Depth Anything Small HF
| Property | Value |
|---|---|
| Parameter Count | 24.8M parameters |
| License | Apache-2.0 |
| Paper | View Paper |
| Architecture | DPT with DINOv2 backbone |
| Tensor Type | F32 |
What is depth-anything-small-hf?
Depth Anything Small HF is a compact yet powerful depth estimation model that represents a significant advancement in computer vision technology. Developed by Lihe Yang and team, this model leverages the DPT (Dense Prediction Transformer) architecture combined with a DINOv2 backbone to deliver state-of-the-art depth estimation capabilities while maintaining a relatively small footprint of 24.8M parameters.
Implementation Details
The model has been trained on an impressive dataset of approximately 62 million images, enabling it to achieve exceptional performance in both relative and absolute depth estimation tasks. It utilizes a transformer-based architecture that has been optimized for efficient processing and accurate depth prediction.
- Transformer-based architecture using DPT and DINOv2
- Trained on 62M images for robust performance
- Optimized for both relative and absolute depth estimation
- Implements efficient F32 tensor operations
Core Capabilities
- Zero-shot depth estimation without fine-tuning
- Seamless integration with HuggingFace Transformers pipeline
- Efficient processing of various image sizes
- Real-time depth map generation
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its efficient architecture that achieves state-of-the-art depth estimation results while maintaining a relatively small parameter count of 24.8M. Its training on a massive dataset of 62M images enables robust performance across diverse scenarios.
Q: What are the recommended use cases?
The model is ideal for zero-shot depth estimation tasks in computer vision applications, including robotics, augmented reality, and scene understanding. It can be easily integrated into existing pipelines using the HuggingFace Transformers library.
