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# smids_10x_deit_base_sgd_0001_fold3

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: 0.3897
- Accuracy: 0.87

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0499        | 1.0   | 750   | 1.0678          | 0.4483   |
| 1.0069        | 2.0   | 1500  | 1.0201          | 0.5333   |
| 0.9342        | 3.0   | 2250  | 0.9596          | 0.605    |
| 0.8668        | 4.0   | 3000  | 0.8944          | 0.65     |
| 0.8215        | 5.0   | 3750  | 0.8304          | 0.6833   |
| 0.7386        | 6.0   | 4500  | 0.7725          | 0.7233   |
| 0.7073        | 7.0   | 5250  | 0.7219          | 0.75     |
| 0.6732        | 8.0   | 6000  | 0.6781          | 0.7667   |
| 0.6382        | 9.0   | 6750  | 0.6410          | 0.7817   |
| 0.5894        | 10.0  | 7500  | 0.6100          | 0.795    |
| 0.5595        | 11.0  | 8250  | 0.5840          | 0.7967   |
| 0.5115        | 12.0  | 9000  | 0.5616          | 0.81     |
| 0.5204        | 13.0  | 9750  | 0.5423          | 0.8133   |
| 0.5584        | 14.0  | 10500 | 0.5257          | 0.8183   |
| 0.4766        | 15.0  | 11250 | 0.5110          | 0.8233   |
| 0.4665        | 16.0  | 12000 | 0.4984          | 0.825    |
| 0.4224        | 17.0  | 12750 | 0.4872          | 0.8267   |
| 0.4651        | 18.0  | 13500 | 0.4771          | 0.8283   |
| 0.4708        | 19.0  | 14250 | 0.4682          | 0.8333   |
| 0.4353        | 20.0  | 15000 | 0.4602          | 0.8367   |
| 0.3692        | 21.0  | 15750 | 0.4528          | 0.8417   |
| 0.4104        | 22.0  | 16500 | 0.4465          | 0.8433   |
| 0.4734        | 23.0  | 17250 | 0.4407          | 0.845    |
| 0.4505        | 24.0  | 18000 | 0.4355          | 0.8467   |
| 0.4228        | 25.0  | 18750 | 0.4307          | 0.85     |
| 0.4446        | 26.0  | 19500 | 0.4264          | 0.85     |
| 0.3547        | 27.0  | 20250 | 0.4224          | 0.85     |
| 0.3891        | 28.0  | 21000 | 0.4188          | 0.8517   |
| 0.4084        | 29.0  | 21750 | 0.4156          | 0.8533   |
| 0.3837        | 30.0  | 22500 | 0.4127          | 0.855    |
| 0.3728        | 31.0  | 23250 | 0.4101          | 0.8583   |
| 0.3097        | 32.0  | 24000 | 0.4075          | 0.8583   |
| 0.4329        | 33.0  | 24750 | 0.4053          | 0.8583   |
| 0.4029        | 34.0  | 25500 | 0.4033          | 0.86     |
| 0.3546        | 35.0  | 26250 | 0.4014          | 0.8617   |
| 0.3774        | 36.0  | 27000 | 0.3998          | 0.8633   |
| 0.3804        | 37.0  | 27750 | 0.3982          | 0.8633   |
| 0.3895        | 38.0  | 28500 | 0.3968          | 0.865    |
| 0.3722        | 39.0  | 29250 | 0.3955          | 0.8667   |
| 0.4041        | 40.0  | 30000 | 0.3944          | 0.8683   |
| 0.3565        | 41.0  | 30750 | 0.3935          | 0.87     |
| 0.4081        | 42.0  | 31500 | 0.3926          | 0.87     |
| 0.3738        | 43.0  | 32250 | 0.3919          | 0.87     |
| 0.3617        | 44.0  | 33000 | 0.3913          | 0.87     |
| 0.3447        | 45.0  | 33750 | 0.3908          | 0.87     |
| 0.3297        | 46.0  | 34500 | 0.3904          | 0.87     |
| 0.3716        | 47.0  | 35250 | 0.3901          | 0.87     |
| 0.3734        | 48.0  | 36000 | 0.3899          | 0.87     |
| 0.3771        | 49.0  | 36750 | 0.3898          | 0.87     |
| 0.3572        | 50.0  | 37500 | 0.3897          | 0.87     |


### 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