
<!-- 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_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: 1.0513
- Accuracy: 0.8902

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2658        | 1.0   | 375   | 0.3126          | 0.8819   |
| 0.1908        | 2.0   | 750   | 0.3283          | 0.8918   |
| 0.0825        | 3.0   | 1125  | 0.4361          | 0.8702   |
| 0.0963        | 4.0   | 1500  | 0.5286          | 0.8619   |
| 0.0865        | 5.0   | 1875  | 0.5193          | 0.8885   |
| 0.0848        | 6.0   | 2250  | 0.5531          | 0.8835   |
| 0.072         | 7.0   | 2625  | 0.6299          | 0.8669   |
| 0.0017        | 8.0   | 3000  | 0.6200          | 0.8819   |
| 0.0259        | 9.0   | 3375  | 0.6078          | 0.9002   |
| 0.0609        | 10.0  | 3750  | 0.5800          | 0.8918   |
| 0.045         | 11.0  | 4125  | 0.6268          | 0.8802   |
| 0.068         | 12.0  | 4500  | 0.5720          | 0.8869   |
| 0.0342        | 13.0  | 4875  | 0.7774          | 0.8652   |
| 0.007         | 14.0  | 5250  | 0.7661          | 0.8769   |
| 0.0136        | 15.0  | 5625  | 0.7670          | 0.8885   |
| 0.025         | 16.0  | 6000  | 0.9672          | 0.8752   |
| 0.0117        | 17.0  | 6375  | 0.6723          | 0.9018   |
| 0.01          | 18.0  | 6750  | 0.7201          | 0.8835   |
| 0.1057        | 19.0  | 7125  | 0.7988          | 0.8686   |
| 0.0079        | 20.0  | 7500  | 0.8529          | 0.8735   |
| 0.0114        | 21.0  | 7875  | 0.9574          | 0.8835   |
| 0.0141        | 22.0  | 8250  | 0.8344          | 0.8819   |
| 0.0006        | 23.0  | 8625  | 0.9308          | 0.8769   |
| 0.0008        | 24.0  | 9000  | 0.8418          | 0.8752   |
| 0.0001        | 25.0  | 9375  | 0.7076          | 0.8869   |
| 0.0123        | 26.0  | 9750  | 0.9006          | 0.8686   |
| 0.0003        | 27.0  | 10125 | 0.9386          | 0.8702   |
| 0.0081        | 28.0  | 10500 | 1.0332          | 0.8735   |
| 0.0           | 29.0  | 10875 | 0.9316          | 0.8752   |
| 0.0272        | 30.0  | 11250 | 0.9157          | 0.8835   |
| 0.0042        | 31.0  | 11625 | 0.9011          | 0.8752   |
| 0.0038        | 32.0  | 12000 | 0.9259          | 0.8769   |
| 0.0044        | 33.0  | 12375 | 0.9290          | 0.8869   |
| 0.0           | 34.0  | 12750 | 0.9423          | 0.8852   |
| 0.0           | 35.0  | 13125 | 0.8933          | 0.8902   |
| 0.0           | 36.0  | 13500 | 0.8976          | 0.8885   |
| 0.0           | 37.0  | 13875 | 0.8889          | 0.8835   |
| 0.0004        | 38.0  | 14250 | 1.0859          | 0.8802   |
| 0.0           | 39.0  | 14625 | 1.0992          | 0.8869   |
| 0.0031        | 40.0  | 15000 | 1.0003          | 0.8952   |
| 0.0           | 41.0  | 15375 | 1.0009          | 0.8985   |
| 0.0027        | 42.0  | 15750 | 1.0542          | 0.8885   |
| 0.0026        | 43.0  | 16125 | 1.0230          | 0.8852   |
| 0.0026        | 44.0  | 16500 | 1.0414          | 0.8885   |
| 0.0027        | 45.0  | 16875 | 1.0121          | 0.8885   |
| 0.0           | 46.0  | 17250 | 1.0455          | 0.8885   |
| 0.006         | 47.0  | 17625 | 1.0427          | 0.8918   |
| 0.0           | 48.0  | 18000 | 1.0481          | 0.8918   |
| 0.0024        | 49.0  | 18375 | 1.0499          | 0.8918   |
| 0.0022        | 50.0  | 18750 | 1.0513          | 0.8902   |


### 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