Commit
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1dfc285
1
Parent(s):
8db51c7
AutoAnchor BPR to 4 significant figures
Browse files- utils/utils.py +2 -2
utils/utils.py
CHANGED
@@ -66,7 +66,7 @@ def check_anchors(dataset, model, thr=4.0, imgsz=640):
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return (best > 1. / thr).float().mean() # best possible recall
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bpr = metric(anchors.clone().cpu().view(-1, 2))
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print('Best Possible Recall (BPR) = %.
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if bpr < 0.99: # threshold to recompute
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print('. Attempting to generate improved anchors, please wait...' % bpr)
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new_anchors = kmean_anchors(dataset, n=9, img_size=640, thr=4.0, gen=1000, verbose=False)
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@@ -733,7 +733,7 @@ def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=10
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k = k[np.argsort(k.prod(1))] # sort small to large
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x, best = metric(k, wh0)
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bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n # best possible recall, anch > thr
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print('thr=%.2f: %.
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print('n=%g, img_size=%s, metric_all=%.3f/%.3f-mean/best, past_thr=%.3f-mean: ' %
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(n, img_size, x.mean(), best.mean(), x[x > thr].mean()), end='')
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for i, x in enumerate(k):
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return (best > 1. / thr).float().mean() # best possible recall
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bpr = metric(anchors.clone().cpu().view(-1, 2))
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+
print('Best Possible Recall (BPR) = %.4f' % bpr, end='')
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if bpr < 0.99: # threshold to recompute
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print('. Attempting to generate improved anchors, please wait...' % bpr)
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new_anchors = kmean_anchors(dataset, n=9, img_size=640, thr=4.0, gen=1000, verbose=False)
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k = k[np.argsort(k.prod(1))] # sort small to large
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x, best = metric(k, wh0)
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bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n # best possible recall, anch > thr
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+
print('thr=%.2f: %.4f best possible recall, %.2f anchors past thr' % (thr, bpr, aat))
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print('n=%g, img_size=%s, metric_all=%.3f/%.3f-mean/best, past_thr=%.3f-mean: ' %
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(n, img_size, x.mean(), best.mean(), x[x > thr].mean()), end='')
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for i, x in enumerate(k):
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