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# smids_5x_deit_base_rms_00001_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: 1.0612
- Accuracy: 0.8852

## 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: 1e-05
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2146        | 1.0   | 375   | 0.2894          | 0.8952   |
| 0.1437        | 2.0   | 750   | 0.3102          | 0.8935   |
| 0.0617        | 3.0   | 1125  | 0.4639          | 0.8902   |
| 0.0286        | 4.0   | 1500  | 0.5331          | 0.8935   |
| 0.0028        | 5.0   | 1875  | 0.6331          | 0.8802   |
| 0.0027        | 6.0   | 2250  | 0.6763          | 0.8902   |
| 0.0012        | 7.0   | 2625  | 0.7498          | 0.8835   |
| 0.0002        | 8.0   | 3000  | 0.6659          | 0.8952   |
| 0.0           | 9.0   | 3375  | 0.7256          | 0.8935   |
| 0.0209        | 10.0  | 3750  | 0.7936          | 0.8769   |
| 0.0042        | 11.0  | 4125  | 0.8361          | 0.8802   |
| 0.001         | 12.0  | 4500  | 0.8162          | 0.8852   |
| 0.0011        | 13.0  | 4875  | 0.8014          | 0.8968   |
| 0.0           | 14.0  | 5250  | 0.8392          | 0.8885   |
| 0.0018        | 15.0  | 5625  | 0.9229          | 0.8752   |
| 0.0           | 16.0  | 6000  | 0.8989          | 0.8869   |
| 0.0029        | 17.0  | 6375  | 0.8923          | 0.8902   |
| 0.0           | 18.0  | 6750  | 0.8680          | 0.8852   |
| 0.0112        | 19.0  | 7125  | 0.9026          | 0.8852   |
| 0.0           | 20.0  | 7500  | 0.9170          | 0.8902   |
| 0.0           | 21.0  | 7875  | 1.0300          | 0.8735   |
| 0.0           | 22.0  | 8250  | 0.8953          | 0.8885   |
| 0.0           | 23.0  | 8625  | 0.9292          | 0.8918   |
| 0.0001        | 24.0  | 9000  | 0.9442          | 0.8935   |
| 0.0           | 25.0  | 9375  | 0.9984          | 0.8952   |
| 0.0           | 26.0  | 9750  | 1.0751          | 0.8885   |
| 0.0           | 27.0  | 10125 | 1.0903          | 0.8819   |
| 0.0           | 28.0  | 10500 | 1.0301          | 0.8852   |
| 0.0           | 29.0  | 10875 | 1.0019          | 0.8885   |
| 0.0           | 30.0  | 11250 | 0.9825          | 0.8902   |
| 0.0045        | 31.0  | 11625 | 1.0018          | 0.8835   |
| 0.0032        | 32.0  | 12000 | 1.0070          | 0.8885   |
| 0.0037        | 33.0  | 12375 | 0.9955          | 0.8902   |
| 0.0           | 34.0  | 12750 | 1.0401          | 0.8802   |
| 0.0           | 35.0  | 13125 | 1.0361          | 0.8835   |
| 0.0           | 36.0  | 13500 | 1.0263          | 0.8869   |
| 0.0           | 37.0  | 13875 | 1.0646          | 0.8802   |
| 0.0           | 38.0  | 14250 | 1.0823          | 0.8835   |
| 0.0           | 39.0  | 14625 | 1.0786          | 0.8852   |
| 0.0029        | 40.0  | 15000 | 1.0585          | 0.8869   |
| 0.0           | 41.0  | 15375 | 1.0567          | 0.8852   |
| 0.0023        | 42.0  | 15750 | 1.0631          | 0.8852   |
| 0.0027        | 43.0  | 16125 | 1.0573          | 0.8885   |
| 0.0026        | 44.0  | 16500 | 1.0579          | 0.8902   |
| 0.0023        | 45.0  | 16875 | 1.0642          | 0.8852   |
| 0.0           | 46.0  | 17250 | 1.0620          | 0.8852   |
| 0.0049        | 47.0  | 17625 | 1.0628          | 0.8852   |
| 0.0           | 48.0  | 18000 | 1.0622          | 0.8852   |
| 0.0022        | 49.0  | 18375 | 1.0616          | 0.8852   |
| 0.0024        | 50.0  | 18750 | 1.0612          | 0.8852   |


### 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