The Five Minutes Datature Demo

OCR isn’t the bottleneck - structure is: raw engines like PaddleOCR read text reliably, but collapse under real-world conditions like multi-label scenes where context is lost. A lightweight detection-first pipeline (detect → crop → OCR → structure) turns that same text into production-ready JSON with minimal data and training, eliminating regex hacks and manual entry.

Most defect detection models need thousands of labeled examples of what's broken, but what if you only have images of good parts? We put three anomaly detection models (PatchCore, PaDiM, and EfficientAd) head to head using Anomalib and MVTec AD to see which one strikes the best balance between accuracy and training speed.

Qwen3-VL is Alibaba’s newer vision-language model family, and Datature Vi gives teams an end-to-end way to annotate VLM data, fine-tune Qwen3 with LoRA or full training, monitor evaluation, and export them for deployment. The main shift is from traditional CV’s fixed boxes-and-labels workflow to flexible multimodal outputs like phrase grounding, VQA, and free-text reasoning, with DPO alignment and RAG-based retrieval planned next. In this tutorial, we show you how you can easily train your own VLM model on our platform.