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# Entrnal_5class_agumm_last_newV6_model

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: 0.0410
- Train Accuracy: 0.9612
- Train Top-3-accuracy: 0.9962
- Validation Loss: 0.3703
- Validation Accuracy: 0.9623
- Validation Top-3-accuracy: 0.9963
- Epoch: 12

## 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': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 1209, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32

### Training results

| Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch |
|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
| 1.0109     | 0.5898         | 0.8913               | 0.5771          | 0.7468              | 0.9576                    | 0     |
| 0.4103     | 0.7997         | 0.9708               | 0.4029          | 0.8329              | 0.9786                    | 1     |
| 0.2249     | 0.8581         | 0.9827               | 0.3677          | 0.8769              | 0.9857                    | 2     |
| 0.1584     | 0.8905         | 0.9877               | 0.3730          | 0.9010              | 0.9893                    | 3     |
| 0.1164     | 0.9097         | 0.9904               | 0.3957          | 0.9169              | 0.9913                    | 4     |
| 0.0841     | 0.9231         | 0.9920               | 0.3896          | 0.9285              | 0.9927                    | 5     |
| 0.0676     | 0.9331         | 0.9932               | 0.3718          | 0.9373              | 0.9937                    | 6     |
| 0.0561     | 0.9408         | 0.9941               | 0.3701          | 0.9440              | 0.9944                    | 7     |
| 0.0500     | 0.9468         | 0.9947               | 0.3691          | 0.9493              | 0.9949                    | 8     |
| 0.0461     | 0.9516         | 0.9952               | 0.3698          | 0.9535              | 0.9954                    | 9     |
| 0.0435     | 0.9554         | 0.9956               | 0.3694          | 0.9570              | 0.9958                    | 10    |
| 0.0418     | 0.9585         | 0.9959               | 0.3705          | 0.9598              | 0.9961                    | 11    |
| 0.0410     | 0.9612         | 0.9962               | 0.3703          | 0.9623              | 0.9963                    | 12    |


### Framework versions

- Transformers 4.44.2
- TensorFlow 2.15.1
- Datasets 3.0.0
- Tokenizers 0.19.1

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