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---
license: other
base_model: nvidia/mit-b1
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b1-miic-tl
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. -->
# segformer-b1-miic-tl
This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on the yijisuk/ic-chip-sample dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1915
- Mean Iou: 0.4765
- Mean Accuracy: 0.9531
- Overall Accuracy: 0.9531
- Accuracy Unlabeled: nan
- Accuracy Circuit: 0.9531
- Iou Unlabeled: 0.0
- Iou Circuit: 0.9531
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:-------------:|:-----------:|
| 0.7961 | 1.0 | 20 | 0.5776 | 0.3160 | 0.6320 | 0.6320 | nan | 0.6320 | 0.0 | 0.6320 |
| 0.7261 | 2.0 | 40 | 0.4222 | 0.4655 | 0.9310 | 0.9310 | nan | 0.9310 | 0.0 | 0.9310 |
| 0.3132 | 3.0 | 60 | 0.2869 | 0.4478 | 0.8956 | 0.8956 | nan | 0.8956 | 0.0 | 0.8956 |
| 0.2224 | 4.0 | 80 | 0.2898 | 0.4817 | 0.9635 | 0.9635 | nan | 0.9635 | 0.0 | 0.9635 |
| 0.1641 | 5.0 | 100 | 0.2861 | 0.4733 | 0.9466 | 0.9466 | nan | 0.9466 | 0.0 | 0.9466 |
| 0.9802 | 6.0 | 120 | 0.3005 | 0.4790 | 0.9581 | 0.9581 | nan | 0.9581 | 0.0 | 0.9581 |
| 0.1633 | 7.0 | 140 | 0.2953 | 0.4397 | 0.8794 | 0.8794 | nan | 0.8794 | 0.0 | 0.8794 |
| 0.3674 | 8.0 | 160 | 0.2951 | 0.4809 | 0.9619 | 0.9619 | nan | 0.9619 | 0.0 | 0.9619 |
| 0.1632 | 9.0 | 180 | 0.3007 | 0.4740 | 0.9480 | 0.9480 | nan | 0.9480 | 0.0 | 0.9480 |
| 0.3719 | 10.0 | 200 | 0.2633 | 0.4687 | 0.9374 | 0.9374 | nan | 0.9374 | 0.0 | 0.9374 |
| 0.2061 | 11.0 | 220 | 0.2544 | 0.4575 | 0.9150 | 0.9150 | nan | 0.9150 | 0.0 | 0.9150 |
| 0.1756 | 12.0 | 240 | 0.2587 | 0.4856 | 0.9711 | 0.9711 | nan | 0.9711 | 0.0 | 0.9711 |
| 0.366 | 13.0 | 260 | 0.2458 | 0.4883 | 0.9765 | 0.9765 | nan | 0.9765 | 0.0 | 0.9765 |
| 0.2532 | 14.0 | 280 | 0.2742 | 0.4771 | 0.9543 | 0.9543 | nan | 0.9543 | 0.0 | 0.9543 |
| 0.144 | 15.0 | 300 | 0.2424 | 0.4612 | 0.9223 | 0.9223 | nan | 0.9223 | 0.0 | 0.9223 |
| 0.1314 | 16.0 | 320 | 0.2130 | 0.4745 | 0.9489 | 0.9489 | nan | 0.9489 | 0.0 | 0.9489 |
| 1.4391 | 17.0 | 340 | 0.2156 | 0.4813 | 0.9626 | 0.9626 | nan | 0.9626 | 0.0 | 0.9626 |
| 0.211 | 18.0 | 360 | 0.1995 | 0.4767 | 0.9533 | 0.9533 | nan | 0.9533 | 0.0 | 0.9533 |
| 0.0792 | 19.0 | 380 | 0.2052 | 0.4855 | 0.9710 | 0.9710 | nan | 0.9710 | 0.0 | 0.9710 |
| 1.1 | 20.0 | 400 | 0.1972 | 0.4712 | 0.9424 | 0.9424 | nan | 0.9424 | 0.0 | 0.9424 |
| 0.067 | 21.0 | 420 | 0.2015 | 0.4697 | 0.9394 | 0.9394 | nan | 0.9394 | 0.0 | 0.9394 |
| 0.1783 | 22.0 | 440 | 0.2100 | 0.4821 | 0.9642 | 0.9642 | nan | 0.9642 | 0.0 | 0.9642 |
| 0.1594 | 23.0 | 460 | 0.1989 | 0.4746 | 0.9491 | 0.9491 | nan | 0.9491 | 0.0 | 0.9491 |
| 0.2306 | 24.0 | 480 | 0.1957 | 0.4668 | 0.9337 | 0.9337 | nan | 0.9337 | 0.0 | 0.9337 |
| 0.9809 | 25.0 | 500 | 0.1971 | 0.4802 | 0.9603 | 0.9603 | nan | 0.9603 | 0.0 | 0.9603 |
| 0.1154 | 26.0 | 520 | 0.1957 | 0.4792 | 0.9585 | 0.9585 | nan | 0.9585 | 0.0 | 0.9585 |
| 0.2142 | 27.0 | 540 | 0.1945 | 0.4827 | 0.9655 | 0.9655 | nan | 0.9655 | 0.0 | 0.9655 |
| 0.177 | 28.0 | 560 | 0.1930 | 0.4725 | 0.9451 | 0.9451 | nan | 0.9451 | 0.0 | 0.9451 |
| 0.2003 | 29.0 | 580 | 0.1965 | 0.4827 | 0.9654 | 0.9654 | nan | 0.9654 | 0.0 | 0.9654 |
| 0.1977 | 30.0 | 600 | 0.1995 | 0.4861 | 0.9722 | 0.9722 | nan | 0.9722 | 0.0 | 0.9722 |
| 0.1671 | 31.0 | 620 | 0.1946 | 0.4760 | 0.9520 | 0.9520 | nan | 0.9520 | 0.0 | 0.9520 |
| 0.1449 | 32.0 | 640 | 0.1895 | 0.4642 | 0.9285 | 0.9285 | nan | 0.9285 | 0.0 | 0.9285 |
| 0.2587 | 33.0 | 660 | 0.1920 | 0.4810 | 0.9619 | 0.9619 | nan | 0.9619 | 0.0 | 0.9619 |
| 1.2053 | 34.0 | 680 | 0.1931 | 0.4790 | 0.9579 | 0.9579 | nan | 0.9579 | 0.0 | 0.9579 |
| 0.1107 | 35.0 | 700 | 0.1951 | 0.4824 | 0.9647 | 0.9647 | nan | 0.9647 | 0.0 | 0.9647 |
| 0.0821 | 36.0 | 720 | 0.1926 | 0.4788 | 0.9577 | 0.9577 | nan | 0.9577 | 0.0 | 0.9577 |
| 0.5034 | 37.0 | 740 | 0.1903 | 0.4656 | 0.9311 | 0.9311 | nan | 0.9311 | 0.0 | 0.9311 |
| 0.137 | 38.0 | 760 | 0.1892 | 0.4684 | 0.9368 | 0.9368 | nan | 0.9368 | 0.0 | 0.9368 |
| 0.2861 | 39.0 | 780 | 0.1911 | 0.4762 | 0.9524 | 0.9524 | nan | 0.9524 | 0.0 | 0.9524 |
| 0.965 | 40.0 | 800 | 0.1928 | 0.4716 | 0.9432 | 0.9432 | nan | 0.9432 | 0.0 | 0.9432 |
| 0.138 | 41.0 | 820 | 0.1926 | 0.4742 | 0.9483 | 0.9483 | nan | 0.9483 | 0.0 | 0.9483 |
| 0.0291 | 42.0 | 840 | 0.1888 | 0.4689 | 0.9378 | 0.9378 | nan | 0.9378 | 0.0 | 0.9378 |
| 0.0624 | 43.0 | 860 | 0.1895 | 0.4684 | 0.9369 | 0.9369 | nan | 0.9369 | 0.0 | 0.9369 |
| 0.0611 | 44.0 | 880 | 0.1915 | 0.4772 | 0.9545 | 0.9545 | nan | 0.9545 | 0.0 | 0.9545 |
| 0.0322 | 45.0 | 900 | 0.1893 | 0.4670 | 0.9340 | 0.9340 | nan | 0.9340 | 0.0 | 0.9340 |
| 0.0927 | 46.0 | 920 | 0.1901 | 0.4714 | 0.9428 | 0.9428 | nan | 0.9428 | 0.0 | 0.9428 |
| 0.1752 | 47.0 | 940 | 0.1897 | 0.4758 | 0.9516 | 0.9516 | nan | 0.9516 | 0.0 | 0.9516 |
| 0.1343 | 48.0 | 960 | 0.1906 | 0.4779 | 0.9559 | 0.9559 | nan | 0.9559 | 0.0 | 0.9559 |
| 0.0765 | 49.0 | 980 | 0.1903 | 0.4732 | 0.9464 | 0.9464 | nan | 0.9464 | 0.0 | 0.9464 |
| 0.048 | 50.0 | 1000 | 0.1915 | 0.4765 | 0.9531 | 0.9531 | nan | 0.9531 | 0.0 | 0.9531 |
### Framework versions
- Transformers 4.36.2
- Pytorch 1.11.0+cu115
- Datasets 2.15.0
- Tokenizers 0.15.0
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