---
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-ucf101-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-ucf101-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: 0.3402
- Accuracy: 0.9054

## 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: 3530

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.2221        | 0.0989  | 349  | 1.0476          | 0.6204   |
| 0.7062        | 1.0989  | 698  | 0.7049          | 0.7679   |
| 0.626         | 2.0989  | 1047 | 0.4938          | 0.8394   |
| 0.4414        | 3.0989  | 1396 | 0.5213          | 0.8365   |
| 0.4938        | 4.0989  | 1745 | 0.5057          | 0.8248   |
| 0.4516        | 5.0989  | 2094 | 0.4209          | 0.8730   |
| 0.2555        | 6.0989  | 2443 | 0.4210          | 0.8701   |
| 0.3595        | 7.0989  | 2792 | 0.3525          | 0.8993   |
| 0.2301        | 8.0989  | 3141 | 0.3473          | 0.9036   |
| 0.1837        | 9.0989  | 3490 | 0.3532          | 0.8949   |
| 0.3946        | 10.0113 | 3530 | 0.3517          | 0.8964   |


### Framework versions

- Transformers 4.44.1
- Pytorch 2.0.1+cu117
- Datasets 2.21.0
- Tokenizers 0.19.1