Coreml Depth Anything Small
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 results in relative and absolute depth estimation tasks.
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Release Time : 5/3/2024
Model Overview
This model adopts the DPT architecture and is based on the DINOv2 backbone network, focusing on image depth estimation tasks, capable of predicting depth information from a single image.
Model Features
Large-scale training data
Trained on approximately 62 million images, ensuring strong generalization capabilities.
Efficient architecture
Based on DPT architecture and DINOv2 backbone, achieving efficient inference while maintaining accuracy.
Multi-platform support
Provides Core ML format models for efficient operation on Apple devices.
Precision optimization
Offers both Float32 and Float16 precision versions to balance accuracy and performance needs.
Model Capabilities
Monocular depth estimation
Relative depth prediction
Absolute depth prediction
Image depth analysis
Use Cases
Computer vision
Augmented reality
Provides scene depth information for AR applications.
Enhances realism in AR object placement and interaction.
3D reconstruction
Generates depth maps from single images.
Assists in 3D scene reconstruction.
Autonomous driving
Assists vehicles in perceiving surrounding environments.
Provides scene depth information.
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