From 477ed41e7e4e8a8443bc633846eb01e2182dc68a Mon Sep 17 00:00:00 2001 From: Jonathan Huang Date: Tue, 20 Jun 2017 16:14:33 -0700 Subject: [PATCH] Replace Oxford-IIT by Oxford-IIIT. (#1708) --- object_detection/g3doc/preparing_inputs.md | 6 +++--- object_detection/g3doc/running_locally.md | 2 +- object_detection/g3doc/running_on_cloud.md | 2 +- object_detection/g3doc/running_pets.md | 12 ++++++------ ...aster_rcnn_inception_resnet_v2_atrous_pets.config | 2 +- .../configs/faster_rcnn_resnet101_pets.config | 2 +- .../configs/faster_rcnn_resnet152_pets.config | 2 +- .../samples/configs/faster_rcnn_resnet50_pets.config | 2 +- .../samples/configs/rfcn_resnet101_pets.config | 2 +- .../samples/configs/ssd_inception_v2_pets.config | 2 +- .../samples/configs/ssd_mobilenet_v1_pets.config | 2 +- 11 files changed, 18 insertions(+), 18 deletions(-) diff --git a/object_detection/g3doc/preparing_inputs.md b/object_detection/g3doc/preparing_inputs.md index a1f8f17e1a..77ba7f39fd 100644 --- a/object_detection/g3doc/preparing_inputs.md +++ b/object_detection/g3doc/preparing_inputs.md @@ -2,7 +2,7 @@ Tensorflow Object Detection API reads data using the TFRecord file format. Two sample scripts (`create_pascal_tf_record.py` and `create_pet_tf_record.py`) are -provided to convert from the PASCAL VOC dataset and Oxford-IIT Pet dataset to +provided to convert from the PASCAL VOC dataset and Oxford-IIIT Pet dataset to TFRecords. ## Generating the PASCAL VOC TFRecord files. @@ -26,9 +26,9 @@ pascal_val.record in the tensorflow/models/object_detection directory. The label map for the PASCAL VOC data set can be found at data/pascal_label_map.pbtxt. -## Generation the Oxford-IIT Pet TFRecord files. +## Generation the Oxford-IIIT Pet TFRecord files. -The Oxford-IIT Pet data set can be downloaded from +The Oxford-IIIT Pet data set can be downloaded from [their website](http://www.robots.ox.ac.uk/~vgg/data/pets/). Extract the tar file and run the `create_pet_tf_record` script to generate TFRecords. diff --git a/object_detection/g3doc/running_locally.md b/object_detection/g3doc/running_locally.md index 7143b6d858..dd53225b33 100644 --- a/object_detection/g3doc/running_locally.md +++ b/object_detection/g3doc/running_locally.md @@ -10,7 +10,7 @@ dependencies, compiling the configuration protobufs and setting up the Python environment. 2. A valid data set has been created. See [this page](preparing_inputs.md) for instructions on how to generate a dataset for the PASCAL VOC challenge or the -Oxford-IIT Pet dataset. +Oxford-IIIT Pet dataset. 3. A Object Detection pipeline configuration has been written. See [this page](configuring_jobs.md) for details on how to write a pipeline configuration. diff --git a/object_detection/g3doc/running_on_cloud.md b/object_detection/g3doc/running_on_cloud.md index 0d74ac4e21..b96725eafe 100644 --- a/object_detection/g3doc/running_on_cloud.md +++ b/object_detection/g3doc/running_on_cloud.md @@ -11,7 +11,7 @@ See [the Cloud ML quick start guide](https://cloud.google.com/ml-engine/docs/qui in the [installation instructions](installation.md). 3. The reader has a valid data set and stored it in a Google Cloud Storage bucket. See [this page](preparing_inputs.md) for instructions on how to generate -a dataset for the PASCAL VOC challenge or the Oxford-IIT Pet dataset. +a dataset for the PASCAL VOC challenge or the Oxford-IIIT Pet dataset. 4. The reader has configured a valid Object Detection pipeline, and stored it in a Google Cloud Storage bucket. See [this page](configuring_jobs.md) for details on how to write a pipeline configuration. diff --git a/object_detection/g3doc/running_pets.md b/object_detection/g3doc/running_pets.md index 08fe34eb36..eae858af77 100644 --- a/object_detection/g3doc/running_pets.md +++ b/object_detection/g3doc/running_pets.md @@ -1,7 +1,7 @@ -# Quick Start: Distributed Training on the Oxford-IIT Pets Dataset on Google Cloud +# Quick Start: Distributed Training on the Oxford-IIIT Pets Dataset on Google Cloud This page is a walkthrough for training an object detector using the Tensorflow -Object Detection API. In this tutorial, we'll be training on the Oxford-IIT Pets +Object Detection API. In this tutorial, we'll be training on the Oxford-IIIT Pets dataset to build a system to detect various breeds of cats and dogs. The output of the detector will look like the following: @@ -43,11 +43,11 @@ Please run through the [installation instructions](installation.md) to install Tensorflow and all it dependencies. Ensure the Protobuf libraries are compiled and the library directories are added to `PYTHONPATH`. -## Getting the Oxford-IIT Pets Dataset and Uploading it to Google Cloud Storage +## Getting the Oxford-IIIT Pets Dataset and Uploading it to Google Cloud Storage In order to train a detector, we require a dataset of images, bounding boxes and -classifications. For this demo, we'll use the Oxford-IIT Pets dataset. The raw -dataset for Oxford-IIT Pets lives +classifications. For this demo, we'll use the Oxford-IIIT Pets dataset. The raw +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) @@ -65,7 +65,7 @@ the tarballs, your object_detection directory should appear 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-IIT Pet dataset into TFRecords. Run the following commands from the +Oxford-IIIT Pet dataset into TFRecords. Run the following commands from the object_detection directory: ``` bash diff --git a/object_detection/samples/configs/faster_rcnn_inception_resnet_v2_atrous_pets.config b/object_detection/samples/configs/faster_rcnn_inception_resnet_v2_atrous_pets.config index a091333746..fc7e14e259 100644 --- a/object_detection/samples/configs/faster_rcnn_inception_resnet_v2_atrous_pets.config +++ b/object_detection/samples/configs/faster_rcnn_inception_resnet_v2_atrous_pets.config @@ -1,5 +1,5 @@ # Faster R-CNN with Inception Resnet v2, Atrous version; -# Configured for Oxford-IIT Pets Dataset. +# Configured for Oxford-IIIT Pets Dataset. # Users should configure the fine_tune_checkpoint field in the train config as # well as the label_map_path and input_path fields in the train_input_reader and # eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that diff --git a/object_detection/samples/configs/faster_rcnn_resnet101_pets.config b/object_detection/samples/configs/faster_rcnn_resnet101_pets.config index b90304c2ef..cee6604a43 100644 --- a/object_detection/samples/configs/faster_rcnn_resnet101_pets.config +++ b/object_detection/samples/configs/faster_rcnn_resnet101_pets.config @@ -1,4 +1,4 @@ -# Faster R-CNN with Resnet-101 (v1) configured for the Oxford-IIT Pet Dataset. +# Faster R-CNN with Resnet-101 (v1) configured for the Oxford-IIIT Pet Dataset. # Users should configure the fine_tune_checkpoint field in the train config as # well as the label_map_path and input_path fields in the train_input_reader and # eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that diff --git a/object_detection/samples/configs/faster_rcnn_resnet152_pets.config b/object_detection/samples/configs/faster_rcnn_resnet152_pets.config index 128380b9cc..aae28489ee 100644 --- a/object_detection/samples/configs/faster_rcnn_resnet152_pets.config +++ b/object_detection/samples/configs/faster_rcnn_resnet152_pets.config @@ -1,4 +1,4 @@ -# Faster R-CNN with Resnet-152 (v1), configured for Oxford-IIT Pets Dataset. +# Faster R-CNN with Resnet-152 (v1), configured for Oxford-IIIT Pets Dataset. # Users should configure the fine_tune_checkpoint field in the train config as # well as the label_map_path and input_path fields in the train_input_reader and # eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that diff --git a/object_detection/samples/configs/faster_rcnn_resnet50_pets.config b/object_detection/samples/configs/faster_rcnn_resnet50_pets.config index 5e929301a4..110c1b4bb4 100644 --- a/object_detection/samples/configs/faster_rcnn_resnet50_pets.config +++ b/object_detection/samples/configs/faster_rcnn_resnet50_pets.config @@ -1,4 +1,4 @@ -# Faster R-CNN with Resnet-50 (v1), configured for Oxford-IIT Pets Dataset. +# Faster R-CNN with Resnet-50 (v1), configured for Oxford-IIIT Pets Dataset. # Users should configure the fine_tune_checkpoint field in the train config as # well as the label_map_path and input_path fields in the train_input_reader and # eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that diff --git a/object_detection/samples/configs/rfcn_resnet101_pets.config b/object_detection/samples/configs/rfcn_resnet101_pets.config index 2b9df17ef8..a2b88f9df3 100644 --- a/object_detection/samples/configs/rfcn_resnet101_pets.config +++ b/object_detection/samples/configs/rfcn_resnet101_pets.config @@ -1,4 +1,4 @@ -# R-FCN with Resnet-101 (v1), configured for Oxford-IIT Pets Dataset. +# R-FCN with Resnet-101 (v1), configured for Oxford-IIIT Pets Dataset. # Users should configure the fine_tune_checkpoint field in the train config as # well as the label_map_path and input_path fields in the train_input_reader and # eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that diff --git a/object_detection/samples/configs/ssd_inception_v2_pets.config b/object_detection/samples/configs/ssd_inception_v2_pets.config index 49bdf7e064..b14fa480d8 100644 --- a/object_detection/samples/configs/ssd_inception_v2_pets.config +++ b/object_detection/samples/configs/ssd_inception_v2_pets.config @@ -1,4 +1,4 @@ -# SSD with Inception v2 configured for Oxford-IIT Pets Dataset. +# SSD with Inception v2 configured for Oxford-IIIT Pets Dataset. # Users should configure the fine_tune_checkpoint field in the train config as # well as the label_map_path and input_path fields in the train_input_reader and # eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that diff --git a/object_detection/samples/configs/ssd_mobilenet_v1_pets.config b/object_detection/samples/configs/ssd_mobilenet_v1_pets.config index c8d83ddb14..429075c645 100644 --- a/object_detection/samples/configs/ssd_mobilenet_v1_pets.config +++ b/object_detection/samples/configs/ssd_mobilenet_v1_pets.config @@ -1,4 +1,4 @@ -# SSD with Mobilenet v1, configured for Oxford-IIT Pets Dataset. +# SSD with Mobilenet v1, configured for Oxford-IIIT Pets Dataset. # Users should configure the fine_tune_checkpoint field in the train config as # well as the label_map_path and input_path fields in the train_input_reader and # eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that