irfa50

Awesome-Computer-Vision-Paper-List

This repository contains all the papers accepted in top conference of computer vision, with convenience to search related papers.

DiffPool

TensorFlow 2.0 implementation of MNIST classification using Graph Convolutional Network

Deep-Graph-Learning

A notebook containing implementations of different graph deep node embeddings along with benchmark graph neural network models in tensorflow. This has been taken from https://www.kaggle.com/abhilash1910/nlp-workshop-ml-india-deep-graph-learning to apply GNNs/node embeddings on NLP task.

IMAGE-PROCESSING-USING-CONVOLUTION

Contains the various IMAGE PROCESSING FILTERS which are of great research interest in the field of Computer Vision.

gnn-for-darknet

This repo contains the codes and the notebooks used for the paper "Exploring Temporal GNN Embeddings for Darknet Traffic Analysis".

gnn_cnn_ocean_temp_forecasting

Jupyter notebooks for ocean temperature anomaly forecasting with GNNs and CNNs and data preprocessing from NetCDF multidimensional data, last updated 07/2022

graph-neural-network

The repository is a collection of Jupyter notebooks showcasing various projects related to graph neural networks (GNNs). Each notebook provides a detailed explanation of the project and its implementation, making it easy for users to understand and replicate the results.

gnn_explore_era

This repo contains google collab notebooks on gnns

gnn-notebooks

some gnn notebooks with pytorch-geometric

gnn_node_classification_pyg

pytorch-geometric GNN tutorial for node classification with Jupyter notebook.

awesome-satellite-imagery-datasets

🛰️ List of satellite image training datasets with annotations for computer vision and deep learning

cloudless

Deep learning pipeline for orbital satellite data for detecting clouds

DeepOSM

Train a deep learning net with OpenStreetMap features and satellite imagery.

fast-labeling-workflow

Building large-scale datasets is a time-consuming endeavour, especially for tasks like image segmentation where the labels need to be very precise. This tutorial shows how you can speed up your labeling workflow for image segmentation with Segments.ai, using model training in the loop.

label-maker

Data Preparation for Satellite Machine Learning

PlotNeuralNet

Latex code for making neural networks diagrams

robosat

Semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads, water, clouds

sat-seg-thesis

Deep learning for semantic segmentation of satellite imagery

satellite-image-deep-learning

Resources for deep learning with satellite & aerial imagery

segmenter

[ICCV2021] Official PyTorch implementation of Segmenter: Transformer for Semantic Segmentation

semantic-segmentation

CNNs for pixel-by-pixel prediction on satellite imagery

simrdwn

Rapid satellite imagery object detection

ssai-cnn

Semantic Segmentation for Aerial / Satellite Images with Convolutional Neural Networks including an unofficial implementation of Volodymyr Mnih's methods

TextureBasedSegmentation_ImageClassification

utilities

Packages intended to assist in the preprocessing of SpaceNet satellite imagery data corpus to a format that is consumable by machine learning algorithms.

X-ray-Classification

Classify various radiology images into respective categories.Being done using various shape and texture features for feature extraction and SVM for classification.

yolt

You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery

gnn_material

Notebooks with some basic notes on GNNs

gnn_CiteSeer_classifier

This Jupyter notebook implements a GNN classifier on CiteSeer Dataset using PyTorch.

GNN_beginning

Here are some notebooks from when I started working on GNNs.