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

18
docs/en/datasets/detect/african-wildlife.md
View file

@ -1,7 +1,7 @@
---
comments: true
description: Explore our African Wildlife Dataset featuring images of buffalo, elephant, rhino, and zebra for training computer vision models. Ideal for research and conservation.
keywords: African Wildlife Dataset, South African animals, object detection, computer vision, YOLOv8, wildlife research, conservation, dataset
keywords: African Wildlife Dataset, South African animals, object detection, computer vision, YOLO11, wildlife research, conservation, dataset
---
# African Wildlife Dataset
@ -16,7 +16,7 @@ This dataset showcases four common animal classes typically found in South Afric
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> African Wildlife Animals Detection using Ultralytics YOLOv8
<strong>Watch:</strong> African Wildlife Animals Detection using Ultralytics YOLO11
</p>
## Dataset Structure
@ -43,7 +43,7 @@ A YAML (Yet Another Markup Language) file defines the dataset configuration, inc
## Usage
To train a YOLOv8n model on the African wildlife dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the provided code samples. For a comprehensive list of available parameters, refer to the model's [Training](../../modes/train.md) page.
To train a YOLO11n model on the African wildlife dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the provided code samples. For a comprehensive list of available parameters, refer to the model's [Training](../../modes/train.md) page.
!!! example "Train Example"
@ -53,7 +53,7 @@ To train a YOLOv8n model on the African wildlife dataset for 100 [epochs](https:
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="african-wildlife.yaml", epochs=100, imgsz=640)
@ -63,7 +63,7 @@ To train a YOLOv8n model on the African wildlife dataset for 100 [epochs](https:
```bash
# Start training from a pretrained *.pt model
yolo detect train data=african-wildlife.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=african-wildlife.yaml model=yolo11n.pt epochs=100 imgsz=640
```
!!! example "Inference Example"
@ -107,9 +107,9 @@ The dataset has been released available under the [AGPL-3.0 License](https://git
The African Wildlife Dataset includes images of four common animal species found in South African nature reserves: buffalo, elephant, rhino, and zebra. It is a valuable resource for training computer vision algorithms in object detection and animal identification. The dataset supports various tasks like object tracking, research, and conservation efforts. For more information on its structure and applications, refer to the [Dataset Structure](#dataset-structure) section and [Applications](#applications) of the dataset.
### How do I train a YOLOv8 model using the African Wildlife Dataset?
### How do I train a YOLO11 model using the African Wildlife Dataset?
You can train a YOLOv8 model on the African Wildlife Dataset by using the `african-wildlife.yaml` configuration file. Below is an example of how to train the YOLOv8n model for 100 epochs with an image size of 640:
You can train a YOLO11 model on the African Wildlife Dataset by using the `african-wildlife.yaml` configuration file. Below is an example of how to train the YOLO11n model for 100 epochs with an image size of 640:
!!! example
@ -119,7 +119,7 @@ You can train a YOLOv8 model on the African Wildlife Dataset by using the `afric
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="african-wildlife.yaml", epochs=100, imgsz=640)
@ -129,7 +129,7 @@ You can train a YOLOv8 model on the African Wildlife Dataset by using the `afric
```bash
# Start training from a pretrained *.pt model
yolo detect train data=african-wildlife.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=african-wildlife.yaml model=yolo11n.pt epochs=100 imgsz=640
```
For additional training parameters and options, refer to the [Training](../../modes/train.md) documentation.