- Developed a high-performance Vehicle Image Recognizer Web App using deep learning techniques.
- Designed and implemented a Convolutional Neural Network (CNN) using TensorFlow and Keras.
- Integrated a Flask server for backend functionality, enabling users to upload vehicle images and receive real-time predictions.
- Supports recognition for various vehicle types, including bus, family sedan, fire engine, heavy truck, jeep, minibus, racing car, SUV, taxi, and truck.
Watch the demo video of the project: Demo Video
- Real-Time Predictions: Implemented a Flask server to handle image uploads and provide real-time predictions.
- Interactive Web App: Created an intuitive web interface for users to upload images and receive immediate classification results.
- Programming Language: Python
- Machine Learning Library: TensorFlow, Keras
- Web Framework: Flask
- Frontend: HTML5, CSS3, JavaScript
- Dataset: 1400 images for training, 200 images for validation
