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After [training](./model-training-tips.md) and [evaluating](./model-evaluation-insights.md) your model, it's time to test it. Model testing involves assessing how well it performs in real-world scenarios. Testing considers factors like accuracy, reliability, fairness, and how easy it is to understand the model's decisions. The goal is to make sure the model performs as intended, delivers the expected results, and fits into the [overall objective of your application](./defining-project-goals.md) or project.
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+ Watch: How to Test Machine Learning Models | Avoid Data Leakage in Computer Vision 🚀
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Model testing is quite similar to model evaluation, but they are two distinct [steps in a computer vision project](./steps-of-a-cv-project.md). Model evaluation involves metrics and plots to assess the model's accuracy. On the other hand, model testing checks if the model's learned behavior is the same as expectations. In this guide, we'll explore strategies for testing your [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models.
## Model Testing Vs. Model Evaluation
diff --git a/docs/en/solutions/index.md b/docs/en/solutions/index.md
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+ Watch: How to Run Ultralytics Solutions from the Command Line (CLI) | Ultralytics YOLO11 🚀
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## Solutions
Here's our curated list of Ultralytics solutions that can be used to create awesome [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) projects.