Update README.md

README.md

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-# 🧠 SmartVision AI  
-### Intelligent Multi-Class Object Recognition System
-
-SmartVision AI is an **end-to-end Computer Vision application** that performs  
-**image classification**, **object detection**, and **real-time inference** using
-state-of-the-art deep learning models.  
-The project demonstrates the complete AI lifecycle — from **model training** to
-**optimized deployment** using **Streamlit**.
-
 ---
-
-## 🚀 Key Features
-
-- 🖼️ **Image Classification**
-  - Custom-trained deep learning models
-  - Top-5 prediction display with confidence scores
-  - Side-by-side comparison of multiple CNN architectures
-
-- 📦 **Object Detection**
-  - Pretrained YOLOv8 model for real-time object detection
-  - Bounding boxes, class labels, and confidence scores
-  - Adjustable confidence threshold
-
-- 📸 **Live Webcam Detection (Optimized)**
-  - Real-time detection using webcam
-  - FPS monitoring and CPU-friendly optimizations
-  - Frame skipping and resolution scaling
-
-- 📊 **Model Performance Dashboard**
-  - Accuracy comparison (Train / Validation / Test)
-  - Inference speed analysis
-  - Visual performance insights
-
-- ⚡ **Optimized Inference**
-  - Lightweight models for CPU execution
-  - Streamlit caching for faster loading
-  - Performance-focused design decisions
-
+title: SmartVision AI
+emoji: 👁️
+colorFrom: indigo
+colorTo: blue
+sdk: streamlit
+sdk_version: "1.32.0"
+app_file: src/streamlit_app.py
+pinned: false
 ---
 
-## 🏗️ Model Architectures Used
+# SmartVision AI 👁️
 
-### 🔹 Image Classification
-- **VGG16 (Custom Trained)**
-- **ResNet50**
-- **MobileNetV2**
-- **EfficientNet-B0**
+**SmartVision AI** is an intelligent multi-class object recognition system built using
+Deep Learning and Computer Vision techniques.
 
-### 🔹 Object Detection
-- **YOLOv8 (Pretrained on COCO Dataset)**
-
----
+## 🚀 Features
+- Image Classification using CNN models (VGG16, ResNet50, MobileNetV2, EfficientNet-B0)
+- Object Detection using YOLOv8
+- Model performance comparison dashboard
+- Optimized inference for deployment
+- Streamlit-based interactive UI
 
-## 📂 Dataset Information
-
-- **Image Classification Dataset**
-  - Domain-specific dataset
-  - 25 object classes
-  - Train / Validation / Test split
-  - Image preprocessing and augmentation applied
-
-- **Object Detection Dataset**
-  - COCO Dataset
-  - 80 general-purpose object classes
-  - Bounding box annotations
-
----
-
-## 🛠️ Tech Stack
-
-**Programming Language**
-- Python 🐍
-
-**Deep Learning & Computer Vision**
+## 🛠 Tech Stack
+- Python
 - PyTorch
-- Torchvision
 - Ultralytics YOLOv8
-- OpenCV
-
-**Data Analysis & Visualization**
-- NumPy
-- Pandas
-- Matplotlib
-- Seaborn
-
-**Web & Deployment**
 - Streamlit
-- VS Code
-- Git & GitHub
-
----
-
-## ⚡ Performance Optimization Techniques
+- OpenCV
+- NumPy, Pandas, Matplotlib
 
-- Frame skipping for real-time webcam inference
-- Reduced image resolution for faster detection
-- Lightweight YOLOv8n model for CPU execution
-- Streamlit resource caching
-- Confidence-based filtering of predictions
+## 📦 Deployment
+This app is deployed using **Hugging Face Spaces (Streamlit SDK)**.
 
 ---
 
-## 📁 Project Structure
-SmartVisionAI/
-│
-├── app.py # Main Streamlit application
-├── requirements.txt # Python dependencies
-├── README.md # Project documentation
-├── Image.txt/ # Images, icons, logos
-├── yolo.ipynb
-└── smartvisionAI.ipynb(Downloading and training process of data)
-
-## Screenshots
-<img width="1920" height="1080" alt="Screenshot (176)" src="https://github.com/user-attachments/assets/feb97730-b862-4504-ac37-bc733fe21aba" />
-
-<img width="1920" height="1080" alt="Screenshot (178)" src="https://github.com/user-attachments/assets/60abc70b-3d2c-4aee-aaf2-53dade77d7e3" />
-Demo Images
-
-<img width="1920" height="1080" alt="Screenshot (177)" src="https://github.com/user-attachments/assets/5c03e4b4-eaa5-4eb8-942f-b3fae16db210" />
-Detection
-
-<img width="1253" height="825" alt="Screenshot 2025-12-14 at 08-17-06 SmartVision AI - Intelligent Multi-Class Object Recognition System" src="https://github.com/user-attachments/assets/158bdfca-c160-4968-a508-d3cd47878768" />
-
-
-
-
-## 📌 Note on Model Files
-
--Due to size constraints, trained model weights (.pt, .pth) are not included
-in this repository.
-
-## 🎓 Academic & Practical Relevance
-
-- This project was built to:
-
-- Demonstrate practical Deep Learning & Computer Vision skills
-
-- Showcase model deployment and optimization
-
-- Serve as a portfolio project for interviews and evaluations
-
-## 👨‍💻 Developer
-
-Rahul Kumar
-B.Tech in Information Technology
-IIEST Shibpur
+👨‍💻 Developed by **Rahul Kumar**