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.

翻译：编织图案是针织材料创作与设计中的关键组成部分。传统上，这些图案通过非正式方式传授，但随着技术进步，任何对编织感兴趣的人都可以利用图案作为指南开始编织。或许因为编织主要是一种爱好（除了使用专业编织机进行工业制造外），人工智能在编织领域的应用不如在其他领域那样广泛。然而，确定是否可以通过自动化系统识别和分类编织图案具有重要意义。为了识别和分类编织图案，本研究提出了一种深度学习模型。该模型使用数据增强和迁移学习技术，主要特征提取和分类算法为Inception ResNet-V2。模型评估采用了准确率、对数损失、F1分数、精确率和召回率等指标。评估结果表明，模型在准确率、精确率、召回率和F1分数上表现优异。此外，大多数类别的AUC分数在0.7-0.9之间。通过与其他预训练模型（包括使用迁移学习的ResNet-50模型）进行对比分析，所提模型的评估结果超越了所有其他模型。本项目的主要限制是时间因素——若拥有更多时间，模型可能在更多训练周期中实现更高的准确率。