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README.md

@@ -11,29 +11,36 @@ base_model:
 ---
 # depth_anything_v2_small — ExecuTorch XNNPACK
 
-`depth_anything_v2_small_xnnpack_fp32.pte` (99.0 MB, fp32, XNNPACK-delegated)
-
 - **Source**: depth-anything/Depth-Anything-V2-Small-hf
 - **License**: Apache-2.0
 - **Input**: [[1, 3, 518, 518]] — RGB, ImageNet norm, 518x518
 - **Output**: relative inverse depth [1,518,518]
 
-## Verification (Mac arm64, executorch 1.4.0, torch 2.13.0)
+## Variants
+
+All variants take and return fp32 tensors — swap the `.pte` file, keep your app code.
+
+| precision | file | size (MB) | parity vs fp32 eager (worst corr) | Mac median (ms)* |
+|-----------|------|-----------|------------------------------------|------------------|
+| fp32 | `depth_anything_v2_small_xnnpack_fp32.pte` | 99.0 | 1.000000 | 167.3 |
+| fp16 | `depth_anything_v2_small_xnnpack_fp16.pte` | 55.5 | 0.999992 | 289.7 |
+| int8 (dynamic) | `depth_anything_v2_small_xnnpack_int8.pte` | 35.5 | 0.999979 | 166.2 |
+
+\*Mac arm64, single process, median of 10 — a reference point for relative cost
+only, not a device number (torch eager fp32 on the same machine: 85.0 ms).
 
-Parity vs torch fp32 eager on random input:
+## Verification (executorch 1.4.0, torch 2.13.0)
+
+Parity is measured against the fp32 eager model on random input; `corr` is
+the correlation over all elements of each output tensor.
 
 | output | shape | max_abs_diff | corr |
 |--------|-------|--------------|------|
-| 0 | [1, 518, 518] | 6.914e-06 | 1.000000 |
+| 0 | [1, 518, 518] | 4.768e-06 | 1.000000 |
 
-Median latency over 10 runs (single Mac process, reference only — device numbers to follow):
-ExecuTorch 173.7 ms vs torch eager 88.0 ms.
+XNNPACK delegate coverage (fp32): 73.4% (482/657 ops); ops left on the portable kernels: `aten.expand_copy.default` x49, `aten.native_layer_norm.default` x28, `aten.mul.Scalar` x24, `aten.logical_not.default` x24, `aten.eq.Scalar` x12, `aten.full_like.default` x12, `aten.any.dim` x12, `aten.where.self` x12, `dim_order_ops._to_dim_order_copy.default` x1, `aten.squeeze_copy.dims` x1
 
 ## Conversion
 
 torch.export -> to_edge_transform_and_lower(XnnpackPartitioner) -> .pte
 (conversion scripts: [executorch-models](https://github.com/john-rocky/executorch-models))
-
-
----
-Part of [executorch-models](https://github.com/john-rocky/executorch-models) — a verified .pte zoo for ExecuTorch. Conversion scripts and all models are indexed there.