---
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:
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  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()
```