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LabelFlow

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Labelling interface

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AI Assistants

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Workspaces

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Import a dataset

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Export a dataset

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API

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Support

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Sign in

Online workspaces allow users to collaborate by uploading images to LabelFlow's secure servers, unlocking new levels of productivity. Sign up to use these collaborative tools.

Sign in

You must first be signed in to create a workspace, which can be done with Google, GitHub, or with your email.

Should you choose to enter your email, you will receive an invitation with a magic link as displayed below:

Import Images from File URLs

Simply paste the image URLs in the Image uploader

These are the simple steps you should follow to import from your database:

  1. Press the Add images button on the top right

  2. Select Import from a list of URLs instead button

  3. Copy and paste the image URLs

  4. Select Start Import

It's that easy. 🔥👌

You can also import 🖊 annotations in the COCO format, .

it's explained here

Import Annotations

LabelFlow can ingest Labels in the COCO format into a dataset. For that you must drop a COCO .json file in the uploader. The COCO .json file contains the annotations of your dataset images.

  • Open an existing dataset or create a new one

  • First import your images

    • Press the Add images button on the top right

    • Drag and drop your images in the uploader

    • Select Start Labeling once the import is finished

  • Second import your annotations

    • Press the Add images button again

    • Drag and drop your annotation file in the COCO format

  • Select Start Labeling once the import is finished

It's that easy. 🔥👌

This feature allows you to share a dataset with someone: from LabelFlow export the dataset in COCO format, your colleague can then import the annotation file in a new dataset.

Intro to LabelFlow

The beginner's guide to the image labelling annotation tool LabelFlow

What is our mission at LabelFlow?

At LabelFlow we are on a mission to build GitHub for Visual Data. We first started building an open source image labeling platform focused on user experience. We are now working on more advanced features to manage datasets and allow you build the next big thing with AI ! You can find the code on Github here, feel free to ping us on Discord as well, there are impactful contributions to bring to the project.

Who are we?

After 5 years building , an AI-powered platform for the energy space helping to identify hazards on power grids and wind turbines, LabelFlow founding team figured out that it was possible to bring AI to scale in complex use cases. It usually brings better reliability, more productivity and hence a better user experience but this has a price and there is no secret sauce: the dataset must be large, homogeneous, and accurately labeled. Machine Learning models performance mainly comes from the quality of the datasets, less from the model itself.

Let's get started !

Head on over to and let us take a test case to see how you can leverage LabelFlow to bring your machine learning models to scale today.

Sterblue
Quick start

Quick Start

Let us take the case that you are making an AI model that identifies rhinos from an image for wildlife protection services. You need to train your model with high-quality labeled images so you can produce an effective tool against poaching. These are the steps you can follow to get prepare the high-quality labeled images.

1. Gather Image Dataset

The first step is to gather a dataset of images containing rhinos.

2. Create a dataset

Navigate to and you will be welcomed with this interface. Press “Start Labeling”. Click on “Create new dataset” and give your dataset a name. Now press “Start Labeling” and enter your dataset.

3. Import your images

Press “Add images” and you drop all the images that you will label for this project. You can drag and drop the images onto the screen or paste the URL to your database. You can also drop the URL to your database if your images are on the cloud.

It will lead you to a gallery where you can see all your pictures.

Select the image you would like to label first and away we go!

4. Label Images

Bounding Box

Select the bounding box tool and click on the edges of the Rhinos you would like to label. Two guiding lines will be available to help you select the edges. Right-click on the bounding box to specify the class of the label. Labels are colored by class for convenience.

Polygon

Select the small arrow on the drawing tool and choose the polygon tool. Draw a polygon around the rhino and adjust the vertices at the end by dragging them.

Navigate between your images by simply choosing the next image you would like to label or clicking the small arrows at the bottom left. You can also use the left and right arrow keys.

Click on the keyboard icon on the top right to see the complete list of shortcuts

Labels can overlap, intersect or encompass other labels. Zoom in on your images by using the top right button or by using the trackpad. Create as many label classes as you need.

5. Export Labels

Once you've finished, select the export tool on the top right.

A small pop-up will appear with information about the number of labels and will prompt you to specify the export format, such as .

Select your desired file type and it will download to your device.

Now you are ready to train your AI model! It’s that easy! 👌

LabelFlow
COCO

Edit labels

Once you have created a label for your object, you may want to change its shape, size, location, or class.

Changing Label Class

Right-click on the label and select the appropriate class from the pop-up menu.

You can also press the number next to the class name on your keyboard to select that class

Changing Label Shape and Size

Select and drag a vertex to move it to correct the shape and size of the label.

If the label you are editing is a polygon, it is possible to add additional vertices to it.

Simply select an edge and drag the vertex that appears on your chosen position.

Changing Label location

Click anywhere inside the label and drag it to another position

Label Types

Choosing the right label type for your image annotation project is important. Classification tags will maximize your labeling productivity, while polygons will boost your AI performance and bounding boxes may be for some an acceptable compromise.

Good news, check the Auto polygons tool which creates a polygon by simply adding a bounding box.

LabelFlow supports the following labeling types:

  • Image classification

  • Bounding boxes

  • Polygons

  • Auto polygons (a polygon generated from a bounding box)

You can read more about the different label types on our dedicated post:

Polygons

The polygon label type is defined by several vertices, which allows the user to accurately label complex shapes.

By definition, polygon labels are closer in shape to the subject of the label than a simple bounding box.

These are the simple steps you need to take to make a polygon label:

  1. Select the small arrow on the drawing tool on the left and select the polygon tool

  2. Click on the outermost edge of the subject of the label

  3. Follow the edges of the subject and select the vertices of the polygon

  4. When you are finished click on the vertex near the pointer and the polygon will be created

  5. Right-click on the label to select/add its class

You can create vertices and move existing ones to change the shape and size of the polygon!

  • Add vertices to the polygon by selecting an edge and dragging the vertex that appears

  • Select and drag an existing vertex to move it

Auto Polygons

The Auto polygon tool allows you to boost your labeling productivity.

You create a bounding box around the object you wish to label and an AI automatically transforms this bounding box into a precise polygon around your object.

Auto Polygon. The fastest way to create a polygon around your object.

Refinements

Move or add Auto Polygon keypoints

You can refine the automatically generated polygons by simply moving the target keypoint and/or adding inside keypoints and outside keypoints .

This is as simple as moving the target keypoint and/or doing a click inside or outside the created polygon.

  • Target keypoint. It must always be on the object you wish to label. Else move it on the object.

  • Outside keypoint. A click inside the polygon will add an outside keypoint and exclude this area from the polygon label.

  • Inside keypoint. A click outside the polygon will add an inside keypoint and include this area to the polygon label.

Always start by moving the target keypoint to another position on the object if the generated polygon is not good enough. Only after adjusting the target keypoint should you start adding inside and outside keypoints.

Manually adjust the polygon's geometry

  • Click on to activate the selection mode (or press v for the shortcut)

  • Click on the polygon you want to edit

  • Select the polygon edition mode (next to the class selection menu in the top left). You can press e to switch between both edition modes.

This edition mode is also illustrated in the above video.

Image Classification

The classification label type is the simplest and quickest way to label images. As opposed to the bounding box and polygon label types, the classification type doesn't have an associated geometry within the image. This label type simply consists of a general label for the entire image.

These are the simple steps you need to take to make a classification label:

  1. Select the small arrow on the drawing tool on the left and select the classification tool

  2. Click on the label box in the top left of the image or right-click on the image to select/add its class

It's that easy!

Check out the page to find out how to increase your productivity!

Shortcuts

There are many shortcuts in LabelFlow you can take advantage of to optimize your productivity. Click on the keyboard icon on the top right to see the complete list of shortcuts

Tools

  • Press v to activate the selection tool

  • Press b to select the bounding box tool

  • Press p to select the polygon tool

  • Press k to select the classification tool

  • Press a to select the Auto-Polygon tool

  • Press i to select the AI Assistant tool and r to run the AI Assistant

  • Press c to change the class of the currently selected label

  • Press del or Backspace to delete the selected label

  • Press from 0 to 9 to select the corresponding class for the selected label

  • Press / or f to search for labels in the label class selection

Navigation

  • Press ⬅️ Left arrow key to navigate to the previous image

  • Press ➡️ Right arrow key to navigate to the next image

Edit

  • Press Esc to cancel current action

  • Press ctrl + z or cmd + z to undo last action

  • Press ctrl + y, ctrl + shift + y

You can now drag any points on the contour to adjust the polygon geometry

,
cmd + y
, or
cmd + shift + z
to redo last action
shortcuts

Introduction to Workspaces

Online workspaces allow users to collaborate by uploading images to LabelFlow's secure servers, unlocking new levels of productivity. Sign up to use these collaborative tools for free now.

By default, LabelFlow users have one local or offline workspace, where they can label and export their datasets from their computers without ever having to sign in.

Signing in to LabelFlow allows users to create unlimited online workspaces, upload their images to LabelFlow's secure servers and collaborate with teammates. Here's a brief walkthrough of the new Workspace feature.

DETR - COCO

Presentation

The DEtection TRansformer (DETR) model was trained by Facebook on the COCO 2017 dataset (118,000 images) and is hosted by Hugging Face. You can find its complete description card here.

DETR - COCO AI Assistant outputs by default bounding boxes around the detected objects. If the detected object class does not exist in the dataset, the AI assistant will create it.

Generate Polygons instead of Bounding Boxes

It is possible to transform a bounding box into a polygon by selecting the Post-process option. Note that the inference time will be slightly longer by selecting this option.

Sample Image

Classes

DETR - COCO AI Assistant is able to detect 90 different classes of objects (some better than others we must say...!)

Classes

A class is a category in which labels are classified. It helps group similar labels together, which is essential for any labeling project. A class is defined by its name and has its own color to help differentiate it from the others.

Create New Classes

When you create a new labeling project, it comes with the default class "None". These are the simple steps you need to take to create more labels

Post-processing option to generate polygons instead of bounding boxes
Automatic cat and dog detection by DETR - COCO AI Assistant
person
bicycle
car
motorcycle
airplane
bus
train
truck
boat
trafficlight
firehydrant
streetsign
stopsign
parkingmeter
bench
bird
cat
dog
horse
sheep
cow
elephant
bear
zebra
giraffe
hat
backpack
umbrella
shoe
eyeglasses
handbag
tie
suitcase
frisbee
skis
snowboard
sportsball
kite
baseballbat
baseballglove
skateboard
surfboard
tennisracket
bottle
plate
wineglass
cup
fork
knife
spoon
bowl
banana
apple
sandwich
orange
broccoli
carrot
hotdog
pizza
donut
cake
chair
couch
pottedplant
bed
mirror
diningtable
window
desk
toilet
door
tv
laptop
mouse
remote
keyboard
cellphone
microwave
oven
toaster
sink
refrigerator
blender
book
clock
vase
scissors
teddybear
hairdrier
toothbrush
Right-click on an existing label
  • Click on the search field and write the name of the new class

  • Press enter or click on the button that says "Create class [New_Class] "

  • You can easily import classes to a new dataset from an existing dataset.

    1. In your existing dataset having the classes you wish to copy

      1. Click on Export

      2. Select the COCO export format, and activate the option Avoid image names collision

    2. In your new dataset

      1. Click on Add Images

      2. Drag and drop the COCO export file created at step 1

    Edit Classes

    The classes tab in your project space shows all the information about all the classes in your project. The total number of classes is also visible to help you, as well as the number of occurrences for each class. Furthermore, this screen details the keyboard shortcuts that can be used to quickly select the appropriate class.

    You can change the name of a class by selecting the edit icon and giving your class a new name.

    You can also delete a class and all labels associated with it will be classified as "None".

    Managing Complex Taxonomies

    LabelFlow can accommodate complex taxonomies, allowing users to manage very specific use cases.

    The use of the / to separate classes and subclasses is widely used and easily comprehensible: Class/Subclass1/Subclass2

    An example schema could be: Object/Material/Defect for infrastructure inspections, or even Object/Type/Material/Defect.

    Using this methodology to manage complex taxonomies and use-cases allows for the classes to be easily interpreted once exported.

    Datasets

    Each labeling project has its own dataset which is a collection of images that are prepared to be labeled. You can view all the images in your dataset on the Images tab of your project.

    You can add images to your dataset by clicking the "add images" button on the top right of the images page.

    When you create a new project, you are greeted with the suggestion to add images to your dataset.

    Import Images

    There are 2 ways to import your images

    There are two ways to import images and labels to your dataset.

    1. Import Images from files. Simply Drag and drop the files in the uploader.

    ViT - ImageNet

    Presentation

    Vision Transformer (ViT) model was pre-trained by Google on ImageNet-21k (14 million images, 21,843 classes), and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes). The model is hosted by Hugging Face and you can find its complete description card here.

    ViT - ImageNet performs image classification, it add tags corresponding to the detected object onto the image. If the detected object class does not exist in the dataset, the AI assistant will create it.

    An image automatically tagged as "Ox" by the 'ViT - ImageNet' AI Assistant

    Classes

    ViT - ImageNet AI Assistant is able to detect 1000 different classes of objects (some better than others we must say...!).

    kit fox, Vulpes macrotis
    English setter
    Australian terrier
    grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus
    lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens
    Egyptian cat
    ibex, Capra ibex
    Persian cat
    cougar, puma, catamount, mountain lion, painter, panther, Felis concolor
    gazelle
    porcupine, hedgehog
    sea lion
    badger
    Great Dane
    Scottish deerhound, deerhound
    killer whale, killer, orca, grampus, sea wolf, Orcinus orca
    mink
    African elephant, Loxodonta africana
    red wolf, maned wolf, Canis rufus, Canis niger
    jaguar, panther, Panthera onca, Felis onca
    hyena, hyaena
    titi, titi monkey
    three-toed sloth, ai, Bradypus tridactylus
    sorrel
    black-footed ferret, ferret, Mustela nigripes
    dalmatian, coach dog, carriage dog
    Staffordshire bullterrier, Staffordshire bull terrier
    Bouvier des Flandres, Bouviers des Flandres
    weasel
    miniature poodle
    bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis
    fox squirrel, eastern fox squirrel, Sciurus niger
    colobus, colobus monkey
    tiger cat
    impala, Aepyceros melampus
    coyote, prairie wolf, brush wolf, Canis latrans
    Yorkshire terrier
    Newfoundland, Newfoundland dog
    red fox, Vulpes vulpes
    hartebeest
    grey fox, gray fox, Urocyon cinereoargenteus
    Pekinese, Pekingese, Peke
    guenon, guenon monkey
    mongoose
    indri, indris, Indri indri, Indri brevicaudatus
    tiger, Panthera tigris
    wild boar, boar, Sus scrofa
    zebra
    ram, tup
    orangutan, orang, orangutang, Pongo pygmaeus
    basenji
    leopard, Panthera pardus
    vizsla, Hungarian pointer
    squirrel monkey, Saimiri sciureus
    Siamese cat, Siamese
    chimpanzee, chimp, Pan troglodytes
    komondor
    proboscis monkey, Nasalis larvatus
    guinea pig, Cavia cobaya
    white wolf, Arctic wolf, Canis lupus tundrarum
    ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus
    gorilla, Gorilla gorilla
    ox
    Tibetan mastiff
    spider monkey, Ateles geoffroyi
    Doberman, Doberman pinscher
    warthog
    Arabian camel, dromedary, Camelus dromedarius
    siamang, Hylobates syndactylus, Symphalangus syndactylus
    golden retriever
    Border collie
    hare
    boxer
    patas, hussar monkey, Erythrocebus patas
    baboon
    macaque
    capuchin, ringtail, Cebus capucinus
    flat-coated retriever
    hog, pig, grunter, squealer, Sus scrofa
    Eskimo dog, husky
    Brittany spaniel
    dial telephone, dial phone
    maze, labyrinth
    Gordon setter
    dingo, warrigal, warragal, Canis dingo
    hamster
    Arctic fox, white fox, Alopex lagopus
    water buffalo, water ox, Asiatic buffalo, Bubalus bubalis
    American black bear, black bear, Ursus americanus, Euarctos americanus
    Angora, Angora rabbit
    bison
    howler monkey, howler
    hippopotamus, hippo, river horse, Hippopotamus amphibius
    giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca
    tabby, tabby cat
    marmoset
    Saint Bernard, St Bernard
    armadillo
    redbone
    polecat, fitch, foulmart, foumart, Mustela putorius
    marmot
    gibbon, Hylobates lar
    llama
    wood rabbit, cottontail, cottontail rabbit
    lion, king of beasts, Panthera leo
    Irish setter, red setter
    dugong, Dugong dugon
    Indian elephant, Elephas maximus
    beaver
    Madagascar cat, ring-tailed lemur, Lemur catta
    Rhodesian ridgeback
    lynx, catamount
    African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus
    langur
    timber wolf, grey wolf, gray wolf, Canis lupus
    cheetah, chetah, Acinonyx jubatus
    sloth bear, Melursus ursinus, Ursus ursinus
    German shepherd, German shepherd dog, German police dog, alsatian
    otter
    koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus
    tusker
    echidna, spiny anteater, anteater
    wallaby, brush kangaroo
    platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus
    wombat
    revolver, six-gun, six-shooter
    umbrella
    schooner
    soccer ball
    accordion, piano accordion, squeeze box
    ant, emmet, pismire
    starfish, sea star
    chambered nautilus, pearly nautilus, nautilus
    grand piano, grand
    laptop, laptop computer
    strawberry
    airliner
    warplane, military plane
    airship, dirigible
    balloon
    space shuttle
    fireboat
    gondola
    speedboat
    lifeboat
    canoe
    yawl
    catamaran
    trimaran
    container ship, containership, container vessel
    liner, ocean liner
    pirate, pirate ship
    aircraft carrier, carrier, flattop, attack aircraft carrier
    submarine, pigboat, sub, U-boat
    wreck
    half track
    tank, army tank, armored combat vehicle, armoured combat vehicle
    missile
    bobsled, bobsleigh, bob
    dogsled, dog sled, dog sleigh
    bicycle-built-for-two, tandem bicycle, tandem
    mountain bike, all-terrain bike, off-roader
    freight car
    passenger car, coach, carriage
    barrow, garden cart, lawn cart, wheelbarrow
    shopping cart
    motor scooter, scooter
    forklift
    electric locomotive
    steam locomotive
    amphibian, amphibious vehicle
    ambulance
    beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
    cab, hack, taxi, taxicab
    convertible
    jeep, landrover
    limousine, limo
    minivan
    Model T
    racer, race car, racing car
    sports car, sport car
    go-kart
    golfcart, golf cart
    moped
    snowplow, snowplough
    fire engine, fire truck
    garbage truck, dustcart
    pickup, pickup truck
    tow truck, tow car, wrecker
    trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi
    moving van
    police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria
    recreational vehicle, RV, R.V.
    streetcar, tram, tramcar, trolley, trolley car
    snowmobile
    tractor
    mobile home, manufactured home
    tricycle, trike, velocipede
    unicycle, monocycle
    horse cart, horse-cart
    mosquito net
    oxcart
    bassinet
    cradle
    crib, cot
    four-poster
    bookcase
    china cabinet, china closet
    medicine chest, medicine cabinet
    chiffonier, commode
    table lamp
    file, file cabinet, filing cabinet
    pay-phone, pay-station
    park bench
    barber chair
    throne
    folding chair
    rocking chair, rocker
    studio couch, day bed
    toilet seat
    desk
    pool table, billiard table, snooker table
    dining table, board
    entertainment center
    wardrobe, closet, press
    Granny Smith
    orange
    lemon
    fig
    pineapple, ananas
    banana
    jackfruit, jak, jack
    custard apple
    pomegranate
    acorn
    hip, rose hip, rosehip
    ear, spike, capitulum
    rapeseed
    corn
    buckeye, horse chestnut, conker
    organ, pipe organ
    upright, upright piano
    chime, bell, gong
    drum, membranophone, tympan
    gong, tam-tam
    maraca
    marimba, xylophone
    steel drum
    banjo
    cello, violoncello
    lampshade, lamp shade
    harp
    acoustic guitar
    electric guitar
    cornet, horn, trumpet, trump
    French horn, horn
    trombone
    harmonica, mouth organ, harp, mouth harp
    ocarina, sweet potato
    panpipe, pandean pipe, syrinx
    bassoon
    sax, saxophone
    flute, transverse flute
    daisy
    yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum
    cliff, drop, drop-off
    valley, vale
    alp
    volcano
    promontory, headland, head, foreland
    sandbar, sand bar
    coral reef
    lakeside, lakeshore
    seashore, coast, seacoast, sea-coast
    geyser
    hatchet
    cleaver, meat cleaver, chopper
    letter opener, paper knife, paperknife
    plane, carpenter's plane, woodworking plane
    power drill
    lawn mower, mower
    hammer
    corkscrew, bottle screw
    can opener, tin opener
    plunger, plumber's helper
    screwdriver
    shovel
    plow, plough
    chain saw, chainsaw
    cock
    hen
    ostrich, Struthio camelus
    brambling, Fringilla montifringilla
    goldfinch, Carduelis carduelis
    house finch, linnet, Carpodacus mexicanus
    junco, snowbird
    indigo bunting, indigo finch, indigo bird, Passerina cyanea
    robin, American robin, Turdus migratorius
    bulbul
    jay
    magpie
    chickadee
    water ouzel, dipper
    kite
    bald eagle, American eagle, Haliaeetus leucocephalus
    vulture
    great grey owl, great gray owl, Strix nebulosa
    black grouse
    ptarmigan
    ruffed grouse, partridge, Bonasa umbellus
    prairie chicken, prairie grouse, prairie fowl
    peacock
    quail
    partridge
    African grey, African gray, Psittacus erithacus
    macaw
    sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita
    lorikeet
    coucal
    bee eater
    hornbill
    hummingbird
    jacamar
    toucan
    drake
    red-breasted merganser, Mergus serrator
    goose
    black swan, Cygnus atratus
    white stork, Ciconia ciconia
    black stork, Ciconia nigra
    spoonbill
    flamingo
    American egret, great white heron, Egretta albus
    little blue heron, Egretta caerulea
    bittern
    crane
    limpkin, Aramus pictus
    American coot, marsh hen, mud hen, water hen, Fulica americana
    bustard
    ruddy turnstone, Arenaria interpres
    red-backed sandpiper, dunlin, Erolia alpina
    redshank, Tringa totanus
    dowitcher
    oystercatcher, oyster catcher
    European gallinule, Porphyrio porphyrio
    pelican
    king penguin, Aptenodytes patagonica
    albatross, mollymawk
    great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias
    tiger shark, Galeocerdo cuvieri
    hammerhead, hammerhead shark
    electric ray, crampfish, numbfish, torpedo
    stingray
    barracouta, snoek
    coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch
    tench, Tinca tinca
    goldfish, Carassius auratus
    eel
    rock beauty, Holocanthus tricolor
    anemone fish
    lionfish
    puffer, pufferfish, blowfish, globefish
    sturgeon
    gar, garfish, garpike, billfish, Lepisosteus osseus
    loggerhead, loggerhead turtle, Caretta caretta
    mud turtle
    terrapin
    box turtle, box tortoise
    banded gecko
    common iguana, iguana, Iguana iguana
    American chameleon, anole, Anolis carolinensis
    whiptail, whiptail lizard
    agama
    frilled lizard, Chlamydosaurus kingi
    alligator lizard
    Gila monster, Heloderma suspectum
    green lizard, Lacerta viridis
    African chameleon, Chamaeleo chamaeleon
    Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis
    triceratops
    African crocodile, Nile crocodile, Crocodylus niloticus
    American alligator, Alligator mississipiensis
    thunder snake, worm snake, Carphophis amoenus
    ringneck snake, ring-necked snake, ring snake
    hognose snake, puff adder, sand viper
    green snake, grass snake
    king snake, kingsnake
    garter snake, grass snake
    water snake
    vine snake
    night snake, Hypsiglena torquata
    boa constrictor, Constrictor constrictor
    rock python, rock snake, Python sebae
    Indian cobra, Naja naja
    green mamba
    sea snake
    horned viper, cerastes, sand viper, horned asp, Cerastes cornutus
    stone wall
    sidewinder, horned rattlesnake, Crotalus cerastes
    European fire salamander, Salamandra salamandra
    common newt, Triturus vulgaris
    eft
    spotted salamander, Ambystoma maculatum
    axolotl, mud puppy, Ambystoma mexicanum
    bullfrog, Rana catesbeiana
    tree frog, tree-frog
    tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui
    whistle
    wing
    paintbrush
    oxygen mask
    snorkel
    loudspeaker, speaker, speaker unit, loudspeaker system, speaker system
    microphone, mike
    screen, CRT screen
    mouse, computer mouse
    electric fan, blower
    oil filter
    strainer
    space heater
    stove
    guillotine
    barometer
    rule, ruler
    odometer, hodometer, mileometer, milometer
    scale, weighing machine
    analog clock
    digital clock
    wall clock
    hourglass
    sundial
    parking meter
    stopwatch, stop watch
    digital watch
    stethoscope
    syringe
    magnetic compass
    binoculars, field glasses, opera glasses
    projector
    sunglasses, dark glasses, shades
    loupe, jeweler's loupe
    radio telescope, radio reflector
    bow
    cannon
    assault rifle, assault gun
    rifle
    projectile, missile
    computer keyboard, keypad
    typewriter keyboard
    crane
    lighter, light, igniter, ignitor
    abacus
    cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM
    slide rule, slipstick
    desktop computer
    hand-held computer, hand-held microcomputer
    notebook, notebook computer
    padlock
    harvester, reaper
    thresher, thrasher, threshing machine
    printer
    slot, one-armed bandit
    vending machine
    sewing machine
    joystick
    switch, electric switch, electrical switch
    hook, claw
    car wheel
    paddlewheel, paddle wheel
    pinwheel
    potter's wheel
    gas pump, gasoline pump, petrol pump, island dispenser
    carousel, carrousel, merry-go-round, roundabout, whirligig
    swing
    reel
    radiator
    puck, hockey puck
    hard disc, hard disk, fixed disk
    sunglass
    pick, plectrum, plectron
    car mirror
    solar dish, solar collector, solar furnace
    remote control, remote
    disk brake, disc brake
    buckle
    hair slide
    knot
    combination lock
    web site, website, internet site, site
    nail
    safety pin
    screw
    muzzle
    seat belt, seatbelt
    ski
    candle, taper, wax light
    jack-o'-lantern
    spotlight, spot
    torch
    neck brace
    pier
    tripod
    maypole
    hand blower, blow dryer, blow drier, hair dryer, hair drier
    mousetrap
    spider web, spider's web
    trilobite
    harvestman, daddy longlegs, Phalangium opilio
    scorpion
    black and gold garden spider, Argiope aurantia
    barn spider, Araneus cavaticus
    garden spider, Aranea diademata
    black widow, Latrodectus mactans
    tarantula
    wolf spider, hunting spider
    tick
    centipede
    isopod
    Dungeness crab, Cancer magister
    rock crab, Cancer irroratus
    fiddler crab
    king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica
    American lobster, Northern lobster, Maine lobster, Homarus americanus
    spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish
    crayfish, crawfish, crawdad, crawdaddy
    hermit crab
    tiger beetle
    ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle
    ground beetle, carabid beetle
    long-horned beetle, longicorn, longicorn beetle
    leaf beetle, chrysomelid
    dung beetle
    rhinoceros beetle
    weevil
    fly
    bee
    grasshopper, hopper
    cricket
    walking stick, walkingstick, stick insect
    cockroach, roach
    mantis, mantid
    cicada, cicala
    leafhopper
    lacewing, lacewing fly
    dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk
    damselfly
    admiral
    ringlet, ringlet butterfly
    monarch, monarch butterfly, milkweed butterfly, Danaus plexippus
    cabbage butterfly
    sulphur butterfly, sulfur butterfly
    lycaenid, lycaenid butterfly
    jellyfish
    sea anemone, anemone
    brain coral
    flatworm, platyhelminth
    nematode, nematode worm, roundworm
    conch
    snail
    slug
    sea slug, nudibranch
    chiton, coat-of-mail shell, sea cradle, polyplacophore
    sea urchin
    sea cucumber, holothurian
    iron, smoothing iron
    espresso maker
    microwave, microwave oven
    Dutch oven
    rotisserie
    toaster
    waffle iron
    vacuum, vacuum cleaner
    dishwasher, dish washer, dishwashing machine
    refrigerator, icebox
    washer, automatic washer, washing machine
    Crock Pot
    frying pan, frypan, skillet
    wok
    caldron, cauldron
    coffeepot
    teapot
    spatula
    altar
    triumphal arch
    patio, terrace
    steel arch bridge
    suspension bridge
    viaduct
    barn
    greenhouse, nursery, glasshouse
    palace
    monastery
    library
    apiary, bee house
    boathouse
    church, church building
    mosque
    stupa, tope
    planetarium
    restaurant, eating house, eating place, eatery
    cinema, movie theater, movie theatre, movie house, picture palace
    home theater, home theatre
    lumbermill, sawmill
    coil, spiral, volute, whorl, helix
    obelisk
    totem pole
    castle
    prison, prison house
    grocery store, grocery, food market, market
    bakery, bakeshop, bakehouse
    barbershop
    bookshop, bookstore, bookstall
    butcher shop, meat market
    confectionery, confectionary, candy store
    shoe shop, shoe-shop, shoe store
    tobacco shop, tobacconist shop, tobacconist
    toyshop
    fountain
    cliff dwelling
    yurt
    dock, dockage, docking facility
    brass, memorial tablet, plaque
    megalith, megalithic structure
    bannister, banister, balustrade, balusters, handrail
    breakwater, groin, groyne, mole, bulwark, seawall, jetty
    dam, dike, dyke
    chainlink fence
    picket fence, paling
    worm fence, snake fence, snake-rail fence, Virginia fence
    diamondback, diamondback rattlesnake, Crotalus adamanteus
    grille, radiator grille
    sliding door
    turnstile
    mountain tent
    scoreboard
    honeycomb
    plate rack
    pedestal, plinth, footstall
    beacon, lighthouse, beacon light, pharos
    leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea
    mashed potato
    bell pepper
    head cabbage
    broccoli
    cauliflower
    zucchini, courgette
    spaghetti squash
    acorn squash
    butternut squash
    cucumber, cuke
    artichoke, globe artichoke
    cardoon
    mushroom
    shower curtain
    jean, blue jean, denim
    carton
    handkerchief, hankie, hanky, hankey
    sandal
    ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin
    safe
    plate
    necklace
    croquet ball
    fur coat
    thimble
    pajama, pyjama, pj's, jammies
    running shoe
    oboe, hautboy, hautbois
    cocktail shaker
    chest
    manhole cover
    modem
    tub, vat
    tray
    balance beam, beam
    bagel, beigel
    violin, fiddle
    prayer rug, prayer mat
    kimono
    hot pot, hotpot
    whiskey jug
    knee pad
    book jacket, dust cover, dust jacket, dust wrapper
    spindle
    ski mask
    beer bottle
    crash helmet
    bottlecap
    tile roof
    mask
    maillot
    Petri dish
    football helmet
    bathing cap, swimming cap
    teddy, teddy bear
    holster
    pop bottle, soda bottle
    photocopier
    vestment
    crossword puzzle, crossword
    golf ball
    trifle
    suit, suit of clothes
    water tower
    feather boa, boa
    cloak
    red wine
    drumstick
    shield, buckler
    Christmas stocking
    hoopskirt, crinoline
    menu
    stage
    bonnet, poke bonnet
    meat loaf, meatloaf
    baseball
    face powder
    scabbard
    sunscreen, sunblock, sun blocker
    beer glass
    hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa
    guacamole
    wool, woolen, woollen
    hay
    bow tie, bow-tie, bowtie
    mailbag, postbag
    water jug
    bucket, pail
    dishrag, dishcloth
    soup bowl
    eggnog
    mortar
    trench coat
    paddle, boat paddle
    chain
    swab, swob, mop
    mixing bowl
    potpie
    wine bottle
    shoji
    bulletproof vest
    drilling platform, offshore rig
    binder, ring-binder
    cardigan
    sweatshirt
    pot, flowerpot
    birdhouse
    jinrikisha, ricksha, rickshaw
    hamper
    ping-pong ball
    pencil box, pencil case
    consomme
    apron
    punching bag, punch bag, punching ball, punchball
    backpack, back pack, knapsack, packsack, rucksack, haversack
    groom, bridegroom
    bearskin, busby, shako
    pencil sharpener
    broom
    abaya
    mortarboard
    poncho
    crutch
    Polaroid camera, Polaroid Land camera
    space bar
    cup
    racket, racquet
    traffic light, traffic signal, stoplight
    quill, quill pen
    radio, wireless
    snow leopard, ounce, Panthera uncia
    dough
    cuirass
    military uniform
    lipstick, lip rouge
    shower cap
    monitor
    oscilloscope, scope, cathode-ray oscilloscope, CRO
    mitten
    brassiere, bra, bandeau
    French loaf
    vase
    milk can
    rugby ball
    paper towel
    earthstar
    envelope
    miniskirt, mini
    cowboy hat, ten-gallon hat
    trolleybus, trolley coach, trackless trolley
    perfume, essence
    bathtub, bathing tub, bath, tub
    hotdog, hot dog, red hot
    coral fungus
    bullet train, bullet
    pillow
    toilet tissue, toilet paper, bathroom tissue
    cassette
    carpenter's kit, tool kit
    ladle
    stinkhorn, carrion fungus
    lotion
    hair spray
    academic gown, academic robe, judge's robe
    dome
    crate
    wig
    burrito
    pill bottle
    chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour
    theater curtain, theatre curtain
    window shade
    barrel, cask
    washbasin, handbasin, washbowl, lavabo, wash-hand basin
    ballpoint, ballpoint pen, ballpen, Biro
    basketball
    bath towel
    cowboy boot
    gown
    window screen
    agaric
    standard poodle
    cellular telephone, cellular phone, cellphone, cell, mobile phone
    nipple
    barbell
    mailbox, letter box
    lab coat, laboratory coat
    fire screen, fireguard
    minibus
    packet
    brown bear, bruin, Ursus arctos
    pole
    horizontal bar, high bar
    sombrero
    pickelhaube
    rain barrel
    wallet, billfold, notecase, pocketbook
    cassette player
    comic book
    piggy bank, penny bank
    street sign
    bell cote, bell cot
    fountain pen
    Windsor tie
    volleyball
    overskirt
    sarong
    purse
    bolo tie, bolo, bola tie, bola
    bib
    parachute, chute
    sleeping bag
    television, television system
    swimming trunks, bathing trunks
    measuring cup
    espresso
    pizza, pizza pie
    breastplate, aegis, egis
    shopping basket
    wooden spoon
    saltshaker, salt shaker
    chocolate sauce, chocolate syrup
    ballplayer, baseball player
    goblet
    gyromitra
    stretcher
    water bottle
    skunk, polecat, wood pussy
    soap dispenser
    jersey, T-shirt, tee shirt
    school bus
    jigsaw puzzle
    plastic bag
    reflex camera
    diaper, nappy, napkin
    Band Aid
    ice lolly, lolly, lollipop, popsicle
    velvet
    tennis ball
    gasmask, respirator, gas helmet
    doormat, welcome mat
    Loafer
    ice cream, icecream
    pretzel
    quilt, comforter, comfort, puff
    maillot, tank suit
    tape player
    clog, geta, patten, sabot
    iPod
    bolete
    meerkat, mierkat
    scuba diver
    pitcher, ewer
    matchstick
    bikini, two-piece
    sock
    CD player
    lens cap, lens cover
    thatch, thatched roof
    vault
    beaker
    bubble
    cheeseburger
    parallel bars, bars
    flagpole, flagstaff
    coffee mug
    rubber eraser, rubber, pencil eraser
    stole
    carbonara
    dumbbell
    
    English springer, English springer spaniel
    malamute, malemute, Alaskan malamute
    Walker hound, Walker foxhound
    Welsh springer spaniel
    whippet
    Weimaraner
    soft-coated wheaten terrier
    Dandie Dinmont, Dandie Dinmont terrier
    Old English sheepdog, bobtail
    otterhound, otter hound
    bloodhound, sleuthhound
    Airedale, Airedale terrier
    giant schnauzer
    black-and-tan coonhound
    papillon
    Mexican hairless
    Cardigan, Cardigan Welsh corgi
    malinois
    Lhasa, Lhasa apso
    Norwegian elkhound, elkhound
    Rottweiler
    Saluki, gazelle hound
    schipperke
    Brabancon griffon
    West Highland white terrier
    Sealyham terrier, Sealyham
    Irish wolfhound
    EntleBucher
    French bulldog
    Bernese mountain dog
    Maltese dog, Maltese terrier, Maltese
    Norfolk terrier
    toy terrier
    cairn, cairn terrier
    groenendael
    clumber, clumber spaniel
    Afghan hound, Afghan
    Japanese spaniel
    borzoi, Russian wolfhound
    toy poodle
    Kerry blue terrier
    Scotch terrier, Scottish terrier, Scottie
    Boston bull, Boston terrier
    Greater Swiss Mountain dog
    Appenzeller
    Shih-Tzu
    Irish water spaniel
    Pomeranian
    Bedlington terrier
    miniature schnauzer
    collie
    Irish terrier
    affenpinscher, monkey pinscher, monkey dog
    silky terrier, Sydney silky
    beagle
    Leonberg
    German short-haired pointer
    dhole, Cuon alpinus
    Chesapeake Bay retriever
    bull mastiff
    kuvasz
    pug, pug-dog
    curly-coated retriever
    Norwich terrier
    keeshond
    Lakeland terrier
    standard schnauzer
    Tibetan terrier, chrysanthemum dog
    wire-haired fox terrier
    basset, basset hound
    chow, chow chow
    American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier
    Shetland sheepdog, Shetland sheep dog, Shetland
    Great Pyrenees
    Chihuahua
    Labrador retriever
    Samoyed, Samoyede
    bluetick
    kelpie
    miniature pinscher
    Italian greyhound
    cocker spaniel, English cocker spaniel, cocker
    Sussex spaniel
    Pembroke, Pembroke Welsh corgi
    Blenheim spaniel
    Ibizan hound, Ibizan Podenco
    English foxhound
    briard
    Border terrier

    Getting Started

    GraphQL Playground

    LabelFlow GraphQL playground is the best way to get started with the API. Just connect to https://labelflow.ai/graphiql using any modern web browser, and get started. The interface should look like something like this.

    As the images on LabelFlow are stored locally only, there is no authentication needed. All queries and mutations will be done on a database stored locally in your browser.

    Perform our first GraphQL Query

    Let’s write a graphQL query which asks for the ids, names, and dataset id of the first 10 images you have access to:

    You can now run the query by pressing the ▶️“Run Query Button” in the top left of the screen. You should see the response appear on the right side of the screen.

    Access the interactive documentation

    Our Graph API is self-documented. You can access all the documentation in the GraphQL playground. There are several ways to benefit from the documentation:

    • Automatic autocompletion of your queries

    • Manual autocompletion of your queries, triggered by pressing CTRL-SPACE when writing a query in the query area

    • Schema documentation, using the “Schema” Button on the right side of the screen.

    Here is a screenshot showcasing both Autocompletion (on the left side, in the query area), and the schema documentation (on the right side, in the sidebar). Our API is based on the principle of OpenCrud ().

    Workspace Settings & Billing

    Online workspaces allow users to collaborate by uploading images to LabelFlow's secure servers, unlocking new levels of productivity. Sign up to use these collaborative tools for free now.

    The settings tab is where you'll be able to update your workspace's name, your plan and also delete your workspace.

    Name & Avatar

    You can change the name of your workspace from the settings tab. Making modifications to the name will also automatically update the Avatar, which helps to quickly identify specific workspaces.

    Billing

    You'll also be able to consult and update your current plan, payment methods and billing information from the settings tab to match your needs.

    LabelFlow offers plans ranging from 1000 images hosted for free, up to 50,000 images. For needs above 50,000 hosted images, feel free to contact the team on our to discuss the options or by email contact@labelflow.ai .

    Delete Workspace

    This is what we call the Danger Zone. You can delete a workspace along with all of its data from the settings tab. In order to delete the workspace for good, you will be prompted to type the workspace's name to avoid any potential accidents.

    • Deleting the Workspace workspace will delete all of its images and labels

    • The workspace plan will be canceled

    Migrate a Dataset

    How to migrate a dataset to another workspace

    Let's imagine you want to move a dataset from a workspace that we'll call old-workspace to new-workspace. For now you'll need to export your dataset in the COCO format and import them back into the new workspace.

    1. Export your datasets

    • Go to your old workspace https://labelflow.ai/old-workspace

    • Open the dataset you wish to migrate

    • Click on Export in the top right

      • Select the COCO format

      • In the displayed options, choose to export the annotations only, it will save a JSON file to your computer

    2. Create a new workspace (go to if you already have one)

    • Click on Create workspace by opening the workspace switcher (top left - or )

    • Name your workspace (it has to be unique across the entire LabelFlow community)

    • Sign-in if you are not already signed-in

    3. Import your dataset to the new-workspace

    • Create a new dataset. This dataset is hosted in your new-workspace. Make sure you have selected your new-workspace in the workspace switcher (see above)

    • Click on the dataset card to open the image gallery view

    • Click on the Add images button

    You should repeat this operation for every dataset you wish to migrate.

    Create a Workspace

    Online workspaces allow users to collaborate by uploading images to LabelFlow's secure servers, unlocking new levels of productivity. Sign up to use these collaborative tools for free now.

    Create a Workspace

    Once you've signed in, you will be able to consult your local workspace, as well as create a new online workspace.

    Clicking on create workspace will prompt you to choose a name for your new workspace. The workspace name can be modified in the settings tab later on.

    Every member will lose access to the workspace
    Discord server
    You now have an online workspace in the workspace switcher. Select it.

    Start by uploading the images you had in your old-workspace dataset you have exported at step 1. Once done, close the import modal to come back to the image gallery view.

  • Click on Add images a 2nd time and drop the COCO (.json) annotation file you have exported at step 1.

  • That's it 🎉 . Click on your images, your annotations and the classes have been imported.

  • step 3
    click here
    Export your local datasets in the COCO format.
    https://github.com/opencrud/opencrud

    Import Images from Files

    Simply drag and drop the image files in the image uploader

    These are the simple steps you should follow to import from your computer:

    1. Press the Add images button on the top right

    2. Drag and drop your images and/or folders.

    3. Select Start Labeling once the import is finished

    It's that easy. 🔥👌

    You can also import 🖊 annotations in the COCO format, .

    COCO

    Exporting Labels

    COCO is a file format for object detections within an image. It describes the boundaries and attributes of labels inside an image which makes it perfect as an input for training AI models.

    Once you are finished with your labeling project, select the export tool on the top right.

    A small pop-up will appear with information about the number of labels and prompts you to specify the export format. Select the COCO file type and it will download your labels in the COCO format.

    Exporting Images

    As an option, you can export the images together with the COCO detection file in a zip file.

    Note that it may take several minutes ⏱ when you export the images of a large dataset (1000+ images).

    Bounding Boxes

    A bounding box is the simplest type of label that is defined by just two points.

    These are the simple steps you need to take to make a bounding box label:

    1. Select the bounding box tool on the left

    2. Click on the outermost edge of the subject of the label

    3. Click the opposite edge of the object to create a rectangle covering the subject

    4. Right-click on the label to select/add its class

    You can move and resize the bounding box to make changes after it is created

    • Select and drag a vertex to its correct position

    • Click anywhere in the label to drag it to the correct position

    YOLO

    Exporting your labels

    You Only Look Once, or YOLO is a widely used labeling format, where a .txt file is created for each image file in the same directory. Each .txt file contains the annotations for the corresponding image file, that is object class, object coordinates, height, and width.

    Once you are finished with your labeling project, select the export tool on the top right.

    A small pop-up will appear with information about the number of labels and prompts you to specify the export format. Select the YOLO file type and it will download your labels in the YOLO format.

    query images {
      images(first:10){
        id
        datasetId
        name
      }
    }

    Exporting Images

    As an option, you can export the images together with the YOLO detection file in a zip file.

    Note that it may take several minutes ⏱ when you export the images of a large dataset (1000+ images).

    it's explained here

    Invite Members

    Online workspaces allow users to collaborate by uploading images to LabelFlow's secure servers, unlocking new levels of productivity. Sign up to use these collaborative tools for free now.

    Online Workspaces can be shared with teammates, unlocking new levels of productivity through collaboration.

    Simply enter your teammates' email addresses to add them to the workspace.

    • Users invited to a workspace have access to all of its datasets, so be mindful of who you invite

    • Don't hesitate to create multiple workspaces for different teams and projects

    One email address per line when inviting new members

    For now, all invitees are given the Owner role, giving them the ability to edit & delete datasets as well as update the plan, payment methods and billing information. More roles will be added in the near future.

    Head over to to let us know what roles you'd like to see on LabelFlow, and vote on other new features.

    our feedback page

    Presentation

    The AI Assistant tool 🥁 assists (...) you in the labelling process! Each Assistant is an AI trained on a specific task.

    For now there are 2 general purpose AI Assistants (more will come, vote below!).

    • DETR - COCO an object detection AI Assistant trained across 80 classes. More here.

    • ViT - ImageNet an image classification AI Assistant trained across 1000 classes. More here.

    As we are launching this new feature and we want to collect your feedbacks, every plans (Community, Starter, Pro) have access to the current AI Assistants.

    Productivity tip From the labeling view: press the i key to select the AI Assistant tool and r to run the AI Assistant.

    Specific AI Assistant Requests

    If you have specific needs in terms of AI Assistants, upvote or add your own request ⬇️

    Sample Images

    here
    Object detection with DETR - COCO AI Assistant
    Image classification with ViT - ImageNet AI Assistant

    Presentation

    Overview

    The LabelFlow Graph API is available at the following URL: https://labelflow.ai/graphiql. It allows front-end applications to access all LabelFlow back-end services in a unified way. The Graph API is a gateway to LabelFlow services.

    GraphQL standard

    The Graph API uses the GraphQL query language, at modern, open specification initiated by Facebook but now used by a large part of the Web, as a successor to REST APIs. The specification and documentation for GraphQL can be found at .The Graph API is implemented over HTTPS, in practice, most tools made for REST APIs are compatible with GraphQL APIs out of the box.

    Security

    The API is available as a GraphQL API over HTTPS. All communications with the API are encrypted and secured using state-of-the-art standards. The API is used by LabelFlow's own applications. It is open to integration with customers' IT systems.

    https://graphql.org/

    CSV

    The CSV export format lists the labels contained in your dataset. Note that the polygon and classification labels are exported as bounding boxes.

    You'll find several columns for every label:

    • imageId: a unique identifier for every image

    • imageName: speaks for itself...!

    • width: width of the image in pixel

    • height: height of the image in pixel

    • class: name of the label class

    • xmin, ymin, xmax, ymax: bounding box coordinates in pixel of its top left corner and bottom right corner

    • imageURL: link to the image stored in LabelFlow

    For security reasons, the image URL is valid for 7 days only. You can generate a new CSV export at any time to reset the validity dates of the URLs.

    Definition of xmin, ymin, xmax, ymax locating a bounding box in an image

    FAQ

    Can I work on a dataset with my team?

    Yes! You need to sign-in, create an online workspace and invite your team to the newly created workspace.

    Can I request a new feature?

    Please do! You can ask or upvote a specific feature here:

    Is your roadmap public?

    Yes! You can see the features/Bugs that are planned or that we are working on here:

    Discord Channel

    Discover our discord server! The place where you can interact with the LabelFlow community and its developers.

    Images uploaded by providing their URLs to LabelFlow
    https://youtu.be/hWJfY6jhaagyoutu.be
    Easily adjust an Auto Polygon geometry by adding inside or outside keypoints
    https://youtu.be/L721GiushNYyoutu.be
    https://youtu.be/SmPY7czZFjIyoutu.be
    https://labelflow.ai/posts/choosing-the-right-image-labeling-types-for-ai-training-1labelflow.ai
    Uploading images from your computer
    Join the Labelflow Discord Server!Discord
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