| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: google/vit-base-patch16-224-in21k |
| tags: |
| - generated_from_keras_callback |
| model-index: |
| - name: VIT_fourclass_classifier |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information Keras had access to. You should |
| probably proofread and complete it, then remove this comment. --> |
|
|
| # VIT_fourclass_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: 0.0945 |
| - Validation Loss: 1.7241 |
| - Train Accuracy: 0.6974 |
| - 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': np.float32(0.01), 'momentum': 0.0, 'nesterov': False} |
| - training_precision: float32 |
| |
| ### Training results |
| |
| | Train Loss | Validation Loss | Train Accuracy | Epoch | |
| |:----------:|:---------------:|:--------------:|:-----:| |
| | 0.7946 | 1.1484 | 0.6272 | 0 | |
| | 0.3246 | 1.1792 | 0.6769 | 1 | |
| | 0.2266 | 1.2812 | 0.6842 | 2 | |
| | 0.1841 | 1.5085 | 0.6754 | 3 | |
| | 0.1589 | 1.4224 | 0.6944 | 4 | |
| | 0.1244 | 1.4229 | 0.6901 | 5 | |
| | 0.1174 | 1.4858 | 0.6784 | 6 | |
| | 0.1133 | 1.4221 | 0.6974 | 7 | |
| | 0.1026 | 1.4273 | 0.7003 | 8 | |
| | 0.1083 | 1.5406 | 0.7003 | 9 | |
| | 0.1038 | 1.6223 | 0.6974 | 10 | |
| | 0.0876 | 1.5613 | 0.6959 | 11 | |
| | 0.1018 | 1.4540 | 0.7149 | 12 | |
| | 0.0808 | 1.4853 | 0.7193 | 13 | |
| | 0.0945 | 1.7241 | 0.6974 | 14 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.52.4 |
| - TensorFlow 2.18.0 |
| - Datasets 3.6.0 |
| - Tokenizers 0.21.1 |
| |
