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# smids_5x_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: 0.5438
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9114        | 1.0   | 376   | 0.8706          | 0.5559   |
| 0.8509        | 2.0   | 752   | 1.2414          | 0.3456   |
| 0.8099        | 3.0   | 1128  | 1.0576          | 0.4007   |
| 0.8085        | 4.0   | 1504  | 0.8246          | 0.5442   |
| 0.8886        | 5.0   | 1880  | 0.8245          | 0.5376   |
| 0.7819        | 6.0   | 2256  | 0.7875          | 0.5977   |
| 0.7498        | 7.0   | 2632  | 0.8002          | 0.6344   |
| 0.7083        | 8.0   | 3008  | 0.8113          | 0.6027   |
| 0.7609        | 9.0   | 3384  | 0.7440          | 0.6594   |
| 0.7953        | 10.0  | 3760  | 0.7639          | 0.5993   |
| 0.694         | 11.0  | 4136  | 0.7065          | 0.6594   |
| 0.7315        | 12.0  | 4512  | 0.7188          | 0.6277   |
| 0.7192        | 13.0  | 4888  | 0.6863          | 0.7229   |
| 0.6504        | 14.0  | 5264  | 0.6661          | 0.6828   |
| 0.6524        | 15.0  | 5640  | 0.6777          | 0.6661   |
| 0.5701        | 16.0  | 6016  | 0.7272          | 0.6561   |
| 0.5543        | 17.0  | 6392  | 0.7125          | 0.6878   |
| 0.6439        | 18.0  | 6768  | 0.6430          | 0.7028   |
| 0.648         | 19.0  | 7144  | 0.6863          | 0.6928   |
| 0.5899        | 20.0  | 7520  | 0.6226          | 0.7162   |
| 0.6393        | 21.0  | 7896  | 0.6018          | 0.7312   |
| 0.5884        | 22.0  | 8272  | 0.5610          | 0.7412   |
| 0.5288        | 23.0  | 8648  | 0.5975          | 0.7379   |
| 0.5965        | 24.0  | 9024  | 0.6473          | 0.7028   |
| 0.58          | 25.0  | 9400  | 0.5765          | 0.7396   |
| 0.5899        | 26.0  | 9776  | 0.6331          | 0.7245   |
| 0.5507        | 27.0  | 10152 | 0.5858          | 0.7396   |
| 0.5002        | 28.0  | 10528 | 0.5674          | 0.7396   |
| 0.5229        | 29.0  | 10904 | 0.5711          | 0.7629   |
| 0.5096        | 30.0  | 11280 | 0.5570          | 0.7312   |
| 0.5311        | 31.0  | 11656 | 0.5601          | 0.7396   |
| 0.5742        | 32.0  | 12032 | 0.6065          | 0.7346   |
| 0.4585        | 33.0  | 12408 | 0.5565          | 0.7462   |
| 0.5294        | 34.0  | 12784 | 0.5555          | 0.7446   |
| 0.5171        | 35.0  | 13160 | 0.5723          | 0.7462   |
| 0.4899        | 36.0  | 13536 | 0.5748          | 0.7279   |
| 0.4582        | 37.0  | 13912 | 0.5789          | 0.7396   |
| 0.5149        | 38.0  | 14288 | 0.5146          | 0.7679   |
| 0.4968        | 39.0  | 14664 | 0.6020          | 0.7613   |
| 0.5645        | 40.0  | 15040 | 0.5459          | 0.7546   |
| 0.4741        | 41.0  | 15416 | 0.5562          | 0.7479   |
| 0.4423        | 42.0  | 15792 | 0.5487          | 0.7412   |
| 0.4186        | 43.0  | 16168 | 0.5329          | 0.7479   |
| 0.4763        | 44.0  | 16544 | 0.5469          | 0.7462   |
| 0.4775        | 45.0  | 16920 | 0.5538          | 0.7496   |
| 0.4053        | 46.0  | 17296 | 0.5298          | 0.7613   |
| 0.429         | 47.0  | 17672 | 0.5338          | 0.7663   |
| 0.4194        | 48.0  | 18048 | 0.5631          | 0.7496   |
| 0.3965        | 49.0  | 18424 | 0.5407          | 0.7629   |
| 0.356         | 50.0  | 18800 | 0.5438          | 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