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+GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
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+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<http://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<http://www.gnu.org/philosophy/why-not-lgpl.html>.

manifest.json

@@ -0,0 +1,134 @@
+[
+  {
+    "name": "yolov5m-cls",
+    "family": "yolov5",
+    "task": "classify",
+    "chip": "rk3576",
+    "precision": "fp16",
+    "path": "models/classify/yolov5m-cls/rk3576/fp16",
+    "weights": "source/weights/yolov5m-cls.pt",
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+  },
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+    "family": "yolov5",
+    "task": "classify",
+    "chip": "rk3576",
+    "precision": "int8",
+    "path": "models/classify/yolov5m-cls/rk3576/int8",
+    "weights": "source/weights/yolov5m-cls.pt",
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+    "metadata_sha256": "817cec2b00fc7d028def92cec388d294b184e4142d3dd45b4f2297b90a1967e1"
+  },
+  {
+    "name": "yolov5n-cls",
+    "family": "yolov5",
+    "task": "classify",
+    "chip": "rk3576",
+    "precision": "fp16",
+    "path": "models/classify/yolov5n-cls/rk3576/fp16",
+    "weights": "source/weights/yolov5n-cls.pt",
+    "sha256": "30438f1ec5050fa92c7fb12934a90141d16177a3d5353efe72788b8667d66551",
+    "metadata_sha256": "8d8711568c225625aca257275da457598732f08cf20bf6836cafb1c623dac0f9"
+  },
+  {
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+    "family": "yolov5",
+    "task": "classify",
+    "chip": "rk3576",
+    "precision": "int8",
+    "path": "models/classify/yolov5n-cls/rk3576/int8",
+    "weights": "source/weights/yolov5n-cls.pt",
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+    "metadata_sha256": "2fb739abdbe1263eabfeedc29c34cd0c6a8616bcbe72142954c2319f5a345bb7"
+  },
+  {
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+    "family": "yolov5",
+    "task": "classify",
+    "chip": "rk3576",
+    "precision": "fp16",
+    "path": "models/classify/yolov5s-cls/rk3576/fp16",
+    "weights": "source/weights/yolov5s-cls.pt",
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+    "metadata_sha256": "07425a1a1ad0406790befa2724d5c8f2786082be0900bf2a354967ba9ef4096f"
+  },
+  {
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+    "family": "yolov5",
+    "task": "classify",
+    "chip": "rk3576",
+    "precision": "int8",
+    "path": "models/classify/yolov5s-cls/rk3576/int8",
+    "weights": "source/weights/yolov5s-cls.pt",
+    "sha256": "f47392d425c1e2d76e6ae39f84cf1f0acaa105b9e6f4b55b30764bef89af8a40",
+    "metadata_sha256": "d2630b3d41138942eba8bd78d9b6477f84605fd6d3f29e5a4f9a16ed6f3f5c9b"
+  },
+  {
+    "name": "yolov5m-cls",
+    "family": "yolov5",
+    "task": "classify",
+    "chip": "rk3588",
+    "precision": "fp16",
+    "path": "models/classify/yolov5m-cls/rk3588/fp16",
+    "weights": "source/weights/yolov5m-cls.pt",
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+    "metadata_sha256": "2f5d028de3b6b9054550983ff2a382331385435c0f5bfff9a35429f86a4464cf"
+  },
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+    "family": "yolov5",
+    "task": "classify",
+    "chip": "rk3588",
+    "precision": "int8",
+    "path": "models/classify/yolov5m-cls/rk3588/int8",
+    "weights": "source/weights/yolov5m-cls.pt",
+    "sha256": "1affc434a7e66a9cb7f1c6de416cec5b2894e06a66405dcbf04232958447907b",
+    "metadata_sha256": "137d63cfd35692d9f843a6a142d8bd7a7e99dd5119659c7526b0822bd7a82c1e"
+  },
+  {
+    "name": "yolov5n-cls",
+    "family": "yolov5",
+    "task": "classify",
+    "chip": "rk3588",
+    "precision": "fp16",
+    "path": "models/classify/yolov5n-cls/rk3588/fp16",
+    "weights": "source/weights/yolov5n-cls.pt",
+    "sha256": "1ce2871b6c02bb46e945db76f824e1144d3c88c1b06ccefa3c96a108a1213daa",
+    "metadata_sha256": "5eb38c549fd038be769503a41e9fcb68d17c33be197b4148138b19295a150c3e"
+  },
+  {
+    "name": "yolov5n-cls",
+    "family": "yolov5",
+    "task": "classify",
+    "chip": "rk3588",
+    "precision": "int8",
+    "path": "models/classify/yolov5n-cls/rk3588/int8",
+    "weights": "source/weights/yolov5n-cls.pt",
+    "sha256": "b0d66530d0f87b03d96b9f57ebfc72586403655eb775280e9b9f6d1d5d4fd178",
+    "metadata_sha256": "64d9a151a7449422957564363725985da16cd618bcee20ecca11119d14b32b54"
+  },
+  {
+    "name": "yolov5s-cls",
+    "family": "yolov5",
+    "task": "classify",
+    "chip": "rk3588",
+    "precision": "fp16",
+    "path": "models/classify/yolov5s-cls/rk3588/fp16",
+    "weights": "source/weights/yolov5s-cls.pt",
+    "sha256": "61b7c3b33ac44eaa240cc3e1e4299c9853f514639cabfd60ab75960230447a4b",
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+    "family": "yolov5",
+    "task": "classify",
+    "chip": "rk3588",
+    "precision": "int8",
+    "path": "models/classify/yolov5s-cls/rk3588/int8",
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+  }
+]

source/calibration.json

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+}

source/convert-extended.py

@@ -0,0 +1,274 @@
+#!/usr/bin/env python3
+"""Export task heads that have no Model Zoo example and compile them with RKNN."""
+
+import argparse
+import hashlib
+import os
+from pathlib import Path
+import sys
+import tempfile
+
+
+TASKS = {
+    "yolov5n-cls": "classify",
+    "yolov8n-cls": "classify",
+    "yolo11n-cls": "classify",
+}
+YOLO26_TASKS = {"cls": "classify", "seg": "segment", "pose": "pose", "obb": "obb",
+                "sem": "semantic", "depth": "depth"}
+TASKS.update({f"{family}{size}-cls": "classify"
+              for family in ("yolov5", "yolov8", "yolo11") for size in "sm"})
+TASKS.update({f"yolo26{size}-{suffix}": task
+              for size in "nsm" for suffix, task in YOLO26_TASKS.items()})
+SIZES = {"classify": 224, "semantic": 512, "depth": 384}
+CHIPS = ("rk3576", "rk3588")
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
+
+
+def load_yolov5_classifier(checkpoint, source):
+    """Bake YOLOv5's ImageNet normalization into the input graph."""
+    import torch
+
+    sys.path.insert(0, str(source))
+    try:
+        from models.experimental import attempt_load
+
+        classifier = attempt_load(str(checkpoint), device=torch.device("cpu"), fuse=True)
+    finally:
+        sys.path.pop(0)
+
+    class NormalizeClassifier(torch.nn.Module):
+        def __init__(self, model):
+            super().__init__()
+            self.model = model
+            self.register_buffer("mean", torch.tensor(IMAGENET_MEAN).reshape(1, 3, 1, 1))
+            self.register_buffer("std", torch.tensor(IMAGENET_STD).reshape(1, 3, 1, 1))
+
+        def forward(self, images):
+            return self.model((images - self.mean) / self.std).softmax(dim=1)
+
+    return NormalizeClassifier(classifier).eval(), classifier.names
+
+
+def split_yolo26(head, task, size):
+    """Expose one-to-one heads before TopK, following the Zoo YOLO26 detection layout."""
+    import torch
+    from ultralytics.utils.tal import make_anchors
+
+    def forward(features):
+        predictions = head.forward_head(features, **head.one2one)
+        anchors, strides = make_anchors(features, head.stride, 0.5)
+        head.anchors = anchors.transpose(0, 1)
+        head.strides = strides.transpose(0, 1)
+        raw_boxes = predictions["boxes"]
+        left_top, right_bottom = raw_boxes.split(2, dim=1)
+        anchor = head.anchors.unsqueeze(0)
+        if task == "obb":
+            angle = predictions["angle"]
+            offset_x, offset_y = ((right_bottom - left_top) / 2).split(1, dim=1)
+            cosine, sine = angle.cos(), angle.sin()
+            rotated = torch.cat((offset_x * cosine - offset_y * sine,
+                                 offset_x * sine + offset_y * cosine), dim=1)
+            boxes = torch.cat((anchor + rotated, left_top + right_bottom), dim=1)
+        else:
+            boxes = torch.cat((anchor - left_top, anchor + right_bottom), dim=1)
+
+        outputs = (predictions["scores"].sigmoid(), boxes * (head.strides / size))
+        if task == "segment":
+            return (*outputs, predictions["mask_coefficient"], head.proto(features))
+        if task == "obb":
+            return (*outputs, angle)
+
+        points = head.kpts_decode(predictions["kpts"]).reshape(1, *head.kpt_shape, -1)
+        # Keep visibility and x/y on comparable scales for INT8 quantization.
+        points = torch.cat((points[:, :, :2] / size, points[:, :, 2:]), dim=2)
+        return (*outputs, points.reshape(1, head.nk, -1))
+
+    return forward
+
+
+def export_checkpoint(checkpoint, destination, size, yolov5_source):
+    os.environ.setdefault("YOLO_AUTOINSTALL", "false")
+    import onnx
+    import torch
+    import ultralytics
+    import yaml
+    from ultralytics import YOLO
+
+    task = TASKS[checkpoint.stem]
+    if checkpoint.stem.startswith("yolov5"):
+        model, names = load_yolov5_classifier(checkpoint, yolov5_source)
+        head = None
+    else:
+        model = YOLO(str(checkpoint), task=task).model.cpu().float().eval()
+        head = model.model[-1]
+        names = getattr(model, "names", None) or {0: "depth"}
+    split = task in {"segment", "pose", "obb"}
+    if split:
+        if head.reg_max != 1 or getattr(head, "one2one_cv2", None) is None:
+            raise ValueError("Expected a DFL-free, one-to-one YOLO26 head")
+        # Fusion removes whichever trained branch is inactive.
+        head.end2end = True
+    if head is not None:
+        model.fuse(verbose=False)
+    with checkpoint.open("rb") as file:
+        digest = hashlib.file_digest(file, "sha256").hexdigest()
+    metadata = {
+        "task": task,
+        "batch": 1,
+        "channels": 3,
+        "imgsz": [size, size],
+        "names": names,
+        "preprocess": "center_crop" if task == "classify" else "letterbox",
+        "coordinates": "pixels",
+        "end2end": False,
+        "export_source": "model-zoo-inspired-pytorch",
+        "checkpoint": checkpoint.name,
+        "checkpoint_sha256": digest,
+        "ultralytics": ultralytics.__version__,
+        "pytorch": str(torch.__version__),
+    }
+    if split:
+        head.export = True
+        head.forward = split_yolo26(head, task, size)
+        metadata.update(coordinates="normalized", end2end=True)
+        output_names = ["Sigmoid_scores", "boxes"]
+        if task == "segment":
+            output_names.extend(("mask_coefficients", "prototypes"))
+        elif task == "pose":
+            output_names.append("keypoints")
+            metadata["kpt_shape"] = list(head.kpt_shape)
+        else:
+            output_names.append("angle")
+    elif task == "classify":
+        if head is not None:
+            head.export = True
+        else:
+            metadata["checkpoint_source"] = (
+                f"https://github.com/ultralytics/yolov5/releases/download/v7.0/{checkpoint.name}"
+            )
+            metadata["normalization"] = "ImageNet mean/std baked into ONNX"
+        output_names = ["probabilities"]
+    else:
+        # Evaluation mode emits the native grid before terminal full-image resize.
+        head.export = False
+        metadata["dense_resize"] = "half_pixel" if task == "semantic" else "align_corners"
+        output_names = ["logits" if task == "semantic" else "depth"]
+
+    image = torch.zeros(1, 3, size, size)
+    with torch.inference_mode():
+        expected = model(image)
+        tensors = expected if isinstance(expected, tuple) else (expected,)
+        if len(tensors) != len(output_names) or not all(torch.is_tensor(x) for x in tensors):
+            raise ValueError("Unexpected output contract from checkpoint head")
+        for name, tensor in zip(output_names, tensors):
+            print(f"{name}: {tuple(tensor.shape)}")
+        torch.onnx.export(model, image, str(destination / "model.onnx"), opset_version=13,
+                          input_names=["images"], output_names=output_names,
+                          dynamo=False, do_constant_folding=True)
+
+    graph = onnx.load(destination / "model.onnx")
+    onnx.checker.check_model(graph)
+    (destination / "metadata.yaml").write_text(yaml.safe_dump(metadata, sort_keys=False))
+
+
+def compile_graph(source, destination, chip, precision, dataset):
+    import yaml
+    from rknn.api import RKNN
+
+    metadata = yaml.safe_load((source / "metadata.yaml").read_text())
+    metadata.update(target_platform=chip, precision="int8" if precision == "i8" else "fp16")
+
+    def check(code, step):
+        if code != 0:
+            raise RuntimeError(f"RKNN {step} failed ({code})")
+
+    toolkit = RKNN(verbose=False)
+    try:
+        check(toolkit.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]],
+                             target_platform=chip), "config")
+        check(toolkit.load_onnx(model=str(source / "model.onnx")), "load ONNX")
+        with tempfile.TemporaryDirectory(prefix="nvr-calibration-") as directory:
+            if precision == "i8" and metadata["checkpoint"].startswith("yolov5"):
+                dataset = center_crop_calibration(dataset, Path(directory), metadata["imgsz"][0])
+            check(toolkit.build(do_quantization=precision == "i8",
+                                dataset=str(dataset) if dataset else None), "build")
+        check(toolkit.export_rknn(str(destination / "model.rknn")), "export")
+    finally:
+        toolkit.release()
+    (destination / "metadata.yaml").write_text(yaml.safe_dump(metadata, sort_keys=False))
+
+
+def center_crop_calibration(dataset, directory, size):
+    """Use the same square crop and linear resize as YOLOv5 classify/predict.py."""
+    import cv2
+
+    paths = []
+    for index, line in enumerate(dataset.read_text().splitlines()):
+        if not line.strip():
+            continue
+        source = dataset.parent / line.strip()
+        image = cv2.imread(str(source), cv2.IMREAD_COLOR)
+        if image is None:
+            raise ValueError(f"Cannot read calibration image: {source}")
+        height, width = image.shape[:2]
+        side = min(height, width)
+        top, left = (height - side) // 2, (width - side) // 2
+        image = cv2.resize(image[top:top + side, left:left + side], (size, size))
+        output = directory / f"{index:04}.png"
+        if not cv2.imwrite(str(output), image):
+            raise RuntimeError(f"Cannot write calibration image: {output}")
+        paths.append(str(output))
+    cropped = directory / "images.txt"
+    cropped.write_text("\n".join(paths) + "\n")
+    return cropped
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    commands = parser.add_subparsers(dest="command", required=True)
+    export = commands.add_parser("export", help="Export one original checkpoint to ONNX")
+    export.add_argument("checkpoint", type=Path)
+    export.add_argument("output", type=Path, help="New directory for the ONNX and metadata")
+    export.add_argument("--imgsz", type=int, help="Override the square input size")
+    export.add_argument("--yolov5-source", type=Path, help="YOLOv5 v7.0 source checkout")
+    compile_parser = commands.add_parser("compile", help="Build one RKNN from an exported directory")
+    compile_parser.add_argument("onnx_directory", type=Path)
+    compile_parser.add_argument("output", type=Path, help="New directory for the RKNN and metadata")
+    compile_parser.add_argument("--chip", required=True, choices=CHIPS)
+    compile_parser.add_argument("--precision", required=True, choices=("fp", "i8"))
+    compile_parser.add_argument("--dataset", type=Path, help="RKNN calibration image list")
+    args = parser.parse_args()
+
+    destination = args.output.resolve()
+    if destination.exists():
+        parser.error(f"Output already exists: {destination}")
+    destination.parent.mkdir(parents=True, exist_ok=True)
+    with tempfile.TemporaryDirectory(prefix=".convert-", dir=destination.parent) as directory:
+        work = Path(directory) / "output"
+        work.mkdir()
+        if args.command == "export":
+            checkpoint = args.checkpoint.resolve()
+            if not checkpoint.is_file() or checkpoint.stem not in TASKS:
+                parser.error("Provide one of these local checkpoints: " + ", ".join(TASKS))
+            size = args.imgsz or SIZES.get(TASKS[checkpoint.stem], 640)
+            if size < 32 or size > 4096 or size % 32:
+                parser.error("--imgsz must be a multiple of 32 from 32 to 4096")
+            yolov5_source = args.yolov5_source.resolve() if args.yolov5_source else None
+            if checkpoint.stem.startswith("yolov5") and (
+                yolov5_source is None or not (yolov5_source / "models/experimental.py").is_file()
+            ):
+                parser.error("YOLOv5 classification requires a --yolov5-source v7.0 checkout")
+            export_checkpoint(checkpoint, work, size, yolov5_source)
+        else:
+            dataset = args.dataset.resolve() if args.dataset else None
+            if args.precision == "i8" and (dataset is None or not dataset.is_file()):
+                parser.error("INT8 requires an existing --dataset image list")
+            compile_graph(args.onnx_directory.resolve(), work, args.chip, args.precision, dataset)
+        work.rename(destination)
+    print(destination)
+
+
+if __name__ == "__main__":
+    main()

source/weights.json

@@ -0,0 +1,20 @@
+[
+  {
+    "name": "yolov5m-cls",
+    "source": "https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-cls.pt",
+    "sha256": "dbfdf14826b47fd45b178f652048339d83dc437d93a893f5dfbea6729578794d",
+    "file": "source/weights/yolov5m-cls.pt"
+  },
+  {
+    "name": "yolov5n-cls",
+    "source": "https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-cls.pt",
+    "sha256": "77b15f65325b37c24e616cbe0e5349d63c35508607458b9f2bcb03875ab75f33",
+    "file": "source/weights/yolov5n-cls.pt"
+  },
+  {
+    "name": "yolov5s-cls",
+    "source": "https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt",
+    "sha256": "53ebfe1c19d60bea82c821b93b52bf58d81d5a06b693668e516fd8d44769881e",
+    "file": "source/weights/yolov5s-cls.pt"
+  }
+]