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+---
+comments: true
+description: A detailed guide on defining the problem and setting objectives in a computer vision project, highlighting the importance of clear problem statements and measurable objectives.
+keywords: Computer Vision Project, Defining Problems, Setting Objectives, SMART Objectives, Project Scope, How Does Computer Vision Work, Computer Vision Techniques, Computer Vision Basics
+---
+
+# A Practical Guide for Defining Your Computer Vision Project
+
+## Introduction
+
+The first step in any computer vision project is defining what you want to achieve. It's crucial to have a clear roadmap from the start, which includes everything from data collection to deploying your model.
+
+If you need a quick refresher on the basics of a computer vision project, take a moment to read our guide on [the key steps in a computer vision project](./steps-of-a-cv-project.md). It’ll give you a solid overview of the whole process. Once you’re caught up, come back here so we can dive into how exactly you can define and refine the goals for your project.
+
+Now, let’s get to the heart of defining a clear problem statement for your project and exploring the key decisions you’ll need to make along the way.
+
+## Defining A Clear Problem Statement
+
+Setting clear goals and objectives for your project is the first big step toward finding the most effective solutions. Let’s understand how you can clearly define your project’s problem statement:
+
+- **Identify the Core Issue:** Pinpoint the specific challenge your computer vision project aims to solve.
+- **Determine the Scope:** Define the boundaries of your problem.
+- **Consider End Users and Stakeholders:** Identify who will be affected by the solution.
+- **Analyze Project Requirements and Constraints:** Assess available resources (time, budget, personnel) and identify any technical or regulatory constraints.
+
+### Example of a Business Problem Statement
+
+Let’s walk through an example.
+
+Consider a computer vision project where you want to [estimate the speed of vehicles](./speed-estimation.md) on a highway. The core issue is that current speed monitoring methods are inefficient and error-prone due to outdated radar systems and manual processes. The project aims to develop a real-time computer vision system that can replace legacy [speed estimation](https://www.ultralytics.com/blog/ultralytics-yolov8-for-speed-estimation-in-computer-vision-projects) systems.
+
+
+ 
+
+
+Primary users include traffic management authorities and law enforcement, while secondary stakeholders are highway planners and the public benefiting from safer roads. Key requirements involve evaluating budget, time, and personnel, as well as addressing technical needs like high-resolution cameras and real-time data processing. Additionally, regulatory constraints on privacy and data security must be considered.
+
+### Setting Measurable Objectives
+
+Setting measurable objectives is key to the success of a computer vision project. These goals should be clear, achievable, and time-bound.
+
+For example, if you are developing a system to estimate vehicle speeds on a highway. You could consider the following measurable objectives:
+
+- To achieve at least 95% accuracy in speed detection within six months, using a dataset of 10,000 vehicle images.
+- The system should be able to process real-time video feeds at 30 frames per second with minimal delay.
+
+By setting specific and quantifiable goals, you can effectively track progress, identify areas for improvement, and ensure the project stays on course.
+
+## The Connection Between The Problem Statement and The Computer Vision Tasks
+
+Your problem statement helps you conceptualize which computer vision task can solve your issue.
+
+For example, if your problem is monitoring vehicle speeds on a highway, the relevant computer vision task is object tracking. [Object tracking](../modes/track.md) is suitable because it allows the system to continuously follow each vehicle in the video feed, which is crucial for accurately calculating their speeds.
+
+
+ 
+
+
+Other tasks, like [object detection](../tasks/detect.md), are not suitable as they don't provide continuous location or movement information. Once you’ve identified the appropriate computer vision task, it guides several critical aspects of your project, like model selection, dataset preparation, and model training approaches.
+
+## Which Comes First: Model Selection, Dataset Preparation, or Model Training Approach?
+
+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.
+ - **Example**: In a project exploring new methods for detecting manufacturing defects, start with experimenting on a small data subset. Once you find a promising technique, select a model tailored to those findings and prepare a comprehensive dataset.
+
+## Common Discussion Points in the Community
+
+Next, let's look at a few common discussion points in the community regarding computer vision tasks and project planning.
+
+### What Are the Different Computer Vision Tasks?
+
+The most popular computer vision tasks include image classification, object detection, and image segmentation.
+
+
+ 
+
+
+For a detailed explanation of various tasks, please take a look at the Ultralytics Docs page on [YOLOv8 Tasks](../tasks/index.md).
+
+### Can a Pre-trained Model Remember Classes It Knew Before Custom Training?
+
+No, pre-trained models don't "remember" classes in the traditional sense. They learn patterns from massive datasets, and during custom training (fine-tuning), these patterns are adjusted for your specific task. The model's capacity is limited, and focusing on new information can overwrite some previous learnings.
+
+
+ 
+
+
+If you want to use the classes the model was pre-trained on, a practical approach is to use two models: one retains the original performance, and the other is fine-tuned for your specific task. This way, you can combine the outputs of both models. There are also other options like freezing layers, using the pre-trained model as a feature extractor, and task-specific branching, but these are more complex solutions and require more expertise.
+
+### How Do Deployment Options Affect My Computer Vision Project?
+
+[Model deployment options](./model-deployment-options.md) critically impact the performance of your computer vision project. For instance, the deployment environment must handle the computational load of your model. Here are some practical examples:
+
+- **Edge Devices**: Deploying on edge devices like smartphones or IoT devices requires lightweight models due to their limited computational resources. Example technologies include [TensorFlow Lite](../integrations/tflite.md) and [ONNX Runtime](../integrations/onnx.md), which are optimized for such environments.
+- **Cloud Servers**: Cloud deployments can handle more complex models with larger computational demands. Cloud platforms like [AWS](../integrations/amazon-sagemaker.md), Google Cloud, and Azure offer robust hardware options that can scale based on the project’s needs.
+- **On-Premise Servers**: For scenarios requiring high data privacy and security, deploying on-premise might be necessary. This involves significant upfront hardware investment but allows full control over the data and infrastructure.
+- **Hybrid Solutions**: Some projects might benefit from a hybrid approach, where some processing is done on the edge, while more complex analyses are offloaded to the cloud. This can balance performance needs with cost and latency considerations.
+
+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.
+
+
+ 
+
+
+- **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:
+
+### 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.
+
+### Comprehensive Guides and Documentation
+
+- **Ultralytics YOLOv8 Documentation:** Explore the [official YOLOv8 documentation](./index.md) for in-depth guides and valuable tips on various computer vision tasks and projects.
+
+## 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.
diff --git a/docs/en/guides/index.md b/docs/en/guides/index.md
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--- a/docs/en/guides/index.md
+++ b/docs/en/guides/index.md
@@ -42,6 +42,8 @@ Here's a compilation of in-depth guides to help you master different aspects of
- [Edge TPU on Raspberry Pi](coral-edge-tpu-on-raspberry-pi.md): [Google Edge TPU](https://coral.ai/products/accelerator) accelerates YOLO inference on [Raspberry Pi](https://www.raspberrypi.com/).
- [View Inference Images in a Terminal](view-results-in-terminal.md): Use VSCode's integrated terminal to view inference results when using Remote Tunnel or SSH sessions.
- [OpenVINO Latency vs Throughput Modes](optimizing-openvino-latency-vs-throughput-modes.md) - Learn latency and throughput optimization techniques for peak YOLO inference performance.
+- [Steps of a Computer Vision Project ](steps-of-a-cv-project.md) 🚀 NEW: Learn about the key steps involved in a computer vision project, including defining goals, selecting models, preparing data, and evaluating results.
+- [Defining A Computer Vision Project's Goals](defining-project-goals.md) 🚀 NEW: Walk through how to effectively define clear and measurable goals for your computer vision project. Learn the importance of a well-defined problem statement and how it creates a roadmap for your project.
## Real-World Projects
diff --git a/docs/en/guides/steps-of-a-cv-project.md b/docs/en/guides/steps-of-a-cv-project.md
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+---
+comments: true
+description: A guide that walks through the key steps involved in a computer vision project, including steps like defining the problem, data collection, model training, and deployment.
+keywords: Computer Vision Steps, How Does Computer Vision Work, Computer Vision Techniques, Computer Vision Basics, Model Deployment, Data Annotation, Model Evaluation
+---
+
+# Understanding the Key Steps in a Computer Vision Project
+
+## Introduction
+
+Computer vision is a subfield of artificial intelligence (AI) that helps computers see and understand the world like humans do. It processes and analyzes images or videos to extract information, recognize patterns, and make decisions based on that data.
+
+Computer vision techniques like [object detection](../tasks/detect.md), [image classification](../tasks/classify.md), and [instance segmentation](../tasks/segment.md) can be applied across various industries, from [autonomous driving](https://www.ultralytics.com/solutions/ai-in-self-driving) to [medical imaging](https://www.ultralytics.com/solutions/ai-in-healthcare), to gain valuable insights.
+
+
+ 
+
+
+Working on your own computer vision projects is a great way to understand and learn more about computer vision. However, a computer vision project can consist of many steps, and it might seem confusing at first. By the end of this guide, you’ll be familiar with the steps involved in a computer vision project. We’ll walk through everything from the beginning to the end of a project, explaining why each part is important. Let’s get started and make your computer vision project a success!
+
+## An Overview of a Computer Vision Project
+
+Before discussing the details of each step involved in a computer vision project, let's look at the overall process. If you started a computer vision project today, you’d take the following steps:
+
+- Your first priority would be to understand your project’s requirements.
+- Then, you’d collect and accurately label the images that will help train your model.
+- Next, you’d clean your data and apply augmentation techniques to prepare it for model training.
+- After model training, you’d thoroughly test and evaluate your model to make sure it performs consistently under different conditions.
+- Finally, you’d deploy your model into the real world and update it based on new insights and feedback.
+
+
+ .jpeg)
+
+
+Now that we know what to expect, let's dive right into the steps and get your project moving forward.
+
+## Step 1: Defining Your Project’s Goals
+
+The first step in any computer vision project is clearly defining the problem you’re trying to solve. Knowing the end goal helps you start to build a solution. This is especially true when it comes to computer vision because your project’s objective will directly affect which computer vision task you need to focus on.
+
+Here are some examples of project objectives and the computer vision tasks that can be used to reach these objectives:
+
+- **Objective:** To develop a system that can monitor and manage the flow of different vehicle types on highways, improving traffic management and safety.
+ - **Computer Vision Task:** Object detection is ideal for traffic monitoring because it efficiently locates and identifies multiple vehicles. It is less computationally demanding than image segmentation, which provides unnecessary detail for this task, ensuring faster, real-time analysis.
+
+- **Objective:** To develop a tool that assists radiologists by providing precise, pixel-level outlines of tumors in medical imaging scans.
+ - **Computer Vision Task:** Image segmentation is suitable for medical imaging because it provides accurate and detailed boundaries of tumors that are crucial for assessing size, shape, and treatment planning.
+
+- **Objective:** To create a digital system that categorizes various documents (e.g., invoices, receipts, legal paperwork) to improve organizational efficiency and document retrieval.
+ - **Computer Vision Task:** Image classification is ideal here as it handles one document at a time, without needing to consider the document's position in the image. This approach simplifies and accelerates the sorting process.
+
+### Step 1.5: Selecting the Right Model and Training Approach
+
+After understanding the project objective and suitable computer vision tasks, an essential part of defining the project goal is [selecting the right model](../models/index.md) and training approach.
+
+Depending on the objective, you might choose to select the model first or after seeing what data you are able to collect in Step 2. For example, suppose your project is highly dependent on the availability of specific types of data. In that case, it may be more practical to gather and analyze the data first before selecting a model. On the other hand, if you have a clear understanding of the model requirements, you can choose the model first and then collect data that fits those specifications.
+
+Choosing between training from scratch or using transfer learning affects how you prepare your data. Training from scratch requires a diverse dataset to build the model’s understanding from the ground up. Transfer learning, on the other hand, allows you to use a pre-trained model and adapt it with a smaller, more specific dataset. Also, choosing a specific model to train will determine how you need to prepare your data, such as resizing images or adding annotations, according to the model’s specific requirements.
+
+
+ 
+
+
+Note: When choosing a model, consider its [deployment](./model-deployment-options.md) to ensure compatibility and performance. For example, lightweight models are ideal for edge computing due to their efficiency on resource-constrained devices. To learn more about the key points related to defining your project, read [our guide](./defining-project-goals.md) on defining your project’s goals and selecting the right model.
+
+Before getting into the hands-on work of a computer vision project, it's important to have a clear understanding of these details. Double-check that you've considered the following before moving on to Step 2:
+
+- Clearly define the problem you’re trying to solve.
+- Determine the end goal of your project.
+- Identify the specific computer vision task needed (e.g., object detection, image classification, image segmentation).
+- Decide whether to train a model from scratch or use transfer learning.
+- Select the appropriate model for your task and deployment needs.
+
+## Step 2: Data Collection and Data Annotation
+
+The quality of your computer vision models depend on the quality of your dataset. You can either collect images from the internet, take your own pictures, or use pre-existing datasets. Here are some great resources for downloading high-quality datasets: [Google Dataset Search Engine](https://datasetsearch.research.google.com/), [UC Irvine Machine Learning Repository](https://archive.ics.uci.edu/), and [Kaggle Datasets](https://www.kaggle.com/datasets).
+
+Some libraries, like Ultralytics, provide [built-in support for various datasets](../datasets/index.md), making it easier to get started with high-quality data. These libraries often include utilities for using popular datasets seamlessly, which can save you a lot of time and effort in the initial stages of your project.
+
+However, if you choose to collect images or take your own pictures, you’ll need to annotate your data. Data annotation is the process of labeling your data to impart knowledge to your model. The type of data annotation you’ll work with depends on your specific computer vision technique. Here are some examples:
+
+- **Image Classification:** You’ll label the entire image as a single class.
+- **Object Detection:** You’ll draw bounding boxes around each object in the image and label each box.
+- **Image Segmentation:** You’ll label each pixel in the image according to the object it belongs to, creating detailed object boundaries.
+
+
+ 
+
+
+Data annotation can be a time-consuming manual effort. Annotation tools can help make this process easier. Here are some useful open annotation tools: [LabeI Studio](https://github.com/HumanSignal/label-studio), [CVAT](https://github.com/cvat-ai/cvat), and [Labelme](https://github.com/labelmeai/labelme).
+
+## Step 3: Data Augmentation and Splitting Your Dataset
+
+After collecting and annotating your image data, it's important to first split your dataset into training, validation, and test sets before performing data augmentation. Splitting your dataset before augmentation is crucial to test and validate your model on original, unaltered data. It helps accurately assess how well the model generalizes to new, unseen data.
+
+Here's how to split your data:
+
+- **Training Set:** It is the largest portion of your data, typically 70-80% of the total, used to train your model.
+- **Validation Set:** Usually around 10-15% of your data; this set is used to tune hyperparameters and validate the model during training, helping to prevent overfitting.
+- **Test Set:** The remaining 10-15% of your data is set aside as the test set. It is used to evaluate the model's performance on unseen data after training is complete.
+
+After splitting your data, you can perform data augmentation by applying transformations like rotating, scaling, and flipping images to artificially increase the size of your dataset. Data augmentation makes your model more robust to variations and improves its performance on unseen images.
+
+
+ 
+
+
+Libraries like OpenCV, Albumentations, and TensorFlow offer flexible augmentation functions that you can use. Additionally, some libraries, such as Ultralytics, have [built-in augmentation settings](../modes/train.md) directly within its model training function, simplifying the process.
+
+To understand your data better, you can use tools like [Matplotlib](https://matplotlib.org/) or [Seaborn](https://seaborn.pydata.org/) to visualize the images and analyze their distribution and characteristics. Visualizing your data helps identify patterns, anomalies, and the effectiveness of your augmentation techniques. You can also use [Ultralytics Explorer](../datasets/explorer/index.md), a tool for exploring computer vision datasets with semantic search, SQL queries, and vector similarity search.
+
+
+ 
+
+
+By properly understanding, splitting, and augmenting your data, you can develop a well-trained, validated, and tested model that performs well in real-world applications.
+
+## Step 4: Model Training
+
+Once your dataset is ready for training, you can focus on setting up the necessary environment, managing your datasets, and training your model.
+
+First, you’ll need to make sure your environment is configured correctly. Typically, this includes the following:
+
+- Installing essential libraries and frameworks like TensorFlow, PyTorch, or [Ultralytics](../quickstart.md).
+- If you are using a GPU, installing libraries like CUDA and cuDNN will help enable GPU acceleration and speed up the training process.
+
+Then, you can load your training and validation datasets into your environment. Normalize and preprocess the data through resizing, format conversion, or augmentation. With your model selected, configure the layers and specify hyperparameters. Compile the model by setting the loss function, optimizer, and performance metrics.
+
+Libraries like Ultralytics simplify the training process. You can [start training](../modes/train.md) by feeding data into the model with minimal code. These libraries handle weight adjustments, backpropagation, and validation automatically. They also offer tools to monitor progress and adjust hyperparameters easily. After training, save the model and its weights with a few commands.
+
+It's important to keep in mind that proper dataset management is vital for efficient training. Use version control for datasets to track changes and ensure reproducibility. Tools like [DVC (Data Version Control)](../integrations/dvc.md) can help manage large datasets.
+
+## Step 5: Model Evaluation and Model Finetuning
+
+It’s important to assess your model's performance using various metrics and refine it to improve accuracy. [Evaluating](../modes/val.md) helps identify areas where the model excels and where it may need improvement. Fine-tuning ensures the model is optimized for the best possible performance.
+
+- **[Performance Metrics](./yolo-performance-metrics.md):** Use metrics like accuracy, precision, recall, and F1-score to evaluate your model’s performance. These metrics provide insights into how well your model is making predictions.
+- **[Hyperparameter Tuning](./hyperparameter-tuning.md):** Adjust hyperparameters to optimize model performance. Techniques like grid search or random search can help find the best hyperparameter values.
+
+- Fine-Tuning: Make small adjustments to the model architecture or training process to enhance performance. This might involve tweaking learning rates, batch sizes, or other model parameters.
+
+## Step 6: Model Testing
+
+In this step, you can make sure that your model performs well on completely unseen data, confirming its readiness for deployment. The difference between model testing and model evaluation is that it focuses on verifying the final model's performance rather than iteratively improving it.
+
+It's important to thoroughly test and debug any common issues that may arise. Test your model on a separate test dataset that was not used during training or validation. This dataset should represent real-world scenarios to ensure the model's performance is consistent and reliable.
+
+Also, address common problems such as overfitting, underfitting, and data leakage. Use techniques like cross-validation and anomaly detection to identify and fix these issues.
+
+## Step 7: Model Deployment
+
+Once your model has been thoroughly tested, it's time to deploy it. Deployment involves making your model available for use in a production environment. Here are the steps to deploy a computer vision model:
+
+- Setting Up the Environment: Configure the necessary infrastructure for your chosen deployment option, whether it's cloud-based (AWS, Google Cloud, Azure) or edge-based (local devices, IoT).
+
+- **[Exporting the Model](../modes/export.md):** Export your model to the appropriate format (e.g., ONNX, TensorRT, CoreML for YOLOv8) to ensure compatibility with your deployment platform.
+- **Deploying the Model:** Deploy the model by setting up APIs or endpoints and integrating it with your application.
+- **Ensuring Scalability**: Implement load balancers, auto-scaling groups, and monitoring tools to manage resources and handle increasing data and user requests.
+
+## Step 8: Monitoring, Maintenance, and Documentation
+
+Once your model is deployed, it’s important to continuously monitor its performance, maintain it to handle any issues, and document the entire process for future reference and improvements.
+
+Monitoring tools can help you track key performance indicators (KPIs) and detect anomalies or drops in accuracy. By monitoring the model, you can be aware of model drift, where the model's performance declines over time due to changes in the input data. Periodically retrain the model with updated data to maintain accuracy and relevance.
+
+
+ 
+
+
+In addition to monitoring and maintenance, documentation is also key. Thoroughly document the entire process, including model architecture, training procedures, hyperparameters, data preprocessing steps, and any changes made during deployment and maintenance. Good documentation ensures reproducibility and makes future updates or troubleshooting easier. By effectively monitoring, maintaining, and documenting your model, you can ensure it remains accurate, reliable, and easy to manage over its lifecycle.
+
+## FAQs
+
+Here are some common questions that might arise during a computer vision project:
+
+- **Q1:** How do the steps change if I already have a dataset or data when starting a computer vision project?
+ - **A1:** Starting with a pre-existing dataset or data affects the initial steps of your project. In Step 1, along with deciding the computer vision task and model, you’ll also need to explore your dataset thoroughly. Understanding its quality, variety, and limitations will guide your choice of model and training approach. Your approach should align closely with the data's characteristics for more effective outcomes. Depending on your data or dataset, you may be able to skip Step 2 as well.
+
+- **Q2:** I’m not sure what computer vision project to start my AI learning journey with.
+ - **A2:** Check out our [guides on Real-World Projects](./index.md) for inspiration and guidance.
+
+- **Q3:** I don’t want to train a model. I just want to try running a model on an image. How can I do that?
+ - **A3:** You can use a pre-trained model to run predictions on an image without training a new model. Check out the [YOLOv8 predict docs page](../modes/predict.md) for instructions on how to use a pre-trained YOLOv8 model to make predictions on your images.
+
+- **Q4:** Where can I find more detailed articles and updates about computer vision applications and YOLOv8?
+ - **A4:** For more detailed articles, updates, and insights about computer vision applications and YOLOv8, visit the [Ultralytics blog page](https://www.ultralytics.com/blog). The blog covers a wide range of topics and provides valuable information to help you stay updated and improve your projects.
+
+## Engaging with the Community
+
+Connecting with a community of computer vision enthusiasts can help you tackle any issues you face while working on your computer vision project with confidence. Here are some ways to learn, troubleshoot, and network effectively.
+
+### Community Resources
+
+- **GitHub Issues:** Check out the [YOLOv8 GitHub repository](https://github.com/ultralytics/ultralytics/issues) and use the Issues tab to ask questions, report bugs, and suggest new features. The active community and maintainers are there to help with specific issues.
+- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://ultralytics.com/discord/) to interact with other users and developers, get support, and share insights.
+
+### Official Documentation
+
+- **Ultralytics YOLOv8 Documentation:** Explore the [official YOLOv8 documentation](./index.md) for detailed guides with helpful tips on different computer vision tasks and projects.
+
+Using these resources will help you overcome challenges and stay updated with the latest trends and best practices in the computer vision community.
+
+## Kickstart Your Computer Vision Project Today!
+
+Taking on a computer vision project can be exciting and rewarding. By following the steps in this guide, you can build a solid foundation for success. Each step is crucial for developing a solution that meets your objectives and works well in real-world scenarios. As you gain experience, you'll discover advanced techniques and tools to improve your projects. Stay curious, keep learning, and explore new methods and innovations!
diff --git a/mkdocs.yml b/mkdocs.yml
index dd112aad3e..f7489681fe 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -293,6 +293,8 @@ nav:
- Edge TPU on Raspberry Pi: guides/coral-edge-tpu-on-raspberry-pi.md
- Viewing Inference Images in a Terminal: guides/view-results-in-terminal.md
- OpenVINO Latency vs Throughput modes: guides/optimizing-openvino-latency-vs-throughput-modes.md
+ - Steps of a Computer Vision Project: guides/steps-of-a-cv-project.md
+ - Defining A Computer Vision Project's Goals: guides/defining-project-goals.md
- Real-World Projects:
- NEW 🚀 Analytics: guides/analytics.md
- Object Counting: guides/object-counting.md