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# akar49/mri_classifier

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:
- Train Loss: 0.1032
- Validation Loss: 0.1556
- Train Accuracy: 0.9367
- Epoch: 14

## 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:
- optimizer: {'name': 'SGD', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 0.001, 'momentum': 0.0, 'nesterov': False}
- training_precision: float32

### Training results

| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.6447     | 0.6133          | 0.7004         | 0     |
| 0.5405     | 0.5010          | 0.8256         | 1     |
| 0.4181     | 0.3917          | 0.8650         | 2     |
| 0.3122     | 0.3189          | 0.9058         | 3     |
| 0.2474     | 0.3069          | 0.8875         | 4     |
| 0.2021     | 0.2733          | 0.9044         | 5     |
| 0.1745     | 0.2455          | 0.9100         | 6     |
| 0.1591     | 0.2203          | 0.9212         | 7     |
| 0.1450     | 0.2350          | 0.9142         | 8     |
| 0.1397     | 0.2122          | 0.9198         | 9     |
| 0.1227     | 0.2098          | 0.9212         | 10    |
| 0.1169     | 0.1754          | 0.9325         | 11    |
| 0.1080     | 0.1782          | 0.9339         | 12    |
| 0.0971     | 0.1705          | 0.9353         | 13    |
| 0.1032     | 0.1556          | 0.9367         | 14    |


### Framework versions

- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.1
- Tokenizers 0.13.3

notumor tumor