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110 lines
6.0 KiB
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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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## 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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