Depth-Anything-V2-Metric-Indoor-Large-hf
| Property | Value |
|---|---|
| Parameters | 335.3M |
| Architecture | DPT with DINOv2 backbone |
| Training Data | ~600K synthetic + ~62M real unlabeled images |
| Paper | Depth Anything V2 |
What is Depth-Anything-V2-Metric-Indoor-Large-hf?
This is a state-of-the-art depth estimation model specifically fine-tuned for indoor metric depth estimation. It represents the large variant of the Depth Anything V2 family, utilizing the powerful DPT architecture combined with a DINOv2 backbone. The model has been trained on a massive dataset combining synthetic labeled images and real unlabeled images, making it particularly robust for real-world applications.
Implementation Details
The model is implemented using the transformers library and features a sophisticated architecture designed for precise depth estimation. It processes images through a pipeline that converts 2D images into accurate depth maps, leveraging the latest advances in computer vision and transformer architectures.
- Requires transformers >= 4.45.0
- Supports zero-shot depth estimation
- Provides both relative and absolute depth estimation capabilities
- Compatible with standard image processing pipelines
Core Capabilities
- High-precision indoor depth estimation
- Metric depth prediction for real-world applications
- Zero-shot inference support
- Efficient processing of various image sizes
- Robust performance on complex indoor scenes
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its large-scale architecture (335.3M parameters) and specialized training for indoor metric depth estimation using the Hypersim dataset. It combines synthetic and real-world training data to achieve superior depth estimation accuracy.
Q: What are the recommended use cases?
The model is ideal for indoor scene understanding, robotics navigation, AR/VR applications, and any scenario requiring accurate metric depth estimation in indoor environments. It's particularly well-suited for applications requiring precise distance measurements rather than just relative depth understanding.
