---
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: 0.9544
- Accuracy: 0.6216

## 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: 780

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.1454        | 0.1013 | 79   | 1.1138          | 0.4324   |
| 0.9601        | 1.1013 | 158  | 1.2666          | 0.3514   |
| 0.7864        | 2.1013 | 237  | 1.1963          | 0.4054   |
| 1.0221        | 3.1013 | 316  | 1.1635          | 0.3514   |
| 0.8762        | 4.1013 | 395  | 1.0229          | 0.4595   |
| 0.9761        | 5.1013 | 474  | 0.9012          | 0.5676   |
| 0.6493        | 6.1013 | 553  | 1.0638          | 0.6486   |
| 0.7152        | 7.1013 | 632  | 1.0122          | 0.6216   |
| 0.3646        | 8.1013 | 711  | 0.8835          | 0.6216   |
| 0.2113        | 9.0885 | 780  | 0.9544          | 0.6216   |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0