This week, I focused on developing the furniture classification component of our project, which involves a multi-label image classification model. This model’s objective is to analyze images of rooms and identify the presence of specific furniture items from a predefined list. I downloaded a dataset of room images that will be used to train the model and a .csv file containing the names of the training images and their corresponding true labels, which are essential for training and validating the model’s accuracy. I began constructing the model architecture using Keras.
Currently, the classification part of the project is behind schedule. The initial steps of creating the model architecture and organizing the training data have been completed, but there’s still significant work to be done in terms of training and testing the model. To get back on track, I plan to begin the process of training the model with the downloaded image dataset and evaluating its performance using the true labels from the .csv file. I aim to reach a point where the model can be tested to assess its effectiveness in classifying furniture within room images accurately.