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Depth Anything Base Hf

Developed by LiheYoung
Depth Anything is a depth estimation model based on the DPT architecture and DINOv2 backbone network, trained on approximately 62 million images, achieving state-of-the-art performance in zero-shot depth estimation.
Downloads 4,101
Release Time : 1/22/2024

Model Overview

This model is primarily used for depth estimation tasks, capable of predicting depth information from a single image, suitable for various applications in the field of computer vision.

Model Features

Large-scale training data
The model is trained on approximately 62 million images, providing strong generalization capabilities.
Zero-shot depth estimation
Can be directly applied to depth estimation tasks in various scenarios without domain-specific fine-tuning.
Advanced architecture
Combines the DPT architecture and DINOv2 backbone network to achieve high-performance depth prediction.

Model Capabilities

Single-image depth estimation
Zero-shot prediction
Computer vision analysis

Use Cases

Computer vision
3D scene reconstruction
Predict depth information from a single 2D image to assist in 3D scene reconstruction
Augmented reality
Provide scene depth information for AR applications
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