DOTA database training with yolo | 基于DOTA数据集的yolo训练
-
Updated
Jan 7, 2020 - Python
DOTA database training with yolo | 基于DOTA数据集的yolo训练
Toolkit for working with the DOTA aerial object detection dataset
Successfully optimized deep learning models to detect 15 distinct objects through implementation of image tiling and innovative Strategic Aerial Homogenization for Inference (SAHI) approach to improve mean average precision by 36%
This repository contains implementations of Mosaic and Cutout data augmentation techniques applied to the DOTA v1.5 dataset. It provides Python code for generating augmented images and labels, helping enhance object detection tasks in aerial imagery. Explore the examples to see how these methods improve dataset diversity.
一个用于旋转目标检测任务的标注格式转换工具,支持在 RoLabelImg (类VOC XML) 格式和 DOTA (TXT) 格式之间进行双向、批量的转换。
Add a description, image, and links to the dota-dataset topic page so that developers can more easily learn about it.
To associate your repository with the dota-dataset topic, visit your repo's landing page and select "manage topics."