Build Instructions

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• 1

  Prepare Data / Photos

  In this project we will use a smartphone camera to capture the images for data collection for ease of use. Take picture from above in different positions with backgrounds of varying angles lighting condition to ensure that the model can work under slightly different conditions (to prevent overfitting). FYI, lighting and object size are crucial aspect to ensure the performance of this model.

  Note: Keep the size of the objects similar in size in the pictures. Significant difference in object size will confuse the FOMO (Edge Impulse Faster Objects More Objects) algorithm.

• 2

  Data acquisition, and labelling

  Open studio.edgeimpulse.com , login (or create an account first) then create a new project.

  Choose Images project option, then Classify Multiple Objects. In Dashboard > Project Info, choose Bounding Boxes for labelling method and Nicla Vision for target device. Then in Data acquisition, click on Upload Data tab, choose your photo files, auto split, then click Begin upload.

  Click on Labelling queue tab then start drag a box around an object and label it (person) and Save. Repeat.. until all images labelled.

  Make sure that the ratio between Training and  Test data is ideal, around 80/20.

• 3

  Training and building model using FOMO Object Detection

  Once you have the dataset ready, go to Create Impulse and set 96 x 96 as image width - height (this help in keeping the ML model small in memory size). Then choose Fit shortest axis, and choose Image and Object Detection as learning blocks.

  Go to Image parameter section, select color depth as Grayscale then press Save parameters. Then click on Generate and navigate to Object Detection section, and leave training setting for Neural Network as it is — in our case is quite balanced pre-trained model, then we choose FOMO (MobileNet V2 0.35). Train the  model by press the Start training.. and you can see the progress.

  If everything is OK, then we can test the model, go to Model Testing section and click Classify all. Our result is above 90%, then we can move on to the next step — deployment.

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