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# smids_1x_beit_base_adamax_0001_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: 0.7859
- Accuracy: 0.8902

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.357         | 1.0   | 75   | 0.2942          | 0.8852   |
| 0.196         | 2.0   | 150  | 0.2977          | 0.8769   |
| 0.1343        | 3.0   | 225  | 0.3454          | 0.8835   |
| 0.1165        | 4.0   | 300  | 0.4770          | 0.8586   |
| 0.0357        | 5.0   | 375  | 0.3863          | 0.8819   |
| 0.0407        | 6.0   | 450  | 0.5588          | 0.8785   |
| 0.0487        | 7.0   | 525  | 0.5410          | 0.8769   |
| 0.0422        | 8.0   | 600  | 0.5327          | 0.8835   |
| 0.0252        | 9.0   | 675  | 0.5671          | 0.8885   |
| 0.0072        | 10.0  | 750  | 0.5229          | 0.8852   |
| 0.0013        | 11.0  | 825  | 0.5397          | 0.9018   |
| 0.0233        | 12.0  | 900  | 0.6716          | 0.8902   |
| 0.0031        | 13.0  | 975  | 0.6232          | 0.8935   |
| 0.0106        | 14.0  | 1050 | 0.6722          | 0.8835   |
| 0.0052        | 15.0  | 1125 | 0.5873          | 0.9101   |
| 0.0117        | 16.0  | 1200 | 0.6014          | 0.8935   |
| 0.0056        | 17.0  | 1275 | 0.6190          | 0.8952   |
| 0.018         | 18.0  | 1350 | 0.6714          | 0.8902   |
| 0.0034        | 19.0  | 1425 | 0.6903          | 0.8918   |
| 0.0034        | 20.0  | 1500 | 0.6789          | 0.8902   |
| 0.0018        | 21.0  | 1575 | 0.7049          | 0.8852   |
| 0.0015        | 22.0  | 1650 | 0.8451          | 0.8802   |
| 0.0032        | 23.0  | 1725 | 0.6725          | 0.8885   |
| 0.0116        | 24.0  | 1800 | 0.7163          | 0.8952   |
| 0.0001        | 25.0  | 1875 | 0.6827          | 0.8918   |
| 0.004         | 26.0  | 1950 | 0.7084          | 0.8885   |
| 0.012         | 27.0  | 2025 | 0.7239          | 0.8968   |
| 0.0099        | 28.0  | 2100 | 0.7371          | 0.8918   |
| 0.0044        | 29.0  | 2175 | 0.7635          | 0.8869   |
| 0.0039        | 30.0  | 2250 | 0.7043          | 0.8918   |
| 0.0035        | 31.0  | 2325 | 0.7276          | 0.8902   |
| 0.0           | 32.0  | 2400 | 0.7428          | 0.8935   |
| 0.0           | 33.0  | 2475 | 0.7968          | 0.8852   |
| 0.014         | 34.0  | 2550 | 0.7553          | 0.8918   |
| 0.0048        | 35.0  | 2625 | 0.7230          | 0.8968   |
| 0.0029        | 36.0  | 2700 | 0.7674          | 0.8869   |
| 0.0           | 37.0  | 2775 | 0.7425          | 0.8918   |
| 0.0023        | 38.0  | 2850 | 0.7970          | 0.8902   |
| 0.0047        | 39.0  | 2925 | 0.8047          | 0.8869   |
| 0.0021        | 40.0  | 3000 | 0.7994          | 0.8885   |
| 0.0           | 41.0  | 3075 | 0.7761          | 0.8852   |
| 0.0025        | 42.0  | 3150 | 0.7890          | 0.8885   |
| 0.0046        | 43.0  | 3225 | 0.7889          | 0.8885   |
| 0.0           | 44.0  | 3300 | 0.7915          | 0.8852   |
| 0.0047        | 45.0  | 3375 | 0.7967          | 0.8885   |
| 0.0           | 46.0  | 3450 | 0.7946          | 0.8869   |
| 0.002         | 47.0  | 3525 | 0.7884          | 0.8885   |
| 0.0           | 48.0  | 3600 | 0.7873          | 0.8885   |
| 0.0           | 49.0  | 3675 | 0.7859          | 0.8902   |
| 0.0           | 50.0  | 3750 | 0.7859          | 0.8902   |


### 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