Core ML versions of Depth Anything V2 (small)

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README.md

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+---
+tags:
+- depth-estimation
+library_name: coreml
+license: apache-2.0
+---
+
+# Depth Anything V2 Core ML Models
+
+Depth Anything V2 was introduced in [the paper of the same name](https://arxiv.org/abs/2406.09414) by Lihe Yang et al. It uses the same architecture as the original Depth Anything release, but uses synthetic data and a larger capacity teacher model to achieve much finer and robust depth predictions. The original Depth Anything model was introduced in the paper [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://arxiv.org/abs/2401.10891) by Lihe Yang et al., and was first released in [this repository](https://github.com/LiheYoung/Depth-Anything).
+
+## Model description
+
+Depth Anything V2 leverages the [DPT](https://huggingface.co/docs/transformers/model_doc/dpt) architecture with a [DINOv2](https://huggingface.co/docs/transformers/model_doc/dinov2) backbone.
+
+The model is trained on ~600K synthetic labeled images and ~62 million real unlabeled images, obtaining state-of-the-art results for both relative and absolute depth estimation.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/depth_anything_overview.jpg"
+alt="drawing" width="600"/>
+
+<small> Depth Anything overview. Taken from the <a href="https://arxiv.org/abs/2401.10891">original paper</a>.</small>
+
+## Evaluation - Variants
+
+| Variant                                                 | Parameters | Size (MB) | Weight precision | Act. precision | abs-rel error | abs-rel reference |
+| ------------------------------------------------------- | ---------: | --------: | ---------------- | -------------- | ------------: | ----------------: |
+| [small-original](https://huggingface.co/pcuenq/Depth-Anything-V2-Small-hf) (PyTorch)                                 |      24.8M |      99.2 | Float32          | Float32        |               |                   |
+| [DepthAnythingV2SmallF32](DepthAnythingV2SmallF32.mlpackage) |      24.8M |      99.2 | Float32          | Float32        |        0.0072 |    small-original |
+| [DepthAnythingV2SmallF16](DepthAnythingV2SmallF16.mlpackage) |      24.8M |      49.8 | Float16          | Float16        |        0.0089 |    small-original |
+
+Evaluated on 512 landscape images from the COCO dataset with aspect ratio similar to 4:3. Images were streched to a fixed size of 518x396, and the groundtruth corresponds to the results from the PyTorch model running on CUDA with `float32` precision. 
+
+## Evaluation - Inference time
+
+The following results use the small-float16 variant.
+
+| Device               | OS   | Inference time (ms) | Dominant compute unit |
+| -------------------- | ---- | ------------------: | --------------------- |
+| iPhone 12 Pro Max    | 18.0 |               31.10 | Neural Engine         |
+| iPhone 15 Pro Max    | 17.4 |               33.90 | Neural Engine         |
+| MacBook Pro (M1 Max) | 15.0 |               32.80 | Neural Engine         |
+| MacBook Pro (M3 Max) | 15.0 |               24.58 | Neural Engine         |
+
+
+## Download
+
+Install `huggingface-cli`
+
+```bash
+brew install huggingface-cli
+```
+
+To download one of the `.mlpackage` folders to the `models` directory:
+
+```bash
+huggingface-cli download \
+  --local-dir models --local-dir-use-symlinks False \
+  apple/coreml-depth-anything-small \
+  --include "DepthAnythingV2SmallF16.mlpackage/*"
+```
+
+To download everything, skip the `--include` argument.
+
+## Integrate in Swift apps
+
+The [`huggingface/coreml-examples`](https://github.com/huggingface/coreml-examples/blob/main/depth-anything-example/README.md) repository contains sample Swift code for `coreml-depth-anything-small-v2` and other models. See [the instructions there](https://github.com/huggingface/coreml-examples/tree/main/depth-anything-example) to build the demo app, which shows how to use the model in your own Swift apps.