update

README.md

@@ -15,7 +15,105 @@ dataset_info:
     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
 ---
-# Dataset Card for "wheel-chair-images-annotation4object-detec"
+# Wheelchair Dataset for Object Detection
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+## 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.
+
+## loading the Dataset
+'''
+# Load the dataset
+hf_dataset = load_dataset("Falah/wheel-chair-images-annotation4object-detec", 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}")
+
+'''
+
+## Dataset Citation
+
+If you use this wheelchair dataset for object detection in your work, please cite it using the following template:
+
+```
+@dataset{falah_salieh_wheelchair_dataset_2023,
+  author = {Falah G. Salieh},
+  title = {Wheelchair Dataset for Object Detection},
+  year = {2023},
+  publisher = {Huggingface.co},
+  url = {\url{https://huggingface.co/datasets/Falah/wheel-chair-images-annotation4object-detec}},
+}
+```
+
+
+---