brails.processors.occupancy_classifier.occupancy_classifier module

Class object to use and retrain the occupancy classifier model.

class brails.processors.occupancy_classifier.occupancy_classifier.OccupancyClassifier(input_data: dict | None = None)

Bases: ImageClassifier

Class for predicting building occupancy types from street-level imagery.

The OccupancyClassifier classifier attempts to predict occupancy types of buildings as 1 of 2 types: Residential or Other. The classification is done by the ImageClassifier class. This class is a wrapper that just sets up the inputs for that class.

Variables

model_path (str):

Path to the model file

Methods:

predict(ImageSet):

To return the predictions for the set of images provided

predict(images: ImageSet) → dict

Predict building occupancy type.

Args:

images (ImageSet):

ImageSet containing the set of images for which a prediction is required

Returns:

dict:

Dictionary with keys set to image names in ImageSet.images and values set to the predicted occupancy types

retrain(data_dir: str, batch_size: int = 8, nepochs: int = 100, plot_loss: bool = True)

Retrain the current occupancy model using new data.

Args:

data_dir (str):

Path to the directory containing the new training data. The directory should be structured with subdirectories for each class, each containing the respective images.

batch_size (int, optional, default=8):

The number of samples per batch used during training.

nepochs (int, optional, default=100):

The number of epochs for which the model will be retrained.

plot_loss (bool, optional, default=True):

If True, displays a plot of the training loss over epochs.

Notes:

• This method uses an instance of ImageClassifier to perform the

  retraining.

• The current model is loaded from self.modelPath and updated with

  the new data.

• Ensure data_dir is properly prepared and contains sufficient data

  for effective retraining.

Example:

retrainer = OccupancyClassifier({‘modelPath’:’path/to/current/model’}) retrainer.retrain(data_dir=”path/to/new/data”,

batch_size=16, nepochs=50, plot_loss=True)