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FGVC-Aircraft Benchmark

Fine-Grained Visual Classification of Aircraft (FGVC-Aircraft) is a benchmark dataset for the fine grained visual categorization of aircraft.

Please use the following citation when referring to this dataset:

Fine-Grained Visual Classification of Aircraft, S. Maji, J. Kannala, E. Rahtu, M. Blaschko, A. Vedaldi, arXiv.org, 2013

@techreport{maji13fine-grained,
   title         = {Fine-Grained Visual Classification of Aircraft},
   author        = {S. Maji and J. Kannala and E. Rahtu
                    and M. Blaschko and A. Vedaldi},
   year          = {2013},
   archivePrefix = {arXiv},
   eprint        = {1306.5151},
   primaryClass  = "cs-cv",
}

For further information see:

Note. This data has been used as part of the ImageNet FGVC challenge in conjuction with the International Conference on Computer Vision (ICCV) 2013. Test labels were not made available until the challenge due to the ImageNet challenge policy. They have now been released as part of the download above. If you arelady downloaded the iamge archive and want to have access to the test labels, simply download the annotations archive again.

Note. Images in the benchmark are generously made available for non-commercial research purposes only by a number of airplane spotters. Please note that the original authors retain the copyright of the respective photographs and should be contacted for any other use. For further details see the copyright note below.

Quick start

The dataset contains 10,200 images of aircraft, with 100 images for each of 102 different aircraft model variants, most of which are airplanes. The (main) aircraft in each image is annotated with a tight bounding box and a hierarchical airplane model label.

Aircraft models are organized in a four-levels hierarchy. The four levels, from finer to coarser, are:

  • Model, e.g. Boeing 737-76J. Since certain models are nearly visually indistinguishable, this level is not used in the evaluation.
  • Variant, e.g. Boeing 737-700. A variant collapses all the models that are visually indistinguishable into one class. The dataset comprises 102 different variants.
  • Family, e.g. Boeing 737. The dataset comprises 70 different families.
  • Manufacturer, e.g. Boeing. The dataset comprises 41 different manufacturers.

The data is divided into three equally-sized training, validation and test subsets. The first two sets can be used for development, and the latter should be used for final evaluation only. The format of the data is described next.

The performance of a fine-grained classification algorithm is evaluated in term of average class-prediction accuracy. This is defined as the average of the diagonal of the row-normalized confusion matrix, as used for example in Caltech-101. Three classification challenges are considered: variant, family, and manufacturer. An evaluation script in MATLAB is provided.

About aircraft

Aircraft, and in particular airplanes, are alternative to objects typically considered for fine-grained categorization such as birds and pets. There are several aspects that make aircraft model recognition particularly interesting. Firstly, aircraft designs span a hundred years, including many thousand different models and hundreds of different makes and airlines. Secondly, aircraft designs vary significantly depending on the size (from home-built to large carriers), destination (private, civil, military), purpose (transporter, carrier, training, sport, fighter, etc.), propulsion (glider, propeller, jet), and many other factors including technology. One particular axis of variation, which is is not shared with categories such as animals, is the fact that the structure of the aircraft changes with their design (number of wings, undercarriages, wheel per undercarriage, engines, etc.). Thirdly, any given aircraft model can be re-purposed or used by different companies, which causes further variations in appearance (livery). These, depending on the identification task, may be consider as noise or as useful information to be extracted. Finally, aircraft are largely rigid objects, which simplifies certain aspects of their modeling (compared to highly-deformable animals such as cats), allowing one to focus on the core aspects of the fine-grained recognition problem.

Data format

The directory data contains the images as well as a number of text files with the data annotations.

Images are contained in the data/images sub-directory. They are in JPEG format and have a name composed of seven digits and the .jpg suffix (e.g. data/images/1187707.jpg). The image resolution is about 1-2MP. Each image has at the bottom a banner 20 pixels high containing copyright information. Please make sure to remove this banner when using the images to train and evaluate algorithms.

The annotations come in a number of text files. Each line of these files contains an image name optionally followed by an image annotation, either a textual label or a sequence of numbers.

data/images_train.txt contains the list of training images:

0787226
1481091
1548899
0674300
...

Similar files data/images_val.txt and data/images_test.txt contain the list of validation and test images.

data/images_variant_train.txt, data/images_family_train.txt, and data/images_manufacturer_train.txt contain the list of training images annotated with the model variant, family, and manufacturer names respectively:

0787226 Abingdon Spherical Free Balloon
1481091 AEG Wagner Eule
1548899 Aeris Naviter AN-2 Enara
0674300 Aeritalia F-104S Starfighter
...

Similar files are provided for the validation and test subsets.

Finally, data/images_box.txt contains the aircraft bounding boxes, one per image. The bounding box is specified by four numbers: xmin, ymin, xmax and ymax. The top-left pixel of an image has coordinate (1,1).

Evaluation

The performance of a classifier is measured in term of its average classification accuracy, as detailed next.

Evaluation metric

The output of a classification algorithm must be a list of triplets of the type (image,label,score), where

  • image is an image label, i.e. a seven-digit number,
  • label is an image label, i.e.. an aircraft model variant, family, or manufacturer, and
  • score is a real number expressing the belief in the judgment.

When computing the classification accuracy, an image is assigned the label contained in its highest-scoring triplet. An image that has no triplets is considered unclassified and always count as a classification error (therefore it is better to guess at least one label for each image rather than leaving it unclassified).

The quality of the predictions is measured in term of average accuracy, obtained as follows:

  • The confusion matrix is square, with one row per class.
  • Each element of the confusion matrix is the number of time aircraft of a given class (specified by the row) are classified as a second class (column). Ideally, the confusion matrix should be diagonal.
  • The confusion matrix is row-normalized by the number of images of the corresponding aircraft class (each row therefore sums to one if there are no unclassified images).
  • The average accuracy is computed as the average of the diagonal of the confusion matrix.

There are three challenges: classifying the aircraft variant, family, and manufacturer.

Evaluation code

The evaluation protocol has been implemented in the MATLAB m-file evaluation.m. This function takes the path to the data folder, a composite name indicating the evaluation subset and challenge (e.g. 'manufacturer_test' or 'family_val'), and the list of triplets, and returns the confusion matrix. For example

images = {'2074164'} ;
labels = {'McDonnell Douglas MD-90-30'} ;
scores = 1 ;
confusion = evaluate('/path/fgcv-aircraft/data', 'test', images, labels, scores) ;
accuracy = mean(diag(confusion)) ;

evaluates a classifier output containing exactly one triplet (image, label, score), where the image is '2074164', its predicted class is 'McDonnell Douglas MD-90-30', and the score of the prediction is 1. In practice, a complete set of predictions (one for each image-class pair) is usually evaluated.

See the builtin help of the evaluation MATLAB functions for further practical details. See also example_evaluation.m for examples on how to use this function.

Acknowledgments

The creation of this dataset started during the Johns Hopkins CLSP Summer Workshop 2012 Towards a Detailed Understanding of Objects and Scenes in Natural Images with, in alphabetical order, Matthew B. Blaschko, Ross B. Girshick, Juho Kannala, Iasonas Kokkinos, Siddharth Mahendran, Subhransu Maji, Sammy Mohamed, Esa Rahtu, Naomi Saphra, Karen Simonyan, Ben Taskar, Andrea Vedaldi, and David Weiss.

The CLSP workshop was supported by the National Science Foundation via Grant No 1005411, the Office of the Director of National Intelligence via the JHU Human Language Technology Center of Excellence; and Google Inc.

A special thanks goes to Pekka Rantalankila for helping with the creation of the airplane hieararchy.

Many thanks to the photographers that kindly made available their images for research purposes. Each photographer is listed below, along with a link to his/her airlners.net page:

Please note that the images are made available exclusively for non-commercial research purposes. The original authors retain the copyright on the respective pictures and should be contacted for any other usage of them.

Release notes

  • FGVC-Aircraft 2013b - The same as 2013a, but with test annotations included.
  • FGVC-Aircraft 2013a - First public release of the data.