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Fair comparison of different algorithms on the HRSC2016 dataset.

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HRSC2016_SOTA

This repo collects some state-of-the-art results on remote sensing ship dataset HRSC2016 for convenient comparison.

Benchmark

(Ranked according to the mAP(07)).

model backbone input_size mAP(07) mAP(12) FPS paper link code
R2CNN ResNet101 800x800 73.07 79.73 2 arxiv tf
RC1&RC2 VGG16 - 75.7 - - ICPRAM -
Axis Learning ResNet101 800x800 78.15 - 14 Remote Sensing -
RRPN ResNet101 800x800 79.08 85.64 3.5 TMM -
TOSO ResNet101 800x800 79.29 - 17 ICASSP2020 -
RRD VGG16 800x800 84.30 - - CVPR2018 C++
RoI-Transformer ResNet101 512x800 86.20 - 6 CVPR2019 pytorch
RSDet ResNet50 800x800 86.5 - - AAAI2021 tf
Gliding Vertex ResNet101 512×800 88.20 - 10 TPAMI pytorch
OPLD ResNet50 1024x1333 88.44 - 7.3 JSTAR pytorch
BBAVectors ResNet101 608x608 88.60 - 11.7 WACV2021 pytorch
DRN Hourglass104 768x768 - 92.70 10 CVPR2020 pytorch
DAL ResNet101 416x416 88.95 - 34 AAAI2021 pytorch
RIDet-Q ResNet101 800x800 89.10 - 8.5 arxiv pytorch
R3Det ResNet101 800x800 89.26 96.01 12 AAAI2021 tf, pytorch
DCL ResNet101 800x800 89.46 96.41 - CVPR2021 tf
SLA ResNet101 768x768 89.51 - - Remote Sensing pytorch
CSL ResNet50 800x800 89.62 96.10 - ECCV2020 tf
RIDet-O ResNet101 800x800 89.63 - - arxiv pytorch
CFC-Net ResNet101 800x800 89.70 - 28 TGRS pytorch
GWD ResNet101 800x800 89.85 97.37 - ICML2021 tf
TIOE-Det ResNet101 800x800 90.16 96.65 - ISPRS&RS2023 pytorch
S2ANet ResNet101 512x800 90.17 95.01 12.7 TGRS pytorch
ReDet ResNet101 512x800 90.46 97.63 - CVPR2021 pytorch
Oriented R-CNN ResNet101 1333x800 90.50 97.60 15.1 ICCV2021 pytorch

Notes

For fair comparison, experiments on the HRSC2016 dataset should meet the following requirements:

  • Strictly following the official dataset division. Specifically, training and testing should be performed on the training set and test set respectively. It is unreasonable to use the validation set for training.
  • Many previous work used the VOC07 method to calculate mAP, and some recent work used the VOC12 standard to calculate mAP. These two results should not be compared equally.
  • All results are obtained at level 1, that is, there is only one class (ship).

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Fair comparison of different algorithms on the HRSC2016 dataset.

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