A plant or object identification app — photograph something, get back what it is — is a well-scoped, visually compelling student project achievable without training a model from scratch.
A realistic build
- Start with a pretrained image classification or embedding model, not a from-scratch architecture.
- Build (or find) a well-curated reference dataset for the category you're identifying, with clean labelled examples.
- Use similarity search against the reference set (via embeddings) rather than a fixed closed-set classifier, so new categories can be added without retraining.
- Show a confidence score and a few alternative matches, not just one guess — this is more honest and more useful when the model is uncertain.
What makes this project stand out
Handling the 'I don't know' case gracefully — recognising low confidence and saying so — is the detail that separates a genuinely useful demo from a toy that always confidently guesses something.
See image embeddings for visual search.
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU Kanpur) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. Email pranjulrathour41@gmail.com.
