Subway-Station-Hazard-Detection
Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning
Our great sponsors
Subway-Station-Hazard-Detection | Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning | |
---|---|---|
4 | 8 | |
11 | 57 | |
- | - | |
0.0 | 3.6 | |
about 3 years ago | about 3 years ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Subway-Station-Hazard-Detection
- Subway Station Hazard Detection - 3D simulation based recognition of hazards in subway stations with the help of a convolutional neural network
-
Subway Station Hazard Detection
Check it out on my GitHub: https://github.com/Psarpei/Subway-Station-Hazard-Detection
- Subway Station Hazard Detection - A Simulation based hazard Detection with convolutional neural networks for Computer Vision
Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning
- Real-time Object Detection for Autonomous Driving using Deep Learning, Performance comparison of YOLO and Faster R-CNN based on the BDD100K dataset
- [P] Real-time Object Detection for Autonomous Driving using Deep Learning, Performance comparison of YOLO and Faster R-CNN on the BDD100K dataset, Goethe University Frankfurt Germany (Fall 2020)
- Real-time Object Detection for Autonomous Driving using Deep Learning, Performance comparison of YOLO and Faster R-CNN on the BDD100K dataset, Goethe University Frankfurt Germany (Fall 2020)
- Real-time Object Detection for Autonomous Driving using Deep Learning, Goethe University Frankfurt Germany (Fall 2020)
What are some alternatives?
ml-course - Open Machine Learning course
get-started-with-JAX - The purpose of this repo is to make it easy to get started with JAX, Flax, and Haiku. It contains my "Machine Learning with JAX" series of tutorials (YouTube videos and Jupyter Notebooks) as well as the content I found useful while learning about the JAX ecosystem.
3D-Public-Transport-Simulator - The 3D Public Transport Simulator is a Unity-based simulation, which uses OpenStreetMap data in order to support the simulation of worldwide locations. The development was part of a Bachelor thesis.
yolo-tf2 - yolo(all versions) implementation in keras and tensorflow 2.x
pytorch-segmentation - :art: Semantic segmentation models, datasets and losses implemented in PyTorch.
HugsVision - HugsVision is a easy to use huggingface wrapper for state-of-the-art computer vision
simple-faster-rcnn-pytorch - A simplified implemention of Faster R-CNN that replicate performance from origin paper
lama - 🦙 LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022
NYU-DLSP20 - NYU Deep Learning Spring 2020
Mask-RCNN-Implementation - Mask RCNN Implementation on Custom Data(Labelme)
dl-colab-notebooks - Try out deep learning models online on Google Colab
fashionpedia-api - Python API for Fashionpedia Dataset