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Muhammad Rizwan Munawar 2024-05-02 18:31:09 +05:00 committed by GitHub
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@ -8,6 +8,17 @@ keywords: Ultralytics, LVIS dataset, object detection, YOLO, YOLO model training
The [LVIS dataset](https://www.lvisdataset.org/) is a large-scale, fine-grained vocabulary-level annotation dataset developed and released by Facebook AI Research (FAIR). It is primarily used as a research benchmark for object detection and instance segmentation with a large vocabulary of categories, aiming to drive further advancements in computer vision field.
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<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/cfTKj96TjSE"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen>
</iframe>
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<strong>Watch:</strong> YOLO World training workflow with LVIS dataset
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<p align="center">
<img width="640" src="https://github.com/ultralytics/ultralytics/assets/26833433/40230a80-e7bc-4310-a860-4cc0ef4bb02a" alt="LVIS Dataset example images">
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