| --- |
| license: apache-2.0 |
| base_model: litert-community/MODNet-LiteRT |
| base_model_relation: quantized |
| library_name: openvino |
| pipeline_tag: image-segmentation |
| tags: |
| - openvino |
| - onnx |
| - intel-npu |
| --- |
| |
| # MODNet-OpenVINO |
|
|
| MODNet for Intel NPUs, GPUs and CPUs: modnet from [litert-community/MODNet-LiteRT](https://huggingface.co/litert-community/MODNet-LiteRT/tree/4d5bd22bde15c8ae5e215ca4799513844979a431) 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 16.60 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 | |
| |---|---|---| |
| | `modnet.onnx` | 26,503,485 | `062f6d9f76a43ec14b67376a2da77d604e9f0968cf835fa190e654875e3fc090` | |
| | `openvino/modnet.xml` | 1,442,411 | `7488ce4a32b6f4443f33a7e872ee0bdd2de0cb9bbf0f14ceabed05cca47997ab` | |
| | `openvino/modnet.bin` | 25,848,312 | `74ce169252d038dcd11f539a00480ba171c846b63d3114dddf82e3f7b35d6547` | |
| | `npu/modnet.blob` | 19,796,097 | `f3de63c41292d1b2b368035331a220bb9be0f66fc18354b0a0c8584b782c242f` | |
|
|
| 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`) | 16.60 | 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 | 15.80 | 15.41 | PASS (cos ≥ 0.9999) | |
| | Intel Graphics (iGPU) | ONNX Runtime 1.31.1 + OpenVINO EP 1.8.1 | `.onnx` | 32.06 | 2.31 | PASS (cos ≥ 0.9985) | |
| | RTX 5080 Laptop (95 W limit) | ONNX Runtime 1.31.1, CUDA EP | fp16 copy of the `.onnx` (not in this repo) | 8.59 | 1.17 | PASS (cos ≥ 0.9997) | |
| | RTX 5080 Laptop (95 W limit) | ONNX Runtime 1.31.1, CUDA EP | `.onnx` | 10.79 | 1.20 | PASS (cos ≥ 0.9999) | |
| | Core Ultra 9 386H CPU | ONNX Runtime 1.31.1, CPU EP | `.onnx` | 68.72 | 0.15 | reference | |
| | Snapdragon X2 Elite NPU | Qualcomm AI Hub, precompiled QNN ONNX | `.onnx` compiled by AI Hub | 8.30 | 0.35 | 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 | 9.76 | 6.63 | not compared (AI Hub profile job, 2026-10-30) | |
| | Galaxy S26 NPU | Qualcomm AI Hub, precompiled QNN ONNX | `.onnx` compiled by AI Hub | 8.86 | 0.21 | 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: |
|
|
| ```sh |
| pip install huggingface_hub |
| hf download mlboydaisuke/MODNet-OpenVINO --local-dir MODNet-OpenVINO |
| cd MODNet-OpenVINO |
| ``` |
|
|
| OpenVINO (`pip install openvino==2026.4.1`): |
|
|
| ```python |
| 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/modnet.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/modnet.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 with the OpenVINO EP (`pip install onnxruntime==1.31.1 onnxruntime-ep-openvino==1.8.1`): |
|
|
| ```python |
| import numpy as np |
| import onnxruntime as ort |
| import onnxruntime_ep_openvino as ov_ep |
| |
| ort.register_execution_provider_library(ov_ep.get_ep_name(), ov_ep.get_library_path()) |
| npu = [d for d in ort.get_ep_devices() |
| if d.ep_name == ov_ep.get_ep_name() and d.ep_metadata.get("ov_device") == "NPU"] |
| if not npu: |
| raise SystemExit("no Intel NPU visible to the OpenVINO EP") |
| so = ort.SessionOptions() |
| so.add_provider_for_devices(npu, {}) |
| sess = ort.InferenceSession("modnet.onnx", sess_options=so) # compiles for the NPU here |
| # replace the zeros with your preprocessed input (float32) |
| feeds = {i.name: np.zeros(i.shape, np.float32) for i in sess.get_inputs()} |
| outputs = sess.run(None, feeds) |
| print([o.shape for o in outputs]) |
| ``` |
|
|
| ## Licence |
|
|
| Apache-2.0, the licence of [litert-community/MODNet-LiteRT](https://huggingface.co/litert-community/MODNet-LiteRT). |
|
|
