Organize RKNN models by Toolkit2 version

.gitattributes

@@ -168,3 +168,143 @@ models/detect/yolo26x/rk3576/fp16/model.rknn filter=lfs diff=lfs merge=lfs -text
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README.md

@@ -12,7 +12,7 @@ tags:
 
 Converted with the Ultralytics export workflow and task-specific RKNN conversion tools. This repository contains 140 FP16 and INT8 exports for RK3576 and RK3588. Tasks include classify, depth, detect, obb, pose, segment, semantic.
 
-Choose a model under `models/<task>/<variant>/<chip>/<precision>/` and download both `model.rknn` and `metadata.yaml`. The [manifest](manifest.json) lists the supported combinations and SHA-256 hashes.
+Choose a model under `models/<toolkit-version>/<task>/<variant>/<chip>/<precision>/` and download both `model.rknn` and `metadata.yaml`. The [manifest](manifest.json) lists the supported combinations and SHA-256 hashes.
 
 ## Source and license
 
@@ -25,3 +25,5 @@ The [source directory](tree/main/source) contains the original checkpoints, conv
 ### INT8 detection
 
 Use the `metadata.yaml` from the same INT8 model folder. The detector outputs normalized box coordinates, so the app needs `coordinates: normalized`. Import both files together in the warehouse. For the CLI, keep them in the same directory. Interpreting the coordinates as pixels can result in no boxes.
+
+Models are organized by RKNN Toolkit2 version under `models/v<version>/`. Each model and its metadata share a folder.

manifest.json

@@ -15,8 +15,9 @@
     "decode_precision": null,
     "fp16_decode_tensors": null,
     "classes": 80,
-    "path": "models/detect/yolo26n/rk3576/fp16",
-    "weights": "source/weights/yolo26n.pt"
+    "path": "models/v2.3.2/detect/yolo26n/rk3576/fp16",
+    "weights": "source/weights/yolo26n.pt",
+    "toolkit_version": "2.3.2"
   },
   {
     "name": "yolo26n",
@@ -34,8 +35,9 @@
     "decode_precision": null,
     "fp16_decode_tensors": null,
     "classes": 80,
-    "path": "models/detect/yolo26n/rk3588/fp16",
-    "weights": "source/weights/yolo26n.pt"
+    "path": "models/v2.3.2/detect/yolo26n/rk3588/fp16",
+    "weights": "source/weights/yolo26n.pt",
+    "toolkit_version": "2.3.2"
   },
   {
     "name": "yolo26n-seg",
@@ -53,8 +55,9 @@
     "decode_precision": null,
     "fp16_decode_tensors": null,
     "classes": 80,
-    "path": "models/segment/yolo26n-seg/rk3576/fp16",
-    "weights": "source/weights/yolo26n-seg.pt"
+    "path": "models/v2.3.2/segment/yolo26n-seg/rk3576/fp16",
+    "weights": "source/weights/yolo26n-seg.pt",
+    "toolkit_version": "2.3.2"
   },
   {
     "name": "yolo26n-seg",
@@ -72,8 +75,9 @@
     "decode_precision": "fp16",
     "fp16_decode_tensors": 26,
     "classes": 80,
-    "path": "models/segment/yolo26n-seg/rk3576/int8",
-    "weights": "source/weights/yolo26n-seg.pt"
+    "path": "models/v2.3.2/segment/yolo26n-seg/rk3576/int8",
+    "weights": "source/weights/yolo26n-seg.pt",
+    "toolkit_version": "2.3.2"
   },
   {
     "name": "yolo26n-seg",
@@ -91,8 +95,9 @@
     "decode_precision": "fp16",
     "fp16_decode_tensors": 26,
     "classes": 80,
-    "path": "models/segment/yolo26n-seg/rk3588/int8",
-    "weights": "source/weights/yolo26n-seg.pt"
+    "path": "models/v2.3.2/segment/yolo26n-seg/rk3588/int8",
+    "weights": "source/weights/yolo26n-seg.pt",
+    "toolkit_version": "2.3.2"
   },
   {
     "name": "yolo26n-seg",
@@ -110,8 +115,9 @@
     "decode_precision": null,
     "fp16_decode_tensors": null,
     "classes": 80,
-    "path": "models/segment/yolo26n-seg/rk3588/fp16",
-    "weights": "source/weights/yolo26n-seg.pt"
+    "path": "models/v2.3.2/segment/yolo26n-seg/rk3588/fp16",
+    "weights": "source/weights/yolo26n-seg.pt",
+    "toolkit_version": "2.3.2"
   },
   {
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     "name": "yolo26x-obb",
@@ -2641,8 +2779,9 @@
     "decode_precision": "fp16",
     "fp16_decode_tensors": 36,
     "classes": 15,
-    "path": "models/obb/yolo26x-obb/rk3588/int8",
-    "weights": "source/weights/yolo26x-obb.pt"
+    "path": "models/v2.3.2/obb/yolo26x-obb/rk3588/int8",
+    "weights": "source/weights/yolo26x-obb.pt",
+    "toolkit_version": "2.3.2"
   },
   {
     "name": "yolo26x-obb",
@@ -2660,7 +2799,8 @@
     "decode_precision": "fp16",
     "fp16_decode_tensors": 36,
     "classes": 15,
-    "path": "models/obb/yolo26x-obb/rk3576/int8",
-    "weights": "source/weights/yolo26x-obb.pt"
+    "path": "models/v2.3.2/obb/yolo26x-obb/rk3576/int8",
+    "weights": "source/weights/yolo26x-obb.pt",
+    "toolkit_version": "2.3.2"
   }
 ]