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# smids_10x_beit_large_adamax_001_fold3

This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0324
- Accuracy: 0.9017

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3438        | 1.0   | 750   | 0.3826          | 0.8517   |
| 0.2931        | 2.0   | 1500  | 0.3034          | 0.89     |
| 0.2025        | 3.0   | 2250  | 0.3971          | 0.8783   |
| 0.2582        | 4.0   | 3000  | 0.3086          | 0.8867   |
| 0.2483        | 5.0   | 3750  | 0.3346          | 0.8917   |
| 0.1606        | 6.0   | 4500  | 0.3908          | 0.8717   |
| 0.1236        | 7.0   | 5250  | 0.4286          | 0.8783   |
| 0.1197        | 8.0   | 6000  | 0.3887          | 0.9      |
| 0.0412        | 9.0   | 6750  | 0.4924          | 0.885    |
| 0.0384        | 10.0  | 7500  | 0.5551          | 0.89     |
| 0.0583        | 11.0  | 8250  | 0.4882          | 0.9017   |
| 0.0806        | 12.0  | 9000  | 0.5902          | 0.88     |
| 0.0489        | 13.0  | 9750  | 0.5212          | 0.88     |
| 0.0353        | 14.0  | 10500 | 0.5171          | 0.9      |
| 0.0094        | 15.0  | 11250 | 0.6341          | 0.895    |
| 0.0154        | 16.0  | 12000 | 0.5409          | 0.9133   |
| 0.0118        | 17.0  | 12750 | 0.6110          | 0.8833   |
| 0.0159        | 18.0  | 13500 | 0.6873          | 0.9033   |
| 0.0026        | 19.0  | 14250 | 0.7871          | 0.8983   |
| 0.0163        | 20.0  | 15000 | 0.6341          | 0.895    |
| 0.0002        | 21.0  | 15750 | 0.7139          | 0.9017   |
| 0.0006        | 22.0  | 16500 | 0.6717          | 0.9033   |
| 0.0266        | 23.0  | 17250 | 0.6268          | 0.895    |
| 0.0051        | 24.0  | 18000 | 0.6425          | 0.905    |
| 0.0           | 25.0  | 18750 | 0.7506          | 0.91     |
| 0.0004        | 26.0  | 19500 | 0.6864          | 0.9017   |
| 0.0002        | 27.0  | 20250 | 0.6111          | 0.9117   |
| 0.0163        | 28.0  | 21000 | 0.6875          | 0.9017   |
| 0.0001        | 29.0  | 21750 | 0.8050          | 0.8967   |
| 0.0002        | 30.0  | 22500 | 0.7397          | 0.8967   |
| 0.0004        | 31.0  | 23250 | 0.8218          | 0.8983   |
| 0.0           | 32.0  | 24000 | 0.8725          | 0.8983   |
| 0.0           | 33.0  | 24750 | 0.9662          | 0.8967   |
| 0.0           | 34.0  | 25500 | 0.9148          | 0.9083   |
| 0.0           | 35.0  | 26250 | 0.8492          | 0.9083   |
| 0.0001        | 36.0  | 27000 | 0.8264          | 0.9067   |
| 0.0           | 37.0  | 27750 | 0.8650          | 0.895    |
| 0.0004        | 38.0  | 28500 | 0.9030          | 0.91     |
| 0.0           | 39.0  | 29250 | 0.9540          | 0.9      |
| 0.0           | 40.0  | 30000 | 1.0292          | 0.8883   |
| 0.0           | 41.0  | 30750 | 1.0282          | 0.8917   |
| 0.0           | 42.0  | 31500 | 1.0128          | 0.8933   |
| 0.0           | 43.0  | 32250 | 1.0147          | 0.8983   |
| 0.0           | 44.0  | 33000 | 0.9709          | 0.8983   |
| 0.0           | 45.0  | 33750 | 0.9643          | 0.9067   |
| 0.0           | 46.0  | 34500 | 0.9770          | 0.9017   |
| 0.0           | 47.0  | 35250 | 1.0000          | 0.8983   |
| 0.0           | 48.0  | 36000 | 1.0223          | 0.9017   |
| 0.0           | 49.0  | 36750 | 1.0291          | 0.9017   |
| 0.0           | 50.0  | 37500 | 1.0324          | 0.9017   |


### 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