Datasets:
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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:
```text
160 × 120
```
and are stored as grayscale images.
## Dataset Structure
Each sample contains:
```text
image
image_id
task
sample_type
width
height
objects
```
`sample_type` is either:
```text
positive
negative
```
The `objects` field contains:
```text
bbox
category
```
Bounding boxes use the format:
```text
[x, y, width, height]
```
with coordinates expressed in pixels.
The bicycle class is represented with category ID:
```text
0
```
Negative samples contain no bounding boxes.
## Example
```python
{
"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. |