Datasets:
metadata
annotations_creators:
- derived
language:
- eng
license: unknown
multilinguality: monolingual
source_datasets:
- HuggingFaceM4/FGVC-Aircraft
task_categories:
- zero-shot-image-classification
- image-to-text
- text-to-image
task_ids: []
dataset_info:
- config_name: default
features:
- name: image
dtype: image
- name: bbox
struct:
- name: ymin
dtype: int64
- name: xmin
dtype: int64
- name: ymax
dtype: int64
- name: xmax
dtype: int64
- name: family
dtype:
class_label:
names:
'0': A300
'1': A310
'2': A320
'3': A330
'4': A340
'5': A380
'6': ATR-42
'7': ATR-72
'8': An-12
'9': BAE 146
'10': BAE-125
'11': Beechcraft 1900
'12': Boeing 707
'13': Boeing 717
'14': Boeing 727
'15': Boeing 737
'16': Boeing 747
'17': Boeing 757
'18': Boeing 767
'19': Boeing 777
'20': C-130
'21': C-47
'22': CRJ-200
'23': CRJ-700
'24': Cessna 172
'25': Cessna 208
'26': Cessna Citation
'27': Challenger 600
'28': DC-10
'29': DC-3
'30': DC-6
'31': DC-8
'32': DC-9
'33': DH-82
'34': DHC-1
'35': DHC-6
'36': DR-400
'37': Dash 8
'38': Dornier 328
'39': EMB-120
'40': Embraer E-Jet
'41': Embraer ERJ 145
'42': Embraer Legacy 600
'43': Eurofighter Typhoon
'44': F-16
'45': F/A-18
'46': Falcon 2000
'47': Falcon 900
'48': Fokker 100
'49': Fokker 50
'50': Fokker 70
'51': Global Express
'52': Gulfstream
'53': Hawk T1
'54': Il-76
'55': King Air
'56': L-1011
'57': MD-11
'58': MD-80
'59': MD-90
'60': Metroliner
'61': PA-28
'62': SR-20
'63': Saab 2000
'64': Saab 340
'65': Spitfire
'66': Tornado
'67': Tu-134
'68': Tu-154
'69': Yak-42
- name: manufacturer
dtype:
class_label:
names:
'0': ATR
'1': Airbus
'2': Antonov
'3': Beechcraft
'4': Boeing
'5': Bombardier Aerospace
'6': British Aerospace
'7': Canadair
'8': Cessna
'9': Cirrus Aircraft
'10': Dassault Aviation
'11': Dornier
'12': Douglas Aircraft Company
'13': Embraer
'14': Eurofighter
'15': Fairchild
'16': Fokker
'17': Gulfstream Aerospace
'18': Ilyushin
'19': Lockheed Corporation
'20': Lockheed Martin
'21': McDonnell Douglas
'22': Panavia
'23': Piper
'24': Robin
'25': Saab
'26': Supermarine
'27': Tupolev
'28': Yakovlev
'29': de Havilland
- name: variant
dtype:
class_label:
names:
'0': 707-320
'1': 727-200
'2': 737-200
'3': 737-300
'4': 737-400
'5': 737-500
'6': 737-600
'7': 737-700
'8': 737-800
'9': 737-900
'10': 747-100
'11': 747-200
'12': 747-300
'13': 747-400
'14': 757-200
'15': 757-300
'16': 767-200
'17': 767-300
'18': 767-400
'19': 777-200
'20': 777-300
'21': A300B4
'22': A310
'23': A318
'24': A319
'25': A320
'26': A321
'27': A330-200
'28': A330-300
'29': A340-200
'30': A340-300
'31': A340-500
'32': A340-600
'33': A380
'34': ATR-42
'35': ATR-72
'36': An-12
'37': BAE 146-200
'38': BAE 146-300
'39': BAE-125
'40': Beechcraft 1900
'41': Boeing 717
'42': C-130
'43': C-47
'44': CRJ-200
'45': CRJ-700
'46': CRJ-900
'47': Cessna 172
'48': Cessna 208
'49': Cessna 525
'50': Cessna 560
'51': Challenger 600
'52': DC-10
'53': DC-3
'54': DC-6
'55': DC-8
'56': DC-9-30
'57': DH-82
'58': DHC-1
'59': DHC-6
'60': DHC-8-100
'61': DHC-8-300
'62': DR-400
'63': Dornier 328
'64': E-170
'65': E-190
'66': E-195
'67': EMB-120
'68': ERJ 135
'69': ERJ 145
'70': Embraer Legacy 600
'71': Eurofighter Typhoon
'72': F-16A/B
'73': F/A-18
'74': Falcon 2000
'75': Falcon 900
'76': Fokker 100
'77': Fokker 50
'78': Fokker 70
'79': Global Express
'80': Gulfstream IV
'81': Gulfstream V
'82': Hawk T1
'83': Il-76
'84': L-1011
'85': MD-11
'86': MD-80
'87': MD-87
'88': MD-90
'89': Metroliner
'90': Model B200
'91': PA-28
'92': SR-20
'93': Saab 2000
'94': Saab 340
'95': Spitfire
'96': Tornado
'97': Tu-134
'98': Tu-154
'99': Yak-42
splits:
- name: train
num_bytes: 879465178.444
num_examples: 3334
- name: validation
num_bytes: 866544460.931
num_examples: 3333
- name: test
num_bytes: 874244327.534
num_examples: 3333
download_size: 2761622313
dataset_size: 2620253966.909
- config_name: labels
features:
- name: labels
dtype: string
splits:
- name: train
num_bytes: 4546
num_examples: 100
download_size: 1931
dataset_size: 4546
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
- config_name: labels
data_files:
- split: train
path: labels/train-*
tags:
- mteb
- text
- image
Classifying aircraft images from 41 manufacturers and 102 variants.
| Task category | i2t |
| Domains | Encyclopaedic |
| Reference | https://arxiv.org/abs/1306.5151 |
Source datasets:
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_task("FGVCAircraftZeroShot")
evaluator = mteb.MTEB([task])
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
To learn more about how to run models on mteb task check out the GitHub repository.
Citation
If you use this dataset, please cite the dataset as well as mteb, as this dataset likely includes additional processing as a part of the MMTEB Contribution.
@misc{maji2013finegrainedvisualclassificationaircraft,
archiveprefix = {arXiv},
author = {Subhransu Maji and Esa Rahtu and Juho Kannala and Matthew Blaschko and Andrea Vedaldi},
eprint = {1306.5151},
primaryclass = {cs.CV},
title = {Fine-Grained Visual Classification of Aircraft},
url = {https://arxiv.org/abs/1306.5151},
year = {2013},
}
@article{enevoldsen2025mmtebmassivemultilingualtext,
title={MMTEB: Massive Multilingual Text Embedding Benchmark},
author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
publisher = {arXiv},
journal={arXiv preprint arXiv:2502.13595},
year={2025},
url={https://arxiv.org/abs/2502.13595},
doi = {10.48550/arXiv.2502.13595},
}
@article{muennighoff2022mteb,
author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Loïc and Reimers, Nils},
title = {MTEB: Massive Text Embedding Benchmark},
publisher = {arXiv},
journal={arXiv preprint arXiv:2210.07316},
year = {2022}
url = {https://arxiv.org/abs/2210.07316},
doi = {10.48550/ARXIV.2210.07316},
}
Dataset Statistics
Dataset Statistics
The following code contains the descriptive statistics from the task. These can also be obtained using:
import mteb
task = mteb.get_task("FGVCAircraftZeroShot")
desc_stats = task.metadata.descriptive_stats
{
"test": {
"num_samples": 3333,
"unique_num_labels": 100,
"min_image_width": 800,
"average_image_width": 1098.5760576057605,
"max_image_width": 1600,
"min_image_height": 413,
"average_image_height": 746.996699669967,
"max_image_height": 1197,
"min_label_text_length": 38,
"average_label_text_length": 41.46,
"max_label_text_length": 53,
"labels": {
"0": {
"count": 33
},
"1": {
"count": 33
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"2": {
"count": 34
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"3": {
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"4": {
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}
}
}
}
This dataset card was automatically generated using MTEB