Geometric Computer Vision Library for Spatial AI

PyTorch extensions for fast R&D prototyping and Kaggle farming

Multithreaded Python without the GIL

Computer Vision dataset analysis

torch-optimizer -- collection of optimizers for Pytorch

Official code for "Tiny Object Detection in Aerial Images".

Official repo for DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single Image (CVPR 2022).

Official repo for FEAR: Fast, Efficient, Accurate and Robust Visual Tracker (ECCV 2022)

Contains source code for the winning solution of the xView3 challenge https://iuu.xview.us/.

Implementations of Recent Papers in Computer Vision

A baseline segmentation example using the comma10k dataset (WIP)

Speech Recognition for Ukrainian

Tensors and Dynamic neural networks in Python with strong GPU acceleration

An efficient video loader for deep learning with smart shuffling that's super easy to digest

SSD: Single Shot MultiBox Detector | a PyTorch Tutorial to Object Detection

Source code for the paper "Divide and Conquer the Embedding Space for Metric Learning", CVPR 2019

A collection of various deep learning architectures, models, and tips

YOLOv3 in PyTorch > ONNX > CoreML > TFLite

PyTorch implementation of a deep metric learning technique called "Magnet Loss" from Facebook AI Research (FAIR) in ICLR 2016.

tensorboard for pytorch (and chainer, mxnet, numpy, ...)

Materials from talks I gave

Continual Learning tutorials and demo running on Google Colaboratory.

Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125

A Clone version from Original SegCaps source code with enhancements on MS COCO dataset.

Implement 'Single Shot Text Detector with Regional Attention, ICCV 2017 Spotlight'

Winning Solutions from SpaceNet Road Detection and Routing Challenge

Keras Implementation of The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation by (Simon Jégou, Michal Drozdzal, David Vazquez, Adriana Romero, Yoshua Bengio)

Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!