psk1998 Goto Github PK

carma-simulation

efficientps

PyTorch code for training EfficientPS for Panoptic Segmentation https://rl.uni-freiburg.de/research/panoptic

flow

Computational framework for reinforcement learning in traffic control

gscnn

Gated-Shape CNN for Semantic Segmentation (ICCV 2019)

image-segmentation-keras

Implementation of Segnet, FCN, UNet , PSPNet and other models in Keras.

models

Models and examples built with TensorFlow

nhtsa-sensitivity-project

Framework to compute the sensitivities of various semantic segmentation algorithms over different image datasets with respect to the changes in Image Properties,

rgbd_semantic_segmentation_pytorch

PyTorch Implementation of some RGBD Semantic Segmentation models.

rubikscube

sumo-traci_osm

This repository contains a Python Script and a SUMO configuration (.sumocfg) file. On running the Python Script, you are asked to input any location name (better keep it specific like - New York City, Carnegie Mellon University). This geographic location is converted into coordinates using geocoder library. Then using Selenium library, a map of the given location in .osm format is downloaded into your default downloads folder. This file is then moved into your working directory (for which you will have to change the variable 'destination' in the python script). After the map.osm file is moved to the working directory, the network from this map is extracted into .net.xml format. Using randomTrips.py, random routes are generated in the network. In the .sumocfg file, the network file, route file and output files are declared. Now, in the Python Script, TraCI is used to simulate the .sumocfg file and the output is stored in .out.xml format file.