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...

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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Finetuning Your Own Cosmos-Reason2 Model
18
MIN READ
February 13, 2026
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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.

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Visual Question Answering: A Comprehensive Guide to Fine-tuning VLMs for Intelligent Image Understanding
12
MIN READ
December 19, 2025
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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.

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