Algorithm 1 Object Classification and Localization Using Processed Image Data.

Require:  Zipped image dataset $D_{zip}$ Ensure:  Trained multi-output CNN model   1:Extract images from $D_{zip}$ and load as dataset D   2:Parse filenames to extract object types and coordinates, storing in DataFrame $df$   3:Preprocess images in D and extract labels, resulting in feature array X, type labels $y_{\mathrm{type}}$, and coordinate labels $y_{\mathrm{coords}}$   4:Split X, $y_{\mathrm{type}}$, and $y_{\mathrm{coords}}$ into training and testing sets   5:Convert $y_{\mathrm{type}}$ to one-hot encoded labels for each object type   6:Define CNN model with convolutional layers for feature extraction   7:Add classification and coordinate output layers   8:Compile model with loss functions for classification and coordinate regression   9:Train model on training data, using testing data for validation 10:Return trained model