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ViPE: Video Pose Engine for Geometric 3D Perception
The main features of nv-tlabs/vipe are: Data Annotation and Synthesis.
Open-source alternatives to nv-tlabs/vipe include: cleanlab/cleanlab — Cleanlab is a data-centric AI library and toolkit designed to improve machine learning model performance by detecting… code-kern-ai/refinery — The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like… cvat-ai/cvat — CVAT is an open-source, web-based platform designed for annotating images, videos, and 3D point clouds to create… diyago/tabular-data-generation — We well know GANs for success in the realistic image generation. However, they can be applied in tabular data… doccano/doccano — Doccano is a collaborative data labeling platform and machine learning dataset management system. It provides a… argilla-io/argilla — Argilla is a collaborative AI feedback tool and data curation management system. It serves as a human-in-the-loop…
Cleanlab is a data-centric AI library and toolkit designed to improve machine learning model performance by detecting label errors and increasing overall dataset quality. It implements a confident learning framework that iteratively refines label noise estimates by comparing model predictions with estimated label probabilities to identify mislabeled examples. The project provides specialized utilities for active learning optimization, allowing for the selection of the most impactful examples for labeling or re-labeling. It also includes an outlier detection tool to identify atypical data poin
The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.
CVAT is an open-source, web-based platform designed for annotating images, videos, and 3D point clouds to create high-quality training datasets for machine learning. It functions as a containerized server that orchestrates the entire lifecycle of computer vision data, from initial task creation and manual labeling to quality assurance and final dataset export. The platform distinguishes itself through deep integration with machine learning models, allowing users to deploy custom AI models as serverless functions for automated object detection, tracking, and skeleton annotation. It supports co
Argilla is a collaborative AI feedback tool and data curation management system. It serves as a human-in-the-loop dataset platform designed to coordinate workforce annotators and domain experts in labeling, rating, and refining data samples for machine learning projects. The platform focuses on large language model dataset curation and reinforcement learning from human feedback workflows. It provides a shared workspace for integrating human expertise into AI development to validate model outputs and correct data errors. The system manages the end-to-end machine learning data pipeline, includ