| # FGVC-Aircraft Benchmark | |
| **Fine-Grained Visual Classification of Aircraft (FGVC-Aircraft)** is | |
| a benchmark dataset for the fine grained visual categorization of | |
| aircraft. | |
| * [Data, annotations, and evaluation code](archives/fgvc-aircraft-2013b.tar.gz) [2.75 GB | [MD5 Sum](archives/fgvc-aircraft-2013b.html)]. | |
| * [Annotations and evaluation code only](archives/fgvc-aircraft-2013b-annotations.tar.gz) [375 KB | [MD5 Sum](archives/fgvc-aircraft-2013b-annotations.html)]. | |
| * Project [home page](http://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/). | |
| * This data was used as part of the fine-grained recognition challenge | |
| [FGComp 2013](https://sites.google.com/site/fgcomp2013/) which ran | |
| jointly with the ImageNet Challenge 2013 | |
| ([results](https://sites.google.com/site/fgcomp2013/results)). Please | |
| note that *the evaluation code provided here may differ* from the | |
| one used in the challenge. | |
| 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](http://arxiv.org/abs/1306.5151), 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: | |
| * [Quick start](#quick) | |
| * [About aircraft](#aircraft) | |
| * [Data and annotation format](#format) | |
| * [Evaluation](#evaluation) | |
| * [Evaluation metric](#metric) | |
| * [Evaluation code](#code) | |
| * [Ackwonledgments](#ack) | |
| * [Release notes](#release) | |
| **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](#ack) below. | |
| # <a id=quick></a> 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](#format). | |
| 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](#software) in MATLAB is provided. | |
| ## <a href=aircraft></a> 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. | |
| # <a id=format></a> 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](#ack) 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: | |
| <pre> | |
| 0787226 | |
| 1481091 | |
| 1548899 | |
| 0674300 | |
| ... | |
| </pre> | |
| 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: | |
| <pre> | |
| 0787226 Abingdon Spherical Free Balloon | |
| 1481091 AEG Wagner Eule | |
| 1548899 Aeris Naviter AN-2 Enara | |
| 0674300 Aeritalia F-104S Starfighter | |
| ... | |
| </pre> | |
| 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). | |
| # <a id=evaluation></a> Evaluation | |
| The performance of a classifier is measured in term of its average | |
| classification accuracy, as detailed next. | |
| ## <a id=metric></a> 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. | |
| ## <a id=code></a> 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 | |
| <pre> | |
| images = {'2074164'} ; | |
| labels = {'McDonnell Douglas MD-90-30'} ; | |
| scores = 1 ; | |
| confusion = evaluate('/path/fgcv-aircraft/data', 'test', images, labels, scores) ; | |
| accuracy = mean(diag(confusion)) ; | |
| </pre> | |
| 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. | |
| # <a id=ack></a> 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](http://www.clsp.jhu.edu/workshops/archive/ws-12/groups/tduosn/) | |
| 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](http://airliners.net) page: | |
| * [Mick Bajcar](http://www.airliners.net/profile/dendrobatid) | |
| * [Aldo Bidini](http://www.airliners.net/profile/aldobid) | |
| * [Wim Callaert](http://www.airliners.net/profile/minoeke) | |
| * [Tommy Desmet](http://www.airliners.net/profile/tommypilot) | |
| * [Thomas Posch](http://www.airliners.net/profile/snorre) | |
| * [James Richard Covington](http://www.airliners.net/profile/lemonkitty) | |
| * [Gerry Stegmeier](http://www.airliners.net/profile/stegi) | |
| * [Ben Wang](http://www.airliners.net/profile/aal151heavy) | |
| * [Darren Wilson](http://www.airliners.net/profile/dazbo5) | |
| * [Konstantin von Wedelstaedt](http://www.airliners.net/profile/fly-k) | |
| 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. | |
| # <a id=release></a> Release notes | |
| * *FGVC-Aircraft 2013b* - The same as 2013a, but with test annotations included. | |
| * *FGVC-Aircraft 2013a* - First public release of the data. | |