README.md

metadata

dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': Lower
            '1': Upper
  splits:
    - name: original
      num_bytes: 2575827
      num_examples: 32
    - name: augmented
      num_bytes: 22191126
      num_examples: 320
  download_size: 24632318
  dataset_size: 24766953
configs:
  - config_name: default
    data_files:
      - split: original
        path: data/original-*
      - split: augmented
        path: data/augmented-*

Gym Machines Image Dataset

Dataset Summary

This dataset includes 30+ original, student-created images of gym machines (objects and small arrangements/scenes) with a binary classification target for each image:

• 0 = Lower body machine
• 1 = Upper body machine

The dataset is stored on Hugging Face with two splits:

• original: 32 manually collected and labeled images
• augmented: 320 synthetic samples generated via label-preserving transformations

Total: 350+ images

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Purpose

This dataset was created as part of a course assignment to demonstrate:

• Safe collection of original image data
• Application of augmentation techniques for dataset expansion
• Preparation and publishing of datasets to Hugging Face for reproducibility and sharing

It is intended for educational use in computer vision, data preprocessing, and augmentation workflows.

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Composition

• Subjects: Common gym machines (e.g., leg press, hack squat, chest press, lat pulldown).
• Labels: Binary (Lower, Upper).
• Images: 224×224 RGB, .jpg format.
• Counts:
  • Original split: 32 images
  • Augmented split: 320 images

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Data Collection

• Images were captured safely by the student, without any people or personally identifiable information (PII).
• Only objects and gym machines were included.
• All images were resized to 224×224 pixels.

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Preprocessing & Augmentation

Preprocessing

• Converted to RGB
• Resized to 224×224

Augmentation Techniques

Applied using PyTorch/TorchVision:

• Random horizontal flip (p=0.5)
• Random rotation (±20°)
• Random color jitter (brightness, contrast, saturation ±0.3)
• Random resized crop (scale = 0.8–1.0)
• Gaussian blur

These transformations expanded the dataset from 32 originals to 320 augmented samples, while preserving labels.

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Labels

• Binary target:
  • 0 → Lower body machine
  • 1 → Upper body machine

Labels were manually assigned by the student based on machine function.

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Splits

• original → 32 images
• augmented → 320 images
• Published as a DatasetDict on Hugging Face.

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Intended Use & Limitations

• Use cases: Educational exercises in dataset handling, preprocessing, augmentation, and Hugging Face dataset publishing.
• Not intended for: Medical, health, or workout guidance.
• Limitations:
  • Small dataset size → not suitable for production training
  • Labels are simplified (Upper vs Lower) and may not capture full machine usage

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Ethical Considerations

• No people or personal information were included.
• No sensitive content.
• Strictly object-based dataset.

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License

• Released under CC BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike).
• You may use and adapt for educational/research purposes with attribution.
• Not for commercial use.

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AI Usage Disclosure

• AI tools (e.g., ChatGPT) assisted in:
  • Structuring the dataset card
• All images were student-created, not AI-generated.