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Chaitanya798800
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videomae-large_5class_UCFCrime

Video Classification
Transformers
Safetensors
videomae
Generated from Trainer
Inference Endpoints
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videomae-large_5class_UCFCrime / README.md
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metadata
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-large
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
model-index:
  - name: videomae-large_6class_UCFCrime
    results: []

videomae-large_6class_UCFCrime

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

  • Loss: 1.6826
  • Accuracy: 0.7523
  • Precision: 0.7853
  • Recall: 0.7523
  • F1 Score: 0.7364

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: 2850

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1 Score
1.5794 0.03 95 1.4947 0.4128 0.2370 0.4128 0.2975
1.7243 1.03 190 1.3362 0.5321 0.4612 0.5321 0.4143
1.6818 2.03 285 1.3317 0.3119 0.4356 0.3119 0.2364
1.0727 3.03 380 1.1666 0.5046 0.5546 0.5046 0.4337
1.1121 4.03 475 1.3545 0.4587 0.6563 0.4587 0.3678
1.0062 5.03 570 1.4656 0.5963 0.5376 0.5963 0.5325
1.2532 6.03 665 1.6516 0.5780 0.5596 0.5780 0.4982
1.9184 7.03 760 1.5020 0.5872 0.7283 0.5872 0.5449
1.0223 8.03 855 1.4417 0.5872 0.5636 0.5872 0.5027
0.9406 9.03 950 1.9402 0.5780 0.5800 0.5780 0.4906
1.3058 10.03 1045 1.7611 0.4679 0.6914 0.4679 0.4463
0.9196 11.03 1140 1.0373 0.6330 0.5648 0.6330 0.5625
0.4191 12.03 1235 0.9139 0.6789 0.7553 0.6789 0.6824
0.4816 13.03 1330 1.0840 0.7248 0.8001 0.7248 0.7176
1.0577 14.03 1425 0.9822 0.7339 0.7724 0.7339 0.7216
0.719 15.03 1520 1.4597 0.6789 0.6954 0.6789 0.6715
0.3427 16.03 1615 1.4807 0.6789 0.7114 0.6789 0.6818
0.6303 17.03 1710 1.9664 0.6881 0.7482 0.6881 0.6604
0.0025 18.03 1805 1.5750 0.7156 0.7403 0.7156 0.7067
0.2404 19.03 1900 2.2045 0.6606 0.7090 0.6606 0.6292
0.0313 20.03 1995 1.6007 0.7248 0.7776 0.7248 0.7091
0.3372 21.03 2090 1.6536 0.7156 0.7811 0.7156 0.6864
0.5431 22.03 2185 2.1961 0.6514 0.7559 0.6514 0.6161
0.0003 23.03 2280 1.6826 0.7523 0.7853 0.7523 0.7364
0.0013 24.03 2375 1.8359 0.7431 0.7906 0.7431 0.7291
0.0025 25.03 2470 2.0891 0.6881 0.7433 0.6881 0.6611
0.0153 26.03 2565 1.8442 0.7431 0.7855 0.7431 0.7296
0.0147 27.03 2660 1.8483 0.7431 0.7816 0.7431 0.7243
0.0002 28.03 2755 1.9301 0.7339 0.7765 0.7339 0.7150
0.0004 29.03 2850 1.9169 0.7339 0.7765 0.7339 0.7150

Framework versions

  • Transformers 4.39.2
  • Pytorch 2.2.1+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2