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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-12-30 13:09:16 -08:00
parent 88579bd24e
commit e4a797fc1e
1 changed files with 4 additions and 2 deletions
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6
utils/utils.py
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@ -744,7 +744,9 @@ def kmeans_targets(path='data/coco64.txt', n=9, img_size=416): # from utils.uti
for s, l in zip(dataset.shapes, dataset.labels):
l[:, [1, 3]] *= s[0] # normalized to pixels
l[:, [2, 4]] *= s[1]
l[:, 1:] *= img_size / max(s) * random.uniform(0.5, 1.5) # nominal img_size for training
l[:, 1:] *= img_size / max(s)
l = l.repeat(10, axis=0) # augment 10x
l *= np.random.uniform(0.5, 1.5, size=(l.shape[0], 1)) # multi-scale box
wh = np.concatenate(dataset.labels, 0)[:, 3:5] # wh from cxywh
# Kmeans calculation
@ -762,7 +764,7 @@ def kmeans_targets(path='data/coco64.txt', n=9, img_size=416): # from utils.uti
# Measure IoUs
iou = wh_iou(torch.Tensor(wh), torch.Tensor(k))
biou = iou.max(0)[0] # closest anchor IoU
print('Best Possible Recall (BPR): %.3f' % (biou > 0.2635).float().mean()) # BPR (best possible recall)
print('Best Possible Recall (BPR): %.3f' % (biou > 0.225).float().mean()) # BPR (best possible recall)
# Print
print('kmeans anchors (n=%g, img_size=%g, IoU=%.2f/%.2f/%.2f-min/mean/best): ' %