Datasets:

maryzhang
/

hw1-24679-image-dataset

language:
  - en
license: cc-by-4.0
tags:
  - image-classification
  - food
  - cuisine-classification
  - binary-classification
  - computer-vision
  - augmented-dataset
  - cmu-24679
annotations_creators:
  - expert-generated
language_creators:
  - found
pretty_name: Asian vs Western Food Classification Dataset
size_categories:
  - n<1K
source_datasets:
  - original
task_categories:
  - image-classification
task_ids:
  - multi-class-image-classification
paperswithcode_id: null
configs:
  - config_name: default
    data_files:
      - split: original
        path: data/original-*
      - split: augmented
        path: data/augmented-*
dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype: int32
  config_name: default
  splits:
    - name: original
      num_bytes: 469379
      num_examples: 40
    - name: augmented
      num_bytes: 27013178
      num_examples: 320
  download_size: 27483853
  dataset_size: 27482557

Asian vs Western Food Classification Dataset

Dataset Summary

Purpose: This dataset was created for binary classification of food images into Asian or Western cuisine categories, developed as part of CMU 24-679 coursework to explore computer vision techniques in food recognition.

Quick Stats:

360 total images (40 original + 320 augmented)
Binary classification task
224x224 RGB images
Balanced classes (~50% each category)

Contact: [email protected]

Sample Contact Sheet

Sample grid showing 4 Asian cuisine images (top row) and 4 Western cuisine images (bottom row) from the original dataset

Dataset Composition

Features

image: PIL Image object (224x224 RGB)
label: Integer (0=Western, 1=Asian)

Class Distribution

Cuisine Type	Original	Augmented	Label
Western	20	160	0
Asian	20	160	1

Data Splits

original: 40 hand-collected food images
augmented: 320 synthetically augmented images (8x augmentation per original)

Data Collection Process

Collection Methodology

Images were collected between January-February 2025 using:

Personal photography of restaurant meals
Home-cooked dishes from both culinary traditions
Mobile phone cameras (various models)
Natural lighting conditions when possible

Selection Criteria

Clear food presentation
Distinctive cuisine characteristics
No people or identifying information in frame
Variety in dish types (appetizers, mains, desserts)
Representative of common dishes from each cuisine

Preprocessing and Augmentation

Preprocessing Pipeline

Resize to 224x224 pixels (bilinear interpolation)
Convert to RGB format
Normalize pixel values to [0, 255]

Augmentation Techniques

Each original image generated 7 augmented variants using:

Geometric: RandomResizedCrop (0.7-1.0), RandomRotation (±15°), RandomHorizontalFlip (p=0.5)
Color: ColorJitter (brightness=0.2, contrast=0.2, saturation=0.15, hue=0.05)
Advanced: RandAugment (num_ops=2, magnitude=7), RandomErasing (p=0.2)

Labels and Annotation

Labeling Schema

0: Western cuisine (European, American dishes)
1: Asian cuisine (East Asian, Southeast Asian dishes)

Annotation Process

Manual labeling by dataset creator
Verification based on known cuisine origins
Edge cases resolved by primary ingredients and cooking methods

Intended Use and Limitations

Intended Use Cases

Educational projects in computer vision
Binary food classification research
Transfer learning experiments
Baseline model development

Limitations

Small dataset size requires transfer learning for good performance
Binary classification oversimplifies cuisine diversity
May not generalize to fusion cuisine or ambiguous dishes
Limited to common dishes, not comprehensive of all cuisine varieties
Potential bias toward restaurant-style presentation

Out-of-Scope Uses

Commercial food recognition systems
Medical or dietary assessment applications
Fine-grained cuisine classification (regional variations)
Production deployment without additional training data

Ethical Considerations

Representation

Effort made to balance representation between cuisine types
Acknowledges that binary classification may perpetuate oversimplified cultural categories
"Asian" and "Western" are broad categorizations that don't capture cuisine diversity

Privacy

No personally identifiable information included
No restaurant branding or identifying markers
Images taken in public settings or personal kitchens

Cultural Sensitivity

Dataset created for educational purposes
Users should be aware of cultural simplification in binary categorization
Not intended to make value judgments about cuisine types

AI Usage Disclosure

AI-Assisted Components

Augmentation code: Partially generated using AI assistance for transform pipeline
Documentation: README structure and sections refined with AI assistance
Data collection: All images are original, not AI-generated
Labels: Manually assigned, not AI-generated

Human Oversight

All images personally collected and verified
Labels manually reviewed for accuracy
Augmentation parameters tuned based on empirical testing
Final dataset curated and quality-checked by human creator

Usage Example

from datasets import load_dataset
from torchvision import transforms

# Load dataset
dataset = load_dataset("maryzhang/hw1-24679-image-dataset")

# Setup transforms
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

# Access data
sample = dataset['original'][0]
image, label = sample['image'], sample['label']
cuisine = "Asian" if label == 1 else "Western"

print(f"Sample cuisine type: {cuisine}")

Citation

@dataset{zhang2025food,
  author = {Mary Zhang},
  title = {Asian vs Western Food Classification Dataset},
  year = {2025},
  publisher = {Hugging Face},
  note = {CMU 24-679 Homework 1},
  url = {https://huggingface.co/datasets/maryzhang/hw1-24679-image-dataset}
}

License

This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Contact

Dataset created by Mary Zhang for CMU 24-679. For questions or issues, please use the discussion forum on Hugging Face.