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# smids_3x_deit_small_sgd_00001_fold2

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: 1.0145
- Accuracy: 0.4875

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0768        | 1.0   | 225   | 1.0706          | 0.4276   |
| 1.0941        | 2.0   | 450   | 1.0680          | 0.4293   |
| 1.0918        | 3.0   | 675   | 1.0655          | 0.4309   |
| 1.0511        | 4.0   | 900   | 1.0630          | 0.4376   |
| 1.0876        | 5.0   | 1125  | 1.0606          | 0.4393   |
| 1.0339        | 6.0   | 1350  | 1.0583          | 0.4393   |
| 1.0616        | 7.0   | 1575  | 1.0561          | 0.4459   |
| 1.0897        | 8.0   | 1800  | 1.0540          | 0.4459   |
| 1.053         | 9.0   | 2025  | 1.0518          | 0.4493   |
| 1.0515        | 10.0  | 2250  | 1.0498          | 0.4509   |
| 1.0879        | 11.0  | 2475  | 1.0479          | 0.4542   |
| 1.0316        | 12.0  | 2700  | 1.0459          | 0.4542   |
| 1.0424        | 13.0  | 2925  | 1.0441          | 0.4576   |
| 1.0786        | 14.0  | 3150  | 1.0424          | 0.4609   |
| 1.061         | 15.0  | 3375  | 1.0407          | 0.4626   |
| 1.064         | 16.0  | 3600  | 1.0390          | 0.4642   |
| 1.0184        | 17.0  | 3825  | 1.0374          | 0.4626   |
| 1.0313        | 18.0  | 4050  | 1.0359          | 0.4626   |
| 1.0429        | 19.0  | 4275  | 1.0344          | 0.4642   |
| 1.0308        | 20.0  | 4500  | 1.0330          | 0.4642   |
| 1.049         | 21.0  | 4725  | 1.0317          | 0.4659   |
| 1.0164        | 22.0  | 4950  | 1.0304          | 0.4642   |
| 1.0457        | 23.0  | 5175  | 1.0292          | 0.4642   |
| 1.0471        | 24.0  | 5400  | 1.0280          | 0.4626   |
| 1.0294        | 25.0  | 5625  | 1.0269          | 0.4642   |
| 1.0309        | 26.0  | 5850  | 1.0258          | 0.4659   |
| 1.0318        | 27.0  | 6075  | 1.0248          | 0.4659   |
| 1.0436        | 28.0  | 6300  | 1.0238          | 0.4676   |
| 1.0288        | 29.0  | 6525  | 1.0229          | 0.4725   |
| 1.0425        | 30.0  | 6750  | 1.0220          | 0.4742   |
| 1.0267        | 31.0  | 6975  | 1.0212          | 0.4792   |
| 1.0174        | 32.0  | 7200  | 1.0204          | 0.4809   |
| 1.0197        | 33.0  | 7425  | 1.0197          | 0.4809   |
| 1.0313        | 34.0  | 7650  | 1.0190          | 0.4809   |
| 1.0296        | 35.0  | 7875  | 1.0184          | 0.4809   |
| 1.0429        | 36.0  | 8100  | 1.0179          | 0.4842   |
| 1.0312        | 37.0  | 8325  | 1.0173          | 0.4859   |
| 1.0214        | 38.0  | 8550  | 1.0169          | 0.4842   |
| 1.0321        | 39.0  | 8775  | 1.0164          | 0.4859   |
| 1.0329        | 40.0  | 9000  | 1.0161          | 0.4859   |
| 1.0094        | 41.0  | 9225  | 1.0157          | 0.4875   |
| 0.9973        | 42.0  | 9450  | 1.0154          | 0.4875   |
| 1.0326        | 43.0  | 9675  | 1.0152          | 0.4875   |
| 1.0086        | 44.0  | 9900  | 1.0150          | 0.4875   |
| 1.0104        | 45.0  | 10125 | 1.0148          | 0.4875   |
| 1.0211        | 46.0  | 10350 | 1.0147          | 0.4875   |
| 0.9952        | 47.0  | 10575 | 1.0146          | 0.4875   |
| 0.9977        | 48.0  | 10800 | 1.0146          | 0.4875   |
| 1.0187        | 49.0  | 11025 | 1.0146          | 0.4875   |
| 1.0188        | 50.0  | 11250 | 1.0145          | 0.4875   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2

abnormal_sperm non-sperm normal_sperm