---
library_name: transformers
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: videomae-base-videoMAE
  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-videoMAE

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: 1.0497
- Accuracy: 0.6667
- Precision: 0.8056
- Recall: 0.6667
- F1: 0.6348

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1275

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.4685        | 1.0    | 256  | 0.2059          | 1.0      | 1.0       | 1.0    | 1.0    |
| 0.4451        | 2.0    | 512  | 0.1075          | 0.9333   | 0.9407    | 0.9333 | 0.9327 |
| 0.1107        | 3.0    | 768  | 3.2224          | 0.4667   | 0.2178    | 0.4667 | 0.2970 |
| 0.0004        | 4.0    | 1024 | 0.7245          | 0.7333   | 0.8303    | 0.7333 | 0.7185 |
| 0.0002        | 4.9805 | 1275 | 1.0497          | 0.6667   | 0.8056    | 0.6667 | 0.6348 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1