DPT-BEiT-Base-384
| Property | Value |
|---|---|
| Parameter Count | 111M |
| License | MIT |
| Paper | Vision Transformers for Dense Prediction |
| Tensor Type | F32 |
What is dpt-beit-base-384?
DPT-BEiT-Base-384 is a Dense Prediction Transformer model developed by Intel for monocular depth estimation. Trained on 1.4 million images, it leverages the BEiT (Bidirectional Encoder representation from Image Transformers) backbone architecture to analyze single images and predict depth information.
Implementation Details
The model combines a BEiT backbone with a specialized neck and head architecture designed for depth estimation tasks. It processes images through a transformer-based architecture and outputs detailed depth maps that can be interpolated to match original image dimensions.
- Zero-shot depth estimation capability
- 384x384 input resolution
- Transformer-based architecture with dense prediction capabilities
- Compatible with HuggingFace's pipeline API
Core Capabilities
- Monocular depth estimation from single images
- High-quality depth map generation
- Efficient processing with 111M parameters
- Seamless integration with popular deep learning frameworks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its use of the BEiT backbone combined with DPT architecture, offering efficient depth estimation without requiring stereo images or multiple viewpoints. The model's architecture is specifically optimized for dense prediction tasks, making it particularly effective for depth estimation.
Q: What are the recommended use cases?
The model is ideal for applications requiring depth estimation from single images, such as: 3D scene understanding, robotics navigation, augmented reality applications, and computer vision tasks requiring depth information. It's particularly useful when stereo camera setups are not available or practical.
