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ruisv/
bcdl-superres

Name: bcdl-superres
Availability: InStock

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x4 super-resolution for RDK S100/S100P — Real-ESRGAN Compact and SPAN

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, a C++17 inference and media library for the RDK S-series with Python bindings.

Upstream: Real-ESRGAN general-x4v3 (BSD-3) and SPAN x4 ch48 (Apache-2.0)

[!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.

Files

file	what it is
`realesr_general_x4v3_nashm_128.hbm`	Real-ESRGAN Compact, 128x128 tile — 37.1 MB
`spanx4_ch48_nashm_128.hbm`	SPAN ch48, 128x128 tile — 5.8 MB

Measured on an S100P

stage	latency	throughput
Compact	2.01 ms/tile	498 FPS
SPAN	1.09 ms/tile	915 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.

Use it

conda install -c https://mirrors.ruis.ai/conda -c conda-forge bcdl

python

import bcdl
engine = bcdl.Engine("realesr_general_x4v3_nashm_128.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 (中文).

What to know before deploying

Two models, and neither supersedes the other. SPAN is fidelity-oriented and wins on a clean downscale (32.95 dB against Compact's 30.01 and bicubic's 32.29) at a sixth of the size. Compact is perceptual, trained on real degradations, and wins on blurred or JPEG-compressed input (28.54 against 27.60). Pick by your input domain, not by benchmark rank.

The 128x128 tile is deliberate. A compiled .hbm is mostly instruction stream rather than weights, and it scales with tile area: the same network at 256x256 is a 148 MB model against 37 MB here, for identical per-pixel throughput. If your runtime already tiles — BCDL's SuperResolver does, with overlapped cross-fading — take the small tile.

PSNR alone is the wrong headline for a perceptual upscaler, which is why the two numbers above are quoted per input domain.

Licence

Real-ESRGAN is BSD-3 with weights released by the copyright holder; SPAN is Apache-2.0 by project statement, though its checkpoints ship from a separate file host with no terms attached to the artefact.

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, so this build can be reproduced or retargeted rather than taken on trust.

ruisv/bcdl-superres