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UCAS-AOD-benchmark

A benchmark of UCAS-AOD dataset. (Only Oriented box is tested)

To be continued...

Introduction

There is no official division of the UCAS-AOD dataset, thus it's troublesome to compare the performance on different models. You can directly make comparison with our test results if you adopt the same division strategy.

Dataset repare

  1. Download UCAS-AOD dataset .
  2. Unzip dataset package into your root_dir, and rename the folder to UCAS_AOD.
  3. Put our imageset files train.txt, val.txt and test.txt into ImageSets folder in UCAS_AOD.
  4. Run data_prepare.py (modify the dataset dir to your own), and you will obtain directory as follow:
UCAS_AOD
└───AllImages
│   │   P0001.png
│   │   P0002.png
│   │	...
│   └───P1510.png
└───Annotations
│   │   P0001.txt
│   │   P0002.txt
│   │	...
│   └───P1510.txt       
└───ImageSets 
│   │   train.txt
│   │   val.txt
│   └───test.txt  
└───Test
│   │   P0003.png
│   │	...
│   └───P1508.txt 
└───CAR
└───PLANE
└───Neg
  1. Train, eval and test you model according to ImageSets settings.

notes: The integrated dataset contains 1510 images, with train set 755, val set 302, test set 452(following DOTA division 5:2:3). Files are numbered from 1-1510, in which 1-510 are cars, 511-1510 are airplanes. Besides, classname is attached to label file in format of classname x1 y1 x2 y2 x3 y3 x4 y4 theta lx ly w h ,

for example:

car  2.763971e+02	9.125021e+01	2.911375e+02	3.823406e+01	3.308891e+02	4.928647e+01	3.161486e+02	1.023026e+02	1.055379e+02	2.787673e+02	3.876027e+01	4.975157e+01	6.301615e+01	
car  3.002141e+02	1.003123e+02	3.209637e+02	4.665470e+01	3.566901e+02	6.047021e+01	3.359405e+02	1.141279e+02	1.111416e+02	3.055889e+02	4.856245e+01	4.572642e+01	6.365764e+01	
...

Experiment

Environment

  • NVIDIA 2080 Ti
  • pytorch>1.1.0
  • CUDA 10.0

Details

  • Models are Trained on trainset , and test on testset, valset is used for parameter optimization.
  • All models are available at Baidu Drive with passward sd4f.
  • na denotes number of anchors preset at each location of feature maps.
  • Data augment is adopted (random flip, hsv augment, translation, rotation).
  • All models are evaluated via VOC07 metric.

Benchmark

model backbone input_size na car airplane mAP paper link code
R-Yolov3 Darknet53 800*800 9 74.63 89.52 82.08 arxiv code1, code2
R-RetinaNet ResNet50 800*800 3 84.64 90.51 87.57 ICCV 2017 code
Faster RCNN ResNet50 800*800 3 86.87 89.86 88.36 CVPR 2018 code
RoI Transformer ResNet50 800*800 3 88.02 90.02 89.02 CVPR 2019 code
RIDet-Q ResNet50 800*800 9 88.50 89.96 89.23 GRSL code
SLA ResNet50 800*800 9 88.57 90.30 89.44 RS code
CFC-Net ResNet50 800*800 1 89.29 88.69 89.49 TGRS code
TIOE-Det ResNet50 800*800 9 88.83 90.15 89.49 ISPRS&RS2023 pytorch
RIDet-O ResNet50 800*800 9 88.88 90.35 89.62 GRSL code
DAL ResNet50 800*800 3 89.25 90.49 89.87 AAAI 2021 code
S2ANet ResNet50 800*800 1 89.56 90.42 89.99 TGRS code

Some Results

car

airplane


Notes : More results and PRs are welcomed if you test with imagesets division here.