---
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: 469379.0
    num_examples: 40
  - name: augmented
    num_bytes: 27013178.0
    num_examples: 320
  download_size: 27483853
  dataset_size: 27482557.0
---

# 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**: maryzhang@cmu.edu

## 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
1. Resize to 224x224 pixels (bilinear interpolation)
2. Convert to RGB format
3. 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

```python
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

```bibtex
@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)](https://creativecommons.org/licenses/by/4.0/) license.

## Contact

Dataset created by Mary Zhang for CMU 24-679.
For questions or issues, please use the [discussion forum](https://huggingface.co/datasets/maryzhang/hw1-24679-image-dataset/discussions) on Hugging Face.