README.md

---
# Dataset card metadata
language:
  - en
license: cc-by-4.0          # your dataset’s license
library_name: huggingface/datasets      # or “huggingface/datasets” if you published via that library
pretty_name: Central Asian Food Scenes Dataset
tags:
  - food recognition
  - central asian food dataset
  - object-detection
paper: https://doi.org/10.1038/s41598-025-95770-9 
homepage: https://github.com/IS2AI/Central_Asian_Food_Scenes_Dataset  
---



# Central Asian Food Scenes Dataset

In this work, we propose the first Central Asia Food Scenes Dataset that contains **21,306 images** with **69,856 instances** across **239 food classes**. To make sure that the dataset contains various food items, we took as a benchmark the ontology of Global Individual Food Tool developed by Food and Agriculture Organization (FAO) together with the World Health Organization (WHO) [1]. The dataset contains food items across **18 coarse classes**: 
🍅 Vegetables • 🥖 Baked goods • 🍲 Cooked dishes • 🍎 Fruits • 🌿 Herbs • 🍖 Meat dishes • 🍰 Desserts • 🥗 Salads • 🥫 Sauces • 🥤 Drinks • 🧀 Dairy • 🍔 Fast food • 🍜 Soups • 🍟 Sides • 🥜 Nuts • 🥒 Pickled & fermented • 🥚 Egg products • 🌾 Cereals  

The images come from:  
- 🤖 **15,939** web-scraped images (i.e., Google, YouTube, Yandex)  
- 📸 **2,324** everyday-life photos  
- 🎥 **3,043** video frames (1 fps)  

The dataset has been checked and cleaned for duplicates using the Python Hash Image library. Furthermore, we have also filtered out images less than 30 kB in size and replaced them by performing additional iterative data scraping and duplicate check to make sure the **high quality** of the dataset.

---

## 📊 Class Balance

The dataset is unbalaced. The statistics across high-level 18 classes is shown on Figure below.

<div align="center">
  <img
    src="https://raw.githubusercontent.com/IS2AI/Central_Asian_Food_Scenes_Dataset/main/figures/statistics.png"
    alt="Class distribution"
    width="700"
  >
</div>

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## 🖼️ Annotation Examples

Sample Food scenes annotated images are shown below. Figures illustrate annotated food scenes samples based on our annotation rules that we have followed to create the dataset: the liquid objects such as beverages and soups are annotated together with the dish itself (see upper image), solid food items are annotated without the plate, in case one class is located on top of another class the annotations are made as shown on lower image; in case one class is obscured by another class and the rest of the background class is not visible we highlight only the visible part (see `Salad leaves' class annotation on the lower image). 

<div align="center">
  <img
    src="https://raw.githubusercontent.com/IS2AI/Central_Asian_Food_Scenes_Dataset/main/figures/protocol_1.png"
    alt="Protocol 1"
    width="700"
  /><br><br>
  <img
    src="https://raw.githubusercontent.com/IS2AI/Central_Asian_Food_Scenes_Dataset/main/figures/protocol_2.png"
    alt="Protocol 2"
    width="700"
  >
</div>

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## 🔀 Dataset Splits

|Fold| Train| Valid| Test| 
|----|-----------------|-----------------|--------------------|
|images|17,046|2,084|2,176|
|instances|55,422|7,062|7,381|

---


## 📥 Download the Dataset

**Direct download**  
   Grab the full dataset archive here:  
   ▶️ [Download ZIP](https://issai.nu.edu.kz/wp-content/themes/issai-new/data/models/CAFSD/CAFSD.zip)



## 📈 Training Results

Training results of different versions of the YOLOv8 model on the Central Asian Food Scenes Dataset, model sizes, and inference times.

<div align="center">
  <img
    src="https://raw.githubusercontent.com/IS2AI/Central_Asian_Food_Scenes_Dataset/main/figures/table_results.png"
    alt="Training results"
    width="800"
  >
</div>

---

## 🛠️ Pre-trained Models

Pre-trained weights of the YOLOv8 model can be downloaded using these links:

- **YOLOv8n**: https://issai.nu.edu.kz/wp-content/themes/issai-new/data/models/CAFSD/yolov8n.pt
- **YOLOv8s**: https://issai.nu.edu.kz/wp-content/themes/issai-new/data/models/CAFSD/yolov8s.pt
- **YOLOv8m**: https://issai.nu.edu.kz/wp-content/themes/issai-new/data/models/CAFSD/yolov8m.pt  
- **YOLOv8l**: https://issai.nu.edu.kz/wp-content/themes/issai-new/data/models/CAFSD/yolov8l.pt  
- **YOLOv8x**: https://issai.nu.edu.kz/wp-content/themes/issai-new/data/models/CAFSD/yolov8x.pt  

---

## 📖 References

[1] European Commission: Impact assessment on measures addressing food waste to complete swd (2014) 207 regarding the review of EU waste management target (2014). Accessed 5-22-2024.

---

## 📝 Citation

In case of using our dataset and/or pre-trained models, please cite our work:

```bibtex
@article{Karabay2025,
  author    = {Karabay, Aknur and Varol, Huseyin Atakan and Chan, Mei Yen},
  title     = {Improved food image recognition by leveraging deep learning and data-driven methods with an application to Central Asian Food Scene},
  journal   = {Scientific Reports},
  volume    = {15},
  number    = {1},
  pages     = {14043},
  year      = {2025},
  doi       = {10.1038/s41598-025-95770-9},
  url       = {https://doi.org/10.1038/s41598-025-95770-9},
  issn      = {2045-2322}
}
```

[📚 View the publication](https://doi.org/10.1038/s41598-025-95770-9)