---
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-ucfsuc-subset
  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-ucfsuc-subset

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: 0.6334
- Accuracy: 0.7079

## 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 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: 310

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.7131        | 0.1032 | 32   | 0.7573          | 0.3596   |
| 0.7351        | 1.1032 | 64   | 0.6929          | 0.5393   |
| 0.6892        | 2.1032 | 96   | 0.7539          | 0.5393   |
| 0.693         | 3.1032 | 128  | 0.7079          | 0.5169   |
| 0.5903        | 4.1032 | 160  | 0.7080          | 0.6180   |
| 0.626         | 5.1032 | 192  | 0.6610          | 0.6854   |
| 0.6316        | 6.1032 | 224  | 0.5789          | 0.7303   |
| 0.5287        | 7.1032 | 256  | 0.6366          | 0.7079   |
| 0.6648        | 8.1032 | 288  | 0.6215          | 0.7191   |
| 0.4605        | 9.0710 | 310  | 0.6334          | 0.7079   |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3