---
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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/gauravkum2002-personal/huggingface/runs/0lr8dfax)
# 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: 0.2412
- Accuracy: 0.9484

## 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: 16
- eval_batch_size: 16
- 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: 148

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 2.3597        | 0.1284 | 19   | 2.0514          | 0.4714   |
| 1.9928        | 1.1284 | 38   | 1.4878          | 0.5429   |
| 1.1016        | 2.1284 | 57   | 0.7900          | 0.7857   |
| 0.4919        | 3.1284 | 76   | 0.5123          | 0.8286   |
| 0.324         | 4.1284 | 95   | 0.3623          | 0.8714   |
| 0.167         | 5.1284 | 114  | 0.2944          | 0.8857   |
| 0.1024        | 6.1284 | 133  | 0.2258          | 0.9      |
| 0.0915        | 7.1014 | 148  | 0.2249          | 0.9      |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1