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# smids_5x_deit_base_adamax_001_fold2

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1252
- Accuracy: 0.8852

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3593        | 1.0   | 375   | 0.2868          | 0.8902   |
| 0.3012        | 2.0   | 750   | 0.2473          | 0.9085   |
| 0.3836        | 3.0   | 1125  | 0.3500          | 0.8619   |
| 0.1484        | 4.0   | 1500  | 0.3561          | 0.8819   |
| 0.142         | 5.0   | 1875  | 0.3496          | 0.8619   |
| 0.1054        | 6.0   | 2250  | 0.5030          | 0.8519   |
| 0.1132        | 7.0   | 2625  | 0.4021          | 0.8769   |
| 0.0387        | 8.0   | 3000  | 0.5600          | 0.8752   |
| 0.0412        | 9.0   | 3375  | 0.4804          | 0.8935   |
| 0.049         | 10.0  | 3750  | 0.4670          | 0.8902   |
| 0.0223        | 11.0  | 4125  | 0.5161          | 0.8852   |
| 0.0227        | 12.0  | 4500  | 0.5268          | 0.8802   |
| 0.029         | 13.0  | 4875  | 0.5511          | 0.8819   |
| 0.0101        | 14.0  | 5250  | 0.5655          | 0.8935   |
| 0.0239        | 15.0  | 5625  | 0.5903          | 0.8885   |
| 0.0204        | 16.0  | 6000  | 0.6826          | 0.8869   |
| 0.0387        | 17.0  | 6375  | 0.6581          | 0.8835   |
| 0.0045        | 18.0  | 6750  | 0.5940          | 0.8869   |
| 0.0004        | 19.0  | 7125  | 0.7563          | 0.8885   |
| 0.0271        | 20.0  | 7500  | 0.5791          | 0.9035   |
| 0.0211        | 21.0  | 7875  | 0.5981          | 0.8869   |
| 0.0086        | 22.0  | 8250  | 0.6990          | 0.8869   |
| 0.0146        | 23.0  | 8625  | 0.6527          | 0.8935   |
| 0.0006        | 24.0  | 9000  | 0.5903          | 0.8885   |
| 0.02          | 25.0  | 9375  | 0.6548          | 0.8952   |
| 0.0007        | 26.0  | 9750  | 0.7230          | 0.8952   |
| 0.0           | 27.0  | 10125 | 0.7646          | 0.9002   |
| 0.0           | 28.0  | 10500 | 0.8095          | 0.8852   |
| 0.0           | 29.0  | 10875 | 0.8926          | 0.8835   |
| 0.0           | 30.0  | 11250 | 0.8629          | 0.8819   |
| 0.0041        | 31.0  | 11625 | 0.8782          | 0.8819   |
| 0.0047        | 32.0  | 12000 | 0.8948          | 0.8819   |
| 0.0063        | 33.0  | 12375 | 0.9158          | 0.8752   |
| 0.0001        | 34.0  | 12750 | 0.9726          | 0.8918   |
| 0.0           | 35.0  | 13125 | 1.0164          | 0.8819   |
| 0.0           | 36.0  | 13500 | 1.0004          | 0.8869   |
| 0.0           | 37.0  | 13875 | 1.0193          | 0.8869   |
| 0.0           | 38.0  | 14250 | 1.0151          | 0.8935   |
| 0.0           | 39.0  | 14625 | 1.0231          | 0.8902   |
| 0.0035        | 40.0  | 15000 | 1.0298          | 0.8852   |
| 0.0           | 41.0  | 15375 | 1.0402          | 0.8902   |
| 0.0028        | 42.0  | 15750 | 1.0577          | 0.8869   |
| 0.0026        | 43.0  | 16125 | 1.0687          | 0.8819   |
| 0.0027        | 44.0  | 16500 | 1.0626          | 0.8852   |
| 0.0029        | 45.0  | 16875 | 1.0972          | 0.8835   |
| 0.0           | 46.0  | 17250 | 1.0976          | 0.8819   |
| 0.0055        | 47.0  | 17625 | 1.1056          | 0.8819   |
| 0.0           | 48.0  | 18000 | 1.1143          | 0.8852   |
| 0.0025        | 49.0  | 18375 | 1.1213          | 0.8835   |
| 0.0024        | 50.0  | 18750 | 1.1252          | 0.8852   |


### 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