Datasets:

TESS-Computer
/

quickdraw-circles-delta

license: mit
task_categories:
  - robotics
tags:
  - trajectory-prediction
  - mouse-control
  - computer-control
  - quick-draw
  - diffusion
size_categories:
  - 10K<n<100K

Quick, Draw! Circles - Trajectory Dataset

Dataset for training trajectory prediction models, specifically designed for the Qwen-DiT-Draw project.

Dataset Description

This dataset contains chunked trajectory data from the Quick, Draw! circle category, formatted for training diffusion-based trajectory prediction models.

Key Features

Variable-length trajectories with stop signals (GR00T-style)
16-point chunks with (x, y, state) format
Loss masking for handling variable-length final chunks
512×512 canvas images showing drawing progression

Dataset Statistics

Metric	Value
Total samples	21207
Source circles	10000
Chunk size	16 points
Canvas size	512×512
Avg chunks/circle	2.1

Data Format

Each sample contains:

{
    "image": Image,           # 512×512 canvas (white for first chunk, partial drawing for rest)
    "instruction": str,       # "draw a circle"
    "trajectory": [[x, y, state], ...],  # 16 points, normalized [0, 1]
    "mask": [1, 1, ..., 0, 0],           # 1=real point, 0=ignore in loss
    "is_last": bool,          # True if final chunk of trajectory
    "n_real_points": int,     # Number of real points in this chunk (1-16)
    "circle_idx": int,        # Source circle index
    "chunk_idx": int,         # Chunk index within circle
}

State Signal

state = 0: Continue drawing
state = 1: Stroke complete (STOP)

The model learns WHERE to place the stop signal, not a fixed position.

Loss Masking

For final chunks with fewer than 16 real points:

mask = [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]
        ↑ real points (count in loss)  ↑ ignored

Usage

from datasets import load_dataset

dataset = load_dataset("TESS-Computer/quickdraw-circles")

# Access a sample
sample = dataset["train"][0]
image = sample["image"]           # PIL Image
trajectory = sample["trajectory"] # List of [x, y, state]
mask = sample["mask"]             # Loss mask

Source

Data sourced from Google Quick, Draw! Dataset (circle category only).

License

MIT License

Citation

@misc{quickdraw-circles-trajectory,
    title={Quick, Draw! Circles Trajectory Dataset},
    author={TESS Computer},
    year={2025},
    url={https://huggingface.co/datasets/TESS-Computer/quickdraw-circles}
}