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Intoxicated_Classification

Video Classification
Transformers
TensorBoard
Safetensors
videomae
Generated from Trainer
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Intoxicated_Classification / README.md
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metadata
library_name: transformers
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: Intoxicated_Classification
    results: []

Intoxicated_Classification

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: 1.7660
  • Accuracy: 0.6971
  • F1 Macro: 0.6962
  • F1 Weighted: 0.6990
  • Precision Macro: 0.7040
  • Precision Weighted: 0.7216
  • Recall Macro: 0.7094
  • Recall Weighted: 0.6971

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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • training_steps: 6980

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Macro F1 Weighted Precision Macro Precision Weighted Recall Macro Recall Weighted
0.3101 0.1 698 1.1498 0.6133 0.6127 0.6100 0.6448 0.6663 0.6396 0.6133
0.0223 1.1 1396 2.0621 0.6154 0.6114 0.6046 0.6727 0.6992 0.6528 0.6154
0.071 2.1 2094 1.1017 0.6650 0.6649 0.6641 0.6905 0.7124 0.6884 0.6650
0.1562 3.1 2792 0.9922 0.7803 0.7713 0.7792 0.7746 0.7791 0.7691 0.7803
0.1505 4.1 3490 0.6705 0.8203 0.8157 0.8208 0.8143 0.8217 0.8176 0.8203
0.0634 5.1 4188 2.0951 0.6155 0.6122 0.6059 0.6687 0.6945 0.6515 0.6155
0.0002 6.1 4886 2.2097 0.6473 0.6472 0.6483 0.6627 0.6818 0.6645 0.6473
0.0366 7.1 5584 1.7946 0.6842 0.6837 0.6858 0.6947 0.7133 0.6988 0.6842
0.0002 8.1 6282 1.8526 0.6838 0.6832 0.6856 0.6926 0.7107 0.6972 0.6838
0.0004 9.1 6980 1.7660 0.6971 0.6962 0.6990 0.7040 0.7216 0.7094 0.6971

Framework versions

  • Transformers 4.51.3
  • Pytorch 2.6.0+cu124
  • Datasets 3.5.1
  • Tokenizers 0.21.1