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.
Qi Ming, Junjie Song, Yunpeng Dong.
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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.
- 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).
Please refer to install.md for installation
change the dataset classes in dataset_classes