updates
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@ -669,7 +669,7 @@ def coco_single_class_labels(path='../coco/labels/train2014/', label_class=43):
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shutil.copyfile(src=img_file, dst='new/images/' + Path(file).name.replace('txt', 'jpg')) # copy images
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shutil.copyfile(src=img_file, dst='new/images/' + Path(file).name.replace('txt', 'jpg')) # copy images
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def kmeans_targets(path='../coco/trainvalno5k.txt', n=9, img_size=416): # from utils.utils import *; kmeans_targets()
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def kmeans_targets(path='../coco/trainvalno5k.txt', n=9, img_size=512): # from utils.utils import *; kmeans_targets()
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# Produces a list of target kmeans suitable for use in *.cfg files
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# Produces a list of target kmeans suitable for use in *.cfg files
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from utils.datasets import LoadImagesAndLabels
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from utils.datasets import LoadImagesAndLabels
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from scipy import cluster
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from scipy import cluster
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@ -679,7 +679,7 @@ def kmeans_targets(path='../coco/trainvalno5k.txt', n=9, img_size=416): # from
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for s, l in zip(dataset.shapes, dataset.labels):
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for s, l in zip(dataset.shapes, dataset.labels):
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l[:, [1, 3]] *= s[0] # normalized to pixels
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l[:, [1, 3]] *= s[0] # normalized to pixels
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l[:, [2, 4]] *= s[1]
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l[:, [2, 4]] *= s[1]
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l[:, 1:] *= img_size / max(s) # nominal img_size for training
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l[:, 1:] *= img_size / max(s) * random.uniform(0.99, 1.01) # nominal img_size for training
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wh = np.concatenate(dataset.labels, 0)[:, 3:5] # wh from cxywh
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wh = np.concatenate(dataset.labels, 0)[:, 3:5] # wh from cxywh
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# Kmeans calculation
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# Kmeans calculation
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