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---
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comments: true
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description: Dive into Google's Open Images V7, a comprehensive dataset offering a broad scope for computer vision research. Understand its usage with deep learning models.
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keywords: Open Images V7, object detection, segmentation masks, visual relationships, localized narratives, computer vision, deep learning, annotations, bounding boxes
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---
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# Open Images V7 Dataset
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[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.
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## Open Images V7 Pretrained Models
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| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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|-------------------------------------------------------------------------------------------|-----------------------|----------------------|--------------------------------|-------------------------------------|--------------------|-------------------|
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| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8n-oiv7.pt) | 640 | 18.4 | 142.4 | 1.21 | 3.5 | 10.5 |
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| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8s-oiv7.pt) | 640 | 27.7 | 183.1 | 1.40 | 11.4 | 29.7 |
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| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8m-oiv7.pt) | 640 | 33.6 | 408.5 | 2.26 | 26.2 | 80.6 |
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| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8l-oiv7.pt) | 640 | 34.9 | 596.9 | 2.43 | 44.1 | 167.4 |
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| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8x-oiv7.pt) | 640 | 36.3 | 860.6 | 3.56 | 68.7 | 260.6 |
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## Key Features
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- Encompasses ~9M images annotated in various ways to suit multiple computer vision tasks.
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- 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.
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- Visual relationship annotations totaling 3.3M are available, detailing 1,466 unique relationship triplets, object properties, and human activities.
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- V5 introduced segmentation masks for 2.8M objects across 350 classes.
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- V6 introduced 675k localized narratives that amalgamate voice, text, and mouse traces highlighting described objects.
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- V7 introduced 66.4M point-level labels on 1.4M images, spanning 5,827 classes.
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- Encompasses 61.4M image-level labels across a diverse set of 20,638 classes.
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- Provides a unified platform for image classification, object detection, relationship detection, instance segmentation, and multimodal image descriptions.
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## Dataset Structure
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Open Images V7 is structured in multiple components catering to varied computer vision challenges:
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- **Images**: About 9 million images, often showcasing intricate scenes with an average of 8.3 objects per image.
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- **Bounding Boxes**: Over 16 million boxes that demarcate objects across 600 categories.
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- **Segmentation Masks**: These detail the exact boundary of 2.8M objects across 350 classes.
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- **Visual Relationships**: 3.3M annotations indicating object relationships, properties, and actions.
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- **Localized Narratives**: 675k descriptions combining voice, text, and mouse traces.
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- **Point-Level Labels**: 66.4M labels across 1.4M images, suitable for zero/few-shot semantic segmentation.
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## Applications
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Open Images V7 is a cornerstone for training and evaluating state-of-the-art models in various computer vision tasks. The dataset's broad scope and high-quality annotations make it indispensable for researchers and developers specializing in computer vision.
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## Dataset YAML
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Typically, datasets come with a YAML (Yet Another Markup Language) file that delineates the dataset's configuration. For the case of Open Images V7, a hypothetical `OpenImagesV7.yaml` might exist. For accurate paths and configurations, one should refer to the dataset's official repository or documentation.
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!!! Example "OpenImagesV7.yaml"
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```yaml
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--8<-- "ultralytics/cfg/datasets/open-images-v7.yaml"
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```
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## Usage
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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.
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!!! Warning
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The complete Open Images V7 dataset comprises 1,743,042 training images and 41,620 validation images, requiring approximately **561 GB of storage space** upon download.
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Executing the commands provided below will trigger an automatic download of the full dataset if it's not already present locally. Before running the below example it's crucial to:
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- Verify that your device has enough storage capacity.
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- Ensure a robust and speedy internet connection.
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!!! Example "Train Example"
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a COCO-pretrained YOLOv8n model
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model = YOLO('yolov8n.pt')
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# Train the model on the Open Images V7 dataset
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results = model.train(data='open-images-v7.yaml', epochs=100, imgsz=640)
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```
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=== "CLI"
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```bash
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# Train a COCO-pretrained YOLOv8n model on the Open Images V7 dataset
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yolo detect train data=open-images-v7.yaml model=yolov8n.pt epochs=100 imgsz=640
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```
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## Sample Data and Annotations
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Illustrations of the dataset help provide insights into its richness:
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- **Open Images V7**: This image exemplifies the depth and detail of annotations available, including bounding boxes, relationships, and segmentation masks.
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Researchers can gain invaluable insights into the array of computer vision challenges that the dataset addresses, from basic object detection to intricate relationship identification.
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## Citations and Acknowledgments
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For those employing Open Images V7 in their work, it's prudent to cite the relevant papers and acknowledge the creators:
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!!! Quote ""
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=== "BibTeX"
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```bibtex
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@article{OpenImages,
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author = {Alina Kuznetsova and Hassan Rom and Neil Alldrin and Jasper Uijlings and Ivan Krasin and Jordi Pont-Tuset and Shahab Kamali and Stefan Popov and Matteo Malloci and Alexander Kolesnikov and Tom Duerig and Vittorio Ferrari},
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title = {The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale},
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year = {2020},
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journal = {IJCV}
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}
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```
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A heartfelt acknowledgment goes out to the Google AI team for creating and maintaining the Open Images V7 dataset. For a deep dive into the dataset and its offerings, navigate to the [official Open Images V7 website](https://storage.googleapis.com/openimages/web/index.html).
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