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Key Features
- AI-powered recognition of 33 traditional Bangladeshi dishes
- Custom-curated dataset of Bangladeshi cuisine for model training
- NextViT deep learning architecture achieving 89.76% accuracy
- Real-time inference engine for instant food classification from images
- Personalized nutrition recommendations based on classified dishes
- Health suggestions tailored to identified food items
- NEON PostgreSQL + Drizzle ORM for scalable data operations
- Modern, responsive UI deployed on Vercel
Challenges Faced
- No existing labeled dataset for Bangladeshi dishes, requiring data to be collected and curated from scratch
- Visually similar dishes (e.g., different curries) were hard to distinguish with a lightweight model
- Serving real-time inference without a heavy backend footprint
Solutions Implemented
- Curated and labeled a custom dataset of 33 traditional dishes covering diverse presentations
- Adopted the NextViT architecture, tuned for a strong accuracy/speed trade-off, reaching 89.76% accuracy
- Paired the classifier with a lightweight Next.js + PostgreSQL stack for instant nutrition lookups after inference
Technologies Used
Next.jsTypeScriptNextViTPyTorchDeep LearningPostgreSQLDrizzle ORMOpenRouter AIComputer Vision
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Get in touch
Curious how it was built?
I'd be glad to walk through the architecture and decisions behind DeshiPlate AI: Bangladeshi Food Recognition.