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  "title": "How to label data for YOLOv8 models | Roboflow",
  "body_markdown": "How to label data for YOLOv8 training\nBefore you can train a computer vision model, you need labeled data on which to train your model. The more accurate the labels, or annotations, are, the higher the performance the model will achieve.\nOverview\nIn this guide, we are going to show how to use\nRoboflow Annotate\na free tool you can use to create a dataset for YOLOv8 Object Detection training. You can use data annotated in Roboflow for training a model in Roboflow using Roboflow Train. You can also export your annotations so you can use them in your own YOLOv8 Object Detection custom training process.\nRoboflow offers a robust annotation platform that:\nUses state-of-the-art technology\nComplies with SOC 2 Type 2 requirements\nTrusted by over 2M developers\nTo label data for YOLOv8, you will:\nImport data into Roboflow Annotate\nOpen an image\nLabel data with bounding boxes or polygons\nTry\nLabel Assist\nfor automated labeling\nSave the annotated data\n(Optional) Train a model or export your data\nLet's get started!\nStep 1\n:\nImport data into Roboflow Annotate\nFirst, create a\nfree Roboflow account\n. Then, create a new project from the Roboflow dashboard:\nOnce you have created a project, you will be taken to a page where you can upload your images. Drag-and-drop any images into the box:\nYou can also drag in annotation files if you want to view or amend annotations in Roboflow Annotate.\nWhen you have uploaded your files, click \"Save and Continue\".\nYour images will be uploaded to Roboflow.\nStep 2\n:\nOpen an Image\nAfter you have uploaded all of your images, click on one to open:\nOnce you have opened an image, you will see the Roboflow Annotate interface, through which you can create bounding boxes, segmentation masks, classification labels, and polygon annotations, depending on your project type.\nSince this is an Object Detection project, we can either create bounding box or polygon annotations.\nStep 3\n:\nLabel Data with Boxes or Polygons\nTo draw a bounding box, select the box tool in the right sidebar of Roboflow Annotate, or press \"b\" on your keyboard. Then, drag where you want to draw your bounding box:\nYour bounding box should be drawn tightly around the object you want to annotate. Tight bounding boxes allow the model you are training to better understand what the specific object is you want to identify. Review our\nlabeling best practices\nfor more data annotation tips.\nYou can draw a polygon for object detection or semantic segmentation using the Polygon annotation tool. To toggle polygon annotation mode, press \"p\" on your keyboard, or click on the icon below the bounding box icon in the Roboflow Annotate sidebar.\nYou can also use\nSmart Polygon\nto generate annotations by clicking an object of interest.\nTo enable Smart Polygon, click the cursor icon in the right sidebar. There are two versions of Smart Polygon:\nStandard, which is ideal for small items\nEnhanced, which is ideal for most use cases\nLet's use Enhanced Smart Polygon to label solar panels.\nWith Smart Polygon enabled, you can click on an object to create a polygon annotation.\nPolygon annotations can be used for object detection and segmentation tasks\n.\nStep 4\n:\nAnnotate Images with Label Assist\nRoboflow Annotate comes with a tool called Label Assist with which you can label images. Label Assist lets you use:\nA model trained on the\nMicrosoft COCO dataset\n, that can identify 80 classes\nAny of the 50,000+ public trained models on\nRoboflow Universe\nPrevious versions of your model\nTo use Label Assist, click the magic wand icon in the sidebar. Roboflow Annotate will recommend new annotations based on the model you have selected as your assistant.\nStep 5\n:\nSave Your Annotation\nOnce you have finished annotating your image, you can move on to the next image you want to annotate! To do so, click the forward or backward arrows at the top of the Annotate interface, or use the forward and backward arrows on your keyboard.\nStep 6\n:\nTrain a Model or Export Data\nWith all of your data labeled, you are now ready to train a model on Roboflow or export your data elsewhere. To train a model in Roboflow with your data, follow our\nRoboflow Train guide\n.\nAlternatively, you can export your data into over\n30 different formats\n, depending on the needs for your project.\nAutomatically Label Data with Autodistill\nUsing\nAutodistill\n, you can automatically label data for object detection, segmentation, and classification. To learn more about using Autodistill, check out our\nAutodistill guide\n.\nResources\nWhat is YOLOv8?\nHow to Train YOLOv8 Object Detection on a Custom Dataset\nYOLOv8 PyTorch TXT\nYOLOv8 Model Licensing and Pricing\nLearn how to label data for other models\nEach guide walks through the same steps in Roboflow Annotate for a different model.\nDetectron2 Instance Segmentation\nDetectron2 Object Detection\nDETR\nFlorence-2\nGPT-4o\nMMDetection\nMT-YOLOv6\nPaliGemma\nRF-DETR\nRTMDet\nVision Transformer\nYOLO-NAS\nYOLO11\nYOLO11 OBB\nYOLOR\nYOLOS\nYOLOv3 PyTorch\nYOLOv4 PyTorch\nYOLOv5\nYOLOv5 Classification\nYOLOv5 Instance Segmentation\nYOLOv7\nYOLOv7 Instance Segmentation\nYOLOv8 Classification\nYOLOv8 Instance Segmentation\nYOLOv8 Oriented Bounding Boxes\nYOLOv9\nYOLOv10\nYOLOv12\nYOLOX",
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