HongChuang/ultralytics-ascend
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 3

Update YOLO11 Actions and Docs (#16596)

Browse source 
Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
This commit is contained in:
Ultralytics Assistant 2024-10-01 16:58:12 +02:00 • committed by GitHub
parent 51e93d6111
commit 97f38409fb
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
124 changed files with 1948 additions and 1948 deletions
Download patch file Download diff file Expand all files Collapse all files

8
docs/en/datasets/classify/mnist.md
View file

@ -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.