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# Image-Arousal-new

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6535
- Accuracy: 0.4591

## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.2322        | 0.1855 | 100  | 1.2411          | 0.4452   |
| 1.1613        | 0.3711 | 200  | 1.2600          | 0.3987   |
| 1.2851        | 0.5566 | 300  | 1.2428          | 0.4052   |
| 1.1931        | 0.7421 | 400  | 1.2041          | 0.4559   |
| 1.1098        | 0.9276 | 500  | 1.1918          | 0.4586   |
| 1.1714        | 1.1132 | 600  | 1.1806          | 0.4721   |
| 1.1216        | 1.2987 | 700  | 1.1692          | 0.4651   |
| 1.2208        | 1.4842 | 800  | 1.1801          | 0.4614   |
| 1.0644        | 1.6698 | 900  | 1.1775          | 0.4596   |
| 1.1638        | 1.8553 | 1000 | 1.2031          | 0.4721   |
| 0.9559        | 2.0408 | 1100 | 1.2392          | 0.4521   |
| 0.8442        | 2.2263 | 1200 | 1.2544          | 0.4661   |
| 0.8713        | 2.4119 | 1300 | 1.2792          | 0.4744   |
| 0.8442        | 2.5974 | 1400 | 1.2618          | 0.4647   |
| 0.831         | 2.7829 | 1500 | 1.3202          | 0.4554   |
| 0.7774        | 2.9685 | 1600 | 1.3087          | 0.4572   |
| 0.5501        | 3.1540 | 1700 | 1.4975          | 0.4600   |
| 0.6069        | 3.3395 | 1800 | 1.5869          | 0.4512   |
| 0.4397        | 3.5250 | 1900 | 1.6458          | 0.4387   |
| 0.4468        | 3.7106 | 2000 | 1.6341          | 0.4493   |
| 0.4198        | 3.8961 | 2100 | 1.6535          | 0.4591   |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1

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