Create README.md

README.md

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+# Pillars Dataset
+
+This dataset is designed for detecting roadside vertical structures - pillars, poles, and pylons - which are critical static obstacles for autonomous vehicle navigation. It contains 12K bounding box annotations from two side/rear-facing cameras across 6 driving sessions in urban and suburban environments.
+
+## Dataset Description
+
+- **Source:** Fleet of autonomous vehicles, urban and suburban roads
+- **Sensor:** 2x LUCID TRI054S-CC cameras (2880×1860), front/side facing
+- **Coverage:** ~12K bounding box annotations, ~1.4K unique images, single class
+- **Splits:** Train 70% (11,080 rows, 1,242 images), Test 30% (1,035 rows, 139 images) - split by driving session to prevent temporal leakage
+- **Format:** Parquet (annotations) + JPEG (images) in `train/` and `test/` subdirectories
+
+### Classes (1)
+
+| Class | Count | Description |
+|-------|-------|-------------|
+| pillar | 12,115 | Roadside pillar / pole / pylon |
+
+### Data Fields
+
+| Field | Type | Description |
+|-------|------|-------------|
+| bbox_msg_id | VARCHAR | UUID linking to the original bounding box message |
+| object_id | BIGINT | Unique object tracking ID |
+| label | VARCHAR | Object class (`pillar`) |
+| bbox_coords | DOUBLE[4] | Bounding box [x, y, width, height] in pixel coordinates |
+| timestamp_ns | BIGINT | ROS bag timestamp (nanoseconds) |
+| image_path | VARCHAR | Relative path to JPEG in `train/` or `test/` |
+
+### Data Splits
+
+| Split | Rows | Images |
+|-------|------|--------|
+| train | 11,080 | 1,242 |
+| test  | 1,035  | 139   |
+
+### Dataset Structure
+
+```
+pillars_dataset/
+├── annotations.parquet   # All annotations (12,115 rows)
+├── train/
+│   ├── camera_1C0FAF5250E2/   # 41 images
+│   └── camera_1C0FAF57D6F8/   # 1,201 images
+└── test/
+    ├── camera_1C0FAF5250E2/   # 40 images
+    └── camera_1C0FAF57D6F8/   # 99 images
+```
+
+### Usage
+
+```python
+import pandas as pd
+
+df = pd.read_parquet("annotations.parquet")
+print(f"{len(df)} annotations, {df.image_path.nunique()} unique images")
+```