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# smids_5x_deit_base_sgd_00001_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: 1.0653
- Accuracy: 0.475

## 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: 1e-05
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1101        | 1.0   | 375   | 1.1105          | 0.3333   |
| 1.1112        | 2.0   | 750   | 1.1084          | 0.3483   |
| 1.1096        | 3.0   | 1125  | 1.1064          | 0.3533   |
| 1.1002        | 4.0   | 1500  | 1.1045          | 0.3633   |
| 1.1197        | 5.0   | 1875  | 1.1027          | 0.3667   |
| 1.0859        | 6.0   | 2250  | 1.1009          | 0.3733   |
| 1.0856        | 7.0   | 2625  | 1.0992          | 0.3833   |
| 1.0857        | 8.0   | 3000  | 1.0975          | 0.3883   |
| 1.0995        | 9.0   | 3375  | 1.0959          | 0.3933   |
| 1.1123        | 10.0  | 3750  | 1.0943          | 0.4      |
| 1.0849        | 11.0  | 4125  | 1.0928          | 0.405    |
| 1.0866        | 12.0  | 4500  | 1.0914          | 0.4067   |
| 1.08          | 13.0  | 4875  | 1.0899          | 0.42     |
| 1.0796        | 14.0  | 5250  | 1.0886          | 0.4217   |
| 1.082         | 15.0  | 5625  | 1.0872          | 0.425    |
| 1.0851        | 16.0  | 6000  | 1.0859          | 0.4283   |
| 1.0643        | 17.0  | 6375  | 1.0847          | 0.4283   |
| 1.0676        | 18.0  | 6750  | 1.0834          | 0.4283   |
| 1.0655        | 19.0  | 7125  | 1.0823          | 0.43     |
| 1.0761        | 20.0  | 7500  | 1.0811          | 0.4283   |
| 1.069         | 21.0  | 7875  | 1.0800          | 0.4333   |
| 1.0634        | 22.0  | 8250  | 1.0790          | 0.435    |
| 1.0576        | 23.0  | 8625  | 1.0780          | 0.4383   |
| 1.0581        | 24.0  | 9000  | 1.0770          | 0.4367   |
| 1.0635        | 25.0  | 9375  | 1.0761          | 0.44     |
| 1.0553        | 26.0  | 9750  | 1.0752          | 0.4417   |
| 1.0268        | 27.0  | 10125 | 1.0743          | 0.4433   |
| 1.0755        | 28.0  | 10500 | 1.0735          | 0.4483   |
| 1.0483        | 29.0  | 10875 | 1.0727          | 0.455    |
| 1.0533        | 30.0  | 11250 | 1.0720          | 0.455    |
| 1.0638        | 31.0  | 11625 | 1.0713          | 0.455    |
| 1.0617        | 32.0  | 12000 | 1.0707          | 0.4567   |
| 1.0624        | 33.0  | 12375 | 1.0700          | 0.46     |
| 1.052         | 34.0  | 12750 | 1.0695          | 0.4667   |
| 1.0462        | 35.0  | 13125 | 1.0689          | 0.4683   |
| 1.0582        | 36.0  | 13500 | 1.0684          | 0.4683   |
| 1.0432        | 37.0  | 13875 | 1.0680          | 0.4683   |
| 1.0468        | 38.0  | 14250 | 1.0675          | 0.47     |
| 1.0664        | 39.0  | 14625 | 1.0671          | 0.4717   |
| 1.0442        | 40.0  | 15000 | 1.0668          | 0.47     |
| 1.0443        | 41.0  | 15375 | 1.0665          | 0.4717   |
| 1.0372        | 42.0  | 15750 | 1.0662          | 0.4717   |
| 1.0389        | 43.0  | 16125 | 1.0660          | 0.4717   |
| 1.044         | 44.0  | 16500 | 1.0658          | 0.4717   |
| 1.0519        | 45.0  | 16875 | 1.0656          | 0.4717   |
| 1.0549        | 46.0  | 17250 | 1.0655          | 0.475    |
| 1.0727        | 47.0  | 17625 | 1.0654          | 0.475    |
| 1.0653        | 48.0  | 18000 | 1.0653          | 0.475    |
| 1.0698        | 49.0  | 18375 | 1.0653          | 0.475    |
| 1.0509        | 50.0  | 18750 | 1.0653          | 0.475    |


### 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