---
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: 0.7149
- Accuracy: 0.9038

## 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: 1
- eval_batch_size: 1
- 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: 15000

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0174        | 0.02  | 300   | 0.2750          | 0.9423   |
| 0.0218        | 1.02  | 600   | 0.7020          | 0.8269   |
| 0.5348        | 2.02  | 900   | 1.1836          | 0.7692   |
| 0.9667        | 3.02  | 1200  | 1.9316          | 0.5962   |
| 2.0242        | 4.02  | 1500  | 1.5680          | 0.6923   |
| 0.7852        | 5.02  | 1800  | 0.5868          | 0.9038   |
| 1.9104        | 6.02  | 2100  | 1.8121          | 0.7115   |
| 1.1466        | 7.02  | 2400  | 1.3801          | 0.75     |
| 0.0025        | 8.02  | 2700  | 1.2799          | 0.7692   |
| 0.0005        | 9.02  | 3000  | 1.8073          | 0.7115   |
| 0.0005        | 10.02 | 3300  | 0.4820          | 0.9231   |
| 0.5816        | 11.02 | 3600  | 0.7625          | 0.8846   |
| 0.0014        | 12.02 | 3900  | 0.4762          | 0.9231   |
| 0.0694        | 13.02 | 4200  | 1.3250          | 0.8077   |
| 0.0002        | 14.02 | 4500  | 0.9637          | 0.8654   |
| 0.0003        | 15.02 | 4800  | 0.4808          | 0.9231   |
| 0.0003        | 16.02 | 5100  | 0.8623          | 0.8846   |
| 0.0002        | 17.02 | 5400  | 0.6881          | 0.9231   |
| 0.0004        | 18.02 | 5700  | 0.5577          | 0.9038   |
| 0.0001        | 19.02 | 6000  | 0.5069          | 0.9231   |
| 0.4994        | 20.02 | 6300  | 0.3667          | 0.9423   |
| 0.0002        | 21.02 | 6600  | 0.3666          | 0.9423   |
| 1.0279        | 22.02 | 6900  | 1.0781          | 0.8654   |
| 0.0135        | 23.02 | 7200  | 2.2670          | 0.7308   |
| 0.0002        | 24.02 | 7500  | 0.1732          | 0.9615   |
| 0.0002        | 25.02 | 7800  | 0.4422          | 0.9423   |
| 0.0001        | 26.02 | 8100  | 0.8196          | 0.8846   |
| 0.0001        | 27.02 | 8400  | 0.8037          | 0.8846   |
| 0.0001        | 28.02 | 8700  | 0.8696          | 0.8846   |
| 0.0002        | 29.02 | 9000  | 0.7887          | 0.9231   |
| 0.7745        | 30.02 | 9300  | 0.3868          | 0.9423   |
| 0.0001        | 31.02 | 9600  | 0.4386          | 0.9423   |
| 0.0002        | 32.02 | 9900  | 0.4036          | 0.9423   |
| 0.0001        | 33.02 | 10200 | 0.3513          | 0.9423   |
| 0.0001        | 34.02 | 10500 | 0.3075          | 0.9423   |
| 0.0001        | 35.02 | 10800 | 0.5712          | 0.9231   |
| 0.0005        | 36.02 | 11100 | 0.6482          | 0.9231   |
| 0.0001        | 37.02 | 11400 | 0.8843          | 0.9038   |
| 0.0001        | 38.02 | 11700 | 0.9147          | 0.8846   |
| 0.0001        | 39.02 | 12000 | 0.6891          | 0.9038   |
| 0.0001        | 40.02 | 12300 | 0.8976          | 0.8846   |
| 0.0001        | 41.02 | 12600 | 1.6405          | 0.8077   |
| 0.0001        | 42.02 | 12900 | 1.0550          | 0.8654   |
| 0.0           | 43.02 | 13200 | 1.0356          | 0.8654   |
| 0.0           | 44.02 | 13500 | 1.0037          | 0.8462   |
| 0.0           | 45.02 | 13800 | 0.9632          | 0.8654   |
| 0.0           | 46.02 | 14100 | 0.6649          | 0.9231   |
| 0.0           | 47.02 | 14400 | 0.8702          | 0.8846   |
| 0.0           | 48.02 | 14700 | 0.7201          | 0.9038   |
| 0.0           | 49.02 | 15000 | 0.7149          | 0.9038   |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1