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220 results found

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

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 Masks

Import Volumes with .nrrd 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 Pointcloud Project

Import Pointcloud 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

Slicer 3D 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

NN Inference on videos in project or dataset

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 in Supervisely format

Export project or dataset in Supervisely volumes format

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

3D BBox Interpolation

Deploy interpolation method as REST API service

RITM interactive segmentation SmartTool

State of the art object segmentation model in Labeleing 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

ImgAug Studio

Visualize and build augmentation pipeline with ImgAug

Synthetic retail products

Generate synthetic data for classification of retail products on grocery shelves

Sliding window merge

Merge images and labels that were split by sliding window before

Sliding window split

Configure, preview and split images and annotations with sliding window

YOLOv5 collection

All you need to work with YOLOv5

Flying objects

Generate synthetic data: flying foregrounds on top of backgrounds

Visual Tagging

Assign tags to images using example images

Create foreground mask

Create foreground mask from alpha channel of image

Serve YOLOv5

Deploy model as REST API service

Train YOLOv5

Dashboard to configure and monitor training

NN Image Labeling

Use deployed neural network in labeling interface

Apply NN to Images Project

NN Inference on images in project or dataset

Review Retail Tags

Review Retail Tags

Review and correct tags (supports multi-user mode)

Retail Tagging

Retail Tagging

Supports multi-user mode

Mark Reference Objects for Retail

Mark Reference Objects for Retail

Prepare examples for products from catalog

Extract frames from videos

Read every n-th frame and save to images project

Tags co-occurrence matrix

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

Movie genre from its poster

Application imports kaggle dataset 'Movie genre from its poster' as supervisely project

While True Script

While True Script

Used to create infinite task for debug

Import videos by URLs from txt file

Downloads videos by URLs and uploads them to Supervisely Storage

Diff and Merge Images Projects

Visual diff and merge tool helps compare images in two projects

Diff and Merge Project Meta

Visual diff and merge tool helps compare project tags and classes

Classes co-occurrence matrix

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

Copy project between instances

Copies images + annotations + images metadata

Render Video Labels to MP4

Creates presentation mp4 file based on labeled video

Video objects stats for every class

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

Group reference objects into batches

Group items by selected columns from CSV catalog

Videos project to images project

Creates images project from video project

Create JSON with reference items

Objects with specific tag will be treated as reference items

Copy image tags to objects

Tags and object classes can be customized

Add properties to image from CSV

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

Convert YOLO v5 to Supervisely format

Transform YOLO v5 format to supervisely project

Convert Supervisely to YOLO v5 format

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

Create users from CSV

Only instance admin has permissions to run it

App Template (No GUI)

template for your headless app

Remote import

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

Assign train/val tags to images

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

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).

Labeling Events Stats

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

Import from Google Cloud Storage

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

Labeling Jobs Stats

General statistics for all labeling jobs in team

Create Trainset for SmartTool

Prepare training data for SmartTool

Object Size Stats

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

Classes stats for images

Detailed statistics for all classes in images project

Merge classes

Merge multiple classes with same shape to a single one

Rasterize objects on images

Convert classes to bitmap and rasterize objects without intersections

Print Progress and Error

Print Progress and Error

Prints progress and then raises error

Unpack AnyShape Classes

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

Convert Class Shape

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

Hello World!

Demonstrates how to turn your python script into Supervisely App

While True App

While True App

nocode app that ignores soft stop

Tomatoes (Annotated)

Tomatoes (Annotated)

Project with 66 annotated tomatoes (424 images)

Demo LYFT 3D dataset annotated

Demo LYFT 3D dataset annotated

Demo project with pointcloud episodes from LYFT 3D dataset with labels

Demo LYFT 3D dataset

Demo LYFT 3D dataset

Demo project with pointcloud episodes from LYFT 3D dataset without labels

Demo KITTI pointcloud episodes annotated

Demo KITTI pointcloud episodes annotated

Demo project with pointcloud episodes from KITTI dataset with labels

Demo KITTI pointcloud episodes

Demo KITTI pointcloud episodes

Demo project with pointcloud episodes from KITTI dataset without labels

Demo video pairs

Demo video pairs

Video pairs for multicamera labeling

Demo pointcloud project

30 pointclouds without annotations

Demo pointcloud project with labels

30 pointclouds with annotations

PascalVOC GT Masks (Sample)

PascalVOC GT Masks (Sample)

726 sample gt-labeled images