YOLOv3使用笔记——Kmeans聚类计算anchor boxes

        anchor boxes用来预测bounding box,faster rcnn中用128*128,256*256,512*512,分三个尺度变换1:1,1:2,2:1,共计9个anchor来预测框,每个anchor预测2000个框左右,使得检出率提高很多。YOLOv2开始增加了anchor机制,在v3中增加到9个anchor。例如yolov3-voc.cfg中这组anchor,anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326,由作者通过聚类VOC数据集得到的,20类目标中大到bicycle、bus,小到bird、cat,目标大小差距很大,如果用自己的数据集训练检测目标,其中部分anchor并不合理,本文记录下在自己的数据集上聚类计算anchor,提高bounding box的检出率。

 

原工程:https://github.com/lars76/kmeans-anchor-boxes

Joseph Redmon论文数据avg iou在67.2,该作者验证在k=9时,多次迭代在VOC 2007数据集上得到avg iou在67.13,相差无几。

 

修改的example.py

import glob
import xml.etree.ElementTree as ET

import numpy as np

from kmeans import kmeans, avg_iou

ANNOTATIONS_PATH = "Annotations"
CLUSTERS = 9


def load_dataset(path):
  dataset = []
  for xml_file in glob.glob("{}/*xml".format(path)):
    tree = ET.parse(xml_file)

    height = int(tree.findtext("./size/height"))
    width = int(tree.findtext("./size/width"))

    for obj in tree.iter("object"):
      xmin = int(obj.findtext("bndbox/xmin")) / width
      ymin = int(obj.findtext("bndbox/ymin")) / height
      xmax = int(obj.findtext("bndbox/xmax")) / width
      ymax = int(obj.findtext("bndbox/ymax")) / height

      xmin = np.float64(xmin)
      ymin = np.float64(ymin)
      xmax = np.float64(xmax)
      ymax = np.float64(ymax)
      if xmax == xmin or ymax == ymin:
         print(xml_file)
      dataset.append([xmax - xmin, ymax - ymin])
  return np.array(dataset)

if __name__ == '__main__':
  #print(__file__)
  data = load_dataset(ANNOTATIONS_PATH)
  out = kmeans(data, k=CLUSTERS)
  #clusters = [[10,13],[16,30],[33,23],[30,61],[62,45],[59,119],[116,90],[156,198],[373,326]]
  #out= np.array(clusters)/416.0
  print(out)
  print("Accuracy: {:.2f}%".format(avg_iou(data, out) * 100))
  print("Boxes:\n {}-{}".format(out[:, 0]*416, out[:, 1]*416))

  ratios = np.around(out[:, 0] / out[:, 1], decimals=2).tolist()
  print("Ratios:\n {}".format(sorted(ratios)))

 

Kmeans因为初始点敏感,所以每次运行得到的anchor值不一样,但是对应的avg iou稳定。用于训练的话就需要统计多组anchor,针对固定的测试集比较了。

可以计算下VOC的这组anchor在自己数据集上的avg iou,对比直接在数据集上聚类得到的anchor以及avg iou。

 

 

参考:

https://blog.csdn.net/hrsstudy/article/details/71173305?utm_source=itdadao&utm_medium=referral

https://blog.csdn.net/sinat_24143931/article/details/78773936

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