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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224-in21k |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: VIT_fourclass_Jun25 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information Keras had access to. You should |
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probably proofread and complete it, then remove this comment. --> |
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# VIT_fourclass_Jun25 |
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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. |
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It achieves the following results on the evaluation set: |
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- Train Loss: 0.0938 |
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- Validation Loss: 2.3960 |
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- Train Accuracy: 0.51 |
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- Epoch: 14 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- 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} |
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- training_precision: float32 |
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### Training results |
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| Train Loss | Validation Loss | Train Accuracy | Epoch | |
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|:----------:|:---------------:|:--------------:|:-----:| |
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| 0.9196 | 1.2060 | 0.38 | 0 | |
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| 0.3792 | 1.4807 | 0.48 | 1 | |
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| 0.2729 | 1.7396 | 0.45 | 2 | |
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| 0.2006 | 2.4379 | 0.29 | 3 | |
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| 0.1996 | 2.4795 | 0.36 | 4 | |
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| 0.1734 | 2.7916 | 0.35 | 5 | |
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| 0.1860 | 4.1270 | 0.09 | 6 | |
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| 0.1490 | 2.7235 | 0.37 | 7 | |
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| 0.1077 | 3.5380 | 0.26 | 8 | |
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| 0.1173 | 2.7697 | 0.42 | 9 | |
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| 0.1526 | 2.7868 | 0.42 | 10 | |
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| 0.1161 | 3.1132 | 0.36 | 11 | |
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| 0.1093 | 3.5738 | 0.33 | 12 | |
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| 0.0884 | 3.1227 | 0.37 | 13 | |
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| 0.0938 | 2.3960 | 0.51 | 14 | |
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### Framework versions |
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- Transformers 4.52.4 |
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- TensorFlow 2.18.0 |
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- Datasets 3.6.0 |
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- Tokenizers 0.21.1 |
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