Style Transfer with Neural Network #405
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## Pull Request for PyVerse 💡 ### Requesting to submit a pull request to the PyVerse repository. --- #### Issue Title **Please enter the title of the issue related to your pull request.** *Style Transfer with Neural Network* - [x] I have provided the issue title. --- #### Info about the Related Issue **What's the goal of the project?** *This project aims to implement a style transfer technique using neural networks to blend content and style images, allowing users to generate artistic images by combining different visual elements.* - [x] I have described the aim of the project. --- #### Name **Please mention your name.** *Alolika Bhowmik* - [x] I have provided my name. --- #### GitHub ID **Please mention your GitHub ID.** *alo7lika* - [x] I have provided my GitHub ID. --- #### Email ID **Please mention your email ID for further communication.** *alolikabhowmik72@gmail.com* - [x] I have provided my email ID. --- #### Identify Yourself **Mention in which program you are contributing (e.g., WoB, GSSOC, SSOC, SWOC).** *GSSOC* - [x] I have mentioned my participant role. --- #### Closes **Enter the issue number that will be closed through this PR.** #405 *Closes: - [x] I have provided the issue number. --- #### Describe the Add-ons or Changes You've Made **Give a clear description of what you have added or modified.** *Implemented a Jupyter Notebook that demonstrates style transfer using PyTorch. Added sample images, updated documentation, and included a requirements.txt file for dependencies.* - [x] I have described my changes. --- #### Type of Change **Select the type of change:** - [ ] Bug fix (non-breaking change which fixes an issue) - [x] New feature (non-breaking change which adds functionality) - [ ] Code style update (formatting, local variables) - [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected) - [ ] This change requires a documentation update --- #### How Has This Been Tested? **Describe how your changes have been tested.** *Tested the style transfer implementation with various content and style images, ensuring that the output is generated correctly and meets expectations.* - [x] I have described my testing process. --- #### Checklist **Please confirm the following:** - [x] My code follows the guidelines of this project. - [x] I have performed a self-review of my own code. - [x] I have commented my code, particularly wherever it was hard to understand. - [x] I have made corresponding changes to the documentation. - [x] My changes generate no new warnings. - [x] I have added things that prove my fix is effective or that my feature works. - [x] Any dependent changes have been merged and published in downstream modules.
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Guidelines
Latest Merged PR Link
#292
Project Description
The Style Transfer with Neural Networks project explores the use of deep learning techniques to combine the content of one image with the artistic style of another. By utilizing Convolutional Neural Networks (CNNs), the model separates the content features from the structure and the style features from patterns like brushstrokes or colors. The style transfer process involves optimizing a new image that retains the content of the first image while applying the artistic qualities of the second. The project leverages a pre-trained model, such as VGG16 or VGG19, to extract content and style representations, and it uses loss functions to balance the content and style contributions in the final output. This approach has wide applications in digital art, enabling users to create unique artwork by blending different artistic styles with real-world images.
Full Name
alolika bhowmik
Participant Role
Contributor GSSOC EXT 24
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