Comprehensive technical guide to VLM evaluation and fine-tuning, covering key metrics (BLEU, METEOR, CIDEr, SPICE, BERTScore, CLIPScore, VQA Accuracy, ANLS) and core loss functions (cross-entropy, contrastive, focal, KL divergence, DPO). Includes mathematical formulations, step-by-step worked examples, and practical code snippets for implementation.

Our annual data-driven analysis of how enterprises are actually deploying computer vision in 2026 - covering the five dominant deployment patterns, sample ROI numbers by vertical, technology choices between YOLO26 and RF-DETR, edge vs cloud splits, and the no-code vs custom engineering debate.

Vision-language models can see, but without reasoning they often hallucinate, miss spatial details, or fail silently. This post shows how Chain-of-Thought prompting transforms VLMs into interpretable, step-by-step reasoners, with real examples of grounding, VQA, and physical AI using Cosmos Reason1 - and how to train and deploy them in practice.