diff --git a/.github/workflows/greetings.yml b/.github/workflows/greetings.yml index 00e0f9da237..ec84cf1779e 100644 --- a/.github/workflows/greetings.yml +++ b/.github/workflows/greetings.yml @@ -30,7 +30,7 @@ jobs: If this is a 🐛 Bug Report, please provide a [minimum reproducible example](https://docs.ultralytics.com/help/minimum_reproducible_example/) to help us debug it. - If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our [Tips for Best Training Results](https://docs.ultralytics.com/yolov5/tutorials/tips_for_best_training_results/). + If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our [Tips for Best Training Results](https://docs.ultralytics.com/guides/model-training-tips//). Join the vibrant [Ultralytics Discord](https://ultralytics.com/discord) 🎧 community for real-time conversations and collaborations. This platform offers a perfect space to inquire, showcase your work, and connect with fellow Ultralytics users. diff --git a/README.md b/README.md index 32ba8d4a50c..a2e818679f0 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,6 @@
Ultralytics CI - Ultralytics Code Coverage Ultralytics YOLOv8 Citation Ultralytics Docker Pulls Ultralytics Discord diff --git a/README.zh-CN.md b/README.zh-CN.md index 6a10506f1ec..8e9e4e65fc6 100644 --- a/README.zh-CN.md +++ b/README.zh-CN.md @@ -8,10 +8,10 @@
Ultralytics CI - Ultralytics Code Coverage YOLOv8 Citation Docker Pulls Discord + Ultralytics Forums
Run on Gradient Open In Colab diff --git a/docs/README.md b/docs/README.md index 9d84419bab3..2b6209eaa54 100644 --- a/docs/README.md +++ b/docs/README.md @@ -10,6 +10,7 @@ [![Check Domains](https://github.com/ultralytics/docs/actions/workflows/check_domains.yml/badge.svg)](https://github.com/ultralytics/docs/actions/workflows/check_domains.yml) [![Ultralytics Actions](https://github.com/ultralytics/docs/actions/workflows/format.yml/badge.svg)](https://github.com/ultralytics/docs/actions/workflows/format.yml) [![Discord](https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue)](https://ultralytics.com/discord) +[![Forums](https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue)](https://community.ultralytics.com) ## 🛠️ Installation diff --git a/docs/en/guides/data-collection-and-annotation.md b/docs/en/guides/data-collection-and-annotation.md index 3aad44beaa8..58a14ac93ce 100644 --- a/docs/en/guides/data-collection-and-annotation.md +++ b/docs/en/guides/data-collection-and-annotation.md @@ -130,26 +130,6 @@ If you are working with multiple people, consistency between different annotator While reviewing, if you find errors, correct them and update the guidelines to avoid future mistakes. Provide feedback to annotators and offer regular training to help reduce errors. Having a strong process for handling errors keeps your dataset accurate and reliable. -## FAQs - -Here are some questions that might encounter while collecting and annotating data: - -- **Q1:** What is active learning in the context of data annotation? - - - **A1:** Active learning in data annotation is a technique where a machine learning model iteratively selects the most informative data points for labeling. This improves the model's performance with fewer labeled examples. By focusing on the most valuable data, active learning accelerates the training process and improves the model's ability to generalize from limited data. - -

- Overview of Active Learning -

- -- **Q2:** How does automated annotation work? - - - **A2:** Automated annotation uses pre-trained models and algorithms to label data without needing human effort. These models, which have been trained on large datasets, can identify patterns and features in new data. Techniques like transfer learning adjust these models for specific tasks, and active learning helps by selecting the most useful data points for labeling. However, this approach is only possible in certain cases where the model has been trained on sufficiently similar data and tasks. - -- **Q3:** How many images do I need to collect for [YOLOv8 custom training](../modes/train.md)? - - - **A3:** For transfer learning and object detection, a good general rule of thumb is to have a minimum of a few hundred annotated objects per class. However, when training a model to detect just one class, it is advisable to start with at least 100 annotated images and train for around 100 epochs. For complex tasks, you may need thousands of images per class to achieve reliable model performance. - ## Share Your Thoughts with the Community Bouncing your ideas and queries off other computer vision enthusiasts can help accelerate your projects. Here are some great ways to learn, troubleshoot, and network: @@ -166,3 +146,38 @@ Bouncing your ideas and queries off other computer vision enthusiasts can help a ## Conclusion By following the best practices for collecting and annotating data, avoiding bias, and using the right tools and techniques, you can significantly improve your model's performance. Engaging with the community and using available resources will keep you informed and help you troubleshoot issues effectively. Remember, quality data is the foundation of a successful project, and the right strategies will help you build robust and reliable models. + +## FAQ + +### What is the best way to avoid bias in data collection for computer vision projects? + +Avoiding bias in data collection ensures that your computer vision model performs well across various scenarios. To minimize bias, consider collecting data from diverse sources to capture different perspectives and scenarios. Ensure balanced representation among all relevant groups, such as different ages, genders, and ethnicities. Regularly review and update your dataset to identify and address any emerging biases. Techniques such as oversampling underrepresented classes, data augmentation, and fairness-aware algorithms can also help mitigate bias. By employing these strategies, you maintain a robust and fair dataset that enhances your model's generalization capability. + +### How can I ensure high consistency and accuracy in data annotation? + +Ensuring high consistency and accuracy in data annotation involves establishing clear and objective labeling guidelines. Your instructions should be detailed, with examples and illustrations to clarify expectations. Consistency is achieved by setting standard criteria for annotating various data types, ensuring all annotations follow the same rules. To reduce personal biases, train annotators to stay neutral and objective. Regular reviews and updates of labeling rules help maintain accuracy and alignment with project goals. Using automated tools to check for consistency and getting feedback from other annotators also contribute to maintaining high-quality annotations. + +### How many images do I need for training Ultralytics YOLO models? + +For effective transfer learning and object detection with Ultralytics YOLO models, start with a minimum of a few hundred annotated objects per class. If training for just one class, begin with at least 100 annotated images and train for approximately 100 epochs. More complex tasks might require thousands of images per class to achieve high reliability and performance. Quality annotations are crucial, so ensure your data collection and annotation processes are rigorous and aligned with your project's specific goals. Explore detailed training strategies in the [YOLOv8 training guide](../modes/train.md). + +### What are some popular tools for data annotation? + +Several popular open-source tools can streamline the data annotation process: + +- **[Label Studio](https://github.com/HumanSignal/label-studio)**: A flexible tool supporting various annotation tasks, project management, and quality control features. +- **[CVAT](https://www.cvat.ai/)**: Offers multiple annotation formats and customizable workflows, making it suitable for complex projects. +- **[Labelme](https://github.com/labelmeai/labelme)**: Ideal for quick and straightforward image annotation with polygons. + +These tools can help enhance the efficiency and accuracy of your annotation workflows. For extensive feature lists and guides, refer to our [data annotation tools documentation](../datasets/index.md). + +### What types of data annotation are commonly used in computer vision? + +Different types of data annotation cater to various computer vision tasks: + +- **Bounding Boxes**: Used primarily for object detection, these are rectangular boxes around objects in an image. +- **Polygons**: Provide more precise object outlines suitable for instance segmentation tasks. +- **Masks**: Offer pixel-level detail, used in semantic segmentation to differentiate objects from the background. +- **Keypoints**: Identify specific points of interest within an image, useful for tasks like pose estimation and facial landmark detection. + +Selecting the appropriate annotation type depends on your project's requirements. Learn more about how to implement these annotations and their formats in our [data annotation guide](#what-is-data-annotation). diff --git a/docs/en/guides/defining-project-goals.md b/docs/en/guides/defining-project-goals.md index 7af2509ab91..cb6dbc2dcaf 100644 --- a/docs/en/guides/defining-project-goals.md +++ b/docs/en/guides/defining-project-goals.md @@ -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. - -

- Overview of SMART criteria -

- -- **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). diff --git a/docs/en/guides/model-training-tips.md b/docs/en/guides/model-training-tips.md index a25cb9d9917..d7329f3668a 100644 --- a/docs/en/guides/model-training-tips.md +++ b/docs/en/guides/model-training-tips.md @@ -28,7 +28,7 @@ Now that we know what is happening behind the scenes when we train a model, let' ## Training on Large Datasets -There are a few different aspects to think about when you are planning on using a large dataset to train a model. For example, you can adjust the batch size, control the GPU utilization, choose to use multi-scale training, etc. Let's walk through each of these options in detail. +There are a few different aspects to think about when you are planning on using a large dataset to train a model. For example, you can adjust the batch size, control the GPU utilization, choose to use multiscale training, etc. Let's walk through each of these options in detail. ### Batch Size and GPU Utilization @@ -45,9 +45,9 @@ When it comes to YOLOv8, you can easily implement subset training by using the ` ### Multi-scale Training -Multi-scale training is a technique that improves your model's ability to generalize by training it on images of varying sizes. Your model can learn to detect objects at different scales and distances and become more robust. +Multiscale training is a technique that improves your model's ability to generalize by training it on images of varying sizes. Your model can learn to detect objects at different scales and distances and become more robust. -For example, when you train YOLOv8, you can enable multi-scale training by setting the `scale` parameter. This parameter adjusts the size of training images by a specified factor, simulating objects at different distances. For example, setting `scale=0.5` will reduce the image size by half, while `scale=2.0` will double it. Configuring this parameter allows your model to experience a variety of image scales and improve its detection capabilities across different object sizes and scenarios. +For example, when you train YOLOv8, you can enable multiscale training by setting the `scale` parameter. This parameter adjusts the size of training images by a specified factor, simulating objects at different distances. For example, setting `scale=0.5` will reduce the image size by half, while `scale=2.0` will double it. Configuring this parameter allows your model to experience a variety of image scales and improve its detection capabilities across different object sizes and scenarios. ### Caching @@ -140,19 +140,6 @@ Different optimizers have various strengths and weaknesses. Let's take a glimpse For YOLOv8, the `optimizer` parameter lets you choose from various optimizers, including SGD, Adam, AdamW, NAdam, RAdam, and RMSProp, or you can set it to `auto` for automatic selection based on model configuration. -## FAQs - -- **Q1:** Does the Size of Training Images Affect Predictions on High-Quality Images? - - - **A1:** The size of training images can indeed impact the prediction accuracy of high-resolution images. Training on low-resolution images might not capture the finer details that high-resolution images contain, potentially leading to less accurate predictions when the model is applied to high-resolution images. To achieve optimal performance, it's generally recommended to train your model on images that are similar in resolution to those you expect to encounter during inference. - -- **Q2:** Does 'Auto' Optimizer Switch Between during Training? - - - **A2:** No, the 'Auto' setting does not switch between different optimizers during training. It selects a single optimizer at the start of the training process and uses it consistently throughout the entire training session, ensuring stability and consistency in the optimization process. - -- **Q3:** Can Yolov8 handle various image sizes? - - **A3:** Yes, YOLOv8 can handle images of various sizes. During training and prediction, the model automatically resizes the images to the specified `imgsz` parameter. - ## Connecting with the Community Being part of a community of computer vision enthusiasts can help you solve problems and learn faster. Here are some ways to connect, get help, and share ideas. @@ -171,3 +158,25 @@ Using these resources will help you solve challenges and stay up-to-date with th ## Key Takeaways Training computer vision models involves following good practices, optimizing your strategies, and solving problems as they arise. Techniques like adjusting batch sizes, mixed precision training, and starting with pre-trained weights can make your models work better and train faster. Methods like subset training and early stopping help you save time and resources. Staying connected with the community and keeping up with new trends will help you keep improving your model training skills. + +## FAQ + +### How can I improve GPU utilization when training a large dataset with Ultralytics YOLO? + +To improve GPU utilization, set the `batch_size` parameter in your training configuration to the maximum size supported by your GPU. This ensures that you make full use of the GPU's capabilities, reducing training time. If you encounter memory errors, incrementally reduce the batch size until training runs smoothly. For YOLOv8, setting `batch=-1` in your training script will automatically determine the optimal batch size for efficient processing. For further information, refer to the [training configuration](../modes/train.md). + +### What is mixed precision training, and how do I enable it in YOLOv8? + +Mixed precision training utilizes both 16-bit (FP16) and 32-bit (FP32) floating-point types to balance computational speed and precision. This approach speeds up training and reduces memory usage without sacrificing model accuracy. To enable mixed precision training in YOLOv8, set the `amp` parameter to `True` in your training configuration. This activates Automatic Mixed Precision (AMP) training. For more details on this optimization technique, see the [training configuration](../modes/train.md). + +### How does multiscale training enhance YOLOv8 model performance? + +Multiscale training enhances model performance by training on images of varying sizes, allowing the model to better generalize across different scales and distances. In YOLOv8, you can enable multiscale training by setting the `scale` parameter in the training configuration. For example, `scale=0.5` reduces the image size by half, while `scale=2.0` doubles it. This technique simulates objects at different distances, making the model more robust across various scenarios. For settings and more details, check out the [training configuration](../modes/train.md). + +### How can I use pre-trained weights to speed up training in YOLOv8? + +Using pre-trained weights can significantly reduce training times and improve model performance by starting from a model that already understands basic features. In YOLOv8, you can set the `pretrained` parameter to `True` or specify a path to custom pre-trained weights in your training configuration. This approach, known as transfer learning, leverages knowledge from large datasets to adapt to your specific task. Learn more about pre-trained weights and their advantages [here](../modes/train.md). + +### What is the recommended number of epochs for training a model, and how do I set this in YOLOv8? + +The number of epochs refers to the complete passes through the training dataset during model training. A typical starting point is 300 epochs. If your model overfits early, you can reduce the number. Alternatively, if overfitting isn’t observed, you might extend training to 600, 1200, or more epochs. To set this in YOLOv8, use the `epochs` parameter in your training script. For additional advice on determining the ideal number of epochs, refer to this section on [number of epochs](#the-number-of-epochs-to-train-for). diff --git a/docs/en/guides/preprocessing_annotated_data.md b/docs/en/guides/preprocessing_annotated_data.md index 1156c2a0b7b..5c66e9fbf54 100644 --- a/docs/en/guides/preprocessing_annotated_data.md +++ b/docs/en/guides/preprocessing_annotated_data.md @@ -126,18 +126,6 @@ For a more advanced approach to EDA, you can use the Ultralytics Explorer tool. Overview of Ultralytics Explorer

-## FAQs - -Here are some questions that might come up while you prepare your dataset: - -- **Q1:** How much preprocessing is too much? - - - **A1:** Preprocessing is essential but should be balanced. Overdoing it can lead to loss of critical information, overfitting, increased complexity, and higher computational costs. Focus on necessary steps like resizing, normalization, and basic augmentation, adjusting based on model performance. - -- **Q2:** What are the common pitfalls in EDA? - - - **A2:** Common pitfalls in Exploratory Data Analysis (EDA) include ignoring data quality issues like missing values and outliers, confirmation bias, overfitting visualizations, neglecting data distribution, and overlooking correlations. A systematic approach helps gain accurate and valuable insights. - ## Reach Out and Connect Having discussions about your project with other computer vision enthusiasts can give you new ideas from different perspectives. Here are some great ways to learn, troubleshoot, and network: @@ -154,3 +142,30 @@ Having discussions about your project with other computer vision enthusiasts can ## Your Dataset Is Ready! Properly resized, normalized, and augmented data improves model performance by reducing noise and improving generalization. By following the preprocessing techniques and best practices outlined in this guide, you can create a solid dataset. With your preprocessed dataset ready, you can confidently proceed to the next steps in your project. + +## FAQ + +### What is the importance of data preprocessing in computer vision projects? + +Data preprocessing is essential in computer vision projects because it ensures that the data is clean, consistent, and in a format that is optimal for model training. By addressing issues such as noise, inconsistency, and imbalance in raw data, preprocessing steps like resizing, normalization, augmentation, and dataset splitting help reduce computational load and improve model performance. For more details, visit the [steps of a computer vision project](../guides/steps-of-a-cv-project.md). + +### How can I use Ultralytics YOLO for data augmentation? + +For data augmentation with Ultralytics YOLOv8, you need to modify the dataset configuration file (.yaml). In this file, you can specify various augmentation techniques such as random crops, horizontal flips, and brightness adjustments. This can be effectively done using the training configurations [explained here](../modes/train.md). Data augmentation helps create a more robust dataset, reduce overfitting, and improve model generalization. + +### What are the best data normalization techniques for computer vision data? + +Normalization scales pixel values to a standard range for faster convergence and improved performance during training. Common techniques include: + +- **Min-Max Scaling**: Scales pixel values to a range of 0 to 1. +- **Z-Score Normalization**: Scales pixel values based on their mean and standard deviation. + +For YOLOv8, normalization is handled automatically, including conversion to RGB and pixel value scaling. Learn more about it in the [model training section](../modes/train.md). + +### How should I split my annotated dataset for training? + +To split your dataset, a common practice is to divide it into 70% for training, 20% for validation, and 10% for testing. It is important to maintain the data distribution of classes across these splits and avoid data leakage by performing augmentation only on the training set. Use tools like scikit-learn or TensorFlow for efficient dataset splitting. See the detailed guide on [dataset preparation](../guides/data-collection-and-annotation.md). + +### Can I handle varying image sizes in YOLOv8 without manual resizing? + +Yes, Ultralytics YOLOv8 can handle varying image sizes through the 'imgsz' parameter during model training. This parameter ensures that images are resized so their largest dimension matches the specified size (e.g., 640 pixels), while maintaining the aspect ratio. For more flexible input handling and automatic adjustments, check the [model training section](../modes/train.md). diff --git a/docs/en/guides/steps-of-a-cv-project.md b/docs/en/guides/steps-of-a-cv-project.md index 8ed50f4944b..c6b45db41a5 100644 --- a/docs/en/guides/steps-of-a-cv-project.md +++ b/docs/en/guides/steps-of-a-cv-project.md @@ -171,26 +171,6 @@ Monitoring tools can help you track key performance indicators (KPIs) and detect 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. @@ -209,3 +189,42 @@ Using these resources will help you overcome challenges and stay updated with th ## 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! + +## FAQ + +### How do I choose the right computer vision task for my project? + +Choosing the right computer vision task depends on your project's end goal. For instance, if you want to monitor traffic, **object detection** is suitable as it can locate and identify multiple vehicle types in real-time. For medical imaging, **image segmentation** is ideal for providing detailed boundaries of tumors, aiding in diagnosis and treatment planning. Learn more about specific tasks like [object detection](../tasks/detect.md), [image classification](../tasks/classify.md), and [instance segmentation](../tasks/segment.md). + +### Why is data annotation crucial in computer vision projects? + +Data annotation is vital for teaching your model to recognize patterns. The type of annotation varies with the task: + +- **Image Classification**: Entire image labeled as a single class. +- **Object Detection**: Bounding boxes drawn around objects. +- **Image Segmentation**: Each pixel labeled according to the object it belongs to. + +Tools like [Label Studio](https://github.com/HumanSignal/label-studio), [CVAT](https://github.com/cvat-ai/cvat), and [Labelme](https://github.com/labelmeai/labelme) can assist in this process. For more details, refer to our [data collection and annotation guide](./data-collection-and-annotation.md). + +### What steps should I follow to augment and split my dataset effectively? + +Splitting your dataset before augmentation helps validate model performance on original, unaltered data. Follow these steps: + +- **Training Set**: 70-80% of your data. +- **Validation Set**: 10-15% for hyperparameter tuning. +- **Test Set**: Remaining 10-15% for final evaluation. + +After splitting, apply data augmentation techniques like rotation, scaling, and flipping to increase dataset diversity. Libraries such as Albumentations and OpenCV can help. Ultralytics also offers [built-in augmentation settings](../modes/train.md) for convenience. + +### How can I export my trained computer vision model for deployment? + +Exporting your model ensures compatibility with different deployment platforms. Ultralytics provides multiple formats, including ONNX, TensorRT, and CoreML. To export your YOLOv8 model, follow this guide: + +- Use the `export` function with the desired format parameter. +- Ensure the exported model fits the specifications of your deployment environment (e.g., edge devices, cloud). + +For more information, check out the [model export guide](../modes/export.md). + +### What are the best practices for monitoring and maintaining a deployed computer vision model? + +Continuous monitoring and maintenance are essential for a model's long-term success. Implement tools for tracking Key Performance Indicators (KPIs) and detecting anomalies. Regularly retrain the model with updated data to counteract model drift. Document the entire process, including model architecture, hyperparameters, and changes, to ensure reproducibility and ease of future updates. Learn more in our [monitoring and maintenance guide](#monitoring-maintenance-and-documentation). diff --git a/docs/en/guides/workouts-monitoring.md b/docs/en/guides/workouts-monitoring.md index a0175955a8d..419c5624756 100644 --- a/docs/en/guides/workouts-monitoring.md +++ b/docs/en/guides/workouts-monitoring.md @@ -199,7 +199,7 @@ Using Ultralytics YOLOv8 for workout monitoring provides several key benefits: - **Health Awareness:** Early detection of patterns that indicate potential health issues or over-training. - **Informed Decisions:** Make data-driven decisions to adjust routines and set realistic goals. -You can watch a [YouTube video demonstration](https://www.youtube.com/embed/LGGxqLZtvuw) to see these benefits in action. +You can watch a [YouTube video demonstration](https://www.youtube.com/watch?v=LGGxqLZtvuw) to see these benefits in action. ### How accurate is Ultralytics YOLOv8 in detecting and tracking exercises? diff --git a/docs/en/hub/index.md b/docs/en/hub/index.md index 98470f8be77..a88fce70ca1 100644 --- a/docs/en/hub/index.md +++ b/docs/en/hub/index.md @@ -23,7 +23,7 @@ keywords: Ultralytics HUB, YOLO models, train YOLO, YOLOv5, YOLOv8, object detec

-CI CPU Open In Colab Discord +CI CPU Open In Colab Discord Ultralytics Forums
diff --git a/docs/en/index.md b/docs/en/index.md index 17749c95c9f..3153378e9b4 100644 --- a/docs/en/index.md +++ b/docs/en/index.md @@ -21,10 +21,10 @@ keywords: Ultralytics, YOLOv8, object detection, image segmentation, deep learni

Ultralytics CI -Ultralytics Code Coverage YOLOv8 Citation Docker Pulls Discord +Ultralytics Forums
Run on Gradient Open In Colab diff --git a/docs/en/integrations/google-colab.md b/docs/en/integrations/google-colab.md index 988a86f8819..90eee63860c 100644 --- a/docs/en/integrations/google-colab.md +++ b/docs/en/integrations/google-colab.md @@ -138,7 +138,7 @@ For more tips on managing your Colab session, visit the [Google Colab FAQ page]( ### Can I use custom datasets for training YOLOv8 models in Google Colab? -Yes, you can use custom datasets to train YOLOv8 models in Google Colab. Upload your dataset to Google Drive and load it directly into your Colab notebook. You can follow Nicolai's YouTube guide, [How to Train YOLOv8 Models on Your Custom Dataset](https://www.youtube.com/embed/LNwODJXcvt4?si=lB9UAc4hatSSEr2a), or refer to the [Custom Dataset Training guide](https://www.ultralytics.com/blog/training-custom-datasets-with-ultralytics-yolov8-in-google-colab) for detailed steps. +Yes, you can use custom datasets to train YOLOv8 models in Google Colab. Upload your dataset to Google Drive and load it directly into your Colab notebook. You can follow Nicolai's YouTube guide, [How to Train YOLOv8 Models on Your Custom Dataset](https://www.youtube.com/watch?v=LNwODJXcvt4?si=lB9UAc4hatSSEr2a), or refer to the [Custom Dataset Training guide](https://www.ultralytics.com/blog/training-custom-datasets-with-ultralytics-yolov8-in-google-colab) for detailed steps. ### What should I do if my Google Colab training session is interrupted? diff --git a/docs/en/yolov5/index.md b/docs/en/yolov5/index.md index 593d13b66e7..5bbc13b7d09 100644 --- a/docs/en/yolov5/index.md +++ b/docs/en/yolov5/index.md @@ -90,3 +90,25 @@ Your journey with YOLOv5 doesn't have to be a solitary one. Join our vibrant com Interested in contributing? We welcome contributions of all forms; from code improvements and bug reports to documentation updates. Check out our [contributing guidelines](../help/contributing.md/) for more information. We're excited to see the innovative ways you'll use YOLOv5. Dive in, experiment, and revolutionize your computer vision projects! 🚀 + +## FAQ + +### What are the key features of Ultralytics YOLOv5? + +Ultralytics YOLOv5 is renowned for its high-speed and high-accuracy object detection capabilities. Built on PyTorch, it is versatile and user-friendly, making it suitable for various computer vision projects. Key features include real-time inference, support for multiple training tricks like Test-Time Augmentation (TTA) and Model Ensembling, and compatibility with export formats such as TFLite, ONNX, CoreML, and TensorRT. To delve deeper into how Ultralytics YOLOv5 can elevate your project, explore our [TFLite, ONNX, CoreML, TensorRT Export guide](tutorials/model_export.md). + +### How can I train a custom YOLOv5 model on my dataset? + +Training a custom YOLOv5 model on your dataset involves a few key steps. First, prepare your dataset in the required format, annotated with labels. Then, configure the YOLOv5 training parameters and start the training process using the `train.py` script. For an in-depth tutorial on this process, consult our [Train Custom Data guide](tutorials/train_custom_data.md). It provides step-by-step instructions to ensure optimal results for your specific use case. + +### Why should I use Ultralytics YOLOv5 over other object detection models like RCNN? + +Ultralytics YOLOv5 is preferred over models like RCNN due to its superior speed and accuracy in real-time object detection. YOLOv5 processes the entire image in one go, making it significantly faster compared to the region-based approach of RCNN, which involves multiple passes. Additionally, YOLOv5's seamless integration with various export formats and extensive documentation make it an excellent choice for both beginners and professionals. Learn more about the architectural advantages in our [Architecture Summary](tutorials/architecture_description.md). + +### How can I optimize YOLOv5 model performance during training? + +Optimizing YOLOv5 model performance involves tuning various hyperparameters and incorporating techniques like data augmentation and transfer learning. Ultralytics provides comprehensive resources on hyperparameter evolution and pruning/sparsity to improve model efficiency. You can discover practical tips in our [Tips for Best Training Results guide](tutorials/tips_for_best_training_results.md), which offers actionable insights for achieving optimal performance during training. + +### What environments are supported for running YOLOv5 applications? + +Ultralytics YOLOv5 supports a variety of environments, including free GPU notebooks on Gradient, Google Colab, Kaggle, as well as major cloud platforms like Google Cloud, Amazon AWS, and Azure. Docker images are also available for convenient setup. For a detailed guide on setting up these environments, check our [Supported Environments](tutorials/roboflow_datasets_integration.md) section, which includes step-by-step instructions for each platform. diff --git a/docs/en/yolov5/tutorials/roboflow_datasets_integration.md b/docs/en/yolov5/tutorials/roboflow_datasets_integration.md index 901adab38b4..4ea5297a93f 100644 --- a/docs/en/yolov5/tutorials/roboflow_datasets_integration.md +++ b/docs/en/yolov5/tutorials/roboflow_datasets_integration.md @@ -71,3 +71,35 @@ Ultralytics provides a range of ready-to-use environments, each pre-installed wi YOLOv5 CI This badge indicates that all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are successfully passing. These CI tests rigorously check the functionality and performance of YOLOv5 across various key aspects: [training](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py), and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py). They ensure consistent and reliable operation on macOS, Windows, and Ubuntu, with tests conducted every 24 hours and upon each new commit. + +## FAQ + +### How do I upload data to Roboflow for training YOLOv5 models? + +You can upload your data to Roboflow using three different methods: via the website, the REST API, or through Python. These options offer flexibility depending on your technical preference or project requirements. Once your data is uploaded, you can organize, label, and version it to prepare for training with Ultralytics YOLOv5 models. For more details, visit the [Upload](#upload) section of the documentation. + +### What are the advantages of using Roboflow for data labeling and versioning? + +Roboflow provides a comprehensive platform for data organization, labeling, and versioning which is essential for efficient machine learning workflows. By using Roboflow with YOLOv5, you can streamline the process of dataset preparation, ensuring that your data is accurately annotated and consistently versioned. The platform also supports various preprocessing and offline augmentation options to enhance your dataset's quality. For a deeper dive into these features, see the [Labeling](#labeling) and [Versioning](#versioning) sections of the documentation. + +### How can I export my dataset from Roboflow to YOLOv5 format? + +Exporting your dataset from Roboflow to YOLOv5 format is straightforward. You can use the Python code snippet provided in the documentation: + +```python +from roboflow import Roboflow + +rf = Roboflow(api_key="YOUR API KEY HERE") +project = rf.workspace().project("YOUR PROJECT") +dataset = project.version("YOUR VERSION").download("yolov5") +``` + +This code will download your dataset in a format compatible with YOLOv5, allowing you to quickly begin training your model. For more details, refer to the [Exporting Data](#exporting-data) section. + +### What is active learning and how does it work with YOLOv5 and Roboflow? + +Active learning is a machine learning strategy that iteratively improves a model by intelligently selecting the most informative data points to label. With the Roboflow and YOLOv5 integration, you can implement active learning to continuously enhance your model's performance. This involves deploying a model, capturing new data, using the model to make predictions, and then manually verifying or correcting those predictions to further train the model. For more insights into active learning see the [Active Learning](#active-learning) section above. + +### How can I use Ultralytics environments for training YOLOv5 models on different platforms? + +Ultralytics provides ready-to-use environments with pre-installed dependencies like CUDA, CUDNN, Python, and PyTorch, making it easier to kickstart your training projects. These environments are available on various platforms such as Google Cloud, AWS, Azure, and Docker. You can also access free GPU notebooks via [Paperspace](https://bit.ly/yolov5-paperspace-notebook), [Google Colab](https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb), and [Kaggle](https://www.kaggle.com/ultralytics/yolov5). For specific setup instructions, visit the [Supported Environments](#supported-environments) section of the documentation. diff --git a/docs/en/yolov5/tutorials/train_custom_data.md b/docs/en/yolov5/tutorials/train_custom_data.md index 0ea64abe535..1b9e47bc3fc 100644 --- a/docs/en/yolov5/tutorials/train_custom_data.md +++ b/docs/en/yolov5/tutorials/train_custom_data.md @@ -222,3 +222,53 @@ Ultralytics provides a range of ready-to-use environments, each pre-installed wi YOLOv5 CI This badge indicates that all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are successfully passing. These CI tests rigorously check the functionality and performance of YOLOv5 across various key aspects: [training](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py), and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py). They ensure consistent and reliable operation on macOS, Windows, and Ubuntu, with tests conducted every 24 hours and upon each new commit. + +## FAQ + +### How do I train YOLOv5 on my custom dataset? + +Training YOLOv5 on a custom dataset involves several steps: + +1. **Prepare Your Dataset**: Collect and label images. Use tools like [Roboflow](https://roboflow.com/?ref=ultralytics) to organize data and export in [YOLOv5 format](https://roboflow.com/formats/yolov5-pytorch-txt?ref=ultralytics). +2. **Setup Environment**: Clone the YOLOv5 repo and install dependencies: + ```bash + git clone https://github.com/ultralytics/yolov5 + cd yolov5 + pip install -r requirements.txt + ``` +3. **Create Dataset Configuration**: Write a `dataset.yaml` file defining train/val paths and class names. +4. **Train the Model**: + ```bash + python train.py --img 640 --epochs 3 --data dataset.yaml --weights yolov5s.pt + ``` + +### What tools can I use to annotate my YOLOv5 dataset? + +You can use [Roboflow Annotate](https://roboflow.com/annotate?ref=ultralytics), an intuitive web-based tool for labeling images. It supports team collaboration and exports in YOLOv5 format. After collecting images, use Roboflow to create and manage annotations efficiently. Other options include tools like LabelImg and CVAT for local annotations. + +### Why should I use Ultralytics HUB for training my YOLO models? + +Ultralytics HUB offers an end-to-end platform for training, deploying, and managing YOLO models without needing extensive coding skills. Benefits of using Ultralytics HUB include: + +- **Easy Model Training**: Simplifies the training process with preconfigured environments. +- **Data Management**: Effortlessly manage datasets and version control. +- **Real-time Monitoring**: Integrates tools like [Comet](https://bit.ly/yolov5-readme-comet) for real-time metrics tracking and visualization. +- **Collaboration**: Ideal for team projects with shared resources and easy management. + +### How do I convert my annotated data to YOLOv5 format? + +To convert annotated data to YOLOv5 format using Roboflow: + +1. **Upload Your Dataset** to a Roboflow workspace. +2. **Label Images** if not already labeled. +3. **Generate and Export** the dataset in `YOLOv5 Pytorch` format. Ensure preprocessing steps like Auto-Orient and Resize (Stretch) to the square input size (e.g., 640x640) are applied. +4. **Download the Dataset** and integrate it into your YOLOv5 training script. + +### What are the licensing options for using YOLOv5 in commercial applications? + +Ultralytics offers two licensing options: + +- **AGPL-3.0 License**: An open-source license suitable for non-commercial use, ideal for students and enthusiasts. +- **Enterprise License**: Tailored for businesses seeking to integrate YOLOv5 into commercial products and services. For detailed information, visit our [Licensing page](https://ultralytics.com/license). + +For more details, refer to our guide on [Ultralytics Licensing](https://ultralytics.com/license). diff --git a/mkdocs.yml b/mkdocs.yml index 0706a5388b1..1f2f3918dde 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -173,6 +173,8 @@ nav: - 🇫🇷  Français: https://docs.ultralytics.com/fr/ - 🇪🇸  Español: https://docs.ultralytics.com/es/ - 🇵🇹  Português: https://docs.ultralytics.com/pt/ + - 🇮🇹  Italiano: https://docs.ultralytics.com/it/ + - 🇳🇱  Nederlands: https://docs.ultralytics.com/nl/ - 🇹🇷  Türkçe: https://docs.ultralytics.com/tr/ - 🇻🇳  Tiếng Việt: https://docs.ultralytics.com/vi/ - 🇮🇳  हिन्दी: https://docs.ultralytics.com/hi/