Update README.MD

README.MD

@@ -1,4 +1,3 @@
-```markdown
 ---
 license: mit
 task_categories:
@@ -9,6 +8,7 @@ tags:
 - computer-vision
 - cleanlab
 - data-centric-ai
+- bounding-boxes
 pretty_name: Object Detection Tutorial Dataset
 size_categories:
 - n<1K
@@ -16,15 +16,22 @@ size_categories:
 
 # Object Detection Tutorial Dataset
 
-Dataset used in cleanlab's [Object Detection tutorial](https://docs.cleanlab.ai/stable/tutorials/object_detection.html).
+## Dataset Description
 
-## Dataset Contents
+This dataset contains object detection annotations and predictions used in the cleanlab tutorial: [Object Detection](https://docs.cleanlab.ai/stable/tutorials/object_detection.html).
 
-- **labels.pkl**: Ground truth bounding box labels
-- **predictions.pkl**: Model predictions for bounding boxes
-- **example_images.zip**: Sample images for object detection
+The dataset demonstrates how to use cleanlab to identify and correct label issues in object detection datasets, where labels consist of bounding boxes around objects in images.
 
-## Usage
+### Dataset Summary
+
+- **Total Examples**: 118 images with bounding box annotations
+- **Task**: Object detection with bounding boxes
+- **Files**:
+  - `labels.pkl`: Ground truth bounding box labels
+  - `predictions.pkl`: Model predictions for bounding boxes
+  - `example_images.zip`: Sample images for object detection
+
+### Dataset Structure
 
 ```python
 from huggingface_hub import hf_hub_download
@@ -41,14 +48,70 @@ predictions_path = hf_hub_download('Cleanlab/object-detection-tutorial', 'predic
 with open(predictions_path, 'rb') as f:
     predictions = pickle.load(f)
 
-# Download images
+# Download and extract images
 images_path = hf_hub_download('Cleanlab/object-detection-tutorial', 'example_images.zip')
 with zipfile.ZipFile(images_path, 'r') as zip_ref:
     zip_ref.extractall('example_images/')
 ```
 
+### Data Format
+
+- **labels.pkl**: Dictionary containing ground truth bounding boxes in format `[x_min, y_min, x_max, y_max, class_id]`
+- **predictions.pkl**: Dictionary containing predicted bounding boxes with confidence scores
+- **example_images.zip**: Compressed folder containing image files
+
+## Dataset Creation
+
+This dataset was created for educational purposes to demonstrate cleanlab's capabilities for detecting issues in object detection datasets, such as:
+- Incorrectly labeled bounding boxes
+- Missing annotations
+- Poor quality predictions
+- Annotation inconsistencies
+
+## Uses
+
+### Primary Use Case
+
+This dataset is designed for:
+1. Learning data-centric AI techniques for object detection
+2. Demonstrating cleanlab's object detection issue detection
+3. Teaching proper annotation quality assessment workflows
+
+### Example Usage
+
+```python
+from huggingface_hub import hf_hub_download
+import pickle
+from cleanlab.object_detection.summary import object_detection_health_summary
+
+# Download files
+labels_path = hf_hub_download('Cleanlab/object-detection-tutorial', 'labels.pkl')
+predictions_path = hf_hub_download('Cleanlab/object-detection-tutorial', 'predictions.pkl')
+
+# Load data
+with open(labels_path, 'rb') as f:
+    labels = pickle.load(f)
+with open(predictions_path, 'rb') as f:
+    predictions = pickle.load(f)
+
+# Use cleanlab to analyze object detection data quality
+summary = object_detection_health_summary(labels, predictions)
+print(summary)
+```
+
+## Tutorial
+
+For a complete tutorial using this dataset, see:
+[Object Detection Tutorial](https://docs.cleanlab.ai/stable/tutorials/object_detection.html)
+
+## Licensing Information
+
+MIT License
+
 ## Citation
 
+If you use this dataset in your research, please cite the cleanlab library:
+
 ```bibtex
 @software{cleanlab,
   author = {Northcutt, Curtis G. and Athalye, Anish and Mueller, Jonas},
@@ -60,6 +123,7 @@ with zipfile.ZipFile(images_path, 'r') as zip_ref:
 
 ## Contact
 
-- Repository: https://github.com/cleanlab/cleanlab
-- Documentation: https://docs.cleanlab.ai
-```
+- **Maintainers**: Cleanlab Team
+- **Repository**: https://github.com/cleanlab/cleanlab
+- **Documentation**: https://docs.cleanlab.ai
+- **Issues**: https://github.com/cleanlab/cleanlab/issues