ZoeDepth NYU-KITTI Model
| Property | Value |
|---|---|
| Parameters | 345M |
| License | MIT |
| Paper | View Paper |
| Framework | DPT-based Architecture |
| Training Data | NYU and KITTI datasets |
What is zoedepth-nyu-kitti?
ZoeDepth is an advanced depth estimation model that combines the power of relative and metric depth estimation. Developed by Intel, this model extends the DPT (Dense Prediction Transformer) framework to provide accurate depth measurements in actual metric values. The model has been specifically fine-tuned on both NYU and KITTI datasets, making it particularly robust for various real-world applications.
Implementation Details
The model utilizes a transformer-based architecture built upon the DPT framework, incorporating 345M parameters for precise depth estimation. It processes images using F32 tensor types and provides both relative and absolute depth measurements.
- Zero-shot transfer capability for depth estimation
- Integration with Hugging Face's pipeline API
- Support for monocular depth estimation
- State-of-the-art performance on benchmark datasets
Core Capabilities
- Accurate metric depth estimation from single images
- Zero-shot transfer learning capabilities
- Seamless integration with transformation pipelines
- Support for both indoor (NYU) and outdoor (KITTI) scenarios
Frequently Asked Questions
Q: What makes this model unique?
This model uniquely combines relative and metric depth estimation capabilities, allowing for accurate depth measurements in real-world units. Its dual-dataset training on NYU and KITTI makes it versatile for both indoor and outdoor applications.
Q: What are the recommended use cases?
The model is ideal for zero-shot monocular depth estimation tasks, particularly in applications requiring accurate depth measurements from single images. It's suitable for robotics, autonomous navigation, scene understanding, and computer vision applications.
