chore: Reflect regularization option

The notebooks now explicitly reflect the L2 regularization option.
nicocurat · Jan 17, 2018 · 2164ff4 · 2164ff4
1 parent d1b2e19
commit 2164ff4
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Showing 5 changed files with 16 additions and 7 deletions.
diff --git a/ssd300_evaluation_COCO.ipynb b/ssd300_evaluation_COCO.ipynb
@@ -18,7 +18,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "from keras import backend as K\n",
@@ -81,7 +83,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# 1: Build the Keras model\n",
@@ -90,6 +94,7 @@
     "\n",
     "model = ssd_300(image_size=(img_height, img_width, 3),\n",
     "                n_classes=80,\n",
+    "                l2_regularization=0.0005,\n",
     "                scales=[0.07, 0.15, 0.33, 0.51, 0.69, 0.87, 1.05], # The scales for Pascal VOC are [0.1, 0.2, 0.37, 0.54, 0.71, 0.88, 1.05]\n",
     "                aspect_ratios_per_layer=[[1.0, 2.0, 0.5],\n",
     "                                         [1.0, 2.0, 0.5, 3.0, 1.0/3.0],\n",

diff --git a/ssd300_inference.ipynb b/ssd300_inference.ipynb
@@ -80,6 +80,7 @@
     "\n",
     "model = ssd_300(image_size=(img_height, img_width, 3),\n",
     "                n_classes=20,\n",
+    "                l2_regularization=0.0005,\n",
     "                scales=[0.1, 0.2, 0.37, 0.54, 0.71, 0.88, 1.05], # The scales for MS COCO are [0.07, 0.15, 0.33, 0.51, 0.69, 0.87, 1.05]\n",
     "                aspect_ratios_per_layer=[[1.0, 2.0, 0.5],\n",
     "                                         [1.0, 2.0, 0.5, 3.0, 1.0/3.0],\n",
@@ -320,8 +321,8 @@
     "           'sheep', 'sofa', 'train', 'tvmonitor']\n",
     "\n",
     "dataset.parse_xml(images_dirs=[VOC_2007_test_images_dir],\n",
-    "                  annotations_dirs=[VOC_2007_test_annotations_dir],\n",
     "                  image_set_filenames=[VOC_2007_test_image_set_filename],\n",
+    "                  annotations_dirs=[VOC_2007_test_annotations_dir],\n",
     "                  classes=classes,\n",
     "                  include_classes='all',\n",
     "                  exclude_truncated=False,\n",

diff --git a/ssd300_training.ipynb b/ssd300_training.ipynb
@@ -136,6 +136,7 @@
     "\n",
     "model = ssd_300(image_size=(img_height, img_width, img_channels),\n",
     "                n_classes=n_classes,\n",
+    "                l2_regularization=0.0005,\n",
     "                scales=scales,\n",
     "                aspect_ratios_per_layer=aspect_ratios,\n",
     "                two_boxes_for_ar1=two_boxes_for_ar1,\n",
@@ -269,19 +270,19 @@
     "\n",
     "train_dataset.parse_xml(images_dirs=[VOC_2007_images_dir,\n",
     "                                     VOC_2012_images_dir],\n",
-    "                        annotations_dirs=[VOC_2007_annotations_dir,\n",
-    "                                          VOC_2012_annotations_dir],\n",
     "                        image_set_filenames=[VOC_2007_trainval_image_set_filename,\n",
     "                                             VOC_2012_trainval_image_set_filename],\n",
+    "                        annotations_dirs=[VOC_2007_annotations_dir,\n",
+    "                                          VOC_2012_annotations_dir],\n",
     "                        classes=classes,\n",
     "                        include_classes='all',\n",
     "                        exclude_truncated=False,\n",
     "                        exclude_difficult=False,\n",
     "                        ret=False)\n",
     "\n",
     "val_dataset.parse_xml(images_dirs=[VOC_2007_test_images_dir],\n",
-    "                      annotations_dirs=[VOC_2007_test_annotations_dir],\n",
     "                      image_set_filenames=[VOC_2007_test_image_set_filename],\n",
+    "                      annotations_dirs=[VOC_2007_test_annotations_dir],\n",
     "                      classes=classes,\n",
     "                      include_classes='all',\n",
     "                      exclude_truncated=False,\n",

diff --git a/ssd512_inference.ipynb b/ssd512_inference.ipynb
@@ -80,6 +80,7 @@
     "\n",
     "model = ssd_512(image_size=(img_height, img_width, 3),\n",
     "                n_classes=20,\n",
+    "                l2_regularization=0.0005,\n",
     "                scales=[0.07, 0.15, 0.3, 0.45, 0.6, 0.75, 0.9, 1.05], # The scales for MS COCO are [0.04, 0.1, 0.26, 0.42, 0.58, 0.74, 0.9, 1.06]\n",
     "                aspect_ratios_per_layer=[[1.0, 2.0, 0.5],\n",
     "                                         [1.0, 2.0, 0.5, 3.0, 1.0/3.0],\n",
@@ -321,8 +322,8 @@
     "           'sheep', 'sofa', 'train', 'tvmonitor']\n",
     "\n",
     "dataset.parse_xml(images_dirs=[VOC_2007_test_images_dir],\n",
-    "                  annotations_dirs=[VOC_2007_test_annotations_dir],\n",
     "                  image_set_filenames=[VOC_2007_test_image_set_filename],\n",
+    "                  annotations_dirs=[VOC_2007_test_annotations_dir],\n",
     "                  classes=classes,\n",
     "                  include_classes='all',\n",
     "                  exclude_truncated=False,\n",

diff --git a/ssd7_training.ipynb b/ssd7_training.ipynb
@@ -134,6 +134,7 @@
     "\n",
     "model = build_model(image_size=(img_height, img_width, img_channels),\n",
     "                    n_classes=n_classes,\n",
+    "                    l2_regularization=0.0,\n",
     "                    scales=scales,\n",
     "                    aspect_ratios_global=aspect_ratios,\n",
     "                    aspect_ratios_per_layer=None,\n",