Awesome Object Detection
A curated list of papers, code, and resources for object detection algorithms in computer vision.
What is Awesome Object Detection?
Awesome Object Detection is a curated GitHub repository that aggregates academic papers, code implementations, and survey articles related to object detection in computer vision. It serves as a reference hub for tracking the evolution of detection algorithms—from classic models like R-CNN and YOLO to contemporary approaches—helping researchers and engineers stay updated with the field.
Computer vision researchers, machine learning engineers, and students who need a structured overview of object detection literature and accessible codebases for experimentation or benchmarking.
It saves significant time by compiling scattered resources into a single, well-organized list, offering direct links to papers, official repositories, and alternative implementations across multiple deep learning frameworks.
Overview
Awesome Object Detection based on handong1587 github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html
Use Cases
Best For
- Finding code implementations of specific object detection algorithms (e.g., YOLOv3, Faster R-CNN)
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