Update docs in object_detection to reflect new path.` (#2434)

research/object_detection/g3doc/defining_your_own_model.md

@@ -94,7 +94,7 @@ definition as one example. Some remarks:
 
 * We typically initialize the weights of this feature extractor
   using those from the
-  [Slim Resnet-101 classification checkpoint](https://github.com/tensorflow/models/tree/master/slim#pre-trained-models),
+  [Slim Resnet-101 classification checkpoint](https://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models),
   and we know
   that images were preprocessed when training this checkpoint
   by subtracting a channel mean from each input

research/object_detection/g3doc/exporting_models.md

@@ -8,10 +8,10 @@ graph proto. A checkpoint will typically consist of three files:
 * model.ckpt-${CHECKPOINT_NUMBER}.meta
 
 After you've identified a candidate checkpoint to export, run the following
-command from tensorflow/models/object_detection:
+command from tensorflow/models/research/object_detection:
 
 ``` bash
-# From tensorflow/models
+# From tensorflow/models/research/
 python object_detection/export_inference_graph.py \
     --input_type image_tensor \
     --pipeline_config_path ${PIPELINE_CONFIG_PATH} \

research/object_detection/g3doc/installation.md

@@ -7,7 +7,7 @@ Tensorflow Object Detection API depends on the following libraries:
 * Protobuf 2.6
 * Pillow 1.0
 * lxml
-* tf Slim (which is included in the "tensorflow/models" checkout)
+* tf Slim (which is included in the "tensorflow/models/research/" checkout)
 * Jupyter notebook
 * Matplotlib
 * Tensorflow
@@ -45,23 +45,23 @@ sudo pip install matplotlib
 The Tensorflow Object Detection API uses Protobufs to configure model and
 training parameters. Before the framework can be used, the Protobuf libraries
 must be compiled. This should be done by running the following command from
-the tensorflow/models directory:
+the tensorflow/models/research/ directory:
 
 
 ``` bash
-# From tensorflow/models/
+# From tensorflow/models/research/
 protoc object_detection/protos/*.proto --python_out=.
 ```
 
 ## Add Libraries to PYTHONPATH
 
-When running locally, the tensorflow/models/ and slim directories should be
-appended to PYTHONPATH. This can be done by running the following from
-tensorflow/models/:
+When running locally, the tensorflow/models/research/ and slim directories
+should be appended to PYTHONPATH. This can be done by running the following from
+tensorflow/models/research/:
 
 
 ``` bash
-# From tensorflow/models/
+# From tensorflow/models/research/
 export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
 ```
 

research/object_detection/g3doc/preparing_inputs.md

@@ -13,7 +13,7 @@ To download, extract and convert it to TFRecords, run the following commands
 below:
 
 ```bash
-# From tensorflow/models
+# From tensorflow/models/research/
 wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
 tar -xvf VOCtrainval_11-May-2012.tar
 python object_detection/create_pascal_tf_record.py \
@@ -27,7 +27,7 @@ python object_detection/create_pascal_tf_record.py \
 ```
 
 You should end up with two TFRecord files named `pascal_train.record` and
-`pascal_val.record` in the `tensorflow/models` directory.
+`pascal_val.record` in the `tensorflow/models/research/` directory.
 
 The label map for the PASCAL VOC data set can be found at
 `object_detection/data/pascal_label_map.pbtxt`.
@@ -39,7 +39,7 @@ The Oxford-IIIT Pet data set is located
 convert it to TFRecrods, run the following commands below:
 
 ```bash
-# From tensorflow/models
+# From tensorflow/models/research/
 wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz
 wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz
 tar -xvf annotations.tar.gz
@@ -51,7 +51,7 @@ python object_detection/create_pet_tf_record.py \
 ```
 
 You should end up with two TFRecord files named `pet_train.record` and
-`pet_val.record` in the `tensorflow/models` directory.
+`pet_val.record` in the `tensorflow/models/research/` directory.
 
 The label map for the Pet dataset can be found at
 `object_detection/data/pet_label_map.pbtxt`.

research/object_detection/g3doc/running_locally.md

@@ -33,7 +33,7 @@ Oxford-IIIT Pet dataset.
 A local training job can be run with the following command:
 
 ```bash
-# From the tensorflow/models/ directory
+# From the tensorflow/models/research/ directory
 python object_detection/train.py \
     --logtostderr \
     --pipeline_config_path=${PATH_TO_YOUR_PIPELINE_CONFIG} \
@@ -52,7 +52,7 @@ train directory for new checkpoints and evaluate them on a test dataset. The
 job can be run using the following command:
 
 ```bash
-# From the tensorflow/models/ directory
+# From the tensorflow/models/research/ directory
 python object_detection/eval.py \
     --logtostderr \
     --pipeline_config_path=${PATH_TO_YOUR_PIPELINE_CONFIG} \

research/object_detection/g3doc/running_notebook.md

@@ -3,10 +3,10 @@
 If you'd like to hit the ground running and run detection on a few example
 images right out of the box, we recommend trying out the Jupyter notebook demo.
 To run the Jupyter notebook, run the following command from
-`tensorflow/models/object_detection`:
+`tensorflow/models/research/object_detection`:
 
 ```
-# From tensorflow/models/object_detection
+# From tensorflow/models/research/object_detection
 jupyter notebook
 ```
 

research/object_detection/g3doc/running_on_cloud.md

@@ -27,7 +27,7 @@ packaged (along with it's TF-Slim dependency). The required packages can be
 created with the following command
 
 ``` bash
-# From tensorflow/models/
+# From tensorflow/models/research/
 python setup.py sdist
 (cd slim && python setup.py sdist)
 ```
@@ -69,7 +69,7 @@ been written, a user can start a training job on Cloud ML Engine using the
 following command:
 
 ``` bash
-# From tensorflow/models/
+# From tensorflow/models/research/
 gcloud ml-engine jobs submit training object_detection_`date +%s` \
     --job-dir=gs://${TRAIN_DIR} \
     --packages dist/object_detection-0.1.tar.gz,slim/dist/slim-0.1.tar.gz \

research/object_detection/g3doc/running_pets.md

@@ -51,18 +51,19 @@ dataset for Oxford-IIIT Pets lives
 [here](http://www.robots.ox.ac.uk/~vgg/data/pets/). You will need to download
 both the image dataset [`images.tar.gz`](http://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz)
 and the groundtruth data [`annotations.tar.gz`](http://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz)
-to the `tensorflow/models` directory and unzip them. This may take some time.
+to the `tensorflow/models/research/` directory and unzip them. This may take
+some time.
 
 ``` bash
-# From tensorflow/models/
+# From tensorflow/models/research/
 wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz
 wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz
 tar -xvf images.tar.gz
 tar -xvf annotations.tar.gz
 ```
 
-After downloading the tarballs, your `tensorflow/models` directory should appear
-as follows:
+After downloading the tarballs, your `tensorflow/models/research/` directory
+should appear as follows:
 
 ```lang-none
 - images.tar.gz
@@ -76,10 +77,10 @@ as follows:
 The Tensorflow Object Detection API expects data to be in the TFRecord format,
 so we'll now run the `create_pet_tf_record` script to convert from the raw
 Oxford-IIIT Pet dataset into TFRecords. Run the following commands from the
-`tensorflow/models` directory:
+`tensorflow/models/research/` directory:
 
 ``` bash
-# From tensorflow/models/
+# From tensorflow/models/research/
 python object_detection/create_pet_tf_record.py \
     --label_map_path=object_detection/data/pet_label_map.pbtxt \
     --data_dir=`pwd` \
@@ -90,14 +91,14 @@ Note: It is normal to see some warnings when running this script. You may ignore
 them.
 
 Two TFRecord files named `pet_train.record` and `pet_val.record` should be
-generated in the `tensorflow/models` directory.
+generated in the `tensorflow/models/research/` directory.
 
 Now that the data has been generated, we'll need to upload it to Google Cloud
 Storage so the data can be accessed by ML Engine. Run the following command to
 copy the files into your GCS bucket (substituting `${YOUR_GCS_BUCKET}`):
 
 ``` bash
-# From tensorflow/models/
+# From tensorflow/models/research/
 gsutil cp pet_train.record gs://${YOUR_GCS_BUCKET}/data/pet_train.record
 gsutil cp pet_val.record gs://${YOUR_GCS_BUCKET}/data/pet_val.record
 gsutil cp object_detection/data/pet_label_map.pbtxt gs://${YOUR_GCS_BUCKET}/data/pet_label_map.pbtxt
@@ -145,7 +146,7 @@ upload your edited file onto GCS, making note of the path it was uploaded to
 (we'll need it when starting the training/eval jobs).
 
 ``` bash
-# From tensorflow/models/
+# From tensorflow/models/research/
 
 # Edit the faster_rcnn_resnet101_pets.config template. Please note that there
 # are multiple places where PATH_TO_BE_CONFIGURED needs to be set.
@@ -187,10 +188,10 @@ Before we can start a job on Google Cloud ML Engine, we must:
 2. Write a cluster configuration for our Google Cloud ML job.
 
 To package the Tensorflow Object Detection code, run the following commands from
-the `tensorflow/models/` directory:
+the `tensorflow/models/research/` directory:
 
 ``` bash
-# From tensorflow/models/
+# From tensorflow/models/research/
 python setup.py sdist
 (cd slim && python setup.py sdist)
 ```
@@ -202,11 +203,11 @@ For running the training Cloud ML job, we'll configure the cluster to use 10
 training jobs (1 master + 9 workers) and three parameters servers. The
 configuration file can be found at `object_detection/samples/cloud/cloud.yml`.
 
-To start training, execute the following command from the `tensorflow/models/`
-directory:
+To start training, execute the following command from the
+`tensorflow/models/research/` directory:
 
 ``` bash
-# From tensorflow/models/
+# From tensorflow/models/research/
 gcloud ml-engine jobs submit training `whoami`_object_detection_`date +%s` \
     --job-dir=gs://${YOUR_GCS_BUCKET}/train \
     --packages dist/object_detection-0.1.tar.gz,slim/dist/slim-0.1.tar.gz \
@@ -221,7 +222,7 @@ gcloud ml-engine jobs submit training `whoami`_object_detection_`date +%s` \
 Once training has started, we can run an evaluation concurrently:
 
 ``` bash
-# From tensorflow/models/
+# From tensorflow/models/research/
 gcloud ml-engine jobs submit training `whoami`_object_detection_eval_`date +%s` \
     --job-dir=gs://${YOUR_GCS_BUCKET}/train \
     --packages dist/object_detection-0.1.tar.gz,slim/dist/slim-0.1.tar.gz \
@@ -288,10 +289,10 @@ three files:
 * `model.ckpt-${CHECKPOINT_NUMBER}.meta`
 
 After you've identified a candidate checkpoint to export, run the following
-command from `tensorflow/models`:
+command from `tensorflow/models/research/`:
 
 ``` bash
-# From tensorflow/models
+# From tensorflow/models/research/
 gsutil cp gs://${YOUR_GCS_BUCKET}/train/model.ckpt-${CHECKPOINT_NUMBER}.* .
 python object_detection/export_inference_graph.py \
     --input_type image_tensor \