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# smids_10x_deit_small_adamax_001_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: 1.1653
- Accuracy: 0.8983

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2967        | 1.0   | 750   | 0.3026          | 0.8833   |
| 0.2176        | 2.0   | 1500  | 0.3634          | 0.865    |
| 0.1482        | 3.0   | 2250  | 0.3577          | 0.8917   |
| 0.1878        | 4.0   | 3000  | 0.3366          | 0.89     |
| 0.2072        | 5.0   | 3750  | 0.4027          | 0.8783   |
| 0.1159        | 6.0   | 4500  | 0.4215          | 0.9017   |
| 0.1092        | 7.0   | 5250  | 0.4522          | 0.875    |
| 0.1055        | 8.0   | 6000  | 0.5168          | 0.8733   |
| 0.0404        | 9.0   | 6750  | 0.4834          | 0.8867   |
| 0.0353        | 10.0  | 7500  | 0.5506          | 0.895    |
| 0.0316        | 11.0  | 8250  | 0.5770          | 0.88     |
| 0.0204        | 12.0  | 9000  | 0.7013          | 0.88     |
| 0.0769        | 13.0  | 9750  | 0.7049          | 0.8833   |
| 0.0408        | 14.0  | 10500 | 0.5508          | 0.8983   |
| 0.014         | 15.0  | 11250 | 0.6644          | 0.8883   |
| 0.0112        | 16.0  | 12000 | 0.7305          | 0.895    |
| 0.0118        | 17.0  | 12750 | 0.6466          | 0.8967   |
| 0.0015        | 18.0  | 13500 | 0.7382          | 0.89     |
| 0.0022        | 19.0  | 14250 | 0.9099          | 0.8967   |
| 0.0028        | 20.0  | 15000 | 0.8123          | 0.8883   |
| 0.0003        | 21.0  | 15750 | 0.7936          | 0.895    |
| 0.0021        | 22.0  | 16500 | 0.8670          | 0.89     |
| 0.0001        | 23.0  | 17250 | 0.8387          | 0.89     |
| 0.0001        | 24.0  | 18000 | 0.9036          | 0.8867   |
| 0.0071        | 25.0  | 18750 | 0.9933          | 0.8967   |
| 0.0003        | 26.0  | 19500 | 0.9103          | 0.8933   |
| 0.0           | 27.0  | 20250 | 0.9486          | 0.8983   |
| 0.0           | 28.0  | 21000 | 0.9480          | 0.89     |
| 0.0           | 29.0  | 21750 | 1.0149          | 0.8983   |
| 0.0           | 30.0  | 22500 | 0.9710          | 0.89     |
| 0.0           | 31.0  | 23250 | 0.8903          | 0.9017   |
| 0.0           | 32.0  | 24000 | 0.9900          | 0.8983   |
| 0.0           | 33.0  | 24750 | 0.9812          | 0.8967   |
| 0.0           | 34.0  | 25500 | 1.0802          | 0.8917   |
| 0.0           | 35.0  | 26250 | 1.0127          | 0.9      |
| 0.0           | 36.0  | 27000 | 1.0499          | 0.8917   |
| 0.0           | 37.0  | 27750 | 1.0711          | 0.895    |
| 0.0018        | 38.0  | 28500 | 1.1040          | 0.8983   |
| 0.0           | 39.0  | 29250 | 1.0513          | 0.9017   |
| 0.0           | 40.0  | 30000 | 1.1398          | 0.9      |
| 0.0           | 41.0  | 30750 | 1.1537          | 0.9      |
| 0.0           | 42.0  | 31500 | 1.1196          | 0.9      |
| 0.0           | 43.0  | 32250 | 1.1395          | 0.8967   |
| 0.0           | 44.0  | 33000 | 1.1136          | 0.9017   |
| 0.0           | 45.0  | 33750 | 1.1523          | 0.895    |
| 0.0           | 46.0  | 34500 | 1.1446          | 0.9      |
| 0.0           | 47.0  | 35250 | 1.1542          | 0.8967   |
| 0.0           | 48.0  | 36000 | 1.1560          | 0.8983   |
| 0.0           | 49.0  | 36750 | 1.1589          | 0.8983   |
| 0.0           | 50.0  | 37500 | 1.1653          | 0.8983   |


### 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