Depth-Anything-V2-Small-hf
| Property | Value |
|---|---|
| Parameter Count | 24.8M |
| License | Apache 2.0 |
| Architecture | DPT with DINOv2 backbone |
| Paper | Depth Anything V2 |
What is Depth-Anything-V2-Small-hf?
Depth-Anything-V2-Small-hf is a lightweight yet powerful monocular depth estimation model that represents a significant advancement in computer vision technology. Built upon the DPT architecture with a DINOv2 backbone, this model has been trained on an extensive dataset comprising 595K synthetic labeled images and over 62M real unlabeled images.
Implementation Details
The model leverages transformer-based architecture to process visual information and estimate depth from single images. It operates at F32 tensor precision and provides an efficient solution that's 10x faster than SD-based alternatives while maintaining high accuracy.
- Trained on synthetic and real-world data for robust performance
- Implements DPT architecture with DINOv2 backbone
- Offers improved fine-grained detail detection compared to V1
- Provides both relative and absolute depth estimation capabilities
Core Capabilities
- Zero-shot depth estimation from single images
- Fine-grained detail preservation in depth maps
- Efficient processing with minimal computational overhead
- Robust performance across diverse scene types
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its ability to combine efficient processing (24.8M parameters) with state-of-the-art depth estimation accuracy, while being 10x faster than comparable SD-based models. Its training on both synthetic and real-world data ensures robust performance across various scenarios.
Q: What are the recommended use cases?
The model is ideal for applications requiring monocular depth estimation, including robotics, autonomous navigation, augmented reality, and computer vision research. It's particularly suitable for scenarios requiring real-time depth estimation with limited computational resources.
