Datasets:

ccm
/

2025-24679-image-dataset

dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': recycling
            '1': trash
  splits:
    - name: original
      num_bytes: 2095716
      num_examples: 174
    - name: augmented
      num_bytes: 32947958
      num_examples: 522
  download_size: 35030975
  dataset_size: 35043674
configs:
  - config_name: default
    data_files:
      - split: original
        path: data/original-*
      - split: augmented
        path: data/augmented-*
license: mit
task_categories:
  - image-classification
language:
  - en
pretty_name: 24-679 Image Dataset
size_categories:
  - n<1K

Dataset Card for `ccm/2025-24679-image-dataset`

Dataset Details

Dataset Description

This dataset consists of images labeled as recycling (0) or trash (1). It was created as part of a classroom exercise in supervised learning and data augmentation, with the goal of giving students practice in building and evaluating image classification pipelines.

Curated by: Fall 2025 24-679 course at Carnegie Mellon University
Shared by [optional]: Christopher McComb
License: MIT
Language(s): N/A (image dataset)

Uses

Direct Use

Training and evaluating image classification models (binary classification: recycling vs. trash).
Experimenting with image preprocessing (resizing, normalization, augmentation).
Teaching end-to-end ML workflows: data loading, training, validation, and evaluation.

Out-of-Scope Use

Production deployment in real recycling or waste-sorting systems.
Generalization to real-world trash/recycling classification without larger and more diverse datasets.
Use in safety-critical or automated decision-making contexts.

Dataset Structure

The dataset includes two splits:

original: 174 examples (collected by students).
augmented: 522 examples (synthetically generated to balance and expand the dataset).

Each row includes:

image: an image file (e.g., JPEG/PNG).
label: integer class label (0 = recycling, 1 = trash).

Dataset Creation

Curation Rationale

The dataset was curated to provide a simple, hands-on dataset for practicing image classification methods in an educational setting. Recycling/trash was chosen because it is easy to photograph and conceptually straightforward.

Source Data

Data Collection and Processing

Original images were collected on campus by students (e.g., photographs of bins, bottles, cans, paper, etc.).
Labels were assigned manually during the collection process.
Augmented data was generated with transformations such as rotations, flips, brightness/contrast changes, and cropping.

Who are the source data producers?

Original data: Students in the 24-679 course.
Augmented data: Generated by course instructors and teaching assistants using standard augmentation tools.

Bias, Risks, and Limitations

Small sample size: Only 174 original images.
Synthetic augmentation: Does not capture real-world variation in lighting, backgrounds, or object diversity.
Domain bias: Limited to CMU campus items, not representative of recycling/trash globally.

Recommendations

Use primarily for teaching and demonstration purposes.
Do not generalize beyond the dataset scope.
Highlight dataset limitations during instruction to reinforce lessons about data quality and bias.

Dataset Card Contact

Christopher McComb (Carnegie Mellon University) — [email protected]

Dataset Card for ccm/2025-24679-image-dataset