README.md

metadata

configs:
  - config_name: default
    data_files:
      - split: full
        path: data/full-*
      - split: curated_10
        path: data/curated_10-*
      - split: thumbnail_3
        path: data/thumbnail_3-*
task_categories:
  - image-to-image
pretty_name: Triangle Reconstruction Benchmark
size_categories:
  - n<1K

Triangle Reconstruction Benchmark

A small, diverse dataset curated manually from existing datasets and images found online, with item-level rights and provenance information.

The purpose of this dataset is to evaluate the quality of algorithms for approximating images using sets of coloured triangles, such as Genetic Algorithms or greedy ones such as Geometrize.

Composition

The benchmark set consists of 75 images, all rendered and post-processed to RGB PNG with a 1024-pixel longest edge. Aspect ratio is preserved. Exact source files are retained under originals/.

Each image has a stable integer index from 1 to 75 for convenient reference (for example, “sample 23”), as well as a content-stable hexadecimal id. The same image retains its full-set index in every subset.

There are three sets:

• full: the entire 75-image collection,
• curated_10: a 10-image development subset useful when iterating on new algorithms, and
• thumbnail_3: three visually distinct headline images selected for thumbnails and quick comparisons. This includes the Mona Lisa, a target famously used by projects such as EvoLisa.

Rights and attribution

This dataset contains images from multiple sources. No new or collective license is asserted over the source images; each image remains subject to its original license or rights status. Source, rights, and attribution information is provided at the item level. Canonical images have been resized and/or rasterized for evaluation.

See THIRD_PARTY_NOTICES.md, third_party_licenses/, and the Parquet metadata columns. In particular, DIV2K is described by its publisher as academic-research-only, Kodak has no first-party license file in the acquired mirror, and logos may remain subject to trademark rights.

Loading

from datasets import load_dataset

dataset = load_dataset("benchislett/TrianglePaintBench")

When working from a local clone, use load_dataset("path/to/triangle_reconstruction_hf") instead.

Integrity hashes for the original and canonical image files are in checksums.sha256.