psk1998
Mechanical Engineering | Autonomous Vehicles | Smart Connected Systems | Electric Vehicles | Battery Systems Engineering | PSU | OSU
The Pennsylvania State UniversityState College, PA
Pinned Repositories
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)
iampsk98.github.io
Personal Webpage
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,
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.
psk1998's Repositories
psk1998/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.
psk1998/carma-simulation
psk1998/EfficientPS
PyTorch code for training EfficientPS for Panoptic Segmentation https://rl.uni-freiburg.de/research/panoptic
psk1998/flow
Computational framework for reinforcement learning in traffic control
psk1998/GSCNN
Gated-Shape CNN for Semantic Segmentation (ICCV 2019)
psk1998/iampsk98.github.io
Personal Webpage
psk1998/image-segmentation-keras
Implementation of Segnet, FCN, UNet , PSPNet and other models in Keras.
psk1998/models
Models and examples built with TensorFlow
psk1998/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,
psk1998/RGBD_Semantic_Segmentation_PyTorch
PyTorch Implementation of some RGBD Semantic Segmentation models.
psk1998/RubiksCube
psk1998/skills-github-pages
My clone repository
psk1998/skills-introduction-to-github
My clone repository