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# smids_5x_beit_base_adamax_00001_fold2

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: 0.8394
- Accuracy: 0.8985

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3036        | 1.0   | 375   | 0.2703          | 0.8918   |
| 0.2118        | 2.0   | 750   | 0.2674          | 0.8968   |
| 0.1557        | 3.0   | 1125  | 0.2889          | 0.8918   |
| 0.074         | 4.0   | 1500  | 0.2842          | 0.9002   |
| 0.0616        | 5.0   | 1875  | 0.3403          | 0.8935   |
| 0.036         | 6.0   | 2250  | 0.3534          | 0.9101   |
| 0.0382        | 7.0   | 2625  | 0.4309          | 0.8985   |
| 0.0686        | 8.0   | 3000  | 0.4834          | 0.8985   |
| 0.022         | 9.0   | 3375  | 0.5298          | 0.8935   |
| 0.0159        | 10.0  | 3750  | 0.5866          | 0.8985   |
| 0.0173        | 11.0  | 4125  | 0.5610          | 0.8968   |
| 0.0241        | 12.0  | 4500  | 0.6962          | 0.8869   |
| 0.0123        | 13.0  | 4875  | 0.6252          | 0.8952   |
| 0.0054        | 14.0  | 5250  | 0.6170          | 0.9002   |
| 0.0251        | 15.0  | 5625  | 0.6453          | 0.8952   |
| 0.0003        | 16.0  | 6000  | 0.6804          | 0.8952   |
| 0.0563        | 17.0  | 6375  | 0.6912          | 0.8985   |
| 0.0079        | 18.0  | 6750  | 0.6905          | 0.9018   |
| 0.0009        | 19.0  | 7125  | 0.7171          | 0.8935   |
| 0.0206        | 20.0  | 7500  | 0.7602          | 0.8985   |
| 0.0222        | 21.0  | 7875  | 0.7242          | 0.8952   |
| 0.0005        | 22.0  | 8250  | 0.7227          | 0.9002   |
| 0.0001        | 23.0  | 8625  | 0.7725          | 0.9002   |
| 0.0002        | 24.0  | 9000  | 0.7700          | 0.8935   |
| 0.0001        | 25.0  | 9375  | 0.7746          | 0.8985   |
| 0.0001        | 26.0  | 9750  | 0.7609          | 0.9018   |
| 0.017         | 27.0  | 10125 | 0.8256          | 0.8918   |
| 0.0019        | 28.0  | 10500 | 0.7444          | 0.8952   |
| 0.0254        | 29.0  | 10875 | 0.7839          | 0.9035   |
| 0.0041        | 30.0  | 11250 | 0.7929          | 0.9002   |
| 0.0018        | 31.0  | 11625 | 0.7983          | 0.8968   |
| 0.0163        | 32.0  | 12000 | 0.8337          | 0.8968   |
| 0.0122        | 33.0  | 12375 | 0.8065          | 0.8918   |
| 0.0021        | 34.0  | 12750 | 0.8472          | 0.8968   |
| 0.0003        | 35.0  | 13125 | 0.8572          | 0.8968   |
| 0.0036        | 36.0  | 13500 | 0.8680          | 0.8935   |
| 0.0086        | 37.0  | 13875 | 0.8533          | 0.8935   |
| 0.0002        | 38.0  | 14250 | 0.8606          | 0.8885   |
| 0.0065        | 39.0  | 14625 | 0.8465          | 0.8869   |
| 0.0212        | 40.0  | 15000 | 0.8444          | 0.8952   |
| 0.0163        | 41.0  | 15375 | 0.8576          | 0.8918   |
| 0.0071        | 42.0  | 15750 | 0.8227          | 0.8952   |
| 0.0234        | 43.0  | 16125 | 0.8305          | 0.8935   |
| 0.0019        | 44.0  | 16500 | 0.8174          | 0.9002   |
| 0.0226        | 45.0  | 16875 | 0.8559          | 0.8902   |
| 0.0176        | 46.0  | 17250 | 0.8405          | 0.8918   |
| 0.0236        | 47.0  | 17625 | 0.8413          | 0.8952   |
| 0.0179        | 48.0  | 18000 | 0.8437          | 0.8985   |
| 0.0141        | 49.0  | 18375 | 0.8368          | 0.8968   |
| 0.0007        | 50.0  | 18750 | 0.8394          | 0.8985   |


### 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