Datasets:
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---
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
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