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# smids_1x_deit_small_adamax_00001_fold2

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: 0.7991
- Accuracy: 0.8569

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5561        | 1.0   | 75   | 0.5421          | 0.7804   |
| 0.3586        | 2.0   | 150  | 0.4304          | 0.8286   |
| 0.3328        | 3.0   | 225  | 0.3782          | 0.8586   |
| 0.266         | 4.0   | 300  | 0.3735          | 0.8536   |
| 0.2451        | 5.0   | 375  | 0.3596          | 0.8536   |
| 0.2001        | 6.0   | 450  | 0.3408          | 0.8669   |
| 0.1658        | 7.0   | 525  | 0.3450          | 0.8552   |
| 0.1239        | 8.0   | 600  | 0.3523          | 0.8519   |
| 0.0994        | 9.0   | 675  | 0.3736          | 0.8602   |
| 0.0972        | 10.0  | 750  | 0.3715          | 0.8636   |
| 0.0323        | 11.0  | 825  | 0.4031          | 0.8569   |
| 0.0293        | 12.0  | 900  | 0.4332          | 0.8586   |
| 0.0328        | 13.0  | 975  | 0.4440          | 0.8636   |
| 0.0162        | 14.0  | 1050 | 0.4947          | 0.8552   |
| 0.02          | 15.0  | 1125 | 0.5267          | 0.8586   |
| 0.014         | 16.0  | 1200 | 0.5586          | 0.8586   |
| 0.0039        | 17.0  | 1275 | 0.5843          | 0.8536   |
| 0.0174        | 18.0  | 1350 | 0.6241          | 0.8586   |
| 0.003         | 19.0  | 1425 | 0.6332          | 0.8502   |
| 0.0035        | 20.0  | 1500 | 0.6412          | 0.8569   |
| 0.0013        | 21.0  | 1575 | 0.6514          | 0.8519   |
| 0.0012        | 22.0  | 1650 | 0.6772          | 0.8502   |
| 0.0007        | 23.0  | 1725 | 0.6821          | 0.8502   |
| 0.0007        | 24.0  | 1800 | 0.7040          | 0.8569   |
| 0.0006        | 25.0  | 1875 | 0.6982          | 0.8569   |
| 0.0005        | 26.0  | 1950 | 0.7116          | 0.8519   |
| 0.0162        | 27.0  | 2025 | 0.7266          | 0.8602   |
| 0.0086        | 28.0  | 2100 | 0.7241          | 0.8536   |
| 0.0081        | 29.0  | 2175 | 0.7251          | 0.8602   |
| 0.0111        | 30.0  | 2250 | 0.7386          | 0.8602   |
| 0.0161        | 31.0  | 2325 | 0.7520          | 0.8586   |
| 0.0003        | 32.0  | 2400 | 0.7496          | 0.8519   |
| 0.0003        | 33.0  | 2475 | 0.7540          | 0.8552   |
| 0.0003        | 34.0  | 2550 | 0.7601          | 0.8602   |
| 0.0123        | 35.0  | 2625 | 0.7663          | 0.8519   |
| 0.0002        | 36.0  | 2700 | 0.7752          | 0.8586   |
| 0.0002        | 37.0  | 2775 | 0.7743          | 0.8502   |
| 0.0002        | 38.0  | 2850 | 0.7784          | 0.8519   |
| 0.0004        | 39.0  | 2925 | 0.7826          | 0.8536   |
| 0.0002        | 40.0  | 3000 | 0.7838          | 0.8536   |
| 0.0044        | 41.0  | 3075 | 0.7838          | 0.8586   |
| 0.0002        | 42.0  | 3150 | 0.7911          | 0.8552   |
| 0.001         | 43.0  | 3225 | 0.7929          | 0.8569   |
| 0.0106        | 44.0  | 3300 | 0.7934          | 0.8552   |
| 0.0002        | 45.0  | 3375 | 0.7947          | 0.8552   |
| 0.0002        | 46.0  | 3450 | 0.7979          | 0.8536   |
| 0.001         | 47.0  | 3525 | 0.7983          | 0.8536   |
| 0.0002        | 48.0  | 3600 | 0.7989          | 0.8569   |
| 0.0022        | 49.0  | 3675 | 0.7991          | 0.8569   |
| 0.0004        | 50.0  | 3750 | 0.7991          | 0.8569   |


### 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