Datasets:
license: cc-by-4.0
task_categories:
- object-detection
- image-classification
- image-to-image
- text-to-image
language:
- en
pretty_name: >-
AI-generated Dataset of Outpainted Vehicles for Eye-level Classification and
Localization
size_categories:
- 10K<n<100K
viewer: true
tags:
- transportation
- autonomous-driving
- traffic-surveillance
- vehicle-image
AIDOVECL: AI-generated Dataset of Outpainted Vehicles for Eye-level Classification and Localization
We introduce an annotated AI-generated dataset of eye-level vehicle images using outpainting, offering versatile generation of diverse vehicle classes in varied contexts with pretrained models.
Citation Notice
Please ensure that all publications and presentations using this data reference the following paper:
Dataset Details
Dataset Description
Our dataset consists of AI-generated, eye-level vehicle images created by detecting and cropping vehicles from manually chosen and classified seed images. These vehicles are then outpainted onto larger canvases to replicate diverse real-world scenarios. The resulting images are meticulously annotated automatically, offering high-quality ground truth data. Through the use of sophisticated outpainting techniques and stringent image quality assessments, we ensure exceptional visual fidelity and contextual accuracy. Moreover, the efficiency and utility of the dataset are demonstrated through benchmark experiments presented in the accompanying paper.
- Curated by: Amir Kazemi, Qurat ul ain Fatima, Volodymyr Kindratenko, Christopher W Tessum.
- Funded by: The authors acknowledge support from the University of Illinois National Center for Supercomputing Applications (NCSA) Faculty Fellowship Program, the Zhejiang University and University of Illinois Dynamic Research Enterprise for Multidisciplinary Engineering Sciences (DREMES), and the University of Illinois Discovery Partners Institute (DPI). This work utilizes resources supported by the National Science Foundation’s (NSF) Major Research Instrumentation program, grant #1725729, as well as the University of Illinois at Urbana-Champaign.
- License: Creative Commons Attribution 4.0 International
Dataset Sources
- Repository: https://huggingface.co/datasets/amir-kazemi/aidovecl/tree/main
- Paper: https://arxiv.org/abs/2410.24116
- Demo: https://github.com/amir-kazemi/aidovecl
Uses
Direct Use
This curated dataset combines real, manually classified vehicle images with AI-generated outpainted variants, offering a diverse yet structured resource for computer vision and mobility-related applications. The images maintain contextual plausibility and consistent labeling, making the dataset suitable for training, evaluation, and augmentation workflows. In autonomous driving, it supports vehicle detection and recognition by exposing models to varied environments and viewpoints. The inclusion of fine-grained classes—reflecting differences in vehicle size, passenger capacity, and utility—enables more detailed analysis in urban planning and transportation modeling, such as estimating fleet composition, mode share, and curb usage. For environmental research, the dataset facilitates modeling of vehicle-type-specific emissions and spatial distribution patterns. By combining real-world fidelity with scalable synthetic augmentation, it contributes to more reproducible and generalizable pipelines across both technical and policy-oriented domains.
Out-of-Scope Use
While the dataset improves access to diverse and well-annotated vehicle imagery, it is not intended for all uses. Malicious or unethical applications—such as unauthorized surveillance or activities that infringe on privacy—are strictly prohibited. The dataset is most suitable for domains like autonomous driving, urban planning, and environmental monitoring, where the vehicle-level annotations and scene contexts are relevant. Applying it in unrelated fields, especially those misaligned with its structure or labeling scheme, may lead to poor performance or misleading outcomes.
Dataset Structure
The root directory contains the augmented dataset, curated by manually excluding rare generation failures to ensure aesthetic quality and realism, and organized into train, val, and test splits. In contrast, the datasets.zip archive contains the benchmarking datasets used in the paper, structured for reproducibility. It includes real_raw, which holds the original manually classified images grouped by vehicle type; real, a YOLO-formatted selection of these images with corresponding labels and a configuration file (real.yaml); and augmented, which extends the real set with AI-generated outpainted images and labels, accompanied by augmented.yaml.
Vehicle Classification and Subcategories
| ID | Vehicle Class | Subcategories |
|---|---|---|
| 0 | COUPE | Coupe, Convertible, Cabriolet, or other two-door passenger cars |
| 1 | SEDAN | Sedan, or other four-door passenger cars |
| 2 | SUV | SUV or Crossover |
| 3 | MINIVAN | Minivan or Wagon |
| 4 | MINIBUS | Minibus, Shuttles, or large passenger vans |
| 5 | BUS | City, Coach, Double-Decker, Articulated, or School Bus |
| 6 | VAN | Work, Camper, or Conversion Van |
| 7 | PICKUP | Regular, Crew Cab, or Extended Cab Pickup Truck |
| 8 | TRUCK | Single Unit, Trailer, Articulated, Dump, Tanker, or Mixer Truck |
Split Summary (image/object counts per class)
| split | background | bus | coupe | minibus | minivan | pickup | sedan | suv | truck | van | TOTAL |
|---|---|---|---|---|---|---|---|---|---|---|---|
| train | 784 | 1715 | 1209 | 330 | 350 | 356 | 1642 | 726 | 1071 | 269 | 8452 |
| val | 98 | 214 | 152 | 40 | 45 | 44 | 205 | 91 | 134 | 32 | 1055 |
| test | 98 | 213 | 150 | 41 | 43 | 44 | 205 | 91 | 133 | 33 | 1051 |
Dataset Creation
Curation Rationale
Publicly available vehicle recognition datasets have driven progress in the field but often fall short in key areas, including lacking contextual richness, offering limited vehicle classifications, and presenting suboptimal viewing angles. These shortcomings underscore the necessity for customizable vehicle datasets tailored to meet the specific requirements of contemporary vehicle recognition applications.
Source Data
Data Collection and Processing
The data collection and processing procedures are documented in detail in the manuscript.
Who are the source data producers?
The origin of the source data is described in detail in the manuscript.
Annotations
Annotation process
All annotations in this study were generated automatically using the pipeline described in the manuscript.
Who are the annotators?
No manual annotators were involved.
Personal and Sensitive Information
To the best of our knowledge, the dataset does not contain information that is likely to be personal or sensitive, nor is any content intended to identify individuals.
Bias, Risks, and Limitations
Our dataset currently cannot outpaint multiple vehicles in a single image due to the limitations of the inpainting model, which struggles with generating coherent scenes with multiple vehicles. This also stems from the challenge of selecting seed images that naturally correlate. Consequently, our dataset lacks rich occlusion scenarios crucial for training robust detection systems. Additionally, our reliance on pretrained detection and inpainting models means that objects not included in the original model training may be missed or appear unrealistic. We also face a shortage of seed images, especially for certain vehicle classes like vans, leading to class imbalances. Addressing these issues is crucial for improving our dataset's effectiveness and the accuracy of detection models.
Recommendations
As future work, we recommend exploring more advanced generative methods or complementary augmentations to better capture multi-vehicle and occlusion scenarios, expanding seed image collections to address class imbalance, and fine-tuning pretrained models with domain-specific data for broader coverage. These efforts should be paired with systematic quality checks, human oversight, and transparent documentation to ensure realism, mitigate bias, and guide appropriate dataset use.
Citation
BibTeX:
@article{kazemi2024aidovecl,
title={AIDOVECL: AI-generated Dataset of Outpainted Vehicles for Eye-level Classification and Localization},
author={Kazemi, Amir and Kindratenko, Volodymyr and Tessum, Christopher and others},
journal={arXiv preprint arXiv:2410.24116},
year={2024}
}
APA:
Kazemi, A., Kindratenko, V., & Tessum, C. (2024). AIDOVECL: AI-generated Dataset of Outpainted Vehicles for Eye-level Classification and Localization. arXiv preprint arXiv:2410.24116.
Dataset Card Authors
Amir Kazemi