QЦi z S SK Jr S SKrS SKJr S SKJrJrJrJ r J
r
S SK r SSK
JrJr SSKJr " S S \5 rg)
) annotationsN)Path)AnyCallableOptionalTupleUnion ) download_and_extract_archiveverify_str_arg)
VisionDatasetc ^ \ rS rSrSrSr S
S U 4S jjjrS S jrS
S jrSS jr SS jr
S r U =r $ ) FGVCAircraft
a `FGVC Aircraft `_ Dataset.
The dataset contains 10,000 images of aircraft, with 100 images for each of 100
different aircraft model variants, most of which are airplanes.
Aircraft models are organized in a three-levels hierarchy. The three levels, from
finer to coarser, are:
- ``variant``, e.g. Boeing 737-700. A variant collapses all the models that are visually
indistinguishable into one class. The dataset comprises 100 different variants.
- ``family``, e.g. Boeing 737. The dataset comprises 70 different families.
- ``manufacturer``, e.g. Boeing. The dataset comprises 30 different manufacturers.
Args:
root (str or ``pathlib.Path``): Root directory of the FGVC Aircraft dataset.
split (string, optional): The dataset split, supports ``train``, ``val``,
``trainval`` and ``test``.
annotation_level (str, optional): The annotation level, supports ``variant``,
``family`` and ``manufacturer``.
transform (callable, optional): A function/transform that takes in a PIL image
and returns a transformed version. E.g, ``transforms.RandomCrop``
target_transform (callable, optional): A function/transform that takes in the
target and transforms it.
download (bool, optional): If True, downloads the dataset from the internet and
puts it in root directory. If dataset is already downloaded, it is not
downloaded again.
zWhttps://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/archives/fgvc-aircraft-2013b.tar.gzc
6 > [ TU ] XUS9 [ USS5 U l [ USS5 U l [
R R U R S5 U l U( a U R 5 U R 5 ( d [ S5 e[
R R U R SS S
S S.U R 5 n[ US 5 nU V s/ s H oR 5 PM sn U l S S S 5 [! [# U R [% [' U R 5 5 5 5 U l [
R R U R SS
5 n
[
R R U R SSU R SU R S35 n / U l / U l [ U S 5 nU H n U R 5 R/ SS5 u pU R* R1 [
R R X S35 5 U R, R1 U R( U
5 M S S S 5 g s sn f ! , ( d f GNw= f! , ( d f g = f)N) transformtarget_transformsplit)trainvaltrainvaltestannotation_level)variantfamily manufacturerzfgvc-aircraft-2013bz;Dataset not found. You can use download=True to download itdataz variants.txtz families.txtzmanufacturers.txtrimagesimages__z.txt r
z.jpg)super__init__r _split_annotation_levelospathjoinroot
_data_path _download
_check_exists RuntimeErroropenstripclassesdictziprangelen class_to_idx _image_files_labelsr append)selfr* r r r r downloadannotation_fileflineimage_data_folder labels_file
image_name
label_name __class__s a/var/www/html/ai-image-ml/venv/lib/python3.13/site-packages/torchvision/datasets/fgvc_aircraft.pyr$ FGVCAircraft.__init__+ s EUV$UG5YZ !/ 02W"
'',,tyy2GH NN !!## \] ] '',, OO )( 3
$$
&
/3
'1567QTJJLQ7DL ( !T\\5T\\9J3K!L M GGLL &( Kggll4??FgdF\F\E]]^_c_j_j^kko>NN5)E ))%0E|rK c p U R 5 ( a g[ U R U R 5 g)zG
Download the FGVC Aircraft dataset archive and extract it under root.
N)r- r _URLr* rH s rD r, FGVCAircraft._downloadj s( $TYY :rK c [ R R U R 5 =( a) [ R R U R 5 $ rG )r' r( existsr+ isdirrH s rD r- FGVCAircraft._check_existsr s/ ww~~doo.Q277== 3QQrK )r& r+ r7 r8 r% r6 r1 )r r NNF)r* zUnion[str, Path]r strr r^ r Optional[Callable]r r_ r; boolreturnNone)ra int)rQ rc ra zTuple[Any, Any])ra rb )ra r` )
__name__
__module__ __qualname____firstlineno____doc__rX r$ rI rU r, r- __static_attributes__
__classcell__)rC s @rD r r
s 6 eD
)(,/3 .C .C .C .C
& .C -
.C .C
.C .C`&
;R RrK r )
__future__r r' pathlibr typingr r r r r PIL.ImagerN utilsr r visionr
r rK rD rr s- " 8 8 ? !fR= fRrK