Create README.md

README.md

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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.