Add comprehensive README with GRAID statistics

README.md

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+---
+pretty_name: "GRAID Waymo Perception Dataset Question-Answer Dataset"
+language:
+- en
+license: "cc-by-nc-4.0"
+task_categories:
+- visual-question-answering
+- object-detection
+tags:
+- visual-reasoning
+- spatial-reasoning
+- object-detection
+- computer-vision
+- autonomous-driving
+- waymo
+---
+
+# GRAID Waymo Perception Dataset Question-Answer Dataset
+
+## Overview
+
+This dataset was generated using **GRAID** (**G**enerating **R**easoning questions from **A**nalysis of **I**mages via **D**iscriminative artificial intelligence), a framework for creating spatial reasoning datasets from object detection annotations.
+
+**GRAID** transforms raw object detection data into structured question-answer pairs that test various aspects of object localization, visual reasoning, spatial reasoning, and object relationship comprehension.
+
+## Dataset Details
+
+- **Total QA Pairs**: 13,855
+- **Source Dataset**: Waymo Perception Dataset
+- **Generation Date**: 2025-09-23
+- **Image Format**: Embedded in parquet files (no separate image files)
+- **Question Types**: 17 different reasoning patterns
+
+## Dataset Splits
+
+- **train**: 11,049 (79.75%)
+- **val**: 2,806 (20.25%)
+
+## Question Type Distribution
+
+- **Are there {target} or more {object_1}(s) in this image? Respond Yes/No.**: 3,080 (22.23%)
+- **Are there less than {target} {object_1}(s) in this image? Respond Yes/No.**: 3,080 (22.23%)
+- **How many {object_1}(s) are there in this image?**: 1,540 (11.12%)
+- **How many {object_1}(s) are in the image? Choose one: A) {range_a}, B) {range_b}, C) {range_c}, D) Unsure / Not Visible. Respond with the letter only.**: 1,186 (8.56%)
+- **Is there at least one {object_1} to the left of any {object_2}?**: 1,074 (7.75%)
+- **Is there at least one {object_1} to the right of any {object_2}?**: 1,074 (7.75%)
+- **Rank the {k} kinds of objects that appear the largest (by pixel area) in the image from largest to smallest. Provide your answer as a comma-separated list of object names only.**: 531 (3.83%)
+- **What kind of object appears the most frequently in the image?**: 497 (3.59%)
+- **What kind of object appears the least frequently in the image?**: 497 (3.59%)
+- **Are there more {object_1}(s) than {object_2}(s) in this image?**: 497 (3.59%)
+- **If you were to draw a tight box around each object in the image, which type of object would have the biggest box?**: 423 (3.05%)
+- **Divide the image into a grid of {N} rows x {M} columns. Number the cells from left to right, then top to bottom, starting with 1. In what cell does the {object_1} appear?**: 181 (1.31%)
+- **Divide the image into thirds. In which third does the {object_1} primarily appear? Respond with the letter only: A) left third, B) middle third, C) right third.**: 104 (0.75%)
+- **What is the leftmost object in the image?**: 38 (0.27%)
+- **What is the rightmost object in the image?**: 37 (0.27%)
+- **Does the rightmost object in the image appear to be wider than it is tall?**: 9 (0.06%)
+- **Does the leftmost object in the image appear to be wider than it is tall?**: 7 (0.05%)
+
+## Performance Analysis
+
+### Question Processing Efficiency
+
+| Question Type | is_applicable Avg (ms) | apply Avg (ms) | Predicate -> QA Hit Rate | Empty cases |
+|---------------|------------------------|----------------|--------------------------|-------------|
+| Divide the image into thirds. In which third does the {object_1} primarily appear? Respond with the letter only: A) left third, B) middle third, C) right third. | 0.03 | 0.32 | 66.2% | 53 |
+| Divide the image into a grid of {N} rows x {M} columns. Number the cells from left to right, then top to bottom, starting with 1. In what cell does the {object_1} appear? | 0.01 | 1.82 | 28.8% | 447 |
+| If you were to draw a tight box around each object in the image, which type of object would have the biggest box? | 0.02 | 18.40 | 78.3% | 117 |
+| Rank the {k} kinds of objects that appear the largest (by pixel area) in the image from largest to smallest. Provide your answer as a comma-separated list of object names only. | 0.02 | 15.74 | 98.3% | 9 |
+| What kind of object appears the most frequently in the image? | 0.01 | 0.02 | 92.0% | 43 |
+| What kind of object appears the least frequently in the image? | 0.01 | 0.02 | 92.0% | 43 |
+| Is there at least one {object_1} to the left of any {object_2}? | 1.60 | 9.09 | 100.0% | 0 |
+| Is there at least one {object_1} to the right of any {object_2}? | 1.43 | 8.01 | 100.0% | 0 |
+| What is the leftmost object in the image? | 0.02 | 1.47 | 24.2% | 119 |
+| What is the rightmost object in the image? | 0.01 | 1.36 | 23.6% | 120 |
+| How many {object_1}(s) are there in this image? | 0.01 | 0.02 | 100.0% | 0 |
+| Are there more {object_1}(s) than {object_2}(s) in this image? | 0.01 | 0.02 | 92.0% | 43 |
+| What appears the most in this image: {object_1}s, {object_2}s, or {object_3}s? | 0.01 | 0.02 | 0.0% | 540 |
+| Does the leftmost object in the image appear to be wider than it is tall? | 0.01 | 0.50 | 4.5% | 150 |
+| Does the rightmost object in the image appear to be wider than it is tall? | 0.01 | 0.55 | 5.7% | 148 |
+| Are there {target} or more {object_1}(s) in this image? Respond Yes/No. | 0.01 | 0.02 | 100.0% | 0 |
+| Are there less than {target} {object_1}(s) in this image? Respond Yes/No. | 0.01 | 0.02 | 100.0% | 0 |
+| How many {object_1}(s) are in the image? Choose one: A) {range_a}, B) {range_b}, C) {range_c}, D) Unsure / Not Visible. Respond with the letter only. | 0.01 | 0.13 | 88.8% | 112 |
+**Notes:**
+- `is_applicable` checks if a question type can be applied to an image
+- `apply` generates the actual question-answer pairs
+- Predicate -> QA Hit Rate = Percentage of applicable cases that generated at least one QA pair
+- Empty cases = Number of times is_applicable=True but apply returned no QA pairs
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load the complete dataset
+dataset = load_dataset("kd7/graid-waymo-unique")
+
+# Access individual splits
+train_data = dataset["train"]
+val_data = dataset["val"]
+
+# Example of accessing a sample
+sample = dataset["train"][0]  # or "val"
+print(f"Question: {sample['question']}")
+print(f"Answer: {sample['answer']}")  
+print(f"Question Type: {sample['question_type']}")
+
+# The image is embedded as a PIL Image object
+image = sample["image"]
+image.show()  # Display the image
+```
+
+## Dataset Schema
+
+- **image**: PIL Image object (embedded, no separate files)
+- **annotations**: COCO-style bounding box annotations  
+- **question**: Generated question text
+- **answer**: Corresponding answer text
+- **reasoning**: Additional reasoning information (if applicable)
+- **question_type**: Type of question (e.g., "HowMany", "LeftOf", "Quadrants")
+- **source_id**: Original image identifier from Waymo Perception Dataset
+
+## License
+
+This generated dataset is licensed under **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)**, which permits free use for non-commercial purposes including academic research and education.
+
+**Commercial Use Policy**: Commercial entities (including startups and companies) that wish to use this dataset for commercial purposes must obtain a paid license from **MESH**. The CC BY-NC license prohibits commercial use without explicit permission.
+
+To request a commercial license, please contact **Karim Elmaaroufi**.
+
+**Original Source Compliance**: The original source datasets and their licenses still apply to the underlying images and annotations. You must comply with both the CC BY-NC terms and the source dataset terms:
+
+This dataset is derived from the Waymo Perception Dataset. Please refer to the [Waymo Perception Dataset license terms](https://waymo.com/open/terms/) for usage restrictions.
+
+## Citation
+
+If you use this dataset in your research, please cite both the original dataset and the GRAID framework:
+
+```bibtex
+@dataset{graid_waymo,
+    title={GRAID Waymo Perception Dataset Question-Answer Dataset},
+    author={GRAID Framework},
+    year={2025},
+    note={Generated using GRAID: Generating Reasoning questions from Analysis of Images via Discriminative artificial intelligence}
+}
+
+@inproceedings{waymo,
+    title={Scalability in Perception for Autonomous Driving: Waymo Open Dataset},
+    author={Sun, Pei and Kretzschmar, Henrik and Dotiwalla, Xerxes and Chouard, Aurelien and Patnaik, Vijaysai and Tsui, Paul and Guo, James and Zhou, Yin and Chai, Yuning and Caine, Benjamin and others},
+    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+    pages={2446--2454},
+    year={2020}
+}
+```
+
+## Contact
+
+For questions about this dataset or the GRAID framework, please open an issue in the repository.