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Stars
A library for audio and music analysis, feature extraction.
OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
A library for efficient similarity search and clustering of dense vectors.
A discussion between John Ousterhout and Robert Martin about differences between John's book "A Philosophy of Software Design" and Bob's book "Clean Code".
Categorized overview of programming principles & design patterns
Thin-wrapper around the mock package for easier use with pytest
An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
An open source implementation of CLIP.
An example how to use DataChain and DVC to version data, make project reproducible, track experiments and models
ETL, Analytics, Versioning for Unstructured Data
(AAAI' 20) A Python Toolbox for Machine Learning Model Combination
Framework agnostic sliced/tiled inference + interactive ui + error analysis plots
Image restoration with neural networks but without learning.
A (PyTorch) imbalanced dataset sampler for oversampling low frequent classes and undersampling high frequent ones.
DeepInverse: a PyTorch library for solving imaging inverse problems using deep learning
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
This is a python implementation for stitching images.
Efficient vision foundation models for high-resolution generation and perception.
The C++ Core Guidelines are a set of tried-and-true guidelines, rules, and best practices about coding in C++
VimTeX: A modern Vim and neovim filetype plugin for LaTeX files.
The balance python package offers a simple workflow and methods for dealing with biased data samples when looking to infer from them to some target population of interest.
A python library for self-supervised learning on images.
Blazing fast framework for fine-tuning similarity learning models