A Deep Learning based Fashion Recommender System using code that demonstrates the integration of PyTorch and TensorFlow to implement the CP-VTON (Cloth-Person Virtual Try-On) framework.
By combining the strengths of these two industry-standard deep learning libraries, I have developed a robust solution that facilitates accurate cloth-person alignment and realistic garment fitting.
Key Features:
PyTorch and TensorFlow Fusion: Leveraging the strengths of both frameworks, I have optimized the architecture for real-time inference while maintaining the precision of PyTorch's dynamic computation graph and TensorFlow's efficient deployment capabilities.
CP-VTON Implementation: I have meticulously implemented the CP-VTON framework, which involves parsing the clothing items and generating realistic draped garments on the target person's image. This process ensures that the virtual try-on outcome is both visually appealing and true to life.
Model Training and Optimization: The repository includes detailed code for training the CP-VTON model on custom datasets, and optimizing it for high-quality output. I have also explored techniques for handling diverse clothing styles, poses, and lighting conditions to enhance the versatility of the virtual try-on solution.
Documentation and Usage: Clear and concise documentation guides you through setting up the environment, data preprocessing, model training, and deployment. I have included example code snippets for running inference on sample images, allowing recruiters to quickly grasp the project's functionality.
By exploring this repository, you will witness my expertise in deep learning, fashion technology, and software engineering. My commitment to creating a captivating virtual try-on experience can contribute significantly to enhancing the e-commerce landscape.
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