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Update YOLO11 Actions and Docs (#16596)

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docs/en/datasets/classify/imagenette.md
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@ -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).