---
license: mit
task_categories:
- object-detection
language:
- en
tags:
- computer-vision
- cleanlab
- data-centric-ai
- bounding-boxes
pretty_name: Object Detection Tutorial Dataset
size_categories:
- n<1K
---

# Object Detection Tutorial Dataset

## Dataset Description

This dataset contains object detection annotations and predictions used in the cleanlab tutorial: [Object Detection](https://docs.cleanlab.ai/stable/tutorials/object_detection.html).

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.

### 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
import pickle
import zipfile

# Download labels
labels_path = hf_hub_download('Cleanlab/object-detection-tutorial', 'labels.pkl')
with open(labels_path, 'rb') as f:
    labels = pickle.load(f)

# Download predictions
predictions_path = hf_hub_download('Cleanlab/object-detection-tutorial', 'predictions.pkl')
with open(predictions_path, 'rb') as f:
    predictions = pickle.load(f)

# 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},
  title = {cleanlab},
  year = {2021},
  url = {https://github.com/cleanlab/cleanlab},
}
```

## Contact

- **Maintainers**: Cleanlab Team
- **Repository**: https://github.com/cleanlab/cleanlab
- **Documentation**: https://docs.cleanlab.ai
- **Issues**: https://github.com/cleanlab/cleanlab/issues