<commit_before>from __future__ import division
from __future__ import print_function

# Train and evaluate a classification model for VOC2012
import argparse
import config
import numpy as np

parser = argparse.ArgumentParser(description='Train and evaluate a classification model for VOC')
# Model parameters
parser.add_argument('prototxt', help='prototxt file of the model to evaluate (deploy.prototxt preferred)')
parser.add_argument('caffemodel', help='model weights of the model to evaluate')

# Training parameters
parser.add_argument('--output-dir', help='Do we want to save the model?')
parser.add_argument('--voc-dir', default=config.VOC_DIR, help='The VOC2007 or VOC2012 data directory')
parser.add_argument('--gpu', type=int, help='What GPU do we train on')

# Solver parameters
parser.add_argument('--solver', default='SGD', help='Solver type')
parser.add_argument('-lr', type=float, default=0.001, help='Base LR')
parser.add_argument('-bs', type=int, default=16, help='batch size')
parser.add_argument('-nit', type=int, default=80000, help='Number of training iterations')

# Initialization parameters
parser.add_argument('--mean_value', help='A comma-separated list of floats specifying the per-channel values of the mean to subtract')
parser.add_argument('--no-mean', action='store_true', help='Do not mean center')
parser.add_argument('--clip', default='drop7', help='clip the network at this layer')
parser.add_argument('--train-from', default=None, help='Train only layers after this layer')
parser.add_argument('--random-from', default='drop7', help='Initialize all layers after this layer randomly')
parser.add_argument('--scale', type=float, default=1.0, help='Scale of the input data')
parser.add_argument('--min-scale', type=float, default=0.5, help='Minimum scale transformation of image')
parser.add_argument('--max-scale', type=float, default=2.0, help='Maximum scale transformation of image')
parser.add_argument('--resize', type=int, help='Resize the images before passing them to the CNN')<commit_msg>don't randomize fc8 by default<commit_after>from __future__ import division
from __future__ import print_function

# Train and evaluate a classification model for VOC2012
import argparse
import config
import numpy as np

parser = argparse.ArgumentParser(description='Train and evaluate a classification model for VOC')
# Model parameters
parser.add_argument('prototxt', help='prototxt file of the model to evaluate (deploy.prototxt preferred)')
parser.add_argument('caffemodel', help='model weights of the model to evaluate')

# Training parameters
parser.add_argument('--output-dir', help='Do we want to save the model?')
parser.add_argument('--voc-dir', default=config.VOC_DIR, help='The VOC2007 or VOC2012 data directory')
parser.add_argument('--gpu', type=int, help='What GPU do we train on')

# Solver parameters
parser.add_argument('--solver', default='SGD', help='Solver type')
parser.add_argument('-lr', type=float, default=0.001, help='Base LR')
parser.add_argument('-bs', type=int, default=16, help='batch size')
parser.add_argument('-nit', type=int, default=80000, help='Number of training iterations')

# Initialization parameters
parser.add_argument('--mean_value', help='A comma-separated list of floats specifying the per-channel values of the mean to subtract')
parser.add_argument('--no-mean', action='store_true', help='Do not mean center')
parser.add_argument('--clip', default='drop7', help='clip the network at this layer')
parser.add_argument('--train-from', default=None, help='Train only layers after this layer')
parser.add_argument('--random-from', default=None, help='Initialize all layers after this layer randomly')
parser.add_argument('--scale', type=float, default=1.0, help='Scale of the input data')
parser.add_argument('--min-scale', type=float, default=0.5, help='Minimum scale transformation of image')
parser.add_argument('--max-scale', type=float, default=2.0, help='Maximum scale transformation of image')
parser.add_argument('--resize', type=int, help='Resize the images before passing them to the CNN')
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