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# smids_10x_deit_tiny_adamax_001_fold2

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: 1.0691
- Accuracy: 0.9002

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3362        | 1.0   | 750   | 0.3519          | 0.8652   |
| 0.2971        | 2.0   | 1500  | 0.3131          | 0.8918   |
| 0.1771        | 3.0   | 2250  | 0.2717          | 0.8885   |
| 0.2985        | 4.0   | 3000  | 0.3652          | 0.8652   |
| 0.1399        | 5.0   | 3750  | 0.3216          | 0.9018   |
| 0.1317        | 6.0   | 4500  | 0.3948          | 0.8802   |
| 0.1309        | 7.0   | 5250  | 0.3860          | 0.8902   |
| 0.1165        | 8.0   | 6000  | 0.4557          | 0.8852   |
| 0.0308        | 9.0   | 6750  | 0.5032          | 0.8686   |
| 0.0315        | 10.0  | 7500  | 0.4981          | 0.8769   |
| 0.0974        | 11.0  | 8250  | 0.6363          | 0.8769   |
| 0.1017        | 12.0  | 9000  | 0.5021          | 0.8869   |
| 0.0475        | 13.0  | 9750  | 0.5896          | 0.8885   |
| 0.0086        | 14.0  | 10500 | 0.6931          | 0.8918   |
| 0.0301        | 15.0  | 11250 | 0.6531          | 0.8902   |
| 0.0049        | 16.0  | 12000 | 0.7157          | 0.8819   |
| 0.0307        | 17.0  | 12750 | 0.7054          | 0.8935   |
| 0.0113        | 18.0  | 13500 | 0.7646          | 0.8869   |
| 0.0492        | 19.0  | 14250 | 0.7424          | 0.8885   |
| 0.0093        | 20.0  | 15000 | 0.6366          | 0.8952   |
| 0.011         | 21.0  | 15750 | 0.8426          | 0.8885   |
| 0.0191        | 22.0  | 16500 | 0.7557          | 0.8952   |
| 0.0047        | 23.0  | 17250 | 0.7578          | 0.8885   |
| 0.0163        | 24.0  | 18000 | 0.8275          | 0.8902   |
| 0.0001        | 25.0  | 18750 | 0.8176          | 0.8935   |
| 0.0023        | 26.0  | 19500 | 0.8054          | 0.8968   |
| 0.0181        | 27.0  | 20250 | 0.8270          | 0.8952   |
| 0.0           | 28.0  | 21000 | 0.8173          | 0.9035   |
| 0.0001        | 29.0  | 21750 | 0.8348          | 0.9018   |
| 0.0           | 30.0  | 22500 | 0.8105          | 0.9101   |
| 0.0           | 31.0  | 23250 | 0.7837          | 0.9118   |
| 0.0           | 32.0  | 24000 | 0.9929          | 0.8935   |
| 0.0           | 33.0  | 24750 | 0.8103          | 0.9085   |
| 0.0           | 34.0  | 25500 | 0.8769          | 0.9035   |
| 0.0           | 35.0  | 26250 | 0.8987          | 0.8985   |
| 0.0           | 36.0  | 27000 | 1.0129          | 0.9002   |
| 0.0053        | 37.0  | 27750 | 0.9506          | 0.9068   |
| 0.0           | 38.0  | 28500 | 1.0495          | 0.8935   |
| 0.0           | 39.0  | 29250 | 0.9869          | 0.9018   |
| 0.0           | 40.0  | 30000 | 1.0087          | 0.8968   |
| 0.0           | 41.0  | 30750 | 1.0348          | 0.8985   |
| 0.0           | 42.0  | 31500 | 1.0299          | 0.8985   |
| 0.0           | 43.0  | 32250 | 1.0437          | 0.8968   |
| 0.0           | 44.0  | 33000 | 1.0468          | 0.8985   |
| 0.0028        | 45.0  | 33750 | 1.0539          | 0.9002   |
| 0.0           | 46.0  | 34500 | 1.0588          | 0.9002   |
| 0.0           | 47.0  | 35250 | 1.0567          | 0.9002   |
| 0.0           | 48.0  | 36000 | 1.0631          | 0.9002   |
| 0.0           | 49.0  | 36750 | 1.0673          | 0.9002   |
| 0.0           | 50.0  | 37500 | 1.0691          | 0.9002   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2

abnormal_sperm non-sperm normal_sperm