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# smids_3x_deit_base_adamax_0001_fold3

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7452
- Accuracy: 0.915

## 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:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2572        | 1.0   | 225   | 0.2588          | 0.9117   |
| 0.1813        | 2.0   | 450   | 0.2945          | 0.9033   |
| 0.0684        | 3.0   | 675   | 0.3525          | 0.8917   |
| 0.0167        | 4.0   | 900   | 0.3756          | 0.9083   |
| 0.0335        | 5.0   | 1125  | 0.4340          | 0.9233   |
| 0.0108        | 6.0   | 1350  | 0.5239          | 0.905    |
| 0.0092        | 7.0   | 1575  | 0.5269          | 0.915    |
| 0.0041        | 8.0   | 1800  | 0.5932          | 0.905    |
| 0.0041        | 9.0   | 2025  | 0.5685          | 0.9083   |
| 0.0072        | 10.0  | 2250  | 0.5896          | 0.9017   |
| 0.0071        | 11.0  | 2475  | 0.6208          | 0.8933   |
| 0.0024        | 12.0  | 2700  | 0.8087          | 0.885    |
| 0.0           | 13.0  | 2925  | 0.5984          | 0.9167   |
| 0.0097        | 14.0  | 3150  | 0.5788          | 0.9183   |
| 0.0           | 15.0  | 3375  | 0.5731          | 0.9183   |
| 0.0           | 16.0  | 3600  | 0.5894          | 0.915    |
| 0.0           | 17.0  | 3825  | 0.6180          | 0.91     |
| 0.0           | 18.0  | 4050  | 0.6105          | 0.9083   |
| 0.0           | 19.0  | 4275  | 0.6354          | 0.9117   |
| 0.0           | 20.0  | 4500  | 0.6230          | 0.9117   |
| 0.0           | 21.0  | 4725  | 0.6553          | 0.91     |
| 0.0           | 22.0  | 4950  | 0.6378          | 0.9117   |
| 0.0           | 23.0  | 5175  | 0.6483          | 0.91     |
| 0.0           | 24.0  | 5400  | 0.6523          | 0.9133   |
| 0.0037        | 25.0  | 5625  | 0.6514          | 0.91     |
| 0.0           | 26.0  | 5850  | 0.6779          | 0.9117   |
| 0.0           | 27.0  | 6075  | 0.6547          | 0.9133   |
| 0.0           | 28.0  | 6300  | 0.6677          | 0.9083   |
| 0.0036        | 29.0  | 6525  | 0.7232          | 0.9117   |
| 0.0033        | 30.0  | 6750  | 0.6928          | 0.915    |
| 0.003         | 31.0  | 6975  | 0.7388          | 0.9117   |
| 0.0           | 32.0  | 7200  | 0.6975          | 0.9133   |
| 0.0           | 33.0  | 7425  | 0.6914          | 0.9117   |
| 0.0           | 34.0  | 7650  | 0.7362          | 0.9083   |
| 0.0           | 35.0  | 7875  | 0.7090          | 0.9133   |
| 0.0           | 36.0  | 8100  | 0.6994          | 0.915    |
| 0.0           | 37.0  | 8325  | 0.7110          | 0.9083   |
| 0.0           | 38.0  | 8550  | 0.7083          | 0.915    |
| 0.0           | 39.0  | 8775  | 0.7138          | 0.9133   |
| 0.0           | 40.0  | 9000  | 0.7202          | 0.915    |
| 0.0           | 41.0  | 9225  | 0.7321          | 0.9117   |
| 0.0           | 42.0  | 9450  | 0.7313          | 0.9117   |
| 0.0           | 43.0  | 9675  | 0.7345          | 0.9117   |
| 0.0           | 44.0  | 9900  | 0.7321          | 0.9133   |
| 0.0           | 45.0  | 10125 | 0.7382          | 0.9117   |
| 0.0           | 46.0  | 10350 | 0.7417          | 0.9117   |
| 0.0024        | 47.0  | 10575 | 0.7424          | 0.9133   |
| 0.0           | 48.0  | 10800 | 0.7445          | 0.9117   |
| 0.0           | 49.0  | 11025 | 0.7452          | 0.9133   |
| 0.0           | 50.0  | 11250 | 0.7452          | 0.915    |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "facebook/deit-base-patch16-224", "model-index": [{"name": "smids_3x_deit_base_adamax_0001_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.915, "name": "Accuracy"}]}]}]}
transformers pytorch vit image-classification generated_from_trainer dataset:imagefolder base_model:facebook/deit-base-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us