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# smids_10x_beit_large_adamax_001_fold2

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: 1.4058
- Accuracy: 0.8536

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6414        | 1.0   | 750   | 0.6828          | 0.6639   |
| 0.5428        | 2.0   | 1500  | 0.5438          | 0.7754   |
| 0.4614        | 3.0   | 2250  | 0.4523          | 0.8336   |
| 0.4233        | 4.0   | 3000  | 0.4215          | 0.8236   |
| 0.4304        | 5.0   | 3750  | 0.4599          | 0.7903   |
| 0.3335        | 6.0   | 4500  | 0.4118          | 0.8336   |
| 0.3481        | 7.0   | 5250  | 0.4939          | 0.8253   |
| 0.3092        | 8.0   | 6000  | 0.4308          | 0.8486   |
| 0.2568        | 9.0   | 6750  | 0.4756          | 0.8353   |
| 0.331         | 10.0  | 7500  | 0.4715          | 0.8619   |
| 0.2403        | 11.0  | 8250  | 0.5349          | 0.8469   |
| 0.2162        | 12.0  | 9000  | 0.5922          | 0.8136   |
| 0.2489        | 13.0  | 9750  | 0.5818          | 0.8419   |
| 0.0972        | 14.0  | 10500 | 0.6218          | 0.8419   |
| 0.1212        | 15.0  | 11250 | 0.5371          | 0.8436   |
| 0.1175        | 16.0  | 12000 | 0.6818          | 0.8286   |
| 0.1011        | 17.0  | 12750 | 0.8719          | 0.8120   |
| 0.179         | 18.0  | 13500 | 0.7106          | 0.8486   |
| 0.1325        | 19.0  | 14250 | 0.6119          | 0.8552   |
| 0.111         | 20.0  | 15000 | 0.7905          | 0.8552   |
| 0.0431        | 21.0  | 15750 | 0.8636          | 0.8469   |
| 0.0973        | 22.0  | 16500 | 0.9921          | 0.8403   |
| 0.0529        | 23.0  | 17250 | 0.7563          | 0.8536   |
| 0.1212        | 24.0  | 18000 | 1.1228          | 0.8103   |
| 0.0377        | 25.0  | 18750 | 1.0572          | 0.8386   |
| 0.035         | 26.0  | 19500 | 0.8767          | 0.8536   |
| 0.0591        | 27.0  | 20250 | 0.9535          | 0.8652   |
| 0.0188        | 28.0  | 21000 | 1.1035          | 0.8536   |
| 0.0402        | 29.0  | 21750 | 1.1575          | 0.8586   |
| 0.0333        | 30.0  | 22500 | 1.1473          | 0.8669   |
| 0.0255        | 31.0  | 23250 | 1.0948          | 0.8469   |
| 0.0283        | 32.0  | 24000 | 1.4345          | 0.8419   |
| 0.0262        | 33.0  | 24750 | 1.1277          | 0.8552   |
| 0.0004        | 34.0  | 25500 | 1.2002          | 0.8519   |
| 0.0058        | 35.0  | 26250 | 1.1085          | 0.8586   |
| 0.0265        | 36.0  | 27000 | 1.2506          | 0.8436   |
| 0.0298        | 37.0  | 27750 | 1.1890          | 0.8602   |
| 0.0146        | 38.0  | 28500 | 1.5719          | 0.8486   |
| 0.0266        | 39.0  | 29250 | 1.2137          | 0.8486   |
| 0.0079        | 40.0  | 30000 | 1.2207          | 0.8586   |
| 0.0077        | 41.0  | 30750 | 1.1783          | 0.8636   |
| 0.0004        | 42.0  | 31500 | 1.2606          | 0.8552   |
| 0.0014        | 43.0  | 32250 | 1.6455          | 0.8453   |
| 0.0004        | 44.0  | 33000 | 1.4264          | 0.8436   |
| 0.015         | 45.0  | 33750 | 1.4403          | 0.8536   |
| 0.0002        | 46.0  | 34500 | 1.2419          | 0.8552   |
| 0.002         | 47.0  | 35250 | 1.3338          | 0.8536   |
| 0.0101        | 48.0  | 36000 | 1.5464          | 0.8469   |
| 0.0086        | 49.0  | 36750 | 1.3979          | 0.8536   |
| 0.0061        | 50.0  | 37500 | 1.4058          | 0.8536   |


### 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