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# smids_5x_deit_tiny_rms_0001_fold4

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: 1.4357
- Accuracy: 0.8667

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3287        | 1.0   | 375   | 0.3863          | 0.85     |
| 0.2455        | 2.0   | 750   | 0.3649          | 0.8717   |
| 0.1213        | 3.0   | 1125  | 0.4642          | 0.8583   |
| 0.1727        | 4.0   | 1500  | 0.5805          | 0.8617   |
| 0.1128        | 5.0   | 1875  | 0.6371          | 0.8483   |
| 0.0689        | 6.0   | 2250  | 0.6331          | 0.8683   |
| 0.0983        | 7.0   | 2625  | 0.6829          | 0.865    |
| 0.1105        | 8.0   | 3000  | 0.6645          | 0.8617   |
| 0.0716        | 9.0   | 3375  | 0.9136          | 0.8583   |
| 0.0639        | 10.0  | 3750  | 0.7869          | 0.8867   |
| 0.0325        | 11.0  | 4125  | 0.8744          | 0.8733   |
| 0.0627        | 12.0  | 4500  | 0.9757          | 0.8567   |
| 0.0409        | 13.0  | 4875  | 0.9654          | 0.8633   |
| 0.0848        | 14.0  | 5250  | 0.8074          | 0.8667   |
| 0.0374        | 15.0  | 5625  | 0.9236          | 0.8667   |
| 0.037         | 16.0  | 6000  | 1.0898          | 0.8617   |
| 0.0497        | 17.0  | 6375  | 1.1236          | 0.8583   |
| 0.0095        | 18.0  | 6750  | 1.0183          | 0.87     |
| 0.0289        | 19.0  | 7125  | 1.0208          | 0.8783   |
| 0.0255        | 20.0  | 7500  | 1.1375          | 0.8667   |
| 0.0016        | 21.0  | 7875  | 1.1251          | 0.8617   |
| 0.0005        | 22.0  | 8250  | 1.0252          | 0.8717   |
| 0.015         | 23.0  | 8625  | 1.1223          | 0.865    |
| 0.0375        | 24.0  | 9000  | 1.0372          | 0.8733   |
| 0.0379        | 25.0  | 9375  | 0.9869          | 0.8667   |
| 0.0001        | 26.0  | 9750  | 1.0331          | 0.8733   |
| 0.0134        | 27.0  | 10125 | 0.9754          | 0.885    |
| 0.0           | 28.0  | 10500 | 1.0742          | 0.8583   |
| 0.0001        | 29.0  | 10875 | 1.0378          | 0.88     |
| 0.0           | 30.0  | 11250 | 1.1203          | 0.875    |
| 0.0077        | 31.0  | 11625 | 1.1471          | 0.8783   |
| 0.0003        | 32.0  | 12000 | 1.1437          | 0.8783   |
| 0.0           | 33.0  | 12375 | 1.1521          | 0.875    |
| 0.0003        | 34.0  | 12750 | 1.2362          | 0.865    |
| 0.0           | 35.0  | 13125 | 1.2535          | 0.8567   |
| 0.0           | 36.0  | 13500 | 1.2428          | 0.865    |
| 0.0002        | 37.0  | 13875 | 1.3504          | 0.8583   |
| 0.0191        | 38.0  | 14250 | 1.2705          | 0.87     |
| 0.0           | 39.0  | 14625 | 1.3466          | 0.8667   |
| 0.0           | 40.0  | 15000 | 1.3575          | 0.8617   |
| 0.0           | 41.0  | 15375 | 1.3681          | 0.8667   |
| 0.0           | 42.0  | 15750 | 1.3681          | 0.87     |
| 0.0           | 43.0  | 16125 | 1.3799          | 0.865    |
| 0.0           | 44.0  | 16500 | 1.3559          | 0.8667   |
| 0.0           | 45.0  | 16875 | 1.3770          | 0.865    |
| 0.0           | 46.0  | 17250 | 1.4044          | 0.8667   |
| 0.0           | 47.0  | 17625 | 1.4188          | 0.8683   |
| 0.0           | 48.0  | 18000 | 1.4286          | 0.8667   |
| 0.0           | 49.0  | 18375 | 1.4343          | 0.8667   |
| 0.0           | 50.0  | 18750 | 1.4357          | 0.8667   |


### 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