marigold depth v1 0

Introduction

Marigold is a diffusion model for monocular depth estimation, which repurposes diffusion-based image generators. It leverages the visual information in generative models, providing state-of-the-art results in depth estimation from a single image. The model can generalize to unseen data using a zero-shot approach.

Architecture

Marigold is developed from Stable Diffusion, adapted for depth estimation. The model is fine-tuned with synthetic data to enhance its performance in various scenarios, including in-the-wild and zero-shot settings. It uses a specialized pipeline, MarigoldPipeline, to achieve these results.

Training

The training process involves fine-tuning the model with synthetic data, enabling it to perform monocular depth estimation effectively. The approach allows the model to utilize the extensive visual knowledge inherent in modern generative models, which aids in achieving high accuracy in depth estimation tasks.

Guide: Running Locally

1. Installation: Clone the repository from GitHub and navigate to the project directory.
2. Dependencies: Install the required dependencies using a package manager like pip.
3. Configuration: Set up the model configuration files as specified in the repository documentation.
4. Execution: Run the model using a Python script or a Jupyter notebook.
5. Cloud GPUs: Utilize cloud services like Google Colab or AWS for GPU resources to speed up processing, especially for larger datasets.

License

The Marigold model is licensed under the Apache License, Version 2.0. Users must adhere to the terms outlined in the LICENSE file when using the code and model.