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Temo27Anas
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tsf-gs-rot-flip-wtoken-DRPT-r128-f198-4.4-h768-i3072-p32-b4-e60

Video Classification
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timesformer
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tsf-gs-rot-flip-wtoken-DRPT-r128-f198-4.4-h768-i3072-p32-b4-e60 / 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-rot-flip-wtoken-DRPT-r128-f198-4.4-h768-i3072-p32-b4-e60
    results: []

tsf-gs-rot-flip-wtoken-DRPT-r128-f198-4.4-h768-i3072-p32-b4-e60

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: 1.4736
  • Accuracy: 0.7487

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: 4
  • eval_batch_size: 4
  • 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: 13020
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.0904 0.0167 217 1.1132 0.3262
1.1105 1.0167 434 1.1408 0.3262
1.2079 2.0167 651 1.1040 0.3422
1.1561 3.0167 868 1.1015 0.3316
1.1399 4.0167 1085 1.1023 0.3262
1.1942 5.0167 1302 1.1053 0.3743
1.09 6.0167 1519 1.0816 0.3690
1.1052 7.0167 1736 1.1205 0.3369
0.9883 8.0167 1953 1.1582 0.3904
1.0268 9.0167 2170 0.9488 0.5775
0.8247 10.0167 2387 0.9106 0.6417
0.841 11.0167 2604 0.8541 0.6471
0.8026 12.0167 2821 0.7250 0.6898
0.942 13.0167 3038 0.7447 0.6578
0.7162 14.0167 3255 0.8518 0.6043
1.0489 15.0167 3472 0.9505 0.6096
0.8231 16.0167 3689 1.0083 0.5882
0.8702 17.0167 3906 0.7928 0.6684
0.5014 18.0167 4123 0.7290 0.7433
0.5914 19.0167 4340 0.6092 0.7701
0.6259 20.0167 4557 1.0359 0.7487
0.7433 21.0167 4774 1.0086 0.7326
0.2929 22.0167 4991 1.7072 0.6364
0.3763 23.0167 5208 0.9161 0.7487
0.4253 24.0167 5425 1.1377 0.7219
0.7882 25.0167 5642 1.2351 0.7059
0.5644 26.0167 5859 1.5853 0.6150
0.6372 27.0167 6076 0.8027 0.7540
0.3907 28.0167 6293 1.6891 0.6096
0.7374 29.0167 6510 1.2730 0.6791
0.6292 30.0167 6727 1.1093 0.7326
0.2892 31.0167 6944 1.2396 0.7219
0.6779 32.0167 7161 1.6986 0.6524
0.2337 33.0167 7378 1.7567 0.6898
0.3919 34.0167 7595 1.1040 0.7380
0.6117 35.0167 7812 1.0982 0.7701
0.5463 36.0167 8029 1.1052 0.7380
0.3559 37.0167 8246 1.6846 0.6578
0.3356 38.0167 8463 0.9712 0.7487
0.0266 39.0167 8680 1.7524 0.6684
0.3919 40.0167 8897 1.6011 0.7112
0.1367 41.0167 9114 1.1935 0.7433
0.1936 42.0167 9331 1.5143 0.7326
0.7236 43.0167 9548 1.1621 0.7861
0.5877 44.0167 9765 1.5272 0.7326
0.3265 45.0167 9982 1.2536 0.7754
0.4684 46.0167 10199 1.1489 0.7647
0.3436 47.0167 10416 1.3433 0.7701
0.0272 48.0167 10633 1.5171 0.7380
0.3487 49.0167 10850 1.1279 0.7647
0.024 50.0167 11067 1.6780 0.7273
0.4644 51.0167 11284 1.3860 0.7914
0.5177 52.0167 11501 1.7174 0.7219
0.263 53.0167 11718 1.4811 0.7326
0.3323 54.0167 11935 1.4279 0.7807
0.2919 55.0167 12152 1.4855 0.7166
0.1878 56.0167 12369 1.4966 0.7594
0.1307 57.0167 12586 1.3280 0.7594
0.4798 58.0167 12803 1.6149 0.7487
0.0599 59.0167 13020 1.3786 0.7540

Framework versions

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