The third place winning solution (3/220) in the track of Fine-grained Object Recognition in High-Resolution Optical Images, 2021 Gaofen Challenge on Automated High-Resolution Earth Observation Image Interpretation.
- models
config location:Under folder config/fair1m
Strong backbone: add swin transformer as backbone
storng head: adapt double head to ROI transformer and ReDet - Training trick
config location:Under folder:config/fair1m
trick:Fine-tuning with SWA and cosine learning rate - Trick
file location: competition_trick
model emsemble: WBF(make WBF support oriented bounding box)
version tool: Visualize objects for analysis
- models
ReDet, Oriented RCNN, ROITranformer - Tricks
Multi-scale training and testing: Limited by the computing power using two scales of 1.0 and 1.5, and crop images into 1024*1024 with stride 800
Lower confidence: Set the output threshold into 0.005.
Off-line date augmentation: Random combination of affine transformation, flip, scaling, optical distortion for data augmentation.
install: readme_install.md
Dataset format:only support DOTA (Changed the label format of the FAIR1M dataset to DOTA format during the competition)