# FoodX-251: A Dataset for Fine-grained Food Classification

arXiv: https://arxiv.org/abs/1907.06167 · HTML: https://mlsanity.com/paper/1907.06167/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2019.

## Built on by (most cited first, 30 of 33)

- [ImageNet-21K Pretraining for the Masses](https://mlsanity.com/paper/2104.10972/) (2021)
- [Large Scale Visual Food Recognition](https://mlsanity.com/paper/2103.16107/) (2021)
- [A Large-Scale Benchmark for Food Image Segmentation](https://mlsanity.com/paper/2105.05409/) (2021)
- [INTERN: A New Learning Paradigm Towards General Vision](https://mlsanity.com/paper/2111.08687/) (2021)
- [Res-VMamba: Fine-Grained Food Category Visual Classification Using Selective State Space Models with Deep Residual Learning](https://mlsanity.com/paper/2402.15761/) (2024)
- [Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy](https://mlsanity.com/paper/2203.07845/) (2022)
- [Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets](https://mlsanity.com/paper/2104.12690/) (2021)
- [Learning Program Representations for Food Images and Cooking Recipes](https://mlsanity.com/paper/2203.16071/) (2022)
- [MD3: The Multi-Dialect Dataset of Dialogues](https://mlsanity.com/paper/2305.11355/) (2023)
- [From Canteen Food to Daily Meals: Generalizing Food Recognition to More Practical Scenarios](https://mlsanity.com/paper/2403.07403/) (2024)
- [A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment](https://mlsanity.com/paper/2106.11776/) (2021)
- [It's All in the Head: Representation Knowledge Distillation through Classifier Sharing](https://mlsanity.com/paper/2201.06945/) (2022)
- [Representation Consolidation for Training Expert Students](https://mlsanity.com/paper/2107.08039/) (2021)
- [NutritionVerse-3D: A 3D Food Model Dataset for Nutritional Intake Estimation](https://mlsanity.com/paper/2304.05619/) (2023)
- [RTRA: Rapid Training of Regularization-based Approaches in Continual Learning](https://mlsanity.com/paper/2312.09361/) (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)
- [Pruning by Block Benefit: Exploring the Properties of Vision Transformer Blocks during Domain Adaptation](https://mlsanity.com/paper/2506.23675/) (2025)
- [NutritionVerse: Empirical Study of Various Dietary Intake Estimation Approaches](https://mlsanity.com/paper/2309.07704/) (2023)
- [CLDyB: Towards Dynamic Benchmarking for Continual Learning with Pre-trained Models](https://mlsanity.com/paper/2503.04655/) (2025)
- [SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights](https://mlsanity.com/paper/2507.04412/) (2025)
- [Enhancing Food Intake Tracking in Long-Term Care with Automated Food Imaging and Nutrient Intake Tracking (AFINI-T) Technology](https://mlsanity.com/paper/2112.04608/) (2021)
- [A Mobile Food Recognition System for Dietary Assessment](https://mlsanity.com/paper/2204.09432/) (2022)
- [ViM: Vision Middleware for Unified Downstream Transferring](https://mlsanity.com/paper/2303.06911/) (2023)
- [Batch Model Consolidation: A Multi-Task Model Consolidation Framework](https://mlsanity.com/paper/2305.16484/) (2023)
- [Precision at Scale: Domain-Specific Datasets On-Demand](https://mlsanity.com/paper/2407.03463/) (2024)
- [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)
- [Drawing Attention to Detail: Pose Alignment through Self-Attention for Fine-Grained Object Classification](https://mlsanity.com/paper/2302.04800/) (2023)

## Abstract (arXiv)

Food classification is a challenging problem due to the large number of categories, high visual similarity between different foods, as well as the lack of datasets for training state-of-the-art deep models. Solving this problem will require advances in both computer vision models as well as datasets for evaluating these models. In this paper we focus on the second aspect and introduce FoodX-251, a dataset of 251 fine-grained food categories with 158k images collected from the web. We use 118k images as a training set and provide human verified labels for 40k images that can be used for validation and testing. In this work, we outline the procedure of creating this dataset and provide relevant baselines with deep learning models. The FoodX-251 dataset has been used for organizing iFood-2019 challenge in the Fine-Grained Visual Categorization workshop (FGVC6 at CVPR 2019) and is available for download.