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Alexey's Projects

dijkstra icon dijkstra

Find the shortest path using Dijkstra algorithm (Crossplatform: Qt4.8.4 + OpenGL)

dldt icon dldt

Deep Learning Deployment Toolkit

dlwin icon dlwin

GPU-accelerated Deep Learning on Windows 10 native

dpt icon dpt

Dense Prediction Transformers

dsmnet icon dsmnet

Domain-invariant Stereo Matching Networks

elicuda icon elicuda

ELICuda - an example for unified using Thrust code on host backends CPU(CPP/OpenMP) and GPU(CUDA)

frrn icon frrn

Full Resolution Residual Networks for Semantic Image Segmentation

gen-efficientnet-pytorch icon gen-efficientnet-pytorch

Pretrained EfficientNet, EfficientNet-Lite, MixNet, MobileNetV3 / V2, MNASNet A1 and B1, FBNet, Single-Path NAS

gpt-2 icon gpt-2

Code for the paper "Language Models are Unsupervised Multitask Learners"

hip icon hip

HIP : Convert CUDA to Portable C++ Code

hrnet-maskrcnn-benchmark icon hrnet-maskrcnn-benchmark

Object detection with multi-level representations generated from deep high-resolution representation learning (HRNetV2h).

hrnet-semantic-segmentation icon hrnet-semantic-segmentation

This is an official implementation of semantic segmentation for our TPAMI paper "Deep High-Resolution Representation Learning for Visual Recognition". https://arxiv.org/abs/1908.07919

hub icon hub

A library for transfer learning by reusing parts of TensorFlow models.

hub-1 icon hub-1

Submission to https://pytorch.org/hub/

keras-yolo3 icon keras-yolo3

A Keras implementation of YOLOv3 (Tensorflow backend)

lambda-networks icon lambda-networks

Implementation of LambdaNetworks, a new approach to image recognition that reaches SOTA with less compute

libcds icon libcds

A C++ library of Concurrent Data Structures

libvma icon libvma

Linux user space library for network socket acceleration based on RDMA compatible network adaptors

mapbox-gl-native icon mapbox-gl-native

Render Mapbox styles in mobile, desktop, and node applications using C++ and OpenGL

midas icon midas

Code for robust monocular depth estimation described in "Ranftl et. al., Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer, arXiv:1907.01341"

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