Designed with simplicity and ease of use in mind, the Python interface enables users to quickly implement object detection, segmentation, and classification in their projects.

YOLOv8's Python interface allows for seamless integration into your Python projects, making it easy to load, run, and process the model's output. yolo predict -model yolov8n.pt -imgsz 640 -conf 0.25 ❌ (do not use -).yolo predict model=yolov8n.pt, imgsz=640, conf=0.25 ❌ (do not use ,).yolo predict model yolov8n.pt imgsz 640 conf 0.25 ❌ (missing =).yolo predict model=yolov8n.pt imgsz=640 conf=0.25 ✅.Do not use - argument prefixes or commas, between arguments. Dockerfile-conda: Based on Miniconda3 with conda installation of ultralytics package.īelow are the commands to get the latest image and execute it:Īrguments must be passed as arg=val pairs, split by an equals = sign and delimited by spaces between pairs.Dockerfile-python: Minimal image with just Python and necessary dependencies, ideal for lightweight applications and development.Dockerfile-jetson: Tailored for NVIDIA Jetson devices, integrating GPU support optimized for these platforms.Dockerfile-cpu: Ubuntu-based CPU-only version suitable for inference and environments without GPUs.Dockerfile-arm64: Optimized for ARM64 architecture, allowing deployment on devices like Raspberry Pi and other ARM64-based platforms.Dockerfile: GPU image recommended for training.Ultralytics offers 5 main supported Docker images, each designed to provide high compatibility and efficiency for different platforms and use cases: By choosing one of the official ultralytics images from Docker Hub, you not only avoid the complexity of local installation but also benefit from access to a verified working environment. Utilize Docker to effortlessly execute the ultralytics package in an isolated container, ensuring consistent and smooth performance across various environments.

Install conda windows