Compiled BPU models for RDK S100/S100P

.gitattributes

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README.md

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+---
+license: other
+library_name: bcdl
+tags:
+- rdk-s100
+- rdk-s100p
+- d-robotics
+- bpu
+- hbm
+- image-segmentation
+- semantic-segmentation
+---
+
+# PIDNet-S for RDK S100/S100P — real-time semantic segmentation
+
+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: [PIDNet](https://github.com/XuJiacong/PIDNet)-S, Cityscapes 19 classes
+
+> [!WARNING]
+> **Non-commercial.** These weights carry a restriction the upstream *code*
+> licence never mentions — see [Licence](#licence) at the bottom. Redistribution
+> is permitted; commercial use is not. If you need a commercial build, the
+> recipe is public and you can retrain or substitute the backbone.
+
+## Files
+
+| file | what it is |
+|---|---|
+| `pidnet_s_nashm_1024x2048_nv12_v3.hbm` | 2048x1024 NV12, 19 classes — 18.0 MB |
+
+## Measured on an S100P
+
+| stage | latency | throughput |
+|---|---|---|
+| segmentation | 4.48 ms | 223 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("pidnet_s_nashm_1024x2048_nv12_v3.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
+
+**11x faster than the DeepLabV3+ build it replaces** (4.48 ms against 49.6 ms) at
+half the model size, for a 0.9859 output cosine and 94.6% pixel agreement.
+
+The output is at 1/8 resolution — `[1, 19, 128, 256]` — and the label map is
+upsampled by the caller. Argmax costs 0.18 ms here, so folding it into the graph
+would buy nothing.
+
+**The `_v3` suffix is load-bearing.** Earlier builds were calibrated on data that
+had not been pre-normalised. When `cal_data_type` is float32 the compiler's
+`norm_type` does **not** apply to the calibration data, so the input thresholds
+come out wrong — and the model still compiles without a warning and segments to
+noise. If you rebuild this, check the input threshold in `quant_info.json`.
+
+**Non-commercial.** See the licence note above.
+
+## Licence
+
+MIT on the code, but the weights are trained on **Cityscapes**, whose terms permit distributing a trained model and bar commercial use of it in the same breath.
+
+**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.

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