Object detection system using deformable part models (DPMs) and latent SVM (voc-release5). You may want to use the latest tarball on my website. The github code may include code changes that have not been tested as thoroughly and will not necessarily reproduce the results on the website.
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README

Information
===========

Welcome to voc-release5!

This is the companion code-release for my Ph.D. dissertation ("Appendix
C").

Project webpage: http://www.cs.uchicago.edu/~rbg/latent/

Release 5 highlights (see docs/changelog for more details)
 * Weak-label structural SVM (wl-ssvm) [4]
 * Person grammar model (NIPS 2011) [4]
 * Convex optimizer improvements (faster convergence)
 * Code cleanup, reorganization, and speed improvements
 * Training is done in memory (no more large .dat files on disk!)
 * Scale (and location) prior
 * Star-cascade included
 * Bug fixes

Summary
=======

This is an implementation of our object detection system based on
mixtures of deformable part models. This release extends the system
in [2], and is described in my dissertation [5]. The models in this
implementation are represented using the grammar formalism presented
in [3,4,5]. The learning framework supports both binary latent SVM
and weak-label structural SVM (WL-SSVM), which was introduced in
[4,5]. The code also supports the person object detection grammar
from my NIPS 2011 paper [4].

This distribution contains object detection and model learning code,
as well as pretrained models for the PASCAL{07,10} and INRIA Person
datasets.  This release also includes code for rescoring detections
based on contextual information and the star-cascade detection
algorithm of [6].

The system is implemented in MATLAB, with various helper functions
written in MEX C++ for efficiency reasons.

More details, especially about the learning algorithm and model
strcuture, can be found in my dissertation [5].

For questions concerning the code please read the FAQ first
http://people.cs.uchicago.edu/~rbg/latent/voc-release5-faq.html,
and then, if you still have questions, contact Ross Girshick at
<ross.girshick AT gmail DOT com>.

This project has been supported by the National Science Foundation
under Grant No. 0534820, 0746569 and 0811340.


How to Cite
===========
If you use this code or the pretrained models in your research,
please cite [2] and this specific release:

  @misc{voc-release5,
    author       = "Girshick, R. B. and Felzenszwalb, P. F. and McAllester, D.",
    title        = "Discriminatively Trained Deformable Part Models, Release 5",
    howpublished = "http://people.cs.uchicago.edu/~rbg/latent-release5/"
  }

If you use an intermediate release downloaded from github you may
also want to cite the date or git commit hash.

You should also cite some of the following depending on what aspects
of this system you are using or comparing against:
 * [4] for the NIPS 2011 person grammar model and/or Weak-Label
       Structural SVM
 * [6] for the cascade detection algorithm
 * [5] if you discuss specific parts of the system that are not
       published elsewhere (e.g., max regularization, latent
       orientation)


References
==========

[1] P. Felzenszwalb, D. McAllester, D. Ramaman.  
A Discriminatively Trained, Multiscale, Deformable Part Model.  
Proceedings of the IEEE CVPR 2008.

[2] P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan
Object Detection with Discriminatively Trained Part Based Models.
IEEE Transactions on Pattern Analysis and Machine Intelligence,
Vol. 32, No. 9, September 2010.

[3] P. Felzenszwalb, D. McAllester
Object Detection Grammars.
University of Chicago, Computer Science TR-2010-02, February 2010

[4] R. Girshick, P. Felzenszwalb, D. McAllester
Object Detection with Grammar Models.
Proceedings of Neural Information Processing Systems (NIPS) 2011.

[5] R. Girshick
From Rigid Templates to Grammars: Object Detection with Structured Models.
Ph.D. dissertation, The University of Chicago, April 2012.

[6] P. Felzenszwalb, R. Girshick, D. McAllester
Cascade Object Detection with Deformable Part Models.
In Proceedings of the IEEE CVPR 2010.


System Requirements
===================
 * Linux or OS X
 * MATLAB
 * GCC >= 4.2 (or an older version if it has OpenMP support)
 * At least 4GB of memory (plus an additional ~0.75GB for each
   parallel matlab worker)

The software was tested on several versions of Linux and Mac OS X
using MATLAB versions R2011a. There may be compatibility issues
with older versions of MATLAB, though I have successfully run it
back to R2008b.


Getting started
===============

1. Unpack the code
2. Start matlab
3. Run the 'compile' function to compile the helper functions.
   (you may need to edit compile.m to use a different convolution 
   routine depending on your system)
4. Run demo.m or demo_cascade.m to see the detection code in action

Note: If you don't start matlab in the code directory, you may need
to manually run startup.m to ensure all paths are correctly set.

When you run the code for the first time you will likely see this
message:
"""
~~~~~~~~~~~ Hello ~~~~~~~~~~~
voc-release5 is not yet configured for learning.
You can still run demo.m, but please read
the section "Using the learning code" in README.
(Could not find the PASCAL VOC devkit in %s)
"""

This is warning you that you have not configured voc_config.m to
look in the correct path for the PASCAL VOCdevkit. You will need
to download the PASCAL VOCdevkit, unpack it, and set the BASE_DIR
variable in voc_config.m accordingly.


Example detection usage
=======================

>> load VOC2007/car_final.mat;       % car model trained on the PASCAL 2007 dataset
>> im = imread('000034.jpg');        % test image
>> bbox = process(im, model, -0.5);  % detect objects
>> showboxes(im, bbox);              % display results

The main functions defined in the object detection code are:

boxes = imgdetect(im, model, thresh)              % detect objects in image im
bbox = bboxpred_get(model.bboxpred, dets, boxes)  % bounding box location regression
I = nms(bbox, overlap)                            % non-maximal suppression
bbox = clipboxes(im, bbox)                        % clip boxes to image boundary
showboxes(im, boxes)                              % visualize detections
visualizemodel(model)                             % visualize models

Their usage is demonstrated in the 'demo' script.  

The directories 'VOC20??' contain matlab .mat file with models
trained on several PASCAL datasets (the train+val subsets).  Loading
one of these files from within matlab will define a variable 'model'
with the model trained for a particular object category in the
current workspace.  The value 'model.thresh' defines a threshold
that can be used in the 'imgdetect' function to obtain a high recall
rate.


Using the learning code
=======================

1. Download and install the 2006-2011 PASCAL VOC devkit and dataset
   (you should set VOCopts.testset='test' in VOCinit.m)
   The code expects to find the VOCdevkit in the path 
   <BASE_DIR>/VOC<PASCAL_YEAR>/VOCdevkit
2. Modify 'voc_config.m' so BASE_DIR and PASCAL_YEAR are set
   to where you've unpacked the VOCdevkit
3. Start matlab
4. Run the 'compile' function to compile the helper functions
   (you may need to edit compile.m to use a different convolution 
    routine depending on your system)
5. Use the 'pascal' script to train and evaluate a model

example:
>> pascal('bicycle', 3);   % train and evaluate a 6 component bicycle model

The learning code saves a number of intermediate models in a model
cache directory defined in 'voc_config.m'.


Context Rescoring
=================

This release includes code for rescoring detections based on contextual
information.  Context rescoring is performed by class-specific SVMs.
To train these SVMs, the following steps are required.
1) Models for all 20 PASCAL object classes must be trained.
2) Detections must be computed on the PASCAL trainval and test datasets.
   (The function trainval.m can be used for computing detections on the
    trainval dataset.)
3) Compile the included libsvm matlab interface:
   >> cd external/libsvm-3.12/matlab/
   >> libsvm_make

After these steps have been completed, the context rescoring can be
executed by calling 'context_rescore()'.

Example:
>> context_rescore();


Cascaded Detection
==================

The star-cascade algorithm [7] is now included with the rest of
object detection system.


Multicore Support
=================

In addition to multithreaded convolutions (see notes in compile.m),
multicore support is also available through the Matlab Parallel
Computing Toolbox.  Various loops (e.g., negative example data mining,
positive latent labeling, and testing) are implemented using the 'parfor'
parallel for-loop construct.  To take advantage of the parfor loops,
use the 'matlabpool' command.

example:
>> matlabpool open 8   % start 8 parallel matlab instances

The parfor loops work without any changes when running a single
Matlab instance.  Note that due to the use of parfor loops you may
see non-sequential ordering of loop indexes in the terminal output when
training and testing.  This is expected behavior.  The parallel computing
toolbox has been tested on Linux using Matlab 2011a.

The learning code, which uses Mark Schmidt's minConf for LBGFS with
simple box constraints, now computes function gradients using OMP
based multithreading. By default a single thread is used unless a
matlabpool has already been opened. Note that when computing the
function gradient with different numbers of threads, the resulting
gradients will be very slightly different. In practice this leads
to small variations in the resulting AP scores.


Example training time
=====================

I just trained a 2007 bicycle model on a new 6-core Intel(R) Core(TM)
i7-3930K CPU @ 3.20GHz system using a pool of 8 matlab workers in
71 minutes. Testing on the 2007 test set took an additional 38
minutes.