---
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_BdSLW60_FrameRateCorrected_withoutAug
  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_BdSLW60_FrameRateCorrected_withoutAug

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: 0.7568
- Accuracy: 0.8080

## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 22400

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 3.1009        | 0.0400 | 897  | 2.3993          | 0.5082   |
| 0.3152        | 1.0401 | 1795 | 0.2117          | 0.9612   |
| 0.0363        | 2.0401 | 2693 | 0.0375          | 0.9918   |
| 0.0431        | 3.0401 | 3591 | 0.0841          | 0.9765   |
| 0.0554        | 4.0400 | 4488 | 0.0323          | 0.9941   |
| 0.0467        | 5.0401 | 5386 | 0.0614          | 0.9835   |
| 0.0324        | 6.0401 | 6284 | 0.0944          | 0.9859   |
| 0.0381        | 7.0401 | 7182 | 0.0583          | 0.9871   |
| 0.0039        | 8.0400 | 8079 | 0.0307          | 0.9918   |
| 0.0149        | 9.0401 | 8977 | 0.0694          | 0.9859   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1