Object-detection tutorials benchmark on data-center GPUs. Students run on a free-tier notebook GPU or a laptop's integrated graphics, and the achievable frame rate is a different number entirely.
What's realistic
- A small, quantized detection model on a free-tier GPU: often 15-30 FPS, enough for real-time demo purposes.
- The same model on CPU only: usually 1-5 FPS — fine for batch processing, not for a live demo.
- A larger, higher-accuracy model on a budget GPU: may drop below 10 FPS; test before promising 'real time' in a pitch.
Getting more out of limited hardware
Quantize the model, reduce input resolution if your task tolerates it, and skip frames rather than processing every one at full resolution (see frame sampling). Measure actual FPS on your target hardware before a demo, not on whatever GPU you trained with.
See video frame sampling for vision models and edge deployment for vision models.
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.
