AutonomousRacing_Literature
com.unity.perception
| AutonomousRacing_Literature | com.unity.perception | |
|---|---|---|
| 3 | 8 | |
| 88 | 875 | |
| - | 1.5% | |
| 2.5 | 0.0 | |
| 11 months ago | 12 months ago | |
| TeX | C# | |
| MIT License | GNU General Public License v3.0 or later |
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AutonomousRacing_Literature
com.unity.perception
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Synthetic image Generation
Unity
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dataset collection for transfer learning
If you are interested, there are open source solutions on top of Unity, Blender, Unreal. You can generate yourself the data you described easier than it looks (the amount of options and settings can be intimidating with these tools).
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Help with Yolov4 Training and Synthetic Data
I see that you are already selected Unity for your AR app development, so you can go ahead and generate your data in Unity as well. They have an open source package to do that. Repo has tutorials to get you started and perception team is responsive to issues and questions.
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Unity provides People Generator & Home Interior Generator for Computer Vision tasks!
Check the Unity Perception Package here.
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How to generate images and labels using unity (or other game engine) to train YOLO5 object detection model? (Synthetic Data generation using unity for neural network learning). Are there any existing solutions? Or something similar
Hey, you can use Unity’s official Perception package. They have tutorials in the repo, but if you need more there are couple of tutorials on YT. If you don’t mind, what kind of images are you going to generate? I am developing a synthetic data generation tool for Unity, may I ask you couple of questions?
- Are there any tools to generate images and labels from 3d models/games?
- [D] Use of (machine learning + Game engines) for automatic 2D/3D content creation
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How to get started with synthetic data generation?
Using Unity: https://github.com/Unity-Technologies/com.unity.perception
What are some alternatives?
BlenderProc - A procedural Blender pipeline for photorealistic training image generation
Towards-Explainable-AI-System-for-Traffic-Sign-Recognition-and-Deployment-in-a-Simulated-Environment - This project is part of the CS course 'Systems Engineering Meets Life Sciences I' at Goethe University Frankfurt. In this Computer Vision project, we present our first attempt at tackling the problem of traffic sign recognition using a systems engineering approach.
PeopleSansPeople - Unity's privacy-preserving human-centric synthetic data generator
kubric - A data generation pipeline for creating semi-realistic synthetic multi-object videos with rich annotations such as instance segmentation masks, depth maps, and optical flow.
EasySynth - Unreal Engine plugin for easy creation of synthetic image datasets
SynthDet - SynthDet - An end-to-end object detection pipeline using synthetic data
unrealcv - UnrealCV: Connecting Computer Vision to Unreal Engine