Guide: Create a request

only project managers and workforce managers can create new requests annotation request is the core concept in the organization of annotation work at kognic the request's configuration specifies what data to annotate (input batch), how to annotate the data (annotation instruction taxonomy and guideline), which process the data should be produced by (workflow), and who should annotate and quality assure the data (team) you can create requests in multiple ways create a new request in an already existing project create from scratch create using an existing request as a template create a new request in a new project this chapter explains how to create a request in an existing project if you want to create a new project and add one or multiple requests to it, we recommend you start by reading the chapter guide create a new project docid\ f4u4tgqwmj1zxpcnznjsh create a request from scratch when creating a request from scratch, you can freely specify what input type and workflow the request should use if a request in the project uses the same input type and workflow as you want for your new request, you can also create a new request using the existing one as a template 1\ open the project in which you want to create the request to get started, open the project in which you want to create a new request then locate the "create request" button at the top right corner of the project page 2\ configure the request add request and input batch information name the request when naming the request, ideally, use a straightforward name that refers to the type of annotation you plan to use it to produce try to avoid using names made up of random letters and numbers input batch external id optional external id that can be used to target the project when using external apis name the input batch when creating a request, you need to create a new input batch alongside it the input batch is referenced when uploading your data to the request an input batch is a group of inputs an input is a set of sensor data you want to annotate it usually consists of at least one image/frame it can also contain images from several cameras, a point cloud, a video, or a sequence of images read more in our api documentation select an annotation type kognic uses annotation types to categorize the annotations produced in different requests select the one that best matches the type of annotations you plan to produce in your request select request producer the organization with the request role producer is responsible for producing annotated data in the request users from this organization are allowed more detailed monitoring and management options and can configure the request team in detail all to ensure they can successfully monitor and manage the production process select an input type the input type decides what data type you can upload to your newly created input batch and later annotate in the request the available input types are input type description lidar and cameras a single frame, containing both camera data (from one or multiple cameras) and lidar data lidar and cameras sequence a sequence of frames, containing both camera data (from one or multiple cameras) and lidar data cameras a single frame of camera data the data can be from one or multiple cameras cameras sequence a sequence of camera frames the data can be from one or multiple cameras aggregated lidars and cameras sequence a sequence of frames, containing both camera data (from one or multiple cameras) and lidar data the lidar data will be aggregated with respect to the first frame of the sequence select annotation instruction in this step you can select a revision of a annotation instruction you have created with our tools an annotation instruction is a specification of what you want to annotate and a guidance of how you want it to be annotated a revision is a version of your instruction that has been used in production as you learn more about your dataset, you probably want to update your instruction, which is handled by creating a new revision select workflow the request workflow determines the steps used to produce annotations and in what order they define what type of steps (tasks) to complete before we have produced a deliverable annotation below we will describe the available workflows annotate + review every input is first annotated and then reviewed if the annotation's quality is insufficient, the reviewer can reject the annotation if rejected, the annotation is sent for a review correction and then reviewed again this loop continues until the annotation is accepted, at which point the annotated input becomes delivery ready annotate + correct + review every input is first annotated, then corrected, and lastly, reviewed if the annotation's quality is insufficient, the reviewer can reject the annotation if rejected, the annotation is sent for a review correction and then reviewed again this loop continues until the annotation is accepted, at which point the annotated input becomes delivery ready flexible workflows our flexible workflows consist of phases, which can be added, removed or displayed in any order to suit your unique needs we currerntly have two flexible workflows available for use, with more planned for the future you can learn more about our flexible workflows in the chapter flexible workflows docid\ nfxqay29jqu2ulbmqvpn7 select review error types the error types are available in the feedback tools during review tasks, and during follow up correction tasks if your organization has many error types configured, you might want to select a subset of error types that are relevant for this request you can learn more about error types on the page error types docid 7sa8 gk8s48crtht3ypmz 3\ create the request once everything above has been specified, you can create your new request 🎉 4\ prepare the request for production after creating your request, you need to ensure the following things are done upload data to the input batch you can read more about this process in our api documentation if questions remain, reach out to your kognic contact prepare an annotation instruction docid\ aibhomp8oboyzmbeuvl j a guideline and a taxonomy are needed to start the production of annotations in the request these specify what types of annotations should be created in the request and are needed to ensure the relevant information and tools are available to the team members the taxonomy and guideline are part of what we call an annotation instruction you can create and publish annotation instructions in the app, see annotation instruction docid\ aibhomp8oboyzmbeuvl j when you have set the revision in annotation instruction to published, kognic will then add the additional settings and set it to ready for production you will then be able to select the revision when creating a request add team members to get the data label according to your selected workflow, you need a team this team will be responsible for completing tasks during production in the tab team docid\ fiopd5ytqela7kyahrpyl you can add team members and configure what type of tasks they should access and if they should get them automatically or not read more about adding team members in the chapter team docid\ fiopd5ytqela7kyahrpyl activate automatic task allocation when all configuration is completed and you are ready to start producing annotations in the request we encourage you to activate our automatic task allocation system you can read how the system works and how to activate it in guide automatic task allocation docid\ c7ebfn74omk zk2b3n 9o create a request using an existing request as a template when using an existing request as a template, the new request will use the same input type and workflow as the original request you also have the option of using the same annotation instruction, which saves you time in preparing the request for production if you don't want your new request to use the same input type and workflow as an existing request in the project, create the request from scratch instead 1\ open the request which you want to use as a template to get started, open the request you want to use as a template for your new request then open the context menu at the top right corner of the page, and select the menu option "use as template" the new request will be created in the same project as the request that is used as a template 2\ configure the request add request and input batch information name the request when naming the request, ideally, use a straightforward name that refers to the type of annotation you plan to use it to produce try to avoid using names made up of random letters and numbers name the input batch when creating a request, you need to create a new input batch alongside it to this input batch, you later upload your data to have it annotated in the request an input batch is a group of inputs an input is a set of input data you want to annotate it usually consists of at least one image/frame it can also contain images from several cameras, a point cloud, a video, or a sequence of images select an annotation type (only applicable if you don't use the same annotation instruction) kognic uses annotation types to categorize the annotations produced in different requests select the one that best matches the type annotations you plan to produce in your request 3\ create the request once everything above has been specified, you can create your new request 🎉 4\ prepare the request for production after creating your request, you need to collaborate with kognic to ensure the request is ready for production this means ensuring the following things are done upload data to the input batch you can read more about this process in our api documentation if questions remain, reach out to your kognic contact prepare an annotation instruction docid\ aibhomp8oboyzmbeuvl j and ask kognic to connect it to the request note! this step can be skipped if you selected "use same instruction" in the "use as template" dialog a guideline and a taxonomy are needed to start the production of annotations in the request these specify what types of annotations should be created in the request and are needed to ensure the relevant information and tools are available to the team members the taxonomy and guideline are part of what we call an annotation instruction you can create and publish annotation instructions in the app, see annotation instruction docid\ aibhomp8oboyzmbeuvl j when the annotation instruction is published and ready for production, you need to communicate to your kognic contact which request you want to use it for add team members to get the data label according to your selected workflow, you need a team this team will be responsible for completing tasks during production in the tab team docid\ fiopd5ytqela7kyahrpyl you can add team members and configure what type of tasks they should access and if they should get them automatically or not read more about adding team members in the chapter team docid\ fiopd5ytqela7kyahrpyl activate automatic task allocation when all configuration is completed and you are ready to start producing annotations in the request we encourage you to activate our automatic task allocation system you can read how the system works and how to activate it in guide automatic task allocation docid\ c7ebfn74omk zk2b3n 9o