Roboflow

On-premise computer vision lets you run vision AI on your own servers, edge devices, or factory hardware. Learn how local inference works, which deployment fits, what hardware you need, how to set up Roboflow Inference, and how to manage models securely at scale.

Learn how to verify torque marks with computer vision using RF-DETR and a Vision-Language Model.

Learn how to deploy self-hosted computer vision models for privacy, low latency, and offline use. See how Roboflow Inference simplifies local deployment

Run an inference server yourself for low latency, on-prem data, or offline use; use a hosted API otherwise. Start one locally with Docker.

[email protected] uses a single 0.50 IoU threshold; [email protected]:0.95 averages ten thresholds from 0.50 to 0.95. See how each is calculated and when to use it.

Automated visual inspection validation under QMSR: an inspection method change is a letter to file, not a 510(k). Here is the evidence package.

Build a local basketball shot tracker using an RF-DETR detector and zero-shot keypoint tracking. Learn how to calibrate distance from the rim, detect shot release with pose data, handle net occlusions using physics, and render live mechanics overlays onto your video.

In this guide, we show how to build a defect detection and visual inspection system with computer vision using Roboflow.

In this guide, learn how to use a wood surface inspection system to identify defects on wood.

Labeling is the slowest part of building a vision model. The fix is letting a foundation model take the first pass while a human reviews. We benchmarked every top vision model on object detection to find which ones you can trust with the job, and how to pick between them.

Upload one slow walkaround video of a rental car and get back a signed, timestamped report of every scratch and dent, with reflections filtered out by physics. Here's how to build it with RF-DETR, Roboflow Workflows, and Gemini.