TR VISION
Khushi SharmaCreate a complete Traffic Sign Recognition System using Deep Learning and Convolutional Neural Networks (CNN). Project Title: Traffic Sign Recognition using CNN and Deep Learning Objective: Develop a web application that can recognize and classify traffic signs from uploaded images using a trained CNN model. Frontend Requirements: - React.js frontend - Modern and responsive UI - Attractive dashboard design - Drag and Drop image upload - Browse image upload option - Image preview before prediction - Loading animation during prediction - Display predicted traffic sign name - Display confidence percentage - Error handling for invalid image uploads - Mobile and desktop responsive design Backend Requirements: - Python FastAPI backend - REST API architecture - Image upload endpoint - Prediction endpoint - Health check endpoint - Proper error handling - CORS support for React frontend Deep Learning Requirements: - TensorFlow/Keras framework - Convolutional Neural Network (CNN) built from scratch - Use: - Convolution Layers - ReLU Activation - MaxPooling Layers - Dropout Layers - Dense Layers - Softmax Output Layer - Multi-class traffic sign classification - Model saved as traffic_sign_model.h5 Dataset Requirements: - Use GTSRB (German Traffic Sign Recognition Benchmark) dataset - Image resizing to 32x32 - Image normalization - Data preprocessing pipeline - Data augmentation using ImageDataGenerator - Train/Test split Model Training Features: - CNN training script - Model checkpoint saving - Early stopping - Accuracy and loss tracking - Training history visualization Evaluation Features: - Accuracy graph - Loss graph - Confusion Matrix - Classification Report - Final test accuracy display Prediction Features: - Upload traffic sign image - Real-time prediction - Display sign class name - Display confidence score - Show uploaded image with result Project Structure: TrafficSignRecognition/ │ ├── frontend/ │ ├── src/ │ │ ├── components/ │ │ ├── pages/ │ │ ├── App.js │ │ ├── index.js │ │ └── api.js │ │ │ ├── public/ │ ├── package.json │ └── README.md │ ├── backend/ │ ├── server.py │ ├── classifier.py │ ├── requirements.txt │ ├── uploads/ │ └── models/ │ └── traffic_sign_model.h5 │ ├── training/ │ ├── train_model.py │ ├── evaluate_model.py │ ├── plots/ │ └── saved_models/ │ ├── dataset/ │ └── README.md API Endpoints: - GET /health - POST /predict Required Libraries: Backend: - TensorFlow - Keras - OpenCV - NumPy - Pandas - Matplotlib - FastAPI - Uvicorn - Pillow Frontend: - React.js - Axios - React Icons Additional Requirements: - Clean code with comments - Professional folder structure - Production-ready project - Fully functional frontend-backend integration - Step-by-step setup instructions - Complete source code for every file - No placeholder code - All files should be fully implemented and ready to run Generate the complete code for frontend, backend, CNN model training, dataset preprocessing, API integration, and project documentation. and also given the zip file to copy paste in the vs code plss help me broo
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See the Road
Clearly
Upload any traffic sign image and get instant, accurate classification powered by deep learning and convolutional neural networks trained on the GTSRB dataset.
Three Steps to Recognition
Upload Image
Drag and drop or browse to upload a traffic sign image for analysis.
AI Analysis
Our CNN model processes the image through convolution layers, ReLU activation, and softmax classification.
Get Results
Receive instant classification with confidence scores and detailed analysis of the traffic sign.
Built for Precision
Everything you need to recognize, train, evaluate, and deploy traffic sign classification models — all in one platform.
Deep Learning CNN
Multi-layer convolutional neural network with ReLU activation, max pooling, and dropout layers trained on the GTSRB dataset.
Real-time Predictions
Get instant traffic sign classification with confidence percentages in milliseconds.
Model Training
Train and fine-tune models with data augmentation, checkpoint saving, and early stopping.
Performance Analytics
Visualize accuracy graphs, loss curves, confusion matrices, and detailed classification reports.
REST API
Full API access with health checks, image upload, and prediction endpoints with CORS support.
Model Deployment
Export trained models and deploy to production with an optimized inference pipeline.
See It in Action
Our model classifies 43 different traffic sign types from the GTSRB dataset with high confidence scores.
Stop
RegulatorySpeed Limit 30
Speed LimitYield
PriorityNo Entry
ProhibitoryTurn Right
MandatoryPedestrian
WarningReady to Identify
Traffic Signs?
Start by uploading a traffic sign image or explore the full API documentation to integrate TR VISION into your application.
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