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<h1>FGVC-Aircraft Benchmark</h1> |
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<p><strong>Fine-Grained Visual Classification of Aircraft (FGVC-Aircraft)</strong> is |
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a benchmark dataset for the fine grained visual categorization of |
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aircraft.</p> |
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<ul> |
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<li><a href="archives/fgvc-aircraft-2013b.tar.gz">Data, annotations, and evaluation code</a> [2.75 GB | <a href="archives/fgvc-aircraft-2013b.html">MD5 Sum</a>].</li> |
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<li><a href="archives/fgvc-aircraft-2013b-annotations.tar.gz">Annotations and evaluation code only</a> [375 KB | <a href="archives/fgvc-aircraft-2013b-annotations.html">MD5 Sum</a>].</li> |
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<li>Project <a href="http://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/">home page</a>.</li> |
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<li>This data was used as part of the fine-grained recognition challenge |
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<a href="https://sites.google.com/site/fgcomp2013/">FGComp 2013</a> which ran |
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jointly with the ImageNet Challenge 2013 |
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(<a href="https://sites.google.com/site/fgcomp2013/results">results</a>). Please |
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note that <em>the evaluation code provided here may differ</em> from the |
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one used in the challenge.</li> |
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</ul> |
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<p>Please use the following citation when referring to this dataset:</p> |
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<p><em>Fine-Grained Visual Classification of Aircraft</em>, S. Maji, J. Kannala, |
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E. Rahtu, M. Blaschko, A. Vedaldi, <a href="http://arxiv.org/abs/1306.5151">arXiv.org</a>, 2013</p> |
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<pre><code>@techreport{maji13fine-grained, |
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title = {Fine-Grained Visual Classification of Aircraft}, |
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author = {S. Maji and J. Kannala and E. Rahtu |
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and M. Blaschko and A. Vedaldi}, |
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year = {2013}, |
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archivePrefix = {arXiv}, |
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eprint = {1306.5151}, |
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primaryClass = "cs-cv", |
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} |
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</code></pre> |
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<p>For further information see:</p> |
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<ul> |
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<li><a href="#quick">Quick start</a> |
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<ul> |
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<li><a href="#aircraft">About aircraft</a></li> |
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</ul></li> |
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<li><a href="#format">Data and annotation format</a></li> |
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<li><a href="#evaluation">Evaluation</a> |
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<ul> |
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<li><a href="#metric">Evaluation metric</a></li> |
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<li><a href="#code">Evaluation code</a></li> |
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</ul></li> |
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<li><a href="#ack">Ackwonledgments</a></li> |
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<li><a href="#release">Release notes</a></li> |
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</ul> |
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<p><strong>Note.</strong> This data has been used as part of the <em>ImageNet FGVC |
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challenge in conjuction with the International Conference on Computer |
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Vision (ICCV) 2013</em>. Test labels were not made available until the |
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challenge due to the ImageNet challenge policy. They have now been |
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released as part of the download above. If you arelady downloaded the |
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iamge archive and want to have access to the test labels, simply |
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download the annotations archive again.</p> |
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<p><strong>Note.</strong> Images in the benchmark are generously made available <strong>for |
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non-commercial research purposes only</strong> by a number of <em>airplane |
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spotters</em>. Please note that the original authors retain the copyright |
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of the respective photographs and should be contacted for any other |
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use. For further details see the <a href="#ack">copyright note</a> below.</p> |
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<h1><a id=quick></a> Quick start</h1> |
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<p>The dataset contains 10,200 images of aircraft, with 100 images for |
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each of 102 different aircraft model variants, most of which are |
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airplanes. The (main) aircraft in each image is annotated with a tight |
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bounding box and a hierarchical airplane model label.</p> |
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<p>Aircraft models are organized in a four-levels hierarchy. The four |
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levels, from finer to coarser, are:</p> |
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<ul> |
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<li><strong>Model</strong>, e.g. <em>Boeing 737-76J</em>. Since certain models are nearly visually |
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indistinguishable, this level is not used in the evaluation.</li> |
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<li><strong>Variant</strong>, e.g. <em>Boeing 737-700</em>. A variant collapses all the |
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models that are visually indistinguishable into one class. The |
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dataset comprises 102 different variants.</li> |
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<li><strong>Family</strong>, e.g. <em>Boeing 737</em>. The dataset comprises 70 different |
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families.</li> |
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<li><strong>Manufacturer</strong>, e.g. <em>Boeing</em>. The dataset comprises 41 |
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different manufacturers.</li> |
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</ul> |
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<p>The data is divided into three equally-sized <em>training</em>, <em>validation</em> |
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and <em>test</em> subsets. The first two sets can be used for development, |
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and the latter should be used for final evaluation only. The format of |
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the data is described <a href="#format">next</a>.</p> |
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<p>The performance of a fine-grained classification algorithm is |
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evaluated in term of average class-prediction accuracy. This is |
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defined as the average of the diagonal of the row-normalized confusion |
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matrix, as used for example in Caltech-101. Three classification |
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challenges are considered: variant, family, and manufacturer. An |
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<a href="#software">evaluation script</a> in MATLAB is provided.</p> |
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<h2><a href=aircraft></a> About aircraft</h2> |
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<p>Aircraft, and in particular airplanes, are alternative to objects |
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typically considered for fine-grained categorization such as birds and |
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pets. There are several aspects that make aircraft model recognition |
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particularly interesting. Firstly, aircraft designs span a hundred |
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years, including many thousand different models and hundreds of |
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different makes and airlines. Secondly, aircraft designs vary |
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significantly depending on the size (from home-built to large |
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carriers), destination (private, civil, military), purpose |
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(transporter, carrier, training, sport, fighter, etc.), propulsion |
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(glider, propeller, jet), and many other factors including |
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technology. One particular axis of variation, which is is not shared |
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with categories such as animals, is the fact that the <em>structure</em> of |
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the aircraft changes with their design (number of wings, |
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undercarriages, wheel per undercarriage, engines, etc.). Thirdly, any |
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given aircraft model can be re-purposed or used by different |
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companies, which causes further variations in appearance |
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(livery). These, depending on the identification task, may be consider |
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as noise or as useful information to be extracted. Finally, aircraft |
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are largely rigid objects, which simplifies certain aspects of their |
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modeling (compared to highly-deformable animals such as cats), |
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allowing one to focus on the core aspects of the fine-grained |
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recognition problem.</p> |
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<h1><a id=format></a> Data format</h1> |
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<p>The directory <code>data</code> contains the images as well as a number of text |
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files with the data annotations.</p> |
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<p>Images are contained in the <code>data/images</code> sub-directory. They are in |
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JPEG format and have a name composed of seven digits and the <code>.jpg</code> |
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suffix (e.g. <code>data/images/1187707.jpg</code>). The image resolution is about |
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1-2MP. Each image has at the bottom a banner 20 pixels high containing |
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<a href="#ack">copyright</a> information. Please make sure to remove this banner |
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when using the images to train and evaluate algorithms.</p> |
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<p>The annotations come in a number of text files. Each line of these |
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files contains an image name optionally followed by an image |
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annotation, either a textual label or a sequence of numbers.</p> |
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<p><code>data/images_train.txt</code> contains the list of training images:</p> |
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<pre> |
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0787226 |
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1481091 |
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1548899 |
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0674300 |
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... |
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</pre> |
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<p>Similar files <code>data/images_val.txt</code> and <code>data/images_test.txt</code> contain the list |
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of validation and test images.</p> |
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<p><code>data/images_variant_train.txt</code>, <code>data/images_family_train.txt</code>, and |
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<code>data/images_manufacturer_train.txt</code> contain the list of training |
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images annotated with the model variant, family, and manufacturer |
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names respectively:</p> |
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<pre> |
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0787226 Abingdon Spherical Free Balloon |
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1481091 AEG Wagner Eule |
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1548899 Aeris Naviter AN-2 Enara |
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0674300 Aeritalia F-104S Starfighter |
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... |
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</pre> |
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<p>Similar files are provided for the validation and test subsets.</p> |
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<p>Finally, <code>data/images_box.txt</code> contains the aircraft bounding |
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boxes, one per image. The bounding box is specified by four numbers: |
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<em>xmin</em>, <em>ymin</em>, <em>xmax</em> and <em>ymax</em>. The top-left pixel of an image has |
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coordinate (1,1).</p> |
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<h1><a id=evaluation></a> Evaluation</h1> |
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<p>The performance of a classifier is measured in term of its average |
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classification accuracy, as detailed next.</p> |
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<h2><a id=metric></a> Evaluation metric</h2> |
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<p>The output of a classification algorithm must be a list of triplets of |
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the type (<em>image</em>,<em>label</em>,<em>score</em>), where</p> |
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<ul> |
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<li><em>image</em> is an image label, i.e. a seven-digit number,</li> |
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<li><em>label</em> is an image label, i.e.. an aircraft model variant, family, or manufacturer, and</li> |
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<li><em>score</em> is a real number expressing the belief in the judgment.</li> |
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</ul> |
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<p>When computing the classification accuracy, an image is assigned the |
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label contained in its highest-scoring triplet. An image that has no |
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triplets is considered unclassified and always count as a |
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classification error (therefore it is better to guess at least one |
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label for each image rather than leaving it unclassified).</p> |
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<p>The quality of the predictions is measured in term of <em>average |
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accuracy</em>, obtained as follows:</p> |
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<ul> |
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<li>The confusion matrix is square, with one row per class.</li> |
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<li>Each element of the confusion matrix is the number of time aircraft |
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of a given class (specified by the row) are classified as a second |
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class (column). Ideally, the confusion matrix should be diagonal.</li> |
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<li>The confusion matrix is row-normalized by the number of images of |
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the corresponding aircraft class (each row therefore sums to one if |
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there are no unclassified images).</li> |
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<li>The average accuracy is computed as the average of the diagonal of |
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the confusion matrix.</li> |
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</ul> |
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<p>There are three challenges: classifying the aircraft variant, family, and manufacturer.</p> |
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<h2><a id=code></a> Evaluation code</h2> |
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<p>The evaluation protocol has been implemented in the MATLAB m-file |
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<code>evaluation.m</code>. This function takes the path to the <code>data</code> folder, a |
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composite name indicating the evaluation subset and challenge |
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(e.g. <code>'manufacturer_test'</code> or <code>'family_val'</code>), and the list of |
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triplets, and returns the confusion matrix. For example</p> |
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<pre> |
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images = {'2074164'} ; |
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labels = {'McDonnell Douglas MD-90-30'} ; |
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scores = 1 ; |
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confusion = evaluate('/path/fgcv-aircraft/data', 'test', images, labels, scores) ; |
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accuracy = mean(diag(confusion)) ; |
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</pre> |
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<p>evaluates a classifier output containing exactly one triplet (image, |
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label, score), where the image is <code>'2074164'</code>, its predicted class is |
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<code>'McDonnell Douglas MD-90-30'</code>, and the score of the prediction is |
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<code>1</code>. In practice, a complete set of predictions (one for each |
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image-class pair) is usually evaluated.</p> |
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<p>See the builtin help of the <code>evaluation</code> MATLAB functions for further |
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practical details. See also <code>example_evaluation.m</code> for examples on how |
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to use this function.</p> |
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<h1><a id=ack></a> Acknowledgments</h1> |
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<p>The creation of this dataset started during the <em>Johns Hopkins CLSP |
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Summer Workshop 2012</em> |
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<a href="http://www.clsp.jhu.edu/workshops/archive/ws-12/groups/tduosn/">Towards a Detailed Understanding of Objects and Scenes in Natural Images</a> |
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with, in alphabetical order, Matthew B. Blaschko, Ross B. Girshick, |
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Juho Kannala, Iasonas Kokkinos, Siddharth Mahendran, Subhransu Maji, |
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Sammy Mohamed, Esa Rahtu, Naomi Saphra, Karen Simonyan, Ben Taskar, |
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Andrea Vedaldi, and David Weiss.</p> |
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<p>The CLSP workshop was supported by the National Science Foundation via |
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Grant No 1005411, the Office of the Director of National Intelligence |
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via the JHU Human Language Technology Center of Excellence; and Google |
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Inc.</p> |
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<p>A special thanks goes to Pekka Rantalankila for helping with the |
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creation of the airplane hieararchy.</p> |
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<p>Many thanks to the photographers that kindly made available their |
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images for research purposes. Each photographer is listed below, along |
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with a link to his/her <a href="http://airliners.net">airlners.net</a> page:</p> |
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<ul> |
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<li><a href="http://www.airliners.net/profile/dendrobatid">Mick Bajcar</a></li> |
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<li><a href="http://www.airliners.net/profile/aldobid">Aldo Bidini</a></li> |
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<li><a href="http://www.airliners.net/profile/minoeke">Wim Callaert</a></li> |
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<li><a href="http://www.airliners.net/profile/tommypilot">Tommy Desmet</a></li> |
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<li><a href="http://www.airliners.net/profile/snorre">Thomas Posch</a></li> |
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<li><a href="http://www.airliners.net/profile/lemonkitty">James Richard Covington</a></li> |
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<li><a href="http://www.airliners.net/profile/stegi">Gerry Stegmeier</a></li> |
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<li><a href="http://www.airliners.net/profile/aal151heavy">Ben Wang</a></li> |
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<li><a href="http://www.airliners.net/profile/dazbo5">Darren Wilson</a></li> |
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<li><a href="http://www.airliners.net/profile/fly-k">Konstantin von Wedelstaedt</a></li> |
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</ul> |
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<p>Please note that the images are made available <strong>exclusively for |
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non-commercial research purposes</strong>. The original authors retain the |
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copyright on the respective pictures and should be contacted for any |
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other usage of them.</p> |
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<h1><a id=release></a> Release notes</h1> |
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<ul> |
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<li><em>FGVC-Aircraft 2013b</em> - The same as 2013a, but with test annotations included.</li> |
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<li><em>FGVC-Aircraft 2013a</em> - First public release of the data.</li> |
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</ul> |
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