---
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: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Intoxicated_Classification

This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/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