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Update YOLO11 Actions and Docs (#16596)

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docs/en/datasets/detect/objects365.md
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---
comments: true
description: Explore the Objects365 Dataset with 2M images and 30M bounding boxes across 365 categories. Enhance your object detection models with diverse, high-quality data.
keywords: Objects365 dataset, object detection, machine learning, deep learning, computer vision, annotated images, bounding boxes, YOLOv8, high-resolution images, dataset configuration
keywords: Objects365 dataset, object detection, machine learning, deep learning, computer vision, annotated images, bounding boxes, YOLO11, high-resolution images, dataset configuration
---
# Objects365 Dataset
@ -38,7 +38,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
## Usage
To train a YOLOv8n model on the Objects365 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 model on the Objects365 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"
@ -48,7 +48,7 @@ To train a YOLOv8n model on the Objects365 dataset for 100 [epochs](https://www.
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="Objects365.yaml", epochs=100, imgsz=640)
@ -58,7 +58,7 @@ To train a YOLOv8n model on the Objects365 dataset for 100 [epochs](https://www.
```bash
# Start training from a pretrained *.pt model
yolo detect train data=Objects365.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=Objects365.yaml model=yolo11n.pt epochs=100 imgsz=640
```
## Sample Data and Annotations
@ -97,9 +97,9 @@ We would like to acknowledge the team of researchers who created and maintain th
The [Objects365 dataset](https://www.objects365.org/) is designed for object detection tasks in [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and computer vision. It provides a large-scale, high-quality dataset with 2 million annotated images and 30 million bounding boxes across 365 categories. Leveraging such a diverse dataset helps improve the performance and generalization of object detection models, making it invaluable for research and development in the field.
### How can I train a YOLOv8 model on the Objects365 dataset?
### How can I train a YOLO11 model on the Objects365 dataset?
To train a YOLOv8n model using the Objects365 dataset for 100 epochs with an image size of 640, follow these instructions:
To train a YOLO11n model using the Objects365 dataset for 100 epochs with an image size of 640, follow these instructions:
!!! example "Train Example"
@ -109,7 +109,7 @@ To train a YOLOv8n model using the Objects365 dataset for 100 epochs with an ima
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="Objects365.yaml", epochs=100, imgsz=640)
@ -119,7 +119,7 @@ To train a YOLOv8n model using the Objects365 dataset for 100 epochs with an ima
```bash
# Start training from a pretrained *.pt model
yolo detect train data=Objects365.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=Objects365.yaml model=yolo11n.pt epochs=100 imgsz=640
```
Refer to the [Training](../../modes/train.md) page for a comprehensive list of available arguments.