Build with RF-DETR
Build a Food Quality Inspection App Free with RF-DETR
Grade produce on the line as fresh, at risk, or rotten and pull bad items before they ship. 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 a Food Quality Inspection App with Roboflow’s Free RF-DETR
In short
A food quality inspection app uses computer vision to check each item on a processing or packing line and grade it, for example sorting tomatoes into fresh, at risk, and rotten, then flags anything that should be pulled. 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 defect count, and a PASS or FAIL result.
How it works
The workflow behind the food quality inspection app
Use this Food Quality Inspection with RF-DETR workflow: each image goes to an RF-DETR model that grades every tomato as fresh, at risk, or rotten, then through a per-class confidence filter and into two tracks. One counts the at-risk and rotten items and turns the count into PASS or FAIL, the other draws boxes and grade 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
- defect_count
- predictions
How to build a food quality inspection 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 produce on the line, taken with the camera and lighting you will use, covering every grade you sort into.
Label grades and train RF-DETR
Use Auto Label to box each item as fresh, at risk, or rotten, or with your own grades and defect types, then review and correct the results. Train an RF-DETR model and select it in the detect_produce block. RF-DETR fine-tunes well on small custom datasets, so you can start with the images you have.
Tune the thresholds and the pass/fail rule
Set the per-grade thresholds in filter_by_confidence. Change count_defects or pass_fail_result if, for example, at-risk items should go to a separate lane instead of failing the batch.
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, defect_count, and predictions for your sorter or quality system to act on.
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 food quality inspection app in natural language, without leaving your editor.
Ask an assistant like Codex or ChatGPT to search Roboflow Universe for produce quality datasets, upload and auto-label your line images, and train an RF-DETR model on fresh, at risk, and rotten classes. It can then create and run the food quality inspection 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 food quality inspection app can do
Trained on your images, an RF-DETR model handles the specific, hard-to-catch cases that matter on your line:
- Catch early soft spots and discoloration before produce turns rotten
- Tell at-risk produce apart from fresh items with natural color variation
- Grade every item when produce sits touching or overlapping on the belt
- Keep grading accurately as lighting and produce size vary
- Spot rotten items mixed into an otherwise fresh batch
- Inspect every item at line speed
Want to customize your food quality inspection app?
Describe the food quality inspection app you want in plain English: the product, the grades or defects, and what should be pulled. 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
A food quality inspection app has to grade every item on a moving line, often with small, subtle differences between fresh and at-risk produce, so the model needs to be both accurate and fast. Here is how RF-DETR, fine-tuned on your own produce 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 keeps up with produce moving down a line. It fine-tunes well on small custom datasets and is open source, so you can train on your own products and grades and run the model wherever you need it.
Build your food quality inspection app free today
Label, train, and deploy your food quality inspection app in one place.