Update README.md

README.md

@@ -1,10 +1,43 @@
 ---
+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
@@ -14,32 +47,175 @@ dataset_info:
     num_examples: 320
   download_size: 27483853
   dataset_size: 27482557.0
-configs:
-- config_name: default
-  data_files:
-  - split: original
-    path: data/original-*
-  - split: augmented
-    path: data/augmented-*
 ---
-# Asian vs Western Food Image Dataset with Augmentations
-This dataset contains food images for binary classification between Asian and Western cuisine styles.
-
-## Dataset Structure
-- **original**: 40 original food images (224x224 pixels)
-- **augmented**: 320 synthetic samples generated using image augmentation techniques
-
-## Features
-- `image`: Food image (PIL Image, 224x224 RGB)
-- `label`: Binary classification (int32)
-  - 0: Western cuisine
-  - 1: Asian cuisine
-
-## Augmentation Techniques Used
-Image augmentations were applied to create 8x synthetic samples:
-- **Random cropping**: Scale 0.7-1.0 with aspect ratio preservation
-- **Rotation**: ±15 degrees random rotation
-- **Flipping**: Horizontal (50%) and vertical (10%) flips
-- **Color jittering**: Brightness, contrast, saturation, and hue adjustments
-- **RandAugment**: Advanced augmentation with magnitude 7
-- **Random erasing**: 20% probability for occlusion robustness
+
+# 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
+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.