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# smids_3x_deit_base_rms_001_fold2

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6036
- Accuracy: 0.8020

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1186        | 1.0   | 225   | 0.8982          | 0.5075   |
| 0.9141        | 2.0   | 450   | 0.8195          | 0.5557   |
| 0.8927        | 3.0   | 675   | 0.8189          | 0.5441   |
| 0.8323        | 4.0   | 900   | 0.8095          | 0.5541   |
| 0.8947        | 5.0   | 1125  | 0.7623          | 0.5757   |
| 0.7287        | 6.0   | 1350  | 0.8273          | 0.5591   |
| 0.7585        | 7.0   | 1575  | 0.7770          | 0.5973   |
| 0.8103        | 8.0   | 1800  | 0.7290          | 0.6106   |
| 0.7335        | 9.0   | 2025  | 0.7908          | 0.5807   |
| 0.7359        | 10.0  | 2250  | 0.7312          | 0.5874   |
| 0.8194        | 11.0  | 2475  | 0.9398          | 0.5557   |
| 0.7512        | 12.0  | 2700  | 0.7107          | 0.5923   |
| 0.7169        | 13.0  | 2925  | 0.7015          | 0.6639   |
| 0.6759        | 14.0  | 3150  | 0.6767          | 0.6672   |
| 0.7072        | 15.0  | 3375  | 0.6493          | 0.6955   |
| 0.6502        | 16.0  | 3600  | 0.6076          | 0.7404   |
| 0.6691        | 17.0  | 3825  | 0.6396          | 0.6855   |
| 0.6248        | 18.0  | 4050  | 0.5525          | 0.7621   |
| 0.5977        | 19.0  | 4275  | 0.7766          | 0.6373   |
| 0.582         | 20.0  | 4500  | 0.5758          | 0.7438   |
| 0.5383        | 21.0  | 4725  | 0.5521          | 0.7554   |
| 0.6208        | 22.0  | 4950  | 0.5508          | 0.7521   |
| 0.6018        | 23.0  | 5175  | 0.5519          | 0.7604   |
| 0.5417        | 24.0  | 5400  | 0.5813          | 0.7471   |
| 0.6149        | 25.0  | 5625  | 0.5077          | 0.7820   |
| 0.5061        | 26.0  | 5850  | 0.5197          | 0.7804   |
| 0.5327        | 27.0  | 6075  | 0.5610          | 0.7454   |
| 0.487         | 28.0  | 6300  | 0.5448          | 0.7654   |
| 0.5248        | 29.0  | 6525  | 0.5394          | 0.7704   |
| 0.4978        | 30.0  | 6750  | 0.5209          | 0.7804   |
| 0.523         | 31.0  | 6975  | 0.5417          | 0.7604   |
| 0.502         | 32.0  | 7200  | 0.5080          | 0.7770   |
| 0.4674        | 33.0  | 7425  | 0.5071          | 0.7820   |
| 0.4329        | 34.0  | 7650  | 0.4947          | 0.8003   |
| 0.4583        | 35.0  | 7875  | 0.5207          | 0.7854   |
| 0.4868        | 36.0  | 8100  | 0.4819          | 0.8087   |
| 0.4542        | 37.0  | 8325  | 0.4836          | 0.7987   |
| 0.4328        | 38.0  | 8550  | 0.5050          | 0.7937   |
| 0.3395        | 39.0  | 8775  | 0.5073          | 0.7953   |
| 0.339         | 40.0  | 9000  | 0.5849          | 0.7870   |
| 0.3908        | 41.0  | 9225  | 0.5523          | 0.7820   |
| 0.4049        | 42.0  | 9450  | 0.5288          | 0.7920   |
| 0.3295        | 43.0  | 9675  | 0.5405          | 0.8053   |
| 0.3742        | 44.0  | 9900  | 0.5541          | 0.8020   |
| 0.3832        | 45.0  | 10125 | 0.5567          | 0.7953   |
| 0.3742        | 46.0  | 10350 | 0.5578          | 0.7920   |
| 0.3317        | 47.0  | 10575 | 0.5698          | 0.8103   |
| 0.2873        | 48.0  | 10800 | 0.5859          | 0.8037   |
| 0.3255        | 49.0  | 11025 | 0.6015          | 0.7970   |
| 0.3175        | 50.0  | 11250 | 0.6036          | 0.8020   |


### 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