Machine learning books and papers

⚡️ Apple Depth Pro

# setting up a venv:
conda create -n depth-pro -y python=3.9
conda activate depth-pro
pip install -e .

# Download pretrained checkpoints:
source get_pretrained_models.sh

# Run the inference from CLI on a single image:
depth-pro-run -i ./data/example.jpg

# Running from python
from PIL import Image
import depth_pro

model, transform = depth_pro.create_model_and_transforms()
model.eval()
image, _, f_px = depth_pro.load_rgb(image_path)
image = transform(image)
prediction = model.infer(image, f_px=f_px)
depth = prediction["depth"]  # Depth in [m].
focallength_px = prediction["focallength_px"]  # Focal length in pixels.

🟡

Demo

🟡

Arxiv

🖥

GitHub

@Machine_learn

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3.4K viewsOct 10, 2024 at 05:54

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Create: 2024-10-10
Last Update: 2025-07-03 15:43:47

⚡️ Apple Depth Pro

# setting up a venv: conda create -n depth-pro -y python=3.9 conda activate depth-pro pip install -e . # Download pretrained checkpoints: source get_pretrained_models.sh # Run the inference from CLI on a single image: depth-pro-run -i ./data/example.jpg # Running from python from PIL import Image import depth_pro model, transform = depth_pro.create_model_and_transforms() model.eval() image, _, f_px = depth_pro.load_rgb(image_path) image = transform(image) prediction = model.infer(image, f_px=f_px) depth = prediction["depth"] # Depth in [m]. focallength_px = prediction["focallength_px"] # Focal length in pixels.

🟡Demo
🟡Arxiv
🖥GitHub

@Machine_learn

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