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

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

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.

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
 <a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a>
 
 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.

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
 <a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a>
 
 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).