Planogram compliance: Datasets & Models
Automated planogram compliance detection uses object detection to verify that products are placed on retail shelves according to the intended planogram. It checks facings, positions, and stock presence from shelf images.
Planogram compliance
This planogram compliance detection workflow takes retail shelf images as input and applies object detection to locate individual products, price tags, and empty shelf slots. It then flags misplaced items, missing facings, out-of-stock gaps, and incorrect positioning by comparing the detected shelf state against the approved planogram layout.
Try it out
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Why automate planogram compliance
Manual planogram audits are slow, inconsistent, and expensive: staff walk aisles with clipboards or tablets comparing shelves to reference diagrams, and errors slip through while stockouts and misplaced products cost sales. Automating planogram compliance detection with computer vision turns a subjective, time-consuming audit into a repeatable measurement that runs on every store visit. For retail tech teams, this means faster audits, objective compliance scoring across hundreds of stores, and near real-time alerts when shelves drift from the intended layout.
Key benefits
Detection models flag misplaced items, empty facings, and share-of-shelf issues in seconds, letting field teams fix problems on the spot instead of after a delayed report. The workflow runs on existing hardware, associate phones, shelf-mounted cameras, or scanning robots, so it scales across large store fleets without new infrastructure. Consistent, image-based evidence also gives merchandising and brand teams an auditable record of on-shelf availability and compliance over time.
How to use it
- Upload an image: Drop any photo into the widget above to try planogram compliance for free, right on this page.
- Preview instant results: The model detects product package, price tag, shelf label, empty shelf slot, promotional display within seconds and returns an annotated image with structured predictions, so you can immediately see how well it works. Adjust the detection classes and re-run until the results fit your scenario.
- Like what you see?: With a free Roboflow account, you can deploy this workflow with the Serverless Cloud API, customize it in your workspace, or train RF-DETR on your own images for production accuracy.
- Get started for free: No code or training data required to begin: upload, review the results, and make it part of your stack in a single click.
Recommended model for planogram compliance
RF-DETR is state of the art for real-time object detection, instance segmentation, and keypoint detection, with higher accuracy and lower latency than comparable models, and it's Apache 2.0 licensed. When you're ready to build a production model for planogram compliance, fine-tune it on your own labeled images, then deploy to a Serverless Cloud API endpoint.
Not sure where to start?
Describe your Planogram Compliance project to Roboflow Agent.