`ultralytics 8.0.18` new python callbacks and minor fixes (#580)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Jeroen Rombouts <36196499+jarombouts@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

README.md

@@ -108,7 +108,7 @@ yolo predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg"
 #### Python
 
 YOLOv8 may also be used directly in a Python environment, and accepts the
-same [arguments](https://docs.ultralytics.com/config/) as in the CLI example above:
+same [arguments](https://docs.ultralytics.com/cfg/) as in the CLI example above:
 
 ```python
 from ultralytics import YOLO

README.zh-CN.md

@@ -70,7 +70,7 @@ YOLOv8 可以直接在命令行界面（CLI）中使用 `yolo` 命令运行：
 yolo predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg"
 ```
 
-`yolo`可以用于各种任务和模式，并接受额外的参数，例如 `imgsz=640`。参见 YOLOv8 [文档](https://docs.ultralytics.com)中可用`yolo`[参数](https://docs.ultralytics.com/config/)的完整列表。
+`yolo`可以用于各种任务和模式，并接受额外的参数，例如 `imgsz=640`。参见 YOLOv8 [文档](https://docs.ultralytics.com)中可用`yolo`[参数](https://docs.ultralytics.com/cfg/)的完整列表。
 
 ```bash
 yolo task=detect    mode=train    model=yolov8n.pt        args...
@@ -79,7 +79,7 @@ yolo task=detect    mode=train    model=yolov8n.pt        args...
                          export         yolov8n.pt        format=onnx  args...
 ```
 
-YOLOv8 也可以在 Python 环境中直接使用，并接受与上面 CLI 例子中相同的[参数](https://docs.ultralytics.com/config/)：
+YOLOv8 也可以在 Python 环境中直接使用，并接受与上面 CLI 例子中相同的[参数](https://docs.ultralytics.com/cfg/)：
 
 ```python
 from ultralytics import YOLO

docs/cli.md

@@ -167,7 +167,7 @@ Default arguments can be overriden by simply passing them as arguments in the CL
     === "Example 2"
         Predict a YouTube video using a pretrained segmentation model at image size 320:
         ```bash
-        yolo segment predict model=yolov8n-seg.pt source=https://youtu.be/Zgi9g1ksQHc imgsz=320
+        yolo segment predict model=yolov8n-seg.pt source='https://youtu.be/Zgi9g1ksQHc' imgsz=320
         ```
 
     === "Example 3"

examples/tutorial.ipynb

@@ -101,7 +101,7 @@
       "source": [
         "# 1. Predict\n",
         "\n",
-        "YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See a full list of available `yolo` [arguments](https://docs.ultralytics.com/config/) in the YOLOv8 [Docs](https://docs.ultralytics.com).\n"
+        "YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See a full list of available `yolo` [arguments](https://docs.ultralytics.com/cfg/) in the YOLOv8 [Docs](https://docs.ultralytics.com).\n"
       ]
     },
     {

tests/test_python.py

@@ -127,7 +127,3 @@ def test_workflow():
     model.val()
     model.predict(SOURCE)
     model.export(format="onnx", opset=12)  # export a model to ONNX format
-
-
-if __name__ == "__main__":
-    test_predict_img()

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
-__version__ = "8.0.17"
+__version__ = "8.0.18"
 
 from ultralytics.yolo.engine.model import YOLO
 from ultralytics.yolo.utils import ops

ultralytics/models/README.md

@@ -24,7 +24,7 @@ yolo task=detect mode=train model=yolov8n.yaml data=coco128.yaml epochs=100
 ```
 
 They may also be used directly in a Python environment, and accepts the same
-[arguments](https://docs.ultralytics.com/config/) as in the CLI example above:
+[arguments](https://docs.ultralytics.com/cfg/) as in the CLI example above:
 
 ```python
 from ultralytics import YOLO

ultralytics/nn/autobackend.py

@@ -222,7 +222,8 @@ class AutoBackend(nn.Module):
             nhwc = model.runtime.startswith("tensorflow")
             '''
         else:
-            raise NotImplementedError(f'ERROR: {w} is not a supported format')
+            raise NotImplementedError(f"ERROR: '{w}' is not a supported format. For supported formats see "
+                                      f"https://docs.ultralytics.com/reference/nn/")
 
         # class names
         if 'names' not in locals():

ultralytics/yolo/cfg/__init__.py

@@ -28,7 +28,7 @@ CLI_HELP_MSG = \
         yolo detect train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01
 
     2. Predict a YouTube video using a pretrained segmentation model at image size 320:
-        yolo segment predict model=yolov8n-seg.pt source=https://youtu.be/Zgi9g1ksQHc imgsz=320
+        yolo segment predict model=yolov8n-seg.pt source='https://youtu.be/Zgi9g1ksQHc' imgsz=320
 
     3. Val a pretrained detection model at batch-size 1 and image size 640:
         yolo detect val model=yolov8n.pt data=coco128.yaml batch=1 imgsz=640
@@ -126,13 +126,13 @@ def merge_equals_args(args: List[str]) -> List[str]:
     """
     new_args = []
     for i, arg in enumerate(args):
-        if arg == '=' and 0 < i < len(args) - 1:
+        if arg == '=' and 0 < i < len(args) - 1:  # merge ['arg', '=', 'val']
             new_args[-1] += f"={args[i + 1]}"
             del args[i + 1]
-        elif arg.endswith('=') and i < len(args) - 1:
+        elif arg.endswith('=') and i < len(args) - 1 and '=' not in args[i + 1]:  # merge ['arg=', 'val']
             new_args.append(f"{arg}{args[i + 1]}")
             del args[i + 1]
-        elif arg.startswith('=') and i > 0:
+        elif arg.startswith('=') and i > 0:  # merge ['arg', '=val']
             new_args[-1] += arg
         else:
             new_args.append(arg)
@@ -178,7 +178,7 @@ def entrypoint(debug=False):
         if '=' in a:
             try:
                 re.sub(r' *= *', '=', a)  # remove spaces around equals sign
-                k, v = a.split('=')
+                k, v = a.split('=', 1)  # split on first '=' sign
                 if k == 'cfg':  # custom.yaml passed
                     LOGGER.info(f"{PREFIX}Overriding {DEFAULT_CFG_PATH} with {v}")
                     overrides = {k: val for k, val in yaml_load(v).items() if k != 'cfg'}

ultralytics/yolo/cfg/default.yaml

@@ -59,8 +59,9 @@ line_thickness: 3  # bounding box thickness (pixels)
 visualize: False  # visualize model features
 augment: False  # apply image augmentation to prediction sources
 agnostic_nms: False  # class-agnostic NMS
-retina_masks: False  # use high-resolution segmentation masks
 classes: null  # filter results by class, i.e. class=0, or class=[0,2,3]
+retina_masks: False  # use high-resolution segmentation masks
+boxes: True # Show boxes in segmentation predictions
 
 # Export settings ------------------------------------------------------------------------------------------------------
 format: torchscript  # format to export to

ultralytics/yolo/data/dataloaders/v5loader.py

@@ -28,7 +28,7 @@ from PIL import ExifTags, Image, ImageOps
 from torch.utils.data import DataLoader, Dataset, dataloader, distributed
 from tqdm import tqdm
 
-from ultralytics.yolo.data.utils import check_dataset, unzip_file
+from ultralytics.yolo.data.utils import check_det_dataset, unzip_file
 from ultralytics.yolo.utils import (DATASETS_DIR, LOGGER, NUM_THREADS, TQDM_BAR_FORMAT, is_colab, is_dir_writeable,
                                     is_kaggle)
 from ultralytics.yolo.utils.checks import check_requirements, check_yaml
@@ -1061,7 +1061,7 @@ class HUBDatasetStats():
         except Exception as e:
             raise Exception("error/HUB/dataset_stats/yaml_load") from e
 
-        check_dataset(data, autodownload)  # download dataset if missing
+        check_det_dataset(data, autodownload)  # download dataset if missing
         self.hub_dir = Path(data['path'] + '-hub')
         self.im_dir = self.hub_dir / 'images'
         self.im_dir.mkdir(parents=True, exist_ok=True)  # makes /images

ultralytics/yolo/data/utils.py

@@ -185,7 +185,7 @@ def polygons2masks_overlap(imgsz, segments, downsample_ratio=1):
     return masks, index
 
 
-def check_dataset_yaml(dataset, autodownload=True):
+def check_det_dataset(dataset, autodownload=True):
     # Download, check and/or unzip dataset if not found locally
     data = check_file(dataset)
 
@@ -254,7 +254,7 @@ def check_dataset_yaml(dataset, autodownload=True):
     return data  # dictionary
 
 
-def check_dataset(dataset: str):
+def check_cls_dataset(dataset: str):
     """
     Check a classification dataset such as Imagenet.
 

ultralytics/yolo/engine/exporter.py

@@ -69,31 +69,25 @@ from ultralytics.nn.modules import Detect, Segment
 from ultralytics.nn.tasks import ClassificationModel, DetectionModel, SegmentationModel
 from ultralytics.yolo.cfg import get_cfg
 from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages
-from ultralytics.yolo.data.utils import check_dataset
+from ultralytics.yolo.data.utils import check_det_dataset
 from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, callbacks, colorstr, get_default_args, yaml_save
 from ultralytics.yolo.utils.checks import check_imgsz, check_requirements, check_version, check_yaml
 from ultralytics.yolo.utils.files import file_size
 from ultralytics.yolo.utils.ops import Profile
-from ultralytics.yolo.utils.torch_utils import guess_task_from_head, select_device, smart_inference_mode
+from ultralytics.yolo.utils.torch_utils import guess_task_from_model_yaml, select_device, smart_inference_mode
 
 MACOS = platform.system() == 'Darwin'  # macOS environment
 
 
 def export_formats():
     # YOLOv8 export formats
-    x = [
-        ['PyTorch', '-', '.pt', True, True],
-        ['TorchScript', 'torchscript', '.torchscript', True, True],
-        ['ONNX', 'onnx', '.onnx', True, True],
-        ['OpenVINO', 'openvino', '_openvino_model', True, False],
-        ['TensorRT', 'engine', '.engine', False, True],
-        ['CoreML', 'coreml', '.mlmodel', True, False],
-        ['TensorFlow SavedModel', 'saved_model', '_saved_model', True, True],
-        ['TensorFlow GraphDef', 'pb', '.pb', True, True],
-        ['TensorFlow Lite', 'tflite', '.tflite', True, False],
-        ['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', False, False],
-        ['TensorFlow.js', 'tfjs', '_web_model', False, False],
-        ['PaddlePaddle', 'paddle', '_paddle_model', True, True],]
+    x = [['PyTorch', '-', '.pt', True, True], ['TorchScript', 'torchscript', '.torchscript', True, True],
+         ['ONNX', 'onnx', '.onnx', True, True], ['OpenVINO', 'openvino', '_openvino_model', True, False],
+         ['TensorRT', 'engine', '.engine', False, True], ['CoreML', 'coreml', '.mlmodel', True, False],
+         ['TensorFlow SavedModel', 'saved_model', '_saved_model', True, True],
+         ['TensorFlow GraphDef', 'pb', '.pb', True, True], ['TensorFlow Lite', 'tflite', '.tflite', True, False],
+         ['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', False, False],
+         ['TensorFlow.js', 'tfjs', '_web_model', False, False], ['PaddlePaddle', 'paddle', '_paddle_model', True, True]]
     return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'CPU', 'GPU'])
 
 
@@ -135,7 +129,7 @@ class Exporter:
             overrides (dict, optional): Configuration overrides. Defaults to None.
         """
         self.args = get_cfg(cfg, overrides)
-        self.callbacks = defaultdict(list, {k: [v] for k, v in callbacks.default_callbacks.items()})  # add callbacks
+        self.callbacks = defaultdict(list, {k: v for k, v in callbacks.default_callbacks.items()})  # add callbacks
         callbacks.add_integration_callbacks(self)
 
     @smart_inference_mode()
@@ -241,7 +235,7 @@ class Exporter:
         # Finish
         f = [str(x) for x in f if x]  # filter out '' and None
         if any(f):
-            task = guess_task_from_head(model.yaml["head"][-1][-2])
+            task = guess_task_from_model_yaml(model)
             s = "-WARNING ⚠️ not yet supported for YOLOv8 exported models"
             LOGGER.info(f'\nExport complete ({time.time() - t:.1f}s)'
                         f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
@@ -570,7 +564,7 @@ class Exporter:
                     if n >= n_images:
                         break
 
-            dataset = LoadImages(check_dataset(check_yaml(data))['train'], imgsz=imgsz, auto=False)
+            dataset = LoadImages(check_det_dataset(check_yaml(data))['train'], imgsz=imgsz, auto=False)
             converter.representative_dataset = lambda: representative_dataset_gen(dataset, n_images=100)
             converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
             converter.target_spec.supported_types = []

ultralytics/yolo/engine/model.py

@@ -6,9 +6,9 @@ from ultralytics import yolo  # noqa
 from ultralytics.nn.tasks import ClassificationModel, DetectionModel, SegmentationModel, attempt_load_one_weight
 from ultralytics.yolo.cfg import get_cfg
 from ultralytics.yolo.engine.exporter import Exporter
-from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, yaml_load
+from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, callbacks, yaml_load
 from ultralytics.yolo.utils.checks import check_yaml
-from ultralytics.yolo.utils.torch_utils import guess_task_from_head, smart_inference_mode
+from ultralytics.yolo.utils.torch_utils import guess_task_from_model_yaml, smart_inference_mode
 
 # Map head to model, trainer, validator, and predictor classes
 MODEL_MAP = {
@@ -68,7 +68,7 @@ class YOLO:
         """
         cfg = check_yaml(cfg)  # check YAML
         cfg_dict = yaml_load(cfg, append_filename=True)  # model dict
-        self.task = guess_task_from_head(cfg_dict["head"][-1][-2])
+        self.task = guess_task_from_model_yaml(cfg_dict)
         self.ModelClass, self.TrainerClass, self.ValidatorClass, self.PredictorClass = \
             self._guess_ops_from_task(self.task)
         self.model = self.ModelClass(cfg_dict, verbose=verbose)  # initialize
@@ -228,6 +228,12 @@ class YOLO:
         """
         return self.model.names
 
+    def add_callback(self, event: str, func):
+        """
+        Add callback
+        """
+        callbacks.default_callbacks[event].append(func)
+
     @staticmethod
     def _reset_ckpt_args(args):
         args.pop("project", None)

ultralytics/yolo/engine/predictor.py

@@ -88,7 +88,7 @@ class BasePredictor:
         self.vid_path, self.vid_writer = None, None
         self.annotator = None
         self.data_path = None
-        self.callbacks = defaultdict(list, {k: [v] for k, v in callbacks.default_callbacks.items()})  # add callbacks
+        self.callbacks = defaultdict(list, {k: v for k, v in callbacks.default_callbacks.items()})  # add callbacks
         callbacks.add_integration_callbacks(self)
 
     def preprocess(self, img):
@@ -172,16 +172,17 @@ class BasePredictor:
         # setup source. Run every time predict is called
         self.setup_source(source)
         # check if save_dir/ label file exists
-        if self.args.save:
+        if self.args.save or self.args.save_txt:
             (self.save_dir / 'labels' if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True)
         # warmup model
         if not self.done_warmup:
             self.model.warmup(imgsz=(1 if self.model.pt or self.model.triton else self.bs, 3, *self.imgsz))
             self.done_warmup = True
 
-        self.seen, self.windows, self.dt = 0, [], (ops.Profile(), ops.Profile(), ops.Profile())
+        self.seen, self.windows, self.dt, self.batch = 0, [], (ops.Profile(), ops.Profile(), ops.Profile()), None
         for batch in self.dataset:
             self.run_callbacks("on_predict_batch_start")
+            self.batch = batch
             path, im, im0s, vid_cap, s = batch
             visualize = increment_path(self.save_dir / Path(path).stem, mkdir=True) if self.args.visualize else False
             with self.dt[0]:
@@ -195,13 +196,13 @@ class BasePredictor:
 
             # postprocess
             with self.dt[2]:
-                results = self.postprocess(preds, im, im0s, self.classes)
+                self.results = self.postprocess(preds, im, im0s, self.classes)
             for i in range(len(im)):
                 p, im0 = (path[i], im0s[i]) if self.webcam or self.from_img else (path, im0s)
                 p = Path(p)
 
                 if verbose or self.args.save or self.args.save_txt or self.args.show:
-                    s += self.write_results(i, results, (p, im, im0))
+                    s += self.write_results(i, self.results, (p, im, im0))
 
                 if self.args.show:
                     self.show(p)
@@ -209,22 +210,21 @@ class BasePredictor:
                 if self.args.save:
                     self.save_preds(vid_cap, i, str(self.save_dir / p.name))
 
-            yield from results
+            self.run_callbacks("on_predict_batch_end")
+            yield from self.results
 
             # Print time (inference-only)
             if verbose:
                 LOGGER.info(f"{s}{'' if len(preds) else '(no detections), '}{self.dt[1].dt * 1E3:.1f}ms")
 
-            self.run_callbacks("on_predict_batch_end")
-
         # Print results
         if verbose and self.seen:
             t = tuple(x.t / self.seen * 1E3 for x in self.dt)  # speeds per image
             LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms postprocess per image at shape '
                         f'{(1, 3, *self.imgsz)}' % t)
         if self.args.save_txt or self.args.save:
-            s = f"\n{len(list(self.save_dir.glob('labels/*.txt')))} labels saved to {self.save_dir / 'labels'}" \
-                if self.args.save_txt else ''
+            nl = len(list(self.save_dir.glob('labels/*.txt')))  # number of labels
+            s = f"\n{nl} label{'s' * (nl > 1)} saved to {self.save_dir / 'labels'}" if self.args.save_txt else ''
             LOGGER.info(f"Results saved to {colorstr('bold', self.save_dir)}{s}")
 
         self.run_callbacks("on_predict_end")

ultralytics/yolo/engine/trainer.py

@@ -20,19 +20,18 @@ from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.optim import lr_scheduler
 from tqdm import tqdm
 
-import ultralytics.yolo.utils as utils
 from ultralytics import __version__
 from ultralytics.nn.tasks import attempt_load_one_weight
 from ultralytics.yolo.cfg import get_cfg
-from ultralytics.yolo.data.utils import check_dataset, check_dataset_yaml
+from ultralytics.yolo.data.utils import check_cls_dataset, check_det_dataset
 from ultralytics.yolo.utils import (DEFAULT_CFG_PATH, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks, colorstr,
-                                    yaml_save)
+                                    emojis, yaml_save)
 from ultralytics.yolo.utils.autobatch import check_train_batch_size
 from ultralytics.yolo.utils.checks import check_file, check_imgsz, print_args
 from ultralytics.yolo.utils.dist import ddp_cleanup, generate_ddp_command
 from ultralytics.yolo.utils.files import get_latest_run, increment_path
 from ultralytics.yolo.utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, init_seeds, one_cycle,
-                                                strip_optimizer)
+                                                select_device, strip_optimizer)
 
 
 class BaseTrainer:
@@ -81,7 +80,7 @@ class BaseTrainer:
             overrides (dict, optional): Configuration overrides. Defaults to None.
         """
         self.args = get_cfg(cfg, overrides)
-        self.device = utils.torch_utils.select_device(self.args.device, self.args.batch)
+        self.device = select_device(self.args.device, self.args.batch)
         self.check_resume()
         self.console = LOGGER
         self.validator = None
@@ -120,9 +119,11 @@ class BaseTrainer:
         self.model = self.args.model
         self.data = self.args.data
         if self.data.endswith(".yaml"):
-            self.data = check_dataset_yaml(self.data)
+            self.data = check_det_dataset(self.data)
+        elif self.args.task == 'classify':
+            self.data = check_cls_dataset(self.data)
         else:
-            self.data = check_dataset(self.data)
+            raise FileNotFoundError(emojis(f"Dataset '{self.args.data}' not found ❌"))
         self.trainset, self.testset = self.get_dataset(self.data)
         self.ema = None
 
@@ -140,7 +141,7 @@ class BaseTrainer:
         self.plot_idx = [0, 1, 2]
 
         # Callbacks
-        self.callbacks = defaultdict(list, {k: [v] for k, v in callbacks.default_callbacks.items()})  # add callbacks
+        self.callbacks = defaultdict(list, {k: v for k, v in callbacks.default_callbacks.items()})  # add callbacks
         if RANK in {0, -1}:
             callbacks.add_integration_callbacks(self)
 

ultralytics/yolo/engine/validator.py

@@ -9,8 +9,8 @@ from tqdm import tqdm
 
 from ultralytics.nn.autobackend import AutoBackend
 from ultralytics.yolo.cfg import get_cfg
-from ultralytics.yolo.data.utils import check_dataset, check_dataset_yaml
-from ultralytics.yolo.utils import DEFAULT_CFG_PATH, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks
+from ultralytics.yolo.data.utils import check_cls_dataset, check_det_dataset
+from ultralytics.yolo.utils import DEFAULT_CFG_PATH, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks, emojis
 from ultralytics.yolo.utils.checks import check_imgsz
 from ultralytics.yolo.utils.files import increment_path
 from ultralytics.yolo.utils.ops import Profile
@@ -70,7 +70,7 @@ class BaseValidator:
         if self.args.conf is None:
             self.args.conf = 0.001  # default conf=0.001
 
-        self.callbacks = defaultdict(list, {k: [v] for k, v in callbacks.default_callbacks.items()})  # add callbacks
+        self.callbacks = defaultdict(list, {k: v for k, v in callbacks.default_callbacks.items()})  # add callbacks
 
     @smart_inference_mode()
     def __call__(self, trainer=None, model=None):
@@ -109,9 +109,11 @@ class BaseValidator:
                         f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models')
 
             if isinstance(self.args.data, str) and self.args.data.endswith(".yaml"):
-                self.data = check_dataset_yaml(self.args.data)
+                self.data = check_det_dataset(self.args.data)
+            elif self.args.task == 'classify':
+                self.data = check_cls_dataset(self.args.data)
             else:
-                self.data = check_dataset(self.args.data)
+                raise FileNotFoundError(emojis(f"Dataset '{self.args.data}' not found ❌"))
 
             if self.device.type == 'cpu':
                 self.args.workers = 0  # faster CPU val as time dominated by inference, not dataloading

ultralytics/yolo/utils/__init__.py

@@ -68,7 +68,7 @@ HELP_MSG = \
             yolo detect train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01
 
         - Predict a YouTube video using a pretrained segmentation model at image size 320:
-            yolo segment predict model=yolov8n-seg.pt source=https://youtu.be/Zgi9g1ksQHc imgsz=320
+            yolo segment predict model=yolov8n-seg.pt source='https://youtu.be/Zgi9g1ksQHc' imgsz=320
 
         - Val a pretrained detection model at batch-size 1 and image size 640:
             yolo detect val model=yolov8n.pt data=coco128.yaml batch=1 imgsz=640
@@ -109,6 +109,9 @@ class IterableSimpleNamespace(SimpleNamespace):
     def __str__(self):
         return '\n'.join(f"{k}={v}" for k, v in vars(self).items())
 
+    def get(self, key, default=None):
+        return getattr(self, key, default)
+
 
 # Default configuration
 with open(DEFAULT_CFG_PATH, errors='ignore') as f:

ultralytics/yolo/utils/callbacks/base.py

@@ -106,36 +106,36 @@ def on_export_end(exporter):
 
 default_callbacks = {
     # Run in trainer
-    'on_pretrain_routine_start': on_pretrain_routine_start,
-    'on_pretrain_routine_end': on_pretrain_routine_end,
-    'on_train_start': on_train_start,
-    'on_train_epoch_start': on_train_epoch_start,
-    'on_train_batch_start': on_train_batch_start,
-    'optimizer_step': optimizer_step,
-    'on_before_zero_grad': on_before_zero_grad,
-    'on_train_batch_end': on_train_batch_end,
-    'on_train_epoch_end': on_train_epoch_end,
-    'on_fit_epoch_end': on_fit_epoch_end,  # fit = train + val
-    'on_model_save': on_model_save,
-    'on_train_end': on_train_end,
-    'on_params_update': on_params_update,
-    'teardown': teardown,
+    'on_pretrain_routine_start': [on_pretrain_routine_start],
+    'on_pretrain_routine_end': [on_pretrain_routine_end],
+    'on_train_start': [on_train_start],
+    'on_train_epoch_start': [on_train_epoch_start],
+    'on_train_batch_start': [on_train_batch_start],
+    'optimizer_step': [optimizer_step],
+    'on_before_zero_grad': [on_before_zero_grad],
+    'on_train_batch_end': [on_train_batch_end],
+    'on_train_epoch_end': [on_train_epoch_end],
+    'on_fit_epoch_end': [on_fit_epoch_end],  # fit = train + val
+    'on_model_save': [on_model_save],
+    'on_train_end': [on_train_end],
+    'on_params_update': [on_params_update],
+    'teardown': [teardown],
 
     # Run in validator
-    'on_val_start': on_val_start,
-    'on_val_batch_start': on_val_batch_start,
-    'on_val_batch_end': on_val_batch_end,
-    'on_val_end': on_val_end,
+    'on_val_start': [on_val_start],
+    'on_val_batch_start': [on_val_batch_start],
+    'on_val_batch_end': [on_val_batch_end],
+    'on_val_end': [on_val_end],
 
     # Run in predictor
-    'on_predict_start': on_predict_start,
-    'on_predict_batch_start': on_predict_batch_start,
-    'on_predict_batch_end': on_predict_batch_end,
-    'on_predict_end': on_predict_end,
+    'on_predict_start': [on_predict_start],
+    'on_predict_batch_start': [on_predict_batch_start],
+    'on_predict_batch_end': [on_predict_batch_end],
+    'on_predict_end': [on_predict_end],
 
     # Run in exporter
-    'on_export_start': on_export_start,
-    'on_export_end': on_export_end}
+    'on_export_start': [on_export_start],
+    'on_export_end': [on_export_end]}
 
 
 def add_integration_callbacks(instance):

ultralytics/yolo/utils/torch_utils.py

@@ -307,18 +307,20 @@ def strip_optimizer(f='best.pt', s=''):
     LOGGER.info(f"Optimizer stripped from {f},{f' saved as {s},' if s else ''} {mb:.1f}MB")
 
 
-def guess_task_from_head(head):
-    task = None
-    if head.lower() in ["classify", "classifier", "cls", "fc"]:
-        task = "classify"
-    if head.lower() in ["detect"]:
-        task = "detect"
-    if head.lower() in ["segment"]:
-        task = "segment"
-
-    if not task:
-        raise SyntaxError("task or model not recognized! Please refer the docs at : ")  # TODO: add docs links
-
+def guess_task_from_model_yaml(model):
+    try:
+        cfg = model if isinstance(model, dict) else model.yaml  # model cfg dict
+        m = cfg["head"][-1][-2].lower()  # output module name
+        task = None
+        if m in ["classify", "classifier", "cls", "fc"]:
+            task = "classify"
+        if m in ["detect"]:
+            task = "detect"
+        if m in ["segment"]:
+            task = "segment"
+    except Exception as e:
+        raise SyntaxError('Unknown task. Define task explicitly, i.e. task=detect when running your command. '
+                          'Valid tasks are detect, segment, classify.') from e
     return task
 
 
@@ -374,14 +376,36 @@ def profile(input, ops, n=10, device=None):
 
 
 class EarlyStopping:
-    # early stopper
+    """
+    Early stopping class that stops training when a specified number of epochs have passed without improvement.
+    """
+
     def __init__(self, patience=30):
+        """
+        Initialize early stopping object
+
+        Args:
+            patience (int, optional): Number of epochs to wait after fitness stops improving before stopping. Default is 30.
+        """
         self.best_fitness = 0.0  # i.e. mAP
         self.best_epoch = 0
         self.patience = patience or float('inf')  # epochs to wait after fitness stops improving to stop
         self.possible_stop = False  # possible stop may occur next epoch
 
     def __call__(self, epoch, fitness):
+        """
+        Check whether to stop training
+
+        Args:
+            epoch (int): Current epoch of training
+            fitness (float): Fitness value of current epoch
+
+        Returns:
+            bool: True if training should stop, False otherwise
+        """
+        if fitness is None:  # check if fitness=None (happens when val=False)
+            return False
+
         if fitness >= self.best_fitness:  # >= 0 to allow for early zero-fitness stage of training
             self.best_epoch = epoch
             self.best_fitness = fitness

ultralytics/yolo/v8/classify/val.py

@@ -10,6 +10,7 @@ class ClassificationValidator(BaseValidator):
 
     def __init__(self, dataloader=None, save_dir=None, pbar=None, logger=None, args=None):
         super().__init__(dataloader, save_dir, pbar, logger, args)
+        self.args.task = 'classify'
         self.metrics = ClassifyMetrics()
 
     def get_desc(self):

ultralytics/yolo/v8/detect/val.py

@@ -20,6 +20,7 @@ class DetectionValidator(BaseValidator):
 
     def __init__(self, dataloader=None, save_dir=None, pbar=None, logger=None, args=None):
         super().__init__(dataloader, save_dir, pbar, logger, args)
+        self.args.task = 'detect'
         self.data_dict = yaml_load(check_file(self.args.data), append_filename=True) if self.args.data else None
         self.is_coco = False
         self.class_map = None

ultralytics/yolo/v8/segment/predict.py

@@ -87,7 +87,7 @@ class SegmentationPredictor(DetectionPredictor):
                 c = int(cls)  # integer class
                 label = None if self.args.hide_labels else (
                     self.model.names[c] if self.args.hide_conf else f'{self.model.names[c]} {conf:.2f}')
-                self.annotator.box_label(d.xyxy.squeeze(), label, color=colors(c, True))
+                self.annotator.box_label(d.xyxy.squeeze(), label, color=colors(c, True)) if self.args.boxes else None
             if self.args.save_crop:
                 imc = im0.copy()
                 save_one_box(d.xyxy,

ultralytics/yolo/v8/segment/val.py

@@ -19,7 +19,7 @@ class SegmentationValidator(DetectionValidator):
 
     def __init__(self, dataloader=None, save_dir=None, pbar=None, logger=None, args=None):
         super().__init__(dataloader, save_dir, pbar, logger, args)
-        self.args.task = "segment"
+        self.args.task = 'segment'
         self.metrics = SegmentMetrics(save_dir=self.save_dir)
 
     def preprocess(self, batch):