Enterprise Computer Vision Begins with Robust Data Curation & Quality Annotation

Plainsight lays the foundation for production-ready computer vision with data collection, AI-powered image and video labeling, and dataset management.

AI-Powered Annotation Increases Project Speed 20X

SmartPoly Object Selection

We label polygons for instance segmentation by drawing a simple rectangle that resolves to an object.

With Plainsight you can label polygons for instance segmentation by drawing a simple rectangle that resolves to an object

TrackForward Labeling

We track labeled objects from frame-to-frame and automatically apply polygons or bounding boxes.

With Plainsight you can track labeled objects from frame-to-frame and automatically apply polygons or bounding boxes.

AutoLabel Common Classes

We select common objects and recognize them with pre-trained models that automatically apply labels.

We select common objects and recognize them with pre-trained models that automatically apply labels.

We Label Data Faster, Smarter, Better

Designed to maximize your productivity and scalability, Plainsight’s end-to-end enterprise computer vision services begin with a robust process of data discovery.

1. Data Ingestion and Discovery

We connect to multiple sources from Amazon S3, Google Cloud Storage, or local files. We build robust datasets and apply custom filters for any project.

2. Managed Data Labeling

We configure labels to your dataset and unique challenges for the best results, including: Rectangle, Polygon, Point, Feature Points, Text, and Classification labels.

3. AI-Powered Data Management

We get results faster with AI-Powered tools like smart polygon selection, frame-to-frame object tracking, and common object auto-labeling in addition to our proven best practices and expertise.

4. Model Training and Deployment

We immediately put your dataset to work and train your model using automated training features.

Plainsight is Trusted by Industry Leaders & Partners:

Flexible Label Types

Plainsight supports all major definitions used in computer vision labeling. We use Rectangles, Polygons, Point, Feature Points, Text, Class, Multi-Class labels and more to build robust datasets for our enterprise customers.

Boxes detect specific objects within larger images.

Rectangles

Boxes detect specific objects within larger images.

Segment objects for detection within image frames.

Polygons

Segment objects for detection within image frames.

Feature point labeling

Feature Points

Points help assess body language, gait, and more.

object detection labeling

Text

Text is available for accessible image captions

Multi-Class labeling

Classification

Used for image classification & attribute identification.

Sub-labels add to the definitions of rectangles, polygons, and point labels.

Sub-Labels

Sub-labels add to the definitions of rectangles, polygons, and point labels.

Easy Exports

Plainsight supports the most common label formats for computer vision: COCO, Pascal VOC, YOLO, Create ML or our own JSON format.

A large-scale object detection, segmentation and captioning dataset

COCO

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

Pattern Analysis, Statistical Modeling and Computational Learning Visual Object Classes

Pascal VOC

Pattern Analysis, Statistical Modeling and Computational Learning Visual Object Classes

A popular real-time object detection algorithm

YOLO

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

Apple's machine learning model creating and training framework

Create ML

Apple’s machine learning model creation and training framework

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.

Data Source Connections

We connect to both local storage and remote data sources for projects.

Supported image formats: JPG, PNG, GIF, BMP, TIFF, WEBP

Supported video formats from S3 & GCS : AVI, FLV, MKV, MOV, MP4, OGG, WEBM

We connect both local storage and remote data sources for projects.

Amazon S3

Amazon S3 bucket

Google Cloud Storage

GCP bucket

Local Assets

Upload files

Local CSV

CSV file