yolov5-qat

utils/aws/resume.py

+# Resume all interrupted trainings in yolov5/ dir including DDP trainings
+# Usage: $ python utils/aws/resume.py
+
+import os
+import sys
+from pathlib import Path
+
+import torch
+import yaml
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[2]  # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+
+port = 0  # --master_port
+path = Path("").resolve()
+for last in path.rglob("*/**/last.pt"):
+    ckpt = torch.load(last)
+    if ckpt["optimizer"] is None:
+        continue
+
+    # Load opt.yaml
+    with open(last.parent.parent / "opt.yaml", errors="ignore") as f:
+        opt = yaml.safe_load(f)
+
+    # Get device count
+    d = opt["device"].split(",")  # devices
+    nd = len(d)  # number of devices
+    ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1)  # distributed data parallel
+
+    if ddp:  # multi-GPU
+        port += 1
+        cmd = f"python -m torch.distributed.run --nproc_per_node {nd} --master_port {port} train.py --resume {last}"
+    else:  # single-GPU
+        cmd = f"python train.py --resume {last}"
+
+    cmd += " > /dev/null 2>&1 &"  # redirect output to dev/null and run in daemon thread
+    print(cmd)
+    os.system(cmd)

utils/aws/userdata.sh

+#!/bin/bash
+# AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html
+# This script will run only once on first instance start (for a re-start script see mime.sh)
+# /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir
+# Use >300 GB SSD
+
+cd home/ubuntu
+if [ ! -d yolov5 ]; then
+  echo "Running first-time script." # install dependencies, download COCO, pull Docker
+  git clone https://github.com/ultralytics/yolov5 -b master && sudo chmod -R 777 yolov5
+  cd yolov5
+  bash data/scripts/get_coco.sh && echo "COCO done." &
+  sudo docker pull ultralytics/yolov5:latest && echo "Docker done." &
+  python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." &
+  wait && echo "All tasks done." # finish background tasks
+else
+  echo "Running re-start script." # resume interrupted runs
+  i=0
+  list=$(sudo docker ps -qa) # container list i.e. $'one\ntwo\nthree\nfour'
+  while IFS= read -r id; do
+    ((i++))
+    echo "restarting container $i: $id"
+    sudo docker start $id
+    # sudo docker exec -it $id python train.py --resume # single-GPU
+    sudo docker exec -d $id python utils/aws/resume.py # multi-scenario
+  done <<<"$list"
+fi

utils/callbacks.py

+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+"""Callback utils."""
+
+import threading
+
+
+class Callbacks:
+    """Handles all registered callbacks for YOLOv5 Hooks."""
+
+    def __init__(self):
+        """Initializes a Callbacks object to manage registered YOLOv5 training event hooks."""
+        self._callbacks = {
+            "on_pretrain_routine_start": [],
+            "on_pretrain_routine_end": [],
+            "on_train_start": [],
+            "on_train_epoch_start": [],
+            "on_train_batch_start": [],
+            "optimizer_step": [],
+            "on_before_zero_grad": [],
+            "on_train_batch_end": [],
+            "on_train_epoch_end": [],
+            "on_val_start": [],
+            "on_val_batch_start": [],
+            "on_val_image_end": [],
+            "on_val_batch_end": [],
+            "on_val_end": [],
+            "on_fit_epoch_end": [],  # fit = train + val
+            "on_model_save": [],
+            "on_train_end": [],
+            "on_params_update": [],
+            "teardown": [],
+        }
+        self.stop_training = False  # set True to interrupt training
+
+    def register_action(self, hook, name="", callback=None):
+        """
+        Register a new action to a callback hook.
+
+        Args:
+            hook: The callback hook name to register the action to
+            name: The name of the action for later reference
+            callback: The callback to fire
+        """
+        assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}"
+        assert callable(callback), f"callback '{callback}' is not callable"
+        self._callbacks[hook].append({"name": name, "callback": callback})
+
+    def get_registered_actions(self, hook=None):
+        """
+        Returns all the registered actions by callback hook.
+
+        Args:
+            hook: The name of the hook to check, defaults to all
+        """
+        return self._callbacks[hook] if hook else self._callbacks
+
+    def run(self, hook, *args, thread=False, **kwargs):
+        """
+        Loop through the registered actions and fire all callbacks on main thread.
+
+        Args:
+            hook: The name of the hook to check, defaults to all
+            args: Arguments to receive from YOLOv5
+            thread: (boolean) Run callbacks in daemon thread
+            kwargs: Keyword Arguments to receive from YOLOv5
+        """
+
+        assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}"
+        for logger in self._callbacks[hook]:
+            if thread:
+                threading.Thread(target=logger["callback"], args=args, kwargs=kwargs, daemon=True).start()
+            else:
+                logger["callback"](*args, **kwargs)

utils/dataloaders.py

+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+"""Dataloaders and dataset utils."""
+
+import contextlib
+import glob
+import hashlib
+import json
+import math
+import os
+import random
+import shutil
+import time
+from itertools import repeat
+from multiprocessing.pool import Pool, ThreadPool
+from pathlib import Path
+from threading import Thread
+from urllib.parse import urlparse
+
+import numpy as np
+import psutil
+import torch
+import torch.nn.functional as F
+import torchvision
+import yaml
+from PIL import ExifTags, Image, ImageOps
+from torch.utils.data import DataLoader, Dataset, dataloader, distributed
+from tqdm import tqdm
+
+from utils.augmentations import (
+    Albumentations,
+    augment_hsv,
+    classify_albumentations,
+    classify_transforms,
+    copy_paste,
+    letterbox,
+    mixup,
+    random_perspective,
+)
+from utils.general import (
+    DATASETS_DIR,
+    LOGGER,
+    NUM_THREADS,
+    TQDM_BAR_FORMAT,
+    check_dataset,
+    check_requirements,
+    check_yaml,
+    clean_str,
+    cv2,
+    is_colab,
+    is_kaggle,
+    segments2boxes,
+    unzip_file,
+    xyn2xy,
+    xywh2xyxy,
+    xywhn2xyxy,
+    xyxy2xywhn,
+)
+from utils.torch_utils import torch_distributed_zero_first
+
+# Parameters
+HELP_URL = "See https://docs.ultralytics.com/yolov5/tutorials/train_custom_data"
+IMG_FORMATS = "bmp", "dng", "jpeg", "jpg", "mpo", "png", "tif", "tiff", "webp", "pfm"  # include image suffixes
+VID_FORMATS = "asf", "avi", "gif", "m4v", "mkv", "mov", "mp4", "mpeg", "mpg", "ts", "wmv"  # include video suffixes
+LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1))  # https://pytorch.org/docs/stable/elastic/run.html
+RANK = int(os.getenv("RANK", -1))
+WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1))
+PIN_MEMORY = str(os.getenv("PIN_MEMORY", True)).lower() == "true"  # global pin_memory for dataloaders
+
+# Get orientation exif tag
+for orientation in ExifTags.TAGS.keys():
+    if ExifTags.TAGS[orientation] == "Orientation":
+        break
+
+
+def get_hash(paths):
+    """Generates a single SHA256 hash for a list of file or directory paths by combining their sizes and paths."""
+    size = sum(os.path.getsize(p) for p in paths if os.path.exists(p))  # sizes
+    h = hashlib.sha256(str(size).encode())  # hash sizes
+    h.update("".join(paths).encode())  # hash paths
+    return h.hexdigest()  # return hash
+
+
+def exif_size(img):
+    """Returns corrected PIL image size (width, height) considering EXIF orientation."""
+    s = img.size  # (width, height)
+    with contextlib.suppress(Exception):
+        rotation = dict(img._getexif().items())[orientation]
+        if rotation in [6, 8]:  # rotation 270 or 90
+            s = (s[1], s[0])
+    return s
+
+
+def exif_transpose(image):
+    """
+    Transpose a PIL image accordingly if it has an EXIF Orientation tag.
+    Inplace version of https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py exif_transpose()
+
+    :param image: The image to transpose.
+    :return: An image.
+    """
+    exif = image.getexif()
+    orientation = exif.get(0x0112, 1)  # default 1
+    if orientation > 1:
+        method = {
+            2: Image.FLIP_LEFT_RIGHT,
+            3: Image.ROTATE_180,
+            4: Image.FLIP_TOP_BOTTOM,
+            5: Image.TRANSPOSE,
+            6: Image.ROTATE_270,
+            7: Image.TRANSVERSE,
+            8: Image.ROTATE_90,
+        }.get(orientation)
+        if method is not None:
+            image = image.transpose(method)
+            del exif[0x0112]
+            image.info["exif"] = exif.tobytes()
+    return image
+
+
+def seed_worker(worker_id):
+    """
+    Sets the seed for a dataloader worker to ensure reproducibility, based on PyTorch's randomness notes.
+
+    See https://pytorch.org/docs/stable/notes/randomness.html#dataloader.
+    """
+    worker_seed = torch.initial_seed() % 2**32
+    np.random.seed(worker_seed)
+    random.seed(worker_seed)
+
+
+# Inherit from DistributedSampler and override iterator
+# https://github.com/pytorch/pytorch/blob/master/torch/utils/data/distributed.py
+class SmartDistributedSampler(distributed.DistributedSampler):
+    def __iter__(self):
+        """Yields indices for distributed data sampling, shuffled deterministically based on epoch and seed."""
+        g = torch.Generator()
+        g.manual_seed(self.seed + self.epoch)
+
+        # determine the the eventual size (n) of self.indices (DDP indices)
+        n = int((len(self.dataset) - self.rank - 1) / self.num_replicas) + 1  # num_replicas == WORLD_SIZE
+        idx = torch.randperm(n, generator=g)
+        if not self.shuffle:
+            idx = idx.sort()[0]
+
+        idx = idx.tolist()
+        if self.drop_last:
+            idx = idx[: self.num_samples]
+        else:
+            padding_size = self.num_samples - len(idx)
+            if padding_size <= len(idx):
+                idx += idx[:padding_size]
+            else:
+                idx += (idx * math.ceil(padding_size / len(idx)))[:padding_size]
+
+        return iter(idx)
+
+
+def create_dataloader(
+    path,
+    imgsz,
+    batch_size,
+    stride,
+    single_cls=False,
+    hyp=None,
+    augment=False,
+    cache=False,
+    pad=0.0,
+    rect=False,
+    rank=-1,
+    workers=8,
+    image_weights=False,
+    quad=False,
+    prefix="",
+    shuffle=False,
+    seed=0,
+):
+    if rect and shuffle:
+        LOGGER.warning("WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False")
+        shuffle = False
+    with torch_distributed_zero_first(rank):  # init dataset *.cache only once if DDP
+        dataset = LoadImagesAndLabels(
+            path,
+            imgsz,
+            batch_size,
+            augment=augment,  # augmentation
+            hyp=hyp,  # hyperparameters
+            rect=rect,  # rectangular batches
+            cache_images=cache,
+            single_cls=single_cls,
+            stride=int(stride),
+            pad=pad,
+            image_weights=image_weights,
+            prefix=prefix,
+            rank=rank,
+        )
+
+    batch_size = min(batch_size, len(dataset))
+    nd = torch.cuda.device_count()  # number of CUDA devices
+    nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers])  # number of workers
+    sampler = None if rank == -1 else SmartDistributedSampler(dataset, shuffle=shuffle)
+    loader = DataLoader if image_weights else InfiniteDataLoader  # only DataLoader allows for attribute updates
+    generator = torch.Generator()
+    generator.manual_seed(6148914691236517205 + seed + RANK)
+    return loader(
+        dataset,
+        batch_size=batch_size,
+        shuffle=shuffle and sampler is None,
+        num_workers=nw,
+        sampler=sampler,
+        pin_memory=PIN_MEMORY,
+        collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn,
+        worker_init_fn=seed_worker,
+        generator=generator,
+    ), dataset
+
+
+class InfiniteDataLoader(dataloader.DataLoader):
+    """
+    Dataloader that reuses workers.
+
+    Uses same syntax as vanilla DataLoader
+    """
+
+    def __init__(self, *args, **kwargs):
+        """Initializes an InfiniteDataLoader that reuses workers with standard DataLoader syntax, augmenting with a
+        repeating sampler.
+        """
+        super().__init__(*args, **kwargs)
+        object.__setattr__(self, "batch_sampler", _RepeatSampler(self.batch_sampler))
+        self.iterator = super().__iter__()
+
+    def __len__(self):
+        """Returns the length of the batch sampler's sampler in the InfiniteDataLoader."""
+        return len(self.batch_sampler.sampler)
+
+    def __iter__(self):
+        """Yields batches of data indefinitely in a loop by resetting the sampler when exhausted."""
+        for _ in range(len(self)):
+            yield next(self.iterator)
+
+
+class _RepeatSampler:
+    """
+    Sampler that repeats forever.
+
+    Args:
+        sampler (Sampler)
+    """
+
+    def __init__(self, sampler):
+        """Initializes a perpetual sampler wrapping a provided `Sampler` instance for endless data iteration."""
+        self.sampler = sampler
+
+    def __iter__(self):
+        """Returns an infinite iterator over the dataset by repeatedly yielding from the given sampler."""
+        while True:
+            yield from iter(self.sampler)
+
+
+class LoadScreenshots:
+    # YOLOv5 screenshot dataloader, i.e. `python detect.py --source "screen 0 100 100 512 256"`
+    def __init__(self, source, img_size=640, stride=32, auto=True, transforms=None):
+        """
+        Initializes a screenshot dataloader for YOLOv5 with specified source region, image size, stride, auto, and
+        transforms.
+
+        Source = [screen_number left top width height] (pixels)
+        """
+        check_requirements("mss")
+        import mss
+
+        source, *params = source.split()
+        self.screen, left, top, width, height = 0, None, None, None, None  # default to full screen 0
+        if len(params) == 1:
+            self.screen = int(params[0])
+        elif len(params) == 4:
+            left, top, width, height = (int(x) for x in params)
+        elif len(params) == 5:
+            self.screen, left, top, width, height = (int(x) for x in params)
+        self.img_size = img_size
+        self.stride = stride
+        self.transforms = transforms
+        self.auto = auto
+        self.mode = "stream"
+        self.frame = 0
+        self.sct = mss.mss()
+
+        # Parse monitor shape
+        monitor = self.sct.monitors[self.screen]
+        self.top = monitor["top"] if top is None else (monitor["top"] + top)
+        self.left = monitor["left"] if left is None else (monitor["left"] + left)
+        self.width = width or monitor["width"]
+        self.height = height or monitor["height"]
+        self.monitor = {"left": self.left, "top": self.top, "width": self.width, "height": self.height}
+
+    def __iter__(self):
+        """Iterates over itself, enabling use in loops and iterable contexts."""
+        return self
+
+    def __next__(self):
+        """Captures and returns the next screen frame as a BGR numpy array, cropping to only the first three channels
+        from BGRA.
+        """
+        im0 = np.array(self.sct.grab(self.monitor))[:, :, :3]  # [:, :, :3] BGRA to BGR
+        s = f"screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: "
+
+        if self.transforms:
+            im = self.transforms(im0)  # transforms
+        else:
+            im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0]  # padded resize
+            im = im.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB
+            im = np.ascontiguousarray(im)  # contiguous
+        self.frame += 1
+        return str(self.screen), im, im0, None, s  # screen, img, original img, im0s, s
+
+
+class LoadImages:
+    """YOLOv5 image/video dataloader, i.e. `python detect.py --source image.jpg/vid.mp4`"""
+
+    def __init__(self, path, img_size=640, stride=32, auto=True, transforms=None, vid_stride=1):
+        """Initializes YOLOv5 loader for images/videos, supporting glob patterns, directories, and lists of paths."""
+        if isinstance(path, str) and Path(path).suffix == ".txt":  # *.txt file with img/vid/dir on each line
+            path = Path(path).read_text().rsplit()
+        files = []
+        for p in sorted(path) if isinstance(path, (list, tuple)) else [path]:
+            p = str(Path(p).resolve())
+            if "*" in p:
+                files.extend(sorted(glob.glob(p, recursive=True)))  # glob
+            elif os.path.isdir(p):
+                files.extend(sorted(glob.glob(os.path.join(p, "*.*"))))  # dir
+            elif os.path.isfile(p):
+                files.append(p)  # files
+            else:
+                raise FileNotFoundError(f"{p} does not exist")
+
+        images = [x for x in files if x.split(".")[-1].lower() in IMG_FORMATS]
+        videos = [x for x in files if x.split(".")[-1].lower() in VID_FORMATS]
+        ni, nv = len(images), len(videos)
+
+        self.img_size = img_size
+        self.stride = stride
+        self.files = images + videos
+        self.nf = ni + nv  # number of files
+        self.video_flag = [False] * ni + [True] * nv
+        self.mode = "image"
+        self.auto = auto
+        self.transforms = transforms  # optional
+        self.vid_stride = vid_stride  # video frame-rate stride
+        if any(videos):
+            self._new_video(videos[0])  # new video
+        else:
+            self.cap = None
+        assert self.nf > 0, (
+            f"No images or videos found in {p}. "
+            f"Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {VID_FORMATS}"
+        )
+
+    def __iter__(self):
+        """Initializes iterator by resetting count and returns the iterator object itself."""
+        self.count = 0
+        return self
+
+    def __next__(self):
+        """Advances to the next file in the dataset, raising StopIteration if at the end."""
+        if self.count == self.nf:
+            raise StopIteration
+        path = self.files[self.count]
+
+        if self.video_flag[self.count]:
+            # Read video
+            self.mode = "video"
+            for _ in range(self.vid_stride):
+                self.cap.grab()
+            ret_val, im0 = self.cap.retrieve()
+            while not ret_val:
+                self.count += 1
+                self.cap.release()
+                if self.count == self.nf:  # last video
+                    raise StopIteration
+                path = self.files[self.count]
+                self._new_video(path)
+                ret_val, im0 = self.cap.read()
+
+            self.frame += 1
+            # im0 = self._cv2_rotate(im0)  # for use if cv2 autorotation is False
+            s = f"video {self.count + 1}/{self.nf} ({self.frame}/{self.frames}) {path}: "
+
+        else:
+            # Read image
+            self.count += 1
+            im0 = cv2.imread(path)  # BGR
+            assert im0 is not None, f"Image Not Found {path}"
+            s = f"image {self.count}/{self.nf} {path}: "
+
+        if self.transforms:
+            im = self.transforms(im0)  # transforms
+        else:
+            im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0]  # padded resize
+            im = im.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB
+            im = np.ascontiguousarray(im)  # contiguous
+
+        return path, im, im0, self.cap, s
+
+    def _new_video(self, path):
+        """Initializes a new video capture object with path, frame count adjusted by stride, and orientation
+        metadata.
+        """
+        self.frame = 0
+        self.cap = cv2.VideoCapture(path)
+        self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT) / self.vid_stride)
+        self.orientation = int(self.cap.get(cv2.CAP_PROP_ORIENTATION_META))  # rotation degrees
+        # self.cap.set(cv2.CAP_PROP_ORIENTATION_AUTO, 0)  # disable https://github.com/ultralytics/yolov5/issues/8493
+
+    def _cv2_rotate(self, im):
+        """Rotates a cv2 image based on its orientation; supports 0, 90, and 180 degrees rotations."""
+        if self.orientation == 0:
+            return cv2.rotate(im, cv2.ROTATE_90_CLOCKWISE)
+        elif self.orientation == 180:
+            return cv2.rotate(im, cv2.ROTATE_90_COUNTERCLOCKWISE)
+        elif self.orientation == 90:
+            return cv2.rotate(im, cv2.ROTATE_180)
+        return im
+
+    def __len__(self):
+        """Returns the number of files in the dataset."""
+        return self.nf  # number of files
+
+
+class LoadStreams:
+    # YOLOv5 streamloader, i.e. `python detect.py --source 'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP streams`
+    def __init__(self, sources="file.streams", img_size=640, stride=32, auto=True, transforms=None, vid_stride=1):
+        """Initializes a stream loader for processing video streams with YOLOv5, supporting various sources including
+        YouTube.
+        """
+        torch.backends.cudnn.benchmark = True  # faster for fixed-size inference
+        self.mode = "stream"
+        self.img_size = img_size
+        self.stride = stride
+        self.vid_stride = vid_stride  # video frame-rate stride
+        sources = Path(sources).read_text().rsplit() if os.path.isfile(sources) else [sources]
+        n = len(sources)
+        self.sources = [clean_str(x) for x in sources]  # clean source names for later
+        self.imgs, self.fps, self.frames, self.threads = [None] * n, [0] * n, [0] * n, [None] * n
+        for i, s in enumerate(sources):  # index, source
+            # Start thread to read frames from video stream
+            st = f"{i + 1}/{n}: {s}... "
+            if urlparse(s).hostname in ("www.youtube.com", "youtube.com", "youtu.be"):  # if source is YouTube video
+                # YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/LNwODJXcvt4'
+                check_requirements(("pafy", "youtube_dl==2020.12.2"))
+                import pafy
+
+                s = pafy.new(s).getbest(preftype="mp4").url  # YouTube URL
+            s = eval(s) if s.isnumeric() else s  # i.e. s = '0' local webcam
+            if s == 0:
+                assert not is_colab(), "--source 0 webcam unsupported on Colab. Rerun command in a local environment."
+                assert not is_kaggle(), "--source 0 webcam unsupported on Kaggle. Rerun command in a local environment."
+            cap = cv2.VideoCapture(s)
+            assert cap.isOpened(), f"{st}Failed to open {s}"
+            w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+            h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+            fps = cap.get(cv2.CAP_PROP_FPS)  # warning: may return 0 or nan
+            self.frames[i] = max(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)), 0) or float("inf")  # infinite stream fallback
+            self.fps[i] = max((fps if math.isfinite(fps) else 0) % 100, 0) or 30  # 30 FPS fallback
+
+            _, self.imgs[i] = cap.read()  # guarantee first frame
+            self.threads[i] = Thread(target=self.update, args=([i, cap, s]), daemon=True)
+            LOGGER.info(f"{st} Success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)")
+            self.threads[i].start()
+        LOGGER.info("")  # newline
+
+        # check for common shapes
+        s = np.stack([letterbox(x, img_size, stride=stride, auto=auto)[0].shape for x in self.imgs])
+        self.rect = np.unique(s, axis=0).shape[0] == 1  # rect inference if all shapes equal
+        self.auto = auto and self.rect
+        self.transforms = transforms  # optional
+        if not self.rect:
+            LOGGER.warning("WARNING ⚠️ Stream shapes differ. For optimal performance supply similarly-shaped streams.")
+
+    def update(self, i, cap, stream):
+        """Reads frames from stream `i`, updating imgs array; handles stream reopening on signal loss."""
+        n, f = 0, self.frames[i]  # frame number, frame array
+        while cap.isOpened() and n < f:
+            n += 1
+            cap.grab()  # .read() = .grab() followed by .retrieve()
+            if n % self.vid_stride == 0:
+                success, im = cap.retrieve()
+                if success:
+                    self.imgs[i] = im
+                else:
+                    LOGGER.warning("WARNING ⚠️ Video stream unresponsive, please check your IP camera connection.")
+                    self.imgs[i] = np.zeros_like(self.imgs[i])
+                    cap.open(stream)  # re-open stream if signal was lost
+            time.sleep(0.0)  # wait time
+
+    def __iter__(self):
+        """Resets and returns the iterator for iterating over video frames or images in a dataset."""
+        self.count = -1
+        return self
+
+    def __next__(self):
+        """Iterates over video frames or images, halting on thread stop or 'q' key press, raising `StopIteration` when
+        done.
+        """
+        self.count += 1
+        if not all(x.is_alive() for x in self.threads) or cv2.waitKey(1) == ord("q"):  # q to quit
+            cv2.destroyAllWindows()
+            raise StopIteration
+
+        im0 = self.imgs.copy()
+        if self.transforms:
+            im = np.stack([self.transforms(x) for x in im0])  # transforms
+        else:
+            im = np.stack([letterbox(x, self.img_size, stride=self.stride, auto=self.auto)[0] for x in im0])  # resize
+            im = im[..., ::-1].transpose((0, 3, 1, 2))  # BGR to RGB, BHWC to BCHW
+            im = np.ascontiguousarray(im)  # contiguous
+
+        return self.sources, im, im0, None, ""
+
+    def __len__(self):
+        """Returns the number of sources in the dataset, supporting up to 32 streams at 30 FPS over 30 years."""
+        return len(self.sources)  # 1E12 frames = 32 streams at 30 FPS for 30 years
+
+
+def img2label_paths(img_paths):
+    """Generates label file paths from corresponding image file paths by replacing `/images/` with `/labels/` and
+    extension with `.txt`.
+    """
+    sa, sb = f"{os.sep}images{os.sep}", f"{os.sep}labels{os.sep}"  # /images/, /labels/ substrings
+    return [sb.join(x.rsplit(sa, 1)).rsplit(".", 1)[0] + ".txt" for x in img_paths]
+
+
+class LoadImagesAndLabels(Dataset):
+    # YOLOv5 train_loader/val_loader, loads images and labels for training and validation
+    cache_version = 0.6  # dataset labels *.cache version
+    rand_interp_methods = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4]
+
+    def __init__(
+        self,
+        path,
+        img_size=640,
+        batch_size=16,
+        augment=False,
+        hyp=None,
+        rect=False,
+        image_weights=False,
+        cache_images=False,
+        single_cls=False,
+        stride=32,
+        pad=0.0,
+        min_items=0,
+        prefix="",
+        rank=-1,
+        seed=0,
+    ):
+        self.img_size = img_size
+        self.augment = augment
+        self.hyp = hyp
+        self.image_weights = image_weights
+        self.rect = False if image_weights else rect
+        self.mosaic = self.augment and not self.rect  # load 4 images at a time into a mosaic (only during training)
+        self.mosaic_border = [-img_size // 2, -img_size // 2]
+        self.stride = stride
+        self.path = path
+        self.albumentations = Albumentations(size=img_size) if augment else None
+
+        try:
+            f = []  # image files
+            for p in path if isinstance(path, list) else [path]:
+                p = Path(p)  # os-agnostic
+                if p.is_dir():  # dir
+                    f += glob.glob(str(p / "**" / "*.*"), recursive=True)
+                    # f = list(p.rglob('*.*'))  # pathlib
+                elif p.is_file():  # file
+                    with open(p) as t:
+                        t = t.read().strip().splitlines()
+                        parent = str(p.parent) + os.sep
+                        f += [x.replace("./", parent, 1) if x.startswith("./") else x for x in t]  # to global path
+                        # f += [p.parent / x.lstrip(os.sep) for x in t]  # to global path (pathlib)
+                else:
+                    raise FileNotFoundError(f"{prefix}{p} does not exist")
+            self.im_files = sorted(x.replace("/", os.sep) for x in f if x.split(".")[-1].lower() in IMG_FORMATS)
+            # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in IMG_FORMATS])  # pathlib
+            assert self.im_files, f"{prefix}No images found"
+        except Exception as e:
+            raise Exception(f"{prefix}Error loading data from {path}: {e}\n{HELP_URL}") from e
+
+        # Check cache
+        self.label_files = img2label_paths(self.im_files)  # labels
+        cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix(".cache")
+        try:
+            cache, exists = np.load(cache_path, allow_pickle=True).item(), True  # load dict
+            assert cache["version"] == self.cache_version  # matches current version
+            assert cache["hash"] == get_hash(self.label_files + self.im_files)  # identical hash
+        except Exception:
+            cache, exists = self.cache_labels(cache_path, prefix), False  # run cache ops
+
+        # Display cache
+        nf, nm, ne, nc, n = cache.pop("results")  # found, missing, empty, corrupt, total
+        if exists and LOCAL_RANK in {-1, 0}:
+            d = f"Scanning {cache_path}... {nf} images, {nm + ne} backgrounds, {nc} corrupt"
+            tqdm(None, desc=prefix + d, total=n, initial=n, bar_format=TQDM_BAR_FORMAT)  # display cache results
+            if cache["msgs"]:
+                LOGGER.info("\n".join(cache["msgs"]))  # display warnings
+        assert nf > 0 or not augment, f"{prefix}No labels found in {cache_path}, can not start training. {HELP_URL}"
+
+        # Read cache
+        [cache.pop(k) for k in ("hash", "version", "msgs")]  # remove items
+        labels, shapes, self.segments = zip(*cache.values())
+        nl = len(np.concatenate(labels, 0))  # number of labels
+        assert nl > 0 or not augment, f"{prefix}All labels empty in {cache_path}, can not start training. {HELP_URL}"
+        self.labels = list(labels)
+        self.shapes = np.array(shapes)
+        self.im_files = list(cache.keys())  # update
+        self.label_files = img2label_paths(cache.keys())  # update
+
+        # Filter images
+        if min_items:
+            include = np.array([len(x) >= min_items for x in self.labels]).nonzero()[0].astype(int)
+            LOGGER.info(f"{prefix}{n - len(include)}/{n} images filtered from dataset")
+            self.im_files = [self.im_files[i] for i in include]
+            self.label_files = [self.label_files[i] for i in include]
+            self.labels = [self.labels[i] for i in include]
+            self.segments = [self.segments[i] for i in include]
+            self.shapes = self.shapes[include]  # wh
+
+        # Create indices
+        n = len(self.shapes)  # number of images
+        bi = np.floor(np.arange(n) / batch_size).astype(int)  # batch index
+        nb = bi[-1] + 1  # number of batches
+        self.batch = bi  # batch index of image
+        self.n = n
+        self.indices = np.arange(n)
+        if rank > -1:  # DDP indices (see: SmartDistributedSampler)
+            # force each rank (i.e. GPU process) to sample the same subset of data on every epoch
+            self.indices = self.indices[np.random.RandomState(seed=seed).permutation(n) % WORLD_SIZE == RANK]
+
+        # Update labels
+        include_class = []  # filter labels to include only these classes (optional)
+        self.segments = list(self.segments)
+        include_class_array = np.array(include_class).reshape(1, -1)
+        for i, (label, segment) in enumerate(zip(self.labels, self.segments)):
+            if include_class:
+                j = (label[:, 0:1] == include_class_array).any(1)
+                self.labels[i] = label[j]
+                if segment:
+                    self.segments[i] = [segment[idx] for idx, elem in enumerate(j) if elem]
+            if single_cls:  # single-class training, merge all classes into 0
+                self.labels[i][:, 0] = 0
+
+        # Rectangular Training
+        if self.rect:
+            # Sort by aspect ratio
+            s = self.shapes  # wh
+            ar = s[:, 1] / s[:, 0]  # aspect ratio
+            irect = ar.argsort()
+            self.im_files = [self.im_files[i] for i in irect]
+            self.label_files = [self.label_files[i] for i in irect]
+            self.labels = [self.labels[i] for i in irect]
+            self.segments = [self.segments[i] for i in irect]
+            self.shapes = s[irect]  # wh
+            ar = ar[irect]
+
+            # Set training image shapes
+            shapes = [[1, 1]] * nb
+            for i in range(nb):
+                ari = ar[bi == i]
+                mini, maxi = ari.min(), ari.max()
+                if maxi < 1:
+                    shapes[i] = [maxi, 1]
+                elif mini > 1:
+                    shapes[i] = [1, 1 / mini]
+
+            self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(int) * stride
+
+        # Cache images into RAM/disk for faster training
+        if cache_images == "ram" and not self.check_cache_ram(prefix=prefix):
+            cache_images = False
+        self.ims = [None] * n
+        self.npy_files = [Path(f).with_suffix(".npy") for f in self.im_files]
+        if cache_images:
+            b, gb = 0, 1 << 30  # bytes of cached images, bytes per gigabytes
+            self.im_hw0, self.im_hw = [None] * n, [None] * n
+            fcn = self.cache_images_to_disk if cache_images == "disk" else self.load_image
+            results = ThreadPool(NUM_THREADS).imap(lambda i: (i, fcn(i)), self.indices)
+            pbar = tqdm(results, total=len(self.indices), bar_format=TQDM_BAR_FORMAT, disable=LOCAL_RANK > 0)
+            for i, x in pbar:
+                if cache_images == "disk":
+                    b += self.npy_files[i].stat().st_size
+                else:  # 'ram'
+                    self.ims[i], self.im_hw0[i], self.im_hw[i] = x  # im, hw_orig, hw_resized = load_image(self, i)
+                    b += self.ims[i].nbytes * WORLD_SIZE
+                pbar.desc = f"{prefix}Caching images ({b / gb:.1f}GB {cache_images})"
+            pbar.close()
+
+    def check_cache_ram(self, safety_margin=0.1, prefix=""):
+        """Checks if available RAM is sufficient for caching images, adjusting for a safety margin."""
+        b, gb = 0, 1 << 30  # bytes of cached images, bytes per gigabytes
+        n = min(self.n, 30)  # extrapolate from 30 random images
+        for _ in range(n):
+            im = cv2.imread(random.choice(self.im_files))  # sample image
+            ratio = self.img_size / max(im.shape[0], im.shape[1])  # max(h, w)  # ratio
+            b += im.nbytes * ratio**2
+        mem_required = b * self.n / n  # GB required to cache dataset into RAM
+        mem = psutil.virtual_memory()
+        cache = mem_required * (1 + safety_margin) < mem.available  # to cache or not to cache, that is the question
+        if not cache:
+            LOGGER.info(
+                f'{prefix}{mem_required / gb:.1f}GB RAM required, '
+                f'{mem.available / gb:.1f}/{mem.total / gb:.1f}GB available, '
+                f"{'caching images ✅' if cache else 'not caching images ⚠️'}"
+            )
+        return cache
+
+    def cache_labels(self, path=Path("./labels.cache"), prefix=""):
+        """Caches dataset labels, verifies images, reads shapes, and tracks dataset integrity."""
+        x = {}  # dict
+        nm, nf, ne, nc, msgs = 0, 0, 0, 0, []  # number missing, found, empty, corrupt, messages
+        desc = f"{prefix}Scanning {path.parent / path.stem}..."
+        with Pool(NUM_THREADS) as pool:
+            pbar = tqdm(
+                pool.imap(verify_image_label, zip(self.im_files, self.label_files, repeat(prefix))),
+                desc=desc,
+                total=len(self.im_files),
+                bar_format=TQDM_BAR_FORMAT,
+            )
+            for im_file, lb, shape, segments, nm_f, nf_f, ne_f, nc_f, msg in pbar:
+                nm += nm_f
+                nf += nf_f
+                ne += ne_f
+                nc += nc_f
+                if im_file:
+                    x[im_file] = [lb, shape, segments]
+                if msg:
+                    msgs.append(msg)
+                pbar.desc = f"{desc} {nf} images, {nm + ne} backgrounds, {nc} corrupt"
+
+        pbar.close()
+        if msgs:
+            LOGGER.info("\n".join(msgs))
+        if nf == 0:
+            LOGGER.warning(f"{prefix}WARNING ⚠️ No labels found in {path}. {HELP_URL}")
+        x["hash"] = get_hash(self.label_files + self.im_files)
+        x["results"] = nf, nm, ne, nc, len(self.im_files)
+        x["msgs"] = msgs  # warnings
+        x["version"] = self.cache_version  # cache version
+        try:
+            np.save(path, x)  # save cache for next time
+            path.with_suffix(".cache.npy").rename(path)  # remove .npy suffix
+            LOGGER.info(f"{prefix}New cache created: {path}")
+        except Exception as e:
+            LOGGER.warning(f"{prefix}WARNING ⚠️ Cache directory {path.parent} is not writeable: {e}")  # not writeable
+        return x
+
+    def __len__(self):
+        """Returns the number of images in the dataset."""
+        return len(self.im_files)
+
+    # def __iter__(self):
+    #     self.count = -1
+    #     print('ran dataset iter')
+    #     #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF)
+    #     return self
+
+    def __getitem__(self, index):
+        """Fetches the dataset item at the given index, considering linear, shuffled, or weighted sampling."""
+        index = self.indices[index]  # linear, shuffled, or image_weights
+
+        hyp = self.hyp
+        mosaic = self.mosaic and random.random() < hyp["mosaic"]
+        if mosaic:
+            # Load mosaic
+            img, labels = self.load_mosaic(index)
+            shapes = None
+
+            # MixUp augmentation
+            if random.random() < hyp["mixup"]:
+                img, labels = mixup(img, labels, *self.load_mosaic(random.choice(self.indices)))
+
+        else:
+            # Load image
+            img, (h0, w0), (h, w) = self.load_image(index)
+
+            # Letterbox
+            shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size  # final letterboxed shape
+            img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment)
+            shapes = (h0, w0), ((h / h0, w / w0), pad)  # for COCO mAP rescaling
+
+            labels = self.labels[index].copy()
+            if labels.size:  # normalized xywh to pixel xyxy format
+                labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1])
+
+            if self.augment:
+                img, labels = random_perspective(
+                    img,
+                    labels,
+                    degrees=hyp["degrees"],
+                    translate=hyp["translate"],
+                    scale=hyp["scale"],
+                    shear=hyp["shear"],
+                    perspective=hyp["perspective"],
+                )
+
+        nl = len(labels)  # number of labels
+        if nl:
+            labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1e-3)
+
+        if self.augment:
+            # Albumentations
+            img, labels = self.albumentations(img, labels)
+            nl = len(labels)  # update after albumentations
+
+            # HSV color-space
+            augment_hsv(img, hgain=hyp["hsv_h"], sgain=hyp["hsv_s"], vgain=hyp["hsv_v"])
+
+            # Flip up-down
+            if random.random() < hyp["flipud"]:
+                img = np.flipud(img)
+                if nl:
+                    labels[:, 2] = 1 - labels[:, 2]
+
+            # Flip left-right
+            if random.random() < hyp["fliplr"]:
+                img = np.fliplr(img)
+                if nl:
+                    labels[:, 1] = 1 - labels[:, 1]
+
+            # Cutouts
+            # labels = cutout(img, labels, p=0.5)
+            # nl = len(labels)  # update after cutout
+
+        labels_out = torch.zeros((nl, 6))
+        if nl:
+            labels_out[:, 1:] = torch.from_numpy(labels)
+
+        # Convert
+        img = img.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB
+        img = np.ascontiguousarray(img)
+
+        return torch.from_numpy(img), labels_out, self.im_files[index], shapes
+
+    def load_image(self, i):
+        """
+        Loads an image by index, returning the image, its original dimensions, and resized dimensions.
+
+        Returns (im, original hw, resized hw)
+        """
+        im, f, fn = (
+            self.ims[i],
+            self.im_files[i],
+            self.npy_files[i],
+        )
+        if im is None:  # not cached in RAM
+            if fn.exists():  # load npy
+                im = np.load(fn)
+            else:  # read image
+                im = cv2.imread(f)  # BGR
+                assert im is not None, f"Image Not Found {f}"
+            h0, w0 = im.shape[:2]  # orig hw
+            r = self.img_size / max(h0, w0)  # ratio
+            if r != 1:  # if sizes are not equal
+                interp = cv2.INTER_LINEAR if (self.augment or r > 1) else cv2.INTER_AREA
+                im = cv2.resize(im, (math.ceil(w0 * r), math.ceil(h0 * r)), interpolation=interp)
+            return im, (h0, w0), im.shape[:2]  # im, hw_original, hw_resized
+        return self.ims[i], self.im_hw0[i], self.im_hw[i]  # im, hw_original, hw_resized
+
+    def cache_images_to_disk(self, i):
+        """Saves an image to disk as an *.npy file for quicker loading, identified by index `i`."""
+        f = self.npy_files[i]
+        if not f.exists():
+            np.save(f.as_posix(), cv2.imread(self.im_files[i]))
+
+    def load_mosaic(self, index):
+        """Loads a 4-image mosaic for YOLOv5, combining 1 selected and 3 random images, with labels and segments."""
+        labels4, segments4 = [], []
+        s = self.img_size
+        yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border)  # mosaic center x, y
+        indices = [index] + random.choices(self.indices, k=3)  # 3 additional image indices
+        random.shuffle(indices)
+        for i, index in enumerate(indices):
+            # Load image
+            img, _, (h, w) = self.load_image(index)
+
+            # place img in img4
+            if i == 0:  # top left
+                img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8)  # base image with 4 tiles
+                x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc  # xmin, ymin, xmax, ymax (large image)
+                x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h  # xmin, ymin, xmax, ymax (small image)
+            elif i == 1:  # top right
+                x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc
+                x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h
+            elif i == 2:  # bottom left
+                x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h)
+                x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h)
+            elif i == 3:  # bottom right
+                x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h)
+                x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h)
+
+            img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b]  # img4[ymin:ymax, xmin:xmax]
+            padw = x1a - x1b
+            padh = y1a - y1b
+
+            # Labels
+            labels, segments = self.labels[index].copy(), self.segments[index].copy()
+            if labels.size:
+                labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh)  # normalized xywh to pixel xyxy format
+                segments = [xyn2xy(x, w, h, padw, padh) for x in segments]
+            labels4.append(labels)
+            segments4.extend(segments)
+
+        # Concat/clip labels
+        labels4 = np.concatenate(labels4, 0)
+        for x in (labels4[:, 1:], *segments4):
+            np.clip(x, 0, 2 * s, out=x)  # clip when using random_perspective()
+        # img4, labels4 = replicate(img4, labels4)  # replicate
+
+        # Augment
+        img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp["copy_paste"])
+        img4, labels4 = random_perspective(
+            img4,
+            labels4,
+            segments4,
+            degrees=self.hyp["degrees"],
+            translate=self.hyp["translate"],
+            scale=self.hyp["scale"],
+            shear=self.hyp["shear"],
+            perspective=self.hyp["perspective"],
+            border=self.mosaic_border,
+        )  # border to remove
+
+        return img4, labels4
+
+    def load_mosaic9(self, index):
+        """Loads 1 image + 8 random images into a 9-image mosaic for augmented YOLOv5 training, returning labels and
+        segments.
+        """
+        labels9, segments9 = [], []
+        s = self.img_size
+        indices = [index] + random.choices(self.indices, k=8)  # 8 additional image indices
+        random.shuffle(indices)
+        hp, wp = -1, -1  # height, width previous
+        for i, index in enumerate(indices):
+            # Load image
+            img, _, (h, w) = self.load_image(index)
+
+            # place img in img9
+            if i == 0:  # center
+                img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8)  # base image with 4 tiles
+                h0, w0 = h, w
+                c = s, s, s + w, s + h  # xmin, ymin, xmax, ymax (base) coordinates
+            elif i == 1:  # top
+                c = s, s - h, s + w, s
+            elif i == 2:  # top right
+                c = s + wp, s - h, s + wp + w, s
+            elif i == 3:  # right
+                c = s + w0, s, s + w0 + w, s + h
+            elif i == 4:  # bottom right
+                c = s + w0, s + hp, s + w0 + w, s + hp + h
+            elif i == 5:  # bottom
+                c = s + w0 - w, s + h0, s + w0, s + h0 + h
+            elif i == 6:  # bottom left
+                c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h
+            elif i == 7:  # left
+                c = s - w, s + h0 - h, s, s + h0
+            elif i == 8:  # top left
+                c = s - w, s + h0 - hp - h, s, s + h0 - hp
+
+            padx, pady = c[:2]
+            x1, y1, x2, y2 = (max(x, 0) for x in c)  # allocate coords
+
+            # Labels
+            labels, segments = self.labels[index].copy(), self.segments[index].copy()
+            if labels.size:
+                labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padx, pady)  # normalized xywh to pixel xyxy format
+                segments = [xyn2xy(x, w, h, padx, pady) for x in segments]
+            labels9.append(labels)
+            segments9.extend(segments)
+
+            # Image
+            img9[y1:y2, x1:x2] = img[y1 - pady :, x1 - padx :]  # img9[ymin:ymax, xmin:xmax]
+            hp, wp = h, w  # height, width previous
+
+        # Offset
+        yc, xc = (int(random.uniform(0, s)) for _ in self.mosaic_border)  # mosaic center x, y
+        img9 = img9[yc : yc + 2 * s, xc : xc + 2 * s]
+
+        # Concat/clip labels
+        labels9 = np.concatenate(labels9, 0)
+        labels9[:, [1, 3]] -= xc
+        labels9[:, [2, 4]] -= yc
+        c = np.array([xc, yc])  # centers
+        segments9 = [x - c for x in segments9]
+
+        for x in (labels9[:, 1:], *segments9):
+            np.clip(x, 0, 2 * s, out=x)  # clip when using random_perspective()
+        # img9, labels9 = replicate(img9, labels9)  # replicate
+
+        # Augment
+        img9, labels9, segments9 = copy_paste(img9, labels9, segments9, p=self.hyp["copy_paste"])
+        img9, labels9 = random_perspective(
+            img9,
+            labels9,
+            segments9,
+            degrees=self.hyp["degrees"],
+            translate=self.hyp["translate"],
+            scale=self.hyp["scale"],
+            shear=self.hyp["shear"],
+            perspective=self.hyp["perspective"],
+            border=self.mosaic_border,
+        )  # border to remove
+
+        return img9, labels9
+
+    @staticmethod
+    def collate_fn(batch):
+        """Batches images, labels, paths, and shapes, assigning unique indices to targets in merged label tensor."""
+        im, label, path, shapes = zip(*batch)  # transposed
+        for i, lb in enumerate(label):
+            lb[:, 0] = i  # add target image index for build_targets()
+        return torch.stack(im, 0), torch.cat(label, 0), path, shapes
+
+    @staticmethod
+    def collate_fn4(batch):
+        """Bundles a batch's data by quartering the number of shapes and paths, preparing it for model input."""
+        im, label, path, shapes = zip(*batch)  # transposed
+        n = len(shapes) // 4
+        im4, label4, path4, shapes4 = [], [], path[:n], shapes[:n]
+
+        ho = torch.tensor([[0.0, 0, 0, 1, 0, 0]])
+        wo = torch.tensor([[0.0, 0, 1, 0, 0, 0]])
+        s = torch.tensor([[1, 1, 0.5, 0.5, 0.5, 0.5]])  # scale
+        for i in range(n):  # zidane torch.zeros(16,3,720,1280)  # BCHW
+            i *= 4
+            if random.random() < 0.5:
+                im1 = F.interpolate(im[i].unsqueeze(0).float(), scale_factor=2.0, mode="bilinear", align_corners=False)[
+                    0
+                ].type(im[i].type())
+                lb = label[i]
+            else:
+                im1 = torch.cat((torch.cat((im[i], im[i + 1]), 1), torch.cat((im[i + 2], im[i + 3]), 1)), 2)
+                lb = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s
+            im4.append(im1)
+            label4.append(lb)
+
+        for i, lb in enumerate(label4):
+            lb[:, 0] = i  # add target image index for build_targets()
+
+        return torch.stack(im4, 0), torch.cat(label4, 0), path4, shapes4
+
+
+# Ancillary functions --------------------------------------------------------------------------------------------------
+def flatten_recursive(path=DATASETS_DIR / "coco128"):
+    """Flattens a directory by copying all files from subdirectories to a new top-level directory, preserving
+    filenames.
+    """
+    new_path = Path(f"{str(path)}_flat")
+    if os.path.exists(new_path):
+        shutil.rmtree(new_path)  # delete output folder
+    os.makedirs(new_path)  # make new output folder
+    for file in tqdm(glob.glob(f"{str(Path(path))}/**/*.*", recursive=True)):
+        shutil.copyfile(file, new_path / Path(file).name)
+
+
+def extract_boxes(path=DATASETS_DIR / "coco128"):
+    """
+    Converts a detection dataset to a classification dataset, creating a directory for each class and extracting
+    bounding boxes.
+
+    Example: from utils.dataloaders import *; extract_boxes()
+    """
+    path = Path(path)  # images dir
+    shutil.rmtree(path / "classification") if (path / "classification").is_dir() else None  # remove existing
+    files = list(path.rglob("*.*"))
+    n = len(files)  # number of files
+    for im_file in tqdm(files, total=n):
+        if im_file.suffix[1:] in IMG_FORMATS:
+            # image
+            im = cv2.imread(str(im_file))[..., ::-1]  # BGR to RGB
+            h, w = im.shape[:2]
+
+            # labels
+            lb_file = Path(img2label_paths([str(im_file)])[0])
+            if Path(lb_file).exists():
+                with open(lb_file) as f:
+                    lb = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32)  # labels
+
+                for j, x in enumerate(lb):
+                    c = int(x[0])  # class
+                    f = (path / "classification") / f"{c}" / f"{path.stem}_{im_file.stem}_{j}.jpg"  # new filename
+                    if not f.parent.is_dir():
+                        f.parent.mkdir(parents=True)
+
+                    b = x[1:] * [w, h, w, h]  # box
+                    # b[2:] = b[2:].max()  # rectangle to square
+                    b[2:] = b[2:] * 1.2 + 3  # pad
+                    b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(int)
+
+                    b[[0, 2]] = np.clip(b[[0, 2]], 0, w)  # clip boxes outside of image
+                    b[[1, 3]] = np.clip(b[[1, 3]], 0, h)
+                    assert cv2.imwrite(str(f), im[b[1] : b[3], b[0] : b[2]]), f"box failure in {f}"
+
+
+def autosplit(path=DATASETS_DIR / "coco128/images", weights=(0.9, 0.1, 0.0), annotated_only=False):
+    """Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files
+    Usage: from utils.dataloaders import *; autosplit()
+    Arguments
+        path:            Path to images directory
+        weights:         Train, val, test weights (list, tuple)
+        annotated_only:  Only use images with an annotated txt file
+    """
+    path = Path(path)  # images dir
+    files = sorted(x for x in path.rglob("*.*") if x.suffix[1:].lower() in IMG_FORMATS)  # image files only
+    n = len(files)  # number of files
+    random.seed(0)  # for reproducibility
+    indices = random.choices([0, 1, 2], weights=weights, k=n)  # assign each image to a split
+
+    txt = ["autosplit_train.txt", "autosplit_val.txt", "autosplit_test.txt"]  # 3 txt files
+    for x in txt:
+        if (path.parent / x).exists():
+            (path.parent / x).unlink()  # remove existing
+
+    print(f"Autosplitting images from {path}" + ", using *.txt labeled images only" * annotated_only)
+    for i, img in tqdm(zip(indices, files), total=n):
+        if not annotated_only or Path(img2label_paths([str(img)])[0]).exists():  # check label
+            with open(path.parent / txt[i], "a") as f:
+                f.write(f"./{img.relative_to(path.parent).as_posix()}" + "\n")  # add image to txt file
+
+
+def verify_image_label(args):
+    """Verifies a single image-label pair, ensuring image format, size, and legal label values."""
+    im_file, lb_file, prefix = args
+    nm, nf, ne, nc, msg, segments = 0, 0, 0, 0, "", []  # number (missing, found, empty, corrupt), message, segments
+    try:
+        # verify images
+        im = Image.open(im_file)
+        im.verify()  # PIL verify
+        shape = exif_size(im)  # image size
+        assert (shape[0] > 9) & (shape[1] > 9), f"image size {shape} <10 pixels"
+        assert im.format.lower() in IMG_FORMATS, f"invalid image format {im.format}"
+        if im.format.lower() in ("jpg", "jpeg"):
+            with open(im_file, "rb") as f:
+                f.seek(-2, 2)
+                if f.read() != b"\xff\xd9":  # corrupt JPEG
+                    ImageOps.exif_transpose(Image.open(im_file)).save(im_file, "JPEG", subsampling=0, quality=100)
+                    msg = f"{prefix}WARNING ⚠️ {im_file}: corrupt JPEG restored and saved"
+
+        # verify labels
+        if os.path.isfile(lb_file):
+            nf = 1  # label found
+            with open(lb_file) as f:
+                lb = [x.split() for x in f.read().strip().splitlines() if len(x)]
+                if any(len(x) > 6 for x in lb):  # is segment
+                    classes = np.array([x[0] for x in lb], dtype=np.float32)
+                    segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in lb]  # (cls, xy1...)
+                    lb = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1)  # (cls, xywh)
+                lb = np.array(lb, dtype=np.float32)
+            nl = len(lb)
+            if nl:
+                assert lb.shape[1] == 5, f"labels require 5 columns, {lb.shape[1]} columns detected"
+                assert (lb >= 0).all(), f"negative label values {lb[lb < 0]}"
+                assert (lb[:, 1:] <= 1).all(), f"non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}"
+                _, i = np.unique(lb, axis=0, return_index=True)
+                if len(i) < nl:  # duplicate row check
+                    lb = lb[i]  # remove duplicates
+                    if segments:
+                        segments = [segments[x] for x in i]
+                    msg = f"{prefix}WARNING ⚠️ {im_file}: {nl - len(i)} duplicate labels removed"
+            else:
+                ne = 1  # label empty
+                lb = np.zeros((0, 5), dtype=np.float32)
+        else:
+            nm = 1  # label missing
+            lb = np.zeros((0, 5), dtype=np.float32)
+        return im_file, lb, shape, segments, nm, nf, ne, nc, msg
+    except Exception as e:
+        nc = 1
+        msg = f"{prefix}WARNING ⚠️ {im_file}: ignoring corrupt image/label: {e}"
+        return [None, None, None, None, nm, nf, ne, nc, msg]
+
+
+class HUBDatasetStats:
+    """
+    Class for generating HUB dataset JSON and `-hub` dataset directory.
+
+    Arguments
+        path:           Path to data.yaml or data.zip (with data.yaml inside data.zip)
+        autodownload:   Attempt to download dataset if not found locally
+
+    Usage
+        from utils.dataloaders import HUBDatasetStats
+        stats = HUBDatasetStats('coco128.yaml', autodownload=True)  # usage 1
+        stats = HUBDatasetStats('path/to/coco128.zip')  # usage 2
+        stats.get_json(save=False)
+        stats.process_images()
+    """
+
+    def __init__(self, path="coco128.yaml", autodownload=False):
+        """Initializes HUBDatasetStats with optional auto-download for datasets, given a path to dataset YAML or ZIP
+        file.
+        """
+        zipped, data_dir, yaml_path = self._unzip(Path(path))
+        try:
+            with open(check_yaml(yaml_path), errors="ignore") as f:
+                data = yaml.safe_load(f)  # data dict
+                if zipped:
+                    data["path"] = data_dir
+        except Exception as e:
+            raise Exception("error/HUB/dataset_stats/yaml_load") from e
+
+        check_dataset(data, autodownload)  # download dataset if missing
+        self.hub_dir = Path(data["path"] + "-hub")
+        self.im_dir = self.hub_dir / "images"
+        self.im_dir.mkdir(parents=True, exist_ok=True)  # makes /images
+        self.stats = {"nc": data["nc"], "names": list(data["names"].values())}  # statistics dictionary
+        self.data = data
+
+    @staticmethod
+    def _find_yaml(dir):
+        """Finds and returns the path to a single '.yaml' file in the specified directory, preferring files that match
+        the directory name.
+        """
+        files = list(dir.glob("*.yaml")) or list(dir.rglob("*.yaml"))  # try root level first and then recursive
+        assert files, f"No *.yaml file found in {dir}"
+        if len(files) > 1:
+            files = [f for f in files if f.stem == dir.stem]  # prefer *.yaml files that match dir name
+            assert files, f"Multiple *.yaml files found in {dir}, only 1 *.yaml file allowed"
+        assert len(files) == 1, f"Multiple *.yaml files found: {files}, only 1 *.yaml file allowed in {dir}"
+        return files[0]
+
+    def _unzip(self, path):
+        """Unzips a .zip file at 'path', returning success status, unzipped directory, and path to YAML file within."""
+        if not str(path).endswith(".zip"):  # path is data.yaml
+            return False, None, path
+        assert Path(path).is_file(), f"Error unzipping {path}, file not found"
+        unzip_file(path, path=path.parent)
+        dir = path.with_suffix("")  # dataset directory == zip name
+        assert dir.is_dir(), f"Error unzipping {path}, {dir} not found. path/to/abc.zip MUST unzip to path/to/abc/"
+        return True, str(dir), self._find_yaml(dir)  # zipped, data_dir, yaml_path
+
+    def _hub_ops(self, f, max_dim=1920):
+        """Resizes and saves an image at reduced quality for web/app viewing, supporting both PIL and OpenCV."""
+        f_new = self.im_dir / Path(f).name  # dataset-hub image filename
+        try:  # use PIL
+            im = Image.open(f)
+            r = max_dim / max(im.height, im.width)  # ratio
+            if r < 1.0:  # image too large
+                im = im.resize((int(im.width * r), int(im.height * r)))
+            im.save(f_new, "JPEG", quality=50, optimize=True)  # save
+        except Exception as e:  # use OpenCV
+            LOGGER.info(f"WARNING ⚠️ HUB ops PIL failure {f}: {e}")
+            im = cv2.imread(f)
+            im_height, im_width = im.shape[:2]
+            r = max_dim / max(im_height, im_width)  # ratio
+            if r < 1.0:  # image too large
+                im = cv2.resize(im, (int(im_width * r), int(im_height * r)), interpolation=cv2.INTER_AREA)
+            cv2.imwrite(str(f_new), im)
+
+    def get_json(self, save=False, verbose=False):
+        """Generates dataset JSON for Ultralytics HUB, optionally saves or prints it; save=bool, verbose=bool."""
+
+        def _round(labels):
+            """Rounds class labels to integers and coordinates to 4 decimal places for improved label accuracy."""
+            return [[int(c), *(round(x, 4) for x in points)] for c, *points in labels]
+
+        for split in "train", "val", "test":
+            if self.data.get(split) is None:
+                self.stats[split] = None  # i.e. no test set
+                continue
+            dataset = LoadImagesAndLabels(self.data[split])  # load dataset
+            x = np.array(
+                [
+                    np.bincount(label[:, 0].astype(int), minlength=self.data["nc"])
+                    for label in tqdm(dataset.labels, total=dataset.n, desc="Statistics")
+                ]
+            )  # shape(128x80)
+            self.stats[split] = {
+                "instance_stats": {"total": int(x.sum()), "per_class": x.sum(0).tolist()},
+                "image_stats": {
+                    "total": dataset.n,
+                    "unlabelled": int(np.all(x == 0, 1).sum()),
+                    "per_class": (x > 0).sum(0).tolist(),
+                },
+                "labels": [{str(Path(k).name): _round(v.tolist())} for k, v in zip(dataset.im_files, dataset.labels)],
+            }
+
+        # Save, print and return
+        if save:
+            stats_path = self.hub_dir / "stats.json"
+            print(f"Saving {stats_path.resolve()}...")
+            with open(stats_path, "w") as f:
+                json.dump(self.stats, f)  # save stats.json
+        if verbose:
+            print(json.dumps(self.stats, indent=2, sort_keys=False))
+        return self.stats
+
+    def process_images(self):
+        """Compresses images for Ultralytics HUB across 'train', 'val', 'test' splits and saves to specified
+        directory.
+        """
+        for split in "train", "val", "test":
+            if self.data.get(split) is None:
+                continue
+            dataset = LoadImagesAndLabels(self.data[split])  # load dataset
+            desc = f"{split} images"
+            for _ in tqdm(ThreadPool(NUM_THREADS).imap(self._hub_ops, dataset.im_files), total=dataset.n, desc=desc):
+                pass
+        print(f"Done. All images saved to {self.im_dir}")
+        return self.im_dir
+
+
+# Classification dataloaders -------------------------------------------------------------------------------------------
+class ClassificationDataset(torchvision.datasets.ImageFolder):
+    """
+    YOLOv5 Classification Dataset.
+
+    Arguments
+        root:  Dataset path
+        transform:  torchvision transforms, used by default
+        album_transform: Albumentations transforms, used if installed
+    """
+
+    def __init__(self, root, augment, imgsz, cache=False):
+        """Initializes YOLOv5 Classification Dataset with optional caching, augmentations, and transforms for image
+        classification.
+        """
+        super().__init__(root=root)
+        self.torch_transforms = classify_transforms(imgsz)
+        self.album_transforms = classify_albumentations(augment, imgsz) if augment else None
+        self.cache_ram = cache is True or cache == "ram"
+        self.cache_disk = cache == "disk"
+        self.samples = [list(x) + [Path(x[0]).with_suffix(".npy"), None] for x in self.samples]  # file, index, npy, im
+
+    def __getitem__(self, i):
+        """Fetches and transforms an image sample by index, supporting RAM/disk caching and Augmentations."""
+        f, j, fn, im = self.samples[i]  # filename, index, filename.with_suffix('.npy'), image
+        if self.cache_ram and im is None:
+            im = self.samples[i][3] = cv2.imread(f)
+        elif self.cache_disk:
+            if not fn.exists():  # load npy
+                np.save(fn.as_posix(), cv2.imread(f))
+            im = np.load(fn)
+        else:  # read image
+            im = cv2.imread(f)  # BGR
+        if self.album_transforms:
+            sample = self.album_transforms(image=cv2.cvtColor(im, cv2.COLOR_BGR2RGB))["image"]
+        else:
+            sample = self.torch_transforms(im)
+        return sample, j
+
+
+def create_classification_dataloader(
+    path, imgsz=224, batch_size=16, augment=True, cache=False, rank=-1, workers=8, shuffle=True
+):
+    # Returns Dataloader object to be used with YOLOv5 Classifier
+    with torch_distributed_zero_first(rank):  # init dataset *.cache only once if DDP
+        dataset = ClassificationDataset(root=path, imgsz=imgsz, augment=augment, cache=cache)
+    batch_size = min(batch_size, len(dataset))
+    nd = torch.cuda.device_count()
+    nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers])
+    sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)
+    generator = torch.Generator()
+    generator.manual_seed(6148914691236517205 + RANK)
+    return InfiniteDataLoader(
+        dataset,
+        batch_size=batch_size,
+        shuffle=shuffle and sampler is None,
+        num_workers=nw,
+        sampler=sampler,
+        pin_memory=PIN_MEMORY,
+        worker_init_fn=seed_worker,
+        generator=generator,
+    )  # or DataLoader(persistent_workers=True)

utils/docker/Dockerfile

+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+# Builds ultralytics/yolov5:latest image on DockerHub https://hub.docker.com/r/ultralytics/yolov5
+# Image is CUDA-optimized for YOLOv5 single/multi-GPU training and inference
+
+# Start FROM PyTorch image https://hub.docker.com/r/pytorch/pytorch
+FROM pytorch/pytorch:2.0.0-cuda11.7-cudnn8-runtime
+
+# Downloads to user config dir
+ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
+
+# Install linux packages
+ENV DEBIAN_FRONTEND noninteractive
+RUN apt update
+RUN TZ=Etc/UTC apt install -y tzdata
+RUN apt install --no-install-recommends -y gcc git zip curl htop libgl1 libglib2.0-0 libpython3-dev gnupg
+# RUN alias python=python3
+
+# Security updates
+# https://security.snyk.io/vuln/SNYK-UBUNTU1804-OPENSSL-3314796
+RUN apt upgrade --no-install-recommends -y openssl
+
+# Create working directory
+RUN rm -rf /usr/src/app && mkdir -p /usr/src/app
+WORKDIR /usr/src/app
+
+# Copy contents
+COPY . /usr/src/app
+
+# Install pip packages
+COPY requirements.txt .
+RUN python3 -m pip install --upgrade pip wheel
+RUN pip install --no-cache -r requirements.txt albumentations comet gsutil notebook \
+    coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2023.0'
+    # tensorflow tensorflowjs \
+
+# Set environment variables
+ENV OMP_NUM_THREADS=1
+
+# Cleanup
+ENV DEBIAN_FRONTEND teletype
+
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/yolov5:latest && sudo docker build -f utils/docker/Dockerfile -t $t . && sudo docker push $t
+
+# Pull and Run
+# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t
+
+# Pull and Run with local directory access
+# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/datasets:/usr/src/datasets $t
+
+# Kill all
+# sudo docker kill $(sudo docker ps -q)
+
+# Kill all image-based
+# sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/yolov5:latest)
+
+# DockerHub tag update
+# t=ultralytics/yolov5:latest tnew=ultralytics/yolov5:v6.2 && sudo docker pull $t && sudo docker tag $t $tnew && sudo docker push $tnew
+
+# Clean up
+# sudo docker system prune -a --volumes
+
+# Update Ubuntu drivers
+# https://www.maketecheasier.com/install-nvidia-drivers-ubuntu/
+
+# DDP test
+# python -m torch.distributed.run --nproc_per_node 2 --master_port 1 train.py --epochs 3
+
+# GCP VM from Image
+# docker.io/ultralytics/yolov5:latest

utils/docker/Dockerfile-arm64

+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+# Builds ultralytics/yolov5:latest-arm64 image on DockerHub https://hub.docker.com/r/ultralytics/yolov5
+# Image is aarch64-compatible for Apple M1 and other ARM architectures i.e. Jetson Nano and Raspberry Pi
+
+# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu
+FROM arm64v8/ubuntu:22.10
+
+# Downloads to user config dir
+ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
+
+# Install linux packages
+ENV DEBIAN_FRONTEND noninteractive
+RUN apt update
+RUN TZ=Etc/UTC apt install -y tzdata
+RUN apt install --no-install-recommends -y python3-pip git zip curl htop gcc libgl1 libglib2.0-0 libpython3-dev
+# RUN alias python=python3
+
+# Install pip packages
+COPY requirements.txt .
+RUN python3 -m pip install --upgrade pip wheel
+RUN pip install --no-cache -r requirements.txt albumentations gsutil notebook \
+    coremltools onnx onnxruntime
+    # tensorflow-aarch64 tensorflowjs \
+
+# Create working directory
+RUN mkdir -p /usr/src/app
+WORKDIR /usr/src/app
+
+# Copy contents
+COPY . /usr/src/app
+ENV DEBIAN_FRONTEND teletype
+
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/yolov5:latest-arm64 && sudo docker build --platform linux/arm64 -f utils/docker/Dockerfile-arm64 -t $t . && sudo docker push $t
+
+# Pull and Run
+# t=ultralytics/yolov5:latest-arm64 && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t

utils/docker/Dockerfile-cpu

+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+# Builds ultralytics/yolov5:latest-cpu image on DockerHub https://hub.docker.com/r/ultralytics/yolov5
+# Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv5 deployments
+
+# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu
+FROM ubuntu:23.10
+
+# Downloads to user config dir
+ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
+
+# Install linux packages
+# g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package
+RUN apt update \
+    && apt install --no-install-recommends -y python3-pip git zip curl htop libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0
+# RUN alias python=python3
+
+# Remove python3.11/EXTERNALLY-MANAGED or use 'pip install --break-system-packages' avoid 'externally-managed-environment' Ubuntu nightly error
+RUN rm -rf /usr/lib/python3.11/EXTERNALLY-MANAGED
+
+# Install pip packages
+COPY requirements.txt .
+RUN python3 -m pip install --upgrade pip wheel
+RUN pip install --no-cache -r requirements.txt albumentations gsutil notebook \
+    coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2023.0' \
+    # tensorflow tensorflowjs \
+    --extra-index-url https://download.pytorch.org/whl/cpu
+
+# Create working directory
+RUN mkdir -p /usr/src/app
+WORKDIR /usr/src/app
+
+# Copy contents
+COPY . /usr/src/app
+
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/yolov5:latest-cpu && sudo docker build -f utils/docker/Dockerfile-cpu -t $t . && sudo docker push $t
+
+# Pull and Run
+# t=ultralytics/yolov5:latest-cpu && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t

utils/downloads.py

+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+"""Download utils."""
+
+import logging
+import subprocess
+import urllib
+from pathlib import Path
+
+import requests
+import torch
+
+
+def is_url(url, check=True):
+    """Determines if a string is a URL and optionally checks its existence online, returning a boolean."""
+    try:
+        url = str(url)
+        result = urllib.parse.urlparse(url)
+        assert all([result.scheme, result.netloc])  # check if is url
+        return (urllib.request.urlopen(url).getcode() == 200) if check else True  # check if exists online
+    except (AssertionError, urllib.request.HTTPError):
+        return False
+
+
+def gsutil_getsize(url=""):
+    """
+    Returns the size in bytes of a file at a Google Cloud Storage URL using `gsutil du`.
+
+    Returns 0 if the command fails or output is empty.
+    """
+    output = subprocess.check_output(["gsutil", "du", url], shell=True, encoding="utf-8")
+    return int(output.split()[0]) if output else 0
+
+
+def url_getsize(url="https://ultralytics.com/images/bus.jpg"):
+    """Returns the size in bytes of a downloadable file at a given URL; defaults to -1 if not found."""
+    response = requests.head(url, allow_redirects=True)
+    return int(response.headers.get("content-length", -1))
+
+
+def curl_download(url, filename, *, silent: bool = False) -> bool:
+    """Download a file from a url to a filename using curl."""
+    silent_option = "sS" if silent else ""  # silent
+    proc = subprocess.run(
+        [
+            "curl",
+            "-#",
+            f"-{silent_option}L",
+            url,
+            "--output",
+            filename,
+            "--retry",
+            "9",
+            "-C",
+            "-",
+        ]
+    )
+    return proc.returncode == 0
+
+
+def safe_download(file, url, url2=None, min_bytes=1e0, error_msg=""):
+    """
+    Downloads a file from a URL (or alternate URL) to a specified path if file is above a minimum size.
+
+    Removes incomplete downloads.
+    """
+    from utils.general import LOGGER
+
+    file = Path(file)
+    assert_msg = f"Downloaded file '{file}' does not exist or size is < min_bytes={min_bytes}"
+    try:  # url1
+        LOGGER.info(f"Downloading {url} to {file}...")
+        torch.hub.download_url_to_file(url, str(file), progress=LOGGER.level <= logging.INFO)
+        assert file.exists() and file.stat().st_size > min_bytes, assert_msg  # check
+    except Exception as e:  # url2
+        if file.exists():
+            file.unlink()  # remove partial downloads
+        LOGGER.info(f"ERROR: {e}\nRe-attempting {url2 or url} to {file}...")
+        # curl download, retry and resume on fail
+        curl_download(url2 or url, file)
+    finally:
+        if not file.exists() or file.stat().st_size < min_bytes:  # check
+            if file.exists():
+                file.unlink()  # remove partial downloads
+            LOGGER.info(f"ERROR: {assert_msg}\n{error_msg}")
+        LOGGER.info("")
+
+
+def attempt_download(file, repo="ultralytics/yolov5", release="v7.0"):
+    """Downloads a file from GitHub release assets or via direct URL if not found locally, supporting backup
+    versions.
+    """
+    from utils.general import LOGGER
+
+    def github_assets(repository, version="latest"):
+        # Return GitHub repo tag (i.e. 'v7.0') and assets (i.e. ['yolov5s.pt', 'yolov5m.pt', ...])
+        if version != "latest":
+            version = f"tags/{version}"  # i.e. tags/v7.0
+        response = requests.get(f"https://api.github.com/repos/{repository}/releases/{version}").json()  # github api
+        return response["tag_name"], [x["name"] for x in response["assets"]]  # tag, assets
+
+    file = Path(str(file).strip().replace("'", ""))
+    if not file.exists():
+        # URL specified
+        name = Path(urllib.parse.unquote(str(file))).name  # decode '%2F' to '/' etc.
+        if str(file).startswith(("http:/", "https:/")):  # download
+            url = str(file).replace(":/", "://")  # Pathlib turns :// -> :/
+            file = name.split("?")[0]  # parse authentication https://url.com/file.txt?auth...
+            if Path(file).is_file():
+                LOGGER.info(f"Found {url} locally at {file}")  # file already exists
+            else:
+                safe_download(file=file, url=url, min_bytes=1e5)
+            return file
+
+        # GitHub assets
+        assets = [f"yolov5{size}{suffix}.pt" for size in "nsmlx" for suffix in ("", "6", "-cls", "-seg")]  # default
+        try:
+            tag, assets = github_assets(repo, release)
+        except Exception:
+            try:
+                tag, assets = github_assets(repo)  # latest release
+            except Exception:
+                try:
+                    tag = subprocess.check_output("git tag", shell=True, stderr=subprocess.STDOUT).decode().split()[-1]
+                except Exception:
+                    tag = release
+
+        if name in assets:
+            file.parent.mkdir(parents=True, exist_ok=True)  # make parent dir (if required)
+            safe_download(
+                file,
+                url=f"https://github.com/{repo}/releases/download/{tag}/{name}",
+                min_bytes=1e5,
+                error_msg=f"{file} missing, try downloading from https://github.com/{repo}/releases/{tag}",
+            )
+
+    return str(file)

utils/flask_rest_api/README.md

+# Flask REST API
+
+[REST](https://en.wikipedia.org/wiki/Representational_state_transfer) [API](https://en.wikipedia.org/wiki/API)s are commonly used to expose Machine Learning (ML) models to other services. This folder contains an example REST API created using Flask to expose the YOLOv5s model from [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/).
+
+## Requirements
+
+[Flask](https://palletsprojects.com/p/flask/) is required. Install with:
+
+```shell
+$ pip install Flask
+```
+
+## Run
+
+After Flask installation run:
+
+```shell
+$ python3 restapi.py --port 5000
+```
+
+Then use [curl](https://curl.se/) to perform a request:
+
+```shell
+$ curl -X POST -F image=@zidane.jpg 'http://localhost:5000/v1/object-detection/yolov5s'
+```
+
+The model inference results are returned as a JSON response:
+
+```json
+[
+  {
+    "class": 0,
+    "confidence": 0.8900438547,
+    "height": 0.9318675399,
+    "name": "person",
+    "width": 0.3264600933,
+    "xcenter": 0.7438579798,
+    "ycenter": 0.5207948685
+  },
+  {
+    "class": 0,
+    "confidence": 0.8440024257,
+    "height": 0.7155083418,
+    "name": "person",
+    "width": 0.6546785235,
+    "xcenter": 0.427829951,
+    "ycenter": 0.6334488392
+  },
+  {
+    "class": 27,
+    "confidence": 0.3771208823,
+    "height": 0.3902671337,
+    "name": "tie",
+    "width": 0.0696444362,
+    "xcenter": 0.3675483763,
+    "ycenter": 0.7991207838
+  },
+  {
+    "class": 27,
+    "confidence": 0.3527112305,
+    "height": 0.1540903747,
+    "name": "tie",
+    "width": 0.0336618312,
+    "xcenter": 0.7814827561,
+    "ycenter": 0.5065554976
+  }
+]
+```
+
+An example python script to perform inference using [requests](https://docs.python-requests.org/en/master/) is given in `example_request.py`

utils/flask_rest_api/example_request.py

+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+"""Perform test request."""
+
+import pprint
+
+import requests
+
+DETECTION_URL = "http://localhost:5000/v1/object-detection/yolov5s"
+IMAGE = "zidane.jpg"
+
+# Read image
+with open(IMAGE, "rb") as f:
+    image_data = f.read()
+
+response = requests.post(DETECTION_URL, files={"image": image_data}).json()
+
+pprint.pprint(response)

utils/flask_rest_api/restapi.py

+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+"""Run a Flask REST API exposing one or more YOLOv5s models."""
+
+import argparse
+import io
+
+import torch
+from flask import Flask, request
+from PIL import Image
+
+app = Flask(__name__)
+models = {}
+
+DETECTION_URL = "/v1/object-detection/<model>"
+
+
+@app.route(DETECTION_URL, methods=["POST"])
+def predict(model):
+    """Predict and return object detections in JSON format given an image and model name via a Flask REST API POST
+    request.
+    """
+    if request.method != "POST":
+        return
+
+    if request.files.get("image"):
+        # Method 1
+        # with request.files["image"] as f:
+        #     im = Image.open(io.BytesIO(f.read()))
+
+        # Method 2
+        im_file = request.files["image"]
+        im_bytes = im_file.read()
+        im = Image.open(io.BytesIO(im_bytes))
+
+        if model in models:
+            results = models[model](im, size=640)  # reduce size=320 for faster inference
+            return results.pandas().xyxy[0].to_json(orient="records")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Flask API exposing YOLOv5 model")
+    parser.add_argument("--port", default=5000, type=int, help="port number")
+    parser.add_argument("--model", nargs="+", default=["yolov5s"], help="model(s) to run, i.e. --model yolov5n yolov5s")
+    opt = parser.parse_args()
+
+    for m in opt.model:
+        models[m] = torch.hub.load("ultralytics/yolov5", m, force_reload=True, skip_validation=True)
+
+    app.run(host="0.0.0.0", port=opt.port)  # debug=True causes Restarting with stat

utils/general.py

+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+"""General utils."""
+
+import contextlib
+import glob
+import inspect
+import logging
+import logging.config
+import math
+import os
+import platform
+import random
+import re
+import signal
+import subprocess
+import sys
+import time
+import urllib
+from copy import deepcopy
+from datetime import datetime
+from itertools import repeat
+from multiprocessing.pool import ThreadPool
+from pathlib import Path
+from subprocess import check_output
+from tarfile import is_tarfile
+from typing import Optional
+from zipfile import ZipFile, is_zipfile
+
+import cv2
+import numpy as np
+import pandas as pd
+import pkg_resources as pkg
+import torch
+import torchvision
+import yaml
+
+# Import 'ultralytics' package or install if missing
+try:
+    import ultralytics
+
+    assert hasattr(ultralytics, "__version__")  # verify package is not directory
+except (ImportError, AssertionError):
+    os.system("pip install -U ultralytics")
+    import ultralytics
+
+from ultralytics.utils.checks import check_requirements
+
+from utils import TryExcept, emojis
+from utils.downloads import curl_download, gsutil_getsize
+from utils.metrics import box_iou, fitness
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[1]  # YOLOv5 root directory
+RANK = int(os.getenv("RANK", -1))
+
+# Settings
+NUM_THREADS = min(8, max(1, os.cpu_count() - 1))  # number of YOLOv5 multiprocessing threads
+DATASETS_DIR = Path(os.getenv("YOLOv5_DATASETS_DIR", ROOT.parent / "datasets"))  # global datasets directory
+AUTOINSTALL = str(os.getenv("YOLOv5_AUTOINSTALL", True)).lower() == "true"  # global auto-install mode
+VERBOSE = str(os.getenv("YOLOv5_VERBOSE", True)).lower() == "true"  # global verbose mode
+TQDM_BAR_FORMAT = "{l_bar}{bar:10}{r_bar}"  # tqdm bar format
+FONT = "Arial.ttf"  # https://ultralytics.com/assets/Arial.ttf
+
+torch.set_printoptions(linewidth=320, precision=5, profile="long")
+np.set_printoptions(linewidth=320, formatter={"float_kind": "{:11.5g}".format})  # format short g, %precision=5
+pd.options.display.max_columns = 10
+cv2.setNumThreads(0)  # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader)
+os.environ["NUMEXPR_MAX_THREADS"] = str(NUM_THREADS)  # NumExpr max threads
+os.environ["OMP_NUM_THREADS"] = "1" if platform.system() == "darwin" else str(NUM_THREADS)  # OpenMP (PyTorch and SciPy)
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"  # suppress verbose TF compiler warnings in Colab
+
+
+def is_ascii(s=""):
+    """Checks if input string `s` contains only ASCII characters; returns `True` if so, otherwise `False`."""
+    s = str(s)  # convert list, tuple, None, etc. to str
+    return len(s.encode().decode("ascii", "ignore")) == len(s)
+
+
+def is_chinese(s="人工智能"):
+    """Determines if a string `s` contains any Chinese characters; returns `True` if so, otherwise `False`."""
+    return bool(re.search("[\u4e00-\u9fff]", str(s)))
+
+
+def is_colab():
+    """Checks if the current environment is a Google Colab instance; returns `True` for Colab, otherwise `False`."""
+    return "google.colab" in sys.modules
+
+
+def is_jupyter():
+    """
+    Check if the current script is running inside a Jupyter Notebook. Verified on Colab, Jupyterlab, Kaggle, Paperspace.
+
+    Returns:
+        bool: True if running inside a Jupyter Notebook, False otherwise.
+    """
+    with contextlib.suppress(Exception):
+        from IPython import get_ipython
+
+        return get_ipython() is not None
+    return False
+
+
+def is_kaggle():
+    """Checks if the current environment is a Kaggle Notebook by validating environment variables."""
+    return os.environ.get("PWD") == "/kaggle/working" and os.environ.get("KAGGLE_URL_BASE") == "https://www.kaggle.com"
+
+
+def is_docker() -> bool:
+    """Check if the process runs inside a docker container."""
+    if Path("/.dockerenv").exists():
+        return True
+    try:  # check if docker is in control groups
+        with open("/proc/self/cgroup") as file:
+            return any("docker" in line for line in file)
+    except OSError:
+        return False
+
+
+def is_writeable(dir, test=False):
+    """Checks if a directory is writable, optionally testing by creating a temporary file if `test=True`."""
+    if not test:
+        return os.access(dir, os.W_OK)  # possible issues on Windows
+    file = Path(dir) / "tmp.txt"
+    try:
+        with open(file, "w"):  # open file with write permissions
+            pass
+        file.unlink()  # remove file
+        return True
+    except OSError:
+        return False
+
+
+LOGGING_NAME = "yolov5"
+
+
+def set_logging(name=LOGGING_NAME, verbose=True):
+    """Configures logging with specified verbosity; `name` sets the logger's name, `verbose` controls logging level."""
+    rank = int(os.getenv("RANK", -1))  # rank in world for Multi-GPU trainings
+    level = logging.INFO if verbose and rank in {-1, 0} else logging.ERROR
+    logging.config.dictConfig(
+        {
+            "version": 1,
+            "disable_existing_loggers": False,
+            "formatters": {name: {"format": "%(message)s"}},
+            "handlers": {
+                name: {
+                    "class": "logging.StreamHandler",
+                    "formatter": name,
+                    "level": level,
+                }
+            },
+            "loggers": {
+                name: {
+                    "level": level,
+                    "handlers": [name],
+                    "propagate": False,
+                }
+            },
+        }
+    )
+
+
+set_logging(LOGGING_NAME)  # run before defining LOGGER
+LOGGER = logging.getLogger(LOGGING_NAME)  # define globally (used in train.py, val.py, detect.py, etc.)
+if platform.system() == "Windows":
+    for fn in LOGGER.info, LOGGER.warning:
+        setattr(LOGGER, fn.__name__, lambda x: fn(emojis(x)))  # emoji safe logging
+
+
+def user_config_dir(dir="Ultralytics", env_var="YOLOV5_CONFIG_DIR"):
+    """Returns user configuration directory path, preferring environment variable `YOLOV5_CONFIG_DIR` if set, else OS-
+    specific.
+    """
+    env = os.getenv(env_var)
+    if env:
+        path = Path(env)  # use environment variable
+    else:
+        cfg = {"Windows": "AppData/Roaming", "Linux": ".config", "Darwin": "Library/Application Support"}  # 3 OS dirs
+        path = Path.home() / cfg.get(platform.system(), "")  # OS-specific config dir
+        path = (path if is_writeable(path) else Path("/tmp")) / dir  # GCP and AWS lambda fix, only /tmp is writeable
+    path.mkdir(exist_ok=True)  # make if required
+    return path
+
+
+CONFIG_DIR = user_config_dir()  # Ultralytics settings dir
+
+
+class Profile(contextlib.ContextDecorator):
+    # YOLOv5 Profile class. Usage: @Profile() decorator or 'with Profile():' context manager
+    def __init__(self, t=0.0, device: torch.device = None):
+        """Initializes a profiling context for YOLOv5 with optional timing threshold and device specification."""
+        self.t = t
+        self.device = device
+        self.cuda = bool(device and str(device).startswith("cuda"))
+
+    def __enter__(self):
+        """Initializes timing at the start of a profiling context block for performance measurement."""
+        self.start = self.time()
+        return self
+
+    def __exit__(self, type, value, traceback):
+        """Concludes timing, updating duration for profiling upon exiting a context block."""
+        self.dt = self.time() - self.start  # delta-time
+        self.t += self.dt  # accumulate dt
+
+    def time(self):
+        """Measures and returns the current time, synchronizing CUDA operations if `cuda` is True."""
+        if self.cuda:
+            torch.cuda.synchronize(self.device)
+        return time.time()
+
+
+class Timeout(contextlib.ContextDecorator):
+    # YOLOv5 Timeout class. Usage: @Timeout(seconds) decorator or 'with Timeout(seconds):' context manager
+    def __init__(self, seconds, *, timeout_msg="", suppress_timeout_errors=True):
+        """Initializes a timeout context/decorator with defined seconds, optional message, and error suppression."""
+        self.seconds = int(seconds)
+        self.timeout_message = timeout_msg
+        self.suppress = bool(suppress_timeout_errors)
+
+    def _timeout_handler(self, signum, frame):
+        """Raises a TimeoutError with a custom message when a timeout event occurs."""
+        raise TimeoutError(self.timeout_message)
+
+    def __enter__(self):
+        """Initializes timeout mechanism on non-Windows platforms, starting a countdown to raise TimeoutError."""
+        if platform.system() != "Windows":  # not supported on Windows
+            signal.signal(signal.SIGALRM, self._timeout_handler)  # Set handler for SIGALRM
+            signal.alarm(self.seconds)  # start countdown for SIGALRM to be raised
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        """Disables active alarm on non-Windows systems and optionally suppresses TimeoutError if set."""
+        if platform.system() != "Windows":
+            signal.alarm(0)  # Cancel SIGALRM if it's scheduled
+            if self.suppress and exc_type is TimeoutError:  # Suppress TimeoutError
+                return True
+
+
+class WorkingDirectory(contextlib.ContextDecorator):
+    # Usage: @WorkingDirectory(dir) decorator or 'with WorkingDirectory(dir):' context manager
+    def __init__(self, new_dir):
+        """Initializes a context manager/decorator to temporarily change the working directory."""
+        self.dir = new_dir  # new dir
+        self.cwd = Path.cwd().resolve()  # current dir
+
+    def __enter__(self):
+        """Temporarily changes the working directory within a 'with' statement context."""
+        os.chdir(self.dir)
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        """Restores the original working directory upon exiting a 'with' statement context."""
+        os.chdir(self.cwd)
+
+
+def methods(instance):
+    """Returns list of method names for a class/instance excluding dunder methods."""
+    return [f for f in dir(instance) if callable(getattr(instance, f)) and not f.startswith("__")]
+
+
+def print_args(args: Optional[dict] = None, show_file=True, show_func=False):
+    """Logs the arguments of the calling function, with options to include the filename and function name."""
+    x = inspect.currentframe().f_back  # previous frame
+    file, _, func, _, _ = inspect.getframeinfo(x)
+    if args is None:  # get args automatically
+        args, _, _, frm = inspect.getargvalues(x)
+        args = {k: v for k, v in frm.items() if k in args}
+    try:
+        file = Path(file).resolve().relative_to(ROOT).with_suffix("")
+    except ValueError:
+        file = Path(file).stem
+    s = (f"{file}: " if show_file else "") + (f"{func}: " if show_func else "")
+    LOGGER.info(colorstr(s) + ", ".join(f"{k}={v}" for k, v in args.items()))
+
+
+def init_seeds(seed=0, deterministic=False):
+    """
+    Initializes RNG seeds and sets deterministic options if specified.
+
+    See https://pytorch.org/docs/stable/notes/randomness.html
+    """
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)  # for Multi-GPU, exception safe
+    # torch.backends.cudnn.benchmark = True  # AutoBatch problem https://github.com/ultralytics/yolov5/issues/9287
+    if deterministic and check_version(torch.__version__, "1.12.0"):  # https://github.com/ultralytics/yolov5/pull/8213
+        torch.use_deterministic_algorithms(True)
+        torch.backends.cudnn.deterministic = True
+        os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"
+        os.environ["PYTHONHASHSEED"] = str(seed)
+
+
+def intersect_dicts(da, db, exclude=()):
+    """Returns intersection of `da` and `db` dicts with match_boxesing keys and shapes, excluding `exclude` keys; uses `da`
+    values.
+    """
+    return {k: v for k, v in da.items() if k in db and all(x not in k for x in exclude) and v.shape == db[k].shape}
+
+
+def get_default_args(func):
+    """Returns a dict of `func` default arguments by inspecting its signature."""
+    signature = inspect.signature(func)
+    return {k: v.default for k, v in signature.parameters.items() if v.default is not inspect.Parameter.empty}
+
+
+def get_latest_run(search_dir="."):
+    """Returns the path to the most recent 'last.pt' file in /runs to resume from, searches in `search_dir`."""
+    last_list = glob.glob(f"{search_dir}/**/last*.pt", recursive=True)
+    return max(last_list, key=os.path.getctime) if last_list else ""
+
+
+def file_age(path=__file__):
+    """Calculates and returns the age of a file in days based on its last modification time."""
+    dt = datetime.now() - datetime.fromtimestamp(Path(path).stat().st_mtime)  # delta
+    return dt.days  # + dt.seconds / 86400  # fractional days
+
+
+def file_date(path=__file__):
+    """Returns a human-readable file modification date in 'YYYY-M-D' format, given a file path."""
+    t = datetime.fromtimestamp(Path(path).stat().st_mtime)
+    return f"{t.year}-{t.month}-{t.day}"
+
+
+def file_size(path):
+    """Returns file or directory size in megabytes (MB) for a given path, where directories are recursively summed."""
+    mb = 1 << 20  # bytes to MiB (1024 ** 2)
+    path = Path(path)
+    if path.is_file():
+        return path.stat().st_size / mb
+    elif path.is_dir():
+        return sum(f.stat().st_size for f in path.glob("**/*") if f.is_file()) / mb
+    else:
+        return 0.0
+
+
+def check_online():
+    """Checks internet connectivity by attempting to create a connection to "1.1.1.1" on port 443, retries once if the
+    first attempt fails.
+    """
+    import socket
+
+    def run_once():
+        # Check once
+        try:
+            socket.create_connection(("1.1.1.1", 443), 5)  # check host accessibility
+            return True
+        except OSError:
+            return False
+
+    return run_once() or run_once()  # check twice to increase robustness to intermittent connectivity issues
+
+
+def git_describe(path=ROOT):
+    """
+    Returns a human-readable git description of the repository at `path`, or an empty string on failure.
+
+    Example output is 'fv5.0-5-g3e25f1e'. See https://git-scm.com/docs/git-describe.
+    """
+    try:
+        assert (Path(path) / ".git").is_dir()
+        return check_output(f"git -C {path} describe --tags --long --always", shell=True).decode()[:-1]
+    except Exception:
+        return ""
+
+
+@TryExcept()
+@WorkingDirectory(ROOT)
+def check_git_status(repo="ultralytics/yolov5", branch="master"):
+    """Checks if YOLOv5 code is up-to-date with the repository, advising 'git pull' if behind; errors return informative
+    messages.
+    """
+    url = f"https://github.com/{repo}"
+    msg = f", for updates see {url}"
+    s = colorstr("github: ")  # string
+    assert Path(".git").exists(), s + "skipping check (not a git repository)" + msg
+    assert check_online(), s + "skipping check (offline)" + msg
+
+    splits = re.split(pattern=r"\s", string=check_output("git remote -v", shell=True).decode())
+    matches = [repo in s for s in splits]
+    if any(matches):
+        remote = splits[matches.index(True) - 1]
+    else:
+        remote = "ultralytics"
+        check_output(f"git remote add {remote} {url}", shell=True)
+    check_output(f"git fetch {remote}", shell=True, timeout=5)  # git fetch
+    local_branch = check_output("git rev-parse --abbrev-ref HEAD", shell=True).decode().strip()  # checked out
+    n = int(check_output(f"git rev-list {local_branch}..{remote}/{branch} --count", shell=True))  # commits behind
+    if n > 0:
+        pull = "git pull" if remote == "origin" else f"git pull {remote} {branch}"
+        s += f"⚠️ YOLOv5 is out of date by {n} commit{'s' * (n > 1)}. Use '{pull}' or 'git clone {url}' to update."
+    else:
+        s += f"up to date with {url} ✅"
+    LOGGER.info(s)
+
+
+@WorkingDirectory(ROOT)
+def check_git_info(path="."):
+    """Checks YOLOv5 git info, returning a dict with remote URL, branch name, and commit hash."""
+    check_requirements("gitpython")
+    import git
+
+    try:
+        repo = git.Repo(path)
+        remote = repo.remotes.origin.url.replace(".git", "")  # i.e. 'https://github.com/ultralytics/yolov5'
+        commit = repo.head.commit.hexsha  # i.e. '3134699c73af83aac2a481435550b968d5792c0d'
+        try:
+            branch = repo.active_branch.name  # i.e. 'main'
+        except TypeError:  # not on any branch
+            branch = None  # i.e. 'detached HEAD' state
+        return {"remote": remote, "branch": branch, "commit": commit}
+    except git.exc.InvalidGitRepositoryError:  # path is not a git dir
+        return {"remote": None, "branch": None, "commit": None}
+
+
+def check_python(minimum="3.8.0"):
+    """Checks if current Python version meets the minimum required version, exits if not."""
+    check_version(platform.python_version(), minimum, name="Python ", hard=True)
+
+
+def check_version(current="0.0.0", minimum="0.0.0", name="version ", pinned=False, hard=False, verbose=False):
+    """Checks if the current version meets the minimum required version, exits or warns based on parameters."""
+    current, minimum = (pkg.parse_version(x) for x in (current, minimum))
+    result = (current == minimum) if pinned else (current >= minimum)  # bool
+    s = f"WARNING ⚠️ {name}{minimum} is required by YOLOv5, but {name}{current} is currently installed"  # string
+    if hard:
+        assert result, emojis(s)  # assert min requirements met
+    if verbose and not result:
+        LOGGER.warning(s)
+    return result
+
+
+def check_img_size(imgsz, s=32, floor=0):
+    """Adjusts image size to be divisible by stride `s`, supports int or list/tuple input, returns adjusted size."""
+    if isinstance(imgsz, int):  # integer i.e. img_size=640
+        new_size = max(make_divisible(imgsz, int(s)), floor)
+    else:  # list i.e. img_size=[640, 480]
+        imgsz = list(imgsz)  # convert to list if tuple
+        new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz]
+    if new_size != imgsz:
+        LOGGER.warning(f"WARNING ⚠️ --img-size {imgsz} must be multiple of max stride {s}, updating to {new_size}")
+    return new_size
+
+
+def check_imshow(warn=False):
+    """Checks environment support for image display; warns on failure if `warn=True`."""
+    try:
+        assert not is_jupyter()
+        assert not is_docker()
+        cv2.imshow("test", np.zeros((1, 1, 3)))
+        cv2.waitKey(1)
+        cv2.destroyAllWindows()
+        cv2.waitKey(1)
+        return True
+    except Exception as e:
+        if warn:
+            LOGGER.warning(f"WARNING ⚠️ Environment does not support cv2.imshow() or PIL Image.show()\n{e}")
+        return False
+
+
+def check_suffix(file="yolov5s.pt", suffix=(".pt",), msg=""):
+    """Validates if a file or files have an acceptable suffix, raising an error if not."""
+    if file and suffix:
+        if isinstance(suffix, str):
+            suffix = [suffix]
+        for f in file if isinstance(file, (list, tuple)) else [file]:
+            s = Path(f).suffix.lower()  # file suffix
+            if len(s):
+                assert s in suffix, f"{msg}{f} acceptable suffix is {suffix}"
+
+
+def check_yaml(file, suffix=(".yaml", ".yml")):
+    """Searches/downloads a YAML file, verifies its suffix (.yaml or .yml), and returns the file path."""
+    return check_file(file, suffix)
+
+
+def check_file(file, suffix=""):
+    """Searches/downloads a file, checks its suffix (if provided), and returns the file path."""
+    check_suffix(file, suffix)  # optional
+    file = str(file)  # convert to str()
+    if os.path.isfile(file) or not file:  # exists
+        return file
+    elif file.startswith(("http:/", "https:/")):  # download
+        url = file  # warning: Pathlib turns :// -> :/
+        file = Path(urllib.parse.unquote(file).split("?")[0]).name  # '%2F' to '/', split https://url.com/file.txt?auth
+        if os.path.isfile(file):
+            LOGGER.info(f"Found {url} locally at {file}")  # file already exists
+        else:
+            LOGGER.info(f"Downloading {url} to {file}...")
+            torch.hub.download_url_to_file(url, file)
+            assert Path(file).exists() and Path(file).stat().st_size > 0, f"File download failed: {url}"  # check
+        return file
+    elif file.startswith("clearml://"):  # ClearML Dataset ID
+        assert (
+            "clearml" in sys.modules
+        ), "ClearML is not installed, so cannot use ClearML dataset. Try running 'pip install clearml'."
+        return file
+    else:  # search
+        files = []
+        for d in "data", "models", "utils":  # search directories
+            files.extend(glob.glob(str(ROOT / d / "**" / file), recursive=True))  # find file
+        assert len(files), f"File not found: {file}"  # assert file was found
+        assert len(files) == 1, f"Multiple files match '{file}', specify exact path: {files}"  # assert unique
+        return files[0]  # return file
+
+
+def check_font(font=FONT, progress=False):
+    """Ensures specified font exists or downloads it from Ultralytics assets, optionally displaying progress."""
+    font = Path(font)
+    file = CONFIG_DIR / font.name
+    if not font.exists() and not file.exists():
+        url = f"https://ultralytics.com/assets/{font.name}"
+        LOGGER.info(f"Downloading {url} to {file}...")
+        torch.hub.download_url_to_file(url, str(file), progress=progress)
+
+
+def check_dataset(data, autodownload=True):
+    """Validates and/or auto-downloads a dataset, returning its configuration as a dictionary."""
+
+    # Download (optional)
+    extract_dir = ""
+    if isinstance(data, (str, Path)) and (is_zipfile(data) or is_tarfile(data)):
+        download(data, dir=f"{DATASETS_DIR}/{Path(data).stem}", unzip=True, delete=False, curl=False, threads=1)
+        data = next((DATASETS_DIR / Path(data).stem).rglob("*.yaml"))
+        extract_dir, autodownload = data.parent, False
+
+    # Read yaml (optional)
+    if isinstance(data, (str, Path)):
+        data = yaml_load(data)  # dictionary
+
+    # Checks
+    for k in "train", "val", "names":
+        assert k in data, emojis(f"data.yaml '{k}:' field missing ❌")
+    if isinstance(data["names"], (list, tuple)):  # old array format
+        data["names"] = dict(enumerate(data["names"]))  # convert to dict
+    assert all(isinstance(k, int) for k in data["names"].keys()), "data.yaml names keys must be integers, i.e. 2: car"
+    data["nc"] = len(data["names"])
+
+    # Resolve paths
+    path = Path(extract_dir or data.get("path") or "")  # optional 'path' default to '.'
+    if not path.is_absolute():
+        path = (ROOT / path).resolve()
+        data["path"] = path  # download scripts
+    for k in "train", "val", "test":
+        if data.get(k):  # prepend path
+            if isinstance(data[k], str):
+                x = (path / data[k]).resolve()
+                if not x.exists() and data[k].startswith("../"):
+                    x = (path / data[k][3:]).resolve()
+                data[k] = str(x)
+            else:
+                data[k] = [str((path / x).resolve()) for x in data[k]]
+
+    # Parse yaml
+    train, val, test, s = (data.get(x) for x in ("train", "val", "test", "download"))
+    if val:
+        val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])]  # val path
+        if not all(x.exists() for x in val):
+            LOGGER.info("\nDataset not found ⚠️, missing paths %s" % [str(x) for x in val if not x.exists()])
+            if not s or not autodownload:
+                raise Exception("Dataset not found ❌")
+            t = time.time()
+            if s.startswith("http") and s.endswith(".zip"):  # URL
+                f = Path(s).name  # filename
+                LOGGER.info(f"Downloading {s} to {f}...")
+                torch.hub.download_url_to_file(s, f)
+                Path(DATASETS_DIR).mkdir(parents=True, exist_ok=True)  # create root
+                unzip_file(f, path=DATASETS_DIR)  # unzip
+                Path(f).unlink()  # remove zip
+                r = None  # success
+            elif s.startswith("bash "):  # bash script
+                LOGGER.info(f"Running {s} ...")
+                r = subprocess.run(s, shell=True)
+            else:  # python script
+                r = exec(s, {"yaml": data})  # return None
+            dt = f"({round(time.time() - t, 1)}s)"
+            s = f"success ✅ {dt}, saved to {colorstr('bold', DATASETS_DIR)}" if r in (0, None) else f"failure {dt} ❌"
+            LOGGER.info(f"Dataset download {s}")
+    check_font("Arial.ttf" if is_ascii(data["names"]) else "Arial.Unicode.ttf", progress=True)  # download fonts
+    return data  # dictionary
+
+
+def check_amp(model):
+    """Checks PyTorch AMP functionality for a model, returns True if AMP operates correctly, otherwise False."""
+    from models.common import AutoShape, DetectMultiBackend
+
+    def amp_allclose(model, im):
+        # All close FP32 vs AMP results
+        m = AutoShape(model, verbose=False)  # model
+        a = m(im).xywhn[0]  # FP32 inference
+        m.amp = True
+        b = m(im).xywhn[0]  # AMP inference
+        return a.shape == b.shape and torch.allclose(a, b, atol=0.1)  # close to 10% absolute tolerance
+
+    prefix = colorstr("AMP: ")
+    device = next(model.parameters()).device  # get model device
+    if device.type in ("cpu", "mps"):
+        return False  # AMP only used on CUDA devices
+    f = ROOT / "data" / "images" / "bus.jpg"  # image to check
+    im = f if f.exists() else "https://ultralytics.com/images/bus.jpg" if check_online() else np.ones((640, 640, 3))
+    try:
+        assert amp_allclose(deepcopy(model), im) or amp_allclose(DetectMultiBackend("yolov5n.pt", device), im)
+        LOGGER.info(f"{prefix}checks passed ✅")
+        return True
+    except Exception:
+        help_url = "https://github.com/ultralytics/yolov5/issues/7908"
+        LOGGER.warning(f"{prefix}checks failed ❌, disabling Automatic Mixed Precision. See {help_url}")
+        return False
+
+
+def yaml_load(file="data.yaml"):
+    """Safely loads and returns the contents of a YAML file specified by `file` argument."""
+    with open(file, errors="ignore") as f:
+        return yaml.safe_load(f)
+
+
+def yaml_save(file="data.yaml", data={}):
+    """Safely saves `data` to a YAML file specified by `file`, converting `Path` objects to strings; `data` is a
+    dictionary.
+    """
+    with open(file, "w") as f:
+        yaml.safe_dump({k: str(v) if isinstance(v, Path) else v for k, v in data.items()}, f, sort_keys=False)
+
+
+def unzip_file(file, path=None, exclude=(".DS_Store", "__MACOSX")):
+    """Unzips `file` to `path` (default: file's parent), excluding filenames containing any in `exclude` (`.DS_Store`,
+    `__MACOSX`).
+    """
+    if path is None:
+        path = Path(file).parent  # default path
+    with ZipFile(file) as zipObj:
+        for f in zipObj.namelist():  # list all archived filenames in the zip
+            if all(x not in f for x in exclude):
+                zipObj.extract(f, path=path)
+
+
+def url2file(url):
+    """
+    Converts a URL string to a valid filename by stripping protocol, domain, and any query parameters.
+
+    Example https://url.com/file.txt?auth -> file.txt
+    """
+    url = str(Path(url)).replace(":/", "://")  # Pathlib turns :// -> :/
+    return Path(urllib.parse.unquote(url)).name.split("?")[0]  # '%2F' to '/', split https://url.com/file.txt?auth
+
+
+def download(url, dir=".", unzip=True, delete=True, curl=False, threads=1, retry=3):
+    """Downloads and optionally unzips files concurrently, supporting retries and curl fallback."""
+
+    def download_one(url, dir):
+        # Download 1 file
+        success = True
+        if os.path.isfile(url):
+            f = Path(url)  # filename
+        else:  # does not exist
+            f = dir / Path(url).name
+            LOGGER.info(f"Downloading {url} to {f}...")
+            for i in range(retry + 1):
+                if curl:
+                    success = curl_download(url, f, silent=(threads > 1))
+                else:
+                    torch.hub.download_url_to_file(url, f, progress=threads == 1)  # torch download
+                    success = f.is_file()
+                if success:
+                    break
+                elif i < retry:
+                    LOGGER.warning(f"⚠️ Download failure, retrying {i + 1}/{retry} {url}...")
+                else:
+                    LOGGER.warning(f"❌ Failed to download {url}...")
+
+        if unzip and success and (f.suffix == ".gz" or is_zipfile(f) or is_tarfile(f)):
+            LOGGER.info(f"Unzipping {f}...")
+            if is_zipfile(f):
+                unzip_file(f, dir)  # unzip
+            elif is_tarfile(f):
+                subprocess.run(["tar", "xf", f, "--directory", f.parent], check=True)  # unzip
+            elif f.suffix == ".gz":
+                subprocess.run(["tar", "xfz", f, "--directory", f.parent], check=True)  # unzip
+            if delete:
+                f.unlink()  # remove zip
+
+    dir = Path(dir)
+    dir.mkdir(parents=True, exist_ok=True)  # make directory
+    if threads > 1:
+        pool = ThreadPool(threads)
+        pool.imap(lambda x: download_one(*x), zip(url, repeat(dir)))  # multithreaded
+        pool.close()
+        pool.join()
+    else:
+        for u in [url] if isinstance(url, (str, Path)) else url:
+            download_one(u, dir)
+
+
+def make_divisible(x, divisor):
+    """Adjusts `x` to be divisible by `divisor`, returning the nearest greater or equal value."""
+    if isinstance(divisor, torch.Tensor):
+        divisor = int(divisor.max())  # to int
+    return math.ceil(x / divisor) * divisor
+
+
+def clean_str(s):
+    """Cleans a string by replacing special characters with underscore, e.g., `clean_str('#example!')` returns
+    '_example_'.
+    """
+    return re.sub(pattern="[|@#!¡·$€%&()=?¿^*;:,¨´><+]", repl="_", string=s)
+
+
+def one_cycle(y1=0.0, y2=1.0, steps=100):
+    """
+    Generates a lambda for a sinusoidal ramp from y1 to y2 over 'steps'.
+
+    See https://arxiv.org/pdf/1812.01187.pdf for details.
+    """
+    return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1
+
+
+def colorstr(*input):
+    """
+    Colors a string using ANSI escape codes, e.g., colorstr('blue', 'hello world').
+
+    See https://en.wikipedia.org/wiki/ANSI_escape_code.
+    """
+    *args, string = input if len(input) > 1 else ("blue", "bold", input[0])  # color arguments, string
+    colors = {
+        "black": "\033[30m",  # basic colors
+        "red": "\033[31m",
+        "green": "\033[32m",
+        "yellow": "\033[33m",
+        "blue": "\033[34m",
+        "magenta": "\033[35m",
+        "cyan": "\033[36m",
+        "white": "\033[37m",
+        "bright_black": "\033[90m",  # bright colors
+        "bright_red": "\033[91m",
+        "bright_green": "\033[92m",
+        "bright_yellow": "\033[93m",
+        "bright_blue": "\033[94m",
+        "bright_magenta": "\033[95m",
+        "bright_cyan": "\033[96m",
+        "bright_white": "\033[97m",
+        "end": "\033[0m",  # misc
+        "bold": "\033[1m",
+        "underline": "\033[4m",
+    }
+    return "".join(colors[x] for x in args) + f"{string}" + colors["end"]
+
+
+def labels_to_class_weights(labels, nc=80):
+    """Calculates class weights from labels to handle class imbalance in training; input shape: (n, 5)."""
+    if labels[0] is None:  # no labels loaded
+        return torch.Tensor()
+
+    labels = np.concatenate(labels, 0)  # labels.shape = (866643, 5) for COCO
+    classes = labels[:, 0].astype(int)  # labels = [class xywh]
+    weights = np.bincount(classes, minlength=nc)  # occurrences per class
+
+    # Prepend gridpoint count (for uCE training)
+    # gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum()  # gridpoints per image
+    # weights = np.hstack([gpi * len(labels)  - weights.sum() * 9, weights * 9]) ** 0.5  # prepend gridpoints to start
+
+    weights[weights == 0] = 1  # replace empty bins with 1
+    weights = 1 / weights  # number of targets per class
+    weights /= weights.sum()  # normalize
+    return torch.from_numpy(weights).float()
+
+
+def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
+    """Calculates image weights from labels using class weights for weighted sampling."""
+    # Usage: index = random.choices(range(n), weights=image_weights, k=1)  # weighted image sample
+    class_counts = np.array([np.bincount(x[:, 0].astype(int), minlength=nc) for x in labels])
+    return (class_weights.reshape(1, nc) * class_counts).sum(1)
+
+
+def coco80_to_coco91_class():
+    """
+    Converts COCO 80-class index to COCO 91-class index used in the paper.
+
+    Reference: https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
+    """
+    # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n')
+    # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n')
+    # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)]  # darknet to coco
+    # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)]  # coco to darknet
+    return [
+        1,
+        2,
+        3,
+        4,
+        5,
+        6,
+        7,
+        8,
+        9,
+        10,
+        11,
+        13,
+        14,
+        15,
+        16,
+        17,
+        18,
+        19,
+        20,
+        21,
+        22,
+        23,
+        24,
+        25,
+        27,
+        28,
+        31,
+        32,
+        33,
+        34,
+        35,
+        36,
+        37,
+        38,
+        39,
+        40,
+        41,
+        42,
+        43,
+        44,
+        46,
+        47,
+        48,
+        49,
+        50,
+        51,
+        52,
+        53,
+        54,
+        55,
+        56,
+        57,
+        58,
+        59,
+        60,
+        61,
+        62,
+        63,
+        64,
+        65,
+        67,
+        70,
+        72,
+        73,
+        74,
+        75,
+        76,
+        77,
+        78,
+        79,
+        80,
+        81,
+        82,
+        84,
+        85,
+        86,
+        87,
+        88,
+        89,
+        90,
+    ]
+
+
+def xyxy2xywh(x):
+    """Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right."""
+    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
+    y[..., 0] = (x[..., 0] + x[..., 2]) / 2  # x center
+    y[..., 1] = (x[..., 1] + x[..., 3]) / 2  # y center
+    y[..., 2] = x[..., 2] - x[..., 0]  # width
+    y[..., 3] = x[..., 3] - x[..., 1]  # height
+    return y
+
+
+def xywh2xyxy(x):
+    """Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right."""
+    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
+    y[..., 0] = x[..., 0] - x[..., 2] / 2  # top left x
+    y[..., 1] = x[..., 1] - x[..., 3] / 2  # top left y
+    y[..., 2] = x[..., 0] + x[..., 2] / 2  # bottom right x
+    y[..., 3] = x[..., 1] + x[..., 3] / 2  # bottom right y
+    return y
+
+
+def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
+    """Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right."""
+    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
+    y[..., 0] = w * (x[..., 0] - x[..., 2] / 2) + padw  # top left x
+    y[..., 1] = h * (x[..., 1] - x[..., 3] / 2) + padh  # top left y
+    y[..., 2] = w * (x[..., 0] + x[..., 2] / 2) + padw  # bottom right x
+    y[..., 3] = h * (x[..., 1] + x[..., 3] / 2) + padh  # bottom right y
+    return y
+
+
+def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0):
+    """Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] normalized where xy1=top-left, xy2=bottom-right."""
+    if clip:
+        clip_boxes(x, (h - eps, w - eps))  # warning: inplace clip
+    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
+    y[..., 0] = ((x[..., 0] + x[..., 2]) / 2) / w  # x center
+    y[..., 1] = ((x[..., 1] + x[..., 3]) / 2) / h  # y center
+    y[..., 2] = (x[..., 2] - x[..., 0]) / w  # width
+    y[..., 3] = (x[..., 3] - x[..., 1]) / h  # height
+    return y
+
+
+def xyn2xy(x, w=640, h=640, padw=0, padh=0):
+    """Convert normalized segments into pixel segments, shape (n,2)."""
+    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
+    y[..., 0] = w * x[..., 0] + padw  # top left x
+    y[..., 1] = h * x[..., 1] + padh  # top left y
+    return y
+
+
+def segment2box(segment, width=640, height=640):
+    """Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy)."""
+    x, y = segment.T  # segment xy
+    inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height)
+    (
+        x,
+        y,
+    ) = x[inside], y[inside]
+    return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4))  # xyxy
+
+
+def segments2boxes(segments):
+    """Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh)."""
+    boxes = []
+    for s in segments:
+        x, y = s.T  # segment xy
+        boxes.append([x.min(), y.min(), x.max(), y.max()])  # cls, xyxy
+    return xyxy2xywh(np.array(boxes))  # cls, xywh
+
+
+def resample_segments(segments, n=1000):
+    """Resamples an (n,2) segment to a fixed number of points for consistent representation."""
+    for i, s in enumerate(segments):
+        s = np.concatenate((s, s[0:1, :]), axis=0)
+        x = np.linspace(0, len(s) - 1, n)
+        xp = np.arange(len(s))
+        segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T  # segment xy
+    return segments
+
+
+def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None):
+    """Rescales (xyxy) bounding boxes from img1_shape to img0_shape, optionally using provided `ratio_pad`."""
+    if ratio_pad is None:  # calculate from img0_shape
+        gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1])  # gain  = old / new
+        pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2  # wh padding
+    else:
+        gain = ratio_pad[0][0]
+        pad = ratio_pad[1]
+
+    boxes[..., [0, 2]] -= pad[0]  # x padding
+    boxes[..., [1, 3]] -= pad[1]  # y padding
+    boxes[..., :4] /= gain
+    clip_boxes(boxes, img0_shape)
+    return boxes
+
+
+def scale_segments(img1_shape, segments, img0_shape, ratio_pad=None, normalize=False):
+    """Rescales segment coordinates from img1_shape to img0_shape, optionally normalizing them with custom padding."""
+    if ratio_pad is None:  # calculate from img0_shape
+        gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1])  # gain  = old / new
+        pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2  # wh padding
+    else:
+        gain = ratio_pad[0][0]
+        pad = ratio_pad[1]
+
+    segments[:, 0] -= pad[0]  # x padding
+    segments[:, 1] -= pad[1]  # y padding
+    segments /= gain
+    clip_segments(segments, img0_shape)
+    if normalize:
+        segments[:, 0] /= img0_shape[1]  # width
+        segments[:, 1] /= img0_shape[0]  # height
+    return segments
+
+
+def clip_boxes(boxes, shape):
+    """Clips bounding box coordinates (xyxy) to fit within the specified image shape (height, width)."""
+    if isinstance(boxes, torch.Tensor):  # faster individually
+        boxes[..., 0].clamp_(0, shape[1])  # x1
+        boxes[..., 1].clamp_(0, shape[0])  # y1
+        boxes[..., 2].clamp_(0, shape[1])  # x2
+        boxes[..., 3].clamp_(0, shape[0])  # y2
+    else:  # np.array (faster grouped)
+        boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1])  # x1, x2
+        boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0])  # y1, y2
+
+
+def clip_segments(segments, shape):
+    """Clips segment coordinates (xy1, xy2, ...) to an image's boundaries given its shape (height, width)."""
+    if isinstance(segments, torch.Tensor):  # faster individually
+        segments[:, 0].clamp_(0, shape[1])  # x
+        segments[:, 1].clamp_(0, shape[0])  # y
+    else:  # np.array (faster grouped)
+        segments[:, 0] = segments[:, 0].clip(0, shape[1])  # x
+        segments[:, 1] = segments[:, 1].clip(0, shape[0])  # y
+
+
+def non_max_suppression(
+    prediction,
+    conf_thres=0.25,
+    iou_thres=0.45,
+    classes=None,
+    agnostic=False,
+    multi_label=False,
+    labels=(),
+    max_det=300,
+    nm=0,  # number of masks
+):
+    """
+    Non-Maximum Suppression (NMS) on inference results to reject overlapping detections.
+
+    Returns:
+         list of detections, on (n,6) tensor per image [xyxy, conf, cls]
+    """
+
+    # Checks
+    assert 0 <= conf_thres <= 1, f"Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0"
+    assert 0 <= iou_thres <= 1, f"Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0"
+    if isinstance(prediction, (list, tuple)):  # YOLOv5 model in validation model, output = (inference_out, loss_out)
+        prediction = prediction[0]  # select only inference output
+
+    device = prediction.device
+    mps = "mps" in device.type  # Apple MPS
+    if mps:  # MPS not fully supported yet, convert tensors to CPU before NMS
+        prediction = prediction.cpu()
+    bs = prediction.shape[0]  # batch size
+    nc = prediction.shape[2] - nm - 5  # number of classes
+    xc = prediction[..., 4] > conf_thres  # candidates
+
+    # Settings
+    # min_wh = 2  # (pixels) minimum box width and height
+    max_wh = 7680  # (pixels) maximum box width and height
+    max_nms = 30000  # maximum number of boxes into torchvision.ops.nms()
+    time_limit = 0.5 + 0.05 * bs  # seconds to quit after
+    redundant = True  # require redundant detections
+    multi_label &= nc > 1  # multiple labels per box (adds 0.5ms/img)
+    merge = False  # use merge-NMS
+
+    t = time.time()
+    mi = 5 + nc  # mask start index
+    output = [torch.zeros((0, 6 + nm), device=prediction.device)] * bs
+    for xi, x in enumerate(prediction):  # image index, image inference
+        # Apply constraints
+        # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0  # width-height
+        x = x[xc[xi]]  # confidence
+
+        # Cat apriori labels if autolabelling
+        if labels and len(labels[xi]):
+            lb = labels[xi]
+            v = torch.zeros((len(lb), nc + nm + 5), device=x.device)
+            v[:, :4] = lb[:, 1:5]  # box
+            v[:, 4] = 1.0  # conf
+            v[range(len(lb)), lb[:, 0].long() + 5] = 1.0  # cls
+            x = torch.cat((x, v), 0)
+
+        # If none remain process next image
+        if not x.shape[0]:
+            continue
+
+        # Compute conf
+        x[:, 5:] *= x[:, 4:5]  # conf = obj_conf * cls_conf
+
+        # Box/Mask
+        box = xywh2xyxy(x[:, :4])  # center_x, center_y, width, height) to (x1, y1, x2, y2)
+        mask = x[:, mi:]  # zero columns if no masks
+
+        # Detections matrix nx6 (xyxy, conf, cls)
+        if multi_label:
+            i, j = (x[:, 5:mi] > conf_thres).nonzero(as_tuple=False).T
+            x = torch.cat((box[i], x[i, 5 + j, None], j[:, None].float(), mask[i]), 1)
+        else:  # best class only
+            conf, j = x[:, 5:mi].max(1, keepdim=True)
+            x = torch.cat((box, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres]
+
+        # Filter by class
+        if classes is not None:
+            x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
+
+        # Apply finite constraint
+        # if not torch.isfinite(x).all():
+        #     x = x[torch.isfinite(x).all(1)]
+
+        # Check shape
+        n = x.shape[0]  # number of boxes
+        if not n:  # no boxes
+            continue
+        x = x[x[:, 4].argsort(descending=True)[:max_nms]]  # sort by confidence and remove excess boxes
+
+        # Batched NMS
+        c = x[:, 5:6] * (0 if agnostic else max_wh)  # classes
+        boxes, scores = x[:, :4] + c, x[:, 4]  # boxes (offset by class), scores
+        i = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS
+        i = i[:max_det]  # limit detections
+        if merge and (1 < n < 3e3):  # Merge NMS (boxes merged using weighted mean)
+            # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
+            iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix
+            weights = iou * scores[None]  # box weights
+            x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True)  # merged boxes
+            if redundant:
+                i = i[iou.sum(1) > 1]  # require redundancy
+
+        output[xi] = x[i]
+        if mps:
+            output[xi] = output[xi].to(device)
+        if (time.time() - t) > time_limit:
+            LOGGER.warning(f"WARNING ⚠️ NMS time limit {time_limit:.3f}s exceeded")
+            break  # time limit exceeded
+
+    return output
+
+
+def strip_optimizer(f="best.pt", s=""):
+    """
+    Strips optimizer and optionally saves checkpoint to finalize training; arguments are file path 'f' and save path
+    's'.
+
+    Example: from utils.general import *; strip_optimizer()
+    """
+    x = torch.load(f, map_location=torch.device("cpu"))
+    if x.get("ema"):
+        x["model"] = x["ema"]  # replace model with ema
+    for k in "optimizer", "best_fitness", "ema", "updates":  # keys
+        x[k] = None
+    x["epoch"] = -1
+    x["model"].half()  # to FP16
+    for p in x["model"].parameters():
+        p.requires_grad = False
+    torch.save(x, s or f)
+    mb = os.path.getsize(s or f) / 1e6  # filesize
+    LOGGER.info(f"Optimizer stripped from {f},{f' saved as {s},' if s else ''} {mb:.1f}MB")
+
+
+def print_mutation(keys, results, hyp, save_dir, bucket, prefix=colorstr("evolve: ")):
+    """Logs evolution results and saves to CSV and YAML in `save_dir`, optionally syncs with `bucket`."""
+    evolve_csv = save_dir / "evolve.csv"
+    evolve_yaml = save_dir / "hyp_evolve.yaml"
+    keys = tuple(keys) + tuple(hyp.keys())  # [results + hyps]
+    keys = tuple(x.strip() for x in keys)
+    vals = results + tuple(hyp.values())
+    n = len(keys)
+
+    # Download (optional)
+    if bucket:
+        url = f"gs://{bucket}/evolve.csv"
+        if gsutil_getsize(url) > (evolve_csv.stat().st_size if evolve_csv.exists() else 0):
+            subprocess.run(["gsutil", "cp", f"{url}", f"{save_dir}"])  # download evolve.csv if larger than local
+
+    # Log to evolve.csv
+    s = "" if evolve_csv.exists() else (("%20s," * n % keys).rstrip(",") + "\n")  # add header
+    with open(evolve_csv, "a") as f:
+        f.write(s + ("%20.5g," * n % vals).rstrip(",") + "\n")
+
+    # Save yaml
+    with open(evolve_yaml, "w") as f:
+        data = pd.read_csv(evolve_csv, skipinitialspace=True)
+        data = data.rename(columns=lambda x: x.strip())  # strip keys
+        i = np.argmax(fitness(data.values[:, :4]))  #
+        generations = len(data)
+        f.write(
+            "# YOLOv5 Hyperparameter Evolution Results\n"
+            + f"# Best generation: {i}\n"
+            + f"# Last generation: {generations - 1}\n"
+            + "# "
+            + ", ".join(f"{x.strip():>20s}" for x in keys[:7])
+            + "\n"
+            + "# "
+            + ", ".join(f"{x:>20.5g}" for x in data.values[i, :7])
+            + "\n\n"
+        )
+        yaml.safe_dump(data.loc[i][7:].to_dict(), f, sort_keys=False)
+
+    # Print to screen
+    LOGGER.info(
+        prefix
+        + f"{generations} generations finished, current result:\n"
+        + prefix
+        + ", ".join(f"{x.strip():>20s}" for x in keys)
+        + "\n"
+        + prefix
+        + ", ".join(f"{x:20.5g}" for x in vals)
+        + "\n\n"
+    )
+
+    if bucket:
+        subprocess.run(["gsutil", "cp", f"{evolve_csv}", f"{evolve_yaml}", f"gs://{bucket}"])  # upload
+
+
+def apply_classifier(x, model, img, im0):
+    """Applies second-stage classifier to YOLO outputs, filtering detections by class match."""
+    # Example model = torchvision.models.__dict__['efficientnet_b0'](pretrained=True).to(device).eval()
+    im0 = [im0] if isinstance(im0, np.ndarray) else im0
+    for i, d in enumerate(x):  # per image
+        if d is not None and len(d):
+            d = d.clone()
+
+            # Reshape and pad cutouts
+            b = xyxy2xywh(d[:, :4])  # boxes
+            b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1)  # rectangle to square
+            b[:, 2:] = b[:, 2:] * 1.3 + 30  # pad
+            d[:, :4] = xywh2xyxy(b).long()
+
+            # Rescale boxes from img_size to im0 size
+            scale_boxes(img.shape[2:], d[:, :4], im0[i].shape)
+
+            # Classes
+            pred_cls1 = d[:, 5].long()
+            ims = []
+            for a in d:
+                cutout = im0[i][int(a[1]) : int(a[3]), int(a[0]) : int(a[2])]
+                im = cv2.resize(cutout, (224, 224))  # BGR
+
+                im = im[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
+                im = np.ascontiguousarray(im, dtype=np.float32)  # uint8 to float32
+                im /= 255  # 0 - 255 to 0.0 - 1.0
+                ims.append(im)
+
+            pred_cls2 = model(torch.Tensor(ims).to(d.device)).argmax(1)  # classifier prediction
+            x[i] = x[i][pred_cls1 == pred_cls2]  # retain matching class detections
+
+    return x
+
+
+def increment_path(path, exist_ok=False, sep="", mkdir=False):
+    """
+    Generates an incremented file or directory path if it exists, with optional mkdir; args: path, exist_ok=False,
+    sep="", mkdir=False.
+
+    Example: runs/exp --> runs/exp{sep}2, runs/exp{sep}3, ... etc
+    """
+    path = Path(path)  # os-agnostic
+    if path.exists() and not exist_ok:
+        path, suffix = (path.with_suffix(""), path.suffix) if path.is_file() else (path, "")
+
+        # Method 1
+        for n in range(2, 9999):
+            p = f"{path}{sep}{n}{suffix}"  # increment path
+            if not os.path.exists(p):  #
+                break
+        path = Path(p)
+
+        # Method 2 (deprecated)
+        # dirs = glob.glob(f"{path}{sep}*")  # similar paths
+        # matches = [re.search(rf"{path.stem}{sep}(\d+)", d) for d in dirs]
+        # i = [int(m.groups()[0]) for m in matches if m]  # indices
+        # n = max(i) + 1 if i else 2  # increment number
+        # path = Path(f"{path}{sep}{n}{suffix}")  # increment path
+
+    if mkdir:
+        path.mkdir(parents=True, exist_ok=True)  # make directory
+
+    return path
+
+
+# OpenCV Multilanguage-friendly functions ------------------------------------------------------------------------------------
+imshow_ = cv2.imshow  # copy to avoid recursion errors
+
+
+def imread(filename, flags=cv2.IMREAD_COLOR):
+    """Reads an image from a file and returns it as a numpy array, using OpenCV's imdecode to support multilanguage
+    paths.
+    """
+    return cv2.imdecode(np.fromfile(filename, np.uint8), flags)
+
+
+def imwrite(filename, img):
+    """Writes an image to a file, returns True on success and False on failure, supports multilanguage paths."""
+    try:
+        cv2.imencode(Path(filename).suffix, img)[1].tofile(filename)
+        return True
+    except Exception:
+        return False
+
+
+def imshow(path, im):
+    """Displays an image using Unicode path, requires encoded path and image matrix as input."""
+    imshow_(path.encode("unicode_escape").decode(), im)
+
+
+if Path(inspect.stack()[0].filename).parent.parent.as_posix() in inspect.stack()[-1].filename:
+    cv2.imread, cv2.imwrite, cv2.imshow = imread, imwrite, imshow  # redefine
+
+# Variables ------------------------------------------------------------------------------------------------------------

utils/google_app_engine/Dockerfile

+FROM gcr.io/google-appengine/python
+
+# Create a virtualenv for dependencies. This isolates these packages from
+# system-level packages.
+# Use -p python3 or -p python3.7 to select python version. Default is version 2.
+RUN virtualenv /env -p python3
+
+# Setting these environment variables are the same as running
+# source /env/bin/activate.
+ENV VIRTUAL_ENV /env
+ENV PATH /env/bin:$PATH
+
+RUN apt-get update && apt-get install -y python-opencv
+
+# Copy the application's requirements.txt and run pip to install all
+# dependencies into the virtualenv.
+ADD requirements.txt /app/requirements.txt
+RUN pip install -r /app/requirements.txt
+
+# Add the application source code.
+ADD . /app
+
+# Run a WSGI server to serve the application. gunicorn must be declared as
+# a dependency in requirements.txt.
+CMD gunicorn -b :$PORT main:app

utils/google_app_engine/additional_requirements.txt

+# add these requirements in your app on top of the existing ones
+pip==23.3
+Flask==2.3.2
+gunicorn==19.10.0
+werkzeug>=3.0.1 # not directly required, pinned by Snyk to avoid a vulnerability

utils/google_app_engine/app.yaml

+runtime: custom
+env: flex
+
+service: yolov5app
+
+liveness_check:
+  initial_delay_sec: 600
+
+manual_scaling:
+  instances: 1
+resources:
+  cpu: 1
+  memory_gb: 4
+  disk_size_gb: 20

utils/loggers/__init__.py

+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+"""Logging utils."""
+import json
+import os
+import warnings
+from pathlib import Path
+
+import pkg_resources as pkg
+import torch
+
+from utils.general import LOGGER, colorstr, cv2
+from utils.loggers.clearml.clearml_utils import ClearmlLogger
+from utils.loggers.wandb.wandb_utils import WandbLogger
+from utils.plots import plot_images, plot_labels, plot_results
+from utils.torch_utils import de_parallel
+
+LOGGERS = ("csv", "tb", "wandb", "clearml", "comet")  # *.csv, TensorBoard, Weights & Biases, ClearML
+RANK = int(os.getenv("RANK", -1))
+
+try:
+    from torch.utils.tensorboard import SummaryWriter
+except ImportError:
+    SummaryWriter = lambda *args: None  # None = SummaryWriter(str)
+
+try:
+    import wandb
+
+    assert hasattr(wandb, "__version__")  # verify package import not local dir
+    if pkg.parse_version(wandb.__version__) >= pkg.parse_version("0.12.2") and RANK in {0, -1}:
+        try:
+            wandb_login_success = wandb.login(timeout=30)
+        except wandb.errors.UsageError:  # known non-TTY terminal issue
+            wandb_login_success = False
+        if not wandb_login_success:
+            wandb = None
+except (ImportError, AssertionError):
+    wandb = None
+
+try:
+    import clearml
+
+    assert hasattr(clearml, "__version__")  # verify package import not local dir
+except (ImportError, AssertionError):
+    clearml = None
+
+try:
+    if RANK in {0, -1}:
+        import comet_ml
+
+        assert hasattr(comet_ml, "__version__")  # verify package import not local dir
+        from utils.loggers.comet import CometLogger
+
+    else:
+        comet_ml = None
+except (ImportError, AssertionError):
+    comet_ml = None
+
+
+def _json_default(value):
+    """
+    Format `value` for JSON serialization (e.g. unwrap tensors).
+
+    Fall back to strings.
+    """
+    if isinstance(value, torch.Tensor):
+        try:
+            value = value.item()
+        except ValueError:  # "only one element tensors can be converted to Python scalars"
+            pass
+    return value if isinstance(value, float) else str(value)
+
+
+class Loggers:
+    # YOLOv5 Loggers class
+    def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, include=LOGGERS):
+        """Initializes loggers for YOLOv5 training and validation metrics, paths, and options."""
+        self.save_dir = save_dir
+        self.weights = weights
+        self.opt = opt
+        self.hyp = hyp
+        self.plots = not opt.noplots  # plot results
+        self.logger = logger  # for printing results to console
+        self.include = include
+        self.keys = [
+            "train/box_loss",
+            "train/obj_loss",
+            "train/cls_loss",  # train loss
+            "metrics/precision",
+            "metrics/recall",
+            "metrics/mAP_0.5",
+            "metrics/mAP_0.5:0.95",  # metrics
+            "val/box_loss",
+            "val/obj_loss",
+            "val/cls_loss",  # val loss
+            "x/lr0",
+            "x/lr1",
+            "x/lr2",
+        ]  # params
+        self.best_keys = ["best/epoch", "best/precision", "best/recall", "best/mAP_0.5", "best/mAP_0.5:0.95"]
+        for k in LOGGERS:
+            setattr(self, k, None)  # init empty logger dictionary
+        self.csv = True  # always log to csv
+        self.ndjson_console = "ndjson_console" in self.include  # log ndjson to console
+        self.ndjson_file = "ndjson_file" in self.include  # log ndjson to file
+
+        # Messages
+        if not comet_ml:
+            prefix = colorstr("Comet: ")
+            s = f"{prefix}run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet"
+            self.logger.info(s)
+        # TensorBoard
+        s = self.save_dir
+        if "tb" in self.include and not self.opt.evolve:
+            prefix = colorstr("TensorBoard: ")
+            self.logger.info(f"{prefix}Start with 'tensorboard --logdir {s.parent}', view at http://localhost:6006/")
+            self.tb = SummaryWriter(str(s))
+
+        # W&B
+        if wandb and "wandb" in self.include:
+            self.opt.hyp = self.hyp  # add hyperparameters
+            self.wandb = WandbLogger(self.opt)
+        else:
+            self.wandb = None
+
+        # ClearML
+        if clearml and "clearml" in self.include:
+            try:
+                self.clearml = ClearmlLogger(self.opt, self.hyp)
+            except Exception:
+                self.clearml = None
+                prefix = colorstr("ClearML: ")
+                LOGGER.warning(
+                    f"{prefix}WARNING ⚠️ ClearML is installed but not configured, skipping ClearML logging."
+                    f" See https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration#readme"
+                )
+
+        else:
+            self.clearml = None
+
+        # Comet
+        if comet_ml and "comet" in self.include:
+            if isinstance(self.opt.resume, str) and self.opt.resume.startswith("comet://"):
+                run_id = self.opt.resume.split("/")[-1]
+                self.comet_logger = CometLogger(self.opt, self.hyp, run_id=run_id)
+
+            else:
+                self.comet_logger = CometLogger(self.opt, self.hyp)
+
+        else:
+            self.comet_logger = None
+
+    @property
+    def remote_dataset(self):
+        """Fetches dataset dictionary from remote logging services like ClearML, Weights & Biases, or Comet ML."""
+        data_dict = None
+        if self.clearml:
+            data_dict = self.clearml.data_dict
+        if self.wandb:
+            data_dict = self.wandb.data_dict
+        if self.comet_logger:
+            data_dict = self.comet_logger.data_dict
+
+        return data_dict
+
+    def on_train_start(self):
+        """Initializes the training process for Comet ML logger if it's configured."""
+        if self.comet_logger:
+            self.comet_logger.on_train_start()
+
+    def on_pretrain_routine_start(self):
+        """Invokes pre-training routine start hook for Comet ML logger if available."""
+        if self.comet_logger:
+            self.comet_logger.on_pretrain_routine_start()
+
+    def on_pretrain_routine_end(self, labels, names):
+        """Callback that runs at the end of pre-training routine, logging label plots if enabled."""
+        if self.plots:
+            plot_labels(labels, names, self.save_dir)
+            paths = self.save_dir.glob("*labels*.jpg")  # training labels
+            if self.wandb:
+                self.wandb.log({"Labels": [wandb.Image(str(x), caption=x.name) for x in paths]})
+            if self.comet_logger:
+                self.comet_logger.on_pretrain_routine_end(paths)
+            if self.clearml:
+                for path in paths:
+                    self.clearml.log_plot(title=path.stem, plot_path=path)
+
+    def on_train_batch_end(self, model, ni, imgs, targets, paths, vals):
+        """Logs training batch end events, plots images, and updates external loggers with batch-end data."""
+        log_dict = dict(zip(self.keys[:3], vals))
+        # Callback runs on train batch end
+        # ni: number integrated batches (since train start)
+        if self.plots:
+            if ni < 3:
+                f = self.save_dir / f"train_batch{ni}.jpg"  # filename
+                plot_images(imgs, targets, paths, f)
+                if ni == 0 and self.tb and not self.opt.sync_bn:
+                    log_tensorboard_graph(self.tb, model, imgsz=(self.opt.imgsz, self.opt.imgsz))
+            if ni == 10 and (self.wandb or self.clearml):
+                files = sorted(self.save_dir.glob("train*.jpg"))
+                if self.wandb:
+                    self.wandb.log({"Mosaics": [wandb.Image(str(f), caption=f.name) for f in files if f.exists()]})
+                if self.clearml:
+                    self.clearml.log_debug_samples(files, title="Mosaics")
+
+        if self.comet_logger:
+            self.comet_logger.on_train_batch_end(log_dict, step=ni)
+
+    def on_train_epoch_end(self, epoch):
+        """Callback that updates the current epoch in Weights & Biases at the end of a training epoch."""
+        if self.wandb:
+            self.wandb.current_epoch = epoch + 1
+
+        if self.comet_logger:
+            self.comet_logger.on_train_epoch_end(epoch)
+
+    def on_val_start(self):
+        """Callback that signals the start of a validation phase to the Comet logger."""
+        if self.comet_logger:
+            self.comet_logger.on_val_start()
+
+    def on_val_image_end(self, pred, predn, path, names, im):
+        """Callback that logs a validation image and its predictions to WandB or ClearML."""
+        if self.wandb:
+            self.wandb.val_one_image(pred, predn, path, names, im)
+        if self.clearml:
+            self.clearml.log_image_with_boxes(path, pred, names, im)
+
+    def on_val_batch_end(self, batch_i, im, targets, paths, shapes, out):
+        """Logs validation batch results to Comet ML during training at the end of each validation batch."""
+        if self.comet_logger:
+            self.comet_logger.on_val_batch_end(batch_i, im, targets, paths, shapes, out)
+
+    def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix):
+        """Logs validation results to WandB or ClearML at the end of the validation process."""
+        if self.wandb or self.clearml:
+            files = sorted(self.save_dir.glob("val*.jpg"))
+        if self.wandb:
+            self.wandb.log({"Validation": [wandb.Image(str(f), caption=f.name) for f in files]})
+        if self.clearml:
+            self.clearml.log_debug_samples(files, title="Validation")
+
+        if self.comet_logger:
+            self.comet_logger.on_val_end(nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix)
+
+    def on_fit_epoch_end(self, vals, epoch, best_fitness, fi):
+        """Callback that logs metrics and saves them to CSV or NDJSON at the end of each fit (train+val) epoch."""
+        x = dict(zip(self.keys, vals))
+        if self.csv:
+            file = self.save_dir / "results.csv"
+            n = len(x) + 1  # number of cols
+            s = "" if file.exists() else (("%20s," * n % tuple(["epoch"] + self.keys)).rstrip(",") + "\n")  # add header
+            with open(file, "a") as f:
+                f.write(s + ("%20.5g," * n % tuple([epoch] + vals)).rstrip(",") + "\n")
+        if self.ndjson_console or self.ndjson_file:
+            json_data = json.dumps(dict(epoch=epoch, **x), default=_json_default)
+        if self.ndjson_console:
+            print(json_data)
+        if self.ndjson_file:
+            file = self.save_dir / "results.ndjson"
+            with open(file, "a") as f:
+                print(json_data, file=f)
+
+        if self.tb:
+            for k, v in x.items():
+                self.tb.add_scalar(k, v, epoch)
+        elif self.clearml:  # log to ClearML if TensorBoard not used
+            self.clearml.log_scalars(x, epoch)
+
+        if self.wandb:
+            if best_fitness == fi:
+                best_results = [epoch] + vals[3:7]
+                for i, name in enumerate(self.best_keys):
+                    self.wandb.wandb_run.summary[name] = best_results[i]  # log best results in the summary
+            self.wandb.log(x)
+            self.wandb.end_epoch()
+
+        if self.clearml:
+            self.clearml.current_epoch_logged_images = set()  # reset epoch image limit
+            self.clearml.current_epoch += 1
+
+        if self.comet_logger:
+            self.comet_logger.on_fit_epoch_end(x, epoch=epoch)
+
+    def on_model_save(self, last, epoch, final_epoch, best_fitness, fi):
+        """Callback that handles model saving events, logging to Weights & Biases or ClearML if enabled."""
+        if (epoch + 1) % self.opt.save_period == 0 and not final_epoch and self.opt.save_period != -1:
+            if self.wandb:
+                self.wandb.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi)
+            if self.clearml:
+                self.clearml.task.update_output_model(
+                    model_path=str(last), model_name="Latest Model", auto_delete_file=False
+                )
+
+        if self.comet_logger:
+            self.comet_logger.on_model_save(last, epoch, final_epoch, best_fitness, fi)
+
+    def on_train_end(self, last, best, epoch, results):
+        """Callback that runs at the end of training to save plots and log results."""
+        if self.plots:
+            plot_results(file=self.save_dir / "results.csv")  # save results.png
+        files = ["results.png", "confusion_matrix.png", *(f"{x}_curve.png" for x in ("F1", "PR", "P", "R"))]
+        files = [(self.save_dir / f) for f in files if (self.save_dir / f).exists()]  # filter
+        self.logger.info(f"Results saved to {colorstr('bold', self.save_dir)}")
+
+        if self.tb and not self.clearml:  # These images are already captured by ClearML by now, we don't want doubles
+            for f in files:
+                self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats="HWC")
+
+        if self.wandb:
+            self.wandb.log(dict(zip(self.keys[3:10], results)))
+            self.wandb.log({"Results": [wandb.Image(str(f), caption=f.name) for f in files]})
+            # Calling wandb.log. TODO: Refactor this into WandbLogger.log_model
+            if not self.opt.evolve:
+                wandb.log_artifact(
+                    str(best if best.exists() else last),
+                    type="model",
+                    name=f"run_{self.wandb.wandb_run.id}_model",
+                    aliases=["latest", "best", "stripped"],
+                )
+            self.wandb.finish_run()
+
+        if self.clearml and not self.opt.evolve:
+            self.clearml.log_summary(dict(zip(self.keys[3:10], results)))
+            [self.clearml.log_plot(title=f.stem, plot_path=f) for f in files]
+            self.clearml.log_model(
+                str(best if best.exists() else last), "Best Model" if best.exists() else "Last Model", epoch
+            )
+
+        if self.comet_logger:
+            final_results = dict(zip(self.keys[3:10], results))
+            self.comet_logger.on_train_end(files, self.save_dir, last, best, epoch, final_results)
+
+    def on_params_update(self, params: dict):
+        """Updates experiment hyperparameters or configurations in WandB, Comet, or ClearML."""
+        if self.wandb:
+            self.wandb.wandb_run.config.update(params, allow_val_change=True)
+        if self.comet_logger:
+            self.comet_logger.on_params_update(params)
+        if self.clearml:
+            self.clearml.task.connect(params)
+
+
+class GenericLogger:
+    """
+    YOLOv5 General purpose logger for non-task specific logging
+    Usage: from utils.loggers import GenericLogger; logger = GenericLogger(...)
+    Arguments
+        opt:             Run arguments
+        console_logger:  Console logger
+        include:         loggers to include
+    """
+
+    def __init__(self, opt, console_logger, include=("tb", "wandb", "clearml")):
+        """Initializes a generic logger with optional TensorBoard, W&B, and ClearML support."""
+        self.save_dir = Path(opt.save_dir)
+        self.include = include
+        self.console_logger = console_logger
+        self.csv = self.save_dir / "results.csv"  # CSV logger
+        if "tb" in self.include:
+            prefix = colorstr("TensorBoard: ")
+            self.console_logger.info(
+                f"{prefix}Start with 'tensorboard --logdir {self.save_dir.parent}', view at http://localhost:6006/"
+            )
+            self.tb = SummaryWriter(str(self.save_dir))
+
+        if wandb and "wandb" in self.include:
+            self.wandb = wandb.init(
+                project=web_project_name(str(opt.project)), name=None if opt.name == "exp" else opt.name, config=opt
+            )
+        else:
+            self.wandb = None
+
+        if clearml and "clearml" in self.include:
+            try:
+                # Hyp is not available in classification mode
+                hyp = {} if "hyp" not in opt else opt.hyp
+                self.clearml = ClearmlLogger(opt, hyp)
+            except Exception:
+                self.clearml = None
+                prefix = colorstr("ClearML: ")
+                LOGGER.warning(
+                    f"{prefix}WARNING ⚠️ ClearML is installed but not configured, skipping ClearML logging."
+                    f" See https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml#readme"
+                )
+        else:
+            self.clearml = None
+
+    def log_metrics(self, metrics, epoch):
+        """Logs metrics to CSV, TensorBoard, W&B, and ClearML; `metrics` is a dict, `epoch` is an int."""
+        if self.csv:
+            keys, vals = list(metrics.keys()), list(metrics.values())
+            n = len(metrics) + 1  # number of cols
+            s = "" if self.csv.exists() else (("%23s," * n % tuple(["epoch"] + keys)).rstrip(",") + "\n")  # header
+            with open(self.csv, "a") as f:
+                f.write(s + ("%23.5g," * n % tuple([epoch] + vals)).rstrip(",") + "\n")
+
+        if self.tb:
+            for k, v in metrics.items():
+                self.tb.add_scalar(k, v, epoch)
+
+        if self.wandb:
+            self.wandb.log(metrics, step=epoch)
+
+        if self.clearml:
+            self.clearml.log_scalars(metrics, epoch)
+
+    def log_images(self, files, name="Images", epoch=0):
+        """Logs images to all loggers with optional naming and epoch specification."""
+        files = [Path(f) for f in (files if isinstance(files, (tuple, list)) else [files])]  # to Path
+        files = [f for f in files if f.exists()]  # filter by exists
+
+        if self.tb:
+            for f in files:
+                self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats="HWC")
+
+        if self.wandb:
+            self.wandb.log({name: [wandb.Image(str(f), caption=f.name) for f in files]}, step=epoch)
+
+        if self.clearml:
+            if name == "Results":
+                [self.clearml.log_plot(f.stem, f) for f in files]
+            else:
+                self.clearml.log_debug_samples(files, title=name)
+
+    def log_graph(self, model, imgsz=(640, 640)):
+        """Logs model graph to all configured loggers with specified input image size."""
+        if self.tb:
+            log_tensorboard_graph(self.tb, model, imgsz)
+
+    def log_model(self, model_path, epoch=0, metadata=None):
+        """Logs the model to all configured loggers with optional epoch and metadata."""
+        if metadata is None:
+            metadata = {}
+        # Log model to all loggers
+        if self.wandb:
+            art = wandb.Artifact(name=f"run_{wandb.run.id}_model", type="model", metadata=metadata)
+            art.add_file(str(model_path))
+            wandb.log_artifact(art)
+        if self.clearml:
+            self.clearml.log_model(model_path=model_path, model_name=model_path.stem)
+
+    def update_params(self, params):
+        """Updates logged parameters in WandB and/or ClearML if enabled."""
+        if self.wandb:
+            wandb.run.config.update(params, allow_val_change=True)
+        if self.clearml:
+            self.clearml.task.connect(params)
+
+
+def log_tensorboard_graph(tb, model, imgsz=(640, 640)):
+    """Logs the model graph to TensorBoard with specified image size and model."""
+    try:
+        p = next(model.parameters())  # for device, type
+        imgsz = (imgsz, imgsz) if isinstance(imgsz, int) else imgsz  # expand
+        im = torch.zeros((1, 3, *imgsz)).to(p.device).type_as(p)  # input image (WARNING: must be zeros, not empty)
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")  # suppress jit trace warning
+            tb.add_graph(torch.jit.trace(de_parallel(model), im, strict=False), [])
+    except Exception as e:
+        LOGGER.warning(f"WARNING ⚠️ TensorBoard graph visualization failure {e}")
+
+
+def web_project_name(project):
+    """Converts a local project name to a standardized web project name with optional suffixes."""
+    if not project.startswith("runs/train"):
+        return project
+    suffix = "-Classify" if project.endswith("-cls") else "-Segment" if project.endswith("-seg") else ""
+    return f"YOLOv5{suffix}"

utils/loggers/clearml/README.md

+# ClearML Integration
+
+<img align="center" src="https://github.com/thepycoder/clearml_screenshots/raw/main/logos_dark.png#gh-light-mode-only" alt="Clear|ML"><img align="center" src="https://github.com/thepycoder/clearml_screenshots/raw/main/logos_light.png#gh-dark-mode-only" alt="Clear|ML">
+
+## About ClearML
+
+[ClearML](https://cutt.ly/yolov5-tutorial-clearml) is an [open-source](https://github.com/allegroai/clearml) toolbox designed to save you time ⏱️.
+
+🔨 Track every YOLOv5 training run in the <b>experiment manager</b>
+
+🔧 Version and easily access your custom training data with the integrated ClearML <b>Data Versioning Tool</b>
+
+🔦 <b>Remotely train and monitor</b> your YOLOv5 training runs using ClearML Agent
+
+🔬 Get the very best mAP using ClearML <b>Hyperparameter Optimization</b>
+
+🔭 Turn your newly trained <b>YOLOv5 model into an API</b> with just a few commands using ClearML Serving
+
+<br />
+And so much more. It's up to you how many of these tools you want to use, you can stick to the experiment manager, or chain them all together into an impressive pipeline!
+<br />
+<br />
+
+![ClearML scalars dashboard](https://github.com/thepycoder/clearml_screenshots/raw/main/experiment_manager_with_compare.gif)
+
+<br />
+<br />
+
+## 🦾 Setting Things Up
+
+To keep track of your experiments and/or data, ClearML needs to communicate to a server. You have 2 options to get one:
+
+Either sign up for free to the [ClearML Hosted Service](https://cutt.ly/yolov5-tutorial-clearml) or you can set up your own server, see [here](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server). Even the server is open-source, so even if you're dealing with sensitive data, you should be good to go!
+
+1. Install the `clearml` python package:
+
+    ```bash
+    pip install clearml
+    ```
+
+2. Connect the ClearML SDK to the server by [creating credentials](https://app.clear.ml/settings/workspace-configuration) (go right top to Settings -> Workspace -> Create new credentials), then execute the command below and follow the instructions:
+
+    ```bash
+    clearml-init
+    ```
+
+That's it! You're done 😎
+
+<br />
+
+## 🚀 Training YOLOv5 With ClearML
+
+To enable ClearML experiment tracking, simply install the ClearML pip package.
+
+```bash
+pip install clearml>=1.2.0
+```
+
+This will enable integration with the YOLOv5 training script. Every training run from now on, will be captured and stored by the ClearML experiment manager.
+
+If you want to change the `project_name` or `task_name`, use the `--project` and `--name` arguments of the `train.py` script, by default the project will be called `YOLOv5` and the task `Training`. PLEASE NOTE: ClearML uses `/` as a delimiter for subprojects, so be careful when using `/` in your project name!
+
+```bash
+python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache
+```
+
+or with custom project and task name:
+
+```bash
+python train.py --project my_project --name my_training --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache
+```
+
+This will capture:
+
+- Source code + uncommitted changes
+- Installed packages
+- (Hyper)parameters
+- Model files (use `--save-period n` to save a checkpoint every n epochs)
+- Console output
+- Scalars (mAP_0.5, mAP_0.5:0.95, precision, recall, losses, learning rates, ...)
+- General info such as machine details, runtime, creation date etc.
+- All produced plots such as label correlogram and confusion matrix
+- Images with bounding boxes per epoch
+- Mosaic per epoch
+- Validation images per epoch
+- ...
+
+That's a lot right? 🤯 Now, we can visualize all of this information in the ClearML UI to get an overview of our training progress. Add custom columns to the table view (such as e.g. mAP_0.5) so you can easily sort on the best performing model. Or select multiple experiments and directly compare them!
+
+There even more we can do with all of this information, like hyperparameter optimization and remote execution, so keep reading if you want to see how that works!
+
+<br />
+
+## 🔗 Dataset Version Management
+
+Versioning your data separately from your code is generally a good idea and makes it easy to acquire the latest version too. This repository supports supplying a dataset version ID, and it will make sure to get the data if it's not there yet. Next to that, this workflow also saves the used dataset ID as part of the task parameters, so you will always know for sure which data was used in which experiment!
+
+![ClearML Dataset Interface](https://github.com/thepycoder/clearml_screenshots/raw/main/clearml_data.gif)
+
+### Prepare Your Dataset
+
+The YOLOv5 repository supports a number of different datasets by using yaml files containing their information. By default datasets are downloaded to the `../datasets` folder in relation to the repository root folder. So if you downloaded the `coco128` dataset using the link in the yaml or with the scripts provided by yolov5, you get this folder structure:
+
+```
+..
+|_ yolov5
+|_ datasets
+    |_ coco128
+        |_ images
+        |_ labels
+        |_ LICENSE
+        |_ README.txt
+```
+
+But this can be any dataset you wish. Feel free to use your own, as long as you keep to this folder structure.
+
+Next, ⚠️**copy the corresponding yaml file to the root of the dataset folder**⚠️. This yaml files contains the information ClearML will need to properly use the dataset. You can make this yourself too, of course, just follow the structure of the example yamls.
+
+Basically we need the following keys: `path`, `train`, `test`, `val`, `nc`, `names`.
+
+```
+..
+|_ yolov5
+|_ datasets
+    |_ coco128
+        |_ images
+        |_ labels
+        |_ coco128.yaml  # <---- HERE!
+        |_ LICENSE
+        |_ README.txt
+```
+
+### Upload Your Dataset
+
+To get this dataset into ClearML as a versioned dataset, go to the dataset root folder and run the following command:
+
+```bash
+cd coco128
+clearml-data sync --project YOLOv5 --name coco128 --folder .
+```
+
+The command `clearml-data sync` is actually a shorthand command. You could also run these commands one after the other:
+
+```bash
+# Optionally add --parent <parent_dataset_id> if you want to base
+# this version on another dataset version, so no duplicate files are uploaded!
+clearml-data create --name coco128 --project YOLOv5
+clearml-data add --files .
+clearml-data close
+```
+
+### Run Training Using A ClearML Dataset
+
+Now that you have a ClearML dataset, you can very simply use it to train custom YOLOv5 🚀 models!
+
+```bash
+python train.py --img 640 --batch 16 --epochs 3 --data clearml://<your_dataset_id> --weights yolov5s.pt --cache
+```
+
+<br />
+
+## 👀 Hyperparameter Optimization
+
+Now that we have our experiments and data versioned, it's time to take a look at what we can build on top!
+
+Using the code information, installed packages and environment details, the experiment itself is now **completely reproducible**. In fact, ClearML allows you to clone an experiment and even change its parameters. We can then just rerun it with these new parameters automatically, this is basically what HPO does!
+
+To **run hyperparameter optimization locally**, we've included a pre-made script for you. Just make sure a training task has been run at least once, so it is in the ClearML experiment manager, we will essentially clone it and change its hyperparameters.
+
+You'll need to fill in the ID of this `template task` in the script found at `utils/loggers/clearml/hpo.py` and then just run it :) You can change `task.execute_locally()` to `task.execute()` to put it in a ClearML queue and have a remote agent work on it instead.
+
+```bash
+# To use optuna, install it first, otherwise you can change the optimizer to just be RandomSearch
+pip install optuna
+python utils/loggers/clearml/hpo.py
+```
+
+![HPO](https://github.com/thepycoder/clearml_screenshots/raw/main/hpo.png)
+
+## 🤯 Remote Execution (advanced)
+
+Running HPO locally is really handy, but what if we want to run our experiments on a remote machine instead? Maybe you have access to a very powerful GPU machine on-site, or you have some budget to use cloud GPUs. This is where the ClearML Agent comes into play. Check out what the agent can do here:
+
+- [YouTube video](https://youtu.be/MX3BrXnaULs)
+- [Documentation](https://clear.ml/docs/latest/docs/clearml_agent)
+
+In short: every experiment tracked by the experiment manager contains enough information to reproduce it on a different machine (installed packages, uncommitted changes etc.). So a ClearML agent does just that: it listens to a queue for incoming tasks and when it finds one, it recreates the environment and runs it while still reporting scalars, plots etc. to the experiment manager.
+
+You can turn any machine (a cloud VM, a local GPU machine, your own laptop ... ) into a ClearML agent by simply running:
+
+```bash
+clearml-agent daemon --queue <queues_to_listen_to> [--docker]
+```
+
+### Cloning, Editing And Enqueuing
+
+With our agent running, we can give it some work. Remember from the HPO section that we can clone a task and edit the hyperparameters? We can do that from the interface too!
+
+🪄 Clone the experiment by right-clicking it
+
+🎯 Edit the hyperparameters to what you wish them to be
+
+⏳ Enqueue the task to any of the queues by right-clicking it
+
+![Enqueue a task from the UI](https://github.com/thepycoder/clearml_screenshots/raw/main/enqueue.gif)
+
+### Executing A Task Remotely
+
+Now you can clone a task like we explained above, or simply mark your current script by adding `task.execute_remotely()` and on execution it will be put into a queue, for the agent to start working on!
+
+To run the YOLOv5 training script remotely, all you have to do is add this line to the training.py script after the clearml logger has been instantiated:
+
+```python
+# ...
+# Loggers
+data_dict = None
+if RANK in {-1, 0}:
+    loggers = Loggers(save_dir, weights, opt, hyp, LOGGER)  # loggers instance
+    if loggers.clearml:
+        loggers.clearml.task.execute_remotely(queue="my_queue")  # <------ ADD THIS LINE
+        # Data_dict is either None is user did not choose for ClearML dataset or is filled in by ClearML
+        data_dict = loggers.clearml.data_dict
+# ...
+```
+
+When running the training script after this change, python will run the script up until that line, after which it will package the code and send it to the queue instead!
+
+### Autoscaling workers
+
+ClearML comes with autoscalers too! This tool will automatically spin up new remote machines in the cloud of your choice (AWS, GCP, Azure) and turn them into ClearML agents for you whenever there are experiments detected in the queue. Once the tasks are processed, the autoscaler will automatically shut down the remote machines, and you stop paying!
+
+Check out the autoscalers getting started video below.
+
+[![Watch the video](https://img.youtube.com/vi/j4XVMAaUt3E/0.jpg)](https://youtu.be/j4XVMAaUt3E)

utils/loggers/clearml/clearml_utils.py

+"""Main Logger class for ClearML experiment tracking."""
+import glob
+import re
+from pathlib import Path
+
+import matplotlib.image as mpimg
+import matplotlib.pyplot as plt
+import numpy as np
+import yaml
+from ultralytics.utils.plotting import Annotator, colors
+
+try:
+    import clearml
+    from clearml import Dataset, Task
+
+    assert hasattr(clearml, "__version__")  # verify package import not local dir
+except (ImportError, AssertionError):
+    clearml = None
+
+
+def construct_dataset(clearml_info_string):
+    """Load in a clearml dataset and fill the internal data_dict with its contents."""
+    dataset_id = clearml_info_string.replace("clearml://", "")
+    dataset = Dataset.get(dataset_id=dataset_id)
+    dataset_root_path = Path(dataset.get_local_copy())
+
+    # We'll search for the yaml file definition in the dataset
+    yaml_filenames = list(glob.glob(str(dataset_root_path / "*.yaml")) + glob.glob(str(dataset_root_path / "*.yml")))
+    if len(yaml_filenames) > 1:
+        raise ValueError(
+            "More than one yaml file was found in the dataset root, cannot determine which one contains "
+            "the dataset definition this way."
+        )
+    elif not yaml_filenames:
+        raise ValueError(
+            "No yaml definition found in dataset root path, check that there is a correct yaml file "
+            "inside the dataset root path."
+        )
+    with open(yaml_filenames[0]) as f:
+        dataset_definition = yaml.safe_load(f)
+
+    assert set(
+        dataset_definition.keys()
+    ).issuperset(
+        {"train", "test", "val", "nc", "names"}
+    ), "The right keys were not found in the yaml file, make sure it at least has the following keys: ('train', 'test', 'val', 'nc', 'names')"
+
+    data_dict = {}
+    data_dict["train"] = (
+        str((dataset_root_path / dataset_definition["train"]).resolve()) if dataset_definition["train"] else None
+    )
+    data_dict["test"] = (
+        str((dataset_root_path / dataset_definition["test"]).resolve()) if dataset_definition["test"] else None
+    )
+    data_dict["val"] = (
+        str((dataset_root_path / dataset_definition["val"]).resolve()) if dataset_definition["val"] else None
+    )
+    data_dict["nc"] = dataset_definition["nc"]
+    data_dict["names"] = dataset_definition["names"]
+
+    return data_dict
+
+
+class ClearmlLogger:
+    """
+    Log training runs, datasets, models, and predictions to ClearML.
+
+    This logger sends information to ClearML at app.clear.ml or to your own hosted server. By default, this information
+    includes hyperparameters, system configuration and metrics, model metrics, code information and basic data metrics
+    and analyses.
+
+    By providing additional command line arguments to train.py, datasets, models and predictions can also be logged.
+    """
+
+    def __init__(self, opt, hyp):
+        """
+        - Initialize ClearML Task, this object will capture the experiment
+        - Upload dataset version to ClearML Data if opt.upload_dataset is True
+
+        arguments:
+        opt (namespace) -- Commandline arguments for this run
+        hyp (dict) -- Hyperparameters for this run
+
+        """
+        self.current_epoch = 0
+        # Keep tracked of amount of logged images to enforce a limit
+        self.current_epoch_logged_images = set()
+        # Maximum number of images to log to clearML per epoch
+        self.max_imgs_to_log_per_epoch = 16
+        # Get the interval of epochs when bounding box images should be logged
+        # Only for detection task though!
+        if "bbox_interval" in opt:
+            self.bbox_interval = opt.bbox_interval
+        self.clearml = clearml
+        self.task = None
+        self.data_dict = None
+        if self.clearml:
+            self.task = Task.init(
+                project_name="YOLOv5" if str(opt.project).startswith("runs/") else opt.project,
+                task_name=opt.name if opt.name != "exp" else "Training",
+                tags=["YOLOv5"],
+                output_uri=True,
+                reuse_last_task_id=opt.exist_ok,
+                auto_connect_frameworks={"pytorch": False, "matplotlib": False},
+                # We disconnect pytorch auto-detection, because we added manual model save points in the code
+            )
+            # ClearML's hooks will already grab all general parameters
+            # Only the hyperparameters coming from the yaml config file
+            # will have to be added manually!
+            self.task.connect(hyp, name="Hyperparameters")
+            self.task.connect(opt, name="Args")
+
+            # Make sure the code is easily remotely runnable by setting the docker image to use by the remote agent
+            self.task.set_base_docker(
+                "ultralytics/yolov5:latest",
+                docker_arguments='--ipc=host -e="CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=1"',
+                docker_setup_bash_script="pip install clearml",
+            )
+
+            # Get ClearML Dataset Version if requested
+            if opt.data.startswith("clearml://"):
+                # data_dict should have the following keys:
+                # names, nc (number of classes), test, train, val (all three relative paths to ../datasets)
+                self.data_dict = construct_dataset(opt.data)
+                # Set data to data_dict because wandb will crash without this information and opt is the best way
+                # to give it to them
+                opt.data = self.data_dict
+
+    def log_scalars(self, metrics, epoch):
+        """
+        Log scalars/metrics to ClearML.
+
+        arguments:
+        metrics (dict) Metrics in dict format: {"metrics/mAP": 0.8, ...}
+        epoch (int) iteration number for the current set of metrics
+        """
+        for k, v in metrics.items():
+            title, series = k.split("/")
+            self.task.get_logger().report_scalar(title, series, v, epoch)
+
+    def log_model(self, model_path, model_name, epoch=0):
+        """
+        Log model weights to ClearML.
+
+        arguments:
+        model_path (PosixPath or str) Path to the model weights
+        model_name (str) Name of the model visible in ClearML
+        epoch (int) Iteration / epoch of the model weights
+        """
+        self.task.update_output_model(
+            model_path=str(model_path), name=model_name, iteration=epoch, auto_delete_file=False
+        )
+
+    def log_summary(self, metrics):
+        """
+        Log final metrics to a summary table.
+
+        arguments:
+        metrics (dict) Metrics in dict format: {"metrics/mAP": 0.8, ...}
+        """
+        for k, v in metrics.items():
+            self.task.get_logger().report_single_value(k, v)
+
+    def log_plot(self, title, plot_path):
+        """
+        Log image as plot in the plot section of ClearML.
+
+        arguments:
+        title (str) Title of the plot
+        plot_path (PosixPath or str) Path to the saved image file
+        """
+        img = mpimg.imread(plot_path)
+        fig = plt.figure()
+        ax = fig.add_axes([0, 0, 1, 1], frameon=False, aspect="auto", xticks=[], yticks=[])  # no ticks
+        ax.imshow(img)
+
+        self.task.get_logger().report_matplotlib_figure(title, "", figure=fig, report_interactive=False)
+
+    def log_debug_samples(self, files, title="Debug Samples"):
+        """
+        Log files (images) as debug samples in the ClearML task.
+
+        arguments:
+        files (List(PosixPath)) a list of file paths in PosixPath format
+        title (str) A title that groups together images with the same values
+        """
+        for f in files:
+            if f.exists():
+                it = re.search(r"_batch(\d+)", f.name)
+                iteration = int(it.groups()[0]) if it else 0
+                self.task.get_logger().report_image(
+                    title=title, series=f.name.replace(f"_batch{iteration}", ""), local_path=str(f), iteration=iteration
+                )
+
+    def log_image_with_boxes(self, image_path, boxes, class_names, image, conf_threshold=0.25):
+        """
+        Draw the bounding boxes on a single image and report the result as a ClearML debug sample.
+
+        arguments:
+        image_path (PosixPath) the path the original image file
+        boxes (list): list of scaled predictions in the format - [xmin, ymin, xmax, ymax, confidence, class]
+        class_names (dict): dict containing mapping of class int to class name
+        image (Tensor): A torch tensor containing the actual image data
+        """
+        if (
+            len(self.current_epoch_logged_images) < self.max_imgs_to_log_per_epoch
+            and self.current_epoch >= 0
+            and (self.current_epoch % self.bbox_interval == 0 and image_path not in self.current_epoch_logged_images)
+        ):
+            im = np.ascontiguousarray(np.moveaxis(image.mul(255).clamp(0, 255).byte().cpu().numpy(), 0, 2))
+            annotator = Annotator(im=im, pil=True)
+            for i, (conf, class_nr, box) in enumerate(zip(boxes[:, 4], boxes[:, 5], boxes[:, :4])):
+                color = colors(i)
+
+                class_name = class_names[int(class_nr)]
+                confidence_percentage = round(float(conf) * 100, 2)
+                label = f"{class_name}: {confidence_percentage}%"
+
+                if conf > conf_threshold:
+                    annotator.rectangle(box.cpu().numpy(), outline=color)
+                    annotator.box_label(box.cpu().numpy(), label=label, color=color)
+
+            annotated_image = annotator.result()
+            self.task.get_logger().report_image(
+                title="Bounding Boxes", series=image_path.name, iteration=self.current_epoch, image=annotated_image
+            )
+            self.current_epoch_logged_images.add(image_path)

utils/loggers/clearml/hpo.py

+from clearml import Task
+
+# Connecting ClearML with the current process,
+# from here on everything is logged automatically
+from clearml.automation import HyperParameterOptimizer, UniformParameterRange
+from clearml.automation.optuna import OptimizerOptuna
+
+task = Task.init(
+    project_name="Hyper-Parameter Optimization",
+    task_name="YOLOv5",
+    task_type=Task.TaskTypes.optimizer,
+    reuse_last_task_id=False,
+)
+
+# Example use case:
+optimizer = HyperParameterOptimizer(
+    # This is the experiment we want to optimize
+    base_task_id="<your_template_task_id>",
+    # here we define the hyper-parameters to optimize
+    # Notice: The parameter name should exactly match what you see in the UI: <section_name>/<parameter>
+    # For Example, here we see in the base experiment a section Named: "General"
+    # under it a parameter named "batch_size", this becomes "General/batch_size"
+    # If you have `argparse` for example, then arguments will appear under the "Args" section,
+    # and you should instead pass "Args/batch_size"
+    hyper_parameters=[
+        UniformParameterRange("Hyperparameters/lr0", min_value=1e-5, max_value=1e-1),
+        UniformParameterRange("Hyperparameters/lrf", min_value=0.01, max_value=1.0),
+        UniformParameterRange("Hyperparameters/momentum", min_value=0.6, max_value=0.98),
+        UniformParameterRange("Hyperparameters/weight_decay", min_value=0.0, max_value=0.001),
+        UniformParameterRange("Hyperparameters/warmup_epochs", min_value=0.0, max_value=5.0),
+        UniformParameterRange("Hyperparameters/warmup_momentum", min_value=0.0, max_value=0.95),
+        UniformParameterRange("Hyperparameters/warmup_bias_lr", min_value=0.0, max_value=0.2),
+        UniformParameterRange("Hyperparameters/box", min_value=0.02, max_value=0.2),
+        UniformParameterRange("Hyperparameters/cls", min_value=0.2, max_value=4.0),
+        UniformParameterRange("Hyperparameters/cls_pw", min_value=0.5, max_value=2.0),
+        UniformParameterRange("Hyperparameters/obj", min_value=0.2, max_value=4.0),
+        UniformParameterRange("Hyperparameters/obj_pw", min_value=0.5, max_value=2.0),
+        UniformParameterRange("Hyperparameters/iou_t", min_value=0.1, max_value=0.7),
+        UniformParameterRange("Hyperparameters/anchor_t", min_value=2.0, max_value=8.0),
+        UniformParameterRange("Hyperparameters/fl_gamma", min_value=0.0, max_value=4.0),
+        UniformParameterRange("Hyperparameters/hsv_h", min_value=0.0, max_value=0.1),
+        UniformParameterRange("Hyperparameters/hsv_s", min_value=0.0, max_value=0.9),
+        UniformParameterRange("Hyperparameters/hsv_v", min_value=0.0, max_value=0.9),
+        UniformParameterRange("Hyperparameters/degrees", min_value=0.0, max_value=45.0),
+        UniformParameterRange("Hyperparameters/translate", min_value=0.0, max_value=0.9),
+        UniformParameterRange("Hyperparameters/scale", min_value=0.0, max_value=0.9),
+        UniformParameterRange("Hyperparameters/shear", min_value=0.0, max_value=10.0),
+        UniformParameterRange("Hyperparameters/perspective", min_value=0.0, max_value=0.001),
+        UniformParameterRange("Hyperparameters/flipud", min_value=0.0, max_value=1.0),
+        UniformParameterRange("Hyperparameters/fliplr", min_value=0.0, max_value=1.0),
+        UniformParameterRange("Hyperparameters/mosaic", min_value=0.0, max_value=1.0),
+        UniformParameterRange("Hyperparameters/mixup", min_value=0.0, max_value=1.0),
+        UniformParameterRange("Hyperparameters/copy_paste", min_value=0.0, max_value=1.0),
+    ],
+    # this is the objective metric we want to maximize/minimize
+    objective_metric_title="metrics",
+    objective_metric_series="mAP_0.5",
+    # now we decide if we want to maximize it or minimize it (accuracy we maximize)
+    objective_metric_sign="max",
+    # let us limit the number of concurrent experiments,
+    # this in turn will make sure we do dont bombard the scheduler with experiments.
+    # if we have an auto-scaler connected, this, by proxy, will limit the number of machine
+    max_number_of_concurrent_tasks=1,
+    # this is the optimizer class (actually doing the optimization)
+    # Currently, we can choose from GridSearch, RandomSearch or OptimizerBOHB (Bayesian optimization Hyper-Band)
+    optimizer_class=OptimizerOptuna,
+    # If specified only the top K performing Tasks will be kept, the others will be automatically archived
+    save_top_k_tasks_only=5,  # 5,
+    compute_time_limit=None,
+    total_max_jobs=20,
+    min_iteration_per_job=None,
+    max_iteration_per_job=None,
+)
+
+# report every 10 seconds, this is way too often, but we are testing here
+optimizer.set_report_period(10 / 60)
+# You can also use the line below instead to run all the optimizer tasks locally, without using queues or agent
+# an_optimizer.start_locally(job_complete_callback=job_complete_callback)
+# set the time limit for the optimization process (2 hours)
+optimizer.set_time_limit(in_minutes=120.0)
+# Start the optimization process in the local environment
+optimizer.start_locally()
+# wait until process is done (notice we are controlling the optimization process in the background)
+optimizer.wait()
+# make sure background optimization stopped
+optimizer.stop()
+
+print("We are done, good bye")