Datasets:

ibrahimdaud
/

multi-label-food-recognition

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

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:

@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