arthasmil/rsipac2022

RSIPAC 2022 @Track3

RSIPAC2022

RSIPAC 2022 @Track3

Track 3: Semantic Segmentation

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TODO: upload the tuned class weights for CE loss and the DICE loss after the final phase.

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Update 2022/11/20

• We uploaded the final version of our model configuration. We used a cas-training approach: the model is initially trained @512x512 and then tuned @768x768.

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Update 2022/11/19

• The score is the weighted average F1-Score over all classes, thus the small classes will contribute little to the final score. So we can drop 3-4 classes for simplification. In our solution, we dropped the photovolts, the airports and the railway-station. As the percentage of the natural-bare-soil is also very low, we believe this could also be dropped.
• To solve the class imbalance problem, we tuned the class weight manually using the confusion matrix.
• Multi-scales training and inferencing could also improve the performance.

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Update 2022/11/17

Main features of Basic Model:

• CE + DICE loss (1:2)
• Multiple augmentations with Albu to avoid overfitting
• TTA
• SWA
• Drop some small samples, such as the photovolts, the airports and the railway-station. We only classify the objects into 15 classes with the class 0 to be the playground.

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WHOLE CODEBASE WILL BE RELEASED AFTER THE COMPETITION.