Update computer-vision-augmentation-guide.html

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@@ -123,7 +123,7 @@ <h1 class="fw-bolder mb-1">Computer Vision Augmentation: A Practical Guide for A
 
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-                <div class="container"><div class="row"><div class="col-lg-10 mx-auto"><p class="fs-5 mb-4">In the rapidly evolving world of Artificial Intelligence (AI), Computer Vision stands out as a key technology enabling machines to "see" and interpret images and videos. The performance of computer vision models heavily relies on the quantity and quality of training data. However, acquiring large, diverse, and accurately labeled datasets can be challenging and expensive. That's where data augmentation techniques come to the rescue.</p><p class="fs-5 mb-4">Computer vision augmentations are techniques used to artificially expand the size of a training dataset by creating modified versions of existing images. These modifications can include transformations like flipping, rotating, cropping, changing brightness, adding noise, and more. By introducing these variations, we can significantly improve the robustness and generalization ability of computer vision models, allowing them to perform well on unseen data and in challenging real-world scenarios.</p><p class="fs-5 mb-4">This blog post will delve into the world of computer vision augmentations, exploring various techniques and demonstrating how they can be used to enhance the performance of your AI models. We'll cover both basic image-level augmentations and more advanced bounding box-level augmentations. Let's dive in!</p><nav><ul class="fs-5 mb-4" style="list-style: none; padding-left: 0;"><li><a href="#table-of-contents" class="text-body-emphasis fw-bold">Table of Contents</a></li><li><a href="#explore-computer-vision-augmentations" class="text-body-emphasis fw-bold">Explore Computer Vision Augmentations</a></li><li><a href="#flip" class="text-body-emphasis fw-bold">Flip (horizontal or vertical)</a></li><li><a href="#rotation" class="text-body-emphasis fw-bold">Rotation</a></li><li><a href="#brightness" class="text-body-emphasis fw-bold">Brightness</a></li><li><a href="#contrast" class="text-body-emphasis fw-bold">Contrast</a></li><li><a href="#grayscale" class="text-body-emphasis fw-bold">Grayscale</a></li><li><a href="#random-crop" class="text-body-emphasis fw-bold">Random Crop</a></li><li><a href="#random-noise" class="text-body-emphasis fw-bold">Random Noise</a></li><li><a href="#blur" class="text-body-emphasis fw-bold">Blur</a></li><li><a href="#bounding-box-level-augmentations" class="text-body-emphasis fw-bold">Bounding Box Level Augmentations</a></li><li><a href="#mosaic" class="text-body-emphasis fw-bold">Mosaic</a></li><li><a href="#how-to-use-computer-vision-augmentations" class="text-body-emphasis fw-bold">How to Use Computer Vision Augmentations</a></li></ul></nav></div></div></div>
+                <div class="container"><div class="row"><div class="col-lg-10 mx-auto"><p class="fs-5 mb-4">In the rapidly evolving world of Artificial Intelligence (AI), Computer Vision stands out as a key technology enabling machines to "see" and interpret images and videos. The performance of computer vision models heavily relies on the quantity and quality of training data. However, acquiring large, diverse, and accurately labeled datasets can be challenging and expensive. That's where data augmentation techniques come to the rescue.</p><p class="fs-5 mb-4">Computer vision augmentations are techniques used to artificially expand the size of a training dataset by creating modified versions of existing images. These modifications can include transformations like flipping, rotating, cropping, changing brightness, adding noise, and more. By introducing these variations, we can significantly improve the robustness and generalization ability of computer vision models, allowing them to perform well on unseen data and in challenging real-world scenarios.</p><p class="fs-5 mb-4">This blog post will delve into the world of computer vision augmentations, exploring various techniques and demonstrating how they can be used to enhance the performance of your AI models. We'll cover both basic image-level augmentations and more advanced bounding box-level augmentations. Let's dive in!</p></div></div></div>
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             <h2 class="fw-bolder mb-4 mt-5">Explore Computer Vision Augmentations</h2>