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README.md

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 Relative monocular depth from a single image. DA3 Main Series, Base (0.12B params, DINOv2 ViT-B/14 + DualDPT head). Higher quality than Small at ~3× the model size.
 
+<p><img src="https://huggingface.co/mlboydaisuke/Depth-Anything-3-Base-CoreML/resolve/main/media/e3fad96333.jpg" alt="Depth Anything 3 Base (504×504) demo"> <img src="https://huggingface.co/mlboydaisuke/Depth-Anything-3-Base-CoreML/resolve/main/media/718482c065.jpg" alt="Depth Anything 3 Base (504×504) demo"> <img src="https://huggingface.co/mlboydaisuke/Depth-Anything-3-Base-CoreML/resolve/main/media/642cf23afa.jpg" alt="Depth Anything 3 Base (504×504) demo"> <img src="https://huggingface.co/mlboydaisuke/Depth-Anything-3-Base-CoreML/resolve/main/media/6c8d97048e.jpg" alt="Depth Anything 3 Base (504×504) demo"></p>
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 Core ML conversion of [ByteDance-Seed/Depth-Anything-3](https://github.com/ByteDance-Seed/Depth-Anything-3) for on-device inference on iPhone, iPad and Mac. Converted with `coremltools`; the packages are stateless, so all sequencing and buffering lives in your Swift code.
 
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