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# smids_1x_deit_small_sgd_0001_fold3

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: 0.8065
- Accuracy: 0.6733

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.08          | 1.0   | 75   | 1.0697          | 0.42     |
| 1.068         | 2.0   | 150  | 1.0540          | 0.4567   |
| 1.0267        | 3.0   | 225  | 1.0400          | 0.4817   |
| 0.9978        | 4.0   | 300  | 1.0279          | 0.5117   |
| 1.0069        | 5.0   | 375  | 1.0166          | 0.5167   |
| 0.9928        | 6.0   | 450  | 1.0059          | 0.5333   |
| 0.9852        | 7.0   | 525  | 0.9957          | 0.535    |
| 0.9289        | 8.0   | 600  | 0.9859          | 0.54     |
| 0.9567        | 9.0   | 675  | 0.9764          | 0.5533   |
| 0.9517        | 10.0  | 750  | 0.9672          | 0.5583   |
| 0.9256        | 11.0  | 825  | 0.9583          | 0.565    |
| 0.9357        | 12.0  | 900  | 0.9498          | 0.565    |
| 0.9377        | 13.0  | 975  | 0.9415          | 0.57     |
| 0.9369        | 14.0  | 1050 | 0.9334          | 0.575    |
| 0.8836        | 15.0  | 1125 | 0.9259          | 0.5667   |
| 0.922         | 16.0  | 1200 | 0.9184          | 0.575    |
| 0.8518        | 17.0  | 1275 | 0.9112          | 0.5833   |
| 0.8741        | 18.0  | 1350 | 0.9043          | 0.5883   |
| 0.8604        | 19.0  | 1425 | 0.8978          | 0.5933   |
| 0.8822        | 20.0  | 1500 | 0.8914          | 0.5983   |
| 0.8613        | 21.0  | 1575 | 0.8855          | 0.61     |
| 0.8383        | 22.0  | 1650 | 0.8797          | 0.6117   |
| 0.8466        | 23.0  | 1725 | 0.8742          | 0.615    |
| 0.8373        | 24.0  | 1800 | 0.8689          | 0.6217   |
| 0.8379        | 25.0  | 1875 | 0.8638          | 0.6283   |
| 0.8435        | 26.0  | 1950 | 0.8590          | 0.6217   |
| 0.7992        | 27.0  | 2025 | 0.8544          | 0.6267   |
| 0.8242        | 28.0  | 2100 | 0.8500          | 0.6317   |
| 0.8405        | 29.0  | 2175 | 0.8460          | 0.635    |
| 0.8059        | 30.0  | 2250 | 0.8421          | 0.6383   |
| 0.8193        | 31.0  | 2325 | 0.8384          | 0.6383   |
| 0.8107        | 32.0  | 2400 | 0.8351          | 0.64     |
| 0.777         | 33.0  | 2475 | 0.8318          | 0.6433   |
| 0.799         | 34.0  | 2550 | 0.8288          | 0.6483   |
| 0.7972        | 35.0  | 2625 | 0.8260          | 0.6533   |
| 0.8308        | 36.0  | 2700 | 0.8234          | 0.6533   |
| 0.761         | 37.0  | 2775 | 0.8210          | 0.6567   |
| 0.8092        | 38.0  | 2850 | 0.8187          | 0.6567   |
| 0.8047        | 39.0  | 2925 | 0.8167          | 0.66     |
| 0.7661        | 40.0  | 3000 | 0.8149          | 0.6633   |
| 0.7897        | 41.0  | 3075 | 0.8132          | 0.6633   |
| 0.7801        | 42.0  | 3150 | 0.8117          | 0.665    |
| 0.8176        | 43.0  | 3225 | 0.8105          | 0.6683   |
| 0.7701        | 44.0  | 3300 | 0.8093          | 0.6717   |
| 0.8017        | 45.0  | 3375 | 0.8084          | 0.6717   |
| 0.7892        | 46.0  | 3450 | 0.8077          | 0.6717   |
| 0.7778        | 47.0  | 3525 | 0.8071          | 0.6717   |
| 0.7798        | 48.0  | 3600 | 0.8068          | 0.6733   |
| 0.7777        | 49.0  | 3675 | 0.8066          | 0.6733   |
| 0.7532        | 50.0  | 3750 | 0.8065          | 0.6733   |


### 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