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Add Discourse at https://community.ultralytics.com (#14231)

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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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@ -108,24 +108,6 @@ If you want to use the classes the model was pre-trained on, a practical approac
Each deployment option offers different benefits and challenges, and the choice depends on specific project requirements like performance, cost, and security.
## FAQs
Here are some questions that might encounter while defining your computer vision project:
- **Q1:** How do I set effective and measurable objectives for my computer vision project?
- **A1:** To set effective and measurable objectives, follow the SMART criteria: Specific, Measurable, Achievable, Relevant, and Time-bound. Define what success looks like, how it will be measured, ensure the goals are attainable with available resources, align them with broader project aims, and set a deadline.
<p align="center">
<img width="100%" src="https://ugc.futurelearn.com/uploads/assets/58/fa/58fa21ca-e284-4086-a1ce-58689ea49982.png" alt="Overview of SMART criteria">
</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?
- **A3:** Common challenges include vague or overly broad problem statements, unrealistic objectives, lack of stakeholder alignment, insufficient understanding of technical constraints, and underestimating data requirements. Addressing these challenges requires thorough initial research, clear communication with stakeholders, and iterative refinement of the problem statement and objectives.
## Connecting with the Community
Connecting with other computer vision enthusiasts can be incredibly helpful for your projects by providing support, solutions, and new ideas. Here are some great ways to learn, troubleshoot, and network:
@ -142,3 +124,55 @@ Connecting with other computer vision enthusiasts can be incredibly helpful for
## Conclusion
Defining a clear problem and setting measurable goals is key to a successful computer vision project. We've highlighted the importance of being clear and focused from the start. Having specific goals helps avoid oversight. Also, staying connected with others in the community through platforms like GitHub or Discord is important for learning and staying current. In short, good planning and engaging with the community is a huge part of successful computer vision projects.
## FAQ
### How do I define a clear problem statement for my Ultralytics computer vision project?
To define a clear problem statement for your Ultralytics computer vision project, follow these steps:
1. **Identify the Core Issue:** Pinpoint the specific challenge your project aims to solve.
2. **Determine the Scope:** Clearly outline the boundaries of your problem.
3. **Consider End Users and Stakeholders:** Identify who will be affected by your solution.
4. **Analyze Project Requirements and Constraints:** Assess available resources and any technical or regulatory limitations.
Providing a well-defined problem statement ensures that the project remains focused and aligned with your objectives. For a detailed guide, refer to our [practical guide](#defining-a-clear-problem-statement).
### Why should I use Ultralytics YOLOv8 for speed estimation in my computer vision project?
Ultralytics YOLOv8 is ideal for speed estimation because of its real-time object tracking capabilities, high accuracy, and robust performance in detecting and monitoring vehicle speeds. It overcomes inefficiencies and inaccuracies of traditional radar systems by leveraging cutting-edge computer vision technology. Check out our blog on [speed estimation using YOLOv8](https://www.ultralytics.com/blog/ultralytics-yolov8-for-speed-estimation-in-computer-vision-projects) for more insights and practical examples.
### How do I set effective measurable objectives for my computer vision project with Ultralytics YOLOv8?
Set effective and measurable objectives using the SMART criteria:
- **Specific:** Define clear and detailed goals.
- **Measurable:** Ensure objectives are quantifiable.
- **Achievable:** Set realistic targets within your capabilities.
- **Relevant:** Align objectives with your overall project goals.
- **Time-bound:** Set deadlines for each objective.
For example, "Achieve 95% accuracy in speed detection within six months using a 10,000 vehicle image dataset." This approach helps track progress and identifies areas for improvement. Read more about [setting measurable objectives](#setting-measurable-objectives).
### How do deployment options affect the performance of my Ultralytics YOLO models?
Deployment options critically impact the performance of your Ultralytics YOLO models. Here are key options:
- **Edge Devices:** Use lightweight models like TensorFlow Lite or ONNX Runtime for deployment on devices with limited resources.
- **Cloud Servers:** Utilize robust cloud platforms like AWS, Google Cloud, or Azure for handling complex models.
- **On-Premise Servers:** High data privacy and security needs may require on-premise deployments.
- **Hybrid Solutions:** Combine edge and cloud approaches for balanced performance and cost-efficiency.
For more information, refer to our [detailed guide on model deployment options](./model-deployment-options.md).
### What are the most common challenges in defining the problem for a computer vision project with Ultralytics?
Common challenges include:
- Vague or overly broad problem statements.
- Unrealistic objectives.
- Lack of stakeholder alignment.
- Insufficient understanding of technical constraints.
- Underestimating data requirements.
Address these challenges through thorough initial research, clear communication with stakeholders, and iterative refinement of the problem statement and objectives. Learn more about these challenges [here](#common-challenges).