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docs/en/datasets/detect/objects365.md
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@ -24,7 +24,7 @@ The Objects365 dataset is organized into a single set of images with correspondi
## Applications
The Objects365 dataset is widely used for training and evaluating deep learning models in object detection tasks. The dataset's diverse set of object categories and high-quality annotations make it a valuable resource for researchers and practitioners in the field of computer vision.
The Objects365 dataset is widely used for training and evaluating deep learning models in object detection tasks. The dataset's diverse set of object categories and high-quality annotations make it a valuable resource for researchers and practitioners in the field of [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv).
## Dataset YAML
@ -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 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 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.
!!! example "Train Example"
@ -63,7 +63,7 @@ To train a YOLOv8n model on the Objects365 dataset for 100 epochs with an image
## Sample Data and Annotations
The Objects365 dataset contains a diverse set of high-resolution images with objects from 365 categories, providing rich context for object detection tasks. Here are some examples of the images in the dataset:
The Objects365 dataset contains a diverse set of high-resolution images with objects from 365 categories, providing rich context for [object detection](https://www.ultralytics.com/glossary/object-detection) tasks. Here are some examples of the images in the dataset:
![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/objects365-sample-image.avif)
@ -95,7 +95,7 @@ We would like to acknowledge the team of researchers who created and maintain th
### What is the Objects365 dataset used for?
The [Objects365 dataset](https://www.objects365.org/) is designed for object detection tasks in machine learning 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.
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?
@ -138,4 +138,4 @@ The YAML configuration file for the Objects365 dataset is available at [Objects3
### How does the dataset structure of Objects365 enhance object detection modeling?
The [Objects365 dataset](https://www.objects365.org/) is organized with 2 million high-resolution images and comprehensive annotations of over 30 million bounding boxes. This structure ensures a robust dataset for training deep learning models in object detection, offering a wide variety of objects and scenarios. Such diversity and volume help in developing models that are more accurate and capable of generalizing well to real-world applications. For more details on the dataset structure, refer to the [Dataset YAML](#dataset-yaml) section.
The [Objects365 dataset](https://www.objects365.org/) is organized with 2 million high-resolution images and comprehensive annotations of over 30 million bounding boxes. This structure ensures a robust dataset for training [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in object detection, offering a wide variety of objects and scenarios. Such diversity and volume help in developing models that are more accurate and capable of generalizing well to real-world applications. For more details on the dataset structure, refer to the [Dataset YAML](#dataset-yaml) section.