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Explanation of the segmentation parrameters #228
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Hi team,
I am looking to train using my own data and wanted to get some basic explanation on the relevance of the parrameters
sample_num = 2048
batch_size = 12
num_epochs = 256
In my settings file i have :
num_class = 2
sample_num = 2048
batch_size = 12
num_epochs = 256
label_weights = []
for c in range(num_class):
label_weights.append(1.0)
learning_rate_base = 0.001
decay_steps = 20000
decay_rate = 0.7
learning_rate_min = 1e-6
step_val = 500
weight_decay = 0.0
Trying to train on aerial data with 8 points per sq meter density and i have a training dataset of 800 000 000 million points.
I am seing some over-fitting very early on with these values:
num_class = 2
sample_num = 12288
batch_size = 6
num_epochs = 8096
label_weights = []
for c in range(num_class):
label_weights.append(1.0)
learning_rate_base = 0.001
decay_steps = 20000
decay_rate = 0.7
learning_rate_min = 1e-6
step_val = 500
weight_decay = 0.0
jitter = 0.0
jitter_val = 0.0
rotation_range = [0, math.pi/32., 0, 'u']
rotation_range_val = [0, 0, 0, 'u']
rotation_order = 'rxyz'
scaling_range = [0.0, 0.0, 0.0, 'g']
scaling_range_val = [0, 0, 0, 'u']
sample_num_variance = 1 // 8
sample_num_clip = 1 // 4
Any advice?
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