Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,56 @@
+---
+license: mit
+tags:
+  - coreai
+  - depth-estimation
+  - apple
+  - macos27
+library_name: coreai
+---
+
+# DA3-LARGE · CoreAI (.aimodel)
+
+[Depth Anything 3 (DA3-LARGE)](https://huggingface.co/depth-anything/DA3-LARGE) any-view depth model (ViT-L backbone), converted to Apple's **CoreAI** `.aimodel` format (macOS 27 / iOS 27+, device only — no simulator). Single-image input (N=1).
+
+Converted with `coreai_torch 0.4.2` / `torch 2.14` via `torch.export → run_decompositions → TorchConverter → optimize → save_asset`. Two precision builds: **fp32** (1.35 GB) and **fp16** (677.8 MB, half-precision compute with fp32 IO).
+
+## IO contract
+
+- Function: `main`
+- Input: `image` — float32 `[1, 3, 504, 504]`, ImageNet-normalized planar RGB (normalization is caller-side)
+- Output: `depth` float32 `[504, 504]` (relative depth; any-view head, no sky output)
+
+## Validation (vs PyTorch fp32 reference, fixed-seed golden input)
+
+| Build | max abs err | median rel err | load (cold) | p50 latency (30f) |
+|---|---|---|---|---|
+| fp32 | **8.35e-07** | 1.94e-07 | 1.73 s | 116.9 ms |
+| fp16 | **6.81e-04** | 1.54e-04 | 1.21 s | 39.1 ms |
+
+- Export pipeline self-checks: torch.export vs wrapper = 0.0; decomposed graph vs wrapper ≤ 4.9e-04 (fp16)
+- Zero missing operators (39 aten op kinds / 2391 nodes accepted by coreai_torch)
+- Model config: HF snapshot ships no `config.json`; conversion uses the `da3-large.yaml` registry config from the DA3 source tree (weights load with unexpected=0)
+- Any-view RoPE `max_position` is fixed to a static constant (37) at export time — numerically identical, verified eager max|err| = 0
+
+## Usage (Swift)
+
+```swift
+import CoreAI
+
+let model = try await AIModel(
+    contentsOf: url,
+    options: SpecializationOptions(preferredComputeUnitKind: .neuralEngine) // or .gpu / .default
+)
+guard let f = try model.loadFunction(named: "main") else { … }
+let input = NDArray(scalars: pixels, shape: [1, 3, 504, 504])
+var outputs = try await f.run(inputs: ["image": input])
+let depth = outputs.remove("depth")?.ndArray
+```
+
+`preferredComputeUnitKind` is a *preference*: with the fp32 build, `auto` / `.gpu` / `.neuralEngine` land within noise (the model runs on GPU). The **fp16** build is ~3× faster (39.1 ms vs 116.9 ms p50) and is the one that can actually benefit from ANE.
+
+## Files
+
+- `da3large-fp32.aimodel/` — fp32 bundle (`main.mlirb` + `main.hash` + `metadata.json`)
+- `da3large-fp16.aimodel/` — fp16 bundle
+- `recipe.toml` — full conversion recipe (sources, versions, validation gates)