updates
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@ -1,5 +1,4 @@
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import argparse
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import argparse
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import time
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from sys import platform
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from sys import platform
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from models import * # set ONNX_EXPORT in models.py
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from models import * # set ONNX_EXPORT in models.py
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2
train.py
2
train.py
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@ -430,7 +430,7 @@ if __name__ == '__main__':
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# Mutate
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# Mutate
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init_seeds(seed=int(time.time()))
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init_seeds(seed=int(time.time()))
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s = [.15, .15, .15, .15, .15, .15, .15, .00, .02, .20, .15, .0, .0, .0, .0, .0, .0] # sigmas
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s = [.15, .15, .15, .15, .15, .15, .15, .00, .02, .20, .20, .20, .20, .20, .20, .20, .20] # sigmas
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for i, k in enumerate(hyp.keys()):
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for i, k in enumerate(hyp.keys()):
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x = (np.random.randn(1) * s[i] + 1) ** 2.0 # plt.hist(x.ravel(), 300)
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x = (np.random.randn(1) * s[i] + 1) ** 2.0 # plt.hist(x.ravel(), 300)
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hyp[k] *= float(x) # vary by sigmas
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hyp[k] *= float(x) # vary by sigmas
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@ -88,6 +88,9 @@ def coco_class_weights(): # frequency of each class in coco train2014
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1877, 17630, 4337, 4624, 1075, 3468, 135, 1380]
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1877, 17630, 4337, 4624, 1075, 3468, 135, 1380]
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weights = 1 / torch.Tensor(n)
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weights = 1 / torch.Tensor(n)
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weights /= weights.sum()
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weights /= weights.sum()
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# with open('data/coco.names', 'r') as f:
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# for k, v in zip(f.read().splitlines(), n):
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# print('%20s: %g' % (k, v))
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return weights
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return weights
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