---
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](https://github.com/HusseinLezzaik/qwen-dit-draw) project.

## 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")

# 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}
}
```