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Add https://youtu.be/R42s2zFtNIY to hub/datasets.md and CoreML image fix (#8085)

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Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Muhammad Rizwan Munawar 2024-02-07 22:32:13 +05:00 committed by GitHub
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11
docs/en/hub/datasets.md
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@ -10,6 +10,17 @@ keywords: Ultralytics, HUB datasets, YOLO model training, upload datasets, datas
Once uploaded, datasets can be immediately utilized for model training. This integrated approach facilitates a seamless transition from dataset management to model training, significantly simplifying the entire process.
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/R42s2zFtNIY"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> Watch: Upload Datasets to Ultralytics HUB | Complete Walkthrough of Dataset Upload Feature
</p>
## Upload Dataset
Ultralytics HUB datasets are just like YOLOv5 and YOLOv8 🚀 datasets. They use the same structure and the same label formats to keep everything simple.

2
docs/en/integrations/coreml.md
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@ -13,7 +13,7 @@ The CoreML export format allows you to optimize your [Ultralytics YOLOv8](https:
## CoreML
<p align="center">
<img width="100%" src="https://docs-assets.developer.apple.com/published/b8a49e3417/renderedDark2x-1638462887.png" alt="CoreML Overview">
<img width="100%" src="https://github.com/RizwanMunawar/ultralytics/assets/62513924/0c757e32-3a9f-422e-9526-efde5f663ccd" alt="CoreML Overview">
</p>
[CoreML](https://developer.apple.com/documentation/coreml) is Apple's foundational machine learning framework that builds upon Accelerate, BNNS, and Metal Performance Shaders. It provides a machine-learning model format that seamlessly integrates into iOS applications and supports tasks such as image analysis, natural language processing, audio-to-text conversion, and sound analysis.

4
docs/en/integrations/gradio.md
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@ -16,7 +16,9 @@ This Gradio interface provides an easy and interactive way to perform object det
* **Real-Time Adjustments:** Parameters such as confidence and IoU thresholds can be adjusted on the fly, allowing for immediate feedback and optimization of detection results.
* **Broad Accessibility:** The Gradio web interface can be accessed by anyone, making it an excellent tool for demonstrations, educational purposes, and quick experiments.
<img width="800" alt="Gradio example screenshot" src="https://github.com/WangQvQ/ultralytics/assets/58406737/5d906f10-fd62-4bcc-8856-ef3233102c1d">
<p align="center">
<img width="800" alt="Gradio example screenshot" src="https://github.com/RizwanMunawar/ultralytics/assets/26833433/52ee3cd2-ac59-4c27-9084-0fd05c6c33be">
</p>
## How to Install the Gradio