Subway-Station-Hazard-Detection VS ml-mipt

Compare Subway-Station-Hazard-Detection vs ml-mipt and see what are their differences.

Subway-Station-Hazard-Detection

This project is part of the CS course 'Systems Engineering Meets Life Sciences II' at Goethe University Frankfurt. In this Computer Vision project, we developed a first prototype of a security system which uses the surveillance cameras at subway stations to recognize dangerous situations. The training data was artificially generated by a Unity-based simulation. (by Psarpei)

ml-mipt

Former repository of ML course. Redirect link included (by girafe-ai)
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Subway-Station-Hazard-Detection ml-mipt
4 18
11 8
- -
0.0 0.0
about 3 years ago over 1 year ago
Jupyter Notebook Jupyter Notebook
MIT License MIT License
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Subway-Station-Hazard-Detection

Posts with mentions or reviews of Subway-Station-Hazard-Detection. We have used some of these posts to build our list of alternatives and similar projects.

ml-mipt

Posts with mentions or reviews of ml-mipt. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing Subway-Station-Hazard-Detection and ml-mipt you can also consider the following projects:

ml-course - Open Machine Learning course

Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning - My Computer Vision project from my Computer Vision Course (Fall 2020) at Goethe University Frankfurt, Germany. Performance comparison between state-of-the-art Object Detection algorithms YOLO and Faster R-CNN based on the Berkeley DeepDrive (BDD100K) Dataset.

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.

MachineLearningWithPython - Get started with Machine Learning with Python - An introduction with Python programming examples

pytorch-segmentation - :art: Semantic segmentation models, datasets and losses implemented in PyTorch.

Deep-Learning-Computer-Vision - My assignment solutions for Stanford’s CS231n (CNNs for Visual Recognition) and Michigan’s EECS 498-007/598-005 (Deep Learning for Computer Vision), version 2020.

mlops-course - Learn how to design, develop, deploy and iterate on production-grade ML applications.

pytorch-implementations - A collection of paper implementations using the PyTorch framework

d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.

deepcourse - Learn the Deep Learning for Computer Vision in three steps: theory from base to SotA, code in PyTorch, and space-repetition with Anki