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# smids_5x_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: 0.2734
- Accuracy: 0.91

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6727        | 1.0   | 375   | 0.6864          | 0.7317   |
| 0.6041        | 2.0   | 750   | 0.5073          | 0.8033   |
| 0.4189        | 3.0   | 1125  | 0.4416          | 0.8267   |
| 0.3943        | 4.0   | 1500  | 0.3997          | 0.8433   |
| 0.4289        | 5.0   | 1875  | 0.3785          | 0.855    |
| 0.3775        | 6.0   | 2250  | 0.3585          | 0.8617   |
| 0.3696        | 7.0   | 2625  | 0.3398          | 0.8767   |
| 0.3454        | 8.0   | 3000  | 0.3318          | 0.8767   |
| 0.3235        | 9.0   | 3375  | 0.3277          | 0.8717   |
| 0.3129        | 10.0  | 3750  | 0.3193          | 0.8733   |
| 0.2807        | 11.0  | 4125  | 0.3194          | 0.8833   |
| 0.2877        | 12.0  | 4500  | 0.3054          | 0.8867   |
| 0.2952        | 13.0  | 4875  | 0.3044          | 0.89     |
| 0.2764        | 14.0  | 5250  | 0.3000          | 0.89     |
| 0.2449        | 15.0  | 5625  | 0.2977          | 0.8933   |
| 0.2466        | 16.0  | 6000  | 0.2964          | 0.8983   |
| 0.2903        | 17.0  | 6375  | 0.2961          | 0.8883   |
| 0.2759        | 18.0  | 6750  | 0.2922          | 0.895    |
| 0.2465        | 19.0  | 7125  | 0.2899          | 0.9      |
| 0.2405        | 20.0  | 7500  | 0.2859          | 0.8967   |
| 0.1951        | 21.0  | 7875  | 0.2887          | 0.9      |
| 0.2066        | 22.0  | 8250  | 0.2843          | 0.9017   |
| 0.2962        | 23.0  | 8625  | 0.2838          | 0.9033   |
| 0.2403        | 24.0  | 9000  | 0.2814          | 0.9033   |
| 0.2279        | 25.0  | 9375  | 0.2878          | 0.9033   |
| 0.2748        | 26.0  | 9750  | 0.2833          | 0.9017   |
| 0.2265        | 27.0  | 10125 | 0.2865          | 0.905    |
| 0.2583        | 28.0  | 10500 | 0.2821          | 0.9083   |
| 0.1555        | 29.0  | 10875 | 0.2828          | 0.905    |
| 0.2329        | 30.0  | 11250 | 0.2785          | 0.9117   |
| 0.235         | 31.0  | 11625 | 0.2766          | 0.9067   |
| 0.245         | 32.0  | 12000 | 0.2760          | 0.91     |
| 0.2626        | 33.0  | 12375 | 0.2763          | 0.9067   |
| 0.2391        | 34.0  | 12750 | 0.2794          | 0.905    |
| 0.1618        | 35.0  | 13125 | 0.2760          | 0.9117   |
| 0.1815        | 36.0  | 13500 | 0.2756          | 0.9067   |
| 0.1967        | 37.0  | 13875 | 0.2796          | 0.9033   |
| 0.1639        | 38.0  | 14250 | 0.2740          | 0.91     |
| 0.2156        | 39.0  | 14625 | 0.2769          | 0.9083   |
| 0.1866        | 40.0  | 15000 | 0.2767          | 0.915    |
| 0.1684        | 41.0  | 15375 | 0.2758          | 0.9083   |
| 0.194         | 42.0  | 15750 | 0.2754          | 0.9067   |
| 0.2588        | 43.0  | 16125 | 0.2756          | 0.9083   |
| 0.213         | 44.0  | 16500 | 0.2746          | 0.91     |
| 0.1893        | 45.0  | 16875 | 0.2745          | 0.9083   |
| 0.2111        | 46.0  | 17250 | 0.2742          | 0.9083   |
| 0.2091        | 47.0  | 17625 | 0.2740          | 0.91     |
| 0.2023        | 48.0  | 18000 | 0.2739          | 0.9083   |
| 0.1852        | 49.0  | 18375 | 0.2735          | 0.91     |
| 0.197         | 50.0  | 18750 | 0.2734          | 0.91     |


### 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