SmartPoly Object Selection
Save time labeling polygons for instance segmentation by drawing a simple rectangle that resolves to an object.
Track a labeled objects from frame-to-frame to automatically apply polygons or bounding boxes.
AutoLabel Common Classes
Select common objects to recognize and Plainsight will use pre-trained models to automatically apply labels.
Label Your Data Faster, Smarter, Better
Designed to maximize your productivity and scale, a full set of features to equip all your computer vision labeling projects with step-by-step capabilities.
1. Ingest Data
2. Define Labels
Configure labels for your dataset. Select from: Rectangle, Polygon, Point, Feature Points, Text, Classification
3. AI- Powered Data Annotation
4. Train a Model in Plainsight or Export Your Data
Plainsight is Trusted by Industry Leaders & Partners:
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.
Draw boxes to detect specific objects within images.
Segment objects for detection within image frames.
Use for highly detailed detection such as emotion & body pose.
Leverage for free text production for image captions ideal for accessibility.
Apply for image classification & attribute identification.
Add sub-labels into the label definitions for rectangles, polygons, and point labels.
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.
A large-scale object detection, segmentation, and captioning dataset
Pattern Analysis, Statistical Modeling and Computational Learning Visual Object Classes
“You Only Look Once”. A popular real-time object detection algorithm
Apple’s machine learning model creation and training framework
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.
Sync data sources across teams
Add labeling and project details
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
Connect an Amazon S3 bucket
Google Cloud Storage
Connect a GCP bucket
Upload files for labeling
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.