Launch: Semantic Segmentation for Labeling, Training, Deployment

Roboflow has expanded its capabilities to fully support semantic segmentation projects, allowing users to annotate data in Roboflow Annotate, train models with Roboflow Train, and deploy them using Roboflow Deploy. Semantic segmentation, which classifies each pixel in an image to a specific class, differs from instance segmentation by not identifying individual objects with bounding boxes. This technology is particularly useful in fields like autonomous vehicles, medical imaging, and satellite imagery analysis. Roboflow offers AI-assisted labeling tools, such as the Smart Polygon tool, to streamline the labeling process, which is crucial for enhancing model performance. The platform's one-click model training solution, Roboflow Train, utilizes transfer learning and AutoML tools to optimize model development and reduce costs. After training, models can be deployed for inference through Roboflow Deploy, which provides autoscaling infrastructure and API endpoints for seamless integration into applications. Users can continuously improve their models through active learning by sampling images from production back into Roboflow. Roboflow supports free access to over 100,000 open-source datasets for various computer vision projects, enabling users to explore applications like infrastructure analysis and medical diagnostics.