Explore constantly growing catalog of Supervisely Apps: open-source web-applications that provide new functionality to Supervisely platform. Import and export data, train neural networks, run data transformations and many more — all done by Supervisely Apps!
The most popular applications among them all
Import Images without annotations to Supervisely
Images with corresponding annotations
Detailed statistics for all classes in images project
Dashboard to configure and monitor training
Import images from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
Transform project to YOLO v5 format and prepares tar archive for download
Upload images using .CSV file
Import videos with annotations in Supervisely format
Deploy model as REST API service
Import videos from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
The newest applications in continually growing ecosystem
Export pointclouds project and prepares downloadable tar archive
Export videos project and prepares downloadable tar archive
complete solution for medical DICOM annotation
complete solution for image annotation with advanced features
complete solution for LiDAR annotation with photo context
complete solution for video annotation
complete solution for LiDAR episodes annotation with photo context
complete solution for image annotation
Batched smart labeling tool for Videos
Import selected videos from Team Files to selected destination
Import Images without annotations to Supervisely
Images with corresponding annotations
Import images from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
Upload images using .CSV file
Import videos with annotations in Supervisely format
Import videos from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
Image project with person instances
Import pointclouds without annotations in .ply format from Team Files
Import volumes in DICOM and NRRD formats without annotations
Downloads videos by URLs and uploads them to Supervisely Storage
Transform project to YOLO v5 format and prepares tar archive for download
Converts Supervisely Project to Pascal VOC format
Converts Supervisely to COCO format and prepares tar archive for download
Creates presentation mp4 file based on labeled video
For semantic segmentation task
Converts Supervisely annotations to Cityscapes format and prepares downloadable tar archive
Export project or dataset in Supervisely volumes format
Export project or dataset in Supervisely pointcloud episode format
Download activity as csv file
Converts Supervisely Pointcloud format to KITTI 3D
Training, inference, serving, performance analysis, smart tools…
Dashboard to configure and monitor training
Deploy model as REST API service
State of the art object segmentation model in Labeleing Interface
Prepare training data for SmartTool
serve and use in videos annotator
NN Inference on images in project or dataset
Use deployed neural network in labeling interface
Generate synthetic data: flying foregrounds on top of backgrounds
Batched smart labeling tool for Images
to TorchScript and ONNX formats
NN Inference on images in project or dataset
Use deployed neural network in labeling interface
Batched smart labeling tool for Images
Image Pixel Classification using ilastik
Use neural network in labeling interface to classify images and objects
Assign tags to images using example images
Filter objects and tags by user and copy them to working area
Use metric learning models to classify images
Label videos for Action Recognition task
Label and Review videos for Action Recognition task
Team members, annotator performance & stats, exams, issues…
General statistics for all labeling jobs in team
Total number of labeling actions and annotated unique images in a time interval
First Time Through ratio shows how many items labeler annotated right the first time (i.e. reviewer accepted his work on first round).
Download activity as csv file
Only instance admin has permissions to run it
Group items by selected columns from CSV catalog
Annotate Project using Queues
Invite users to team
Synthetic training data generation
Transform data and annotations, perform augmentations, filtering and querying…
Prepare training data for SmartTool
Converts shapes of classes (e.g. polygon to bitmap) and all corresponding objects
Creates images project from video project
Assigns tags (train/val) to images. Training apps will use these tags to split data.
Generate synthetic data: flying foregrounds on top of backgrounds
Merge multiple classes with same shape to a single one
Visualize and build augmentation pipeline with ImgAug
for both images and their annotations
Configure, preview and split images and annotations with sliding window
Read every n-th frame and save to images project
Data exploration and insights, visualization, statistics, quality assurance
Detailed statistics for all classes in images project
General statistics for all labeling jobs in team
Detailed statistics and distribution of object sizes (width, height, area)
Total number of labeling actions and annotated unique images in a time interval
Explore images for every combination of classes pairs in co-occurrence table
Creates presentation mp4 file based on labeled video
The number of objects, figures and frames for every class for every dataset
First Time Through ratio shows how many items labeler annotated right the first time (i.e. reviewer accepted his work on first round).
Explore images for every combination of tags pairs in co-occurrence table
Review images annotations object by object with ease
Development environment, template apps, widgets how-to
Template application to serve custom detection models
nocode app that ignores soft stop
Working demo, use it as a template for your custom apps
template for your headless app
Prints progress and then raises error
Used to create infinite task for debug
serve and use in videos annotator
Presentation, content generation, administration
Image project with person instances
Labeled roads (sample: 100 images, full version: 1000 images)
594 unlabeled images
6 images with annotated lemons and kiwifruits
10 images with labeled road
Labeled images of products on the shelve: snacks, chips, crisps
Sample images project without labels
What breed is this cat? demo for visual tagging app
Unlabeled images: sunflower / pumpkin (peeled + unpeeled) / mix
1171 sample gt-labeled images
