---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- precision
- recall
model-index:
- name: UNetOscillatoryNeuron
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# UNetOscillatoryNeuron

This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5575
- Dice: 0.6904
- Iou: 0.5364
- Precision: 0.9958
- Recall: 0.5374

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Dice   | Iou    | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:---------:|:------:|
| 0.5468        | 1.0   | 27   | 0.6598          | 0.4436 | 0.2873 | 0.2982    | 0.9041 |
| 0.324         | 2.0   | 54   | 0.5230          | 0.5408 | 0.3726 | 0.6035    | 0.5030 |
| 0.2251        | 3.0   | 81   | 0.6158          | 0.7171 | 0.5703 | 0.9217    | 0.6041 |
| 0.4844        | 4.0   | 108  | 0.6834          | 0.7842 | 0.6496 | 0.8024    | 0.7784 |
| 0.5407        | 5.0   | 135  | 0.5531          | 0.6705 | 0.5126 | 0.9877    | 0.5157 |
| 0.5445        | 6.0   | 162  | 0.6066          | 0.7390 | 0.5945 | 0.9493    | 0.6140 |
| 0.543         | 7.0   | 189  | 0.5387          | 0.6575 | 0.4977 | 0.9919    | 0.4999 |
| 0.5402        | 8.0   | 216  | 0.5975          | 0.7322 | 0.5865 | 0.9599    | 0.6016 |
| 0.5479        | 9.0   | 243  | 0.5710          | 0.7032 | 0.5515 | 0.9845    | 0.5563 |
| 0.5428        | 10.0  | 270  | 0.5723          | 0.7053 | 0.5527 | 0.9849    | 0.5570 |
| 0.5412        | 11.0  | 297  | 0.5991          | 0.7363 | 0.5911 | 0.9633    | 0.6044 |
| 0.5448        | 12.0  | 324  | 0.5792          | 0.7143 | 0.5638 | 0.9817    | 0.5694 |
| 0.5439        | 13.0  | 351  | 0.5896          | 0.7273 | 0.5801 | 0.9764    | 0.5877 |
| 0.5422        | 14.0  | 378  | 0.5863          | 0.7235 | 0.5758 | 0.9789    | 0.5827 |
| 0.5433        | 15.0  | 405  | 0.5615          | 0.6933 | 0.5389 | 0.9925    | 0.5408 |
| 0.5429        | 16.0  | 432  | 0.5913          | 0.7298 | 0.5835 | 0.9752    | 0.5917 |
| 0.5426        | 17.0  | 459  | 0.5643          | 0.6976 | 0.5446 | 0.9920    | 0.5469 |
| 0.541         | 18.0  | 486  | 0.5971          | 0.7359 | 0.5911 | 0.9689    | 0.6022 |
| 0.543         | 19.0  | 513  | 0.6000          | 0.7390 | 0.5947 | 0.9665    | 0.6064 |
| 0.5414        | 20.0  | 540  | 0.5751          | 0.7117 | 0.5614 | 0.9892    | 0.5646 |
| 0.5443        | 21.0  | 567  | 0.5708          | 0.7066 | 0.5558 | 0.9908    | 0.5585 |
| 0.5418        | 22.0  | 594  | 0.5826          | 0.7203 | 0.5714 | 0.9820    | 0.5770 |
| 0.5455        | 23.0  | 621  | 0.5835          | 0.7219 | 0.5738 | 0.9832    | 0.5790 |
| 0.5414        | 24.0  | 648  | 0.5673          | 0.7025 | 0.5508 | 0.9932    | 0.5528 |
| 0.5457        | 25.0  | 675  | 0.5911          | 0.7307 | 0.5844 | 0.9779    | 0.5917 |
| 0.5422        | 26.0  | 702  | 0.5705          | 0.7066 | 0.5554 | 0.9921    | 0.5576 |
| 0.5427        | 27.0  | 729  | 0.5716          | 0.7079 | 0.5566 | 0.9911    | 0.5591 |
| 0.5421        | 28.0  | 756  | 0.5687          | 0.7041 | 0.5528 | 0.9904    | 0.5557 |
| 0.5451        | 29.0  | 783  | 0.5626          | 0.6968 | 0.5438 | 0.9942    | 0.5453 |
| 0.5424        | 30.0  | 810  | 0.5704          | 0.7068 | 0.5554 | 0.9914    | 0.5578 |
| 0.5438        | 31.0  | 837  | 0.5693          | 0.7054 | 0.5541 | 0.9923    | 0.5563 |
| 0.5432        | 32.0  | 864  | 0.5668          | 0.7022 | 0.5497 | 0.9930    | 0.5516 |
| 0.5431        | 33.0  | 891  | 0.5708          | 0.7075 | 0.5561 | 0.9917    | 0.5584 |
| 0.5408        | 34.0  | 918  | 0.5758          | 0.7137 | 0.5640 | 0.9898    | 0.5671 |
| 0.5423        | 35.0  | 945  | 0.5779          | 0.7161 | 0.5664 | 0.9886    | 0.5698 |
| 0.5466        | 36.0  | 972  | 0.5599          | 0.6936 | 0.5399 | 0.9957    | 0.5410 |
| 0.5434        | 37.0  | 999  | 0.5766          | 0.7149 | 0.5651 | 0.9898    | 0.5681 |
| 0.5437        | 38.0  | 1026 | 0.5602          | 0.6940 | 0.5405 | 0.9951    | 0.5418 |
| 0.5432        | 39.0  | 1053 | 0.5690          | 0.7055 | 0.5539 | 0.9923    | 0.5560 |
| 0.5446        | 40.0  | 1080 | 0.5607          | 0.6944 | 0.5410 | 0.9948    | 0.5423 |
| 0.5437        | 41.0  | 1107 | 0.5650          | 0.7002 | 0.5475 | 0.9939    | 0.5491 |
| 0.545         | 42.0  | 1134 | 0.5626          | 0.6972 | 0.5443 | 0.9946    | 0.5457 |
| 0.5457        | 43.0  | 1161 | 0.5565          | 0.6892 | 0.5348 | 0.9961    | 0.5357 |
| 0.5444        | 44.0  | 1188 | 0.5600          | 0.6938 | 0.5403 | 0.9952    | 0.5415 |
| 0.5419        | 45.0  | 1215 | 0.5621          | 0.6966 | 0.5435 | 0.9948    | 0.5449 |
| 0.543         | 46.0  | 1242 | 0.5596          | 0.6932 | 0.5394 | 0.9950    | 0.5407 |
| 0.5436        | 47.0  | 1269 | 0.5587          | 0.6919 | 0.5380 | 0.9959    | 0.5390 |
| 0.5444        | 48.0  | 1296 | 0.5585          | 0.6917 | 0.5379 | 0.9957    | 0.5390 |
| 0.5441        | 49.0  | 1323 | 0.5602          | 0.6941 | 0.5406 | 0.9951    | 0.5419 |
| 0.5425        | 50.0  | 1350 | 0.5575          | 0.6904 | 0.5364 | 0.9958    | 0.5374 |


### Framework versions

- Transformers 4.51.1
- Pytorch 2.5.1+cu124
- Datasets 3.5.0
- Tokenizers 0.21.0