diff --git a/data/trained_openclip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt b/data/trained_openclip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt new file mode 100644 index 0000000000000000000000000000000000000000..0a794a0941d4ddd27b40b759b92e67d3ab285541 --- /dev/null +++ b/data/trained_openclip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e520c5f14ebfa4aaa17603db60fb1192698c292dd6746fd865d58571652b4c9b +size 5135895330 diff --git a/data/trained_openclip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt b/data/trained_openclip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt new file mode 100644 index 0000000000000000000000000000000000000000..3c70b4c1dd95b84cafbe83e036b3092fb1f497a3 --- /dev/null +++ b/data/trained_openclip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:43851e0e37bd2295f25bfc7dfe490fed761f49c7ed6f7e3f1cab64b70134739c +size 5135895394 diff --git a/data/trained_openclip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_3.pt b/data/trained_openclip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_3.pt new file mode 100644 index 0000000000000000000000000000000000000000..af420954e5c5815948553da6ce95d242b6e760c2 --- /dev/null +++ b/data/trained_openclip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_3.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2c31bb3d275dcca08bbcc12c275792dc1f711f0bac3b8768f98cd5da40791b84 +size 5135895394 diff --git a/data/trained_openclip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log b/data/trained_openclip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log new file mode 100644 index 0000000000000000000000000000000000000000..b97eaed1305f85e0abfafdf669fa9ca3e0d9d200 --- /dev/null +++ b/data/trained_openclip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log @@ -0,0 +1,582 @@ +2024-09-02,17:00:30 | INFO | Running in distributed mode with multiple processes. Device: cuda:0.Process (global: 0, local 0), total 4. +2024-09-02,17:00:30 | INFO | Loaded ViT-L-14-336 model config. +2024-09-02,17:00:33 | INFO | Loading pretrained ViT-L-14-336 weights (/project/deemreason/junteng/Vision4Math/data/openclip-vit-14-336/openclip_model.pt). +2024-09-02,17:00:44 | INFO | Model: +2024-09-02,17:00:44 | INFO | CLIP( + (visual): VisionTransformer( + (conv1): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14), bias=False) + (patch_dropout): Identity() + (ln_pre): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) + (transformer): Transformer( + (resblocks): ModuleList( + (0-23): 24 x ResidualAttentionBlock( + (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) + (attn): MultiheadAttention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) + ) + (ls_1): Identity() + (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=1024, out_features=4096, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=4096, out_features=1024, bias=True) + ) + (ls_2): Identity() + ) + ) + ) + (ln_post): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) + ) + (transformer): Transformer( + (resblocks): ModuleList( + (0-11): 12 x ResidualAttentionBlock( + (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (attn): MultiheadAttention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (ls_1): Identity() + (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (mlp): Sequential( + (c_fc): Linear(in_features=768, out_features=3072, bias=True) + (gelu): QuickGELU() + (c_proj): Linear(in_features=3072, out_features=768, bias=True) + ) + (ls_2): Identity() + ) + ) + ) + (token_embedding): Embedding(49408, 768) + (ln_final): LayerNorm((768,), eps=1e-05, elementwise_affine=True) +) +2024-09-02,17:00:44 | INFO | Params: +2024-09-02,17:00:44 | INFO | accum_freq: 1 +2024-09-02,17:00:44 | INFO | aug_cfg: {} +2024-09-02,17:00:44 | INFO | batch_size: 64 +2024-09-02,17:00:44 | INFO | beta1: 0.9 +2024-09-02,17:00:44 | INFO | beta2: 0.98 +2024-09-02,17:00:44 | INFO | checkpoint_path: /project/deemreason/junteng/Vision4Math/train_clip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints +2024-09-02,17:00:44 | INFO | coca_caption_loss_weight: 2.0 +2024-09-02,17:00:44 | INFO | coca_contrastive_loss_weight: 1.0 +2024-09-02,17:00:44 | INFO | copy_codebase: False +2024-09-02,17:00:44 | INFO | csv_caption_key: caption +2024-09-02,17:00:44 | INFO | csv_img_key: img_path +2024-09-02,17:00:44 | INFO | csv_separator: , +2024-09-02,17:00:44 | INFO | dataset_resampled: False +2024-09-02,17:00:44 | INFO | dataset_type: csv +2024-09-02,17:00:44 | INFO | ddp_static_graph: False +2024-09-02,17:00:44 | INFO | debug: False +2024-09-02,17:00:44 | INFO | delete_previous_checkpoint: False +2024-09-02,17:00:44 | INFO | device: cuda:0 +2024-09-02,17:00:44 | INFO | dist_backend: nccl +2024-09-02,17:00:44 | INFO | dist_url: env:// +2024-09-02,17:00:44 | INFO | distill: False +2024-09-02,17:00:44 | INFO | distill_model: None +2024-09-02,17:00:44 | INFO | distill_pretrained: None +2024-09-02,17:00:44 | INFO | distributed: True +2024-09-02,17:00:44 | INFO | epochs: 3 +2024-09-02,17:00:44 | INFO | epochs_cooldown: None +2024-09-02,17:00:44 | INFO | eps: 1e-06 +2024-09-02,17:00:44 | INFO | force_custom_text: False +2024-09-02,17:00:44 | INFO | force_image_size: None +2024-09-02,17:00:44 | INFO | force_patch_dropout: None +2024-09-02,17:00:44 | INFO | force_quick_gelu: True +2024-09-02,17:00:44 | INFO | gather_with_grad: False +2024-09-02,17:00:44 | INFO | grad_checkpointing: False +2024-09-02,17:00:44 | INFO | grad_clip_norm: None +2024-09-02,17:00:44 | INFO | horovod: False +2024-09-02,17:00:44 | INFO | image_interpolation: None +2024-09-02,17:00:44 | INFO | image_mean: None +2024-09-02,17:00:44 | INFO | image_resize_mode: None +2024-09-02,17:00:44 | INFO | image_std: None +2024-09-02,17:00:44 | INFO | imagenet_v2: None +2024-09-02,17:00:44 | INFO | imagenet_val: None +2024-09-02,17:00:44 | INFO | local_loss: False +2024-09-02,17:00:44 | INFO | local_rank: 0 +2024-09-02,17:00:44 | INFO | lock_image: False +2024-09-02,17:00:44 | INFO | lock_image_freeze_bn_stats: False +2024-09-02,17:00:44 | INFO | lock_image_unlocked_groups: 0 +2024-09-02,17:00:44 | INFO | lock_text: False +2024-09-02,17:00:44 | INFO | lock_text_freeze_layer_norm: False +2024-09-02,17:00:44 | INFO | lock_text_unlocked_layers: 0 +2024-09-02,17:00:44 | INFO | log_every_n_steps: 100 +2024-09-02,17:00:44 | INFO | log_level: 20 +2024-09-02,17:00:44 | INFO | log_local: False +2024-09-02,17:00:44 | INFO | log_path: /project/deemreason/junteng/Vision4Math/train_clip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log +2024-09-02,17:00:44 | INFO | logs: /project/deemreason/junteng/Vision4Math/train_clip/no_hard_negative_logs/plotqa_v2 +2024-09-02,17:00:44 | INFO | lr: 1e-06 +2024-09-02,17:00:44 | INFO | lr_cooldown_end: 0.0 +2024-09-02,17:00:44 | INFO | lr_cooldown_power: 1.0 +2024-09-02,17:00:44 | INFO | lr_scheduler: cosine +2024-09-02,17:00:44 | INFO | model: ViT-L-14-336 +2024-09-02,17:00:44 | INFO | name: 2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp +2024-09-02,17:00:44 | INFO | no_set_device_rank: False +2024-09-02,17:00:44 | INFO | precision: amp +2024-09-02,17:00:44 | INFO | pretrained: /project/deemreason/junteng/Vision4Math/data/openclip-vit-14-336/openclip_model.pt +2024-09-02,17:00:44 | INFO | pretrained_image: False +2024-09-02,17:00:44 | INFO | rank: 0 +2024-09-02,17:00:44 | INFO | remote_sync: None +2024-09-02,17:00:44 | INFO | remote_sync_frequency: 300 +2024-09-02,17:00:44 | INFO | remote_sync_protocol: s3 +2024-09-02,17:00:44 | INFO | report_to: wandb +2024-09-02,17:00:44 | INFO | resume: None +2024-09-02,17:00:44 | INFO | save_frequency: 1 +2024-09-02,17:00:44 | INFO | save_most_recent: False +2024-09-02,17:00:44 | INFO | seed: 0 +2024-09-02,17:00:44 | INFO | siglip: False +2024-09-02,17:00:44 | INFO | skip_scheduler: False +2024-09-02,17:00:44 | INFO | tensorboard: False +2024-09-02,17:00:44 | INFO | tensorboard_path: +2024-09-02,17:00:44 | INFO | torchcompile: False +2024-09-02,17:00:44 | INFO | torchscript: False +2024-09-02,17:00:44 | INFO | trace: False +2024-09-02,17:00:44 | INFO | train_data: /project/deemreason/junteng/Vision4Math/csv_data/plotqa_train_v2.csv +2024-09-02,17:00:44 | INFO | train_data_upsampling_factors: None +2024-09-02,17:00:44 | INFO | train_num_samples: None +2024-09-02,17:00:44 | INFO | use_bn_sync: False +2024-09-02,17:00:44 | INFO | use_bnb_linear: None +2024-09-02,17:00:44 | INFO | val_data: None +2024-09-02,17:00:44 | INFO | val_frequency: 1 +2024-09-02,17:00:44 | INFO | val_num_samples: None +2024-09-02,17:00:44 | INFO | wandb: True +2024-09-02,17:00:44 | INFO | wandb_notes: +2024-09-02,17:00:44 | INFO | wandb_project_name: open-clip--no-hard-sum +2024-09-02,17:00:44 | INFO | warmup: 0 +2024-09-02,17:00:44 | INFO | wd: 0.1 +2024-09-02,17:00:44 | INFO | workers: 4 +2024-09-02,17:00:44 | INFO | world_size: 4 +2024-09-02,17:00:44 | INFO | zeroshot_frequency: 2 +2024-09-02,17:01:05 | INFO | Start epoch 0 +2024-09-02,17:01:17 | INFO | Train Epoch: 0 [ 256/3655823 (0%)] Data (t): 1.754 Batch (t): 12.248, 20.9012/s, 5.22530/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 5.2595 (5.2595) Loss: 5.2595 (5.2595) +2024-09-02,17:01:56 | INFO | Train Epoch: 0 [ 25856/3655823 (1%)] Data (t): 0.000 Batch (t): 0.380, 677.052/s, 169.263/s/gpu LR: 0.000001 Logit Scale: 99.997 Contrastive_loss: 2.9758 (4.1176) Loss: 2.9758 (4.1176) +2024-09-02,17:02:33 | INFO | Train Epoch: 0 [ 51456/3655823 (1%)] Data (t): 0.000 Batch (t): 0.378, 676.905/s, 169.226/s/gpu LR: 0.000001 Logit Scale: 99.997 Contrastive_loss: 2.4698 (3.5684) Loss: 2.4698 (3.5684) +2024-09-02,17:03:11 | INFO | Train Epoch: 0 [ 77056/3655823 (2%)] Data (t): 0.000 Batch (t): 0.379, 676.556/s, 169.139/s/gpu LR: 0.000001 Logit Scale: 99.998 Contrastive_loss: 2.1856 (3.2227) Loss: 2.1856 (3.2227) +2024-09-02,17:03:49 | INFO | Train Epoch: 0 [ 102656/3655823 (3%)] Data (t): 0.000 Batch (t): 0.379, 676.309/s, 169.077/s/gpu LR: 0.000001 Logit Scale: 99.998 Contrastive_loss: 2.1842 (3.0150) Loss: 2.1842 (3.0150) +2024-09-02,17:04:27 | INFO | Train Epoch: 0 [ 128256/3655823 (4%)] Data (t): 0.000 Batch (t): 0.378, 676.523/s, 169.131/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 2.0812 (2.8593) Loss: 2.0812 (2.8593) +2024-09-02,17:05:05 | INFO | Train Epoch: 0 [ 153856/3655823 (4%)] Data (t): 0.000 Batch (t): 0.379, 676.623/s, 169.156/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 2.1902 (2.7638) Loss: 2.1902 (2.7638) +2024-09-02,17:05:43 | INFO | Train Epoch: 0 [ 179456/3655823 (5%)] Data (t): 0.000 Batch (t): 0.379, 675.999/s, 169.000/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 2.1777 (2.6905) Loss: 2.1777 (2.6905) +2024-09-02,17:06:20 | INFO | Train Epoch: 0 [ 205056/3655823 (6%)] Data (t): 0.000 Batch (t): 0.379, 677.692/s, 169.423/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.8749 (2.5999) Loss: 1.8749 (2.5999) +2024-09-02,17:06:58 | INFO | Train Epoch: 0 [ 230656/3655823 (6%)] Data (t): 0.000 Batch (t): 0.379, 675.463/s, 168.866/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.8951 (2.5294) Loss: 1.8951 (2.5294) +2024-09-02,17:07:36 | INFO | Train Epoch: 0 [ 256256/3655823 (7%)] Data (t): 0.000 Batch (t): 0.379, 676.451/s, 169.113/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.9112 (2.4732) Loss: 1.9112 (2.4732) +2024-09-02,17:08:14 | INFO | Train Epoch: 0 [ 281856/3655823 (8%)] Data (t): 0.000 Batch (t): 0.381, 677.074/s, 169.268/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.7566 (2.4135) Loss: 1.7566 (2.4135) +2024-09-02,17:08:52 | INFO | Train Epoch: 0 [ 307456/3655823 (8%)] Data (t): 0.000 Batch (t): 0.381, 676.286/s, 169.071/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.7460 (2.3621) Loss: 1.7460 (2.3621) +2024-09-02,17:09:30 | INFO | Train Epoch: 0 [ 333056/3655823 (9%)] Data (t): 0.000 Batch (t): 0.379, 676.268/s, 169.067/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.6631 (2.3122) Loss: 1.6631 (2.3122) +2024-09-02,17:10:08 | INFO | Train Epoch: 0 [ 358656/3655823 (10%)] Data (t): 0.000 Batch (t): 0.378, 677.603/s, 169.401/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5462 (2.2611) Loss: 1.5462 (2.2611) +2024-09-02,17:10:46 | INFO | Train Epoch: 0 [ 384256/3655823 (11%)] Data (t): 0.000 Batch (t): 0.378, 675.742/s, 168.935/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.6793 (2.2248) Loss: 1.6793 (2.2248) +2024-09-02,17:11:24 | INFO | Train Epoch: 0 [ 409856/3655823 (11%)] Data (t): 0.000 Batch (t): 0.379, 677.675/s, 169.419/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.7648 (2.1977) Loss: 1.7648 (2.1977) +2024-09-02,17:12:02 | INFO | Train Epoch: 0 [ 435456/3655823 (12%)] Data (t): 0.000 Batch (t): 0.378, 676.553/s, 169.138/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.6476 (2.1672) Loss: 1.6476 (2.1672) +2024-09-02,17:12:39 | INFO | Train Epoch: 0 [ 461056/3655823 (13%)] Data (t): 0.000 Batch (t): 0.379, 676.094/s, 169.023/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.6472 (2.1398) Loss: 1.6472 (2.1398) +2024-09-02,17:13:17 | INFO | Train Epoch: 0 [ 486656/3655823 (13%)] Data (t): 0.000 Batch (t): 0.378, 676.813/s, 169.203/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.8936 (2.1275) Loss: 1.8936 (2.1275) +2024-09-02,17:13:55 | INFO | Train Epoch: 0 [ 512256/3655823 (14%)] Data (t): 0.000 Batch (t): 0.379, 677.019/s, 169.255/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.6228 (2.1034) Loss: 1.6228 (2.1034) +2024-09-02,17:14:33 | INFO | Train Epoch: 0 [ 537856/3655823 (15%)] Data (t): 0.000 Batch (t): 0.379, 676.537/s, 169.134/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.7543 (2.0876) Loss: 1.7543 (2.0876) +2024-09-02,17:15:11 | INFO | Train Epoch: 0 [ 563456/3655823 (15%)] Data (t): 0.000 Batch (t): 0.379, 675.239/s, 168.810/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.7627 (2.0735) Loss: 1.7627 (2.0735) +2024-09-02,17:15:49 | INFO | Train Epoch: 0 [ 589056/3655823 (16%)] Data (t): 0.000 Batch (t): 0.381, 676.143/s, 169.036/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.6217 (2.0546) Loss: 1.6217 (2.0546) +2024-09-02,17:16:27 | INFO | Train Epoch: 0 [ 614656/3655823 (17%)] Data (t): 0.000 Batch (t): 0.381, 677.080/s, 169.270/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5036 (2.0326) Loss: 1.5036 (2.0326) +2024-09-02,17:17:05 | INFO | Train Epoch: 0 [ 640256/3655823 (18%)] Data (t): 0.000 Batch (t): 0.379, 676.838/s, 169.209/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5986 (2.0159) Loss: 1.5986 (2.0159) +2024-09-02,17:17:43 | INFO | Train Epoch: 0 [ 665856/3655823 (18%)] Data (t): 0.000 Batch (t): 0.379, 675.593/s, 168.898/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.8321 (2.0091) Loss: 1.8321 (2.0091) +2024-09-02,17:18:21 | INFO | Train Epoch: 0 [ 691456/3655823 (19%)] Data (t): 0.000 Batch (t): 0.379, 675.721/s, 168.930/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.7241 (1.9989) Loss: 1.7241 (1.9989) +2024-09-02,17:18:59 | INFO | Train Epoch: 0 [ 717056/3655823 (20%)] Data (t): 0.000 Batch (t): 0.379, 675.916/s, 168.979/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5365 (1.9830) Loss: 1.5365 (1.9830) +2024-09-02,17:19:36 | INFO | Train Epoch: 0 [ 742656/3655823 (20%)] Data (t): 0.000 Batch (t): 0.378, 676.591/s, 169.148/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.4757 (1.9661) Loss: 1.4757 (1.9661) +2024-09-02,17:20:14 | INFO | Train Epoch: 0 [ 768256/3655823 (21%)] Data (t): 0.000 Batch (t): 0.378, 676.128/s, 169.032/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.7370 (1.9587) Loss: 1.7370 (1.9587) +2024-09-02,17:20:52 | INFO | Train Epoch: 0 [ 793856/3655823 (22%)] Data (t): 0.000 Batch (t): 0.378, 675.958/s, 168.989/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.5011 (1.9444) Loss: 1.5011 (1.9444) +2024-09-02,17:21:30 | INFO | Train Epoch: 0 [ 819456/3655823 (22%)] Data (t): 0.000 Batch (t): 0.378, 677.304/s, 169.326/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.7089 (1.9372) Loss: 1.7089 (1.9372) +2024-09-02,17:22:08 | INFO | Train Epoch: 0 [ 845056/3655823 (23%)] Data (t): 0.000 Batch (t): 0.378, 677.401/s, 169.350/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.7800 (1.9326) Loss: 1.7800 (1.9326) +2024-09-02,17:22:46 | INFO | Train Epoch: 0 [ 870656/3655823 (24%)] Data (t): 0.000 Batch (t): 0.378, 676.107/s, 169.027/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4916 (1.9200) Loss: 1.4916 (1.9200) +2024-09-02,17:23:23 | INFO | Train Epoch: 0 [ 896256/3655823 (25%)] Data (t): 0.000 Batch (t): 0.378, 675.706/s, 168.927/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3761 (1.9049) Loss: 1.3761 (1.9049) +2024-09-02,17:24:02 | INFO | Train Epoch: 0 [ 921856/3655823 (25%)] Data (t): 0.000 Batch (t): 0.386, 677.235/s, 169.309/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3380 (1.8896) Loss: 1.3380 (1.8896) +2024-09-02,17:24:40 | INFO | Train Epoch: 0 [ 947456/3655823 (26%)] Data (t): 0.000 Batch (t): 0.378, 677.327/s, 169.332/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.7603 (1.8862) Loss: 1.7603 (1.8862) +2024-09-02,17:25:18 | INFO | Train Epoch: 0 [ 973056/3655823 (27%)] Data (t): 0.000 Batch (t): 0.378, 676.194/s, 169.049/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3942 (1.8736) Loss: 1.3942 (1.8736) +2024-09-02,17:25:56 | INFO | Train Epoch: 0 [ 998656/3655823 (27%)] Data (t): 0.000 Batch (t): 0.379, 675.893/s, 168.973/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.7801 (1.8712) Loss: 1.7801 (1.8712) +2024-09-02,17:26:34 | INFO | Train Epoch: 0 [1024256/3655823 (28%)] Data (t): 0.000 Batch (t): 0.379, 676.507/s, 169.127/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5607 (1.8637) Loss: 1.5607 (1.8637) +2024-09-02,17:27:11 | INFO | Train Epoch: 0 [1049856/3655823 (29%)] Data (t): 0.000 Batch (t): 0.379, 675.506/s, 168.877/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5423 (1.8560) Loss: 1.5423 (1.8560) +2024-09-02,17:27:49 | INFO | Train Epoch: 0 [1075456/3655823 (29%)] Data (t): 0.000 Batch (t): 0.379, 676.067/s, 169.017/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5785 (1.8496) Loss: 1.5785 (1.8496) +2024-09-02,17:28:27 | INFO | Train Epoch: 0 [1101056/3655823 (30%)] Data (t): 0.000 Batch (t): 0.379, 675.764/s, 168.941/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5327 (1.8424) Loss: 1.5327 (1.8424) +2024-09-02,17:29:05 | INFO | Train Epoch: 0 [1126656/3655823 (31%)] Data (t): 0.000 Batch (t): 0.379, 675.960/s, 168.990/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5360 (1.8355) Loss: 1.5360 (1.8355) +2024-09-02,17:29:43 | INFO | Train Epoch: 0 [1152256/3655823 (32%)] Data (t): 0.000 Batch (t): 0.379, 676.676/s, 169.169/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5511 (1.8294) Loss: 1.5511 (1.8294) +2024-09-02,17:30:21 | INFO | Train Epoch: 0 [1177856/3655823 (32%)] Data (t): 0.000 Batch (t): 0.379, 676.297/s, 169.074/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.5411 (1.8232) Loss: 1.5411 (1.8232) +2024-09-02,17:30:59 | INFO | Train Epoch: 0 [1203456/3655823 (33%)] Data (t): 0.000 Batch (t): 0.379, 676.870/s, 169.217/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.6671 (1.8200) Loss: 1.6671 (1.8200) +2024-09-02,17:31:37 | INFO | Train Epoch: 0 [1229056/3655823 (34%)] Data (t): 0.000 Batch (t): 0.387, 675.279/s, 168.820/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5602 (1.8147) Loss: 1.5602 (1.8147) +2024-09-02,17:32:15 | INFO | Train Epoch: 0 [1254656/3655823 (34%)] Data (t): 0.000 Batch (t): 0.379, 676.180/s, 169.045/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3065 (1.8045) Loss: 1.3065 (1.8045) +2024-09-02,17:32:53 | INFO | Train Epoch: 0 [1280256/3655823 (35%)] Data (t): 0.000 Batch (t): 0.379, 675.648/s, 168.912/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.6546 (1.8016) Loss: 1.6546 (1.8016) +2024-09-02,17:33:31 | INFO | Train Epoch: 0 [1305856/3655823 (36%)] Data (t): 0.000 Batch (t): 0.379, 676.430/s, 169.108/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4001 (1.7939) Loss: 1.4001 (1.7939) +2024-09-02,17:34:09 | INFO | Train Epoch: 0 [1331456/3655823 (36%)] Data (t): 0.000 Batch (t): 0.379, 677.057/s, 169.264/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.6090 (1.7904) Loss: 1.6090 (1.7904) +2024-09-02,17:34:47 | INFO | Train Epoch: 0 [1357056/3655823 (37%)] Data (t): 0.000 Batch (t): 0.379, 675.635/s, 168.909/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3077 (1.7814) Loss: 1.3077 (1.7814) +2024-09-02,17:35:25 | INFO | Train Epoch: 0 [1382656/3655823 (38%)] Data (t): 0.000 Batch (t): 0.378, 676.305/s, 169.076/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5069 (1.7764) Loss: 1.5069 (1.7764) +2024-09-02,17:36:02 | INFO | Train Epoch: 0 [1408256/3655823 (39%)] Data (t): 0.000 Batch (t): 0.379, 676.620/s, 169.155/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5540 (1.7725) Loss: 1.5540 (1.7725) +2024-09-02,17:36:40 | INFO | Train Epoch: 0 [1433856/3655823 (39%)] Data (t): 0.000 Batch (t): 0.378, 676.559/s, 169.140/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.6383 (1.7701) Loss: 1.6383 (1.7701) +2024-09-02,17:37:18 | INFO | Train Epoch: 0 [1459456/3655823 (40%)] Data (t): 0.000 Batch (t): 0.378, 676.126/s, 169.031/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5248 (1.7659) Loss: 1.5248 (1.7659) +2024-09-02,17:37:56 | INFO | Train Epoch: 0 [1485056/3655823 (41%)] Data (t): 0.000 Batch (t): 0.378, 675.003/s, 168.751/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5814 (1.7628) Loss: 1.5814 (1.7628) +2024-09-02,17:38:34 | INFO | Train Epoch: 0 [1510656/3655823 (41%)] Data (t): 0.000 Batch (t): 0.378, 677.089/s, 169.272/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4005 (1.7567) Loss: 1.4005 (1.7567) +2024-09-02,17:39:12 | INFO | Train Epoch: 0 [1536256/3655823 (42%)] Data (t): 0.000 Batch (t): 0.381, 677.374/s, 169.343/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.1673 (1.7471) Loss: 1.1673 (1.7471) +2024-09-02,17:39:50 | INFO | Train Epoch: 0 [1561856/3655823 (43%)] Data (t): 0.000 Batch (t): 0.385, 675.953/s, 168.988/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3990 (1.7414) Loss: 1.3990 (1.7414) +2024-09-02,17:40:28 | INFO | Train Epoch: 0 [1587456/3655823 (43%)] Data (t): 0.000 Batch (t): 0.379, 675.146/s, 168.787/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.4165 (1.7363) Loss: 1.4165 (1.7363) +2024-09-02,17:41:06 | INFO | Train Epoch: 0 [1613056/3655823 (44%)] Data (t): 0.000 Batch (t): 0.378, 677.154/s, 169.288/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3328 (1.7300) Loss: 1.3328 (1.7300) +2024-09-02,17:41:44 | INFO | Train Epoch: 0 [1638656/3655823 (45%)] Data (t): 0.000 Batch (t): 0.379, 675.483/s, 168.871/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5460 (1.7271) Loss: 1.5460 (1.7271) +2024-09-02,17:42:22 | INFO | Train Epoch: 0 [1664256/3655823 (46%)] Data (t): 0.000 Batch (t): 0.378, 677.595/s, 169.399/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3846 (1.7220) Loss: 1.3846 (1.7220) +2024-09-02,17:43:00 | INFO | Train Epoch: 0 [1689856/3655823 (46%)] Data (t): 0.000 Batch (t): 0.378, 677.483/s, 169.371/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.7580 (1.7225) Loss: 1.7580 (1.7225) +2024-09-02,17:43:37 | INFO | Train Epoch: 0 [1715456/3655823 (47%)] Data (t): 0.000 Batch (t): 0.378, 677.225/s, 169.306/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3731 (1.7174) Loss: 1.3731 (1.7174) +2024-09-02,17:44:15 | INFO | Train Epoch: 0 [1741056/3655823 (48%)] Data (t): 0.000 Batch (t): 0.378, 676.380/s, 169.095/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2451 (1.7105) Loss: 1.2451 (1.7105) +2024-09-02,17:44:53 | INFO | Train Epoch: 0 [1766656/3655823 (48%)] Data (t): 0.000 Batch (t): 0.378, 676.181/s, 169.045/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5197 (1.7078) Loss: 1.5197 (1.7078) +2024-09-02,17:45:31 | INFO | Train Epoch: 0 [1792256/3655823 (49%)] Data (t): 0.000 Batch (t): 0.379, 676.012/s, 169.003/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2161 (1.7009) Loss: 1.2161 (1.7009) +2024-09-02,17:46:09 | INFO | Train Epoch: 0 [1817856/3655823 (50%)] Data (t): 0.000 Batch (t): 0.379, 677.011/s, 169.253/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3967 (1.6966) Loss: 1.3967 (1.6966) +2024-09-02,17:46:47 | INFO | Train Epoch: 0 [1843456/3655823 (50%)] Data (t): 0.000 Batch (t): 0.380, 675.726/s, 168.931/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.6459 (1.6959) Loss: 1.6459 (1.6959) +2024-09-02,17:47:25 | INFO | Train Epoch: 0 [1869056/3655823 (51%)] Data (t): 0.000 Batch (t): 0.383, 676.035/s, 169.009/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3637 (1.6915) Loss: 1.3637 (1.6915) +2024-09-02,17:48:03 | INFO | Train Epoch: 0 [1894656/3655823 (52%)] Data (t): 0.000 Batch (t): 0.378, 677.644/s, 169.411/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.3728 (1.6872) Loss: 1.3728 (1.6872) +2024-09-02,17:48:41 | INFO | Train Epoch: 0 [1920256/3655823 (53%)] Data (t): 0.000 Batch (t): 0.378, 678.242/s, 169.560/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2112 (1.6809) Loss: 1.2112 (1.6809) +2024-09-02,17:49:19 | INFO | Train Epoch: 0 [1945856/3655823 (53%)] Data (t): 0.000 Batch (t): 0.378, 677.097/s, 169.274/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4954 (1.6785) Loss: 1.4954 (1.6785) +2024-09-02,17:49:56 | INFO | Train Epoch: 0 [1971456/3655823 (54%)] Data (t): 0.000 Batch (t): 0.378, 675.711/s, 168.928/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5867 (1.6774) Loss: 1.5867 (1.6774) +2024-09-02,17:50:34 | INFO | Train Epoch: 0 [1997056/3655823 (55%)] Data (t): 0.000 Batch (t): 0.378, 675.975/s, 168.994/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3394 (1.6731) Loss: 1.3394 (1.6731) +2024-09-02,17:51:12 | INFO | Train Epoch: 0 [2022656/3655823 (55%)] Data (t): 0.000 Batch (t): 0.378, 677.547/s, 169.387/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2307 (1.6675) Loss: 1.2307 (1.6675) +2024-09-02,17:51:50 | INFO | Train Epoch: 0 [2048256/3655823 (56%)] Data (t): 0.000 Batch (t): 0.378, 677.933/s, 169.483/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5318 (1.6659) Loss: 1.5318 (1.6659) +2024-09-02,17:52:28 | INFO | Train Epoch: 0 [2073856/3655823 (57%)] Data (t): 0.000 Batch (t): 0.378, 675.782/s, 168.946/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2522 (1.6608) Loss: 1.2522 (1.6608) +2024-09-02,17:53:06 | INFO | Train Epoch: 0 [2099456/3655823 (57%)] Data (t): 0.000 Batch (t): 0.378, 676.487/s, 169.122/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3010 (1.6565) Loss: 1.3010 (1.6565) +2024-09-02,17:53:43 | INFO | Train Epoch: 0 [2125056/3655823 (58%)] Data (t): 0.000 Batch (t): 0.378, 677.672/s, 169.418/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3918 (1.6533) Loss: 1.3918 (1.6533) +2024-09-02,17:54:22 | INFO | Train Epoch: 0 [2150656/3655823 (59%)] Data (t): 0.000 Batch (t): 0.381, 676.016/s, 169.004/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3925 (1.6503) Loss: 1.3925 (1.6503) +2024-09-02,17:55:00 | INFO | Train Epoch: 0 [2176256/3655823 (60%)] Data (t): 0.000 Batch (t): 0.384, 676.428/s, 169.107/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4327 (1.6477) Loss: 1.4327 (1.6477) +2024-09-02,17:55:38 | INFO | Train Epoch: 0 [2201856/3655823 (60%)] Data (t): 0.000 Batch (t): 0.378, 676.436/s, 169.109/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.6019 (1.6472) Loss: 1.6019 (1.6472) +2024-09-02,17:56:16 | INFO | Train Epoch: 0 [2227456/3655823 (61%)] Data (t): 0.000 Batch (t): 0.378, 677.053/s, 169.263/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4854 (1.6454) Loss: 1.4854 (1.6454) +2024-09-02,17:56:54 | INFO | Train Epoch: 0 [2253056/3655823 (62%)] Data (t): 0.000 Batch (t): 0.378, 676.850/s, 169.212/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.3348 (1.6419) Loss: 1.3348 (1.6419) +2024-09-02,17:57:31 | INFO | Train Epoch: 0 [2278656/3655823 (62%)] Data (t): 0.000 Batch (t): 0.379, 677.696/s, 169.424/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.3502 (1.6386) Loss: 1.3502 (1.6386) +2024-09-02,17:58:09 | INFO | Train Epoch: 0 [2304256/3655823 (63%)] Data (t): 0.000 Batch (t): 0.379, 675.946/s, 168.986/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.1628 (1.6334) Loss: 1.1628 (1.6334) +2024-09-02,17:58:47 | INFO | Train Epoch: 0 [2329856/3655823 (64%)] Data (t): 0.000 Batch (t): 0.379, 676.944/s, 169.236/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3943 (1.6308) Loss: 1.3943 (1.6308) +2024-09-02,17:59:25 | INFO | Train Epoch: 0 [2355456/3655823 (64%)] Data (t): 0.000 Batch (t): 0.378, 676.151/s, 169.038/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5322 (1.6298) Loss: 1.5322 (1.6298) +2024-09-02,18:00:03 | INFO | Train Epoch: 0 [2381056/3655823 (65%)] Data (t): 0.000 Batch (t): 0.379, 676.359/s, 169.090/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3759 (1.6271) Loss: 1.3759 (1.6271) +2024-09-02,18:00:41 | INFO | Train Epoch: 0 [2406656/3655823 (66%)] Data (t): 0.000 Batch (t): 0.378, 676.859/s, 169.215/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4201 (1.6249) Loss: 1.4201 (1.6249) +2024-09-02,18:01:18 | INFO | Train Epoch: 0 [2432256/3655823 (67%)] Data (t): 0.000 Batch (t): 0.379, 676.175/s, 169.044/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.5969 (1.6246) Loss: 1.5969 (1.6246) +2024-09-02,18:01:57 | INFO | Train Epoch: 0 [2457856/3655823 (67%)] Data (t): 0.000 Batch (t): 0.380, 677.101/s, 169.275/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.3824 (1.6221) Loss: 1.3824 (1.6221) +2024-09-02,18:02:35 | INFO | Train Epoch: 0 [2483456/3655823 (68%)] Data (t): 0.000 Batch (t): 0.383, 676.666/s, 169.167/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.7469 (1.6234) Loss: 1.7469 (1.6234) +2024-09-02,18:03:13 | INFO | Train Epoch: 0 [2509056/3655823 (69%)] Data (t): 0.000 Batch (t): 0.378, 676.162/s, 169.041/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3684 (1.6208) Loss: 1.3684 (1.6208) +2024-09-02,18:03:50 | INFO | Train Epoch: 0 [2534656/3655823 (69%)] Data (t): 0.000 Batch (t): 0.378, 676.999/s, 169.250/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.6684 (1.6213) Loss: 1.6684 (1.6213) +2024-09-02,18:04:28 | INFO | Train Epoch: 0 [2560256/3655823 (70%)] Data (t): 0.000 Batch (t): 0.378, 677.823/s, 169.456/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4214 (1.6193) Loss: 1.4214 (1.6193) +2024-09-02,18:05:06 | INFO | Train Epoch: 0 [2585856/3655823 (71%)] Data (t): 0.000 Batch (t): 0.378, 677.033/s, 169.258/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3613 (1.6168) Loss: 1.3613 (1.6168) +2024-09-02,18:05:44 | INFO | Train Epoch: 0 [2611456/3655823 (71%)] Data (t): 0.000 Batch (t): 0.378, 676.606/s, 169.151/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4383 (1.6150) Loss: 1.4383 (1.6150) +2024-09-02,18:06:22 | INFO | Train Epoch: 0 [2637056/3655823 (72%)] Data (t): 0.000 Batch (t): 0.378, 675.724/s, 168.931/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4383 (1.6133) Loss: 1.4383 (1.6133) +2024-09-02,18:07:00 | INFO | Train Epoch: 0 [2662656/3655823 (73%)] Data (t): 0.000 Batch (t): 0.378, 676.319/s, 169.080/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.5502 (1.6127) Loss: 1.5502 (1.6127) +2024-09-02,18:07:37 | INFO | Train Epoch: 0 [2688256/3655823 (74%)] Data (t): 0.000 Batch (t): 0.378, 676.899/s, 169.225/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4213 (1.6109) Loss: 1.4213 (1.6109) +2024-09-02,18:08:15 | INFO | Train Epoch: 0 [2713856/3655823 (74%)] Data (t): 0.000 Batch (t): 0.378, 677.534/s, 169.383/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.2989 (1.6080) Loss: 1.2989 (1.6080) +2024-09-02,18:08:53 | INFO | Train Epoch: 0 [2739456/3655823 (75%)] Data (t): 0.000 Batch (t): 0.379, 677.148/s, 169.287/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.5259 (1.6072) Loss: 1.5259 (1.6072) +2024-09-02,18:09:31 | INFO | Train Epoch: 0 [2765056/3655823 (76%)] Data (t): 0.000 Batch (t): 0.381, 677.541/s, 169.385/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.5917 (1.6071) Loss: 1.5917 (1.6071) +2024-09-02,18:10:10 | INFO | Train Epoch: 0 [2790656/3655823 (76%)] Data (t): 0.000 Batch (t): 0.384, 677.485/s, 169.371/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.5645 (1.6067) Loss: 1.5645 (1.6067) +2024-09-02,18:10:47 | INFO | Train Epoch: 0 [2816256/3655823 (77%)] Data (t): 0.000 Batch (t): 0.378, 677.393/s, 169.348/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.7347 (1.6079) Loss: 1.7347 (1.6079) +2024-09-02,18:11:25 | INFO | Train Epoch: 0 [2841856/3655823 (78%)] Data (t): 0.000 Batch (t): 0.378, 676.566/s, 169.142/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.6727 (1.6084) Loss: 1.6727 (1.6084) +2024-09-02,18:12:03 | INFO | Train Epoch: 0 [2867456/3655823 (78%)] Data (t): 0.000 Batch (t): 0.378, 676.489/s, 169.122/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3092 (1.6058) Loss: 1.3092 (1.6058) +2024-09-02,18:12:41 | INFO | Train Epoch: 0 [2893056/3655823 (79%)] Data (t): 0.000 Batch (t): 0.378, 677.232/s, 169.308/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4066 (1.6040) Loss: 1.4066 (1.6040) +2024-09-02,18:13:19 | INFO | Train Epoch: 0 [2918656/3655823 (80%)] Data (t): 0.000 Batch (t): 0.378, 676.959/s, 169.240/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5859 (1.6039) Loss: 1.5859 (1.6039) +2024-09-02,18:13:57 | INFO | Train Epoch: 0 [2944256/3655823 (81%)] Data (t): 0.000 Batch (t): 0.378, 676.232/s, 169.058/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4356 (1.6024) Loss: 1.4356 (1.6024) +2024-09-02,18:14:34 | INFO | Train Epoch: 0 [2969856/3655823 (81%)] Data (t): 0.000 Batch (t): 0.378, 677.882/s, 169.471/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3906 (1.6006) Loss: 1.3906 (1.6006) +2024-09-02,18:15:12 | INFO | Train Epoch: 0 [2995456/3655823 (82%)] Data (t): 0.000 Batch (t): 0.378, 677.295/s, 169.324/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5649 (1.6003) Loss: 1.5649 (1.6003) +2024-09-02,18:15:50 | INFO | Train Epoch: 0 [3021056/3655823 (83%)] Data (t): 0.000 Batch (t): 0.379, 674.552/s, 168.638/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5406 (1.5998) Loss: 1.5406 (1.5998) +2024-09-02,18:16:28 | INFO | Train Epoch: 0 [3046656/3655823 (83%)] Data (t): 0.000 Batch (t): 0.379, 675.588/s, 168.897/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3607 (1.5978) Loss: 1.3607 (1.5978) +2024-09-02,18:17:06 | INFO | Train Epoch: 0 [3072256/3655823 (84%)] Data (t): 0.000 Batch (t): 0.379, 675.415/s, 168.854/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4144 (1.5963) Loss: 1.4144 (1.5963) +2024-09-02,18:17:45 | INFO | Train Epoch: 0 [3097856/3655823 (85%)] Data (t): 0.000 Batch (t): 0.387, 676.759/s, 169.190/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3620 (1.5944) Loss: 1.3620 (1.5944) +2024-09-02,18:18:22 | INFO | Train Epoch: 0 [3123456/3655823 (85%)] Data (t): 0.000 Batch (t): 0.379, 677.187/s, 169.297/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.1696 (1.5909) Loss: 1.1696 (1.5909) +2024-09-02,18:19:00 | INFO | Train Epoch: 0 [3149056/3655823 (86%)] Data (t): 0.000 Batch (t): 0.379, 676.087/s, 169.022/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2539 (1.5882) Loss: 1.2539 (1.5882) +2024-09-02,18:19:38 | INFO | Train Epoch: 0 [3174656/3655823 (87%)] Data (t): 0.000 Batch (t): 0.379, 676.190/s, 169.048/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5336 (1.5878) Loss: 1.5336 (1.5878) +2024-09-02,18:20:16 | INFO | Train Epoch: 0 [3200256/3655823 (88%)] Data (t): 0.000 Batch (t): 0.379, 675.661/s, 168.915/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5062 (1.5871) Loss: 1.5062 (1.5871) +2024-09-02,18:20:54 | INFO | Train Epoch: 0 [3225856/3655823 (88%)] Data (t): 0.000 Batch (t): 0.378, 677.067/s, 169.267/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3511 (1.5853) Loss: 1.3511 (1.5853) +2024-09-02,18:21:32 | INFO | Train Epoch: 0 [3251456/3655823 (89%)] Data (t): 0.000 Batch (t): 0.378, 675.913/s, 168.978/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3807 (1.5837) Loss: 1.3807 (1.5837) +2024-09-02,18:22:09 | INFO | Train Epoch: 0 [3277056/3655823 (90%)] Data (t): 0.000 Batch (t): 0.378, 676.783/s, 169.196/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4780 (1.5829) Loss: 1.4780 (1.5829) +2024-09-02,18:22:47 | INFO | Train Epoch: 0 [3302656/3655823 (90%)] Data (t): 0.000 Batch (t): 0.378, 676.323/s, 169.081/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.6936 (1.5837) Loss: 1.6936 (1.5837) +2024-09-02,18:23:25 | INFO | Train Epoch: 0 [3328256/3655823 (91%)] Data (t): 0.000 Batch (t): 0.378, 676.284/s, 169.071/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3626 (1.5820) Loss: 1.3626 (1.5820) +2024-09-02,18:24:03 | INFO | Train Epoch: 0 [3353856/3655823 (92%)] Data (t): 0.000 Batch (t): 0.379, 675.473/s, 168.868/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2329 (1.5794) Loss: 1.2329 (1.5794) +2024-09-02,18:24:41 | INFO | Train Epoch: 0 [3379456/3655823 (92%)] Data (t): 0.000 Batch (t): 0.379, 676.611/s, 169.153/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2383 (1.5768) Loss: 1.2383 (1.5768) +2024-09-02,18:25:19 | INFO | Train Epoch: 0 [3405056/3655823 (93%)] Data (t): 0.000 Batch (t): 0.380, 676.167/s, 169.042/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3239 (1.5749) Loss: 1.3239 (1.5749) +2024-09-02,18:25:57 | INFO | Train Epoch: 0 [3430656/3655823 (94%)] Data (t): 0.000 Batch (t): 0.383, 674.988/s, 168.747/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3073 (1.5729) Loss: 1.3073 (1.5729) +2024-09-02,18:26:35 | INFO | Train Epoch: 0 [3456256/3655823 (95%)] Data (t): 0.000 Batch (t): 0.379, 676.414/s, 169.103/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.4496 (1.5720) Loss: 1.4496 (1.5720) +2024-09-02,18:27:13 | INFO | Train Epoch: 0 [3481856/3655823 (95%)] Data (t): 0.000 Batch (t): 0.379, 677.595/s, 169.399/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2305 (1.5695) Loss: 1.2305 (1.5695) +2024-09-02,18:27:51 | INFO | Train Epoch: 0 [3507456/3655823 (96%)] Data (t): 0.000 Batch (t): 0.379, 676.493/s, 169.123/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.6572 (1.5702) Loss: 1.6572 (1.5702) +2024-09-02,18:28:29 | INFO | Train Epoch: 0 [3533056/3655823 (97%)] Data (t): 0.000 Batch (t): 0.379, 676.216/s, 169.054/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3553 (1.5686) Loss: 1.3553 (1.5686) +2024-09-02,18:29:07 | INFO | Train Epoch: 0 [3558656/3655823 (97%)] Data (t): 0.000 Batch (t): 0.379, 676.980/s, 169.245/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3982 (1.5674) Loss: 1.3982 (1.5674) +2024-09-02,18:29:44 | INFO | Train Epoch: 0 [3584256/3655823 (98%)] Data (t): 0.000 Batch (t): 0.379, 675.737/s, 168.934/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.6576 (1.5681) Loss: 1.6576 (1.5681) +2024-09-02,18:30:22 | INFO | Train Epoch: 0 [3609856/3655823 (99%)] Data (t): 0.000 Batch (t): 0.379, 675.732/s, 168.933/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.4003 (1.5669) Loss: 1.4003 (1.5669) +2024-09-02,18:31:00 | INFO | Train Epoch: 0 [3635456/3655823 (99%)] Data (t): 0.000 Batch (t): 0.378, 676.460/s, 169.115/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2079 (1.5644) Loss: 1.2079 (1.5644) +2024-09-02,18:31:30 | INFO | Train Epoch: 0 [3655680/3655823 (100%)] Data (t): 0.001 Batch (t): 0.378, 680.387/s, 170.097/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3869 (1.5631) Loss: 1.3869 (1.5631) +2024-09-02,18:31:38 | INFO | Start epoch 1 +2024-09-02,18:31:40 | INFO | Train Epoch: 1 [ 256/3655823 (0%)] Data (t): 1.318 Batch (t): 1.708, 149.884/s, 37.4710/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3206 (1.3206) Loss: 1.3206 (1.3206) +2024-09-02,18:32:18 | INFO | Train Epoch: 1 [ 25856/3655823 (1%)] Data (t): 0.000 Batch (t): 0.381, 676.180/s, 169.045/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3646 (1.3426) Loss: 1.3646 (1.3426) +2024-09-02,18:32:56 | INFO | Train Epoch: 1 [ 51456/3655823 (1%)] Data (t): 0.000 Batch (t): 0.378, 676.441/s, 169.110/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3668 (1.3507) Loss: 1.3668 (1.3507) +2024-09-02,18:33:34 | INFO | Train Epoch: 1 [ 77056/3655823 (2%)] Data (t): 0.000 Batch (t): 0.383, 676.097/s, 169.024/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5541 (1.4015) Loss: 1.5541 (1.4015) +2024-09-02,18:34:12 | INFO | Train Epoch: 1 [ 102656/3655823 (3%)] Data (t): 0.000 Batch (t): 0.378, 677.952/s, 169.488/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.1364 (1.3485) Loss: 1.1364 (1.3485) +2024-09-02,18:34:50 | INFO | Train Epoch: 1 [ 128256/3655823 (4%)] Data (t): 0.000 Batch (t): 0.379, 676.564/s, 169.141/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4352 (1.3630) Loss: 1.4352 (1.3630) +2024-09-02,18:35:28 | INFO | Train Epoch: 1 [ 153856/3655823 (4%)] Data (t): 0.000 Batch (t): 0.378, 677.698/s, 169.424/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.1965 (1.3392) Loss: 1.1965 (1.3392) +2024-09-02,18:36:05 | INFO | Train Epoch: 1 [ 179456/3655823 (5%)] Data (t): 0.000 Batch (t): 0.379, 676.961/s, 169.240/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2519 (1.3283) Loss: 1.2519 (1.3283) +2024-09-02,18:36:43 | INFO | Train Epoch: 1 [ 205056/3655823 (6%)] Data (t): 0.000 Batch (t): 0.378, 676.971/s, 169.243/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3401 (1.3296) Loss: 1.3401 (1.3296) +2024-09-02,18:37:21 | INFO | Train Epoch: 1 [ 230656/3655823 (6%)] Data (t): 0.000 Batch (t): 0.378, 677.208/s, 169.302/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5534 (1.3520) Loss: 1.5534 (1.3520) +2024-09-02,18:37:59 | INFO | Train Epoch: 1 [ 256256/3655823 (7%)] Data (t): 0.000 Batch (t): 0.378, 677.115/s, 169.279/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3837 (1.3548) Loss: 1.3837 (1.3548) +2024-09-02,18:38:37 | INFO | Train Epoch: 1 [ 281856/3655823 (8%)] Data (t): 0.000 Batch (t): 0.378, 676.050/s, 169.012/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.1810 (1.3404) Loss: 1.1810 (1.3404) +2024-09-02,18:39:15 | INFO | Train Epoch: 1 [ 307456/3655823 (8%)] Data (t): 0.000 Batch (t): 0.378, 676.067/s, 169.017/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.5743 (1.3584) Loss: 1.5743 (1.3584) +2024-09-02,18:39:53 | INFO | Train Epoch: 1 [ 333056/3655823 (9%)] Data (t): 0.000 Batch (t): 0.381, 673.184/s, 168.296/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5649 (1.3731) Loss: 1.5649 (1.3731) +2024-09-02,18:40:31 | INFO | Train Epoch: 1 [ 358656/3655823 (10%)] Data (t): 0.000 Batch (t): 0.378, 677.106/s, 169.276/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3331 (1.3704) Loss: 1.3331 (1.3704) +2024-09-02,18:41:09 | INFO | Train Epoch: 1 [ 384256/3655823 (11%)] Data (t): 0.000 Batch (t): 0.382, 676.942/s, 169.236/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5151 (1.3795) Loss: 1.5151 (1.3795) +2024-09-02,18:41:47 | INFO | Train Epoch: 1 [ 409856/3655823 (11%)] Data (t): 0.000 Batch (t): 0.379, 676.163/s, 169.041/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3618 (1.3784) Loss: 1.3618 (1.3784) +2024-09-02,18:42:25 | INFO | Train Epoch: 1 [ 435456/3655823 (12%)] Data (t): 0.000 Batch (t): 0.378, 678.376/s, 169.594/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3043 (1.3743) Loss: 1.3043 (1.3743) +2024-09-02,18:43:02 | INFO | Train Epoch: 1 [ 461056/3655823 (13%)] Data (t): 0.000 Batch (t): 0.379, 676.245/s, 169.061/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2998 (1.3704) Loss: 1.2998 (1.3704) +2024-09-02,18:43:40 | INFO | Train Epoch: 1 [ 486656/3655823 (13%)] Data (t): 0.000 Batch (t): 0.379, 675.095/s, 168.774/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3554 (1.3697) Loss: 1.3554 (1.3697) +2024-09-02,18:44:18 | INFO | Train Epoch: 1 [ 512256/3655823 (14%)] Data (t): 0.000 Batch (t): 0.378, 677.371/s, 169.343/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.1097 (1.3573) Loss: 1.1097 (1.3573) +2024-09-02,18:44:56 | INFO | Train Epoch: 1 [ 537856/3655823 (15%)] Data (t): 0.000 Batch (t): 0.379, 676.012/s, 169.003/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4220 (1.3602) Loss: 1.4220 (1.3602) +2024-09-02,18:45:34 | INFO | Train Epoch: 1 [ 563456/3655823 (15%)] Data (t): 0.000 Batch (t): 0.379, 676.611/s, 169.153/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2330 (1.3547) Loss: 1.2330 (1.3547) +2024-09-02,18:46:12 | INFO | Train Epoch: 1 [ 589056/3655823 (16%)] Data (t): 0.000 Batch (t): 0.379, 675.899/s, 168.975/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4963 (1.3606) Loss: 1.4963 (1.3606) +2024-09-02,18:46:50 | INFO | Train Epoch: 1 [ 614656/3655823 (17%)] Data (t): 0.000 Batch (t): 0.379, 676.690/s, 169.173/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4153 (1.3628) Loss: 1.4153 (1.3628) +2024-09-02,18:47:28 | INFO | Train Epoch: 1 [ 640256/3655823 (18%)] Data (t): 0.000 Batch (t): 0.381, 676.393/s, 169.098/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2886 (1.3599) Loss: 1.2886 (1.3599) +2024-09-02,18:48:05 | INFO | Train Epoch: 1 [ 665856/3655823 (18%)] Data (t): 0.000 Batch (t): 0.379, 676.566/s, 169.142/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3555 (1.3598) Loss: 1.3555 (1.3598) +2024-09-02,18:48:44 | INFO | Train Epoch: 1 [ 691456/3655823 (19%)] Data (t): 0.000 Batch (t): 0.385, 676.789/s, 169.197/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3131 (1.3581) Loss: 1.3131 (1.3581) +2024-09-02,18:49:22 | INFO | Train Epoch: 1 [ 717056/3655823 (20%)] Data (t): 0.000 Batch (t): 0.379, 676.749/s, 169.187/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4689 (1.3619) Loss: 1.4689 (1.3619) +2024-09-02,18:50:00 | INFO | Train Epoch: 1 [ 742656/3655823 (20%)] Data (t): 0.000 Batch (t): 0.379, 676.051/s, 169.013/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3362 (1.3611) Loss: 1.3362 (1.3611) +2024-09-02,18:50:38 | INFO | Train Epoch: 1 [ 768256/3655823 (21%)] Data (t): 0.000 Batch (t): 0.379, 675.688/s, 168.922/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.0804 (1.3520) Loss: 1.0804 (1.3520) +2024-09-02,18:51:15 | INFO | Train Epoch: 1 [ 793856/3655823 (22%)] Data (t): 0.000 Batch (t): 0.379, 675.277/s, 168.819/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2939 (1.3502) Loss: 1.2939 (1.3502) +2024-09-02,18:51:53 | INFO | Train Epoch: 1 [ 819456/3655823 (22%)] Data (t): 0.000 Batch (t): 0.379, 676.982/s, 169.245/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4794 (1.3541) Loss: 1.4794 (1.3541) +2024-09-02,18:52:31 | INFO | Train Epoch: 1 [ 845056/3655823 (23%)] Data (t): 0.000 Batch (t): 0.379, 676.448/s, 169.112/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2374 (1.3507) Loss: 1.2374 (1.3507) +2024-09-02,18:53:09 | INFO | Train Epoch: 1 [ 870656/3655823 (24%)] Data (t): 0.000 Batch (t): 0.378, 676.251/s, 169.063/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3711 (1.3513) Loss: 1.3711 (1.3513) +2024-09-02,18:53:47 | INFO | Train Epoch: 1 [ 896256/3655823 (25%)] Data (t): 0.000 Batch (t): 0.378, 677.706/s, 169.427/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.0320 (1.3424) Loss: 1.0320 (1.3424) +2024-09-02,18:54:25 | INFO | Train Epoch: 1 [ 921856/3655823 (25%)] Data (t): 0.000 Batch (t): 0.379, 676.659/s, 169.165/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2438 (1.3397) Loss: 1.2438 (1.3397) +2024-09-02,18:55:03 | INFO | Train Epoch: 1 [ 947456/3655823 (26%)] Data (t): 0.000 Batch (t): 0.379, 677.129/s, 169.282/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.0724 (1.3327) Loss: 1.0724 (1.3327) +2024-09-02,18:55:41 | INFO | Train Epoch: 1 [ 973056/3655823 (27%)] Data (t): 0.000 Batch (t): 0.381, 675.555/s, 168.889/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3111 (1.3321) Loss: 1.3111 (1.3321) +2024-09-02,18:56:19 | INFO | Train Epoch: 1 [ 998656/3655823 (27%)] Data (t): 0.000 Batch (t): 0.383, 676.767/s, 169.192/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3787 (1.3333) Loss: 1.3787 (1.3333) +2024-09-02,18:56:57 | INFO | Train Epoch: 1 [1024256/3655823 (28%)] Data (t): 0.000 Batch (t): 0.379, 675.381/s, 168.845/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2580 (1.3315) Loss: 1.2580 (1.3315) +2024-09-02,18:57:35 | INFO | Train Epoch: 1 [1049856/3655823 (29%)] Data (t): 0.000 Batch (t): 0.379, 677.230/s, 169.308/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.1818 (1.3279) Loss: 1.1818 (1.3279) +2024-09-02,18:58:12 | INFO | Train Epoch: 1 [1075456/3655823 (29%)] Data (t): 0.000 Batch (t): 0.379, 674.269/s, 168.567/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.1513 (1.3238) Loss: 1.1513 (1.3238) +2024-09-02,18:58:50 | INFO | Train Epoch: 1 [1101056/3655823 (30%)] Data (t): 0.000 Batch (t): 0.379, 676.215/s, 169.054/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3069 (1.3234) Loss: 1.3069 (1.3234) +2024-09-02,18:59:28 | INFO | Train Epoch: 1 [1126656/3655823 (31%)] Data (t): 0.000 Batch (t): 0.379, 676.861/s, 169.215/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.1968 (1.3206) Loss: 1.1968 (1.3206) +2024-09-02,19:00:06 | INFO | Train Epoch: 1 [1152256/3655823 (32%)] Data (t): 0.000 Batch (t): 0.378, 676.704/s, 169.176/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3242 (1.3207) Loss: 1.3242 (1.3207) +2024-09-02,19:00:44 | INFO | Train Epoch: 1 [1177856/3655823 (32%)] Data (t): 0.000 Batch (t): 0.379, 676.378/s, 169.094/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4719 (1.3239) Loss: 1.4719 (1.3239) +2024-09-02,19:01:22 | INFO | Train Epoch: 1 [1203456/3655823 (33%)] Data (t): 0.000 Batch (t): 0.379, 665.932/s, 166.483/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4825 (1.3272) Loss: 1.4825 (1.3272) +2024-09-02,19:02:00 | INFO | Train Epoch: 1 [1229056/3655823 (34%)] Data (t): 0.000 Batch (t): 0.378, 675.871/s, 168.968/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3335 (1.3273) Loss: 1.3335 (1.3273) +2024-09-02,19:02:37 | INFO | Train Epoch: 1 [1254656/3655823 (34%)] Data (t): 0.000 Batch (t): 0.379, 676.136/s, 169.034/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3935 (1.3287) Loss: 1.3935 (1.3287) +2024-09-02,19:03:16 | INFO | Train Epoch: 1 [1280256/3655823 (35%)] Data (t): 0.000 Batch (t): 0.380, 676.243/s, 169.061/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.1273 (1.3247) Loss: 1.1273 (1.3247) +2024-09-02,19:03:54 | INFO | Train Epoch: 1 [1305856/3655823 (36%)] Data (t): 0.000 Batch (t): 0.383, 676.823/s, 169.206/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3286 (1.3248) Loss: 1.3286 (1.3248) +2024-09-02,19:04:32 | INFO | Train Epoch: 1 [1331456/3655823 (36%)] Data (t): 0.000 Batch (t): 0.378, 675.300/s, 168.825/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2069 (1.3226) Loss: 1.2069 (1.3226) +2024-09-02,19:05:09 | INFO | Train Epoch: 1 [1357056/3655823 (37%)] Data (t): 0.000 Batch (t): 0.378, 677.890/s, 169.472/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5278 (1.3264) Loss: 1.5278 (1.3264) +2024-09-02,19:05:47 | INFO | Train Epoch: 1 [1382656/3655823 (38%)] Data (t): 0.000 Batch (t): 0.378, 677.210/s, 169.302/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3578 (1.3269) Loss: 1.3578 (1.3269) +2024-09-02,19:06:25 | INFO | Train Epoch: 1 [1408256/3655823 (39%)] Data (t): 0.000 Batch (t): 0.378, 677.765/s, 169.441/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5584 (1.3311) Loss: 1.5584 (1.3311) +2024-09-02,19:07:03 | INFO | Train Epoch: 1 [1433856/3655823 (39%)] Data (t): 0.000 Batch (t): 0.378, 676.684/s, 169.171/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.0684 (1.3265) Loss: 1.0684 (1.3265) +2024-09-02,19:07:41 | INFO | Train Epoch: 1 [1459456/3655823 (40%)] Data (t): 0.000 Batch (t): 0.378, 676.138/s, 169.034/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4312 (1.3283) Loss: 1.4312 (1.3283) +2024-09-02,19:08:18 | INFO | Train Epoch: 1 [1485056/3655823 (41%)] Data (t): 0.000 Batch (t): 0.378, 678.006/s, 169.502/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3832 (1.3292) Loss: 1.3832 (1.3292) +2024-09-02,19:08:56 | INFO | Train Epoch: 1 [1510656/3655823 (41%)] Data (t): 0.000 Batch (t): 0.378, 678.038/s, 169.510/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3224 (1.3291) Loss: 1.3224 (1.3291) +2024-09-02,19:09:34 | INFO | Train Epoch: 1 [1536256/3655823 (42%)] Data (t): 0.000 Batch (t): 0.378, 676.621/s, 169.155/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.1920 (1.3268) Loss: 1.1920 (1.3268) +2024-09-02,19:10:12 | INFO | Train Epoch: 1 [1561856/3655823 (43%)] Data (t): 0.000 Batch (t): 0.378, 677.554/s, 169.388/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.1530 (1.3240) Loss: 1.1530 (1.3240) +2024-09-02,19:10:50 | INFO | Train Epoch: 1 [1587456/3655823 (43%)] Data (t): 0.000 Batch (t): 0.380, 678.509/s, 169.627/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2508 (1.3229) Loss: 1.2508 (1.3229) +2024-09-02,19:11:28 | INFO | Train Epoch: 1 [1613056/3655823 (44%)] Data (t): 0.000 Batch (t): 0.378, 678.060/s, 169.515/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2690 (1.3220) Loss: 1.2690 (1.3220) +2024-09-02,19:12:06 | INFO | Train Epoch: 1 [1638656/3655823 (45%)] Data (t): 0.000 Batch (t): 0.384, 677.897/s, 169.474/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3444 (1.3224) Loss: 1.3444 (1.3224) +2024-09-02,19:12:44 | INFO | Train Epoch: 1 [1664256/3655823 (46%)] Data (t): 0.000 Batch (t): 0.378, 678.119/s, 169.530/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3062 (1.3221) Loss: 1.3062 (1.3221) +2024-09-02,19:13:21 | INFO | Train Epoch: 1 [1689856/3655823 (46%)] Data (t): 0.000 Batch (t): 0.378, 678.805/s, 169.701/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2509 (1.3211) Loss: 1.2509 (1.3211) +2024-09-02,19:13:59 | INFO | Train Epoch: 1 [1715456/3655823 (47%)] Data (t): 0.000 Batch (t): 0.378, 677.433/s, 169.358/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.0719 (1.3174) Loss: 1.0719 (1.3174) +2024-09-02,19:14:37 | INFO | Train Epoch: 1 [1741056/3655823 (48%)] Data (t): 0.000 Batch (t): 0.378, 676.230/s, 169.058/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3447 (1.3178) Loss: 1.3447 (1.3178) +2024-09-02,19:15:15 | INFO | Train Epoch: 1 [1766656/3655823 (48%)] Data (t): 0.000 Batch (t): 0.378, 677.450/s, 169.362/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3510 (1.3183) Loss: 1.3510 (1.3183) +2024-09-02,19:15:53 | INFO | Train Epoch: 1 [1792256/3655823 (49%)] Data (t): 0.000 Batch (t): 0.378, 676.687/s, 169.172/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4492 (1.3201) Loss: 1.4492 (1.3201) +2024-09-02,19:16:31 | INFO | Train Epoch: 1 [1817856/3655823 (50%)] Data (t): 0.000 Batch (t): 0.378, 677.659/s, 169.415/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3111 (1.3200) Loss: 1.3111 (1.3200) +2024-09-02,19:17:08 | INFO | Train Epoch: 1 [1843456/3655823 (50%)] Data (t): 0.000 Batch (t): 0.378, 677.358/s, 169.340/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3456 (1.3203) Loss: 1.3456 (1.3203) +2024-09-02,19:17:46 | INFO | Train Epoch: 1 [1869056/3655823 (51%)] Data (t): 0.000 Batch (t): 0.378, 677.352/s, 169.338/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.5031 (1.3228) Loss: 1.5031 (1.3228) +2024-09-02,19:18:24 | INFO | Train Epoch: 1 [1894656/3655823 (52%)] Data (t): 0.000 Batch (t): 0.380, 677.869/s, 169.467/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3079 (1.3226) Loss: 1.3079 (1.3226) +2024-09-02,19:19:02 | INFO | Train Epoch: 1 [1920256/3655823 (53%)] Data (t): 0.000 Batch (t): 0.378, 678.186/s, 169.547/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4192 (1.3239) Loss: 1.4192 (1.3239) +2024-09-02,19:19:40 | INFO | Train Epoch: 1 [1945856/3655823 (53%)] Data (t): 0.000 Batch (t): 0.384, 676.751/s, 169.188/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3721 (1.3245) Loss: 1.3721 (1.3245) +2024-09-02,19:20:18 | INFO | Train Epoch: 1 [1971456/3655823 (54%)] Data (t): 0.000 Batch (t): 0.378, 676.469/s, 169.117/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4978 (1.3267) Loss: 1.4978 (1.3267) +2024-09-02,19:20:56 | INFO | Train Epoch: 1 [1997056/3655823 (55%)] Data (t): 0.000 Batch (t): 0.378, 678.038/s, 169.509/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3179 (1.3266) Loss: 1.3179 (1.3266) +2024-09-02,19:21:34 | INFO | Train Epoch: 1 [2022656/3655823 (55%)] Data (t): 0.000 Batch (t): 0.378, 678.658/s, 169.664/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.6068 (1.3301) Loss: 1.6068 (1.3301) +2024-09-02,19:22:12 | INFO | Train Epoch: 1 [2048256/3655823 (56%)] Data (t): 0.000 Batch (t): 0.378, 678.065/s, 169.516/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.5001 (1.3322) Loss: 1.5001 (1.3322) +2024-09-02,19:22:49 | INFO | Train Epoch: 1 [2073856/3655823 (57%)] Data (t): 0.000 Batch (t): 0.378, 677.912/s, 169.478/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4725 (1.3339) Loss: 1.4725 (1.3339) +2024-09-02,19:23:27 | INFO | Train Epoch: 1 [2099456/3655823 (57%)] Data (t): 0.000 Batch (t): 0.378, 673.685/s, 168.421/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4260 (1.3350) Loss: 1.4260 (1.3350) +2024-09-02,19:24:05 | INFO | Train Epoch: 1 [2125056/3655823 (58%)] Data (t): 0.000 Batch (t): 0.378, 678.333/s, 169.583/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1183 (1.3325) Loss: 1.1183 (1.3325) +2024-09-02,19:24:43 | INFO | Train Epoch: 1 [2150656/3655823 (59%)] Data (t): 0.000 Batch (t): 0.378, 677.766/s, 169.441/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.6450 (1.3361) Loss: 1.6450 (1.3361) +2024-09-02,19:25:21 | INFO | Train Epoch: 1 [2176256/3655823 (60%)] Data (t): 0.000 Batch (t): 0.378, 676.160/s, 169.040/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4786 (1.3378) Loss: 1.4786 (1.3378) +2024-09-02,19:25:59 | INFO | Train Epoch: 1 [2201856/3655823 (60%)] Data (t): 0.000 Batch (t): 0.380, 675.471/s, 168.868/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2688 (1.3370) Loss: 1.2688 (1.3370) +2024-09-02,19:26:36 | INFO | Train Epoch: 1 [2227456/3655823 (61%)] Data (t): 0.000 Batch (t): 0.378, 677.298/s, 169.324/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2953 (1.3365) Loss: 1.2953 (1.3365) +2024-09-02,19:27:15 | INFO | Train Epoch: 1 [2253056/3655823 (62%)] Data (t): 0.000 Batch (t): 0.384, 676.164/s, 169.041/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4181 (1.3374) Loss: 1.4181 (1.3374) +2024-09-02,19:27:53 | INFO | Train Epoch: 1 [2278656/3655823 (62%)] Data (t): 0.000 Batch (t): 0.378, 676.951/s, 169.238/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2973 (1.3370) Loss: 1.2973 (1.3370) +2024-09-02,19:28:30 | INFO | Train Epoch: 1 [2304256/3655823 (63%)] Data (t): 0.000 Batch (t): 0.378, 676.441/s, 169.110/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2935 (1.3365) Loss: 1.2935 (1.3365) +2024-09-02,19:29:08 | INFO | Train Epoch: 1 [2329856/3655823 (64%)] Data (t): 0.000 Batch (t): 0.378, 677.252/s, 169.313/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2943 (1.3361) Loss: 1.2943 (1.3361) +2024-09-02,19:29:46 | INFO | Train Epoch: 1 [2355456/3655823 (64%)] Data (t): 0.000 Batch (t): 0.378, 675.950/s, 168.987/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4487 (1.3373) Loss: 1.4487 (1.3373) +2024-09-02,19:30:24 | INFO | Train Epoch: 1 [2381056/3655823 (65%)] Data (t): 0.000 Batch (t): 0.378, 677.526/s, 169.382/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4694 (1.3387) Loss: 1.4694 (1.3387) +2024-09-02,19:31:02 | INFO | Train Epoch: 1 [2406656/3655823 (66%)] Data (t): 0.000 Batch (t): 0.378, 677.123/s, 169.281/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4633 (1.3400) Loss: 1.4633 (1.3400) +2024-09-02,19:31:40 | INFO | Train Epoch: 1 [2432256/3655823 (67%)] Data (t): 0.000 Batch (t): 0.378, 677.191/s, 169.298/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3039 (1.3396) Loss: 1.3039 (1.3396) +2024-09-02,19:32:17 | INFO | Train Epoch: 1 [2457856/3655823 (67%)] Data (t): 0.000 Batch (t): 0.378, 677.727/s, 169.432/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2910 (1.3391) Loss: 1.2910 (1.3391) +2024-09-02,19:32:55 | INFO | Train Epoch: 1 [2483456/3655823 (68%)] Data (t): 0.000 Batch (t): 0.378, 677.183/s, 169.296/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3252 (1.3390) Loss: 1.3252 (1.3390) +2024-09-02,19:33:33 | INFO | Train Epoch: 1 [2509056/3655823 (69%)] Data (t): 0.000 Batch (t): 0.379, 675.692/s, 168.923/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2814 (1.3384) Loss: 1.2814 (1.3384) +2024-09-02,19:34:11 | INFO | Train Epoch: 1 [2534656/3655823 (69%)] Data (t): 0.000 Batch (t): 0.381, 671.090/s, 167.773/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1405 (1.3364) Loss: 1.1405 (1.3364) +2024-09-02,19:34:50 | INFO | Train Epoch: 1 [2560256/3655823 (70%)] Data (t): 0.000 Batch (t): 0.385, 676.786/s, 169.196/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4687 (1.3377) Loss: 1.4687 (1.3377) +2024-09-02,19:35:27 | INFO | Train Epoch: 1 [2585856/3655823 (71%)] Data (t): 0.000 Batch (t): 0.378, 677.773/s, 169.443/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4841 (1.3391) Loss: 1.4841 (1.3391) +2024-09-02,19:36:05 | INFO | Train Epoch: 1 [2611456/3655823 (71%)] Data (t): 0.000 Batch (t): 0.378, 677.054/s, 169.264/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3552 (1.3393) Loss: 1.3552 (1.3393) +2024-09-02,19:36:43 | INFO | Train Epoch: 1 [2637056/3655823 (72%)] Data (t): 0.000 Batch (t): 0.378, 673.968/s, 168.492/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4770 (1.3406) Loss: 1.4770 (1.3406) +2024-09-02,19:37:21 | INFO | Train Epoch: 1 [2662656/3655823 (73%)] Data (t): 0.000 Batch (t): 0.378, 676.861/s, 169.215/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.5643 (1.3428) Loss: 1.5643 (1.3428) +2024-09-02,19:37:59 | INFO | Train Epoch: 1 [2688256/3655823 (74%)] Data (t): 0.000 Batch (t): 0.378, 676.341/s, 169.085/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.0868 (1.3403) Loss: 1.0868 (1.3403) +2024-09-02,19:38:37 | INFO | Train Epoch: 1 [2713856/3655823 (74%)] Data (t): 0.000 Batch (t): 0.378, 677.789/s, 169.447/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4392 (1.3413) Loss: 1.4392 (1.3413) +2024-09-02,19:39:14 | INFO | Train Epoch: 1 [2739456/3655823 (75%)] Data (t): 0.000 Batch (t): 0.378, 677.783/s, 169.446/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3094 (1.3410) Loss: 1.3094 (1.3410) +2024-09-02,19:39:52 | INFO | Train Epoch: 1 [2765056/3655823 (76%)] Data (t): 0.000 Batch (t): 0.378, 694.539/s, 173.635/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3531 (1.3411) Loss: 1.3531 (1.3411) +2024-09-02,19:40:30 | INFO | Train Epoch: 1 [2790656/3655823 (76%)] Data (t): 0.000 Batch (t): 0.378, 677.922/s, 169.480/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4230 (1.3418) Loss: 1.4230 (1.3418) +2024-09-02,19:41:08 | INFO | Train Epoch: 1 [2816256/3655823 (77%)] Data (t): 0.000 Batch (t): 0.378, 676.674/s, 169.168/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3340 (1.3418) Loss: 1.3340 (1.3418) +2024-09-02,19:41:46 | INFO | Train Epoch: 1 [2841856/3655823 (78%)] Data (t): 0.000 Batch (t): 0.380, 675.274/s, 168.819/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.6616 (1.3446) Loss: 1.6616 (1.3446) +2024-09-02,19:42:24 | INFO | Train Epoch: 1 [2867456/3655823 (78%)] Data (t): 0.000 Batch (t): 0.380, 677.948/s, 169.487/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3492 (1.3447) Loss: 1.3492 (1.3447) +2024-09-02,19:43:02 | INFO | Train Epoch: 1 [2893056/3655823 (79%)] Data (t): 0.000 Batch (t): 0.382, 677.514/s, 169.379/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2959 (1.3442) Loss: 1.2959 (1.3442) +2024-09-02,19:43:40 | INFO | Train Epoch: 1 [2918656/3655823 (80%)] Data (t): 0.000 Batch (t): 0.378, 676.429/s, 169.107/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2472 (1.3434) Loss: 1.2472 (1.3434) +2024-09-02,19:44:18 | INFO | Train Epoch: 1 [2944256/3655823 (81%)] Data (t): 0.000 Batch (t): 0.378, 677.526/s, 169.382/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4289 (1.3441) Loss: 1.4289 (1.3441) +2024-09-02,19:44:55 | INFO | Train Epoch: 1 [2969856/3655823 (81%)] Data (t): 0.000 Batch (t): 0.378, 676.835/s, 169.209/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3872 (1.3445) Loss: 1.3872 (1.3445) +2024-09-02,19:45:33 | INFO | Train Epoch: 1 [2995456/3655823 (82%)] Data (t): 0.000 Batch (t): 0.378, 676.346/s, 169.086/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2906 (1.3440) Loss: 1.2906 (1.3440) +2024-09-02,19:46:11 | INFO | Train Epoch: 1 [3021056/3655823 (83%)] Data (t): 0.000 Batch (t): 0.378, 677.331/s, 169.333/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4437 (1.3449) Loss: 1.4437 (1.3449) +2024-09-02,19:46:49 | INFO | Train Epoch: 1 [3046656/3655823 (83%)] Data (t): 0.000 Batch (t): 0.378, 676.768/s, 169.192/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2601 (1.3442) Loss: 1.2601 (1.3442) +2024-09-02,19:47:27 | INFO | Train Epoch: 1 [3072256/3655823 (84%)] Data (t): 0.000 Batch (t): 0.378, 677.406/s, 169.351/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1880 (1.3429) Loss: 1.1880 (1.3429) +2024-09-02,19:48:05 | INFO | Train Epoch: 1 [3097856/3655823 (85%)] Data (t): 0.000 Batch (t): 0.378, 677.267/s, 169.317/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.5226 (1.3443) Loss: 1.5226 (1.3443) +2024-09-02,19:48:42 | INFO | Train Epoch: 1 [3123456/3655823 (85%)] Data (t): 0.000 Batch (t): 0.378, 677.367/s, 169.342/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4617 (1.3453) Loss: 1.4617 (1.3453) +2024-09-02,19:49:20 | INFO | Train Epoch: 1 [3149056/3655823 (86%)] Data (t): 0.000 Batch (t): 0.380, 677.908/s, 169.477/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4784 (1.3464) Loss: 1.4784 (1.3464) +2024-09-02,19:49:58 | INFO | Train Epoch: 1 [3174656/3655823 (87%)] Data (t): 0.000 Batch (t): 0.378, 677.313/s, 169.328/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3648 (1.3465) Loss: 1.3648 (1.3465) +2024-09-02,19:50:36 | INFO | Train Epoch: 1 [3200256/3655823 (88%)] Data (t): 0.000 Batch (t): 0.384, 675.626/s, 168.907/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1654 (1.3451) Loss: 1.1654 (1.3451) +2024-09-02,19:51:14 | INFO | Train Epoch: 1 [3225856/3655823 (88%)] Data (t): 0.000 Batch (t): 0.378, 677.134/s, 169.284/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2349 (1.3442) Loss: 1.2349 (1.3442) +2024-09-02,19:51:52 | INFO | Train Epoch: 1 [3251456/3655823 (89%)] Data (t): 0.000 Batch (t): 0.378, 676.566/s, 169.142/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3140 (1.3440) Loss: 1.3140 (1.3440) +2024-09-02,19:52:30 | INFO | Train Epoch: 1 [3277056/3655823 (90%)] Data (t): 0.000 Batch (t): 0.378, 677.606/s, 169.402/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2842 (1.3435) Loss: 1.2842 (1.3435) +2024-09-02,19:53:08 | INFO | Train Epoch: 1 [3302656/3655823 (90%)] Data (t): 0.000 Batch (t): 0.378, 678.056/s, 169.514/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4530 (1.3444) Loss: 1.4530 (1.3444) +2024-09-02,19:53:45 | INFO | Train Epoch: 1 [3328256/3655823 (91%)] Data (t): 0.000 Batch (t): 0.378, 678.353/s, 169.588/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3112 (1.3441) Loss: 1.3112 (1.3441) +2024-09-02,19:54:23 | INFO | Train Epoch: 1 [3353856/3655823 (92%)] Data (t): 0.000 Batch (t): 0.378, 676.432/s, 169.108/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1092 (1.3423) Loss: 1.1092 (1.3423) +2024-09-02,19:55:01 | INFO | Train Epoch: 1 [3379456/3655823 (92%)] Data (t): 0.000 Batch (t): 0.378, 678.571/s, 169.643/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2593 (1.3417) Loss: 1.2593 (1.3417) +2024-09-02,19:55:39 | INFO | Train Epoch: 1 [3405056/3655823 (93%)] Data (t): 0.000 Batch (t): 0.378, 676.829/s, 169.207/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3618 (1.3419) Loss: 1.3618 (1.3419) +2024-09-02,19:56:17 | INFO | Train Epoch: 1 [3430656/3655823 (94%)] Data (t): 0.000 Batch (t): 0.378, 677.256/s, 169.314/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3486 (1.3419) Loss: 1.3486 (1.3419) +2024-09-02,19:56:54 | INFO | Train Epoch: 1 [3456256/3655823 (95%)] Data (t): 0.000 Batch (t): 0.378, 677.226/s, 169.307/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1109 (1.3402) Loss: 1.1109 (1.3402) +2024-09-02,19:57:32 | INFO | Train Epoch: 1 [3481856/3655823 (95%)] Data (t): 0.000 Batch (t): 0.380, 677.971/s, 169.493/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1857 (1.3391) Loss: 1.1857 (1.3391) +2024-09-02,19:58:11 | INFO | Train Epoch: 1 [3507456/3655823 (96%)] Data (t): 0.000 Batch (t): 0.384, 678.350/s, 169.588/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1857 (1.3380) Loss: 1.1857 (1.3380) +2024-09-02,19:58:49 | INFO | Train Epoch: 1 [3533056/3655823 (97%)] Data (t): 0.000 Batch (t): 0.378, 676.603/s, 169.151/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3314 (1.3379) Loss: 1.3314 (1.3379) +2024-09-02,19:59:26 | INFO | Train Epoch: 1 [3558656/3655823 (97%)] Data (t): 0.000 Batch (t): 0.378, 677.256/s, 169.314/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3126 (1.3377) Loss: 1.3126 (1.3377) +2024-09-02,20:00:04 | INFO | Train Epoch: 1 [3584256/3655823 (98%)] Data (t): 0.000 Batch (t): 0.378, 677.511/s, 169.378/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2343 (1.3370) Loss: 1.2343 (1.3370) +2024-09-02,20:00:42 | INFO | Train Epoch: 1 [3609856/3655823 (99%)] Data (t): 0.000 Batch (t): 0.378, 679.617/s, 169.904/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2410 (1.3363) Loss: 1.2410 (1.3363) +2024-09-02,20:01:20 | INFO | Train Epoch: 1 [3635456/3655823 (99%)] Data (t): 0.000 Batch (t): 0.378, 677.979/s, 169.495/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3356 (1.3363) Loss: 1.3356 (1.3363) +2024-09-02,20:01:50 | INFO | Train Epoch: 1 [3655680/3655823 (100%)] Data (t): 0.001 Batch (t): 0.378, 682.050/s, 170.513/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3771 (1.3366) Loss: 1.3771 (1.3366) +2024-09-02,20:02:00 | INFO | Start epoch 2 +2024-09-02,20:02:02 | INFO | Train Epoch: 2 [ 256/3655823 (0%)] Data (t): 1.319 Batch (t): 1.710, 149.718/s, 37.4296/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2449 (1.2449) Loss: 1.2449 (1.2449) +2024-09-02,20:02:40 | INFO | Train Epoch: 2 [ 25856/3655823 (1%)] Data (t): 0.000 Batch (t): 0.378, 676.861/s, 169.215/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3189 (1.2819) Loss: 1.3189 (1.2819) +2024-09-02,20:03:17 | INFO | Train Epoch: 2 [ 51456/3655823 (1%)] Data (t): 0.000 Batch (t): 0.378, 676.681/s, 169.170/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3308 (1.2982) Loss: 1.3308 (1.2982) +2024-09-02,20:03:55 | INFO | Train Epoch: 2 [ 77056/3655823 (2%)] Data (t): 0.000 Batch (t): 0.378, 677.320/s, 169.330/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3594 (1.3135) Loss: 1.3594 (1.3135) +2024-09-02,20:04:33 | INFO | Train Epoch: 2 [ 102656/3655823 (3%)] Data (t): 0.000 Batch (t): 0.380, 677.514/s, 169.379/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4559 (1.3420) Loss: 1.4559 (1.3420) +2024-09-02,20:05:11 | INFO | Train Epoch: 2 [ 128256/3655823 (4%)] Data (t): 0.000 Batch (t): 0.378, 676.716/s, 169.179/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4018 (1.3519) Loss: 1.4018 (1.3519) +2024-09-02,20:05:50 | INFO | Train Epoch: 2 [ 153856/3655823 (4%)] Data (t): 0.000 Batch (t): 0.385, 677.741/s, 169.435/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4088 (1.3601) Loss: 1.4088 (1.3601) +2024-09-02,20:06:27 | INFO | Train Epoch: 2 [ 179456/3655823 (5%)] Data (t): 0.000 Batch (t): 0.378, 677.493/s, 169.373/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2922 (1.3516) Loss: 1.2922 (1.3516) +2024-09-02,20:07:05 | INFO | Train Epoch: 2 [ 205056/3655823 (6%)] Data (t): 0.000 Batch (t): 0.378, 678.098/s, 169.525/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2421 (1.3394) Loss: 1.2421 (1.3394) +2024-09-02,20:07:43 | INFO | Train Epoch: 2 [ 230656/3655823 (6%)] Data (t): 0.000 Batch (t): 0.378, 678.278/s, 169.569/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3272 (1.3382) Loss: 1.3272 (1.3382) +2024-09-02,20:08:21 | INFO | Train Epoch: 2 [ 256256/3655823 (7%)] Data (t): 0.000 Batch (t): 0.378, 677.266/s, 169.317/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.5243 (1.3551) Loss: 1.5243 (1.3551) +2024-09-02,20:08:58 | INFO | Train Epoch: 2 [ 281856/3655823 (8%)] Data (t): 0.000 Batch (t): 0.378, 677.565/s, 169.391/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4867 (1.3661) Loss: 1.4867 (1.3661) +2024-09-02,20:09:36 | INFO | Train Epoch: 2 [ 307456/3655823 (8%)] Data (t): 0.000 Batch (t): 0.378, 678.309/s, 169.577/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2077 (1.3539) Loss: 1.2077 (1.3539) +2024-09-02,20:10:14 | INFO | Train Epoch: 2 [ 333056/3655823 (9%)] Data (t): 0.000 Batch (t): 0.378, 677.762/s, 169.441/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3039 (1.3503) Loss: 1.3039 (1.3503) +2024-09-02,20:10:52 | INFO | Train Epoch: 2 [ 358656/3655823 (10%)] Data (t): 0.000 Batch (t): 0.378, 678.020/s, 169.505/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3097 (1.3476) Loss: 1.3097 (1.3476) +2024-09-02,20:11:30 | INFO | Train Epoch: 2 [ 384256/3655823 (11%)] Data (t): 0.000 Batch (t): 0.378, 677.468/s, 169.367/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2229 (1.3398) Loss: 1.2229 (1.3398) +2024-09-02,20:12:08 | INFO | Train Epoch: 2 [ 409856/3655823 (11%)] Data (t): 0.000 Batch (t): 0.380, 678.148/s, 169.537/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4866 (1.3485) Loss: 1.4866 (1.3485) +2024-09-02,20:12:45 | INFO | Train Epoch: 2 [ 435456/3655823 (12%)] Data (t): 0.000 Batch (t): 0.378, 677.281/s, 169.320/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2608 (1.3436) Loss: 1.2608 (1.3436) +2024-09-02,20:13:23 | INFO | Train Epoch: 2 [ 461056/3655823 (13%)] Data (t): 0.000 Batch (t): 0.380, 677.689/s, 169.422/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.5716 (1.3556) Loss: 1.5716 (1.3556) +2024-09-02,20:14:02 | INFO | Train Epoch: 2 [ 486656/3655823 (13%)] Data (t): 0.000 Batch (t): 0.382, 677.783/s, 169.446/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3118 (1.3534) Loss: 1.3118 (1.3534) +2024-09-02,20:14:39 | INFO | Train Epoch: 2 [ 512256/3655823 (14%)] Data (t): 0.000 Batch (t): 0.378, 677.796/s, 169.449/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3275 (1.3522) Loss: 1.3275 (1.3522) +2024-09-02,20:15:17 | INFO | Train Epoch: 2 [ 537856/3655823 (15%)] Data (t): 0.000 Batch (t): 0.378, 678.541/s, 169.635/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4273 (1.3556) Loss: 1.4273 (1.3556) +2024-09-02,20:15:55 | INFO | Train Epoch: 2 [ 563456/3655823 (15%)] Data (t): 0.000 Batch (t): 0.378, 677.061/s, 169.265/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2714 (1.3519) Loss: 1.2714 (1.3519) +2024-09-02,20:16:33 | INFO | Train Epoch: 2 [ 589056/3655823 (16%)] Data (t): 0.000 Batch (t): 0.378, 678.619/s, 169.655/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3372 (1.3513) Loss: 1.3372 (1.3513) +2024-09-02,20:17:10 | INFO | Train Epoch: 2 [ 614656/3655823 (17%)] Data (t): 0.000 Batch (t): 0.378, 678.221/s, 169.555/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.0998 (1.3412) Loss: 1.0998 (1.3412) +2024-09-02,20:17:48 | INFO | Train Epoch: 2 [ 640256/3655823 (18%)] Data (t): 0.000 Batch (t): 0.378, 676.796/s, 169.199/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.5302 (1.3485) Loss: 1.5302 (1.3485) +2024-09-02,20:18:26 | INFO | Train Epoch: 2 [ 665856/3655823 (18%)] Data (t): 0.000 Batch (t): 0.378, 677.731/s, 169.433/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4029 (1.3505) Loss: 1.4029 (1.3505) +2024-09-02,20:19:04 | INFO | Train Epoch: 2 [ 691456/3655823 (19%)] Data (t): 0.000 Batch (t): 0.378, 677.991/s, 169.498/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.5002 (1.3559) Loss: 1.5002 (1.3559) +2024-09-02,20:19:42 | INFO | Train Epoch: 2 [ 717056/3655823 (20%)] Data (t): 0.000 Batch (t): 0.380, 678.621/s, 169.655/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3825 (1.3568) Loss: 1.3825 (1.3568) +2024-09-02,20:20:20 | INFO | Train Epoch: 2 [ 742656/3655823 (20%)] Data (t): 0.000 Batch (t): 0.378, 677.083/s, 169.271/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4232 (1.3590) Loss: 1.4232 (1.3590) +2024-09-02,20:20:57 | INFO | Train Epoch: 2 [ 768256/3655823 (21%)] Data (t): 0.000 Batch (t): 0.380, 435.518/s, 108.880/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.0991 (1.3506) Loss: 1.0991 (1.3506) +2024-09-02,20:21:36 | INFO | Train Epoch: 2 [ 793856/3655823 (22%)] Data (t): 0.000 Batch (t): 0.382, 676.688/s, 169.172/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4253 (1.3530) Loss: 1.4253 (1.3530) +2024-09-02,20:22:13 | INFO | Train Epoch: 2 [ 819456/3655823 (22%)] Data (t): 0.000 Batch (t): 0.378, 678.179/s, 169.545/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3273 (1.3522) Loss: 1.3273 (1.3522) +2024-09-02,20:22:51 | INFO | Train Epoch: 2 [ 845056/3655823 (23%)] Data (t): 0.000 Batch (t): 0.378, 677.533/s, 169.383/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1739 (1.3469) Loss: 1.1739 (1.3469) +2024-09-02,20:23:29 | INFO | Train Epoch: 2 [ 870656/3655823 (24%)] Data (t): 0.000 Batch (t): 0.378, 677.902/s, 169.476/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2975 (1.3455) Loss: 1.2975 (1.3455) +2024-09-02,20:24:07 | INFO | Train Epoch: 2 [ 896256/3655823 (25%)] Data (t): 0.000 Batch (t): 0.378, 676.265/s, 169.066/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1678 (1.3406) Loss: 1.1678 (1.3406) +2024-09-02,20:24:45 | INFO | Train Epoch: 2 [ 921856/3655823 (25%)] Data (t): 0.000 Batch (t): 0.378, 677.108/s, 169.277/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2021 (1.3368) Loss: 1.2021 (1.3368) +2024-09-02,20:25:22 | INFO | Train Epoch: 2 [ 947456/3655823 (26%)] Data (t): 0.000 Batch (t): 0.378, 678.126/s, 169.531/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1532 (1.3320) Loss: 1.1532 (1.3320) +2024-09-02,20:26:00 | INFO | Train Epoch: 2 [ 973056/3655823 (27%)] Data (t): 0.000 Batch (t): 0.378, 677.229/s, 169.307/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3183 (1.3317) Loss: 1.3183 (1.3317) +2024-09-02,20:26:38 | INFO | Train Epoch: 2 [ 998656/3655823 (27%)] Data (t): 0.000 Batch (t): 0.378, 677.786/s, 169.447/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2127 (1.3287) Loss: 1.2127 (1.3287) +2024-09-02,20:27:16 | INFO | Train Epoch: 2 [1024256/3655823 (28%)] Data (t): 0.000 Batch (t): 0.378, 677.390/s, 169.347/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2672 (1.3272) Loss: 1.2672 (1.3272) +2024-09-02,20:27:54 | INFO | Train Epoch: 2 [1049856/3655823 (29%)] Data (t): 0.000 Batch (t): 0.380, 677.524/s, 169.381/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2595 (1.3256) Loss: 1.2595 (1.3256) +2024-09-02,20:28:31 | INFO | Train Epoch: 2 [1075456/3655823 (29%)] Data (t): 0.000 Batch (t): 0.378, 677.786/s, 169.446/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.6476 (1.3331) Loss: 1.6476 (1.3331) +2024-09-02,20:29:10 | INFO | Train Epoch: 2 [1101056/3655823 (30%)] Data (t): 0.000 Batch (t): 0.384, 677.664/s, 169.416/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4891 (1.3366) Loss: 1.4891 (1.3366) +2024-09-02,20:29:48 | INFO | Train Epoch: 2 [1126656/3655823 (31%)] Data (t): 0.000 Batch (t): 0.378, 677.655/s, 169.414/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4163 (1.3384) Loss: 1.4163 (1.3384) +2024-09-02,20:30:25 | INFO | Train Epoch: 2 [1152256/3655823 (32%)] Data (t): 0.000 Batch (t): 0.378, 678.294/s, 169.573/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2264 (1.3359) Loss: 1.2264 (1.3359) +2024-09-02,20:31:03 | INFO | Train Epoch: 2 [1177856/3655823 (32%)] Data (t): 0.000 Batch (t): 0.378, 675.881/s, 168.970/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.5726 (1.3410) Loss: 1.5726 (1.3410) +2024-09-02,20:31:41 | INFO | Train Epoch: 2 [1203456/3655823 (33%)] Data (t): 0.000 Batch (t): 0.378, 677.321/s, 169.330/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2750 (1.3396) Loss: 1.2750 (1.3396) +2024-09-02,20:32:19 | INFO | Train Epoch: 2 [1229056/3655823 (34%)] Data (t): 0.000 Batch (t): 0.378, 678.268/s, 169.567/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4289 (1.3414) Loss: 1.4289 (1.3414) +2024-09-02,20:32:56 | INFO | Train Epoch: 2 [1254656/3655823 (34%)] Data (t): 0.000 Batch (t): 0.378, 677.645/s, 169.411/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3045 (1.3407) Loss: 1.3045 (1.3407) +2024-09-02,20:33:34 | INFO | Train Epoch: 2 [1280256/3655823 (35%)] Data (t): 0.000 Batch (t): 0.378, 675.694/s, 168.923/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2525 (1.3390) Loss: 1.2525 (1.3390) +2024-09-02,20:34:12 | INFO | Train Epoch: 2 [1305856/3655823 (36%)] Data (t): 0.000 Batch (t): 0.378, 677.159/s, 169.290/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.6430 (1.3448) Loss: 1.6430 (1.3448) +2024-09-02,20:34:50 | INFO | Train Epoch: 2 [1331456/3655823 (36%)] Data (t): 0.000 Batch (t): 0.378, 678.489/s, 169.622/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3494 (1.3449) Loss: 1.3494 (1.3449) +2024-09-02,20:35:28 | INFO | Train Epoch: 2 [1357056/3655823 (37%)] Data (t): 0.000 Batch (t): 0.380, 677.029/s, 169.257/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.0992 (1.3403) Loss: 1.0992 (1.3403) +2024-09-02,20:36:06 | INFO | Train Epoch: 2 [1382656/3655823 (38%)] Data (t): 0.000 Batch (t): 0.378, 677.663/s, 169.416/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.5391 (1.3440) Loss: 1.5391 (1.3440) +2024-09-02,20:36:44 | INFO | Train Epoch: 2 [1408256/3655823 (39%)] Data (t): 0.000 Batch (t): 0.384, 677.486/s, 169.372/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2099 (1.3416) Loss: 1.2099 (1.3416) +2024-09-02,20:37:22 | INFO | Train Epoch: 2 [1433856/3655823 (39%)] Data (t): 0.000 Batch (t): 0.378, 678.183/s, 169.546/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3242 (1.3413) Loss: 1.3242 (1.3413) +2024-09-02,20:37:59 | INFO | Train Epoch: 2 [1459456/3655823 (40%)] Data (t): 0.000 Batch (t): 0.378, 677.552/s, 169.388/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2776 (1.3402) Loss: 1.2776 (1.3402) +2024-09-02,20:38:37 | INFO | Train Epoch: 2 [1485056/3655823 (41%)] Data (t): 0.000 Batch (t): 0.378, 678.564/s, 169.641/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2871 (1.3393) Loss: 1.2871 (1.3393) +2024-09-02,20:39:15 | INFO | Train Epoch: 2 [1510656/3655823 (41%)] Data (t): 0.000 Batch (t): 0.378, 677.467/s, 169.367/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1060 (1.3354) Loss: 1.1060 (1.3354) +2024-09-02,20:39:53 | INFO | Train Epoch: 2 [1536256/3655823 (42%)] Data (t): 0.000 Batch (t): 0.378, 677.348/s, 169.337/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.0803 (1.3312) Loss: 1.0803 (1.3312) +2024-09-02,20:40:31 | INFO | Train Epoch: 2 [1561856/3655823 (43%)] Data (t): 0.000 Batch (t): 0.378, 678.910/s, 169.728/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1991 (1.3291) Loss: 1.1991 (1.3291) +2024-09-02,20:41:08 | INFO | Train Epoch: 2 [1587456/3655823 (43%)] Data (t): 0.000 Batch (t): 0.378, 677.717/s, 169.429/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2045 (1.3271) Loss: 1.2045 (1.3271) +2024-09-02,20:41:46 | INFO | Train Epoch: 2 [1613056/3655823 (44%)] Data (t): 0.000 Batch (t): 0.378, 676.006/s, 169.002/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4872 (1.3296) Loss: 1.4872 (1.3296) +2024-09-02,20:42:24 | INFO | Train Epoch: 2 [1638656/3655823 (45%)] Data (t): 0.000 Batch (t): 0.378, 678.952/s, 169.738/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3148 (1.3294) Loss: 1.3148 (1.3294) +2024-09-02,20:43:02 | INFO | Train Epoch: 2 [1664256/3655823 (46%)] Data (t): 0.000 Batch (t): 0.380, 677.703/s, 169.426/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2000 (1.3274) Loss: 1.2000 (1.3274) +2024-09-02,20:43:40 | INFO | Train Epoch: 2 [1689856/3655823 (46%)] Data (t): 0.000 Batch (t): 0.378, 677.683/s, 169.421/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1651 (1.3250) Loss: 1.1651 (1.3250) +2024-09-02,20:44:18 | INFO | Train Epoch: 2 [1715456/3655823 (47%)] Data (t): 0.000 Batch (t): 0.384, 677.744/s, 169.436/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1259 (1.3220) Loss: 1.1259 (1.3220) +2024-09-02,20:44:56 | INFO | Train Epoch: 2 [1741056/3655823 (48%)] Data (t): 0.000 Batch (t): 0.378, 679.044/s, 169.761/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4703 (1.3242) Loss: 1.4703 (1.3242) +2024-09-02,20:45:34 | INFO | Train Epoch: 2 [1766656/3655823 (48%)] Data (t): 0.000 Batch (t): 0.378, 677.998/s, 169.500/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3210 (1.3241) Loss: 1.3210 (1.3241) +2024-09-02,20:46:12 | INFO | Train Epoch: 2 [1792256/3655823 (49%)] Data (t): 0.000 Batch (t): 0.378, 677.743/s, 169.436/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3419 (1.3244) Loss: 1.3419 (1.3244) +2024-09-02,20:46:49 | INFO | Train Epoch: 2 [1817856/3655823 (50%)] Data (t): 0.000 Batch (t): 0.378, 677.919/s, 169.480/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1527 (1.3220) Loss: 1.1527 (1.3220) +2024-09-02,20:47:27 | INFO | Train Epoch: 2 [1843456/3655823 (50%)] Data (t): 0.000 Batch (t): 0.378, 676.520/s, 169.130/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3024 (1.3217) Loss: 1.3024 (1.3217) +2024-09-02,20:48:05 | INFO | Train Epoch: 2 [1869056/3655823 (51%)] Data (t): 0.000 Batch (t): 0.378, 677.188/s, 169.297/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4475 (1.3234) Loss: 1.4475 (1.3234) +2024-09-02,20:48:43 | INFO | Train Epoch: 2 [1894656/3655823 (52%)] Data (t): 0.000 Batch (t): 0.378, 677.400/s, 169.350/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4376 (1.3250) Loss: 1.4376 (1.3250) +2024-09-02,20:49:20 | INFO | Train Epoch: 2 [1920256/3655823 (53%)] Data (t): 0.000 Batch (t): 0.378, 676.134/s, 169.033/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2484 (1.3240) Loss: 1.2484 (1.3240) +2024-09-02,20:49:58 | INFO | Train Epoch: 2 [1945856/3655823 (53%)] Data (t): 0.000 Batch (t): 0.378, 678.072/s, 169.518/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4302 (1.3253) Loss: 1.4302 (1.3253) +2024-09-02,20:50:36 | INFO | Train Epoch: 2 [1971456/3655823 (54%)] Data (t): 0.000 Batch (t): 0.378, 678.164/s, 169.541/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.5555 (1.3283) Loss: 1.5555 (1.3283) +2024-09-02,20:51:14 | INFO | Train Epoch: 2 [1997056/3655823 (55%)] Data (t): 0.000 Batch (t): 0.380, 678.117/s, 169.529/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2496 (1.3273) Loss: 1.2496 (1.3273) +2024-09-02,20:51:52 | INFO | Train Epoch: 2 [2022656/3655823 (55%)] Data (t): 0.000 Batch (t): 0.382, 436.934/s, 109.234/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2623 (1.3265) Loss: 1.2623 (1.3265) +2024-09-02,20:52:30 | INFO | Train Epoch: 2 [2048256/3655823 (56%)] Data (t): 0.000 Batch (t): 0.380, 677.176/s, 169.294/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4024 (1.3274) Loss: 1.4024 (1.3274) +2024-09-02,20:53:08 | INFO | Train Epoch: 2 [2073856/3655823 (57%)] Data (t): 0.000 Batch (t): 0.378, 678.544/s, 169.636/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4059 (1.3284) Loss: 1.4059 (1.3284) +2024-09-02,20:53:46 | INFO | Train Epoch: 2 [2099456/3655823 (57%)] Data (t): 0.000 Batch (t): 0.378, 678.434/s, 169.608/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1841 (1.3266) Loss: 1.1841 (1.3266) +2024-09-02,20:54:24 | INFO | Train Epoch: 2 [2125056/3655823 (58%)] Data (t): 0.000 Batch (t): 0.378, 675.548/s, 168.887/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4134 (1.3277) Loss: 1.4134 (1.3277) +2024-09-02,20:55:01 | INFO | Train Epoch: 2 [2150656/3655823 (59%)] Data (t): 0.000 Batch (t): 0.378, 676.085/s, 169.021/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2536 (1.3268) Loss: 1.2536 (1.3268) +2024-09-02,20:55:39 | INFO | Train Epoch: 2 [2176256/3655823 (60%)] Data (t): 0.000 Batch (t): 0.378, 677.768/s, 169.442/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2858 (1.3263) Loss: 1.2858 (1.3263) +2024-09-02,20:56:17 | INFO | Train Epoch: 2 [2201856/3655823 (60%)] Data (t): 0.000 Batch (t): 0.378, 678.261/s, 169.565/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2195 (1.3251) Loss: 1.2195 (1.3251) +2024-09-02,20:56:55 | INFO | Train Epoch: 2 [2227456/3655823 (61%)] Data (t): 0.000 Batch (t): 0.378, 676.778/s, 169.195/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4445 (1.3265) Loss: 1.4445 (1.3265) +2024-09-02,20:57:33 | INFO | Train Epoch: 2 [2253056/3655823 (62%)] Data (t): 0.000 Batch (t): 0.378, 676.155/s, 169.039/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.5238 (1.3287) Loss: 1.5238 (1.3287) +2024-09-02,20:58:10 | INFO | Train Epoch: 2 [2278656/3655823 (62%)] Data (t): 0.000 Batch (t): 0.378, 676.956/s, 169.239/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2976 (1.3283) Loss: 1.2976 (1.3283) +2024-09-02,20:58:48 | INFO | Train Epoch: 2 [2304256/3655823 (63%)] Data (t): 0.000 Batch (t): 0.380, 677.609/s, 169.402/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2780 (1.3278) Loss: 1.2780 (1.3278) +2024-09-02,20:59:26 | INFO | Train Epoch: 2 [2329856/3655823 (64%)] Data (t): 0.000 Batch (t): 0.379, 677.970/s, 169.493/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2000 (1.3264) Loss: 1.2000 (1.3264) +2024-09-02,21:00:04 | INFO | Train Epoch: 2 [2355456/3655823 (64%)] Data (t): 0.000 Batch (t): 0.382, 679.044/s, 169.761/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4016 (1.3272) Loss: 1.4016 (1.3272) +2024-09-02,21:00:42 | INFO | Train Epoch: 2 [2381056/3655823 (65%)] Data (t): 0.000 Batch (t): 0.377, 678.672/s, 169.668/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4712 (1.3287) Loss: 1.4712 (1.3287) +2024-09-02,21:01:20 | INFO | Train Epoch: 2 [2406656/3655823 (66%)] Data (t): 0.000 Batch (t): 0.378, 678.801/s, 169.700/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3562 (1.3290) Loss: 1.3562 (1.3290) +2024-09-02,21:01:58 | INFO | Train Epoch: 2 [2432256/3655823 (67%)] Data (t): 0.000 Batch (t): 0.377, 679.188/s, 169.797/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2049 (1.3277) Loss: 1.2049 (1.3277) +2024-09-02,21:02:35 | INFO | Train Epoch: 2 [2457856/3655823 (67%)] Data (t): 0.000 Batch (t): 0.377, 677.978/s, 169.494/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3495 (1.3279) Loss: 1.3495 (1.3279) +2024-09-02,21:03:13 | INFO | Train Epoch: 2 [2483456/3655823 (68%)] Data (t): 0.000 Batch (t): 0.378, 678.681/s, 169.670/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1933 (1.3266) Loss: 1.1933 (1.3266) +2024-09-02,21:03:51 | INFO | Train Epoch: 2 [2509056/3655823 (69%)] Data (t): 0.000 Batch (t): 0.378, 678.829/s, 169.707/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.6302 (1.3296) Loss: 1.6302 (1.3296) +2024-09-02,21:04:29 | INFO | Train Epoch: 2 [2534656/3655823 (69%)] Data (t): 0.000 Batch (t): 0.378, 677.655/s, 169.414/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4722 (1.3311) Loss: 1.4722 (1.3311) +2024-09-02,21:05:06 | INFO | Train Epoch: 2 [2560256/3655823 (70%)] Data (t): 0.000 Batch (t): 0.377, 678.714/s, 169.678/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2526 (1.3303) Loss: 1.2526 (1.3303) +2024-09-02,21:05:44 | INFO | Train Epoch: 2 [2585856/3655823 (71%)] Data (t): 0.000 Batch (t): 0.378, 677.076/s, 169.269/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.5646 (1.3326) Loss: 1.5646 (1.3326) +2024-09-02,21:06:22 | INFO | Train Epoch: 2 [2611456/3655823 (71%)] Data (t): 0.000 Batch (t): 0.380, 678.035/s, 169.509/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.0440 (1.3298) Loss: 1.0440 (1.3298) +2024-09-02,21:07:00 | INFO | Train Epoch: 2 [2637056/3655823 (72%)] Data (t): 0.000 Batch (t): 0.378, 678.280/s, 169.570/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3705 (1.3302) Loss: 1.3705 (1.3302) +2024-09-02,21:07:38 | INFO | Train Epoch: 2 [2662656/3655823 (73%)] Data (t): 0.000 Batch (t): 0.384, 678.158/s, 169.540/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3095 (1.3300) Loss: 1.3095 (1.3300) +2024-09-02,21:08:16 | INFO | Train Epoch: 2 [2688256/3655823 (74%)] Data (t): 0.000 Batch (t): 0.378, 677.534/s, 169.384/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2514 (1.3292) Loss: 1.2514 (1.3292) +2024-09-02,21:08:54 | INFO | Train Epoch: 2 [2713856/3655823 (74%)] Data (t): 0.000 Batch (t): 0.378, 676.951/s, 169.238/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2341 (1.3283) Loss: 1.2341 (1.3283) +2024-09-02,21:09:32 | INFO | Train Epoch: 2 [2739456/3655823 (75%)] Data (t): 0.000 Batch (t): 0.378, 676.280/s, 169.070/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3958 (1.3290) Loss: 1.3958 (1.3290) +2024-09-02,21:10:09 | INFO | Train Epoch: 2 [2765056/3655823 (76%)] Data (t): 0.000 Batch (t): 0.378, 676.302/s, 169.076/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2967 (1.3287) Loss: 1.2967 (1.3287) +2024-09-02,21:10:47 | INFO | Train Epoch: 2 [2790656/3655823 (76%)] Data (t): 0.000 Batch (t): 0.378, 677.827/s, 169.457/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3618 (1.3290) Loss: 1.3618 (1.3290) +2024-09-02,21:11:25 | INFO | Train Epoch: 2 [2816256/3655823 (77%)] Data (t): 0.000 Batch (t): 0.378, 676.040/s, 169.010/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2839 (1.3286) Loss: 1.2839 (1.3286) +2024-09-02,21:12:03 | INFO | Train Epoch: 2 [2841856/3655823 (78%)] Data (t): 0.000 Batch (t): 0.378, 677.107/s, 169.277/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3783 (1.3290) Loss: 1.3783 (1.3290) +2024-09-02,21:12:41 | INFO | Train Epoch: 2 [2867456/3655823 (78%)] Data (t): 0.000 Batch (t): 0.378, 678.247/s, 169.562/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.5307 (1.3308) Loss: 1.5307 (1.3308) +2024-09-02,21:13:18 | INFO | Train Epoch: 2 [2893056/3655823 (79%)] Data (t): 0.000 Batch (t): 0.378, 678.268/s, 169.567/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2354 (1.3300) Loss: 1.2354 (1.3300) +2024-09-02,21:13:56 | INFO | Train Epoch: 2 [2918656/3655823 (80%)] Data (t): 0.000 Batch (t): 0.378, 676.731/s, 169.183/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2266 (1.3291) Loss: 1.2266 (1.3291) +2024-09-02,21:14:34 | INFO | Train Epoch: 2 [2944256/3655823 (81%)] Data (t): 0.000 Batch (t): 0.380, 678.249/s, 169.562/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2062 (1.3280) Loss: 1.2062 (1.3280) +2024-09-02,21:15:13 | INFO | Train Epoch: 2 [2969856/3655823 (81%)] Data (t): 0.000 Batch (t): 0.384, 677.535/s, 169.384/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3600 (1.3283) Loss: 1.3600 (1.3283) +2024-09-02,21:15:50 | INFO | Train Epoch: 2 [2995456/3655823 (82%)] Data (t): 0.000 Batch (t): 0.378, 677.787/s, 169.447/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3766 (1.3287) Loss: 1.3766 (1.3287) +2024-09-02,21:16:28 | INFO | Train Epoch: 2 [3021056/3655823 (83%)] Data (t): 0.000 Batch (t): 0.378, 678.284/s, 169.571/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3064 (1.3285) Loss: 1.3064 (1.3285) +2024-09-02,21:17:06 | INFO | Train Epoch: 2 [3046656/3655823 (83%)] Data (t): 0.000 Batch (t): 0.378, 676.858/s, 169.215/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3456 (1.3286) Loss: 1.3456 (1.3286) +2024-09-02,21:17:44 | INFO | Train Epoch: 2 [3072256/3655823 (84%)] Data (t): 0.000 Batch (t): 0.378, 677.567/s, 169.392/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2079 (1.3276) Loss: 1.2079 (1.3276) +2024-09-02,21:18:22 | INFO | Train Epoch: 2 [3097856/3655823 (85%)] Data (t): 0.000 Batch (t): 0.378, 677.997/s, 169.499/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4054 (1.3283) Loss: 1.4054 (1.3283) +2024-09-02,21:19:00 | INFO | Train Epoch: 2 [3123456/3655823 (85%)] Data (t): 0.010 Batch (t): 0.389, 678.416/s, 169.604/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3007 (1.3281) Loss: 1.3007 (1.3281) +2024-09-02,21:19:38 | INFO | Train Epoch: 2 [3149056/3655823 (86%)] Data (t): 0.000 Batch (t): 0.378, 676.870/s, 169.218/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2144 (1.3271) Loss: 1.2144 (1.3271) +2024-09-02,21:20:16 | INFO | Train Epoch: 2 [3174656/3655823 (87%)] Data (t): 0.000 Batch (t): 0.378, 678.335/s, 169.584/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.0321 (1.3248) Loss: 1.0321 (1.3248) +2024-09-02,21:20:54 | INFO | Train Epoch: 2 [3200256/3655823 (88%)] Data (t): 0.000 Batch (t): 0.378, 677.500/s, 169.375/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1940 (1.3237) Loss: 1.1940 (1.3237) +2024-09-02,21:21:32 | INFO | Train Epoch: 2 [3225856/3655823 (88%)] Data (t): 0.000 Batch (t): 0.378, 666.167/s, 166.542/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3978 (1.3243) Loss: 1.3978 (1.3243) +2024-09-02,21:22:10 | INFO | Train Epoch: 2 [3251456/3655823 (89%)] Data (t): 0.000 Batch (t): 0.380, 676.621/s, 169.155/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1450 (1.3229) Loss: 1.1450 (1.3229) +2024-09-02,21:22:48 | INFO | Train Epoch: 2 [3277056/3655823 (90%)] Data (t): 0.000 Batch (t): 0.384, 676.788/s, 169.197/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1781 (1.3218) Loss: 1.1781 (1.3218) +2024-09-02,21:23:26 | INFO | Train Epoch: 2 [3302656/3655823 (90%)] Data (t): 0.000 Batch (t): 0.378, 678.198/s, 169.550/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.5485 (1.3235) Loss: 1.5485 (1.3235) +2024-09-02,21:24:04 | INFO | Train Epoch: 2 [3328256/3655823 (91%)] Data (t): 0.000 Batch (t): 0.378, 677.541/s, 169.385/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3852 (1.3240) Loss: 1.3852 (1.3240) +2024-09-02,21:24:42 | INFO | Train Epoch: 2 [3353856/3655823 (92%)] Data (t): 0.000 Batch (t): 0.378, 677.359/s, 169.340/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2017 (1.3231) Loss: 1.2017 (1.3231) +2024-09-02,21:25:19 | INFO | Train Epoch: 2 [3379456/3655823 (92%)] Data (t): 0.000 Batch (t): 0.378, 677.775/s, 169.444/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.5104 (1.3245) Loss: 1.5104 (1.3245) +2024-09-02,21:25:57 | INFO | Train Epoch: 2 [3405056/3655823 (93%)] Data (t): 0.000 Batch (t): 0.378, 676.563/s, 169.141/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4114 (1.3251) Loss: 1.4114 (1.3251) +2024-09-02,21:26:35 | INFO | Train Epoch: 2 [3430656/3655823 (94%)] Data (t): 0.000 Batch (t): 0.378, 678.026/s, 169.506/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2695 (1.3247) Loss: 1.2695 (1.3247) +2024-09-02,21:27:13 | INFO | Train Epoch: 2 [3456256/3655823 (95%)] Data (t): 0.000 Batch (t): 0.378, 677.805/s, 169.451/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3511 (1.3249) Loss: 1.3511 (1.3249) +2024-09-02,21:27:54 | INFO | Train Epoch: 2 [3481856/3655823 (95%)] Data (t): 0.031 Batch (t): 0.414, 676.920/s, 169.230/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2732 (1.3245) Loss: 1.2732 (1.3245) +2024-09-02,21:28:32 | INFO | Train Epoch: 2 [3507456/3655823 (96%)] Data (t): 0.000 Batch (t): 0.378, 676.889/s, 169.222/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2559 (1.3241) Loss: 1.2559 (1.3241) +2024-09-02,21:29:10 | INFO | Train Epoch: 2 [3533056/3655823 (97%)] Data (t): 0.000 Batch (t): 0.378, 677.979/s, 169.495/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2239 (1.3233) Loss: 1.2239 (1.3233) +2024-09-02,21:29:48 | INFO | Train Epoch: 2 [3558656/3655823 (97%)] Data (t): 0.000 Batch (t): 0.380, 676.143/s, 169.036/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3523 (1.3235) Loss: 1.3523 (1.3235) +2024-09-02,21:30:26 | INFO | Train Epoch: 2 [3584256/3655823 (98%)] Data (t): 0.000 Batch (t): 0.384, 677.621/s, 169.405/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4448 (1.3244) Loss: 1.4448 (1.3244) +2024-09-02,21:31:04 | INFO | Train Epoch: 2 [3609856/3655823 (99%)] Data (t): 0.000 Batch (t): 0.378, 677.564/s, 169.391/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2651 (1.3240) Loss: 1.2651 (1.3240) +2024-09-02,21:31:42 | INFO | Train Epoch: 2 [3635456/3655823 (99%)] Data (t): 0.000 Batch (t): 0.378, 677.751/s, 169.438/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1543 (1.3228) Loss: 1.1543 (1.3228) +2024-09-02,21:32:12 | INFO | Train Epoch: 2 [3655680/3655823 (100%)] Data (t): 0.001 Batch (t): 0.378, 681.823/s, 170.456/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 0.98438 (1.3204) Loss: 0.98438 (1.3204) diff --git a/data/trained_openclip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/params.txt b/data/trained_openclip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/params.txt new file mode 100644 index 0000000000000000000000000000000000000000..76f93176a2087e52d95699cab7c46ae29c2fe52a --- /dev/null +++ b/data/trained_openclip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/params.txt @@ -0,0 +1,96 @@ +accum_freq: 1 +aug_cfg: {} +batch_size: 64 +beta1: 0.9 +beta2: 0.98 +checkpoint_path: /project/deemreason/junteng/Vision4Math/train_clip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints +coca_caption_loss_weight: 2.0 +coca_contrastive_loss_weight: 1.0 +copy_codebase: False +csv_caption_key: caption +csv_img_key: img_path +csv_separator: , +dataset_resampled: False +dataset_type: csv +ddp_static_graph: False +debug: False +delete_previous_checkpoint: False +device: cuda:0 +dist_backend: nccl +dist_url: env:// +distill: False +distill_model: None +distill_pretrained: None +distributed: True +epochs: 3 +epochs_cooldown: None +eps: 1e-06 +force_custom_text: False +force_image_size: None +force_patch_dropout: None +force_quick_gelu: True +gather_with_grad: False +grad_checkpointing: False +grad_clip_norm: None +horovod: False +image_interpolation: None +image_mean: None +image_resize_mode: None +image_std: None +imagenet_v2: None +imagenet_val: None +local_loss: False +local_rank: 0 +lock_image: False +lock_image_freeze_bn_stats: False +lock_image_unlocked_groups: 0 +lock_text: False +lock_text_freeze_layer_norm: False +lock_text_unlocked_layers: 0 +log_every_n_steps: 100 +log_level: 20 +log_local: False +log_path: /project/deemreason/junteng/Vision4Math/train_clip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log +logs: /project/deemreason/junteng/Vision4Math/train_clip/no_hard_negative_logs/plotqa_v2 +lr: 1e-06 +lr_cooldown_end: 0.0 +lr_cooldown_power: 1.0 +lr_scheduler: cosine +model: ViT-L-14-336 +name: 2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp +no_set_device_rank: False +precision: amp +pretrained: /project/deemreason/junteng/Vision4Math/data/openclip-vit-14-336/openclip_model.pt +pretrained_image: False +rank: 0 +remote_sync: None +remote_sync_frequency: 300 +remote_sync_protocol: s3 +report_to: wandb +resume: None +save_frequency: 1 +save_most_recent: False +seed: 0 +siglip: False +skip_scheduler: False +tensorboard: False +tensorboard_path: +torchcompile: False +torchscript: False +trace: False +train_data: /project/deemreason/junteng/Vision4Math/csv_data/plotqa_train_v2.csv +train_data_upsampling_factors: None +train_num_samples: None +use_bn_sync: False +use_bnb_linear: None +val_data: None +val_frequency: 1 +val_num_samples: None +wandb: True +wandb_notes: +wandb_project_name: open-clip--no-hard-sum +warmup: 0 +wd: 0.1 +workers: 4 +world_size: 4 +zeroshot_frequency: 2