Knife vs Pistol Detection

Dataset Structure

The dataset is divided into four key folders, each containing images meant for both training and evaluation of machine learning models:

• Knife: Contains images specifically focused on knives. These are used to train the knife recognition model.
• eval_Knife: Designed for evaluating the knife detection model’s accuracy and its ability to make reliable predictions.
• Pistol: Contains images of pistols, design to train the model in distinguishing pistols from other objects.
• eval_Pistol: Use to test and evaluate the pistol detection model, ensuring that it can effectively predict pistol-related outcomes.

Additional Features

1. Image Variations: The dataset includes various angles, lighting conditions, and backgrounds to ensure robustness in diverse real-world scenarios.
2. Data Augmentation: To improve model generalization, data augmentation techniques such as rotation, scaling, and cropping can be apply to simulate different environments.
3. Annotation Files: The dataset includes label annotations, providing bounding boxes around the objects (knife or pistol) within each image, facilitating precise object localization tasks.
4. Use Case Examples: This dataset is particularly suited for applications in airport security, automate surveillance systems, and law enforcement technologies where accurate weapon detection is critical.