Build with RF-DETR
Build a Package Damage Detection App Free with RF-DETR
Catch crushed, dented, and torn boxes on the conveyor or at the dock before they ship or get signed for. 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 Package Damage Detection App with Roboflow’s Free RF-DETR
In short
A package damage detection app uses computer vision to inspect every box on a conveyor or at a dock and flag the ones that are crushed, dented, or torn, so damage is caught before a package ships or a delivery is accepted. 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 damaged boxes, and a PASS or FAIL result.
How it works
The workflow behind the package damage detection app
Use this Package Damage Detection with RF-DETR workflow: each image goes to an RF-DETR model that finds every box and labels it damaged or intact, then through a per-class confidence filter and into two tracks. One counts the damaged boxes 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
- damaged_count
- predictions
How to build a package damage detection 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 from the cameras over your conveyor or dock, including both intact boxes and every kind of damage you see.
Label damage and train RF-DETR
Use Auto Label to box each package as damaged or intact, then review and correct the results, looking closely at crushed corners and torn seams. Train an RF-DETR model and select it in the detect_packages block. RF-DETR fine-tunes well on small custom datasets, so you can start with the examples you have.
Tune the thresholds and the pass/fail rule
Set the thresholds in filter_by_confidence for damaged and intact boxes. Change the rule in pass_fail_result if, for example, minor damage should be flagged for review instead of failing the shipment.
Deploy and connect it to your operation
Deploy the workflow as a Serverless Cloud API, or on your own hardware through Inference, to run on live camera streams. Each run returns output_image, inspection_result, damaged_count, and predictions for your warehouse system, claims process, or reject lane 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 package damage detection app in natural language, without leaving your editor.
Ask an assistant like Codex or ChatGPT to search Roboflow Universe for package damage datasets, upload and auto-label your conveyor or dock images, and train an RF-DETR model on damaged and intact box classes. It can then create and run the package damage detection 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 package damage detection app can do
Trained on your images, an RF-DETR model handles the specific, hard-to-catch cases that matter on your dock:
- Spot crushed corners and dented sides at belt speed
- Catch torn flaps and split tape seams before contents spill
- Tell real damage apart from scuffs, tape, and shipping labels
- Inspect boxes of every size, touching or close together on the belt
- Keep detecting as lighting changes at dock doors
- Check every box at receiving before a delivery is signed for
Want to customize your package damage detection app?
Describe the package damage detection app you want in plain English: where your cameras are, the damage you need to catch, and what should happen to a damaged box. 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 package damage detection app has to inspect every box on a moving belt and spot damage that is often small next to the box itself, so the model needs to be both accurate and fast. Here is how RF-DETR, fine-tuned on your own parcel 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 packages moving down a conveyor. It fine-tunes well on small custom datasets and is open source, so you can train on your own boxes and damage types and run the model wherever your cameras are.
Build your package damage detection app free today
Label, train, and deploy your package damage detection app in one place.