This Jupyter Notebook demonstrates the implementation of object detection using the YOLO (You Only Look Once) deep learning model. YOLO is a real-time object detection system capable of detecting multiple objects within an image or video stream. This notebook provides step-by-step instructions to load a pre-trained YOLO model, process images, and identify objects within those images.
- Loading YOLO Model: This notebook explains how to load the YOLOv3 model along with its configuration and weights files.
- Image Preprocessing: The notebook includes a section on image preprocessing, where the input image is resized, normalized, and converted into a blob for YOLO.
- Object Detection: It covers the process of detecting objects within an image, including extracting bounding boxes, confidence scores, and class labels.
- Post-Processing: The notebook includes post-processing steps such as applying non-max suppression (NMS) to filter overlapping boxes and enhance detection accuracy.
- Visualization: The final step involves visualizing the detected objects by drawing bounding boxes and labels on the image.
- Python 3.x
- OpenCV
- NumPy
- Matplotlib
- YOLOv3 weights and configuration files (
yolov3.weights,yolov3.cfg)
- Clone the Repository: Clone this repository to your local machine.
- Download YOLO Weights: Ensure you have the YOLOv3 weights file (
yolov3.weights) and configuration file (yolov3.cfg). - Run the Notebook: Open the notebook in Jupyter and run each cell sequentially. Ensure that the necessary libraries are installed.
- Loading the YOLO Model: Detailed steps on how to load the YOLO model and set up the required files.
- Processing Images: Steps to preprocess images before feeding them to the YOLO model.
- Detection Logic: Explanation of the detection logic, including how to interpret the model's output.
- Visualization: Final step to draw bounding boxes and labels on the detected objects.
- The YOLO model was developed by Joseph Redmon and Ali Farhadi.
- This notebook utilizes the pre-trained YOLOv3 model for demonstration purposes.
