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机器视觉
Computer vision is an interdisciplinary field that deals with how computers can be made to gain high-level understanding of digital images and videos.
Learn OpenCV : C++ and Python Examples
Go package for computer vision using OpenCV 4 and beyond. Includes support for DNN, CUDA, OpenCV Contrib, and OpenVINO.
👨 Code for "Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression"
A list of resources in different fields of Computer Science
📚 The list of vision-based SLAM / Visual Odometry open source, blogs, and papers
Contrastive unpaired image-to-image translation, faster and lighter training than cyclegan (ECCV 2020, in PyTorch)
[Siggraph 2017] BundleFusion: Real-time Globally Consistent 3D Reconstruction using Online Surface Re-integration
AI for GNU Image Manipulation Program
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.
This repository allows you to get started with a gui based training a State-of-the-art Deep Learning model with little to no configuration needed! NoCode training with TensorFlow has never been so easy.
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.
A one-stop repository for low-code easily-installable object detection pipelines.
Monk is a low code Deep Learning tool and a unified wrapper for Computer Vision.
Darknet/YOLO object detection framework
This is tensorflow implementation for cvpr2017 paper "Deeply Supervised Salient Object Detection with Short Connections"
CosmiQ Works Geospatial Machine Learning Analysis Toolkit
PyTorch (and PyTorch Lightning) implementation of Neural Style Transfer, Pix2Pix, CycleGAN, and Deep Dream!
