`ultralytics 8.3.10` Apple iPhone HEIC support (#16853)

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

docs/en/modes/predict.md

@@ -408,6 +408,10 @@ YOLO11 supports various image and video formats, as specified in [ultralytics/da
 
 The below table contains valid Ultralytics image formats.
 
+!!! note
+
+    HEIC images are supported for inference only, not for training.
+
 | Image Suffixes | Example Predict Command          | Reference                                                                  |
 | -------------- | -------------------------------- | -------------------------------------------------------------------------- |
 | `.bmp`         | `yolo predict source=image.bmp`  | [Microsoft BMP File Format](https://en.wikipedia.org/wiki/BMP_file_format) |
@@ -420,6 +424,7 @@ The below table contains valid Ultralytics image formats.
 | `.tiff`        | `yolo predict source=image.tiff` | [Tag Image File Format](https://en.wikipedia.org/wiki/TIFF)                |
 | `.webp`        | `yolo predict source=image.webp` | [WebP](https://en.wikipedia.org/wiki/WebP)                                 |
 | `.pfm`         | `yolo predict source=image.pfm`  | [Portable FloatMap](https://en.wikipedia.org/wiki/Netpbm#File_formats)     |
+| `.HEIC`        | `yolo predict source=image.HEIC` | [High Efficiency Image Format](https://en.wikipedia.org/wiki/HEIF)         |
 
 ### Videos
 

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.3.9"
+__version__ = "8.3.10"
 
 import os
 

ultralytics/data/loaders.py

@@ -18,11 +18,29 @@ from PIL import Image
 from ultralytics.data.utils import FORMATS_HELP_MSG, IMG_FORMATS, VID_FORMATS
 from ultralytics.utils import IS_COLAB, IS_KAGGLE, LOGGER, ops
 from ultralytics.utils.checks import check_requirements
+from ultralytics.utils.patches import imread
 
 
 @dataclass
 class SourceTypes:
-    """Class to represent various types of input sources for predictions."""
+    """
+    Class to represent various types of input sources for predictions.
+
+    This class uses dataclass to define boolean flags for different types of input sources that can be used for
+    making predictions with YOLO models.
+
+    Attributes:
+        stream (bool): Flag indicating if the input source is a video stream.
+        screenshot (bool): Flag indicating if the input source is a screenshot.
+        from_img (bool): Flag indicating if the input source is an image file.
+
+    Examples:
+        >>> source_types = SourceTypes(stream=True, screenshot=False, from_img=False)
+        >>> print(source_types.stream)
+        True
+        >>> print(source_types.from_img)
+        False
+    """
 
     stream: bool = False
     screenshot: bool = False
@@ -32,38 +50,47 @@ class SourceTypes:
 
 class LoadStreams:
     """
-    Stream Loader for various types of video streams, Supports RTSP, RTMP, HTTP, and TCP streams.
+    Stream Loader for various types of video streams.
+
+    Supports RTSP, RTMP, HTTP, and TCP streams. This class handles the loading and processing of multiple video
+    streams simultaneously, making it suitable for real-time video analysis tasks.
 
     Attributes:
-        sources (str): The source input paths or URLs for the video streams.
-        vid_stride (int): Video frame-rate stride, defaults to 1.
-        buffer (bool): Whether to buffer input streams, defaults to False.
+        sources (List[str]): The source input paths or URLs for the video streams.
+        vid_stride (int): Video frame-rate stride.
+        buffer (bool): Whether to buffer input streams.
         running (bool): Flag to indicate if the streaming thread is running.
         mode (str): Set to 'stream' indicating real-time capture.
-        imgs (list): List of image frames for each stream.
-        fps (list): List of FPS for each stream.
-        frames (list): List of total frames for each stream.
-        threads (list): List of threads for each stream.
-        shape (list): List of shapes for each stream.
-        caps (list): List of cv2.VideoCapture objects for each stream.
+        imgs (List[List[np.ndarray]]): List of image frames for each stream.
+        fps (List[float]): List of FPS for each stream.
+        frames (List[int]): List of total frames for each stream.
+        threads (List[Thread]): List of threads for each stream.
+        shape (List[Tuple[int, int, int]]): List of shapes for each stream.
+        caps (List[cv2.VideoCapture]): List of cv2.VideoCapture objects for each stream.
         bs (int): Batch size for processing.
 
     Methods:
-        __init__: Initialize the stream loader.
         update: Read stream frames in daemon thread.
         close: Close stream loader and release resources.
         __iter__: Returns an iterator object for the class.
         __next__: Returns source paths, transformed, and original images for processing.
         __len__: Return the length of the sources object.
 
-    Example:
-         ```bash
-         yolo predict source='rtsp://example.com/media.mp4'
-         ```
+    Examples:
+        >>> stream_loader = LoadStreams("rtsp://example.com/stream1.mp4")
+        >>> for sources, imgs, _ in stream_loader:
+        ...     # Process the images
+        ...     pass
+        >>> stream_loader.close()
+
+    Notes:
+        - The class uses threading to efficiently load frames from multiple streams simultaneously.
+        - It automatically handles YouTube links, converting them to the best available stream URL.
+        - The class implements a buffer system to manage frame storage and retrieval.
     """
 
     def __init__(self, sources="file.streams", vid_stride=1, buffer=False):
-        """Initialize instance variables and check for consistent input stream shapes."""
+        """Initialize stream loader for multiple video sources, supporting various stream types."""
         torch.backends.cudnn.benchmark = True  # faster for fixed-size inference
         self.buffer = buffer  # buffer input streams
         self.running = True  # running flag for Thread
@@ -114,7 +141,7 @@ class LoadStreams:
         LOGGER.info("")  # newline
 
     def update(self, i, cap, stream):
-        """Read stream `i` frames in daemon thread."""
+        """Read stream frames in daemon thread and update image buffer."""
         n, f = 0, self.frames[i]  # frame number, frame array
         while self.running and cap.isOpened() and n < (f - 1):
             if len(self.imgs[i]) < 30:  # keep a <=30-image buffer
@@ -134,7 +161,7 @@ class LoadStreams:
                 time.sleep(0.01)  # wait until the buffer is empty
 
     def close(self):
-        """Close stream loader and release resources."""
+        """Terminates stream loader, stops threads, and releases video capture resources."""
         self.running = False  # stop flag for Thread
         for thread in self.threads:
             if thread.is_alive():
@@ -152,7 +179,7 @@ class LoadStreams:
         return self
 
     def __next__(self):
-        """Returns source paths, transformed and original images for processing."""
+        """Returns the next batch of frames from multiple video streams for processing."""
         self.count += 1
 
         images = []
@@ -179,16 +206,16 @@ class LoadStreams:
         return self.sources, images, [""] * self.bs
 
     def __len__(self):
-        """Return the length of the sources object."""
+        """Return the number of video streams in the LoadStreams object."""
         return self.bs  # 1E12 frames = 32 streams at 30 FPS for 30 years
 
 
 class LoadScreenshots:
     """
-    YOLOv8 screenshot dataloader.
+    Ultralytics screenshot dataloader for capturing and processing screen images.
 
-    This class manages the loading of screenshot images for processing with YOLOv8.
-    Suitable for use with `yolo predict source=screen`.
+    This class manages the loading of screenshot images for processing with YOLO. It is suitable for use with
+    `yolo predict source=screen`.
 
     Attributes:
         source (str): The source input indicating which screen to capture.
@@ -201,15 +228,21 @@ class LoadScreenshots:
         frame (int): Counter for captured frames.
         sct (mss.mss): Screen capture object from `mss` library.
         bs (int): Batch size, set to 1.
-        monitor (dict): Monitor configuration details.
+        fps (int): Frames per second, set to 30.
+        monitor (Dict[str, int]): Monitor configuration details.
 
     Methods:
         __iter__: Returns an iterator object.
         __next__: Captures the next screenshot and returns it.
+
+    Examples:
+        >>> loader = LoadScreenshots("0 100 100 640 480")  # screen 0, top-left (100,100), 640x480
+        >>> for source, im, im0s, vid_cap, s in loader:
+        ...     print(f"Captured frame: {im.shape}")
     """
 
     def __init__(self, source):
-        """Source = [screen_number left top width height] (pixels)."""
+        """Initialize screenshot capture with specified screen and region parameters."""
         check_requirements("mss")
         import mss  # noqa
 
@@ -236,11 +269,11 @@ class LoadScreenshots:
         self.monitor = {"left": self.left, "top": self.top, "width": self.width, "height": self.height}
 
     def __iter__(self):
-        """Returns an iterator of the object."""
+        """Yields the next screenshot image from the specified screen or region for processing."""
         return self
 
     def __next__(self):
-        """Screen capture with 'mss' to get raw pixels from the screen as np array."""
+        """Captures and returns the next screenshot as a numpy array using the mss library."""
         im0 = np.asarray(self.sct.grab(self.monitor))[:, :, :3]  # BGRA to BGR
         s = f"screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: "
 
@@ -250,29 +283,45 @@ class LoadScreenshots:
 
 class LoadImagesAndVideos:
     """
-    YOLOv8 image/video dataloader.
+    A class for loading and processing images and videos for YOLO object detection.
 
-    This class manages the loading and pre-processing of image and video data for YOLOv8. It supports loading from
-    various formats, including single image files, video files, and lists of image and video paths.
+    This class manages the loading and pre-processing of image and video data from various sources, including
+    single image files, video files, and lists of image and video paths.
 
     Attributes:
-        files (list): List of image and video file paths.
+        files (List[str]): List of image and video file paths.
         nf (int): Total number of files (images and videos).
-        video_flag (list): Flags indicating whether a file is a video (True) or an image (False).
+        video_flag (List[bool]): Flags indicating whether a file is a video (True) or an image (False).
         mode (str): Current mode, 'image' or 'video'.
-        vid_stride (int): Stride for video frame-rate, defaults to 1.
-        bs (int): Batch size, set to 1 for this class.
+        vid_stride (int): Stride for video frame-rate.
+        bs (int): Batch size.
         cap (cv2.VideoCapture): Video capture object for OpenCV.
         frame (int): Frame counter for video.
         frames (int): Total number of frames in the video.
-        count (int): Counter for iteration, initialized at 0 during `__iter__()`.
+        count (int): Counter for iteration, initialized at 0 during __iter__().
+        ni (int): Number of images.
 
     Methods:
-        _new_video(path): Create a new cv2.VideoCapture object for a given video path.
+        __init__: Initialize the LoadImagesAndVideos object.
+        __iter__: Returns an iterator object for VideoStream or ImageFolder.
+        __next__: Returns the next batch of images or video frames along with their paths and metadata.
+        _new_video: Creates a new video capture object for the given path.
+        __len__: Returns the number of batches in the object.
+
+    Examples:
+        >>> loader = LoadImagesAndVideos("path/to/data", batch=32, vid_stride=1)
+        >>> for paths, imgs, info in loader:
+        ...     # Process batch of images or video frames
+        ...     pass
+
+    Notes:
+        - Supports various image formats including HEIC.
+        - Handles both local files and directories.
+        - Can read from a text file containing paths to images and videos.
     """
 
     def __init__(self, path, batch=1, vid_stride=1):
-        """Initialize the Dataloader and raise FileNotFoundError if file not found."""
+        """Initialize dataloader for images and videos, supporting various input formats."""
         parent = None
         if isinstance(path, str) and Path(path).suffix == ".txt":  # *.txt file with img/vid/dir on each line
             parent = Path(path).parent
@@ -316,12 +365,12 @@ class LoadImagesAndVideos:
             raise FileNotFoundError(f"No images or videos found in {p}. {FORMATS_HELP_MSG}")
 
     def __iter__(self):
-        """Returns an iterator object for VideoStream or ImageFolder."""
+        """Iterates through image/video files, yielding source paths, images, and metadata."""
         self.count = 0
         return self
 
     def __next__(self):
-        """Returns the next batch of images or video frames along with their paths and metadata."""
+        """Returns the next batch of images or video frames with their paths and metadata."""
         paths, imgs, info = [], [], []
         while len(imgs) < self.bs:
             if self.count >= self.nf:  # end of file list
@@ -336,6 +385,7 @@ class LoadImagesAndVideos:
                 if not self.cap or not self.cap.isOpened():
                     self._new_video(path)
 
+                success = False
                 for _ in range(self.vid_stride):
                     success = self.cap.grab()
                     if not success:
@@ -359,8 +409,19 @@ class LoadImagesAndVideos:
                     if self.count < self.nf:
                         self._new_video(self.files[self.count])
             else:
+                # Handle image files (including HEIC)
                 self.mode = "image"
-                im0 = cv2.imread(path)  # BGR
+                if path.split(".")[-1].lower() == "heic":
+                    # Load HEIC image using Pillow with pillow-heif
+                    check_requirements("pillow-heif")
+
+                    from pillow_heif import register_heif_opener
+
+                    register_heif_opener()  # Register HEIF opener with Pillow
+                    with Image.open(path) as img:
+                        im0 = cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)  # convert image to BGR nparray
+                else:
+                    im0 = imread(path)  # BGR
                 if im0 is None:
                     LOGGER.warning(f"WARNING ⚠️ Image Read Error {path}")
                 else:
@@ -374,7 +435,7 @@ class LoadImagesAndVideos:
         return paths, imgs, info
 
     def _new_video(self, path):
-        """Creates a new video capture object for the given path."""
+        """Creates a new video capture object for the given path and initializes video-related attributes."""
         self.frame = 0
         self.cap = cv2.VideoCapture(path)
         self.fps = int(self.cap.get(cv2.CAP_PROP_FPS))
@@ -383,30 +444,39 @@ class LoadImagesAndVideos:
         self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT) / self.vid_stride)
 
     def __len__(self):
-        """Returns the number of batches in the object."""
-        return math.ceil(self.nf / self.bs)  # number of files
+        """Returns the number of files (images and videos) in the dataset."""
+        return math.ceil(self.nf / self.bs)  # number of batches
 
 
 class LoadPilAndNumpy:
     """
     Load images from PIL and Numpy arrays for batch processing.
 
-    This class is designed to manage loading and pre-processing of image data from both PIL and Numpy formats.
-    It performs basic validation and format conversion to ensure that the images are in the required format for
-    downstream processing.
+    This class manages loading and pre-processing of image data from both PIL and Numpy formats. It performs basic
+    validation and format conversion to ensure that the images are in the required format for downstream processing.
 
     Attributes:
-        paths (list): List of image paths or autogenerated filenames.
-        im0 (list): List of images stored as Numpy arrays.
-        mode (str): Type of data being processed, defaults to 'image'.
+        paths (List[str]): List of image paths or autogenerated filenames.
+        im0 (List[np.ndarray]): List of images stored as Numpy arrays.
+        mode (str): Type of data being processed, set to 'image'.
         bs (int): Batch size, equivalent to the length of `im0`.
 
     Methods:
-        _single_check(im): Validate and format a single image to a Numpy array.
+        _single_check: Validate and format a single image to a Numpy array.
+
+    Examples:
+        >>> from PIL import Image
+        >>> import numpy as np
+        >>> pil_img = Image.new("RGB", (100, 100))
+        >>> np_img = np.random.randint(0, 255, (100, 100, 3), dtype=np.uint8)
+        >>> loader = LoadPilAndNumpy([pil_img, np_img])
+        >>> paths, images, _ = next(iter(loader))
+        >>> print(f"Loaded {len(images)} images")
+        Loaded 2 images
     """
 
     def __init__(self, im0):
-        """Initialize PIL and Numpy Dataloader."""
+        """Initializes a loader for PIL and Numpy images, converting inputs to a standardized format."""
         if not isinstance(im0, list):
             im0 = [im0]
         # use `image{i}.jpg` when Image.filename returns an empty path.
@@ -417,7 +487,7 @@ class LoadPilAndNumpy:
 
     @staticmethod
     def _single_check(im):
-        """Validate and format an image to numpy array."""
+        """Validate and format an image to numpy array, ensuring RGB order and contiguous memory."""
         assert isinstance(im, (Image.Image, np.ndarray)), f"Expected PIL/np.ndarray image type, but got {type(im)}"
         if isinstance(im, Image.Image):
             if im.mode != "RGB":
@@ -427,41 +497,48 @@ class LoadPilAndNumpy:
         return im
 
     def __len__(self):
-        """Returns the length of the 'im0' attribute."""
+        """Returns the length of the 'im0' attribute, representing the number of loaded images."""
         return len(self.im0)
 
     def __next__(self):
-        """Returns batch paths, images, processed images, None, ''."""
+        """Returns the next batch of images, paths, and metadata for processing."""
         if self.count == 1:  # loop only once as it's batch inference
             raise StopIteration
         self.count += 1
         return self.paths, self.im0, [""] * self.bs
 
     def __iter__(self):
-        """Enables iteration for class LoadPilAndNumpy."""
+        """Iterates through PIL/numpy images, yielding paths, raw images, and metadata for processing."""
         self.count = 0
         return self
 
 
 class LoadTensor:
     """
-    Load images from torch.Tensor data.
+    A class for loading and processing tensor data for object detection tasks.
 
-    This class manages the loading and pre-processing of image data from PyTorch tensors for further processing.
+    This class handles the loading and pre-processing of image data from PyTorch tensors, preparing them for
+    further processing in object detection pipelines.
 
     Attributes:
-        im0 (torch.Tensor): The input tensor containing the image(s).
+        im0 (torch.Tensor): The input tensor containing the image(s) with shape (B, C, H, W).
         bs (int): Batch size, inferred from the shape of `im0`.
-        mode (str): Current mode, set to 'image'.
-        paths (list): List of image paths or filenames.
-        count (int): Counter for iteration, initialized at 0 during `__iter__()`.
+        mode (str): Current processing mode, set to 'image'.
+        paths (List[str]): List of image paths or auto-generated filenames.
 
     Methods:
-        _single_check(im, stride): Validate and possibly modify the input tensor.
+        _single_check: Validates and formats an input tensor.
+
+    Examples:
+        >>> import torch
+        >>> tensor = torch.rand(1, 3, 640, 640)
+        >>> loader = LoadTensor(tensor)
+        >>> paths, images, info = next(iter(loader))
+        >>> print(f"Processed {len(images)} images")
     """
 
     def __init__(self, im0) -> None:
-        """Initialize Tensor Dataloader."""
+        """Initialize LoadTensor object for processing torch.Tensor image data."""
         self.im0 = self._single_check(im0)
         self.bs = self.im0.shape[0]
         self.mode = "image"
@@ -469,7 +546,7 @@ class LoadTensor:
 
     @staticmethod
     def _single_check(im, stride=32):
-        """Validate and format an image to torch.Tensor."""
+        """Validates and formats a single image tensor, ensuring correct shape and normalization."""
         s = (
             f"WARNING ⚠️ torch.Tensor inputs should be BCHW i.e. shape(1, 3, 640, 640) "
             f"divisible by stride {stride}. Input shape{tuple(im.shape)} is incompatible."
@@ -491,24 +568,24 @@ class LoadTensor:
         return im
 
     def __iter__(self):
-        """Returns an iterator object."""
+        """Yields an iterator object for iterating through tensor image data."""
         self.count = 0
         return self
 
     def __next__(self):
-        """Return next item in the iterator."""
+        """Yields the next batch of tensor images and metadata for processing."""
         if self.count == 1:
             raise StopIteration
         self.count += 1
         return self.paths, self.im0, [""] * self.bs
 
     def __len__(self):
-        """Returns the batch size."""
+        """Returns the batch size of the tensor input."""
         return self.bs
 
 
 def autocast_list(source):
-    """Merges a list of source of different types into a list of numpy arrays or PIL images."""
+    """Merges a list of sources into a list of numpy arrays or PIL images for Ultralytics prediction."""
     files = []
     for im in source:
         if isinstance(im, (str, Path)):  # filename or uri
@@ -528,21 +605,24 @@ def get_best_youtube_url(url, method="pytube"):
     """
     Retrieves the URL of the best quality MP4 video stream from a given YouTube video.
 
-    This function uses the specified method to extract the video info from YouTube. It supports the following methods:
-    - "pytube": Uses the pytube library to fetch the video streams.
-    - "pafy": Uses the pafy library to fetch the video streams.
-    - "yt-dlp": Uses the yt-dlp library to fetch the video streams.
-
-    The function then finds the highest quality MP4 format that has a video codec but no audio codec, and returns the
-    URL of this video stream.
-
     Args:
         url (str): The URL of the YouTube video.
-        method (str): The method to use for extracting video info. Default is "pytube". Other options are "pafy" and
-            "yt-dlp".
+        method (str): The method to use for extracting video info. Options are "pytube", "pafy", and "yt-dlp".
+            Defaults to "pytube".
 
     Returns:
-        (str): The URL of the best quality MP4 video stream, or None if no suitable stream is found.
+        (str | None): The URL of the best quality MP4 video stream, or None if no suitable stream is found.
+
+    Examples:
+        >>> url = "https://www.youtube.com/watch?v=dQw4w9WgXcQ"
+        >>> best_url = get_best_youtube_url(url)
+        >>> print(best_url)
+        https://rr4---sn-q4flrnek.googlevideo.com/videoplayback?expire=...
+
+    Notes:
+        - Requires additional libraries based on the chosen method: pytubefix, pafy, or yt-dlp.
+        - The function prioritizes streams with at least 1080p resolution when available.
+        - For the "yt-dlp" method, it looks for formats with video codec, no audio, and *.mp4 extension.
     """
     if method == "pytube":
         # Switched from pytube to pytubefix to resolve https://github.com/pytube/pytube/issues/1954

ultralytics/data/utils.py

@@ -35,7 +35,7 @@ from ultralytics.utils.downloads import download, safe_download, unzip_file
 from ultralytics.utils.ops import segments2boxes
 
 HELP_URL = "See https://docs.ultralytics.com/datasets for dataset formatting guidance."
-IMG_FORMATS = {"bmp", "dng", "jpeg", "jpg", "mpo", "png", "tif", "tiff", "webp", "pfm"}  # image suffixes
+IMG_FORMATS = {"bmp", "dng", "jpeg", "jpg", "mpo", "png", "tif", "tiff", "webp", "pfm", "heic"}  # image suffixes
 VID_FORMATS = {"asf", "avi", "gif", "m4v", "mkv", "mov", "mp4", "mpeg", "mpg", "ts", "wmv", "webm"}  # video suffixes
 PIN_MEMORY = str(os.getenv("PIN_MEMORY", True)).lower() == "true"  # global pin_memory for dataloaders
 FORMATS_HELP_MSG = f"Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {VID_FORMATS}"

ultralytics/engine/predictor.py

@@ -381,7 +381,7 @@ class BasePredictor:
 
         # Save images
         else:
-            cv2.imwrite(save_path, im)
+            cv2.imwrite(str(Path(save_path).with_suffix(".jpg")), im)  # save to JPG for best support
 
     def show(self, p=""):
         """Display an image in a window using the OpenCV imshow function."""

ultralytics/utils/checks.py

@@ -238,12 +238,14 @@ def check_version(
     c = parse_version(current)  # '1.2.3' -> (1, 2, 3)
     for r in required.strip(",").split(","):
         op, version = re.match(r"([^0-9]*)([\d.]+)", r).groups()  # split '>=22.04' -> ('>=', '22.04')
+        if not op:
+            op = ">="  # assume >= if no op passed
         v = parse_version(version)  # '1.2.3' -> (1, 2, 3)
         if op == "==" and c != v:
             result = False
         elif op == "!=" and c == v:
             result = False
-        elif op in {">=", ""} and not (c >= v):  # if no constraint passed assume '>=required'
+        elif op == ">=" and not (c >= v):
             result = False
         elif op == "<=" and not (c <= v):
             result = False
@@ -333,18 +335,19 @@ def check_font(font="Arial.ttf"):
         return file
 
 
-def check_python(minimum: str = "3.8.0", hard: bool = True) -> bool:
+def check_python(minimum: str = "3.8.0", hard: bool = True, verbose: bool = True) -> bool:
     """
     Check current python version against the required minimum version.
 
     Args:
         minimum (str): Required minimum version of python.
         hard (bool, optional): If True, raise an AssertionError if the requirement is not met.
+        verbose (bool, optional): If True, print warning message if requirement is not met.
 
     Returns:
         (bool): Whether the installed Python version meets the minimum constraints.
     """
-    return check_version(PYTHON_VERSION, minimum, name="Python", hard=hard)
+    return check_version(PYTHON_VERSION, minimum, name="Python", hard=hard, verbose=verbose)
 
 
 @TryExcept()
@@ -374,8 +377,6 @@ def check_requirements(requirements=ROOT.parent / "requirements.txt", exclude=()
         ```
     """
     prefix = colorstr("red", "bold", "requirements:")
-    check_python()  # check python version
-    check_torchvision()  # check torch-torchvision compatibility
     if isinstance(requirements, Path):  # requirements.txt file
         file = requirements.resolve()
         assert file.exists(), f"{prefix} {file} not found, check failed."
@@ -770,6 +771,8 @@ def cuda_is_available() -> bool:
     return cuda_device_count() > 0
 
 
-# Define constants
+# Run checks and define constants
+check_python("3.8", hard=False, verbose=True)  # check python version
+check_torchvision()  # check torch-torchvision compatibility
 IS_PYTHON_MINIMUM_3_10 = check_python("3.10", hard=False)
 IS_PYTHON_3_12 = PYTHON_VERSION.startswith("3.12")