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
For semantic and instance segmentation tasks
Images with corresponding annotations
complete solution for LiDAR annotation with photo context
images and JSON annotations
complete solution for video annotation
Detailed statistics for all classes in images project
Dashboard to configure and monitor training
The newest applications in continually growing ecosystem
Converts COCO format to Supervisely
30 pointclouds without annotations
30 pointclouds with annotations
Import images with binary masks as annotations
Filters images and provides results in selected format
Export project to Google Cloud Storage, Amazon S3, Microsoft Azure, ...
Slicer 3D algorithms for volume interpolation
Export pointclouds project and prepares downloadable tar archive
Export videos project and prepares downloadable tar archive
complete solution for medical DICOM annotation
Drag and drop images to Supervisely, supported formats: .jpg, .jpeg, jpe, .mpo, .bmp, .png, .tiff, .tif, .webp, .nrrd
Images with corresponding annotations
Import images from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
Upload images using .CSV file
Import videos from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
Import Videos without annotations to Supervisely
Image project with person instances
Downloads videos by URLs and uploads them to Supervisely Storage
Transform YOLO v5 format to supervisely project
Import pointclouds in PCD format without annotations
For semantic and instance segmentation tasks
images and JSON annotations
Export pointclouds project and prepares downloadable tar archive
Export videos project and prepares downloadable tar archive
Transform project to YOLO v5 format and prepares tar archive for download
Converts Supervisely to COCO format and prepares tar archive for download
Converts Supervisely Project to Pascal VOC format
Creates presentation mp4 file based on labeled video
Converts Supervisely Pointcloud format to KITTI 3D
Download activity as csv file
Training, inference, serving, performance analysis, smart tools…
complete solution for image annotation
complete solution for image annotation with advanced features
complete solution for LiDAR annotation with photo context
complete solution for video annotation
Dashboard to configure and monitor training
Deploy model as REST API service
State of the art object segmentation model in Labeleing Interface
serve and use in videos annotator
Prepare training data for SmartTool
NN Inference on images in project or dataset
complete solution for image annotation
complete solution for image annotation with advanced features
complete solution for LiDAR annotation with photo context
complete solution for video annotation
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
complete solution for medical DICOM annotation
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
Read every n-th frame and save to images project
Configure, preview and split images and annotations with sliding window
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)
Explore images for every combination of classes pairs in co-occurrence table
Total number of labeling actions and annotated unique images in a time interval
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
Demonstrates how to turn your python script into Supervisely App
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)
6 images with annotated lemons and kiwifruits
594 unlabeled images
10 images with labeled road
Labeled images of products on the shelve: snacks, chips, crisps
Sample images project without labels
Unlabeled images: sunflower / pumpkin (peeled + unpeeled) / mix
What breed is this cat? demo for visual tagging app
1171 sample gt-labeled images
