Build with RF-DETR
Build an Assembly Verification App Free with RF-DETR
Check every circuit board for missing capacitors, resistors, and diodes and get a pass or fail result. Label images, train RF-DETR free, and deploy it as a workflow.
- No credit card
- No ML team required
- Edge, on-prem or API
Build an Assembly Verification App with Roboflow’s Free RF-DETR
In short
An assembly verification app uses computer vision to check that every part of an assembly is present and in the right place, then fails any unit where a component is missing. The model to train for it is RF-DETR, Roboflow's open-source real-time detection model. With Roboflow you label your images, train RF-DETR for free, and deploy it as a workflow that returns an annotated image, a count of missing components, and a PASS or FAIL result.
How it works
The workflow behind the assembly verification app
Use this Assembly Verification with RF-DETR workflow: each image of a circuit board goes to an RF-DETR model that checks every component location, then through a confidence filter and into two tracks. One counts the missing components and turns the count into PASS or FAIL, the other draws boxes and labels, and both meet where the verdict is written onto the image. Every block is editable in the low-code builder, and the Roboflow Agent can build it from a description.
- output_image
- inspection_result
- missing_component_count
- predictions
How to build an assembly verification app with RF-DETR
Open the workflow and add your images
Click "Use this free template" to copy the workflow into your Roboflow workspace. Upload images of your assembly from the station camera, including complete units and examples of each part that can go missing.
Label parts and train RF-DETR
Use Auto Label to box each component location, marking it as a good part or as missing, then review and correct the results. Train an RF-DETR model and select it in the detect_components block. RF-DETR fine-tunes well on small custom datasets, so you can start with the examples you have.
Tune the threshold and the pass/fail rule
Set the threshold in filter_by_confidence so missing parts are caught without failing good units. Update the class list in count_missing_components to match the parts on your assembly.
Deploy and connect it to your line
Deploy the workflow as a Serverless Cloud API, or on your own hardware through Inference. Each run returns output_image, inspection_result, missing_component_count, and predictions for your reject station or quality records.
Roboflow MCP
Build it from your AI assistant with the Roboflow MCP
Connect the Roboflow MCP Server to Claude, Cursor, or any MCP-compatible assistant and build the whole assembly verification app in natural language, without leaving your editor.
Ask an assistant like Codex or ChatGPT to search Roboflow Universe for assembly and missing component datasets, upload and auto-label your station images, and train an RF-DETR model to check each part. It can then create and run the assembly verification workflow on test images and deploy it to your devices.
Paste it into your coding agent and it walks you through the rest. Setting up by hand? The server is https://mcp.roboflow.com/mcp.
Open the Roboflow listing, select Connect, then sign in to Roboflow. Available on desktop, web and mobile.
Run these commands in your terminal, then sign in to Roboflow. Claude Code CLI has no documented browser install link.
Opens the Roboflow plugin listing. Sign in if prompted and confirm the install on Cursor to add MCP and skills.
Opens the Roboflow plugin listing. Install the plugin and sign in to Roboflow when prompted to connect your account.
Opens the Roboflow plugin listing shared by ChatGPT and Codex. Install the plugin and sign in to Roboflow when prompted to connect your account.
Then run /mcp auth roboflow in Gemini CLI to sign in. Gemini CLI has no documented browser install link.
What your assembly verification app can do
Trained on your images, an RF-DETR model handles the specific, hard-to-catch cases that matter on your line:
- Spot a missing capacitor, resistor, or diode on a crowded board
- Tell an empty component location apart from a good part
- Name which component is missing, not just that something is wrong
- Check every location on every unit, not a sample
- Catch missing parts before the unit moves to the next station
- Adapt to new assemblies by labeling a few examples
Want to customize your assembly verification app?
Describe the assembly verification app you want in plain English: your product, the parts that must be present, and what should happen when one is missing. The Roboflow Agent finds the data, trains an RF-DETR model, and assembles the workflow for you.
Open source
Roboflow’s RF-DETR is open source, Apache-2.0, and free to use
RF-DETR is an open-source, Apache-2.0 licensed real-time object detection model developed by Roboflow. The rfdetr Python package and most RF-DETR models are free to use, including in commercial products, and you can train and run everything on your own hardware. No account or lock-in required.
Apache-2.0 and free
The rfdetr package, Nano to Large detection, every segmentation size and Keypoint are Apache-2.0 licensed and free to use, including commercially.
Install and train locally
pip install rfdetr on Python 3.10+. Fine-tune on your own data in a few lines and run it entirely on your machine.
State of the art on COCO
RF-DETR is a real-time detector that is SOTA on COCO. RF-DETR-Large reaches 56.5 AP at 6.8 ms on an NVIDIA T4, and uses a DINOv2 backbone.
Deploy anywhere
Export to ONNX or TensorRT and run on GPU, CPU, or NVIDIA Jetson at the edge, or serve it through the Roboflow hosted API.
| Models | Licence |
|---|---|
| Detection: Nano, Small, Medium, Large | Apache-2.0 |
| Segmentation: every size, Nano to 2XLarge | Apache-2.0 |
| Keypoint (preview) | Apache-2.0 |
| Detection: Atto, Femto, Pico, XL, 2XL (rfdetr[plus]) | PML 1.0: commercial use included on Roboflow; commercial self-hosting needs the Enterprise add-on |
RF-DETR vs. other free detectors
An assembly verification app has to check many small, closely packed parts on every unit at line speed, so the model needs to be both accurate and fast. Here is how RF-DETR, fine-tuned on your own assembly images, compares for real-time inspection.
| Model | Tasks | Best for | License |
|---|---|---|---|
| RF-DETR | Detection, segmentation (keypoints in preview) | Real-time detection you fine-tune on your own data, one model across tasks | Apache-2.0 (Nano to Large, all segmentation); PML 1.0 (Atto, Femto, Pico, XL, 2XL) |
| D-FINE | Detection | Precision localization when you do not need to fine-tune | Apache-2.0 |
| YOLO | Detection, segmentation, pose | Fast CNN-based detection with a large ecosystem | AGPL-3.0 (commercial license required for closed-source use) |
| GroundingDINO | Open-vocabulary detection | Finding objects by text prompt with no training, not real-time | Apache-2.0 |
| SAM 2 / SAM 3 | Segmentation | Pixel-precise masks; SAM 3 adds text-prompted segmentation | Apache-2.0 (SAM 2), SAM License (SAM 3) |
| Florence-2 | Vision-language | Reading, captioning, and OCR on images | MIT |
The takeaway for this app: RF-DETR runs in real time, so it can check every unit as it passes. It fine-tunes well on small custom datasets and is open source, so you can train on your own assemblies and run the model wherever you need it.
Build your assembly verification app free today
Label, train, and deploy your assembly verification app in one place.