Face Recognition
simdjson
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Face Recognition | simdjson | |
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34 | 65 | |
51,693 | 18,362 | |
- | 1.2% | |
0.0 | 9.2 | |
about 2 months ago | 14 days ago | |
Python | C++ | |
MIT License | Apache License 2.0 |
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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.
Face Recognition
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Security Image Recognition
Camera connected to a PI? Something like this could run locally: https://github.com/ageitgey/face_recognition
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Facial recognition software/API for face-blind teacher?
Have you tried this repo: github
- GitHub - ageitgey/face_recognition: The world's simplest facial recognition api for Python and the command line
- The simplest facial recognition API for Python
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Every thing you need to know about Machine Learning Pipeline
One of the most common challenges is the black-box problem, when the pipeline becomes too complex to understand it would happen. This can make it difficult to identify issues with the system or to understand why it isn't working as we expected or make accurate predictions that saiwa company find out the solution for Face Recognition. Another challenge is the time required for organizations to deploy a machine learning model, which is increasing and make real-time computing difficult . To overcome these challenges, it's important to have an efficient and rigorous ML pipeline . ML level 0 involves a manual process with its own set of challenges, while ML level 1 involves ML pipeline automation and additional components . A well-defined machine learning pipeline can help to abstract the complex process into a series of steps, allowing each team to work independently on specific tasks such as data collection, data preparation, model training, model evaluation, and model deployment.
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Reverse image search / facial recognition
Second link is an easy to implement python library is you want to build it yourself https://github.com/ageitgey/face_recognition
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Made a easy to use face recognition library
It is similar to https://github.com/ageitgey/face_recognition, except that Ageitgey's cli only compares the first face found in the image to the first one found the the second.
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Salisbury council meeting minutes addressing conspiracy theorist councillors
You'd have alot more luck with something like DLIB or an open source implementation such as: https://github.com/ageitgey/face_recognition
- Face comparison in Stable Diffusion
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Understanding different Algorithms for Facial Recognition
To know more about face_recognition module https://github.com/ageitgey/face_recognition
simdjson
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Tips on adding JSON output to your command line utility. (2021)
It's also supported by simdjson [0] (which has a lot of language bindings [1]):
> Multithreaded processing of gigantic Newline-Delimited JSON (ndjson) and related formats at 3.5 GB/s
[0] https://simdjson.org/
[0] https://github.com/simdjson/simdjson?tab=readme-ov-file#bind...
- 1BRC Merykitty's Magic SWAR: 8 Lines of Code Explained in 3k Words
- Training great LLMs from ground zero in the wilderness as a startup
- simdjson: Parsing Gigabytes of JSON per Second
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Use any web browser as GUI, with Zig in the back end and HTML5 in the front end
String parsing is negligible compared to the speed of the DOM which is glacially slow: https://news.ycombinator.com/item?id=38835920
Come on, people, make an effort to learn how insanely fast computers are, and how insanely inefficient our software is.
String parsing can be done at gigabytes per second: https://github.com/simdjson/simdjson If you think that is the slowest operation in the browser, please find some resources that talk about what is actually happening in the browser?
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Cray-1 performance vs. modern CPUs
Thanks for all the detailed information! That answers a bunch of my questions and the implementation of strlen is nice.
The instruction I was thinking of is pshufb. An example ‘weird’ use can be found for detecting white space in simdjson: https://github.com/simdjson/simdjson/blob/24b44309fb52c3e2c5...
This works as follows:
1. Observe that each ascii whitespace character ends with a different nibble.
2. Make some vector of 16 bytes which has the white space character whose final nibble is the index of the byte, or some other character with a different final nibble from the byte (eg first element is space =0x20, next could be eg 0xff but not 0xf1 as that ends in the same nibble as index)
3. For each block where you want to find white space, compute pcmpeqb(pshufb(whitespace, input), input). The rules of pshufb mean (a) non-ascii (ie bit 7 set) characters go to 0 so will compare false, (b) other characters are replaced with an element of whitespace according to their last nibble so will compare equal only if they are that whitespace character.
I’m not sure how easy it would be to do such tricks with vgather.vv. In particular, the length of the input doesn’t matter (could be longer) but the length of white space must be 16 bytes. I’m not sure how the whole vlen stuff interacts with tricks like this where you (a) require certain fixed lengths and (b) may have different lengths for tables and input vectors. (and indeed there might just be better ways, eg you could imagine an operation with a 256-bit register where you permute some vector of bytes by sign-extending the nth bit of the 256-bit register into the result where the input byte is n).
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Codebases to read
Additionally, if you like low level stuff, check out libfmt (https://github.com/fmtlib/fmt) - not a big project, not difficult to understand. Or something like simdjson (https://github.com/simdjson/simdjson).
- Simdjson: Parsing Gigabytes of JSON per Second
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Building a high performance JSON parser
Everything you said is totally reasonable. I'm a big fan of napkin math and theoretical upper bounds on performance.
simdjson (https://github.com/simdjson/simdjson) claims to fully parse JSON on the order of 3 GB/sec. Which is faster than OP's Go whitespace parsing! These tests are running on different hardware so it's not apples-to-apples.
The phrase "cannot go faster than this" is just begging for a "well ackshully". Which I hate to do. But the fact that there is an existence proof of Problem A running faster in C++ SIMD than OP's Probably B scalar Go is quite interesting and worth calling out imho. But I admit it doesn't change the rest of the post.
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New package : lspce - a simple LSP Client for Emacs
I have same question as /u/JDRiverRun : how do you deal with JSON, do you parse json on Rust side or on Emacs side. I see that you are requiring json.el in your lspce.el, but I haven't looked through entire file carefully. If you parse on Rust side, do you use simdjson (there are at least two Rust bindings to it)? If yes, what are your impressions, experiences compared to more "standard" json library?
What are some alternatives?
insightface - State-of-the-art 2D and 3D Face Analysis Project
RapidJSON - A fast JSON parser/generator for C++ with both SAX/DOM style API
CompreFace - Leading free and open-source face recognition system
jsoniter - jsoniter (json-iterator) is fast and flexible JSON parser available in Java and Go
Milvus - A cloud-native vector database, storage for next generation AI applications
json - JSON for Modern C++
OpenCV - Open Source Computer Vision Library
json-schema-validator - JSON schema validator for JSON for Modern C++
tesseract-ocr - Tesseract Open Source OCR Engine (main repository)
JsonCpp - A C++ library for interacting with JSON.
Kornia - Geometric Computer Vision Library for Spatial AI
json - A C++11 library for parsing and serializing JSON to and from a DOM container in memory.