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abduldattijo
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videomae-base-finetuned-ucf101-subset-V3KILLER

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videomae-base-finetuned-ucf101-subset-V3KILLER / README.md
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metadata
license: cc-by-nc-4.0
base_model: abduldattijo/videomae-base-finetuned-ucf101-subset
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: videomae-base-finetuned-ucf101-subset-V3KILLER
    results: []

videomae-base-finetuned-ucf101-subset-V3KILLER

This model is a fine-tuned version of abduldattijo/videomae-base-finetuned-ucf101-subset on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2181
  • Accuracy: 0.9615

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: 5960

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.3447 0.03 150 0.1339 0.9579
0.3161 1.03 300 0.1538 0.9465
0.3386 2.03 450 0.3260 0.9019
0.3572 3.03 600 0.1967 0.9311
0.3699 4.03 750 0.1661 0.9505
0.3125 5.03 900 0.3292 0.9205
0.4785 6.03 1050 0.2029 0.9324
0.3477 7.03 1200 0.1534 0.9385
0.2909 8.03 1350 0.1265 0.9571
0.2646 9.03 1500 0.1239 0.9586
0.3339 10.03 1650 0.1341 0.9628
0.0954 11.03 1800 0.1835 0.9423
0.3861 12.03 1950 0.2241 0.9467
0.248 13.03 2100 0.1258 0.9620
0.2513 14.03 2250 0.2217 0.9357
0.1133 15.03 2400 0.2129 0.9406
0.1421 16.03 2550 0.3006 0.9264
0.0248 17.03 2700 0.3868 0.9142
0.0166 18.03 2850 0.2594 0.9518
0.0874 19.03 3000 0.3652 0.9252
0.0889 20.03 3150 0.2249 0.9533
0.0804 21.03 3300 0.2027 0.9628
0.0019 22.03 3450 0.4682 0.9212
0.0405 23.03 3600 0.2425 0.9493
0.0847 24.03 3750 0.2456 0.9558
0.1656 25.03 3900 0.2623 0.9505
0.1007 26.03 4050 0.2389 0.9484
0.0616 27.03 4200 0.2529 0.9543
0.0005 28.03 4350 0.1521 0.9732
0.0006 29.03 4500 0.4115 0.9165
0.0007 30.03 4650 0.4279 0.9220
0.0004 31.03 4800 0.3572 0.9372
0.0003 32.03 4950 0.3314 0.9419
0.0002 33.03 5100 0.4008 0.9347
0.0611 34.03 5250 0.4632 0.9239
0.0003 35.03 5400 0.3756 0.9368
0.0003 36.03 5550 0.3745 0.9429
0.163 37.03 5700 0.3967 0.9383
0.0059 38.03 5850 0.3808 0.9389
0.0003 39.02 5960 0.3824 0.9395

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.1+cu118
  • Datasets 2.15.0
  • Tokenizers 0.15.0