Train a TFlite model

Many machines have cameras through which they can monitor their environment. With machine leaning, you can train models on patterns within that visual data. You can collect data from the camera stream and label any patterns within the images.

If a camera is pointed at a food display, for example, you can label the image of the display with full or empty, or label items such as individual pizza_slices.

Using a model trained on such images, machines can make inferences about their environments. Your machines can then automatically trigger alerts or perform other actions. If a food display is empty, the machine could, for example, alert a supervisor to restock the display.

Common use cases for this are security, quality assurance, and food service applications.

Follow this guide to use your image data to train an ML model, so that your machine can make inferences about its environment.

Prerequisites

Train a machine learning (ML) model

Now that you have a dataset with your labeled images, you are ready to train a machine learning model.

1. Train an ML model

In the Viam app, navigate to your list of DATASETS and select the one you want to train on.

Click Train model and follow the prompts.

You can train a TFLite model using Built-in training.

Click Next steps.

2. Fill in the details for your ML model

Enter a name for your new model.

Select a Task Type:

• Single Label Classification: The resulting model predicts one of the selected labels or UNKNOWN per image. Select this if you only have one label on each image. Ensure that the dataset you are training on also contains unlabeled images.
• Multi Label Classification: The resulting model predicts one or more of the selected labels per image.
• Object Detection: The resulting model predicts either no detected objects or any number of object labels alongside their locations per image.

Select the labels you want to train your model on from the Labels section. Unselected labels will be ignored, and will not be part of the resulting model.

Click Train model.

3. Wait for your model to train

The model now starts training and you can follow its process on the TRAINING tab.

Once the model has finished training, it becomes visible on the MODELS tab.

You will receive an email when your model finishes training.

4. Debug your training job

From the TRAINING tab, click on your training job’s ID to see its logs.

Note

Your training script may output logs at the error level but still succeed.

You can also view your training jobs’ logs with the viam train logs command.

Test your ML model

Once your model has finished training, you can test it.

Ideally, you want your ML model to be able to work with a high level of confidence. As you test it, if you notice faulty predictions or confidence scores, you will need to adjust your dataset and retrain your model.

If you trained a classification model, you can test it with the following instructions.

1. Navigate to the DATA tab and click on the Images subtab.
2. Click on an image to open the side menu, and select the Actions tab.
3. In the Run model section, select your model and specify a confidence threshold.
4. Click Run model

If the results exceed the confidence threshold, the Run model section shows a label and the responding confidence threshold.

You can test both detection models and classifier models using the following resources together:

• a camera
• a tflite_cpu ML model with the model you just trained
• an mlmodel vision service using the tflite_cpu model

Next steps

Now your machine can make inferences about its environment. The next step is to deploy the ML model and then act or alert based on these inferences.

See the following tutorials for examples of using machine learning models to make your machine do things based on its inferences about its environment:

Helmet Monitor

Use computer vision to detect problems such as people not wearing safety gear and get email alerts.

Colored Object Follower

Instructions for detecting and following a colored object with a rover, like a SCUTTLE robot.

Smart Pet Feeder

Use a Raspberry Pi, a motor, and machine learning to build a smart pet feeder.