# ChineseFoodNet: A large-scale Image Dataset for Chinese Food Recognition

arXiv: https://arxiv.org/abs/1705.02743 · HTML: https://mlsanity.com/paper/1705.02743/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2017.

## Built on by (most cited first, 21 of 21)

- [AuroraCap: Efficient, Performant Video Detailed Captioning and a New Benchmark](https://mlsanity.com/paper/2410.03051/) (2024)
- [M3IT: A Large-Scale Dataset towards Multi-Modal Multilingual Instruction Tuning](https://mlsanity.com/paper/2306.04387/) (2023)
- [Large Scale Visual Food Recognition](https://mlsanity.com/paper/2103.16107/) (2021)
- [FoodLMM: A Versatile Food Assistant using Large Multi-modal Model](https://mlsanity.com/paper/2312.14991/) (2023)
- [Revamping Cross-Modal Recipe Retrieval with Hierarchical Transformers and Self-supervised Learning](https://mlsanity.com/paper/2103.13061/) (2021)
- [FoodieQA: A Multimodal Dataset for Fine-Grained Understanding of Chinese Food Culture](https://mlsanity.com/paper/2406.11030/) (2024)
- [Res-VMamba: Fine-Grained Food Category Visual Classification Using Selective State Space Models with Deep Residual Learning](https://mlsanity.com/paper/2402.15761/) (2024)
- [Learning Program Representations for Food Images and Cooking Recipes](https://mlsanity.com/paper/2203.16071/) (2022)
- [Food-500 Cap: A Fine-Grained Food Caption Benchmark for Evaluating Vision-Language Models](https://mlsanity.com/paper/2308.03151/) (2023)
- [AuroraLong: Bringing RNNs Back to Efficient Open-Ended Video Understanding](https://mlsanity.com/paper/2507.02591/) (2025)
- [NutritionVerse-3D: A 3D Food Model Dataset for Nutritional Intake Estimation](https://mlsanity.com/paper/2304.05619/) (2023)
- [FooDI-ML: a large multi-language dataset of food, drinks and groceries images and descriptions](https://mlsanity.com/paper/2110.02035/) (2021)
- [Leveraging Automatic Personalised Nutrition: Food Image Recognition Benchmark and Dataset based on Nutrition Taxonomy](https://mlsanity.com/paper/2211.07440/) (2022)
- [Deep Learning based Food Instance Segmentation using Synthetic Data](https://mlsanity.com/paper/2107.07191/) (2021)
- [NutritionVerse: Empirical Study of Various Dietary Intake Estimation Approaches](https://mlsanity.com/paper/2309.07704/) (2023)
- [A Mobile Food Recognition System for Dietary Assessment](https://mlsanity.com/paper/2204.09432/) (2022)
- [MALM: Mask Augmentation based Local Matching for Food-Recipe Retrieval](https://mlsanity.com/paper/2305.11327/) (2023)
- [Khana: A Comprehensive Indian Cuisine Dataset](https://mlsanity.com/paper/2509.06006/) (2025)
- [VinaFood21: A Novel Dataset for Evaluating Vietnamese Food Recognition](https://mlsanity.com/paper/2108.02929/) (2021)
- [Feature-Enhanced TResNet for Fine-Grained Food Image Classification](https://mlsanity.com/paper/2507.12828/) (2025)
- [CrossCult-KIBench: A Benchmark for Cross-Cultural Knowledge Insertion in MLLMs](https://mlsanity.com/paper/2605.06115/) (2026)

## Abstract (arXiv)

In this paper, we introduce a new and challenging large-scale food image dataset called "ChineseFoodNet", which aims to automatically recognizing pictured Chinese dishes. Most of the existing food image datasets collected food images either from recipe pictures or selfie. In our dataset, images of each food category of our dataset consists of not only web recipe and menu pictures but photos taken from real dishes, recipe and menu as well. ChineseFoodNet contains over 180,000 food photos of 208 categories, with each category covering a large variations in presentations of same Chinese food. We present our efforts to build this large-scale image dataset, including food category selection, data collection, and data clean and label, in particular how to use machine learning methods to reduce manual labeling work that is an expensive process. We share a detailed benchmark of several state-of-the-art deep convolutional neural networks (CNNs) on ChineseFoodNet. We further propose a novel two-step data fusion approach referred as "TastyNet", which combines prediction results from different CNNs with voting method. Our proposed approach achieves top-1 accuracies of 81.43% on the validation set and 81.55% on the test set, respectively. The latest dataset is public available for research and can be achieved at https://sites.google.com/view/chinesefoodnet.