Launch: Use YOLO26 Semantic Segmentation with Roboflow. You can now label data for semantic segmentation, train YOLO26 semantic segmentation models, and deploy them with Roboflow. Roboflow, Inc. is excited to announce support for YOLO26 semantic segmentation in Roboflow. You can now label data for semantic segmentation, train YOLO26 semantic segmentation models, and deploy them with Roboflow, all in one place. What is YOLO26 Semantic Segmentation? Semantic segmentation assigns a class label to every pixel in an image, producing a dense class map that covers the entire scene. Rather than drawing a box around an object or outlining each individual instance, a semantic segmentation model classifies every pixel into a category, grouping all pixels of the same class together regardless of how many distinct objects are present. This differs from instance segmentation, which separates individual objects of the same class into distinct masks. Because semantic segmentation produces a single dense class map for the full image, it is well suited to scene-level understanding tasks such as autonomous driving, land-cover mapping, and medical imaging, where the goal is to understand every region of an image rather than to count or track separate objects. YOLO26 is the latest model architecture from the team behind YOLO11 and YOLOv8. Its semantic segmentation models extend the YOLO family into pixel-wise, dense prediction, bringing the real-time performance the architecture is known for to whole-scene labeling. Label data for YOLO26 Semantic Segmentation with Roboflow. Building an accurate semantic segmentation model starts with high-quality labeled data. Roboflow provides an extensive suite of annotation tools to help teams label images for semantic segmentation, including AI-assisted and SAM-powered labeling to accelerate the process. Existing datasets can also be brought in and converted, and trained models can be used as label assistants to speed up labeling further. Centralizing annotation in Roboflow gives teams a consistent place to manage datasets, review labels, and prepare data for training without stitching together separate tools. Train YOLO26 Semantic Segmentation models with Roboflow. Once your dataset is ready, you can train YOLO26 semantic segmentation models on Roboflow's hosted training platform. Roboflow manages the underlying training infrastructure and GPU access, so teams can train models without provisioning or maintaining their own hardware. When training is complete, the model is available for testing directly in the Roboflow web interface, making it straightforward to evaluate performance before moving toward deployment. Deploy YOLO26 Semantic Segmentation models with Roboflow. After a model has trained, it can be deployed through the Roboflow cloud API or run on your own hardware with Roboflow Inference. Inference supports deployment across CPU and GPU devices, including edge hardware, so models can run close to where images are captured. For the lowest latency, Roboflow recommends deploying on device with Inference. YOLO26 semantic segmentation models can also be incorporated into Roboflow Workflows, a low-code interface for building multi-step computer vision pipelines and applications that can then be deployed in the cloud or on your own hardware. Get started with YOLO26 Semantic Segmentation. With support for YOLO26 semantic segmentation, Roboflow covers the full path from labeling data to training a model to deploying it in production. Semantic segmentation brings dense, pixel-level scene understanding to the YOLO family, and Roboflow makes each step of working with these models available in a single platform. Cite this post. Use the following entry to cite this post in your research: Contributing Writer. (Jun 11, 2026). Launch: Use YOLO26 Semantic Segmentation with Roboflow. Roboflow Blog: https://blog.roboflow.com/use-yolo26-semantic-segmentation-with-roboflow/ Stay Connected Get the Latest in Computer Vision First Contributing Writer Topics.

Vision Events: Turning Images into Insights. Today Roboflow, Inc. is announcing Roboflow Vision Events, a centralized view into what's happening across your vision deployments. By storing model predictions, images, and relevant metadata in one place, Vision Events transforms the output of your computer vision applications into searchable, historical business insights that drive better operational decisions. A secure, searchable record of your vision deployments. When deploying a computer vision application, you need a reliable place to store model outputs and connect them to your broader goals. For example, if your vision application monitors manufacturing processes across various facilities, your team might want to answer operational questions like, "How many quality issues did our vision app catch today?", "Which facility experienced the most downtime this month?", or "How can we verify compliance issues detected on a specific assembly line?" Traditionally, answering those questions meant building a custom solution to store model predictions alongside the associated image files and relevant metadata. Standard tools often make it challenging to co-locate tabular data with the actual production images from your vision model. That is why Roboflow, Inc. built Vision Events. Now you can aggregate data from all your vision applications running across different cameras or geographic regions into a single, centralized hub. It gives you one place to store model predictions, associated input and output images, device info, and custom metadata. Vision Events closes the loop, connecting the initial problem you set out to solve directly to high-level operational visibility. See all events, images, and metadata in one place. If you have similar data coming from multiple deployments, Vision Events lets you group that information into specific views. These views consolidate the results of your vision model, related images, and additional contextual details. You can easily track standard information like the date, time, and device that captured the imagery, as well as customizable properties like shift times, lot numbers, or manufacturing lines. Filter and find specific events. If you need to review specific information, you can filter your data by a wide range of properties, view the results, and save those filters for easy access later. This makes it easy to not only review the overall performance of your model, but also pinpoint specific events when necessary. For example, if you are tracking quality issues in a battery manufacturing facility and need to review images from a specific time, assembly line, or range of serial numbers, you can filter by the relevant metadata to quickly locate the exact information you need. While reviewing data in Vision Events, if you notice false positives or negatives, you can select images and add them directly to a training dataset. This allows you to simplify the process of continuously improving your vision model. How to bring data into Vision Events. Depending on how your vision application is deployed, there are several options for feeding data into the system. * Workflows: For applications built in the drag-and-drop Workflows editor, you can use the Vision Events Workflow block. * Developer Tools: For more customizable integrations, you can use the REST API and Python SDK to bring data into Vision Events. * Edge Device Backup: For applications running on edge hardware, you can automatically synchronize events from each device using the Edge Device Backup option. Start turning images into insights today. Vision Events is available to all Roboflow users. If you want to get started, log into Roboflow and head to the Vision Events tab. Additional information is available in documentation and pricing. To discuss customizable enterprise deployments, feel free to contact Roboflow, Inc.. Cite this post. Use the following entry to cite this post in your research: Patrick Deschere. (Apr 20, 2026). Vision Events: Turning Images into Insights. Roboflow Blog: https://blog.roboflow.com/vision-events/ Stay Connected Get the Latest in Computer Vision First Patrick Deschere Patrick makes content about solving business challenges with vision AI. He spends his time hosting webinars, editing slides, and drawing bounding boxes around objects. Topics.

Roboflow Ranked #9 AI Product in G2's 2026 Awards. At Roboflow, its mission has always been to democratize computer vision. Roboflow, Inc. believe that giving developers the tools to make the world programmable will solve some of the world's hardest problems. Today, Roboflow, Inc. is proud to share a significant milestone in that journey: After being named the #1 Image Recognition on G2 in 2025, Roboflow is the #9 AI Product on the G2 2026 Best Software Awards list. Being ranked alongside OpenAI, ElevenLabs, Canva, and Notion is a testament to the success of the 1,000,000+ developers who are using Roboflow to bring vision AI into the mainstream. The AI landscape is more crowded than at any point in history with 18,312 total products being evaluated by G2. Vision entering the top 10 is a signal that AI's next major wave of understanding and interacting with the physical world has arrived. Visual AI adoption is growing rapidly. For years, vision AI was sidelined as a future technology reserved for big tech research labs with massive budgets. But that era has officially ended. This shift is critical because, while most AI currently lives in a browser tab, the most valuable AI of the next decade will live in the physical world. As agentic AI and robotics start to take center stage, vision becomes the primary sensory input for every autonomous system. In its 2026 Vision AI Trends report, Roboflow, Inc. analyzed 200,000 real-world projects to show how enterprises are successfully deploying AI to production across 10 global industries. Roboflow's climb into the Top 10 of G2's rankings is another direct reflection of the growing adoption and the real-world value users are finding in vision AI. With over a million vision projects and 250,000 models build with Roboflow, it's clear that the tools are ready for anyone to deploy models that track inventory or identify defects in real time. When you build with Roboflow, you are building on a platform trusted by the world's largest logistics companies such as BNSF, category leading manufacturers like USG, autonomous robotics companies like Peer Robotics, and AI-native healthcare companies like Wellth. A community-driven win. G2 rankings are unique because they aren't decided by a panel of judges; they are determined by the people actually using the software to build the future. Roboflow's ranking is thanks to the researchers, the hobbyists, and the enterprise engineers who have been contributing to the growth of the vision community. Roboflow, Inc. is honored to be your partner in building the foundational vision layer of the AI stack. Ready to see why Roboflow, Inc. is a Top 10 AI product? Start building for free or talk to its sales team to scale your vision AI project today. Cite this post. Use the following entry to cite this post in your research: Trevor Lynn. (Apr 10, 2026). Roboflow Ranked #9 AI Product in G2's 2026 Awards. Roboflow Blog: https://blog.roboflow.com/roboflow-g2-2026-top-ai-software/ Stay Connected Get the Latest in Computer Vision First Trevor Lynn Trevor leads Marketing at Roboflow. He focuses on sharing insights from Roboflow customers to inspire the broader AI community and help advance visual AI. Topics.