---
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base-finetuned-kinetics
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-kinetics-finetuned-round2
  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-kinetics-finetuned-round2

This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4130
- Accuracy: 0.8846

## 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: 9e-05
- train_batch_size: 4
- eval_batch_size: 4
- 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: 1804

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7036        | 0.09  | 165  | 1.0471          | 0.7005   |
| 0.4684        | 1.09  | 330  | 0.5678          | 0.8214   |
| 0.4301        | 2.09  | 495  | 0.7029          | 0.7857   |
| 0.1361        | 3.09  | 660  | 0.4828          | 0.8599   |
| 0.0118        | 4.09  | 825  | 0.5531          | 0.8324   |
| 0.0721        | 5.09  | 990  | 0.3536          | 0.8956   |
| 0.001         | 6.09  | 1155 | 0.4194          | 0.8929   |
| 0.0017        | 7.09  | 1320 | 0.3476          | 0.9038   |
| 0.0007        | 8.09  | 1485 | 0.4356          | 0.8819   |
| 0.0008        | 9.09  | 1650 | 0.4232          | 0.8874   |
| 0.0007        | 10.09 | 1804 | 0.4130          | 0.8846   |


### Framework versions

- Transformers 4.38.2
- Pytorch 1.13.1+cu117
- Datasets 2.18.0
- Tokenizers 0.15.2