Basic Image labeling tool
complete solution for image annotation
complete solution for image annotation
complete solution for image annotation with advanced features
Drag and drop images to Supervisely, supported formats: .jpg, .jpeg, jpe, .mpo, .bmp, .png, .tiff, .tif, .webp, .nrrd
For semantic and instance segmentation tasks
Images with corresponding annotations
images and JSON annotations
Dashboard to configure and monitor training
Deploy model as REST API service
Detailed statistics for all classes in images project
Import images from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
State of the art object segmentation model in Labeleing Interface
Upload images using .CSV file
Transform YOLO v5 format to supervisely project
Transform project to YOLO v5 format and prepares tar archive for download
Converts shapes of classes (e.g. polygon to bitmap) and all corresponding objects
NN Inference on images in project or dataset
Prepare training data for SmartTool
to TorchScript and ONNX formats
Creates images project from video project
Use deployed neural network in labeling interface
Converts Supervisely to COCO format and prepares tar archive for download
Generate synthetic data: flying foregrounds on top of backgrounds
Batched smart labeling tool for Images
Converts Supervisely Project to Pascal VOC format
Convert DICOM data to nrrd format and creates a new project with images grouped by selected metadata
Assigns tags (train/val) to images. Training apps will use these tags to split data.
Dashboard to configure, start and monitor training
Merge multiple classes with same shape to a single one
Visualize and build augmentation pipeline with ImgAug
Connect your remote storage and import data without duplication. Data is stored on your server but visible in Supervisely
Detailed statistics and distribution of object sizes (width, height, area)
Upload images by reading links (Google Cloud Storage) from CSV file
Read every n-th frame and save to images project
Explore images for every combination of classes pairs in co-occurrence table
Merge selected datasets with images or videos into a single one
for both images and their annotations
Import images with binary masks as annotations
Dashboard to configure, start and monitor training
Configure, preview and split images and annotations with sliding window
Dashboard to configure, start and monitor training
Match image tag with CSV columns and add row values to image
Dashboard to configure, start and monitor training
Image Pixel Classification using ilastik
Use neural network in labeling interface to classify images and objects
Visual diff and merge tool helps compare images in two projects
Generate synthetic data for classification of retail products on grocery shelves
Convert classes to bitmap and rasterize objects without intersections
Visual diff and merge tool helps compare project tags and classes
Filters images and provides results in selected format
Interactive Confusion matrix, mAP, ROC and more
Dashboard to configure, start and monitor training
Deploy model as REST API service
Explore images with certain number of objects of specific class
Download activity as csv file
Objects with specific tag will be treated as reference items
Tags and object classes can be customized
Converts COCO format to Supervisely
Export only labeled items and prepares downloadable tar archive
Creates video project from images project
SmartTool integration of Efficient Interactive Segmentation (EISeg)
Split "AnyShape" classes to classes with strictly defined shapes (polygon, bitmap, ...)
Copies images + annotations + images metadata
Import public or custom data in Pascal VOC format to Supervisely
Import Metadata for Images in Project
Deploy model as REST API service
Create foreground mask from alpha channel of image
Assign tags to images using example images
Deploy model as REST API service
Creates new project with cropped objects
Merge images and labels that were split by sliding window before
interactive metrics analysis
Review images annotations object by object with ease
Deploy model as REST API service
Explore images for every combination of tags pairs in co-occurrence table
Application imports kaggle dataset 'Movie genre from its poster' as supervisely project
Filter objects and tags by user and copy them to working area
Use metric learning models to classify images
Convert .CSV catalog to Images Project
label project images or objects using NN
Deploy model as REST API service
Converts Supervisely annotations to Cityscapes format and prepares downloadable tar archive
Preview images as a grid gallery
Export Images Metadata from Project
Label project images using detector and classify predicted boxes
Creates video from images in dataset with selected frame rate and configurable label opacity
Dashboard to configure, start and monitor training
Recommends matching items from the catalog
Calculate embeddings for images project
Put images with labels into collage and renders comparison videos
Import images groups connected via user defined tag
Rename "Key:Value" tags to key_value (fruit: lemon -> fruit_lemon)
Import Cityscapes to Supervisely
Build labels distribution heatmap for dataset.
Saves tag to images mapping to a json file
Google landmarks challenge models
Export images in DOTA format and prepares downloadable archive
Calculate and visualize embeddings
Export project to Google Cloud Storage, Amazon S3, Microsoft Azure, ...
Convert polygon and bitmap labels to semantic segmentation
Edit tags of each object on image
Convert all labels in the project or dataset to rotated bounding boxes
This app perspective transforms and warps your images using qr code in them.
Create new object classes from tags associated with objects
Split one or multiple datasets into parts
Add dataset name tag to all images in project or dataset
Binds nested objects into groups
Convert each class name to tag associated with objects, and merge existing classes into single one
Evaluate your classification model
Converts COCO Keypoints format to Supervisely
Prepare examples for products from catalog
Supports multi-user mode
Review and correct tags (supports multi-user mode)
Image project with person instances
Labeled roads (sample: 100 images, full version: 1000 images)
6 images with annotated lemons and kiwifruits
594 unlabeled images
Labeled images of products on the shelve: snacks, chips, crisps
10 images with labeled road
Sample images project without labels
Unlabeled images: sunflower / pumpkin (peeled + unpeeled) / mix
What breed is this cat? demo for visual tagging app
1171 sample gt-labeled images
Illustrates alpha support in Supervisely
156 unlabeled images with roads
Labeled images: snacks: chips / crisps / mix
Tag (name of breed) is assigned to every image
17 unlabeled images for quick tests
726 sample gt-labeled images
1171 sample prediction-labeled images
726 sample pred-labeled images