README.md

metadata

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
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: val
        path: data/val-*
dataset_info:
  features:
    - name: image
      dtype: image
    - name: annotations
      list:
        - name: area
          dtype: float64
        - name: bbox
          list: float64
        - name: category
          dtype: string
        - name: category_id
          dtype: int64
        - name: iscrowd
          dtype: int64
        - name: score
          dtype: float64
    - name: question
      dtype: string
    - name: answer
      dtype: string
    - name: question_type
      dtype: string
    - name: source_id
      dtype: string
    - name: id
      dtype: int64
  splits:
    - name: train
      num_bytes: 1153019078219.5
      num_examples: 4627468
    - name: val
      num_bytes: 167621242099.75
      num_examples: 672330
  download_size: 30647805316
  dataset_size: 1320640320319.25

GRAID BDD100K 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: 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

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 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:

@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.