# Roboflow 100: A Rich, Multi-Domain Object Detection Benchmark

arXiv: https://arxiv.org/abs/2211.13523 · HTML: https://mlsanity.com/paper/2211.13523/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 10 references that are in the map.
- First posted on arXiv 24 Nov 2022.

## Builds on (most cited first, 5 of 5)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Grounded Language-Image Pre-training](https://mlsanity.com/paper/2112.03857/) (2021)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Florence: A New Foundation Model for Computer Vision](https://mlsanity.com/paper/2111.11432/) (2021)

## Built on by (most cited first, 26 of 26)

- [T-Rex2: Towards Generic Object Detection via Text-Visual Prompt Synergy](https://mlsanity.com/paper/2403.14610/) (2024)
- [Aligning and Prompting Everything All at Once for Universal Visual Perception](https://mlsanity.com/paper/2312.02153/) (2023)
- [LW-DETR: A Transformer Replacement to YOLO for Real-Time Detection](https://mlsanity.com/paper/2406.03459/) (2024)
- [AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models](https://mlsanity.com/paper/2404.16233/) (2024)
- [VISION Datasets: A Benchmark for Vision-based InduStrial InspectiON](https://mlsanity.com/paper/2306.07890/) (2023)
- [What a MESS: Multi-Domain Evaluation of Zero-Shot Semantic Segmentation](https://mlsanity.com/paper/2306.15521/) (2023)
- [Roboflow100-VL: A Multi-Domain Object Detection Benchmark for Vision-Language Models](https://mlsanity.com/paper/2505.20612/) (2025)
- [Cascade-DETR: Delving into High-Quality Universal Object Detection](https://mlsanity.com/paper/2307.11035/) (2023)
- [ODGEN: Domain-specific Object Detection Data Generation with Diffusion Models](https://mlsanity.com/paper/2405.15199/) (2024)
- [Open World Object Detection in the Era of Foundation Models](https://mlsanity.com/paper/2312.05745/) (2023)
- [AVOIDDS: Aircraft Vision-based Intruder Detection Dataset and Simulator](https://mlsanity.com/paper/2306.11203/) (2023)
- [YOLOv11 Optimization for Efficient Resource Utilization](https://mlsanity.com/paper/2412.14790/) (2024)
- [A Structured Review of Underwater Object Detection Challenges and Solutions: From Traditional to Large Vision Language Models](https://mlsanity.com/paper/2509.08490/) (2025)
- [TextDoctor: Unified Document Image Inpainting via Patch Pyramid Diffusion Models](https://mlsanity.com/paper/2503.04021/) (2025)
- [Cost-Sensitive Freeze-thaw Bayesian Optimization for Efficient Hyperparameter Tuning](https://mlsanity.com/paper/2510.21379/) (2025)
- [DETR-ViP: Detection Transformer with Robust Discriminative Visual Prompts](https://mlsanity.com/paper/2604.14684/) (2026)
- [Pseudo-Labeling Driven Refinement of Benchmark Object Detection Datasets via Analysis of Learning Patterns](https://mlsanity.com/paper/2506.00997/) (2025)
- [Improving the performance of object detection by preserving label distribution](https://mlsanity.com/paper/2308.14466/) (2023)
- [SMDDFNet: State-space Modeling and Dynamic Dual Fusion Network for Traffic Sign Detection](https://mlsanity.com/paper/2505.05491/) (2025)
- [Deep learning framework for crater detection and identification on the Moon and Mars](https://mlsanity.com/paper/2508.03920/) (2025)
- [FLORA: Efficient Synthetic Data Generation for Object Detection in Low-Data Regimes via finetuning Flux LoRA](https://mlsanity.com/paper/2508.21712/) (2025)
- [Auto-FlexSwitch: Efficient Dynamic Model Merging via Learnable Task Vector Compression](https://mlsanity.com/paper/2604.28109/) (2026)
- [DRAFE: Domain-Robust Asymmetric Fusion of Heterogeneous Detection Transformers for Cross-City Fine-Grained Traffic Object Detection](https://mlsanity.com/paper/2608.16632/) (2026)
- [CODE: Cross-Modal Calibration and Dynamic Suppression for Open World Object Detection](https://mlsanity.com/paper/2608.27214/) (2026)
- [Pro-Bench: Prompt-Robust Open-Vocabulary Visual Grounding Across Real-World Heterogeneous Environments](https://mlsanity.com/paper/2609.27076/) (2026)
- [PCB-MC: Missing Component Analysis in Printed Circuit Boards](https://mlsanity.com/paper/2609.39427/) (2026)

## Abstract (arXiv)

The evaluation of object detection models is usually performed by optimizing a single metric, e.g. mAP, on a fixed set of datasets, e.g. Microsoft COCO and Pascal VOC. Due to image retrieval and annotation costs, these datasets consist largely of images found on the web and do not represent many real-life domains that are being modelled in practice, e.g. satellite, microscopic and gaming, making it difficult to assert the degree of generalization learned by the model. We introduce the Roboflow-100 (RF100) consisting of 100 datasets, 7 imagery domains, 224,714 images, and 805 class labels with over 11,170 labelling hours. We derived RF100 from over 90,000 public datasets, 60 million public images that are actively being assembled and labelled by computer vision practitioners in the open on the web application Roboflow Universe. By releasing RF100, we aim to provide a semantically diverse, multi-domain benchmark of datasets to help researchers test their model's generalizability with real-life data. RF100 download and benchmark replication are available on GitHub.