Using Artificial Intelligence for the Automation of Knitting Patterns

TL;DR

This paper presents a deep learning approach using transfer learning and data augmentation to classify knitting patterns with high accuracy, demonstrating the viability of AI in a traditionally hobbyist domain.

Contribution

It introduces a novel application of Inception ResNet-V2 for knitting pattern classification, outperforming other pretrained models in accuracy and related metrics.

Findings

High accuracy, precision, recall, and F1 scores achieved

AUC scores mostly between 0.7 and 0.9 for classes

Proven effectiveness of transfer learning in knitting pattern recognition

Abstract

Knitting patterns are a crucial component in the creation and design of knitted materials. Traditionally, these patterns were taught informally, but thanks to advancements in technology, anyone interested in knitting can use the patterns as a guide to start knitting. Perhaps because knitting is mostly a hobby, with the exception of industrial manufacturing utilising specialised knitting machines, the use of Al in knitting is less widespread than its application in other fields. However, it is important to determine whether knitted pattern classification using an automated system is viable. In order to recognise and classify knitting patterns. Using data augmentation and a transfer learning technique, this study proposes a deep learning model. The Inception ResNet-V2 is the main feature extraction and classification algorithm used in the model. Metrics like accuracy, logarithmic loss, F1-score, precision, and recall score were used to evaluate the model. The model evaluation's findings demonstrate high model accuracy, precision, recall, and F1 score. In addition, the AUC score for majority of the classes was in the range (0.7-0.9). A comparative analysis was done using other pretrained models and a ResNet-50 model with transfer learning and the proposed model evaluation results surpassed all others. The major limitation for this project is time, as with more time, there might have been better accuracy over a larger number of epochs.