Computervision Collection

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“Learn OpenCV : C++ and Python Examples”

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🌊Water

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“Go package for computer vision using OpenCV 4 and beyond. Includes support for DNN, CUDA, OpenCV Contrib, and OpenVINO.”

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★★

“:man: Code for "Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression"”

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“Collection of resources spanning key areas of Computer Science”

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🔮Psychic

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“Contrastive unpaired image-to-image translation, faster and lighter training than cyclegan (ECCV 2020, in PyTorch)”

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★★

“:books: The list of vision-based SLAM / Visual Odometry open source, blogs, and papers”

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🔥Fire

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“[Siggraph 2017] BundleFusion: Real-time Globally Consistent 3D Reconstruction using Online Surface Re-integration”

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🔮Psychic

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“AI for GNU Image Manipulation Program”

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🔮Psychic

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“This is a Computer vision package that makes its easy to run Image processing and AI functions. At the core it uses OpenCV and Mediapipe libraries.”

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🔥Fire

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“Darknet/YOLO object detection framework”

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🔮Psychic

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“A set of tools to visualize and interact with sequences of 3D data.”

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🔮Psychic

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“This repository allows you to get started with training a state-of-the-art Deep Learning model with little to no configuration needed! You provide your labeled dataset or label your dataset using our BMW-LabelTool-Lite and you can start the training right away and monitor it in many different ways like TensorBoard or a custom REST API and GUI. NoCode training with YOLOv4 and YOLOV3 has never been so easy.”

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“A one-stop repository for low-code easily-installable object detection pipelines.”

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“Monk is a low code Deep Learning tool and a unified wrapper for Computer Vision.”

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“This repository contains all the deep learning projects done as tutorial”

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🔮Psychic

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“This is tensorflow implementation for cvpr2017 paper "Deeply Supervised Salient Object Detection with Short Connections"”

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“CosmiQ Works Geospatial Machine Learning Analysis Toolkit”

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🔮Psychic

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“PyTorch (and PyTorch Lightning) implementation of Neural Style Transfer, Pix2Pix, CycleGAN, and Deep Dream!”