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# smids_10x_deit_small_rms_001_fold4

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: 2.4181
- Accuracy: 0.7883

## 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: 0.001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.808         | 1.0   | 750   | 0.7526          | 0.6333   |
| 0.7422        | 2.0   | 1500  | 0.7345          | 0.58     |
| 0.7146        | 3.0   | 2250  | 0.7176          | 0.6433   |
| 0.701         | 4.0   | 3000  | 0.6566          | 0.6933   |
| 0.6799        | 5.0   | 3750  | 0.5734          | 0.745    |
| 0.6007        | 6.0   | 4500  | 0.6368          | 0.72     |
| 0.5519        | 7.0   | 5250  | 0.5547          | 0.7833   |
| 0.6119        | 8.0   | 6000  | 0.5502          | 0.7667   |
| 0.5599        | 9.0   | 6750  | 0.5552          | 0.75     |
| 0.508         | 10.0  | 7500  | 0.5666          | 0.7383   |
| 0.5209        | 11.0  | 8250  | 0.5288          | 0.7683   |
| 0.6053        | 12.0  | 9000  | 0.5408          | 0.765    |
| 0.4938        | 13.0  | 9750  | 0.5449          | 0.755    |
| 0.5012        | 14.0  | 10500 | 0.6211          | 0.7533   |
| 0.4544        | 15.0  | 11250 | 0.6619          | 0.725    |
| 0.4855        | 16.0  | 12000 | 0.5101          | 0.8083   |
| 0.3525        | 17.0  | 12750 | 0.5278          | 0.7833   |
| 0.4312        | 18.0  | 13500 | 0.5051          | 0.7917   |
| 0.4593        | 19.0  | 14250 | 0.5277          | 0.7867   |
| 0.3939        | 20.0  | 15000 | 0.5517          | 0.7833   |
| 0.5185        | 21.0  | 15750 | 0.5418          | 0.7667   |
| 0.424         | 22.0  | 16500 | 0.5465          | 0.7917   |
| 0.3637        | 23.0  | 17250 | 0.5971          | 0.785    |
| 0.4457        | 24.0  | 18000 | 0.5681          | 0.7933   |
| 0.4023        | 25.0  | 18750 | 0.5160          | 0.805    |
| 0.3012        | 26.0  | 19500 | 0.5373          | 0.8283   |
| 0.2933        | 27.0  | 20250 | 0.5885          | 0.8067   |
| 0.3104        | 28.0  | 21000 | 0.6014          | 0.8017   |
| 0.292         | 29.0  | 21750 | 0.6093          | 0.8033   |
| 0.3506        | 30.0  | 22500 | 0.6800          | 0.7633   |
| 0.2777        | 31.0  | 23250 | 0.6858          | 0.795    |
| 0.2396        | 32.0  | 24000 | 0.6442          | 0.805    |
| 0.3316        | 33.0  | 24750 | 0.6440          | 0.81     |
| 0.2958        | 34.0  | 25500 | 0.6532          | 0.8117   |
| 0.2121        | 35.0  | 26250 | 0.7494          | 0.8083   |
| 0.1764        | 36.0  | 27000 | 0.7942          | 0.7933   |
| 0.1963        | 37.0  | 27750 | 0.7817          | 0.7883   |
| 0.1829        | 38.0  | 28500 | 0.8010          | 0.7917   |
| 0.1937        | 39.0  | 29250 | 0.8544          | 0.795    |
| 0.1493        | 40.0  | 30000 | 0.9520          | 0.7967   |
| 0.1419        | 41.0  | 30750 | 0.9695          | 0.81     |
| 0.1784        | 42.0  | 31500 | 1.0763          | 0.8017   |
| 0.1485        | 43.0  | 32250 | 1.1404          | 0.825    |
| 0.0665        | 44.0  | 33000 | 1.3155          | 0.7933   |
| 0.1004        | 45.0  | 33750 | 1.5689          | 0.7933   |
| 0.0917        | 46.0  | 34500 | 1.5920          | 0.7917   |
| 0.0556        | 47.0  | 35250 | 1.8022          | 0.7967   |
| 0.0427        | 48.0  | 36000 | 2.0148          | 0.8067   |
| 0.032         | 49.0  | 36750 | 2.3253          | 0.795    |
| 0.012         | 50.0  | 37500 | 2.4181          | 0.7883   |


### 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