Datature

Wrapping up an incredible experience at FIRA - World Ag Robotics Forum in Woodlands, CA! It was inspiring to meet so many innovators driving change in agriculture and to showcase how Datature is enabling precision through our deep-learning vision platform. Connecting in person with our existing users and engaging with new potential partners in the #AgTech space gave us valuable insights into how our platform can support their work on the field and beyond. Big thanks to everyone who visited our stand and to our dedicated team (Brandon Neustadter, Max Wasilko, Leonard S., Eric Voorheis) for making this event such a success - we’re all excited for the future of AgriTech 🥦

Datature will join the Asia-Pacific Agri-Food Innovation Summit as a Featured Company from November 19th to 21st in Singapore 🇸🇬 📍 Marina Bay Sands, Singapore Join Cody Kamin and the team - Thursday, November 21st, as we showcase how Datature transforms agriculture with cutting-edge computer vision. We're helping companies unlock new efficiencies, safety, and yield-optimization through AI-driven solutions. Don’t miss how we’re reshaping the future of the industry 🥦

Introducing Class Metrics & Low Confidence Sampling on Datature Nexus 🔍 We’re thrilled to roll out new features that elevate your model evaluation process. With Class Metrics, you can gain deeper insights by analyzing performance at a granular level, helping you understand which classes of objects your models excel or struggle to detect. With Low Confidence Sampling, users can easily identify and prioritize misclassified or uncertain predictions, optimizing labeling and fine-tuning efforts. These tools are designed to streamline your workflow, enabling quicker iterations and better-performing models. Read The Blog → https://lnkd.in/gsNGJe2F #machinelearning #computervision #modeloptimization

We are excited to announce that Datature will join the FIRA - World Ag Robotics Forum as a partner this year from October 22nd to 24th in Woodland, CA! If you're passionate about computer vision and deep learning in agriculture, make sure to connect with us at the event! 🥦

At Datature, we're thrilled to announce the Beta Launch of our Whole Slide Image (WSI) Dataset Support, tailored specifically for the intricate needs of radiology and pathology professionals. This new tool offers comprehensive support for high-fidelity labeling of WSI files, including formats like .SVS and .NDPI integrate seamlessly across our platform—from Intellibrush to Model Fine-Tuning and Evaluation on WSI. This launch empowers experts to manage vast WSI datasets effortlessly, preserving every detail without compression. Our platform also supports multi-resolution pyramid TIFF files, facilitating rapid navigation and analysis, and securely integrates into your existing workflows. Stay tuned as we unveil more updates in the coming weeks on how #digitalpathology teams can leverage Datature Nexus as a complete solution for developing sophisticated vision models 🔬.

At Datature, we are excited to announce our Beta Launch of Whole Slide Image (WSI) Dataset Support designed specifically for the demands of radiology and pathology experts. Our latest tool not only supports high-fidelity labeling of WSI files, including formats like .SVS and .NDPI, but a full platform support from Intellibrush, to Model Fine-Tuning, to Evaluating Models on WSI. With this, we’re enabling experts to work seamlessly with vast WSI datasets, ensuring that no detail is compressed. Our platform supports multi-resolution pyramid TIFF files, allowing for rapid navigation and analysis, and integrates directly into existing workflows securely. We will be sharing more details on other improvements over the next few weeks on how #digitalpathology teams can leverage an end-to-end tool like Datature Nexus for building complex vision models 🔬

Datature's users can now efficiently label tons of visual objects in images, videos, medical scans, and even satellite tiles easily with our latest SAM 2.0 integration into Intellibrush 🖌️ Since launching Intellibrush in 2022, we have continually enhanced our capabilities to ensure data labeling and ground truth preparation are performed with unparalleled speed and accuracy - If this resonates with you and your team, check out our latest tutorial 👇 https://lnkd.in/gZ5RdXza

At Datature, we are thrilled to announce the integration of Meta's Segment Anything Model 2 (SAM-2) into our annotator interface for advanced object segmentation in video #datasets. Teams working with video datasets will experience significantly faster labeling and higher mask accuracy thanks to our latest video object tracking feature, powered by the updated Intellibrush with SAM-2 as its backbone. Kudos to the Meta AI team for open-sourcing such incredible technology, enabling developers to build critical applications for developers ⚡️ Here's a preview of the enhanced features, and we look forward to bringing more innovations to our users 👇

Meta's Latest Segment Anything Model 2.0 (SAM-2.0), is a groundbreaking advancement in computer vision that supports real-time object segmentation in both images and videos. SAM-2.0’s new memory mechanisms, including the memory bank, memory encoder, and memory attention module, retain information across video frames, ensuring accurate and consistent segmentation even with object motion and occlusions. This enhancement significantly improves object tracking and segmentation across all frames in a video. SAM-2.0 also excels in zero-shot generalization, accurately segmenting unseen objects and visual domains without custom adaptation. Additionally, it can generate multiple valid masks for ambiguous objects, enhancing its adaptability. Explore how SAM-2.0’s latest improvements and use cases on our Datature Blog 👇 https://lnkd.in/gmQVSYmg

Dive into the technical depths of Microsoft's Florence-2, a multi-modal compact visual-language model designed for efficiency and high performance. Florence-2 leverages advanced multi-modal learning techniques to deliver impressive results in various visual-language tasks. Our blog post delves into Florence-2's architecture, its approach to embedding visual and textual data, and the unique methods it uses for optimizing performance. Whether you’re integrating this model into a computer vision project or enhancing your existing ML pipeline, our detailed analysis will provide the insights you need to get started 🤖 Read the Blog 👉 https://lnkd.in/gjwKgTAN