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Import Multispectral Images

Import multispectral images as channels or as separate images.

V7 to Supervisely Migration Tool

Convert and copy multiple V7 datasets into Supervisely at once.

Import V7

Import images and videos with annotations in V7 format.

Train YOLOv5 2.0

Dashboard to configure, start and monitor YOLOv5 2.0 training

Serve YOLOv5 2.0

Deploy YOLOv5 2.0 as REST API service

Data Nodes

[Beta] Drag and drop interface for building custom DataOps pipelines

Labelbox to Supervisely Migration Tool

Convert and copy multiple Labelbox projects into Supervisely at once.

Roboflow to Supervisely Migration Tool

Convert and copy multiple Roboflow projects into Supervisely at once.

Render previews GUI

Service to render annotations on the fly and show them in Supervisely Dashboard

CoTracker object tracking

Track points, polygons and skeletons (keypoints) on videos

Labeling Consensus for Videos

Compare annotations of multiple labelers

Sample images from project

Sample images from project with different methods

Object Classification Metrics

Evaluate your classification model in Detector + Classifier Pipeline

MBPTrack 3D Point Cloud Tracking

Deploy MBPTrack as REST API service

CVAT to Supervisely Migration Tool

Convert and copy multiple CVAT projects into Supervisely at once.

Import CVAT

Import images and videos with annotations in CVAT format.

Copy team between instances

Copy team from one Supervisely Instance to another (including workspaces, team members and team files)

Export COCO Keypoints

Converts Supervisely format to COCO Keypoints

Merge Image Projects

Merge multiple image projects into a single one

MMOCR Inference

Text Detection and Recognition on images

Serve HRDA

Deploy HRDA model for inference

Train HRDA

Train HRDA model for segmentation in semi-supervised mode

Serve Segment Anything in High Quality

Run HQ-SAM and then use in labeling tool

Import PDF as Images

Drag and drop PDFs to import pages as images to Supervisely

XMem Video Object Segmentation

Semi-supervised, works with both long and short videos

Develop and Debug

Develop and Debug

Used to create infinite task for debug

Serve MMDetection 3.0

Deploy MMDetection 3.0 model as a REST API service

Train MMDetection 3.0

Training dashboard for mmdetection framework (v3.0.0 and above).

Serve YOLOv8

Deploy YOLOv8 as REST API service

Train YOLOv8

Dashboard to configure, start and monitor YOLOv8 training

MixFormer object tracking (CVPR2022)

CVPR2022 SOTA video object tracking

Labeling Consensus

Compare annotations of multiple labelers

Export items after review

Export items after the passing labeling job review

Labeling exams

App for creating and managing annotation exams

Import image projects in Supervisely format from cloud storage

Import image projects in Supervisely format from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)

Download image links in CSV

Download CSV file with download links for images

Download images

Download images from project or dataset.

TAP-Net object tracking

Track points and polygons on videos

Export to COCO mask

Converts annotations from Supervisely to COCO format as RLE masks with preserving holes

Instance Segmentation Metrics

Interactive evaluation of your instance segmentation model

Export to YOLOv8 format

Transform Supervisely format to YOLOv8 format

Transfer Assets between Instances

Transfer and filter assets(images) between Supervisely instances

PIPs object tracking

Track points and polygons on videos

Prompt-based Image Filtering with CLIP

Filter and rank images by text prompts with CLIP models

Stable diffusion UI

Run Stable Diffusion model with User Interface

Apply VIAME

Apply pretrained models for underwater species detection

Serve Segment Anything Model

Deploy model as REST API service

Objects Interpolation on video

Track polygons, rectangles and points using linear interpolation

Merge Tags

Merge Tags in videos or images project

Apply OWL-ViT To Images Project

Class-angnostic object detection model

Serve ClickSEG

Deploy ClickSEG models for interactive instance segmentation

Serve OWL-ViT

Class-agnostic interactive detection for auto-prelabeling

Archive Projects to Dropbox

Archive old projects on community

Serve SelfReformer

Deploy SelfReformer for salient object segmentation as a REST API service

Serve IS-Net

Deploy model as REST API service

Serve InSPyReNet

Deploy InSPyReNet for salient object segmentation as a REST API service

Apply Object Segmentor to Images Project

Label project images using object segmentor

YouTube Downloader

Downloads and trim video from Youtube.

Pexels downloader

Downloads images from the Pexels to the dataset.

Serve Transfiner

Deploy Transfiner for instance segmentation as a REST API service

Flickr downloader

Downloads images from the Flickr to the dataset.

Training tensorboard template

Simple integration of NN training with tensorboard support.

Tensorboard

Open metrics in tensorboard

Rotate images

Rotates images along with the annotations in the dataset

Create project from template

Create a new empty project with a meta of original project

Serve ViTPose

Deploy model as REST API service

Apply Detection and Pose Estimation Models to Images Project

Label project images using detector and pose estimator

Cleaner

Remove temporary files from Team files

Slice volume

Slice volumes to 2d images

Import COCO Keypoints

Converts COCO Keypoints format to Supervisely

Image Classification Metrics

Evaluate your classification model

Export volume project to cloud storage

Export volume project to Google Cloud Storage, Amazon S3, Microsoft Azure, ...

Explore data with embeddings

Calculate and visualize embeddings

Mark attributed segments on multi-camera videos

Tag segments (begin and end) with custom attributes on single or multiple videos in dual-panel view

Import volumes from cloud storage

Import volumes from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)

Object tags editor

Edit tags of each object on image

Group nested objects

Binds nested objects into groups

Split datasets

Split one or multiple datasets into parts

Render video to compare projects

Put images with labels into collage and renders comparison videos

Perspective transform using QR code

This app perspective transforms and warps your images using qr code in them.

Tag images by dataset name

Add dataset name tag to all images in project or dataset

Take fragment from video

Extract video fragment to selected project or dataset

Convert to semantic segmentation

Convert polygon and bitmap labels to semantic segmentation

Import YouTube videos

Import videos by urls provided in text file

Import Volumes with 3D Masks

Import Volumes with .nrrd 3D Masks to Supervisely

Convert labels to rotated bboxes

Convert all labels in the project or dataset to rotated bounding boxes

Export to DOTA

Export images in DOTA format and prepares downloadable archive

Insert YouTube logo

Puts YouTube logo on all images in directory

Clone

Clone project or dataset to selected workspace or project, works with all project types: images / videos / 3d / dicom

Project 3D statistics

Detailed statistics for all classes in pointcloud or episodes project

Import Point Cloud Project

Import Point Cloud Project with Annotations and Photo context in Supervisely format

Jupyter datascience notebook

Run Jupyterlab server on your computer with Supervisely Agent and access it from anywhere

Train MMDetection3D

Dashboard to configure, start and monitor training

Mark segments on multi-camera videos

Tag segments (begin and end) on single or multiple videos in dual-panel view

Interactive objects distribution

Explore images with certain number of objects of specific class

Import COCO

Converts COCO format to Supervisely

Import images with masks

Import images with binary masks as annotations

Filter images

Filters images and provides results in selected format

Export project to cloud storage

Export project to Google Cloud Storage, Amazon S3, Microsoft Azure, ...

Volume Interpolation

ITK algorithms for volume interpolation

Export pointclouds project in Supervisely format

Export pointclouds project and prepares downloadable tar archive

Export videos project in Supervisely format

Export videos project and prepares downloadable tar archive

DICOM labeling tool

complete solution for medical DICOM annotation

Advanced Image labeling tool

complete solution for image annotation with advanced features

3D Point Cloud labeling tool

complete solution for LiDAR annotation with photo context

Video labeling tool

complete solution for video annotation

3D Point Cloud Episodes labeling tool

complete solution for LiDAR episodes annotation with photo context

Basic Image labeling tool

complete solution for image annotation

Batched Smart Tool for Videos

Batched smart labeling tool for Videos

Import Videos from Team Files

Import selected videos from Team Files to selected destination

Import Pointclouds PCD

Import pointclouds in PCD format without annotations

Import Videos

Import Videos without annotations to Supervisely

[beta] Serve OpenCV

[beta] Serve OpenCV

serve and use in videos annotator

Convert Point Clouds project to Episodes

Creates sequence of connected point clouds with tracklets

Apply NN to Videos Project

Predictions on every frame are combined with DeepSort into tracks automatically

Tags to object classes

Create new object classes from tags associated with objects

Serve Custom Detection Model Template

Template application to serve custom detection models

Object classes to tags

Convert each class name to tag associated with objects, and merge existing classes into single one

Apply 3D Detection to Pointcloud Project

Run 3D Detection and tracking algorithm on pointclouds or pointcloud episodes project

Serve MMDetection3D

Deploy model as REST API service

Change video framerate

Change video framerate with preserving duration (recodes video)

Apply Detection and Classification Models to Images Project

Label project images using detector and classify predicted boxes

Apply Classifier to Images Project

Apply Classifier to Images Project

label project images or objects using NN

Semantic Segmentation Metrics

Semantic Segmentation Metrics

interactive metrics analysis

Import videos in Supervisely format

Import videos with annotations in Supervisely format

Import images in Supervisely format

Images with corresponding annotations

Import Volumes in Supervisely format

Import Supervisely volumes project with annotations

Import DICOM Volumes

Import volumes in DICOM and NRRD formats without annotations

Import Pointclouds PLY

Import pointclouds without annotations in .ply format from Team Files

Import Images

Drag and drop images to Supervisely, supported formats: .jpg, .jpeg, jpe, .mpo, .bmp, .png, .tiff, .tif, .webp, .nrrd

Batched Smart Tool

Batched smart labeling tool for Images

Serve MMDetection

Deploy model as REST API service

Train MMDetection

Dashboard to configure, start and monitor training

Import dicom studies

Convert DICOM data to nrrd format and creates a new project with images grouped by selected metadata

Import images groups

Import images groups connected via user defined tag

Train RITM

Dashboard to configure, start and monitor training

Serve MMSegmentation

Deploy model as REST API service

Train MMSegmentation

Dashboard to configure, start and monitor training

EiSeg interactive segmentation SmartTool

SmartTool integration of Efficient Interactive Segmentation (EISeg)

Import KITTI-360

Import Pointcloud Episodes from KITTI-360 format

Import Images from CSV

Upload images using .CSV file

Action Recognition Stats Tool

Analyse videos labeled for Action Recognition task

Action Recognition Labeling Tool

Label videos for Action Recognition task

Classic Labeling Queues

Annotate Project using Queues

Action Recognition

Label and Review videos for Action Recognition task

Labels spatial distribution

Build labels distribution heatmap for dataset.

Serve Detectron2

Deploy model as REST API service

Instance Segmentation Collection

Solve Instance Segmentation tasks

Train Detectron2

Dashboard to configure, start and monitor training

Embeddings Calculator

Calculate embeddings for images project

Serve Metric Learning

Google landmarks challenge models

Retail Collection

Retail Collection

Label images using updatable Reference Database

AI Recommendations

Recommends matching items from the catalog

Metric Learning Labeling Tool

Use metric learning models to classify images

CSV Products Catalog to Images Project

Convert .CSV catalog to Images Project

Export Volumes with 3D Annotations

To Supervisely format, compatible with 3D Slicer, MITK

Images thumbnail preview

Preview images as a grid gallery

Objects thumbnail preview

Review images annotations object by object with ease

Export Pointcloud Episodes in Supervisely format

Export project or dataset in Supervisely pointcloud episode format

Import Pointcloud Episodes

Import Pointcloud Episodes with Annotations and Photo context

Import KITTI 3D

Converts KITTI 3D format to Supervisely pointcloud format

Export to KITTI 3D

Converts Supervisely Pointcloud format to KITTI 3D

Crop objects on images

Creates new project with cropped objects

3D BBox Interpolation

Deploy interpolation method as REST API service

RITM interactive segmentation SmartTool

State-of-the art object segmentation model in Labeling Interface

TransT object tracking (CVPR2021)

TransT object tracking (CVPR2021)

serve and use in videos annotator

Object detection metrics

Interactive Confusion matrix, mAP, ROC and more

Images project to videos project

Creates video project from images project

Render video from images

Creates video from images in dataset with selected frame rate and configurable label opacity

Export to COCO

Converts Supervisely to COCO format and prepares tar archive for download

UNet collection

UNet collection

Training, inference, data exploration, synthetic data, and more

Serve UNet

Deploy model as REST API service

Train UNet

Dashboard to configure, start and monitor training

Synthetic videos for tracking

Synthesize videos on annotated data

Convert Video Class Shape

Converts shapes of classes on videos (e.g. polygon to bitmap) and all corresponding objects

Import Pascal VOC

Import public or custom data in Pascal VOC format to Supervisely

Classification collection

Training, inference, ai-assisted labeling, synthetic data and more

Serve MMClassification

Deploy model as REST API service

Train MMClassification

Dashboard to configure, start and monitor training

Export YOLOv5 weights

to TorchScript and ONNX formats

Import images from cloud storage

Import images from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)

AI assisted classification

Use neural network in labeling interface to classify images and objects

Tags to image URLs

Saves tag to images mapping to a json file

Unpack key value tags

Rename "Key:Value" tags to key_value (fruit: lemon -> fruit_lemon)

Import LAS/LAZ format

Import LAS/LAZ format files to Supervisely 3D point cloud labeling tool

Review labels side-by-side

Filter objects and tags by user and copy them to working area

ilastik pixel classification

Image Pixel Classification using ilastik

Export activity as csv

Download activity as csv file

Import Cityscapes

Import Cityscapes to Supervisely

Invite users to team from CSV

Invite users to team

Export to Supervisely format

images and JSON annotations

Export only labeled items

Export only labeled items and prepares downloadable tar archive

Export as masks

For semantic and instance segmentation tasks

Merge datasets

Merge selected datasets with images or videos into a single one

Resize images

for both images and their annotations

Import videos from cloud storage

Import videos from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)

Export Metadata

Export Images Metadata from Project

Import Metadata

Import Metadata for Images in Project

Export to Cityscapes

Converts Supervisely annotations to Cityscapes format and prepares downloadable tar archive

Export to Pascal VOC

Converts Supervisely Project to Pascal VOC format