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# smids_10x_deit_small_rms_001_fold3

This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5590
- Accuracy: 0.7767

## 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: 0.001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8839        | 1.0   | 750   | 0.8956          | 0.4917   |
| 0.8402        | 2.0   | 1500  | 0.8459          | 0.5383   |
| 0.827         | 3.0   | 2250  | 0.8365          | 0.5417   |
| 0.7595        | 4.0   | 3000  | 0.8404          | 0.5617   |
| 0.8496        | 5.0   | 3750  | 0.9112          | 0.505    |
| 0.7825        | 6.0   | 4500  | 0.8246          | 0.6233   |
| 0.8185        | 7.0   | 5250  | 0.7843          | 0.6233   |
| 0.7863        | 8.0   | 6000  | 0.7862          | 0.6183   |
| 0.7304        | 9.0   | 6750  | 0.7478          | 0.6433   |
| 0.7486        | 10.0  | 7500  | 0.7941          | 0.625    |
| 0.7979        | 11.0  | 8250  | 0.7438          | 0.6817   |
| 0.6928        | 12.0  | 9000  | 0.8898          | 0.58     |
| 0.683         | 13.0  | 9750  | 0.7126          | 0.68     |
| 0.7194        | 14.0  | 10500 | 0.7634          | 0.6367   |
| 0.7001        | 15.0  | 11250 | 0.6906          | 0.68     |
| 0.7209        | 16.0  | 12000 | 0.6988          | 0.675    |
| 0.693         | 17.0  | 12750 | 0.7227          | 0.6733   |
| 0.6594        | 18.0  | 13500 | 0.7119          | 0.675    |
| 0.6733        | 19.0  | 14250 | 0.6769          | 0.695    |
| 0.6368        | 20.0  | 15000 | 0.6310          | 0.7183   |
| 0.5529        | 21.0  | 15750 | 0.6379          | 0.73     |
| 0.674         | 22.0  | 16500 | 0.6200          | 0.7233   |
| 0.6173        | 23.0  | 17250 | 0.6390          | 0.7117   |
| 0.7017        | 24.0  | 18000 | 0.6234          | 0.7217   |
| 0.6672        | 25.0  | 18750 | 0.6159          | 0.7117   |
| 0.6143        | 26.0  | 19500 | 0.6119          | 0.7133   |
| 0.5447        | 27.0  | 20250 | 0.6511          | 0.7      |
| 0.616         | 28.0  | 21000 | 0.5943          | 0.7317   |
| 0.6257        | 29.0  | 21750 | 0.6135          | 0.7417   |
| 0.5784        | 30.0  | 22500 | 0.6236          | 0.7383   |
| 0.5488        | 31.0  | 23250 | 0.5814          | 0.7483   |
| 0.5683        | 32.0  | 24000 | 0.6409          | 0.725    |
| 0.5657        | 33.0  | 24750 | 0.6193          | 0.7583   |
| 0.7061        | 34.0  | 25500 | 0.7958          | 0.6533   |
| 0.5815        | 35.0  | 26250 | 0.6092          | 0.7467   |
| 0.545         | 36.0  | 27000 | 0.5902          | 0.7567   |
| 0.574         | 37.0  | 27750 | 0.5865          | 0.7483   |
| 0.5654        | 38.0  | 28500 | 0.6161          | 0.7467   |
| 0.5393        | 39.0  | 29250 | 0.5677          | 0.7667   |
| 0.6213        | 40.0  | 30000 | 0.5702          | 0.7633   |
| 0.5565        | 41.0  | 30750 | 0.5675          | 0.75     |
| 0.5323        | 42.0  | 31500 | 0.5645          | 0.7583   |
| 0.5444        | 43.0  | 32250 | 0.5820          | 0.76     |
| 0.4988        | 44.0  | 33000 | 0.5588          | 0.765    |
| 0.5249        | 45.0  | 33750 | 0.5669          | 0.7583   |
| 0.5246        | 46.0  | 34500 | 0.5504          | 0.7733   |
| 0.4975        | 47.0  | 35250 | 0.5697          | 0.7717   |
| 0.5083        | 48.0  | 36000 | 0.5554          | 0.7717   |
| 0.4948        | 49.0  | 36750 | 0.5551          | 0.775    |
| 0.4147        | 50.0  | 37500 | 0.5590          | 0.7767   |


### 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-small-patch16-224", "model-index": [{"name": "smids_10x_deit_small_rms_001_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.7766666666666666, "name": "Accuracy"}]}]}]}
transformers pytorch vit image-classification generated_from_trainer dataset:imagefolder base_model:facebook/deit-small-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us