Popular GitHub Repositories for Sensor Coverage

Qodo-Cover: An AI-Powered Tool for Automated Test Generation and Code Coverage Enhancement! ????

[ICRA'23] BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation

This repository provides code for machine learning algorithms for edge devices developed at Microsoft Research India.

[PAMI'23] TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving; [CVPR'21] Multi-Modal Fusion Transformer for End-to-End Autonomous Driving

[CoRL 2022] InterFuser: Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer

[NeurIPS 2021] Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation

Predict dense depth maps from sparse and noisy LiDAR frames guided by RGB images. (Ranked 1st place on KITTI) [MVA 2019]

Home automated terrarium/aquarium or other enclosed environment with a Raspberry Pi

Pointer-generator reinforced seq2seq summarization in PyTorch

Bayesian Optimization as a Coverage Tool for Evaluating LLMs. Accurate evaluation (benchmarking) that's 10 times faster with just a few lines of modular code.

[TNNLS] A Comprehensive Survey of Awesome Visual Transformer Literatures.

Code base for the precision, recall, density, and coverage metrics for generative models. ICML 2020.

A modern, lightweight, and customizable C# Discord library with Native AOT support, immutable caching, voice capabilities, and complete API coverage.

Tensorflow and PyTorch implementation of Unsupervised Depth Completion from Visual Inertial Odometry (in RA-L January 2020 & ICRA 2020)

sensor calibration tools for autonomous driving and robotics

Open-Source RAG app with LLM Observability (Langfuse), support for 100+ providers (LiteLLM), Dockerized, Full Type-checking, 100% Test coverage, and more...

The AI sensor processing SDK for low latency streaming workflows

An expanded version of the previously released Kazakh text-to-speech (KazakhTTS) synthesis corpus. In KazakhTTS2, the overall size has increased from 93 hours to 271 hours, the number of speakers has risen from two to five (three females and two males), and the topic coverage has been diversified.

Library for multi-dimensional, multi-sensor, uni/multivariate time series data analysis, unsupervised feature selection, unsupervised deep anomaly detection, and prototype of explainable AI for anomaly detector

Deep learning for grasp detection within MoveIt.