from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals from edict import AttrDict import six import numpy as np _C = AttrDict() cfg = _C """ Training options """ _C.TRAIN = AttrDict() # scales an image's shortest side _C.TRAIN.scales = [800] # max size of longest side _C.TRAIN.max_size = 1333 # images per GPU in minibatch _C.TRAIN.im_per_batch = 1 # stopgrad at a spcified stage _C.TRAIN.freeze_at = 2 # number of RPN proposals to keep before NMS _C.TRAIN.rpn_pre_nms_top_n = 12000 # number of RPN proposals to keep after NMS
# http://www.apache.org/licenses/LICENSE-2.0 #Unless required by applicable law or agreed to in writing, software #distributed under the License is distributed on an "AS IS" BASIS, #WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #See the License for the specific language governing permissions and #limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals from edict import AttrDict import six import numpy as np _C = AttrDict() cfg = _C # # Training options # # Snapshot period _C.snapshot_iter = 2000 # min valid area for gt boxes _C.gt_min_area = -1 # max target box number in an image _C.max_box_num = 50
from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import sys import distutils.util import numpy as np import six from collections import deque import argparse import functools from edict import AttrDict import pdb _C = AttrDict() cfg = _C # # Training options # _C.data_dir = './veri' # Snapshot period _C.snapshot_iter = 2000 _C.num_instances = 1 _C.batch_size = 64 # pixel mean values
# http://www.apache.org/licenses/LICENSE-2.0 #Unless required by applicable law or agreed to in writing, software #distributed under the License is distributed on an "AS IS" BASIS, #WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #See the License for the specific language governing permissions and #limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals from edict import AttrDict import six import numpy as np _C = AttrDict() cfg = _C # # Training options # _C.TRAIN = AttrDict() # scales an image's shortest side _C.TRAIN.scales = [800] # max size of longest side _C.TRAIN.max_size = 1333 # images per GPU in minibatch _C.TRAIN.im_per_batch = 1
# http://www.apache.org/licenses/LICENSE-2.0 #Unless required by applicable law or agreed to in writing, software #distributed under the License is distributed on an "AS IS" BASIS, #WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #See the License for the specific language governing permissions and #limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals from edict import AttrDict import six import numpy as np _C = AttrDict() cfg = _C # # Training options # _C.TRAIN = AttrDict() # scales an image's shortest side _C.TRAIN.scales = [800] # max size of longest side _C.TRAIN.max_size = 1333 # images per GPU in minibatch _C.TRAIN.im_per_batch = 1