--- license: other base_model: nvidia/mit-b0 tags: - vision - image-segmentation - generated_from_trainer model-index: - name: segformer-b0-miic-tl results: [] --- # segformer-b0-miic-tl This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the yijisuk/ic-chip-sample dataset. It achieves the following results on the evaluation set: - Loss: 0.4495 - Mean Iou: 0.4202 - Mean Accuracy: 0.8404 - Overall Accuracy: 0.8404 - Accuracy Unlabeled: nan - Accuracy Circuit: 0.8404 - Iou Unlabeled: 0.0 - Iou Circuit: 0.8404 ## 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: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Circuit | Iou Unlabeled | Iou Circuit | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:-------------:|:-----------:| | 0.465 | 1.0 | 20 | 0.6113 | 0.3296 | 0.6593 | 0.6593 | nan | 0.6593 | 0.0 | 0.6593 | | 0.4071 | 2.0 | 40 | 0.4866 | 0.3345 | 0.6689 | 0.6689 | nan | 0.6689 | 0.0 | 0.6689 | | 0.4429 | 3.0 | 60 | 0.3963 | 0.3623 | 0.7245 | 0.7245 | nan | 0.7245 | 0.0 | 0.7245 | | 0.2408 | 4.0 | 80 | 0.3606 | 0.4257 | 0.8515 | 0.8515 | nan | 0.8515 | 0.0 | 0.8515 | | 0.2002 | 5.0 | 100 | 0.3594 | 0.4345 | 0.8690 | 0.8690 | nan | 0.8690 | 0.0 | 0.8690 | | 0.1885 | 6.0 | 120 | 0.3702 | 0.4291 | 0.8583 | 0.8583 | nan | 0.8583 | 0.0 | 0.8583 | | 0.4626 | 7.0 | 140 | 0.3858 | 0.4128 | 0.8256 | 0.8256 | nan | 0.8256 | 0.0 | 0.8256 | | 0.0865 | 8.0 | 160 | 0.3578 | 0.4456 | 0.8912 | 0.8912 | nan | 0.8912 | 0.0 | 0.8912 | | 0.0752 | 9.0 | 180 | 0.3595 | 0.4387 | 0.8774 | 0.8774 | nan | 0.8774 | 0.0 | 0.8774 | | 0.2567 | 10.0 | 200 | 0.4103 | 0.3981 | 0.7961 | 0.7961 | nan | 0.7961 | 0.0 | 0.7961 | | 0.1419 | 11.0 | 220 | 0.4053 | 0.4229 | 0.8458 | 0.8458 | nan | 0.8458 | 0.0 | 0.8458 | | 0.0623 | 12.0 | 240 | 0.3798 | 0.4415 | 0.8830 | 0.8830 | nan | 0.8830 | 0.0 | 0.8830 | | 0.3336 | 13.0 | 260 | 0.3855 | 0.4374 | 0.8748 | 0.8748 | nan | 0.8748 | 0.0 | 0.8748 | | 0.1283 | 14.0 | 280 | 0.3931 | 0.4368 | 0.8736 | 0.8736 | nan | 0.8736 | 0.0 | 0.8736 | | 0.5155 | 15.0 | 300 | 0.4108 | 0.4268 | 0.8535 | 0.8535 | nan | 0.8535 | 0.0 | 0.8535 | | 1.2662 | 16.0 | 320 | 0.4062 | 0.4328 | 0.8656 | 0.8656 | nan | 0.8656 | 0.0 | 0.8656 | | 0.2631 | 17.0 | 340 | 0.3825 | 0.4464 | 0.8929 | 0.8929 | nan | 0.8929 | 0.0 | 0.8929 | | 0.1751 | 18.0 | 360 | 0.3981 | 0.4335 | 0.8669 | 0.8669 | nan | 0.8669 | 0.0 | 0.8669 | | 0.243 | 19.0 | 380 | 0.3963 | 0.4436 | 0.8872 | 0.8872 | nan | 0.8872 | 0.0 | 0.8872 | | 0.1779 | 20.0 | 400 | 0.4413 | 0.4060 | 0.8119 | 0.8119 | nan | 0.8119 | 0.0 | 0.8119 | | 0.0682 | 21.0 | 420 | 0.4106 | 0.4363 | 0.8725 | 0.8725 | nan | 0.8725 | 0.0 | 0.8725 | | 0.2943 | 22.0 | 440 | 0.4052 | 0.4386 | 0.8771 | 0.8771 | nan | 0.8771 | 0.0 | 0.8771 | | 0.118 | 23.0 | 460 | 0.4260 | 0.4197 | 0.8394 | 0.8394 | nan | 0.8394 | 0.0 | 0.8394 | | 0.0865 | 24.0 | 480 | 0.4023 | 0.4270 | 0.8540 | 0.8540 | nan | 0.8540 | 0.0 | 0.8540 | | 0.1693 | 25.0 | 500 | 0.4276 | 0.4199 | 0.8399 | 0.8399 | nan | 0.8399 | 0.0 | 0.8399 | | 0.1778 | 26.0 | 520 | 0.4044 | 0.4409 | 0.8818 | 0.8818 | nan | 0.8818 | 0.0 | 0.8818 | | 0.3617 | 27.0 | 540 | 0.4405 | 0.4121 | 0.8242 | 0.8242 | nan | 0.8242 | 0.0 | 0.8242 | | 0.1688 | 28.0 | 560 | 0.4333 | 0.4234 | 0.8467 | 0.8467 | nan | 0.8467 | 0.0 | 0.8467 | | 0.282 | 29.0 | 580 | 0.4060 | 0.4365 | 0.8730 | 0.8730 | nan | 0.8730 | 0.0 | 0.8730 | | 0.0992 | 30.0 | 600 | 0.4297 | 0.4196 | 0.8393 | 0.8393 | nan | 0.8393 | 0.0 | 0.8393 | | 1.379 | 31.0 | 620 | 0.4389 | 0.4193 | 0.8386 | 0.8386 | nan | 0.8386 | 0.0 | 0.8386 | | 0.1355 | 32.0 | 640 | 0.4438 | 0.4205 | 0.8410 | 0.8410 | nan | 0.8410 | 0.0 | 0.8410 | | 0.1067 | 33.0 | 660 | 0.4271 | 0.4299 | 0.8598 | 0.8598 | nan | 0.8598 | 0.0 | 0.8598 | | 1.0659 | 34.0 | 680 | 0.4490 | 0.4063 | 0.8125 | 0.8125 | nan | 0.8125 | 0.0 | 0.8125 | | 0.1481 | 35.0 | 700 | 0.4317 | 0.4279 | 0.8557 | 0.8557 | nan | 0.8557 | 0.0 | 0.8557 | | 1.385 | 36.0 | 720 | 0.4215 | 0.4322 | 0.8644 | 0.8644 | nan | 0.8644 | 0.0 | 0.8644 | | 0.3081 | 37.0 | 740 | 0.4564 | 0.4089 | 0.8178 | 0.8178 | nan | 0.8178 | 0.0 | 0.8178 | | 0.1989 | 38.0 | 760 | 0.4345 | 0.4241 | 0.8482 | 0.8482 | nan | 0.8482 | 0.0 | 0.8482 | | 0.1752 | 39.0 | 780 | 0.4230 | 0.4302 | 0.8605 | 0.8605 | nan | 0.8605 | 0.0 | 0.8605 | | 0.1489 | 40.0 | 800 | 0.4253 | 0.4231 | 0.8462 | 0.8462 | nan | 0.8462 | 0.0 | 0.8462 | | 0.1769 | 41.0 | 820 | 0.4184 | 0.4275 | 0.8549 | 0.8549 | nan | 0.8549 | 0.0 | 0.8549 | | 0.1927 | 42.0 | 840 | 0.4162 | 0.4314 | 0.8629 | 0.8629 | nan | 0.8629 | 0.0 | 0.8629 | | 0.2442 | 43.0 | 860 | 0.4321 | 0.4234 | 0.8468 | 0.8468 | nan | 0.8468 | 0.0 | 0.8468 | | 0.2513 | 44.0 | 880 | 0.4280 | 0.4258 | 0.8515 | 0.8515 | nan | 0.8515 | 0.0 | 0.8515 | | 0.7221 | 45.0 | 900 | 0.4449 | 0.4190 | 0.8380 | 0.8380 | nan | 0.8380 | 0.0 | 0.8380 | | 0.0675 | 46.0 | 920 | 0.4369 | 0.4210 | 0.8419 | 0.8419 | nan | 0.8419 | 0.0 | 0.8419 | | 0.1256 | 47.0 | 940 | 0.4159 | 0.4313 | 0.8625 | 0.8625 | nan | 0.8625 | 0.0 | 0.8625 | | 0.1251 | 48.0 | 960 | 0.4312 | 0.4249 | 0.8498 | 0.8498 | nan | 0.8498 | 0.0 | 0.8498 | | 0.2183 | 49.0 | 980 | 0.4340 | 0.4262 | 0.8524 | 0.8524 | nan | 0.8524 | 0.0 | 0.8524 | | 0.2148 | 50.0 | 1000 | 0.4495 | 0.4202 | 0.8404 | 0.8404 | nan | 0.8404 | 0.0 | 0.8404 | ### Framework versions - Transformers 4.36.2 - Pytorch 1.11.0+cu115 - Datasets 2.15.0 - Tokenizers 0.15.0