---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': a1
          '1': a2
          '2': a3
          '3': a4
          '4': ammonite
          '5': bamboofern
          '6': bedder
          '7': binary
          '8': branch
          '9': broccoli
          '10': bud
          '11': c_curve
          '12': castle
          '13': cedarleaf
          '14': coral
          '15': crystal
          '16': deerfern
          '17': dragon_curve
          '18': drumlin
          '19': fern
          '20': filmyfern
          '21': fleabane
          '22': flower
          '23': gaku
          '24': ginkgo
          '25': gold_dragon
          '26': grassfern
          '27': greygoldenrod
          '28': groundpine
          '29': involucre
          '30': koch_curve
          '31': koch_snowflake
          '32': maple_leaf
          '33': mcWorter_pedigree
          '34': morningglory
          '35': newyorkfern
          '36': octopuslegs
          '37': penta
          '38': pinetree
          '39': rose
          '40': shieldfern
          '41': sierpinski_carpet
          '42': sierpinski_gasket
          '43': sierpinski_pentagon
          '44': snail
          '45': snowcap
          '46': snowdrift
          '47': spiderbrake
          '48': spiral
          '49': spleenwort_fern
          '50': star
          '51': sticks
          '52': sunflower
          '53': supernova
          '54': swirl
          '55': tree
          '56': turbanshell
          '57': umbrellafern
          '58': watersprite
          '59': zigzag
  splits:
  - name: train
    num_bytes: 3588623140
    num_examples: 60000
  download_size: 1829671228
  dataset_size: 3588623140
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- image-classification
pretty_name: 'FractalDB 60 '
size_categories:
- 10K<n<100K
---

# FractalDB 60

FractalDB 60 dataset from [Pre-training without Natural Images](https://hirokatsukataoka16.github.io/Pretraining-without-Natural-Images/).

[Original repo](https://github.com/hirokatsukataoka16/FractalDB-Pretrained-ResNet-PyTorch) | [Project page](https://hirokatsukataoka16.github.io/Pretraining-without-Natural-Images/) | [arXiv](https://arxiv.org/abs/2101.08515)

## Citing

```bibtex
@article{KataokaIJCV2022,
  author={Kataoka, Hirokatsu and Okayasu, Kazushige and Matsumoto, Asato and Yamagata, Eisuke and Yamada, Ryosuke and Inoue, Nakamasa and Nakamura, Akio and Satoh, Yutaka},
  title={Pre-training without Natural Images},
  article={International Journal on Computer Vision (IJCV)},
  year={2022},
}

@inproceedings{KataokaACCV2020,
  author={Kataoka, Hirokatsu and Okayasu, Kazushige and Matsumoto, Asato and Yamagata, Eisuke and Yamada, Ryosuke and Inoue, Nakamasa and Nakamura, Akio and Satoh, Yutaka},
  title={Pre-training without Natural Images},
  booktitle={Asian Conference on Computer Vision (ACCV)},
  year={2020},
}

@misc{kataoka2021pretraining,
  title={Pre-training without Natural Images}, 
  author={Hirokatsu Kataoka and Kazushige Okayasu and Asato Matsumoto and Eisuke Yamagata and Ryosuke Yamada and Nakamasa Inoue and Akio Nakamura and Yutaka Satoh},
  year={2021},
  eprint={2101.08515},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}
```