Label data, train YOLO26 semantic segmentation models, and deploy them with Roboflow, all in one place. Pixel-level, whole-scene understanding from labeling to the edge.
Roboflow covers the full path from labeling data to training a YOLO26 semantic model to deploying it in production, so you do not have to stitch separate tools together or prepare mask files by hand.
Use Roboflow's annotation tools with AI-assisted and SAM-powered labeling to segment regions with a few clicks instead of painting every pixel. Bring in and convert existing datasets, or use a trained model as a label assistant.
Train YOLO26 semantic segmentation models on Roboflow's hosted training platform. Roboflow manages the infrastructure and GPU access, so your team trains without provisioning or maintaining hardware.
When training finishes, the model is available to test directly in the Roboflow web interface. Review mean IoU and pixel accuracy on images that match your real deployment conditions before moving to production.
Deploy through the Roboflow cloud API or run on your own hardware with Inference across CPU, GPU, and edge devices. For the lowest latency, deploy on device, or chain the model into Workflows.
Assigns a class label to every pixel, producing a single dense class map for the whole scene. All pixels of the same class are grouped together regardless of how many objects are present. Built for scene-level understanding: drivable area, land cover, and medical regions.
Separates individual objects of the same class into distinct masks, so you can count, track, or measure each one. The right choice for object-level tasks. For instance segmentation, train RF-DETR, Roboflow's state-of-the-art, commercial-safe model for segmentation and detection.
The rule of thumb: use semantic segmentation when you care about every region of the scene, and instance segmentation when you need to tell one object from the next. If your task is instance segmentation, reach for RF-DETR: it leads current models on accuracy and latency and ships under a permissive Apache 2.0 license. Roboflow supports both tasks, end to end.
| Semantic segmentation | Instance segmentation | |
|---|---|---|
| Output | One dense class map for the whole image | A separate mask per detected object |
| Same-class objects | Merged into one class region | Kept as separate instances |
| Counts objects? | No | Yes |
| Best for | Drivable area, land cover, medical regions | Counting, tracking, per-object measurement |
Dense scene understanding, faster labeling, hosted training, and deployment anywhere.
YOLO26 semantic models classify every pixel into a category, producing a dense map of the entire scene. That brings the real-time speed of the YOLO architecture to whole-scene labeling, not just a handful of objects.
Dense pixel labeling is the most time-consuming kind. AI-assisted and SAM-powered tools let you segment regions with a few clicks instead of painting every pixel, and Roboflow exports the mask format semantic training expects, no hand-built class-ID masks.
Train YOLO26 semantic models on Roboflow's hosted platform. Roboflow manages the training infrastructure and GPU access, so your team ships models without provisioning or maintaining hardware, then tests results in the web interface.
Run models through the Roboflow cloud API or on your own hardware with Inference, across CPU, GPU, and edge devices, close to where images are captured. Chain semantic models into Workflows for multi-step pipelines.
Half the Fortune 100 build computer vision with Roboflow, with segmentation models deployed in driving, mapping, agriculture, and on the edge.
Trusted by teams at BNSF, Rivian, GE Vernova, Cummins, USG, Pella, and Peer Robotics.
YOLO semantic segmentation uses a YOLO26 model with a -sem suffix to assign a class label to every pixel in an image, producing a single dense class map for the whole scene rather than separate object masks. It brings the real-time speed of the YOLO architecture to pixel-wise, whole-scene labeling, and is suited to autonomous driving, land-cover mapping, and medical imaging. With Roboflow you can label data, train a YOLO26 semantic model, and deploy it in one platform.
Semantic segmentation assigns a class to every pixel but does not separate individual objects, so all cars in a scene share one class region. Instance segmentation produces a distinct mask for each object, even when objects share a class, which is what you need to count, track, or measure individual objects. Semantic segmentation is best for scene understanding; instance segmentation is best for object-level tasks. For instance segmentation, train RF-DETR, Roboflow's recommended model.
Label in Roboflow using AI-assisted and SAM-powered tools that let you segment regions with a few clicks rather than painting every pixel by hand. You can also import and convert existing datasets, or use a trained model as a label assistant. Roboflow then exports the data in the mask format semantic training expects, so you do not have to produce single-channel class-ID masks manually.
Yes. After training on Roboflow, deploy through the cloud API or run on your own hardware with Roboflow Inference, which supports CPU, GPU, and edge devices. For the lowest latency, Roboflow recommends deploying on device, and you can chain the model into a larger pipeline with Roboflow Workflows.
Label data, train a YOLO26 semantic model, and deploy it to the cloud or the edge, all in one platform.
Ask the Roboflow agent about labeling, training, and deploying YOLO26 semantic models.
See how to label, train, and deploy YOLO26 semantic models in one place.
How per-pixel class maps work and where they fit in computer vision.
Explore drivable-area and scene-understanding deployments on robots.
