Displaying 1 to 13 from 13 results

luminoth - Deep Learning toolkit for Computer Vision

  •    Python

Luminoth is an open source toolkit for computer vision. Currently, we support object detection, but we are aiming for much more. It is built in Python, using TensorFlow and Sonnet. Read the full documentation here.

AlphaPose - Multi-Person Pose Estimation System

  •    Jupyter

Alpha Pose is an accurate multi-person pose estimator, which is the first open-source system that achieves 70+ mAP (72.3 mAP) on COCO dataset and 80+ mAP (82.1 mAP) on MPII dataset. To match poses that correspond to the same person across frames, we also provide an efficient online pose tracker called Pose Flow. It is the first open-source online pose tracker that achieves both 60+ mAP (66.5 mAP) and 50+ MOTA (58.3 MOTA) on PoseTrack Challenge dataset. Note: Please read PoseFlow/README.md for details.

tf-faster-rcnn - Tensorflow Faster RCNN for Object Detection

  •    Python

For a good and more up-to-date implementation for faster/mask RCNN with multi-gpu support, please see the example in TensorPack here. A Tensorflow implementation of faster RCNN detection framework by Xinlei Chen (xinleic@cs.cmu.edu). This repository is based on the python Caffe implementation of faster RCNN available here.

faster-rcnn.pytorch - A faster pytorch implementation of faster r-cnn

  •    Python

It supports multi-image batch training. We revise all the layers, including dataloader, rpn, roi-pooling, etc., to support multiple images in each minibatch. It supports multiple GPUs training. We use a multiple GPU wrapper (nn.DataParallel here) to make it flexible to use one or more GPUs, as a merit of the above two features.

simple-faster-rcnn-pytorch - A simplified implemention of Faster R-CNN that replicate performance from origin paper

  •    Jupyter

VGG16 train on trainval and test on test split. Note: the training shows great randomness, you may need a bit of luck and more epoches of training to reach the highest mAP. However, it should be easy to surpass the lower bound.

tensornets - High level network definitions with pre-trained weights in TensorFlow

  •    Python

High level network definitions with pre-trained weights in TensorFlow (tested with >= 1.1.0). You can install TensorNets from PyPI (pip install tensornets) or directly from GitHub (pip install git+https://github.com/taehoonlee/tensornets.git).

PVANet-FACE - A face detection model based on PVANet

  •    Python

Training a face detection model using PVANet. This repository contains source files of face detection using the PVANet. It is developed based on the awesome pva-faster-rcnn repository.

Shadowless - A Fast and Open Source Autonomous Perception System.

  •    C++

The more fast your are, the more shadowless you got. Shadowless is a new generation auto-drive perception system that feel things only in vision(more features maybe add in). We building shadowless on the purpose of establish a fully intelligent and fast drive system.

py-R-FCN-multiGPU - Code for training py-faster-rcnn and py-R-FCN on multiple GPUs in caffe

  •    Jupyter

py-R-FCN now supports both joint training and alternative optimization. The official R-FCN code (written in MATLAB) is available here.

caffe-faster-rcnn - faster rcnn c++ version

  •    C++

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors. and step-by-step examples.

Faster_RCNN_for_DOTA - Code used for training Faster R-CNN on DOTA

  •    Python

This is the official repo of paper DOTA: A Large-scale Dataset for Object Detection in Aerial Images. This repo contains code for training Faster R-CNN on oriented bounding boxes and horizontal bounding boxes as reported in our paper. This code is mostly modified by Zhen Zhu and Jian Ding.

dockerface - An easy to use docker solution for deep learning face detection.


Dockerface is a deep learning replacement for dlib and OpenCV non-deep face detection. It deploys a trained Faster R-CNN network on Caffe through an easy to use docker image. Bring your videos and images, run dockerface and obtain videos and images with bounding boxes of face detections and an easy to use face detection annotation text file. The docker image is large for now because OpenCV has to be compiled and stored in the image to be able to use video and it takes up a lot of space.