Upload and Configure an RKNN Model
If you already have a .rknn model file (see Choose a Model Deployment Path), upload it to reCamera Pro and configure it via the Web UI.
Upload a model
- In the Web UI, go to AI Inference → Model Management.
- Click Upload Model.
- Drag and drop or select your
.rknnfile.
After uploading, the model appears in the Model Management list.
Model fields
| Field | Description |
|---|---|
| Model File | Filename on the device |
| Model Name | Display name |
| Framework | Runtime framework (RKNN) |
| Algorithm | Algorithm type, e.g. YOLO, nanodet |
| Type | Task type, e.g. Detection |
| Version | Model version |
| Size | File size |
| Operations | Configure or delete |
Configure the model
Each model has a Configure page for setting basic information, detection categories, and post-processing parameters.
| Configuration Item | Description |
|---|---|
| Model Name | Display name |
| Framework | Runtime framework, e.g. RKNN |
| Version | Model version |
| Type | Task type, e.g. Object Detection |
| Algorithm | Algorithm, e.g. YOLOv5 |
| Author | Author information |
| Description | Model description |
| Detection Categories | Names of the model's output categories |
| Post-processing Configuration | IOU, Confidence, max_obj |
Detection categories
Categories can be added manually or imported in bulk from a TXT file. After configuring categories, inference results are displayed and output using your category names.
Post-processing parameters
| Parameter | Description |
|---|---|
| IOU | Intersection over Union threshold for bounding box non-maximum suppression |
| Confidence | Object detection confidence threshold |
| max_obj | Maximum number of objects output per frame |
Click Save to apply.
Run the model
- Go to AI Inference → Inference Configuration.
- Enable inference, select your uploaded model as the Running Model, and set the inference frequency.
- Click Save Configuration.
Related pages
- Choose a Model Deployment Path
- SenseCraft ONNX-to-RKNN Conversion
- Configure Detection
- Validate Model Performance
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