Formatting and remove yolov5

docs/docs/configuration/object_detectors.md

@@ -131,7 +131,7 @@ model:
   labelmap_path: /openvino-model/coco_91cl_bkgr.txt
 ```
 
-This detector also supports some YOLO variants: YOLOX and YOLOv5 specifically. Other YOLO variants are not officially supported/tested. Frigate does not come with any yolo models preloaded, so you will need to supply your own models. This detector has been verified to work with the [yolox_tiny](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/yolox-tiny) model from Intel's Open Model Zoo. You can follow [these instructions](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/yolox-tiny#download-a-model-and-convert-it-into-openvino-ir-format) to retrieve the OpenVINO-compatible `yolox_tiny` model. Make sure that the model input dimensions match the `width` and `height` parameters, and `model_type` is set accordingly. See [Full Configuration Reference](/configuration/reference.md) for a list of possible `model_type` options. Below is an example of how `yolox_tiny` can be used in Frigate:
+This detector also supports YOLOX. Other YOLO variants are not officially supported/tested. Frigate does not come with any yolo models preloaded, so you will need to supply your own models. This detector has been verified to work with the [yolox_tiny](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/yolox-tiny) model from Intel's Open Model Zoo. You can follow [these instructions](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/yolox-tiny#download-a-model-and-convert-it-into-openvino-ir-format) to retrieve the OpenVINO-compatible `yolox_tiny` model. Make sure that the model input dimensions match the `width` and `height` parameters, and `model_type` is set accordingly. See [Full Configuration Reference](/configuration/reference.md) for a list of possible `model_type` options. Below is an example of how `yolox_tiny` can be used in Frigate:
 
 ```yaml
 detectors:

docs/docs/configuration/reference.md

@@ -80,7 +80,7 @@ model:
   # Valid values are nhwc or nchw (default: shown below)
   input_tensor: nhwc
   # Optional: Object detection model type, currently only used with the OpenVINO detector
-  # Valid values are ssd, yolox or yolov5 (default: shown below)
+  # Valid values are ssd, yolox (default: shown below)
   model_type: ssd
   # Optional: Label name modifications. These are merged into the standard labelmap.
   labelmap:

frigate/detectors/detector_config.py

@@ -30,7 +30,6 @@ class InputTensorEnum(str, Enum):
 class ModelTypeEnum(str, Enum):
     ssd = "ssd"
     yolox = "yolox"
-    yolov5 = "yolov5"
 
 
 class ModelConfig(BaseModel):

frigate/detectors/plugins/onnx.py

@@ -31,7 +31,6 @@ class ONNXDetector(DetectionApi):
             )
             raise
 
-
         path = detector_config.model.path
         logger.info(f"ONNX: loading {detector_config.model.path}")
         self.model = onnxruntime.InferenceSession(path)
@@ -45,4 +44,6 @@ class ONNXDetector(DetectionApi):
 
         tensor_output = self.model.run(None, {model_input_name: tensor_input})[0]
 
-        raise Exception("No models are currently supported via onnx. See the docs for more info.")
+        raise Exception(
+            "No models are currently supported via onnx. See the docs for more info."
+        )

frigate/detectors/plugins/openvino.py

@@ -131,21 +131,3 @@ class OvDetector(DetectionApi):
                     object_detected[6], object_detected[5], object_detected[:4]
                 )
             return detections
-        elif self.ov_model_type == ModelTypeEnum.yolov5:
-            out_tensor = infer_request.get_output_tensor()
-            output_data = out_tensor.data[0]
-            # filter out lines with scores below threshold
-            conf_mask = (output_data[:, 4] >= 0.5).squeeze()
-            output_data = output_data[conf_mask]
-            # limit to top 20 scores, descending order
-            ordered = output_data[output_data[:, 4].argsort()[::-1]][:20]
-
-            detections = np.zeros((20, 6), np.float32)
-
-            for i, object_detected in enumerate(ordered):
-                detections[i] = self.process_yolo(
-                    np.argmax(object_detected[5:]),
-                    object_detected[4],
-                    object_detected[:4],
-                )
-            return detections

frigate/detectors/plugins/rknn.py

@@ -98,7 +98,9 @@ class Rknn(DetectionApi):
                 "Error initializing rknn runtime. Do you run docker in privileged mode?"
             )
 
-        raise Exception("RKNN does not currently support any models. Please see the docs for more info.")
+        raise Exception(
+            "RKNN does not currently support any models. Please see the docs for more info."
+        )
 
     def __del__(self):
         self.rknn.release()

frigate/detectors/plugins/rocm.py

@@ -118,4 +118,6 @@ class ROCmDetector(DetectionApi):
             addr, shape=detector_result.get_shape().lens()
         )
 
-        raise Exception("No models are currently supported for rocm. See the docs for more info.")
+        raise Exception(
+            "No models are currently supported for rocm. See the docs for more info."
+        )