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videomae-base-short-ssv2-finetuned-ct_cpc

Safetensors
videomae
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metadata
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base-short-ssv2
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: videomae-base-short-ssv2-finetuned-ct_cpc
    results: []

videomae-base-short-ssv2-finetuned-ct_cpc

This model is a fine-tuned version of MCG-NJU/videomae-base-short-ssv2 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 1.1547
  • Accuracy: 0.6508

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: 1e-05
  • train_batch_size: 6
  • eval_batch_size: 6
  • 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: 2300

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.4832 0.02 47 1.1203 0.6667
1.32 1.02 94 1.0505 0.6667
1.1539 2.02 141 1.0525 0.6667
1.2399 3.02 188 1.0354 0.6667
0.9928 4.02 235 1.0516 0.6667
1.0054 5.02 282 1.0297 0.6667
1.1166 6.02 329 1.0247 0.6667
1.2715 7.02 376 1.0098 0.6667
1.0317 8.02 423 1.0404 0.6667
1.1258 9.02 470 1.0054 0.6667
0.8991 10.02 517 1.0477 0.6667
0.8957 11.02 564 1.0608 0.6667
1.078 12.02 611 1.0166 0.6667
0.9067 13.02 658 1.2110 0.6667
1.0383 14.02 705 1.0380 0.6
1.2244 15.02 752 1.1041 0.6667
1.0364 16.02 799 1.0123 0.6
0.9662 17.02 846 1.1803 0.6333
0.9299 18.02 893 1.0910 0.6333
0.877 19.02 940 1.0183 0.65
0.8649 20.02 987 1.1415 0.6167
0.8841 21.02 1034 1.0975 0.6333
0.6276 22.02 1081 1.0728 0.6
0.7974 23.02 1128 1.1676 0.5833
0.7381 24.02 1175 1.1624 0.55
0.4807 25.02 1222 1.1930 0.55
0.6812 26.02 1269 1.1511 0.6
0.5802 27.02 1316 1.2315 0.6167
0.5943 28.02 1363 1.1641 0.5667
0.5416 29.02 1410 1.0963 0.6
0.6676 30.02 1457 1.1131 0.5667
0.4085 31.02 1504 1.2948 0.5833
0.4548 32.02 1551 1.2788 0.6333
0.4351 33.02 1598 1.1922 0.5667
0.5641 34.02 1645 1.2677 0.55
0.4471 35.02 1692 1.2766 0.6167
0.361 36.02 1739 1.2525 0.55
0.5668 37.02 1786 1.2928 0.55
0.5262 38.02 1833 1.2429 0.6333
0.4901 39.02 1880 1.1457 0.5667
0.481 40.02 1927 1.2137 0.5833
0.3566 41.02 1974 1.1830 0.6167
0.5147 42.02 2021 1.1629 0.6
0.4013 43.02 2068 1.2735 0.6
0.3895 44.02 2115 1.2758 0.6167
0.3727 45.02 2162 1.2519 0.5833
0.2379 46.02 2209 1.2318 0.5833
0.2985 47.02 2256 1.2594 0.5833
0.2991 48.02 2300 1.2510 0.5833

Framework versions

  • Transformers 4.37.2
  • Pytorch 2.0.1+cu117
  • Datasets 3.0.1
  • Tokenizers 0.15.1