# LogoDet-3K: A Large-Scale Image Dataset for Logo Detection

arXiv: https://arxiv.org/abs/2008.05359 · HTML: https://mlsanity.com/paper/2008.05359/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2020.

## Built on by (most cited first, 25 of 25)

- [Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark](https://mlsanity.com/paper/2207.11169/) (2022)
- [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)
- [Cross-domain Multi-modal Few-shot Object Detection via Rich Text](https://mlsanity.com/paper/2403.16188/) (2024)
- [RL-LOGO: Deep Reinforcement Learning Localization for Logo Recognition](https://mlsanity.com/paper/2312.16792/) (2023)
- [EMMA: Concept Erasure Benchmark with Comprehensive Semantic Metrics and Diverse Categories](https://mlsanity.com/paper/2512.17320/) (2025)
- [PV-RCNN++: Semantical Point-Voxel Feature Interaction for 3D Object Detection](https://mlsanity.com/paper/2208.13414/) (2022)
- [PP-ShiTu: A Practical Lightweight Image Recognition System](https://mlsanity.com/paper/2111.00775/) (2021)
- [Image-Text Pre-Training for Logo Recognition](https://mlsanity.com/paper/2309.10206/) (2023)
- [Makeup216: Logo Recognition with Adversarial Attention Representations](https://mlsanity.com/paper/2112.06533/) (2021)
- [SEPT: Towards Scalable and Efficient Visual Pre-Training](https://mlsanity.com/paper/2212.05473/) (2022)
- [ViM: Vision Middleware for Unified Downstream Transferring](https://mlsanity.com/paper/2303.06911/) (2023)
- [FashionLOGO: Prompting Multimodal Large Language Models for Fashion Logo Embeddings](https://mlsanity.com/paper/2308.09012/) (2023)
- [TMID: A Comprehensive Real-world Dataset for Trademark Infringement Detection in E-Commerce](https://mlsanity.com/paper/2312.05103/) (2023)
- [Vision Language Models Map Logos to Text via Semantic Entanglement in the Visual Projector](https://mlsanity.com/paper/2510.12287/) (2025)
- [Rooms with Text: A Dataset for Overlaying Text Detection](https://mlsanity.com/paper/2211.11350/) (2022)
- [Contrastive Multi-View Textual-Visual Encoding: Towards One Hundred Thousand-Scale One-Shot Logo Identification](https://mlsanity.com/paper/2211.12926/) (2022)
- [A Cross-direction Task Decoupling Network for Small Logo Detection](https://mlsanity.com/paper/2305.02503/) (2023)
- [Denoising-While-Completing Network (DWCNet): Robust Point Cloud Completion Under Corruption](https://mlsanity.com/paper/2507.16743/) (2025)
- [CIDER: A Causal Cure for Brand-Obsessed Text-to-Image Models](https://mlsanity.com/paper/2509.15803/) (2025)
- [Logo-VGR: Visual Grounded Reasoning for Open-world Logo Recognition](https://mlsanity.com/paper/2509.25811/) (2025)
- [ExposureEngine: Oriented Logo Detection and Sponsor Visibility Analytics in Sports Broadcasts](https://mlsanity.com/paper/2510.04739/) (2025)
- [WildSVG: Towards Reliable SVG Generation Under Real-Word Conditions](https://mlsanity.com/paper/2602.21416/) (2026)
- [LU-500: A Logo Benchmark for Concept Unlearning](https://mlsanity.com/paper/2607.24101/) (2026)

## Abstract (arXiv)

Logo detection has been gaining considerable attention because of its wide range of applications in the multimedia field, such as copyright infringement detection, brand visibility monitoring, and product brand management on social media. In this paper, we introduce LogoDet-3K, the largest logo detection dataset with full annotation, which has 3,000 logo categories, about 200,000 manually annotated logo objects and 158,652 images. LogoDet-3K creates a more challenging benchmark for logo detection, for its higher comprehensive coverage and wider variety in both logo categories and annotated objects compared with existing datasets. We describe the collection and annotation process of our dataset, analyze its scale and diversity in comparison to other datasets for logo detection. We further propose a strong baseline method Logo-Yolo, which incorporates Focal loss and CIoU loss into the state-of-the-art YOLOv3 framework for large-scale logo detection. Logo-Yolo can solve the problems of multi-scale objects, logo sample imbalance and inconsistent bounding-box regression. It obtains about 4% improvement on the average performance compared with YOLOv3, and greater improvements compared with reported several deep detection models on LogoDet-3K. The evaluations on other three existing datasets further verify the effectiveness of our method, and demonstrate better generalization ability of LogoDet-3K on logo detection and retrieval tasks. The LogoDet-3K dataset is used to promote large-scale logo-related research and it can be found at https://github.com/Wangjing1551/LogoDet-3K-Dataset.