connectednotes
AI-basketball-analysis
| connectednotes | AI-basketball-analysis | |
|---|---|---|
| 2 | 12 | |
| 26 | 923 | |
| - | - | |
| 0.0 | 0.0 | |
| almost 2 years ago | about 1 year ago | |
| TypeScript | Python | |
| MIT License | GNU General Public License v3.0 or later |
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connectednotes
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Looking for feedback and/or test users for my free & open source Zettelkasten app
Hey, thanks for taking a look! Good to know the mobile view is fine, most of the time I test on my laptop. The tech stack is Typescript/Angular plus some libraries like Codemirror for the editor and Cytoscape for graphing. The github is at https://github.com/tsiki/connectednotes is you're interested in taking a closer look.
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Ask HN: Show me your Half Baked project
https://connectednotes.net (github: https://github.com/tsiki/connectednotes)
It's essentially Zettelkasten based note taking + flashcards + FOSS
It's a bit beyond half-baked but I'm currently trying to crush most annoying bugs for the alpha release and there's plenty of those, so it's not exactly baked either.
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
What are some alternatives?
DIY-arcade - How to build your own full-size arcade machine from scratch
Deep-SORT-YOLOv4 - People detection and optional tracking with Tensorflow backend.
hof - Framework that joins data models, schemas, code generation, and a task engine. Language and technology agnostic.
openpifpaf - Official implementation of "OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association" in PyTorch.
dflex - The sophisticated Drag and Drop library you've been waiting for 🥳
go-live - 🗂️ go-live is an ultra-light server utility that serves files, HTML or anything else, over HTTP.
ws-monitoring - A simple & lightweight realtime monitoring web UI + server in Node.js
veems - An open-source platform for online video.
dupver - Deduplicating VCS for large binary files in Go
FastMOT - High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀
SynthDet - SynthDet - An end-to-end object detection pipeline using synthetic data
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