---
library_name: transformers
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-judo
  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. -->

# videomae-base-finetuned-judo

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: 0.9681
- Accuracy: 0.6765

## 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: 4
- eval_batch_size: 4
- 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: 1360

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3504        | 0.1   | 136  | 1.5076          | 0.2353   |
| 1.276         | 1.1   | 272  | 1.3571          | 0.4559   |
| 1.3092        | 2.1   | 408  | 1.3368          | 0.3676   |
| 1.0253        | 3.1   | 544  | 1.2489          | 0.4853   |
| 0.8738        | 4.1   | 680  | 1.2059          | 0.4559   |
| 0.8798        | 5.1   | 816  | 1.0972          | 0.5441   |
| 0.6182        | 6.1   | 952  | 1.0025          | 0.5882   |
| 0.6829        | 7.1   | 1088 | 1.0121          | 0.6176   |
| 0.387         | 8.1   | 1224 | 1.0217          | 0.6176   |
| 0.484         | 9.1   | 1360 | 0.9681          | 0.6765   |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.4.0
- Tokenizers 0.21.0