#high accuracy

This project introduces a high-speed and precise local feature matcher using LightGlue, a deep neural network optimized for matching sparse features across image pairs. Employing adaptive pruning for network width and depth, it delivers fast inference for simple image pairs while effectively managing complexity for more challenging ones. The project includes pretrained weights compatible with SuperPoint, DISK, ALIKED, and SIFT features. The repository provides all necessary code for training and evaluation, delivering faster performance, particularly with the integration of FlashAttention and PyTorch compilation. Experience high-performance feature matching in visual localization and more.

DeepVariant, a deep learning-based variant caller, effectively identifies genetic variants across multiple sequencing data types such as NGS, RNA-seq, PacBio, and Oxford Nanopore. It is optimized for variant calling in diploid organisms, supporting whole genome and exome sequences. DeepVariant uses CNNs to transform aligned reads into pileup images, classifying and reporting genotypes with precision. Though primarily trained on human data, its functionality is enhanced for trio or duo data by tools like DeepTrio and GLnexus. It operates efficiently on various infrastructure, including cloud computing, offering a robust solution for precision genomic analysis.