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.

simpledet - A Simple and Versatile Framework for Object Detection and Instance Recognition

•    Python

Everything is configurable from the config file, all the changes should be out of source. One experiment is a directory in experiments folder with the same name as the config file.

labelme - Image Polygonal Annotation with Python (polygon, rectangle, line, point and image-level flag annotation)

•    Python

Labelme is a graphical image annotation tool inspired by http://labelme.csail.mit.edu. It is written in Python and uses Qt for its graphical interface. Fig 2. VOC dataset example of instance segmentation.

SoTA-Point-Cloud - 🔥Deep Learning for 3D Point Clouds (IEEE TPAMI, 2020)

🔥Deep Learning for 3D Point Clouds (IEEE TPAMI, 2020)

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.

yolact - A simple, fully convolutional model for real-time instance segmentation.

•    Python

YOLACT++'s resnet50 model runs at 33.5 fps on a Titan Xp and achieves 34.1 mAP on COCO's test-dev (check out our journal paper here).

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

•    Python

Object detection and instance segmentation toolkit based on PaddlePaddle.

tide - A General Toolbox for Identifying Object Detection Errors

•    Python

An easy-to-use, general toolbox to compute and evaluate the effect of object detection and instance segmentation on overall performance. This is the code for our paper: TIDE: A General Toolbox for Identifying Object Detection Errors (ArXiv) [ECCV2020 Spotlight]. The current version is v1.0.1 (changelog).

data-science-bowl-2018 - End-to-end one-class instance segmentation based on U-Net architecture for Data Science Bowl 2018 in Kaggle

•    Python

41th on Data Science Bowl 2018 in Kaggle. Implementations of U-Net based models and ensemble methods.

tfvos - Semi-Supervised Video Object Segmentation (VOS) with Tensorflow

•    Jupyter

The aim of this project is to implement and compare implementations of several video object segmentation (VOS) algorithms using Tensorflow. As part of the NIPS Paper Implementation Challenge, we chose MaskRNN: Instance Level Video Object Segmentation (NIPS 2017) [2017h] as our first implementation. See the MaskRNN Tensorflow Implementation Section for more info.

tfwss - Weakly Supervised Segmentation with Tensorflow

•    Jupyter

This repo contains a TensorFlow implementation of weakly supervised instance segmentation as described in Simple Does It: Weakly Supervised Instance and Semantic Segmentation, by Khoreva et al. (CVPR 2017). The idea behind weakly supervised segmentation is to train a model using cheap-to-generate label approximations (e.g., bounding boxes) as substitute/guiding labels for computer vision classification tasks that usually require very detailed labels. In semantic labelling, each image pixel is assigned to a specific class (e.g., boat, car, background, etc.). In instance segmentation, all the pixels belonging to the same object instance are given the same instance ID.

chainer-fcis - Chainer Implementation of Fully Convolutional Instance-aware Semantic Segmentation

•    Python

This is Chainer implementation of Fully Convolutional Instance-aware Semantic Segmentation. Original Mxnet repository is msracver/FCIS.

seg-by-interaction - Unsupervised instance segmentation via active robot interaction

•    Python

This is the implementation for the paper on Learning Instance Segmentation by Interaction. We present an approach for building an active agent that learns to segment its visual observations into individual objects by interacting with its environment in a completely self-supervised manner. The agent uses its current segmentation model to infer pixels that constitute objects and refines the segmentation model by interacting with these pixels. To be released soon. Email me in case you need early access.

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

•    Python

Chainer Implementation of Mask R-CNN.

ShapeProp - Learning Saliency Propagation for Semi-supervised Instance Segmentation

•    Python

Please follow the instruction below to install it and run the experiment demo.

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.

DiscoBox - The Official PyTorch Implementation of DiscoBox.

•    Python

This repository contains the official Pytorch implementation of training & evaluation code and pretrained models for DiscoBox. DiscoBox is a state of the art framework that can jointly predict high quality instance segmentation and semantic correspondence from box annotations. We use MMDetection v2.10.0 as the codebase.

RIO - Official PyTorch implementation of RIO

•    Python

Figure 1: Our proposed Resampling at image-level and obect-level (RIO). Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection. Nadine Chang, Zhiding Yu, Yu-Xiong Wang, Anima Anandkumar, Sanja Fidler, Jose M. Alvarez. ICML 2021.