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Artificial Intelligence · Major Project
Car Model Recognition
Fine-grained classification of car make, model and year
3-4 weeks build
Overview
Tells apart cars that look almost identical using the public Stanford Cars dataset of 196 make, model and year classes. The pipeline crops each car with its bounding box, applies heavy augmentation, and fine-tunes pretrained ResNet and EfficientNet backbones. Grad-CAM heatmaps show which parts of the car drove each prediction, and a web app returns the top five guesses for an uploaded photo.
What makes it stand up
- 01Fine-grained classification over 196 car classes
- 02Transfer learning with ResNet and EfficientNet backbones
- 03Bounding-box cropping and targeted augmentation
- 04Grad-CAM heatmaps explaining each prediction
- 05Top-1 and top-5 accuracy with confusion analysis in the report
Module breakdown
- 01
Data pipeline
Loads annotations, crops cars and augments images.
- 02
Training
Fine-tunes backbones with staged unfreezing.
- 03
Evaluation
Top-k accuracy and the most-confused class pairs.
- 04
Explainability
Grad-CAM overlays for correct and wrong predictions.
- 05
Web app
FastAPI inference behind a React upload page.
After this, you will be able to
- Explain what makes fine-grained classification hard
- Defend your transfer learning and unfreezing schedule
- Demo a prediction with its Grad-CAM heatmap
Technology stack
- Python
- PyTorch
- OpenCV
- FastAPI
- React
- Level
- Major Project
- Domain
- Artificial Intelligence
- Build time
- 3-4 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support