The Satellite Imagery Multiscale Rapid Detection with Windowed Networks (SIMRDWN) codebase combines some of the leading object detection algorithms into a unified framework designed to detect objects both large and small in overhead imagery. This work seeks to extend the YOLT modification of…
الميزات الرئيسية لـ avanetten/simrdwn هي: Geospatial Machine Learning, Object Detection.
تشمل البدائل مفتوحة المصدر لـ avanetten/simrdwn: weecology/deepforest — Python Package for Airborne RGB machine learning. torchgeo/torchgeo — TorchGeo is a PyTorch library designed for deep learning on geospatial data, providing a framework for building and… eriklindernoren/pytorch-yolov3 — This project is a PyTorch implementation of the YOLOv3 object detection architecture. It functions as a real-time… alexeyab/darknet — Darknet is a high-performance C-based inference engine and computer vision library designed for real-time object… atten4vis/groupdetr. ansleliu/lightnet.
TorchGeo is a PyTorch library designed for deep learning on geospatial data, providing a framework for building and training neural networks for tasks such as semantic segmentation, object detection, and change detection. It serves as a comprehensive pipeline for remote sensing, featuring specialized dataset loaders and multispectral image preprocessing tools. The library is distinguished by a dedicated remote sensing model zoo and extensive support for transfer learning, allowing users to integrate pre-trained weights optimized for specific satellite sensors. It also includes support for sel
This project is a PyTorch implementation of the YOLOv3 object detection architecture. It functions as a real-time object detector and computer vision framework designed to identify and locate multiple objects within images using bounding boxes and class labels. The system allows for both the use of pretrained weights for immediate image analysis and the training of custom models using datasets with bounding box annotations. It provides a programmatic interface to integrate detection capabilities directly into other software applications. The framework includes tools for model evaluation to m
Darknet is a high-performance C-based inference engine and computer vision library designed for real-time object identification and localization. It serves as a neural network framework for training and deploying detection models using the YOLO architecture, providing a toolset for deep learning training and deployment. The project differentiates itself through a C and CUDA implementation that enables hardware acceleration for matrix multiplication and inference speed optimization. It provides a shared library interface for embedding detection capabilities into external applications and suppo
