Light-Head RCNN: In Defense of two-stage object detector
Light-Head RCNN: In Defense of two-stage object detector 文章基于faster-rcnn和r-fcn进行改进 本文研究two-stage慢于one-stage的原因，发现faster rcnn和r-fcn会在roi warping之前或之后进行高强度的计算，faster-rcnn包含两个fc用于ROI的识别，r-fcn则会生成一个大型得分图（
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相关文章
- 1. 【Light-Head RCNN】《Light-Head R-CNN: In Defense of Two-Stage Object Detector》
- 2. Light-Head R-CNN(In Defense of Two-Stage Object Detector) 学习笔记
- 3. Light-Head R-CNN: In Defense of Two-Stage Object Detector
- 4. 目标检测--Light-Head R-CNN: In Defense of Two-Stage Object Detector
- 5. 深度学习【55】物体检测：Light-Head R-CNN: In Defense of Two-Stage Object Detector
- 6. 【论文学习记录】Light-Head R-CNN: In Defense of Two-Stage Object Detector
- 7. In Defense of MOT：读CVPR17《Deep Network Flow for Multi-Object Tracking》有感
- 8. 【论文笔记】物体检测系列 Light-Head R-CNN: In Defense of Two-Stage Object Detector
- 9. In Defense of the Triplet Loss for Person Re-Identification
- 10. How to implement a YOLO (v3) object detector from scratch in PyTorch: Part 1