| --- |
| license: apache-2.0 |
| base_model: litert-community/DIS-ISNet-LiteRT |
| base_model_relation: quantized |
| library_name: openvino |
| pipeline_tag: image-segmentation |
| tags: |
| - openvino |
| - onnx |
| - intel-npu |
| --- |
| |
| # DIS-ISNet-OpenVINO |
|
|
| DIS-ISNet for Intel NPUs, GPUs and CPUs: dis from [litert-community/DIS-ISNet-LiteRT](https://huggingface.co/litert-community/DIS-ISNet-LiteRT/tree/1b966dbe2f33bd5ca1299cf94fbab59265210b6b) 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 41.77 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 | |
| |---|---|---| |
| | `dis.onnx` | 176,368,980 | `4101d87d06f48169718b0a6dbe844b0c861b69289493f9ed6fe9e5ec08dda95e` | |
| | `openvino/dis.xml` | 1,138,382 | `91ab641e25eeab1d359379038a8a5d4428a99e3ac7aa4ade28c72dd600b367da` | |
| | `openvino/dis.bin` | 176,019,080 | `df408e5ef680a4a0e393ab11a2263de656610f075bf1dc5483f440bfb5d38e30` | |
| | `npu/dis.blob` | 110,022,785 | `b521145f276f138242427719ef127b443ac3689858a5ef34f18c12bbd8e246d8` | |
|
|
| 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), 2026-11-07. 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`) | 41.77 | 0.06 | PASS (cos ≥ 0.9999) | |
| | Intel AI Boost NPU | ONNX Runtime 1.31.1 + OpenVINO EP 1.8.1 | `.onnx`, compiled when the session loads | 40.92 | 60.40 | PASS (cos ≥ 0.9999) | |
| | Intel Graphics (iGPU) | ONNX Runtime 1.31.1 + OpenVINO EP 1.8.1 | `.onnx` | 30.17 | 2.94 | PASS (cos ≥ 0.9999) | |
| | RTX 5080 Laptop (95 W limit) | ONNX Runtime 1.31.1, CUDA EP | fp16 copy of the `.onnx` (not in this repo) | 20.54 | 1.13 | PASS (cos ≥ 0.9999) | |
| | RTX 5080 Laptop (95 W limit) | ONNX Runtime 1.31.1, CUDA EP | `.onnx` | 32.05 | 1.14 | PASS (cos ≥ 0.9999) | |
| | Core Ultra 9 386H CPU | ONNX Runtime 1.31.1, CPU EP | `.onnx` | 1040.20 | 0.30 | reference | |
| | Snapdragon X2 Elite NPU | Qualcomm AI Hub, precompiled QNN ONNX | `.onnx` compiled by AI Hub | 21.53 | 0.37 | 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 | 21.52 | 9.28 | not compared (AI Hub profile job, 2026-10-30) | |
| | Galaxy S26 NPU | Qualcomm AI Hub, precompiled QNN ONNX | `.onnx` compiled by AI Hub | 19.17 | 0.26 | 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/DIS-ISNet-OpenVINO --local-dir DIS-ISNet-OpenVINO |
| cd DIS-ISNet-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/dis.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/dis.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("dis.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/DIS-ISNet-LiteRT](https://huggingface.co/litert-community/DIS-ISNet-LiteRT). |
|
|
