Learn OpenCV : C++ and Python Examples
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Updated
Jul 24, 2021 - Jupyter Notebook
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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.
A list of resources in different fields of Computer Science (multiple languages)
[Siggraph 2017] BundleFusion: Real-time Globally Consistent 3D Reconstruction using Online Surface Re-integration
Contrastive unpaired image-to-image translation, faster and lighter training than cyclegan (ECCV 2020, in PyTorch)
AI for GNU Image Manipulation Program
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.
Monk is a low code Deep Learning tool and a unified wrapper for Computer Vision.
This is tensorflow implementation for cvpr2017 paper "Deeply Supervised Salient Object Detection with Short Connections"
CosmiQ Works Geospatial Machine Learning Analysis Toolkit
Bonnet and then some! Deep Learning Framework for various Image Recognition Tasks. Photogrammetry and Robotics Lab, University of Bonn
PyTorch (and PyTorch Lightning) implementation of Neural Style Transfer, Pix2Pix, CycleGAN, and Deep Dream!
AdaLAM is a fully handcrafted realtime outlier filter integrating several best practices into a single efficient and effective framework. It detects inliers by searching for significant local affine patterns in image correspondences.
This is a repository for an object detection inference API using the Tensorflow framework.
Here lies all the exercises I implement and share in my website
Plant Disease Detector Web Application
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.
Code for the IJCAI 2018 paper "R^3Net: Recurrent Residual Refinement Network for Saliency Detection"
Vision framework example for my article. https://medium.com/compileswift/swift-world-whats-new-in-ios-11-vision-456ba4156bad
capsule networks that achieves outstanding performance on the German traffic sign dataset
RoboMaster2019 Infantry Vision OpenSource Code of Shenzhen University
Sample projects for Computer Vision with Raspberry Pi and Movidius Neural Compute Stick
PyTorch Implementation of Deformable GAN https://arxiv.org/abs/1801.00055
On using resume feature of mxrcnn to start training my model again after some number of epochs, it produced really bad inferences. Whereas when I trained the whole model in one go, the results we satisfactory. I think there is some issue in the resume feature of mxrcnn pipeline.