metadata

annotations_creators:
  - crowdsourced
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language:
  - en
language_creators:
  - crowdsourced
license: []
multilinguality:
  - monolingual
paperswithcode_id: imagenet
pretty_name: Tiny-ImageNet
size_categories:
  - 100K<n<1M
source_datasets:
  - extended|imagenet-1k
task_categories:
  - image-classification
task_ids:
  - multi-class-image-classification

Dataset Card for tiny-imagenet

Dataset Description

Homepage: https://www.kaggle.com/c/tiny-imagenet
Repository: [Needs More Information]
Paper: http://cs231n.stanford.edu/reports/2017/pdfs/930.pdf
Leaderboard: https://paperswithcode.com/sota/image-classification-on-tiny-imagenet-1

Dataset Summary

Tiny ImageNet contains 100000 images of 200 classes (500 for each class) downsized to 64×64 colored images. Each class has 500 training images, 50 validation images, and 50 test images.

Languages

The class labels in the dataset are in English.

Dataset Structure

Data Instances

{
  'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=64x64 at 0x1A800E8E190,
  'label': 15
}

Data Fields

image: A PIL.Image.Image object containing the image. Note that when accessing the image column: dataset[0]["image"] the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the "image" column, i.e. dataset[0]["image"] should always be preferred over dataset["image"][0].
label: an int classification label. -1 for test set as the labels are missing. Check classes.py for the map of numbers & labels.

Data Splits

	Train	Valid
# of samples	100000	10000

Usage

Example

Load Dataset

def example_usage():
    tiny_imagenet = load_dataset('Maysee/tiny-imagenet', split='train')
    print(tiny_imagenet[0])

if __name__ == '__main__':
    example_usage()