Tutorials

The Five Minutes Datature Demo

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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 Deploy Vision AI Models at The Edge with Datature Outpost
10
MIN READ
February 25, 2026
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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.

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YOLO26: The Edge-First Evolution of Real-Time Object Detection
7
MIN READ
February 22, 2026
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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.

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VLM Training Metrics and Loss Functions: A Technical Reference [2026]
15
MIN READ
February 16, 2026
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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.

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