Datasets:
metadata
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': n01532829
'1': n01558993
'2': n01704323
'3': n01749939
'4': n01770081
'5': n01843383
'6': n01855672
'7': n01910747
'8': n01930112
'9': n01981276
'10': n02074367
'11': n02089867
'12': n02091244
'13': n02091831
'14': n02099601
'15': n02101006
'16': n02105505
'17': n02108089
'18': n02108551
'19': n02108915
'20': n02110063
'21': n02110341
'22': n02111277
'23': n02113712
'24': n02114548
'25': n02116738
'26': n02120079
'27': n02129165
'28': n02138441
'29': n02165456
'30': n02174001
'31': n02219486
'32': n02443484
'33': n02457408
'34': n02606052
'35': n02687172
'36': n02747177
'37': n02795169
'38': n02823428
'39': n02871525
'40': n02950826
'41': n02966193
'42': n02971356
'43': n02981792
'44': n03017168
'45': n03047690
'46': n03062245
'47': n03075370
'48': n03127925
'49': n03146219
'50': n03207743
'51': n03220513
'52': n03272010
'53': n03337140
'54': n03347037
'55': n03400231
'56': n03417042
'57': n03476684
'58': n03527444
'59': n03535780
'60': n03544143
'61': n03584254
'62': n03676483
'63': n03770439
'64': n03773504
'65': n03775546
'66': n03838899
'67': n03854065
'68': n03888605
'69': n03908618
'70': n03924679
'71': n03980874
'72': n03998194
'73': n04067472
'74': n04146614
'75': n04149813
'76': n04243546
'77': n04251144
'78': n04258138
'79': n04275548
'80': n04296562
'81': n04389033
'82': n04418357
'83': n04435653
'84': n04443257
'85': n04509417
'86': n04515003
'87': n04522168
'88': n04596742
'89': n04604644
'90': n04612504
'91': n06794110
'92': n07584110
'93': n07613480
'94': n07697537
'95': n07747607
'96': n09246464
'97': n09256479
'98': n13054560
'99': n13133613
splits:
- name: train
num_bytes: 6284840508
num_examples: 50000
- name: validation
num_bytes: 1286953696
num_examples: 10000
- name: test
num_bytes: 670707560
num_examples: 5000
download_size: 7433461683
dataset_size: 8242501764
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
license: other
license_name: imagenet
license_link: https://www.image-net.org/download.php
task_categories:
- image-classification
pretty_name: Mini-ImageNet
size_categories:
- 10K<n<100K
Dataset Description
A mini version of ImageNet-1k with 100 of 1000 classes present.
Unlike some 'mini' variants this one includes the original images at their original sizes. Many such subsets downsample to 84x84 or other smaller resolutions.
Data Splits
Train
- 50000 samples from ImageNet-1k train split
Validation
- 10000 samples from ImageNet-1k train split
Test
- 5000 samples from ImageNet-1k validation split (all 50 samples per class)
Usage
This dataset is good for testing hparams and models in timm
Train
python train.py --dataset hfds/timm/mini-imagenet --model resnet50 --amp --num-classes 100
Citation Information
For the specific instance of this mini variant I am not sure what the origin is. It is different from commonly referenced Vinyales et al.,2016 as it doesn't match the classes / splits.
Train & validation splits match train & test of https://www.kaggle.com/datasets/ctrnngtrung/miniimagenet ... it is not clear where that originated though.
Original ImageNet citation:
@article{imagenet15russakovsky,
Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
Title = { {ImageNet Large Scale Visual Recognition Challenge} },
Year = {2015},
journal = {International Journal of Computer Vision (IJCV)},
doi = {10.1007/s11263-015-0816-y},
volume={115},
number={3},
pages={211-252}
}