metadata
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
'2': '2'
'3': '3'
'4': '4'
'5': '5'
'6': '6'
'7': '7'
'8': '8'
'9': '9'
'10': '10'
'11': '11'
'12': '12'
'13': '13'
'14': '14'
'15': '15'
'16': '16'
'17': '17'
'18': '18'
'19': '19'
'20': '20'
'21': '21'
'22': '22'
'23': '23'
'24': '24'
'25': '25'
'26': '26'
'27': '27'
'28': '28'
'29': '29'
'30': '30'
'31': '31'
'32': '32'
'33': '33'
'34': '34'
'35': '35'
'36': '36'
'37': '37'
'38': '38'
'39': '39'
'40': '40'
'41': '41'
'42': '42'
'43': '43'
'44': '44'
'45': '45'
'46': '46'
'47': '47'
'48': '48'
'49': '49'
'50': '50'
'51': '51'
'52': '52'
'53': '53'
'54': '54'
'55': '55'
'56': '56'
'57': '57'
'58': '58'
'59': '59'
'60': '60'
'61': '61'
'62': '62'
'63': '63'
'64': '64'
'65': '65'
'66': '66'
'67': '67'
'68': '68'
'69': '69'
'70': '70'
'71': '71'
'72': '72'
'73': '73'
'74': '74'
'75': '75'
'76': '76'
'77': '77'
'78': '78'
'79': '79'
'80': '80'
'81': '81'
'82': '82'
'83': '83'
'84': '84'
'85': '85'
'86': '86'
'87': '87'
'88': '88'
'89': '89'
'90': '90'
'91': '91'
'92': '92'
'93': '93'
'94': '94'
'95': '95'
'96': '96'
'97': '97'
'98': '98'
'99': '99'
'100': '100'
'101': '101'
splits:
- name: train
num_bytes: 276768171.75
num_examples: 2754
- name: val
num_bytes: 31942022
num_examples: 306
- name: trainval
num_bytes: 308710193.5
num_examples: 3060
- name: test
num_bytes: 670168354.5
num_examples: 6084
- name: train800
num_bytes: 80944215
num_examples: 800
- name: val200
num_bytes: 20954456
num_examples: 200
- name: train800val200
num_bytes: 101898671
num_examples: 1000
download_size: 1491513351
dataset_size: 1491386083.75
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: trainval
path: data/trainval-*
- split: test
path: data/test-*
- split: train800
path: data/train800-*
- split: val200
path: data/val200-*
- split: train800val200
path: data/train800val200-*
license: cc-by-4.0
VTAB Caltech101
This dataset has been used for the paper Fantastic Features and Where to Find Them: A Probing Method to combine Features from Multiple Foundation Models (NeurIPS 2025).
It reproduces the settings (splits, labels) used for the Visual Task Adaptation Benchmark (VTAB).
- VTAB Paper: A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark
- VTAB Repository: google-research/task_adaptation
Details of the original dataset:
- Original Citation: Fei-Fei, Li, Robert Fergus, and Pietro Perona. "One-shot learning of object categories." IEEE Transactions on Pattern Analysis and Machine Intelligence.
- Caltech 101 Homepage: Caltech101 on CaltechDATA