Light-Head RCNN: In Defense of two-stage object detector
Light-Head RCNN: In Defense of two-stage object detector 文章基於faster-rcnn和r-fcn進行改進 本文研究two-stage慢於one-stage的原因，發現faster rcnn和r-fcn會在roi warping之前或之後進行高強度的計算，faster-rcnn包含兩個fc用於ROI的識別，r-fcn則會生成一個大型得分圖（
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相關文章
- 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) 學習筆記
- 3. Light-Head R-CNN: In Defense of Two-Stage Object Detector
- 4. 目標檢測--Light-Head R-CNN: In Defense of Two-Stage Object Detector
- 5. 深度學習【55】物體檢測：Light-Head R-CNN: In Defense of Two-Stage Object Detector
- 6. 【論文學習記錄】Light-Head R-CNN: In Defense of Two-Stage Object Detector
- 7. In Defense of MOT：讀CVPR17《Deep Network Flow for Multi-Object Tracking》有感
- 8. 【論文筆記】物體檢測系列 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