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Platform News and Updates
Aug 24, 2022 Update Notes
Plainsight Pipelines
Plainsight Pipelines is a guided step-by-step interface that streamlines the setup, processing and management of visual data workflows, making it easier than ever to operationalize computer vision models, create custom data transformations and prediction pipelines at scale.
Types of Pipelines 
Streaming: for always available predictions when response time is important 
Batch: Create scheduled or one-time batch pipelines
Inputs
Cloud buckets (Google Cloud and Amazon S3), Pub/Sub, API Endpoints 
Images or video (videos can be split at custom frame rate) 
Setup filename and file extension filters when syncing from cloud sources
Pipeline Blocks
Transform your data with resize and crop blocks 
Generate predictions from you data using pre built models or your own custom models
Perform tiled inferencing to identify small objects on high resolution images 
The block library will continue to grow in future releases
Outputs
Cloud buckets (Google Cloud and Amazon S3), Pub/Sub, API 
Option to include output image files along with predictions
Enhanced Model Metrics
We’ve updated our reporting for computer vision models trained in the Plainsight platform. You’ll find plenty of new metrics that vary by model type, improvements include:
New metrics and tools for each supported model type to enable you to identify low model performance AND diagnose potential causes of low performance 
View your training settings in the evaluation page to know how your model was trained
Improved training image gallery metadata, sorting and filtering
Classification 
Added classification matrix for comparing class performance and identifying confusion between classes 
Added Confusion matrix that can be reordered and filtered to find problematic classes
Added summary metrics for each class
Object detection
Added summary statistics per class
Regression
Added metrics support for this new model type
Semantic Segmentation
Added metrics support for this new model type
Billing
Improved communication when credits are running low or are exhausted
May 5, 2022 Update Notes
New features 
Semantic segmentation 
You can now label, train and deploy semantic segmentation models!
Overview:
Semantic segmentation classifies each pixel in an image that matches a defined label. These models do not track unique instances of objects, so they are not recommended for counting/tracking unique items. Rather, these models are useful for calculating the coverage area of defined objects within a frame.
Why Use Semantic Segmentation? 
Semantic segmentation models typically are more performant than instance segmentation models as they are not classifying unique labels for each detection and instead are painting all instances of a defined object. Common uses for semantic segmentation segmentation include:
Autonomous vehicles: Self driving cars need low latency and pixel accuracy to ensure the vehicles stay on the road, obey traffic laws and avoid collisions. 
Precision assembly: Combining robotics with semantic segmentation models allows users to pin-point the location of materials in a frame and pass those coordinates along for a robot to take action. 
Calculating area: When properly calibrated, semantic segmentation models can help users understand how much of a frame is covered by a defined object with pixel perfect accuracy, enabling a large range of use cases from measuring the movement and growth of wildfires all of the way down to measuring the growth of bacterial colonies in lab environments.
​Read more about utilizing instance and semantic segmentation models.
Improvements and bug fixes
Model training: We've made improvements, optimizations, and fixed some issues related to model training. 
Storage usage: We've fixed an issue with storage usage reporting and have also made it easier to see your current usage compared to your billable usage in the Billing details:
April 5, 2022 Update Notes
We are excited to share the notes for our April release of the Plainsight vision AI platform. This update includes support for training new model types, UI improvements and updates to our billing and pricing. 
Along with updates to our Enterprise vision AI platform, we’ll be launching new On-Demand features over the coming weeks to streamline deployment and adoption of Plainsight’s intuitive computer vision solutions. 
We’d love to hear what you think about these new features and hear about your experience with the Plainsight vision AI platform. Join the official Plainsight Slack channel to connect directly with the Plainsight team.
Dataset
Free Labels: Accelerate the creation of robust datasets with free unlimited labels.
Large File Format Support: Faster loading speed for large and high resolution datasets and/or projects with a large number of labels.
Zoom and Pan: Improved pan and scroll mouse interactions for more accurate labeling.
Training
Mobile Model Training and Download*: Train and download TFLite models specifically designed and optimized for mobile devices including iOS and Android phones and tablets. Read about how to train TensorFlow Lite mobile models.
*Plainsight Enterprise Feature Only. Contact us for more information.​
Training and Inferencing with Tiled Images: Slice high-resolution images into tiles in order to detect small objects. Tiled models take image tiles as inputs, creates inferences on each tile, stitches the original image back together, and returns detections for the image as a whole. Read more about how to use tiling.
Regression Models: Label and train models to predict values in images such as an object size, age, or weight. Read about how to train a regression model in our walk-through guide.
Terms and Pricing
Labels Are Now Free and Unlimited: Stop worrying about cost-per-label pricing and create richer datasets for high-performance models.
$100 in Credits: Jump-start your next computer vision project with $100 in credits that let you explore each section of our vision AI platform. 
Plainsight On-Demand: With pay-as-you-go pricing, explore our full vision AI platform and pay only for usage and functionality that fits your needs.
Read about our new Usage and Pricing.
Documentation Updates
New Regression and Regression Labeling docs
New Walk-through: Training a Regression Model​
New Tiling doc
New Train a Mobile Model doc
Updated Billing doc and subpages
Added User Roles to User Management
Added Changing Organizations to Account Management
Updated Train Your Model with SmartML guide
New Walk-through: Training a Classification Model​
Video walk-throughs: Upload local files, Upload files via CSV, Sync files from GCS​
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Outline
Aug 24, 2022 Update Notes
May 5, 2022 Update Notes
New features
Improvements and bug fixes
April 5, 2022 Update Notes
Dataset
Training
Terms and Pricing
Documentation Updates