Merge branch 'main' into quan

docs/en/models/sam-2.md

@@ -250,15 +250,18 @@ To auto-annotate your dataset using SAM 2, follow this example:
     ```python
     from ultralytics.data.annotator import auto_annotate
 
-    auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model="sam2_b.pt")
+    auto_annotate(data="path/to/images", det_model="yolo11x.pt", sam_model="sam2_b.pt")
     ```
 
 | Argument     | Type                    | Description                                                                                             | Default        |
 | ------------ | ----------------------- | ------------------------------------------------------------------------------------------------------- | -------------- |
 | `data`       | `str`                   | Path to a folder containing images to be annotated.                                                     |                |
-| `det_model`  | `str`, optional         | Pre-trained YOLO detection model. Defaults to 'yolov8x.pt'.                                             | `'yolov8x.pt'` |
+| `det_model`  | `str`, optional         | Pre-trained YOLO detection model. Defaults to 'yolo11x.pt'.                                             | `'yolo11x.pt'` |
 | `sam_model`  | `str`, optional         | Pre-trained SAM 2 segmentation model. Defaults to 'sam2_b.pt'.                                          | `'sam2_b.pt'`  |
 | `device`     | `str`, optional         | Device to run the models on. Defaults to an empty string (CPU or GPU, if available).                    |                |
+| `conf`       | `float`, optional       | Confidence threshold for detection model; default is 0.25.                                              | `0.25`         |
+| `iou`        | `float`, optional       | IoU threshold for filtering overlapping boxes in detection results; default is 0.45.                    | `0.45`         |
+| `imgsz`      | `int`, optional         | Input image resize dimension; default is 640.                                                           | `640`          |
 | `output_dir` | `str`, `None`, optional | Directory to save the annotated results. Defaults to a 'labels' folder in the same directory as 'data'. | `None`         |
 
 This function facilitates the rapid creation of high-quality segmentation datasets, ideal for researchers and developers aiming to accelerate their projects.

docs/en/models/sam.md

@@ -205,15 +205,18 @@ To auto-annotate your dataset with the Ultralytics framework, use the `auto_anno
         ```python
         from ultralytics.data.annotator import auto_annotate
 
-        auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model="sam_b.pt")
+        auto_annotate(data="path/to/images", det_model="yolo11x.pt", sam_model="sam_b.pt")
         ```
 
 | Argument     | Type                  | Description                                                                                             | Default        |
 | ------------ | --------------------- | ------------------------------------------------------------------------------------------------------- | -------------- |
 | `data`       | `str`                 | Path to a folder containing images to be annotated.                                                     |                |
-| `det_model`  | `str`, optional       | Pre-trained YOLO detection model. Defaults to 'yolov8x.pt'.                                             | `'yolov8x.pt'` |
+| `det_model`  | `str`, optional       | Pre-trained YOLO detection model. Defaults to 'yolo11x.pt'.                                             | `'yolo11x.pt'` |
 | `sam_model`  | `str`, optional       | Pre-trained SAM segmentation model. Defaults to 'sam_b.pt'.                                             | `'sam_b.pt'`   |
 | `device`     | `str`, optional       | Device to run the models on. Defaults to an empty string (CPU or GPU, if available).                    |                |
+| `conf`       | `float`, optional     | Confidence threshold for detection model; default is 0.25.                                              | `0.25`         |
+| `iou`        | `float`, optional     | IoU threshold for filtering overlapping boxes in detection results; default is 0.45.                    | `0.45`         |
+| `imgsz`      | `int`, optional       | Input image resize dimension; default is 640.                                                           | `640`          |
 | `output_dir` | `str`, None, optional | Directory to save the annotated results. Defaults to a 'labels' folder in the same directory as 'data'. | `None`         |
 
 The `auto_annotate` function takes the path to your images, with optional arguments for specifying the pre-trained detection and SAM segmentation models, the device to run the models on, and the output directory for saving the annotated results.

examples/README.md

@@ -8,7 +8,7 @@ This directory features a collection of real-world applications and walkthroughs
 | ----------------------------------------------------------------------------------------------------------------------------------------- | ------------------ | ----------------------------------------------------------------------------------------- |
 | [YOLO ONNX Detection Inference with C++](./YOLOv8-CPP-Inference)                                                                          | C++/ONNX           | [Justas Bartnykas](https://github.com/JustasBart)                                         |
 | [YOLO OpenCV ONNX Detection Python](./YOLOv8-OpenCV-ONNX-Python)                                                                          | OpenCV/Python/ONNX | [Farid Inawan](https://github.com/frdteknikelektro)                                       |
-| [YOLOv8 .NET ONNX ImageSharp](https://github.com/dme-compunet/YOLOv8)                                                                     | C#/ONNX/ImageSharp | [Compunet](https://github.com/dme-compunet)                                               |
+| [YOLO C# ONNX-Runtime](https://github.com/dme-compunet/YoloSharp)                                                                         | .NET/ONNX-Runtime  | [Compunet](https://github.com/dme-compunet)                                               |
 | [YOLO .Net ONNX Detection C#](https://www.nuget.org/packages/Yolov8.Net)                                                                  | C# .Net            | [Samuel Stainback](https://github.com/sstainba)                                           |
 | [YOLOv8 on NVIDIA Jetson(TensorRT and DeepStream)](https://wiki.seeedstudio.com/YOLOv8-DeepStream-TRT-Jetson/)                            | Python             | [Lakshantha](https://github.com/lakshanthad)                                              |
 | [YOLOv8 ONNXRuntime Python](./YOLOv8-ONNXRuntime)                                                                                         | Python/ONNXRuntime | [Semih Demirel](https://github.com/semihhdemirel)                                         |

tests/test_exports.py

@@ -193,16 +193,16 @@ def test_export_paddle():
 
 
 @pytest.mark.slow
-def test_export_ncnn():
+def test_export_mnn():
-    """Test YOLO exports to NCNN format."""
+    """Test YOLO exports to MNN format (WARNING: MNN test must precede NCNN test or CI error on Windows)."""
-    file = YOLO(MODEL).export(format="ncnn", imgsz=32)
+    file = YOLO(MODEL).export(format="mnn", imgsz=32)
     YOLO(file)(SOURCE, imgsz=32)  # exported model inference
 
 
 @pytest.mark.slow
-def test_export_mnn():
+def test_export_ncnn():
-    """Test YOLO exports to MNN format."""
+    """Test YOLO exports to NCNN format."""
-    file = YOLO(MODEL).export(format="mnn", imgsz=32)
+    file = YOLO(MODEL).export(format="ncnn", imgsz=32)
     YOLO(file)(SOURCE, imgsz=32)  # exported model inference
 
 

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.3.25"
+__version__ = "8.3.26"
 
 import os
 

ultralytics/data/annotator.py

@@ -5,7 +5,9 @@ from pathlib import Path
 from ultralytics import SAM, YOLO
 
 
-def auto_annotate(data, det_model="yolov8x.pt", sam_model="sam_b.pt", device="", output_dir=None):
+def auto_annotate(
+    data, det_model="yolo11x.pt", sam_model="sam_b.pt", device="", conf=0.25, iou=0.45, imgsz=640, output_dir=None
+):
     """
     Automatically annotates images using a YOLO object detection model and a SAM segmentation model.
 
@@ -17,6 +19,9 @@ def auto_annotate(data, det_model="yolov8x.pt", sam_model="sam_b.pt", device="",
         det_model (str): Path or name of the pre-trained YOLO detection model.
         sam_model (str): Path or name of the pre-trained SAM segmentation model.
         device (str): Device to run the models on (e.g., 'cpu', 'cuda', '0').
+        conf (float): Confidence threshold for detection model; default is 0.25.
+        iou (float): IoU threshold for filtering overlapping boxes in detection results; default is 0.45.
+        imgsz (int): Input image resize dimension; default is 640.
         output_dir (str | None): Directory to save the annotated results. If None, a default directory is created.
 
     Examples:
@@ -36,7 +41,7 @@ def auto_annotate(data, det_model="yolov8x.pt", sam_model="sam_b.pt", device="",
         output_dir = data.parent / f"{data.stem}_auto_annotate_labels"
     Path(output_dir).mkdir(exist_ok=True, parents=True)
 
-    det_results = det_model(data, stream=True, device=device)
+    det_results = det_model(data, stream=True, device=device, conf=conf, iou=iou, imgsz=imgsz)
 
     for result in det_results:
         class_ids = result.boxes.cls.int().tolist()  # noqa

ultralytics/engine/trainer.py

@@ -791,6 +791,8 @@ class BaseTrainer:
                 else:  # weight (with decay)
                     g[0].append(param)
 
+        optimizers = {"Adam", "Adamax", "AdamW", "NAdam", "RAdam", "RMSProp", "SGD", "auto"}
+        name = {x.lower(): x for x in optimizers}.get(name.lower(), None)
         if name in {"Adam", "Adamax", "AdamW", "NAdam", "RAdam"}:
             optimizer = getattr(optim, name, optim.Adam)(g[2], lr=lr, betas=(momentum, 0.999), weight_decay=0.0)
         elif name == "RMSProp":
@@ -799,9 +801,8 @@ class BaseTrainer:
             optimizer = optim.SGD(g[2], lr=lr, momentum=momentum, nesterov=True)
         else:
             raise NotImplementedError(
-                f"Optimizer '{name}' not found in list of available optimizers "
+                f"Optimizer '{name}' not found in list of available optimizers {optimizers}. "
-                f"[Adam, AdamW, NAdam, RAdam, RMSProp, SGD, auto]."
+                "Request support for addition optimizers at https://github.com/ultralytics/ultralytics."
-                "To request support for addition optimizers please visit https://github.com/ultralytics/ultralytics."
             )
 
         optimizer.add_param_group({"params": g[0], "weight_decay": decay})  # add g0 with weight_decay

ultralytics/nn/autobackend.py

@@ -684,6 +684,9 @@ class AutoBackend(nn.Module):
                         else:
                             x[:, [0, 2]] *= w
                             x[:, [1, 3]] *= h
+                            if self.task == "pose":
+                                x[:, 5::3] *= w
+                                x[:, 6::3] *= h
                     y.append(x)
             # TF segment fixes: export is reversed vs ONNX export and protos are transposed
             if len(y) == 2:  # segment with (det, proto) output order reversed

ultralytics/nn/modules/head.py

@@ -252,9 +252,21 @@ class Pose(Detect):
     def kpts_decode(self, bs, kpts):
         """Decodes keypoints."""
         ndim = self.kpt_shape[1]
-        if self.export:  # required for TFLite export to avoid 'PLACEHOLDER_FOR_GREATER_OP_CODES' bug
+        if self.export:
-            y = kpts.view(bs, *self.kpt_shape, -1)
+            if self.format in {
-            a = (y[:, :, :2] * 2.0 + (self.anchors - 0.5)) * self.strides
+                "tflite",
+                "edgetpu",
+            }:  # required for TFLite export to avoid 'PLACEHOLDER_FOR_GREATER_OP_CODES' bug
+                # Precompute normalization factor to increase numerical stability
+                y = kpts.view(bs, *self.kpt_shape, -1)
+                grid_h, grid_w = self.shape[2], self.shape[3]
+                grid_size = torch.tensor([grid_w, grid_h], device=y.device).reshape(1, 2, 1)
+                norm = self.strides / (self.stride[0] * grid_size)
+                a = (y[:, :, :2] * 2.0 + (self.anchors - 0.5)) * norm
+            else:
+                # NCNN fix
+                y = kpts.view(bs, *self.kpt_shape, -1)
+                a = (y[:, :, :2] * 2.0 + (self.anchors - 0.5)) * self.strides
             if ndim == 3:
                 a = torch.cat((a, y[:, :, 2:3].sigmoid()), 2)
             return a.view(bs, self.nk, -1)