Learn how to train a custom YOLOv11 model for skin disease detection using advanced computer vision techniques. Perfect for AI enthusiasts and developers!
đź”— Training Notebook: [Colab Notebook]
🛠️ Code Repository: [GitHub Repository]
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date 2024-12-14 13:16:16
views 1381
author UCyB_7yHs7y8u9rONDSXgkCg
source
Here is a summary of the video transcript in 300 words:
The video is about using computer vision and AI to detect skin diseases. The host is a 30-year-old Web 3 DeFi tech editor, and he is exploring new use cases in medical healthcare. He is analyzing images with MRI and skin diseases and using AI with Python platform Py Research.
In this video, the host is training a custom model using YOLO11, a prevalent real-time object detection system. He explains how to implement the interface and use the code. The host discusses the concept of skin disease, such as acne and other skin conditions, and how AI can be used to detect them. He explains how to upload images and videos to the platform, train the model, and export it as a Python file.
The host also talks about the interface and how to launch the interface, which is currently available on the platform’s GitHub site. He also mentions the importance of labeled data for better results and how to collect and annotate images. The host also discusses how to implement the code and run the model on a GPU, using a Google Colab platform.
The video concludes with a demonstration of the model’s performance, showing the detection of skin diseases and the mixed results. The host also mentions the need for more data and the potential applications of this technology in the healthcare industry. Overall, the video provides an overview of the process of training a custom model using YOLO11 and computer vision for skin disease detection.
