--- license: apache-2.0 base_model: litert-community/RTMPose-s-LiteRT base_model_relation: quantized library_name: openvino pipeline_tag: keypoint-detection tags: - openvino - onnx - intel-npu --- # RTMPose-s-OpenVINO RTMPose-s for Intel NPUs, GPUs and CPUs: rtmpose_s_fp16 from [litert-community/RTMPose-s-LiteRT](https://huggingface.co/litert-community/RTMPose-s-LiteRT/tree/9cc07fa96de442bcc568f2cbb442e94fbfe4dac2) 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 0.89 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 | |---|---|---| | `rtmpose_s_fp16.onnx` | 22,197,006 | `6c802b86a0f976f700afc336916a6db593df098b11568803e96d749ef6c8b9f3` | | `openvino/rtmpose_s_fp16.xml` | 645,133 | `43bc352fc506a2dba6c3c4a6d28c8841d7355862788cf264d61cb2ea0dc2b85e` | | `openvino/rtmpose_s_fp16.bin` | 21,870,864 | `e67abb3cc341620f5e759c33aafdf66535a6f2e93eb7c2ef99cc8ce5c5a3536c` | | `npu/rtmpose_s_fp16.blob` | 11,858,056 | `e080cbbab273a719570224f9795a1b88873b0ff8246f051a7b0316d5b78239ed` | 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`) | 0.89 | 0.01 | FAIL: output_0_output: cos 0.9367 < 0.99; output_0_output: argmax agreement 15/17; output_1_output: cos 0.9374 < 0.99; output_1_output: argmax agreement 10/17 | | Intel AI Boost NPU | ONNX Runtime 1.31.1 + OpenVINO EP 1.8.1 | `.onnx`, compiled when the session loads | 1.17 | 0.95 | FAIL: output_0_output: cos 0.9367 < 0.99; output_0_output: argmax agreement 15/17; output_1_output: cos 0.9374 < 0.99; output_1_output: argmax agreement 10/17 | | Intel Graphics (iGPU) | ONNX Runtime 1.31.1 + OpenVINO EP 1.8.1 | `.onnx` | 0.66 | 0.45 | 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) | 2.72 | 1.26 | FAIL: output_0_output: argmax agreement 16/17; output_1_output: argmax agreement 15/17 | | RTX 5080 Laptop (95 W limit) | ONNX Runtime 1.31.1, CUDA EP | `.onnx` | 2.94 | 1.16 | FAIL: output_1_output: argmax agreement 16/17 | | Core Ultra 9 386H CPU | ONNX Runtime 1.31.1, CPU EP | `.onnx` | 3.23 | 0.07 | reference | | Snapdragon X2 Elite NPU | Qualcomm AI Hub, precompiled QNN ONNX | `.onnx` compiled by AI Hub | 0.44 | 0.44 | 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 | 0.42 | 0.79 | not compared (AI Hub profile job, 2026-10-30) | | Galaxy S26 NPU | Qualcomm AI Hub, precompiled QNN ONNX | `.onnx` compiled by AI Hub | 0.36 | 0.15 | 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/RTMPose-s-OpenVINO --local-dir RTMPose-s-OpenVINO cd RTMPose-s-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/rtmpose_s_fp16.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/rtmpose_s_fp16.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("rtmpose_s_fp16.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/RTMPose-s-LiteRT](https://huggingface.co/litert-community/RTMPose-s-LiteRT). The source card names the upstream model as `open-mmlab/mmpose` (not a Hugging Face repo).