README.md

---
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.