/CVPR21Chal-Agrivision

This repo contains the code to reproduce our results in CVPR21 Challenge on Agriculture-Vision.

Primary LanguagePythonMIT LicenseMIT

CVPR 2021 Challenge on Agriculture Vision

This repo contains the code to reproduce our results in CVPR21 Challenge on Agriculture-Vision. We ranked 4th in the supervised track.

By Songyao Jiang, Bin Sun, and Yun Fu, from Smile Lab @ Northeastern University

Introduction

The first model is modified MSCG-Net, please see MSCG-Net/README.md to train and test the model. The second model is modified DeepLabv3, please see Deeplabv3_Ensemble/Readme.txt to train and test the model. The results of the above models are assembled together to improve the overall mIoU using the ensemble code in Deeplabv3_Ensemble. We used ensemble results as our final submission during the challenge

Pretrained models

Google Drive

Code structure

├── MSCGNet                # Model 1
├── Deeplabv3_Ensemble	   # Model 2 and ensemble
└── challenge_report       # Detailed report submitted

Results Summary

Model Backbone #Params mIoU
MSCG-Net ResNet-101 31M 0.464
DeepLabv3 ResNet-101 60M 0.494
Ensemble N/A 91M 0.507

Splits: 56,944/18,334/19,708 train/val/test

Resolution: 512 x 512

Modalities: 1. RGB, 2. NIR (Near-infrared)

Annotations:

0 - background, 1 - double_plant, 2 - drydown, 3 - endrow, 4 - nutrient_deficiency, 5 - planter_skip, 6 - water, 7 - waterway, 8 - weed_cluster


This model is modified from MSCG-Net models (MSCG-Net-50 and MSCG-Net-101) for semantic segmentation in Agriculture-Vision Challenge and Workshop (CVPR 2021).

Pretrained model

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

This folder contains code modified from Deeplabv3 for the CVPR 2021 Challenge on Agriculture Vision. This folder also contains ensemble code to obtain our final results.

Pretrained models

https://drive.google.com/drive/folders/1VnPKVErUHEjbCe5ailsSvhXCjZWnw0qH?usp=sharing

Reference

https://github.com/samleoqh/MSCG-Net

https://github.com/LAOS-Y/AgriVision

https://github.com/HRNet/HRNet-Semantic-Segmentation