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