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videomae-base-finetuned-ucf_crime

Video Classification
Transformers
PyTorch
TensorBoard
videomae
Generated from Trainer
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videomae-base-finetuned-ucf_crime / README.md
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metadata
license: cc-by-nc-4.0
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: videomae-base-finetuned-ucf_crime
    results: []

videomae-base-finetuned-ucf_crime

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

  • Loss: 2.0871
  • Accuracy: 0.5058

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: 2
  • eval_batch_size: 2
  • 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: 6000

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.6487 0.02 120 0.6813 0.6989
0.4529 1.02 240 0.7467 0.5678
0.5165 2.02 360 0.7600 0.7264
0.9203 3.02 480 0.6189 0.6517
1.2667 4.02 600 0.6702 0.6511
1.0452 5.02 720 0.5703 0.7592
0.3893 6.02 840 0.5524 0.7397
1.2013 7.02 960 0.7470 0.6109
1.4903 8.02 1080 1.0094 0.5695
0.5434 9.02 1200 0.5189 0.7586
0.8344 10.02 1320 0.7674 0.5552
1.1621 11.02 1440 0.9610 0.5874
0.8743 12.02 1560 0.8160 0.6448
0.7118 13.02 1680 0.6015 0.6592
0.8404 14.02 1800 0.7149 0.7661
1.0397 15.02 1920 0.6360 0.7770
1.3511 16.02 2040 0.9110 0.6523
1.0257 17.02 2160 1.0642 0.5891
1.0726 18.02 2280 0.7299 0.8213
0.5609 19.02 2400 0.8921 0.6408
0.495 20.02 2520 0.7762 0.7632
1.6306 21.02 2640 0.9976 0.7092
1.0072 22.02 2760 0.5697 0.8029
0.4503 23.02 2880 0.8448 0.6914
0.8306 24.02 3000 0.9617 0.7351
0.8824 25.02 3120 1.1839 0.6759
0.837 26.02 3240 1.6406 0.6075
0.4214 27.02 3360 1.1276 0.6920
0.5166 28.02 3480 1.0815 0.7626
0.6925 29.02 3600 1.1492 0.7086
0.2864 30.02 3720 1.1919 0.7345
0.463 31.02 3840 1.3524 0.6937
1.1162 32.02 3960 1.8301 0.5822
0.0033 33.02 4080 1.4447 0.6891
0.002 34.02 4200 1.6565 0.6960
0.0017 35.02 4320 1.5357 0.7282
0.2289 36.02 4440 1.9812 0.6397
0.4801 37.02 4560 2.2316 0.6167
1.0323 38.02 4680 2.1380 0.5822
0.0715 39.02 4800 1.9264 0.6345
0.0024 40.02 4920 2.6257 0.5023
0.4734 41.02 5040 1.8666 0.6316
0.3108 42.02 5160 1.5493 0.7299
0.2318 43.02 5280 2.0831 0.6333
0.6623 44.02 5400 2.3276 0.6029
0.0011 45.02 5520 2.0469 0.6684
0.32 46.02 5640 2.4646 0.6138
0.0054 47.02 5760 2.5998 0.5897
0.2733 48.02 5880 2.7594 0.5638
0.0005 49.02 6000 2.8625 0.55

Framework versions

  • Transformers 4.29.2
  • Pytorch 1.13.1+cu117
  • Datasets 2.12.0
  • Tokenizers 0.13.3