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 and JSON annotations
Images with corresponding annotations
Transform project to YOLO v5 format and prepares tar archive for download
Clone project or dataset to selected workspace or project, works with all project types: images / videos / 3d / dicom
Transform YOLO v5 format to supervisely project
Converts Supervisely to COCO format and prepares tar archive for download
Import images from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
Export only labeled items and prepares downloadable tar archive
Upload images using .CSV file
Dashboard to configure and monitor training
Use deployed neural network in labeling interface
Import images with binary masks as annotations
NN Inference on images in project or dataset
Converts Supervisely Project to Pascal VOC format
Download activity as csv file
Convert DICOM data to nrrd format and creates a new project with images grouped by selected metadata
Deploy model as REST API service
Merge selected datasets with images or videos into a single one
Converts COCO format to Supervisely
Converts shapes of classes (e.g. polygon to bitmap) and all corresponding objects
Assigns tags (train/val) to images. Training apps will use these tags to split data.
Creates images project from video project
Transform Supervisely format to YOLOv8 format
Detailed statistics for all classes in images project
State-of-the art object segmentation model in Labeling Interface
Dashboard to configure, start and monitor training
Read every n-th frame and save to images project
Import public or custom data in Pascal VOC format to Supervisely
Connect your remote storage and import data without duplication. Data is stored on your server but visible in Supervisely
Upload images by reading links (Google Cloud Storage) from CSV file
Merge multiple classes with same shape to a single one
Filters images and provides results in selected format
Prepare training data for SmartTool
Dashboard to configure, start and monitor training
Export images in DOTA format and prepares downloadable archive
Deploy model as REST API service
Converts Supervisely annotations to Cityscapes format and prepares downloadable tar archive
Use neural network in labeling interface to classify images and objects
Dashboard to configure, start and monitor training
Use metric learning models to classify images
Copies images + annotations + images metadata
Dashboard to configure, start and monitor YOLOv8 training
Generate synthetic data: flying foregrounds on top of backgrounds
Download images from project or dataset.
Dashboard to configure, start and monitor training
Deploy model as REST API service
Dashboard to configure, start and monitor training
Deploy model as REST API service
Visual diff and merge tool helps compare images in two projects
Visual diff and merge tool helps compare project tags and classes
Import Cityscapes to Supervisely
Convert .CSV catalog to Images Project
Visualize and build augmentation pipeline with ImgAug
Batched smart labeling tool for Images
Image Pixel Classification using ilastik
Configure, preview and split images and annotations with sliding window
Deploy model as REST API service
Detailed statistics and distribution of object sizes (width, height, area)
for both images and their annotations
label project images or objects using NN
Interactive Confusion matrix, mAP, ROC and more
Dashboard to configure, start and monitor training
Review images annotations object by object with ease
Creates new project with cropped objects
Deploy ClickSEG models for interactive instance segmentation
Deploy YOLOv8 as REST API service
Explore images for every combination of classes pairs in co-occurrence table
Class-agnostic interactive detection for auto-prelabeling
Split "AnyShape" classes to classes with strictly defined shapes (polygon, bitmap, ...)
Deploy model as REST API service
Creates video project from images project
Split one or multiple datasets into parts
Training dashboard for mmdetection framework (v3.0.0 and above).
Explore images with certain number of objects of specific class
Export Images Metadata from Project
Convert classes to bitmap and rasterize objects without intersections
Deploy model as REST API service
Label project images using detector and classify predicted boxes
Put images with labels into collage and renders comparison videos
Assign tags to images using example images
Import images groups connected via user defined tag
Import Metadata for Images in Project
Edit tags of each object on image
to TorchScript and ONNX formats
Preview images as a grid gallery
Class-angnostic object detection model
Calculate and visualize embeddings
Filter objects and tags by user and copy them to working area
Creates video from images in dataset with selected frame rate and configurable label opacity
SmartTool integration of Efficient Interactive Segmentation (EISeg)
Convert polygon and bitmap labels to semantic segmentation
Deploy model as REST API service
Filter and rank images by text prompts with CLIP models
Download CSV file with download links for images
Downloads images from the Pexels to the dataset.
interactive metrics analysis
Recommends matching items from the catalog
Build labels distribution heatmap for dataset.
Converts COCO Keypoints format to Supervisely
Create foreground mask from alpha channel of image
Rename "Key:Value" tags to key_value (fruit: lemon -> fruit_lemon)
Label project images using detector and pose estimator
Deploy MMDetection 3.0 model as a REST API service
Run HQ-SAM and then use in labeling tool
Google landmarks challenge models
Calculate embeddings for images project
Export project to Google Cloud Storage, Amazon S3, Microsoft Azure, ...
Evaluate your classification model
Convert all labels in the project or dataset to rotated bounding boxes
Merge images and labels that were split by sliding window before
Objects with specific tag will be treated as reference items
Add dataset name tag to all images in project or dataset
Compare annotations of multiple labelers
Interactive evaluation of your instance segmentation model
Label project images using object segmentor
Explore images for every combination of tags pairs in co-occurrence table
Converts annotations from Supervisely to COCO format as RLE masks with preserving holes
Generate synthetic data for classification of retail products on grocery shelves
Export items after the passing labeling job review
Saves tag to images mapping to a json file
Rotates images along with the annotations in the dataset
Match image tag with CSV columns and add row values to image
Text Detection and Recognition on images
Transfer and filter assets(images) between Supervisely instances
Converts Supervisely format to COCO Keypoints
Tags and object classes can be customized
This app perspective transforms and warps your images using qr code in them.
Merge Tags in videos or images project
Deploy model as REST API service
Binds nested objects into groups
Slice volumes to 2d images
Deploy InSPyReNet for salient object segmentation as a REST API service
Application imports kaggle dataset 'Movie genre from its poster' as supervisely project
Create new object classes from tags associated with objects
Downloads images from the Flickr to the dataset.
Convert each class name to tag associated with objects, and merge existing classes into single one
Create a new empty project with a meta of original project
Apply pretrained models for underwater species detection
Run Stable Diffusion model with User Interface
Drag and drop PDFs to import pages as images to Supervisely
Prepare examples for products from catalog
Import image projects in Supervisely format from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
Train HRDA model for segmentation in semi-supervised mode
Supports multi-user mode
Merge multiple image projects into a single one
Deploy HRDA model for inference
Deploy Transfiner for instance segmentation as a REST API service
Deploy SelfReformer for salient object segmentation as a REST API service
Review and correct tags (supports multi-user mode)
6 images with annotated lemons and kiwifruits
Image project with person instances
Labeled images: snacks: chips / crisps / mix
Sample images project without labels
Labeled roads (sample: 100 images, full version: 1000 images)
17 unlabeled images for quick tests
Labeled images of products on the shelve: snacks, chips, crisps
10 images with labeled road
Tag (name of breed) is assigned to every image
594 unlabeled images
156 unlabeled images with roads
726 sample gt-labeled images
Project with 66 annotated tomatoes (424 images)
Unlabeled images: sunflower / pumpkin (peeled + unpeeled) / mix
1171 sample gt-labeled images
1171 sample prediction-labeled images
What breed is this cat? demo for visual tagging app
726 sample pred-labeled images
Synthetic dataset for cracks segmentation
Illustrates alpha support in Supervisely
For object detection tutorials
For object detection tutorials
Images of wheat for training and validation
Wheat images for test