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# smids_10x_deit_small_sgd_0001_fold5

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: 0.4025
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.999         | 1.0   | 750   | 1.0177          | 0.4867   |
| 0.9125        | 2.0   | 1500  | 0.9538          | 0.56     |
| 0.8354        | 3.0   | 2250  | 0.8848          | 0.64     |
| 0.7909        | 4.0   | 3000  | 0.8172          | 0.685    |
| 0.7315        | 5.0   | 3750  | 0.7535          | 0.7183   |
| 0.6641        | 6.0   | 4500  | 0.7023          | 0.7433   |
| 0.61          | 7.0   | 5250  | 0.6582          | 0.755    |
| 0.5883        | 8.0   | 6000  | 0.6232          | 0.7783   |
| 0.6057        | 9.0   | 6750  | 0.5936          | 0.79     |
| 0.5434        | 10.0  | 7500  | 0.5693          | 0.795    |
| 0.5298        | 11.0  | 8250  | 0.5500          | 0.7917   |
| 0.4881        | 12.0  | 9000  | 0.5324          | 0.8      |
| 0.5014        | 13.0  | 9750  | 0.5180          | 0.8      |
| 0.4862        | 14.0  | 10500 | 0.5060          | 0.8083   |
| 0.4712        | 15.0  | 11250 | 0.4949          | 0.81     |
| 0.4371        | 16.0  | 12000 | 0.4864          | 0.8117   |
| 0.4626        | 17.0  | 12750 | 0.4789          | 0.815    |
| 0.4294        | 18.0  | 13500 | 0.4706          | 0.815    |
| 0.4498        | 19.0  | 14250 | 0.4650          | 0.815    |
| 0.425         | 20.0  | 15000 | 0.4594          | 0.815    |
| 0.4212        | 21.0  | 15750 | 0.4532          | 0.8167   |
| 0.4517        | 22.0  | 16500 | 0.4489          | 0.82     |
| 0.4104        | 23.0  | 17250 | 0.4443          | 0.8167   |
| 0.4051        | 24.0  | 18000 | 0.4407          | 0.82     |
| 0.4019        | 25.0  | 18750 | 0.4371          | 0.8217   |
| 0.3884        | 26.0  | 19500 | 0.4338          | 0.825    |
| 0.3154        | 27.0  | 20250 | 0.4302          | 0.825    |
| 0.3994        | 28.0  | 21000 | 0.4273          | 0.8283   |
| 0.4061        | 29.0  | 21750 | 0.4246          | 0.83     |
| 0.4059        | 30.0  | 22500 | 0.4225          | 0.8283   |
| 0.3637        | 31.0  | 23250 | 0.4202          | 0.8267   |
| 0.3501        | 32.0  | 24000 | 0.4181          | 0.8283   |
| 0.4209        | 33.0  | 24750 | 0.4163          | 0.8317   |
| 0.3255        | 34.0  | 25500 | 0.4145          | 0.8317   |
| 0.3933        | 35.0  | 26250 | 0.4127          | 0.8317   |
| 0.3766        | 36.0  | 27000 | 0.4115          | 0.8317   |
| 0.3145        | 37.0  | 27750 | 0.4102          | 0.8317   |
| 0.3874        | 38.0  | 28500 | 0.4090          | 0.83     |
| 0.3898        | 39.0  | 29250 | 0.4079          | 0.83     |
| 0.365         | 40.0  | 30000 | 0.4069          | 0.8317   |
| 0.3728        | 41.0  | 30750 | 0.4059          | 0.8317   |
| 0.3865        | 42.0  | 31500 | 0.4051          | 0.8317   |
| 0.3813        | 43.0  | 32250 | 0.4045          | 0.8317   |
| 0.3607        | 44.0  | 33000 | 0.4040          | 0.8317   |
| 0.3955        | 45.0  | 33750 | 0.4034          | 0.8333   |
| 0.3317        | 46.0  | 34500 | 0.4031          | 0.835    |
| 0.4022        | 47.0  | 35250 | 0.4028          | 0.835    |
| 0.3888        | 48.0  | 36000 | 0.4026          | 0.835    |
| 0.3745        | 49.0  | 36750 | 0.4025          | 0.835    |
| 0.3           | 50.0  | 37500 | 0.4025          | 0.835    |


### 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