DPT-BEiT-Large-512
| Property | Value |
|---|---|
| Parameter Count | 344M |
| License | MIT |
| Author | Intel |
| Paper | MiDaS v3.1 |
What is dpt-beit-large-512?
DPT-BEiT-Large-512 is a state-of-the-art monocular depth estimation model that combines Dense Prediction Transformer (DPT) architecture with a BEiT backbone. Trained on 1.4 million images, this model represents the latest advancement in transformer-based depth estimation technology.
Implementation Details
The model utilizes a BEiT transformer backbone with a 512x512 training resolution, complemented by a specialized neck and head architecture for depth estimation. It operates at F32 precision and has been optimized for robust performance across various scenarios.
- 344M trainable parameters
- 512x512 input resolution
- Zero-shot transfer capability with 10.82 score
- Transformer-based architecture with BEiT backbone
Core Capabilities
- High-quality monocular depth estimation from single images
- Zero-shot transfer to new scenarios
- Efficient processing of high-resolution inputs
- Superior performance compared to conventional convolutional approaches
Frequently Asked Questions
Q: What makes this model unique?
This model combines the power of BEiT transformers with DPT architecture, achieving superior depth estimation performance compared to traditional approaches. It's particularly notable for its high-resolution processing capability and robust zero-shot transfer abilities.
Q: What are the recommended use cases?
The model is ideal for applications in generative AI, 3D reconstruction, autonomous driving, and any scenario requiring accurate depth estimation from single images. It's particularly well-suited for zero-shot applications where fine-tuning isn't possible.
