centbueze/nigeria-sign-language2

Computer vision project for Nigeria Sign Language gesture recognition.(continuous) 中文 | 한국어 | 日本語 | Русский | Deutsch | Français | Español | Português | Türkçe | Tiếng Việt | العربية Ultralytics creates cutting-edge, state-of-the-art (SOTA) YOLO models built on years of foundational research in computer vision and AI. Constantly updated for performance and flexibility, our models are **fast**, **accurate**, and **easy to use**. They excel at object detection, tracking, instance segmentation, image classification, and pose estimation tasks. Find detailed documentation in the Ultralytics Docs. Get support via GitHub Issues. Join discussions on Discord, Reddit, and the Ultralytics Community Forums! Request an Enterprise License for commercial use at Ultralytics Licensing. ## 📄 Documentation See below for quickstart installation and usage examples. For comprehensive guidance on training, validation, prediction, and deployment, refer to our full Ultralytics Docs. Install Install the `ultralytics` package, including all requirements, in a **Python>=3.8** environment with **PyTorch>=1.8**. ```bash pip install ultralytics ``` For alternative installation methods, including Conda, Docker, and building from source via Git, please consult the Quickstart Guide. Usage ### CLI You can use Ultralytics YOLO directly from the Command Line Interface (CLI) with the `yolo` command: ```bash # Predict using a pretrained YOLO model (e.g., YOLO11n) on an image yolo predict model=yolo11n.pt source='ultralytics.com' ``` The `yolo` command supports various tasks and modes, accepting additional arguments like `imgsz=640`. Explore the YOLO CLI Docs for more examples. ### Python Ultralytics YOLO can also be integrated directly into your Python projects. It accepts the same configuration arguments as the CLI: ```python from ultralytics import YOLO # Load a pretrained YOLO11n model model = YOLO("yolo11n.pt") # Train the model on the COCO8 dataset for 100 epochs train_results = model.train( data …