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
complete solution for image annotation
complete solution for image annotation with advanced features
Drag and drop images to Supervisely, supported formats: .jpg, .jpeg, jpe, .mpo, .bmp, .png, .tiff, .tif, .webp, .nrrd
complete solution for video annotation
complete solution for LiDAR annotation with photo context
For semantic and instance segmentation tasks
images and JSON annotations
complete solution for LiDAR episodes annotation with photo context
Images with corresponding annotations
Import Videos without annotations to Supervisely
complete solution for medical DICOM annotation
Import pointclouds in PCD format without annotations
Transform project to YOLO v5 format and prepares tar archive for download
Clone project or dataset to selected workspace or project, works with all project types: images / videos / 3d / dicom
Export pointclouds project and prepares downloadable tar archive
Transform YOLO v5 format to supervisely project
Export videos project and prepares downloadable tar archive
Converts Supervisely to COCO format and prepares tar archive for download
Import images from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
Export only labeled items and prepares downloadable tar archive
Upload images using .CSV file
Dashboard to configure and monitor training
Use deployed neural network in labeling interface
Import images with binary masks as annotations
NN Inference on images in project or dataset
The newest applications in continually growing ecosystem
Copy team from one Supervisely Instance to another (including workspaces, team members and team files)
Converts Supervisely format to COCO Keypoints
Merge multiple image projects into a single one
Text Detection and Recognition on images
Deploy HRDA model for inference
Train HRDA model for segmentation in semi-supervised mode
Run HQ-SAM and then use in labeling tool
Drag and drop PDFs to import pages as images to Supervisely
Semi-supervised, works with both long and short videos
Used to create infinite task for debug
Deploy MMDetection 3.0 model as a REST API service
Training dashboard for mmdetection framework (v3.0.0 and above).
Deploy YOLOv8 as REST API service
Dashboard to configure, start and monitor YOLOv8 training
CVPR2022 SOTA video object tracking
Compare annotations of multiple labelers
Export items after the passing labeling job review
App for creating and managing annotation exams
Import image projects in Supervisely format from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
Download CSV file with download links for images
Download images from project or dataset.
Track points and polygons on videos
Converts annotations from Supervisely to COCO format as RLE masks with preserving holes
Interactive evaluation of your instance segmentation model
Transform Supervisely format to YOLOv8 format
Upload your assets from PC or cloud storage, in many formats
Drag and drop images to Supervisely, supported formats: .jpg, .jpeg, jpe, .mpo, .bmp, .png, .tiff, .tif, .webp, .nrrd
Images with corresponding annotations
Import Videos without annotations to Supervisely
Import pointclouds in PCD format without annotations
Transform YOLO v5 format to supervisely project
Import images from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
Upload images using .CSV file
Import images with binary masks as annotations
Import videos from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
Import videos with annotations in Supervisely format
Convert DICOM data to nrrd format and creates a new project with images grouped by selected metadata
Import Pointcloud Episodes with Annotations and Photo context
Converts COCO format to Supervisely
Downloads videos by URLs and uploads them to Supervisely Storage
Import pointclouds without annotations in .ply format from Team Files
Import public or custom data in Pascal VOC format to Supervisely
Connect your remote storage and import data without duplication. Data is stored on your server but visible in Supervisely
Import volumes in DICOM and NRRD formats without annotations
Upload images by reading links (Google Cloud Storage) from CSV file
Import Point Cloud Project with Annotations and Photo context in Supervisely format
Copies images + annotations + images metadata
Import Cityscapes to Supervisely
Convert .CSV catalog to Images Project
Import Supervisely volumes project with annotations
Converts KITTI 3D format to Supervisely pointcloud format
Save your assets and labels in different formats
For semantic and instance segmentation tasks
images and JSON annotations
Transform project to YOLO v5 format and prepares tar archive for download
Export pointclouds project and prepares downloadable tar archive
Export videos project and prepares downloadable tar archive
Converts Supervisely to COCO format and prepares tar archive for download
Export only labeled items and prepares downloadable tar archive
Converts Supervisely Project to Pascal VOC format
Download activity as csv file
Transform Supervisely format to YOLOv8 format
Export project or dataset in Supervisely pointcloud episode format
Export images in DOTA format and prepares downloadable archive
Converts Supervisely annotations to Cityscapes format and prepares downloadable tar archive
To Supervisely format, compatible with 3D Slicer, MITK
Converts Supervisely Pointcloud format to KITTI 3D
Download images from project or dataset.
Creates presentation mp4 file based on labeled video
Export Images Metadata from Project
Creates video from images in dataset with selected frame rate and configurable label opacity
Download CSV file with download links for images
Export project to Google Cloud Storage, Amazon S3, Microsoft Azure, ...
Objects with specific tag will be treated as reference items
Converts annotations from Supervisely to COCO format as RLE masks with preserving holes
Export items after the passing labeling job review
Saves tag to images mapping to a json file
Training, inference, serving, performance analysis, smart tools…
complete solution for image annotation
complete solution for image annotation with advanced features
complete solution for video annotation
complete solution for LiDAR annotation with photo context
complete solution for LiDAR episodes annotation with photo context
complete solution for medical DICOM annotation
Dashboard to configure and monitor training
Use deployed neural network in labeling interface
NN Inference on images in project or dataset
Deploy model as REST API service
State-of-the art object segmentation model in Labeling Interface
Dashboard to configure, start and monitor training
Prepare training data for SmartTool
Dashboard to configure, start and monitor training
Run 3D Detection and tracking algorithm on pointclouds or pointcloud episodes project
Predictions on every frame are combined with DeepSort into tracks automatically
serve and use in videos annotator
Deploy model as REST API service
Use neural network in labeling interface to classify images and objects
Dashboard to configure, start and monitor training
Use metric learning models to classify images
Dashboard to configure, start and monitor YOLOv8 training
Generate synthetic data: flying foregrounds on top of backgrounds
Dashboard to configure, start and monitor training
Deploy model as REST API service
For any type of data - image, video, 3d point cloud, dicom, custom interfaces, AI assistance…
complete solution for image annotation
complete solution for image annotation with advanced features
complete solution for video annotation
complete solution for LiDAR annotation with photo context
complete solution for LiDAR episodes annotation with photo context
complete solution for medical DICOM annotation
Use deployed neural network in labeling interface
NN Inference on images in project or dataset
Predictions on every frame are combined with DeepSort into tracks automatically
Use neural network in labeling interface to classify images and objects
Use metric learning models to classify images
Batched smart labeling tool for Images
Image Pixel Classification using ilastik
Tag segments (begin and end) with custom attributes on single or multiple videos in dual-panel view
Assign tags to images using example images
Edit tags of each object on image
Tag segments (begin and end) on single or multiple videos in dual-panel view
Filter objects and tags by user and copy them to working area
Batched smart labeling tool for Videos
Label videos for Action Recognition task
Prepare examples for products from catalog
Supports multi-user mode
Label and Review videos for Action Recognition task
Review and correct tags (supports multi-user mode)
Team members, annotator performance & stats, exams, issues…
Download activity as csv file
General statistics for all labeling jobs in team
Annotate Project using Queues
Total number of labeling actions and annotated unique images in a time interval
App for creating and managing annotation exams
Compare annotations of multiple labelers
First Time Through ratio shows how many items labeler annotated right the first time (i.e. reviewer accepted his work on first round).
Export items after the passing labeling job review
Invite users to team
Only instance admin has permissions to run it
Group items by selected columns from CSV catalog
Synthetic training data generation
Generate synthetic data: flying foregrounds on top of backgrounds
Generate synthetic data for classification of retail products on grocery shelves
Run Stable Diffusion model with User Interface
Synthesize videos on annotated data
Transform data and annotations, perform augmentations, filtering and querying…
Clone project or dataset to selected workspace or project, works with all project types: images / videos / 3d / dicom
Merge selected datasets with images or videos into a single one
Converts shapes of classes (e.g. polygon to bitmap) and all corresponding objects
Assigns tags (train/val) to images. Training apps will use these tags to split data.
Creates images project from video project
Read every n-th frame and save to images project
Merge multiple classes with same shape to a single one
Filters images and provides results in selected format
Prepare training data for SmartTool
Generate synthetic data: flying foregrounds on top of backgrounds
Visual diff and merge tool helps compare images in two projects
Visual diff and merge tool helps compare project tags and classes
Visualize and build augmentation pipeline with ImgAug
Configure, preview and split images and annotations with sliding window
for both images and their annotations
Creates new project with cropped objects
Split "AnyShape" classes to classes with strictly defined shapes (polygon, bitmap, ...)
Creates video project from images project
Split one or multiple datasets into parts
Convert classes to bitmap and rasterize objects without intersections
Creates sequence of connected point clouds with tracklets
Put images with labels into collage and renders comparison videos
Edit tags of each object on image
Creates video from images in dataset with selected frame rate and configurable label opacity
Convert polygon and bitmap labels to semantic segmentation
Data exploration and insights, visualization, statistics, quality assurance
Detailed statistics for all classes in images project
Creates presentation mp4 file based on labeled video
General statistics for all labeling jobs in team
Detailed statistics and distribution of object sizes (width, height, area)
The number of objects, figures and frames for every class for every dataset
Review images annotations object by object with ease
Explore images for every combination of classes pairs in co-occurrence table
Explore images with certain number of objects of specific class
Put images with labels into collage and renders comparison videos
Preview images as a grid gallery
Calculate and visualize embeddings
Build labels distribution heatmap for dataset.
Total number of labeling actions and annotated unique images in a time interval
Evaluate your classification model
Compare annotations of multiple labelers
Interactive evaluation of your instance segmentation model
Detailed statistics for all classes in pointcloud or episodes project
Explore images for every combination of tags pairs in co-occurrence table
First Time Through ratio shows how many items labeler annotated right the first time (i.e. reviewer accepted his work on first round).
Analyse videos labeled for Action Recognition task
Development environment, template apps, widgets how-to
Used to create infinite task for debug
Run Jupyterlab server on your computer with Supervisely Agent and access it from anywhere
Prints progress and then raises error
Demonstrates how to turn your python script into Supervisely App
serve and use in videos annotator
nocode app that ignores soft stop
Template application to serve custom detection models
Puts YouTube logo on all images in directory
Simple integration of NN training with tensorboard support.
Used to create infinite task for debug
template for your headless app
Presentation, content generation, administration
Archive old projects on community
Remove temporary files from Team files
Create a new empty project with a meta of original project
Label images using updatable Reference Database
Solve Instance Segmentation tasks
Data samples and full datasets to get you up and running
6 images with annotated lemons and kiwifruits
30 pointclouds without annotations
Image project with person instances
30 pointclouds with annotations
Labeled images: snacks: chips / crisps / mix
Demo project with pointcloud episodes from LYFT 3D dataset without labels
Sample images project without labels
Sample videos with labels
Demo project with pointcloud episodes from LYFT 3D dataset with labels
Demo project with pointcloud episodes from KITTI dataset with labels
Labeled roads (sample: 100 images, full version: 1000 images)
Demo project with pointcloud episodes from KITTI dataset without labels
17 unlabeled images for quick tests
Project with labeled dicom and nrrd volumes
Demo project with dicom / nrrd volumes without labels
Video pairs for multicamera labeling
Labeled images of products on the shelve: snacks, chips, crisps
10 images with labeled road
Tag (name of breed) is assigned to every image
594 unlabeled images
156 unlabeled images with roads
726 sample gt-labeled images
Project with 66 annotated tomatoes (424 images)
Unlabeled images: sunflower / pumpkin (peeled + unpeeled) / mix
1171 sample gt-labeled images
