Model

 

Load Model:

Tflite provides us loadModel method to load our model. It takes two values model file path and labels file path.

Future loadModel() async {
  Tflite.close();
  await Tflite.loadModel(
      model: "assets/ssd_mobilenet.tflite",
      labels: "assets/ssd_mobilenet.txt");
}

Run Model:

In this method, we will run the model using Tflite. Here we are using the live stream of the image so we will have to use the detectObjectOnFrame method to run our model.

runModel() async {
  recognitionsList = await Tflite.detectObjectOnFrame(
    bytesList: cameraImage.planes.map((plane) {
      return plane.bytes;
    }).toList(),
    imageHeight: cameraImage.height,
    imageWidth: cameraImage.width,
    imageMean: 127.5,
    imageStd: 127.5,
    numResultsPerClass: 1,
    threshold: 0.4,
  );

  setState(() {
    cameraImage;
  });
}