AlphaRotate: A Rotation Detection Benchmark using TensorFlow
arXiv:2111.06677
Abstract
AlphaRotate is an open-source Tensorflow benchmark for performing scalable rotation detection on various datasets. It currently provides more than 18 popular rotation detection models under a single, well-documented API designed for use by both practitioners and researchers. AlphaRotate regards high performance, robustness, sustainability and scalability as the core concept of design, and all models are covered by unit testing, continuous integration, code coverage, maintainability checks, and visual monitoring and analysis. AlphaRotate can be installed from PyPI and is released under the Apache-2.0 License. Source code is available at https://github.com/yangxue0827/RotationDetection.
arXiv comment7 pages, 1 figure, 1 table
References in corpus (7)
- R-FCN: Object Detection via Region-based Fully Convolutional Networks
- MMDetection: Open MMLab Detection Toolbox and Benchmark
- Automatic Ship Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networks
- R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection
- Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence
- Position Detection and Direction Prediction for Arbitrary-Oriented Ships via Multitask Rotation Region Convolutional Neural Network
- Optimization for Arbitrary-Oriented Object Detection via Representation Invariance Loss