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ruisv/bcdl-superres
bcdl-superres is a image-to-image model from ruisv. Use it when you need one image transformed into another. It is set up for bcdl. The card lists the license as other.
Compiled BPU models (.hbm) for the D-Robotics RDK S100 / S100P, ready to load — no ONNX export, no calibration, no hbcompile. Built and measured with BCDL, a C++17 inference and media library for the RDK S-series with…
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Updated Aug 13, 2026
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From the Hugging Face model README
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.
| 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 |
| 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.
conda install -c https://mirrors.ruis.ai/conda -c conda-forge bcdl
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).
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.
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.