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# smids_3x_deit_tiny_sgd_0001_fold4

This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6133
- Accuracy: 0.745

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2288        | 1.0   | 225   | 1.2017          | 0.3733   |
| 1.1101        | 2.0   | 450   | 1.1316          | 0.3717   |
| 1.1013        | 3.0   | 675   | 1.0936          | 0.395    |
| 1.0586        | 4.0   | 900   | 1.0632          | 0.42     |
| 1.0359        | 5.0   | 1125  | 1.0361          | 0.4367   |
| 0.9983        | 6.0   | 1350  | 1.0106          | 0.4633   |
| 1.019         | 7.0   | 1575  | 0.9868          | 0.475    |
| 0.9201        | 8.0   | 1800  | 0.9635          | 0.4933   |
| 0.9378        | 9.0   | 2025  | 0.9411          | 0.5117   |
| 0.9004        | 10.0  | 2250  | 0.9202          | 0.535    |
| 0.8802        | 11.0  | 2475  | 0.9003          | 0.5517   |
| 0.9236        | 12.0  | 2700  | 0.8818          | 0.565    |
| 0.8457        | 13.0  | 2925  | 0.8636          | 0.5817   |
| 0.7849        | 14.0  | 3150  | 0.8469          | 0.5917   |
| 0.8073        | 15.0  | 3375  | 0.8311          | 0.6017   |
| 0.8252        | 16.0  | 3600  | 0.8159          | 0.61     |
| 0.7763        | 17.0  | 3825  | 0.8016          | 0.6267   |
| 0.785         | 18.0  | 4050  | 0.7881          | 0.6333   |
| 0.7828        | 19.0  | 4275  | 0.7746          | 0.64     |
| 0.7255        | 20.0  | 4500  | 0.7617          | 0.6483   |
| 0.7412        | 21.0  | 4725  | 0.7495          | 0.6567   |
| 0.7373        | 22.0  | 4950  | 0.7382          | 0.665    |
| 0.7527        | 23.0  | 5175  | 0.7270          | 0.6667   |
| 0.6946        | 24.0  | 5400  | 0.7170          | 0.67     |
| 0.6784        | 25.0  | 5625  | 0.7075          | 0.6817   |
| 0.6623        | 26.0  | 5850  | 0.6988          | 0.6933   |
| 0.6734        | 27.0  | 6075  | 0.6906          | 0.6983   |
| 0.6676        | 28.0  | 6300  | 0.6828          | 0.7083   |
| 0.6739        | 29.0  | 6525  | 0.6758          | 0.7117   |
| 0.6897        | 30.0  | 6750  | 0.6692          | 0.715    |
| 0.6092        | 31.0  | 6975  | 0.6631          | 0.7167   |
| 0.5885        | 32.0  | 7200  | 0.6574          | 0.7217   |
| 0.618         | 33.0  | 7425  | 0.6519          | 0.7217   |
| 0.5886        | 34.0  | 7650  | 0.6471          | 0.7233   |
| 0.587         | 35.0  | 7875  | 0.6427          | 0.7233   |
| 0.6289        | 36.0  | 8100  | 0.6385          | 0.7233   |
| 0.6176        | 37.0  | 8325  | 0.6348          | 0.7267   |
| 0.6111        | 38.0  | 8550  | 0.6313          | 0.7283   |
| 0.5626        | 39.0  | 8775  | 0.6283          | 0.7317   |
| 0.5867        | 40.0  | 9000  | 0.6256          | 0.735    |
| 0.6103        | 41.0  | 9225  | 0.6232          | 0.7367   |
| 0.6145        | 42.0  | 9450  | 0.6210          | 0.7417   |
| 0.5742        | 43.0  | 9675  | 0.6191          | 0.7417   |
| 0.6152        | 44.0  | 9900  | 0.6175          | 0.745    |
| 0.6028        | 45.0  | 10125 | 0.6162          | 0.745    |
| 0.5964        | 46.0  | 10350 | 0.6151          | 0.745    |
| 0.5533        | 47.0  | 10575 | 0.6143          | 0.745    |
| 0.597         | 48.0  | 10800 | 0.6137          | 0.745    |
| 0.617         | 49.0  | 11025 | 0.6134          | 0.745    |
| 0.5745        | 50.0  | 11250 | 0.6133          | 0.745    |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.1+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-tiny-patch16-224", "model-index": [{"name": "smids_3x_deit_tiny_sgd_0001_fold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.745, "name": "Accuracy"}]}]}]}
transformers pytorch vit image-classification generated_from_trainer dataset:imagefolder base_model:facebook/deit-tiny-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us