Distill-Any-Depth
| Property | Value |
|---|---|
| Author | xingyang1 |
| Paper | arXiv:2502.19204 |
| Repository | https://huggingface.co/xingyang1/Distill-Any-Depth |
What is Distill-Any-Depth?
Distill-Any-Depth represents a breakthrough in monocular depth estimation, achieving state-of-the-art performance through innovative knowledge distillation algorithms. This model specializes in predicting depth from single images, offering various model sizes to accommodate different computational requirements and use cases.
Implementation Details
The model implementation leverages advanced knowledge distillation techniques to create more efficient and accurate depth estimators. It can be easily installed through the Hugging Face repository and requires minimal setup with standard Python dependencies.
- Knowledge distillation-based architecture for optimal performance
- Multiple model size options for different deployment scenarios
- Simple installation process via git clone and pip install
Core Capabilities
- State-of-the-art monocular depth estimation
- Flexible deployment options with various model sizes
- Efficient inference for real-world applications
- Robust depth prediction from single images
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness lies in its novel knowledge distillation approach, which creates stronger monocular depth estimators while maintaining efficiency across different model sizes.
Q: What are the recommended use cases?
This model is ideal for applications requiring accurate depth estimation from single images, such as autonomous navigation, augmented reality, and computer vision tasks where depth information is crucial.
