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# smids_3x_deit_base_rms_001_fold1

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: 0.6681
- Accuracy: 0.7730

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.858         | 1.0   | 226   | 2.2476          | 0.3289   |
| 0.8652        | 2.0   | 452   | 0.8983          | 0.5242   |
| 0.8184        | 3.0   | 678   | 0.9071          | 0.5008   |
| 0.8998        | 4.0   | 904   | 0.8629          | 0.5142   |
| 0.8039        | 5.0   | 1130  | 0.9089          | 0.4958   |
| 0.8516        | 6.0   | 1356  | 0.8861          | 0.5025   |
| 0.7034        | 7.0   | 1582  | 0.8149          | 0.6010   |
| 0.7133        | 8.0   | 1808  | 0.9764          | 0.5559   |
| 0.6649        | 9.0   | 2034  | 0.8042          | 0.6160   |
| 0.7571        | 10.0  | 2260  | 0.7526          | 0.6644   |
| 0.7133        | 11.0  | 2486  | 0.7559          | 0.6678   |
| 0.7109        | 12.0  | 2712  | 0.7911          | 0.6411   |
| 0.6513        | 13.0  | 2938  | 0.7985          | 0.6461   |
| 0.642         | 14.0  | 3164  | 0.6827          | 0.7028   |
| 0.6582        | 15.0  | 3390  | 0.7203          | 0.6728   |
| 0.6538        | 16.0  | 3616  | 0.7567          | 0.6678   |
| 0.5556        | 17.0  | 3842  | 0.7078          | 0.6694   |
| 0.6192        | 18.0  | 4068  | 0.6570          | 0.7129   |
| 0.6471        | 19.0  | 4294  | 0.7189          | 0.6995   |
| 0.5592        | 20.0  | 4520  | 0.7057          | 0.6995   |
| 0.6811        | 21.0  | 4746  | 0.6584          | 0.7262   |
| 0.6379        | 22.0  | 4972  | 0.6924          | 0.6912   |
| 0.641         | 23.0  | 5198  | 0.6895          | 0.7212   |
| 0.5889        | 24.0  | 5424  | 0.6980          | 0.6995   |
| 0.639         | 25.0  | 5650  | 0.6309          | 0.7279   |
| 0.6445        | 26.0  | 5876  | 0.6685          | 0.7379   |
| 0.524         | 27.0  | 6102  | 0.6179          | 0.7362   |
| 0.5828        | 28.0  | 6328  | 0.6999          | 0.6761   |
| 0.5112        | 29.0  | 6554  | 0.7255          | 0.6945   |
| 0.5736        | 30.0  | 6780  | 0.6697          | 0.7012   |
| 0.5437        | 31.0  | 7006  | 0.6621          | 0.7262   |
| 0.4721        | 32.0  | 7232  | 0.6063          | 0.7412   |
| 0.4483        | 33.0  | 7458  | 0.6550          | 0.7062   |
| 0.4826        | 34.0  | 7684  | 0.7265          | 0.6845   |
| 0.4436        | 35.0  | 7910  | 0.5926          | 0.7713   |
| 0.479         | 36.0  | 8136  | 0.5652          | 0.7579   |
| 0.4615        | 37.0  | 8362  | 0.5901          | 0.7563   |
| 0.4618        | 38.0  | 8588  | 0.6669          | 0.7346   |
| 0.4189        | 39.0  | 8814  | 0.6189          | 0.7646   |
| 0.486         | 40.0  | 9040  | 0.6150          | 0.7613   |
| 0.4426        | 41.0  | 9266  | 0.6125          | 0.7663   |
| 0.4773        | 42.0  | 9492  | 0.6744          | 0.7396   |
| 0.4281        | 43.0  | 9718  | 0.6291          | 0.7730   |
| 0.3986        | 44.0  | 9944  | 0.6315          | 0.7880   |
| 0.4375        | 45.0  | 10170 | 0.6494          | 0.7679   |
| 0.3887        | 46.0  | 10396 | 0.6596          | 0.7746   |
| 0.4097        | 47.0  | 10622 | 0.6522          | 0.7679   |
| 0.4037        | 48.0  | 10848 | 0.6698          | 0.7763   |
| 0.3545        | 49.0  | 11074 | 0.6604          | 0.7730   |
| 0.3857        | 50.0  | 11300 | 0.6681          | 0.7730   |


### 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