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# smids_5x_deit_small_sgd_0001_fold3

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: 0.4989
- Accuracy: 0.835

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.059         | 1.0   | 375   | 1.0700          | 0.43     |
| 1.011         | 2.0   | 750   | 1.0336          | 0.4883   |
| 0.9442        | 3.0   | 1125  | 0.9972          | 0.5233   |
| 0.9044        | 4.0   | 1500  | 0.9613          | 0.5667   |
| 0.9034        | 5.0   | 1875  | 0.9273          | 0.6067   |
| 0.8397        | 6.0   | 2250  | 0.8960          | 0.6217   |
| 0.8344        | 7.0   | 2625  | 0.8666          | 0.6533   |
| 0.8102        | 8.0   | 3000  | 0.8388          | 0.66     |
| 0.7533        | 9.0   | 3375  | 0.8130          | 0.675    |
| 0.7704        | 10.0  | 3750  | 0.7889          | 0.6783   |
| 0.6922        | 11.0  | 4125  | 0.7657          | 0.695    |
| 0.7058        | 12.0  | 4500  | 0.7444          | 0.7067   |
| 0.7015        | 13.0  | 4875  | 0.7244          | 0.7183   |
| 0.7084        | 14.0  | 5250  | 0.7056          | 0.725    |
| 0.6276        | 15.0  | 5625  | 0.6882          | 0.74     |
| 0.6138        | 16.0  | 6000  | 0.6721          | 0.745    |
| 0.6401        | 17.0  | 6375  | 0.6573          | 0.7533   |
| 0.6373        | 18.0  | 6750  | 0.6430          | 0.7633   |
| 0.569         | 19.0  | 7125  | 0.6303          | 0.7633   |
| 0.5819        | 20.0  | 7500  | 0.6185          | 0.77     |
| 0.5294        | 21.0  | 7875  | 0.6077          | 0.7817   |
| 0.5473        | 22.0  | 8250  | 0.5978          | 0.7883   |
| 0.5629        | 23.0  | 8625  | 0.5888          | 0.7967   |
| 0.5783        | 24.0  | 9000  | 0.5802          | 0.8017   |
| 0.509         | 25.0  | 9375  | 0.5724          | 0.8067   |
| 0.5255        | 26.0  | 9750  | 0.5652          | 0.805    |
| 0.5612        | 27.0  | 10125 | 0.5585          | 0.81     |
| 0.5914        | 28.0  | 10500 | 0.5523          | 0.815    |
| 0.4839        | 29.0  | 10875 | 0.5467          | 0.815    |
| 0.4781        | 30.0  | 11250 | 0.5414          | 0.8167   |
| 0.5423        | 31.0  | 11625 | 0.5367          | 0.8183   |
| 0.5434        | 32.0  | 12000 | 0.5323          | 0.8183   |
| 0.5812        | 33.0  | 12375 | 0.5281          | 0.82     |
| 0.4776        | 34.0  | 12750 | 0.5244          | 0.8183   |
| 0.4385        | 35.0  | 13125 | 0.5209          | 0.8217   |
| 0.4956        | 36.0  | 13500 | 0.5178          | 0.8217   |
| 0.4746        | 37.0  | 13875 | 0.5150          | 0.8233   |
| 0.4824        | 38.0  | 14250 | 0.5124          | 0.8233   |
| 0.49          | 39.0  | 14625 | 0.5101          | 0.8217   |
| 0.4379        | 40.0  | 15000 | 0.5080          | 0.8233   |
| 0.4149        | 41.0  | 15375 | 0.5062          | 0.825    |
| 0.4917        | 42.0  | 15750 | 0.5046          | 0.8267   |
| 0.5208        | 43.0  | 16125 | 0.5031          | 0.8283   |
| 0.4676        | 44.0  | 16500 | 0.5020          | 0.8283   |
| 0.4552        | 45.0  | 16875 | 0.5009          | 0.8317   |
| 0.4563        | 46.0  | 17250 | 0.5002          | 0.8333   |
| 0.5467        | 47.0  | 17625 | 0.4996          | 0.835    |
| 0.5056        | 48.0  | 18000 | 0.4992          | 0.835    |
| 0.4817        | 49.0  | 18375 | 0.4990          | 0.835    |
| 0.4808        | 50.0  | 18750 | 0.4989          | 0.835    |


### 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