← Back to catalog


130 results found

Basic Image labeling tool

complete solution for image annotation


Advanced Image labeling tool

complete solution for image annotation with advanced features


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


Import images in Supervisely format

Images with corresponding annotations


Export to Supervisely format

images and JSON annotations


Train YOLOv5

Dashboard to configure and monitor training


Serve YOLOv5

Deploy model as REST API service


Classes stats for images

Detailed statistics for all classes in images project


Import images from cloud storage

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


RITM interactive segmentation SmartTool

State of the art object segmentation model in Labeleing Interface


Import Images from CSV

Upload images using .CSV file


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


Convert Class Shape

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


Apply NN to Images Project

NN Inference on images in project or dataset


Create Trainset for SmartTool

Prepare training data for SmartTool


Export YOLOv5 weights

to TorchScript and ONNX formats


Videos project to images project

Creates images project from video project


NN Image Labeling

Use deployed neural network in labeling interface


Export to COCO

Converts Supervisely to COCO format and prepares tar archive for download


Flying objects

Generate synthetic data: flying foregrounds on top of backgrounds


Batched Smart Tool

Batched smart labeling tool for Images


Export to Pascal VOC

Converts Supervisely Project to Pascal VOC format


Import dicom studies

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


Assign train/val tags to images

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


Train MMClassification

Dashboard to configure, start and monitor training


Merge classes

Merge multiple classes with same shape to a single one


ImgAug Studio

Visualize and build augmentation pipeline with ImgAug


Remote import

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


Object Size Stats

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


Import from Google Cloud Storage

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


Extract frames from videos

Read every n-th frame and save to images project


Classes co-occurrence matrix

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


Merge datasets

Merge selected datasets with images or videos into a single one


Resize images

for both images and their annotations


Import images with masks

Import images with binary masks as annotations


Train MMSegmentation

Dashboard to configure, start and monitor training


Sliding window split

Configure, preview and split images and annotations with sliding window


Train UNet

Dashboard to configure, start and monitor training


Add properties to image from CSV

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


Train MMDetection

Dashboard to configure, start and monitor training


ilastik pixel classification

Image Pixel Classification using ilastik


AI assisted classification

Use neural network in labeling interface to classify images and objects


Diff and Merge Images Projects

Visual diff and merge tool helps compare images in two projects


Synthetic retail products

Generate synthetic data for classification of retail products on grocery shelves


Rasterize objects on images

Convert classes to bitmap and rasterize objects without intersections


Diff and Merge Project Meta

Visual diff and merge tool helps compare project tags and classes


Filter images

Filters images and provides results in selected format


Object detection metrics

Interactive Confusion matrix, mAP, ROC and more


Train Detectron2

Dashboard to configure, start and monitor training


Serve Detectron2

Deploy model as REST API service


Interactive objects distribution

Explore images with certain number of objects of specific class


Export activity as csv

Download activity as csv file


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


Import COCO

Converts COCO format to Supervisely


Export only labeled items

Export only labeled items and prepares downloadable tar archive


Images project to videos project

Creates video project from images project


EiSeg interactive segmentation SmartTool

SmartTool integration of Efficient Interactive Segmentation (EISeg)


Unpack AnyShape Classes

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


Copy project between instances

Copies images + annotations + images metadata


Import Pascal VOC

Import public or custom data in Pascal VOC format to Supervisely


Import Metadata

Import Metadata for Images in Project


Serve MMClassification

Deploy model as REST API service


Create foreground mask

Create foreground mask from alpha channel of image


Visual Tagging

Assign tags to images using example images


Serve MMDetection

Deploy model as REST API service


Crop objects on images

Creates new project with cropped objects


Sliding window merge

Merge images and labels that were split by sliding window before


Semantic Segmentation Metrics

Semantic Segmentation Metrics

interactive metrics analysis


Objects thumbnail preview

Review images annotations object by object with ease


Serve UNet

Deploy model as REST API service


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


Review labels side-by-side

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


Metric Learning Labeling Tool

Use metric learning models to classify images


CSV Products Catalog to Images Project

Convert .CSV catalog to Images Project


Apply Classifier to Images Project

Apply Classifier to Images Project

label project images or objects using NN


Serve MMSegmentation

Deploy model as REST API service


Export to Cityscapes

Converts Supervisely annotations to Cityscapes format and prepares downloadable tar archive


Images thumbnail preview

Preview images as a grid gallery


Export Metadata

Export Images Metadata from Project


Apply Detection and Classification Models to Images Project

Label project images using detector and classify predicted boxes


Render video from images

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


Train RITM

Dashboard to configure, start and monitor training


AI Recommendations

Recommends matching items from the catalog


Embeddings Calculator

Calculate embeddings for images project


Render video to compare projects

Put images with labels into collage and renders comparison videos


Import images groups

Import images groups connected via user defined tag


Unpack key value tags

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


Import Cityscapes

Import Cityscapes to Supervisely


Labels spatial distribution

Build labels distribution heatmap for dataset.


Tags to image URLs

Saves tag to images mapping to a json file


Serve Metric Learning

Google landmarks challenge models


Export to DOTA

Export images in DOTA format and prepares downloadable archive


Explore data with embeddings

Calculate and visualize embeddings


Export project to cloud storage

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


Convert to semantic segmentation

Convert polygon and bitmap labels to semantic segmentation


Object tags editor

Edit tags of each object on image


Convert labels to rotated bboxes

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


Perspective transform using QR code

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


Tags to object classes

Create new object classes from tags associated with objects


Split datasets

Split one or multiple datasets into parts


Tag images by dataset name

Add dataset name tag to all images in project or dataset


Group nested objects

Binds nested objects into groups


Object classes to tags

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


Classification metrics

Evaluate your classification model


Import COCO Keypoints

Converts COCO Keypoints format to Supervisely


Mark Reference Objects for Retail

Mark Reference Objects for Retail

Prepare examples for products from catalog


Retail Tagging

Retail Tagging

Supports multi-user mode


Review Retail Tags

Review Retail Tags

Review and correct tags (supports multi-user mode)




Image project with person instances


Country Roads

Country Roads

Labeled roads (sample: 100 images, full version: 1000 images)


Lemons (Annotated)

Lemons (Annotated)

6 images with annotated lemons and kiwifruits


Country Roads (Test)

Country Roads (Test)

594 unlabeled images


Grocery store shelves

Labeled images of products on the shelve: snacks, chips, crisps


Roads (Annotated)

Roads (Annotated)

10 images with labeled road


Lemons (Test)

Lemons (Test)

Sample images project without labels




Unlabeled images: sunflower / pumpkin (peeled + unpeeled) / mix


Cats quiz

Cats quiz

What breed is this cat? demo for visual tagging app


PascalVOC GT BBoxes (Sample)

PascalVOC GT BBoxes (Sample)

1171 sample gt-labeled images


Images with alpha channel

Images with alpha channel

Illustrates alpha support in Supervisely


Roads (Test)

Roads (Test)

156 unlabeled images with roads


Snacks catalog

Snacks catalog

Labeled images: snacks: chips / crisps / mix


Top 10 cat breeds

Top 10 cat breeds

Tag (name of breed) is assigned to every image


Demo Images

Demo Images

17 unlabeled images for quick tests


PascalVOC GT Masks (Sample)

PascalVOC GT Masks (Sample)

726 sample gt-labeled images


PascalVOC PRED BBoxes (Sample)

PascalVOC PRED BBoxes (Sample)

1171 sample prediction-labeled images


PascalVOC PRED Masks (Sample)

PascalVOC PRED Masks (Sample)

726 sample pred-labeled images