Marigold Depth Estimation Model
| Property | Value |
|---|---|
| License | Apache-2.0 |
| Paper | Research Paper |
| Downloads | 34,551 |
| Likes | 116 |
What is marigold-depth-v1-0?
Marigold is an innovative diffusion-based model specifically designed for monocular depth estimation. Built upon the foundation of Stable Diffusion, it represents a breakthrough in leveraging generative AI for depth perception tasks. The model has been fine-tuned with synthetic data and demonstrates remarkable zero-shot transfer capabilities to unseen images.
Implementation Details
The model employs a specialized pipeline called MarigoldPipeline and utilizes the Diffusers framework. It's implemented using safetensors for efficient model weight storage and handling. The architecture repurposes existing diffusion-based image generators, demonstrating an innovative approach to depth estimation.
- Zero-shot capability for immediate deployment
- Built on Stable Diffusion architecture
- Synthetic data fine-tuning
- Efficient implementation using safetensors
Core Capabilities
- Single-image depth estimation
- Monocular depth perception
- In-the-wild image processing
- Zero-shot transfer learning
- State-of-the-art depth estimation results
Frequently Asked Questions
Q: What makes this model unique?
Marigold stands out for its ability to leverage pre-existing knowledge from generative image models for depth estimation, offering zero-shot capabilities without requiring additional training on target domains.
Q: What are the recommended use cases?
The model is ideal for applications requiring depth estimation from single images, including computer vision systems, robotics, augmented reality, and scene understanding tasks. It's particularly valuable when working with in-the-wild images where traditional depth estimation methods might struggle.
