implement RKNN downloads for yolov9 and yolox models (#17875)

* Add other rockchip download models

* Specify newer release version

* Specify newer release version

* Update docs for rknn downloads

* Update hardware docs

docs/docs/configuration/object_detectors.md

@@ -845,13 +845,13 @@ detectors: # required
 The inference time was determined on a rk3588 with 3 NPU cores.
 
 | Model                 | Size in mb | Inference time in ms |
-| ------------------- | ---------- | -------------------- |
+| --------------------- | ---------- | -------------------- |
 | deci-fp16-yolonas_s   | 24         | 25                   |
 | deci-fp16-yolonas_m   | 62         | 35                   |
 | deci-fp16-yolonas_l   | 81         | 45                   |
-| yolov9_tiny         | 8          | 35                   |
-| yolox_nano          | 3          | 16                   |
-| yolox_tiny          | 6          | 20                   |
+| frigate-fp16-yolov9-t | 6          | 35                   |
+| rock-i8-yolox_nano    | 3          | 14                   |
+| rock-i8_yolox_tiny    | 6          | 18                   |
 
 - All models are automatically downloaded and stored in the folder `config/model_cache/rknn_cache`. After upgrading Frigate, you should remove older models to free up space.
 - You can also provide your own `.rknn` model. You should not save your own models in the `rknn_cache` folder, store them directly in the `model_cache` folder or another subfolder. To convert a model to `.rknn` format see the `rknn-toolkit2` (requires a x86 machine). Note, that there is only post-processing for the supported models.
@@ -887,10 +887,13 @@ The pre-trained YOLO-NAS weights from DeciAI are subject to their license and ca
 model: # required
   # name of model (will be automatically downloaded) or path to your own .rknn model file
   # possible values are:
-  # - yolov9-t
-  # - yolov9-s
+  # - frigate-fp16-yolov9-t
+  # - frigate-fp16-yolov9-s
+  # - frigate-fp16-yolov9-m
+  # - frigate-fp16-yolov9-c
+  # - frigate-fp16-yolov9-e
   # your yolo_model.rknn
-  path: /config/model_cache/rknn_cache/yolov9-t.rknn
+  path: frigate-fp16-yolov9-t
   model_type: yolo-generic
   width: 320
   height: 320
@@ -905,10 +908,12 @@ model: # required
 model: # required
   # name of model (will be automatically downloaded) or path to your own .rknn model file
   # possible values are:
-  # - yolox_nano
-  # - yolox_tiny
+  # - rock-i8-yolox_nano
+  # - rock-i8-yolox_tiny
+  # - rock-fp16-yolox_nano
+  # - rock-fp16-yolox_tiny
   # your yolox_model.rknn
-  path: yolox_tiny
+  path: rock-i8-yolox_nano
   model_type: yolox
   width: 416
   height: 416

docs/docs/frigate/hardware.md

@@ -168,7 +168,7 @@ Frigate supports hardware video processing on all Rockchip boards. However, hard
 
 | Name            | YOLOv9 Inference Time | YOLO-NAS Inference Time     | YOLOx Inference Time      |
 | --------------- | --------------------- | --------------------------- | ------------------------- |
-| rk3588 3 cores  | ~ 35 ms               | small: ~ 20 ms med: ~ 30 ms | nano: 18 ms tiny: 20 ms   |
+| rk3588 3 cores  | tiny: ~ 35 ms         | small: ~ 20 ms med: ~ 30 ms | nano: 14 ms tiny: 18 ms   |
 | rk3566 1 core   |                       | small: ~ 96 ms              |                           |
 
 

frigate/detectors/plugins/rknn.py

@@ -19,7 +19,11 @@ DETECTOR_KEY = "rknn"
 
 supported_socs = ["rk3562", "rk3566", "rk3568", "rk3576", "rk3588"]
 
-supported_models = {ModelTypeEnum.yolonas: "^deci-fp16-yolonas_[sml]$"}
+supported_models = {
+    ModelTypeEnum.yologeneric: "^frigate-fp16-yolov9-[cemst]$",
+    ModelTypeEnum.yolonas: "^deci-fp16-yolonas_[sml]$",
+    ModelTypeEnum.yolox: "^rock-(fp16|i8)-yolox_(nano|tiny)$",
+}
 
 model_cache_dir = os.path.join(MODEL_CACHE_DIR, "rknn_cache/")
 
@@ -115,7 +119,7 @@ class Rknn(DetectionApi):
                     model_props["model_type"] = model_type
 
             if model_matched:
-                model_props["filename"] = model_path + f"-{soc}-v2.3.0-1.rknn"
+                model_props["filename"] = model_path + f"-{soc}-v2.3.2-1.rknn"
 
                 model_props["path"] = model_cache_dir + model_props["filename"]
 
@@ -136,7 +140,7 @@ class Rknn(DetectionApi):
             os.mkdir(model_cache_dir)
 
         urllib.request.urlretrieve(
-            f"https://github.com/MarcA711/rknn-models/releases/download/v2.3.0/{filename}",
+            f"https://github.com/MarcA711/rknn-models/releases/download/v2.3.2/{filename}",
             model_cache_dir + filename,
         )