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# smids_5x_deit_small_rms_001_fold5

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: 0.9266
- Accuracy: 0.7933

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8478        | 1.0   | 375   | 0.8288          | 0.5567   |
| 0.8902        | 2.0   | 750   | 0.8202          | 0.5483   |
| 0.8343        | 3.0   | 1125  | 0.7711          | 0.65     |
| 0.8476        | 4.0   | 1500  | 0.8002          | 0.5683   |
| 0.7349        | 5.0   | 1875  | 0.7526          | 0.62     |
| 0.7146        | 6.0   | 2250  | 0.7401          | 0.645    |
| 0.6924        | 7.0   | 2625  | 0.7402          | 0.6467   |
| 0.8092        | 8.0   | 3000  | 0.7366          | 0.6267   |
| 0.7302        | 9.0   | 3375  | 0.7094          | 0.67     |
| 0.6674        | 10.0  | 3750  | 0.6785          | 0.6733   |
| 0.7108        | 11.0  | 4125  | 0.6584          | 0.6967   |
| 0.5797        | 12.0  | 4500  | 0.7184          | 0.68     |
| 0.6078        | 13.0  | 4875  | 0.6814          | 0.6767   |
| 0.6457        | 14.0  | 5250  | 0.6261          | 0.72     |
| 0.6638        | 15.0  | 5625  | 0.5980          | 0.7217   |
| 0.5829        | 16.0  | 6000  | 0.5841          | 0.7517   |
| 0.5785        | 17.0  | 6375  | 0.5759          | 0.7283   |
| 0.5879        | 18.0  | 6750  | 0.5909          | 0.7233   |
| 0.5859        | 19.0  | 7125  | 0.6185          | 0.7183   |
| 0.5569        | 20.0  | 7500  | 0.5506          | 0.745    |
| 0.5537        | 21.0  | 7875  | 0.5606          | 0.7617   |
| 0.5092        | 22.0  | 8250  | 0.5522          | 0.7483   |
| 0.61          | 23.0  | 8625  | 0.6297          | 0.7383   |
| 0.5833        | 24.0  | 9000  | 0.5399          | 0.7667   |
| 0.5315        | 25.0  | 9375  | 0.5551          | 0.7517   |
| 0.5313        | 26.0  | 9750  | 0.5176          | 0.7717   |
| 0.5327        | 27.0  | 10125 | 0.5547          | 0.775    |
| 0.4821        | 28.0  | 10500 | 0.5221          | 0.77     |
| 0.5852        | 29.0  | 10875 | 0.4983          | 0.7717   |
| 0.4777        | 30.0  | 11250 | 0.5766          | 0.75     |
| 0.4511        | 31.0  | 11625 | 0.5104          | 0.7733   |
| 0.5002        | 32.0  | 12000 | 0.5870          | 0.76     |
| 0.465         | 33.0  | 12375 | 0.4942          | 0.7917   |
| 0.4934        | 34.0  | 12750 | 0.5302          | 0.7783   |
| 0.4217        | 35.0  | 13125 | 0.5314          | 0.7883   |
| 0.3994        | 36.0  | 13500 | 0.5461          | 0.7917   |
| 0.3823        | 37.0  | 13875 | 0.5187          | 0.7933   |
| 0.3965        | 38.0  | 14250 | 0.5803          | 0.7917   |
| 0.3576        | 39.0  | 14625 | 0.5564          | 0.79     |
| 0.3853        | 40.0  | 15000 | 0.5425          | 0.8033   |
| 0.3694        | 41.0  | 15375 | 0.5885          | 0.7967   |
| 0.3496        | 42.0  | 15750 | 0.6131          | 0.7967   |
| 0.3293        | 43.0  | 16125 | 0.6330          | 0.8033   |
| 0.2565        | 44.0  | 16500 | 0.6562          | 0.795    |
| 0.3188        | 45.0  | 16875 | 0.7306          | 0.7933   |
| 0.2833        | 46.0  | 17250 | 0.8042          | 0.7917   |
| 0.2208        | 47.0  | 17625 | 0.7887          | 0.79     |
| 0.1436        | 48.0  | 18000 | 0.8206          | 0.7933   |
| 0.1521        | 49.0  | 18375 | 0.8762          | 0.8083   |
| 0.1603        | 50.0  | 18750 | 0.9266          | 0.7933   |


### 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