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Python Package for Airborne RGB machine learning
The main features of weecology/deepforest are: Computer Vision Frameworks, Geospatial Machine Learning, Object Detection.
Projects with overlapping indexed features include: msracver/deformable-convnets — Deformable-ConvNets is a computer vision framework and a collection of neural network components designed to implement… facebookresearch/detectron2 — Detectron2 is a PyTorch computer vision framework and visual recognition platform designed for training and deploying… avanetten/simrdwn — The Satellite Imagery Multiscale Rapid Detection with Windowed Networks (SIMRDWN) codebase combines some of the… 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… ansleliu/lightnet — LightNet: Light-weight Networks for Semantic Image Segmentation (Cityscapes and Mapillary Vistas Dataset).
Deformable-ConvNets is a computer vision framework and a collection of neural network components designed to implement deformable convolutional neural networks. It provides adaptive convolutional layers and pooling implementations that modify their receptive fields based on input features to better capture the geometry of objects within images. The project enables the use of learnable sampling offsets and modulation masks to align convolutional grids with target object shapes. It includes specialized tools for visualizing learned offsets in convolutions and pooling layers, allowing for the an
Detectron2 is a PyTorch computer vision framework and visual recognition platform designed for training and deploying models for object detection, image segmentation, and visual recognition. It provides a research-oriented environment for training complex vision models with multi-GPU acceleration. The project includes a specialized object detection library for identifying and locating multiple objects via bounding boxes, as well as an image segmentation toolkit for creating pixel-level masks through instance, semantic, and panoptic segmentation. Additionally, it features a human pose estimati
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…
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