
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# smids_1x_deit_small_adamax_0001_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: 0.6669
- Accuracy: 0.8983

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4029        | 1.0   | 75   | 0.3535          | 0.845    |
| 0.3043        | 2.0   | 150  | 0.3006          | 0.88     |
| 0.16          | 3.0   | 225  | 0.2739          | 0.8917   |
| 0.1191        | 4.0   | 300  | 0.3321          | 0.8867   |
| 0.1124        | 5.0   | 375  | 0.4055          | 0.885    |
| 0.0475        | 6.0   | 450  | 0.4499          | 0.89     |
| 0.0249        | 7.0   | 525  | 0.4528          | 0.89     |
| 0.0478        | 8.0   | 600  | 0.5581          | 0.8883   |
| 0.0013        | 9.0   | 675  | 0.5854          | 0.8867   |
| 0.0299        | 10.0  | 750  | 0.6219          | 0.8833   |
| 0.0306        | 11.0  | 825  | 0.6492          | 0.8833   |
| 0.0013        | 12.0  | 900  | 0.5930          | 0.89     |
| 0.006         | 13.0  | 975  | 0.6079          | 0.8933   |
| 0.018         | 14.0  | 1050 | 0.6720          | 0.885    |
| 0.009         | 15.0  | 1125 | 0.7215          | 0.8917   |
| 0.0001        | 16.0  | 1200 | 0.5817          | 0.8967   |
| 0.0052        | 17.0  | 1275 | 0.5765          | 0.8983   |
| 0.0097        | 18.0  | 1350 | 0.5975          | 0.8967   |
| 0.0033        | 19.0  | 1425 | 0.6180          | 0.8983   |
| 0.0           | 20.0  | 1500 | 0.6163          | 0.9      |
| 0.0043        | 21.0  | 1575 | 0.5951          | 0.9      |
| 0.0041        | 22.0  | 1650 | 0.6227          | 0.8983   |
| 0.0044        | 23.0  | 1725 | 0.6229          | 0.895    |
| 0.0           | 24.0  | 1800 | 0.6143          | 0.9      |
| 0.0068        | 25.0  | 1875 | 0.6320          | 0.9017   |
| 0.0           | 26.0  | 1950 | 0.6233          | 0.8983   |
| 0.0062        | 27.0  | 2025 | 0.6313          | 0.8967   |
| 0.0083        | 28.0  | 2100 | 0.6337          | 0.8983   |
| 0.0062        | 29.0  | 2175 | 0.6268          | 0.9033   |
| 0.0034        | 30.0  | 2250 | 0.6363          | 0.9      |
| 0.0           | 31.0  | 2325 | 0.6524          | 0.8983   |
| 0.0           | 32.0  | 2400 | 0.6386          | 0.9      |
| 0.0057        | 33.0  | 2475 | 0.6534          | 0.9      |
| 0.0           | 34.0  | 2550 | 0.6542          | 0.8967   |
| 0.0048        | 35.0  | 2625 | 0.6498          | 0.9017   |
| 0.0           | 36.0  | 2700 | 0.6512          | 0.9033   |
| 0.003         | 37.0  | 2775 | 0.6532          | 0.9017   |
| 0.0           | 38.0  | 2850 | 0.6543          | 0.8967   |
| 0.0           | 39.0  | 2925 | 0.6569          | 0.8967   |
| 0.0           | 40.0  | 3000 | 0.6561          | 0.9      |
| 0.0028        | 41.0  | 3075 | 0.6588          | 0.8967   |
| 0.0           | 42.0  | 3150 | 0.6601          | 0.9017   |
| 0.007         | 43.0  | 3225 | 0.6640          | 0.9017   |
| 0.0           | 44.0  | 3300 | 0.6634          | 0.8967   |
| 0.0024        | 45.0  | 3375 | 0.6647          | 0.9033   |
| 0.0           | 46.0  | 3450 | 0.6647          | 0.8983   |
| 0.0051        | 47.0  | 3525 | 0.6655          | 0.8983   |
| 0.0049        | 48.0  | 3600 | 0.6656          | 0.8983   |
| 0.0           | 49.0  | 3675 | 0.6665          | 0.8967   |
| 0.0043        | 50.0  | 3750 | 0.6669          | 0.8983   |


### 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