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.

mask-rcnn tensorflow object-detection instance-segmentation keras

Detectron

•    Python

---

Example output of e2e_mask_rcnn-R-101-FPN_2x using Detectron pretrained weight. Corresponding example output from Detectron.

mask-rcnn pytorch detection pose-estimation segmentation detectron

mmdetection - OpenMMLab Detection Toolbox and Benchmark

•    Python

---

News: We released the technical report on ArXiv. MMDetection is an open source object detection toolbox based on PyTorch. It is a part of the OpenMMLab project.

pytorch fast-rcnn ssd faster-rcnn rpn object-detection instance-segmentation mask-rcnn retinanet cascade-rcnn

PaddleDetection - Object detection and instance segmentation toolkit based on PaddlePaddle.

•    Python

---

Object detection and instance segmentation toolkit based on PaddlePaddle.

ssd faster-rcnn face-detection object-detection multi-object-tracking instance-segmentation mask-rcnn retinanet faceboxes yolov3 cascade-rcnn fcos blazeface cornernet-lite efficientdet yolov4 libra-rcnn pp-yolo ttfnet fairmot

TACO - 🌮 Trash Annotations in Context Dataset Toolkit

•    Jupyter

---

🌮 Trash Annotations in Context Dataset Toolkit

deep-learning trash dataset object-detection garbage mask-rcnn litter

realtime-detectron - Real-time Detectron using webcam.

•    Python

---

This is a demo project of a real-time Mask R-CNN using Detectron. We will be using consumer grade webcam for capturing the video stream. Here's an example of demo created by reddit's user _sshin_.

detectron mask-rcnn real-time webcam demo object-detection object-segmentation deeplearning

open-solution-googleai-object-detection - Open solution to the Google AI Object Detection Challenge :maple_leaf:

•    Python

---

In this open source solution you will find references to the neptune.ml. It is free platform for community Users, which we use daily to keep track of our experiments. Please note that using neptune.ml is not necessary to proceed with this solution. You may run it as plain Python script 🐍. This competition is special, because it used Open Images Dataset V4, which is quite large: >1.8M images and >0.5TB 😲 To make it more approachable, we are hosting entire dataset in the neptune's public directory 😎. You can use this dataset in neptune.ml with no additional setup 👍.

data-science machine-learning deep-learning deep-neural-networks pipeline pipeline-framework reproducible-research reproducible-experiments reproducibility object-detection google-ai-challange challenge detection-network retina-net mask-rcnn

chainer-mask-rcnn - Chainer Implementation of Mask R-CNN

•    Python

---

Chainer Implementation of Mask R-CNN.

chainer instance-segmentation computer-vision deep-learning mask-rcnn object-detection iccv-2017 coco detectron

SALSA-Semantic-Assisted-SLAM - SALSA: Semantic Assisted Life-Long SLAM for Indoor Environments (16-833 SLAM Project at CMU)

•    C++

---

We propose a learning augmented lifelong SLAM method for indoor environments. Most of the existing SLAM methods assume a static environment and disregard dynamic objects. Another problem is that most feature and semantic based SLAM methods fail in repetitive environments. The unexpected changes of surroundings corrupts the quality of the tracking and leads to system failure. This project aims to use learning methods to classify landmarks and objects as dynamic and/or repeatable in nature to better handle optimization, achieve robust performance in a changing environment, and to re-localize in a lifelong-setting. We propose using semantic information and assigning scores to object feature points based on their probability to be dynamic and/or repeatable. We update the front-end, optimization cost functions, and BOW feature generation for loop closures from the original ORB-SLAM2 pipeline. Please see our paper for more details. Mask-RCNN was used to segment object instances and the scores were mapped according to the scale shown on the right. The left column shows score maps for the OpenLORIS-Scene [3] cafe1-2 sequence and the right column shows the score maps for TUM-RGBD walking_static sequence.

slam orb-slam2 salsa mask-rcnn loop-closure semantic-slam slam-methods indoor-environments openloris-scene lifelong-slam tum-rgbd

SAMaskRCNNKitti - Transfer Learning on a COCO pre-trained Mask-RCNN to detect cars and pedestrians of the KITTI Dataset

•    Jupyter

---

Transfer Learning on a COCO pre-trained Mask-RCNN to detect cars and pedestrians of the KITTI Dataset

machine-learning object-detection transfer-learning kitti mask-rcnn google-colab coco- instanc

mask_rcnn_ros - ROS Wrapped Mask RCNN

•    Python

---

A ROS Node for detecting objects using Mask RCNN. The detector is build on the Tensorflow and Keras ecosystem. Source to activate the virtualenv containing the installed deps.

deep-learning ros object-detection instance-segmentation mask-rcnn

Semantic-Segmentation-PyTorch - PyTorch implementation for Semantic Segmentation, include FCN, U-Net, SegNet, GCN, PSPNet, Deeplabv3, Deeplabv3+, Mask R-CNN, DUC, GoogleNet, and more dataset

•    Python

---

I'm going to implement The Image Segmentation Paper Top10 Net in PyTorch firstly.

computer-vision deep-learning image-processing pytorch image-classification fcn segnet semantic-segmentation mask-rcnn unet-pytorch pytorch-implementation charmve