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# smids_10x_deit_small_rms_00001_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: 1.5041
- Accuracy: 0.8817

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1992        | 1.0   | 750   | 0.3642          | 0.8683   |
| 0.1164        | 2.0   | 1500  | 0.4047          | 0.88     |
| 0.0544        | 3.0   | 2250  | 0.5775          | 0.8733   |
| 0.0424        | 4.0   | 3000  | 0.7191          | 0.8717   |
| 0.0428        | 5.0   | 3750  | 0.8609          | 0.88     |
| 0.0178        | 6.0   | 4500  | 0.9003          | 0.8767   |
| 0.002         | 7.0   | 5250  | 0.9741          | 0.8883   |
| 0.0106        | 8.0   | 6000  | 1.0810          | 0.88     |
| 0.015         | 9.0   | 6750  | 1.0442          | 0.8767   |
| 0.0           | 10.0  | 7500  | 1.1184          | 0.875    |
| 0.0002        | 11.0  | 8250  | 1.1373          | 0.88     |
| 0.0           | 12.0  | 9000  | 1.0569          | 0.8833   |
| 0.0004        | 13.0  | 9750  | 1.1443          | 0.8783   |
| 0.0211        | 14.0  | 10500 | 1.1371          | 0.8717   |
| 0.0           | 15.0  | 11250 | 1.1887          | 0.8717   |
| 0.074         | 16.0  | 12000 | 1.0864          | 0.8767   |
| 0.0           | 17.0  | 12750 | 1.1543          | 0.8817   |
| 0.0061        | 18.0  | 13500 | 1.0371          | 0.8833   |
| 0.0           | 19.0  | 14250 | 1.2499          | 0.875    |
| 0.006         | 20.0  | 15000 | 1.1799          | 0.8733   |
| 0.0265        | 21.0  | 15750 | 1.2064          | 0.8783   |
| 0.0           | 22.0  | 16500 | 1.2139          | 0.875    |
| 0.0           | 23.0  | 17250 | 1.2392          | 0.8833   |
| 0.0           | 24.0  | 18000 | 1.3220          | 0.8717   |
| 0.0002        | 25.0  | 18750 | 1.3064          | 0.8717   |
| 0.0           | 26.0  | 19500 | 1.3677          | 0.8767   |
| 0.0           | 27.0  | 20250 | 1.2718          | 0.89     |
| 0.0           | 28.0  | 21000 | 1.2639          | 0.8783   |
| 0.0           | 29.0  | 21750 | 1.3047          | 0.8817   |
| 0.0           | 30.0  | 22500 | 1.3577          | 0.885    |
| 0.0           | 31.0  | 23250 | 1.3501          | 0.88     |
| 0.0           | 32.0  | 24000 | 1.3749          | 0.875    |
| 0.0           | 33.0  | 24750 | 1.4487          | 0.875    |
| 0.0           | 34.0  | 25500 | 1.3473          | 0.8867   |
| 0.0           | 35.0  | 26250 | 1.4090          | 0.885    |
| 0.0           | 36.0  | 27000 | 1.4862          | 0.8733   |
| 0.0           | 37.0  | 27750 | 1.3778          | 0.8783   |
| 0.0           | 38.0  | 28500 | 1.4730          | 0.8767   |
| 0.0           | 39.0  | 29250 | 1.4305          | 0.89     |
| 0.0           | 40.0  | 30000 | 1.4453          | 0.8833   |
| 0.0           | 41.0  | 30750 | 1.4782          | 0.8833   |
| 0.0           | 42.0  | 31500 | 1.4838          | 0.88     |
| 0.0           | 43.0  | 32250 | 1.4721          | 0.8867   |
| 0.0           | 44.0  | 33000 | 1.4749          | 0.8867   |
| 0.0           | 45.0  | 33750 | 1.4889          | 0.8867   |
| 0.0           | 46.0  | 34500 | 1.4951          | 0.885    |
| 0.0           | 47.0  | 35250 | 1.4970          | 0.8833   |
| 0.0           | 48.0  | 36000 | 1.4998          | 0.8833   |
| 0.0           | 49.0  | 36750 | 1.5024          | 0.8833   |
| 0.0           | 50.0  | 37500 | 1.5041          | 0.8817   |


### 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