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
Clone project or dataset to selected workspace or project, works with all project types: images / videos / 3d / dicom
Transform project to YOLO v5 format and prepares tar archive for download
Converts Supervisely to COCO format and prepares tar archive for download
Transform YOLO v5 format to supervisely project
Export only labeled items and prepares downloadable tar archive
Use deployed neural network in labeling interface
Import images from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
NN Inference on images in project or dataset
Transform Supervisely format to YOLOv8 format
Upload images using .CSV file
Dashboard to configure and monitor training
Import images with binary masks as annotations
Dashboard to configure, start and monitor YOLO (v8, v9) training
Converts Supervisely Project to Pascal VOC format
Converts COCO format to Supervisely
Merge selected datasets with images or videos into a single one
Converts shapes of classes (e.g. polygon to bitmap) and all corresponding objects
Download activity as csv file
Deploy model as REST API service
Creates images project from video project
Download images from project or dataset.
Creates project with images grouped by selected metadata, converting DICOM data to NRRD format in the process.
State-of-the art object segmentation model in Labeling Interface
Assigns tags (train/val) to images. Training apps will use these tags to split data.
Deploy YOLO (v8, v9) as REST API service
Filters images and provides results in selected format
Detailed statistics for all classes in images project
Dashboard to configure, start and monitor training
Read every n-th frame and save to images project
Deploy model as REST API service
Import public or custom data in Pascal VOC format to Supervisely
Dashboard to configure, start and monitor training
Merge multiple classes with same shape to a single one
Dashboard to configure, start and monitor training
Use neural network in labeling interface to classify images and objects
Deploy ClickSEG models for interactive instance segmentation
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
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
Dashboard to configure, start and monitor training
Training dashboard for mmdetection framework (v3.0.0 and above).
Use metric learning models to classify images
Converts Supervisely annotations to Cityscapes format and prepares downloadable tar archive
Copies images + annotations + images metadata
Generate synthetic data: flying foregrounds on top of backgrounds
Converts Supervisely format to COCO Keypoints
Deploy model as REST API service
Visualize and build augmentation pipeline with ImgAug
Deploy model as REST API service
Batched smart labeling tool for Images
Dashboard to configure, start and monitor training
Class-agnostic interactive detection for auto-prelabeling
label project images or objects using NN
Configure, preview and split images and annotations with sliding window
Visual diff and merge tool helps compare images in two projects
Visual diff and merge tool helps compare project tags and classes
Run HQ-SAM and then use in labeling tool
Convert .CSV catalog to Images Project
Deploy model as REST API service
Split one or multiple datasets into parts
Import Cityscapes to Supervisely
for both images and their annotations
Detailed statistics and distribution of object sizes (width, height, area)
Interactive Confusion matrix, mAP, ROC and more
Image Pixel Classification using ilastik
Creates new project with cropped objects
Deploy MMDetection 3.0 model as a REST API service
Class-angnostic object detection model
Convert classes to bitmap and rasterize objects without intersections
Download CSV file with download links for images
Deploy model as REST API service
Review images annotations object by object with ease
Label project images using detector and pose estimator
Import images groups connected via user defined tag
Explore images for every combination of classes pairs in co-occurrence table
Export Images Metadata from Project
Deploy model as REST API service
Import Metadata for Images in Project
Creates video project from images project
Split "AnyShape" classes to classes with strictly defined shapes (polygon, bitmap, ...)
Assign tags to images using example images
Label project images using detector and classify predicted boxes
Converts annotations from Supervisely to COCO format as RLE masks with preserving holes
Interactive evaluation of your instance segmentation model
Edit tags of each object on image
Calculate and visualize embeddings
interactive metrics analysis
Explore images with certain number of objects of specific class
Export items after the passing labeling job review
Filter objects and tags by user and copy them to working area
Merge multiple image projects into a single one
Convert polygon and bitmap labels to semantic segmentation
Evaluate your classification model
to TorchScript and ONNX formats
Downloads images from the Pexels to the dataset.
SmartTool integration of Efficient Interactive Segmentation (EISeg)
Rotates images along with the annotations in the dataset
Put images with labels into collage and renders comparison videos
Train HRDA model for segmentation in semi-supervised mode
Creates video from images in dataset with selected frame rate and configurable label opacity
Filter and rank images by text prompts with CLIP models
Convert and copy multiple Roboflow projects into Supervisely at once.
Convert all labels in the project or dataset to rotated bounding boxes
Converts COCO Keypoints format to Supervisely
Preview images as a grid gallery
Objects with specific tag will be treated as reference items
Google landmarks challenge models
Add dataset name tag to all images in project or dataset
Build labels distribution heatmap for dataset.
Binds nested objects into groups
Label project images using object segmentor
Create foreground mask from alpha channel of image
Merge images and labels that were split by sliding window before
App to obscure data on images and videos
Compare annotations of multiple labelers
Calculate embeddings for images project
Recommends matching items from the catalog
Dashboard to configure, start and monitor YOLOv5 2.0 training
Rename "Key:Value" tags to key_value (fruit: lemon -> fruit_lemon)
Generate synthetic data for classification of retail products on grocery shelves
Export project to Google Cloud Storage, Amazon S3, Microsoft Azure, ...
Text Detection and Recognition on images
Deploy YOLOv5 2.0 as REST API service
Saves tag to images mapping to a json file
Import images and videos with annotations in CVAT format.
Deploy HRDA model for inference
Explore images for every combination of tags pairs in co-occurrence table
Drag and drop PDFs to import pages as images to Supervisely
Match image tag with CSV columns and add row values to image
Convert and copy multiple Labelbox projects into Supervisely at once.
No description available
Import image projects in Supervisely format from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
Transfer and filter assets(images) between Supervisely instances
Import multispectral images as channels or as separate images.
Tags and object classes can be customized
Merge Tags in videos or images project
Create new object classes from tags associated with objects
Deploy model as REST API service
Slice volumes to 2d images
Create a new empty project with a meta of original project
Prepare examples for products from catalog
Run Stable Diffusion model with User Interface
This app perspective transforms and warps your images using qr code in them.
Convert each class name to tag associated with objects, and merge existing classes into single one
Deploy InSPyReNet for salient object segmentation as a REST API service
Downloads images from the Flickr to the dataset.
Convert and copy multiple V7 datasets into Supervisely at once.
Application imports kaggle dataset 'Movie genre from its poster' as supervisely project
Import images and videos with annotations in V7 format.
Convert and copy multiple CVAT projects into Supervisely at once.
Evaluate your classification model in Detector + Classifier Pipeline
Application that visualizes the most recently updated images
Apply pretrained models for underwater species detection
Sample images from project with different methods
to TorchScript and ONNX formats
Deploy Matte Anything as REST API service
Deploy Transfiner for instance segmentation as a REST API service
Supports multi-user mode
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
Sample images project without labels
Labeled images: snacks: chips / crisps / mix
17 unlabeled images for quick tests
Labeled roads (sample: 100 images, full version: 1000 images)
156 unlabeled images with roads
Labeled images of products on the shelve: snacks, chips, crisps
Tag (name of breed) is assigned to every image
10 images with labeled road
594 unlabeled images
Project with 66 annotated tomatoes (424 images)
Unlabeled images: sunflower / pumpkin (peeled + unpeeled) / mix
726 sample gt-labeled images
Synthetic dataset for cracks segmentation
For object detection tutorials
1171 sample gt-labeled images
What breed is this cat? demo for visual tagging app
1171 sample prediction-labeled images
Images of wheat for training and validation
For object detection tutorials
Wheat images for test
726 sample pred-labeled images
Illustrates alpha support in Supervisely