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This codebase is created to build benchmarks for object detection in aerial images. It is modified from mmdetection. The master branch works with PyTorch 1.1 or higher. If you would like to use PyTorch 0.4.1, please checkout to the pytorch-0.4.1 branch.
The main features of dingjiansw101/aerialdetection are: Object Detection.
Projects with overlapping indexed features include: 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… allenai/reclip — This repository contains the code for the paper ReCLIP: A Strong Zero-shot Baseline for Referring Expression… atten4vis/groupdetr. atten4vis/ms-detr. akhtarvision/cal-detr.
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
This repository contains the code for the paper ReCLIP: A Strong Zero-shot Baseline for Referring Expression Comprehension (ACL 2022).