kd7/graid-waymo-unique

GRAID Waymo Perception Dataset Question-Answer Dataset

Overview

This dataset was generated using GRAID (Generating Reasoning questions from Analysis of Images via Discriminative 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

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 for usage restrictions.

Citation

If you use this dataset in your research, please cite both the original dataset and the GRAID framework:

@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.