v1.0

ultralytics/engine/validator.py

+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+"""
+Check a model's accuracy on a test or val split of a dataset.
+
+Usage:
+    $ yolo mode=val model=yolov8n.pt data=coco8.yaml imgsz=640
+
+Usage - formats:
+    $ yolo mode=val model=yolov8n.pt                 # PyTorch
+                          yolov8n.torchscript        # TorchScript
+                          yolov8n.onnx               # ONNX Runtime or OpenCV DNN with dnn=True
+                          yolov8n_openvino_model     # OpenVINO
+                          yolov8n.engine             # TensorRT
+                          yolov8n.mlpackage          # CoreML (macOS-only)
+                          yolov8n_saved_model        # TensorFlow SavedModel
+                          yolov8n.pb                 # TensorFlow GraphDef
+                          yolov8n.tflite             # TensorFlow Lite
+                          yolov8n_edgetpu.tflite     # TensorFlow Edge TPU
+                          yolov8n_paddle_model       # PaddlePaddle
+                          yolov8n.mnn                # MNN
+                          yolov8n_ncnn_model         # NCNN
+"""
+
+import json
+import time
+from pathlib import Path
+
+import numpy as np
+import torch
+
+from ultralytics.cfg import get_cfg, get_save_dir
+from ultralytics.data.utils import check_cls_dataset, check_det_dataset
+from ultralytics.nn.autobackend import AutoBackend
+from ultralytics.utils import LOGGER, TQDM, callbacks, colorstr, emojis
+from ultralytics.utils.checks import check_imgsz
+from ultralytics.utils.ops import Profile
+from ultralytics.utils.torch_utils import de_parallel, select_device, smart_inference_mode
+
+
+class BaseValidator:
+    """
+    BaseValidator.
+
+    A base class for creating validators.
+
+    Attributes:
+        args (SimpleNamespace): Configuration for the validator.
+        dataloader (DataLoader): Dataloader to use for validation.
+        pbar (tqdm): Progress bar to update during validation.
+        model (nn.Module): Model to validate.
+        data (dict): Data dictionary.
+        device (torch.device): Device to use for validation.
+        batch_i (int): Current batch index.
+        training (bool): Whether the model is in training mode.
+        names (dict): Class names.
+        seen: Records the number of images seen so far during validation.
+        stats: Placeholder for statistics during validation.
+        confusion_matrix: Placeholder for a confusion matrix.
+        nc: Number of classes.
+        iouv: (torch.Tensor): IoU thresholds from 0.50 to 0.95 in spaces of 0.05.
+        jdict (dict): Dictionary to store JSON validation results.
+        speed (dict): Dictionary with keys 'preprocess', 'inference', 'loss', 'postprocess' and their respective
+                      batch processing times in milliseconds.
+        save_dir (Path): Directory to save results.
+        plots (dict): Dictionary to store plots for visualization.
+        callbacks (dict): Dictionary to store various callback functions.
+    """
+
+    def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
+        """
+        Initializes a BaseValidator instance.
+
+        Args:
+            dataloader (torch.utils.data.DataLoader): Dataloader to be used for validation.
+            save_dir (Path, optional): Directory to save results.
+            pbar (tqdm.tqdm): Progress bar for displaying progress.
+            args (SimpleNamespace): Configuration for the validator.
+            _callbacks (dict): Dictionary to store various callback functions.
+        """
+        self.args = get_cfg(overrides=args)
+        self.dataloader = dataloader
+        self.pbar = pbar
+        self.stride = None
+        self.data = None
+        self.device = None
+        self.batch_i = None
+        self.training = True
+        self.names = None
+        self.seen = None
+        self.stats = None
+        self.confusion_matrix = None
+        self.nc = None
+        self.iouv = None
+        self.jdict = None
+        self.speed = {"preprocess": 0.0, "inference": 0.0, "loss": 0.0, "postprocess": 0.0}
+
+        self.save_dir = save_dir or get_save_dir(self.args)
+        (self.save_dir / "labels" if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True)
+        if self.args.conf is None:
+            self.args.conf = 0.001  # default conf=0.001
+        self.args.imgsz = check_imgsz(self.args.imgsz, max_dim=1)
+
+        self.plots = {}
+        self.callbacks = _callbacks or callbacks.get_default_callbacks()
+
+    @smart_inference_mode()
+    def __call__(self, trainer=None, model=None):
+        """Executes validation process, running inference on dataloader and computing performance metrics."""
+        self.training = trainer is not None
+        augment = self.args.augment and (not self.training)
+        if self.training:
+            self.device = trainer.device
+            self.data = trainer.data
+            # force FP16 val during training
+            self.args.half = self.device.type != "cpu" and trainer.amp
+            model = trainer.ema.ema or trainer.model
+            model = model.half() if self.args.half else model.float()
+            # self.model = model
+            self.loss = torch.zeros_like(trainer.loss_items, device=trainer.device)
+            self.args.plots &= trainer.stopper.possible_stop or (trainer.epoch == trainer.epochs - 1)
+            model.eval()
+        else:
+            if str(self.args.model).endswith(".yaml") and model is None:
+                LOGGER.warning("WARNING ⚠️ validating an untrained model YAML will result in 0 mAP.")
+            callbacks.add_integration_callbacks(self)
+            model = AutoBackend(
+                weights=model or self.args.model,
+                device=select_device(self.args.device, self.args.batch),
+                dnn=self.args.dnn,
+                data=self.args.data,
+                fp16=self.args.half,
+            )
+            # self.model = model
+            self.device = model.device  # update device
+            self.args.half = model.fp16  # update half
+            stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine
+            imgsz = check_imgsz(self.args.imgsz, stride=stride)
+            if engine:
+                self.args.batch = model.batch_size
+            elif not pt and not jit:
+                self.args.batch = model.metadata.get("batch", 1)  # export.py models default to batch-size 1
+                LOGGER.info(f"Setting batch={self.args.batch} input of shape ({self.args.batch}, 3, {imgsz}, {imgsz})")
+
+            if str(self.args.data).split(".")[-1] in {"yaml", "yml"}:
+                self.data = check_det_dataset(self.args.data)
+            elif self.args.task == "classify":
+                self.data = check_cls_dataset(self.args.data, split=self.args.split)
+            else:
+                raise FileNotFoundError(emojis(f"Dataset '{self.args.data}' for task={self.args.task} not found ❌"))
+
+            if self.device.type in {"cpu", "mps"}:
+                self.args.workers = 0  # faster CPU val as time dominated by inference, not dataloading
+            if not pt:
+                self.args.rect = False
+            self.stride = model.stride  # used in get_dataloader() for padding
+            self.dataloader = self.dataloader or self.get_dataloader(self.data.get(self.args.split), self.args.batch)
+
+            model.eval()
+            model.warmup(imgsz=(1 if pt else self.args.batch, 3, imgsz, imgsz))  # warmup
+
+        self.run_callbacks("on_val_start")
+        dt = (
+            Profile(device=self.device),
+            Profile(device=self.device),
+            Profile(device=self.device),
+            Profile(device=self.device),
+        )
+        bar = TQDM(self.dataloader, desc=self.get_desc(), total=len(self.dataloader))
+        self.init_metrics(de_parallel(model))
+        self.jdict = []  # empty before each val
+        for batch_i, batch in enumerate(bar):
+            self.run_callbacks("on_val_batch_start")
+            self.batch_i = batch_i
+            # Preprocess
+            with dt[0]:
+                batch = self.preprocess(batch)
+
+            # Inference
+            with dt[1]:
+                preds = model(batch["img"], augment=augment)
+
+            # Loss
+            with dt[2]:
+                if self.training:
+                    self.loss += model.loss(batch, preds)[1]
+
+            # Postprocess
+            with dt[3]:
+                preds = self.postprocess(preds)
+
+            self.update_metrics(preds, batch)
+            if self.args.plots and batch_i < 3:
+                self.plot_val_samples(batch, batch_i)
+                self.plot_predictions(batch, preds, batch_i)
+
+            self.run_callbacks("on_val_batch_end")
+        stats = self.get_stats()
+        self.check_stats(stats)
+        self.speed = dict(zip(self.speed.keys(), (x.t / len(self.dataloader.dataset) * 1e3 for x in dt)))
+        self.finalize_metrics()
+        self.print_results()
+        self.run_callbacks("on_val_end")
+        if self.training:
+            model.float()
+            results = {**stats, **trainer.label_loss_items(self.loss.cpu() / len(self.dataloader), prefix="val")}
+            return {k: round(float(v), 5) for k, v in results.items()}  # return results as 5 decimal place floats
+        else:
+            LOGGER.info(
+                "Speed: {:.1f}ms preprocess, {:.1f}ms inference, {:.1f}ms loss, {:.1f}ms postprocess per image".format(
+                    *tuple(self.speed.values())
+                )
+            )
+            if self.args.save_json and self.jdict:
+                with open(str(self.save_dir / "predictions.json"), "w") as f:
+                    LOGGER.info(f"Saving {f.name}...")
+                    json.dump(self.jdict, f)  # flatten and save
+                stats = self.eval_json(stats)  # update stats
+            if self.args.plots or self.args.save_json:
+                LOGGER.info(f"Results saved to {colorstr('bold', self.save_dir)}")
+            return stats
+
+    def match_predictions(self, pred_classes, true_classes, iou, use_scipy=False):
+        """
+        Matches predictions to ground truth objects (pred_classes, true_classes) using IoU.
+
+        Args:
+            pred_classes (torch.Tensor): Predicted class indices of shape(N,).
+            true_classes (torch.Tensor): Target class indices of shape(M,).
+            iou (torch.Tensor): An NxM tensor containing the pairwise IoU values for predictions and ground of truth
+            use_scipy (bool): Whether to use scipy for matching (more precise).
+
+        Returns:
+            (torch.Tensor): Correct tensor of shape(N,10) for 10 IoU thresholds.
+        """
+        # Dx10 matrix, where D - detections, 10 - IoU thresholds
+        correct = np.zeros((pred_classes.shape[0], self.iouv.shape[0])).astype(bool)
+        # LxD matrix where L - labels (rows), D - detections (columns)
+        correct_class = true_classes[:, None] == pred_classes
+        iou = iou * correct_class  # zero out the wrong classes
+        iou = iou.cpu().numpy()
+        for i, threshold in enumerate(self.iouv.cpu().tolist()):
+            if use_scipy:
+                # WARNING: known issue that reduces mAP in https://github.com/ultralytics/ultralytics/pull/4708
+                import scipy  # scope import to avoid importing for all commands
+
+                cost_matrix = iou * (iou >= threshold)
+                if cost_matrix.any():
+                    labels_idx, detections_idx = scipy.optimize.linear_sum_assignment(cost_matrix)
+                    valid = cost_matrix[labels_idx, detections_idx] > 0
+                    if valid.any():
+                        correct[detections_idx[valid], i] = True
+            else:
+                matches = np.nonzero(iou >= threshold)  # IoU > threshold and classes match
+                matches = np.array(matches).T
+                if matches.shape[0]:
+                    if matches.shape[0] > 1:
+                        matches = matches[iou[matches[:, 0], matches[:, 1]].argsort()[::-1]]
+                        matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
+                        # matches = matches[matches[:, 2].argsort()[::-1]]
+                        matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
+                    correct[matches[:, 1].astype(int), i] = True
+        return torch.tensor(correct, dtype=torch.bool, device=pred_classes.device)
+
+    def add_callback(self, event: str, callback):
+        """Appends the given callback."""
+        self.callbacks[event].append(callback)
+
+    def run_callbacks(self, event: str):
+        """Runs all callbacks associated with a specified event."""
+        for callback in self.callbacks.get(event, []):
+            callback(self)
+
+    def get_dataloader(self, dataset_path, batch_size):
+        """Get data loader from dataset path and batch size."""
+        raise NotImplementedError("get_dataloader function not implemented for this validator")
+
+    def build_dataset(self, img_path):
+        """Build dataset."""
+        raise NotImplementedError("build_dataset function not implemented in validator")
+
+    def preprocess(self, batch):
+        """Preprocesses an input batch."""
+        return batch
+
+    def postprocess(self, preds):
+        """Preprocesses the predictions."""
+        return preds
+
+    def init_metrics(self, model):
+        """Initialize performance metrics for the YOLO model."""
+        pass
+
+    def update_metrics(self, preds, batch):
+        """Updates metrics based on predictions and batch."""
+        pass
+
+    def finalize_metrics(self, *args, **kwargs):
+        """Finalizes and returns all metrics."""
+        pass
+
+    def get_stats(self):
+        """Returns statistics about the model's performance."""
+        return {}
+
+    def check_stats(self, stats):
+        """Checks statistics."""
+        pass
+
+    def print_results(self):
+        """Prints the results of the model's predictions."""
+        pass
+
+    def get_desc(self):
+        """Get description of the YOLO model."""
+        pass
+
+    @property
+    def metric_keys(self):
+        """Returns the metric keys used in YOLO training/validation."""
+        return []
+
+    def on_plot(self, name, data=None):
+        """Registers plots (e.g. to be consumed in callbacks)."""
+        self.plots[Path(name)] = {"data": data, "timestamp": time.time()}
+
+    # TODO: may need to put these following functions into callback
+    def plot_val_samples(self, batch, ni):
+        """Plots validation samples during training."""
+        pass
+
+    def plot_predictions(self, batch, preds, ni):
+        """Plots YOLO model predictions on batch images."""
+        pass
+
+    def pred_to_json(self, preds, batch):
+        """Convert predictions to JSON format."""
+        pass
+
+    def eval_json(self, stats):
+        """Evaluate and return JSON format of prediction statistics."""
+        pass

ultralytics/hub/__init__.py

+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+import requests
+
+from ultralytics.data.utils import HUBDatasetStats
+from ultralytics.hub.auth import Auth
+from ultralytics.hub.session import HUBTrainingSession
+from ultralytics.hub.utils import HUB_API_ROOT, HUB_WEB_ROOT, PREFIX, events
+from ultralytics.utils import LOGGER, SETTINGS, checks
+
+__all__ = (
+    "PREFIX",
+    "HUB_WEB_ROOT",
+    "HUBTrainingSession",
+    "login",
+    "logout",
+    "reset_model",
+    "export_fmts_hub",
+    "export_model",
+    "get_export",
+    "check_dataset",
+    "events",
+)
+
+
+def login(api_key: str = None, save=True) -> bool:
+    """
+    Log in to the Ultralytics HUB API using the provided API key.
+
+    The session is not stored; a new session is created when needed using the saved SETTINGS or the HUB_API_KEY
+    environment variable if successfully authenticated.
+
+    Args:
+        api_key (str, optional): API key to use for authentication.
+            If not provided, it will be retrieved from SETTINGS or HUB_API_KEY environment variable.
+        save (bool, optional): Whether to save the API key to SETTINGS if authentication is successful.
+
+    Returns:
+        (bool): True if authentication is successful, False otherwise.
+    """
+    checks.check_requirements("hub-sdk>=0.0.12")
+    from hub_sdk import HUBClient
+
+    api_key_url = f"{HUB_WEB_ROOT}/settings?tab=api+keys"  # set the redirect URL
+    saved_key = SETTINGS.get("api_key")
+    active_key = api_key or saved_key
+    credentials = {"api_key": active_key} if active_key and active_key != "" else None  # set credentials
+
+    client = HUBClient(credentials)  # initialize HUBClient
+
+    if client.authenticated:
+        # Successfully authenticated with HUB
+
+        if save and client.api_key != saved_key:
+            SETTINGS.update({"api_key": client.api_key})  # update settings with valid API key
+
+        # Set message based on whether key was provided or retrieved from settings
+        log_message = (
+            "New authentication successful ✅" if client.api_key == api_key or not credentials else "Authenticated ✅"
+        )
+        LOGGER.info(f"{PREFIX}{log_message}")
+
+        return True
+    else:
+        # Failed to authenticate with HUB
+        LOGGER.info(f"{PREFIX}Get API key from {api_key_url} and then run 'yolo login API_KEY'")
+        return False
+
+
+def logout():
+    """
+    Log out of Ultralytics HUB by removing the API key from the settings file. To log in again, use 'yolo login'.
+
+    Example:
+        ```python
+        from ultralytics import hub
+
+        hub.logout()
+        ```
+    """
+    SETTINGS["api_key"] = ""
+    LOGGER.info(f"{PREFIX}logged out ✅. To log in again, use 'yolo login'.")
+
+
+def reset_model(model_id=""):
+    """Reset a trained model to an untrained state."""
+    r = requests.post(f"{HUB_API_ROOT}/model-reset", json={"modelId": model_id}, headers={"x-api-key": Auth().api_key})
+    if r.status_code == 200:
+        LOGGER.info(f"{PREFIX}Model reset successfully")
+        return
+    LOGGER.warning(f"{PREFIX}Model reset failure {r.status_code} {r.reason}")
+
+
+def export_fmts_hub():
+    """Returns a list of HUB-supported export formats."""
+    from ultralytics.engine.exporter import export_formats
+
+    return list(export_formats()["Argument"][1:]) + ["ultralytics_tflite", "ultralytics_coreml"]
+
+
+def export_model(model_id="", format="torchscript"):
+    """Export a model to all formats."""
+    assert format in export_fmts_hub(), f"Unsupported export format '{format}', valid formats are {export_fmts_hub()}"
+    r = requests.post(
+        f"{HUB_API_ROOT}/v1/models/{model_id}/export", json={"format": format}, headers={"x-api-key": Auth().api_key}
+    )
+    assert r.status_code == 200, f"{PREFIX}{format} export failure {r.status_code} {r.reason}"
+    LOGGER.info(f"{PREFIX}{format} export started ✅")
+
+
+def get_export(model_id="", format="torchscript"):
+    """Get an exported model dictionary with download URL."""
+    assert format in export_fmts_hub(), f"Unsupported export format '{format}', valid formats are {export_fmts_hub()}"
+    r = requests.post(
+        f"{HUB_API_ROOT}/get-export",
+        json={"apiKey": Auth().api_key, "modelId": model_id, "format": format},
+        headers={"x-api-key": Auth().api_key},
+    )
+    assert r.status_code == 200, f"{PREFIX}{format} get_export failure {r.status_code} {r.reason}"
+    return r.json()
+
+
+def check_dataset(path: str, task: str) -> None:
+    """
+    Function for error-checking HUB dataset Zip file before upload. It checks a dataset for errors before it is uploaded
+    to the HUB. Usage examples are given below.
+
+    Args:
+        path (str): Path to data.zip (with data.yaml inside data.zip).
+        task (str): Dataset task. Options are 'detect', 'segment', 'pose', 'classify', 'obb'.
+
+    Example:
+        Download *.zip files from https://github.com/ultralytics/hub/tree/main/example_datasets
+            i.e. https://github.com/ultralytics/hub/raw/main/example_datasets/coco8.zip for coco8.zip.
+        ```python
+        from ultralytics.hub import check_dataset
+
+        check_dataset("path/to/coco8.zip", task="detect")  # detect dataset
+        check_dataset("path/to/coco8-seg.zip", task="segment")  # segment dataset
+        check_dataset("path/to/coco8-pose.zip", task="pose")  # pose dataset
+        check_dataset("path/to/dota8.zip", task="obb")  # OBB dataset
+        check_dataset("path/to/imagenet10.zip", task="classify")  # classification dataset
+        ```
+    """
+    HUBDatasetStats(path=path, task=task).get_json()
+    LOGGER.info(f"Checks completed correctly ✅. Upload this dataset to {HUB_WEB_ROOT}/datasets/.")

ultralytics/hub/auth.py

+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+import requests
+
+from ultralytics.hub.utils import HUB_API_ROOT, HUB_WEB_ROOT, PREFIX, request_with_credentials
+from ultralytics.utils import IS_COLAB, LOGGER, SETTINGS, emojis
+
+API_KEY_URL = f"{HUB_WEB_ROOT}/settings?tab=api+keys"
+
+
+class Auth:
+    """
+    Manages authentication processes including API key handling, cookie-based authentication, and header generation.
+
+    The class supports different methods of authentication:
+    1. Directly using an API key.
+    2. Authenticating using browser cookies (specifically in Google Colab).
+    3. Prompting the user to enter an API key.
+
+    Attributes:
+        id_token (str or bool): Token used for identity verification, initialized as False.
+        api_key (str or bool): API key for authentication, initialized as False.
+        model_key (bool): Placeholder for model key, initialized as False.
+    """
+
+    id_token = api_key = model_key = False
+
+    def __init__(self, api_key="", verbose=False):
+        """
+        Initialize Auth class and authenticate user.
+
+        Handles API key validation, Google Colab authentication, and new key requests. Updates SETTINGS upon successful
+        authentication.
+
+        Args:
+            api_key (str): API key or combined key_id format.
+            verbose (bool): Enable verbose logging.
+        """
+        # Split the input API key in case it contains a combined key_model and keep only the API key part
+        api_key = api_key.split("_")[0]
+
+        # Set API key attribute as value passed or SETTINGS API key if none passed
+        self.api_key = api_key or SETTINGS.get("api_key", "")
+
+        # If an API key is provided
+        if self.api_key:
+            # If the provided API key matches the API key in the SETTINGS
+            if self.api_key == SETTINGS.get("api_key"):
+                # Log that the user is already logged in
+                if verbose:
+                    LOGGER.info(f"{PREFIX}Authenticated ✅")
+                return
+            else:
+                # Attempt to authenticate with the provided API key
+                success = self.authenticate()
+        # If the API key is not provided and the environment is a Google Colab notebook
+        elif IS_COLAB:
+            # Attempt to authenticate using browser cookies
+            success = self.auth_with_cookies()
+        else:
+            # Request an API key
+            success = self.request_api_key()
+
+        # Update SETTINGS with the new API key after successful authentication
+        if success:
+            SETTINGS.update({"api_key": self.api_key})
+            # Log that the new login was successful
+            if verbose:
+                LOGGER.info(f"{PREFIX}New authentication successful ✅")
+        elif verbose:
+            LOGGER.info(f"{PREFIX}Get API key from {API_KEY_URL} and then run 'yolo login API_KEY'")
+
+    def request_api_key(self, max_attempts=3):
+        """
+        Prompt the user to input their API key.
+
+        Returns the model ID.
+        """
+        import getpass
+
+        for attempts in range(max_attempts):
+            LOGGER.info(f"{PREFIX}Login. Attempt {attempts + 1} of {max_attempts}")
+            input_key = getpass.getpass(f"Enter API key from {API_KEY_URL} ")
+            self.api_key = input_key.split("_")[0]  # remove model id if present
+            if self.authenticate():
+                return True
+        raise ConnectionError(emojis(f"{PREFIX}Failed to authenticate ❌"))
+
+    def authenticate(self) -> bool:
+        """
+        Attempt to authenticate with the server using either id_token or API key.
+
+        Returns:
+            (bool): True if authentication is successful, False otherwise.
+        """
+        try:
+            if header := self.get_auth_header():
+                r = requests.post(f"{HUB_API_ROOT}/v1/auth", headers=header)
+                if not r.json().get("success", False):
+                    raise ConnectionError("Unable to authenticate.")
+                return True
+            raise ConnectionError("User has not authenticated locally.")
+        except ConnectionError:
+            self.id_token = self.api_key = False  # reset invalid
+            LOGGER.warning(f"{PREFIX}Invalid API key ⚠️")
+            return False
+
+    def auth_with_cookies(self) -> bool:
+        """
+        Attempt to fetch authentication via cookies and set id_token. User must be logged in to HUB and running in a
+        supported browser.
+
+        Returns:
+            (bool): True if authentication is successful, False otherwise.
+        """
+        if not IS_COLAB:
+            return False  # Currently only works with Colab
+        try:
+            authn = request_with_credentials(f"{HUB_API_ROOT}/v1/auth/auto")
+            if authn.get("success", False):
+                self.id_token = authn.get("data", {}).get("idToken", None)
+                self.authenticate()
+                return True
+            raise ConnectionError("Unable to fetch browser authentication details.")
+        except ConnectionError:
+            self.id_token = False  # reset invalid
+            return False
+
+    def get_auth_header(self):
+        """
+        Get the authentication header for making API requests.
+
+        Returns:
+            (dict): The authentication header if id_token or API key is set, None otherwise.
+        """
+        if self.id_token:
+            return {"authorization": f"Bearer {self.id_token}"}
+        elif self.api_key:
+            return {"x-api-key": self.api_key}
+        # else returns None

ultralytics/hub/google/__init__.py

+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+import concurrent.futures
+import statistics
+import time
+from typing import List, Optional, Tuple
+
+import requests
+
+
+class GCPRegions:
+    """
+    A class for managing and analyzing Google Cloud Platform (GCP) regions.
+
+    This class provides functionality to initialize, categorize, and analyze GCP regions based on their
+    geographical location, tier classification, and network latency.
+
+    Attributes:
+        regions (Dict[str, Tuple[int, str, str]]): A dictionary of GCP regions with their tier, city, and country.
+
+    Methods:
+        tier1: Returns a list of tier 1 GCP regions.
+        tier2: Returns a list of tier 2 GCP regions.
+        lowest_latency: Determines the GCP region(s) with the lowest network latency.
+
+    Examples:
+        >>> from ultralytics.hub.google import GCPRegions
+        >>> regions = GCPRegions()
+        >>> lowest_latency_region = regions.lowest_latency(verbose=True, attempts=3)
+        >>> print(f"Lowest latency region: {lowest_latency_region[0][0]}")
+    """
+
+    def __init__(self):
+        """Initializes the GCPRegions class with predefined Google Cloud Platform regions and their details."""
+        self.regions = {
+            "asia-east1": (1, "Taiwan", "China"),
+            "asia-east2": (2, "Hong Kong", "China"),
+            "asia-northeast1": (1, "Tokyo", "Japan"),
+            "asia-northeast2": (1, "Osaka", "Japan"),
+            "asia-northeast3": (2, "Seoul", "South Korea"),
+            "asia-south1": (2, "Mumbai", "India"),
+            "asia-south2": (2, "Delhi", "India"),
+            "asia-southeast1": (2, "Jurong West", "Singapore"),
+            "asia-southeast2": (2, "Jakarta", "Indonesia"),
+            "australia-southeast1": (2, "Sydney", "Australia"),
+            "australia-southeast2": (2, "Melbourne", "Australia"),
+            "europe-central2": (2, "Warsaw", "Poland"),
+            "europe-north1": (1, "Hamina", "Finland"),
+            "europe-southwest1": (1, "Madrid", "Spain"),
+            "europe-west1": (1, "St. Ghislain", "Belgium"),
+            "europe-west10": (2, "Berlin", "Germany"),
+            "europe-west12": (2, "Turin", "Italy"),
+            "europe-west2": (2, "London", "United Kingdom"),
+            "europe-west3": (2, "Frankfurt", "Germany"),
+            "europe-west4": (1, "Eemshaven", "Netherlands"),
+            "europe-west6": (2, "Zurich", "Switzerland"),
+            "europe-west8": (1, "Milan", "Italy"),
+            "europe-west9": (1, "Paris", "France"),
+            "me-central1": (2, "Doha", "Qatar"),
+            "me-west1": (1, "Tel Aviv", "Israel"),
+            "northamerica-northeast1": (2, "Montreal", "Canada"),
+            "northamerica-northeast2": (2, "Toronto", "Canada"),
+            "southamerica-east1": (2, "São Paulo", "Brazil"),
+            "southamerica-west1": (2, "Santiago", "Chile"),
+            "us-central1": (1, "Iowa", "United States"),
+            "us-east1": (1, "South Carolina", "United States"),
+            "us-east4": (1, "Northern Virginia", "United States"),
+            "us-east5": (1, "Columbus", "United States"),
+            "us-south1": (1, "Dallas", "United States"),
+            "us-west1": (1, "Oregon", "United States"),
+            "us-west2": (2, "Los Angeles", "United States"),
+            "us-west3": (2, "Salt Lake City", "United States"),
+            "us-west4": (2, "Las Vegas", "United States"),
+        }
+
+    def tier1(self) -> List[str]:
+        """Returns a list of GCP regions classified as tier 1 based on predefined criteria."""
+        return [region for region, info in self.regions.items() if info[0] == 1]
+
+    def tier2(self) -> List[str]:
+        """Returns a list of GCP regions classified as tier 2 based on predefined criteria."""
+        return [region for region, info in self.regions.items() if info[0] == 2]
+
+    @staticmethod
+    def _ping_region(region: str, attempts: int = 1) -> Tuple[str, float, float, float, float]:
+        """Pings a specified GCP region and returns latency statistics: mean, min, max, and standard deviation."""
+        url = f"https://{region}-docker.pkg.dev"
+        latencies = []
+        for _ in range(attempts):
+            try:
+                start_time = time.time()
+                _ = requests.head(url, timeout=5)
+                latency = (time.time() - start_time) * 1000  # convert latency to milliseconds
+                if latency != float("inf"):
+                    latencies.append(latency)
+            except requests.RequestException:
+                pass
+        if not latencies:
+            return region, float("inf"), float("inf"), float("inf"), float("inf")
+
+        std_dev = statistics.stdev(latencies) if len(latencies) > 1 else 0
+        return region, statistics.mean(latencies), std_dev, min(latencies), max(latencies)
+
+    def lowest_latency(
+        self,
+        top: int = 1,
+        verbose: bool = False,
+        tier: Optional[int] = None,
+        attempts: int = 1,
+    ) -> List[Tuple[str, float, float, float, float]]:
+        """
+        Determines the GCP regions with the lowest latency based on ping tests.
+
+        Args:
+            top (int): Number of top regions to return.
+            verbose (bool): If True, prints detailed latency information for all tested regions.
+            tier (int | None): Filter regions by tier (1 or 2). If None, all regions are tested.
+            attempts (int): Number of ping attempts per region.
+
+        Returns:
+            (List[Tuple[str, float, float, float, float]]): List of tuples containing region information and
+            latency statistics. Each tuple contains (region, mean_latency, std_dev, min_latency, max_latency).
+
+        Examples:
+            >>> regions = GCPRegions()
+            >>> results = regions.lowest_latency(top=3, verbose=True, tier=1, attempts=2)
+            >>> print(results[0][0])  # Print the name of the lowest latency region
+        """
+        if verbose:
+            print(f"Testing GCP regions for latency (with {attempts} {'retry' if attempts == 1 else 'attempts'})...")
+
+        regions_to_test = [k for k, v in self.regions.items() if v[0] == tier] if tier else list(self.regions.keys())
+        with concurrent.futures.ThreadPoolExecutor(max_workers=50) as executor:
+            results = list(executor.map(lambda r: self._ping_region(r, attempts), regions_to_test))
+
+        sorted_results = sorted(results, key=lambda x: x[1])
+
+        if verbose:
+            print(f"{'Region':<25} {'Location':<35} {'Tier':<5} Latency (ms)")
+            for region, mean, std, min_, max_ in sorted_results:
+                tier, city, country = self.regions[region]
+                location = f"{city}, {country}"
+                if mean == float("inf"):
+                    print(f"{region:<25} {location:<35} {tier:<5} Timeout")
+                else:
+                    print(f"{region:<25} {location:<35} {tier:<5} {mean:.0f} ± {std:.0f} ({min_:.0f} - {max_:.0f})")
+            print(f"\nLowest latency region{'s' if top > 1 else ''}:")
+            for region, mean, std, min_, max_ in sorted_results[:top]:
+                tier, city, country = self.regions[region]
+                location = f"{city}, {country}"
+                print(f"{region} ({location}, {mean:.0f} ± {std:.0f} ms ({min_:.0f} - {max_:.0f}))")
+
+        return sorted_results[:top]
+
+
+# Usage example
+if __name__ == "__main__":
+    regions = GCPRegions()
+    top_3_latency_tier1 = regions.lowest_latency(top=3, verbose=True, tier=1, attempts=3)

ultralytics/hub/session.py

+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+import shutil
+import threading
+import time
+from http import HTTPStatus
+from pathlib import Path
+from urllib.parse import parse_qs, urlparse
+
+import requests
+
+from ultralytics.hub.utils import HELP_MSG, HUB_WEB_ROOT, PREFIX, TQDM
+from ultralytics.utils import IS_COLAB, LOGGER, SETTINGS, __version__, checks, emojis
+from ultralytics.utils.errors import HUBModelError
+
+AGENT_NAME = f"python-{__version__}-colab" if IS_COLAB else f"python-{__version__}-local"
+
+
+class HUBTrainingSession:
+    """
+    HUB training session for Ultralytics HUB YOLO models. Handles model initialization, heartbeats, and checkpointing.
+
+    Attributes:
+        model_id (str): Identifier for the YOLO model being trained.
+        model_url (str): URL for the model in Ultralytics HUB.
+        rate_limits (dict): Rate limits for different API calls (in seconds).
+        timers (dict): Timers for rate limiting.
+        metrics_queue (dict): Queue for the model's metrics.
+        model (dict): Model data fetched from Ultralytics HUB.
+    """
+
+    def __init__(self, identifier):
+        """
+        Initialize the HUBTrainingSession with the provided model identifier.
+
+        Args:
+            identifier (str): Model identifier used to initialize the HUB training session.
+                It can be a URL string or a model key with specific format.
+
+        Raises:
+            ValueError: If the provided model identifier is invalid.
+            ConnectionError: If connecting with global API key is not supported.
+            ModuleNotFoundError: If hub-sdk package is not installed.
+        """
+        from hub_sdk import HUBClient
+
+        self.rate_limits = {"metrics": 3, "ckpt": 900, "heartbeat": 300}  # rate limits (seconds)
+        self.metrics_queue = {}  # holds metrics for each epoch until upload
+        self.metrics_upload_failed_queue = {}  # holds metrics for each epoch if upload failed
+        self.timers = {}  # holds timers in ultralytics/utils/callbacks/hub.py
+        self.model = None
+        self.model_url = None
+        self.model_file = None
+        self.train_args = None
+
+        # Parse input
+        api_key, model_id, self.filename = self._parse_identifier(identifier)
+
+        # Get credentials
+        active_key = api_key or SETTINGS.get("api_key")
+        credentials = {"api_key": active_key} if active_key else None  # set credentials
+
+        # Initialize client
+        self.client = HUBClient(credentials)
+
+        # Load models
+        try:
+            if model_id:
+                self.load_model(model_id)  # load existing model
+            else:
+                self.model = self.client.model()  # load empty model
+        except Exception:
+            if identifier.startswith(f"{HUB_WEB_ROOT}/models/") and not self.client.authenticated:
+                LOGGER.warning(
+                    f"{PREFIX}WARNING ⚠️ Please log in using 'yolo login API_KEY'. "
+                    "You can find your API Key at: https://hub.ultralytics.com/settings?tab=api+keys."
+                )
+
+    @classmethod
+    def create_session(cls, identifier, args=None):
+        """Class method to create an authenticated HUBTrainingSession or return None."""
+        try:
+            session = cls(identifier)
+            if args and not identifier.startswith(f"{HUB_WEB_ROOT}/models/"):  # not a HUB model URL
+                session.create_model(args)
+                assert session.model.id, "HUB model not loaded correctly"
+            return session
+        # PermissionError and ModuleNotFoundError indicate hub-sdk not installed
+        except (PermissionError, ModuleNotFoundError, AssertionError):
+            return None
+
+    def load_model(self, model_id):
+        """Loads an existing model from Ultralytics HUB using the provided model identifier."""
+        self.model = self.client.model(model_id)
+        if not self.model.data:  # then model does not exist
+            raise ValueError(emojis("❌ The specified HUB model does not exist"))  # TODO: improve error handling
+
+        self.model_url = f"{HUB_WEB_ROOT}/models/{self.model.id}"
+        if self.model.is_trained():
+            print(emojis(f"Loading trained HUB model {self.model_url} 🚀"))
+            url = self.model.get_weights_url("best")  # download URL with auth
+            self.model_file = checks.check_file(url, download_dir=Path(SETTINGS["weights_dir"]) / "hub" / self.model.id)
+            return
+
+        # Set training args and start heartbeats for HUB to monitor agent
+        self._set_train_args()
+        self.model.start_heartbeat(self.rate_limits["heartbeat"])
+        LOGGER.info(f"{PREFIX}View model at {self.model_url} 🚀")
+
+    def create_model(self, model_args):
+        """Initializes a HUB training session with the specified model identifier."""
+        payload = {
+            "config": {
+                "batchSize": model_args.get("batch", -1),
+                "epochs": model_args.get("epochs", 300),
+                "imageSize": model_args.get("imgsz", 640),
+                "patience": model_args.get("patience", 100),
+                "device": str(model_args.get("device", "")),  # convert None to string
+                "cache": str(model_args.get("cache", "ram")),  # convert True, False, None to string
+            },
+            "dataset": {"name": model_args.get("data")},
+            "lineage": {
+                "architecture": {"name": self.filename.replace(".pt", "").replace(".yaml", "")},
+                "parent": {},
+            },
+            "meta": {"name": self.filename},
+        }
+
+        if self.filename.endswith(".pt"):
+            payload["lineage"]["parent"]["name"] = self.filename
+
+        self.model.create_model(payload)
+
+        # Model could not be created
+        # TODO: improve error handling
+        if not self.model.id:
+            return None
+
+        self.model_url = f"{HUB_WEB_ROOT}/models/{self.model.id}"
+
+        # Start heartbeats for HUB to monitor agent
+        self.model.start_heartbeat(self.rate_limits["heartbeat"])
+
+        LOGGER.info(f"{PREFIX}View model at {self.model_url} 🚀")
+
+    @staticmethod
+    def _parse_identifier(identifier):
+        """
+        Parses the given identifier to determine the type of identifier and extract relevant components.
+
+        The method supports different identifier formats:
+            - A HUB model URL https://hub.ultralytics.com/models/MODEL
+            - A HUB model URL with API Key https://hub.ultralytics.com/models/MODEL?api_key=APIKEY
+            - A local filename that ends with '.pt' or '.yaml'
+
+        Args:
+            identifier (str): The identifier string to be parsed.
+
+        Returns:
+            (tuple): A tuple containing the API key, model ID, and filename as applicable.
+
+        Raises:
+            HUBModelError: If the identifier format is not recognized.
+        """
+        api_key, model_id, filename = None, None, None
+        if Path(identifier).suffix in {".pt", ".yaml"}:
+            filename = identifier
+        elif identifier.startswith(f"{HUB_WEB_ROOT}/models/"):
+            parsed_url = urlparse(identifier)
+            model_id = Path(parsed_url.path).stem  # handle possible final backslash robustly
+            query_params = parse_qs(parsed_url.query)  # dictionary, i.e. {"api_key": ["API_KEY_HERE"]}
+            api_key = query_params.get("api_key", [None])[0]
+        else:
+            raise HUBModelError(f"model='{identifier} invalid, correct format is {HUB_WEB_ROOT}/models/MODEL_ID")
+        return api_key, model_id, filename
+
+    def _set_train_args(self):
+        """
+        Initializes training arguments and creates a model entry on the Ultralytics HUB.
+
+        This method sets up training arguments based on the model's state and updates them with any additional
+        arguments provided. It handles different states of the model, such as whether it's resumable, pretrained,
+        or requires specific file setup.
+
+        Raises:
+            ValueError: If the model is already trained, if required dataset information is missing, or if there are
+                issues with the provided training arguments.
+        """
+        if self.model.is_resumable():
+            # Model has saved weights
+            self.train_args = {"data": self.model.get_dataset_url(), "resume": True}
+            self.model_file = self.model.get_weights_url("last")
+        else:
+            # Model has no saved weights
+            self.train_args = self.model.data.get("train_args")  # new response
+
+            # Set the model file as either a *.pt or *.yaml file
+            self.model_file = (
+                self.model.get_weights_url("parent") if self.model.is_pretrained() else self.model.get_architecture()
+            )
+
+        if "data" not in self.train_args:
+            # RF bug - datasets are sometimes not exported
+            raise ValueError("Dataset may still be processing. Please wait a minute and try again.")
+
+        self.model_file = checks.check_yolov5u_filename(self.model_file, verbose=False)  # YOLOv5->YOLOv5u
+        self.model_id = self.model.id
+
+    def request_queue(
+        self,
+        request_func,
+        retry=3,
+        timeout=30,
+        thread=True,
+        verbose=True,
+        progress_total=None,
+        stream_response=None,
+        *args,
+        **kwargs,
+    ):
+        """Attempts to execute `request_func` with retries, timeout handling, optional threading, and progress."""
+
+        def retry_request():
+            """Attempts to call `request_func` with retries, timeout, and optional threading."""
+            t0 = time.time()  # Record the start time for the timeout
+            response = None
+            for i in range(retry + 1):
+                if (time.time() - t0) > timeout:
+                    LOGGER.warning(f"{PREFIX}Timeout for request reached. {HELP_MSG}")
+                    break  # Timeout reached, exit loop
+
+                response = request_func(*args, **kwargs)
+                if response is None:
+                    LOGGER.warning(f"{PREFIX}Received no response from the request. {HELP_MSG}")
+                    time.sleep(2**i)  # Exponential backoff before retrying
+                    continue  # Skip further processing and retry
+
+                if progress_total:
+                    self._show_upload_progress(progress_total, response)
+                elif stream_response:
+                    self._iterate_content(response)
+
+                if HTTPStatus.OK <= response.status_code < HTTPStatus.MULTIPLE_CHOICES:
+                    # if request related to metrics upload
+                    if kwargs.get("metrics"):
+                        self.metrics_upload_failed_queue = {}
+                    return response  # Success, no need to retry
+
+                if i == 0:
+                    # Initial attempt, check status code and provide messages
+                    message = self._get_failure_message(response, retry, timeout)
+
+                    if verbose:
+                        LOGGER.warning(f"{PREFIX}{message} {HELP_MSG} ({response.status_code})")
+
+                if not self._should_retry(response.status_code):
+                    LOGGER.warning(f"{PREFIX}Request failed. {HELP_MSG} ({response.status_code}")
+                    break  # Not an error that should be retried, exit loop
+
+                time.sleep(2**i)  # Exponential backoff for retries
+
+            # if request related to metrics upload and exceed retries
+            if response is None and kwargs.get("metrics"):
+                self.metrics_upload_failed_queue.update(kwargs.get("metrics"))
+
+            return response
+
+        if thread:
+            # Start a new thread to run the retry_request function
+            threading.Thread(target=retry_request, daemon=True).start()
+        else:
+            # If running in the main thread, call retry_request directly
+            return retry_request()
+
+    @staticmethod
+    def _should_retry(status_code):
+        """Determines if a request should be retried based on the HTTP status code."""
+        retry_codes = {
+            HTTPStatus.REQUEST_TIMEOUT,
+            HTTPStatus.BAD_GATEWAY,
+            HTTPStatus.GATEWAY_TIMEOUT,
+        }
+        return status_code in retry_codes
+
+    def _get_failure_message(self, response: requests.Response, retry: int, timeout: int):
+        """
+        Generate a retry message based on the response status code.
+
+        Args:
+            response: The HTTP response object.
+            retry: The number of retry attempts allowed.
+            timeout: The maximum timeout duration.
+
+        Returns:
+            (str): The retry message.
+        """
+        if self._should_retry(response.status_code):
+            return f"Retrying {retry}x for {timeout}s." if retry else ""
+        elif response.status_code == HTTPStatus.TOO_MANY_REQUESTS:  # rate limit
+            headers = response.headers
+            return (
+                f"Rate limit reached ({headers['X-RateLimit-Remaining']}/{headers['X-RateLimit-Limit']}). "
+                f"Please retry after {headers['Retry-After']}s."
+            )
+        else:
+            try:
+                return response.json().get("message", "No JSON message.")
+            except AttributeError:
+                return "Unable to read JSON."
+
+    def upload_metrics(self):
+        """Upload model metrics to Ultralytics HUB."""
+        return self.request_queue(self.model.upload_metrics, metrics=self.metrics_queue.copy(), thread=True)
+
+    def upload_model(
+        self,
+        epoch: int,
+        weights: str,
+        is_best: bool = False,
+        map: float = 0.0,
+        final: bool = False,
+    ) -> None:
+        """
+        Upload a model checkpoint to Ultralytics HUB.
+
+        Args:
+            epoch (int): The current training epoch.
+            weights (str): Path to the model weights file.
+            is_best (bool): Indicates if the current model is the best one so far.
+            map (float): Mean average precision of the model.
+            final (bool): Indicates if the model is the final model after training.
+        """
+        weights = Path(weights)
+        if not weights.is_file():
+            last = weights.with_name(f"last{weights.suffix}")
+            if final and last.is_file():
+                LOGGER.warning(
+                    f"{PREFIX} WARNING ⚠️ Model 'best.pt' not found, copying 'last.pt' to 'best.pt' and uploading. "
+                    "This often happens when resuming training in transient environments like Google Colab. "
+                    "For more reliable training, consider using Ultralytics HUB Cloud. "
+                    "Learn more at https://docs.ultralytics.com/hub/cloud-training."
+                )
+                shutil.copy(last, weights)  # copy last.pt to best.pt
+            else:
+                LOGGER.warning(f"{PREFIX} WARNING ⚠️ Model upload issue. Missing model {weights}.")
+                return
+
+        self.request_queue(
+            self.model.upload_model,
+            epoch=epoch,
+            weights=str(weights),
+            is_best=is_best,
+            map=map,
+            final=final,
+            retry=10,
+            timeout=3600,
+            thread=not final,
+            progress_total=weights.stat().st_size if final else None,  # only show progress if final
+            stream_response=True,
+        )
+
+    @staticmethod
+    def _show_upload_progress(content_length: int, response: requests.Response) -> None:
+        """
+        Display a progress bar to track the upload progress of a file download.
+
+        Args:
+            content_length (int): The total size of the content to be downloaded in bytes.
+            response (requests.Response): The response object from the file download request.
+
+        Returns:
+            None
+        """
+        with TQDM(total=content_length, unit="B", unit_scale=True, unit_divisor=1024) as pbar:
+            for data in response.iter_content(chunk_size=1024):
+                pbar.update(len(data))
+
+    @staticmethod
+    def _iterate_content(response: requests.Response) -> None:
+        """
+        Process the streamed HTTP response data.
+
+        Args:
+            response (requests.Response): The response object from the file download request.
+
+        Returns:
+            None
+        """
+        for _ in response.iter_content(chunk_size=1024):
+            pass  # Do nothing with data chunks

ultralytics/hub/utils.py

+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+import os
+import platform
+import random
+import threading
+import time
+from pathlib import Path
+
+import requests
+
+from ultralytics.utils import (
+    ARGV,
+    ENVIRONMENT,
+    IS_COLAB,
+    IS_GIT_DIR,
+    IS_PIP_PACKAGE,
+    LOGGER,
+    ONLINE,
+    RANK,
+    SETTINGS,
+    TESTS_RUNNING,
+    TQDM,
+    TryExcept,
+    __version__,
+    colorstr,
+    get_git_origin_url,
+)
+from ultralytics.utils.downloads import GITHUB_ASSETS_NAMES
+
+HUB_API_ROOT = os.environ.get("ULTRALYTICS_HUB_API", "https://api.ultralytics.com")
+HUB_WEB_ROOT = os.environ.get("ULTRALYTICS_HUB_WEB", "https://hub.ultralytics.com")
+
+PREFIX = colorstr("Ultralytics HUB: ")
+HELP_MSG = "If this issue persists please visit https://github.com/ultralytics/hub/issues for assistance."
+
+
+def request_with_credentials(url: str) -> any:
+    """
+    Make an AJAX request with cookies attached in a Google Colab environment.
+
+    Args:
+        url (str): The URL to make the request to.
+
+    Returns:
+        (any): The response data from the AJAX request.
+
+    Raises:
+        OSError: If the function is not run in a Google Colab environment.
+    """
+    if not IS_COLAB:
+        raise OSError("request_with_credentials() must run in a Colab environment")
+    from google.colab import output  # noqa
+    from IPython import display  # noqa
+
+    display.display(
+        display.Javascript(
+            f"""
+            window._hub_tmp = new Promise((resolve, reject) => {{
+                const timeout = setTimeout(() => reject("Failed authenticating existing browser session"), 5000)
+                fetch("{url}", {{
+                    method: 'POST',
+                    credentials: 'include'
+                }})
+                    .then((response) => resolve(response.json()))
+                    .then((json) => {{
+                    clearTimeout(timeout);
+                    }}).catch((err) => {{
+                    clearTimeout(timeout);
+                    reject(err);
+                }});
+            }});
+            """
+        )
+    )
+    return output.eval_js("_hub_tmp")
+
+
+def requests_with_progress(method, url, **kwargs):
+    """
+    Make an HTTP request using the specified method and URL, with an optional progress bar.
+
+    Args:
+        method (str): The HTTP method to use (e.g. 'GET', 'POST').
+        url (str): The URL to send the request to.
+        **kwargs (any): Additional keyword arguments to pass to the underlying `requests.request` function.
+
+    Returns:
+        (requests.Response): The response object from the HTTP request.
+
+    Note:
+        - If 'progress' is set to True, the progress bar will display the download progress for responses with a known
+        content length.
+        - If 'progress' is a number then progress bar will display assuming content length = progress.
+    """
+    progress = kwargs.pop("progress", False)
+    if not progress:
+        return requests.request(method, url, **kwargs)
+    response = requests.request(method, url, stream=True, **kwargs)
+    total = int(response.headers.get("content-length", 0) if isinstance(progress, bool) else progress)  # total size
+    try:
+        pbar = TQDM(total=total, unit="B", unit_scale=True, unit_divisor=1024)
+        for data in response.iter_content(chunk_size=1024):
+            pbar.update(len(data))
+        pbar.close()
+    except requests.exceptions.ChunkedEncodingError:  # avoid 'Connection broken: IncompleteRead' warnings
+        response.close()
+    return response
+
+
+def smart_request(method, url, retry=3, timeout=30, thread=True, code=-1, verbose=True, progress=False, **kwargs):
+    """
+    Makes an HTTP request using the 'requests' library, with exponential backoff retries up to a specified timeout.
+
+    Args:
+        method (str): The HTTP method to use for the request. Choices are 'post' and 'get'.
+        url (str): The URL to make the request to.
+        retry (int, optional): Number of retries to attempt before giving up. Default is 3.
+        timeout (int, optional): Timeout in seconds after which the function will give up retrying. Default is 30.
+        thread (bool, optional): Whether to execute the request in a separate daemon thread. Default is True.
+        code (int, optional): An identifier for the request, used for logging purposes. Default is -1.
+        verbose (bool, optional): A flag to determine whether to print out to console or not. Default is True.
+        progress (bool, optional): Whether to show a progress bar during the request. Default is False.
+        **kwargs (any): Keyword arguments to be passed to the requests function specified in method.
+
+    Returns:
+        (requests.Response): The HTTP response object. If the request is executed in a separate thread, returns None.
+    """
+    retry_codes = (408, 500)  # retry only these codes
+
+    @TryExcept(verbose=verbose)
+    def func(func_method, func_url, **func_kwargs):
+        """Make HTTP requests with retries and timeouts, with optional progress tracking."""
+        r = None  # response
+        t0 = time.time()  # initial time for timer
+        for i in range(retry + 1):
+            if (time.time() - t0) > timeout:
+                break
+            r = requests_with_progress(func_method, func_url, **func_kwargs)  # i.e. get(url, data, json, files)
+            if r.status_code < 300:  # return codes in the 2xx range are generally considered "good" or "successful"
+                break
+            try:
+                m = r.json().get("message", "No JSON message.")
+            except AttributeError:
+                m = "Unable to read JSON."
+            if i == 0:
+                if r.status_code in retry_codes:
+                    m += f" Retrying {retry}x for {timeout}s." if retry else ""
+                elif r.status_code == 429:  # rate limit
+                    h = r.headers  # response headers
+                    m = (
+                        f"Rate limit reached ({h['X-RateLimit-Remaining']}/{h['X-RateLimit-Limit']}). "
+                        f"Please retry after {h['Retry-After']}s."
+                    )
+                if verbose:
+                    LOGGER.warning(f"{PREFIX}{m} {HELP_MSG} ({r.status_code} #{code})")
+                if r.status_code not in retry_codes:
+                    return r
+            time.sleep(2**i)  # exponential standoff
+        return r
+
+    args = method, url
+    kwargs["progress"] = progress
+    if thread:
+        threading.Thread(target=func, args=args, kwargs=kwargs, daemon=True).start()
+    else:
+        return func(*args, **kwargs)
+
+
+class Events:
+    """
+    A class for collecting anonymous event analytics. Event analytics are enabled when sync=True in settings and
+    disabled when sync=False. Run 'yolo settings' to see and update settings.
+
+    Attributes:
+        url (str): The URL to send anonymous events.
+        rate_limit (float): The rate limit in seconds for sending events.
+        metadata (dict): A dictionary containing metadata about the environment.
+        enabled (bool): A flag to enable or disable Events based on certain conditions.
+    """
+
+    url = "https://www.google-analytics.com/mp/collect?measurement_id=G-X8NCJYTQXM&api_secret=QLQrATrNSwGRFRLE-cbHJw"
+
+    def __init__(self):
+        """Initializes the Events object with default values for events, rate_limit, and metadata."""
+        self.events = []  # events list
+        self.rate_limit = 30.0  # rate limit (seconds)
+        self.t = 0.0  # rate limit timer (seconds)
+        self.metadata = {
+            "cli": Path(ARGV[0]).name == "yolo",
+            "install": "git" if IS_GIT_DIR else "pip" if IS_PIP_PACKAGE else "other",
+            "python": ".".join(platform.python_version_tuple()[:2]),  # i.e. 3.10
+            "version": __version__,
+            "env": ENVIRONMENT,
+            "session_id": round(random.random() * 1e15),
+            "engagement_time_msec": 1000,
+        }
+        self.enabled = (
+            SETTINGS["sync"]
+            and RANK in {-1, 0}
+            and not TESTS_RUNNING
+            and ONLINE
+            and (IS_PIP_PACKAGE or get_git_origin_url() == "https://github.com/ultralytics/ultralytics.git")
+        )
+
+    def __call__(self, cfg):
+        """
+        Attempts to add a new event to the events list and send events if the rate limit is reached.
+
+        Args:
+            cfg (IterableSimpleNamespace): The configuration object containing mode and task information.
+        """
+        if not self.enabled:
+            # Events disabled, do nothing
+            return
+
+        # Attempt to add to events
+        if len(self.events) < 25:  # Events list limited to 25 events (drop any events past this)
+            params = {
+                **self.metadata,
+                "task": cfg.task,
+                "model": cfg.model if cfg.model in GITHUB_ASSETS_NAMES else "custom",
+            }
+            if cfg.mode == "export":
+                params["format"] = cfg.format
+            self.events.append({"name": cfg.mode, "params": params})
+
+        # Check rate limit
+        t = time.time()
+        if (t - self.t) < self.rate_limit:
+            # Time is under rate limiter, wait to send
+            return
+
+        # Time is over rate limiter, send now
+        data = {"client_id": SETTINGS["uuid"], "events": self.events}  # SHA-256 anonymized UUID hash and events list
+
+        # POST equivalent to requests.post(self.url, json=data)
+        smart_request("post", self.url, json=data, retry=0, verbose=False)
+
+        # Reset events and rate limit timer
+        self.events = []
+        self.t = t
+
+
+# Run below code on hub/utils init -------------------------------------------------------------------------------------
+events = Events()

ultralytics/models/__init__.py

+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from .fastsam import FastSAM
+from .nas import NAS
+from .rtdetr import RTDETR
+from .sam import SAM
+from .yolo import YOLO, YOLOWorld
+
+__all__ = "YOLO", "RTDETR", "SAM", "FastSAM", "NAS", "YOLOWorld"  # allow simpler import

ultralytics/models/fastsam/__init__.py

+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from .model import FastSAM
+from .predict import FastSAMPredictor
+from .val import FastSAMValidator
+
+__all__ = "FastSAMPredictor", "FastSAM", "FastSAMValidator"

ultralytics/models/fastsam/model.py

+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from pathlib import Path
+
+from ultralytics.engine.model import Model
+
+from .predict import FastSAMPredictor
+from .val import FastSAMValidator
+
+
+class FastSAM(Model):
+    """
+    FastSAM model interface.
+
+    Example:
+        ```python
+        from ultralytics import FastSAM
+
+        model = FastSAM("last.pt")
+        results = model.predict("ultralytics/assets/bus.jpg")
+        ```
+    """
+
+    def __init__(self, model="FastSAM-x.pt"):
+        """Call the __init__ method of the parent class (YOLO) with the updated default model."""
+        if str(model) == "FastSAM.pt":
+            model = "FastSAM-x.pt"
+        assert Path(model).suffix not in {".yaml", ".yml"}, "FastSAM models only support pre-trained models."
+        super().__init__(model=model, task="segment")
+
+    def predict(self, source, stream=False, bboxes=None, points=None, labels=None, texts=None, **kwargs):
+        """
+        Perform segmentation prediction on image or video source.
+
+        Supports prompted segmentation with bounding boxes, points, labels, and texts.
+
+        Args:
+            source (str | PIL.Image | numpy.ndarray): Input source.
+            stream (bool): Enable real-time streaming.
+            bboxes (list): Bounding box coordinates for prompted segmentation.
+            points (list): Points for prompted segmentation.
+            labels (list): Labels for prompted segmentation.
+            texts (list): Texts for prompted segmentation.
+            **kwargs (Any): Additional keyword arguments.
+
+        Returns:
+            (list): Model predictions.
+        """
+        prompts = dict(bboxes=bboxes, points=points, labels=labels, texts=texts)
+        return super().predict(source, stream, prompts=prompts, **kwargs)
+
+    @property
+    def task_map(self):
+        """Returns a dictionary mapping segment task to corresponding predictor and validator classes."""
+        return {"segment": {"predictor": FastSAMPredictor, "validator": FastSAMValidator}}

ultralytics/models/fastsam/predict.py

+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+import torch
+from PIL import Image
+
+from ultralytics.models.yolo.segment import SegmentationPredictor
+from ultralytics.utils import DEFAULT_CFG, checks
+from ultralytics.utils.metrics import box_iou
+from ultralytics.utils.ops import scale_masks
+
+from .utils import adjust_bboxes_to_image_border
+
+
+class FastSAMPredictor(SegmentationPredictor):
+    """
+    FastSAMPredictor is specialized for fast SAM (Segment Anything Model) segmentation prediction tasks in Ultralytics
+    YOLO framework.
+
+    This class extends the SegmentationPredictor, customizing the prediction pipeline specifically for fast SAM. It
+    adjusts post-processing steps to incorporate mask prediction and non-max suppression while optimizing for single-
+    class segmentation.
+    """
+
+    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
+        """Initializes a FastSAMPredictor for fast SAM segmentation tasks in Ultralytics YOLO framework."""
+        super().__init__(cfg, overrides, _callbacks)
+        self.prompts = {}
+
+    def postprocess(self, preds, img, orig_imgs):
+        """Applies box postprocess for FastSAM predictions."""
+        bboxes = self.prompts.pop("bboxes", None)
+        points = self.prompts.pop("points", None)
+        labels = self.prompts.pop("labels", None)
+        texts = self.prompts.pop("texts", None)
+        results = super().postprocess(preds, img, orig_imgs)
+        for result in results:
+            full_box = torch.tensor(
+                [0, 0, result.orig_shape[1], result.orig_shape[0]], device=preds[0].device, dtype=torch.float32
+            )
+            boxes = adjust_bboxes_to_image_border(result.boxes.xyxy, result.orig_shape)
+            idx = torch.nonzero(box_iou(full_box[None], boxes) > 0.9).flatten()
+            if idx.numel() != 0:
+                result.boxes.xyxy[idx] = full_box
+
+        return self.prompt(results, bboxes=bboxes, points=points, labels=labels, texts=texts)
+
+    def prompt(self, results, bboxes=None, points=None, labels=None, texts=None):
+        """
+        Internal function for image segmentation inference based on cues like bounding boxes, points, and masks.
+        Leverages SAM's specialized architecture for prompt-based, real-time segmentation.
+
+        Args:
+            results (Results | List[Results]): The original inference results from FastSAM models without any prompts.
+            bboxes (np.ndarray | List, optional): Bounding boxes with shape (N, 4), in XYXY format.
+            points (np.ndarray | List, optional): Points indicating object locations with shape (N, 2), in pixels.
+            labels (np.ndarray | List, optional): Labels for point prompts, shape (N, ). 1 = foreground, 0 = background.
+            texts (str | List[str], optional): Textual prompts, a list contains string objects.
+
+        Returns:
+            (List[Results]): The output results determined by prompts.
+        """
+        if bboxes is None and points is None and texts is None:
+            return results
+        prompt_results = []
+        if not isinstance(results, list):
+            results = [results]
+        for result in results:
+            if len(result) == 0:
+                prompt_results.append(result)
+                continue
+            masks = result.masks.data
+            if masks.shape[1:] != result.orig_shape:
+                masks = scale_masks(masks[None], result.orig_shape)[0]
+            # bboxes prompt
+            idx = torch.zeros(len(result), dtype=torch.bool, device=self.device)
+            if bboxes is not None:
+                bboxes = torch.as_tensor(bboxes, dtype=torch.int32, device=self.device)
+                bboxes = bboxes[None] if bboxes.ndim == 1 else bboxes
+                bbox_areas = (bboxes[:, 3] - bboxes[:, 1]) * (bboxes[:, 2] - bboxes[:, 0])
+                mask_areas = torch.stack([masks[:, b[1] : b[3], b[0] : b[2]].sum(dim=(1, 2)) for b in bboxes])
+                full_mask_areas = torch.sum(masks, dim=(1, 2))
+
+                union = bbox_areas[:, None] + full_mask_areas - mask_areas
+                idx[torch.argmax(mask_areas / union, dim=1)] = True
+            if points is not None:
+                points = torch.as_tensor(points, dtype=torch.int32, device=self.device)
+                points = points[None] if points.ndim == 1 else points
+                if labels is None:
+                    labels = torch.ones(points.shape[0])
+                labels = torch.as_tensor(labels, dtype=torch.int32, device=self.device)
+                assert len(labels) == len(points), (
+                    f"Excepted `labels` got same size as `point`, but got {len(labels)} and {len(points)}"
+                )
+                point_idx = (
+                    torch.ones(len(result), dtype=torch.bool, device=self.device)
+                    if labels.sum() == 0  # all negative points
+                    else torch.zeros(len(result), dtype=torch.bool, device=self.device)
+                )
+                for point, label in zip(points, labels):
+                    point_idx[torch.nonzero(masks[:, point[1], point[0]], as_tuple=True)[0]] = bool(label)
+                idx |= point_idx
+            if texts is not None:
+                if isinstance(texts, str):
+                    texts = [texts]
+                crop_ims, filter_idx = [], []
+                for i, b in enumerate(result.boxes.xyxy.tolist()):
+                    x1, y1, x2, y2 = (int(x) for x in b)
+                    if masks[i].sum() <= 100:
+                        filter_idx.append(i)
+                        continue
+                    crop_ims.append(Image.fromarray(result.orig_img[y1:y2, x1:x2, ::-1]))
+                similarity = self._clip_inference(crop_ims, texts)
+                text_idx = torch.argmax(similarity, dim=-1)  # (M, )
+                if len(filter_idx):
+                    text_idx += (torch.tensor(filter_idx, device=self.device)[None] <= int(text_idx)).sum(0)
+                idx[text_idx] = True
+
+            prompt_results.append(result[idx])
+
+        return prompt_results
+
+    def _clip_inference(self, images, texts):
+        """
+        CLIP Inference process.
+
+        Args:
+            images (List[PIL.Image]): A list of source images and each of them should be PIL.Image type with RGB channel order.
+            texts (List[str]): A list of prompt texts and each of them should be string object.
+
+        Returns:
+            (torch.Tensor): The similarity between given images and texts.
+        """
+        try:
+            import clip
+        except ImportError:
+            checks.check_requirements("git+https://github.com/ultralytics/CLIP.git")
+            import clip
+        if (not hasattr(self, "clip_model")) or (not hasattr(self, "clip_preprocess")):
+            self.clip_model, self.clip_preprocess = clip.load("ViT-B/32", device=self.device)
+        images = torch.stack([self.clip_preprocess(image).to(self.device) for image in images])
+        tokenized_text = clip.tokenize(texts).to(self.device)
+        image_features = self.clip_model.encode_image(images)
+        text_features = self.clip_model.encode_text(tokenized_text)
+        image_features /= image_features.norm(dim=-1, keepdim=True)  # (N, 512)
+        text_features /= text_features.norm(dim=-1, keepdim=True)  # (M, 512)
+        return (image_features * text_features[:, None]).sum(-1)  # (M, N)
+
+    def set_prompts(self, prompts):
+        """Set prompts in advance."""
+        self.prompts = prompts