--- language: - en license: cc-by-nc-sa-4.0 task_categories: - visual-question-answering - object-detection - image-text-to-text pretty_name: GRAID BDD100K Question-Answer Dataset 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 [Paper](https://huggingface.co/papers/2510.22118) | [Project Page](https://ke7.github.io/graid/) ## 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, the BDD100K dataset, and the GRAID framework paper: ```bibtex @article{graid2025spatial, title={GRAID: Enhancing Spatial Reasoning of VLMs Through High-Fidelity Data Generation}, author={{Anonymous}}, journal={arXiv preprint arXiv:2510.22118}, year={2025}, url={https://huggingface.co/papers/2510.22118} } @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.