Search Results for "proposal"
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Showing 4 open source projects for "proposal"
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Faster R-CNN
Object detection framework based on deep convolutional networks
This repository provides a MATLAB / Caffe re-implementation of the Faster R-CNN object detection framework (originally from Ren et al. 2015). The Faster R-CNN architecture combines a Region Proposal Network (RPN) with a Fast R-CNN style detection network to share convolutional feature maps and thus speed up detection. The repo includes code to train, test, and deploy Faster R-CNN models under the MATLAB / Caffe environment, example configuration files, and model checkpoints. Multiple configuration files for different datasets and architectures. ... -
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R-FCN
R-FCN: Object Detection via Region-based Fully Convolutional Networks
R-FCN (“Region-based Fully Convolutional Networks”) is an object detection framework that makes almost all computation fully convolutional and shared across the image, unlike prior region-based approaches (e.g. Faster R-CNN) which run per-region sub-networks. The repository provides an implementation (in Python) supporting end-to-end training and inference of R-FCN models on standard datasets. The authors propose position-sensitive score maps to reconcile the need for translation variance... -
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Edges
Structured Edge Detection Toolbox
Structured Edge Detection (Edges) is a MATLAB toolbox implementing the structured forests method for fast and accurate edge detection (up to ~60 fps in many settings). The toolbox also includes the Edge Boxes object proposal method, fast superpixel generation, and utilities for training, evaluation, and integration with vision pipelines. High performance (frames per second performance depending on settings). Integration with MATLAB and compatibility with external vision pipelines. Fast edge detection using structured forests (predict structured edge maps). -
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Rcnn
R-CNN: Regions with Convolutional Neural Network Features
This repository contains the original MATLAB implementation of R-CNN (Regions with Convolutional Neural Networks), a pioneering deep learning-based object detection framework. Developed by Ross Girshick, R-CNN combines region proposals with convolutional neural networks to detect objects in images. It was one of the first approaches to significantly improve performance on object detection benchmarks like PASCAL VOC. -
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