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Search results with tag "Yolov3"

FCOS: Fully Convolutional One-Stage Object Detection

FCOS: Fully Convolutional One-Stage Object Detection

openaccess.thecvf.com

YOLOv3, and Faster R-CNN rely on pre-defined anchor boxes. In contrast, our proposed detector FCOS is anchor box free, as well as proposal free. By eliminating the pre-defined set of anchor boxes, FCOS completely avoids the complicated computation related to anchor boxes such as calculating overlapping during training. More importantly,

  Yolov3

YOLOv3: An Incremental Improvement

YOLOv3: An Incremental Improvement

pjreddie.com

YOLOv3-320 YOLOv3-416 YOLOv3-608 mAP 28.0 28.0 29.9 31.2 33.2 36.2 32.5 34.4 37.8 28.2 31.0 33.0 time 61 85 85 125 156 172 73 90 198 22 29 51 Figure 1. We adapt this figure from the Focal Loss paper [9]. YOLOv3 runs significantly faster than other detection methods with comparable performance. Times from either an M40 or Titan X, they are ...

  Yolov3

YOLOv3 を用いた圃場におけるレタスの位置検出と精度評価

YOLOv3 を用いた圃場におけるレタスの位置検出と精度評価

ais.shinshu-u.ac.jp

YOLOv3はdarknet53と呼ばれる独自の特徴抽出機を ベースに学習をおこなう.darknet53はResidual Blockを導入することで層が深くなり,検出精度の向 上に寄与している.表1にdarknet53 のネットワー ク構造の詳細を示す. 表1 darknet53 2.2. 中心位置と検出対象範囲

  Yolov3

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