updates
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@ -594,13 +594,16 @@ def kmeans_targets(path='data/coco_64img.txt', n=9, img_size=416): # from utils
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# Measure IoUs
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# Measure IoUs
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iou = torch.stack([wh_iou(torch.Tensor(wh).T, torch.Tensor(x).T) for x in k], 0)
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iou = torch.stack([wh_iou(torch.Tensor(wh).T, torch.Tensor(x).T) for x in k], 0)
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miou = iou.mean() # mean IoU with all anchors
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biou = iou.max(0)[0] # closest anchor IoU
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biou = iou.max(0)[0].mean() # mean IoU with the closest anchor
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# Print results
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# Print
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print('kmeans anchors (n=%g, img_size=%g, IoU=%.2f/%.2f mean/best): ' % (n, img_size, miou, biou), end='')
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print('kmeans anchors (n=%g, img_size=%g, IoU=%.2f/%.2f/%.2f-min/mean/best): ' %
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for x in k.ravel():
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(n, img_size, biou.min(), iou.mean(), biou.mean()), end='')
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print('%.1f, ' % x, end='') # drop-in replacement for *.cfg anchors
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for i, x in enumerate(k):
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print('%i,%i' % (round(x[0]), round(x[1])), end=', ' if i < len(k) - 1 else '\n') # use in *.cfg
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# Plot
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# plt.hist(biou.numpy().ravel(), 100)
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def print_mutation(hyp, results, bucket=''):
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def print_mutation(hyp, results, bucket=''):
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