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# smids_1x_deit_small_adamax_00001_fold1

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: 0.6979
- Accuracy: 0.8648

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5894        | 1.0   | 76   | 0.5595          | 0.7830   |
| 0.4626        | 2.0   | 152  | 0.4242          | 0.8314   |
| 0.2923        | 3.0   | 228  | 0.3702          | 0.8531   |
| 0.2746        | 4.0   | 304  | 0.3590          | 0.8464   |
| 0.2111        | 5.0   | 380  | 0.3390          | 0.8631   |
| 0.1639        | 6.0   | 456  | 0.3346          | 0.8614   |
| 0.108         | 7.0   | 532  | 0.3491          | 0.8698   |
| 0.1272        | 8.0   | 608  | 0.3559          | 0.8681   |
| 0.132         | 9.0   | 684  | 0.3508          | 0.8648   |
| 0.0579        | 10.0  | 760  | 0.3614          | 0.8715   |
| 0.0748        | 11.0  | 836  | 0.3587          | 0.8681   |
| 0.0507        | 12.0  | 912  | 0.3817          | 0.8715   |
| 0.0272        | 13.0  | 988  | 0.4000          | 0.8781   |
| 0.0136        | 14.0  | 1064 | 0.4159          | 0.8731   |
| 0.0187        | 15.0  | 1140 | 0.4377          | 0.8648   |
| 0.0055        | 16.0  | 1216 | 0.4760          | 0.8698   |
| 0.0039        | 17.0  | 1292 | 0.5096          | 0.8648   |
| 0.0182        | 18.0  | 1368 | 0.5153          | 0.8698   |
| 0.0025        | 19.0  | 1444 | 0.5136          | 0.8698   |
| 0.0086        | 20.0  | 1520 | 0.5513          | 0.8664   |
| 0.0136        | 21.0  | 1596 | 0.5585          | 0.8765   |
| 0.0139        | 22.0  | 1672 | 0.5755          | 0.8648   |
| 0.0008        | 23.0  | 1748 | 0.5786          | 0.8765   |
| 0.0007        | 24.0  | 1824 | 0.6119          | 0.8748   |
| 0.013         | 25.0  | 1900 | 0.6090          | 0.8681   |
| 0.0086        | 26.0  | 1976 | 0.6766          | 0.8581   |
| 0.0066        | 27.0  | 2052 | 0.6214          | 0.8681   |
| 0.0074        | 28.0  | 2128 | 0.6445          | 0.8664   |
| 0.0116        | 29.0  | 2204 | 0.6664          | 0.8614   |
| 0.0003        | 30.0  | 2280 | 0.6454          | 0.8648   |
| 0.006         | 31.0  | 2356 | 0.6504          | 0.8681   |
| 0.0144        | 32.0  | 2432 | 0.6501          | 0.8698   |
| 0.0003        | 33.0  | 2508 | 0.6602          | 0.8698   |
| 0.0003        | 34.0  | 2584 | 0.6626          | 0.8648   |
| 0.0124        | 35.0  | 2660 | 0.6658          | 0.8698   |
| 0.0103        | 36.0  | 2736 | 0.6772          | 0.8698   |
| 0.0136        | 37.0  | 2812 | 0.6878          | 0.8648   |
| 0.0002        | 38.0  | 2888 | 0.6900          | 0.8681   |
| 0.0083        | 39.0  | 2964 | 0.6827          | 0.8631   |
| 0.0002        | 40.0  | 3040 | 0.6875          | 0.8698   |
| 0.0045        | 41.0  | 3116 | 0.6912          | 0.8664   |
| 0.0002        | 42.0  | 3192 | 0.6876          | 0.8614   |
| 0.0047        | 43.0  | 3268 | 0.6912          | 0.8631   |
| 0.0002        | 44.0  | 3344 | 0.7110          | 0.8598   |
| 0.0002        | 45.0  | 3420 | 0.6957          | 0.8648   |
| 0.0002        | 46.0  | 3496 | 0.6969          | 0.8648   |
| 0.0002        | 47.0  | 3572 | 0.6980          | 0.8648   |
| 0.0039        | 48.0  | 3648 | 0.6956          | 0.8681   |
| 0.0002        | 49.0  | 3724 | 0.6983          | 0.8648   |
| 0.0002        | 50.0  | 3800 | 0.6979          | 0.8648   |


### 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