Merge commit 'ed6b0410d252e408793bec0ac630a40137dfee36'

* commit 'ed6b0410d252e408793bec0ac630a40137dfee36':
  Update README.md
  update readme
  update models.
  Fix typo.
  Fix default options.
  Update new models.
  Update new models.
  Update readme.
  change threshold for testing
  Update README.md
  BUG FIX: Include regularization in the loss.
  Update config.py

# Conflicts:
#	README.md
#	lib/model/test.py
#	lib/nets/network.py
#	lib/nets/resnet_v1.py
#	lib/nets/vgg16.py

Author: ruotianluo

experiments/cfgs/res101-lg.yml

@@ -9,7 +9,6 @@ TRAIN:
   BG_THRESH_LO: 0.0
   DISPLAY: 20
   BATCH_SIZE: 256
-  WEIGHT_DECAY: 0.0001
   DOUBLE_BIAS: False
   SNAPSHOT_PREFIX: res101_faster_rcnn
   SCALES: [800]

experiments/cfgs/res101.yml

@@ -9,7 +9,6 @@ TRAIN:
   BG_THRESH_LO: 0.0
   DISPLAY: 20
   BATCH_SIZE: 256
-  WEIGHT_DECAY: 0.0001
   DOUBLE_BIAS: False
   SNAPSHOT_PREFIX: res101_faster_rcnn
 TEST:

experiments/cfgs/res50.yml

@@ -9,7 +9,6 @@ TRAIN:
   BG_THRESH_LO: 0.0
   DISPLAY: 20
   BATCH_SIZE: 256
-  WEIGHT_DECAY: 0.0001
   DOUBLE_BIAS: False
   SNAPSHOT_PREFIX: res50_faster_rcnn
 TEST:

lib/model/config.py

@@ -25,7 +25,7 @@
 __C.TRAIN.MOMENTUM = 0.9
 
 # Weight decay, for regularization
-__C.TRAIN.WEIGHT_DECAY = 0.0005
+__C.TRAIN.WEIGHT_DECAY = 0.0001
 
 # Factor for reducing the learning rate
 __C.TRAIN.GAMMA = 0.1
@@ -232,7 +232,7 @@
 # Whether to regularize the depth-wise filters during training
 __C.MOBILENET.REGU_DEPTH = False
 
-# Number of fixed layers during training, by default the first of all 14 layers is fixed
+# Number of fixed layers during training, by default the bottom 5 of 14 layers is fixed
 # Range: 0 (none) to 12 (all)
 __C.MOBILENET.FIXED_LAYERS = 5
 

lib/model/test.py

@@ -137,7 +137,7 @@ def apply_nms(all_boxes, thresh):
       nms_boxes[cls_ind][im_ind] = dets[keep, :].copy()
   return nms_boxes
 
-def test_net(net, imdb, weights_filename, max_per_image=100, thresh=0.05):
+def test_net(net, imdb, weights_filename, max_per_image=100, thresh=0.):
   np.random.seed(cfg.RNG_SEED)
   """Test a Fast R-CNN network on an image database."""
   num_images = len(imdb.image_index)

lib/nets/mobilenet_v1.py

@@ -85,7 +85,7 @@ def mobilenet_v1_base(inputs,
                       min_depth=8,
                       depth_multiplier=1.0,
                       output_stride=None,
-                      reuse=False,
+                      reuse=None,
                       scope=None):
   """Mobilenet v1.
   Constructs a Mobilenet v1 network from inputs to the given final endpoint.
@@ -211,7 +211,7 @@ def __init__(self):
     self._depth_multiplier = cfg.MOBILENET.DEPTH_MULTIPLIER
     self._scope = 'MobilenetV1'
 
-  def _image_to_head(self, is_training, reuse=False):
+  def _image_to_head(self, is_training, reuse=None):
     # Base bottleneck
     assert (0 <= cfg.MOBILENET.FIXED_LAYERS <= 12)
     net_conv = self._image
@@ -237,7 +237,7 @@ def _image_to_head(self, is_training, reuse=False):
 
     return net_conv
 
-  def _head_to_tail(self, pool5, is_training, reuse=False):
+  def _head_to_tail(self, pool5, is_training, reuse=None):
     with slim.arg_scope(mobilenet_v1_arg_scope(is_training=is_training)):
       fc7 = mobilenet_v1_base(pool5,
                               _CONV_DEFS[12:],

lib/utils/visualization.py

@@ -8,6 +8,7 @@
 from __future__ import print_function
 
 import numpy as np
+from six.moves import range
 import PIL.Image as Image
 import PIL.ImageColor as ImageColor
 import PIL.ImageDraw as ImageDraw
@@ -39,7 +40,14 @@
     'WhiteSmoke', 'Yellow', 'YellowGreen'
 ]
 
-def draw_single_box(image, xmin, ymin, xmax, ymax, display_str, font, color='black', thickness=4):
+NUM_COLORS = len(STANDARD_COLORS)
+
+try:
+  FONT = ImageFont.truetype('arial.ttf', 24)
+except IOError:
+  FONT = ImageFont.load_default()
+
+def _draw_single_box(image, xmin, ymin, xmax, ymax, display_str, font, color='black', thickness=4):
   draw = ImageDraw.Draw(image)
   (left, right, top, bottom) = (xmin, xmax, ymin, ymax)
   draw.line([(left, top), (left, bottom), (right, bottom),
@@ -62,15 +70,10 @@ def draw_single_box(image, xmin, ymin, xmax, ymax, display_str, font, color='bla
 
 def draw_bounding_boxes(image, gt_boxes, im_info):
   num_boxes = gt_boxes.shape[0]
-  num_colors = len(STANDARD_COLORS)
-  im_info = im_info[0]
+  gt_boxes_new = gt_boxes.copy()
+  gt_boxes_new[:,:4] = np.round(gt_boxes_new[:,:4].copy() / im_info[2])
   disp_image = Image.fromarray(np.uint8(image[0]))
 
-  try:
-    font = ImageFont.truetype('arial.ttf', 24)
-  except IOError:
-    font = ImageFont.load_default()
-
   for i in range(num_boxes):
     this_class = int(gt_boxes[i, 4])
     disp_image = draw_single_box(disp_image,