CBL-Conditional-Boundary-Loss

The official implementation of the accepted IEEE-TIP paper Conditional Boundary Loss for Semantic Segmentation.
We provide the code of our CBL based on our_maskformer and MMsegmentation. The whole CBL MMsegmentation code base will be uploaded soon. For now, your can use our CBL in MMsegmentation following the instructions down below.

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Our Results

More results of other models like Swin-B and PSP, together with the trained model weights file, will be updated soon once we finished the orgnization of our project.
Temporary result table:

Cityscapes

model	Backbone	iter	Crop Size	mIoU MMseg (single scale)	mIoU +CBL Ours (single scale)
OCRNet	HRNetW48	80K	512x1024	80.70	81.95

ADE20K

model	Backbone	mIoU(SS)	mIoU(MS)
MaskFormer	Swin-B	--	53.83(official)
MaskFormer +CBL	Swin-B	53.49	54.89 Trained Model Code:CBL0
Mask2Former	Swin-B	--	55.07(official)
Mask2Former +CBL	Swin-B	54.79	56.05 Trained Model Code:CBL0

How to train MaskFormer +CBL model

We build our implementation based on the official code base of MaskFormer. Please refer to our MaskFormer code base at our_maskformer This implementation enables easy reproduction of our CBL on MaskFormer, which do not need the above complicated steps for mmsegmentation. The trained MaskFormer+CBL model can also be found at MaskFormer+CBL Trained Model Code:CBL0 The trained Mask2Foremer+CBL model is also provided at Mask2Former+CBL Trained Model Code:CBL0

How to use our code in MMsegmentation

We follow the implementation of MMsegmentation. Here we provide the code of CBL based on the OCRHead in CBLocr_head.py.
The class name of the OCRHead with our CBL is New_ER5OCRHead.

1. Download our code and add the CBLocr_head.py to Path mmseg/models/decode_head/ocr_head.py in your MMsegmentation source code project.
2. Import the ER5OCRHead class in the mmseg/models/decode_head/init.py:
   change the line from .ocr_head import OCRHead as from .ocr_head import OCRHead, New_ER5OCRHead
3. Add the code for generating Ground Truth boundary for training:
4. Download the biou.py and add it to mmseg/core/evaluation/biou.py
5. Import the functions in biou.py:
   add the following line to the mmseg/core/evaluation/init.py
   from .biou import multi_class_gt_to_boundary
   then add 'multi_class_gt_to_boundary'to the list of __all__ = [xxx]
6. Download the boundary.py and add it to mmseg/datasets/pipelines/boundary.py
7. Import the GenerateBoundary class in the mmseg/datasets/pipelines/init.py:
   add the following line to the mmseg/datasets/pipelines/init.py
   from .boundary import GenerateBoundary
   then add 'GenerateBoundary' to the list of __all__ = [xxx]
8. Using our config.py to train a OCRNet.
   For example, to train a OCRNet-HRNetW48 on cityscapes, please run the following code:
   sh tools/dist_train.sh YOUR_PATH_TO_THE_CONFIG/erocrnet_hr48_512x1024_80k_cityscapes_fp16.py 8

TO-DO List(after accepted)

1.Upload the whole CBL project based on MMsegmentation (including CBL trained models with PSPNet, DeeplabV3+, Swin-B)

2.Upload the whole CBL project based on MaskFormer (including CBL trained models with MaskFormer) (Done)

3.Write a new instruction about how to run our CBL on the above-mentioned projects.