Depth Anything Base Model
| Property | Value |
|---|---|
| Author | Lihe Yang et al. |
| Architecture | DPT with DINOv2 backbone |
| Training Data | ~62 million images |
| Paper | arXiv:2401.10891 |
What is depth-anything-base-hf?
Depth Anything is a state-of-the-art depth estimation model that represents a significant advancement in computer vision technology. It utilizes the DPT (Dense Prediction Transformer) architecture combined with a DINOv2 backbone to perform highly accurate depth estimation on images. The model has been trained on an extensive dataset of approximately 62 million images, enabling it to achieve superior performance in both relative and absolute depth estimation tasks.
Implementation Details
The model is implemented using the Transformers library and can be easily integrated into existing pipelines. It supports both high-level pipeline API and direct model usage through AutoImageProcessor and AutoModelForDepthEstimation classes. The model processes images and returns depth predictions that can be interpolated to match original image dimensions.
- Leverages DPT architecture with DINOv2 backbone for robust feature extraction
- Supports zero-shot depth estimation without fine-tuning
- Provides flexible API integration options
- Outputs depth maps that can be easily post-processed
Core Capabilities
- Zero-shot depth estimation on arbitrary images
- High-quality relative and absolute depth prediction
- Efficient processing through optimized architecture
- Seamless integration with Hugging Face Transformers ecosystem
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness lies in its extensive training on 62 million images and its ability to perform zero-shot depth estimation without requiring task-specific fine-tuning. 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 particularly well-suited for applications requiring depth estimation from single images, such as 3D scene understanding, autonomous navigation, augmented reality, and computer vision research. It can be used directly without additional training for zero-shot depth estimation tasks.
