| --- |
| 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 |
| |
