EnglishAI Model Download, Debugging, and Deployment Guide
Choose a Model Deployment Workflow
Before deploying a local model on MaixCAM / MaixCAM-Pro / MaixCAM2, first identify the model source, target device, and deployment path. Choose the workflow that matches your current resources: do not retrain a model if a ready-made one already works; train first when you need custom classes; after obtaining a .pt file, export it to ONNX and prefer the online conversion platform to generate deployable model files. Manual command-line conversion is an advanced workflow for custom conversion parameters or deeper debugging.
| Goal | Recommended workflow | Documentation |
|---|---|---|
| Use built-in or ready-made models | Use built-in models first. For more resolutions or class sets, choose the matching device platform in MaixHub Model Zoo. MaixCAM / MaixCAM-Pro model packages usually include .mud and .cvimodel files, while MaixCAM2 model packages usually include .mud and .axmodel files. Place the files from the same package in the same directory on the device |
Model and dataset sources |
| Train a custom recognition target | Use MaixHub online training to complete data collection, annotation, training, and deployment | MaixHub online training |
| Train a YOLO model offline | Prepare the dataset and train the YOLO model on a computer. Use the recommended Ultralytics versions for training and export. After training produces a .pt file, export it to an ONNX model with a fixed input size |
YOLO model offline training |
| Convert a YOLO model online | Upload the ONNX model and a ZIP archive containing 20-100 calibration images. The platform generates .mud + .cvimodel for MaixCAM / MaixCAM-Pro, or .mud + .axmodel for MaixCAM2 |
Online graphical model conversion platform |
| Convert an ONNX model manually | Use command-line conversion when you need custom output nodes, conversion parameters, toolchain configuration, or when online conversion does not meet the requirement | MaixCAM2 model conversion / MaixCAM model conversion / Trim ONNX model output nodes |
| Self-host the conversion platform | Use this when uploading models to the online service is not suitable, or when you need intranet deployment, a self-managed conversion server, or platform-source debugging | Self-hosted graphical model conversion platform |
| Port a new AI model | Use this for model types not yet wrapped by MaixPy, where you need to handle preprocessing, postprocessing, MUD description, and inference code yourself | Port a new AI model |
After choosing your workflow, continue with the corresponding document.
