Hugging Face's logo

aarondevstack
/
DepthPro-512x512-coreml

Depth Estimation
Core ML
Depth Pro
visionos
apple-silicon
amlr
computer-vision
512x512
ane-optimized
Model card Files
xet
 Community
How to use from the
Use from the
Depth Pro library
# 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"]

DepthPro CoreML (512x512 Real-Time)

This repository contains the Fast (512x512) version of the DepthPro model, specifically optimized for the Apple Neural Engine (ANE).

DepthPro is a state-of-the-art monocular depth estimation model. This 512px version is designed for Real-Time Previews and high-speed video conversion on Apple Vision Pro and Apple Silicon Macs.

🚀 Key Features

  • ANE Accelerated: Leveraging the Apple Neural Engine for ultra-low power and high-speed inference.
  • Real-Time Performance: Ideal for interactive parameter tuning (Max Disparity, Convergence Plane).
  • VisionOS Ready: Fully compatible with Apple Vision Pro via the DepthProPipeline.

📊 Performance & Requirements

Metric Specification
Input Resolution 512 x 512 pixels
Compute Units All (Optimized for ANE)
Average Latency < 1.0s per frame (on M2/M3 chips)
Target Use Case Real-time 3D Preview / Quick Video Conversion

This model is the best choice for the initial phase of your 3D conversion workflow, allowing for near-instant feedback while adjusting 3D rendering parameters.

📦 Repository Contents

The repository contains the following core components:

  1. DepthPro_transform.mlpackage: Image preprocessing.
  2. DepthPro_encoder.mlpackage: Feature extraction (ANE Optimized).
  3. DepthPro_decoder.mlpackage: Multiresolution fusion.
  4. DepthPro_depth.mlpackage: Final depth output.

🛠 Usage with Swift Transformers

You can download and cache this model dynamically using swift-transformers:

let hub = Hub()
let modelDir = try await hub.snapshot(repoId: "aarondevstack/DepthPro-512x512-coreml")
// Load models from the downloaded directory
Downloads last month
195
Inference Providers NEW
Depth Estimation
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support