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and more..
In conclusion, the analysis results demonstrate the success of our image recognition-based shopping project in providing a seamless and personalized shopping experience to users. The high accuracy of the image recognition system and the effectiveness of the recommendation system contribute to a more efficient and satisfying shopping journey for users while offering valuable insights to retailers for business optimization.
Picture-to-Product: An image recognition-based shopping experience
Project Advisor: Ishie Eswar
Introduction
This project aims to provide customers with a seamless and personalised shopping experience by using image recognition technology to identify products quickly and accurately. The existing shopping experiences can be frustrating and time-consuming, and customers often need to go through multiple shelves and compare prices, leading to a negative shopping experience. However, the Picture-to-Product experience aims to solve this problem by using advanced machine learning models to analyse user-submitted images and identify the product.
Methodology
Analysis and Results
Summary/Conclusions
Key References
Acknowledgements
The image recognition-based shopping project uses AI for accurate object identification and personalized recommendations. It offers a user-friendly interface and seamless integration for an efficient shopping experience, benefiting both users and retailers.
With seamless integration into online shopping platforms, users can easily purchase recognized items, saving time and effort. Meanwhile, retailers benefit from valuable insights into customer preferences, allowing them to optimize product offerings and marketing strategies.
Our sincere appreciation goes out to OpenAI for their exceptional work in AI, laying the foundation for our language model and influencing our image recognition-based shopping experience. Special appreciation to the developers of pioneering image recognition technologies like Google Lens, Pinterest Lens, Bixby Vision, and Amazon Flow, inspiring and guiding us in integrating this technology into our platform, and offering valuable insights for continuous enhancement. | |
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The Picture-to-Product system benefits both customers and retailers. By analyzing user interactions and data, retailers gain valuable insights into customer preferences, popular products, and emerging trends, enabling them to optimize product offerings, marketing strategies, and inventory management. This user-friendly and secure project has the potential to revolutionize the future of retail and enhance customer satisfaction, ultimately boosting revenue.
AI Model Selection
In the initial phase, we researched and evaluated various AI-based image recognition models. After careful consideration, we selected a state-of-the-art deep learning model with high accuracy and efficiency for object identification.
Model Training
The selected AI model underwent extensive training using the annotated dataset. We fine-tuned the model's parameters and utilized transfer learning techniques to optimize its performance for our specific shopping use case.
User Interface Development
We designed an intuitive and user-friendly interface to facilitate the image recognition process. The interface allows users to easily upload images and receive real-time product identifications and personalized recommendations.
Performance Metrics
For the evaluation of our image recognition and recommendation systems, we used the following key performance metrics:
Data Collection and Preprocessing�To train the AI model effectively, we gathered a diverse and extensive dataset of product images from various categories. This dataset was carefully curated, annotated, and preprocessed to ensure the model's robustness and generalization.
Methodology
Recommendation System Implementation
We developed a personalized recommendation system that analyzes user behavior and purchase history. This system suggests relevant products based on individual preferences, further enhancing the shopping experience.
Deployment and Continuous Improvement
After successful testing and refinement, we deployed the image recognition-based shopping project to the target online shopping platforms. We continue to monitor its performance and gather user feedback for continuous improvement and updates.
By following this methodology, we have created an innovative and efficient shopping solution that benefits users with a seamless experience and retailers with valuable insights for business growth and optimization. The integration of AI-based image recognition and personalized recommendations opens new possibilities for the future of online shopping.
Image Recognition Accuracy
The image recognition system showcased impressive accuracy across various product categories:
The confusion matrix and performance graphs revealed consistent and reliable performance, ensuring accurate identification of products.
Computer Engineering Department
Aggarwal Ilisha (MS Software Engineering)
Mehendale Nalini (MS Software Engineering)
Pathradkar Shraddha (MS Computer Engineering)
Shah Rushil (MS Software Engineering)
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