13.8.5. Deploying to the camera

The trained model lives on Roboflow’s servers. Getting it onto the camera takes one download, then the same steps as loading any other model.

13.8.5.1. Downloading the weights

On the Deployments page, choose Deploy to 3rd Party Platforms and select the OpenMV tab. It downloads the model’s weights as a single integer-quantized .tflite file, named after the project and version – the int8 format the camera’s TFLite engine runs.

The OpenMV deploy target – Download Files saves the camera-ready .tflite weights.

13.8.5.2. Loading it on the camera

Add the .tflite file to the camera with the IDE’s ROMFS editor, which converts it for the board’s NPU when the board has one, then load it in a script with ml.Model. Models also run from the camera’s flash drive – copy the file over and point ml.Model at the path – but ROMFS is the better home: models there execute straight from flash without a RAM copy.

A detection model’s raw output is a tensor of box coordinates and class scores that still needs decoding. Roboflow’s YOLO-family detectors decode with the post-processors the camera ships in ml.postprocessing.ultralytics, so a few lines wire the model to its decoder and you have boxes and labels.

See also

The machine learning chapter for running models with the ml module – loading, the inference pipeline, and the walkthrough of decoding YOLO-family output.