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
Images with corresponding annotations
complete solution for LiDAR episodes annotation with photo context
Import Videos without annotations to Supervisely
The newest applications in continually growing ecosystem
Downloads images from the Flickr to the dataset.
Simple integration of NN training with tensorboard support.
Open metrics in tensorboard
Rotates images along with the annotations in the dataset
Create a new empty project with a meta of original project
Deploy model as REST API service
Label project images using detector and pose estimator
Remove temporary files from Team files
Slice volumes to 2d images
Converts COCO Keypoints format to Supervisely
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
Import images from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
Transform YOLO v5 format to supervisely project
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
For semantic and instance segmentation tasks
images and JSON annotations
Export pointclouds project and prepares downloadable tar archive
Transform project to YOLO v5 format and prepares tar archive for download
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
Export project or dataset in Supervisely pointcloud episode format
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
State of the art object segmentation model in Labeleing Interface
complete solution for medical DICOM annotation
serve and use in videos annotator
Batched smart labeling tool for Images
Dashboard to configure and monitor training
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
Tag segments (begin and end) on single or multiple videos in dual-panel view
Batched smart labeling tool for Images
Use deployed neural network in labeling interface
NN Inference on images in project or dataset
Team members, annotator performance & stats, exams, issues…
Download activity as csv file
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).
Only instance admin has permissions to run it
Group items by selected columns from CSV catalog
General statistics for all labeling jobs in team
Invite users to team
Annotate Project using Queues
Synthetic training data generation
Transform data and annotations, perform augmentations, filtering and querying…
Visualize and build augmentation pipeline with ImgAug
Merge selected datasets with images or videos into a single one
Creates images project from video project
Converts shapes of classes (e.g. polygon to bitmap) and all corresponding objects
Read every n-th frame and save to images project
Assigns tags (train/val) to images. Training apps will use these tags to split data.
Generate synthetic data for classification of retail products on grocery shelves
Prepare training data for SmartTool
Merge multiple classes with same shape to a single one
Tags and object classes can be customized
Data exploration and insights, visualization, statistics, quality assurance
Detailed statistics for all classes in images project
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).
Creates presentation mp4 file based on labeled video
Explore images for every combination of tags pairs in co-occurrence table
General statistics for all labeling jobs in team
Put images with labels into collage and renders comparison videos
Detailed statistics and distribution of object sizes (width, height, area)
Review images annotations object by object with ease
The number of objects, figures and frames for every class for every dataset
Development environment, template apps, widgets how-to
Run Jupyterlab server on your computer with Supervisely Agent and access it from anywhere
Template application to serve custom detection models
Deploy model as REST API service
Used to create infinite task for debug
Puts YouTube logo on all images in directory
Simple integration of NN training with tensorboard support.
Demonstrates how to turn your python script into Supervisely App
template for your headless app
Prints progress and then raises error
nocode app that ignores soft stop
Presentation, content generation, administration
Clone project or dataset to selected workspace or project, works with all project types: images / videos / 3d / dicom
Label images using updatable Reference Database
Remove temporary files from Team files
Create a new empty project with a meta of original project
Solve Instance Segmentation tasks
Image project with person instances
Labeled roads (sample: 100 images, full version: 1000 images)
6 images with annotated lemons and kiwifruits
594 unlabeled images
Labeled images of products on the shelve: snacks, chips, crisps
10 images with labeled road
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
