updates
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@ -755,7 +755,7 @@ def kmean_anchors(path='data/coco64.txt', n=9, img_size=(320, 640)):
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wh *= np.random.uniform(img_size[0], img_size[1], size=(wh.shape[0], 1)) # normalized to pixels (multi-scale)
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# Kmeans calculation
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print('Running kmeans on %g boxes...' % len(wh))
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print('Running kmeans on %g points...' % len(wh))
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s = wh.std(0) # sigmas for whitening
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k, dist = kmeans(wh / s, n, iter=30) # points, mean distance
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k = k[np.argsort(k.prod(1))] * s # sort small to large
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