Merge branch 'main' into quan

docs/en/hub/datasets.md

@@ -98,6 +98,18 @@ Next, [train a model](./models.md#train-model) on your dataset.
 
 ![Ultralytics HUB screenshot of the Dataset page with an arrow pointing to the Train Model button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_upload_dataset_9.jpg)
 
+## Download Dataset
+
+Navigate to the Dataset page of the dataset you want to download, open the dataset actions dropdown and click on the **Download** option. This action will start downloading your dataset.
+
+![Ultralytics HUB screenshot of the Dataset page with an arrow pointing to the Download option](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_download_dataset_1.jpg)
+
+??? tip "Tip"
+
+    You can download a dataset directly from the [Datasets](https://hub.ultralytics.com/datasets) page.
+
+    ![Ultralytics HUB screenshot of the Datasets page with an arrow pointing to the Download option of one of the datasets](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_download_dataset_2.jpg)
+
 ## Share Dataset
 
 !!! info "Info"

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.2.79"
+__version__ = "8.2.80"
 
 import os
 

ultralytics/engine/model.py

@@ -6,6 +6,7 @@ from typing import List, Union
 
 import numpy as np
 import torch
+from PIL import Image
 
 from ultralytics.cfg import TASK2DATA, get_cfg, get_save_dir
 from ultralytics.engine.results import Results
@@ -143,7 +144,7 @@ class Model(nn.Module):
 
     def __call__(
         self,
-        source: Union[str, Path, int, list, tuple, np.ndarray, torch.Tensor] = None,
+        source: Union[str, Path, int, Image.Image, list, tuple, np.ndarray, torch.Tensor] = None,
         stream: bool = False,
         **kwargs,
     ) -> list:
@@ -504,7 +505,7 @@ class Model(nn.Module):
 
     def predict(
         self,
-        source: Union[str, Path, int, list, tuple, np.ndarray, torch.Tensor] = None,
+        source: Union[str, Path, int, Image.Image, list, tuple, np.ndarray, torch.Tensor] = None,
         stream: bool = False,
         predictor=None,
         **kwargs,
@@ -517,7 +518,7 @@ class Model(nn.Module):
         types of image sources and can operate in a streaming mode.
 
         Args:
-            source (str | Path | int | List[str] | List[Path] | List[int] | np.ndarray | torch.Tensor): The source
+            source (str | Path | int | PIL.Image | np.ndarray | torch.Tensor | List | Tuple): The source
                 of the image(s) to make predictions on. Accepts various types including file paths, URLs, PIL
                 images, numpy arrays, and torch tensors.
             stream (bool): If True, treats the input source as a continuous stream for predictions.
@@ -900,7 +901,7 @@ class Model(nn.Module):
         initialized, it sets it up before retrieving the names.
 
         Returns:
-            (List[str]): A list of class names associated with the model.
+            (Dict[int, str]): A dict of class names associated with the model.
 
         Raises:
             AttributeError: If the model or predictor does not have a 'names' attribute.
@@ -908,7 +909,7 @@ class Model(nn.Module):
         Examples:
             >>> model = YOLO('yolov8n.pt')
             >>> print(model.names)
-            ['person', 'bicycle', 'car', ...]
+            {0: 'person', 1: 'bicycle', 2: 'car', ...}
         """
         from ultralytics.nn.autobackend import check_class_names
 

ultralytics/utils/metrics.py

@@ -445,7 +445,7 @@ def smooth(y, f=0.05):
 
 
 @plt_settings()
-def plot_pr_curve(px, py, ap, save_dir=Path("pr_curve.png"), names=(), on_plot=None):
+def plot_pr_curve(px, py, ap, save_dir=Path("pr_curve.png"), names={}, on_plot=None):
     """Plots a precision-recall curve."""
     fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
     py = np.stack(py, axis=1)
@@ -470,7 +470,7 @@ def plot_pr_curve(px, py, ap, save_dir=Path("pr_curve.png"), names=(), on_plot=N
 
 
 @plt_settings()
-def plot_mc_curve(px, py, save_dir=Path("mc_curve.png"), names=(), xlabel="Confidence", ylabel="Metric", on_plot=None):
+def plot_mc_curve(px, py, save_dir=Path("mc_curve.png"), names={}, xlabel="Confidence", ylabel="Metric", on_plot=None):
     """Plots a metric-confidence curve."""
     fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
 
@@ -528,7 +528,7 @@ def compute_ap(recall, precision):
 
 
 def ap_per_class(
-    tp, conf, pred_cls, target_cls, plot=False, on_plot=None, save_dir=Path(), names=(), eps=1e-16, prefix=""
+    tp, conf, pred_cls, target_cls, plot=False, on_plot=None, save_dir=Path(), names={}, eps=1e-16, prefix=""
 ):
     """
     Computes the average precision per class for object detection evaluation.
@@ -541,7 +541,7 @@ def ap_per_class(
         plot (bool, optional): Whether to plot PR curves or not. Defaults to False.
         on_plot (func, optional): A callback to pass plots path and data when they are rendered. Defaults to None.
         save_dir (Path, optional): Directory to save the PR curves. Defaults to an empty path.
-        names (tuple, optional): Tuple of class names to plot PR curves. Defaults to an empty tuple.
+        names (dict, optional): Dict of class names to plot PR curves. Defaults to an empty tuple.
         eps (float, optional): A small value to avoid division by zero. Defaults to 1e-16.
         prefix (str, optional): A prefix string for saving the plot files. Defaults to an empty string.
 
@@ -799,13 +799,13 @@ class DetMetrics(SimpleClass):
         save_dir (Path): A path to the directory where the output plots will be saved. Defaults to current directory.
         plot (bool): A flag that indicates whether to plot precision-recall curves for each class. Defaults to False.
         on_plot (func): An optional callback to pass plots path and data when they are rendered. Defaults to None.
-        names (tuple of str): A tuple of strings that represents the names of the classes. Defaults to an empty tuple.
+        names (dict of str): A dict of strings that represents the names of the classes. Defaults to an empty tuple.
 
     Attributes:
         save_dir (Path): A path to the directory where the output plots will be saved.
         plot (bool): A flag that indicates whether to plot the precision-recall curves for each class.
         on_plot (func): An optional callback to pass plots path and data when they are rendered.
-        names (tuple of str): A tuple of strings that represents the names of the classes.
+        names (dict of str): A dict of strings that represents the names of the classes.
         box (Metric): An instance of the Metric class for storing the results of the detection metrics.
         speed (dict): A dictionary for storing the execution time of different parts of the detection process.
 
@@ -822,7 +822,7 @@ class DetMetrics(SimpleClass):
         curves_results: TODO
     """
 
-    def __init__(self, save_dir=Path("."), plot=False, on_plot=None, names=()) -> None:
+    def __init__(self, save_dir=Path("."), plot=False, on_plot=None, names={}) -> None:
         """Initialize a DetMetrics instance with a save directory, plot flag, callback function, and class names."""
         self.save_dir = save_dir
         self.plot = plot