Training

Lab setup

First, make sure you have completed the initial setup.

If you are part of a course

  1. Open Terminal. Run the update command to make sure you have the latest code. 
    $ mwc update
  2. Move to this lab's directory. 
    $ cd ~/Desktop/making_with_code/mwc2/unit3/lab_training
  3. Enter this lab's shell environment. 
    $ poetry shell

If you are working on your own

  1. Move to your MWC directory. 
    $ cd ~/Desktop/making_with_code
  2. Get a copy of this lab's materials. 
    git clone https://git.makingwithcode.org/mwc/lab_training.git

Object classification

In the models lab, we used textual features to classify text. We can do the same thing with images--but what would the features be? All we start with are pixel values. In the models lab, we used a neural net to create higher-order features; we'll do the same thing here. Video is just a bunch of images...

The higher-order features here are things like edges, lines, and object boundaries. The human visual system does some similar things!

A pre-trained model

Use the YOLO model and Streamlit to detect objects in images. Docs

Training