.. _sec_automm_detection_prepare_voc:

AutoMM Detection - Prepare Pascal VOC Dataset
=============================================


`Pascal VOC <http://host.robots.ox.ac.uk/pascal/VOC/>`__ is a collection
of datasets for object detection. The most commonly combination for
benchmarking is using VOC2007 trainval and VOC2012 trainval for training
and VOC2007 test for validation. Both VOC2007 and VOC2012 have the same
20 classes, and they have 16551 training images in total. This tutorial
will walk through the steps of preparing both VOC2007 and VOC2012 for
Autogluon MultiModalPredictor.

You need 8.4 GB disk space to download and extract this dataset. SSD is
preferred over HDD because of its better performance. The total time to
prepare the dataset depends on your Internet speed and disk performance.
For example, it often takes 10 min on AWS EC2 with EBS.

VOC has an `official
webpage <http://host.robots.ox.ac.uk/pascal/VOC/>`__ to download the
data, but it’s always easier to perform a one-step setup. We prepared a
script to download both VOC2007 and VOC2012 in our examples:
`download_voc0712.sh <https://raw.githubusercontent.com/autogluon/autogluon/master/examples/automm/object_detection/download_voc0712.sh>`__.
You can also download them separately:
`download_voc07.sh <https://raw.githubusercontent.com/autogluon/autogluon/master/examples/automm/object_detection/download_voc07.sh>`__,
`download_voc12.sh <https://raw.githubusercontent.com/autogluon/autogluon/master/examples/automm/object_detection/download_voc12.sh>`__.
Or you can also use our cli tool ``prepare_detection_dataset`` that can
download all datasets mentioned in our tutorials. This python script is
in our code:
`prepare_detection_dataset.py <https://raw.githubusercontent.com/autogluon/autogluon/master/multimodal/src/autogluon/multimodal/cli/prepare_detection_dataset.py>`__,
and you can also run it as a cli:
``python3 -m autogluon.multimodal.cli.prepare_detection_dataset``.

Download with Python Script
~~~~~~~~~~~~~~~~~~~~~~~~~~~

The python script does not show progress bar, but is promised to work on
all major platforms. If you are working on a Unix system and needs a
progress bar, try the bash script!

You could either extract it under current directory by running:

::

   python3 -m autogluon.multimodal.cli.prepare_detection_dataset --dataset_name voc0712

or extract it under a provided output path:

::

   python3 -m autogluon.multimodal.cli.prepare_detection_dataset --dataset_name voc0712 --output_path ~/data

or make it shorter:

::

   python3 -m autogluon.multimodal.cli.prepare_detection_dataset -d voc -o ~/data

or download them separately

::

   python3 -m autogluon.multimodal.cli.prepare_detection_dataset -d voc07 -o ~/data
   python3 -m autogluon.multimodal.cli.prepare_detection_dataset -d voc12 -o ~/data

Download with Bash Script
~~~~~~~~~~~~~~~~~~~~~~~~~

You could either extract it under current directory by running:

::

   bash download_voc0712.sh

or extract it under a provided output path:

::

   bash download_voc0712.sh ~/data

The command line output will show the progress bar:

::

   extract data in current directory
   Downloading VOC2007 trainval ...
     % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                    Dload  Upload   Total   Spent    Left  Speed
   100  438M  100  438M    0     0  92.3M      0  0:00:04  0:00:04 --:--:-- 95.5M
   Downloading VOC2007 test data ...
     % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                    Dload  Upload   Total   Spent    Left  Speed
   100  430M  100  430M    0     0  96.5M      0  0:00:04  0:00:04 --:--:-- 99.1M
   Downloading VOC2012 trainval ...
     % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                    Dload  Upload   Total   Spent    Left  Speed
    73 1907M   73 1401M    0     0   108M      0  0:00:17  0:00:12  0:00:05  118M

And after it finished, VOC datasets are extracted in folder
``VOCdevkit``, it contains

::

   VOC2007  VOC2012

And both of them contains:

::

   Annotations  ImageSets  JPEGImages  SegmentationClass  SegmentationObject

.. _sec_automm_detection_prepare_coco17: and

The VOC Format
~~~~~~~~~~~~~~

VOC also refers to the specific format (in ``.xml`` file) the VOC
dataset is using.

**In Autogluon MultiModalPredictor, we strongly recommend using COCO as
your data format instead. Check
:ref:`convert_data_to_coco_format` for more information about COCO
dataset and how to convert a VOC dataset to COCO.**

However, for fast proof testing we also have limit support for VOC
format. While using VOC format dataset, the input is the root path of
the dataset, and contains at least:

::

   Annotations  ImageSets  JPEGImages

Other Examples
~~~~~~~~~~~~~~

You may go to `AutoMM
Examples <https://github.com/autogluon/autogluon/tree/master/examples/automm>`__
to explore other examples about AutoMM.

Customization
~~~~~~~~~~~~~

To learn how to customize AutoMM, please refer to
:ref:`sec_automm_customization`.

Citation
~~~~~~~~

::

   @Article{Everingham10,
      author = "Everingham, M. and Van~Gool, L. and Williams, C. K. I. and Winn, J. and Zisserman, A.",
      title = "The Pascal Visual Object Classes (VOC) Challenge",
      journal = "International Journal of Computer Vision",
      volume = "88",
      year = "2010",
      number = "2",
      month = jun,
      pages = "303--338",
   }