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# vit-base-patch16-224-in21k-leukemia

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 Leukemia Dataset hosted on kaggle https://www.kaggle.com/datasets/andrewmvd/leukemia-classification.
It achieves the following results on the evaluation set:
- Train Loss: 0.3256
- Train Accuracy: 0.8795
- Validation Loss: 0.6907
- Validation Accuracy: 0.6848
- Epoch: 13

## Model description

Google Vision Transormer (ViT). fine-tuned on the white blood cancer - Leukemia - dataset

## Intended uses & limitations

This model was fine-tuned as a part of my project `LeukemiaAI`, a fully integrated pipeline
to detect Leukemia.

**Github Repo**:
https://github.com/MohammedSaLah-Eldeen/LeukemiaAI

### Training hyperparameters

- training_precision: mixed_float16
- optimizer: {
      'inner_optimizer': {
          'module': 'keras.optimizers.experimental',
          'class_name': 'SGD',
          'config': {
              'name': 'SGD',
              'weight_decay': None,
              'clipnorm': None,
              'global_clipnorm': 1,
              'clipvalue': None,
              'use_ema': False,
              'ema_momentum': 0.99,
              'ema_overwrite_frequency': None,
              'jit_compile': True,
              'is_legacy_optimizer': False,
              'learning_rate': {
                  'module': 'keras.optimizers.schedules',
                  'class_name': 'CosineDecay',
                  'config': {
                      'initial_learning_rate': 0.001,
                      'decay_steps': 896,
                      'alpha': 0.0,
                      'name': None,
                      'warmup_target': None,
                      'warmup_steps': 0
                  },
                  'registered_name': None
              },
              'momentum': 0.9,
              'nesterov': False
          },
          'registered_name': None
      },
      'dynamic': True,
      'initial_scale': 32768.0,
      'dynamic_growth_steps': 2000
}
### Training results

| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5007     | 0.7629         | 0.7206          | 0.6643              | 0     |
| 0.3958     | 0.8418         | 0.7137          | 0.6686              | 1     |
| 0.3578     | 0.8632         | 0.6998          | 0.6789              | 2     |
| 0.3377     | 0.8713         | 0.6899          | 0.6843              | 3     |
| 0.3274     | 0.8778         | 0.6869          | 0.6832              | 4     |
| 0.3261     | 0.8792         | 0.6880          | 0.6859              | 5     |
| 0.3257     | 0.8797         | 0.6906          | 0.6848              | 6     |
| 0.3255     | 0.8796         | 0.6896          | 0.6859              | 7     |
| 0.3256     | 0.8794         | 0.6901          | 0.6848              | 8     |
| 0.3258     | 0.8795         | 0.6867          | 0.6864              | 9     |
| 0.3258     | 0.8793         | 0.6896          | 0.6859              | 10    |
| 0.3256     | 0.8796         | 0.6871          | 0.6864              | 11    |
| 0.3255     | 0.8795         | 0.6897          | 0.6853              | 12    |
| 0.3256     | 0.8795         | 0.6907          | 0.6848              | 13    |


### Framework versions

- Transformers 4.35.0
- TensorFlow 2.13.0
- Datasets 2.1.0
- Tokenizers 0.14.1

hem all