Add comprehensive README with GRAID statistics

README.md

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+---
+pretty_name: "GRAID BDD100K Question-Answer Dataset"
+language:
+- en
+license: "bsd-3-clause"
+task_categories:
+- visual-question-answering
+- object-detection
+tags:
+- visual-reasoning
+- spatial-reasoning
+- object-detection
+- computer-vision
+- autonomous-driving
+- bdd100k
+---
+
+# GRAID BDD100K 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**: 5,299,798
+- **Source Dataset**: BDD100K (Berkeley DeepDrive)
+- **Generation Date**: 2025-09-11
+- **Image Format**: Embedded in parquet files (no separate image files)
+- **Question Types**: 22 different reasoning patterns
+
+## Dataset Splits
+
+- **train**: 4,627,468 (87.31%)
+- **val**: 672,330 (12.69%)
+
+## Question Type Distribution
+
+- **Is there at least one {object_1} to the left of any {object_2}?**: 708,762 (13.37%)
+- **Is there at least one {object_1} to the right of any {object_2}?**: 708,762 (13.37%)
+- **Is there at least one {object_1} that appears closer to the camera than any {object_2}?**: 708,762 (13.37%)
+- **Is there at least one {object_1} that appears farther from the camera than any {object_2}?**: 708,762 (13.37%)
+- **Are there more than {target} {object_1}(s) in this image? Respond Yes/No.**: 510,268 (9.63%)
+- **Are there less than {target} {object_1}(s) in this image? Respond Yes/No.**: 510,268 (9.63%)
+- **Are there more {object_1}(s) than {object_2}(s) in this image?**: 305,574 (5.77%)
+- **How many {object_1}(s) are there in this image?**: 255,134 (4.81%)
+- **What appears the most in this image: {object_1}s, {object_2}s, or {object_3}s?**: 250,197 (4.72%)
+- **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.**: 136,928 (2.58%)
+- **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.**: 111,176 (2.10%)
+- **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?**: 82,442 (1.56%)
+- **What kind of object appears the most frequently in the image?**: 64,405 (1.22%)
+- **Rank the {k} kinds of objects that appear the closest to the camera in the image from closest to farthest. Provide your answer as a comma-separated list of object names only.**: 60,120 (1.13%)
+- **If you were to draw a tight box around each object in the image, which type of object would have the biggest box?**: 57,994 (1.09%)
+- **What kind of object appears the least frequently in the image?**: 53,454 (1.01%)
+- **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.**: 37,730 (0.71%)
+- **What is the rightmost object in the image?**: 8,287 (0.16%)
+- **What is the leftmost object in the image?**: 7,327 (0.14%)
+- **Is the width of the {object_1} appear to be larger than the height?**: 7,059 (0.13%)
+- **Does the leftmost object in the image appear to be wider than it is tall?**: 3,682 (0.07%)
+- **Does the rightmost object in the image appear to be wider than it is tall?**: 2,705 (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 | 0.92 | 71.7% | 11535 |
+| Is the width of the {object_1} appear to be larger than the height? | 0.01 | 1.15 | 16.7% | 34017 |
+| 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 | 5.19 | 42.5% | 93933 |
+| 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 | 28.15 | 78.8% | 15593 |
+| 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 | 26.79 | 87.0% | 16663 |
+| What kind of object appears the most frequently in the image? | 0.02 | 0.01 | 87.5% | 9182 |
+| What kind of object appears the least frequently in the image? | 0.01 | 0.01 | 72.6% | 20133 |
+| Is there at least one {object_1} to the left of any {object_2}? | 6.47 | 69.93 | 100.0% | 0 |
+| Is there at least one {object_1} to the right of any {object_2}? | 5.17 | 46.95 | 100.0% | 0 |
+| What is the leftmost object in the image? | 0.03 | 2.19 | 18.0% | 33486 |
+| What is the rightmost object in the image? | 0.02 | 2.12 | 20.3% | 32526 |
+| How many {object_1}(s) are there in this image? | 0.02 | 0.02 | 100.0% | 0 |
+| Are there more {object_1}(s) than {object_2}(s) in this image? | 0.01 | 0.02 | 97.7% | 1708 |
+| What appears the most in this image: {object_1}s, {object_2}s, or {object_3}s? | 0.01 | 0.02 | 69.5% | 22432 |
+| Does the leftmost object in the image appear to be wider than it is tall? | 0.01 | 1.62 | 9.0% | 37131 |
+| Does the rightmost object in the image appear to be wider than it is tall? | 0.01 | 1.43 | 6.6% | 38108 |
+| Are there more than {target} {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 | 0.14 | 94.3% | 4504 |
+| Is there at least one {object_1} that appears closer to the camera than any {object_2}? | 4.09 | 8627.26 | 100.0% | 0 |
+| Is there at least one {object_1} that appears farther from the camera than any {object_2}? | 1.92 | 420.61 | 100.0% | 0 |
+| Rank the {k} kinds of objects that appear the closest to the camera in the image from closest to farthest. Provide your answer as a comma-separated list of object names only. | 0.04 | 54.12 | 47.0% | 67719 |
+**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-bdd")
+
+# 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 BDD100K (Berkeley DeepDrive)
+
+## License
+
+This dataset is derived from the BDD100K dataset. Please refer to the [BDD100K license terms](https://github.com/bdd100k/bdd100k) for usage restrictions. The GRAID-generated questions and metadata are provided under the same terms.
+
+## Citation
+
+If you use this dataset in your research, please cite both the original dataset and the GRAID framework:
+
+```bibtex
+@dataset{graid_bdd,
+    title={GRAID BDD100K Question-Answer Dataset},
+    author={GRAID Framework},
+    year={2025},
+    note={Generated using GRAID: Generating Reasoning questions from Analysis of Images via Discriminative artificial intelligence}
+}
+
+@INPROCEEDINGS{9156329,
+    author={Yu, Fisher and Chen, Haofeng and Wang, Xin and Xian, Wenqi and Chen, Yingying and Liu, Fangchen and Madhavan, Vashisht and Darrell, Trevor},
+    booktitle={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, 
+    title={BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning}, 
+    year={2020},
+    volume={},
+    number={},
+    pages={2633-2642},
+    keywords={Task analysis;Visualization;Roads;Image segmentation;Meteorology;Training;Benchmark testing},
+    doi={10.1109/CVPR42600.2020.00271}
+}
+```
+
+## Contact
+
+For questions about this dataset or the GRAID framework, please open an issue in the repository.