new
The latest tools that help you and your team build software better, together.
Import Cityscapes
Import Cityscapes to Supervisely
Invite users to team from CSV
Invite users to team
Export to Supervisely format
images and JSON annotations
Export only labeled items
Export only labeled items and prepares downloadable tar archive
Export as masks
For semantic segmentation task
Merge datasets
Merge selected datasets with images or videos into a single one
Resize images
for both images and their annotations
Import videos from cloud storage
Import videos from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
Export Metadata
Export Images Metadata from Project
Import Metadata
Import Metadata for Images in Project
Export to Cityscapes
Converts Supervisely annotations to Cityscapes format and prepares downloadable tar archive
Export to Pascal VOC
Converts Supervisely Project to Pascal VOC format
ImgAug Studio
Visualize and build augmentation pipeline with ImgAug
Synthetic retail products
Generate synthetic data for classification of retail products on grocery shelves
Grocery store shelves
Labeled images of products on the shelve: snacks, chips, crisps
Snacks catalog
Labeled images: snacks: chips / crisps / mix
Sliding window merge
Merge images and labels that were split by sliding window before
Sliding window split
Configure, preview and split images and annotations with sliding window
YOLOv5 collection
All you need to work with YOLOv5
Flying objects
Generate synthetic data: flying foregrounds on top of backgrounds
Seeds
Unlabeled images: sunflower / pumpkin (peeled + unpeeled) / mix
Visual Tagging
Assign tags to images using example images
Cats quiz
What breed is this cat? demo for visual tagging app
Top 10 cat breeds
Tag (name of breed) is assigned to every image
Create foreground mask
Create foreground mask from alpha channel of image
Images with alpha channel
Illustrates alpha support in Supervisely
Serve YOLOv5 GPU
Deploy model as REST API service
Train YOLOv5 GPU
Dashboard to configure and monitor training
NN Image Labeling
Use deployed neural network in labeling interface
Apply NN to images project
NN Inference on images in project or dataset
Demo Images
17 unlabeled images for quick tests
Review Retail Tags
Review and correct tags (supports multi-user mode)
Retail Tagging
Supports multi-user mode
Mark Reference Objects for Retail
Prepare examples for products from catalog
Extract frames from videos
Read every n-th frame and save to images project
Tags co-occurrence matrix
Explore images for every combination of tags pairs in co-occurrence table
Movie genre from its poster
Application imports kaggle dataset 'Movie genre from its poster' as supervisely project
While True Script
Used to create infinite task for debug
Import videos by URLs from txt file
Downloads videos by URLs and uploads them to Supervisely Storage
Diff and Merge Images Projects
Visual diff and merge tool helps compare images in two projects
Diff and Merge Project Meta
Visual diff and merge tool helps compare project tags and classes
Classes co-occurrence matrix
Explore images for every combination of classes pairs in co-occurrence table
Copy project between instances
Copies images + annotations + images metadata
Render Video Labels to MP4
Creates presentation mp4 file based on labeled video
Video objects stats for every class
The number of objects, figures and frames for every class for every dataset
Group reference objects into batches
Group items by selected columns from CSV catalog
Turn video project into images
Labeled video frames to labeled images
Create JSON with reference items
Objects with specific tag will be treated as reference items
Copy image tags to objects
Tags and object classes can be customized
Add properties to image from CSV
Match image tag with CSV columns and add row values to image
Convert YOLO v5 to Supervisely format
Transform YOLO v5 format to supervisely project
Convert Supervisely to YOLO v5 format
Transform project to YOLO v5 format and prepares tar archive for download
Create users from CSV
Only instance admin has permissions to run it
App Template (No GUI)
template for your headless app
Remote import
Connect your remote storage and import data without duplication. Data is stored on your server but visible in Supervisely
Assign train/val tags to images
Assigns tags (train/val) to images. Training apps will use these tags to split data.
Labeler First Time Through
First Time Through ratio shows how many items labeler annotated right the first time (i.e. reviewer accepted his work on first round).
Labeling Events Stats
Total number of labeling actions and annotated unique images in a time interval
Import from Google Cloud Storage
Upload images by reading links (Google Cloud Storage) from CSV file
Labeling Jobs Stats
General statistics for all labeling jobs in team
Create Trainset for SmartTool
Prepare training data for SmartTool
Object Size Stats
Detailed statistics and distribution of object sizes (width, height, area)
Classes stats for images
Detailed statistics for all classes in images project
Merge classes
Merge multiple classes with same shape to a single one
Rasterize objects on images
Convert classes to bitmap and rasterize objects without intersections
Lemons (Annotated)
6 images with annotated lemons and kiwifruits
Lemons (Test)
Sample images project without labels
Print Progress and Error
Prints progress and then raises error
Videos example
Sample videos with labels
Unpack AnyShape Classes
Split "AnyShape" classes to classes with strictly defined shapes (polygon, bitmap, ...)
Convert Class Shape
Converts shapes of classes (e.g. polygon to bitmap) and all corresponding objects
Hello World App
Working demo, use it as a template for your custom apps
Roads (Test)
156 unlabeled images with roads
While True App
nocode app that ignores soft stop
Country Roads
Labeled roads (sample: 100 images, full version: 1000 images)
Persons
Image project with person instances
Roads (Annotated)
10 images with labeled road
Country Roads (Test)
594 unlabeled images
