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# smids_5x_deit_tiny_rms_001_fold2

This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4964
- Accuracy: 0.8486

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0328        | 1.0   | 375   | 0.9569          | 0.4459   |
| 0.8929        | 2.0   | 750   | 0.8978          | 0.5374   |
| 0.8224        | 3.0   | 1125  | 0.7888          | 0.5574   |
| 0.8327        | 4.0   | 1500  | 0.8571          | 0.5641   |
| 0.7266        | 5.0   | 1875  | 1.1729          | 0.5025   |
| 0.6507        | 6.0   | 2250  | 0.7875          | 0.6456   |
| 0.6983        | 7.0   | 2625  | 0.6489          | 0.6972   |
| 0.6312        | 8.0   | 3000  | 0.7326          | 0.6789   |
| 0.641         | 9.0   | 3375  | 0.5505          | 0.7488   |
| 0.6354        | 10.0  | 3750  | 0.5766          | 0.7354   |
| 0.5813        | 11.0  | 4125  | 0.4910          | 0.7920   |
| 0.6084        | 12.0  | 4500  | 0.5458          | 0.7720   |
| 0.4944        | 13.0  | 4875  | 0.4657          | 0.8020   |
| 0.5555        | 14.0  | 5250  | 0.5401          | 0.7621   |
| 0.526         | 15.0  | 5625  | 0.4958          | 0.7837   |
| 0.3751        | 16.0  | 6000  | 0.4911          | 0.8037   |
| 0.4264        | 17.0  | 6375  | 0.5204          | 0.7837   |
| 0.4312        | 18.0  | 6750  | 0.5011          | 0.7953   |
| 0.3686        | 19.0  | 7125  | 0.4979          | 0.7970   |
| 0.3954        | 20.0  | 7500  | 0.4812          | 0.8120   |
| 0.3782        | 21.0  | 7875  | 0.4706          | 0.8120   |
| 0.3544        | 22.0  | 8250  | 0.4461          | 0.8353   |
| 0.3759        | 23.0  | 8625  | 0.4516          | 0.8319   |
| 0.3473        | 24.0  | 9000  | 0.4332          | 0.8270   |
| 0.2572        | 25.0  | 9375  | 0.5951          | 0.8203   |
| 0.3628        | 26.0  | 9750  | 0.5630          | 0.7887   |
| 0.2737        | 27.0  | 10125 | 0.5304          | 0.8336   |
| 0.2272        | 28.0  | 10500 | 0.5597          | 0.8319   |
| 0.2226        | 29.0  | 10875 | 0.5680          | 0.8419   |
| 0.1778        | 30.0  | 11250 | 0.6295          | 0.8170   |
| 0.2382        | 31.0  | 11625 | 0.6223          | 0.8270   |
| 0.1721        | 32.0  | 12000 | 0.6049          | 0.8469   |
| 0.219         | 33.0  | 12375 | 0.5556          | 0.8569   |
| 0.0972        | 34.0  | 12750 | 0.6389          | 0.8502   |
| 0.1781        | 35.0  | 13125 | 0.7873          | 0.8253   |
| 0.1052        | 36.0  | 13500 | 0.8815          | 0.8236   |
| 0.1087        | 37.0  | 13875 | 0.7444          | 0.8453   |
| 0.09          | 38.0  | 14250 | 0.9779          | 0.8253   |
| 0.0859        | 39.0  | 14625 | 0.8817          | 0.8386   |
| 0.0521        | 40.0  | 15000 | 0.9849          | 0.8453   |
| 0.081         | 41.0  | 15375 | 1.0555          | 0.8203   |
| 0.0225        | 42.0  | 15750 | 1.1081          | 0.8303   |
| 0.0521        | 43.0  | 16125 | 1.2294          | 0.8253   |
| 0.0259        | 44.0  | 16500 | 1.3035          | 0.8336   |
| 0.0403        | 45.0  | 16875 | 1.3613          | 0.8253   |
| 0.0225        | 46.0  | 17250 | 1.4500          | 0.8103   |
| 0.0235        | 47.0  | 17625 | 1.5096          | 0.8270   |
| 0.0002        | 48.0  | 18000 | 1.5022          | 0.8469   |
| 0.0101        | 49.0  | 18375 | 1.4968          | 0.8469   |
| 0.0029        | 50.0  | 18750 | 1.4964          | 0.8486   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2

abnormal_sperm non-sperm normal_sperm