An object oriented (OOP) approach to train Tensorflow models and serve them using Tensorflow Serving.

Few-shot object detection (FSOD) has attracted significant research attention in remote sensing due to its potential to reduce reliance on large annotated datasets. However, two challenges remain in ...

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Single point-supervised object detection is gaining attention due to its cost-effectiveness. However, existing approaches focus on generating horizontal bounding boxes (HBBs) while ignoring oriented ...