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# smids_1x_beit_base_sgd_001_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: 0.3943
- Accuracy: 0.84

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0814        | 1.0   | 75   | 1.0547          | 0.4367   |
| 0.8585        | 2.0   | 150  | 0.8397          | 0.63     |
| 0.7866        | 3.0   | 225  | 0.7414          | 0.6717   |
| 0.7611        | 4.0   | 300  | 0.6885          | 0.71     |
| 0.6598        | 5.0   | 375  | 0.6453          | 0.7433   |
| 0.6441        | 6.0   | 450  | 0.6118          | 0.7433   |
| 0.6302        | 7.0   | 525  | 0.5846          | 0.7667   |
| 0.58          | 8.0   | 600  | 0.5727          | 0.7717   |
| 0.6043        | 9.0   | 675  | 0.5453          | 0.7783   |
| 0.5566        | 10.0  | 750  | 0.5288          | 0.7917   |
| 0.5653        | 11.0  | 825  | 0.5189          | 0.795    |
| 0.5734        | 12.0  | 900  | 0.5084          | 0.8      |
| 0.526         | 13.0  | 975  | 0.4978          | 0.8067   |
| 0.5307        | 14.0  | 1050 | 0.4908          | 0.805    |
| 0.441         | 15.0  | 1125 | 0.4849          | 0.8167   |
| 0.4731        | 16.0  | 1200 | 0.4739          | 0.8183   |
| 0.4795        | 17.0  | 1275 | 0.4707          | 0.82     |
| 0.4461        | 18.0  | 1350 | 0.4612          | 0.8267   |
| 0.4796        | 19.0  | 1425 | 0.4551          | 0.82     |
| 0.4848        | 20.0  | 1500 | 0.4529          | 0.8217   |
| 0.4879        | 21.0  | 1575 | 0.4473          | 0.8217   |
| 0.424         | 22.0  | 1650 | 0.4423          | 0.8217   |
| 0.4384        | 23.0  | 1725 | 0.4358          | 0.8233   |
| 0.4332        | 24.0  | 1800 | 0.4326          | 0.83     |
| 0.4502        | 25.0  | 1875 | 0.4313          | 0.83     |
| 0.4747        | 26.0  | 1950 | 0.4225          | 0.83     |
| 0.4021        | 27.0  | 2025 | 0.4213          | 0.8367   |
| 0.3712        | 28.0  | 2100 | 0.4180          | 0.83     |
| 0.4664        | 29.0  | 2175 | 0.4162          | 0.835    |
| 0.4161        | 30.0  | 2250 | 0.4131          | 0.8317   |
| 0.3674        | 31.0  | 2325 | 0.4116          | 0.8383   |
| 0.3951        | 32.0  | 2400 | 0.4117          | 0.8317   |
| 0.4114        | 33.0  | 2475 | 0.4085          | 0.8333   |
| 0.3877        | 34.0  | 2550 | 0.4062          | 0.84     |
| 0.4073        | 35.0  | 2625 | 0.4053          | 0.8383   |
| 0.4212        | 36.0  | 2700 | 0.4054          | 0.8367   |
| 0.3625        | 37.0  | 2775 | 0.4024          | 0.8417   |
| 0.4369        | 38.0  | 2850 | 0.4016          | 0.8383   |
| 0.393         | 39.0  | 2925 | 0.3994          | 0.8417   |
| 0.3655        | 40.0  | 3000 | 0.3982          | 0.84     |
| 0.3762        | 41.0  | 3075 | 0.3980          | 0.84     |
| 0.3881        | 42.0  | 3150 | 0.3976          | 0.8417   |
| 0.4602        | 43.0  | 3225 | 0.3971          | 0.845    |
| 0.3865        | 44.0  | 3300 | 0.3963          | 0.845    |
| 0.4135        | 45.0  | 3375 | 0.3957          | 0.8433   |
| 0.3962        | 46.0  | 3450 | 0.3950          | 0.8467   |
| 0.3933        | 47.0  | 3525 | 0.3951          | 0.8433   |
| 0.3744        | 48.0  | 3600 | 0.3945          | 0.84     |
| 0.4098        | 49.0  | 3675 | 0.3944          | 0.84     |
| 0.3625        | 50.0  | 3750 | 0.3943          | 0.84     |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

abnormal_sperm non-sperm normal_sperm