XFeat for RDK S100/S100P — compiled BPU model

.gitattributes

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README.md

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+---
+license: apache-2.0
+library_name: bcdl
+tags:
+- rdk-s100
+- rdk-s100p
+- d-robotics
+- bpu
+- hbm
+- image-feature-extraction
+- keypoint-detection
+---
+
+# XFeat for RDK S100/S100P — sparse local features
+
+Compiled BPU models (`.hbm`) for the **D-Robotics RDK S100 / S100P**, ready to
+load — no ONNX export, no calibration, no `hb_compile`. Built and measured with
+[**BCDL**](https://github.com/ruisv/bcdl), a C++17 inference and media library
+for the RDK S-series with Python bindings.
+
+Upstream: [XFeat / accelerated_features](https://github.com/verlab/accelerated_features)
+
+> [!TIP]
+> **Redistributable, including commercially.** The licence chain was checked on
+> the code, the pretrained weights it started from, and the data it was trained
+> on — all three, because a permissive repository badge does not by itself say
+> anything about the weights. See [Licence](#licence).
+
+## Files
+
+| file | what it is |
+|---|---|
+| `xfeat_nashm_640x480.hbm` | backbone, 640x480, 3 outputs — 3.0 MB |
+
+## Measured on an S100P
+
+| stage | latency | throughput |
+|---|---|---|
+| backbone | 0.99 ms | 1013 FPS |
+
+`hrt_model_exec perf`, one thread, minimum of three runs, on a board first gated
+against its own previously recorded numbers. **BPU time only** — CPU
+pre/post-processing is on top and is listed per task in BCDL's
+[benchmark results](https://github.com/ruisv/bcdl/blob/main/benchmarks/RESULTS.md).
+
+## Use it
+
+```bash
+conda install -c https://mirrors.ruis.ai/conda -c conda-forge bcdl
+```
+
+```python
+import bcdl
+engine = bcdl.Engine("xfeat_nashm_640x480.hbm")
+print(engine.input_shape(0), engine.output_shape(0))
+```
+
+Each task has a decoder in BCDL that turns those raw outputs into boxes,
+keypoints, masks, disparity or text — see the
+[Python API](https://github.com/ruisv/bcdl/blob/main/docs/API.md)
+([中文](https://github.com/ruisv/bcdl/blob/main/docs/API.zh.md)).
+
+## What to know before deploying
+
+**Only the convolutional backbone is compiled** — 3 MB of it. Keypoint NMS,
+top-k selection and sparse descriptor sampling stay on the CPU, which is what
+keeps the graph free of dynamic control flow. BCDL does that half for you.
+
+Two rewrites were needed to export it at all, and both were checked numerically
+before being trusted: the input `InstanceNorm` was lifted out of the graph into
+CPU preprocessing, and `_unfold2d` became `pixel_unshuffle` (a single
+`SpaceToDepth`), asserted equal to the original beforehand.
+
+**The descriptor sampler is bicubic, not bilinear.** Upstream takes the default
+mode of `InterpolateSparse2d`, which is bicubic, while the *reliability* map in
+the same file uses bilinear. Getting that wrong leaves shapes, counts and
+keypoints all correct and the descriptor cosine stuck at 0.9965 — which reads
+like quantisation noise. It is not.
+
+## Licence
+
+Apache-2.0, and the pretrained weights are committed inside that repository — so the same grant covers them.
+
+**BCDL itself is Apache-2.0 and is unrelated to these terms** — it is a
+general-purpose runtime that loads any `.hbm`. The licence above constrains
+*these weights and this compiled artefact*.
+
+The conversion recipe — ONNX export, calibration, `hb_compile` config and the
+acceptance numbers — is public in
+[**bcdl-model-zoo**](https://github.com/ruisv/bcdl-model-zoo), so this build can
+be reproduced or retargeted rather than taken on trust.

xfeat_nashm_640x480.hbm

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