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YOLO Module

Yolo

Class for YOLO PyTorch model operations.

This class provides methods to load a YOLO model, get class names, export to various formats,
and run inference. Also, this class contains constants related to YOLO models, directories, colors, and utility functions
for checking model names, versions, dataset statuses, and dataset names.

Source code in devices\raspberry_pi_5\src\yolo\__init__.py 
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class Yolo:
    """
    Class for YOLO PyTorch model operations.

    This class provides methods to load a YOLO model, get class names, export to various formats,
    and run inference. Also, this class contains constants related to YOLO models, directories, colors, and utility functions
    for checking model names, versions, dataset statuses, and dataset names.
    """

    @staticmethod
    def load(model_path: str | os.PathLike[str], task='detect') -> YOLO:
        """
        Load YOLO PyTorch model.

        Args:
            model_path (str | os.PathLike[str]): Path to the YOLO model file.
            task (str): Task type, default is 'detect'.
        Returns:
            YOLO: Loaded YOLO model.
        """
        # Check if the model path exists
        Files.ensure_directory_exists(model_path)

        # Load the model
        return YOLO(model_path, task=task, verbose=True)

    @staticmethod
    def get_class_names(model: YOLO) -> Dict[int, str]:
        """
        Get YOLO PyTorch model class names.

        Args:
            model (YOLO): Loaded YOLO model.
        Returns:
            Dict[int, str]: Dictionary mapping class indices to class names.
        """
        return model.names

    @staticmethod
    def export_tensor_rt(model: YOLO, quantized: bool = True) -> str:
        """
        Export the model to TensorRT format.

        Args:
            model (YOLO): Loaded YOLO model.
            quantized (bool): Whether to quantize the model, default is True.
        Returns:
            str: Path to the exported TensorRT engine file.
        """
        return model.export(format="engine", int8=quantized)

    @staticmethod
    def export_onnx(model: YOLO) -> str:
        """
        Export the model to ONNX format.

        Args:
            model (YOLO): Loaded YOLO model.
        Returns:
            str: Path to the exported ONNX model file.
        """
        return model.export(format="onnx")

    @staticmethod
    def export_tflite(model: YOLO, quantized: bool = True) -> str:
        """
        Export the model to TFLite format.

        Args:
            model (YOLO): Loaded YOLO model.
            quantized (bool): Whether to quantize the model, default is True.
        Returns:
            str: Path to the exported TFLite model file.
        """
        return model.export(format="tflite", int8=quantized)

    @staticmethod
    def run_inference(
        model: YOLO,
        preprocessed_image: torch.Tensor
    ) -> tuple[List, float]:
        """
        Run inference from PyTorch model.

        Args:
            model (YOLO): Loaded YOLO model.
            preprocessed_image (torch.Tensor): Preprocessed image tensor.
        Returns:
            tuple(List, float): Inferences and elapsed time in seconds.
        """
        # Get time
        start_time = time.time()

        # Run inference
        inferences = model(torch.from_numpy(preprocessed_image).float())

        # Get time
        end_time = time.time()
        elapsed_time = end_time - start_time

        return inferences, elapsed_time

export_onnx(model) staticmethod

Export the model to ONNX format.

Parameters:

Name Type Description Default
model YOLO

Loaded YOLO model.

 required

Returns:
str: Path to the exported ONNX model file.

Source code in devices\raspberry_pi_5\src\yolo\__init__.py 
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@staticmethod
def export_onnx(model: YOLO) -> str:
    """
    Export the model to ONNX format.

    Args:
        model (YOLO): Loaded YOLO model.
    Returns:
        str: Path to the exported ONNX model file.
    """
    return model.export(format="onnx")

export_tensor_rt(model, quantized=True) staticmethod

Export the model to TensorRT format.

Parameters:

Name Type Description Default
model YOLO

Loaded YOLO model.

 required
quantized bool

Whether to quantize the model, default is True.

 True

Returns:
str: Path to the exported TensorRT engine file.

Source code in devices\raspberry_pi_5\src\yolo\__init__.py 
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@staticmethod
def export_tensor_rt(model: YOLO, quantized: bool = True) -> str:
    """
    Export the model to TensorRT format.

    Args:
        model (YOLO): Loaded YOLO model.
        quantized (bool): Whether to quantize the model, default is True.
    Returns:
        str: Path to the exported TensorRT engine file.
    """
    return model.export(format="engine", int8=quantized)

export_tflite(model, quantized=True) staticmethod

Export the model to TFLite format.

Parameters:

Name Type Description Default
model YOLO

Loaded YOLO model.

 required
quantized bool

Whether to quantize the model, default is True.

 True

Returns:
str: Path to the exported TFLite model file.

Source code in devices\raspberry_pi_5\src\yolo\__init__.py 
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@staticmethod
def export_tflite(model: YOLO, quantized: bool = True) -> str:
    """
    Export the model to TFLite format.

    Args:
        model (YOLO): Loaded YOLO model.
        quantized (bool): Whether to quantize the model, default is True.
    Returns:
        str: Path to the exported TFLite model file.
    """
    return model.export(format="tflite", int8=quantized)

get_class_names(model) staticmethod

Get YOLO PyTorch model class names.

Parameters:

Name Type Description Default
model YOLO

Loaded YOLO model.

 required

Returns:
Dict[int, str]: Dictionary mapping class indices to class names.

Source code in devices\raspberry_pi_5\src\yolo\__init__.py 
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@staticmethod
def get_class_names(model: YOLO) -> Dict[int, str]:
    """
    Get YOLO PyTorch model class names.

    Args:
        model (YOLO): Loaded YOLO model.
    Returns:
        Dict[int, str]: Dictionary mapping class indices to class names.
    """
    return model.names

load(model_path, task='detect') staticmethod

Load YOLO PyTorch model.

Parameters:

Name Type Description Default
model_path str | PathLike[str]

Path to the YOLO model file.

 required
task str

Task type, default is 'detect'.

 'detect'

Returns:
YOLO: Loaded YOLO model.

Source code in devices\raspberry_pi_5\src\yolo\__init__.py 
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@staticmethod
def load(model_path: str | os.PathLike[str], task='detect') -> YOLO:
    """
    Load YOLO PyTorch model.

    Args:
        model_path (str | os.PathLike[str]): Path to the YOLO model file.
        task (str): Task type, default is 'detect'.
    Returns:
        YOLO: Loaded YOLO model.
    """
    # Check if the model path exists
    Files.ensure_directory_exists(model_path)

    # Load the model
    return YOLO(model_path, task=task, verbose=True)

run_inference(model, preprocessed_image) staticmethod

Run inference from PyTorch model.

Parameters:

Name Type Description Default
model YOLO

Loaded YOLO model.

 required
preprocessed_image Tensor

Preprocessed image tensor.

 required

Returns:
tuple(List, float): Inferences and elapsed time in seconds.

Source code in devices\raspberry_pi_5\src\yolo\__init__.py 
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@staticmethod
def run_inference(
    model: YOLO,
    preprocessed_image: torch.Tensor
) -> tuple[List, float]:
    """
    Run inference from PyTorch model.

    Args:
        model (YOLO): Loaded YOLO model.
        preprocessed_image (torch.Tensor): Preprocessed image tensor.
    Returns:
        tuple(List, float): Inferences and elapsed time in seconds.
    """
    # Get time
    start_time = time.time()

    # Run inference
    inferences = model(torch.from_numpy(preprocessed_image).float())

    # Get time
    end_time = time.time()
    elapsed_time = end_time - start_time

    return inferences, elapsed_time

args

Args

Bases: Args

Class to handle command line arguments.

Source code in devices\raspberry_pi_5\src\yolo\args\__init__.py 
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class Args(A):
    """
    Class to handle command line arguments.
    """

    def add_yolo_input_model_pt_argument(self) -> None:
        """
        Add YOLO input PyTorch model argument to the parser.
        """
        self._add_non_boolean_argument(
            Flag.INPUT_MODEL_PT,
            type=str,
            required=True,
            help='YOLO input PyTorch model',
        )

    def get_yolo_input_model_pt(self) -> str:
        """
        Get the YOLO input PyTorch model argument.

        Returns:
            str: The YOLO input PyTorch model.
        """
        return self._get_attribute_from_args_dict(Flag.INPUT_MODEL_PT)

    def add_yolo_output_model_argument(self) -> None:
        """
        Add YOLO output model argument to the parser.
        """
        self._add_non_boolean_argument(
            Flag.OUTPUT_MODEL,
            type=str,
            required=True, help='YOLO output model',
            choices=MODELS_NAME
        )

    def get_yolo_output_model(self) -> str:
        """
        Get the YOLO output model argument.

        Returns:
            str: The YOLO output model.
        """
        return self._get_attribute_from_args_dict(Flag.OUTPUT_MODEL)

    def add_yolo_format_argument(
        self,
        required: bool = False
    ) -> None:
        """
        Add YOLO format argument to the parser.

        Args:
            required (bool): Whether the argument is required or not. Defaults to False.
        """
        self._add_non_boolean_argument(
            Flag.FORMAT,
            type=str,
            required=required,
            help='YOLO format',
            choices=FORMATS,
            default=FORMAT_PT
        )

    def get_yolo_format(self) -> str:
        """
        Get the YOLO format argument.

        Returns:
            str: The YOLO format.
        """
        return self._get_attribute_from_args_dict(Flag.FORMAT)

    def add_yolo_quantized_argument(
        self,
        default: bool = False
    ) -> None:
        """
        Add YOLO quantized argument to the parser.

        Args:
            default (bool): Default value for the quantized argument. Defaults to False.
        """
        self._add_boolean_argument(Flag.QUANTIZED, default=default)

    def get_yolo_quantized(self) -> bool:
        """
        Get the YOLO quantized argument.

        Returns:
            bool: The YOLO quantized flag.
        """
        return self._get_attribute_from_args_dict(Flag.QUANTIZED)

    def add_yolo_retraining_argument(
        self,
        default: bool = False
    ) -> None:
        """
        Add YOLO retraining argument to the parser.

        Args:
            default (bool): Default value for the retraining argument. Defaults to False.
        """
        self._add_boolean_argument(Flag.RETRAINING, default=default)

    def get_yolo_retraining(self) -> bool:
        """
        Get the YOLO retraining argument.

        Returns:
            bool: The YOLO retraining flag.
        """
        return self._get_attribute_from_args_dict(Flag.RETRAINING)

    def add_yolo_classes_argument(
        self,
        required: bool = True
    ) -> None:
        """
        Add YOLO classes argument to the parser.

        Args:
            required (bool): Whether the argument is required or not. Defaults to True.
        """
        self._add_non_boolean_argument(
            Flag.CLASSES,
            type=str,
            required=required,
            help='YOLO classes',
            nargs="*"
        )

    def get_yolo_classes(self) -> List[str]:
        """
        Get the YOLO classes argument.

        Returns:
            List[str]: The YOLO classes.
        """
        return self._get_attribute_from_args_dict(Flag.CLASSES)

    def add_yolo_ignore_classes_argument(
        self,
        required: bool = True
    ) -> None:
        """
        Add YOLO ignore classes argument to the parser.

        Args:
            required (bool): Whether the argument is required or not. Defaults to True.
        """
        self._add_non_boolean_argument(
            Flag.IGNORE_CLASSES,
            type=str,
            required=required,
            help='YOLO ignore classes',
            nargs="*"
        )

    def get_yolo_ignore_classes(self) -> List[str]:
        """
        Get the YOLO ignore classes argument.

        Returns:
            List[str]: The YOLO ignore classes.
        """
        return self._get_attribute_from_args_dict(Flag.IGNORE_CLASSES)

    def add_yolo_epochs_argument(
        self,
        required: bool = True
    ) -> None:
        """
        Add YOLO epochs argument to the parser.

        Args:
            required (bool): Whether the argument is required or not. Defaults to True.
        """
        self._add_non_boolean_argument(
            Flag.EPOCHS,
            type=int,
            required=required,
            help='YOLO epochs',
            default=100
        )

    def get_yolo_epochs(self) -> int:
        """
        Get the YOLO epochs argument.

        Returns:
            int: The number of YOLO epochs.
        """
        return self._get_attribute_from_args_dict(Flag.EPOCHS)

    def add_yolo_device_argument(
        self,
        required: bool = True
    ) -> None:
        """
        Add YOLO device argument to the parser.

        Args:
            required (bool): Whether the argument is required or not. Defaults to True.
        """
        self._add_non_boolean_argument(
            Flag.DEVICE,
            type=str,
            required=required,
            help='YOLO device',
            choices=['0', 'cpu', 'cuda'],
            default='0'
        )

    def get_yolo_device(self) -> str:
        """
        Get the YOLO device argument.

        Returns:
            str: The YOLO device.
        """
        return self._get_attribute_from_args_dict(Flag.DEVICE)

    def add_yolo_image_size_argument(
        self,
        required: bool = True
    ) -> None:
        """
        Add YOLO image size argument to the parser.

        Args:
            required (bool): Whether the argument is required or not. Defaults to True.
        """
        self._add_non_boolean_argument(
            Flag.IMAGE_SIZE,
            type=int,
            required=required,
            help='YOLO image size',
            default=SIZE
        )

    def get_yolo_image_size(self) -> int:
        """
        Get the YOLO image size argument.

        Returns:
            int: The YOLO image size.
        """
        return self._get_attribute_from_args_dict(Flag.IMAGE_SIZE)

add_yolo_classes_argument(required=True)

Add YOLO classes argument to the parser.

Parameters:

Name Type Description Default
required bool

Whether the argument is required or not. Defaults to True.

 True
Source code in devices\raspberry_pi_5\src\yolo\args\__init__.py 
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def add_yolo_classes_argument(
    self,
    required: bool = True
) -> None:
    """
    Add YOLO classes argument to the parser.

    Args:
        required (bool): Whether the argument is required or not. Defaults to True.
    """
    self._add_non_boolean_argument(
        Flag.CLASSES,
        type=str,
        required=required,
        help='YOLO classes',
        nargs="*"
    )

add_yolo_device_argument(required=True)

Add YOLO device argument to the parser.

Parameters:

Name Type Description Default
required bool

Whether the argument is required or not. Defaults to True.

 True
Source code in devices\raspberry_pi_5\src\yolo\args\__init__.py 
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def add_yolo_device_argument(
    self,
    required: bool = True
) -> None:
    """
    Add YOLO device argument to the parser.

    Args:
        required (bool): Whether the argument is required or not. Defaults to True.
    """
    self._add_non_boolean_argument(
        Flag.DEVICE,
        type=str,
        required=required,
        help='YOLO device',
        choices=['0', 'cpu', 'cuda'],
        default='0'
    )

add_yolo_epochs_argument(required=True)

Add YOLO epochs argument to the parser.

Parameters:

Name Type Description Default
required bool

Whether the argument is required or not. Defaults to True.

 True
Source code in devices\raspberry_pi_5\src\yolo\args\__init__.py 
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def add_yolo_epochs_argument(
    self,
    required: bool = True
) -> None:
    """
    Add YOLO epochs argument to the parser.

    Args:
        required (bool): Whether the argument is required or not. Defaults to True.
    """
    self._add_non_boolean_argument(
        Flag.EPOCHS,
        type=int,
        required=required,
        help='YOLO epochs',
        default=100
    )

add_yolo_format_argument(required=False)

Add YOLO format argument to the parser.

Parameters:

Name Type Description Default
required bool

Whether the argument is required or not. Defaults to False.

 False
Source code in devices\raspberry_pi_5\src\yolo\args\__init__.py 
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def add_yolo_format_argument(
    self,
    required: bool = False
) -> None:
    """
    Add YOLO format argument to the parser.

    Args:
        required (bool): Whether the argument is required or not. Defaults to False.
    """
    self._add_non_boolean_argument(
        Flag.FORMAT,
        type=str,
        required=required,
        help='YOLO format',
        choices=FORMATS,
        default=FORMAT_PT
    )

add_yolo_ignore_classes_argument(required=True)

Add YOLO ignore classes argument to the parser.

Parameters:

Name Type Description Default
required bool

Whether the argument is required or not. Defaults to True.

 True
Source code in devices\raspberry_pi_5\src\yolo\args\__init__.py 
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def add_yolo_ignore_classes_argument(
    self,
    required: bool = True
) -> None:
    """
    Add YOLO ignore classes argument to the parser.

    Args:
        required (bool): Whether the argument is required or not. Defaults to True.
    """
    self._add_non_boolean_argument(
        Flag.IGNORE_CLASSES,
        type=str,
        required=required,
        help='YOLO ignore classes',
        nargs="*"
    )

add_yolo_image_size_argument(required=True)

Add YOLO image size argument to the parser.

Parameters:

Name Type Description Default
required bool

Whether the argument is required or not. Defaults to True.

 True
Source code in devices\raspberry_pi_5\src\yolo\args\__init__.py 
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def add_yolo_image_size_argument(
    self,
    required: bool = True
) -> None:
    """
    Add YOLO image size argument to the parser.

    Args:
        required (bool): Whether the argument is required or not. Defaults to True.
    """
    self._add_non_boolean_argument(
        Flag.IMAGE_SIZE,
        type=int,
        required=required,
        help='YOLO image size',
        default=SIZE
    )

add_yolo_input_model_pt_argument()

Add YOLO input PyTorch model argument to the parser.

Source code in devices\raspberry_pi_5\src\yolo\args\__init__.py 
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def add_yolo_input_model_pt_argument(self) -> None:
    """
    Add YOLO input PyTorch model argument to the parser.
    """
    self._add_non_boolean_argument(
        Flag.INPUT_MODEL_PT,
        type=str,
        required=True,
        help='YOLO input PyTorch model',
    )

add_yolo_output_model_argument()

Add YOLO output model argument to the parser.

Source code in devices\raspberry_pi_5\src\yolo\args\__init__.py 
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def add_yolo_output_model_argument(self) -> None:
    """
    Add YOLO output model argument to the parser.
    """
    self._add_non_boolean_argument(
        Flag.OUTPUT_MODEL,
        type=str,
        required=True, help='YOLO output model',
        choices=MODELS_NAME
    )

add_yolo_quantized_argument(default=False)

Add YOLO quantized argument to the parser.

Parameters:

Name Type Description Default
default bool

Default value for the quantized argument. Defaults to False.

 False
Source code in devices\raspberry_pi_5\src\yolo\args\__init__.py 
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def add_yolo_quantized_argument(
    self,
    default: bool = False
) -> None:
    """
    Add YOLO quantized argument to the parser.

    Args:
        default (bool): Default value for the quantized argument. Defaults to False.
    """
    self._add_boolean_argument(Flag.QUANTIZED, default=default)

add_yolo_retraining_argument(default=False)

Add YOLO retraining argument to the parser.

Parameters:

Name Type Description Default
default bool

Default value for the retraining argument. Defaults to False.

 False
Source code in devices\raspberry_pi_5\src\yolo\args\__init__.py 
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def add_yolo_retraining_argument(
    self,
    default: bool = False
) -> None:
    """
    Add YOLO retraining argument to the parser.

    Args:
        default (bool): Default value for the retraining argument. Defaults to False.
    """
    self._add_boolean_argument(Flag.RETRAINING, default=default)

get_yolo_classes()

Get the YOLO classes argument.

Returns:

Type Description
List[str]

List[str]: The YOLO classes.
Source code in devices\raspberry_pi_5\src\yolo\args\__init__.py 
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def get_yolo_classes(self) -> List[str]:
    """
    Get the YOLO classes argument.

    Returns:
        List[str]: The YOLO classes.
    """
    return self._get_attribute_from_args_dict(Flag.CLASSES)

get_yolo_device()

Get the YOLO device argument.

Returns:

Name Type Description
str str

The YOLO device.
Source code in devices\raspberry_pi_5\src\yolo\args\__init__.py 
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def get_yolo_device(self) -> str:
    """
    Get the YOLO device argument.

    Returns:
        str: The YOLO device.
    """
    return self._get_attribute_from_args_dict(Flag.DEVICE)

get_yolo_epochs()

Get the YOLO epochs argument.

Returns:

Name Type Description
int int

The number of YOLO epochs.
Source code in devices\raspberry_pi_5\src\yolo\args\__init__.py 
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def get_yolo_epochs(self) -> int:
    """
    Get the YOLO epochs argument.

    Returns:
        int: The number of YOLO epochs.
    """
    return self._get_attribute_from_args_dict(Flag.EPOCHS)

get_yolo_format()

Get the YOLO format argument.

Returns:

Name Type Description
str str

The YOLO format.
Source code in devices\raspberry_pi_5\src\yolo\args\__init__.py 
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def get_yolo_format(self) -> str:
    """
    Get the YOLO format argument.

    Returns:
        str: The YOLO format.
    """
    return self._get_attribute_from_args_dict(Flag.FORMAT)

get_yolo_ignore_classes()

Get the YOLO ignore classes argument.

Returns:

Type Description
List[str]

List[str]: The YOLO ignore classes.
Source code in devices\raspberry_pi_5\src\yolo\args\__init__.py 
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def get_yolo_ignore_classes(self) -> List[str]:
    """
    Get the YOLO ignore classes argument.

    Returns:
        List[str]: The YOLO ignore classes.
    """
    return self._get_attribute_from_args_dict(Flag.IGNORE_CLASSES)

get_yolo_image_size()

Get the YOLO image size argument.

Returns:

Name Type Description
int int

The YOLO image size.
Source code in devices\raspberry_pi_5\src\yolo\args\__init__.py 
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def get_yolo_image_size(self) -> int:
    """
    Get the YOLO image size argument.

    Returns:
        int: The YOLO image size.
    """
    return self._get_attribute_from_args_dict(Flag.IMAGE_SIZE)

get_yolo_input_model_pt()

Get the YOLO input PyTorch model argument.

Returns:

Name Type Description
str str

The YOLO input PyTorch model.
Source code in devices\raspberry_pi_5\src\yolo\args\__init__.py 
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def get_yolo_input_model_pt(self) -> str:
    """
    Get the YOLO input PyTorch model argument.

    Returns:
        str: The YOLO input PyTorch model.
    """
    return self._get_attribute_from_args_dict(Flag.INPUT_MODEL_PT)

get_yolo_output_model()

Get the YOLO output model argument.

Returns:

Name Type Description
str str

The YOLO output model.
Source code in devices\raspberry_pi_5\src\yolo\args\__init__.py 
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def get_yolo_output_model(self) -> str:
    """
    Get the YOLO output model argument.

    Returns:
        str: The YOLO output model.
    """
    return self._get_attribute_from_args_dict(Flag.OUTPUT_MODEL)

get_yolo_quantized()

Get the YOLO quantized argument.

Returns:

Name Type Description
bool bool

The YOLO quantized flag.
Source code in devices\raspberry_pi_5\src\yolo\args\__init__.py 
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def get_yolo_quantized(self) -> bool:
    """
    Get the YOLO quantized argument.

    Returns:
        bool: The YOLO quantized flag.
    """
    return self._get_attribute_from_args_dict(Flag.QUANTIZED)

get_yolo_retraining()

Get the YOLO retraining argument.

Returns:

Name Type Description
bool bool

The YOLO retraining flag.
Source code in devices\raspberry_pi_5\src\yolo\args\__init__.py 
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def get_yolo_retraining(self) -> bool:
    """
    Get the YOLO retraining argument.

    Returns:
        bool: The YOLO retraining flag.
    """
    return self._get_attribute_from_args_dict(Flag.RETRAINING)

enums

Flag

Bases: Enum

Enum to represent command line flags.

Source code in devices\raspberry_pi_5\src\yolo\args\enums.py 
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@unique
class Flag(Enum):
    """
    Enum to represent command line flags.
    """

    DEBUG = BaseFlags.DEBUG.value
    INPUT_MODEL = BaseFlags.INPUT_MODEL.value
    VERSION = BaseFlags.VERSION.value
    FORMAT = 4
    QUANTIZED = 5
    INPUT_MODEL_PT = 6
    OUTPUT_MODEL = 7
    RETRAINING = 8
    CLASSES = 9
    IGNORE_CLASSES = 10
    EPOCHS = 11
    DEVICE = 12
    IMAGE_SIZE = 13

    @property
    def parsed_name(self) -> str:
        """
        Get the flag name with the prefix.

        Returns:
            str: The flag name with the prefix.
        """
        return self.name.lower().replace("_", "-")
parsed_name property

Get the flag name with the prefix.

Returns:

Name Type Description
str str

The flag name with the prefix.

files

Files

Bases: Files

Files utility class.

Source code in devices\raspberry_pi_5\src\yolo\files\__init__.py 
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class Files(F):
    """
    Files utility class.
    """

    @staticmethod
    def check_dataset_status(dataset_status: str | None) -> None:
        """
        Check the validity of dataset status.

        Args:
            dataset_status (str | None): The dataset status to check.
        Raises:
            ValueError: If the dataset status is invalid.
        """
        if dataset_status:
            if dataset_status not in [DATASET_TO_PROCESS, DATASET_PROCESSED]:
                mapped_yolo_dataset_statuses = add_single_quotes_to_list_elements(
                    DATASET_STATUSES
                )
                raise ValueError(
                    f"Invalid dataset status: {dataset_status}. Must be one of the following: {', '.join(mapped_yolo_dataset_statuses)}."
                )

    @staticmethod
    def check_model_dataset_status(
        dataset_name: str,
        dataset_status: str | None
    ) -> None:
        """
        Check the validity of model dataset status.

        Args:
            dataset_name (str): The name of the dataset.
            dataset_status (str | None): The dataset status to check.
        Raises:
            ValueError: If the dataset name is invalid for the given dataset status.
        """
        # Check if the dataset name is split by dataset status
        if dataset_status:
            if dataset_name in [DATASET_AUGMENTED, DATASET_ORGANIZED]:
                raise ValueError(
                    f"Invalid dataset path. The dataset name '{dataset_name}' should not be used with dataset status '{dataset_status}'."
                )

    @classmethod
    def check_dataset_name(cls, dataset_name: str) -> None:
        """
        Check the validity of dataset name.

        Args:
            dataset_name (str): The name of the dataset to check.
        Raises:
            ValueError: If the dataset name is invalid.
        """
        # Check the validity of dataset name
        if dataset_name not in [DATASET_ORIGINAL, DATASET_RESIZED,
                                DATASET_LABELED, DATASET_AUGMENTED,
                                DATASET_ORGANIZED]:
            raise ValueError(
                f"Invalid dataset name: {dataset_name}. Must be one of the defined dataset folders."
            )

    @classmethod
    def check_model_dataset_name(
        cls,
        dataset_name: str,
        model_name: str | None
    ) -> None:
        """
        Check the validity of the model dataset name.

        Args:
            dataset_name (str): The name of the dataset.
            model_name (str | None): The name of the model.
        Raises:
            ValueError: If the dataset name is invalid for the given model name.
        """
        # Check if the dataset name is split by model name
        if model_name:
            if dataset_name in [DATASET_ORIGINAL, DATASET_RESIZED]:
                raise ValueError(
                    f"Invalid dataset path. The dataset name '{dataset_name}' should not be used with model name '{model_name}'."
                )

        # Check if the dataset name is split by model name
        elif dataset_name not in [DATASET_ORIGINAL, DATASET_RESIZED]:
            raise ValueError(
                f"Invalid dataset path. The dataset name '{dataset_name}' should not be used without a model name."
            )

    @classmethod
    def get_dataset_model_dir_path(
        cls,
        dataset_name: str,
        dataset_status: str | None,
        model_name: str | None
    ) -> str | PathLike[str]:
        """
        Get the dataset model directory path.

        Args:
            dataset_name (str): The name of the dataset.
            dataset_status (str | None): The status of the dataset (e.g., 'to_process', 'processed').
            model_name (str | None): The name of the YOLO model.
        Returns:
            str | PathLike[str]: The path to the dataset model directory.
        """
        # Check the validity of the model name
        if model_name:
            Args.check_model_name(model_name)

        # Check the validity of the dataset status
        if dataset_status:
            cls.check_dataset_status(dataset_status)
            cls.check_model_dataset_status(dataset_name, dataset_status)

        # Check the validity of the dataset name
        cls.check_dataset_name(dataset_name)
        cls.check_model_dataset_name(dataset_name, model_name)

        # Check if the dataset is split by model name
        if dataset_status and model_name:
            return os.path.join(
                DATASET_DIR, model_name, dataset_name,
                dataset_status
            )

        if dataset_status:
            return os.path.join(
                DATASET_DIR, DATASET_GENERAL, dataset_name,
                dataset_status
            )

        if model_name:
            return os.path.join(DATASET_DIR, model_name, dataset_name)

        return os.path.join(DATASET_DIR, DATASET_GENERAL, dataset_name)

    @classmethod
    def get_yolo_runs_new_name_dir_path(
        cls,
        yolo_version: str
    ) -> str | PathLike:
        """
        Get the YOLO runs folder path with the new name.

        Args:
            yolo_version (str): The version of the YOLO model.
        Returns:
            str | PathLike: The path to the YOLO runs folder with a new name.
        """
        # Get the YOLO version folder path
        yolo_version_dir = cls.get_yolo_version_dir_path(yolo_version)

        # Get the current Unix timestamp
        unix_timestamp = int(time())

        return os.path.join(yolo_version_dir, f'{RUNS}_{unix_timestamp}')

    @classmethod
    def get_yolo_old_runs_dir_path(cls, yolo_version: str) -> str | PathLike:
        """
        Get the YOLO old runs folder path.

        Args:
            yolo_version (str): The version of the YOLO model.
        Returns:
            str | PathLike: The path to the YOLO old runs folder.
        """
        # Get the YOLO version folder path
        yolo_version_dir = cls.get_yolo_version_dir_path(yolo_version)

        return os.path.join(yolo_version_dir, RUNS_OLD)

    @classmethod
    def get_model_best_pt_path(
        cls, model_name: str,
        yolo_version: str
    ) -> str | PathLike:
        """
        Get the model best PyTorch path.

        Args:
            model_name (str): Name of the YOLO model.
            yolo_version (str): Version of the YOLO model.
        Returns:
            str | PathLike: The path to the model best PyTorch file.
        """
        # Get the model weight path
        model_weight_path = cls.get_model_weight_dir_path(
            model_name,
            yolo_version
        )

        return os.path.join(model_weight_path, BEST_PT)

    @classmethod
    def get_model_best_onnx_path(
        cls, model_name: str,
        yolo_version: str
    ) -> str | PathLike:
        """
        Get  the model best ONNX path.

        Args:
            model_name (str): Name of the YOLO model.
            yolo_version (str): Version of the YOLO model.
        Returns:
            str | PathLike: The path to the model best ONNX file.
        """
        # Get the model weight path
        model_weight_path = cls.get_model_weight_dir_path(
            model_name,
            yolo_version
        )

        return os.path.join(model_weight_path, BEST_ONNX)

    @classmethod
    def get_yolo_zip_dir_path(cls, yolo_version: str) -> str | PathLike:
        """
        Get the YOLO zip folder path.

        Args:
            yolo_version (str): The version of the YOLO model.
        Returns:
            str | PathLike: The path to the YOLO zip folder.
        """
        # Check the validity of the YOLO version
        Args.check_yolo_version(yolo_version)

        return os.path.join(YOLO_DIR, yolo_version, ZIP)

    @classmethod
    def get_yolo_data_dir_path(cls) -> str | PathLike:
        """
        Get the YOLO data folder path.

        Returns:
            str | PathLike: The path to the YOLO data folder.
        """
        return os.path.join(YOLO_DIR, DATA)

    @classmethod
    def get_yolo_colab_data_dir_path(cls) -> str | PathLike:
        """
        Get the YOLO colab data folder path.

        Returns:
            str | PathLike: The path to the YOLO colab data folder.
        """
        # Get the model data folder path
        yolo_data_dir = cls.get_yolo_data_dir_path()

        return os.path.join(yolo_data_dir, COLAB)

    @classmethod
    def get_yolo_local_data_dir_path(cls) -> str | PathLike:
        """
        Get the YOLO local data folder path.

        Returns:
            str | PathLike: The path to the YOLO local data folder.
        """
        # Get the model data folder path
        yolo_data_dir = cls.get_yolo_data_dir_path()

        return os.path.join(yolo_data_dir, LOCAL)

    @classmethod
    def get_model_data_name(cls, model_name: str) -> str:
        """
        Get the model data name.

        Args:
            model_name (str): Name of the YOLO model.
        Returns:
            str: The name of the model data file.
        """
        # Check the validity of the model name
        Args.check_model_name(model_name)

        return model_name + '.yaml'

    @classmethod
    def get_model_colab_data_path(cls, model_name: str) -> str | PathLike:
        """
        Get the model Colab data path.

        Args:
            model_name (str): Name of the YOLO model.
        Returns:
            str | PathLike: The path to the model Colab data file.
        """
        # Get the model Colab data folder path
        yolo_colab_data_dir = cls.get_yolo_colab_data_dir_path()

        # Get the model data name
        model_data_name = cls.get_model_data_name(model_name)

        return os.path.join(yolo_colab_data_dir, model_data_name)

    @classmethod
    def get_model_local_data_path(cls, model_name: str) -> str | PathLike:
        """
        Get the model local data path.

        Args:
            model_name (str): Name of the YOLO model.
        Returns:
            str | PathLike: The path to the model local data file.
        """
        # Get the model local data folder path
        yolo_local_data_dir = cls.get_yolo_local_data_dir_path()

        # Get the model data name
        model_data_name = cls.get_model_data_name(model_name)

        return os.path.join(yolo_local_data_dir, model_data_name)

    @classmethod
    def get_yolo_notebooks_dir_path(cls, yolo_version: str) -> str | PathLike:
        """
        Get the YOLO notebooks folder path.

        Args:
            yolo_version (str): The version of the YOLO model.
        Returns:
            str | PathLike: The path to the YOLO notebooks folder.
        """
        # Check the validity of the YOLO version
        Args.check_yolo_version(yolo_version)

        return os.path.join(YOLO_DIR, yolo_version, NOTEBOOKS)

    @classmethod
    def get_tf_record_path(
        cls,
        model_name: str,
        yolo_version: str
    ) -> str | PathLike:
        """
        Get the TensorFlow Record path.

        Args:
            model_name (str): Name of the YOLO model.
            yolo_version (str): Version of the YOLO model.
        Returns:
            str | PathLike: The path to the TensorFlow Record file.
        """
        # Get the YOLO version folder path
        yolo_version_dir = cls.get_yolo_version_dir_path(yolo_version)

        # Check the validity of the model name
        Args.check_model_name(model_name)

        return os.path.join(
            yolo_version_dir, TF_RECORDS,
            model_name + '.tfrecord'
        )

    @classmethod
    def get_yolo_dataset_notes_file_path(
        cls,
        dataset_name: str,
        dataset_status: str | None,
        model_name: str | None
    ) -> str | PathLike:
        """
        Get the YOLO dataset notes file path.

        Args:
            dataset_name (str): The name of the dataset.
            dataset_status (str | None): The status of the dataset (e.g., 'to_process', 'processed').
            model_name (str | None): The name of the YOLO model.
        Returns:
            str | PathLike: The path to the YOLO dataset notes file.
        """
        # Get the dataset model directory path
        dataset_model_dir_path = cls.get_dataset_model_dir_path(
            dataset_name,
            dataset_status,
            model_name
        )

        return os.path.join(dataset_model_dir_path, DATASET_NOTES_JSON)

    @classmethod
    def get_yolo_dataset_classes_file_path(
        cls,
        dataset_name: str,
        dataset_status: str | None,
        model_name: str | None
    ) -> str | PathLike:
        """
        Get the YOLO dataset classes file path.

        Args:
            dataset_name (str): The name of the dataset.
            dataset_status (str | None): The status of the dataset (e.g., 'to_process', 'processed').
            model_name (str | None): The name of the YOLO model.
        Returns:
            str | PathLike: The path to the YOLO dataset classes file.
        """
        # Get the dataset model directory path
        dataset_model_dir_path = cls.get_dataset_model_dir_path(
            dataset_name,
            dataset_status,
            model_name
        )

        return os.path.join(dataset_model_dir_path, DATASET_CLASSES_TXT)

check_dataset_name(dataset_name) classmethod

Check the validity of dataset name.

Parameters:

Name Type Description Default
dataset_name str

The name of the dataset to check.

 required

Raises:
ValueError: If the dataset name is invalid.

Source code in devices\raspberry_pi_5\src\yolo\files\__init__.py 
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@classmethod
def check_dataset_name(cls, dataset_name: str) -> None:
    """
    Check the validity of dataset name.

    Args:
        dataset_name (str): The name of the dataset to check.
    Raises:
        ValueError: If the dataset name is invalid.
    """
    # Check the validity of dataset name
    if dataset_name not in [DATASET_ORIGINAL, DATASET_RESIZED,
                            DATASET_LABELED, DATASET_AUGMENTED,
                            DATASET_ORGANIZED]:
        raise ValueError(
            f"Invalid dataset name: {dataset_name}. Must be one of the defined dataset folders."
        )

check_dataset_status(dataset_status) staticmethod

Check the validity of dataset status.

Parameters:

Name Type Description Default
dataset_status str | None

The dataset status to check.

 required

Raises:
ValueError: If the dataset status is invalid.

Source code in devices\raspberry_pi_5\src\yolo\files\__init__.py 
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@staticmethod
def check_dataset_status(dataset_status: str | None) -> None:
    """
    Check the validity of dataset status.

    Args:
        dataset_status (str | None): The dataset status to check.
    Raises:
        ValueError: If the dataset status is invalid.
    """
    if dataset_status:
        if dataset_status not in [DATASET_TO_PROCESS, DATASET_PROCESSED]:
            mapped_yolo_dataset_statuses = add_single_quotes_to_list_elements(
                DATASET_STATUSES
            )
            raise ValueError(
                f"Invalid dataset status: {dataset_status}. Must be one of the following: {', '.join(mapped_yolo_dataset_statuses)}."
            )

check_model_dataset_name(dataset_name, model_name) classmethod

Check the validity of the model dataset name.

Parameters:

Name Type Description Default
dataset_name str

The name of the dataset.

 required
model_name str | None

The name of the model.

 required

Raises:
ValueError: If the dataset name is invalid for the given model name.

Source code in devices\raspberry_pi_5\src\yolo\files\__init__.py 
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@classmethod
def check_model_dataset_name(
    cls,
    dataset_name: str,
    model_name: str | None
) -> None:
    """
    Check the validity of the model dataset name.

    Args:
        dataset_name (str): The name of the dataset.
        model_name (str | None): The name of the model.
    Raises:
        ValueError: If the dataset name is invalid for the given model name.
    """
    # Check if the dataset name is split by model name
    if model_name:
        if dataset_name in [DATASET_ORIGINAL, DATASET_RESIZED]:
            raise ValueError(
                f"Invalid dataset path. The dataset name '{dataset_name}' should not be used with model name '{model_name}'."
            )

    # Check if the dataset name is split by model name
    elif dataset_name not in [DATASET_ORIGINAL, DATASET_RESIZED]:
        raise ValueError(
            f"Invalid dataset path. The dataset name '{dataset_name}' should not be used without a model name."
        )

check_model_dataset_status(dataset_name, dataset_status) staticmethod

Check the validity of model dataset status.

Parameters:

Name Type Description Default
dataset_name str

The name of the dataset.

 required
dataset_status str | None

The dataset status to check.

 required

Raises:
ValueError: If the dataset name is invalid for the given dataset status.

Source code in devices\raspberry_pi_5\src\yolo\files\__init__.py 
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@staticmethod
def check_model_dataset_status(
    dataset_name: str,
    dataset_status: str | None
) -> None:
    """
    Check the validity of model dataset status.

    Args:
        dataset_name (str): The name of the dataset.
        dataset_status (str | None): The dataset status to check.
    Raises:
        ValueError: If the dataset name is invalid for the given dataset status.
    """
    # Check if the dataset name is split by dataset status
    if dataset_status:
        if dataset_name in [DATASET_AUGMENTED, DATASET_ORGANIZED]:
            raise ValueError(
                f"Invalid dataset path. The dataset name '{dataset_name}' should not be used with dataset status '{dataset_status}'."
            )

get_dataset_model_dir_path(dataset_name, dataset_status, model_name) classmethod

Get the dataset model directory path.

Parameters:

Name Type Description Default
dataset_name str

The name of the dataset.

 required
dataset_status str | None

The status of the dataset (e.g., 'to_process', 'processed').

 required
model_name str | None

The name of the YOLO model.

 required

Returns:
str | PathLike[str]: The path to the dataset model directory.

Source code in devices\raspberry_pi_5\src\yolo\files\__init__.py 
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@classmethod
def get_dataset_model_dir_path(
    cls,
    dataset_name: str,
    dataset_status: str | None,
    model_name: str | None
) -> str | PathLike[str]:
    """
    Get the dataset model directory path.

    Args:
        dataset_name (str): The name of the dataset.
        dataset_status (str | None): The status of the dataset (e.g., 'to_process', 'processed').
        model_name (str | None): The name of the YOLO model.
    Returns:
        str | PathLike[str]: The path to the dataset model directory.
    """
    # Check the validity of the model name
    if model_name:
        Args.check_model_name(model_name)

    # Check the validity of the dataset status
    if dataset_status:
        cls.check_dataset_status(dataset_status)
        cls.check_model_dataset_status(dataset_name, dataset_status)

    # Check the validity of the dataset name
    cls.check_dataset_name(dataset_name)
    cls.check_model_dataset_name(dataset_name, model_name)

    # Check if the dataset is split by model name
    if dataset_status and model_name:
        return os.path.join(
            DATASET_DIR, model_name, dataset_name,
            dataset_status
        )

    if dataset_status:
        return os.path.join(
            DATASET_DIR, DATASET_GENERAL, dataset_name,
            dataset_status
        )

    if model_name:
        return os.path.join(DATASET_DIR, model_name, dataset_name)

    return os.path.join(DATASET_DIR, DATASET_GENERAL, dataset_name)

get_model_best_onnx_path(model_name, yolo_version) classmethod

Get the model best ONNX path.

Parameters:

Name Type Description Default
model_name str

Name of the YOLO model.

 required
yolo_version str

Version of the YOLO model.

 required

Returns:
str | PathLike: The path to the model best ONNX file.

Source code in devices\raspberry_pi_5\src\yolo\files\__init__.py 
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@classmethod
def get_model_best_onnx_path(
    cls, model_name: str,
    yolo_version: str
) -> str | PathLike:
    """
    Get  the model best ONNX path.

    Args:
        model_name (str): Name of the YOLO model.
        yolo_version (str): Version of the YOLO model.
    Returns:
        str | PathLike: The path to the model best ONNX file.
    """
    # Get the model weight path
    model_weight_path = cls.get_model_weight_dir_path(
        model_name,
        yolo_version
    )

    return os.path.join(model_weight_path, BEST_ONNX)

get_model_best_pt_path(model_name, yolo_version) classmethod

Get the model best PyTorch path.

Parameters:

Name Type Description Default
model_name str

Name of the YOLO model.

 required
yolo_version str

Version of the YOLO model.

 required

Returns:
str | PathLike: The path to the model best PyTorch file.

Source code in devices\raspberry_pi_5\src\yolo\files\__init__.py 
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@classmethod
def get_model_best_pt_path(
    cls, model_name: str,
    yolo_version: str
) -> str | PathLike:
    """
    Get the model best PyTorch path.

    Args:
        model_name (str): Name of the YOLO model.
        yolo_version (str): Version of the YOLO model.
    Returns:
        str | PathLike: The path to the model best PyTorch file.
    """
    # Get the model weight path
    model_weight_path = cls.get_model_weight_dir_path(
        model_name,
        yolo_version
    )

    return os.path.join(model_weight_path, BEST_PT)

get_model_colab_data_path(model_name) classmethod

Get the model Colab data path.

Parameters:

Name Type Description Default
model_name str

Name of the YOLO model.

 required

Returns:
str | PathLike: The path to the model Colab data file.

Source code in devices\raspberry_pi_5\src\yolo\files\__init__.py 
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@classmethod
def get_model_colab_data_path(cls, model_name: str) -> str | PathLike:
    """
    Get the model Colab data path.

    Args:
        model_name (str): Name of the YOLO model.
    Returns:
        str | PathLike: The path to the model Colab data file.
    """
    # Get the model Colab data folder path
    yolo_colab_data_dir = cls.get_yolo_colab_data_dir_path()

    # Get the model data name
    model_data_name = cls.get_model_data_name(model_name)

    return os.path.join(yolo_colab_data_dir, model_data_name)

get_model_data_name(model_name) classmethod

Get the model data name.

Parameters:

Name Type Description Default
model_name str

Name of the YOLO model.

 required

Returns:
str: The name of the model data file.

Source code in devices\raspberry_pi_5\src\yolo\files\__init__.py 
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@classmethod
def get_model_data_name(cls, model_name: str) -> str:
    """
    Get the model data name.

    Args:
        model_name (str): Name of the YOLO model.
    Returns:
        str: The name of the model data file.
    """
    # Check the validity of the model name
    Args.check_model_name(model_name)

    return model_name + '.yaml'

get_model_local_data_path(model_name) classmethod

Get the model local data path.

Parameters:

Name Type Description Default
model_name str

Name of the YOLO model.

 required

Returns:
str | PathLike: The path to the model local data file.

Source code in devices\raspberry_pi_5\src\yolo\files\__init__.py 
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@classmethod
def get_model_local_data_path(cls, model_name: str) -> str | PathLike:
    """
    Get the model local data path.

    Args:
        model_name (str): Name of the YOLO model.
    Returns:
        str | PathLike: The path to the model local data file.
    """
    # Get the model local data folder path
    yolo_local_data_dir = cls.get_yolo_local_data_dir_path()

    # Get the model data name
    model_data_name = cls.get_model_data_name(model_name)

    return os.path.join(yolo_local_data_dir, model_data_name)

get_tf_record_path(model_name, yolo_version) classmethod

Get the TensorFlow Record path.

Parameters:

Name Type Description Default
model_name str

Name of the YOLO model.

 required
yolo_version str

Version of the YOLO model.

 required

Returns:
str | PathLike: The path to the TensorFlow Record file.

Source code in devices\raspberry_pi_5\src\yolo\files\__init__.py 
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@classmethod
def get_tf_record_path(
    cls,
    model_name: str,
    yolo_version: str
) -> str | PathLike:
    """
    Get the TensorFlow Record path.

    Args:
        model_name (str): Name of the YOLO model.
        yolo_version (str): Version of the YOLO model.
    Returns:
        str | PathLike: The path to the TensorFlow Record file.
    """
    # Get the YOLO version folder path
    yolo_version_dir = cls.get_yolo_version_dir_path(yolo_version)

    # Check the validity of the model name
    Args.check_model_name(model_name)

    return os.path.join(
        yolo_version_dir, TF_RECORDS,
        model_name + '.tfrecord'
    )

get_yolo_colab_data_dir_path() classmethod

Get the YOLO colab data folder path.

Returns:

Type Description
str | PathLike

str | PathLike: The path to the YOLO colab data folder.
Source code in devices\raspberry_pi_5\src\yolo\files\__init__.py 
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@classmethod
def get_yolo_colab_data_dir_path(cls) -> str | PathLike:
    """
    Get the YOLO colab data folder path.

    Returns:
        str | PathLike: The path to the YOLO colab data folder.
    """
    # Get the model data folder path
    yolo_data_dir = cls.get_yolo_data_dir_path()

    return os.path.join(yolo_data_dir, COLAB)

get_yolo_data_dir_path() classmethod

Get the YOLO data folder path.

Returns:

Type Description
str | PathLike

str | PathLike: The path to the YOLO data folder.
Source code in devices\raspberry_pi_5\src\yolo\files\__init__.py 
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@classmethod
def get_yolo_data_dir_path(cls) -> str | PathLike:
    """
    Get the YOLO data folder path.

    Returns:
        str | PathLike: The path to the YOLO data folder.
    """
    return os.path.join(YOLO_DIR, DATA)

get_yolo_dataset_classes_file_path(dataset_name, dataset_status, model_name) classmethod

Get the YOLO dataset classes file path.

Parameters:

Name Type Description Default
dataset_name str

The name of the dataset.

 required
dataset_status str | None

The status of the dataset (e.g., 'to_process', 'processed').

 required
model_name str | None

The name of the YOLO model.

 required

Returns:
str | PathLike: The path to the YOLO dataset classes file.

Source code in devices\raspberry_pi_5\src\yolo\files\__init__.py 
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@classmethod
def get_yolo_dataset_classes_file_path(
    cls,
    dataset_name: str,
    dataset_status: str | None,
    model_name: str | None
) -> str | PathLike:
    """
    Get the YOLO dataset classes file path.

    Args:
        dataset_name (str): The name of the dataset.
        dataset_status (str | None): The status of the dataset (e.g., 'to_process', 'processed').
        model_name (str | None): The name of the YOLO model.
    Returns:
        str | PathLike: The path to the YOLO dataset classes file.
    """
    # Get the dataset model directory path
    dataset_model_dir_path = cls.get_dataset_model_dir_path(
        dataset_name,
        dataset_status,
        model_name
    )

    return os.path.join(dataset_model_dir_path, DATASET_CLASSES_TXT)

get_yolo_dataset_notes_file_path(dataset_name, dataset_status, model_name) classmethod

Get the YOLO dataset notes file path.

Parameters:

Name Type Description Default
dataset_name str

The name of the dataset.

 required
dataset_status str | None

The status of the dataset (e.g., 'to_process', 'processed').

 required
model_name str | None

The name of the YOLO model.

 required

Returns:
str | PathLike: The path to the YOLO dataset notes file.

Source code in devices\raspberry_pi_5\src\yolo\files\__init__.py 
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@classmethod
def get_yolo_dataset_notes_file_path(
    cls,
    dataset_name: str,
    dataset_status: str | None,
    model_name: str | None
) -> str | PathLike:
    """
    Get the YOLO dataset notes file path.

    Args:
        dataset_name (str): The name of the dataset.
        dataset_status (str | None): The status of the dataset (e.g., 'to_process', 'processed').
        model_name (str | None): The name of the YOLO model.
    Returns:
        str | PathLike: The path to the YOLO dataset notes file.
    """
    # Get the dataset model directory path
    dataset_model_dir_path = cls.get_dataset_model_dir_path(
        dataset_name,
        dataset_status,
        model_name
    )

    return os.path.join(dataset_model_dir_path, DATASET_NOTES_JSON)

get_yolo_local_data_dir_path() classmethod

Get the YOLO local data folder path.

Returns:

Type Description
str | PathLike

str | PathLike: The path to the YOLO local data folder.
Source code in devices\raspberry_pi_5\src\yolo\files\__init__.py 
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@classmethod
def get_yolo_local_data_dir_path(cls) -> str | PathLike:
    """
    Get the YOLO local data folder path.

    Returns:
        str | PathLike: The path to the YOLO local data folder.
    """
    # Get the model data folder path
    yolo_data_dir = cls.get_yolo_data_dir_path()

    return os.path.join(yolo_data_dir, LOCAL)

get_yolo_notebooks_dir_path(yolo_version) classmethod

Get the YOLO notebooks folder path.

Parameters:

Name Type Description Default
yolo_version str

The version of the YOLO model.

 required

Returns:
str | PathLike: The path to the YOLO notebooks folder.

Source code in devices\raspberry_pi_5\src\yolo\files\__init__.py 
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@classmethod
def get_yolo_notebooks_dir_path(cls, yolo_version: str) -> str | PathLike:
    """
    Get the YOLO notebooks folder path.

    Args:
        yolo_version (str): The version of the YOLO model.
    Returns:
        str | PathLike: The path to the YOLO notebooks folder.
    """
    # Check the validity of the YOLO version
    Args.check_yolo_version(yolo_version)

    return os.path.join(YOLO_DIR, yolo_version, NOTEBOOKS)

get_yolo_old_runs_dir_path(yolo_version) classmethod

Get the YOLO old runs folder path.

Parameters:

Name Type Description Default
yolo_version str

The version of the YOLO model.

 required

Returns:
str | PathLike: The path to the YOLO old runs folder.

Source code in devices\raspberry_pi_5\src\yolo\files\__init__.py 
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@classmethod
def get_yolo_old_runs_dir_path(cls, yolo_version: str) -> str | PathLike:
    """
    Get the YOLO old runs folder path.

    Args:
        yolo_version (str): The version of the YOLO model.
    Returns:
        str | PathLike: The path to the YOLO old runs folder.
    """
    # Get the YOLO version folder path
    yolo_version_dir = cls.get_yolo_version_dir_path(yolo_version)

    return os.path.join(yolo_version_dir, RUNS_OLD)

get_yolo_runs_new_name_dir_path(yolo_version) classmethod

Get the YOLO runs folder path with the new name.

Parameters:

Name Type Description Default
yolo_version str

The version of the YOLO model.

 required

Returns:
str | PathLike: The path to the YOLO runs folder with a new name.

Source code in devices\raspberry_pi_5\src\yolo\files\__init__.py 
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@classmethod
def get_yolo_runs_new_name_dir_path(
    cls,
    yolo_version: str
) -> str | PathLike:
    """
    Get the YOLO runs folder path with the new name.

    Args:
        yolo_version (str): The version of the YOLO model.
    Returns:
        str | PathLike: The path to the YOLO runs folder with a new name.
    """
    # Get the YOLO version folder path
    yolo_version_dir = cls.get_yolo_version_dir_path(yolo_version)

    # Get the current Unix timestamp
    unix_timestamp = int(time())

    return os.path.join(yolo_version_dir, f'{RUNS}_{unix_timestamp}')

get_yolo_zip_dir_path(yolo_version) classmethod

Get the YOLO zip folder path.

Parameters:

Name Type Description Default
yolo_version str

The version of the YOLO model.

 required

Returns:
str | PathLike: The path to the YOLO zip folder.

Source code in devices\raspberry_pi_5\src\yolo\files\__init__.py 
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@classmethod
def get_yolo_zip_dir_path(cls, yolo_version: str) -> str | PathLike:
    """
    Get the YOLO zip folder path.

    Args:
        yolo_version (str): The version of the YOLO model.
    Returns:
        str | PathLike: The path to the YOLO zip folder.
    """
    # Check the validity of the YOLO version
    Args.check_yolo_version(yolo_version)

    return os.path.join(YOLO_DIR, yolo_version, ZIP)