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!
Curated catalog of high-quality computer vision datasets with deep analysis and visualizations.
New datasets every day!
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The most popular applications among them all
complete solution for image annotation
complete solution for image annotation with advanced features
complete solution for video annotation
Drag and drop images to Supervisely, supported formats: .jpg, .jpeg, jpe, .mpo, .bmp, .png, .tiff, .tif, .webp, .nrrd
complete solution for LiDAR annotation with photo context
complete solution for LiDAR episodes annotation with photo context
For semantic and instance segmentation tasks
images and JSON annotations
Images with corresponding annotations
complete solution for medical DICOM annotation
Clone project or dataset to selected workspace or project, works with all project types: images / videos / 3d / dicom
Import Videos without annotations to Supervisely
Transform project to YOLO v5 format and prepares tar archive for download
Import pointclouds in PCD format without annotations
Converts Supervisely to COCO format and prepares tar archive for download
Transform Supervisely format to YOLOv8 format
Export videos project and prepares downloadable tar archive
Transform YOLO v5 format to supervisely project
Export only labeled items and prepares downloadable tar archive
Use deployed neural network in labeling interface
Export pointclouds project and prepares downloadable tar archive
NN Inference on images in project or dataset
Dashboard to configure, start and monitor YOLOv8 | v9 | v10 | v11 training
Import images from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
Dashboard to configure and monitor training
The newest applications in continually growing ecosystem
Export images project to extended Supervisely format with Blobs
Mouse action recognition with RT-DETRv2 and MVD
Merge multispectral images into one
Deploy model as REST API service
Dashboard to configure, start and monitor training
For testing releases and configs
Deploy YOLO models as a REST API service
Train YOLO models on your data
This application is used to measure performance on the Supervisely instance.
Real-time colormap postprocessing visualization for Image Labeling Toolbox
Deploy MCITrack as REST API service
Deploy Kosmos-2 as a REST API service
Deploy Grounding DINO as a REST API service
Deploy Molmo as a REST API service
Synchronize Teams between Supervisely Instances including Workspaces and Team members
Open experiment metrics in tensorboard
Dashboard for SAM 2.1 fine-tuning
Text-Prompted Object Detection with Mask Segmentation
Deploy Florence-2 as a REST API service
Train RT-DETRv2 model on your data
Deploy RT-DETRv2 as a REST API service
Real-time CLAHE filter visualization
Propagate bboxes to multiview images group
Transcode videos to mp4 format
Train RT-DETR model on your data
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
Converts COCO format to Supervisely
Import videos from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
Import videos with annotations in Supervisely format
Import Pointcloud Episodes with Annotations and Photo context
Creates project with images grouped by selected metadata, converting DICOM data to NRRD format in the process.
Import volumes in DICOM and NRRD formats without annotations
Import pointclouds without annotations in .ply format from Team Files
Downloads videos by URLs and uploads them to Supervisely Storage
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
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
Convert .CSV catalog to Images Project
Import Supervisely volumes project with annotations
Converts KITTI 3D format to Supervisely pointcloud format
Import Cityscapes to Supervisely
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
Converts Supervisely to COCO format and prepares tar archive for download
Transform Supervisely format to YOLOv8 format
Export videos project and prepares downloadable tar archive
Export only labeled items and prepares downloadable tar archive
Export pointclouds project and prepares downloadable tar archive
Converts Supervisely Project to Pascal VOC format
Download images from project or dataset.
Download activity as csv file
Export project or dataset in Supervisely pointcloud episode format
Converts Supervisely format to COCO Keypoints
To Supervisely format, compatible with 3D Slicer, MITK
Converts Supervisely Pointcloud format to KITTI 3D
Export images in DOTA format and prepares downloadable archive
Converts Supervisely annotations to Cityscapes format and prepares downloadable tar archive
Download CSV file with download links for images
Creates presentation mp4 file based on labeled video
Converts annotations from Supervisely to COCO format as RLE masks with preserving holes
Export items after the passing labeling job review
Export Images Metadata from Project
Objects with specific tag will be treated as reference items
Creates video from images in dataset with selected frame rate and configurable label opacity
Export Pointclouds and Pointcloud episodes to ROS Bag 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
complete solution for medical DICOM annotation
Use deployed neural network in labeling interface
NN Inference on images in project or dataset
Dashboard to configure, start and monitor YOLOv8 | v9 | v10 | v11 training
Dashboard to configure and monitor training
Deploy YOLOv8 | v9 | v10 | v11 as REST API service
Deploy model as REST API service
State-of-the art object segmentation model in Labeling Interface
Dashboard to configure, start and monitor training
Use metric learning models to classify images
Deploy model as REST API service
Training dashboard for mmdetection framework (v3.0.0 and above).
Dashboard to configure, start and monitor training
Predictions on every frame are combined with BoT-SORT/DeepSort into tracks automatically
Deploy ClickSEG models for interactive instance segmentation
Run 3D Detection and tracking algorithm on pointclouds or pointcloud episodes project
Dashboard to configure, start and monitor training
Dashboard to configure, start and monitor training
Dashboard to configure, start and monitor training
Use neural network in labeling interface to classify images and objects
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
Use metric learning models to classify images
Predictions on every frame are combined with BoT-SORT/DeepSort into tracks automatically
Use neural network in labeling interface to classify images and objects
Batched smart labeling tool for Images
Tracking settings for video annotation tool
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
Apply NN models to video frames
Edit tags of each object on image
Filter objects and tags by user and copy them to working area
Label videos for Action Recognition task
Batched smart labeling tool for Videos
Tag segments (begin and end) on single or multiple videos in dual-panel view
Prepare examples for products from catalog
Sample images from project with different methods
Automated real-time annotation tests.
Team members, annotator performance & stats, exams, issues…
Download activity as csv file
General statistics for all labeling jobs in team
Export items after the passing labeling job review
App for creating and managing annotation exams
Annotate Project using Queues
Compare annotations of multiple labelers
Total number of labeling actions and annotated unique images in a time interval
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).
Invite users to team
Allows you to review annotation results in a user interface specifically designed for such tasks
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
Creates images project from video project
Assigns tags (train/val) to images. Training apps will use these tags to split data.
Filters images and provides results in selected format
Merge multiple classes with same shape to a single one
Read every n-th frame and save to images project
Prepare training data for SmartTool
Visualize and build augmentation pipeline with ImgAug
Split one or multiple datasets into parts
Generate synthetic data: flying foregrounds on top of backgrounds
Configure, preview and split images and annotations with sliding window
Creates new project with cropped objects
Visual diff and merge tool helps compare images in two projects
Visual diff and merge tool helps compare project tags and classes
for both images and their annotations
Convert classes to bitmap and rasterize objects without intersections
Create new object classes from tags associated with objects
Split "AnyShape" classes to classes with strictly defined shapes (polygon, bitmap, ...)
Edit tags of each object on image
Creates video project from images project
Merge multiple image projects into a single one
Convert polygon and bitmap labels to semantic segmentation
App to obscure data on images and videos
Data exploration and insights, visualization, statistics, quality assurance
Detailed statistics for all classes in images project
The number of objects, figures and frames for every class for every dataset
Creates presentation mp4 file based on labeled video
Detailed statistics and distribution of object sizes (width, height, area)
General statistics for all labeling jobs in team
Calculate and visualize embeddings
Review images annotations object by object with ease
Explore images for every combination of classes pairs in co-occurrence table
Interactive evaluation of your instance segmentation model
Explore images with certain number of objects of specific class
Evaluate your classification model
Put images with labels into collage and renders comparison videos
Preview images as a grid gallery
Build labels distribution heatmap for dataset.
Compare annotations of multiple labelers
Total number of labeling actions and annotated unique images in a time interval
Explore images for every combination of tags pairs in co-occurrence table
Compare annotations of multiple labelers
Detailed statistics for all classes in pointcloud or episodes project
Analyse videos labeled for Action Recognition task
First Time Through ratio shows how many items labeler annotated right the first time (i.e. reviewer accepted his work on first round).
Evaluate your classification model in Detector + Classifier Pipeline
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
Used to create infinite task for debug
Prints progress and then raises error
nocode app that ignores soft stop
Demonstrates how to turn your python script into Supervisely App
serve and use in videos annotator
Template application to serve custom detection models
Simple integration of NN training with tensorboard support.
Puts YouTube logo on all images in directory
template for your headless app
For testing releases and configs
Presentation, content generation, administration
[Beta] Drag and drop interface for building custom DataOps pipelines
Get instant DatasetNinja statistics for your project
Archive old projects on community
Service to render annotations on the fly and show them in Supervisely
Remove temporary files from Team files
Effortlessly track and interpolate labeled objects on a conveyor belt in real-time
Delete unused projects or their datasets in large batches
Create a new empty project with a meta of original project
Propagate bboxes to multiview images group
Application that visualizes the most recently updated images
Label images using updatable Reference Database
This application is used to measure performance on the Supervisely instance.
Solve Instance Segmentation tasks
Real-time CLAHE filter visualization
Real-time colormap postprocessing visualization for Image Labeling Toolbox
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
Sample images project without labels
Sample videos with labels
Demo project with pointcloud episodes from LYFT 3D dataset without labels
Labeled images: snacks: chips / crisps / mix
Demo project with pointcloud episodes from KITTI dataset with labels
Demo project with pointcloud episodes from LYFT 3D dataset with labels
17 unlabeled images for quick tests
Demo project with pointcloud episodes from KITTI dataset without labels
Labeled roads (sample: 100 images, full version: 1000 images)
Project with labeled dicom and nrrd volumes
Demo project with dicom / nrrd volumes without labels
156 unlabeled images with roads
Video pairs for multicamera labeling
Tag (name of breed) is assigned to every image
Labeled images of products on the shelve: snacks, chips, crisps
10 images with labeled road
594 unlabeled images
Project with 66 annotated tomatoes (424 images)
For object detection tutorials
Synthetic dataset for cracks segmentation
Unlabeled images: sunflower / pumpkin (peeled + unpeeled) / mix
