SwinTransformer + OBBDet

The sixth place winning solution (6/220) in the track of Fine-grained Object Recognition in High-Resolution Optical Images, 2021 Gaofen Challenge on Automated High-Resolution Earth Observation Image Interpretation.

Members

Qi Ming, Junjie Song, Yunpeng Dong.

Solution

• Off-line date augmentation
  We use random combination of affine transformation, flip, scaling, optical distortion for data augmentation.

• Multi-scale training and testing
  The training images are resized into sizes of 600, 800, and 1024 for training and testing.

• Strong backbone
  Swin transformer is adopt in ORCNN and RoI Transformer for better performance.

• Model ensemble
  We have merged the results from RoI Transformer, ORCNN, S2ANet, and ReDet.

• Lower confidence
  Set the output threshold into 0.005.

Tried but didn't work

• Soft-NMS.
• Adjust NMS threshold.
• Class-agnostic NMS.
• Mosaic, and mix up for data augmentation.
• Oversample the categories with fewer instances.
• Train the detectors for specific classes with low AP.
• Multi-scale training and testing on SwinTransformer-based detectors (even dropped by about 1% mAP).

Detections