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Docs Prettier reformat (#13483)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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docs/en/guides/defining-project-goals.md
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@ -63,9 +63,11 @@ Other tasks, like [object detection](../tasks/detect.md), are not suitable as th
The order of model selection, dataset preparation, and training approach depends on the specifics of your project. Here are a few tips to help you decide:
- **Clear Understanding of the Problem**: If your problem and objectives are well-defined, start with model selection. Then, prepare your dataset and decide on the training approach based on the model's requirements.
- **Example**: Start by selecting a model for a traffic monitoring system that estimates vehicle speeds. Choose an object tracking model, gather and annotate highway videos, and then train the model with techniques for real-time video processing.
- **Unique or Limited Data**: If your project is constrained by unique or limited data, begin with dataset preparation. For instance, if you have a rare dataset of medical images, annotate and prepare the data first. Then, select a model that performs well on such data, followed by choosing a suitable training approach.
- **Example**: Prepare the data first for a facial recognition system with a small dataset. Annotate it, then select a model that works well with limited data, such as a pre-trained model for transfer learning. Finally, decide on a training approach, including data augmentation, to expand the dataset.
- **Need for Experimentation**: In projects where experimentation is crucial, start with the training approach. This is common in research projects where you might initially test different training techniques. Refine your model selection after identifying a promising method and prepare the dataset based on your findings.
@ -118,6 +120,7 @@ Here are some questions that might encounter while defining your computer vision
</p>
- **Q2:** Can the scope of a computer vision project change after the problem statement is defined?
- **A2:** Yes, the scope of a computer vision project can change as new information becomes available or as project requirements evolve. It's important to regularly review and adjust the problem statement and objectives to reflect any new insights or changes in project direction.
- **Q3:** What are some common challenges in defining the problem for a computer vision project?
@ -130,7 +133,7 @@ Connecting with other computer vision enthusiasts can be incredibly helpful for
### Community Support Channels
- **GitHub Issues:** Head over to the YOLOv8 GitHub repository. You can use the [Issues tab](https://github.com/ultralytics/ultralytics/issues) to raise questions, report bugs, and suggest features. The community and maintainers can assist with specific problems you encounter.
- **Ultralytics Discord Server:** Become part of the [Ultralytics Discord server](https://ultralytics.com/discord/). Connect with fellow users and developers, seek support, exchange knowledge, and discuss ideas.
- **Ultralytics Discord Server:** Become part of the [Ultralytics Discord server](https://ultralytics.com/discord/). Connect with fellow users and developers, seek support, exchange knowledge, and discuss ideas.
### Comprehensive Guides and Documentation