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| comments | description | keywords |
|---|---|---|
| true | Get to know Roboflow 100, a comprehensive object detection benchmark that brings together 100 datasets from different domains. | Ultralytics, YOLOv8, YOLO models, Roboflow 100, object detection, benchmark, computer vision, datasets, deep learning models |
Roboflow 100 Dataset
Roboflow 100, developed by Roboflow and sponsored by Intel, is a groundbreaking object detection benchmark. It includes 100 diverse datasets sampled from over 90,000 public datasets. This benchmark is designed to test the adaptability of models to various domains, including healthcare, aerial imagery, and video games.
Key Features
- Includes 100 datasets across seven domains: Aerial, Videogames, Microscopic, Underwater, Documents, Electromagnetic, and Real World.
- The benchmark comprises 224,714 images across 805 classes, thanks to over 11,170 hours of labeling efforts.
- All images are resized to 640x640 pixels, with a focus on eliminating class ambiguity and filtering out underrepresented classes.
- Annotations include bounding boxes for objects, making it suitable for training and evaluating object detection models.
Dataset Structure
The Roboflow 100 dataset is organized into seven categories, each with a distinct set of datasets, images, and classes:
- Aerial: Consists of 7 datasets with a total of 9,683 images, covering 24 distinct classes.
- Videogames: Includes 7 datasets, featuring 11,579 images across 88 classes.
- Microscopic: Comprises 11 datasets with 13,378 images, spanning 28 classes.
- Underwater: Contains 5 datasets, encompassing 18,003 images in 39 classes.
- Documents: Consists of 8 datasets with 24,813 images, divided into 90 classes.
- Electromagnetic: Made up of 12 datasets, totaling 36,381 images in 41 classes.
- Real World: The largest category with 50 datasets, offering 110,615 images across 495 classes.
This structure enables a diverse and extensive testing ground for object detection models, reflecting real-world application scenarios.
Applications
Roboflow 100 is invaluable for various applications related to computer vision and deep learning. Researchers and engineers can use this benchmark to:
- Evaluate the performance of object detection models in a multi-domain context.
- Test the adaptability of models to real-world scenarios beyond common object recognition.
- Benchmark the capabilities of object detection models across diverse datasets, including those in healthcare, aerial imagery, and video games.
For more ideas and inspiration on real-world applications, be sure to check out our guides on real-world projects.
Usage
The Roboflow 100 dataset is available on both GitHub and Roboflow Universe.
You can access it directly from the Roboflow 100 GitHub repository. In addition, on Roboflow Universe, you have the flexibility to download individual datasets by simply clicking the export button within each dataset.
Sample Data and Annotations
Roboflow 100 consists of datasets with diverse images and videos captured from various angles and domains. Here’s a look at examples of annotated images in the RF100 benchmark.
The diversity in the Roboflow 100 benchmark that can be seen above is a significant advancement from traditional benchmarks which often focus on optimizing a single metric within a limited domain.
Citations and Acknowledgments
If you use the Roboflow 100 dataset in your research or development work, please cite the following paper:
!!! Quote ""
=== "BibTeX"
```bibtex
@misc{2211.13523,
Author = {Floriana Ciaglia and Francesco Saverio Zuppichini and Paul Guerrie and Mark McQuade and Jacob Solawetz},
Title = {Roboflow 100: A Rich, Multi-Domain Object Detection Benchmark},
Eprint = {arXiv:2211.13523},
}
```
Our thanks go to the Roboflow team and all the contributors for their hard work in creating and sustaining the Roboflow 100 dataset.
If you are interested in exploring more datasets to enhance your object detection and machine learning projects, feel free to visit our comprehensive dataset collection.