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--- |
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license: cc-by-nc-4.0 |
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tags: |
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- depth-estimation |
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- coreml |
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- apple-silicon |
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- vision |
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- computer-vision |
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library_name: coreml |
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--- |
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# Depth Anything V2 - CoreML |
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Depth Anything V2 models (Base and Large) converted to CoreML format for optimized inference on Apple Silicon (M-series chips). |
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## Models |
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| Model | Size | Parameters | Performance (M4 Pro est.) | License | |
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|-------|------|------------|---------------------------|---------| |
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| Small F16 | 48 MB | 24.8M | ~30ms (~33 fps) | Apache-2.0 | |
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| Base F16 | 172 MB | 97.5M | ~60-90ms (~14 fps) | CC-BY-NC-4.0 | |
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| Large F16 | 590 MB | 335.3M | ~200-300ms (~4 fps) | CC-BY-NC-4.0 | |
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All models use Float16 precision and run on Apple's Neural Engine + GPU + CPU. |
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## License |
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Both **Base** and **Large** models are **CC-BY-NC-4.0** (non-commercial only), following the [official Depth Anything V2 licensing](https://github.com/DepthAnything/Depth-Anything-V2#license). |
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**For commercial use**, you must use the Small model (Apache-2.0), which is available directly from [Apple's CoreML model zoo](https://developer.apple.com/machine-learning/models/). |
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## Download |
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**Base model**: |
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```bash |
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curl -L -o DepthAnythingV2BaseF16.mlpackage.tar.gz \ |
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"https://huggingface.co/mrgnw/depth-anything-v2-coreml/resolve/main/DepthAnythingV2BaseF16.mlpackage.tar.gz" |
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tar -xzf DepthAnythingV2BaseF16.mlpackage.tar.gz |
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``` |
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**Large model**: |
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```bash |
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curl -L -o DepthAnythingV2LargeF16.mlpackage.tar.gz \ |
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"https://huggingface.co/mrgnw/depth-anything-v2-coreml/resolve/main/DepthAnythingV2LargeF16.mlpackage.tar.gz" |
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tar -xzf DepthAnythingV2LargeF16.mlpackage.tar.gz |
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``` |
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**Small model** (from Apple): |
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```bash |
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curl -L -o DepthAnythingV2SmallF16.mlpackage.zip \ |
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"https://ml-assets.apple.com/coreml/models/Image/DepthEstimation/DepthAnything/DepthAnythingV2SmallF16.mlpackage.zip" |
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unzip DepthAnythingV2SmallF16.mlpackage.zip |
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``` |
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## Usage |
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### Swift |
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```swift |
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import CoreML |
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let modelURL = URL(fileURLWithPath: "DepthAnythingV2BaseF16.mlpackage") |
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let config = MLModelConfiguration() |
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config.computeUnits = .all // Use Neural Engine + GPU + CPU |
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let model = try MLModel(contentsOf: modelURL, configuration: config) |
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// Input: RGB image (1, 3, 518, 518) |
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// Output: depth map (1, 518, 518) |
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``` |
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## Performance |
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**M4 Pro (estimated):** |
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- Small: ~25-30ms per frame |
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- Base: ~60-90ms per frame |
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- Large: ~200-300ms per frame |
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These are **10-20x faster** than ONNX CPU inference because they use the Apple Neural Engine. |
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## Citation |
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```bibtex |
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@article{yang2024depth, |
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title={Depth Anything V2}, |
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author={Yang, Lihe and Kang, Bingyi and Huang, Zilong and Zhao, Zhen and Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang}, |
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journal={arXiv:2406.09414}, |
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year={2024} |
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} |
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``` |
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## Related |
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- [Original Depth Anything V2](https://github.com/DepthAnything/Depth-Anything-V2) |
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- [spatial-maker](https://github.com/mrgnw/spatial-maker) - Uses these models for spatial video/photo conversion |
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