Release 0.8.1

• Server API refactoring
• Blueprint support: WITH GREAT THANKS to @graingert
• LS version auto-checker
• Object detector ML backend for image recognition based on MMDetection

Minor:

• Make short version for task and completion paths on tasks page with tooltips with @kasuteru
• Pretty print for "Show task data"-
• Fix prediction api with @hepaajan
• Fixed ascending order on Tasks page for some cases
• Task ID added to CSV export
• External hostname correct processing
• UI fixes

Release 0.8.0 - Time Series Data Labeling

Introducing Time Series data labeling for classification, segmentation, forecasting, outlier/anomaly detection, and many more!

Read more on our introductory blog post. Play with predefined templates on /setup page. Import your data from CSV, TSV or JSON formatted files.

Features

• Multiple projects support and project sharing for multi-session mode (@ktl014)

Improvements

• Now you can set s3://-like URLs in your tasks and they are going to be resolved as Presigned URLs according to the connected S3 storage
• Specify AWS S3 bucket region within cloud storage settings UI
• Drag-n-drop data folder to import your images, audios, etc. from local file storage
• Docs updates

Release 0.7.5.post0

We put a big effort to rewrite some parts of the labeling engine for easier and better work with results, deserialization, an inner structure and so on. That led us to faster initialization (but we have a lot of space for improvements), more predictable regions operations, more stable per-region labeling, non-breaking region removal.

Features

• Relations visualizations
• Nested labeling improvements
• Granularity fixes - now you can enable granularity per label
• New deserialization mechanics for LSF
• LSF faster initialization,
• More predictable regions operations
• More stable per-region labeling
• Non-breaking region removal

Improvements

• Show errors about content loading and CORS problems
• Detailed logs from ML backend on ML page
• Task navigation improvements with hotkeys on the tasks page
• Minimum formatting of completed_at (@TTouka)
• Maintain ordering in source.json and task.json for sequential sampling (@theconsummatedhruv)
• Move external fonts to LS
• Improving documentation clarity and readability
• Add ML predictions on the tasks page
• Table tag support on import and its example
• Previews for audio and texts on the tasks page
• Don't keep premade predictions in completion + Keep premade predictions if we can't get fresh (@hepaajan)
• Support hostname environment variable to docker
• COCO export support for segmentation (@vkhizanov)

Fixes

• Fixed dates in non-Chromium browsers
• Don't stretch out text object with long lines
• Don't save broken regions (polygons, brushes)
• Fixed per-region audio labeling with predictions
• Update s3 signature version to s3v4 to support more AWS regions (@wdvr)
• Fixes for task validation in cloud storages
• Update pip and six packages to avoid fails while LS install
• Fix storage paths with trailing slashes
• Fix Project.next_task to sort task IDs for "sequential" sampling (@k4200)

Contributors
@john-sandall @wdvr @jeannefukumaru @TTouka @theconsummate @kathoum
@k4200 @hepaajan @vkhizanov

Release 0.7.4

Features

• quickview for task page
• WSGIServer support by @k4200
• https with ssl support by @abduhbm
• project setup api added
• brush RLE converter for export (label-studio-converters module)

Improvements

• significant speed up of task importing with @lightforever
• significant speed up of next task & tasks page

Fixes

• correct ordering for skip
• relative url links for pages and api
• encoding fixes
• allow one label in labeling config validation
• misspelling errors fixes by @Necmttn
• ML backend loads previously trained model before starting the new one

Label Studio Frontend

• we released BrushLabels a while ago, but they were not ready for production use and had some harmful problems. So now they are all fixed. Now they are stable after submit or resize and won't lose your labeling.
• fixed problems with completions created from predictions — they won't break the app from now.
• clickable href links
• previously we used to disable all links in your HyperText documents to protect you from leaving the labeling suddenly. But now if you really need to follow these links you can use . Anyway all links will open by ctrl/cmd+click.
• now you can hide regions for easier labeling if you have them a lot. Just use the eye button in regions list. Works only for images.
• other minor bugs

Contributors
@lightforever @k4200 @RasmusEdvardsen @abduhbm @hepaajan

Release 0.7.3

Features:

• Tree-like view of regions for grouping and better semantic organization. Also overall appearance of regions and relations are improved.
• Choices layout="select" for huge lists of choices; filtering included.
• New rotatecontrol="yes" added to rotate all the canvas instead of your head while trying to get the whole picture.
• display skipped completions in task manager
• implementation of tests
• prev/next buttons for label stream
• #288 (html upload as files)
• generate signed URLs on GCE instance (#326)
• relative path to js static
• image previews in tasks (if label config contains images)
• basic auth with username and password

Fixes:

• fix with Text and Header floats in import tasks
• fix uri resolver on s3 with custom credentials passed
• #330
• #329
• ML backend with locally served images
• Weird bugs on zoomed images are fixed: moving, resizing, polygon modification — they all are good finally.
• Also creation of new regions works well on the edge of the image now.
• Almost all warnings and errors are fixed, so your console won't be flooded.
• Other issues were fixed as well, especially related to NER.

Release 0.7.2

• Storage & bucket selector in runtime via UI
• Fixed RQ jobs & docker compose version of ML backend
• Pagination and ordering in tasks API
• Examples with embedded PDF/Website labeling
• Fixed drag-n-drop file upload
• More fixes for overlapping spans - creation, selecting, correct deserialisation and relations

Release 0.7.1

Fixed Issues:

• Empty completions from target cloud storage
• Launching ML backend from docker-compose
• Unsynchronized completion id with task id
• "undefined" popup with error

Release 0.7.0

New Features

• Connect cloud storage like s3 / gcp
• Frontend testing framework
• Putting different shapes on the same image
• maxUsages parameter for the labels
• Paragraphs

Bugfixes

• Allow different types of shapes to be used in the same image. For example you can label the same image using both rectangles and ellipses.
• Fixing double text deserialization HumanSignal/label-studio-frontend#85
• Fix bug with groups of required choices HumanSignal/label-studio-frontend#74
• Several fixes for NER labeling — empty captured text, double clicks, labels appearance

Read More

https://labelstud.io/blog/release-070-cloud-storage-enablement.html

Release 0.6.1

Post release 0.6.0 fixes & improvements:

• Remove individual task from UI #267
• More options for task sampling - enable active learning mode using --sampling=prediction-score-min
• Enabling serving locally stored images and audios by --allow-serving-local-files
• Repaired broken default localhost #287
• Fixed one-click deploy on Heroku & Google Cloud #257
• Fixed docker compose start #291
• Fixed examples for ML backend runs #294
• Fixed audio wave rendering #195
• Added HTML classification example #289
• Compatibility with Anaconda environments
• CONLL export now works fine with consecutive spans HumanSignal/label-studio-converter#4

Release 0.6.0

Nested Labeling

Nested labeling enables you to specify multiple classification options that show up after you’ve selected a connected parent class. It can match based on the selected Choice or Label value, and works with a required attribute too, smart selecting the region that you’ve not labeled. To try it out check Choices documentation and look for the following attributes: visibleWhen, whenTagName, whenLabelValue, whenChoiceValue.

Per region labeling

With per region labeling you can now provide additional attributes to the labeled regions. For example, when doing audio segmentation you can further classify the region. Per region is available for any data type and the following control tags: Choices, TextArea, and Rating.

It nicely integrates with the nested labeling, for example, you can provide multiple levels of classification for any particular region.

Machine learning backend updates

• Creating & connecting machine learning backend becomes way easier - simply define your model.py script with .fit() / .predict() methods and run ML backend with label-studio-ml start --init --script=model.py. Check quickstart and tutorials on how to connect sklearn and PyTorch models
• New ML page in UI, where you can specify URLs to connect ML backends, manually trigger model training, explore training statuses and quickly check predictions by drag-n-dropping tasks.
  
• Label Studio now supports multiple ML backends connected together. You can simultaneously get multiple predictions for each task and do comparative performance analysis for different models or different hyperparameters of a single model. It's possible to connect as many backends as you want by using --ml-backend url1 url2 ... command line option or adding them via UI.

Filtering

When the number of labels or choices is big, looking for a particular one becomes tedious. New tag to the rescue. It works with any list of Labels / Choices, and is keyboard-driven. Here is an example of the interaction. Hitting shift+f puts focus, then hitting Enter key selects the first matching item.

Display Label Names

Displaying labels on top of the labeled regions proved to be a useful feature if you’d like to do a verification of the labeling. Visually inspecting the regions takes smaller amounts of time than doing so through switching between regions.

Models Scores

Along with the names of the labels you can provide a prediction score for specific regions. That score may either come from the data that you upload or from the model that you’ve connected. When it’s available you can Sort by the score, and quickly verify/adjust the labeling for the most “uncertain” regions.

Keeping the label active

If you label the same type of data it may be cumbersome to keep selecting the same label over and over again, now you can choose to keep the last label active and use it for new labeling.

Don’t forget to unselect the region when you want to select a new label, otherwise, you’d change the label of the existing region.

Bug fixes & improvements

• --host argument now available via command-line argument (thanks to @hachreak)
• fixed upload with plain text tasks (thanks to @gauthamsuresh09)
• fixed one-click deploy on Google Cloud (thanks to @iCorv)
• fixed URL paths for proxy safety (thanks to ezavesky)