MSCG-Net

MSCG-Net for Semantic Segmentation

Introduce

This repository contains code modified from MSCG-Net models (MSCG-Net-50 and MSCG-Net-101) for semantic segmentation in Agriculture-Vision Challenge and Workshop (CVPR 2021). Original readme file can be found here

Code structure

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

Environments

• python 3.7
• pytorch 1.7.1
• opencv
• tensorboardx
• albumentations
• pretrainedmodels
• others (see requirements.txt)

Pretrained model

https://drive.google.com/file/d/1oW503NxUfwANfKQZ8zT3gG_XDWSuwwsQ/view?usp=sharing

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 structure 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 all available GPUs

python train_R101.py

Test

python test_submission.py

Trained weights for R101 (save to ./ckpt/R101_baseline before run test_submission)

ckpt

Results Summary

mIoU: 0.464251