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# smids_3x_deit_small_rms_001_fold1

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: 1.0295
- Accuracy: 0.7663

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9037        | 1.0   | 226   | 1.1611          | 0.4224   |
| 0.8436        | 2.0   | 452   | 0.8419          | 0.5442   |
| 0.8202        | 3.0   | 678   | 0.8414          | 0.5359   |
| 0.8734        | 4.0   | 904   | 0.8332          | 0.5326   |
| 0.8282        | 5.0   | 1130  | 0.7907          | 0.6127   |
| 0.8721        | 6.0   | 1356  | 0.8061          | 0.5559   |
| 0.7744        | 7.0   | 1582  | 0.7612          | 0.6260   |
| 0.7444        | 8.0   | 1808  | 0.8606          | 0.5492   |
| 0.7266        | 9.0   | 2034  | 0.7492          | 0.6427   |
| 0.7385        | 10.0  | 2260  | 0.7643          | 0.6344   |
| 0.6851        | 11.0  | 2486  | 0.7983          | 0.5843   |
| 0.6844        | 12.0  | 2712  | 0.7946          | 0.6561   |
| 0.6727        | 13.0  | 2938  | 0.8087          | 0.6244   |
| 0.6244        | 14.0  | 3164  | 0.6709          | 0.6912   |
| 0.6712        | 15.0  | 3390  | 0.6742          | 0.7095   |
| 0.6346        | 16.0  | 3616  | 0.6684          | 0.7162   |
| 0.5408        | 17.0  | 3842  | 0.6615          | 0.7028   |
| 0.63          | 18.0  | 4068  | 0.6480          | 0.7295   |
| 0.6263        | 19.0  | 4294  | 0.7205          | 0.6611   |
| 0.5327        | 20.0  | 4520  | 0.6519          | 0.7078   |
| 0.6622        | 21.0  | 4746  | 0.6350          | 0.7179   |
| 0.6299        | 22.0  | 4972  | 0.8817          | 0.6210   |
| 0.6304        | 23.0  | 5198  | 0.6476          | 0.7362   |
| 0.5526        | 24.0  | 5424  | 0.6677          | 0.7145   |
| 0.6295        | 25.0  | 5650  | 0.6118          | 0.7546   |
| 0.6308        | 26.0  | 5876  | 0.6212          | 0.7362   |
| 0.5383        | 27.0  | 6102  | 0.7015          | 0.7179   |
| 0.5618        | 28.0  | 6328  | 0.8218          | 0.6711   |
| 0.4879        | 29.0  | 6554  | 0.7043          | 0.6928   |
| 0.5827        | 30.0  | 6780  | 0.6552          | 0.7229   |
| 0.5364        | 31.0  | 7006  | 0.6340          | 0.7379   |
| 0.4905        | 32.0  | 7232  | 0.6047          | 0.7529   |
| 0.4492        | 33.0  | 7458  | 0.7039          | 0.7028   |
| 0.4914        | 34.0  | 7684  | 0.6660          | 0.7379   |
| 0.3519        | 35.0  | 7910  | 0.6494          | 0.7479   |
| 0.3791        | 36.0  | 8136  | 0.6497          | 0.7513   |
| 0.4111        | 37.0  | 8362  | 0.6075          | 0.7646   |
| 0.4433        | 38.0  | 8588  | 0.6728          | 0.7679   |
| 0.3357        | 39.0  | 8814  | 0.6576          | 0.7529   |
| 0.3901        | 40.0  | 9040  | 0.6972          | 0.7596   |
| 0.4094        | 41.0  | 9266  | 0.6481          | 0.7696   |
| 0.3576        | 42.0  | 9492  | 0.6871          | 0.7746   |
| 0.335         | 43.0  | 9718  | 0.7307          | 0.7846   |
| 0.2737        | 44.0  | 9944  | 0.7687          | 0.7746   |
| 0.3485        | 45.0  | 10170 | 0.7785          | 0.7780   |
| 0.278         | 46.0  | 10396 | 0.8580          | 0.7730   |
| 0.2622        | 47.0  | 10622 | 0.8921          | 0.7713   |
| 0.2496        | 48.0  | 10848 | 0.9544          | 0.7730   |
| 0.1441        | 49.0  | 11074 | 0.9744          | 0.7730   |
| 0.1894        | 50.0  | 11300 | 1.0295          | 0.7663   |


### 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