Instructions to use aarondevstack/DepthPro-1024x1024-coreml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Depth Pro
How to use aarondevstack/DepthPro-1024x1024-coreml with Depth Pro:
# Download checkpoint pip install huggingface_hub hf download aarondevstack/DepthPro-1024x1024-coreml --local-dir checkpoints
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
Download DepthPro_encoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin from aarondevstack/DepthPro-1024x1024-coreml: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/aarondevstack/DepthPro-1024x1024-coreml/resolve/main/DepthPro_encoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin
- Command line
-
hf download hf://aarondevstack/DepthPro-1024x1024-coreml/DepthPro_encoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin
-
curl -L -o weight.bin https://huggingface.co/aarondevstack/DepthPro-1024x1024-coreml/resolve/main/DepthPro_encoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin
1.26 GB
- Xet hash:
- a3b87645c2ce964d1829c46b07bee96dcac3c733606d67e5b810129d3c152298
- Size of remote file:
- 1.26 GB
- SHA256:
- 7e582e80dfe67489c4afd6f4e551c7669ae84da80728451d737790b4f5438285
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