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Bias amp 2 settings for metal4/9/2024 ![]() ![]() While leveraging the computational power of the M1/M2 chips, this enables more efficient processing of the training tasks. # Start training from a pretrained *.pt model using GPUs 0 and 1 yolo detect train data =coco128.yaml model =yolov8n.pt epochs = 100 imgsz = 640 device =mps Visualization and Monitoring: Real-time tracking of training metrics and visualization of the learning process for better insights.Hyperparameter Configuration: The option to modify hyperparameters through YAML configuration files or CLI arguments.Multi-GPU Support: Scale your training efforts seamlessly across multiple GPUs to expedite the process.Automatic Dataset Download: Standard datasets like COCO, VOC, and ImageNet are downloaded automatically on first use.The following are some notable features of YOLOv8's Train mode: Hyperparameter Flexibility: A broad range of customizable hyperparameters to fine-tune model performance.User-Friendly: Simple yet powerful CLI and Python interfaces for a straightforward training experience.Versatility: Train on custom datasets in addition to readily available ones like COCO, VOC, and ImageNet.Efficiency: Make the most out of your hardware, whether you're on a single-GPU setup or scaling across multiple GPUs.Here are some compelling reasons to opt for YOLOv8's Train mode: ![]() Why Choose Ultralytics YOLO for Training? ![]() Watch: How to Train a YOLOv8 model on Your Custom Dataset in Google Colab. This guide aims to cover all the details you need to get started with training your own models using YOLOv8's robust set of features. Train mode in Ultralytics YOLOv8 is engineered for effective and efficient training of object detection models, fully utilizing modern hardware capabilities. Training a deep learning model involves feeding it data and adjusting its parameters so that it can make accurate predictions. ![]()
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