Datature

The @nvidia Cosmos Cookoff is here - a 4-week physical AI challenge that could reshape how we approach autonomous systems, and for us it's especially exciting since our CPO and Co-Founder @eranshir is judging alongside experts from @DatatureAI , @huggingface, and @nvidia. This isn't just another AI competition. With NVIDIA Cosmos Reason 2 and real-world validation requirements, teams will tackle the hardest problems in robotics and AV development - egocentric reasoning, traffic-aware models, and physical plausibility checks. Eran brings a unique perspective: while others simulate edge cases, Nexar has captured them from 10B+ miles of real-world driving. That's the foundation serious AV development needs. The industry is finally recognizing what forward-thinking leaders like Eran have advocated for years - simulated data isn't enough. Real roads demand real models. Jan 29 - Feb 26. Solo or teams up to 4. $5K grand prize plus NVIDIA DGX Spark. Sign up, and be a part of the future building the future of physical AI: luma.com/nvidia-cosmos-… #PhysicalAI #Robotics #AV #EdgeCases #AIChallenge

We’re excited to be part of the NVIDIA Cosmos Cookoff, alongside @nvidia , @huggingface, @nebiusai, and @getnexar What stands out about Cosmos Reason 2 is its focus on physical AI - moving beyond static perception toward reasoning that accounts for real-world constraints, egocentric understanding, and physical plausibility. This shift is clear - models must reason in environments governed by physics, not just pixels. Looking forward to seeing what the community builds over the coming weeks! 🤖 We will also be releasing tutorials on how to leverage cookbooks and blueprints to swiftly build your projects leverage CR2!

Our latest blog post shows how to fine-tune Vision-Language Models for VQA - so models can answer image-based questions with accuracy & context, not just describe what they see. Fine-tuning VLMs for VQA is not as simple as feeding models a bunch of image-question pairs. The real challenge lies in dataset preparation and system prompts. Our guide walks through a practical use case: Fine-tuning Qwen 2.5-VL 7B to answer medical imaging questions using Datature Vi 🤖 Tutorial → datature.io/blog/visual-qu…

If you are interested to train your own VLMs, we just launched Vi, a platform that handles annotation → fine-tuning → deployment. Sign Up For Beta 👉 vi.datature.com

Most vision-language models don’t fail because they can’t see. They fail because they don’t reason. Our latest post breaks down Chain-of-Thought (CoT) for VLMs - how explicit reasoning improves grounding ↘ 🔗 datature.io/blog/introduct…

ARBOR Technology Corp., a developer of Industrial Internet of Things (IIoT) and edge AI computing solutions, has formed a strategic alliance with @DatatureAI and MemryX Inc. #MachineVision #AI #Software Read more here: photonics.com/Articles/ARBOR…

One of the most requested features on @DatatureAI is finally here 🎉 Our new t-SNE Embedding Visualization lets you instantly see how your #datasets cluster - making it easier to spot duplicates, catch anomalies, and speed up labeling. Really excited for our community to start using this → it’s been on the wishlist for a long time, and the team delivered 🚀

Engineering Blog → datature.io/blog/introduci…

🚀 New on Datature: t-SNE Embedding Visualization Turn thousands of image embeddings into an interactive 2D map - spot duplicates, detect anomalies, visualize clusters, and speed up curation. Now Live → Find it under “Projector” in your Asset Tab

AI model accuracy and robustness can be improved by integrating metadata like location enums, IMU data, geo-coordinates, and sensors. Read more about our experiments with model fusion methods (early, late, hybrid) and best practices in our latest findings ↘

If you missed us at @HIMSS, join us for our webinar to learn more about how Datature can speed up and improve your 3D Medical Model AI Pipeline 🩻 Webinar → linkedin.com/events/7293768…

At @DatatureAI, we are unifying multi-planar reconstruction annotation, volumetric deep learning, and seamless deployment into a single, integrated intelligence layer. ⚡ Advanced Annotation – AI-driven, scalable labeling for CT, MRI, PET, with strict consensus/review enforcement. ⚡ Volumetric Model Training – Expanded #3DUNET architectures, fine-tuned with 3D augmentation for unparalleled precision. ⚡ Seamless Deployment – Native #3DSlicer integration & PACS support to operationalize insights at scale. This is just the beginning.

Exciting news! @DatatureAI is headed to #MDMWest in Anaheim, Feb 4-6 at the Anaheim Convention Center, Booth #5529. Discover cutting-edge machine learning and computer vision for medical, manufacturing & materials solutions with live demos!

Learn how to train an object detection model with YOLOv8 on Datature’s Nexus platform. In this tutorial, Leonard walks you through: - Training a model to identify and locate objects within images. - Optimizing workflows for speed and accuracy. Applications include warehouse management, autonomous vehicles, and retail analytics. Watch now and see how Nexus simplifies complex tasks #ObjectDetection #AI #ComputerVision #Datature #DatatureTutorials #YOLOv8

Learn how to train a video classification model using MoviNet on Datature’s Nexus platform. youtu.be/QXIavCwMzoY In this tutorial, Leonard guides you through: Training a model to analyze and classify actions or events in video sequences. Enhancing workflows for real-time video processing and accuracy. Applications include sports analytics, surveillance systems, and content moderation. Watch now and explore how Nexus simplifies advanced video analysis. #VideoClassification #AI #ComputerVision #Datature #DatatureTutorials #MoviNet

Discover PaliGemma 2: The next-generation vision-language model setting new benchmarks in scalability, high-resolution processing, and domain-specific adaptability. In this article, we break down its cutting-edge architecture, explore its benchmarks, and highlight the innovations driving its performance. Perfect for ML practitioners and researchers, this deep dive unveils the model’s capabilities and transformative potential. Learn more: datature.io/blog/introduci…