Merge branch 'master' into cifar10_experiment

object_detection/data/pascal_label_map.pbtxt

-item {
-  id: 0
-  name: 'none_of_the_above'
-}
-
 item {
   id: 1
   name: 'aeroplane'

object_detection/data/pet_label_map.pbtxt

-item {
-  id: 0
-  name: 'none_of_the_above'
-}
-
 item {
   id: 1
   name: 'Abyssinian'

object_detection/g3doc/configuring_jobs.md

@@ -157,6 +157,6 @@ number of workers, gpu type).
 
 ## Configuring the Evaluator
 
-Currently evaluation is fixed to generating metrics as defined by the PASCAL
-VOC challenge. The parameters for `eval_config` are set to reasonable defaults
-and typically do not need to be configured.
+Currently evaluation is fixed to generating metrics as defined by the PASCAL VOC
+challenge. The parameters for `eval_config` are set to reasonable defaults and
+typically do not need to be configured.

object_detection/g3doc/installation.md

@@ -74,6 +74,6 @@ to avoid running this manually, you can add it as a new line to the end of your
 You can test that you have correctly installed the Tensorflow Object Detection\
 API by running the following command:
 
-``` bash
+```bash
 python object_detection/builders/model_builder_test.py
 ```

object_detection/g3doc/running_locally.md

@@ -77,5 +77,5 @@ tensorboard --logdir=${PATH_TO_MODEL_DIRECTORY}
 ```
 
 where `${PATH_TO_MODEL_DIRECTORY}` points to the directory that contains the
-train and eval directories. Please note it make take Tensorboard a couple
-minutes to populate with data.
+train and eval directories. Please note it may take Tensorboard a couple minutes
+to populate with data.

object_detection/g3doc/running_notebook.md

@@ -11,5 +11,5 @@ jupyter notebook
 ```
 
 The notebook should open in your favorite web browser. Click the
-[`object_detection_tutorial.ipynb`](../object_detection_tutorial.ipynb) link
-to open the demo.
+[`object_detection_tutorial.ipynb`](../object_detection_tutorial.ipynb) link to
+open the demo.

object_detection/g3doc/running_on_cloud.md

@@ -88,7 +88,7 @@ training checkpoints and events will be written to and
 Google Cloud Storage.
 
 Users can monitor the progress of their training job on the [ML Engine
-Dashboard](https://pantheon.corp.google.com/mlengine/jobs).
+Dashboard](https://console.cloud.google.com/mlengine/jobs).
 
 ## Running an Evaluation Job on Cloud
 

object_detection/meta_architectures/BUILD

@@ -13,12 +13,11 @@ py_library(
     srcs = ["ssd_meta_arch.py"],
     deps = [
         "//tensorflow",
-        "//tensorflow_models/object_detection/core:box_coder",
         "//tensorflow_models/object_detection/core:box_list",
         "//tensorflow_models/object_detection/core:box_predictor",
         "//tensorflow_models/object_detection/core:model",
         "//tensorflow_models/object_detection/core:target_assigner",
-        "//tensorflow_models/object_detection/utils:variables_helper",
+        "//tensorflow_models/object_detection/utils:shape_utils",
     ],
 )
 
@@ -56,7 +55,7 @@ py_library(
         "//tensorflow_models/object_detection/core:standard_fields",
         "//tensorflow_models/object_detection/core:target_assigner",
         "//tensorflow_models/object_detection/utils:ops",
-        "//tensorflow_models/object_detection/utils:variables_helper",
+        "//tensorflow_models/object_detection/utils:shape_utils",
     ],
 )