Vision-Language Model Fine-Tuning for Food Recognition

Fine-tuned and compressed a vision-language model for on-device food recognition.

Led end-to-end fine-tuning of a vision-language model for food recognition: curated 300,000+ training samples with quantity-aware supervision, then quantized and packaged the model for edge deployment — compressing it roughly 68% (6GB to 1.9GB via INT4 GGUF) while keeping end-to-end inference latency at 6 seconds or less on-device.