ultralytics 8.3.110 New dataset file access speed checks (#20197)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

docs/en/help/code-of-conduct.md

@@ -76,11 +76,11 @@ Community leaders will follow these Community Impact Guidelines in determining t
 
 ## Attribution
 
-This Code of Conduct is adapted from the [Contributor Covenant](https://www.contributor-covenant.org/), version 2.0, available at https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
+This Code of Conduct is adapted from the [Contributor Covenant](https://www.contributor-covenant.org/), version 2.0, available at https://www.contributor-covenant.org/version/2/0/code_of_conduct/.
 
 Community Impact Guidelines were inspired by [Mozilla's code of conduct enforcement ladder](https://github.com/mozilla/inclusion).
 
-For answers to common questions about this code of conduct, see the FAQ at https://www.contributor-covenant.org/faq. Translations are available at https://www.contributor-covenant.org/translations.
+For answers to common questions about this code of conduct, see the FAQ at https://www.contributor-covenant.org/faq/. Translations are available at https://www.contributor-covenant.org/translations/.
 
 ## FAQ
 

docs/en/models/yoloe.md

@@ -236,13 +236,13 @@ YOLOE supports both text-based and visual prompting. Using prompts is straightfo
         # Define visual prompts based on a separate reference image
         visual_prompts = dict(
             bboxes=np.array([[221.52, 405.8, 344.98, 857.54]]),  # Box enclosing person
-            cls=np.array([0])  # ID to be assigned for person
+            cls=np.array([0]),  # ID to be assigned for person
         )
 
         # Run prediction on a different image, using reference image to guide what to look for
         results = model.predict(
-            "ultralytics/assets/zidane.jpg",            # Target image for detection
-            refer_image="ultralytics/assets/bus.jpg",   # Reference image used to get visual prompts
+            "ultralytics/assets/zidane.jpg",  # Target image for detection
+            refer_image="ultralytics/assets/bus.jpg",  # Reference image used to get visual prompts
             visual_prompts=visual_prompts,
             predictor=YOLOEVPSegPredictor,
         )
@@ -263,7 +263,7 @@ YOLOE supports both text-based and visual prompting. Using prompts is straightfo
         model = YOLOE("yoloe-11l-seg.pt")
 
         # Define visual prompts using bounding boxes and their corresponding class IDs.
-        # Each box highlights an example of the object you want the model to detect.    
+        # Each box highlights an example of the object you want the model to detect.
         visual_prompts = dict(
             bboxes=[
                 np.array(

docs/en/reference/data/utils.md

@@ -19,6 +19,10 @@ keywords: Ultralytics, dataset utils, data handling, image verification, Python,
 
 <br><br><hr><br>
 
+## ::: ultralytics.data.utils.check_file_speeds
+
+<br><br><hr><br>
+
 ## ::: ultralytics.data.utils.get_hash
 
 <br><br><hr><br>

docs/en/yolov5/environments/google_cloud_quickstart_tutorial.md

@@ -109,7 +109,7 @@ For efficient data management, especially with large datasets or numerous experi
 
 ```bash
 # Ensure Google Cloud SDK is installed and initialized
-# If not installed: curl https://sdk.cloud.google.com | bash
+# If not installed: curl https://sdk.cloud.google.com/ | bash
 # Then initialize: gcloud init
 
 # Example: Copy your dataset from a GCS bucket to your VM

docs/overrides/main.html

@@ -5,7 +5,7 @@
 <div class="banner-wrapper">
   <div
     class="banner-content-wrapper"
-    onclick="window.open('https://docs.ultralytics.com/models/yolo11')"
+    onclick="window.open('https://docs.ultralytics.com/models/yolo11/')"
   >
     <p>Introducing</p>
     <img

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
 
-__version__ = "8.3.109"
+__version__ = "8.3.110"
 
 import os
 

ultralytics/data/base.py

@@ -14,7 +14,7 @@ import numpy as np
 import psutil
 from torch.utils.data import Dataset
 
-from ultralytics.data.utils import FORMATS_HELP_MSG, HELP_URL, IMG_FORMATS
+from ultralytics.data.utils import FORMATS_HELP_MSG, HELP_URL, IMG_FORMATS, check_file_speeds
 from ultralytics.utils import DEFAULT_CFG, LOCAL_RANK, LOGGER, NUM_THREADS, TQDM
 
 
@@ -172,6 +172,7 @@ class BaseDataset(Dataset):
             raise FileNotFoundError(f"{self.prefix}Error loading data from {img_path}\n{HELP_URL}") from e
         if self.fraction < 1:
             im_files = im_files[: round(len(im_files) * self.fraction)]  # retain a fraction of the dataset
+        check_file_speeds(im_files, prefix=self.prefix)  # check image read speeds
         return im_files
 
     def update_labels(self, include_class: Optional[list]):

ultralytics/data/dataset.py

@@ -31,6 +31,7 @@ from .converter import merge_multi_segment
 from .utils import (
     HELP_URL,
     LOGGER,
+    check_file_speeds,
     get_hash,
     img2label_paths,
     load_dataset_cache_file,
@@ -794,6 +795,7 @@ class ClassificationDataset:
         path = Path(self.root).with_suffix(".cache")  # *.cache file path
 
         try:
+            check_file_speeds([file for (file, _) in self.samples[:5]], prefix=self.prefix)  # check image read speeds
             cache = load_dataset_cache_file(path)  # attempt to load a *.cache file
             assert cache["version"] == DATASET_CACHE_VERSION  # matches current version
             assert cache["hash"] == get_hash([x[0] for x in self.samples])  # identical hash

ultralytics/data/utils.py

@@ -47,6 +47,81 @@ def img2label_paths(img_paths):
     return [sb.join(x.rsplit(sa, 1)).rsplit(".", 1)[0] + ".txt" for x in img_paths]
 
 
+def check_file_speeds(files, threshold_ms=10, max_files=5, prefix=""):
+    """
+    Check dataset file access speed and provide performance feedback.
+
+    This function tests the access speed of dataset files by measuring ping (stat call) time and read speed.
+    It samples up to 5 files from the provided list and warns if access times exceed the threshold.
+
+    Args:
+        files (list): List of file paths to check for access speed.
+        threshold_ms (float, optional): Threshold in milliseconds for ping time warnings.
+        max_files (int, optional): The maximum number of files to check.
+        prefix (str, optional): Prefix string to add to log messages.
+
+    Examples:
+        >>> from pathlib import Path
+        >>> image_files = list(Path("dataset/images").glob("*.jpg"))
+        >>> check_file_speeds(image_files, threshold_ms=15)
+    """
+    if not files or len(files) == 0:
+        LOGGER.warning(f"{prefix}WARNING ⚠️ Image speed checks: No files to check")
+        return
+
+    # Sample files (max 5)
+    files = random.sample(files, min(max_files, len(files)))
+
+    # Test ping (stat time)
+    ping_times = []
+    file_sizes = []
+    read_speeds = []
+
+    for f in files:
+        try:
+            # Measure ping (stat call)
+            start = time.perf_counter()
+            file_size = os.stat(f).st_size
+            ping_times.append((time.perf_counter() - start) * 1000)  # ms
+            file_sizes.append(file_size)
+
+            # Measure read speed
+            start = time.perf_counter()
+            with open(f, "rb") as file_obj:
+                _ = file_obj.read()
+            read_time = time.perf_counter() - start
+            if read_time > 0:  # Avoid division by zero
+                read_speeds.append(file_size / (1 << 20) / read_time)  # MB/s
+        except Exception:
+            pass
+
+    if not ping_times:
+        LOGGER.warning(f"{prefix}WARNING ⚠️ Image speed checks: failed to access files")
+        return
+
+    # Calculate stats with uncertainties
+    avg_ping = np.mean(ping_times)
+    std_ping = np.std(ping_times, ddof=1) if len(ping_times) > 1 else 0
+    size_msg = f", size: {np.mean(file_sizes) / (1 << 10):.1f} KB"
+    ping_msg = f"ping: {avg_ping:.1f}±{std_ping:.1f} ms"
+
+    if read_speeds:
+        avg_speed = np.mean(read_speeds)
+        std_speed = np.std(read_speeds, ddof=1) if len(read_speeds) > 1 else 0
+        speed_msg = f", read: {avg_speed:.1f}±{std_speed:.1f} MB/s"
+    else:
+        speed_msg = ""
+
+    if avg_ping < threshold_ms:
+        LOGGER.info(f"{prefix}Fast image access ✅ ({ping_msg}{speed_msg}{size_msg})")
+    else:
+        LOGGER.warning(
+            f"{prefix}WARNING ⚠️ Slow image access detected ({ping_msg}{speed_msg}{size_msg}). "
+            f"Use local storage instead of remote/mounted storage for better performance. "
+            f"See https://docs.ultralytics.com/guides/model-training-tips/"
+        )
+
+
 def get_hash(paths):
     """Returns a single hash value of a list of paths (files or dirs)."""
     size = 0

ultralytics/engine/exporter.py

@@ -1000,7 +1000,7 @@ class Exporter:
 
     @try_export
     def export_pb(self, keras_model, prefix=colorstr("TensorFlow GraphDef:")):
-        """YOLO TensorFlow GraphDef *.pb export https://github.com/leimao/Frozen_Graph_TensorFlow."""
+        """YOLO TensorFlow GraphDef *.pb export https://github.com/leimao/Frozen-Graph-TensorFlow."""
         import tensorflow as tf  # noqa
         from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2  # noqa
 
@@ -1281,7 +1281,7 @@ class Exporter:
         return f, None
 
     def _add_tflite_metadata(self, file):
-        """Add metadata to *.tflite models per https://www.tensorflow.org/lite/models/convert/metadata."""
+        """Add metadata to *.tflite models per https://ai.google.dev/edge/litert/models/metadata."""
         import flatbuffers
 
         try:

ultralytics/nn/autobackend.py

@@ -397,7 +397,7 @@ class AutoBackend(nn.Module):
                 pass
 
         # TFLite or TFLite Edge TPU
-        elif tflite or edgetpu:  # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python
+        elif tflite or edgetpu:  # https://ai.google.dev/edge/litert/microcontrollers/python
             try:  # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu
                 from tflite_runtime.interpreter import Interpreter, load_delegate
             except ImportError:

ultralytics/nn/modules/block.py

@@ -422,7 +422,7 @@ class C3Ghost(C3):
 
 
 class GhostBottleneck(nn.Module):
-    """Ghost Bottleneck https://github.com/huawei-noah/ghostnet."""
+    """Ghost Bottleneck https://github.com/huawei-noah/Efficient-AI-Backbones."""
 
     def __init__(self, c1, c2, k=3, s=1):
         """

ultralytics/nn/modules/conv.py

@@ -337,7 +337,7 @@ class GhostConv(nn.Module):
         cv2 (Conv): Cheap operation convolution.
 
     References:
-        https://github.com/huawei-noah/ghostnet
+        https://github.com/huawei-noah/Efficient-AI-Backbones
     """
 
     def __init__(self, c1, c2, k=1, s=1, g=1, act=True):

ultralytics/solutions/parking_management.py

@@ -58,7 +58,7 @@ class ParkingPtsSelection:
                 "Linux": "sudo apt install python3-tk (Debian/Ubuntu) | sudo dnf install python3-tkinter (Fedora) | "
                 "sudo pacman -S tk (Arch)",
                 "Windows": "reinstall Python and enable the checkbox `tcl/tk and IDLE` on **Optional Features** during installation",
-                "Darwin": "reinstall Python from https://www.python.org/downloads/mac-osx/ or `brew install python-tk`",
+                "Darwin": "reinstall Python from https://www.python.org/downloads/macos/ or `brew install python-tk`",
             }.get(platform.system(), "Unknown OS. Check your Python installation.")
 
             LOGGER.warning(f"WARNING ⚠️  Tkinter is not configured or supported. Potential fix: {install_cmd}")

ultralytics/utils/dist.py

@@ -87,7 +87,7 @@ def generate_ddp_command(world_size, trainer):
         cmd (List[str]): The command to execute for distributed training.
         file (str): Path to the temporary file created for DDP training.
     """
-    import __main__  # noqa local import to avoid https://github.com/Lightning-AI/lightning/issues/15218
+    import __main__  # noqa local import to avoid https://github.com/Lightning-AI/pytorch-lightning/issues/15218
 
     if not trainer.resume:
         shutil.rmtree(trainer.save_dir)  # remove the save_dir

ultralytics/utils/metrics.py

@@ -52,7 +52,7 @@ def bbox_ioa(box1, box2, iou=False, eps=1e-7):
 def box_iou(box1, box2, eps=1e-7):
     """
     Calculate intersection-over-union (IoU) of boxes. Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
-    Based on https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py.
+    Based on https://github.com/pytorch/vision/blob/main/torchvision/ops/boxes.py.
 
     Args:
         box1 (torch.Tensor): A tensor of shape (N, 4) representing N bounding boxes.