Upload DewarpNet-OpenVINO: ONNX, OpenVINO IR, Intel NPU blob, card and manifest

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+npu/dewarp.blob filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,82 @@
+---
+license: mit
+base_model: litert-community/DewarpNet-LiteRT
+base_model_relation: quantized
+library_name: openvino
+pipeline_tag: image-to-image
+tags:
+- openvino
+- onnx
+- intel-npu
+---
+
+# DewarpNet-OpenVINO
+
+DewarpNet for Intel NPUs, GPUs and CPUs: dewarp from [litert-community/DewarpNet-LiteRT](https://huggingface.co/litert-community/DewarpNet-LiteRT/tree/dfc68e3e462a6eb7eb8c4dbbff4049e3bd3cc9e3) 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 8.24 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 |
+|---|---|---|
+| `dewarp.onnx` | 189,339,174 | `4dde2be5fa86a4b4b1dd870b0ba89dc5da6e2165b90992c13a96450935eb04c2` |
+| `openvino/dewarp.xml` | 1,139,702 | `36060c9fd58b3fec9df7c95314892385f25240e9956a8005a05d2af914188e75` |
+| `openvino/dewarp.bin` | 188,529,172 | `840b093d86a174c4694c0ff3a37a5270576bac67e4c2b4ff9737a279929f89bd` |
+| `npu/dewarp.blob` | 19,378,305 | `345b2e8bb9239bf6df1e91a2ca8bad42ff83906e29087b755459cb61c9b669f3` |
+
+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`) | 8.24 | 0.01 | 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` | 9.07 | 1.74 | FAIL: output_0_output: cos 0.9891 < 0.99 |
+| RTX 5080 Laptop (95 W limit) | ONNX Runtime 1.31.1, CUDA EP | fp16 copy of the `.onnx` (not in this repo) | 6.74 | 1.51 | PASS (cos ≥ 0.9999) |
+| RTX 5080 Laptop (95 W limit) | ONNX Runtime 1.31.1, CUDA EP | `.onnx` | 8.53 | 1.47 | PASS (cos ≥ 0.9999) |
+| Core Ultra 9 386H CPU | ONNX Runtime 1.31.1, CPU EP | `.onnx` | 201.36 | 0.65 | reference |
+| Snapdragon X2 Elite NPU | Qualcomm AI Hub, precompiled QNN ONNX | `.onnx` compiled by AI Hub | 5.04 | 0.34 | 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 | 24.36 (19 layers on the CPU) | 6.26 | not compared (AI Hub profile job, 2026-10-30) |
+| Galaxy S26 NPU | Qualcomm AI Hub, precompiled QNN ONNX | `.onnx` compiled by AI Hub | 4.76 | 0.23 | 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/DewarpNet-OpenVINO --local-dir DewarpNet-OpenVINO
+cd DewarpNet-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/dewarp.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/dewarp.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 `dewarp.onnx` on the NPU: the session fails to load (`Compilation failed`). The OpenVINO lines above run it.
+
+## Licence
+
+MIT, the licence of [litert-community/DewarpNet-LiteRT](https://huggingface.co/litert-community/DewarpNet-LiteRT).

dewarp.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4dde2be5fa86a4b4b1dd870b0ba89dc5da6e2165b90992c13a96450935eb04c2
+size 189339174

manifest.json

@@ -0,0 +1,39 @@
+{
+ "source_repo": "litert-community/DewarpNet-LiteRT",
+ "source_revision": "dfc68e3e462a6eb7eb8c4dbbff4049e3bd3cc9e3",
+ "graphs": {
+  "dewarp": {
+   "from": "dewarp",
+   "ir": {
+    "openvino": "2026.4.1-22982-e213a147257-releases/2026/4",
+    "compress_to_fp16": false
+   },
+   "blob_compiled_with": {
+    "openvino": "2026.4.1-22982-e213a147257-releases/2026/4",
+    "FULL_DEVICE_NAME": "Intel(R) AI Boost",
+    "NPU_DRIVER_VERSION": "1004841",
+    "NPU_COMPILER_VERSION": "524292",
+    "NPU_COMPILER_TYPE": "CompilerType.PREFER_PLUGIN",
+    "INFERENCE_PRECISION_HINT": "<Type: 'float16'>"
+   },
+   "files": {
+    "dewarp.onnx": {
+     "bytes": 189339174,
+     "sha256": "4dde2be5fa86a4b4b1dd870b0ba89dc5da6e2165b90992c13a96450935eb04c2"
+    },
+    "openvino/dewarp.xml": {
+     "bytes": 1139702,
+     "sha256": "36060c9fd58b3fec9df7c95314892385f25240e9956a8005a05d2af914188e75"
+    },
+    "openvino/dewarp.bin": {
+     "bytes": 188529172,
+     "sha256": "840b093d86a174c4694c0ff3a37a5270576bac67e4c2b4ff9737a279929f89bd"
+    },
+    "npu/dewarp.blob": {
+     "bytes": 19378305,
+     "sha256": "345b2e8bb9239bf6df1e91a2ca8bad42ff83906e29087b755459cb61c9b669f3"
+    }
+   }
+  }
+ }
+}

npu/dewarp.blob

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:345b2e8bb9239bf6df1e91a2ca8bad42ff83906e29087b755459cb61c9b669f3
+size 19378305

openvino/dewarp.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:840b093d86a174c4694c0ff3a37a5270576bac67e4c2b4ff9737a279929f89bd
+size 188529172