Merge branch 'main' into uv-pytests

.github/workflows/docs.yml

@@ -23,7 +23,7 @@ on:
     inputs:
       publish_docs:
         description: "Publish live to https://docs.ultralytics.com"
-        default: "true"
+        default: true
         type: boolean
 
 jobs:

docs/en/guides/triton-inference-server.md

@@ -48,6 +48,16 @@ from ultralytics import YOLO
 # Load a model
 model = YOLO("yolo11n.pt")  # load an official model
 
+# Retreive metadata during export
+metadata = []
+
+
+def export_cb(exporter):
+    metadata.append(exporter.metadata)
+
+
+model.add_callback("on_export_end", export_cb)
+
 # Export the model
 onnx_file = model.export(format="onnx", dynamic=True)
 ```
@@ -107,7 +117,13 @@ The Triton Model Repository is a storage location where Triton can access and lo
         }
       }
     }
-    """
+    parameters {
+      key: "metadata"
+      value: {
+        string_value: "%s"
+      }
+    }
+    """ % metadata[0]
 
     with open(triton_model_path / "config.pbtxt", "w") as f:
         f.write(data)

docs/en/macros/augmentation-args.md

@@ -13,7 +13,7 @@
 | `bgr`             | `float` | `0.0`         | `0.0 - 1.0`   | Flips the image channels from RGB to BGR with the specified probability, useful for increasing robustness to incorrect channel ordering.                                  |
 | `mosaic`          | `float` | `1.0`         | `0.0 - 1.0`   | Combines four training images into one, simulating different scene compositions and object interactions. Highly effective for complex scene understanding.                |
 | `mixup`           | `float` | `0.0`         | `0.0 - 1.0`   | Blends two images and their labels, creating a composite image. Enhances the model's ability to generalize by introducing label noise and visual variability.             |
-| `copy_paste`      | `float` | `0.0`         | `0.0 - 1.0`   | Copies objects from one image and pastes them onto another, useful for increasing object instances and learning object occlusion.                                         |
+| `copy_paste`      | `float` | `0.0`         | `0.0 - 1.0`   | Copies and pastes objects across images, useful for increasing object instances and learning object occlusion. Requires segmentation labels.                              |
 | `copy_paste_mode` | `str`   | `flip`        | -             | Copy-Paste augmentation method selection among the options of (`"flip"`, `"mixup"`).                                                                                      |
 | `auto_augment`    | `str`   | `randaugment` | -             | Automatically applies a predefined augmentation policy (`randaugment`, `autoaugment`, `augmix`), optimizing for classification tasks by diversifying the visual features. |
 | `erasing`         | `float` | `0.4`         | `0.0 - 0.9`   | Randomly erases a portion of the image during classification training, encouraging the model to focus on less obvious features for recognition.                           |

docs/en/reference/utils/ops.md

@@ -129,4 +129,8 @@ keywords: Ultralytics, utility operations, non-max suppression, bounding box tra
 
 ## ::: ultralytics.utils.ops.clean_str
 
+<br><br><hr><br>
+
+## ::: ultralytics.utils.ops.empty_like
+
 <br><br>

docs/en/solutions/index.md

@@ -29,7 +29,6 @@ Here's our curated list of Ultralytics solutions that can be used to create awes
 - [Parking Management](../guides/parking-management.md) 🚀: Organize and direct vehicle flow in parking areas with YOLO11, optimizing space utilization and user experience.
 - [Analytics](../guides/analytics.md) 📊: Conduct comprehensive data analysis to discover patterns and make informed decisions, leveraging YOLO11 for descriptive, predictive, and prescriptive analytics.
 - [Live Inference with Streamlit](../guides/streamlit-live-inference.md) 🚀: Leverage the power of YOLO11 for real-time [object detection](https://www.ultralytics.com/glossary/object-detection) directly through your web browser with a user-friendly Streamlit interface.
-- [Live Inference with Streamlit](../guides/streamlit-live-inference.md) 🚀: Leverage the power of YOLO11 for real-time [object detection](https://www.ultralytics.com/glossary/object-detection) directly through your web browser with a user-friendly Streamlit interface.
 - [Track Objects in Zone](../guides/trackzone.md) 🎯 NEW: Learn how to track objects within specific zones of video frames using YOLO11 for precise and efficient monitoring.
 
 ## Solutions Usage
@@ -39,7 +38,7 @@ Here's our curated list of Ultralytics solutions that can be used to create awes
     `yolo SOLUTIONS SOLUTION_NAME ARGS`
 
     - **SOLUTIONS** is a required keyword.
-    - **SOLUTION_NAME** (optional) is one of: `['count', 'heatmap', 'queue', 'speed', 'workout', 'analytics']`.
+    - **SOLUTION_NAME** (optional) is one of: `['count', 'heatmap', 'queue', 'speed', 'workout', 'analytics', 'trackzone']`.
     - **ARGS** (optional) are custom `arg=value` pairs, such as `show_in=True`, to override default settings.
 
     === "CLI"

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.3.43"
+__version__ = "8.3.47"
 
 import os
 

ultralytics/engine/exporter.py

@@ -73,7 +73,7 @@ from ultralytics.data import build_dataloader
 from ultralytics.data.dataset import YOLODataset
 from ultralytics.data.utils import check_cls_dataset, check_det_dataset
 from ultralytics.nn.autobackend import check_class_names, default_class_names
-from ultralytics.nn.modules import C2f, Detect, RTDETRDecoder
+from ultralytics.nn.modules import C2f, Classify, Detect, RTDETRDecoder
 from ultralytics.nn.tasks import DetectionModel, SegmentationModel, WorldModel
 from ultralytics.utils import (
     ARM64,
@@ -287,6 +287,8 @@ class Exporter:
 
             model = FXModel(model)
         for m in model.modules():
+            if isinstance(m, Classify):
+                m.export = True
             if isinstance(m, (Detect, RTDETRDecoder)):  # includes all Detect subclasses like Segment, Pose, OBB
                 m.dynamic = self.args.dynamic
                 m.export = True

ultralytics/engine/model.py

@@ -136,6 +136,7 @@ class Model(nn.Module):
         # Check if Triton Server model
         elif self.is_triton_model(model):
             self.model_name = self.model = model
+            self.overrides["task"] = task or "detect"  # set `task=detect` if not explicitly set
             return
 
         # Load or create new YOLO model

ultralytics/models/sam/predict.py

@@ -1105,7 +1105,7 @@ class SAM2VideoPredictor(SAM2Predictor):
             for obj_temp_output_dict in temp_output_dict_per_obj.values():
                 temp_frame_inds.update(obj_temp_output_dict[storage_key].keys())
             consolidated_frame_inds[storage_key].update(temp_frame_inds)
-            # consolidate the temprary output across all objects on this frame
+            # consolidate the temporary output across all objects on this frame
             for frame_idx in temp_frame_inds:
                 consolidated_out = self._consolidate_temp_output_across_obj(
                     frame_idx, is_cond=is_cond, run_mem_encoder=True

ultralytics/models/yolo/classify/predict.py

@@ -53,7 +53,8 @@ class ClassificationPredictor(BasePredictor):
         if not isinstance(orig_imgs, list):  # input images are a torch.Tensor, not a list
             orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
 
+        preds = preds[0] if isinstance(preds, (list, tuple)) else preds
         return [
-            Results(orig_img, path=img_path, names=self.model.names, probs=pred.softmax(0))
+            Results(orig_img, path=img_path, names=self.model.names, probs=pred)
             for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0])
         ]

ultralytics/models/yolo/classify/val.py

@@ -71,6 +71,10 @@ class ClassificationValidator(BaseValidator):
         self.metrics.confusion_matrix = self.confusion_matrix
         self.metrics.save_dir = self.save_dir
 
+    def postprocess(self, preds):
+        """Preprocesses the classification predictions."""
+        return preds[0] if isinstance(preds, (list, tuple)) else preds
+
     def get_stats(self):
         """Returns a dictionary of metrics obtained by processing targets and predictions."""
         self.metrics.process(self.targets, self.pred)

ultralytics/nn/autobackend.py

@@ -96,7 +96,7 @@ class AutoBackend(nn.Module):
         Initialize the AutoBackend for inference.
 
         Args:
-            weights (str): Path to the model weights file. Defaults to 'yolov8n.pt'.
+            weights (str | torch.nn.Module): Path to the model weights file or a module instance. Defaults to 'yolo11n.pt'.
             device (torch.device): Device to run the model on. Defaults to CPU.
             dnn (bool): Use OpenCV DNN module for ONNX inference. Defaults to False.
             data (str | Path | optional): Path to the additional data.yaml file containing class names. Optional.
@@ -462,6 +462,7 @@ class AutoBackend(nn.Module):
             from ultralytics.utils.triton import TritonRemoteModel
 
             model = TritonRemoteModel(w)
+            metadata = model.metadata
 
         # Any other format (unsupported)
         else:

ultralytics/nn/modules/head.py

@@ -282,6 +282,8 @@ class Pose(Detect):
 class Classify(nn.Module):
     """YOLO classification head, i.e. x(b,c1,20,20) to x(b,c2)."""
 
+    export = False  # export mode
+
     def __init__(self, c1, c2, k=1, s=1, p=None, g=1):
         """Initializes YOLO classification head to transform input tensor from (b,c1,20,20) to (b,c2) shape."""
         super().__init__()
@@ -296,7 +298,10 @@ class Classify(nn.Module):
         if isinstance(x, list):
             x = torch.cat(x, 1)
         x = self.linear(self.drop(self.pool(self.conv(x)).flatten(1)))
-        return x
+        if self.training:
+            return x
+        y = x.softmax(1)  # get final output
+        return y if self.export else (y, x)
 
 
 class WorldDetect(Detect):

ultralytics/utils/loss.py

@@ -604,6 +604,7 @@ class v8ClassificationLoss:
 
     def __call__(self, preds, batch):
         """Compute the classification loss between predictions and true labels."""
+        preds = preds[1] if isinstance(preds, (list, tuple)) else preds
         loss = F.cross_entropy(preds, batch["cls"], reduction="mean")
         loss_items = loss.detach()
         return loss, loss_items

ultralytics/utils/ops.py

@@ -400,7 +400,7 @@ def xyxy2xywh(x):
         y (np.ndarray | torch.Tensor): The bounding box coordinates in (x, y, width, height) format.
     """
     assert x.shape[-1] == 4, f"input shape last dimension expected 4 but input shape is {x.shape}"
-    y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x)  # faster than clone/copy
+    y = empty_like(x)  # faster than clone/copy
     y[..., 0] = (x[..., 0] + x[..., 2]) / 2  # x center
     y[..., 1] = (x[..., 1] + x[..., 3]) / 2  # y center
     y[..., 2] = x[..., 2] - x[..., 0]  # width
@@ -420,7 +420,7 @@ def xywh2xyxy(x):
         y (np.ndarray | torch.Tensor): The bounding box coordinates in (x1, y1, x2, y2) format.
     """
     assert x.shape[-1] == 4, f"input shape last dimension expected 4 but input shape is {x.shape}"
-    y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x)  # faster than clone/copy
+    y = empty_like(x)  # faster than clone/copy
     xy = x[..., :2]  # centers
     wh = x[..., 2:] / 2  # half width-height
     y[..., :2] = xy - wh  # top left xy
@@ -443,7 +443,7 @@ def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
             x1,y1 is the top-left corner, x2,y2 is the bottom-right corner of the bounding box.
     """
     assert x.shape[-1] == 4, f"input shape last dimension expected 4 but input shape is {x.shape}"
-    y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x)  # faster than clone/copy
+    y = empty_like(x)  # faster than clone/copy
     y[..., 0] = w * (x[..., 0] - x[..., 2] / 2) + padw  # top left x
     y[..., 1] = h * (x[..., 1] - x[..., 3] / 2) + padh  # top left y
     y[..., 2] = w * (x[..., 0] + x[..., 2] / 2) + padw  # bottom right x
@@ -469,7 +469,7 @@ def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0):
     if clip:
         x = clip_boxes(x, (h - eps, w - eps))
     assert x.shape[-1] == 4, f"input shape last dimension expected 4 but input shape is {x.shape}"
-    y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x, dtype=float)  # faster than clone/copy
+    y = empty_like(x)  # faster than clone/copy
     y[..., 0] = ((x[..., 0] + x[..., 2]) / 2) / w  # x center
     y[..., 1] = ((x[..., 1] + x[..., 3]) / 2) / h  # y center
     y[..., 2] = (x[..., 2] - x[..., 0]) / w  # width
@@ -838,3 +838,10 @@ def clean_str(s):
         (str): a string with special characters replaced by an underscore _
     """
     return re.sub(pattern="[|@#!¡·$€%&()=?¿^*;:,¨´><+]", repl="_", string=s)
+
+
+def empty_like(x):
+    """Creates empty torch.Tensor or np.ndarray with same shape as input and float32 dtype."""
+    return (
+        torch.empty_like(x, dtype=torch.float32) if isinstance(x, torch.Tensor) else np.empty_like(x, dtype=np.float32)
+    )

ultralytics/utils/triton.py

@@ -66,6 +66,7 @@ class TritonRemoteModel:
         self.np_input_formats = [type_map[x] for x in self.input_formats]
         self.input_names = [x["name"] for x in config["input"]]
         self.output_names = [x["name"] for x in config["output"]]
+        self.metadata = eval(config.get("parameters", {}).get("metadata", {}).get("string_value", "None"))
 
     def __call__(self, *inputs: np.ndarray) -> List[np.ndarray]:
         """