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.

通过数据增强和迁移学习技术，本研究提出了一种深度学习模型来识别和分类编织图案，模型使用 Inception ResNet-V2 作为主要特征提取和分类算法，并利用准确度、对数损失、F1 分数、精确度和召回率等度量指标对模型进行评估，结果显示高模型准确度、精确度、召回率和 F1 分数，与其他预训练模型和 ResNet-50 模型相比，提出的模型评估结果更好。