15.1.17. The dataset editor¶

Training a custom classifier starts with a labelled dataset – hundreds of images of each thing the model should recognise, captured by the camera that will run it, sorted into one folder per class. The dataset editor is the IDE’s capture workflow for building one.

Tools → Dataset Editor → New Dataset asks for a folder to build the dataset in – warning that a non-empty folder’s contents will be deleted – and sets it up: a file browser pane docks on the left of the main window, and a capture script (dataset_capture_script.py) opens in the editor. The script is a plain capture loop, and it is meant to be edited – apply the same lens correction, cropping, or filtering the deployed application will use, so the model trains on the images it will actually see. Open Dataset reopens an existing dataset folder later, and Close Dataset puts the window back to normal.

Screenshot needed

figures/dataset-editor.png – the main window with a dataset open: the dataset pane on the left showing two or three class folders with a few captured images, the image preview below it, and the capture script in the editor. Crop to show the dataset pane and part of the editor.

15.1.17.1. Capturing¶

While a dataset is open, two buttons join the toolbar on the window’s left edge, below the run controls. New Class Folder (Ctrl+Shift+N) creates a class – one per category the model should learn, named for the label. With the capture script running and a class folder selected, Capture Data (Ctrl+Shift+S) saves the current frame buffer image into that class, and the preview pane under the file browser shows each capture as it lands.

The capture rhythm is: select a class, point the camera at an example of it, capture; move the object, vary the angle, the distance, the background, the lighting, capture again – variation in the dataset is what buys robustness in the model. Repeat per class, including a background class of scenes containing none of the targets if the application needs to know when nothing is there.

15.1.17.2. Exporting and training¶

The Export submenu sends the finished dataset to training. Export Dataset to Zip File packs it into an archive with class-prefixed file names – the neutral format every training service and framework accepts. For Edge Impulse, the IDE integrates directly: log in to an Edge Impulse account from the same submenu, and Upload to Edge Impulse Project pushes the dataset straight into a project (an API-key upload exists for accounts where the email-and-password login is not an option). Train there, export the model, and the NPU converter makes it camera-ready when the board needs it.