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# smids_10x_deit_small_adamax_001_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: 1.0087
- Accuracy: 0.8985

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3033        | 1.0   | 750   | 0.3519          | 0.8552   |
| 0.1821        | 2.0   | 1500  | 0.2712          | 0.9151   |
| 0.17          | 3.0   | 2250  | 0.2785          | 0.8985   |
| 0.2112        | 4.0   | 3000  | 0.3938          | 0.8735   |
| 0.1304        | 5.0   | 3750  | 0.4058          | 0.8719   |
| 0.1351        | 6.0   | 4500  | 0.3464          | 0.8835   |
| 0.0944        | 7.0   | 5250  | 0.4185          | 0.9018   |
| 0.08          | 8.0   | 6000  | 0.4321          | 0.9052   |
| 0.0621        | 9.0   | 6750  | 0.4755          | 0.8902   |
| 0.0267        | 10.0  | 7500  | 0.5448          | 0.8669   |
| 0.0347        | 11.0  | 8250  | 0.4694          | 0.8968   |
| 0.1206        | 12.0  | 9000  | 0.6302          | 0.8819   |
| 0.032         | 13.0  | 9750  | 0.6552          | 0.8819   |
| 0.0071        | 14.0  | 10500 | 0.5891          | 0.8869   |
| 0.0007        | 15.0  | 11250 | 0.6706          | 0.8935   |
| 0.0152        | 16.0  | 12000 | 0.6742          | 0.8852   |
| 0.0005        | 17.0  | 12750 | 0.6672          | 0.8952   |
| 0.0068        | 18.0  | 13500 | 0.7499          | 0.8885   |
| 0.0002        | 19.0  | 14250 | 0.7790          | 0.9018   |
| 0.0003        | 20.0  | 15000 | 0.7692          | 0.8835   |
| 0.0056        | 21.0  | 15750 | 0.8482          | 0.8752   |
| 0.0136        | 22.0  | 16500 | 0.8127          | 0.8835   |
| 0.0063        | 23.0  | 17250 | 0.6687          | 0.8952   |
| 0.0072        | 24.0  | 18000 | 0.8624          | 0.8869   |
| 0.0           | 25.0  | 18750 | 0.8382          | 0.8902   |
| 0.0001        | 26.0  | 19500 | 0.8780          | 0.8769   |
| 0.0107        | 27.0  | 20250 | 0.8313          | 0.8935   |
| 0.0           | 28.0  | 21000 | 0.9547          | 0.8869   |
| 0.0           | 29.0  | 21750 | 0.9878          | 0.8952   |
| 0.0           | 30.0  | 22500 | 0.8456          | 0.9035   |
| 0.0           | 31.0  | 23250 | 1.0397          | 0.8918   |
| 0.0           | 32.0  | 24000 | 0.9157          | 0.9018   |
| 0.0           | 33.0  | 24750 | 0.9451          | 0.9018   |
| 0.0           | 34.0  | 25500 | 0.9702          | 0.8985   |
| 0.0           | 35.0  | 26250 | 0.9002          | 0.9085   |
| 0.0           | 36.0  | 27000 | 0.9202          | 0.8985   |
| 0.004         | 37.0  | 27750 | 0.9200          | 0.8968   |
| 0.0           | 38.0  | 28500 | 0.9595          | 0.8968   |
| 0.0           | 39.0  | 29250 | 0.9656          | 0.9018   |
| 0.0           | 40.0  | 30000 | 0.9661          | 0.9018   |
| 0.0           | 41.0  | 30750 | 0.9523          | 0.9018   |
| 0.0           | 42.0  | 31500 | 0.9556          | 0.9002   |
| 0.0           | 43.0  | 32250 | 0.9641          | 0.8985   |
| 0.0           | 44.0  | 33000 | 0.9727          | 0.9002   |
| 0.0026        | 45.0  | 33750 | 0.9812          | 0.8985   |
| 0.0           | 46.0  | 34500 | 0.9958          | 0.8985   |
| 0.0           | 47.0  | 35250 | 0.9951          | 0.8985   |
| 0.0           | 48.0  | 36000 | 1.0005          | 0.8985   |
| 0.0           | 49.0  | 36750 | 1.0064          | 0.8985   |
| 0.0           | 50.0  | 37500 | 1.0087          | 0.8985   |


### 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