Yolov7 Flask

Overview

The Burn Detection Classification tool is a practical web application designed for detecting burn injuries using the powerful Yolov7 model. This setup is user-friendly and efficient, allowing individuals to harness advanced machine learning capabilities without needing extensive technical know-how. By leveraging a pre-trained model or your trained Yolov7 models, the app simplifies the process of image recognition and classification, making it an ideal solution for healthcare professionals and researchers alike.

With its straightforward installation process and intuitive interface, this app allows users to quickly load their models and start testing with images right away. The aim is to provide quick insights into burn detection, ensuring that critical data can be obtained promptly, thereby improving response times and decision-making in medical situations.

Features

• Automatic Model Detection: The app autonomously searches for the model file within your directory, requiring no manual configuration from the user.

• Simple Setup Process: Follow clear instructions to install the necessary requirements, including PyTorch and Flask, making it easy for users to get started.

• Support for Pre-trained Models: Users can choose to utilize pre-trained Yolov7 models, eliminating the need for custom training while still delivering impressive results.

• Real-time Model Feedback: A green bar at the top of the app provides real-time updates on the model currently being used for inference, enhancing user experience.

• Image Testing Capabilities: Users can easily test the app using photographs relevant to the model's training, allowing for immediate validation of results.

• Future Expansion Possibilities: While current features focus on static images, there are plans to potentially include support for video and webcam input based on user feedback.

• Designed for Versatility: By accommodating both trained and pre-trained models, the app is flexible enough to meet the diverse needs of users in the health and research fields.