
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# smids_5x_deit_tiny_rms_0001_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: 1.1259
- Accuracy: 0.8735

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3139        | 1.0   | 375   | 0.2920          | 0.8835   |
| 0.213         | 2.0   | 750   | 0.3450          | 0.8785   |
| 0.2004        | 3.0   | 1125  | 0.4306          | 0.8719   |
| 0.1151        | 4.0   | 1500  | 0.4856          | 0.8702   |
| 0.1363        | 5.0   | 1875  | 0.5483          | 0.8752   |
| 0.0415        | 6.0   | 2250  | 0.6014          | 0.8719   |
| 0.0888        | 7.0   | 2625  | 0.6594          | 0.8636   |
| 0.0129        | 8.0   | 3000  | 0.7394          | 0.8702   |
| 0.0606        | 9.0   | 3375  | 0.7551          | 0.8619   |
| 0.0273        | 10.0  | 3750  | 0.7977          | 0.8536   |
| 0.0575        | 11.0  | 4125  | 0.7927          | 0.8702   |
| 0.0142        | 12.0  | 4500  | 0.8285          | 0.8619   |
| 0.006         | 13.0  | 4875  | 0.8594          | 0.8819   |
| 0.0339        | 14.0  | 5250  | 0.8600          | 0.8686   |
| 0.0029        | 15.0  | 5625  | 0.9289          | 0.8719   |
| 0.0348        | 16.0  | 6000  | 0.7828          | 0.8819   |
| 0.0273        | 17.0  | 6375  | 0.7381          | 0.8885   |
| 0.029         | 18.0  | 6750  | 0.9087          | 0.8686   |
| 0.0306        | 19.0  | 7125  | 0.9194          | 0.8785   |
| 0.0034        | 20.0  | 7500  | 1.0978          | 0.8619   |
| 0.0052        | 21.0  | 7875  | 0.9530          | 0.8785   |
| 0.0001        | 22.0  | 8250  | 0.9575          | 0.8752   |
| 0.0447        | 23.0  | 8625  | 0.9869          | 0.8819   |
| 0.0122        | 24.0  | 9000  | 0.8869          | 0.8785   |
| 0.0018        | 25.0  | 9375  | 1.0324          | 0.8669   |
| 0.0117        | 26.0  | 9750  | 0.9387          | 0.8852   |
| 0.0206        | 27.0  | 10125 | 1.0468          | 0.8719   |
| 0.0002        | 28.0  | 10500 | 0.9421          | 0.8785   |
| 0.0001        | 29.0  | 10875 | 0.8621          | 0.8968   |
| 0.0027        | 30.0  | 11250 | 0.9653          | 0.8769   |
| 0.0116        | 31.0  | 11625 | 0.9958          | 0.8785   |
| 0.0019        | 32.0  | 12000 | 1.1300          | 0.8752   |
| 0.0084        | 33.0  | 12375 | 1.0346          | 0.8802   |
| 0.0           | 34.0  | 12750 | 1.0458          | 0.8719   |
| 0.0           | 35.0  | 13125 | 1.0740          | 0.8719   |
| 0.0001        | 36.0  | 13500 | 1.0706          | 0.8719   |
| 0.0           | 37.0  | 13875 | 1.2116          | 0.8735   |
| 0.0           | 38.0  | 14250 | 1.1598          | 0.8735   |
| 0.0           | 39.0  | 14625 | 1.1682          | 0.8785   |
| 0.0029        | 40.0  | 15000 | 1.0573          | 0.8835   |
| 0.0           | 41.0  | 15375 | 1.1307          | 0.8735   |
| 0.0028        | 42.0  | 15750 | 1.1484          | 0.8702   |
| 0.0032        | 43.0  | 16125 | 1.1289          | 0.8752   |
| 0.0031        | 44.0  | 16500 | 1.1224          | 0.8769   |
| 0.0027        | 45.0  | 16875 | 1.1287          | 0.8719   |
| 0.0           | 46.0  | 17250 | 1.1176          | 0.8752   |
| 0.006         | 47.0  | 17625 | 1.1207          | 0.8752   |
| 0.0           | 48.0  | 18000 | 1.1234          | 0.8752   |
| 0.0024        | 49.0  | 18375 | 1.1256          | 0.8752   |
| 0.0022        | 50.0  | 18750 | 1.1259          | 0.8735   |


### 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