Organize RKNN models by family

.gitattributes

@@ -33,3 +33,23 @@ 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
+models/classify/yolov8m-cls/rk3576/fp16/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/classify/yolov8m-cls/rk3576/int8/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/classify/yolov8n-cls/rk3576/fp16/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/classify/yolov8n-cls/rk3576/int8/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/classify/yolov8s-cls/rk3576/fp16/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/classify/yolov8s-cls/rk3576/int8/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/classify/yolov8m-cls/rk3588/fp16/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/classify/yolov8m-cls/rk3588/int8/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/classify/yolov8n-cls/rk3588/fp16/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/classify/yolov8n-cls/rk3588/int8/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/classify/yolov8s-cls/rk3588/fp16/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/classify/yolov8s-cls/rk3588/int8/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/obb/yolov8m-obb/rk3576/fp16/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/obb/yolov8m-obb/rk3576/int8/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/pose/yolov8m-pose/rk3576/fp16/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/pose/yolov8m-pose/rk3576/int8/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/obb/yolov8s-obb/rk3576/fp16/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/obb/yolov8s-obb/rk3576/int8/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/pose/yolov8s-pose/rk3576/fp16/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/pose/yolov8s-pose/rk3576/int8/model.rknn filter=lfs diff=lfs merge=lfs -text

LICENSE

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models/classify/yolov8m-cls/rk3576/fp16/metadata.yaml

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+export_source: model-zoo-inspired-pytorch
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+precision: fp16

models/classify/yolov8m-cls/rk3576/fp16/model.rknn

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models/classify/yolov8m-cls/rk3576/int8/metadata.yaml

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+preprocess: center_crop
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+end2end: false
+export_source: model-zoo-inspired-pytorch
+checkpoint: yolov8m-cls.pt
+checkpoint_sha256: 864010e833b46be542ed849b2eb0fdacf51e4679dc677c18dcc4505244d08ec1
+ultralytics: 8.4.184
+pytorch: 2.4.0+cu121
+target_platform: rk3576
+precision: int8

models/classify/yolov8m-cls/rk3576/int8/model.rknn

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+oid sha256:4627b7efc88dbad44dd2809c25d7f119d79f445e87c3715915d4c61f78beba41
+size 19367686

models/classify/yolov8m-cls/rk3588/fp16/metadata.yaml

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+export_source: model-zoo-inspired-pytorch
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models/classify/yolov8m-cls/rk3588/fp16/model.rknn

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+oid sha256:f2547a0fdbe17e4753241fc59445649296d1f71212b74b96411eee9be85dfe02
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+export_source: model-zoo-inspired-pytorch
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+preprocess: center_crop
+coordinates: pixels
+end2end: false
+export_source: model-zoo-inspired-pytorch
+checkpoint: yolov8n-cls.pt
+checkpoint_sha256: 11fa19f2aea79bc960d680a13f82f22105982b325eb9e17a4a5e1a9f8245980a
+ultralytics: 8.4.184
+pytorch: 2.4.0+cu121
+target_platform: rk3576
+precision: fp16

models/classify/yolov8n-cls/rk3576/fp16/model.rknn

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+oid sha256:468f585137ee994e659fa86ec6667407f2aa0267b319d14fb96df066ba3723d1
+size 6972537

models/classify/yolov8n-cls/rk3576/int8/metadata.yaml

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+preprocess: center_crop
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+export_source: model-zoo-inspired-pytorch
+checkpoint: yolov8n-cls.pt
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+ultralytics: 8.4.184
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+precision: int8

models/classify/yolov8n-cls/rk3576/int8/model.rknn

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+oid sha256:17d23dd2d14413fb9e1165ad4eaabd0051cd91a19e005919eb3514eaded82d9c
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+export_source: model-zoo-inspired-pytorch
+checkpoint: yolov8n-cls.pt
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+ultralytics: 8.4.184
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+target_platform: rk3588
+precision: int8

models/classify/yolov8n-cls/rk3588/int8/model.rknn

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+export_source: model-zoo-inspired-pytorch
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models/classify/yolov8s-cls/rk3576/fp16/model.rknn

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+oid sha256:9330b5945661ec3cac18b1d0cf84b2af8925c22b31ac95876b37c729801221c9
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+preprocess: center_crop
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+export_source: model-zoo-inspired-pytorch
+checkpoint: yolov8s-cls.pt
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+ultralytics: 8.4.184
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+target_platform: rk3588
+precision: fp16

models/classify/yolov8s-cls/rk3588/fp16/model.rknn

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models/classify/yolov8s-cls/rk3588/int8/metadata.yaml

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+preprocess: center_crop
+coordinates: pixels
+end2end: false
+export_source: model-zoo-inspired-pytorch
+checkpoint: yolov8s-cls.pt
+checkpoint_sha256: 8079e739ded3ce0837ceaade760d6d17d496f38aaa4ed73c3da5b91ee5a9ca3c
+ultralytics: 8.4.184
+pytorch: 2.4.0+cu121
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+      "sha256": "079af237aafcdddfe5a528ef5e40f38ca449fce9ad5a49fbdaaeef068fac7c76",
+      "coco128_file": "000000000387.jpg"
+    },
+    {
+      "file": "0068.jpg",
+      "sha256": "f6d313afba47fe0747d1d78cd342f420eb305de8faadb5cb164de428522b40d6",
+      "coco128_file": "000000000389.jpg"
+    },
+    {
+      "file": "0069.jpg",
+      "sha256": "2cf78c1cfa98714d2ed8a643f931c39511bbb6085fddf157b27ed709089e0343",
+      "coco128_file": "000000000394.jpg"
+    },
+    {
+      "file": "0070.jpg",
+      "sha256": "8d823d141724cc8656ccd48b49d6e44556ca399fbbd8458cc488f0d9816809a4",
+      "coco128_file": "000000000395.jpg"
+    },
+    {
+      "file": "0071.jpg",
+      "sha256": "5011e5e9eac564d30fa652281a7fd1d7693545077971741a241f400611e4c33e",
+      "coco128_file": "000000000397.jpg"
+    },
+    {
+      "file": "0072.jpg",
+      "sha256": "7b78338d4c7a30f7c38eb3bf11a7623a195e10926359adfd7281d6ce99e42dcb",
+      "coco128_file": "000000000400.jpg"
+    },
+    {
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+      "sha256": "c46b3242d195c8c81b6776f4bfa385ef886ff1e7cb814e2c29e03303b85403b2",
+      "coco128_file": "000000000404.jpg"
+    },
+    {
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+      "sha256": "cc75d90eac7419395e81ddd7e33c81dbec387892e086b40cbf12dc3fe09f8bb1",
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+    },
+    {
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+      "sha256": "4b4e5c24a3aafa6d591ab385cc3756f90a804212a8e060ba374d1658b69b308e",
+      "coco128_file": "000000000419.jpg"
+    },
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+    },
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+    },
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+    },
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+      "sha256": "ee66d39f65c8ce3421fda1ca85b1942f6dff29813c97b6fb4ea4ed015c3a822a",
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+    },
+    {
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+      "coco128_file": "000000000443.jpg"
+    },
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+    },
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+      "sha256": "3b1fe58cb414f373c61990a5fed4f1a6418a02b2e8a3ed3ecef613f4e7198068",
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+    },
+    {
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+    },
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+    },
+    {
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+      "coco128_file": "000000000472.jpg"
+    },
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+    },
+    {
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+      "sha256": "cb30e9ff9b1182fc9aa328f601f0ca640236eea29ec695b57edc11a572dfbfed",
+      "coco128_file": "000000000486.jpg"
+    },
+    {
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+      "sha256": "9e1c9d4c88d38e6a2cd2f775bea82c137c14dc3b13f8430bcdadcb89ab0b7022",
+      "coco128_file": "000000000488.jpg"
+    },
+    {
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+    },
+    {
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+    },
+    {
+      "file": "0091.jpg",
+      "sha256": "f7ce0feeb7699cd56886c64ac4ea923c76be97b34ee3a2ea0909c45e08411ec0",
+      "coco128_file": "000000000502.jpg"
+    },
+    {
+      "file": "0092.jpg",
+      "sha256": "0fc73f660a10ed91c52f76a2b34b5fa252bf70ff84f7b2a35374726388528ca2",
+      "coco128_file": "000000000508.jpg"
+    },
+    {
+      "file": "0093.jpg",
+      "sha256": "c2fcc1d44e81016f4c53e7e610c44dc28a2ca1bc5e73f73623129d261bff15e2",
+      "coco128_file": "000000000510.jpg"
+    },
+    {
+      "file": "0094.jpg",
+      "sha256": "073520d67d44551b4e0bc24d4eb3a6bb68bf00481a8b15d9f34479e787d2cba9",
+      "coco128_file": "000000000514.jpg"
+    },
+    {
+      "file": "0095.jpg",
+      "sha256": "2b971302ba56e1049294f9e0b198f37316148c8257b8d7344002aed624c45844",
+      "coco128_file": "000000000520.jpg"
+    },
+    {
+      "file": "0096.jpg",
+      "sha256": "5d9958bdec3062b05090258293a19af01d231de69b943d1ff0bb4a7b943e6db3",
+      "coco128_file": "000000000529.jpg"
+    },
+    {
+      "file": "0097.jpg",
+      "sha256": "111bb407e512f23fcc14bbc51ad4b312b0797332aab86ecd9373808286ed30b1",
+      "coco128_file": "000000000531.jpg"
+    },
+    {
+      "file": "0098.jpg",
+      "sha256": "60745e50cb857e4683d3622ac6c1e7ade63bd73ee458eb76a70455badaee2869",
+      "coco128_file": "000000000532.jpg"
+    },
+    {
+      "file": "0099.jpg",
+      "sha256": "745e538b3979ffa93f63bb294163e173d9a99413bce6cf3289215b541c9f1eb1",
+      "coco128_file": "000000000536.jpg"
+    },
+    {
+      "file": "0100.jpg",
+      "sha256": "32099f009fcb436bad1bd59b5d48b3e5620fa667a83db5a09879e158dd346103",
+      "coco128_file": "000000000540.jpg"
+    },
+    {
+      "file": "0101.jpg",
+      "sha256": "ef93fd2248a41119a34a86d11478b87bd0c715ff3c2c65f3f5f54a3f31a3dedf",
+      "coco128_file": "000000000542.jpg"
+    },
+    {
+      "file": "0102.jpg",
+      "sha256": "f599082d9c87db6029fc3d6e468074d1294dc98a8755152ca7324811aa1d10cd",
+      "coco128_file": "000000000544.jpg"
+    },
+    {
+      "file": "0103.jpg",
+      "sha256": "96801af258ded041845af111c4535dcb12a512cf22f6fd9854e5723920532d60",
+      "coco128_file": "000000000560.jpg"
+    },
+    {
+      "file": "0104.jpg",
+      "sha256": "e31320272d768add7113d41a9c65407fd4868da1229f8658fc877e9b07c4c794",
+      "coco128_file": "000000000562.jpg"
+    },
+    {
+      "file": "0105.jpg",
+      "sha256": "1f67bf49851ff43dba866f0cd6cc1b6f1d35ee75255a6092483550548584f477",
+      "coco128_file": "000000000564.jpg"
+    },
+    {
+      "file": "0106.jpg",
+      "sha256": "07655c58e2286ec5e8b74eee71a03841ccf732cbc502d6c380d894a14041ad5f",
+      "coco128_file": "000000000569.jpg"
+    },
+    {
+      "file": "0107.jpg",
+      "sha256": "b779e782a66c196373580de6ede5c4faacabf05fb521eab8df4d11b71d8d2770",
+      "coco128_file": "000000000572.jpg"
+    },
+    {
+      "file": "0108.jpg",
+      "sha256": "839789fe6192bbd5776761d675aa76017b67810b556f3e25e2f3a31acffd86f3",
+      "coco128_file": "000000000575.jpg"
+    },
+    {
+      "file": "0109.jpg",
+      "sha256": "60ad7a794ae3032c302c4e1d5111099921ce7e8df23df4401f60bdc74dc83bf1",
+      "coco128_file": "000000000581.jpg"
+    },
+    {
+      "file": "0110.jpg",
+      "sha256": "f1ae69b71470e4d4e88e342b69fa47187fe5c7a56882eac0e548107075f4716f",
+      "coco128_file": "000000000584.jpg"
+    },
+    {
+      "file": "0111.jpg",
+      "sha256": "b028805b2fb2c666ef72d4a7a01677bcf0ed56fb4d14cadc8646acdf379999c7",
+      "coco128_file": "000000000589.jpg"
+    },
+    {
+      "file": "0112.jpg",
+      "sha256": "dd687875b5ff4fd0a91dcc910690da36858271562ea908ff61661b4cec9b4cee",
+      "coco128_file": "000000000590.jpg"
+    },
+    {
+      "file": "0113.jpg",
+      "sha256": "8152ff314326c47105e065ec16ef28e1baa9dd640f40475fe385b612d5e65cf1",
+      "coco128_file": "000000000595.jpg"
+    },
+    {
+      "file": "0114.jpg",
+      "sha256": "f611f233ff6657baa61f32f573a213ef1b1af031e892f8b4e287c21a856497e7",
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+    },
+    {
+      "file": "0115.jpg",
+      "sha256": "9d924b4353cca7295c149e3659866a18e78234f0dc701df119191152f4a31fec",
+      "coco128_file": "000000000599.jpg"
+    },
+    {
+      "file": "0116.jpg",
+      "sha256": "9135dd0723240d2d4f64ce2173901a0f23a222dea98b72e1259c28f28fa2aab6",
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+    },
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+      "sha256": "9ee977eaa2947ffa1508dfc8b6e3c179a9945ee3a55529f91ede04689f00e667",
+      "coco128_file": "000000000612.jpg"
+    },
+    {
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+      "sha256": "4c7d0e5b1ac23a287f95640c4aaab93a0e120c384558e3fff70b96f6f1eb80ce",
+      "coco128_file": "000000000620.jpg"
+    },
+    {
+      "file": "0119.jpg",
+      "sha256": "c11d16bc49acabc2a52b990174110753dbe3a9a71eb7aa37af8f5fc4c616fb20",
+      "coco128_file": "000000000623.jpg"
+    },
+    {
+      "file": "0120.jpg",
+      "sha256": "0938276afa762c367dac4968bc9a3d4abf9d83db1f4bea67262bff976568e24c",
+      "coco128_file": "000000000625.jpg"
+    },
+    {
+      "file": "0121.jpg",
+      "sha256": "47b4a3b3fe24c6b1f77a9d89aa2e5d1a3b410b7ba611f54c252c2e13e975afc8",
+      "coco128_file": "000000000626.jpg"
+    },
+    {
+      "file": "0122.jpg",
+      "sha256": "be76e3e5bed8c8a9a0d8081844bfca7ce013093e746b66aad13922373644ad32",
+      "coco128_file": "000000000629.jpg"
+    },
+    {
+      "file": "0123.jpg",
+      "sha256": "d769636e9b9d22fa6fd4dcc2e683b2c95a5a9c247b3cc95df7367b1dba52f9bc",
+      "coco128_file": "000000000634.jpg"
+    },
+    {
+      "file": "0124.jpg",
+      "sha256": "8e948d6888e7bf449e91883d3a2b0d61804308aada31fbf289add4da4dd978e8",
+      "coco128_file": "000000000636.jpg"
+    },
+    {
+      "file": "0125.jpg",
+      "sha256": "8eae4239a000d602acb4c58801a3c070e4bf3da608e57e7a3dcbd5ef719895cc",
+      "coco128_file": "000000000641.jpg"
+    },
+    {
+      "file": "0126.jpg",
+      "sha256": "53d61e69ccf08b42a1cefa414c69a1c03d72aba2852e1fde35c4da3df8753b3f",
+      "coco128_file": "000000000643.jpg"
+    },
+    {
+      "file": "0127.jpg",
+      "sha256": "9bb45795336c1d05189fe51a77e04270cd1da8929d62060926798f3dd2d25204",
+      "coco128_file": "000000000650.jpg"
+    }
+  ]
+}

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()

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