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docs/en/datasets/detect/open-images-v7.md
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@ -6,7 +6,7 @@ keywords: Open Images V7, Google dataset, computer vision, YOLOv8 models, object
# Open Images V7 Dataset
[Open Images V7](https://storage.googleapis.com/openimages/web/index.html) is a versatile and expansive dataset championed by Google. Aimed at propelling research in the realm of computer vision, it boasts a vast collection of images annotated with a plethora of data, including image-level labels, object bounding boxes, object segmentation masks, visual relationships, and localized narratives.
[Open Images V7](https://storage.googleapis.com/openimages/web/index.html) is a versatile and expansive dataset championed by Google. Aimed at propelling research in the realm of [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv), it boasts a vast collection of images annotated with a plethora of data, including image-level labels, object bounding boxes, object segmentation masks, visual relationships, and localized narratives.
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@ -16,7 +16,7 @@ keywords: Open Images V7, Google dataset, computer vision, YOLOv8 models, object
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<br>
<strong>Watch:</strong> Object Detection using OpenImagesV7 Pretrained Model
<strong>Watch:</strong> [Object Detection](https://www.ultralytics.com/glossary/object-detection) using OpenImagesV7 Pretrained Model
</p>
## Open Images V7 Pretrained Models
@ -34,13 +34,13 @@ keywords: Open Images V7, Google dataset, computer vision, YOLOv8 models, object
## Key Features
- Encompasses ~9M images annotated in various ways to suit multiple computer vision tasks.
- Houses a staggering 16M bounding boxes across 600 object classes in 1.9M images. These boxes are primarily hand-drawn by experts ensuring high precision.
- Houses a staggering 16M bounding boxes across 600 object classes in 1.9M images. These boxes are primarily hand-drawn by experts ensuring high [precision](https://www.ultralytics.com/glossary/precision).
- Visual relationship annotations totaling 3.3M are available, detailing 1,466 unique relationship triplets, object properties, and human activities.
- V5 introduced segmentation masks for 2.8M objects across 350 classes.
- V6 introduced 675k localized narratives that amalgamate voice, text, and mouse traces highlighting described objects.
- V7 introduced 66.4M point-level labels on 1.4M images, spanning 5,827 classes.
- Encompasses 61.4M image-level labels across a diverse set of 20,638 classes.
- Provides a unified platform for image classification, object detection, relationship detection, instance segmentation, and multimodal image descriptions.
- Provides a unified platform for image classification, object detection, relationship detection, [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation), and multimodal image descriptions.
## Dataset Structure
@ -51,7 +51,7 @@ Open Images V7 is structured in multiple components catering to varied computer
- **Segmentation Masks**: These detail the exact boundary of 2.8M objects across 350 classes.
- **Visual Relationships**: 3.3M annotations indicating object relationships, properties, and actions.
- **Localized Narratives**: 675k descriptions combining voice, text, and mouse traces.
- **Point-Level Labels**: 66.4M labels across 1.4M images, suitable for zero/few-shot semantic segmentation.
- **Point-Level Labels**: 66.4M labels across 1.4M images, suitable for zero/few-shot [semantic segmentation](https://www.ultralytics.com/glossary/semantic-segmentation).
## Applications
@ -69,7 +69,7 @@ Typically, datasets come with a YAML (Yet Another Markup Language) file that del
## Usage
To train a YOLOv8n model on the Open Images V7 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 Open Images V7 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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@ -191,10 +191,10 @@ Ultralytics provides several YOLOv8 pretrained models for the Open Images V7 dat
The Open Images V7 dataset supports a variety of computer vision tasks including:
- **Image Classification**
- **[Image Classification](https://www.ultralytics.com/glossary/image-classification)**
- **Object Detection**
- **Instance Segmentation**
- **Visual Relationship Detection**
- **Multimodal Image Descriptions**
Its comprehensive annotations and broad scope make it suitable for training and evaluating advanced machine learning models, as highlighted in practical use cases detailed in our [applications](#applications) section.
Its comprehensive annotations and broad scope make it suitable for training and evaluating advanced [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) models, as highlighted in practical use cases detailed in our [applications](#applications) section.