Object Detection Using YOLOv7 and Flask

YOLOv7 is a state-of-the-art real-time object detection system that can detect multiple objects in an image with high accuracy. In this article, we will explore how to use YOLOv7 with Flask to create a web application for object detection.

Setting Up YOLOv7

First, you will need to install YOLOv7 and its dependencies. You can find the installation instructions on the official YOLOv7 repository on GitHub. Once you have installed YOLOv7, you can use pre-trained models to detect objects in images.

Creating a Flask Web Application

Flask is a popular web framework for Python that allows you to create web applications with ease. You can create a new Flask project and set up routes to handle object detection requests. You will also need to create a simple HTML form for users to upload images for object detection.

Integrating YOLOv7 with Flask

Once you have set up the Flask web application, you can integrate YOLOv7 to handle object detection requests. When a user uploads an image, the web application will pass the image to YOLOv7 for object detection. YOLOv7 will then return the detected objects along with their bounding boxes and confidence scores.

Displaying Object Detection Results

Finally, you can display the object detection results on the web application using HTML and CSS. You can create a simple interface to show the original image along with the detected objects and their bounding boxes. This will allow users to easily visualize the object detection results.

Conclusion

Object detection using YOLOv7 and Flask is a powerful combination that allows you to create a real-time object detection web application. With YOLOv7’s accuracy and speed, and Flask’s simplicity, you can easily create a web application for object detection that can be accessed from any device with a web browser.