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# smids_1x_deit_tiny_sgd_001_fold1

This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4210
- Accuracy: 0.8331

## 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: 0.001
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0811        | 1.0   | 76   | 1.0552          | 0.4808   |
| 0.945         | 2.0   | 152  | 0.9389          | 0.5793   |
| 0.8473        | 3.0   | 228  | 0.8609          | 0.6361   |
| 0.7999        | 4.0   | 304  | 0.8002          | 0.6561   |
| 0.6841        | 5.0   | 380  | 0.7558          | 0.6745   |
| 0.6329        | 6.0   | 456  | 0.7159          | 0.6895   |
| 0.6112        | 7.0   | 532  | 0.6747          | 0.7112   |
| 0.5645        | 8.0   | 608  | 0.6404          | 0.7195   |
| 0.5735        | 9.0   | 684  | 0.6198          | 0.7162   |
| 0.4448        | 10.0  | 760  | 0.5963          | 0.7362   |
| 0.4826        | 11.0  | 836  | 0.5775          | 0.7446   |
| 0.4884        | 12.0  | 912  | 0.5596          | 0.7546   |
| 0.419         | 13.0  | 988  | 0.5449          | 0.7629   |
| 0.4668        | 14.0  | 1064 | 0.5322          | 0.7713   |
| 0.3648        | 15.0  | 1140 | 0.5206          | 0.7780   |
| 0.435         | 16.0  | 1216 | 0.5120          | 0.7796   |
| 0.3985        | 17.0  | 1292 | 0.5037          | 0.7846   |
| 0.3605        | 18.0  | 1368 | 0.4957          | 0.7913   |
| 0.4239        | 19.0  | 1444 | 0.4882          | 0.7980   |
| 0.3983        | 20.0  | 1520 | 0.4823          | 0.7980   |
| 0.3854        | 21.0  | 1596 | 0.4759          | 0.8047   |
| 0.3728        | 22.0  | 1672 | 0.4711          | 0.8114   |
| 0.3399        | 23.0  | 1748 | 0.4667          | 0.8047   |
| 0.3623        | 24.0  | 1824 | 0.4632          | 0.8114   |
| 0.3017        | 25.0  | 1900 | 0.4575          | 0.8164   |
| 0.388         | 26.0  | 1976 | 0.4531          | 0.8147   |
| 0.2998        | 27.0  | 2052 | 0.4507          | 0.8147   |
| 0.3486        | 28.0  | 2128 | 0.4459          | 0.8180   |
| 0.2911        | 29.0  | 2204 | 0.4440          | 0.8214   |
| 0.2957        | 30.0  | 2280 | 0.4418          | 0.8230   |
| 0.3626        | 31.0  | 2356 | 0.4385          | 0.8264   |
| 0.2887        | 32.0  | 2432 | 0.4361          | 0.8214   |
| 0.3011        | 33.0  | 2508 | 0.4347          | 0.8247   |
| 0.3216        | 34.0  | 2584 | 0.4321          | 0.8230   |
| 0.4399        | 35.0  | 2660 | 0.4322          | 0.8314   |
| 0.3353        | 36.0  | 2736 | 0.4294          | 0.8297   |
| 0.3238        | 37.0  | 2812 | 0.4281          | 0.8280   |
| 0.3158        | 38.0  | 2888 | 0.4267          | 0.8297   |
| 0.316         | 39.0  | 2964 | 0.4263          | 0.8331   |
| 0.3078        | 40.0  | 3040 | 0.4248          | 0.8297   |
| 0.2618        | 41.0  | 3116 | 0.4247          | 0.8331   |
| 0.2721        | 42.0  | 3192 | 0.4237          | 0.8314   |
| 0.2921        | 43.0  | 3268 | 0.4227          | 0.8347   |
| 0.3099        | 44.0  | 3344 | 0.4222          | 0.8347   |
| 0.3081        | 45.0  | 3420 | 0.4219          | 0.8347   |
| 0.2984        | 46.0  | 3496 | 0.4214          | 0.8347   |
| 0.2929        | 47.0  | 3572 | 0.4213          | 0.8331   |
| 0.3041        | 48.0  | 3648 | 0.4210          | 0.8331   |
| 0.2743        | 49.0  | 3724 | 0.4210          | 0.8331   |
| 0.3962        | 50.0  | 3800 | 0.4210          | 0.8331   |


### 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