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# smids_5x_deit_base_sgd_0001_fold4

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4947
- Accuracy: 0.8217

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0807        | 1.0   | 375   | 1.0913          | 0.4067   |
| 1.0601        | 2.0   | 750   | 1.0697          | 0.46     |
| 1.0228        | 3.0   | 1125  | 1.0461          | 0.515    |
| 0.9878        | 4.0   | 1500  | 1.0193          | 0.5683   |
| 1.0067        | 5.0   | 1875  | 0.9894          | 0.6      |
| 0.9245        | 6.0   | 2250  | 0.9567          | 0.6167   |
| 0.9152        | 7.0   | 2625  | 0.9222          | 0.6367   |
| 0.8904        | 8.0   | 3000  | 0.8874          | 0.665    |
| 0.8675        | 9.0   | 3375  | 0.8548          | 0.69     |
| 0.8528        | 10.0  | 3750  | 0.8243          | 0.7217   |
| 0.7884        | 11.0  | 4125  | 0.7961          | 0.7333   |
| 0.7678        | 12.0  | 4500  | 0.7701          | 0.7483   |
| 0.7258        | 13.0  | 4875  | 0.7462          | 0.75     |
| 0.7401        | 14.0  | 5250  | 0.7242          | 0.7633   |
| 0.7231        | 15.0  | 5625  | 0.7037          | 0.7683   |
| 0.6595        | 16.0  | 6000  | 0.6848          | 0.7817   |
| 0.6613        | 17.0  | 6375  | 0.6674          | 0.785    |
| 0.6469        | 18.0  | 6750  | 0.6514          | 0.7867   |
| 0.6638        | 19.0  | 7125  | 0.6367          | 0.79     |
| 0.6264        | 20.0  | 7500  | 0.6233          | 0.7917   |
| 0.615         | 21.0  | 7875  | 0.6112          | 0.795    |
| 0.6208        | 22.0  | 8250  | 0.6000          | 0.795    |
| 0.573         | 23.0  | 8625  | 0.5898          | 0.805    |
| 0.5775        | 24.0  | 9000  | 0.5804          | 0.8033   |
| 0.6018        | 25.0  | 9375  | 0.5718          | 0.8033   |
| 0.5747        | 26.0  | 9750  | 0.5639          | 0.8033   |
| 0.5711        | 27.0  | 10125 | 0.5567          | 0.805    |
| 0.5703        | 28.0  | 10500 | 0.5501          | 0.81     |
| 0.5047        | 29.0  | 10875 | 0.5441          | 0.81     |
| 0.5419        | 30.0  | 11250 | 0.5386          | 0.81     |
| 0.5562        | 31.0  | 11625 | 0.5335          | 0.8167   |
| 0.4909        | 32.0  | 12000 | 0.5288          | 0.8183   |
| 0.5437        | 33.0  | 12375 | 0.5245          | 0.82     |
| 0.5223        | 34.0  | 12750 | 0.5206          | 0.8183   |
| 0.4818        | 35.0  | 13125 | 0.5170          | 0.8183   |
| 0.4831        | 36.0  | 13500 | 0.5138          | 0.8183   |
| 0.5242        | 37.0  | 13875 | 0.5109          | 0.82     |
| 0.4897        | 38.0  | 14250 | 0.5083          | 0.8217   |
| 0.5618        | 39.0  | 14625 | 0.5059          | 0.8217   |
| 0.5176        | 40.0  | 15000 | 0.5038          | 0.8217   |
| 0.4753        | 41.0  | 15375 | 0.5019          | 0.8217   |
| 0.464         | 42.0  | 15750 | 0.5003          | 0.8217   |
| 0.5062        | 43.0  | 16125 | 0.4989          | 0.8217   |
| 0.4853        | 44.0  | 16500 | 0.4977          | 0.8217   |
| 0.5132        | 45.0  | 16875 | 0.4967          | 0.8217   |
| 0.4927        | 46.0  | 17250 | 0.4960          | 0.8217   |
| 0.5364        | 47.0  | 17625 | 0.4954          | 0.8217   |
| 0.5219        | 48.0  | 18000 | 0.4950          | 0.8217   |
| 0.4998        | 49.0  | 18375 | 0.4948          | 0.8217   |
| 0.495         | 50.0  | 18750 | 0.4947          | 0.8217   |


### 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