---
library_name: transformers
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-large
tags:
- generated_from_trainer
model-index:
- name: videomae-large-finetuned-deepfake-subset
  results: []
metrics:
- f1
---


# videomae-large-finetuned-deepfake-subset

This model is a fine-tuned version of [MCG-NJU/videomae-large](https://huggingface.co/MCG-NJU/videomae-large) on the [Deepfake
Detection Challenge](https://www.kaggle.com/competitions/deepfake-detection-challenge) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2588

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data


## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 4470
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6169        | 0.1   | 447  | 0.6023          |
| 0.6086        | 1.1   | 894  | 0.5055          |
| 0.376         | 2.1   | 1341 | 0.4250          |
| 0.3863        | 3.1   | 1788 | 0.6712          |
| 0.249         | 4.1   | 2235 | 0.3951          |
| 0.3233        | 5.1   | 2682 | 0.4969          |
| 0.1995        | 6.1   | 3129 | 0.3744          |
| 0.0874        | 7.1   | 3576 | 0.4104          |
| 0.2518        | 8.1   | 4023 | 0.2647          |
| 0.0118        | 9.1   | 4470 | 0.3337          |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1