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# smids_1x_deit_tiny_sgd_0001_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: 0.9030
- Accuracy: 0.59

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2891        | 1.0   | 75   | 1.2964          | 0.35     |
| 1.1841        | 2.0   | 150  | 1.2323          | 0.3683   |
| 1.1375        | 3.0   | 225  | 1.1862          | 0.3917   |
| 1.1786        | 4.0   | 300  | 1.1551          | 0.3933   |
| 1.0678        | 5.0   | 375  | 1.1335          | 0.3867   |
| 1.1099        | 6.0   | 450  | 1.1171          | 0.41     |
| 1.0735        | 7.0   | 525  | 1.1035          | 0.43     |
| 1.0557        | 8.0   | 600  | 1.0918          | 0.4283   |
| 1.0742        | 9.0   | 675  | 1.0815          | 0.435    |
| 1.0667        | 10.0  | 750  | 1.0716          | 0.45     |
| 1.0307        | 11.0  | 825  | 1.0624          | 0.465    |
| 1.0285        | 12.0  | 900  | 1.0538          | 0.48     |
| 1.0155        | 13.0  | 975  | 1.0454          | 0.4883   |
| 1.0004        | 14.0  | 1050 | 1.0371          | 0.4983   |
| 0.9896        | 15.0  | 1125 | 1.0296          | 0.5      |
| 0.9962        | 16.0  | 1200 | 1.0219          | 0.5033   |
| 0.9993        | 17.0  | 1275 | 1.0142          | 0.51     |
| 0.982         | 18.0  | 1350 | 1.0069          | 0.5067   |
| 0.9813        | 19.0  | 1425 | 0.9999          | 0.51     |
| 0.9516        | 20.0  | 1500 | 0.9928          | 0.5183   |
| 0.9735        | 21.0  | 1575 | 0.9864          | 0.53     |
| 0.9641        | 22.0  | 1650 | 0.9800          | 0.5367   |
| 0.9696        | 23.0  | 1725 | 0.9741          | 0.5417   |
| 0.9132        | 24.0  | 1800 | 0.9683          | 0.55     |
| 0.9427        | 25.0  | 1875 | 0.9629          | 0.55     |
| 0.956         | 26.0  | 1950 | 0.9577          | 0.5483   |
| 0.9026        | 27.0  | 2025 | 0.9527          | 0.5517   |
| 0.9342        | 28.0  | 2100 | 0.9481          | 0.5517   |
| 0.9171        | 29.0  | 2175 | 0.9437          | 0.5517   |
| 0.9183        | 30.0  | 2250 | 0.9395          | 0.5517   |
| 0.9037        | 31.0  | 2325 | 0.9358          | 0.555    |
| 0.8583        | 32.0  | 2400 | 0.9322          | 0.555    |
| 0.8838        | 33.0  | 2475 | 0.9289          | 0.5567   |
| 0.9061        | 34.0  | 2550 | 0.9258          | 0.56     |
| 0.877         | 35.0  | 2625 | 0.9229          | 0.5667   |
| 0.8993        | 36.0  | 2700 | 0.9203          | 0.57     |
| 0.8879        | 37.0  | 2775 | 0.9178          | 0.575    |
| 0.9187        | 38.0  | 2850 | 0.9156          | 0.5767   |
| 0.8776        | 39.0  | 2925 | 0.9136          | 0.5817   |
| 0.8807        | 40.0  | 3000 | 0.9117          | 0.5833   |
| 0.9149        | 41.0  | 3075 | 0.9100          | 0.5867   |
| 0.9426        | 42.0  | 3150 | 0.9085          | 0.5867   |
| 0.9085        | 43.0  | 3225 | 0.9072          | 0.5883   |
| 0.8614        | 44.0  | 3300 | 0.9060          | 0.5883   |
| 0.9002        | 45.0  | 3375 | 0.9051          | 0.59     |
| 0.8489        | 46.0  | 3450 | 0.9043          | 0.59     |
| 0.8489        | 47.0  | 3525 | 0.9037          | 0.59     |
| 0.8906        | 48.0  | 3600 | 0.9033          | 0.59     |
| 0.8819        | 49.0  | 3675 | 0.9031          | 0.59     |
| 0.8423        | 50.0  | 3750 | 0.9030          | 0.59     |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

abnormal_sperm non-sperm normal_sperm