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# smids_10x_deit_base_sgd_0001_fold4

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: 0.3973
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0764        | 1.0   | 750   | 1.0715          | 0.4467   |
| 1.0016        | 2.0   | 1500  | 1.0200          | 0.565    |
| 0.9544        | 3.0   | 2250  | 0.9543          | 0.625    |
| 0.8923        | 4.0   | 3000  | 0.8833          | 0.665    |
| 0.8291        | 5.0   | 3750  | 0.8138          | 0.7317   |
| 0.7577        | 6.0   | 4500  | 0.7525          | 0.7483   |
| 0.6942        | 7.0   | 5250  | 0.6996          | 0.775    |
| 0.6967        | 8.0   | 6000  | 0.6552          | 0.785    |
| 0.6475        | 9.0   | 6750  | 0.6183          | 0.7967   |
| 0.5601        | 10.0  | 7500  | 0.5880          | 0.8017   |
| 0.5611        | 11.0  | 8250  | 0.5631          | 0.8033   |
| 0.5838        | 12.0  | 9000  | 0.5423          | 0.8117   |
| 0.4987        | 13.0  | 9750  | 0.5248          | 0.8133   |
| 0.5348        | 14.0  | 10500 | 0.5101          | 0.815    |
| 0.4457        | 15.0  | 11250 | 0.4972          | 0.8167   |
| 0.4244        | 16.0  | 12000 | 0.4863          | 0.82     |
| 0.394         | 17.0  | 12750 | 0.4768          | 0.8217   |
| 0.4347        | 18.0  | 13500 | 0.4683          | 0.8267   |
| 0.455         | 19.0  | 14250 | 0.4609          | 0.8283   |
| 0.4228        | 20.0  | 15000 | 0.4543          | 0.8317   |
| 0.3783        | 21.0  | 15750 | 0.4484          | 0.8333   |
| 0.4279        | 22.0  | 16500 | 0.4432          | 0.835    |
| 0.4396        | 23.0  | 17250 | 0.4385          | 0.8333   |
| 0.4808        | 24.0  | 18000 | 0.4342          | 0.8367   |
| 0.4208        | 25.0  | 18750 | 0.4304          | 0.8367   |
| 0.3784        | 26.0  | 19500 | 0.4269          | 0.8367   |
| 0.3843        | 27.0  | 20250 | 0.4238          | 0.8367   |
| 0.3813        | 28.0  | 21000 | 0.4210          | 0.8333   |
| 0.3843        | 29.0  | 21750 | 0.4183          | 0.835    |
| 0.3906        | 30.0  | 22500 | 0.4160          | 0.835    |
| 0.3475        | 31.0  | 23250 | 0.4138          | 0.8367   |
| 0.3808        | 32.0  | 24000 | 0.4118          | 0.8383   |
| 0.373         | 33.0  | 24750 | 0.4100          | 0.8383   |
| 0.4094        | 34.0  | 25500 | 0.4083          | 0.84     |
| 0.3294        | 35.0  | 26250 | 0.4068          | 0.84     |
| 0.3714        | 36.0  | 27000 | 0.4054          | 0.84     |
| 0.3219        | 37.0  | 27750 | 0.4042          | 0.84     |
| 0.3856        | 38.0  | 28500 | 0.4031          | 0.84     |
| 0.3967        | 39.0  | 29250 | 0.4021          | 0.84     |
| 0.3872        | 40.0  | 30000 | 0.4012          | 0.8417   |
| 0.3755        | 41.0  | 30750 | 0.4004          | 0.8417   |
| 0.3647        | 42.0  | 31500 | 0.3997          | 0.8417   |
| 0.4013        | 43.0  | 32250 | 0.3991          | 0.8417   |
| 0.3537        | 44.0  | 33000 | 0.3986          | 0.8417   |
| 0.3786        | 45.0  | 33750 | 0.3982          | 0.8417   |
| 0.344         | 46.0  | 34500 | 0.3978          | 0.8417   |
| 0.2971        | 47.0  | 35250 | 0.3976          | 0.8417   |
| 0.3345        | 48.0  | 36000 | 0.3974          | 0.8417   |
| 0.3788        | 49.0  | 36750 | 0.3974          | 0.8417   |
| 0.2837        | 50.0  | 37500 | 0.3973          | 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