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metadata
annotations_creators:
  - crowdsourced
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_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': n01443537
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            '2': n01641577
            '3': n01644900
            '4': n01698640
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            '197': n09332890
            '198': n09428293
            '199': n12267677
  splits:
    - name: train
      num_bytes: 192793264.38
      num_examples: 98179
    - name: validation
      num_bytes: 9626623.079
      num_examples: 4909
    - name: test
      num_bytes: 9642629.914
      num_examples: 4923
  download_size: 165987322
  dataset_size: 212062517.373
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*

Dataset Card for tiny-imagenet-200-clean

Dataset Description

Dataset Summary

The original Tiny ImageNet contained 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.

This clean version removed grey scale images and only kept RGB 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.
  • label: an int classification label.

Data Splits

Train Validation Test
# of samples 98179 4909 4923

Usage

Example

Load Dataset

def example_usage():
    tiny_imagenet = load_dataset('slegroux/tiny-imagenet-200-clean', split='train')
    print(tiny_imagenet[0])

if __name__ == '__main__':
    example_usage()