AI-basketball-analysis
SynthDet
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| AI-basketball-analysis | SynthDet | |
|---|---|---|
| 12 | 3 | |
| 923 | 350 | |
| - | 0.9% | |
| 0.0 | 2.9 | |
| 12 months ago | 10 months ago | |
| Python | C# | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.
AI-basketball-analysis
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[P] Basketball Shots Detection and Shooting Pose Analysis (Open Source)
Source code: https://github.com/chonyy/AI-basketball-analysis
- Show HN: Visualizing Basketball Trajectory and Analyzing Shooting Pose
- Automatically Overlaying Baseball Pitch Motion and Trajectory in Realtime (Open Source)
- Show HN: AI Basketball Analysis Web App and API
- Show HN: Visualize and Analyze Basketball Shots and Shooting Pose with ML
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Ask HN: Show me your Half Baked project
I built an app to visualize and analyze basketball shots and shooting pose with machine learning.
https://github.com/chonyy/AI-basketball-analysis
The result is pretty nice. However, the only problem is the slow inference speed. I'm now refactoring the project structure and changing the model to a much faster YOLO model.
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Show HN: Automatic Baseball Pitching Motion and Trajectory Overlay in Realtime
Thanks for asking! This is not a noob question.
I would say that the similar workflow could be applied to any ball-related sports. The object detection and the tracking algorithm is basically the same. Then, you could add any sport-specific feature!
For example, I have used a similar method to build AI Basketball Analysis.
https://github.com/chonyy/AI-basketball-analysis
- Show HN: AI Basketball Analysis in Realtime
- Show HN: AI Basketball Visualization
SynthDet
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Looking for 3D food packaging assets
Didn't try but Unity did a object recognition project with store products: https://github.com/Unity-Technologies/SynthDet. You can find the models and texture in the "Assets/Foreground Objects" folder. And you may need to check the license depending on your usage.
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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 guides?
This is probably what you're looking for . https://github.com/Unity-Technologies/SynthDet
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Imported materials for objects can't edit.
So I'm working on Unity with this Project (SynthDet) https://github.com/Unity-Technologies/SynthDet and I'm working on imported objects (obj. ), trying to edit materials of those objects, but I can't. The materials are gredy out. Don't know what to do. Do you guys have any ideas what to do, to allow to edit imported materials?
What are some alternatives?
Deep-SORT-YOLOv4 - People detection and optional tracking with Tensorflow backend.
Deep-Learning-Push-Up-Counter - Deep Learning approach to count the number of repetitions in a video of push ups or pull ups.
openpifpaf - Official implementation of "OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association" in PyTorch.
yolov3-tf2 - YoloV3 Implemented in Tensorflow 2.0
go-live - 🗂️ go-live is an ultra-light server utility that serves files, HTML or anything else, over HTTP.
machine-learning-for-trading - Code for Machine Learning for Algorithmic Trading, 2nd edition.
veems - An open-source platform for online video.
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.
FastMOT - High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀
make-sense - Free to use online tool for labelling photos. https://makesense.ai
live_data
Mask-RCNN-Implementation - Mask RCNN Implementation on Custom Data(Labelme)