Labellerr: training data platform with smart feedback loop

What is Data Annotation

Data annotation is a process to turn unstructured data (like images, videos, text and audio) into structured data to make it ready for model training. Today we find ourselves surrounded by large volume of raw data. Our data annotation platform helps AI teams to create high quality labelled data for computer vision model training.

Types of Data Annotation

Based on project requirement one can multiple different annotation on data for example classification, bounding boxes, polygon and key point annotation. One can perform data labeling on different modalities like image annotation, text classification, semantic annotation, video annotation etc.

Data Annotation Solution

Image Annotation

Object detection, object tracking, segmentation and classification are some of the most popular image labeling methods. Labellerr support all kind of image labeling.

Video Annotation

Labellerr support classification, tracking, key-point annotation, and object detection on a video or image sequence with auto labeling feature.

Text annotation

Name entity recognition, sentiment analysis, classification, intent annotation and more on text data with automation can be performed on our platform

Healthcare

Let Labellerr be your trustworthy partner in healthcare innovation as we turn unprocessed data into invaluable insight with our thorough annotation services, opening the door for ground-breaking study and individualized patient care.

Agritech

Labellerr's annotation services bridge the gap between cutting-edge technology and compassionate agriculture for a better future using the synergy between human knowledge and artificial intelligence.

Vision for Retail

Labellerr's smart feedback loop helps ML team to automate their computer vision data pipeline efficiently. Even at production!

Automotive

With Labellerr, computer vision team manages their data pipeline at ease. Our "smart feedback loop" is designed to remove the manual data curation, quality check and label process.

Security & Surveillance

Some of the production usecase example where Labellerr's revolutionary "Smart Feedback Loop" helping ML teams to get ground truth labels and model training super fast and simple.

Data annotation process

Easy data ingestion from
multi-cloud environments

Enable the ML team to
perform exploratory data analysis

Detect labels using model-assisted
& zero-shot approaches

Indicate the data that
need manual labeling

Automate the custom
workflow

Connectmodel for smart data
curation for upcoming data batches

Repeat

Enterprise ready

On-premise data, integrations in AWS Sagemaker, GCP Vertex, Azure ML, and more

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For enterprises that work in environments without internet access, Labellerr offers a fully on-premise solution. The software can be deployed either in your cloud infrastructure or on your own hardware

Labellerr allows you to upload data to its encrypted storage through UI, CLI, or Python SDK. The uploaded data can be versioned, used for model training, model versioning, and deployment. All data or models created through Labellerr’s encrypted storage solely belong to its creator