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---
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
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