TESS-Computer/qwen-dit-draw-delta
Robotics • Updated • 3
Error code: ClientConnectionError
Dataset for training trajectory prediction models, specifically designed for the Qwen-DiT-Draw project.
This dataset contains chunked trajectory data from the Quick, Draw! circle category, formatted for training diffusion-based trajectory prediction models.
| Metric | Value |
|---|---|
| Total samples | 21207 |
| Source circles | 10000 |
| Chunk size | 16 points |
| Canvas size | 512×512 |
| Avg chunks/circle | 2.1 |
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 = 0: Continue drawingstate = 1: Stroke complete (STOP)The model learns WHERE to place the stop signal, not a fixed position.
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
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
Data sourced from Google Quick, Draw! Dataset (circle category only).
MIT License
@misc{quickdraw-circles-trajectory,
title={Quick, Draw! Circles Trajectory Dataset},
author={TESS Computer},
year={2025},
url={https://huggingface.co/datasets/TESS-Computer/quickdraw-circles}
}