pajama912/FoodLogAthl-218

FoodLogAthl Meal Image Dataset

Overview

FoodLogAthl-218 is a real-world meal image dataset containing 6,925 original photos, each annotated with one or more food bounding boxes and rich metadata (capture date, user ID). Unlike web-sourced collections, these images come from an actual dietary management app, FoodLog Athl, reflecting everyday photo conditions and meal diversity.

Background & Motivation

Most public food image datasets (e.g. Food-101, UEC Food256, Food2K) are built from carefully curated web images, often favoring visually polished, single-dish photos. Such benchmarks fail to capture the messy, multi-dish snapshots people take for personal food logs.
FoodLogAthl-218 bridges this gap by leveraging images uploaded by real users of the FoodLog Athl app, yielding a challenging, authentic benchmark for both classification and object-detection models in dietary-management applications.

Data Collection

• Source: FoodLog Athl (dietary-management app)
• Period: May 2023 – Oct 2024
• User selection: all meal records linked to dietitians with ≥3 active users
• Record contents: user ID, recording date, original image, crop-based bounding boxes, dish names
• Multi-dish handling: each detected dish becomes a separate sample, but images with any invalid crop are discarded as a whole to preserve real meal frequency

Directory Structure

FoodLogAthl-218/
├── images/
│   ├── image_XXXXX.jpg
│   └── ... (total 6,925 images)
├── annotations/
│   └── instances_coco.json
├── metadata.csv
├── class_map.csv
├── .gitattributes
├── .gitignore
└── README.md

File Descriptions

• images/
  All original .jpg files named image_<ID>.jpg.

• annotations/instances_coco.json
  Standard COCO fields:

  • images (id, file_name, width, height)
  • annotations (id, image_id, category_id, bbox [x_min, y_min, w, h], area, iscrowd)
  • categories (id, name)

• metadata.csv

  file_name	record_date	user_id
  image_125683.jpg	2024-06-17	248
  image_121223.jpg	2023-05-12	105
  image_123574.jpg	2024-06-17	105
  …	…	…

• class_map.csv:

  • Mapping information between class names and IDs

• .gitattributes:

  • Git attributes configuration file

• .gitignore:

  • Git ignore configuration file

• README.md
  This overview, plus usage and license details.

Usage Example

from datasets import load_dataset

ds = load_dataset("your-username/foodlogathl")
print(ds)
# ➞ DatasetDict({
#       train: Dataset(...)
#   })
# Access:
images      = ds["train"]["images"]
annotations = ds["train"]["annotations"]
metadata    = ds["train"]["metadata"]

User Instructions for FoodLogAthl-218

Please fill out the application form with the following information and click Apply.
Once we review your request (typically within 2–3 business days), you will receive an email notification with access instructions.

1. Go to the Request access button at the top of this page.
2. Complete the form fields:
   • Full Name
   • Email
   • Affiliation (Company / University)
   • Department
   • Title
   • Purpose of Use
   • Purpose of Use (detail)
3. Click Apply.

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After You Apply

• Review period: 2–3 business days
• Contact: For any questions about access, please email [email protected]
• Access granted: You'll get read-only access to the images/ and annotations/ folders via your Hugging Face account.