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# smids_3x_deit_small_rms_001_fold2

This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6000
- Accuracy: 0.7720

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1321        | 1.0   | 225   | 1.1948          | 0.4293   |
| 0.871         | 2.0   | 450   | 0.8948          | 0.5175   |
| 0.8008        | 3.0   | 675   | 0.7992          | 0.5408   |
| 0.8238        | 4.0   | 900   | 0.8283          | 0.5574   |
| 0.8152        | 5.0   | 1125  | 0.7604          | 0.6023   |
| 0.7386        | 6.0   | 1350  | 0.8796          | 0.5491   |
| 0.8772        | 7.0   | 1575  | 0.8860          | 0.5874   |
| 0.8376        | 8.0   | 1800  | 0.7510          | 0.6356   |
| 0.7551        | 9.0   | 2025  | 0.8017          | 0.6223   |
| 0.7343        | 10.0  | 2250  | 0.7106          | 0.6506   |
| 0.7921        | 11.0  | 2475  | 0.7927          | 0.6223   |
| 0.7457        | 12.0  | 2700  | 0.6623          | 0.6972   |
| 0.7029        | 13.0  | 2925  | 0.7255          | 0.6855   |
| 0.6991        | 14.0  | 3150  | 0.6397          | 0.7471   |
| 0.6797        | 15.0  | 3375  | 0.6573          | 0.6988   |
| 0.7545        | 16.0  | 3600  | 0.6382          | 0.7038   |
| 0.68          | 17.0  | 3825  | 0.6756          | 0.6805   |
| 0.624         | 18.0  | 4050  | 0.6219          | 0.7488   |
| 0.6708        | 19.0  | 4275  | 0.6554          | 0.7088   |
| 0.6335        | 20.0  | 4500  | 0.6777          | 0.6905   |
| 0.6626        | 21.0  | 4725  | 0.6194          | 0.7438   |
| 0.7083        | 22.0  | 4950  | 0.6072          | 0.7488   |
| 0.7252        | 23.0  | 5175  | 0.5921          | 0.7471   |
| 0.6519        | 24.0  | 5400  | 0.5775          | 0.7454   |
| 0.6375        | 25.0  | 5625  | 0.6257          | 0.7188   |
| 0.6035        | 26.0  | 5850  | 0.5599          | 0.7554   |
| 0.6072        | 27.0  | 6075  | 0.6182          | 0.7338   |
| 0.5614        | 28.0  | 6300  | 0.5694          | 0.7654   |
| 0.5851        | 29.0  | 6525  | 0.5778          | 0.7488   |
| 0.5267        | 30.0  | 6750  | 0.5678          | 0.7521   |
| 0.6187        | 31.0  | 6975  | 0.5810          | 0.7571   |
| 0.5995        | 32.0  | 7200  | 0.5883          | 0.7587   |
| 0.5524        | 33.0  | 7425  | 0.5665          | 0.7671   |
| 0.5635        | 34.0  | 7650  | 0.5545          | 0.7671   |
| 0.5514        | 35.0  | 7875  | 0.5682          | 0.7654   |
| 0.5935        | 36.0  | 8100  | 0.5461          | 0.7687   |
| 0.533         | 37.0  | 8325  | 0.5437          | 0.7820   |
| 0.4461        | 38.0  | 8550  | 0.5819          | 0.7571   |
| 0.4417        | 39.0  | 8775  | 0.5848          | 0.7554   |
| 0.4385        | 40.0  | 9000  | 0.5831          | 0.7754   |
| 0.4422        | 41.0  | 9225  | 0.6218          | 0.7504   |
| 0.5095        | 42.0  | 9450  | 0.5522          | 0.7787   |
| 0.4571        | 43.0  | 9675  | 0.5702          | 0.7854   |
| 0.4352        | 44.0  | 9900  | 0.5650          | 0.7920   |
| 0.4947        | 45.0  | 10125 | 0.6244          | 0.7504   |
| 0.4183        | 46.0  | 10350 | 0.5769          | 0.7754   |
| 0.4309        | 47.0  | 10575 | 0.5669          | 0.7820   |
| 0.441         | 48.0  | 10800 | 0.5895          | 0.7737   |
| 0.475         | 49.0  | 11025 | 0.5930          | 0.7704   |
| 0.4102        | 50.0  | 11250 | 0.6000          | 0.7720   |


### 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