YOLOPv2 - Python Similar Projects List
Panoptic PolarNet 92
Panoptic-PolarNet is a fast and robust LiDAR point cloud panoptic segmentation framework. We learn both semantic segmentation and class-agnostic instance clustering in a single inference network using a polar Bird's Eye View (BEV) representation. Predictions from the semantic and instance head are then fused through a majority voting to create the final panopticsegmentation.
Greenpack - Go 109
Cross-language serialization for Golang: greenpack adds versioning, stronger typing, and optional schema atop msgpack2. `greenpack -msgpack2` produces classic msgpack2, and handles nils. Cousin to ZebraPack (https://github.com/glycerine/zebrapack), greenpack's advantage is fully self-describing data. Oh, and faster than protobufs. .
greenpack news, April 2019
Version 5.0.9 includes native support for time.Duration serialization.
Version 5.0.4 includes two new features:
a) interfaces are supported, and automatically detected
Self Driving Car 3D Simulator With CNN - Python 147
Implementing a self driving car using a 3D Driving Simulator. CNN will be used for training.
Self Driving car after 50 epochs of training
Some point in our life as programmers we all wonder how a self driving car is actually programmed. I went th
Carma platform - C++ 293
CARMA Platform is built on robot operating system (ROS) and utilizes open source software (OSS) that enables Cooperative Driving Automation (CDA) features to allow Automated Driving Systems to interact and cooperate with infrastructure and other vehicles through communication. The newest inception of CARMA is now live on Github and open for collaborating. The CARMAPlatform is created on a robot operating system (ROS) and utilizes open source software (OSS) that enables cooperative automated driving plug-ins.
Weakly Supervised Panoptic Segmentation - MATLAB 156
Weakly- and Semi-Supervised Panoptic Segmentation (ECCV18).
Weakly- and Semi-Supervised Panoptic Segmentation
by Qizhu Li*, Anurag Arnab*, Philip H.S. Torr
This repository demonstrates the weakly supervised ground truth generation scheme presented in our paper Weakly- and Semi-Supervised