ultralytics
hyperfine
ultralytics | hyperfine | |
---|---|---|
27 | 74 | |
22,973 | 20,020 | |
7.1% | - | |
9.8 | 8.1 | |
4 days ago | 3 days ago | |
Python | Rust | |
GNU Affero General Public License v3.0 | Apache License 2.0 |
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.
ultralytics
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The CEO of Ultralytics (yolov8) using LLMs to engage with commenters on GitHub
Yep, I noticed this a while ago. It posts easily identifiable ChatGPT responses. It also posts garbage wrong answers which makes it worse than useless. Totally disrespectful to the userbase.
https://github.com/ultralytics/ultralytics/issues/5748#issue...
- FLaNK Weekly 08 Jan 2024
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My kid sounds like ChatGPT, and soon yours might, too
There are obvious places it is being used that I have noticed organically. For instance, check out the answers in this repo:
https://github.com/ultralytics/ultralytics/issues/5748#issue...
If you read the answers there, the style of answering is always to repeat the question in a very specific way. Once you see it you can’t in-see it.
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
When browsing the state-of-the-art in object detection on Papers with Code, I found the YOLO model to be one of the most popular, accurate, and fastest. That being said, I would recommend having a look at Ultralytics, which provides the tools to evaluate, predict, and export the latest versions of YOLO models with only a few lines of code.
- Instance segmentation of small objects in grainy drone imagery
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Breaking the Myth: Object Detection Isn't Hard as Thought
YOLOv8 (You Only Look Once) is an open-source Computer Vision AI model released on January 10th, 2023. It’s called YOLO because it detects everything inside an image in a single pass. The new version can perform image detection, classification, instance segmentation, tracking, and pose estimation tasks.
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How I use "AI" to entertain my cat
Next, I needed to figure out, how can I access the stream, recognize an animal, then let Max know? There are tons of examples of recognizing an object via camera frames, but I ultimately found this python library called ultralytics that supports RTSP streams and classifying objects in the video frames using pre-built models. The docs looked like it would be pretty low effort, so after some experimentation, I was successful in having the ultralytics library recognize objects from my cheap camera!
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How to load the optimizer state_dicts in yolov8?
I have created an issue in their Github as well but so far not much help has been recieved. You can check that here
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Autodistill: A new way to create CV models
And the target models include: * YOLOv8 (You Only Look Once) * YOLO-NAS * YOLOv5 * and DETR
hyperfine
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Measuring startup and shutdown overhead of several code interpreters
Check out the official hyperfine Github repo
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Bun - The One Tool for All Your JavaScript/Typescript Project's Needs?
And then I used hyperfine to run the benchmarks on my MacBook Pro 14 M2 Max, and here are the results:
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Faster tetranucleotide (k-mer) frequencies!
Search "benchmarking tools for linux" and decide that hyperfine is good for what I'm doing. Run Jennifer's new python script against my refactored perl and find that the python is 1.26 times faster for k=3 and 1.47 times faster for k=4. For the Covid-19 sequence, these are both on the order of hundreds of milliseconds.
- Hyperfine: A command-line benchmarking tool
- FLaNK Weekly 08 Jan 2024
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Show HN: Inshellisense – IDE style shell autocomplete
> It is very possible to write sub 100ms procedures in TS, […]
I will not disagree with this statement because I don’t have a way to test inshellisense right now. Could you (or anyone with a working Node + NPM installation) please install inshellisense and post the actual numbers? Perhaps using a tool like hyperfine (https://github.com/sharkdp/hyperfine).
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Firefox has surpassed Chrome on Speedometer
Yeah, while it's not as thorough as these tools, the method is at least reproducible and sane, and with ~10 or so samples, you get an interval with a nice confidence.
Another through method will be hyperfine[0], yet I wanted to provide a method which requires no installation and can be done in a whim, without jumps and hoops, with the tools already at hand.
[0]: https://github.com/sharkdp/hyperfine
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How to optimize your config? What are mistakes to avoid when optimizing your config?
That is native and inbuild but I would suggest below options instead 1. Using lazy's Profile tab instead https://github.com/folke/lazy.nvim 2. Using a dedicated plugin to do this https://github.com/dstein64/vim-startuptime. 3. Using an external program hyperfine is one that I use https://github.com/sharkdp/hyperfine
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How to remove all <br> from all of my .html files
Fair enough, although might I recommend using hyperfine for your testing? ;p
What are some alternatives?
segment-anything - The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
criterion.rs - Statistics-driven benchmarking library for Rust
super-gradients - Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.
fd - A simple, fast and user-friendly alternative to 'find'
yolo_tracking - BoxMOT: pluggable SOTA tracking modules for segmentation, object detection and pose estimation models
ripgrep - ripgrep recursively searches directories for a regex pattern while respecting your gitignore
GroundingDINO - Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"
awesome-mac -  Now we have become very big, Different from the original idea. Collect premium software in various categories.
Auto-GPT - An experimental open-source attempt to make GPT-4 fully autonomous. [Moved to: https://github.com/Significant-Gravitas/Auto-GPT]
kubeconform - A FAST Kubernetes manifests validator, with support for Custom Resources!
yolov8_onnx_python - YOLOv8 inference using Python
quinn - Async-friendly QUIC implementation in Rust