12 open-source projects similar to seanbell/opensurfaces-segmentation-ui, ranked by how many features they have in common. Compare stars, activity and what each one does to find the best Opensurfaces Segmentation Ui alternative.
Labelme is a Python-based image annotation tool used to create computer vision datasets. It serves as a visual editor for semantic segmentation, allowing users to define object boundaries using polygons, rectangles, points, and circles. The application also functions as a multispectral image annotator, supporting high-bit depth TIFF files used in satellite and scientific imagery. The tool incorporates AI-assisted labeling capabilities to automate the creation of masks and polygons. These features allow for shape generation driven by text prompts or interactive point selections, which propose
Source code for the LabelMe annotation tool.
AirSim is a high-fidelity simulation platform designed for the development and testing of autonomous vehicles. Built as a plugin for game engines, it provides a physics-based environment that models vehicle dynamics and sensor data, serving as a foundation for robotics research, computer vision training, and reinforcement learning. The platform distinguishes itself through its support for hardware-in-the-loop and software-in-the-loop testing, allowing developers to validate control logic and firmware against real-world signals or concurrent processes. It offers extensive programmatic control
CVAT is an open-source computer vision annotation tool and visual dataset management platform. It provides a self-hosted interface for labeling images, videos, and 3D data to create datasets for vision AI models. The platform features AI-assisted data labeling to automate the creation of masks and bounding boxes, utilizing a plug-in system to connect external machine learning models. It includes a consensus-based quality assurance system that verifies label accuracy by comparing independent annotations. The system covers collaborative team management, project organization through task decomp
ZenML is an extensible machine learning orchestration framework designed to manage the end-to-end lifecycle of data pipelines and AI agent workflows. It functions as a durable orchestrator that executes machine learning tasks as directed acyclic graphs, ensuring that every step is containerized for consistent performance across local, cloud, and hybrid infrastructure. By decoupling pipeline code from underlying compute and storage backends, the platform allows developers to define infrastructure-agnostic stacks that remain portable across diverse environments. The project distinguishes itself