Paper doll parser implementation from ICCV 2013
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PaperDoll clothing parser

Unconstrained clothing parser for a full-body picture.

Paper Doll Parsing: Retrieving Similar Styles to Parse Clothing Items
Kota Yamaguchi, M. Hadi Kiapour, Tamara L. Berg
ICCV 2013

This package only contains source codes. Download additional data files to use the parser or to run an experiment.

To parse a new image using a pre-trained models, only download the model file (Caution: ~70GB).

cd paperdoll/
for i in 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14

Check the MD5SUM to make sure download is successful.

md5sum -b models-v1.0.tar.*
3f14f5d90e4c3c3ce014311dce0df1bf *models-v1.0.tar.00
46bb5d046dc6f9a6e6cb3c9832ab4c6d *models-v1.0.tar.01
85f089dd4a589e02fe5da1fb16b7dbae *models-v1.0.tar.02
b0f0d18bd9ec13fbc6c63e0a1fd6356d *models-v1.0.tar.03
1b7838c2d4c8287f900992f3e7969f9c *models-v1.0.tar.04
5e7f9c7a87e3cc753b4508daa65c247a *models-v1.0.tar.05
e7ae269f42e1b7bdf30f9cac3b7ea62a *models-v1.0.tar.06
96c92e94ae179fd805f731da65636604 *models-v1.0.tar.07
b3c5f7a89a78a7dc60ee57641b6297e9 *models-v1.0.tar.08
0371ddec6c5ce04cf185f30cfd8e92ce *models-v1.0.tar.09
e9b7a90856b58d7d47f5f28902ccc561 *models-v1.0.tar.10
6ced6bf6292c3893cc4ba429ac4617b8 *models-v1.0.tar.11
57d4b0617d984c767b4617da2e44158f *models-v1.0.tar.12
1ee83b90fd49b0fe4310c89ceaf69a17 *models-v1.0.tar.13
7db0e3291730e53ffed526144c2c8e10 *models-v1.0.tar.14

If files are clean, unarchive.

cat models-v1.0.tar.* | tar xf -

To run an experiment from scratch, download the training data.

cd paperdoll/
tar xvf data-v1.0.tar
rm data-v1.0.tar


data/        Directory to place data.
lib/         Library directory.
log/         Log directory.
tasks/       Experimental scripts.
tmp/         Temporary data directory.    This file.
LICENSE.txt  Lincense notice.
make.m       Build script.
startup.m    Runtime initialization script.


The software is originally developed on Ubuntu 12.04 LTS and also tested using Ubuntu 14.04 LTS with Matlab R2014a.

The following are the prerequisites for clothing parser.

  • Matlab
  • OpenCV
  • Berkeley DB
  • Boost C++ library

Also, to run all the experiments in the paper, it is required to have a computing grid with Sun Grid Engine (SGE) or compatible distributed environment. In Ubuntu, search for how to use grindengine package.

To install these requirements in Ubuntu,

sudo apt-get install build-essential libopencv-dev libdb-dev libboost-all-dev

After installing prerequisites, the attached make.m script will compile all the necessary binaries within Matlab.


Runtime error

Depending on the Matlab installation, it is probably necessary to resolve conflicting library dependency. Use LD_PRELOAD environmental variable to prevent conflict at runtime. For example, in Ubuntu,

LD_PRELOAD=/usr/lib/x86_64-linux-gnu/ matlab -singleCompThread

To find a conflicting library, use ldd tool within Matlab and also from outside of Matlab, then compare the output. Append suspicious library to the LD_PRELOAD variable.

!ldd lib/mexopencv/+cv/imread.mex*
ldd lib/mexopencv/+cv/imread.mex*


Launch Matlab from the project root directory (i.e., paperdoll-v1.0/). This will automatically call startup to initialize necessary environment.

Run a pre-trained parser for a new image

load data/paperdoll_pipeline.mat config;
input_image = imread('/path/to/new_image.jpg');
input_sample = struct('image', imencode(input_image, 'jpg'));
result = feature_calculator.apply(config, input_sample)

The result is a struct with the following fields.

  • image: input image in JPEG-format.
  • pose: estimated pose.
  • refined_labels: predicted clothing items.
  • final_labeling: PNG-encoded labeling.

To get a per-pixel labeling, use imdecode. For example, the following example access the label of the pixel at (100, 100).

labeling = imdecode(result.final_labeling, 'png');
label = result.refined_labels{labeling(100, 100)};

To visualize the parsing result.

show_parsing(result.image, result.final_labeling, result.refined_labels);


The pose estimator is set up to process roughly 600x400 pixels in the pre-trained model. Change the configuration by setting the image scaling parameter. Also, lower the threshold value if the pipeline throws an error in pose estimation.

config{1}.scale = [200,200]; % Set the maximum image size in the pose estimator.
                             % It is best to specify no larger than 200 pixels.
config{1}.model.thresh = -2; % Change the threshold value if pose estimation fails.

Run an experiment from scratch

Due to the copyright concern, we only provide image URLs in the PaperDoll dataset. We also provide a script to download images. Please note that some of the images might not be accessible at the provided URL since they might be deleted by users. Depending on the network connection, downloading images takes a day or more.

echo task100_download_paperdoll_photos | matlab -nodisplay

After getting training images, use tasks/ to run an experiment from scratch. The script is designed to run on an SGE cluster environment with Ubuntu 12.04 and all the required libraries.

nohup ./tasks/ < /dev/null > log/paperdoll_main.log 2>&1 &

Again, depending on the configuration, this can take a few days. Note that because of the randomness in some of the algorithms and also the data availability, we don't guarantee this reproduces the exact numbers reported in the paper. However, the resulting model should give a similar figure.

SGE cluster with Debian/Ubuntu

To build an SGE grid in Debian/Ubuntu, install the following packages.


sudo apt-get install gridengine-* default-jre


apt-get install gridengine-exec gridengine-client default-jre

See Documentation for configuration details. The qmon tool can be used to set up the environment. Sometimes it is necessary to change how the hostname is resolved in /etc/hosts.

Data format


This file contains the Fashionista dataset from [Yamaguchi et. al. CVPR 2011] with ground truth annotation and also their parsing results in unconstrained parsing. The file contains three variables:

  • truths: ground truth annotation in struct array.
  • predictions: predicted parsing results in struct array.
  • test_index: samples used for training.

The sample struct has the following fields.

  • index: index of the sample.
  • url: URL of the original image.
  • image: JPEG-encoded image data.
  • pose: struct of pose annotation or prediction.
  • annotation: struct of clothing segmentation.
  • id: unique sample ID.

The pose annotation contains 14 points in image coordinates (x,y). The order of annotation is the following.


The clothing segmentation struct consists of the following fields.

  • superpixel_map: PNG-encoded superpixel segmentation.
  • superpixel_labels: Clothing annotation for each superpixel.
  • labels: Cell strings of clothing names.
  • marginals: Marginal probability of clothing labels at each superpixel.

To access per-pixel annotation of sample i,

segmentation = imdecode(truths(i).superpixel_map, 'png');
clothing_annotation = truhts(i).superpixel_labels(segmentation);

To get a label at pixel (100, 100),

label = truths(i).labels{clothing_annotation(100, 100)}


The file contains two variables:

  • labels: cell strings of all clothing labels in the dataset.
  • samples: struct array of data samples with following fields.

Each sample has the following fields.

  • id: unique sample ID.
  • url: URL of the jpg file.
  • post_url: URL of the blog post.
  • tagging: indices of the associated tags.

To access tags of the sample i:

tags = labels(samples(i).tagging);


The file contains negative samples to train a pose estimator. There is one variable:

  • samples: struct array of samples. The im field contains JPEG-encoded images. The point is empty.


The PaperDoll codes are distributed under BSD license. However, some of the dependent libraries in lib/ might be protected by other license. Check each directory for detail.