Organize RKNN models by family

.gitattributes

@@ -76,3 +76,8 @@ models/pose/yolo11n-pose/rk3588/int8/model.rknn filter=lfs diff=lfs merge=lfs -t
 models/segment/yolo11n-seg/rk3588/fp16/model.rknn filter=lfs diff=lfs merge=lfs -text
 models/segment/yolo11n-seg/rk3588/int8/model.rknn filter=lfs diff=lfs merge=lfs -text
 models/obb/yolo11s-obb/rk3588/fp16/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/obb/yolo11s-obb/rk3588/int8/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/pose/yolo11s-pose/rk3588/fp16/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/pose/yolo11s-pose/rk3588/int8/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/segment/yolo11s-seg/rk3588/fp16/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/segment/yolo11s-seg/rk3588/int8/model.rknn filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,22 @@
+---
+license: agpl-3.0
+tags:
+- rknn
+- rknn2
+- rockchip
+- rk3576
+- rk3588
+---
+
+# YOLO11-RKNN2
+
+Rockchip-guided and custom YOLO exports for RK3576 and RK3588.
+Converted with RKNN Toolkit2 2.3.2.
+
+Available tasks: classify, obb, pose, segment.
+
+Download `model.rknn` and `metadata.yaml` from the same folder under `models/<task>/<variant>/<chip>/<precision>/`.
+
+The manifest records model and metadata checksums. Original checkpoints, conversion scripts, and their provenance are in `source/`.
+
+The models and checkpoints are distributed under AGPL-3.0. See `LICENSE` and `source/weights.json`.

manifest.json

@@ -0,0 +1,614 @@
+[
+  {
+    "name": "yolo11m-cls",
+    "family": "yolo11",
+    "task": "classify",
+    "chip": "rk3576",
+    "precision": "fp16",
+    "path": "models/classify/yolo11m-cls/rk3576/fp16",
+    "weights": "source/weights/yolo11m-cls.pt",
+    "sha256": "29bf33ec50736ef26b9ce2a94617b6ba410b1dc588d14a583f7f1f56870bdfaa",
+    "metadata_sha256": "3fb043109343a4bcbcd4893e0637ae8f336d1eb59d17f13b2f60892175975dab",
+    "conversion": "custom"
+  },
+  {
+    "name": "yolo11m-cls",
+    "family": "yolo11",
+    "task": "classify",
+    "chip": "rk3576",
+    "precision": "int8",
+    "path": "models/classify/yolo11m-cls/rk3576/int8",
+    "weights": "source/weights/yolo11m-cls.pt",
+    "sha256": "8d41294cebc8254e3e8752d8ea39f0b4a33a2b05ff7e1479d6645d0189590a1b",
+    "metadata_sha256": "2efeb534fdd1fa4cb380abfcac6d113d3165a8738c85ab53f19ea0438c2aaaa8",
+    "conversion": "custom"
+  },
+  {
+    "name": "yolo11n-cls",
+    "family": "yolo11",
+    "task": "classify",
+    "chip": "rk3576",
+    "precision": "fp16",
+    "path": "models/classify/yolo11n-cls/rk3576/fp16",
+    "weights": "source/weights/yolo11n-cls.pt",
+    "sha256": "68a996c1fe36b94f9bd6867858f7d947fd56b6d2e4da3f2f19a73ed2a7a37cfa",
+    "metadata_sha256": "326eeb190487b7d6ae52872abaccf3f6995fcdd665c14e3883b76b1ee2aa07bb",
+    "conversion": "custom"
+  },
+  {
+    "name": "yolo11n-cls",
+    "family": "yolo11",
+    "task": "classify",
+    "chip": "rk3576",
+    "precision": "int8",
+    "path": "models/classify/yolo11n-cls/rk3576/int8",
+    "weights": "source/weights/yolo11n-cls.pt",
+    "sha256": "7ef550741e14cbd3864c903d78382c993f66f44305b9bc78095e384b1d00c718",
+    "metadata_sha256": "42ccc34d3daa1b7cadb1b1ac003a3bc8fbe769ba51f8903ffd5a11c5c9ebe821",
+    "conversion": "custom"
+  },
+  {
+    "name": "yolo11s-cls",
+    "family": "yolo11",
+    "task": "classify",
+    "chip": "rk3576",
+    "precision": "fp16",
+    "path": "models/classify/yolo11s-cls/rk3576/fp16",
+    "weights": "source/weights/yolo11s-cls.pt",
+    "sha256": "9e84c92a4377429fcdce1c5aec1b1e126bc884c957a3ef4b9002add6f34bfdb8",
+    "metadata_sha256": "4afa20c4f4d508c4692d1b15d884096c1b4da33868a6302d928cde26fdac39ed",
+    "conversion": "custom"
+  },
+  {
+    "name": "yolo11s-cls",
+    "family": "yolo11",
+    "task": "classify",
+    "chip": "rk3576",
+    "precision": "int8",
+    "path": "models/classify/yolo11s-cls/rk3576/int8",
+    "weights": "source/weights/yolo11s-cls.pt",
+    "sha256": "48880ccd6fd3b130672195652804f24d90a73fed39de45dd1b666ee05757a1a7",
+    "metadata_sha256": "80980d225a70f37a0d4ab5346df9a6f509bcacdd738b34c0598dae08913f0b6c",
+    "conversion": "custom"
+  },
+  {
+    "name": "yolo11m-cls",
+    "family": "yolo11",
+    "task": "classify",
+    "chip": "rk3588",
+    "precision": "fp16",
+    "path": "models/classify/yolo11m-cls/rk3588/fp16",
+    "weights": "source/weights/yolo11m-cls.pt",
+    "sha256": "63e2d88bc016d3339ef51a54afcddb7676f983be564d84c8d6c1166de2d2a3bf",
+    "metadata_sha256": "fde933ed4e6408a8d8bf409662e971d3db070d7c00e1ca0e3c827ad86e5a7c30",
+    "conversion": "custom"
+  },
+  {
+    "name": "yolo11m-cls",
+    "family": "yolo11",
+    "task": "classify",
+    "chip": "rk3588",
+    "precision": "int8",
+    "path": "models/classify/yolo11m-cls/rk3588/int8",
+    "weights": "source/weights/yolo11m-cls.pt",
+    "sha256": "76d8964408a128fc32a268ea367351768362a9aceacbbc59031fc905b58ef2f4",
+    "metadata_sha256": "886fcad56d528d8c76b51ad32d8fc25a96aa9c851f25f02d175cfe6e2af5fed7",
+    "conversion": "custom"
+  },
+  {
+    "name": "yolo11n-cls",
+    "family": "yolo11",
+    "task": "classify",
+    "chip": "rk3588",
+    "precision": "fp16",
+    "path": "models/classify/yolo11n-cls/rk3588/fp16",
+    "weights": "source/weights/yolo11n-cls.pt",
+    "sha256": "8d540134af0708598106b953f1ff2601bbdf79acb298adea30e3f72e31be4b69",
+    "metadata_sha256": "795a2fb8bfed216874db34130a46517101c2f2bfa236a88e3b714bb95cee5937",
+    "conversion": "custom"
+  },
+  {
+    "name": "yolo11n-cls",
+    "family": "yolo11",
+    "task": "classify",
+    "chip": "rk3588",
+    "precision": "int8",
+    "path": "models/classify/yolo11n-cls/rk3588/int8",
+    "weights": "source/weights/yolo11n-cls.pt",
+    "sha256": "6b377ebbbb67198f005af80842902f40dbf1e398c3ee279ad40f2eea3ee52b80",
+    "metadata_sha256": "09dac57947f429ab2d1a4c5b3d0297288345d014728b69ed0eb3acef1568612f",
+    "conversion": "custom"
+  },
+  {
+    "name": "yolo11s-cls",
+    "family": "yolo11",
+    "task": "classify",
+    "chip": "rk3588",
+    "precision": "fp16",
+    "path": "models/classify/yolo11s-cls/rk3588/fp16",
+    "weights": "source/weights/yolo11s-cls.pt",
+    "sha256": "e0362b0b099b3bb3329263a3dfda088cfe5a159215908e6eedecd2c112ae1f39",
+    "metadata_sha256": "b08b812f181d8b672a630574e682d71b6e683493097e50f30c4f0ca89c3aa8f6",
+    "conversion": "custom"
+  },
+  {
+    "name": "yolo11s-cls",
+    "family": "yolo11",
+    "task": "classify",
+    "chip": "rk3588",
+    "precision": "int8",
+    "path": "models/classify/yolo11s-cls/rk3588/int8",
+    "weights": "source/weights/yolo11s-cls.pt",
+    "sha256": "8de2398dc2acb495435c4695b4dc7940939ca31c6cbddb1d1e259e602d2811dc",
+    "metadata_sha256": "96be5526e5b525f1077f23ddc19630143c001b500e398eed5a82e05c8123a364",
+    "conversion": "custom"
+  },
+  {
+    "name": "yolo11m-obb",
+    "family": "yolo11",
+    "task": "obb",
+    "chip": "rk3576",
+    "precision": "fp16",
+    "path": "models/obb/yolo11m-obb/rk3576/fp16",
+    "weights": "source/weights/yolo11m-obb.pt",
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+    "conversion": "rockchip-guided"
+  },
+  {
+    "name": "yolo11m-obb",
+    "family": "yolo11",
+    "task": "obb",
+    "chip": "rk3576",
+    "precision": "int8",
+    "path": "models/obb/yolo11m-obb/rk3576/int8",
+    "weights": "source/weights/yolo11m-obb.pt",
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+    "conversion": "rockchip-guided"
+  },
+  {
+    "name": "yolo11m-pose",
+    "family": "yolo11",
+    "task": "pose",
+    "chip": "rk3576",
+    "precision": "fp16",
+    "path": "models/pose/yolo11m-pose/rk3576/fp16",
+    "weights": "source/weights/yolo11m-pose.pt",
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+    "conversion": "rockchip-guided"
+  },
+  {
+    "name": "yolo11m-pose",
+    "family": "yolo11",
+    "task": "pose",
+    "chip": "rk3576",
+    "precision": "int8",
+    "path": "models/pose/yolo11m-pose/rk3576/int8",
+    "weights": "source/weights/yolo11m-pose.pt",
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+    "conversion": "rockchip-guided"
+  },
+  {
+    "name": "yolo11m-seg",
+    "family": "yolo11",
+    "task": "segment",
+    "chip": "rk3576",
+    "precision": "fp16",
+    "path": "models/segment/yolo11m-seg/rk3576/fp16",
+    "weights": "source/weights/yolo11m-seg.pt",
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+    "conversion": "rockchip-guided"
+  },
+  {
+    "name": "yolo11m-seg",
+    "family": "yolo11",
+    "task": "segment",
+    "chip": "rk3576",
+    "precision": "int8",
+    "path": "models/segment/yolo11m-seg/rk3576/int8",
+    "weights": "source/weights/yolo11m-seg.pt",
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+    "conversion": "rockchip-guided"
+  },
+  {
+    "name": "yolo11n-obb",
+    "family": "yolo11",
+    "task": "obb",
+    "chip": "rk3576",
+    "precision": "fp16",
+    "path": "models/obb/yolo11n-obb/rk3576/fp16",
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+    "conversion": "rockchip-guided"
+  },
+  {
+    "name": "yolo11n-obb",
+    "family": "yolo11",
+    "task": "obb",
+    "chip": "rk3576",
+    "precision": "int8",
+    "path": "models/obb/yolo11n-obb/rk3576/int8",
+    "weights": "source/weights/yolo11n-obb.pt",
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+    "conversion": "rockchip-guided"
+  },
+  {
+    "name": "yolo11n-pose",
+    "family": "yolo11",
+    "task": "pose",
+    "chip": "rk3576",
+    "precision": "fp16",
+    "path": "models/pose/yolo11n-pose/rk3576/fp16",
+    "weights": "source/weights/yolo11n-pose.pt",
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+    "conversion": "rockchip-guided"
+  },
+  {
+    "name": "yolo11n-pose",
+    "family": "yolo11",
+    "task": "pose",
+    "chip": "rk3576",
+    "precision": "int8",
+    "path": "models/pose/yolo11n-pose/rk3576/int8",
+    "weights": "source/weights/yolo11n-pose.pt",
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+    "conversion": "rockchip-guided"
+  },
+  {
+    "name": "yolo11n-seg",
+    "family": "yolo11",
+    "task": "segment",
+    "chip": "rk3576",
+    "precision": "fp16",
+    "path": "models/segment/yolo11n-seg/rk3576/fp16",
+    "weights": "source/weights/yolo11n-seg.pt",
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+    "conversion": "rockchip-guided"
+  },
+  {
+    "name": "yolo11n-seg",
+    "family": "yolo11",
+    "task": "segment",
+    "chip": "rk3576",
+    "precision": "int8",
+    "path": "models/segment/yolo11n-seg/rk3576/int8",
+    "weights": "source/weights/yolo11n-seg.pt",
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+    "conversion": "rockchip-guided"
+  },
+  {
+    "name": "yolo11s-obb",
+    "family": "yolo11",
+    "task": "obb",
+    "chip": "rk3576",
+    "precision": "fp16",
+    "path": "models/obb/yolo11s-obb/rk3576/fp16",
+    "weights": "source/weights/yolo11s-obb.pt",
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+    "conversion": "rockchip-guided"
+  },
+  {
+    "name": "yolo11s-obb",
+    "family": "yolo11",
+    "task": "obb",
+    "chip": "rk3576",
+    "precision": "int8",
+    "path": "models/obb/yolo11s-obb/rk3576/int8",
+    "weights": "source/weights/yolo11s-obb.pt",
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+    "conversion": "rockchip-guided"
+  },
+  {
+    "name": "yolo11s-pose",
+    "family": "yolo11",
+    "task": "pose",
+    "chip": "rk3576",
+    "precision": "fp16",
+    "path": "models/pose/yolo11s-pose/rk3576/fp16",
+    "weights": "source/weights/yolo11s-pose.pt",
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+    "conversion": "rockchip-guided"
+  },
+  {
+    "name": "yolo11s-pose",
+    "family": "yolo11",
+    "task": "pose",
+    "chip": "rk3576",
+    "precision": "int8",
+    "path": "models/pose/yolo11s-pose/rk3576/int8",
+    "weights": "source/weights/yolo11s-pose.pt",
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+    "conversion": "rockchip-guided"
+  },
+  {
+    "name": "yolo11s-seg",
+    "family": "yolo11",
+    "task": "segment",
+    "chip": "rk3576",
+    "precision": "fp16",
+    "path": "models/segment/yolo11s-seg/rk3576/fp16",
+    "weights": "source/weights/yolo11s-seg.pt",
+    "sha256": "7a1f8a7e28810f730f860a8f5d82dda1551c4cbe273dc4a317d8f78791b24a96",
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+    "conversion": "rockchip-guided"
+  },
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+    "conversion": "rockchip-guided"
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+    "task": "obb",
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+    "precision": "int8",
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+    "task": "pose",
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+    "precision": "fp16",
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+    "conversion": "rockchip-guided"
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+    "family": "yolo11",
+    "task": "pose",
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+    "family": "yolo11",
+    "task": "segment",
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+  },
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+    "family": "yolo11",
+    "task": "segment",
+    "chip": "rk3588",
+    "precision": "int8",
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+    "conversion": "rockchip-guided"
+  },
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+    "family": "yolo11",
+    "task": "obb",
+    "chip": "rk3588",
+    "precision": "fp16",
+    "path": "models/obb/yolo11s-obb/rk3588/fp16",
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+    "conversion": "rockchip-guided"
+  },
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+    "name": "yolo11s-obb",
+    "family": "yolo11",
+    "task": "obb",
+    "chip": "rk3588",
+    "precision": "int8",
+    "path": "models/obb/yolo11s-obb/rk3588/int8",
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+    "conversion": "rockchip-guided"
+  },
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+    "family": "yolo11",
+    "task": "pose",
+    "chip": "rk3588",
+    "precision": "fp16",
+    "path": "models/pose/yolo11s-pose/rk3588/fp16",
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+    "conversion": "rockchip-guided"
+  },
+  {
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+    "family": "yolo11",
+    "task": "pose",
+    "chip": "rk3588",
+    "precision": "int8",
+    "path": "models/pose/yolo11s-pose/rk3588/int8",
+    "weights": "source/weights/yolo11s-pose.pt",
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+    "conversion": "rockchip-guided"
+  },
+  {
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+    "family": "yolo11",
+    "task": "segment",
+    "chip": "rk3588",
+    "precision": "fp16",
+    "path": "models/segment/yolo11s-seg/rk3588/fp16",
+    "weights": "source/weights/yolo11s-seg.pt",
+    "sha256": "d2078175d484cf5cd17f1c01bb6f3baee925dde7f2a36cab549132edfd5dbb41",
+    "onnx_sha256": "dd4cc9ccefbbe803c2033fd3e73f8d165e6c1bbfd11631049c8d1e9a302a82bc",
+    "metadata_sha256": "50d11ac8e4de14089503551b8b6bbd2daf75fe9d9a7b7e00e7cb746497a92f51",
+    "conversion": "rockchip-guided"
+  },
+  {
+    "name": "yolo11s-seg",
+    "family": "yolo11",
+    "task": "segment",
+    "chip": "rk3588",
+    "precision": "int8",
+    "path": "models/segment/yolo11s-seg/rk3588/int8",
+    "weights": "source/weights/yolo11s-seg.pt",
+    "sha256": "660baf675cb9097d0e4ba2c22f711bf91ea4dbdacf08072f187e08e6db7e5ed5",
+    "onnx_sha256": "dd4cc9ccefbbe803c2033fd3e73f8d165e6c1bbfd11631049c8d1e9a302a82bc",
+    "metadata_sha256": "4aebb96a20530b74caf48417309a57af77bccd0059539342b7e8ba5cbac16b0a",
+    "conversion": "rockchip-guided"
+  }
+]

models/obb/yolo11s-obb/rk3588/fp16/metadata.yaml

@@ -0,0 +1,25 @@
+task: obb
+batch: 1
+channels: 3
+imgsz:
+- 640
+- 640
+names:
+- plane
+- ship
+- storage tank
+- baseball diamond
+- tennis court
+- basketball court
+- ground track field
+- harbor
+- bridge
+- large vehicle
+- small vehicle
+- helicopter
+- roundabout
+- soccer ball field
+- swimming pool
+target_platform: rk3588
+coordinates: pixels
+precision: fp16

models/obb/yolo11s-obb/rk3588/int8/metadata.yaml

@@ -0,0 +1,25 @@
+task: obb
+batch: 1
+channels: 3
+imgsz:
+- 640
+- 640
+names:
+- plane
+- ship
+- storage tank
+- baseball diamond
+- tennis court
+- basketball court
+- ground track field
+- harbor
+- bridge
+- large vehicle
+- small vehicle
+- helicopter
+- roundabout
+- soccer ball field
+- swimming pool
+target_platform: rk3588
+coordinates: pixels
+precision: int8

models/obb/yolo11s-obb/rk3588/int8/model.rknn

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bd023ebd210afb7d2e93c2fbc8a846d28b2a3b55f6a15450229134eeb6353bd0
+size 11678625

models/pose/yolo11s-pose/rk3588/fp16/metadata.yaml

@@ -0,0 +1,14 @@
+task: pose
+batch: 1
+channels: 3
+imgsz:
+- 640
+- 640
+names:
+- person
+target_platform: rk3588
+coordinates: pixels
+precision: fp16
+kpt_shape:
+- 17
+- 3

models/pose/yolo11s-pose/rk3588/fp16/model.rknn

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+oid sha256:0ed226f07086f589bb751da6ac8ba493121b2c50da959120c980bfbaecd6aa91
+size 22613987

models/pose/yolo11s-pose/rk3588/int8/metadata.yaml

@@ -0,0 +1,14 @@
+task: pose
+batch: 1
+channels: 3
+imgsz:
+- 640
+- 640
+names:
+- person
+target_platform: rk3588
+coordinates: pixels
+precision: int8
+kpt_shape:
+- 17
+- 3

models/pose/yolo11s-pose/rk3588/int8/model.rknn

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dd01cae8b4e0c811bc8cfdf359041da96e0c3d04fdaf93a863342a9b22f9c5bb
+size 12465815

models/segment/yolo11s-seg/rk3588/fp16/metadata.yaml

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+task: segment
+batch: 1
+channels: 3
+imgsz:
+- 640
+- 640
+names:
+- person
+- bicycle
+- car
+- motorcycle
+- airplane
+- bus
+- train
+- truck
+- boat
+- traffic light
+- fire hydrant
+- stop sign
+- parking meter
+- bench
+- bird
+- cat
+- dog
+- horse
+- sheep
+- cow
+- elephant
+- bear
+- zebra
+- giraffe
+- backpack
+- umbrella
+- handbag
+- tie
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+- sports ball
+- kite
+- baseball bat
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+- skateboard
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+- apple
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+- broccoli
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+- pizza
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+- chair
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+- potted plant
+- bed
+- dining table
+- toilet
+- tv
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+- mouse
+- remote
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+- cell phone
+- microwave
+- oven
+- toaster
+- sink
+- refrigerator
+- book
+- clock
+- vase
+- scissors
+- teddy bear
+- hair drier
+- toothbrush
+target_platform: rk3588
+coordinates: pixels
+precision: fp16

models/segment/yolo11s-seg/rk3588/fp16/model.rknn

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+size 22584691

models/segment/yolo11s-seg/rk3588/int8/metadata.yaml

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+task: segment
+batch: 1
+channels: 3
+imgsz:
+- 640
+- 640
+names:
+- person
+- bicycle
+- car
+- motorcycle
+- airplane
+- bus
+- train
+- truck
+- boat
+- traffic light
+- fire hydrant
+- stop sign
+- parking meter
+- bench
+- bird
+- cat
+- dog
+- horse
+- sheep
+- cow
+- elephant
+- bear
+- zebra
+- giraffe
+- backpack
+- umbrella
+- handbag
+- tie
+- suitcase
+- frisbee
+- skis
+- snowboard
+- sports ball
+- kite
+- baseball bat
+- baseball glove
+- skateboard
+- surfboard
+- tennis racket
+- bottle
+- wine glass
+- cup
+- fork
+- knife
+- spoon
+- bowl
+- banana
+- apple
+- sandwich
+- orange
+- broccoli
+- carrot
+- hot dog
+- pizza
+- donut
+- cake
+- chair
+- couch
+- potted plant
+- bed
+- dining table
+- toilet
+- tv
+- laptop
+- mouse
+- remote
+- keyboard
+- cell phone
+- microwave
+- oven
+- toaster
+- sink
+- refrigerator
+- book
+- clock
+- vase
+- scissors
+- teddy bear
+- hair drier
+- toothbrush
+target_platform: rk3588
+coordinates: pixels
+precision: int8

models/segment/yolo11s-seg/rk3588/int8/model.rknn

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+version https://git-lfs.github.com/spec/v1
+oid sha256:660baf675cb9097d0e4ba2c22f711bf91ea4dbdacf08072f187e08e6db7e5ed5
+size 12087849

source/convert-official.py

@@ -0,0 +1,194 @@
+#!/usr/bin/env python3
+"""Download the ONNX listed by Rockchip and invoke its unmodified converters."""
+import argparse
+import concurrent.futures
+import hashlib
+from importlib.metadata import version
+import json
+from pathlib import Path
+import re
+import shutil
+import subprocess
+import sys
+import tempfile
+import yaml
+
+DETECTORS = ("yolov5", "yolov6", "yolov7", "yolov8", "yolov10", "yolo11",
+             "yolo26", "yolox", "ppyoloe", "yolo_world")
+TASKS = ("yolov5_seg", "yolov8_seg", "yolov8_pose", "yolov8_obb")
+SUPPLIED_ONNX = {
+    "yolo26n": "https://meta.zbox.filez.com/v/link/view/dddfdaa76107404a8a358673fa31a041",
+    "yolo26m": "https://meta.zbox.filez.com/v/link/view/198e90ee6bc94c71ad2002d5f9897ac4",
+}
+
+
+def inventory(zoo):
+    for family in (*DETECTORS, *TASKS):
+        readme = zoo / "examples" / family / "README.md"
+        urls = re.findall(r"\((https://[^)]+\.onnx)\)", readme.read_text())
+        assets = {Path(url).stem: url for url in urls}
+        if family in ("yolov8_pose", "yolov8_obb"):
+            assets = {name: url for name, url in assets.items() if "v5n-" in name or "v8n-" in name}
+        if family == "yolo26":
+            assets.update({"yolo26n": None, "yolo26m": None})
+        for name, url in assets.items():
+            yield family, name, url
+
+
+def download(root, zoo, family, name, url):
+    destination = root / "onnx" / family / f"{name}.onnx"
+    destination.parent.mkdir(parents=True, exist_ok=True)
+    if not destination.is_file():
+        local = zoo / "examples" / family / "model" / destination.name
+        if not url and local.is_file():
+            shutil.copy2(local, destination)
+        elif url:
+            partial = destination.with_suffix(".part")
+            subprocess.run(["curl", "-fLsS", "--retry", "2", "--connect-timeout", "12",
+                            "--max-time", "180", "-o", partial, url], check=True)
+            partial.replace(destination)
+        else:
+            raise FileNotFoundError(
+                f"Download {SUPPLIED_ONNX[name]} and save its ONNX as {destination}")
+    import onnx
+    onnx.load(str(destination), load_external_data=False)
+    return destination
+
+
+def digest(path):
+    checksum = hashlib.sha256()
+    with path.open("rb") as stream:
+        for block in iter(lambda: stream.read(1024 * 1024), b""):
+            checksum.update(block)
+    return checksum.hexdigest()
+
+
+def write_metadata(zoo, family, model, destination, chip):
+    if family == "yolo_world":
+        return
+    import onnx
+    inputs = onnx.load(str(model), load_external_data=False).graph.input
+    shape = [dim.dim_value for dim in inputs[0].type.tensor_type.shape.dim]
+    if len(shape) != 4 or shape[:2] != [1, 3] or min(shape[2:]) <= 0:
+        raise RuntimeError(f"Expected static NCHW RGB model, got {shape}")
+    assets = zoo / "examples" / family / "model"
+    labels = next(assets.glob("*labels_list.txt"))
+    tasks = {"yolov5_seg": "segment", "yolov8_seg": "segment",
+             "yolov8_pose": "pose", "yolov8_obb": "obb"}
+    task = tasks.get(family, "detect")
+    info = {
+        "task": task, "batch": 1, "channels": 3, "imgsz": shape[2:],
+        "names": [line.strip() for line in labels.read_text().splitlines() if line.strip()],
+        "target_platform": chip,
+        "coordinates": "pixels",
+    }
+    if family in ("yolov5", "yolov5_seg", "yolov7"):
+        values = [float(value) for value in next(assets.glob("anchors_*.txt")).read_text().split()]
+        info.update(head="anchored", anchors=[values[i:i + 6] for i in range(0, len(values), 6)])
+    if family == "yolox":
+        info["head"] = "yolox"
+    if family in ("yolov10", "yolo26"):
+        info["end2end"] = True
+    if task == "pose":
+        info["kpt_shape"] = [17, 3]
+    (destination / "metadata.yaml").write_text(yaml.safe_dump(info, sort_keys=False))
+
+
+def convert(root, zoo, asset, chip, precision, refresh_metadata=False):
+    family, name, model, source = asset
+    destination = root / "rknn" / chip / family / name / precision
+    destination.mkdir(parents=True, exist_ok=True)
+    output = destination / "model.rknn"
+    receipt = destination / "conversion.json"
+    if not refresh_metadata and output.is_file() and receipt.is_file() and (
+            family == "yolo_world" or (destination / "metadata.yaml").is_file()):
+        return f"cached {family}/{name} {chip} {precision}"
+    work_root = root / "scratch"
+    work_root.mkdir(exist_ok=True)
+    with tempfile.TemporaryDirectory(dir=work_root, prefix=f"{name}-{chip}-{precision}-") as scratch:
+        # Keep the relative dataset and model paths expected by the official scripts.
+        tree = Path(scratch)
+        (tree / "datasets").symlink_to(zoo / "datasets")
+        example = tree / "examples" / family
+        example.mkdir(parents=True)
+        (example / "model").symlink_to(zoo / "examples" / family / "model")
+        working = example / "python"
+        shutil.copytree(zoo / "examples" / family / "python", working)
+        staged = destination / "model.partial.rknn"
+        commands = []
+        if family == "yolo_world":
+            subdir = "clip_text" if name == "clip_text" else "yolo_world"
+            script = f"{subdir}/convert.py"
+            commands.append([script, str(model), chip, precision, str(staged)])
+        elif family == "yolov8_pose":
+            if precision == "i8":
+                commands.append(["convert.py", str(model), chip, "i8", "1"])
+                commands.append(["convert.py", str(model), chip, "i8", "2", str(staged)])
+            else:
+                commands.append(["convert.py", str(model), chip, "fp", "1", str(staged)])
+        else:
+            commands.append(["convert.py", str(model), chip, precision, str(staged)])
+        if not output.is_file():
+            with (destination / "convert.log").open("w") as log:
+                for command in commands:
+                    print(subprocess.list2cmdline(command), file=log, flush=True)
+                    subprocess.run([sys.executable, *command], cwd=working,
+                                   stdout=log, stderr=subprocess.STDOUT, check=True)
+            if not staged.is_file() or not staged.stat().st_size:
+                raise RuntimeError("Converter did not produce an RKNN file")
+            staged.replace(output)
+        write_metadata(zoo, family, model, destination, chip)
+        receipt.write_text(json.dumps({
+            "source": source, "onnx_sha256": digest(model), "rknn_sha256": digest(output),
+            "commands": commands, "toolkit_python": sys.executable,
+            "toolkit_version": version("rknn-toolkit2"),
+            "zoo_commit": subprocess.check_output(
+                ["git", "-C", str(zoo), "rev-parse", "HEAD"], text=True).strip(),
+        }, indent=2) + "\n")
+    return f"ok {family}/{name} {chip} {precision}"
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    root = Path(__file__).resolve().parent
+    parser.add_argument("--zoo", type=Path, default=root.parent / "rknn_model_zoo",
+                        help="Model Zoo checkout (default: ../rknn_model_zoo)")
+    parser.add_argument("--jobs", type=int, default=4)
+    parser.add_argument("--model", action="append", help="Limit the run to named variants")
+    parser.add_argument("--chip", choices=("rk3576", "rk3588"), action="append")
+    parser.add_argument("--precision", choices=("i8", "fp"), action="append")
+    parser.add_argument("--refresh-metadata", action="store_true")
+    args = parser.parse_args()
+    zoo = args.zoo.resolve()
+    inventory_rows = [item for item in inventory(zoo) if not args.model or item[1] in args.model]
+    failures = 0
+    with concurrent.futures.ThreadPoolExecutor(max_workers=6) as downloads, \
+            concurrent.futures.ThreadPoolExecutor(max_workers=args.jobs) as workers:
+        pending = {downloads.submit(download, root, zoo, *item): item for item in inventory_rows}
+        builds = {}
+        for future in concurrent.futures.as_completed(pending):
+            family, name, source = pending[future]
+            try:
+                model = future.result()
+                asset = family, name, model, source or SUPPLIED_ONNX.get(name) or str(model)
+                for chip in args.chip or ("rk3576", "rk3588"):
+                    for precision in args.precision or ("i8", "fp"):
+                        if name == "clip_text" and precision == "i8":
+                            continue
+                        job = workers.submit(convert, root, zoo, asset, chip, precision,
+                                             args.refresh_metadata)
+                        builds[job] = family, name, chip, precision
+            except Exception as error:
+                print(f"asset failed {family}/{name}: {error}", flush=True)
+                failures += 1
+        for future in concurrent.futures.as_completed(builds):
+            try:
+                print(future.result(), flush=True)
+            except Exception as error:
+                print(f"build failed {' '.join(builds[future])}: {error}", flush=True)
+                failures += 1
+    return bool(failures)
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

source/exporters.json

@@ -0,0 +1,15 @@
+{
+  "yolov8": {
+    "exporter": "https://github.com/airockchip/ultralytics_yolov8/tree/4674fe6e003dfbc5f2250d3b39dd31faaf7a9877",
+    "script": "source/extensions/yolov8/convert-guided-tasks.py"
+  },
+  "yolo11": {
+    "exporter": "https://github.com/airockchip/ultralytics_yolo11/tree/0692e9297670acf4cc6d0cec773d7a9493cb8a5f",
+    "script": "source/extensions/yolo11/convert-yolo11.py"
+  },
+  "yolov10": {
+    "exporter": "https://github.com/airockchip/yolov10/tree/81f32c4ee396e679b489ff786faa0f9fa0eec298",
+    "script": "source/extensions/yolov10/convert-yolov10.py"
+  },
+  "model_zoo": "https://github.com/airockchip/rknn_model_zoo/tree/837db8e992815ff8647eadb709f07067f4bc3a71"
+}

source/extensions/yolo11/convert-yolo11.py

@@ -0,0 +1,195 @@
+"""Compile the Rockchip YOLO11 task exports using their Model Zoo counterparts."""
+
+import argparse
+import concurrent.futures
+import hashlib
+import itertools
+import json
+import os
+from pathlib import Path
+import re
+import subprocess
+import sys
+
+import onnx
+import yaml
+
+
+RECIPES = {
+    "seg": ("segment", "yolov8_seg", "coco_80_labels_list.txt"),
+    "pose": ("pose", "yolov8_pose", "yolov8_pose_labels_list.txt"),
+    "obb": ("obb", "yolov8_obb", "yolov8_obb_labels_list.txt"),
+}
+
+
+def sha256(path):
+    with path.open("rb") as handle:
+        return hashlib.file_digest(handle, "sha256").hexdigest()
+
+
+def export_onnx(root, weights):
+    upstream = root / "upstream"
+    config = upstream / "ultralytics" / "cfg" / "default.yaml"
+    original = config.read_text()
+    pristine = subprocess.check_output(
+        ["git", "show", "HEAD:ultralytics/cfg/default.yaml"], cwd=upstream, text=True,
+    )
+    if original != pristine:
+        raise RuntimeError(f"Restore the Rockchip exporter config before running: {config}")
+    (root / "onnx").mkdir(exist_ok=True)
+    try:
+        for task in RECIPES:
+            name = f"yolo11n-{task}"
+            checkpoint = weights / f"{name}.pt"
+            if not checkpoint.is_file():
+                raise FileNotFoundError(checkpoint)
+            model = root / "onnx" / checkpoint.name
+            if model.is_symlink() and model.resolve() != checkpoint.resolve():
+                model.unlink()
+            if not model.exists():
+                model.symlink_to(checkpoint)
+            config.write_text(
+                re.sub(r"^model:.*$", f"model: {json.dumps(str(model))}", original, flags=re.M)
+            )
+            print(f"Rockchip RKOPT export: {name}", flush=True)
+            log = root / "logs" / f"export-{task}.log"
+            with log.open("w") as handle:
+                subprocess.run(
+                    [sys.executable, "ultralytics/engine/exporter.py"], cwd=upstream,
+                    stdout=handle, stderr=subprocess.STDOUT, check=True,
+                    env={
+                        **os.environ, "PYTHONPATH": ".", "YOLO_AUTOINSTALL": "false",
+                        "YOLO_CONFIG_DIR": str(root / "settings"), "OMP_NUM_THREADS": "2",
+                    },
+                )
+            onnx.checker.check_model(str(model.with_suffix(".onnx")))
+    finally:
+        config.write_text(original)
+
+
+def pose_recipe(recipe, model, output):
+    graph = onnx.load(model).graph
+    tensor_names = {name for node in graph.node for name in node.output}
+    keypoints = [
+        value.name for value in graph.output
+        if [dim.dim_value for dim in value.type.tensor_type.shape.dim] == [1, 17, 3, 8400]
+    ]
+    if len(keypoints) != 1:
+        raise ValueError("Cannot identify the decoded YOLO11 keypoint tensor")
+
+    # The upstream YOLOv8 ranges keep each keypoint branch through its decoded
+    # output in floating point. YOLO11 has the same branches but a different
+    # head number and final Concat, so check both endpoints in the ONNX graph.
+    script = recipe.read_text()
+    starts = [f"/model.23/cv4.{i}/cv4.{i}.0/act/Mul_output_0" for i in range(3)]
+    if not all(name in tensor_names for name in [*starts, keypoints[0]]):
+        raise ValueError("YOLO11 pose hybrid range is missing from the graph")
+    if len(re.findall(r"/model\.22/cv4\.", script)) != 3:
+        raise ValueError("Unexpected Model Zoo pose recipe")
+    output.write_text(
+        script.replace("/model.22/cv4.", "/model.23/cv4.").replace(
+            "/model.22/Concat_6_output_0", keypoints[0]
+        )
+    )
+
+
+def convert(root, zoo, task, chip, precision):
+    task_name, example, labels = RECIPES[task]
+    name = f"yolo11n-{task}"
+    model = root / "onnx" / f"{name}.onnx"
+    example_root = zoo / "examples" / example
+    recipe = example_root / "python" / "convert.py"
+    job = root / "scratch" / name / chip / precision
+    cwd = job / "examples" / example / "python"
+    cwd.mkdir(parents=True, exist_ok=True)
+    dataset = job / "datasets"
+    if not dataset.is_symlink():
+        dataset.symlink_to(zoo / "datasets", target_is_directory=True)
+    destination = root / "rknn" / chip / name / precision
+    destination.mkdir(parents=True, exist_ok=True)
+    output = destination / "model.rknn"
+    pending = destination / "model.partial.rknn"
+    if pending.exists():
+        pending.unlink()
+
+    if task == "pose" and precision == "i8":
+        adapted = cwd / "convert.py"
+        pose_recipe(recipe, model, adapted)
+        commands = [
+            [adapted, model, chip, precision, "1"],
+            [adapted, model, chip, precision, "2", pending],
+        ]
+    elif task == "pose":
+        commands = [[recipe, model, chip, precision, "1", pending]]
+    else:
+        commands = [[recipe, model, chip, precision, pending]]
+
+    log = root / "logs" / f"{name}-{chip}-{precision}.log"
+    with log.open("w") as handle:
+        for command in commands:
+            subprocess.run(
+                [sys.executable, *command], cwd=cwd, stdout=handle,
+                stderr=subprocess.STDOUT, check=True,
+                env={**os.environ, "OMP_NUM_THREADS": "2"},
+            )
+    if not pending.is_file() or not pending.stat().st_size:
+        raise RuntimeError(f"No RKNN file written; see {log}")
+    pending.replace(output)
+
+    metadata = {
+        "task": task_name,
+        "batch": 1,
+        "channels": 3,
+        "imgsz": [640, 640],
+        "names": (example_root / "model" / labels).read_text().splitlines(),
+        "target_platform": chip,
+        "coordinates": "pixels",
+        "precision": "fp16" if precision == "fp" else "int8",
+    }
+    if task == "pose":
+        metadata["kpt_shape"] = [17, 3]
+    (destination / "metadata.yaml").write_text(yaml.safe_dump(metadata, sort_keys=False))
+    return {
+        "name": name, "chip": chip, "precision": precision, "status": "ok",
+        "onnx": str(model), "onnx_sha256": sha256(model),
+        "recipe": str(recipe), "recipe_sha256": sha256(recipe),
+        "rknn": str(output), "rknn_sha256": sha256(output),
+        "bytes": output.stat().st_size, "log": str(log),
+    }
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--zoo", type=Path, required=True)
+    parser.add_argument(
+        "--weights", type=Path,
+        help="Re-export ONNX from YOLO11 nano .pt files using the adjacent Rockchip upstream clone",
+    )
+    parser.add_argument("--workers", type=int, default=3)
+    args = parser.parse_args()
+    root = Path(__file__).resolve().parent
+    zoo = args.zoo.resolve()
+    (root / "logs").mkdir(exist_ok=True)
+    if args.weights:
+        export_onnx(root, args.weights.resolve())
+    jobs = itertools.product(RECIPES, ("rk3576", "rk3588"), ("i8", "fp"))
+    results = []
+    with concurrent.futures.ThreadPoolExecutor(args.workers) as pool:
+        running = {pool.submit(convert, root, zoo, *job): job for job in jobs}
+        for future in concurrent.futures.as_completed(running):
+            task, chip, precision = running[future]
+            try:
+                result = future.result()
+            except Exception as error:
+                result = {
+                    "name": f"yolo11n-{task}", "chip": chip, "precision": precision,
+                    "status": "failed", "error": str(error),
+                }
+            print(json.dumps(result), flush=True)
+            results.append(result)
+    (root / "manifest.json").write_text(json.dumps(results, indent=2) + "\n")
+    return int(any(result["status"] != "ok" for result in results))
+
+
+if __name__ == "__main__":
+    sys.exit(main())

source/weights.json

@@ -0,0 +1,134 @@
+[
+  {
+    "name": "yolo11m-cls",
+    "family": "yolo11",
+    "size": "m",
+    "task": "classify",
+    "imgsz": 224,
+    "bytes": 23487128,
+    "sha256": "80583b974e44292d56fc5ba4d26cd0bb04e77edc8bbf4317f7ce678147156dd5",
+    "source": "https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo11m-cls.pt",
+    "file": "source/weights/yolo11m-cls.pt"
+  },
+  {
+    "name": "yolo11n-cls",
+    "family": "yolo11",
+    "size": "n",
+    "task": "classify",
+    "imgsz": 224,
+    "bytes": 5790624,
+    "sha256": "c62d41bf9625777760018bf914d2e6cd472420ccd01706d97a61cb6c82502bd7",
+    "source": "https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo11n-cls.pt",
+    "file": "source/weights/yolo11n-cls.pt"
+  },
+  {
+    "name": "yolo11s-cls",
+    "family": "yolo11",
+    "size": "s",
+    "task": "classify",
+    "imgsz": 224,
+    "bytes": 13630112,
+    "sha256": "e2b605d1c8c212b434a75a32759a6f7adf1d2b29c35f76bdccd4c794cb653cf2",
+    "source": "https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo11s-cls.pt",
+    "file": "source/weights/yolo11s-cls.pt"
+  },
+  {
+    "name": "yolo11m-obb",
+    "family": "yolo11",
+    "size": "m",
+    "task": "obb",
+    "imgsz": 640,
+    "bytes": 42415346,
+    "sha256": "41832a4349c08190335bbc11a8e64726750702eb49cf09abb262bc394a13498c",
+    "source": "https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo11m-obb.pt",
+    "file": "source/weights/yolo11m-obb.pt"
+  },
+  {
+    "name": "yolo11m-pose",
+    "family": "yolo11",
+    "size": "m",
+    "task": "pose",
+    "imgsz": 640,
+    "bytes": 42459307,
+    "sha256": "29b17eaf3a3117cbea906090dbedf9159f7c6a49db58ec8b99ed2dfde1cf6eb2",
+    "source": "https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo11m-pose.pt",
+    "file": "source/weights/yolo11m-pose.pt"
+  },
+  {
+    "name": "yolo11m-seg",
+    "family": "yolo11",
+    "size": "m",
+    "task": "segment",
+    "imgsz": 640,
+    "bytes": 45400152,
+    "sha256": "eb9a06f63e2206c35d68d839b08c362429ebecf933ad54c1ad68b2fd001c17cf",
+    "source": "https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo11m-seg.pt",
+    "file": "source/weights/yolo11m-seg.pt"
+  },
+  {
+    "name": "yolo11n-obb",
+    "family": "yolo11",
+    "size": "n",
+    "task": "obb",
+    "imgsz": 640,
+    "bytes": 5795654,
+    "sha256": "b62898ebf38940ca4df323863e45ee9d84a1a46d5d11ebdde529fb33aa9f3a32",
+    "source": "https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo11n-obb.pt",
+    "file": "source/weights/yolo11n-obb.pt"
+  },
+  {
+    "name": "yolo11n-pose",
+    "family": "yolo11",
+    "size": "n",
+    "task": "pose",
+    "imgsz": 640,
+    "bytes": 6255593,
+    "sha256": "869e83fcdffdc7371fa4e34cd8e51c838cc729571d1635e5141e3075e9319dc0",
+    "source": "https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo11n-pose.pt",
+    "file": "source/weights/yolo11n-pose.pt"
+  },
+  {
+    "name": "yolo11n-seg",
+    "family": "yolo11",
+    "size": "n",
+    "task": "segment",
+    "imgsz": 640,
+    "bytes": 6182636,
+    "sha256": "55ed65c56c91713d23e8402371c6c49a6fd84f257f7dce452e8d70e41dcbe152",
+    "source": "https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo11n-seg.pt",
+    "file": "source/weights/yolo11n-seg.pt"
+  },
+  {
+    "name": "yolo11s-obb",
+    "family": "yolo11",
+    "size": "s",
+    "task": "obb",
+    "imgsz": 640,
+    "bytes": 19934534,
+    "sha256": "43fa63102922e0701501241b307420d24fc55e080816888b18bf8c6f96b1a45a",
+    "source": "https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo11s-obb.pt",
+    "file": "source/weights/yolo11s-obb.pt"
+  },
+  {
+    "name": "yolo11s-pose",
+    "family": "yolo11",
+    "size": "s",
+    "task": "pose",
+    "imgsz": 640,
+    "bytes": 20373161,
+    "sha256": "1060bda4a27012060eca246f9b2adeea22eabb045a1e58f8d229be29b7ebc2ba",
+    "source": "https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo11s-pose.pt",
+    "file": "source/weights/yolo11s-pose.pt"
+  },
+  {
+    "name": "yolo11s-seg",
+    "family": "yolo11",
+    "size": "s",
+    "task": "segment",
+    "imgsz": 640,
+    "bytes": 20669228,
+    "sha256": "1caa81c0195412efa411b632bcfb8c184939dddb6ae41f6a80c41b211ff257c3",
+    "source": "https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo11s-seg.pt",
+    "file": "source/weights/yolo11s-seg.pt"
+  }
+]