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PyTorch implementation of popular datasets and models in remote sensing
Train to 94% on CIFAR-10 in <6.3 seconds on a single A100. Or ~95.79% in ~110 seconds (or less!)
This is the official implementation of TrivialAugment and a mini-library for the application of multiple image augmentation strategies including RandAugment and TrivialAugment.
Awesome resources on normalizing flows.
Large-Scale Machine and Deep Learning in PyTorch.
Traditional Machine Learning Models for Large-Scale Datasets in PyTorch.
[NeurIPS 2020] This project provides a strong single-stage baseline for Long-Tailed Classification, Detection, and Instance Segmentation (LVIS). It is also a PyTorch implementation of the NeurIPS 2…
Code for the Scene Graph Generation part of CVPR 2019 oral paper: "Learning to Compose Dynamic Tree Structures for Visual Contexts"
A new codebase for popular Scene Graph Generation methods (2020). Visualization & Scene Graph Extraction on custom images/datasets are provided. It's also a PyTorch implementation of paper “Unbiase…
PyTorch Implementation for the paper "C3VQG: Category Consistent Cyclic Visual Question Generation" (ACM MM Asia'20).
PRML algorithms implemented in Python
scikit-learn: machine learning in Python
Bayesian Modeling and Probabilistic Programming in Python
Prettier is an opinionated code formatter.
2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
⚡ A Fast, Extensible Progress Bar for Python and CLI
VGGFace implementation with Keras Framework
A curated list of awesome Python asyncio frameworks, libraries, software and resources
😎 Awesome lists about all kinds of interesting topics
A curated list of awesome C++ (or C) frameworks, libraries, resources, and shiny things. Inspired by awesome-... stuff.
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
SqueezeNet implementation with Keras Framework
Everything you need to prepare for your technical interview