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# smids_10x_deit_small_sgd_0001_fold4

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: 0.4063
- Accuracy: 0.8417

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9715        | 1.0   | 750   | 1.0172          | 0.455    |
| 0.9076        | 2.0   | 1500  | 0.9524          | 0.5267   |
| 0.8403        | 3.0   | 2250  | 0.8812          | 0.625    |
| 0.7987        | 4.0   | 3000  | 0.8125          | 0.6817   |
| 0.7256        | 5.0   | 3750  | 0.7521          | 0.7183   |
| 0.6364        | 6.0   | 4500  | 0.7018          | 0.7483   |
| 0.5752        | 7.0   | 5250  | 0.6571          | 0.775    |
| 0.63          | 8.0   | 6000  | 0.6211          | 0.7817   |
| 0.6197        | 9.0   | 6750  | 0.5901          | 0.79     |
| 0.5118        | 10.0  | 7500  | 0.5651          | 0.7983   |
| 0.5006        | 11.0  | 8250  | 0.5449          | 0.8017   |
| 0.5617        | 12.0  | 9000  | 0.5276          | 0.8033   |
| 0.4842        | 13.0  | 9750  | 0.5134          | 0.8083   |
| 0.5031        | 14.0  | 10500 | 0.5016          | 0.81     |
| 0.4417        | 15.0  | 11250 | 0.4908          | 0.8083   |
| 0.4457        | 16.0  | 12000 | 0.4818          | 0.8083   |
| 0.3768        | 17.0  | 12750 | 0.4743          | 0.8117   |
| 0.4232        | 18.0  | 13500 | 0.4671          | 0.8167   |
| 0.4491        | 19.0  | 14250 | 0.4614          | 0.8167   |
| 0.4472        | 20.0  | 15000 | 0.4557          | 0.8233   |
| 0.3954        | 21.0  | 15750 | 0.4506          | 0.8267   |
| 0.405         | 22.0  | 16500 | 0.4463          | 0.83     |
| 0.4169        | 23.0  | 17250 | 0.4425          | 0.8317   |
| 0.4563        | 24.0  | 18000 | 0.4389          | 0.8333   |
| 0.3987        | 25.0  | 18750 | 0.4356          | 0.8333   |
| 0.39          | 26.0  | 19500 | 0.4325          | 0.8317   |
| 0.4056        | 27.0  | 20250 | 0.4297          | 0.8317   |
| 0.3872        | 28.0  | 21000 | 0.4272          | 0.8317   |
| 0.3817        | 29.0  | 21750 | 0.4249          | 0.835    |
| 0.4035        | 30.0  | 22500 | 0.4229          | 0.8367   |
| 0.3636        | 31.0  | 23250 | 0.4211          | 0.835    |
| 0.4122        | 32.0  | 24000 | 0.4193          | 0.8367   |
| 0.3917        | 33.0  | 24750 | 0.4176          | 0.8383   |
| 0.3839        | 34.0  | 25500 | 0.4161          | 0.84     |
| 0.3217        | 35.0  | 26250 | 0.4147          | 0.84     |
| 0.3641        | 36.0  | 27000 | 0.4136          | 0.84     |
| 0.3379        | 37.0  | 27750 | 0.4124          | 0.84     |
| 0.3959        | 38.0  | 28500 | 0.4115          | 0.84     |
| 0.3972        | 39.0  | 29250 | 0.4106          | 0.84     |
| 0.3899        | 40.0  | 30000 | 0.4098          | 0.84     |
| 0.3662        | 41.0  | 30750 | 0.4090          | 0.84     |
| 0.3473        | 42.0  | 31500 | 0.4084          | 0.8417   |
| 0.3905        | 43.0  | 32250 | 0.4078          | 0.8417   |
| 0.3794        | 44.0  | 33000 | 0.4074          | 0.8417   |
| 0.3783        | 45.0  | 33750 | 0.4070          | 0.8417   |
| 0.3309        | 46.0  | 34500 | 0.4067          | 0.8417   |
| 0.3086        | 47.0  | 35250 | 0.4065          | 0.8417   |
| 0.3454        | 48.0  | 36000 | 0.4063          | 0.8417   |
| 0.3559        | 49.0  | 36750 | 0.4063          | 0.8417   |
| 0.323         | 50.0  | 37500 | 0.4063          | 0.8417   |


### 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