@ -10,6 +10,17 @@ keywords: Model Evaluation, Machine Learning Model Evaluation, Fine Tuning Machi
Once you've [trained](./model-training-tips.md) your computer vision model, evaluating and refining it to perform optimally is essential. Just training your model isn't enough. You need to make sure that your model is accurate, efficient, and fulfills the [objective](./defining-project-goals.md) of your computer vision project. By evaluating and fine-tuning your model, you can identify weaknesses, improve its accuracy, and boost overall performance.
<strong>Watch:</strong> Insights into Model Evaluation and Fine-Tuning | Tips for Improving Mean Average Precision
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In this guide, we'll share insights on model evaluation and fine-tuning that'll make this [step of a computer vision project](./steps-of-a-cv-project.md) more approachable. We'll discuss how to understand evaluation metrics and implement fine-tuning techniques, giving you the knowledge to elevate your model's capabilities.
@ -13,28 +13,16 @@ Pose estimation is a task that involves identifying the location of specific poi
The output of a pose estimation model is a set of points that represent the keypoints on an object in the image, usually along with the confidence scores for each point. Pose estimation is a good choice when you need to identify specific parts of an object in a scene, and their location in relation to each other.
Muhammad Rizwan Munawar
7 hours ago
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GitHub