# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license from pathlib import Path from ultralytics.data.build import load_inference_source from ultralytics.engine.model import Model from ultralytics.models import yolo from ultralytics.nn.tasks import ( ClassificationModel, DetectionModel, OBBModel, PoseModel, SegmentationModel, WorldModel, YOLOEModel, YOLOESegModel, ) from ultralytics.utils import ROOT, yaml_load class YOLO(Model): """YOLO (You Only Look Once) object detection model.""" def __init__(self, model="yolo11n.pt", task=None, verbose=False): """ Initialize a YOLO model. This constructor initializes a YOLO model, automatically switching to specialized model types (YOLOWorld or YOLOE) based on the model filename. Args: model (str | Path): Model name or path to model file, i.e. 'yolo11n.pt', 'yolov8n.yaml'. task (str | None): YOLO task specification, i.e. 'detect', 'segment', 'classify', 'pose', 'obb'. Defaults to auto-detection based on model. verbose (bool): Display model info on load. Examples: >>> from ultralytics import YOLO >>> model = YOLO("yolov8n.pt") # load a pretrained YOLOv8n detection model >>> model = YOLO("yolov8n-seg.pt") # load a pretrained YOLOv8n segmentation model >>> model = YOLO("yolo11n.pt") # load a pretrained YOLOv11n detection model """ path = Path(model) if "-world" in path.stem and path.suffix in {".pt", ".yaml", ".yml"}: # if YOLOWorld PyTorch model new_instance = YOLOWorld(path, verbose=verbose) self.__class__ = type(new_instance) self.__dict__ = new_instance.__dict__ elif "yoloe" in path.stem and path.suffix in {".pt", ".yaml", ".yml"}: # if YOLOE PyTorch model new_instance = YOLOE(path, task=task, verbose=verbose) self.__class__ = type(new_instance) self.__dict__ = new_instance.__dict__ else: # Continue with default YOLO initialization super().__init__(model=model, task=task, verbose=verbose) @property def task_map(self): """Map head to model, trainer, validator, and predictor classes.""" return { "classify": { "model": ClassificationModel, "trainer": yolo.classify.ClassificationTrainer, "validator": yolo.classify.ClassificationValidator, "predictor": yolo.classify.ClassificationPredictor, }, "detect": { "model": DetectionModel, "trainer": yolo.detect.DetectionTrainer, "validator": yolo.detect.DetectionValidator, "predictor": yolo.detect.DetectionPredictor, }, "segment": { "model": SegmentationModel, "trainer": yolo.segment.SegmentationTrainer, "validator": yolo.segment.SegmentationValidator, "predictor": yolo.segment.SegmentationPredictor, }, "pose": { "model": PoseModel, "trainer": yolo.pose.PoseTrainer, "validator": yolo.pose.PoseValidator, "predictor": yolo.pose.PosePredictor, }, "obb": { "model": OBBModel, "trainer": yolo.obb.OBBTrainer, "validator": yolo.obb.OBBValidator, "predictor": yolo.obb.OBBPredictor, }, } class YOLOWorld(Model): """YOLO-World object detection model.""" def __init__(self, model="yolov8s-world.pt", verbose=False) -> None: """ Initialize YOLOv8-World model with a pre-trained model file. Loads a YOLOv8-World model for object detection. If no custom class names are provided, it assigns default COCO class names. Args: model (str | Path): Path to the pre-trained model file. Supports *.pt and *.yaml formats. verbose (bool): If True, prints additional information during initialization. """ super().__init__(model=model, task="detect", verbose=verbose) # Assign default COCO class names when there are no custom names if not hasattr(self.model, "names"): self.model.names = yaml_load(ROOT / "cfg/datasets/coco8.yaml").get("names") @property def task_map(self): """Map head to model, validator, and predictor classes.""" return { "detect": { "model": WorldModel, "validator": yolo.detect.DetectionValidator, "predictor": yolo.detect.DetectionPredictor, "trainer": yolo.world.WorldTrainer, } } def set_classes(self, classes): """ Set the model's class names for detection. Args: classes (list[str]): A list of categories i.e. ["person"]. """ self.model.set_classes(classes) # Remove background if it's given background = " " if background in classes: classes.remove(background) self.model.names = classes # Reset method class names if self.predictor: self.predictor.model.names = classes class YOLOE(Model): """YOLOE object detection and segmentation model.""" def __init__(self, model="yoloe-11s-seg.pt", task=None, verbose=False) -> None: """ Initialize YOLOE model with a pre-trained model file. Args: model (str | Path): Path to the pre-trained model file. Supports *.pt and *.yaml formats. task (str, optional): Task type for the model. Auto-detected if None. verbose (bool): If True, prints additional information during initialization. """ super().__init__(model=model, task=task, verbose=verbose) # Assign default COCO class names when there are no custom names if not hasattr(self.model, "names"): self.model.names = yaml_load(ROOT / "cfg/datasets/coco8.yaml").get("names") @property def task_map(self): """Map head to model, validator, and predictor classes.""" return { "detect": { "model": YOLOEModel, "validator": yolo.yoloe.YOLOEDetectValidator, "predictor": yolo.detect.DetectionPredictor, "trainer": yolo.yoloe.YOLOETrainer, }, "segment": { "model": YOLOESegModel, "validator": yolo.yoloe.YOLOESegValidator, "predictor": yolo.segment.SegmentationPredictor, "trainer": yolo.yoloe.YOLOESegTrainer, }, } def get_text_pe(self, texts): """Get text positional embeddings for the given texts.""" assert isinstance(self.model, YOLOEModel) return self.model.get_text_pe(texts) def get_visual_pe(self, img, visual): """ Get visual positional embeddings for the given image and visual features. This method extracts positional embeddings from visual features based on the input image. It requires that the model is an instance of YOLOEModel. Args: img (torch.Tensor): Input image tensor. visual (torch.Tensor): Visual features extracted from the image. Returns: (torch.Tensor): Visual positional embeddings. Examples: >>> model = YOLOE("yoloe-11s-seg.pt") >>> img = torch.rand(1, 3, 640, 640) >>> visual_features = model.model.backbone(img) >>> pe = model.get_visual_pe(img, visual_features) """ assert isinstance(self.model, YOLOEModel) return self.model.get_visual_pe(img, visual) def set_vocab(self, vocab, names): """ Set vocabulary and class names for the YOLOE model. This method configures the vocabulary and class names used by the model for text processing and classification tasks. The model must be an instance of YOLOEModel. Args: vocab (list): Vocabulary list containing tokens or words used by the model for text processing. names (list): List of class names that the model can detect or classify. Raises: AssertionError: If the model is not an instance of YOLOEModel. Examples: >>> model = YOLOE("yoloe-11s-seg.pt") >>> model.set_vocab(["person", "car", "dog"], ["person", "car", "dog"]) """ assert isinstance(self.model, YOLOEModel) self.model.set_vocab(vocab, names=names) def get_vocab(self, names): """Get vocabulary for the given class names.""" assert isinstance(self.model, YOLOEModel) return self.model.get_vocab(names) def set_classes(self, classes, embeddings): """ Set the model's class names and embeddings for detection. Args: classes (list[str]): A list of categories i.e. ["person"]. embeddings (torch.Tensor): Embeddings corresponding to the classes. """ assert isinstance(self.model, YOLOEModel) self.model.set_classes(classes, embeddings) # Verify no background class is present assert " " not in classes self.model.names = classes # Reset method class names if self.predictor: self.predictor.model.names = classes def val( self, validator=None, load_vp=False, refer_data=None, **kwargs, ): """ Validate the model using text or visual prompts. Args: validator (callable, optional): A callable validator function. If None, a default validator is loaded. load_vp (bool): Whether to load visual prompts. If False, text prompts are used. refer_data (str, optional): Path to the reference data for visual prompts. **kwargs (Any): Additional keyword arguments to override default settings. Returns: (dict): Validation statistics containing metrics computed during validation. """ custom = {"rect": not load_vp} # method defaults args = {**self.overrides, **custom, **kwargs, "mode": "val"} # highest priority args on the right validator = (validator or self._smart_load("validator"))(args=args, _callbacks=self.callbacks) validator(model=self.model, load_vp=load_vp, refer_data=refer_data) self.metrics = validator.metrics return validator.metrics def predict( self, source=None, stream: bool = False, visual_prompts: dict = {}, refer_image=None, predictor=None, **kwargs, ): """ Run prediction on images, videos, directories, streams, etc. Args: source (str | int | PIL.Image | np.ndarray, optional): Source for prediction. Accepts image paths, directory paths, URL/YouTube streams, PIL images, numpy arrays, or webcam indices. stream (bool): Whether to stream the prediction results. If True, results are yielded as a generator as they are computed. visual_prompts (dict): Dictionary containing visual prompts for the model. Must include 'bboxes' and 'cls' keys when non-empty. refer_image (str | PIL.Image | np.ndarray, optional): Reference image for visual prompts. predictor (callable, optional): Custom predictor function. If None, a predictor is automatically loaded based on the task. **kwargs (Any): Additional keyword arguments passed to the predictor. Returns: (List | generator): List of Results objects or generator of Results objects if stream=True. Examples: >>> model = YOLOE("yoloe-11s-seg.pt") >>> results = model.predict("path/to/image.jpg") >>> # With visual prompts >>> prompts = {"bboxes": [[10, 20, 100, 200]], "cls": ["person"]} >>> results = model.predict("path/to/image.jpg", visual_prompts=prompts) """ if len(visual_prompts): assert "bboxes" in visual_prompts and "cls" in visual_prompts, ( f"Expected 'bboxes' and 'cls' in visual prompts, but got {visual_prompts.keys()}" ) assert len(visual_prompts["bboxes"]) == len(visual_prompts["cls"]), ( f"Expected equal number of bounding boxes and classes, but got {len(visual_prompts['bboxes'])} and " f"{len(visual_prompts['cls'])} respectively" ) self.predictor = (predictor or self._smart_load("predictor"))( overrides={ "task": self.model.task, "mode": "predict", "save": False, "verbose": refer_image is None, "batch": 1, }, _callbacks=self.callbacks, ) if len(visual_prompts): num_cls = ( max(len(set(c)) for c in visual_prompts["cls"]) if isinstance(source, list) # means multiple images else len(set(visual_prompts["cls"])) ) self.model.model[-1].nc = num_cls self.model.names = [f"object{i}" for i in range(num_cls)] self.predictor.set_prompts(visual_prompts.copy()) self.predictor.setup_model(model=self.model) if refer_image is None and source is not None: dataset = load_inference_source(source) if dataset.mode in {"video", "stream"}: # NOTE: set the first frame as refer image for videos/streams inference refer_image = next(iter(dataset))[1][0] if refer_image is not None and len(visual_prompts): vpe = self.predictor.get_vpe(refer_image) self.model.set_classes(self.model.names, vpe) self.task = "segment" if isinstance(self.predictor, yolo.segment.SegmentationPredictor) else "detect" self.predictor = None # reset predictor return super().predict(source, stream, **kwargs)