license: mit
base_model: litert-community/DewarpNet-LiteRT
base_model_relation: quantized
library_name: openvino
pipeline_tag: image-to-image
tags:
- openvino
- onnx
- intel-npu
DewarpNet-OpenVINO
DewarpNet for Intel NPUs, GPUs and CPUs: dewarp from litert-community/DewarpNet-LiteRT as ONNX (tf2onnx 1.17.0, opset 17), OpenVINO IR with fp32 weights, and a precompiled Intel NPU blob. On the NPU of a Core Ultra 9 386H the blob runs in 8.24 ms per run, numpy input to numpy output.
Scripts, raw records, and the same table for all 52 graphs of LiteRT-Models: https://github.com/john-rocky/windows-zoo
Files
| file | bytes | sha256 |
|---|---|---|
dewarp.onnx |
189,339,174 | 4dde2be5fa86a4b4b1dd870b0ba89dc5da6e2165b90992c13a96450935eb04c2 |
openvino/dewarp.xml |
1,139,702 | 36060c9fd58b3fec9df7c95314892385f25240e9956a8005a05d2af914188e75 |
openvino/dewarp.bin |
188,529,172 | 840b093d86a174c4694c0ff3a37a5270576bac67e4c2b4ff9737a279929f89bd |
npu/dewarp.blob |
19,378,305 | 345b2e8bb9239bf6df1e91a2ca8bad42ff83906e29087b755459cb61c9b669f3 |
The NPU blob was compiled with OpenVINO 2026.4.1, Intel NPU driver 32.0.100.4841 (OpenVINO reports NPU_DRIVER_VERSION 1004841, NPU_COMPILER_VERSION 524292). If import_model refuses a blob on another driver or OpenVINO version, compile the IR instead (the commented line in the snippet).
How it was measured
MSI Stealth 16 AI+ (Core Ultra 9 386H, RTX 5080 Laptop GPU at a 95 W power limit, Windows 11 build 26200.9711, on AC power), 2026-11-06 to 2026-11-07. On the laptop, each row is one process: load, one run, 5 warmup runs, then the median of 30 timed runs (2 warmup + 10 timed runs when the first run takes more than 3 s), numpy input to numpy output, every runtime at its default settings, one process at a time, each started after the CPU, the RTX and the Intel GPU had gone idle (the thresholds are in METHOD.md of the GitHub repo). Output check: each output against the CPU output in fp32 on the same input (ONNX Runtime's CPU EP); PASS = cosine ≥ 0.99, no NaN or Inf, and top-1 agreement ≥ 0.99 for class scores. The Snapdragon rows are Qualcomm AI Hub profile jobs of the same ONNX files (their own timer).
Speed and output check
| processor | runtime | file | ms | load s | output check |
|---|---|---|---|---|---|
| Intel AI Boost NPU | OpenVINO 2026.4.1 | npu/ blob (import_model) |
8.24 | 0.01 | PASS (cos ≥ 0.9999) |
| Intel AI Boost NPU | ONNX Runtime 1.31.1 + OpenVINO EP 1.8.1 | .onnx, compiled when the session loads |
- | - | did not run |
| Intel Graphics (iGPU) | ONNX Runtime 1.31.1 + OpenVINO EP 1.8.1 | .onnx |
9.07 | 1.74 | FAIL: output_0_output: cos 0.9891 < 0.99 |
| RTX 5080 Laptop (95 W limit) | ONNX Runtime 1.31.1, CUDA EP | fp16 copy of the .onnx (not in this repo) |
6.74 | 1.51 | PASS (cos ≥ 0.9999) |
| RTX 5080 Laptop (95 W limit) | ONNX Runtime 1.31.1, CUDA EP | .onnx |
8.53 | 1.47 | PASS (cos ≥ 0.9999) |
| Core Ultra 9 386H CPU | ONNX Runtime 1.31.1, CPU EP | .onnx |
201.36 | 0.65 | reference |
| Snapdragon X2 Elite NPU | Qualcomm AI Hub, precompiled QNN ONNX | .onnx compiled by AI Hub |
5.04 | 0.34 | not compared (AI Hub profile job, 2026-10-30) |
| Snapdragon X2 Elite NPU | Qualcomm AI Hub, ONNX Runtime QNN EP | .onnx, compiled when it loads |
24.36 (19 layers on the CPU) | 6.26 | not compared (AI Hub profile job, 2026-10-30) |
| Galaxy S26 NPU | Qualcomm AI Hub, precompiled QNN ONNX | .onnx compiled by AI Hub |
4.76 | 0.23 | not compared (AI Hub profile job, 2026-10-30) |
The ONNX file against the source .tflite (LiteRT interpreter on the CPU, the same input): PASS, cosine ≥ 0.9999 for every output.
Run it on the Intel NPU
Download this repo, then run the snippets from its folder:
pip install huggingface_hub
hf download mlboydaisuke/DewarpNet-OpenVINO --local-dir DewarpNet-OpenVINO
cd DewarpNet-OpenVINO
OpenVINO (pip install openvino==2026.4.1):
import numpy as np
import openvino as ov
core = ov.Core()
if "NPU" not in core.available_devices:
raise SystemExit(f"no Intel NPU visible to OpenVINO: {core.available_devices}")
with open("npu/dewarp.blob", "rb") as f:
compiled = core.import_model(f.read(), "NPU") # precompiled: no compile at load
# or compile the IR on this machine: compiled = core.compile_model("openvino/dewarp.xml", "NPU")
# replace the zeros with your preprocessed input (float32, the shapes printed by compiled.inputs)
feeds = {i.get_any_name(): np.zeros(list(i.shape), np.float32) for i in compiled.inputs}
outputs = compiled(feeds)
print({o.get_any_name(): outputs[o].shape for o in compiled.outputs})
ONNX Runtime 1.31.1 with the OpenVINO EP 1.8.1 does not run dewarp.onnx on the NPU: the session fails to load (Compilation failed). The OpenVINO lines above run it.
Licence
MIT, the licence of litert-community/DewarpNet-LiteRT.