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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-12-30 15:39:17 -08:00
parent 2cf31ab7bc
commit d30e4eea37
1 changed files with 6 additions and 4 deletions
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10
utils/utils.py
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@ -734,7 +734,7 @@ def coco_single_class_labels(path='../coco/labels/train2014/', label_class=43):
shutil.copyfile(src=img_file, dst='new/images/' + Path(file).name.replace('txt', 'jpg')) # copy images
def kmean_anchors(path='data/coco64.txt', n=9, img_size=(288, 640)): # from utils.utils import *; kmean_anchors()
def kmean_anchors(path='../coco/train2017.txt', n=12, img_size=(320, 640)): # from utils.utils import *; kmean_anchors()
# Produces a list of target kmeans suitable for use in *.cfg files
from utils.datasets import LoadImagesAndLabels
from scipy import cluster
@ -762,12 +762,14 @@ def kmean_anchors(path='data/coco64.txt', n=9, img_size=(288, 640)): # from uti
# Measure IoUs
iou = wh_iou(torch.Tensor(wh), torch.Tensor(k))
biou = iou.max(1)[0] # closest anchor IoU
print('Best Possible Recall (BPR): %.3f' % (biou > 0.225).float().mean()) # BPR (best possible recall)
max_iou = iou.max(1)[0] # best IoU
min_iou = iou.min(1)[0] # worst IoU
print('Best Possible Recall (BPR): %.3f' % (max_iou > 0.225).float().mean()) # BPR (best possible recall)
print('Mean anchors over threshold: %.3f' % ((iou > 0.225).float().mean() * n)) # BPR (best possible recall)
# Print
print('kmeans anchors (n=%g, img_size=%s, IoU=%.2f/%.2f/%.2f-min/mean/best): ' %
(n, img_size, biou.min(), iou.mean(), biou.mean()), end='')
(n, img_size, min_iou.mean(), iou.mean(), max_iou.mean()), end='')
for i, x in enumerate(k):
print('%i,%i' % (round(x[0]), round(x[1])), end=', ' if i < len(k) - 1 else '\n') # use in *.cfg