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

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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docs/en/yolov5/index.md
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@ -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.