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images

199 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

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

Convert Supervisely to YOLO v5 format

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

Export to COCO

Converts Supervisely to COCO format and prepares tar archive for download

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 to YOLOv8 format

Transform Supervisely format to YOLOv8 format

Import images from cloud storage

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

Apply NN to Images Project

NN Inference on images in project or dataset

Train YOLOv5

Dashboard to configure and monitor training

Import Images from CSV

Upload images using .CSV file

Train YOLOv8 | v9 | v10

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

Import images with masks

Import images with binary masks as annotations

Export to Pascal VOC

Converts Supervisely Project to Pascal VOC format

Import COCO

Converts COCO format to Supervisely

Convert Class Shape

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

Merge datasets

Merge selected datasets with images or videos into a single one

Export activity as csv

Download activity as csv file

Download images

Download images from project or dataset.

Serve YOLOv5

Deploy model as REST API service

Videos project to images project

Creates images project from video project

Import DICOM studies

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

Serve YOLOv8 | v9 | v10

Deploy YOLOv8 | v9 | v10 as REST API service

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.

Filter images

Filters images and provides results in selected format

Classes stats for images

Detailed statistics for all classes in images project

Train MMSegmentation

Dashboard to configure, start and monitor training

Train MMDetection

Dashboard to configure, start and monitor training

Serve Segment Anything Model

Deploy model as REST API service

Extract frames from videos

Read every n-th frame and save to images project

Merge classes

Merge multiple classes with same shape to a single one

Import Pascal VOC

Import public or custom data in Pascal VOC format to Supervisely

Train Detectron2

Dashboard to configure, start and monitor training

Serve ClickSEG

Deploy ClickSEG models for interactive instance segmentation

Train MMDetection 3.0

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

Metric Learning Labeling Tool

Use metric learning models to classify images

AI assisted classification

Use neural network in labeling interface to classify images and objects

Remote import

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

Train MMClassification

Dashboard to configure, start and monitor training

Train UNet

Dashboard to configure, start and monitor training

Create Trainset for SmartTool

Prepare training data for SmartTool

Import from Google Cloud Storage

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

Export to DOTA

Export images in DOTA format and prepares downloadable archive

Serve MMDetection

Deploy model as REST API service

Export COCO Keypoints

Converts Supervisely format to COCO Keypoints

Copy project between instances

Copies images + annotations + images metadata

Export to Cityscapes

Converts Supervisely annotations to Cityscapes format and prepares downloadable tar archive

Serve MMSegmentation

Deploy model as REST API service

Serve Detectron2

Deploy model as REST API service

Flying objects

Generate synthetic data: flying foregrounds on top of backgrounds

ImgAug Studio

Visualize and build augmentation pipeline with ImgAug

Train RITM

Dashboard to configure, start and monitor training

Serve OWL-ViT

Class-agnostic interactive detection for auto-prelabeling

Batched Smart Tool

Batched smart labeling tool for Images

Sliding window split

Configure, preview and split images and annotations with sliding window

Apply Classifier to Images Project

Apply Classifier to Images Project

label project images or objects using NN

Serve Segment Anything in High Quality

Run HQ-SAM and then use in labeling tool

Diff and Merge Images Projects

Visual diff and merge tool helps compare images in two projects

Split datasets

Split one or multiple datasets into parts

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

Serve MMDetection 3.0

Deploy MMDetection 3.0 model as a REST API service

Serve MMClassification

Deploy model as REST API service

Download image links in CSV

Download CSV file with download links for images

Import Cityscapes

Import Cityscapes to Supervisely

Resize images

for both images and their annotations

Crop objects on images

Creates new project with cropped objects

Apply OWL-ViT To Images Project

Class-angnostic object detection model

Object Size Stats

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

ilastik pixel classification

Image Pixel Classification using ilastik

Object detection metrics

Interactive Confusion matrix, mAP, ROC and more

Rasterize objects on images

Convert classes to bitmap and rasterize objects without intersections

Serve ViTPose

Deploy model as REST API service

Export to COCO mask

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

Apply Detection and Pose Estimation Models to Images Project

Label project images using detector and pose estimator

Objects thumbnail preview

Review images annotations object by object with ease

Import Metadata

Import Metadata for Images in Project

Classes co-occurrence matrix

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

Export Metadata

Export Images Metadata from Project

Images project to videos project

Creates video project from images project

Import images groups

Import images groups connected via user defined tag

Serve UNet

Deploy model as REST API service

Visual Tagging

Assign tags to images using example images

Instance Segmentation Metrics

Interactive evaluation of your instance segmentation model

Object tags editor

Edit tags of each object on image

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

Export items after review

Export items after the passing labeling job review

Explore data with embeddings

Calculate and visualize embeddings

Train HRDA

Train HRDA model for segmentation in semi-supervised mode

Semantic Segmentation Metrics

Semantic Segmentation Metrics

interactive metrics analysis

Merge Image Projects

Merge multiple image projects into a single one

Interactive objects distribution

Explore images with certain number of objects of specific class

Review labels side-by-side

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

Serve Metric Learning

Google landmarks challenge models

Convert to semantic segmentation

Convert polygon and bitmap labels to semantic segmentation

Prompt-based Image Filtering with CLIP

Filter and rank images by text prompts with CLIP models

Pexels downloader

Downloads images from the Pexels to the dataset.

Create JSON with reference items

Objects with specific tag will be treated as reference items

Rotate images

Rotates images along with the annotations in the dataset

Export YOLOv5 weights

to TorchScript and ONNX formats

Embeddings Calculator

Calculate embeddings for images project

Image Classification Metrics

Evaluate your classification model

Roboflow to Supervisely Migration Tool

Convert and copy multiple Roboflow projects into Supervisely at once.

Convert labels to rotated bboxes

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

EiSeg interactive segmentation SmartTool

SmartTool integration of Efficient Interactive Segmentation (EISeg)

Render video to compare projects

Put images with labels into collage and renders comparison videos

Render video from images

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

Images thumbnail preview

Preview images as a grid gallery

Import COCO Keypoints

Converts COCO Keypoints format to Supervisely

Evaluator for Model Benchmark

Evaluate the performance of the NN model

Apply Object Segmentor to Images Project

Label project images using object segmentor

Serve Segment Anything 2

Deploy SAM 2 model as REST API service

AI Recommendations

Recommends matching items from the catalog

Tag images by dataset name

Add dataset name tag to all images in project or dataset

Sliding window merge

Merge images and labels that were split by sliding window before

Labels spatial distribution

Build labels distribution heatmap for dataset.

Group nested objects

Binds nested objects into groups

Anonymize Data

App to obscure data on images and videos

Create foreground mask

Create foreground mask from alpha channel of image

Train YOLOv5 2.0

Dashboard to configure, start and monitor YOLOv5 2.0 training

Labeling Consensus

Compare annotations of multiple labelers

Unpack key value tags

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

Synthetic retail products

Generate synthetic data for classification of retail products on grocery shelves

Add properties to image from CSV

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

Export project to cloud storage

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

Serve HRDA

Deploy HRDA model for inference

Tags to object classes

Create new object classes from tags associated with objects

MMOCR Inference

Text Detection and Recognition on images

Group Images for Multiview Labeling

No description available

Tags to image URLs

Saves tag to images mapping to a json file

Import PDF as Images

Drag and drop PDFs to import pages as images to Supervisely

Import CVAT

Import images and videos with annotations in CVAT format.

Serve YOLOv5 2.0

Deploy YOLOv5 2.0 as REST API service

Tags co-occurrence matrix

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

Mark Reference Objects for Retail

Mark Reference Objects for Retail

Prepare examples for products from catalog

Labelbox to Supervisely Migration Tool

Convert and copy multiple Labelbox projects into Supervisely at once.

Merge Tags

Merge Tags in videos or images project

Transfer Assets between Instances

Transfer and filter assets(images) between Supervisely instances

Import Multispectral Images

Import multispectral images as channels or as separate images.

Import image projects in Supervisely format from cloud storage

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

Copy image tags to objects

Tags and object classes can be customized

Serve IS-Net

Deploy model as REST API service

Slice volume

Slice volumes to 2d images

Create project from template

Create a new empty project with a meta of original project

Stable diffusion UI

Run Stable Diffusion model with User Interface

Object classes to tags

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

Perspective transform using QR code

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

Flickr downloader

Downloads images from the Flickr to the dataset.

Serve InSPyReNet

Deploy InSPyReNet for salient object segmentation as a REST API service

V7 to Supervisely Migration Tool

Convert and copy multiple V7 datasets into Supervisely at once.

Sample images from project

Sample images from project with different methods

Movie genre from its poster

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

Import V7

Import images and videos with annotations in V7 format.

Annotation Validator

Validate annotations in a project

Visualize Image Updates

Application that visualizes the most recently updated images

Object Classification Metrics

Evaluate your classification model in Detector + Classifier Pipeline

Export YOLOv8 weights

to TorchScript and ONNX formats

CVAT to Supervisely Migration Tool

Convert and copy multiple CVAT projects into Supervisely at once.

Apply VIAME

Apply pretrained models for underwater species detection

Real-time Labeling Quality Check

Automated real-time annotation tests.

Serve Matte Anything

Deploy Matte Anything as REST API service

Review Image Annotations in Batches

Allows you to review annotation results in a user interface specifically designed for such tasks

Retail Tagging

Retail Tagging

Supports multi-user mode

Serve Transfiner

Deploy Transfiner for instance segmentation as a REST API service

Conveyor Belt Auto Tracking

Effortlessly track and interpolate labeled objects on a conveyor belt in real-time

Serve SelfReformer

Deploy SelfReformer for salient object segmentation as a REST API service

Review Retail Tags

Review Retail Tags

Review and correct tags (supports multi-user mode)

Lemons (Annotated)

Lemons (Annotated)

6 images with annotated lemons and kiwifruits

Persons

Persons

Image project with person instances

Lemons (Test)

Lemons (Test)

Sample images project without labels

Snacks catalog

Snacks catalog

Labeled images: snacks: chips / crisps / mix

Demo Images

Demo Images

17 unlabeled images for quick tests

Country Roads

Country Roads

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

Roads (Test)

Roads (Test)

156 unlabeled images with roads

Top 10 cat breeds

Top 10 cat breeds

Tag (name of breed) is assigned to every image

Grocery store shelves

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

Roads (Annotated)

Roads (Annotated)

10 images with labeled road

Country Roads (Test)

Country Roads (Test)

594 unlabeled images

Tomatoes (Annotated)

Tomatoes (Annotated)

Project with 66 annotated tomatoes (424 images)

Seeds

Seeds

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

Cracks Synthetic Dataset

Synthetic dataset for cracks segmentation

PascalVOC GT Masks (Sample)

PascalVOC GT Masks (Sample)

726 sample gt-labeled images

Train dataset - Insulator-Defect Detection

For object detection tutorials

PascalVOC GT BBoxes (Sample)

PascalVOC GT BBoxes (Sample)

1171 sample gt-labeled images

Train dataset - Eschikon Wheat Segmentation (EWS)

Images of wheat for training and validation

Cats quiz

Cats quiz

What breed is this cat? demo for visual tagging app

PascalVOC PRED BBoxes (Sample)

PascalVOC PRED BBoxes (Sample)

1171 sample prediction-labeled images

Test dataset - Insulator-Defect Detection

For object detection tutorials

Test dataset - Eschikon Wheat Segmentation (EWS)

Wheat images for test

PascalVOC PRED Masks (Sample)

PascalVOC PRED Masks (Sample)

726 sample pred-labeled images

Images with alpha channel

Images with alpha channel

Illustrates alpha support in Supervisely