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
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
Converts Supervisely to COCO format and prepares tar archive for download
Import pointclouds in PCD format without annotations
Transform Supervisely format to YOLOv8 format
Export videos project and prepares downloadable tar archive
Export only labeled items and prepares downloadable tar archive
Use deployed neural network in labeling interface
NN Inference on images in project or dataset
Transform YOLO v5 format to supervisely project
Dashboard to configure, start and monitor YOLOv8 | v9 | v10 | v11 training
Export pointclouds project and prepares downloadable tar archive
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.
Deploy YOLOv8 | v9 | v10 | v11 as REST API service
Merge selected datasets with images or videos into a single one
Converts shapes of classes (e.g. polygon to bitmap) and all corresponding objects
Download activity as csv file
Converts COCO format to Supervisely
Import videos from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
Creates images project from video project
Deploy model as REST API service
Import videos with annotations in Supervisely format
Import Pointcloud Episodes with Annotations and Photo context
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.
Dashboard to configure, start and monitor training
Import volumes in DICOM and NRRD formats without annotations
Use metric learning models to classify images
Filters images and provides results in selected format
Deploy model as REST API service
Detailed statistics for all classes in images project
Drag and drop interface for building custom DataOps pipelines
Training dashboard for mmdetection framework (v3.0.0 and above).
Export project or dataset in Supervisely pointcloud episode format
Import pointclouds without annotations in .ply format from Team Files
Downloads videos by URLs and uploads them to Supervisely Storage
Merge multiple classes with same shape to a single one
Predictions on every frame are combined with BoT-SORT/DeepSort into tracks automatically
Dashboard to configure, start and monitor training
Read every n-th frame and save to images project
Converts Supervisely format to COCO Keypoints
Deploy ClickSEG models for interactive instance segmentation
To Supervisely format, compatible with 3D Slicer, MITK
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
Deploy SAM 2 model as REST API service
Dashboard to configure, start and monitor training
Dashboard to configure, start and monitor training
Run Jupyterlab server on your computer with Supervisely Agent and access it from anywhere
Used to create infinite task for debug
Use neural network in labeling interface to classify images and objects
Converts Supervisely Pointcloud format to KITTI 3D
Deploy model as REST API service
Prepare training data for SmartTool
Deploy model as REST API service
Connect your remote storage and import data without duplication. Data is stored on your server but visible in Supervisely
Export images in DOTA format and prepares downloadable archive
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
The number of objects, figures and frames for every class for every dataset
Class-agnostic interactive detection for auto-prelabeling
serve and use in videos annotator
Deploy model as REST API service
Split one or multiple datasets into parts
Visualize and build augmentation pipeline with ImgAug
Dashboard to configure, start and monitor training
Converts Supervisely annotations to Cityscapes format and prepares downloadable tar archive
Configure, preview and split images and annotations with sliding window
Run HQ-SAM and then use in labeling tool
Download CSV file with download links for images
Deploy MMDetection 3.0 model as a REST API service
Generate synthetic data: flying foregrounds on top of backgrounds
Deploy model as REST API service
Semi-supervised, works with both long and short videos
Creates presentation mp4 file based on labeled video
Convert .CSV catalog to Images Project
Batched smart labeling tool for Images
label project images or objects using NN
Creates new project with cropped objects
Tracking settings for video annotation tool
CVPR2022 SOTA video object tracking
Converts annotations from Supervisely to COCO format as RLE masks with preserving holes
Class-angnostic object detection model
Evaluate the performance of the NN model and compare it with the results of other models
Visual diff and merge tool helps compare images in two projects
Deploy model as REST API service
for both images and their annotations
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
Deploy model as REST API service
Detailed statistics and distribution of object sizes (width, height, area)
Interactive Confusion matrix, mAP, ROC and more
Convert classes to bitmap and rasterize objects without intersections
Import Cityscapes to Supervisely
Import Metadata for Images in Project
Google landmarks challenge models
Image Pixel Classification using ilastik
Dashboard to configure, start and monitor training
Label project images using detector and pose estimator
General statistics for all labeling jobs in team
Tag segments (begin and end) with custom attributes on single or multiple videos in dual-panel view
Calculate embeddings for images project
Export items after the passing labeling job review
Recommends matching items from the catalog
Get instant DatasetNinja statistics for your project
Apply NN models to video frames
Downloads images and videos from Pexels.
Calculate and visualize embeddings
Train HRDA model for segmentation in semi-supervised mode
App for creating and managing annotation exams
Assign tags to images using example images
Deploy model as REST API service
Used to create infinite task for debug
Edit tags of each object on image
Review images annotations object by object with ease
Label project images using detector and classify predicted boxes
Interactive evaluation of your instance segmentation model
Split "AnyShape" classes to classes with strictly defined shapes (polygon, bitmap, ...)
Explore images for every combination of classes pairs in co-occurrence table
Export Images Metadata from Project
Convert polygon and bitmap labels to semantic segmentation
Merge multiple image projects into a single one
Create new object classes from tags associated with objects
Import multiview image groups connected via user defined tag
App to obscure data on images and videos
Creates video project from images project
Deploy interpolation method as REST API service
Rotates images along with the annotations in the dataset
interactive metrics analysis
Convert and copy multiple Roboflow projects into Supervisely at once.
Import LAS/LAZ format files to Supervisely 3D point cloud labeling tool
Creates sequence of connected point clouds with tracklets
Explore images with certain number of objects of specific class
Merge images and labels that were split by sliding window before
Filter objects and tags by user and copy them to working area
ITK algorithms for volume interpolation
Filter and rank images by text prompts with CLIP models
Archive old projects on community
Objects with specific tag will be treated as reference items
Train RT-DETRv2 model on your data
Service to render annotations on the fly and show them in Supervisely
Evaluate your classification model
Convert all labels in the project or dataset to rotated bounding boxes
Label videos for Action Recognition task
Label project images using object segmentor
Creates video from images in dataset with selected frame rate and configurable label opacity
to TorchScript and ONNX formats
Deploy RT-DETRv2 as a REST API service
Train YOLO models on your data
Change video framerate with preserving duration (recodes video)
SmartTool integration of Efficient Interactive Segmentation (EISeg)
Batched smart labeling tool for Videos
Add dataset name tag to all images in project or dataset
Annotate Project using Queues
Export project to Google Cloud Storage, Amazon S3, Microsoft Azure, ...
Put images with labels into collage and renders comparison videos
Extract video fragment to selected project or dataset
Converts COCO Keypoints format to Supervisely
Dashboard to configure, start and monitor YOLOv5 2.0 training
Export Pointclouds and Pointcloud episodes to ROS Bag format
Preview images as a grid gallery
Build labels distribution heatmap for dataset.
Tag segments (begin and end) on single or multiple videos in dual-panel view
Track points and polygons on videos
Import Volumes with .nrrd 3D Masks to Supervisely
Import Pointcloud Episodes from KITTI-360 format
Dashboard for SAM 2.1 fine-tuning
Prints progress and then raises error
Track polygons, rectangles and points using linear interpolation
Remove temporary files from Team files
Binds nested objects into groups
Compare annotations of multiple labelers
Import volumes from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
Create foreground mask from alpha channel of image
Dashboard to configure, start and monitor training
Import videos by urls provided in text file
Track points, polygons and skeletons (keypoints) on videos
Drag and drop PDFs to import pages as images to Supervisely
Deploy HRDA model for inference
Import multispectral images as channels or as separate images.