Datasets:

Adaptation99
/

OVCL

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+<body>
+<h1>FGVC-Aircraft Benchmark</h1>
+<p><strong>Fine-Grained Visual Classification of Aircraft (FGVC-Aircraft)</strong> is
+a benchmark dataset for the fine grained visual categorization of
+aircraft.</p>
+<ul>
+<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>
+<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>
+<li>Project <a href="http://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/">home page</a>.</li>
+<li>This data was used as part of the fine-grained recognition challenge
+<a href="https://sites.google.com/site/fgcomp2013/">FGComp 2013</a> which ran
+jointly with the ImageNet Challenge 2013
+(<a href="https://sites.google.com/site/fgcomp2013/results">results</a>). Please
+note that <em>the evaluation code provided here may differ</em> from the
+one used in the challenge.</li>
+</ul>
+<p>Please use the following citation when referring to this dataset:</p>
+<p><em>Fine-Grained Visual Classification of Aircraft</em>, S. Maji, J. Kannala,
+E. Rahtu, M. Blaschko, A. Vedaldi, <a href="http://arxiv.org/abs/1306.5151">arXiv.org</a>, 2013</p>
+<pre><code>@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",
+}
+</code></pre>
+<p>For further information see:</p>
+<ul>
+<li><a href="#quick">Quick start</a>
+<ul>
+<li><a href="#aircraft">About aircraft</a></li>
+</ul></li>
+<li><a href="#format">Data and annotation format</a></li>
+<li><a href="#evaluation">Evaluation</a>
+<ul>
+<li><a href="#metric">Evaluation metric</a></li>
+<li><a href="#code">Evaluation code</a></li>
+</ul></li>
+<li><a href="#ack">Ackwonledgments</a></li>
+<li><a href="#release">Release notes</a></li>
+</ul>
+<p><strong>Note.</strong> This data has been used as part of the <em>ImageNet FGVC
+challenge in conjuction with the International Conference on Computer
+Vision (ICCV) 2013</em>. 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.</p>
+<p><strong>Note.</strong> Images in the benchmark are generously made available <strong>for
+non-commercial research purposes only</strong> by a number of <em>airplane
+spotters</em>. 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 <a href="#ack">copyright note</a> below.</p>
+<h1><a id=quick></a> Quick start</h1>
+<p>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.</p>
+<p>Aircraft models are organized in a four-levels hierarchy. The four
+levels, from finer to coarser, are:</p>
+<ul>
+<li><strong>Model</strong>, e.g. <em>Boeing 737-76J</em>. Since certain models are nearly visually
+indistinguishable, this level is not used in the evaluation.</li>
+<li><strong>Variant</strong>, e.g. <em>Boeing 737-700</em>. A variant collapses all the
+models that are visually indistinguishable into one class. The
+dataset comprises 102 different variants.</li>
+<li><strong>Family</strong>, e.g. <em>Boeing 737</em>. The dataset comprises 70 different
+families.</li>
+<li><strong>Manufacturer</strong>, e.g. <em>Boeing</em>. The dataset comprises 41
+different manufacturers.</li>
+</ul>
+<p>The data is divided into three equally-sized <em>training</em>, <em>validation</em>
+and <em>test</em> 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 <a href="#format">next</a>.</p>
+<p>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
+<a href="#software">evaluation script</a> in MATLAB is provided.</p>
+<h2><a href=aircraft></a> About aircraft</h2>
+<p>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 <em>structure</em> 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.</p>
+<h1><a id=format></a> Data format</h1>
+<p>The directory <code>data</code> contains the images as well as a number of text
+files with the data annotations.</p>
+<p>Images are contained in the <code>data/images</code> sub-directory. They are in
+JPEG format and have a name composed of seven digits and the <code>.jpg</code>
+suffix (e.g. <code>data/images/1187707.jpg</code>). The image resolution is about
+1-2MP. Each image has at the bottom a banner 20 pixels high containing
+<a href="#ack">copyright</a> information. Please make sure to remove this banner
+when using the images to train and evaluate algorithms.</p>
+<p>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.</p>
+<p><code>data/images_train.txt</code> contains the list of training images:</p>
+<pre>
+0787226
+1481091
+1548899
+0674300
+...
+</pre>
+<p>Similar files <code>data/images_val.txt</code> and <code>data/images_test.txt</code> contain the list
+of validation and test images.</p>
+<p><code>data/images_variant_train.txt</code>, <code>data/images_family_train.txt</code>, and
+<code>data/images_manufacturer_train.txt</code> contain the list of training
+images annotated with the model variant, family, and manufacturer
+names respectively:</p>
+<pre>
+0787226 Abingdon Spherical Free Balloon
+1481091 AEG Wagner Eule
+1548899 Aeris Naviter AN-2 Enara
+0674300 Aeritalia F-104S Starfighter
+...
+</pre>
+<p>Similar files are provided for the validation and test subsets.</p>
+<p>Finally, <code>data/images_box.txt</code> contains the aircraft bounding
+boxes, one per image. The bounding box is specified by four numbers:
+<em>xmin</em>, <em>ymin</em>, <em>xmax</em> and <em>ymax</em>. The top-left pixel of an image has
+coordinate (1,1).</p>
+<h1><a id=evaluation></a> Evaluation</h1>
+<p>The performance of a classifier is measured in term of its average
+classification accuracy, as detailed next.</p>
+<h2><a id=metric></a> Evaluation metric</h2>
+<p>The output of a classification algorithm must be a list of triplets of
+the type (<em>image</em>,<em>label</em>,<em>score</em>), where</p>
+<ul>
+<li><em>image</em> is an image label, i.e. a seven-digit number,</li>
+<li><em>label</em> is an image label, i.e.. an aircraft model variant, family, or manufacturer, and</li>
+<li><em>score</em> is a real number expressing the belief in the judgment.</li>
+</ul>
+<p>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).</p>
+<p>The quality of the predictions is measured in term of <em>average
+accuracy</em>, obtained as follows:</p>
+<ul>
+<li>The confusion matrix is square, with one row per class.</li>
+<li>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.</li>
+<li>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).</li>
+<li>The average accuracy is computed as the average of the diagonal of
+the confusion matrix.</li>
+</ul>
+<p>There are three challenges: classifying the aircraft variant, family, and manufacturer.</p>
+<h2><a id=code></a> Evaluation code</h2>
+<p>The evaluation protocol has been implemented in the MATLAB m-file
+<code>evaluation.m</code>. This function takes the path to the <code>data</code> folder, a
+composite name indicating the evaluation subset and challenge
+(e.g. <code>'manufacturer_test'</code> or <code>'family_val'</code>), and the list of
+triplets, and returns the confusion matrix. For example</p>
+<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>
+<p>evaluates a classifier output containing exactly one triplet (image,
+label, score), where the image is <code>'2074164'</code>, its predicted class is
+<code>'McDonnell Douglas MD-90-30'</code>, and the score of the prediction is
+<code>1</code>. In practice, a complete set of predictions (one for each
+image-class pair) is usually evaluated.</p>
+<p>See the builtin help of the <code>evaluation</code> MATLAB functions for further
+practical details. See also <code>example_evaluation.m</code> for examples on how
+to use this function.</p>
+<h1><a id=ack></a> Acknowledgments</h1>
+<p>The creation of this dataset started during the <em>Johns Hopkins CLSP
+Summer Workshop 2012</em>
+<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>
+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.</p>
+<p>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.</p>
+<p>A special thanks goes to Pekka Rantalankila for helping with the
+creation of the airplane hieararchy.</p>
+<p>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 <a href="http://airliners.net">airlners.net</a> page:</p>
+<ul>
+<li><a href="http://www.airliners.net/profile/dendrobatid">Mick Bajcar</a></li>
+<li><a href="http://www.airliners.net/profile/aldobid">Aldo Bidini</a></li>
+<li><a href="http://www.airliners.net/profile/minoeke">Wim Callaert</a></li>
+<li><a href="http://www.airliners.net/profile/tommypilot">Tommy Desmet</a></li>
+<li><a href="http://www.airliners.net/profile/snorre">Thomas Posch</a></li>
+<li><a href="http://www.airliners.net/profile/lemonkitty">James Richard Covington</a></li>
+<li><a href="http://www.airliners.net/profile/stegi">Gerry Stegmeier</a></li>
+<li><a href="http://www.airliners.net/profile/aal151heavy">Ben Wang</a></li>
+<li><a href="http://www.airliners.net/profile/dazbo5">Darren Wilson</a></li>
+<li><a href="http://www.airliners.net/profile/fly-k">Konstantin von Wedelstaedt</a></li>
+</ul>
+<p>Please note that the images are made available <strong>exclusively for
+non-commercial research purposes</strong>. The original authors retain the
+copyright on the respective pictures and should be contacted for any
+other usage of them.</p>
+<h1><a id=release></a> Release notes</h1>
+<ul>
+<li><em>FGVC-Aircraft 2013b</em> - The same as 2013a, but with test annotations included.</li>
+<li><em>FGVC-Aircraft 2013a</em> - First public release of the data.</li>
+</ul>
+</body>
+</html>

fgvc-aircraft-2013b/README.md ADDED Viewed

	@@ -0,0 +1,261 @@

+# 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.

fgvc-aircraft-2013b/data/families.txt ADDED Viewed

	@@ -0,0 +1,70 @@

+A300
+A310
+A320
+A330
+A340
+A380
+ATR-42
+ATR-72
+An-12
+BAE 146
+BAE-125
+Beechcraft 1900
+Boeing 707
+Boeing 717
+Boeing 727
+Boeing 737
+Boeing 747
+Boeing 757
+Boeing 767
+Boeing 777
+C-130
+C-47
+CRJ-200
+CRJ-700
+Cessna 172
+Cessna 208
+Cessna Citation
+Challenger 600
+DC-10
+DC-3
+DC-6
+DC-8
+DC-9
+DH-82
+DHC-1
+DHC-6
+DR-400
+Dash 8
+Dornier 328
+EMB-120
+Embraer E-Jet
+Embraer ERJ 145
+Embraer Legacy 600
+Eurofighter Typhoon
+F-16
+F/A-18
+Falcon 2000
+Falcon 900
+Fokker 100
+Fokker 50
+Fokker 70
+Global Express
+Gulfstream
+Hawk T1
+Il-76
+King Air
+L-1011
+MD-11
+MD-80
+MD-90
+Metroliner
+PA-28
+SR-20
+Saab 2000
+Saab 340
+Spitfire
+Tornado
+Tu-134
+Tu-154
+Yak-42

fgvc-aircraft-2013b/data/images/0034309.jpg ADDED Viewed

Git LFS Details

SHA256: 07a4a1beea732fa877ad2fb2e67533c754f8fd31a8c8df5dd5b1131c6896aa64
Pointer size: 130 Bytes
Size of remote file: 76 kB

fgvc-aircraft-2013b/data/images/0034958.jpg ADDED Viewed

Git LFS Details

SHA256: 74c386b468a2ca08bc58ed9e643846ab09a6a26cda94c359713a04b95821e4ff
Pointer size: 130 Bytes
Size of remote file: 82.2 kB

fgvc-aircraft-2013b/data/images/0037511.jpg ADDED Viewed

Git LFS Details

SHA256: ad56d673641ad2a5899acf749f50c92a39a62881d6b56a5ab661d19eef9e0a1f
Pointer size: 131 Bytes
Size of remote file: 118 kB

fgvc-aircraft-2013b/data/images/0037512.jpg ADDED Viewed

Git LFS Details

SHA256: a00ab2543de43c8e5ef2cb8da78e06222d8749bef6060f95eaa169520d9cb83a
Pointer size: 131 Bytes
Size of remote file: 113 kB

fgvc-aircraft-2013b/data/images/0038598.jpg ADDED Viewed

Git LFS Details

SHA256: eae70fa0c7d849357a5fbcaa208b4166b499c41a62a8b877587afcc510f8efc0
Pointer size: 130 Bytes
Size of remote file: 76.3 kB

fgvc-aircraft-2013b/data/images/0038626.jpg ADDED Viewed

Git LFS Details

SHA256: cf0513b4c49a2920dffca56a4f6ab343d8eec3935ae1bbaa54007553381ede1c
Pointer size: 131 Bytes
Size of remote file: 112 kB

fgvc-aircraft-2013b/data/images/0038671.jpg ADDED Viewed

Git LFS Details

SHA256: f3bcf3ff9e6da89e66e2c68e3c1a5d2c397a5ce2982d4c7030b27181360aa48b
Pointer size: 131 Bytes
Size of remote file: 106 kB

fgvc-aircraft-2013b/data/images/0041419.jpg ADDED Viewed

Git LFS Details

SHA256: 50fa8eabc135f6014f6b0b821db47320f324e850175b67d2f79f8d213bdbc5db
Pointer size: 130 Bytes
Size of remote file: 92.1 kB

fgvc-aircraft-2013b/data/images/0043750.jpg ADDED Viewed

Git LFS Details

SHA256: eb797efd2fb5035aabf06aa50cccef72e3cfff5f43e559c6d397da5169e817fb
Pointer size: 131 Bytes
Size of remote file: 103 kB

fgvc-aircraft-2013b/data/images/0043890.jpg ADDED Viewed

Git LFS Details

SHA256: 50b3088462871eecff26964d60827af05e6de290a63d1bbcecaee6b09ac9c0c0
Pointer size: 130 Bytes
Size of remote file: 65.4 kB

fgvc-aircraft-2013b/data/images/0043892.jpg ADDED Viewed

Git LFS Details

SHA256: 0048799d536bd1958556e449d31eec404e88635707c018dd65db4dc4390d3a84
Pointer size: 130 Bytes
Size of remote file: 70.2 kB

fgvc-aircraft-2013b/data/images/0045128.jpg ADDED Viewed

Git LFS Details

SHA256: 7fa4b3e08da952720d566136a5bbe6a5611e9355fdf35400e8a9389732e6a214
Pointer size: 130 Bytes
Size of remote file: 70 kB

fgvc-aircraft-2013b/data/images/0045133.jpg ADDED Viewed

Git LFS Details

SHA256: 7df5fb31cd8bb936c108ee4ad4c218d1a90539a947428f50c742624d8a360583
Pointer size: 131 Bytes
Size of remote file: 103 kB

fgvc-aircraft-2013b/data/images/0046946.jpg ADDED Viewed

Git LFS Details

SHA256: a6420abdf5278a40ed1e8b970479b0d25dfa18f5eff31e902efe7fdf787d1fd3
Pointer size: 131 Bytes
Size of remote file: 246 kB

fgvc-aircraft-2013b/data/images/0048339.jpg ADDED Viewed

Git LFS Details

SHA256: 94e05fd5656c7af1562ec58fa3dc058b0a9d26c7afcfc517e0154abcf1ef9935
Pointer size: 131 Bytes
Size of remote file: 112 kB

fgvc-aircraft-2013b/data/images/0048340.jpg ADDED Viewed

Git LFS Details

SHA256: f7befd1e73c0f89947cedea4d54041e8aee85cb12c9549924fbd3e5c7255597c
Pointer size: 131 Bytes
Size of remote file: 100 kB

fgvc-aircraft-2013b/data/images/0048341.jpg ADDED Viewed

Git LFS Details

SHA256: 4e5461ebb1b23c6e1f22db4b126c4100cb66a27c060e323c09c2c91e01fd655b
Pointer size: 130 Bytes
Size of remote file: 43.6 kB

fgvc-aircraft-2013b/data/images/0054367.jpg ADDED Viewed

Git LFS Details

SHA256: 2d67ab6e3d495a0106f1d37bd80d8fd191568bcfaa5e80bf629bb9fd01b91efb
Pointer size: 130 Bytes
Size of remote file: 83.6 kB

fgvc-aircraft-2013b/data/images/0056297.jpg ADDED Viewed

Git LFS Details

SHA256: 6b5215cf2503123026b42f7a3a949dc8a96314c172b7d21dc899dd3140098179
Pointer size: 131 Bytes
Size of remote file: 420 kB

fgvc-aircraft-2013b/data/images/0056299.jpg ADDED Viewed

Git LFS Details

SHA256: a0e4e0207a53944300c4f17d47d86bd3601d0b688766c4ee3028d42bcfb4d400
Pointer size: 131 Bytes
Size of remote file: 408 kB

fgvc-aircraft-2013b/data/images/0056323.jpg ADDED Viewed

Git LFS Details

SHA256: a7cf36c4eb1645700653a7d10e46246c2d7617c9899746a6e44d10ebd468523c
Pointer size: 131 Bytes
Size of remote file: 291 kB

fgvc-aircraft-2013b/data/images/0056324.jpg ADDED Viewed

Git LFS Details

SHA256: 0144cc6a0114a2e99c294451ac54ee180157fa93b143ea064172acabb0c91d3a
Pointer size: 130 Bytes
Size of remote file: 51.5 kB

fgvc-aircraft-2013b/data/images/0056331.jpg ADDED Viewed

Git LFS Details

SHA256: f4db6d081a1229d9bfc0caf4ff2d4d6f3328f4c06c24c923f921ca398349084f
Pointer size: 131 Bytes
Size of remote file: 608 kB

fgvc-aircraft-2013b/data/images/0056333.jpg ADDED Viewed

Git LFS Details

SHA256: 2714aa370fa2f1d2f4961d8f7d1e07021da1f2c5a77a0890325201b702b82413
Pointer size: 130 Bytes
Size of remote file: 97 kB

fgvc-aircraft-2013b/data/images/0056337.jpg ADDED Viewed

Git LFS Details

SHA256: 0089b64ad952c3afb2c860ab71a693033c13b94da81c8d99536f96e09f535cdc
Pointer size: 131 Bytes
Size of remote file: 478 kB

fgvc-aircraft-2013b/data/images/0056589.jpg ADDED Viewed

Git LFS Details

SHA256: fd47c983f45ddd5ea47bd3e5f6b997724548353bbb049e3873e83ce5047cd2ae
Pointer size: 131 Bytes
Size of remote file: 101 kB

fgvc-aircraft-2013b/data/images/0056782.jpg ADDED Viewed

Git LFS Details

SHA256: f52a5dc00fd45cc1718a28695f4b1206e9cf39e5d328a02cb91712d4a047bf8a
Pointer size: 130 Bytes
Size of remote file: 73.5 kB

fgvc-aircraft-2013b/data/images/0056978.jpg ADDED Viewed

Git LFS Details

SHA256: c12f9edc9dd1bf138e792c9899c545458ca25846dac7b9a3745809a342fd3557
Pointer size: 130 Bytes
Size of remote file: 88 kB

fgvc-aircraft-2013b/data/images/0059101.jpg ADDED Viewed

Git LFS Details

SHA256: 899bc4d6f1c84514128a613d4cd4ce21e151bb0e599aa48315a9a9acedc1edeb
Pointer size: 131 Bytes
Size of remote file: 113 kB

fgvc-aircraft-2013b/data/images/0059107.jpg ADDED Viewed

Git LFS Details

SHA256: 51ac4a5e238a6d998bf22c4b919ecb82cc63f1cddc99fc91ab50a704fe224daf
Pointer size: 131 Bytes
Size of remote file: 128 kB

fgvc-aircraft-2013b/data/images/0059112.jpg ADDED Viewed

Git LFS Details

SHA256: b62a84e597df80eecad7d4f51c22b6cebc802ba8938504b0f1365a6966a886c3
Pointer size: 130 Bytes
Size of remote file: 83.4 kB

fgvc-aircraft-2013b/data/images/0061136.jpg ADDED Viewed

Git LFS Details

SHA256: 19e634c81bd952ac842bad1c03e4b9acce560ad18f2e3c0429abe5aa37d001bc
Pointer size: 131 Bytes
Size of remote file: 112 kB

fgvc-aircraft-2013b/data/images/0061139.jpg ADDED Viewed

Git LFS Details

SHA256: ad2aa6bf8754a362516e4d8368be46f6b24951c338cfce41d53185ffc45f7afd
Pointer size: 131 Bytes
Size of remote file: 148 kB

fgvc-aircraft-2013b/data/images/0061307.jpg ADDED Viewed

Git LFS Details

SHA256: 3e9ef438b911ce057f11873e8341f6ed269701336ef8969f94218103a308e5c1
Pointer size: 131 Bytes
Size of remote file: 108 kB

fgvc-aircraft-2013b/data/images/0061399.jpg ADDED Viewed

Git LFS Details

SHA256: 6c070b3f7722e4748779970a004d2ec7a913e5e3f8236592c9634b7db9885374
Pointer size: 131 Bytes
Size of remote file: 127 kB

fgvc-aircraft-2013b/data/images/0061401.jpg ADDED Viewed

Git LFS Details

SHA256: 865f3541f3bfbc7aa16b83bb7f4e09aaa5cb8819030e6d9f18afcfd981c60401
Pointer size: 131 Bytes
Size of remote file: 120 kB

fgvc-aircraft-2013b/data/images/0061580.jpg ADDED Viewed

Git LFS Details

SHA256: 911a5d746623890984edfc408ba875dda4f0ff40ee566c86401db138c73c18fd
Pointer size: 131 Bytes
Size of remote file: 117 kB

fgvc-aircraft-2013b/data/images/0061581.jpg ADDED Viewed

Git LFS Details

SHA256: 5d4f84981d7b30788e62c15c513ed40ba91c879199e7ff8d676a3f43d12f2984
Pointer size: 131 Bytes
Size of remote file: 149 kB

fgvc-aircraft-2013b/data/images/0062125.jpg ADDED Viewed

Git LFS Details

SHA256: 5800ff3947f2dfd24aa8217341fbcf3d3e01393a4240fecf7b173e79f32aba8f
Pointer size: 130 Bytes
Size of remote file: 99 kB

fgvc-aircraft-2013b/data/images/0062131.jpg ADDED Viewed

Git LFS Details

SHA256: 43a0eda57ebf5addc3ec2d22640e9ce28abb7c1ce503b85bb4e7648a28141235
Pointer size: 130 Bytes
Size of remote file: 50.3 kB

fgvc-aircraft-2013b/data/images/0062134.jpg ADDED Viewed

Git LFS Details

SHA256: d0feff0d407ee4aa59da5cb3b4d05d0a870de1b2afeb060565b15d1169580dab
Pointer size: 131 Bytes
Size of remote file: 122 kB

fgvc-aircraft-2013b/data/images/0062223.jpg ADDED Viewed

Git LFS Details

SHA256: 7ea418f10cf79c3a136a8e8082c3583762dc563e0db8b9bafcb988607240bb82
Pointer size: 131 Bytes
Size of remote file: 154 kB

fgvc-aircraft-2013b/data/images/0062224.jpg ADDED Viewed

Git LFS Details

SHA256: 1f61ee35dbd10b18ea7ed46c2e64e4e8e975897a488be40ddcca98772e0943a2
Pointer size: 131 Bytes
Size of remote file: 127 kB

fgvc-aircraft-2013b/data/images/0062226.jpg ADDED Viewed

Git LFS Details

SHA256: f91cf5c07cc1b45df6749a2bd6b0c56072e966972527aa0749e42128b97b3bd0
Pointer size: 130 Bytes
Size of remote file: 86.8 kB

fgvc-aircraft-2013b/data/images/0062255.jpg ADDED Viewed

Git LFS Details

SHA256: 38cc70f7b47e6041dd24983e21e4a8b8b07da3833858e3522b7f09c7681a3103
Pointer size: 131 Bytes
Size of remote file: 138 kB

fgvc-aircraft-2013b/data/images/0062258.jpg ADDED Viewed

Git LFS Details

SHA256: 069e5ae63a158a4933526896ec7149baf049e0d05ca3fd04f25810a1e7764788
Pointer size: 130 Bytes
Size of remote file: 54.2 kB

fgvc-aircraft-2013b/data/images/0062260.jpg ADDED Viewed

Git LFS Details

SHA256: fab442cca7a2d418be0a898d35f3d407007198792fdb53deee3e419d1ea38178
Pointer size: 130 Bytes
Size of remote file: 85.3 kB