FineFractalDB

FineFractalDB is a synthetic image dataset for pre-training vision models without any natural images. It consists of 1,000 classes × 1,000 images = 1,000,000 images. Each class corresponds to one Iterated Function System (IFS), and each image is rendered from that IFS with randomized instance-level parameters. Unlike FractalDB, every image is paired with the exact parameters used to render it, which enables parameter-aware objectives such as the Parameter-Aware Triplet Loss proposed in FDSML.

• Paper: arXiv link coming soon
• Project page: https://shineiarakawa.github.io/fdsml/
• Code: https://github.com/ShineiArakawa/fdsml
• Pre-trained models: https://huggingface.co/fdsml

Compared with FractalDB-1k, the generation process is refined for higher intra-class variation and more stable structures:

• 1,000 independent parameter perturbations per class (instead of 25), so every instance is a distinct point in parameter space.
• Strict contraction constraints on every affine map, which keep the attractor stable.
• Rejection sampling on pixel occupancy, which removes nearly empty renderings.

Contents

The dataset is provided in two equivalent forms:

• finefractaldb.zip (about 11.5 GB): the original image-folder archive used by the official code.
• data/train-*.parquet (about 11.5 GB in total): the same images and parameters as Parquet shards. This is what the Dataset Viewer above shows and what datasets.load_dataset reads.

Parquet shards

Each row has the following columns:

Column	Type	Description
image	image	362×362 RGB PNG
label	int64	class (IFS) index, 0–999
weight_id	int64	per-class instance index, 0–999
params	list of 6 float32	instance parameters wa, wb, wc, wd, we, wf (last row of the corresponding CSV)
file_name	string	path of the image inside finefractaldb.zip

Reading the Parquet shards requires the datasets library (pip install datasets), which decodes the image column into PIL images:

from datasets import load_dataset

ds = load_dataset("fdsml/FineFractalDB", split="train", streaming=True)
example = next(iter(ds))
example["image"].show()
print(example["label"], example["params"])

The shards are generated from the zip with tools/create_hf_parquet.py in the official code. Note that the official training code reads finefractaldb.zip directly and does not depend on the datasets library or the Parquet shards.

Zip archive

finefractaldb.zip extracts to:

finefractaldb/
├── images/                 # image-folder format, 1,000 class directories
│   ├── 00000/
│   │   ├── 00000_00000_00000000.png
│   │   └── ...
│   └── ...
└── params/                 # per-image instance parameters
    ├── 00000/
    │   ├── 00000_00000.csv
    │   └── ...
    ├── ...
    └── params.pt           # cache of all parameters for fast loading

Images are rendered with the 3×3 patch option (--patch) and random seed 0, exactly as used in the paper. The mean and std of the instance parameters, needed for normalization in pretrain.py, are:

--param_mean -0.00020501 -0.01737614 0.01417852 -0.00803842 0.01674297 0.02324245
--param_std 0.44203399 0.45483486 0.45470102 0.44323780 0.44413787 0.42966211

Download

To train with the official code, download and extract the zip archive:

hf download fdsml/FineFractalDB finefractaldb.zip --repo-type dataset --local-dir datasets
unzip datasets/finefractaldb.zip -d datasets

To download only the Parquet shards:

hf download fdsml/FineFractalDB --include "data/*.parquet" --repo-type dataset --local-dir datasets/finefractaldb_parquet

The dataset can also be regenerated from scratch with the official code in about 15 minutes on a 4-core CPU.

Citation

@inproceedings{Otake2027fdsml,
  title     = {FDSML: Formula-Driven Supervised Metric Learning with Parameter-Aware Triplet Loss},
  author    = {Hina Otake and Shinei Arakawa and Keitaro Tanaka and Yoshihiro Fukuhara and Hirokatsu Kataoka and Shigeo Morishima},
  booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
  year      = {2027},
}

License

Released under CC BY-NC-SA 4.0. Non-commercial use only.