I developed a fashion recommendation system that utilizes the power of transfer learning using ResNet-50 architecture along with Annoy an optimized K-Nearest Neighbours algorithm to deliver personalized recommendations based on user input. By performing feature extraction on a large dataset of over 45,000 images using transfer learning with ResNet-50, I was able to effectively analyze the image data. To identify the top 5 closest matches to a user's input, I implemented a similarity search approach using K-Nearest Neighbours, providing personalized fashion recommendations. The system is user-friendly and intuitive, and allows for accurate and effective analysis of image data.
This recommendation system showcases the versatility and power of transfer learning, similarity search, and convolutional neural networks (CNNs), providing a solid foundation for building larger and more comprehensive recommendation systems
In this project, we propose a model that uses Convolutional Neural Network and the Nearest neighbour backed recommender. As shown in the figure Initially, the neural networks are trained and then an inventory is selected for generating recommendations and a database is created for the items in inventory. The nearest neighbour’s algorithm is used to find the most relevant products based on the input image and recommendations are generated.
To generate recommendations, our proposed approach uses Sklearn Nearest neighbours . This allows us to find the nearest neighbours for the given input image. The similarity measure used in this Project is the Cosine Similarity measure. The top 5 recommendations are extracted from the database and their images are displayed.
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Convolutional Neural Network is a specialized neural network designed for visual data, such as images & videos. But CNNs also work well for non-image data (especially in NLP & text classification).
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Its concept is similar to that of a vanilla neural network (multilayer perceptron) – It follows the same general principle of forwarding & backward propagation.
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Once the data is pre-processed, the neural networks are trained, utilizing transfer learning from ResNet50. More additional layers are added in the last layers that replace the architecture and weights from ResNet50 in order to fine-tune the network model to serve the current issue. The figure shows the ResNet50 architecture.
The images from Kaggle Fashion Product Images Dataset. The inventory is then run through the neural networks to classify and generate embeddings and the output is then used to generate recommendations.
The concept of Transfer learning is used to overcome the issues of the small size Fashion dataset. Therefore we pre-train the classification models on the DeepFashion dataset that consists of 44,441 garment images. The networks are trained and validated on the dataset taken. The training results show a great accuracy of the model with low error, loss and good f-score.
Use pip to install the requirements.
pip install -r requirements.txtTo run the web server, simply execute streamlit with the main recommender app:
streamlit run main.py-
OpenCV - Open Source Computer Vision and Machine Learning software library
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Tensorflow - TensorFlow is an end-to-end open source platform for machine learning.
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Tqdm - tqdm is a Python library that allows you to output a smart progress bar by wrapping around any iterable.
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streamlit - Streamlit is an open-source app framework for Machine Learning and Data Science teams. Create beautiful data apps in hours, not weeks.
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pandas - pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.
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Pillow - PIL is the Python Imaging Library by Fredrik Lundh and Contributors.
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scikit-learn - Scikit-learn is a free software machine learning library for the Python programming language.
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opencv-python - OpenCV is a huge open-source library for computer vision, machine learning, and image processing.
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Had very basic knowledge of Deep Learning before the Microsoft Engage Program's qualification announcement. Spentquality time on learning the new concepts attached to Deep Learning and then began the design-build process of this project.
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Had to learn streamlit for creating graphical UI.
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Setting the dependencies with proper version is the most critical.
In this project, we have presented a novel framework for fashion recommendation that is driven by data, visually related and simple effective recommendation systems for generating fashion product images. The proposed approach uses a two-stage phase. Initially, our proposed approach extracts the features of the image using CNN classifier ie., for instance allowing the customers to upload any random fashion image from any E-commerce website and later generating similar images to the uploaded image based on the features and texture of the input image. It is imperative that such research goes forward to facilitate greater recommendation accuracy and improve the overall experience of fashion exploration for direct and indirect consumers alike.