In response to the client's need for diverse map image generation from complex architectural diagrams, we developed a solution using advanced machine learning techniques. The challenge lay in refining the dataset to remove unwanted objects and augmenting it to facilitate the creation of varied map images. By implementing a tailored solution, we aimed to meet the client's requirements efficiently while ensuring scalability and customization.
The client provided architectural diagrams containing diverse objects, complicating the process of generating map images. They sought to enhance the diversity of their dataset to enable the AI model to produce a broader range of map images. Additionally, the client required customization options to tailor the generated images to their specific needs. Addressing these challenges while maintaining efficiency and scalability was essential to meeting the client’s expectations.
To meet the client’s needs, we used a combination of Generative Adversarial Networks (GANs) and image-to-image translation models. Firstly, we carefully cleaned up the architectural diagrams to get rid of any unwanted objects, making sure our dataset was top-notch for training. Then, we beefed up the dataset to make it more diverse, allowing the AI model to create a wider variety of map images. For training, we made the most of Google Colab, which helped us streamline the process, saving time and resources. We also tweaked the model settings to make sure it performed at its best, delivering accurate results. Our solution allowed for customized outputs, perfectly tailored to the client’s specific preferences and requirements.
By cleaning the architectural diagrams and generating new data, our solution significantly increased the diversity of the dataset, enabling the AI model to produce a wide range of map images.
The image-to-image translation model provided flexibility in adjusting the output according to the client’s specific requirements, ensuring tailored results.
Leveraging Google Colab for training streamlined the process, optimizing resource utilization and reducing development time.
The developed model could generate a large number of images, providing scalability to accommodate future expansion or changes in client demands.
