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images

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)

Persons

Persons

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

Seeds

Seeds

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