Similar images in online fashion catalog

Finding the perfect dress can be a challenging task, especially when shopping online with a vast selection of options. To make the process easier, we have developed a procedure to find similar dress apparel from an online catalog using Bootstrap Your Own Latent (BYOL), implemented in PyTorch.

Our aim is to provide a tool that can help customers find dresses that are similar in style, color, and other attributes to a reference dress. BYOL is a self-supervised learning approach that allows us to learn useful representations of the data without the need for labeled examples. By using BYOL, our tool is able to learn the patterns and trends in the data and make accurate recommendations for similar dresses.

To implement the tool, we made the following assumptions:

The online catalog contains accurate and sufficient data on the images and attributes of the dresses, such as style, color, and material.

The images and attributes of the dresses can be used to learn useful representations of the data that capture the patterns and trends in the data.

BYOL, implemented in PyTorch, can effectively learn the representations and make accurate recommendations for similar dresses.

To develop the tool, we followed the following steps:

Data preparation: We prepared the data for BYOL by extracting the images and attributes of the dresses from the online catalog.

Model selection: We selected a suitable architecture for BYOL, such as a convolutional neural network (CNN), based on the characteristics of the data.

Model training: We trained the selected model using BYOL in PyTorch, using the images and attributes of the dresses as inputs. The model learned the representations of the data by predicting the images and attributes of the dresses using the learned representations.

Recommendation: We used the trained model to recommend similar dresses from the online catalog based on the input data.

With the use of BYOL, implemented in PyTorch, our tool is able to learn the patterns and trends in the data and make accurate recommendations for similar dresses.