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---
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
          '1': n01629819
          '2': n01641577
          '3': n01644900
          '4': n01698640
          '5': n01742172
          '6': n01768244
          '7': n01770393
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          '13': n01910747
          '14': n01917289
          '15': n01944390
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          '23': n02074367
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          '26': n02099601
          '27': n02099712
          '28': n02106662
          '29': n02113799
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          '32': n02124075
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          '34': n02129165
          '35': n02132136
          '36': n02165456
          '37': n02190166
          '38': n02206856
          '39': n02226429
          '40': n02231487
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          '43': n02268443
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          '51': n02415577
          '52': n02423022
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          '54': n02480495
          '55': n02481823
          '56': n02486410
          '57': n02504458
          '58': n02509815
          '59': n02666196
          '60': n02669723
          '61': n02699494
          '62': n02730930
          '63': n02769748
          '64': n02788148
          '65': n02791270
          '66': n02793495
          '67': n02795169
          '68': n02802426
          '69': n02808440
          '70': n02814533
          '71': n02814860
          '72': n02815834
          '73': n02823428
          '74': n02837789
          '75': n02841315
          '76': n02843684
          '77': n02883205
          '78': n02892201
          '79': n02906734
          '80': n02909870
          '81': n02917067
          '82': n02927161
          '83': n02948072
          '84': n02950826
          '85': n02963159
          '86': n02977058
          '87': n02988304
          '88': n02999410
          '89': n03014705
          '90': n03026506
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          '92': n03085013
          '93': n03089624
          '94': n03100240
          '95': n03126707
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          '97': n03179701
          '98': n03201208
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          '103': n03393912
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          '109': n03544143
          '110': n03584254
          '111': n03599486
          '112': n03617480
          '113': n03637318
          '114': n03649909
          '115': n03662601
          '116': n03670208
          '117': n03706229
          '118': n03733131
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          '120': n03770439
          '121': n03796401
          '122': n03804744
          '123': n03814639
          '124': n03837869
          '125': n03838899
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          '127': n03891332
          '128': n03902125
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          '131': n03970156
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          '136': n03992509
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          '152': n04275548
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          '155': n04328186
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          '160': n04398044
          '161': n04399382
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          '163': n04456115
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          '165': n04486054
          '166': n04487081
          '167': n04501370
          '168': n04507155
          '169': n04532106
          '170': n04532670
          '171': n04540053
          '172': n04560804
          '173': n04562935
          '174': n04596742
          '175': n04597913
          '176': n06596364
          '177': n07579787
          '178': n07583066
          '179': n07614500
          '180': n07615774
          '181': n07695742
          '182': n07711569
          '183': n07715103
          '184': n07720875
          '185': n07734744
          '186': n07747607
          '187': n07749582
          '188': n07753592
          '189': n07768694
          '190': n07871810
          '191': n07873807
          '192': n07875152
          '193': n07920052
          '194': n09193705
          '195': n09246464
          '196': n09256479
          '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

- **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

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

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

if __name__ == '__main__':
    example_usage()
```