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Apps help you extend functionality with custom UI

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Import Images

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

Export as masks

For semantic and instance segmentation tasks

Export to Supervisely format

images and JSON annotations

Import images in Supervisely format

Images with corresponding annotations

Clone

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

Import Videos

Import Videos without annotations to Supervisely

Convert Supervisely to YOLO v5 format

Transform project to YOLO v5 format and prepares tar archive for download

Import Pointclouds PCD

Import pointclouds in PCD format without annotations

Export to COCO

Converts Supervisely to COCO format and prepares tar archive for download

Export to YOLOv8 format

Transform Supervisely format to YOLOv8 format

Export videos project in Supervisely format

Export videos project and prepares downloadable tar archive

Convert YOLO v5 to Supervisely format

Transform YOLO v5 format to supervisely project

Export only labeled items

Export only labeled items and prepares downloadable tar archive

NN Image Labeling

Use deployed neural network in labeling interface

Export pointclouds project in Supervisely format

Export pointclouds project and prepares downloadable tar archive

Apply NN to Images Project

NN Inference on images in project or dataset

Train YOLOv8 | v9 | v10 | v11

Dashboard to configure, start and monitor YOLOv8 | v9 | v10 | v11 training

Import images from cloud storage

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

Train YOLOv5

Dashboard to configure and monitor training

Import Images from CSV

Upload images using .CSV file

Import images with masks

Import images with binary masks as annotations

Export to Pascal VOC

Converts Supervisely Project to Pascal VOC format

Download images

Download images from project or dataset.

Merge datasets

Merge selected datasets with images or videos into a single one

Convert Class Shape

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

Export activity as csv

Download activity as csv file

Import COCO

Converts COCO format to Supervisely

Serve YOLOv8 | v9 | v10 | v11

Deploy YOLOv8 | v9 | v10 | v11 as REST API service

Import videos from cloud storage

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

Serve YOLOv5

Deploy model as REST API service

Import videos in Supervisely format

Import videos with annotations in Supervisely format

Import Pointcloud Episodes

Import Pointcloud Episodes with Annotations and Photo context

Videos project to images project

Creates images project from video project

RITM interactive segmentation SmartTool

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

Assign train/val tags to images

Assigns tags (train/val) to images. Training apps will use these tags to split data.

Import DICOM studies

Creates project with images grouped by selected metadata, converting DICOM data to NRRD format in the process.

Train MMSegmentation

Dashboard to configure, start and monitor training

Metric Learning Labeling Tool

Use metric learning models to classify images

Filter images

Filters images and provides results in selected format

Classes stats for images

Detailed statistics for all classes in images project

Import DICOM Volumes

Import volumes in DICOM and NRRD formats without annotations

Serve Segment Anything Model

Deploy model as REST API service

Train MMDetection 3.0

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

Import Pointclouds PLY

Import pointclouds without annotations in .ply format from Team Files

Export Pointcloud Episodes in Supervisely format

Export project or dataset in Supervisely pointcloud episode format

Import videos by URLs from txt file

Downloads videos by URLs and uploads them to Supervisely Storage

ML Pipelines

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

Merge classes

Merge multiple classes with same shape to a single one

Train MMDetection

Dashboard to configure, start and monitor training

Extract frames from videos

Read every n-th frame and save to images project

Apply NN to Videos Project

Predictions on every frame are combined with BoT-SORT/DeepSort into tracks automatically

Serve ClickSEG

Deploy ClickSEG models for interactive instance segmentation

Apply 3D Detection to Pointcloud Project

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

Import Pascal VOC

Import public or custom data in Pascal VOC format to Supervisely

Train Detectron2

Dashboard to configure, start and monitor training

Export COCO Keypoints

Converts Supervisely format to COCO Keypoints

Export Volumes with 3D Annotations

To Supervisely format, compatible with 3D Slicer, MITK

Train UNet

Dashboard to configure, start and monitor training

Train MMClassification

Dashboard to configure, start and monitor training

AI assisted classification

Use neural network in labeling interface to classify images and objects

While True Script

While True Script

Used to create infinite task for debug

Export to KITTI 3D

Converts Supervisely Pointcloud format to KITTI 3D

Serve MMSegmentation

Deploy model as REST API service

Create Trainset for SmartTool

Prepare training data for SmartTool

Remote import

Connect your remote storage and import data without duplication. Data is stored on your server but visible in Supervisely

Serve MMDetection

Deploy model as REST API service

Export to DOTA

Export images in DOTA format and prepares downloadable archive

Serve Segment Anything 2.1

Deploy SAM 2 model as REST API service

Import from Google Cloud Storage

Upload images by reading links (Google Cloud Storage) from CSV file

Import Point Cloud Project

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

Copy project between instances

Copies images + annotations + images metadata

TransT object tracking (CVPR2021)

TransT object tracking (CVPR2021)

serve and use in videos annotator

Serve MMDetection3D

Deploy model as REST API service

Export to Cityscapes

Converts Supervisely annotations to Cityscapes format and prepares downloadable tar archive

Serve OWL-ViT

Class-agnostic interactive detection for auto-prelabeling

ImgAug Studio

Visualize and build augmentation pipeline with ImgAug

Train RITM

Dashboard to configure, start and monitor training

Split datasets

Split one or multiple datasets into parts

Serve Segment Anything in High Quality

Run HQ-SAM and then use in labeling tool

Flying objects

Generate synthetic data: flying foregrounds on top of backgrounds

Serve MMDetection 3.0

Deploy MMDetection 3.0 model as a REST API service

Video objects stats for every class

The number of objects, figures and frames for every class for every dataset

Serve Detectron2

Deploy model as REST API service

Jupyter datascience notebook

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

XMem Video Object Segmentation

Semi-supervised, works with both long and short videos

Sliding window split

Configure, preview and split images and annotations with sliding window

CSV Products Catalog to Images Project

Convert .CSV catalog to Images Project

Batched Smart Tool

Batched smart labeling tool for Images

Download image links in CSV

Download CSV file with download links for images

Render Video Labels to MP4

Creates presentation mp4 file based on labeled video

Crop objects on images

Creates new project with cropped objects

Apply Classifier to Images Project

Apply Classifier to Images Project

label project images or objects using NN

Apply OWL-ViT To Images Project

Class-angnostic object detection model

MixFormer object tracking

CVPR2022 SOTA video object tracking

Auto Track

Tracking settings for video annotation tool

Diff and Merge Images Projects

Visual diff and merge tool helps compare images in two projects

Serve MMClassification

Deploy model as REST API service

Diff and Merge Project Meta

Visual diff and merge tool helps compare project tags and classes

Resize images

for both images and their annotations

Import KITTI 3D

Converts KITTI 3D format to Supervisely pointcloud format

Import Volumes in Supervisely format

Import Supervisely volumes project with annotations

Object Size Stats

Detailed statistics and distribution of object sizes (width, height, area)

Export to COCO mask

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

Evaluator for Model Benchmark

Evaluate the performance of the NN model and compare it with the results of other models

Rasterize objects on images

Convert classes to bitmap and rasterize objects without intersections

Serve ViTPose

Deploy model as REST API service

Import Cityscapes

Import Cityscapes to Supervisely

Serve Metric Learning

Google landmarks challenge models

Import Metadata

Import Metadata for Images in Project

ilastik pixel classification

Image Pixel Classification using ilastik

Train MMDetection3D

Dashboard to configure, start and monitor training

Object detection metrics

Interactive Confusion matrix, mAP, ROC and more

Labeling Jobs Stats

General statistics for all labeling jobs in team

Mark attributed segments on multi-camera videos

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

Embeddings Calculator

Calculate embeddings for images project

AI Recommendations

Recommends matching items from the catalog

Apply Detection and Pose Estimation Models to Images Project

Label project images using detector and pose estimator

On-the-Fly Quality Assurance

On-the-Fly Quality Assurance

Get instant DatasetNinja statistics for your project

Explore data with embeddings

Calculate and visualize embeddings

Export Items After Review

Export items after the passing labeling job review

Train HRDA

Train HRDA model for segmentation in semi-supervised mode

Visual Tagging

Assign tags to images using example images

Labeling exams

App for creating and managing annotation exams

Apply NN to video frames

Apply NN models to video frames

Objects thumbnail preview

Review images annotations object by object with ease

Serve UNet

Deploy model as REST API service

Classes co-occurrence matrix

Explore images for every combination of classes pairs in co-occurrence table

Instance Segmentation Metrics

Interactive evaluation of your instance segmentation model

Export Metadata

Export Images Metadata from Project

Unpack AnyShape Classes

Split "AnyShape" classes to classes with strictly defined shapes (polygon, bitmap, ...)

Apply Detection and Classification Models to Images Project

Label project images using detector and classify predicted boxes

Tags to object classes

Create new object classes from tags associated with objects

Object tags editor

Edit tags of each object on image

Import Multiview Image Groups

Import multiview image groups connected via user defined tag

Pexels downloader

Downloads images from the Pexels to the dataset.

Merge Image Projects

Merge multiple image projects into a single one

Images project to videos project

Creates video project from images project

Convert to semantic segmentation

Convert polygon and bitmap labels to semantic segmentation

3D BBox Interpolation

Deploy interpolation method as REST API service

Anonymize Data

App to obscure data on images and videos

Semantic Segmentation Metrics

Semantic Segmentation Metrics

interactive metrics analysis

Convert Point Clouds project to Episodes

Creates sequence of connected point clouds with tracklets

Import LAS/LAZ format

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

Rotate images

Rotates images along with the annotations in the dataset

Interactive objects distribution

Explore images with certain number of objects of specific class

Roboflow to Supervisely Migration Tool

Convert and copy multiple Roboflow projects into Supervisely at once.

Review labels side-by-side

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

Archive Projects to Dropbox

Archive old projects on community

Create JSON with reference items

Objects with specific tag will be treated as reference items

Prompt-based Image Filtering with CLIP

Filter and rank images by text prompts with CLIP models

Develop and Debug

Develop and Debug

Used to create infinite task for debug

Volume Interpolation

ITK algorithms for volume interpolation

Image Classification Metrics

Evaluate your classification model

Action Recognition Labeling Tool

Label videos for Action Recognition task

Render previews GUI

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

Convert labels to rotated bboxes

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

Export YOLOv5 weights

to TorchScript and ONNX formats

Apply Object Segmentor to Images Project

Label project images using object segmentor

Render video from images

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

Batched Smart Tool for Videos

Batched smart labeling tool for Videos

EiSeg interactive segmentation SmartTool

SmartTool integration of Efficient Interactive Segmentation (EISeg)

Change video framerate

Change video framerate with preserving duration (recodes video)

Sliding window merge

Merge images and labels that were split by sliding window before

Classic Labeling Queues

Annotate Project using Queues

Render video to compare projects

Put images with labels into collage and renders comparison videos

Take fragment from video

Extract video fragment to selected project or dataset

Train YOLOv5 2.0

Dashboard to configure, start and monitor YOLOv5 2.0 training

Tag images by dataset name

Add dataset name tag to all images in project or dataset

Import COCO Keypoints

Converts COCO Keypoints format to Supervisely

Images thumbnail preview

Preview images as a grid gallery

Mark segments on multi-camera videos

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

PIPs object tracking

Track points and polygons on videos

Labels spatial distribution

Build labels distribution heatmap for dataset.

Import KITTI-360

Import Pointcloud Episodes from KITTI-360 format

Import Volumes with 3D Masks

Import Volumes with .nrrd 3D Masks to Supervisely

Print Progress and Error

Print Progress and Error

Prints progress and then raises error

Export project to cloud storage

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

Objects Interpolation on video

Track polygons, rectangles and points using linear interpolation

Cleaner

Remove temporary files from Team files

Group nested objects

Binds nested objects into groups

Export Point Clouds to ROS Bag

Export Pointclouds and Pointcloud episodes to ROS Bag format

Import volumes from cloud storage

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

Create foreground mask

Create foreground mask from alpha channel of image

Import YouTube videos

Import videos by urls provided in text file

Labeling Consensus

Compare annotations of multiple labelers

Import PDF as Images

Drag and drop PDFs to import pages as images to Supervisely

Import Multispectral Images

Import multispectral images as channels or as separate images.

Serve HRDA

Deploy HRDA model for inference

MMOCR Inference

Text Detection and Recognition on images

Labeling Events Stats

Total number of labeling actions and annotated unique images in a time interval

Unpack key value tags

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

Fine-tune Segment Anything 2.1

Dashboard for SAM 2.1 fine-tuning

YouTube Downloader

Downloads and trim video from Youtube.

Synthetic retail products

Generate synthetic data for classification of retail products on grocery shelves

Group Images for Multiview Labeling

No description available

Tags to image URLs

Saves tag to images mapping to a json file

MBPTrack 3D Point Cloud Tracking

Deploy MBPTrack as REST API service

Labelbox to Supervisely Migration Tool

Convert and copy multiple Labelbox projects into Supervisely at once.

Add properties to image from CSV

Match image tag with CSV columns and add row values to image

Train RT-DETR

Train RT-DETR model on your data