mmrotate仓库中 “主要模型” 及其 “配置文件” 的列表

这里写目录标题

• mmrotate 仓库中的主要模型和配置
• • Background and Motivation 背景与动机
  • Methods Overview 方法概述
  • • 1. CFA
    • • CFA: Convex-hull Feature Adaptation for Oriented and Densely Packed Object Detection
      • CFA：用于定向和密集对象检测的凸包特征适应
    • 2. ConvNeXt
    • • ConvNeXt: A ConvNet for the 2020s
      • ConvNeXt：面向2020年代的卷积神经网络
    • 3. Circular Smooth Label (CSL)
    • • Circular Smooth Label: Solving Boundary Discontinuity in Oriented Object Detection
      • 圆形平滑标签：解决定向目标检测中的边界不连续性问题
    • 4. G-Reppoints
    • • G-Rep: Gaussian Representation for Arbitrary-Oriented Object Detection
      • G-Rep：用于任意方向目标检测的高斯表示
    • 5. Gliding Vertex
    • • Gliding Vertex: Gliding Vertex on the Horizontal Bounding Box for Multi-Oriented Object Detection
      • Gliding Vertex：在水平边界框上滑动顶点用于多方向目标检测
    • 6. GWD
    • • GWD: Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss
      • GWD：用高斯沃瑟斯坦距离损失重新思考旋转目标检测
    • 7. K-FIoU
    • • KFIoU: The KFIoU Loss for Rotated Object Detection
      • KFIoU：用于旋转目标检测的KFIoU损失
    • 8. KLD
    • • KLD: Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence
      • KLD：通过Kullback-Leibler散度学习高精度旋转目标检测边界框
    • 9. Oriented R-CNN
    • • Oriented R-CNN: Oriented R-CNN for Object Detection
      • Oriented R-CNN：用于目标检测的定向R-CNN
    • 10. Oriented Reppoints
    • • Oriented Reppoints: Oriented Reppoints for Aerial Object Detection
      • Oriented Reppoints：用于航空目标检测的定向Reppoints
    • 11. R3Det
    • • R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object
      • R3Det：具有特征优化的精细单阶段旋转目标检测器
    • 12. ReDet (Rotation-equivariant Network)
    • • ReDet: A Rotation-equivariant Detector for Aerial Object Detection
      • ReDet：用于航空目标检测的旋转等变检测器
    • 13. RoI Transformer
    • • RoI Transformer: Learning RoI Transformer for Oriented Object Detection in Aerial Images
      • RoI Transformer：在航空图像中用于定向目标检测的RoI Transformer
    • 14. Rotated ATSS
    • • Rotated ATSS: Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection
      • Rotated ATSS：通过自适应训练样本选择弥合基于锚点和无锚点检测之间的差距
    • 15. Rotated Faster R-CNN
    • • Rotated Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
      • Rotated Faster R-CNN：用于实时目标检测的旋转区域提议网络
    • 16. Rotated FCOS
    • • Rotated FCOS: Fully Convolutional One-Stage Object Detection
      • Rotated FCOS：全卷积单阶段目标检测
    • 17. Rotated Reppoints
    • • Rotated Reppoints: RepPoints for Arbitrary-Oriented Object Detection
      • Rotated Reppoints：用于任意方向目标检测的Reppoints
    • 18. Rotated RetinaNet
    • • Rotated RetinaNet: Focal Loss for Dense Object Detection
      • Rotated RetinaNet：用于密集目标检测的焦点损失
    • 19. S2A-Net (Single-stage Anchor-free Network)
    • • S2A-Net: Align Deep Features for Oriented Object Detection
      • S2A-Net：对齐深度特征用于定向目标检测
    • 20. SASM Reppoints
    • • SASM: Shape-Adaptive Selection and Measurement for Oriented Object Detection
      • SASM：用于定向目标检测的形状自适应选择和测量