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videomae-base-finetuned-kinetics-fight_22-01-2024

Video Classification
Transformers
Safetensors
videomae
Generated from Trainer
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videomae-base-finetuned-kinetics-fight_22-01-2024 / README.md
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metadata
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base-finetuned-kinetics
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
model-index:
  - name: videomae-base-finetuned-kinetics-fight_22-01-2024
    results: []

videomae-base-finetuned-kinetics-fight_22-01-2024

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

  • Loss: 0.2265
  • Accuracy: 0.9159
  • Precision: 0.9507
  • Recall: 0.8773

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-07
  • train_batch_size: 14
  • eval_batch_size: 14
  • 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: 15820

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall
0.6066 0.05 792 0.5969 0.7509 0.7817 0.6961
0.429 1.05 1584 0.3898 0.8422 0.8937 0.7767
0.184 2.05 2376 0.2700 0.8859 0.9281 0.8367
0.1911 3.05 3168 0.2280 0.9028 0.9405 0.8601
0.1115 4.05 3960 0.2218 0.9063 0.9436 0.8642
0.1799 5.05 4752 0.2293 0.9090 0.9604 0.8532
0.1282 6.05 5544 0.2265 0.9159 0.9507 0.8773
0.1211 7.05 6336 0.2554 0.9087 0.9562 0.8567
0.076 8.05 7128 0.2738 0.9063 0.9588 0.8491
0.1152 9.05 7920 0.2785 0.9090 0.9541 0.8594
0.0281 10.05 8712 0.2852 0.9118 0.9537 0.8656
0.0806 11.05 9504 0.2994 0.9094 0.9548 0.8594
0.0755 12.05 10296 0.3124 0.9104 0.9556 0.8608
0.0986 13.05 11088 0.3134 0.9114 0.9462 0.8725
0.0222 14.05 11880 0.3241 0.9111 0.9550 0.8629
0.0272 15.05 12672 0.3269 0.9125 0.9503 0.8704
0.0657 16.05 13464 0.3401 0.9097 0.9556 0.8594
0.1083 17.05 14256 0.3424 0.9097 0.9549 0.8601
0.0059 18.05 15048 0.3461 0.9094 0.9555 0.8587
0.0143 19.05 15820 0.3462 0.9094 0.9555 0.8587

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2