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# smids_5x_deit_base_rms_0001_fold3

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: 1.0537
- Accuracy: 0.9033

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2482        | 1.0   | 375   | 0.3175          | 0.8933   |
| 0.1437        | 2.0   | 750   | 0.2994          | 0.91     |
| 0.0829        | 3.0   | 1125  | 0.4831          | 0.8717   |
| 0.0657        | 4.0   | 1500  | 0.4645          | 0.87     |
| 0.0415        | 5.0   | 1875  | 0.5452          | 0.895    |
| 0.0518        | 6.0   | 2250  | 0.4649          | 0.89     |
| 0.0246        | 7.0   | 2625  | 0.4579          | 0.8933   |
| 0.0124        | 8.0   | 3000  | 0.5092          | 0.8933   |
| 0.0196        | 9.0   | 3375  | 0.6123          | 0.885    |
| 0.0528        | 10.0  | 3750  | 0.5846          | 0.89     |
| 0.0162        | 11.0  | 4125  | 0.6461          | 0.89     |
| 0.0269        | 12.0  | 4500  | 0.6644          | 0.89     |
| 0.0189        | 13.0  | 4875  | 0.6691          | 0.8867   |
| 0.013         | 14.0  | 5250  | 0.5509          | 0.895    |
| 0.0125        | 15.0  | 5625  | 0.6239          | 0.8867   |
| 0.042         | 16.0  | 6000  | 0.5644          | 0.8967   |
| 0.0002        | 17.0  | 6375  | 0.7253          | 0.8967   |
| 0.0067        | 18.0  | 6750  | 0.7652          | 0.8983   |
| 0.0166        | 19.0  | 7125  | 0.7033          | 0.8983   |
| 0.0136        | 20.0  | 7500  | 0.7542          | 0.8867   |
| 0.0035        | 21.0  | 7875  | 0.8364          | 0.8883   |
| 0.0092        | 22.0  | 8250  | 0.7788          | 0.89     |
| 0.0366        | 23.0  | 8625  | 0.7487          | 0.8933   |
| 0.0057        | 24.0  | 9000  | 0.8195          | 0.8933   |
| 0.0002        | 25.0  | 9375  | 0.6186          | 0.9      |
| 0.0001        | 26.0  | 9750  | 0.7244          | 0.9017   |
| 0.0002        | 27.0  | 10125 | 0.8368          | 0.8883   |
| 0.0004        | 28.0  | 10500 | 0.8205          | 0.895    |
| 0.0421        | 29.0  | 10875 | 0.8404          | 0.89     |
| 0.0033        | 30.0  | 11250 | 0.8091          | 0.8967   |
| 0.0           | 31.0  | 11625 | 0.7929          | 0.8967   |
| 0.0109        | 32.0  | 12000 | 0.8783          | 0.8883   |
| 0.0           | 33.0  | 12375 | 0.8591          | 0.8917   |
| 0.0           | 34.0  | 12750 | 0.9822          | 0.89     |
| 0.0           | 35.0  | 13125 | 0.9216          | 0.8933   |
| 0.0           | 36.0  | 13500 | 0.9855          | 0.895    |
| 0.0           | 37.0  | 13875 | 0.8868          | 0.9017   |
| 0.0           | 38.0  | 14250 | 0.9047          | 0.9017   |
| 0.0           | 39.0  | 14625 | 0.9416          | 0.9017   |
| 0.0033        | 40.0  | 15000 | 0.8937          | 0.91     |
| 0.0117        | 41.0  | 15375 | 1.0141          | 0.8967   |
| 0.0           | 42.0  | 15750 | 1.0456          | 0.9      |
| 0.0           | 43.0  | 16125 | 1.0341          | 0.9017   |
| 0.0           | 44.0  | 16500 | 1.0394          | 0.9033   |
| 0.0           | 45.0  | 16875 | 1.0421          | 0.905    |
| 0.0           | 46.0  | 17250 | 1.0425          | 0.9017   |
| 0.0           | 47.0  | 17625 | 1.0481          | 0.905    |
| 0.0           | 48.0  | 18000 | 1.0506          | 0.9033   |
| 0.0           | 49.0  | 18375 | 1.0518          | 0.905    |
| 0.0           | 50.0  | 18750 | 1.0537          | 0.9033   |


### 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