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