Efficient adaptive non-maximal suppression algorithms for homogeneous spatial keypoint distribution
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Updated
Jan 30, 2022 - C++
Efficient adaptive non-maximal suppression algorithms for homogeneous spatial keypoint distribution
Python library for YOLO small object detection and instance segmentation
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Each week I create sketches covering key Computer Vision concepts. If you want to learn more about CV stick around!
[CVPR 2021] Official PyTorch Code of GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection
Non-maximum suppression for object detection in a neural network
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PyTorch implementation of the YOLOv1 architecture presented in "You Only Look Once: Unified, Real-Time Object Detection" by Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi
Image SSD object detection in Java using Tensorrflow
This repository contains projects related to various aspects of image processing, from basic operations to advanced techniques like active contours. Examples and case studies focus on applications in medical imaging.
Wheat detection using Faster RCNN
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Detection algorithms and applications from famous papers; simple theory; solid code.
Code to reproduce the experiments described in "Do We Still Need Non-Maximum Suppression? Accurate Confidence Estimates and Implicit Duplication Modeling with IoU-Aware Calibration" (https://arxiv.org/pdf/2309.03110.pdf)
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Fast and simple implementation of Non Maximum Suppression algorithm in C++ with DLL for python usage.
This repo is based on https://github.com/jeetkanjani7/Parallel_NMS but add PyCUDA implementation
Parallel CUDA implementation of NON maximum Suppression. PyCUDA version is now moved to https://github.com/keineahnung2345/PyCUDA_NMS
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