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# smids_10x_deit_tiny_sgd_00001_fold3

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: 0.9716
- Accuracy: 0.5233

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.3912        | 1.0   | 750   | 1.3088          | 0.3483   |
| 1.282         | 2.0   | 1500  | 1.2523          | 0.3583   |
| 1.2674        | 3.0   | 2250  | 1.2106          | 0.375    |
| 1.2413        | 4.0   | 3000  | 1.1803          | 0.3833   |
| 1.1441        | 5.0   | 3750  | 1.1586          | 0.39     |
| 1.1769        | 6.0   | 4500  | 1.1422          | 0.3967   |
| 1.1041        | 7.0   | 5250  | 1.1291          | 0.395    |
| 1.1052        | 8.0   | 6000  | 1.1182          | 0.4083   |
| 1.1033        | 9.0   | 6750  | 1.1088          | 0.4133   |
| 1.0691        | 10.0  | 7500  | 1.1002          | 0.42     |
| 1.0707        | 11.0  | 8250  | 1.0922          | 0.4333   |
| 1.055         | 12.0  | 9000  | 1.0847          | 0.4417   |
| 1.0312        | 13.0  | 9750  | 1.0777          | 0.4433   |
| 1.0496        | 14.0  | 10500 | 1.0710          | 0.4417   |
| 1.0476        | 15.0  | 11250 | 1.0646          | 0.45     |
| 1.0102        | 16.0  | 12000 | 1.0585          | 0.455    |
| 0.9929        | 17.0  | 12750 | 1.0527          | 0.4617   |
| 1.009         | 18.0  | 13500 | 1.0471          | 0.47     |
| 0.9872        | 19.0  | 14250 | 1.0418          | 0.4767   |
| 0.9829        | 20.0  | 15000 | 1.0367          | 0.485    |
| 0.9531        | 21.0  | 15750 | 1.0318          | 0.4867   |
| 0.9943        | 22.0  | 16500 | 1.0272          | 0.4917   |
| 0.9746        | 23.0  | 17250 | 1.0227          | 0.4933   |
| 0.9878        | 24.0  | 18000 | 1.0185          | 0.4933   |
| 0.9395        | 25.0  | 18750 | 1.0145          | 0.5017   |
| 0.9881        | 26.0  | 19500 | 1.0107          | 0.505    |
| 0.9509        | 27.0  | 20250 | 1.0071          | 0.51     |
| 0.9714        | 28.0  | 21000 | 1.0038          | 0.5117   |
| 0.9762        | 29.0  | 21750 | 1.0005          | 0.5133   |
| 0.9566        | 30.0  | 22500 | 0.9975          | 0.5133   |
| 0.9136        | 31.0  | 23250 | 0.9946          | 0.5133   |
| 0.9478        | 32.0  | 24000 | 0.9920          | 0.5167   |
| 0.9511        | 33.0  | 24750 | 0.9895          | 0.5167   |
| 0.9608        | 34.0  | 25500 | 0.9872          | 0.5183   |
| 0.9345        | 35.0  | 26250 | 0.9850          | 0.5183   |
| 0.941         | 36.0  | 27000 | 0.9831          | 0.5217   |
| 0.9373        | 37.0  | 27750 | 0.9813          | 0.5217   |
| 0.9836        | 38.0  | 28500 | 0.9796          | 0.5233   |
| 0.9294        | 39.0  | 29250 | 0.9782          | 0.5217   |
| 0.9394        | 40.0  | 30000 | 0.9769          | 0.5233   |
| 0.9196        | 41.0  | 30750 | 0.9757          | 0.5233   |
| 0.941         | 42.0  | 31500 | 0.9747          | 0.5233   |
| 0.9468        | 43.0  | 32250 | 0.9739          | 0.5233   |
| 0.9204        | 44.0  | 33000 | 0.9732          | 0.5233   |
| 0.9194        | 45.0  | 33750 | 0.9727          | 0.5233   |
| 0.9493        | 46.0  | 34500 | 0.9722          | 0.5233   |
| 0.9121        | 47.0  | 35250 | 0.9719          | 0.5233   |
| 0.9108        | 48.0  | 36000 | 0.9717          | 0.5233   |
| 0.8965        | 49.0  | 36750 | 0.9716          | 0.5233   |
| 0.896         | 50.0  | 37500 | 0.9716          | 0.5233   |


### 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-tiny-patch16-224", "model-index": [{"name": "smids_10x_deit_tiny_sgd_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": 0.5233333333333333, "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