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# smids_10x_deit_small_rms_0001_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: 1.0878
- Accuracy: 0.8968

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2199        | 1.0   | 750   | 0.3087          | 0.8702   |
| 0.1415        | 2.0   | 1500  | 0.3270          | 0.8952   |
| 0.0884        | 3.0   | 2250  | 0.4634          | 0.8735   |
| 0.1125        | 4.0   | 3000  | 0.5598          | 0.8735   |
| 0.0577        | 5.0   | 3750  | 0.5910          | 0.8785   |
| 0.0338        | 6.0   | 4500  | 0.6886          | 0.8802   |
| 0.0535        | 7.0   | 5250  | 0.5872          | 0.8835   |
| 0.0197        | 8.0   | 6000  | 0.6155          | 0.8952   |
| 0.0062        | 9.0   | 6750  | 0.7198          | 0.8852   |
| 0.019         | 10.0  | 7500  | 0.6897          | 0.8985   |
| 0.011         | 11.0  | 8250  | 0.6348          | 0.8985   |
| 0.0457        | 12.0  | 9000  | 0.6894          | 0.8835   |
| 0.0048        | 13.0  | 9750  | 0.7305          | 0.8918   |
| 0.0312        | 14.0  | 10500 | 0.8235          | 0.8835   |
| 0.0002        | 15.0  | 11250 | 0.8886          | 0.8819   |
| 0.0307        | 16.0  | 12000 | 0.8040          | 0.8769   |
| 0.0004        | 17.0  | 12750 | 0.6236          | 0.9068   |
| 0.0012        | 18.0  | 13500 | 0.8211          | 0.8785   |
| 0.0004        | 19.0  | 14250 | 0.6599          | 0.8985   |
| 0.0198        | 20.0  | 15000 | 0.6886          | 0.8985   |
| 0.0006        | 21.0  | 15750 | 0.8044          | 0.8852   |
| 0.007         | 22.0  | 16500 | 0.7019          | 0.8885   |
| 0.0333        | 23.0  | 17250 | 0.7287          | 0.8819   |
| 0.0           | 24.0  | 18000 | 1.0716          | 0.8652   |
| 0.0           | 25.0  | 18750 | 0.9627          | 0.8752   |
| 0.0006        | 26.0  | 19500 | 1.0237          | 0.8686   |
| 0.0027        | 27.0  | 20250 | 0.9748          | 0.8769   |
| 0.0004        | 28.0  | 21000 | 0.9776          | 0.8902   |
| 0.0           | 29.0  | 21750 | 0.9254          | 0.8785   |
| 0.0001        | 30.0  | 22500 | 0.9772          | 0.8902   |
| 0.0132        | 31.0  | 23250 | 0.7890          | 0.9035   |
| 0.0002        | 32.0  | 24000 | 0.8329          | 0.8985   |
| 0.0           | 33.0  | 24750 | 0.8259          | 0.9101   |
| 0.0           | 34.0  | 25500 | 0.9870          | 0.8852   |
| 0.0067        | 35.0  | 26250 | 1.0178          | 0.8918   |
| 0.0           | 36.0  | 27000 | 0.9706          | 0.9035   |
| 0.0039        | 37.0  | 27750 | 0.9405          | 0.8952   |
| 0.0           | 38.0  | 28500 | 1.0909          | 0.8869   |
| 0.0           | 39.0  | 29250 | 1.0161          | 0.9002   |
| 0.0           | 40.0  | 30000 | 0.9672          | 0.9068   |
| 0.0           | 41.0  | 30750 | 1.0373          | 0.8952   |
| 0.0           | 42.0  | 31500 | 1.0954          | 0.8968   |
| 0.0           | 43.0  | 32250 | 1.0521          | 0.9035   |
| 0.0           | 44.0  | 33000 | 1.0547          | 0.8968   |
| 0.0028        | 45.0  | 33750 | 1.0695          | 0.8952   |
| 0.0           | 46.0  | 34500 | 1.0676          | 0.8968   |
| 0.0           | 47.0  | 35250 | 1.0805          | 0.8968   |
| 0.0           | 48.0  | 36000 | 1.0830          | 0.8968   |
| 0.0           | 49.0  | 36750 | 1.0856          | 0.8968   |
| 0.0           | 50.0  | 37500 | 1.0878          | 0.8968   |


### 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