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tsf-gs-rots-wtoken-DRPT0.3-r224-f150-6.6-h768-i3072-p32-b8-e50

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timesformer
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tsf-gs-rots-wtoken-DRPT0.3-r224-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-r224-f150-6.6-h768-i3072-p32-b8-e50
    results: []

tsf-gs-rots-wtoken-DRPT0.3-r224-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: 1.2019
  • Accuracy: 0.6043

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
1.1714 0.0202 109 1.1130 0.3369
1.1431 1.0202 218 1.1018 0.3369
1.1363 2.0202 327 1.1018 0.3369
1.1117 3.0202 436 1.1062 0.3369
1.0901 4.0202 545 1.1024 0.3369
1.061 5.0202 654 1.1049 0.3262
1.1047 6.0202 763 1.0996 0.3262
1.0911 7.0202 872 1.1056 0.3369
1.1305 8.0202 981 1.0994 0.3262
1.1145 9.0202 1090 1.0978 0.3262
1.1075 10.0202 1199 1.0921 0.3369
1.0886 11.0202 1308 1.0732 0.4118
1.2003 12.0202 1417 1.1430 0.3369
1.1006 13.0202 1526 1.0699 0.4599
1.0594 14.0202 1635 1.0786 0.3743
1.0283 15.0202 1744 1.0056 0.4813
1.0804 16.0202 1853 1.0239 0.4332
1.1055 17.0202 1962 1.0384 0.5027
1.0577 18.0202 2071 0.9683 0.4813
0.9961 19.0202 2180 1.1497 0.3422
0.9365 20.0202 2289 1.1324 0.4064
1.0121 21.0202 2398 1.0838 0.5080
0.8774 22.0202 2507 1.1312 0.5722
0.8922 23.0202 2616 1.1480 0.5241
1.0667 24.0202 2725 0.9482 0.6096
0.825 25.0202 2834 0.8572 0.6524
0.985 26.0202 2943 0.9455 0.6043
0.8331 27.0202 3052 0.7787 0.6310
0.8799 28.0202 3161 0.8728 0.5989
0.7616 29.0202 3270 0.7851 0.6952
0.6198 30.0202 3379 0.8857 0.6524
0.9501 31.0202 3488 0.7847 0.6791
0.708 32.0202 3597 0.7956 0.6684
0.697 33.0202 3706 0.8540 0.6471
0.7463 34.0202 3815 0.7873 0.7005
0.6234 35.0202 3924 1.0743 0.5615
0.7174 36.0202 4033 0.7159 0.7219
0.6577 37.0202 4142 0.8100 0.6417
0.7482 38.0202 4251 0.8508 0.6631
0.6993 39.0202 4360 0.8796 0.6684
0.6782 40.0202 4469 0.9814 0.6578
0.614 41.0202 4578 0.8482 0.6631
0.7516 42.0202 4687 0.9654 0.6471
0.7425 43.0202 4796 1.0050 0.6257
0.6818 44.0202 4905 1.1266 0.6096
0.4599 45.0202 5014 1.1824 0.6364
0.7561 46.0202 5123 1.1567 0.6417
0.7543 47.0202 5232 1.2946 0.5882
0.4198 48.0202 5341 1.2598 0.5882
0.6238 49.0109 5400 1.2019 0.6043

Framework versions

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