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# ansilmbabl/vit-base-patch16-224-in21k-Cards

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: 0.0861
- Train Accuracy: 0.9775
- Train Top-3-accuracy: 0.9991
- Validation Loss: 0.6749
- Validation Accuracy: 0.8113
- Validation Top-3-accuracy: 0.9740
- Epoch: 5

## 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: {'inner_optimizer': {'module': 'transformers.optimization_tf', 'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 53200, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.8999999761581421, 'beta_2': 0.9990000128746033, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}, 'registered_name': 'AdamWeightDecay'}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16

### Training results

| Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch |
|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
| 1.3188     | 0.6043         | 0.8822               | 0.8883          | 0.7130              | 0.9537                    | 0     |
| 0.6864     | 0.7853         | 0.9705               | 0.6807          | 0.7647              | 0.9650                    | 1     |
| 0.3966     | 0.8815         | 0.9896               | 0.5364          | 0.8120              | 0.9777                    | 2     |
| 0.2310     | 0.9338         | 0.9964               | 0.4965          | 0.8427              | 0.9817                    | 3     |
| 0.1333     | 0.9649         | 0.9981               | 0.4799          | 0.8513              | 0.9837                    | 4     |
| 0.0861     | 0.9775         | 0.9991               | 0.6749          | 0.8113              | 0.9740                    | 5     |


### Framework versions

- Transformers 4.41.2
- TensorFlow 2.14.0
- Datasets 2.19.1
- Tokenizers 0.19.1

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