Depth Anything Large Model
| Property | Value |
|---|---|
| Parameter Count | 335M |
| License | Apache 2.0 |
| Paper | View Paper |
| Author | LiheYoung |
| Architecture | DPT with DINOv2 backbone |
What is depth-anything-large-hf?
Depth Anything is a cutting-edge computer vision model designed for depth estimation tasks. This large variant, containing 335M parameters, represents the state-of-the-art in both relative and absolute depth estimation. Trained on an impressive dataset of approximately 62 million images, it leverages the powerful combination of DPT architecture and DINOv2 backbone to achieve exceptional performance in zero-shot depth estimation scenarios.
Implementation Details
The model utilizes a transformer-based architecture that combines the Dense Prediction Transformer (DPT) with a DINOv2 backbone. It processes images through a sophisticated pipeline that enables accurate depth estimation without requiring specific fine-tuning for new scenarios.
- F32 tensor type for precise depth calculations
- Transformer-based architecture for robust feature extraction
- Supports both relative and absolute depth estimation
- Implements efficient zero-shot capabilities
Core Capabilities
- Zero-shot depth estimation on arbitrary images
- High-resolution depth map generation
- Seamless integration with the Hugging Face Transformers library
- Supports both pipeline and direct model usage approaches
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its extensive training on 62M images and its ability to perform zero-shot depth estimation without additional training. The combination of DPT architecture with DINOv2 backbone enables state-of-the-art performance in both relative and absolute depth estimation tasks.
Q: What are the recommended use cases?
The model is ideal for applications requiring depth estimation from single images, such as 3D reconstruction, autonomous navigation, augmented reality, and computer vision research. It can be easily integrated into existing pipelines using the Hugging Face Transformers library.
