Synthetic shape datasets¶

fuse_augmentations.data draws colored shapes on a canvas and writes ready-to-train datasets. It is a standalone generation utility: no file-backed dataset loader, no model, no training loop. Use it for pipeline smoke tests, augmentation demos, teaching material, and quick detector sanity checks where a real dataset is overkill.

It supports two annotation formats — COCO and YOLO — across four tasks — detection, segmentation, oriented bounding box (OBB), and keypoints / pose.

Install¶

Rendering uses Pillow, which ships as a base dependency — nothing extra to install:

pip install fuse-augmentations

Quickstart¶

import tempfile

from fuse_augmentations import generate_dataset

with tempfile.TemporaryDirectory() as out_dir:
    counts = generate_dataset(
        out_dir,
        num_images=100,
        fmt="coco",
        task="detection",
        class_mode="shape",
        seed=0,
    )

print(counts)

Per-split image counts

{'train': 70, 'val': 20, 'test': 10}

Pass a real path instead of the temporary directory to keep the dataset. The same call with fmt="yolo" writes an Ultralytics-style layout; generate_dataset returns the number of images written per split.

In this section¶

Shape families — the four vocabularies (geometric, animals, symbols, letters), the visual shape reference, and how to select shapes and colors.
Tasks and keypoints — the four annotation tasks and every family's keypoint schema.
Annotation formats — COCO and YOLO on-disk layouts, plus the in-memory streaming training feed.
Customization and extension — reproducibility knobs, custom splits and fills, registering new families and writers, and editing the packaged assets.