Build a Multi-Model Image Classification App with YOLO & Streamlit | Deep Learning Project Tutorial
In this video, I’ll show you how to build a Multi-Model Image Classification App using YOLO and Streamlit — all in one interface! 🚀
This app allows you to choose from multiple trained YOLO models and classify different types of images such as Brain Tumor, Pneumonia, Emotions, Flowers, Nature Scenes, and even Rock-Paper-Scissors gestures 🧠🌸🏞️✊📄✂️
We’ll walk through:
✅ How to load YOLO classification models dynamically
✅ How to integrate multiple datasets in a single Streamlit UI
✅ How to classify uploaded images in real-time
✅ How to display predictions, confidence scores, and model info
✅ How to use custom background and styling for a modern look
🧩 Tech Stack Used:
Python 🐍
Streamlit 🌐
YOLO (Ultralytics) ⚡
PIL (Image Processing)
Base64 (for background image encoding)
💡 Features
Supports multiple pretrained YOLO classification models
Interactive image uploader
Beautiful Streamlit UI with background styling
Displays prediction results and confidence
Modular and reusable classifier() function
📚 Models Included
🧠 Brain Tumor Classification
🫁 Pneumonia Detection
😀 Face Emotion Recognition
🌼 Flower Classification
🌄 Nature Scene Classification
✊ Rock Paper Scissors Classification
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🎥 Watch till the end to learn how to customize, expand, and deploy this app easily for your own projects!
📎 GitHub Code Link: https://github.com/NitinCVOrbit/Multi...
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