ShapeY

ShapeY is a benchmark that tests a vision system's shape recognition capacity. It consists of ~68k images of 200 3D objects rendered from ShapeNet.

ShapeY is not a training set. It validates that an object recognition system has developed a genuine capacity for shape understanding, using tasks that are hard for systems that rely on texture or contrast cues instead of shape.

Code: https://github.com/njw0709/ShapeY

Contents

Directory	Images	Description
ShapeY200/dataset/	68,200	The standard benchmark.
ShapeY200CR/dataset/	68,200	Contrast-reversed counterparts, for the CR experiment.

200 objects across 20 categories (10 objects per category), each rendered in 341 viewpoint series of 11 views: 68,200 images per set.

Filename convention

<category>_<shapenet_id>-<axis><NN>.png

• <axis> is a non-empty subset of the five transformation axes x, y, p, r, w (translation in x and y, pitch, roll, and scale/width), always in that canonical order — 31 combinations in total.
• <NN> is the view index within the series, 01–11, increasing distance from the reference view.

Example: airplane_1021a0914a7207aff927ed529ad90a11-pr03.png is view 3 of the combined pitch+roll series for that airplane.

The parser for these names is shapeymodular.utils.ImageNameHelper.parse_imgname.

Usage

git clone https://github.com/njw0709/ShapeY && cd ShapeY
uv sync
uv run scripts/download_data.py --variant all --out data
export SHAPEY_IMG_DIR=$PWD/data/ShapeY200/dataset
export SHAPEY_IMG_DIR_CR=$PWD/data/ShapeY200CR/dataset

Or directly:

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="GamGyulNN/ShapeY",
    repo_type="dataset",
    allow_patterns=["ShapeY200/**"],
    local_dir="data",
)

License

MIT for the benchmark itself. The renders are derived from ShapeNet — please also observe ShapeNet's terms of use.