MSCG-Net for Semantic Segmentation

Introduce

This repository contains MSCG-Net models (MSCG-Net-50 and MSCG-Net-101) for semantic segmentation in Agriculture-Vision Challenge and Workshop (CVPR 2020), and the pipeline of training and testing models, implemented in PyTorch.

Please consider citing our work if you find the code helps you

Multi-view Self-Constructing Graph Convolutional Networks with Adaptive Class Weighting Loss for Semantic Segmentation

@inproceedings{liu2020CVPRW,
  title={Multi-view Self-Constructing Graph Convolutional Networks with Adaptive Class Weighting Loss for Semantic Segmentation},
  author={Qinghui Liu and Michael Kampffmeyer and Robert Jenssen and Arnt-Børre Salberg},
  booktitle={Proceedings of CVPRW 2020 on Agriculture-Vision},
  year={2020}
}

Self-Constructing Graph Convolutional Networks for Semantic Labeling

@inproceedings{liu2020scg,
  title={Self-Constructing Graph Convolutional Networks for Semantic Labeling},
  author={Qinghui Liu and Michael Kampffmeyer and Robert Jenssen and Arnt-Børre Salberg},
  booktitle={Proceedings of IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium},
  year={2020}
}

Environments

• python 3.5+
• pytorch >= 1.2.0
• opencv 3.4+
• tensorboardx 1.9
• scikit-learn
• numpy
• others (see requirements.txt)

Code structure

├── config		# config code
├── data		# dataset loader and pre-processing code
├── tools		# train and test code
├── lib			# model, loss, utils code
├── submission	        # output test results for submission
└── ckpt 		# output check point, trained weights, log files, etc

Dataset prepare

1. change DATASET_ROOT to your dataset path in ./data/AgricultureVision/pre_process.py

DATASET_ROOT = '/your/path/to/Agriculture-Vision'

2. keep the dataset sturcture as the same with the official structure shown as below

Agriculture-Vision
|-- train
|   |-- masks
|   |-- labels
|   |-- boundaries
|   |-- images
|   |   |-- nir
|   |   |-- rgb
|-- val
|   |-- masks
|   |-- labels
|   |-- boundaries
|   |-- images
|   |   |-- nir
|   |   |-- rgb
|-- test
|   |-- boundaries
|   |-- images
|   |   |-- nir
|   |   |-- rgb
|   |-- masks

Train with a single GPU

CUDA_VISIBLE_DEVICES=0 python ./tools/train_R50.py

Test with a single GPU

CUDA_VISIBLE_DEVICES=0 python ./tools/test_submission.py

Pre-trained weights download (save them to ./ckpt before run test)

ckpt1 ckpt2 ckpt3