---
license: mit
base_model: litert-community/YOLACT-ResNet50-LiteRT
base_model_relation: quantized
library_name: openvino
pipeline_tag: image-segmentation
tags:
- openvino
- onnx
- intel-npu
---

# YOLACT-ResNet50-OpenVINO

YOLACT-ResNet50 for Intel NPUs, GPUs and CPUs: yolact from [litert-community/YOLACT-ResNet50-LiteRT](https://huggingface.co/litert-community/YOLACT-ResNet50-LiteRT/tree/3ecb14b9c4635cd87b515e1d12201c2c1631e19c) 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 16.29 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 |
|---|---|---|
| `yolact.onnx` | 124,705,334 | `414d1e9ad7c8e9075e0f285ae794307beaa5befe785b5fda22f1e0cc7cfb6eed` |
| `openvino/yolact.xml` | 789,193 | `4ff0eaf04fbf3f1f20531b03bf7bc799665aab73cc5872e14f1092e58206a6c3` |
| `openvino/yolact.bin` | 124,471,816 | `7d0014086613dd81ec686a5280679415df07d08b202883e4cbfe708007db2751` |
| `npu/yolact.blob` | 71,868,566 | `5a9dfa91a4a2a238c519439f48b795724890a4e15d5a0bb3e669958933f21961` |

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. 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 (OpenVINO on the CPU, as ONNX Runtime 1.31.1 cannot load this file); PASS = cosine ≥ 0.99, no NaN or Inf, and top-1 agreement ≥ 0.99 for class scores.

## Speed and output check

| processor | runtime | file | ms | load s | output check |
|---|---|---|---|---|---|
| Intel AI Boost NPU | OpenVINO 2026.4.1 | `npu/` blob (`import_model`) | 16.29 | 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 | - | - | did not run |
| Intel Graphics (iGPU) | ONNX Runtime 1.31.1 + OpenVINO EP 1.8.1 | `.onnx` | - | - | did not run |
| RTX 5080 Laptop (95 W limit) | ONNX Runtime 1.31.1, CUDA EP | fp16 copy of the `.onnx` (not in this repo) | - | - | did not run |
| RTX 5080 Laptop (95 W limit) | ONNX Runtime 1.31.1, CUDA EP | `.onnx` | - | - | did not run |
| Core Ultra 9 386H CPU | ONNX Runtime 1.31.1, CPU EP | `.onnx` | - | - | did not run |
| Core Ultra 9 386H CPU | OpenVINO 2026.4.1 | `.onnx`, f32 | - | - | reference (ONNX Runtime 1.31.1 cannot load this file) |

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/YOLACT-ResNet50-OpenVINO --local-dir YOLACT-ResNet50-OpenVINO
cd YOLACT-ResNet50-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/yolact.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/yolact.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 1.31.1 with the OpenVINO EP 1.8.1 does not run `yolact.onnx` on the NPU: the session fails to load (`onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : Load model from C:\src\zoo\onnx\yolact.onnx failed:Node (__main__.Wrap/yolact.`). The OpenVINO lines above run it.

## Licence

MIT, the licence of [litert-community/YOLACT-ResNet50-LiteRT](https://huggingface.co/litert-community/YOLACT-ResNet50-LiteRT). The upstream model: [dbolya/yolact-resnet50](https://huggingface.co/dbolya/yolact-resnet50).