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.
We 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 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.
Segment objects for detection within image frames.
Points help assess body language, gait, and more.
Text is available for accessible image captions
Used for image classification & attribute identification.
Sub-labels add to the definitions of rectangles, polygons, and point labels.
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
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
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
Amazon S3 bucket
Google Cloud Storage