Depth-Anything-V2-Metric-Outdoor-Large-hf
| Property | Value |
|---|---|
| Parameter Count | 335.3M |
| Architecture | DPT with DINOv2 backbone |
| Training Data | ~600K synthetic + ~62M real unlabeled images |
| Paper | arXiv:2406.09414 |
What is Depth-Anything-V2-Metric-Outdoor-Large-hf?
Depth-Anything-V2-Metric-Outdoor-Large-hf is a state-of-the-art depth estimation model specifically fine-tuned for outdoor scenes using the Virtual KITTI datasets. It represents the large-scale variant of the Depth Anything V2 family, designed to provide highly accurate metric depth predictions for outdoor environments.
Implementation Details
The model utilizes a DPT (Dense Prediction Transformer) architecture combined with a DINOv2 backbone, leveraging the power of transformers for dense visual predictions. It has been trained on an extensive dataset comprising approximately 600,000 synthetic labeled images and 62 million real unlabeled images, making it robust and versatile for real-world applications.
- Large model variant with 335.3M parameters
- Compatible with the transformers library (requires version >=4.45.0)
- Optimized for outdoor metric depth estimation
- Supports zero-shot depth estimation capabilities
Core Capabilities
- High-precision metric depth estimation for outdoor scenes
- Zero-shot depth prediction without additional training
- Efficient processing of various image sizes
- Seamless integration with the Hugging Face transformers pipeline
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its specialized fine-tuning for outdoor metric depth estimation, large parameter count (335.3M), and training on both synthetic and real-world data, making it particularly effective for outdoor scene understanding.
Q: What are the recommended use cases?
The model is ideal for applications requiring accurate depth estimation in outdoor environments, such as autonomous navigation, 3D scene reconstruction, and augmented reality applications focused on outdoor scenarios.
