SHGNN: Structure-Aware Heterogeneous Graph Neural Network
The source code and dataset of the paper: SHGNN: Structure-Aware Heterogeneous Graph Neural Network. Requirements The framework is implemented using python3 with dependencies specified in requirements.txt. git clone https://github.com/Wentao-Xu/SHGNN.git cd SHGNN conda create -n shgnn python=3.8 conda activate shgnn pip install -r requirements.txt Dataset preparation source prepare_data.sh tar -zxvf data.tar.gz mkdir checkpoint Running the code
Read moreHost Your Django Project on Heroku
As a novice web developer, you’ve built your portfolio app and shared your code on GitHub. Perhaps, you’re hoping to attract technical recruiters to land your first programming job. Many coding bootcamp graduates are likely doing the same thing. To differentiate yourself from the crowd and boost your chances of getting noticed, you can start hosting your Django project online. For a hobby Django project, you’ll want a hosting service that’s free of charge, quick to set up, user-friendly, and […]
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A AntiChannelBan Telegram Group Bot Open Source
This is a Anti Channel Ban Robots delete and ban message sent by channels Deployment Method 𝚂𝚄𝙿𝙿𝙾𝚁𝚃 CREDIT BrayDen Blaze Shubhanshu Achu Biju Zaid VPS Deployment ⚒️
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Archive of the image generator stuff from my API
Archive of the image generator stuff from my API Q: Why?A: Because I am removing these components from the API Q: How do I run it?A: You use quart (Flask, but with async) and blueprints. I won’t copy-paste the entire API for people to instantly steal it, if that’s what you thought you could do. Q: Can I use it for my API?A: Yes, but give credits in some way GitHub View Github
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Life Dynamics for python
run command must be like this: /usr/bin/python3 /home/nmakagonov/Daphny/daphny_counter/Daphny_counter.py -o /home/nmakagonov/Daphny/out.txt –path /media/nmakagonov/DiskD/NN_DATA/daphniacounter/DataForTraining/DataForTraining/MediumPopulation/medium_normal_RawImage/ -st 300 -sp 500where -o :path to the output text file where the counting result will be saved –path path to the directory with daphny-images -st number of the first frame for analysis -sp number of the last frame for analysis GitHub View Github
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Deep Level Set for Box-supervised Instance Segmentation in Aerial Images
Wentong Li, Yijie Chen, Wenyu Liu, Jianke Zhu* Any questions or discussions are welcomed! Installation Please refer to INSTALL.md for environment installation. This code is based on MMdetection and AerialDetection. Getting Started Please see GETTING_STARTED.md for the dataset preparation and basic usage. Visualization Instance segmentation results only under the supervision of box annotations on iSAID dataset. To Do COCO dataset Medical images Text images Citation
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