wangshgeo's repositories
awesome-ml4spo
Awesome Machine Learning for Spatial Optimization Resources
BDCI-2020-TimeSeries-Prediction
Matrix Factorization for High-Dimensional and Sparse Time Series Prediction
city-roads
Visualization of all roads within any city
clusternet
Code release for NeurIPS 2019 paper "End to End Learning and Optimization on Graphs"
Detectron2-Train-a-Instance-Segmentation-Model
Learn how to train a custom instance segmentation model with Detectron2
DROO
Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks
geospatial_libs
Some issues for geospatial libs
Hybrid-learn2branch
Hybrid Models for Learning to Branch (NeurIPS 2020)
IBD-of-p-center-and-p-median
The code of Improved Benders decomposition algorithm for city emergency service facility location
kaggle-satellite-imagery-feature-detection
Satellite Imagery Feature Detection (68 out of 419)
landcover
Land Cover Mapping
Learning-to-Optimize-Arxiv
The repository archives papers regarding the combination of combinatorial optimization and machine learning and corresponding reading notes.
maps
maps
osm-population-predictor
Population prediction model based on extracted OSM features
paper-dgmm2021
Repository for the extra material of our paper submitted to DGMM 2021.
pgrouting4dev
pgrouting4dev, fixing issues and bugs
Plant-Disease-Detection-Web-application
Deep learning with convolutional neural networks (CNNs) has achieved great success in the classification of various plant diseases. However, a limited number of studies have elucidated the process of inference, leaving it as an untouchable black box. Revealing the CNN to extract the learned feature as an interpretable form not only ensures its reliability but also enables the validation of the model authenticity and the training dataset by human intervention. In this study, a variety of neuron-wise and layer-wise visualization methods were applied using a CNN, trained with a publicly available plant disease image dataset. We showed that neural networks can capture the colors and textures of lesions specific to respective diseases upon diagnosis, which resembles human decision-making. While several visualization methods were used as they are, others had to be optimized to target a specific layer that fully captures the features to generate consequential outputs. Moreover, by interpreting the generated attention maps, we identified several layers that were not contributing to inference and removed such layers inside the network, decreasing the number of parameters by 75% without affecting the classification accuracy. The results provide an impetus for the CNN black box users in the field of plant science to better understand the diagnosis process and lead to further efficient use of deep learning for plant disease diagnosis.
RotationDetection
This is a tensorflow-based rotation detection benchmark, also called UranusDet.
Web-Crawler
Web Crawler for baidu qunar and ctrip