bodywork-core Bodywork deploys machine learning projects developed in Python, to Kubernetes. It helps you: serve models as microservices execute batch jobs run reproducible pipelines On demand, or on a schedule. It automates repetitive DevOps tasks and frees machine learning engineers to focus on what they do best – solving data problems with machine learning. Where does Bodywork Fit? Bodywork is aimed at teams who want to deploy machine learning projects in containers. It will deliver your project’s Python modules directly […]

Vector Neurons We introduce a general framework built on top of what we call Vector Neurons for creating SO(3) equivariant neural networks. Extending neurons from single scalars to 3D vectors, our vector neurons transport SO(3) actions to latent spaces and provide a framework for building equivariance in common neural operations including linear layers, non-linearities, pooling, and normalization. Created by Congyue Deng, Or Litany, Yueqi Duan, Adrien Poulenard, Andrea Tagliasacchi, and Leonidas Guibas. Overview vnn is the author’s implementation of Vector […]

Scene Graph Benchmark in PyTorch 1.4 This project aims at providing the necessary building blocks for easily creating detection and segmentation models using PyTorch 1.0. Highlights Upgrad to pytorch 1.4 (can also upgrade to 1.7) Multi-GPU training and inference Batched inference: can perform inference using multiple images per batch per GPU. Fast and flexible tsv dataset format Remove FasterRCNN detector dependency: during relation head training, can plugin bounding boxes from any detector. Provides pre-trained models for different scene graph detection […]