Update YOLO11 Actions and Docs (#16596)
Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
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124 changed files with 1948 additions and 1948 deletions
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@ -108,8 +108,8 @@ To train a model on the DOTA v1 dataset, you can utilize the following code snip
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```python
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from ultralytics import YOLO
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# Create a new YOLOv8n-OBB model from scratch
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model = YOLO("yolov8n-obb.yaml")
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# Create a new YOLO11n-OBB model from scratch
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model = YOLO("yolo11n-obb.yaml")
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# Train the model on the DOTAv1 dataset
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results = model.train(data="DOTAv1.yaml", epochs=100, imgsz=1024)
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@ -118,8 +118,8 @@ To train a model on the DOTA v1 dataset, you can utilize the following code snip
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=== "CLI"
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```bash
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# Train a new YOLOv8n-OBB model on the DOTAv1 dataset
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yolo obb train data=DOTAv1.yaml model=yolov8n-obb.pt epochs=100 imgsz=1024
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# Train a new YOLO11n-OBB model on the DOTAv1 dataset
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yolo obb train data=DOTAv1.yaml model=yolo11n-obb.pt epochs=100 imgsz=1024
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```
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## Sample Data and Annotations
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@ -176,8 +176,8 @@ To train a model on the DOTA dataset, you can use the following example with Ult
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```python
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from ultralytics import YOLO
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# Create a new YOLOv8n-OBB model from scratch
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model = YOLO("yolov8n-obb.yaml")
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# Create a new YOLO11n-OBB model from scratch
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model = YOLO("yolo11n-obb.yaml")
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# Train the model on the DOTAv1 dataset
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results = model.train(data="DOTAv1.yaml", epochs=100, imgsz=1024)
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@ -186,8 +186,8 @@ To train a model on the DOTA dataset, you can use the following example with Ult
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=== "CLI"
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```bash
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# Train a new YOLOv8n-OBB model on the DOTAv1 dataset
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yolo obb train data=DOTAv1.yaml model=yolov8n-obb.pt epochs=100 imgsz=1024
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# Train a new YOLO11n-OBB model on the DOTAv1 dataset
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yolo obb train data=DOTAv1.yaml model=yolo11n-obb.pt epochs=100 imgsz=1024
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```
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For more details on how to split and preprocess the DOTA images, refer to the [split DOTA images section](#split-dota-images).
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@ -1,7 +1,7 @@
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---
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comments: true
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description: Explore the DOTA8 dataset - a small, versatile oriented object detection dataset ideal for testing and debugging object detection models using Ultralytics YOLOv8.
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keywords: DOTA8 dataset, Ultralytics, YOLOv8, object detection, debugging, training models, oriented object detection, dataset YAML
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description: Explore the DOTA8 dataset - a small, versatile oriented object detection dataset ideal for testing and debugging object detection models using Ultralytics YOLO11.
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keywords: DOTA8 dataset, Ultralytics, YOLO11, object detection, debugging, training models, oriented object detection, dataset YAML
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---
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# DOTA8 Dataset
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@ -10,7 +10,7 @@ keywords: DOTA8 dataset, Ultralytics, YOLOv8, object detection, debugging, train
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[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.
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This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLOv8](https://github.com/ultralytics/ultralytics).
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This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLO11](https://github.com/ultralytics/ultralytics).
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## Dataset YAML
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@ -24,7 +24,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
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## Usage
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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.
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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.
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!!! example "Train Example"
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@ -34,7 +34,7 @@ To train a YOLOv8n-obb model on the DOTA8 dataset for 100 [epochs](https://www.u
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n-obb.pt") # load a pretrained model (recommended for training)
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model = YOLO("yolo11n-obb.pt") # load a pretrained model (recommended for training)
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# Train the model
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results = model.train(data="dota8.yaml", epochs=100, imgsz=640)
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@ -44,7 +44,7 @@ To train a YOLOv8n-obb model on the DOTA8 dataset for 100 [epochs](https://www.u
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```bash
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# Start training from a pretrained *.pt model
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yolo obb train data=dota8.yaml model=yolov8n-obb.pt epochs=100 imgsz=640
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yolo obb train data=dota8.yaml model=yolo11n-obb.pt epochs=100 imgsz=640
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```
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## Sample Images and Annotations
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@ -84,11 +84,11 @@ A special note of gratitude to the team behind the DOTA datasets for their comme
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### What is the DOTA8 dataset and how can it be used?
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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).
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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).
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### How do I train a YOLOv8 model using the DOTA8 dataset?
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### How do I train a YOLO11 model using the DOTA8 dataset?
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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.
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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.
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!!! example "Train Example"
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@ -98,7 +98,7 @@ To train a YOLOv8n-obb model on the DOTA8 dataset for 100 epochs with an image s
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n-obb.pt") # load a pretrained model (recommended for training)
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model = YOLO("yolo11n-obb.pt") # load a pretrained model (recommended for training)
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# Train the model
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results = model.train(data="dota8.yaml", epochs=100, imgsz=640)
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@ -108,7 +108,7 @@ To train a YOLOv8n-obb model on the DOTA8 dataset for 100 epochs with an image s
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```bash
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# Start training from a pretrained *.pt model
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yolo obb train data=dota8.yaml model=yolov8n-obb.pt epochs=100 imgsz=640
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yolo obb train data=dota8.yaml model=yolo11n-obb.pt epochs=100 imgsz=640
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```
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### What are the key features of the DOTA dataset and where can I access the YAML file?
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@ -119,6 +119,6 @@ The DOTA dataset is known for its large-scale benchmark and the challenges it pr
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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.
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### Why should I use Ultralytics YOLOv8 for object detection tasks?
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### Why should I use Ultralytics YOLO11 for object detection tasks?
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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).
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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).
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@ -39,8 +39,8 @@ To train a model using these OBB formats:
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```python
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from ultralytics import YOLO
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# Create a new YOLOv8n-OBB model from scratch
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model = YOLO("yolov8n-obb.yaml")
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# Create a new YOLO11n-OBB model from scratch
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model = YOLO("yolo11n-obb.yaml")
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# Train the model on the DOTAv1 dataset
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results = model.train(data="DOTAv1.yaml", epochs=100, imgsz=1024)
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@ -49,8 +49,8 @@ To train a model using these OBB formats:
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=== "CLI"
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```bash
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# Train a new YOLOv8n-OBB model on the DOTAv1 dataset
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yolo obb train data=DOTAv1.yaml model=yolov8n-obb.pt epochs=100 imgsz=1024
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# Train a new YOLO11n-OBB model on the DOTAv1 dataset
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yolo obb train data=DOTAv1.yaml model=yolo11n-obb.pt epochs=100 imgsz=1024
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```
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## Supported Datasets
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@ -92,7 +92,7 @@ It's imperative to validate the compatibility of the dataset with your model and
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Oriented Bounding Boxes (OBB) are a type of bounding box annotation where the box can be rotated to align more closely with the object being detected, rather than just being axis-aligned. This is particularly useful in aerial or satellite imagery where objects might not be aligned with the image axes. In Ultralytics YOLO models, OBBs are represented by their four corner points in the YOLO OBB format. This allows for more accurate object detection since the bounding boxes can rotate to fit the objects better.
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### How do I convert my existing DOTA dataset labels to YOLO OBB format for use with Ultralytics YOLOv8?
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### How do I convert my existing DOTA dataset labels to YOLO OBB format for use with Ultralytics YOLO11?
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You can convert DOTA dataset labels to YOLO OBB format using the `convert_dota_to_yolo_obb` function from Ultralytics. This conversion ensures compatibility with the Ultralytics YOLO models, enabling you to leverage the OBB capabilities for enhanced object detection. Here's a quick example:
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@ -104,9 +104,9 @@ convert_dota_to_yolo_obb("path/to/DOTA")
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This script will reformat your DOTA annotations into a YOLO-compatible format.
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### How do I train a YOLOv8 model with oriented bounding boxes (OBB) on my dataset?
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### How do I train a YOLO11 model with oriented bounding boxes (OBB) on my dataset?
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Training a YOLOv8 model with OBBs involves ensuring your dataset is in the YOLO OBB format and then using the Ultralytics API to train the model. Here's an example in both Python and CLI:
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Training a YOLO11 model with OBBs involves ensuring your dataset is in the YOLO OBB format and then using the Ultralytics API to train the model. Here's an example in both Python and CLI:
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!!! example
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@ -115,8 +115,8 @@ Training a YOLOv8 model with OBBs involves ensuring your dataset is in the YOLO
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```python
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from ultralytics import YOLO
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# Create a new YOLOv8n-OBB model from scratch
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model = YOLO("yolov8n-obb.yaml")
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# Create a new YOLO11n-OBB model from scratch
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model = YOLO("yolo11n-obb.yaml")
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# Train the model on the custom dataset
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results = model.train(data="your_dataset.yaml", epochs=100, imgsz=640)
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@ -125,8 +125,8 @@ Training a YOLOv8 model with OBBs involves ensuring your dataset is in the YOLO
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=== "CLI"
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```bash
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# Train a new YOLOv8n-OBB model on the custom dataset
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yolo obb train data=your_dataset.yaml model=yolov8n-obb.yaml epochs=100 imgsz=640
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# Train a new YOLO11n-OBB model on the custom dataset
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yolo obb train data=your_dataset.yaml model=yolo11n-obb.yaml epochs=100 imgsz=640
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```
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This ensures your model leverages the detailed OBB annotations for improved detection [accuracy](https://www.ultralytics.com/glossary/accuracy).
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@ -142,6 +142,6 @@ Currently, Ultralytics supports the following datasets for OBB training:
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These datasets are tailored for scenarios where OBBs offer a significant advantage, such as aerial and satellite image analysis.
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### Can I use my own dataset with oriented bounding boxes for YOLOv8 training, and if so, how?
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### Can I use my own dataset with oriented bounding boxes for YOLO11 training, and if so, how?
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Yes, you can use your own dataset with oriented bounding boxes for YOLOv8 training. Ensure your dataset annotations are converted to the YOLO OBB format, which involves defining bounding boxes by their four corner points. You can then create a YAML configuration file specifying the dataset paths, classes, and other necessary details. For more information on creating and configuring your datasets, refer to the [Supported Datasets](#supported-datasets) section.
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Yes, you can use your own dataset with oriented bounding boxes for YOLO11 training. Ensure your dataset annotations are converted to the YOLO OBB format, which involves defining bounding boxes by their four corner points. You can then create a YAML configuration file specifying the dataset paths, classes, and other necessary details. For more information on creating and configuring your datasets, refer to the [Supported Datasets](#supported-datasets) section.
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