 0.1559 refrigerator_rec_0.50: 0.3684 counter_rec_0.50: 0.1154 sink_rec_0.50: 0.2653 window_rec_0.50: 0.1028 desk_rec_0.50: 0.4567 bed_rec_0.50: 0.5185 toilet_rec_0.50: 0.4828 showercurtrain_rec_0.50: 0.1071 bathtub_rec_0.50: 0.3548 mAR_0.50: 0.2454 data_time: 0.2435 time: 2.7229 2025/05/12 20:56:45 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_108.pth is removed 2025/05/12 20:57:09 - mmengine - INFO - The best checkpoint with 0.4325 mAP_0.25 at 110 epoch is saved to best_mAP_0.25_epoch_110.pth. 2025/05/12 20:59:57 - mmengine - INFO - Epoch(train) [111][10/91] base_lr: 2.0615e-04 lr: 2.0615e-04 eta: 3 days, 2:17:03 time: 10.3884 data_time: 1.4915 memory: 68702 grad_norm: 1.5664 loss: 1.8395 center_loss: 0.5028 size_loss: 0.1523 cls_loss: 0.5790 giou_loss: 0.6053 2025/05/12 21:01:35 - mmengine - INFO - Epoch(train) [111][20/91] base_lr: 2.0615e-04 lr: 2.0615e-04 eta: 3 days, 2:15:12 time: 10.4062 data_time: 1.4928 memory: 68702 grad_norm: 1.5015 loss: 1.8453 center_loss: 0.5058 size_loss: 0.1529 cls_loss: 0.5810 giou_loss: 0.6056 2025/05/12 21:03:12 - mmengine - INFO - Epoch(train) [111][30/91] base_lr: 2.0615e-04 lr: 2.0615e-04 eta: 3 days, 2:13:19 time: 10.4109 data_time: 1.4880 memory: 68702 grad_norm: 1.5873 loss: 1.8372 center_loss: 0.5092 size_loss: 0.1500 cls_loss: 0.5781 giou_loss: 0.5999 2025/05/12 21:04:48 - mmengine - INFO - Epoch(train) [111][40/91] base_lr: 2.0615e-04 lr: 2.0615e-04 eta: 3 days, 2:11:25 time: 10.4259 data_time: 1.4821 memory: 68703 grad_norm: 1.6010 loss: 1.8470 center_loss: 0.5163 size_loss: 0.1505 cls_loss: 0.5775 giou_loss: 0.6027 2025/05/12 21:06:25 - mmengine - INFO - Epoch(train) [111][50/91] base_lr: 2.0615e-04 lr: 2.0615e-04 eta: 3 days, 2:09:33 time: 10.6116 data_time: 1.5059 memory: 68702 grad_norm: 1.6205 loss: 1.8720 center_loss: 0.5336 size_loss: 0.1514 cls_loss: 0.5821 giou_loss: 0.6049 2025/05/12 21:08:03 - mmengine - INFO - Epoch(train) [111][60/91] base_lr: 2.0615e-04 lr: 2.0615e-04 eta: 3 days, 2:07:42 time: 9.7262 data_time: 0.6011 memory: 68702 grad_norm: 1.6015 loss: 1.8772 center_loss: 0.5359 size_loss: 0.1522 cls_loss: 0.5818 giou_loss: 0.6074 2025/05/12 21:09:40 - mmengine - INFO - Epoch(train) [111][70/91] base_lr: 2.0615e-04 lr: 2.0615e-04 eta: 3 days, 2:05:49 time: 9.7046 data_time: 0.6005 memory: 68703 grad_norm: 1.6195 loss: 1.8551 center_loss: 0.5269 size_loss: 0.1511 cls_loss: 0.5735 giou_loss: 0.6036 2025/05/12 21:11:16 - mmengine - INFO - Epoch(train) [111][80/91] base_lr: 2.0615e-04 lr: 2.0615e-04 eta: 3 days, 2:03:53 time: 9.6882 data_time: 0.6056 memory: 68702 grad_norm: 1.6349 loss: 1.8588 center_loss: 0.5249 size_loss: 0.1523 cls_loss: 0.5733 giou_loss: 0.6083 2025/05/12 21:12:51 - mmengine - INFO - Epoch(train) [111][90/91] base_lr: 2.0615e-04 lr: 2.0615e-04 eta: 3 days, 2:01:55 time: 9.6499 data_time: 0.5984 memory: 68702 grad_norm: 1.5852 loss: 1.8512 center_loss: 0.5178 size_loss: 0.1512 cls_loss: 0.5742 giou_loss: 0.6080 2025/05/12 21:12:53 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 21:15:15 - mmengine - INFO - Epoch(train) [112][10/91] base_lr: 2.0540e-04 lr: 2.0540e-04 eta: 3 days, 2:01:29 time: 10.4012 data_time: 1.4756 memory: 68703 grad_norm: 1.5305 loss: 1.8191 center_loss: 0.4953 size_loss: 0.1492 cls_loss: 0.5718 giou_loss: 0.6028 2025/05/12 21:16:52 - mmengine - INFO - Epoch(train) [112][20/91] base_lr: 2.0540e-04 lr: 2.0540e-04 eta: 3 days, 1:59:36 time: 10.3789 data_time: 1.4554 memory: 68702 grad_norm: 1.5473 loss: 1.8123 center_loss: 0.4929 size_loss: 0.1482 cls_loss: 0.5712 giou_loss: 0.5999 2025/05/12 21:18:29 - mmengine - INFO - Epoch(train) [112][30/91] base_lr: 2.0540e-04 lr: 2.0540e-04 eta: 3 days, 1:57:42 time: 10.3807 data_time: 1.4556 memory: 68702 grad_norm: 1.7320 loss: 1.8426 center_loss: 0.5025 size_loss: 0.1510 cls_loss: 0.5830 giou_loss: 0.6061 2025/05/12 21:20:07 - mmengine - INFO - Epoch(train) [112][40/91] base_lr: 2.0540e-04 lr: 2.0540e-04 eta: 3 days, 1:55:52 time: 10.4162 data_time: 1.4521 memory: 68702 grad_norm: 1.6261 loss: 1.8438 center_loss: 0.5026 size_loss: 0.1511 cls_loss: 0.5866 giou_loss: 0.6034 2025/05/12 21:21:44 - mmengine - INFO - Epoch(train) [112][50/91] base_lr: 2.0540e-04 lr: 2.0540e-04 eta: 3 days, 1:54:01 time: 10.6256 data_time: 1.4699 memory: 68702 grad_norm: 1.5888 loss: 1.8486 center_loss: 0.5076 size_loss: 0.1521 cls_loss: 0.5868 giou_loss: 0.6021 2025/05/12 21:23:21 - mmengine - INFO - Epoch(train) [112][60/91] base_lr: 2.0540e-04 lr: 2.0540e-04 eta: 3 days, 1:52:07 time: 9.7093 data_time: 0.5811 memory: 68702 grad_norm: 1.6196 loss: 1.8656 center_loss: 0.5156 size_loss: 0.1529 cls_loss: 0.5903 giou_loss: 0.6067 2025/05/12 21:24:58 - mmengine - INFO - Epoch(train) [112][70/91] base_lr: 2.0540e-04 lr: 2.0540e-04 eta: 3 days, 1:50:15 time: 9.7146 data_time: 0.5908 memory: 68702 grad_norm: 1.6148 loss: 1.8705 center_loss: 0.5216 size_loss: 0.1539 cls_loss: 0.5901 giou_loss: 0.6049 2025/05/12 21:26:34 - mmengine - INFO - Epoch(train) [112][80/91] base_lr: 2.0540e-04 lr: 2.0540e-04 eta: 3 days, 1:48:21 time: 9.7113 data_time: 0.5909 memory: 68702 grad_norm: 1.4883 loss: 1.8408 center_loss: 0.5121 size_loss: 0.1509 cls_loss: 0.5799 giou_loss: 0.5979 2025/05/12 21:28:09 - mmengine - INFO - Epoch(train) [112][90/91] base_lr: 2.0540e-04 lr: 2.0540e-04 eta: 3 days, 1:46:23 time: 9.6525 data_time: 0.5799 memory: 68702 grad_norm: 1.4722 loss: 1.8388 center_loss: 0.5112 size_loss: 0.1504 cls_loss: 0.5785 giou_loss: 0.5987 2025/05/12 21:28:11 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 21:28:11 - mmengine - INFO - Saving checkpoint at 112 epochs 2025/05/12 21:29:08 - mmengine - INFO - Epoch(val) [112][10/39] eta: 0:01:36 time: 2.8449 data_time: 0.3564 memory: 15952 2025/05/12 21:29:34 - mmengine - INFO - Epoch(val) [112][20/39] eta: 0:00:56 time: 2.7132 data_time: 0.2210 memory: 13407 2025/05/12 21:30:00 - mmengine - INFO - Epoch(val) [112][30/39] eta: 0:00:25 time: 2.7172 data_time: 0.2217 memory: 13407 2025/05/12 21:30:26 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.2199 | 0.4057 | 0.0132 | 0.0962 | | table | 0.4623 | 0.6143 | 0.1431 | 0.3029 | | sofa | 0.7524 | 0.8557 | 0.2331 | 0.4227 | | chair | 0.5540 | 0.7113 | 0.1436 | 0.3077 | | curtain | 0.1977 | 0.4478 | 0.0868 | 0.1493 | | bookshelf | 0.2905 | 0.5974 | 0.0809 | 0.2597 | | picture | 0.0236 | 0.1081 | 0.0045 | 0.0045 | | cabinet | 0.2767 | 0.4651 | 0.0623 | 0.1962 | | window | 0.1242 | 0.3262 | 0.0106 | 0.0957 | | desk | 0.6934 | 0.8425 | 0.2807 | 0.4882 | | door | 0.1368 | 0.4090 | 0.0125 | 0.1071 | | refrigerator | 0.4507 | 0.5789 | 0.2284 | 0.3158 | | sink | 0.4608 | 0.5714 | 0.1719 | 0.2755 | | counter | 0.1961 | 0.3462 | 0.0108 | 0.0769 | | bed | 0.8305 | 0.8519 | 0.3607 | 0.5309 | | toilet | 0.8769 | 0.9310 | 0.4815 | 0.5345 | | bathtub | 0.7813 | 0.8387 | 0.3360 | 0.5161 | | showercurtrain | 0.2034 | 0.4643 | 0.0030 | 0.0357 | +----------------+---------+---------+---------+---------+ | Overall | 0.4184 | 0.5759 | 0.1480 | 0.2620 | +----------------+---------+---------+---------+---------+ 2025/05/12 21:30:26 - mmengine - INFO - Epoch(val) [112][39/39] chair_AP_0.25: 0.5540 sofa_AP_0.25: 0.7524 table_AP_0.25: 0.4623 garbagebin_AP_0.25: 0.2199 bookshelf_AP_0.25: 0.2905 picture_AP_0.25: 0.0236 curtain_AP_0.25: 0.1977 door_AP_0.25: 0.1368 cabinet_AP_0.25: 0.2767 refrigerator_AP_0.25: 0.4507 counter_AP_0.25: 0.1961 sink_AP_0.25: 0.4608 window_AP_0.25: 0.1242 desk_AP_0.25: 0.6934 bed_AP_0.25: 0.8305 toilet_AP_0.25: 0.8769 showercurtrain_AP_0.25: 0.2034 bathtub_AP_0.25: 0.7813 mAP_0.25: 0.4184 chair_rec_0.25: 0.7113 sofa_rec_0.25: 0.8557 table_rec_0.25: 0.6143 garbagebin_rec_0.25: 0.4057 bookshelf_rec_0.25: 0.5974 picture_rec_0.25: 0.1081 curtain_rec_0.25: 0.4478 door_rec_0.25: 0.4090 cabinet_rec_0.25: 0.4651 refrigerator_rec_0.25: 0.5789 counter_rec_0.25: 0.3462 sink_rec_0.25: 0.5714 window_rec_0.25: 0.3262 desk_rec_0.25: 0.8425 bed_rec_0.25: 0.8519 toilet_rec_0.25: 0.9310 showercurtrain_rec_0.25: 0.4643 bathtub_rec_0.25: 0.8387 mAR_0.25: 0.5759 chair_AP_0.50: 0.1436 sofa_AP_0.50: 0.2331 table_AP_0.50: 0.1431 garbagebin_AP_0.50: 0.0132 bookshelf_AP_0.50: 0.0809 picture_AP_0.50: 0.0045 curtain_AP_0.50: 0.0868 door_AP_0.50: 0.0125 cabinet_AP_0.50: 0.0623 refrigerator_AP_0.50: 0.2284 counter_AP_0.50: 0.0108 sink_AP_0.50: 0.1719 window_AP_0.50: 0.0106 desk_AP_0.50: 0.2807 bed_AP_0.50: 0.3607 toilet_AP_0.50: 0.4815 showercurtrain_AP_0.50: 0.0030 bathtub_AP_0.50: 0.3360 mAP_0.50: 0.1480 chair_rec_0.50: 0.3077 sofa_rec_0.50: 0.4227 table_rec_0.50: 0.3029 garbagebin_rec_0.50: 0.0962 bookshelf_rec_0.50: 0.2597 picture_rec_0.50: 0.0045 curtain_rec_0.50: 0.1493 door_rec_0.50: 0.1071 cabinet_rec_0.50: 0.1962 refrigerator_rec_0.50: 0.3158 counter_rec_0.50: 0.0769 sink_rec_0.50: 0.2755 window_rec_0.50: 0.0957 desk_rec_0.50: 0.4882 bed_rec_0.50: 0.5309 toilet_rec_0.50: 0.5345 showercurtrain_rec_0.50: 0.0357 bathtub_rec_0.50: 0.5161 mAR_0.50: 0.2620 data_time: 0.2587 time: 2.7550 2025/05/12 21:32:53 - mmengine - INFO - Epoch(train) [113][10/91] base_lr: 2.0465e-04 lr: 2.0465e-04 eta: 3 days, 1:46:07 time: 10.4786 data_time: 1.4723 memory: 68702 grad_norm: 1.5540 loss: 1.8517 center_loss: 0.5130 size_loss: 0.1518 cls_loss: 0.5857 giou_loss: 0.6012 2025/05/12 21:34:30 - mmengine - INFO - Epoch(train) [113][20/91] base_lr: 2.0465e-04 lr: 2.0465e-04 eta: 3 days, 1:44:16 time: 10.5052 data_time: 1.4687 memory: 68703 grad_norm: 1.6437 loss: 1.8313 center_loss: 0.5067 size_loss: 0.1504 cls_loss: 0.5766 giou_loss: 0.5976 2025/05/12 21:36:07 - mmengine - INFO - Epoch(train) [113][30/91] base_lr: 2.0465e-04 lr: 2.0465e-04 eta: 3 days, 1:42:23 time: 10.5045 data_time: 1.4748 memory: 68702 grad_norm: 1.6797 loss: 1.8358 center_loss: 0.5042 size_loss: 0.1517 cls_loss: 0.5782 giou_loss: 0.6017 2025/05/12 21:37:44 - mmengine - INFO - Epoch(train) [113][40/91] base_lr: 2.0465e-04 lr: 2.0465e-04 eta: 3 days, 1:40:30 time: 10.4982 data_time: 1.4649 memory: 68702 grad_norm: 1.6748 loss: 1.8533 center_loss: 0.5090 size_loss: 0.1539 cls_loss: 0.5839 giou_loss: 0.6065 2025/05/12 21:39:21 - mmengine - INFO - Epoch(train) [113][50/91] base_lr: 2.0465e-04 lr: 2.0465e-04 eta: 3 days, 1:38:38 time: 10.7017 data_time: 1.4836 memory: 68702 grad_norm: 1.6270 loss: 1.8486 center_loss: 0.5120 size_loss: 0.1543 cls_loss: 0.5760 giou_loss: 0.6064 2025/05/12 21:40:59 - mmengine - INFO - Epoch(train) [113][60/91] base_lr: 2.0465e-04 lr: 2.0465e-04 eta: 3 days, 1:36:47 time: 9.7194 data_time: 0.5884 memory: 68702 grad_norm: 1.7009 loss: 1.8416 center_loss: 0.5079 size_loss: 0.1533 cls_loss: 0.5733 giou_loss: 0.6072 2025/05/12 21:42:36 - mmengine - INFO - Epoch(train) [113][70/91] base_lr: 2.0465e-04 lr: 2.0465e-04 eta: 3 days, 1:34:54 time: 9.7038 data_time: 0.5909 memory: 68702 grad_norm: 1.7396 loss: 1.8602 center_loss: 0.5158 size_loss: 0.1548 cls_loss: 0.5799 giou_loss: 0.6098 2025/05/12 21:44:11 - mmengine - INFO - Epoch(train) [113][80/91] base_lr: 2.0465e-04 lr: 2.0465e-04 eta: 3 days, 1:32:59 time: 9.6852 data_time: 0.5910 memory: 68702 grad_norm: 1.6764 loss: 1.8502 center_loss: 0.5096 size_loss: 0.1544 cls_loss: 0.5822 giou_loss: 0.6040 2025/05/12 21:45:47 - mmengine - INFO - Epoch(train) [113][90/91] base_lr: 2.0465e-04 lr: 2.0465e-04 eta: 3 days, 1:31:04 time: 9.6703 data_time: 0.5887 memory: 68702 grad_norm: 1.6313 loss: 1.8507 center_loss: 0.5131 size_loss: 0.1546 cls_loss: 0.5779 giou_loss: 0.6051 2025/05/12 21:45:49 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 21:48:16 - mmengine - INFO - Epoch(train) [114][10/91] base_lr: 2.0389e-04 lr: 2.0389e-04 eta: 3 days, 1:30:48 time: 10.5133 data_time: 1.5097 memory: 68702 grad_norm: 1.7549 loss: 1.8472 center_loss: 0.5112 size_loss: 0.1537 cls_loss: 0.5772 giou_loss: 0.6051 2025/05/12 21:49:53 - mmengine - INFO - Epoch(train) [114][20/91] base_lr: 2.0389e-04 lr: 2.0389e-04 eta: 3 days, 1:28:56 time: 10.5028 data_time: 1.5050 memory: 68702 grad_norm: 1.6800 loss: 1.8458 center_loss: 0.5094 size_loss: 0.1535 cls_loss: 0.5783 giou_loss: 0.6045 2025/05/12 21:51:31 - mmengine - INFO - Epoch(train) [114][30/91] base_lr: 2.0389e-04 lr: 2.0389e-04 eta: 3 days, 1:27:05 time: 10.5217 data_time: 1.5002 memory: 68702 grad_norm: 1.6606 loss: 1.8317 center_loss: 0.5025 size_loss: 0.1519 cls_loss: 0.5741 giou_loss: 0.6031 2025/05/12 21:53:08 - mmengine - INFO - Epoch(train) [114][40/91] base_lr: 2.0389e-04 lr: 2.0389e-04 eta: 3 days, 1:25:13 time: 10.5431 data_time: 1.4950 memory: 68702 grad_norm: 1.6994 loss: 1.8518 center_loss: 0.5117 size_loss: 0.1527 cls_loss: 0.5788 giou_loss: 0.6086 2025/05/12 21:54:46 - mmengine - INFO - Epoch(train) [114][50/91] base_lr: 2.0389e-04 lr: 2.0389e-04 eta: 3 days, 1:23:22 time: 10.7352 data_time: 1.5182 memory: 68702 grad_norm: 1.6437 loss: 1.8450 center_loss: 0.5066 size_loss: 0.1515 cls_loss: 0.5805 giou_loss: 0.6064 2025/05/12 21:56:24 - mmengine - INFO - Epoch(train) [114][60/91] base_lr: 2.0389e-04 lr: 2.0389e-04 eta: 3 days, 1:21:32 time: 9.7446 data_time: 0.5900 memory: 68703 grad_norm: 1.6094 loss: 1.8662 center_loss: 0.5153 size_loss: 0.1543 cls_loss: 0.5869 giou_loss: 0.6096 2025/05/12 21:58:01 - mmengine - INFO - Epoch(train) [114][70/91] base_lr: 2.0389e-04 lr: 2.0389e-04 eta: 3 days, 1:19:39 time: 9.7413 data_time: 0.5932 memory: 68702 grad_norm: 1.4405 loss: 1.8578 center_loss: 0.5152 size_loss: 0.1543 cls_loss: 0.5798 giou_loss: 0.6085 2025/05/12 21:59:37 - mmengine - INFO - Epoch(train) [114][80/91] base_lr: 2.0389e-04 lr: 2.0389e-04 eta: 3 days, 1:17:46 time: 9.7248 data_time: 0.6063 memory: 68702 grad_norm: 1.4443 loss: 1.8622 center_loss: 0.5154 size_loss: 0.1543 cls_loss: 0.5849 giou_loss: 0.6077 2025/05/12 22:01:13 - mmengine - INFO - Epoch(train) [114][90/91] base_lr: 2.0389e-04 lr: 2.0389e-04 eta: 3 days, 1:15:49 time: 9.6844 data_time: 0.5997 memory: 68703 grad_norm: 1.4245 loss: 1.8410 center_loss: 0.5028 size_loss: 0.1524 cls_loss: 0.5827 giou_loss: 0.6030 2025/05/12 22:01:15 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 22:01:15 - mmengine - INFO - Saving checkpoint at 114 epochs 2025/05/12 22:02:10 - mmengine - INFO - Epoch(val) [114][10/39] eta: 0:01:32 time: 2.8452 data_time: 0.3496 memory: 15952 2025/05/12 22:02:36 - mmengine - INFO - Epoch(val) [114][20/39] eta: 0:00:54 time: 2.6919 data_time: 0.2035 memory: 13407 2025/05/12 22:03:01 - mmengine - INFO - Epoch(val) [114][30/39] eta: 0:00:24 time: 2.6876 data_time: 0.2031 memory: 13407 2025/05/12 22:03:28 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | sofa | 0.6786 | 0.8660 | 0.2345 | 0.4021 | | garbagebin | 0.2407 | 0.4321 | 0.0353 | 0.1264 | | table | 0.4617 | 0.5971 | 0.1500 | 0.2943 | | chair | 0.5296 | 0.6952 | 0.1438 | 0.3114 | | curtain | 0.2229 | 0.5224 | 0.0096 | 0.0896 | | bookshelf | 0.2441 | 0.5325 | 0.0491 | 0.1948 | | picture | 0.0196 | 0.0901 | 0.0020 | 0.0180 | | cabinet | 0.2736 | 0.4973 | 0.0601 | 0.1962 | | window | 0.1092 | 0.3085 | 0.0214 | 0.1028 | | door | 0.1247 | 0.3854 | 0.0114 | 0.1006 | | counter | 0.2826 | 0.4423 | 0.0449 | 0.1346 | | sink | 0.5135 | 0.6429 | 0.1234 | 0.2755 | | refrigerator | 0.3516 | 0.4912 | 0.1972 | 0.2807 | | bed | 0.8113 | 0.8642 | 0.3354 | 0.5185 | | desk | 0.6538 | 0.8583 | 0.1895 | 0.4252 | | toilet | 0.8401 | 0.9138 | 0.3568 | 0.4828 | | bathtub | 0.7706 | 0.9032 | 0.2188 | 0.4194 | | showercurtrain | 0.2854 | 0.6071 | 0.0179 | 0.0714 | +----------------+---------+---------+---------+---------+ | Overall | 0.4119 | 0.5916 | 0.1223 | 0.2469 | +----------------+---------+---------+---------+---------+ 2025/05/12 22:03:28 - mmengine - INFO - Epoch(val) [114][39/39] chair_AP_0.25: 0.5296 sofa_AP_0.25: 0.6786 table_AP_0.25: 0.4617 garbagebin_AP_0.25: 0.2407 bookshelf_AP_0.25: 0.2441 picture_AP_0.25: 0.0196 curtain_AP_0.25: 0.2229 door_AP_0.25: 0.1247 cabinet_AP_0.25: 0.2736 refrigerator_AP_0.25: 0.3516 counter_AP_0.25: 0.2826 sink_AP_0.25: 0.5135 window_AP_0.25: 0.1092 desk_AP_0.25: 0.6538 bed_AP_0.25: 0.8113 toilet_AP_0.25: 0.8401 showercurtrain_AP_0.25: 0.2854 bathtub_AP_0.25: 0.7706 mAP_0.25: 0.4119 chair_rec_0.25: 0.6952 sofa_rec_0.25: 0.8660 table_rec_0.25: 0.5971 garbagebin_rec_0.25: 0.4321 bookshelf_rec_0.25: 0.5325 picture_rec_0.25: 0.0901 curtain_rec_0.25: 0.5224 door_rec_0.25: 0.3854 cabinet_rec_0.25: 0.4973 refrigerator_rec_0.25: 0.4912 counter_rec_0.25: 0.4423 sink_rec_0.25: 0.6429 window_rec_0.25: 0.3085 desk_rec_0.25: 0.8583 bed_rec_0.25: 0.8642 toilet_rec_0.25: 0.9138 showercurtrain_rec_0.25: 0.6071 bathtub_rec_0.25: 0.9032 mAR_0.25: 0.5916 chair_AP_0.50: 0.1438 sofa_AP_0.50: 0.2345 table_AP_0.50: 0.1500 garbagebin_AP_0.50: 0.0353 bookshelf_AP_0.50: 0.0491 picture_AP_0.50: 0.0020 curtain_AP_0.50: 0.0096 door_AP_0.50: 0.0114 cabinet_AP_0.50: 0.0601 refrigerator_AP_0.50: 0.1972 counter_AP_0.50: 0.0449 sink_AP_0.50: 0.1234 window_AP_0.50: 0.0214 desk_AP_0.50: 0.1895 bed_AP_0.50: 0.3354 toilet_AP_0.50: 0.3568 showercurtrain_AP_0.50: 0.0179 bathtub_AP_0.50: 0.2188 mAP_0.50: 0.1223 chair_rec_0.50: 0.3114 sofa_rec_0.50: 0.4021 table_rec_0.50: 0.2943 garbagebin_rec_0.50: 0.1264 bookshelf_rec_0.50: 0.1948 picture_rec_0.50: 0.0180 curtain_rec_0.50: 0.0896 door_rec_0.50: 0.1006 cabinet_rec_0.50: 0.1962 refrigerator_rec_0.50: 0.2807 counter_rec_0.50: 0.1346 sink_rec_0.50: 0.2755 window_rec_0.50: 0.1028 desk_rec_0.50: 0.4252 bed_rec_0.50: 0.5185 toilet_rec_0.50: 0.4828 showercurtrain_rec_0.50: 0.0714 bathtub_rec_0.50: 0.4194 mAR_0.50: 0.2469 data_time: 0.2350 time: 2.7136 2025/05/12 22:06:01 - mmengine - INFO - Epoch(train) [115][10/91] base_lr: 2.0312e-04 lr: 2.0312e-04 eta: 3 days, 1:15:47 time: 10.6347 data_time: 1.6352 memory: 68702 grad_norm: 1.5291 loss: 1.8376 center_loss: 0.4979 size_loss: 0.1511 cls_loss: 0.5906 giou_loss: 0.5979 2025/05/12 22:07:39 - mmengine - INFO - Epoch(train) [115][20/91] base_lr: 2.0312e-04 lr: 2.0312e-04 eta: 3 days, 1:13:57 time: 10.6475 data_time: 1.6283 memory: 68702 grad_norm: 1.4988 loss: 1.8376 center_loss: 0.4960 size_loss: 0.1510 cls_loss: 0.5932 giou_loss: 0.5974 2025/05/12 22:09:16 - mmengine - INFO - Epoch(train) [115][30/91] base_lr: 2.0312e-04 lr: 2.0312e-04 eta: 3 days, 1:12:05 time: 10.6494 data_time: 1.6208 memory: 68702 grad_norm: 1.5074 loss: 1.8390 center_loss: 0.4959 size_loss: 0.1524 cls_loss: 0.5956 giou_loss: 0.5950 2025/05/12 22:10:52 - mmengine - INFO - Epoch(train) [115][40/91] base_lr: 2.0312e-04 lr: 2.0312e-04 eta: 3 days, 1:10:12 time: 10.6412 data_time: 1.6101 memory: 68702 grad_norm: 1.5063 loss: 1.8350 center_loss: 0.4983 size_loss: 0.1527 cls_loss: 0.5892 giou_loss: 0.5949 2025/05/12 22:12:30 - mmengine - INFO - Epoch(train) [115][50/91] base_lr: 2.0312e-04 lr: 2.0312e-04 eta: 3 days, 1:08:21 time: 10.8429 data_time: 1.6300 memory: 68702 grad_norm: 1.4541 loss: 1.8300 center_loss: 0.4980 size_loss: 0.1520 cls_loss: 0.5854 giou_loss: 0.5946 2025/05/12 22:14:07 - mmengine - INFO - Epoch(train) [115][60/91] base_lr: 2.0312e-04 lr: 2.0312e-04 eta: 3 days, 1:06:29 time: 9.7225 data_time: 0.5828 memory: 68702 grad_norm: 1.3962 loss: 1.8195 center_loss: 0.4949 size_loss: 0.1500 cls_loss: 0.5791 giou_loss: 0.5956 2025/05/12 22:15:44 - mmengine - INFO - Epoch(train) [115][70/91] base_lr: 2.0312e-04 lr: 2.0312e-04 eta: 3 days, 1:04:36 time: 9.6971 data_time: 0.5922 memory: 68702 grad_norm: 1.4347 loss: 1.8063 center_loss: 0.4913 size_loss: 0.1480 cls_loss: 0.5741 giou_loss: 0.5929 2025/05/12 22:17:20 - mmengine - INFO - Epoch(train) [115][80/91] base_lr: 2.0312e-04 lr: 2.0312e-04 eta: 3 days, 1:02:43 time: 9.6898 data_time: 0.5979 memory: 68702 grad_norm: 1.4732 loss: 1.8063 center_loss: 0.4909 size_loss: 0.1466 cls_loss: 0.5731 giou_loss: 0.5956 2025/05/12 22:18:56 - mmengine - INFO - Epoch(train) [115][90/91] base_lr: 2.0312e-04 lr: 2.0312e-04 eta: 3 days, 1:00:47 time: 9.6682 data_time: 0.5888 memory: 68702 grad_norm: 1.5173 loss: 1.8081 center_loss: 0.4890 size_loss: 0.1465 cls_loss: 0.5780 giou_loss: 0.5945 2025/05/12 22:18:58 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 22:21:27 - mmengine - INFO - Epoch(train) [116][10/91] base_lr: 2.0236e-04 lr: 2.0236e-04 eta: 3 days, 1:00:34 time: 10.5502 data_time: 1.5352 memory: 68702 grad_norm: 1.7017 loss: 1.8108 center_loss: 0.4885 size_loss: 0.1471 cls_loss: 0.5790 giou_loss: 0.5962 2025/05/12 22:23:03 - mmengine - INFO - Epoch(train) [116][20/91] base_lr: 2.0236e-04 lr: 2.0236e-04 eta: 3 days, 0:58:39 time: 10.5296 data_time: 1.5191 memory: 68703 grad_norm: 1.6569 loss: 1.8104 center_loss: 0.4877 size_loss: 0.1484 cls_loss: 0.5803 giou_loss: 0.5940 2025/05/12 22:24:41 - mmengine - INFO - Epoch(train) [116][30/91] base_lr: 2.0236e-04 lr: 2.0236e-04 eta: 3 days, 0:56:48 time: 10.5517 data_time: 1.5156 memory: 68703 grad_norm: 1.6199 loss: 1.8175 center_loss: 0.4894 size_loss: 0.1493 cls_loss: 0.5827 giou_loss: 0.5962 2025/05/12 22:26:17 - mmengine - INFO - Epoch(train) [116][40/91] base_lr: 2.0236e-04 lr: 2.0236e-04 eta: 3 days, 0:54:55 time: 10.5445 data_time: 1.5039 memory: 68703 grad_norm: 1.5886 loss: 1.8167 center_loss: 0.4885 size_loss: 0.1487 cls_loss: 0.5866 giou_loss: 0.5929 2025/05/12 22:27:55 - mmengine - INFO - Epoch(train) [116][50/91] base_lr: 2.0236e-04 lr: 2.0236e-04 eta: 3 days, 0:53:05 time: 10.7446 data_time: 1.5056 memory: 68702 grad_norm: 1.5218 loss: 1.8279 center_loss: 0.4964 size_loss: 0.1495 cls_loss: 0.5863 giou_loss: 0.5957 2025/05/12 22:29:32 - mmengine - INFO - Epoch(train) [116][60/91] base_lr: 2.0236e-04 lr: 2.0236e-04 eta: 3 days, 0:51:13 time: 9.6952 data_time: 0.5452 memory: 68700 grad_norm: 1.4354 loss: 1.8220 center_loss: 0.4936 size_loss: 0.1485 cls_loss: 0.5835 giou_loss: 0.5963 2025/05/12 22:31:09 - mmengine - INFO - Epoch(train) [116][70/91] base_lr: 2.0236e-04 lr: 2.0236e-04 eta: 3 days, 0:49:21 time: 9.7198 data_time: 0.5638 memory: 68702 grad_norm: 1.4984 loss: 1.8204 center_loss: 0.4954 size_loss: 0.1485 cls_loss: 0.5788 giou_loss: 0.5978 2025/05/12 22:32:47 - mmengine - INFO - Epoch(train) [116][80/91] base_lr: 2.0236e-04 lr: 2.0236e-04 eta: 3 days, 0:47:30 time: 9.7157 data_time: 0.5580 memory: 68702 grad_norm: 1.4846 loss: 1.8232 center_loss: 0.4996 size_loss: 0.1483 cls_loss: 0.5780 giou_loss: 0.5973 2025/05/12 22:34:22 - mmengine - INFO - Epoch(train) [116][90/91] base_lr: 2.0236e-04 lr: 2.0236e-04 eta: 3 days, 0:45:34 time: 9.6952 data_time: 0.5565 memory: 68702 grad_norm: 1.5350 loss: 1.8045 center_loss: 0.4937 size_loss: 0.1473 cls_loss: 0.5696 giou_loss: 0.5940 2025/05/12 22:34:24 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 22:34:24 - mmengine - INFO - Saving checkpoint at 116 epochs 2025/05/12 22:35:18 - mmengine - INFO - Epoch(val) [116][10/39] eta: 0:01:33 time: 2.8162 data_time: 0.3328 memory: 15952 2025/05/12 22:35:43 - mmengine - INFO - Epoch(val) [116][20/39] eta: 0:00:55 time: 2.6905 data_time: 0.2075 memory: 13407 2025/05/12 22:36:09 - mmengine - INFO - Epoch(val) [116][30/39] eta: 0:00:25 time: 2.6912 data_time: 0.2062 memory: 13407 2025/05/12 22:36:35 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.2360 | 0.4491 | 0.0288 | 0.1377 | | sofa | 0.6822 | 0.8351 | 0.1820 | 0.3711 | | table | 0.4393 | 0.5743 | 0.1186 | 0.2629 | | chair | 0.5676 | 0.7273 | 0.1186 | 0.2902 | | curtain | 0.2595 | 0.4925 | 0.0439 | 0.1493 | | picture | 0.0195 | 0.1171 | 0.0024 | 0.0135 | | bookshelf | 0.2873 | 0.5714 | 0.0900 | 0.2338 | | cabinet | 0.2550 | 0.4946 | 0.0612 | 0.1828 | | window | 0.1589 | 0.3759 | 0.0267 | 0.1028 | | door | 0.1225 | 0.4026 | 0.0104 | 0.1092 | | desk | 0.6277 | 0.8346 | 0.1954 | 0.4409 | | refrigerator | 0.4224 | 0.5614 | 0.1566 | 0.2807 | | sink | 0.5163 | 0.6633 | 0.0943 | 0.2347 | | counter | 0.2729 | 0.4423 | 0.0485 | 0.0962 | | bed | 0.8308 | 0.8395 | 0.4086 | 0.5556 | | toilet | 0.7917 | 0.8793 | 0.3930 | 0.4828 | | bathtub | 0.7391 | 0.8710 | 0.2139 | 0.4194 | | showercurtrain | 0.3261 | 0.6071 | 0.0510 | 0.1429 | +----------------+---------+---------+---------+---------+ | Overall | 0.4197 | 0.5966 | 0.1247 | 0.2504 | +----------------+---------+---------+---------+---------+ 2025/05/12 22:36:35 - mmengine - INFO - Epoch(val) [116][39/39] chair_AP_0.25: 0.5676 sofa_AP_0.25: 0.6822 table_AP_0.25: 0.4393 garbagebin_AP_0.25: 0.2360 bookshelf_AP_0.25: 0.2873 picture_AP_0.25: 0.0195 curtain_AP_0.25: 0.2595 door_AP_0.25: 0.1225 cabinet_AP_0.25: 0.2550 refrigerator_AP_0.25: 0.4224 counter_AP_0.25: 0.2729 sink_AP_0.25: 0.5163 window_AP_0.25: 0.1589 desk_AP_0.25: 0.6277 bed_AP_0.25: 0.8308 toilet_AP_0.25: 0.7917 showercurtrain_AP_0.25: 0.3261 bathtub_AP_0.25: 0.7391 mAP_0.25: 0.4197 chair_rec_0.25: 0.7273 sofa_rec_0.25: 0.8351 table_rec_0.25: 0.5743 garbagebin_rec_0.25: 0.4491 bookshelf_rec_0.25: 0.5714 picture_rec_0.25: 0.1171 curtain_rec_0.25: 0.4925 door_rec_0.25: 0.4026 cabinet_rec_0.25: 0.4946 refrigerator_rec_0.25: 0.5614 counter_rec_0.25: 0.4423 sink_rec_0.25: 0.6633 window_rec_0.25: 0.3759 desk_rec_0.25: 0.8346 bed_rec_0.25: 0.8395 toilet_rec_0.25: 0.8793 showercurtrain_rec_0.25: 0.6071 bathtub_rec_0.25: 0.8710 mAR_0.25: 0.5966 chair_AP_0.50: 0.1186 sofa_AP_0.50: 0.1820 table_AP_0.50: 0.1186 garbagebin_AP_0.50: 0.0288 bookshelf_AP_0.50: 0.0900 picture_AP_0.50: 0.0024 curtain_AP_0.50: 0.0439 door_AP_0.50: 0.0104 cabinet_AP_0.50: 0.0612 refrigerator_AP_0.50: 0.1566 counter_AP_0.50: 0.0485 sink_AP_0.50: 0.0943 window_AP_0.50: 0.0267 desk_AP_0.50: 0.1954 bed_AP_0.50: 0.4086 toilet_AP_0.50: 0.3930 showercurtrain_AP_0.50: 0.0510 bathtub_AP_0.50: 0.2139 mAP_0.50: 0.1247 chair_rec_0.50: 0.2902 sofa_rec_0.50: 0.3711 table_rec_0.50: 0.2629 garbagebin_rec_0.50: 0.1377 bookshelf_rec_0.50: 0.2338 picture_rec_0.50: 0.0135 curtain_rec_0.50: 0.1493 door_rec_0.50: 0.1092 cabinet_rec_0.50: 0.1828 refrigerator_rec_0.50: 0.2807 counter_rec_0.50: 0.0962 sink_rec_0.50: 0.2347 window_rec_0.50: 0.1028 desk_rec_0.50: 0.4409 bed_rec_0.50: 0.5556 toilet_rec_0.50: 0.4828 showercurtrain_rec_0.50: 0.1429 bathtub_rec_0.50: 0.4194 mAR_0.50: 0.2504 data_time: 0.2394 time: 2.7244 2025/05/12 22:39:02 - mmengine - INFO - Epoch(train) [117][10/91] base_lr: 2.0158e-04 lr: 2.0158e-04 eta: 3 days, 0:45:14 time: 10.5239 data_time: 1.4167 memory: 68703 grad_norm: 1.5797 loss: 1.8029 center_loss: 0.4904 size_loss: 0.1481 cls_loss: 0.5716 giou_loss: 0.5927 2025/05/12 22:40:40 - mmengine - INFO - Epoch(train) [117][20/91] base_lr: 2.0158e-04 lr: 2.0158e-04 eta: 3 days, 0:43:24 time: 10.5372 data_time: 1.4316 memory: 68702 grad_norm: 1.5842 loss: 1.8009 center_loss: 0.4903 size_loss: 0.1472 cls_loss: 0.5726 giou_loss: 0.5907 2025/05/12 22:42:17 - mmengine - INFO - Epoch(train) [117][30/91] base_lr: 2.0158e-04 lr: 2.0158e-04 eta: 3 days, 0:41:33 time: 10.5478 data_time: 1.4265 memory: 68702 grad_norm: 1.5684 loss: 1.7874 center_loss: 0.4849 size_loss: 0.1459 cls_loss: 0.5695 giou_loss: 0.5871 2025/05/12 22:43:55 - mmengine - INFO - Epoch(train) [117][40/91] base_lr: 2.0158e-04 lr: 2.0158e-04 eta: 3 days, 0:39:42 time: 10.5484 data_time: 1.4297 memory: 68702 grad_norm: 1.5141 loss: 1.8082 center_loss: 0.4936 size_loss: 0.1475 cls_loss: 0.5750 giou_loss: 0.5920 2025/05/12 22:45:33 - mmengine - INFO - Epoch(train) [117][50/91] base_lr: 2.0158e-04 lr: 2.0158e-04 eta: 3 days, 0:37:54 time: 10.7601 data_time: 1.4452 memory: 68702 grad_norm: 1.5265 loss: 1.8179 center_loss: 0.4985 size_loss: 0.1471 cls_loss: 0.5763 giou_loss: 0.5960 2025/05/12 22:47:10 - mmengine - INFO - Epoch(train) [117][60/91] base_lr: 2.0158e-04 lr: 2.0158e-04 eta: 3 days, 0:36:02 time: 9.7623 data_time: 0.5878 memory: 68703 grad_norm: 1.5286 loss: 1.8257 center_loss: 0.4992 size_loss: 0.1460 cls_loss: 0.5825 giou_loss: 0.5980 2025/05/12 22:48:48 - mmengine - INFO - Epoch(train) [117][70/91] base_lr: 2.0158e-04 lr: 2.0158e-04 eta: 3 days, 0:34:11 time: 9.7601 data_time: 0.5687 memory: 68703 grad_norm: 1.5550 loss: 1.8204 center_loss: 0.4990 size_loss: 0.1459 cls_loss: 0.5806 giou_loss: 0.5949 2025/05/12 22:50:25 - mmengine - INFO - Epoch(train) [117][80/91] base_lr: 2.0158e-04 lr: 2.0158e-04 eta: 3 days, 0:32:19 time: 9.7451 data_time: 0.5840 memory: 68702 grad_norm: 1.5887 loss: 1.8159 center_loss: 0.4978 size_loss: 0.1451 cls_loss: 0.5796 giou_loss: 0.5934 2025/05/12 22:52:00 - mmengine - INFO - Epoch(train) [117][90/91] base_lr: 2.0158e-04 lr: 2.0158e-04 eta: 3 days, 0:30:24 time: 9.7101 data_time: 0.5791 memory: 68702 grad_norm: 1.5777 loss: 1.7958 center_loss: 0.4867 size_loss: 0.1445 cls_loss: 0.5766 giou_loss: 0.5880 2025/05/12 22:52:02 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 22:54:29 - mmengine - INFO - Epoch(train) [118][10/91] base_lr: 2.0080e-04 lr: 2.0080e-04 eta: 3 days, 0:30:03 time: 10.5308 data_time: 1.4733 memory: 68703 grad_norm: 1.5597 loss: 1.8233 center_loss: 0.4972 size_loss: 0.1485 cls_loss: 0.5841 giou_loss: 0.5935 2025/05/12 22:56:06 - mmengine - INFO - Epoch(train) [118][20/91] base_lr: 2.0080e-04 lr: 2.0080e-04 eta: 3 days, 0:28:11 time: 10.5299 data_time: 1.4728 memory: 68700 grad_norm: 1.6031 loss: 1.8227 center_loss: 0.4999 size_loss: 0.1499 cls_loss: 0.5795 giou_loss: 0.5935 2025/05/12 22:57:44 - mmengine - INFO - Epoch(train) [118][30/91] base_lr: 2.0080e-04 lr: 2.0080e-04 eta: 3 days, 0:26:20 time: 10.5229 data_time: 1.4607 memory: 68703 grad_norm: 1.5625 loss: 1.8488 center_loss: 0.5115 size_loss: 0.1537 cls_loss: 0.5819 giou_loss: 0.6016 2025/05/12 22:59:22 - mmengine - INFO - Epoch(train) [118][40/91] base_lr: 2.0080e-04 lr: 2.0080e-04 eta: 3 days, 0:24:30 time: 10.5444 data_time: 1.4567 memory: 68703 grad_norm: 1.5225 loss: 1.8484 center_loss: 0.5115 size_loss: 0.1536 cls_loss: 0.5826 giou_loss: 0.6007 2025/05/12 23:01:00 - mmengine - INFO - Epoch(train) [118][50/91] base_lr: 2.0080e-04 lr: 2.0080e-04 eta: 3 days, 0:22:42 time: 10.7575 data_time: 1.4622 memory: 68702 grad_norm: 1.6203 loss: 1.8572 center_loss: 0.5223 size_loss: 0.1543 cls_loss: 0.5783 giou_loss: 0.6022 2025/05/12 23:02:38 - mmengine - INFO - Epoch(train) [118][60/91] base_lr: 2.0080e-04 lr: 2.0080e-04 eta: 3 days, 0:20:52 time: 9.7687 data_time: 0.5653 memory: 68702 grad_norm: 1.7383 loss: 1.8250 center_loss: 0.5100 size_loss: 0.1500 cls_loss: 0.5693 giou_loss: 0.5958 2025/05/12 23:04:16 - mmengine - INFO - Epoch(train) [118][70/91] base_lr: 2.0080e-04 lr: 2.0080e-04 eta: 3 days, 0:19:03 time: 9.7938 data_time: 0.5662 memory: 68702 grad_norm: 1.6592 loss: 1.8267 center_loss: 0.5116 size_loss: 0.1506 cls_loss: 0.5687 giou_loss: 0.5958 2025/05/12 23:05:54 - mmengine - INFO - Epoch(train) [118][80/91] base_lr: 2.0080e-04 lr: 2.0080e-04 eta: 3 days, 0:17:12 time: 9.7961 data_time: 0.5799 memory: 68702 grad_norm: 1.7255 loss: 1.8286 center_loss: 0.5079 size_loss: 0.1503 cls_loss: 0.5737 giou_loss: 0.5967 2025/05/12 23:07:30 - mmengine - INFO - Epoch(train) [118][90/91] base_lr: 2.0080e-04 lr: 2.0080e-04 eta: 3 days, 0:15:19 time: 9.7655 data_time: 0.5699 memory: 68703 grad_norm: 1.7105 loss: 1.8293 center_loss: 0.5061 size_loss: 0.1491 cls_loss: 0.5750 giou_loss: 0.5991 2025/05/12 23:07:32 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 23:07:32 - mmengine - INFO - Saving checkpoint at 118 epochs 2025/05/12 23:08:29 - mmengine - INFO - Epoch(val) [118][10/39] eta: 0:01:34 time: 2.8340 data_time: 0.3404 memory: 15952 2025/05/12 23:08:55 - mmengine - INFO - Epoch(val) [118][20/39] eta: 0:00:55 time: 2.7086 data_time: 0.2118 memory: 13407 2025/05/12 23:09:21 - mmengine - INFO - Epoch(val) [118][30/39] eta: 0:00:25 time: 2.7122 data_time: 0.2106 memory: 13407 2025/05/12 23:09:47 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.2255 | 0.4226 | 0.0211 | 0.1075 | | sofa | 0.7103 | 0.8351 | 0.2035 | 0.3814 | | curtain | 0.2713 | 0.4478 | 0.0546 | 0.1343 | | table | 0.4573 | 0.5914 | 0.1321 | 0.2686 | | chair | 0.5527 | 0.6937 | 0.1337 | 0.2829 | | bookshelf | 0.3476 | 0.6753 | 0.0609 | 0.1818 | | picture | 0.0158 | 0.1261 | 0.0005 | 0.0180 | | door | 0.1285 | 0.4026 | 0.0131 | 0.1092 | | cabinet | 0.2929 | 0.5081 | 0.0553 | 0.1882 | | window | 0.1107 | 0.3262 | 0.0181 | 0.1064 | | counter | 0.3173 | 0.4423 | 0.0157 | 0.1154 | | sink | 0.5180 | 0.6020 | 0.0915 | 0.2347 | | refrigerator | 0.5306 | 0.6667 | 0.1433 | 0.2105 | | desk | 0.7319 | 0.8661 | 0.2513 | 0.4252 | | bed | 0.8224 | 0.8395 | 0.4171 | 0.5679 | | toilet | 0.8122 | 0.8966 | 0.3063 | 0.4655 | | bathtub | 0.8114 | 0.8387 | 0.2998 | 0.4516 | | showercurtrain | 0.2973 | 0.5357 | 0.1000 | 0.1429 | +----------------+---------+---------+---------+---------+ | Overall | 0.4419 | 0.5954 | 0.1288 | 0.2440 | +----------------+---------+---------+---------+---------+ 2025/05/12 23:09:47 - mmengine - INFO - Epoch(val) [118][39/39] chair_AP_0.25: 0.5527 sofa_AP_0.25: 0.7103 table_AP_0.25: 0.4573 garbagebin_AP_0.25: 0.2255 bookshelf_AP_0.25: 0.3476 picture_AP_0.25: 0.0158 curtain_AP_0.25: 0.2713 door_AP_0.25: 0.1285 cabinet_AP_0.25: 0.2929 refrigerator_AP_0.25: 0.5306 counter_AP_0.25: 0.3173 sink_AP_0.25: 0.5180 window_AP_0.25: 0.1107 desk_AP_0.25: 0.7319 bed_AP_0.25: 0.8224 toilet_AP_0.25: 0.8122 showercurtrain_AP_0.25: 0.2973 bathtub_AP_0.25: 0.8114 mAP_0.25: 0.4419 chair_rec_0.25: 0.6937 sofa_rec_0.25: 0.8351 table_rec_0.25: 0.5914 garbagebin_rec_0.25: 0.4226 bookshelf_rec_0.25: 0.6753 picture_rec_0.25: 0.1261 curtain_rec_0.25: 0.4478 door_rec_0.25: 0.4026 cabinet_rec_0.25: 0.5081 refrigerator_rec_0.25: 0.6667 counter_rec_0.25: 0.4423 sink_rec_0.25: 0.6020 window_rec_0.25: 0.3262 desk_rec_0.25: 0.8661 bed_rec_0.25: 0.8395 toilet_rec_0.25: 0.8966 showercurtrain_rec_0.25: 0.5357 bathtub_rec_0.25: 0.8387 mAR_0.25: 0.5954 chair_AP_0.50: 0.1337 sofa_AP_0.50: 0.2035 table_AP_0.50: 0.1321 garbagebin_AP_0.50: 0.0211 bookshelf_AP_0.50: 0.0609 picture_AP_0.50: 0.0005 curtain_AP_0.50: 0.0546 door_AP_0.50: 0.0131 cabinet_AP_0.50: 0.0553 refrigerator_AP_0.50: 0.1433 counter_AP_0.50: 0.0157 sink_AP_0.50: 0.0915 window_AP_0.50: 0.0181 desk_AP_0.50: 0.2513 bed_AP_0.50: 0.4171 toilet_AP_0.50: 0.3063 showercurtrain_AP_0.50: 0.1000 bathtub_AP_0.50: 0.2998 mAP_0.50: 0.1288 chair_rec_0.50: 0.2829 sofa_rec_0.50: 0.3814 table_rec_0.50: 0.2686 garbagebin_rec_0.50: 0.1075 bookshelf_rec_0.50: 0.1818 picture_rec_0.50: 0.0180 curtain_rec_0.50: 0.1343 door_rec_0.50: 0.1092 cabinet_rec_0.50: 0.1882 refrigerator_rec_0.50: 0.2105 counter_rec_0.50: 0.1154 sink_rec_0.50: 0.2347 window_rec_0.50: 0.1064 desk_rec_0.50: 0.4252 bed_rec_0.50: 0.5679 toilet_rec_0.50: 0.4655 showercurtrain_rec_0.50: 0.1429 bathtub_rec_0.50: 0.4516 mAR_0.50: 0.2440 data_time: 0.2453 time: 2.7493 2025/05/12 23:09:47 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_110.pth is removed 2025/05/12 23:10:08 - mmengine - INFO - The best checkpoint with 0.4419 mAP_0.25 at 118 epoch is saved to best_mAP_0.25_epoch_118.pth. 2025/05/12 23:12:58 - mmengine - INFO - Epoch(train) [119][10/91] base_lr: 2.0002e-04 lr: 2.0002e-04 eta: 3 days, 0:14:57 time: 10.5783 data_time: 1.5501 memory: 68701 grad_norm: 1.6701 loss: 1.8224 center_loss: 0.4951 size_loss: 0.1473 cls_loss: 0.5818 giou_loss: 0.5983 2025/05/12 23:14:34 - mmengine - INFO - Epoch(train) [119][20/91] base_lr: 2.0002e-04 lr: 2.0002e-04 eta: 3 days, 0:13:03 time: 10.5506 data_time: 1.5441 memory: 68703 grad_norm: 1.7433 loss: 1.8321 center_loss: 0.4998 size_loss: 0.1482 cls_loss: 0.5830 giou_loss: 0.6012 2025/05/12 23:16:11 - mmengine - INFO - Epoch(train) [119][30/91] base_lr: 2.0002e-04 lr: 2.0002e-04 eta: 3 days, 0:11:10 time: 10.5209 data_time: 1.5424 memory: 68700 grad_norm: 1.9983 loss: 1.8499 center_loss: 0.5036 size_loss: 0.1501 cls_loss: 0.5913 giou_loss: 0.6049 2025/05/12 23:17:48 - mmengine - INFO - Epoch(train) [119][40/91] base_lr: 2.0002e-04 lr: 2.0002e-04 eta: 3 days, 0:09:19 time: 10.5068 data_time: 1.5304 memory: 68702 grad_norm: 2.0034 loss: 1.8464 center_loss: 0.5012 size_loss: 0.1490 cls_loss: 0.5906 giou_loss: 0.6056 2025/05/12 23:19:26 - mmengine - INFO - Epoch(train) [119][50/91] base_lr: 2.0002e-04 lr: 2.0002e-04 eta: 3 days, 0:07:29 time: 10.6973 data_time: 1.5312 memory: 68702 grad_norm: 2.0103 loss: 1.8654 center_loss: 0.5082 size_loss: 0.1514 cls_loss: 0.5981 giou_loss: 0.6078 2025/05/12 23:21:03 - mmengine - INFO - Epoch(train) [119][60/91] base_lr: 2.0002e-04 lr: 2.0002e-04 eta: 3 days, 0:05:38 time: 9.7015 data_time: 0.5561 memory: 68703 grad_norm: 2.0301 loss: 1.8719 center_loss: 0.5155 size_loss: 0.1537 cls_loss: 0.5943 giou_loss: 0.6084 2025/05/12 23:22:40 - mmengine - INFO - Epoch(train) [119][70/91] base_lr: 2.0002e-04 lr: 2.0002e-04 eta: 3 days, 0:03:46 time: 9.7156 data_time: 0.5665 memory: 68702 grad_norm: 1.9307 loss: 1.8644 center_loss: 0.5141 size_loss: 0.1536 cls_loss: 0.5904 giou_loss: 0.6062 2025/05/12 23:24:17 - mmengine - INFO - Epoch(train) [119][80/91] base_lr: 2.0002e-04 lr: 2.0002e-04 eta: 3 days, 0:01:54 time: 9.7187 data_time: 0.5766 memory: 68703 grad_norm: 1.7376 loss: 1.8474 center_loss: 0.5109 size_loss: 0.1515 cls_loss: 0.5805 giou_loss: 0.6044 2025/05/12 23:25:53 - mmengine - INFO - Epoch(train) [119][90/91] base_lr: 2.0002e-04 lr: 2.0002e-04 eta: 3 days, 0:00:00 time: 9.7008 data_time: 0.5747 memory: 68702 grad_norm: 1.6559 loss: 1.8393 center_loss: 0.5106 size_loss: 0.1512 cls_loss: 0.5761 giou_loss: 0.6013 2025/05/12 23:25:55 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 23:28:20 - mmengine - INFO - Epoch(train) [120][10/91] base_lr: 1.9923e-04 lr: 1.9923e-04 eta: 2 days, 23:59:33 time: 10.4917 data_time: 1.4082 memory: 68702 grad_norm: 1.7646 loss: 1.8523 center_loss: 0.5137 size_loss: 0.1519 cls_loss: 0.5808 giou_loss: 0.6059 2025/05/12 23:29:57 - mmengine - INFO - Epoch(train) [120][20/91] base_lr: 1.9923e-04 lr: 1.9923e-04 eta: 2 days, 23:57:42 time: 10.4968 data_time: 1.4053 memory: 68702 grad_norm: 1.6925 loss: 1.8488 center_loss: 0.5126 size_loss: 0.1516 cls_loss: 0.5803 giou_loss: 0.6044 2025/05/12 23:31:35 - mmengine - INFO - Epoch(train) [120][30/91] base_lr: 1.9923e-04 lr: 1.9923e-04 eta: 2 days, 23:55:53 time: 10.5199 data_time: 1.3996 memory: 68702 grad_norm: 1.6546 loss: 1.8524 center_loss: 0.5108 size_loss: 0.1508 cls_loss: 0.5843 giou_loss: 0.6065 2025/05/12 23:33:13 - mmengine - INFO - Epoch(train) [120][40/91] base_lr: 1.9923e-04 lr: 1.9923e-04 eta: 2 days, 23:54:03 time: 10.5363 data_time: 1.3970 memory: 68702 grad_norm: 1.8326 loss: 1.8515 center_loss: 0.5099 size_loss: 0.1508 cls_loss: 0.5849 giou_loss: 0.6058 2025/05/12 23:34:51 - mmengine - INFO - Epoch(train) [120][50/91] base_lr: 1.9923e-04 lr: 1.9923e-04 eta: 2 days, 23:52:13 time: 10.7221 data_time: 1.4119 memory: 68702 grad_norm: 1.8160 loss: 1.8628 center_loss: 0.5174 size_loss: 0.1527 cls_loss: 0.5826 giou_loss: 0.6100 2025/05/12 23:36:29 - mmengine - INFO - Epoch(train) [120][60/91] base_lr: 1.9923e-04 lr: 1.9923e-04 eta: 2 days, 23:50:23 time: 9.7710 data_time: 0.5900 memory: 68702 grad_norm: 1.8548 loss: 1.8470 center_loss: 0.5148 size_loss: 0.1518 cls_loss: 0.5753 giou_loss: 0.6051 2025/05/12 23:38:08 - mmengine - INFO - Epoch(train) [120][70/91] base_lr: 1.9923e-04 lr: 1.9923e-04 eta: 2 days, 23:48:36 time: 9.8098 data_time: 0.5846 memory: 68702 grad_norm: 1.9090 loss: 1.8483 center_loss: 0.5160 size_loss: 0.1520 cls_loss: 0.5736 giou_loss: 0.6067 2025/05/12 23:39:44 - mmengine - INFO - Epoch(train) [120][80/91] base_lr: 1.9923e-04 lr: 1.9923e-04 eta: 2 days, 23:46:44 time: 9.7779 data_time: 0.5930 memory: 68702 grad_norm: 1.8685 loss: 1.8561 center_loss: 0.5193 size_loss: 0.1530 cls_loss: 0.5796 giou_loss: 0.6043 2025/05/12 23:41:20 - mmengine - INFO - Epoch(train) [120][90/91] base_lr: 1.9923e-04 lr: 1.9923e-04 eta: 2 days, 23:44:48 time: 9.7316 data_time: 0.5811 memory: 68702 grad_norm: 1.7330 loss: 1.8641 center_loss: 0.5195 size_loss: 0.1532 cls_loss: 0.5863 giou_loss: 0.6051 2025/05/12 23:41:22 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 23:41:22 - mmengine - INFO - Saving checkpoint at 120 epochs 2025/05/12 23:42:16 - mmengine - INFO - Epoch(val) [120][10/39] eta: 0:01:36 time: 2.8655 data_time: 0.3625 memory: 15952 2025/05/12 23:42:42 - mmengine - INFO - Epoch(val) [120][20/39] eta: 0:00:56 time: 2.7317 data_time: 0.2315 memory: 13407 2025/05/12 23:43:08 - mmengine - INFO - Epoch(val) [120][30/39] eta: 0:00:25 time: 2.7338 data_time: 0.2303 memory: 13407 2025/05/12 23:43:34 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.2456 | 0.4283 | 0.0235 | 0.1075 | | table | 0.4304 | 0.5800 | 0.1430 | 0.2743 | | sofa | 0.7203 | 0.8247 | 0.2031 | 0.3918 | | chair | 0.5544 | 0.6988 | 0.1564 | 0.3289 | | curtain | 0.2595 | 0.4627 | 0.0740 | 0.1194 | | picture | 0.0123 | 0.1036 | 0.0004 | 0.0090 | | bookshelf | 0.2550 | 0.5325 | 0.0502 | 0.1818 | | door | 0.1337 | 0.4176 | 0.0244 | 0.1199 | | cabinet | 0.2997 | 0.5000 | 0.0590 | 0.1801 | | window | 0.1449 | 0.3582 | 0.0160 | 0.0816 | | counter | 0.3590 | 0.4808 | 0.0207 | 0.1154 | | sink | 0.5593 | 0.6633 | 0.1165 | 0.2653 | | refrigerator | 0.4443 | 0.5965 | 0.2632 | 0.3860 | | bed | 0.7709 | 0.8148 | 0.4203 | 0.5926 | | desk | 0.7428 | 0.8661 | 0.2792 | 0.4803 | | toilet | 0.8745 | 0.9655 | 0.3234 | 0.4655 | | bathtub | 0.7350 | 0.8065 | 0.3232 | 0.5161 | | showercurtrain | 0.2187 | 0.5000 | 0.0073 | 0.0714 | +----------------+---------+---------+---------+---------+ | Overall | 0.4311 | 0.5889 | 0.1391 | 0.2604 | +----------------+---------+---------+---------+---------+ 2025/05/12 23:43:34 - mmengine - INFO - Epoch(val) [120][39/39] chair_AP_0.25: 0.5544 sofa_AP_0.25: 0.7203 table_AP_0.25: 0.4304 garbagebin_AP_0.25: 0.2456 bookshelf_AP_0.25: 0.2550 picture_AP_0.25: 0.0123 curtain_AP_0.25: 0.2595 door_AP_0.25: 0.1337 cabinet_AP_0.25: 0.2997 refrigerator_AP_0.25: 0.4443 counter_AP_0.25: 0.3590 sink_AP_0.25: 0.5593 window_AP_0.25: 0.1449 desk_AP_0.25: 0.7428 bed_AP_0.25: 0.7709 toilet_AP_0.25: 0.8745 showercurtrain_AP_0.25: 0.2187 bathtub_AP_0.25: 0.7350 mAP_0.25: 0.4311 chair_rec_0.25: 0.6988 sofa_rec_0.25: 0.8247 table_rec_0.25: 0.5800 garbagebin_rec_0.25: 0.4283 bookshelf_rec_0.25: 0.5325 picture_rec_0.25: 0.1036 curtain_rec_0.25: 0.4627 door_rec_0.25: 0.4176 cabinet_rec_0.25: 0.5000 refrigerator_rec_0.25: 0.5965 counter_rec_0.25: 0.4808 sink_rec_0.25: 0.6633 window_rec_0.25: 0.3582 desk_rec_0.25: 0.8661 bed_rec_0.25: 0.8148 toilet_rec_0.25: 0.9655 showercurtrain_rec_0.25: 0.5000 bathtub_rec_0.25: 0.8065 mAR_0.25: 0.5889 chair_AP_0.50: 0.1564 sofa_AP_0.50: 0.2031 table_AP_0.50: 0.1430 garbagebin_AP_0.50: 0.0235 bookshelf_AP_0.50: 0.0502 picture_AP_0.50: 0.0004 curtain_AP_0.50: 0.0740 door_AP_0.50: 0.0244 cabinet_AP_0.50: 0.0590 refrigerator_AP_0.50: 0.2632 counter_AP_0.50: 0.0207 sink_AP_0.50: 0.1165 window_AP_0.50: 0.0160 desk_AP_0.50: 0.2792 bed_AP_0.50: 0.4203 toilet_AP_0.50: 0.3234 showercurtrain_AP_0.50: 0.0073 bathtub_AP_0.50: 0.3232 mAP_0.50: 0.1391 chair_rec_0.50: 0.3289 sofa_rec_0.50: 0.3918 table_rec_0.50: 0.2743 garbagebin_rec_0.50: 0.1075 bookshelf_rec_0.50: 0.1818 picture_rec_0.50: 0.0090 curtain_rec_0.50: 0.1194 door_rec_0.50: 0.1199 cabinet_rec_0.50: 0.1801 refrigerator_rec_0.50: 0.3860 counter_rec_0.50: 0.1154 sink_rec_0.50: 0.2653 window_rec_0.50: 0.0816 desk_rec_0.50: 0.4803 bed_rec_0.50: 0.5926 toilet_rec_0.50: 0.4655 showercurtrain_rec_0.50: 0.0714 bathtub_rec_0.50: 0.5161 mAR_0.50: 0.2604 data_time: 0.2676 time: 2.7662 2025/05/12 23:46:01 - mmengine - INFO - Epoch(train) [121][10/91] base_lr: 1.9844e-04 lr: 1.9844e-04 eta: 2 days, 23:44:24 time: 10.5614 data_time: 1.4592 memory: 68700 grad_norm: 1.7234 loss: 1.8403 center_loss: 0.5058 size_loss: 0.1499 cls_loss: 0.5863 giou_loss: 0.5983 2025/05/12 23:47:38 - mmengine - INFO - Epoch(train) [121][20/91] base_lr: 1.9844e-04 lr: 1.9844e-04 eta: 2 days, 23:42:31 time: 10.5382 data_time: 1.4578 memory: 68702 grad_norm: 1.6290 loss: 1.8523 center_loss: 0.5080 size_loss: 0.1509 cls_loss: 0.5922 giou_loss: 0.6012 2025/05/12 23:49:15 - mmengine - INFO - Epoch(train) [121][30/91] base_lr: 1.9844e-04 lr: 1.9844e-04 eta: 2 days, 23:40:39 time: 10.4934 data_time: 1.4524 memory: 68703 grad_norm: 1.5835 loss: 1.8330 center_loss: 0.5005 size_loss: 0.1475 cls_loss: 0.5880 giou_loss: 0.5969 2025/05/12 23:50:52 - mmengine - INFO - Epoch(train) [121][40/91] base_lr: 1.9844e-04 lr: 1.9844e-04 eta: 2 days, 23:38:49 time: 10.5107 data_time: 1.4451 memory: 68702 grad_norm: 1.5658 loss: 1.8215 center_loss: 0.4968 size_loss: 0.1462 cls_loss: 0.5806 giou_loss: 0.5979 2025/05/12 23:52:29 - mmengine - INFO - Epoch(train) [121][50/91] base_lr: 1.9844e-04 lr: 1.9844e-04 eta: 2 days, 23:36:58 time: 10.7017 data_time: 1.4588 memory: 68702 grad_norm: 1.6500 loss: 1.8257 center_loss: 0.5008 size_loss: 0.1480 cls_loss: 0.5787 giou_loss: 0.5982 2025/05/12 23:54:07 - mmengine - INFO - Epoch(train) [121][60/91] base_lr: 1.9844e-04 lr: 1.9844e-04 eta: 2 days, 23:35:08 time: 9.7164 data_time: 0.5730 memory: 68703 grad_norm: 1.7789 loss: 1.8339 center_loss: 0.5040 size_loss: 0.1485 cls_loss: 0.5794 giou_loss: 0.6020 2025/05/12 23:55:45 - mmengine - INFO - Epoch(train) [121][70/91] base_lr: 1.9844e-04 lr: 1.9844e-04 eta: 2 days, 23:33:18 time: 9.7341 data_time: 0.5668 memory: 68702 grad_norm: 1.8759 loss: 1.8332 center_loss: 0.5069 size_loss: 0.1501 cls_loss: 0.5728 giou_loss: 0.6035 2025/05/12 23:57:22 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/12 23:57:22 - mmengine - INFO - Epoch(train) [121][80/91] base_lr: 1.9844e-04 lr: 1.9844e-04 eta: 2 days, 23:31:26 time: 9.7383 data_time: 0.5673 memory: 68702 grad_norm: 1.9718 loss: 1.8513 center_loss: 0.5147 size_loss: 0.1524 cls_loss: 0.5757 giou_loss: 0.6085 2025/05/12 23:58:58 - mmengine - INFO - Epoch(train) [121][90/91] base_lr: 1.9844e-04 lr: 1.9844e-04 eta: 2 days, 23:29:32 time: 9.7097 data_time: 0.5606 memory: 68702 grad_norm: 2.0010 loss: 1.8513 center_loss: 0.5151 size_loss: 0.1526 cls_loss: 0.5754 giou_loss: 0.6083 2025/05/12 23:59:00 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 00:01:32 - mmengine - INFO - Epoch(train) [122][10/91] base_lr: 1.9764e-04 lr: 1.9764e-04 eta: 2 days, 23:29:20 time: 10.6584 data_time: 1.5713 memory: 68700 grad_norm: 1.8723 loss: 1.8536 center_loss: 0.5208 size_loss: 0.1526 cls_loss: 0.5693 giou_loss: 0.6109 2025/05/13 00:03:09 - mmengine - INFO - Epoch(train) [122][20/91] base_lr: 1.9764e-04 lr: 1.9764e-04 eta: 2 days, 23:27:27 time: 10.6363 data_time: 1.5554 memory: 68700 grad_norm: 1.8234 loss: 1.8579 center_loss: 0.5227 size_loss: 0.1529 cls_loss: 0.5708 giou_loss: 0.6114 2025/05/13 00:04:46 - mmengine - INFO - Epoch(train) [122][30/91] base_lr: 1.9764e-04 lr: 1.9764e-04 eta: 2 days, 23:25:36 time: 10.6296 data_time: 1.5585 memory: 68702 grad_norm: 1.8264 loss: 1.8522 center_loss: 0.5161 size_loss: 0.1500 cls_loss: 0.5780 giou_loss: 0.6082 2025/05/13 00:06:23 - mmengine - INFO - Epoch(train) [122][40/91] base_lr: 1.9764e-04 lr: 1.9764e-04 eta: 2 days, 23:23:44 time: 10.6175 data_time: 1.5688 memory: 68703 grad_norm: 1.7618 loss: 1.8461 center_loss: 0.5109 size_loss: 0.1499 cls_loss: 0.5795 giou_loss: 0.6058 2025/05/13 00:08:01 - mmengine - INFO - Epoch(train) [122][50/91] base_lr: 1.9764e-04 lr: 1.9764e-04 eta: 2 days, 23:21:54 time: 10.8183 data_time: 1.5919 memory: 68702 grad_norm: 1.6994 loss: 1.8544 center_loss: 0.5072 size_loss: 0.1502 cls_loss: 0.5915 giou_loss: 0.6054 2025/05/13 00:09:37 - mmengine - INFO - Epoch(train) [122][60/91] base_lr: 1.9764e-04 lr: 1.9764e-04 eta: 2 days, 23:20:03 time: 9.7057 data_time: 0.5735 memory: 68702 grad_norm: 1.7162 loss: 1.8508 center_loss: 0.5093 size_loss: 0.1496 cls_loss: 0.5873 giou_loss: 0.6045 2025/05/13 00:11:14 - mmengine - INFO - Epoch(train) [122][70/91] base_lr: 1.9764e-04 lr: 1.9764e-04 eta: 2 days, 23:18:11 time: 9.7086 data_time: 0.5889 memory: 68703 grad_norm: 1.6151 loss: 1.8399 center_loss: 0.5054 size_loss: 0.1487 cls_loss: 0.5872 giou_loss: 0.5986 2025/05/13 00:12:51 - mmengine - INFO - Epoch(train) [122][80/91] base_lr: 1.9764e-04 lr: 1.9764e-04 eta: 2 days, 23:16:19 time: 9.7024 data_time: 0.5971 memory: 68702 grad_norm: 1.6133 loss: 1.8265 center_loss: 0.5014 size_loss: 0.1489 cls_loss: 0.5806 giou_loss: 0.5956 2025/05/13 00:14:27 - mmengine - INFO - Epoch(train) [122][90/91] base_lr: 1.9764e-04 lr: 1.9764e-04 eta: 2 days, 23:14:24 time: 9.6779 data_time: 0.5831 memory: 68703 grad_norm: 1.6945 loss: 1.8394 center_loss: 0.5037 size_loss: 0.1485 cls_loss: 0.5896 giou_loss: 0.5976 2025/05/13 00:14:28 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 00:14:28 - mmengine - INFO - Saving checkpoint at 122 epochs 2025/05/13 00:15:17 - mmengine - INFO - Epoch(val) [122][10/39] eta: 0:01:30 time: 2.8356 data_time: 0.3385 memory: 15952 2025/05/13 00:15:43 - mmengine - INFO - Epoch(val) [122][20/39] eta: 0:00:54 time: 2.6864 data_time: 0.1894 memory: 13407 2025/05/13 00:16:09 - mmengine - INFO - Epoch(val) [122][30/39] eta: 0:00:24 time: 2.6802 data_time: 0.1852 memory: 13407 2025/05/13 00:16:35 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | chair | 0.5646 | 0.7251 | 0.1700 | 0.3253 | | bookshelf | 0.2869 | 0.5325 | 0.0748 | 0.1948 | | sofa | 0.6626 | 0.8454 | 0.1388 | 0.3402 | | table | 0.4161 | 0.5629 | 0.1271 | 0.2486 | | garbagebin | 0.2620 | 0.4566 | 0.0249 | 0.1226 | | curtain | 0.2072 | 0.4776 | 0.0230 | 0.1493 | | picture | 0.0171 | 0.1081 | 0.0000 | 0.0000 | | door | 0.1812 | 0.4690 | 0.0180 | 0.1370 | | cabinet | 0.2996 | 0.5242 | 0.0419 | 0.1694 | | window | 0.1683 | 0.3617 | 0.0336 | 0.1206 | | counter | 0.2921 | 0.4808 | 0.0355 | 0.1731 | | sink | 0.5555 | 0.6735 | 0.0805 | 0.2449 | | refrigerator | 0.4367 | 0.6316 | 0.2393 | 0.3684 | | bed | 0.8058 | 0.8272 | 0.3900 | 0.5432 | | desk | 0.6933 | 0.8346 | 0.2732 | 0.4724 | | toilet | 0.8609 | 0.9310 | 0.4643 | 0.5690 | | showercurtrain | 0.2836 | 0.5357 | 0.0161 | 0.1071 | | bathtub | 0.7682 | 0.8387 | 0.3187 | 0.4516 | +----------------+---------+---------+---------+---------+ | Overall | 0.4312 | 0.6009 | 0.1372 | 0.2632 | +----------------+---------+---------+---------+---------+ 2025/05/13 00:16:35 - mmengine - INFO - Epoch(val) [122][39/39] chair_AP_0.25: 0.5646 sofa_AP_0.25: 0.6626 table_AP_0.25: 0.4161 garbagebin_AP_0.25: 0.2620 bookshelf_AP_0.25: 0.2869 picture_AP_0.25: 0.0171 curtain_AP_0.25: 0.2072 door_AP_0.25: 0.1812 cabinet_AP_0.25: 0.2996 refrigerator_AP_0.25: 0.4367 counter_AP_0.25: 0.2921 sink_AP_0.25: 0.5555 window_AP_0.25: 0.1683 desk_AP_0.25: 0.6933 bed_AP_0.25: 0.8058 toilet_AP_0.25: 0.8609 showercurtrain_AP_0.25: 0.2836 bathtub_AP_0.25: 0.7682 mAP_0.25: 0.4312 chair_rec_0.25: 0.7251 sofa_rec_0.25: 0.8454 table_rec_0.25: 0.5629 garbagebin_rec_0.25: 0.4566 bookshelf_rec_0.25: 0.5325 picture_rec_0.25: 0.1081 curtain_rec_0.25: 0.4776 door_rec_0.25: 0.4690 cabinet_rec_0.25: 0.5242 refrigerator_rec_0.25: 0.6316 counter_rec_0.25: 0.4808 sink_rec_0.25: 0.6735 window_rec_0.25: 0.3617 desk_rec_0.25: 0.8346 bed_rec_0.25: 0.8272 toilet_rec_0.25: 0.9310 showercurtrain_rec_0.25: 0.5357 bathtub_rec_0.25: 0.8387 mAR_0.25: 0.6009 chair_AP_0.50: 0.1700 sofa_AP_0.50: 0.1388 table_AP_0.50: 0.1271 garbagebin_AP_0.50: 0.0249 bookshelf_AP_0.50: 0.0748 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0230 door_AP_0.50: 0.0180 cabinet_AP_0.50: 0.0419 refrigerator_AP_0.50: 0.2393 counter_AP_0.50: 0.0355 sink_AP_0.50: 0.0805 window_AP_0.50: 0.0336 desk_AP_0.50: 0.2732 bed_AP_0.50: 0.3900 toilet_AP_0.50: 0.4643 showercurtrain_AP_0.50: 0.0161 bathtub_AP_0.50: 0.3187 mAP_0.50: 0.1372 chair_rec_0.50: 0.3253 sofa_rec_0.50: 0.3402 table_rec_0.50: 0.2486 garbagebin_rec_0.50: 0.1226 bookshelf_rec_0.50: 0.1948 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.1493 door_rec_0.50: 0.1370 cabinet_rec_0.50: 0.1694 refrigerator_rec_0.50: 0.3684 counter_rec_0.50: 0.1731 sink_rec_0.50: 0.2449 window_rec_0.50: 0.1206 desk_rec_0.50: 0.4724 bed_rec_0.50: 0.5432 toilet_rec_0.50: 0.5690 showercurtrain_rec_0.50: 0.1071 bathtub_rec_0.50: 0.4516 mAR_0.50: 0.2632 data_time: 0.2126 time: 2.6988 2025/05/13 00:19:09 - mmengine - INFO - Epoch(train) [123][10/91] base_lr: 1.9684e-04 lr: 1.9684e-04 eta: 2 days, 23:14:13 time: 10.6378 data_time: 1.5979 memory: 68702 grad_norm: 1.7420 loss: 1.8399 center_loss: 0.5073 size_loss: 0.1491 cls_loss: 0.5860 giou_loss: 0.5974 2025/05/13 00:20:47 - mmengine - INFO - Epoch(train) [123][20/91] base_lr: 1.9684e-04 lr: 1.9684e-04 eta: 2 days, 23:12:25 time: 10.6717 data_time: 1.5947 memory: 68702 grad_norm: 1.6740 loss: 1.8509 center_loss: 0.5082 size_loss: 0.1511 cls_loss: 0.5932 giou_loss: 0.5985 2025/05/13 00:22:24 - mmengine - INFO - Epoch(train) [123][30/91] base_lr: 1.9684e-04 lr: 1.9684e-04 eta: 2 days, 23:10:34 time: 10.6754 data_time: 1.5842 memory: 68702 grad_norm: 1.6753 loss: 1.8447 center_loss: 0.5056 size_loss: 0.1506 cls_loss: 0.5873 giou_loss: 0.6012 2025/05/13 00:24:02 - mmengine - INFO - Epoch(train) [123][40/91] base_lr: 1.9684e-04 lr: 1.9684e-04 eta: 2 days, 23:08:42 time: 10.6802 data_time: 1.5799 memory: 68703 grad_norm: 1.7047 loss: 1.8636 center_loss: 0.5146 size_loss: 0.1508 cls_loss: 0.5903 giou_loss: 0.6079 2025/05/13 00:25:39 - mmengine - INFO - Epoch(train) [123][50/91] base_lr: 1.9684e-04 lr: 1.9684e-04 eta: 2 days, 23:06:52 time: 10.8784 data_time: 1.5923 memory: 68702 grad_norm: 1.5434 loss: 1.8421 center_loss: 0.5046 size_loss: 0.1490 cls_loss: 0.5856 giou_loss: 0.6029 2025/05/13 00:27:17 - mmengine - INFO - Epoch(train) [123][60/91] base_lr: 1.9684e-04 lr: 1.9684e-04 eta: 2 days, 23:05:02 time: 9.7496 data_time: 0.5609 memory: 68702 grad_norm: 1.5250 loss: 1.8369 center_loss: 0.5013 size_loss: 0.1477 cls_loss: 0.5866 giou_loss: 0.6014 2025/05/13 00:28:53 - mmengine - INFO - Epoch(train) [123][70/91] base_lr: 1.9684e-04 lr: 1.9684e-04 eta: 2 days, 23:03:10 time: 9.7200 data_time: 0.5668 memory: 68703 grad_norm: 1.6188 loss: 1.8328 center_loss: 0.4994 size_loss: 0.1459 cls_loss: 0.5879 giou_loss: 0.5996 2025/05/13 00:30:30 - mmengine - INFO - Epoch(train) [123][80/91] base_lr: 1.9684e-04 lr: 1.9684e-04 eta: 2 days, 23:01:18 time: 9.7117 data_time: 0.5829 memory: 68702 grad_norm: 1.6848 loss: 1.8359 center_loss: 0.4986 size_loss: 0.1458 cls_loss: 0.5921 giou_loss: 0.5994 2025/05/13 00:32:05 - mmengine - INFO - Epoch(train) [123][90/91] base_lr: 1.9684e-04 lr: 1.9684e-04 eta: 2 days, 22:59:22 time: 9.6722 data_time: 0.5750 memory: 68702 grad_norm: 1.5609 loss: 1.8260 center_loss: 0.4939 size_loss: 0.1457 cls_loss: 0.5887 giou_loss: 0.5977 2025/05/13 00:32:07 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 00:34:37 - mmengine - INFO - Epoch(train) [124][10/91] base_lr: 1.9604e-04 lr: 1.9604e-04 eta: 2 days, 22:59:01 time: 10.5539 data_time: 1.5115 memory: 68702 grad_norm: 1.6750 loss: 1.8458 center_loss: 0.5008 size_loss: 0.1475 cls_loss: 0.5970 giou_loss: 0.6004 2025/05/13 00:36:14 - mmengine - INFO - Epoch(train) [124][20/91] base_lr: 1.9604e-04 lr: 1.9604e-04 eta: 2 days, 22:57:10 time: 10.5587 data_time: 1.5031 memory: 68703 grad_norm: 1.7057 loss: 1.8614 center_loss: 0.5121 size_loss: 0.1513 cls_loss: 0.5934 giou_loss: 0.6046 2025/05/13 00:37:50 - mmengine - INFO - Epoch(train) [124][30/91] base_lr: 1.9604e-04 lr: 1.9604e-04 eta: 2 days, 22:55:16 time: 10.5294 data_time: 1.4963 memory: 68703 grad_norm: 1.6333 loss: 1.8324 center_loss: 0.5011 size_loss: 0.1487 cls_loss: 0.5826 giou_loss: 0.6000 2025/05/13 00:39:27 - mmengine - INFO - Epoch(train) [124][40/91] base_lr: 1.9604e-04 lr: 1.9604e-04 eta: 2 days, 22:53:24 time: 10.5367 data_time: 1.4912 memory: 68702 grad_norm: 1.5576 loss: 1.8558 center_loss: 0.5128 size_loss: 0.1513 cls_loss: 0.5881 giou_loss: 0.6037 2025/05/13 00:41:03 - mmengine - INFO - Epoch(train) [124][50/91] base_lr: 1.9604e-04 lr: 1.9604e-04 eta: 2 days, 22:51:32 time: 10.7242 data_time: 1.5083 memory: 68700 grad_norm: 1.5879 loss: 1.8505 center_loss: 0.5129 size_loss: 0.1525 cls_loss: 0.5835 giou_loss: 0.6016 2025/05/13 00:42:40 - mmengine - INFO - Epoch(train) [124][60/91] base_lr: 1.9604e-04 lr: 1.9604e-04 eta: 2 days, 22:49:41 time: 9.6699 data_time: 0.5633 memory: 68702 grad_norm: 1.4780 loss: 1.8536 center_loss: 0.5153 size_loss: 0.1520 cls_loss: 0.5857 giou_loss: 0.6006 2025/05/13 00:44:17 - mmengine - INFO - Epoch(train) [124][70/91] base_lr: 1.9604e-04 lr: 1.9604e-04 eta: 2 days, 22:47:48 time: 9.6514 data_time: 0.5765 memory: 68702 grad_norm: 1.4422 loss: 1.8203 center_loss: 0.4992 size_loss: 0.1461 cls_loss: 0.5806 giou_loss: 0.5944 2025/05/13 00:45:53 - mmengine - INFO - Epoch(train) [124][80/91] base_lr: 1.9604e-04 lr: 1.9604e-04 eta: 2 days, 22:45:56 time: 9.6690 data_time: 0.5823 memory: 68702 grad_norm: 1.4611 loss: 1.8274 center_loss: 0.4997 size_loss: 0.1474 cls_loss: 0.5847 giou_loss: 0.5956 2025/05/13 00:47:28 - mmengine - INFO - Epoch(train) [124][90/91] base_lr: 1.9604e-04 lr: 1.9604e-04 eta: 2 days, 22:44:01 time: 9.6366 data_time: 0.5694 memory: 68703 grad_norm: 1.5863 loss: 1.8068 center_loss: 0.4919 size_loss: 0.1452 cls_loss: 0.5776 giou_loss: 0.5922 2025/05/13 00:47:30 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 00:47:30 - mmengine - INFO - Saving checkpoint at 124 epochs 2025/05/13 00:48:24 - mmengine - INFO - Epoch(val) [124][10/39] eta: 0:01:34 time: 2.8118 data_time: 0.3222 memory: 15952 2025/05/13 00:48:50 - mmengine - INFO - Epoch(val) [124][20/39] eta: 0:00:55 time: 2.7220 data_time: 0.2307 memory: 13407 2025/05/13 00:49:16 - mmengine - INFO - Epoch(val) [124][30/39] eta: 0:00:25 time: 2.7211 data_time: 0.2224 memory: 13407 2025/05/13 00:49:42 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.1849 | 0.4113 | 0.0185 | 0.1189 | | table | 0.4500 | 0.6086 | 0.1416 | 0.3000 | | chair | 0.5563 | 0.7047 | 0.1461 | 0.3114 | | sofa | 0.7112 | 0.8557 | 0.1649 | 0.3814 | | curtain | 0.1895 | 0.4627 | 0.0465 | 0.0896 | | picture | 0.0187 | 0.0991 | 0.0000 | 0.0000 | | bookshelf | 0.2404 | 0.5584 | 0.0660 | 0.2078 | | cabinet | 0.2619 | 0.4973 | 0.0500 | 0.1774 | | window | 0.1238 | 0.3440 | 0.0066 | 0.0674 | | door | 0.1255 | 0.4261 | 0.0132 | 0.1092 | | counter | 0.2211 | 0.4231 | 0.0207 | 0.1154 | | refrigerator | 0.4553 | 0.6491 | 0.1528 | 0.2982 | | sink | 0.4642 | 0.6224 | 0.1142 | 0.2551 | | bed | 0.8234 | 0.8519 | 0.4128 | 0.5679 | | desk | 0.7202 | 0.8425 | 0.2678 | 0.4331 | | toilet | 0.8682 | 0.9310 | 0.4543 | 0.5345 | | bathtub | 0.7772 | 0.8387 | 0.2236 | 0.3871 | | showercurtrain | 0.3276 | 0.6429 | 0.0171 | 0.1071 | +----------------+---------+---------+---------+---------+ | Overall | 0.4177 | 0.5983 | 0.1287 | 0.2479 | +----------------+---------+---------+---------+---------+ 2025/05/13 00:49:42 - mmengine - INFO - Epoch(val) [124][39/39] chair_AP_0.25: 0.5563 sofa_AP_0.25: 0.7112 table_AP_0.25: 0.4500 garbagebin_AP_0.25: 0.1849 bookshelf_AP_0.25: 0.2404 picture_AP_0.25: 0.0187 curtain_AP_0.25: 0.1895 door_AP_0.25: 0.1255 cabinet_AP_0.25: 0.2619 refrigerator_AP_0.25: 0.4553 counter_AP_0.25: 0.2211 sink_AP_0.25: 0.4642 window_AP_0.25: 0.1238 desk_AP_0.25: 0.7202 bed_AP_0.25: 0.8234 toilet_AP_0.25: 0.8682 showercurtrain_AP_0.25: 0.3276 bathtub_AP_0.25: 0.7772 mAP_0.25: 0.4177 chair_rec_0.25: 0.7047 sofa_rec_0.25: 0.8557 table_rec_0.25: 0.6086 garbagebin_rec_0.25: 0.4113 bookshelf_rec_0.25: 0.5584 picture_rec_0.25: 0.0991 curtain_rec_0.25: 0.4627 door_rec_0.25: 0.4261 cabinet_rec_0.25: 0.4973 refrigerator_rec_0.25: 0.6491 counter_rec_0.25: 0.4231 sink_rec_0.25: 0.6224 window_rec_0.25: 0.3440 desk_rec_0.25: 0.8425 bed_rec_0.25: 0.8519 toilet_rec_0.25: 0.9310 showercurtrain_rec_0.25: 0.6429 bathtub_rec_0.25: 0.8387 mAR_0.25: 0.5983 chair_AP_0.50: 0.1461 sofa_AP_0.50: 0.1649 table_AP_0.50: 0.1416 garbagebin_AP_0.50: 0.0185 bookshelf_AP_0.50: 0.0660 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0465 door_AP_0.50: 0.0132 cabinet_AP_0.50: 0.0500 refrigerator_AP_0.50: 0.1528 counter_AP_0.50: 0.0207 sink_AP_0.50: 0.1142 window_AP_0.50: 0.0066 desk_AP_0.50: 0.2678 bed_AP_0.50: 0.4128 toilet_AP_0.50: 0.4543 showercurtrain_AP_0.50: 0.0171 bathtub_AP_0.50: 0.2236 mAP_0.50: 0.1287 chair_rec_0.50: 0.3114 sofa_rec_0.50: 0.3814 table_rec_0.50: 0.3000 garbagebin_rec_0.50: 0.1189 bookshelf_rec_0.50: 0.2078 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.0896 door_rec_0.50: 0.1092 cabinet_rec_0.50: 0.1774 refrigerator_rec_0.50: 0.2982 counter_rec_0.50: 0.1154 sink_rec_0.50: 0.2551 window_rec_0.50: 0.0674 desk_rec_0.50: 0.4331 bed_rec_0.50: 0.5679 toilet_rec_0.50: 0.5345 showercurtrain_rec_0.50: 0.1071 bathtub_rec_0.50: 0.3871 mAR_0.50: 0.2479 data_time: 0.2591 time: 2.7567 2025/05/13 00:52:12 - mmengine - INFO - Epoch(train) [125][10/91] base_lr: 1.9523e-04 lr: 1.9523e-04 eta: 2 days, 22:43:39 time: 10.5427 data_time: 1.4760 memory: 68702 grad_norm: 1.6410 loss: 1.8174 center_loss: 0.4990 size_loss: 0.1466 cls_loss: 0.5731 giou_loss: 0.5986 2025/05/13 00:53:48 - mmengine - INFO - Epoch(train) [125][20/91] base_lr: 1.9523e-04 lr: 1.9523e-04 eta: 2 days, 22:41:46 time: 10.5317 data_time: 1.4704 memory: 68703 grad_norm: 1.6707 loss: 1.8060 center_loss: 0.4943 size_loss: 0.1452 cls_loss: 0.5698 giou_loss: 0.5968 2025/05/13 00:55:25 - mmengine - INFO - Epoch(train) [125][30/91] base_lr: 1.9523e-04 lr: 1.9523e-04 eta: 2 days, 22:39:54 time: 10.5289 data_time: 1.4577 memory: 68703 grad_norm: 1.7031 loss: 1.8161 center_loss: 0.4944 size_loss: 0.1467 cls_loss: 0.5785 giou_loss: 0.5965 2025/05/13 00:57:02 - mmengine - INFO - Epoch(train) [125][40/91] base_lr: 1.9523e-04 lr: 1.9523e-04 eta: 2 days, 22:38:04 time: 10.5510 data_time: 1.4553 memory: 68702 grad_norm: 1.7477 loss: 1.8260 center_loss: 0.5021 size_loss: 0.1481 cls_loss: 0.5765 giou_loss: 0.5994 2025/05/13 00:58:39 - mmengine - INFO - Epoch(train) [125][50/91] base_lr: 1.9523e-04 lr: 1.9523e-04 eta: 2 days, 22:36:12 time: 10.7388 data_time: 1.4666 memory: 68702 grad_norm: 1.6339 loss: 1.8371 center_loss: 0.5116 size_loss: 0.1483 cls_loss: 0.5769 giou_loss: 0.6003 2025/05/13 01:00:16 - mmengine - INFO - Epoch(train) [125][60/91] base_lr: 1.9523e-04 lr: 1.9523e-04 eta: 2 days, 22:34:20 time: 9.6728 data_time: 0.5453 memory: 68702 grad_norm: 1.5987 loss: 1.8160 center_loss: 0.5008 size_loss: 0.1456 cls_loss: 0.5749 giou_loss: 0.5947 2025/05/13 01:01:52 - mmengine - INFO - Epoch(train) [125][70/91] base_lr: 1.9523e-04 lr: 1.9523e-04 eta: 2 days, 22:32:27 time: 9.6727 data_time: 0.5520 memory: 68702 grad_norm: 1.8160 loss: 1.8209 center_loss: 0.5035 size_loss: 0.1483 cls_loss: 0.5754 giou_loss: 0.5937 2025/05/13 01:03:28 - mmengine - INFO - Epoch(train) [125][80/91] base_lr: 1.9523e-04 lr: 1.9523e-04 eta: 2 days, 22:30:34 time: 9.6605 data_time: 0.5585 memory: 68703 grad_norm: 1.7961 loss: 1.8330 center_loss: 0.5120 size_loss: 0.1504 cls_loss: 0.5732 giou_loss: 0.5974 2025/05/13 01:05:03 - mmengine - INFO - Epoch(train) [125][90/91] base_lr: 1.9523e-04 lr: 1.9523e-04 eta: 2 days, 22:28:39 time: 9.6204 data_time: 0.5476 memory: 68702 grad_norm: 1.7093 loss: 1.8418 center_loss: 0.5137 size_loss: 0.1505 cls_loss: 0.5792 giou_loss: 0.5984 2025/05/13 01:05:05 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 01:07:34 - mmengine - INFO - Epoch(train) [126][10/91] base_lr: 1.9441e-04 lr: 1.9441e-04 eta: 2 days, 22:28:13 time: 10.4922 data_time: 1.3822 memory: 68703 grad_norm: 1.7471 loss: 1.8381 center_loss: 0.5089 size_loss: 0.1508 cls_loss: 0.5783 giou_loss: 0.6001 2025/05/13 01:09:10 - mmengine - INFO - Epoch(train) [126][20/91] base_lr: 1.9441e-04 lr: 1.9441e-04 eta: 2 days, 22:26:20 time: 10.4894 data_time: 1.3943 memory: 68703 grad_norm: 1.7413 loss: 1.8604 center_loss: 0.5195 size_loss: 0.1518 cls_loss: 0.5866 giou_loss: 0.6026 2025/05/13 01:10:47 - mmengine - INFO - Epoch(train) [126][30/91] base_lr: 1.9441e-04 lr: 1.9441e-04 eta: 2 days, 22:24:28 time: 10.4979 data_time: 1.3908 memory: 68702 grad_norm: 1.6187 loss: 1.8641 center_loss: 0.5181 size_loss: 0.1501 cls_loss: 0.5901 giou_loss: 0.6058 2025/05/13 01:12:23 - mmengine - INFO - Epoch(train) [126][40/91] base_lr: 1.9441e-04 lr: 1.9441e-04 eta: 2 days, 22:22:36 time: 10.4984 data_time: 1.3867 memory: 68702 grad_norm: 1.7353 loss: 1.8730 center_loss: 0.5204 size_loss: 0.1511 cls_loss: 0.5946 giou_loss: 0.6069 2025/05/13 01:14:00 - mmengine - INFO - Epoch(train) [126][50/91] base_lr: 1.9441e-04 lr: 1.9441e-04 eta: 2 days, 22:20:44 time: 10.6878 data_time: 1.3984 memory: 68703 grad_norm: 1.7436 loss: 1.8470 center_loss: 0.5067 size_loss: 0.1478 cls_loss: 0.5937 giou_loss: 0.5987 2025/05/13 01:15:37 - mmengine - INFO - Epoch(train) [126][60/91] base_lr: 1.9441e-04 lr: 1.9441e-04 eta: 2 days, 22:18:54 time: 9.6706 data_time: 0.5662 memory: 68702 grad_norm: 1.7237 loss: 1.8586 center_loss: 0.5162 size_loss: 0.1480 cls_loss: 0.5963 giou_loss: 0.5980 2025/05/13 01:17:14 - mmengine - INFO - Epoch(train) [126][70/91] base_lr: 1.9441e-04 lr: 1.9441e-04 eta: 2 days, 22:17:02 time: 9.6747 data_time: 0.5527 memory: 68700 grad_norm: 1.7319 loss: 1.8365 center_loss: 0.5021 size_loss: 0.1464 cls_loss: 0.5940 giou_loss: 0.5940 2025/05/13 01:18:50 - mmengine - INFO - Epoch(train) [126][80/91] base_lr: 1.9441e-04 lr: 1.9441e-04 eta: 2 days, 22:15:09 time: 9.6638 data_time: 0.5588 memory: 68702 grad_norm: 1.7919 loss: 1.8124 center_loss: 0.4939 size_loss: 0.1444 cls_loss: 0.5842 giou_loss: 0.5899 2025/05/13 01:20:24 - mmengine - INFO - Epoch(train) [126][90/91] base_lr: 1.9441e-04 lr: 1.9441e-04 eta: 2 days, 22:13:13 time: 9.6256 data_time: 0.5578 memory: 68702 grad_norm: 1.6542 loss: 1.7890 center_loss: 0.4883 size_loss: 0.1416 cls_loss: 0.5727 giou_loss: 0.5864 2025/05/13 01:20:26 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 01:20:26 - mmengine - INFO - Saving checkpoint at 126 epochs 2025/05/13 01:21:23 - mmengine - INFO - Epoch(val) [126][10/39] eta: 0:01:36 time: 2.8739 data_time: 0.3696 memory: 15952 2025/05/13 01:21:49 - mmengine - INFO - Epoch(val) [126][20/39] eta: 0:00:56 time: 2.7453 data_time: 0.2342 memory: 13407 2025/05/13 01:22:15 - mmengine - INFO - Epoch(val) [126][30/39] eta: 0:00:25 time: 2.7432 data_time: 0.2251 memory: 13407 2025/05/13 01:22:41 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.2379 | 0.4264 | 0.0269 | 0.1189 | | table | 0.4371 | 0.5971 | 0.1203 | 0.2686 | | chair | 0.5537 | 0.7091 | 0.1331 | 0.3070 | | sofa | 0.6485 | 0.8041 | 0.1823 | 0.3814 | | bookshelf | 0.2912 | 0.5455 | 0.0920 | 0.1948 | | curtain | 0.1952 | 0.4478 | 0.0366 | 0.1045 | | picture | 0.0218 | 0.1261 | 0.0002 | 0.0090 | | door | 0.1306 | 0.4325 | 0.0140 | 0.1199 | | window | 0.1466 | 0.3333 | 0.0131 | 0.0816 | | cabinet | 0.2795 | 0.4812 | 0.0439 | 0.1667 | | counter | 0.2550 | 0.4231 | 0.0246 | 0.1154 | | refrigerator | 0.4829 | 0.6491 | 0.1864 | 0.3158 | | sink | 0.4828 | 0.6327 | 0.0839 | 0.2449 | | bed | 0.8280 | 0.8519 | 0.3358 | 0.5062 | | desk | 0.7037 | 0.8504 | 0.1676 | 0.3701 | | toilet | 0.8328 | 0.9138 | 0.4446 | 0.5172 | | showercurtrain | 0.2257 | 0.5357 | 0.0045 | 0.0357 | | bathtub | 0.8243 | 0.9032 | 0.2373 | 0.4194 | +----------------+---------+---------+---------+---------+ | Overall | 0.4209 | 0.5924 | 0.1193 | 0.2376 | +----------------+---------+---------+---------+---------+ 2025/05/13 01:22:42 - mmengine - INFO - Epoch(val) [126][39/39] chair_AP_0.25: 0.5537 sofa_AP_0.25: 0.6485 table_AP_0.25: 0.4371 garbagebin_AP_0.25: 0.2379 bookshelf_AP_0.25: 0.2912 picture_AP_0.25: 0.0218 curtain_AP_0.25: 0.1952 door_AP_0.25: 0.1306 cabinet_AP_0.25: 0.2795 refrigerator_AP_0.25: 0.4829 counter_AP_0.25: 0.2550 sink_AP_0.25: 0.4828 window_AP_0.25: 0.1466 desk_AP_0.25: 0.7037 bed_AP_0.25: 0.8280 toilet_AP_0.25: 0.8328 showercurtrain_AP_0.25: 0.2257 bathtub_AP_0.25: 0.8243 mAP_0.25: 0.4209 chair_rec_0.25: 0.7091 sofa_rec_0.25: 0.8041 table_rec_0.25: 0.5971 garbagebin_rec_0.25: 0.4264 bookshelf_rec_0.25: 0.5455 picture_rec_0.25: 0.1261 curtain_rec_0.25: 0.4478 door_rec_0.25: 0.4325 cabinet_rec_0.25: 0.4812 refrigerator_rec_0.25: 0.6491 counter_rec_0.25: 0.4231 sink_rec_0.25: 0.6327 window_rec_0.25: 0.3333 desk_rec_0.25: 0.8504 bed_rec_0.25: 0.8519 toilet_rec_0.25: 0.9138 showercurtrain_rec_0.25: 0.5357 bathtub_rec_0.25: 0.9032 mAR_0.25: 0.5924 chair_AP_0.50: 0.1331 sofa_AP_0.50: 0.1823 table_AP_0.50: 0.1203 garbagebin_AP_0.50: 0.0269 bookshelf_AP_0.50: 0.0920 picture_AP_0.50: 0.0002 curtain_AP_0.50: 0.0366 door_AP_0.50: 0.0140 cabinet_AP_0.50: 0.0439 refrigerator_AP_0.50: 0.1864 counter_AP_0.50: 0.0246 sink_AP_0.50: 0.0839 window_AP_0.50: 0.0131 desk_AP_0.50: 0.1676 bed_AP_0.50: 0.3358 toilet_AP_0.50: 0.4446 showercurtrain_AP_0.50: 0.0045 bathtub_AP_0.50: 0.2373 mAP_0.50: 0.1193 chair_rec_0.50: 0.3070 sofa_rec_0.50: 0.3814 table_rec_0.50: 0.2686 garbagebin_rec_0.50: 0.1189 bookshelf_rec_0.50: 0.1948 picture_rec_0.50: 0.0090 curtain_rec_0.50: 0.1045 door_rec_0.50: 0.1199 cabinet_rec_0.50: 0.1667 refrigerator_rec_0.50: 0.3158 counter_rec_0.50: 0.1154 sink_rec_0.50: 0.2449 window_rec_0.50: 0.0816 desk_rec_0.50: 0.3701 bed_rec_0.50: 0.5062 toilet_rec_0.50: 0.5172 showercurtrain_rec_0.50: 0.0357 bathtub_rec_0.50: 0.4194 mAR_0.50: 0.2376 data_time: 0.2613 time: 2.7740 2025/05/13 01:25:16 - mmengine - INFO - Epoch(train) [127][10/91] base_lr: 1.9360e-04 lr: 1.9360e-04 eta: 2 days, 22:12:58 time: 10.6154 data_time: 1.4484 memory: 68703 grad_norm: 1.6860 loss: 1.7933 center_loss: 0.4856 size_loss: 0.1518 cls_loss: 0.5674 giou_loss: 0.5885 2025/05/13 01:26:51 - mmengine - INFO - Epoch(train) [127][20/91] base_lr: 1.9360e-04 lr: 1.9360e-04 eta: 2 days, 22:11:03 time: 10.5761 data_time: 1.4316 memory: 68702 grad_norm: 1.6882 loss: 1.7912 center_loss: 0.4859 size_loss: 0.1534 cls_loss: 0.5643 giou_loss: 0.5875 2025/05/13 01:28:27 - mmengine - INFO - Epoch(train) [127][30/91] base_lr: 1.9360e-04 lr: 1.9360e-04 eta: 2 days, 22:09:11 time: 10.5688 data_time: 1.4363 memory: 68702 grad_norm: 1.7859 loss: 1.8042 center_loss: 0.4927 size_loss: 0.1549 cls_loss: 0.5651 giou_loss: 0.5914 2025/05/13 01:30:04 - mmengine - INFO - Epoch(train) [127][40/91] base_lr: 1.9360e-04 lr: 1.9360e-04 eta: 2 days, 22:07:18 time: 10.5744 data_time: 1.4223 memory: 68702 grad_norm: 1.6825 loss: 1.8236 center_loss: 0.5019 size_loss: 0.1575 cls_loss: 0.5681 giou_loss: 0.5961 2025/05/13 01:31:40 - mmengine - INFO - Epoch(train) [127][50/91] base_lr: 1.9360e-04 lr: 1.9360e-04 eta: 2 days, 22:05:27 time: 10.7731 data_time: 1.4306 memory: 68702 grad_norm: 1.6548 loss: 1.8112 center_loss: 0.4959 size_loss: 0.1482 cls_loss: 0.5696 giou_loss: 0.5974 2025/05/13 01:33:17 - mmengine - INFO - Epoch(train) [127][60/91] base_lr: 1.9360e-04 lr: 1.9360e-04 eta: 2 days, 22:03:36 time: 9.6293 data_time: 0.5320 memory: 68700 grad_norm: 1.6603 loss: 1.8142 center_loss: 0.4953 size_loss: 0.1478 cls_loss: 0.5753 giou_loss: 0.5958 2025/05/13 01:34:54 - mmengine - INFO - Epoch(train) [127][70/91] base_lr: 1.9360e-04 lr: 1.9360e-04 eta: 2 days, 22:01:45 time: 9.6666 data_time: 0.5516 memory: 68703 grad_norm: 1.7394 loss: 1.8252 center_loss: 0.5000 size_loss: 0.1465 cls_loss: 0.5808 giou_loss: 0.5979 2025/05/13 01:36:31 - mmengine - INFO - Epoch(train) [127][80/91] base_lr: 1.9360e-04 lr: 1.9360e-04 eta: 2 days, 21:59:53 time: 9.6673 data_time: 0.5427 memory: 68703 grad_norm: 1.6837 loss: 1.8183 center_loss: 0.4978 size_loss: 0.1450 cls_loss: 0.5778 giou_loss: 0.5976 2025/05/13 01:38:06 - mmengine - INFO - Epoch(train) [127][90/91] base_lr: 1.9360e-04 lr: 1.9360e-04 eta: 2 days, 21:57:58 time: 9.6433 data_time: 0.5380 memory: 68703 grad_norm: 1.6533 loss: 1.8035 center_loss: 0.4902 size_loss: 0.1437 cls_loss: 0.5782 giou_loss: 0.5914 2025/05/13 01:38:08 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 01:40:33 - mmengine - INFO - Epoch(train) [128][10/91] base_lr: 1.9277e-04 lr: 1.9277e-04 eta: 2 days, 21:57:23 time: 10.4581 data_time: 1.3757 memory: 68701 grad_norm: 1.6626 loss: 1.7985 center_loss: 0.4897 size_loss: 0.1422 cls_loss: 0.5787 giou_loss: 0.5879 2025/05/13 01:42:11 - mmengine - INFO - Epoch(train) [128][20/91] base_lr: 1.9277e-04 lr: 1.9277e-04 eta: 2 days, 21:55:35 time: 10.4850 data_time: 1.3713 memory: 68702 grad_norm: 1.7324 loss: 1.8041 center_loss: 0.4994 size_loss: 0.1440 cls_loss: 0.5715 giou_loss: 0.5892 2025/05/13 01:43:49 - mmengine - INFO - Epoch(train) [128][30/91] base_lr: 1.9277e-04 lr: 1.9277e-04 eta: 2 days, 21:53:46 time: 10.5041 data_time: 1.3805 memory: 68702 grad_norm: 1.7086 loss: 1.7930 center_loss: 0.4931 size_loss: 0.1432 cls_loss: 0.5688 giou_loss: 0.5879 2025/05/13 01:45:27 - mmengine - INFO - Epoch(train) [128][40/91] base_lr: 1.9277e-04 lr: 1.9277e-04 eta: 2 days, 21:51:56 time: 10.5258 data_time: 1.3942 memory: 68702 grad_norm: 1.7133 loss: 1.8158 center_loss: 0.5069 size_loss: 0.1467 cls_loss: 0.5710 giou_loss: 0.5913 2025/05/13 01:47:05 - mmengine - INFO - Epoch(train) [128][50/91] base_lr: 1.9277e-04 lr: 1.9277e-04 eta: 2 days, 21:50:08 time: 10.7511 data_time: 1.4289 memory: 68700 grad_norm: 1.6669 loss: 1.8341 center_loss: 0.5166 size_loss: 0.1484 cls_loss: 0.5726 giou_loss: 0.5965 2025/05/13 01:48:43 - mmengine - INFO - Epoch(train) [128][60/91] base_lr: 1.9277e-04 lr: 1.9277e-04 eta: 2 days, 21:48:19 time: 9.7936 data_time: 0.5932 memory: 68702 grad_norm: 1.6658 loss: 1.8395 center_loss: 0.5181 size_loss: 0.1500 cls_loss: 0.5708 giou_loss: 0.6007 2025/05/13 01:50:19 - mmengine - INFO - Epoch(train) [128][70/91] base_lr: 1.9277e-04 lr: 1.9277e-04 eta: 2 days, 21:46:27 time: 9.7599 data_time: 0.5973 memory: 68702 grad_norm: 1.5944 loss: 1.8355 center_loss: 0.5105 size_loss: 0.1488 cls_loss: 0.5747 giou_loss: 0.6014 2025/05/13 01:51:56 - mmengine - INFO - Epoch(train) [128][80/91] base_lr: 1.9277e-04 lr: 1.9277e-04 eta: 2 days, 21:44:35 time: 9.7300 data_time: 0.5901 memory: 68702 grad_norm: 1.5289 loss: 1.8367 center_loss: 0.5162 size_loss: 0.1490 cls_loss: 0.5686 giou_loss: 0.6029 2025/05/13 01:53:31 - mmengine - INFO - Epoch(train) [128][90/91] base_lr: 1.9277e-04 lr: 1.9277e-04 eta: 2 days, 21:42:41 time: 9.6851 data_time: 0.5737 memory: 68703 grad_norm: 1.4753 loss: 1.8238 center_loss: 0.5063 size_loss: 0.1467 cls_loss: 0.5695 giou_loss: 0.6013 2025/05/13 01:53:33 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 01:53:33 - mmengine - INFO - Saving checkpoint at 128 epochs 2025/05/13 01:54:27 - mmengine - INFO - Epoch(val) [128][10/39] eta: 0:01:36 time: 2.8864 data_time: 0.3780 memory: 15952 2025/05/13 01:54:53 - mmengine - INFO - Epoch(val) [128][20/39] eta: 0:00:56 time: 2.7328 data_time: 0.2308 memory: 13407 2025/05/13 01:55:19 - mmengine - INFO - Epoch(val) [128][30/39] eta: 0:00:25 time: 2.7263 data_time: 0.2291 memory: 13407 2025/05/13 01:55:46 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.2729 | 0.4679 | 0.0394 | 0.1509 | | table | 0.4608 | 0.5971 | 0.1767 | 0.3029 | | sofa | 0.7299 | 0.8454 | 0.2504 | 0.4227 | | chair | 0.5930 | 0.7266 | 0.1682 | 0.3268 | | curtain | 0.2294 | 0.4627 | 0.0562 | 0.1493 | | picture | 0.0207 | 0.1171 | 0.0012 | 0.0315 | | bookshelf | 0.2423 | 0.5974 | 0.0591 | 0.2208 | | door | 0.1326 | 0.4433 | 0.0201 | 0.1370 | | window | 0.1509 | 0.3475 | 0.0113 | 0.0887 | | cabinet | 0.2909 | 0.5108 | 0.0698 | 0.1909 | | counter | 0.2657 | 0.5192 | 0.0216 | 0.1346 | | refrigerator | 0.5382 | 0.6316 | 0.1402 | 0.2632 | | sink | 0.5100 | 0.6531 | 0.1014 | 0.2551 | | desk | 0.7003 | 0.8189 | 0.2088 | 0.4409 | | bed | 0.8071 | 0.8272 | 0.3302 | 0.4938 | | toilet | 0.8726 | 0.9310 | 0.4406 | 0.5345 | | bathtub | 0.7656 | 0.7742 | 0.2111 | 0.3871 | | showercurtrain | 0.3038 | 0.6071 | 0.0372 | 0.1786 | +----------------+---------+---------+---------+---------+ | Overall | 0.4381 | 0.6043 | 0.1302 | 0.2616 | +----------------+---------+---------+---------+---------+ 2025/05/13 01:55:46 - mmengine - INFO - Epoch(val) [128][39/39] chair_AP_0.25: 0.5930 sofa_AP_0.25: 0.7299 table_AP_0.25: 0.4608 garbagebin_AP_0.25: 0.2729 bookshelf_AP_0.25: 0.2423 picture_AP_0.25: 0.0207 curtain_AP_0.25: 0.2294 door_AP_0.25: 0.1326 cabinet_AP_0.25: 0.2909 refrigerator_AP_0.25: 0.5382 counter_AP_0.25: 0.2657 sink_AP_0.25: 0.5100 window_AP_0.25: 0.1509 desk_AP_0.25: 0.7003 bed_AP_0.25: 0.8071 toilet_AP_0.25: 0.8726 showercurtrain_AP_0.25: 0.3038 bathtub_AP_0.25: 0.7656 mAP_0.25: 0.4381 chair_rec_0.25: 0.7266 sofa_rec_0.25: 0.8454 table_rec_0.25: 0.5971 garbagebin_rec_0.25: 0.4679 bookshelf_rec_0.25: 0.5974 picture_rec_0.25: 0.1171 curtain_rec_0.25: 0.4627 door_rec_0.25: 0.4433 cabinet_rec_0.25: 0.5108 refrigerator_rec_0.25: 0.6316 counter_rec_0.25: 0.5192 sink_rec_0.25: 0.6531 window_rec_0.25: 0.3475 desk_rec_0.25: 0.8189 bed_rec_0.25: 0.8272 toilet_rec_0.25: 0.9310 showercurtrain_rec_0.25: 0.6071 bathtub_rec_0.25: 0.7742 mAR_0.25: 0.6043 chair_AP_0.50: 0.1682 sofa_AP_0.50: 0.2504 table_AP_0.50: 0.1767 garbagebin_AP_0.50: 0.0394 bookshelf_AP_0.50: 0.0591 picture_AP_0.50: 0.0012 curtain_AP_0.50: 0.0562 door_AP_0.50: 0.0201 cabinet_AP_0.50: 0.0698 refrigerator_AP_0.50: 0.1402 counter_AP_0.50: 0.0216 sink_AP_0.50: 0.1014 window_AP_0.50: 0.0113 desk_AP_0.50: 0.2088 bed_AP_0.50: 0.3302 toilet_AP_0.50: 0.4406 showercurtrain_AP_0.50: 0.0372 bathtub_AP_0.50: 0.2111 mAP_0.50: 0.1302 chair_rec_0.50: 0.3268 sofa_rec_0.50: 0.4227 table_rec_0.50: 0.3029 garbagebin_rec_0.50: 0.1509 bookshelf_rec_0.50: 0.2208 picture_rec_0.50: 0.0315 curtain_rec_0.50: 0.1493 door_rec_0.50: 0.1370 cabinet_rec_0.50: 0.1909 refrigerator_rec_0.50: 0.2632 counter_rec_0.50: 0.1346 sink_rec_0.50: 0.2551 window_rec_0.50: 0.0887 desk_rec_0.50: 0.4409 bed_rec_0.50: 0.4938 toilet_rec_0.50: 0.5345 showercurtrain_rec_0.50: 0.1786 bathtub_rec_0.50: 0.3871 mAR_0.50: 0.2616 data_time: 0.2775 time: 2.7669 2025/05/13 01:58:11 - mmengine - INFO - Epoch(train) [129][10/91] base_lr: 1.9195e-04 lr: 1.9195e-04 eta: 2 days, 21:42:05 time: 10.4554 data_time: 1.4084 memory: 68703 grad_norm: 1.4793 loss: 1.8237 center_loss: 0.5036 size_loss: 0.1482 cls_loss: 0.5711 giou_loss: 0.6008 2025/05/13 01:59:48 - mmengine - INFO - Epoch(train) [129][20/91] base_lr: 1.9195e-04 lr: 1.9195e-04 eta: 2 days, 21:40:13 time: 10.4424 data_time: 1.4002 memory: 68702 grad_norm: 1.4219 loss: 1.8186 center_loss: 0.5014 size_loss: 0.1487 cls_loss: 0.5699 giou_loss: 0.5985 2025/05/13 02:01:24 - mmengine - INFO - Epoch(train) [129][30/91] base_lr: 1.9195e-04 lr: 1.9195e-04 eta: 2 days, 21:38:20 time: 10.4376 data_time: 1.3894 memory: 68702 grad_norm: 1.4503 loss: 1.8086 center_loss: 0.4979 size_loss: 0.1479 cls_loss: 0.5672 giou_loss: 0.5956 2025/05/13 02:03:00 - mmengine - INFO - Epoch(train) [129][40/91] base_lr: 1.9195e-04 lr: 1.9195e-04 eta: 2 days, 21:36:29 time: 10.4375 data_time: 1.3815 memory: 68702 grad_norm: 1.4323 loss: 1.8041 center_loss: 0.4922 size_loss: 0.1481 cls_loss: 0.5712 giou_loss: 0.5925 2025/05/13 02:04:37 - mmengine - INFO - Epoch(train) [129][50/91] base_lr: 1.9195e-04 lr: 1.9195e-04 eta: 2 days, 21:34:37 time: 10.6221 data_time: 1.4012 memory: 68703 grad_norm: 1.4037 loss: 1.7863 center_loss: 0.4903 size_loss: 0.1458 cls_loss: 0.5634 giou_loss: 0.5868 2025/05/13 02:06:14 - mmengine - INFO - Epoch(train) [129][60/91] base_lr: 1.9195e-04 lr: 1.9195e-04 eta: 2 days, 21:32:46 time: 9.6513 data_time: 0.5461 memory: 68702 grad_norm: 1.4573 loss: 1.7897 center_loss: 0.4897 size_loss: 0.1446 cls_loss: 0.5686 giou_loss: 0.5868 2025/05/13 02:07:50 - mmengine - INFO - Epoch(train) [129][70/91] base_lr: 1.9195e-04 lr: 1.9195e-04 eta: 2 days, 21:30:54 time: 9.6520 data_time: 0.5478 memory: 68702 grad_norm: 1.4949 loss: 1.7892 center_loss: 0.4878 size_loss: 0.1431 cls_loss: 0.5705 giou_loss: 0.5877 2025/05/13 02:09:27 - mmengine - INFO - Epoch(train) [129][80/91] base_lr: 1.9195e-04 lr: 1.9195e-04 eta: 2 days, 21:29:02 time: 9.6543 data_time: 0.5598 memory: 68702 grad_norm: 1.5365 loss: 1.7848 center_loss: 0.4850 size_loss: 0.1429 cls_loss: 0.5715 giou_loss: 0.5854 2025/05/13 02:11:01 - mmengine - INFO - Epoch(train) [129][90/91] base_lr: 1.9195e-04 lr: 1.9195e-04 eta: 2 days, 21:27:06 time: 9.6136 data_time: 0.5589 memory: 68702 grad_norm: 1.6075 loss: 1.7903 center_loss: 0.4886 size_loss: 0.1429 cls_loss: 0.5710 giou_loss: 0.5878 2025/05/13 02:11:03 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 02:13:33 - mmengine - INFO - Epoch(train) [130][10/91] base_lr: 1.9111e-04 lr: 1.9111e-04 eta: 2 days, 21:26:40 time: 10.5268 data_time: 1.5039 memory: 68702 grad_norm: 1.8428 loss: 1.8097 center_loss: 0.4936 size_loss: 0.1466 cls_loss: 0.5781 giou_loss: 0.5914 2025/05/13 02:15:09 - mmengine - INFO - Epoch(train) [130][20/91] base_lr: 1.9111e-04 lr: 1.9111e-04 eta: 2 days, 21:24:46 time: 10.5032 data_time: 1.5005 memory: 68702 grad_norm: 1.9045 loss: 1.7983 center_loss: 0.4912 size_loss: 0.1463 cls_loss: 0.5702 giou_loss: 0.5906 2025/05/13 02:16:46 - mmengine - INFO - Epoch(train) [130][30/91] base_lr: 1.9111e-04 lr: 1.9111e-04 eta: 2 days, 21:22:55 time: 10.5160 data_time: 1.5226 memory: 68702 grad_norm: 1.9055 loss: 1.7814 center_loss: 0.4888 size_loss: 0.1456 cls_loss: 0.5601 giou_loss: 0.5869 2025/05/13 02:18:22 - mmengine - INFO - Epoch(train) [130][40/91] base_lr: 1.9111e-04 lr: 1.9111e-04 eta: 2 days, 21:21:04 time: 10.5208 data_time: 1.5189 memory: 68702 grad_norm: 1.9143 loss: 1.7993 center_loss: 0.4951 size_loss: 0.1469 cls_loss: 0.5654 giou_loss: 0.5919 2025/05/13 02:20:00 - mmengine - INFO - Epoch(train) [130][50/91] base_lr: 1.9111e-04 lr: 1.9111e-04 eta: 2 days, 21:19:14 time: 10.7310 data_time: 1.5344 memory: 68702 grad_norm: 1.8882 loss: 1.7911 center_loss: 0.4903 size_loss: 0.1443 cls_loss: 0.5644 giou_loss: 0.5920 2025/05/13 02:21:36 - mmengine - INFO - Epoch(train) [130][60/91] base_lr: 1.9111e-04 lr: 1.9111e-04 eta: 2 days, 21:17:22 time: 9.6486 data_time: 0.5856 memory: 68702 grad_norm: 1.8024 loss: 1.7960 center_loss: 0.4944 size_loss: 0.1444 cls_loss: 0.5650 giou_loss: 0.5922 2025/05/13 02:23:14 - mmengine - INFO - Epoch(train) [130][70/91] base_lr: 1.9111e-04 lr: 1.9111e-04 eta: 2 days, 21:15:34 time: 9.7059 data_time: 0.6175 memory: 68702 grad_norm: 1.7426 loss: 1.8120 center_loss: 0.5021 size_loss: 0.1451 cls_loss: 0.5712 giou_loss: 0.5936 2025/05/13 02:24:50 - mmengine - INFO - Epoch(train) [130][80/91] base_lr: 1.9111e-04 lr: 1.9111e-04 eta: 2 days, 21:13:41 time: 9.6900 data_time: 0.6013 memory: 68702 grad_norm: 1.7145 loss: 1.8187 center_loss: 0.5006 size_loss: 0.1449 cls_loss: 0.5800 giou_loss: 0.5933 2025/05/13 02:26:25 - mmengine - INFO - Epoch(train) [130][90/91] base_lr: 1.9111e-04 lr: 1.9111e-04 eta: 2 days, 21:11:46 time: 9.6509 data_time: 0.6013 memory: 68702 grad_norm: 1.6493 loss: 1.8012 center_loss: 0.4970 size_loss: 0.1434 cls_loss: 0.5743 giou_loss: 0.5864 2025/05/13 02:26:27 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 02:26:27 - mmengine - INFO - Saving checkpoint at 130 epochs 2025/05/13 02:27:24 - mmengine - INFO - Epoch(val) [130][10/39] eta: 0:01:38 time: 2.8896 data_time: 0.3963 memory: 15952 2025/05/13 02:27:50 - mmengine - INFO - Epoch(val) [130][20/39] eta: 0:00:56 time: 2.7396 data_time: 0.2434 memory: 13407 2025/05/13 02:28:16 - mmengine - INFO - Epoch(val) [130][30/39] eta: 0:00:25 time: 2.7430 data_time: 0.2436 memory: 13407 2025/05/13 02:28:42 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.1884 | 0.4151 | 0.0209 | 0.1302 | | chair | 0.5304 | 0.7069 | 0.1114 | 0.3012 | | sofa | 0.7339 | 0.8454 | 0.2085 | 0.3918 | | table | 0.4476 | 0.6086 | 0.1420 | 0.2829 | | curtain | 0.1864 | 0.4478 | 0.0255 | 0.0746 | | bookshelf | 0.2374 | 0.5844 | 0.0588 | 0.2468 | | picture | 0.0093 | 0.1036 | 0.0006 | 0.0045 | | window | 0.1197 | 0.3546 | 0.0066 | 0.0674 | | bed | 0.8143 | 0.8395 | 0.3593 | 0.4938 | | desk | 0.7007 | 0.8583 | 0.2433 | 0.4252 | | door | 0.1260 | 0.4368 | 0.0124 | 0.1263 | | cabinet | 0.2450 | 0.5027 | 0.0352 | 0.1720 | | counter | 0.3249 | 0.4808 | 0.0105 | 0.0962 | | refrigerator | 0.3551 | 0.5614 | 0.2460 | 0.3860 | | sink | 0.4329 | 0.6429 | 0.0562 | 0.2245 | | toilet | 0.7669 | 0.8621 | 0.3730 | 0.5172 | | bathtub | 0.8029 | 0.8710 | 0.2816 | 0.5161 | | showercurtrain | 0.1707 | 0.5357 | 0.0212 | 0.1429 | +----------------+---------+---------+---------+---------+ | Overall | 0.3996 | 0.5921 | 0.1229 | 0.2555 | +----------------+---------+---------+---------+---------+ 2025/05/13 02:28:42 - mmengine - INFO - Epoch(val) [130][39/39] chair_AP_0.25: 0.5304 sofa_AP_0.25: 0.7339 table_AP_0.25: 0.4476 garbagebin_AP_0.25: 0.1884 bookshelf_AP_0.25: 0.2374 picture_AP_0.25: 0.0093 curtain_AP_0.25: 0.1864 door_AP_0.25: 0.1260 cabinet_AP_0.25: 0.2450 refrigerator_AP_0.25: 0.3551 counter_AP_0.25: 0.3249 sink_AP_0.25: 0.4329 window_AP_0.25: 0.1197 desk_AP_0.25: 0.7007 bed_AP_0.25: 0.8143 toilet_AP_0.25: 0.7669 showercurtrain_AP_0.25: 0.1707 bathtub_AP_0.25: 0.8029 mAP_0.25: 0.3996 chair_rec_0.25: 0.7069 sofa_rec_0.25: 0.8454 table_rec_0.25: 0.6086 garbagebin_rec_0.25: 0.4151 bookshelf_rec_0.25: 0.5844 picture_rec_0.25: 0.1036 curtain_rec_0.25: 0.4478 door_rec_0.25: 0.4368 cabinet_rec_0.25: 0.5027 refrigerator_rec_0.25: 0.5614 counter_rec_0.25: 0.4808 sink_rec_0.25: 0.6429 window_rec_0.25: 0.3546 desk_rec_0.25: 0.8583 bed_rec_0.25: 0.8395 toilet_rec_0.25: 0.8621 showercurtrain_rec_0.25: 0.5357 bathtub_rec_0.25: 0.8710 mAR_0.25: 0.5921 chair_AP_0.50: 0.1114 sofa_AP_0.50: 0.2085 table_AP_0.50: 0.1420 garbagebin_AP_0.50: 0.0209 bookshelf_AP_0.50: 0.0588 picture_AP_0.50: 0.0006 curtain_AP_0.50: 0.0255 door_AP_0.50: 0.0124 cabinet_AP_0.50: 0.0352 refrigerator_AP_0.50: 0.2460 counter_AP_0.50: 0.0105 sink_AP_0.50: 0.0562 window_AP_0.50: 0.0066 desk_AP_0.50: 0.2433 bed_AP_0.50: 0.3593 toilet_AP_0.50: 0.3730 showercurtrain_AP_0.50: 0.0212 bathtub_AP_0.50: 0.2816 mAP_0.50: 0.1229 chair_rec_0.50: 0.3012 sofa_rec_0.50: 0.3918 table_rec_0.50: 0.2829 garbagebin_rec_0.50: 0.1302 bookshelf_rec_0.50: 0.2468 picture_rec_0.50: 0.0045 curtain_rec_0.50: 0.0746 door_rec_0.50: 0.1263 cabinet_rec_0.50: 0.1720 refrigerator_rec_0.50: 0.3860 counter_rec_0.50: 0.0962 sink_rec_0.50: 0.2245 window_rec_0.50: 0.0674 desk_rec_0.50: 0.4252 bed_rec_0.50: 0.4938 toilet_rec_0.50: 0.5172 showercurtrain_rec_0.50: 0.1429 bathtub_rec_0.50: 0.5161 mAR_0.50: 0.2555 data_time: 0.2753 time: 2.7693 2025/05/13 02:31:08 - mmengine - INFO - Epoch(train) [131][10/91] base_lr: 1.9028e-04 lr: 1.9028e-04 eta: 2 days, 21:11:09 time: 10.4599 data_time: 1.4952 memory: 68702 grad_norm: 1.4797 loss: 1.8108 center_loss: 0.5009 size_loss: 0.1442 cls_loss: 0.5754 giou_loss: 0.5903 2025/05/13 02:32:44 - mmengine - INFO - Epoch(train) [131][20/91] base_lr: 1.9028e-04 lr: 1.9028e-04 eta: 2 days, 21:09:16 time: 10.4514 data_time: 1.4884 memory: 68702 grad_norm: 1.4988 loss: 1.8065 center_loss: 0.4961 size_loss: 0.1438 cls_loss: 0.5768 giou_loss: 0.5899 2025/05/13 02:34:20 - mmengine - INFO - Epoch(train) [131][30/91] base_lr: 1.9028e-04 lr: 1.9028e-04 eta: 2 days, 21:07:24 time: 10.4211 data_time: 1.4669 memory: 68702 grad_norm: 1.4268 loss: 1.8002 center_loss: 0.4925 size_loss: 0.1434 cls_loss: 0.5738 giou_loss: 0.5905 2025/05/13 02:35:57 - mmengine - INFO - Epoch(train) [131][40/91] base_lr: 1.9028e-04 lr: 1.9028e-04 eta: 2 days, 21:05:33 time: 10.4282 data_time: 1.4660 memory: 68700 grad_norm: 1.4721 loss: 1.8053 center_loss: 0.4949 size_loss: 0.1450 cls_loss: 0.5704 giou_loss: 0.5951 2025/05/13 02:37:33 - mmengine - INFO - Epoch(train) [131][50/91] base_lr: 1.9028e-04 lr: 1.9028e-04 eta: 2 days, 21:03:42 time: 10.6223 data_time: 1.4700 memory: 68703 grad_norm: 1.4679 loss: 1.8218 center_loss: 0.4998 size_loss: 0.1460 cls_loss: 0.5756 giou_loss: 0.6003 2025/05/13 02:39:10 - mmengine - INFO - Epoch(train) [131][60/91] base_lr: 1.9028e-04 lr: 1.9028e-04 eta: 2 days, 21:01:50 time: 9.6410 data_time: 0.5766 memory: 68702 grad_norm: 1.5241 loss: 1.8125 center_loss: 0.4961 size_loss: 0.1458 cls_loss: 0.5729 giou_loss: 0.5978 2025/05/13 02:40:46 - mmengine - INFO - Epoch(train) [131][70/91] base_lr: 1.9028e-04 lr: 1.9028e-04 eta: 2 days, 20:59:59 time: 9.6488 data_time: 0.5807 memory: 68702 grad_norm: 1.6097 loss: 1.8200 center_loss: 0.4983 size_loss: 0.1469 cls_loss: 0.5755 giou_loss: 0.5994 2025/05/13 02:42:23 - mmengine - INFO - Epoch(train) [131][80/91] base_lr: 1.9028e-04 lr: 1.9028e-04 eta: 2 days, 20:58:07 time: 9.6500 data_time: 0.5792 memory: 68702 grad_norm: 1.6220 loss: 1.8238 center_loss: 0.5002 size_loss: 0.1475 cls_loss: 0.5762 giou_loss: 0.5999 2025/05/13 02:43:57 - mmengine - INFO - Epoch(train) [131][90/91] base_lr: 1.9028e-04 lr: 1.9028e-04 eta: 2 days, 20:56:12 time: 9.6130 data_time: 0.5681 memory: 68702 grad_norm: 1.6102 loss: 1.8317 center_loss: 0.5037 size_loss: 0.1483 cls_loss: 0.5799 giou_loss: 0.5997 2025/05/13 02:43:59 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 02:46:30 - mmengine - INFO - Epoch(train) [132][10/91] base_lr: 1.8944e-04 lr: 1.8944e-04 eta: 2 days, 20:55:44 time: 10.5316 data_time: 1.3851 memory: 68702 grad_norm: 1.9040 loss: 1.8211 center_loss: 0.5015 size_loss: 0.1491 cls_loss: 0.5751 giou_loss: 0.5954 2025/05/13 02:48:06 - mmengine - INFO - Epoch(train) [132][20/91] base_lr: 1.8944e-04 lr: 1.8944e-04 eta: 2 days, 20:53:52 time: 10.5337 data_time: 1.3931 memory: 68702 grad_norm: 1.8099 loss: 1.8189 center_loss: 0.4960 size_loss: 0.1485 cls_loss: 0.5795 giou_loss: 0.5949 2025/05/13 02:49:42 - mmengine - INFO - Epoch(train) [132][30/91] base_lr: 1.8944e-04 lr: 1.8944e-04 eta: 2 days, 20:52:00 time: 10.5256 data_time: 1.3932 memory: 68701 grad_norm: 1.7031 loss: 1.8353 center_loss: 0.5084 size_loss: 0.1498 cls_loss: 0.5784 giou_loss: 0.5986 2025/05/13 02:51:19 - mmengine - INFO - Epoch(train) [132][40/91] base_lr: 1.8944e-04 lr: 1.8944e-04 eta: 2 days, 20:50:09 time: 10.5337 data_time: 1.3980 memory: 68702 grad_norm: 1.7017 loss: 1.8355 center_loss: 0.5091 size_loss: 0.1495 cls_loss: 0.5788 giou_loss: 0.5981 2025/05/13 02:52:57 - mmengine - INFO - Epoch(train) [132][50/91] base_lr: 1.8944e-04 lr: 1.8944e-04 eta: 2 days, 20:48:20 time: 10.7505 data_time: 1.4120 memory: 68703 grad_norm: 1.5235 loss: 1.8273 center_loss: 0.5066 size_loss: 0.1484 cls_loss: 0.5745 giou_loss: 0.5979 2025/05/13 02:54:33 - mmengine - INFO - Epoch(train) [132][60/91] base_lr: 1.8944e-04 lr: 1.8944e-04 eta: 2 days, 20:46:29 time: 9.6709 data_time: 0.5970 memory: 68702 grad_norm: 1.4144 loss: 1.8542 center_loss: 0.5251 size_loss: 0.1497 cls_loss: 0.5785 giou_loss: 0.6010 2025/05/13 02:56:10 - mmengine - INFO - Epoch(train) [132][70/91] base_lr: 1.8944e-04 lr: 1.8944e-04 eta: 2 days, 20:44:38 time: 9.6790 data_time: 0.5920 memory: 68703 grad_norm: 1.4546 loss: 1.8592 center_loss: 0.5291 size_loss: 0.1500 cls_loss: 0.5768 giou_loss: 0.6033 2025/05/13 02:57:37 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 02:57:46 - mmengine - INFO - Epoch(train) [132][80/91] base_lr: 1.8944e-04 lr: 1.8944e-04 eta: 2 days, 20:42:46 time: 9.6780 data_time: 0.5915 memory: 68702 grad_norm: 1.5045 loss: 1.8494 center_loss: 0.5253 size_loss: 0.1511 cls_loss: 0.5729 giou_loss: 0.6000 2025/05/13 02:59:21 - mmengine - INFO - Epoch(train) [132][90/91] base_lr: 1.8944e-04 lr: 1.8944e-04 eta: 2 days, 20:40:51 time: 9.6336 data_time: 0.5820 memory: 68702 grad_norm: 1.7298 loss: 1.8310 center_loss: 0.5152 size_loss: 0.1494 cls_loss: 0.5699 giou_loss: 0.5966 2025/05/13 02:59:23 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 02:59:23 - mmengine - INFO - Saving checkpoint at 132 epochs 2025/05/13 03:00:17 - mmengine - INFO - Epoch(val) [132][10/39] eta: 0:01:37 time: 2.8848 data_time: 0.3811 memory: 15952 2025/05/13 03:00:44 - mmengine - INFO - Epoch(val) [132][20/39] eta: 0:00:56 time: 2.7309 data_time: 0.2222 memory: 13407 2025/05/13 03:01:09 - mmengine - INFO - Epoch(val) [132][30/39] eta: 0:00:25 time: 2.7323 data_time: 0.2225 memory: 13407 2025/05/13 03:01:35 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | chair | 0.5085 | 0.6674 | 0.1101 | 0.2522 | | sofa | 0.6475 | 0.8351 | 0.1724 | 0.3196 | | garbagebin | 0.1802 | 0.3811 | 0.0135 | 0.0868 | | table | 0.4336 | 0.5629 | 0.1164 | 0.2429 | | curtain | 0.1559 | 0.4925 | 0.0286 | 0.1343 | | bookshelf | 0.2420 | 0.5455 | 0.1028 | 0.2597 | | picture | 0.0180 | 0.1081 | 0.0001 | 0.0045 | | door | 0.1103 | 0.3790 | 0.0087 | 0.0899 | | showercurtrain | 0.2558 | 0.5000 | 0.0226 | 0.1071 | | cabinet | 0.2647 | 0.4731 | 0.0463 | 0.1694 | | window | 0.1206 | 0.3582 | 0.0143 | 0.0922 | | counter | 0.2548 | 0.4038 | 0.0060 | 0.0385 | | refrigerator | 0.4580 | 0.6491 | 0.2106 | 0.3333 | | sink | 0.3973 | 0.5612 | 0.0409 | 0.1633 | | bed | 0.8418 | 0.8642 | 0.4070 | 0.5679 | | desk | 0.7316 | 0.8583 | 0.2772 | 0.4488 | | toilet | 0.7665 | 0.8448 | 0.3099 | 0.3448 | | bathtub | 0.7218 | 0.7742 | 0.1446 | 0.3548 | +----------------+---------+---------+---------+---------+ | Overall | 0.3949 | 0.5699 | 0.1129 | 0.2228 | +----------------+---------+---------+---------+---------+ 2025/05/13 03:01:35 - mmengine - INFO - Epoch(val) [132][39/39] chair_AP_0.25: 0.5085 sofa_AP_0.25: 0.6475 table_AP_0.25: 0.4336 garbagebin_AP_0.25: 0.1802 bookshelf_AP_0.25: 0.2420 picture_AP_0.25: 0.0180 curtain_AP_0.25: 0.1559 door_AP_0.25: 0.1103 cabinet_AP_0.25: 0.2647 refrigerator_AP_0.25: 0.4580 counter_AP_0.25: 0.2548 sink_AP_0.25: 0.3973 window_AP_0.25: 0.1206 desk_AP_0.25: 0.7316 bed_AP_0.25: 0.8418 toilet_AP_0.25: 0.7665 showercurtrain_AP_0.25: 0.2558 bathtub_AP_0.25: 0.7218 mAP_0.25: 0.3949 chair_rec_0.25: 0.6674 sofa_rec_0.25: 0.8351 table_rec_0.25: 0.5629 garbagebin_rec_0.25: 0.3811 bookshelf_rec_0.25: 0.5455 picture_rec_0.25: 0.1081 curtain_rec_0.25: 0.4925 door_rec_0.25: 0.3790 cabinet_rec_0.25: 0.4731 refrigerator_rec_0.25: 0.6491 counter_rec_0.25: 0.4038 sink_rec_0.25: 0.5612 window_rec_0.25: 0.3582 desk_rec_0.25: 0.8583 bed_rec_0.25: 0.8642 toilet_rec_0.25: 0.8448 showercurtrain_rec_0.25: 0.5000 bathtub_rec_0.25: 0.7742 mAR_0.25: 0.5699 chair_AP_0.50: 0.1101 sofa_AP_0.50: 0.1724 table_AP_0.50: 0.1164 garbagebin_AP_0.50: 0.0135 bookshelf_AP_0.50: 0.1028 picture_AP_0.50: 0.0001 curtain_AP_0.50: 0.0286 door_AP_0.50: 0.0087 cabinet_AP_0.50: 0.0463 refrigerator_AP_0.50: 0.2106 counter_AP_0.50: 0.0060 sink_AP_0.50: 0.0409 window_AP_0.50: 0.0143 desk_AP_0.50: 0.2772 bed_AP_0.50: 0.4070 toilet_AP_0.50: 0.3099 showercurtrain_AP_0.50: 0.0226 bathtub_AP_0.50: 0.1446 mAP_0.50: 0.1129 chair_rec_0.50: 0.2522 sofa_rec_0.50: 0.3196 table_rec_0.50: 0.2429 garbagebin_rec_0.50: 0.0868 bookshelf_rec_0.50: 0.2597 picture_rec_0.50: 0.0045 curtain_rec_0.50: 0.1343 door_rec_0.50: 0.0899 cabinet_rec_0.50: 0.1694 refrigerator_rec_0.50: 0.3333 counter_rec_0.50: 0.0385 sink_rec_0.50: 0.1633 window_rec_0.50: 0.0922 desk_rec_0.50: 0.4488 bed_rec_0.50: 0.5679 toilet_rec_0.50: 0.3448 showercurtrain_rec_0.50: 0.1071 bathtub_rec_0.50: 0.3548 mAR_0.50: 0.2228 data_time: 0.2580 time: 2.7695 2025/05/13 03:04:00 - mmengine - INFO - Epoch(train) [133][10/91] base_lr: 1.8860e-04 lr: 1.8860e-04 eta: 2 days, 20:40:10 time: 10.4098 data_time: 1.4549 memory: 68703 grad_norm: 1.9351 loss: 1.8389 center_loss: 0.5197 size_loss: 0.1510 cls_loss: 0.5676 giou_loss: 0.6005 2025/05/13 03:05:36 - mmengine - INFO - Epoch(train) [133][20/91] base_lr: 1.8860e-04 lr: 1.8860e-04 eta: 2 days, 20:38:17 time: 10.3944 data_time: 1.4536 memory: 68703 grad_norm: 2.0401 loss: 1.8310 center_loss: 0.5122 size_loss: 0.1503 cls_loss: 0.5691 giou_loss: 0.5993 2025/05/13 03:07:12 - mmengine - INFO - Epoch(train) [133][30/91] base_lr: 1.8860e-04 lr: 1.8860e-04 eta: 2 days, 20:36:24 time: 10.3772 data_time: 1.4486 memory: 68703 grad_norm: 2.1098 loss: 1.8265 center_loss: 0.5087 size_loss: 0.1503 cls_loss: 0.5712 giou_loss: 0.5962 2025/05/13 03:08:48 - mmengine - INFO - Epoch(train) [133][40/91] base_lr: 1.8860e-04 lr: 1.8860e-04 eta: 2 days, 20:34:32 time: 10.3803 data_time: 1.4527 memory: 68702 grad_norm: 2.1145 loss: 1.8149 center_loss: 0.5003 size_loss: 0.1469 cls_loss: 0.5744 giou_loss: 0.5933 2025/05/13 03:10:25 - mmengine - INFO - Epoch(train) [133][50/91] base_lr: 1.8860e-04 lr: 1.8860e-04 eta: 2 days, 20:32:42 time: 10.5844 data_time: 1.4888 memory: 68703 grad_norm: 1.8619 loss: 1.8378 center_loss: 0.5096 size_loss: 0.1486 cls_loss: 0.5822 giou_loss: 0.5973 2025/05/13 03:12:01 - mmengine - INFO - Epoch(train) [133][60/91] base_lr: 1.8860e-04 lr: 1.8860e-04 eta: 2 days, 20:30:50 time: 9.6207 data_time: 0.6022 memory: 68702 grad_norm: 1.7548 loss: 1.8227 center_loss: 0.5046 size_loss: 0.1461 cls_loss: 0.5793 giou_loss: 0.5927 2025/05/13 03:13:37 - mmengine - INFO - Epoch(train) [133][70/91] base_lr: 1.8860e-04 lr: 1.8860e-04 eta: 2 days, 20:28:58 time: 9.6272 data_time: 0.6062 memory: 68703 grad_norm: 1.6073 loss: 1.8033 center_loss: 0.4925 size_loss: 0.1453 cls_loss: 0.5752 giou_loss: 0.5903 2025/05/13 03:15:12 - mmengine - INFO - Epoch(train) [133][80/91] base_lr: 1.8860e-04 lr: 1.8860e-04 eta: 2 days, 20:27:05 time: 9.6163 data_time: 0.5994 memory: 68703 grad_norm: 1.5337 loss: 1.8006 center_loss: 0.4916 size_loss: 0.1449 cls_loss: 0.5726 giou_loss: 0.5915 2025/05/13 03:16:47 - mmengine - INFO - Epoch(train) [133][90/91] base_lr: 1.8860e-04 lr: 1.8860e-04 eta: 2 days, 20:25:10 time: 9.5919 data_time: 0.5880 memory: 68702 grad_norm: 1.6994 loss: 1.8255 center_loss: 0.5056 size_loss: 0.1479 cls_loss: 0.5733 giou_loss: 0.5987 2025/05/13 03:16:49 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 03:19:13 - mmengine - INFO - Epoch(train) [134][10/91] base_lr: 1.8775e-04 lr: 1.8775e-04 eta: 2 days, 20:24:27 time: 10.3675 data_time: 1.3901 memory: 68700 grad_norm: 1.7036 loss: 1.8087 center_loss: 0.4991 size_loss: 0.1480 cls_loss: 0.5674 giou_loss: 0.5942 2025/05/13 03:20:50 - mmengine - INFO - Epoch(train) [134][20/91] base_lr: 1.8775e-04 lr: 1.8775e-04 eta: 2 days, 20:22:37 time: 10.3852 data_time: 1.3849 memory: 68703 grad_norm: 1.6928 loss: 1.8143 center_loss: 0.5004 size_loss: 0.1481 cls_loss: 0.5690 giou_loss: 0.5968 2025/05/13 03:22:27 - mmengine - INFO - Epoch(train) [134][30/91] base_lr: 1.8775e-04 lr: 1.8775e-04 eta: 2 days, 20:20:46 time: 10.4004 data_time: 1.3862 memory: 68702 grad_norm: 1.6874 loss: 1.8198 center_loss: 0.5031 size_loss: 0.1488 cls_loss: 0.5703 giou_loss: 0.5975 2025/05/13 03:24:03 - mmengine - INFO - Epoch(train) [134][40/91] base_lr: 1.8775e-04 lr: 1.8775e-04 eta: 2 days, 20:18:55 time: 10.4251 data_time: 1.3921 memory: 68702 grad_norm: 1.6684 loss: 1.8137 center_loss: 0.5013 size_loss: 0.1471 cls_loss: 0.5688 giou_loss: 0.5965 2025/05/13 03:25:40 - mmengine - INFO - Epoch(train) [134][50/91] base_lr: 1.8775e-04 lr: 1.8775e-04 eta: 2 days, 20:17:05 time: 10.6194 data_time: 1.4039 memory: 68702 grad_norm: 1.5024 loss: 1.8012 center_loss: 0.4936 size_loss: 0.1469 cls_loss: 0.5647 giou_loss: 0.5959 2025/05/13 03:27:17 - mmengine - INFO - Epoch(train) [134][60/91] base_lr: 1.8775e-04 lr: 1.8775e-04 eta: 2 days, 20:15:14 time: 9.6760 data_time: 0.5674 memory: 68702 grad_norm: 1.4767 loss: 1.7967 center_loss: 0.4931 size_loss: 0.1469 cls_loss: 0.5621 giou_loss: 0.5945 2025/05/13 03:28:53 - mmengine - INFO - Epoch(train) [134][70/91] base_lr: 1.8775e-04 lr: 1.8775e-04 eta: 2 days, 20:13:23 time: 9.6715 data_time: 0.5810 memory: 68700 grad_norm: 1.4197 loss: 1.7890 center_loss: 0.4896 size_loss: 0.1466 cls_loss: 0.5606 giou_loss: 0.5923 2025/05/13 03:30:30 - mmengine - INFO - Epoch(train) [134][80/91] base_lr: 1.8775e-04 lr: 1.8775e-04 eta: 2 days, 20:11:32 time: 9.6598 data_time: 0.5752 memory: 68702 grad_norm: 1.6179 loss: 1.7825 center_loss: 0.4872 size_loss: 0.1446 cls_loss: 0.5586 giou_loss: 0.5922 2025/05/13 03:32:05 - mmengine - INFO - Epoch(train) [134][90/91] base_lr: 1.8775e-04 lr: 1.8775e-04 eta: 2 days, 20:09:39 time: 9.6412 data_time: 0.5726 memory: 68703 grad_norm: 1.7002 loss: 1.7964 center_loss: 0.4939 size_loss: 0.1477 cls_loss: 0.5606 giou_loss: 0.5942 2025/05/13 03:32:07 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 03:32:07 - mmengine - INFO - Saving checkpoint at 134 epochs 2025/05/13 03:33:03 - mmengine - INFO - Epoch(val) [134][10/39] eta: 0:01:35 time: 2.8728 data_time: 0.3640 memory: 15952 2025/05/13 03:33:29 - mmengine - INFO - Epoch(val) [134][20/39] eta: 0:00:55 time: 2.7178 data_time: 0.2187 memory: 13407 2025/05/13 03:33:54 - mmengine - INFO - Epoch(val) [134][30/39] eta: 0:00:25 time: 2.7060 data_time: 0.2180 memory: 13407 2025/05/13 03:34:20 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.2237 | 0.4396 | 0.0217 | 0.1113 | | table | 0.4774 | 0.6057 | 0.1808 | 0.3200 | | sofa | 0.6775 | 0.8351 | 0.2192 | 0.4433 | | chair | 0.5601 | 0.7032 | 0.1495 | 0.3319 | | curtain | 0.2992 | 0.6567 | 0.0760 | 0.1493 | | bookshelf | 0.2854 | 0.6234 | 0.0554 | 0.2208 | | picture | 0.0263 | 0.0991 | 0.0002 | 0.0135 | | door | 0.1230 | 0.4240 | 0.0145 | 0.1135 | | window | 0.1264 | 0.3369 | 0.0071 | 0.0780 | | cabinet | 0.2655 | 0.5027 | 0.0317 | 0.1667 | | counter | 0.2497 | 0.4808 | 0.0290 | 0.1538 | | sink | 0.4490 | 0.6122 | 0.0747 | 0.2551 | | refrigerator | 0.4798 | 0.6316 | 0.2036 | 0.3158 | | desk | 0.6888 | 0.8425 | 0.2728 | 0.4882 | | bed | 0.8031 | 0.8519 | 0.3670 | 0.5185 | | toilet | 0.8675 | 0.9483 | 0.4652 | 0.5345 | | bathtub | 0.8003 | 0.8710 | 0.2691 | 0.4516 | | showercurtrain | 0.3306 | 0.6786 | 0.0232 | 0.1429 | +----------------+---------+---------+---------+---------+ | Overall | 0.4296 | 0.6191 | 0.1367 | 0.2671 | +----------------+---------+---------+---------+---------+ 2025/05/13 03:34:20 - mmengine - INFO - Epoch(val) [134][39/39] chair_AP_0.25: 0.5601 sofa_AP_0.25: 0.6775 table_AP_0.25: 0.4774 garbagebin_AP_0.25: 0.2237 bookshelf_AP_0.25: 0.2854 picture_AP_0.25: 0.0263 curtain_AP_0.25: 0.2992 door_AP_0.25: 0.1230 cabinet_AP_0.25: 0.2655 refrigerator_AP_0.25: 0.4798 counter_AP_0.25: 0.2497 sink_AP_0.25: 0.4490 window_AP_0.25: 0.1264 desk_AP_0.25: 0.6888 bed_AP_0.25: 0.8031 toilet_AP_0.25: 0.8675 showercurtrain_AP_0.25: 0.3306 bathtub_AP_0.25: 0.8003 mAP_0.25: 0.4296 chair_rec_0.25: 0.7032 sofa_rec_0.25: 0.8351 table_rec_0.25: 0.6057 garbagebin_rec_0.25: 0.4396 bookshelf_rec_0.25: 0.6234 picture_rec_0.25: 0.0991 curtain_rec_0.25: 0.6567 door_rec_0.25: 0.4240 cabinet_rec_0.25: 0.5027 refrigerator_rec_0.25: 0.6316 counter_rec_0.25: 0.4808 sink_rec_0.25: 0.6122 window_rec_0.25: 0.3369 desk_rec_0.25: 0.8425 bed_rec_0.25: 0.8519 toilet_rec_0.25: 0.9483 showercurtrain_rec_0.25: 0.6786 bathtub_rec_0.25: 0.8710 mAR_0.25: 0.6191 chair_AP_0.50: 0.1495 sofa_AP_0.50: 0.2192 table_AP_0.50: 0.1808 garbagebin_AP_0.50: 0.0217 bookshelf_AP_0.50: 0.0554 picture_AP_0.50: 0.0002 curtain_AP_0.50: 0.0760 door_AP_0.50: 0.0145 cabinet_AP_0.50: 0.0317 refrigerator_AP_0.50: 0.2036 counter_AP_0.50: 0.0290 sink_AP_0.50: 0.0747 window_AP_0.50: 0.0071 desk_AP_0.50: 0.2728 bed_AP_0.50: 0.3670 toilet_AP_0.50: 0.4652 showercurtrain_AP_0.50: 0.0232 bathtub_AP_0.50: 0.2691 mAP_0.50: 0.1367 chair_rec_0.50: 0.3319 sofa_rec_0.50: 0.4433 table_rec_0.50: 0.3200 garbagebin_rec_0.50: 0.1113 bookshelf_rec_0.50: 0.2208 picture_rec_0.50: 0.0135 curtain_rec_0.50: 0.1493 door_rec_0.50: 0.1135 cabinet_rec_0.50: 0.1667 refrigerator_rec_0.50: 0.3158 counter_rec_0.50: 0.1538 sink_rec_0.50: 0.2551 window_rec_0.50: 0.0780 desk_rec_0.50: 0.4882 bed_rec_0.50: 0.5185 toilet_rec_0.50: 0.5345 showercurtrain_rec_0.50: 0.1429 bathtub_rec_0.50: 0.4516 mAR_0.50: 0.2671 data_time: 0.2532 time: 2.7290 2025/05/13 03:36:50 - mmengine - INFO - Epoch(train) [135][10/91] base_lr: 1.8690e-04 lr: 1.8690e-04 eta: 2 days, 20:09:07 time: 10.5424 data_time: 1.5297 memory: 68703 grad_norm: 1.7331 loss: 1.7974 center_loss: 0.4890 size_loss: 0.1481 cls_loss: 0.5696 giou_loss: 0.5907 2025/05/13 03:38:28 - mmengine - INFO - Epoch(train) [135][20/91] base_lr: 1.8690e-04 lr: 1.8690e-04 eta: 2 days, 20:07:19 time: 10.5742 data_time: 1.5443 memory: 68703 grad_norm: 1.7376 loss: 1.8292 center_loss: 0.5021 size_loss: 0.1504 cls_loss: 0.5790 giou_loss: 0.5976 2025/05/13 03:40:05 - mmengine - INFO - Epoch(train) [135][30/91] base_lr: 1.8690e-04 lr: 1.8690e-04 eta: 2 days, 20:05:28 time: 10.5727 data_time: 1.5372 memory: 68703 grad_norm: 1.7967 loss: 1.8535 center_loss: 0.5170 size_loss: 0.1521 cls_loss: 0.5810 giou_loss: 0.6034 2025/05/13 03:41:41 - mmengine - INFO - Epoch(train) [135][40/91] base_lr: 1.8690e-04 lr: 1.8690e-04 eta: 2 days, 20:03:38 time: 10.5768 data_time: 1.5359 memory: 68702 grad_norm: 1.7918 loss: 1.8561 center_loss: 0.5174 size_loss: 0.1534 cls_loss: 0.5812 giou_loss: 0.6041 2025/05/13 03:43:19 - mmengine - INFO - Epoch(train) [135][50/91] base_lr: 1.8690e-04 lr: 1.8690e-04 eta: 2 days, 20:01:50 time: 10.7838 data_time: 1.5433 memory: 68702 grad_norm: 1.7033 loss: 1.8479 center_loss: 0.5153 size_loss: 0.1514 cls_loss: 0.5784 giou_loss: 0.6028 2025/05/13 03:44:56 - mmengine - INFO - Epoch(train) [135][60/91] base_lr: 1.8690e-04 lr: 1.8690e-04 eta: 2 days, 20:00:00 time: 9.7332 data_time: 0.5896 memory: 68702 grad_norm: 1.7262 loss: 1.8485 center_loss: 0.5156 size_loss: 0.1509 cls_loss: 0.5794 giou_loss: 0.6027 2025/05/13 03:46:33 - mmengine - INFO - Epoch(train) [135][70/91] base_lr: 1.8690e-04 lr: 1.8690e-04 eta: 2 days, 19:58:09 time: 9.7062 data_time: 0.5711 memory: 68702 grad_norm: 1.7640 loss: 1.8324 center_loss: 0.5073 size_loss: 0.1475 cls_loss: 0.5770 giou_loss: 0.6006 2025/05/13 03:48:10 - mmengine - INFO - Epoch(train) [135][80/91] base_lr: 1.8690e-04 lr: 1.8690e-04 eta: 2 days, 19:56:18 time: 9.6987 data_time: 0.5792 memory: 68702 grad_norm: 1.5729 loss: 1.8240 center_loss: 0.5007 size_loss: 0.1478 cls_loss: 0.5772 giou_loss: 0.5983 2025/05/13 03:49:45 - mmengine - INFO - Epoch(train) [135][90/91] base_lr: 1.8690e-04 lr: 1.8690e-04 eta: 2 days, 19:54:24 time: 9.6639 data_time: 0.5733 memory: 68702 grad_norm: 1.5178 loss: 1.8216 center_loss: 0.5005 size_loss: 0.1478 cls_loss: 0.5785 giou_loss: 0.5948 2025/05/13 03:49:46 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 03:52:14 - mmengine - INFO - Epoch(train) [136][10/91] base_lr: 1.8604e-04 lr: 1.8604e-04 eta: 2 days, 19:53:48 time: 10.5069 data_time: 1.4791 memory: 68702 grad_norm: 1.5911 loss: 1.8001 center_loss: 0.4919 size_loss: 0.1447 cls_loss: 0.5732 giou_loss: 0.5902 2025/05/13 03:53:51 - mmengine - INFO - Epoch(train) [136][20/91] base_lr: 1.8604e-04 lr: 1.8604e-04 eta: 2 days, 19:51:56 time: 10.4877 data_time: 1.4783 memory: 68703 grad_norm: 1.5823 loss: 1.8124 center_loss: 0.5005 size_loss: 0.1455 cls_loss: 0.5743 giou_loss: 0.5921 2025/05/13 03:55:28 - mmengine - INFO - Epoch(train) [136][30/91] base_lr: 1.8604e-04 lr: 1.8604e-04 eta: 2 days, 19:50:06 time: 10.4974 data_time: 1.4835 memory: 68703 grad_norm: 1.5567 loss: 1.8168 center_loss: 0.5034 size_loss: 0.1475 cls_loss: 0.5728 giou_loss: 0.5932 2025/05/13 03:57:05 - mmengine - INFO - Epoch(train) [136][40/91] base_lr: 1.8604e-04 lr: 1.8604e-04 eta: 2 days, 19:48:16 time: 10.5077 data_time: 1.4780 memory: 68702 grad_norm: 1.5882 loss: 1.8184 center_loss: 0.5052 size_loss: 0.1482 cls_loss: 0.5709 giou_loss: 0.5940 2025/05/13 03:58:42 - mmengine - INFO - Epoch(train) [136][50/91] base_lr: 1.8604e-04 lr: 1.8604e-04 eta: 2 days, 19:46:27 time: 10.7068 data_time: 1.4846 memory: 68703 grad_norm: 1.5344 loss: 1.8165 center_loss: 0.5033 size_loss: 0.1484 cls_loss: 0.5689 giou_loss: 0.5959 2025/05/13 04:00:18 - mmengine - INFO - Epoch(train) [136][60/91] base_lr: 1.8604e-04 lr: 1.8604e-04 eta: 2 days, 19:44:36 time: 9.6801 data_time: 0.5776 memory: 68703 grad_norm: 1.4358 loss: 1.8029 center_loss: 0.4980 size_loss: 0.1480 cls_loss: 0.5656 giou_loss: 0.5913 2025/05/13 04:01:55 - mmengine - INFO - Epoch(train) [136][70/91] base_lr: 1.8604e-04 lr: 1.8604e-04 eta: 2 days, 19:42:46 time: 9.6963 data_time: 0.5803 memory: 68703 grad_norm: 1.4428 loss: 1.7881 center_loss: 0.4910 size_loss: 0.1472 cls_loss: 0.5617 giou_loss: 0.5882 2025/05/13 04:03:32 - mmengine - INFO - Epoch(train) [136][80/91] base_lr: 1.8604e-04 lr: 1.8604e-04 eta: 2 days, 19:40:55 time: 9.6834 data_time: 0.5763 memory: 68703 grad_norm: 1.4139 loss: 1.7882 center_loss: 0.4869 size_loss: 0.1466 cls_loss: 0.5668 giou_loss: 0.5878 2025/05/13 04:05:07 - mmengine - INFO - Epoch(train) [136][90/91] base_lr: 1.8604e-04 lr: 1.8604e-04 eta: 2 days, 19:39:02 time: 9.6522 data_time: 0.5682 memory: 68702 grad_norm: 1.3860 loss: 1.7816 center_loss: 0.4850 size_loss: 0.1457 cls_loss: 0.5642 giou_loss: 0.5866 2025/05/13 04:05:09 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 04:05:09 - mmengine - INFO - Saving checkpoint at 136 epochs 2025/05/13 04:06:04 - mmengine - INFO - Epoch(val) [136][10/39] eta: 0:01:36 time: 2.8495 data_time: 0.3664 memory: 15952 2025/05/13 04:06:30 - mmengine - INFO - Epoch(val) [136][20/39] eta: 0:00:56 time: 2.7186 data_time: 0.2322 memory: 13407 2025/05/13 04:06:56 - mmengine - INFO - Epoch(val) [136][30/39] eta: 0:00:25 time: 2.7244 data_time: 0.2324 memory: 13407 2025/05/13 04:07:22 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.2363 | 0.4396 | 0.0159 | 0.1075 | | table | 0.4752 | 0.6057 | 0.1799 | 0.3029 | | sofa | 0.7498 | 0.8557 | 0.1985 | 0.3711 | | chair | 0.5911 | 0.7215 | 0.1471 | 0.3202 | | bookshelf | 0.3587 | 0.5714 | 0.1030 | 0.2468 | | curtain | 0.3082 | 0.4776 | 0.0215 | 0.0896 | | picture | 0.0317 | 0.1261 | 0.0010 | 0.0225 | | cabinet | 0.2924 | 0.5161 | 0.0535 | 0.1909 | | window | 0.1718 | 0.3901 | 0.0250 | 0.1135 | | door | 0.1368 | 0.4111 | 0.0168 | 0.1263 | | counter | 0.3805 | 0.5000 | 0.0199 | 0.0769 | | refrigerator | 0.4435 | 0.5789 | 0.1700 | 0.3158 | | sink | 0.5130 | 0.6224 | 0.1346 | 0.2857 | | desk | 0.6630 | 0.8031 | 0.2905 | 0.4567 | | bed | 0.8143 | 0.8395 | 0.3839 | 0.5432 | | toilet | 0.8855 | 0.9483 | 0.4127 | 0.5345 | | bathtub | 0.7408 | 0.8065 | 0.2738 | 0.4839 | | showercurtrain | 0.2075 | 0.5000 | 0.1022 | 0.1786 | +----------------+---------+---------+---------+---------+ | Overall | 0.4445 | 0.5952 | 0.1416 | 0.2648 | +----------------+---------+---------+---------+---------+ 2025/05/13 04:07:23 - mmengine - INFO - Epoch(val) [136][39/39] chair_AP_0.25: 0.5911 sofa_AP_0.25: 0.7498 table_AP_0.25: 0.4752 garbagebin_AP_0.25: 0.2363 bookshelf_AP_0.25: 0.3587 picture_AP_0.25: 0.0317 curtain_AP_0.25: 0.3082 door_AP_0.25: 0.1368 cabinet_AP_0.25: 0.2924 refrigerator_AP_0.25: 0.4435 counter_AP_0.25: 0.3805 sink_AP_0.25: 0.5130 window_AP_0.25: 0.1718 desk_AP_0.25: 0.6630 bed_AP_0.25: 0.8143 toilet_AP_0.25: 0.8855 showercurtrain_AP_0.25: 0.2075 bathtub_AP_0.25: 0.7408 mAP_0.25: 0.4445 chair_rec_0.25: 0.7215 sofa_rec_0.25: 0.8557 table_rec_0.25: 0.6057 garbagebin_rec_0.25: 0.4396 bookshelf_rec_0.25: 0.5714 picture_rec_0.25: 0.1261 curtain_rec_0.25: 0.4776 door_rec_0.25: 0.4111 cabinet_rec_0.25: 0.5161 refrigerator_rec_0.25: 0.5789 counter_rec_0.25: 0.5000 sink_rec_0.25: 0.6224 window_rec_0.25: 0.3901 desk_rec_0.25: 0.8031 bed_rec_0.25: 0.8395 toilet_rec_0.25: 0.9483 showercurtrain_rec_0.25: 0.5000 bathtub_rec_0.25: 0.8065 mAR_0.25: 0.5952 chair_AP_0.50: 0.1471 sofa_AP_0.50: 0.1985 table_AP_0.50: 0.1799 garbagebin_AP_0.50: 0.0159 bookshelf_AP_0.50: 0.1030 picture_AP_0.50: 0.0010 curtain_AP_0.50: 0.0215 door_AP_0.50: 0.0168 cabinet_AP_0.50: 0.0535 refrigerator_AP_0.50: 0.1700 counter_AP_0.50: 0.0199 sink_AP_0.50: 0.1346 window_AP_0.50: 0.0250 desk_AP_0.50: 0.2905 bed_AP_0.50: 0.3839 toilet_AP_0.50: 0.4127 showercurtrain_AP_0.50: 0.1022 bathtub_AP_0.50: 0.2738 mAP_0.50: 0.1416 chair_rec_0.50: 0.3202 sofa_rec_0.50: 0.3711 table_rec_0.50: 0.3029 garbagebin_rec_0.50: 0.1075 bookshelf_rec_0.50: 0.2468 picture_rec_0.50: 0.0225 curtain_rec_0.50: 0.0896 door_rec_0.50: 0.1263 cabinet_rec_0.50: 0.1909 refrigerator_rec_0.50: 0.3158 counter_rec_0.50: 0.0769 sink_rec_0.50: 0.2857 window_rec_0.50: 0.1135 desk_rec_0.50: 0.4567 bed_rec_0.50: 0.5432 toilet_rec_0.50: 0.5345 showercurtrain_rec_0.50: 0.1786 bathtub_rec_0.50: 0.4839 mAR_0.50: 0.2648 data_time: 0.2720 time: 2.7689 2025/05/13 04:07:23 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_118.pth is removed 2025/05/13 04:07:44 - mmengine - INFO - The best checkpoint with 0.4445 mAP_0.25 at 136 epoch is saved to best_mAP_0.25_epoch_136.pth. 2025/05/13 04:10:36 - mmengine - INFO - Epoch(train) [137][10/91] base_lr: 1.8519e-04 lr: 1.8519e-04 eta: 2 days, 19:38:19 time: 10.4517 data_time: 1.5004 memory: 68703 grad_norm: 1.4536 loss: 1.7812 center_loss: 0.4862 size_loss: 0.1464 cls_loss: 0.5637 giou_loss: 0.5850 2025/05/13 04:12:13 - mmengine - INFO - Epoch(train) [137][20/91] base_lr: 1.8519e-04 lr: 1.8519e-04 eta: 2 days, 19:36:29 time: 10.4626 data_time: 1.4843 memory: 68702 grad_norm: 1.5322 loss: 1.7950 center_loss: 0.4927 size_loss: 0.1478 cls_loss: 0.5646 giou_loss: 0.5899 2025/05/13 04:13:50 - mmengine - INFO - Epoch(train) [137][30/91] base_lr: 1.8519e-04 lr: 1.8519e-04 eta: 2 days, 19:34:39 time: 10.4544 data_time: 1.4798 memory: 68702 grad_norm: 1.5184 loss: 1.7952 center_loss: 0.4911 size_loss: 0.1474 cls_loss: 0.5665 giou_loss: 0.5902 2025/05/13 04:15:26 - mmengine - INFO - Epoch(train) [137][40/91] base_lr: 1.8519e-04 lr: 1.8519e-04 eta: 2 days, 19:32:48 time: 10.4491 data_time: 1.4825 memory: 68702 grad_norm: 1.5270 loss: 1.8058 center_loss: 0.5034 size_loss: 0.1482 cls_loss: 0.5609 giou_loss: 0.5934 2025/05/13 04:17:03 - mmengine - INFO - Epoch(train) [137][50/91] base_lr: 1.8519e-04 lr: 1.8519e-04 eta: 2 days, 19:30:58 time: 10.6393 data_time: 1.4932 memory: 68700 grad_norm: 1.6451 loss: 1.8088 center_loss: 0.5025 size_loss: 0.1480 cls_loss: 0.5647 giou_loss: 0.5936 2025/05/13 04:18:39 - mmengine - INFO - Epoch(train) [137][60/91] base_lr: 1.8519e-04 lr: 1.8519e-04 eta: 2 days, 19:29:07 time: 9.6738 data_time: 0.5550 memory: 68702 grad_norm: 1.6536 loss: 1.8211 center_loss: 0.5095 size_loss: 0.1488 cls_loss: 0.5660 giou_loss: 0.5969 2025/05/13 04:20:16 - mmengine - INFO - Epoch(train) [137][70/91] base_lr: 1.8519e-04 lr: 1.8519e-04 eta: 2 days, 19:27:17 time: 9.6708 data_time: 0.5718 memory: 68702 grad_norm: 1.5855 loss: 1.8383 center_loss: 0.5141 size_loss: 0.1515 cls_loss: 0.5710 giou_loss: 0.6017 2025/05/13 04:21:53 - mmengine - INFO - Epoch(train) [137][80/91] base_lr: 1.8519e-04 lr: 1.8519e-04 eta: 2 days, 19:25:26 time: 9.6617 data_time: 0.5720 memory: 68702 grad_norm: 1.5370 loss: 1.8297 center_loss: 0.5092 size_loss: 0.1499 cls_loss: 0.5719 giou_loss: 0.5986 2025/05/13 04:23:28 - mmengine - INFO - Epoch(train) [137][90/91] base_lr: 1.8519e-04 lr: 1.8519e-04 eta: 2 days, 19:23:33 time: 9.6422 data_time: 0.5602 memory: 68702 grad_norm: 1.5306 loss: 1.8200 center_loss: 0.5015 size_loss: 0.1497 cls_loss: 0.5726 giou_loss: 0.5962 2025/05/13 04:23:30 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 04:25:53 - mmengine - INFO - Epoch(train) [138][10/91] base_lr: 1.8432e-04 lr: 1.8432e-04 eta: 2 days, 19:22:46 time: 10.4089 data_time: 1.4210 memory: 68702 grad_norm: 1.5130 loss: 1.8242 center_loss: 0.5008 size_loss: 0.1503 cls_loss: 0.5762 giou_loss: 0.5968 2025/05/13 04:27:29 - mmengine - INFO - Epoch(train) [138][20/91] base_lr: 1.8432e-04 lr: 1.8432e-04 eta: 2 days, 19:20:54 time: 10.3986 data_time: 1.4129 memory: 68702 grad_norm: 1.4726 loss: 1.8074 center_loss: 0.4905 size_loss: 0.1471 cls_loss: 0.5754 giou_loss: 0.5943 2025/05/13 04:29:06 - mmengine - INFO - Epoch(train) [138][30/91] base_lr: 1.8432e-04 lr: 1.8432e-04 eta: 2 days, 19:19:04 time: 10.4023 data_time: 1.4101 memory: 68702 grad_norm: 1.4825 loss: 1.7996 center_loss: 0.4911 size_loss: 0.1439 cls_loss: 0.5716 giou_loss: 0.5931 2025/05/13 04:30:43 - mmengine - INFO - Epoch(train) [138][40/91] base_lr: 1.8432e-04 lr: 1.8432e-04 eta: 2 days, 19:17:14 time: 10.4116 data_time: 1.4075 memory: 68702 grad_norm: 1.5395 loss: 1.8111 center_loss: 0.4970 size_loss: 0.1458 cls_loss: 0.5723 giou_loss: 0.5960 2025/05/13 04:32:20 - mmengine - INFO - Epoch(train) [138][50/91] base_lr: 1.8432e-04 lr: 1.8432e-04 eta: 2 days, 19:15:25 time: 10.6024 data_time: 1.4329 memory: 68702 grad_norm: 1.5602 loss: 1.7955 center_loss: 0.4908 size_loss: 0.1455 cls_loss: 0.5676 giou_loss: 0.5916 2025/05/13 04:33:57 - mmengine - INFO - Epoch(train) [138][60/91] base_lr: 1.8432e-04 lr: 1.8432e-04 eta: 2 days, 19:13:35 time: 9.6802 data_time: 0.5639 memory: 68702 grad_norm: 1.5763 loss: 1.7963 center_loss: 0.4955 size_loss: 0.1463 cls_loss: 0.5631 giou_loss: 0.5914 2025/05/13 04:35:34 - mmengine - INFO - Epoch(train) [138][70/91] base_lr: 1.8432e-04 lr: 1.8432e-04 eta: 2 days, 19:11:45 time: 9.6894 data_time: 0.5734 memory: 68703 grad_norm: 1.6669 loss: 1.8062 center_loss: 0.5009 size_loss: 0.1478 cls_loss: 0.5650 giou_loss: 0.5925 2025/05/13 04:37:10 - mmengine - INFO - Epoch(train) [138][80/91] base_lr: 1.8432e-04 lr: 1.8432e-04 eta: 2 days, 19:09:54 time: 9.6779 data_time: 0.5843 memory: 68702 grad_norm: 1.7561 loss: 1.8165 center_loss: 0.5045 size_loss: 0.1506 cls_loss: 0.5684 giou_loss: 0.5931 2025/05/13 04:38:46 - mmengine - INFO - Epoch(train) [138][90/91] base_lr: 1.8432e-04 lr: 1.8432e-04 eta: 2 days, 19:08:01 time: 9.6545 data_time: 0.5733 memory: 68702 grad_norm: 1.7285 loss: 1.8101 center_loss: 0.4984 size_loss: 0.1494 cls_loss: 0.5706 giou_loss: 0.5916 2025/05/13 04:38:47 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 04:38:47 - mmengine - INFO - Saving checkpoint at 138 epochs 2025/05/13 04:39:43 - mmengine - INFO - Epoch(val) [138][10/39] eta: 0:01:34 time: 2.8678 data_time: 0.3671 memory: 15952 2025/05/13 04:40:09 - mmengine - INFO - Epoch(val) [138][20/39] eta: 0:00:55 time: 2.7168 data_time: 0.2191 memory: 13407 2025/05/13 04:40:35 - mmengine - INFO - Epoch(val) [138][30/39] eta: 0:00:25 time: 2.7211 data_time: 0.2181 memory: 13407 2025/05/13 04:41:02 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.2160 | 0.4321 | 0.0164 | 0.1000 | | table | 0.4605 | 0.6143 | 0.1519 | 0.2800 | | sofa | 0.6815 | 0.8557 | 0.1678 | 0.4021 | | curtain | 0.2616 | 0.5224 | 0.0482 | 0.1343 | | chair | 0.5442 | 0.7076 | 0.1534 | 0.3173 | | picture | 0.0200 | 0.1351 | 0.0053 | 0.0270 | | bookshelf | 0.3014 | 0.5844 | 0.0798 | 0.2597 | | window | 0.1540 | 0.3582 | 0.0227 | 0.0922 | | cabinet | 0.2570 | 0.4839 | 0.0474 | 0.1613 | | door | 0.1798 | 0.4775 | 0.0189 | 0.1306 | | counter | 0.3224 | 0.5577 | 0.0657 | 0.1923 | | refrigerator | 0.4242 | 0.5965 | 0.2250 | 0.3509 | | sink | 0.4205 | 0.5816 | 0.1039 | 0.2551 | | desk | 0.7310 | 0.8740 | 0.2175 | 0.4331 | | bed | 0.7979 | 0.8272 | 0.4195 | 0.6049 | | toilet | 0.8840 | 0.9483 | 0.4376 | 0.5172 | | bathtub | 0.8258 | 0.8710 | 0.2565 | 0.4839 | | showercurtrain | 0.2754 | 0.5357 | 0.0140 | 0.1071 | +----------------+---------+---------+---------+---------+ | Overall | 0.4310 | 0.6091 | 0.1362 | 0.2694 | +----------------+---------+---------+---------+---------+ 2025/05/13 04:41:02 - mmengine - INFO - Epoch(val) [138][39/39] chair_AP_0.25: 0.5442 sofa_AP_0.25: 0.6815 table_AP_0.25: 0.4605 garbagebin_AP_0.25: 0.2160 bookshelf_AP_0.25: 0.3014 picture_AP_0.25: 0.0200 curtain_AP_0.25: 0.2616 door_AP_0.25: 0.1798 cabinet_AP_0.25: 0.2570 refrigerator_AP_0.25: 0.4242 counter_AP_0.25: 0.3224 sink_AP_0.25: 0.4205 window_AP_0.25: 0.1540 desk_AP_0.25: 0.7310 bed_AP_0.25: 0.7979 toilet_AP_0.25: 0.8840 showercurtrain_AP_0.25: 0.2754 bathtub_AP_0.25: 0.8258 mAP_0.25: 0.4310 chair_rec_0.25: 0.7076 sofa_rec_0.25: 0.8557 table_rec_0.25: 0.6143 garbagebin_rec_0.25: 0.4321 bookshelf_rec_0.25: 0.5844 picture_rec_0.25: 0.1351 curtain_rec_0.25: 0.5224 door_rec_0.25: 0.4775 cabinet_rec_0.25: 0.4839 refrigerator_rec_0.25: 0.5965 counter_rec_0.25: 0.5577 sink_rec_0.25: 0.5816 window_rec_0.25: 0.3582 desk_rec_0.25: 0.8740 bed_rec_0.25: 0.8272 toilet_rec_0.25: 0.9483 showercurtrain_rec_0.25: 0.5357 bathtub_rec_0.25: 0.8710 mAR_0.25: 0.6091 chair_AP_0.50: 0.1534 sofa_AP_0.50: 0.1678 table_AP_0.50: 0.1519 garbagebin_AP_0.50: 0.0164 bookshelf_AP_0.50: 0.0798 picture_AP_0.50: 0.0053 curtain_AP_0.50: 0.0482 door_AP_0.50: 0.0189 cabinet_AP_0.50: 0.0474 refrigerator_AP_0.50: 0.2250 counter_AP_0.50: 0.0657 sink_AP_0.50: 0.1039 window_AP_0.50: 0.0227 desk_AP_0.50: 0.2175 bed_AP_0.50: 0.4195 toilet_AP_0.50: 0.4376 showercurtrain_AP_0.50: 0.0140 bathtub_AP_0.50: 0.2565 mAP_0.50: 0.1362 chair_rec_0.50: 0.3173 sofa_rec_0.50: 0.4021 table_rec_0.50: 0.2800 garbagebin_rec_0.50: 0.1000 bookshelf_rec_0.50: 0.2597 picture_rec_0.50: 0.0270 curtain_rec_0.50: 0.1343 door_rec_0.50: 0.1306 cabinet_rec_0.50: 0.1613 refrigerator_rec_0.50: 0.3509 counter_rec_0.50: 0.1923 sink_rec_0.50: 0.2551 window_rec_0.50: 0.0922 desk_rec_0.50: 0.4331 bed_rec_0.50: 0.6049 toilet_rec_0.50: 0.5172 showercurtrain_rec_0.50: 0.1071 bathtub_rec_0.50: 0.4839 mAR_0.50: 0.2694 data_time: 0.2530 time: 2.7513 2025/05/13 04:43:33 - mmengine - INFO - Epoch(train) [139][10/91] base_lr: 1.8346e-04 lr: 1.8346e-04 eta: 2 days, 19:07:29 time: 10.5876 data_time: 1.5082 memory: 68702 grad_norm: 1.7708 loss: 1.8035 center_loss: 0.4961 size_loss: 0.1478 cls_loss: 0.5668 giou_loss: 0.5927 2025/05/13 04:45:10 - mmengine - INFO - Epoch(train) [139][20/91] base_lr: 1.8346e-04 lr: 1.8346e-04 eta: 2 days, 19:05:39 time: 10.5876 data_time: 1.5243 memory: 68700 grad_norm: 1.7253 loss: 1.7867 center_loss: 0.4865 size_loss: 0.1439 cls_loss: 0.5670 giou_loss: 0.5893 2025/05/13 04:46:47 - mmengine - INFO - Epoch(train) [139][30/91] base_lr: 1.8346e-04 lr: 1.8346e-04 eta: 2 days, 19:03:49 time: 10.5840 data_time: 1.5101 memory: 68702 grad_norm: 1.6610 loss: 1.7787 center_loss: 0.4834 size_loss: 0.1448 cls_loss: 0.5626 giou_loss: 0.5879 2025/05/13 04:48:23 - mmengine - INFO - Epoch(train) [139][40/91] base_lr: 1.8346e-04 lr: 1.8346e-04 eta: 2 days, 19:01:58 time: 10.5881 data_time: 1.5023 memory: 68702 grad_norm: 1.6638 loss: 1.7644 center_loss: 0.4781 size_loss: 0.1426 cls_loss: 0.5574 giou_loss: 0.5862 2025/05/13 04:50:01 - mmengine - INFO - Epoch(train) [139][50/91] base_lr: 1.8346e-04 lr: 1.8346e-04 eta: 2 days, 19:00:10 time: 10.7883 data_time: 1.5272 memory: 68702 grad_norm: 1.5042 loss: 1.7644 center_loss: 0.4800 size_loss: 0.1418 cls_loss: 0.5557 giou_loss: 0.5868 2025/05/13 04:51:38 - mmengine - INFO - Epoch(train) [139][60/91] base_lr: 1.8346e-04 lr: 1.8346e-04 eta: 2 days, 18:58:21 time: 9.7046 data_time: 0.5867 memory: 68702 grad_norm: 1.5311 loss: 1.7564 center_loss: 0.4756 size_loss: 0.1405 cls_loss: 0.5553 giou_loss: 0.5849 2025/05/13 04:53:15 - mmengine - INFO - Epoch(train) [139][70/91] base_lr: 1.8346e-04 lr: 1.8346e-04 eta: 2 days, 18:56:31 time: 9.6995 data_time: 0.5686 memory: 68703 grad_norm: 1.6034 loss: 1.7681 center_loss: 0.4789 size_loss: 0.1423 cls_loss: 0.5568 giou_loss: 0.5901 2025/05/13 04:54:52 - mmengine - INFO - Epoch(train) [139][80/91] base_lr: 1.8346e-04 lr: 1.8346e-04 eta: 2 days, 18:54:40 time: 9.6954 data_time: 0.5768 memory: 68703 grad_norm: 1.5670 loss: 1.7669 center_loss: 0.4785 size_loss: 0.1413 cls_loss: 0.5570 giou_loss: 0.5901 2025/05/13 04:56:28 - mmengine - INFO - Epoch(train) [139][90/91] base_lr: 1.8346e-04 lr: 1.8346e-04 eta: 2 days, 18:52:49 time: 9.6854 data_time: 0.5663 memory: 68703 grad_norm: 1.4757 loss: 1.7645 center_loss: 0.4783 size_loss: 0.1442 cls_loss: 0.5566 giou_loss: 0.5855 2025/05/13 04:56:29 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 04:58:56 - mmengine - INFO - Epoch(train) [140][10/91] base_lr: 1.8259e-04 lr: 1.8259e-04 eta: 2 days, 18:52:06 time: 10.4915 data_time: 1.3803 memory: 68702 grad_norm: 1.5409 loss: 1.7700 center_loss: 0.4816 size_loss: 0.1446 cls_loss: 0.5573 giou_loss: 0.5864 2025/05/13 05:00:32 - mmengine - INFO - Epoch(train) [140][20/91] base_lr: 1.8259e-04 lr: 1.8259e-04 eta: 2 days, 18:50:16 time: 10.4863 data_time: 1.3763 memory: 68703 grad_norm: 1.5320 loss: 1.7754 center_loss: 0.4859 size_loss: 0.1439 cls_loss: 0.5575 giou_loss: 0.5880 2025/05/13 05:02:09 - mmengine - INFO - Epoch(train) [140][30/91] base_lr: 1.8259e-04 lr: 1.8259e-04 eta: 2 days, 18:48:26 time: 10.4881 data_time: 1.3790 memory: 68703 grad_norm: 1.4604 loss: 1.7651 center_loss: 0.4833 size_loss: 0.1443 cls_loss: 0.5530 giou_loss: 0.5845 2025/05/13 05:03:46 - mmengine - INFO - Epoch(train) [140][40/91] base_lr: 1.8259e-04 lr: 1.8259e-04 eta: 2 days, 18:46:35 time: 10.4876 data_time: 1.3757 memory: 68700 grad_norm: 1.5008 loss: 1.7688 center_loss: 0.4837 size_loss: 0.1447 cls_loss: 0.5559 giou_loss: 0.5845 2025/05/13 05:05:23 - mmengine - INFO - Epoch(train) [140][50/91] base_lr: 1.8259e-04 lr: 1.8259e-04 eta: 2 days, 18:44:46 time: 10.6670 data_time: 1.3928 memory: 68703 grad_norm: 1.5143 loss: 1.7791 center_loss: 0.4892 size_loss: 0.1444 cls_loss: 0.5575 giou_loss: 0.5880 2025/05/13 05:07:00 - mmengine - INFO - Epoch(train) [140][60/91] base_lr: 1.8259e-04 lr: 1.8259e-04 eta: 2 days, 18:42:56 time: 9.6830 data_time: 0.5669 memory: 68702 grad_norm: 1.6499 loss: 1.7828 center_loss: 0.4905 size_loss: 0.1454 cls_loss: 0.5585 giou_loss: 0.5884 2025/05/13 05:08:37 - mmengine - INFO - Epoch(train) [140][70/91] base_lr: 1.8259e-04 lr: 1.8259e-04 eta: 2 days, 18:41:06 time: 9.6810 data_time: 0.5825 memory: 68702 grad_norm: 1.6895 loss: 1.8024 center_loss: 0.4986 size_loss: 0.1481 cls_loss: 0.5664 giou_loss: 0.5893 2025/05/13 05:10:13 - mmengine - INFO - Epoch(train) [140][80/91] base_lr: 1.8259e-04 lr: 1.8259e-04 eta: 2 days, 18:39:16 time: 9.6752 data_time: 0.5873 memory: 68702 grad_norm: 1.7562 loss: 1.8048 center_loss: 0.4975 size_loss: 0.1459 cls_loss: 0.5728 giou_loss: 0.5887 2025/05/13 05:11:49 - mmengine - INFO - Epoch(train) [140][90/91] base_lr: 1.8259e-04 lr: 1.8259e-04 eta: 2 days, 18:37:24 time: 9.6608 data_time: 0.5801 memory: 68702 grad_norm: 1.7152 loss: 1.8235 center_loss: 0.5072 size_loss: 0.1466 cls_loss: 0.5745 giou_loss: 0.5952 2025/05/13 05:11:51 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 05:11:51 - mmengine - INFO - Saving checkpoint at 140 epochs 2025/05/13 05:12:44 - mmengine - INFO - Epoch(val) [140][10/39] eta: 0:01:32 time: 2.8418 data_time: 0.3392 memory: 15952 2025/05/13 05:13:09 - mmengine - INFO - Epoch(val) [140][20/39] eta: 0:00:54 time: 2.7043 data_time: 0.2059 memory: 13407 2025/05/13 05:13:35 - mmengine - INFO - Epoch(val) [140][30/39] eta: 0:00:25 time: 2.7087 data_time: 0.2065 memory: 13407 2025/05/13 05:14:02 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.2737 | 0.4604 | 0.0233 | 0.1151 | | chair | 0.5565 | 0.7054 | 0.1568 | 0.3136 | | sofa | 0.6941 | 0.8247 | 0.2097 | 0.4227 | | table | 0.4526 | 0.5743 | 0.1271 | 0.2486 | | bookshelf | 0.3094 | 0.5455 | 0.1196 | 0.2468 | | curtain | 0.2595 | 0.4776 | 0.0730 | 0.1493 | | picture | 0.0177 | 0.1216 | 0.0002 | 0.0135 | | window | 0.1465 | 0.3794 | 0.0305 | 0.1064 | | cabinet | 0.2794 | 0.5027 | 0.0373 | 0.1667 | | door | 0.1468 | 0.4775 | 0.0170 | 0.1285 | | counter | 0.2899 | 0.5000 | 0.0284 | 0.1154 | | sink | 0.5209 | 0.6224 | 0.1336 | 0.2449 | | refrigerator | 0.4441 | 0.5965 | 0.2567 | 0.3509 | | desk | 0.6556 | 0.8110 | 0.2002 | 0.4488 | | bed | 0.8504 | 0.8642 | 0.4240 | 0.5926 | | toilet | 0.8941 | 0.9310 | 0.4338 | 0.5172 | | bathtub | 0.6998 | 0.8065 | 0.2550 | 0.4516 | | showercurtrain | 0.3402 | 0.5714 | 0.0271 | 0.1071 | +----------------+---------+---------+---------+---------+ | Overall | 0.4351 | 0.5985 | 0.1419 | 0.2633 | +----------------+---------+---------+---------+---------+ 2025/05/13 05:14:02 - mmengine - INFO - Epoch(val) [140][39/39] chair_AP_0.25: 0.5565 sofa_AP_0.25: 0.6941 table_AP_0.25: 0.4526 garbagebin_AP_0.25: 0.2737 bookshelf_AP_0.25: 0.3094 picture_AP_0.25: 0.0177 curtain_AP_0.25: 0.2595 door_AP_0.25: 0.1468 cabinet_AP_0.25: 0.2794 refrigerator_AP_0.25: 0.4441 counter_AP_0.25: 0.2899 sink_AP_0.25: 0.5209 window_AP_0.25: 0.1465 desk_AP_0.25: 0.6556 bed_AP_0.25: 0.8504 toilet_AP_0.25: 0.8941 showercurtrain_AP_0.25: 0.3402 bathtub_AP_0.25: 0.6998 mAP_0.25: 0.4351 chair_rec_0.25: 0.7054 sofa_rec_0.25: 0.8247 table_rec_0.25: 0.5743 garbagebin_rec_0.25: 0.4604 bookshelf_rec_0.25: 0.5455 picture_rec_0.25: 0.1216 curtain_rec_0.25: 0.4776 door_rec_0.25: 0.4775 cabinet_rec_0.25: 0.5027 refrigerator_rec_0.25: 0.5965 counter_rec_0.25: 0.5000 sink_rec_0.25: 0.6224 window_rec_0.25: 0.3794 desk_rec_0.25: 0.8110 bed_rec_0.25: 0.8642 toilet_rec_0.25: 0.9310 showercurtrain_rec_0.25: 0.5714 bathtub_rec_0.25: 0.8065 mAR_0.25: 0.5985 chair_AP_0.50: 0.1568 sofa_AP_0.50: 0.2097 table_AP_0.50: 0.1271 garbagebin_AP_0.50: 0.0233 bookshelf_AP_0.50: 0.1196 picture_AP_0.50: 0.0002 curtain_AP_0.50: 0.0730 door_AP_0.50: 0.0170 cabinet_AP_0.50: 0.0373 refrigerator_AP_0.50: 0.2567 counter_AP_0.50: 0.0284 sink_AP_0.50: 0.1336 window_AP_0.50: 0.0305 desk_AP_0.50: 0.2002 bed_AP_0.50: 0.4240 toilet_AP_0.50: 0.4338 showercurtrain_AP_0.50: 0.0271 bathtub_AP_0.50: 0.2550 mAP_0.50: 0.1419 chair_rec_0.50: 0.3136 sofa_rec_0.50: 0.4227 table_rec_0.50: 0.2486 garbagebin_rec_0.50: 0.1151 bookshelf_rec_0.50: 0.2468 picture_rec_0.50: 0.0135 curtain_rec_0.50: 0.1493 door_rec_0.50: 0.1285 cabinet_rec_0.50: 0.1667 refrigerator_rec_0.50: 0.3509 counter_rec_0.50: 0.1154 sink_rec_0.50: 0.2449 window_rec_0.50: 0.1064 desk_rec_0.50: 0.4488 bed_rec_0.50: 0.5926 toilet_rec_0.50: 0.5172 showercurtrain_rec_0.50: 0.1071 bathtub_rec_0.50: 0.4516 mAR_0.50: 0.2633 data_time: 0.2314 time: 2.7296 2025/05/13 05:16:32 - mmengine - INFO - Epoch(train) [141][10/91] base_lr: 1.8172e-04 lr: 1.8172e-04 eta: 2 days, 18:36:47 time: 10.5669 data_time: 1.4189 memory: 68702 grad_norm: 1.7385 loss: 1.8239 center_loss: 0.5075 size_loss: 0.1466 cls_loss: 0.5758 giou_loss: 0.5941 2025/05/13 05:18:08 - mmengine - INFO - Epoch(train) [141][20/91] base_lr: 1.8172e-04 lr: 1.8172e-04 eta: 2 days, 18:34:56 time: 10.5495 data_time: 1.4065 memory: 68702 grad_norm: 1.6750 loss: 1.8134 center_loss: 0.4988 size_loss: 0.1463 cls_loss: 0.5773 giou_loss: 0.5910 2025/05/13 05:19:46 - mmengine - INFO - Epoch(train) [141][30/91] base_lr: 1.8172e-04 lr: 1.8172e-04 eta: 2 days, 18:33:08 time: 10.5642 data_time: 1.4080 memory: 68703 grad_norm: 1.6388 loss: 1.8097 center_loss: 0.4951 size_loss: 0.1466 cls_loss: 0.5765 giou_loss: 0.5915 2025/05/13 05:21:22 - mmengine - INFO - Epoch(train) [141][40/91] base_lr: 1.8172e-04 lr: 1.8172e-04 eta: 2 days, 18:31:17 time: 10.5614 data_time: 1.4007 memory: 68702 grad_norm: 1.5479 loss: 1.8150 center_loss: 0.4978 size_loss: 0.1480 cls_loss: 0.5770 giou_loss: 0.5923 2025/05/13 05:22:59 - mmengine - INFO - Epoch(train) [141][50/91] base_lr: 1.8172e-04 lr: 1.8172e-04 eta: 2 days, 18:29:28 time: 10.7426 data_time: 1.4128 memory: 68702 grad_norm: 1.5081 loss: 1.8012 center_loss: 0.4926 size_loss: 0.1466 cls_loss: 0.5742 giou_loss: 0.5878 2025/05/13 05:24:32 - mmengine - INFO - Epoch(train) [141][60/91] base_lr: 1.8172e-04 lr: 1.8172e-04 eta: 2 days, 18:27:32 time: 9.6098 data_time: 0.5518 memory: 68702 grad_norm: 1.4390 loss: 1.7924 center_loss: 0.4877 size_loss: 0.1438 cls_loss: 0.5739 giou_loss: 0.5871 2025/05/13 05:26:08 - mmengine - INFO - Epoch(train) [141][70/91] base_lr: 1.8172e-04 lr: 1.8172e-04 eta: 2 days, 18:25:40 time: 9.6001 data_time: 0.5464 memory: 68703 grad_norm: 1.4210 loss: 1.7836 center_loss: 0.4863 size_loss: 0.1426 cls_loss: 0.5690 giou_loss: 0.5857 2025/05/13 05:27:45 - mmengine - INFO - Epoch(train) [141][80/91] base_lr: 1.8172e-04 lr: 1.8172e-04 eta: 2 days, 18:23:50 time: 9.5830 data_time: 0.5437 memory: 68703 grad_norm: 1.4588 loss: 1.7612 center_loss: 0.4796 size_loss: 0.1403 cls_loss: 0.5606 giou_loss: 0.5808 2025/05/13 05:29:20 - mmengine - INFO - Epoch(train) [141][90/91] base_lr: 1.8172e-04 lr: 1.8172e-04 eta: 2 days, 18:21:57 time: 9.5614 data_time: 0.5387 memory: 68700 grad_norm: 1.6985 loss: 1.7589 center_loss: 0.4770 size_loss: 0.1397 cls_loss: 0.5613 giou_loss: 0.5809 2025/05/13 05:29:22 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 05:31:50 - mmengine - INFO - Epoch(train) [142][10/91] base_lr: 1.8084e-04 lr: 1.8084e-04 eta: 2 days, 18:21:16 time: 10.4196 data_time: 1.3493 memory: 68702 grad_norm: 1.8056 loss: 1.7618 center_loss: 0.4779 size_loss: 0.1400 cls_loss: 0.5606 giou_loss: 0.5835 2025/05/13 05:33:26 - mmengine - INFO - Epoch(train) [142][20/91] base_lr: 1.8084e-04 lr: 1.8084e-04 eta: 2 days, 18:19:25 time: 10.5061 data_time: 1.3635 memory: 68702 grad_norm: 1.8320 loss: 1.7849 center_loss: 0.4876 size_loss: 0.1430 cls_loss: 0.5675 giou_loss: 0.5869 2025/05/13 05:35:03 - mmengine - INFO - Epoch(train) [142][30/91] base_lr: 1.8084e-04 lr: 1.8084e-04 eta: 2 days, 18:17:36 time: 10.5083 data_time: 1.3776 memory: 68702 grad_norm: 1.8826 loss: 1.8110 center_loss: 0.5035 size_loss: 0.1431 cls_loss: 0.5723 giou_loss: 0.5921 2025/05/13 05:36:40 - mmengine - INFO - Epoch(train) [142][40/91] base_lr: 1.8084e-04 lr: 1.8084e-04 eta: 2 days, 18:15:46 time: 10.5133 data_time: 1.3817 memory: 68703 grad_norm: 1.7887 loss: 1.8178 center_loss: 0.5088 size_loss: 0.1436 cls_loss: 0.5707 giou_loss: 0.5947 2025/05/13 05:38:17 - mmengine - INFO - Epoch(train) [142][50/91] base_lr: 1.8084e-04 lr: 1.8084e-04 eta: 2 days, 18:13:57 time: 10.7034 data_time: 1.3914 memory: 68701 grad_norm: 1.5569 loss: 1.8240 center_loss: 0.5151 size_loss: 0.1459 cls_loss: 0.5652 giou_loss: 0.5978 2025/05/13 05:39:54 - mmengine - INFO - Epoch(train) [142][60/91] base_lr: 1.8084e-04 lr: 1.8084e-04 eta: 2 days, 18:12:07 time: 9.6856 data_time: 0.5817 memory: 68702 grad_norm: 1.5021 loss: 1.8146 center_loss: 0.5124 size_loss: 0.1462 cls_loss: 0.5620 giou_loss: 0.5941 2025/05/13 05:41:31 - mmengine - INFO - Epoch(train) [142][70/91] base_lr: 1.8084e-04 lr: 1.8084e-04 eta: 2 days, 18:10:17 time: 9.6859 data_time: 0.5919 memory: 68702 grad_norm: 1.5622 loss: 1.8006 center_loss: 0.5039 size_loss: 0.1440 cls_loss: 0.5596 giou_loss: 0.5931 2025/05/13 05:43:07 - mmengine - INFO - Epoch(train) [142][80/91] base_lr: 1.8084e-04 lr: 1.8084e-04 eta: 2 days, 18:08:27 time: 9.6739 data_time: 0.5836 memory: 68702 grad_norm: 1.5781 loss: 1.7889 center_loss: 0.4924 size_loss: 0.1448 cls_loss: 0.5606 giou_loss: 0.5910 2025/05/13 05:44:42 - mmengine - INFO - Epoch(train) [142][90/91] base_lr: 1.8084e-04 lr: 1.8084e-04 eta: 2 days, 18:06:34 time: 9.6397 data_time: 0.5720 memory: 68703 grad_norm: 1.5681 loss: 1.8096 center_loss: 0.4987 size_loss: 0.1479 cls_loss: 0.5678 giou_loss: 0.5953 2025/05/13 05:44:44 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 05:44:44 - mmengine - INFO - Saving checkpoint at 142 epochs 2025/05/13 05:45:38 - mmengine - INFO - Epoch(val) [142][10/39] eta: 0:01:35 time: 2.8403 data_time: 0.3336 memory: 15952 2025/05/13 05:46:04 - mmengine - INFO - Epoch(val) [142][20/39] eta: 0:00:55 time: 2.7206 data_time: 0.2135 memory: 13407 2025/05/13 05:46:30 - mmengine - INFO - Epoch(val) [142][30/39] eta: 0:00:25 time: 2.7359 data_time: 0.2124 memory: 13407 2025/05/13 05:46:56 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | chair | 0.5729 | 0.7193 | 0.1482 | 0.3319 | | table | 0.4726 | 0.6114 | 0.1415 | 0.2771 | | garbagebin | 0.2515 | 0.4377 | 0.0293 | 0.1415 | | sofa | 0.7387 | 0.8763 | 0.1955 | 0.3814 | | curtain | 0.2681 | 0.5224 | 0.0155 | 0.0746 | | bookshelf | 0.2881 | 0.5714 | 0.0862 | 0.2597 | | picture | 0.0175 | 0.1216 | 0.0039 | 0.0270 | | window | 0.1458 | 0.3865 | 0.0125 | 0.0851 | | cabinet | 0.3092 | 0.5108 | 0.0433 | 0.1747 | | door | 0.1784 | 0.4797 | 0.0272 | 0.1349 | | counter | 0.2948 | 0.5000 | 0.0397 | 0.1731 | | sink | 0.4920 | 0.6122 | 0.0966 | 0.2449 | | refrigerator | 0.4967 | 0.6667 | 0.2253 | 0.4035 | | desk | 0.6710 | 0.8346 | 0.2741 | 0.4567 | | bed | 0.8421 | 0.8765 | 0.3793 | 0.5309 | | toilet | 0.8251 | 0.8966 | 0.3413 | 0.4483 | | bathtub | 0.7857 | 0.8387 | 0.2749 | 0.4516 | | showercurtrain | 0.3167 | 0.5357 | 0.0333 | 0.2143 | +----------------+---------+---------+---------+---------+ | Overall | 0.4426 | 0.6110 | 0.1315 | 0.2673 | +----------------+---------+---------+---------+---------+ 2025/05/13 05:46:56 - mmengine - INFO - Epoch(val) [142][39/39] chair_AP_0.25: 0.5729 sofa_AP_0.25: 0.7387 table_AP_0.25: 0.4726 garbagebin_AP_0.25: 0.2515 bookshelf_AP_0.25: 0.2881 picture_AP_0.25: 0.0175 curtain_AP_0.25: 0.2681 door_AP_0.25: 0.1784 cabinet_AP_0.25: 0.3092 refrigerator_AP_0.25: 0.4967 counter_AP_0.25: 0.2948 sink_AP_0.25: 0.4920 window_AP_0.25: 0.1458 desk_AP_0.25: 0.6710 bed_AP_0.25: 0.8421 toilet_AP_0.25: 0.8251 showercurtrain_AP_0.25: 0.3167 bathtub_AP_0.25: 0.7857 mAP_0.25: 0.4426 chair_rec_0.25: 0.7193 sofa_rec_0.25: 0.8763 table_rec_0.25: 0.6114 garbagebin_rec_0.25: 0.4377 bookshelf_rec_0.25: 0.5714 picture_rec_0.25: 0.1216 curtain_rec_0.25: 0.5224 door_rec_0.25: 0.4797 cabinet_rec_0.25: 0.5108 refrigerator_rec_0.25: 0.6667 counter_rec_0.25: 0.5000 sink_rec_0.25: 0.6122 window_rec_0.25: 0.3865 desk_rec_0.25: 0.8346 bed_rec_0.25: 0.8765 toilet_rec_0.25: 0.8966 showercurtrain_rec_0.25: 0.5357 bathtub_rec_0.25: 0.8387 mAR_0.25: 0.6110 chair_AP_0.50: 0.1482 sofa_AP_0.50: 0.1955 table_AP_0.50: 0.1415 garbagebin_AP_0.50: 0.0293 bookshelf_AP_0.50: 0.0862 picture_AP_0.50: 0.0039 curtain_AP_0.50: 0.0155 door_AP_0.50: 0.0272 cabinet_AP_0.50: 0.0433 refrigerator_AP_0.50: 0.2253 counter_AP_0.50: 0.0397 sink_AP_0.50: 0.0966 window_AP_0.50: 0.0125 desk_AP_0.50: 0.2741 bed_AP_0.50: 0.3793 toilet_AP_0.50: 0.3413 showercurtrain_AP_0.50: 0.0333 bathtub_AP_0.50: 0.2749 mAP_0.50: 0.1315 chair_rec_0.50: 0.3319 sofa_rec_0.50: 0.3814 table_rec_0.50: 0.2771 garbagebin_rec_0.50: 0.1415 bookshelf_rec_0.50: 0.2597 picture_rec_0.50: 0.0270 curtain_rec_0.50: 0.0746 door_rec_0.50: 0.1349 cabinet_rec_0.50: 0.1747 refrigerator_rec_0.50: 0.4035 counter_rec_0.50: 0.1731 sink_rec_0.50: 0.2449 window_rec_0.50: 0.0851 desk_rec_0.50: 0.4567 bed_rec_0.50: 0.5309 toilet_rec_0.50: 0.4483 showercurtrain_rec_0.50: 0.2143 bathtub_rec_0.50: 0.4516 mAR_0.50: 0.2673 data_time: 0.2448 time: 2.7676 2025/05/13 05:49:22 - mmengine - INFO - Epoch(train) [143][10/91] base_lr: 1.7996e-04 lr: 1.7996e-04 eta: 2 days, 18:05:47 time: 10.4438 data_time: 1.4239 memory: 68703 grad_norm: 1.6292 loss: 1.8127 center_loss: 0.4988 size_loss: 0.1481 cls_loss: 0.5702 giou_loss: 0.5955 2025/05/13 05:50:58 - mmengine - INFO - Epoch(train) [143][20/91] base_lr: 1.7996e-04 lr: 1.7996e-04 eta: 2 days, 18:03:57 time: 10.4472 data_time: 1.4081 memory: 68702 grad_norm: 1.7375 loss: 1.8125 center_loss: 0.4943 size_loss: 0.1483 cls_loss: 0.5767 giou_loss: 0.5932 2025/05/13 05:52:35 - mmengine - INFO - Epoch(train) [143][30/91] base_lr: 1.7996e-04 lr: 1.7996e-04 eta: 2 days, 18:02:07 time: 10.4478 data_time: 1.4007 memory: 68702 grad_norm: 1.6261 loss: 1.8141 center_loss: 0.4999 size_loss: 0.1483 cls_loss: 0.5737 giou_loss: 0.5922 2025/05/13 05:54:12 - mmengine - INFO - Epoch(train) [143][40/91] base_lr: 1.7996e-04 lr: 1.7996e-04 eta: 2 days, 18:00:17 time: 10.4514 data_time: 1.4014 memory: 68703 grad_norm: 1.5884 loss: 1.8155 center_loss: 0.4983 size_loss: 0.1487 cls_loss: 0.5761 giou_loss: 0.5924 2025/05/13 05:55:49 - mmengine - INFO - Epoch(train) [143][50/91] base_lr: 1.7996e-04 lr: 1.7996e-04 eta: 2 days, 17:58:28 time: 10.6426 data_time: 1.4150 memory: 68703 grad_norm: 1.5815 loss: 1.7895 center_loss: 0.4923 size_loss: 0.1461 cls_loss: 0.5654 giou_loss: 0.5857 2025/05/13 05:57:26 - mmengine - INFO - Epoch(train) [143][60/91] base_lr: 1.7996e-04 lr: 1.7996e-04 eta: 2 days, 17:56:39 time: 9.6849 data_time: 0.5584 memory: 68700 grad_norm: 1.5389 loss: 1.7798 center_loss: 0.4878 size_loss: 0.1431 cls_loss: 0.5675 giou_loss: 0.5814 2025/05/13 05:59:02 - mmengine - INFO - Epoch(train) [143][70/91] base_lr: 1.7996e-04 lr: 1.7996e-04 eta: 2 days, 17:54:49 time: 9.6791 data_time: 0.5652 memory: 68702 grad_norm: 1.3514 loss: 1.7744 center_loss: 0.4890 size_loss: 0.1426 cls_loss: 0.5613 giou_loss: 0.5815 2025/05/13 06:00:20 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 06:00:39 - mmengine - INFO - Epoch(train) [143][80/91] base_lr: 1.7996e-04 lr: 1.7996e-04 eta: 2 days, 17:52:58 time: 9.6702 data_time: 0.5696 memory: 68702 grad_norm: 1.3779 loss: 1.7734 center_loss: 0.4876 size_loss: 0.1444 cls_loss: 0.5589 giou_loss: 0.5826 2025/05/13 06:02:14 - mmengine - INFO - Epoch(train) [143][90/91] base_lr: 1.7996e-04 lr: 1.7996e-04 eta: 2 days, 17:51:06 time: 9.6523 data_time: 0.5606 memory: 68702 grad_norm: 1.4659 loss: 1.7527 center_loss: 0.4785 size_loss: 0.1410 cls_loss: 0.5552 giou_loss: 0.5780 2025/05/13 06:02:16 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 06:04:44 - mmengine - INFO - Epoch(train) [144][10/91] base_lr: 1.7908e-04 lr: 1.7908e-04 eta: 2 days, 17:50:24 time: 10.5222 data_time: 1.4810 memory: 68703 grad_norm: 1.5110 loss: 1.7723 center_loss: 0.4843 size_loss: 0.1441 cls_loss: 0.5616 giou_loss: 0.5823 2025/05/13 06:06:20 - mmengine - INFO - Epoch(train) [144][20/91] base_lr: 1.7908e-04 lr: 1.7908e-04 eta: 2 days, 17:48:33 time: 10.4985 data_time: 1.4758 memory: 68702 grad_norm: 1.5331 loss: 1.7686 center_loss: 0.4802 size_loss: 0.1440 cls_loss: 0.5601 giou_loss: 0.5844 2025/05/13 06:07:57 - mmengine - INFO - Epoch(train) [144][30/91] base_lr: 1.7908e-04 lr: 1.7908e-04 eta: 2 days, 17:46:44 time: 10.5077 data_time: 1.4719 memory: 68703 grad_norm: 1.5611 loss: 1.7625 center_loss: 0.4741 size_loss: 0.1429 cls_loss: 0.5614 giou_loss: 0.5841 2025/05/13 06:09:34 - mmengine - INFO - Epoch(train) [144][40/91] base_lr: 1.7908e-04 lr: 1.7908e-04 eta: 2 days, 17:44:53 time: 10.5003 data_time: 1.4651 memory: 68702 grad_norm: 1.6095 loss: 1.7571 center_loss: 0.4694 size_loss: 0.1417 cls_loss: 0.5659 giou_loss: 0.5801 2025/05/13 06:11:11 - mmengine - INFO - Epoch(train) [144][50/91] base_lr: 1.7908e-04 lr: 1.7908e-04 eta: 2 days, 17:43:05 time: 10.7009 data_time: 1.4731 memory: 68702 grad_norm: 1.5051 loss: 1.7640 center_loss: 0.4719 size_loss: 0.1426 cls_loss: 0.5648 giou_loss: 0.5846 2025/05/13 06:12:48 - mmengine - INFO - Epoch(train) [144][60/91] base_lr: 1.7908e-04 lr: 1.7908e-04 eta: 2 days, 17:41:16 time: 9.6745 data_time: 0.5345 memory: 68703 grad_norm: 1.5517 loss: 1.7516 center_loss: 0.4707 size_loss: 0.1404 cls_loss: 0.5571 giou_loss: 0.5835 2025/05/13 06:14:25 - mmengine - INFO - Epoch(train) [144][70/91] base_lr: 1.7908e-04 lr: 1.7908e-04 eta: 2 days, 17:39:27 time: 9.6947 data_time: 0.5438 memory: 68702 grad_norm: 1.5310 loss: 1.7621 center_loss: 0.4785 size_loss: 0.1424 cls_loss: 0.5562 giou_loss: 0.5850 2025/05/13 06:16:01 - mmengine - INFO - Epoch(train) [144][80/91] base_lr: 1.7908e-04 lr: 1.7908e-04 eta: 2 days, 17:37:36 time: 9.6765 data_time: 0.5400 memory: 68702 grad_norm: 1.5510 loss: 1.7645 center_loss: 0.4816 size_loss: 0.1401 cls_loss: 0.5569 giou_loss: 0.5860 2025/05/13 06:17:36 - mmengine - INFO - Epoch(train) [144][90/91] base_lr: 1.7908e-04 lr: 1.7908e-04 eta: 2 days, 17:35:43 time: 9.6533 data_time: 0.5384 memory: 68701 grad_norm: 1.4933 loss: 1.7855 center_loss: 0.4922 size_loss: 0.1421 cls_loss: 0.5584 giou_loss: 0.5928 2025/05/13 06:17:38 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 06:17:38 - mmengine - INFO - Saving checkpoint at 144 epochs 2025/05/13 06:18:35 - mmengine - INFO - Epoch(val) [144][10/39] eta: 0:01:37 time: 2.8846 data_time: 0.3561 memory: 15952 2025/05/13 06:19:02 - mmengine - INFO - Epoch(val) [144][20/39] eta: 0:00:56 time: 2.7554 data_time: 0.2246 memory: 13407 2025/05/13 06:19:27 - mmengine - INFO - Epoch(val) [144][30/39] eta: 0:00:25 time: 2.7495 data_time: 0.2225 memory: 13407 2025/05/13 06:19:53 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | table | 0.4751 | 0.6257 | 0.1503 | 0.2886 | | sofa | 0.6979 | 0.8247 | 0.2026 | 0.3711 | | garbagebin | 0.2565 | 0.4415 | 0.0268 | 0.1264 | | bookshelf | 0.2816 | 0.6104 | 0.0762 | 0.2468 | | chair | 0.5797 | 0.7098 | 0.1796 | 0.3392 | | curtain | 0.2220 | 0.4179 | 0.0117 | 0.0597 | | picture | 0.0259 | 0.1081 | 0.0012 | 0.0135 | | window | 0.1346 | 0.3901 | 0.0111 | 0.0851 | | door | 0.1584 | 0.4690 | 0.0249 | 0.1456 | | cabinet | 0.2599 | 0.4919 | 0.0525 | 0.1882 | | sink | 0.5350 | 0.6327 | 0.1491 | 0.2857 | | refrigerator | 0.4791 | 0.6140 | 0.1855 | 0.3333 | | counter | 0.2679 | 0.4231 | 0.0064 | 0.0769 | | toilet | 0.8874 | 0.9483 | 0.3806 | 0.5172 | | desk | 0.6597 | 0.8504 | 0.2601 | 0.5197 | | bed | 0.8323 | 0.8519 | 0.3594 | 0.5309 | | bathtub | 0.7325 | 0.8387 | 0.2602 | 0.4839 | | showercurtrain | 0.2536 | 0.5714 | 0.0536 | 0.1786 | +----------------+---------+---------+---------+---------+ | Overall | 0.4299 | 0.6011 | 0.1329 | 0.2661 | +----------------+---------+---------+---------+---------+ 2025/05/13 06:19:53 - mmengine - INFO - Epoch(val) [144][39/39] chair_AP_0.25: 0.5797 sofa_AP_0.25: 0.6979 table_AP_0.25: 0.4751 garbagebin_AP_0.25: 0.2565 bookshelf_AP_0.25: 0.2816 picture_AP_0.25: 0.0259 curtain_AP_0.25: 0.2220 door_AP_0.25: 0.1584 cabinet_AP_0.25: 0.2599 refrigerator_AP_0.25: 0.4791 counter_AP_0.25: 0.2679 sink_AP_0.25: 0.5350 window_AP_0.25: 0.1346 desk_AP_0.25: 0.6597 bed_AP_0.25: 0.8323 toilet_AP_0.25: 0.8874 showercurtrain_AP_0.25: 0.2536 bathtub_AP_0.25: 0.7325 mAP_0.25: 0.4299 chair_rec_0.25: 0.7098 sofa_rec_0.25: 0.8247 table_rec_0.25: 0.6257 garbagebin_rec_0.25: 0.4415 bookshelf_rec_0.25: 0.6104 picture_rec_0.25: 0.1081 curtain_rec_0.25: 0.4179 door_rec_0.25: 0.4690 cabinet_rec_0.25: 0.4919 refrigerator_rec_0.25: 0.6140 counter_rec_0.25: 0.4231 sink_rec_0.25: 0.6327 window_rec_0.25: 0.3901 desk_rec_0.25: 0.8504 bed_rec_0.25: 0.8519 toilet_rec_0.25: 0.9483 showercurtrain_rec_0.25: 0.5714 bathtub_rec_0.25: 0.8387 mAR_0.25: 0.6011 chair_AP_0.50: 0.1796 sofa_AP_0.50: 0.2026 table_AP_0.50: 0.1503 garbagebin_AP_0.50: 0.0268 bookshelf_AP_0.50: 0.0762 picture_AP_0.50: 0.0012 curtain_AP_0.50: 0.0117 door_AP_0.50: 0.0249 cabinet_AP_0.50: 0.0525 refrigerator_AP_0.50: 0.1855 counter_AP_0.50: 0.0064 sink_AP_0.50: 0.1491 window_AP_0.50: 0.0111 desk_AP_0.50: 0.2601 bed_AP_0.50: 0.3594 toilet_AP_0.50: 0.3806 showercurtrain_AP_0.50: 0.0536 bathtub_AP_0.50: 0.2602 mAP_0.50: 0.1329 chair_rec_0.50: 0.3392 sofa_rec_0.50: 0.3711 table_rec_0.50: 0.2886 garbagebin_rec_0.50: 0.1264 bookshelf_rec_0.50: 0.2468 picture_rec_0.50: 0.0135 curtain_rec_0.50: 0.0597 door_rec_0.50: 0.1456 cabinet_rec_0.50: 0.1882 refrigerator_rec_0.50: 0.3333 counter_rec_0.50: 0.0769 sink_rec_0.50: 0.2857 window_rec_0.50: 0.0851 desk_rec_0.50: 0.5197 bed_rec_0.50: 0.5309 toilet_rec_0.50: 0.5172 showercurtrain_rec_0.50: 0.1786 bathtub_rec_0.50: 0.4839 mAR_0.50: 0.2661 data_time: 0.2589 time: 2.7703 2025/05/13 06:22:20 - mmengine - INFO - Epoch(train) [145][10/91] base_lr: 1.7819e-04 lr: 1.7819e-04 eta: 2 days, 17:34:58 time: 10.4723 data_time: 1.3806 memory: 68702 grad_norm: 1.5333 loss: 1.8007 center_loss: 0.5037 size_loss: 0.1462 cls_loss: 0.5551 giou_loss: 0.5956 2025/05/13 06:23:56 - mmengine - INFO - Epoch(train) [145][20/91] base_lr: 1.7819e-04 lr: 1.7819e-04 eta: 2 days, 17:33:08 time: 10.4696 data_time: 1.3723 memory: 68703 grad_norm: 1.4719 loss: 1.8167 center_loss: 0.5073 size_loss: 0.1488 cls_loss: 0.5655 giou_loss: 0.5950 2025/05/13 06:25:33 - mmengine - INFO - Epoch(train) [145][30/91] base_lr: 1.7819e-04 lr: 1.7819e-04 eta: 2 days, 17:31:18 time: 10.4624 data_time: 1.3738 memory: 68702 grad_norm: 1.6741 loss: 1.8170 center_loss: 0.5059 size_loss: 0.1484 cls_loss: 0.5660 giou_loss: 0.5966 2025/05/13 06:27:09 - mmengine - INFO - Epoch(train) [145][40/91] base_lr: 1.7819e-04 lr: 1.7819e-04 eta: 2 days, 17:29:27 time: 10.4668 data_time: 1.3675 memory: 68702 grad_norm: 1.6566 loss: 1.8186 center_loss: 0.5041 size_loss: 0.1494 cls_loss: 0.5687 giou_loss: 0.5964 2025/05/13 06:28:46 - mmengine - INFO - Epoch(train) [145][50/91] base_lr: 1.7819e-04 lr: 1.7819e-04 eta: 2 days, 17:27:38 time: 10.6579 data_time: 1.3766 memory: 68702 grad_norm: 1.5238 loss: 1.7969 center_loss: 0.4916 size_loss: 0.1467 cls_loss: 0.5673 giou_loss: 0.5912 2025/05/13 06:30:23 - mmengine - INFO - Epoch(train) [145][60/91] base_lr: 1.7819e-04 lr: 1.7819e-04 eta: 2 days, 17:25:48 time: 9.6579 data_time: 0.5333 memory: 68702 grad_norm: 1.5102 loss: 1.7757 center_loss: 0.4822 size_loss: 0.1430 cls_loss: 0.5633 giou_loss: 0.5872 2025/05/13 06:31:59 - mmengine - INFO - Epoch(train) [145][70/91] base_lr: 1.7819e-04 lr: 1.7819e-04 eta: 2 days, 17:23:59 time: 9.6612 data_time: 0.5485 memory: 68702 grad_norm: 1.5668 loss: 1.7615 center_loss: 0.4747 size_loss: 0.1406 cls_loss: 0.5587 giou_loss: 0.5875 2025/05/13 06:33:36 - mmengine - INFO - Epoch(train) [145][80/91] base_lr: 1.7819e-04 lr: 1.7819e-04 eta: 2 days, 17:22:08 time: 9.6584 data_time: 0.5498 memory: 68702 grad_norm: 1.6787 loss: 1.7487 center_loss: 0.4703 size_loss: 0.1400 cls_loss: 0.5569 giou_loss: 0.5815 2025/05/13 06:35:11 - mmengine - INFO - Epoch(train) [145][90/91] base_lr: 1.7819e-04 lr: 1.7819e-04 eta: 2 days, 17:20:17 time: 9.6390 data_time: 0.5517 memory: 68702 grad_norm: 1.6919 loss: 1.7457 center_loss: 0.4671 size_loss: 0.1409 cls_loss: 0.5550 giou_loss: 0.5826 2025/05/13 06:35:13 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 06:37:40 - mmengine - INFO - Epoch(train) [146][10/91] base_lr: 1.7730e-04 lr: 1.7730e-04 eta: 2 days, 17:19:31 time: 10.4933 data_time: 1.4758 memory: 68702 grad_norm: 1.8148 loss: 1.7747 center_loss: 0.4785 size_loss: 0.1443 cls_loss: 0.5635 giou_loss: 0.5883 2025/05/13 06:39:17 - mmengine - INFO - Epoch(train) [146][20/91] base_lr: 1.7730e-04 lr: 1.7730e-04 eta: 2 days, 17:17:42 time: 10.5001 data_time: 1.4807 memory: 68702 grad_norm: 1.8777 loss: 1.7899 center_loss: 0.4821 size_loss: 0.1453 cls_loss: 0.5703 giou_loss: 0.5922 2025/05/13 06:40:54 - mmengine - INFO - Epoch(train) [146][30/91] base_lr: 1.7730e-04 lr: 1.7730e-04 eta: 2 days, 17:15:52 time: 10.5009 data_time: 1.4666 memory: 68702 grad_norm: 1.9537 loss: 1.8065 center_loss: 0.4903 size_loss: 0.1469 cls_loss: 0.5763 giou_loss: 0.5931 2025/05/13 06:42:30 - mmengine - INFO - Epoch(train) [146][40/91] base_lr: 1.7730e-04 lr: 1.7730e-04 eta: 2 days, 17:14:02 time: 10.4984 data_time: 1.4658 memory: 68703 grad_norm: 1.6349 loss: 1.8079 center_loss: 0.4899 size_loss: 0.1459 cls_loss: 0.5751 giou_loss: 0.5970 2025/05/13 06:44:07 - mmengine - INFO - Epoch(train) [146][50/91] base_lr: 1.7730e-04 lr: 1.7730e-04 eta: 2 days, 17:12:13 time: 10.6882 data_time: 1.4794 memory: 68702 grad_norm: 1.5633 loss: 1.8050 center_loss: 0.4921 size_loss: 0.1457 cls_loss: 0.5731 giou_loss: 0.5941 2025/05/13 06:45:44 - mmengine - INFO - Epoch(train) [146][60/91] base_lr: 1.7730e-04 lr: 1.7730e-04 eta: 2 days, 17:10:24 time: 9.6728 data_time: 0.5574 memory: 68703 grad_norm: 1.5348 loss: 1.8042 center_loss: 0.4964 size_loss: 0.1435 cls_loss: 0.5718 giou_loss: 0.5926 2025/05/13 06:47:21 - mmengine - INFO - Epoch(train) [146][70/91] base_lr: 1.7730e-04 lr: 1.7730e-04 eta: 2 days, 17:08:35 time: 9.6715 data_time: 0.5452 memory: 68702 grad_norm: 1.4656 loss: 1.8016 center_loss: 0.4955 size_loss: 0.1440 cls_loss: 0.5705 giou_loss: 0.5917 2025/05/13 06:48:57 - mmengine - INFO - Epoch(train) [146][80/91] base_lr: 1.7730e-04 lr: 1.7730e-04 eta: 2 days, 17:06:44 time: 9.6647 data_time: 0.5559 memory: 68702 grad_norm: 1.3647 loss: 1.7848 center_loss: 0.4866 size_loss: 0.1423 cls_loss: 0.5656 giou_loss: 0.5903 2025/05/13 06:50:33 - mmengine - INFO - Epoch(train) [146][90/91] base_lr: 1.7730e-04 lr: 1.7730e-04 eta: 2 days, 17:04:52 time: 9.6444 data_time: 0.5553 memory: 68702 grad_norm: 1.5031 loss: 1.7962 center_loss: 0.4933 size_loss: 0.1438 cls_loss: 0.5680 giou_loss: 0.5912 2025/05/13 06:50:34 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 06:50:34 - mmengine - INFO - Saving checkpoint at 146 epochs 2025/05/13 06:51:27 - mmengine - INFO - Epoch(val) [146][10/39] eta: 0:01:33 time: 2.8605 data_time: 0.3531 memory: 15952 2025/05/13 06:51:53 - mmengine - INFO - Epoch(val) [146][20/39] eta: 0:00:55 time: 2.7073 data_time: 0.2099 memory: 13407 2025/05/13 06:52:19 - mmengine - INFO - Epoch(val) [146][30/39] eta: 0:00:25 time: 2.6944 data_time: 0.2076 memory: 13407 2025/05/13 06:52:45 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | chair | 0.5488 | 0.7047 | 0.1338 | 0.3019 | | garbagebin | 0.2629 | 0.4642 | 0.0328 | 0.1340 | | sofa | 0.7192 | 0.8660 | 0.2610 | 0.4330 | | table | 0.4692 | 0.5971 | 0.1421 | 0.2771 | | bookshelf | 0.2505 | 0.5584 | 0.0630 | 0.2338 | | curtain | 0.2446 | 0.4776 | 0.0424 | 0.0896 | | picture | 0.0195 | 0.0991 | 0.0001 | 0.0090 | | window | 0.1203 | 0.3546 | 0.0177 | 0.0816 | | cabinet | 0.2543 | 0.5054 | 0.0356 | 0.1667 | | door | 0.1510 | 0.4411 | 0.0173 | 0.1306 | | counter | 0.4438 | 0.5962 | 0.0080 | 0.0769 | | sink | 0.4748 | 0.6224 | 0.1015 | 0.2347 | | refrigerator | 0.4725 | 0.6140 | 0.1062 | 0.2807 | | desk | 0.6884 | 0.8583 | 0.2324 | 0.4567 | | bed | 0.8214 | 0.8395 | 0.4343 | 0.6049 | | toilet | 0.8490 | 0.9483 | 0.4158 | 0.5517 | | bathtub | 0.7110 | 0.8065 | 0.1304 | 0.3548 | | showercurtrain | 0.1742 | 0.5000 | 0.0157 | 0.1071 | +----------------+---------+---------+---------+---------+ | Overall | 0.4264 | 0.6030 | 0.1217 | 0.2514 | +----------------+---------+---------+---------+---------+ 2025/05/13 06:52:45 - mmengine - INFO - Epoch(val) [146][39/39] chair_AP_0.25: 0.5488 sofa_AP_0.25: 0.7192 table_AP_0.25: 0.4692 garbagebin_AP_0.25: 0.2629 bookshelf_AP_0.25: 0.2505 picture_AP_0.25: 0.0195 curtain_AP_0.25: 0.2446 door_AP_0.25: 0.1510 cabinet_AP_0.25: 0.2543 refrigerator_AP_0.25: 0.4725 counter_AP_0.25: 0.4438 sink_AP_0.25: 0.4748 window_AP_0.25: 0.1203 desk_AP_0.25: 0.6884 bed_AP_0.25: 0.8214 toilet_AP_0.25: 0.8490 showercurtrain_AP_0.25: 0.1742 bathtub_AP_0.25: 0.7110 mAP_0.25: 0.4264 chair_rec_0.25: 0.7047 sofa_rec_0.25: 0.8660 table_rec_0.25: 0.5971 garbagebin_rec_0.25: 0.4642 bookshelf_rec_0.25: 0.5584 picture_rec_0.25: 0.0991 curtain_rec_0.25: 0.4776 door_rec_0.25: 0.4411 cabinet_rec_0.25: 0.5054 refrigerator_rec_0.25: 0.6140 counter_rec_0.25: 0.5962 sink_rec_0.25: 0.6224 window_rec_0.25: 0.3546 desk_rec_0.25: 0.8583 bed_rec_0.25: 0.8395 toilet_rec_0.25: 0.9483 showercurtrain_rec_0.25: 0.5000 bathtub_rec_0.25: 0.8065 mAR_0.25: 0.6030 chair_AP_0.50: 0.1338 sofa_AP_0.50: 0.2610 table_AP_0.50: 0.1421 garbagebin_AP_0.50: 0.0328 bookshelf_AP_0.50: 0.0630 picture_AP_0.50: 0.0001 curtain_AP_0.50: 0.0424 door_AP_0.50: 0.0173 cabinet_AP_0.50: 0.0356 refrigerator_AP_0.50: 0.1062 counter_AP_0.50: 0.0080 sink_AP_0.50: 0.1015 window_AP_0.50: 0.0177 desk_AP_0.50: 0.2324 bed_AP_0.50: 0.4343 toilet_AP_0.50: 0.4158 showercurtrain_AP_0.50: 0.0157 bathtub_AP_0.50: 0.1304 mAP_0.50: 0.1217 chair_rec_0.50: 0.3019 sofa_rec_0.50: 0.4330 table_rec_0.50: 0.2771 garbagebin_rec_0.50: 0.1340 bookshelf_rec_0.50: 0.2338 picture_rec_0.50: 0.0090 curtain_rec_0.50: 0.0896 door_rec_0.50: 0.1306 cabinet_rec_0.50: 0.1667 refrigerator_rec_0.50: 0.2807 counter_rec_0.50: 0.0769 sink_rec_0.50: 0.2347 window_rec_0.50: 0.0816 desk_rec_0.50: 0.4567 bed_rec_0.50: 0.6049 toilet_rec_0.50: 0.5517 showercurtrain_rec_0.50: 0.1071 bathtub_rec_0.50: 0.3548 mAR_0.50: 0.2514 data_time: 0.2415 time: 2.7280 2025/05/13 06:55:07 - mmengine - INFO - Epoch(train) [147][10/91] base_lr: 1.7641e-04 lr: 1.7641e-04 eta: 2 days, 17:03:58 time: 10.3961 data_time: 1.4383 memory: 68702 grad_norm: 1.6116 loss: 1.7850 center_loss: 0.4831 size_loss: 0.1449 cls_loss: 0.5689 giou_loss: 0.5881 2025/05/13 06:56:44 - mmengine - INFO - Epoch(train) [147][20/91] base_lr: 1.7641e-04 lr: 1.7641e-04 eta: 2 days, 17:02:09 time: 10.4029 data_time: 1.4550 memory: 68702 grad_norm: 1.5852 loss: 1.7677 center_loss: 0.4780 size_loss: 0.1439 cls_loss: 0.5601 giou_loss: 0.5857 2025/05/13 06:58:21 - mmengine - INFO - Epoch(train) [147][30/91] base_lr: 1.7641e-04 lr: 1.7641e-04 eta: 2 days, 17:00:20 time: 10.4011 data_time: 1.4553 memory: 68702 grad_norm: 1.5974 loss: 1.7754 center_loss: 0.4834 size_loss: 0.1452 cls_loss: 0.5577 giou_loss: 0.5891 2025/05/13 06:59:58 - mmengine - INFO - Epoch(train) [147][40/91] base_lr: 1.7641e-04 lr: 1.7641e-04 eta: 2 days, 16:58:31 time: 10.4146 data_time: 1.4614 memory: 68702 grad_norm: 1.6057 loss: 1.7647 center_loss: 0.4817 size_loss: 0.1426 cls_loss: 0.5537 giou_loss: 0.5867 2025/05/13 07:01:36 - mmengine - INFO - Epoch(train) [147][50/91] base_lr: 1.7641e-04 lr: 1.7641e-04 eta: 2 days, 16:56:43 time: 10.6193 data_time: 1.4804 memory: 68702 grad_norm: 1.3937 loss: 1.7580 center_loss: 0.4779 size_loss: 0.1407 cls_loss: 0.5543 giou_loss: 0.5850 2025/05/13 07:03:13 - mmengine - INFO - Epoch(train) [147][60/91] base_lr: 1.7641e-04 lr: 1.7641e-04 eta: 2 days, 16:54:54 time: 9.7108 data_time: 0.5980 memory: 68702 grad_norm: 1.3984 loss: 1.7545 center_loss: 0.4802 size_loss: 0.1409 cls_loss: 0.5515 giou_loss: 0.5818 2025/05/13 07:04:49 - mmengine - INFO - Epoch(train) [147][70/91] base_lr: 1.7641e-04 lr: 1.7641e-04 eta: 2 days, 16:53:05 time: 9.7072 data_time: 0.5796 memory: 68703 grad_norm: 1.4241 loss: 1.7570 center_loss: 0.4796 size_loss: 0.1409 cls_loss: 0.5549 giou_loss: 0.5816 2025/05/13 07:06:25 - mmengine - INFO - Epoch(train) [147][80/91] base_lr: 1.7641e-04 lr: 1.7641e-04 eta: 2 days, 16:51:14 time: 9.6876 data_time: 0.5825 memory: 68702 grad_norm: 1.4554 loss: 1.7464 center_loss: 0.4763 size_loss: 0.1393 cls_loss: 0.5542 giou_loss: 0.5765 2025/05/13 07:08:01 - mmengine - INFO - Epoch(train) [147][90/91] base_lr: 1.7641e-04 lr: 1.7641e-04 eta: 2 days, 16:49:22 time: 9.6502 data_time: 0.5720 memory: 68702 grad_norm: 1.4525 loss: 1.7800 center_loss: 0.4914 size_loss: 0.1443 cls_loss: 0.5628 giou_loss: 0.5815 2025/05/13 07:08:02 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 07:10:27 - mmengine - INFO - Epoch(train) [148][10/91] base_lr: 1.7551e-04 lr: 1.7551e-04 eta: 2 days, 16:48:30 time: 10.4275 data_time: 1.4503 memory: 68702 grad_norm: 1.6434 loss: 1.7894 center_loss: 0.4946 size_loss: 0.1439 cls_loss: 0.5637 giou_loss: 0.5873 2025/05/13 07:12:04 - mmengine - INFO - Epoch(train) [148][20/91] base_lr: 1.7551e-04 lr: 1.7551e-04 eta: 2 days, 16:46:41 time: 10.4326 data_time: 1.4648 memory: 68702 grad_norm: 1.6696 loss: 1.7888 center_loss: 0.4919 size_loss: 0.1443 cls_loss: 0.5630 giou_loss: 0.5896 2025/05/13 07:13:40 - mmengine - INFO - Epoch(train) [148][30/91] base_lr: 1.7551e-04 lr: 1.7551e-04 eta: 2 days, 16:44:52 time: 10.4290 data_time: 1.4704 memory: 68702 grad_norm: 1.6382 loss: 1.8081 center_loss: 0.4986 size_loss: 0.1458 cls_loss: 0.5689 giou_loss: 0.5947 2025/05/13 07:15:17 - mmengine - INFO - Epoch(train) [148][40/91] base_lr: 1.7551e-04 lr: 1.7551e-04 eta: 2 days, 16:43:02 time: 10.4367 data_time: 1.4667 memory: 68702 grad_norm: 1.5951 loss: 1.8207 center_loss: 0.5005 size_loss: 0.1475 cls_loss: 0.5737 giou_loss: 0.5990 2025/05/13 07:16:55 - mmengine - INFO - Epoch(train) [148][50/91] base_lr: 1.7551e-04 lr: 1.7551e-04 eta: 2 days, 16:41:15 time: 10.6549 data_time: 1.4829 memory: 68702 grad_norm: 1.4925 loss: 1.8000 center_loss: 0.4885 size_loss: 0.1459 cls_loss: 0.5722 giou_loss: 0.5934 2025/05/13 07:18:32 - mmengine - INFO - Epoch(train) [148][60/91] base_lr: 1.7551e-04 lr: 1.7551e-04 eta: 2 days, 16:39:26 time: 9.7014 data_time: 0.6082 memory: 68702 grad_norm: 1.3971 loss: 1.8073 center_loss: 0.4937 size_loss: 0.1465 cls_loss: 0.5731 giou_loss: 0.5939 2025/05/13 07:20:09 - mmengine - INFO - Epoch(train) [148][70/91] base_lr: 1.7551e-04 lr: 1.7551e-04 eta: 2 days, 16:37:37 time: 9.6946 data_time: 0.5979 memory: 68700 grad_norm: 1.3368 loss: 1.8181 center_loss: 0.5012 size_loss: 0.1476 cls_loss: 0.5737 giou_loss: 0.5956 2025/05/13 07:21:44 - mmengine - INFO - Epoch(train) [148][80/91] base_lr: 1.7551e-04 lr: 1.7551e-04 eta: 2 days, 16:35:46 time: 9.6785 data_time: 0.5942 memory: 68702 grad_norm: 1.5021 loss: 1.8131 center_loss: 0.5005 size_loss: 0.1485 cls_loss: 0.5700 giou_loss: 0.5941 2025/05/13 07:23:20 - mmengine - INFO - Epoch(train) [148][90/91] base_lr: 1.7551e-04 lr: 1.7551e-04 eta: 2 days, 16:33:55 time: 9.6552 data_time: 0.5992 memory: 68702 grad_norm: 1.5516 loss: 1.8021 center_loss: 0.4975 size_loss: 0.1474 cls_loss: 0.5672 giou_loss: 0.5899 2025/05/13 07:23:22 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 07:23:22 - mmengine - INFO - Saving checkpoint at 148 epochs 2025/05/13 07:24:19 - mmengine - INFO - Epoch(val) [148][10/39] eta: 0:01:37 time: 2.8578 data_time: 0.3657 memory: 15952 2025/05/13 07:24:45 - mmengine - INFO - Epoch(val) [148][20/39] eta: 0:00:56 time: 2.7358 data_time: 0.2361 memory: 13407 2025/05/13 07:25:11 - mmengine - INFO - Epoch(val) [148][30/39] eta: 0:00:25 time: 2.7402 data_time: 0.2349 memory: 13407 2025/05/13 07:25:36 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.2045 | 0.4358 | 0.0143 | 0.1019 | | sofa | 0.7665 | 0.8660 | 0.1935 | 0.3814 | | table | 0.4959 | 0.6171 | 0.1507 | 0.2971 | | chair | 0.5662 | 0.7003 | 0.1220 | 0.2909 | | bookshelf | 0.2920 | 0.5584 | 0.0721 | 0.1688 | | curtain | 0.2293 | 0.4478 | 0.0264 | 0.0896 | | picture | 0.0195 | 0.1036 | 0.0000 | 0.0000 | | window | 0.1355 | 0.3652 | 0.0119 | 0.0816 | | bed | 0.8156 | 0.8272 | 0.3956 | 0.5556 | | door | 0.1513 | 0.4390 | 0.0240 | 0.1263 | | cabinet | 0.2465 | 0.4731 | 0.0486 | 0.1667 | | counter | 0.3769 | 0.5192 | 0.0145 | 0.0962 | | sink | 0.5444 | 0.6531 | 0.0415 | 0.1837 | | refrigerator | 0.4783 | 0.5789 | 0.0943 | 0.2105 | | desk | 0.6985 | 0.8504 | 0.2907 | 0.4961 | | toilet | 0.8563 | 0.9138 | 0.3740 | 0.4655 | | bathtub | 0.7641 | 0.9032 | 0.1275 | 0.3548 | | showercurtrain | 0.1888 | 0.4286 | 0.0064 | 0.0714 | +----------------+---------+---------+---------+---------+ | Overall | 0.4350 | 0.5934 | 0.1116 | 0.2299 | +----------------+---------+---------+---------+---------+ 2025/05/13 07:25:36 - mmengine - INFO - Epoch(val) [148][39/39] chair_AP_0.25: 0.5662 sofa_AP_0.25: 0.7665 table_AP_0.25: 0.4959 garbagebin_AP_0.25: 0.2045 bookshelf_AP_0.25: 0.2920 picture_AP_0.25: 0.0195 curtain_AP_0.25: 0.2293 door_AP_0.25: 0.1513 cabinet_AP_0.25: 0.2465 refrigerator_AP_0.25: 0.4783 counter_AP_0.25: 0.3769 sink_AP_0.25: 0.5444 window_AP_0.25: 0.1355 desk_AP_0.25: 0.6985 bed_AP_0.25: 0.8156 toilet_AP_0.25: 0.8563 showercurtrain_AP_0.25: 0.1888 bathtub_AP_0.25: 0.7641 mAP_0.25: 0.4350 chair_rec_0.25: 0.7003 sofa_rec_0.25: 0.8660 table_rec_0.25: 0.6171 garbagebin_rec_0.25: 0.4358 bookshelf_rec_0.25: 0.5584 picture_rec_0.25: 0.1036 curtain_rec_0.25: 0.4478 door_rec_0.25: 0.4390 cabinet_rec_0.25: 0.4731 refrigerator_rec_0.25: 0.5789 counter_rec_0.25: 0.5192 sink_rec_0.25: 0.6531 window_rec_0.25: 0.3652 desk_rec_0.25: 0.8504 bed_rec_0.25: 0.8272 toilet_rec_0.25: 0.9138 showercurtrain_rec_0.25: 0.4286 bathtub_rec_0.25: 0.9032 mAR_0.25: 0.5934 chair_AP_0.50: 0.1220 sofa_AP_0.50: 0.1935 table_AP_0.50: 0.1507 garbagebin_AP_0.50: 0.0143 bookshelf_AP_0.50: 0.0721 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0264 door_AP_0.50: 0.0240 cabinet_AP_0.50: 0.0486 refrigerator_AP_0.50: 0.0943 counter_AP_0.50: 0.0145 sink_AP_0.50: 0.0415 window_AP_0.50: 0.0119 desk_AP_0.50: 0.2907 bed_AP_0.50: 0.3956 toilet_AP_0.50: 0.3740 showercurtrain_AP_0.50: 0.0064 bathtub_AP_0.50: 0.1275 mAP_0.50: 0.1116 chair_rec_0.50: 0.2909 sofa_rec_0.50: 0.3814 table_rec_0.50: 0.2971 garbagebin_rec_0.50: 0.1019 bookshelf_rec_0.50: 0.1688 picture_rec_0.50: 0.0000 curtain_rec_0.50: 0.0896 door_rec_0.50: 0.1263 cabinet_rec_0.50: 0.1667 refrigerator_rec_0.50: 0.2105 counter_rec_0.50: 0.0962 sink_rec_0.50: 0.1837 window_rec_0.50: 0.0816 desk_rec_0.50: 0.4961 bed_rec_0.50: 0.5556 toilet_rec_0.50: 0.4655 showercurtrain_rec_0.50: 0.0714 bathtub_rec_0.50: 0.3548 mAR_0.50: 0.2299 data_time: 0.2732 time: 2.7778 2025/05/13 07:28:04 - mmengine - INFO - Epoch(train) [149][10/91] base_lr: 1.7461e-04 lr: 1.7461e-04 eta: 2 days, 16:33:08 time: 10.4950 data_time: 1.5200 memory: 68702 grad_norm: 1.7726 loss: 1.7872 center_loss: 0.4912 size_loss: 0.1461 cls_loss: 0.5609 giou_loss: 0.5891 2025/05/13 07:29:42 - mmengine - INFO - Epoch(train) [149][20/91] base_lr: 1.7461e-04 lr: 1.7461e-04 eta: 2 days, 16:31:20 time: 10.5068 data_time: 1.5131 memory: 68702 grad_norm: 1.8038 loss: 1.7940 center_loss: 0.4949 size_loss: 0.1472 cls_loss: 0.5621 giou_loss: 0.5898 2025/05/13 07:31:19 - mmengine - INFO - Epoch(train) [149][30/91] base_lr: 1.7461e-04 lr: 1.7461e-04 eta: 2 days, 16:29:31 time: 10.5174 data_time: 1.5051 memory: 68703 grad_norm: 1.8569 loss: 1.7977 center_loss: 0.4933 size_loss: 0.1463 cls_loss: 0.5657 giou_loss: 0.5923 2025/05/13 07:32:55 - mmengine - INFO - Epoch(train) [149][40/91] base_lr: 1.7461e-04 lr: 1.7461e-04 eta: 2 days, 16:27:41 time: 10.5258 data_time: 1.5039 memory: 68702 grad_norm: 1.7634 loss: 1.7708 center_loss: 0.4834 size_loss: 0.1425 cls_loss: 0.5593 giou_loss: 0.5855 2025/05/13 07:34:32 - mmengine - INFO - Epoch(train) [149][50/91] base_lr: 1.7461e-04 lr: 1.7461e-04 eta: 2 days, 16:25:53 time: 10.7202 data_time: 1.5090 memory: 68702 grad_norm: 1.6949 loss: 1.7785 center_loss: 0.4844 size_loss: 0.1432 cls_loss: 0.5634 giou_loss: 0.5875 2025/05/13 07:36:10 - mmengine - INFO - Epoch(train) [149][60/91] base_lr: 1.7461e-04 lr: 1.7461e-04 eta: 2 days, 16:24:06 time: 9.7176 data_time: 0.5831 memory: 68703 grad_norm: 1.5680 loss: 1.7679 center_loss: 0.4810 size_loss: 0.1419 cls_loss: 0.5613 giou_loss: 0.5837 2025/05/13 07:37:48 - mmengine - INFO - Epoch(train) [149][70/91] base_lr: 1.7461e-04 lr: 1.7461e-04 eta: 2 days, 16:22:18 time: 9.7227 data_time: 0.5781 memory: 68702 grad_norm: 1.5634 loss: 1.7637 center_loss: 0.4754 size_loss: 0.1418 cls_loss: 0.5644 giou_loss: 0.5821 2025/05/13 07:39:24 - mmengine - INFO - Epoch(train) [149][80/91] base_lr: 1.7461e-04 lr: 1.7461e-04 eta: 2 days, 16:20:28 time: 9.7037 data_time: 0.5837 memory: 68702 grad_norm: 1.5232 loss: 1.7526 center_loss: 0.4710 size_loss: 0.1418 cls_loss: 0.5610 giou_loss: 0.5788 2025/05/13 07:41:00 - mmengine - INFO - Epoch(train) [149][90/91] base_lr: 1.7461e-04 lr: 1.7461e-04 eta: 2 days, 16:18:37 time: 9.6885 data_time: 0.5800 memory: 68703 grad_norm: 1.4646 loss: 1.7732 center_loss: 0.4798 size_loss: 0.1455 cls_loss: 0.5640 giou_loss: 0.5840 2025/05/13 07:41:01 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 07:43:26 - mmengine - INFO - Epoch(train) [150][10/91] base_lr: 1.7371e-04 lr: 1.7371e-04 eta: 2 days, 16:17:45 time: 10.4864 data_time: 1.4186 memory: 68702 grad_norm: 1.5140 loss: 1.8089 center_loss: 0.4985 size_loss: 0.1506 cls_loss: 0.5692 giou_loss: 0.5906 2025/05/13 07:45:03 - mmengine - INFO - Epoch(train) [150][20/91] base_lr: 1.7371e-04 lr: 1.7371e-04 eta: 2 days, 16:15:55 time: 10.4565 data_time: 1.4090 memory: 68702 grad_norm: 1.5042 loss: 1.8079 center_loss: 0.4964 size_loss: 0.1499 cls_loss: 0.5723 giou_loss: 0.5892 2025/05/13 07:46:40 - mmengine - INFO - Epoch(train) [150][30/91] base_lr: 1.7371e-04 lr: 1.7371e-04 eta: 2 days, 16:14:06 time: 10.4538 data_time: 1.4139 memory: 68702 grad_norm: 1.4594 loss: 1.8025 center_loss: 0.4964 size_loss: 0.1488 cls_loss: 0.5694 giou_loss: 0.5879 2025/05/13 07:48:16 - mmengine - INFO - Epoch(train) [150][40/91] base_lr: 1.7371e-04 lr: 1.7371e-04 eta: 2 days, 16:12:17 time: 10.4531 data_time: 1.4008 memory: 68702 grad_norm: 1.4393 loss: 1.8059 center_loss: 0.4993 size_loss: 0.1491 cls_loss: 0.5689 giou_loss: 0.5886 2025/05/13 07:49:54 - mmengine - INFO - Epoch(train) [150][50/91] base_lr: 1.7371e-04 lr: 1.7371e-04 eta: 2 days, 16:10:30 time: 10.6571 data_time: 1.4172 memory: 68702 grad_norm: 1.3753 loss: 1.7646 center_loss: 0.4787 size_loss: 0.1441 cls_loss: 0.5642 giou_loss: 0.5776 2025/05/13 07:51:32 - mmengine - INFO - Epoch(train) [150][60/91] base_lr: 1.7371e-04 lr: 1.7371e-04 eta: 2 days, 16:08:41 time: 9.7023 data_time: 0.5832 memory: 68702 grad_norm: 1.4000 loss: 1.7682 center_loss: 0.4785 size_loss: 0.1421 cls_loss: 0.5695 giou_loss: 0.5782 2025/05/13 07:53:09 - mmengine - INFO - Epoch(train) [150][70/91] base_lr: 1.7371e-04 lr: 1.7371e-04 eta: 2 days, 16:06:53 time: 9.7146 data_time: 0.5784 memory: 68703 grad_norm: 1.3254 loss: 1.7657 center_loss: 0.4774 size_loss: 0.1426 cls_loss: 0.5678 giou_loss: 0.5779 2025/05/13 07:54:45 - mmengine - INFO - Epoch(train) [150][80/91] base_lr: 1.7371e-04 lr: 1.7371e-04 eta: 2 days, 16:05:03 time: 9.7003 data_time: 0.5715 memory: 68702 grad_norm: 1.3632 loss: 1.7656 center_loss: 0.4739 size_loss: 0.1421 cls_loss: 0.5711 giou_loss: 0.5784 2025/05/13 07:56:20 - mmengine - INFO - Epoch(train) [150][90/91] base_lr: 1.7371e-04 lr: 1.7371e-04 eta: 2 days, 16:03:12 time: 9.6808 data_time: 0.5767 memory: 68702 grad_norm: 1.3847 loss: 1.7690 center_loss: 0.4743 size_loss: 0.1413 cls_loss: 0.5736 giou_loss: 0.5798 2025/05/13 07:56:22 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 07:56:22 - mmengine - INFO - Saving checkpoint at 150 epochs 2025/05/13 07:57:17 - mmengine - INFO - Epoch(val) [150][10/39] eta: 0:01:34 time: 2.8763 data_time: 0.3715 memory: 15952 2025/05/13 07:57:42 - mmengine - INFO - Epoch(val) [150][20/39] eta: 0:00:55 time: 2.7154 data_time: 0.2156 memory: 13407 2025/05/13 07:58:08 - mmengine - INFO - Epoch(val) [150][30/39] eta: 0:00:25 time: 2.7108 data_time: 0.2143 memory: 13407 2025/05/13 07:58:34 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.2946 | 0.4698 | 0.0242 | 0.1264 | | table | 0.4852 | 0.6200 | 0.1559 | 0.3029 | | sofa | 0.6650 | 0.8144 | 0.2355 | 0.4433 | | chair | 0.6153 | 0.7507 | 0.1928 | 0.3604 | | curtain | 0.2584 | 0.4478 | 0.0823 | 0.1791 | | bookshelf | 0.3464 | 0.5844 | 0.0582 | 0.1818 | | window | 0.1404 | 0.3688 | 0.0048 | 0.0567 | | picture | 0.0389 | 0.1441 | 0.0066 | 0.0315 | | cabinet | 0.2471 | 0.4919 | 0.0451 | 0.1694 | | door | 0.1462 | 0.4368 | 0.0184 | 0.1285 | | counter | 0.3993 | 0.6154 | 0.0467 | 0.1923 | | refrigerator | 0.4713 | 0.5789 | 0.1837 | 0.3158 | | sink | 0.5304 | 0.6224 | 0.1805 | 0.3061 | | desk | 0.6692 | 0.8504 | 0.2309 | 0.4567 | | bed | 0.8054 | 0.8395 | 0.3470 | 0.5062 | | toilet | 0.8926 | 0.9483 | 0.3839 | 0.4828 | | showercurtrain | 0.3205 | 0.5714 | 0.0230 | 0.1071 | | bathtub | 0.7744 | 0.8387 | 0.2160 | 0.4516 | +----------------+---------+---------+---------+---------+ | Overall | 0.4500 | 0.6108 | 0.1353 | 0.2666 | +----------------+---------+---------+---------+---------+ 2025/05/13 07:58:34 - mmengine - INFO - Epoch(val) [150][39/39] chair_AP_0.25: 0.6153 sofa_AP_0.25: 0.6650 table_AP_0.25: 0.4852 garbagebin_AP_0.25: 0.2946 bookshelf_AP_0.25: 0.3464 picture_AP_0.25: 0.0389 curtain_AP_0.25: 0.2584 door_AP_0.25: 0.1462 cabinet_AP_0.25: 0.2471 refrigerator_AP_0.25: 0.4713 counter_AP_0.25: 0.3993 sink_AP_0.25: 0.5304 window_AP_0.25: 0.1404 desk_AP_0.25: 0.6692 bed_AP_0.25: 0.8054 toilet_AP_0.25: 0.8926 showercurtrain_AP_0.25: 0.3205 bathtub_AP_0.25: 0.7744 mAP_0.25: 0.4500 chair_rec_0.25: 0.7507 sofa_rec_0.25: 0.8144 table_rec_0.25: 0.6200 garbagebin_rec_0.25: 0.4698 bookshelf_rec_0.25: 0.5844 picture_rec_0.25: 0.1441 curtain_rec_0.25: 0.4478 door_rec_0.25: 0.4368 cabinet_rec_0.25: 0.4919 refrigerator_rec_0.25: 0.5789 counter_rec_0.25: 0.6154 sink_rec_0.25: 0.6224 window_rec_0.25: 0.3688 desk_rec_0.25: 0.8504 bed_rec_0.25: 0.8395 toilet_rec_0.25: 0.9483 showercurtrain_rec_0.25: 0.5714 bathtub_rec_0.25: 0.8387 mAR_0.25: 0.6108 chair_AP_0.50: 0.1928 sofa_AP_0.50: 0.2355 table_AP_0.50: 0.1559 garbagebin_AP_0.50: 0.0242 bookshelf_AP_0.50: 0.0582 picture_AP_0.50: 0.0066 curtain_AP_0.50: 0.0823 door_AP_0.50: 0.0184 cabinet_AP_0.50: 0.0451 refrigerator_AP_0.50: 0.1837 counter_AP_0.50: 0.0467 sink_AP_0.50: 0.1805 window_AP_0.50: 0.0048 desk_AP_0.50: 0.2309 bed_AP_0.50: 0.3470 toilet_AP_0.50: 0.3839 showercurtrain_AP_0.50: 0.0230 bathtub_AP_0.50: 0.2160 mAP_0.50: 0.1353 chair_rec_0.50: 0.3604 sofa_rec_0.50: 0.4433 table_rec_0.50: 0.3029 garbagebin_rec_0.50: 0.1264 bookshelf_rec_0.50: 0.1818 picture_rec_0.50: 0.0315 curtain_rec_0.50: 0.1791 door_rec_0.50: 0.1285 cabinet_rec_0.50: 0.1694 refrigerator_rec_0.50: 0.3158 counter_rec_0.50: 0.1923 sink_rec_0.50: 0.3061 window_rec_0.50: 0.0567 desk_rec_0.50: 0.4567 bed_rec_0.50: 0.5062 toilet_rec_0.50: 0.4828 showercurtrain_rec_0.50: 0.1071 bathtub_rec_0.50: 0.4516 mAR_0.50: 0.2666 data_time: 0.2489 time: 2.7363 2025/05/13 07:58:34 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_136.pth is removed 2025/05/13 07:58:59 - mmengine - INFO - The best checkpoint with 0.4500 mAP_0.25 at 150 epoch is saved to best_mAP_0.25_epoch_150.pth. 2025/05/13 08:01:53 - mmengine - INFO - Epoch(train) [151][10/91] base_lr: 1.7280e-04 lr: 1.7280e-04 eta: 2 days, 16:02:24 time: 10.5226 data_time: 1.5912 memory: 68703 grad_norm: 1.5929 loss: 1.7823 center_loss: 0.4781 size_loss: 0.1425 cls_loss: 0.5763 giou_loss: 0.5854 2025/05/13 08:03:30 - mmengine - INFO - Epoch(train) [151][20/91] base_lr: 1.7280e-04 lr: 1.7280e-04 eta: 2 days, 16:00:35 time: 10.5165 data_time: 1.5943 memory: 68703 grad_norm: 1.5786 loss: 1.7820 center_loss: 0.4790 size_loss: 0.1420 cls_loss: 0.5746 giou_loss: 0.5865 2025/05/13 08:05:07 - mmengine - INFO - Epoch(train) [151][30/91] base_lr: 1.7280e-04 lr: 1.7280e-04 eta: 2 days, 15:58:47 time: 10.5203 data_time: 1.6119 memory: 68703 grad_norm: 1.6541 loss: 1.8034 center_loss: 0.4900 size_loss: 0.1434 cls_loss: 0.5793 giou_loss: 0.5906 2025/05/13 08:06:44 - mmengine - INFO - Epoch(train) [151][40/91] base_lr: 1.7280e-04 lr: 1.7280e-04 eta: 2 days, 15:56:58 time: 10.5183 data_time: 1.6225 memory: 68702 grad_norm: 1.6782 loss: 1.7838 center_loss: 0.4818 size_loss: 0.1420 cls_loss: 0.5745 giou_loss: 0.5855 2025/05/13 08:08:22 - mmengine - INFO - Epoch(train) [151][50/91] base_lr: 1.7280e-04 lr: 1.7280e-04 eta: 2 days, 15:55:11 time: 10.7357 data_time: 1.6590 memory: 68703 grad_norm: 1.6044 loss: 1.7770 center_loss: 0.4832 size_loss: 0.1410 cls_loss: 0.5694 giou_loss: 0.5835 2025/05/13 08:09:59 - mmengine - INFO - Epoch(train) [151][60/91] base_lr: 1.7280e-04 lr: 1.7280e-04 eta: 2 days, 15:53:23 time: 9.7193 data_time: 0.6424 memory: 68702 grad_norm: 1.4856 loss: 1.7878 center_loss: 0.4875 size_loss: 0.1421 cls_loss: 0.5726 giou_loss: 0.5856 2025/05/13 08:11:36 - mmengine - INFO - Epoch(train) [151][70/91] base_lr: 1.7280e-04 lr: 1.7280e-04 eta: 2 days, 15:51:34 time: 9.7281 data_time: 0.6418 memory: 68702 grad_norm: 1.5263 loss: 1.7753 center_loss: 0.4817 size_loss: 0.1422 cls_loss: 0.5690 giou_loss: 0.5824 2025/05/13 08:13:13 - mmengine - INFO - Epoch(train) [151][80/91] base_lr: 1.7280e-04 lr: 1.7280e-04 eta: 2 days, 15:49:45 time: 9.7147 data_time: 0.6364 memory: 68702 grad_norm: 1.5116 loss: 1.7560 center_loss: 0.4740 size_loss: 0.1399 cls_loss: 0.5627 giou_loss: 0.5794 2025/05/13 08:14:48 - mmengine - INFO - Epoch(train) [151][90/91] base_lr: 1.7280e-04 lr: 1.7280e-04 eta: 2 days, 15:47:54 time: 9.6889 data_time: 0.6298 memory: 68702 grad_norm: 1.5049 loss: 1.7492 center_loss: 0.4751 size_loss: 0.1396 cls_loss: 0.5567 giou_loss: 0.5779 2025/05/13 08:14:50 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 08:17:19 - mmengine - INFO - Epoch(train) [152][10/91] base_lr: 1.7190e-04 lr: 1.7190e-04 eta: 2 days, 15:47:07 time: 10.5519 data_time: 1.4403 memory: 68703 grad_norm: 1.5970 loss: 1.7361 center_loss: 0.4644 size_loss: 0.1382 cls_loss: 0.5586 giou_loss: 0.5749 2025/05/13 08:18:56 - mmengine - INFO - Epoch(train) [152][20/91] base_lr: 1.7190e-04 lr: 1.7190e-04 eta: 2 days, 15:45:18 time: 10.5341 data_time: 1.4293 memory: 68702 grad_norm: 1.7315 loss: 1.7439 center_loss: 0.4705 size_loss: 0.1387 cls_loss: 0.5590 giou_loss: 0.5757 2025/05/13 08:20:33 - mmengine - INFO - Epoch(train) [152][30/91] base_lr: 1.7190e-04 lr: 1.7190e-04 eta: 2 days, 15:43:29 time: 10.5321 data_time: 1.4180 memory: 68701 grad_norm: 1.7746 loss: 1.7715 center_loss: 0.4816 size_loss: 0.1410 cls_loss: 0.5659 giou_loss: 0.5830 2025/05/13 08:22:09 - mmengine - INFO - Epoch(train) [152][40/91] base_lr: 1.7190e-04 lr: 1.7190e-04 eta: 2 days, 15:41:40 time: 10.5268 data_time: 1.4032 memory: 68702 grad_norm: 1.7754 loss: 1.7800 center_loss: 0.4838 size_loss: 0.1413 cls_loss: 0.5705 giou_loss: 0.5843 2025/05/13 08:23:46 - mmengine - INFO - Epoch(train) [152][50/91] base_lr: 1.7190e-04 lr: 1.7190e-04 eta: 2 days, 15:39:51 time: 10.7250 data_time: 1.4122 memory: 68702 grad_norm: 1.7638 loss: 1.8020 center_loss: 0.4926 size_loss: 0.1438 cls_loss: 0.5766 giou_loss: 0.5890 2025/05/13 08:25:23 - mmengine - INFO - Epoch(train) [152][60/91] base_lr: 1.7190e-04 lr: 1.7190e-04 eta: 2 days, 15:38:03 time: 9.6784 data_time: 0.5709 memory: 68702 grad_norm: 1.6820 loss: 1.8062 center_loss: 0.4998 size_loss: 0.1436 cls_loss: 0.5724 giou_loss: 0.5904 2025/05/13 08:27:00 - mmengine - INFO - Epoch(train) [152][70/91] base_lr: 1.7190e-04 lr: 1.7190e-04 eta: 2 days, 15:36:14 time: 9.6927 data_time: 0.5802 memory: 68702 grad_norm: 1.5604 loss: 1.7911 center_loss: 0.4901 size_loss: 0.1420 cls_loss: 0.5715 giou_loss: 0.5875 2025/05/13 08:28:37 - mmengine - INFO - Epoch(train) [152][80/91] base_lr: 1.7190e-04 lr: 1.7190e-04 eta: 2 days, 15:34:25 time: 9.6811 data_time: 0.5786 memory: 68702 grad_norm: 1.4822 loss: 1.7763 center_loss: 0.4837 size_loss: 0.1413 cls_loss: 0.5669 giou_loss: 0.5844 2025/05/13 08:30:12 - mmengine - INFO - Epoch(train) [152][90/91] base_lr: 1.7190e-04 lr: 1.7190e-04 eta: 2 days, 15:32:34 time: 9.6654 data_time: 0.5760 memory: 68702 grad_norm: 1.4730 loss: 1.7905 center_loss: 0.4917 size_loss: 0.1431 cls_loss: 0.5672 giou_loss: 0.5884 2025/05/13 08:30:14 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 08:30:14 - mmengine - INFO - Saving checkpoint at 152 epochs 2025/05/13 08:31:11 - mmengine - INFO - Epoch(val) [152][10/39] eta: 0:01:36 time: 2.8579 data_time: 0.3608 memory: 15952 2025/05/13 08:31:36 - mmengine - INFO - Epoch(val) [152][20/39] eta: 0:00:56 time: 2.7219 data_time: 0.2229 memory: 13407 2025/05/13 08:32:02 - mmengine - INFO - Epoch(val) [152][30/39] eta: 0:00:25 time: 2.7209 data_time: 0.2216 memory: 13407 2025/05/13 08:32:28 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | chair | 0.5434 | 0.7142 | 0.1415 | 0.3216 | | sofa | 0.7166 | 0.8763 | 0.1837 | 0.3711 | | garbagebin | 0.2716 | 0.4736 | 0.0197 | 0.1075 | | table | 0.4598 | 0.6114 | 0.1382 | 0.2886 | | bookshelf | 0.3223 | 0.6364 | 0.1226 | 0.2727 | | curtain | 0.2697 | 0.5224 | 0.0542 | 0.1343 | | picture | 0.0166 | 0.1351 | 0.0008 | 0.0135 | | window | 0.1289 | 0.3475 | 0.0113 | 0.0887 | | door | 0.1358 | 0.4454 | 0.0141 | 0.1328 | | cabinet | 0.2557 | 0.4677 | 0.0383 | 0.1586 | | counter | 0.4792 | 0.6154 | 0.0347 | 0.1538 | | refrigerator | 0.4243 | 0.6140 | 0.2495 | 0.3860 | | sink | 0.4472 | 0.6122 | 0.0701 | 0.2245 | | desk | 0.6922 | 0.8583 | 0.2409 | 0.4488 | | bed | 0.8694 | 0.8889 | 0.3554 | 0.5432 | | toilet | 0.8509 | 0.9138 | 0.4634 | 0.5690 | | showercurtrain | 0.2346 | 0.5000 | 0.0071 | 0.0714 | | bathtub | 0.7484 | 0.9032 | 0.1603 | 0.3548 | +----------------+---------+---------+---------+---------+ | Overall | 0.4370 | 0.6187 | 0.1281 | 0.2578 | +----------------+---------+---------+---------+---------+ 2025/05/13 08:32:28 - mmengine - INFO - Epoch(val) [152][39/39] chair_AP_0.25: 0.5434 sofa_AP_0.25: 0.7166 table_AP_0.25: 0.4598 garbagebin_AP_0.25: 0.2716 bookshelf_AP_0.25: 0.3223 picture_AP_0.25: 0.0166 curtain_AP_0.25: 0.2697 door_AP_0.25: 0.1358 cabinet_AP_0.25: 0.2557 refrigerator_AP_0.25: 0.4243 counter_AP_0.25: 0.4792 sink_AP_0.25: 0.4472 window_AP_0.25: 0.1289 desk_AP_0.25: 0.6922 bed_AP_0.25: 0.8694 toilet_AP_0.25: 0.8509 showercurtrain_AP_0.25: 0.2346 bathtub_AP_0.25: 0.7484 mAP_0.25: 0.4370 chair_rec_0.25: 0.7142 sofa_rec_0.25: 0.8763 table_rec_0.25: 0.6114 garbagebin_rec_0.25: 0.4736 bookshelf_rec_0.25: 0.6364 picture_rec_0.25: 0.1351 curtain_rec_0.25: 0.5224 door_rec_0.25: 0.4454 cabinet_rec_0.25: 0.4677 refrigerator_rec_0.25: 0.6140 counter_rec_0.25: 0.6154 sink_rec_0.25: 0.6122 window_rec_0.25: 0.3475 desk_rec_0.25: 0.8583 bed_rec_0.25: 0.8889 toilet_rec_0.25: 0.9138 showercurtrain_rec_0.25: 0.5000 bathtub_rec_0.25: 0.9032 mAR_0.25: 0.6187 chair_AP_0.50: 0.1415 sofa_AP_0.50: 0.1837 table_AP_0.50: 0.1382 garbagebin_AP_0.50: 0.0197 bookshelf_AP_0.50: 0.1226 picture_AP_0.50: 0.0008 curtain_AP_0.50: 0.0542 door_AP_0.50: 0.0141 cabinet_AP_0.50: 0.0383 refrigerator_AP_0.50: 0.2495 counter_AP_0.50: 0.0347 sink_AP_0.50: 0.0701 window_AP_0.50: 0.0113 desk_AP_0.50: 0.2409 bed_AP_0.50: 0.3554 toilet_AP_0.50: 0.4634 showercurtrain_AP_0.50: 0.0071 bathtub_AP_0.50: 0.1603 mAP_0.50: 0.1281 chair_rec_0.50: 0.3216 sofa_rec_0.50: 0.3711 table_rec_0.50: 0.2886 garbagebin_rec_0.50: 0.1075 bookshelf_rec_0.50: 0.2727 picture_rec_0.50: 0.0135 curtain_rec_0.50: 0.1343 door_rec_0.50: 0.1328 cabinet_rec_0.50: 0.1586 refrigerator_rec_0.50: 0.3860 counter_rec_0.50: 0.1538 sink_rec_0.50: 0.2245 window_rec_0.50: 0.0887 desk_rec_0.50: 0.4488 bed_rec_0.50: 0.5432 toilet_rec_0.50: 0.5690 showercurtrain_rec_0.50: 0.0714 bathtub_rec_0.50: 0.3548 mAR_0.50: 0.2578 data_time: 0.2587 time: 2.7558 2025/05/13 08:34:56 - mmengine - INFO - Epoch(train) [153][10/91] base_lr: 1.7098e-04 lr: 1.7098e-04 eta: 2 days, 15:31:45 time: 10.5163 data_time: 1.5534 memory: 68703 grad_norm: 1.5422 loss: 1.7776 center_loss: 0.4873 size_loss: 0.1434 cls_loss: 0.5621 giou_loss: 0.5847 2025/05/13 08:36:32 - mmengine - INFO - Epoch(train) [153][20/91] base_lr: 1.7098e-04 lr: 1.7098e-04 eta: 2 days, 15:29:55 time: 10.5023 data_time: 1.5432 memory: 68703 grad_norm: 1.5852 loss: 1.7783 center_loss: 0.4843 size_loss: 0.1453 cls_loss: 0.5628 giou_loss: 0.5859 2025/05/13 08:38:08 - mmengine - INFO - Epoch(train) [153][30/91] base_lr: 1.7098e-04 lr: 1.7098e-04 eta: 2 days, 15:28:05 time: 10.4926 data_time: 1.5342 memory: 68702 grad_norm: 1.6109 loss: 1.7703 center_loss: 0.4843 size_loss: 0.1452 cls_loss: 0.5573 giou_loss: 0.5835 2025/05/13 08:39:45 - mmengine - INFO - Epoch(train) [153][40/91] base_lr: 1.7098e-04 lr: 1.7098e-04 eta: 2 days, 15:26:16 time: 10.4850 data_time: 1.5309 memory: 68702 grad_norm: 1.5689 loss: 1.7553 center_loss: 0.4777 size_loss: 0.1436 cls_loss: 0.5552 giou_loss: 0.5788 2025/05/13 08:41:23 - mmengine - INFO - Epoch(train) [153][50/91] base_lr: 1.7098e-04 lr: 1.7098e-04 eta: 2 days, 15:24:29 time: 10.6955 data_time: 1.5424 memory: 68702 grad_norm: 1.5022 loss: 1.7534 center_loss: 0.4754 size_loss: 0.1424 cls_loss: 0.5557 giou_loss: 0.5799 2025/05/13 08:43:00 - mmengine - INFO - Epoch(train) [153][60/91] base_lr: 1.7098e-04 lr: 1.7098e-04 eta: 2 days, 15:22:40 time: 9.6738 data_time: 0.5495 memory: 68702 grad_norm: 1.4283 loss: 1.7655 center_loss: 0.4796 size_loss: 0.1437 cls_loss: 0.5612 giou_loss: 0.5810 2025/05/13 08:44:37 - mmengine - INFO - Epoch(train) [153][70/91] base_lr: 1.7098e-04 lr: 1.7098e-04 eta: 2 days, 15:20:52 time: 9.6955 data_time: 0.5630 memory: 68702 grad_norm: 1.4359 loss: 1.7497 center_loss: 0.4720 size_loss: 0.1417 cls_loss: 0.5575 giou_loss: 0.5785 2025/05/13 08:46:13 - mmengine - INFO - Epoch(train) [153][80/91] base_lr: 1.7098e-04 lr: 1.7098e-04 eta: 2 days, 15:19:02 time: 9.6940 data_time: 0.5558 memory: 68702 grad_norm: 1.4182 loss: 1.7458 center_loss: 0.4700 size_loss: 0.1416 cls_loss: 0.5572 giou_loss: 0.5771 2025/05/13 08:47:48 - mmengine - INFO - Epoch(train) [153][90/91] base_lr: 1.7098e-04 lr: 1.7098e-04 eta: 2 days, 15:17:11 time: 9.6707 data_time: 0.5533 memory: 68703 grad_norm: 1.4255 loss: 1.7620 center_loss: 0.4807 size_loss: 0.1419 cls_loss: 0.5576 giou_loss: 0.5818 2025/05/13 08:47:50 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 08:50:16 - mmengine - INFO - Epoch(train) [154][10/91] base_lr: 1.7007e-04 lr: 1.7007e-04 eta: 2 days, 15:16:18 time: 10.4629 data_time: 1.3740 memory: 68703 grad_norm: 1.4436 loss: 1.7903 center_loss: 0.4891 size_loss: 0.1453 cls_loss: 0.5703 giou_loss: 0.5857 2025/05/13 08:51:51 - mmengine - INFO - Epoch(train) [154][20/91] base_lr: 1.7007e-04 lr: 1.7007e-04 eta: 2 days, 15:14:26 time: 10.4308 data_time: 1.3506 memory: 68702 grad_norm: 1.5290 loss: 1.7903 center_loss: 0.4929 size_loss: 0.1445 cls_loss: 0.5668 giou_loss: 0.5861 2025/05/13 08:53:28 - mmengine - INFO - Epoch(train) [154][30/91] base_lr: 1.7007e-04 lr: 1.7007e-04 eta: 2 days, 15:12:38 time: 10.4294 data_time: 1.3468 memory: 68702 grad_norm: 1.5055 loss: 1.8055 center_loss: 0.4979 size_loss: 0.1451 cls_loss: 0.5702 giou_loss: 0.5922 2025/05/13 08:55:05 - mmengine - INFO - Epoch(train) [154][40/91] base_lr: 1.7007e-04 lr: 1.7007e-04 eta: 2 days, 15:10:49 time: 10.4385 data_time: 1.3521 memory: 68703 grad_norm: 1.4851 loss: 1.8192 center_loss: 0.5042 size_loss: 0.1469 cls_loss: 0.5714 giou_loss: 0.5967 2025/05/13 08:56:43 - mmengine - INFO - Epoch(train) [154][50/91] base_lr: 1.7007e-04 lr: 1.7007e-04 eta: 2 days, 15:09:03 time: 10.6625 data_time: 1.3735 memory: 68702 grad_norm: 1.4442 loss: 1.7872 center_loss: 0.4880 size_loss: 0.1441 cls_loss: 0.5633 giou_loss: 0.5918 2025/05/13 08:58:20 - mmengine - INFO - Epoch(train) [154][60/91] base_lr: 1.7007e-04 lr: 1.7007e-04 eta: 2 days, 15:07:15 time: 9.6936 data_time: 0.5401 memory: 68702 grad_norm: 1.4630 loss: 1.7775 center_loss: 0.4852 size_loss: 0.1444 cls_loss: 0.5593 giou_loss: 0.5887 2025/05/13 08:59:57 - mmengine - INFO - Epoch(train) [154][70/91] base_lr: 1.7007e-04 lr: 1.7007e-04 eta: 2 days, 15:05:26 time: 9.7177 data_time: 0.5578 memory: 68703 grad_norm: 1.3719 loss: 1.7652 center_loss: 0.4762 size_loss: 0.1423 cls_loss: 0.5618 giou_loss: 0.5849 2025/05/13 09:01:05 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 09:01:34 - mmengine - INFO - Epoch(train) [154][80/91] base_lr: 1.7007e-04 lr: 1.7007e-04 eta: 2 days, 15:03:37 time: 9.7113 data_time: 0.5647 memory: 68703 grad_norm: 1.3498 loss: 1.7740 center_loss: 0.4903 size_loss: 0.1416 cls_loss: 0.5577 giou_loss: 0.5845 2025/05/13 09:03:09 - mmengine - INFO - Epoch(train) [154][90/91] base_lr: 1.7007e-04 lr: 1.7007e-04 eta: 2 days, 15:01:46 time: 9.6790 data_time: 0.5549 memory: 68703 grad_norm: 1.3781 loss: 1.7750 center_loss: 0.4921 size_loss: 0.1407 cls_loss: 0.5577 giou_loss: 0.5846 2025/05/13 09:03:11 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 09:03:11 - mmengine - INFO - Saving checkpoint at 154 epochs 2025/05/13 09:04:04 - mmengine - INFO - Epoch(val) [154][10/39] eta: 0:01:33 time: 2.8499 data_time: 0.3537 memory: 15952 2025/05/13 09:04:30 - mmengine - INFO - Epoch(val) [154][20/39] eta: 0:00:55 time: 2.6966 data_time: 0.2077 memory: 13407 2025/05/13 09:04:55 - mmengine - INFO - Epoch(val) [154][30/39] eta: 0:00:25 time: 2.6910 data_time: 0.2078 memory: 13407 2025/05/13 09:05:21 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.2292 | 0.4472 | 0.0212 | 0.1302 | | sofa | 0.7256 | 0.8454 | 0.1868 | 0.3505 | | table | 0.4630 | 0.6000 | 0.1302 | 0.2829 | | chair | 0.5719 | 0.7237 | 0.1505 | 0.3304 | | bookshelf | 0.2531 | 0.5714 | 0.0635 | 0.2468 | | curtain | 0.1870 | 0.5373 | 0.0137 | 0.1045 | | picture | 0.0201 | 0.1486 | 0.0011 | 0.0315 | | window | 0.1172 | 0.3617 | 0.0151 | 0.0993 | | door | 0.1135 | 0.4261 | 0.0109 | 0.1221 | | cabinet | 0.2437 | 0.5000 | 0.0428 | 0.1801 | | counter | 0.3025 | 0.4808 | 0.0206 | 0.1154 | | refrigerator | 0.4325 | 0.6140 | 0.1466 | 0.2982 | | sink | 0.5030 | 0.6633 | 0.0752 | 0.2449 | | desk | 0.6517 | 0.8346 | 0.1967 | 0.4331 | | bed | 0.8220 | 0.8642 | 0.4292 | 0.5926 | | toilet | 0.8396 | 0.9310 | 0.4791 | 0.6034 | | bathtub | 0.8626 | 0.9355 | 0.3162 | 0.5484 | | showercurtrain | 0.2036 | 0.5000 | 0.0391 | 0.1786 | +----------------+---------+---------+---------+---------+ | Overall | 0.4190 | 0.6103 | 0.1299 | 0.2718 | +----------------+---------+---------+---------+---------+ 2025/05/13 09:05:21 - mmengine - INFO - Epoch(val) [154][39/39] chair_AP_0.25: 0.5719 sofa_AP_0.25: 0.7256 table_AP_0.25: 0.4630 garbagebin_AP_0.25: 0.2292 bookshelf_AP_0.25: 0.2531 picture_AP_0.25: 0.0201 curtain_AP_0.25: 0.1870 door_AP_0.25: 0.1135 cabinet_AP_0.25: 0.2437 refrigerator_AP_0.25: 0.4325 counter_AP_0.25: 0.3025 sink_AP_0.25: 0.5030 window_AP_0.25: 0.1172 desk_AP_0.25: 0.6517 bed_AP_0.25: 0.8220 toilet_AP_0.25: 0.8396 showercurtrain_AP_0.25: 0.2036 bathtub_AP_0.25: 0.8626 mAP_0.25: 0.4190 chair_rec_0.25: 0.7237 sofa_rec_0.25: 0.8454 table_rec_0.25: 0.6000 garbagebin_rec_0.25: 0.4472 bookshelf_rec_0.25: 0.5714 picture_rec_0.25: 0.1486 curtain_rec_0.25: 0.5373 door_rec_0.25: 0.4261 cabinet_rec_0.25: 0.5000 refrigerator_rec_0.25: 0.6140 counter_rec_0.25: 0.4808 sink_rec_0.25: 0.6633 window_rec_0.25: 0.3617 desk_rec_0.25: 0.8346 bed_rec_0.25: 0.8642 toilet_rec_0.25: 0.9310 showercurtrain_rec_0.25: 0.5000 bathtub_rec_0.25: 0.9355 mAR_0.25: 0.6103 chair_AP_0.50: 0.1505 sofa_AP_0.50: 0.1868 table_AP_0.50: 0.1302 garbagebin_AP_0.50: 0.0212 bookshelf_AP_0.50: 0.0635 picture_AP_0.50: 0.0011 curtain_AP_0.50: 0.0137 door_AP_0.50: 0.0109 cabinet_AP_0.50: 0.0428 refrigerator_AP_0.50: 0.1466 counter_AP_0.50: 0.0206 sink_AP_0.50: 0.0752 window_AP_0.50: 0.0151 desk_AP_0.50: 0.1967 bed_AP_0.50: 0.4292 toilet_AP_0.50: 0.4791 showercurtrain_AP_0.50: 0.0391 bathtub_AP_0.50: 0.3162 mAP_0.50: 0.1299 chair_rec_0.50: 0.3304 sofa_rec_0.50: 0.3505 table_rec_0.50: 0.2829 garbagebin_rec_0.50: 0.1302 bookshelf_rec_0.50: 0.2468 picture_rec_0.50: 0.0315 curtain_rec_0.50: 0.1045 door_rec_0.50: 0.1221 cabinet_rec_0.50: 0.1801 refrigerator_rec_0.50: 0.2982 counter_rec_0.50: 0.1154 sink_rec_0.50: 0.2449 window_rec_0.50: 0.0993 desk_rec_0.50: 0.4331 bed_rec_0.50: 0.5926 toilet_rec_0.50: 0.6034 showercurtrain_rec_0.50: 0.1786 bathtub_rec_0.50: 0.5484 mAR_0.50: 0.2718 data_time: 0.2406 time: 2.7209 2025/05/13 09:07:46 - mmengine - INFO - Epoch(train) [155][10/91] base_lr: 1.6915e-04 lr: 1.6915e-04 eta: 2 days, 15:00:50 time: 10.4487 data_time: 1.4289 memory: 68702 grad_norm: 1.4139 loss: 1.7975 center_loss: 0.5044 size_loss: 0.1443 cls_loss: 0.5627 giou_loss: 0.5861 2025/05/13 09:09:24 - mmengine - INFO - Epoch(train) [155][20/91] base_lr: 1.6915e-04 lr: 1.6915e-04 eta: 2 days, 14:59:04 time: 10.4676 data_time: 1.4416 memory: 68702 grad_norm: 1.4036 loss: 1.7999 center_loss: 0.5065 size_loss: 0.1438 cls_loss: 0.5636 giou_loss: 0.5860 2025/05/13 09:11:01 - mmengine - INFO - Epoch(train) [155][30/91] base_lr: 1.6915e-04 lr: 1.6915e-04 eta: 2 days, 14:57:16 time: 10.4806 data_time: 1.4398 memory: 68703 grad_norm: 1.4146 loss: 1.7796 center_loss: 0.5010 size_loss: 0.1437 cls_loss: 0.5503 giou_loss: 0.5845 2025/05/13 09:12:38 - mmengine - INFO - Epoch(train) [155][40/91] base_lr: 1.6915e-04 lr: 1.6915e-04 eta: 2 days, 14:55:27 time: 10.4838 data_time: 1.4465 memory: 68700 grad_norm: 1.4598 loss: 1.7449 center_loss: 0.4767 size_loss: 0.1422 cls_loss: 0.5498 giou_loss: 0.5763 2025/05/13 09:14:15 - mmengine - INFO - Epoch(train) [155][50/91] base_lr: 1.6915e-04 lr: 1.6915e-04 eta: 2 days, 14:53:39 time: 10.6825 data_time: 1.4664 memory: 68702 grad_norm: 1.4246 loss: 1.7410 center_loss: 0.4737 size_loss: 0.1398 cls_loss: 0.5536 giou_loss: 0.5739 2025/05/13 09:15:53 - mmengine - INFO - Epoch(train) [155][60/91] base_lr: 1.6915e-04 lr: 1.6915e-04 eta: 2 days, 14:51:51 time: 9.7270 data_time: 0.5878 memory: 68702 grad_norm: 1.4221 loss: 1.7247 center_loss: 0.4621 size_loss: 0.1390 cls_loss: 0.5507 giou_loss: 0.5728 2025/05/13 09:17:29 - mmengine - INFO - Epoch(train) [155][70/91] base_lr: 1.6915e-04 lr: 1.6915e-04 eta: 2 days, 14:50:02 time: 9.7001 data_time: 0.5888 memory: 68702 grad_norm: 1.4237 loss: 1.7277 center_loss: 0.4667 size_loss: 0.1395 cls_loss: 0.5489 giou_loss: 0.5727 2025/05/13 09:19:06 - mmengine - INFO - Epoch(train) [155][80/91] base_lr: 1.6915e-04 lr: 1.6915e-04 eta: 2 days, 14:48:13 time: 9.6871 data_time: 0.5974 memory: 68702 grad_norm: 1.3777 loss: 1.7346 center_loss: 0.4665 size_loss: 0.1392 cls_loss: 0.5554 giou_loss: 0.5736 2025/05/13 09:20:41 - mmengine - INFO - Epoch(train) [155][90/91] base_lr: 1.6915e-04 lr: 1.6915e-04 eta: 2 days, 14:46:22 time: 9.6602 data_time: 0.5915 memory: 68702 grad_norm: 1.5293 loss: 1.7400 center_loss: 0.4670 size_loss: 0.1386 cls_loss: 0.5595 giou_loss: 0.5749 2025/05/13 09:20:43 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 09:23:15 - mmengine - INFO - Epoch(train) [156][10/91] base_lr: 1.6823e-04 lr: 1.6823e-04 eta: 2 days, 14:45:38 time: 10.6020 data_time: 1.5710 memory: 68702 grad_norm: 1.9198 loss: 1.7622 center_loss: 0.4785 size_loss: 0.1407 cls_loss: 0.5619 giou_loss: 0.5812 2025/05/13 09:24:51 - mmengine - INFO - Epoch(train) [156][20/91] base_lr: 1.6823e-04 lr: 1.6823e-04 eta: 2 days, 14:43:48 time: 10.5763 data_time: 1.5543 memory: 68702 grad_norm: 2.0298 loss: 1.7717 center_loss: 0.4847 size_loss: 0.1411 cls_loss: 0.5620 giou_loss: 0.5840 2025/05/13 09:26:28 - mmengine - INFO - Epoch(train) [156][30/91] base_lr: 1.6823e-04 lr: 1.6823e-04 eta: 2 days, 14:41:59 time: 10.5827 data_time: 1.5451 memory: 68703 grad_norm: 2.0823 loss: 1.7833 center_loss: 0.4910 size_loss: 0.1406 cls_loss: 0.5642 giou_loss: 0.5875 2025/05/13 09:28:04 - mmengine - INFO - Epoch(train) [156][40/91] base_lr: 1.6823e-04 lr: 1.6823e-04 eta: 2 days, 14:40:10 time: 10.5738 data_time: 1.5401 memory: 68702 grad_norm: 2.1098 loss: 1.8078 center_loss: 0.5019 size_loss: 0.1434 cls_loss: 0.5720 giou_loss: 0.5905 2025/05/13 09:29:42 - mmengine - INFO - Epoch(train) [156][50/91] base_lr: 1.6823e-04 lr: 1.6823e-04 eta: 2 days, 14:38:23 time: 10.7754 data_time: 1.5550 memory: 68702 grad_norm: 1.8479 loss: 1.7957 center_loss: 0.4971 size_loss: 0.1431 cls_loss: 0.5683 giou_loss: 0.5872 2025/05/13 09:31:18 - mmengine - INFO - Epoch(train) [156][60/91] base_lr: 1.6823e-04 lr: 1.6823e-04 eta: 2 days, 14:36:34 time: 9.6619 data_time: 0.5585 memory: 68702 grad_norm: 1.5227 loss: 1.7888 center_loss: 0.4955 size_loss: 0.1427 cls_loss: 0.5629 giou_loss: 0.5877 2025/05/13 09:32:55 - mmengine - INFO - Epoch(train) [156][70/91] base_lr: 1.6823e-04 lr: 1.6823e-04 eta: 2 days, 14:34:45 time: 9.6774 data_time: 0.5733 memory: 68702 grad_norm: 1.3701 loss: 1.7973 center_loss: 0.4949 size_loss: 0.1435 cls_loss: 0.5694 giou_loss: 0.5895 2025/05/13 09:34:31 - mmengine - INFO - Epoch(train) [156][80/91] base_lr: 1.6823e-04 lr: 1.6823e-04 eta: 2 days, 14:32:56 time: 9.6720 data_time: 0.5781 memory: 68702 grad_norm: 1.3166 loss: 1.7843 center_loss: 0.4862 size_loss: 0.1442 cls_loss: 0.5667 giou_loss: 0.5873 2025/05/13 09:36:07 - mmengine - INFO - Epoch(train) [156][90/91] base_lr: 1.6823e-04 lr: 1.6823e-04 eta: 2 days, 14:31:05 time: 9.6420 data_time: 0.5741 memory: 68700 grad_norm: 1.3351 loss: 1.7731 center_loss: 0.4827 size_loss: 0.1434 cls_loss: 0.5602 giou_loss: 0.5868 2025/05/13 09:36:08 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 09:36:08 - mmengine - INFO - Saving checkpoint at 156 epochs 2025/05/13 09:37:05 - mmengine - INFO - Epoch(val) [156][10/39] eta: 0:01:33 time: 2.8224 data_time: 0.3353 memory: 15952 2025/05/13 09:37:31 - mmengine - INFO - Epoch(val) [156][20/39] eta: 0:00:55 time: 2.6960 data_time: 0.2050 memory: 13407 2025/05/13 09:37:57 - mmengine - INFO - Epoch(val) [156][30/39] eta: 0:00:25 time: 2.7047 data_time: 0.2048 memory: 13407 2025/05/13 09:38:22 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.2494 | 0.4453 | 0.0293 | 0.1302 | | table | 0.4615 | 0.5943 | 0.1501 | 0.2743 | | chair | 0.5759 | 0.7164 | 0.1628 | 0.3341 | | sofa | 0.6959 | 0.8247 | 0.1818 | 0.3402 | | curtain | 0.3098 | 0.5672 | 0.0863 | 0.1493 | | bookshelf | 0.2854 | 0.5584 | 0.1144 | 0.2597 | | picture | 0.0176 | 0.1171 | 0.0000 | 0.0045 | | window | 0.1516 | 0.3652 | 0.0233 | 0.1028 | | cabinet | 0.2933 | 0.5027 | 0.0470 | 0.1505 | | counter | 0.3428 | 0.5192 | 0.0335 | 0.1731 | | door | 0.1560 | 0.4347 | 0.0184 | 0.1328 | | refrigerator | 0.5247 | 0.6316 | 0.1628 | 0.2807 | | sink | 0.4646 | 0.5918 | 0.1116 | 0.2653 | | desk | 0.6943 | 0.8346 | 0.2787 | 0.4646 | | bed | 0.8078 | 0.8395 | 0.4230 | 0.5802 | | toilet | 0.8425 | 0.8966 | 0.4117 | 0.4828 | | bathtub | 0.7953 | 0.8387 | 0.2267 | 0.4194 | | showercurtrain | 0.3474 | 0.6071 | 0.0705 | 0.2143 | +----------------+---------+---------+---------+---------+ | Overall | 0.4453 | 0.6047 | 0.1407 | 0.2644 | +----------------+---------+---------+---------+---------+ 2025/05/13 09:38:22 - mmengine - INFO - Epoch(val) [156][39/39] chair_AP_0.25: 0.5759 sofa_AP_0.25: 0.6959 table_AP_0.25: 0.4615 garbagebin_AP_0.25: 0.2494 bookshelf_AP_0.25: 0.2854 picture_AP_0.25: 0.0176 curtain_AP_0.25: 0.3098 door_AP_0.25: 0.1560 cabinet_AP_0.25: 0.2933 refrigerator_AP_0.25: 0.5247 counter_AP_0.25: 0.3428 sink_AP_0.25: 0.4646 window_AP_0.25: 0.1516 desk_AP_0.25: 0.6943 bed_AP_0.25: 0.8078 toilet_AP_0.25: 0.8425 showercurtrain_AP_0.25: 0.3474 bathtub_AP_0.25: 0.7953 mAP_0.25: 0.4453 chair_rec_0.25: 0.7164 sofa_rec_0.25: 0.8247 table_rec_0.25: 0.5943 garbagebin_rec_0.25: 0.4453 bookshelf_rec_0.25: 0.5584 picture_rec_0.25: 0.1171 curtain_rec_0.25: 0.5672 door_rec_0.25: 0.4347 cabinet_rec_0.25: 0.5027 refrigerator_rec_0.25: 0.6316 counter_rec_0.25: 0.5192 sink_rec_0.25: 0.5918 window_rec_0.25: 0.3652 desk_rec_0.25: 0.8346 bed_rec_0.25: 0.8395 toilet_rec_0.25: 0.8966 showercurtrain_rec_0.25: 0.6071 bathtub_rec_0.25: 0.8387 mAR_0.25: 0.6047 chair_AP_0.50: 0.1628 sofa_AP_0.50: 0.1818 table_AP_0.50: 0.1501 garbagebin_AP_0.50: 0.0293 bookshelf_AP_0.50: 0.1144 picture_AP_0.50: 0.0000 curtain_AP_0.50: 0.0863 door_AP_0.50: 0.0184 cabinet_AP_0.50: 0.0470 refrigerator_AP_0.50: 0.1628 counter_AP_0.50: 0.0335 sink_AP_0.50: 0.1116 window_AP_0.50: 0.0233 desk_AP_0.50: 0.2787 bed_AP_0.50: 0.4230 toilet_AP_0.50: 0.4117 showercurtrain_AP_0.50: 0.0705 bathtub_AP_0.50: 0.2267 mAP_0.50: 0.1407 chair_rec_0.50: 0.3341 sofa_rec_0.50: 0.3402 table_rec_0.50: 0.2743 garbagebin_rec_0.50: 0.1302 bookshelf_rec_0.50: 0.2597 picture_rec_0.50: 0.0045 curtain_rec_0.50: 0.1493 door_rec_0.50: 0.1328 cabinet_rec_0.50: 0.1505 refrigerator_rec_0.50: 0.2807 counter_rec_0.50: 0.1731 sink_rec_0.50: 0.2653 window_rec_0.50: 0.1028 desk_rec_0.50: 0.4646 bed_rec_0.50: 0.5802 toilet_rec_0.50: 0.4828 showercurtrain_rec_0.50: 0.2143 bathtub_rec_0.50: 0.4194 mAR_0.50: 0.2644 data_time: 0.2375 time: 2.7415 2025/05/13 09:40:49 - mmengine - INFO - Epoch(train) [157][10/91] base_lr: 1.6731e-04 lr: 1.6731e-04 eta: 2 days, 14:30:11 time: 10.4735 data_time: 1.4843 memory: 68702 grad_norm: 1.3926 loss: 1.7650 center_loss: 0.4771 size_loss: 0.1414 cls_loss: 0.5609 giou_loss: 0.5856 2025/05/13 09:42:25 - mmengine - INFO - Epoch(train) [157][20/91] base_lr: 1.6731e-04 lr: 1.6731e-04 eta: 2 days, 14:28:21 time: 10.4647 data_time: 1.4821 memory: 68702 grad_norm: 1.4220 loss: 1.7635 center_loss: 0.4754 size_loss: 0.1418 cls_loss: 0.5618 giou_loss: 0.5844 2025/05/13 09:44:02 - mmengine - INFO - Epoch(train) [157][30/91] base_lr: 1.6731e-04 lr: 1.6731e-04 eta: 2 days, 14:26:33 time: 10.4701 data_time: 1.4742 memory: 68700 grad_norm: 1.5366 loss: 1.7733 center_loss: 0.4833 size_loss: 0.1422 cls_loss: 0.5620 giou_loss: 0.5859 2025/05/13 09:45:39 - mmengine - INFO - Epoch(train) [157][40/91] base_lr: 1.6731e-04 lr: 1.6731e-04 eta: 2 days, 14:24:44 time: 10.4672 data_time: 1.4701 memory: 68702 grad_norm: 1.5509 loss: 1.7507 center_loss: 0.4720 size_loss: 0.1388 cls_loss: 0.5607 giou_loss: 0.5793 2025/05/13 09:47:16 - mmengine - INFO - Epoch(train) [157][50/91] base_lr: 1.6731e-04 lr: 1.6731e-04 eta: 2 days, 14:22:56 time: 10.6697 data_time: 1.4891 memory: 68702 grad_norm: 1.4841 loss: 1.7740 center_loss: 0.4858 size_loss: 0.1403 cls_loss: 0.5660 giou_loss: 0.5819 2025/05/13 09:48:52 - mmengine - INFO - Epoch(train) [157][60/91] base_lr: 1.6731e-04 lr: 1.6731e-04 eta: 2 days, 14:21:08 time: 9.6695 data_time: 0.5749 memory: 68700 grad_norm: 1.5139 loss: 1.7877 center_loss: 0.4923 size_loss: 0.1428 cls_loss: 0.5688 giou_loss: 0.5839 2025/05/13 09:50:29 - mmengine - INFO - Epoch(train) [157][70/91] base_lr: 1.6731e-04 lr: 1.6731e-04 eta: 2 days, 14:19:19 time: 9.6794 data_time: 0.5799 memory: 68700 grad_norm: 1.5686 loss: 1.7790 center_loss: 0.4860 size_loss: 0.1411 cls_loss: 0.5690 giou_loss: 0.5829 2025/05/13 09:52:00 - mmengine - INFO - Epoch(train) [157][80/91] base_lr: 1.6731e-04 lr: 1.6731e-04 eta: 2 days, 14:17:22 time: 9.5689 data_time: 0.5674 memory: 68703 grad_norm: 1.4988 loss: 1.7645 center_loss: 0.4778 size_loss: 0.1393 cls_loss: 0.5685 giou_loss: 0.5788 2025/05/13 09:53:26 - mmengine - INFO - Epoch(train) [157][90/91] base_lr: 1.6731e-04 lr: 1.6731e-04 eta: 2 days, 14:15:16 time: 9.3505 data_time: 0.5101 memory: 68702 grad_norm: 1.4942 loss: 1.7749 center_loss: 0.4861 size_loss: 0.1415 cls_loss: 0.5668 giou_loss: 0.5806 2025/05/13 09:53:27 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 09:55:20 - mmengine - INFO - Epoch(train) [158][10/91] base_lr: 1.6639e-04 lr: 1.6639e-04 eta: 2 days, 14:13:28 time: 9.4849 data_time: 0.9003 memory: 68700 grad_norm: 1.5523 loss: 1.7588 center_loss: 0.4734 size_loss: 0.1406 cls_loss: 0.5657 giou_loss: 0.5792 2025/05/13 09:56:46 - mmengine - INFO - Epoch(train) [158][20/91] base_lr: 1.6639e-04 lr: 1.6639e-04 eta: 2 days, 14:11:23 time: 9.2689 data_time: 0.8425 memory: 68702 grad_norm: 1.4999 loss: 1.7555 center_loss: 0.4737 size_loss: 0.1416 cls_loss: 0.5613 giou_loss: 0.5789 2025/05/13 09:58:12 - mmengine - INFO - Epoch(train) [158][30/91] base_lr: 1.6639e-04 lr: 1.6639e-04 eta: 2 days, 14:09:18 time: 9.0628 data_time: 0.7852 memory: 68700 grad_norm: 1.5478 loss: 1.7570 center_loss: 0.4792 size_loss: 0.1412 cls_loss: 0.5581 giou_loss: 0.5785 2025/05/13 09:59:38 - mmengine - INFO - Epoch(train) [158][40/91] base_lr: 1.6639e-04 lr: 1.6639e-04 eta: 2 days, 14:07:13 time: 8.9658 data_time: 0.7489 memory: 68702 grad_norm: 1.5559 loss: 1.7537 center_loss: 0.4762 size_loss: 0.1416 cls_loss: 0.5581 giou_loss: 0.5777 2025/05/13 10:01:05 - mmengine - INFO - Epoch(train) [158][50/91] base_lr: 1.6639e-04 lr: 1.6639e-04 eta: 2 days, 14:05:10 time: 9.1431 data_time: 0.7561 memory: 68702 grad_norm: 1.5276 loss: 1.7544 center_loss: 0.4807 size_loss: 0.1417 cls_loss: 0.5524 giou_loss: 0.5796 2025/05/13 10:02:31 - mmengine - INFO - Epoch(train) [158][60/91] base_lr: 1.6639e-04 lr: 1.6639e-04 eta: 2 days, 14:03:05 time: 8.6217 data_time: 0.2978 memory: 68702 grad_norm: 1.5482 loss: 1.7365 center_loss: 0.4748 size_loss: 0.1399 cls_loss: 0.5490 giou_loss: 0.5728 2025/05/13 10:03:57 - mmengine - INFO - Epoch(train) [158][70/91] base_lr: 1.6639e-04 lr: 1.6639e-04 eta: 2 days, 14:01:00 time: 8.6219 data_time: 0.2989 memory: 68703 grad_norm: 1.6176 loss: 1.7317 center_loss: 0.4717 size_loss: 0.1371 cls_loss: 0.5523 giou_loss: 0.5706 2025/05/13 10:05:23 - mmengine - INFO - Epoch(train) [158][80/91] base_lr: 1.6639e-04 lr: 1.6639e-04 eta: 2 days, 13:58:55 time: 8.6143 data_time: 0.2973 memory: 68702 grad_norm: 1.5592 loss: 1.7441 center_loss: 0.4721 size_loss: 0.1394 cls_loss: 0.5585 giou_loss: 0.5740 2025/05/13 10:06:48 - mmengine - INFO - Epoch(train) [158][90/91] base_lr: 1.6639e-04 lr: 1.6639e-04 eta: 2 days, 13:56:49 time: 8.6028 data_time: 0.2950 memory: 68702 grad_norm: 1.5235 loss: 1.7550 center_loss: 0.4793 size_loss: 0.1407 cls_loss: 0.5573 giou_loss: 0.5778 2025/05/13 10:06:49 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 10:06:49 - mmengine - INFO - Saving checkpoint at 158 epochs 2025/05/13 10:07:26 - mmengine - INFO - Epoch(val) [158][10/39] eta: 0:01:14 time: 2.7063 data_time: 0.2498 memory: 15952 2025/05/13 10:07:49 - mmengine - INFO - Epoch(val) [158][20/39] eta: 0:00:46 time: 2.5243 data_time: 0.1160 memory: 13407 2025/05/13 10:08:12 - mmengine - INFO - Epoch(val) [158][30/39] eta: 0:00:21 time: 2.4683 data_time: 0.1078 memory: 13407 2025/05/13 10:08:35 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.2597 | 0.4226 | 0.0353 | 0.1321 | | table | 0.4257 | 0.5714 | 0.1041 | 0.2400 | | chair | 0.5595 | 0.7171 | 0.1462 | 0.3202 | | sofa | 0.6720 | 0.8454 | 0.1810 | 0.3918 | | curtain | 0.2941 | 0.5672 | 0.0585 | 0.1194 | | bookshelf | 0.3335 | 0.5844 | 0.1158 | 0.2338 | | picture | 0.0267 | 0.1306 | 0.0034 | 0.0180 | | door | 0.1706 | 0.4561 | 0.0191 | 0.1328 | | cabinet | 0.2622 | 0.4866 | 0.0664 | 0.1989 | | window | 0.1518 | 0.3830 | 0.0260 | 0.1028 | | refrigerator | 0.5474 | 0.6491 | 0.1277 | 0.2807 | | sink | 0.4923 | 0.6429 | 0.0922 | 0.2449 | | counter | 0.2097 | 0.4615 | 0.0057 | 0.0769 | | toilet | 0.9187 | 0.9655 | 0.3775 | 0.4828 | | desk | 0.6684 | 0.8346 | 0.2622 | 0.4409 | | bed | 0.8266 | 0.8519 | 0.3724 | 0.5432 | | bathtub | 0.7804 | 0.8387 | 0.2284 | 0.4516 | | showercurtrain | 0.3354 | 0.6071 | 0.0258 | 0.1429 | +----------------+---------+---------+---------+---------+ | Overall | 0.4408 | 0.6120 | 0.1249 | 0.2530 | +----------------+---------+---------+---------+---------+ 2025/05/13 10:08:35 - mmengine - INFO - Epoch(val) [158][39/39] chair_AP_0.25: 0.5595 sofa_AP_0.25: 0.6720 table_AP_0.25: 0.4257 garbagebin_AP_0.25: 0.2597 bookshelf_AP_0.25: 0.3335 picture_AP_0.25: 0.0267 curtain_AP_0.25: 0.2941 door_AP_0.25: 0.1706 cabinet_AP_0.25: 0.2622 refrigerator_AP_0.25: 0.5474 counter_AP_0.25: 0.2097 sink_AP_0.25: 0.4923 window_AP_0.25: 0.1518 desk_AP_0.25: 0.6684 bed_AP_0.25: 0.8266 toilet_AP_0.25: 0.9187 showercurtrain_AP_0.25: 0.3354 bathtub_AP_0.25: 0.7804 mAP_0.25: 0.4408 chair_rec_0.25: 0.7171 sofa_rec_0.25: 0.8454 table_rec_0.25: 0.5714 garbagebin_rec_0.25: 0.4226 bookshelf_rec_0.25: 0.5844 picture_rec_0.25: 0.1306 curtain_rec_0.25: 0.5672 door_rec_0.25: 0.4561 cabinet_rec_0.25: 0.4866 refrigerator_rec_0.25: 0.6491 counter_rec_0.25: 0.4615 sink_rec_0.25: 0.6429 window_rec_0.25: 0.3830 desk_rec_0.25: 0.8346 bed_rec_0.25: 0.8519 toilet_rec_0.25: 0.9655 showercurtrain_rec_0.25: 0.6071 bathtub_rec_0.25: 0.8387 mAR_0.25: 0.6120 chair_AP_0.50: 0.1462 sofa_AP_0.50: 0.1810 table_AP_0.50: 0.1041 garbagebin_AP_0.50: 0.0353 bookshelf_AP_0.50: 0.1158 picture_AP_0.50: 0.0034 curtain_AP_0.50: 0.0585 door_AP_0.50: 0.0191 cabinet_AP_0.50: 0.0664 refrigerator_AP_0.50: 0.1277 counter_AP_0.50: 0.0057 sink_AP_0.50: 0.0922 window_AP_0.50: 0.0260 desk_AP_0.50: 0.2622 bed_AP_0.50: 0.3724 toilet_AP_0.50: 0.3775 showercurtrain_AP_0.50: 0.0258 bathtub_AP_0.50: 0.2284 mAP_0.50: 0.1249 chair_rec_0.50: 0.3202 sofa_rec_0.50: 0.3918 table_rec_0.50: 0.2400 garbagebin_rec_0.50: 0.1321 bookshelf_rec_0.50: 0.2338 picture_rec_0.50: 0.0180 curtain_rec_0.50: 0.1194 door_rec_0.50: 0.1328 cabinet_rec_0.50: 0.1989 refrigerator_rec_0.50: 0.2807 counter_rec_0.50: 0.0769 sink_rec_0.50: 0.2449 window_rec_0.50: 0.1028 desk_rec_0.50: 0.4409 bed_rec_0.50: 0.5432 toilet_rec_0.50: 0.4828 showercurtrain_rec_0.50: 0.1429 bathtub_rec_0.50: 0.4516 mAR_0.50: 0.2530 data_time: 0.1082 time: 2.3786 2025/05/13 10:10:28 - mmengine - INFO - Epoch(train) [159][10/91] base_lr: 1.6546e-04 lr: 1.6546e-04 eta: 2 days, 13:55:04 time: 8.9928 data_time: 0.7482 memory: 68703 grad_norm: 1.5985 loss: 1.7652 center_loss: 0.4816 size_loss: 0.1438 cls_loss: 0.5587 giou_loss: 0.5811 2025/05/13 10:11:55 - mmengine - INFO - Epoch(train) [159][20/91] base_lr: 1.6546e-04 lr: 1.6546e-04 eta: 2 days, 13:52:59 time: 8.9938 data_time: 0.7455 memory: 68703 grad_norm: 1.4701 loss: 1.7820 center_loss: 0.4871 size_loss: 0.1453 cls_loss: 0.5628 giou_loss: 0.5868 2025/05/13 10:13:21 - mmengine - INFO - Epoch(train) [159][30/91] base_lr: 1.6546e-04 lr: 1.6546e-04 eta: 2 days, 13:50:55 time: 8.9981 data_time: 0.7475 memory: 68702 grad_norm: 1.5066 loss: 1.7799 center_loss: 0.4881 size_loss: 0.1460 cls_loss: 0.5580 giou_loss: 0.5877 2025/05/13 10:14:47 - mmengine - INFO - Epoch(train) [159][40/91] base_lr: 1.6546e-04 lr: 1.6546e-04 eta: 2 days, 13:48:51 time: 9.0005 data_time: 0.7408 memory: 68702 grad_norm: 2.3438 loss: 1.7741 center_loss: 0.4839 size_loss: 0.1439 cls_loss: 0.5616 giou_loss: 0.5846 2025/05/13 10:16:14 - mmengine - INFO - Epoch(train) [159][50/91] base_lr: 1.6546e-04 lr: 1.6546e-04 eta: 2 days, 13:46:48 time: 9.1802 data_time: 0.7554 memory: 68703 grad_norm: 2.4176 loss: 1.7976 center_loss: 0.4913 size_loss: 0.1437 cls_loss: 0.5739 giou_loss: 0.5887 2025/05/13 10:17:40 - mmengine - INFO - Epoch(train) [159][60/91] base_lr: 1.6546e-04 lr: 1.6546e-04 eta: 2 days, 13:44:44 time: 8.6310 data_time: 0.2977 memory: 68702 grad_norm: 2.4661 loss: 1.8325 center_loss: 0.4999 size_loss: 0.1460 cls_loss: 0.5904 giou_loss: 0.5962 2025/05/13 10:19:06 - mmengine - INFO - Epoch(train) [159][70/91] base_lr: 1.6546e-04 lr: 1.6546e-04 eta: 2 days, 13:42:39 time: 8.6295 data_time: 0.2981 memory: 68703 grad_norm: 2.4841 loss: 1.8399 center_loss: 0.5013 size_loss: 0.1459 cls_loss: 0.5932 giou_loss: 0.5995 2025/05/13 10:20:32 - mmengine - INFO - Epoch(train) [159][80/91] base_lr: 1.6546e-04 lr: 1.6546e-04 eta: 2 days, 13:40:35 time: 8.6233 data_time: 0.2912 memory: 68702 grad_norm: 2.4720 loss: 1.8608 center_loss: 0.5086 size_loss: 0.1471 cls_loss: 0.6017 giou_loss: 0.6034 2025/05/13 10:21:57 - mmengine - INFO - Epoch(train) [159][90/91] base_lr: 1.6546e-04 lr: 1.6546e-04 eta: 2 days, 13:38:29 time: 8.5997 data_time: 0.2914 memory: 68702 grad_norm: 1.6099 loss: 1.8544 center_loss: 0.5092 size_loss: 0.1473 cls_loss: 0.5962 giou_loss: 0.6017 2025/05/13 10:21:58 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 10:23:51 - mmengine - INFO - Epoch(train) [160][10/91] base_lr: 1.6453e-04 lr: 1.6453e-04 eta: 2 days, 13:36:42 time: 8.9711 data_time: 0.7732 memory: 68702 grad_norm: 1.7514 loss: 1.8467 center_loss: 0.5046 size_loss: 0.1459 cls_loss: 0.5938 giou_loss: 0.6023 2025/05/13 10:25:17 - mmengine - INFO - Epoch(train) [160][20/91] base_lr: 1.6453e-04 lr: 1.6453e-04 eta: 2 days, 13:34:38 time: 8.9636 data_time: 0.7716 memory: 68703 grad_norm: 1.6965 loss: 1.8523 center_loss: 0.5102 size_loss: 0.1489 cls_loss: 0.5892 giou_loss: 0.6040 2025/05/13 10:26:43 - mmengine - INFO - Epoch(train) [160][30/91] base_lr: 1.6453e-04 lr: 1.6453e-04 eta: 2 days, 13:32:33 time: 8.9644 data_time: 0.7739 memory: 68702 grad_norm: 1.6538 loss: 1.8500 center_loss: 0.5096 size_loss: 0.1491 cls_loss: 0.5902 giou_loss: 0.6011 2025/05/13 10:28:09 - mmengine - INFO - Epoch(train) [160][40/91] base_lr: 1.6453e-04 lr: 1.6453e-04 eta: 2 days, 13:30:29 time: 8.9633 data_time: 0.7783 memory: 68703 grad_norm: 1.6074 loss: 1.8372 center_loss: 0.5072 size_loss: 0.1490 cls_loss: 0.5822 giou_loss: 0.5988 2025/05/13 10:29:36 - mmengine - INFO - Epoch(train) [160][50/91] base_lr: 1.6453e-04 lr: 1.6453e-04 eta: 2 days, 13:28:27 time: 9.1472 data_time: 0.7933 memory: 68703 grad_norm: 1.4487 loss: 1.8234 center_loss: 0.5030 size_loss: 0.1471 cls_loss: 0.5763 giou_loss: 0.5970 2025/05/13 10:31:02 - mmengine - INFO - Epoch(train) [160][60/91] base_lr: 1.6453e-04 lr: 1.6453e-04 eta: 2 days, 13:26:23 time: 8.6195 data_time: 0.3074 memory: 68702 grad_norm: 1.3752 loss: 1.8068 center_loss: 0.4979 size_loss: 0.1459 cls_loss: 0.5733 giou_loss: 0.5896 2025/05/13 10:32:28 - mmengine - INFO - Epoch(train) [160][70/91] base_lr: 1.6453e-04 lr: 1.6453e-04 eta: 2 days, 13:24:20 time: 8.6253 data_time: 0.3094 memory: 68702 grad_norm: 1.4240 loss: 1.7799 center_loss: 0.4876 size_loss: 0.1418 cls_loss: 0.5687 giou_loss: 0.5818 2025/05/13 10:33:54 - mmengine - INFO - Epoch(train) [160][80/91] base_lr: 1.6453e-04 lr: 1.6453e-04 eta: 2 days, 13:22:15 time: 8.6218 data_time: 0.3150 memory: 68703 grad_norm: 1.5318 loss: 1.7598 center_loss: 0.4778 size_loss: 0.1394 cls_loss: 0.5636 giou_loss: 0.5790 2025/05/13 10:35:19 - mmengine - INFO - Epoch(train) [160][90/91] base_lr: 1.6453e-04 lr: 1.6453e-04 eta: 2 days, 13:20:10 time: 8.6043 data_time: 0.3097 memory: 68700 grad_norm: 1.5226 loss: 1.7749 center_loss: 0.4805 size_loss: 0.1395 cls_loss: 0.5712 giou_loss: 0.5837 2025/05/13 10:35:20 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 10:35:20 - mmengine - INFO - Saving checkpoint at 160 epochs 2025/05/13 10:35:59 - mmengine - INFO - Epoch(val) [160][10/39] eta: 0:01:14 time: 2.4143 data_time: 0.1432 memory: 15952 2025/05/13 10:36:22 - mmengine - INFO - Epoch(val) [160][20/39] eta: 0:00:46 time: 2.3596 data_time: 0.0915 memory: 13407 2025/05/13 10:36:45 - mmengine - INFO - Epoch(val) [160][30/39] eta: 0:00:21 time: 2.3601 data_time: 0.0909 memory: 13407 2025/05/13 10:37:08 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.2622 | 0.4396 | 0.0319 | 0.1434 | | table | 0.4470 | 0.5743 | 0.1456 | 0.2914 | | chair | 0.5788 | 0.7237 | 0.1588 | 0.3231 | | bookshelf | 0.2926 | 0.5714 | 0.0819 | 0.2468 | | sofa | 0.7402 | 0.8660 | 0.2449 | 0.4433 | | curtain | 0.2470 | 0.5522 | 0.0607 | 0.1045 | | picture | 0.0271 | 0.1306 | 0.0015 | 0.0180 | | window | 0.1426 | 0.3546 | 0.0072 | 0.0922 | | cabinet | 0.2761 | 0.4839 | 0.0584 | 0.1935 | | door | 0.1847 | 0.4946 | 0.0155 | 0.1221 | | sink | 0.5012 | 0.6327 | 0.1147 | 0.2755 | | refrigerator | 0.4842 | 0.6140 | 0.1870 | 0.3333 | | counter | 0.2834 | 0.4808 | 0.0217 | 0.1346 | | desk | 0.6783 | 0.8661 | 0.2609 | 0.4567 | | bed | 0.8313 | 0.8519 | 0.3748 | 0.5432 | | toilet | 0.8497 | 0.9138 | 0.4330 | 0.4828 | | bathtub | 0.7893 | 0.8710 | 0.2480 | 0.4839 | | showercurtrain | 0.2713 | 0.5357 | 0.0264 | 0.1429 | +----------------+---------+---------+---------+---------+ | Overall | 0.4382 | 0.6087 | 0.1374 | 0.2684 | +----------------+---------+---------+---------+---------+ 2025/05/13 10:37:08 - mmengine - INFO - Epoch(val) [160][39/39] chair_AP_0.25: 0.5788 sofa_AP_0.25: 0.7402 table_AP_0.25: 0.4470 garbagebin_AP_0.25: 0.2622 bookshelf_AP_0.25: 0.2926 picture_AP_0.25: 0.0271 curtain_AP_0.25: 0.2470 door_AP_0.25: 0.1847 cabinet_AP_0.25: 0.2761 refrigerator_AP_0.25: 0.4842 counter_AP_0.25: 0.2834 sink_AP_0.25: 0.5012 window_AP_0.25: 0.1426 desk_AP_0.25: 0.6783 bed_AP_0.25: 0.8313 toilet_AP_0.25: 0.8497 showercurtrain_AP_0.25: 0.2713 bathtub_AP_0.25: 0.7893 mAP_0.25: 0.4382 chair_rec_0.25: 0.7237 sofa_rec_0.25: 0.8660 table_rec_0.25: 0.5743 garbagebin_rec_0.25: 0.4396 bookshelf_rec_0.25: 0.5714 picture_rec_0.25: 0.1306 curtain_rec_0.25: 0.5522 door_rec_0.25: 0.4946 cabinet_rec_0.25: 0.4839 refrigerator_rec_0.25: 0.6140 counter_rec_0.25: 0.4808 sink_rec_0.25: 0.6327 window_rec_0.25: 0.3546 desk_rec_0.25: 0.8661 bed_rec_0.25: 0.8519 toilet_rec_0.25: 0.9138 showercurtrain_rec_0.25: 0.5357 bathtub_rec_0.25: 0.8710 mAR_0.25: 0.6087 chair_AP_0.50: 0.1588 sofa_AP_0.50: 0.2449 table_AP_0.50: 0.1456 garbagebin_AP_0.50: 0.0319 bookshelf_AP_0.50: 0.0819 picture_AP_0.50: 0.0015 curtain_AP_0.50: 0.0607 door_AP_0.50: 0.0155 cabinet_AP_0.50: 0.0584 refrigerator_AP_0.50: 0.1870 counter_AP_0.50: 0.0217 sink_AP_0.50: 0.1147 window_AP_0.50: 0.0072 desk_AP_0.50: 0.2609 bed_AP_0.50: 0.3748 toilet_AP_0.50: 0.4330 showercurtrain_AP_0.50: 0.0264 bathtub_AP_0.50: 0.2480 mAP_0.50: 0.1374 chair_rec_0.50: 0.3231 sofa_rec_0.50: 0.4433 table_rec_0.50: 0.2914 garbagebin_rec_0.50: 0.1434 bookshelf_rec_0.50: 0.2468 picture_rec_0.50: 0.0180 curtain_rec_0.50: 0.1045 door_rec_0.50: 0.1221 cabinet_rec_0.50: 0.1935 refrigerator_rec_0.50: 0.3333 counter_rec_0.50: 0.1346 sink_rec_0.50: 0.2755 window_rec_0.50: 0.0922 desk_rec_0.50: 0.4567 bed_rec_0.50: 0.5432 toilet_rec_0.50: 0.4828 showercurtrain_rec_0.50: 0.1429 bathtub_rec_0.50: 0.4839 mAR_0.50: 0.2684 data_time: 0.1010 time: 2.3702 2025/05/13 10:39:01 - mmengine - INFO - Epoch(train) [161][10/91] base_lr: 1.6360e-04 lr: 1.6360e-04 eta: 2 days, 13:18:24 time: 8.9926 data_time: 0.7602 memory: 68702 grad_norm: 1.5231 loss: 1.7803 center_loss: 0.4826 size_loss: 0.1413 cls_loss: 0.5721 giou_loss: 0.5842 2025/05/13 10:40:28 - mmengine - INFO - Epoch(train) [161][20/91] base_lr: 1.6360e-04 lr: 1.6360e-04 eta: 2 days, 13:16:22 time: 8.9996 data_time: 0.7630 memory: 68702 grad_norm: 1.4539 loss: 1.7632 center_loss: 0.4759 size_loss: 0.1405 cls_loss: 0.5644 giou_loss: 0.5824 2025/05/13 10:41:54 - mmengine - INFO - Epoch(train) [161][30/91] base_lr: 1.6360e-04 lr: 1.6360e-04 eta: 2 days, 13:14:18 time: 8.9996 data_time: 0.7621 memory: 68700 grad_norm: 1.3620 loss: 1.7679 center_loss: 0.4811 size_loss: 0.1420 cls_loss: 0.5614 giou_loss: 0.5833 2025/05/13 10:43:20 - mmengine - INFO - Epoch(train) [161][40/91] base_lr: 1.6360e-04 lr: 1.6360e-04 eta: 2 days, 13:12:15 time: 9.0050 data_time: 0.7528 memory: 68703 grad_norm: 1.3890 loss: 1.7748 center_loss: 0.4849 size_loss: 0.1431 cls_loss: 0.5617 giou_loss: 0.5851 2025/05/13 10:44:47 - mmengine - INFO - Epoch(train) [161][50/91] base_lr: 1.6360e-04 lr: 1.6360e-04 eta: 2 days, 13:10:12 time: 9.1772 data_time: 0.7658 memory: 68703 grad_norm: 1.3963 loss: 1.7715 center_loss: 0.4853 size_loss: 0.1441 cls_loss: 0.5581 giou_loss: 0.5841 2025/05/13 10:46:12 - mmengine - INFO - Epoch(train) [161][60/91] base_lr: 1.6360e-04 lr: 1.6360e-04 eta: 2 days, 13:08:08 time: 8.6242 data_time: 0.3038 memory: 68703 grad_norm: 1.4415 loss: 1.7627 center_loss: 0.4840 size_loss: 0.1444 cls_loss: 0.5526 giou_loss: 0.5818 2025/05/13 10:47:38 - mmengine - INFO - Epoch(train) [161][70/91] base_lr: 1.6360e-04 lr: 1.6360e-04 eta: 2 days, 13:06:04 time: 8.6083 data_time: 0.3018 memory: 68702 grad_norm: 1.4618 loss: 1.7823 center_loss: 0.4947 size_loss: 0.1464 cls_loss: 0.5560 giou_loss: 0.5851 2025/05/13 10:49:04 - mmengine - INFO - Epoch(train) [161][80/91] base_lr: 1.6360e-04 lr: 1.6360e-04 eta: 2 days, 13:04:01 time: 8.6013 data_time: 0.3021 memory: 68702 grad_norm: 1.4585 loss: 1.7697 center_loss: 0.4868 size_loss: 0.1439 cls_loss: 0.5548 giou_loss: 0.5842 2025/05/13 10:50:29 - mmengine - INFO - Epoch(train) [161][90/91] base_lr: 1.6360e-04 lr: 1.6360e-04 eta: 2 days, 13:01:56 time: 8.5754 data_time: 0.2988 memory: 68702 grad_norm: 1.3429 loss: 1.7620 center_loss: 0.4870 size_loss: 0.1427 cls_loss: 0.5493 giou_loss: 0.5831 2025/05/13 10:50:30 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 10:52:22 - mmengine - INFO - Epoch(train) [162][10/91] base_lr: 1.6267e-04 lr: 1.6267e-04 eta: 2 days, 13:00:07 time: 8.9268 data_time: 0.7567 memory: 68702 grad_norm: 1.3933 loss: 1.7680 center_loss: 0.4878 size_loss: 0.1435 cls_loss: 0.5540 giou_loss: 0.5827 2025/05/13 10:53:47 - mmengine - INFO - Epoch(train) [162][20/91] base_lr: 1.6267e-04 lr: 1.6267e-04 eta: 2 days, 12:58:03 time: 8.9306 data_time: 0.7638 memory: 68702 grad_norm: 1.4149 loss: 1.7580 center_loss: 0.4781 size_loss: 0.1409 cls_loss: 0.5568 giou_loss: 0.5823 2025/05/13 10:55:13 - mmengine - INFO - Epoch(train) [162][30/91] base_lr: 1.6267e-04 lr: 1.6267e-04 eta: 2 days, 12:56:00 time: 8.9325 data_time: 0.7617 memory: 68702 grad_norm: 1.4104 loss: 1.7410 center_loss: 0.4688 size_loss: 0.1384 cls_loss: 0.5564 giou_loss: 0.5773 2025/05/13 10:56:39 - mmengine - INFO - Epoch(train) [162][40/91] base_lr: 1.6267e-04 lr: 1.6267e-04 eta: 2 days, 12:53:57 time: 8.9333 data_time: 0.7594 memory: 68700 grad_norm: 1.4976 loss: 1.7442 center_loss: 0.4695 size_loss: 0.1385 cls_loss: 0.5583 giou_loss: 0.5779 2025/05/13 10:58:06 - mmengine - INFO - Epoch(train) [162][50/91] base_lr: 1.6267e-04 lr: 1.6267e-04 eta: 2 days, 12:51:55 time: 9.1098 data_time: 0.7704 memory: 68702 grad_norm: 1.5611 loss: 1.7519 center_loss: 0.4764 size_loss: 0.1403 cls_loss: 0.5592 giou_loss: 0.5760 2025/05/13 10:59:32 - mmengine - INFO - Epoch(train) [162][60/91] base_lr: 1.6267e-04 lr: 1.6267e-04 eta: 2 days, 12:49:51 time: 8.6024 data_time: 0.3103 memory: 68703 grad_norm: 1.5294 loss: 1.7367 center_loss: 0.4715 size_loss: 0.1390 cls_loss: 0.5529 giou_loss: 0.5734 2025/05/13 11:00:58 - mmengine - INFO - Epoch(train) [162][70/91] base_lr: 1.6267e-04 lr: 1.6267e-04 eta: 2 days, 12:47:48 time: 8.6068 data_time: 0.3095 memory: 68702 grad_norm: 1.4939 loss: 1.7470 center_loss: 0.4746 size_loss: 0.1398 cls_loss: 0.5576 giou_loss: 0.5750 2025/05/13 11:02:24 - mmengine - INFO - Epoch(train) [162][80/91] base_lr: 1.6267e-04 lr: 1.6267e-04 eta: 2 days, 12:45:45 time: 8.6049 data_time: 0.3045 memory: 68703 grad_norm: 1.4974 loss: 1.7544 center_loss: 0.4782 size_loss: 0.1406 cls_loss: 0.5591 giou_loss: 0.5764 2025/05/13 11:03:49 - mmengine - INFO - Epoch(train) [162][90/91] base_lr: 1.6267e-04 lr: 1.6267e-04 eta: 2 days, 12:43:41 time: 8.5862 data_time: 0.3001 memory: 68700 grad_norm: 1.3971 loss: 1.7634 center_loss: 0.4786 size_loss: 0.1420 cls_loss: 0.5640 giou_loss: 0.5789 2025/05/13 11:03:50 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 11:03:50 - mmengine - INFO - Saving checkpoint at 162 epochs 2025/05/13 11:04:27 - mmengine - INFO - Epoch(val) [162][10/39] eta: 0:01:14 time: 2.4079 data_time: 0.1387 memory: 15952 2025/05/13 11:04:50 - mmengine - INFO - Epoch(val) [162][20/39] eta: 0:00:46 time: 2.3591 data_time: 0.0907 memory: 13407 2025/05/13 11:05:13 - mmengine - INFO - Epoch(val) [162][30/39] eta: 0:00:21 time: 2.3571 data_time: 0.0892 memory: 13407 2025/05/13 11:05:35 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.2643 | 0.4264 | 0.0217 | 0.1075 | | chair | 0.5938 | 0.7310 | 0.1465 | 0.3202 | | curtain | 0.2383 | 0.4627 | 0.0543 | 0.1493 | | table | 0.4725 | 0.6086 | 0.1670 | 0.3057 | | sofa | 0.7247 | 0.8557 | 0.2167 | 0.4021 | | bookshelf | 0.2726 | 0.5584 | 0.0925 | 0.2208 | | picture | 0.0105 | 0.1126 | 0.0002 | 0.0045 | | desk | 0.6880 | 0.8504 | 0.2561 | 0.4646 | | window | 0.1683 | 0.3865 | 0.0170 | 0.0709 | | cabinet | 0.3051 | 0.5269 | 0.0617 | 0.1909 | | door | 0.1847 | 0.4625 | 0.0172 | 0.1242 | | counter | 0.2424 | 0.5192 | 0.0154 | 0.1346 | | refrigerator | 0.4842 | 0.5965 | 0.1364 | 0.2807 | | sink | 0.5039 | 0.6224 | 0.1829 | 0.3163 | | bed | 0.8360 | 0.8519 | 0.4394 | 0.6049 | | toilet | 0.9435 | 0.9655 | 0.4515 | 0.5690 | | bathtub | 0.7272 | 0.8387 | 0.2488 | 0.4839 | | showercurtrain | 0.3476 | 0.6786 | 0.0829 | 0.2500 | +----------------+---------+---------+---------+---------+ | Overall | 0.4449 | 0.6141 | 0.1449 | 0.2778 | +----------------+---------+---------+---------+---------+ 2025/05/13 11:05:35 - mmengine - INFO - Epoch(val) [162][39/39] chair_AP_0.25: 0.5938 sofa_AP_0.25: 0.7247 table_AP_0.25: 0.4725 garbagebin_AP_0.25: 0.2643 bookshelf_AP_0.25: 0.2726 picture_AP_0.25: 0.0105 curtain_AP_0.25: 0.2383 door_AP_0.25: 0.1847 cabinet_AP_0.25: 0.3051 refrigerator_AP_0.25: 0.4842 counter_AP_0.25: 0.2424 sink_AP_0.25: 0.5039 window_AP_0.25: 0.1683 desk_AP_0.25: 0.6880 bed_AP_0.25: 0.8360 toilet_AP_0.25: 0.9435 showercurtrain_AP_0.25: 0.3476 bathtub_AP_0.25: 0.7272 mAP_0.25: 0.4449 chair_rec_0.25: 0.7310 sofa_rec_0.25: 0.8557 table_rec_0.25: 0.6086 garbagebin_rec_0.25: 0.4264 bookshelf_rec_0.25: 0.5584 picture_rec_0.25: 0.1126 curtain_rec_0.25: 0.4627 door_rec_0.25: 0.4625 cabinet_rec_0.25: 0.5269 refrigerator_rec_0.25: 0.5965 counter_rec_0.25: 0.5192 sink_rec_0.25: 0.6224 window_rec_0.25: 0.3865 desk_rec_0.25: 0.8504 bed_rec_0.25: 0.8519 toilet_rec_0.25: 0.9655 showercurtrain_rec_0.25: 0.6786 bathtub_rec_0.25: 0.8387 mAR_0.25: 0.6141 chair_AP_0.50: 0.1465 sofa_AP_0.50: 0.2167 table_AP_0.50: 0.1670 garbagebin_AP_0.50: 0.0217 bookshelf_AP_0.50: 0.0925 picture_AP_0.50: 0.0002 curtain_AP_0.50: 0.0543 door_AP_0.50: 0.0172 cabinet_AP_0.50: 0.0617 refrigerator_AP_0.50: 0.1364 counter_AP_0.50: 0.0154 sink_AP_0.50: 0.1829 window_AP_0.50: 0.0170 desk_AP_0.50: 0.2561 bed_AP_0.50: 0.4394 toilet_AP_0.50: 0.4515 showercurtrain_AP_0.50: 0.0829 bathtub_AP_0.50: 0.2488 mAP_0.50: 0.1449 chair_rec_0.50: 0.3202 sofa_rec_0.50: 0.4021 table_rec_0.50: 0.3057 garbagebin_rec_0.50: 0.1075 bookshelf_rec_0.50: 0.2208 picture_rec_0.50: 0.0045 curtain_rec_0.50: 0.1493 door_rec_0.50: 0.1242 cabinet_rec_0.50: 0.1909 refrigerator_rec_0.50: 0.2807 counter_rec_0.50: 0.1346 sink_rec_0.50: 0.3163 window_rec_0.50: 0.0709 desk_rec_0.50: 0.4646 bed_rec_0.50: 0.6049 toilet_rec_0.50: 0.5690 showercurtrain_rec_0.50: 0.2500 bathtub_rec_0.50: 0.4839 mAR_0.50: 0.2778 data_time: 0.1010 time: 2.3678 2025/05/13 11:07:26 - mmengine - INFO - Epoch(train) [163][10/91] base_lr: 1.6173e-04 lr: 1.6173e-04 eta: 2 days, 12:41:51 time: 8.9287 data_time: 0.7631 memory: 68702 grad_norm: 1.3362 loss: 1.7636 center_loss: 0.4756 size_loss: 0.1424 cls_loss: 0.5658 giou_loss: 0.5798 2025/05/13 11:08:52 - mmengine - INFO - Epoch(train) [163][20/91] base_lr: 1.6173e-04 lr: 1.6173e-04 eta: 2 days, 12:39:49 time: 8.9258 data_time: 0.7507 memory: 68702 grad_norm: 1.3351 loss: 1.7559 center_loss: 0.4737 size_loss: 0.1420 cls_loss: 0.5607 giou_loss: 0.5795 2025/05/13 11:10:18 - mmengine - INFO - Epoch(train) [163][30/91] base_lr: 1.6173e-04 lr: 1.6173e-04 eta: 2 days, 12:37:46 time: 8.9303 data_time: 0.7568 memory: 68702 grad_norm: 1.3272 loss: 1.7630 center_loss: 0.4789 size_loss: 0.1434 cls_loss: 0.5569 giou_loss: 0.5837 2025/05/13 11:11:44 - mmengine - INFO - Epoch(train) [163][40/91] base_lr: 1.6173e-04 lr: 1.6173e-04 eta: 2 days, 12:35:43 time: 8.9295 data_time: 0.7609 memory: 68702 grad_norm: 1.3476 loss: 1.7660 center_loss: 0.4789 size_loss: 0.1431 cls_loss: 0.5578 giou_loss: 0.5862 2025/05/13 11:13:11 - mmengine - INFO - Epoch(train) [163][50/91] base_lr: 1.6173e-04 lr: 1.6173e-04 eta: 2 days, 12:33:41 time: 9.1106 data_time: 0.7831 memory: 68702 grad_norm: 1.3578 loss: 1.7690 center_loss: 0.4816 size_loss: 0.1444 cls_loss: 0.5586 giou_loss: 0.5844 2025/05/13 11:14:37 - mmengine - INFO - Epoch(train) [163][60/91] base_lr: 1.6173e-04 lr: 1.6173e-04 eta: 2 days, 12:31:39 time: 8.6167 data_time: 0.3143 memory: 68703 grad_norm: 1.3963 loss: 1.7684 center_loss: 0.4802 size_loss: 0.1445 cls_loss: 0.5606 giou_loss: 0.5831 2025/05/13 11:16:03 - mmengine - INFO - Epoch(train) [163][70/91] base_lr: 1.6173e-04 lr: 1.6173e-04 eta: 2 days, 12:29:36 time: 8.6150 data_time: 0.3218 memory: 68702 grad_norm: 1.3915 loss: 1.7698 center_loss: 0.4789 size_loss: 0.1432 cls_loss: 0.5665 giou_loss: 0.5813 2025/05/13 11:17:29 - mmengine - INFO - Epoch(train) [163][80/91] base_lr: 1.6173e-04 lr: 1.6173e-04 eta: 2 days, 12:27:33 time: 8.6110 data_time: 0.3181 memory: 68701 grad_norm: 1.3779 loss: 1.7685 center_loss: 0.4766 size_loss: 0.1432 cls_loss: 0.5698 giou_loss: 0.5789 2025/05/13 11:18:54 - mmengine - INFO - Epoch(train) [163][90/91] base_lr: 1.6173e-04 lr: 1.6173e-04 eta: 2 days, 12:25:30 time: 8.5950 data_time: 0.3062 memory: 68703 grad_norm: 1.4096 loss: 1.7727 center_loss: 0.4819 size_loss: 0.1439 cls_loss: 0.5683 giou_loss: 0.5787 2025/05/13 11:18:55 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 11:20:47 - mmengine - INFO - Epoch(train) [164][10/91] base_lr: 1.6079e-04 lr: 1.6079e-04 eta: 2 days, 12:23:42 time: 8.9518 data_time: 0.7523 memory: 68702 grad_norm: 1.5266 loss: 1.7951 center_loss: 0.4877 size_loss: 0.1432 cls_loss: 0.5818 giou_loss: 0.5823 2025/05/13 11:22:13 - mmengine - INFO - Epoch(train) [164][20/91] base_lr: 1.6079e-04 lr: 1.6079e-04 eta: 2 days, 12:21:40 time: 8.9551 data_time: 0.7589 memory: 68702 grad_norm: 1.6013 loss: 1.7998 center_loss: 0.4899 size_loss: 0.1429 cls_loss: 0.5825 giou_loss: 0.5844 2025/05/13 11:23:40 - mmengine - INFO - Epoch(train) [164][30/91] base_lr: 1.6079e-04 lr: 1.6079e-04 eta: 2 days, 12:19:38 time: 8.9666 data_time: 0.7607 memory: 68702 grad_norm: 1.5833 loss: 1.7941 center_loss: 0.4883 size_loss: 0.1426 cls_loss: 0.5781 giou_loss: 0.5851 2025/05/13 11:25:06 - mmengine - INFO - Epoch(train) [164][40/91] base_lr: 1.6079e-04 lr: 1.6079e-04 eta: 2 days, 12:17:36 time: 8.9705 data_time: 0.7582 memory: 68703 grad_norm: 1.5919 loss: 1.7943 center_loss: 0.4878 size_loss: 0.1419 cls_loss: 0.5786 giou_loss: 0.5860 2025/05/13 11:26:33 - mmengine - INFO - Epoch(train) [164][50/91] base_lr: 1.6079e-04 lr: 1.6079e-04 eta: 2 days, 12:15:36 time: 9.1564 data_time: 0.7825 memory: 68702 grad_norm: 1.4689 loss: 1.7567 center_loss: 0.4774 size_loss: 0.1381 cls_loss: 0.5616 giou_loss: 0.5795 2025/05/13 11:27:59 - mmengine - INFO - Epoch(train) [164][60/91] base_lr: 1.6079e-04 lr: 1.6079e-04 eta: 2 days, 12:13:34 time: 8.6453 data_time: 0.3309 memory: 68702 grad_norm: 1.4141 loss: 1.7492 center_loss: 0.4761 size_loss: 0.1387 cls_loss: 0.5580 giou_loss: 0.5764 2025/05/13 11:29:26 - mmengine - INFO - Epoch(train) [164][70/91] base_lr: 1.6079e-04 lr: 1.6079e-04 eta: 2 days, 12:11:32 time: 8.6426 data_time: 0.3280 memory: 68702 grad_norm: 1.3029 loss: 1.7490 center_loss: 0.4769 size_loss: 0.1387 cls_loss: 0.5562 giou_loss: 0.5772 2025/05/13 11:30:51 - mmengine - INFO - Epoch(train) [164][80/91] base_lr: 1.6079e-04 lr: 1.6079e-04 eta: 2 days, 12:09:29 time: 8.6284 data_time: 0.3240 memory: 68702 grad_norm: 1.3732 loss: 1.7490 center_loss: 0.4774 size_loss: 0.1398 cls_loss: 0.5570 giou_loss: 0.5747 2025/05/13 11:32:16 - mmengine - INFO - Epoch(train) [164][90/91] base_lr: 1.6079e-04 lr: 1.6079e-04 eta: 2 days, 12:07:26 time: 8.6049 data_time: 0.3165 memory: 68702 grad_norm: 1.4403 loss: 1.7646 center_loss: 0.4843 size_loss: 0.1414 cls_loss: 0.5607 giou_loss: 0.5782 2025/05/13 11:32:18 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 11:32:18 - mmengine - INFO - Saving checkpoint at 164 epochs 2025/05/13 11:32:56 - mmengine - INFO - Epoch(val) [164][10/39] eta: 0:01:14 time: 2.4087 data_time: 0.1424 memory: 15952 2025/05/13 11:33:19 - mmengine - INFO - Epoch(val) [164][20/39] eta: 0:00:46 time: 2.3584 data_time: 0.0927 memory: 13407 2025/05/13 11:33:43 - mmengine - INFO - Epoch(val) [164][30/39] eta: 0:00:21 time: 2.3613 data_time: 0.0960 memory: 13407 2025/05/13 11:34:05 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.2784 | 0.4396 | 0.0279 | 0.1189 | | table | 0.4499 | 0.5829 | 0.1420 | 0.2829 | | sofa | 0.7165 | 0.8454 | 0.1974 | 0.3814 | | chair | 0.5705 | 0.7230 | 0.1708 | 0.3458 | | bookshelf | 0.2975 | 0.5974 | 0.0979 | 0.2727 | | curtain | 0.2243 | 0.4925 | 0.0171 | 0.0597 | | picture | 0.0336 | 0.1171 | 0.0005 | 0.0180 | | window | 0.1819 | 0.3830 | 0.0252 | 0.1206 | | cabinet | 0.2777 | 0.5188 | 0.0469 | 0.1882 | | door | 0.1598 | 0.4433 | 0.0192 | 0.1263 | | counter | 0.2521 | 0.4808 | 0.0321 | 0.1731 | | sink | 0.5673 | 0.6633 | 0.1345 | 0.2959 | | refrigerator | 0.5412 | 0.6667 | 0.1601 | 0.2982 | | desk | 0.6811 | 0.8425 | 0.2806 | 0.4961 | | bed | 0.8519 | 0.8642 | 0.3532 | 0.5185 | | toilet | 0.8922 | 0.9483 | 0.4489 | 0.5690 | | bathtub | 0.8453 | 0.9355 | 0.2793 | 0.4194 | | showercurtrain | 0.4130 | 0.5714 | 0.0343 | 0.1786 | +----------------+---------+---------+---------+---------+ | Overall | 0.4575 | 0.6175 | 0.1371 | 0.2702 | +----------------+---------+---------+---------+---------+ 2025/05/13 11:34:05 - mmengine - INFO - Epoch(val) [164][39/39] chair_AP_0.25: 0.5705 sofa_AP_0.25: 0.7165 table_AP_0.25: 0.4499 garbagebin_AP_0.25: 0.2784 bookshelf_AP_0.25: 0.2975 picture_AP_0.25: 0.0336 curtain_AP_0.25: 0.2243 door_AP_0.25: 0.1598 cabinet_AP_0.25: 0.2777 refrigerator_AP_0.25: 0.5412 counter_AP_0.25: 0.2521 sink_AP_0.25: 0.5673 window_AP_0.25: 0.1819 desk_AP_0.25: 0.6811 bed_AP_0.25: 0.8519 toilet_AP_0.25: 0.8922 showercurtrain_AP_0.25: 0.4130 bathtub_AP_0.25: 0.8453 mAP_0.25: 0.4575 chair_rec_0.25: 0.7230 sofa_rec_0.25: 0.8454 table_rec_0.25: 0.5829 garbagebin_rec_0.25: 0.4396 bookshelf_rec_0.25: 0.5974 picture_rec_0.25: 0.1171 curtain_rec_0.25: 0.4925 door_rec_0.25: 0.4433 cabinet_rec_0.25: 0.5188 refrigerator_rec_0.25: 0.6667 counter_rec_0.25: 0.4808 sink_rec_0.25: 0.6633 window_rec_0.25: 0.3830 desk_rec_0.25: 0.8425 bed_rec_0.25: 0.8642 toilet_rec_0.25: 0.9483 showercurtrain_rec_0.25: 0.5714 bathtub_rec_0.25: 0.9355 mAR_0.25: 0.6175 chair_AP_0.50: 0.1708 sofa_AP_0.50: 0.1974 table_AP_0.50: 0.1420 garbagebin_AP_0.50: 0.0279 bookshelf_AP_0.50: 0.0979 picture_AP_0.50: 0.0005 curtain_AP_0.50: 0.0171 door_AP_0.50: 0.0192 cabinet_AP_0.50: 0.0469 refrigerator_AP_0.50: 0.1601 counter_AP_0.50: 0.0321 sink_AP_0.50: 0.1345 window_AP_0.50: 0.0252 desk_AP_0.50: 0.2806 bed_AP_0.50: 0.3532 toilet_AP_0.50: 0.4489 showercurtrain_AP_0.50: 0.0343 bathtub_AP_0.50: 0.2793 mAP_0.50: 0.1371 chair_rec_0.50: 0.3458 sofa_rec_0.50: 0.3814 table_rec_0.50: 0.2829 garbagebin_rec_0.50: 0.1189 bookshelf_rec_0.50: 0.2727 picture_rec_0.50: 0.0180 curtain_rec_0.50: 0.0597 door_rec_0.50: 0.1263 cabinet_rec_0.50: 0.1882 refrigerator_rec_0.50: 0.2982 counter_rec_0.50: 0.1731 sink_rec_0.50: 0.2959 window_rec_0.50: 0.1206 desk_rec_0.50: 0.4961 bed_rec_0.50: 0.5185 toilet_rec_0.50: 0.5690 showercurtrain_rec_0.50: 0.1786 bathtub_rec_0.50: 0.4194 mAR_0.50: 0.2702 data_time: 0.1111 time: 2.3752 2025/05/13 11:34:05 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_150.pth is removed 2025/05/13 11:34:16 - mmengine - INFO - The best checkpoint with 0.4575 mAP_0.25 at 164 epoch is saved to best_mAP_0.25_epoch_164.pth. 2025/05/13 11:36:18 - mmengine - INFO - Epoch(train) [165][10/91] base_lr: 1.5985e-04 lr: 1.5985e-04 eta: 2 days, 12:05:35 time: 8.9088 data_time: 0.7729 memory: 68702 grad_norm: 1.5326 loss: 1.7611 center_loss: 0.4744 size_loss: 0.1413 cls_loss: 0.5676 giou_loss: 0.5779 2025/05/13 11:37:44 - mmengine - INFO - Epoch(train) [165][20/91] base_lr: 1.5985e-04 lr: 1.5985e-04 eta: 2 days, 12:03:33 time: 8.9089 data_time: 0.7648 memory: 68702 grad_norm: 1.5665 loss: 1.7757 center_loss: 0.4837 size_loss: 0.1416 cls_loss: 0.5674 giou_loss: 0.5830 2025/05/13 11:39:10 - mmengine - INFO - Epoch(train) [165][30/91] base_lr: 1.5985e-04 lr: 1.5985e-04 eta: 2 days, 12:01:31 time: 8.9107 data_time: 0.7648 memory: 68702 grad_norm: 1.6243 loss: 1.7848 center_loss: 0.4864 size_loss: 0.1424 cls_loss: 0.5703 giou_loss: 0.5857 2025/05/13 11:40:36 - mmengine - INFO - Epoch(train) [165][40/91] base_lr: 1.5985e-04 lr: 1.5985e-04 eta: 2 days, 11:59:30 time: 8.9127 data_time: 0.7657 memory: 68702 grad_norm: 1.6265 loss: 1.7780 center_loss: 0.4822 size_loss: 0.1406 cls_loss: 0.5730 giou_loss: 0.5822 2025/05/13 11:42:03 - mmengine - INFO - Epoch(train) [165][50/91] base_lr: 1.5985e-04 lr: 1.5985e-04 eta: 2 days, 11:57:28 time: 9.0865 data_time: 0.7819 memory: 68702 grad_norm: 1.6076 loss: 1.7402 center_loss: 0.4712 size_loss: 0.1383 cls_loss: 0.5571 giou_loss: 0.5737 2025/05/13 11:43:29 - mmengine - INFO - Epoch(train) [165][60/91] base_lr: 1.5985e-04 lr: 1.5985e-04 eta: 2 days, 11:55:27 time: 8.6255 data_time: 0.3127 memory: 68702 grad_norm: 1.6356 loss: 1.7702 center_loss: 0.4826 size_loss: 0.1425 cls_loss: 0.5645 giou_loss: 0.5805 2025/05/13 11:44:56 - mmengine - INFO - Epoch(train) [165][70/91] base_lr: 1.5985e-04 lr: 1.5985e-04 eta: 2 days, 11:53:26 time: 8.6290 data_time: 0.3144 memory: 68702 grad_norm: 1.6419 loss: 1.7822 center_loss: 0.4880 size_loss: 0.1429 cls_loss: 0.5694 giou_loss: 0.5819 2025/05/13 11:45:47 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 11:46:21 - mmengine - INFO - Epoch(train) [165][80/91] base_lr: 1.5985e-04 lr: 1.5985e-04 eta: 2 days, 11:51:24 time: 8.6185 data_time: 0.3155 memory: 68702 grad_norm: 1.6419 loss: 1.7719 center_loss: 0.4830 size_loss: 0.1416 cls_loss: 0.5691 giou_loss: 0.5782 2025/05/13 11:47:46 - mmengine - INFO - Epoch(train) [165][90/91] base_lr: 1.5985e-04 lr: 1.5985e-04 eta: 2 days, 11:49:21 time: 8.5995 data_time: 0.3070 memory: 68700 grad_norm: 1.5498 loss: 1.7681 center_loss: 0.4819 size_loss: 0.1409 cls_loss: 0.5647 giou_loss: 0.5806 2025/05/13 11:47:48 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 11:49:40 - mmengine - INFO - Epoch(train) [166][10/91] base_lr: 1.5890e-04 lr: 1.5890e-04 eta: 2 days, 11:47:34 time: 8.9680 data_time: 0.7689 memory: 68702 grad_norm: 1.6030 loss: 1.7750 center_loss: 0.4793 size_loss: 0.1406 cls_loss: 0.5685 giou_loss: 0.5865 2025/05/13 11:51:07 - mmengine - INFO - Epoch(train) [166][20/91] base_lr: 1.5890e-04 lr: 1.5890e-04 eta: 2 days, 11:45:33 time: 8.9724 data_time: 0.7669 memory: 68702 grad_norm: 1.5442 loss: 1.7588 center_loss: 0.4750 size_loss: 0.1375 cls_loss: 0.5656 giou_loss: 0.5807 2025/05/13 11:52:33 - mmengine - INFO - Epoch(train) [166][30/91] base_lr: 1.5890e-04 lr: 1.5890e-04 eta: 2 days, 11:43:32 time: 8.9717 data_time: 0.7626 memory: 68702 grad_norm: 1.4828 loss: 1.7373 center_loss: 0.4639 size_loss: 0.1366 cls_loss: 0.5608 giou_loss: 0.5759 2025/05/13 11:53:59 - mmengine - INFO - Epoch(train) [166][40/91] base_lr: 1.5890e-04 lr: 1.5890e-04 eta: 2 days, 11:41:30 time: 8.9733 data_time: 0.7585 memory: 68702 grad_norm: 1.4634 loss: 1.7491 center_loss: 0.4670 size_loss: 0.1373 cls_loss: 0.5661 giou_loss: 0.5787 2025/05/13 11:55:26 - mmengine - INFO - Epoch(train) [166][50/91] base_lr: 1.5890e-04 lr: 1.5890e-04 eta: 2 days, 11:39:30 time: 9.1589 data_time: 0.7745 memory: 68702 grad_norm: 1.4602 loss: 1.7548 center_loss: 0.4752 size_loss: 0.1390 cls_loss: 0.5653 giou_loss: 0.5752 2025/05/13 11:56:52 - mmengine - INFO - Epoch(train) [166][60/91] base_lr: 1.5890e-04 lr: 1.5890e-04 eta: 2 days, 11:37:29 time: 8.6390 data_time: 0.3077 memory: 68702 grad_norm: 1.4159 loss: 1.7496 center_loss: 0.4697 size_loss: 0.1391 cls_loss: 0.5665 giou_loss: 0.5742 2025/05/13 11:58:18 - mmengine - INFO - Epoch(train) [166][70/91] base_lr: 1.5890e-04 lr: 1.5890e-04 eta: 2 days, 11:35:28 time: 8.6355 data_time: 0.3059 memory: 68702 grad_norm: 1.4508 loss: 1.7561 center_loss: 0.4696 size_loss: 0.1400 cls_loss: 0.5692 giou_loss: 0.5774 2025/05/13 11:59:44 - mmengine - INFO - Epoch(train) [166][80/91] base_lr: 1.5890e-04 lr: 1.5890e-04 eta: 2 days, 11:33:26 time: 8.6233 data_time: 0.3050 memory: 68702 grad_norm: 1.4914 loss: 1.7589 center_loss: 0.4702 size_loss: 0.1393 cls_loss: 0.5695 giou_loss: 0.5799 2025/05/13 12:01:09 - mmengine - INFO - Epoch(train) [166][90/91] base_lr: 1.5890e-04 lr: 1.5890e-04 eta: 2 days, 11:31:24 time: 8.6067 data_time: 0.2976 memory: 68702 grad_norm: 1.5045 loss: 1.7517 center_loss: 0.4693 size_loss: 0.1393 cls_loss: 0.5650 giou_loss: 0.5781 2025/05/13 12:01:10 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 12:01:10 - mmengine - INFO - Saving checkpoint at 166 epochs 2025/05/13 12:01:47 - mmengine - INFO - Epoch(val) [166][10/39] eta: 0:01:13 time: 2.4093 data_time: 0.1452 memory: 15952 2025/05/13 12:02:10 - mmengine - INFO - Epoch(val) [166][20/39] eta: 0:00:46 time: 2.3567 data_time: 0.0923 memory: 13407 2025/05/13 12:02:33 - mmengine - INFO - Epoch(val) [166][30/39] eta: 0:00:21 time: 2.3547 data_time: 0.0909 memory: 13407 2025/05/13 12:02:56 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.2859 | 0.4679 | 0.0301 | 0.1340 | | chair | 0.6105 | 0.7427 | 0.1673 | 0.3348 | | table | 0.4633 | 0.6000 | 0.1075 | 0.2629 | | bookshelf | 0.2280 | 0.5195 | 0.0697 | 0.2468 | | sofa | 0.7098 | 0.8660 | 0.2593 | 0.4227 | | curtain | 0.2478 | 0.5224 | 0.0293 | 0.0746 | | picture | 0.0219 | 0.1396 | 0.0015 | 0.0135 | | door | 0.1487 | 0.4454 | 0.0175 | 0.1370 | | window | 0.1573 | 0.3298 | 0.0350 | 0.0957 | | cabinet | 0.2766 | 0.4892 | 0.0523 | 0.1855 | | counter | 0.3143 | 0.5385 | 0.0088 | 0.0962 | | sink | 0.4697 | 0.6429 | 0.1449 | 0.2959 | | refrigerator | 0.4828 | 0.6140 | 0.1870 | 0.2982 | | desk | 0.6836 | 0.8661 | 0.1887 | 0.4016 | | bed | 0.8166 | 0.8519 | 0.4047 | 0.5679 | | bathtub | 0.7553 | 0.8710 | 0.3231 | 0.5484 | | toilet | 0.9483 | 0.9828 | 0.5114 | 0.6034 | | showercurtrain | 0.2873 | 0.5000 | 0.0381 | 0.1429 | +----------------+---------+---------+---------+---------+ | Overall | 0.4393 | 0.6105 | 0.1431 | 0.2701 | +----------------+---------+---------+---------+---------+ 2025/05/13 12:02:56 - mmengine - INFO - Epoch(val) [166][39/39] chair_AP_0.25: 0.6105 sofa_AP_0.25: 0.7098 table_AP_0.25: 0.4633 garbagebin_AP_0.25: 0.2859 bookshelf_AP_0.25: 0.2280 picture_AP_0.25: 0.0219 curtain_AP_0.25: 0.2478 door_AP_0.25: 0.1487 cabinet_AP_0.25: 0.2766 refrigerator_AP_0.25: 0.4828 counter_AP_0.25: 0.3143 sink_AP_0.25: 0.4697 window_AP_0.25: 0.1573 desk_AP_0.25: 0.6836 bed_AP_0.25: 0.8166 toilet_AP_0.25: 0.9483 showercurtrain_AP_0.25: 0.2873 bathtub_AP_0.25: 0.7553 mAP_0.25: 0.4393 chair_rec_0.25: 0.7427 sofa_rec_0.25: 0.8660 table_rec_0.25: 0.6000 garbagebin_rec_0.25: 0.4679 bookshelf_rec_0.25: 0.5195 picture_rec_0.25: 0.1396 curtain_rec_0.25: 0.5224 door_rec_0.25: 0.4454 cabinet_rec_0.25: 0.4892 refrigerator_rec_0.25: 0.6140 counter_rec_0.25: 0.5385 sink_rec_0.25: 0.6429 window_rec_0.25: 0.3298 desk_rec_0.25: 0.8661 bed_rec_0.25: 0.8519 toilet_rec_0.25: 0.9828 showercurtrain_rec_0.25: 0.5000 bathtub_rec_0.25: 0.8710 mAR_0.25: 0.6105 chair_AP_0.50: 0.1673 sofa_AP_0.50: 0.2593 table_AP_0.50: 0.1075 garbagebin_AP_0.50: 0.0301 bookshelf_AP_0.50: 0.0697 picture_AP_0.50: 0.0015 curtain_AP_0.50: 0.0293 door_AP_0.50: 0.0175 cabinet_AP_0.50: 0.0523 refrigerator_AP_0.50: 0.1870 counter_AP_0.50: 0.0088 sink_AP_0.50: 0.1449 window_AP_0.50: 0.0350 desk_AP_0.50: 0.1887 bed_AP_0.50: 0.4047 toilet_AP_0.50: 0.5114 showercurtrain_AP_0.50: 0.0381 bathtub_AP_0.50: 0.3231 mAP_0.50: 0.1431 chair_rec_0.50: 0.3348 sofa_rec_0.50: 0.4227 table_rec_0.50: 0.2629 garbagebin_rec_0.50: 0.1340 bookshelf_rec_0.50: 0.2468 picture_rec_0.50: 0.0135 curtain_rec_0.50: 0.0746 door_rec_0.50: 0.1370 cabinet_rec_0.50: 0.1855 refrigerator_rec_0.50: 0.2982 counter_rec_0.50: 0.0962 sink_rec_0.50: 0.2959 window_rec_0.50: 0.0957 desk_rec_0.50: 0.4016 bed_rec_0.50: 0.5679 toilet_rec_0.50: 0.6034 showercurtrain_rec_0.50: 0.1429 bathtub_rec_0.50: 0.5484 mAR_0.50: 0.2701 data_time: 0.0986 time: 2.3623 2025/05/13 12:04:48 - mmengine - INFO - Epoch(train) [167][10/91] base_lr: 1.5796e-04 lr: 1.5796e-04 eta: 2 days, 11:29:38 time: 8.9753 data_time: 0.7550 memory: 68703 grad_norm: 1.5182 loss: 1.7436 center_loss: 0.4681 size_loss: 0.1393 cls_loss: 0.5585 giou_loss: 0.5777 2025/05/13 12:06:15 - mmengine - INFO - Epoch(train) [167][20/91] base_lr: 1.5796e-04 lr: 1.5796e-04 eta: 2 days, 11:27:37 time: 8.9766 data_time: 0.7597 memory: 68702 grad_norm: 1.4836 loss: 1.7355 center_loss: 0.4641 size_loss: 0.1386 cls_loss: 0.5593 giou_loss: 0.5735 2025/05/13 12:07:41 - mmengine - INFO - Epoch(train) [167][30/91] base_lr: 1.5796e-04 lr: 1.5796e-04 eta: 2 days, 11:25:37 time: 8.9788 data_time: 0.7540 memory: 68703 grad_norm: 1.4409 loss: 1.7389 center_loss: 0.4688 size_loss: 0.1395 cls_loss: 0.5553 giou_loss: 0.5752 2025/05/13 12:09:07 - mmengine - INFO - Epoch(train) [167][40/91] base_lr: 1.5796e-04 lr: 1.5796e-04 eta: 2 days, 11:23:36 time: 8.9856 data_time: 0.7584 memory: 68702 grad_norm: 1.4730 loss: 1.7411 center_loss: 0.4731 size_loss: 0.1410 cls_loss: 0.5513 giou_loss: 0.5756 2025/05/13 12:10:34 - mmengine - INFO - Epoch(train) [167][50/91] base_lr: 1.5796e-04 lr: 1.5796e-04 eta: 2 days, 11:21:35 time: 9.1639 data_time: 0.7823 memory: 68702 grad_norm: 1.4470 loss: 1.7607 center_loss: 0.4798 size_loss: 0.1426 cls_loss: 0.5575 giou_loss: 0.5808 2025/05/13 12:12:00 - mmengine - INFO - Epoch(train) [167][60/91] base_lr: 1.5796e-04 lr: 1.5796e-04 eta: 2 days, 11:19:35 time: 8.6394 data_time: 0.3146 memory: 68700 grad_norm: 1.4591 loss: 1.7641 center_loss: 0.4799 size_loss: 0.1425 cls_loss: 0.5607 giou_loss: 0.5810 2025/05/13 12:13:27 - mmengine - INFO - Epoch(train) [167][70/91] base_lr: 1.5796e-04 lr: 1.5796e-04 eta: 2 days, 11:17:34 time: 8.6395 data_time: 0.3131 memory: 68703 grad_norm: 1.4903 loss: 1.7681 center_loss: 0.4832 size_loss: 0.1430 cls_loss: 0.5597 giou_loss: 0.5822 2025/05/13 12:14:52 - mmengine - INFO - Epoch(train) [167][80/91] base_lr: 1.5796e-04 lr: 1.5796e-04 eta: 2 days, 11:15:33 time: 8.6279 data_time: 0.3224 memory: 68701 grad_norm: 1.5162 loss: 1.7665 center_loss: 0.4820 size_loss: 0.1414 cls_loss: 0.5622 giou_loss: 0.5810 2025/05/13 12:16:18 - mmengine - INFO - Epoch(train) [167][90/91] base_lr: 1.5796e-04 lr: 1.5796e-04 eta: 2 days, 11:13:31 time: 8.6083 data_time: 0.3155 memory: 68703 grad_norm: 1.4734 loss: 1.7696 center_loss: 0.4811 size_loss: 0.1420 cls_loss: 0.5643 giou_loss: 0.5823 2025/05/13 12:16:19 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 12:18:10 - mmengine - INFO - Epoch(train) [168][10/91] base_lr: 1.5701e-04 lr: 1.5701e-04 eta: 2 days, 11:11:44 time: 8.9589 data_time: 0.7690 memory: 68702 grad_norm: 1.5309 loss: 1.7827 center_loss: 0.4861 size_loss: 0.1420 cls_loss: 0.5702 giou_loss: 0.5844 2025/05/13 12:19:36 - mmengine - INFO - Epoch(train) [168][20/91] base_lr: 1.5701e-04 lr: 1.5701e-04 eta: 2 days, 11:09:43 time: 8.9467 data_time: 0.7718 memory: 68702 grad_norm: 1.4345 loss: 1.7786 center_loss: 0.4838 size_loss: 0.1429 cls_loss: 0.5713 giou_loss: 0.5806 2025/05/13 12:21:02 - mmengine - INFO - Epoch(train) [168][30/91] base_lr: 1.5701e-04 lr: 1.5701e-04 eta: 2 days, 11:07:42 time: 8.9444 data_time: 0.7711 memory: 68700 grad_norm: 1.4933 loss: 1.7771 center_loss: 0.4802 size_loss: 0.1417 cls_loss: 0.5743 giou_loss: 0.5809 2025/05/13 12:22:28 - mmengine - INFO - Epoch(train) [168][40/91] base_lr: 1.5701e-04 lr: 1.5701e-04 eta: 2 days, 11:05:41 time: 8.9448 data_time: 0.7690 memory: 68702 grad_norm: 1.5958 loss: 1.7797 center_loss: 0.4858 size_loss: 0.1437 cls_loss: 0.5704 giou_loss: 0.5798 2025/05/13 12:23:55 - mmengine - INFO - Epoch(train) [168][50/91] base_lr: 1.5701e-04 lr: 1.5701e-04 eta: 2 days, 11:03:42 time: 9.1228 data_time: 0.7894 memory: 68702 grad_norm: 1.5062 loss: 1.7360 center_loss: 0.4685 size_loss: 0.1381 cls_loss: 0.5584 giou_loss: 0.5710 2025/05/13 12:25:21 - mmengine - INFO - Epoch(train) [168][60/91] base_lr: 1.5701e-04 lr: 1.5701e-04 eta: 2 days, 11:01:41 time: 8.6117 data_time: 0.3250 memory: 68702 grad_norm: 1.5376 loss: 1.7373 center_loss: 0.4715 size_loss: 0.1401 cls_loss: 0.5535 giou_loss: 0.5721 2025/05/13 12:26:47 - mmengine - INFO - Epoch(train) [168][70/91] base_lr: 1.5701e-04 lr: 1.5701e-04 eta: 2 days, 10:59:40 time: 8.6089 data_time: 0.3252 memory: 68702 grad_norm: 1.5816 loss: 1.7460 center_loss: 0.4732 size_loss: 0.1388 cls_loss: 0.5569 giou_loss: 0.5771 2025/05/13 12:28:13 - mmengine - INFO - Epoch(train) [168][80/91] base_lr: 1.5701e-04 lr: 1.5701e-04 eta: 2 days, 10:57:39 time: 8.6094 data_time: 0.3275 memory: 68702 grad_norm: 1.5108 loss: 1.7460 center_loss: 0.4760 size_loss: 0.1385 cls_loss: 0.5541 giou_loss: 0.5775 2025/05/13 12:29:38 - mmengine - INFO - Epoch(train) [168][90/91] base_lr: 1.5701e-04 lr: 1.5701e-04 eta: 2 days, 10:55:38 time: 8.5885 data_time: 0.3158 memory: 68702 grad_norm: 1.4280 loss: 1.7362 center_loss: 0.4671 size_loss: 0.1366 cls_loss: 0.5544 giou_loss: 0.5781 2025/05/13 12:29:39 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 12:29:39 - mmengine - INFO - Saving checkpoint at 168 epochs 2025/05/13 12:30:18 - mmengine - INFO - Epoch(val) [168][10/39] eta: 0:01:14 time: 2.4004 data_time: 0.1368 memory: 15952 2025/05/13 12:30:41 - mmengine - INFO - Epoch(val) [168][20/39] eta: 0:00:46 time: 2.3534 data_time: 0.0901 memory: 13407 2025/05/13 12:31:04 - mmengine - INFO - Epoch(val) [168][30/39] eta: 0:00:21 time: 2.3521 data_time: 0.0890 memory: 13407 2025/05/13 12:31:26 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | sofa | 0.7556 | 0.8557 | 0.2033 | 0.3711 | | table | 0.4428 | 0.5914 | 0.1066 | 0.2600 | | chair | 0.5426 | 0.6930 | 0.1300 | 0.2939 | | curtain | 0.2674 | 0.5075 | 0.0468 | 0.1045 | | garbagebin | 0.2217 | 0.4189 | 0.0143 | 0.1075 | | bookshelf | 0.2930 | 0.5195 | 0.1086 | 0.2208 | | picture | 0.0235 | 0.1351 | 0.0002 | 0.0090 | | window | 0.1207 | 0.3333 | 0.0286 | 0.1170 | | cabinet | 0.2905 | 0.5215 | 0.0461 | 0.1747 | | door | 0.1508 | 0.4197 | 0.0204 | 0.1349 | | counter | 0.3458 | 0.5769 | 0.0101 | 0.0962 | | sink | 0.5024 | 0.6224 | 0.0718 | 0.2245 | | refrigerator | 0.4645 | 0.5965 | 0.2619 | 0.3509 | | desk | 0.6766 | 0.8346 | 0.2338 | 0.4488 | | bed | 0.8200 | 0.8519 | 0.3660 | 0.5679 | | toilet | 0.8749 | 0.9138 | 0.3863 | 0.4655 | | bathtub | 0.7694 | 0.8710 | 0.2069 | 0.3871 | | showercurtrain | 0.1886 | 0.5357 | 0.0271 | 0.1786 | +----------------+---------+---------+---------+---------+ | Overall | 0.4306 | 0.5999 | 0.1261 | 0.2507 | +----------------+---------+---------+---------+---------+ 2025/05/13 12:31:26 - mmengine - INFO - Epoch(val) [168][39/39] chair_AP_0.25: 0.5426 sofa_AP_0.25: 0.7556 table_AP_0.25: 0.4428 garbagebin_AP_0.25: 0.2217 bookshelf_AP_0.25: 0.2930 picture_AP_0.25: 0.0235 curtain_AP_0.25: 0.2674 door_AP_0.25: 0.1508 cabinet_AP_0.25: 0.2905 refrigerator_AP_0.25: 0.4645 counter_AP_0.25: 0.3458 sink_AP_0.25: 0.5024 window_AP_0.25: 0.1207 desk_AP_0.25: 0.6766 bed_AP_0.25: 0.8200 toilet_AP_0.25: 0.8749 showercurtrain_AP_0.25: 0.1886 bathtub_AP_0.25: 0.7694 mAP_0.25: 0.4306 chair_rec_0.25: 0.6930 sofa_rec_0.25: 0.8557 table_rec_0.25: 0.5914 garbagebin_rec_0.25: 0.4189 bookshelf_rec_0.25: 0.5195 picture_rec_0.25: 0.1351 curtain_rec_0.25: 0.5075 door_rec_0.25: 0.4197 cabinet_rec_0.25: 0.5215 refrigerator_rec_0.25: 0.5965 counter_rec_0.25: 0.5769 sink_rec_0.25: 0.6224 window_rec_0.25: 0.3333 desk_rec_0.25: 0.8346 bed_rec_0.25: 0.8519 toilet_rec_0.25: 0.9138 showercurtrain_rec_0.25: 0.5357 bathtub_rec_0.25: 0.8710 mAR_0.25: 0.5999 chair_AP_0.50: 0.1300 sofa_AP_0.50: 0.2033 table_AP_0.50: 0.1066 garbagebin_AP_0.50: 0.0143 bookshelf_AP_0.50: 0.1086 picture_AP_0.50: 0.0002 curtain_AP_0.50: 0.0468 door_AP_0.50: 0.0204 cabinet_AP_0.50: 0.0461 refrigerator_AP_0.50: 0.2619 counter_AP_0.50: 0.0101 sink_AP_0.50: 0.0718 window_AP_0.50: 0.0286 desk_AP_0.50: 0.2338 bed_AP_0.50: 0.3660 toilet_AP_0.50: 0.3863 showercurtrain_AP_0.50: 0.0271 bathtub_AP_0.50: 0.2069 mAP_0.50: 0.1261 chair_rec_0.50: 0.2939 sofa_rec_0.50: 0.3711 table_rec_0.50: 0.2600 garbagebin_rec_0.50: 0.1075 bookshelf_rec_0.50: 0.2208 picture_rec_0.50: 0.0090 curtain_rec_0.50: 0.1045 door_rec_0.50: 0.1349 cabinet_rec_0.50: 0.1747 refrigerator_rec_0.50: 0.3509 counter_rec_0.50: 0.0962 sink_rec_0.50: 0.2245 window_rec_0.50: 0.1170 desk_rec_0.50: 0.4488 bed_rec_0.50: 0.5679 toilet_rec_0.50: 0.4655 showercurtrain_rec_0.50: 0.1786 bathtub_rec_0.50: 0.3871 mAR_0.50: 0.2507 data_time: 0.1020 time: 2.3646 2025/05/13 12:33:19 - mmengine - INFO - Epoch(train) [169][10/91] base_lr: 1.5606e-04 lr: 1.5606e-04 eta: 2 days, 10:53:52 time: 8.9607 data_time: 0.7782 memory: 68702 grad_norm: 1.5549 loss: 1.7515 center_loss: 0.4718 size_loss: 0.1373 cls_loss: 0.5610 giou_loss: 0.5814 2025/05/13 12:34:45 - mmengine - INFO - Epoch(train) [169][20/91] base_lr: 1.5606e-04 lr: 1.5606e-04 eta: 2 days, 10:51:51 time: 8.9669 data_time: 0.7704 memory: 68703 grad_norm: 1.6383 loss: 1.7414 center_loss: 0.4651 size_loss: 0.1353 cls_loss: 0.5621 giou_loss: 0.5790 2025/05/13 12:36:11 - mmengine - INFO - Epoch(train) [169][30/91] base_lr: 1.5606e-04 lr: 1.5606e-04 eta: 2 days, 10:49:51 time: 8.9627 data_time: 0.7677 memory: 68702 grad_norm: 1.6671 loss: 1.7343 center_loss: 0.4641 size_loss: 0.1355 cls_loss: 0.5589 giou_loss: 0.5759 2025/05/13 12:37:36 - mmengine - INFO - Epoch(train) [169][40/91] base_lr: 1.5606e-04 lr: 1.5606e-04 eta: 2 days, 10:47:50 time: 8.9561 data_time: 0.7635 memory: 68703 grad_norm: 1.7011 loss: 1.7327 center_loss: 0.4624 size_loss: 0.1354 cls_loss: 0.5603 giou_loss: 0.5745 2025/05/13 12:39:03 - mmengine - INFO - Epoch(train) [169][50/91] base_lr: 1.5606e-04 lr: 1.5606e-04 eta: 2 days, 10:45:51 time: 9.1345 data_time: 0.7845 memory: 68702 grad_norm: 1.6014 loss: 1.7341 center_loss: 0.4656 size_loss: 0.1371 cls_loss: 0.5574 giou_loss: 0.5739 2025/05/13 12:40:29 - mmengine - INFO - Epoch(train) [169][60/91] base_lr: 1.5606e-04 lr: 1.5606e-04 eta: 2 days, 10:43:50 time: 8.6073 data_time: 0.3072 memory: 68702 grad_norm: 1.5078 loss: 1.7266 center_loss: 0.4633 size_loss: 0.1363 cls_loss: 0.5573 giou_loss: 0.5697 2025/05/13 12:41:55 - mmengine - INFO - Epoch(train) [169][70/91] base_lr: 1.5606e-04 lr: 1.5606e-04 eta: 2 days, 10:41:50 time: 8.6057 data_time: 0.3098 memory: 68702 grad_norm: 1.3771 loss: 1.7344 center_loss: 0.4630 size_loss: 0.1374 cls_loss: 0.5619 giou_loss: 0.5720 2025/05/13 12:43:21 - mmengine - INFO - Epoch(train) [169][80/91] base_lr: 1.5606e-04 lr: 1.5606e-04 eta: 2 days, 10:39:50 time: 8.6095 data_time: 0.3106 memory: 68702 grad_norm: 1.3457 loss: 1.7357 center_loss: 0.4669 size_loss: 0.1388 cls_loss: 0.5589 giou_loss: 0.5711 2025/05/13 12:44:46 - mmengine - INFO - Epoch(train) [169][90/91] base_lr: 1.5606e-04 lr: 1.5606e-04 eta: 2 days, 10:37:49 time: 8.5960 data_time: 0.3068 memory: 68702 grad_norm: 1.3327 loss: 1.7546 center_loss: 0.4750 size_loss: 0.1415 cls_loss: 0.5600 giou_loss: 0.5782 2025/05/13 12:44:48 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 12:46:40 - mmengine - INFO - Epoch(train) [170][10/91] base_lr: 1.5511e-04 lr: 1.5511e-04 eta: 2 days, 10:36:03 time: 8.9741 data_time: 0.7881 memory: 68702 grad_norm: 1.4808 loss: 1.7762 center_loss: 0.4859 size_loss: 0.1427 cls_loss: 0.5637 giou_loss: 0.5839 2025/05/13 12:48:06 - mmengine - INFO - Epoch(train) [170][20/91] base_lr: 1.5511e-04 lr: 1.5511e-04 eta: 2 days, 10:34:03 time: 8.9696 data_time: 0.7892 memory: 68702 grad_norm: 1.5352 loss: 1.7983 center_loss: 0.4949 size_loss: 0.1453 cls_loss: 0.5677 giou_loss: 0.5904 2025/05/13 12:49:32 - mmengine - INFO - Epoch(train) [170][30/91] base_lr: 1.5511e-04 lr: 1.5511e-04 eta: 2 days, 10:32:03 time: 8.9661 data_time: 0.7895 memory: 68703 grad_norm: 1.5980 loss: 1.7890 center_loss: 0.4907 size_loss: 0.1448 cls_loss: 0.5662 giou_loss: 0.5872 2025/05/13 12:50:58 - mmengine - INFO - Epoch(train) [170][40/91] base_lr: 1.5511e-04 lr: 1.5511e-04 eta: 2 days, 10:30:03 time: 8.9668 data_time: 0.7911 memory: 68702 grad_norm: 1.5352 loss: 1.7950 center_loss: 0.4908 size_loss: 0.1445 cls_loss: 0.5702 giou_loss: 0.5895 2025/05/13 12:52:25 - mmengine - INFO - Epoch(train) [170][50/91] base_lr: 1.5511e-04 lr: 1.5511e-04 eta: 2 days, 10:28:04 time: 9.1476 data_time: 0.8186 memory: 68702 grad_norm: 1.4753 loss: 1.7538 center_loss: 0.4760 size_loss: 0.1402 cls_loss: 0.5587 giou_loss: 0.5789 2025/05/13 12:53:51 - mmengine - INFO - Epoch(train) [170][60/91] base_lr: 1.5511e-04 lr: 1.5511e-04 eta: 2 days, 10:26:05 time: 8.6208 data_time: 0.3330 memory: 68702 grad_norm: 1.4130 loss: 1.7398 center_loss: 0.4666 size_loss: 0.1394 cls_loss: 0.5584 giou_loss: 0.5755 2025/05/13 12:55:17 - mmengine - INFO - Epoch(train) [170][70/91] base_lr: 1.5511e-04 lr: 1.5511e-04 eta: 2 days, 10:24:05 time: 8.6191 data_time: 0.3320 memory: 68702 grad_norm: 1.4579 loss: 1.7187 center_loss: 0.4600 size_loss: 0.1379 cls_loss: 0.5521 giou_loss: 0.5687 2025/05/13 12:56:43 - mmengine - INFO - Epoch(train) [170][80/91] base_lr: 1.5511e-04 lr: 1.5511e-04 eta: 2 days, 10:22:04 time: 8.6113 data_time: 0.3280 memory: 68702 grad_norm: 1.4648 loss: 1.7147 center_loss: 0.4595 size_loss: 0.1362 cls_loss: 0.5503 giou_loss: 0.5688 2025/05/13 12:58:08 - mmengine - INFO - Epoch(train) [170][90/91] base_lr: 1.5511e-04 lr: 1.5511e-04 eta: 2 days, 10:20:03 time: 8.5905 data_time: 0.3148 memory: 68702 grad_norm: 1.5071 loss: 1.7269 center_loss: 0.4686 size_loss: 0.1361 cls_loss: 0.5489 giou_loss: 0.5734 2025/05/13 12:58:09 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 12:58:09 - mmengine - INFO - Saving checkpoint at 170 epochs 2025/05/13 12:58:46 - mmengine - INFO - Epoch(val) [170][10/39] eta: 0:01:14 time: 2.4048 data_time: 0.1406 memory: 15952 2025/05/13 12:59:09 - mmengine - INFO - Epoch(val) [170][20/39] eta: 0:00:46 time: 2.3555 data_time: 0.0912 memory: 13407 2025/05/13 12:59:32 - mmengine - INFO - Epoch(val) [170][30/39] eta: 0:00:21 time: 2.3540 data_time: 0.0892 memory: 13407 2025/05/13 12:59:54 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.2422 | 0.4264 | 0.0378 | 0.1453 | | table | 0.4944 | 0.6257 | 0.1622 | 0.2886 | | chair | 0.5578 | 0.7083 | 0.1550 | 0.3326 | | sofa | 0.6791 | 0.8247 | 0.2076 | 0.3814 | | curtain | 0.2796 | 0.5672 | 0.0272 | 0.0896 | | picture | 0.0204 | 0.1351 | 0.0021 | 0.0315 | | bookshelf | 0.3603 | 0.5974 | 0.0991 | 0.2597 | | window | 0.1584 | 0.3830 | 0.0213 | 0.0993 | | cabinet | 0.2898 | 0.5269 | 0.0487 | 0.1855 | | door | 0.1547 | 0.4839 | 0.0196 | 0.1499 | | counter | 0.4099 | 0.6154 | 0.0157 | 0.1346 | | sink | 0.4679 | 0.6122 | 0.0917 | 0.2449 | | refrigerator | 0.4070 | 0.5965 | 0.1893 | 0.3158 | | desk | 0.7070 | 0.8189 | 0.2445 | 0.4173 | | bed | 0.8529 | 0.8765 | 0.4206 | 0.5802 | | toilet | 0.9278 | 0.9828 | 0.3388 | 0.5000 | | bathtub | 0.7476 | 0.8710 | 0.2493 | 0.4839 | | showercurtrain | 0.2121 | 0.6071 | 0.0339 | 0.1786 | +----------------+---------+---------+---------+---------+ | Overall | 0.4427 | 0.6255 | 0.1314 | 0.2677 | +----------------+---------+---------+---------+---------+ 2025/05/13 12:59:54 - mmengine - INFO - Epoch(val) [170][39/39] chair_AP_0.25: 0.5578 sofa_AP_0.25: 0.6791 table_AP_0.25: 0.4944 garbagebin_AP_0.25: 0.2422 bookshelf_AP_0.25: 0.3603 picture_AP_0.25: 0.0204 curtain_AP_0.25: 0.2796 door_AP_0.25: 0.1547 cabinet_AP_0.25: 0.2898 refrigerator_AP_0.25: 0.4070 counter_AP_0.25: 0.4099 sink_AP_0.25: 0.4679 window_AP_0.25: 0.1584 desk_AP_0.25: 0.7070 bed_AP_0.25: 0.8529 toilet_AP_0.25: 0.9278 showercurtrain_AP_0.25: 0.2121 bathtub_AP_0.25: 0.7476 mAP_0.25: 0.4427 chair_rec_0.25: 0.7083 sofa_rec_0.25: 0.8247 table_rec_0.25: 0.6257 garbagebin_rec_0.25: 0.4264 bookshelf_rec_0.25: 0.5974 picture_rec_0.25: 0.1351 curtain_rec_0.25: 0.5672 door_rec_0.25: 0.4839 cabinet_rec_0.25: 0.5269 refrigerator_rec_0.25: 0.5965 counter_rec_0.25: 0.6154 sink_rec_0.25: 0.6122 window_rec_0.25: 0.3830 desk_rec_0.25: 0.8189 bed_rec_0.25: 0.8765 toilet_rec_0.25: 0.9828 showercurtrain_rec_0.25: 0.6071 bathtub_rec_0.25: 0.8710 mAR_0.25: 0.6255 chair_AP_0.50: 0.1550 sofa_AP_0.50: 0.2076 table_AP_0.50: 0.1622 garbagebin_AP_0.50: 0.0378 bookshelf_AP_0.50: 0.0991 picture_AP_0.50: 0.0021 curtain_AP_0.50: 0.0272 door_AP_0.50: 0.0196 cabinet_AP_0.50: 0.0487 refrigerator_AP_0.50: 0.1893 counter_AP_0.50: 0.0157 sink_AP_0.50: 0.0917 window_AP_0.50: 0.0213 desk_AP_0.50: 0.2445 bed_AP_0.50: 0.4206 toilet_AP_0.50: 0.3388 showercurtrain_AP_0.50: 0.0339 bathtub_AP_0.50: 0.2493 mAP_0.50: 0.1314 chair_rec_0.50: 0.3326 sofa_rec_0.50: 0.3814 table_rec_0.50: 0.2886 garbagebin_rec_0.50: 0.1453 bookshelf_rec_0.50: 0.2597 picture_rec_0.50: 0.0315 curtain_rec_0.50: 0.0896 door_rec_0.50: 0.1499 cabinet_rec_0.50: 0.1855 refrigerator_rec_0.50: 0.3158 counter_rec_0.50: 0.1346 sink_rec_0.50: 0.2449 window_rec_0.50: 0.0993 desk_rec_0.50: 0.4173 bed_rec_0.50: 0.5802 toilet_rec_0.50: 0.5000 showercurtrain_rec_0.50: 0.1786 bathtub_rec_0.50: 0.4839 mAR_0.50: 0.2677 data_time: 0.1020 time: 2.3673 2025/05/13 13:01:47 - mmengine - INFO - Epoch(train) [171][10/91] base_lr: 1.5416e-04 lr: 1.5416e-04 eta: 2 days, 10:18:18 time: 8.9629 data_time: 0.7567 memory: 68703 grad_norm: 1.6513 loss: 1.7481 center_loss: 0.4709 size_loss: 0.1365 cls_loss: 0.5633 giou_loss: 0.5774 2025/05/13 13:03:13 - mmengine - INFO - Epoch(train) [171][20/91] base_lr: 1.5416e-04 lr: 1.5416e-04 eta: 2 days, 10:16:18 time: 8.9567 data_time: 0.7475 memory: 68703 grad_norm: 1.5551 loss: 1.7527 center_loss: 0.4738 size_loss: 0.1366 cls_loss: 0.5624 giou_loss: 0.5799 2025/05/13 13:04:39 - mmengine - INFO - Epoch(train) [171][30/91] base_lr: 1.5416e-04 lr: 1.5416e-04 eta: 2 days, 10:14:18 time: 8.9585 data_time: 0.7492 memory: 68703 grad_norm: 1.5399 loss: 1.7498 center_loss: 0.4695 size_loss: 0.1366 cls_loss: 0.5620 giou_loss: 0.5817 2025/05/13 13:06:05 - mmengine - INFO - Epoch(train) [171][40/91] base_lr: 1.5416e-04 lr: 1.5416e-04 eta: 2 days, 10:12:19 time: 8.9670 data_time: 0.7489 memory: 68702 grad_norm: 1.4724 loss: 1.7460 center_loss: 0.4735 size_loss: 0.1356 cls_loss: 0.5593 giou_loss: 0.5777 2025/05/13 13:07:31 - mmengine - INFO - Epoch(train) [171][50/91] base_lr: 1.5416e-04 lr: 1.5416e-04 eta: 2 days, 10:10:20 time: 9.1406 data_time: 0.7706 memory: 68702 grad_norm: 1.4059 loss: 1.7252 center_loss: 0.4692 size_loss: 0.1354 cls_loss: 0.5452 giou_loss: 0.5754 2025/05/13 13:08:57 - mmengine - INFO - Epoch(train) [171][60/91] base_lr: 1.5416e-04 lr: 1.5416e-04 eta: 2 days, 10:08:20 time: 8.6048 data_time: 0.3075 memory: 68702 grad_norm: 1.4493 loss: 1.7401 center_loss: 0.4721 size_loss: 0.1367 cls_loss: 0.5543 giou_loss: 0.5769 2025/05/13 13:10:23 - mmengine - INFO - Epoch(train) [171][70/91] base_lr: 1.5416e-04 lr: 1.5416e-04 eta: 2 days, 10:06:20 time: 8.6033 data_time: 0.3082 memory: 68703 grad_norm: 1.4570 loss: 1.7276 center_loss: 0.4662 size_loss: 0.1345 cls_loss: 0.5525 giou_loss: 0.5744 2025/05/13 13:11:49 - mmengine - INFO - Epoch(train) [171][80/91] base_lr: 1.5416e-04 lr: 1.5416e-04 eta: 2 days, 10:04:21 time: 8.6077 data_time: 0.3140 memory: 68703 grad_norm: 1.3998 loss: 1.7424 center_loss: 0.4742 size_loss: 0.1364 cls_loss: 0.5553 giou_loss: 0.5766 2025/05/13 13:13:14 - mmengine - INFO - Epoch(train) [171][90/91] base_lr: 1.5416e-04 lr: 1.5416e-04 eta: 2 days, 10:02:21 time: 8.5897 data_time: 0.3123 memory: 68702 grad_norm: 1.3668 loss: 1.7408 center_loss: 0.4691 size_loss: 0.1376 cls_loss: 0.5565 giou_loss: 0.5777 2025/05/13 13:13:16 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 13:15:06 - mmengine - INFO - Epoch(train) [172][10/91] base_lr: 1.5320e-04 lr: 1.5320e-04 eta: 2 days, 10:00:33 time: 8.9289 data_time: 0.7559 memory: 68702 grad_norm: 1.4358 loss: 1.7397 center_loss: 0.4687 size_loss: 0.1372 cls_loss: 0.5587 giou_loss: 0.5751 2025/05/13 13:16:33 - mmengine - INFO - Epoch(train) [172][20/91] base_lr: 1.5320e-04 lr: 1.5320e-04 eta: 2 days, 9:58:34 time: 8.9409 data_time: 0.7531 memory: 68702 grad_norm: 1.4331 loss: 1.7295 center_loss: 0.4648 size_loss: 0.1377 cls_loss: 0.5534 giou_loss: 0.5736 2025/05/13 13:17:59 - mmengine - INFO - Epoch(train) [172][30/91] base_lr: 1.5320e-04 lr: 1.5320e-04 eta: 2 days, 9:56:35 time: 8.9454 data_time: 0.7571 memory: 68702 grad_norm: 1.4449 loss: 1.7405 center_loss: 0.4689 size_loss: 0.1398 cls_loss: 0.5577 giou_loss: 0.5741 2025/05/13 13:19:25 - mmengine - INFO - Epoch(train) [172][40/91] base_lr: 1.5320e-04 lr: 1.5320e-04 eta: 2 days, 9:54:36 time: 8.9420 data_time: 0.7488 memory: 68702 grad_norm: 1.4991 loss: 1.7458 center_loss: 0.4736 size_loss: 0.1421 cls_loss: 0.5556 giou_loss: 0.5745 2025/05/13 13:20:52 - mmengine - INFO - Epoch(train) [172][50/91] base_lr: 1.5320e-04 lr: 1.5320e-04 eta: 2 days, 9:52:37 time: 9.1203 data_time: 0.7653 memory: 68702 grad_norm: 1.4004 loss: 1.7501 center_loss: 0.4780 size_loss: 0.1434 cls_loss: 0.5540 giou_loss: 0.5747 2025/05/13 13:22:18 - mmengine - INFO - Epoch(train) [172][60/91] base_lr: 1.5320e-04 lr: 1.5320e-04 eta: 2 days, 9:50:38 time: 8.6265 data_time: 0.3050 memory: 68702 grad_norm: 1.4161 loss: 1.7428 center_loss: 0.4752 size_loss: 0.1413 cls_loss: 0.5531 giou_loss: 0.5731 2025/05/13 13:23:44 - mmengine - INFO - Epoch(train) [172][70/91] base_lr: 1.5320e-04 lr: 1.5320e-04 eta: 2 days, 9:48:40 time: 8.6240 data_time: 0.3120 memory: 68701 grad_norm: 1.4046 loss: 1.7424 center_loss: 0.4757 size_loss: 0.1411 cls_loss: 0.5531 giou_loss: 0.5725 2025/05/13 13:25:10 - mmengine - INFO - Epoch(train) [172][80/91] base_lr: 1.5320e-04 lr: 1.5320e-04 eta: 2 days, 9:46:40 time: 8.6147 data_time: 0.3056 memory: 68702 grad_norm: 1.4127 loss: 1.7239 center_loss: 0.4668 size_loss: 0.1383 cls_loss: 0.5487 giou_loss: 0.5702 2025/05/13 13:26:35 - mmengine - INFO - Epoch(train) [172][90/91] base_lr: 1.5320e-04 lr: 1.5320e-04 eta: 2 days, 9:44:40 time: 8.5970 data_time: 0.2991 memory: 68702 grad_norm: 1.4066 loss: 1.7173 center_loss: 0.4625 size_loss: 0.1362 cls_loss: 0.5494 giou_loss: 0.5692 2025/05/13 13:26:36 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 13:26:36 - mmengine - INFO - Saving checkpoint at 172 epochs 2025/05/13 13:27:13 - mmengine - INFO - Epoch(val) [172][10/39] eta: 0:01:14 time: 2.4086 data_time: 0.1428 memory: 15952 2025/05/13 13:27:36 - mmengine - INFO - Epoch(val) [172][20/39] eta: 0:00:46 time: 2.3566 data_time: 0.0918 memory: 13407 2025/05/13 13:27:59 - mmengine - INFO - Epoch(val) [172][30/39] eta: 0:00:21 time: 2.3557 data_time: 0.0910 memory: 13407 2025/05/13 13:28:21 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | sofa | 0.6717 | 0.8247 | 0.2318 | 0.4124 | | garbagebin | 0.2475 | 0.4283 | 0.0270 | 0.1189 | | table | 0.4366 | 0.6029 | 0.1401 | 0.2914 | | chair | 0.5808 | 0.7420 | 0.1704 | 0.3582 | | curtain | 0.2472 | 0.5075 | 0.0236 | 0.1343 | | bookshelf | 0.3066 | 0.6234 | 0.1008 | 0.3117 | | picture | 0.0240 | 0.1171 | 0.0003 | 0.0135 | | window | 0.1631 | 0.3794 | 0.0231 | 0.1135 | | cabinet | 0.3027 | 0.5134 | 0.0549 | 0.1774 | | door | 0.1626 | 0.4604 | 0.0201 | 0.1392 | | counter | 0.3540 | 0.5577 | 0.0265 | 0.1154 | | sink | 0.4980 | 0.6224 | 0.1063 | 0.2449 | | refrigerator | 0.4521 | 0.6140 | 0.1971 | 0.3333 | | bed | 0.8106 | 0.8395 | 0.4209 | 0.5432 | | desk | 0.6691 | 0.8031 | 0.3216 | 0.5039 | | toilet | 0.9149 | 0.9828 | 0.4266 | 0.5345 | | bathtub | 0.8292 | 0.8710 | 0.4517 | 0.6129 | | showercurtrain | 0.3025 | 0.5357 | 0.0148 | 0.1071 | +----------------+---------+---------+---------+---------+ | Overall | 0.4430 | 0.6125 | 0.1532 | 0.2814 | +----------------+---------+---------+---------+---------+ 2025/05/13 13:28:21 - mmengine - INFO - Epoch(val) [172][39/39] chair_AP_0.25: 0.5808 sofa_AP_0.25: 0.6717 table_AP_0.25: 0.4366 garbagebin_AP_0.25: 0.2475 bookshelf_AP_0.25: 0.3066 picture_AP_0.25: 0.0240 curtain_AP_0.25: 0.2472 door_AP_0.25: 0.1626 cabinet_AP_0.25: 0.3027 refrigerator_AP_0.25: 0.4521 counter_AP_0.25: 0.3540 sink_AP_0.25: 0.4980 window_AP_0.25: 0.1631 desk_AP_0.25: 0.6691 bed_AP_0.25: 0.8106 toilet_AP_0.25: 0.9149 showercurtrain_AP_0.25: 0.3025 bathtub_AP_0.25: 0.8292 mAP_0.25: 0.4430 chair_rec_0.25: 0.7420 sofa_rec_0.25: 0.8247 table_rec_0.25: 0.6029 garbagebin_rec_0.25: 0.4283 bookshelf_rec_0.25: 0.6234 picture_rec_0.25: 0.1171 curtain_rec_0.25: 0.5075 door_rec_0.25: 0.4604 cabinet_rec_0.25: 0.5134 refrigerator_rec_0.25: 0.6140 counter_rec_0.25: 0.5577 sink_rec_0.25: 0.6224 window_rec_0.25: 0.3794 desk_rec_0.25: 0.8031 bed_rec_0.25: 0.8395 toilet_rec_0.25: 0.9828 showercurtrain_rec_0.25: 0.5357 bathtub_rec_0.25: 0.8710 mAR_0.25: 0.6125 chair_AP_0.50: 0.1704 sofa_AP_0.50: 0.2318 table_AP_0.50: 0.1401 garbagebin_AP_0.50: 0.0270 bookshelf_AP_0.50: 0.1008 picture_AP_0.50: 0.0003 curtain_AP_0.50: 0.0236 door_AP_0.50: 0.0201 cabinet_AP_0.50: 0.0549 refrigerator_AP_0.50: 0.1971 counter_AP_0.50: 0.0265 sink_AP_0.50: 0.1063 window_AP_0.50: 0.0231 desk_AP_0.50: 0.3216 bed_AP_0.50: 0.4209 toilet_AP_0.50: 0.4266 showercurtrain_AP_0.50: 0.0148 bathtub_AP_0.50: 0.4517 mAP_0.50: 0.1532 chair_rec_0.50: 0.3582 sofa_rec_0.50: 0.4124 table_rec_0.50: 0.2914 garbagebin_rec_0.50: 0.1189 bookshelf_rec_0.50: 0.3117 picture_rec_0.50: 0.0135 curtain_rec_0.50: 0.1343 door_rec_0.50: 0.1392 cabinet_rec_0.50: 0.1774 refrigerator_rec_0.50: 0.3333 counter_rec_0.50: 0.1154 sink_rec_0.50: 0.2449 window_rec_0.50: 0.1135 desk_rec_0.50: 0.5039 bed_rec_0.50: 0.5432 toilet_rec_0.50: 0.5345 showercurtrain_rec_0.50: 0.1071 bathtub_rec_0.50: 0.6129 mAR_0.50: 0.2814 data_time: 0.1050 time: 2.3690 2025/05/13 13:30:13 - mmengine - INFO - Epoch(train) [173][10/91] base_lr: 1.5225e-04 lr: 1.5225e-04 eta: 2 days, 9:42:54 time: 8.9595 data_time: 0.7478 memory: 68703 grad_norm: 1.4888 loss: 1.7291 center_loss: 0.4651 size_loss: 0.1362 cls_loss: 0.5574 giou_loss: 0.5703 2025/05/13 13:31:39 - mmengine - INFO - Epoch(train) [173][20/91] base_lr: 1.5225e-04 lr: 1.5225e-04 eta: 2 days, 9:40:55 time: 8.9564 data_time: 0.7447 memory: 68702 grad_norm: 1.4594 loss: 1.7238 center_loss: 0.4616 size_loss: 0.1370 cls_loss: 0.5559 giou_loss: 0.5693 2025/05/13 13:33:05 - mmengine - INFO - Epoch(train) [173][30/91] base_lr: 1.5225e-04 lr: 1.5225e-04 eta: 2 days, 9:38:56 time: 8.9516 data_time: 0.7426 memory: 68702 grad_norm: 1.8409 loss: 1.7243 center_loss: 0.4619 size_loss: 0.1362 cls_loss: 0.5582 giou_loss: 0.5679 2025/05/13 13:34:31 - mmengine - INFO - Epoch(train) [173][40/91] base_lr: 1.5225e-04 lr: 1.5225e-04 eta: 2 days, 9:36:57 time: 8.9544 data_time: 0.7445 memory: 68702 grad_norm: 1.9155 loss: 1.7262 center_loss: 0.4650 size_loss: 0.1364 cls_loss: 0.5546 giou_loss: 0.5703 2025/05/13 13:35:58 - mmengine - INFO - Epoch(train) [173][50/91] base_lr: 1.5225e-04 lr: 1.5225e-04 eta: 2 days, 9:34:59 time: 9.1311 data_time: 0.7619 memory: 68703 grad_norm: 1.9978 loss: 1.7189 center_loss: 0.4629 size_loss: 0.1351 cls_loss: 0.5517 giou_loss: 0.5693 2025/05/13 13:37:24 - mmengine - INFO - Epoch(train) [173][60/91] base_lr: 1.5225e-04 lr: 1.5225e-04 eta: 2 days, 9:33:00 time: 8.6147 data_time: 0.3057 memory: 68702 grad_norm: 1.9851 loss: 1.7206 center_loss: 0.4631 size_loss: 0.1360 cls_loss: 0.5496 giou_loss: 0.5720 2025/05/13 13:38:50 - mmengine - INFO - Epoch(train) [173][70/91] base_lr: 1.5225e-04 lr: 1.5225e-04 eta: 2 days, 9:31:01 time: 8.6133 data_time: 0.3148 memory: 68702 grad_norm: 2.0158 loss: 1.7305 center_loss: 0.4676 size_loss: 0.1372 cls_loss: 0.5518 giou_loss: 0.5739 2025/05/13 13:40:16 - mmengine - INFO - Epoch(train) [173][80/91] base_lr: 1.5225e-04 lr: 1.5225e-04 eta: 2 days, 9:29:02 time: 8.6078 data_time: 0.3132 memory: 68702 grad_norm: 1.7994 loss: 1.7360 center_loss: 0.4724 size_loss: 0.1389 cls_loss: 0.5476 giou_loss: 0.5772 2025/05/13 13:41:41 - mmengine - INFO - Epoch(train) [173][90/91] base_lr: 1.5225e-04 lr: 1.5225e-04 eta: 2 days, 9:27:03 time: 8.5917 data_time: 0.3076 memory: 68700 grad_norm: 1.8091 loss: 1.7553 center_loss: 0.4780 size_loss: 0.1391 cls_loss: 0.5576 giou_loss: 0.5806 2025/05/13 13:41:42 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 13:43:34 - mmengine - INFO - Epoch(train) [174][10/91] base_lr: 1.5129e-04 lr: 1.5129e-04 eta: 2 days, 9:25:17 time: 8.9520 data_time: 0.7718 memory: 68703 grad_norm: 1.8440 loss: 1.7712 center_loss: 0.4841 size_loss: 0.1418 cls_loss: 0.5617 giou_loss: 0.5836 2025/05/13 13:45:00 - mmengine - INFO - Epoch(train) [174][20/91] base_lr: 1.5129e-04 lr: 1.5129e-04 eta: 2 days, 9:23:18 time: 8.9499 data_time: 0.7694 memory: 68703 grad_norm: 1.8158 loss: 1.7734 center_loss: 0.4842 size_loss: 0.1406 cls_loss: 0.5646 giou_loss: 0.5840 2025/05/13 13:46:26 - mmengine - INFO - Epoch(train) [174][30/91] base_lr: 1.5129e-04 lr: 1.5129e-04 eta: 2 days, 9:21:19 time: 8.9513 data_time: 0.7653 memory: 68703 grad_norm: 1.7309 loss: 1.7666 center_loss: 0.4808 size_loss: 0.1391 cls_loss: 0.5635 giou_loss: 0.5833 2025/05/13 13:47:52 - mmengine - INFO - Epoch(train) [174][40/91] base_lr: 1.5129e-04 lr: 1.5129e-04 eta: 2 days, 9:19:21 time: 8.9537 data_time: 0.7616 memory: 68703 grad_norm: 1.5694 loss: 1.7782 center_loss: 0.4827 size_loss: 0.1411 cls_loss: 0.5700 giou_loss: 0.5844 2025/05/13 13:49:19 - mmengine - INFO - Epoch(train) [174][50/91] base_lr: 1.5129e-04 lr: 1.5129e-04 eta: 2 days, 9:17:23 time: 9.1250 data_time: 0.7781 memory: 68702 grad_norm: 1.3997 loss: 1.7491 center_loss: 0.4730 size_loss: 0.1392 cls_loss: 0.5611 giou_loss: 0.5759 2025/05/13 13:50:45 - mmengine - INFO - Epoch(train) [174][60/91] base_lr: 1.5129e-04 lr: 1.5129e-04 eta: 2 days, 9:15:25 time: 8.6126 data_time: 0.3014 memory: 68703 grad_norm: 1.3155 loss: 1.7602 center_loss: 0.4795 size_loss: 0.1399 cls_loss: 0.5633 giou_loss: 0.5775 2025/05/13 13:52:11 - mmengine - INFO - Epoch(train) [174][70/91] base_lr: 1.5129e-04 lr: 1.5129e-04 eta: 2 days, 9:13:27 time: 8.6231 data_time: 0.3048 memory: 68702 grad_norm: 1.3348 loss: 1.7562 center_loss: 0.4806 size_loss: 0.1409 cls_loss: 0.5593 giou_loss: 0.5755 2025/05/13 13:53:37 - mmengine - INFO - Epoch(train) [174][80/91] base_lr: 1.5129e-04 lr: 1.5129e-04 eta: 2 days, 9:11:29 time: 8.6194 data_time: 0.3072 memory: 68703 grad_norm: 1.3549 loss: 1.7549 center_loss: 0.4794 size_loss: 0.1415 cls_loss: 0.5592 giou_loss: 0.5748 2025/05/13 13:55:02 - mmengine - INFO - Epoch(train) [174][90/91] base_lr: 1.5129e-04 lr: 1.5129e-04 eta: 2 days, 9:09:29 time: 8.5978 data_time: 0.3045 memory: 68702 grad_norm: 1.3255 loss: 1.7369 center_loss: 0.4733 size_loss: 0.1393 cls_loss: 0.5517 giou_loss: 0.5726 2025/05/13 13:55:03 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 13:55:03 - mmengine - INFO - Saving checkpoint at 174 epochs 2025/05/13 13:55:42 - mmengine - INFO - Epoch(val) [174][10/39] eta: 0:01:14 time: 2.4065 data_time: 0.1420 memory: 15952 2025/05/13 13:56:05 - mmengine - INFO - Epoch(val) [174][20/39] eta: 0:00:46 time: 2.3520 data_time: 0.0888 memory: 13407 2025/05/13 13:56:28 - mmengine - INFO - Epoch(val) [174][30/39] eta: 0:00:21 time: 2.3527 data_time: 0.0895 memory: 13407 2025/05/13 13:56:50 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.2976 | 0.4528 | 0.0431 | 0.1434 | | chair | 0.5802 | 0.7135 | 0.1846 | 0.3428 | | sofa | 0.7526 | 0.8660 | 0.2231 | 0.4021 | | table | 0.4738 | 0.6057 | 0.1689 | 0.2943 | | bookshelf | 0.3216 | 0.5584 | 0.1325 | 0.2857 | | curtain | 0.2346 | 0.5075 | 0.0537 | 0.1045 | | picture | 0.0259 | 0.1126 | 0.0003 | 0.0135 | | window | 0.1480 | 0.3617 | 0.0081 | 0.0638 | | bed | 0.8396 | 0.8519 | 0.4126 | 0.5926 | | door | 0.1985 | 0.5225 | 0.0258 | 0.1799 | | cabinet | 0.3036 | 0.5108 | 0.0573 | 0.1828 | | sink | 0.5167 | 0.6327 | 0.1353 | 0.2959 | | refrigerator | 0.4640 | 0.5965 | 0.2439 | 0.3333 | | counter | 0.3355 | 0.5769 | 0.0033 | 0.0577 | | desk | 0.6905 | 0.8346 | 0.2409 | 0.4488 | | toilet | 0.8437 | 0.8966 | 0.4134 | 0.5000 | | bathtub | 0.8378 | 0.9032 | 0.2820 | 0.4839 | | showercurtrain | 0.3359 | 0.5714 | 0.0393 | 0.1786 | +----------------+---------+---------+---------+---------+ | Overall | 0.4556 | 0.6153 | 0.1482 | 0.2724 | +----------------+---------+---------+---------+---------+ 2025/05/13 13:56:50 - mmengine - INFO - Epoch(val) [174][39/39] chair_AP_0.25: 0.5802 sofa_AP_0.25: 0.7526 table_AP_0.25: 0.4738 garbagebin_AP_0.25: 0.2976 bookshelf_AP_0.25: 0.3216 picture_AP_0.25: 0.0259 curtain_AP_0.25: 0.2346 door_AP_0.25: 0.1985 cabinet_AP_0.25: 0.3036 refrigerator_AP_0.25: 0.4640 counter_AP_0.25: 0.3355 sink_AP_0.25: 0.5167 window_AP_0.25: 0.1480 desk_AP_0.25: 0.6905 bed_AP_0.25: 0.8396 toilet_AP_0.25: 0.8437 showercurtrain_AP_0.25: 0.3359 bathtub_AP_0.25: 0.8378 mAP_0.25: 0.4556 chair_rec_0.25: 0.7135 sofa_rec_0.25: 0.8660 table_rec_0.25: 0.6057 garbagebin_rec_0.25: 0.4528 bookshelf_rec_0.25: 0.5584 picture_rec_0.25: 0.1126 curtain_rec_0.25: 0.5075 door_rec_0.25: 0.5225 cabinet_rec_0.25: 0.5108 refrigerator_rec_0.25: 0.5965 counter_rec_0.25: 0.5769 sink_rec_0.25: 0.6327 window_rec_0.25: 0.3617 desk_rec_0.25: 0.8346 bed_rec_0.25: 0.8519 toilet_rec_0.25: 0.8966 showercurtrain_rec_0.25: 0.5714 bathtub_rec_0.25: 0.9032 mAR_0.25: 0.6153 chair_AP_0.50: 0.1846 sofa_AP_0.50: 0.2231 table_AP_0.50: 0.1689 garbagebin_AP_0.50: 0.0431 bookshelf_AP_0.50: 0.1325 picture_AP_0.50: 0.0003 curtain_AP_0.50: 0.0537 door_AP_0.50: 0.0258 cabinet_AP_0.50: 0.0573 refrigerator_AP_0.50: 0.2439 counter_AP_0.50: 0.0033 sink_AP_0.50: 0.1353 window_AP_0.50: 0.0081 desk_AP_0.50: 0.2409 bed_AP_0.50: 0.4126 toilet_AP_0.50: 0.4134 showercurtrain_AP_0.50: 0.0393 bathtub_AP_0.50: 0.2820 mAP_0.50: 0.1482 chair_rec_0.50: 0.3428 sofa_rec_0.50: 0.4021 table_rec_0.50: 0.2943 garbagebin_rec_0.50: 0.1434 bookshelf_rec_0.50: 0.2857 picture_rec_0.50: 0.0135 curtain_rec_0.50: 0.1045 door_rec_0.50: 0.1799 cabinet_rec_0.50: 0.1828 refrigerator_rec_0.50: 0.3333 counter_rec_0.50: 0.0577 sink_rec_0.50: 0.2959 window_rec_0.50: 0.0638 desk_rec_0.50: 0.4488 bed_rec_0.50: 0.5926 toilet_rec_0.50: 0.5000 showercurtrain_rec_0.50: 0.1786 bathtub_rec_0.50: 0.4839 mAR_0.50: 0.2724 data_time: 0.1027 time: 2.3658 2025/05/13 13:58:42 - mmengine - INFO - Epoch(train) [175][10/91] base_lr: 1.5033e-04 lr: 1.5033e-04 eta: 2 days, 9:07:43 time: 8.9672 data_time: 0.7670 memory: 68702 grad_norm: 1.4986 loss: 1.7324 center_loss: 0.4662 size_loss: 0.1385 cls_loss: 0.5547 giou_loss: 0.5730 2025/05/13 14:00:09 - mmengine - INFO - Epoch(train) [175][20/91] base_lr: 1.5033e-04 lr: 1.5033e-04 eta: 2 days, 9:05:45 time: 8.9638 data_time: 0.7631 memory: 68702 grad_norm: 1.4910 loss: 1.7311 center_loss: 0.4699 size_loss: 0.1392 cls_loss: 0.5506 giou_loss: 0.5714 2025/05/13 14:01:35 - mmengine - INFO - Epoch(train) [175][30/91] base_lr: 1.5033e-04 lr: 1.5033e-04 eta: 2 days, 9:03:47 time: 8.9565 data_time: 0.7592 memory: 68702 grad_norm: 1.4827 loss: 1.7420 center_loss: 0.4708 size_loss: 0.1391 cls_loss: 0.5570 giou_loss: 0.5751 2025/05/13 14:03:00 - mmengine - INFO - Epoch(train) [175][40/91] base_lr: 1.5033e-04 lr: 1.5033e-04 eta: 2 days, 9:01:49 time: 8.9552 data_time: 0.7554 memory: 68702 grad_norm: 1.4747 loss: 1.7530 center_loss: 0.4764 size_loss: 0.1397 cls_loss: 0.5592 giou_loss: 0.5777 2025/05/13 14:04:27 - mmengine - INFO - Epoch(train) [175][50/91] base_lr: 1.5033e-04 lr: 1.5033e-04 eta: 2 days, 8:59:51 time: 9.1307 data_time: 0.7744 memory: 68702 grad_norm: 1.3840 loss: 1.7440 center_loss: 0.4706 size_loss: 0.1384 cls_loss: 0.5586 giou_loss: 0.5764 2025/05/13 14:05:53 - mmengine - INFO - Epoch(train) [175][60/91] base_lr: 1.5033e-04 lr: 1.5033e-04 eta: 2 days, 8:57:53 time: 8.6086 data_time: 0.3007 memory: 68702 grad_norm: 1.4203 loss: 1.7531 center_loss: 0.4769 size_loss: 0.1411 cls_loss: 0.5564 giou_loss: 0.5787 2025/05/13 14:07:19 - mmengine - INFO - Epoch(train) [175][70/91] base_lr: 1.5033e-04 lr: 1.5033e-04 eta: 2 days, 8:55:55 time: 8.6079 data_time: 0.3051 memory: 68702 grad_norm: 1.4055 loss: 1.7480 center_loss: 0.4673 size_loss: 0.1403 cls_loss: 0.5620 giou_loss: 0.5784 2025/05/13 14:08:45 - mmengine - INFO - Epoch(train) [175][80/91] base_lr: 1.5033e-04 lr: 1.5033e-04 eta: 2 days, 8:53:57 time: 8.6018 data_time: 0.3073 memory: 68702 grad_norm: 1.4012 loss: 1.7226 center_loss: 0.4574 size_loss: 0.1374 cls_loss: 0.5543 giou_loss: 0.5734 2025/05/13 14:10:10 - mmengine - INFO - Epoch(train) [175][90/91] base_lr: 1.5033e-04 lr: 1.5033e-04 eta: 2 days, 8:51:58 time: 8.5891 data_time: 0.3041 memory: 68702 grad_norm: 1.4378 loss: 1.7069 center_loss: 0.4498 size_loss: 0.1369 cls_loss: 0.5511 giou_loss: 0.5690 2025/05/13 14:10:11 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 14:12:03 - mmengine - INFO - Epoch(train) [176][10/91] base_lr: 1.4937e-04 lr: 1.4937e-04 eta: 2 days, 8:50:11 time: 8.9495 data_time: 0.7407 memory: 68702 grad_norm: 1.7253 loss: 1.7525 center_loss: 0.4745 size_loss: 0.1419 cls_loss: 0.5573 giou_loss: 0.5787 2025/05/13 14:13:29 - mmengine - INFO - Epoch(train) [176][20/91] base_lr: 1.4937e-04 lr: 1.4937e-04 eta: 2 days, 8:48:14 time: 8.9492 data_time: 0.7493 memory: 68703 grad_norm: 1.7584 loss: 1.7629 center_loss: 0.4786 size_loss: 0.1437 cls_loss: 0.5603 giou_loss: 0.5803 2025/05/13 14:14:55 - mmengine - INFO - Epoch(train) [176][30/91] base_lr: 1.4937e-04 lr: 1.4937e-04 eta: 2 days, 8:46:16 time: 8.9527 data_time: 0.7500 memory: 68702 grad_norm: 1.9016 loss: 1.7687 center_loss: 0.4843 size_loss: 0.1439 cls_loss: 0.5581 giou_loss: 0.5824 2025/05/13 14:16:21 - mmengine - INFO - Epoch(train) [176][40/91] base_lr: 1.4937e-04 lr: 1.4937e-04 eta: 2 days, 8:44:18 time: 8.9526 data_time: 0.7477 memory: 68702 grad_norm: 1.9197 loss: 1.7835 center_loss: 0.4926 size_loss: 0.1452 cls_loss: 0.5581 giou_loss: 0.5876 2025/05/13 14:17:48 - mmengine - INFO - Epoch(train) [176][50/91] base_lr: 1.4937e-04 lr: 1.4937e-04 eta: 2 days, 8:42:21 time: 9.1325 data_time: 0.7718 memory: 68702 grad_norm: 1.6254 loss: 1.7806 center_loss: 0.4919 size_loss: 0.1438 cls_loss: 0.5598 giou_loss: 0.5850 2025/05/13 14:19:14 - mmengine - INFO - Epoch(train) [176][60/91] base_lr: 1.4937e-04 lr: 1.4937e-04 eta: 2 days, 8:40:24 time: 8.6276 data_time: 0.3301 memory: 68702 grad_norm: 1.5649 loss: 1.7686 center_loss: 0.4841 size_loss: 0.1434 cls_loss: 0.5594 giou_loss: 0.5817 2025/05/13 14:20:40 - mmengine - INFO - Epoch(train) [176][70/91] base_lr: 1.4937e-04 lr: 1.4937e-04 eta: 2 days, 8:38:26 time: 8.6302 data_time: 0.3256 memory: 68703 grad_norm: 1.4968 loss: 1.7637 center_loss: 0.4823 size_loss: 0.1420 cls_loss: 0.5574 giou_loss: 0.5819 2025/05/13 14:21:24 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 14:22:06 - mmengine - INFO - Epoch(train) [176][80/91] base_lr: 1.4937e-04 lr: 1.4937e-04 eta: 2 days, 8:36:28 time: 8.6196 data_time: 0.3292 memory: 68702 grad_norm: 1.3316 loss: 1.7733 center_loss: 0.4880 size_loss: 0.1425 cls_loss: 0.5594 giou_loss: 0.5833 2025/05/13 14:23:31 - mmengine - INFO - Epoch(train) [176][90/91] base_lr: 1.4937e-04 lr: 1.4937e-04 eta: 2 days, 8:34:30 time: 8.6066 data_time: 0.3230 memory: 68703 grad_norm: 1.3446 loss: 1.7686 center_loss: 0.4827 size_loss: 0.1436 cls_loss: 0.5623 giou_loss: 0.5800 2025/05/13 14:23:33 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 14:23:33 - mmengine - INFO - Saving checkpoint at 176 epochs 2025/05/13 14:24:09 - mmengine - INFO - Epoch(val) [176][10/39] eta: 0:01:13 time: 2.4023 data_time: 0.1390 memory: 15952 2025/05/13 14:24:33 - mmengine - INFO - Epoch(val) [176][20/39] eta: 0:00:46 time: 2.3532 data_time: 0.0893 memory: 13407 2025/05/13 14:24:56 - mmengine - INFO - Epoch(val) [176][30/39] eta: 0:00:21 time: 2.3554 data_time: 0.0893 memory: 13407 2025/05/13 14:25:18 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.2746 | 0.4698 | 0.0289 | 0.1302 | | sofa | 0.7063 | 0.8557 | 0.1780 | 0.3918 | | table | 0.4945 | 0.6000 | 0.1541 | 0.2857 | | chair | 0.5599 | 0.7091 | 0.1577 | 0.3333 | | bookshelf | 0.2841 | 0.5974 | 0.0874 | 0.2727 | | curtain | 0.3436 | 0.5522 | 0.0728 | 0.1194 | | picture | 0.0195 | 0.1532 | 0.0008 | 0.0135 | | window | 0.1558 | 0.3723 | 0.0144 | 0.0922 | | door | 0.1624 | 0.4647 | 0.0224 | 0.1627 | | cabinet | 0.2924 | 0.5108 | 0.0574 | 0.1828 | | counter | 0.4040 | 0.6154 | 0.0299 | 0.1346 | | sink | 0.5537 | 0.6429 | 0.1188 | 0.2755 | | refrigerator | 0.5068 | 0.6667 | 0.1781 | 0.3333 | | bed | 0.8300 | 0.8395 | 0.3921 | 0.5432 | | desk | 0.6803 | 0.8346 | 0.2446 | 0.4252 | | toilet | 0.9371 | 0.9655 | 0.4242 | 0.5000 | | bathtub | 0.8886 | 0.9032 | 0.3385 | 0.5161 | | showercurtrain | 0.3318 | 0.6429 | 0.0220 | 0.1071 | +----------------+---------+---------+---------+---------+ | Overall | 0.4681 | 0.6331 | 0.1401 | 0.2678 | +----------------+---------+---------+---------+---------+ 2025/05/13 14:25:18 - mmengine - INFO - Epoch(val) [176][39/39] chair_AP_0.25: 0.5599 sofa_AP_0.25: 0.7063 table_AP_0.25: 0.4945 garbagebin_AP_0.25: 0.2746 bookshelf_AP_0.25: 0.2841 picture_AP_0.25: 0.0195 curtain_AP_0.25: 0.3436 door_AP_0.25: 0.1624 cabinet_AP_0.25: 0.2924 refrigerator_AP_0.25: 0.5068 counter_AP_0.25: 0.4040 sink_AP_0.25: 0.5537 window_AP_0.25: 0.1558 desk_AP_0.25: 0.6803 bed_AP_0.25: 0.8300 toilet_AP_0.25: 0.9371 showercurtrain_AP_0.25: 0.3318 bathtub_AP_0.25: 0.8886 mAP_0.25: 0.4681 chair_rec_0.25: 0.7091 sofa_rec_0.25: 0.8557 table_rec_0.25: 0.6000 garbagebin_rec_0.25: 0.4698 bookshelf_rec_0.25: 0.5974 picture_rec_0.25: 0.1532 curtain_rec_0.25: 0.5522 door_rec_0.25: 0.4647 cabinet_rec_0.25: 0.5108 refrigerator_rec_0.25: 0.6667 counter_rec_0.25: 0.6154 sink_rec_0.25: 0.6429 window_rec_0.25: 0.3723 desk_rec_0.25: 0.8346 bed_rec_0.25: 0.8395 toilet_rec_0.25: 0.9655 showercurtrain_rec_0.25: 0.6429 bathtub_rec_0.25: 0.9032 mAR_0.25: 0.6331 chair_AP_0.50: 0.1577 sofa_AP_0.50: 0.1780 table_AP_0.50: 0.1541 garbagebin_AP_0.50: 0.0289 bookshelf_AP_0.50: 0.0874 picture_AP_0.50: 0.0008 curtain_AP_0.50: 0.0728 door_AP_0.50: 0.0224 cabinet_AP_0.50: 0.0574 refrigerator_AP_0.50: 0.1781 counter_AP_0.50: 0.0299 sink_AP_0.50: 0.1188 window_AP_0.50: 0.0144 desk_AP_0.50: 0.2446 bed_AP_0.50: 0.3921 toilet_AP_0.50: 0.4242 showercurtrain_AP_0.50: 0.0220 bathtub_AP_0.50: 0.3385 mAP_0.50: 0.1401 chair_rec_0.50: 0.3333 sofa_rec_0.50: 0.3918 table_rec_0.50: 0.2857 garbagebin_rec_0.50: 0.1302 bookshelf_rec_0.50: 0.2727 picture_rec_0.50: 0.0135 curtain_rec_0.50: 0.1194 door_rec_0.50: 0.1627 cabinet_rec_0.50: 0.1828 refrigerator_rec_0.50: 0.3333 counter_rec_0.50: 0.1346 sink_rec_0.50: 0.2755 window_rec_0.50: 0.0922 desk_rec_0.50: 0.4252 bed_rec_0.50: 0.5432 toilet_rec_0.50: 0.5000 showercurtrain_rec_0.50: 0.1071 bathtub_rec_0.50: 0.5161 mAR_0.50: 0.2678 data_time: 0.1017 time: 2.3685 2025/05/13 14:25:18 - mmengine - INFO - The previous best checkpoint /data/yang/codes/MVSDet/work_dirs/imsize448_4layers_v2_simpler_projector_project_to_scannnet_axis_no_norm_predpc_c2lr_400epochs_atten_fps_lm_dis08_one2morematch_task_query/ModelName/best_mAP_0.25_epoch_164.pth is removed 2025/05/13 14:25:30 - mmengine - INFO - The best checkpoint with 0.4681 mAP_0.25 at 176 epoch is saved to best_mAP_0.25_epoch_176.pth. 2025/05/13 14:27:31 - mmengine - INFO - Epoch(train) [177][10/91] base_lr: 1.4841e-04 lr: 1.4841e-04 eta: 2 days, 8:32:41 time: 8.9166 data_time: 0.7700 memory: 68702 grad_norm: 1.4168 loss: 1.7997 center_loss: 0.4952 size_loss: 0.1475 cls_loss: 0.5703 giou_loss: 0.5867 2025/05/13 14:28:57 - mmengine - INFO - Epoch(train) [177][20/91] base_lr: 1.4841e-04 lr: 1.4841e-04 eta: 2 days, 8:30:44 time: 8.9095 data_time: 0.7593 memory: 68702 grad_norm: 1.4272 loss: 1.7868 center_loss: 0.4900 size_loss: 0.1454 cls_loss: 0.5649 giou_loss: 0.5864 2025/05/13 14:30:23 - mmengine - INFO - Epoch(train) [177][30/91] base_lr: 1.4841e-04 lr: 1.4841e-04 eta: 2 days, 8:28:46 time: 8.9097 data_time: 0.7559 memory: 68700 grad_norm: 1.4607 loss: 1.7531 center_loss: 0.4752 size_loss: 0.1407 cls_loss: 0.5592 giou_loss: 0.5780 2025/05/13 14:31:49 - mmengine - INFO - Epoch(train) [177][40/91] base_lr: 1.4841e-04 lr: 1.4841e-04 eta: 2 days, 8:26:49 time: 8.9131 data_time: 0.7518 memory: 68702 grad_norm: 1.5978 loss: 1.7315 center_loss: 0.4653 size_loss: 0.1399 cls_loss: 0.5521 giou_loss: 0.5742 2025/05/13 14:33:16 - mmengine - INFO - Epoch(train) [177][50/91] base_lr: 1.4841e-04 lr: 1.4841e-04 eta: 2 days, 8:24:52 time: 9.0903 data_time: 0.7666 memory: 68702 grad_norm: 1.5663 loss: 1.7219 center_loss: 0.4638 size_loss: 0.1388 cls_loss: 0.5463 giou_loss: 0.5730 2025/05/13 14:34:42 - mmengine - INFO - Epoch(train) [177][60/91] base_lr: 1.4841e-04 lr: 1.4841e-04 eta: 2 days, 8:22:55 time: 8.6234 data_time: 0.3026 memory: 68702 grad_norm: 1.5348 loss: 1.7079 center_loss: 0.4559 size_loss: 0.1366 cls_loss: 0.5442 giou_loss: 0.5712 2025/05/13 14:36:09 - mmengine - INFO - Epoch(train) [177][70/91] base_lr: 1.4841e-04 lr: 1.4841e-04 eta: 2 days, 8:20:58 time: 8.6292 data_time: 0.3075 memory: 68702 grad_norm: 1.5568 loss: 1.6959 center_loss: 0.4499 size_loss: 0.1358 cls_loss: 0.5431 giou_loss: 0.5671 2025/05/13 14:37:34 - mmengine - INFO - Epoch(train) [177][80/91] base_lr: 1.4841e-04 lr: 1.4841e-04 eta: 2 days, 8:19:01 time: 8.6224 data_time: 0.3091 memory: 68702 grad_norm: 1.7094 loss: 1.7175 center_loss: 0.4581 size_loss: 0.1378 cls_loss: 0.5484 giou_loss: 0.5732 2025/05/13 14:39:00 - mmengine - INFO - Epoch(train) [177][90/91] base_lr: 1.4841e-04 lr: 1.4841e-04 eta: 2 days, 8:17:02 time: 8.6067 data_time: 0.3046 memory: 68702 grad_norm: 1.7373 loss: 1.7298 center_loss: 0.4658 size_loss: 0.1380 cls_loss: 0.5525 giou_loss: 0.5735 2025/05/13 14:39:01 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 14:40:52 - mmengine - INFO - Epoch(train) [178][10/91] base_lr: 1.4744e-04 lr: 1.4744e-04 eta: 2 days, 8:15:15 time: 8.9439 data_time: 0.7580 memory: 68702 grad_norm: 1.7674 loss: 1.7372 center_loss: 0.4702 size_loss: 0.1384 cls_loss: 0.5532 giou_loss: 0.5754 2025/05/13 14:42:18 - mmengine - INFO - Epoch(train) [178][20/91] base_lr: 1.4744e-04 lr: 1.4744e-04 eta: 2 days, 8:13:19 time: 8.9471 data_time: 0.7674 memory: 68700 grad_norm: 1.7424 loss: 1.7438 center_loss: 0.4758 size_loss: 0.1404 cls_loss: 0.5519 giou_loss: 0.5756 2025/05/13 14:43:45 - mmengine - INFO - Epoch(train) [178][30/91] base_lr: 1.4744e-04 lr: 1.4744e-04 eta: 2 days, 8:11:22 time: 8.9472 data_time: 0.7719 memory: 68702 grad_norm: 1.7099 loss: 1.7564 center_loss: 0.4797 size_loss: 0.1407 cls_loss: 0.5564 giou_loss: 0.5795 2025/05/13 14:45:11 - mmengine - INFO - Epoch(train) [178][40/91] base_lr: 1.4744e-04 lr: 1.4744e-04 eta: 2 days, 8:09:25 time: 8.9501 data_time: 0.7747 memory: 68702 grad_norm: 1.5012 loss: 1.7630 center_loss: 0.4825 size_loss: 0.1421 cls_loss: 0.5557 giou_loss: 0.5826 2025/05/13 14:46:38 - mmengine - INFO - Epoch(train) [178][50/91] base_lr: 1.4744e-04 lr: 1.4744e-04 eta: 2 days, 8:07:29 time: 9.1391 data_time: 0.7958 memory: 68702 grad_norm: 1.4022 loss: 1.7546 center_loss: 0.4742 size_loss: 0.1425 cls_loss: 0.5602 giou_loss: 0.5777 2025/05/13 14:48:04 - mmengine - INFO - Epoch(train) [178][60/91] base_lr: 1.4744e-04 lr: 1.4744e-04 eta: 2 days, 8:05:33 time: 8.6486 data_time: 0.3418 memory: 68702 grad_norm: 1.3882 loss: 1.7431 center_loss: 0.4720 size_loss: 0.1410 cls_loss: 0.5548 giou_loss: 0.5753 2025/05/13 14:49:31 - mmengine - INFO - Epoch(train) [178][70/91] base_lr: 1.4744e-04 lr: 1.4744e-04 eta: 2 days, 8:03:36 time: 8.6490 data_time: 0.3390 memory: 68703 grad_norm: 1.4983 loss: 1.7466 center_loss: 0.4700 size_loss: 0.1397 cls_loss: 0.5606 giou_loss: 0.5764 2025/05/13 14:50:57 - mmengine - INFO - Epoch(train) [178][80/91] base_lr: 1.4744e-04 lr: 1.4744e-04 eta: 2 days, 8:01:39 time: 8.6417 data_time: 0.3348 memory: 68701 grad_norm: 1.5600 loss: 1.7337 center_loss: 0.4669 size_loss: 0.1389 cls_loss: 0.5558 giou_loss: 0.5721 2025/05/13 14:52:22 - mmengine - INFO - Epoch(train) [178][90/91] base_lr: 1.4744e-04 lr: 1.4744e-04 eta: 2 days, 7:59:41 time: 8.6259 data_time: 0.3242 memory: 68702 grad_norm: 1.5947 loss: 1.7323 center_loss: 0.4672 size_loss: 0.1376 cls_loss: 0.5573 giou_loss: 0.5702 2025/05/13 14:52:23 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 14:52:23 - mmengine - INFO - Saving checkpoint at 178 epochs 2025/05/13 14:53:02 - mmengine - INFO - Epoch(val) [178][10/39] eta: 0:01:13 time: 2.4028 data_time: 0.1366 memory: 15952 2025/05/13 14:53:25 - mmengine - INFO - Epoch(val) [178][20/39] eta: 0:00:46 time: 2.3559 data_time: 0.0890 memory: 13407 2025/05/13 14:53:48 - mmengine - INFO - Epoch(val) [178][30/39] eta: 0:00:21 time: 2.3540 data_time: 0.0874 memory: 13407 2025/05/13 14:54:10 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | sofa | 0.6759 | 0.8454 | 0.2358 | 0.4433 | | garbagebin | 0.2892 | 0.4547 | 0.0222 | 0.1208 | | table | 0.4605 | 0.6029 | 0.1694 | 0.3114 | | chair | 0.6037 | 0.7398 | 0.1601 | 0.3377 | | curtain | 0.2424 | 0.5224 | 0.0196 | 0.0746 | | picture | 0.0224 | 0.1351 | 0.0037 | 0.0180 | | bookshelf | 0.3439 | 0.6623 | 0.0938 | 0.2727 | | door | 0.1510 | 0.4433 | 0.0140 | 0.1263 | | window | 0.1533 | 0.3440 | 0.0219 | 0.0922 | | cabinet | 0.2856 | 0.5161 | 0.0487 | 0.1801 | | sink | 0.4975 | 0.6327 | 0.1206 | 0.2857 | | refrigerator | 0.5009 | 0.6667 | 0.2088 | 0.3684 | | counter | 0.3261 | 0.5385 | 0.0380 | 0.1731 | | desk | 0.6823 | 0.8346 | 0.2149 | 0.4331 | | bed | 0.8272 | 0.8395 | 0.4047 | 0.5802 | | toilet | 0.8661 | 0.9310 | 0.4128 | 0.5517 | | bathtub | 0.7155 | 0.8065 | 0.3259 | 0.5161 | | showercurtrain | 0.2919 | 0.5000 | 0.0089 | 0.0714 | +----------------+---------+---------+---------+---------+ | Overall | 0.4409 | 0.6120 | 0.1402 | 0.2754 | +----------------+---------+---------+---------+---------+ 2025/05/13 14:54:10 - mmengine - INFO - Epoch(val) [178][39/39] chair_AP_0.25: 0.6037 sofa_AP_0.25: 0.6759 table_AP_0.25: 0.4605 garbagebin_AP_0.25: 0.2892 bookshelf_AP_0.25: 0.3439 picture_AP_0.25: 0.0224 curtain_AP_0.25: 0.2424 door_AP_0.25: 0.1510 cabinet_AP_0.25: 0.2856 refrigerator_AP_0.25: 0.5009 counter_AP_0.25: 0.3261 sink_AP_0.25: 0.4975 window_AP_0.25: 0.1533 desk_AP_0.25: 0.6823 bed_AP_0.25: 0.8272 toilet_AP_0.25: 0.8661 showercurtrain_AP_0.25: 0.2919 bathtub_AP_0.25: 0.7155 mAP_0.25: 0.4409 chair_rec_0.25: 0.7398 sofa_rec_0.25: 0.8454 table_rec_0.25: 0.6029 garbagebin_rec_0.25: 0.4547 bookshelf_rec_0.25: 0.6623 picture_rec_0.25: 0.1351 curtain_rec_0.25: 0.5224 door_rec_0.25: 0.4433 cabinet_rec_0.25: 0.5161 refrigerator_rec_0.25: 0.6667 counter_rec_0.25: 0.5385 sink_rec_0.25: 0.6327 window_rec_0.25: 0.3440 desk_rec_0.25: 0.8346 bed_rec_0.25: 0.8395 toilet_rec_0.25: 0.9310 showercurtrain_rec_0.25: 0.5000 bathtub_rec_0.25: 0.8065 mAR_0.25: 0.6120 chair_AP_0.50: 0.1601 sofa_AP_0.50: 0.2358 table_AP_0.50: 0.1694 garbagebin_AP_0.50: 0.0222 bookshelf_AP_0.50: 0.0938 picture_AP_0.50: 0.0037 curtain_AP_0.50: 0.0196 door_AP_0.50: 0.0140 cabinet_AP_0.50: 0.0487 refrigerator_AP_0.50: 0.2088 counter_AP_0.50: 0.0380 sink_AP_0.50: 0.1206 window_AP_0.50: 0.0219 desk_AP_0.50: 0.2149 bed_AP_0.50: 0.4047 toilet_AP_0.50: 0.4128 showercurtrain_AP_0.50: 0.0089 bathtub_AP_0.50: 0.3259 mAP_0.50: 0.1402 chair_rec_0.50: 0.3377 sofa_rec_0.50: 0.4433 table_rec_0.50: 0.3114 garbagebin_rec_0.50: 0.1208 bookshelf_rec_0.50: 0.2727 picture_rec_0.50: 0.0180 curtain_rec_0.50: 0.0746 door_rec_0.50: 0.1263 cabinet_rec_0.50: 0.1801 refrigerator_rec_0.50: 0.3684 counter_rec_0.50: 0.1731 sink_rec_0.50: 0.2857 window_rec_0.50: 0.0922 desk_rec_0.50: 0.4331 bed_rec_0.50: 0.5802 toilet_rec_0.50: 0.5517 showercurtrain_rec_0.50: 0.0714 bathtub_rec_0.50: 0.5161 mAR_0.50: 0.2754 data_time: 0.0990 time: 2.3634 2025/05/13 14:56:01 - mmengine - INFO - Epoch(train) [179][10/91] base_lr: 1.4648e-04 lr: 1.4648e-04 eta: 2 days, 7:57:55 time: 8.9579 data_time: 0.7691 memory: 68703 grad_norm: 1.6644 loss: 1.7587 center_loss: 0.4768 size_loss: 0.1389 cls_loss: 0.5665 giou_loss: 0.5765 2025/05/13 14:57:27 - mmengine - INFO - Epoch(train) [179][20/91] base_lr: 1.4648e-04 lr: 1.4648e-04 eta: 2 days, 7:55:58 time: 8.9512 data_time: 0.7579 memory: 68702 grad_norm: 1.6899 loss: 1.7716 center_loss: 0.4805 size_loss: 0.1414 cls_loss: 0.5716 giou_loss: 0.5781 2025/05/13 14:58:53 - mmengine - INFO - Epoch(train) [179][30/91] base_lr: 1.4648e-04 lr: 1.4648e-04 eta: 2 days, 7:54:01 time: 8.9464 data_time: 0.7483 memory: 68702 grad_norm: 1.6658 loss: 1.7644 center_loss: 0.4776 size_loss: 0.1405 cls_loss: 0.5685 giou_loss: 0.5779 2025/05/13 15:00:19 - mmengine - INFO - Epoch(train) [179][40/91] base_lr: 1.4648e-04 lr: 1.4648e-04 eta: 2 days, 7:52:04 time: 8.9441 data_time: 0.7440 memory: 68703 grad_norm: 1.6827 loss: 1.7617 center_loss: 0.4776 size_loss: 0.1415 cls_loss: 0.5646 giou_loss: 0.5780 2025/05/13 15:01:46 - mmengine - INFO - Epoch(train) [179][50/91] base_lr: 1.4648e-04 lr: 1.4648e-04 eta: 2 days, 7:50:08 time: 9.1234 data_time: 0.7581 memory: 68702 grad_norm: 1.5720 loss: 1.7481 center_loss: 0.4743 size_loss: 0.1404 cls_loss: 0.5599 giou_loss: 0.5734 2025/05/13 15:03:12 - mmengine - INFO - Epoch(train) [179][60/91] base_lr: 1.4648e-04 lr: 1.4648e-04 eta: 2 days, 7:48:12 time: 8.6248 data_time: 0.2974 memory: 68702 grad_norm: 1.5554 loss: 1.7378 center_loss: 0.4736 size_loss: 0.1402 cls_loss: 0.5492 giou_loss: 0.5748 2025/05/13 15:04:39 - mmengine - INFO - Epoch(train) [179][70/91] base_lr: 1.4648e-04 lr: 1.4648e-04 eta: 2 days, 7:46:15 time: 8.6279 data_time: 0.3057 memory: 68703 grad_norm: 1.5375 loss: 1.7318 center_loss: 0.4727 size_loss: 0.1396 cls_loss: 0.5450 giou_loss: 0.5745 2025/05/13 15:06:04 - mmengine - INFO - Epoch(train) [179][80/91] base_lr: 1.4648e-04 lr: 1.4648e-04 eta: 2 days, 7:44:18 time: 8.6176 data_time: 0.3076 memory: 68702 grad_norm: 1.5335 loss: 1.7288 center_loss: 0.4728 size_loss: 0.1399 cls_loss: 0.5432 giou_loss: 0.5729 2025/05/13 15:07:29 - mmengine - INFO - Epoch(train) [179][90/91] base_lr: 1.4648e-04 lr: 1.4648e-04 eta: 2 days, 7:42:20 time: 8.5935 data_time: 0.3016 memory: 68702 grad_norm: 1.5255 loss: 1.7457 center_loss: 0.4782 size_loss: 0.1403 cls_loss: 0.5487 giou_loss: 0.5784 2025/05/13 15:07:30 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 15:09:22 - mmengine - INFO - Epoch(train) [180][10/91] base_lr: 1.4551e-04 lr: 1.4551e-04 eta: 2 days, 7:40:34 time: 8.9419 data_time: 0.7456 memory: 68702 grad_norm: 1.5317 loss: 1.7422 center_loss: 0.4752 size_loss: 0.1417 cls_loss: 0.5469 giou_loss: 0.5783 2025/05/13 15:10:48 - mmengine - INFO - Epoch(train) [180][20/91] base_lr: 1.4551e-04 lr: 1.4551e-04 eta: 2 days, 7:38:38 time: 8.9359 data_time: 0.7415 memory: 68702 grad_norm: 1.6070 loss: 1.7448 center_loss: 0.4760 size_loss: 0.1419 cls_loss: 0.5502 giou_loss: 0.5767 2025/05/13 15:12:14 - mmengine - INFO - Epoch(train) [180][30/91] base_lr: 1.4551e-04 lr: 1.4551e-04 eta: 2 days, 7:36:41 time: 8.9348 data_time: 0.7382 memory: 68702 grad_norm: 1.6916 loss: 1.7319 center_loss: 0.4702 size_loss: 0.1388 cls_loss: 0.5455 giou_loss: 0.5774 2025/05/13 15:13:40 - mmengine - INFO - Epoch(train) [180][40/91] base_lr: 1.4551e-04 lr: 1.4551e-04 eta: 2 days, 7:34:44 time: 8.9359 data_time: 0.7359 memory: 68703 grad_norm: 1.6109 loss: 1.7338 center_loss: 0.4758 size_loss: 0.1395 cls_loss: 0.5404 giou_loss: 0.5781 2025/05/13 15:15:06 - mmengine - INFO - Epoch(train) [180][50/91] base_lr: 1.4551e-04 lr: 1.4551e-04 eta: 2 days, 7:32:49 time: 9.1156 data_time: 0.7512 memory: 68703 grad_norm: 1.5109 loss: 1.7135 center_loss: 0.4689 size_loss: 0.1374 cls_loss: 0.5350 giou_loss: 0.5722 2025/05/13 15:16:32 - mmengine - INFO - Epoch(train) [180][60/91] base_lr: 1.4551e-04 lr: 1.4551e-04 eta: 2 days, 7:30:52 time: 8.6062 data_time: 0.3017 memory: 68703 grad_norm: 1.5088 loss: 1.7032 center_loss: 0.4648 size_loss: 0.1361 cls_loss: 0.5333 giou_loss: 0.5690 2025/05/13 15:17:59 - mmengine - INFO - Epoch(train) [180][70/91] base_lr: 1.4551e-04 lr: 1.4551e-04 eta: 2 days, 7:28:56 time: 8.6150 data_time: 0.3017 memory: 68702 grad_norm: 1.4132 loss: 1.7066 center_loss: 0.4624 size_loss: 0.1351 cls_loss: 0.5388 giou_loss: 0.5703 2025/05/13 15:19:24 - mmengine - INFO - Epoch(train) [180][80/91] base_lr: 1.4551e-04 lr: 1.4551e-04 eta: 2 days, 7:26:59 time: 8.6041 data_time: 0.2984 memory: 68702 grad_norm: 1.3194 loss: 1.7175 center_loss: 0.4652 size_loss: 0.1363 cls_loss: 0.5455 giou_loss: 0.5705 2025/05/13 15:20:49 - mmengine - INFO - Epoch(train) [180][90/91] base_lr: 1.4551e-04 lr: 1.4551e-04 eta: 2 days, 7:25:02 time: 8.5882 data_time: 0.2940 memory: 68703 grad_norm: 1.3197 loss: 1.7092 center_loss: 0.4610 size_loss: 0.1348 cls_loss: 0.5459 giou_loss: 0.5675 2025/05/13 15:20:50 - mmengine - INFO - Exp name: scannet_vggtdet 2025/05/13 15:20:50 - mmengine - INFO - Saving checkpoint at 180 epochs 2025/05/13 15:21:28 - mmengine - INFO - Epoch(val) [180][10/39] eta: 0:01:14 time: 2.4046 data_time: 0.1400 memory: 15952 2025/05/13 15:21:51 - mmengine - INFO - Epoch(val) [180][20/39] eta: 0:00:46 time: 2.3583 data_time: 0.0928 memory: 13407 2025/05/13 15:22:14 - mmengine - INFO - Epoch(val) [180][30/39] eta: 0:00:21 time: 2.3563 data_time: 0.0913 memory: 13407 2025/05/13 15:22:36 - mmengine - INFO - +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | garbagebin | 0.2756 | 0.4585 | 0.0281 | 0.1491 | | sofa | 0.7401 | 0.9072 | 0.1790 | 0.3505 | | chair | 0.6193 | 0.7493 | 0.1712 | 0.3458 | | curtain | 0.3137 | 0.5821 | 0.0554 | 0.1343 | | table | 0.5029 | 0.6314 | 0.1367 | 0.2800 | | picture | 0.0259 | 0.1306 | 0.0013 | 0.0180 | | bookshelf | 0.3688 | 0.5325 | 0.1158 | 0.2987 | | window | 0.1764 | 0.3901 | 0.0339 | 0.1170 | | cabinet | 0.2783 | 0.5027 | 0.0445 | 0.1855 | | door | 0.1829 | 0.4839 | 0.0230 | 0.1606 | | refrigerator | 0.4897 | 0.6491 | 0.2048 | 0.3684 | | sink | 0.5076 | 0.6429 | 0.0864 | 0.2449 | | counter | 0.4031 | 0.6346 | 0.0188 | 0.1154 | | desk | 0.6690 | 0.8346 | 0.2225 | 0.4409 | | bed | 0.8195 | 0.8395 | 0.4643 | 0.6173 | | toilet | 0.9029 | 0.9655 | 0.3782 | 0.5345 | | bathtub | 0.8322 | 0.8710 | 0.4942 | 0.6452 | | showercurtrain | 0.3286 | 0.6429 | 0.0614 | 0.2143 | +----------------+---------+---------+---------+---------+ | Overall | 0.4687 | 0.6360 | 0.1511 | 0.2900 | +----------------+---------+---------+---------+---------+