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