{"id":58,"date":"2026-08-01T07:50:15","date_gmt":"2026-08-01T07:50:15","guid":{"rendered":"https:\/\/www.graveiensai.com\/blog\/?p=58"},"modified":"2026-08-01T07:56:22","modified_gmt":"2026-08-01T07:56:22","slug":"types-of-image-annotation","status":"publish","type":"post","link":"https:\/\/www.graveiensai.com\/blog\/types-of-image-annotation\/","title":{"rendered":"Types of Image Annotation: The Complete 2026 Guide"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><strong>Quick answer:<\/strong> The main types of image anno\u00adta\u00adtion are bound\u00ading box anno\u00adta\u00adtion, poly\u00adgon anno\u00adta\u00adtion, 3D cuboid anno\u00adta\u00adtion, seman\u00adtic seg\u00admen\u00adta\u00adtion, instance seg\u00admen\u00adta\u00adtion, panop\u00adtic seg\u00admen\u00adta\u00adtion, key\u00adpoint (land\u00admark) anno\u00adta\u00adtion, poly\u00adline anno\u00adta\u00adtion, and image clas\u00adsi\u00adfi\u00adca\u00adtion. Each label visu\u00adal\u00adizes data dif\u00adfer\u00adent\u00adly so a com\u00adput\u00ader vision mod\u00adel learns to detect, out\u00adline, or under\u00adstand objects at the lev\u00adel of accu\u00adra\u00adcy your use case demands.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><em>This guide is writ\u00adten for machine learn\u00ading engi\u00adneers, com\u00adput\u00ader vision prod\u00aduct own\u00aders, and data oper\u00ada\u00adtions leads who need to choose the right label\u00ading method before a train\u00ading run, not a sur\u00adface lev\u00adel def\u00adi\u00adn\u00adi\u00adtions post.<\/em><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Choos\u00ading between the many types of image anno\u00adta\u00adtion is one of the high\u00adest lever\u00adage deci\u00adsions in any com\u00adput\u00ader vision project. Get it right, and your mod\u00adel reach\u00ades pro\u00adduc\u00adtion accu\u00adra\u00adcy on sched\u00adule. Get it wrong and you pay twice: once for the labels and again to redo them. This guide breaks down every major anno\u00adta\u00adtion method, when to use it, the trade\u00adoffs, and the real world use cas\u00ades behind each so you can match the tech\u00adnique to the mod\u00adel instead of guess\u00ading.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">At Graveiens AI, our anno\u00adta\u00adtion teams label mil\u00adlions of images a year through a four stage qual\u00adi\u00adty work\u00adflow, and the pat\u00adterns below reflect what actu\u00adal\u00adly holds up in pro\u00adduc\u00adtion. If you want the ser\u00advice view first, see our <a href=\"https:\/\/www.graveiensai.com\/data-annotation\">data anno\u00adta\u00adtion and label\u00ading ser\u00advices<\/a> for how these meth\u00adods are deliv\u00adered at scale.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Key takeaways<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Bound\u00ading box anno\u00adta\u00adtion<\/strong> is the fastest, most com\u00admon form of object label\u00ading and pow\u00aders most object detec\u00adtion mod\u00adels.<\/li>\n\n\n\n<li><strong>Seman\u00adtic seg\u00admen\u00adta\u00adtion<\/strong> labels every pix\u00adel by class; <strong>instance seg\u00admen\u00adta\u00adtion<\/strong> sep\u00ada\u00adrates indi\u00advid\u00adual objects; <strong>panop\u00adtic seg\u00admen\u00adta\u00adtion<\/strong> does both.<\/li>\n\n\n\n<li><strong>3D cuboid anno\u00adta\u00adtion<\/strong> adds depth for autonomous dri\u00adving and robot\u00adics, while <strong>key\u00adpoint anno\u00adta\u00adtion<\/strong> cap\u00adtures pose and facial land\u00admarks.<\/li>\n\n\n\n<li><strong>Image clas\u00adsi\u00adfi\u00adca\u00adtion<\/strong> tags a whole image with one or more labels and is the sim\u00adplest anno\u00adta\u00adtion type to pro\u00adduce.<\/li>\n\n\n\n<li>The right choice depends on the task, bud\u00adget, and the pre\u00adci\u00adsion your mod\u00adel actu\u00adal\u00adly needs not on pick\u00ading the most detailed method avail\u00adable.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Table of contents<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/docs.google.com\/document\/d\/1wUH0scdomtQs707BGgdjjSiEQm51aEl9BKo3S1A7jqA\/edit#what-is-image-annotation\" target=\"_blank\" rel=\"noopener\">What is image anno\u00adta\u00adtion?<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/docs.google.com\/document\/d\/1wUH0scdomtQs707BGgdjjSiEQm51aEl9BKo3S1A7jqA\/edit#why-it-matters\" target=\"_blank\" rel=\"noopener\">Why the types of image anno\u00adta\u00adtion mat\u00adter<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/docs.google.com\/document\/d\/1wUH0scdomtQs707BGgdjjSiEQm51aEl9BKo3S1A7jqA\/edit#at-a-glance\" target=\"_blank\" rel=\"noopener\">The main anno\u00adta\u00adtion types at a glance<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/docs.google.com\/document\/d\/1wUH0scdomtQs707BGgdjjSiEQm51aEl9BKo3S1A7jqA\/edit#bounding-box\" target=\"_blank\" rel=\"noopener\">1. Bound\u00ading box anno\u00adta\u00adtion<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/docs.google.com\/document\/d\/1wUH0scdomtQs707BGgdjjSiEQm51aEl9BKo3S1A7jqA\/edit#polygon\" target=\"_blank\" rel=\"noopener\">2. Poly\u00adgon anno\u00adta\u00adtion<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/docs.google.com\/document\/d\/1wUH0scdomtQs707BGgdjjSiEQm51aEl9BKo3S1A7jqA\/edit#cuboid\" target=\"_blank\" rel=\"noopener\">3. 3D cuboid anno\u00adta\u00adtion<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/docs.google.com\/document\/d\/1wUH0scdomtQs707BGgdjjSiEQm51aEl9BKo3S1A7jqA\/edit#semantic-segmentation\" target=\"_blank\" rel=\"noopener\">4. Seman\u00adtic seg\u00admen\u00adta\u00adtion<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/docs.google.com\/document\/d\/1wUH0scdomtQs707BGgdjjSiEQm51aEl9BKo3S1A7jqA\/edit#instance-segmentation\" target=\"_blank\" rel=\"noopener\">5. Instance seg\u00admen\u00adta\u00adtion<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/docs.google.com\/document\/d\/1wUH0scdomtQs707BGgdjjSiEQm51aEl9BKo3S1A7jqA\/edit#panoptic\" target=\"_blank\" rel=\"noopener\">6. Panop\u00adtic seg\u00admen\u00adta\u00adtion<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/docs.google.com\/document\/d\/1wUH0scdomtQs707BGgdjjSiEQm51aEl9BKo3S1A7jqA\/edit#keypoint\" target=\"_blank\" rel=\"noopener\">7. Key\u00adpoint and land\u00admark anno\u00adta\u00adtion<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/docs.google.com\/document\/d\/1wUH0scdomtQs707BGgdjjSiEQm51aEl9BKo3S1A7jqA\/edit#polyline\" target=\"_blank\" rel=\"noopener\">8. Poly\u00adline and line anno\u00adta\u00adtion<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/docs.google.com\/document\/d\/1wUH0scdomtQs707BGgdjjSiEQm51aEl9BKo3S1A7jqA\/edit#image-classification\" target=\"_blank\" rel=\"noopener\">9. Image clas\u00adsi\u00adfi\u00adca\u00adtion<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/docs.google.com\/document\/d\/1wUH0scdomtQs707BGgdjjSiEQm51aEl9BKo3S1A7jqA\/edit#how-to-choose\" target=\"_blank\" rel=\"noopener\">How to choose the right anno\u00adta\u00adtion type<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/docs.google.com\/document\/d\/1wUH0scdomtQs707BGgdjjSiEQm51aEl9BKo3S1A7jqA\/edit#formats\" target=\"_blank\" rel=\"noopener\">Anno\u00adta\u00adtion for\u00admats: COCO, YOLO, Pas\u00adcal VOC<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/docs.google.com\/document\/d\/1wUH0scdomtQs707BGgdjjSiEQm51aEl9BKo3S1A7jqA\/edit#best-practices\" target=\"_blank\" rel=\"noopener\">Best prac\u00adtices and qual\u00adi\u00adty assur\u00adance<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/docs.google.com\/document\/d\/1wUH0scdomtQs707BGgdjjSiEQm51aEl9BKo3S1A7jqA\/edit#faqs\" target=\"_blank\" rel=\"noopener\">FAQs<\/a><\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>What is image annotation?<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Image anno\u00adta\u00adtion is the process of adding human ver\u00adi\u00adfied labels to images so a machine learn\u00ading mod\u00adel can learn to rec\u00adog\u00adnize objects, bound\u00adaries, and scenes. In prac\u00adtice it means draw\u00ading box\u00ades, trac\u00ading out\u00adlines, mark\u00ading points, or tag\u00adging pix\u00adels, then stor\u00ading those labels in a struc\u00adtured for\u00admat the mod\u00adel can read dur\u00ading train\u00ading.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Every super\u00advised com\u00adput\u00ader vision sys\u00adtem depends on it. With\u00adout accu\u00adrate labels, even the best archi\u00adtec\u00adture has noth\u00ading mean\u00ading\u00adful to learn, which is why the var\u00adi\u00adous anno\u00adta\u00adtion meth\u00adods exist in the first place: dif\u00adfer\u00adent mod\u00adels need visu\u00adal infor\u00adma\u00adtion described in dif\u00adfer\u00adent ways. Object labelling for a retail shelf detec\u00adtion mod\u00adel looks noth\u00ading like pix\u00adellev\u00adel anno\u00adta\u00adtion for a self dri\u00adving car, even though both start from a pho\u00adto\u00adgraph.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Why the types of image annotation matter<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Pick\u00ading among these anno\u00adta\u00adtion meth\u00adods is real\u00adly a deci\u00adsion about the trade\u00adoff between cost, speed, and pre\u00adci\u00adsion. A bound\u00ading box takes a few sec\u00adonds to draw; a detailed seg\u00admen\u00adta\u00adtion mask can take sev\u00ader\u00adal min\u00adutes per object. Mul\u00adti\u00adply that across a dataset of 100,000 images and the choice defines your entire bud\u00adget and time\u00adline.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The method you choose also caps your mod\u00adel\u2019s ceil\u00ading. A detec\u00adtor trained only on rec\u00adtan\u00adgu\u00adlar object labelling can tell you <em>where<\/em> a tumshaped region is, but not its exact con\u00adtour, so a health\u00adcare team build\u00ading diag\u00adnos\u00adtic tool\u00ading almost always needs seg\u00admen\u00adta\u00adtion instead. Match\u00ading the anno\u00adta\u00adtion type to the out\u00adcome is the core skill, and it is where an expe\u00adri\u00adenced <a href=\"https:\/\/www.graveiensai.com\/computer-vision\">com\u00adput\u00ader vision data part\u00adner<\/a> earns its keep. The sec\u00adtions below give you the deci\u00adsion cri\u00adte\u00adria for each method so the match is delib\u00ader\u00adate rather than acci\u00adden\u00adtal.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>The main types of image annotation at a glance<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Before the deep dive, here is how the most com\u00admon anno\u00adta\u00adtion meth\u00adods com\u00adpare across effort and best fit tasks.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Anno\u00adta\u00adtion type<\/strong><\/td><td><strong>What it labels<\/strong><\/td><td><strong>Pre\u00adci\u00adsion<\/strong><\/td><td><strong>Typ\u00adi\u00adcal use case<\/strong><\/td><\/tr><tr><td>Bound\u00ading box<\/td><td>Rec\u00adtan\u00adgu\u00adlar region around an object<\/td><td>Low\u2013medium<\/td><td>Object detec\u00adtion, count\u00ading<\/td><\/tr><tr><td>Poly\u00adgon<\/td><td>Exact object out\u00adline<\/td><td>High<\/td><td>Irreg\u00adu\u00adlar shapes, instance masks<\/td><\/tr><tr><td>3D cuboid<\/td><td>Object with depth and ori\u00aden\u00adta\u00adtion<\/td><td>High<\/td><td>Autonomous dri\u00adving, robot\u00adics<\/td><\/tr><tr><td>Seman\u00adtic seg\u00admen\u00adta\u00adtion<\/td><td>Every pix\u00adel by class<\/td><td>Very high<\/td><td>Scene under\u00adstand\u00ading, med\u00adical<\/td><\/tr><tr><td>Instance seg\u00admen\u00adta\u00adtion<\/td><td>Each object sep\u00ada\u00adrate\u00adly at pix\u00adel lev\u00adel<\/td><td>Very high<\/td><td>Crowd\u00aded scenes, count\u00ading<\/td><\/tr><tr><td>Panop\u00adtic seg\u00admen\u00adta\u00adtion<\/td><td>Class\u00ades + indi\u00advid\u00adual instances<\/td><td>Very high<\/td><td>Fullscene per\u00adcep\u00adtion<\/td><\/tr><tr><td>Key\u00adpoint<\/td><td>Spe\u00adcif\u00adic points on an object<\/td><td>High<\/td><td>Pose, facial land\u00admarks<\/td><\/tr><tr><td>Poly\u00adline<\/td><td>Open con\u00adnect\u00aded lines<\/td><td>Medi\u00adum<\/td><td>Lanes, roads, cracks<\/td><\/tr><tr><td>Image clas\u00adsi\u00adfi\u00adca\u00adtion<\/td><td>The whole image<\/td><td>Low<\/td><td>Tag\u00adging, con\u00adtent sort\u00ading<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Under\u00adstand\u00ading these dif\u00adfer\u00adences is the foun\u00adda\u00adtion of good <a href=\"https:\/\/www.graveiensai.com\/data-collection\">data col\u00adlec\u00adtion and label\u00ading plan\u00adning<\/a>, because the anno\u00adta\u00adtion type you choose should be decid\u00aded before a sin\u00adgle image is cap\u00adtured. Now let\u2019s look at each method in detail.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>1. Bounding box annotation<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Bound\u00ading box anno\u00adta\u00adtion draws a rec\u00adtan\u00adgle around each tar\u00adget object, defined by its topleft and bot\u00adtom\u00adright coor\u00addi\u00adnates. It is the most wide\u00adly used of all anno\u00adta\u00adtion meth\u00adods because it is fast, cheap, and good enough for the major\u00adi\u00adty of object detec\u00adtion tasks.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This form of object labelling pow\u00aders mod\u00adels that count prod\u00aducts on a shelf, spot pedes\u00adtri\u00adans in a frame, or flag defects on a pro\u00adduc\u00adtion line. A trained anno\u00adta\u00adtor can place dozens of box\u00ades per minute, which makes bound\u00ading box object labelling the default start\u00ading point for most teams test\u00ading a new detec\u00adtion idea.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The lim\u00adi\u00adta\u00adtion is pre\u00adci\u00adsion: a rec\u00adtan\u00adgle always includes back\u00adground pix\u00adels around any\u00adthing that isn\u2019t itself rec\u00adtan\u00adgu\u00adlar. For a car pho\u00adtographed at an angle or an ani\u00admal mid\u00adstride, a large share of the box is not the object. When that back\u00adground noise hurts mod\u00adel per\u00adfor\u00admance, teams grad\u00adu\u00adate to poly\u00adgons or seg\u00admen\u00adta\u00adtion. Even so, straight\u00adfor\u00adward object labelling with box\u00ades remains the work\u00adhorse behind <a href=\"https:\/\/www.graveiensai.com\/retail-ecommerce\">retail and ecom\u00admerce com\u00adput\u00ader vision<\/a>, where speed and vol\u00adume mat\u00adter more than pix\u00adelper\u00adfect out\u00adlines.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Best for:<\/strong> object detec\u00adtion, object count\u00ading, and rapid dataset boot\u00adstrap\u00adping.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>2. Polygon annotation<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Poly\u00adgon anno\u00adta\u00adtion traces the exact out\u00adline of an object using a series of con\u00adnect\u00aded points, cap\u00adtur\u00ading irreg\u00adu\u00adlar shapes far more pre\u00adcise\u00adly than a rec\u00adtan\u00adgle. Where a box wastes pix\u00adels on the back\u00adground, a poly\u00adgon hugs the true bound\u00adary of a build\u00ading, a gar\u00adment, or an ani\u00admal.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">That pre\u00adci\u00adsion comes at a cost poly\u00adgons take longer to draw and demand more skilled anno\u00adta\u00adtors, but they are indis\u00adpens\u00adable when shape mat\u00adters. Fash\u00adion cat\u00ada\u00adlogs, aer\u00adi\u00adal imagery, and agri\u00adcul\u00adtur\u00adal crop analy\u00adsis all rely on poly\u00adgon object label\u00ading to iso\u00adlate objects clean\u00adly. Poly\u00adgons are also the man\u00adu\u00adal foun\u00adda\u00adtion for many instance seg\u00admen\u00adta\u00adtion datasets, since a closed poly\u00adgon can be con\u00advert\u00aded into a pix\u00adel mask.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For <a href=\"https:\/\/www.graveiensai.com\/geospatial\">geospa\u00adtial and satel\u00adlite imagery projects<\/a>, poly\u00adgon anno\u00adta\u00adtion is often the only viable method, because rooftops, roads, and field bound\u00adaries almost nev\u00ader fit inside a clean rec\u00adtan\u00adgle.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Best for:<\/strong> irreg\u00adu\u00adlar objects, over\u00adhead imagery, and pre\u00adcise object out\u00adlines.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>3. 3D cuboid annotation<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">3D cuboid anno\u00adta\u00adtion extends the bound\u00ading box into three dimen\u00adsions, cap\u00adtur\u00ading an objec\u00adt\u2019s posi\u00adtion, ori\u00aden\u00adta\u00adtion, and vol\u00adume rather than just its 2D loca\u00adtion. Each cuboid encodes depth, so a mod\u00adel can rea\u00adson about how far away a car is and which direc\u00adtion it faces not just that it appears in the frame.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This is essen\u00adtial for autonomous sys\u00adtems. Per\u00adcep\u00adtion stacks for self\u00addriv\u00ading vehi\u00adcles use cuboids on both cam\u00adera images and LiDAR point clouds to build a spa\u00adtial map of the world. Robot\u00adics arms use them to grasp objects at the cor\u00adrect depth. Because cuboid work is often fused with sen\u00adsor data, it pairs nat\u00adu\u00adral\u00adly with <a href=\"https:\/\/www.graveiensai.com\/sensor-fusion-lidar\">3D point cloud and LiDAR anno\u00adta\u00adtion<\/a>, where objects are labeled direct\u00adly in a depthac\u00adcu\u00adrate 3D space.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Among all anno\u00adta\u00adtion meth\u00adods, cuboids demand the most spa\u00adtial rea\u00adson\u00ading from anno\u00adta\u00adtors, which is why <a href=\"https:\/\/www.graveiensai.com\/adas\">ADAS and autonomous dri\u00adving pro\u00adgrams<\/a> typ\u00adi\u00adcal\u00adly rely on spe\u00adcial\u00adized, heav\u00adi\u00adly QAre\u00adviewed teams rather than a gen\u00ader\u00adal crowd.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Best for:<\/strong> autonomous dri\u00adving, robot\u00adics, and any task need\u00ading depth and ori\u00aden\u00adta\u00adtion.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>4. Semantic segmentation<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Seman\u00adtic seg\u00admen\u00adta\u00adtion assigns a class label to every sin\u00adgle pix\u00adel in an image, so a \u201croad,\u201d \u201csky,\u201d \u201ccar,\u201d or \u201ctree\u201d is under\u00adstood at the pix\u00adel lev\u00adel rather than as a box. There are no sep\u00ada\u00adrate object iden\u00adti\u00adties \u2014 all pix\u00adels of the same class share one label which makes it ide\u00adal for under\u00adstand\u00ading the over\u00adall com\u00adpo\u00adsi\u00adtion of a scene.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This is the most detailed way to teach a mod\u00adel <em>what<\/em> is present every\u00adwhere in a frame. Autonomous vehi\u00adcles use it to know exact\u00adly where the dri\u00advable road sur\u00adface ends. Med\u00adical imag\u00ading teams use it to out\u00adline organs and lesions with pix\u00adel accu\u00adra\u00adcy, which is why <a href=\"https:\/\/www.graveiensai.com\/healthcare\">health\u00adcare AI data pro\u00adgrams<\/a> lean on seg\u00admen\u00adta\u00adtion far more than on box\u00ades. Pub\u00adlic bench\u00admarks like Cityscapes pro\u00advide dense pix\u00adellev\u00adel label\u00ading across rough\u00adly 30 class\u00ades, giv\u00ading teams a ref\u00ader\u00adence for how demand\u00ading this label\u00ading can be.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The trade\u00adoff is effort: seman\u00adtic seg\u00admen\u00adta\u00adtion is among the slow\u00adest and most expen\u00adsive anno\u00adta\u00adtion meth\u00adods, often tak\u00ading min\u00adutes per image. When bud\u00adgets are tight, teams reserve it for the class\u00ades where pix\u00adel pre\u00adci\u00adsion gen\u00aduine\u00adly changes the mod\u00adel\u2019s behav\u00adior.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Best for:<\/strong> scene under\u00adstand\u00ading, med\u00adical imag\u00ading, and dri\u00advablearea detec\u00adtion.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>5. Instance segmentation<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Instance seg\u00admen\u00adta\u00adtion com\u00adbines the pix\u00adel pre\u00adci\u00adsion of seman\u00adtic label\u00ading with object iden\u00adti\u00adty: it labels every pix\u00adel <em>and<\/em> sep\u00ada\u00adrates each object of the same class into a dis\u00adtinct instance. Ten peo\u00adple in a crowd become ten indi\u00advid\u00adu\u00adal\u00adly masked peo\u00adple, not one undi\u00advid\u00aded \u201cper\u00adson\u201d region.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This mat\u00adters when\u00adev\u00ader count\u00ading or track\u00ading indi\u00advid\u00adual objects is the goal. A ware\u00adhouse robot needs to dis\u00adtin\u00adguish one box from the box behind it; a cell\u00adbi\u00adol\u00ado\u00adgy mod\u00adel needs each cell count\u00aded sep\u00ada\u00adrate\u00adly. Instance seg\u00admen\u00adta\u00adtion gives mod\u00adels that per\u00adob\u00adject clar\u00adi\u00adty while keep\u00ading pix\u00adellev\u00adel accu\u00adra\u00adcy, at the cost of being one of the most labor\u00adin\u00adten\u00adsive anno\u00adta\u00adtion meth\u00adods avail\u00adable.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Because it blends two dis\u00adci\u00adplines, instance seg\u00admen\u00adta\u00adtion projects ben\u00ade\u00adfit from a rig\u00ador\u00adous review loop the kind of <a href=\"https:\/\/www.graveiensai.com\/data-validation\">inde\u00adpen\u00addent data val\u00adi\u00adda\u00adtion and qual\u00adi\u00adty review<\/a> that catch\u00ades merged or split masks before they poi\u00adson a train\u00ading set.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Best for:<\/strong> crowd\u00aded scenes, per\u00adob\u00adject count\u00ading, and pre\u00adcise track\u00ading.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>6. Panoptic segmentation<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Panop\u00adtic seg\u00admen\u00adta\u00adtion uni\u00adfies seman\u00adtic and instance seg\u00admen\u00adta\u00adtion into a sin\u00adgle out\u00adput: count\u00adable \u201cthings\u201d (cars, peo\u00adple) get indi\u00advid\u00adual instance masks, while amor\u00adphous \u201cstuff\u201d (road, sky, grass) gets a sin\u00adgle seman\u00adtic label. The result is a com\u00adplete, gapfree under\u00adstand\u00ading of every pix\u00adel in the scene.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This is the most com\u00adpre\u00adhen\u00adsive of all anno\u00adta\u00adtion meth\u00adods and the most demand\u00ading to pro\u00adduce, so it is usu\u00adal\u00adly reserved for high\u00adstakes per\u00adcep\u00adtion sys\u00adtems full self\u00addriv\u00ading stacks, advanced robot\u00adics, and research bench\u00admarks where noth\u00ading in the frame can be left unla\u00adbeled. Teams that reach for panop\u00adtic seg\u00admen\u00adta\u00adtion almost always run it through a mature, <a href=\"https:\/\/www.graveiensai.com\/process\">doc\u00adu\u00adment\u00aded anno\u00adta\u00adtion process<\/a> to keep qual\u00adi\u00adty con\u00adsis\u00adtent across large vol\u00adumes.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Best for:<\/strong> com\u00adplete scene per\u00adcep\u00adtion where both objects and back\u00adground mat\u00adter.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>7. Keypoint and landmark annotation<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Key\u00adpoint anno\u00adta\u00adtion, also called land\u00admark anno\u00adta\u00adtion, marks spe\u00adcif\u00adic points of inter\u00adest on an object the cor\u00adners of the eyes and mouth on a face, or the joints of a human body. Con\u00adnect\u00ading those points reveals struc\u00adture, pose, and move\u00adment.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">It is the back\u00adbone of pose esti\u00adma\u00adtion, ges\u00adture recog\u00adni\u00adtion, and facial analy\u00adsis. Fit\u00adness apps track body key\u00adpoints to check exer\u00adcise form; AR fil\u00adters map facial land\u00admarks to place effects accu\u00adrate\u00adly, a com\u00admon need in <a href=\"https:\/\/www.graveiensai.com\/ar-vr\">AR\/VR and spa\u00adtial com\u00adput\u00ading<\/a> datasets. Key\u00adpoint anno\u00adta\u00adtion is pre\u00adcise but sen\u00adsi\u00adtive: a land\u00admark placed a few pix\u00adels off can mean\u00ading\u00adful\u00adly degrade a pose mod\u00adel, so con\u00adsis\u00adten\u00adcy across anno\u00adta\u00adtors is crit\u00adi\u00adcal.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Best for:<\/strong> pose esti\u00adma\u00adtion, facial land\u00admark detec\u00adtion, and ges\u00adture recog\u00adni\u00adtion.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>8. Polyline and line annotation<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Poly\u00adline anno\u00adta\u00adtion draws open, con\u00adnect\u00aded line seg\u00adments that fol\u00adlow a path rather than enclos\u00ading an area. Unlike a poly\u00adgon, a poly\u00adline nev\u00ader clos\u00ades, which makes it the nat\u00adur\u00adal fit for lin\u00adear fea\u00adtures.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Lane detec\u00adtion is the clas\u00adsic exam\u00adple: self dri\u00adving and dri\u00adver assis\u00adtance sys\u00adtems trace lane mark\u00adings, road edges, and rail tracks as poly\u00adlines. Infra\u00adstruc\u00adture inspec\u00adtion uses the same method to mark cracks in pave\u00adment or pipelines. Because lane geom\u00ade\u00adtry feeds direct\u00adly into steer\u00ading deci\u00adsions, poly\u00adline work is a sta\u00adple of <a href=\"https:\/\/www.graveiensai.com\/automotive\">auto\u00admo\u00adtive and in cab\u00adin data pro\u00adgrams<\/a>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Best for:<\/strong> lanes, roads, rails, wiring, and crack or defect lines.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>9. Image classification<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Image clas\u00adsi\u00adfi\u00adca\u00adtion is the sim\u00adplest form of anno\u00adta\u00adtion: instead of locat\u00ading any\u00adthing, it assigns one or more labels to the entire image. A pho\u00adto is tagged \u201ccat,\u201d a scan is tagged \u201cpneu\u00admo\u00adnia \/ nor\u00admal,\u201d or a prod\u00aduct shot is tagged by cat\u00ade\u00adgo\u00adry. There is no local\u00adiza\u00adtion it answers <em>what<\/em> the image con\u00adtains, not <em>where<\/em>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Because image clas\u00adsi\u00adfi\u00adca\u00adtion only requires a deci\u00adsion rather than a draw\u00ading, it is the fastest and cheap\u00adest anno\u00adta\u00adtion type to pro\u00adduce at scale. It pow\u00aders con\u00adtent sort\u00ading, local-iza\u00adtion ital tilage, and the tag\u00adging lay\u00aders behind search and rec\u00adom\u00admen\u00adda\u00adtion. Many teams com\u00adbine it with detec\u00adtion: a first mod\u00adel uses clas\u00adsi\u00adfi\u00adca\u00adtion to fil\u00adter rel\u00ade\u00advant frames, then a detec\u00adtor local\u00adizes objects inside them.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Image clas\u00adsi\u00adfi\u00adca\u00adtion also under\u00adpins con\u00adtent safe\u00adty work\u00adflows, where images are labeled as safe or unsafe before they reach users, the same prin\u00adci\u00adple behind <a href=\"https:\/\/www.graveiensai.com\/content-moderation\">con\u00adtent mod\u00ader\u00ada\u00adtion data ser\u00advices<\/a>. Despite its sim\u00adplic\u00adi\u00adty, it still demands clear guide\u00adlines, since ambigu\u00adous labels are the most com\u00admon cause of a noisy clas\u00adsi\u00adfi\u00adca\u00adtion dataset.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Best for:<\/strong> whole image tag\u00adging, con\u00adtent sort\u00ading, triage, and rec\u00adom\u00admen\u00adda\u00adtion.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>How to choose the right annotation type<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The right method fol\u00adlows from three ques\u00adtions: What does the mod\u00adel need to <em>do<\/em>? How much pre\u00adci\u00adsion does that require? And what is your bud\u00adget per image?<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Detect or count objects?<\/strong> Start with bound\u00ading box object labelling; move to poly\u00adgons only if rec\u00adtan\u00adgles hurt accu\u00adra\u00adcy.<\/li>\n\n\n\n<li><strong>Under\u00adstand every pix\u00adel of a scene?<\/strong> Use seman\u00adtic seg\u00admen\u00adta\u00adtion, or instance\/panoptic seg\u00admen\u00adta\u00adtion if you also need object iden\u00adti\u00adty.<\/li>\n\n\n\n<li><strong>Rea\u00adson about depth?<\/strong> Use 3D cuboids, usu\u00adal\u00adly fused with LiDAR.<\/li>\n\n\n\n<li><strong>Track pose or struc\u00adture?<\/strong> Use key\u00adpoint anno\u00adta\u00adtion.<\/li>\n\n\n\n<li><strong>Fol\u00adlow lin\u00adear fea\u00adtures?<\/strong> Use poly\u00adlines.<\/li>\n\n\n\n<li><strong>Just label the whole image?<\/strong> Use image clas\u00adsi\u00adfi\u00adca\u00adtion.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">A prac\u00adti\u00adcal tac\u00adtic is to start sim\u00adple and esca\u00adlate. Many pro\u00adduc\u00adtion pipelines boot\u00adstrap with bound\u00ading box\u00ades, mea\u00adsure mod\u00adel per\u00adfor\u00admance, and upgrade only the class\u00ades that need fin\u00ader anno\u00adta\u00adtion meth\u00adods. This staged approach pro\u00adtects bud\u00adget while still reach\u00ading the accu\u00adra\u00adcy tar\u00adget. Sus\u00adtained anno\u00adta\u00adtion at scale also depends on a reli\u00adable, vet\u00adted label\u00ading team \u2014 the kind of <a href=\"https:\/\/www.graveiensai.com\/workforce\">spe\u00adcial\u00adized anno\u00adta\u00adtion work\u00adforce<\/a> that keeps qual\u00adi\u00adty steady as vol\u00adumes grow.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Annotation formats: COCO, YOLO, and Pascal VOC<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Whichev\u00ader anno\u00adta\u00adtion type you choose, the labels are stored in a stan\u00addard\u00adized for\u00admat so your train\u00ading frame\u00adwork can read them. The three dom\u00adi\u00adnant for\u00admats are COCO (JSON, sup\u00adports box\u00ades, seg\u00admen\u00adta\u00adtion, and key\u00adpoints \u2014 the COCO dataset spans 80 object cat\u00ade\u00adgories), YOLO (light\u00adweight text files, one row per object, opti\u00admized for the YOLO detec\u00adtor fam\u00adi\u00adly), and Pas\u00adcal VOC (XML per image, a long\u00adstand\u00ading detec\u00adtion bench\u00admark).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Get\u00adting the for\u00admat right mat\u00adters as much as the labels them\u00adselves. A per\u00adfect set of seg\u00admen\u00adta\u00adtion masks in the wrong schema will fail to load, so con\u00adfirm the tar\u00adget for\u00admat before anno\u00adta\u00adtion begins. These for\u00admats also make datasets portable across anno\u00adta\u00adtion and <a href=\"https:\/\/www.graveiensai.com\/generative-ai\">gen\u00ader\u00ada\u00adtive AI and mod\u00adel train\u00ading work\u00adflows<\/a>, which increas\u00ading\u00adly con\u00adsume the same labeled visu\u00adal data for mul\u00adti\u00admodal train\u00ading.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Best practices and quality assurance<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The best anno\u00adta\u00adtion type still fails with\u00adout dis\u00adci\u00adplined qual\u00adi\u00adty con\u00adtrol. Across thou\u00adsands of pro\u00adduc\u00adtion pro\u00adgrams, the same prac\u00adtices sep\u00ada\u00adrate mod\u00adel ready datasets from noisy ones.<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Write a pre\u00adcise anno\u00adta\u00adtion guide mod\u00adel Ready <\/strong>beled exam\u00adples and edge cas\u00ades before work starts ambi\u00adgu\u00adi\u00adty is the top source of label noise.<\/li>\n\n\n\n<li><strong>Run a small pilot batch<\/strong> and mea\u00adsure inter\u00adan\u00adno\u00adta\u00adtor agree\u00adment before scal\u00ading.<\/li>\n\n\n\n<li><strong>Use a mul\u00adti\u00adstage review loop cre\u00adate,<\/strong> inter\u00adnal review, client review, and rework \u2014 rather than sin\u00adglepass label\u00ading.<\/li>\n\n\n\n<li><strong>Track qual\u00adi\u00adty with met\u00adrics<\/strong> such as Inter\u00adsec\u00adtion over Union (IoU) for box\u00ades and masks; an IoU above 0.90 against a gold set is a com\u00admon pro\u00adduc\u00adtion bar.<\/li>\n\n\n\n<li><strong>Ver\u00adsion your dataset and guide\u00adlines<\/strong> so changes are auditable over time.<\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\">This is exact\u00adly the fourstage QA work\u00adflow Graveiens AI runs under its ISO 9001:2017 cer\u00adti\u00adfi\u00adca\u00adtion, and it is <a href=\"https:\/\/www.graveiensai.com\/why-choose-us\">why teams choose us<\/a> for high\u00adpre\u00adci\u00adsion pro\u00adgrams where a sin\u00adgle sys\u00adtem\u00adat\u00adic label\u00ading error can com\u00adpro\u00admise an entire train\u00ading run. For textheavy pipelines, the same rig\u00ador extends to our <a href=\"https:\/\/www.graveiensai.com\/nlp\">nat\u00adur\u00adal lan\u00adguage pro\u00adcess\u00ading anno\u00adta\u00adtion<\/a> work.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Down\u00adload:<\/strong> Use our free <strong>Image Anno\u00adta\u00adtion Type Selec\u00adtion Check\u00adlist task,<\/strong> required pre\u00adci\u00adsion, bud\u00adget per image, rec\u00adom\u00admend\u00aded anno\u00adta\u00adtion type, and out\u00adput for\u00admat to stan\u00addard\u00adize the deci\u00adsion across your team. <a href=\"https:\/\/www.graveiensai.com\/contact-us\">Request the check\u00adlist<\/a>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>F<\/strong>AQS for Types of Image Annotation<\/h2>\n\n\n<div id=\"rank-math-faq\" class=\"rank-math-block\">\n<ol class=\"rank-math-list \">\n<li id=\"faq-question-1785569520977\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \">What are the main types of image annotation?<\/h3>\n<div class=\"rank-math-answer \">\n\n<p>The main types of image anno\u00adta\u00adtion are bound\u00ading box anno\u00adta\u00adtion, poly\u00adgon anno\u00adta\u00adtion, 3D cuboid anno\u00adta\u00adtion, seman\u00adtic seg\u00admen\u00adta\u00adtion, instance seg\u00admen\u00adta\u00adtion, panop\u00adtic seg\u00admen\u00adta\u00adtion, key\u00adpoint (land\u00admark) anno\u00adta\u00adtion, poly\u00adline anno\u00adta\u00adtion, and image clas\u00adsi\u00adfi\u00adca\u00adtion. Each labels images dif\u00adfer\u00adent\u00adly to train a spe\u00adcif\u00adic kind of com\u00adput\u00ader vision mod\u00adel.<\/p>\n\n<\/div>\n<\/li>\n<li id=\"faq-question-1785569572700\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \">What is the difference between object labeling and image classification?<\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Object labelling locates objects with\u00adin an image, for exam\u00adple, draw\u00ading a box around each car so the mod\u00adel learns <em>where<\/em> things are. Image clas\u00adsi\u00adfi\u00adca\u00adtion assigns a sin\u00adgle label to the whole image and answers only <em>what<\/em> it con\u00adtains, with no loca\u00adtion infor\u00adma\u00adtion. Detec\u00adtion tasks need object\u00adla\u00adbel\u00ading; sort\u00adingg or tag\u00adging tasks can use whole image tag\u00adging.<\/p>\n\n<\/div>\n<\/li>\n<li id=\"faq-question-1785569597745\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \">When should I use semantic segmentation instead of bounding boxes?<\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Use seman\u00adtic seg\u00admen\u00adta\u00adtion when your mod\u00adel needs pix\u00adellev\u00adel under\u00adstand\u00ading, such as iden\u00adti\u00adfy\u00ading the exact dri\u00advable road sur\u00adface or out\u00adlin\u00ading an organ in a med\u00adical scan. Bound\u00ading box\u00ades are faster and cheap\u00ader, but they include back\u00adground pix\u00adels and can\u00adnot cap\u00adture pre\u00adcise shape. If exact bound\u00adaries change your mod\u00adel\u2019s deci\u00adsions, it is worth the extra cost.<\/p>\n\n<\/div>\n<\/li>\n<li id=\"faq-question-1785569620196\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \">What is the difference between semantic and instance segmentation?<\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Seman\u00adtic seg\u00admen\u00adta\u00adtion labels every pix\u00adel by class but treats all objects of one class as a sin\u00adgle region. Instance seg\u00admen\u00adta\u00adtion also labels pix\u00adels but sep\u00ada\u00adrates each object into its own instance, so ten cars become ten dis\u00adtinct masks. Choose instance seg\u00admen\u00adta\u00adtion when you need to count or track indi\u00advid\u00adual objects.<\/p>\n\n<\/div>\n<\/li>\n<li id=\"faq-question-1785569644417\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \">Which type of image annotation is the most accurate?<\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Panop\u00adtic seg\u00admen\u00adta\u00adtion is the most com\u00adpre\u00adhen\u00adsive, because it labels every pix\u00adel and sep\u00ada\u00adrates indi\u00advid\u00adual objects, com\u00adbin\u00ading seman\u00adtic and instance seg\u00admen\u00adta\u00adtion. It is also the most expen\u00adsive and time\u00adcon\u00adsum\u00ading, so it is reserved for high\u00adstakes per\u00adcep\u00adtion sys\u00adtems like autonomous dri\u00adving where com\u00adplete scene under\u00adstand\u00ading is required.<\/p>\n\n<\/div>\n<\/li>\n<li id=\"faq-question-1785569670482\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \">How much does image annotation cost?<\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Cost depends almost entire\u00adly on the anno\u00adta\u00adtion type and com\u00adplex\u00adi\u00adty. A bound\u00ading box may take sec\u00adonds, while a detailed seg\u00admen\u00adta\u00adtion mask can take sev\u00ader\u00adal min\u00adutes per object \u2014 so seg\u00admen\u00adta\u00adtion can cost 10x or more than object labelling object soe image. Providers that invoice only for approved deliv\u00ader\u00adables reduce the risk of pay\u00ading for rework.<\/p>\n\n<\/div>\n<\/li>\n<li id=\"faq-question-1785569689699\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \">Which annotation type is best for autonomous vehicles?<\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Autonomous vehi\u00adcles use sev\u00ader\u00adal anno\u00adta\u00adtion meth\u00adods togeth\u00ader: 3D cuboids and seman\u00adtic seg\u00admen\u00adta\u00adtion for per\u00adcep\u00adtion, poly\u00adlines for lane detec\u00adtion, and bound\u00ading box\u00ades for fast object detec\u00adtion. These are usu\u00adal\u00adly fused with LiDAR point\u00adcloud anno\u00adta\u00adtion to give the vehi\u00adcle a depthac\u00adcu\u00adrate 3D mod\u00adel of its sur\u00adround\u00adings.<\/p>\n\n<\/div>\n<\/li>\n<\/ol>\n<\/div>\n\n\n<h2 class=\"wp-block-heading\">Conclusion<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">There is no sin\u00adgle best method \u2014 only the right one for your task. The types of image anno\u00adta\u00adtion range from fast, low\u00adcost object labelling with bound\u00ading box\u00ades to pix\u00adelper\u00adfect seg\u00admen\u00adta\u00adtion and full panop\u00adtic scene under\u00adstand\u00ading, and the smart move is to match the tech\u00adnique to the pre\u00adci\u00adsion your mod\u00adel actu\u00adal\u00adly needs, then esca\u00adlate only where accu\u00adra\u00adcy demands it. Start sim\u00adple, mea\u00adsure, and upgrade delib\u00ader\u00adate\u00adly.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">If you\u2019re plan\u00adning a com\u00adput\u00ader vision pro\u00adgram and want an expe\u00adri\u00adenced part\u00adner to han\u00addle the label\u00ading, Graveiens AI deliv\u00aders every anno\u00adta\u00adtion type in this guide through an ISO 9001:2017 fourstage QA work\u00adflow \u2014 and invoic\u00ades only for the work you approve. <a href=\"https:\/\/www.graveiensai.com\/contact-us\">Book a lowrisk pilot<\/a> with a sam\u00adple batch on your own data.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><em>Sources: <\/em><a href=\"https:\/\/docs.ultralytics.com\/guides\/data-collection-and-annotation\/\" target=\"_blank\" rel=\"noopener\"><em>Ultr\u00ada\u00adlyt\u00adics \u2014 Data Col\u00adlec\u00adtion and Anno\u00adta\u00adtion Guide<\/em><\/a><em>, <\/em><a href=\"https:\/\/www.telusdigital.com\/insights\/data-and-ai\/article\/an-introduction-to-5-types-of-image-annotation\" target=\"_blank\" rel=\"noopener\"><em>TELUS Dig\u00adi\u00adtal \u2014 5 Types of Image Anno\u00adta\u00adtion<\/em><\/a><em>, <\/em><a href=\"https:\/\/labelbox.com\/guides\/image-annotation\/\" target=\"_blank\" rel=\"noopener\"><em>Label\u00adbox \u2014 Image Anno\u00adta\u00adtion Best Prac\u00adtices<\/em><\/a><em>, <\/em><a href=\"https:\/\/cocodataset.org\/\" target=\"_blank\" rel=\"noopener\"><em>COCO Dataset<\/em><\/a><em>, <\/em><a href=\"https:\/\/www.cityscapes-dataset.com\/\" target=\"_blank\" rel=\"noopener\"><em>Cityscapes Dataset<\/em><\/a><em>.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Quick answer: The main types of image anno\u00adta\u00adtion are bound\u00ading box anno\u00adta\u00adtion, poly\u00adgon anno\u00adta\u00adtion, 3D cuboid anno\u00adta\u00adtion, seman\u00adtic seg\u00admen\u00adta\u00adtion, instance seg\u00admen\u00adta\u00adtion, panop\u00adtic seg\u00admen\u00adta\u00adtion, key\u00adpoint (land\u00admark) anno\u00adta\u00adtion, poly\u00adline anno\u00adta\u00adtion,\u2026<\/p>\n","protected":false},"author":1,"featured_media":62,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"wp_typography_post_enhancements_disabled":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-58","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"_links":{"self":[{"href":"https:\/\/www.graveiensai.com\/blog\/wp-json\/wp\/v2\/posts\/58","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.graveiensai.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.graveiensai.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.graveiensai.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.graveiensai.com\/blog\/wp-json\/wp\/v2\/comments?post=58"}],"version-history":[{"count":1,"href":"https:\/\/www.graveiensai.com\/blog\/wp-json\/wp\/v2\/posts\/58\/revisions"}],"predecessor-version":[{"id":60,"href":"https:\/\/www.graveiensai.com\/blog\/wp-json\/wp\/v2\/posts\/58\/revisions\/60"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.graveiensai.com\/blog\/wp-json\/wp\/v2\/media\/62"}],"wp:attachment":[{"href":"https:\/\/www.graveiensai.com\/blog\/wp-json\/wp\/v2\/media?parent=58"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.graveiensai.com\/blog\/wp-json\/wp\/v2\/categories?post=58"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.graveiensai.com\/blog\/wp-json\/wp\/v2\/tags?post=58"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}