🚀 Final Goal
An app that allows users to point their smartphone camera at a car and instantly display the car model name, new car price, and used car market price.
#1 tip is here
✅ Development Steps (By Phase)
🔹 STEP 1: Build a High-Accuracy Car Model Recognition AI
- Collect car image data (with a focus on Japanese vehicles)
- Preprocess images and train classification models such as ResNet or YOLO
- Evaluate accuracy and analyze common misclassification patterns
- Visualize decision regions using tools like Grad-CAM to improve interpretability
🔹 STEP 2: Establish a Method for Retrieving Price Data
- Gather information on new and used car prices via web scraping or public APIs (e.g., CarSensor, Goo-net)
- Create a mapping system that links car model names to pricing data
- Account for variations in price depending on year and trim level; for the MVP, use representative or average prices
🔹 STEP 3: Build a Minimum Viable Product (PoC) with Smartphone Camera Input
- Capture a car image using a smartphone → upload the image or send it via API
- Run car model inference and price estimation on a Python backend
- Return results to the user (can be displayed via a simple web UI)
💡 Streamlit + FastAPI or Flutter + Flask are good tools for this phase
🔹 STEP 4: Develop a Full-Fledged Mobile App
- Build a mobile app with integrated camera functionality (using Flutter or React Native)
- Capture → send image to inference API → display results
- Design a user-friendly UI/UX (e.g., thumbnail images, price tags, “purchase” button-style display)
- Consider features like login, history tracking, and location tagging
🔹 STEP 5: Additional Features & Monetization
- 📈 Price history tracking with graphs
- 🛠 Estimate year/odometer reading based on visual cues or OCR
- 💰 Monetization via ads or affiliate links to used car listings and dealers
- 📊 Extend functionality to include popularity, user reviews, fuel efficiency info, etc.
🔑 Technical Components Summary
| Component | Technology Used |
|---|---|
| Car Recognition AI | PyTorch / YOLOv8 / ResNet |
| Price Retrieval | Web Scraping (Selenium / BeautifulSoup) or API |
| Server API | FastAPI / Flask |
| Mobile App | Flutter / React Native / Expo |
| Deployment | Google Firebase / Vercel / AWS Lambda |
💡 First Milestone (MVP Idea)
✅ “Upload a car photo → Instantly get the car model name and estimated used price” — all within a simple web app.
