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# smids_3x_beit_base_adamax_00001_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: 0.7848
- Accuracy: 0.9183

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2779        | 1.0   | 375   | 0.3054          | 0.8733   |
| 0.2162        | 2.0   | 750   | 0.2359          | 0.92     |
| 0.1285        | 3.0   | 1125  | 0.2539          | 0.9217   |
| 0.0945        | 4.0   | 1500  | 0.2722          | 0.9233   |
| 0.1011        | 5.0   | 1875  | 0.3075          | 0.92     |
| 0.0628        | 6.0   | 2250  | 0.3567          | 0.9167   |
| 0.0288        | 7.0   | 2625  | 0.3944          | 0.915    |
| 0.0403        | 8.0   | 3000  | 0.4745          | 0.9083   |
| 0.0254        | 9.0   | 3375  | 0.4777          | 0.92     |
| 0.0101        | 10.0  | 3750  | 0.5260          | 0.9233   |
| 0.0079        | 11.0  | 4125  | 0.5710          | 0.92     |
| 0.0161        | 12.0  | 4500  | 0.5888          | 0.915    |
| 0.0114        | 13.0  | 4875  | 0.6115          | 0.92     |
| 0.0178        | 14.0  | 5250  | 0.6193          | 0.915    |
| 0.0098        | 15.0  | 5625  | 0.6503          | 0.9183   |
| 0.0165        | 16.0  | 6000  | 0.6581          | 0.9233   |
| 0.0022        | 17.0  | 6375  | 0.6879          | 0.9217   |
| 0.0225        | 18.0  | 6750  | 0.7059          | 0.92     |
| 0.0007        | 19.0  | 7125  | 0.7568          | 0.9117   |
| 0.0104        | 20.0  | 7500  | 0.6995          | 0.92     |
| 0.0014        | 21.0  | 7875  | 0.7129          | 0.9183   |
| 0.0053        | 22.0  | 8250  | 0.7485          | 0.9133   |
| 0.0549        | 23.0  | 8625  | 0.7098          | 0.9183   |
| 0.0039        | 24.0  | 9000  | 0.7046          | 0.9183   |
| 0.0037        | 25.0  | 9375  | 0.7588          | 0.915    |
| 0.0003        | 26.0  | 9750  | 0.7455          | 0.92     |
| 0.0253        | 27.0  | 10125 | 0.8244          | 0.9033   |
| 0.025         | 28.0  | 10500 | 0.7649          | 0.915    |
| 0.0003        | 29.0  | 10875 | 0.7615          | 0.9183   |
| 0.0276        | 30.0  | 11250 | 0.7366          | 0.92     |
| 0.0005        | 31.0  | 11625 | 0.7763          | 0.915    |
| 0.0305        | 32.0  | 12000 | 0.7932          | 0.91     |
| 0.0001        | 33.0  | 12375 | 0.7611          | 0.9183   |
| 0.0308        | 34.0  | 12750 | 0.7888          | 0.905    |
| 0.0002        | 35.0  | 13125 | 0.7612          | 0.9183   |
| 0.0004        | 36.0  | 13500 | 0.7891          | 0.9167   |
| 0.0001        | 37.0  | 13875 | 0.7612          | 0.9183   |
| 0.0           | 38.0  | 14250 | 0.7623          | 0.9167   |
| 0.0009        | 39.0  | 14625 | 0.7611          | 0.9167   |
| 0.0068        | 40.0  | 15000 | 0.7732          | 0.9167   |
| 0.0008        | 41.0  | 15375 | 0.7647          | 0.92     |
| 0.0059        | 42.0  | 15750 | 0.7690          | 0.915    |
| 0.0001        | 43.0  | 16125 | 0.7709          | 0.92     |
| 0.0042        | 44.0  | 16500 | 0.7831          | 0.9183   |
| 0.0002        | 45.0  | 16875 | 0.7842          | 0.92     |
| 0.0105        | 46.0  | 17250 | 0.7861          | 0.9183   |
| 0.0007        | 47.0  | 17625 | 0.7770          | 0.915    |
| 0.0           | 48.0  | 18000 | 0.7805          | 0.9183   |
| 0.0           | 49.0  | 18375 | 0.7842          | 0.9183   |
| 0.0           | 50.0  | 18750 | 0.7848          | 0.9183   |


### 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