NEW OpenAI Structured Outputs with Label Studio 🚀

YOLO ML backend for Label Studio

The YOLO ML backend for Label Studio is designed to integrate advanced object detection, segmentation, classification, and video object tracking capabilities directly into Label Studio.

This integration allows you to leverage powerful YOLOv8 models for various machine learning tasks, making it easier to annotate large datasets and ensure high-quality predictions.

Supported Features

YOLO Task Name LS Control Tag Prediction Supported LS Import Supported LS Export Supported
Object Detection <RectangleLabels> ✅ YOLO, COCO YOLO, COCO
Oriented Bounding Boxes (OBB) <RectangleLabels model_obb="true"> ✅ YOLO YOLO
Image Instance Segmentation: Polygons <PolygonLabels> ✅ COCO YOLO, COCO
Image Semantic Segmentation: Masks <BrushLabels> ❌ Native Native
Image Classification <Choices> ✅ Native Native
Pose Detection <KeyPoints> ✅ Native Native
Video Object Tracking <VideoRectangle> ✅ Native Native
Video Temporal Classification <TimelineLabels> ✅ Native Native
  • LS Control Tag: Label Studio control tag from the labeling configuration.
  • LS Import Supported: Indicates whether Label Studio supports Import from YOLO format to Label Studio (using the LS converter).
  • LS Export Supported: Indicates whether Label Studio supports Export from Label Studio to YOLO format (the Export button on the Data Manager and using the LS converter).
  • Native: Native means that only native Label Studio JSON format is supported.

Before you begin

Before you begin, you need to install the Label Studio ML backend.

This tutorial uses the YOLO example.

Quick start

  1. Add LABEL_STUDIO_URL and LABEL_STUDIO_API_KEY to the docker-compose.yml file. These variables should point to your Label Studio instance and its API key, respectively. For more information about finding your Label Studio API key, see our documentation.

  2. Run docker compose

    docker-compose up --build

  3. Open Label Studio and create a new project with the following labeling config:

    <View>
  <Image name="image" value="$image"/>
  <RectangleLabels name="label" toName="image" model_score_threshold="0.25">
    <Label value="Car" background="blue" predicted_values="jeep,cab,limousine,truck"/>
  </RectangleLabels>
</View>

  4. Then from the Model page in the project settings, connect the model. The default URL is http://localhost:9090.

  5. Add images or video (depending on tasks you are going to solve) to Label Studio.

  6. Open any task in the Data Manager and see the predictions from the YOLO model.

Labeling configurations

Supported object & control tags

Object tags

Control tags

How to skip the control tag?

If you don’t want to use the ML backend for some control tags, you can force skipping by adding the model_skip="true" attribute to the control tag:

<Choices name="choice" toName="image" model_skip="true">

Mixed object and control tags

You can mix different object and control tags in one project. The YOLO model will detect all known control tags and make predictions for them. For example: 

<View>
  <Image name="image1" value="$image"/>
  <RectangleLabels name="label" toName="image1" model_score_threshold="0.1">
    <Label value="person" background="red"/>
    <Label value="car" background="blue"/>
  </RectangleLabels>
  
  <Image name="image2" value="$image"/>
  <Choices name="choice" toName="image2" model_score_threshold="0.1">
    <Choice value="airship"/>
    <Choice value="passenger_car"/>
  </Choices>
</View>

In this example, both RectangleLabels and Choices will be detected and predicted by the YOLO model.

You can also use different YOLO models for the same task to compare them visually:

<View>
  <Image name="image1" value="$image"/>
  <RectangleLabels name="label1" toName="image1" model_path="yolov8n.pt" model_score_threshold="0.1">
    <Label value="car" background="blue"/>
  </RectangleLabels>
  <RectangleLabels name="label2" toName="image1" model_path="yolov8m.pt" model_score_threshold="0.1">
    <Label value="car" background="red"/>
  </RectangleLabels>
</View>

Label and choice mapping

graph TD
    A[Label Studio :: Labeling Config :: Labels or Choices] <--> B[ML Model :: Names]

If you use a common YOLO model, you have to add mapping between your labels and the ML model labels. By default, the YOLO ML backend will use the same (or lowercased) names as you specified in the value attribute.

In this example the label “Jeep” will be mapped to “jeep” in the ML model:

<Choice value="Jeep"/>

For more precise control you can use the predicted_values attribute to specify multiple and different labels from the ML model:

<Choice value="Car" predicted_values="jeep,cab,limousine"/>
Tip: How to find all YOLO model names?
Labels are printed in the ML model logs when you start using the ML backend with the INFO logging level.

Or you can find some labels in YOLO_CLASSES.md

Tip: How to map my labels to YOLO names using an LLM?
You can use an LLM model (e.g. ChatGPT) to automatically build mapping between Label Studio labels and ML model labels. Here is an example of a prompt for this. It includes 1000 labels from YOLOv8 classification model (`yolov8n-cls`). 
**Task:**

1. **ML Model Labels:**
   - I have the following labels in my ML model:
['tench', 'goldfish', 'great_white_shark', 'tiger_shark', 'hammerhead', 'electric_ray', 'stingray', 'cock', 'hen', 'ostrich', 'brambling', 'goldfinch', 'house_finch', 'junco', 'indigo_bunting', 'robin', 'bulbul', 'jay', 'magpie', 'chickadee', 'water_ouzel', 'kite', 'bald_eagle', 'vulture', 'great_grey_owl', 'European_fire_salamander', 'common_newt', 'eft', 'spotted_salamander', 'axolotl', 'bullfrog', 'tree_frog', 'tailed_frog', 'loggerhead', 'leatherback_turtle', 'mud_turtle', 'terrapin', 'box_turtle', 'banded_gecko', 'common_iguana', 'American_chameleon', 'whiptail', 'agama', 'frilled_lizard', 'alligator_lizard', 'Gila_monster', 'green_lizard', 'African_chameleon', 'Komodo_dragon', 'African_crocodile', 'American_alligator', 'triceratops', 'thunder_snake', 'ringneck_snake', 'hognose_snake', 'green_snake', 'king_snake', 'garter_snake', 'water_snake', 'vine_snake', 'night_snake', 'boa_constrictor', 'rock_python', 'Indian_cobra', 'green_mamba', 'sea_snake', 'horned_viper', 'diamondback', 'sidewinder', 'trilobite', 'harvestman', 'scorpion', 'black_and_gold_garden_spider', 'barn_spider', 'garden_spider', 'black_widow', 'tarantula', 'wolf_spider', 'tick', 'centipede', 'black_grouse', 'ptarmigan', 'ruffed_grouse', 'prairie_chicken', 'peacock', 'quail', 'partridge', 'African_grey', 'macaw', 'sulphur-crested_cockatoo', 'lorikeet', 'coucal', 'bee_eater', 'hornbill', 'hummingbird', 'jacamar', 'toucan', 'drake', 'red-breasted_merganser', 'goose', 'black_swan', 'tusker', 'echidna', 'platypus', 'wallaby', 'koala', 'wombat', 'jellyfish', 'sea_anemone', 'brain_coral', 'flatworm', 'nematode', 'conch', 'snail', 'slug', 'sea_slug', 'chiton', 'chambered_nautilus', 'Dungeness_crab', 'rock_crab', 'fiddler_crab', 'king_crab', 'American_lobster', 'spiny_lobster', 'crayfish', 'hermit_crab', 'isopod', 'white_stork', 'black_stork', 'spoonbill', 'flamingo', 'little_blue_heron', 'American_egret', 'bittern', 'crane_(bird)', 'limpkin', 'European_gallinule', 'American_coot', 'bustard', 'ruddy_turnstone', 'red-backed_sandpiper', 'redshank', 'dowitcher', 'oystercatcher', 'pelican', 'king_penguin', 'albatross', 'grey_whale', 'killer_whale', 'dugong', 'sea_lion', 'Chihuahua', 'Japanese_spaniel', 'Maltese_dog', 'Pekinese', 'Shih-Tzu', 'Blenheim_spaniel', 'papillon', 'toy_terrier', 'Rhodesian_ridgeback', 'Afghan_hound', 'basset', 'beagle', 'bloodhound', 'bluetick', 'black-and-tan_coonhound', 'Walker_hound', 'English_foxhound', 'redbone', 'borzoi', 'Irish_wolfhound', 'Italian_greyhound', 'whippet', 'Ibizan_hound', 'Norwegian_elkhound', 'otterhound', 'Saluki', 'Scottish_deerhound', 'Weimaraner', 'Staffordshire_bullterrier', 'American_Staffordshire_terrier', 'Bedlington_terrier', 'Border_terrier', 'Kerry_blue_terrier', 'Irish_terrier', 'Norfolk_terrier', 'Norwich_terrier', 'Yorkshire_terrier', 'wire-haired_fox_terrier', 'Lakeland_terrier', 'Sealyham_terrier', 'Airedale', 'cairn', 'Australian_terrier', 'Dandie_Dinmont', 'Boston_bull', 'miniature_schnauzer', 'giant_schnauzer', 'standard_schnauzer', 'Scotch_terrier', 'Tibetan_terrier', 'silky_terrier', 'soft-coated_wheaten_terrier', 'West_Highland_white_terrier', 'Lhasa', 'flat-coated_retriever', 'curly-coated_retriever', 'golden_retriever', 'Labrador_retriever', 'Chesapeake_Bay_retriever', 'German_short-haired_pointer', 'vizsla', 'English_setter', 'Irish_setter', 'Gordon_setter', 'Brittany_spaniel', 'clumber', 'English_springer', 'Welsh_springer_spaniel', 'cocker_spaniel', 'Sussex_spaniel', 'Irish_water_spaniel', 'kuvasz', 'schipperke', 'groenendael', 'malinois', 'briard', 'kelpie', 'komondor', 'Old_English_sheepdog', 'Shetland_sheepdog', 'collie', 'Border_collie', 'Bouvier_des_Flandres', 'Rottweiler', 'German_shepherd', 'Doberman', 'miniature_pinscher', 'Greater_Swiss_Mountain_dog', 'Bernese_mountain_dog', 'Appenzeller', 'EntleBucher', 'boxer', 'bull_mastiff', 'Tibetan_mastiff', 'French_bulldog', 'Great_Dane', 'Saint_Bernard', 'Eskimo_dog', 'malamute', 'Siberian_husky', 'dalmatian', 'affenpinscher', 'basenji', 'pug', 'Leonberg', 'Newfoundland', 'Great_Pyrenees', 'Samoyed', 'Pomeranian', 'chow', 'keeshond', 'Brabancon_griffon', 'Pembroke', 'Cardigan', 'toy_poodle', 'miniature_poodle', 'standard_poodle', 'Mexican_hairless', 'timber_wolf', 'white_wolf', 'red_wolf', 'coyote', 'dingo', 'dhole', 'African_hunting_dog', 'hyena', 'red_fox', 'kit_fox', 'Arctic_fox', 'grey_fox', 'tabby', 'tiger_cat', 'Persian_cat', 'Siamese_cat', 'Egyptian_cat', 'cougar', 'lynx', 'leopard', 'snow_leopard', 'jaguar', 'lion', 'tiger', 'cheetah', 'brown_bear', 'American_black_bear', 'ice_bear', 'sloth_bear', 'mongoose', 'meerkat', 'tiger_beetle', 'ladybug', 'ground_beetle', 'long-horned_beetle', 'leaf_beetle', 'dung_beetle', 'rhinoceros_beetle', 'weevil', 'fly', 'bee', 'ant', 'grasshopper', 'cricket', 'walking_stick', 'cockroach', 'mantis', 'cicada', 'leafhopper', 'lacewing', 'dragonfly', 'damselfly', 'admiral', 'ringlet', 'monarch', 'cabbage_butterfly', 'sulphur_butterfly', 'lycaenid', 'starfish', 'sea_urchin', 'sea_cucumber', 'wood_rabbit', 'hare', 'Angora', 'hamster', 'porcupine', 'fox_squirrel', 'marmot', 'beaver', 'guinea_pig', 'sorrel', 'zebra', 'hog', 'wild_boar', 'warthog', 'hippopotamus', 'ox', 'water_buffalo', 'bison', 'ram', 'bighorn', 'ibex', 'hartebeest', 'impala', 'gazelle', 'Arabian_camel', 'llama', 'weasel', 'mink', 'polecat', 'black-footed_ferret', 'otter', 'skunk', 'badger', 'armadillo', 'three-toed_sloth', 'orangutan', 'gorilla', 'chimpanzee', 'gibbon', 'siamang', 'guenon', 'patas', 'baboon', 'macaque', 'langur', 'colobus', 'proboscis_monkey', 'marmoset', 'capuchin', 'howler_monkey', 'titi', 'spider_monkey', 'squirrel_monkey', 'Madagascar_cat', 'indri', 'Indian_elephant', 'African_elephant', 'lesser_panda', 'giant_panda', 'barracouta', 'eel', 'coho', 'rock_beauty', 'anemone_fish', 'sturgeon', 'gar', 'lionfish', 'puffer', 'abacus', 'abaya', 'academic_gown', 'accordion', 'acoustic_guitar', 'aircraft_carrier', 'airliner', 'airship', 'altar', 'ambulance', 'amphibian', 'analog_clock', 'apiary', 'apron', 'ashcan', 'assault_rifle', 'backpack', 'bakery', 'balance_beam', 'balloon', 'ballpoint', 'Band_Aid', 'banjo', 'bannister', 'barbell', 'barber_chair', 'barbershop', 'barn', 'barometer', 'barrel', 'barrow', 'baseball', 'basketball', 'bassinet', 'bassoon', 'bathing_cap', 'bath_towel', 'bathtub', 'beach_wagon', 'beacon', 'beaker', 'bearskin', 'beer_bottle', 'beer_glass', 'bell_cote', 'bib', 'bicycle-built-for-two', 'bikini', 'binder', 'binoculars', 'birdhouse', 'boathouse', 'bobsled', 'bolo_tie', 'bonnet', 'bookcase', 'bookshop', 'bottlecap', 'bow', 'bow_tie', 'brass', 'brassiere', 'breakwater', 'breastplate', 'broom', 'bucket', 'buckle', 'bulletproof_vest', 'bullet_train', 'butcher_shop', 'cab', 'caldron', 'candle', 'cannon', 'canoe', 'can_opener', 'cardigan', 'car_mirror', 'carousel', "carpenter's_kit", 'carton', 'car_wheel', 'cash_machine', 'cassette', 'cassette_player', 'castle', 'catamaran', 'CD_player', 'cello', 'cellular_telephone', 'chain', 'chainlink_fence', 'chain_mail', 'chain_saw', 'chest', 'chiffonier', 'chime', 'china_cabinet', 'Christmas_stocking', 'church', 'cinema', 'cleaver', 'cliff_dwelling', 'cloak', 'clog', 'cocktail_shaker', 'coffee_mug', 'coffeepot', 'coil', 'combination_lock', 'computer_keyboard', 'confectionery', 'container_ship', 'convertible', 'corkscrew', 'cornet', 'cowboy_boot', 'cowboy_hat', 'cradle', 'crane_(machine)', 'crash_helmet', 'crate', 'crib', 'Crock_Pot', 'croquet_ball', 'crutch', 'cuirass', 'dam', 'desk', 'desktop_computer', 'dial_telephone', 'diaper', 'digital_clock', 'digital_watch', 'dining_table', 'dishrag', 'dishwasher', 'disk_brake', 'dock', 'dogsled', 'dome', 'doormat', 'drilling_platform', 'drum', 'drumstick', 'dumbbell', 'Dutch_oven', 'electric_fan', 'electric_guitar', 'electric_locomotive', 'entertainment_center', 'envelope', 'espresso_maker', 'face_powder', 'feather_boa', 'file', 'fireboat', 'fire_engine', 'fire_screen', 'flagpole', 'flute', 'folding_chair', 'football_helmet', 'forklift', 'fountain', 'fountain_pen', 'four-poster', 'freight_car', 'French_horn', 'frying_pan', 'fur_coat', 'garbage_truck', 'gasmask', 'gas_pump', 'goblet', 'go-kart', 'golf_ball', 'golfcart', 'gondola', 'gong', 'gown', 'grand_piano', 'greenhouse', 'grille', 'grocery_store', 'guillotine', 'hair_slide', 'hair_spray', 'half_track', 'hammer', 'hamper', 'hand_blower', 'hand-held_computer', 'handkerchief', 'hard_disc', 'harmonica', 'harp', 'harvester', 'hatchet', 'holster', 'home_theater', 'honeycomb', 'hook', 'hoopskirt', 'horizontal_bar', 'horse_cart', 'hourglass', 'iPod', 'iron', "jack-o'-lantern", 'jean', 'jeep', 'jersey', 'jigsaw_puzzle', 'jinrikisha', 'joystick', 'kimono', 'knee_pad', 'knot', 'lab_coat', 'ladle', 'lampshade', 'laptop', 'lawn_mower', 'lens_cap', 'letter_opener', 'library', 'lifeboat', 'lighter', 'limousine', 'liner', 'lipstick', 'Loafer', 'lotion', 'loudspeaker', 'loupe', 'lumbermill', 'magnetic_compass', 'mailbag', 'mailbox', 'maillot_(tights)', 'maillot_(tank_suit)', 'manhole_cover', 'maraca', 'marimba', 'mask', 'matchstick', 'maypole', 'maze', 'measuring_cup', 'medicine_chest', 'megalith', 'microphone', 'microwave', 'military_uniform', 'milk_can', 'minibus', 'miniskirt', 'minivan', 'missile', 'mitten', 'mixing_bowl', 'mobile_home', 'Model_T', 'modem', 'monastery', 'monitor', 'moped', 'mortar', 'mortarboard', 'mosque', 'mosquito_net', 'motor_scooter', 'mountain_bike', 'mountain_tent', 'mouse', 'mousetrap', 'moving_van', 'muzzle', 'nail', 'neck_brace', 'necklace', 'nipple', 'notebook', 'obelisk', 'oboe', 'ocarina', 'odometer', 'oil_filter', 'organ', 'oscilloscope', 'overskirt', 'oxcart', 'oxygen_mask', 'packet', 'paddle', 'paddlewheel', 'padlock', 'paintbrush', 'pajama', 'palace', 'panpipe', 'paper_towel', 'parachute', 'parallel_bars', 'park_bench', 'parking_meter', 'passenger_car', 'patio', 'pay-phone', 'pedestal', 'pencil_box', 'pencil_sharpener', 'perfume', 'Petri_dish', 'photocopier', 'pick', 'pickelhaube', 'picket_fence', 'pickup', 'pier', 'piggy_bank', 'pill_bottle', 'pillow', 'ping-pong_ball', 'pinwheel', 'pirate', 'pitcher', 'plane', 'planetarium', 'plastic_bag', 'plate_rack', 'plow', 'plunger', 'Polaroid_camera', 'pole', 'police_van', 'poncho', 'pool_table', 'pop_bottle', 'pot', "potter's_wheel", 'power_drill', 'prayer_rug', 'printer', 'prison', 'projectile', 'projector', 'puck', 'punching_bag', 'purse', 'quill', 'quilt', 'racer', 'racket', 'radiator', 'radio', 'radio_telescope', 'rain_barrel', 'recreational_vehicle', 'reel', 'reflex_camera', 'refrigerator', 'remote_control', 'restaurant', 'revolver', 'rifle', 'rocking_chair', 'rotisserie', 'rubber_eraser', 'rugby_ball', 'rule', 'running_shoe', 'safe', 'safety_pin', 'saltshaker', 'sandal', 'sarong', 'sax', 'scabbard', 'scale', 'school_bus', 'schooner', 'scoreboard', 'screen', 'screw', 'screwdriver', 'seat_belt', 'sewing_machine', 'shield', 'shoe_shop', 'shoji', 'shopping_basket', 'shopping_cart', 'shovel', 'shower_cap', 'shower_curtain', 'ski', 'ski_mask', 'sleeping_bag', 'slide_rule', 'sliding_door', 'slot', 'snorkel', 'snowmobile', 'snowplow', 'soap_dispenser', 'soccer_ball', 'sock', 'solar_dish', 'sombrero', 'soup_bowl', 'space_bar', 'space_heater', 'space_shuttle', 'spatula', 'speedboat', 'spider_web', 'spindle', 'sports_car', 'spotlight', 'stage', 'steam_locomotive', 'steel_arch_bridge', 'steel_drum', 'stethoscope', 'stole', 'stone_wall', 'stopwatch', 'stove', 'strainer', 'streetcar', 'stretcher', 'studio_couch', 'stupa', 'submarine', 'suit', 'sundial', 'sunglass', 'sunglasses', 'sunscreen', 'suspension_bridge', 'swab', 'sweatshirt', 'swimming_trunks', 'swing', 'switch', 'syringe', 'table_lamp', 'tank', 'tape_player', 'teapot', 'teddy', 'television', 'tennis_ball', 'thatch', 'theater_curtain', 'thimble', 'thresher', 'throne', 'tile_roof', 'toaster', 'tobacco_shop', 'toilet_seat', 'torch', 'totem_pole', 'tow_truck', 'toyshop', 'tractor', 'trailer_truck', 'tray', 'trench_coat', 'tricycle', 'trimaran', 'tripod', 'triumphal_arch', 'trolleybus', 'trombone', 'tub', 'turnstile', 'typewriter_keyboard', 'umbrella', 'unicycle', 'upright', 'vacuum', 'vase', 'vault', 'velvet', 'vending_machine', 'vestment', 'viaduct', 'violin', 'volleyball', 'waffle_iron', 'wall_clock', 'wallet', 'wardrobe', 'warplane', 'washbasin', 'washer', 'water_bottle', 'water_jug', 'water_tower', 'whiskey_jug', 'whistle', 'wig', 'window_screen', 'window_shade', 'Windsor_tie', 'wine_bottle', 'wing', 'wok', 'wooden_spoon', 'wool', 'worm_fence', 'wreck', 'yawl', 'yurt', 'web_site', 'comic_book', 'crossword_puzzle', 'street_sign', 'traffic_light', 'book_jacket', 'menu', 'plate', 'guacamole', 'consomme', 'hot_pot', 'trifle', 'ice_cream', 'ice_lolly', 'French_loaf', 'bagel', 'pretzel', 'cheeseburger', 'hotdog', 'mashed_potato', 'head_cabbage', 'broccoli', 'cauliflower', 'zucchini', 'spaghetti_squash', 'acorn_squash', 'butternut_squash', 'cucumber', 'artichoke', 'bell_pepper', 'cardoon', 'mushroom', 'Granny_Smith', 'strawberry', 'orange', 'lemon', 'fig', 'pineapple', 'banana', 'jackfruit', 'custard_apple', 'pomegranate', 'hay', 'carbonara', 'chocolate_sauce', 'dough', 'meat_loaf', 'pizza', 'potpie', 'burrito', 'red_wine', 'espresso', 'cup', 'eggnog', 'alp', 'bubble', 'cliff', 'coral_reef', 'geyser', 'lakeside', 'promontory', 'sandbar', 'seashore', 'valley', 'volcano', 'ballplayer', 'groom', 'scuba_diver', 'rapeseed', 'daisy', "yellow_lady's_slipper", 'corn', 'acorn', 'hip', 'buckeye', 'coral_fungus', 'agaric', 'gyromitra', 'stinkhorn', 'earthstar', 'hen-of-the-woods', 'bolete', 'ear', 'toilet_tissue']


2. **Labeling Config:**
   - I have this labeling config from Label Studio:
   <View>
     <Image name="image" value="$image"/>
     <Choices name="choice" toName="image">
       <Choice value="Airplane"/>
       <Choice value="Car"/>
     </Choices>
   </View>

3. **Mapping Instructions:**
   - Map the labels from the Label Studio config to the closest matching ML model labels as follows:
     1. Use the `value` attribute from each `<Choice>` tag to identify the label.
     2. Find all similar and relevant labels from the ML model corresponding to each `<Choice>` label.
     3. Add a `predicted_values="<relevant_label1_from_ml_model>,<relevant_label2_from_ml_model>"` attribute inside each `<Choice>` tag using only labels from the ML model.

4. **Output:**
   - Provide the final labeling config with the `predicted_values` attribute added, using all relevant labels from the ML model, without any explanations.

Supported YOLO Versions: YOLOv5, YOLO11, and others

  • YOLOv5: This model is supported for object detection tasks. Make sure to specify model_path="yolov5nu.pt" (don’t forget the u) to use the YOLOv5 model.

  • YOLOv8: These models have been successfully tested with the current ML backend. Check the full list of v8 models here.

  • YOLO11: YOLO11 models have also been successfully tested with this ML backend. Check the full list of v11 models here.

    Warning 1: You must upgrade the ultralytics package to the latest version (pip install -U ultralytics) or rebuild the ML backend Docker from scratch (docker-compose build --no-cache) if you used it before the latest Ultralytics update on Monday, September 30, 2024.

    Warning 2: YOLO11 models do not use the v in their naming convention. For example, use yolo11n.pt instead of yolov11n.pt, unlike the naming convention in YOLOv8.

  • For a full list of supported YOLO versions and models, refer to the Ultralytics documentation: Ultralytics Supported YOLO Models

    Note: Some YOLO models listed in the Ultralytics documentation have not been tested with this ML backend, but they might still work.

Your own custom YOLO models

You can load your own YOLO labels using the following steps:

  1. Mount your model as /app/models/<your-model>.pt inside of your Docker.
  2. Add model_path="<your-model>.pt" to the control tag in the labeling configuration, e.g.:
<RectangleLabels model_path="my_model.pt">
Step by step guide: Using your own custom YOLOv8 model
You can integrate your own custom-trained YOLOv8 models with the YOLO ML backend for Label Studio. Follow these detailed steps to set up your custom model in the ML backend Docker:

Step 0: Install Label Studio and clone this Github repository

  1. Install and run Label Studio (or see more ways here).
pip install label-studio
label-studio
  1. Clone the Label Studio ML backend repository and go to the yolo example folder:
git clone https://github.com/HumanSignal/label-studio-ml-backend.git
cd label-studio-ml-backend/examples/yolo

Step 1: Prepare your custom YOLOv8 model

Ensure that your custom YOLOv8 model is saved as a .pt file, which is the standard format for PyTorch models. Let’s assume your model file is named my_custom_model.pt.

Step 2: Place your model

  1. Create a ‘models’ directory:

    In your project root directory yolo (the same directory where your docker-compose.yml is located), create a new folder named models and place your my_custom_model.pt file inside it.

    yolo/
├── docker-compose.yml
├── models/
    └── my_custom_model.pt

  2. Modify docker-compose.yml to mount the ‘models’ directory:

    Update your docker-compose.yml file to include a volume that maps the local models directory to the /app/models directory inside the Docker container.

    services:
  yolo:
    container_name: yolo
    ...
    volumes:
      - ./models:/app/models  # Mount the local 'models' directory
    environment:
      - ALLOW_CUSTOM_MODEL_PATH=true
    ...

Step 3: Ensure environment variables are set correctly

  • ALLOW_CUSTOM_MODEL_PATH=true: Ensure this is set in your Docker environment variables to allow the ML backend to load custom model paths (it’s true by default).
  • LABEL_STUDIO_URL: This is necessary to specify the external IP or domain of your Label Studio instance. If you run Label Studio and ML backend locally, you can try setting it to LABEL_STUDIO_URL=http://host.docker.internal:8080.
  • LABEL_STUDIO_API_KEY: This is necessary to specify the API key of your Label Studio instance. You can find it in Label Studio on the User Account page.
  • LOG_LEVEL: (optional) Set the logging level for the ML backend. You can use DEBUG, INFO, WARNING, ERROR.

Example docker-compose.yml:

environment:
  - ALLOW_CUSTOM_MODEL_PATH=true
  - LABEL_STUDIO_URL=http://host.docker.internal:8080
  - LABEL_STUDIO_API_KEY=your_api_key
  - LOG_LEVEL=DEBUG  # optional

Step 4: Update your Labeling Configuration in Label Studio

In your Label Studio project, specify the path to your custom model in the labeling configuration by adding the model_path parameter to the control tag you’re using (e.g., <RectangleLabels>, <PolygonLabels>, <Choices>, etc.).

Example for Object Detection with <RectangleLabels>:

<View>
  <Image name="image" value="$image"/>
  <RectangleLabels 
          name="label" toName="image" 
          model_path="my_custom_model.pt" 
          model_score_threshold="0.25">
    <Label value="Cat"/>
    <Label value="Dog"/>
  </RectangleLabels>
</View>

Important Notes:

  • model_path: The model_path should match the filename of your custom model and must be located in the /app/models directory inside the Docker container.
  • Class names mapping: Ensure that the value attributes in your <Label> tags correspond to the class names your model predicts. If they differ, use the predicted_values attribute to map them. Example:
    <Label value="Cat" predicted_values="feline"/>
<Label value="Dog" predicted_values="canine"/>
  • Where to find class names: See ML backend logs to know the exact class names your model predicts, and then you can use these names in the predicted_values attribute and in the value of Label tags directly.

Step 5: Restart the ML Backend

After making these changes, restart the ML backend to apply the new configuration.

docker-compose down
docker-compose up --build

Step 6: Connect the ML Backend to your Label Studio project

  1. Open your Label Studio instance.
  2. Go to the Settings of your project.
  3. Navigate to the Model tab.
  4. Connect to the ML backend by entering the ML backend URL (if you run it locally, it’s most likely http://localhost:9090).

Step 7: Test your setup

Add some tasks (images or other data) to your Label Studio project and open a task in the labeling interface. The ML backend should now use your custom model to generate predictions.

Common pitfalls

  • Incorrect model path: Ensure that the model_path in your labeling configuration exactly matches the filename of your model inside /app/models.
  • Label mismatch: Double-check that your labels in Label Studio match the classes your model predicts, or use predicted_values to map them.
  • Keypoints models and model_index: If you use a keypoints model, you should specify the model_index parameter in each Label tag.

Training

The current Label Studio ML backend doesn’t support training YOLO models. You have to do it manually on your side. Or you can contribute to this repository and add training support for this ML backend.



Classification using <Choices>

YOLO provides a classification model and Label Studio supports this with the Choices control tag.

More info: https://docs.ultralytics.com/tasks/classify/

Labeling config

<View>
  <Image name="image" value="$image"/>
  <Choices name="choice" toName="image" model_score_threshold="0.25">
    <Choice value="Airplane" predicted_values="aircraft_carrier,airliner,airship,warplane"/>
    <Choice value="Car" predicted_values="limousine,minivan,jeep,sports_car,passenger_car,police_van"/>
  </Choices>
</View>

Parameters

Parameter Type Default Description
model_score_threshold float 0.5 Sets the minimum confidence threshold for detections. Objects detected with confidence below this threshold will be disregarded. Adjusting this value can help reduce false positives.
model_path string None Path to the custom YOLO model. See more in the section Your own custom YOLO models.
choice string single Possible values: single, single-radio, multiple. If you use choice="single" (default) you can select only one label. The ML backend will return the label with the highest confidence using argmax strategy. If you use choice="multiple" you can select multiple labels. The ML backend will return all labels with confidence above the model_score_threshold.

For example:

<Choices name="choice" toName="image" model_score_threshold="0.25" model_path="my_model.pt">

Default model

yolov8n-cls.pt is the default classification model.



Object detection using RectangleLabels

YOLO models provide bounding box detection, also known as “object detection.” Label Studio supports this with the RectangleLabels control tag.

YOLO OBB models are also supported.

More info: https://docs.ultralytics.com/tasks/detect/

Labeling config

<View>
  <Image name="image" value="$image"/>
  <RectangleLabels name="label" toName="image" model_score_threshold="0.25" opacity="0.1">
    <Label value="Person" background="red"/>
    <Label value="Car" background="blue"/>
  </RectangleLabels>
</View>

Parameters

Parameter Type Default Description
model_score_threshold float 0.5 Sets the minimum confidence threshold for detections. Objects detected with confidence below this threshold will be disregarded. Adjusting this value can help reduce false positives.
model_path string None Path to the custom YOLO model. See more in the section Your own custom YOLO models.
model_obb bool False Enables Oriented Bounding Boxes (OBB) mode. Typically it uses *-obb.pt yolo models.

For example:

<RectangleLabels name="label" toName="image" model_score_threshold="0.25" model_path="my_model.pt">

Default model

yolov8m.pt is the default object detection model. yolov8n-obb.pt is the default OBB object detection model.

Oriented Bounding Boxes (YOLO OBB)

Oriented (rotated) bounding boxes will be generated automatically if you use an OBB model. Specify model_obb="true" in the RectangleLabels tag to enable this mode:

<RectangleLabels name="label" toName="image" model_score_threshold="0.25" model_obb="true">

More info: https://docs.ultralytics.com/tasks/obb/



Segmentation using PolygonLabels

YOLO models provide segmentation detection, also known as “instance segmentation.” Label Studio supports this with the PolygonLabels control tag.

More info: https://docs.ultralytics.com/tasks/segment/

Labeling config

<View>
  <Image name="image" value="$image"/>
  <PolygonLabels name="label" toName="image" model_score_threshold="0.25" opacity="0.1">
    <Label value="Car" background="blue"/>
    <Label value="Person" background="red"/>
  </PolygonLabels>
</View>

Parameters

Parameter Type Default Description
model_score_threshold float 0.5 Sets the minimum confidence threshold for detections. Objects detected with confidence below this threshold will be disregarded. Adjusting this value can help reduce false positives.
model_path string None Path to the custom YOLO model. See more in the section Your own custom YOLO models.

For example:

<PolygonLabels name="label" toName="image" model_score_threshold="0.25" model_path="my_model.pt">

Default model

yolov8n-seg.pt is the default segmentation model.



Keypoint detection using KeyPointLabels

YOLO models provide keypoint detection, also known as “pose estimation.” Label Studio supports this with the KeyPointLabels control tag.

More info: Ultralytics YOLO Keypoint Documentation

image

Labeling config

<View>
  <RectangleLabels name="keypoints_bbox" toName="image" model_skip="true">
    <Label value="person"/>
  </RectangleLabels>
  
  <KeyPointLabels name="keypoints" toName="image"
    model_score_threshold="0.75" model_point_threshold="0.5" 
    model_add_bboxes="true" model_point_size="1"
    model_path="yolov8n-pose.pt"
  >
    <Label value="nose" predicted_values="person" model_index="0" background="red" />

    <Label value="left_eye" predicted_values="person" model_index="1" background="yellow" />
    <Label value="right_eye" predicted_values="person" model_index="2" background="yellow" />

    <Label value="left_ear" predicted_values="person" model_index="3" background="purple" />
    <Label value="right_ear" predicted_values="person" model_index="4" background="purple" />
    
    <View>
      <Label value="left_shoulder" predicted_values="person" model_index="5" background="green" />
      <Label value="left_elbow" predicted_values="person" model_index="7" background="green" />
      <Label value="left_wrist" predicted_values="person" model_index="9" background="green" />

      <Label value="right_shoulder" predicted_values="person" model_index="6" background="blue" />
      <Label value="right_elbow" predicted_values="person" model_index="8" background="blue" />
      <Label value="right_wrist" predicted_values="person" model_index="10" background="blue" />
    </View>
    
    <View>
      <Label value="left_hip" predicted_values="person" model_index="11" background="brown" />
      <Label value="left_knee" predicted_values="person" model_index="13" background="brown" />
      <Label value="left_ankle" predicted_values="person" model_index="15" background="brown" />

      <Label value="right_hip" predicted_values="person" model_index="12" background="orange" />
      <Label value="right_knee" predicted_values="person" model_index="14" background="orange" />
      <Label value="right_ankle" predicted_values="person" model_index="16" background="orange" />
    </View>
  </KeyPointLabels>
  
  <Image name="image" value="$image" />
</View>

Parameters

Parameter Type Default Description
model_path string None Path to the custom YOLO model. See more in the section Your own custom YOLO models.
model_score_threshold float 0.5 Sets the minimum confidence threshold for bounding box detections. Keypoints that are related to the detected bbox with a confidence below this threshold will be disregarded.
model_point_threshold float 0.0 Minimum confidence threshold for keypoints. Keypoints with confidence below this value will be ignored.
model_add_bboxes bool True Adds bounding boxes for detected keypoints. All keypoints will be grouped by parent bounding boxes on the region panel. See details in the tip below.
model_point_size float 1 Size of the keypoints in pixels. Just a visual parameter.
model_index int None Index of the keypoint in the YOLO model output starting from 0. It’s used in Label tags only to build mapping between a Label and an output point.

For example:

<KeyPointLabels name="keypoints" toName="image"
                model_path="yolov8n-pose.pt"
                model_score_threshold="0.25" model_point_threshold="0.5" 
                model_add_bboxes="true" model_point_size="2">

Default model

yolov8n-pose.pt is the default keypoint detection model.

Grouping keypoints with bounding boxes

When using keypoint detection, the ML backend groups keypoints by the bounding box (bbox) associated with each detected person or object. You will see the grouping under the Regions panel on the right side of Label Studio. Note that you can drag and drop region items as necessary.

The bounding boxes are added to the prediction results by default. You can enable or disable this behavior by setting model_add_bboxes:

<KeyPointLabels name="keypoints" toName="image" model_add_bboxes="false">
Tip: How to only keep keypoints and discard bounding boxes?

To enable both keypoint detection and bounding box detection in the same task, you have to

  1. set model_add_bboxes="false" in the KeyPointLabels tag,
  2. remove RectangleLabels tag.

You can use this labeling configuration to get rid of bounding boxes and keep only keypoints:

<View>
  <KeyPointLabels name="keypoints" toName="image"
    model_score_threshold="0.75" model_point_threshold="0.5" 
    model_path="yolov8n-pose.pt" model_point_size="1"
    model_add_bboxes="false"              
  >
    <Label value="nose" predicted_values="person" model_index="0" background="red" />

    <Label value="left_eye" predicted_values="person" model_index="1" background="yellow" />
    <Label value="right_eye" predicted_values="person" model_index="2" background="yellow" />

    <Label value="left_ear" predicted_values="person" model_index="3" background="purple" />
    <Label value="right_ear" predicted_values="person" model_index="4" background="purple" />
    
    <View>
      <Label value="left_shoulder" predicted_values="person" model_index="5" background="green" />
      <Label value="left_elbow" predicted_values="person" model_index="7" background="green" />
      <Label value="left_wrist" predicted_values="person" model_index="9" background="green" />

      <Label value="right_shoulder" predicted_values="person" model_index="6" background="blue" />
      <Label value="right_elbow" predicted_values="person" model_index="8" background="blue" />
      <Label value="right_wrist" predicted_values="person" model_index="10" background="blue" />
    </View>
    
    <View>
      <Label value="left_hip" predicted_values="person" model_index="11" background="brown" />
      <Label value="left_knee" predicted_values="person" model_index="13" background="brown" />
      <Label value="left_ankle" predicted_values="person" model_index="15" background="brown" />

      <Label value="right_hip" predicted_values="person" model_index="12" background="orange" />
      <Label value="right_knee" predicted_values="person" model_index="14" background="orange" />
      <Label value="right_ankle" predicted_values="person" model_index="16" background="orange" />
    </View>
  </KeyPointLabels>
  <Image name="image" value="$image" />
</View>

Point mapping

For precise control, you can map keypoints to specific labels in your Label Studio configuration. Each keypoint can be associated with a specific part of a person or object, and you can define this mapping using the model_index and predicted_values attributes.

<Label value="left_eye" predicted_values="person" model_index="1" />
<Label value="right_eye" predicted_values="person" model_index="2" />

This configuration ensures that the keypoints detected by the YOLO model are correctly labeled in the Label Studio interface. For pose detection models, the model_index attribute is used to map keypoints to specific parts of the body according to the YOLO model output:

0: Nose 1: Left Eye 2: Right Eye 3: Left Ear 4: Right Ear 
5: Left Shoulder 6: Right Shoulder 7: Left Elbow 8: Right Elbow 9: Left Wrist 10: Right Wrist 
11: Left Hip 12: Right Hip 13: Left Knee 14: Right Knee 15: Left Ankle 16: Right Ankle

Recommendations

  • Bounding Box Visualization: Use the model_add_bboxes parameter to visualize the bounding box containing the keypoints. This is especially useful when dealing with multiple detected persons or objects.
  • Threshold Adjustment: Adjust the model_score_threshold and model_point_threshold parameters based on your dataset and the confidence level required for accurate keypoint detection.


Video object tracking using VideoRectangle

YOLO models provide object tracking, also known as “multi-object tracking.” Label Studio supports this with the VideoRectangle + Labels control tags.

More info: https://docs.ultralytics.com/modes/track/

Labeling config

<View>
    <Video name="video" value="$video"/>
    <VideoRectangle name="box" toName="video" model_tracker="botsort" model_conf="0.25" model_iou="0.7" />
    <Labels name="label" toName="video">
      <Label value="Person" background="red"/>
      <Label value="Car" background="blue"/>
    </Labels>
</View>

Trackers

https://docs.ultralytics.com/modes/track/?h=track#tracker-selection

The best tracker to use with Ultralytics YOLO depends on your specific needs.

The default tracker is BoT-SORT, which is generally well-suited for most scenarios.

However, if you’re looking for an alternative with different strengths, ByteTrack is another good choice that you can easily configure. ByteTrack is known for its high performance in multi-object tracking, especially in situations with varying object appearances and reappearances.

Both trackers can be customized using YAML configuration files to fit your specific use cases.

You can specify the tracker in the control tag:

  • <VideoRectangle model_tracker="botsort">
  • <VideoRectangle model_tracker="bytetrack">

Parameters for bounding boxes

The tracker works with the object detection model (bounding boxes).

The first step is to detect bounding boxes, the second step is to track them (find the same boxes among frames). These parameters are related to the first step - bounding box detection.

Read more about these parameters: https://docs.ultralytics.com/modes/track/?h=track#tracking-arguments

Parameter Type Default Description
model_conf float 0.25 Sets the minimum confidence threshold for detections. Objects detected with confidence below this threshold will be disregarded. Adjusting this value can help reduce false positives.
model_iou float 0.7 Intersection Over Union (IoU) threshold for Non-Maximum Suppression (NMS). Lower values result in fewer detections by eliminating overlapping boxes, useful for reducing duplicates.
model_tracker string botsort Sets the tracker to use for multi-object tracking. Options include botsort, bytetrack, or a custom YAML file.
model_path string None Path to the custom YOLO model. See more in the section Your own custom YOLO models.

For example: 

<VideoRectangle name="label" toName="video" model_tracker="botsort" model_conf="0.25" model_iou="0.7" />

Parameters for trackers

For an example of tracker parameters, see https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/trackers.

The main parameter is model_tracker which can be set to

  • botsort
  • bytetrack
  • Or the name of any custom yaml file that you place into models directory (do not include the file extension .yaml when setting this parameter).

As long as they are available within the yaml file, you can specify tracker parameters directly from the labeling config. All parameters should be prefixed with botsort_ or bytetrack_.

For example: 

<VideoRectangle 
    name="label" toName="video" 
    model_tracker="botsort" 
    botsort_track_low_thresh="0.1" botsort_track_high_thresh="0.2" 
/>

Default model

yolov8n.pt is the default object detection model.

Recommendations

  • Video object tracking is a computationally intensive task. Small models like yolov8n.pt are recommended for real-time tracking, however, they may not be as accurate as larger models.

  • Label Studio has timeout limits for ML backend requests. You can adjust the timeout in the Label Studio backend settings.

  • Or use the CLI tool to run predictions asynchronously.


Video temporal classification using TimelineLabels

This ML backend supports temporal multi-label video classification for the <TimelineLabels> control tag in Label Studio. There are two modes available:

  • Simple: In the simple mode, the model uses pre-trained YOLO classes to generate predictions without additional training.
  • Trainable: In the trainable mode, the model can be trained on custom labels and annotations submitted in Label Studio using few-shot learning as training is performed on a small number of annotations.

Labeling config

<View>
  <Video name="video" value="$video"/>
  <TimelineLabels 
          name="label" toName="video" 
          model_trainable="false" model_score_threshold="0.25">
    <Label value="Ball" predicted_values="soccer_ball" />
    <Label value="hamster" />
  </TimelineLabels>
</View>

Model training

For more details on using the TimelineLabels ML backend, including training the model and adjusting neural network classifier parameters, please refer to README_TIMELINE_LABELS.md.

Default model

yolov8n-cls.pt is the default classification model for simple mode.



Run the YOLO ML backend

  1. Start the Machine Learning backend on http://localhost:9090 with the prebuilt image:

    docker-compose up

  2. Validate that the backend is running

    $ curl http://localhost:9090/
{"status":"UP"}

  3. Create a project in Label Studio. Then from the Model page in the project settings, connect the model. The default URL is http://localhost:9090.

Building from source (advanced)

To build the ML backend from source, you have to clone the repository and build the Docker image:

docker-compose build

Running without Docker (advanced)

To run the ML backend without Docker, you have to clone the repository and install all dependencies using pip:

python -m venv ml-backend
source ml-backend/bin/activate
pip install -r requirements-base.txt
pip install -r requirements.txt

Then you can start the ML backend:

label-studio-ml start ./dir_with_your_model

Also, you can check Dockerfile for additional dependencies and install them manually.

Parameters

Check the environment section in the docker-compose.yml file before running the container. All available parameters are listed there.

Note: You can use lots of YOLO model parameters in labeling configurations directly, e.g. model_path or model_score_threshold.

Command line interface for the terminal

Overview

This Command Line Interface (CLI) tool facilitates the integration of YOLO models with Label Studio for machine learning predictions. It provides an alternative method for running YOLO predictions on tasks managed by Label Studio, particularly useful for processing long videos or large datasets.

Running the model predictions directly from the CLI helps to avoid issues like connection timeouts between Label Studio and the ML backend, which can occur during lengthy processing times.

When to use the CLI

When working with extensive media files such as long videos, processing times can be significant. Label Studio may interrupt the connection with the ML backend if the request takes too long, resulting in incomplete predictions.

By running this CLI tool, you can execute model predictions asynchronously without the need for Label Studio to maintain a constant connection to the backend. This ensures that even large or complex tasks are processed fully, and predictions are saved to Label Studio using SDK once completed.

How it works

  1. Label Studio Connection: The tool connects to a running instance of Label Studio using the provided API key and URL.
  2. Task Preparation: Tasks can be provided directly via a JSON file or as a list of task IDs. The tool fetches task data from Label Studio if task IDs are supplied.
  3. Model Loading: The YOLO model is loaded and initialized based on the project’s configuration.
  4. Prediction Process: For each task, the YOLO model generates predictions, which are then post-processed to Label Studio’s expected format.
  5. Asynchronous Upload: The generated predictions are uploaded back to Label Studio, allowing for large tasks to be processed without timing out.

Usage

python cli.py --ls-url http://localhost:8080 --ls-api-key your_api_key --project 1 --tasks tasks.json

or 

python cli.py --ls-url http://localhost:8080 --ls-api-key YOUR_API_KEY --project 1 --tasks 1,2,3

Parameters

  • --ls-url: The URL of the Label Studio instance. Defaults to http://localhost:8080.

  • --ls-api-key: The API key for Label Studio. Used to authenticate the connection.

  • --project: The ID of the Label Studio project where the tasks are managed. Defaults to 1.

  • --tasks:

    1. The path to a JSON file containing a list of tasks or task IDs, e.g.:

    tasks_ids.json 

    [1,2,3]

    tasks.json 

    [{"id": 1, "data": {"image": "https://example.com/1.jpg"}}, {"id": 2, "data": {"image": "https://example.com/2.jpg"}}]

    1. If a file is not provided, you can pass a comma-separated list of task IDs directly, e.g.: 1,2,3

Logging

Use LOG_LEVEL=DEBUG to get detailed logs. Example:

LOG_LEVEL=DEBUG python cli.py --ls-url http://localhost:8080 --ls-api-key YOUR_API_KEY --project 2 --tasks 1,2,3

For developers

The architecture of the project and development guidelines are described in the README_DEVELOP.md file.

In this article

  1. YOLO ML backend for Label Studio
  2. Before you begin
  3. Quick start
  4. Labeling configurations
  5. Supported object & control tags
  6. Mixed object and control tags
  7. Label and choice mapping
  8. Supported YOLO Versions: YOLOv5, YOLO11, and others
  9. Your own custom YOLO models
  10. Step 0: Install Label Studio and clone this Github repository
  11. Step 1: Prepare your custom YOLOv8 model
  12. Step 2: Place your model
  13. Step 3: Ensure environment variables are set correctly
  14. Step 4: Update your Labeling Configuration in Label Studio
  15. Step 5: Restart the ML Backend
  16. Step 6: Connect the ML Backend to your Label Studio project
  17. Step 7: Test your setup
  18. Common pitfalls
  19. Training
  20. Classification using <Choices>
  21. Labeling config
  22. Parameters
  23. Default model
  24. Object detection using RectangleLabels
  25. Labeling config
  26. Parameters
  27. Default model
  28. Oriented Bounding Boxes (YOLO OBB)
  29. Segmentation using PolygonLabels
  30. Labeling config
  31. Parameters
  32. Default model
  33. Keypoint detection using KeyPointLabels
  34. Labeling config
  35. Parameters
  36. Default model
  37. Grouping keypoints with bounding boxes
  38. Point mapping
  39. Recommendations
  40. Video object tracking using VideoRectangle
  41. Labeling config
  42. Trackers
  43. Parameters for bounding boxes
  44. Parameters for trackers
  45. Default model
  46. Recommendations
  47. Video temporal classification using TimelineLabels
  48. Labeling config
  49. Model training
  50. Default model
  51. Run the YOLO ML backend
  52. Running with Docker (recommended)
  53. Building from source (advanced)
  54. Running without Docker (advanced)
  55. Parameters
  56. Command line interface for the terminal
  57. Overview
  58. When to use the CLI
  59. How it works
  60. Usage
  61. Parameters
  62. Logging
  63. For developers
Unlock more with Enterprise Get a Demo