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Yolov3-Polygon

This is a implementation of rotation object detecion based on YOLOv3-quadrangle. I upgraded it to support pytorch 1.1 or higher and fix some bugs. Object detection in arbitrary orientations is achieved by detecting four corner points, the model has been tested on remote sensing dataset UCAS-AOD. The results and trained models can be found here.

training

dataset

The annotations of your own dataset need to be converted into DOTA format.

imagesets

Generate imageset file via utils/generate_imageset.py

config

Run utils/kmeans.py to generate preset anchors.

Modify two parts in cfg/yolov3.cfg : 1. classes 2. conv filter before yolo layer should be (8+cls+1)*3.

Modify training sets in .data file.

Modify classnames in data/*.names.

eval

prepare labels:

python datasets/UCAS_AOD/ucas_aod2gt.py

conduct evaluation:

python eval.py

detect

python detect.py

Detections

Thanks to

YOLOv3-quadrangle

mAP calculation

ultralytics yolov3

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Arbitrary-oriented object detection based on yolov3.

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