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# smids_3x_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: 0.2937
- Accuracy: 0.8967

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8635        | 1.0   | 225   | 0.8411          | 0.6267   |
| 0.6523        | 2.0   | 450   | 0.6173          | 0.7583   |
| 0.5403        | 3.0   | 675   | 0.5212          | 0.8017   |
| 0.4896        | 4.0   | 900   | 0.4692          | 0.8233   |
| 0.4594        | 5.0   | 1125  | 0.4353          | 0.8333   |
| 0.4326        | 6.0   | 1350  | 0.4063          | 0.8467   |
| 0.3692        | 7.0   | 1575  | 0.3897          | 0.855    |
| 0.4088        | 8.0   | 1800  | 0.3756          | 0.8567   |
| 0.4036        | 9.0   | 2025  | 0.3600          | 0.8667   |
| 0.387         | 10.0  | 2250  | 0.3535          | 0.8717   |
| 0.349         | 11.0  | 2475  | 0.3460          | 0.8717   |
| 0.3537        | 12.0  | 2700  | 0.3401          | 0.875    |
| 0.3714        | 13.0  | 2925  | 0.3342          | 0.8783   |
| 0.3497        | 14.0  | 3150  | 0.3327          | 0.8817   |
| 0.2955        | 15.0  | 3375  | 0.3234          | 0.8867   |
| 0.346         | 16.0  | 3600  | 0.3197          | 0.8933   |
| 0.3452        | 17.0  | 3825  | 0.3164          | 0.89     |
| 0.3182        | 18.0  | 4050  | 0.3143          | 0.8867   |
| 0.3047        | 19.0  | 4275  | 0.3110          | 0.8933   |
| 0.3008        | 20.0  | 4500  | 0.3105          | 0.8883   |
| 0.2783        | 21.0  | 4725  | 0.3050          | 0.8917   |
| 0.2751        | 22.0  | 4950  | 0.3037          | 0.8967   |
| 0.2477        | 23.0  | 5175  | 0.3059          | 0.8917   |
| 0.2485        | 24.0  | 5400  | 0.3040          | 0.8917   |
| 0.2841        | 25.0  | 5625  | 0.3099          | 0.8917   |
| 0.2803        | 26.0  | 5850  | 0.3058          | 0.8967   |
| 0.2313        | 27.0  | 6075  | 0.3019          | 0.8933   |
| 0.2302        | 28.0  | 6300  | 0.3005          | 0.895    |
| 0.2775        | 29.0  | 6525  | 0.2994          | 0.895    |
| 0.2039        | 30.0  | 6750  | 0.2961          | 0.9      |
| 0.261         | 31.0  | 6975  | 0.2949          | 0.9      |
| 0.2791        | 32.0  | 7200  | 0.2986          | 0.895    |
| 0.2917        | 33.0  | 7425  | 0.2938          | 0.8983   |
| 0.2364        | 34.0  | 7650  | 0.2966          | 0.895    |
| 0.2087        | 35.0  | 7875  | 0.2917          | 0.9      |
| 0.2544        | 36.0  | 8100  | 0.2944          | 0.8983   |
| 0.2254        | 37.0  | 8325  | 0.2941          | 0.8967   |
| 0.2119        | 38.0  | 8550  | 0.2972          | 0.8933   |
| 0.2445        | 39.0  | 8775  | 0.2905          | 0.9      |
| 0.204         | 40.0  | 9000  | 0.2909          | 0.8967   |
| 0.2353        | 41.0  | 9225  | 0.2968          | 0.895    |
| 0.2574        | 42.0  | 9450  | 0.2926          | 0.9      |
| 0.2197        | 43.0  | 9675  | 0.2953          | 0.8967   |
| 0.2519        | 44.0  | 9900  | 0.2939          | 0.8967   |
| 0.2337        | 45.0  | 10125 | 0.2971          | 0.895    |
| 0.2047        | 46.0  | 10350 | 0.2932          | 0.8967   |
| 0.2633        | 47.0  | 10575 | 0.2935          | 0.8967   |
| 0.2254        | 48.0  | 10800 | 0.2947          | 0.895    |
| 0.2679        | 49.0  | 11025 | 0.2937          | 0.895    |
| 0.2687        | 50.0  | 11250 | 0.2937          | 0.8967   |


### 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