Depth Estimation
Core ML
Depth Pro
visionos
apple-silicon
amlr
computer-vision
512x512
ane-optimized
Instructions to use aarondevstack/DepthPro-512x512-coreml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Depth Pro
How to use aarondevstack/DepthPro-512x512-coreml with Depth Pro:
# Download checkpoint pip install huggingface-hub huggingface-cli download --local-dir checkpoints aarondevstack/DepthPro-512x512-coreml
import depth_pro # Load model and preprocessing transform model, transform = depth_pro.create_model_and_transforms() model.eval() # Load and preprocess an image. image, _, f_px = depth_pro.load_rgb("example.png") image = transform(image) # Run inference. prediction = model.infer(image, f_px=f_px) # Results: 1. Depth in meters depth = prediction["depth"] # Results: 2. Focal length in pixels focallength_px = prediction["focallength_px"] - Notebooks
- Google Colab
- Kaggle
File size: 617 Bytes
14ff16b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | {
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"author": "com.apple.CoreML",
"description": "CoreML Model Weights",
"name": "weights",
"path": "com.apple.CoreML/weights"
},
"6686BC52-DA2A-42B1-9C1E-C50E57DBB1B0": {
"author": "com.apple.CoreML",
"description": "CoreML Model Specification",
"name": "model.mlmodel",
"path": "com.apple.CoreML/model.mlmodel"
}
},
"rootModelIdentifier": "6686BC52-DA2A-42B1-9C1E-C50E57DBB1B0"
}
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