region:us
# AI-Based Security Check Object Detection

![Project Cover](https://huggingface.co/Raminasser99/ObjectDetector/blob/main/cover.png)

## Model Description
This AI model utilizes deep learning, specifically a variant of the VGG16 convolutional neural network (CNN), to identify dangerous objects during airport security checks. It's designed to analyze X-ray images for quick and accurate detection of prohibited items.

## Intended Use
- **Primary Use**: To assist in identifying dangerous objects in X-ray images at airport security checkpoints.
- **Users**: This model is intended for use by airport security personnel and software developers creating security-check software.
- **Out-of-Scope**: Not intended for use outside of controlled airport security environments.

## Model Training
- **Data**: Trained on a dataset of X-ray images, which includes a variety of common and dangerous objects found in luggage.
- **Preprocessing**: Images are preprocessed for normalization and augmentation.
- **Training Details**: Utilizes transfer learning with VGG16 architecture, trained on Google Colab.

## Model Performance
The model's performance is evaluated using accuracy metrics across different optimizers. Below are the visual representations of the model's accuracy and optimizer performance:

![Optimizer Performance](https://huggingface.co/Raminasser99/ObjectDetector/blob/main/image3.png)

## How to Use the Model
To use the model:
1. Clone the repository and upload the script to a Google Colab environment.
2. Install necessary libraries and mount Google Drive for dataset access.
3. Run the script to train the model or use pre-trained weights for inference.

## Limitations and Bias
- The model's performance is heavily dependent on the diversity and quality of the training data.
- Performance may vary significantly on X-ray images that differ from the training dataset in terms of quality, angle, or object composition.

## Ethical Considerations
- This model should be used as an assistive tool, not a complete replacement for human judgment.
- Care should be taken to maintain privacy and avoid misuse of the technology.

## Acknowledgments
Special thanks to the creators of the datasets and the developers of the TensorFlow and Keras libraries, which made this project possible.

## Contributing
Contributions to this project are welcome. Please fork the repository and submit a pull request for suggested improvements or enhancements.