--- license: other base_model: nvidia/mit-b0 tags: - vision - image-segmentation - generated_from_trainer model-index: - name: segformer-b0-finetuned-segments-ic-chip-sample results: [] --- # segformer-b0-finetuned-segments-ic-chip-sample 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: 0.0772 - Mean Iou: 0.4863 - Mean Accuracy: 0.9725 - Overall Accuracy: 0.9725 - Accuracy Unlabeled: nan - Accuracy Circuit: 0.9725 - Iou Unlabeled: 0.0 - Iou Circuit: 0.9725 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:-------------:|:-----------:| | 0.0885 | 1.0 | 20 | 0.0858 | 0.4907 | 0.9815 | 0.9815 | nan | 0.9815 | 0.0 | 0.9815 | | 0.1764 | 2.0 | 40 | 0.0854 | 0.4734 | 0.9469 | 0.9469 | nan | 0.9469 | 0.0 | 0.9469 | | 0.0569 | 3.0 | 60 | 0.0854 | 0.4702 | 0.9404 | 0.9404 | nan | 0.9404 | 0.0 | 0.9404 | | 0.0959 | 4.0 | 80 | 0.0851 | 0.4893 | 0.9786 | 0.9786 | nan | 0.9786 | 0.0 | 0.9786 | | 0.2969 | 5.0 | 100 | 0.0825 | 0.4863 | 0.9727 | 0.9727 | nan | 0.9727 | 0.0 | 0.9727 | | 0.1979 | 6.0 | 120 | 0.0824 | 0.4873 | 0.9746 | 0.9746 | nan | 0.9746 | 0.0 | 0.9746 | | 0.0906 | 7.0 | 140 | 0.0824 | 0.4740 | 0.9480 | 0.9480 | nan | 0.9480 | 0.0 | 0.9480 | | 0.2879 | 8.0 | 160 | 0.0821 | 0.4882 | 0.9764 | 0.9764 | nan | 0.9764 | 0.0 | 0.9764 | | 0.1366 | 9.0 | 180 | 0.0807 | 0.4833 | 0.9666 | 0.9666 | nan | 0.9666 | 0.0 | 0.9666 | | 0.1664 | 10.0 | 200 | 0.0813 | 0.4860 | 0.9720 | 0.9720 | nan | 0.9720 | 0.0 | 0.9720 | | 0.1521 | 11.0 | 220 | 0.0831 | 0.4830 | 0.9660 | 0.9660 | nan | 0.9660 | 0.0 | 0.9660 | | 0.2004 | 12.0 | 240 | 0.0795 | 0.4825 | 0.9651 | 0.9651 | nan | 0.9651 | 0.0 | 0.9651 | | 0.1547 | 13.0 | 260 | 0.0793 | 0.4812 | 0.9625 | 0.9625 | nan | 0.9625 | 0.0 | 0.9625 | | 0.4191 | 14.0 | 280 | 0.0788 | 0.4830 | 0.9659 | 0.9659 | nan | 0.9659 | 0.0 | 0.9659 | | 0.0431 | 15.0 | 300 | 0.0782 | 0.4815 | 0.9630 | 0.9630 | nan | 0.9630 | 0.0 | 0.9630 | | 1.3911 | 16.0 | 320 | 0.0793 | 0.4820 | 0.9640 | 0.9640 | nan | 0.9640 | 0.0 | 0.9640 | | 0.0217 | 17.0 | 340 | 0.0814 | 0.4836 | 0.9671 | 0.9671 | nan | 0.9671 | 0.0 | 0.9671 | | 0.1116 | 18.0 | 360 | 0.0789 | 0.4839 | 0.9678 | 0.9678 | nan | 0.9678 | 0.0 | 0.9678 | | 0.3295 | 19.0 | 380 | 0.0791 | 0.4763 | 0.9526 | 0.9526 | nan | 0.9526 | 0.0 | 0.9526 | | 0.0327 | 20.0 | 400 | 0.0792 | 0.4829 | 0.9658 | 0.9658 | nan | 0.9658 | 0.0 | 0.9658 | | 0.2542 | 21.0 | 420 | 0.0787 | 0.4861 | 0.9722 | 0.9722 | nan | 0.9722 | 0.0 | 0.9722 | | 0.1587 | 22.0 | 440 | 0.0783 | 0.4772 | 0.9543 | 0.9543 | nan | 0.9543 | 0.0 | 0.9543 | | 0.2721 | 23.0 | 460 | 0.0804 | 0.4913 | 0.9825 | 0.9825 | nan | 0.9825 | 0.0 | 0.9825 | | 0.0505 | 24.0 | 480 | 0.0781 | 0.4827 | 0.9655 | 0.9655 | nan | 0.9655 | 0.0 | 0.9655 | | 0.1417 | 25.0 | 500 | 0.0801 | 0.4834 | 0.9669 | 0.9669 | nan | 0.9669 | 0.0 | 0.9669 | | 0.1371 | 26.0 | 520 | 0.0777 | 0.4838 | 0.9676 | 0.9676 | nan | 0.9676 | 0.0 | 0.9676 | | 0.1282 | 27.0 | 540 | 0.0773 | 0.4807 | 0.9613 | 0.9613 | nan | 0.9613 | 0.0 | 0.9613 | | 0.057 | 28.0 | 560 | 0.0772 | 0.4829 | 0.9657 | 0.9657 | nan | 0.9657 | 0.0 | 0.9657 | | 0.2592 | 29.0 | 580 | 0.0807 | 0.4872 | 0.9744 | 0.9744 | nan | 0.9744 | 0.0 | 0.9744 | | 0.1687 | 30.0 | 600 | 0.0794 | 0.4825 | 0.9649 | 0.9649 | nan | 0.9649 | 0.0 | 0.9649 | | 0.499 | 31.0 | 620 | 0.0805 | 0.4853 | 0.9706 | 0.9706 | nan | 0.9706 | 0.0 | 0.9706 | | 0.1584 | 32.0 | 640 | 0.0790 | 0.4845 | 0.9691 | 0.9691 | nan | 0.9691 | 0.0 | 0.9691 | | 0.0689 | 33.0 | 660 | 0.0785 | 0.4845 | 0.9690 | 0.9690 | nan | 0.9690 | 0.0 | 0.9690 | | 1.3764 | 34.0 | 680 | 0.0790 | 0.4848 | 0.9696 | 0.9696 | nan | 0.9696 | 0.0 | 0.9696 | | 0.2597 | 35.0 | 700 | 0.0808 | 0.4875 | 0.9751 | 0.9751 | nan | 0.9751 | 0.0 | 0.9751 | | 1.0757 | 36.0 | 720 | 0.0761 | 0.4841 | 0.9681 | 0.9681 | nan | 0.9681 | 0.0 | 0.9681 | | 0.6112 | 37.0 | 740 | 0.0779 | 0.4825 | 0.9650 | 0.9650 | nan | 0.9650 | 0.0 | 0.9650 | | 0.2899 | 38.0 | 760 | 0.0787 | 0.4796 | 0.9591 | 0.9591 | nan | 0.9591 | 0.0 | 0.9591 | | 0.3402 | 39.0 | 780 | 0.0777 | 0.4838 | 0.9676 | 0.9676 | nan | 0.9676 | 0.0 | 0.9676 | | 0.0183 | 40.0 | 800 | 0.0771 | 0.4829 | 0.9657 | 0.9657 | nan | 0.9657 | 0.0 | 0.9657 | | 0.1407 | 41.0 | 820 | 0.0774 | 0.4809 | 0.9617 | 0.9617 | nan | 0.9617 | 0.0 | 0.9617 | | 0.4045 | 42.0 | 840 | 0.0767 | 0.4819 | 0.9638 | 0.9638 | nan | 0.9638 | 0.0 | 0.9638 | | 0.2159 | 43.0 | 860 | 0.0780 | 0.4850 | 0.9699 | 0.9699 | nan | 0.9699 | 0.0 | 0.9699 | | 0.0541 | 44.0 | 880 | 0.0768 | 0.4812 | 0.9624 | 0.9624 | nan | 0.9624 | 0.0 | 0.9624 | | 0.0638 | 45.0 | 900 | 0.0774 | 0.4863 | 0.9726 | 0.9726 | nan | 0.9726 | 0.0 | 0.9726 | | 0.0409 | 46.0 | 920 | 0.0788 | 0.4875 | 0.9749 | 0.9749 | nan | 0.9749 | 0.0 | 0.9749 | | 0.1662 | 47.0 | 940 | 0.0774 | 0.4871 | 0.9743 | 0.9743 | nan | 0.9743 | 0.0 | 0.9743 | | 0.1636 | 48.0 | 960 | 0.0783 | 0.4860 | 0.9720 | 0.9720 | nan | 0.9720 | 0.0 | 0.9720 | | 0.033 | 49.0 | 980 | 0.0791 | 0.4882 | 0.9764 | 0.9764 | nan | 0.9764 | 0.0 | 0.9764 | | 0.171 | 50.0 | 1000 | 0.0772 | 0.4863 | 0.9725 | 0.9725 | nan | 0.9725 | 0.0 | 0.9725 | ### Framework versions - Transformers 4.36.2 - Pytorch 1.11.0+cu115 - Datasets 2.15.0 - Tokenizers 0.15.0