Open source platform for the machine learning lifecycle

What Do You See in Vehicle? Comprehensive Vision Solution for In-Vehicle Gaze Estimation

A python library wrapping the Confluence REST API

Atlassian Python REST API wrapper

LAVIS - A One-stop Library for Language-Vision Intelligence

simple CAN-Transceiver with BCM sockets written in c99

Offical PyTorch implementation of "BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework"

convert mmdetection model to tensorrt, support fp16, int8, batch input, dynamic shape etc.

流媒体NetFlix解锁检测脚本 / A script used to determine whether your network can watch native Netflix movies or not

Pytorch implementation of "All Tokens Matter: Token Labeling for Training Better Vision Transformers"

A curated list of Resources for Motion Planning

openpilot is an operating system for robotics. Currently, it upgrades the driver assistance system on 275+ supported cars.

L5Kit - https://woven.toyota

WACV Multi-Camera Multi-Target Challenge

LidarView performs real-time reception, recording, visualization and processing of 3D LiDAR data. This repository is a mirror of https://gitlab.kitware.com/LidarView/lidarview.

Task-Aware Monocular Depth Estimation for 3D Object Detection, AAAI2020

Unofficial PyTorch implementation of "RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving" (ECCV 2020)

A pytorch implementation of "D4LCN: Learning Depth-Guided Convolutions for Monocular 3D Object Detection" CVPR 2020

The official PyTorch Implementation of RTM3D and KM3D for Monocular 3D Object Detection

3DSSD: Point-based 3D Single Stage Object Detector (CVPR 2020)

OpenMMLab's next-generation platform for general 3D object detection.

ICRA 2019 "FastDepth: Fast Monocular Depth Estimation on Embedded Systems"

A General-purpose Task-parallel Programming System using Modern C++

Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-identification

fast-cpp-csv-parser

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

Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation

MACE is a deep learning inference framework optimized for mobile heterogeneous computing platforms.

Semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads, water, clouds