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tsf-gs-rots-wtoken-DRPT0.3-r128-f150-6.6-h768-i3072-p32-b8-e50

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timesformer
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tsf-gs-rots-wtoken-DRPT0.3-r128-f150-6.6-h768-i3072-p32-b8-e50 / README.md
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metadata
license: cc-by-nc-4.0
base_model: facebook/timesformer-base-finetuned-k400
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: tsf-gs-rots-wtoken-DRPT0.3-r128-f150-6.6-h768-i3072-p32-b8-e50
    results: []

tsf-gs-rots-wtoken-DRPT0.3-r128-f150-6.6-h768-i3072-p32-b8-e50

This model is a fine-tuned version of facebook/timesformer-base-finetuned-k400 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 1.7417
  • Accuracy: 0.6150

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • training_steps: 5400
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.1273 0.0202 109 1.1116 0.3262
1.1488 1.0202 218 1.0997 0.3369
1.1365 2.0202 327 1.1016 0.3369
1.1232 3.0202 436 1.1009 0.3369
1.0927 4.0202 545 1.1017 0.3369
1.0807 5.0202 654 1.1055 0.3262
1.1257 6.0202 763 1.1005 0.3262
1.0961 7.0202 872 1.0999 0.3369
1.1192 8.0202 981 1.0997 0.3262
1.1039 9.0202 1090 1.0982 0.3369
1.1047 10.0202 1199 1.0988 0.3369
1.0662 11.0202 1308 1.3609 0.3743
1.197 12.0202 1417 1.1068 0.3369
1.1331 13.0202 1526 1.1085 0.3422
1.1174 14.0202 1635 1.1129 0.3262
1.0838 15.0202 1744 1.0893 0.4011
1.0943 16.0202 1853 1.0385 0.3904
1.0989 17.0202 1962 1.0832 0.5027
1.0326 18.0202 2071 0.9577 0.4813
1.0394 19.0202 2180 0.9385 0.6043
0.9952 20.0202 2289 0.8765 0.6096
0.9504 21.0202 2398 0.8307 0.6096
0.9256 22.0202 2507 0.8004 0.6471
0.8924 23.0202 2616 0.9152 0.5989
0.9158 24.0202 2725 0.7679 0.6952
0.8838 25.0202 2834 0.7533 0.6952
1.0359 26.0202 2943 0.7408 0.6845
0.8345 27.0202 3052 0.7069 0.7112
0.8803 28.0202 3161 0.7740 0.6684
0.7475 29.0202 3270 0.6999 0.7112
0.5596 30.0202 3379 0.8609 0.6364
0.8362 31.0202 3488 1.5082 0.4813
0.672 32.0202 3597 0.7459 0.7059
0.6874 33.0202 3706 0.9255 0.6845
0.6259 34.0202 3815 0.8475 0.6364
0.6356 35.0202 3924 0.8400 0.6791
0.6482 36.0202 4033 0.8579 0.6310
0.5495 37.0202 4142 1.5998 0.5241
0.6663 38.0202 4251 0.7969 0.7112
0.6363 39.0202 4360 1.1134 0.6845
0.6794 40.0202 4469 0.9227 0.6952
0.6632 41.0202 4578 1.1304 0.6631
0.7225 42.0202 4687 0.9182 0.7112
0.6032 43.0202 4796 1.2193 0.6578
0.5534 44.0202 4905 1.4561 0.6524
0.4216 45.0202 5014 1.3694 0.6364
0.6082 46.0202 5123 1.5731 0.5989
0.7025 47.0202 5232 1.8556 0.6257
0.4275 48.0202 5341 1.7699 0.6257
0.462 49.0109 5400 1.7417 0.6150

Framework versions

  • Transformers 4.41.2
  • Pytorch 1.13.0+cu117
  • Datasets 2.20.0
  • Tokenizers 0.19.1