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# image_classification

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: 0.1919
- Accuracy: 0.9609

## 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: 0.0001
- 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
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 65   | 0.1913          | 0.9609   |
| No log        | 2.0   | 130  | 0.2691          | 0.9141   |
| No log        | 3.0   | 195  | 0.2559          | 0.9219   |
| No log        | 4.0   | 260  | 0.2436          | 0.9219   |
| No log        | 5.0   | 325  | 0.2779          | 0.9219   |
| No log        | 6.0   | 390  | 0.2464          | 0.9297   |
| No log        | 7.0   | 455  | 0.1589          | 0.9609   |
| 0.1457        | 8.0   | 520  | 0.0968          | 0.9766   |
| 0.1457        | 9.0   | 585  | 0.2216          | 0.9453   |
| 0.1457        | 10.0  | 650  | 0.1490          | 0.9609   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2

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