# Blind People: Clothing Category Classification and Stain Detection Using Transfer Learning
## 2 Related Work  
In the last few years, deep learning techniques have arisen as a great method to solve problems in computer vision, such as image classification, object detection, face recognition and language processing, where convolutional neural networks (CNNs) play an imperative role [14]. The convolutional neural networks (CNNs) have exhibited excellent results and advances in image recognition since 2012 (CNNs) [15; 16], when AlexNet [17] was introduced in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) [18; 19]. The ImageNet competition consists of evaluating several algorithms for large-scale object detection and image classification, allowing researchers to compare detection across a variety of object classes. In recent years, several CNNs have been presented, such as VGG [20], GoogLeNet [21], SqueezeNet [22], Inception [23], ResNet [24], ShuffleNet [25], MobileNet [26; 27], EfficientNet [28] and RegNet [29], among others, using these networks for different image classification problems. In line with this premise, some researchers turned to the fashion world, making use of the more recent advances in computer vision to explore diverse areas such as fashion detection, fashion analysis and fashion recommendations, achieving promising results [30]. Based on the scope of the conducted research, only fashion classification is covered in this work. A quick literature survey allowed the identification of several works that have attempted to handle the classification of fashion images. Most of the authors evaluate their models based on top \(k\) accuracy regarding clothing attribute recognition, normally with top 3 and top 5 scores, which means that the correct label is among the top \(k\) predicted labels. The research of [PERSON] et al. [31] presented a network for describing people based on fine-grained clothing attributes, with an accuracy of 48.32%. Similarly, [PERSON] et al. [32] introduced FashionNet, which learns clothing features by jointly predicting clothing attributes and landmarks. Predicted landmarks are used to pool or gate the learned feature maps. The authors reported a top 3 classification accuracy score of 93.01% and a top 5 score of 97.01%. Another method to detect fashion items in a given image using deep convolutional neural networks (CNN), with a mean Average Precision (mAP) of 31.1%, was performed by [PERSON] et al. [33]. Likewise, [PERSON] et al. [34] proposed another method based on weakly supervised learning for classifying e-commerce products, presenting a top 3 category accuracy score of 86.30% and a top 5 accuracy score of 92.80%. Later, [PERSON] et al. [35] proposed a fashion network to address fashion landmark detection and category classification with the introduction of intermediate attention layers for a better enhancement in clothing features, category classification and attribute estimation. In their work, accuracy scores of 90.99% and 95.75% were reported for top 3 and top 5, respectively. In another study by [PERSON] et al. [36], the authors presented a two-stream convolutional neural with one branch dedicated to landmark detection and the other one to category and attribute classification, allowing the model to learn the correlations among multiple tasks and consequently achieve improvements in the results. Accuracy scores of 93.01% and 97.01% for top 3 and top 5 were reported, respectively. A novel fashion classification model proposed by [PERSON] et al. [37] improves the performance by taking into account the hierarchical dependences between class labels, reaching accuracy of 91.24% and 95.68% in top 3 and top 5, respectively. A multitask deep learning architecture was then proposed by [PERSON] et al. [38] that groups similar tasks and promotes the creation of separated branches for unrelated tasks, with accuracy results of 83.24% and 90.39% for top 3 and top 5, respectively. [PERSON] and [PERSON] [39] proposed a Hierarchical Convolutional Neural Network (H-CNN) for fashion apparel classification.