Add the int8 detectors for the Hexagon, calibrated on real photographs

tools/quantise-models.sh writes the QDQ form QNN's HTP backend takes
whole: opset 17, per-channel int8 weights, uint8 activations, ranges
from running the f32 graph over photographs fed exactly as the app
feeds them. The calibration is strided, four images at a time, because
every ONNX Runtime calibrator holds each image's whole set of
activations until it folds them — a gigabyte an image on the 10g
detector, and an OOM kill with no message when folded once at the end.

Release-time, never on the device (docs/inference.md §5): it needs
real photographs and a person reading the recall measurement that
gates whether each file is offered.

models/face/README.md

@@ -28,6 +28,16 @@ every reader treats "never read" as unknown, never as closed. They are found in
 directory as the pair, so a hand-placed pair does not pick up a package's eye models from a
 directory it otherwise outranks.
 
+    scrfd_500m_640.int8.onnx   0.8 MB   the same three, in the form the Hexagon NPU takes
+    scrfd_2.5g_640.int8.onnx   0.9 MB   (docs/inference.md §5) — opset 17, per-channel int8
+    scrfd_10g_640.int8.onnx    4.3 MB   weights, uint8 activations, calibrated on 96 photographs
+
+The int8 files are **derived** by `tools/quantise-models.sh` from the f32 ones beside them and
+travel with them: the engine loads the `.int8.onnx` sibling when the device's backend wants it and
+the canonical file otherwise, and a library indexed on the int8 form records it as a different
+detector (`scrfd_500m_i8+w600k_mbf`), because it finds a different set of faces. Every other
+platform ignores them. The embedder has no int8 form and never will (§7 of the same document).
+
 **A clone without git-lfs gets a ~130-byte pointer where each model should be.** Both packagers check
 for exactly that and refuse, rather than shipping the pointer and failing inside tract on the user's
 machine. Fix it with `git lfs pull`.