task_categories:object-detection size_categories:1K<n<10K language:en license:apache-2.0 region:us
---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: instances
    list:
    - name: box
      sequence: float64
    - name: label
      dtype: int64
  splits:
  - name: train
    num_bytes: 25165898.049
    num_examples: 1107
  download_size: 0
  dataset_size: 25165898.049
license: apache-2.0
task_categories:
- object-detection
language:
- en
pretty_name: wheel_chair_detection
size_categories:
- 1K<n<10K
---
# Wheelchair Dataset for Object Detection

## Dataset Information

The `dataset_info` file provides information about the wheelchair dataset designed for object detection. Here are the details:

### Features

- **image**: Represents the images in the dataset.
  - Data type: `image`

- **instances**: Represents the instances within each image. Each instance consists of a bounding box and a label.
  - Data type: `list`
  - Sub-features:
    - **box**: Bounding box coordinates for each instance.
      - Data type: `float64`
    - **label**: Label for each instance.
      - Data type: `int64`

### Splits

- **Train**: This split, named "train," contains a total of 1,107 examples.
  - Number of bytes: 25,165,898.049
  - Number of examples: 1,107

### Dataset Size

- Download size: 0 (no download required)
- Dataset size: 25,165,898.049 bytes

## Wheelchair Class Name

The dataset includes the following class names for object detection:

```json
"labels": ClassLabel(names=["person", "wheel_chair", "not wheel chair"])
```

The class labels are defined as follows:
- "person"
- "wheel_chair"
- "not wheel chair"

## Object Detection Application (YOLOv Models)

You can utilize the dataset with YOLOv models for object detection tasks. The class labels for the models correspond to the defined class names mentioned above:

```json
"labels": ClassLabel(names=["person", "wheel_chair", "not wheel chair"])
```

Make sure to follow the appropriate implementation guidelines and examples for YOLOv models to leverage this dataset effectively.

```python
# Load the dataset
hf_dataset = load_dataset("your_dataset_name", split="train")
# Accessing image
image = hf_dataset[1]['image']
# Display the image
image.show()
# Accessing label and bounding box coordinates
instances = hf_dataset[1]['instances']
for instance in instances:
    label = instance['label']
    box = instance['box']
    # Get the class name for the label
    class_name = hf_dataset.features['instances']['label'].int2str(label)
    print(f"Label: {class_name}")
    print(f"Bounding Box: {box}")
```
## Citation

If you use this dataset in your research or any other work, please consider citing it as:

```
@dataset{wheel-chair-images-annotation4object-detec_dataset,
  author = {Falah.G.Salieh},
  title = {Wheelchair Dataset for Object Detection},
  year = {2023},
  publisher = {Hugging Face},
  version = {1.0},
  location = {Online},
  url = {Falah/wheel-chair-images-annotation4object-detec}
}
```

## License

Wheelchair Dataset for Object Detection Dataset is provided under the Apache-2.0 license.


```