meli
AI-basketball-analysis
meli | AI-basketball-analysis | |
---|---|---|
8 | 12 | |
366 | 923 | |
- | - | |
7.5 | 0.0 | |
almost 2 years ago | about 1 year ago | |
Rust | Python | |
GNU General Public License v3.0 only | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
meli
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Easy to use e-mail clients for GNU/Linux?
There's also https://github.com/meli/meli.
- GitHub - meli/meli: 🐝 experimental terminal mail client, mirror of https://git.meli.delivery/meli/meli.git https://crates.io/crates/meli
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Mutt's Secret Sauce (2017)
Try https://github.com/meli/meli/ it's mostly stable and doesn't have the problems described in the article. Development's on hold due to some personal factors but it's going to be picked up again.
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Writing Programs with Ncurses
I don't know if it counts as rich, but I rolled my own terminal UI in my email client meli [0]. There's a web demo here: https://meli.delivery/wasm2.html that is the client compiled to webassembly and the ANSI escape codes translated to equivalent SVG drawing so that you can try it on your browser. [0] https://github.com/meli/meli/
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Best terminal mail client
Meli. It's actively developed, looks good, has TUI, supports multiple accts, Maildir, mbox, notmuch, IMAP and JMAP. Need I say more?
- Mail clients that can show labels?
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Ask HN: Show me your Half Baked project
My terminal mail client. Work has been slow but steady
https://meli.delivery
https://github.com/meli/meli
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?
himalaya - CLI to manage emails
Deep-SORT-YOLOv4 - People detection and optional tracking with Tensorflow backend.
mutt-wizard - A system for automatically configuring mutt and isync with a simple interface and safe passwords
openpifpaf - Official implementation of "OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association" in PyTorch.
mblaze - Unix utilities to deal with Maildir
go-live - 🗂️ go-live is an ultra-light server utility that serves files, HTML or anything else, over HTTP.
astroid - A graphical threads-with-tags style, lightweight and fast, e-mail client for Notmuch
veems - An open-source platform for online video.
imtui - ImTui: Immediate Mode Text-based User Interface C++ Library
FastMOT - High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀
mail-parser - Fast and robust e-mail parsing library for Rust
SynthDet - SynthDet - An end-to-end object detection pipeline using synthetic data