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Optimize Docs images (#15900)

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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Muhammad Rizwan Munawar 2024-08-30 05:52:10 +05:00 • committed by GitHub
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docs/en/datasets/detect/lvis.md
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@ -20,7 +20,7 @@ The [LVIS dataset](https://www.lvisdataset.org/) is a large-scale, fine-grained
</p>
<p align="center">
<img width="640" src="https://github.com/ultralytics/ultralytics/assets/26833433/40230a80-e7bc-4310-a860-4cc0ef4bb02a" alt="LVIS Dataset example images">
<img width="640" src="https://github.com/ultralytics/docs/releases/download/0/lvis-dataset-example-images.avif" alt="LVIS Dataset example images">
</p>
## Key Features
@ -83,7 +83,7 @@ To train a YOLOv8n model on the LVIS dataset for 100 epochs with an image size o
The LVIS dataset contains a diverse set of images with various object categories and complex scenes. Here are some examples of images from the dataset, along with their corresponding annotations:
![LVIS Dataset sample image](https://github.com/ultralytics/ultralytics/assets/26833433/38cc033a-68b0-47f3-a5b8-4ef554362e40)
![LVIS Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/lvis-mosaiced-training-batch.avif)
- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
@ -154,6 +154,6 @@ Ultralytics YOLO models, including the latest YOLOv8, are optimized for real-tim
Yes, the LVIS dataset includes a variety of images with diverse object categories and complex scenes. Here is an example of a sample image along with its annotations:
![LVIS Dataset sample image](https://github.com/ultralytics/ultralytics/assets/26833433/38cc033a-68b0-47f3-a5b8-4ef554362e40)
![LVIS Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/lvis-mosaiced-training-batch.avif)
This mosaiced image demonstrates a training batch composed of multiple dataset images combined into one. Mosaicing increases the variety of objects and scenes within each training batch, enhancing the model's ability to generalize across different contexts. For more details on the LVIS dataset, explore the [LVIS dataset documentation](#key-features).