【論文閱讀】You Only Look Once: Unified, Real-Time Object Detection
論文簡介： YOLO是作者針對R-CNN系列等的Two-stage的目標檢測算法的時間性能較低，訓練、預測Pipeline分散複雜等的缺點，提出的一種更接近端到端的目標檢測算法。其具有更加統一、清晰的結構，具有較高的時間性能，可以達到實時檢測，但具有比Faster R-CNN較低的準確性（MAP）。 一、YOLO V1 簡介 在R-CNN系列等的Two-stage的目標檢測算法中，基本都遵循需要進
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相關文章
- 1. 論文閱讀筆記：You Only Look Once: Unified, Real-Time Object Detection(YOLO)
- 2. 【論文閱讀】YOLOv1-You Only Look Once: Unified, Real-Time Object Detection Joseph
- 3. 論文閱讀筆記：You Only Look Once: Unified, Real-Time Object Detection
- 4. You Only Look Once: Unified, Real-Time Object Detection
- 5. You Only Look Once Unified, Real-Time Object Detection
- 6. 【YOLOv1】《You Only Look Once: Unified, Real-Time Object Detection》
- 7. YOLOv1：You Only Look Once: Unified, Real-Time Object Detection
- 8. You Only Look Once: Unified, Real-Time Object Detection 論文詳讀
- 9. 【論文閱讀筆記】You Only Look Once: Unified，Real-Time Object Dection
- 10. You Only Look Once: Unified, Real-Time Object Detection閱讀筆記