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# smids_1x_deit_small_sgd_0001_fold4

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: 0.7697
- Accuracy: 0.685

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0786        | 1.0   | 75   | 1.0493          | 0.4483   |
| 1.0628        | 2.0   | 150  | 1.0330          | 0.4767   |
| 1.0285        | 3.0   | 225  | 1.0186          | 0.5233   |
| 0.9944        | 4.0   | 300  | 1.0062          | 0.53     |
| 1.0232        | 5.0   | 375  | 0.9947          | 0.5517   |
| 0.9927        | 6.0   | 450  | 0.9839          | 0.56     |
| 0.9942        | 7.0   | 525  | 0.9736          | 0.5767   |
| 0.9843        | 8.0   | 600  | 0.9634          | 0.595    |
| 0.9686        | 9.0   | 675  | 0.9535          | 0.6033   |
| 0.9669        | 10.0  | 750  | 0.9439          | 0.6067   |
| 0.9496        | 11.0  | 825  | 0.9345          | 0.6117   |
| 0.9424        | 12.0  | 900  | 0.9255          | 0.615    |
| 0.9379        | 13.0  | 975  | 0.9166          | 0.615    |
| 0.9246        | 14.0  | 1050 | 0.9079          | 0.6217   |
| 0.9261        | 15.0  | 1125 | 0.8998          | 0.63     |
| 0.8974        | 16.0  | 1200 | 0.8916          | 0.6333   |
| 0.9045        | 17.0  | 1275 | 0.8836          | 0.6367   |
| 0.8617        | 18.0  | 1350 | 0.8760          | 0.6417   |
| 0.885         | 19.0  | 1425 | 0.8688          | 0.6433   |
| 0.8736        | 20.0  | 1500 | 0.8617          | 0.6467   |
| 0.8843        | 21.0  | 1575 | 0.8551          | 0.6433   |
| 0.8472        | 22.0  | 1650 | 0.8488          | 0.6417   |
| 0.8796        | 23.0  | 1725 | 0.8428          | 0.6417   |
| 0.8784        | 24.0  | 1800 | 0.8370          | 0.6467   |
| 0.8408        | 25.0  | 1875 | 0.8316          | 0.65     |
| 0.8377        | 26.0  | 1950 | 0.8263          | 0.655    |
| 0.8101        | 27.0  | 2025 | 0.8213          | 0.6583   |
| 0.8334        | 28.0  | 2100 | 0.8166          | 0.66     |
| 0.8187        | 29.0  | 2175 | 0.8122          | 0.6567   |
| 0.8337        | 30.0  | 2250 | 0.8080          | 0.6583   |
| 0.8018        | 31.0  | 2325 | 0.8041          | 0.665    |
| 0.8384        | 32.0  | 2400 | 0.8003          | 0.67     |
| 0.813         | 33.0  | 2475 | 0.7968          | 0.6767   |
| 0.7997        | 34.0  | 2550 | 0.7936          | 0.6817   |
| 0.7882        | 35.0  | 2625 | 0.7905          | 0.6833   |
| 0.7651        | 36.0  | 2700 | 0.7878          | 0.6817   |
| 0.7706        | 37.0  | 2775 | 0.7852          | 0.6817   |
| 0.7916        | 38.0  | 2850 | 0.7828          | 0.6783   |
| 0.8116        | 39.0  | 2925 | 0.7807          | 0.6783   |
| 0.7662        | 40.0  | 3000 | 0.7787          | 0.6783   |
| 0.7857        | 41.0  | 3075 | 0.7769          | 0.6817   |
| 0.7862        | 42.0  | 3150 | 0.7753          | 0.6817   |
| 0.8172        | 43.0  | 3225 | 0.7740          | 0.685    |
| 0.7812        | 44.0  | 3300 | 0.7728          | 0.6867   |
| 0.803         | 45.0  | 3375 | 0.7718          | 0.685    |
| 0.7949        | 46.0  | 3450 | 0.7710          | 0.685    |
| 0.779         | 47.0  | 3525 | 0.7704          | 0.685    |
| 0.7941        | 48.0  | 3600 | 0.7700          | 0.685    |
| 0.7892        | 49.0  | 3675 | 0.7698          | 0.685    |
| 0.7766        | 50.0  | 3750 | 0.7697          | 0.685    |


### 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