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# smids_10x_deit_tiny_sgd_00001_fold4

This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9601
- Accuracy: 0.51

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.4156        | 1.0   | 750   | 1.2978          | 0.34     |
| 1.3315        | 2.0   | 1500  | 1.2425          | 0.3483   |
| 1.2993        | 3.0   | 2250  | 1.2021          | 0.37     |
| 1.2642        | 4.0   | 3000  | 1.1733          | 0.3717   |
| 1.1084        | 5.0   | 3750  | 1.1526          | 0.375    |
| 1.1915        | 6.0   | 4500  | 1.1373          | 0.3733   |
| 1.1121        | 7.0   | 5250  | 1.1248          | 0.3817   |
| 1.1023        | 8.0   | 6000  | 1.1144          | 0.39     |
| 1.0611        | 9.0   | 6750  | 1.1051          | 0.3867   |
| 1.0698        | 10.0  | 7500  | 1.0965          | 0.39     |
| 1.0512        | 11.0  | 8250  | 1.0884          | 0.4017   |
| 1.0962        | 12.0  | 9000  | 1.0808          | 0.405    |
| 1.0873        | 13.0  | 9750  | 1.0735          | 0.4117   |
| 1.0536        | 14.0  | 10500 | 1.0664          | 0.4183   |
| 1.0525        | 15.0  | 11250 | 1.0596          | 0.4283   |
| 1.026         | 16.0  | 12000 | 1.0532          | 0.4317   |
| 1.0131        | 17.0  | 12750 | 1.0470          | 0.44     |
| 0.9786        | 18.0  | 13500 | 1.0410          | 0.4433   |
| 0.9869        | 19.0  | 14250 | 1.0353          | 0.4467   |
| 0.9996        | 20.0  | 15000 | 1.0299          | 0.4517   |
| 1.0078        | 21.0  | 15750 | 1.0247          | 0.4533   |
| 0.9709        | 22.0  | 16500 | 1.0197          | 0.4617   |
| 1.009         | 23.0  | 17250 | 1.0149          | 0.4633   |
| 1.0068        | 24.0  | 18000 | 1.0104          | 0.4633   |
| 0.9737        | 25.0  | 18750 | 1.0061          | 0.47     |
| 0.9634        | 26.0  | 19500 | 1.0021          | 0.4767   |
| 0.9648        | 27.0  | 20250 | 0.9982          | 0.4783   |
| 0.931         | 28.0  | 21000 | 0.9946          | 0.485    |
| 0.993         | 29.0  | 21750 | 0.9911          | 0.4867   |
| 0.9852        | 30.0  | 22500 | 0.9879          | 0.49     |
| 0.9579        | 31.0  | 23250 | 0.9848          | 0.49     |
| 0.9747        | 32.0  | 24000 | 0.9819          | 0.4933   |
| 0.9501        | 33.0  | 24750 | 0.9793          | 0.5017   |
| 0.9432        | 34.0  | 25500 | 0.9768          | 0.5033   |
| 0.9384        | 35.0  | 26250 | 0.9745          | 0.505    |
| 0.9356        | 36.0  | 27000 | 0.9724          | 0.505    |
| 0.9023        | 37.0  | 27750 | 0.9705          | 0.5067   |
| 0.9257        | 38.0  | 28500 | 0.9687          | 0.5083   |
| 0.9635        | 39.0  | 29250 | 0.9672          | 0.5083   |
| 0.9335        | 40.0  | 30000 | 0.9658          | 0.51     |
| 0.8943        | 41.0  | 30750 | 0.9645          | 0.51     |
| 0.9485        | 42.0  | 31500 | 0.9635          | 0.51     |
| 0.976         | 43.0  | 32250 | 0.9626          | 0.51     |
| 0.9386        | 44.0  | 33000 | 0.9619          | 0.51     |
| 0.9526        | 45.0  | 33750 | 0.9613          | 0.51     |
| 0.9016        | 46.0  | 34500 | 0.9608          | 0.51     |
| 0.9008        | 47.0  | 35250 | 0.9605          | 0.51     |
| 0.9525        | 48.0  | 36000 | 0.9603          | 0.51     |
| 0.8965        | 49.0  | 36750 | 0.9602          | 0.51     |
| 0.8897        | 50.0  | 37500 | 0.9601          | 0.51     |


### 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