Basic Image labeling tool
complete solution for image annotation
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
For semantic and instance segmentation tasks
complete solution for LiDAR episodes annotation with photo context
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
Export videos project and prepares downloadable tar archive
Transform Supervisely format to YOLOv8 format
Transform YOLO v5 format to supervisely project
Export only labeled items and prepares downloadable tar archive
Export pointclouds project and prepares downloadable tar archive
Use deployed neural network in labeling interface
Dashboard to configure, start and monitor YOLOv8 | v9 | v10 | v11 training
NN Inference on images in project or dataset
Import images from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
Dashboard to configure and monitor training
Upload images using .CSV file
Import images with binary masks as annotations
Converts Supervisely Project to Pascal VOC format
Download images from project or dataset.
Merge selected datasets with images or videos into a single one
Converts shapes of classes (e.g. polygon to bitmap) and all corresponding objects
Converts COCO format to Supervisely
Download activity as csv file
Import videos from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
Deploy YOLOv8 | v9 | v10 | v11 as REST API service
Deploy model as REST API service
Import videos with annotations in Supervisely format
Import Pointcloud Episodes with Annotations and Photo context
Creates images project from video project
State-of-the art object segmentation model in Labeling Interface
Assigns tags (train/val) to images. Training apps will use these tags to split data.
Creates project with images grouped by selected metadata, converting DICOM data to NRRD format in the process.
Use metric learning models to classify images
Dashboard to configure, start and monitor training
Filters images and provides results in selected format
Import volumes in DICOM and NRRD formats without annotations
Detailed statistics for all classes in images project
Import pointclouds without annotations in .ply format from Team Files
Deploy model as REST API service
Export project or dataset in Supervisely pointcloud episode format
Downloads videos by URLs and uploads them to Supervisely Storage
Training dashboard for mmdetection framework (v3.0.0 and above).
Dashboard to configure, start and monitor training
Merge multiple classes with same shape to a single one
[Beta] Drag and drop interface for building custom DataOps pipelines
Read every n-th frame and save to images project
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
Import public or custom data in Pascal VOC format to Supervisely
Dashboard to configure, start and monitor training
To Supervisely format, compatible with 3D Slicer, MITK
Converts Supervisely format to COCO Keypoints
Use neural network in labeling interface to classify images and objects
Dashboard to configure, start and monitor training
Dashboard to configure, start and monitor training
Used to create infinite task for debug
Converts Supervisely Pointcloud format to KITTI 3D
Prepare training data for SmartTool
Connect your remote storage and import data without duplication. Data is stored on your server but visible in Supervisely
Deploy model as REST API service
Export images in DOTA format and prepares downloadable archive
Upload images by reading links (Google Cloud Storage) from CSV file
Deploy model as REST API service
Import Point Cloud Project with Annotations and Photo context in Supervisely format
serve and use in videos annotator
Copies images + annotations + images metadata
Deploy model as REST API service
Converts Supervisely annotations to Cityscapes format and prepares downloadable tar archive
Visualize and build augmentation pipeline with ImgAug
Dashboard to configure, start and monitor training
Class-agnostic interactive detection for auto-prelabeling
Generate synthetic data: flying foregrounds on top of backgrounds
Deploy SAM 2 model as REST API service
The number of objects, figures and frames for every class for every dataset
Run HQ-SAM and then use in labeling tool
Split one or multiple datasets into parts
Deploy model as REST API service
Deploy MMDetection 3.0 model as a REST API service
Convert .CSV catalog to Images Project
Batched smart labeling tool for Images
Creates presentation mp4 file based on labeled video
Semi-supervised, works with both long and short videos
Configure, preview and split images and annotations with sliding window
Download CSV file with download links for images
Run Jupyterlab server on your computer with Supervisely Agent and access it from anywhere
Creates new project with cropped objects
label project images or objects using NN
Class-angnostic object detection model
CVPR2022 SOTA video object tracking
Visual diff and merge tool helps compare images in two projects
Tracking settings for video annotation tool
Deploy model as REST API service
Visual diff and merge tool helps compare project tags and classes
Import Supervisely volumes project with annotations
for both images and their annotations
Converts KITTI 3D format to Supervisely pointcloud format
Detailed statistics and distribution of object sizes (width, height, area)
Converts annotations from Supervisely to COCO format as RLE masks with preserving holes
Import Cityscapes to Supervisely
Convert classes to bitmap and rasterize objects without intersections
Google landmarks challenge models
Import Metadata for Images in Project
Deploy model as REST API service
Image Pixel Classification using ilastik
Dashboard to configure, start and monitor training
Interactive Confusion matrix, mAP, ROC and more
Evaluate the performance of the NN model and compare it with the results of other models
General statistics for all labeling jobs in team
Calculate embeddings for images project
Tag segments (begin and end) with custom attributes on single or multiple videos in dual-panel view
Recommends matching items from the catalog
Label project images using detector and pose estimator
Calculate and visualize embeddings
Export items after the passing labeling job review
Train HRDA model for segmentation in semi-supervised mode
App for creating and managing annotation exams
Review images annotations object by object with ease
Assign tags to images using example images
Apply NN models to video frames
Explore images for every combination of classes pairs in co-occurrence table
Export Images Metadata from Project
Create new object classes from tags associated with objects
Interactive evaluation of your instance segmentation model
Split "AnyShape" classes to classes with strictly defined shapes (polygon, bitmap, ...)
Get instant DatasetNinja statistics for your project
Deploy model as REST API service
Import multiview image groups connected via user defined tag
Edit tags of each object on image
Label project images using detector and classify predicted boxes
Creates video project from images project
Merge multiple image projects into a single one
Deploy interpolation method as REST API service
App to obscure data on images and videos
Convert polygon and bitmap labels to semantic segmentation
interactive metrics analysis
Downloads images from the Pexels to the dataset.
Creates sequence of connected point clouds with tracklets
Import LAS/LAZ format files to Supervisely 3D point cloud labeling tool
Explore images with certain number of objects of specific class
Rotates images along with the annotations in the dataset
Archive old projects on community
Filter objects and tags by user and copy them to working area
Convert and copy multiple Roboflow projects into Supervisely at once.
Objects with specific tag will be treated as reference items
ITK algorithms for volume interpolation
Filter and rank images by text prompts with CLIP models
Label videos for Action Recognition task
Used to create infinite task for debug
Convert all labels in the project or dataset to rotated bounding boxes
to TorchScript and ONNX formats
Service to render annotations on the fly and show them in Supervisely
Evaluate your classification model
Batched smart labeling tool for Videos
SmartTool integration of Efficient Interactive Segmentation (EISeg)
Creates video from images in dataset with selected frame rate and configurable label opacity
Change video framerate with preserving duration (recodes video)
Label project images using object segmentor
Put images with labels into collage and renders comparison videos
Annotate Project using Queues
Dashboard to configure, start and monitor YOLOv5 2.0 training
Converts COCO Keypoints format to Supervisely
Merge images and labels that were split by sliding window before
Preview images as a grid gallery
Extract video fragment to selected project or dataset
Add dataset name tag to all images in project or dataset
Tag segments (begin and end) on single or multiple videos in dual-panel view
Track points and polygons on videos
Build labels distribution heatmap for dataset.
Import Pointcloud Episodes from KITTI-360 format
Import Volumes with .nrrd 3D Masks to Supervisely
Prints progress and then raises error
Binds nested objects into groups
Track polygons, rectangles and points using linear interpolation
Import volumes from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
Export Pointclouds and Pointcloud episodes to ROS Bag format
Remove temporary files from Team files
Create foreground mask from alpha channel of image
Export project to Google Cloud Storage, Amazon S3, Microsoft Azure, ...
Import videos by urls provided in text file
Compare annotations of multiple labelers
Drag and drop PDFs to import pages as images to Supervisely
Import multispectral images as channels or as separate images.
Deploy HRDA model for inference
Total number of labeling actions and annotated unique images in a time interval
Text Detection and Recognition on images
Rename "Key:Value" tags to key_value (fruit: lemon -> fruit_lemon)
Downloads and trim video from Youtube.
All you need to work with YOLOv5
Generate synthetic data for classification of retail products on grocery shelves