Build High Quality Labeled Dataset
Building great machine learning models requires a huge amount of high quality labeled training data. In fact, eighty percent of the time spent on developing machine learning is related to data management, which slows innovation and results in long build-test cycles. This translates to 2-4 weeks of calendar time, a preposterous amount when compared to software development.
Skyl’s Data Labeling platform provides data scientists to build great machine models through faster iteration cycles and powerful tooling that unlocks new capabilities.
How Labelwise works
Image Classification Labeling Job
Image Classification Labelling jobs help you label images and get them ready for machine learning training. It deals with the annotation of images from the provided categories.
Skyl provides you with a lucid interface for image classification labelling jobs through which you can get your dataset labeled as per the instructions your provide.
Named Entity Extraction Labeling Job
Named Entity Extraction Jobs deal with the extraction of named-entities from unstructured text. These categories could be anything from individuals, organizations, places to quantities, monetary values, percentages and others.
With Skyl’s NER labelling jobs, you can create jobs that make it extremely easy to label, tag and annotate text in an interface that unifies all your labelling jobs in one place.
Object Detection Labeling Job
Object Detection labeling jobs enables you to label individual objects in a given image along with its bounding box and label. These objects could be from real world like traffic lights, vehicles, pedestrians, docments, logos etc.
With Skyl’s Labelwise Object detection job, your colloaborator (human labelers) get easy and intitutive labeling tool which can be used to annotate bounding box for your images as objects. These annotated dataset then can be downloaded as you preferened choice of format YOLO, PASCAL. TF Record.