mmrotate仓库中 “主要模型” 及其 “配置文件” 的列表

mmrotate

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
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