Stars
A paper list of object detection using deep learning.
Simple yet powerful and really extendable application for managing a blog within your Django Web site.
业内为数不多致力于极致体验的超强全自研跨平台(windows/linux/android/iOS)流媒体内核,通过模块化自由组合,支持实时RTMP推流、RTSP推流、RTMP播放器、RTSP播放器、录像、多路流媒体转发、音视频导播、动态视频合成、音频混音、直播互动、内置轻量级RTSP服务等,比快更快,业界真正靠谱的超低延迟直播SDK(1秒内,低延迟模式下150~300ms)。
Bare bones introduction to machine learning from linear regression to convolutional neural networks using Theano.
xianyi / caffe-android-lib
Forked from sh1r0/caffe-android-libPorting caffe to android platform
CCSUZJJ / caffe-android-lib
Forked from xianyi/caffe-android-libPorting caffe to android platform
A ROS package implementing the OpenCV HOG pedestrian detector and HAAR face detector.
MatConvNet-based codebase to encode FV-CNN and CNN features from Flickr8k images.
AIHGF / caffe-android-demo
Forked from sh1r0/caffe-android-demoAn android caffe demo app exploiting caffe pre-trained ImageNet model for image classification
A simple code to visualize net for matconvnet.
Code for several state-of-the-art papers in object detection and semantic segmentation.
Simple matconvnet implementation of human detection using convolutional neural networks
Train your own data with MatConvNet
⏬ Utils to help download images by id, crop bounding box, label images, etc.
LabelImg is now part of the Label Studio community. The popular image annotation tool created by Tzutalin is no longer actively being developed, but you can check out Label Studio, the open source …
⛄ OpenCV RGBD-Odometry (Visual Odometry based RGB-D images)
Trained model files for dlib example programs.
R-FCN: Object Detection via Region-based Fully Convolutional Networks
Implementation based on the paper Li et al., “A Convolutional Neural Network Cascade for Face Detection, ” 2015 CVPR
ZhangSirM / bgslibrary
Forked from myfavouritekk/bgslibraryA Background Subtraction Library
The source code for our CVPR 2015 work "Deeply Learned Attributes for Crowded Scene Understanding" with a two-branch CNN model.
Matlab code for our CVPR 2014 work "Scene-Independent Group Profiling in Crowd".