# The Open Brands Dataset: Unified brand detection and recognition at scale

arXiv: https://arxiv.org/abs/2012.07350 · HTML: https://mlsanity.com/paper/2012.07350/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2020.

## Built on by (most cited first, 7 of 7)

- [FoodLogoDet-1500: A Dataset for Large-Scale Food Logo Detection via Multi-Scale Feature Decoupling Network](https://mlsanity.com/paper/2108.04644/) (2021)
- [Deep Learning for Logo Detection: A Survey](https://mlsanity.com/paper/2210.04399/) (2022)
- [Brand Visibility in Packaging: A Deep Learning Approach for Logo Detection, Saliency-Map Prediction, and Logo Placement Analysis](https://mlsanity.com/paper/2403.02336/) (2024)
- [An Effective and Robust Detector for Logo Detection](https://mlsanity.com/paper/2108.00422/) (2021)
- [FashionLOGO: Prompting Multimodal Large Language Models for Fashion Logo Embeddings](https://mlsanity.com/paper/2308.09012/) (2023)
- [Logo-VGR: Visual Grounded Reasoning for Open-world Logo Recognition](https://mlsanity.com/paper/2509.25811/) (2025)
- [LU-500: A Logo Benchmark for Concept Unlearning](https://mlsanity.com/paper/2607.24101/) (2026)

## Abstract (arXiv)

Intellectual property protection(IPP) have received more and more attention recently due to the development of the global e-commerce platforms. brand recognition plays a significant role in IPP. Recent studies for brand recognition and detection are based on small-scale datasets that are not comprehensive enough when exploring emerging deep learning techniques. Moreover, it is challenging to evaluate the true performance of brand detection methods in realistic and open scenes. In order to tackle these problems, we first define the special issues of brand detection and recognition compared with generic object detection. Second, a novel brands benchmark called "Open Brands" is established. The dataset contains 1,437,812 images which have brands and 50,000 images without any brand. The part with brands in Open Brands contains 3,113,828 instances annotated in 3 dimensions: 4 types, 559 brands and 1216 logos. To the best of our knowledge, it is the largest dataset for brand detection and recognition with rich annotations. We provide in-depth comprehensive statistics about the dataset, validate the quality of the annotations and study how the performance of many modern models evolves with an increasing amount of training data. Third, we design a network called "Brand Net" to handle brand recognition. Brand Net gets state-of-art mAP on Open Brand compared with existing detection methods.