D-FINE-S-OpenVINO

D-FINE-S for Intel NPUs, GPUs and CPUs: dfine_graphA_fp16 and dfine_graphB_fp16 from litert-community/D-FINE-S-LiteRT as ONNX (tf2onnx 1.17.0, opset 17), OpenVINO IR with fp32 weights, and precompiled Intel NPU blobs. tf2onnx 1.17.0 drops the bias of 15 FULLY_CONNECTED layers of dfine_graphA_fp16 and dfine_graphB_fp16; these files were converted with a one-line fix of that handler (convert/patch_tf2onnx.py in the GitHub repo), and their outputs match the .tflite. On the NPU of a Core Ultra 9 386H the blob runs in 11.14 ms (dfine_graphA_fp16) and 96.94 ms (dfine_graphB_fp16) 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
dfine_graphA_fp16.onnx	25,224,269	a17ae288f3885fc9fac1947b809804ec22fcc7a2e349769157df0852c9342f3a
openvino/dfine_graphA_fp16.xml	1,733,404	d826377da28183918bbc2acd6e89ae8d0d6c826efdbebc34b907c949aea0680c
openvino/dfine_graphA_fp16.bin	24,222,828	a8784deaba145f7186fdfd005108eba308eada86e9be98c587146430a393c402
npu/dfine_graphA_fp16.blob	18,370,696	7a1041fd1917e0122a9c41b723027e607290dc08d0d3012047d6c89eb635fb0e
dfine_graphB_fp16.onnx	17,758,341	440bc1c6db016bc069027df3ce791b09d0b36727dcabfb340c31c7e158cfdb12
openvino/dfine_graphB_fp16.xml	1,467,491	7dc7752dd3e923b95df0b4ff75843df16b153f8f76510f8ad501bbad2566969d
openvino/dfine_graphB_fp16.bin	16,665,968	91b4a6e2eb7246d1ba77d6669ff5594f209f05e9b15588fafbf938906819bf87
npu/dfine_graphB_fp16.blob	60,096,662	077cfe34c93d875266a5bbfa383bda686fd670cc3259d0c08528b704ca6af0d0

The NPU blobs were 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. No Snapdragon rows: Qualcomm AI Hub measured the files before the bias fix (its numbers are in the GitHub table).

dfine_graphA_fp16

processor	runtime	file	ms	load s	output check
Intel AI Boost NPU	OpenVINO 2026.4.1	npu/ blob (import_model)	11.14	0.01	PASS (cos ≥ 0.9973)
Intel AI Boost NPU	ONNX Runtime 1.31.1 + OpenVINO EP 1.8.1	.onnx, compiled when the session loads	8.61	12.75	PASS (cos ≥ 0.9973)
Intel Graphics (iGPU)	ONNX Runtime 1.31.1 + OpenVINO EP 1.8.1	.onnx	8.89	1.36	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)	6.80	1.29	PASS (cos ≥ 0.9999)
RTX 5080 Laptop (95 W limit)	ONNX Runtime 1.31.1, CUDA EP	.onnx	7.22	1.37	PASS (cos ≥ 0.9999)
Core Ultra 9 386H CPU	ONNX Runtime 1.31.1, CPU EP	.onnx	44.49	0.26	reference

The ONNX file against the source .tflite (LiteRT interpreter on the CPU, the same input): PASS, cosine ≥ 0.9999 for every output.

dfine_graphB_fp16

processor	runtime	file	ms	load s	output check
Intel AI Boost NPU	OpenVINO 2026.4.1	npu/ blob (import_model)	96.94	0.04	PASS (cos ≥ 0.9999)
Intel AI Boost NPU	ONNX Runtime 1.31.1 + OpenVINO EP 1.8.1	.onnx, compiled when the session loads	97.99	51.92	PASS (cos ≥ 0.9999)
Intel Graphics (iGPU)	ONNX Runtime 1.31.1 + OpenVINO EP 1.8.1	.onnx	88.00	3.14	FAIL: output_1_output: 0 NaN, 80 Inf; output_1_output: argmax agreement 189/300
RTX 5080 Laptop (95 W limit)	ONNX Runtime 1.31.1, CUDA EP	fp16 copy of the .onnx (not in this repo)	9.60	1.27	FAIL: output_1_output: 0 NaN, 80 Inf
RTX 5080 Laptop (95 W limit)	ONNX Runtime 1.31.1, CUDA EP	.onnx	9.66	1.23	PASS (cos ≥ 0.9999)
Core Ultra 9 386H CPU	ONNX Runtime 1.31.1, CPU EP	.onnx	67.99	0.11	reference

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/D-FINE-S-OpenVINO --local-dir D-FINE-S-OpenVINO
cd D-FINE-S-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/dfine_graphA_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/dfine_graphA_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):

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("dfine_graphA_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])

The same lines run dfine_graphB_fp16 with its own file names.

Licence

Apache-2.0, the licence of litert-community/D-FINE-S-LiteRT. The upstream model: ustc-community/dfine-small-coco.