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# smids_5x_deit_base_rms_0001_fold2

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2231
- Accuracy: 0.8819

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.244         | 1.0   | 375   | 0.3336          | 0.8735   |
| 0.1999        | 2.0   | 750   | 0.3073          | 0.8952   |
| 0.0877        | 3.0   | 1125  | 0.5180          | 0.8486   |
| 0.0613        | 4.0   | 1500  | 0.6173          | 0.8735   |
| 0.0492        | 5.0   | 1875  | 0.4636          | 0.8769   |
| 0.0237        | 6.0   | 2250  | 0.5520          | 0.8869   |
| 0.0332        | 7.0   | 2625  | 0.6932          | 0.8769   |
| 0.0234        | 8.0   | 3000  | 0.5512          | 0.8902   |
| 0.0167        | 9.0   | 3375  | 0.6767          | 0.8819   |
| 0.0254        | 10.0  | 3750  | 0.4652          | 0.8985   |
| 0.0244        | 11.0  | 4125  | 0.6296          | 0.8819   |
| 0.0022        | 12.0  | 4500  | 0.7077          | 0.8852   |
| 0.0032        | 13.0  | 4875  | 0.5101          | 0.8968   |
| 0.0197        | 14.0  | 5250  | 0.7253          | 0.8735   |
| 0.0071        | 15.0  | 5625  | 0.6712          | 0.8968   |
| 0.0257        | 16.0  | 6000  | 0.7898          | 0.8686   |
| 0.0086        | 17.0  | 6375  | 0.7760          | 0.8869   |
| 0.028         | 18.0  | 6750  | 0.6224          | 0.8719   |
| 0.0186        | 19.0  | 7125  | 0.8173          | 0.8785   |
| 0.0482        | 20.0  | 7500  | 0.7586          | 0.8719   |
| 0.1001        | 21.0  | 7875  | 0.8040          | 0.8835   |
| 0.0024        | 22.0  | 8250  | 0.8709          | 0.8652   |
| 0.0013        | 23.0  | 8625  | 0.7956          | 0.8752   |
| 0.0002        | 24.0  | 9000  | 0.8317          | 0.8802   |
| 0.0002        | 25.0  | 9375  | 0.7874          | 0.8819   |
| 0.0164        | 26.0  | 9750  | 0.8324          | 0.8869   |
| 0.0002        | 27.0  | 10125 | 0.7963          | 0.8902   |
| 0.0001        | 28.0  | 10500 | 0.8631          | 0.8952   |
| 0.0312        | 29.0  | 10875 | 0.8641          | 0.8902   |
| 0.0005        | 30.0  | 11250 | 0.9305          | 0.8852   |
| 0.0052        | 31.0  | 11625 | 1.0338          | 0.8869   |
| 0.0033        | 32.0  | 12000 | 0.8216          | 0.8752   |
| 0.0052        | 33.0  | 12375 | 0.9970          | 0.8819   |
| 0.0065        | 34.0  | 12750 | 0.8099          | 0.8918   |
| 0.0           | 35.0  | 13125 | 0.9129          | 0.8852   |
| 0.0           | 36.0  | 13500 | 0.8964          | 0.8885   |
| 0.0           | 37.0  | 13875 | 0.9774          | 0.8785   |
| 0.0           | 38.0  | 14250 | 1.0097          | 0.8852   |
| 0.0           | 39.0  | 14625 | 1.0835          | 0.8802   |
| 0.0031        | 40.0  | 15000 | 1.0742          | 0.8769   |
| 0.0           | 41.0  | 15375 | 1.1287          | 0.8802   |
| 0.0028        | 42.0  | 15750 | 1.0739          | 0.8819   |
| 0.0028        | 43.0  | 16125 | 1.1899          | 0.8769   |
| 0.0028        | 44.0  | 16500 | 1.1924          | 0.8769   |
| 0.003         | 45.0  | 16875 | 1.1778          | 0.8802   |
| 0.0           | 46.0  | 17250 | 1.2129          | 0.8819   |
| 0.0058        | 47.0  | 17625 | 1.2164          | 0.8819   |
| 0.0           | 48.0  | 18000 | 1.2195          | 0.8819   |
| 0.0025        | 49.0  | 18375 | 1.2217          | 0.8819   |
| 0.0023        | 50.0  | 18750 | 1.2231          | 0.8819   |


### 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