My CV written using jinja2 to render yaml as LaTeX

PyTorch implementation of RandLA-Net

This is an unofficial implementation of the Point Transformer paper.

🍀 Pytorch Implementation of RandLA-Net (https://arxiv.org/abs/1911.11236)

Scalable Neuroglancer compatible Downsampling, Meshing, Skeletonizing, Contrast Normalization, Transfers and more.

a lightweight ChatGPT plugin for neovim

vim plugin for async synchronisation of remote files and local files using rsync

PyTorch Connectomics: segmentation toolbox for EM connectomics

Skeletonize densely labeled 3D image segmentations with TEASAR. (Medial Axis Transform)

SAM-Med3D: An Efficient General-purpose Promptable Segmentation Model for 3D Volumetric Medical Image

Invariant representation learning from imaging and spectral data

Track-Anything is a flexible and interactive tool for video object tracking and segmentation, based on Segment Anything, XMem, and E2FGVI.

utility code for AxonEM challenge

Read and write Neuroglancer datasets programmatically.

A PyTorch implementation of Dynamic Graph CNN for Learning on Point Clouds (DGCNN)

PointNet and PointNet++ implemented by pytorch (pure python) and on ModelNet, ShapeNet and S3DIS.

Weave, developed by the team at Weights and Biases, is a new open-source toolkit designed for performant, interactive data exploration. Our mission is to equip Machine Learning practitioners with t…

PyTorch implementation of normalizing flow models

The largest pre-trained medical image segmentation model (1.4B parameters) based on the largest public dataset (>100k annotations), up until April 2023.