updates
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@ -30,7 +30,6 @@ def modelinfo(model):
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print('\n%g layers, %g parameters, %g gradients' % (i + 1, nparams, ngradients))
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print('\n%g layers, %g parameters, %g gradients' % (i + 1, nparams, ngradients))
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def xview_class_weights(indices): # weights of each class in the training set, normalized to mu = 1
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def xview_class_weights(indices): # weights of each class in the training set, normalized to mu = 1
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weights = 1 / torch.FloatTensor(
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weights = 1 / torch.FloatTensor(
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[74, 364, 713, 71, 2925, 209767, 6925, 1101, 3612, 12134, 5871, 3640, 860, 4062, 895, 149, 174, 17, 1624, 1846,
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[74, 364, 713, 71, 2925, 209767, 6925, 1101, 3612, 12134, 5871, 3640, 860, 4062, 895, 149, 174, 17, 1624, 1846,
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@ -40,7 +39,6 @@ def xview_class_weights(indices): # weights of each class in the training set,
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return weights[indices]
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return weights[indices]
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def plot_one_box(x, im, color=None, label=None, line_thickness=None):
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def plot_one_box(x, im, color=None, label=None, line_thickness=None):
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tl = line_thickness or round(0.003 * max(im.shape[0:2])) # line thickness
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tl = line_thickness or round(0.003 * max(im.shape[0:2])) # line thickness
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color = color or [random.randint(0, 255) for _ in range(3)]
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color = color or [random.randint(0, 255) for _ in range(3)]
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