---
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-scratch
  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-scratch

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: 1.0187
- Accuracy: 0.7808

## 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: 12
- eval_batch_size: 12
- 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: 3952

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6869        | 0.08  | 330  | 0.6326          | 0.6490   |
| 0.6342        | 1.08  | 660  | 0.6356          | 0.6447   |
| 0.6718        | 2.08  | 990  | 0.6112          | 0.6648   |
| 0.5003        | 3.08  | 1320 | 0.5741          | 0.6991   |
| 0.4131        | 4.08  | 1650 | 0.5480          | 0.7077   |
| 0.3233        | 5.08  | 1980 | 0.5564          | 0.7464   |
| 0.2411        | 6.08  | 2310 | 0.4929          | 0.7923   |
| 0.3402        | 7.08  | 2640 | 0.7592          | 0.7593   |
| 0.2174        | 8.08  | 2970 | 0.7752          | 0.7779   |
| 0.1706        | 9.08  | 3300 | 0.8511          | 0.7923   |
| 0.1127        | 10.08 | 3630 | 0.9263          | 0.7994   |
| 0.062         | 11.08 | 3952 | 1.0187          | 0.7808   |


### Framework versions

- Transformers 4.39.0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2