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Type: Organization
Caffe fork that supports Fast R-CNN
faster rcnn c++ version. joint train; please checkout into dev branch (git checkout dev)
Forked from Wei Liu's repository for SSD and ParseNet. For further information, please respectively refer to the original paper and the repository in README.md
MS COCO API - http://mscoco.org/
Convolutional Neural Networks
http://web.engr.illinois.edu/~dhoiem/projects/detectionAnalysis/
Copy of original work at
FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.
DSOD: Learning Deeply Supervised Object Detectors from Scratch. In ICCV 2017.
Fast R-CNN
Faster R-CNN
Frustum PointNets for 3D Object Detection from RGB-D Data
Caffe implementation of Google MobileNet SSD detection network, with pretrained weights on VOC0712 and mAP=0.727.
Object detection via a multi-region & semantic segmentation-aware CNN model
BING Objectness proposal estimator linux/mac/windows version implementation, runs at 1000 FPS. More in http://mmcheng.net/bing/ and also http://www.robots.ox.ac.uk/~szheng/DepthProposals.html
OHEM support for Fast R-CNN
Faster R-CNN (Python implementation) -- see https://github.com/ShaoqingRen/faster_rcnn for the official MATLAB version
R-CNN: Regions with Convolutional Neural Network Features
RON: Reverse Connection with Objectness Prior Networks for Object Detection, CVPR 2017
Simple Python script to compute Selective Search proposals in Matlab.
A tensorflow implementation for SqueezeDet, a convolutional neural network for object detection.
Caffe implementation of Google VGG/MobileNet/ShuffleNet SSD detection network. Ref: (https://github.com/chuanqi305/MobileNet-SSD) (https://github.com/weiliu89/caffe) SSD QQ交流群:581437405
Object detection system using deformable part models (DPMs) and latent SVM (voc-release5). You may want to use the latest tarball on my website. The github code may include code changes that have not been tested as thoroughly and will not necessarily reproduce the results on the website.
http://host.robots.ox.ac.uk/pascal/VOC/index.html
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