Ankle/yolov5_birdproject
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AutoAnchor BPR to 4 significant figures

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Glenn Jocher committed 2020-06-16 16:19:58 -07:00
1 parent 8db51c7002
commit 1dfc28527f
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utils/utils.py
+2 -2
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@@ -66,7 +66,7 @@ def check_anchors(dataset, model, thr=4.0, imgsz=640):
return (best > 1. / thr).float().mean() #  best possible recall return (best > 1. / thr).float().mean() #  best possible recall
bpr = metric(anchors.clone().cpu().view(-1, 2)) bpr = metric(anchors.clone().cpu().view(-1, 2))
print('Best Possible Recall (BPR) = %.3f' % bpr, end='') print('Best Possible Recall (BPR) = %.4f' % bpr, end='')
if bpr < 0.99: # threshold to recompute if bpr < 0.99: # threshold to recompute
print('. Attempting to generate improved anchors, please wait...' % bpr) print('. Attempting to generate improved anchors, please wait...' % bpr)
new_anchors = kmean_anchors(dataset, n=9, img_size=640, thr=4.0, gen=1000, verbose=False) new_anchors = kmean_anchors(dataset, n=9, img_size=640, thr=4.0, gen=1000, verbose=False)
@@ -733,7 +733,7 @@ def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=10
k = k[np.argsort(k.prod(1))] # sort small to large k = k[np.argsort(k.prod(1))] # sort small to large
x, best = metric(k, wh0) x, best = metric(k, wh0)
bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n # best possible recall, anch > thr bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n # best possible recall, anch > thr
print('thr=%.2f: %.3f best possible recall, %.2f anchors past thr' % (thr, bpr, aat)) print('thr=%.2f: %.4f best possible recall, %.2f anchors past thr' % (thr, bpr, aat))
print('n=%g, img_size=%s, metric_all=%.3f/%.3f-mean/best, past_thr=%.3f-mean: ' % print('n=%g, img_size=%s, metric_all=%.3f/%.3f-mean/best, past_thr=%.3f-mean: ' %
(n, img_size, x.mean(), best.mean(), x[x > thr].mean()), end='') (n, img_size, x.mean(), best.mean(), x[x > thr].mean()), end='')
for i, x in enumerate(k): for i, x in enumerate(k):