trees
AI-basketball-analysis
| trees | AI-basketball-analysis | |
|---|---|---|
| 1 | 12 | |
| 0 | 923 | |
| - | - | |
| 3.8 | 0.0 | |
| 12 months ago | about 1 year ago | |
| C | Python | |
| - | GNU General Public License v3.0 or later |
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trees
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Ask HN: Show me your Half Baked project
I've always wanted my own gradient boosting machine implementation to compete with Xgboost and LightGBM. There's a huge space of unexplored tricks around regularization, randomization, tree structure, etc., that few people are exploring because neural nets are exploding.
So far I've roughly caught up in speed and accuracy with a few original tricks (and 1/20 of the features), but no real breakthroughs: https://github.com/benpastel/trees
On the plus side
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?
observable-state-tree - An observable state tree is a normal object except that listeners can be bound to any subtree of the state tree.
Deep-SORT-YOLOv4 - People detection and optional tracking with Tensorflow backend.
go-live - 🗂️ go-live is an ultra-light server utility that serves files, HTML or anything else, over HTTP.
openpifpaf - Official implementation of "OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association" in PyTorch.
pastty - Copy and paste across devices
veems - An open-source platform for online video.
hof - Framework that joins data models, schemas, code generation, and a task engine. Language and technology agnostic.
dflex - The sophisticated Drag and Drop library you've been waiting for 🥳
FastMOT - High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀
morphy - A simple static site generator
SynthDet - SynthDet - An end-to-end object detection pipeline using synthetic data