物体検出で重なったバウンディングボックスを除去・集約するアルゴリズムのまとめ (NMS, Soft-NMS, NMW, WBF) - け日記
物体検出の分野では、検出した物体をバウンディングボックス (BBox) で囲んで、それぞれに信頼度 (スコア) を算出します。 このとき重複したBBoxを除去あるいは集約するアルゴリズムにはバリエーションがあります。物体検出モデルの後処理やコンペなどでよく使われる4つを紹介します。 NMS Soft-NMS NMW WBF 最初におさらい: IoU (Intersection over Union) 2つのBBoxがどれくらい重複しているかを表す指標の1つで、1.0に近づくほど重複しています。 分子が重なっている面積 分母が2つのBBoxの総面積 実装 def iou(a: tuple, b: tuple) -> float: a_x1, a_y1, a_x2, a_y2 = a b_x1, b_y1, b_x2, b_y2 = b if a == b: return 1.0 elif (
GitHub - ZFTurbo/Weighted-Boxes-Fusion: Set of methods to ensemble boxes from different object detection models, including implementation of "Weighted boxes fusion (WBF)" method.
Coordinates for boxes expected to be normalized e.g in range [0; 1]. Order: x1, y1, x2, y2. Example of boxes ensembling for 2 models below. First model predicts 5 boxes, second model predicts 4 boxes. Confidence scores for each box model 1: [0.9, 0.8, 0.2, 0.4, 0.7] Confidence scores for each box model 2: [0.5, 0.8, 0.7, 0.3] Labels (classes) for each box model 1: [0, 1, 0, 1, 1] Labels (classes)
Non-Maximum Suppression for Object Detection in Python - PyImageSearch
Machine Learning Tutorials by Adrian Rosebrock on November 17, 2014 Connecticut is cold. Very cold. Sometimes it’s hard to even get out of bed in the morning. And honestly, without the aide of copious amounts of pumpkin spice lattes and the beautiful sunrise over the crisp autumn leaves, I don’t think I would leave my cozy bed. But I have work to do. And today that work includes writing a blog pos
(Faster) Non-Maximum Suppression in Python - PyImageSearch
PyImageSearch You can master Computer Vision, Deep Learning, and OpenCV - PyImageSearch I have issues — I can’t stop thinking about object detection. You see, last night I was watching The Walking Dead and instead of enjoying the zombie brutality, the forced cannibalism, or the enthralling storyline, all I wanted to do was build an object detection system to recognize zombies. Would it be very use
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NMS by Representative Region: Towards Crowded Pedestrian Detection by Proposal Pairing
Learning non-maximum suppression
