Organize RKNN models by Toolkit2 version

.gitattributes

@@ -45,3 +45,15 @@ models/classify/yolov5s-cls/rk3576/fp16/model.rknn filter=lfs diff=lfs merge=lfs
 models/classify/yolov5m-cls/rk3588/int8/model.rknn filter=lfs diff=lfs merge=lfs -text
 models/classify/yolov5m-cls/rk3588/fp16/model.rknn filter=lfs diff=lfs merge=lfs -text
 models/classify/yolov5m-cls/rk3576/fp16/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/v2.3.2/classify/yolov5m-cls/rk3576/fp16/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/v2.3.2/classify/yolov5m-cls/rk3576/int8/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/v2.3.2/classify/yolov5m-cls/rk3588/fp16/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/v2.3.2/classify/yolov5m-cls/rk3588/int8/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/v2.3.2/classify/yolov5n-cls/rk3576/fp16/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/v2.3.2/classify/yolov5n-cls/rk3576/int8/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/v2.3.2/classify/yolov5n-cls/rk3588/fp16/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/v2.3.2/classify/yolov5n-cls/rk3588/int8/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/v2.3.2/classify/yolov5s-cls/rk3576/fp16/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/v2.3.2/classify/yolov5s-cls/rk3576/int8/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/v2.3.2/classify/yolov5s-cls/rk3588/fp16/model.rknn filter=lfs diff=lfs merge=lfs -text
+models/v2.3.2/classify/yolov5s-cls/rk3588/int8/model.rknn filter=lfs diff=lfs merge=lfs -text

README.md

@@ -12,10 +12,12 @@ tags:
 
 # YOLOv5-RKNN2
 
-Custom conversion in the Rockchip Model Zoo collection. Converted with RKNN Toolkit2 2.3.2. YOLOv5 n, s and m classifiers in FP16 and INT8 for RK3576 and RK3588. Download the model and metadata from the same directory under `models/classify/<variant>/<chip>/<precision>/`.
+Custom conversion in the Rockchip Model Zoo collection. Converted with RKNN Toolkit2 2.3.2. YOLOv5 n, s and m classifiers in FP16 and INT8 for RK3576 and RK3588. Download the model and metadata from the same directory under `models/v2.3.2/classify/<variant>/<chip>/<precision>/`.
 
 The original classification weights came from the [YOLOv5 v7.0 release](https://github.com/ultralytics/yolov5/releases/tag/v7.0) and are included with their hashes and original URLs in [source](tree/main/source). The distribution includes the [GPL-3.0 license](LICENSE) from that release.
 
 The custom conversion exports an ONNX graph and compiles it with RKNN Toolkit2. See `source/convert-extended.py --help` and use the [v7.0 source checkout](https://github.com/ultralytics/yolov5/tree/v7.0) with `--yolov5-source`. INT8 uses the COCO128 calibration list in `source/calibration.json`. The [manifest](manifest.json) lists all output hashes.
 
 The Model Zoo detection exports are currently unpublished while their upstream checkpoint provenance is being verified.
+
+Models are organized by RKNN Toolkit2 version under `models/v<version>/`. Each model and its metadata share a folder.

manifest.json

@@ -5,10 +5,11 @@
     "task": "classify",
     "chip": "rk3576",
     "precision": "fp16",
-    "path": "models/classify/yolov5m-cls/rk3576/fp16",
+    "path": "models/v2.3.2/classify/yolov5m-cls/rk3576/fp16",
     "weights": "source/weights/yolov5m-cls.pt",
     "sha256": "1bb480befd681d8b4d39fba140cb89d56a300aa177e5f292ae629d5b20abec53",
-    "metadata_sha256": "4c345953cccc8f64c8bb5cc4d1abba2de8fa914a0052666b2b5bf2fa5ab2c0b7"
+    "metadata_sha256": "4c345953cccc8f64c8bb5cc4d1abba2de8fa914a0052666b2b5bf2fa5ab2c0b7",
+    "toolkit_version": "2.3.2"
   },
   {
     "name": "yolov5m-cls",
@@ -16,10 +17,11 @@
     "task": "classify",
     "chip": "rk3576",
     "precision": "int8",
-    "path": "models/classify/yolov5m-cls/rk3576/int8",
+    "path": "models/v2.3.2/classify/yolov5m-cls/rk3576/int8",
     "weights": "source/weights/yolov5m-cls.pt",
     "sha256": "27c1a46e19435a636e7481f3b4deceae72715e0623645e75f85983976eff1304",
-    "metadata_sha256": "817cec2b00fc7d028def92cec388d294b184e4142d3dd45b4f2297b90a1967e1"
+    "metadata_sha256": "817cec2b00fc7d028def92cec388d294b184e4142d3dd45b4f2297b90a1967e1",
+    "toolkit_version": "2.3.2"
   },
   {
     "name": "yolov5n-cls",
@@ -27,10 +29,11 @@
     "task": "classify",
     "chip": "rk3576",
     "precision": "fp16",
-    "path": "models/classify/yolov5n-cls/rk3576/fp16",
+    "path": "models/v2.3.2/classify/yolov5n-cls/rk3576/fp16",
     "weights": "source/weights/yolov5n-cls.pt",
     "sha256": "30438f1ec5050fa92c7fb12934a90141d16177a3d5353efe72788b8667d66551",
-    "metadata_sha256": "8d8711568c225625aca257275da457598732f08cf20bf6836cafb1c623dac0f9"
+    "metadata_sha256": "8d8711568c225625aca257275da457598732f08cf20bf6836cafb1c623dac0f9",
+    "toolkit_version": "2.3.2"
   },
   {
     "name": "yolov5n-cls",
@@ -38,10 +41,11 @@
     "task": "classify",
     "chip": "rk3576",
     "precision": "int8",
-    "path": "models/classify/yolov5n-cls/rk3576/int8",
+    "path": "models/v2.3.2/classify/yolov5n-cls/rk3576/int8",
     "weights": "source/weights/yolov5n-cls.pt",
     "sha256": "059697b54a95857cc93b03c5ce78838d7697664953256ba80a593456cd2a2d3d",
-    "metadata_sha256": "2fb739abdbe1263eabfeedc29c34cd0c6a8616bcbe72142954c2319f5a345bb7"
+    "metadata_sha256": "2fb739abdbe1263eabfeedc29c34cd0c6a8616bcbe72142954c2319f5a345bb7",
+    "toolkit_version": "2.3.2"
   },
   {
     "name": "yolov5s-cls",
@@ -49,10 +53,11 @@
     "task": "classify",
     "chip": "rk3576",
     "precision": "fp16",
-    "path": "models/classify/yolov5s-cls/rk3576/fp16",
+    "path": "models/v2.3.2/classify/yolov5s-cls/rk3576/fp16",
     "weights": "source/weights/yolov5s-cls.pt",
     "sha256": "e3aaa516d8550e34aefce3947bc533e888bb7bdb2fd104747e925d883658a52e",
-    "metadata_sha256": "07425a1a1ad0406790befa2724d5c8f2786082be0900bf2a354967ba9ef4096f"
+    "metadata_sha256": "07425a1a1ad0406790befa2724d5c8f2786082be0900bf2a354967ba9ef4096f",
+    "toolkit_version": "2.3.2"
   },
   {
     "name": "yolov5s-cls",
@@ -60,10 +65,11 @@
     "task": "classify",
     "chip": "rk3576",
     "precision": "int8",
-    "path": "models/classify/yolov5s-cls/rk3576/int8",
+    "path": "models/v2.3.2/classify/yolov5s-cls/rk3576/int8",
     "weights": "source/weights/yolov5s-cls.pt",
     "sha256": "f47392d425c1e2d76e6ae39f84cf1f0acaa105b9e6f4b55b30764bef89af8a40",
-    "metadata_sha256": "d2630b3d41138942eba8bd78d9b6477f84605fd6d3f29e5a4f9a16ed6f3f5c9b"
+    "metadata_sha256": "d2630b3d41138942eba8bd78d9b6477f84605fd6d3f29e5a4f9a16ed6f3f5c9b",
+    "toolkit_version": "2.3.2"
   },
   {
     "name": "yolov5m-cls",
@@ -71,10 +77,11 @@
     "task": "classify",
     "chip": "rk3588",
     "precision": "fp16",
-    "path": "models/classify/yolov5m-cls/rk3588/fp16",
+    "path": "models/v2.3.2/classify/yolov5m-cls/rk3588/fp16",
     "weights": "source/weights/yolov5m-cls.pt",
     "sha256": "d13f0b8878e3946374278121f98a53caa566716db59b8b85478907cac05cbab9",
-    "metadata_sha256": "2f5d028de3b6b9054550983ff2a382331385435c0f5bfff9a35429f86a4464cf"
+    "metadata_sha256": "2f5d028de3b6b9054550983ff2a382331385435c0f5bfff9a35429f86a4464cf",
+    "toolkit_version": "2.3.2"
   },
   {
     "name": "yolov5m-cls",
@@ -82,10 +89,11 @@
     "task": "classify",
     "chip": "rk3588",
     "precision": "int8",
-    "path": "models/classify/yolov5m-cls/rk3588/int8",
+    "path": "models/v2.3.2/classify/yolov5m-cls/rk3588/int8",
     "weights": "source/weights/yolov5m-cls.pt",
     "sha256": "1affc434a7e66a9cb7f1c6de416cec5b2894e06a66405dcbf04232958447907b",
-    "metadata_sha256": "137d63cfd35692d9f843a6a142d8bd7a7e99dd5119659c7526b0822bd7a82c1e"
+    "metadata_sha256": "137d63cfd35692d9f843a6a142d8bd7a7e99dd5119659c7526b0822bd7a82c1e",
+    "toolkit_version": "2.3.2"
   },
   {
     "name": "yolov5n-cls",
@@ -93,10 +101,11 @@
     "task": "classify",
     "chip": "rk3588",
     "precision": "fp16",
-    "path": "models/classify/yolov5n-cls/rk3588/fp16",
+    "path": "models/v2.3.2/classify/yolov5n-cls/rk3588/fp16",
     "weights": "source/weights/yolov5n-cls.pt",
     "sha256": "1ce2871b6c02bb46e945db76f824e1144d3c88c1b06ccefa3c96a108a1213daa",
-    "metadata_sha256": "5eb38c549fd038be769503a41e9fcb68d17c33be197b4148138b19295a150c3e"
+    "metadata_sha256": "5eb38c549fd038be769503a41e9fcb68d17c33be197b4148138b19295a150c3e",
+    "toolkit_version": "2.3.2"
   },
   {
     "name": "yolov5n-cls",
@@ -104,10 +113,11 @@
     "task": "classify",
     "chip": "rk3588",
     "precision": "int8",
-    "path": "models/classify/yolov5n-cls/rk3588/int8",
+    "path": "models/v2.3.2/classify/yolov5n-cls/rk3588/int8",
     "weights": "source/weights/yolov5n-cls.pt",
     "sha256": "b0d66530d0f87b03d96b9f57ebfc72586403655eb775280e9b9f6d1d5d4fd178",
-    "metadata_sha256": "64d9a151a7449422957564363725985da16cd618bcee20ecca11119d14b32b54"
+    "metadata_sha256": "64d9a151a7449422957564363725985da16cd618bcee20ecca11119d14b32b54",
+    "toolkit_version": "2.3.2"
   },
   {
     "name": "yolov5s-cls",
@@ -115,10 +125,11 @@
     "task": "classify",
     "chip": "rk3588",
     "precision": "fp16",
-    "path": "models/classify/yolov5s-cls/rk3588/fp16",
+    "path": "models/v2.3.2/classify/yolov5s-cls/rk3588/fp16",
     "weights": "source/weights/yolov5s-cls.pt",
     "sha256": "61b7c3b33ac44eaa240cc3e1e4299c9853f514639cabfd60ab75960230447a4b",
-    "metadata_sha256": "05077db64170b689c4df100201e6f93cef4f039f5ff6f01a93dbfcb6f06956e9"
+    "metadata_sha256": "05077db64170b689c4df100201e6f93cef4f039f5ff6f01a93dbfcb6f06956e9",
+    "toolkit_version": "2.3.2"
   },
   {
     "name": "yolov5s-cls",
@@ -126,9 +137,10 @@
     "task": "classify",
     "chip": "rk3588",
     "precision": "int8",
-    "path": "models/classify/yolov5s-cls/rk3588/int8",
+    "path": "models/v2.3.2/classify/yolov5s-cls/rk3588/int8",
     "weights": "source/weights/yolov5s-cls.pt",
     "sha256": "52bb9d48e6515a304e1f7ac27900cbd358c9de5862ca198f27ed428c0036cf76",
-    "metadata_sha256": "9e735a6c2d4ae494bd914e32533fb2e48083f75f65c37f69d8c4df701be02133"
+    "metadata_sha256": "9e735a6c2d4ae494bd914e32533fb2e48083f75f65c37f69d8c4df701be02133",
+    "toolkit_version": "2.3.2"
   }
-]
+]