@@ -459,8 +459,20 @@ def deploy(self, model_type: str, model_path: str, filename: str = "weights/best
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model_path (str): File path to the model weights to be uploaded.
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filename (str, optional): The name of the weights file. Defaults to "weights/best.pt".
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"""
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-
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- supported_models = ["yolov5" , "yolov7-seg" , "yolov8" , "yolov9" , "yolonas" , "paligemma" , "yolov10" , "florence-2" ]
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+ if model_type .startswith ("yolo11" ):
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+ model_type = model_type .replace ("yolo11" , "yolov11" )
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+
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+ supported_models = [
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+ "yolov5" ,
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+ "yolov7-seg" ,
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+ "yolov8" ,
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+ "yolov9" ,
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+ "yolonas" ,
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+ "paligemma" ,
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+ "yolov10" ,
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+ "florence-2" ,
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+ "yolov11" ,
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+ ]
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if not any (supported_model in model_type for supported_model in supported_models ):
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raise (ValueError (f"Model type { model_type } not supported. Supported models are" f" { supported_models } " ))
@@ -519,6 +531,19 @@ def deploy(self, model_type: str, model_path: str, filename: str = "weights/best
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" Please install it with `pip install torch`"
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)
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+ elif "yolov11" in model_type :
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+ try :
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+ import torch
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+ import ultralytics
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+
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+ except ImportError :
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+ raise RuntimeError (
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+ "The ultralytics python package is required to deploy yolov10"
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+ " models. Please install it with `pip install ultralytics`"
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+ )
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+
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+ print_warn_for_wrong_dependencies_versions ([("ultralytics" , ">=" , "8.3.0" )], ask_to_continue = True )
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+
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model = torch .load (os .path .join (model_path , filename ))
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if isinstance (model ["model" ].names , list ):
@@ -530,9 +555,9 @@ def deploy(self, model_type: str, model_path: str, filename: str = "weights/best
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class_names .sort (key = lambda x : x [0 ])
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class_names = [x [1 ] for x in class_names ]
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- if "yolov8" in model_type or "yolov10" in model_type :
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+ if "yolov8" in model_type or "yolov10" in model_type or "yolov11" in model_type :
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# try except for backwards compatibility with older versions of ultralytics
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- if "-cls" in model_type or model_type .startswith ("yolov10" ):
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+ if "-cls" in model_type or model_type .startswith ("yolov10" ) or model_type . startswith ( "yolov11" ) :
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nc = model ["model" ].yaml ["nc" ]
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args = model ["train_args" ]
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else :
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