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akar49/mri_classifier
This model is a fine-tuned version of 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
