---
license: apache-2.0
base_model: litert-community/D-FINE-S-LiteRT
base_model_relation: quantized
library_name: openvino
pipeline_tag: object-detection
tags:
- openvino
- onnx
- intel-npu
---

# 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](https://huggingface.co/litert-community/D-FINE-S-LiteRT/tree/b662bc69f7d8b8278acaf57ca0831e194a0bbed4) as ONNX, OpenVINO IR with fp32 weights, and precompiled Intel NPU blobs. Call graph A (backbone and encoder) first, on a 640×640 image. A host step then builds graph B's inputs from A's outputs and that repo's `host_params.bin`. Graph B (decoder) returns 300 boxes and their class logits. The NPU of a Core Ultra 9 386H runs the blobs in 11.14 ms (A) and 96.94 ms (B).

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).

## Conversion

The ONNX files were converted from the `.tflite` files with tf2onnx 1.17.0 (opset 17). 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`.

## 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:

```sh
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`):

```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/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`):

```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("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.

The Python on the [LiteRT card](https://huggingface.co/litert-community/D-FINE-S-LiteRT) has the rest: the preprocessing (`img`, `x`), the loading of `host_params.bin` from that repo (`p`, `take`), and the host step between the graphs (from `enc_cls, mem =` to `ref =`). Swap its `run()` for the lines above and give each graph its inputs by shape.

## Branch `windows-model-zoo`

The branch [`windows-model-zoo`](https://huggingface.co/mlboydaisuke/D-FINE-S-OpenVINO/tree/windows-model-zoo) adds the 5 files that [windows-model-zoo](https://github.com/john-rocky/windows-model-zoo) runs. `unfixed/`: the conversion without the bias fix above, with its fp16 copy and NPU blob. `fp16/`: the fp16 copy that the RTX fp16 row of dfine_graphB_fp16 above ran. To use the model, take main's files.

## Licence

Apache-2.0, the licence of [litert-community/D-FINE-S-LiteRT](https://huggingface.co/litert-community/D-FINE-S-LiteRT). The upstream model: [ustc-community/dfine-small-coco](https://huggingface.co/ustc-community/dfine-small-coco).