Esempio n. 1
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# Factor for reducing the learning rate
__C.TRAIN.GAMMA = 0.1

# Whether to double the learning rate for bias
__C.TRAIN.DOUBLE_BIAS = True

# Whether to initialize the weights with truncated normal distribution
__C.TRAIN.TRUNCATED = False

# Whether to have weight decay on bias as well
__C.TRAIN.BIAS_DECAY = False

##### RPN OPTION ####
__C.ANCHOR_SCALES = np.array([8, 16, 32])

__C.ANCHOR_RATIOS = [0.5, 1, 2]

# Down-sampling ratio
__C.FEAT_STRIDE = [
    16,
]

__C.TRAIN.RPN_CLOBBER_POSITIVES = False

__C.TRAIN.RPN_POSITIVE_OVERLAP = 0.7

__C.TRAIN.RPN_NEGATIVE_OVERLAP = 0.3

__C.TRAIN.RPN_BATCHSIZE = 256

__C.TRAIN.RPN_FG_FRACTION = 0.5