| --- |
| 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. |
