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# smids_10x_beit_large_adamax_001_fold4

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: 1.6842
- Accuracy: 0.8717

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3361        | 1.0   | 750   | 0.4333          | 0.8367   |
| 0.2968        | 2.0   | 1500  | 0.4495          | 0.8467   |
| 0.288         | 3.0   | 2250  | 0.4264          | 0.8383   |
| 0.2379        | 4.0   | 3000  | 0.4907          | 0.85     |
| 0.1893        | 5.0   | 3750  | 0.4876          | 0.8533   |
| 0.1419        | 6.0   | 4500  | 0.4376          | 0.8667   |
| 0.1288        | 7.0   | 5250  | 0.5742          | 0.84     |
| 0.079         | 8.0   | 6000  | 0.6426          | 0.86     |
| 0.0885        | 9.0   | 6750  | 0.6694          | 0.8617   |
| 0.0513        | 10.0  | 7500  | 0.7772          | 0.8483   |
| 0.0371        | 11.0  | 8250  | 0.7425          | 0.8667   |
| 0.0559        | 12.0  | 9000  | 0.7844          | 0.8633   |
| 0.0437        | 13.0  | 9750  | 0.9475          | 0.8617   |
| 0.0237        | 14.0  | 10500 | 0.8539          | 0.86     |
| 0.0064        | 15.0  | 11250 | 1.1662          | 0.8683   |
| 0.0766        | 16.0  | 12000 | 1.1003          | 0.8683   |
| 0.0045        | 17.0  | 12750 | 1.1294          | 0.8633   |
| 0.0012        | 18.0  | 13500 | 1.0595          | 0.8717   |
| 0.0107        | 19.0  | 14250 | 1.0246          | 0.875    |
| 0.0098        | 20.0  | 15000 | 0.9670          | 0.8633   |
| 0.0227        | 21.0  | 15750 | 1.0829          | 0.8633   |
| 0.0004        | 22.0  | 16500 | 1.0091          | 0.855    |
| 0.0026        | 23.0  | 17250 | 1.0123          | 0.8667   |
| 0.001         | 24.0  | 18000 | 1.0183          | 0.8783   |
| 0.0083        | 25.0  | 18750 | 1.2133          | 0.8533   |
| 0.0076        | 26.0  | 19500 | 1.0638          | 0.865    |
| 0.0045        | 27.0  | 20250 | 1.1546          | 0.8717   |
| 0.0001        | 28.0  | 21000 | 1.0902          | 0.8567   |
| 0.0003        | 29.0  | 21750 | 1.1809          | 0.86     |
| 0.0           | 30.0  | 22500 | 1.2715          | 0.8733   |
| 0.0001        | 31.0  | 23250 | 1.1922          | 0.8767   |
| 0.0           | 32.0  | 24000 | 1.4076          | 0.87     |
| 0.0075        | 33.0  | 24750 | 1.3961          | 0.8617   |
| 0.0           | 34.0  | 25500 | 1.4345          | 0.875    |
| 0.0           | 35.0  | 26250 | 1.6125          | 0.8683   |
| 0.0           | 36.0  | 27000 | 1.5456          | 0.8567   |
| 0.0           | 37.0  | 27750 | 1.5632          | 0.865    |
| 0.0           | 38.0  | 28500 | 1.6349          | 0.8617   |
| 0.0           | 39.0  | 29250 | 1.5362          | 0.8617   |
| 0.0           | 40.0  | 30000 | 1.6434          | 0.8667   |
| 0.0           | 41.0  | 30750 | 1.6815          | 0.87     |
| 0.0           | 42.0  | 31500 | 1.6593          | 0.8667   |
| 0.0           | 43.0  | 32250 | 1.6757          | 0.87     |
| 0.0           | 44.0  | 33000 | 1.6503          | 0.8683   |
| 0.0           | 45.0  | 33750 | 1.6999          | 0.8667   |
| 0.0           | 46.0  | 34500 | 1.6868          | 0.8667   |
| 0.0           | 47.0  | 35250 | 1.6803          | 0.87     |
| 0.0           | 48.0  | 36000 | 1.6872          | 0.8733   |
| 0.0           | 49.0  | 36750 | 1.6911          | 0.8717   |
| 0.0           | 50.0  | 37500 | 1.6842          | 0.8717   |


### 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