---
license: mit
task_categories:
- image-classification
- multi-label-classification
tags:
- food-recognition
- multi-label
- computer-vision
- food-classification
size_categories:
- 10K<n<100K
---

# Multi-Label Food Recognition Dataset


This is a multi-label food recognition dataset generated from single-class food images.
Each image contains 2-5 different food items composited together using natural composition methods.


## Dataset Details

- **Total Images**: 13,000
- **Training Images**: 10,400 (80%)
- **Validation Images**: 2,600 (20%)
- **Number of Classes**: 90
- **Labels per Image**: 2-5 labels
- **Image Format**: RGB, 512x512 pixels
- **File Format**: Parquet

## Dataset Structure

Each sample contains:
- `image`: PIL Image (RGB, 512x512)
- `labels`: List of integer label IDs (multi-hot encoded)
- `label_names`: List of string class names
- `num_labels`: Number of labels in the image (2-5)

## Usage

```python
from datasets import load_dataset

# Load dataset
dataset = load_dataset("ibrahimdaud/multi-label-food-recognition")

# Access splits
train_data = dataset['train']
val_data = dataset['validation']

# Example: Get first training sample
sample = train_data[0]
print(f"Image: {sample['image']}")
print(f"Labels: {sample['label_names']}")
print(f"Label IDs: {sample['labels']}")
```

## Citation

If you use this dataset, please cite:

```bibtex
@dataset{multi_label_food_recognition,
  title={Multi-Label Food Recognition Dataset},
  author={Your Name},
  year={2024},
  url={https://huggingface.co/datasets/ibrahimdaud/multi-label-food-recognition}
}
```

## License

MIT License