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# smids_10x_beit_large_adamax_00001_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: 0.7342
- Accuracy: 0.93

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1234        | 1.0   | 750   | 0.2380          | 0.9133   |
| 0.0658        | 2.0   | 1500  | 0.2732          | 0.9317   |
| 0.0204        | 3.0   | 2250  | 0.3498          | 0.9217   |
| 0.0213        | 4.0   | 3000  | 0.4104          | 0.925    |
| 0.0054        | 5.0   | 3750  | 0.4509          | 0.9317   |
| 0.0002        | 6.0   | 4500  | 0.5343          | 0.9233   |
| 0.0104        | 7.0   | 5250  | 0.5450          | 0.9267   |
| 0.0001        | 8.0   | 6000  | 0.6214          | 0.9217   |
| 0.0002        | 9.0   | 6750  | 0.5669          | 0.9333   |
| 0.0           | 10.0  | 7500  | 0.5842          | 0.9233   |
| 0.0003        | 11.0  | 8250  | 0.5405          | 0.9267   |
| 0.0007        | 12.0  | 9000  | 0.6365          | 0.9233   |
| 0.0           | 13.0  | 9750  | 0.6437          | 0.9267   |
| 0.0006        | 14.0  | 10500 | 0.6868          | 0.92     |
| 0.0           | 15.0  | 11250 | 0.6484          | 0.93     |
| 0.0           | 16.0  | 12000 | 0.6945          | 0.925    |
| 0.0           | 17.0  | 12750 | 0.6473          | 0.925    |
| 0.0           | 18.0  | 13500 | 0.7329          | 0.9233   |
| 0.0           | 19.0  | 14250 | 0.6697          | 0.9283   |
| 0.0           | 20.0  | 15000 | 0.7054          | 0.9317   |
| 0.0           | 21.0  | 15750 | 0.7229          | 0.9267   |
| 0.0001        | 22.0  | 16500 | 0.6657          | 0.9267   |
| 0.0           | 23.0  | 17250 | 0.6845          | 0.925    |
| 0.0           | 24.0  | 18000 | 0.7071          | 0.9233   |
| 0.0           | 25.0  | 18750 | 0.7119          | 0.9267   |
| 0.0           | 26.0  | 19500 | 0.7250          | 0.9283   |
| 0.0           | 27.0  | 20250 | 0.7491          | 0.93     |
| 0.0           | 28.0  | 21000 | 0.7325          | 0.9267   |
| 0.0           | 29.0  | 21750 | 0.7225          | 0.93     |
| 0.0           | 30.0  | 22500 | 0.7702          | 0.93     |
| 0.0           | 31.0  | 23250 | 0.7702          | 0.93     |
| 0.0           | 32.0  | 24000 | 0.7279          | 0.93     |
| 0.0           | 33.0  | 24750 | 0.7215          | 0.9283   |
| 0.0           | 34.0  | 25500 | 0.7215          | 0.9267   |
| 0.0           | 35.0  | 26250 | 0.7456          | 0.9267   |
| 0.0           | 36.0  | 27000 | 0.7430          | 0.9267   |
| 0.0           | 37.0  | 27750 | 0.7363          | 0.9283   |
| 0.0           | 38.0  | 28500 | 0.7489          | 0.93     |
| 0.0           | 39.0  | 29250 | 0.7854          | 0.9267   |
| 0.0           | 40.0  | 30000 | 0.7378          | 0.9283   |
| 0.0           | 41.0  | 30750 | 0.7334          | 0.93     |
| 0.0           | 42.0  | 31500 | 0.7235          | 0.9333   |
| 0.0           | 43.0  | 32250 | 0.7203          | 0.93     |
| 0.0           | 44.0  | 33000 | 0.7319          | 0.9267   |
| 0.0           | 45.0  | 33750 | 0.7326          | 0.93     |
| 0.0           | 46.0  | 34500 | 0.7443          | 0.93     |
| 0.0           | 47.0  | 35250 | 0.7511          | 0.93     |
| 0.0           | 48.0  | 36000 | 0.7575          | 0.93     |
| 0.0           | 49.0  | 36750 | 0.7357          | 0.93     |
| 0.0           | 50.0  | 37500 | 0.7342          | 0.93     |


### 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