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

  •        192

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.

https://arxiv.org/abs/1711.10398
https://github.com/jessemelpolio/Faster_RCNN_for_DOTA

Tags
Implementation
License
Platform

   




Related Projects

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.

pytorch-faster-rcnn - 0.4 updated. Support cpu test and demo.

  •    Jupyter

The main differences between new and old master branch are in this two commits: 9d4c24e, c899ce7 The change is related to this issue; master now matches all the details in tf-faster-rcnn so that we can now convert pretrained tf model to pytorch model. A pytorch implementation of faster RCNN detection framework based on Xinlei Chen's tf-faster-rcnn. Xinlei Chen's repository is based on the python Caffe implementation of faster RCNN available here.

chainer-faster-rcnn - Object Detection with Faster R-CNN in Chainer

  •    Python

This is an experimental implementation of Faster R-CNN in Chainer based on Ross Girshick's work: py-faster-rcnn codes. Using anaconda is strongly recommended.

adversarial-frcnn - A-Fast-RCNN (CVPR 2017)

  •    Python

This is a Caffe based version of A-Fast-RCNN (arxiv_link). Although we originally implement it on torch, this Caffe re-implementation is much simpler, faster and easier to use. We release the code for training A-Fast-RCNN with Adversarial Spatial Dropout Network.

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.


RON - RON: Reverse Connection with Objectness Prior Networks for Object Detection, CVPR 2017

  •    Python

RON is a state-of-the-art visual object detection system for efficient object detection framework. The code is modified from py-faster-rcnn. You can use the code to train/evaluate a network for object detection task. For more details, please refer to our CVPR paper. Note: SSD300 and SSD500 are the original SSD model from SSD.

cascade-rcnn - Caffe implementation of multiple popular object detection frameworks

  •    C++

This repository is written by Zhaowei Cai at UC San Diego. This repository implements mulitple popular object detection algorithms, including Faster R-CNN, R-FCN, FPN, and our recently proposed Cascade R-CNN, on the MS-COCO and PASCAL VOC datasets. Multiple choices are available for backbone network, including AlexNet, VGG-Net and ResNet. It is written in C++ and powered by Caffe deep learning toolbox.

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.

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.

mx-maskrcnn - An MXNet implementation of Mask R-CNN

  •    Python

An MXNet implementation of Mask R-CNN. This repository is based largely on the mx-rcnn implementation of Faster RCNN available here.

soft-nms - Object Detection

  •    Jupyter

This repository includes the code for Soft-NMS. Soft-NMS is integrated with two object detectors, R-FCN and Faster-RCNN. The Soft-NMS paper can be found here. Soft-NMS is complementary to multi-scale testing and iterative bounding box regression. Check MSRA slides from the COCO 2017 challenge.

py-faster-rcnn - Faster R-CNN (Python implementation) -- see https://github

  •    Python

The official Faster R-CNN code (written in MATLAB) is available here. If your goal is to reproduce the results in our NIPS 2015 paper, please use the official code. This Python implementation contains contributions from Sean Bell (Cornell) written during an MSR internship.

Android-Object-Detection - :coffee: Fast-RCNN and Scene Recognition using Caffe

  •    Java

Get the Caffe model and push it to Phone SDCard. For object detection, network(*.prototxt) should use ROILayer, you can refer to Fast-RCNN. For scene recognition(object recognition), it can use any caffe network and weight with memory input layer. Scene recognition - Convolutional neural networks trained on Places Input a picture of a place or scene and predicts it.

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).

keras-rcnn - Keras package for region-based convolutional neural networks (RCNNs)

  •    Python

keras-rcnn is the Keras package for region-based convolutional neural networks. The data is made up of a list of dictionaries corresponding to images.

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.

SSH - SSH: Single Stage Headless Face Detector

  •    Python

This repository includes the code for training and evaluating the SSH face detector introduced in our ICCV 2017 paper. The code is adapted based on an intial fork from the py-faster-rcnn repository.

keras_frcnn - Keras Implementation of faster-rcnn

  •    Python

This work has been publiced on StrangeAI - An AI Algorithm Hub, You can found this work at Here (You may found more interesting work on this website, it's a very good resource to learn AI, StrangeAi authors maintainered all applications in AI). This code only support to keras 2.0.3, the newest version will cause some errors. If you can fix it, feel free to send me a PR.

Mask-RCNN - A PyTorch implementation of the architecture of Mask RCNN, serves as an introduction to working with PyTorch

  •    Python

A PyTorch implementation of the architecture of Mask RCNN, serves as an introduction to working with PyTorch

Mask_RCNN - Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

  •    Python

This is an implementation of Mask R-CNN on Python 3, Keras, and TensorFlow. The model generates bounding boxes and segmentation masks for each instance of an object in the image. It's based on Feature Pyramid Network (FPN) and a ResNet101 backbone. The code is documented and designed to be easy to extend. If you use it in your research, please consider citing this repository (bibtex below). If you work on 3D vision, you might find our recently released Matterport3D dataset useful as well. This dataset was created from 3D-reconstructed spaces captured by our customers who agreed to make them publicly available for academic use. You can see more examples here.