Datature Blog

Datature Outpost enables one-click deployment of computer vision models to edge devices for real-time, low-latency, bandwidth-efficient inference. It centralizes fleet management, monitoring, and model updates, making large-scale edge deployment simple and scalable.

YOLO26 is a deployment-first evolution of the YOLO family, eliminating NMS and Distribution Focal Loss while introducing Progressive Loss Balancing, STAL, and the MuSGD optimizer to deliver faster convergence and up to 43% faster CPU inference without sacrificing accuracy.

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.

Learn how to finetune NVIDIA's Cosmos-Reason2 vision-language model on Datature Vi to bring chain-of-thought reasoning to physical AI applications like warehouse automation, enabling robots to not just detect objects but reason about safety, spatial relationships, and physical interactions.

Get real-time visibility into your annotation team's performance with new metrics that show who's doing what, where corrections are happening, and how to optimize your review workflow before quality issues reach production.

Learn how to train Qwen2.5-VL to automatically detect and describe objects in images. This guide covers dataset preparation, training on consumer GPUs, and real-world results with detailed examples and troubleshooting tips

Visual Question Answering (VQA) enables AI models to answer natural language questions about images, powering use cases from healthcare and retail to accessibility and industrial inspection. In this article we show you how you can fine-tune your own VQA model with your dataset on Datature Vi.

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.