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rbgirshick avatar

rbgirshick/rcnn

0
View on GitHub↗
2,414 stars·975 forks·Matlab·BSD-2-Clause·13 views

Rcnn

R-CNN: Regions with Convolutional Neural Network Features

Features

  • Computer Vision Applications - Region-based object detection and semantic segmentation implementation.
  • Object Detection - Rich feature hierarchies for object detection.

Star history

Star history chart for rbgirshick/rcnnStar history chart for rbgirshick/rcnn

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Projects sharing features with Rcnn

These projects share indexed features with Rcnn. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • eriklindernoren/pytorch-yolov3eriklindernoren avatar

    eriklindernoren/PyTorch-YOLOv3

    7,439View on GitHub↗

    This project is a PyTorch implementation of the YOLOv3 object detection architecture. It functions as a real-time object detector and computer vision framework designed to identify and locate multiple objects within images using bounding boxes and class labels. The system allows for both the use of pretrained weights for immediate image analysis and the training of custom models using datasets with bounding box annotations. It provides a programmatic interface to integrate detection capabilities directly into other software applications. The framework includes tools for model evaluation to m

    Python
    View on GitHub↗7,439
  • alexeyab/darknetAlexeyAB avatar

    AlexeyAB/darknet

    22,159View on GitHub↗

    Darknet is a high-performance C-based inference engine and computer vision library designed for real-time object identification and localization. It serves as a neural network framework for training and deploying detection models using the YOLO architecture, providing a toolset for deep learning training and deployment. The project differentiates itself through a C and CUDA implementation that enables hardware acceleration for matrix multiplication and inference speed optimization. It provides a shared library interface for embedding detection capabilities into external applications and suppo

    C
    View on GitHub↗22,159
  • allenai/reclipallenai avatar

    allenai/reclip

    92View on GitHub↗

    This repository contains the code for the paper ReCLIP: A Strong Zero-shot Baseline for Referring Expression Comprehension (ACL 2022).

    Python
    View on GitHub↗92
  • akhtarvision/cal-detrA

    akhtarvision/cal-detr

    0View on GitHub↗
    View on GitHub↗0
Compare all 30 related projects→

Frequently asked questions

What does rbgirshick/rcnn do?

R-CNN: Regions with Convolutional Neural Network Features

What are the main features of rbgirshick/rcnn?

The main features of rbgirshick/rcnn are: Computer Vision Applications, Object Detection.

Which projects share features with rbgirshick/rcnn?

Projects with overlapping indexed features include: eriklindernoren/pytorch-yolov3 — This project is a PyTorch implementation of the YOLOv3 object detection architecture. It functions as a real-time… alexeyab/darknet — Darknet is a high-performance C-based inference engine and computer vision library designed for real-time object… allenai/reclip — This repository contains the code for the paper ReCLIP: A Strong Zero-shot Baseline for Referring Expression… aravindhm/deep-goggle — Source code for "Understanding Deep Image Representations by Inverting Them", CVPR 2015. atten4vis/groupdetr. akhtarvision/cal-detr.