---
license: cc-by-4.0
task_categories:
  - other
tags:
  - food
  - nutrition
  - density
  - FoodSeg103
  - calories
  - computer-vision
  - food-segmentation
pretty_name: FoodSeg103 Food Density & Nutrition Dataset
size_categories:
  - n<1K
---

# FoodSeg103 Food Density & Nutrition Dataset

## Dataset Summary

This dataset provides **physical density** and **nutritional information** for all **103 food ingredient classes** defined in the [FoodSeg103](https://xiongweiwu.github.io/foodseg103.html) benchmark — a large-scale food image segmentation dataset introduced in the ACM MM 2021 paper *"A Large-Scale Benchmark for Food Image Segmentation"* (Wu et al., 2021).

The goal is to enrich food segmentation models with physical and nutritional context, enabling downstream tasks such as **calorie estimation**, **portion size calculation**, and **nutrient profiling** from food images.

---

## What is FoodSeg103?

FoodSeg103 is a large-scale benchmark for fine-grained food image segmentation containing **7,118 images** annotated with **103 ingredient classes** and pixel-wise segmentation masks. Each image contains an average of 6 ingredient labels. The dataset was curated from Recipe1M and annotated by human annotators.

- **Paper:** [A Large-Scale Benchmark for Food Image Segmentation](https://arxiv.org/abs/2105.05409)
- **Authors:** Xiongwei Wu, Xin Fu, Ying Liu, Ee-Peng Lim, Steven C.H. Hoi, Qianru Sun
- **GitHub:** [LARC-CMU-SMU/FoodSeg103-Benchmark-v1](https://github.com/LARC-CMU-SMU/FoodSeg103-Benchmark-v1)
- **License:** Apache 2.0

---

## This Dataset

For each of the 103 FoodSeg103 food classes, this dataset provides:

### Physical Density (from aqua-calc.com / USDA)
Bulk/apparent density measured as grams per 250 mL metric cup, converted to standard units. Useful for estimating **food mass from volume** (e.g., from segmentation masks with depth estimation).

### Nutritional Information (per 100g, from FatSecret)
18 nutrition columns sourced from [FatSecret](https://www.fatsecret.com), which aggregates USDA FoodData Central data.

---

## Dataset Structure

### Columns

| Column | Type | Description |
|--------|------|-------------|
| `class_id` | int | FoodSeg103 class ID (0–102) |
| `class_name` | string | FoodSeg103 class label |
| `food_description` | string | USDA/aqua-calc food name for density lookup |
| `g_per_metric_cup` | float | Grams per 250 mL metric cup (bulk density raw) |
| `density_g_per_cm3` | float | Density in g/cm³ |
| `density_kg_per_m3` | float | Density in kg/m³ |
| `calories_kcal` | float | Energy (kcal per 100g) |
| `fat_total_g` | float | Total fat (g per 100g) |
| `fat_saturated_g` | float | Saturated fat (g per 100g) |
| `fat_polyunsat_g` | float | Polyunsaturated fat (g per 100g) |
| `fat_monounsat_g` | float | Monounsaturated fat (g per 100g) |
| `fat_trans_g` | float | Trans fat (g per 100g) |
| `cholesterol_mg` | float | Cholesterol (mg per 100g) |
| `sodium_mg` | float | Sodium (mg per 100g) |
| `carb_g` | float | Total carbohydrates (g per 100g) |
| `fiber_g` | float | Dietary fiber (g per 100g) |
| `sugar_g` | float | Total sugars (g per 100g) |
| `protein_g` | float | Protein (g per 100g) |
| `vitamin_a_ug` | float | Vitamin A (µg RAE per 100g) |
| `vitamin_c_mg` | float | Vitamin C (mg per 100g) |
| `vitamin_d_ug` | float | Vitamin D (µg per 100g) |
| `calcium_mg` | float | Calcium (mg per 100g) |
| `iron_mg` | float | Iron (mg per 100g) |
| `potassium_mg` | float | Potassium (mg per 100g) |

---

## Data Collection

### Density Data
- **Source:** [aqua-calc.com](https://www.aqua-calc.com/page/density-table) (powered by USDA FoodData Central)
- **Method:** Web scraping — each food matched to closest USDA entry
- **Unit:** Bulk/apparent density (includes air gaps), not true solid density
- **Conversion:** `density (g/cm³) = g_per_metric_cup / 250`

### Nutrition Data
- **Source:** [FatSecret](https://www.fatsecret.com) (aggregates USDA FoodData Central / SR Legacy)
- **Method:** Web scraping — searched by class name, fetched 100g serving page
- **Unit:** Per 100g of food (consistent across all classes)

---

## Usage Examples

```python
from datasets import load_dataset

ds = load_dataset("arunapb/food-density-nutrition", split="train")
df = ds.to_pandas()

# Estimate mass from segmented volume (cm³)
def estimate_mass(class_name, volume_cm3):
    row = df[df["class_name"] == class_name].iloc[0]
    return volume_cm3 * row["density_g_per_cm3"]

# Estimate calories from mass
def estimate_calories(class_name, mass_g):
    row = df[df["class_name"] == class_name].iloc[0]
    return (mass_g / 100) * row["calories_kcal"]

# Example: 200cm³ of apple
mass = estimate_mass("apple", 200)       # ~105.6g
cals = estimate_calories("apple", mass)  # ~54.9 kcal
print(f"Mass: {mass:.1f}g | Calories: {cals:.1f} kcal")
```

---

## Intended Use Cases

- **Calorie estimation** from food segmentation masks (volume → mass → calories)
- **Nutritional profiling** of meals from food images
- **Portion size analysis** using physical density and segmented area
- **Dataset enrichment** — adding nutritional metadata to FoodSeg103 predictions
- **Food computing research** — linking vision models to nutritional knowledge

---

## Limitations

- Density values are **bulk/apparent densities** and vary with food preparation and packing
- Nutrition values are **generic averages** — actual values vary by variety, preparation, and ripeness
- Some classes (e.g. `other ingredients`, `hanamaki baozi`) are broad and have approximate values

---

## Citation

```bibtex
@inproceedings{wu2021foodseg103,
  title     = {A Large-Scale Benchmark for Food Image Segmentation},
  author    = {Xiongwei Wu and Xin Fu and Ying Liu and Ee-Peng Lim and Steven C.H. Hoi and Qianru Sun},
  booktitle = {Proceedings of the 29th ACM International Conference on Multimedia},
  year      = {2021},
  doi       = {10.1145/3474085.3475628}
}
```

---

## License

- **This dataset:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
- **FoodSeg103 class list:** Apache 2.0 (Wu et al., 2021)
- **Nutritional & density data:** USDA FoodData Central (public domain)