Tutorials

The Five Minutes Datature Demo

Video Description Lorem Ipsum

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.

Train Your Own Object Detection Model

Many Ways, One Outcome

After developing computer vision models for different startups and companies, I've come to realize that there's no fixed way to do it. Just the topic of PyTorch vs TensorFlow is polarizing in itself. That's just the tip of the iceberg - atop of the tons of different annotation formats, models and infrastructure setups.

At Datature, we seek to streamline the pipeline and tasks required to build computer vision models effectively. Starting from annotations, dataset management, workflow management, all the way to training and deployment. The interface has been carefully curated to ensure that teams get it right on the first try!

There will be more how-to contents in the upcoming weeks to demonstrate what we believe data teams and researchers should be doing instead. Meanwhile, here's a five minutes introduction video on how you can use our platform to build your models at neck-breaking speed.


An Evolving Product

We believe that the product should constantly evolve to fit more use cases and we are open to suggestions on the next key feature to build. Additionally, we are looking to build a community around the product, so come chat with us on our Slack Channel!

Resources

More reading...

How to Annotate DICOM Images for Medical Image Segmentation
14
MIN READ
April 17, 2026
This is some text inside of a div block.

Medical image segmentation projects live or die by annotation tooling, not model architecture. This guide compares desktop tools like 3D Slicer and cloud platforms like Datature across the full DICOM-to-deployed-model pipeline, covering MPR viewing, SAM-assisted labeling, multi-annotator consensus, and 3D segmentation training.

Read
Reading Shipping Labels with Computer Vision: From PaddleOCR to Production Pipeline
15
MIN READ
April 2, 2026
This is some text inside of a div block.

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.

Read
Visual Anomaly Detection with Anomalib: A Hands-On Guide [2026]
16
MIN READ
March 19, 2026
This is some text inside of a div block.

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.

Read
Get Started Now

Get Started using Datature’s computer vision platform now for free.