mapbox/robosat
Robosat
Semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads, water, clouds
Features
- Computer Vision - Semantic segmentation for satellite and aerial imagery.
How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
This project is a comprehensive computer vision library for the PyTorch ecosystem, providing a standardized collection of neural network architectures, datasets, and high-performance transformation utilities. It serves as a foundational framework for building, training, and deploying deep learning models, offering a centralized model registry that allows developers to instantiate architectures with pre-trained weights for tasks such as image classification, object detection, and semantic segmentation. The library distinguishes itself through its modular approach to data and compute management
Earth observation processing framework for machine learning in Python
Yolact is a computer vision framework and real-time instance segmentation model. It utilizes a fully convolutional neural network to detect objects and generate pixel-level masks for images and video feeds. The system employs prototypical mask generation to create global mask prototypes that are linearly combined for instance-specific results. It incorporates deformable convolutional layers and deformable region-of-interest pooling to adapt spatial sampling to the irregular shapes of objects. The framework covers the full model development lifecycle, including training on custom datasets, ac
YOLOv5 is a comprehensive computer vision framework designed for end-to-end deep learning, specializing in real-time object detection, image classification, and instance segmentation. It provides a unified toolkit that manages the entire lifecycle of a model, from initial dataset configuration and hyperparameter tuning to high-speed inference and deployment. The framework utilizes a modular neural architecture, allowing users to swap backbone and head components to tailor models for specific visual tasks. What distinguishes this project is its focus on production-ready deployment and model ef
Semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads, water, clouds
The main features of mapbox/robosat are: Computer Vision, Computer Vision and Image Processing, Computer Vision Libraries, Deep Learning Frameworks, Geospatial Machine Learning, Specialized Segmentation.
Projects with overlapping indexed features include: sentinel-hub/eo-learn — Earth observation processing framework for machine learning in Python. ultralytics/yolov5 — YOLOv5 is a comprehensive computer vision framework designed for end-to-end deep learning, specializing in real-time… dbolya/yolact — Yolact is a computer vision framework and real-time instance segmentation model. It utilizes a fully convolutional… pytorch/vision — This project is a comprehensive computer vision library for the PyTorch ecosystem, providing a standardized collection… aleju/imgaug — imgaug is a Python library for machine learning data augmentation and computer vision dataset expansion. It provides… alexeyab/darknet — Darknet is a high-performance C-based inference engine and computer vision library designed for real-time object…