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