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AI-Powered Data Annotation

Save time & resources with smart tools for polygon selection, frame-to-frame predictive labeling, & automated object recognition.

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SmartPoly Object Selection

Save time labeling polygons for instance segmentation by drawing a simple rectangle that resolves to an object.

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TrackForward Labeling

Track a labeled objects from frame-to-frame to automatically apply polygons or bounding boxes.

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AutoLabel Common Classes

Select common objects to recognize and Plainsight will use pre-trained models to automatically apply labels. 

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Label Your Data Faster, Smarter, Better

A full set of features to equip all your computer vision labeling projects. Plainsight’s Data Annotation is designed to maximize productivity and scale.

1. Ingest Data

Connect to multiple sources from Amazon S3, Google Cloud Storage, or local files. Apply filters & create data subsets for projects.

2. Define Labels

Configure labels for your dataset. Select from: Rectangle, Polygon, Point, Feature Points, Text, Classification

3. Label Data

Use AI-Powered tools for fast & easy smart polygon selection, frame-to-frame object tracking, & common object auto-labeling

4. Export Datasets

Export labeled data to the format you need including COCO, Pascal VOC, YOLO, Create ML, or Plainsight’s own flexible JSON format

Plainsight is Trusted by Industry Leaders & Partners:

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Flexible Label Types

Plainsight supports all major definitions used in computer vision labeling. Choose from Rectangles, Polygons, Point, Feature Points, Text, Class, or Multi-Class.

object detection labeling

Rectangles

Draw boxes to detect specific objects within images.

Image Segmentation labeling

Polygons

Segment objects for detection within image frames.

Feature point labeling

Feature Points

Use for highly detailed detection such as emotion & body pose.

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Text

Leverage for free text production for image captions ideal for accessibility.

Multi-Class labeling

Classification

Apply for image classification & attribute identification.

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Sub-Labels

Add sub-labels into the label definitions for rectangles, polygons, and point labels.

Easy Exports

Plainsight’s Data Annotation supports the most common label formats for computer vision. Choose to export as COCO, Pascal VOC, YOLO, Create ML or our own JSON format.

Image Segmentation labeling

COCO

A large-scale object detection, segmentation, and captioning dataset

Pascal VOC

Pattern Analysis, Statistical Modeling and Computational Learning Visual Object Classes

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YOLO

“You Only Look Once”. A popular real-time object detection algorithm

Create ML Support

Create ML

Apple’s machine learning model creation and training framework

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Plainsight JSON

Our format is a schema defined JSON document containing all label elements. We also include metadata about the image or frame and source video if applicable.

Team Collaboration

Plainsight’s Data Annotation makes labeling as a team easy. Add new members, sync data sources across teams, manage roles, provide labeling instructions, add comments, review labeling progress and time spent per label.

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Cloud Sync

Sync data sources across teams

Multi-Class labeling

Instructions

Add labeling and project details

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Project Review

Approve or reject labels & track progress

Data Source Connections

Connect both local storage and remote data sources to projects.

Supported image formats: JPG, PNG, GIF, BMP, TIFF, WEBP
Supported video formats from S3 & GCS : AVI, FLV, MKV, MOV, MP4, OGG, WEBM

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Amazon S3

Connect an Amazon S3 bucket

Create ML Support

Google Cloud Storage

Connect a GCP bucket

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Local Assets

Upload files for labeling

Create ML Support

Local CSV

Use a CSV file to upload assets

Get Started for FREE

No matter the size of your labeling projects, if you’re an expert, or just getting started with computer vision, Plainsight provides the speed, automation, and ease you need.