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apps

Apps help you extend functionality with custom UI

239 results found

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

Import Videos

Import Videos without annotations to Supervisely

Import Pointclouds PCD

Import pointclouds in PCD format without annotations

Clone

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

Convert Supervisely to YOLO v5 format

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

Convert YOLO v5 to Supervisely format

Transform YOLO v5 format to supervisely project

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

Export to COCO

Converts Supervisely to COCO format and prepares tar archive for download

Import images from cloud storage

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

Export only labeled items

Export only labeled items and prepares downloadable tar archive

Import Images from CSV

Upload images using .CSV file

NN Image Labeling

Use deployed neural network in labeling interface

Train YOLOv5

Dashboard to configure and monitor training

Apply NN to Images Project

NN Inference on images in project or dataset

Import images with masks

Import images with binary masks as annotations

Import videos from cloud storage

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

Export to Pascal VOC

Converts Supervisely Project to Pascal VOC format

Import videos in Supervisely format

Import videos with annotations in Supervisely format

Export to YOLOv8 format

Transform Supervisely format to YOLOv8 format

Export activity as csv

Download activity as csv file

Serve YOLOv5

Deploy model as REST API service

Import COCO

Converts COCO format to Supervisely

Merge datasets

Merge selected datasets with images or videos into a single one

Import Pointcloud Episodes

Import Pointcloud Episodes with Annotations and Photo context

Import dicom studies

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

Convert Class Shape

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

Import Pointclouds PLY

Import pointclouds without annotations in .ply format from Team Files

Import videos by URLs from txt file

Downloads videos by URLs and uploads them to Supervisely Storage

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.

Classes stats for images

Detailed statistics for all classes in images project

Train MMDetection

Dashboard to configure, start and monitor training

Extract frames from videos

Read every n-th frame and save to images project

Import Pascal VOC

Import public or custom data in Pascal VOC format to Supervisely

Filter images

Filters images and provides results in selected format

Import DICOM Volumes

Import volumes in DICOM and NRRD formats without annotations

Train YOLOv8

Dashboard to configure, start and monitor YOLOv8 training

Merge classes

Merge multiple classes with same shape to a single one

Export Pointcloud Episodes in Supervisely format

Export project or dataset in Supervisely pointcloud episode format

Remote import

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

Import from Google Cloud Storage

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

Train Detectron2

Dashboard to configure, start and monitor training

Create Trainset for SmartTool

Prepare training data for SmartTool

Apply 3D Detection to Pointcloud Project

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

Download images

Download images from project or dataset.

Train MMSegmentation

Dashboard to configure, start and monitor training

Apply NN to Videos Project

Predictions on every frame are combined with DeepSort into tracks automatically

AI assisted classification

Use neural network in labeling interface to classify images and objects

Import Point Cloud Project

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

TransT object tracking (CVPR2021)

TransT object tracking (CVPR2021)

serve and use in videos annotator

Export to DOTA

Export images in DOTA format and prepares downloadable archive

Serve MMDetection

Deploy model as REST API service

Train UNet

Dashboard to configure, start and monitor training

Export Volumes with 3D Annotations

To Supervisely format, compatible with 3D Slicer, MITK

Serve Segment Anything Model

Deploy model as REST API service

Metric Learning Labeling Tool

Use metric learning models to classify images

Export to Cityscapes

Converts Supervisely annotations to Cityscapes format and prepares downloadable tar archive

Copy project between instances

Copies images + annotations + images metadata

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 MMDetection3D

Deploy model as REST API service

Flying objects

Generate synthetic data: flying foregrounds on top of backgrounds

Serve Detectron2

Deploy model as REST API service

Serve YOLOv8

Deploy YOLOv8 as REST API service

Render Video Labels to MP4

Creates presentation mp4 file based on labeled video

Train MMClassification

Dashboard to configure, start and monitor training

Serve ClickSEG

Deploy ClickSEG models for interactive instance segmentation

Diff and Merge Images Projects

Visual diff and merge tool helps compare images in two projects

ImgAug Studio

Visualize and build augmentation pipeline with ImgAug

Diff and Merge Project Meta

Visual diff and merge tool helps compare project tags and classes

CSV Products Catalog to Images Project

Convert .CSV catalog to Images Project

Import Volumes in Supervisely format

Import Supervisely volumes project with annotations

Import Cityscapes

Import Cityscapes to Supervisely

Batched Smart Tool

Batched smart labeling tool for Images

Sliding window split

Configure, preview and split images and annotations with sliding window

ilastik pixel classification

Image Pixel Classification using ilastik

Labeling Jobs Stats

General statistics for all labeling jobs in team

Object Size Stats

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

Train RITM

Dashboard to configure, start and monitor training

Serve MMSegmentation

Deploy model as REST API service

Apply Classifier to Images Project

Apply Classifier to Images Project

label project images or objects using NN

Mark attributed segments on multi-camera videos

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

Resize images

for both images and their annotations

Serve OWL-ViT

Class-agnostic interactive detection for auto-prelabeling

Object detection metrics

Interactive Confusion matrix, mAP, ROC and more

Import KITTI 3D

Converts KITTI 3D format to Supervisely pointcloud format

Video objects stats for every class

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

Crop objects on images

Creates new project with cropped objects

Rasterize objects on images

Convert classes to bitmap and rasterize objects without intersections

Split datasets

Split one or multiple datasets into parts

Objects thumbnail preview

Review images annotations object by object with ease

Train MMDetection3D

Dashboard to configure, start and monitor training

Serve MMClassification

Deploy model as REST API service

Train MMDetection 3.0

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

Classes co-occurrence matrix

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

XMem Video Object Segmentation

Semi-supervised, works with both long and short videos

Unpack AnyShape Classes

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

Export Metadata

Export Images Metadata from Project

Images project to videos project

Creates video project from images project

Archive Projects to Dropbox

Archive old projects on community

Apply OWL-ViT To Images Project

Class-angnostic object detection model

Interactive objects distribution

Explore images with certain number of objects of specific class

Serve UNet

Deploy model as REST API service

Jupyter datascience notebook

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

Convert Point Clouds project to Episodes

Creates sequence of connected point clouds with tracklets

Apply Detection and Classification Models to Images Project

Label project images using detector and classify predicted boxes

Review labels side-by-side

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

Import Metadata

Import Metadata for Images in Project

Visual Tagging

Assign tags to images using example images

Serve ViTPose

Deploy model as REST API service

Semantic Segmentation Metrics

Semantic Segmentation Metrics

interactive metrics analysis

Export COCO Keypoints

Converts Supervisely format to COCO Keypoints

Object tags editor

Edit tags of each object on image

Import images groups

Import images groups connected via user defined tag

Labeling exams

App for creating and managing annotation exams

Render video to compare projects

Put images with labels into collage and renders comparison videos

Import LAS/LAZ format

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

Volume Interpolation

ITK algorithms for volume interpolation

Action Recognition Labeling Tool

Label videos for Action Recognition task

Export YOLOv5 weights

to TorchScript and ONNX formats

Render video from images

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

Mark segments on multi-camera videos

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

Explore data with embeddings

Calculate and visualize embeddings

Images thumbnail preview

Preview images as a grid gallery

Download image links in CSV

Download CSV file with download links for images

3D BBox Interpolation

Deploy interpolation method as REST API service

Apply Detection and Pose Estimation Models to Images Project

Label project images using detector and pose estimator

Serve Segment Anything in High Quality

Run HQ-SAM and then use in labeling tool

EiSeg interactive segmentation SmartTool

SmartTool integration of Efficient Interactive Segmentation (EISeg)

Convert to semantic segmentation

Convert polygon and bitmap labels to semantic segmentation

Print Progress and Error

Print Progress and Error

Prints progress and then raises error

Instance Segmentation Metrics

Interactive evaluation of your instance segmentation model

Serve MMDetection 3.0

Deploy MMDetection 3.0 model as a REST API service

MixFormer object tracking (CVPR2022)

CVPR2022 SOTA video object tracking

Prompt-based Image Filtering with CLIP

Filter and rank images by text prompts with CLIP models

PIPs object tracking

Track points and polygons on videos

Classic Labeling Queues

Annotate Project using Queues

Batched Smart Tool for Videos

Batched smart labeling tool for Videos

Import volumes from cloud storage

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

Serve Metric Learning

Google landmarks challenge models

Import KITTI-360

Import Pointcloud Episodes from KITTI-360 format

Import Volumes with 3D Masks

Import Volumes with .nrrd 3D Masks to Supervisely

Pexels downloader

Downloads images from the Pexels to the dataset.

Take fragment from video

Extract video fragment to selected project or dataset

Labels spatial distribution

Build labels distribution heatmap for dataset.

Image Classification Metrics

Evaluate your classification model

Import COCO Keypoints

Converts COCO Keypoints format to Supervisely

Convert labels to rotated bboxes

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

AI Recommendations

Recommends matching items from the catalog

Rotate images

Rotates images along with the annotations in the dataset

Labeling Events Stats

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

Import YouTube videos

Import videos by urls provided in text file

Apply Object Segmentor to Images Project

Label project images using object segmentor

Labeling Consensus

Compare annotations of multiple labelers

Create foreground mask

Create foreground mask from alpha channel of image

Data Nodes

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

Create JSON with reference items

Objects with specific tag will be treated as reference items

Sliding window merge

Merge images and labels that were split by sliding window before

Unpack key value tags

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

Objects Interpolation on video

Track polygons, rectangles and points using linear interpolation

Export project to cloud storage

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

Export items after review

Export items after the passing labeling job review

Tag images by dataset name

Add dataset name tag to all images in project or dataset

Export to COCO mask

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

Embeddings Calculator

Calculate embeddings for images project

YouTube Downloader

Downloads and trim video from Youtube.

Change video framerate

Change video framerate with preserving duration (recodes video)

TAP-Net object tracking

Track points and polygons on videos

Tags co-occurrence matrix

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

Project 3D statistics

Detailed statistics for all classes in pointcloud or episodes project

Hello World!

Demonstrates how to turn your python script into Supervisely App

Labeler First Time Through

First Time Through ratio shows how many items labeler annotated right the first time (i.e. reviewer accepted his work on first round).

Add properties to image from CSV

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

Render previews GUI

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

Tags to image URLs

Saves tag to images mapping to a json file

Synthetic retail products

Generate synthetic data for classification of retail products on grocery shelves

Labelbox to Supervisely Migration Tool

Convert and copy multiple Labelbox projects into Supervisely at once.

Copy image tags to objects

Tags and object classes can be customized

MMOCR Inference

Text Detection and Recognition on images

Transfer Assets between Instances

Transfer and filter assets(images) between Supervisely instances

Import Videos from Team Files

Import selected videos from Team Files to selected destination

Train HRDA

Train HRDA model for segmentation in semi-supervised mode

Invite users to team from CSV

Invite users to team

Action Recognition Stats Tool

Analyse videos labeled for Action Recognition task

Merge Tags

Merge Tags in videos or images project

Develop and Debug

Develop and Debug

Used to create infinite task for debug

Perspective transform using QR code

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

Convert Video Class Shape

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

Cleaner

Remove temporary files from Team files

Serve IS-Net

Deploy model as REST API service

Labeling Consensus for Videos

Compare annotations of multiple labelers

Merge Image Projects

Merge multiple image projects into a single one

Create project from template

Create a new empty project with a meta of original project

Serve InSPyReNet

Deploy InSPyReNet for salient object segmentation as a REST API service

Create users from CSV

Only instance admin has permissions to run it