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# smids_3x_deit_base_adamax_00001_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: 0.6129
- Accuracy: 0.9183

## 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: 1e-05
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3685        | 1.0   | 225   | 0.3281          | 0.8817   |
| 0.2394        | 2.0   | 450   | 0.2668          | 0.9033   |
| 0.1551        | 3.0   | 675   | 0.2550          | 0.9167   |
| 0.1239        | 4.0   | 900   | 0.2574          | 0.9117   |
| 0.1058        | 5.0   | 1125  | 0.2783          | 0.915    |
| 0.058         | 6.0   | 1350  | 0.2899          | 0.91     |
| 0.0386        | 7.0   | 1575  | 0.3339          | 0.9033   |
| 0.0293        | 8.0   | 1800  | 0.3749          | 0.9117   |
| 0.0298        | 9.0   | 2025  | 0.3833          | 0.9167   |
| 0.013         | 10.0  | 2250  | 0.4190          | 0.91     |
| 0.0241        | 11.0  | 2475  | 0.4405          | 0.91     |
| 0.0009        | 12.0  | 2700  | 0.4672          | 0.9017   |
| 0.0005        | 13.0  | 2925  | 0.4613          | 0.9083   |
| 0.0003        | 14.0  | 3150  | 0.4827          | 0.9133   |
| 0.0004        | 15.0  | 3375  | 0.4942          | 0.915    |
| 0.001         | 16.0  | 3600  | 0.5082          | 0.915    |
| 0.0002        | 17.0  | 3825  | 0.5550          | 0.9117   |
| 0.0001        | 18.0  | 4050  | 0.5345          | 0.915    |
| 0.0001        | 19.0  | 4275  | 0.5425          | 0.915    |
| 0.0001        | 20.0  | 4500  | 0.5481          | 0.9167   |
| 0.0001        | 21.0  | 4725  | 0.5684          | 0.9167   |
| 0.0001        | 22.0  | 4950  | 0.5664          | 0.9117   |
| 0.0           | 23.0  | 5175  | 0.5625          | 0.915    |
| 0.0001        | 24.0  | 5400  | 0.5934          | 0.9133   |
| 0.0037        | 25.0  | 5625  | 0.6215          | 0.9067   |
| 0.0001        | 26.0  | 5850  | 0.5781          | 0.9183   |
| 0.0           | 27.0  | 6075  | 0.5846          | 0.9183   |
| 0.0           | 28.0  | 6300  | 0.5796          | 0.9183   |
| 0.0021        | 29.0  | 6525  | 0.6062          | 0.9167   |
| 0.0066        | 30.0  | 6750  | 0.5817          | 0.9167   |
| 0.0028        | 31.0  | 6975  | 0.6008          | 0.915    |
| 0.0           | 32.0  | 7200  | 0.5847          | 0.9167   |
| 0.0           | 33.0  | 7425  | 0.5946          | 0.9183   |
| 0.0           | 34.0  | 7650  | 0.6041          | 0.9133   |
| 0.0           | 35.0  | 7875  | 0.5926          | 0.915    |
| 0.0           | 36.0  | 8100  | 0.5946          | 0.92     |
| 0.0           | 37.0  | 8325  | 0.6079          | 0.915    |
| 0.0           | 38.0  | 8550  | 0.5973          | 0.9217   |
| 0.0           | 39.0  | 8775  | 0.5993          | 0.9183   |
| 0.0           | 40.0  | 9000  | 0.6003          | 0.9183   |
| 0.0           | 41.0  | 9225  | 0.6120          | 0.915    |
| 0.0           | 42.0  | 9450  | 0.6070          | 0.92     |
| 0.0           | 43.0  | 9675  | 0.6080          | 0.9183   |
| 0.0           | 44.0  | 9900  | 0.6062          | 0.9217   |
| 0.0           | 45.0  | 10125 | 0.6095          | 0.9183   |
| 0.0           | 46.0  | 10350 | 0.6121          | 0.9183   |
| 0.0019        | 47.0  | 10575 | 0.6121          | 0.9183   |
| 0.0           | 48.0  | 10800 | 0.6134          | 0.9183   |
| 0.0           | 49.0  | 11025 | 0.6133          | 0.9183   |
| 0.0           | 50.0  | 11250 | 0.6129          | 0.9183   |


### 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_00001_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9183333333333333, "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