Auto-annotation is the process of using AI models to automatically generate labels (bounding boxes, polygons, segmentation masks) on images, replacing or reducing the need for manual human annotation. It is the single most impactful advancement in making computer vision projects accessible to non-specialists. Why Annotation is the Bottleneck Before a
Computer vision in agriculture is proven — from drone crop scouting to livestock monitoring to post-harvest grading. But most deployments cost six figures and take seasons to validate. This post maps the real use cases, the real costs, and what a different approach looks like.
Computer vision in manufacturing is proven. But most deployments cost six figures and take months — pricing out the majority who need it most. This post maps real use cases, real costs, and what a different approach looks like with small, task-specific models.
Jump into your platform workspace and manage computer vision projects with ease. Here’s how to create a new project, organize your data, use agentic self-annotation, and deploy your trained model with an API key. Step 1: Log in to the platform with your credentials. Step 2: On the dashboard,
MWC 2026 just confirmed what the industry has been building toward for years: smartglasses are no longer a gimmick. Alibaba unveiled the Qwen AI Glasses — balanced, comfortable, and surprisingly normal-looking for a device with a screen embedded in the lens. Google demoed Android XR prototype glasses to packed crowds in
There's a quote from Yann LeCun that has stayed with us since we started building vfrog: "A more efficient world where AI sees." It's three words that capture something most people in the AI industry talk around rather than directly. We're building