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.
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:
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.
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.
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).
The performance of a classifier is measured in term of its average classification accuracy, as detailed next.
The output of a classification algorithm must be a list of triplets of the type (image,label,score), where
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:
There are three challenges: classifying the aircraft variant, family, and manufacturer.
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.
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.