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# smids_5x_beit_base_adamax_0001_fold3

This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2034
- Accuracy: 0.88

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6864        | 1.0   | 375   | 0.6860          | 0.7217   |
| 0.4252        | 2.0   | 750   | 0.6416          | 0.7633   |
| 0.3187        | 3.0   | 1125  | 0.4319          | 0.845    |
| 0.2894        | 4.0   | 1500  | 0.4449          | 0.84     |
| 0.2471        | 5.0   | 1875  | 0.4205          | 0.8483   |
| 0.1902        | 6.0   | 2250  | 0.3865          | 0.8633   |
| 0.2206        | 7.0   | 2625  | 0.4140          | 0.86     |
| 0.1469        | 8.0   | 3000  | 0.4250          | 0.875    |
| 0.0865        | 9.0   | 3375  | 0.5396          | 0.8717   |
| 0.0868        | 10.0  | 3750  | 0.6873          | 0.8583   |
| 0.0906        | 11.0  | 4125  | 0.4484          | 0.8883   |
| 0.0585        | 12.0  | 4500  | 0.6803          | 0.88     |
| 0.1093        | 13.0  | 4875  | 0.5271          | 0.8833   |
| 0.0782        | 14.0  | 5250  | 0.6855          | 0.8633   |
| 0.0163        | 15.0  | 5625  | 0.6773          | 0.865    |
| 0.0996        | 16.0  | 6000  | 0.6530          | 0.8733   |
| 0.0415        | 17.0  | 6375  | 0.8051          | 0.88     |
| 0.0673        | 18.0  | 6750  | 0.7716          | 0.88     |
| 0.1076        | 19.0  | 7125  | 0.7362          | 0.8783   |
| 0.0103        | 20.0  | 7500  | 0.7231          | 0.875    |
| 0.0579        | 21.0  | 7875  | 0.7618          | 0.88     |
| 0.0461        | 22.0  | 8250  | 0.9110          | 0.8717   |
| 0.0544        | 23.0  | 8625  | 0.7656          | 0.8767   |
| 0.0075        | 24.0  | 9000  | 0.8172          | 0.8933   |
| 0.0242        | 25.0  | 9375  | 1.0276          | 0.865    |
| 0.001         | 26.0  | 9750  | 1.1126          | 0.8717   |
| 0.0272        | 27.0  | 10125 | 0.9522          | 0.8833   |
| 0.0034        | 28.0  | 10500 | 0.9353          | 0.87     |
| 0.0223        | 29.0  | 10875 | 0.8506          | 0.8833   |
| 0.0058        | 30.0  | 11250 | 1.1236          | 0.855    |
| 0.0185        | 31.0  | 11625 | 1.0029          | 0.8733   |
| 0.0001        | 32.0  | 12000 | 0.8488          | 0.88     |
| 0.0006        | 33.0  | 12375 | 0.8729          | 0.8767   |
| 0.0005        | 34.0  | 12750 | 1.0735          | 0.875    |
| 0.0003        | 35.0  | 13125 | 1.0286          | 0.885    |
| 0.0           | 36.0  | 13500 | 0.8268          | 0.89     |
| 0.0001        | 37.0  | 13875 | 1.0063          | 0.875    |
| 0.0002        | 38.0  | 14250 | 0.9833          | 0.885    |
| 0.0009        | 39.0  | 14625 | 0.9292          | 0.8917   |
| 0.0034        | 40.0  | 15000 | 0.9953          | 0.8883   |
| 0.0           | 41.0  | 15375 | 1.0555          | 0.885    |
| 0.0           | 42.0  | 15750 | 1.0377          | 0.89     |
| 0.0           | 43.0  | 16125 | 1.1991          | 0.8717   |
| 0.0           | 44.0  | 16500 | 1.1156          | 0.8783   |
| 0.0           | 45.0  | 16875 | 1.1077          | 0.8717   |
| 0.0001        | 46.0  | 17250 | 1.0635          | 0.8817   |
| 0.0           | 47.0  | 17625 | 1.1588          | 0.8817   |
| 0.0           | 48.0  | 18000 | 1.1268          | 0.8867   |
| 0.0           | 49.0  | 18375 | 1.2011          | 0.88     |
| 0.0           | 50.0  | 18750 | 1.2034          | 0.88     |


### 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