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README.md

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+---
+license: apache-2.0
+library_name: litert
+pipeline_tag: keypoint-detection
+tags: [keypoint-detection, image-matching, local-features, xfeat, litert, tflite, on-device, gpu]
+---
+
+# XFeat (Accelerated Features) — LiteRT (CompiledModel GPU)
+
+**XFeat** (Apache-2.0, ~1.5M, a lightweight pure-CNN local feature extractor for image matching —
+SLAM / AR / image registration) re-authored to a **GPU-native** LiteRT `.tflite` via **litert_torch**.
+FP16, **1.4 MB**, input **[1, 480, 640, 1]** NHWC normalized grayscale.
+
+Verified on a Pixel 8a: full **LITERT_CL residency (72/72 nodes, 1 partition), ~0.4 ms**, GPU output
+matches CPU/PyTorch (corr 0.9999).
+
+## I/O
+- **Input** `[1, 480, 640, 1]` NHWC, grayscale, per-image **InstanceNorm** applied host-side
+  (`(g - mean)/sqrt(var+1e-5)` over the image).
+- **Outputs** (all at H/8 × W/8 = 60×80): `feats` `[1,64,60,80]` dense descriptors; `keypoints`
+  `[1,65,60,80]` keypoint logits; `heatmap` `[1,1,60,80]` reliability. Keypoint NMS, descriptor
+  bilinear-sampling, and mutual-nearest-neighbor matching run host-side.
+
+## GPU-clean re-authoring
+- Input gray + InstanceNorm moved **host-side** (its spatial reduction over H·W would overflow fp16
+  on the delegate).
+- `_unfold2d(x, 8)` (space-to-depth via unfold → >4-D / GATHER_ND) → a one-hot `Conv2d(1,64,k=8,s=8)`
+  (exact, single CONV_2D). Result: zero GATHER/SELECT/TopK/Cast, no >4-D — full GPU residency.
+
+## Training data & PII
+XFeat is trained on public correspondence data (MegaDepth + synthetic homographies). It outputs
+geometric keypoints/descriptors only — no faces, identities, or personal attributes. Official
+weights; only the op graph was re-authored for GPU.
+
+## Sample app + conversion script
+https://github.com/google-ai-edge/litert-samples (compiled_model_api, two-image matching).