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# smids_10x_deit_small_rms_00001_fold3

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: 1.0298
- Accuracy: 0.9117

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.17          | 1.0   | 750   | 0.2376          | 0.9167   |
| 0.1586        | 2.0   | 1500  | 0.3127          | 0.9117   |
| 0.0361        | 3.0   | 2250  | 0.4163          | 0.9033   |
| 0.0337        | 4.0   | 3000  | 0.5484          | 0.89     |
| 0.0423        | 5.0   | 3750  | 0.5723          | 0.91     |
| 0.0287        | 6.0   | 4500  | 0.6189          | 0.905    |
| 0.0936        | 7.0   | 5250  | 0.8112          | 0.895    |
| 0.0008        | 8.0   | 6000  | 0.7783          | 0.8967   |
| 0.0117        | 9.0   | 6750  | 0.8326          | 0.8983   |
| 0.0553        | 10.0  | 7500  | 0.8619          | 0.8967   |
| 0.0           | 11.0  | 8250  | 0.7664          | 0.895    |
| 0.0           | 12.0  | 9000  | 0.9064          | 0.8917   |
| 0.022         | 13.0  | 9750  | 0.8767          | 0.905    |
| 0.0003        | 14.0  | 10500 | 0.8256          | 0.8967   |
| 0.0           | 15.0  | 11250 | 0.8920          | 0.8983   |
| 0.0           | 16.0  | 12000 | 0.8919          | 0.8883   |
| 0.0005        | 17.0  | 12750 | 0.7873          | 0.91     |
| 0.0002        | 18.0  | 13500 | 1.0358          | 0.8833   |
| 0.0           | 19.0  | 14250 | 0.9585          | 0.8933   |
| 0.0           | 20.0  | 15000 | 0.9183          | 0.8933   |
| 0.02          | 21.0  | 15750 | 1.0608          | 0.8867   |
| 0.0           | 22.0  | 16500 | 0.9497          | 0.8983   |
| 0.0           | 23.0  | 17250 | 0.9676          | 0.895    |
| 0.0           | 24.0  | 18000 | 0.9490          | 0.8983   |
| 0.001         | 25.0  | 18750 | 1.0068          | 0.8983   |
| 0.0001        | 26.0  | 19500 | 0.9409          | 0.9017   |
| 0.0           | 27.0  | 20250 | 0.9205          | 0.8933   |
| 0.0006        | 28.0  | 21000 | 0.9294          | 0.9033   |
| 0.0           | 29.0  | 21750 | 0.9650          | 0.8917   |
| 0.0           | 30.0  | 22500 | 1.0551          | 0.8933   |
| 0.0           | 31.0  | 23250 | 0.9687          | 0.895    |
| 0.0           | 32.0  | 24000 | 0.9869          | 0.8933   |
| 0.0           | 33.0  | 24750 | 0.9708          | 0.905    |
| 0.0           | 34.0  | 25500 | 0.9496          | 0.9067   |
| 0.0           | 35.0  | 26250 | 0.9626          | 0.91     |
| 0.0           | 36.0  | 27000 | 1.0150          | 0.9033   |
| 0.0           | 37.0  | 27750 | 0.9930          | 0.9017   |
| 0.0           | 38.0  | 28500 | 0.9861          | 0.905    |
| 0.0           | 39.0  | 29250 | 1.0163          | 0.9033   |
| 0.0           | 40.0  | 30000 | 1.0159          | 0.9017   |
| 0.0           | 41.0  | 30750 | 1.0242          | 0.9033   |
| 0.0           | 42.0  | 31500 | 1.0278          | 0.905    |
| 0.0           | 43.0  | 32250 | 1.0282          | 0.9033   |
| 0.0           | 44.0  | 33000 | 1.0251          | 0.9083   |
| 0.0           | 45.0  | 33750 | 1.0218          | 0.91     |
| 0.0           | 46.0  | 34500 | 1.0275          | 0.91     |
| 0.0           | 47.0  | 35250 | 1.0270          | 0.9083   |
| 0.0           | 48.0  | 36000 | 1.0297          | 0.91     |
| 0.0           | 49.0  | 36750 | 1.0292          | 0.91     |
| 0.0           | 50.0  | 37500 | 1.0298          | 0.9117   |


### 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