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# smids_10x_beit_large_adamax_001_fold5

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: 0.8836
- Accuracy: 0.905

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3665        | 1.0   | 750   | 0.3594          | 0.8583   |
| 0.2964        | 2.0   | 1500  | 0.4126          | 0.8483   |
| 0.2817        | 3.0   | 2250  | 0.2955          | 0.895    |
| 0.2107        | 4.0   | 3000  | 0.4285          | 0.8483   |
| 0.2441        | 5.0   | 3750  | 0.2917          | 0.905    |
| 0.2284        | 6.0   | 4500  | 0.3000          | 0.8933   |
| 0.1417        | 7.0   | 5250  | 0.3775          | 0.9033   |
| 0.1212        | 8.0   | 6000  | 0.4010          | 0.9      |
| 0.1114        | 9.0   | 6750  | 0.3900          | 0.8917   |
| 0.1229        | 10.0  | 7500  | 0.5863          | 0.8833   |
| 0.0978        | 11.0  | 8250  | 0.5114          | 0.8883   |
| 0.019         | 12.0  | 9000  | 0.6596          | 0.9033   |
| 0.0244        | 13.0  | 9750  | 0.6428          | 0.9017   |
| 0.0242        | 14.0  | 10500 | 0.6293          | 0.9      |
| 0.0159        | 15.0  | 11250 | 0.5943          | 0.9067   |
| 0.0287        | 16.0  | 12000 | 0.4876          | 0.9033   |
| 0.0161        | 17.0  | 12750 | 0.7094          | 0.8933   |
| 0.0033        | 18.0  | 13500 | 0.7392          | 0.9117   |
| 0.0133        | 19.0  | 14250 | 0.6855          | 0.9017   |
| 0.0009        | 20.0  | 15000 | 0.7025          | 0.895    |
| 0.033         | 21.0  | 15750 | 0.5767          | 0.895    |
| 0.0007        | 22.0  | 16500 | 0.6533          | 0.8983   |
| 0.0005        | 23.0  | 17250 | 0.8501          | 0.8883   |
| 0.0041        | 24.0  | 18000 | 0.6751          | 0.91     |
| 0.0016        | 25.0  | 18750 | 0.8175          | 0.8983   |
| 0.022         | 26.0  | 19500 | 0.7166          | 0.9067   |
| 0.002         | 27.0  | 20250 | 0.7746          | 0.9033   |
| 0.0002        | 28.0  | 21000 | 0.7048          | 0.91     |
| 0.0002        | 29.0  | 21750 | 0.8217          | 0.9083   |
| 0.0187        | 30.0  | 22500 | 0.7107          | 0.8983   |
| 0.0002        | 31.0  | 23250 | 0.7863          | 0.9133   |
| 0.0           | 32.0  | 24000 | 0.8314          | 0.8983   |
| 0.0           | 33.0  | 24750 | 0.7909          | 0.8967   |
| 0.0003        | 34.0  | 25500 | 0.8566          | 0.905    |
| 0.0           | 35.0  | 26250 | 0.7280          | 0.9117   |
| 0.0           | 36.0  | 27000 | 0.8236          | 0.9017   |
| 0.0068        | 37.0  | 27750 | 0.7886          | 0.92     |
| 0.0           | 38.0  | 28500 | 0.8302          | 0.9017   |
| 0.0           | 39.0  | 29250 | 0.8589          | 0.9067   |
| 0.0           | 40.0  | 30000 | 0.8152          | 0.9017   |
| 0.0           | 41.0  | 30750 | 0.8501          | 0.905    |
| 0.0           | 42.0  | 31500 | 0.8563          | 0.91     |
| 0.0           | 43.0  | 32250 | 0.7690          | 0.9117   |
| 0.0           | 44.0  | 33000 | 0.8007          | 0.9083   |
| 0.0           | 45.0  | 33750 | 0.8622          | 0.9033   |
| 0.0001        | 46.0  | 34500 | 0.8624          | 0.905    |
| 0.0           | 47.0  | 35250 | 0.8665          | 0.9067   |
| 0.0           | 48.0  | 36000 | 0.8739          | 0.9067   |
| 0.0           | 49.0  | 36750 | 0.8825          | 0.9067   |
| 0.0           | 50.0  | 37500 | 0.8836          | 0.905    |


### 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