E-commerce Products Image Classification using EfficientNetB5 with Transfer Learning

Aim:

         The primary aim of this project is to develop a deep learning model for accurate product image classification in e-commerce platforms using the ResNetV2 architecture.

Abstract:

         In modern e-commerce, efficient product classification is crucial for managing vast inventories and improving search ability on platforms. While traditional methods of classification require manual feature extraction and complex multi-stage pipelines, deep learning offers a more streamlined and efficient approach. This project focuses on using the ResNetV2 architecture, a variant of residual networks, to perform end-to-end product image classification. By processing raw images directly through ResNetV2’s deep layers, the model can extract hierarchical features and classify them into predefined categories, eliminating the need for external feature extractors or classifiers. The model uses skip connections to mitigate the vanishing gradient problem, which is common in very deep networks, enabling more accurate and scalable classifications. This approach significantly reduces the complexity and time required for classification compared to traditional methods, making it well-suited for e-commerce platforms handling large datasets of diverse product images.

Introduction:

      The rapid growth of e-commerce platforms has led to a significant increase in the number of products available for online shopping, creating an urgent need for effective systems to categorize these products. Proper classification of product images is essential for improving search ability, inventory management, and customer experience. However, as product images continue to vary in terms of size, background, lighting, and other factors, traditional classification methods, which often rely on manual effort or rule-based algorithms, are becoming less effective. These methods struggle to handle the increasing volume and diversity of product images, especially as new categories and products are constantly introduced. Additionally, manually categorizing millions of images is time-consuming and prone to errors.

            To address these challenges, deep learning techniques offer a more efficient solution. These models can learn to automatically recognize features in raw images without the need for manual feature extraction or predefined rules. By processing product images end-to-end, deep learning models can classify them into predefined categories such as electronics, clothing, home goods, and more. This approach not only improves accuracy but also reduces the time and effort involved in categorization, making it ideal for large-scale e-commerce platforms.

Existing System:

           The existing system for product image classification typically relies on traditional Convolutional Neural Networks (CNNs), where multiple layers are used to extract features and classify images. These systems process images through convolutional layers that focus on detecting basic edges and textures in the early layers, while deeper layers capture more complex and abstract features. Pooling layers are used to reduce the dimensionality of the image and enhance the model’s ability to generalize. After feature extraction, fully connected layers are used to map the features to predefined categories. However, traditional CNN models often have a large number of parameters, leading to high computational costs and inefficiencies when handling large-scale datasets. Moreover, these systems are typically not optimized for scaling to varying input data complexities. The introduction of Efficient Net has addressed these challenges by incorporating a compound scaling formula that balances depth, width, and resolution for improved performance with fewer parameters. EfficientNet-B5, a larger version of Efficient Net, offers higher accuracy and is specifically designed to handle diverse input data efficiently. It captures features at multiple levels of abstraction and reduces parameter counts by using depth wise convolutional operations. Efficient Net’s architecture scales and shifts normalized values using fixed parameters, ensuring consistency and reusability during training. The model retains EfficientNet-B5’s structure, using a fully connected layer with 256 neurons to learn object-specific features, with the output layer containing neurons representing different product categories.

Disadvantages:

          Efficient Net offers a more efficient and accurate approach to image classification, it does have several limitations. One significant drawback is the complexity involved in tuning and scaling the model. Efficient Net uses a compound scaling method that requires balancing depth, width, and resolution, which can be a challenging and time-consuming process. Additionally, despite its efficiency, larger variants of Efficient Net, such as EfficientNet-B5, may still have longer training times, making them less suitable for tasks with tight time constraints. While the model is designed to reduce computational costs, it can still experience higher inference latency, particularly when deployed in real-time applications where speed is critical. Furthermore, Efficient Net can be prone to over fitting when applied to smaller datasets, necessitating the use of techniques like data augmentation or transfer learning, which can add to the complexity of model training. Another limitation is the limited interpretability of the model, making it difficult to understand the reasons behind specific predictions, which can be a challenge in fields where model transparency is essential. Additionally, Efficient Net heavily relies on proper image preprocessing, such as resizing and normalization, and deviations from this preprocessing can significantly degrade performance. Finally, for simpler image classification tasks with smaller datasets, Efficient Net may be over-engineered, as smaller and simpler models could provide faster and more efficient results.

Proposed System:

          This project utilizes the ResNetV2 architecture to tackle the challenges of large-scale product image classification in e-commerce. ResNetV2, an enhanced version of the ResNet model, incorporates advanced residual blocks to enable the training of deeper models by effectively mitigating the vanishing gradient problem. The system operates through a series of well-defined steps. First, raw product images are input into the model. These images undergo preprocessing, where they are resized to a uniform dimension, such as 224×224 pixels, and normalized to ensure consistency in input formatting. The preprocessed images are then processed by the ResNetV2 model, which uses multiple residual blocks to extract hierarchical features at various levels of abstraction. The extracted features are subsequently passed through a fully connected layer that classifies the images into one of the predefined product categories. Finally, the system outputs the predicted category with the highest probability, providing an efficient and accurate solution for product image classification in e-commerce applications.

Advantage:

             The proposed model offers several advantages that make it well-suited for large-scale product image classification in e-commerce. By integrating feature extraction and classification into a single, unified process, it eliminates the need for multiple models or steps, enabling end-to-end classification. Leveraging the advanced ResNetV2 architecture, the model can learn complex representations of images, resulting in improved classification accuracy compared to traditional systems. Its scalability allows it to efficiently handle large datasets and seamlessly adapt to new product categories as the e-commerce platform grows. The deep residual blocks enhance processing speed by addressing challenges like vanishing gradients, reducing both training and inference times. Additionally, the model simplifies the overall system by reducing computational overhead and complexity, as it manages both feature extraction and classification within a streamlined framework.