# Products-10K: A Large-scale Product Recognition Dataset

arXiv: https://arxiv.org/abs/2008.10545 · HTML: https://mlsanity.com/paper/2008.10545/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2020.

## Built on by (most cited first, 30 of 42)

- [Fine-Grained Image Analysis with Deep Learning: A Survey](https://mlsanity.com/paper/2111.06119/) (2021)
- [The Met Dataset: Instance-level Recognition for Artworks](https://mlsanity.com/paper/2202.01747/) (2022)
- [Benchmarking Large Vision-Language Models on Fine-Grained Image Tasks: A Comprehensive Evaluation](https://mlsanity.com/paper/2504.14988/) (2025)
- [ARMBench: An Object-centric Benchmark Dataset for Robotic Manipulation](https://mlsanity.com/paper/2303.16382/) (2023)
- [Where's Waldo: Diffusion Features for Personalized Segmentation and Retrieval](https://mlsanity.com/paper/2405.18025/) (2024)
- [Continual Learning for Visual Search with Backward Consistent Feature Embedding](https://mlsanity.com/paper/2205.13384/) (2022)
- [Fine-grained Recognition with Learnable Semantic Data Augmentation](https://mlsanity.com/paper/2309.00399/) (2023)
- [You Don't Need Domain-Specific Data Augmentations When Scaling Self-Supervised Learning](https://mlsanity.com/paper/2406.09294/) (2024)
- [SMEC: Rethinking Matryoshka Representation Learning for Retrieval Embedding Compression](https://mlsanity.com/paper/2510.12474/) (2025)
- [Dynamic Metric Learning: Towards a Scalable Metric Space to Accommodate Multiple Semantic Scales](https://mlsanity.com/paper/2103.11781/) (2021)
- [1st Place Solution in Google Universal Images Embedding](https://mlsanity.com/paper/2210.08473/) (2022)
- [PPA: Preference Profiling Attack Against Federated Learning](https://mlsanity.com/paper/2202.04856/) (2022)
- [Enhancing Fine-Grained Vision-Language Pretraining with Negative Augmented Samples](https://mlsanity.com/paper/2412.10029/) (2024)
- [LookBench: A Live and Holistic Open Benchmark for Fashion Image Retrieval](https://mlsanity.com/paper/2601.14706/) (2026)
- [InsertDiffusion: Identity Preserving Visualization of Objects through a Training-Free Diffusion Architecture](https://mlsanity.com/paper/2407.10592/) (2024)
- [Same or Not? Enhancing Visual Perception in Vision-Language Models](https://mlsanity.com/paper/2512.23592/) (2025)
- [Multi-Granularity Archaeological Dating of Chinese Bronze Dings Based on a Knowledge-Guided Relation Graph](https://mlsanity.com/paper/2303.15266/) (2023)
- [ImageSentinel: Protecting Visual Datasets from Unauthorized Retrieval-Augmented Image Generation](https://mlsanity.com/paper/2510.12119/) (2025)
- [V2L: Leveraging Vision and Vision-language Models into Large-scale Product Retrieval](https://mlsanity.com/paper/2207.12994/) (2022)
- [Enhancing Cost Efficiency in Active Learning with Candidate Set Query](https://mlsanity.com/paper/2502.06209/) (2025)
- [PP-ShiTu: A Practical Lightweight Image Recognition System](https://mlsanity.com/paper/2111.00775/) (2021)
- [Unitail: Detecting, Reading, and Matching in Retail Scene](https://mlsanity.com/paper/2204.00298/) (2022)
- [GreenDB: Toward a Product-by-Product Sustainability Database](https://mlsanity.com/paper/2205.02908/) (2022)
- [5th Place Solution to Kaggle Google Universal Image Embedding Competition](https://mlsanity.com/paper/2210.09495/) (2022)
- [Retail-786k: a Large-Scale Dataset for Visual Entity Matching](https://mlsanity.com/paper/2309.17164/) (2023)
- [Fine-Grained Open-Vocabulary Object Detection with Fined-Grained Prompts: Task, Dataset and Benchmark](https://mlsanity.com/paper/2503.14862/) (2025)
- [SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights](https://mlsanity.com/paper/2507.04412/) (2025)
- [How far have we gone in Generative Image Restoration? A study on its capability, limitations and evaluation practices](https://mlsanity.com/paper/2603.05010/) (2026)
- [6th Place Solution to Google Universal Image Embedding](https://mlsanity.com/paper/2210.09377/) (2022)
- [Efficient and Discriminative Image Feature Extraction for Universal Image Retrieval](https://mlsanity.com/paper/2409.13513/) (2024)

## Abstract (arXiv)

With the rapid development of electronic commerce, the way of shopping has experienced a revolutionary evolution. To fully meet customers' massive and diverse online shopping needs with quick response, the retailing AI system needs to automatically recognize products from images and videos at the stock-keeping unit (SKU) level with high accuracy. However, product recognition is still a challenging task, since many of SKU-level products are fine-grained and visually similar by a rough glimpse. Although there are already some products benchmarks available, these datasets are either too small (limited number of products) or noisy-labeled (lack of human labeling). In this paper, we construct a human-labeled product image dataset named "Products-10K", which contains 10,000 fine-grained SKU-level products frequently bought by online customers in JD.com. Based on our new database, we also introduced several useful tips and tricks for fine-grained product recognition. The products-10K dataset is available via https://products-10k.github.io/.