Mani Fold

Introduction

ManiFold, developed by SVECTOR, features advanced AI models offering high performance across various domains. This documentation provides a detailed guide on the capabilities of the ManiFold models and instructions for their integration into applications.

Architecture

ManiFold models are designed to deliver scalability and efficiency, ensuring state-of-the-art results. They are compatible with modern machine learning frameworks, allowing for flexible integration and diverse applications.

Training

ManiFold's training is focused on achieving state-of-the-art performance. Key features include:

• State-of-the-Art Performance: Ensures efficiency and scalability.
• Versatile Applications: Applicable for tasks ranging from image analysis to AI workflows.

Guide: Running Locally

To run ManiFold models locally, follow these steps:

1. Requirements: Ensure your environment includes:
   • Python version 3.8 or higher.
   • Install dependencies using:

     pip install torch safetensors numpy
     

2. Use Cases:
   • 3D Reconstruction: Generate sparse and dense 3D models.
   • Image Analysis: Use advanced image conditioning.
   • AI Workflow Integration: Streamline AI tasks.

For enhanced performance, consider using cloud GPUs such as those provided by AWS, Google Cloud, or Azure.

License

This project is licensed under the SVECTOR Proprietary License. For more details, refer to the license file included with the project. The license is also noted as CC-BY-NC-3.0, indicating some restrictions on commercial use.