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Update YOLO11 Actions and Docs (#16596)

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docs/en/datasets/obb/dota8.md
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---
comments: true
description: Explore the DOTA8 dataset - a small, versatile oriented object detection dataset ideal for testing and debugging object detection models using Ultralytics YOLOv8.
keywords: DOTA8 dataset, Ultralytics, YOLOv8, object detection, debugging, training models, oriented object detection, dataset YAML
description: Explore the DOTA8 dataset - a small, versatile oriented object detection dataset ideal for testing and debugging object detection models using Ultralytics YOLO11.
keywords: DOTA8 dataset, Ultralytics, YOLO11, object detection, debugging, training models, oriented object detection, dataset YAML
---
# DOTA8 Dataset
@ -10,7 +10,7 @@ keywords: DOTA8 dataset, Ultralytics, YOLOv8, object detection, debugging, train
[Ultralytics](https://www.ultralytics.com/) DOTA8 is a small, but versatile oriented [object detection](https://www.ultralytics.com/glossary/object-detection) dataset composed of the first 8 images of 8 images of the split DOTAv1 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging object detection models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.
This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLOv8](https://github.com/ultralytics/ultralytics).
This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLO11](https://github.com/ultralytics/ultralytics).
## Dataset YAML
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## Usage
To train a YOLOv8n-obb model on the DOTA8 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
To train a YOLO11n-obb model on the DOTA8 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
!!! example "Train Example"
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from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-obb.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n-obb.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="dota8.yaml", epochs=100, imgsz=640)
@ -44,7 +44,7 @@ To train a YOLOv8n-obb model on the DOTA8 dataset for 100 [epochs](https://www.u
```bash
# Start training from a pretrained *.pt model
yolo obb train data=dota8.yaml model=yolov8n-obb.pt epochs=100 imgsz=640
yolo obb train data=dota8.yaml model=yolo11n-obb.pt epochs=100 imgsz=640
```
## Sample Images and Annotations
@ -84,11 +84,11 @@ A special note of gratitude to the team behind the DOTA datasets for their comme
### What is the DOTA8 dataset and how can it be used?
The DOTA8 dataset is a small, versatile oriented object detection dataset made up of the first 8 images from the DOTAv1 split set, with 4 images designated for training and 4 for validation. It's ideal for testing and debugging object detection models like Ultralytics YOLOv8. Due to its manageable size and diversity, it helps in identifying pipeline errors and running sanity checks before deploying larger datasets. Learn more about object detection with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics).
The DOTA8 dataset is a small, versatile oriented object detection dataset made up of the first 8 images from the DOTAv1 split set, with 4 images designated for training and 4 for validation. It's ideal for testing and debugging object detection models like Ultralytics YOLO11. Due to its manageable size and diversity, it helps in identifying pipeline errors and running sanity checks before deploying larger datasets. Learn more about object detection with [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics).
### How do I train a YOLOv8 model using the DOTA8 dataset?
### How do I train a YOLO11 model using the DOTA8 dataset?
To train a YOLOv8n-obb model on the DOTA8 dataset for 100 epochs with an image size of 640, you can use the following code snippets. For comprehensive argument options, refer to the model [Training](../../modes/train.md) page.
To train a YOLO11n-obb model on the DOTA8 dataset for 100 epochs with an image size of 640, you can use the following code snippets. For comprehensive argument options, refer to the model [Training](../../modes/train.md) page.
!!! example "Train Example"
@ -98,7 +98,7 @@ To train a YOLOv8n-obb model on the DOTA8 dataset for 100 epochs with an image s
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-obb.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n-obb.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="dota8.yaml", epochs=100, imgsz=640)
@ -108,7 +108,7 @@ To train a YOLOv8n-obb model on the DOTA8 dataset for 100 epochs with an image s
```bash
# Start training from a pretrained *.pt model
yolo obb train data=dota8.yaml model=yolov8n-obb.pt epochs=100 imgsz=640
yolo obb train data=dota8.yaml model=yolo11n-obb.pt epochs=100 imgsz=640
```
### What are the key features of the DOTA dataset and where can I access the YAML file?
@ -119,6 +119,6 @@ The DOTA dataset is known for its large-scale benchmark and the challenges it pr
Mosaicing combines multiple images into one during training, increasing the variety of objects and contexts within each batch. This improves a model's ability to generalize to different object sizes, aspect ratios, and scenes. This technique can be visually demonstrated through a training batch composed of mosaiced DOTA8 dataset images, helping in robust model development. Explore more about mosaicing and training techniques on our [Training](../../modes/train.md) page.
### Why should I use Ultralytics YOLOv8 for object detection tasks?
### Why should I use Ultralytics YOLO11 for object detection tasks?
Ultralytics YOLOv8 provides state-of-the-art real-time object detection capabilities, including features like oriented bounding boxes (OBB), [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation), and a highly versatile training pipeline. It's suitable for various applications and offers pretrained models for efficient fine-tuning. Explore further about the advantages and usage in the [Ultralytics YOLOv8 documentation](https://github.com/ultralytics/ultralytics).
Ultralytics YOLO11 provides state-of-the-art real-time object detection capabilities, including features like oriented bounding boxes (OBB), [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation), and a highly versatile training pipeline. It's suitable for various applications and offers pretrained models for efficient fine-tuning. Explore further about the advantages and usage in the [Ultralytics YOLO11 documentation](https://github.com/ultralytics/ultralytics).