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DepthProMetric1024.mlpackage/Data/com.apple.CoreML/model.mlmodel

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DepthProMetric1024.mlpackage/Data/com.apple.CoreML/weights/weight.bin

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DepthProMetric1024.mlpackage/Manifest.json

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+    "itemInfoEntries": {
+        "31A01AA5-7FBD-45DB-AA21-B51D03998597": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Weights",
+            "name": "weights",
+            "path": "com.apple.CoreML/weights"
+        },
+        "DF8A79AB-286A-4734-A4EA-B88031155FBF": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Specification",
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LICENSE

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+Copyright (C) 2024 Apple Inc. All Rights Reserved.
+
+Disclaimer: IMPORTANT:  This Apple software is supplied to you by Apple
+Inc. ("Apple") in consideration of your agreement to the following
+terms, and your use, installation, modification or redistribution of
+this Apple software constitutes acceptance of these terms.  If you do
+not agree with these terms, please do not use, install, modify or
+redistribute this Apple software.
+
+In consideration of your agreement to abide by the following terms, and
+subject to these terms, Apple grants you a personal, non-exclusive
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+Software, with or without modifications, in source and/or binary forms;
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+Neither the name, trademarks, service marks or logos of Apple Inc. may
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README.md

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+---
+license: other
+license_name: apple-sample-code-license
+license_link: LICENSE
+pipeline_tag: depth-estimation
+tags:
+  - coreml
+  - depth
+  - metric-depth
+  - apple-silicon
+  - touchdesigner
+---
+
+# Depth Pro — Core ML (metric depth, 1024, 6-bit palettized)
+
+A Core ML conversion of [Apple's Depth Pro](https://github.com/apple/ml-depth-pro)
+— **metric** monocular depth: absolute metres from a single image, with no
+camera intrinsics, plus an estimated focal length.
+
+Converted for [AML (TD Apple ML)](https://github.com/mickeyvanolst), where it
+runs in the CoreML TOP inside TouchDesigner, but there is nothing
+TouchDesigner-specific about the package.
+
+## What it gives you
+
+| output | shape | meaning |
+|---|---|---|
+| `depth_m` | `[1, 1, 1024, 1024]` | depth in **metres** |
+| `focallength_px` | `[1]` | estimated focal length in pixels |
+
+Input: `image`, 1536×1536 RGB (the network's own size — the depth map is
+resized to 1024 inside the graph, while the values are still float).
+
+## How it was converted
+
+1. `depth_pro.pt` from Apple's CDN (the repo's own `get_pretrained_models.sh`).
+2. Traced at 1536×1536 and converted with coremltools 9 (fp16 compute).
+   `deg2rad` has no Core ML conversion and was replaced with a multiply.
+3. The metric arithmetic from `DepthPro.infer` is baked into the graph, so
+   the model returns metres rather than canonical inverse depth:
+   `f_px = 0.5·W / tan(0.5·fov)`, `depth = 1 / (canonical · W / f_px)`.
+4. The depth map is resized to 1024² in-graph.
+5. Weights palettized to 6 bits (kmeans, per-tensor).
+
+## Accuracy and cost
+
+Measured against the PyTorch reference on the same photograph, Apple silicon
+(fanless M-series):
+
+| | size | per frame | vs PyTorch |
+|---|---|---|---|
+| fp16 | 1.8 GB | 4.8 s | median 0.15 % |
+| **6-bit (this package)** | **682 MB** | **3.6 s** | median 1.17 %, p95 4.5 % |
+
+First load compiles for the Neural Engine and takes a couple of minutes;
+afterwards it is cached by the OS.
+
+## Licence
+
+Apple's licence for Depth Pro, redistributed verbatim as `LICENSE`. The
+weights come from the repository's own download script; this package is a
+format conversion of them.
+
+## Citation
+
+    @inproceedings{Bochkovskii2025:depthpro,
+      title     = {Depth Pro: Sharp Monocular Metric Depth in Less Than a Second},
+      author    = {Aleksei Bochkovskii and Ama\"{e}l Delaunoy and Hugo Germain and
+                   Marcel Santos and Yichao Zhou and Stephan R. Richter and
+                   Vladlen Koltun},
+      booktitle = {International Conference on Learning Representations},
+      year      = {2025},
+    }