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# smids_10x_deit_small_rms_001_fold5

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: 0.6302
- Accuracy: 0.7167

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1023        | 1.0   | 750   | 1.0958          | 0.34     |
| 0.9402        | 2.0   | 1500  | 0.9088          | 0.5033   |
| 0.9044        | 3.0   | 2250  | 0.8761          | 0.5383   |
| 0.8247        | 4.0   | 3000  | 0.8349          | 0.5233   |
| 0.7854        | 5.0   | 3750  | 0.8127          | 0.5633   |
| 0.7771        | 6.0   | 4500  | 0.8860          | 0.5383   |
| 0.773         | 7.0   | 5250  | 0.8230          | 0.575    |
| 0.8024        | 8.0   | 6000  | 0.7956          | 0.5883   |
| 0.8797        | 9.0   | 6750  | 0.8015          | 0.6183   |
| 0.7815        | 10.0  | 7500  | 0.7866          | 0.6083   |
| 0.7914        | 11.0  | 8250  | 0.7547          | 0.6267   |
| 0.7411        | 12.0  | 9000  | 0.7615          | 0.59     |
| 0.7343        | 13.0  | 9750  | 0.7214          | 0.6617   |
| 0.7764        | 14.0  | 10500 | 0.7295          | 0.6717   |
| 0.7555        | 15.0  | 11250 | 0.7012          | 0.6617   |
| 0.7373        | 16.0  | 12000 | 0.7948          | 0.6217   |
| 0.6985        | 17.0  | 12750 | 0.7396          | 0.6267   |
| 0.7821        | 18.0  | 13500 | 0.7384          | 0.66     |
| 0.7914        | 19.0  | 14250 | 0.7821          | 0.635    |
| 0.7863        | 20.0  | 15000 | 0.7254          | 0.655    |
| 0.6932        | 21.0  | 15750 | 0.7242          | 0.6633   |
| 0.6744        | 22.0  | 16500 | 0.7009          | 0.6817   |
| 0.6983        | 23.0  | 17250 | 0.6866          | 0.7133   |
| 0.6779        | 24.0  | 18000 | 0.6963          | 0.6983   |
| 0.6937        | 25.0  | 18750 | 0.6942          | 0.6817   |
| 0.6943        | 26.0  | 19500 | 0.6864          | 0.695    |
| 0.6231        | 27.0  | 20250 | 0.7126          | 0.665    |
| 0.6418        | 28.0  | 21000 | 0.6620          | 0.6983   |
| 0.72          | 29.0  | 21750 | 0.6656          | 0.7017   |
| 0.7042        | 30.0  | 22500 | 0.6697          | 0.6867   |
| 0.754         | 31.0  | 23250 | 0.6511          | 0.7033   |
| 0.6987        | 32.0  | 24000 | 0.6765          | 0.69     |
| 0.7166        | 33.0  | 24750 | 0.6802          | 0.7083   |
| 0.6725        | 34.0  | 25500 | 0.6763          | 0.7033   |
| 0.6612        | 35.0  | 26250 | 0.6382          | 0.7083   |
| 0.6967        | 36.0  | 27000 | 0.6445          | 0.705    |
| 0.6491        | 37.0  | 27750 | 0.6443          | 0.7133   |
| 0.7274        | 38.0  | 28500 | 0.6314          | 0.7333   |
| 0.6904        | 39.0  | 29250 | 0.6429          | 0.7267   |
| 0.6516        | 40.0  | 30000 | 0.6385          | 0.7167   |
| 0.6647        | 41.0  | 30750 | 0.6386          | 0.7      |
| 0.666         | 42.0  | 31500 | 0.6656          | 0.695    |
| 0.6901        | 43.0  | 32250 | 0.6568          | 0.715    |
| 0.6021        | 44.0  | 33000 | 0.6375          | 0.7117   |
| 0.6467        | 45.0  | 33750 | 0.6267          | 0.7117   |
| 0.6249        | 46.0  | 34500 | 0.6374          | 0.71     |
| 0.6161        | 47.0  | 35250 | 0.6354          | 0.71     |
| 0.6534        | 48.0  | 36000 | 0.6396          | 0.715    |
| 0.6031        | 49.0  | 36750 | 0.6326          | 0.7117   |
| 0.6145        | 50.0  | 37500 | 0.6302          | 0.7167   |


### 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_fold5", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.7166666666666667, "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