README.md

metadata

language:
  - en
task_categories:
  - object-detection
tags:
  - computer-vision
  - object-detection
  - bicycle
  - grayscale
  - embedded-ai
  - tinyml
pretty_name: Bike Detection Subset
size_categories:
  - n<1K

Bike Detection Subset

Dataset Description

This dataset is a small public subset of a private bicycle detection dataset used for embedded computer vision experiments.

It contains 200 grayscale QQVGA images:

• 100 positive samples containing at least one bicycle
• 100 negative samples without bicycles

The images have a resolution of:

160 × 120

and are stored as grayscale images.

Dataset Structure

Each sample contains:

image
image_id
task
sample_type
width
height
objects

sample_type is either:

positive
negative

The objects field contains:

bbox
category

Bounding boxes use the format:

[x, y, width, height]

with coordinates expressed in pixels.

The bicycle class is represented with category ID:

0

Negative samples contain no bounding boxes.

Example

{
    "image": ...,
    "image_id": "task_01_image_001",
    "task": "task_01",
    "sample_type": "positive",
    "width": 160,
    "height": 120,
    "objects": {
        "bbox": [
            [34.0, 21.0, 48.0, 71.0]
        ],
        "category": [
            0
        ]
    }
}

Dataset Creation

The samples were randomly selected from a larger private dataset.

The original data consists of low-resolution grayscale images prepared for an embedded bicycle detection use case.

Annotations were originally stored in YOLO format and converted to pixel-space bounding boxes when generating this Hugging Face dataset.

Intended Use

This subset is intended for:

• Demonstrating the associated bicycle detection model
• Testing dataset loading and preprocessing pipelines
• Embedded computer vision experiments
• Bicycle object detection examples

It is not intended to be a comprehensive benchmark dataset.

Limitations

This dataset contains only 200 samples and represents a limited subset of the original private dataset.

It may not cover the full diversity of:

• Lighting conditions
• Camera viewpoints
• Bicycle appearances
• Backgrounds
• Object scales
• Acquisition environments

Performance measured on this subset should therefore not be considered representative of general bicycle detection performance.

Source Dataset

The complete training dataset is private and is not distributed with this repository.