Update YOLO11 Actions and Docs (#16596)

Signed-off-by: UltralyticsAssistant <web@ultralytics.com>

docs/en/datasets/classify/caltech101.md

@@ -36,7 +36,7 @@ To train a YOLO model on the Caltech-101 dataset for 100 epochs, you can use the
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="caltech101", epochs=100, imgsz=416)
@@ -46,7 +46,7 @@ To train a YOLO model on the Caltech-101 dataset for 100 epochs, you can use the
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=caltech101 model=yolov8n-cls.pt epochs=100 imgsz=416
+        yolo classify train data=caltech101 model=yolo11n-cls.pt epochs=100 imgsz=416
         ```
 
 ## Sample Images and Annotations
@@ -98,7 +98,7 @@ To train an Ultralytics YOLO model on the Caltech-101 dataset, you can use the p
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="caltech101", epochs=100, imgsz=416)
@@ -108,7 +108,7 @@ To train an Ultralytics YOLO model on the Caltech-101 dataset, you can use the p
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=caltech101 model=yolov8n-cls.pt epochs=100 imgsz=416
+        yolo classify train data=caltech101 model=yolo11n-cls.pt epochs=100 imgsz=416
         ```
 
 For more detailed arguments and options, refer to the model [Training](../../modes/train.md) page.

docs/en/datasets/classify/caltech256.md

@@ -47,7 +47,7 @@ To train a YOLO model on the Caltech-256 dataset for 100 epochs, you can use the
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="caltech256", epochs=100, imgsz=416)
@@ -57,7 +57,7 @@ To train a YOLO model on the Caltech-256 dataset for 100 epochs, you can use the
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=caltech256 model=yolov8n-cls.pt epochs=100 imgsz=416
+        yolo classify train data=caltech256 model=yolo11n-cls.pt epochs=100 imgsz=416
         ```
 
 ## Sample Images and Annotations
@@ -106,7 +106,7 @@ To train a YOLO model on the Caltech-256 dataset for 100 [epochs](https://www.ul
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model
 
         # Train the model
         results = model.train(data="caltech256", epochs=100, imgsz=416)
@@ -116,7 +116,7 @@ To train a YOLO model on the Caltech-256 dataset for 100 [epochs](https://www.ul
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=caltech256 model=yolov8n-cls.pt epochs=100 imgsz=416
+        yolo classify train data=caltech256 model=yolo11n-cls.pt epochs=100 imgsz=416
         ```
 
 ### What are the most common use cases for the Caltech-256 dataset?
@@ -141,6 +141,6 @@ Ultralytics YOLO models offer several advantages for training on the Caltech-256
 - **High Accuracy**: YOLO models are known for their state-of-the-art performance in object detection tasks.
 - **Speed**: They provide real-time inference capabilities, making them suitable for applications requiring quick predictions.
 - **Ease of Use**: With Ultralytics HUB, users can train, validate, and deploy models without extensive coding.
-- **Pretrained Models**: Starting from pretrained models, like `yolov8n-cls.pt`, can significantly reduce training time and improve model [accuracy](https://www.ultralytics.com/glossary/accuracy).
+- **Pretrained Models**: Starting from pretrained models, like `yolo11n-cls.pt`, can significantly reduce training time and improve model [accuracy](https://www.ultralytics.com/glossary/accuracy).
 
 For more details, explore our [comprehensive training guide](../../modes/train.md).

docs/en/datasets/classify/cifar10.md

@@ -16,7 +16,7 @@ The [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) (Canadian Institute
     allowfullscreen>
   </iframe>
   <br>
-  <strong>Watch:</strong> How to Train an <a href="https://www.ultralytics.com/glossary/image-classification">Image Classification</a> Model with CIFAR-10 Dataset using Ultralytics YOLOv8
+  <strong>Watch:</strong> How to Train an <a href="https://www.ultralytics.com/glossary/image-classification">Image Classification</a> Model with CIFAR-10 Dataset using Ultralytics YOLO11
 </p>
 
 ## Key Features
@@ -50,7 +50,7 @@ To train a YOLO model on the CIFAR-10 dataset for 100 epochs with an image size
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="cifar10", epochs=100, imgsz=32)
@@ -60,7 +60,7 @@ To train a YOLO model on the CIFAR-10 dataset for 100 epochs with an image size
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=cifar10 model=yolov8n-cls.pt epochs=100 imgsz=32
+        yolo classify train data=cifar10 model=yolo11n-cls.pt epochs=100 imgsz=32
         ```
 
 ## Sample Images and Annotations
@@ -104,7 +104,7 @@ To train a YOLO model on the CIFAR-10 dataset using Ultralytics, you can follow
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="cifar10", epochs=100, imgsz=32)
@@ -114,7 +114,7 @@ To train a YOLO model on the CIFAR-10 dataset using Ultralytics, you can follow
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=cifar10 model=yolov8n-cls.pt epochs=100 imgsz=32
+        yolo classify train data=cifar10 model=yolo11n-cls.pt epochs=100 imgsz=32
         ```
 
 For more details, refer to the model [Training](../../modes/train.md) page.

docs/en/datasets/classify/cifar100.md

@@ -39,7 +39,7 @@ To train a YOLO model on the CIFAR-100 dataset for 100 [epochs](https://www.ultr
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="cifar100", epochs=100, imgsz=32)
@@ -49,7 +49,7 @@ To train a YOLO model on the CIFAR-100 dataset for 100 [epochs](https://www.ultr
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=cifar100 model=yolov8n-cls.pt epochs=100 imgsz=32
+        yolo classify train data=cifar100 model=yolo11n-cls.pt epochs=100 imgsz=32
         ```
 
 ## Sample Images and Annotations
@@ -97,7 +97,7 @@ You can train a YOLO model on the CIFAR-100 dataset using either Python or CLI c
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="cifar100", epochs=100, imgsz=32)
@@ -107,7 +107,7 @@ You can train a YOLO model on the CIFAR-100 dataset using either Python or CLI c
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=cifar100 model=yolov8n-cls.pt epochs=100 imgsz=32
+        yolo classify train data=cifar100 model=yolo11n-cls.pt epochs=100 imgsz=32
         ```
 
 For a comprehensive list of available arguments, please refer to the model [Training](../../modes/train.md) page.

docs/en/datasets/classify/fashion-mnist.md

@@ -16,7 +16,7 @@ The [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset is
     allowfullscreen>
   </iframe>
   <br>
-  <strong>Watch:</strong> How to do <a href="https://www.ultralytics.com/glossary/image-classification">Image Classification</a> on Fashion MNIST Dataset using Ultralytics YOLOv8
+  <strong>Watch:</strong> How to do <a href="https://www.ultralytics.com/glossary/image-classification">Image Classification</a> on Fashion MNIST Dataset using Ultralytics YOLO11
 </p>
 
 ## Key Features
@@ -64,7 +64,7 @@ To train a CNN model on the Fashion-MNIST dataset for 100 [epochs](https://www.u
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="fashion-mnist", epochs=100, imgsz=28)
@@ -74,7 +74,7 @@ To train a CNN model on the Fashion-MNIST dataset for 100 [epochs](https://www.u
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=fashion-mnist model=yolov8n-cls.pt epochs=100 imgsz=28
+        yolo classify train data=fashion-mnist model=yolo11n-cls.pt epochs=100 imgsz=28
         ```
 
 ## Sample Images and Annotations
@@ -107,7 +107,7 @@ To train an Ultralytics YOLO model on the Fashion-MNIST dataset, you can use bot
         from ultralytics import YOLO
 
         # Load a pretrained model
-        model = YOLO("yolov8n-cls.pt")
+        model = YOLO("yolo11n-cls.pt")
 
         # Train the model on Fashion-MNIST
         results = model.train(data="fashion-mnist", epochs=100, imgsz=28)
@@ -117,7 +117,7 @@ To train an Ultralytics YOLO model on the Fashion-MNIST dataset, you can use bot
     === "CLI"
 
         ```bash
-        yolo classify train data=fashion-mnist model=yolov8n-cls.pt epochs=100 imgsz=28
+        yolo classify train data=fashion-mnist model=yolo11n-cls.pt epochs=100 imgsz=28
         ```
 
 For more detailed training parameters, refer to the [Training page](../../modes/train.md).
@@ -128,7 +128,7 @@ The [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset is
 
 ### Can I use Ultralytics YOLO for image classification tasks like Fashion-MNIST?
 
-Yes, Ultralytics YOLO models can be used for image classification tasks, including those involving the Fashion-MNIST dataset. YOLOv8, for example, supports various vision tasks such as detection, segmentation, and classification. To get started with image classification tasks, refer to the [Classification page](https://docs.ultralytics.com/tasks/classify/).
+Yes, Ultralytics YOLO models can be used for image classification tasks, including those involving the Fashion-MNIST dataset. YOLO11, for example, supports various vision tasks such as detection, segmentation, and classification. To get started with image classification tasks, refer to the [Classification page](https://docs.ultralytics.com/tasks/classify/).
 
 ### What are the key features and structure of the Fashion-MNIST dataset?
 

docs/en/datasets/classify/imagenet.md

@@ -12,11 +12,11 @@ keywords: ImageNet, deep learning, visual recognition, computer vision, pretrain
 
 | Model                                                                                        | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) at 640 |
 | -------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ |
-| [YOLOv8n-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-cls.pt) | 224                   | 69.0             | 88.3             | 12.9                           | 0.31                                | 2.7                | 4.3                      |
-| [YOLOv8s-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-cls.pt) | 224                   | 73.8             | 91.7             | 23.4                           | 0.35                                | 6.4                | 13.5                     |
-| [YOLOv8m-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-cls.pt) | 224                   | 76.8             | 93.5             | 85.4                           | 0.62                                | 17.0               | 42.7                     |
-| [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-cls.pt) | 224                   | 76.8             | 93.5             | 163.0                          | 0.87                                | 37.5               | 99.7                     |
-| [YOLOv8x-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-cls.pt) | 224                   | 79.0             | 94.6             | 232.0                          | 1.01                                | 57.4               | 154.8                    |
+| [YOLO11n-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-cls.pt) | 224                   | 69.0             | 88.3             | 12.9                           | 0.31                                | 2.7                | 4.3                      |
+| [YOLO11s-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-cls.pt) | 224                   | 73.8             | 91.7             | 23.4                           | 0.35                                | 6.4                | 13.5                     |
+| [YOLO11m-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-cls.pt) | 224                   | 76.8             | 93.5             | 85.4                           | 0.62                                | 17.0               | 42.7                     |
+| [YOLO11l-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-cls.pt) | 224                   | 76.8             | 93.5             | 163.0                          | 0.87                                | 37.5               | 99.7                     |
+| [YOLO11x-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-cls.pt) | 224                   | 79.0             | 94.6             | 232.0                          | 1.01                                | 57.4               | 154.8                    |
 
 ## Key Features
 
@@ -49,7 +49,7 @@ To train a deep learning model on the ImageNet dataset for 100 [epochs](https://
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="imagenet", epochs=100, imgsz=224)
@@ -59,7 +59,7 @@ To train a deep learning model on the ImageNet dataset for 100 [epochs](https://
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=imagenet model=yolov8n-cls.pt epochs=100 imgsz=224
+        yolo classify train data=imagenet model=yolo11n-cls.pt epochs=100 imgsz=224
         ```
 
 ## Sample Images and Annotations
@@ -110,7 +110,7 @@ To use a pretrained Ultralytics YOLO model for image classification on the Image
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="imagenet", epochs=100, imgsz=224)
@@ -120,14 +120,14 @@ To use a pretrained Ultralytics YOLO model for image classification on the Image
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=imagenet model=yolov8n-cls.pt epochs=100 imgsz=224
+        yolo classify train data=imagenet model=yolo11n-cls.pt epochs=100 imgsz=224
         ```
 
 For more in-depth training instruction, refer to our [Training page](../../modes/train.md).
 
-### Why should I use the Ultralytics YOLOv8 pretrained models for my ImageNet dataset projects?
+### Why should I use the Ultralytics YOLO11 pretrained models for my ImageNet dataset projects?
 
-Ultralytics YOLOv8 pretrained models offer state-of-the-art performance in terms of speed and [accuracy](https://www.ultralytics.com/glossary/accuracy) for various computer vision tasks. For example, the YOLOv8n-cls model, with a top-1 accuracy of 69.0% and a top-5 accuracy of 88.3%, is optimized for real-time applications. Pretrained models reduce the computational resources required for training from scratch and accelerate development cycles. Learn more about the performance metrics of YOLOv8 models in the [ImageNet Pretrained Models section](#imagenet-pretrained-models).
+Ultralytics YOLO11 pretrained models offer state-of-the-art performance in terms of speed and [accuracy](https://www.ultralytics.com/glossary/accuracy) for various computer vision tasks. For example, the YOLO11n-cls model, with a top-1 accuracy of 69.0% and a top-5 accuracy of 88.3%, is optimized for real-time applications. Pretrained models reduce the computational resources required for training from scratch and accelerate development cycles. Learn more about the performance metrics of YOLO11 models in the [ImageNet Pretrained Models section](#imagenet-pretrained-models).
 
 ### How is the ImageNet dataset structured, and why is it important?
 

docs/en/datasets/classify/imagenet10.md

@@ -35,7 +35,7 @@ To test a deep learning model on the ImageNet10 dataset with an image size of 22
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="imagenet10", epochs=5, imgsz=224)
@@ -45,7 +45,7 @@ To test a deep learning model on the ImageNet10 dataset with an image size of 22
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=imagenet10 model=yolov8n-cls.pt epochs=5 imgsz=224
+        yolo classify train data=imagenet10 model=yolo11n-cls.pt epochs=5 imgsz=224
         ```
 
 ## Sample Images and Annotations
@@ -94,7 +94,7 @@ To test your deep learning model on the ImageNet10 dataset with an image size of
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="imagenet10", epochs=5, imgsz=224)
@@ -104,7 +104,7 @@ To test your deep learning model on the ImageNet10 dataset with an image size of
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=imagenet10 model=yolov8n-cls.pt epochs=5 imgsz=224
+        yolo classify train data=imagenet10 model=yolo11n-cls.pt epochs=5 imgsz=224
         ```
 
 Refer to the [Training](../../modes/train.md) page for a comprehensive list of available arguments.

docs/en/datasets/classify/imagenette.md

@@ -37,7 +37,7 @@ To train a model on the ImageNette dataset for 100 epochs with a standard image
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="imagenette", epochs=100, imgsz=224)
@@ -47,7 +47,7 @@ To train a model on the ImageNette dataset for 100 epochs with a standard image
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=imagenette model=yolov8n-cls.pt epochs=100 imgsz=224
+        yolo classify train data=imagenette model=yolo11n-cls.pt epochs=100 imgsz=224
         ```
 
 ## Sample Images and Annotations
@@ -72,7 +72,7 @@ To use these datasets, simply replace 'imagenette' with 'imagenette160' or 'imag
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model with ImageNette160
         results = model.train(data="imagenette160", epochs=100, imgsz=160)
@@ -82,7 +82,7 @@ To use these datasets, simply replace 'imagenette' with 'imagenette160' or 'imag
 
         ```bash
         # Start training from a pretrained *.pt model with ImageNette160
-        yolo classify train data=imagenette160 model=yolov8n-cls.pt epochs=100 imgsz=160
+        yolo classify train data=imagenette160 model=yolo11n-cls.pt epochs=100 imgsz=160
         ```
 
 !!! example "Train Example with ImageNette320"
@@ -93,7 +93,7 @@ To use these datasets, simply replace 'imagenette' with 'imagenette160' or 'imag
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model with ImageNette320
         results = model.train(data="imagenette320", epochs=100, imgsz=320)
@@ -103,7 +103,7 @@ To use these datasets, simply replace 'imagenette' with 'imagenette160' or 'imag
 
         ```bash
         # Start training from a pretrained *.pt model with ImageNette320
-        yolo classify train data=imagenette320 model=yolov8n-cls.pt epochs=100 imgsz=320
+        yolo classify train data=imagenette320 model=yolo11n-cls.pt epochs=100 imgsz=320
         ```
 
 These smaller versions of the dataset allow for rapid iterations during the development process while still providing valuable and realistic image classification tasks.
@@ -130,7 +130,7 @@ To train a YOLO model on the ImageNette dataset for 100 [epochs](https://www.ult
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="imagenette", epochs=100, imgsz=224)
@@ -140,7 +140,7 @@ To train a YOLO model on the ImageNette dataset for 100 [epochs](https://www.ult
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=imagenette model=yolov8n-cls.pt epochs=100 imgsz=224
+        yolo classify train data=imagenette model=yolo11n-cls.pt epochs=100 imgsz=224
         ```
 
 For more details, see the [Training](../../modes/train.md) documentation page.
@@ -167,7 +167,7 @@ Yes, the ImageNette dataset is also available in two resized versions: ImageNett
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")
+        model = YOLO("yolo11n-cls.pt")
 
         # Train the model with ImageNette160
         results = model.train(data="imagenette160", epochs=100, imgsz=160)
@@ -177,7 +177,7 @@ Yes, the ImageNette dataset is also available in two resized versions: ImageNett
 
         ```bash
         # Start training from a pretrained *.pt model with ImageNette160
-        yolo detect train data=imagenette160 model=yolov8n-cls.pt epochs=100 imgsz=160
+        yolo detect train data=imagenette160 model=yolo11n-cls.pt epochs=100 imgsz=160
         ```
 
 For more information, refer to [Training with ImageNette160 and ImageNette320](#imagenette160-and-imagenette320).

docs/en/datasets/classify/imagewoof.md

@@ -34,7 +34,7 @@ To train a CNN model on the ImageWoof dataset for 100 [epochs](https://www.ultra
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="imagewoof", epochs=100, imgsz=224)
@@ -44,7 +44,7 @@ To train a CNN model on the ImageWoof dataset for 100 [epochs](https://www.ultra
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=imagewoof model=yolov8n-cls.pt epochs=100 imgsz=224
+        yolo classify train data=imagewoof model=yolo11n-cls.pt epochs=100 imgsz=224
         ```
 
 ## Dataset Variants
@@ -67,7 +67,7 @@ To use these variants in your training, simply replace 'imagewoof' in the datase
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # For medium-sized dataset
         model.train(data="imagewoof320", epochs=100, imgsz=224)
@@ -80,7 +80,7 @@ To use these variants in your training, simply replace 'imagewoof' in the datase
 
         ```bash
         # Load a pretrained model and train on the small-sized dataset
-        yolo classify train model=yolov8n-cls.pt data=imagewoof320 epochs=100 imgsz=224
+        yolo classify train model=yolo11n-cls.pt data=imagewoof320 epochs=100 imgsz=224
         ```
 
 It's important to note that using smaller images will likely yield lower performance in terms of classification accuracy. However, it's an excellent way to iterate quickly in the early stages of model development and prototyping.
@@ -116,7 +116,7 @@ To train a [Convolutional Neural Network](https://www.ultralytics.com/glossary/c
         ```python
         from ultralytics import YOLO
 
-        model = YOLO("yolov8n-cls.pt")  # Load a pretrained model
+        model = YOLO("yolo11n-cls.pt")  # Load a pretrained model
         results = model.train(data="imagewoof", epochs=100, imgsz=224)
         ```
 
@@ -124,7 +124,7 @@ To train a [Convolutional Neural Network](https://www.ultralytics.com/glossary/c
     === "CLI"
 
         ```bash
-        yolo classify train data=imagewoof model=yolov8n-cls.pt epochs=100 imgsz=224
+        yolo classify train data=imagewoof model=yolo11n-cls.pt epochs=100 imgsz=224
         ```
 
 For more details on available training arguments, refer to the [Training](../../modes/train.md) page.

docs/en/datasets/classify/index.md

@@ -86,7 +86,7 @@ This structured approach ensures that the model can effectively learn from well-
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="path/to/dataset", epochs=100, imgsz=640)
@@ -96,7 +96,7 @@ This structured approach ensures that the model can effectively learn from well-
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=path/to/data model=yolov8n-cls.pt epochs=100 imgsz=640
+        yolo detect train data=path/to/data model=yolo11n-cls.pt epochs=100 imgsz=640
         ```
 
 ## Supported Datasets
@@ -170,7 +170,7 @@ To use your own dataset with Ultralytics YOLO, ensure it follows the specified d
 from ultralytics import YOLO
 
 # Load a model
-model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
 # Train the model
 results = model.train(data="path/to/your/dataset", epochs=100, imgsz=640)
@@ -182,7 +182,7 @@ More details can be found in the [Adding your own dataset](#adding-your-own-data
 
 Ultralytics YOLO offers several benefits for image classification, including:
 
-- **Pretrained Models**: Load pretrained models like `yolov8n-cls.pt` to jump-start your training process.
+- **Pretrained Models**: Load pretrained models like `yolo11n-cls.pt` to jump-start your training process.
 - **Ease of Use**: Simple API and CLI commands for training and evaluation.
 - **High Performance**: State-of-the-art [accuracy](https://www.ultralytics.com/glossary/accuracy) and speed, ideal for real-time applications.
 - **Support for Multiple Datasets**: Seamless integration with various popular datasets like CIFAR-10, ImageNet, and more.
@@ -202,7 +202,7 @@ Training a model using Ultralytics YOLO can be done easily in both Python and CL
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model
 
         # Train the model
         results = model.train(data="path/to/dataset", epochs=100, imgsz=640)
@@ -213,7 +213,7 @@ Training a model using Ultralytics YOLO can be done easily in both Python and CL
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=path/to/data model=yolov8n-cls.pt epochs=100 imgsz=640
+        yolo detect train data=path/to/data model=yolo11n-cls.pt epochs=100 imgsz=640
         ```
 
 These examples demonstrate the straightforward process of training a YOLO model using either approach. For more information, visit the [Usage](#usage) section.

docs/en/datasets/classify/mnist.md

@@ -42,7 +42,7 @@ To train a CNN model on the MNIST dataset for 100 [epochs](https://www.ultralyti
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="mnist", epochs=100, imgsz=32)
@@ -52,7 +52,7 @@ To train a CNN model on the MNIST dataset for 100 [epochs](https://www.ultralyti
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=mnist model=yolov8n-cls.pt epochs=100 imgsz=28
+        yolo classify train data=mnist model=yolo11n-cls.pt epochs=100 imgsz=28
         ```
 
 ## Sample Images and Annotations
@@ -103,7 +103,7 @@ To train a model on the MNIST dataset using Ultralytics YOLO, you can follow the
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="mnist", epochs=100, imgsz=32)
@@ -113,7 +113,7 @@ To train a model on the MNIST dataset using Ultralytics YOLO, you can follow the
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=mnist model=yolov8n-cls.pt epochs=100 imgsz=28
+        yolo classify train data=mnist model=yolo11n-cls.pt epochs=100 imgsz=28
         ```
 
 For a detailed list of available training arguments, refer to the [Training](../../modes/train.md) page.