Depth-Anything-V2-Small
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Language | English |
| Downloads | 66,309 |
What is Depth-Anything-V2-Small?
Depth-Anything-V2-Small is a cutting-edge monocular depth estimation model that represents a significant advancement in computer vision technology. Trained on an extensive dataset comprising 595K synthetic labeled images and over 62M real unlabeled images, this model offers superior performance while maintaining efficiency.
Implementation Details
The model utilizes a ViTS encoder architecture with features=64 and output channels [48, 96, 192, 384]. Implementation is straightforward through the depth_anything_v2.dpt module, with the model being particularly optimized for efficient inference.
- Lightweight architecture optimized for performance
- Pre-trained weights available for immediate deployment
- Compatible with standard Python ML frameworks
- Supports CPU and GPU inference
Core Capabilities
- Fine-grained detail detection surpassing V1
- 10x faster performance compared to SD-based models
- Enhanced robustness versus Marigold and Geowizard
- Efficient resource utilization with smaller footprint
- Strong performance on fine-tuning tasks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its exceptional balance of speed and accuracy, being 10x faster than SD-based alternatives while providing more detailed depth estimation. The training on both synthetic and real-world data makes it particularly robust.
Q: What are the recommended use cases?
The model is ideal for applications requiring real-time depth estimation, including robotics, augmented reality, autonomous navigation, and computer vision research. Its efficiency makes it suitable for both research and production environments.
