Use Any type-hints for args and kwargs (#18372)

Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

docs/en/guides/streamlit-live-inference.md

@@ -45,7 +45,7 @@ Streamlit makes it simple to build and deploy interactive web applications. Comb
         ```bash
         yolo solutions inference
 
-        yolo solutions inference model="path/to/model/file.pt"
+        yolo solutions inference model="path/to/model.pt"
         ```
 
     === "Python"

ultralytics/engine/model.py

@@ -2,7 +2,7 @@
 
 import inspect
 from pathlib import Path
-from typing import Dict, List, Union
+from typing import Any, Dict, List, Union
 
 import numpy as np
 import torch
@@ -152,7 +152,7 @@ class Model(nn.Module):
         self,
         source: Union[str, Path, int, Image.Image, list, tuple, np.ndarray, torch.Tensor] = None,
         stream: bool = False,
-        **kwargs,
+        **kwargs: Any,
     ) -> list:
         """
         Alias for the predict method, enabling the model instance to be callable for predictions.
@@ -165,7 +165,7 @@ class Model(nn.Module):
                 the image(s) to make predictions on. Can be a file path, URL, PIL image, numpy array, PyTorch
                 tensor, or a list/tuple of these.
             stream (bool): If True, treat the input source as a continuous stream for predictions.
-            **kwargs (Any): Additional keyword arguments to configure the prediction process.
+            **kwargs: Additional keyword arguments to configure the prediction process.
 
         Returns:
             (List[ultralytics.engine.results.Results]): A list of prediction results, each encapsulated in a
@@ -466,7 +466,7 @@ class Model(nn.Module):
         self,
         source: Union[str, Path, int, list, tuple, np.ndarray, torch.Tensor] = None,
         stream: bool = False,
-        **kwargs,
+        **kwargs: Any,
     ) -> list:
         """
         Generates image embeddings based on the provided source.
@@ -478,7 +478,7 @@ class Model(nn.Module):
             source (str | Path | int | List | Tuple | np.ndarray | torch.Tensor): The source of the image for
                 generating embeddings. Can be a file path, URL, PIL image, numpy array, etc.
             stream (bool): If True, predictions are streamed.
-            **kwargs (Any): Additional keyword arguments for configuring the embedding process.
+            **kwargs: Additional keyword arguments for configuring the embedding process.
 
         Returns:
             (List[torch.Tensor]): A list containing the image embeddings.
@@ -501,7 +501,7 @@ class Model(nn.Module):
         source: Union[str, Path, int, Image.Image, list, tuple, np.ndarray, torch.Tensor] = None,
         stream: bool = False,
         predictor=None,
-        **kwargs,
+        **kwargs: Any,
     ) -> List[Results]:
         """
         Performs predictions on the given image source using the YOLO model.
@@ -517,7 +517,7 @@ class Model(nn.Module):
             stream (bool): If True, treats the input source as a continuous stream for predictions.
             predictor (BasePredictor | None): An instance of a custom predictor class for making predictions.
                 If None, the method uses a default predictor.
-            **kwargs (Any): Additional keyword arguments for configuring the prediction process.
+            **kwargs: Additional keyword arguments for configuring the prediction process.
 
         Returns:
             (List[ultralytics.engine.results.Results]): A list of prediction results, each encapsulated in a
@@ -562,7 +562,7 @@ class Model(nn.Module):
         source: Union[str, Path, int, list, tuple, np.ndarray, torch.Tensor] = None,
         stream: bool = False,
         persist: bool = False,
-        **kwargs,
+        **kwargs: Any,
     ) -> List[Results]:
         """
         Conducts object tracking on the specified input source using the registered trackers.
@@ -576,7 +576,7 @@ class Model(nn.Module):
                 tracking. Can be a file path, URL, or video stream.
             stream (bool): If True, treats the input source as a continuous video stream. Defaults to False.
             persist (bool): If True, persists trackers between different calls to this method. Defaults to False.
-            **kwargs (Any): Additional keyword arguments for configuring the tracking process.
+            **kwargs: Additional keyword arguments for configuring the tracking process.
 
         Returns:
             (List[ultralytics.engine.results.Results]): A list of tracking results, each a Results object.
@@ -607,7 +607,7 @@ class Model(nn.Module):
     def val(
         self,
         validator=None,
-        **kwargs,
+        **kwargs: Any,
     ):
         """
         Validates the model using a specified dataset and validation configuration.
@@ -619,7 +619,7 @@ class Model(nn.Module):
         Args:
             validator (ultralytics.engine.validator.BaseValidator | None): An instance of a custom validator class for
                 validating the model.
-            **kwargs (Any): Arbitrary keyword arguments for customizing the validation process.
+            **kwargs: Arbitrary keyword arguments for customizing the validation process.
 
         Returns:
             (ultralytics.utils.metrics.DetMetrics): Validation metrics obtained from the validation process.
@@ -642,7 +642,7 @@ class Model(nn.Module):
 
     def benchmark(
         self,
-        **kwargs,
+        **kwargs: Any,
     ):
         """
         Benchmarks the model across various export formats to evaluate performance.
@@ -653,7 +653,7 @@ class Model(nn.Module):
         defaults, and any additional user-provided keyword arguments.
 
         Args:
-            **kwargs (Any): Arbitrary keyword arguments to customize the benchmarking process. These are combined with
+            **kwargs: Arbitrary keyword arguments to customize the benchmarking process. These are combined with
                 default configurations, model-specific arguments, and method defaults. Common options include:
                 - data (str): Path to the dataset for benchmarking.
                 - imgsz (int | List[int]): Image size for benchmarking.
@@ -691,7 +691,7 @@ class Model(nn.Module):
 
     def export(
         self,
-        **kwargs,
+        **kwargs: Any,
     ) -> str:
         """
         Exports the model to a different format suitable for deployment.
@@ -701,7 +701,7 @@ class Model(nn.Module):
         defaults, and any additional arguments provided.
 
         Args:
-            **kwargs (Dict): Arbitrary keyword arguments to customize the export process. These are combined with
+            **kwargs: Arbitrary keyword arguments to customize the export process. These are combined with
                 the model's overrides and method defaults. Common arguments include:
                 format (str): Export format (e.g., 'onnx', 'engine', 'coreml').
                 half (bool): Export model in half-precision.
@@ -740,7 +740,7 @@ class Model(nn.Module):
     def train(
         self,
         trainer=None,
-        **kwargs,
+        **kwargs: Any,
     ):
         """
         Trains the model using the specified dataset and training configuration.
@@ -755,7 +755,7 @@ class Model(nn.Module):
 
         Args:
             trainer (BaseTrainer | None): Custom trainer instance for model training. If None, uses default.
-            **kwargs (Any): Arbitrary keyword arguments for training configuration. Common options include:
+            **kwargs: Arbitrary keyword arguments for training configuration. Common options include:
                 data (str): Path to dataset configuration file.
                 epochs (int): Number of training epochs.
                 batch_size (int): Batch size for training.
@@ -816,8 +816,8 @@ class Model(nn.Module):
         self,
         use_ray=False,
         iterations=10,
-        *args,
+        *args: Any,
-        **kwargs,
+        **kwargs: Any,
     ):
         """
         Conducts hyperparameter tuning for the model, with an option to use Ray Tune.
@@ -830,8 +830,8 @@ class Model(nn.Module):
         Args:
             use_ray (bool): If True, uses Ray Tune for hyperparameter tuning. Defaults to False.
             iterations (int): The number of tuning iterations to perform. Defaults to 10.
-            *args (List): Variable length argument list for additional arguments.
+            *args: Variable length argument list for additional arguments.
-            **kwargs (Dict): Arbitrary keyword arguments. These are combined with the model's overrides and defaults.
+            **kwargs: Arbitrary keyword arguments. These are combined with the model's overrides and defaults.
 
         Returns:
             (Dict): A dictionary containing the results of the hyperparameter search.

ultralytics/solutions/streamlit_inference.py

@@ -2,6 +2,7 @@
 
 import io
 import time
+from typing import Any
 
 import cv2
 
@@ -37,16 +38,16 @@ class Inference:
         inference: Performs real-time object detection inference.
 
     Examples:
-        >>> inf = solutions.Inference(model="path/to/model/file.pt")  # Model is not necessary argument.
+        >>> inf = solutions.Inference(model="path/to/model.pt")  # Model is not necessary argument.
         >>> inf.inference()
     """
 
-    def __init__(self, **kwargs):
+    def __init__(self, **kwargs: Any):
         """
         Initializes the Inference class, checking Streamlit requirements and setting up the model path.
 
         Args:
-            **kwargs (Dict): Additional keyword arguments for model configuration.
+            **kwargs (Any): Additional keyword arguments for model configuration.
         """
         check_requirements("streamlit>=1.29.0")  # scope imports for faster ultralytics package load speeds
         import streamlit as st