Upload SAM2-hiera-tiny-OpenVINO: ONNX, OpenVINO IR, Intel NPU blob, card and manifest

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---
license: apache-2.0
base_model: mlboydaisuke/SAM2-hiera-tiny-LiteRT
base_model_relation: quantized
library_name: openvino
pipeline_tag: mask-generation
tags:
- openvino
- onnx
- intel-npu
---
# SAM2-hiera-tiny-OpenVINO

SAM2-hiera-tiny for Intel NPUs, GPUs and CPUs: sam2_decoder and sam2_encoder from [mlboydaisuke/SAM2-hiera-tiny-LiteRT](https://huggingface.co/mlboydaisuke/SAM2-hiera-tiny-LiteRT/tree/8736e24606f7edf62e34cb8ef950546e49df7eff) 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 6 FULLY_CONNECTED layers of sam2_decoder; 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 7.97 ms (sam2_decoder) and 1972.66 ms (sam2_encoder) 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 |
|---|---|---|
| `sam2_decoder.onnx` | 24,932,648 | `bef9a8b8f8adb6bb41755f6d9153742ed51d8f51b105fbbfb67ead7d59f449ea` |
| `openvino/sam2_decoder.xml` | 755,917 | `775d174179109655fbb26e1e7bc1880cfa02d2dc2f4c7fd31d0d42de8f1800df` |
| `openvino/sam2_decoder.bin` | 24,421,952 | `ce3bc5ab2832b822eaa6a9413802e2731120cb7184a01c74b1a8d6e8de617c59` |
| `npu/sam2_decoder.blob` | 21,188,737 | `2326a9d867fb98556a44bbb442d2f201477e9ee3453b3e1345f2e5a515675797` |
| `sam2_encoder.onnx` | 134,201,095 | `2377332c91031aa842d01d7bd53c1e3b772a2310d9c2925aed2b11e74fb59b4f` |
| `openvino/sam2_encoder.xml` | 890,720 | `91c371c1af7b9791f5bf214b4edcd7a7f7142f902481b44658630191801e6a26` |
| `openvino/sam2_encoder.bin` | 134,055,208 | `e33d8c1ebbd01b6b272ae008a32dd42b33b67153ae08501c4baeafed7a68163c` |
| `npu/sam2_encoder.blob` | 146,653,313 | `e107d56b61d093e1d03be3b3b5d69c5436deb7126d2258ea9ffb5986da196c3b` |

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. The Snapdragon rows are Qualcomm AI Hub profile jobs of the same ONNX files (their own timer). No Snapdragon rows for sam2_decoder: Qualcomm AI Hub measured the file before the bias fix (its numbers are in the GitHub table).
## sam2_decoder

| processor | runtime | file | ms | load s | output check |
|---|---|---|---|---|---|
| Intel AI Boost NPU | OpenVINO 2026.4.1 | `npu/` blob (`import_model`) | 7.97 | 0.02 | FAIL: output_0_output: cos 0.1192 < 0.99 |
| Intel AI Boost NPU | ONNX Runtime 1.31.1 + OpenVINO EP 1.8.1 | `.onnx`, compiled when the session loads | 9.82 | 4.15 | PASS (cos ≥ 0.9998) |
| Intel Graphics (iGPU) | ONNX Runtime 1.31.1 + OpenVINO EP 1.8.1 | `.onnx` | 11.29 | 1.19 | 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) | 4.58 | 1.23 | PASS (cos ≥ 0.9999) |
| RTX 5080 Laptop (95 W limit) | ONNX Runtime 1.31.1, CUDA EP | `.onnx` | 4.97 | 1.33 | PASS (cos ≥ 0.9999) |
| Core Ultra 9 386H CPU | ONNX Runtime 1.31.1, CPU EP | `.onnx` | 26.88 | 0.27 | reference |

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

## sam2_encoder
| processor | runtime | file | ms | load s | output check |
|---|---|---|---|---|---|
| Intel AI Boost NPU | OpenVINO 2026.4.1 | `npu/` blob (`import_model`) | 1972.66 | 0.14 | PASS (cos ≥ 0.9999) |
| Intel AI Boost NPU | ONNX Runtime 1.31.1 + OpenVINO EP 1.8.1 | `.onnx`, compiled when the session loads | 106.59 | 225.12 | PASS (cos ≥ 0.9999) |
| Intel Graphics (iGPU) | ONNX Runtime 1.31.1 + OpenVINO EP 1.8.1 | `.onnx` | 156.36 | 1.55 | 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) | 28.01 | 1.27 | PASS (cos ≥ 0.9999) |
| RTX 5080 Laptop (95 W limit) | ONNX Runtime 1.31.1, CUDA EP | `.onnx` | 46.41 | 1.26 | PASS (cos ≥ 0.9999) |
| Core Ultra 9 386H CPU | ONNX Runtime 1.31.1, CPU EP | `.onnx` | 2200.98 | 0.33 | reference |
| Snapdragon X2 Elite NPU | Qualcomm AI Hub, ONNX Runtime QNN EP | `.onnx`, compiled when it loads | 191.09 (1 layers on the CPU) | 139.87 | 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/SAM2-hiera-tiny-OpenVINO --local-dir SAM2-hiera-tiny-OpenVINO
cd SAM2-hiera-tiny-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/sam2_decoder.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/sam2_decoder.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("sam2_decoder.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 sam2_encoder with its own file names.
## Licence
Apache-2.0, the licence of [mlboydaisuke/SAM2-hiera-tiny-LiteRT](https://huggingface.co/mlboydaisuke/SAM2-hiera-tiny-LiteRT). The upstream model: [facebook/sam2.1-hiera-tiny](https://huggingface.co/facebook/sam2.1-hiera-tiny).

	---
	license: apache-2.0
	base_model: mlboydaisuke/SAM2-hiera-tiny-LiteRT
	base_model_relation: quantized
	library_name: openvino
	pipeline_tag: mask-generation
	tags:
	- openvino
	- onnx
	- intel-npu
	---

	# SAM2-hiera-tiny-OpenVINO

	SAM2-hiera-tiny for Intel NPUs, GPUs and CPUs: sam2_decoder and sam2_encoder from [mlboydaisuke/SAM2-hiera-tiny-LiteRT](https://huggingface.co/mlboydaisuke/SAM2-hiera-tiny-LiteRT/tree/8736e24606f7edf62e34cb8ef950546e49df7eff) 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 6 FULLY_CONNECTED layers of sam2_decoder; 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 7.97 ms (sam2_decoder) and 1972.66 ms (sam2_encoder) 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 \|
	\|---\|---\|---\|
	\| `sam2_decoder.onnx` \| 24,932,648 \| `bef9a8b8f8adb6bb41755f6d9153742ed51d8f51b105fbbfb67ead7d59f449ea` \|
	\| `openvino/sam2_decoder.xml` \| 755,917 \| `775d174179109655fbb26e1e7bc1880cfa02d2dc2f4c7fd31d0d42de8f1800df` \|
	\| `openvino/sam2_decoder.bin` \| 24,421,952 \| `ce3bc5ab2832b822eaa6a9413802e2731120cb7184a01c74b1a8d6e8de617c59` \|
	\| `npu/sam2_decoder.blob` \| 21,188,737 \| `2326a9d867fb98556a44bbb442d2f201477e9ee3453b3e1345f2e5a515675797` \|
	\| `sam2_encoder.onnx` \| 134,201,095 \| `2377332c91031aa842d01d7bd53c1e3b772a2310d9c2925aed2b11e74fb59b4f` \|
	\| `openvino/sam2_encoder.xml` \| 890,720 \| `91c371c1af7b9791f5bf214b4edcd7a7f7142f902481b44658630191801e6a26` \|
	\| `openvino/sam2_encoder.bin` \| 134,055,208 \| `e33d8c1ebbd01b6b272ae008a32dd42b33b67153ae08501c4baeafed7a68163c` \|
	\| `npu/sam2_encoder.blob` \| 146,653,313 \| `e107d56b61d093e1d03be3b3b5d69c5436deb7126d2258ea9ffb5986da196c3b` \|

	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. The Snapdragon rows are Qualcomm AI Hub profile jobs of the same ONNX files (their own timer). No Snapdragon rows for sam2_decoder: Qualcomm AI Hub measured the file before the bias fix (its numbers are in the GitHub table).

	## sam2_decoder

	\| processor \| runtime \| file \| ms \| load s \| output check \|
	\|---\|---\|---\|---\|---\|---\|
	\| Intel AI Boost NPU \| OpenVINO 2026.4.1 \| `npu/` blob (`import_model`) \| 7.97 \| 0.02 \| FAIL: output_0_output: cos 0.1192 < 0.99 \|
	\| Intel AI Boost NPU \| ONNX Runtime 1.31.1 + OpenVINO EP 1.8.1 \| `.onnx`, compiled when the session loads \| 9.82 \| 4.15 \| PASS (cos ≥ 0.9998) \|
	\| Intel Graphics (iGPU) \| ONNX Runtime 1.31.1 + OpenVINO EP 1.8.1 \| `.onnx` \| 11.29 \| 1.19 \| 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) \| 4.58 \| 1.23 \| PASS (cos ≥ 0.9999) \|
	\| RTX 5080 Laptop (95 W limit) \| ONNX Runtime 1.31.1, CUDA EP \| `.onnx` \| 4.97 \| 1.33 \| PASS (cos ≥ 0.9999) \|
	\| Core Ultra 9 386H CPU \| ONNX Runtime 1.31.1, CPU EP \| `.onnx` \| 26.88 \| 0.27 \| reference \|

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

	## sam2_encoder

	\| processor \| runtime \| file \| ms \| load s \| output check \|
	\|---\|---\|---\|---\|---\|---\|
	\| Intel AI Boost NPU \| OpenVINO 2026.4.1 \| `npu/` blob (`import_model`) \| 1972.66 \| 0.14 \| PASS (cos ≥ 0.9999) \|
	\| Intel AI Boost NPU \| ONNX Runtime 1.31.1 + OpenVINO EP 1.8.1 \| `.onnx`, compiled when the session loads \| 106.59 \| 225.12 \| PASS (cos ≥ 0.9999) \|
	\| Intel Graphics (iGPU) \| ONNX Runtime 1.31.1 + OpenVINO EP 1.8.1 \| `.onnx` \| 156.36 \| 1.55 \| 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) \| 28.01 \| 1.27 \| PASS (cos ≥ 0.9999) \|
	\| RTX 5080 Laptop (95 W limit) \| ONNX Runtime 1.31.1, CUDA EP \| `.onnx` \| 46.41 \| 1.26 \| PASS (cos ≥ 0.9999) \|
	\| Core Ultra 9 386H CPU \| ONNX Runtime 1.31.1, CPU EP \| `.onnx` \| 2200.98 \| 0.33 \| reference \|
	\| Snapdragon X2 Elite NPU \| Qualcomm AI Hub, ONNX Runtime QNN EP \| `.onnx`, compiled when it loads \| 191.09 (1 layers on the CPU) \| 139.87 \| 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/SAM2-hiera-tiny-OpenVINO --local-dir SAM2-hiera-tiny-OpenVINO
	cd SAM2-hiera-tiny-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/sam2_decoder.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/sam2_decoder.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("sam2_decoder.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 sam2_encoder with its own file names.

	## Licence

	Apache-2.0, the licence of [mlboydaisuke/SAM2-hiera-tiny-LiteRT](https://huggingface.co/mlboydaisuke/SAM2-hiera-tiny-LiteRT). The upstream model: [facebook/sam2.1-hiera-tiny](https://huggingface.co/facebook/sam2.1-hiera-tiny).