Add dataset card

README.md

@@ -1,32 +1,103 @@
 ---
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: instruction
-    dtype: string
-  - name: trajectory
-    list:
-      list: float64
-  - name: mask
-    list: float64
-  - name: is_last
-    dtype: bool
-  - name: n_real_points
-    dtype: int64
-  - name: circle_idx
-    dtype: int64
-  - name: chunk_idx
-    dtype: int64
-  splits:
-  - name: train
-    num_bytes: 69788547.232
-    num_examples: 21207
-  download_size: 37762168
-  dataset_size: 69788547.232
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+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}
+}
+```