Datasets:

EricCRX
/

hw1-image

license: mit
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:
        class_label:
          names:
            '0': food
            '1': view
  splits:
    - name: original
      num_bytes: 371487
      num_examples: 32
    - name: augmented
      num_bytes: 4075054
      num_examples: 352
  download_size: 4411701
  dataset_size: 4446541

📄 Model Card: Food vs View Image Dataset

1. Purpose

This dataset was created for educational purposes as part of Homework 1 (Dealing with Data).
It demonstrates how to build, preprocess, augment, and upload an image dataset to Hugging Face.
The dataset supports binary image classification tasks and shows how augmentation can expand small collections into larger, balanced sets.

2. Composition

Domain: Everyday life photos.
Samples: 32 unique, student-created images.
Classes (binary target):
- food → meals, snacks, and beverages.
- view → landscapes, outdoor scenery.
Split Sizes:
- Original: 32 images
- Augmented: 352 images

3. Collection Process

Source: All original images were taken by the student using a phone camera.
Content: Food photos are close-ups of meals, while view photos capture outdoor landscapes.
Format: JPEG/PNG images.
Resolution: Resized to 224×224 pixels.
Ethical Assurance: No faces, people, or personal identifiers are included.

4. Preprocessing & Augmentation

Preprocessing:
- Resized all images to 224×224.
- Converted to RGB format.
Augmentation Techniques (label-preserving):
- Horizontal/vertical flips
- Random rotations (0°, 90°, 180°, 270°)
- Brightness/contrast adjustments
- Random cropping and scaling
Goal: Expand dataset to ≥300 samples while preserving labels.

5. Labels

Target Variable: label
Values:
- 0: food
- 1: view

6. Splits

Original split: 32 manually collected images (original).
Augmented split: 352 synthetic variants via documented augmentation techniques (augmented).

7. Exploratory Data Analysis (EDA)

Below are random samples from both classes:

Class counts: 15 food, 17 view
Observation: Food images tend to be close-up shots under artificial lighting, while view images capture natural scenery with broader perspectives.

8. Intended Use / Limitations

Use:
- Educational demo for binary image classification.
- Practice with preprocessing, augmentation, and Hugging Face uploads.
Limitations:
- Small dataset; not suitable for real-world production.
- Augmentation does not fully capture real-world variability.

9. Ethical Considerations

Images are student-owned and contain no personal or sensitive data.
Dataset is safe for academic and research use.

10. License

Released under the MIT License for educational and research purposes.

11. AI Usage Disclosure

Original images (32): Taken by the student.
Augmentation: Generated via Python libraries (e.g., torchvision.transforms).
Documentation: Drafted with the assistance of AI tools (ChatGPT) but reviewed and finalized manually.