| --- |
| 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 Qwen-DiT-Draw. |
|
|
| ## Dataset Description |
|
|
| This dataset contains chunked trajectory data from the [Quick, Draw!](https://quickdraw.withgoogle.com/data) 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: |
|
|
| ```python |
| { |
| "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 |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset("TESS-Computer/quickdraw-circles-clean") |
| |
| # 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](https://github.com/googlecreativelab/quickdraw-dataset) (circle category only). |
|
|
| ## License |
|
|
| MIT License |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{quickdraw-circles-trajectory, |
| title={Quick, Draw! Circles Trajectory Dataset}, |
| author={TESS Computer}, |
| year={2025}, |
| url={https://huggingface.co/datasets/TESS-Computer/quickdraw-circles-clean} |
| } |
| ``` |
|
|
