`ultralytics 8.0.143` add `Model` base class (#3934)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

docs/reference/engine/model.md

@@ -3,7 +3,7 @@ description: Explore the detailed guide on using the Ultralytics YOLO Engine Mod
 keywords: Ultralytics, YOLO, engine model, documentation, guide, implementation, training, evaluation
 ---
 
-## YOLO
+## Model
 ---
-### ::: ultralytics.engine.model.YOLO
+### ::: ultralytics.engine.model.Model
 <br><br>

docs/reference/models/yolo/model.md

@@ -0,0 +1,9 @@
+---
+description: Discover the Ultralytics YOLO model class. Learn advanced techniques, tips, and tricks for training.
+keywords: Ultralytics YOLO, YOLO, YOLO model, Model Training, Machine Learning, Deep Learning, Computer Vision
+---
+
+## YOLO
+---
+### ::: ultralytics.models.yolo.model.YOLO
+<br><br>

mkdocs.yml

@@ -317,6 +317,7 @@ nav:
                   - predict: reference/models/yolo/detect/predict.md
                   - train: reference/models/yolo/detect/train.md
                   - val: reference/models/yolo/detect/val.md
+              - model: reference/models/yolo/model.md
               - pose:
                   - predict: reference/models/yolo/pose/predict.md
                   - train: reference/models/yolo/pose/train.md

ultralytics/__init__.py

@@ -1,10 +1,9 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = '8.0.142'
+__version__ = '8.0.143'
 
-from ultralytics.engine.model import YOLO
 from ultralytics.hub import start
-from ultralytics.models import RTDETR, SAM
+from ultralytics.models import RTDETR, SAM, YOLO
 from ultralytics.models.fastsam import FastSAM
 from ultralytics.models.nas import NAS
 from ultralytics.utils import SETTINGS as settings

ultralytics/engine/model.py

@@ -1,36 +1,23 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
+import inspect
 import sys
 from pathlib import Path
 from typing import Union
 
 from ultralytics.cfg import get_cfg
 from ultralytics.engine.exporter import Exporter
-from ultralytics.models import yolo  # noqa
+from ultralytics.nn.tasks import attempt_load_one_weight, guess_model_task, nn, yaml_model_load
-from ultralytics.nn.tasks import (ClassificationModel, DetectionModel, PoseModel, SegmentationModel,
-                                  attempt_load_one_weight, guess_model_task, nn, yaml_model_load)
 from ultralytics.utils import (DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, ROOT, callbacks,
                                is_git_dir, yaml_load)
 from ultralytics.utils.checks import check_file, check_imgsz, check_pip_update_available, check_yaml
 from ultralytics.utils.downloads import GITHUB_ASSET_STEMS
 from ultralytics.utils.torch_utils import smart_inference_mode
 
-# Map head to model, trainer, validator, and predictor classes
-TASK_MAP = {
-    'classify': [
-        ClassificationModel, yolo.classify.ClassificationTrainer, yolo.classify.ClassificationValidator,
-        yolo.classify.ClassificationPredictor],
-    'detect':
-    [DetectionModel, yolo.detect.DetectionTrainer, yolo.detect.DetectionValidator, yolo.detect.DetectionPredictor],
-    'segment': [
-        SegmentationModel, yolo.segment.SegmentationTrainer, yolo.segment.SegmentationValidator,
-        yolo.segment.SegmentationPredictor],
-    'pose': [PoseModel, yolo.pose.PoseTrainer, yolo.pose.PoseValidator, yolo.pose.PosePredictor]}
 
-
+class Model:
-class YOLO:
     """
-    YOLO (You Only Look Once) object detection model.
+    A base model class to unify apis for all the models.
 
     Args:
         model (str, Path): Path to the model file to load or create.
@@ -81,13 +68,13 @@ class YOLO:
         self.predictor = None  # reuse predictor
         self.model = None  # model object
         self.trainer = None  # trainer object
-        self.task = None  # task type
         self.ckpt = None  # if loaded from *.pt
         self.cfg = None  # if loaded from *.yaml
         self.ckpt_path = None
         self.overrides = {}  # overrides for trainer object
         self.metrics = None  # validation/training metrics
         self.session = None  # HUB session
+        self.task = task  # task type
         model = str(model).strip()  # strip spaces
 
         # Check if Ultralytics HUB model from https://hub.ultralytics.com
@@ -109,11 +96,6 @@ class YOLO:
         """Calls the 'predict' function with given arguments to perform object detection."""
         return self.predict(source, stream, **kwargs)
 
-    def __getattr__(self, attr):
-        """Raises error if object has no requested attribute."""
-        name = self.__class__.__name__
-        raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")
-
     @staticmethod
     def is_hub_model(model):
         """Check if the provided model is a HUB model."""
@@ -122,19 +104,21 @@ class YOLO:
             [len(x) for x in model.split('_')] == [42, 20],  # APIKEY_MODELID
             len(model) == 20 and not Path(model).exists() and all(x not in model for x in './\\')))  # MODELID
 
-    def _new(self, cfg: str, task=None, verbose=True):
+    def _new(self, cfg: str, task=None, model=None, verbose=True):
         """
         Initializes a new model and infers the task type from the model definitions.
 
         Args:
             cfg (str): model configuration file
             task (str | None): model task
+            model (BaseModel): Customized model.
             verbose (bool): display model info on load
         """
         cfg_dict = yaml_model_load(cfg)
         self.cfg = cfg
         self.task = task or guess_model_task(cfg_dict)
-        self.model = TASK_MAP[self.task][0](cfg_dict, verbose=verbose and RANK == -1)  # build model
+        model = model or self.smart_load('model')
+        self.model = model(cfg_dict, verbose=verbose and RANK == -1)  # build model
         self.overrides['model'] = self.cfg
 
         # Below added to allow export from yamls
@@ -217,7 +201,7 @@ class YOLO:
         self.model.fuse()
 
     @smart_inference_mode()
-    def predict(self, source=None, stream=False, **kwargs):
+    def predict(self, source=None, stream=False, predictor=None, **kwargs):
         """
         Perform prediction using the YOLO model.
 
@@ -225,6 +209,7 @@ class YOLO:
             source (str | int | PIL | np.ndarray): The source of the image to make predictions on.
                           Accepts all source types accepted by the YOLO model.
             stream (bool): Whether to stream the predictions or not. Defaults to False.
+            predictor (BasePredictor): Customized predictor.
             **kwargs : Additional keyword arguments passed to the predictor.
                        Check the 'configuration' section in the documentation for all available options.
 
@@ -236,6 +221,8 @@ class YOLO:
             LOGGER.warning(f"WARNING ⚠️ 'source' is missing. Using 'source={source}'.")
         is_cli = (sys.argv[0].endswith('yolo') or sys.argv[0].endswith('ultralytics')) and any(
             x in sys.argv for x in ('predict', 'track', 'mode=predict', 'mode=track'))
+        # Check prompts for SAM/FastSAM
+        prompts = kwargs.pop('prompts', None)
         overrides = self.overrides.copy()
         overrides['conf'] = 0.25
         overrides.update(kwargs)  # prefer kwargs
@@ -245,12 +232,16 @@ class YOLO:
             overrides['save'] = kwargs.get('save', False)  # do not save by default if called in Python
         if not self.predictor:
             self.task = overrides.get('task') or self.task
-            self.predictor = TASK_MAP[self.task][3](overrides=overrides, _callbacks=self.callbacks)
+            predictor = predictor or self.smart_load('predictor')
+            self.predictor = predictor(overrides=overrides, _callbacks=self.callbacks)
             self.predictor.setup_model(model=self.model, verbose=is_cli)
         else:  # only update args if predictor is already setup
             self.predictor.args = get_cfg(self.predictor.args, overrides)
             if 'project' in overrides or 'name' in overrides:
                 self.predictor.save_dir = self.predictor.get_save_dir()
+        # Set prompts for SAM/FastSAM
+        if len and hasattr(self.predictor, 'set_prompts'):
+            self.predictor.set_prompts(prompts)
         return self.predictor.predict_cli(source=source) if is_cli else self.predictor(source=source, stream=stream)
 
     def track(self, source=None, stream=False, persist=False, **kwargs):
@@ -277,12 +268,13 @@ class YOLO:
         return self.predict(source=source, stream=stream, **kwargs)
 
     @smart_inference_mode()
-    def val(self, data=None, **kwargs):
+    def val(self, data=None, validator=None, **kwargs):
         """
         Validate a model on a given dataset.
 
         Args:
             data (str): The dataset to validate on. Accepts all formats accepted by yolo
+            validator (BaseValidator): Customized validator.
             **kwargs : Any other args accepted by the validators. To see all args check 'configuration' section in docs
         """
         overrides = self.overrides.copy()
@@ -295,11 +287,12 @@ class YOLO:
             self.task = args.task
         else:
             args.task = self.task
+        validator = validator or self.smart_load('validator')
         if args.imgsz == DEFAULT_CFG.imgsz and not isinstance(self.model, (str, Path)):
             args.imgsz = self.model.args['imgsz']  # use trained imgsz unless custom value is passed
         args.imgsz = check_imgsz(args.imgsz, max_dim=1)
 
-        validator = TASK_MAP[self.task][2](args=args, _callbacks=self.callbacks)
+        validator = validator(args=args, _callbacks=self.callbacks)
         validator(model=self.model)
         self.metrics = validator.metrics
 
@@ -349,11 +342,12 @@ class YOLO:
         args.task = self.task
         return Exporter(overrides=args, _callbacks=self.callbacks)(model=self.model)
 
-    def train(self, **kwargs):
+    def train(self, trainer=None, **kwargs):
         """
         Trains the model on a given dataset.
 
         Args:
+            trainer (BaseTrainer, optional): Customized trainer.
             **kwargs (Any): Any number of arguments representing the training configuration.
         """
         self._check_is_pytorch_model()
@@ -373,7 +367,8 @@ class YOLO:
         if overrides.get('resume'):
             overrides['resume'] = self.ckpt_path
         self.task = overrides.get('task') or self.task
-        self.trainer = TASK_MAP[self.task][1](overrides=overrides, _callbacks=self.callbacks)
+        trainer = trainer or self.smart_load('trainer')
+        self.trainer = trainer(overrides=overrides, _callbacks=self.callbacks)
         if not overrides.get('resume'):  # manually set model only if not resuming
             self.trainer.model = self.trainer.get_model(weights=self.model if self.ckpt else None, cfg=self.model.yaml)
             self.model = self.trainer.model
@@ -442,3 +437,27 @@ class YOLO:
         """Reset all registered callbacks."""
         for event in callbacks.default_callbacks.keys():
             self.callbacks[event] = [callbacks.default_callbacks[event][0]]
+
+    def __getattr__(self, attr):
+        """Raises error if object has no requested attribute."""
+        name = self.__class__.__name__
+        raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")
+
+    def smart_load(self, key):
+        """Load model/trainer/validator/predictor."""
+        try:
+            return self.task_map[self.task][key]
+        except Exception:
+            name = self.__class__.__name__
+            mode = inspect.stack()[1][3]  # get the function name.
+            raise NotImplementedError(
+                f'WARNING ⚠️ `{name}` model does not support `{mode}` mode for `{self.task}` task yet.')
+
+    @property
+    def task_map(self):
+        """Map head to model, trainer, validator, and predictor classes
+
+        Returns:
+            task_map (dict)
+        """
+        raise NotImplementedError('Please provide task map for your model!')

ultralytics/models/__init__.py

@@ -1,4 +1,7 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
 from .rtdetr import RTDETR
 from .sam import SAM
+from .yolo import YOLO
 
-__all__ = 'RTDETR', 'SAM'  # allow simpler import
+__all__ = 'YOLO', 'RTDETR', 'SAM'  # allow simpler import

ultralytics/models/fastsam/model.py

@@ -1,111 +1,31 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
-"""
-FastSAM model interface.
 
-Usage - Predict:
+from pathlib import Path
-    from ultralytics import FastSAM
-
-    model = FastSAM('last.pt')
-    results = model.predict('ultralytics/assets/bus.jpg')
-"""
 
-from ultralytics.cfg import get_cfg
+from ultralytics.engine.model import Model
-from ultralytics.engine.exporter import Exporter
-from ultralytics.engine.model import YOLO
-from ultralytics.utils import DEFAULT_CFG, LOGGER, ROOT, is_git_dir
-from ultralytics.utils.checks import check_imgsz
-from ultralytics.utils.torch_utils import model_info, smart_inference_mode
 
 from .predict import FastSAMPredictor
+from .val import FastSAMValidator
 
 
-class FastSAM(YOLO):
+class FastSAM(Model):
-
-    def __init__(self, model='FastSAM-x.pt'):
-        """Call the __init__ method of the parent class (YOLO) with the updated default model"""
-        if model == 'FastSAM.pt':
-            model = 'FastSAM-x.pt'
-        super().__init__(model=model)
-        # any additional initialization code for FastSAM
-
-    @smart_inference_mode()
-    def predict(self, source=None, stream=False, **kwargs):
-        """
-        Perform prediction using the YOLO model.
-
-        Args:
-            source (str | int | PIL | np.ndarray): The source of the image to make predictions on.
-                          Accepts all source types accepted by the YOLO model.
-            stream (bool): Whether to stream the predictions or not. Defaults to False.
-            **kwargs : Additional keyword arguments passed to the predictor.
-                       Check the 'configuration' section in the documentation for all available options.
-
-        Returns:
-            (List[ultralytics.engine.results.Results]): The prediction results.
-        """
-        if source is None:
-            source = ROOT / 'assets' if is_git_dir() else 'https://ultralytics.com/images/bus.jpg'
-            LOGGER.warning(f"WARNING ⚠️ 'source' is missing. Using 'source={source}'.")
-        overrides = self.overrides.copy()
-        overrides['conf'] = 0.25
-        overrides.update(kwargs)  # prefer kwargs
-        overrides['mode'] = kwargs.get('mode', 'predict')
-        assert overrides['mode'] in ['track', 'predict']
-        overrides['save'] = kwargs.get('save', False)  # do not save by default if called in Python
-        self.predictor = FastSAMPredictor(overrides=overrides)
-        self.predictor.setup_model(model=self.model, verbose=False)
-
-        return self.predictor(source, stream=stream)
-
-    def train(self, **kwargs):
-        """Function trains models but raises an error as FastSAM models do not support training."""
-        raise NotImplementedError("FastSAM models don't support training")
-
-    def val(self, **kwargs):
-        """Run validation given dataset."""
-        overrides = dict(task='segment', mode='val')
-        overrides.update(kwargs)  # prefer kwargs
-        args = get_cfg(cfg=DEFAULT_CFG, overrides=overrides)
-        args.imgsz = check_imgsz(args.imgsz, max_dim=1)
-        validator = FastSAM(args=args)
-        validator(model=self.model)
-        self.metrics = validator.metrics
-        return validator.metrics
-
-    @smart_inference_mode()
-    def export(self, **kwargs):
     """
-        Export model.
+    FastSAM model interface.
 
-        Args:
+    Usage - Predict:
-            **kwargs : Any other args accepted by the predictors. To see all args check 'configuration' section in docs
+        from ultralytics import FastSAM
-        """
-        overrides = dict(task='detect')
-        overrides.update(kwargs)
-        overrides['mode'] = 'export'
-        args = get_cfg(cfg=DEFAULT_CFG, overrides=overrides)
-        args.task = self.task
-        if args.imgsz == DEFAULT_CFG.imgsz:
-            args.imgsz = self.model.args['imgsz']  # use trained imgsz unless custom value is passed
-        if args.batch == DEFAULT_CFG.batch:
-            args.batch = 1  # default to 1 if not modified
-        return Exporter(overrides=args)(model=self.model)
-
-    def info(self, detailed=False, verbose=True):
-        """
-        Logs model info.
 
-        Args:
+        model = FastSAM('last.pt')
-            detailed (bool): Show detailed information about model.
+        results = model.predict('ultralytics/assets/bus.jpg')
-            verbose (bool): Controls verbosity.
     """
-        return model_info(self.model, detailed=detailed, verbose=verbose, imgsz=640)
 
-    def __call__(self, source=None, stream=False, **kwargs):
+    def __init__(self, model='FastSAM-x.pt'):
-        """Calls the 'predict' function with given arguments to perform object detection."""
+        """Call the __init__ method of the parent class (YOLO) with the updated default model"""
-        return self.predict(source, stream, **kwargs)
+        if model == 'FastSAM.pt':
+            model = 'FastSAM-x.pt'
+        assert Path(model).suffix != '.yaml', 'FastSAM models only support pre-trained models.'
+        super().__init__(model=model, task='segment')
 
-    def __getattr__(self, attr):
+    @property
-        """Raises error if object has no requested attribute."""
+    def task_map(self):
-        name = self.__class__.__name__
+        return {'segment': {'predictor': FastSAMPredictor, 'validator': FastSAMValidator}}
-        raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")

ultralytics/models/nas/model.py

@@ -13,105 +13,36 @@ from pathlib import Path
 
 import torch
 
-from ultralytics.cfg import get_cfg
+from ultralytics.engine.model import Model
-from ultralytics.engine.exporter import Exporter
-from ultralytics.utils import DEFAULT_CFG, DEFAULT_CFG_DICT, LOGGER, ROOT, is_git_dir
-from ultralytics.utils.checks import check_imgsz
 from ultralytics.utils.torch_utils import model_info, smart_inference_mode
 
 from .predict import NASPredictor
 from .val import NASValidator
 
 
-class NAS:
+class NAS(Model):
 
     def __init__(self, model='yolo_nas_s.pt') -> None:
+        assert Path(model).suffix != '.yaml', 'YOLO-NAS models only support pre-trained models.'
+        super().__init__(model, task='detect')
+
+    @smart_inference_mode()
+    def _load(self, weights: str, task: str):
         # Load or create new NAS model
         import super_gradients
-
+        suffix = Path(weights).suffix
-        self.predictor = None
-        suffix = Path(model).suffix
         if suffix == '.pt':
-            self._load(model)
+            self.model = torch.load(weights)
         elif suffix == '':
-            self.model = super_gradients.training.models.get(model, pretrained_weights='coco')
+            self.model = super_gradients.training.models.get(weights, pretrained_weights='coco')
-        self.task = 'detect'
-        self.model.args = DEFAULT_CFG_DICT  # attach args to model
-
         # Standardize model
         self.model.fuse = lambda verbose=True: self.model
         self.model.stride = torch.tensor([32])
         self.model.names = dict(enumerate(self.model._class_names))
         self.model.is_fused = lambda: False  # for info()
         self.model.yaml = {}  # for info()
-        self.model.pt_path = model  # for export()
+        self.model.pt_path = weights  # for export()
         self.model.task = 'detect'  # for export()
-        self.info()
-
-    @smart_inference_mode()
-    def _load(self, weights: str):
-        self.model = torch.load(weights)
-
-    @smart_inference_mode()
-    def predict(self, source=None, stream=False, **kwargs):
-        """
-        Perform prediction using the YOLO model.
-
-        Args:
-            source (str | int | PIL | np.ndarray): The source of the image to make predictions on.
-                          Accepts all source types accepted by the YOLO model.
-            stream (bool): Whether to stream the predictions or not. Defaults to False.
-            **kwargs : Additional keyword arguments passed to the predictor.
-                       Check the 'configuration' section in the documentation for all available options.
-
-        Returns:
-            (List[ultralytics.engine.results.Results]): The prediction results.
-        """
-        if source is None:
-            source = ROOT / 'assets' if is_git_dir() else 'https://ultralytics.com/images/bus.jpg'
-            LOGGER.warning(f"WARNING ⚠️ 'source' is missing. Using 'source={source}'.")
-        overrides = dict(conf=0.25, task='detect', mode='predict')
-        overrides.update(kwargs)  # prefer kwargs
-        if not self.predictor:
-            self.predictor = NASPredictor(overrides=overrides)
-            self.predictor.setup_model(model=self.model)
-        else:  # only update args if predictor is already setup
-            self.predictor.args = get_cfg(self.predictor.args, overrides)
-        return self.predictor(source, stream=stream)
-
-    def train(self, **kwargs):
-        """Function trains models but raises an error as NAS models do not support training."""
-        raise NotImplementedError("NAS models don't support training")
-
-    def val(self, **kwargs):
-        """Run validation given dataset."""
-        overrides = dict(task='detect', mode='val')
-        overrides.update(kwargs)  # prefer kwargs
-        args = get_cfg(cfg=DEFAULT_CFG, overrides=overrides)
-        args.imgsz = check_imgsz(args.imgsz, max_dim=1)
-        validator = NASValidator(args=args)
-        validator(model=self.model)
-        self.metrics = validator.metrics
-        return validator.metrics
-
-    @smart_inference_mode()
-    def export(self, **kwargs):
-        """
-        Export model.
-
-        Args:
-            **kwargs : Any other args accepted by the predictors. To see all args check 'configuration' section in docs
-        """
-        overrides = dict(task='detect')
-        overrides.update(kwargs)
-        overrides['mode'] = 'export'
-        args = get_cfg(cfg=DEFAULT_CFG, overrides=overrides)
-        args.task = self.task
-        if args.imgsz == DEFAULT_CFG.imgsz:
-            args.imgsz = self.model.args['imgsz']  # use trained imgsz unless custom value is passed
-        if args.batch == DEFAULT_CFG.batch:
-            args.batch = 1  # default to 1 if not modified
-        return Exporter(overrides=args)(model=self.model)
 
     def info(self, detailed=False, verbose=True):
         """
@@ -123,11 +54,6 @@ class NAS:
         """
         return model_info(self.model, detailed=detailed, verbose=verbose, imgsz=640)
 
-    def __call__(self, source=None, stream=False, **kwargs):
+    @property
-        """Calls the 'predict' function with given arguments to perform object detection."""
+    def task_map(self):
-        return self.predict(source, stream, **kwargs)
+        return {'detect': {'predictor': NASPredictor, 'validator': NASValidator}}
-
-    def __getattr__(self, attr):
-        """Raises error if object has no requested attribute."""
-        name = self.__class__.__name__
-        raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")

ultralytics/models/rtdetr/model.py

@@ -2,172 +2,29 @@
 """
 RT-DETR model interface
 """
-
+from ultralytics.engine.model import Model
-from pathlib import Path
+from ultralytics.nn.tasks import RTDETRDetectionModel
-
-import torch.nn as nn
-
-from ultralytics.cfg import get_cfg
-from ultralytics.engine.exporter import Exporter
-from ultralytics.nn.tasks import RTDETRDetectionModel, attempt_load_one_weight, yaml_model_load
-from ultralytics.utils import DEFAULT_CFG, DEFAULT_CFG_DICT, LOGGER, RANK, ROOT, is_git_dir
-from ultralytics.utils.checks import check_imgsz
-from ultralytics.utils.torch_utils import model_info, smart_inference_mode
 
 from .predict import RTDETRPredictor
 from .train import RTDETRTrainer
 from .val import RTDETRValidator
 
 
-class RTDETR:
+class RTDETR(Model):
+    """
+    RTDETR model interface.
+    """
 
     def __init__(self, model='rtdetr-l.pt') -> None:
         if model and not model.endswith('.pt') and not model.endswith('.yaml'):
             raise NotImplementedError('RT-DETR only supports creating from pt file or yaml file.')
-        # Load or create new YOLO model
+        super().__init__(model=model, task='detect')
-        self.predictor = None
+
-        self.ckpt = None
+    @property
-        suffix = Path(model).suffix
+    def task_map(self):
-        if suffix == '.yaml':
+        return {
-            self._new(model)
+            'detect': {
-        else:
+                'predictor': RTDETRPredictor,
-            self._load(model)
+                'validator': RTDETRValidator,
-
+                'trainer': RTDETRTrainer,
-    def _new(self, cfg: str, verbose=True):
+                'model': RTDETRDetectionModel}}
-        cfg_dict = yaml_model_load(cfg)
-        self.cfg = cfg
-        self.task = 'detect'
-        self.model = RTDETRDetectionModel(cfg_dict, verbose=verbose)  # build model
-
-        # Below added to allow export from YAMLs
-        self.model.args = DEFAULT_CFG_DICT  # attach args to model
-        self.model.task = self.task
-
-    @smart_inference_mode()
-    def _load(self, weights: str):
-        self.model, self.ckpt = attempt_load_one_weight(weights)
-        self.model.args = DEFAULT_CFG_DICT  # attach args to model
-        self.task = self.model.args['task']
-
-    @smart_inference_mode()
-    def load(self, weights='yolov8n.pt'):
-        """
-        Transfers parameters with matching names and shapes from 'weights' to model.
-        """
-        if isinstance(weights, (str, Path)):
-            weights, self.ckpt = attempt_load_one_weight(weights)
-        self.model.load(weights)
-        return self
-
-    @smart_inference_mode()
-    def predict(self, source=None, stream=False, **kwargs):
-        """
-        Perform prediction using the YOLO model.
-
-        Args:
-            source (str | int | PIL | np.ndarray): The source of the image to make predictions on.
-                          Accepts all source types accepted by the YOLO model.
-            stream (bool): Whether to stream the predictions or not. Defaults to False.
-            **kwargs : Additional keyword arguments passed to the predictor.
-                       Check the 'configuration' section in the documentation for all available options.
-
-        Returns:
-            (List[ultralytics.engine.results.Results]): The prediction results.
-        """
-        if source is None:
-            source = ROOT / 'assets' if is_git_dir() else 'https://ultralytics.com/images/bus.jpg'
-            LOGGER.warning(f"WARNING ⚠️ 'source' is missing. Using 'source={source}'.")
-        overrides = dict(conf=0.25, task='detect', mode='predict')
-        overrides.update(kwargs)  # prefer kwargs
-        if not self.predictor:
-            self.predictor = RTDETRPredictor(overrides=overrides)
-            self.predictor.setup_model(model=self.model)
-        else:  # only update args if predictor is already setup
-            self.predictor.args = get_cfg(self.predictor.args, overrides)
-        return self.predictor(source, stream=stream)
-
-    def train(self, **kwargs):
-        """
-        Trains the model on a given dataset.
-
-        Args:
-            **kwargs (Any): Any number of arguments representing the training configuration.
-        """
-        overrides = dict(task='detect', mode='train')
-        overrides.update(kwargs)
-        overrides['deterministic'] = False
-        if not overrides.get('data'):
-            raise AttributeError("Dataset required but missing, i.e. pass 'data=coco128.yaml'")
-        if overrides.get('resume'):
-            overrides['resume'] = self.ckpt_path
-        self.task = overrides.get('task') or self.task
-        self.trainer = RTDETRTrainer(overrides=overrides)
-        if not overrides.get('resume'):  # manually set model only if not resuming
-            self.trainer.model = self.trainer.get_model(weights=self.model if self.ckpt else None, cfg=self.model.yaml)
-            self.model = self.trainer.model
-        self.trainer.train()
-        # Update model and cfg after training
-        if RANK in (-1, 0):
-            self.model, _ = attempt_load_one_weight(str(self.trainer.best))
-            self.overrides = self.model.args
-            self.metrics = getattr(self.trainer.validator, 'metrics', None)  # TODO: no metrics returned by DDP
-
-    def val(self, **kwargs):
-        """Run validation given dataset."""
-        overrides = dict(task='detect', mode='val')
-        overrides.update(kwargs)  # prefer kwargs
-        args = get_cfg(cfg=DEFAULT_CFG, overrides=overrides)
-        args.imgsz = check_imgsz(args.imgsz, max_dim=1)
-        validator = RTDETRValidator(args=args)
-        validator(model=self.model)
-        self.metrics = validator.metrics
-        return validator.metrics
-
-    def info(self, verbose=True):
-        """Get model info"""
-        return model_info(self.model, verbose=verbose)
-
-    def _check_is_pytorch_model(self):
-        """
-        Raises TypeError is model is not a PyTorch model
-        """
-        pt_str = isinstance(self.model, (str, Path)) and Path(self.model).suffix == '.pt'
-        pt_module = isinstance(self.model, nn.Module)
-        if not (pt_module or pt_str):
-            raise TypeError(f"model='{self.model}' must be a *.pt PyTorch model, but is a different type. "
-                            f'PyTorch models can be used to train, val, predict and export, i.e. '
-                            f"'yolo export model=yolov8n.pt', but exported formats like ONNX, TensorRT etc. only "
-                            f"support 'predict' and 'val' modes, i.e. 'yolo predict model=yolov8n.onnx'.")
-
-    def fuse(self):
-        """Fuse PyTorch Conv2d and BatchNorm2d layers."""
-        self._check_is_pytorch_model()
-        self.model.fuse()
-
-    @smart_inference_mode()
-    def export(self, **kwargs):
-        """
-        Export model.
-
-        Args:
-            **kwargs : Any other args accepted by the predictors. To see all args check 'configuration' section in docs
-        """
-        overrides = dict(task='detect')
-        overrides.update(kwargs)
-        overrides['mode'] = 'export'
-        args = get_cfg(cfg=DEFAULT_CFG, overrides=overrides)
-        args.task = self.task
-        if args.imgsz == DEFAULT_CFG.imgsz:
-            args.imgsz = self.model.args['imgsz']  # use trained imgsz unless custom value is passed
-        if args.batch == DEFAULT_CFG.batch:
-            args.batch = 1  # default to 1 if not modified
-        return Exporter(overrides=args)(model=self.model)
-
-    def __call__(self, source=None, stream=False, **kwargs):
-        """Calls the 'predict' function with given arguments to perform object detection."""
-        return self.predict(source, stream, **kwargs)
-
-    def __getattr__(self, attr):
-        """Raises error if object has no requested attribute."""
-        name = self.__class__.__name__
-        raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")

ultralytics/models/sam/model.py

@@ -3,51 +3,38 @@
 SAM model interface
 """
 
-from ultralytics.cfg import get_cfg
+from ultralytics.engine.model import Model
 from ultralytics.utils.torch_utils import model_info
 
 from .build import build_sam
 from .predict import Predictor
 
 
-class SAM:
+class SAM(Model):
+    """
+    SAM model interface.
+    """
 
     def __init__(self, model='sam_b.pt') -> None:
         if model and not model.endswith('.pt') and not model.endswith('.pth'):
             # Should raise AssertionError instead?
             raise NotImplementedError('Segment anything prediction requires pre-trained checkpoint')
-        self.model = build_sam(model)
+        super().__init__(model=model, task='segment')
-        self.task = 'segment'  # required
+
-        self.predictor = None  # reuse predictor
+    def _load(self, weights: str, task=None):
+        self.model = build_sam(weights)
 
     def predict(self, source, stream=False, bboxes=None, points=None, labels=None, **kwargs):
         """Predicts and returns segmentation masks for given image or video source."""
         overrides = dict(conf=0.25, task='segment', mode='predict', imgsz=1024)
-        overrides.update(kwargs)  # prefer kwargs
+        kwargs.update(overrides)
-        if not self.predictor:
+        prompts = dict(bboxes=bboxes, points=points, labels=labels)
-            self.predictor = Predictor(overrides=overrides)
+        super().predict(source, stream, prompts=prompts, **kwargs)
-            self.predictor.setup_model(model=self.model)
-        else:  # only update args if predictor is already setup
-            self.predictor.args = get_cfg(self.predictor.args, overrides)
-        return self.predictor(source, stream=stream, bboxes=bboxes, points=points, labels=labels)
-
-    def train(self, **kwargs):
-        """Function trains models but raises an error as SAM models do not support training."""
-        raise NotImplementedError("SAM models don't support training")
-
-    def val(self, **kwargs):
-        """Run validation given dataset."""
-        raise NotImplementedError("SAM models don't support validation")
 
     def __call__(self, source=None, stream=False, bboxes=None, points=None, labels=None, **kwargs):
         """Calls the 'predict' function with given arguments to perform object detection."""
         return self.predict(source, stream, bboxes, points, labels, **kwargs)
 
-    def __getattr__(self, attr):
-        """Raises error if object has no requested attribute."""
-        name = self.__class__.__name__
-        raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")
-
     def info(self, detailed=False, verbose=True):
         """
         Logs model info.
@@ -57,3 +44,7 @@ class SAM:
             verbose (bool): Controls verbosity.
         """
         return model_info(self.model, detailed=detailed, verbose=verbose)
+
+    @property
+    def task_map(self):
+        return {'segment': {'predictor': Predictor}}

ultralytics/models/sam/predict.py

@@ -28,6 +28,8 @@ class Predictor(BasePredictor):
         # Args for set_image
         self.im = None
         self.features = None
+        # Args for set_prompts
+        self.prompts = {}
         # Args for segment everything
         self.segment_all = False
 
@@ -92,6 +94,10 @@ class Predictor(BasePredictor):
                 of masks and H=W=256. These low resolution logits can be passed to
                 a subsequent iteration as mask input.
         """
+        # Get prompts from self.prompts first
+        bboxes = self.prompts.pop('bboxes', bboxes)
+        points = self.prompts.pop('points', points)
+        masks = self.prompts.pop('masks', masks)
         if all(i is None for i in [bboxes, points, masks]):
             return self.generate(im, *args, **kwargs)
         return self.prompt_inference(im, bboxes, points, labels, masks, multimask_output)
@@ -348,6 +354,10 @@ class Predictor(BasePredictor):
             self.im = im
             break
 
+    def set_prompts(self, prompts):
+        """Set prompts in advance."""
+        self.prompts = prompts
+
     def reset_image(self):
         self.im = None
         self.features = None

ultralytics/models/yolo/__init__.py

@@ -2,4 +2,6 @@
 
 from ultralytics.models.yolo import classify, detect, pose, segment
 
-__all__ = 'classify', 'segment', 'detect', 'pose'
+from .model import YOLO
+
+__all__ = 'classify', 'segment', 'detect', 'pose', 'YOLO'

ultralytics/models/yolo/detect/train.py

@@ -1,4 +1,5 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
+
 from copy import copy
 
 import numpy as np

ultralytics/models/yolo/model.py

@@ -0,0 +1,36 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+from ultralytics.engine.model import Model
+from ultralytics.models import yolo  # noqa
+from ultralytics.nn.tasks import ClassificationModel, DetectionModel, PoseModel, SegmentationModel
+
+
+class YOLO(Model):
+    """
+    YOLO (You Only Look Once) object detection model.
+    """
+
+    @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, }, }

ultralytics/models/yolo/segment/train.py

@@ -1,4 +1,5 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
+
 from copy import copy
 
 from ultralytics.models import yolo