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.

Datature now supports Multilabel Classification, allowing models to assign multiple independent labels to a single image using binary cross entropy with logits instead of softmax-based training. With co-occurrence matrices, streamlined workflows, and flexible deployment options, teams can build and deploy richer, multi-attribute vision systems for real-world use cases.

This article introduces the concepts of evaluation class metrics and low confidence sampling and how they can enable deeper model evaluation insights that can improve your computer vision’s model performance using a helmet detection model as an example