From fd0769c4766626213c308e826b1156c5197cf133 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Mon, 6 Jan 2020 13:59:08 -0800 Subject: [PATCH] updates --- utils/utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/utils/utils.py b/utils/utils.py index 2171d15b..0a99f861 100755 --- a/utils/utils.py +++ b/utils/utils.py @@ -782,7 +782,7 @@ def kmean_anchors(path='../coco/train2017.txt', n=9, img_size=(320, 640)): from scipy.cluster.vq import kmeans print('Running kmeans for %g anchors on %g points...' % (n, len(wh))) s = wh.std(0) # sigmas for whitening - k, dist = kmeans(wh / s, n, iter=20) # points, mean distance + k, dist = kmeans(wh / s, n, iter=30) # points, mean distance k *= s k = print_results(thr, wh, k) @@ -798,7 +798,7 @@ def kmean_anchors(path='../coco/train2017.txt', n=9, img_size=(320, 640)): wh = torch.Tensor(wh) f, ng = fitness(thr, wh, k), 1000 # fitness, generations for _ in tqdm(range(ng), desc='Evolving anchors'): - kg = (k.copy() * (1 + np.random.random() * np.random.randn(*k.shape) * 0.20)).clip(min=2.0) + kg = (k.copy() * (1 + np.random.random() * np.random.randn(*k.shape) * 0.30)).clip(min=2.0) fg = fitness(thr, wh, kg) if fg > f: f, k = fg, kg.copy()