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# Image-Valence

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the custom dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4464
- Accuracy: 0.5863

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2256        | 0.78  | 100  | 1.0936          | 0.5451   |
| 0.7315        | 1.56  | 200  | 0.9981          | 0.5882   |
| 0.2118        | 2.34  | 300  | 1.1650          | 0.5902   |
| 0.1119        | 3.12  | 400  | 1.2864          | 0.5863   |
| 0.1116        | 3.91  | 500  | 1.4464          | 0.5863   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1

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